diff --git a/00_Setup/test2.md b/00_Setup/test2.md new file mode 100644 index 0000000..6bd3239 --- /dev/null +++ b/00_Setup/test2.md @@ -0,0 +1 @@ +fasfdsa diff --git a/00_Setup/test_s01.md b/00_Setup/test_s01.md index 802992c..8b72a75 100644 --- a/00_Setup/test_s01.md +++ b/00_Setup/test_s01.md @@ -1 +1,4 @@ -Hello world +Hello worlds +Hello Pythonistas + +Hi diff --git a/01_PythonBasics/01_IntroToPython.ipynb b/01_PythonBasics/01_IntroToPython.ipynb new file mode 100644 index 0000000..191787b --- /dev/null +++ b/01_PythonBasics/01_IntroToPython.ipynb @@ -0,0 +1,2572 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**credits:** Lecture based on Jan Šíla's lecture that was based on my lecture \n", + "\n", + "## Why learn programming\n", + "\n", + "* To solve problems:\n", + " * manipulate data\n", + " * solve algorithmic tasks\n", + " * automate mundane tasks\n", + "\n", + "* To create:\n", + " * science\n", + " * tools\n", + " * software\n", + " * models\n", + " * art\n", + " * business processes\n", + " * fun stuff\n", + "\n", + "* To think:\n", + " * break complex problems into managable pieces\n", + " * think algorithmically\n", + "\n", + "* To make living :) \n", + "\n", + "\n", + "## Course aims and motivation\n", + "\n", + "* To teach thinking like a programmer\n", + " * Basic concepts\n", + " * Google, StackOverflow\n", + " * Ask correct questions\n", + " * Complex programs are lego tiles combined in an efficient manner\n", + " * Build on the shoulders of giants\n", + "\n", + "* To get you above *I can do anything* threshold, or (more probably) close to it\n", + " * Steep part of the learning curve\n", + " * Requires substantial individual investments\n", + "\n", + "* To collaborate\n", + " * \"Code is more often read than written\" Guido van Rossum\n", + " * version control (check issues, pull requests etc.)\n", + " * documentation and docstring\n", + " * code structuring\n", + " * leverage open-source\n", + "\n", + "* To take of advantage of massive amounts of data flowing around. It is a good idea to know:\n", + " * how to get it\n", + " * how to store It\n", + " * how to process it\n", + " * how to analyze it\n", + " * how to share it\n", + "\n", + "# Intro\n", + "*Q: What is a data pipeline??*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Course Outline\n", + "\n", + "* Programming (also in Python) is extremely broad.\n", + "* To keep you in focus, the course will concentrate on **data pipelines** (no worries, you will still have A LOT to learn ... )\n", + "\n", + "The main topics in the course can be roughly divided into the following:\n", + "\n", + "0. **Syntax** - *the Python fundamentals and programming essentials*\n", + "\n", + "1. **Data collection** - *The world is full of data. How to get some? Most common data formats, storages and languages (JSON/XML/CSV/SQL).* \n", + "\n", + "2. **Data processing** - *Working with the data in Python/Pandas ecosystem. Especially Pandas, Matplotlib and Numpy will be covered. Appropriate data analysis*\n", + "\n", + "3. **Data persistance** - how to store the data?\n", + "\n", + "4. **Project management** - how to package programs? How to distribute it? \n", + "\n", + "5. **Project publication** - offering programs to others using API - Flask\n", + "\n", + "3. **Data ** - *Work on your project.* The last block would be helping to get a hands-on experience and to apply the knowledge gained throughout the course.\n", + "\n", + "\n", + "Warning: The sooner you start writing, the better!!\n", + "\n", + "## The Final Project - End-to-end data analytics/data science project/pipeline\n", + "\n", + "**Description:**\n", + "* Students in teams by 2\n", + "* Business oriented. The application is everything\n", + " - You need to plan - what is a goal of the app?\n", + " - How would users use the data that you offer? \n", + "* The task is to \n", + " 1. Download some data - from API etc, web-scraping etc.\n", + " 2. Process data - clean, transform, aggregate etc.\n", + " - Note that appropriate processing requires adequate analysis! \n", + " - Visualization would most likely be useful... Why not use Jupyter Notebooks? \n", + " 3. Persist data - DB etc.,\n", + " 4. Appropriate package of your program - at least `requirements.txt` + `argparse`?\n", + " 5. Offer outputs of your program as an API (Flask? )\n", + "* The selection of the data is entirely up to the students. \n", + "\n", + "**Deadlines:**\n", + "\n", + "* Follow the GitHub repo\n", + "\n", + "**Consultations:** \n", + "\n", + "* Of course, you may contact us any time. We will do our best, to help to help you." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lecture 2: Basics of Python\n", + "\n", + "\n", + "\n", + "* Guido van Rossum\n", + "\n", + "In December 1989, Van Rossum had been looking for a \"'hobby' programming project that would keep [him] occupied during the week around Christmas\" as his office was closed when he decided to write an interpreter for a \"new scripting language [he] had been thinking about lately: a descendant of ABC that would appeal to Unix/C hackers\". He attributes choosing the name \"Python\" to \"being in a slightly irreverent mood (and a big fan of Monty Python's Flying Circus)\".[21]\n", + "\n", + "* Programming gives you freedom to create -> develop\n", + "\n", + "* Python is an amazing general purpose language, ranked among TOP 3 languages.\n", + "\n", + "* Rather high end language - high level of abstraction, but lower control over stuff (memory handling etc. - but do you need it?) \n", + "\n", + "## When to use Python:\n", + " \n", + "* Particularly great for web development (Flask, Django)\n", + "\n", + "* Data Analysis (Pandas), Scientific computing (Scipy), Business Intelligence (Plotly, Bokeh), ETL processes (Airflow)\n", + "\n", + "* Data science / Machine Learning / AI models (keras, tensorflow, torch) / NLP \n", + "\n", + "* Also covers scientific computing + data science (SciPy) + machine learning (Keras, tensorflow) + business intelligence (Plotly, etc.)\n", + "\n", + "* Cloud computing - AWS lambda functions and many more.\n", + "\n", + "* Interface with C, C++ - speed up your code with some low end arithmetic\n", + "\n", + "* Multiplatform (vs C#), interactive (like R, oppose to C++)\n", + "\n", + "* Source code [publicly available](https://github.com/python/cpython/blob/2.7/Python/bltinmodule.c#L1580)\n", + "\n", + "\n", + "\n", + "## Python as a language\n", + "* General purpose\n", + "\n", + "* Open-source\n", + "\n", + "* Interpreted (not-compiled)\n", + " * Type insecure\n", + "\n", + " * Slow\n", + "\n", + "* Object-oriented\n", + "\n", + "* Easily extendable\n", + "\n", + "* Convinient for beginners, yet very powerful\n", + "\n", + "* Huge community!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Resources\n", + "\n", + "* Quite amazing [Wiki](https://wiki.python.org/moin/BeginnersGuide)\n", + "\n", + "* Practically any problem you will be facing now is on [StackOverflow](https://stackoverflow.com/tags/python) \n", + "\n", + "* PEP 8 Style guide for python [HERE](https://www.python.org/dev/peps/pep-0008/)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## General principles\n", + "\n", + "* Code is more often read than written (GvR) - aim for easy-to-read and well-documented code\n", + "\n", + "* EAFP: “it’s easier to ask for forgiveness than permission”\n", + " vs LBYL: “look before you leap\" -> try stuff, handle possible errors\n", + " \n", + "* Syntax is a part of code - indentation matters!\n", + "\n", + "* Do not worry, you will get those under skin as you code\n", + "\n", + "* The only way to learn coding is coding. Ideally often." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The Zen of Python, by Tim Peters\n", + "\n", + "Beautiful is better than ugly.\n", + "Explicit is better than implicit.\n", + "Simple is better than complex.\n", + "Complex is better than complicated.\n", + "Flat is better than nested.\n", + "Sparse is better than dense.\n", + "Readability counts.\n", + "Special cases aren't special enough to break the rules.\n", + "Although practicality beats purity.\n", + "Errors should never pass silently.\n", + "Unless explicitly silenced.\n", + "In the face of ambiguity, refuse the temptation to guess.\n", + "There should be one-- and preferably only one --obvious way to do it.\n", + "Although that way may not be obvious at first unless you're Dutch.\n", + "Now is better than never.\n", + "Although never is often better than *right* now.\n", + "If the implementation is hard to explain, it's a bad idea.\n", + "If the implementation is easy to explain, it may be a good idea.\n", + "Namespaces are one honking great idea -- let's do more of those!\n" + ] + } + ], + "source": [ + "import this" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How to launch Python code\n", + "\n", + "*Q: How do you launch code?*\n", + "\n", + "*Q: What do we mean by `code execution`?*\n", + "\n", + "* All you need is python executable\n", + "* Command line interface\n", + "* IPython/Jupyter\n", + "* Launched by external trigger - AWS Lambda, API etc.\n", + "\n", + "Either write python code directly into console or launch `*.py` files" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HELLO WORLD\n" + ] + } + ], + "source": [ + "print(\"HELLO WORLD\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Standard output - `print`" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello, World!\n" + ] + } + ], + "source": [ + "# First commands executed should always be the Hello world program!\n", + "\n", + "#simplest \"program\" - tell python to print some strings to the console (standard output)\n", + "print(\"Hello, World!\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'2'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'2'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Objects, attributes, methods and functions\n", + "\n", + "`Object` - Everything is an object" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "o1 = 'Hello world!'" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Hello world!'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "o1" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Hello world!Hello world!'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "o1 + o1" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "type" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "builtin_function_or_method" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(print)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "o2 + o2" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "str" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "type(o1)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "o2 = 3\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "int" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(o2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "print(type(o2))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "from datetime import datetime\n", + "o1 = 'Hello world!'\n", + "print(type(o1))\n", + "o2 = 3\n", + "print(type(o2))\n", + "o3 = [1,2,3,4,5]\n", + "print(type(o3))\n", + "o4 = {'a':0,'b':1}\n", + "print(type(o4))\n", + "o5 = datetime(2021,1,23,18,30)\n", + "print(type(o5))\n", + "o6 = o3[1]\n", + "print(type(o6))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`Attribute` - a variable ( == another object) assigned to the object" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "datetime.datetime" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(o5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`Method` - function that the object applies to itself" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Hello world!'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "o1" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Fuck off world!'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "o1.replace('Hello','Fuck off')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'list' object has no attribute 'upper'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32mc:\\Users\\Martin Hronec\\Projects\\phd\\teaching\\PythonDataIES\\01_PythonBasics\\01_IntroToPython.ipynb Cell 30\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> 1\u001b[0m o1\u001b[39m.\u001b[39;49mupper()\u001b[39m.\u001b[39;49mlower()\u001b[39m.\u001b[39;49msplit(\u001b[39m'\u001b[39;49m\u001b[39m \u001b[39;49m\u001b[39m'\u001b[39;49m)\u001b[39m.\u001b[39;49mupper()\n", + "\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'upper'" + ] + } + ], + "source": [ + "o1.upper().lower().split(' ').upper()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['__add__',\n", + " '__class__',\n", + " '__contains__',\n", + " '__delattr__',\n", + " '__dir__',\n", + " '__doc__',\n", + " '__eq__',\n", + " '__format__',\n", + " '__ge__',\n", + " '__getattribute__',\n", + " '__getitem__',\n", + " '__getnewargs__',\n", + " '__gt__',\n", + " '__hash__',\n", + " '__init__',\n", + " '__init_subclass__',\n", + " '__iter__',\n", + " '__le__',\n", + " '__len__',\n", + " '__lt__',\n", + " '__mod__',\n", + " '__mul__',\n", + " '__ne__',\n", + " '__new__',\n", + " '__reduce__',\n", + " '__reduce_ex__',\n", + " '__repr__',\n", + " '__rmod__',\n", + " '__rmul__',\n", + " '__setattr__',\n", + " '__sizeof__',\n", + " '__str__',\n", + " '__subclasshook__',\n", + " 'capitalize',\n", + " 'casefold',\n", + " 'center',\n", + " 'count',\n", + " 'encode',\n", + " 'endswith',\n", + " 'expandtabs',\n", + " 'find',\n", + " 'format',\n", + " 'format_map',\n", + " 'index',\n", + " 'isalnum',\n", + " 'isalpha',\n", + " 'isascii',\n", + " 'isdecimal',\n", + " 'isdigit',\n", + " 'isidentifier',\n", + " 'islower',\n", + " 'isnumeric',\n", + " 'isprintable',\n", + " 'isspace',\n", + " 'istitle',\n", + " 'isupper',\n", + " 'join',\n", + " 'ljust',\n", + " 'lower',\n", + " 'lstrip',\n", + " 'maketrans',\n", + " 'partition',\n", + " 'removeprefix',\n", + " 'removesuffix',\n", + " 'replace',\n", + " 'rfind',\n", + " 'rindex',\n", + " 'rjust',\n", + " 'rpartition',\n", + " 'rsplit',\n", + " 'rstrip',\n", + " 'split',\n", + " 'splitlines',\n", + " 'startswith',\n", + " 'strip',\n", + " 'swapcase',\n", + " 'title',\n", + " 'translate',\n", + " 'upper',\n", + " 'zfill']" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dir(o1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`Functions` == Callable `()` objects " + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "builtin_function_or_method" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(print)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'good202'" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def add_to_num(orig_number,how_much_to_add):\n", + " return orig_number + how_much_to_add\n", + "\n", + "add_to_num(orig_number = \"good\", how_much_to_add = \"202\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`None` object" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "NoneType" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "o7 = None\n", + "type(o7)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/plain": [ + "NoneType" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(print())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Line structure\n", + "\n", + "* Basically most loved/hated? feature of Python\n", + "* Line breaks and identations are a part of the syntax!\n", + "* It is used to separate content of functions, classes, loops, conditions, etc.\n", + "\n", + "\n", + "### Identations\n", + "* both `space` and `tab` are accepted -- check how your editor works!\n", + "* to keep things simple ALWAYS ident with `tab` (translated to spaces by editor)!\n", + "* Typically 1 indent is 4 spaces\n", + "ALWAYS PRECEEDED BY \"`:`\"\n", + "\n", + "```\n", + "for i in range(10):\n", + " print(i)\n", + "```\n", + "\n", + "```\n", + "if i == 0:\n", + " print('i is zero')\n", + "else:\n", + " print('i is not zero')\n", + "```\n", + "\n", + "If you still need to break a line, without triggering code changes use line joins:\n", + "\n", + "### Explicit line joins\n", + "```\n", + "if 1900 < year < 2100 and 1 <= month <= 12 \\\n", + " and 1 <= day <= 31 and 0 <= hour < 24 \\\n", + " and 0 <= minute < 60 and 0 <= second < 60:\n", + " pass\n", + "```\n", + "\n", + "### Implicit line joins\n", + "Expressions in parentheses `()`, square brackets `[]` or curly braces `{}` can be split over more than one physical line without using backslashes. For example:\n", + "\n", + "```\n", + "month_names = ['January', 'February', 'March', \n", + " 'April', 'May', 'June', \n", + " 'July', 'August', 'September', \n", + " 'October', 'November', 'December']\n", + "\n", + "```\n", + "## Keywords and built-in functions\n", + "Do not use as a variable name!" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['False', 'None', 'True', 'and', 'as', 'assert', 'async', 'await', 'break', 'class', 'continue', 'def', 'del', 'elif', 'else', 'except', 'finally', 'for', 'from', 'global', 'if', 'import', 'in', 'is', 'lambda', 'nonlocal', 'not', 'or', 'pass', 'raise', 'return', 'try', 'while', 'with', 'yield'] \n", + "\n", + " ['ArithmeticError', 'AssertionError', 'AttributeError', 'BaseException', 'BlockingIOError', 'BrokenPipeError', 'BufferError', 'BytesWarning', 'ChildProcessError', 'ConnectionAbortedError', 'ConnectionError', 'ConnectionRefusedError', 'ConnectionResetError', 'DeprecationWarning', 'EOFError', 'Ellipsis', 'EncodingWarning', 'EnvironmentError', 'Exception', 'False', 'FileExistsError', 'FileNotFoundError', 'FloatingPointError', 'FutureWarning', 'GeneratorExit', 'IOError', 'ImportError', 'ImportWarning', 'IndentationError', 'IndexError', 'InterruptedError', 'IsADirectoryError', 'KeyError', 'KeyboardInterrupt', 'LookupError', 'MemoryError', 'ModuleNotFoundError', 'NameError', 'None', 'NotADirectoryError', 'NotImplemented', 'NotImplementedError', 'OSError', 'OverflowError', 'PendingDeprecationWarning', 'PermissionError', 'ProcessLookupError', 'RecursionError', 'ReferenceError', 'ResourceWarning', 'RuntimeError', 'RuntimeWarning', 'StopAsyncIteration', 'StopIteration', 'SyntaxError', 'SyntaxWarning', 'SystemError', 'SystemExit', 'TabError', 'TimeoutError', 'True', 'TypeError', 'UnboundLocalError', 'UnicodeDecodeError', 'UnicodeEncodeError', 'UnicodeError', 'UnicodeTranslateError', 'UnicodeWarning', 'UserWarning', 'ValueError', 'Warning', 'WindowsError', 'ZeroDivisionError', '__IPYTHON__', '__build_class__', '__debug__', '__doc__', '__import__', '__loader__', '__name__', '__package__', '__spec__', 'abs', 'aiter', 'all', 'anext', 'any', 'ascii', 'bin', 'bool', 'breakpoint', 'bytearray', 'bytes', 'callable', 'chr', 'classmethod', 'compile', 'complex', 'copyright', 'credits', 'delattr', 'dict', 'dir', 'display', 'divmod', 'enumerate', 'eval', 'exec', 'execfile', 'filter', 'float', 'format', 'frozenset', 'get_ipython', 'getattr', 'globals', 'hasattr', 'hash', 'help', 'hex', 'id', 'input', 'int', 'isinstance', 'issubclass', 'iter', 'len', 'license', 'list', 'locals', 'map', 'max', 'memoryview', 'min', 'next', 'object', 'oct', 'open', 'ord', 'pow', 'print', 'property', 'range', 'repr', 'reversed', 'round', 'runfile', 'set', 'setattr', 'slice', 'sorted', 'staticmethod', 'str', 'sum', 'super', 'tuple', 'type', 'vars', 'zip']\n" + ] + } + ], + "source": [ + "import keyword\n", + "print(f\"{keyword.kwlist} \\n\\n {dir(__builtins__)}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (823646212.py, line 1)", + "output_type": "error", + "traceback": [ + "\u001b[1;36m Cell \u001b[1;32mIn [34], line 1\u001b[1;36m\u001b[0m\n\u001b[1;33m def = 4\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" + ] + } + ], + "source": [ + "yield = 4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Built-in Data Types and Data structures\n", + "\n", + "## Why needed?\n", + "\n", + "* To process data by a program\n", + "* Represent information (some memory) in some structure\n", + "* Each structure has pros and cons about how it works -> choose the most appropriate\n", + "* Save data structures to variables and work with those\n", + "\n", + "## Numerical\n", + "\n", + "Python differentiates 4 built-in numerical types: Integers, Floats, Longs and Complex\n", + "\n", + "We will only consider Integers and Floats as you will most likely only need these two." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Integer: 4 \n", + "Float: 4.0 \n", + "True\n" + ] + } + ], + "source": [ + "integer = 4\n", + "print('Integer: ',integer, type(integer))\n", + "floatn = 4.\n", + "print('Float: ',floatn,type(floatn))\n", + "\n", + "print(integer == floatn)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Q: What if floats and integers are combined?*\n", + "\n", + "** [FPA:](https://docs.python.org/3/tutorial/floatingpoint.html) Just remember, even though the printed result looks like the exact value of 1/10, the actual stored value is the nearest representable binary fraction. **\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.3200000000000003" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "1.32+2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Python standard operators\n", + "\n", + "* `+` \n", + "* `-` \n", + "* `*`\n", + "* `/`\n", + "\n", + "but also:\n", + "\n", + "* `**` (exponent)\n", + "* `%` (modulus)\n", + "* `//` (floor division)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Binary variables - boolean\n", + "\n", + "`False` ... `0`\n", + "\n", + "`True` ... `1`\n", + "\n", + "\n", + "Beware of implicit type casting!" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], + "source": [ + "b = True\n", + "#print(b) \n", + "print(not b -1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "not ok\n", + "this line\n" + ] + } + ], + "source": [ + "if 0:\n", + " print('ok')\n", + "elif False:\n", + " print('also ok')\n", + "elif True:\n", + " print('not ok')\n", + "\n", + " \n", + "print('this line')" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I can tell the truth more times: 2 times to be precise\n" + ] + } + ], + "source": [ + "print(f'I can tell the truth more times: {True + True} times to be precise')" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We are really ok here\n" + ] + } + ], + "source": [ + "a = 'ok'\n", + "\n", + "#evaluate this conditions\n", + "if a == 'ok':\n", + " print('We are really ok here')\n", + "else:\n", + " print('not ok')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Iterables\n", + "\n", + "* All of them can be iterated over (technically objects with implemented `__iter__()` and `__next__()` functions) - you can make your own iterables, with custom iteration logic!\n", + "\n", + "## List (of `objects`)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "l1 = [1,2,3,4,5]\n", + "l2 = ['Hello','World']\n", + "l3 = ['Combination',2,'Data types']\n", + "l4 = [print,list]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Strings" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I do not have a value\n", + "I have myself and I print some value This is a text variable\n" + ] + } + ], + "source": [ + "txt_var = 'This is a text variable'\n", + "#f-string notation - string interpolation\n", + "\n", + "print(f\"I do not have a value\") #actually faster initialization\n", + "print(f\"I have myself and I print some value {txt_var}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'IES FSV UK'" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "' IES FSV UK '.strip() #remove white spaces\n" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'***IES*FSV*UK****'" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#or replace spaces - good idea?\n", + "' IES FSV UK '.replace(\" \",\"*\") #remove white spaces" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "😊\n" + ] + } + ], + "source": [ + "#Python support emojies, beucase it is unicode 8 encoded\n", + "\n", + "# How cool is that? You can put emojies to your applications\n", + "# add +1 to see the next emoji in line\n", + "a = chr(128521) #python can do the encoding for me \n", + "print(a)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Where to put data - in containers\n", + "# List - array" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sequences\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "['red', 'blue', 'green', 'black', 'white'] - ['a', 'b', 'c', 'd', 'e']\n" + ] + } + ], + "source": [ + "colors = ['red', 'blue', 'green', 'black', 'white']\n", + "print(type(colors))\n", + "# but beware\n", + "tmp = list('abcde')\n", + "print(f\"{colors} - {tmp}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "tmp.append(False)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['a', 'b', 'c', 'd', 'e', ['z'], ['z'], 412421, 1, False]" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tmp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### God bless `list comprehension`" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 4, 9, 16, 25]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "l = [1,2,3,4,5]\n", + "new_l = []\n", + "for el in l:\n", + " new_l.append(el**2)\n", + "new_l" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 4, 9, 16, 25]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[el**2 for el in l]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 0-index\n", + "* Unlike R which is 1-indexed\n", + "* first element `l[0]` \n", + "* last element `l[len(l)-1]` or `l[-1]`" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(tmp)" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "ename": "IndexError", + "evalue": "list index out of range", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtmp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m: list index out of range" + ] + } + ], + "source": [ + "tmp[10]" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "blue\n" + ] + } + ], + "source": [ + "print(colors[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "red\n", + "blue\n", + "green\n", + "black\n", + "white\n" + ] + } + ], + "source": [ + "#iterables\n", + "\n", + "for col in colors:\n", + " print(col)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['red', 'blue', 'green', 'black', 'white']\n", + "green white black\n", + "['green', 'black', 'white']\n", + "['black', 'white']\n" + ] + } + ], + "source": [ + "# subset\n", + "print(colors)\n", + "\n", + "print(f\"{colors[2]} {colors[-1]} {colors[-2]}\")\n", + "print(colors[2:])\n", + "print(colors[-2:])" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['red', 'green', 'white']\n" + ] + } + ], + "source": [ + "# colors[start:stop:stride] full signature of the index subscription\n", + "\n", + "\n", + "# all slicing values are optional!\n", + "print(colors[::2]) #take every second elememt " + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[3, -200, 'hello']\n" + ] + } + ], + "source": [ + "#lists can contain any types - everything else works the same - dynamically typed langauge()\n", + "colors = [3, -200, 'hello']\n", + "print(colors)" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[3, -200, 'hello', 'yellow', 'pink', 'purple', 'black']\n" + ] + } + ], + "source": [ + "# adding to a list \n", + "# Beware inplace operation (TRY TO RUN SEVERAL TIMES)\n", + "colors.append('yellow') \n", + "colors.extend(['pink', 'purple']) # extend colors, in-place\n", + "\n", + "colors += ['black'] # colors = colors + ['black']\n", + "\n", + "print(colors)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Dictionaries\n", + "\n", + "## key-value store" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'emmanuelle': 5752, 'sebastian': 5578}\n" + ] + } + ], + "source": [ + "#in variable tel store key-value pairs. \n", + "empty_dict = {}\n", + "#important fro API calls \n", + "tel = {'emmanuelle': 5752, 'sebastian': 5578}\n", + "print(tel)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'emmanuelle': 5752, 'sebastian': 5578, 'francis': 5915}\n", + "5578\n", + "dict_keys(['emmanuelle', 'sebastian', 'francis'])\n", + "dict_values([5752, 5578, 5915])\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#assign new value\n", + "tel['francis'] = 5915\n", + "print(tel)\n", + "#get value by its key\n", + "print(tel['sebastian'])\n", + "\n", + "print(tel.keys())\n", + "\n", + "print(tel.values())\n", + "\n", + "'francis' in tel\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'Honza'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtel\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Honza'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m: 'Honza'" + ] + } + ], + "source": [ + "tel['Honza']" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "None\n", + "do not have this\n" + ] + } + ], + "source": [ + "#get a value with a default, no KeyError!\n", + "print(f\"{tel.get('Honza')}\") #returns None instead of the error\n", + "print(f\"{tel.get('Honza', 'do not have this')}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "under key emmanuelle is value 5752\n", + "under key sebastian is value 5578\n", + "under key francis is value 5915\n" + ] + } + ], + "source": [ + "for key, value in tel.items():\n", + " print(f\"under key {key} is value {value}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## tuples - immutable containers = cannot overwrite them" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['a', 'b', 'c', 'd'] ('a', 'b', 'c', 'd') (50.082, 14.431)\n" + ] + } + ], + "source": [ + "l = list('abcd')\n", + "t = tuple(l) # or declare as ('a', 'b', 'c', 'd')\n", + "coord = (50.082,14.431)\n", + "print(l, t, coord )" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [], + "source": [ + "#Tuples are immutable; you can't change which variables they contain after construction. \n", + "#However, you can concatenate or slice them to form new tuples:\n", + "\n", + "#t[0] = 1\n", + "\n", + "a = (1, 2, 3)\n", + "b = a + (4, 5, 6) # (1, 2, 3, 4, 5, 6)\n", + "c = b[1:] # (2, 3, 4, 5, 6)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 2, 3, 4, 5, 6)\n", + "(2, 3, 4, 5, 6)\n" + ] + } + ], + "source": [ + "print(b);print(c)" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('thisValue',)\n" + ] + } + ], + "source": [ + "#single element tuples - for example database calls later on - psycopg2 library\n", + "\n", + "sql_query_parameter = ('thisValue',) #notice the comma, other\n", + "print(sql_query_parameter)" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "'int' object does not support item assignment", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'z'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'z'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m#read carefully the Traceback log\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: 'int' object does not support item assignment" + ] + } + ], + "source": [ + "l[0]='z'\n", + "t[0]='z'\n", + "\n", + "#read carefully the Traceback log" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparisons" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Obviously!\n" + ] + } + ], + "source": [ + "if 2**2 == 4:\n", + " print('Obviously!')\n", + " " + ] + }, + { + "attachments": { + "image.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Assignments\n", + "![image.png](attachment:image.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "11\n" + ] + } + ], + "source": [ + "x = 5\n", + "x+=6\n", + "print(f'{x}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Control flows\n", + "\n", + "### For loop" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(4):\n", + " print(f\"Running iteration with index {i}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for word in ('cool', 'powerful', 'readable'):\n", + " print(f'Python is {word} ')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for idx, word in enumerate(('cool', 'powerful', 'readable')):\n", + " print(f'Python is {word} - run {idx}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### If statement\n", + "\n", + "A code block is executed if and only if the conditioan statement evaluates as True" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```Evaluates to False:\n", + " any number equal to zero (0, 0.0, 0+0j)\n", + " an empty container (list, tuple, set, dictionary, …)\n", + " False, \n", + " None (check this rather as if somevar is not None: (PEP 8)\n", + "Evaluates to True:\n", + " everything else```" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "empty array\n", + "got b\n" + ] + } + ], + "source": [ + "a = []\n", + "b = [1,2,3]\n", + "\n", + "if a:\n", + " print('got a')\n", + "else:\n", + " print('empty array')\n", + "if b:\n", + " print('got b')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Functions\n", + "\n", + "*Q: What is a function?*\n", + "\n", + "* function as a basic lego-tile of programmes.\n", + "\n", + "* Most often it will `return something`. The better your code, the more useful `something` is.\n", + "\n", + "* in a good code all of the functionality is hidden in a various functions (and classes).\n", + "\n", + "* allows reproducibility - be a lazy programmer and write a lot of functions\n", + "\n", + "* tips of trades (keeps scope of variables which is very useful when working in interactive shells (Python or R)\n", + "\n", + "```\n", + "def functionName(input1,input2):\n", + " do something ...\n", + " return result\n", + "```\n", + "\n", + "* Hence, you can do the same thing for more inputs, do not have to copy paste code!\n", + "\n", + "\n", + "* Is this a good [code design?](https://github.com/AceLewis/my_first_calculator.py/blob/master/my_first_calculator.py)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Function output vs. standard output" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello world!\n" + ] + }, + { + "data": { + "text/plain": [ + "'H e l l o w o r l d !'" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# define function\n", + "def hello_world():\n", + " hello = \"Hello world!\"\n", + " print(hello)\n", + " return ' '.join(hello)\n", + "\n", + "returned_value = hello_world()\n", + "returned_value" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The function above returned value:\n", + "H e l l o w o r l d !\n" + ] + } + ], + "source": [ + "print(f\"The function above returned value:\\n{returned_value}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello world!\n" + ] + }, + { + "data": { + "text/plain": [ + "'H e l l o w o r l d !'" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hello_world()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "# keyword def introduces a function - then name - then (arg1, arg2, arg3='can_have_default_value')\n", + "def describe_employee(d):\n", + " '''\n", + " retrieves important information about the teacher from the input dictionary and return it as a string\n", + " \n", + " Input: dictionary with the name, role and age and courses keys\n", + " Output: String with information\n", + " '''\n", + " result = '{}, {}, is at least {} years old. '.format(d['name'],d['role'],d['age'])\n", + " \n", + " result += 'He teaches {} courses. '.format(len(d['courses']))\n", + " \n", + " if len(d['courses']) > 5:\n", + " result += 'Probably he is a teaching-superhero!'\n", + " return result" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Martin Gregor, director of IES, is at least 18 years old. He teaches 2 courses. \n", + "Jozef Baruník, econometric guru, is at least 15 years old. He teaches 7 courses. Probably he is a teaching-superhero!\n" + ] + } + ], + "source": [ + "list_of_dicts = [\n", + " {\n", + " 'name':'Martin Gregor',\n", + " 'role':'director of IES',\n", + " 'age':18,\n", + " 'courses':['JEM013','JEB064']\n", + " },\n", + " {\n", + " 'name':'Jozef Baruník',\n", + " 'role':'econometric guru',\n", + " 'age':15,\n", + " 'courses':['JEM005','JEM116','JED414','JED415','JED412','JEM059','JEM061']\n", + " }]\n", + "\n", + "for el in list_of_dicts:\n", + " info = describe_employee(el)\n", + " print(info)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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nameroleagecourses
0Martin Gregordirector of IES18[JEM013, JEB064]
1Jozef Baruníkeconometric guru15[JEM005, JEM116, JED414, JED415, JED412, JEM05...
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" + ], + "text/plain": [ + " name role age \\\n", + "0 Martin Gregor director of IES 18 \n", + "1 Jozef Baruník econometric guru 15 \n", + "\n", + " courses \n", + "0 [JEM013, JEB064] \n", + "1 [JEM005, JEM116, JED414, JED415, JED412, JEM05... " + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "pd.DataFrame(list_of_dicts)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How to write functions?\n", + "\n", + "* Function should do just one thing!\n", + "* Use wrapping functions!\n", + "\n", + "#### Example\n", + "We need to aggregate data and plot it.\n", + "\n", + "We will write three functions:\n", + "\n", + "```\n", + "def aggregateData(param1,param2,data):\n", + " ... perform aggregation ...\n", + " return aggregData\n", + " \n", + "def plotAggregatedData(aggregData,plotParam1,plotParam2):\n", + " ... perform plotting ...\n", + " return plot\n", + " \n", + "def plotAndAggregateData(data,param1,param2,plotParam,plotParam2):\n", + " #aggregate data\n", + " aggregData = aggregateData(param1,param2,data)\n", + " \n", + " plot = plotAggregatedData(aggregData,plotParam1,plotParam2)\n", + " return (plot,aggregData)\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Default parameter values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def foo(x=12):\n", + " return x\n", + "\n", + "print(foo())\n", + "print(foo(10))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If interested see *args or *kwargs - passing variables as list or dictionary" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "2\n", + "3\n", + "4\n" + ] + } + ], + "source": [ + "def fee(*args):\n", + " #dont know how many arguments ther will be\n", + " for el in args:\n", + " print(el)\n", + "\n", + "fee(1,2,3,4)" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/Users/jansila/path/to/somewhere'" + ] + }, + "execution_count": 142, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os #import at first cell - sorry PEP8\n", + "os.path.join('/Users','jansila','path','to','somewhere') #takes *args as well" + ] + }, + { + "attachments": { + "image.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Important programming principles\n", + "\n", + "Program is a set of instructions combined with data.\n", + "\n", + "> *“Perfection is achieved, not when there is nothing more to add, but when there is nothing left to take away.”* (Antoine de Saint Exupery)\n", + "> *“Simplicity is the ultimate sophistication”* (Leondardo da Vinci)\n", + "\n", + "1. Set naming standards and keep it - i.e. data object for teachers will be dictionary with keys 'name','age' and 'role'.\n", + "3. Separate algorithmic logic from the data - functions should be *general*, yet targeted on specific purpose.\n", + "4. Programmer is lazy\n", + "5. Plan before you start! Be sure you know how you will proceed before actual coding\n", + "6. Art of programming is essentialy an art of googling. Stack-Overflowing\n", + "\n", + "Your variables names as well as function names should really describe their content - do not use `var1`,`x` etc. \n", + "\n", + "Names like `data` are OK when you have know you have only one source of data. But the more complex the program is, the more you need more explicit variable names\n", + "\n", + "\n", + "## Scope of variables\n", + "\n", + "Is my variable visible outside of the function? \n", + "\n", + "![image.png](attachment:image.png)\n", + "### Global variables" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7" + ] + }, + "execution_count": 143, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = 5\n", + "def doSomethingGlobal():\n", + " global a\n", + " a = a + 2\n", + "doSomethingGlobal()\n", + "a #a got changed in the scope of the function!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Can you modify the value of a variable inside a function? Most languages (C, Java, …) distinguish “passing by value” and “passing by reference”. In Python, such a distinction is somewhat artificial, and it is a bit subtle whether your variables are going to be modified or not. Fortunately, there exist clear rules.\n", + "Parameters to functions are references to objects, which are passed by value. When you pass a variable to a function, python passes the reference to the object to which the variable refers (the value). Not the variable itself." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Local variables" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7\n", + "The a value is still 5\n" + ] + } + ], + "source": [ + "a = 5 \n", + "\n", + "def doSomethingLocal():\n", + " \n", + " \n", + " return a + 2\n", + "print(doSomethingLocal())\n", + "print(f\"The a value is still {a}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Many types are assigned as a reference!!!" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Horváth', 'Kukačka', 'Gregor']\n", + "4441009536 4441009536\n" + ] + } + ], + "source": [ + "x = ['Horváth','Baruník','Gregor']\n", + "y = x #we call this shallow copy\n", + "\n", + "y[1] = 'Kukačka' #y references x\n", + "print(x)\n", + "\n", + "print(id(x), id(y)) #then point ot he same memory" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = ['Horváth','Baruník','Gregor']\n", + "y = x.copy() #deep copy( create new memory )\n", + "\n", + "y[1] = 'Kukačka' #y references x\n", + "print(x)\n", + "print(id(x), id(y))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## id(object)\n", + "Return the “identity” of an object. This is an integer (or long integer) which is guaranteed to be unique and constant for this object during its lifetime. Two objects with non-overlapping lifetimes may have the same id() value.\n", + "\n", + "CPython implementation detail: This is the address of the object in memory." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Error Handling" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* GOOGLE!!!!!\n", + "* Computer is always right, it is you who did not understand the computer\n", + "\n", + "* If error produced, do not panic! Read it: **CAREFULLY**!\n", + "\n", + "* Worse problems when no errors, yet unexpected results - the mistake would be in wrong understanding of the logic => DEBUGGING!!!\n", + "\n", + "Most common errors:\n", + "1. Incorrect identation (SyntaxError) - automatically corrected in Jupyter, but still do not do it!\n", + "2. Incorrect values - the program expects different values (ValueError)\n", + "3. Non-existing key in dictionary (KeyError)\n", + "4. assignment operator `=` instead of `==` (SyntaxError)\n", + "5. Variable not found (NameError) - remember Python is case-sensitive!\n", + "6. Zero-indexing!!!! \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0\n", + "1.25\n", + "1.6666666666666667\n", + "2.5\n", + "5.0\n" + ] + }, + { + "ename": "ZeroDivisionError", + "evalue": "division by zero", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero" + ] + } + ], + "source": [ + "for i in range(5,-5,-1):\n", + " print(5/i)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Try and except" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0\n", + "1.25\n", + "1.6666666666666667\n", + "2.5\n", + "5.0\n", + "division by zero\n", + "-5.0\n", + "-2.5\n", + "-1.6666666666666667\n", + "-1.25\n", + "-1.0\n" + ] + } + ], + "source": [ + "for i in range(5,-6,-1):\n", + " try:\n", + " print(5/i)\n", + " except Exception as e:\n", + " print(e)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Good luck with coding!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.7 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.7" + }, + "vscode": { + "interpreter": { + "hash": "1f0e6d99f3103fd78365fe1cf7b2d51239fa0878786db9cbdfe89bc88a3151d3" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/01_PythonBasics/hello_world.py b/01_PythonBasics/hello_world.py new file mode 100644 index 0000000..ed4c928 --- /dev/null +++ b/01_PythonBasics/hello_world.py @@ -0,0 +1,6 @@ +def greet_the_world(): + return "Hello, world!" + +if __name__=="__main__": + greetings = greet_the_world() + print(greetings) diff --git a/03_NumpyPandasMatplotlib.ipynb b/03_NumpyPandasMatplotlib.ipynb new file mode 100644 index 0000000..2a06058 --- /dev/null +++ b/03_NumpyPandasMatplotlib.ipynb @@ -0,0 +1,1977 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lecture 3 Numpy, Pandas, Matplotlib\n", + "2023-10-17\n", + "\n", + "\n", + "Jan Šíla - jan.sila@fsv.cuni.cz" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5 min warmup - 5 activity points!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "* What is a a keyword?\n", + "\n", + "* How is python indexed?\n", + "\n", + "* What does for-loop do?\n", + "\n", + "* What is an exception?\n", + "\n", + "* What datatype is '5'?" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:45:19.895067Z", + "start_time": "2023-10-17T15:45:19.882181Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['a', 'b', 'c', 'd']\n" + ] + }, + { + "data": { + "text/plain": [ + "['__add__',\n", + " '__class__',\n", + " '__class_getitem__',\n", + " '__contains__',\n", + " '__delattr__',\n", + " '__delitem__',\n", + " '__dir__',\n", + " '__doc__',\n", + " '__eq__',\n", + " '__format__',\n", + " '__ge__',\n", + " '__getattribute__',\n", + " '__getitem__',\n", + " '__gt__',\n", + " '__hash__',\n", + " '__iadd__',\n", + " '__imul__',\n", + " '__init__',\n", + " '__init_subclass__',\n", + " '__iter__',\n", + " '__le__',\n", + " '__len__',\n", + " '__lt__',\n", + " '__mul__',\n", + " '__ne__',\n", + " '__new__',\n", + " '__reduce__',\n", + " '__reduce_ex__',\n", + " '__repr__',\n", + " '__reversed__',\n", + " '__rmul__',\n", + " '__setattr__',\n", + " '__setitem__',\n", + " '__sizeof__',\n", + " '__str__',\n", + " '__subclasshook__',\n", + " 'append',\n", + " 'clear',\n", + " 'copy',\n", + " 'count',\n", + " 'extend',\n", + " 'index',\n", + " 'insert',\n", + " 'pop',\n", + " 'remove',\n", + " 'reverse',\n", + " 'sort']" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#will be useful today\n", + "# helping - similar to R \"str\"\n", + "print(list('abcd'))\n", + "dir(list('abcd')) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Moving forward from Python's primitive data types\n", + "* Q: can you name the primitives?\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:45:20.675268Z", + "start_time": "2023-10-17T15:45:20.669501Z" + } + }, + "outputs": [], + "source": [ + "# Integers\n", + "# Float\n", + "# Strings\n", + "# Boolean" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Numpy " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Num(erical) Py(thon)\n", + "* NumPy is at the base of Python's scientific stack of tools \n", + "* Python already has *high-level number objects* (integers, floating point) and *containers* (lists, dictionaries ) \n", + "* np arrays contain only one type - unlike general lists\n", + "* **Memory-efficient container that provides fast numerical operations.**\n", + "* **ndarray** = block of memory + indexing scheme + data type descriptor\n", + " * raw data \n", + " * how to locate an element\n", + " * how to interpret an element\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:45:24.341342Z", + "start_time": "2023-10-17T15:45:21.289511Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "adlfs==2023.9.0\r\n", + "aiohttp==3.8.6\r\n", + "aiosignal==1.3.1\r\n", + "annotated-types==0.6.0\r\n", + "anyio==3.7.1\r\n", + "appnope==0.1.3\r\n", + "argon2-cffi==23.1.0\r\n", + "argon2-cffi-bindings==21.2.0\r\n", + "arrow==1.3.0\r\n", + "asttokens==2.4.0\r\n", + "async-timeout==4.0.3\r\n", + "attrs==23.1.0\r\n", + "-e git+ssh://git@gitlab.com/secfo/analytics/power-us-auctions/auctions.git@171cb3e662490b742a5366014fe50ace183a7744#egg=auctions\r\n", + "azure-common==1.1.28\r\n", + "azure-core==1.29.4\r\n", + "azure-datalake-store==0.0.53\r\n", + "azure-identity==1.14.1\r\n", + "azure-keyvault-secrets==4.7.0\r\n", + "azure-storage-blob==12.18.3\r\n", + "azure-storage-file-share==12.14.2\r\n", + "backcall==0.2.0\r\n", + "bcrypt==4.0.1\r\n", + "beautifulsoup4==4.12.2\r\n", + "black==22.3.0\r\n", + "bleach==6.1.0\r\n", + "cachetools==5.3.1\r\n", + "catboost==1.2\r\n", + "certifi==2023.7.22\r\n", + "cffi==1.16.0\r\n", + "cfgv==3.4.0\r\n", + "charset-normalizer==3.3.0\r\n", + "click==8.1.7\r\n", + "cloudpickle==2.2.1\r\n", + "comm==0.1.4\r\n", + "contourpy==1.1.1\r\n", + "coverage==7.3.2\r\n", + "cryptography==41.0.4\r\n", + "cycler==0.12.1\r\n", + "dask==2023.7.1\r\n", + "dask-kubernetes==2023.7.3\r\n", + "debugpy==1.8.0\r\n", + "decorator==5.1.1\r\n", + "defusedxml==0.7.1\r\n", + "distlib==0.3.7\r\n", + "distributed==2023.7.1\r\n", + "dynaconf==3.2.3\r\n", + "entrypoints==0.4\r\n", + "exceptiongroup==1.1.3\r\n", + "executing==2.0.0\r\n", + "fastjsonschema==2.18.1\r\n", + "filelock==3.12.4\r\n", + "fonttools==4.43.1\r\n", + "fqdn==1.5.1\r\n", + "frozenlist==1.4.0\r\n", + "fsspec==2023.9.2\r\n", + "gitdb==4.0.10\r\n", + "GitPython==3.1.37\r\n", + "google-auth==2.23.3\r\n", + "graphviz==0.20.1\r\n", + "h11==0.14.0\r\n", + "hocon==0.3.0\r\n", + "holidays==0.33\r\n", + "httpcore==0.17.3\r\n", + "httpx==0.24.1\r\n", + "identify==2.5.30\r\n", + "idna==3.4\r\n", + "importlib-metadata==6.8.0\r\n", + "iniconfig==2.0.0\r\n", + "ipykernel==6.25.2\r\n", + "ipython==8.16.1\r\n", + "ipython-genutils==0.2.0\r\n", + "ipywidgets==8.1.1\r\n", + "iso8601==2.1.0\r\n", + "isodate==0.6.1\r\n", + "isoduration==20.11.0\r\n", + "ixian==0.23.0\r\n", + "jedi==0.19.1\r\n", + "Jinja2==3.1.2\r\n", + "joblib==1.3.2\r\n", + "jsonpointer==2.4\r\n", + "jsonschema==4.19.1\r\n", + "jsonschema-specifications==2023.7.1\r\n", + "jupyter==1.0.0\r\n", + "jupyter-console==6.6.3\r\n", + "jupyter-events==0.7.0\r\n", + "jupyter_client==7.4.9\r\n", + "jupyter_core==5.4.0\r\n", + "jupyter_server==2.7.3\r\n", + "jupyter_server_terminals==0.4.4\r\n", + "jupyterlab-pygments==0.2.2\r\n", + "jupyterlab-widgets==3.0.9\r\n", + "kiwisolver==1.4.5\r\n", + "kopf==1.36.2\r\n", + "kr8s==0.8.6\r\n", + "kubernetes==27.2.0\r\n", + "kubernetes-asyncio==28.2.0\r\n", + "lightgbm==3.3.5\r\n", + "llvmlite==0.40.1\r\n", + "locket==1.0.0\r\n", + "loguru==0.7.2\r\n", + "markdown-it-py==3.0.0\r\n", + "MarkupSafe==2.1.3\r\n", + "matplotlib==3.8.0\r\n", + "matplotlib-inline==0.1.6\r\n", + "mdurl==0.1.2\r\n", + "mistune==3.0.2\r\n", + "msal==1.24.1\r\n", + "msal-extensions==1.0.0\r\n", + "msgpack==1.0.7\r\n", + "multidict==6.0.4\r\n", + "mypy-extensions==1.0.0\r\n", + "nbclassic==1.0.0\r\n", + "nbclient==0.8.0\r\n", + "nbconvert==7.9.2\r\n", + "nbformat==5.9.2\r\n", + "nest-asyncio==1.5.8\r\n", + "nodeenv==1.8.0\r\n", + "notebook==6.5.6\r\n", + "notebook_shim==0.2.3\r\n", + "numba==0.57.1\r\n", + "numpy==1.24.4\r\n", + "oauthlib==3.2.2\r\n", + "orjson==3.9.9\r\n", + "overrides==7.4.0\r\n", + "packaging==23.2\r\n", + "pandas==2.1.1\r\n", + "pandocfilters==1.5.0\r\n", + "paramiko==3.3.1\r\n", + "parso==0.8.3\r\n", + "partd==1.4.1\r\n", + "pathspec==0.11.2\r\n", + "pexpect==4.8.0\r\n", + "pickleshare==0.7.5\r\n", + "Pillow==10.0.1\r\n", + "platformdirs==3.11.0\r\n", + "plotly==5.17.0\r\n", + "pluggy==1.3.0\r\n", + "portalocker==2.8.2\r\n", + "pre-commit==3.5.0\r\n", + "prometheus-client==0.17.1\r\n", + "prompt-toolkit==3.0.39\r\n", + "psutil==5.9.5\r\n", + "psycopg2-binary==2.9.9\r\n", + "ptyprocess==0.7.0\r\n", + "pure-eval==0.2.2\r\n", + "pyasn1==0.5.0\r\n", + "pyasn1-modules==0.3.0\r\n", + "pycparser==2.21\r\n", + "pydantic==2.4.2\r\n", + "pydantic-settings==2.0.3\r\n", + "pydantic_core==2.10.1\r\n", + "Pygments==2.16.1\r\n", + "pyhocon==0.3.60\r\n", + "PyJWT==2.8.0\r\n", + "pykube-ng==23.6.0\r\n", + "PyNaCl==1.5.0\r\n", + "pyodbc==4.0.35\r\n", + "pyparsing==3.1.1\r\n", + "pytest==7.4.2\r\n", + "pytest-cov==4.1.0\r\n", + "python-box==7.1.1\r\n", + "python-dateutil==2.8.2\r\n", + "python-dotenv==1.0.0\r\n", + "python-json-logger==2.0.7\r\n", + "python-jsonpath==0.7.1\r\n", + "python-on-whales==0.64.3\r\n", + "pytz==2023.3.post1\r\n", + "PyYAML==6.0.1\r\n", + "pyzmq==24.0.1\r\n", + "qtconsole==5.4.4\r\n", + "QtPy==2.4.0\r\n", + "questdb==1.1.0\r\n", + "referencing==0.30.2\r\n", + "requests==2.31.0\r\n", + "requests-oauthlib==1.3.1\r\n", + "rfc3339-validator==0.1.4\r\n", + "rfc3986-validator==0.1.1\r\n", + "rich==13.6.0\r\n", + "rpds-py==0.10.6\r\n", + "rsa==4.9\r\n", + "scikit-learn==1.2.2\r\n", + "scipy==1.11.3\r\n", + "Send2Trash==1.8.2\r\n", + "setuptools-scm==8.0.4\r\n", + "sf-config==2.3.1\r\n", + "sf-connectors==1.2.1\r\n", + "sf-logging==2.0.4\r\n", + "sf-model-lego==1.10.0\r\n", + "sfutils==3.0.2\r\n", + "shap==0.41.0\r\n", + "six==1.16.0\r\n", + "slicer==0.0.7\r\n", + "smmap==5.0.1\r\n", + "sniffio==1.3.0\r\n", + "solace-rust==2.0.4\r\n", + "sortedcontainers==2.4.0\r\n", + "soupsieve==2.5\r\n", + "sqlparams==5.1.0\r\n", + "stack-data==0.6.3\r\n", + "tabulate==0.9.0\r\n", + "tblib==2.0.0\r\n", + "tenacity==8.2.3\r\n", + "terminado==0.17.1\r\n", + "threadpoolctl==3.2.0\r\n", + "tinycss2==1.2.1\r\n", + "tomli==2.0.1\r\n", + "tomlkit==0.11.8\r\n", + "toolz==0.12.0\r\n", + "tornado==6.3.3\r\n", + "tqdm==4.66.1\r\n", + "traitlets==5.11.2\r\n", + "typer==0.9.0\r\n", + "types-python-dateutil==2.8.19.14\r\n", + "typing_extensions==4.8.0\r\n", + "tzdata==2023.3\r\n", + "uri-template==1.3.0\r\n", + "urllib3==2.0.6\r\n", + "virtualenv==20.24.5\r\n", + "wcwidth==0.2.8\r\n", + "webcolors==1.13\r\n", + "webencodings==0.5.1\r\n", + "websocket-client==1.6.4\r\n", + "widgetsnbextension==4.0.9\r\n", + "xgboost==1.7.5\r\n", + "yarl==1.9.2\r\n", + "zict==3.0.0\r\n", + "zipp==3.17.0\r\n" + ] + } + ], + "source": [ + "# it is not natively in the python distribution, do you have it installed?\n", + "\n", + "!pip freeze" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:45:24.476367Z", + "start_time": "2023-10-17T15:45:24.342242Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total 6000\r\n", + "drwxr-xr-x@ 29 jansila staff 928B Oct 17 16:21 \u001b[34m.\u001b[m\u001b[m\r\n", + "drwxr-xr-x@ 29 jansila staff 928B Oct 12 12:59 \u001b[34m..\u001b[m\u001b[m\r\n", + "-rw-r--r--@ 1 jansila staff 10K Oct 11 15:47 .DS_Store\r\n", + "-rw-r--r--@ 1 jansila staff 24B Jun 12 17:18 .dockerignore\r\n", + "-rw-r--r--@ 1 jansila staff 119B Jun 12 11:30 .flake8\r\n", + "drwxr-xr-x@ 15 jansila staff 480B Oct 17 16:54 \u001b[34m.git\u001b[m\u001b[m\r\n", + "-rw-r--r--@ 1 jansila staff 33B Aug 2 14:45 .gitignore\r\n", + "-rw-r--r--@ 1 jansila staff 1.8K Oct 17 16:21 .gitlab-ci.yml\r\n", + "drwxr-xr-x@ 15 jansila staff 480B Oct 17 17:44 \u001b[34m.idea\u001b[m\u001b[m\r\n", + "drwxr-xr-x@ 30 jansila staff 960B Oct 17 16:40 \u001b[34m.logs\u001b[m\u001b[m\r\n", + "drwxr-xr-x@ 5 jansila staff 160B Jul 4 11:14 \u001b[34m.mypy_cache\u001b[m\u001b[m\r\n", + "-rw-r--r--@ 1 jansila staff 1.1K Jun 12 11:30 .pre-commit-config.yaml\r\n", + "drwxr-xr-x@ 6 jansila staff 192B Sep 15 12:28 \u001b[34m.pytest_cache\u001b[m\u001b[m\r\n", + "-rw-r--r--@ 1 jansila staff 8B Jul 14 11:51 .python-version\r\n", + "drwxr-xr-x@ 4 jansila staff 128B Jun 12 11:30 \u001b[34m.vscode\u001b[m\u001b[m\r\n", + "-rw-r--r--@ 1 jansila staff 136B Oct 12 12:31 CODEOWNERS\r\n", + "-rw-r--r--@ 1 jansila staff 344B Oct 12 12:31 Dockerfile.ixian\r\n", + "-rw-r--r--@ 1 jansila staff 383B Jun 12 11:30 Makefile\r\n", + "-rw-r--r--@ 1 jansila staff 4.6K Oct 12 12:31 README.md\r\n", + "drwxr-xr-x@ 6 jansila staff 192B Oct 17 16:38 \u001b[34mauctions\u001b[m\u001b[m\r\n", + "-rw-r--r--@ 1 jansila staff 2.4M Jul 20 08:37 bcktest.pickle\r\n", + "drwxr-xr-x@ 11 jansila staff 352B Oct 17 16:21 \u001b[34mconfig\u001b[m\u001b[m\r\n", + "-rw-r--r--@ 1 jansila staff 3.3K Oct 17 16:21 deployment.yaml\r\n", + "-rw-r--r--@ 1 jansila staff 16K Sep 18 14:42 ixian-doctor-report.txt\r\n", + "-rw-r--r--@ 1 jansila staff 408K Oct 13 22:24 poetry.lock\r\n", + "-rw-r--r--@ 1 jansila staff 235B Jun 12 11:30 poetry.toml\r\n", + "-rw-r--r--@ 1 jansila staff 1.5K Oct 13 16:13 pyproject.toml\r\n", + "drwxr-xr-x@ 14 jansila staff 448B Oct 17 16:21 \u001b[34msrc\u001b[m\u001b[m\r\n", + "drwxr-xr-x@ 8 jansila staff 256B Oct 12 12:31 \u001b[34mtests\u001b[m\u001b[m\r\n" + ] + } + ], + "source": [ + "\n", + "# executing shell comands from the jupyter notebook\n", + "!ls -lha\n", + "#!pip install numpy" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:46:18.202888Z", + "start_time": "2023-10-17T15:46:18.194738Z" + } + }, + "outputs": [], + "source": [ + "# np is alias \"\"(used when name of the packages are too long or coders are rightly lazy)\n", + "import numpy as np\n", + "# very common usage" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:46:18.587522Z", + "start_time": "2023-10-17T15:46:18.584025Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0 1 2 3 4]\n" + ] + }, + { + "data": { + "text/plain": [ + "5" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#simple array\n", + "a = np.array([0, 1, 2, 3, 4])\n", + "\n", + "# \n", + "print(a)\n", + "# dir(a)\n", + "\n", + "# a.ndim\n", + "\n", + "a.shape\n", + "\n", + "len(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:46:20.031066Z", + "start_time": "2023-10-17T15:46:20.025751Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0 1 2 3 4]\n" + ] + } + ], + "source": [ + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:46:20.220268Z", + "start_time": "2023-10-17T15:46:20.216466Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['a', 'b']" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list('ab')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How can you define a matrix?\n", + "\n", + "### array of arrays?" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:46:21.482479Z", + "start_time": "2023-10-17T15:46:21.478710Z" + } + }, + "outputs": [], + "source": [ + "#multi dimensional objects\n", + "# array of array is a matrix\n", + "a = np.array([\n", + " [1,3], [2,3]\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:46:22.025355Z", + "start_time": "2023-10-17T15:46:22.014131Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(2, 2)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:46:22.311009Z", + "start_time": "2023-10-17T15:46:22.306517Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 3],\n", + " [2, 3]])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:46:35.199685Z", + "start_time": "2023-10-17T15:46:35.189613Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2, 2, 2)\n", + "3\n" + ] + } + ], + "source": [ + "# construct 3D array manually (focus on the brackets)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:46:23.280250Z", + "start_time": "2023-10-17T15:46:23.136396Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2, 2, 2)\n", + "3\n" + ] + } + ], + "source": [ + "a = np.array([[[1,3], [2,4]], [[3,5], [4,6]]])\n", + "# print(a)\n", + "print(a.shape)\n", + "print(a.ndim)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:46:35.800503Z", + "start_time": "2023-10-17T15:46:35.794169Z" + } + }, + "source": [ + "Construct array like a civilized person. (Martin Hronec's way! :)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:46:36.187018Z", + "start_time": "2023-10-17T15:46:36.183425Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(range(5))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:46:37.051817Z", + "start_time": "2023-10-17T15:46:37.045410Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.arange(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:46:57.771384Z", + "start_time": "2023-10-17T15:46:57.760708Z" + } + }, + "outputs": [ + { + "ename": "ValueError", + "evalue": "cannot reshape array of size 10 into shape (2,6)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[25], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marange\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreshape\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m6\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m#expected error\u001b[39;00m\n", + "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 10 into shape (2,6)" + ] + } + ], + "source": [ + "# evenly spaced\n", + "a = np.arange(10)\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:46:57.931603Z", + "start_time": "2023-10-17T15:46:57.927928Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2.5, 3.5, 4.5, 5.5, 6.5])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.arange(10).reshape((2,6))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:46:58.574904Z", + "start_time": "2023-10-17T15:46:58.568415Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0. , 0.01010101, 0.02020202, 0.03030303, 0.04040404,\n", + " 0.05050505, 0.06060606, 0.07070707, 0.08080808, 0.09090909,\n", + " 0.1010101 , 0.11111111, 0.12121212, 0.13131313, 0.14141414,\n", + " 0.15151515, 0.16161616, 0.17171717, 0.18181818, 0.19191919,\n", + " 0.2020202 , 0.21212121, 0.22222222, 0.23232323, 0.24242424,\n", + " 0.25252525, 0.26262626, 0.27272727, 0.28282828, 0.29292929,\n", + " 0.3030303 , 0.31313131, 0.32323232, 0.33333333, 0.34343434,\n", + " 0.35353535, 0.36363636, 0.37373737, 0.38383838, 0.39393939,\n", + " 0.4040404 , 0.41414141, 0.42424242, 0.43434343, 0.44444444,\n", + " 0.45454545, 0.46464646, 0.47474747, 0.48484848, 0.49494949,\n", + " 0.50505051, 0.51515152, 0.52525253, 0.53535354, 0.54545455,\n", + " 0.55555556, 0.56565657, 0.57575758, 0.58585859, 0.5959596 ,\n", + " 0.60606061, 0.61616162, 0.62626263, 0.63636364, 0.64646465,\n", + " 0.65656566, 0.66666667, 0.67676768, 0.68686869, 0.6969697 ,\n", + " 0.70707071, 0.71717172, 0.72727273, 0.73737374, 0.74747475,\n", + " 0.75757576, 0.76767677, 0.77777778, 0.78787879, 0.7979798 ,\n", + " 0.80808081, 0.81818182, 0.82828283, 0.83838384, 0.84848485,\n", + " 0.85858586, 0.86868687, 0.87878788, 0.88888889, 0.8989899 ,\n", + " 0.90909091, 0.91919192, 0.92929293, 0.93939394, 0.94949495,\n", + " 0.95959596, 0.96969697, 0.97979798, 0.98989899, 1. ])" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# evenly spaced\n", + "\n", + "# chain operations on a single object\n", + "a = np.arange(10).reshape((2,5)).mean(axis=0)\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0. , 0.01010101, 0.02020202, 0.03030303, 0.04040404,\n", + " 0.05050505, 0.06060606, 0.07070707, 0.08080808, 0.09090909,\n", + " 0.1010101 , 0.11111111, 0.12121212, 0.13131313, 0.14141414,\n", + " 0.15151515, 0.16161616, 0.17171717, 0.18181818, 0.19191919,\n", + " 0.2020202 , 0.21212121, 0.22222222, 0.23232323, 0.24242424,\n", + " 0.25252525, 0.26262626, 0.27272727, 0.28282828, 0.29292929,\n", + " 0.3030303 , 0.31313131, 0.32323232, 0.33333333, 0.34343434,\n", + " 0.35353535, 0.36363636, 0.37373737, 0.38383838, 0.39393939,\n", + " 0.4040404 , 0.41414141, 0.42424242, 0.43434343, 0.44444444,\n", + " 0.45454545, 0.46464646, 0.47474747, 0.48484848, 0.49494949,\n", + " 0.50505051, 0.51515152, 0.52525253, 0.53535354, 0.54545455,\n", + " 0.55555556, 0.56565657, 0.57575758, 0.58585859, 0.5959596 ,\n", + " 0.60606061, 0.61616162, 0.62626263, 0.63636364, 0.64646465,\n", + " 0.65656566, 0.66666667, 0.67676768, 0.68686869, 0.6969697 ,\n", + " 0.70707071, 0.71717172, 0.72727273, 0.73737374, 0.74747475,\n", + " 0.75757576, 0.76767677, 0.77777778, 0.78787879, 0.7979798 ,\n", + " 0.80808081, 0.81818182, 0.82828283, 0.83838384, 0.84848485,\n", + " 0.85858586, 0.86868687, 0.87878788, 0.88888889, 0.8989899 ,\n", + " 0.90909091, 0.91919192, 0.92929293, 0.93939394, 0.94949495,\n", + " 0.95959596, 0.96969697, 0.97979798, 0.98989899, 1. ])" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#generate sequences\n", + "\n", + "# number of points from an interval\n", + "start = 0\n", + "end = 1\n", + "n_points = 100\n", + "a = np.linspace(start, end, n_points)\n", + "a\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:47:00.823027Z", + "start_time": "2023-10-17T15:47:00.816999Z" + } + }, + "source": [ + "# why is it useful?" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:47:01.276551Z", + "start_time": "2023-10-17T15:47:01.271764Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['BitGenerator',\n", + " 'Generator',\n", + " 'MT19937',\n", + " 'PCG64',\n", + " 'PCG64DXSM',\n", + " 'Philox',\n", + " 'RandomState',\n", + " 'SFC64',\n", + " 'SeedSequence',\n", + " '__RandomState_ctor',\n", + " '__all__',\n", + " '__builtins__',\n", + " '__cached__',\n", + " '__doc__',\n", + " '__file__',\n", + " '__loader__',\n", + " '__name__',\n", + " '__package__',\n", + " '__path__',\n", + " '__spec__',\n", + " '_bounded_integers',\n", + " '_common',\n", + " '_generator',\n", + " '_mt19937',\n", + " '_pcg64',\n", + " '_philox',\n", + " '_pickle',\n", + " '_sfc64',\n", + " 'beta',\n", + " 'binomial',\n", + " 'bit_generator',\n", + " 'bytes',\n", + " 'chisquare',\n", + " 'choice',\n", + " 'default_rng',\n", + " 'dirichlet',\n", + " 'exponential',\n", + " 'f',\n", + " 'gamma',\n", + " 'geometric',\n", + " 'get_bit_generator',\n", + " 'get_state',\n", + " 'gumbel',\n", + " 'hypergeometric',\n", + " 'laplace',\n", + " 'logistic',\n", + " 'lognormal',\n", + " 'logseries',\n", + " 'mtrand',\n", + " 'multinomial',\n", + " 'multivariate_normal',\n", + " 'negative_binomial',\n", + " 'noncentral_chisquare',\n", + " 'noncentral_f',\n", + " 'normal',\n", + " 'pareto',\n", + " 'permutation',\n", + " 'poisson',\n", + " 'power',\n", + " 'rand',\n", + " 'randint',\n", + " 'randn',\n", + " 'random',\n", + " 'random_integers',\n", + " 'random_sample',\n", + " 'ranf',\n", + " 'rayleigh',\n", + " 'sample',\n", + " 'seed',\n", + " 'set_bit_generator',\n", + " 'set_state',\n", + " 'shuffle',\n", + " 'standard_cauchy',\n", + " 'standard_exponential',\n", + " 'standard_gamma',\n", + " 'standard_normal',\n", + " 'standard_t',\n", + " 'test',\n", + " 'triangular',\n", + " 'uniform',\n", + " 'vonmises',\n", + " 'wald',\n", + " 'weibull',\n", + " 'zipf']" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#R: seq()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:47:02.632899Z", + "start_time": "2023-10-17T15:47:02.619576Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.47143516, -1.19097569, 1.43270697, -0.3126519 ])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dir(np.random)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.47143516, -1.19097569, 1.43270697, -0.3126519 ])" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# random seed is cell-specific! \n", + "np.random.seed(1234)\n", + "\n", + "# random (normal)\n", + "r = np.random.randn(4)\n", + "r" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# A crucial skill" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:47:04.290587Z", + "start_time": "2023-10-17T15:47:04.284221Z" + } + }, + "source": [ + "### Indexing and Slicing\n", + "* In 2D, the first dimension corresponds to rows, the second to columns.\n", + "* in the multidimensional case, `a[0]` gives all elements in the unspecified dimension" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:47:04.733361Z", + "start_time": "2023-10-17T15:47:04.726517Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0 0]\n", + " [0 0]\n", + " [3 0]\n", + " [0 4]]\n" + ] + } + ], + "source": [ + "# create toy diagonal matrix\n", + "a = np.diag([1,2,3,4])\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:47:05.043441Z", + "start_time": "2023-10-17T15:47:05.040094Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 0, 0, 0],\n", + " [0, 0, 3, 0]])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# print(a[2]) \n", + "\n", + "# print(a[2,:]) #slicing - equivalent to first\n", + "\n", + "# print(a[2][2]) #access single element matrix\n", + "# print(a[2,2])\n", + "\n", + "# print(a[:,1])\n", + "\n", + "print(a[:,-2:])" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:47:05.426887Z", + "start_time": "2023-10-17T15:47:05.424302Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 0],\n", + " [0, 3]])" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a[0:3:2]" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:47:05.808079Z", + "start_time": "2023-10-17T15:47:05.800217Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n", + " 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47\n", + " 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71\n", + " 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95\n", + " 96 97 98 99]\n", + "[99 96 93 90 87 84 81]\n" + ] + } + ], + "source": [ + "# select from start to an end with certain step (could be zero instead of missing)\n", + "# advanced tricks\n", + "\n", + "a[:3:2,:3:2] #step n is every n-th observation" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n", + " 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47\n", + " 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71\n", + " 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95\n", + " 96 97 98 99]\n", + "[99 96 93 90 87 84 81]\n" + ] + } + ], + "source": [ + "s = np.arange(100)\n", + "print(s)\n", + "# step can also be negative\n", + "# start:end:step\n", + "print(s[:80:-3])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:47:06.957485Z", + "start_time": "2023-10-17T15:47:06.952648Z" + } + }, + "source": [ + "**Copies vs. views**\n", + "* a slicing creates a **view** on the original array (just a way of accessing array data)\n", + " * the original array is not copied in memory\n", + "* when modifying the view, the original array is modified as well! (SURPRISE, SURPRISE)\n", + " * allows to save memory and time\n", + "* In CS it is called **shallow copy** vs **deep copy**" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:47:07.484979Z", + "start_time": "2023-10-17T15:47:07.473923Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[12 1 2 3 4 5 6 7 8 9]\n", + "[0 1 2 3 4 5 6 7 8 9]\n", + "False\n" + ] + } + ], + "source": [ + "a = np.arange(10)\n", + "print(a)\n", + "# print(np.may_share_memory(a, a[1:]))\n", + "# print(np.may_share_memory(a, a[1:]))\n", + "b = a \n", + "b[2] = 22\n", + "\n", + "# #print(a.data, b.data)\n", + "\n", + "print(np.may_share_memory(a, b))\n", + "\n", + "print(a)\n", + "print(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[12 1 2 3 4 5 6 7 8 9]\n", + "[0 1 2 3 4 5 6 7 8 9]\n", + "False\n" + ] + } + ], + "source": [ + "a = np.arange(10)\n", + "c = a.copy() # force a copy\n", + "c[0] = 12\n", + "print(c)\n", + "print(a)\n", + "\n", + "print(np.may_share_memory(a, c))\n", + "#print(a.data, c.data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### typical mistake in pandas slicing dataframes -> stay tuned!\n", + "\n", + "SettingWithCopyWarning:\n", + " \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "\n", + "Try using .loc[row_indexer,col_indexer] = value instead" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:47:09.594209Z", + "start_time": "2023-10-17T15:47:09.583445Z" + } + }, + "source": [ + "**Speed of basic numpy operations**\n", + " * much faster then in pure python" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:47:11.846163Z", + "start_time": "2023-10-17T15:47:11.714859Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "264 µs ± 34.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "# if unsure about an algo\n", + "# run on a small sample and get a time estimate! before run for days...\n", + "a = np.arange(10000)\n", + "%timeit -n 100 a + 1 \n", + "\n", + "#caching results is good!" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:47:12.019694Z", + "start_time": "2023-10-17T15:47:12.016989Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 614 µs, sys: 6 µs, total: 620 µs\n", + "Wall time: 623 µs\n" + ] + } + ], + "source": [ + "l = range(10000)\n", + "%timeit -n 100 [i+1 for i in l] " + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:47:25.202391Z", + "start_time": "2023-10-17T15:47:12.447288Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.57 µs ± 51.5 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)\n" + ] + } + ], + "source": [ + "# remember the difference between %time and %timeit\n", + "#timeit runs a number of loops\n", + "\n", + "%time res = [i+1 for i in a]" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4.35 µs ± 171 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n" + ] + } + ], + "source": [ + "%timeit res = a + 1 " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:47:32.925562Z", + "start_time": "2023-10-17T15:47:32.918825Z" + } + }, + "source": [ + "**Changing shape of an array**\n", + "* flattening\n", + "* reshaping (inverse of flattening)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:47:33.311619Z", + "start_time": "2023-10-17T15:47:33.308017Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 2 3 4 5 6]\n" + ] + } + ], + "source": [ + "a = np.array([[1, 2, 3], [4, 5, 6]])\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:47:33.807336Z", + "start_time": "2023-10-17T15:47:33.799712Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2, 3)\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[1, 4],\n", + " [2, 5],\n", + " [3, 6]])" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# flattening\n", + "print(a.flatten())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:47:34.371751Z", + "start_time": "2023-10-17T15:47:34.365965Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 4, 2, 5, 3, 6])" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(a.shape)\n", + "a.T" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:47:34.552945Z", + "start_time": "2023-10-17T15:47:34.550027Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 3, 4, 5, 6])" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a.T.flatten() #or usre flatten/ravel order='F' in Fortran column-wise order" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:47:35.027524Z", + "start_time": "2023-10-17T15:47:35.007257Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 2.71828183, 7.3890561 , 20.08553692],\n", + " [ 54.59815003, 148.4131591 , 403.42879349]])" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a.T.flatten(order='F')" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 2.71828183, 7.3890561 , 20.08553692],\n", + " [ 54.59815003, 148.4131591 , 403.42879349]])" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.exp(a) # a bit like R - vectorized operations" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:47:36.872659Z", + "start_time": "2023-10-17T15:47:35.846345Z" + } + }, + "source": [ + "**Pictures? Just pixels.**" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:48:24.337913Z", + "start_time": "2023-10-17T15:48:24.321652Z" + } + }, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Please open the URL for reading and pass the result to Pillow, e.g. with ``np.array(PIL.Image.open(urllib.request.urlopen(url)))``.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[56], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# for more M.C. Escher's pictures: https://www.mcescher.com/\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m img \u001b[38;5;241m=\u001b[39m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimread\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mhttps://github.com/vitekzkytek/PythonDataIES/blob/Winter2022/02_numpy_pandas/auxiliary/mc_escher_print\u001b[39;49m\u001b[38;5;132;43;01m%20g\u001b[39;49;00m\u001b[38;5;124;43mallery.png\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m plt\u001b[38;5;241m.\u001b[39mimshow(img,interpolation\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnearest\u001b[39m\u001b[38;5;124m'\u001b[39m, aspect\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauto\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/auctions-r-Y_AEU4-py3.10/lib/python3.10/site-packages/matplotlib/pyplot.py:2389\u001b[0m, in \u001b[0;36mimread\u001b[0;34m(fname, format)\u001b[0m\n\u001b[1;32m 2385\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(matplotlib\u001b[38;5;241m.\u001b[39mimage\u001b[38;5;241m.\u001b[39mimread)\n\u001b[1;32m 2386\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mimread\u001b[39m(\n\u001b[1;32m 2387\u001b[0m fname: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m|\u001b[39m pathlib\u001b[38;5;241m.\u001b[39mPath \u001b[38;5;241m|\u001b[39m BinaryIO, \u001b[38;5;28mformat\u001b[39m: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 2388\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39mndarray:\n\u001b[0;32m-> 2389\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmatplotlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/auctions-r-Y_AEU4-py3.10/lib/python3.10/site-packages/matplotlib/image.py:1520\u001b[0m, in \u001b[0;36mimread\u001b[0;34m(fname, format)\u001b[0m\n\u001b[1;32m 1516\u001b[0m img_open \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 1517\u001b[0m PIL\u001b[38;5;241m.\u001b[39mPngImagePlugin\u001b[38;5;241m.\u001b[39mPngImageFile \u001b[38;5;28;01mif\u001b[39;00m ext \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpng\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m PIL\u001b[38;5;241m.\u001b[39mImage\u001b[38;5;241m.\u001b[39mopen)\n\u001b[1;32m 1518\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fname, \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(parse\u001b[38;5;241m.\u001b[39murlparse(fname)\u001b[38;5;241m.\u001b[39mscheme) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 1519\u001b[0m \u001b[38;5;66;03m# Pillow doesn't handle URLs directly.\u001b[39;00m\n\u001b[0;32m-> 1520\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 1521\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlease open the URL for reading and pass the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresult to Pillow, e.g. with \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1523\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m``np.array(PIL.Image.open(urllib.request.urlopen(url)))``.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1524\u001b[0m )\n\u001b[1;32m 1525\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m img_open(fname) \u001b[38;5;28;01mas\u001b[39;00m image:\n\u001b[1;32m 1526\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (_pil_png_to_float_array(image)\n\u001b[1;32m 1527\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(image, PIL\u001b[38;5;241m.\u001b[39mPngImagePlugin\u001b[38;5;241m.\u001b[39mPngImageFile) \u001b[38;5;28;01melse\u001b[39;00m\n\u001b[1;32m 1528\u001b[0m pil_to_array(image))\n", + "\u001b[0;31mValueError\u001b[0m: Please open the URL for reading and pass the result to Pillow, e.g. with ``np.array(PIL.Image.open(urllib.request.urlopen(url)))``." + ] + } + ], + "source": [ + "# we will get to the matplotlib and pyplot in the last part of the lecture\n", + "import matplotlib.pyplot as plt\n", + "# another ipython magic\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# for more M.C. Escher's pictures: https://www.mcescher.com/\n", + "import matplotlib.pyplot as plt\n", + "img = plt.imread('./mc_escher_print gallery.png')\n", + "plt.imshow(img,interpolation='nearest', aspect='auto')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[0.7529412 , 0.7529412 , 0.7529412 , 1. ],\n", + " [0.7411765 , 0.7411765 , 0.7411765 , 1. ],\n", + " [0.7372549 , 0.7372549 , 0.7372549 , 1. ],\n", + " ...,\n", + " [0.95686275, 0.95686275, 0.95686275, 1. ],\n", + " [0.95686275, 0.95686275, 0.95686275, 1. ],\n", + " [0.95686275, 0.95686275, 0.95686275, 1. ]],\n", + "\n", + " [[0.75686276, 0.75686276, 0.75686276, 1. ],\n", + " [0.7490196 , 0.7490196 , 0.7490196 , 1. ],\n", + " [0.74509805, 0.74509805, 0.74509805, 1. ],\n", + " ...,\n", + " [0.95686275, 0.95686275, 0.95686275, 1. ],\n", + " [0.95686275, 0.95686275, 0.95686275, 1. ],\n", + " [0.95686275, 0.95686275, 0.95686275, 1. ]],\n", + "\n", + " [[0.76862746, 0.76862746, 0.76862746, 1. ],\n", + " [0.75686276, 0.75686276, 0.75686276, 1. ],\n", + " [0.75686276, 0.75686276, 0.75686276, 1. ],\n", + " ...,\n", + " [0.95686275, 0.95686275, 0.95686275, 1. ],\n", + " [0.95686275, 0.95686275, 0.95686275, 1. ],\n", + " [0.95686275, 0.95686275, 0.95686275, 1. ]],\n", + "\n", + " ...,\n", + "\n", + " [[0.6745098 , 0.6745098 , 0.6745098 , 1. ],\n", + " [0.654902 , 0.654902 , 0.654902 , 1. ],\n", + " [0.62352943, 0.62352943, 0.62352943, 1. ],\n", + " ...,\n", + " [0.89411765, 0.89411765, 0.89411765, 1. ],\n", + " [0.8901961 , 0.8901961 , 0.8901961 , 1. ],\n", + " [0.8901961 , 0.8901961 , 0.8901961 , 1. ]],\n", + "\n", + " [[0.69803923, 0.69803923, 0.69803923, 1. ],\n", + " [0.6745098 , 0.6745098 , 0.6745098 , 1. ],\n", + " [0.6392157 , 0.6392157 , 0.6392157 , 1. ],\n", + " ...,\n", + " [0.89411765, 0.89411765, 0.89411765, 1. ],\n", + " [0.89411765, 0.89411765, 0.89411765, 1. ],\n", + " [0.8901961 , 0.8901961 , 0.8901961 , 1. ]],\n", + "\n", + " [[0.7254902 , 0.7254902 , 0.7254902 , 1. ],\n", + " [0.7019608 , 0.7019608 , 0.7019608 , 1. ],\n", + " [0.654902 , 0.654902 , 0.654902 , 1. ],\n", + " ...,\n", + " [0.8980392 , 0.8980392 , 0.8980392 , 1. ],\n", + " [0.89411765, 0.89411765, 0.89411765, 1. ],\n", + " [0.8901961 , 0.8901961 , 0.8901961 , 1. ]]], dtype=float32)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# image shape as (H, W, D), depth: https://www.wikiwand.com/en/Color_depth\n", + "img.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# just an array!\n", + "img" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:47:37.218409Z", + "start_time": "2023-10-17T15:47:37.208453Z" + } + }, + "outputs": [], + "source": [ + "self_centered = img[200:,200:500]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:47:37.386863Z", + "start_time": "2023-10-17T15:47:37.384761Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(self_centered)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:47:37.808573Z", + "start_time": "2023-10-17T15:47:37.805072Z" + } + }, + "outputs": [], + "source": [ + "lx, ly, ld = img.shape\n", + "X, Y = np.ogrid[0:lx, 0:ly]\n", + "mask = (X - lx/2)**2 + (Y - ly/2)**2 > lx*ly/4\n", + "img[mask] = 0\n", + "img[range(300), range(300)] = 255\n", + "\n", + "plt.figure(figsize=(3, 3))\n", + "plt.axes([0, 0, 1, 1])\n", + "plt.imshow(img, cmap=plt.cm.gray)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:52:31.652677Z", + "start_time": "2023-10-17T15:52:31.647658Z" + } + }, + "source": [ + "" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:52:31.897152Z", + "start_time": "2023-10-17T15:52:31.879555Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([1, 2, 3, 4, 5, 6]), [array([1, 2, 3]), array([4, 5, 6])])" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# Element-wise operations\n", + "array1 = np.array([1, 2, 3])\n", + "array2 = np.array([4, 5, 6])\n", + "\n", + "# Addition\n", + "sum_arrays = array1 + array2\n", + "\n", + "# Multiplication\n", + "product_arrays = array1 * array2\n", + "\n", + "sum_arrays, product_arrays\n" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "ExecuteTime": { + "end_time": "2023-10-17T15:52:32.138674Z", + "start_time": "2023-10-17T15:52:32.128113Z" + } + }, + "outputs": [], + "source": [ + "\n", + "# Joining arrays\n", + "array1 = np.array([1, 2, 3])\n", + "array2 = np.array([4, 5, 6])\n", + "joined_array = np.concatenate((array1, array2)) #concat is a general concept\n", + "\n", + "# Splitting arrays\n", + "split_arrays = np.split(joined_array, 2)\n", + "#from docs:\n", + "# If indices_or_sections is an integer, N, the array will be divided into N equal arrays along axis. If such a split is not possible, an error is raised\n", + "# If indices_or_sections is a 1-D array of sorted integers, the entries indicate where along axis the array is split. For example, [2, 3] would, for axis=0, result in\n", + " # ary[:2]\n", + " # ary[2:3]\n", + " # ary[3:]\n", + "\n", + "joined_array, split_arrays" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#other operations\n", + "array1.sum()\n", + "#2d array ->\n", + "# argmax\n", + "# .mean()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/04_pandas/04_PandasMatplotlib.ipynb b/04_pandas/04_PandasMatplotlib.ipynb new file mode 100644 index 0000000..0fafcc4 --- /dev/null +++ b/04_pandas/04_PandasMatplotlib.ipynb @@ -0,0 +1,5834 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lecture 4 Pandas\n", + "\n", + "2023-10-24\n", + "\n", + "\n", + "### Table of contents\n", + "\n", + "1. [Pandas](#pandas)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import pandas as pd\n", + "\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python version 3.10.7 (tags/v3.10.7:6cc6b13, Sep 5 2022, 14:08:36) [MSC v.1933 64 bit (AMD64)]\n", + "Pandas version 1.4.4\n" + ] + } + ], + "source": [ + "print(f'Python version {sys.version}')\n", + "print(f'Pandas version {pd.__version__}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data structures in pandas\n", + "\n", + "### Series\n", + "* 1D labeled array able to hold any data type (int,str,float, Python objects, etc.)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=4, step=1)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series([1,-1,1,-1]).index" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`data` (in the example above) can be:\n", + " * a dict\n", + " * a list\n", + " * an ndarray\n", + " * a scalar value\n", + "\n", + "\n", + "Examples of from dict and a scalar value below:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "aa 1\n", + "aaa g\n", + "dtype: object" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# in case of dictionary\n", + "pd.Series({'aa':1, 'aaa':'b', 'aaa':'g'})" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "a 0\n", + "b 1\n", + "c 2\n", + "d 3\n", + "e 4\n", + "dtype: int32" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series(np.arange(5), index=['a', 'b', 'c', 'd','e'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* a key difference between Series/pandas and ndarray: operations between Series automatically align the data based on label" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A:\n", + "Vítek 5\n", + "Martin 10\n", + "Honza 0\n", + "dtype: int64\n", + "B:\n", + "Martin 20\n", + "Honza 15\n", + "Vítek 5\n", + "dtype: int64\n" + ] + }, + { + "data": { + "text/plain": [ + "Honza 15\n", + "Martin 30\n", + "Vítek 10\n", + "dtype: int64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = pd.Series({'Vítek':5, 'Martin':10, 'Honza':0})\n", + "b = pd.Series({'Martin':20,'Honza':15,'Vítek':5})\n", + "print(f'A:\\n{a}\\nB:\\n{b}')\n", + "a+ b" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([25, 25, 5])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = [5,10,0]\n", + "b = [20,15,5]\n", + "\n", + "np.array(a) + np.array(b) # now works as expected" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* looping through (value-by-value) usually not necessary, remember the case of np array" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### DataFrame\n", + "\n", + "* a 2D labeled data structure with columns of potentially different types\n", + "* like a spreadsheet or SQL table, or a dict of Series objects\n", + "* the most frequently used pandas object \n", + "* can be created:\n", + " * typically by reading a csv file\n", + " * dict of 1D ndarrays, lists, dicts, Series\n", + " * 2D numpy.ndarray\n", + " * a Series\n", + " * another DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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12JLB003Angličtina pro ekonomy INaNPoslušná,L.5.03.0NaNNaNNaN...5.05.01.05.05.05.05.0NaNNaNcjp
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34JEB023Úvod do studia právaPražák,P.,Wintr,J.NaN3.04.03.03.01.0...NaNNaN1.03.02.03.02.0NaNNaNies
45JEB055Seminář k aktualitám INaNVyhnánek,T.2.03.0NaNNaNNaN...3.01.01.04.02.02.01.0NaNNaNies
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1JEB003Ekonomie IFanta,N.,Kracík,J.,Švarcová,N.Fanta,N.,Kracík,J.,Švarcová,N.3.05.04.05.04.02.04.03.01.04.01.0NaN2.0NaNNaNies
2JLB003Angličtina pro ekonomy INaNPoslušná,L.5.03.0NaNNaNNaN5.05.05.01.05.05.05.05.0NaNNaNcjp
3NMMA701Matematika 1Spurný,J.Rondoš,J.3.05.03.02.01.04.04.05.01.03.02.02.01.0NaNNaNies
4JEB023Úvod do studia právaPražák,P.,Wintr,J.NaN3.04.03.03.01.0NaNNaNNaN1.03.02.03.02.0NaNNaNies
5JEB055Seminář k aktualitám INaNVyhnánek,T.2.03.0NaNNaNNaN2.03.01.01.04.02.02.01.0NaNNaNies
6JPM314Theories of International RelationsDitrych,O.,Plechanovová,B.NaN2.03.02.04.01.0NaNNaNNaN2.03.01.03.01.0NaNNaNkmv
7JEB998Úvod do ekonomieKameníček,J.NaN4.03.02.03.02.0NaNNaNNaN1.03.01.02.03.0NaNNaNies
8JEB058Seminář matematické analýzy INaNStráský,J.4.04.0NaNNaNNaN4.05.05.01.01.02.03.05.0NaNNaNies
9JPM561Regional Security StudiesKarásek,T.,Klosek,K.Karásek,T.,Klosek,K.5.03.05.05.04.05.05.04.01.05.04.05.05.0NaNNaNkbs
10JEB998Úvod do ekonomieKameníček,J.NaN3.04.02.02.02.0NaNNaNNaN1.03.03.03.01.0NaNNaNies
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" + ], + "text/plain": [ + " course_code course_title \\\n", + "number \n", + "1 JEB003 Ekonomie I \n", + "2 JLB003 Angličtina pro ekonomy I \n", + "3 NMMA701 Matematika 1 \n", + "4 JEB023 Úvod do studia práva \n", + "5 JEB055 Seminář k aktualitám I \n", + "6 JPM314 Theories of International Relations \n", + "7 JEB998 Úvod do ekonomie \n", + "8 JEB058 Seminář matematické analýzy I \n", + "9 JPM561 Regional Security Studies \n", + "10 JEB998 Úvod do ekonomie \n", + "\n", + " teachers seminar_leaders q1 \\\n", + "number \n", + "1 Fanta,N.,Kracík,J.,Švarcová,N. Fanta,N.,Kracík,J.,Švarcová,N. 3.0 \n", + "2 NaN Poslušná,L. 5.0 \n", + "3 Spurný,J. Rondoš,J. 3.0 \n", + "4 Pražák,P.,Wintr,J. NaN 3.0 \n", + "5 NaN Vyhnánek,T. 2.0 \n", + "6 Ditrych,O.,Plechanovová,B. NaN 2.0 \n", + "7 Kameníček,J. NaN 4.0 \n", + "8 NaN Stráský,J. 4.0 \n", + "9 Karásek,T.,Klosek,K. Karásek,T.,Klosek,K. 5.0 \n", + "10 Kameníček,J. NaN 3.0 \n", + "\n", + " q2 q3 q4 q5 q6 q7 q8 q9 q10 q11 q12 q13 c_value \\\n", + "number \n", + "1 5.0 4.0 5.0 4.0 2.0 4.0 3.0 1.0 4.0 1.0 NaN 2.0 NaN \n", + "2 3.0 NaN NaN NaN 5.0 5.0 5.0 1.0 5.0 5.0 5.0 5.0 NaN \n", + "3 5.0 3.0 2.0 1.0 4.0 4.0 5.0 1.0 3.0 2.0 2.0 1.0 NaN \n", + "4 4.0 3.0 3.0 1.0 NaN NaN NaN 1.0 3.0 2.0 3.0 2.0 NaN \n", + "5 3.0 NaN NaN NaN 2.0 3.0 1.0 1.0 4.0 2.0 2.0 1.0 NaN \n", + "6 3.0 2.0 4.0 1.0 NaN NaN NaN 2.0 3.0 1.0 3.0 1.0 NaN \n", + "7 3.0 2.0 3.0 2.0 NaN NaN NaN 1.0 3.0 1.0 2.0 3.0 NaN \n", + "8 4.0 NaN NaN NaN 4.0 5.0 5.0 1.0 1.0 2.0 3.0 5.0 NaN \n", + "9 3.0 5.0 5.0 4.0 5.0 5.0 4.0 1.0 5.0 4.0 5.0 5.0 NaN \n", + "10 4.0 2.0 2.0 2.0 NaN NaN NaN 1.0 3.0 3.0 3.0 1.0 NaN \n", + "\n", + " c_improve department_code \n", + "number \n", + "1 NaN ies \n", + "2 NaN cjp \n", + "3 NaN ies \n", + "4 NaN ies \n", + "5 NaN ies \n", + "6 NaN kmv \n", + "7 NaN ies \n", + "8 NaN ies \n", + "9 NaN kbs \n", + "10 NaN ies " + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# raw data have column names in czech, let's rename them\n", + "# if you do not want to reassign, you can provide arg. \"inplace = True\"\n", + "df = df.rename(columns = {\n", + " 'cislo_dot' : 'number',\n", + " 'kod_predm' : 'course_code',\n", + " 'nazev_predm' : 'course_title',\n", + " 'prednasejici' : 'teachers',\n", + " 'cvicici' : 'seminar_leaders',\n", + " 't1': 'c_value',\n", + " 't2': 'c_improve', \n", + " 'katedra_code' : 'department_code'\n", + "})\n", + "df.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Int64Index([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,\n", + " ...\n", + " 6988, 6989, 6990, 6991, 6992, 6993, 6994, 6995, 6996, 6997],\n", + " dtype='int64', name='number', length=6995)" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# iterative\n", + "df.index" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "# set column named \"course_code\" to be an index (or you can use \"inplace\" option again)\n", + "df.set_index('number', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(6995, 20)" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "number\n", + "1 JEB003\n", + "2 JLB003\n", + "3 NMMA701\n", + "4 JEB023\n", + "5 JEB055\n", + " ... \n", + "6993 JJM260\n", + "6994 JJM264\n", + "6995 JJM360\n", + "6996 JJM354\n", + "6997 JJM340\n", + "Name: course_code, Length: 6995, dtype: object" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.course_code" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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course_codecourse_titleteachersseminar_leadersq1q2q3q4q5q6q7q8q9q10q11q12q13c_valuec_improvedepartment_code
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1JEB003Ekonomie IFanta,N.,Kracík,J.,Švarcová,N.Fanta,N.,Kracík,J.,Švarcová,N.3.05.04.05.04.02.04.03.01.04.01.0NaN2.0NaNNaNies
2JLB003Angličtina pro ekonomy INaNPoslušná,L.5.03.0NaNNaNNaN5.05.05.01.05.05.05.05.0NaNNaNcjp
3NMMA701Matematika 1Spurný,J.Rondoš,J.3.05.03.02.01.04.04.05.01.03.02.02.01.0NaNNaNies
4JEB023Úvod do studia právaPražák,P.,Wintr,J.NaN3.04.03.03.01.0NaNNaNNaN1.03.02.03.02.0NaNNaNies
5JEB055Seminář k aktualitám INaNVyhnánek,T.2.03.0NaNNaNNaN2.03.01.01.04.02.02.01.0NaNNaNies
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" + ], + "text/plain": [ + " course_code course_title teachers \\\n", + "number \n", + "1 JEB003 Ekonomie I Fanta,N.,Kracík,J.,Švarcová,N. \n", + "2 JLB003 Angličtina pro ekonomy I NaN \n", + "3 NMMA701 Matematika 1 Spurný,J. \n", + "4 JEB023 Úvod do studia práva Pražák,P.,Wintr,J. \n", + "5 JEB055 Seminář k aktualitám I NaN \n", + "\n", + " seminar_leaders q1 q2 q3 q4 q5 q6 q7 \\\n", + "number \n", + "1 Fanta,N.,Kracík,J.,Švarcová,N. 3.0 5.0 4.0 5.0 4.0 2.0 4.0 \n", + "2 Poslušná,L. 5.0 3.0 NaN NaN NaN 5.0 5.0 \n", + "3 Rondoš,J. 3.0 5.0 3.0 2.0 1.0 4.0 4.0 \n", + "4 NaN 3.0 4.0 3.0 3.0 1.0 NaN NaN \n", + "5 Vyhnánek,T. 2.0 3.0 NaN NaN NaN 2.0 3.0 \n", + "\n", + " q8 q9 q10 q11 q12 q13 c_value c_improve department_code \n", + "number \n", + "1 3.0 1.0 4.0 1.0 NaN 2.0 NaN NaN ies \n", + "2 5.0 1.0 5.0 5.0 5.0 5.0 NaN NaN cjp \n", + "3 5.0 1.0 3.0 2.0 2.0 1.0 NaN NaN ies \n", + "4 NaN 1.0 3.0 2.0 3.0 2.0 NaN NaN ies \n", + "5 1.0 1.0 4.0 2.0 2.0 1.0 NaN NaN ies " + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# look at the data but refrain from drawing the conclusions\n", + "df.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# make a copy of original data, so if you mess up, can go back to this\n", + "# not that smart when you are working with the large data\n", + "df_copy = df.copy(deep = True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* `pd.DataFrame.copy()`:\n", + " * deep: modifications to the data or indices of the copy will not be reflected in the original object\n", + " * shallow: any changes to the data of the original will be reflected in the shallow copy (and vice versa)" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(6995, 20)" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# try to call it as a function\n", + "# df.shape() # it si an attribute not a function\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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q1q2q3q4q5q6q7q8q9q10q11q12q13
count6847.0000006827.0000005458.0000005457.0000005458.0000002600.0000002600.0000002600.0000006776.0000006829.0000006783.0000006801.0000006798.00000
mean4.1086613.2841664.2147314.3593553.8726644.2884624.4942314.1519231.4191264.0209403.5746723.8969274.12739
std1.0197551.0563331.0323990.9992951.2673210.9648800.8872391.1425840.7754231.0690841.2741291.1159771.12351
min1.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.00000
25%4.0000003.0000004.0000004.0000003.0000004.0000004.0000004.0000001.0000003.0000003.0000003.0000004.00000
50%4.0000003.0000005.0000005.0000004.0000005.0000005.0000005.0000001.0000004.0000004.0000004.0000005.00000
75%5.0000004.0000005.0000005.0000005.0000005.0000005.0000005.0000002.0000005.0000005.0000005.0000005.00000
max5.0000005.0000005.0000005.0000005.0000005.0000005.0000005.0000005.0000005.0000005.0000005.0000005.00000
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" + ], + "text/plain": [ + " q1 q2 q3 q4 q5 \\\n", + "count 6847.000000 6827.000000 5458.000000 5457.000000 5458.000000 \n", + "mean 4.108661 3.284166 4.214731 4.359355 3.872664 \n", + "std 1.019755 1.056333 1.032399 0.999295 1.267321 \n", + "min 1.000000 1.000000 1.000000 1.000000 1.000000 \n", + "25% 4.000000 3.000000 4.000000 4.000000 3.000000 \n", + "50% 4.000000 3.000000 5.000000 5.000000 4.000000 \n", + "75% 5.000000 4.000000 5.000000 5.000000 5.000000 \n", + "max 5.000000 5.000000 5.000000 5.000000 5.000000 \n", + "\n", + " q6 q7 q8 q9 q10 \\\n", + "count 2600.000000 2600.000000 2600.000000 6776.000000 6829.000000 \n", + "mean 4.288462 4.494231 4.151923 1.419126 4.020940 \n", + "std 0.964880 0.887239 1.142584 0.775423 1.069084 \n", + "min 1.000000 1.000000 1.000000 1.000000 1.000000 \n", + "25% 4.000000 4.000000 4.000000 1.000000 3.000000 \n", + "50% 5.000000 5.000000 5.000000 1.000000 4.000000 \n", + "75% 5.000000 5.000000 5.000000 2.000000 5.000000 \n", + "max 5.000000 5.000000 5.000000 5.000000 5.000000 \n", + "\n", + " q11 q12 q13 \n", + "count 6783.000000 6801.000000 6798.00000 \n", + "mean 3.574672 3.896927 4.12739 \n", + "std 1.274129 1.115977 1.12351 \n", + "min 1.000000 1.000000 1.00000 \n", + "25% 3.000000 3.000000 4.00000 \n", + "50% 4.000000 4.000000 5.00000 \n", + "75% 5.000000 5.000000 5.00000 \n", + "max 5.000000 5.000000 5.00000 " + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# classical data summarization\n", + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 6995 entries, 1 to 6997\n", + "Data columns (total 20 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 course_code 6995 non-null object \n", + " 1 course_title 6995 non-null object \n", + " 2 teachers 5434 non-null object \n", + " 3 seminar_leaders 2588 non-null object \n", + " 4 q1 6847 non-null float64\n", + " 5 q2 6827 non-null float64\n", + " 6 q3 5458 non-null float64\n", + " 7 q4 5457 non-null float64\n", + " 8 q5 5458 non-null float64\n", + " 9 q6 2600 non-null float64\n", + " 10 q7 2600 non-null float64\n", + " 11 q8 2600 non-null float64\n", + " 12 q9 6776 non-null float64\n", + " 13 q10 6829 non-null float64\n", + " 14 q11 6783 non-null float64\n", + " 15 q12 6801 non-null float64\n", + " 16 q13 6798 non-null float64\n", + " 17 c_value 2183 non-null object \n", + " 18 c_improve 1798 non-null object \n", + " 19 department_code 6995 non-null object \n", + "dtypes: float64(13), object(7)\n", + "memory usage: 1.1+ MB\n" + ] + } + ], + "source": [ + "# \n", + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# memory usage of each column in bytes (useful when working with the larger datasets)\n", + "df.memory_usage()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* you can treat a DataFrame semantically like a dict of like-indexed Series objects\n", + " * getting, setting, and deleting columns works with the same syntax as the analogous dict operations\n", + "\n", + "## Indexing/Selection\n", + "\n", + "| Operation | Syntax | Result |\n", + "|--------------------------------|---------------|-----------|\n", + "| Select column | df[col] | Series |\n", + "| Select row by label | df.loc[label] | Series |\n", + "| Select row by integer location | df.iloc[loc] | Series |\n", + "| Slice rows | df[5:10] | DataFrame |\n", + "| Select rows by boolean vector | df[bool_vec] | DataFrame |" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "number\n", + "1 Ekonomie I\n", + "2 Angličtina pro ekonomy I\n", + "3 Matematika 1\n", + "4 Úvod do studia práva\n", + "5 Seminář k aktualitám I\n", + " ... \n", + "6993 Novinářská etika v praxi\n", + "6994 Diplomový seminář II.\n", + "6995 Ekonomika v médiích\n", + "6996 Dějiny populární hudby\n", + "6997 Tvůrčí dílny – tvůrčí psaní I\n", + "Name: course_title, Length: 6995, dtype: object" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# gives us series\n", + "df['course_title']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#this demonstrates usefullness of proper column naming\n", + "df.course_title" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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course_title
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1Ekonomie I
2Angličtina pro ekonomy I
3Matematika 1
4Úvod do studia práva
5Seminář k aktualitám I
......
6993Novinářská etika v praxi
6994Diplomový seminář II.
6995Ekonomika v médiích
6996Dějiny populární hudby
6997Tvůrčí dílny – tvůrčí psaní I
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6995 rows × 1 columns

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" + ], + "text/plain": [ + " course_title\n", + "number \n", + "1 Ekonomie I\n", + "2 Angličtina pro ekonomy I\n", + "3 Matematika 1\n", + "4 Úvod do studia práva\n", + "5 Seminář k aktualitám I\n", + "... ...\n", + "6993 Novinářská etika v praxi\n", + "6994 Diplomový seminář II.\n", + "6995 Ekonomika v médiích\n", + "6996 Dějiny populární hudby\n", + "6997 Tvůrčí dílny – tvůrčí psaní I\n", + "\n", + "[6995 rows x 1 columns]" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# multple columns -> gives us dataframe\n", + "df[['course_title']]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# just one column: just convenience (if column name has a space or dot, you are screwed)\n", + "#naming conventions: no special character, underscore for spaces, no CZECH chars! informative and short\n", + "df.course_title" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# subset of columns you want \n", + "df[['course_title','teachers']].head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['course_code', 'course_title', 'teachers', 'seminar_leaders', 'q1',\n", + " 'q2', 'q3', 'q4', 'q5', 'q6', 'q7', 'q8', 'q9', 'q10', 'q11', 'q12',\n", + " 'q13', 'c_value', 'c_improve', 'department_code'],\n", + " dtype='object')" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# list of all columns \n", + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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course_codecourse_titleteachersseminar_leadersq1q2q3q4q5q6...q8q9q10q11q12q13c_valuec_improvedepartment_codetmp
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1JEB003Ekonomie IFanta,N.,Kracík,J.,Švarcová,N.Fanta,N.,Kracík,J.,Švarcová,N.3.05.04.05.04.02.0...3.01.04.01.0NaN2.0NaNNaNies11/10
2JLB003Angličtina pro ekonomy INaNPoslušná,L.5.03.0NaNNaNNaN5.0...5.01.05.05.05.05.0NaNNaNcjp11/10
3NMMA701Matematika 1Spurný,J.Rondoš,J.3.05.03.02.01.04.0...5.01.03.02.02.01.0NaNNaNies11/10
4JEB023Úvod do studia právaPražák,P.,Wintr,J.NaN3.04.03.03.01.0NaN...NaN1.03.02.03.02.0NaNNaNies11/10
5JEB055Seminář k aktualitám INaNVyhnánek,T.2.03.0NaNNaNNaN2.0...1.01.04.02.02.01.0NaNNaNies11/10
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5 rows × 21 columns

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" + ], + "text/plain": [ + " course_code course_title teachers \\\n", + "number \n", + "1 JEB003 Ekonomie I Fanta,N.,Kracík,J.,Švarcová,N. \n", + "2 JLB003 Angličtina pro ekonomy I NaN \n", + "3 NMMA701 Matematika 1 Spurný,J. \n", + "4 JEB023 Úvod do studia práva Pražák,P.,Wintr,J. \n", + "5 JEB055 Seminář k aktualitám I NaN \n", + "\n", + " seminar_leaders q1 q2 q3 q4 q5 q6 ... \\\n", + "number ... \n", + "1 Fanta,N.,Kracík,J.,Švarcová,N. 3.0 5.0 4.0 5.0 4.0 2.0 ... \n", + "2 Poslušná,L. 5.0 3.0 NaN NaN NaN 5.0 ... \n", + "3 Rondoš,J. 3.0 5.0 3.0 2.0 1.0 4.0 ... \n", + "4 NaN 3.0 4.0 3.0 3.0 1.0 NaN ... \n", + "5 Vyhnánek,T. 2.0 3.0 NaN NaN NaN 2.0 ... \n", + "\n", + " q8 q9 q10 q11 q12 q13 c_value c_improve department_code tmp \n", + "number \n", + "1 3.0 1.0 4.0 1.0 NaN 2.0 NaN NaN ies 11/10 \n", + "2 5.0 1.0 5.0 5.0 5.0 5.0 NaN NaN cjp 11/10 \n", + "3 5.0 1.0 3.0 2.0 2.0 1.0 NaN NaN ies 11/10 \n", + "4 NaN 1.0 3.0 2.0 3.0 2.0 NaN NaN ies 11/10 \n", + "5 1.0 1.0 4.0 2.0 2.0 1.0 NaN NaN ies 11/10 \n", + "\n", + "[5 rows x 21 columns]" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# adding columns (first adding, so we have something to drop)\n", + "df['tmp'] = '11/10'\n", + "# you can also use assign function, if new column should be a function of original column \n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df['sumq1q2'] = df.q1+df.q2\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# drop column (you can also use 'del' (a general python comand for deleting)\n", + "df.drop('tmp', axis = 1, inplace = True) # axis to specify you want to drop column, inplace operation in this case" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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course_codecourse_titleteachersseminar_leadersq1q2q3q4q5q6...q8q9q10q11q12q13c_valuec_improvedepartment_codetmp
number
36JEB105StatisticsČervinka,M.Červinka,M.4.05.05.04.03.05.0...5.01.03.04.02.03.0NaNNaNies11/10
42JEB105StatisticsČervinka,M.Hanus,L.5.05.05.05.05.05.0...5.02.05.05.05.05.0Přístup vyučujících a pana Červinky k předmětu...Možná jasnější prezentace, slidy byly někdy ve...ies11/10
327JEB105StatisticsČervinka,M.Smutná,Š.5.04.04.05.04.04.0...5.01.05.05.04.05.0Jako student ekonomické teorii považuji tento ...Když tento předmět srovnám s výukou matematiky...ies11/10
387JEB105StatisticsČervinka,M.Hanus,L.5.04.04.05.04.04.0...5.01.05.04.04.04.0Oceňuji komentáře pana Červinky k našim hodnoc...Měl bych poznámku ohledně prezentací. U někter...ies11/10
882JEB105StatisticsČervinka,M.Nevrla,M.5.04.05.05.03.03.0...3.01.05.05.05.05.0I když jsem byla jen na první přednášce a prvn...Možná bych ocenila více tipů na literaturu (na...ies11/10
943JEB105StatisticsČervinka,M.Červinka,M.5.05.05.05.05.05.0...5.01.05.05.04.05.0NaNNaNies11/10
1003JEB105StatisticsČervinka,M.Hanus,L.4.05.04.03.05.04.0...5.01.05.05.05.04.0NaNNaNies11/10
1418JEB105StatisticsČervinka,M.Nevrla,M.4.05.04.05.05.05.0...5.01.0NaNNaNNaNNaNDomácí úkoly byly časově velice náročné, ale m...NaNies11/10
1429JEB105StatisticsČervinka,M.Červinka,M.5.05.05.05.05.05.0...5.01.05.05.05.05.0NaNNaNies11/10
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1JEB003Ekonomie IFanta,N.,Kracík,J.,Švarcová,N.Fanta,N.,Kracík,J.,Švarcová,N.3.05.04.05.04.02.0...3.01.04.01.0NaN2.0NaNNaNies11/10
3NMMA701Matematika 1Spurný,J.Rondoš,J.3.05.03.02.01.04.0...5.01.03.02.02.01.0NaNNaNies11/10
4JEB023Úvod do studia právaPražák,P.,Wintr,J.NaN3.04.03.03.01.0NaN...NaN1.03.02.03.02.0NaNNaNies11/10
5JEB055Seminář k aktualitám INaNVyhnánek,T.2.03.0NaNNaNNaN2.0...1.01.04.02.02.01.0NaNNaNies11/10
7JEB998Úvod do ekonomieKameníček,J.NaN4.03.02.03.02.0NaN...NaN1.03.01.02.03.0NaNNaNies11/10
..................................................................
6973JEB105StatisticsČervinka,M.Smutná,Š.2.04.01.02.02.05.0...5.01.04.04.03.01.0Domácí úkoly, ačkoliv systém hodnocení je extr...Přednášky. Pan Červinka se vše snaží dělat mno...ies11/10
6976NMMA703Matematika 3Zelený,M.Zelený,M.5.05.05.05.05.05.0...5.01.05.05.05.05.0NaNNaNies11/10
6977JEB114Macroeconomics IBrož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.1.01.01.01.01.01.0...1.03.02.02.02.01.0NaNNaNies11/10
6978JEB108Microeconomics IIČechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.Chytilová,J.,Jonášová,J.,Smutná,Š.3.03.05.05.04.03.0...4.01.02.01.03.03.0NaNNaNies11/10
6979JEB111Advanced Data Analysis in MS ExcelNaNPoláková,N.,Polák,P.5.04.0NaNNaNNaN5.0...5.01.03.04.02.04.0NaNNaNies11/10
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1103 rows × 21 columns

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" + ], + "text/plain": [ + " course_code course_title \\\n", + "number \n", + "1 JEB003 Ekonomie I \n", + "3 NMMA701 Matematika 1 \n", + "4 JEB023 Úvod do studia práva \n", + "5 JEB055 Seminář k aktualitám I \n", + "7 JEB998 Úvod do ekonomie \n", + "... ... ... \n", + "6973 JEB105 Statistics \n", + "6976 NMMA703 Matematika 3 \n", + "6977 JEB114 Macroeconomics I \n", + "6978 JEB108 Microeconomics II \n", + "6979 JEB111 Advanced Data Analysis in MS Excel \n", + "\n", + " teachers \\\n", + "number \n", + "1 Fanta,N.,Kracík,J.,Švarcová,N. \n", + "3 Spurný,J. \n", + "4 Pražák,P.,Wintr,J. \n", + "5 NaN \n", + "7 Kameníček,J. \n", + "... ... \n", + "6973 Červinka,M. \n", + "6976 Zelený,M. \n", + "6977 Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J. \n", + "6978 Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P. \n", + "6979 NaN \n", + "\n", + " seminar_leaders q1 q2 q3 q4 q5 \\\n", + "number \n", + "1 Fanta,N.,Kracík,J.,Švarcová,N. 3.0 5.0 4.0 5.0 4.0 \n", + "3 Rondoš,J. 3.0 5.0 3.0 2.0 1.0 \n", + "4 NaN 3.0 4.0 3.0 3.0 1.0 \n", + "5 Vyhnánek,T. 2.0 3.0 NaN NaN NaN \n", + "7 NaN 4.0 3.0 2.0 3.0 2.0 \n", + "... ... ... ... ... ... ... \n", + "6973 Smutná,Š. 2.0 4.0 1.0 2.0 2.0 \n", + "6976 Zelený,M. 5.0 5.0 5.0 5.0 5.0 \n", + "6977 Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J. 1.0 1.0 1.0 1.0 1.0 \n", + "6978 Chytilová,J.,Jonášová,J.,Smutná,Š. 3.0 3.0 5.0 5.0 4.0 \n", + "6979 Poláková,N.,Polák,P. 5.0 4.0 NaN NaN NaN \n", + "\n", + " q6 ... q8 q9 q10 q11 q12 q13 \\\n", + "number ... \n", + "1 2.0 ... 3.0 1.0 4.0 1.0 NaN 2.0 \n", + "3 4.0 ... 5.0 1.0 3.0 2.0 2.0 1.0 \n", + "4 NaN ... NaN 1.0 3.0 2.0 3.0 2.0 \n", + "5 2.0 ... 1.0 1.0 4.0 2.0 2.0 1.0 \n", + "7 NaN ... NaN 1.0 3.0 1.0 2.0 3.0 \n", + "... ... ... ... ... ... ... ... ... \n", + "6973 5.0 ... 5.0 1.0 4.0 4.0 3.0 1.0 \n", + "6976 5.0 ... 5.0 1.0 5.0 5.0 5.0 5.0 \n", + "6977 1.0 ... 1.0 3.0 2.0 2.0 2.0 1.0 \n", + "6978 3.0 ... 4.0 1.0 2.0 1.0 3.0 3.0 \n", + "6979 5.0 ... 5.0 1.0 3.0 4.0 2.0 4.0 \n", + "\n", + " c_value \\\n", + "number \n", + "1 NaN \n", + "3 NaN \n", + "4 NaN \n", + "5 NaN \n", + "7 NaN \n", + "... ... \n", + "6973 Domácí úkoly, ačkoliv systém hodnocení je extr... \n", + "6976 NaN \n", + "6977 NaN \n", + "6978 NaN \n", + "6979 NaN \n", + "\n", + " c_improve department_code \\\n", + "number \n", + "1 NaN ies \n", + "3 NaN ies \n", + "4 NaN ies \n", + "5 NaN ies \n", + "7 NaN ies \n", + "... ... ... \n", + "6973 Přednášky. Pan Červinka se vše snaží dělat mno... ies \n", + "6976 NaN ies \n", + "6977 NaN ies \n", + "6978 NaN ies \n", + "6979 NaN ies \n", + "\n", + " tmp \n", + "number \n", + "1 11/10 \n", + "3 11/10 \n", + "4 11/10 \n", + "5 11/10 \n", + "7 11/10 \n", + "... ... \n", + "6973 11/10 \n", + "6976 11/10 \n", + "6977 11/10 \n", + "6978 11/10 \n", + "6979 11/10 \n", + "\n", + "[1103 rows x 21 columns]" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df.department_code == 'ies']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### loc and Idioms\n", + "* `.loc` selects data by the label of the rows and columns (as opposed to the `.iloc`) integer location\n", + "* we can also use `.loc` for subsetting based on condition(s)" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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department_codeteachers
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5iesNaN
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" + ], + "text/plain": [ + " department_code teachers\n", + "number \n", + "5 ies NaN\n", + "8 ies NaN\n", + "11 cjp NaN\n", + "14 ks Hendl,J.\n", + "17 ks Bureš,J.,Numerato,D.\n", + "20 kvsp Vlk,A.\n", + "23 ks Soukup,P." + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.loc[5:25:3,['department_code','teachers']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Subset using a mask" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [], + "source": [ + "# select only observations for IES only\n", + "df_ies = df.loc[df['department_code'] == 'ies']" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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course_codecourse_titleteachersseminar_leadersq1q2q3q4q5q6...q8q9q10q11q12q13c_valuec_improvedepartment_codetmp
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1JEB003Ekonomie IFanta,N.,Kracík,J.,Švarcová,N.Fanta,N.,Kracík,J.,Švarcová,N.3.05.04.05.04.02.0...3.01.04.01.0NaN2.0NaNNaNies11/10
3NMMA701Matematika 1Spurný,J.Rondoš,J.3.05.03.02.01.04.0...5.01.03.02.02.01.0NaNNaNies11/10
4JEB023Úvod do studia právaPražák,P.,Wintr,J.NaN3.04.03.03.01.0NaN...NaN1.03.02.03.02.0NaNNaNies11/10
5JEB055Seminář k aktualitám INaNVyhnánek,T.2.03.0NaNNaNNaN2.0...1.01.04.02.02.01.0NaNNaNies11/10
7JEB998Úvod do ekonomieKameníček,J.NaN4.03.02.03.02.0NaN...NaN1.03.01.02.03.0NaNNaNies11/10
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5 rows × 21 columns

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" + ], + "text/plain": [ + " course_code course_title teachers \\\n", + "number \n", + "1 JEB003 Ekonomie I Fanta,N.,Kracík,J.,Švarcová,N. \n", + "3 NMMA701 Matematika 1 Spurný,J. \n", + "4 JEB023 Úvod do studia práva Pražák,P.,Wintr,J. \n", + "5 JEB055 Seminář k aktualitám I NaN \n", + "7 JEB998 Úvod do ekonomie Kameníček,J. \n", + "\n", + " seminar_leaders q1 q2 q3 q4 q5 q6 ... \\\n", + "number ... \n", + "1 Fanta,N.,Kracík,J.,Švarcová,N. 3.0 5.0 4.0 5.0 4.0 2.0 ... \n", + "3 Rondoš,J. 3.0 5.0 3.0 2.0 1.0 4.0 ... \n", + "4 NaN 3.0 4.0 3.0 3.0 1.0 NaN ... \n", + "5 Vyhnánek,T. 2.0 3.0 NaN NaN NaN 2.0 ... \n", + "7 NaN 4.0 3.0 2.0 3.0 2.0 NaN ... \n", + "\n", + " q8 q9 q10 q11 q12 q13 c_value c_improve department_code tmp \n", + "number \n", + "1 3.0 1.0 4.0 1.0 NaN 2.0 NaN NaN ies 11/10 \n", + "3 5.0 1.0 3.0 2.0 2.0 1.0 NaN NaN ies 11/10 \n", + "4 NaN 1.0 3.0 2.0 3.0 2.0 NaN NaN ies 11/10 \n", + "5 1.0 1.0 4.0 2.0 2.0 1.0 NaN NaN ies 11/10 \n", + "7 NaN 1.0 3.0 1.0 2.0 3.0 NaN NaN ies 11/10 \n", + "\n", + "[5 rows x 21 columns]" + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_ies.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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course_codecourse_titleteachersseminar_leadersq1q2q3q4q5q6...q8q9q10q11q12q13c_valuec_improvedepartment_codetmp
number
89JEM005Advanced EconometricsBaruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevr...Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.5.05.05.05.04.05.0...5.01.05.05.05.01.0Mr.Barunik is very sexyNaNies11/10
973JEM005Advanced EconometricsBaruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevr...Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.4.04.05.05.05.02.0...4.01.03.05.04.04.0NaNNaNies11/10
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2 rows × 21 columns

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" + ], + "text/plain": [ + " course_code course_title \\\n", + "number \n", + "89 JEM005 Advanced Econometrics \n", + "973 JEM005 Advanced Econometrics \n", + "\n", + " teachers \\\n", + "number \n", + "89 Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevr... \n", + "973 Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevr... \n", + "\n", + " seminar_leaders q1 q2 q3 q4 q5 \\\n", + "number \n", + "89 Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M. 5.0 5.0 5.0 5.0 4.0 \n", + "973 Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M. 4.0 4.0 5.0 5.0 5.0 \n", + "\n", + " q6 ... q8 q9 q10 q11 q12 q13 c_value \\\n", + "number ... \n", + "89 5.0 ... 5.0 1.0 5.0 5.0 5.0 1.0 Mr.Barunik is very sexy \n", + "973 2.0 ... 4.0 1.0 3.0 5.0 4.0 4.0 NaN \n", + "\n", + " c_improve department_code tmp \n", + "number \n", + "89 NaN ies 11/10 \n", + "973 NaN ies 11/10 \n", + "\n", + "[2 rows x 21 columns]" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# select only observations for Advanced Econometrics\n", + "df.loc[df['course_title'] == 'Advanced Econometrics'].head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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course_codecourse_titleteachersseminar_leadersq1q2q3q4q5q6...q8q9q10q11q12q13c_valuec_improvedepartment_codetmp
number
1138JEM005Advanced EconometricsBaruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevr...Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.NaN5.04.04.04.03.0...3.0NaN2.05.05.05.0The lecturers teaching method.More practical exercises and involvement of st...ies11/10
1870JEM005Advanced EconometricsBaruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevr...Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.3.05.05.05.05.03.0...3.01.03.03.02.03.0Positive atmosphere the lecturer providedIt was too theoretical and abstract. Since it ...ies11/10
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2 rows × 21 columns

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" + ], + "text/plain": [ + " course_code course_title \\\n", + "number \n", + "1138 JEM005 Advanced Econometrics \n", + "1870 JEM005 Advanced Econometrics \n", + "\n", + " teachers \\\n", + "number \n", + "1138 Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevr... \n", + "1870 Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevr... \n", + "\n", + " seminar_leaders q1 q2 q3 q4 q5 \\\n", + "number \n", + "1138 Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M. NaN 5.0 4.0 4.0 4.0 \n", + "1870 Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M. 3.0 5.0 5.0 5.0 5.0 \n", + "\n", + " q6 ... q8 q9 q10 q11 q12 q13 \\\n", + "number ... \n", + "1138 3.0 ... 3.0 NaN 2.0 5.0 5.0 5.0 \n", + "1870 3.0 ... 3.0 1.0 3.0 3.0 2.0 3.0 \n", + "\n", + " c_value \\\n", + "number \n", + "1138 The lecturers teaching method. \n", + "1870 Positive atmosphere the lecturer provided \n", + "\n", + " c_improve department_code \\\n", + "number \n", + "1138 More practical exercises and involvement of st... ies \n", + "1870 It was too theoretical and abstract. Since it ... ies \n", + "\n", + " tmp \n", + "number \n", + "1138 11/10 \n", + "1870 11/10 \n", + "\n", + "[2 rows x 21 columns]" + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# subsetting based on multiple conditions: AE and non-missing comment on what to improve\n", + "df.loc[(df['course_title'] == 'Advanced Econometrics') & (~df['c_improve'].isnull())].head(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Sometimes, we don't have a clear list of columns to be selected ready, e.g. how to select columns from q1 to q13? \n", + " * using actual list of column names :(\n", + " * be lazy!\n", + " * or ... " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# list comprehension\n", + "print([x for x in df.columns if 'q' in x]) #by substring\n", + "print([x for x in df.columns if (len(x) == 2) | (len(x) == 3)]) #by length\n", + "print([x for x in df.columns if x.startswith('q')]) #by first letter\n", + "# by regular expression is the safest - q and then at most 2 digit number -> later in course" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['q1',\n", + " 'q2',\n", + " 'q3',\n", + " 'q4',\n", + " 'q5',\n", + " 'q6',\n", + " 'q7',\n", + " 'q8',\n", + " 'q9',\n", + " 'q10',\n", + " 'q11',\n", + " 'q12',\n", + " 'q13']" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[x for x in df.columns if 'q' in x]" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " q1 q2 q3 q4 q5 q6 q7 q8 q9 q10 q11 q12 q13\n", + "number \n", + "1 3.0 5.0 4.0 5.0 4.0 2.0 4.0 3.0 1.0 4.0 1.0 NaN 2.0\n", + "2 5.0 3.0 NaN NaN NaN 5.0 5.0 5.0 1.0 5.0 5.0 5.0 5.0\n", + "3 3.0 5.0 3.0 2.0 1.0 4.0 4.0 5.0 1.0 3.0 2.0 2.0 1.0\n", + "4 3.0 4.0 3.0 3.0 1.0 NaN NaN NaN 1.0 3.0 2.0 3.0 2.0\n", + "5 2.0 3.0 NaN NaN NaN 2.0 3.0 1.0 1.0 4.0 2.0 2.0 1.0" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_q = df[[x for x in df.columns if 'q' in x]]\n", + "df_q.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using functions on pandas objects\n", + "\n", + "| Operation | Function |\n", + "|--------------------|-----------------------|\n", + "| Row or Column-wise | `apply()` |\n", + "| Aggregation | `agg() / transform()` |\n", + "| Elementwise | `applymap()` |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Tablewise**\n", + "* DFs and Series can be arguments of the functions\n", + "* if multiple functions need to be called in a sequence, use `pipe()` method, also called the method chaining\n", + " * often used in the data science setting\n", + " * inspired by unix pipes and dplyr (%>%) operator in R " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Row or Column-wise Function Application**\n", + "* `apply()` is extremely powerful, when used with some brainpower" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_q.apply(np.mean, axis = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# using lambda - anonymous function\n", + "#standardization to unit variance\n", + "df_q.apply(lambda x: (x - np.mean(x)) / np.std(x), axis = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# using custom function, with arguments (could have also be done with lambda)\n", + "def add_and_substract(df, sub = 1, add = 1):\n", + " return df - sub + add\n", + "df_q.apply(add_and_substract, args = (0,0))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# A little bit more sophisticated: show the longest comment\n", + "df.loc[df['c_value'].apply(lambda x: len(str(x))).idxmax(),'c_value']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Aggregation**\n", + "* *`aggregate()`* and *`transform()`*\n", + "* aggregation allows multiple aggregation operations in a single concise way\n", + "* `transform()` method returns an object that is indexed the same as the original\n", + " * allows multiple operations at the same time, instead of one-by-one as `aggregate()` method" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# aggregating simple function is the same as apply\n", + "df_q.agg(np.mean, axis = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# aggregating more functions more interesting (you could do your own describe function easily! )\n", + "df_q.aggregate([np.mean, np.std, np.min, np.max], axis = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# aggregating using dictionary, i.e. column specific aggregation \n", + "df_q.agg({'q1' : [np.mean], 'q2': np.std, 'q3': [np.mean, np.std, np.var]})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# using single function, the same as with apply\n", + "df_q.transform(lambda x: np.power(x,2))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# using multiple functions (can also be done using dictionary as in the case of aggregate)\n", + "df_q.transform([np.abs, lambda x: x + 1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Missing values" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "number\n", + "1 True\n", + "2 False\n", + "3 True\n", + "4 True\n", + "5 False\n", + " ... \n", + "6993 True\n", + "6994 False\n", + "6995 True\n", + "6996 True\n", + "6997 True\n", + "Name: teachers, Length: 6995, dtype: bool" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.teachers.notnull()" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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course_codecourse_titleteachersseminar_leadersq1q2q3q4q5q6...q8q9q10q11q12q13c_valuec_improvedepartment_codetmp
number
1JEB003Ekonomie IFanta,N.,Kracík,J.,Švarcová,N.Fanta,N.,Kracík,J.,Švarcová,N.3.05.04.05.04.02.0...3.01.04.01.0NaN2.0NaNNaNies11/10
3NMMA701Matematika 1Spurný,J.Rondoš,J.3.05.03.02.01.04.0...5.01.03.02.02.01.0NaNNaNies11/10
4JEB023Úvod do studia právaPražák,P.,Wintr,J.NaN3.04.03.03.01.0NaN...NaN1.03.02.03.02.0NaNNaNies11/10
6JPM314Theories of International RelationsDitrych,O.,Plechanovová,B.NaN2.03.02.04.01.0NaN...NaN2.03.01.03.01.0NaNNaNkmv11/10
7JEB998Úvod do ekonomieKameníček,J.NaN4.03.02.03.02.0NaN...NaN1.03.01.02.03.0NaNNaNies11/10
..................................................................
6991JPM620Geopolitical ThoughtKofroň,J.NaN3.04.03.03.03.0NaN...NaN1.04.03.04.03.0NaNNaNkp11/10
6993JJM260Novinářská etika v praxiMoravec,V.NaN5.05.05.05.05.0NaN...NaN1.05.04.05.05.0Tlak ze strany vyucujicicho - eseje, pestovani...Vetsi ucebnu?kz11/10
6995JJM360Ekonomika v médiíchKlimeš,D.NaN5.04.05.05.04.0NaN...NaN2.05.03.05.05.0Hosty a jejich prednasky.NaNkz11/10
6996JJM354Dějiny populární hudbyHalada,A.NaN3.03.05.05.01.0NaN...NaN5.01.03.03.05.0Kurz mel velky potencial, zabavne a prinosne p...Aby prednasky probihaly.kz11/10
6997JJM340Tvůrčí dílny – tvůrčí psaní INovotný,D.Novotný,D.5.05.05.05.05.05.0...5.01.05.05.04.05.0Ja kurz absolvovala s panem docentem Malym - j...NaNkz11/10
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5434 rows × 21 columns

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" + ], + "text/plain": [ + " course_code course_title \\\n", + "number \n", + "1 JEB003 Ekonomie I \n", + "3 NMMA701 Matematika 1 \n", + "4 JEB023 Úvod do studia práva \n", + "6 JPM314 Theories of International Relations \n", + "7 JEB998 Úvod do ekonomie \n", + "... ... ... \n", + "6991 JPM620 Geopolitical Thought \n", + "6993 JJM260 Novinářská etika v praxi \n", + "6995 JJM360 Ekonomika v médiích \n", + "6996 JJM354 Dějiny populární hudby \n", + "6997 JJM340 Tvůrčí dílny – tvůrčí psaní I \n", + "\n", + " teachers seminar_leaders q1 \\\n", + "number \n", + "1 Fanta,N.,Kracík,J.,Švarcová,N. Fanta,N.,Kracík,J.,Švarcová,N. 3.0 \n", + "3 Spurný,J. Rondoš,J. 3.0 \n", + "4 Pražák,P.,Wintr,J. NaN 3.0 \n", + "6 Ditrych,O.,Plechanovová,B. NaN 2.0 \n", + "7 Kameníček,J. NaN 4.0 \n", + "... ... ... ... \n", + "6991 Kofroň,J. NaN 3.0 \n", + "6993 Moravec,V. NaN 5.0 \n", + "6995 Klimeš,D. NaN 5.0 \n", + "6996 Halada,A. NaN 3.0 \n", + "6997 Novotný,D. Novotný,D. 5.0 \n", + "\n", + " q2 q3 q4 q5 q6 ... q8 q9 q10 q11 q12 q13 \\\n", + "number ... \n", + "1 5.0 4.0 5.0 4.0 2.0 ... 3.0 1.0 4.0 1.0 NaN 2.0 \n", + "3 5.0 3.0 2.0 1.0 4.0 ... 5.0 1.0 3.0 2.0 2.0 1.0 \n", + "4 4.0 3.0 3.0 1.0 NaN ... NaN 1.0 3.0 2.0 3.0 2.0 \n", + "6 3.0 2.0 4.0 1.0 NaN ... NaN 2.0 3.0 1.0 3.0 1.0 \n", + "7 3.0 2.0 3.0 2.0 NaN ... NaN 1.0 3.0 1.0 2.0 3.0 \n", + "... ... ... ... ... ... ... ... ... ... ... ... ... \n", + "6991 4.0 3.0 3.0 3.0 NaN ... NaN 1.0 4.0 3.0 4.0 3.0 \n", + "6993 5.0 5.0 5.0 5.0 NaN ... NaN 1.0 5.0 4.0 5.0 5.0 \n", + "6995 4.0 5.0 5.0 4.0 NaN ... NaN 2.0 5.0 3.0 5.0 5.0 \n", + "6996 3.0 5.0 5.0 1.0 NaN ... NaN 5.0 1.0 3.0 3.0 5.0 \n", + "6997 5.0 5.0 5.0 5.0 5.0 ... 5.0 1.0 5.0 5.0 4.0 5.0 \n", + "\n", + " c_value \\\n", + "number \n", + "1 NaN \n", + "3 NaN \n", + "4 NaN \n", + "6 NaN \n", + "7 NaN \n", + "... ... \n", + "6991 NaN \n", + "6993 Tlak ze strany vyucujicicho - eseje, pestovani... \n", + "6995 Hosty a jejich prednasky. \n", + "6996 Kurz mel velky potencial, zabavne a prinosne p... \n", + "6997 Ja kurz absolvovala s panem docentem Malym - j... \n", + "\n", + " c_improve department_code tmp \n", + "number \n", + "1 NaN ies 11/10 \n", + "3 NaN ies 11/10 \n", + "4 NaN ies 11/10 \n", + "6 NaN kmv 11/10 \n", + "7 NaN ies 11/10 \n", + "... ... ... ... \n", + "6991 NaN kp 11/10 \n", + "6993 Vetsi ucebnu? kz 11/10 \n", + "6995 NaN kz 11/10 \n", + "6996 Aby prednasky probihaly. kz 11/10 \n", + "6997 NaN kz 11/10 \n", + "\n", + "[5434 rows x 21 columns]" + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df.teachers.notnull()]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "% of missing observations for specific column" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df['q1'].isnull().sum() / df['q1'].isnull().count()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "% of missing observations for all columns" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 123, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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EJBXDCBEREUnFMEJERERSMYwQERGRVAwjREREJBXDCBEREUnFMEJERERSMYwQERGRVAwjREREJBXDCBEREUnFMEJERERSMYwQERGRVAwjREREJBXDCBEREUnFMEJERERSMYwQERGRVAwjREREJBXDCBEREUnFMEJERERSMYwQERGRVAwjREREJBXDCBEREUnFMEJERERSMYwQERGRVAwjREREJBXDCBEREUnFMEJERERSMYwQERGRVAwjREREJBXDCBEREUnFMEJERERSMYwQERGRVAwjREREJBXDCBEREUnFMEJERERSMYwQERGRVAwjREREJBXDCBEREUnFMEJERERSMYwQERGRVAwjREREJJVRYeTrr7+GRqOBo6MjAgMDcezYsSK9b926dVCpVOjevbsxqyUiIiIrVOwwsn79eoSFhWHChAmIjY1F48aN0aFDB9y5c+eZ77t69SpGjx6N119/3ehmiYiIyPoUO4zMnTsXgwYNQmhoKOrVq4fFixfD2dkZK1asKPQ9ubm56Nu3LyZNmoTq1aub1DARERFZF9viLJydnY0TJ04gPDxcN6ZWq9G2bVscOXKk0Pd9/vnn8Pb2xoABA3Dw4MHnricrKwtZWVm652lpaQAArVYLrVb73PfnL1OUZYtLydpK17fk3pWuz96ts74l9650fXPWdrARhmNqofffgtZtbH0la1tSfUvovajrVAkhDCsW4tatW/Dx8cHhw4cRFBSkGx87diz279+Po0ePGrzn0KFDeO+993Dy5El4eXmhf//+SE1NxdatWwtdz8SJEzFp0iSD8bVr18LZ2bmo7RIREZFEmZmZ6NOnDx48eAB3d/dClyvWnpHievjwIfr164elS5fCy8uryO8LDw9HWFiY7nlaWhp8fX3Rvn37Z34x+bRaLfbs2YN27drBzs7OqN5l1Fa6viX3rnR99m6d9S25d6Xrm7N2g4m7DMYc1AKTA/IQcVyNrDyV3mtnJ3Ywqb6StS2pviX0nn9k43mKFUa8vLxgY2OD5ORkvfHk5GRUrFjRYPn4+HhcvXoVXbp00Y3l5eU9XrGtLS5cuICXXnrJ4H0ODg5wcHAwGLezsyvWP5riLl8cStZWur4l9650ffZunfUtuXel65ujdlauqvDX8lQGrxd3fYXVV7K2JdS3hN6Lus5incBqb28Pf39/REVF6cby8vIQFRWld9gmX506dXDmzBmcPHlS9+jatStat26NkydPwtfXtzirJyIiIitU7MM0YWFhCAkJQUBAAJo1a4Z58+YhIyMDoaGhAIDg4GD4+Phg2rRpcHR0RIMGDfTe7+npCQAG40RERPRiKnYY6dWrF+7evYvIyEgkJSWhSZMm2LlzJypUqAAASExMhFrNiV2JiIioaIw6gXXYsGEYNmxYga9FR0c/872rVq0yZpVERERkpbgLg4iIiKRiGCEiIiKpGEaIiIhIKoYRIiIikophhIiIiKRiGCEiIiKpGEaIiIhIKoYRIiIikophhIiIiKRiGCEiIiKpGEaIiIhIKoYRIiIikophhIiIiKRiGCEiIiKpGEaIiIhIKoYRIiIikophhIiIiKRiGCEiIiKpGEaIiIhIKoYRIiIikophhIiIiKRiGCEiIiKpGEaIiIhIKoYRIiIikophhIiIiKRiGCEiIiKpGEaIiIhIKoYRIiIikophhIiIiKRiGCEiIiKpGEaIiIhIKoYRIiIikophhIiIiKRiGCEiIiKpGEaIiIhIKoYRIiIikophhIiIiKRiGCEiIiKpGEaIiIhIKoYRIiIikophhIiIiKRiGCEiIiKpGEaIiIhIKoYRIiIikophhIiIiKRiGCEiIiKpGEaIiIhIKoYRIiIikophhIiIiKRiGCEiIiKpGEaIiIhIKoYRIiIikophhIiIiKRiGCEiIiKpGEaIiIhIKoYRIiIikophhIiIiKRiGCEiIiKpbGU3QEREytKM324w5mAjMLMZ0GDiLmTlqvReuzr9zZJqjQgAwwgRWQmlf+GWpvoMC2RteJiGiIiIpGIYISIiIqkYRoiIiEgqo8LI119/DY1GA0dHRwQGBuLYsWOFLrt06VK8/vrrKFOmDMqUKYO2bds+c3kiIiJ6sRQ7jKxfvx5hYWGYMGECYmNj0bhxY3To0AF37twpcPno6Gj07t0b+/btw5EjR+Dr64v27dvj5s2bJjdPRERElq/YV9PMnTsXgwYNQmhoKABg8eLF2L59O1asWIHx48cbLL9mzRq958uWLcOmTZsQFRWF4ODgAteRlZWFrKws3fO0tDQAgFarhVarfW6P+csUZdniUrK20vUtuXel67N3y6/vYCMMx9RC778FrdcS61ty7+aob8m9m7O+JfRe1HWqhBCGFQuRnZ0NZ2dnbNy4Ed27d9eNh4SEIDU1Fdu2bXtujYcPH8Lb2xsbNmzAW2+9VeAyEydOxKRJkwzG165dC2dn56K2S0RERBJlZmaiT58+ePDgAdzd3Qtdrlh7Ru7du4fc3FxUqFBBb7xChQo4f/58kWqMGzcOlStXRtu2bQtdJjw8HGFhYbrnaWlpusM7z/pi8mm1WuzZswft2rWDnZ1dkfoqKiVrK13fkntXuj57t/z6DSbuMhhzUAtMDshDxHE1svL05wE5O7GDxda35N7NUd+SezdnfUvoPf/IxvOU6KRn06dPx7p16xAdHQ1HR8dCl3NwcICDg4PBuJ2dXbE+rIq7fHEoWVvp+pbcu9L12bvl1n960jG91/JUBq8Xd12lqb4l927O+pbcuznqW0LvRV1nscKIl5cXbGxskJycrDeenJyMihUrPvO9s2fPxvTp0/Hbb7+hUaNGxVktERERWbFiXU1jb28Pf39/REVF6cby8vIQFRWFoKCgQt83c+ZMTJ48GTt37kRAQIDx3RIREZHVKfZhmrCwMISEhCAgIADNmjXDvHnzkJGRobu6Jjg4GD4+Ppg2bRoAYMaMGYiMjMTatWuh0WiQlJQEAHB1dYWrq6sZvxQiIiKyRMUOI7169cLdu3cRGRmJpKQkNGnSBDt37tSd1JqYmAi1+n87XBYtWoTs7Gy88847enUmTJiAiRMnmtY9ERERWTyjTmAdNmwYhg0bVuBr0dHRes+vXr1qzCqIiIjoBcF70xAREZFUDCNEREQkFcMIERERScUwQkRERFIxjBAREZFUDCNEREQkFcMIERERScUwQkRERFIxjBAREZFUDCNEREQkFcMIERERScUwQkRERFIxjBAREZFUDCNEREQkFcMIERERScUwQkRERFIxjBAREZFUDCNEREQkFcMIERERScUwQkRERFIxjBAREZFUDCNEREQkFcMIERERScUwQkRERFIxjBAREZFUDCNEREQkFcMIERERScUwQkRERFIxjBAREZFUDCNEREQkFcMIERERScUwQkRERFIxjBAREZFUDCNEREQkFcMIERERScUwQkRERFIxjBAREZFUDCNEREQkFcMIERERScUwQkRERFIxjBAREZFUDCNEREQkFcMIERERScUwQkRERFIxjBAREZFUDCNEREQkFcMIERERScUwQkRERFIxjBAREZFUDCNEREQkFcMIERERScUwQkRERFIxjBAREZFUDCNEREQkFcMIERERSWUruwEiKj0047cbjDnYCMxsBjSYuAtZuSq9165Of7NU1Sciy8QwQmRmJf0Lnb/MicjS8TANERERScU9I1QqKbl3gYcKiIhKF4YRMgp/oRMRkbnwMA0RERFJxTBCREREUjGMEBERkVRGhZGvv/4aGo0Gjo6OCAwMxLFjx565/IYNG1CnTh04OjqiYcOG2LFjh1HNEhERkfUpdhhZv349wsLCMGHCBMTGxqJx48bo0KED7ty5U+Dyhw8fRu/evTFgwADExcWhe/fu6N69O86ePWty80RERGT5in01zdy5czFo0CCEhoYCABYvXozt27djxYoVGD9+vMHy8+fPR8eOHTFmzBgAwOTJk7Fnzx589dVXWLx4cYHryMrKQlZWlu75gwcPAAApKSnQarXP7VGr1SIzMxP379+HnZ1dcb9EabWVrm/O2rY5GYZjeQKZmXmw1aqRm6d/Nc39+/dLTX1L7r2g+pbcuznrW3Lvxa1vyb2bo74l927O+pbQ+8OHDwEAQohnr0wUQ1ZWlrCxsRFbtmzRGw8ODhZdu3Yt8D2+vr7iyy+/1BuLjIwUjRo1KnQ9EyZMEAD44IMPPvjggw8reFy/fv2Z+aJYe0bu3buH3NxcVKhQQW+8QoUKOH/+fIHvSUpKKnD5pKSkQtcTHh6OsLAw3fO8vDykpKSgXLlyUKlUhb4vX1paGnx9fXH9+nW4u7s/d/niULK20vUtuXel67N366xvyb0rXZ+9W2f90ta7EAIPHz5E5cqVn7lcqZz0zMHBAQ4ODnpjnp6exa7j7u6uyDdD6dpK17fk3pWuz96ts74l9650ffZunfVLU+8eHh7PXaZYJ7B6eXnBxsYGycnJeuPJycmoWLFige+pWLFisZYnIiKiF0uxwoi9vT38/f0RFRWlG8vLy0NUVBSCgoIKfE9QUJDe8gCwZ8+eQpcnIiKiF0uxD9OEhYUhJCQEAQEBaNasGebNm4eMjAzd1TXBwcHw8fHBtGnTAAAjRoxAy5YtMWfOHLz55ptYt24djh8/jiVLlpj3K3mCg4MDJkyYYHCop7TXVrq+JfeudH32bp31Lbl3peuzd+usb6m9q4R43vU2hr766ivMmjULSUlJaNKkCRYsWIDAwEAAQKtWraDRaLBq1Srd8hs2bMBnn32Gq1evombNmpg5cyY6d+5sti+CiIiILJdRYYSIiIjIXHhvGiIiIpKKYYSIiIikYhghIiIiqRhGiIiISCqGESIiIpLK6sLIo0ePFKudnZ2NCxcuICcnR5H6Qojn39nQCAcPHsT777+PoKAg3Lx5EwDw/fff49ChQ2ZflzkdO3YM8+fPR3h4OMLDwzF//nwcO3bMbPVv3LiB9PR0g3GtVosDBw6YXD8vL6/Q8cTERKPrCiFw5coV3c9hdnY21q9fj9WrV+PevXtG1y2Kv//+G6tXr1Z0Haa4f/8+9u3bh5SUFACP76c1Y8YMfP755zh37pxZ1yWEwL59+7B06VL8+uuvRbqjeHFVr14dly5dMlu92NhYnDlzRvd827Zt6N69Oz755BNkZ2ebbT3mNGfOHFy7dk12GyYpie3+/fffo0WLFqhcubJue82bNw/btm2ziPrFumtvaZWbmys+//xzUblyZWFjYyPi4+OFEEJ89tlnYtmyZSbXz8jIEB988IGwsbHRqz9s2DAxbdo0k+svW7ZM1K9fX9jb2wt7e3tRv359sXTpUpPrCiHExo0bhZOTkxg4cKBwcHDQ9b5w4ULRqVMns6yjIElJSWLSpElGvTc5OVm89tprQqVSiWrVqolmzZqJZs2aiWrVqgmVSiVee+01kZycbHRvt27dEq+88opQq9XCxsZG9OvXTzx8+FCvd7VabXT9Bw8eiH/961/C0dFReHt7i4iICJGTk2OW+ufPnxfVqlUTarVa1KhRQyQkJAh/f3/h4uIinJ2dhZeXl7h48aLRvT/PyZMnTdo2mZmZYvny5SI0NFR07NhRdO7cWQwbNkz89ttvJvd29OhR4eHhIVQqlShTpow4fvy48PPzEzVr1hQvvfSScHJyEidOnDC6fqdOnURqaqoQQoj79++LwMBAoVKpRPny5YVarRZ16tQRd+7cMar2/PnzC3zY2NiI8PBw3XNTBQQEiI0bNwohhIiPjxeOjo6id+/eokaNGmLEiBEm1f7ll19ERESEOHTokBBCiKioKNGpUyfRoUMH8e233xpdV6VSCRsbG9G2bVuxbt06kZWVZVKfT7t+/bq4e/eu7vmBAwdEnz59xGuvvSb69u0rDh8+bPI6lNzuQgjxzTffCC8vLzFlyhTh5OSk+5xfuXKlaNWqVamvL8Tjv8Qt3qRJk0T16tXFDz/8oLeh1q1bJ1599VWT6w8fPlz4+/uLgwcPChcXF139rVu3iiZNmphUOyIiQri4uIjx48eLbdu2iW3btonx48cLV1dXERERYXLvTZo0Ed99950QQghXV1dd77GxsaJChQom1y+MKb+03n77bREUFCTOnz9v8Nr58+dF8+bNxTvvvGN0b8HBwSIwMFD88ccfYs+ePcLf318EBASIlJQUIcTjsKBSqYyuP3z4cFGrVi2xYcMGsXTpUlGtWjXx5ptv6j5ETanfrVs30bVrV3H69Gnx8ccfi7p164pu3bqJ7Oxs8ejRI9GlSxfx/vvvG937gwcPnvk4ePCg0d/XS5cuiWrVqglvb2/h6+srVCqVePPNN0VgYKCwsbER//rXv4RWqzW697Zt24qBAweKtLQ0MWvWLFGlShUxcOBA3euhoaGie/fuRtdXqVS6EDxkyBBRr149kZCQIIR4/AvN399ffPjhh0bXrlKlitBoNHoPlUolfHx8hEajEX5+fkb3ns/d3V1cvnxZCCHE9OnTRfv27YUQQhw6dEhUqVLF6LqLFy8Wtra2wt/fX7i7u4vvv/9euLm5iYEDB4rBgwcLJycnMW/ePKNqq1QqsXLlStGtWzdhZ2cnypUrJ0aMGCHOnDljdL9Patasmfjll1+EEI8/09VqtejatasYN26c6NGjh7Czs9O9biyltnu+unXrii1btggh9D/nz5w5I8qVK1fq6wthJWHkpZde0v1l9eSGOnfunPD09DS5ftWqVcWRI0cM6l+6dEm4ubmZVNvLy0usXbvWYHzt2rVm+SY7OTmJK1euCCH0e4+PjxcODg5G1z116tQzH+vXrzf6l5arq6uIjY0t9PXjx48LV1dXY1sXlStXFkePHtU9z/8l3qRJE3H//n2T94xUrVpV7Nu3T/f87t27olmzZqJ9+/bi0aNHJtUvX768iIuLE0IIkZ6eLlQqlTh48KDu9ZiYGFG1alWje1epVEKtVhf6yH/dGJ06dRKDBw8WeXl5QojHH8r5e+cuXrwoNBqNmDBhgtG9lylTRvz1119CCCGys7OFWq3W+z6fOHFC+Pj4GF3/yTBSu3ZtsW3bNr3Xf/vtN6MDw+DBg0WTJk10/eeztbUVf/75p3ENF8DNzU2356xt27a6gHDt2jXh6OhodN169eqJJUuWCCGE2Lt3r3B0dBRff/217vWVK1eKunXrGlX7ye2enJwsZsyYIerUqSPUarV45ZVXxJIlS0RaWprRvbu4uOhCZWBgoJg+fbre6wsXLhRNmzY1ur4Qym33fI6OjuLq1atCCP3P+YsXL1pEfSGsJIwUtqH+/PNP4eLiYnL9J/e2PFn/5MmTwt3d3aTaHh4eBe5Wv3DhgvDw8DCpthBC+Pn5iT179ggh9Hv/7rvvjP5wEOJ/v7RUKpXBw9RfWuXKlRPR0dGFvr5v3z6TgpqLi4vBNtdqtaJ79+6iUaNG4vTp0yaFEScnJ92HW760tDQRFBQk2rRpIxISEoyu7+TkJK5du6Z77urqqvuLSwghEhMTTQqZ7u7uYsaMGSI6OrrAx9KlS43u3dnZWW+7Z2VlCTs7O3Hv3j0hxOO/SjUajdG9u7i46IK3EPo/70KY/sGvUql0h2G8vb3F2bNn9V6/evWqSdt+8+bNwtfXVyxcuFA3Zu4w0rp1axEcHCxWr14t7OzsxKVLl4QQQkRHR4tq1aoZXffpn0s7Ozu9PRdXrlwRzs7ORtV+Mow86cCBAyIkJES4uLiY9Dnv4eEhTp06JYR4/H3N//98ly9fNrr3fEpt93x169YVW7duFULo/9wvWLDA5CBVEvWFEMIqTmCtV68eDh48aDC+ceNGNG3a1OT6AQEB2L59u+65SqUCACxbtszkuw/369cPixYtMhhfsmQJ+vbta1JtABg0aBBGjBiBo0ePQqVS4datW1izZg1Gjx6NIUOGGF23bNmyWLp0Ka5cuWLwSEhIwK+//mp07V69eiEkJARbtmxBWlqabjwtLQ1btmxBaGgoevfubXT96tWr4/Tp03pjtra22LBhA6pXr4633nrL6NoAULVqVYOTJd3c3LB79278888/6NGjh9G1K1eurHfy68yZM+Ht7a17fvfuXZQpU8bo+i+//DIAoGXLlgU+XnnlFaNPsvb09MTDhw91zzMzM5GTkwN7e3sAQKNGjXD79m2je/f19UVCQoLu+bp161CpUiXd89u3b8PLy8vo+gDQv39/9OzZE1qtFleuXNF7LSkpCZ6enkbX7tGjB44cOYItW7agU6dOSEpKMqnXgsybNw+xsbEYNmwYPv30U9SoUQPA48/K5s2bG123XLlyupMab926hZycHL2f02vXrqFs2bJG1c7/vH3a66+/jlWrVuHWrVv48ssvjaoNPP5Z//HHHwEATZs2RXR0tN7r+/btg4+Pj9H1AeW2e76wsDAMHToU69evhxACx44dwxdffIHw8HCMHTu21NcHYB0nsG7dulV4eHiI6dOnC2dnZzFr1iwxcOBAYW9vL3bv3m1y/YMHDwpXV1fx4YcfCkdHRzFixAjRrl074eLiIo4fP25S7WHDhgl3d3dRv359MWDAADFgwADRoEED4e7uLoYNGyZGjhypexgjLy9PTJkyRbi4uOj2XDg6OorPPvvMpL7bt28vJk+eXOjrJ0+eNPq8iEePHokPP/xQ2NvbC7VaLRwdHYWjo6NQq9XC3t5eDBkyRDx69MjY1sXYsWN1x2yfptVqRdeuXU06Z+Q///lPoee0pKWlicDAQKP3LgwePPiZJzdPmzZNdO7c2ajaQgixZMmSZ54omZSUJCZOnGhU7ZCQENGyZUtx7tw5kZCQIHr16qX3V1V0dLTw9fU1qrYQQkycOFH8+OOPhb7+ySefiJ49expdv3///nqP9evX670+ZswY0aFDB6Pr58vLyxNTp04VFStWFDY2NmbbM5KTkyP279+vOzfqSf/884/Izs42uvbQoUNFzZo1xZQpU0SzZs1ESEiIqFOnjvjvf/8rdu7cKRo2bCg++OADo2oXtmfEXP766y9Rrlw5ERwcLCZPnixcXV3F+++/L7744gsRHBwsHBwcxMqVK42ur+R2f9IPP/wgatSoofuc9/HxMcsFHCVV32pulHfw4EF8/vnnOHXqFNLT0/Hyyy8jMjIS7du3N0v9+Ph4TJ8+Xa/+uHHj0LBhQ5Pqtm7dukjLqVQq7N271+j1ZGdn4/Lly0hPT0e9evXg6upqdC0A2LJlCzIyMvD+++8X+Prff/+Nn3/+GSEhIUavIy0tDSdOnND9hVixYkX4+/vD3d3d6JoAkJOTg8zMzELr5OTk4ObNm6hWrZpR9f/++2/cunUL9evXL/D1hw8fIjY2Fi1btjSq/rNcuXIFjo6OensESos7d+6gW7duOHr0KIDHe5A2b96s2xuzceNG3L59G//5z38UWX9mZiZsbGwUu7V6RkYGbGxs4OjoaJZ6sbGxOHjwIIKDg03a2/UkR0dHnDt3Dn5+fmaply8jIwMjR47EkSNH0Lx5cyxcuBALFizAp59+Cq1Wi5YtW2L9+vV6e/FKk/j4eHz66afYsWOH7nJ/W1tbvPLKKxgzZgy6d+9uUn2ltntBMjMzkZ6erti2Vqq+1YQRouIKCwsr8rJz585VsJMXy6VLl5CVlYU6derA1tbW7PWV/r4qWV/p3gMCAjBjxgy88cYbxX6vMR49egStVgs3NzeTa5XEv1chBO7cuYO8vDx4eXnBzs7OqDpPK+ntbm5TpkxB3759FQ1T5v8ksBJPnqvwPKb+pW5uPXv2LPKymzdvNmldMn6hJycn49tvv0VkZKRJdeLi4hAXFwetVovatWsDAC5evAgbGxvdX+tA4cesjWWu/ku6trnqF3SOVGGM+ZlR+vsaFxeH2NhY5OTkmL2+0r1PmTIFo0ePxuTJk+Hv7w8XFxe91039LFPy80DpbaNk70pvdz8/v2d+3U+eS2WMDRs2YMKECQgMDMT777+Pd9991+Tzr55msXtGypQpU+QfuvzZGItDrVYXuX5ubm6x6+d79OgRFi5ciH379ukS+ZNiY2OLXTM0NLTIy65cubLY9Z/UunXrIn9AmHKY6UmnTp3Cyy+/bNJ2Bx5/oERHR+O7777T7Qb/+++/ERoaitdffx2jRo0yR7sGzNV/Sdc2V32lf2aU/r4qWV/p3tXq/12z8OTnmxACKpXK5J+b1q1bFzmoFfd7q/S2UbJ3pbf7/Pnz9Z5rtVrExcVh586dGDNmDMaPH29SfQD4888/sWbNGqxbtw43btxAu3bt0LdvX3Tv3h3Ozs4m17fYMPLdd9/p/v/+/fuYMmUKOnTooLu65ciRI9i1axciIiIwcuTIYtffv3+/7v+vXr2K8ePHo3///nr1v/vuO0ybNs2k8yL69u2L3bt345133kGFChUMAtCECROMrl0SlPiAePpKl6edP38evXv3NvkfsI+PD3bv3m1wbsfZs2fRvn173Lp1y6i6Svav9LYpiW2v9C8Vpb6vJVFf6d6f/FwriKnnMSn5vVV62yjZu9LbvTBff/01jh8/bvIfnU+LiYnB2rVrsWHDBjx69KhYRxIKZbZTYSXq2bOn3rX5+RYuXCi6detmcv02bdoUODHZmjVrRMuWLU2q7e7urps+WQmtW7cWf//9t8H4gwcPROvWrU2uX7lyZYP5FoR4PDNfpUqVjKqp5BwmT3J1ddWbnCzf3r17TZpUTcn+ld42JbHtlfiZeZJS39eSqK9070pT8nur9LZR+udShvj4eJMn5ixIXFycGDVqlPDx8THbpGdWMc/Irl270LFjR4Pxjh074rfffjO5/pEjRxAQEGAwHhAQYPKN23x8fMxycldhoqOjC7wR06NHjwqcm6W40tLScPfuXYPxu3fv6s0pURxKzmHypB49eiA0NBSbN2/GjRs3cOPGDWzatAkDBgwo1nk3Jdm/0tumJLa9Ej8zT1Lq+1oS9ZXuHfjfjTObN29u9htnKvm9VXrbKP1zqeR2L8zGjRuNnt/laVeuXMEXX3yB+vXrIyAgAHFxcZg0aZL55sMxS6SRrGrVqmL27NkG47NnzzZpaux8tWrVEmPGjDEYHzNmjKhVq5ZJtXfs2CE6duyom0HWXPKnZVepVGLfvn16U7XHxsaKqVOnmmXmv379+gmNRiM2bdokrl+/Lq5fvy42btwo/Pz8RHBwsFE1lZzD5EkZGRliyJAhwsHBQTfdef48Junp6UbXVbJ/pbdNSWx7JX5mnqTU97Uk6ivdu9I3zlTye6v0tlGyd6W3e5MmTUTTpk11jyZNmujmqTHlJoX58udGatKkiZg1a5a4ceOGyTWfZhVhZOXKlcLGxka89dZbYvLkyWLy5MnirbfeEra2tiZNVpNv+/btwtHRUTRo0EA3MVnDhg2Fo6Oj2L59u0m179y5I1q1aiXUarVwdXUVZcqU0XsY68l7jBS0y93Z2VksX77cpN6FUOYDYvPmzeL7778v9PWUlBSxatUqY1s2kJ6ergtq5vhQU7J/pbdNSWx7pX+p5DP397Uk6ytVW+kbZ5bE91apbaNk70pv94kTJ+o9Pv/8c7Fo0SJx7tw5k2sL8XjCQHPelqAgFnsC69OOHj2KBQsW6Kbhrlu3LoYPH47AwECz1L9x4wa++eYbnD9/Xlf/ww8/hK+vr0l127Zti8TERAwYMKDAE1iNPTn22rVrEEKgevXqOHbsGMqXL697zd7eHt7e3rCxsTGp9ydlZGQgPj4eAPDSSy8ZXLpG9DT+zJQ8Z2dn/PXXX9BoNHBzc8OpU6dQvXp1JCQkoF69enj06JFZ1mPJ31slei+p7V4S8iODuac8sJp5RgIDA7FmzRrF6lepUgVTp041e93Dhw/jyJEjaNy4sVnr5s8e+vSlwkpxcXFBo0aNzFrT0icls+TJsUpi2yvxM0PPVrFiRVy+fBkajUZv/NChQ6hevbrZ1mPJ31sleldiu5f0XFirV6/GrFmzcOnSJQBArVq1MGbMGPTr18/k2oAVhZHc3Fxs3bpVt2ekfv366Nq1q9n++k9NTcXy5cv16n/wwQfw8PAwqW6dOnXwzz//mKNFnZ9//hmdOnWCnZ0dfv7552cu27VrV7Ou25xkTUpmLkr2XxITe1nytqeC5d84c8WKFbobZx45cgSjR49GRESE7PaslhLb3dPT87n//oSZ5jGZO3cuIiIiMGzYMLRo0QLA4yD14Ycf4t69e0ZNn1FQsxbv0qVLolatWsLZ2Vl3Ao+zs7OoXbu23u3VjfXHH3+IsmXLCh8fH9GjRw/Ro0cPUaVKFVGuXDlx4sQJk2rv2rVLNG/eXOzbt0/cu3dPPHjwQO9hjCdvLFXQ+SJPXqpZms2ZM0d06dJF7wZTKSkpolu3bgWesFzaKNm/0tvG0rc9FUypG2fSsymx3aOjo4v8MJVGo9Gd8/KkVatWCY1GY3J9IazkBNZOnTqJjh07ivv37+vG7t27Jzp27GjSHUzzvfbaa6J///5Cq9XqxrRarQgJCRGvv/66SbWfDAZPPiwhLCjN0q/7V7J/pbeNpW97erasrCzx559/iqNHj4qHDx/KbueFYanb3cHBQVy6dMlg/OLFi8LBwcEs67CKwzT79+/H77//rnc9dbly5TB9+nTdLiVTHD9+HEuXLtW7qZetrS3Gjh1b4PwjxbFy5Ur4+voaHE7Ky8tDYmKiSbWBx8f5evXqZXCn0uzsbKxbtw7BwcEmr0MpSl/3rzQl+1d621j6tqdns7e3R7169WS38cJRertnZmYiMTHRYG4pU8+BqVGjBn766Sd88skneuPr169HzZo1TaqdzyrCiIODQ4EfkOnp6bC3tze5vru7OxITE1GnTh298evXr5s8YdkHH3yA27dvG9yO+f79+2jbtq1JU80Dj+9T07FjR4P6Dx8+RGhoaKkOI/mTHM2ZMwfNmjUD8PiqqTFjxphtAiglKdm/0tvG0rc9FSwjIwPTp09HVFRUgffCMvWGavQ/JXnD0rt37yI0NBT//e9/C3zd1HNGJk2ahF69euHAgQO6P/BjYmIQFRWFn376yaTa+awijLz11lv497//jeXLl+t9cH744YdmOUGzV69eGDBgAGbPno3mzZsDePyNGDNmDHr37m1SbfH/TzB6Wnp6OhwdHU2q/az6N27cMPnkW6UtXrwYo0ePRp8+faDVagE83iM1YMAAzJo1S3J3z6dk/0pvG0vf9lSwgQMHYv/+/ejXrx8qVarEE5AV9OTnqxACW7ZsgYeHh25v+okTJ5CammqWcP/xxx8jNTUVR48eRatWrbBlyxYkJydjypQpmDNnjsn13377bRw7dgxz587F1q1bATye3uLYsWNo2rSpyfUBC75R3pNSU1MREhKCX375BXZ2dgCAnJwcdO3aFatWrTL5l252djbGjBmDxYsXIycnBwBgZ2eHIUOGYPr06QaHQIoi/9LJ+fPnY9CgQXp3PczNzcXRo0dhY2ODmJgYo3pu2rQpVCoVTp06hfr16+sdYsrNzcWVK1fQsWNHs6VaJVnynAWAsv0rvW0sfduTPk9PT2zfvt0sh6+p6MaNG4eUlBQsXrxYd0g+NzcXH330Edzd3U0O+JUqVcK2bdvQrFkzuLu74/jx46hVqxZ+/vlnzJw506Qp57VaLQYPHoyIiAj4+fmZ1OezWEUYyXf58mW9Sc9q1Khh1vqZmZl6H8ym3Da5devWAB6f7xIUFKR3OMne3h4ajQajR482+njcpEmTdP8dNWoUXF1dDeq//fbbZjmMRUSWwc/PDzt27EDdunVlt/JCKV++PA4dOqS7TD7fhQsX0Lx5c9y/f9+k+u7u7jh9+jQ0Gg2qVauGtWvXokWLFrhy5Qrq16+PzMxMk+p7eHjg5MmTioYRqzhMk69GjRpmDyAA8ODBA+Tm5qJs2bJo2LChbjwlJQW2trZGTSizb98+AI/P6Zg/f75ZJqV50oQJEwAAGo0GvXr1eu4hnx9//BFdu3blX75EVmzy5MmIjIzEd999Z9IfU1Q8OTk5OH/+vEEYOX/+vFkmpqxduzYuXLgAjUaDxo0b49tvv4VGo8HixYtRqVIlk+t3794dW7duNc98IoWwij0jb7/9Npo1a4Zx48bpjc+cORN//PEHNmzYYFL9Tp06oUuXLvjoo4/0xhcvXoyff/4ZO3bsMKl+aeDu7o6TJ0+adRZGIpIv/5BtvsuXL0MIAY1GozusnS82Nrak23shhIWFYfXq1fjkk0/0zmucPn06+vXrZ/IM0j/88ANycnLQv39/nDhxAh07dkRKSgrs7e2xatUq9OrVy6T6+eeevPHGG/D39zf4o3X48OEm1QesJIyUL18ee/fu1dtrAQBnzpxB27ZtkZycbFL9smXLIiYmxmDX5vnz59GiRQuTd7GVBk/eL4GIrEf+IduiyN+jSuaVl5eH2bNnY/78+bh9+zaAx+d5jBgxAqNGjTLrfcKAx6cUnD9/HlWrVoWXl5fJ9Z51eEalUpnlKiyrCCNOTk44efJkgbvAmjZtavJ06y4uLvj9998LDDuBgYEmH48rDRhGiIiUl39PGXMemj906BBee+01s9WTQS27AXNo2LAh1q9fbzC+bt06s0ww06xZMyxZssRgfPHixfD39ze5PhFRSfjjjz9w9OhRg/GjR4/i+PHjEjp68bi7u5v9HME2bdrAz88Pn3zyCf766y+z1n6aeDxzu9nrWsUJrBEREejZsyfi4+PRpk0bAEBUVBR+/PFHk88XAR4fL2vbti1OnTqFN954Q1f/jz/+wO7du02uT0RUEoYOHYqxY8ciMDBQb/zmzZuYMWNGgUGFzGPjxo346aefCpwh1dRzdW7duoV169bhxx9/xPTp09GoUSP07dsXvXv3RpUqVUyqnW/58uX48ssvdXftrVmzJj7++GMMHDjQLPWt4t40Qgjx66+/iubNmwtnZ2dRrlw50bp1a7PcIChfXFyc6NOnj6hXr57w9/cXoaGh4uLFi2arL5urq6uIj4+X3QYRKcjFxaXAf+cJCQnC1dVVQkcvhvnz5wtXV1cxbNgwYW9vLwYPHizatm0rPDw8xCeffGLWdSUkJIgpU6aI+vXrCxsbG9G6dWuTa0ZERAgXFxcxfvx4sW3bNrFt2zYxfvx44erqKiIiIszQtZXcKK+o1q5dK9LT0xWrP23aNPH3338rVl9J9evXF4mJibLbICIFlS1bVhw+fNhgPCYmRnh6ekro6MVQu3ZtsXbtWiGE/h9+ERERYujQoWZfX05Ojvjll19EkyZNzHLDVS8vL13/T1q7dq0oV66cyfWFEMIqzhkpqsGDB5t8Zc2zTJ06FSkpKYrVN1ZqaiqWLVuG8PBwXX+xsbG4efOmbpmzZ8/C19dXVotEVALat2+P8PBwPHjwQDeWmpqKTz75BO3atZPYmXVLTEzU3UrEyclJdy+1fv364ccffzTbemJiYvDRRx+hUqVK6NOnDxo0aIDt27ebXFer1RZ4U1h/f3/drOSmeqHCiFD4wiGl6xvj9OnTqFWrFmbMmIHZs2cjNTUVwOMbM4WHh8ttjohK1OzZs3H9+nVUq1YNrVu3RuvWreHn54ekpCSz3MOEClaxYkXdH4JVq1bF77//DgC4cuWKWX5vhIeHw8/PD23atEFiYiLmz5+PpKQkfP/99+jYsaPJ9fv164dFixYZjC9ZsgR9+/Y1uT5gJSewUuHCwsLQv39/zJw5U+8Ow507d0afPn0kdkZEJc3HxwenT5/GmjVrcOrUKTg5OSE0NBS9e/c2mACNzKdNmzb4+eef0bRpU4SGhmLkyJHYuHEjjh8/bpYb5R04cABjxozBu+++a5Z5RQqyfPly7N69G6+++iqAx1dgJSYmIjg4WHevNQBGT+BmFfOMFJXSc2mUxrk6PDw8EBsbi5deekmvv2vXrqF27dp49OiR7BaJiKxaXl4e8vLydDcsXbduHQ4fPoyaNWti8ODBJXaPsDfffBPLli0r9hTx+fdSex6VSoW9e/ca0xr3jFg7BwcH3SQ7T7p48SLKly8voSMiku2vv/4q8BLTrl27SurIuqnVaqjV/zsr4r333sN7771X4n0cOHDAqElA8++lpiSGESvXtWtXfP755/jpp58APE6uiYmJGDduHN5++23J3RFRSUpISECPHj1w5swZqFQq3fkK+feuyc3NldmeVTt48CC+/fZbxMfHY+PGjfDx8cH3338PPz8/i5891RxeqBNYq1Wrpuhx0ddffx1OTk6K1TfGnDlzkJ6eDm9vb/zzzz9o2bIlatSoATc3N3zxxRey2yOiEjRixAj4+fnhzp07cHZ2xp9//okDBw4gICAA0dHRstuzWps2bUKHDh3g5OSEuLg4ZGVlAXh8R/ipU6dK7u75Hj16hFmzZqFz584ICAjAyy+/rPcwB6s5ZyQ1NRUbN25EfHw8xowZg7JlyyI2NhYVKlSAj4+PSbVtbGxw+/ZteHt7643fv38f3t7eFvHXRExMDE6dOoX09HS8/PLLaNu2reyWiKiEeXl5Ye/evWjUqBE8PDxw7Ngx1K5dG3v37sWoUaMQFxcnu0Wr1LRpU4wcORLBwcF65+7FxcWhU6dOSEpKKpE+jD2vsW/fvti9ezfeeecdVKhQQe8u0IB5brBoFYdpTp8+jbZt28LDwwNXr17FoEGDULZsWWzevBmJiYlYvXq1SfULy2tZWVklduKRqVq0aIEWLVoAgO7yXiJ6seTm5uquqvPy8sKtW7dQu3ZtVKtWDRcuXJDcnfW6cOEC/u///s9g3MPDwyI+j3/99Vfs2LFD9ztECVYRRpS6fHXBggUAHh9PXbZsGVxdXXWv5ebm4sCBA6hTp47xjZeAGTNmQKPRoFevXgCAd999F5s2bULFihWxY8cONG7cWHKHRFRSGjRogFOnTsHPzw+BgYGYOXMm7O3tsWTJklJ1FaC1qVixIi5fvgyNRqM3fujQIYvY7j4+Pnq/WxVhlnlcJXN3dxeXL18WQuhPtXv16lXh4OBgdF2NRiM0Go1QqVTC19dX91yj0YhatWqJ9u3bi99//90sX4NSNBqNiImJEUIIsXv3buHp6Sl27dolBgwYINq1aye5OyIqSTt37hSbNm0SQghx6dIlUbt2baFSqYSXl5eIioqS3J31mjp1qqhXr574/fffhZubmzh48KD44YcfRPny5cWCBQvMUn/58uUG48uXLxfTp0/XW86YW5bs2LFDdOzYUVy9etWUNp/JKsJI+fLlRWxsrBBCP4zs3r1bVKlSxeT6rVq1EikpKSbXkcHR0VF3z5nhw4eLf//730IIIS5cuMB7URCRuH//vsjLy5PdhlXLy8sTU6ZMES4uLkKlUgmVSiUcHR3FZ599Zpb61apV0/3R+aTff/9daDQak+vfuXNHtGrVSqjVauHq6irKlCmj9zAHqzhMo/TlqyVxjbVSypQpg+vXr8PX1xc7d+7ElClTADw+D8YSTrwlIvO7fPky4uPj8X//938oW7ZsqbyVhTVRqVT49NNPMWbMGFy+fBnp6emoV6+e3qF/UyQlJRU4kVn58uVx+/Ztk+v37t0bN2/exNSpUws8gdUcrCKMzJkzB++8847e5atJSUkICgoyy+Wrubm5WLVqFaKionDnzh3k5eXpvW7sjHMloWfPnujTpw9q1qyJ+/fvo1OnTgCAuLg41KhRQ3J3RFSS7t+/j3fffRf79u2DSqXCpUuXUL16dQwYMABlypTh/WkUZm9vj3r16pm9rq+vL2JiYuDn56c3HhMTg8qVK5tc//Dhwzhy5Iii5xhaRRjx8PDAnj17FLt8dcSIEVi1ahXefPNNNGjQQJFUqJQvv/wSfn5+SExMxMyZM3VJ/Pbt2/joo48kd0dEJWnkyJGws7NDYmIi6tatqxvv1asXwsLCGEbMqDj3nNm8ebNJ6xo0aBA+/vhjaLVatGnTBgAQFRWFsWPHYtSoUSbVBoA6deoYNXNrcVjNPCNPS01Nhaenp1lqeXl5YfXq1ejcubNZ6pUUrVaLwYMHIyIiwiAxE9GLp2LFiti1axcaN26sN+dEQkICGjVqhPT0dNktWo3Q0NAiL7ty5UqT1iWEwPjx47FgwQLdFP+Ojo4YN24cIiMjTaoNALt378akSZPwxRdfoGHDhgaTh7q7u5u8DqsII0pfvlq5cmVER0ejVq1a5mi3RHl4eODkyZMMI0QENzc3xMbGombNmnph5Pjx4+jQoQPu378vu8UXWkxMDAICAuDg4GDU+9PT03Hu3Dk4OTmhZs2aRtd5Wv59dZ4+KiCEgEqlMsv5h1YRRvz8/LBmzRo0b94ce/bswbvvvov169fjp59+QmJiInbv3m1S/Tlz5iAhIQFfffWVRR2iAYCQkBA0adIEI0eOlN0KEUnWuXNn+Pv7Y/LkyXBzc8Pp06dRrVo1vPfee8jLy8PGjRtlt/hCc3d3x8mTJ0vd3CP79+9/5ustW7Y0eR1Wcc5IUlISfH19ATyeKe7dd99F+/btodFoEBgYaHL9Q4cOYd++ffjvf/+L+vXrG+yiMvV4n5Jq1qyJzz//HDExMfD394eLi4ve68OHD5fUGRGVtJkzZ+KNN97A8ePHkZ2djbFjx+LPP/9ESkoKYmJiZLf3wiut+wbMETaexyrCiNKXr3p6eqJHjx4m15Fh+fLl8PT0xIkTJ3DixAm911QqFcMI0QvE3d0d586dw6JFi+Dm5ob09HT07NkTQ4cOhVarld0elSKnT59GgwYNoFarcfr06Wcu26hRI5PXZxWHaYYNG4Zff/0VNWvWRFxcHK5evQpXV1esW7cOM2fORGxsrOwWiYiks4abflozY29kpwS1Wo2kpCR4e3tDrVZDpVIVuOfGXOeMWMWekZK4fDUnJwfR0dGIj49Hnz594Obmhlu3bsHd3d1sE9cQESmpsL8909PT4ejoWMLdUGl25coVlC9fXvf/SrP4MPKsy1fNddLmtWvX0LFjRyQmJiIrKwvt2rWDm5sbZsyYgaysLCxevNgs61HCBx988MzXV6xYUUKdEJEsYWFhAB7/FRsZGQlnZ2fda7m5uTh69CiaNGkiqTvKV5oukKhWrVqB//8sb775JpYtW1bgbLDPY/FhxM7ODps2bUJERIRi6xgxYgQCAgJw6tQplCtXTjfeo0cPDBo0SLH1msPff/+t91yr1eLs2bNITU3VTY5DRNYtLi4OwOM9I2fOnIG9vb3uNXt7ezRu3BijR4+W1Z5VE0Lg+vXr8Pb2fu7eJ0s/a+LAgQNGT45m8WEEALp3746tW7cqdvnqwYMHcfjwYb1/wACg0Whw8+ZNRdZpLlu2bDEYy8vLw5AhQ/DSSy9J6IiISlr+/bVCQ0Mxf/58s0xSRUUjhECNGjXw559/ombNms9c9uHDhyXUVeljFWFE6ctX8/LyCjxB58aNG3BzczOptgxqtRphYWFo1aoVxo4dK7sdIiohps70ScWnVqt19wZ7Xhh5kVnF1TTPml1UpVIhISHBpPq9evWCh4cHlixZopsoqHz58ujWrRuqVq1qkf/Ad+zYgZCQENy9e1d2K0REVu2XX37BzJkzsWjRIjRo0EB2O4ox5WogqwgjSrtx4wY6dOgAIQQuXbqEgIAAXLp0CV5eXjhw4IDBZXKlSf6Ja/mEELh9+za2b9+OkJAQfPXVV5I6IyJ6MZQpUwaZmZnIycmBvb09nJyc9F5PSUmR1Jl5mRJGrOIwjdKqVKmCU6dOYd26dTh9+jTS09MxYMAA9O3b1+CHqrTJP3Etn1qtRvny5TFnzpznXmlDRESmmzdvnuwWSj2r2DPCy1eJiIiUMW3aNFSoUMHgd+2KFStw9+5djBs3TrfckCFD4OnpWex1WEUYeXqq9qcvXzXHvWNu3bqFQ4cO4c6dO8jLy9N7zRKmVL979y4uXLgAAKhdu7ZuMhsiIio5jx49QnZ2tt5Yab+6SaPRYO3atWjevLne+NGjR/Hee++ZZVI0qzhMo/Tlq6tWrcLgwYNhb2+PcuXK6U1MU9rv75KRkYH//Oc/WL16tS5E2djYIDg4GAsXLtSb/IiIiMwvIyMD48aNw08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4500 rows × 4 columns

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" + ], + "text/plain": [ + " account_id district_id frequency date\n", + "0 576 55 POPLATEK MESICNE 930101\n", + "1 3818 74 POPLATEK MESICNE 930101\n", + "2 704 55 POPLATEK MESICNE 930101\n", + "3 2378 16 POPLATEK MESICNE 930101\n", + "4 2632 24 POPLATEK MESICNE 930102\n", + "... ... ... ... ...\n", + "4495 124 55 POPLATEK MESICNE 971228\n", + "4496 3958 59 POPLATEK MESICNE 971228\n", + "4497 777 30 POPLATEK MESICNE 971228\n", + "4498 1573 63 POPLATEK MESICNE 971229\n", + "4499 3276 1 POPLATEK MESICNE 971229\n", + "\n", + "[4500 rows x 4 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['account']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Matplotlib \n", + "\n", + "* \"A picture is worth a thousand words.\"\n", + " * more like \"A picture is worth a few lines of code.\"\n", + "* development started in 2003 by John D. Hunter, a neurobiologist (inspired by MATLAB software)\n", + "* generating basic plots in *matplotlib* is simple, mastering the library can be little bit less pleseant (we skip this part)\n", + "* you can have as much control as you want, but you can also concede as much control as you want \n", + "* [**gallery**](https://matplotlib.org/stable/gallery/index.html)\n", + " * can get help to problems like \"I want to make a figure that looks something I've seen somewhere.\" (hard to google)\n", + "* plotting consists of many layers, from general 'contour this 2D array' to very specific 'color this screen pixel'\n", + " * key is allowing both levels to coexist in one package\n", + "* *matplotlib* has 2 interfaces:\n", + " 1. \"state-machine environment\" (based on MATLAB)\n", + " 2. a object-oriented interface\n", + "* this often creates confusion (multiple, conflicting, solutions on the web)\n", + "* another common confusion is the relationship of *Matplotlib, pyplot and pylab*\n", + " * Matplotlib is the whole package\n", + " * `matplotlib.pyplot` is a module in matplotlib\n", + " * `pylab` is a a convenience module doing a bulk import of `pyplot` and `numpy`\n", + "\n", + "* [anatomy of the plot](https://matplotlib.org/examples/showcase/anatomy.html) from matplotlib\n", + "\n", + "\n", + "\n", + "* the *figure* keeps track of all the child *Axes*, titles,legends, etc.\n", + " * the figure can have any number of *Axes*\n", + "* *Axes* is 'a plot', i.e. the region of the image with the data space\n", + " * given *Axes* object can only be in one Figure\n", + " * *Axes* contains 2 (3 in case of 3D) *Axis* objects which take care of the data limits (conrolled via `set_xlim()` method)\n", + " * each *Axes* has a title (`set_title()`), an x- and y-labels (`set_xlabel()`)\n", + "* *Artist* is anything you can see on the figure, e.g. text objects, Line2D objects, etc.\n", + "\n", + "* `matplolib.pyplot` functions make some changes to a figure, e.g. create a figure, plot some lines, etc.\n", + " * the plotting functions are directed to the current axes\n", + "\n", + "* all of plotting functions expect `np.array` or `array-like` data objects (for majority of cases works out of the box)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# increasing the size of the figure\n", + "plt.figure(figsize = (20,10))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "gather": { + "logged": 1677517840825 + } + }, + "outputs": [], + "source": [ + "from time import sleep\n", + "for style in plt.style.available:\n", + " plt.style.use(style)\n", + " print(style)\n", + " plt.figure(figsize=(5,2))\n", + " plt.plot(np.sin(np.linspace(0,2*np.pi)))\n", + " plt.show()\n", + " sleep(1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.style.use('ggplot')\n", + "# minimum example of pyplot\n", + "x = np.linspace(0, 2, 100)\n", + "\n", + "# we can also specify only \"y\" and use default x-axis: plt.plot(x, label='linear')\n", + "plt.plot(x, x, label='linear', linewidth=2.0)\n", + "plt.plot(x, x**2, label='quadratic')\n", + "plt.plot(x, np.sqrt(x),'k^:',label='sqrt')\n", + "\n", + "plt.xlabel('x label')\n", + "plt.ylabel('y label')\n", + "\n", + "plt.title(\"Basic plots\")\n", + "\n", + "plt.legend(loc = 'best');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* for multiple subplots: `fig, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, sharey=True, figsize=(7, 4))`\n", + "* call `plt.subplot()` and specify three numbers:\n", + " * number of rows\n", + " * number of columns\n", + " * subplot number you want to activate.\n", + "* if subplots are too squished `plt.tight_layout()`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range (1, 5):\n", + " plt.subplot(2, 2, i)\n", + " plt.text(0.5,0.5, str((2, 2, i)), ha='center', fontsize = 10) #again, just a plot\n", + " plt.tight_layout() \n", + " plt.grid(True) # add the grid" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# for multiple figures and axes \n", + "def f(x):\n", + " return np.cos(2*np.pi*x)\n", + "\n", + "x1 = np.arange(0.0, 5.0, 0.1)\n", + "x2 = np.arange(0.0, 5.0, 0.02)\n", + "\n", + "plt.figure(1) # optional, since figure(1) will be created by default\n", + "plt.subplot(211)\n", + "plt.plot(x1, f(x1), 'bo', x2, f(x2), 'k')\n", + "\n", + "plt.subplot(212)\n", + "plt.plot(x2, np.tan(2*np.pi*x2), 'r--')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mu, sigma, n = 100, 15, 10000\n", + "x = np.random.normal(mu, sigma, n)\n", + "\n", + "# the histogram of the data\n", + "plt.hist(x, bins = 50, density= True, facecolor='g')\n", + "\n", + "plt.xlabel('x')\n", + "plt.ylabel('Probability')\n", + "plt.title('Histogram of X')\n", + "\n", + "# meaningful text\n", + "plt.text(60, .025, f'$\\mu={mu},\\ \\sigma={sigma}$')\n", + "# tail events text\n", + "plt.text(40, .00025, f\"I've seen better times.\")\n", + "\n", + "plt.grid(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Saving plots" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax = plt.subplot(111)\n", + "t = np.arange(0.0, 5.0, 0.01)\n", + "s = np.cos(2*np.pi*t)\n", + "line, = plt.plot(t, s, lw=2)\n", + "plt.annotate(\"'go home, you are drunk'-arrow'\", xy=(4.5, -1.7), xytext=(0.3, 1.7),\n", + " arrowprops=dict(facecolor='black', shrink=0.05),\n", + " )\n", + "plt.ylim(-2, 2)\n", + "\n", + "# actually saving\n", + "plt.savefig('auxiliary/go_home_you_drunk.png')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernel_info": { + "name": "python3" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.7" + }, + "microsoft": { + "ms_spell_check": { + "ms_spell_check_language": "en" + } + }, + "nteract": { + "version": "nteract-front-end@1.0.0" + }, + "vscode": { + "interpreter": { + "hash": "1f0e6d99f3103fd78365fe1cf7b2d51239fa0878786db9cbdfe89bc88a3151d3" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/04_pandas/auxiliary/data_2017_zs.csv b/04_pandas/auxiliary/data_2017_zs.csv new file mode 100644 index 0000000..0d64691 --- /dev/null +++ b/04_pandas/auxiliary/data_2017_zs.csv @@ -0,0 +1,6998 @@ +"cislo_dot";"kod_predm";"nazev_predm";"prednasejici";"cvicici";"q1";"q2";"q3";"q4";"q5";"q6";"q7";"q8";"q9";"q10";"q11";"q12";"q13";"t1";"t2";"katedra_code" +"1";"JEB003";"Ekonomie I";"Fanta,N.,Kracík,J.,Švarcová,N.";"Fanta,N.,Kracík,J.,Švarcová,N.";"3";"5";"4";"5";"4";"2";"4";"3";"1";"4";"1";NULL;"2";;;"ies" +"2";"JLB003";"Angličtina pro ekonomy I";;"Poslušná,L.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"3";"NMMA701";"Matematika 1";"Spurný,J.";"Rondoš,J.";"3";"5";"3";"2";"1";"4";"4";"5";"1";"3";"2";"2";"1";;;"ies" +"4";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"3";"4";"3";"3";"1";NULL;NULL;NULL;"1";"3";"2";"3";"2";;;"ies" +"5";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"2";"3";NULL;NULL;NULL;"2";"3";"1";"1";"4";"2";"2";"1";;;"ies" +"6";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"2";"3";"2";"4";"1";NULL;NULL;NULL;"2";"3";"1";"3";"1";;;"kmv" +"7";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"4";"3";"2";"3";"2";NULL;NULL;NULL;"1";"3";"1";"2";"3";;;"ies" +"8";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"4";"4";NULL;NULL;NULL;"4";"5";"5";"1";"1";"2";"3";"5";;;"ies" +"9";"JPM561";"Regional Security Studies";"Karásek,T.,Klosek,K.";"Karásek,T.,Klosek,K.";"5";"3";"5";"5";"4";"5";"5";"4";"1";"5";"4";"5";"5";;;"kbs" +"10";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"4";"2";"2";"2";NULL;NULL;NULL;"1";"3";"3";"3";"1";;;"ies" +"11";"JLB001";"Angličtina pro sociology I";;"Štěpánková,D.";"3";"1";NULL;NULL;NULL;"1";"4";"2";"2";"1";"2";"1";"2";;;"cjp" +"12";"JPM698";"Middle East Security";"Daniel,J.";;"5";"3";"4";"5";"5";NULL;NULL;NULL;"2";"5";"4";"4";"4";;;"kbs" +"13";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"1";"3";"2";"1";"1";NULL;NULL;NULL;"2";"1";"1";"1";"1";"Tento kurz naprosto ničí představy prvních ročníků o vysoké škole. Já osobně jsem v prvním ročníku po navštívení tohoto kurzu v přemýšlela, zda školy nezanechat. A po vyslechnutí kurzu v letošním roce jsem byla pouze šťastná, že ho absolvovat znova nemusím - stejné, nepřínosné a přístup vyučujícího otřesný.";"S veškerou úctou k panu Kameníčkovi - změna vyučujícího.";"ies" +"14";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Hanzlík,P.";"3";"3";"2";"2";"1";"5";"5";"4";"2";"4";"2";"1";"3";;;"ks" +"15";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"5";"3";"4";;;"kmv" +"16";"JPM694";"Strategic Studies";"Hynek,N.,Ludvík,J.,Smetana,M.";;"3";"4";"4";"5";"2";NULL;NULL;NULL;"1";"4";"3";"4";"3";;;"kbs" +"17";"JSB530";"Úvod do sociologického myšlení";"Bureš,J.,Numerato,D.";"Bureš,J.";"3";"1";"1";"5";"1";"4";"5";"4";"4";"2";"3";"2";"3";;;"ks" +"18";"JSB407";"Globální problémy životního prostředí a udržitelný rozvoj";"Drhová,Z.";;"3";"2";"3";"4";"4";NULL;NULL;NULL;"1";"4";"2";"4";"4";"zachovala bych skupinové práce a výuku každý týden";"Lépe definovat ze strany vyučujícího požadavky kurzu apod. - často jsme se ztráceli v tom, co máme dělat, ale vše bylo nakonec vykomunikováno.";"kvsp" +"19";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"2";"3";"3";"3";"2";NULL;NULL;NULL;"1";"2";"3";"3";"3";;;"kmv" +"20";"JSB515";"Vysokoškolská vzdělávací politika";"Vlk,A.";;"5";"4";"4";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Přístup vyučujícího, způsob výuky, oborovost a praktické uvedení do problematiky, výjezd";;"kvsp" +"21";"JPM611";"Cyber Security";"Duračinská,Z.,Střítecký,V.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"3";"3";"3";"3";;;"kbs" +"22";"JPM701";"European and Transatlantic Security";"Kazharski,A.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"4";"4";"3";"4";"4";;;"kbs" +"23";"JSB998";"Úvod do sociologie";"Soukup,P.";;"4";"2";"5";"5";"4";NULL;NULL;NULL;"2";"4";"3";"3";"5";;;"ks" +"24";"JJB605";"Novodobá historie médií";"Bednařík,P.,Končelík,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"25";"JJB616";"Vybrané novinářské osobnosti 19. století";"Sekera,M.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"5";"5";;;"kms" +"26";"JJB620";"Mediální produkce - 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diskuse projektů";;"Angelovská,O.,Mouralová,M.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"1";"5";"5";"1";"5";"Především styl průběžných úkolů, které jsou postupně vypracovávány a následně \"propojeny\" do jednoho velkého, takže studentovi dávají pocit, že je napsání bakalářky zvládnutelné + diskutování konkrétních problémů vyvstávajících v průběhu psaní práce";;"ks" +"35";"JJB601";"Rozbor právního prostředí v českých médiích";"Benda,J.";;"2";"3";"2";"3";"3";NULL;NULL;NULL;"1";"2";"1";"1";"2";;;"kms" +"36";"JEB105";"Statistics";"Červinka,M.";"Červinka,M.";"4";"5";"5";"4";"3";"5";"4";"5";"1";"3";"4";"2";"3";;;"ies" +"37";"JJB600";"Úvod do studia médií I.";"Nečas,V.";;"5";"5";"4";"5";"5";NULL;NULL;NULL;"2";"4";"4";"4";"4";;;"kms" +"38";"JEB047";"Účetnictví II";"Kemény,I.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"3";"5";"5";"5";"5";"Důvtip paní Kemény ale také její znalosti a zkušenosti v tomto oboru.";"Možná distribovat papíry přes SIS, výpisky nebo slidy.";"ies" +"39";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"3";"1";"2";"1";"1";"2";"3";"1";"1";"3";"1";"3";"3";;;"ies" +"40";"NMMA703";"Matematika 3";"Zelený,M.";"Turčinová,H.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"41";"JJM080";"Zábava v televizním vysílání";"Kruml,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"1";"4";"5";;;"kms" +"42";"JEB105";"Statistics";"Červinka,M.";"Hanus,L.";"5";"5";"5";"5";"5";"5";"5";"5";"2";"5";"5";"5";"5";"Přístup vyučujících a pana Červinky k předmětu. Bere feedbacky velice vážně a snaží se nám vyjít vstříc, což je docela vzácné.";"Možná jasnější prezentace, slidy byly někdy velmi matoucí. Jinak bezpochyby asi ten nejlepší předmět na IESu.";"ies" +"43";"JMB533";"Česká republika v integračních procesech";"Šlosarčík,I.";;"5";"2";"4";"4";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kzs" +"44";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"4";"3";"4";"4";"2";"2";"2";"1";"1";"3";"3";"4";NULL;"Zaverecny test byl neadekvatne komplikovany a matouci. Po vypoctech, ktere nam na cvicenich vychazely celociselne a davaly smysl (napriklad byly kladne), me zmatlo, ze v testu vysledky nevychazely vubec tak, jak jsme na to byli zvykli. samozrejme chapu, ze priklady mohou vychazet vseljkak, ovsem tato skutecnost zpusobila, ze v testu jsem si pote uz nebyla jista ani temi netrivialnejsimi vypocty, jelikoz jsem mela za fixovano, ze takova cisla by mi vychazet nemela. proto doporucuji bud davat podobne priklady do cviceni, aby studenti nemuseli byt zaskoceni az u zkousky, nebo davat do pisemky takove priklady, ktere se cvici i na seminarich.";;"ies" +"45";"JMB534";"Evropská unie - vybrané problémy";"Mejstřík,M.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"3";"5";"5";;;"kzs" +"46";"JMB535";"Bezpečnostní problémy současného světa";"Weiss,T.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"4";"5";;;"kzs" +"47";"JJM201";"Teorie masové a mediální komunikace";"Jirák,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"2";"5";"5";;;"kms" +"48";"JMB536";"Bakalářský seminář pro kombinovaný obor Teritoriální studia I";;"Vykoukal,J.";"5";"4";NULL;NULL;NULL;"4";"5";"5";"1";"5";"5";"4";"5";;;"krvs" +"49";"JPM127";"Geopolitika a geostrategie 1815 - 1945";"Romancov,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"50";"JJM226";"Teorie účinků médií";"Nečas,V.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kms" +"51";"JJB600";"Úvod do studia médií I.";"Nečas,V.";;"5";"5";"5";"4";"5";NULL;NULL;NULL;"3";"5";"4";"5";"5";"Příklady z praxe, ukázky a videa";;"kms" +"52";"JPM641";"Světový regionalismus";"Riegl,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"53";"JEB003";"Ekonomie I";"Fanta,N.,Kracík,J.,Švarcová,N.";"Fanta,N.,Kracík,J.,Švarcová,N.";"5";"5";"5";"5";"4";"5";"5";"2";"1";"5";"2";"4";"5";;;"ies" +"54";"JSM005";"Sociální struktura ČR: stav, vývoj, srovnání s EU";"Tuček,M.";;"1";"1";"1";"2";"1";NULL;NULL;NULL;"1";"1";"1";"1";"1";"Kurz by měl převzít některý ze schopnějších pedagogů nebo by měl být úplně zrušen.";"Obsah kurzu byl dost vágní, pan docent vykládal náhodné záležitosti podle nedodělaných tabulek z CVVM. Výklad byl nudný, nezajímavý a neměl žádnou hodnotu. Navíc jsem byla na kurzu šokována šovinistickými výroky pana docenta (např. \"holky prostě mají buňky na to krasobruslení\") a popírání existence genderových nerovností na základě výzkumů provedených jím a jeho kolegy v minulém století. Mrzí mě, že všechny jeho hodin probíhají stejným stylem a že se pan docent podobných výroků dopouští a to před studenty sociologie. Kdybych neměla obavy o své budoucí studium, stěžovala bych si na něj, nestojím ale o problémy. Zkouška probíhala na základě zvláštní diskuze nad reflexemi z hodin, známky vyučující dával podle nálady. Sice je hezké, že většina studentů má radost, že se na takovou zkoušku nemusí učit, ale já bych radši ocenila přínosný kurz a náročnější zkoušku, než tohle.";"ks" +"55";"JLB001";"Angličtina pro sociology I";;"Klírová,M.";"4";"2";NULL;NULL;NULL;"5";"5";"4";NULL;"4";"4";"5";"3";;;"cjp" +"56";"JPM574";"Moderní strany a stranické systémy v Evropě";"Brunclík,M.";;"4";"2";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kp" +"57";"JSM016";"Sociology of Science and Scientific Knowledge";;"Maršálek,J.";"3";"4";NULL;NULL;NULL;"4";"5";"4";"1";"3";"4";"3";"2";;;"ks" +"58";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"2";"3";"5";"4";"1";NULL;NULL;NULL;"1";"1";"1";"1";"2";;;"ies" +"59";"JPM579";"Teorie politických stran";"Perottino,M.";;"4";"2";"5";"5";"3";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kp" +"60";"JLB099";"Rozřazovací test z angličtiny";;"Štěpánková,D.";"3";"3";NULL;NULL;NULL;"3";"3";"1";"1";"1";"1";"1";"3";;;"cjp" +"61";"JSM103";"Academic Writing";;"Blokker,P.";"3";"3";NULL;NULL;NULL;"4";"4";"3";"2";"3";"4";"2";"3";;;"ks" +"62";"JSM031";"Analytické metody výzkumu pro mgr.";"Jeřábek,H.";"Daneš,D.";"4";"5";"3";"4";"3";"5";"5";"5";"2";"4";"4";"4";"5";"Semináře s Danešem";;"ks" +"63";"JPM348";"Nové přístupy k místní správě a přímá volba starostů";;"Jüptner,P.";"4";"3";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"4";"4";;;"kp" +"64";"JSM312";"Electoral, Market, Media and Social Research: Paul Lazarsfeld's Methodology";"Jeřábek,H.";;"4";"3";"3";"5";"4";NULL;NULL;NULL;"2";"4";"3";"4";"4";;;"ks" +"65";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"4";"1";NULL;NULL;NULL;"5";"5";"3";"1";"3";"2";"2";"5";;;"ies" +"66";"JMB529";"Současná západní Evropa";"Rovná,L.,Váška,J.";;"4";"3";"5";"4";"2";NULL;NULL;NULL;"1";"5";"4";"5";"4";;;"kzs" +"67";"JSM421";"Contemporary social theory";"Balon,J.";;"4";"3";"3";"5";"3";NULL;NULL;NULL;"2";"4";"3";"4";"4";;;"ks" +"68";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"3";"2";"1";"2";NULL;NULL;NULL;"1";"4";"3";"3";"2";;"Vyměnit vyučujícího.";"ies" +"69";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"4";"3";NULL;NULL;NULL;"5";"5";"3";"1";"3";"3";"3";"5";;;"ies" +"70";"JPM344";"Diplomní seminář II.";;"Brunclík,M.,Franěk,J.,Hroch,M.,Charvát,J.,Jüptner,P.,Kofroň,J.,Kotábová,V.,Krausz Hladká,M.,Kubátová,H.,Kučera,J.,Landovský,J.,Mlejnek,J.,Perottino,M.,Riegl,M.,Romancov,M.,Říchová,B.,Salamon,J.,Shavit,A.,Švec,K.";"3";"2";NULL;NULL;NULL;"5";"5";"5";"1";"3";"3";"3";"5";;;"kp" +"71";"JSM480";"Evaluation Research";;"Remr,J.";"3";"4";NULL;NULL;NULL;"3";"3";"3";"2";"3";"3";"3";"2";;;"ks" +"72";"JLB019";"Francouzština odborná I";;"Bosáková,L.";"4";"2";NULL;NULL;NULL;"5";"5";"4";"1";"3";"3";"3";"4";"Pozitivní přístup vyučující, poměrně zajímavé materiály k výuce, poutavost hodin.";"Největším problémem je velký rozdíl v jazykových znalostech studentů - pro některé mméně zdatané je kurz příliš náročný, pro ty, kteří mluví dobře, je kurz bohužel již trochu nudný. Ocenil bych více diskuzí.";"cjp" +"73";"JSM554";"Diplomový seminář";;"Tuček,M.";"3";"2";NULL;NULL;NULL;"3";"5";"4";"1";"2";"4";"2";"5";;;"ks" +"74";"JLB001";"Angličtina pro sociology I";;"Klírová,M.";"3";"3";NULL;NULL;NULL;"3";"4";"4";"1";"3";"4";"3";"3";;;"cjp" +"75";"JMB530";"Současná Severní Amerika";"Calda,M.";;"3";"4";"5";"2";"4";NULL;NULL;NULL;"1";"2";"5";"3";"2";;;"kas" +"76";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"3";"3";"1";"1";"2";"2";"2";"5";;;"kz" +"77";"JPM721";"Komparace ekonomik zemí EU";"Kučerová,I.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Paní doktorka Kučerová má opravdu široký záběr a informace umí předávat v souvislostech. Kurs hodnotím velmi kladně.";;"kmv" +"78";"JMB528";"Současné problémy německy mluvících zemí";"Nigrin,T.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"4";;;"knrs" +"79";"JJB238";"Kritika marketingové komunikace";"Rosenfeldová,J.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"3";"3";"3";"5";"5";"základni znalosti filozofie";"podrobnost informací";"kmkpr" +"80";"JSM312";"Electoral, Market, Media and Social Research: Paul Lazarsfeld's Methodology";"Jeřábek,H.";;"3";"2";"4";"5";"4";NULL;NULL;NULL;"1";"2";"3";"4";"3";"Prezentace studentů";"Chtělo by to kurz více odlišit od jiných kurzů pana profesora. Kurz je sice zajímavý a přínosný, ale má velmi podobnou koncepci jeho jiným kurzům.";"ks" +"81";"JMB513";"Soudobé dějiny Dálného východu";"Sýkora,J.";;"4";"3";"5";"5";"2";NULL;NULL;NULL;"1";"4";"5";"4";"4";;;"kas" +"82";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Rössler,J.";"4";"3";"4";"5";"3";"5";"4";"5";"3";"4";"5";"4";"5";;;"ks" +"83";"JSM005";"Sociální struktura ČR: stav, vývoj, srovnání s EU";"Tuček,M.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"5";"2";"4";"5";"Příklady z českých výzkumů k dané tématice, historický vývoj problematiky.";"Nic";"ks" +"84";"JEM003";"Advanced Microeconomics";"Janda,K.,Melikhova,O.";"Janda,K.,Melikhova,O.";"4";"5";"5";"5";"2";"5";"5";"5";"1";"2";"3";"2";"1";"Oxana is great teacher";"material is hard and I dont believe its useful in real life";"ies" +"85";"JSM554";"Diplomový seminář";;"Remr,J.";"4";"5";NULL;NULL;NULL;"5";"5";"5";"2";"4";"4";"4";"5";"Prezentace studentů";;"ks" +"86";"JSB007";"Dějiny sociologie I";"Šanderová,J.";"Veselský,M.";"4";"2";"5";"4";"4";"4";"5";"5";"3";"4";"4";"4";"4";;;"ks" +"87";"JSM573";"Výzkumné kolokvium AVM I";;"Remr,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"5";NULL;;;"ks" +"88";"JSB025";"Sociální problémy";"Frič,P.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"5";"4";"4";"4";;;"kvsp" +"89";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"5";"5";"5";"5";"4";"5";"5";"5";"1";"5";"5";"5";"1";"Mr.Barunik is very sexy";;"ies" +"90";"JSM568";"Výzkumné kolokvium SAQ I";;"Numerato,D.";"4";"3";NULL;NULL;NULL;"4";"5";"4";"2";"3";"4";"3";"4";"Velmi oceňuji možnost zapojení do \"reálného\" výzkumu financovaného GAČRem. Propojení výuky s praxí je důležité.";"Chápu, že je velmi obtížné dodržovat harmonogram výzkumu a že situace se vyvíjí, ale není úplně ideální, pokud se předmět táhne až do zkouškového a možná až do dalšího semestru.";"ks" +"91";"JJB240";"Marketing a tvorba značky";"Průša,P.";;"3";"2";"3";"5";"2";NULL;NULL;NULL;"1";"3";"1";"3";"3";"prezentace vlastního hodnocení značky";"Méně rozebírat teoretické koncepty, o kterých si stejně říkáme, že prakticky nefungují a více rozebírat věci na příkladech reálných značek.";"kmkpr" +"92";"JSB028";"Informační gramotnost";"Tomandlová,V.";;"4";"2";"4";"4";"5";NULL;NULL;NULL;"1";"1";"4";"1";"4";;;"kvsp" +"93";"JJB035";"Odvětvové zpravodajství - ekonomie";"Kameníček,J.";;"4";"3";"4";"2";"4";NULL;NULL;NULL;"1";"4";"4";"4";"2";;;"kz" +"94";"JSB530";"Úvod do sociologického myšlení";"Bureš,J.,Numerato,D.";"Bureš,J.";"3";"2";"2";"4";"2";"4";"5";"4";"3";"4";"5";"5";"4";;;"ks" +"95";"JLB003";"Angličtina pro ekonomy I";;"Poslušná,L.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"1";"4";"3";"3";"5";"Měla jsem paní Poslušnou a hodiny byly zábavné a hlavně paní Poslušná byla hrozně přívětivá a milá ke studentům.";;"cjp" +"96";"JEM017";"Business Cycles Theory";"Baxa,J.,Kučera,A.,Vácha,L.";;"3";"4";"3";"3";"3";NULL;NULL;NULL;"1";"3";"3";"2";"1";;;"ies" +"97";"JSM572";"Sociologie organizací";"Čada,K.";;"3";"4";"4";"5";"4";NULL;NULL;NULL;"2";"4";"3";"3";"2";;;"ks" +"98";"JSM095";"Study of Political Mobilization and Social Movements";"Císař,O.";;"5";"4";"4";"3";"4";NULL;NULL;NULL;"3";"4";"4";"4";"5";;;"ks" +"99";"JLB037";"Italština I";;"Přívozníková,P.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"3";"5";"Skvělý přístup vyučující, snaha vyjít všem vstříc a učinit hodiny zajímavé. Zábavné hodiny a zajímavé materiály. Dobrá kombinace všech prvků - videa, texty, poslech, konverzace.";"Problémem opravdu velké rozdíly ve znalostech studentů. Kurz nebyl vyhrazen pouze studentům na úrovni \"mírně pokročilí\", ale nacházela se zde celá řada naprostých začátečníků, což výuku lehce komplikovalo.";"cjp" +"100";"JLB039";"Ruština odborná I - nižší";;"Mistrová,V.";"4";"3";NULL;NULL;NULL;"5";"5";"4";"1";"3";"3";"3";"4";;;"cjp" +"101";"JEM034";"Corporate Finance";"Bychkova,O.,Malířová,T.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"ies" +"102";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"2";NULL;NULL;NULL;"4";"2";"3";"1";"3";"3";"3";"5";;;"kz" +"103";"JEM035";"Financial Markets Instruments I";"Dědek,O.,Poláková,N.,Polák,P.";"Dědek,O.,Poláková,N.,Polák,P.";"4";"5";"3";"3";"3";"3";"3";"3";"1";"4";"4";"4";"1";;;"ies" +"104";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"2";"5";"5";"2";NULL;NULL;NULL;"1";"3";"2";"3";"5";;;"ks" +"105";"JSB051";"Úvod do sociální antropologie";"Uherek,Z.";"Hrešanová,E.";"5";"4";"4";"5";"5";"4";"5";"4";"1";"5";"4";"5";"5";;;"ks" +"106";"JSM421";"Contemporary social theory";"Balon,J.";;"4";"5";"5";"4";"5";NULL;NULL;NULL;"2";"5";"4";"5";"4";;;"ks" +"107";"JPB202";"Politické strany v Evropě";"Perottino,M.";;"1";"1";"3";"5";"1";NULL;NULL;NULL;"2";"2";"1";"1";"1";;"Kurz je příliš obecný a neposkytuje studentům žádné informace, které nejsou v prvnich 3 větách u dané věci na wikipedii. Kurz by se měl zaměřit na fungování stran, jak analyzovat strany a typologii stran";"kp" +"108";"JLB005";"Angličtina pro politology I";;"Stružková,I.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"5";;;"cjp" +"109";"NMMA701";"Matematika 1";"Spurný,J.";"Honzík,P.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"4";"3";"5";;;"ies" +"110";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"4";"4";"2";"1";NULL;NULL;NULL;"1";"1";"1";"1";"1";;;"ies" +"111";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"5";"5";;;"kp" +"112";"JEM062";"Introductory Econometrics";"Kukačka,J.";"Kukačka,J.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"Jiri Kukacka is talented teacher. Always explains material clearly. And always glad to explain again.";;"ies" +"113";"JJB040";"Kreativita v jazyce";"Šoltys,O.";;"4";"2";"3";"3";"3";NULL;NULL;NULL;"2";"3";"3";"2";"4";"Výběr témat, které na jednotlivé hodiny zpracovat.";;"kz" +"114";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Přibáňová,T.";"4";"5";"5";"4";"5";"5";"5";"5";"1";"4";"5";"4";"5";;;"ks" +"115";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Záhlava,J.";"4";"3";NULL;NULL;NULL;"4";"5";"5";"1";"5";"5";"3";"5";;;"ks" +"116";"JSM477";"Sociology of Critique";"Blokker,P.";;"4";"5";"5";"5";"4";NULL;NULL;NULL;"1";"5";"3";"5";"4";;;"ks" +"117";"JJB276";"Public relations v praxi";;"Hejlová,D.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"3";"5";"5";"5";"5";"Hosté a obsah přednášek";"Hosté často nevěděli jak hodnotit úkoly, které zadal host před nimi.Zbytečně moc času jsme strávili sledováním prezentací ostatních studentů, přestože v nich bylo, to samé.Myslím, že by bylo lepší neztrácet čas prezentováním průběžných úkolů a mít jeden větší velký projekt na konci.";"kmkpr" +"118";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"4";"4";"2";"2";NULL;NULL;NULL;"1";"3";"2";"3";"2";;"Komunikaci vyučujícího se studenty.";"ies" +"119";"JPB018";"Dějiny mezinárodních vztahů I";"Soukup,J.";;"5";"2";"4";"5";"4";NULL;NULL;NULL;"3";"4";"3";"5";"5";;;"kmv" +"120";"JJB050";"Tvůrčí dílny tisk";"Kubík,J.,Osvaldová,B.";;"4";"2";"3";"4";"4";NULL;NULL;NULL;"1";"3";"4";"3";"4";;;"kz" +"121";"JEM132";"Company Valuation";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"2";"5";"4";"4";"4";NULL;NULL;NULL;"1";"3";"3";"3";"1";;"Project is too demanding. Too much accounting";"ies" +"122";"JLB001";"Angličtina pro sociology I";;"Klírová,M.";"3";"2";NULL;NULL;NULL;"2";"4";"3";"1";"2";"3";"3";"3";;;"cjp" +"123";"JJB052";"Tvůrčí dílny FOTO I";"Lábová,A.";;"5";"4";"4";"4";"5";NULL;NULL;NULL;"1";"5";"5";"4";"5";;;"kz" +"124";"JPB020";"Úvod do mezinárodních vztahů";"Karlas,J.,Kučera,T.";;"3";"4";"2";"4";"1";NULL;NULL;NULL;"1";"2";"1";"3";"3";;;"kmv" +"125";"JEM137";"Real Estate Investment";"Jandík,T.,Streblov,P.";;"3";"4";"4";"4";"4";NULL;NULL;NULL;"1";"3";"3";"3";"2";;;"ies" +"126";"JJB059";"Kritika v médiích - televizní";"Novotný,D.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"4";"5";;;"kz" +"127";"JLB005";"Angličtina pro politology I";;"Panešová,K.";"5";"4";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"5";"5";;;"cjp" +"128";"JSB007";"Dějiny sociologie I";"Šanderová,J.";"Šrám,K.";"4";"4";"4";"5";"2";"5";"5";"5";"3";"5";"5";"3";"5";;;"ks" +"129";"JJB238";"Kritika marketingové komunikace";"Rosenfeldová,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"3";"5";"4";"5";"5";;;"kmkpr" +"130";"JJB037";"Kritika v médiích I";;;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"5";"5";;;"kz" +"131";"JSB021";"Základy demografie";"Šídlo,L.";;"4";"4";"5";"3";"4";NULL;NULL;NULL;"2";"5";"5";"4";"5";"Praktická cvičení a přednášky i formou konferencí";"Závěrečný test byl příliš komplexní a opravdu hodně složitý";"ks" +"132";"JEM141";"Traditional and Alternative Risk Transfer in the Insurance Sector";"Pompella,M.,Teplý,P.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"2";;;"ies" +"133";"JPB221";"Metodologický proseminář I";;"Bahenský,V.,Kofroň,J.";"4";"3";NULL;NULL;NULL;"4";"5";"5";"2";"5";"5";"4";"5";;;"kmv" +"134";"JMM200";"Roots of American Music - Folklore, Blues, Jazz";"Calda,M.";;"4";"2";"5";"5";"3";NULL;NULL;NULL;"2";"3";"2";"1";"5";;;"kas" +"135";"JJB630";"Krizová komunikace";"Chudinová,E.";;"1";"1";"1";"5";"1";NULL;NULL;NULL;"1";"1";"1";"1";"1";;"veškerý obsah vyučovaného materiáluvyučující";"kmkpr" +"136";"JLM001";"Academic English I";;"Cotte,P.";"1";"1";NULL;NULL;NULL;"2";"4";"1";"3";"2";"1";"1";"1";;"Kurz má navazovat na odbornou angličtinu, ta je mnohem obtížnější, než je tento kurz. Přednášející by si měl zjistit, co se na prerekizitě učí, aby pouze neopakoval, jinak by se měl předmět zrušit";"cjp" +"137";"JJB253";"Markething - online publikování a populární kultura I.";;"Maxa,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kmkpr" +"138";"JJB002";"Dějiny masových médií II";"Sekera,M.";;"2";"2";"1";"4";"1";NULL;NULL;NULL;"1";"1";"1";"1";"2";;;"kms" +"139";"JPB011";"Politická geografie I";"Romancov,M.";;"4";"3";"5";"5";"3";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kp" +"140";"JPB252";"České novověké dějiny I.";"Kučera,J.";;"3";"4";"2";"4";"1";NULL;NULL;NULL;"2";"2";"2";"3";"3";;;"kp" +"141";"JJB003";"Dějiny masových médií III";"Bednařík,P.,Končelík,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"142";"JPB256";"Politické systémy I";"Brunclík,M.,Jeřábek,M.,Kotábová,V.,Perottino,M.";;"4";"4";"3";"4";"3";NULL;NULL;NULL;"3";"4";"2";"3";"4";;;"kp" +"143";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"4";"3";"2";"3";NULL;NULL;NULL;"1";"4";"2";"3";"3";"Systematické poznámky a přirovnání jevů k událostem v ČR i zahraničí, ne jen pouhé definice.";"Zaměřit se více na informace, které lze využít i v praxi.";"ies" +"144";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"3";"5";"3";"3";"2";NULL;NULL;NULL;"1";"3";"2";"4";"2";;;"kp" +"145";"JJB006";"Současný český jazyk III";;"Svobodová,I.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kms" +"146";"JPM719";"Diplomacy of the European Union";"Pajtinka,E.";;"5";"4";"4";"4";"5";NULL;NULL;NULL;"4";"5";"5";"5";"5";"It is important to have such course, as an IR graduate should definitely have some knowledge on how EU diplomacy works";"I think it would be fitting to spend more time on the actual diplomacy of the EU as the syllabus anticipates. Also the test evaluation is a bit harsh with at least 70/100 needed to pass (get E)";"kmv" +"147";"JEM181";"Data Science with R";"Herman,D.,Krištoufek,L.";"Herman,D.,Krištoufek,L.";"4";"4";"4";"4";"4";"4";"4";"4";"1";"4";"4";"4";"4";"whole course is well organized, I liked everything";"DataCamp is buggy. It shows an error. But when you change something completely non-related to error, the error disappears, which is wrong, because you wasted 20 minutes to fix what the error says...";"ies" +"148";"JSM572";"Sociologie organizací";"Čada,K.";;"3";"4";"3";"4";"3";NULL;NULL;NULL;"4";"2";"2";"4";"4";;"Teoretický výklad byl trochu zmatený, ačkoli byl předmět o sdílených ekonomikách, závěrečná práce se musela týkat znalostních ekonomik. Vyučující neodpovídá na emaily,.";"ks" +"149";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"2";"3";"1";"5";"1";NULL;NULL;NULL;"1";"3";"1";"2";"4";;"The lectures could be a bit more understandable, the lecturers are both very nice people, however, their lectures are incredibly boring.";"kmv" +"150";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"2";"5";"2";"3";"1";NULL;NULL;NULL;"2";"3";"1";"3";"2";;;"kp" +"151";"JJB221";"Ekonomická teorie marketingu";"Postler,M.";;"2";"3";"2";"3";"1";NULL;NULL;NULL;"2";"3";"1";"3";"2";;;"kmkpr" +"152";"JPB018";"Dějiny mezinárodních vztahů I";"Soukup,J.";;"5";"1";"5";"5";"4";NULL;NULL;NULL;"2";"4";"4";"5";"5";;;"kmv" +"153";"JJB167";"Moderování zpravodajských relací";;"Moravec,V.,Šobr,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"4";"5";;;"kz" +"154";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"4";"5";;;"ks" +"155";"JSB010";"Současná sociologie";"Balon,J.";;"1";"3";"1";"1";"1";NULL;NULL;NULL;"3";"4";"1";"4";"1";"Celkově se domnívám, že je předmět velice užitečný a zdá se mi nezbytný pro pochopení sociologie, oceňuji možnost odprezentovat téma";"Vyučujícího evidentně přednášení nebaví a nezajímá, výuka by mohla být interaktivnější a zábavnější, inspiraci lze hledat u Magdaleny Mouralové, její semináře jsou vždy zábavné a interaktivní (chci se to naučit vs. musím se to naučit)";"ks" +"156";"JEM001";"Master´s Thesis Seminar I";;"Havránek,T.,Havránková,Z.,Mejstřík,M.";"1";"3";NULL;NULL;NULL;"1";"1";"1";"1";"1";"1";"1";"1";"zero value";;"ies" +"157";"JPB256";"Politické systémy I";"Brunclík,M.,Jeřábek,M.,Kotábová,V.,Perottino,M.";;"3";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"3";"4";"5";;;"kp" +"158";"JLB099";"Rozřazovací test z angličtiny";;"Panešová,K.";"3";"3";NULL;NULL;NULL;"5";"5";"5";"1";"2";"2";"2";"5";;;"cjp" +"159";"JMB410";"Moderní dějiny západní Evropy";"Matějka,O.,Váška,J.";;"3";"4";"3";"3";"3";NULL;NULL;NULL;"2";"3";"2";"4";"4";"Zajímavý pohled na některé otázky spíše z hlediska teorie a jejich přínosu pro současnost.";"Obsah přednášek v podstatě nesouvisí s okruhem učiva pro splnění testu, často záleží dost na náhodě, jestli student dané jméno nebo pojem někdy v průběhu studia někde slyšel nebo ne. Výuka je zvláště v případě pana doktora Vášky značně chaotická a někdy příliš rychlá. rozvrh přednášek dle SIS nedodřžen, některá témata vynechána.Poznámka k průběhu zkoušky - vyučující se velice nepříjemným způsobem \"bavili\" nad chybami studentů v testu, a to právě v moment, kdy tito studenti psali druhou část zkoušky (tj. esej). Působilo to velice neprofesionálním dojmem, rušilo průběh zkoušky a bylo to pro přítomné studenty velice nepříjemné, jelikož se někdo otevřeně smál jejich výkonům v testech. Výkony mohly být sebevíce špatné, to ale vyučujícího neopravňuje se tomu otevřeně vysmívat a navíc tím rušit studenty při zkoušce.";"kzs" +"160";"JJM360";"Ekonomika v médiích";"Klimeš,D.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Pan Klimeš přednáší skvěle a je milý ke studentům. Z jedné jeho přednášky se člověk dozví více než za celý rok na MKPR.";;"kz" +"161";"JPB020";"Úvod do mezinárodních vztahů";"Karlas,J.,Kučera,T.";;"3";"4";"3";"5";"2";NULL;NULL;NULL;"1";"3";"3";"3";"3";;;"kmv" +"162";"JPB252";"České novověké dějiny I.";"Kučera,J.";;"4";"3";"4";"4";"3";NULL;NULL;NULL;"1";"4";"2";"4";"4";;;"kp" +"163";"JPB221";"Metodologický proseminář I";;"Mlejnek,J.,Valková,I.";"3";"3";NULL;NULL;NULL;"4";"5";"4";"1";"3";"3";"3";"3";;;"kmv" +"164";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"3";"2";"2";"2";NULL;NULL;NULL;"1";"2";"1";"2";"2";;;"ies" +"165";"JSM578";"Anthropology of EU";"Uherek,Z.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"ks" +"166";"JSM559";"Kvalitativní výzkum: pokročilé a experimentální metody";"Grygar,J.,Spalová,B.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ks" +"167";"JSM558";"Stát, národ, globalizace: infrapolitika moci a identity";"Grygar,J.,Hájek,M.";"Grygar,J.,Hájek,M.";"5";"4";"5";"5";"5";"5";"5";"5";"2";"5";"4";"5";"5";;;"ks" +"168";"JSM514";"Metody a techniky práce s informacemi";"Tomandlová,V.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"5";"5";;;"kvsp" +"169";"JSM421";"Contemporary social theory";"Balon,J.";;"4";"2";"4";"5";"3";NULL;NULL;NULL;"2";"3";"2";"4";"3";;;"ks" +"170";"JSM027";"Urbánní antropologie";"Uherek,Z.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"ks" +"171";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";NULL;"5";"Zaměření pouze na rozšiřování slovní zásoby.";;"cjp" +"172";"JSM026";"Klíčové otázky sociální antropologie";"Grygar,J.,Hrešanová,E.,Uherek,Z.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"3";"2";"4";"3";;;"ks" +"173";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"3";"3";"2";"5";NULL;NULL;NULL;"1";"3";"3";"3";"2";;;"ies" +"174";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"5";"4";"3";"2";"2";"1";"1";"5";;;"kz" +"175";"JMB218";"Německo a Rakousko po roce 1989";"Emler,D.,Kunštát,M.,Mlsna,P.,Nigrin,T.,Šafařík,P.";;"3";"3";"3";"4";"3";NULL;NULL;NULL;"2";"2";"2";"2";"2";;"Výuka, která byla rozdělena mezi jednotlivé vyučující mi přišla, že na sebe nenavazovala. Někteří vyučující byli mimo téma. Nakonec jsem se toho tolik nedozvěděla.";"knrs" +"176";"JSB454";"Social Web: (Big) Data Mining";"Růžička,J.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Přístup vyučujícího naprosto špičkový - je ochoten pomoci, snaží se kurz vést interaktivně a naprosto skvěle komunikuje";"Možná častější semináře";"ks" +"177";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Interakce se studenty.";;"cjp" +"178";"JJB284";"Firemní komunikace a kultura";"Poucha,T.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kmkpr" +"179";"JLB053";"Angličtina pro sociální vědy I";;"Gloverová,M.";"4";"5";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"3";"4";;;"cjp" +"180";"JMB056";"Reflexe velkých debat v sociálních vědách ve filmu";;"Kozák,K.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"5";;;"kas" +"181";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"4";"5";;;"krvs" +"182";"JMB402";"Úvod do společenských věd II";;"Kocián,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"3";"4";;;"krvs" +"183";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kp" +"184";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"4";"2";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";"Kombinace teorie a informací využitelných v praxi.";;"krvs" +"185";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Šafařík,P.";"4";"5";"4";"3";"5";"4";"5";"4";"1";"5";"4";"5";"4";;;"knrs" +"186";"JJM260";"Novinářská etika v praxi";"Moravec,V.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"5";"5";"Osoba dra Moravce, který poskytuje studentům širší ukotvení novinářské etiky do filozofického rámce; jeho důraz na povinnou četbu mi umožnil přečíst spoustu zajímavých a podnětných titulů, byť se netýkaly přímo médií a žurnalistiky (Komenský, Bauman...). Jeho nadšení do novinařiny a zejména do předávání štafety svobodného novinářství je inspirující a obdivuhodné.";;"kz" +"187";"JPB018";"Dějiny mezinárodních vztahů I";"Soukup,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"3";"4";"3";"4";"5";;;"kmv" +"188";"JPM561";"Regional Security Studies";"Karásek,T.,Klosek,K.";"Karásek,T.,Klosek,K.";"4";"5";"4";"5";"4";"3";"5";"5";"1";"5";"4";"4";"5";;"I cannot evaluate the teaching skills of seminar leader because the seminars did not take place. Instead of a seminar, we wrote a test. That is not necessarily negative, it is just difficult to evaluate.";"kbs" +"189";"JSB031";"Bakalářský seminář I - diskuse projektů";;"Angelovská,O.,Mouralová,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";"Přístup Magdy Mouralové a Olgy Angelové je opravdu skvělý - studentům nabízejí pomocnou ruku vždy, když je třeba, výuku vedou interaktivně, jednají férově, prostě 10 z 10";"Pár much to mělo, ale vše se bez problému vyřešilo. Věřím, že už není třeba to sem vepisovat. Možná jen ty deadlajny.";"ks" +"190";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Kůželová,M.";"4";"5";"4";"5";"5";"4";"5";"4";"2";"5";"4";"5";"4";;;"krvs" +"191";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"1";"5";"1";"1";"1";NULL;NULL;NULL;"1";"1";"1";"1";"1";;"Změnit vyučujiciho";"ies" +"192";"JPB020";"Úvod do mezinárodních vztahů";"Karlas,J.,Kučera,T.";;"3";"4";"2";"3";"2";NULL;NULL;NULL;"1";"4";"2";"5";"4";;;"kmv" +"193";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"3";"3";"2";"4";;;"knrs" +"194";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";;;"cjp" +"195";"JJM214";"Čtení textů ke studiu médií - populární kultura";;"Reifová,I.";"4";"2";NULL;NULL;NULL;"4";"4";"4";"1";"5";"3";"4";"5";"Tématické rozvržení. Vzhledem k tomu, že jsem se předtím setkal se studiem paměti pouze okrajově, mi předmět přinesl hodně nových informací, které vyučující dobře rozprostřela do různých témat.";"Time management vyučující (pozdní příchody, pravidelné přetahování hodin). Aktualizovat název - \"Populární kultura\" je dosti zavádějící, neboť se jedná pouze o jedno z probíraných témat v rámci studií paměti.";"kms" +"196";"JPB221";"Metodologický proseminář I";;"Bahenský,V.,Kofroň,J.";"4";"3";NULL;NULL;NULL;"4";"5";"5";"1";"5";"5";"3";"5";;;"kmv" +"197";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"4";"5";"4";"5";"5";NULL;NULL;NULL;"2";"5";"3";"4";"4";;;"kas" +"198";"JEM004";"Advanced Macroeconomics";"Baxa,J.,Hromádková,E.";"Baxa,J.,Hromádková,E.,Šestořád,T.,Troch,T.,Žáček,J.";"3";"5";"4";"5";"2";"4";"5";"5";"1";"3";"3";"3";"2";;;"ies" +"199";"JMB402";"Úvod do společenských věd II";;"Mertová,V.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"5";;;"krvs" +"200";"JPB252";"České novověké dějiny I.";"Kučera,J.";;"3";"1";"3";"3";"3";NULL;NULL;NULL;"1";"4";"2";"4";"5";;;"kp" +"201";"JEM062";"Introductory Econometrics";"Kukačka,J.";"Kukačka,J.";"4";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"202";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Čížek,M.";"5";"3";"5";"5";"4";"5";"5";"5";"1";"5";"3";"5";"5";;;"knrs" +"203";"JPB242";"Geografie vnitropolitických konfliktů";;"Doboš,B.,Riegl,M.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"5";"5";"Vybraná témata jsou aktuální a pokud má někdo zájem o tuto problematiku, tak se jeho znalosti jistě rozšíří a pomůže mu účast jak na přednáškách, tak cvičeních. S tím je spojená i četba, která je sice občas poměrně dlouhá, ovšem přínosná a člověku jistě pomůže ať už je to na plnění úkolů, zápočet či zapamatování si autorů a jejich využití někdy do budoucna.";"V podstatě mi vedení kurzu vyhovovalo. Vnitropolitických konfliktů je velké množství a nelze řešit/vybírat četbu na úplně všechny, ovšem pokud se témata během let mění, tak by se mi osobně líbilo věnovat jednu hodinu i Libyi či nějaké zemi ze Střední Asie.";"kp" +"204";"JMB402";"Úvod do společenských věd II";;"Kocián,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"3";"5";"Kombinace informatiky a nácviku psaní prací, rozšíření znalostí ohledně vyhledávání zdrojů";;"krvs" +"205";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Čížek,M.";"4";"3";"4";"5";"3";"5";"5";"5";"3";"5";"3";"5";"5";;;"krvs" +"206";"JJM356";"Metodologické přístupy ke studiu žurnalistiky";"Nečas,V.,Veselková,Z.";;"4";"4";"4";"4";"3";NULL;NULL;NULL;"2";"5";"4";"5";"4";"Konečně mi někdo vysvětlil, co je to ona tajemná a obávaná kódovací kniha, jaké jsou druhy výzkumu používané ve společenských vědách a jak vůbec výzkum udělat (kéž by to člověk věděl už před bakalářskou prací). Sláva! Doktor Nečas umí věci vysvětlit, nezabíhá do zbytečných podrobností a vysvětlí gros věci.";;"kz" +"207";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"1";"5";"5";"3";NULL;NULL;NULL;"1";"5";"3";"3";"5";;;"ks" +"208";"JPB256";"Politické systémy I";"Brunclík,M.,Jeřábek,M.,Kotábová,V.,Perottino,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"3";"5";"5";;;"kp" +"209";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"1";"1";"1";"1";"1";"4";"5";"3";"2";"3";"2";"2";"3";"Jako pozitivum vidím poměrnou jednoduchost kurzu, což tento semestr přišlo vhod. Dále příjemné semináře pana Brože a Mareše. Mankiwova učebnice je uživatelsky přívětivá.";"Obecně se jedná zatím o nejtristnější kurz, který jsem na IESu absolvoval. Semestr začal zrušením prvního semináře v pondělí v 8:00, což nám přednášející oznámil mailem v jednu hodinu ráno. Podobný přístup měl přednášející po celý semestr. Přednášky a semináře neměly ani přes snahu cvičících žádnou přidanou hodnotu, jelikož cvičení jsou prostě okopírovaná z Mankiwa. Přednáškové slidy jsou rovněž zkopírované celé věty z Mankiwa, ovšem ne tak, aby se z nich přímo dalo studovat, takže rovněž nemají žádnou hodnotu. Totéž platí pro část testu. Pro příště by bylo lepší řešení udělat kurz Macra I jako korespondenční, s tím, že bychom si učebnici přečetli doma a pouze odeslali úkoly a přišli na závěrečný test - ušetřilo by mi to tento semestr mnoho nepříjemných situací s panem Kudashvilim. Ten soustavně neodpovídá na maily, na opakované žádosti pak odpovídá hrubě a irelevantně. Například na dotaz týden před předtermínem závěrečné zkoušky na témata v SISu (poslední téma napsané v SISu nebylo probráno a nebylo v učebnici), mi v odpověď přišel zcela irelevantní a nekoherentní email ve smyslu, že pan magistr nebude měnit strukturu celého kurzu kvůli mým osobním preferencím, načež o dva dny později rozeslal hromadný mail, ve kterém oznámil, že toto téma ve zkoušce nebude. Obecně byl jeho přístup ke studentům přezíravý až hrubý. Výsledky midterm testu jsem se snažil několik týdnů po opravení, neb je pan Kudashvili odmítl sdělit jinde než na páteční přednášce. Obecně mám dojem, že pan magistr na kurzu ušetřil vskutku maximální množství práce, důkazem čehož budiž i to, že v SISu dodnes není ani stupnice známek.";"ies" +"210";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"3";"5";"5";"5";"5";;;"kas" +"211";"JMB069";"Transatlantic Security Cooperation";"Weiss,T.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"4";"5";"I really improved my knowledge in this field of study.";;"kzs" +"212";"JPB258";"Politické myšlení novověku I";"Franěk,J.,Kučera,J.";;"5";"4";"5";"3";"3";NULL;NULL;NULL;"3";"5";"2";"5";"5";;;"kp" +"213";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"3";"5";;;"krvs" +"214";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"3";"3";"3";"3";NULL;NULL;NULL;"1";"3";"3";"4";"4";;;"ies" +"215";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Kocian,J.";"5";"5";"5";"5";"5";"4";"4";"5";"1";"5";"3";"5";"5";;;"knrs" +"216";"JMM674";"Maritime security: Geopolitics of the Indian and Pacific Oceans";"Hornát,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"I really improved my knowledges in this field of study. It was very interesting !";;"kas" +"217";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"4";"2";"1";"1";NULL;NULL;NULL;"1";"4";"3";"3";"3";"Oceňuji poskytnuté materiály z přednášek na webu, psaní eseje a velké množství termínů na zkoušku.";"Rozhodně se mi nelíbil přístup vyučujícího a jeho styl výuky. Odmítal odpovídat na jakékoli otázky, lidé, kteří se hlásí, jsou podle něho primitivové, kteří na sebe chtějí upozornit...Navíc v hodinách mluvil velmi potichu a příklady na kterých vysvětloval ekonomické pojmy byly často nesmyslné a urážející. Také nesouhlasím s tím, že se na test musíme učit všechno slovo od slova přesně podle toho, jak to vyučující popsal (vlastní příklady neuznává).";"ies" +"218";"JLB100";"Czech as a Foreign Language I";;"Frantesová,E.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"3";"5";"5";"Being in small groups allowed our professor to have an individual follow-up of each student's progress, eventually giving individual tips to those who needed it without slowing the group down as a whole.";"I found it extremely bizarre that this course needed to be paid for. I think that learning the language of the country in which you live and study is essential and I know a lot of students who finally refused to follow this course because of the important fees students had to pay.";"cjp" +"219";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Andrle,J.";"5";"5";"4";"5";"5";"4";"4";"5";"1";"5";"3";"5";"5";;;"krvs" +"220";"JEB003";"Ekonomie I";"Fanta,N.,Kracík,J.,Švarcová,N.";"Fanta,N.,Kracík,J.,Švarcová,N.";"5";"3";"5";"5";"4";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"221";"JJM224";"Politická ekonomie komunikace";"Vochocová,L.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"2";"5";"3";"5";"5";"Aplikaci probírané látky na aktuální příklady. A celkově aktuálnost probírané látky (na rozdíl od ostatních předmětů pokrývá vyrovnaně různá média /tisk-TV-internet/).";"Práci s texty a četbou. Ze začátku jsem četl texty před hodinami, ale bez předešlého vysvětlení kontextu jsem si z nich mnoho neodnesl. Později jsem začal texty číst jen velmi zběžně, nebo rovnou vůbec a dočítal je (někdy) až po přednášce, a mám pocit, že mi tak daly mnohem více.";"kms" +"222";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"5";NULL;NULL;"5";;;"ks" +"223";"JMB402";"Úvod do společenských věd II";;"Šafařík,P.";"5";"4";NULL;NULL;NULL;"5";"5";"4";"1";"5";"5";"5";"5";"Vše";;"krvs" +"224";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"kp" +"225";"JPB227";"Politický system ČR";"Charvát,J.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kp" +"226";"JPB227";"Politický system ČR";"Charvát,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Způsob, jakým je předmět vyučován. Není to jen čistě opisování z prezentací či přednáška, která trvá 80 minut a člověk si ji odsedí. Formou menších debat je látka probírána a často dojde na zajímavá politická témata, která jsou doprovázena občasnými vtípky a ty jsou vzhledem k začátku hodiny (8 ráno) někdy potřeba, aby udržely pozornost a zájem posluchačů.";;"kp" +"227";"JMB212";"Moderní dějiny Japonska";"Labus,D.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kas" +"228";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"5";"5";;;"ies" +"229";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"4";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"3";"4";;;"knrs" +"230";"JPM345";"Diplomní seminář III.";;"Brunclík,M.,Franěk,J.,Hroch,M.,Charvát,J.,Jüptner,P.,Kofroň,J.,Kotábová,V.,Krausz Hladká,M.,Kubátová,H.,Kučera,J.,Landovský,J.,Mlejnek,J.,Perottino,M.,Riegl,M.,Romancov,M.,Říchová,B.,Salamon,J.,Shavit,A.,Švec,K.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kp" +"231";"JEM166";"Master´s Thesis Seminar - IEPS";;"Benáček,V.";"2";"2";NULL;NULL;NULL;"3";"2";"3";"2";"2";"3";"2";"3";;;"ies" +"232";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Šafařík,P.";"4";"4";"5";"5";"4";"5";"5";"4";"1";"4";"4";"4";"4";;"Lepší témata";"knrs" +"233";"JPB228";"Mírové smlouvy a konference v mez. systému";"Jeřábek,M.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kmv" +"234";"JMB242";"Balkans after 1989";"Hofmeisterová,K.,Kocián,J.,Králová,K.";;"5";"4";"4";"5";"4";NULL;NULL;NULL;"1";"5";"4";"4";"4";"It seems that all contemporary aspects and challenges of Internal and International affairs of the Balkans were covered in this course, meaning that students with no background were able to get a comprehensive understanding of this complex area, and students who already had some background in some fields were able to discover new challenges as well.";;"krvs" +"235";"JJM201";"Teorie masové a mediální komunikace";"Jirák,J.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"2";"4";"3";"Ústní zkoušení, o to více ve této formě dialogu.";;"kms" +"236";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"5";"4";"4";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kas" +"237";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"4";"5";;;"ies" +"238";"JPB260";"Politické myšlení novověku III";"Kučera,J.";;"3";"3";"4";"5";"3";NULL;NULL;NULL;"1";"3";"3";"3";"3";;;"kp" +"239";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"4";"5";;;"ks" +"240";"JJB002";"Dějiny masových médií II";"Sekera,M.";;"3";"3";"4";"3";"2";NULL;NULL;NULL;"4";"1";"1";"1";"2";;;"kms" +"241";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Hornát,J.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"Témata + spojení výuky s aktuálním světem";;"krvs" +"242";"JLB053";"Angličtina pro sociální vědy I";;"Gloverová,M.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Vstřícný přístup ke studentům, ochota poradit a pomoci, jasné a srozumitelné instrukce k testu, materiály k samostudiu, učení se praktických dovedností (např. Prezentace), interaktivní výuka.";;"cjp" +"243";"JSM005";"Sociální struktura ČR: stav, vývoj, srovnání s EU";"Tuček,M.";;"5";"3";"4";"5";"4";NULL;NULL;NULL;"1";"3";"3";"3";"5";"Práce s reálnými výzkumy - orientace v tabulkách a interpretování výsledkůAktuálnost";;"ks" +"244";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"ks" +"245";"JPM306";"African Security";"Werkman,K.";;"3";"2";"3";"5";"4";NULL;NULL;NULL;"2";"4";"1";"5";"4";"Out of all the courses about regional security, this was definitely the one tackling more security issues.It was interesting to read about a variety of threats and problems affecting the African continent, and I like that the professor was open to suggestions about topics. I also liked the students' blog where the professor posted the memos. It was very useful for the exam.";"I did not like the discussions, or at least the fact that the students were almost always the only ones talking.It is surely interesting to hear about other people's opinions, but it would have been more beneficial to hear more of what the professor had to say on the topics.";"kbs" +"246";"JJB003";"Dějiny masových médií III";"Bednařík,P.,Končelík,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"vtipný přístup obou vyučujících a zábavné dějinné historky";;"kms" +"247";"JPB589";"Seminář k politickému myšlení: 19. století";;"Novotný,J.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"1";"5";"5";"5";"5";;;"kp" +"248";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"3";"5";"5";"5";"5";;;"kas" +"249";"JJB019";"Práce s agenturními informacemi";"Prázová,I.,Trunečková,L.";"Prázová,I.,Trunečková,L.";"3";"3";"4";"4";"2";"4";"4";"3";"1";"2";"2";"2";"3";"I learned about the sources a journalist can use to his work and how to use them";"The seminars were sometimes too monotonous and filled with redundant information";"kz" +"250";"JJB021";"Bakalářský seminář";;"Prázová,I.";"2";"1";NULL;NULL;NULL;"4";"4";"3";"1";"3";"3";"3";"3";;;"kz" +"251";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"4";"3";"5";"5";"3";NULL;NULL;NULL;"1";"3";"4";"4";"4";;;"knrs" +"252";"JJB133";"DTP";;"Slanec,J.";"4";"1";NULL;NULL;NULL;"4";"4";"4";"1";"3";"3";"3";"3";;;"kz" +"253";"JPM613";"Armed Forces and Society";"Kučera,T.";;"4";"2";"3";"4";"3";NULL;NULL;NULL;"1";"4";"3";"4";"5";;;"kbs" +"254";"JJM211";"Kvalitativní výzkum mediálních publik";;"Reifová,I.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kms" +"255";"JJM204";"Výzkum médií I";"Křeček,J.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"4";"5";"Způsob výuky založený na interakci se studenty, kdy se vyučující ptá studentů i na zdánlivě očividné věci, a nechá nás tak, abychom sami pochopili podstatu probírané látky. O moc příjemnější způsob než pouhý výklad. Díky za to.";"Tady budu mluvit spíš jen za sebe, ale býval bych ocenil vyšší náročnost na to, co v SPSS/PSPP uděláme.";"kms" +"256";"JJB169";"Věda v médiích";"Kasík,P.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"5";"5";;;"kz" +"257";"JPM324";"Geography and Politics in Europe within Global Regionalism";"Doboš,B.,Riegl,M.";;"3";"3";"1";"2";"5";NULL;NULL;NULL;"2";"1";"1";"2";"1";"In the syllabus it looked like a course intended to understand different theories and implementation of geopolitical thought throughout history to the present days, that is certainly valuable. Of course, the class itself was not like that.";"Methodology could be improved by avoiding lectures in which the teacher would recite by heart and sometimes mumbled information. Evaluation mechanisms aim to avoid analysis and comprehension but they endorse learning by heart facts that seem to be useless but could actually be useful if thought and used well for an analysis. There was lack of precise information regarding literature required for the course and absolute lack of reality in the assignment of the amount of readings to be done.";"kp" +"258";"JPB597";"Current Political Extremism";"Charvát,J.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Very interesting way to approach such a complex term like extremism. The lectures were always fun and covered a lot of ground (fascism, communism, nationalism, antisemitism etc). Definitely worth attending.";;"kp" +"259";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"3";"5";"4";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"1";;;"kmv" +"260";"JMM027";"Contemporary Mediterranean";"Králová,K.,Mejstřík,M.";;"3";"2";"3";"4";"2";NULL;NULL;NULL;"1";"3";"2";"3";"3";;"The evaluation criteria and form of the course did not invite students to participate much.";"kzs" +"261";"JMB037";"Moderní dějiny Polska";"Vykoukal,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"krvs" +"262";"JJB006";"Současný český jazyk III";;"Svobodová,I.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"4";"5";"I learned new things about my mother tongue";;"kms" +"263";"JPM656";"Technology and warfare";"Kučera,T.";;"4";"2";"3";"4";"3";NULL;NULL;NULL;"1";"4";"3";"4";"5";;;"kbs" +"264";"JLM011";"Angličtina pro veřejnou a sociální politiku I";;"Klírová,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"The vocabulary related to my program and English speaking skill";"it could be better that there are not too many tasks every week.";"cjp" +"265";"JEM179";"History and Methodology of Economics";"Baxa,J.,Paulus,M.";;"4";"4";"4";"5";"5";NULL;NULL;NULL;"1";"5";"3";"4";"4";;;"ies" +"266";"JMM130";"Ethno-Political Conflicts in the Caucasus";"Brisku,A.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";NULL;"5";"5";;;"krvs" +"267";"JJB009";"Úvod do psychologie";"Vranka,M.";;"3";"4";"4";"1";"1";NULL;NULL;NULL;"2";"2";"2";"2";"3";;;"kz" +"268";"JSB998";"Úvod do sociologie";"Soukup,P.";;"4";"4";"5";"4";"3";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"ks" +"269";"JMM530";"European Comparative Politics and Society";"Rovná,L.";;"2";"2";"3";"4";"2";NULL;NULL;NULL;"3";"2";"3";"4";"3";;;"kzs" +"270";"JPB227";"Politický system ČR";"Charvát,J.";;"4";"3";"4";"5";"3";NULL;NULL;NULL;"1";"3";"3";"3";"5";;;"kp" +"271";"JMM302";"Russia after 1991";"Svoboda,K.";;"4";"4";"3";"4";"4";NULL;NULL;NULL;"2";"5";NULL;"5";"3";;;"krvs" +"272";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"4";"3";"4";"4";"2";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"krvs" +"273";"JLB053";"Angličtina pro sociální vědy I";;"Gloverová,M.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"1";"5";"4";"4";"5";;;"cjp" +"274";"JPM561";"Regional Security Studies";"Karásek,T.,Klosek,K.";"Karásek,T.,Klosek,K.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"I really liked the variety of the topics in the syllabus. It really expanded my knowledge about security issues as I got to study things I had never heard of. It was very useful to know what mistakes we should have avoided in writing the final paper.";"In-class discussions were too broad in relation to the readings. It would be useful to see the correct answers for the tests. It should have been said in advance (before the course started) that a medium-high level knowledge of IR theories was required. The professors specified it only during the first class and it is by no way possible to read tens of IR books before the first test.";"kbs" +"275";"JMB402";"Úvod do společenských věd II";;"Hofmeisterová,K.";NULL;NULL;NULL;NULL;NULL;"4";"5";"4";NULL;NULL;NULL;NULL;NULL;;;"krvs" +"276";"JMMZ042";"Cohesion Policy of the EU in Central and East European Countries.";"Hauser,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"interesting topics; students were able to make suggestions concerning their interests to influence the course plan; lecturer has hands-on experiences in this topic; good interaction via facebook and gdrive --> easy communication; students from Asia and all over Europe;";"cannot think of any aspect that could be improved;";"krvs" +"277";"JMMZ050";"Political Systems of East European Countries in the 20th Century";"Kubát,M.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;"1";"5";NULL;"5";"5";;;"krvs" +"278";"JPM595";"Arms Control and Disarmament";"Hynek,N.,Smetana,M.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"The guest lecturers were amazing, and I did appreciate them a lot. The readings were well picked and corresponded to the topic of each class. Overall, this course was one of the best courses I had this semester.";"I did not really like the way the double-degree students were pinpointed. I understand their knowledge is better and tops the ordinary Erasmus or Czech students (and yes, that could be a great leading example), but every time I or any other student asked a question, the lecturers asked one of them to answer it instead of answering it themselves. This made me feel somewhat stupid (\"oh, I guess I should have known that; I should not have asked, my peers already know it\") and discouraged me from further questions. The same problem occurred in the course Strategic Studies.";"kbs" +"279";"JEM163";"Principles of Microeconomics";"Janský,P.";"Král,M.,Moravcová,H.,Palanský,M.";"1";"4";"2";"2";"1";"1";"2";"3";"1";"1";"1";"2";"1";;;"ies" +"280";"JJB014";"Žurnalistická tvorba III - Časopisecká tvorba";"Osvaldová,B.";"Maňák,V.";"4";"1";"5";"5";"5";"5";"5";"4";"1";"2";"4";"3";"4";;"nepracovat ve dvojicích, přece jen má každý jiný styl psaní a nikdo si na VŠ stále nenašel kamarády, a proto je pro něj těžké s někým spolupracovat :)";"kz" +"281";"JJM295";"Rozhlasový a televizní dokument";"Štoll,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"Zpětnou vazbu! Po dlouhé době (asi tak po třech letech) mi někdo na vysoké škole poslal zpětnou vazbu na seminární práci a závěrečnou zkoušku. Toho si velmi vážím.";"Býval bych byl rád za trochu více ukázek z mezinárodní dokumentární tvorby, ale to je věc osobních preferencí.";"kz" +"282";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;NULL;NULL;"3";"2";"1";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"ies" +"283";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;NULL;NULL;"5";"5";"5";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"krvs" +"284";"JPM323";"Global Political Philosophy";"Salamon,J.";;"4";"3";"2";"2";"4";NULL;NULL;NULL;"1";"5";"2";"4";"4";;;"kp" +"285";"JLB001";"Angličtina pro sociology I";;"Štěpánková,D.";"3";"1";NULL;NULL;NULL;"4";"5";"4";"1";"2";"2";"4";"4";"Paní Štěpánková je mírná a lidská. Oceňuji její snahu zapamatovat si jména studentů. Výuka byla provázána videi, která dobře odrážela brané téma. Také se mi líbí, že stačí napsat 2 testy nad 70%, a nemusíme dělat zápočtový test.";"Kurz nebyl obtížný, ale ne příliš jsem si z něho neodnesl.";"cjp" +"286";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Čížek,M.";NULL;NULL;"5";"5";"5";"5";"5";"5";NULL;NULL;NULL;NULL;NULL;;;"knrs" +"287";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"3";"3";"1";"3";"2";NULL;NULL;NULL;"2";"3";"2";"3";"3";;;"kmv" +"288";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Čížek,M.";NULL;NULL;"4";"5";"4";"5";"5";"5";NULL;NULL;NULL;NULL;NULL;;;"krvs" +"289";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;NULL;NULL;"5";"5";"3";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"knrs" +"290";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;NULL;NULL;"3";"5";"3";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kas" +"291";"JSB998";"Úvod do sociologie";"Soukup,P.";;NULL;NULL;"5";"5";"5";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"ks" +"292";"JPM697";"Asia Security";"Kolmaš,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"I really like how passionate about the subject the Professor was. I like the course because it touched topics I had never studied before. In-class discussions and games were also very useful and fun.";"I would only add a few more security issues affecting South East Asia/South Asia today but I understand that the course was a combination of IR theory and security.";"kbs" +"293";"JJM247";"Český stranický systém";"Just,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"4";"3";"Jasný a přehledný děj politických událostí.";"Možná dostupnější studijní materiály.";"kz" +"294";"JSM692";"Introduction to Social Research Methodology";"Remr,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"the basic knowledge of social science provides an ability to research and study this academic area deeply.";"Too many theories, I think it could be better if the course combined theory and practice.";"ks" +"295";"JMB047";"Vybrané problémy mezinárodních konfliktů.";"Čížek,M.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"krvs" +"296";"JJM199";"Literární a knižní kritika";"Čeňková,J.";;"4";"4";"4";"5";"4";NULL;NULL;NULL;"2";"4";"4";"4";"4";"Orientace na současnější literaturu a polskou literaturu (Olga Tokarczuková, Mariusz Szczygieł atp...)";;"kz" +"297";"JSB051";"Úvod do sociální antropologie";"Uherek,Z.";"Hrešanová,E.";"2";"3";"5";"4";"1";"3";"3";"2";"2";"3";"2";"1";"2";"Zajímavé texty na seminářích.";"Tento kurz se mi příliš nelíbil. Pan Uherek byl vtipný, ale jeho přednášky byly nezáživné a nic jsem si z nich nepamatoval. Seminář také nehodnotím příliš dobře. Texty byly sice zajímavé, ale všechny byly v angličtině a některé byly poměrně složité. Příliš se mi také nelíbil přístup paní Hrešanové, který úplně neodpovídal standardu vysoké školy.";"ks" +"298";"JLB019";"Francouzština odborná I";;"Bosáková,L.";"5";"4";NULL;NULL;NULL;"5";"5";"4";"2";"4";"3";"5";"5";;;"cjp" +"299";"JPM306";"African Security";"Werkman,K.";;"4";"2";"4";"5";"5";NULL;NULL;NULL;"1";"4";"5";"4";"4";"the discussion approach to the course";"maybe more input from the professor and less from the students";"kbs" +"300";"JPM698";"Middle East Security";"Daniel,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"I really like how passionate about the subject the Professor was. He was very available and also ready to help. I liked that the course combined and linked historical discussions to more modern and complex topics of security. Also, I really liked the guest lectures.";"I would only add a lecture on the Gulf region. I understand that it is not really the Professor's expertise so maybe with a guest lecture?";"kbs" +"301";"JMB079";"The Geography of North America";"Pitoňák,M.";;"5";"3";"4";"5";"3";NULL;NULL;NULL;"2";"4";"3";"5";"5";;;"kas" +"302";"JMB069";"Transatlantic Security Cooperation";"Weiss,T.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"4";"5";"It was very interesting learning about EU and Us's approaches on so many subjects. The comparison and this rich knowledge brought me new perspectives to interpret this kind of subject";"Maybe trying to introduce clear parts in each lesson will be fine to make it ever more understandable (French way of thinking as we are used to have clear plans with clear parts)";"kzs" +"303";"JMB212";"Moderní dějiny Japonska";"Labus,D.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"Znalosti a přístup přednášejícího.";"Více zapojit studenty v hodině.";"kas" +"304";"JPM561";"Regional Security Studies";"Karásek,T.,Klosek,K.";"Karásek,T.,Klosek,K.";"4";"3";"4";"5";"4";"4";"5";"4";"1";"5";"5";"5";"4";"learning more theories about International Security";;"kbs" +"305";"JSM095";"Study of Political Mobilization and Social Movements";"Císař,O.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"4";"5";;;"ks" +"306";"NMMA703";"Matematika 3";"Zelený,M.";"Zelený,M.";"4";"4";"5";"5";"5";"5";"5";"4";"1";"4";"3";NULL;"3";"Matematiku konstantně hodnotím jako jeden z nejlépe vedených předmětů. Letos jí ani nemohu dát nejvyšší obtížnost, pod dojmem dalších předmětů.";"K M3 nemám připomínek. Nicméně jako každý rok mám připomínky k struktuře těchto dotazíků, která je naprosto nesmyslná. Spíše než co chceme zlepšit/zachovat by měly být hlavní klady a zápory, protože to je to, co každý píše. Doporučení předmětu ostatním studentům je u povinných předmětů irelevantní. Rozšíření znalostí a nácvik dovedností mi nikdy nebyl jasný, ostatně záleží dost na tom, co o dané problematice člověk ví již před začátkem kurzu, ne nutně reflektuje kvalitu. Co letos přibylo je skvělá otázka na přístup cvičícího. Opravdu nevím, co si představit pod možnostmi \"velký\" nebo \"malý\".";"ies" +"307";"JEB022";"Institutional Economics";"Kopečná,V.,Schwarz,J.";;"3";"3";"2";"3";"2";NULL;NULL;NULL;"2";"4";"3";"3";"3";;;"ies" +"308";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"incredible input from professor from his personal experience";;"kmv" +"309";"JPM693";"Traditional and Critical Concepts in Security Studies";"Rychnovská,D.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kbs" +"310";"JSM554";"Diplomový seminář";;"Tuček,M.";"4";"2";NULL;NULL;NULL;"4";"5";"4";"1";"2";"4";"2";"4";;;"ks" +"311";"JPM710";"Radicalization and Deradicalization";"Aslan,E.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kbs" +"312";"JPM323";"Global Political Philosophy";"Salamon,J.";;"3";"4";"3";"5";"2";NULL;NULL;NULL;"1";"4";"4";"4";"3";"global approach --> Confucian teachings and muslim political theory, not just Western philosophers";"introduce PowerPoint slides; not just talking of the lecturer during class; improve structure of the course; improve possibilities to follow the lecturer by using PowerPoint, hand out summaries and so on";"kp" +"313";"JEB022";"Institutional Economics";"Kopečná,V.,Schwarz,J.";;"3";"3";"3";"3";"3";NULL;NULL;NULL;"2";"3";"3";"3";"3";;;"ies" +"314";"JPM710";"Radicalization and Deradicalization";"Aslan,E.";;"4";"2";"3";"3";"3";NULL;NULL;NULL;"3";"4";"1";"4";"4";"I liked the reading list in the syllabus and that the Professor shared his own on-field experience with the students.";"I usually do not like students presentations and prefer a normal lecture. Also, feedback on presentations should be shared exclusively with the students doing the presentation. I would have also liked some feedback on the mid-term test.";"kbs" +"315";"JJB135";"Filmový seminář I";;"Šobr,M.";"4";"1";NULL;NULL;NULL;"4";"4";"5";"2";"4";"3";"5";"5";;;"kz" +"316";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Rybín,F.,Vlčková,A.";"5";"4";"5";"5";"5";"4";"5";"5";"1";"5";"5";"5";"5";"Tento kurz považuji za asi nejzajímavější a nejpřínosnější v zimním semestru. Pan profesor Jeřábek byl velice vstřícný, hodný a jeho výklad velmi zajímavý. Doporučuji chodit na přednášky - jsou zajímavé a navíc je tento předmět velice důležitý pro další studium.Cvičení byla velice příjemná a přínosná. Naši cvičící byli velmi přátelští, v hodinách byla legrace a uvolněná atmosféra. Byl to předmět na který jsem se těšil.";"Lepší přístup ke studijním materiálům.";"ks" +"317";"JEB120";"Financial Economics";"Žigraiová,D.";;"3";"3";"3";"3";"3";NULL;NULL;NULL;"3";"3";"3";"3";"3";;;"ies" +"318";"JSM031";"Analytické metody výzkumu pro mgr.";"Jeřábek,H.";"Daneš,D.";"2";"5";"3";"5";"4";"2";"5";"2";"3";"2";"2";"2";"3";"Přednášky hostů (Petrúšek, Chylíková) byly nejpřínosnější z celého kurzu - systematické, zajímavé";"- přednášky a semináře na sebe vůbec nenavazují, přednášející a cvičící spolu nekomunikují a i test je každý zvlášť - je to jako dělat dva různé předměty- kdybychom probírali jednotlivé metody teoreticky na přednáškách a potom je prakticky procvičovali na cvičeních, bylo by to mnohem přínosnější- přednášky mi připadaly všechny, že jsou spíš o Lazarsfeldovi než o metodách- cvičení v tak dlouhých blocích nejsou moc dobré, navíc David mluví hrozně rychle, neustále spěchá a je to na úkor toho, jestli jsme schopní to pochopit";"ks" +"319";"JEB026";"European Economic Integration";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"3";"4";"3";"3";"3";"4";"4";"3";"1";"4";"3";"4";"4";;;"ies" +"320";"JMB249";"Seminář k dějinám středo- a jihovýchodní Evropy";;"Balla,P.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Okruh témat, které navazují na přednášky a prohlubují znalosti meziválečného období v SJVE. Povinná četba, která byla velmi přínosná.";"Ocenil bych alespoň nějakou zpětnou vazbu na zasílaná shrnutí z povinné četby.";"krvs" +"321";"JLB003";"Angličtina pro ekonomy I";;"Poslušná,L.";"5";"2";NULL;NULL;NULL;"4";"4";"4";"1";"4";"4";"4";"4";;;"cjp" +"322";"JPB594";"Realism in International Relations";"Odintsov,N.";;"3";"4";"3";"3";"3";NULL;NULL;NULL;"1";"4";"4";"3";"3";;;"kmv" +"323";"JEB039";"International Trade";"Semerák,V.";;"5";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"5";"5";"5";;;"ies" +"324";"JJB004";"Současný český jazyk I";;"Svobodová,I.";NULL;"4";NULL;NULL;NULL;"3";"1";"5";"1";"4";"5";"3";"5";;;"kz" +"325";"JPM648";"Politics of Security in Northeast Asia";"Karásková,I.";;"4";"3";"3";"3";"3";NULL;NULL;NULL;"1";"4";"4";"3";"3";;;"kmv" +"326";"JJB006";"Současný český jazyk III";;"Svobodová,I.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"2";"5";"4";"4";"5";"Kvituji praktická cvičení. Opakování je matka moudrosti. Chválím energický přístup přednášející, který vás po celou dobu udrží při smyslech.";;"kms" +"327";"JEB105";"Statistics";"Červinka,M.";"Smutná,Š.";"5";"4";"4";"5";"4";"4";"5";"5";"1";"5";"5";"4";"5";"Jako student ekonomické teorii považuji tento předmět za jeden z klíčových. Musím ocenit, že jak pan doktor Červinka, tak cvičící nám vyšli vždy vstříc, předmět mě velice bavil";"Když tento předmět srovnám s výukou matematiky, musím konstatovat, že je složitější se orientovat v látce, kterou je potřeba umět, jinak vše dobré.";"ies" +"328";"JEM020";"Ethics and Economics";"Cahlík,T.";"Cahlík,T.";"4";"3";"4";"4";"4";"4";"4";"4";"1";"4";"3";"3";"3";;;"ies" +"329";"JPM118";"Výběrový seminář: Volby v USA";"Kotábová,V.";;"5";"2";"4";"5";"5";NULL;NULL;NULL;"1";"4";"3";"4";"5";"Interaktivní diskuze nad aktualitami z oblasti Americké politiky";"Nic mě nenapadá";"kp" +"330";"JPM694";"Strategic Studies";"Hynek,N.,Ludvík,J.,Smetana,M.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"The location of the course is great; it is nice to have a lecture in the city center instead of going to Jinonice. I also enjoyed the guest lecturers, who brought some very interesting points to the class.";"Just one small point, the classroom was quite stuffy after the ACAD classes that were before us, so opening all windows during the break and keeping at least one open even during the lecture would be a real improvement to the learning experience.";"kbs" +"331";"JMB091";"Religion, secularity and laicity in Europe (19th-21th centuries)";"Bauer,P.";;"4";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"4";"4";"The study of many way to apprehend religious believes and behaviours depending and the evolution of the historical or geographical space";"Maybe add more parts in the lesson, to make it more structured";"kzs" +"332";"JEB022";"Institutional Economics";"Kopečná,V.,Schwarz,J.";;"3";"2";"3";"4";"4";NULL;NULL;NULL;"2";"3";"3";"3";"4";"Difficulty";;"ies" +"333";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"3";"3";"3";"3";"2";"4";"4";"5";"2";"3";"3";"4";"3";;;"ies" +"334";"JPM146";"Přechody k demokracii v teorii a praxi I";"Mlejnek,J.";;"4";"4";"3";"5";"2";NULL;NULL;NULL;"1";"5";"3";"5";"4";;;"kp" +"335";"JEB120";"Financial Economics";"Žigraiová,D.";;"3";"4";"3";"3";"4";NULL;NULL;NULL;"4";"4";"4";"3";"3";"Difficulty";;"ies" +"336";"JLB003";"Angličtina pro ekonomy I";;"Poslušná,L.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"5";"Difficulty";;"cjp" +"337";"JPM160";"Česká komunální politika";"Jüptner,P.";;"3";"5";"4";"4";"3";NULL;NULL;NULL;"2";"5";"5";"4";"3";;;"kp" +"338";"JMMZ205";"Race, Ethnicity, and Gender in American History and Literature";;"Janíčková,M.,Robbins,D.";"3";"3";NULL;NULL;NULL;"5";"5";"5";"2";"3";"5";"4";"4";"Introduction to early American literature.";;"kas" +"339";"JSM421";"Contemporary social theory";"Balon,J.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"5";"5";"Forma testu, i když některé texty byly až moc složité - nejenom pro pochopení, ale i jazykově.";"Ocenila bych, kdyby polovinu přednášek tvořily prezentace studentů a druhou polovinu výklad pana dr. Balona, bylo by to mnohem zajímavější.";"ks" +"340";"JJB010";"Základy filozofie a vzdělanosti";"Halada,J.";;"3";"3";"3";"3";"1";NULL;NULL;NULL;"3";"4";"1";"2";"3";;"Samotné přednášky kladou důraz na něco jiného než závěrečný test (zkouška). Na přednáškách se probírají myšlenky filosofů, jejich životy. Závěrečný test sestává z otázek na fakta - letopočty, díla, atp.";"kz" +"341";"JMB250";"Seminář k dějinám západní Evropy";;"Synkule,M.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"3";"5";"5";"5";"5";"Současná podoba výuky je ideální. Po absolvování kurzu, jsem získal přehled a znalosti o britské politice, které jsem později využil při zkoušce ze ZE. Oceňuji přístup vyučujícího a časté diskuse.";;"kzs" +"342";"JPM342";"Konflikty v demokracii a národní identita: teoretické problémy analýzy";"Říchová,B.";;"3";"3";"4";"4";"3";NULL;NULL;NULL;"3";"4";"4";"4";"3";;;"kp" +"343";"JJJM191";"Media and the Children";"Zezulková,M.";;"3";"3";"3";"4";"3";NULL;NULL;NULL;"4";"3";"4";"3";"3";;;"kms" +"344";"JMM027";"Contemporary Mediterranean";"Králová,K.,Mejstřík,M.";;"3";"2";"4";"5";"4";NULL;NULL;NULL;"1";"3";"3";"3";"2";"Have an approach I didn't have before of the contemporary Mediterranean area";;"kzs" +"345";"JEM123";"Economics of Least Developed Countries";"Bauer,M.";"Bauer,M.";"4";"4";"4";"4";"4";"4";"4";"4";"1";"4";"4";"4";"4";;;"ies" +"346";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Záhlava,J.";"3";"5";"5";"3";"3";"5";"5";"5";"1";"4";"4";"4";"3";"Na kurzu oceňuji především cvičení. Náš cvičící měl velký přehled, uměl látku dobře vysvětlit. Úkoly měl velmi rychle opravené a brzy posílal zadání dalších úkolů. I když matematiku příliš nevyhledávám, tak cvičení mě bavila.";"Kurz byl poměrně těžký. Navíc o přednáškách nikdo nevěděl, o čem pan profesor Hendl mluví. Navíc se neustále měnily podmínky pro složení zkoušky a do poslední chvíle jsme nevěděli, zdali budeme muset skládat i ústní zkoušku.";"ks" +"347";"JPM579";"Teorie politických stran";"Perottino,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"5";"5";"Interaktivní forma přednášek";;"kp" +"348";"JEM141";"Traditional and Alternative Risk Transfer in the Insurance Sector";"Pompella,M.,Teplý,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ies" +"349";"JPM639";"Problémy ústavního inženýrství";"Brunclík,M.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"2";"5";"4";"5";"4";;;"kp" +"350";"JJB012";"Žurnalistická tvorba I";"Osvaldová,B.";"Krobová,T.,Osvaldová,B.,Slanec,J.";"5";"2";"4";"4";"5";"4";"5";"5";"1";"3";"4";"3";"5";"Dělání rozhovorů je skvělá zkušenost, kterou si člověk jinak nemá moc šanci vyzkoušet. Ocenila bych ale, kdyby jejich hodnocení bylo důkladnější a víc mi pomohlo posunout se dál.";;"kz" +"351";"JEM003";"Advanced Microeconomics";"Janda,K.,Melikhova,O.";"Janda,K.,Melikhova,O.";"3";"4";"4";"5";"3";"4";"4";"4";"1";"2";"3";"3";"1";"Friendly teachers (lecture and seminars)";"Asking for definitions in the exam is not improving my knowledge, but I will learn by heart and forget it again immediately.And the writings on the board are challenging to read because of the size.";"ies" +"352";"JPM641";"Světový regionalismus";"Riegl,M.";;"3";"4";"4";"3";"4";NULL;NULL;NULL;"1";"4";"3";"3";"2";;;"kp" +"353";"JLB027";"Ruština odborná I - vyšší";;"Mistrová,V.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"4";"4";;;"cjp" +"354";"JPM717";"Continental Philosophy and IR";;"Ditrych,O.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kmv" +"355";"JSM572";"Sociologie organizací";"Čada,K.";;"4";"3";"4";"4";"5";NULL;NULL;NULL;"4";"4";"4";"2";"5";"Téma sdílené ekonomiky i povinné texty byly velmi dobře vybrané. I když byl kurz veden hodně nesystematicky, tak byl jedním z nejzajímavějších v tomhle semestru.";"Lépe komunikovat";"ks" +"356";"JPM653";"Politika a média";"Švec,K.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"3";"5";;;"kp" +"357";"JSM628";"European policies and practice towards ethnic minorities";"Bernard Thompson Mikes,A.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"4";"5";"5";"5";"5";;;"kvsp" +"358";"JSM018";"Economic Sociology and European Capitalism for MA";"Blokker,P.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"ks" +"359";"JPM324";"Geography and Politics in Europe within Global Regionalism";"Doboš,B.,Riegl,M.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"4";"showing the historical context of geopolitics; explaining theories; great lecturers (lecture + seminar); students from all over the world had the chance to present topics with relevance for their home countries; interesting overall topic; good and quick communication via mail or office hours;";"give students access to PowerPoint slides --> now it is like 80 minutes of writing, difficult to listen to the lecturer at the same time; definitely too much to read; weekly assignments of 2-5 pages just worth 10 percent of the overall grade? improve proportion!!at least 20 percent overall. it takes about 3-5 hours per week, multiplied by 10 assignments --> just 10 percent?! really?!; feedback for presentations via mail;";"kp" +"360";"JMM293";"The Special Relationship between the United States and Great Britain";"Raška,F.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"3";"4";"2";"4";"3";;;"kas" +"361";"JEM020";"Ethics and Economics";"Cahlík,T.";"Cahlík,T.";"5";"4";"3";"5";"5";"4";"5";"4";"1";"5";"4";"5";"5";;;"ies" +"362";"JJB135";"Filmový seminář I";;"Šobr,M.";"3";"1";NULL;NULL;NULL;"4";"4";"3";"3";"2";"1";"1";"4";;"Lepší výběr filmů - až na pár světlých výjimek byly filmy tohoto semestru buď špatné, nebo české (což obvykle taky znamená špatné, viz například hodnocení na ČSFD, nebo jakákoliv recenze co není od Spáčilové). Chápu že pro české filmy je asi snazší sehnat práva, ale to by neměla být jediná proměnná v rozhodování.";"kz" +"363";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"5";"5";"5";"5";"5";"5";"5";"5";"3";"3";"4";"4";"5";;;"ies" +"364";"JJB015";"Česká literatura I";;"Čeňková,J.,Malý,R.";"3";"2";NULL;NULL;NULL;"5";"5";"3";"1";"5";"3";"3";"4";"Psaní seminární práce o jednom konkrétním díle je velmi přínosné, protože student si musí nastudovat mnoho o úzké oblasti. Což je činnost, kterou žurnalisté dělají neustále.";"Tím, že se většina výuky odehrávala formou prezentací, slyšela jsem vykládat spíš své spolužáky než paní doktorku Čeňkovou, což mě mrzí.";"kz" +"365";"JJM254";"Mediální tvorba";"Čásenský,R.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Kurz mi přišel skutečně užitečný, co se týče praktických znalostí a zkušeností pana Čásenského. Tento kurz byl pro mě nejužitečnější ze všech absolvovaných kurzů na FSV.";;"kz" +"366";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"The lecturer was amazing, as well as the whole design of the course. He managed to keep my full attention during the class, he was funny, he answered all our questions (even outside the class). His videos are fantastic, it helped incredibly and must have been a hell lot of work. I still cannot believe how a course that deals with a topic that is rather boring and difficult (if you are not a real statistics enthusiast) became my most favorite course of this semester! Hats off!";"Nothing, the course is absolutely brilliant!";"kmv" +"367";"JPM910";"The Nature and Function of the State";"Franěk,J.,Pettit,P.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"5";"5";"gread lecturer; interesting topic";"nothing";"kp" +"368";"JMM025";"Putin´s Russia";"Veselý,L.";;"5";"3";"4";"5";"4";NULL;NULL;NULL;"2";"4";"4";"4";"5";;;"krvs" +"369";"JJB017";"Grafický design a základy polygrafie I";"Slanec,J.";;"4";"2";"4";"5";"4";NULL;NULL;NULL;"1";"3";"5";"4";"4";"Přednášky jsou velmi dobře vedené, klade se důraz na opravdu podstatné věci.";;"kz" +"370";"JJB606";"Televize jako instituce";"Štoll,M.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";NULL;"5";"5";"Kurz hodnotím jako velmi přínosný. Oceňuji především přístup vyučujícího, který do teorie vkládal vlastní poznatky z praxe.";"Líbilo by se mi, kdyby součástí kurzu byla exkurze, popř. nějaká aktivita (vlastní projekt), která by nám dala možnost otestovat nové poznatky.";"kms" +"371";"JJB018";"Úvod do fotožurnalistiky";"Lábová,A.";;"3";"2";"3";"5";"4";NULL;NULL;NULL;"1";"3";"1";"4";"3";;;"kz" +"372";"JEM027";"Monetary Economics";"Holub,T.,Malovaná,S.";"Břízová,P.,Hájek,J.,Holub,T.,Malovaná,S.";"5";"3";"5";"5";"5";"4";"5";"4";"1";"5";"4";"5";"5";"I really appreciated the insider's view on the topic and that contents were at the current state of the art.";"The homeworks were graded quite harsh and we could not figure out what the correct answers would have been.";"ies" +"373";"JMM663";"Europe in the French mind: a historical–civilizational point of view";"Bauer,P.";;"3";"4";"2";"3";"3";NULL;NULL;NULL;"3";"3";"1";"4";"2";;;"kzs" +"374";"JMM663";"Europe in the French mind: a historical–civilizational point of view";"Bauer,P.";;"4";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"4";"4";"A depth analyse of the French point of view concerning Europe put into perspective with the other main points of view (German one mainly)";"Maybe add more parts in the lesson to make it clearer";"kzs" +"375";"JPB202";"Politické strany v Evropě";"Perottino,M.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"2";"4";"5";"Líbí se mi, že pan docent Perottino začíná přednášku nějakou aktualitou ze světa. Kurz se navíc skvěle doplňuje s povinným kurzem Politické systémy střední Evropy.";;"kp" +"376";"JJB135";"Filmový seminář I";;"Šobr,M.";"4";"1";NULL;NULL;NULL;"4";"4";"1";"2";"1";"1";"1";"3";;;"kz" +"377";"JEB022";"Institutional Economics";"Kopečná,V.,Schwarz,J.";;"2";"4";"3";"3";"2";NULL;NULL;NULL;"1";"3";"2";"3";"2";;;"ies" +"378";"JJM249";"Duchovní kořeny evropské kultury";"Halada,J.";;"3";"2";"2";"2";"1";NULL;NULL;NULL;"1";"3";"1";"3";"3";;"Výuka byla občas příliš zmatená. Do poslední chvíle jsme nevěděli, jaké jsou přesné požadavky ke splnění předmětu.";"kz" +"379";"JPM323";"Global Political Philosophy";"Salamon,J.";;"2";"5";"2";"3";"1";NULL;NULL;NULL;"1";"2";"2";"3";"1";;;"kp" +"380";"JJB009";"Úvod do psychologie";"Vranka,M.";;"3";"4";"4";"4";"3";NULL;NULL;NULL;"2";"3";"3";"3";"4";;;"kz" +"381";"JEM163";"Principles of Microeconomics";"Janský,P.";"Král,M.,Moravcová,H.,Palanský,M.";"4";"3";"3";"5";"1";"1";"2";"4";"1";"4";"5";"4";"3";"The concepts are very helpful for the degree I'm doing and the material felt relevant and well-paired with my other classes";"The seminar for our class was really poor. Most of us felt really confused and didn't feel like we could ask questions because of limited time. The instructor didn't really seem to want to be teaching the course and didn't do a very good job with explaining the concepts. Most of the time he was facing the board and just solving the questions himself and then asking us if we understood and no one did. The lectures felt disorganized at times and kind of felt like they got off track so I stopped going to them and just read the book and went to seminar.";"ies" +"382";"JSM692";"Introduction to Social Research Methodology";"Remr,J.";;"2";"2";"4";"5";"3";NULL;NULL;NULL;"2";"2";"3";"3";"3";;;"ks" +"383";"JEM062";"Introductory Econometrics";"Kukačka,J.";"Kukačka,J.";"4";"4";"5";"5";"4";"5";"4";"5";"1";"4";"5";"5";"5";;;"ies" +"384";"JJM356";"Metodologické přístupy ke studiu žurnalistiky";"Nečas,V.,Veselková,Z.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"5";"4";"Výuka byla přehledná, byly jasně stanoveny požadavky ke splnění předmětu.";;"kz" +"385";"JPM198";"Contemporary Latin America";"Krausz Hladká,M.";;"4";"1";"5";"5";"5";NULL;NULL;NULL;"2";"5";"3";"4";"5";"I didn't know a lot of things about Latin America. This course allow me to discover it.";"Maybe speaking more slowly";"kp" +"386";"JPM650";"Intelligence";"Bahenský,V.,Galeotti,M.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kbs" +"387";"JEB105";"Statistics";"Červinka,M.";"Hanus,L.";"5";"4";"4";"5";"4";"4";"5";"5";"1";"5";"4";"4";"4";"Oceňuji komentáře pana Červinky k našim hodnocením kurzu Introductory Statistics. Jako studentovi mi to pomohlo, protože mi tím částečně vysvětlil jeho přístup k výuce, řekl co nás chce naučit, atd. Dále bych vyzdvihl rychlé opravení midtermu. Není sice životně důležité, jestli výsledky dorazí za den nebo za pět dní, ale mně osobně to od pana Červinky připadá jako projev respektu vůči studentům, od kterých sice očekává velké množství odvedené práce, ale sám ukazuje, že když musí něco udělat on sám, nedělá mu to problém. Závěrečná (obávaná) ústní zkouška probíhala ve velmi přátelském prostředí, za což panu Červinkovi patří velký dík. Ještě větší dík mu patří za to, co všechno nás za ten rok stihl naučit. Co se týče cvičení, chodil jsem k panu Hanusovi a myslím, že jsem díky jeho vysvětlování pochopil mnoho věcí a odnesl jsem si spoustu praktických dovedností.";"Měl bych poznámku ohledně prezentací. U některých slidů jsem měl občas problém je správně pochopit. Např. v souhrnu testování hypotéz je často uveden test, který je připsán k určité hypotéze. Zároveň ovšem tento test platí i pro následující hypotézu. Očekával bych, že test, který je používán pro obě hypotézy, bude jakýsi nadpis, a že pod ním budou uvedeny dvě možnosti, a ne že bude připsán pod jednu z nich. Takových věcí by se dalo najít mnohem více a vzhledem k tomu, že je v kurzu opravdu hodně informací a zároveň hodně souvislostí mezi nimi, bylo by prospěšné, kdyby prezentace byly přehlednější a jasnější. Čímž neříkám, že jsou nepřehledné, pouze bych uvítal drobné změny a drobná ujasnění- i nepatrná poznámka v prezentaci či na tabuli může často výrazně ovlivnit pochopení daného tématu.";"ies" +"388";"JEM179";"History and Methodology of Economics";"Baxa,J.,Paulus,M.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Really enjoyed the interactive class format, students were very encouraged to participate and be active in class discussions. Teacher's style of presenting was also very engaging.";"Maybe a review session in the 13th week would be helpful to summarize everything learned since its quite a large range of information to take in.";"ies" +"389";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"3";"4";"3";"3";"2";"4";"4";"5";"1";"4";"3";"4";"5";;;"ies" +"390";"JPM693";"Traditional and Critical Concepts in Security Studies";"Rychnovská,D.";;"3";"4";"4";"5";"3";NULL;NULL;NULL;"1";"3";"2";"3";"3";;;"kbs" +"391";"JPM687";"Astropolitics";"Doboš,B.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Great course, fantastic topics, the lecturer is a real enthusiast!";;"kp" +"392";"JJB019";"Práce s agenturními informacemi";"Prázová,I.,Trunečková,L.";"Prázová,I.,Trunečková,L.";"2";"3";"3";"5";"2";"3";"5";"2";"1";"3";"2";"3";"3";"Osvěžení přednášek v osobě pana Troníčka. Exkurze do ČTK.";;"kz" +"393";"JEM034";"Corporate Finance";"Bychkova,O.,Malířová,T.";;"4";"5";"4";"4";"5";NULL;NULL;NULL;"1";"4";"3";"3";"1";;;"ies" +"394";"JPM712";"Insurgency and Counterinsurgency";"Aslan,E.";;"4";"4";"4";"4";"3";NULL;NULL;NULL;"2";"4";"3";"3";"4";;;"kbs" +"395";"JEM037";"Financial Markets";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"4";"3";"4";"5";"5";"3";"4";"5";"1";"4";"5";"5";"4";;"Grading for active participation gives wired incentives to participate. I like being active, but it caused sometimes a chaos, as everyone wanted to be heard. And also extra points in the lectures are putting people on the spot that are seriously interested in the topic.";"ies" +"396";"JEB120";"Financial Economics";"Žigraiová,D.";;"4";"4";"1";"2";"5";NULL;NULL;NULL;"1";"4";"4";"4";"1";;;"ies" +"397";"JJB998";"Úvod do ekonomie";"Poljakov,N.";;"5";"3";"3";"5";"5";NULL;NULL;NULL;"2";"5";"1";"4";"4";"Je skvělé, že tento kurz vede člověk z praxe, a tak je kurz orientovaný na znalosti, které se v praxi opravdu hodí.";"Psaní hromadných esejí je podle mého názoru zvláštní. Esej je útvar, který obsahuje osobní pohled na věc, tým tedy musí utvářet názorové kompromisy. Často se navíc stává, že někteří z týmu toho odpracují daleko víc než jiní - ale hodnoceni jsou stejně.";"kz" +"398";"JPB579";"Bc. seminář Politologie a veřejná politika I";;"Kváča,V.,Mouralová,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Přátelský přístup, zajímavá cvičení, přínosné rady a doporučení. Skvělé je také “nucení” začít psát, dost mi to pomohlo.";;"kp" +"399";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"3";"4";"4";"5";"3";NULL;NULL;NULL;"2";"3";"2";"4";"4";"Líbilo se mi, že paní doktorka Gelnarová propojila faktické věci s politologickými termíny - například Sarotiho typologií nebo Lipsetem a Rokkanem v oblasti štěpních linií. Takhle si to člověk dá mnohem více do souvislostí.";"Ty dobové texty jsou určitě zajímavé a určitě nepovažuji za zbytečné si je přečíst. Nicméně si nemyslím, že je na tom nutné stavět zkoušku a nebo o tom tak dlouho diskutovat na přednášce.";"kp" +"400";"JJM355";"Teoretické koncepty ke studiu žurnalistiky I";"Jirků,J.,Němcová Tejkalová,A.";"Jirků,J.";"4";"5";"4";"5";"5";"5";"5";"5";"1";"4";"3";"5";"4";"Oceňuji přehlednost přednášek, a především seminářů.";;"kz" +"401";"JPB260";"Politické myšlení novověku III";"Kučera,J.";;"5";"4";"5";"4";"4";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Líbí se mi přesah k jiným kulturně-společenským jevům.";;"kp" +"402";"JMM530";"European Comparative Politics and Society";"Rovná,L.";;"1";"1";"2";"3";"1";NULL;NULL;NULL;"3";"2";"2";"2";"1";"Presentations by students were interesting";"The class materials are all over ten years old and from the same book, the syllabus also hasn't been updated since 2010 which is ridiculous. It comes off as lazy that the professor hasn't updated any material for years. Grades were also not based on what the syllabus said they would be (a compilation of essay, presentation, participation), and were assigned entirely from the essay grade which the professor had a teaching assistant do.";"kzs" +"403";"JLB009";"Angličtina pro žurnalisty I";;"Prošková,A.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"2";"5";"3";"5";;;"cjp" +"404";"JSB515";"Vysokoškolská vzdělávací politika";"Vlk,A.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kvsp" +"405";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"4";"5";"5";"5";"3";"3";"3";"1";"1";"4";"4";"5";"4";;;"ies" +"406";"JPM300";"Geopolitics of sovereignty, state failure and unrecognized states";"Riegl,M.";;"3";"4";"4";"5";"4";NULL;NULL;NULL;"1";"4";"3";"3";"3";"I learned a lot of things I didn't know before and it allowed me to better understand the geopolitcal configuration of the world";"Maybe be clearer concerning authors (speaking more about their work in class)";"kp" +"407";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Skvělé a plnohodnotné přednášky.";;"ks" +"408";"NMMA701";"Matematika 1";"Spurný,J.";"Skříšovský,E.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"409";"JSB027";"Sociální politika jako společenská praxe";"Dobiášová,K.,Vlčková,K.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kvsp" +"410";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"2";"3";"2";"2";"2";NULL;NULL;NULL;"3";"3";"3";"3";"1";"The lecture slides are very clear";"Both professors did not interact with students at all during class, just talked off of what was on the slides. The organization of documents in SIS was terrible, everything just placed in there and made it extremely hard to find readings. Readings also were missing frequently in the SIS, I spent a lot of time trying to find readings. For the credit assignment of this course there is an extremely large amount of readings, around 200 pages per week.";"kmv" +"411";"JLB041";"Španělština I";;"Mlýnková,L.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Pratelsky pristup, procvicovani";;"cjp" +"412";"JPB227";"Politický system ČR";"Charvát,J.";;"3";"1";"5";"5";"4";NULL;NULL;NULL;"1";"3";"2";"2";"4";"Líbily se mi diskuze, zajímavé otázky a podněty k přemýšlení. Je dobré začínat politickou kulturou.";"Myslím, že by se mohlo mluvit méně o Ústavě - je sice zásadní, ale každý si jí může přečíst. Na přednáškách mi to přišlo trochu jako ZSV na gymplu. Místo toho by se daly třeba aplikovat různé politologické teorie/modely na české prostředí. Přece jen - všichni studenti už jsou ve třetím ročníku. Tady by se mohly některé teoretické věci objevit v praxi.";"kp" +"413";"JJB021";"Bakalářský seminář";;"Prázová,I.";"2";"2";NULL;NULL;NULL;"3";"5";"2";"1";"2";"2";"2";"2";;"Kurz by se dal celkem obstojně nahradit jedním mailem s několikastránkovým dokumentem, přednášky byly zbytečně dlouhé. Navíc mi přijde nešťastné učit se ve druhém ročníku náležitosti psaní seminárních a jiných prací, když už v prvním ročníku mnozí studenti se svými seminárními pracemi vyhořeli na formalitách.";"kz" +"414";"JPM711";"Issues in Russian and Eurasian Security";"Aslan,E.";;"5";"4";"5";"4";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"krvs" +"415";"JEM181";"Data Science with R";"Herman,D.,Krištoufek,L.";"Herman,D.,Krištoufek,L.";"5";"5";"5";"4";"5";"5";"4";"5";"1";"4";"5";"4";"5";"I could seriously improve my skills and knowledge about R and programming in general (e.g. algorithms, efficiency).";"The prerequirements are not clear. I spoke to many students that never worked with R before and seriously struggled with it. On the other hand, the first DataCamp exercise was very boring (but extremly time consuming!!) if you worked with R before. I would have wished, that one was not compulsory.";"ies" +"416";"JPM697";"Asia Security";"Kolmaš,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";"The lecturer is great and really knows his topic.";;"kbs" +"417";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"4";"4";"3";"2";NULL;NULL;NULL;"1";"4";"1";"4";"3";;;"ies" +"418";"JPM561";"Regional Security Studies";"Karásek,T.,Klosek,K.";"Karásek,T.,Klosek,K.";"4";"4";"5";"5";"4";"5";"5";"4";"1";"3";"2";"4";"5";;;"kbs" +"419";"JPB596";"Čínská zahraniční a bezpečnostní politika";"Karmazin,A.";;"5";"2";"4";"5";"4";NULL;NULL;NULL;"1";"3";"4";"4";"5";"Líbilo se mi, že finální hodnocení se skládalo jednak z prezentace, ale i z písemné zkoušky.";"Kurz byl celkově vážně dobrý, jen by bylo super, kdyby pan přednášející nepřetahoval. Já vím, že je těžké si to přesně naplánovat, ale někdy jsme vážně přetahovali skoro o 10 minut a když máte v tu chvíli být už na jiné přednášce o několik pater jinde, tak je to pak trochu problém.";"kbs" +"420";"JLB005";"Angličtina pro politology I";;"Panešová,K.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";;;"cjp" +"421";"JPM323";"Global Political Philosophy";"Salamon,J.";;"3";"4";"4";"4";"5";NULL;NULL;NULL;"1";"5";"5";"5";"4";"I always wanted to take a philosophy course in my undergrad degree and never did and appreciated getting the perspective through this class. The concepts were very interesting and the readings were well-selected to convey what we were supposed to learn. Loved the seminars attached with the course too.";"Some of the lectures felt like they went off-topic, it would have been helpful to break down the readings more directly. Participation of students was stifled a few times so I think that ended up discouraging less-confident students from speaking up.";"kp" +"422";"JPM702";"NATO and EU in Crisis Management";"Karásek,T.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"3";"4";;;"kbs" +"423";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"3";"5";"3";"2";"2";NULL;NULL;NULL;"1";"4";"2";"4";"3";;"Příliš velká naplněnost učebny. Nižší kvalita přednášek.";"kmv" +"424";"JPM707";"Peacekeeping and Peacebuilding";"Bureš,O.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kbs" +"425";"JSB454";"Social Web: (Big) Data Mining";"Růžička,J.";;"4";"5";"4";"4";"5";NULL;NULL;NULL;"1";"4";"2";"4";"3";"Praktické dovednosti a možnost okamžité implementace v praxi.";"Studijní materii rozložit do více vyučovacích hodin, popř. zmenšit její rozsah. Větší průprava a vedení v jednotlivých projektech.";"ks" +"426";"JJB0111";"Journalism Ethics/Úvod do etiky žurnalistické práce";"Neuzil,M.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"3";"3";"4";"5";;;"kz" +"427";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"4";"5";"Přístup přednášejícího, interaktivní forma, práce z domova.";"Známkování v Moodle může být nespravedlivé (špatně zaokrouhleno a podobně).";"kmv" +"428";"JJB138";"Sportovní žurnalistika v televizi I";"Záruba,R.";"Záruba,R.";"5";"5";"5";"4";"5";"5";"4";"5";"1";"5";"5";"5";"5";"Konečně mám pocit, že studuji žurnalistiku. Skvělá praktická cvičení. Sice jste v nich hozen do vody a musíte se spolehnout hlavně sám na sebe, ale jedině tak se můžete něco naučit. Oceňuji individuální přístup vyučujícího, který do detailu rozebere každou chybičku, podobně jako jeho kolegové v elektronické tužce studia Buly.";;"kz" +"429";"JPM324";"Geography and Politics in Europe within Global Regionalism";"Doboš,B.,Riegl,M.";;"2";"3";"2";"1";"5";NULL;NULL;NULL;"1";"4";"3";"3";"2";"The seminar was by far the best part of the course, it was interactive, the readings were direct and interesting. The lecture portion of the course could be eliminated and the seminar replace it and that would be great.";"Students are unable to get the slides from class or take photos of them so the whole class is just focused on exactly repeating the slides which is juvenile. It distracts students from actually being able to participate in class. The readings and syllabus are really a mess. No one read the textbooks because there were just two full textbooks assigned plus other readings which people couldn't find, broken links etc. It leads to a lack of participation with the readings.";"kp" +"430";"JJB148";"Audiovizual Interpreting the Reality";"Štoll,M.";;"4";"2";"3";"5";"5";NULL;NULL;NULL;"2";"2";"2";"5";"4";;"You can add more practice in this course, I suppose";"kz" +"431";"JJB133";"DTP";;"Slanec,J.";"4";"1";NULL;NULL;NULL;"4";"5";"3";"1";"3";"3";"2";"4";;;"kz" +"432";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"3";"2";"2";"4";NULL;NULL;NULL;"1";"2";"1";"1";"2";;;"ies" +"433";"JPM432";"European Public Space: Interest Representation and Public Debate, ES";"Knutelská,V.";;"4";"5";"3";"3";"5";NULL;NULL;NULL;"1";"4";"4";"4";"4";;"Bodování domácích úkolů je mi nejasné. Často jsem si byl jist, že jsem vše splnil, ale dostal jsem třeba 1-2 body.";"kmv" +"434";"JJM363";"Czech-German-Jewish Literary Triangle";;"Peroutková,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"5";"5";"The teacher assigned very interesting readings and was always open to discussion.";"Nothing so far!";"kz" +"435";"JJB066";"Rozhlas a televize ve světě";"Moravec,V.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"2";"5";"5";"Srovnání s českým kontextem, praktické ukázky, film o německé propagandě";;"kz" +"436";"JJM234";"Media and Society: An Introduction";"Jirák,J.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;"1";"4";"5";"5";"5";;;"kms" +"437";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Líbilo se mi střídání obou vyučujících - každý s jiným přístupem. U dr. Švece komplexnější pohled, u dr. Mlejnka zase vyzdvihování některých zajímavých momentů. Přednášky byly podnětné.";"Vůbec se mi nelíbily prezentace na seminářích: asi je to hlavně chyba studentů. Ale unavovalo mě papouškování už dřív řečených informací z přednášek. Některým prezentacím chyběla jakákoliv přidaná hodnota. Bylo taky velmi náročné udělat čtvrtou prezentaci třeba na Polsko orginálně, aby se neopakovaly věcí zmíněné v prezentacích od kolegů. Témata se hrozně překrývala. Proto navrhuju: rozdělit studenty na dvě poloviny - seminářů bude mít každá skupina 6 - tím pádem by se tolik neopakovaly informace z prezentací.";"kp" +"438";"JSB007";"Dějiny sociologie I";"Šanderová,J.";"Šrám,K.";"4";"4";"3";"4";"4";"4";"5";"5";"3";"4";"4";"5";"4";;;"ks" +"439";"JSB028";"Informační gramotnost";"Tomandlová,V.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"2";"4";;;"kvsp" +"440";"JPB263";"Bakalářský seminář II.";;"Brunclík,M.,Bureš,O.,Ditrych,O.,Franěk,J.,Gelnarová,J.,Hynek,N.,Charvát,J.,Jeřábek,M.,Jüptner,P.,Karásek,T.,Karlas,J.,Knutelská,V.,Kofroň,J.,Kotábová,V.,Krausz Hladká,M.,Kubátová,H.,Kučera,J.,Kučerová,I.,Landovský,J.,Ludvík,J.,Makariusová,R.,Mlejnek,J.,Pa";"5";"4";NULL;NULL;NULL;"5";"5";"3";"1";"4";"5";"5";"5";;;"kp" +"441";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"5";"5";"5";"4";"5";"5";"4";"5";"1";"5";"4";"5";"5";"Tento kurz je těžký, ale naprosto klíčový pro pochopení toho, co vlastně studujeme. Pan doktor Švec přednáší uceleně a jasně, takže si z přednášek opravdu něco odneseme.";"Bylo vidět, že ke konci semestru už jsme opravdu nestíhali, takže poslední témata jsme projeli opravdu hopem. Já vím, že si to máme nastudovat v povinné literatuře, ale možná by stálo za úvahu vyřadit některé méně důležité věci, nebo zkrátka věnovat méně času ostatním teoriím, aby zbyl čas na vše.";"kp" +"442";"JJM240";"Cultural studies";"Soukup,M.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"2";"4";"1";"3";"4";;;"kms" +"443";"JJB067";"Mluvní a pohybová výchova I";;"Pavel,L.";"4";"1";NULL;NULL;NULL;"4";"4";"4";"1";"3";"4";"2";"4";;"Více se zaměřit na čtení textů, více trénovat ve studiu";"kz" +"444";"JSB051";"Úvod do sociální antropologie";"Uherek,Z.";"Hrešanová,E.";"4";"3";"5";"5";"4";"3";"3";"3";"1";"5";"2";"5";"4";;;"ks" +"445";"JPM052";"Mezinárodní ekonomické vztahy (MEV)";"Kučerová,I.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"3";"3";"4";"3";;"Probírané učivo se bohužel často překrývalo s mým předešlým studiem. Navrhuji, aby ten předmět byl více zaměřený na konkrétní problémy.";"kmv" +"446";"JJM362";"History of media";;"Neuzil,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"5";"5";;;"kz" +"447";"JPM432";"European Public Space: Interest Representation and Public Debate, ES";"Knutelská,V.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"It is a beneficial course covering EU lobbying and the EU public space, but touching also several other connected topics, i.e. the EU democratic deficit and else. the course leader selected good literature, the in-class discussion was on point, was a good summary of the literature and also an useful followup to other topics we have covered.";"clearer system of students' evaluation during the semester. we have weekly updated table on the SIS, however it is so far impossible to tell which 'grade' is for activity and which for homework. please place explanation of used abbreviations there. also, when assigning a homework, please stick with email form in case somebody misses a class.";"kmv" +"448";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Rössler,J.";"4";"3";"4";"5";"4";"4";"5";"4";"1";"4";"4";"5";"4";;;"ks" +"449";"JJB069";"Tvůrčí dílny I - televizní";"Lokšík,M.";;"4";"4";"4";"3";"3";NULL;NULL;NULL;"1";"3";"5";"3";"4";;;"kz" +"450";"JPM118";"Výběrový seminář: Volby v USA";"Kotábová,V.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";;;"kp" +"451";"JSB530";"Úvod do sociologického myšlení";"Bureš,J.,Numerato,D.";"Bureš,J.";"4";"3";"4";"4";"2";"5";"5";"5";"1";"3";"3";"4";"4";;;"ks" +"452";"JPM690";"Liberalism in International Relations (TIR)";;"Karlas,J.";"4";"5";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"4";"Přístup přednášejícího. Je přísný, ale vše vysvětlí. Pokud tedy student tápe, je to trochu jeho vina. Neocenitelný byl poskytnutý metodologický návod pro psaní research papers.";;"kmv" +"453";"JJB014";"Žurnalistická tvorba III - Časopisecká tvorba";"Osvaldová,B.";;"3";"3";"4";"4";"2";NULL;NULL;NULL;"2";"4";"4";"4";"3";"Rozdělení přednášek a seminářů. Redakční schůze vedoucí studenty k samostatné činnosti. Velká míra zpětné vyzby.";"Uvažoval bych o tom, zda by kurz neměl být už součástí specializace.";"kz" +"454";"JLB101";"Czech as a Foreign Language II";;"Mazúrková,B.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"The teacher tried her best to initiate and stimulate discussion (always in Czech), thus enabling us to discover new words and structures. She also used very diverse teaching methods and materials (games, quizes and classic reading) which made class very entertaining.";"It might be good to have more homework assigned to develop skills acquired in class.";"cjp" +"455";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Záhlava,J.";"3";"4";"4";"4";"3";"3";"5";"5";"1";"4";"3";"2";"3";;;"ks" +"456";"JLB100";"Czech as a Foreign Language I";;"Mazúrková,B.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"I value that the teacher found the best ways to help us understand and learn the language. I am now more able to communicate in Prague thanks to this classes. They really helped me.";;"cjp" +"457";"JPB589";"Seminář k politickému myšlení: 19. století";;"Novotný,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Nejlepší kurz tohoto semestru. Ocenil jsem, že jsme se zabývali autory, kterým nebylo na přednáškách věnováno tolik času. Líbil se mi taky komplexní pohled z mnoha úhlů - např. na Marxe. Skvělé téma byla například kritika marxismu - dalo to skvělé souvislosti.";;"kp" +"458";"JPM725";"Technology and Security: Contemporary Warfare in the 21st Century";;"Csernatoni,R.";"4";"4";NULL;NULL;NULL;"4";"4";"3";"1";"4";"3";"3";"3";"Zaměření na současnost.";"Skupinové prezentace se mi zdály být ztrátou času. Mrzelo mě, že jsem se nikde nemohl dovědět, kolik bodů jsem získával v průběhu semestru (v jiných kurzech je vše k nahlédnutí).";"kmv" +"459";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"4";"3";"4";"4";"5";NULL;NULL;NULL;"1";"4";"3";"3";"3";;;"ies" +"460";"JJB071";"Tvůrčí dílny I - rozhlasové";"Maršík,J.";"Lovaš,K.,Lucký,J.";"4";"2";"4";"4";"4";"4";"4";"4";"1";"3";"4";"3";"4";;"Více se změřit na ústní projev než na psaní textů, a to cvičit s každým studentem ve výuce, ne jen v rámci procvičování před výukou. Nepouštět celé třídě nahrávky těch studentů, kterým se to nepovedlo. Jistě existují i cizí nepovedené nahrávky.";"kz" +"461";"JPM909";"Rousseau and Nationalism: On the Government of Poland";;"Franěk,J.,Kelly,C.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Dr. Kelly je skvělý, kurz byl velmi obohacující.";;"kp" +"462";"JJM247";"Český stranický systém";"Just,P.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kz" +"463";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"2";"3";"2";"1";NULL;NULL;NULL;"1";"3";"3";"2";"2";;;"ies" +"464";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"2";"4";"3";"1";"3";"2";"1";"3";"2";"2";"2";"2";"1";;;"kp" +"465";"JJB083";"Editování zpravodajských relací";"Beneš,P.";;"4";"3";"3";"5";"5";NULL;NULL;NULL;"1";"4";"4";"3";"4";;"Přidala bych ukázky řazení jednotlivých zpráv v rámci relace v praxi";"kz" +"466";"JLB035";"Francouzština I";;"Bosáková,L.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"5";"5";;;"cjp" +"467";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"4";"4";"4";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"How we managed to evaluate data in an easier way.";"I believe that sometimes the wordings in exams or homeworks are tricky, so i can understand something but if i read it for like 5 times i would get a different idea of what he asks each time. Not in all questions but in some. Also if we cannot do something is better if he can help us and explain than just google the answer. If im confused, google is not really going to help me.";"kmv" +"468";"JPB268";"Evropská integrace";"Plechanovová,B.";;"2";"4";"1";"1";"2";NULL;NULL;NULL;"4";"2";"2";"3";"1";;;"kmv" +"469";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"5";"2";"5";"5";"3";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"krvs" +"470";"JPB269";"Současné problémy mezinárodních vztahů";"Knutelská,V.";;"4";"3";"4";"4";"2";NULL;NULL;NULL;"1";"3";"4";"4";"4";;;"kmv" +"471";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"4";"2";"4";"4";"2";NULL;NULL;NULL;"1";"4";"2";"4";"4";;;"ies" +"472";"JMB402";"Úvod do společenských věd II";;"Hrušková,T.";"3";"3";NULL;NULL;NULL;"3";"2";"4";"3";"3";"3";"3";"3";;;"krvs" +"473";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"3";"4";"5";;;"kp" +"474";"JEB044";"Financial Accounting";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"4";"5";"5";"4";"5";NULL;NULL;NULL;"1";"5";"3";"5";"4";"Professor's broad knowledge and effort were valuable for me in this lecture.";;"ies" +"475";"JPM595";"Arms Control and Disarmament";"Hynek,N.,Smetana,M.";;"5";"2";"4";"4";"4";NULL;NULL;NULL;"1";"3";"3";"3";"5";"The end simulation was exciting as it allowed us to bring together all the sections of homework we had completed in the previous weeks. ​";"Some of the students in the class did not seem as focused on the end task and this impacted on the final simulation as some students turned up and where not as prepared as others. This led to some country teams, that should have had a minor role, having a more significant​ role to play.";"kbs" +"476";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";;;"kp" +"477";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Papežová,K.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"knrs" +"478";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"3";"5";;;"kz" +"479";"JJB167";"Moderování zpravodajských relací";;"Moravec,V.,Šobr,M.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"4";"5";"Velké množství cvičení, zpětná reakce, možnost pustit si nahrávky i doma, příjemná atmosféra! :)";;"kz" +"480";"JPB258";"Politické myšlení novověku I";"Franěk,J.,Kučera,J.";;"4";"3";"4";"4";"3";NULL;NULL;NULL;"1";"3";"3";"3";"4";;;"kp" +"481";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"3";"3";NULL;NULL;NULL;"4";"4";"3";"3";"3";"4";"4";"3";;"Lepší sladění s Matematikou 1, více času na propočítávání zk. písemek";"ies" +"482";"JPB268";"Evropská integrace";"Plechanovová,B.";;"2";"5";"2";"1";"1";NULL;NULL;NULL;"4";"3";"1";"3";"2";;"Tento kurz je koncipován opravdu nešťastně. Přitom jde o základní věci, které je nutno znát a vyžadují se u státnic. Za prvé nerozumím tomu, proč paní docentka Plechanovová změnila rozvrh předmětu až v den první přednášky. Systém hodnocení rešerší mi taky není úplně jasný - píšeme tři, ale hodnotí se nám jen jedna?Co mi absolutně nejde do hlavy je to, že témata na rešerše jsou zveřejněna pět dní před odevzdáním. Pak se samozřejmě stane to, že si v knihovně nemůžete půjčit žádnou literaturu, protože v tak krátkém horizontu se na ní nemůžeme vystřídat a zase tolik tam toho na tato témata není. Nebylo by rozumnější vypsat témata rešerší na začátku semestru, abychom se na těch knížkách mohli vystřídat?Co se týče testu, tak hodina na 5 vypisovacích otázek se mi zdá velmi málo. Většina z nás to jen tak tak stihla napsat, o nějakém překontrolování jsme si mohli nechat jen zdát. S tím souvisí i opravování - z klasifikačního řádu bychom měli dostat opravené testy do týdne, chápu, že je to rozepisovací a chvíli trvá, než to pedagog přečte, ale když ani po 11 dnech nemáme výsledky, nepřijde mi to v pořádku. Takhle máte šanci tak leda na jeden opravný termín.Obecně si myslím, že když píšeme tři rešerše, tak by mohl stačit kroužkovací test.";"kmv" +"483";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Lizcová,Z.";"4";"4";"3";"3";"3";"5";"5";"5";"2";"5";"5";"5";"4";;;"krvs" +"484";"JPM910";"The Nature and Function of the State";"Franěk,J.,Pettit,P.";;"5";"2";"4";"5";"5";NULL;NULL;NULL;"1";"3";"1";"2";"5";;;"kp" +"485";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"4";"3";"5";"5";"4";"2";"3";"1";"1";"4";"3";"5";"5";"I valued the ability of the teacher to give us a broad explanation of the lecture in such a short time.";;"ies" +"486";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"4";"2";"2";"5";"1";NULL;NULL;NULL;"2";"3";"3";"3";"3";;;"knrs" +"487";"JPB011";"Politická geografie I";"Romancov,M.";;"4";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Cením si výborných názorných ukázek pomocí map, a neskonale široké znalosti dr. Romancovce";"vše ok";"kp" +"488";"JJB135";"Filmový seminář I";;"Šobr,M.";"4";"2";NULL;NULL;NULL;"5";"3";"4";"1";"1";"3";"1";"5";;;"kz" +"489";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"5";"4";"4";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kas" +"490";"JPB585";"Stáž v praxi";;;"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Díky kurzu stáž v praxi jsem získala neobyčejně skvělou práci při studiu VŠ!";;"kp" +"491";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"3";"3";"5";"5";"2";"4";"5";"4";"1";"4";"3";"4";"3";"I appreciate the attitude of Mr. Kudashvili to the lecture itself and to students. He seems to be really interested in the topic of his lectures and that is really positive. The only thing which he could a bit improve is his English, because he sometimes uses the same words over and over again. His speech would be even more interesting if he could speak more naturally.";"Unfortunately, I would say that this course is quite unusual. On the one hand, the topic of the course itself is not as difficult as Math or Statistics, but on the other hand, it is really complicated because in the presentations there is almost no useful piece of information. Lecturers would probably say, that they want students to attend lectures, take notes, etc., which is absolutely understandable. But despite the fact I did so, I still had a struggle trying to understand, what is actually going on. And I have to say, that I really liked Principles of Economics and I was looking forward to this course, but it did not bring me as much as I expected. Thus I want to highlight, that the presentations should include much more information!!! Students would understand much more and they would know the links between things, which are such important in this field.";"ies" +"492";"JPM650";"Intelligence";"Bahenský,V.,Galeotti,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"The most valuable part of this course, besides the lectures, was the writing work as it was designed to sharpen our analytical writing skills. Thus, helping the students prepare for the world of work in the intelligence​ community.";"-";"kbs" +"493";"JLB003";"Angličtina pro ekonomy I";;"Poslušná,L.";"4";"3";NULL;NULL;NULL;"5";"5";"3";"1";"3";"4";"4";"5";;"1. semestr bych zaměřil více ekonomicky, méně na obecná témata -> byla by potom lepší návaznost na ekonomické předměty vyučované v AJ";"cjp" +"494";"JPB258";"Politické myšlení novověku I";"Franěk,J.,Kučera,J.";;"3";"3";"4";"4";"3";NULL;NULL;NULL;"2";"4";"2";"4";"3";;;"kp" +"495";"JMMZ204";"Imperial Nations and Subject Peoples: Czech nation in the Austrian Empire and After (17th – 21st centuries)";",.,Janíčková,M.";"Janíčková,M.,Robbins,D.";"2";"4";"3";"5";"3";"3";"5";"3";"3";"2";"3";"3";"2";"The teacher was always friendly, open to our questions and ready to recommend and even organize tours and walks related to the subject of the course.";"Rather unfortunately, our seminars became lectures (there was not much difference between two classes), not always connected to the given topic, with long digressions. We skipped some important material required for the final exam which we had to study on our own.";"krvs" +"496";"JSB998";"Úvod do sociologie";"Soukup,P.";;"3";"2";"4";"4";"1";NULL;NULL;NULL;"2";"1";"1";"1";"3";;;"ks" +"497";"JSB537";"Analýza dat v SPSS";"Soukup,P.";"Oreský,J.";"4";"3";"5";"5";"2";"5";"5";"5";"2";"5";"5";"5";"4";"Ráda bych ocenila seminář s J. Oreským. Jeho vědomosti a způsob, jakým látku přednáší jsou na velmi vynikající úrovni a seminář mi mnoho dal. To se nedá říct o doučování, kdy byl u slova M. Dušín, který mi přišel, že látce sám nerozumí na úrovni toho, aby ji mohl přednášet, natož tak vysvětlovat.";"Rozhodně se mi nelíbí, že státnice z SPSS mají být z věcí, které jsme neprobrali. Zaskočilo mě jednodenní doučování, kdy jsem myslel, že projdeme například dvě hodiny, které jsme nestihli a ne celý předmět, všech 11 cvičení. Buď se statistika 2 neměla rušit, anebo k tomu mělo být přihlédnuto a z větší části se ve státnicích věci z ní neobjevit. Přijde mi to nespravedlivé.";"ks" +"498";"JSB133";"Zemědělství a rozvoj venkova (vybraná témata z rurální sociologie)";"Zagata,L.";;"4";"2";"5";"5";"2";NULL;NULL;NULL;"2";"2";"2";"4";"4";;;"ks" +"499";"JPM611";"Cyber Security";"Duračinská,Z.,Střítecký,V.";;"2";"1";"3";"5";"2";NULL;NULL;NULL;"1";"2";"3";"1";"1";"I enjoyed the teachers enthusiasm, and her obvious care for the subject matter";"The material of the course had little to do with politics or international relations, instead it seemed to be focused entirely upon the function of sub-national cyber entities. The course also had very little technical aspects, so did not provide an insight into cyber security itself. The course could have been improved by focusing instead on cyber power, its function in international relations, cyber warfare, and the application of cyber in the future.";"kbs" +"500";"JSB131";"Velké empirické výzkumy ČR";"Tuček,M.";;"4";"3";"3";"3";"1";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Kurz byl jednoduchý, nebylo třeba ani chodit na přednášky. Nic nového mi však nedal.";"Pan Tuček nám zadal práci navíc, kvůli tomu, že nikdo nechodil na přednášky. Ovšem když mu to 4 studenti poslali týden před deadlinem napsal, že nic nezadal a ať už to nikdo neposílá. Naprostá ztráta času, který jsme mohli využít jinde a navíc se z nás udělali idioti, i když bylo jednoduché si starý email se zadáním vyhledat v mailech.";"ks" +"501";"JSB028";"Informační gramotnost";"Tomandlová,V.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"2";"5";"1";"5";"Doporučila bych hlavně třetím ročníkům, kteří se zde mohou vygenerovat správné citace do bakalářky a následně dojde k jejich kontrole. Dva kredity za něco, byste stejně museli udělat.";;"kvsp" +"502";"JPM656";"Technology and warfare";"Kučera,T.";;"2";"2";"3";"3";"1";NULL;NULL;NULL;"1";"2";"1";"2";"1";"The historical background was valuable, however, dominated large parts of the course and became repetitive​.";"Double classes where too long and many students found this disengaging.Assume a level of knowledge of historical events, some of the lectures were​ dominated by basic knowledge limiting the value of attendance.";"kbs" +"503";"JSB007";"Dějiny sociologie I";"Šanderová,J.";"Orcígr,V.";"5";"4";"5";"5";"5";"5";"4";"3";"3";"5";"5";"5";"5";"Velmi oceňuji profesionalitu doc. Šanderové, její znalosti jsou opravdu ohromné. Navíc látku vykládala jednoznačně a plynule ve formě, kterou jsme mohli všichni pochopit. Také oceňuji prezentace vyučující.";;"ks" +"504";"NMMA701";"Matematika 1";"Spurný,J.";"Skříšovský,E.";NULL;"5";"5";"5";"4";"3";"4";"4";"1";"5";"5";"5";"5";"Výborné znalosti přednášejícího, vysvětlování";"Cvičící by mohli být trochu zkušenější, je vidět že někteří učí poprvé a pedagogické schopnosti jsou horší. Někdy je také horší čitelnost zápisu na tabuli";"ies" +"505";"JMMZ278";"System polityczny Republiki Czeskiej w perspektywie Europy Środkowej";"Kubát,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"The lecture was perfectely structured, informative and simply well presented (how good he speaks Polish!).";"Two classes a week instead of one might be a good solution! :)";"knrs" +"506";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"4";"5";"4";"4";"4";NULL;NULL;NULL;"1";"5";"4";"5";"3";;"Zajímaly by mě kritéria, podle kterých se hodnotil výkon na prezentacích. Co se týče zkoušky, tak bych zvažovala změnit formu. K čemu je nám vědět některé zbytečné podrobnosti, které stejně do týdne zapomeneme. Nebylo by lepší třeba napsat esej - komparaci vývoje dvou států? Myslím si, že by nám to dalo daleko víc. A taky mi přijde nefér, že čím pozdější termín, tím těžší zkouška. A to souvisí i s opravou - na výsledky čekáme už téměř 14 dní a stále nic.";"kp" +"507";"JPM611";"Cyber Security";"Duračinská,Z.,Střítecký,V.";;"2";"3";"3";"4";"3";NULL;NULL;NULL;"2";"3";"2";"2";"2";"I appreciated the lecturer's professional background and experience, which gave students confidence in the lecturer's understanding of cyber security due to their sound foundation in the topic.";"The course was very technical for students with zero technical background or understanding. The assumption going into the course was that we would learn the basics on the technical aspects of cyber security, and then thoroughly discuss the impact cyber security has on international relations and politics, and the future of global security with the increasing shift towards cyber threats. While we learned about CSIRTs and the role cyber security can have in certain contexts, I feel like we could have discussed more of the implications on global politics and relations cyber security has.";"kbs" +"508";"JPM699";"Security and Technology";"Střítecký,V.";;"3";"4";"4";"3";"3";NULL;NULL;NULL;"2";"4";"5";"4";"3";"The group presentation allowed for students to work together on new approaches and research a topic area that is unconventional and therefore extremely interesting​.";"Often the technical side of the lectures was too intense, ​and students fell behind very quickly.";"kbs" +"509";"JPM694";"Strategic Studies";"Hynek,N.,Ludvík,J.,Smetana,M.";;"4";"1";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"I enjoyed the weekly assignments, the evaluation process and the final exam. All were very engaging and useful ways of encouraging and testing knowledge.";"Sometimes there could have been more structure to the classes. Part of the problem was that the ability level in the class was very divided, with some students who had seemingly never studied politics before, mixed with very knowledgeable students. As such the seminars were a missed opportunity to really discuss the intricacies of the material.";"kbs" +"510";"JLB005";"Angličtina pro politology I";;"Panešová,K.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";;;"cjp" +"511";"JSB025";"Sociální problémy";"Frič,P.";;"4";"4";"4";"3";"5";NULL;NULL;NULL;"2";"5";"5";"5";"4";"Kurz byl zajímavý a zajímavě strukturovaný. Pan Frič má velký přehled v problematice. Oceňuji především dostupné prezentace a psaní miniesejů.";"Občas jsem nepochopil hodnocení miniesejů, někdy byly známkovány velmi přísně.";"kvsp" +"512";"JEB136";"Topics in Industrial Organization";"Schwarz,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";;;"ies" +"513";"JPM719";"Diplomacy of the European Union";"Pajtinka,E.";;"2";"3";"4";"2";"2";NULL;NULL;NULL;"4";"2";"1";"2";"1";"If you are not really interested into EU affairs and just want some easy credits, go for it. Then read two wikipedia articles and pass one surprise and one regular test, write a quick surprise essay, because the lecturer will give you a deadline that is in two hours, and harvest credits.";"The course was basic. 80-90% of the information you actually can get from just two medium-sized Wikipedia articles, there's not too much of new things. The class donation was supposed to be about 18 hours, but in fact we've spent not even half of the time there. Frequent and 0,5-1h 'coffee breaks' of the lecturer and reluctant time management just wasted our time, classes ended several hours early. Only about a third of the assigned time was spent on lectures. Part of the evaluation is a surprise essay that you have to write in about two hours because that is the deadlinethe lecturer gave you.";"kmv" +"514";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"2";"1";NULL;NULL;NULL;"4";"4";"2";"2";"2";"1";"3";"3";"Znalosti p. Vyhnánka z praxe";"Nevyužitý potencál předmětu. Diskuzní témata by mohla být zveřejněna alespoň pár dní dopředu. Poté bychom se mohli na zajímavá témata ještě lépe připravit. Když bylo diskuzní téma pro nás nezajímavé nebo neznámé, byla to jenom prosezená hodina.Opravování esejí trvá hodně dlouho";"ies" +"515";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"4";"4";"4";"5";"2";"3";"3";"3";"1";"5";"4";"4";"3";"Obsahově mi micro II dávalo víc smysl než micro I. I změnu přednášejícího vnímám jako pozitivní, paní docentka Chytilová byla během semestru vstřícná a velmi reaktivní na došlé stížnosti.";"Kapacita některých cvičících byla nedostatečná, což vyvrcholilo v absurdní konzultaci s panem Gokem před midtermem. Dále nedostatečně pečlivě připravené testy - v midtermu se víckrát opravovalo hodnocení, kvůli multiple choice otázce bez správné možnosti apod.. Předtermín final testu byl v tomto ohledu ještě horší, zejména otázka na monopoly působila tak, že ji předtím nikdo nezkusil počítat. Prezentace a seminářové materiály mi byly také nápomocnější na micro I, kde obsahovaly celý postup. Na druhou stranu jako plus vnímám příklady přímo v prezentacích, které na micro I nebyly, a které často osvětlovaly více než vzorec. Jelikož jsem díky starým materiálům zjistil, že se seminářové úlohy opakují, navrhoval bych zařadit jiné příklady na aukce, neboť čísla zvolené v tom současném nijak neosvětlují posouvání intervalu u počítání očekávané revenue aukce.";"ies" +"516";"JSB010";"Současná sociologie";"Balon,J.";;"4";"3";"3";"4";"5";NULL;NULL;NULL;"2";"5";"3";"4";"3";;;"ks" +"517";"JEB141";"Introduction to Market Design";"Gregor,M.,Kastl,J.";;"5";"2";"5";"4";"3";NULL;NULL;NULL;"1";"3";"3";"3";"5";;;"ies" +"518";"JPM700";"Space Security";"Doboš,B.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"Space security is a remarkably different topic area than what is taught elsewhere in the field of security. Thus, the areas covered are unique to this course. I ​enjoyed the guest lectures as the helped expand our knowledge gained during lectures and readings.";"The group presentation was okay, however, in future perhaps the group presentation and paper could be combined and this could allow for an individual paper for each person and could be written and a topic taken from a lecture.";"kbs" +"519";"JJM208";"Mediální systémy a jejich komparace";"Miessler,J.";;"4";"3";"3";"4";"3";NULL;NULL;NULL;"2";"4";"3";"4";"4";;;"kms" +"520";"JPM707";"Peacekeeping and Peacebuilding";"Bureš,O.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"The lecturer had expansive knowledge on the topics, and I actually learned a lot about the UN and their peacekeeping than I ever had in my Security Studies courses. The class discussions were really helpful in furthering my understanding of the topics and applying them in real life.";"The final exam would have been better suited as a group essay, I believe. The topic was difficult to approach alone, and working in a group would have been beneficial to discuss strategies and utilise a variety of minds to approach the solution to the fictitious UN peacekeeping mission.";"kbs" +"521";"JSB530";"Úvod do sociologického myšlení";"Bureš,J.,Numerato,D.";"Wirthová,J.";"5";"1";"3";"5";"3";"4";"5";"5";"1";"3";"3";"5";"5";"Velice oceňuji především cvičení. Naše cvičící byla velice přátelská a zábavná, hodiny probíhaly v uvolněné atmosféře. Líbila se mi forma sociologických deníků poskytující nám zpětnou vazbu.Co se týče přednášek, oceňuji přátelský přístup pedagogů. Za nejpřínosnější přednášku považuji přednášku doc. Šanderové na téma psaní odborného textu a citací.";"Kurz nebyl složitý, ale příliš jsem si z něho neodnesl, a to především z přednášek, které byly 1 za 2 týdny a každou hodinu byl jiný vyučující.";"ks" +"522";"JJM346";"Sémiotická analýza";;"Podzimek,J.";"5";"3";NULL;NULL;NULL;"4";"5";"4";"1";"5";"5";"5";"5";;;"kms" +"523";"JSB454";"Social Web: (Big) Data Mining";"Růžička,J.";;"1";"5";"5";"3";"1";NULL;NULL;NULL;"1";"1";"1";"1";"1";;;"ks" +"524";"JPM598";"Grand Strategies";"Ditrych,O.";;"2";"2";"2";"4";"3";NULL;NULL;NULL;"1";"1";"2";"2";"1";;"This course was very difficult to pay attention to as the lecturer read from his notes with really no expansion. There was not room for further talk outside of the lecture - which is typically OK in this type of lecture course - but it was near impossible to grasp any concept or pay attention.";"kbs" +"525";"JJM226";"Teorie účinků médií";"Nečas,V.";;"5";"3";"4";"5";"3";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kms" +"526";"JSB023";"Praktika z kvantitativního výzkumu I";;"Tuček,M.";"3";"3";NULL;NULL;NULL;"2";"2";"4";"1";"3";"5";"2";"3";"Možnost vyzkoušet si tvorbu dotazníkového šetření i s jeho následným provedením v terénu.";;"ks" +"527";"JJM295";"Rozhlasový a televizní dokument";"Štoll,M.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"2";"4";"4";"3";"4";;;"kz" +"528";"JPM613";"Armed Forces and Society";"Kučera,T.";;"4";"2";"4";"4";"3";NULL;NULL;NULL;"1";"4";"4";"3";"3";;;"kbs" +"529";"JEM003";"Advanced Microeconomics";"Janda,K.,Melikhova,O.";"Janda,K.,Melikhova,O.";"3";"3";"4";"5";"3";"4";"5";"3";"2";"2";"3";"4";"2";;;"ies" +"530";"JEM004";"Advanced Macroeconomics";"Baxa,J.,Hromádková,E.";"Baxa,J.,Hromádková,E.,Šestořád,T.,Troch,T.,Žáček,J.";"5";"4";"4";"5";"4";"4";"5";"5";"1";"5";"4";"5";"5";;;"ies" +"531";"JLB047";"Ruština obecná I";;"Mistrová,V.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Nejvíce musím ocenit fenomenální vyučující a její přístup. Paní Mistrová má ruštinu naprosto v malíčku, je lidská, přátelská a přizpůsobí se každému. Navíc látku vykládá velmi dobře, aby to každý pochopil. Kurzy byly velice příjemné, prokládané zajímavými texty a nahrávkami.";;"cjp" +"532";"JMB497";"Metodický úvod pro kombinované studium";"Kubát,M.";;"4";"2";"5";"4";"5";NULL;NULL;NULL;"1";NULL;"3";"3";"5";"The professor is articulate.";"I think 1 class is insufficient in learning how to write academic works without errors, especially given we are expected to write a flawless academic work afterwards. These are our first academic works.";"krvs" +"533";"JEM035";"Financial Markets Instruments I";"Dědek,O.,Poláková,N.,Polák,P.";"Dědek,O.,Poláková,N.,Polák,P.";"3";"4";"3";"4";"1";"4";"5";"3";"1";"4";"4";"3";"3";;;"ies" +"534";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"4";"3";NULL;NULL;NULL;"4";"5";"3";"1";"4";"4";"4";"4";;;"cjp" +"535";"JJB221";"Ekonomická teorie marketingu";"Postler,M.";;"3";"1";"3";"5";"2";NULL;NULL;NULL;"1";"3";"1";"4";"4";"názorné příklady z praxe";"upravit podobu přednášek do takové podoby, aby rozdíl v účasti mezi první a dalšími přednáškami nebyl tak velký. Inženýr Postler má neskutečné množství znalostí, které umí dobře vysvětlit, stejně tak oplývá bohatými zkušenostmi z praxe, ale ne příliš šťastně kombinuje jejich množství v přednáškách.";"kmkpr" +"536";"JSB054";"Výzkumný seminář";;"Hrešanová,E.";"5";"4";NULL;NULL;NULL;"4";"5";"5";"1";"4";"4";"5";"5";"Celkový koncept kurzu jakožto diskusních seminářů je skvělý pro prohloubení znalosti probíraných témat. Semináře probíhaly v přátelském duchu.";"Nic.";"ks" +"537";"JEM037";"Financial Markets";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"2";"2";"3";"3";"1";"5";"5";"4";"1";"1";"2";"3";"1";;;"ies" +"538";"JPM697";"Asia Security";"Kolmaš,M.";;"3";"4";"4";"5";"5";NULL;NULL;NULL;"1";"3";"4";"3";"4";"Debates. Fun and interesring way of teaching a class and get them all motivated to participate";;"kbs" +"539";"JPM693";"Traditional and Critical Concepts in Security Studies";"Rychnovská,D.";;"5";"3";"4";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"class discussions allowed us to reflect our point of view and understand a little more about the readings";"I think that we should not only focus on class discussions on how each of us understood or interpreted things, that way, we have a clear understanding of what the concept really is.";"kbs" +"540";"JEM059";"Quantitative Finance I";"Baruník,J.,Vácha,L.";"Baruník,J.,Vácha,L.";"5";"4";"5";"5";"4";"5";"5";"5";"2";"5";"5";"5";"5";;;"ies" +"541";"JJB224";"Psychologie marketingové komunikace";"Vranka,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"1";"5";"5";"Kvalitní materiály, znalosti a vystupování vyučujícího";"Více četby.";"kmkpr" +"542";"JJB618";"Propagandistické působení médií v 1 polovině 20. století";"Suk,P.";;"5";"2";"4";"4";"4";NULL;NULL;NULL;"5";"3";"3";"3";"4";;;"kms" +"543";"JPM689";"Conflict Studies";"Karásek,T.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"4";"Examples given in class were very helpful to understand the concepts";;"kbs" +"544";"JEM181";"Data Science with R";"Herman,D.,Krištoufek,L.";"Herman,D.,Krištoufek,L.";"5";"3";"5";"5";"3";"5";"5";"4";"1";"5";"5";"4";"5";;;"ies" +"545";"JPM696";"Economic Warfare";"Ludvík,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kbs" +"546";"JPM710";"Radicalization and Deradicalization";"Aslan,E.";;"1";"1";"2";"3";"1";NULL;NULL;NULL;"1";"1";"2";"2";"2";;"This course was very upsetting in how it was arranged - I expected valuable lectures but instead weekly there were only student presentations. Its fine, but as students we do not contain the full tools to teach a whole class through presentations. There was very little lecture time and so little to no incentive to go to class knowing that there probably wouldn't be a full/any lecture. Also I have no idea how our presentations were graded since we received only verbal feedback immediately after the presentation infront of whole class - also our midterms weren't handed back and just told we did good.";"kbs" +"547";"JMB499";"Současné metodologie";"Kubát,M.";;"3";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"3";"3";"3";"Well, it is certainly valuable to understand it.";"Not enough time to explain properly. I realized when writing my comparative study that the topic is so broad and in my opinion we did not cover it enough during the relatively short class. That is not the professor’s fault but rather the system set-up. I would be grateful for at least a 2 day class rather than just 1 day class.";"krvs" +"548";"JPM708";"Ethics and Violence";"Karásek,T.,Kučera,T.";;"4";"2";"4";"5";"4";NULL;NULL;NULL;"1";"4";"5";"4";"4";"I enjoyed the oxford style debates and the entire arrangement of the course - it was a lecture but we could still talk and debate - and this is a great thing";;"kbs" +"549";"JPM693";"Traditional and Critical Concepts in Security Studies";"Rychnovská,D.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kbs" +"550";"JMB523";"Mezinárodní aktuality I";"Fojtek,V.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";"All aspects.";"None.";"kas" +"551";"JJB021";"Bakalářský seminář";;"Prázová,I.";"3";"3";NULL;NULL;NULL;"5";"4";"4";"1";"3";"2";"4";"3";;;"kz" +"552";"JJB066";"Rozhlas a televize ve světě";"Moravec,V.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"4";;;"kz" +"553";"JLM063";"English for Chinese Speaking Students";;"Štěpánková,D.";"5";"3";NULL;NULL;NULL;"3";"5";"4";"1";NULL;NULL;NULL;NULL;;;"cjp" +"554";"JJB067";"Mluvní a pohybová výchova I";;"Pavel,L.";"3";"1";NULL;NULL;NULL;"4";"4";"3";"1";"3";"4";"3";"3";;;"kz" +"555";"JPM701";"European and Transatlantic Security";"Kazharski,A.";;"3";"3";"4";"5";"4";NULL;NULL;NULL;"1";"3";"3";"3";"3";;"Nothing bad but nothing great either about the course - it is not the lecturers fault though I feel.. It was just very confusing with the whole course getting changed around and in the end the lecturer did his best to accommodate us all. Which, thank you for that! The material is not fascinating but again I just feel like this was attributed to the accidental course changes/shortening of the semester.";"kbs" +"556";"JPM721";"Komparace ekonomik zemí EU";"Kučerová,I.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"1";"4";"5";"Příjemný přístup vedoucí kurzu, obdržené informace se týkají ekonomik jednotlivých států EU a ekonomického vývoje EU jako celku. Má částečný přesah do moderní historie. Kurz je fajn, obtížnost přiměřená.";"Nejsem si jistý jestli komparace je slovo, které pro tento kurz použít, pokud vím, příliš jsme toho neporovnávali, kurz je spíše přehledový. Zvážil bych, jestli má cenu v hodinách přednášet o ekonomikách a jejich vývoji stát po státě, a nejet spíše po ekonomických etapách vývoje společenství, přičemž ke konkrétním státům zabrousit v momentě, kdy je tam něco mimořádného nebo zajímavého, např. britská nemoc, další informace, ke kterým bychom se jinak nedostali, apod. Charakteristiky ekonomik jednotlivých zemí si lze nastudovat z poskytnutého handoutu a konec konců i z Wikipedie, nejsou to nijak převratné či odborné informace, myslím, že s tím není třeba 'ztrácet' čas, a naopak dát větší prostor právě popisům různých fenoménů, krizí atd.";"kmv" +"557";"JJB069";"Tvůrčí dílny I - televizní";"Lokšík,M.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"4";"5";;;"kz" +"558";"JPM695";"War Studies";"Hays II,G.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"4";"Knowledge of the professor and classroom discussions";"Compared to other courses quite more effort demanded";"kbs" +"559";"JSB311";"Antropologie náboženství";"Spalová,B.";;"5";"5";"4";"5";"5";NULL;NULL;NULL;"1";"5";"5";"4";"4";"Paní doktorka Spalová má ke studentům skvělý přístup. Velmi přínosné byly exkurze a následné psaní terénních poznámek.";"Myslím si, že tento kurz je velmi málo kreditově ohodnocen na to, jak je pro studenty náročný. Pro mě to byl jeden z nejnáročnejších předmětů za mé studium.";"ks" +"560";"JJB071";"Tvůrčí dílny I - rozhlasové";"Maršík,J.";"Lovaš,K.,Lucký,J.";"5";"3";"5";"5";"4";"5";"4";"5";"1";"5";"5";"4";"5";;;"kz" +"561";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"1";"4";"3";"3";"5";NULL;NULL;NULL;"1";"2";"3";"3";"1";;;"kmv" +"562";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;NULL;NULL;"5";"5";"5";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kmv" +"563";"JJB083";"Editování zpravodajských relací";"Beneš,P.";;"3";"2";"5";"5";"1";NULL;NULL;NULL;"1";"2";"2";"2";"2";;;"kz" +"564";"JPM697";"Asia Security";"Kolmaš,M.";;NULL;NULL;"5";"5";"5";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kbs" +"565";"JPM708";"Ethics and Violence";"Karásek,T.,Kučera,T.";;NULL;NULL;"5";"5";"5";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kbs" +"566";"JJB142";"Literatura faktu";"Halada,J.";;"4";"2";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"5";"4";;;"kz" +"567";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"4";"5";"4";"5";"3";NULL;NULL;NULL;"1";"3";"1";"3";"2";"vhodně zvolená četba k tématu";"prezentace p. Ditrycha jsou chaotické, hodina začíná moc brzy ráno";"kmv" +"568";"JJB237";"Právo a etika v marketingu";"Winter,F.";;"4";"2";"5";"5";"4";NULL;NULL;NULL;"1";"4";"2";"5";"5";;;"kmkpr" +"569";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"2";"3";"1";"4";"2";NULL;NULL;NULL;"1";"4";"2";"5";"2";"The general idea of getting an overview";"The teaching style. Could be made much more interesting eg include discussions about the big debates, the changing thought schools and why they changed etc.";"kmv" +"570";"JSB517";"Hudební subkultury mládeže";"Oravcová,A.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"2";"4";"5";"3";"5";"Nejvíce oceňuji zajímavost probíraných témat. Také přednášky byly velmi zajímavé a interaktivní.";"Nic.";"ks" +"571";"JJB243";"Aktuální trendy a vývoj v oboru I.";"Hejlová,D.,Vranka,M.";"Hejlová,D.,Vranka,M.";"5";"2";"5";"5";"5";"5";"5";"5";"1";"5";"1";"5";"5";;;"kmkpr" +"572";"JPM701";"European and Transatlantic Security";"Kazharski,A.";;"5";"2";"4";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";"Extensive Class room discussions";"Clearer evaluation and grading of the discussion panel task";"kbs" +"573";"JJB249";"Úvod do studia českého jazyka I";"Schneiderová,S.";"Schneiderová,S.";"3";"4";"4";"5";"4";"4";"5";"4";"1";"4";"1";"2";"5";;"V kurzu bych se chtěl více věnovat praktickému psaní.";"kmkpr" +"574";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"5";"5";"5";"3";NULL;NULL;NULL;"1";"5";"5";"3";"5";"online úkoly a testy";;"kmv" +"575";"JPM708";"Ethics and Violence";"Karásek,T.,Kučera,T.";;"5";"2";"4";"4";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";"Extensive Class room discussions";"Clearer evaluation and grading of the first task regarding the discussion panel";"kbs" +"576";"JSB530";"Úvod do sociologického myšlení";"Bureš,J.,Numerato,D.";"Moskvina,Y.";"4";"3";"5";"5";"1";"3";"4";"5";"1";"2";"4";"2";"3";;"Semináře se slečnou Moskvinou mi nepřinesly mnoho nových poznatků a její semináře mi celkově nevyhovovaly. Příště bych zvolila jiného cvičícího.";"ks" +"577";"JJB269";"Sociální kontext komunikace";"Vranka,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"4";"5";;;"kmkpr" +"578";"JJB407";"Bakalářský proseminář";"Rosenfeldová,J.";;"3";"4";"4";"4";"3";NULL;NULL;NULL;"4";"3";"5";"1";"5";"Pečlivé zhodnocení prací.";;"kmkpr" +"579";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"3";"5";"3";"4";"Learning about R and excel is always helpful for future. Great lecturer!";"Exams should maybe be written in a classroom and not from home to limit incentives for cheating.";"kmv" +"580";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"3";"4";"4";"4";;;"ies" +"581";"JPM727";"Orchestration in Global Governance";;"Abbott,K.,Parízek,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"3";"4";"5";"It was cool to have Kenneth Abbott here, explaining his theory himself, and I hope Michal Parízek will do as good a job as him in the potential future of this course. If you like theories of IR, this is hot stuff. It gives you another layer of possibilities how to understand mechanisms of interactions in our IR sphere.";"Please don't do courses in Celetná, the building is a goddamn maze. The treacherous medieval halls will absorb you and nobody will ever see you again. I've spent like 10 minutes searching for a coffee machine there and got lost on my way back to the classroom. There should be a navigation app for that place.";"kmv" +"582";"JMM279";"Společnost a ekonomika";"Fiřtová,M.";"Svoboda,K.";"5";NULL;"5";"5";"5";"5";"5";"5";"1";"5";NULL;"5";"5";"Tento semestr to byl můj nejlepší předmět. Nic neměnit!";;"kas" +"583";"JJB135";"Filmový seminář I";;"Šobr,M.";"4";"1";NULL;NULL;NULL;"3";"4";"3";"4";"3";"1";"1";"4";;;"kz" +"584";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"5";"4";NULL;NULL;NULL;"4";"5";"5";"5";"4";"5";"4";"5";;;"ies" +"585";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"4";"3";"2";"5";"1";"3";"5";"4";"1";"4";"4";"4";"4";;;"ies" +"586";"JLB003";"Angličtina pro ekonomy I";;"Poslušná,L.";"3";"2";NULL;NULL;NULL;"5";"5";"3";"1";"3";"3";"4";"4";;;"cjp" +"587";"JJM247";"Český stranický systém";"Just,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Skvělý přístup vyučujícího, diskuze o novinkách, zajímavosti z politiky, člověk se seznámí s celkovým pozadím a pochopí spoustu věcí";;"kz" +"588";"JLB017";"Němčina pro ekonomy vyšší I";;"Faltýnová,R.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"4";"4";;;"cjp" +"589";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"3";"3";"3";"2";"3";NULL;NULL;NULL;"1";"4";"3";"4";"2";;;"kms" +"590";"JJM260";"Novinářská etika v praxi";"Moravec,V.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kz" +"591";"JPB269";"Současné problémy mezinárodních vztahů";"Knutelská,V.";;"4";"3";"3";"4";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kmv" +"592";"JSM514";"Metody a techniky práce s informacemi";"Tomandlová,V.";;"4";"1";"4";"4";"4";NULL;NULL;NULL;"1";"3";"3";"3";"5";;;"kvsp" +"593";"JSB998";"Úvod do sociologie";"Soukup,P.";;"2";"2";"5";"5";"1";NULL;NULL;NULL;"1";"3";"4";"3";"3";;;"ks" +"594";"JSM612";"Kriminalita a současná česká společnost";"Cejp,M.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"5";;;"kvsp" +"595";"NMMA701";"Matematika 1";"Spurný,J.";"Rondoš,J.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"596";"JJM360";"Ekonomika v médiích";"Klimeš,D.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"kz" +"597";"JSB537";"Analýza dat v SPSS";"Soukup,P.";;"4";"4";"5";"4";"3";NULL;NULL;NULL;"2";"5";"5";"3";"4";"Nejvíce oceňuji možnost naučit se základy programu SPSS.";"Díky tomu, že na začátku kurzu nebyli cvičící, kteří by vedli cvičení, několik cvičení odpadlo a nestihla se probrat všechna témata probíraná na přednáškách.Velmi zvláštní mi připadá, že následná státnice z analýzy dat v SPSS obsahuje látku celého jednoho semestru, který jsme neměli a tudíž jsme se ji neměli, jak naučit (naštěstí cvičíčí uspořádali doučování, kde jsme měli možnost se danou látku naučit).";"ks" +"598";"JSM573";"Výzkumné kolokvium AVM I";;"Remr,J.";"5";"3";NULL;NULL;NULL;"4";"4";"5";"2";"5";"5";"4";"5";;;"ks" +"599";"JLB013";"Němčina odborná I";;"Křenková,D.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"5";"5";;;"cjp" +"600";"JJM291";"Tvůrčí dílny I – tisk";;"Matyášová,J.";"3";"3";NULL;NULL;NULL;"1";"1";"3";"1";"3";"4";"2";"3";;;"kz" +"601";"JSM005";"Sociální struktura ČR: stav, vývoj, srovnání s EU";"Tuček,M.";;"3";"2";"2";"4";"2";NULL;NULL;NULL;"1";"2";"2";"2";"4";;;"ks" +"602";"JSB534";"Introduction to Visual Sociology";"Wladyniak,L.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"3";"5";;;"ks" +"603";"JSB515";"Vysokoškolská vzdělávací politika";"Vlk,A.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"4";"5";"Přístup pedagoga! Jednoznačně jeden z nejlepší předmětů, který jsem na FSV absolvoval. Pozvání hostů z různých VŠ, přístup učitele, metody výuky. Více předmětů by mělo být takto vedeno.";;"kvsp" +"604";"JSM103";"Academic Writing";;"Blokker,P.";NULL;NULL;NULL;NULL;NULL;"4";"4";"3";NULL;NULL;NULL;NULL;NULL;;;"ks" +"605";"JSM477";"Sociology of Critique";"Blokker,P.";;NULL;NULL;"4";"4";"4";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"ks" +"606";"JMB410";"Moderní dějiny západní Evropy";"Matějka,O.,Váška,J.";;"2";"4";"3";"4";"3";NULL;NULL;NULL;"2";"3";"3";"3";"3";;"Atraktivitu probíraných témat, více faktografie než filozofických úvah.";"kzs" +"607";"JLB001";"Angličtina pro sociology I";;"Klírová,M.";"5";"4";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"5";"5";"Laskavý přístup paní vyučující";"Nastavovat jednotnou hodinu odesílání úkolů";"cjp" +"608";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"3";"5";;;"knrs" +"609";"JSB007";"Dějiny sociologie I";"Šanderová,J.";"Bureš,J.";"4";"3";"5";"5";"3";"5";"5";"4";"2";"4";"3";"4";"4";"Počet úkolů - abstraktů - 4";"Kdyby to šlo, tak oznámení o zrušení semináře ještě dříve";"ks" +"610";"JSB055";"Současná sociální antropologie";;"Grygar,J.,Hrešanová,E.";"3";"5";NULL;NULL;NULL;"4";"3";"5";"1";"4";"2";"3";"2";;"Kurz předpokládá velmi dobrou znalost anglického jazyka, neboť texty, z nichž se píší průběžné testy, byly ve většině případů v anglickém jazyce a často velmi obtížné k pochopení. Změnila bych především formu, kterou mají průběžné testy (především ty od pana doktora Grygara), protože pokud člověk zcela neporozuměl některému ze čtených textů, jeho šance u testu byla téměř nulová. Testy paní doktorky Hrešanové byly svou formou pro studenty mnohem sympatičtější a lépe srozumitelné.";"ks" +"611";"JJM242";"Comics as a Medium";"Hrdina,M.";;"3";"4";"3";"5";"3";NULL;NULL;NULL;"2";"3";"5";"3";"2";"Je skvělé, že si studenti mohou vybrat nejnovější komiks, který pak pomocí vlastní recenze mohou doporučit ostatním studentům, rozšířit jejich obzory. Zároveň chválím kreativní stránku semináře, kdy si studenti mohou vytvořit vlastní komiks.";"Přednášky byly příliš přehledové, téměř u žádného titulu nedošlo k hlubšímu rozboru. Některé podstatné oblasti v oboru byly vynechány (belgická produkce, současná produkce, komiks v době nových médií).";"kms" +"612";"JSB025";"Sociální problémy";"Frič,P.";;"5";"4";"5";"4";"3";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Počet a délka (mini)esejí, styl přednášení vyučujícího, jeho smysl pro humor";"Abychom dostali zpět všechny opravené (mini)eseje - hlavně tu závěrečnou a ještě před zkouškovým";"kvsp" +"613";"JPM127";"Geopolitika a geostrategie 1815 - 1945";"Romancov,M.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"614";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"3";"4";;;"knrs" +"615";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Rybín,F.,Vlčková,A.";"4";"4";"5";"5";"4";"5";"5";"4";"1";"5";"5";"5";"5";"Vyučujícího";"Aby pan profesor Jeřábek vyučoval na všech přednáškách, protože u něj se mi nejlépe dělaly poznámky";"ks" +"616";"JEB003";"Ekonomie I";"Fanta,N.,Kracík,J.,Švarcová,N.";"Fanta,N.,Kracík,J.,Švarcová,N.";"3";"3";"3";"3";"2";"4";"5";"4";"1";"2";"3";"3";"3";;;"ies" +"617";"JLB053";"Angličtina pro sociální vědy I";;"Gloverová,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"618";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ies" +"619";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Kocian,J.";"2";"5";"2";"3";"3";"5";"5";"5";"1";"5";"4";"3";"3";;;"knrs" +"620";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"4";"2";NULL;NULL;NULL;"4";"4";"4";"2";"4";"3";"3";"5";;;"ies" +"621";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"2";"4";"5";"4";"5";;;"ies" +"622";"JPM712";"Insurgency and Counterinsurgency";"Aslan,E.";;"4";"4";"3";"4";"4";NULL;NULL;NULL;"1";"5";"3";"2";"3";;"It would be greatly interesting if more lectures could be given by professionals";"kbs" +"623";"JLB003";"Angličtina pro ekonomy I";;"Poslušná,L.";"5";"2";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"4";"5";;;"cjp" +"624";"NMMA701";"Matematika 1";"Spurný,J.";"Honzík,P.";"5";"5";"5";"5";"5";"3";"3";"3";"1";"5";"5";"5";"4";;;"ies" +"625";"JLB019";"Francouzština odborná I";;"Bosáková,L.";"5";"4";NULL;NULL;NULL;"5";"4";"5";"1";"5";"5";"4";"4";"Systematickou výuku - jasné a odpovídající materiály, poslechy, čtení, mluvení.";;"cjp" +"626";"JSB530";"Úvod do sociologického myšlení";"Bureš,J.,Numerato,D.";"Bureš,J.";"5";"4";"5";"5";"5";"5";"5";"5";"2";"4";"5";"5";"5";"Délku deníkového záznamu (500 slov) a jejich témata";"Nejlépe dřívější čas přednášky";"ks" +"627";"JSB998";"Úvod do sociologie";"Soukup,P.";;"4";"1";"4";"5";"1";NULL;NULL;NULL;"1";"3";"3";"3";"5";;;"ks" +"628";"JMB409";"Moderní dějiny středo- a jihovýchodní Evropy-dcera JMB013";"Balla,P.,Švec,L.";;NULL;NULL;"4";"4";"3";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"krvs" +"629";"JMB410";"Moderní dějiny západní Evropy";"Matějka,O.,Váška,J.";;NULL;NULL;"3";"3";"3";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kzs" +"630";"JMB408";"Moderní dějiny Ruska";"Litera,B.,Novák,P.,Pečenka,M.";;NULL;NULL;"3";"3";"3";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"krvs" +"631";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"1";"4";"2";"2";"2";NULL;NULL;NULL;"1";"2";"2";"2";"1";;;"ies" +"632";"JMB250";"Seminář k dějinám západní Evropy";;"Synkule,M.";"3";"2";NULL;NULL;NULL;"2";"3";"4";"4";"4";"3";"4";"3";;"Více účasti ze strany cvičícího, rozdělit témata seminárních prací a prezentací hned na začátku semestru.";"kzs" +"633";"JLB005";"Angličtina pro politology I";;"Panešová,K.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"5";;;"cjp" +"634";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"2";"4";"1";"2";NULL;NULL;NULL;"1";"3";"5";"4";"3";;;"ies" +"635";"JPB011";"Politická geografie I";"Romancov,M.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kp" +"636";"JLB033";"Němčina I";;"Křenková,D.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"637";"JMB414";"Seminář k aktualitám I";;"Synkule,M.";"2";"3";NULL;NULL;NULL;"2";"3";"2";"4";"2";"3";"2";"2";;"Cvičící se dle mého názoru na seminářích nudí, učení ho nebaví, celkově je tento kurz o ničem.";"krvs" +"638";"JPB018";"Dějiny mezinárodních vztahů I";"Soukup,J.";;"5";"3";"5";"4";"4";NULL;NULL;NULL;"3";"5";"5";"5";"5";;;"kmv" +"639";"JLB053";"Angličtina pro sociální vědy I";;"Gloverová,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"640";"JJB135";"Filmový seminář I";;"Šobr,M.";"4";"2";NULL;NULL;NULL;"3";"3";"3";"2";"3";"3";"4";"4";;;"kz" +"641";"JPB020";"Úvod do mezinárodních vztahů";"Karlas,J.,Kučera,T.";;"3";"4";"3";"3";"3";NULL;NULL;NULL;"1";"3";"3";"3";"3";;;"kmv" +"642";"JMB402";"Úvod do společenských věd II";;"Hofmeisterová,K.";"5";"4";NULL;NULL;NULL;"4";"5";"5";"1";"5";"5";"5";"5";;;"krvs" +"643";"JPB221";"Metodologický proseminář I";;"Mlejnek,J.,Valková,I.";"2";"3";NULL;NULL;NULL;"3";"5";"1";"2";"2";"1";"1";"2";;;"kmv" +"644";"JJB221";"Ekonomická teorie marketingu";"Postler,M.";;"4";"3";"4";"4";"3";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kmkpr" +"645";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Buřičová,B.";"4";"4";"4";"4";"4";"5";"5";"5";"2";"3";"4";"3";"4";"Milé a svaté cvičící, kteří nás zachránili před údajně brutální ústní zkouškou po úspěšném absolvování testu, hodného pana vyučujícího, který na to nakonec přistoupil";"Trošičku přednášku přizpůsobit méně chápavým studentům";"ks" +"646";"JPB252";"České novověké dějiny I.";"Kučera,J.";;"3";"3";"3";"3";"3";NULL;NULL;NULL;"1";"3";"3";"3";"3";;;"kp" +"647";"JJB237";"Právo a etika v marketingu";"Winter,F.";;"5";"4";"5";"4";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmkpr" +"648";"JPB256";"Politické systémy I";"Brunclík,M.,Jeřábek,M.,Kotábová,V.,Perottino,M.";;"3";"3";"3";"3";"3";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kp" +"649";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"4";"2";"4";"5";"3";NULL;NULL;NULL;"1";"5";"5";"5";"4";;;"krvs" +"650";"JJB249";"Úvod do studia českého jazyka I";"Schneiderová,S.";"Schneiderová,S.";"3";"4";"4";"4";"3";"4";"4";"4";"1";"4";"4";"3";"3";;;"kmkpr" +"651";"JJB269";"Sociální kontext komunikace";"Vranka,M.";;"4";"4";"4";"4";"3";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kmkpr" +"652";"JJB401";"Komerční a nekomerční marketingová komunikace";"Báča,L.,Obluk,O.";;"4";"4";"5";"4";"4";NULL;NULL;NULL;"1";"4";"4";"5";"4";;;"kmkpr" +"653";"JPM127";"Geopolitika a geostrategie 1815 - 1945";"Romancov,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kp" +"654";"JJB407";"Bakalářský proseminář";"Rosenfeldová,J.";;"3";"3";"4";"3";"3";NULL;NULL;NULL;"2";"3";"4";"3";"4";;;"kmkpr" +"655";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"3";"1";"1";"1";NULL;NULL;NULL;"1";"2";"1";"5";"3";;;"ies" +"656";"JLB045";"Angličtina pro marketing I";;"Stružková,I.";"3";"4";NULL;NULL;NULL;"4";"4";"4";"1";"4";"4";"4";"3";;;"cjp" +"657";"JPM574";"Moderní strany a stranické systémy v Evropě";"Brunclík,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kp" +"658";"JPM595";"Arms Control and Disarmament";"Hynek,N.,Smetana,M.";;"5";"2";"4";"4";"4";NULL;NULL;NULL;"1";"4";"5";"4";"5";;;"kbs" +"659";"JPM650";"Intelligence";"Bahenský,V.,Galeotti,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kbs" +"660";"JPM579";"Teorie politických stran";"Perottino,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"Prostor pro diskuzi a hlavně apel na zamýšlení se nad probíraným tématem.";;"kp" +"661";"JPB553";"Elective Seminar: Neoliberalism";"Franěk,J.";;"4";"3";"3";"4";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"very well selected readingintroduction and transfer to every lesson to the topic";"Make sure that all students upload their shortpaper in googledocs. (I asked most of them by myself to read it)Raise more discussion questions to encourage interaction";"kp" +"662";"JPM639";"Problémy ústavního inženýrství";"Brunclík,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"5";"5";"Prostor pro diskuzi.";;"kp" +"663";"JLB005";"Angličtina pro politology I";;"Panešová,K.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"1";"3";"4";"3";"5";;;"cjp" +"664";"JPM150";"Poloprezidentské režimy v postkomunistické Evropě";"Mlejnek,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"665";"JPM694";"Strategic Studies";"Hynek,N.,Ludvík,J.,Smetana,M.";;"2";"3";"3";"4";"2";NULL;NULL;NULL;"2";"3";"2";"3";"2";;"The weekly readings were rarely engaging and became mundane after several weeks, as were the lectures occasionally.";"kbs" +"666";"JLB053";"Angličtina pro sociální vědy I";;"Gloverová,M.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"1";"5";"5";"4";"5";;;"cjp" +"667";"JPM641";"Světový regionalismus";"Riegl,M.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;"Možná trochu zpomalit přepínání slidů.";"kp" +"668";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Kocian,J.";"5";"5";"5";"5";"5";"5";"5";"4";"1";"5";"5";"5";"5";;;"knrs" +"669";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"5";"4";"4";"5";"5";NULL;NULL;NULL;"2";"5";"4";"5";"5";;;"kas" +"670";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"1";"4";"3";"1";"3";NULL;NULL;NULL;"1";"3";"1";"1";"1";"Nic";"Vyučujícího";"ies" +"671";"JMB018";"Bakalářský seminář I";;"Handl,V.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"krvs" +"672";"JEB026";"European Economic Integration";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"2";"3";"2";"4";"2";"4";"4";"2";NULL;"5";"3";"4";"2";"1) The subject itself is very interesting and important for us, as students of economics living in EU.2) 2) The book that was recommended by the lecturers was very nicely written and easy to understand + it has extra materials online, which is great to better grasp the models. 3) I appreciate the requirement to do a presentation on related topics, I enjoyed doing it and finally we had also possibility to work on our presentation/oral skills (which is quite rare in IES).";"1) The presentations provided by lecturer were simply too complicated, full of not always important information, I got easily lost in the quantum of info and didn't know what is really important to know and learn. I got tired after 2 slides of these PPT more than after 20 proofs in Mathematics. (In the end, I gave up the presentations and learn from the book and other materials). + some aspects of graphs were not completely described and could not even be found in the book. 2) It would be very beneficial to make the lectures more interactive, full of current debates and controversies etc., not only to go through the info that we can read in the book or in PPTs.";"ies" +"673";"JMMZ188";"European Union in International Relations";"Weiss,T.";;"2";"3";"2";"4";"1";NULL;NULL;NULL;"1";"3";"1";"3";"2";;"To produce an essay in groups is not a good idea. It is not only hard in terms of coordination (even more in a course with international students who are used to different study-cultures), but in the end unfair in terms of assessment, as there is the same grade for all group members, irespective of their individual efforts and diligence. Even though not as profoundly, the same applies for group presentations.Other drawbacks: no discussion (it is a seminar!), where it makes no sense to actually read the readings.";"kzs" +"674";"JPM146";"Přechody k demokracii v teorii a praxi I";"Mlejnek,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Prostor pro diskuzi.";"U přednášek nemám výhrad, naopak, ale co se týče seminářů, možná by pro příště bylo lepší více hlídat čas pro jednotlivé prezentace. Bývá pak trochu problematické, pokud má student jiné navazující přednášky.";"kp" +"675";"JPM696";"Economic Warfare";"Ludvík,J.";;"3";"4";"2";"5";"2";NULL;NULL;NULL;"1";"5";"1";"4";"4";"I really appreciated the discussions in different small groups that allowed students to speak their own mind and get different insights on the different topics.";"The random choice for making students speak in class could definitely be more random... Choosing people that seemed uncomfortable in public speaking or for participation in general is definitely not a great pedagogic approach. Grading on participation is quite unfair as well for those same students. If you never experienced that type of fear or anxiety, please get some basic information on the Internet, and consider the fact that you are not a therapist.";"kbs" +"676";"JJM208";"Mediální systémy a jejich komparace";"Miessler,J.";;"2";"4";"2";"3";"1";NULL;NULL;NULL;"1";"3";"2";"4";"2";"Ukončení celého kurzu již v prosinci.";"Předmět lze zapsat nejdříve ve druhém ZS po splnění jiných povinností, tudíž většina studentů již řeší diplomovou práci a státnicové otázky. Z tohoto důvodu by si pan vyučující měl uvědomit, že zadávat na každou přednášku několik desítek stran četby z 99 % v anglickém jazyce kvůli získání 2 bodů je pro některé studenty obrovská ztráta času, který potřebují na důležitější aktivity. Navíc, pak celá následující přednáška rozebírá opět texty, které studenti měli číst. Přišlo by mi rozumnější psát alespoň ob týden, aby měl student čas připravovat si i něco jiného než tyto texty. Také bych rozhodně dala větší prostor na zadané prezentace, protože to, co pan vyučující požaduje v obsahu, se do 15 minut vměstnává jen velmi obtížně. Ve většině případů se pak prezentace nestíhaly, byly při přednesu zkracovány, a to byla mnohdy škoda.";"kms" +"677";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"5";"4";"4";"4";"4";"5";"5";"5";"2";"5";"4";"5";"4";"I appreciate that the seminars were linked to lectures - that they were not happening without notice of the other, as it often happens in other subject. E.g. when we were a seminar ahead of a lecture, we did theoretical background directly in the beginning of a seminar to be able to calculate with the knowledge.";;"ies" +"678";"JPB593";"Political Economy of Regionalism";"Miková,I.";;"3";"2";"2";"5";"2";NULL;NULL;NULL;"1";"4";"5";"5";"4";"Very detailed and explicable slides so that it was easier to follow without co-writing the whole time. Overall, the seminar was very well structured and the hours very well built on each other. In addition, I have received a thorough overview of political economic foundations.";"If the presentations are already rated, then they also need to be discussed so that it is clear what is really important. Not all presentations were equally good comprehensible. I'd like it better to pay more attention to the whole course instead of just looking at the own notes. As a result, fewer students have felt addressed and have dealt with other things.";"kmv" +"679";"JJM320";"Propaganda a československá média po roce 1948";"Suk,P.";;"4";"3";"4";"4";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kms" +"680";"JPM696";"Economic Warfare";"Ludvík,J.";;"2";"3";"2";"4";"2";NULL;NULL;NULL;"1";"2";"3";"2";"1";;"Most of the course was self taught and rarely relied on the knowledge of the lecturer who had very little input which was a wasted opportunity. Whilst I can appreciate the thinking behind continually breaking into groups and teaching one another, it disjointed the structure of the course and became mundane with the weekly group tasks. The method of assessment by writing group papers was difficult to arrange times and dates to meet and complete the work and therefore the standard of the work suffered. For the future I would suggest less emphasis on group work and more actual teaching.";"kbs" +"681";"JJM324";"Teorie mediální komunikace";"Jirák,J.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kms" +"682";"JEB026";"European Economic Integration";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"3";"3";"4";"4";"2";"3";"3";"2";"1";"3";"4";"4";"3";"Lectures by Dr. Hedbavny were interesting";"Change presentations to be less dense and messy and don't just blindly read from them every lecture";"ies" +"683";"JPM342";"Konflikty v demokracii a národní identita: teoretické problémy analýzy";"Říchová,B.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"Bližší (praktičtější) pochopení problematiky skrze seminární práci. Práce s teorií má pro studenta nepochybně mnohem větší přínos.";;"kp" +"684";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kms" +"685";"JEM001";"Master´s Thesis Seminar I";;"Havránek,T.,Havránková,Z.,Mejstřík,M.";"4";"4";NULL;NULL;NULL;"3";"4";"2";"1";"3";"4";"2";"4";;;"ies" +"686";"JPM910";"The Nature and Function of the State";"Franěk,J.,Pettit,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kp" +"687";"JJM330";"Trendy současných českých médií";"Aust,O.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"3";"3";"3";"4";;;"kms" +"688";"JJM211";"Kvalitativní výzkum mediálních publik";;"Reifová,I.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"3";"5";"Praktický nácvik výzkumné metody, samozřejmě s ohledem na možnosti kurzu.";;"kms" +"689";"JJM332";"Přirozený jazyk a média";"Podzimek,J.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"kms" +"690";"JJM373";"Ethical Issues in Media, Business and Society";"Orhan,M.";;"4";"2";"4";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kms" +"691";"JEM123";"Economics of Least Developed Countries";"Bauer,M.";"Bauer,M.";"5";"4";"5";"5";"5";"3";"5";"4";"2";"5";"5";"5";"5";"The course was great, one of my favorite that I attended so far in IES.I liked that various approaches to learn were used - quite interactive lectures, models and calculations during seminars, reading sessions with discussion...Furthermore, it was obvious that the lecturer really likes the topics and understand them properly. It was really enriching for me to spend time there!";;"ies" +"692";"JPM712";"Insurgency and Counterinsurgency";"Aslan,E.";;"4";"3";"4";"3";"4";NULL;NULL;NULL;"1";"5";"3";"4";"4";;"Less emphasis on Chechnya and the Caucasus and to present a larger variety of insurgency examples.";"kbs" +"693";"JJM295";"Rozhlasový a televizní dokument";"Štoll,M.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"3";"3";;;"kz" +"694";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"4";"3";"4";"4";"4";"4";"4";"4";"1";"4";"4";"4";"5";;;"ies" +"695";"JMB069";"Transatlantic Security Cooperation";"Weiss,T.";;"4";"2";"4";"4";"4";NULL;NULL;NULL;"4";"2";"2";"2";"4";;;"kzs" +"696";"JEB141";"Introduction to Market Design";"Gregor,M.,Kastl,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"3";"5";"Lectures were interesting and I was not falling asleep as I had expected beforehand.";"Personally I would add 1 more lecture aimed at pure theory.";"ies" +"697";"JMB091";"Religion, secularity and laicity in Europe (19th-21th centuries)";"Bauer,P.";;"3";"2";"3";"3";"4";NULL;NULL;NULL;"1";"2";"3";"4";"4";;;"kzs" +"698";"JMM601";"U.S. and Human Rights";"Raška,F.";;"3";"2";"2";"2";"2";NULL;NULL;NULL;"1";"2";"2";"3";"2";;;"kas" +"699";"JMM663";"Europe in the French mind: a historical–civilizational point of view";"Bauer,P.";;"3";"2";"3";"3";"4";NULL;NULL;NULL;"1";"2";"2";"2";"3";;;"kzs" +"700";"NMMA701";"Matematika 1";"Spurný,J.";"Skříšovský,E.";"5";"5";"5";"4";"5";"5";"5";"4";"2";"5";"5";"5";"5";"I attend Mathematics 1 for the second time and I really started to like this subject. Devoting time to it is not a waste of time, as I can see the huge progress not only in my ability of calculating itself but esp. in my ability to think more coherently and clearly.";;"ies" +"701";"JJM330";"Trendy současných českých médií";"Aust,O.";;"3";"1";"4";"3";"3";NULL;NULL;NULL;"1";"2";"1";"1";"2";;"Komunikaci vyučujícího se studenty. Studenti do posledního dne před zkouškou v podstatě nevěděli, jak bude vypadat závěr kurzu, jelikož v průběhu přednášek padlo několik možností a na e-mail vyučující sice odpověděl, že pošle informace nebo shrnutí ke zkoušce, nicméně jsme se nedočkali ničeho, až na poslední chvíli několik málo hodin před zkouškou. Chápu, že pan vyučující jako externí pracovník má jistě mnoho práce mimo školu, ale to nic nemění na tom, že takový přístup je pro studenty poměrně nepříjemný.";"kms" +"702";"JMM048";"European Union in International Affairs";"Weiss,T.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kzs" +"703";"JMMZ050";"Political Systems of East European Countries in the 20th Century";"Kubát,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"krvs" +"704";"JMMZ149";"EU Institutions";"Šlosarčík,I.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kzs" +"705";"JMMZ226";"Historical Roots of European Integration";"Kasáková,Z.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kzs" +"706";"JMMZ327";"Knowledge Policies in Europe";"Young,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kzs" +"707";"JMMZ331";"Qualitative methods in social sciences";"Weiss,T.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kzs" +"708";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"1";"5";"1";"2";"3";NULL;NULL;NULL;"1";"1";"1";"3";"1";"Připravenost kantora";"Hlasitost přednášejícího, srozumitelnost tématu";"ies" +"709";"JEM179";"History and Methodology of Economics";"Baxa,J.,Paulus,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"One of my favorite subject that I ever attended in IES.It was really valuable not only in terms of understanding the development of economics and learning about all those \"great names\". I esp. liked that through debates about mandatory reading it gave me a broader sense of what we are doing, why it makes sense to study all those models and mathematics. This course gave me definitely higher motivation to be a good economist.";"The compulsory reading should be +/- of the same amount every week. It was sometimes difficult to manage it and plan adequate time for the reading in my diary, when one week it was 40 pages and another week 100.";"ies" +"710";"JEB003";"Ekonomie I";"Fanta,N.,Kracík,J.,Švarcová,N.";"Fanta,N.,Kracík,J.,Švarcová,N.";"5";"3";"5";"5";"5";"5";"5";"4";"1";"4";"4";"4";"4";"Exkurz do centrálně plánované ekonomiky a mikroekonomické výpočty";"Přidat téma mezinárodního obchodu";"ies" +"711";"JSB998";"Úvod do sociologie";"Soukup,P.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"4";"5";"Přístup kantora. Je zábavný, vždy ochotný a je mu rozumět vše, co nám sděluje.";"Nic";"ks" +"712";"JSM644";"Základy politologie";"Kotlas,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"1";"5";"5";"Odbornou erudici přednášejícího, který mně v tom krátkém čase poutavě uvedl do politologie a podnítil můj zájem o ni, a to i v budoucnu.";"Na zkoušku (esej) se nedá kurzem \"naučit\", potenciál k jejímu výbornému složení musíte mít již v sobě. Podle mě možná chybí větší korelace mezi hodnocením a probraným obsahem kurzu.";"kvsp" +"713";"JPB596";"Čínská zahraniční a bezpečnostní politika";"Karmazin,A.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"přátelský a otevřený přístup, zcela logický a jasný styl přednášení, interaktivita, seminární aktivity,";;"kbs" +"714";"JLB035";"Francouzština I";;"Bosáková,L.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"5";"Paní Bosáková byla moc hodná a hlavně vstřícná. Umožňovala hodně dobrovolných úkolů, jejichž body se započítávaly k průběžným testům a tedy to zvýšilo šanci, že student nebude muset absolvovat závěrečný test.";"Navýšit kapacitu vyučujících, aby měli všichni studenti, kteří mají druhý jazyk povinný, možnost se do kurzu zapsat a absolvovat jej.";"cjp" +"715";"JPB269";"Současné problémy mezinárodních vztahů";"Knutelská,V.";;"3";"3";"3";"3";"1";NULL;NULL;NULL;NULL;"3";"5";"3";"4";;;"kmv" +"716";"JLB059";"Sociological Cinema";;"Blokker,P.,Štěpánková,D.";"4";"2";NULL;NULL;NULL;"5";"5";"4";"1";"2";"3";"4";"5";"Možnost psaní krátkých recenzí.";"Vše v pořádku, nic mě nenapadá.";"cjp" +"717";"JLM001";"Academic English I";;"Cotte,P.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"2";"5";"4";"4";"5";"It was really nice time spent there in the lectures. We had no stress, it was relaxed and still we were actively learning something new and useful. I really enjoyed attending the lectures.";"The lecturer did not want to print us quanta of materials for the lecturers - which I absolutely understand. However, it can be problematic when we also needed to do all the time some exercises,... I would recommend to use some online tools, everybody has a smartphone and we did it in the lectures with Mrs. Poslusna in English for Economists and I think that it worked well.";"cjp" +"718";"JPB569";"Workshop Politické a státní instituce v praxi";;"Brunclík,M.";"3";"1";NULL;NULL;NULL;"4";"4";"2";"3";"2";"2";"3";"4";;;"kp" +"719";"JPM146";"Přechody k demokracii v teorii a praxi I";"Mlejnek,J.";;"5";"1";"4";"5";"5";NULL;NULL;NULL;"3";"5";"3";"5";"5";;;"kp" +"720";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Akurátnost a exemplifikace";"Nenavrhuji nic";"ies" +"721";"JMB215";"Major Problems in North America Since the End of the Cold War";"Pondělíček,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"3";"5";"The interaction between the teacher and the students.";;"kas" +"722";"JPM118";"Výběrový seminář: Volby v USA";"Kotábová,V.";;"5";"4";"4";"3";"5";NULL;NULL;NULL;"1";"5";"4";"5";"4";;;"kp" +"723";"JPB593";"Political Economy of Regionalism";"Miková,I.";;"2";"4";"2";"5";"2";NULL;NULL;NULL;NULL;"4";"3";"3";"2";;"Místo přednášení skrytě za počítačem bych doporučoval vstát a být interaktivní.";"kmv" +"724";"JPM150";"Poloprezidentské režimy v postkomunistické Evropě";"Mlejnek,J.";;"5";"1";"4";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kp" +"725";"JPM160";"Česká komunální politika";"Jüptner,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kp" +"726";"JPM910";"The Nature and Function of the State";"Franěk,J.,Pettit,P.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"4";"4";"This course showed me (unfortunately not really thought me as it was quite short) that it is possible to think about things from completely different perspective than what I've though ever before. It was really exciting to find the extent of our understanding if we build our ideas on the very basics and then expand it. Furthermore, I really liked the metaphors that the lecturer used to describe various topics and phenomenon.It was really enriching time for me.";;"kp" +"727";"JSB031";"Bakalářský seminář I - diskuse projektů";;"Angelovská,O.,Mouralová,M.";"4";"4";NULL;NULL;NULL;"4";"3";"3";"1";"4";"5";"4";"4";"Nutnos napsání prvních 10 stran bakalářky";"Paní Angelovskou více pustit ke slovu. Je hrozně vstřícná a vždy dokáže poradit. Paní Mouralová je sice jako vyučující dobrá, bohužel její přístup z nás někdy dělal idioty. To se u Angelovské nikdy nestalo.";"ks" +"728";"JJB607";"Analýzy mediálních obsahů";"Křeček,J.";;"2";"3";"4";"3";"2";NULL;NULL;NULL;"1";"1";"1";"2";"2";;;"kms" +"729";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"5";"5";"Náhled na věci z vyšší perspektivy, diskuze";"Byla by pěkná vánoční exkurze do některého z míst lidového betlémářství";"ies" +"730";"JMM671";"Rebuilding Europe";;"Rovná,L.";"5";"1";NULL;NULL;NULL;"4";"5";"4";"1";"4";"3";"4";"5";"I valued the most the fact that the course was not like the others.The teacher was there just to supervise us and the students were given liberty in the way they approached the subject.";;"kzs" +"731";"JJB334";"Zábava v médiích";"Kruml,M.";;"5";"2";"4";"4";"4";NULL;NULL;NULL;"1";"4";"3";"4";"5";;;"kms" +"732";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"2";"3";"2";"1";"3";"1";"1";"5";;;"kz" +"733";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"3";"5";"Míra procvičování a vtipu";"Tento kurz nepotřebuje zlepšení";"ies" +"734";"JJB606";"Televize jako instituce";"Štoll,M.";;"4";"4";"5";"4";"4";NULL;NULL;NULL;"1";"4";"3";"4";"5";;;"kms" +"735";"JJB617";"Vybrané novinářské osobnosti 20. století";"Železný,J.";;"5";"3";"5";"4";"5";NULL;NULL;NULL;"1";"5";"2";"5";"5";;;"kms" +"736";"JJB618";"Propagandistické působení médií v 1 polovině 20. století";"Suk,P.";;"5";"4";"5";"4";"5";NULL;NULL;NULL;"1";"5";"2";"5";"5";;;"kms" +"737";"JJB221";"Ekonomická teorie marketingu";"Postler,M.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Velke mnozstvi prikladu z praxe.";"3 hodinove prednasky jsou opravdu dlouhe a po pulce se da jen tezko udrzet pozornost. Lepsi by bylo, kdyby byl kurz kazdy tyden ale hodinu a pul. Chapu ale, ze to z casovych duvodu asi nelze.";"kmkpr" +"738";"JEB111";"Advanced Data Analysis in MS Excel";;"Poláková,N.,Polák,P.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"1";"4";"5";"4";"5";"Procvičování práce s oborovým nádechem a práce na vlastním projektu";;"ies" +"739";"JMM025";"Putin´s Russia";"Veselý,L.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"4";"3";"5";"5";"Special guests who showed practical moments on some issues";"Nothing";"krvs" +"740";"JSM527";"Metody analýzy a tvorby politik II.";"Veselý,A.";;"3";"3";"5";"5";"5";NULL;NULL;NULL;"1";"1";"5";"5";"3";"Přispívá k přípravě diplomové práce.";"Práce, a tedy i kurz, hodnotí doktorandi (ne přednášející), jejichž zkušenosti (vč. erudice) by rozhodně mnohdy mohly být na vyšší úrovni.";"kvsp" +"741";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"3";"4";"4";"4";NULL;NULL;NULL;"1";"5";"4";"5";"4";;"U vysledku zkousek rozhodne neuvadet jmeno a prijmeni, ale dat zakovi kod, podle ktereho pak sve vysledky bude moct najit. Tak jak to byva u ostatnich predmetu.";"ies" +"742";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"3";"5";"3";"4";"5";"nejlepší vyučující a předmět v ZS!";;"ks" +"743";"JPB593";"Political Economy of Regionalism";"Miková,I.";;"5";"3";"5";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kmv" +"744";"JJB600";"Úvod do studia médií I.";"Nečas,V.";;"3";"4";"5";"5";"4";NULL;NULL;NULL;"1";"3";"1";"3";"3";"Vyučující a jeho přednášky jsou zajímavé, nicméně teorie, které jsme museli samostudovat jsou pro mě zmatečné a nezáživné.";"Určitě ubrat množství knih / zdrojů k samostudiu. Bylo by fajn i zvýšit počet přednášek, ale to je asi při současném rozsahu výuky neřešitelné.";"kms" +"745";"JLB019";"Francouzština odborná I";;"Bosáková,L.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"vynikající přístup vyučující, forma testů";;"cjp" +"746";"JPM407";"Feminism in International Relations (TIR)";;"Plechanovová,B.";"4";"4";NULL;NULL;NULL;"4";"4";"5";"3";"4";"5";"4";"5";"Learning feminist theories from the IR point of view";"More discussion should be during the lesson";"kmv" +"747";"JSB515";"Vysokoškolská vzdělávací politika";"Vlk,A.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Kurz byl naprosto skvělý! Doporučuji všem, kteří se nebojí mluvit před publikem a zaujala je vzdělávací politika. Přístup vyučujícího byl asi ten nejlepší jaký jsem doposud zažila. Poprvé jsem vydržela dávat tři hodiny v kuse pozor, bez nutkání dělat vše jiné jen ne tohle. Pan Vlk měl velmi osobitý přístup a nejvíce se mi líbilo, že nám práci rozdělil tak, aby jsme si to nejtěžší oddřeli na začátku a v době zkouškového jsme měli již předmět uznán. Prosila bych více takových vyučujících, nebo alespoň více předmětů s panem Vlkem. Moc děkuji za skvělý předmět - byl zatím nejlepší!";"Lépe specifikovat formu a požadavky tohoto předmětu. Není pro každého mluvit před hromadou aktérů. Jinak nic víc nemám, předmět byl skvělý.";"kvsp" +"748";"JMMZ050";"Political Systems of East European Countries in the 20th Century";"Kubát,M.";;"5";"4";"5";"4";"5";NULL;NULL;NULL;"1";"5";"2";"5";"5";"The broad scope of the topic and the well structured way of lecturing was very pleasing. The problems were encapsulated without simplifications.";;"krvs" +"749";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"1";"5";"1";"1";"1";NULL;NULL;NULL;"1";"1";"1";"1";"1";;"zaměřit se na věci z ekonomie, ne ze zoologie";"ies" +"750";"JSM421";"Contemporary social theory";"Balon,J.";;"5";"4";"4";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"I enriched my knowledge of social theories and learned how practically implement them in real life cases";"Nothing should be improved";"ks" +"751";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"4";"5";"Asymetričnost výkladu a osnovy";;"ks" +"752";"JSB455";"Economic Sociology and European Capitalism";"Blokker,P.";;"5";"3";"5";"4";"5";NULL;NULL;NULL;"1";"4";"5";"3";"5";;;"ks" +"753";"JLB003";"Angličtina pro ekonomy I";;"Gloverová,M.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"1";"5";"5";"4";"5";;;"cjp" +"754";"JJB601";"Rozbor právního prostředí v českých médiích";"Benda,J.";;"2";"3";"2";"2";"4";NULL;NULL;NULL;"1";"4";"2";"2";"4";"Rozsah vyučované látky.";"Komunikaci vyučujícího, konzultace domácích úkolů na přednáškách (tj. oznámit správné řešení), více příkladů z praxe.";"kms" +"755";"NMMA701";"Matematika 1";"Spurný,J.";"Skříšovský,E.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"4";"5";"Zevrubné procházení teorie i praxe aplikované matematiky";;"ies" +"756";"JPM694";"Strategic Studies";"Hynek,N.,Ludvík,J.,Smetana,M.";;"2";"2";"1";"2";"1";NULL;NULL;NULL;"1";"1";"1";"1";"2";;"The weekly tasks required every single week could not have been more of a waste of time. It became difficult in term of workload and lack of inspiration coming from the lack of interesting articles. Maybe you could consider giving to students the weekly theme for the class, and let them write the commentary on it, and leave the choice of articles free. Or simply ask for 2 essays over the semester which would be a more effective way to grade academic skills... Additionally, during some lectures, it was evident that the teachers knew less than some of the students. Finally, I wish the answer given to a student who attempts to speak about their issue in public speaking were not \"this is how you can learn\", before allowing them to extend on how much it is a problem and source of anxiety. Your job as a teacher, should be to be open to discussion or at least fake some basic concern, and not to consider yourself a therapist on the subject by expressing the idea that by forcing them, they might get better. Because you seem completely clueless in the face of this problem please check 'social anxiety' on the Internet. (I also hope better conditions will be established for future students.. the classroom were too packed and unbreathable)";"kbs" +"757";"JJB605";"Novodobá historie médií";"Bednařík,P.,Končelík,J.";;"4";"2";"5";"5";"4";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Zajímavé přednášky obou vyučujících.";"Ujasnit novým žákům pravidla:- seminárky- závěrečného testu (např. poznávání osob - nebylo v probíraném materiálu)";"kms" +"758";"JLB011";"Němčina pro ekonomy nižší I";;"Faltýnová,R.";"5";"3";NULL;NULL;NULL;"4";"4";"3";"1";"5";"4";"4";"5";;;"cjp" +"759";"JSM578";"Anthropology of EU";"Uherek,Z.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Not only reading materials, but also usage of documentary films was useful";"I would like to have more discussion during the course";"ks" +"760";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"4";"5";"4";"3";"3";"3";"2";"2";"2";"5";"4";"5";"5";;;"ies" +"761";"JJB620";"Mediální produkce - žurnalistická práce I.";"Vlasák,Z.";;"3";"2";"5";"5";"3";NULL;NULL;NULL;"1";"3";"4";"4";"4";"Příjemný přednášející, příklady z praxe, ukázky.";;"kms" +"762";"JLB029";"Španělština odborná I";;"Mlýnková,L.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";NULL;"5";"Navrhuji zachovat úplně vše, neboť se jedná o nejpřínosnější a nejzábavnější kurz na fakultě. Díky své vřelosti a schopnosti učit je doktorka Mlýnková tím nejlepším pedagogem, kterého jsem kdy potkal.";;"cjp" +"763";"JJB628";"Marketing módních značek - teorie";"Hejlová,D.,Koudelková,P.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"5";"Zajímavý předmět, super zajímaví hosté a také exkurze na Manolo Blahnika byla skvělá. Vyučující měla skvělý přehled. Bylo vidět, že se ve světe módy hodně orientuje a má mnoho teoretických znalostí. Za mě jeden z nejzajímavěhších předmětů!";"Vše bylo v pořádku. Více takových předmětů";"kmkpr" +"764";"JPB578";"Classics of Political Thought";"Salamon,J.";;"4";"3";"4";"3";"5";NULL;NULL;NULL;"2";"5";"3";"5";"4";"The performance-like lecture was never boring. It was easy to follow the main arguments. The lecturer combined the classics with contemporary thinking and made clear why those are important for current debates in political philosophy.";"The lecture could have been more structured. Some problems that were discussed in the readings were not explained in class.";"kp" +"765";"JJB135";"Filmový seminář I";;"Šobr,M.";"4";"1";NULL;NULL;NULL;"2";"2";"1";"2";"1";"1";"1";"3";;;"kz" +"766";"JMB497";"Metodický úvod pro kombinované studium";"Kubát,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"5";"5";"Pochopení základních pravidel při tvorbě prací souvisejících se studovaným oborem.";"Nic.";"krvs" +"767";"JLB059";"Sociological Cinema";;"Blokker,P.,Štěpánková,D.";"3";"2";NULL;NULL;NULL;"3";"2";"2";"1";"2";"2";"2";"2";;"Je potřeba na studenty více tlačit, aby mluvili";"cjp" +"768";"JMB503";"Soudobé české dějiny";"Kocian,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Odbornost přednášejícího";"Dvě přenášky by byly v souvislosti s rozsahem předmětu vhodnější.";"krvs" +"769";"JSB003";"Oborová sociologie";"Numerato,D.";;"4";"4";"5";"5";"2";NULL;NULL;NULL;"3";"4";"4";"5";"5";;;"ks" +"770";"JSB023";"Praktika z kvantitativního výzkumu I";;"Špaček,O.";"3";"3";NULL;NULL;NULL;"4";"4";"4";"1";"3";"4";"2";"3";;;"ks" +"771";"JMB499";"Současné metodologie";"Kubát,M.";;"4";"4";"5";"5";"3";NULL;NULL;NULL;"1";"4";"5";"5";"5";;;"krvs" +"772";"JMB523";"Mezinárodní aktuality I";"Fojtek,V.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"5";"5";;;"kas" +"773";"JSB028";"Informační gramotnost";"Tomandlová,V.";;"4";"1";"3";"4";"5";NULL;NULL;NULL;"1";"3";"4";"1";"5";;;"kvsp" +"774";"JPM695";"War Studies";"Hays II,G.";;"4";"4";"1";"2";"4";NULL;NULL;NULL;"1";"5";"3";"4";"3";;;"kbs" +"775";"JSB407";"Globální problémy životního prostředí a udržitelný rozvoj";"Drhová,Z.";;"5";"3";"4";"5";"5";NULL;NULL;NULL;"3";"4";"3";"3";"5";;;"kvsp" +"776";"JSB537";"Analýza dat v SPSS";"Soukup,P.";;"2";"4";"5";"4";"3";NULL;NULL;NULL;"4";"3";"4";"4";"2";;"Je obtížné mít státnice z předmětu, jehož polovina obsahu nebyla probrána, protože se vyškrtnul semestr látky.";"ks" +"777";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"1";"5";"5";"2";NULL;NULL;NULL;"2";"2";"2";"3";"5";;;"ks" +"778";"JEB022";"Institutional Economics";"Kopečná,V.,Schwarz,J.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"2";"5";"5";"5";"5";"The readers diary - very helpful in later revision for the exam, and would not have done it without the requirement. Also the professor was very friendly and helpful, easy to approach and always willing to answer questions.";"The exam was harder than expected, so maybe the format of the exam could be altered (e.g. multiple choice, or not as detailed but more general questions).";"ies" +"779";"JJB611";"Česká média po roce 1990";"Jirák,J.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";"Systém hodnocení kurzu.";;"kms" +"780";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"4";"2";"5";"4";"5";NULL;NULL;NULL;"1";"3";"2";"1";NULL;;;"ies" +"781";"JJB612";"Média a životní styl";"Knapík,J.";;"3";"2";"4";"4";"3";NULL;NULL;NULL;"1";"3";"3";"3";"4";;;"kms" +"782";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"4";"4";"3";"1";NULL;NULL;NULL;"1";"4";"1";"3";"4";"Ačkoliv je každá povinnost navíc jistou obtíží, doporučil bych zachovat psaní eseje. (Taky proto, že už ten předmět mít nebudu). Byl to v podstatě \"Skočte do vody a plavte\". Nevýhodou bylo, že jsem opravdu netušil, jak vzorově by esej měla být napsána a musel jsem si poradit sám, takže tam chybělo více věcí a do příště vím, z čeho se poučit při případném dalším psaní eseje.";;"ies" +"783";"JEB136";"Topics in Industrial Organization";"Schwarz,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"The set up of the course: the readers diaries and the fact that every lecture is a guest lecture - so interesting! The professor was also very friendly and easily approachable, making the course very enjoyable.";"Nothing to improve - my favorite course on my exchange!";"ies" +"784";"JJB613";"Úvod do studia nových médií";"Jirků,J.";;"3";"4";"3";"3";"3";NULL;NULL;NULL;"1";"3";"3";"3";"3";;;"kms" +"785";"JSB004";"Sociální nerovnosti";"Šanderová,J.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"3";"5";"5";"5";"5";;;"ks" +"786";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"3";"2";NULL;NULL;NULL;"3";"5";"5";"2";"4";"1";"3";"3";;;"ies" +"787";"JSM477";"Sociology of Critique";"Blokker,P.";;"4";"5";"5";"5";"4";NULL;NULL;NULL;"1";"4";"1";"3";"4";"The broad knowledge of the lecturer and his willingness to share book recommendations, recommendations of authors in the field of sociology and philosophy, tips for interesting research topics, videos, etc.The critical perspective :)I value the introduction to the Sociology of Critique because this research programme and sociological theory seems to be quite influential on many contemporary thinkers.";"When I enroled in this course I was expecting that Laurent Thevenot would hold a guest lecture/workshop, like it was written in the course description. It was a bit disappointing that this was not the case.";"ks" +"788";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"5";"3";NULL;NULL;NULL;"5";"4";"5";"3";"5";"5";"4";"5";;;"ies" +"789";"JJB625";"Manipulace v audiovizuálním sdělení";"Štoll,M.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"4";"5";"Celkový výklad pana Štolla a jeho přístup. Praktické ukázky.";;"kms" +"790";"JJM343";"Interkulturní komunikace";"Soukup,M.";;"5";"2";"4";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kms" +"791";"JJM329";"Média a kultura";"Bednařík,P.,Jirák,J.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"3";"2";"3";"3";"Přístup vyučujících byl skvělý, kurz byl plný aktuálních a zajímavých informací";"N/A";"kms" +"792";"JEM020";"Ethics and Economics";"Cahlík,T.";"Cahlík,T.";"5";"3";"5";"5";"4";"5";"5";"5";"1";"5";"5";"5";"5";"Very interesting discussions - the professor was very open to the student's opinions and lead the discussions perfectly. The assignments were fair and interesting to complete.";"Nothing to improve - great course.";"ies" +"793";"JJB231";"Dějiny a teorie public relations";"Halada,J.,Hejlová,D.";;"2";"5";"3";"3";"3";NULL;NULL;NULL;"1";"3";"2";"3";"2";;;"kmkpr" +"794";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"4";"2";"3";"5";"3";"3";"3";"5";"1";"5";"5";"5";NULL;;;"ies" +"795";"JJM216";"Čtení textů ke studiu médií - česká média po roce 1945";;"Bednařík,P.,Končelík,J.";"4";"2";NULL;NULL;NULL;"5";"5";"4";"1";"4";"2";"4";"4";;;"kms" +"796";"JJB235";"Proces tvorby v marketingové komunikaci";"Bezouška,M.";;"4";"2";"4";"4";"2";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kmkpr" +"797";"JSB021";"Základy demografie";"Šídlo,L.";;"4";"5";"4";"4";"4";NULL;NULL;NULL;"1";"4";"2";"4";"4";;;"ks" +"798";"JJB238";"Kritika marketingové komunikace";"Rosenfeldová,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"4";"5";"4";"5";"5";;;"kmkpr" +"799";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"4";"5";"Oceňuji, že se kurz zabýval hlavně zlepšením slovní zásoby a to za pomoci samostudia - krátkého čtení novinových článků. Práce ve dvojici s jinými studenty také napomohla k zapamatování látky.";;"cjp" +"800";"JJM201";"Teorie masové a mediální komunikace";"Jirák,J.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kms" +"801";"JJB253";"Markething - online publikování a populární kultura I.";;"Maxa,M.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"4";"5";;;"kmkpr" +"802";"JJM371";"New Media and Entrepreneurship";"Orhan,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"Very fun course - great discussions with an easygoing professor who was interested in his students' ideas, opinions and questions. The final project was great to complete with the groups, which resulted in a great atmosphere for the students.";"None - great course!";"kms" +"803";"JLB003";"Angličtina pro ekonomy I";;"Poslušná,L.";"4";"2";NULL;NULL;NULL;"5";"5";"4";"1";"4";"3";"3";NULL;;;"cjp" +"804";"JSB131";"Velké empirické výzkumy ČR";"Tuček,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"3";"5";"5";"5";;;"ks" +"805";"JJB243";"Aktuální trendy a vývoj v oboru I.";"Hejlová,D.,Vranka,M.";"Hejlová,D.,Vranka,M.";"4";"2";"4";"4";"5";"4";"4";"4";"1";"4";"4";"4";"4";;;"kmkpr" +"806";"JSB998";"Úvod do sociologie";"Soukup,P.";;"3";"2";"4";"5";"2";NULL;NULL;NULL;"2";"2";"2";"2";"3";;;"ks" +"807";"JLB041";"Španělština I";;"Mlýnková,L.";"4";"1";NULL;NULL;NULL;"5";"5";"5";"1";"3";"5";"4";"5";;;"cjp" +"808";"JJB334";"Zábava v médiích";"Kruml,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kms" +"809";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"5";"3";"4";"3";"4";NULL;NULL;NULL;"1";"5";"3";"5";"4";;;"krvs" +"810";"NMMA701";"Matematika 1";"Spurný,J.";"Honzík,P.";"4";"5";"4";"5";"5";"3";"2";"5";"1";"5";"5";"4";NULL;;;"ies" +"811";"JSB517";"Hudební subkultury mládeže";"Oravcová,A.";;"2";"3";"2";"3";"1";NULL;NULL;NULL;"1";"1";"1";"3";"4";;"Kladen moc velký důraz na závěrečnou seminární práci.";"ks" +"812";"JMB402";"Úvod do společenských věd II";;"Papežová,K.";"4";"2";NULL;NULL;NULL;"5";"4";"5";"2";"5";"5";"4";"4";;;"krvs" +"813";"JLB057";"Academic Writing for Bachelors";;"Goodall,A.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"1";"5";"5";"5";"5";"The course and material was building up very nicely, discussion of the material learned cleared things out";;"cjp" +"814";"JJB630";"Krizová komunikace";"Chudinová,E.";;"4";"3";"2";"3";"3";NULL;NULL;NULL;"2";"4";"4";"4";"4";;"Výuka probíhá ve Slovenštině a některé výrazy mohou ztížit pochopení látky.";"kmkpr" +"815";"JJM330";"Trendy současných českých médií";"Aust,O.";;"5";"2";"4";"4";"4";NULL;NULL;NULL;"1";"3";"1";"4";"4";"Pozvání vybraných hostů z oboru k diskuzi.";"Moc bych byla ocenila podrobnější instrukce k závěrečnému testu.";"kms" +"816";"JJM331";"Výzkum médií II";"Vochocová,L.";;"5";"4";"4";"5";"4";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kms" +"817";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Papežová,K.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"4";"3";"4";"4";;;"knrs" +"818";"JJB635";"Interkulturní marketing";"Rosenfeldová,J.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"3";"5";"4";"4";"5";;;"kmkpr" +"819";"JJB293";"Role výzkumů v politických a komerčních kampaních";;"Rosenfeldová,J.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"3";"4";"4";"4";"4";;;"kmkpr" +"820";"JJB276";"Public relations v praxi";;"Hejlová,D.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"5";"5";;;"kmkpr" +"821";"JJB243";"Aktuální trendy a vývoj v oboru I.";"Hejlová,D.,Vranka,M.";"Hejlová,D.,Vranka,M.";"5";"2";"5";"5";"5";"5";"5";"5";"1";"5";"3";"5";"5";;;"kmkpr" +"822";"JJB238";"Kritika marketingové komunikace";"Rosenfeldová,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"4";"3";"4";"5";;;"kmkpr" +"823";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Hornát,J.";"4";"3";"4";"4";"4";"4";"5";"4";"1";"4";"2";"4";"4";;;"krvs" +"824";"JJM334";"Diplomový seminář";;;"3";"3";"5";"5";"5";NULL;NULL;NULL;"1";"1";"3";"1";"5";"Jako přednášejícího považuji vedoucí své diplomové práce, paní doktorku Markétu Zezulkovou.Její přístup je velice individuální, nezabývá se mou prací jen ve chvíli, kdy se stavím na konzultační hodiny, ale posílá i tipy na zdroje či pomáhá s etickými záležitostmi se subjekty v zahraničí.";;"kms" +"825";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"3";"2";"2";"3";"1";NULL;NULL;NULL;"1";"5";"2";"1";"3";;;"knrs" +"826";"JMB409";"Moderní dějiny středo- a jihovýchodní Evropy-dcera JMB013";"Balla,P.,Švec,L.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"3";"5";"5";"Skvělé propojení mezi odpřednášenou látkou a látkou zkoušenou v testu, snad nejlepší z teritorií v tomto semestru. Skvělý a poutavý přístup k látce, velice zajímavé přednášky.";"Poskytnutí prezentací pro základní orientaci v probrané látce?";"krvs" +"827";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"4";"4";"3";"3";"3";NULL;NULL;NULL;"1";"5";"3";"5";"4";;;"kas" +"828";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"5";"5";"5";"-well-prepared lecturer - well structured course- easy to follow";;"kmv" +"829";"JEM062";"Introductory Econometrics";"Kukačka,J.";"Kukačka,J.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"4";"5";"5";"5";"Exercise parts of the course were very valuable! Also he was always to answer all the questions from students.";"Too many problems sets were released!! They might be a little bit demanding for some students.";"ies" +"830";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"3";"4";"4";"4";"3";NULL;NULL;NULL;"2";"3";"2";"3";"4";;;"kmv" +"831";"JPM702";"NATO and EU in Crisis Management";"Karásek,T.";;"2";"3";"3";"3";"1";NULL;NULL;NULL;"3";"2";"2";"3";"2";;"- was more a lecture than a seminar- discuss the context more in class rather than summarize the assigned reading during the lecture";"kbs" +"832";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"1";"5";"4";"3";NULL;NULL;NULL;"1";"5";"2";"4";"4";"Přednášky vkládané na Youtube. V případě mé nepřítomnosti se dala probíraná látka snadno dohledat a doplnit, a to i s autentickým výkladem přednášejícího.";;"ks" +"833";"JJB334";"Zábava v médiích";"Kruml,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"3";"5";"Viditelnou znalost vyučujícího o tématu. Připravené ukázky.";"Nic";"kms" +"834";"JPM694";"Strategic Studies";"Hynek,N.,Ludvík,J.,Smetana,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"- well prepared lecturers - good variation of teaching style with a lot of group work etc.";"- too much people for a too small room";"kbs" +"835";"JJB600";"Úvod do studia médií I.";"Nečas,V.";;"3";"4";"4";"5";"5";NULL;NULL;NULL;"2";"4";"3";"4";"5";;"Myslím, že závěrečný test na 6 otevřených otázek je dost těžký";"kms" +"836";"JPM696";"Economic Warfare";"Ludvík,J.";;"5";"3";"4";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"- well prepared lecturer- nice teaching style with group works !!";;"kbs" +"837";"JPM701";"European and Transatlantic Security";"Kazharski,A.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"2";"3";"4";"5";"5";;;"kbs" +"838";"JMBZ193";"American Media, Culture and Globalization";"Klvaňa,T.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Tento kurz je jeden z nejlepší, kterých jsem na IMS za ta léta měla. Pan Klvaňa má vše velmi dobře strukturované tématicky, zajímavé čtení, mix diskuze, přednášení a sledování videí je skvělý. Hlavně ten důraz na diskuzi jak o přečtené literatuře, tak i na obecnou diskuzi. Témata jsou vždy zasazena do souvislostí. Z kurzu jsme nadšená.";"Zkouška. Na psaní esejů bylo málo času.";"kas" +"839";"JJB606";"Televize jako instituce";"Štoll,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kms" +"840";"JJB607";"Analýzy mediálních obsahů";"Křeček,J.";;"3";"3";"4";"4";"5";NULL;NULL;NULL;"1";"3";"4";"3";"4";;;"kms" +"841";"JJB617";"Vybrané novinářské osobnosti 20. století";"Železný,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";"Viditelné znalosti přednášejícího, opravdu kvalitní a zajímavé přednášky.";;"kms" +"842";"JJB618";"Propagandistické působení médií v 1 polovině 20. století";"Suk,P.";;"4";"4";"5";"4";"5";NULL;NULL;NULL;"1";"5";"3";"4";"4";;;"kms" +"843";"JEM003";"Advanced Microeconomics";"Janda,K.,Melikhova,O.";"Janda,K.,Melikhova,O.";"3";"4";"2";"5";"3";"4";"5";"5";"1";"2";"1";"4";"3";;;"ies" +"844";"JEM004";"Advanced Macroeconomics";"Baxa,J.,Hromádková,E.";"Baxa,J.,Hromádková,E.,Šestořád,T.,Troch,T.,Žáček,J.";"4";"4";"5";"5";"5";"2";"4";"2";"1";"5";"3";"5";"4";;"I did not gain to much knowledge about current macro activities. Therefore, I would have preferred to focus more on applied knowledge rather than theoretical. I don't think the stress on computational part is really necessary. The empirical part was both interesting and valuable.";"ies" +"845";"JEM037";"Financial Markets";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"5";"3";"5";"5";"5";"4";"5";"4";"1";"4";"4";"5";"5";"Best lecturer";"Nothing, all great";"ies" +"846";"JEM062";"Introductory Econometrics";"Kukačka,J.";"Kukačka,J.";"5";"3";"5";"5";"3";"5";"5";"5";"1";"4";"4";"5";"5";"The dedication of the professor to both lectures and seminars was very appreciated by students. I believe all of us really felt he wanted to share his knowledge and teach in the best proper manner. Since, I did not have any econometrics courses in my bachelor, but had to take the advanced level, it was extremely useful for me. Thanks!";"I believe personalization with some students was not necessary as it felt like he had some preferable students.";"ies" +"847";"JLB100";"Czech as a Foreign Language I";;"Frantesová,E.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"1";"5";"5";;;"cjp" +"848";"JPM696";"Economic Warfare";"Ludvík,J.";;"4";"2";"3";"5";"5";NULL;NULL;NULL;"2";"2";"3";"3";"2";"The lecturer was engaging and enthusiastic. The course had a very good array of reading materials which were well chosen and informative.";"The course work process was often convoluted. The lecturer made us aware that this was a test of a new organisation, so I am not critical of the ad-hoc feel. However it might be more valuable to use smaller groups for the group conclusions (3-4 at most). Also the course could have used a more detailed introduction, a presentation on the notions and theoretical conceptions of economic warfare, before getting too lost in the strategies. This would have helped to contextualise the strategies, and explain why most of them were ineffective.";"kbs" +"849";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"1";"3";"1";"1";"1";NULL;NULL;NULL;"1";"2";"2";"1";"1";;"Vyměnit profesora za takového, který je ochotný studentům odpovídat na jejich dotazy.";"ies" +"850";"JLB001";"Angličtina pro sociology I";;"Klírová,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"5";;;"cjp" +"851";"JJB169";"Věda v médiích";"Kasík,P.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"5";"5";;;"kz" +"852";"JSB544";"Vybrané kapitoly středoškolské matematiky";;"Hendl,J.";"2";"4";NULL;NULL;NULL;"2";"1";"1";"1";"1";"1";"1";"1";;;"ks" +"853";"JPM127";"Geopolitika a geostrategie 1815 - 1945";"Romancov,M.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"5";"Samotný přednes vyučujícího. I přes vcelku pozdní čas kurzu, byly přednášky velmi zajímavé a zábavné";;"kp" +"854";"JJB035";"Odvětvové zpravodajství - ekonomie";"Kameníček,J.";;"4";"3";"4";"2";"3";NULL;NULL;NULL;"1";"4";"4";"5";"3";;;"kz" +"855";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Buřičová,B.";"4";"2";"2";"2";"1";"5";"5";"5";"1";"3";"3";"2";"4";;;"ks" +"856";"JJB006";"Současný český jazyk III";;"Svobodová,I.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"4";"5";;;"kms" +"857";"JPM146";"Přechody k demokracii v teorii a praxi I";"Mlejnek,J.";;"3";"4";"3";"5";"3";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kp" +"858";"JPM595";"Arms Control and Disarmament";"Hynek,N.,Smetana,M.";;"4";"4";"1";"2";"5";NULL;NULL;NULL;"1";"5";"5";"1";"4";"I really appreciated the simulation, which gave us some great insights of real-life negotiations in arms control.";"I was disappointed by the behaviour of the teachers, which did not seem to pay attention during the simulation, which was supposed to be the highlight of this semester. The take-home exam that followed was comprehensible throughout its part 1. However the second part of the exam involved an assessment of each player/ student in our group based on their knowledge and negotiation skills. I found this exercise highly inappropriate as it felt that because they could not follow the simulation, we were to grade each other instead and do their job in their place.";"kbs" +"859";"JJB055";"Tvůrčí dílny tisk I - tvůrčí psaní";"Malý,R.,Novotný,D.";"Malý,R.,Novotný,D.";"5";"3";"5";"5";"5";"5";"5";"5";"2";"5";"5";"5";"5";;;"kz" +"860";"JPM706";"Terrorism and Counterterrorism";"Bureš,O.";;"5";"3";"4";"5";"5";NULL;NULL;NULL;"2";"4";"3";"5";"5";"The knowledge and preparation of the lecturer was extremely good. The readings were very well chosen and extremely incitement, particularly in their broad range of opinions.";"The class time participation was often a regurgitation of the readings, rather than an opportunity for debate. Although this was understandable given the class size, it would have been good to have had more guided discussions. Although the lecturer did attempt to encourage small group discussions, these were often lead by a set of questions which drew entirely from the reading. A more Socratic-type class might be beneficial, encouraging students to confront their biases and challenge each others beliefs.";"kbs" +"861";"JPM342";"Konflikty v demokracii a národní identita: teoretické problémy analýzy";"Říchová,B.";;"3";"4";"3";"3";"3";NULL;NULL;NULL;"3";"3";"2";"3";"3";;;"kp" +"862";"JEM020";"Ethics and Economics";"Cahlík,T.";"Cahlík,T.";"5";"3";"5";"5";"4";"4";"5";"4";"1";"4";"3";"4";"4";"The approach of the teacher to the students and willingness to help.";"The timetable. 8 am lectures are tough.";"ies" +"863";"JJB037";"Kritika v médiích I";;;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kz" +"864";"JPM574";"Moderní strany a stranické systémy v Evropě";"Brunclík,M.";;"5";"3";"3";"5";"4";NULL;NULL;NULL;"1";"4";"3";"3";"5";"strukturu a obsah kurzu";;"kp" +"865";"JPM579";"Teorie politických stran";"Perottino,M.";;"3";"4";"3";"5";"3";NULL;NULL;NULL;"1";"3";"3";"3";"3";;"prezentace";"kp" +"866";"JMM027";"Contemporary Mediterranean";"Králová,K.,Mejstřík,M.";;"3";"2";"4";"3";"2";NULL;NULL;NULL;"1";"3";"2";"3";"2";"I appreciated the possibility to go and talk with the ambassador, as well as the way classes were structured, with an emphasis on the discussion rather than just listening or passively sitting in a class.";"I believe the main issue was the overall level of the class. Although I strongly believe that the teacher had a deep understanding of the topics addressed, the classmates had a very low/no previous knowledge of the debates. This situation, arguably, led to meaningless discussions or debates based on very basic concepts, which definitely were not at a master-level. The topics, I have to say, were very interesting and I very much appreciated the possibility to talk to the Italian ambassador. Though, I was disappointed at the overall attitude of the class, which either did not care to discuss at all or ended up with statements such as: \"the EU is completely undemocratic\", \"Lebanon is coping fine with the migration crisis (concept at least debatable), why should we accept the migration quotas form the EU?\" or: \"As long as Algeria is fine, France should not mind Lybia\". These are not opinions but believes that demonstrate a very low knowledge of the topic and situations in certain areas of the world, and should be avoided in a master class which, on the contrary, should be based on very strong understanding of the geopolitical issues of the Mediterranean and the willingness to debate, discuss and sometimes also argue, in order to defend or attack some points eventually mentioned.";"kzs" +"867";"JPM712";"Insurgency and Counterinsurgency";"Aslan,E.";;"3";"2";"3";"2";"5";NULL;NULL;NULL;"4";"4";"3";"3";"4";"The class discussions were often very good, and helped students to confront each other's beliefs on the subject. The lecturer did a good job of encouraging these discussions, and allowing all students a chance to speak.";"The lecturer did not have a very comprehensive plan for each class, and appeared to focus mainly on a small range of case studies - primarily Chechnya. Though this was very interesting, it was also limited, and did not encompass the range of examples that one might expect from a course on insurgency.";"kbs" +"868";"JPM639";"Problémy ústavního inženýrství";"Brunclík,M.";;"4";"3";"3";"5";"3";NULL;NULL;NULL;"2";"4";"4";"4";"4";;;"kp" +"869";"JPM641";"Světový regionalismus";"Riegl,M.";;"3";"4";"3";"3";"3";NULL;NULL;NULL;"1";"4";"2";"3";"3";;;"kp" +"870";"JPM595";"Arms Control and Disarmament";"Hynek,N.,Smetana,M.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";"I liked the way it was structured, as well as the possibility to do something practical at the end.";;"kbs" +"871";"JPM611";"Cyber Security";"Duračinská,Z.,Střítecký,V.";;"4";"3";"3";"4";"4";NULL;NULL;NULL;"2";"4";"2";"3";"5";"Overall, I truly appreciated the course, especially since the professor herself was \"new\" to teaching. All the information provided were truly interesting and more often than not delivered in a rather clear way.I truly enjoyed the presentation topics, which felt as good additions to what done in class.I enjoyed the Youtube video explaining more technical stuff, more similar audio-visual material could be used.Finally, also the use of humour and jokes or personal experience examples were truly welcomed.";"The course should follow a much more clear structure, one built having in mind that most (if not all) the students attending the course know little to nothing of the covered topics. For instance, more technical aspects of cybersecurity as well as of different kind of attacks need to be explained during the first lessons in order to create a foundation upon which base the rest of the course, which as the name suggests should also cover international relations.I would avoid focusing that much on CERTs, with 2 well structured lessons maximum devoted too them.Overall, the course lacks the bridge between cybersecurity and international relations. Yes we talked about some international organisations and events linked to cybersecurity, still something else could be added such as a lesson offering a comparative analysis on various states' cybersecurity strategies and military approaches to that field. The fact that this aspect of the course was almost entirely left to students' presentations also is not a too good teaching strategy, since students often lack a similar depth of knowledge as that of professors.Powerpoint presentations could also be refined, cutting down many words and keeping only the most important aspects in order for students to fully concentrate on what the professor says.Finally, please have the more technical parts be explained in a way as much simple as possible, again because most of the students will have little knowledge of computer science and informatics.";"kbs" +"872";"JPM694";"Strategic Studies";"Hynek,N.,Ludvík,J.,Smetana,M.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"5";"3";"4";"4";"As I wrote for the ACAD questionnaire, I particularly liked the way the course was structured. Instead of writing down a single paper at the end, the work on a daily basis was extremely helpful for me (although arguably was much longer and required a much greater effort than just sitting down and writing an essay in December).";;"kbs" +"873";"JLB100";"Czech as a Foreign Language I";;"Nováková,K.";"5";"1";NULL;NULL;NULL;"5";"4";"5";"1";"3";"4";"4";"5";"That it gave me basic knowledge of Czech language and helped me to communicate.";"-";"cjp" +"874";"JJB221";"Ekonomická teorie marketingu";"Postler,M.";;"4";"1";"5";"5";"4";NULL;NULL;NULL;"1";"2";"2";"3";"4";"Vyučující je sympatický, milý, má smysl pro humor, umí výklad pěkně podat.";"Je příliš jednoduchý (i bez účasti na přednáškách se na test dá úspěšně připravit za hodinu), což nevadí, ale studenti z jiných oborů z toho mají legraci, zapisují si kurz kvůli kreditům zadarmo a posmívají se oboru MKPR, protože si myslí, že předměty jsou fraška a zvládne je každý. Je to škoda, protože se mi obsah kurzu moc líbil.";"kmkpr" +"875";"JJB226";"Strategie a finanční management marketingu I";"Majerik,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"4";"4";"5";"5";"Skvělý přednášející s fajn přístupem i smyslem pro humor, nedělal z předmětu zbytečné nesplnitelné drama, ale zároveň jsme museli vynaložit úsilí, připravit prezentaci, a u takových praktických úkolů se naučíme mnohem více než učením na testy.";"Asi nic, kurz se mi moc líbil.";"kmkpr" +"876";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"2";"4";"5";;;"kp" +"877";"JJM080";"Zábava v televizním vysílání";"Kruml,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"3";"2";"2";"4";;;"kms" +"878";"JLB005";"Angličtina pro politology I";;"Panešová,K.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"3";"5";;;"cjp" +"879";"NMMA713";"Introductory Mathematics";;"Vlasák,V.";"2";"3";NULL;NULL;NULL;"1";"3";"2";"1";"2";"2";"2";"2";"The teacher was available if we had questions.";"The way the teacher explains and presents his explanation to the class. There should be more cooperation between the students and the teacher because the notes on the board were often very messy and it was hard to catch up even though I paid attention. Also, it would be valuable to evaluate the level of math of students they come in with so that the teacher knows what to explain in detail and which aspects can be skipped.";"ies" +"880";"JPB018";"Dějiny mezinárodních vztahů I";"Soukup,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"2";"5";"5";;;"kmv" +"881";"JJM202";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"4";"5";"3";"3";"4";NULL;NULL;NULL;"1";"5";"2";"4";"4";;"Mrzelo mě, že většinu kurzu odpřednášel p. Cebe, ačkoliv jeho přednášecí schopnosti nejsou příliš kvalitní a většinou pouze předčítá z prezentace. Mnohem vlídnější a příjemnější projev má druhý z profesorů, p. Bednařík - možná bych proto ocenila prohození jejich rolí a věřím, že s panem Bednaříkem by kurz byl velmi příjemný a zajímavý.";"kms" +"882";"JEB105";"Statistics";"Červinka,M.";"Nevrla,M.";"5";"4";"5";"5";"3";"3";"3";"3";"1";"5";"5";"5";"5";"I když jsem byla jen na první přednášce a prvním cvičení, protože mi více vyhovuje samostudium (potřebuju více flexibility a přijít si na věci sama a vlastním tempem), myslím, že pan Červinka je sympatický a milý člověk, líbí se mi jeho přístup ke studentům. Nemohu posoudit kvalitu kurzu, ale jeho vystupování mimo kurz bylo opravdu vstřícné, posunul kvůli nám i termín zkoušky. Taky hodně oceňuju, jak se snaží spíše, abychom látce rozuměli, než abychom uměli kopu vzorečků nazpaměť, na čemž jsou na IESu založené mikroekonomie a makroekonomie.";"Možná bych ocenila více tipů na literaturu (například něco o vzájemných vztazích jednotlivých pravděpodobnostních rozložení a jejich použití), v té obrovské žluté knize navíc byly příklady jen zadané bez řešení, což mi nikdy moc nepomohlo. Kdybych měla dále něco změnit, možná bych přesunula důkazy až k ústní části. Celkově jsem s kurzem byla velmi spokojená.";"ies" +"883";"JJM204";"Výzkum médií I";"Křeček,J.";;"4";"4";"3";"3";"3";NULL;NULL;NULL;"1";"4";"4";"4";"4";"Oceňuji testování praktických dovedností při kódování, je dobré vyzkoušet si praktickou práci s programem a naučit se, jak analýza reálně probíhá.";;"kms" +"884";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"5";"2";"4";"4";"2";NULL;NULL;NULL;"1";"5";"1";"4";"4";;;"ies" +"885";"JPM695";"War Studies";"Hays II,G.";;"5";"4";"5";"4";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"At a first impact, I sincerely got scared by the way the professor presented the course and the amount of work we had to do. A 6000-word-long essay is, indeed, a lot to do. Though, after working every day, preparing a lot of seminar works and reading essay-related papers, I must say, I am very happy I took the decision to follow the course both because of the professor and the very interesting topics addressed throughout the semester.";;"kbs" +"886";"JPM696";"Economic Warfare";"Ludvík,J.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kbs" +"887";"JJB631";"Social Media: Strategy, Tactics and Analytics";"Audyová,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmkpr" +"888";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"3";"5";;;"ies" +"889";"JEM132";"Company Valuation";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"4";"4";"The lectures were interesting and fun. I looked forward to every session. You feel that Prof. Novak cares deeply about the class and the progress of the students.";"The instructions of the assignments were often unclear. We felt that in many cases the given explanations were more confusing than helping.The workload is compared to other courses very high. Since you put in a lot of effort, you would hope to get some feedback for your assignments and not only the points. Additionally it takes many weeks to receive your points, even for smaller assignments. You don't know where you stand before the exam.";"ies" +"890";"JPM712";"Insurgency and Counterinsurgency";"Aslan,E.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"2";"4";"3";"3";"3";;"I must say, I got a bit disappointed at the time needed for the professor to correct the papers and tests.";"kbs" +"891";"JLB003";"Angličtina pro ekonomy I";;"Poslušná,L.";"3";"1";NULL;NULL;NULL;"5";"5";"1";"1";"2";"2";"2";"3";;;"cjp" +"892";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"Skvělý úžasný předmět, nejlepší vyučující! Pan Zelený i pan Johanis to učí úžasně, kéž by takových vyučujících bylo více i na IESu. Přednáška i cvičení byly vždycky dost vtipné a zajímavé, doufám, že matematika 4 bude stejně kvalitní.";"Neumím si představit, že jde něco zlepšit. Možná aby to celé bylo trochu dřív, kdybych si mohla vybrat, protože na cvičení už jsem umírala únavou a špatně jsem se soustředila, takže si pan Johanis někdy musel myslet, že neumím napočítat do deseti. Možná by i bylo fajn, kdyby cvičení byly třeba v různé termíny a nekryly se, protože občas je zajímavé se stavit i na \"cizí\" cvičení. To jsou ale spíš detaily.";"ies" +"893";"JPM099";"Baltic regional cooperation and Russia";"Zájedová,I.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"4";"5";"Most valuable part was the lecturer herself as her knowledge, personal experience and close insight are the huge asset for the students to acquire new knowledge.";"The only, thing I would love to improve is to grow the amount of reading. Lecturer has access and knowledge of the interesting readings and I think adding some more readings would be valuable for the students as well.";"kmv" +"894";"JEB111";"Advanced Data Analysis in MS Excel";;"Poláková,N.,Polák,P.";"5";"3";NULL;NULL;NULL;"4";"5";"5";"1";"5";"5";"3";"5";;;"ies" +"895";"JJM226";"Teorie účinků médií";"Nečas,V.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Tento kurz se mi moc líbil, pan Nečas umí zajímavě a chytlavě přednášet a přednášky dávaly smysl. Bavila mě provázanost s literaturou, je fajn si dopředu vždy načíst texty a následně se na ně podívat na přednášce, to doporučuji zachovat.";;"kms" +"896";"NMMA701";"Matematika 1";"Spurný,J.";"Rondoš,J.";"5";"5";"5";"5";"5";"4";"5";"4";"1";"5";"5";"5";"5";;;"ies" +"897";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"4";"3";"5";"5";"3";"2";"4";"1";"1";"5";NULL;"5";"5";;;"ies" +"898";"JLB035";"Francouzština I";;"Bosáková,L.";"4";"1";NULL;NULL;NULL;"4";"4";"4";"1";"4";"4";"3";"5";;;"cjp" +"899";"JJM229";"Vývoj televizního vysílání v českých zemích";"Štoll,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Pana Štolla považuji za odborníka na svém místě - je vidět, že má obrovský přehled a navíc umí informace poutavě a zajímavě předat. Tento kurz se velmi bavil a rozhodně bych ho všem doporučila.";;"kms" +"900";"JSB031";"Bakalářský seminář I - diskuse projektů";;"Spalová,B.";NULL;NULL;NULL;NULL;NULL;"5";"5";"5";NULL;NULL;NULL;NULL;NULL;;;"ks" +"901";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"3";"2";NULL;NULL;NULL;"4";"4";"3";"1";"2";NULL;"2";"3";;;"ies" +"902";"JSB537";"Analýza dat v SPSS";"Soukup,P.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"4";"4";"5";"4";"5";;;"ks" +"903";"JPM595";"Arms Control and Disarmament";"Hynek,N.,Smetana,M.";;"4";"2";"4";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";"Enthusiasm of the lecturers in the specific topic and openness to engaging yet not totally tried and tested rather freestyle teaching methods.";"Dig deeper into the topic and make us learn more specifics concerning the technology, and the verification process. Longer classes would help/ were needed. Also, it is not just the students who should make sure to be ready for having a class; falling asleep during the simulated negotiations dreadfully damaged the image of a lecturer in the eyes of many.";"kbs" +"904";"JEM035";"Financial Markets Instruments I";"Dědek,O.,Poláková,N.,Polák,P.";"Dědek,O.,Poláková,N.,Polák,P.";"4";"3";"3";"4";"1";"4";"4";"3";"1";"4";"4";"4";"5";"The class is very well structured with the combination of lectures and seminars. The seminars are very helpful for the exam preperation.";"Attendance at the lectures is not necessary since Prof Dedek will go step by step though his very detailed hand out. Maybe it would be helpful to make up new examples and calculations for the lectures to increase attendance rates.";"ies" +"905";"JPM127";"Geopolitika a geostrategie 1815 - 1945";"Romancov,M.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kp" +"906";"JPM656";"Technology and warfare";"Kučera,T.";;"3";"1";"1";"5";"2";NULL;NULL;NULL;"1";"1";"1";"1";"1";"It made me read an interesting book, sadly that's pretty much it.";"A lot of them, the classes were not very engaging, the usage of Moodle did more harm than good. There was an attempt to follow some class structure, but it was definitely not enough. It's a bit shame that the classes had to be coupled and taking place every other week. The lectures were very general and basic, many students were just not challenged by this course at all. It has to be however noted that the lecturer does make an effort, has the enthusiasm and all manners and professionalism expected from a lecturer, and generally strives to make the class good, it would just require some guidance from other colleagues at the department perhaps.";"kbs" +"907";"JMB057";"Cultural Legacies and Developments in the Balkans: Modern and Traditional Entanglements";"Asavei,M.";;"5";"3";"5";"3";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";"Individual work on certain topics for presentations";"Perhaps some time for introduction to the week before exploring the reading";"krvs" +"908";"JMB069";"Transatlantic Security Cooperation";"Weiss,T.";;"5";"3";"5";"4";"5";NULL;NULL;NULL;"1";"3";"4";"4";"4";"Group presentations allowed individual thinking around a broader topic";"More engaging readings";"kzs" +"909";"JMB178";"U.S. in the 1960s and 1970s";"Raška,F.";;"5";"2";"5";"4";"5";NULL;NULL;NULL;"1";"4";"5";"4";"4";"Freedom to explore the readings, open discussion very helpful";"More structure to classes, sometimes weeks repeat topics by accident due to the open discussions";"kas" +"910";"NMMA711";"Mathematics 1";"Bárta,T.";"Bárta,T.,Vlasák,V.";"3";"5";"3";"4";"5";"3";"4";"4";"1";"4";"4";"4";"1";"-";"It seems unfair that the requirements for this course are quite low, however you are expected to have an in-depth knowledge of often new concepts that were not covered before. Maybe the pre-requirements should be higher so that people can be at least prepared to what they would have to deal with. To me, this is the hardest course and it takes up all my time and energy.";"ies" +"911";"JPB593";"Political Economy of Regionalism";"Miková,I.";;"3";"4";"3";"5";"3";NULL;NULL;NULL;"1";"3";"4";"3";"4";"Lectures very detailed";"Perhaps more engagement with reading";"kmv" +"912";"JSB455";"Economic Sociology and European Capitalism";"Blokker,P.";;"4";"4";"5";"4";"3";NULL;NULL;NULL;"1";"3";"4";"3";"4";"Very high detail in the lectures";"Perhaps greater help to non-sociology focused students to help ease them in";"ks" +"913";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"5";"3";"5";"5";"4";"5";"5";"5";"1";"4";"4";"4";"5";"The in-depth explanation of concepts.";"-";"ies" +"914";"JPB202";"Politické strany v Evropě";"Perottino,M.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"2";"4";"3";"4";"4";;;"kp" +"915";"JJM247";"Český stranický systém";"Just,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Předmět je skvělý - jeho obsahem je přesně to, co by novinář/vlastně kdokoliv měl znát a vědět. Přednášky jsou svižné, srozumitelné a zábavné. Pan Just je skutečný odborník a výborný pedagog.";;"kz" +"916";"JPM694";"Strategic Studies";"Hynek,N.,Ludvík,J.,Smetana,M.";;"5";"3";"4";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";"I had a similar course before, so the major value of it for me was a different perspective on concepts in the field. It introduced me to some new academics, and overall significantly increased my knowledge.";"I would leave this course to Michal Smetana, I among others I believe, don't see the point of having Jan Ludvik in the class. Not that he would not bring interesting insights, and more knowledge to the table, but I see it a terrible waste of his time. The course could also benefit from longer classes, since we were frequently running out of time, and more even guest lecturers.";"kbs" +"917";"JJM249";"Duchovní kořeny evropské kultury";"Halada,J.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";"Pan Halada je skvělý pedagog s hlubokými znalostmi, které umí srozumitelně a zábavně předat studentům.";;"kz" +"918";"JEB003";"Ekonomie I";"Fanta,N.,Kracík,J.,Švarcová,N.";"Fanta,N.,Kracík,J.,Švarcová,N.";"4";"4";"3";"4";"4";"3";"5";"4";"1";"4";"4";"4";"4";;;"ies" +"919";"JMM703";"Post-Soviet Central Eurasia";"Lídl,V.,Šír,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"krvs" +"920";"JJM251";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"3";"5";"5";"4";"4";NULL;NULL;NULL;"1";"4";"2";"3";"3";;;"kz" +"921";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"3";"1";NULL;NULL;NULL;"3";"3";"2";"2";"2";"2";"1";"3";;;"ies" +"922";"JMM086";"Diplomový seminář II";;"Králová,K.,Svoboda,K.,Švec,L.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"5";"5";;;"krvs" +"923";"JMM183";"Současná východní Evropa I";"Lídl,V.,Šír,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"krvs" +"924";"NMMA701";"Matematika 1";"Spurný,J.";"Rondoš,J.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"4";"5";;;"ies" +"925";"JSB998";"Úvod do sociologie";"Soukup,P.";;"3";"3";"2";"2";"1";NULL;NULL;NULL;"1";"3";"2";"2";"3";;;"ks" +"926";"JJM356";"Metodologické přístupy ke studiu žurnalistiky";"Nečas,V.,Veselková,Z.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"5";"5";"Oceňuji srozumitelný výklad pedagoga poměrně složité látky. Zvlášť bych chtěl vyzdvihnout jeho vstřícnost a korektní chování.";;"kz" +"927";"JLB003";"Angličtina pro ekonomy I";;"Poslušná,L.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"4";"4";;;"cjp" +"928";"JJM360";"Ekonomika v médiích";"Klimeš,D.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Oceňuji velice vlídný přístup pana Klimeše a rozumné požadavky k udělení známky. Zvlášť jsem ocenil oba hosty na přednáškách. Jejich výklad byl - stejně jako vyučujícího - velice zajímavý a poučný.";;"kz" +"929";"JPM696";"Economic Warfare";"Ludvík,J.";;"4";"3";"4";"4";"5";NULL;NULL;NULL;"1";"5";"4";"5";"4";"I was completely new to the topic, so it was a nice way of introduction.";"I would suggest to rethink the questions for group summaries based on our answers. Not all the questions were answerable by our case studies, and the lecturer should stick with him deciding on the questions, because it will just end up chaotically, as it did when we tried it. The course could go deeper into the specifics and making students work with data recorded in tables is very appreciated and encouraged to be broadened. I believe it could help the students a lot in grasping the topic, by making them work with some hard data. I don't know about the availability of such data relating to the discussed case studies fit for the purpose here, but it's just something to think about. Some bigger group project as a final exam could be fun as well, since the course was not very challenging.";"kbs" +"930";"JJM297";"Novinář jako politický aktér";"Charvát,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Oceňuji přístup pedagoga a možnost diskuze nad probíranými tématy. Zajímaví byli i různorodí hosté, již pan Charvát do hodin zval.";;"kz" +"931";"JEM004";"Advanced Macroeconomics";"Baxa,J.,Hromádková,E.";"Baxa,J.,Hromádková,E.,Šestořád,T.,Troch,T.,Žáček,J.";"5";"4";"5";"5";"4";"5";"5";"5";"1";"4";"3";"3";"4";;;"ies" +"932";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";;;"kp" +"933";"JEM141";"Traditional and Alternative Risk Transfer in the Insurance Sector";"Pompella,M.,Teplý,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"2";"3";"4";;;"ies" +"934";"JPB018";"Dějiny mezinárodních vztahů I";"Soukup,J.";;"4";"2";"5";"5";"5";NULL;NULL;NULL;"2";"3";"4";"4";"5";;;"kmv" +"935";"NMMA701";"Matematika 1";"Spurný,J.";"Peša,D.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"936";"JPB221";"Metodologický proseminář I";;"Komasová,S.,Parízek,M.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"5";"5";;;"kmv" +"937";"JLB027";"Ruština odborná I - vyšší";;"Mistrová,V.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"938";"JJB221";"Ekonomická teorie marketingu";"Postler,M.";;"2";"1";"3";"5";"1";NULL;NULL;NULL;"1";"2";"1";"1";"3";;;"kmkpr" +"939";"JJM355";"Teoretické koncepty ke studiu žurnalistiky I";"Jirků,J.,Němcová Tejkalová,A.";"Jirků,J.";"2";"5";"3";"5";"2";"5";"5";"3";"1";"3";"1";"3";"2";;"Nepochopil jsem účel tohoto předmětu. Oba vyučující jsou vstřícní, chápaví a milí lidé, ale náplň kurzu nelze popsat jinak, než jako nudnou a zbytečnou. Šrocení se jakýchsi abstraktních levicových teorií o konstrukci společnosti vnímám jako ztrátu času. Ani výklad na přednáškách k pochopení učiva nepomáhal.";"kz" +"940";"JPB252";"České novověké dějiny I.";"Kučera,J.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"4";"5";;;"kp" +"941";"JPM127";"Geopolitika a geostrategie 1815 - 1945";"Romancov,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"4";"5";;;"kp" +"942";"JEB026";"European Economic Integration";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"4";"4";"5";"5";"4";"5";"5";"3";"1";"4";"3";"4";"5";"Oceňuji, že se hodnocení skládá i z prezentování prezentace a ne jen ze závěrečného testu.";"Na seminářích by bylo dobré se učit jednotlivé modely týkající se EEI. Např. následky symetrických a asymetrických šoků.";"ies" +"943";"JEB105";"Statistics";"Červinka,M.";"Červinka,M.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"4";"5";;;"ies" +"944";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"3";"2";"4";"4";"3";NULL;NULL;NULL;"2";"2";"1";"3";"4";;;"ies" +"945";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"4";"1";NULL;NULL;NULL;"3";"4";"1";"1";"2";"1";"1";"4";;;"ies" +"946";"JPM146";"Přechody k demokracii v teorii a praxi I";"Mlejnek,J.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kp" +"947";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"5";;;"ies" +"948";"JPM712";"Insurgency and Counterinsurgency";"Aslan,E.";;"1";"1";"1";"3";"1";NULL;NULL;NULL;"3";"2";"1";"3";"1";"If someone does not know how social norms and identities shape insurgency and counterinsurgency, this course explains that well. It provides platform for a freestyle, almost non-moderated weekly discussion on insurgency, but other than that it does not do much. Having an interesting guest lecturer provide experiences from the field was a sole bright spot of the course.";"Most of them should. The classes were very dominantly ran by the students, the lecturer provided very little input. Having a 25 minute presentation with subsequent 40 minutes of Q&A almost every single class does not leave much space for the lecturer to share his knowledge. There was practically no coursework throughout the semester, hence the students were not really challenged. The lecturer once failed to appear in one class for about 20 minutes, without any excuse before or after, leaving only five people in the class I believe. I did not have the chance to read the course description before choosing it, but judging by the name I thought it would not be so overwhelmingly focused on the wars in Chechnya. I understand that this is the main specialization of the lecturer, but when studying insurgency and counterinsurgency in general, I think knowledge from other case studies (which I sincerely believe the lecturer does have) should be brought up as well. I was not so disappointed by this, because I had classes on this topic before, but if this were to be my into to insurgency and counterinsurgency, which for some students I believe it was, this class would leave me very disappointed.";"kbs" +"949";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"4";"3";NULL;NULL;NULL;"5";"5";"4";"1";"3";"1";"2";"4";;;"ies" +"950";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"4";"1";"4";"4";"3";NULL;NULL;NULL;"1";"2";"2";"3";"4";;;"ies" +"951";"JEB044";"Financial Accounting";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ies" +"952";"JJB0111";"Journalism Ethics/Úvod do etiky žurnalistické práce";"Neuzil,M.";;"3";"4";"5";"5";"4";NULL;NULL;NULL;"1";"3";"3";"3";"3";;;"kz" +"953";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"5";"1";NULL;NULL;NULL;"5";"5";"4";"1";"4";"2";"3";"5";;;"ies" +"954";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"4";"2";"4";"5";"2";"4";"4";"1";"1";"3";"3";"4";"5";;;"ies" +"955";"JJM117";"Popular Culture";"Turnau,T.";;"4";"5";"5";"4";"4";NULL;NULL;NULL;"1";"4";"3";"3";"4";;;"kms" +"956";"JEB111";"Advanced Data Analysis in MS Excel";;"Poláková,N.,Polák,P.";"4";"3";NULL;NULL;NULL;"4";"3";"5";"1";"4";"4";"2";"5";;;"ies" +"957";"JEB141";"Introduction to Market Design";"Gregor,M.,Kastl,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ies" +"958";"JLB003";"Angličtina pro ekonomy I";;"Poslušná,L.";"2";"1";NULL;NULL;NULL;"4";"4";"1";"1";"1";"1";"1";"2";;;"cjp" +"959";"NMMA701";"Matematika 1";"Spurný,J.";"Rondoš,J.";"5";"5";"5";"5";"5";"3";"4";"4";"1";"5";"5";"5";"5";;;"ies" +"960";"JMMZ313";"Government in United States";"Sehnálková,J.";;"3";"2";"4";"4";"4";NULL;NULL;NULL;"1";"2";"2";"3";"3";;;"kas" +"961";"JPM198";"Contemporary Latin America";"Krausz Hladká,M.";;"1";"1";"2";"4";"2";NULL;NULL;NULL;"2";"1";"1";"1";"2";;;"kp" +"962";"JJM234";"Media and Society: An Introduction";"Jirák,J.";;"4";"2";"3";"2";"3";NULL;NULL;NULL;"2";"2";"2";"4";"3";;;"kms" +"963";"JJM240";"Cultural studies";"Soukup,M.";;"3";"1";"5";"5";"4";NULL;NULL;NULL;"3";"4";"2";"4";"5";;;"kms" +"964";"JJM362";"History of media";;"Neuzil,M.";"3";"3";NULL;NULL;NULL;"4";"4";"2";"1";"2";"3";"2";"2";;;"kz" +"965";"JJM233";"Intercultural Communication Management";"Lütke Notarp,U.";;"4";"1";"4";"3";"3";NULL;NULL;NULL;"1";"3";"2";"3";"3";;;"kms" +"966";"JEM032";"Banking";"Mejstřík,M.,Pečená,M.,Teplý,P.";"Pečená,M.";"3";"3";"4";"4";"3";"5";"5";"5";"1";"4";"3";"5";"4";;"Credit application a Credit application review bylo ohodnoceno pouze body. Žádná zpětná vazba, žádný návrh ke zlepšení, žádný komentář. V současné podobě tyto 2 projekty slouží spíš jako odškrtnutí políčka než jako získání nových vědomostí a zkušeností. Pokud se má student poučit z chyb, které učinil, potřebuje je znát.";"ies" +"967";"JPM323";"Global Political Philosophy";"Salamon,J.";;"3";"5";"3";"3";"3";NULL;NULL;NULL;"1";"3";"1";"2";"2";;"The lecturer thinks that by making exam requirements difficult the quality of the course can be improved. Interesting attitude!";"kp" +"968";"JPM602";"Masterś Thesis Seminar I.";;"Kofroň,J.";"3";"2";NULL;NULL;NULL;"3";"3";"3";"1";"2";"3";"2";"3";;;"kp" +"969";"JEM003";"Advanced Microeconomics";"Janda,K.,Melikhova,O.";"Janda,K.,Melikhova,O.";"4";"4";"3";"4";"4";"4";"5";"5";"1";"3";"4";"2";"3";;;"ies" +"970";"JPM342";"Konflikty v demokracii a národní identita: teoretické problémy analýzy";"Říchová,B.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"3";"5";"5";"5";"5";;;"kp" +"971";"JPM692";"Internal Security of the EU [ES]";"Hokovský,R.";;"2";"3";"2";"3";"3";NULL;NULL;NULL;"2";"3";"1";"2";"2";;;"kmv" +"972";"JEM004";"Advanced Macroeconomics";"Baxa,J.,Hromádková,E.";"Baxa,J.,Hromádková,E.,Šestořád,T.,Troch,T.,Žáček,J.";"4";"5";"5";"5";"4";"4";"5";"5";"2";"4";"4";"5";"4";;;"ies" +"973";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"4";"4";"5";"5";"5";"2";"3";"4";"1";"3";"5";"4";"4";;;"ies" +"974";"JJB002";"Dějiny masových médií II";"Sekera,M.";;"4";"2";"4";"5";"5";NULL;NULL;NULL;"4";"4";"2";"3";"4";"návštěvu NM a \"Zámečku\" na Praze 7";"aby přednášky tolik neodpadávaly (byl to jeden z důvodů, proč nás ve výsledku chodilo jenom 5)";"kms" +"975";"JEM034";"Corporate Finance";"Bychkova,O.,Malířová,T.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"3";"4";"5";"4";"3";;;"ies" +"976";"JJB003";"Dějiny masových médií III";"Bednařík,P.,Končelík,J.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"5";"5";"Dvojice vyučujících má skvělý smysl pro humor a vyprávěné historky mi pomohly zapamatovat si jednotlivé události, popřípadě dějinné souvislosti";;"kms" +"977";"JJB006";"Současný český jazyk III";;"Svobodová,I.";"5";"4";NULL;NULL;NULL;"5";"4";"5";"2";"5";"5";"3";"5";;;"kms" +"978";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"4";"2";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"5";"Vysvětlování látky na konkrétních případech, možnost absolvování zápočtového testu už před Vánoci";"Mgr. Pražák by nemusel vyvolávat konkrétní studenty k zodpovězení otázky";"ies" +"979";"JJB009";"Úvod do psychologie";"Vranka,M.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"2";"5";"3";"4";"5";"Praktické příklady";"Některá látka byla zbytečně složitě vysvětlována, dalo se to říct jednodušeji";"kz" +"980";"JLB041";"Španělština I";;"Mlýnková,L.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";NULL;"5";;;"cjp" +"981";"JJB014";"Žurnalistická tvorba III - Časopisecká tvorba";"Osvaldová,B.";"Maňák,V.";"3";"5";"3";"4";"4";"3";"4";"5";"2";"4";"4";"3";"3";;"vzhledem k množství práce, které student musí odevzdat, by bylo fér dávat za splněný předmětu více kreditů";"kz" +"982";"JPM716";"The Geopolitics of Defence Industry and Arms Trade";"Kopečný,T.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"3";"3";"3";;"I must admit, over the last few weeks I got a bit disappointed at the Professor as I received no replies to any of my emails. I arguably had no help with the decision of my essay topic, nor had I have the chance to discuss with the Professor any of my doubts. This is an attitude which I found disappointing and surprising because came out of a blue.";"kp" +"983";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";"Struktura kurzu, která je spojena i s četbou dobového tisku";"Nevyhovoval mi pozdní doba přednášek a trvání přednášky 3 hodiny.";"kp" +"984";"JMB065";"Úvod do mezinárodního a evropského práva";"Šlosarčík,I.";;NULL;NULL;"5";"5";"4";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kzs" +"985";"JJB019";"Práce s agenturními informacemi";"Prázová,I.,Trunečková,L.";"Prázová,I.,Trunečková,L.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"4";"5";;;"kz" +"986";"JMB408";"Moderní dějiny Ruska";"Litera,B.,Novák,P.,Pečenka,M.";;NULL;NULL;"3";"3";"2";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"krvs" +"987";"JMB409";"Moderní dějiny středo- a jihovýchodní Evropy-dcera JMB013";"Balla,P.,Švec,L.";;NULL;NULL;"3";"3";"3";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"krvs" +"988";"JMB410";"Moderní dějiny západní Evropy";"Matějka,O.,Váška,J.";;NULL;NULL;"3";"3";"3";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kzs" +"989";"JJB021";"Bakalářský seminář";;"Prázová,I.";"4";"4";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"5";"4";;;"kz" +"990";"JMBZ193";"American Media, Culture and Globalization";"Klvaňa,T.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kas" +"991";"JPB018";"Dějiny mezinárodních vztahů I";"Soukup,J.";;NULL;NULL;"4";"4";"3";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kmv" +"992";"JJB169";"Věda v médiích";"Kasík,P.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"3";"5";"4";"5";"5";;;"kz" +"993";"JPB268";"Evropská integrace";"Plechanovová,B.";;"3";"3";"1";"2";"2";NULL;NULL;NULL;"3";"5";"3";"3";"3";;"Kurz by se měl zaměřit více detailně na aspekty fungování současné EU, nikoliv jen na obecný přehled.";"kmv" +"994";"JPB020";"Úvod do mezinárodních vztahů";"Karlas,J.,Kučera,T.";;NULL;NULL;"4";"4";"2";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kmv" +"995";"JPB252";"České novověké dějiny I.";"Kučera,J.";;NULL;NULL;"3";"3";"2";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kp" +"996";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";NULL;NULL;"3";"3";"3";"3";"3";"3";NULL;NULL;NULL;NULL;NULL;;;"kp" +"997";"JPB258";"Politické myšlení novověku I";"Franěk,J.,Kučera,J.";;NULL;NULL;"3";"3";"2";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kp" +"998";"JPB269";"Současné problémy mezinárodních vztahů";"Knutelská,V.";;NULL;NULL;"4";"4";"3";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kmv" +"999";"JPB592";"US Government and Politics";"Kotábová,V.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kp" +"1000";"NMMA703";"Matematika 3";"Zelený,M.";"Johanis,M.";"4";"4";"5";"5";"4";"3";"4";"4";"1";"4";"3";"3";"4";;;"ies" +"1001";"JPB228";"Mírové smlouvy a konference v mez. systému";"Jeřábek,M.";;"4";"1";"3";"5";"1";NULL;NULL;NULL;"1";"3";"2";"3";"4";;;"kmv" +"1002";"JEB022";"Institutional Economics";"Kopečná,V.,Schwarz,J.";;"4";"1";"3";"4";"2";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"ies" +"1003";"JEB105";"Statistics";"Červinka,M.";"Hanus,L.";"4";"5";"4";"3";"5";"4";"5";"5";"1";"5";"5";"5";"4";;;"ies" +"1004";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"3";"1";"3";"3";"1";"3";"4";"1";"3";"1";"2";"2";"3";;;"ies" +"1005";"NMMA703";"Matematika 3";"Zelený,M.";"Turčinová,H.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"1006";"JPB020";"Úvod do mezinárodních vztahů";"Karlas,J.,Kučera,T.";;"3";"3";"4";"4";"1";NULL;NULL;NULL;"1";"4";"1";"4";"4";;;"kmv" +"1007";"JPB225";"Československý politický systém I";"Gelnarová,J.";;NULL;NULL;"4";"4";"3";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kp" +"1008";"JPB242";"Geografie vnitropolitických konfliktů";;"Doboš,B.,Riegl,M.";NULL;NULL;NULL;NULL;NULL;"3";"3";"3";NULL;NULL;NULL;NULL;NULL;;;"kp" +"1009";"JPB258";"Politické myšlení novověku I";"Franěk,J.,Kučera,J.";;NULL;NULL;"2";"2";"1";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kp" +"1010";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;NULL;NULL;"3";"4";"5";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kp" +"1011";"JPB268";"Evropská integrace";"Plechanovová,B.";;NULL;NULL;"1";"1";"1";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kmv" +"1012";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;NULL;NULL;"3";"3";"4";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kp" +"1013";"JPB594";"Realism in International Relations";"Odintsov,N.";;NULL;NULL;"4";"4";"2";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kmv" +"1014";"JPB227";"Politický system ČR";"Charvát,J.";;"4";"3";"5";"5";"3";NULL;NULL;NULL;"2";"4";"2";"4";"4";;;"kp" +"1015";"JPM574";"Moderní strany a stranické systémy v Evropě";"Brunclík,M.";;"5";"3";"5";"5";"3";NULL;NULL;NULL;"3";"4";"3";"5";"5";;;"kp" +"1016";"JLB047";"Ruština obecná I";;"Mistrová,V.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";NULL;"5";;;"cjp" +"1017";"JMB065";"Úvod do mezinárodního a evropského práva";"Šlosarčík,I.";;"2";"3";"1";"3";"1";NULL;NULL;NULL;"3";"3";"3";"4";"3";;;"kzs" +"1018";"JPB268";"Evropská integrace";"Plechanovová,B.";;"1";"5";"1";"1";"1";NULL;NULL;NULL;"3";"1";"1";"1";"1";;"Přístup vyučujícího k výuce i studentům. Průběh zkouškového období je ze strany vyučujícího naprosto katastrofální. Pominu-li nedostačující počet a kapacitu vypsaných termínů, je naprosto nepřípustné, aby vyučující daného předmětu během zkouškového období \"přehazoval\" termíny a zapsané studenty dle svého vlastního uvážení. I proto jsem po loňské zkušenosti uvedl v otázce obtížnosti kurzu \"velká\". Zdá se, že kurz je hodnocen a probíhá zcela subjektivně dle aktuální nálady vyučujícího, tudíž je otázkou, zda-li právě v daný moment má student větší či menší šanci uspět, nehledě na jeho znalosti.";"kmv" +"1019";"JPB589";"Seminář k politickému myšlení: 19. století";;"Novotný,J.";"4";"3";NULL;NULL;NULL;"4";"5";"4";"1";"5";"4";"5";"5";;;"kp" +"1020";"JPB260";"Politické myšlení novověku III";"Kučera,J.";;"4";"5";"3";"3";"4";NULL;NULL;NULL;"1";"4";"2";"2";"3";"Osnovu. Jedná se o část věnující se dle mého názoru nejdůležitější etapě politického myšlení a která pobízí ke smýšlení nad rámec státem předurčených demokratických hranic";"Omezit množství odboček, které s tématem nemají až tolik společného. Zajímavostí si samozřejmě vážím a zvýrazňují všeobecný rozhled vyučujícího, které mohu jen obdivovat, ale jejich poměr vůči později zkoušenému učivu je znepokojivý";"kp" +"1021";"JMB208";"Dějiny státu a práva v německy mluvících zemích";"Mlsna,P.";;"4";"3";"4";"2";"3";NULL;NULL;NULL;"5";"3";"1";"3";"3";;;"knrs" +"1022";"JEM032";"Banking";"Mejstřík,M.,Pečená,M.,Teplý,P.";"Pečená,M.";"3";"4";"3";"3";"2";"3";"3";"2";"2";"3";"3";"3";"3";;"Prezentace, spousta překlepů a špatná grafika.";"ies" +"1023";"JMBZ202";"Praktikum/Praktický projekt/Praxe";;;"5";"5";NULL;NULL;NULL;"5";"4";"5";"1";"5";"5";"3";"5";;;"knrs" +"1024";"JSM647";"Manažerské metody ve veřejné a sociální politice";"Ochrana,F.";;NULL;NULL;"5";"5";"4";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kvsp" +"1025";"JPM579";"Teorie politických stran";"Perottino,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"3";"5";"5";;;"kp" +"1026";"JSB025";"Sociální problémy";"Frič,P.";;"5";"3";"4";"5";"5";NULL;NULL;NULL;"2";"5";"3";"4";"5";;"Nic mě nenapadá, kurz mi vyhovoval";"kvsp" +"1027";"JEM137";"Real Estate Investment";"Jandík,T.,Streblov,P.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"2";"4";"4";"4";"5";"Vysokou kvalitu vyučujících přímo z oboru";;"ies" +"1028";"JSB998";"Úvod do sociologie";"Soukup,P.";;NULL;NULL;"4";"5";"1";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"ks" +"1029";"JMBZ290";"Konversatorium zu den aktuellen Fragen";;"Renner,T.";NULL;NULL;NULL;NULL;NULL;"2";"5";"4";NULL;NULL;NULL;NULL;NULL;;;"knrs" +"1030";"JPM639";"Problémy ústavního inženýrství";"Brunclík,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"3";"5";"5";"5";"5";;;"kp" +"1031";"JMBZ289";"Central European Culture from the 19th Century to 1945";"Emler,D.";;NULL;NULL;"4";"5";"2";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"knrs" +"1032";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"3";"3";"5";"1";"5";"1";"1";"5";;;"kz" +"1033";"JMBZ200";"Bakalářský seminář pro česko-německá studia I.";;"Nigrin,T.";NULL;NULL;NULL;NULL;NULL;"4";"1";"5";NULL;NULL;NULL;NULL;NULL;;;"knrs" +"1034";"JMBZ264";"Seminar zu den aktuellen Fragen";;"Renner,T.";NULL;NULL;NULL;NULL;NULL;"2";"5";"4";NULL;NULL;NULL;NULL;NULL;;;"knrs" +"1035";"JPB268";"Evropská integrace";"Plechanovová,B.";;"2";"4";"1";"3";"4";NULL;NULL;NULL;"3";"2";"1";"2";"1";"Obsah vyučované látky";"Způsob přednášení; systém oprav rešerší a přesná definice jejich kritérií; Zvýšení kapacity pro zkouškové termíny, jejichž množství resultuje v nemožnosti účasti na 3 termínech";"kmv" +"1036";"JPM641";"Světový regionalismus";"Riegl,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"4";"5";;;"kp" +"1037";"JPB553";"Elective Seminar: Neoliberalism";"Franěk,J.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"2";"4";"4";;;"kp" +"1038";"JJB334";"Zábava v médiích";"Kruml,M.";;"5";"1";"5";"5";"4";NULL;NULL;NULL;"1";"4";"1";"4";"5";;;"kms" +"1039";"JEM034";"Corporate Finance";"Bychkova,O.,Malířová,T.";;"3";"3";"3";"4";"2";NULL;NULL;NULL;"1";"3";"2";"3";"3";"High value of homeworks";"Homeworks should be more balanced, their time consumption was quite high, however it was made up by their high share on final grade";"ies" +"1040";"JLB035";"Francouzština I";;"Bosáková,L.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"1041";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"4";"2";"4";"3";"4";NULL;NULL;NULL;"1";"4";"4";"5";"4";;;"kp" +"1042";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"4";"3";"2";"2";"1";NULL;NULL;NULL;"1";"4";"1";"4";"4";"V přednáškách je řečeno vše podstatné. Zápisy jsou k dostání online.";;"ies" +"1043";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"4";"4";"5";"4";"4";"3";"3";"2";"2";"4";"4";"4";"5";;;"ies" +"1044";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"2";"5";"3";"3";"5";NULL;NULL;NULL;"1";"5";"2";"2";"2";;"Není jasné, jaké podmínky mají pedagogové k úspěšnému složení kurzu.";"kp" +"1045";"JLB053";"Angličtina pro sociální vědy I";;"Gloverová,M.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"4";;;"cjp" +"1046";"JPB597";"Current Political Extremism";"Charvát,J.";;"5";"1";"4";"5";"5";NULL;NULL;NULL;"2";"4";"3";"3";"5";"Samotný obsah výuky. Jednalo se o extrémně zajímavou přednášku podanou bez nějakých zbytečných předsudků, doprovozenou o relevantní zajímavosti a rozšiřující rozhled mimo obvyklé tematické okruhy";"Počáteční sekci předmětu. Jednalo se zpravidla o informace, které studenti často již znají a bývají součástí skoro všech přednášek vyučujícího. Domnívám se, že bez nich by výuka mohla obsahovat mnohem více zajímavostí a informací, které studenty obohatí";"kp" +"1047";"JSB012";"Úvod do empirického výzkumu ve společenských vědách";"Jeřábek,H.";"Přibáňová,T.";"5";"3";"4";"5";"5";"4";"5";"5";"1";"5";"5";"4";"5";"Rozdělení na cvičení a přednášky.";;"ks" +"1048";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"ies" +"1049";"JSB028";"Informační gramotnost";"Tomandlová,V.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"3";"5";;;"kvsp" +"1050";"JJB006";"Současný český jazyk III";;"Svobodová,I.";"4";"3";NULL;NULL;NULL;"3";"3";"4";"3";"5";"5";"5";"4";;;"kms" +"1051";"JPB256";"Politické systémy I";"Brunclík,M.,Jeřábek,M.,Kotábová,V.,Perottino,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"5";"5";;;"kp" +"1052";"JPM721";"Komparace ekonomik zemí EU";"Kučerová,I.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"2";"5";"5";;;"kmv" +"1053";"JJB014";"Žurnalistická tvorba III - Časopisecká tvorba";"Osvaldová,B.";"Maňák,V.";"3";"3";"5";"5";"3";"5";"5";"4";"2";"3";"4";"4";"4";;;"kz" +"1054";"JSB513";"Úvod do akademické práce";"Höfer,K.,Mouralová,M.,Veselý,A.";;"3";"3";"3";"4";"2";NULL;NULL;NULL;"1";"1";"2";"2";"3";;"Kurz mi připadá zbytečný, většina informací je studentům známá již ze středních škol. Závěrečná skupinová práce je zbytečná.";"kvsp" +"1055";"JPM689";"Conflict Studies";"Karásek,T.";;"5";"3";"5";"5";"3";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kbs" +"1056";"JPB592";"US Government and Politics";"Kotábová,V.";;"3";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;"Změnit způsob vypracování mandatorních \"referátů\" na novinové články. Nutnost vybírat především ze zdrojů jako washingtonpost nebo nytimes vede k nutnosti užívat v podstatě placené zdroje, neboť po určitém množství otevřených článků nemůže již student zdroj použít. Tak se práce stává jen otrockým zpracováním článků, které student otevře jako první. Změna oficiálních zdrojů je domnívám se na místě";"kp" +"1057";"JPM429";"Global terrorism (CS)";;"Makariusová,R.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"1";"3";"2";"4";"5";;;"kmv" +"1058";"JJB019";"Práce s agenturními informacemi";"Prázová,I.,Trunečková,L.";"Prázová,I.,Trunečková,L.";"3";"2";"3";"3";"2";"3";"3";"2";"1";"2";"3";"3";"2";;;"kz" +"1059";"JSB998";"Úvod do sociologie";"Soukup,P.";;"4";"2";"5";"5";"4";NULL;NULL;NULL;"1";"3";"2";"4";"4";;;"ks" +"1060";"JLB005";"Angličtina pro politology I";;"Stružková,I.";"4";"4";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"5";"4";;;"cjp" +"1061";"JJB021";"Bakalářský seminář";;"Prázová,I.";"2";"1";NULL;NULL;NULL;"3";"3";"1";"1";"2";"2";"2";"3";;;"kz" +"1062";"JLB053";"Angličtina pro sociální vědy I";;"Gloverová,M.";"4";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"3";"4";;;"cjp" +"1063";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"3";"4";"3";"4";"4";NULL;NULL;NULL;"3";"5";"4";"5";"3";;;"kp" +"1064";"JJB143";"Žurnalistika a feminismus";"Krobová,T.,Osvaldová,B.";;"4";"2";"5";"5";"4";NULL;NULL;NULL;"2";"4";"3";"4";"5";;;"kz" +"1065";"JPB228";"Mírové smlouvy a konference v mez. systému";"Jeřábek,M.";;"5";"3";"4";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmv" +"1066";"JLB009";"Angličtina pro žurnalisty I";;"Prošková,A.";"3";"2";NULL;NULL;NULL;"4";"5";"3";"1";"4";"4";"4";"4";;;"cjp" +"1067";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"1";"4";"2";"1";"5";"Výběr filmů je skvělý, každý si najde film ze žánru, který ho zajímá.";;"kz" +"1068";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"3";"5";"4";"5";"4";"4";"4";"4";"1";"5";"5";"5";"3";;;"kp" +"1069";"JPB258";"Politické myšlení novověku I";"Franěk,J.,Kučera,J.";;NULL;"5";"4";"5";"4";NULL;NULL;NULL;"1";"3";"3";"2";"3";;;"kp" +"1070";"JPB589";"Seminář k politickému myšlení: 19. století";;"Novotný,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"3";"5";"5";"Iniciaci k předmětu relevantní diskuzi, je velmi osvěžující na FSV opravdu participovat při výuce a ne jen absorbovat informace";"Samotný poměr diskuze a vyučované látky. Ač je diskuze osvěžující, vzhledem k menšímu množství informací poskytnutých docentem Kučerou, i trochu přednášení by bylo na místě";"kp" +"1071";"JPB596";"Čínská zahraniční a bezpečnostní politika";"Karmazin,A.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kbs" +"1072";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"2";"5";"3";"2";"4";NULL;NULL;NULL;"1";"3";"3";"3";"3";;;"kp" +"1073";"JMMZ149";"EU Institutions";"Šlosarčík,I.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"4";"The teacher could explain the topic by using a lot of own examples, which helped a lot to understand it. The subject was really interesting";"The structure of the course was a bit confusing. To learn for the exam it would have helped to have some material (besides the two books) with the most important information we need to know. Besides, it would have been helpful to get more information about the exam (e.g. example questions etc.).";"kzs" +"1074";"JPM909";"Rousseau and Nationalism: On the Government of Poland";;"Franěk,J.,Kelly,C.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"5";"5";"The host lecturer, expert on Rousseau, was obviously very educated on the topic, yet able to present the issue so that us, not so informed on said issue, woukd understand it. I would love this program of host lecturers to continue";"Absolutely Nothing";"kp" +"1075";"JPB263";"Bakalářský seminář II.";;"Brunclík,M.,Bureš,O.,Ditrych,O.,Franěk,J.,Gelnarová,J.,Hynek,N.,Charvát,J.,Jeřábek,M.,Jüptner,P.,Karásek,T.,Karlas,J.,Knutelská,V.,Kofroň,J.,Kotábová,V.,Krausz Hladká,M.,Kubátová,H.,Kučera,J.,Kučerová,I.,Landovský,J.,Ludvík,J.,Makariusová,R.,Mlejnek,J.,Pa";"4";"3";NULL;NULL;NULL;"5";"4";"3";"1";"4";"4";"4";"4";;;"kp" +"1076";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"4";"5";"The teacher has a really positive attitude and motivated the students with it. Also, he explained everything very well and he provided a lot of material to learn with. The learning-videos for the programm R were particularly helpful.";"I didn't like, that the homework was so important for the grade. Even with the best grades in midterm and final it's not possible to get a really good grade over all, if someone just forgot the homework a few times.";"kmv" +"1077";"JLB027";"Ruština odborná I - vyšší";;"Mistrová,V.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"5";"5";;;"cjp" +"1078";"JLB033";"Němčina I";;"Faltýnová,R.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"4";"5";;;"cjp" +"1079";"JMB402";"Úvod do společenských věd II";;"Juhás,T.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"5";"5";;;"krvs" +"1080";"JMB078";"Seminář k současné hovorové ruštině";;"Smirnova,T.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"3";"5";"5";;;"krvs" +"1081";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"knrs" +"1082";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"2";"5";"3";"2";"2";NULL;NULL;NULL;"2";"4";"2";"4";"3";;;"kas" +"1083";"JMB248";"Seminář k dějinám Ruska";;"Kolenovská,D.";"5";"4";NULL;NULL;NULL;"4";"5";"5";"2";"5";"5";"5";"5";"Skvělý přístup vyučující (tj. paní doktorky Kolenovské), hodiny byly zajímavé a poutavé. Seminárním pracím (tj. písemným podobám referátů) byla věnována velká pozornost a studenti dostali cenné rady ohledně psaní textů odborného charakteru.";"Hodiny byly občas lehce chaotické, postrádající nějakou přesnou strukturu. Poměrně často docházelo k pozdnímu zadávání četby a bylo tak velice náročné přečíst poměrně dlouhé texty za velice krátkou dobu (samozřejmě s přihlédnutím na další studijní povinnosti, které si již studenti většinou nějak rozvrhli a četbu pak bylo poměrně složité zakomponovat do \"rozvrhu\").";"krvs" +"1084";"JJB606";"Televize jako instituce";"Štoll,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"4";"5";;;"kms" +"1085";"JJB334";"Zábava v médiích";"Kruml,M.";;"4";"1";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kms" +"1086";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"5";"4";"5";"4";"5";NULL;NULL;NULL;"1";"5";"3";"3";"3";;;"krvs" +"1087";"JJB618";"Propagandistické působení médií v 1 polovině 20. století";"Suk,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";;;"kms" +"1088";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Andrle,J.";"5";"5";"5";"5";"5";"4";"4";"5";"1";"5";"2";"5";"5";;;"krvs" +"1089";"JMM047";"Právní a institucionální rámec evropské integrace.";"Šlosarčík,I.";;"4";"1";"5";"5";"2";NULL;NULL;NULL;"2";"3";"3";"4";"4";;;"kzs" +"1090";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Šafařík,P.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"4";"5";"5";;;"knrs" +"1091";"JMB414";"Seminář k aktualitám I";;"Young,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Very nicely structured and prepared lessons. The approach of the lecturer was amazing and very friendly. It was very good that all the students had the opportunity to experience all the different positions (presenter, opponent etc.), had the chance to learn how to asses oral presentations of others etc. Great discussions about recent events and issues.";"Some of the students did not prepare their parts of the lessons very well or did not understand the assignment properly so I would maybe suggest a more attentive control of the preparation.";"krvs" +"1092";"JJB334";"Zábava v médiích";"Kruml,M.";;"4";"2";"4";"4";"4";NULL;NULL;NULL;"1";"4";"3";"3";"4";;;"kms" +"1093";"JMB197";"Kapitoly z moderních dějin Itálie";"Mejstřík,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"3";"4";"3";"5";"5";"Skvěle připravené hodiny plné zajímavých informací a souvislostí. Dobrá opora v prezentaci. Příjemné vystupování vyučujícího, snaha zodpovědět všechny otázky apod.";"Jelikož se kurz lehce opozdil za plánem, zadávané úkoly byly často předmětem až následující hodiny, a tudíž bylo někdy velice obtížné najít správné odpovědi. Doporučil bych upravovat obsah úkolů vždy v závislosti na průběh kurzu. Na druhou stranu na to bral vyučující ohled a otázky, které byly těžko k dohledání vždy následující hodinu objasňoval.";"kzs" +"1094";"JMB408";"Moderní dějiny Ruska";"Litera,B.,Novák,P.,Pečenka,M.";;"4";"5";"4";"3";"5";NULL;NULL;NULL;"2";"4";"3";"4";"4";"Obsáhlé a zajímavé přednášky (zejm. v případě pana dr. Pečenky) plné doplňujících souvislostí.";"Bylo vidět, že přednášející lehce nestíhají, někdy tendence velké úseky zkrátit a stihnout toho co nejvíce. Navrhl bych zrychlení spíše v počátečních, a ne tak důležitých, částech látky, aby zbylo více času na zásadní obsah kurzu, který je následně předmětem zkoušky. Některé hodiny (zejm. pana doktora Litery) postrádaly nějakou přesnější strukturu.";"krvs" +"1095";"JSM406";"Statistics in SPSS";;"Soukup,P.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"4";;;"ks" +"1096";"JPM611";"Cyber Security";"Duračinská,Z.,Střítecký,V.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"2";"4";"3";"2";"4";"Very knowledgeable and friendly teacher. Obviously very skilled in her field. Good to learn some of the tech background.";"Lacking a wider understanding of cyber security in the broader security and international relations context. More could have been included about cyber security strategies and cyber warfare in a global context rather than individual security.";"kbs" +"1097";"JSM514";"Metody a techniky práce s informacemi";"Tomandlová,V.";;"4";"2";"4";"5";"4";NULL;NULL;NULL;"1";"4";"4";"3";"5";;"dát ho povinně všem prvním ročníkům";"kvsp" +"1098";"JSM516";"Sociální politika v perspektivě životního cyklu";"Dobiášová,K.,Kotrusová,M.";;"4";"3";"4";"4";"5";NULL;NULL;NULL;"1";"5";"3";"4";"5";;;"kvsp" +"1099";"JSM518";"Public Policy";"Potůček,M.,Vlčková,K.";;"2";"5";"3";"4";"2";NULL;NULL;NULL;"2";"3";"3";"4";"4";"semináře";"zbytečně složitý systém - špatně transformovatelné na jiné počty studentů, kurz je napůl v ČJ a napůl v AJ - zadání testu v AJ, ale vypracování v ČJ mi přijde nevhodné";"kvsp" +"1100";"JJM240";"Cultural studies";"Soukup,M.";;"3";"2";"4";"5";"4";NULL;NULL;NULL;"2";"4";"2";"3";"2";"The originality of the topics the teacher chose.";"Explain the topics more deeply";"kms" +"1101";"JEB044";"Financial Accounting";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"4";"5";"Přednášky jsou skvělé, k tomu není potřeba nic dodat.V seminářích jsou velmi zajímavé společné diskuse nad reálnými firmami a snaha o jejich komplexnější zhodnocení. (zejména Petra se tomu dokázala patřičně věnovat a vzbudit zájem)";"V seminářích ty méně zajímavé příklady jako strojové počítání dle vzorců, či přílišné zaměření na probádání jedné oblasti dané firmy bez komplexnějšího komentáře o jejím stavu.";"ies" +"1102";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"1";"3";"4";"4";"5";"Vyučujícího.";;"ies" +"1103";"JJB066";"Rozhlas a televize ve světě";"Moravec,V.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kz" +"1104";"NMMA701";"Matematika 1";"Spurný,J.";"Rondoš,J.";"4";"5";"5";"5";"4";"3";"4";"4";"1";"5";"4";"3";"4";"Pana profesora Spurného.Velmi děkujeme.";;"ies" +"1105";"JJB067";"Mluvní a pohybová výchova I";;"Pavel,L.";"3";"2";NULL;NULL;NULL;"3";"3";"3";"1";"3";"4";"2";"3";;;"kz" +"1106";"JJB069";"Tvůrčí dílny I - televizní";"Lokšík,M.";;"5";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"5";"4";"5";;;"kz" +"1107";"JJB071";"Tvůrčí dílny I - rozhlasové";"Maršík,J.";"Lovaš,K.,Lucký,J.";"4";"4";"3";"3";"4";"3";"3";"4";"2";"3";"5";"4";"4";;;"kz" +"1108";"JPM052";"Mezinárodní ekonomické vztahy (MEV)";"Kučerová,I.";;"3";"3";"5";"5";"5";NULL;NULL;NULL;"1";"3";"2";"2";"4";;;"kmv" +"1109";"JPM260";"Vybrané problémy britské zahraniční politiky v 19. a 20. století, ES";"Soukup,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"kmv" +"1110";"JPM430";"Marxism in International Relations (TIR)";;"Střítecký,V.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"4";"4";;;"kmv" +"1111";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"4";"5";;;"kmv" +"1112";"JJB083";"Editování zpravodajských relací";"Beneš,P.";;"3";"1";"4";"5";"4";NULL;NULL;NULL;"2";"3";"3";"2";"3";;;"kz" +"1113";"JPM689";"Conflict Studies";"Karásek,T.";;"4";"5";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kbs" +"1114";"JLB009";"Angličtina pro žurnalisty I";;"Prošková,A.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"3";"5";;;"cjp" +"1115";"JPM725";"Technology and Security: Contemporary Warfare in the 21st Century";;"Csernatoni,R.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kmv" +"1116";"JMM350";"Hospodářský a sociální systém NMZ I";"Mlsna,P.";;"3";"3";"3";"3";"3";NULL;NULL;NULL;"4";"4";"3";"4";"4";;;"knrs" +"1117";"JLB001";"Angličtina pro sociology I";;"Klírová,M.";"5";"3";NULL;NULL;NULL;"5";"4";"4";"1";"4";"5";"5";"5";"Oceňuji organizaci kurzu a výběr znalostí a dovedností, kterým jsme se naučili. Velice oceňuji organizaci prezentace, kterou jsme museli plánovat po malých krocích v rámci domácích úkolů.";;"cjp" +"1118";"JMM718";"Polština I";;"Sitarz,K.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";;;"knrs" +"1119";"JLB041";"Španělština I";;"Mlýnková,L.";"4";"4";NULL;NULL;NULL;"4";"4";"5";"1";"4";"4";"2";"3";;;"cjp" +"1120";"JSB031";"Bakalářský seminář I - diskuse projektů";;"Angelovská,O.,Mouralová,M.";"5";"3";NULL;NULL;NULL;"4";"5";"4";"2";"4";"4";NULL;"5";"Shrnutí všech dosavadních dovedností, které byly podstatné k psaní bakalářské práce.";"Práce s Moodlem, některé slíbené soubory nebyly sdíleny.";"ks" +"1121";"JSB131";"Velké empirické výzkumy ČR";"Tuček,M.";;"4";"3";"5";"4";"2";NULL;NULL;NULL;"2";"3";"3";"3";"3";;;"ks" +"1122";"JSB133";"Zemědělství a rozvoj venkova (vybraná témata z rurální sociologie)";"Zagata,L.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"4";"5";"Kurz byl výborný, pan Zagata velmi příjemný. Oceňuji celkovou organizaci kurzu, občasné příspěvky k tématu v rámci videa. Vybrané témata byla zajímavá.";;"ks" +"1123";"JLB100";"Czech as a Foreign Language I";;"Mazúrková,B.";"5";"3";NULL;NULL;NULL;"4";"4";"4";"1";"4";"3";"3";"4";;;"cjp" +"1124";"JEM062";"Introductory Econometrics";"Kukačka,J.";"Kukačka,J.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"The methodology used. It was excellent for beginners .";"Inclusion of more sample tests.";"ies" +"1125";"JJM080";"Zábava v televizním vysílání";"Kruml,M.";;"4";"1";"5";"4";"3";NULL;NULL;NULL;"3";"3";"1";"4";"5";;;"kms" +"1126";"JMM048";"European Union in International Affairs";"Weiss,T.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kzs" +"1127";"JEM032";"Banking";"Mejstřík,M.,Pečená,M.,Teplý,P.";"Pečená,M.";"4";"3";"4";"4";"3";"3";"4";"3";"1";"4";"4";"4";"3";"The practical calculations.";"Improve the visualization of the slides.";"ies" +"1128";"JMMZ226";"Historical Roots of European Integration";"Kasáková,Z.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"2";"4";"4";"4";"4";;;"kzs" +"1129";"JJM201";"Teorie masové a mediální komunikace";"Jirák,J.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"3";"5";"4";"5";"5";;;"kms" +"1130";"JSB517";"Hudební subkultury mládeže";"Oravcová,A.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"3";"4";"Oceňuji debaty v rámci novinek v hudebním světě. Paní Oravcová je odborník ve svém oboru a řekla nám hodně zajímavých věcí, jak pro studenty zainteresované v hudbě, tak i pro ty hudbou nepolíbené.";;"ks" +"1131";"JEM037";"Financial Markets";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"5";"4";"5";"5";"4";"3";"3";"3";"1";"4";"4";"4";"4";"The empirical facts to every topic.";"More focus on the seminar papers.";"ies" +"1132";"JJM202";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"3";"5";"5";"3";"2";NULL;NULL;NULL;"1";"5";"3";"5";"4";;;"kms" +"1133";"JJM204";"Výzkum médií I";"Křeček,J.";;"3";"2";"3";"4";"2";NULL;NULL;NULL;"3";"1";"5";"2";"3";;;"kms" +"1134";"JEM003";"Advanced Microeconomics";"Janda,K.,Melikhova,O.";"Janda,K.,Melikhova,O.";"4";"4";"3";"5";"3";"5";"5";"5";"1";"4";"4";"4";"4";"The seminars were really informative.";"The lecturers involvement with the students should be bigger.";"ies" +"1135";"JJM214";"Čtení textů ke studiu médií - populární kultura";;"Reifová,I.";"3";"4";NULL;NULL;NULL;"2";"5";"2";"2";"1";"2";"1";"2";;;"kms" +"1136";"JEM004";"Advanced Macroeconomics";"Baxa,J.,Hromádková,E.";"Baxa,J.,Hromádková,E.,Šestořád,T.,Troch,T.,Žáček,J.";"5";"4";"4";"4";"4";"4";"4";"4";"2";"4";"4";"4";"4";"The use of Gretl for assigments.";"Theoretical seminar solutions as well as homework solutions would have been helpful.";"ies" +"1137";"JJM221";"Mediální gramotnost a mediální výchova";;"Wolák,R.";"3";"1";NULL;NULL;NULL;"5";"5";"1";"4";"2";"2";"2";"4";;;"kms" +"1138";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";NULL;"5";"4";"4";"4";"3";"3";"3";NULL;"2";"5";"5";"5";"The lecturers teaching method.";"More practical exercises and involvement of students in the seminars.";"ies" +"1139";"JJM224";"Politická ekonomie komunikace";"Vochocová,L.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"3";"5";"5";;;"kms" +"1140";"JLB005";"Angličtina pro politology I";;"Stružková,I.";"4";"4";NULL;NULL;NULL;"4";"5";"3";"1";"3";"3";"4";"3";;;"cjp" +"1141";"JJM231";"Mediální výchova v rodině";;"Šťastná,L.";"2";"5";NULL;NULL;NULL;"4";"4";"2";"1";"1";"1";"1";"2";;;"kms" +"1142";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kp" +"1143";"JJM343";"Interkulturní komunikace";"Soukup,M.";;"5";"3";"5";"4";"5";NULL;NULL;NULL;"2";"4";"5";"5";"5";;;"kms" +"1144";"JPB221";"Metodologický proseminář I";;"Bahenský,V.,Kofroň,J.";"3";"4";NULL;NULL;NULL;"4";"4";"3";"1";"4";"4";"3";"3";;;"kmv" +"1145";"JEM034";"Corporate Finance";"Bychkova,O.,Malířová,T.";;"2";"3";"3";"3";"1";NULL;NULL;NULL;"2";"2";"3";"2";"1";;"At IES, there are so many courses similar to this one. It definitely should not be mandatory.Give us more diversity, please!";"ies" +"1146";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"3";"4";"4";"4";"3";NULL;NULL;NULL;"2";"4";"3";"3";"3";;;"kp" +"1147";"JPB258";"Politické myšlení novověku I";"Franěk,J.,Kučera,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kp" +"1148";"JPB020";"Úvod do mezinárodních vztahů";"Karlas,J.,Kučera,T.";;"1";"5";"2";"3";"1";NULL;NULL;NULL;"1";"1";"1";"1";"1";;;"kmv" +"1149";"JPB252";"České novověké dějiny I.";"Kučera,J.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kp" +"1150";"JPB018";"Dějiny mezinárodních vztahů I";"Soukup,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"3";"5";"5";"5";"5";;;"kmv" +"1151";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"5";"5";;;"kz" +"1152";"JLB047";"Ruština obecná I";;"Mistrová,V.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Úžasná paní profesorka!! Takhle si představuji vysokoškolské studium.";;"cjp" +"1153";"JLB053";"Angličtina pro sociální vědy I";;"Štěpánková,D.";"3";"3";NULL;NULL;NULL;"4";"3";"4";"1";"4";"4";"3";"4";"Oceňuji snahu, naučit studenty více odborných slovíček, které poté využijí v dalším studiu.";"Více bych zařadila do výuky také opakování gramatiky. Chybělo mi tak odborné vedení a vysvětlení obtížnější gramatiky, kterou jsem doma jenom horko těžko doháněla. Myslím si, že když je to přípravný kurz, tak by se zde měla probírat i gramatika a ne jí nechat pouze na domácí přípravě studenta.";"cjp" +"1154";"JPM710";"Radicalization and Deradicalization";"Aslan,E.";;"4";"3";"4";"3";"3";NULL;NULL;NULL;"3";"4";"4";"4";"4";"The reading materials were interesting and engaging. Enjoyed the structure of the course. Teacher obviously an expert in his field.";"Classes hosted by students meant it was difficult to engage in classed. Would have preferred more teacher taught content, considering that the students presentations were basically the assigned readings for that week which we had already read. Also the public grading in front of peers immediately after presentations meant that pressure was massively increased for a simple task.";"kbs" +"1155";"JSB010";"Současná sociologie";"Balon,J.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"3";"5";"4";"5";"5";"Oceňuji to, že jsme byli v průběhu semestru nuceni přečíst jedno sociologické dílo, na které jsme následně měli napsat anotaci a recenzi.";;"ks" +"1156";"JLB041";"Španělština I";;"Mlýnková,L.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"3";"5";"Vstřícný přístup ke studentům, milé možnosti jak získat nějaké body navíc (testíky, úkoly)";;"cjp" +"1157";"JSB023";"Praktika z kvantitativního výzkumu I";;"Tuček,M.";"3";"3";NULL;NULL;NULL;"4";"3";"3";"3";"3";"4";"3";"4";"Oceňuji to, že je to konečně předmět, kde si prakticky vyzkoušíme sestavit dotazník, reálně sebrat data a také je analyzovat.";"Navrhuji zlepšit komunikaci mezi profesorem a studenty. Občas jsme nechápali, proč jsou naše názory špatné a proč vše musí být podle autority.";"ks" +"1158";"JJB148";"Audiovizual Interpreting the Reality";"Štoll,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"5";"4";"3";"5";"4";;;"kz" +"1159";"JSB033";"Praktika z kvalitativního výzkumu";;"Spalová,B.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"4";"5";"Oceňuji téma a postavení kurzu, který vedla profesorka Spalová. Kurz byl založen na osobní autobiografii a bylo příjemné si výzkum vyzkoušet na sobě a na svých spolužácích. Velice to utužilo kolektiv a vyzkoušeli jsme si spoustu zajímavých metod dotazování a analýzy sebraných dat a materiálů.";;"ks" +"1160";"JLM001";"Academic English I";;"Cotte,P.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"4";"5";"Ms. Cotte is the best - she knows her stuff and she has the best attitude. The lessons are very pleasant, even for someone who is not very comfortable with public speaking.";"Maybe the structure of materials could be improved - sometimes it was a little confusing (many papers, unclear page numbers, etc.) - but that's just detail, overall the course was amazing.";"cjp" +"1161";"JSB054";"Výzkumný seminář";;"Hrešanová,E.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"5";"Oceňuji formu, jakou byl tento kurz veden. Hlavní náplní byla diskuze, která byla zpočátku pro většinu lidí nepříjemná a těžká, ale nakonec to naše dovednosti vybrousilo a zlepšilo, což vidím, jako největší přínos tohoto kurzu.";;"ks" +"1162";"JSB537";"Analýza dat v SPSS";"Soukup,P.";"Oreský,J.";"3";"4";"4";"4";"4";"5";"5";"5";"4";"4";"4";"4";"5";"Oceňuji průběžné testy, které nás donutily se průběžně připravovat a učit. A nejvíce oceňuji houževnatost cvičících, kteří nám ve svém volném čase byli ochotni organizovat doučování, abychom dohnali to, co jsme nestihli nebo nepochopili.";"Letos byl tento kurz absolutně na nic. První měsíc semestru se tento kurz vůbec nevyučoval a když už začal, tak často cvičení i přednášky odpadaly, takže jsme samozřejmě vůbec ani zdaleka nestihli to, co jsme měli probrat. Kdyby to alespoň nebyl státnicový předmět, tak bych to ještě skousla, ale pokud máme jít za pár dní ke státnicím a přitom jsme polovinu učiva neměli odprezentovanou, tak si myslím, že se někde stala ohromná chyba, která by se měla napravit nebo ke které by se alespoň mělo přihlédnout.";"ks" +"1163";"JSB311";"Antropologie náboženství";"Spalová,B.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"4";"5";"Oceňuji průběžné úkoly, které nám pomáhaly pochopit probíranou látku.";;"ks" +"1164";"JSB055";"Současná sociální antropologie";;"Grygar,J.,Hrešanová,E.";"4";"5";NULL;NULL;NULL;"5";"4";"5";"1";"5";"4";"5";"5";;"Navrhuji se vrátit opět k průběžným úkolům na každou hodinu, které vycházejí z povinné literatury. Letos kurz proťaly 4 průběžné testy z povinné literatury, které byly hodně těžké a těžko se na ně připravovalo. Člověk když čte text, který má 40 stran a neví, co bude jeho profesor pokládat za nejdůležitější, tak je to velmi těžké. Myslím si, že by buď měly být texty kratší a výstižnější nebo by se mělo od těchto testů upustit a navrátit se zpět k úkolům.";"ks" +"1165";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"4";"2";"2";"1";NULL;NULL;NULL;"1";"2";"1";"2";"1";;"Nezajímavé monotónní přednášky, profesor mluví velmi potichu, podle mého názoru nespravedlivě ohodnocené testy, byla jsem naučená na všechny pokusy velmi dobře a zatím ani jednou neuspěla. Frustrace stoupá s postupným větším a větším rizikem vyhazovu ze školy kvůli předmětu, který pro můj studovaný obor ani není stěžejní.";"ies" +"1166";"JLB100";"Czech as a Foreign Language I";;"Frantesová,E.";"4";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";NULL;"5";"5";;;"cjp" +"1167";"JPM719";"Diplomacy of the European Union";"Pajtinka,E.";;"3";"4";"4";"4";"5";NULL;NULL;NULL;"5";"4";"2";"3";"4";"The system of one week of lessons is quite good, as it saves time in the rest of the semester.";"I did not understand why every day the lessons ended much (much!) sooner than it was scheduled in the system - not that I would complain, but it was unnecessary as it would be better to schedule SIS correctly. Furthermore, the \"surprise\" final exam during the last session could be announced in advance (if someone missed just a lesson or two, it was difficult to pass the test). And lastly, it should be declared that the content of the course is basically the same as in \"Teorie and praxe diplomacie\" - again, I am not complaining, but it would be nice to know it in advance.";"kmv" +"1168";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"4";"3";"3";"1";NULL;NULL;NULL;"1";"3";"3";"1";"2";;;"ies" +"1169";"JSB007";"Dějiny sociologie I";"Šanderová,J.";"Bureš,J.";"4";"2";"5";"5";"4";"4";"5";"5";"3";"4";"3";"3";"4";;;"ks" +"1170";"JSB025";"Sociální problémy";"Frič,P.";;"5";"3";"5";"4";"5";NULL;NULL;NULL;"2";"4";"5";"5";"5";;;"kvsp" +"1171";"JPM721";"Komparace ekonomik zemí EU";"Kučerová,I.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;"1";"5";"2";"3";"4";"Typický kurz paní Kučerové - lehce \"středoškolské\" přednášky, ale u tohoto tématu to není vůbec na škodu, jelikož jde primárně o pokrytí vývoje ekonomik dvaceti osmi zemí, což si žádá mnoho \"tvrdých\" informací a dat. Super jako doplněk ke státnicím z MEV, i jako samostatný kurz.";"Samotná komparace se sice párkrát objevila, ale spíš okrajově - bylo by fajn tomuto věnovat třeba celou jednu hodinu - a srovnávat při ní jednotlivé vývoje. V této podobě byl přesnější spíš původní název (Vývoj ekonomik ...) :)";"kmv" +"1172";"JEM062";"Introductory Econometrics";"Kukačka,J.";"Kukačka,J.";"1";"2";"5";"5";"5";"5";"5";"5";"1";"2";"5";"5";"5";"The way of teaching and the beginning slides with short recap of previous lectures.";"It was Perfect, but maybe more advanced topics could be added.";"ies" +"1173";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Rybín,F.,Vlčková,A.";"3";"4";"5";"5";"4";"3";"5";"3";"2";"4";"4";"4";"3";;;"ks" +"1174";"JSB530";"Úvod do sociologického myšlení";"Bureš,J.,Numerato,D.";"Moskvina,Y.";"2";"2";"4";"5";"2";"3";"3";"1";"3";"2";"1";"3";"2";;;"ks" +"1175";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Buřičová,B.";"3";"4";"3";"3";"2";"5";"5";"5";"3";"4";"4";"3";"3";;;"ks" +"1176";"JLB005";"Angličtina pro politology I";;"Panešová,K.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"1177";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"3";"3";"2";"3";"3";NULL;NULL;NULL;"2";"3";"2";"2";"3";;"- Unfortunately, it was not easy to follow the lecture, because the lecturers were not able to explain the theories freely very well or were reading from a ready-made script - Time: 8 am is too early";"kmv" +"1178";"JEM003";"Advanced Microeconomics";"Janda,K.,Melikhova,O.";"Janda,K.,Melikhova,O.";"4";"3";"5";"5";"4";"5";"5";"5";"1";"2";"3";"2";"3";"The seminars were much more interesting, as you get insight into the lecture materials. Also the Grading system was good.";;"ies" +"1179";"JPM526";"Justice and Reconciliation in Post-Conflict Societies";;"Werkman,K.";"4";"2";NULL;NULL;NULL;"4";"5";"4";"1";"4";"3";"3";"4";;;"kmv" +"1180";"JEM004";"Advanced Macroeconomics";"Baxa,J.,Hromádková,E.";"Baxa,J.,Hromádková,E.,Šestořád,T.,Troch,T.,Žáček,J.";"4";"4";"5";"5";"5";"5";"5";"5";"1";"4";"5";"4";"4";"The homework assignments were interesting";"The time allotted for midterm was not enough, as the questions were a lot.";"ies" +"1181";"JPM658";"International Economic Relations";"Parízek,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"- Range of topics covered in class- Class size and possibility to discuss certain topics";;"kmv" +"1182";"JEM032";"Banking";"Mejstřík,M.,Pečená,M.,Teplý,P.";"Pečená,M.";"5";"2";"5";"4";"4";"5";"5";"5";"1";"2";"4";"4";"5";"Seminars were interesting.";"More advanced topics can be covered.";"ies" +"1183";"JEM037";"Financial Markets";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"5";"3";"5";"5";"4";"5";"5";"3";"1";"3";"2";"3";"5";"The lectures were interesting and they covered a lot of important topics in finance.";"More focus on lectures and not on seminars.";"ies" +"1184";"JLB003";"Angličtina pro ekonomy I";;"Poslušná,L.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Využití různých aplikací a nových médií - např. rozšířená realita.";;"cjp" +"1185";"JMM079";"Hospodářský a sociální systém NMZ I";"Mlsna,P.";;"3";"5";"1";"1";"3";NULL;NULL;NULL;"4";"4";"1";"4";"2";;;"knrs" +"1186";"JMM083";"Deutsche und mitteleuropäische Geschichte im 20. Jh.";"Barth,B.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"5";"5";"5";;;"knrs" +"1187";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"5";"5";"4";"1";"2";"2";"1";"5";"Je to dobré takhle, jak to je.";"Je to dobré takhle, jak to je.";"kz" +"1188";"JMM279";"Společnost a ekonomika";"Fiřtová,M.";"Kačmárová,P.";"5";"5";"4";"4";"4";"3";"3";"4";"3";"5";"4";"5";"5";;;"kas" +"1189";"JMM081";"Zahraniční politika SRN I";"Handl,V.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"knrs" +"1190";"JSB007";"Dějiny sociologie I";"Šanderová,J.";"Sedláčková,T.";"4";"3";"5";"4";"2";"5";"5";"5";"1";"4";"4";"5";"4";;"Víc porovnávat jednotlivé sociology mezi sebou";"ks" +"1191";"JSB025";"Sociální problémy";"Frič,P.";;"4";"3";"5";"3";"4";NULL;NULL;NULL;"1";"5";"5";"5";"4";;;"kvsp" +"1192";"JSB028";"Informační gramotnost";"Tomandlová,V.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"4";"5";;;"kvsp" +"1193";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Coufalová,L.,Svobodová,T.";"5";"4";"5";"5";"3";"5";"5";"5";"1";"5";"5";"5";"5";;;"ks" +"1194";"JSB530";"Úvod do sociologického myšlení";"Bureš,J.,Numerato,D.";"Wirthová,J.";"3";"3";"5";"5";"2";"4";"5";"4";"2";"2";"2";"3";"2";;"Je to zbytečné, psát deníky o ničem.. s vysokou školou to nemá moc společného";"ks" +"1195";"JPM719";"Diplomacy of the European Union";"Pajtinka,E.";;"1";"2";"1";"2";"1";NULL;NULL;NULL;"4";"2";"1";"1";"1";;"- Teaching method: lecturer was only reading from the slides and was only making comments occasionally. - The slides should be provided to the students, without having them to ask for it several times, especially when the final exam is only based on them - There was no discussion about the topic, although there would have been plenty of time to do so- Teaching time: the class was interrupted by unnecessary breaks - On the last day, students were required to write an exam and a short paper during the actual lecture time, which was not communicated beforehand (What was the value of it?)- The final exam took place together with another class. While we were taking the exam on EU Diplomacy, the people from the other class and their lecturer were talking and laughing, which made it hard to concentrate (poor organization, unfair conditions)- Some of the questions in the final exam were confusing";"kmv" +"1196";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Buřičová,B.";"3";"4";"3";"1";"3";"5";"5";"5";"1";"3";"4";"2";"3";"Bibi byla nejlepší";"Přístup Hendla je zarážející, chápu že matiky k tomuto oboru jaksi patří, ale proč z ní pan Hendl dělá nejdůležitější předmět a tyranizuje na něm žáky, to opravdu nepochopím. Ústní zkouška mi připadá naprosto nesmyslná pokud žák projde testem. To že prošel, snad znamená, že látku zvládl a má splněno. Ale když projde a pak má ještě jít nechat deptat Hendlem, to je na hlavu. Systém 2x neudělá test, jde na ústní je podle mě správný.";"ks" +"1197";"JPM724";"Critical Approaches to International Politics and Security";;"Daniel,J.,Rychnovská,D.";"4";"3";NULL;NULL;NULL;"4";"5";"4";"1";"4";"4";"4";"4";;;"kmv" +"1198";"JPM690";"Liberalism in International Relations (TIR)";;"Karlas,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kmv" +"1199";"JMM339";"American National Security Policy";"Raška,F.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Professor Raska does a wonderful job of making the material engaging and encouraging students to participate as much as possible. It truly is a pleasure to learn from his experience and expertise.";;"kas" +"1200";"JPM099";"Baltic regional cooperation and Russia";"Zájedová,I.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"4";"This course does a wonderful job of introducing students to many perspectives of a complicated situation. It was truly a pleasure to learn from the different voices involved in the Baltic region.";"The course had a lot of information in it. It would be helpful to have a bit more structure in how to approach all of this knowledge.";"kmv" +"1201";"JPM689";"Conflict Studies";"Karásek,T.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"3";"Topics covered in the course";"There are too many (too long) readings, which is overwhelming. Especially because some of the readings are not liked to a certain lesson, but to many. This makes it hard to prepare for the lessons and the final exam. (Shorter) readings directly linked to specific lessons would be better.";"kbs" +"1202";"JPM191";"Geopolitics of Great Powers: Russia";"Baštář Leichtová,M.";;"5";"4";"4";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"This class was a great way to expand my knowledge in a subject that I had never truly explored before. It was full of interesting information.";"The exam did not necessarily reflect information that was covered in the course. It drew on a lot of what was expected to be common knowledge, and not from the lectures.";"kp" +"1203";"JPM425";"Conflict & Cooperation in International River Basins";"Landovský,J.";;"4";"4";"4";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kp" +"1204";"JJM240";"Cultural studies";"Soukup,M.";;"4";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";;"Transparent requirements for Final Paper and Final Exam.";"kms" +"1205";"JMM615";"Democracy Promotion: history, theories, practice";"Najšlová,L.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"5";"5";"3";"4";"Small number of students";"More credits for that much required work";"kzs" +"1206";"JMB011";"Moderní dějiny Ruska";"Litera,B.,Pečenka,M.";"seminář nenavštěvován";"4";"4";"4";"4";"4";"4";"4";"5";"1";"5";"4";"5";"5";;"Vzhledem k tomu, že součástí výstupního testu je esej, bylo by možná dobré mít již např. v prvním ročníku studia nějaký předmět na psaní tohoto slohového útvaru. Právě u zkoušek z teritorií se ukazuje, že mnoho lidí jednoduše u eseje pohoří, přestože má dostatečné množství znalostí. Více bych kurz směřoval do současnosti.";"krvs" +"1207";"JPM706";"Terrorism and Counterterrorism";"Bureš,O.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"This class is a wonderful introduction to Counterterrorism within different governmental structures. It was truly fascinating and helped broaden my understanding of Counterterrorism.";;"kbs" +"1208";"JJM259";"Mediální systémy a jejich komparace";"Miessler,J.";;"3";"5";"3";"5";"4";NULL;NULL;NULL;"1";"4";"3";"5";"4";"Přístup vyučujícího.Testy každý týden, které donutí studenty studovat průběžně.";"Větší čas na výstupy studentů - 15 minut bylo u některých témat příliš málo.Textů na každý týden bylo občas příliš mnoho.";"kz" +"1209";"JMB018";"Bakalářský seminář I";;"Emler,D.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"3";"5";"5";"5";"5";"Během kurzu si student ujasní, jak by měla vypadat jeho bakalařská práce.";"Asi bych byl pro povinnou docházku";"krvs" +"1210";"JPM705";"Human Security";"Hynek,N.";;"2";"3";"5";"5";"5";NULL;NULL;NULL;"4";"3";"3";"4";"3";;"Seperat grades for group work and time and held for finding the group regarding research interest of participants";"kbs" +"1211";"JPM708";"Ethics and Violence";"Karásek,T.,Kučera,T.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kbs" +"1212";"JJM260";"Novinářská etika v praxi";"Moravec,V.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";"Vyučující.Host z prostředí justice (Lenka Bradáčová).Eseje - jak průběžné na hodinách, tak velké. Obojí je přínosné.";;"kz" +"1213";"JPM718";"Critical Perspectives on Violence";;"Ditrych,O.";"4";"3";NULL;NULL;NULL;"4";"5";"4";"1";"4";"5";"3";"4";;"Less reading so everybody read the text and hence more valuable discussion";"kmv" +"1214";"JPM724";"Critical Approaches to International Politics and Security";;"Daniel,J.,Rychnovská,D.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Nice atmosphere and interactive teaching methods";"Less discussion between techer and students, instead more within groups of students";"kmv" +"1215";"JPM716";"The Geopolitics of Defence Industry and Arms Trade";"Kopečný,T.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Professor Kopecny did a wonderful job of making the information in this course accessible and engaging. I was a truly a pleasure to learn from his personal experiences and wide breadth of knowledge.";;"kp" +"1216";"JSM095";"Study of Political Mobilization and Social Movements";"Císař,O.";;"5";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"5";"5";"5";;;"ks" +"1217";"JJM360";"Ekonomika v médiích";"Klimeš,D.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Vyučující - úspěšný v praxi, ale zároveň má velké teoretické znalosti a přehled.Hosté.";;"kz" +"1218";"JJM279";"Divadelní kritika";"Homolová Richtrová,N.";;"5";"3";"4";"5";"4";NULL;NULL;NULL;"2";"5";"5";"3";"4";"Společná návštěva představení.Diskusní charakter předmětu.Společné diskuse nad recenzemi spolužáků.";"Uvítal bych více ukázek ze současného divadla a zároveň ze současné divadelní kritiky.Zajímavý by mohl být také nějaký host z velkých médií.";"kz" +"1219";"JEB003";"Ekonomie I";"Fanta,N.,Kracík,J.,Švarcová,N.";"Fanta,N.,Kracík,J.,Švarcová,N.";"5";"4";"5";"5";"5";"4";"5";"4";"1";"5";"4";"5";"5";;;"ies" +"1220";"JJM340";"Tvůrčí dílny – tvůrčí psaní I";"Malý,R.";"Malý,R.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"4";"5";"4";"5";"Vyučující a jeho přístup ke studentům.Pestrá paleta žánrů, které si studenti mohou vyzkoušet.Diskuse nad pracemi studentů.";"Hodilo by se více času na diskusi a čtení prací studentů, Tvůrčí psaní je předmět, který by si rozšíření časové dotace zasloužil.";"kz" +"1221";"JPB020";"Úvod do mezinárodních vztahů";"Karlas,J.,Kučera,T.";;"1";"5";"1";"3";"4";NULL;NULL;NULL;"1";"3";"1";"1";"1";;;"kmv" +"1222";"NMMA701";"Matematika 1";"Spurný,J.";"Honzík,P.";"5";"5";"5";"4";"5";"2";"1";"3";"2";"4";"5";"3";"4";;;"ies" +"1223";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Tento předmět byl pro mě velmi přínosný, co se týče komplexnějšího porozumnění zdánlivě jednoduchým věcem (např. státní hranice), které se sice na středních školách učí také, ale chybí jim tam právě ono zasazení do širšího kontextu a propojení s dalšími problémy ve světě. Výuka doplňena několika mapami mi velmi vyhovovala, neboť je pro mě daný jev snáze zapamatovalný.";"Nic mě nenapadá.";"kp" +"1224";"JJB221";"Ekonomická teorie marketingu";"Postler,M.";;"5";"2";"4";"5";"5";NULL;NULL;NULL;"1";"4";"2";"3";"5";"Mnoho příkladů a příběhů z praxe.";;"kmkpr" +"1225";"JJB224";"Psychologie marketingové komunikace";"Vranka,M.";;"4";"4";"3";"4";"4";NULL;NULL;NULL;"2";"4";"2";"4";"4";"Velké množství nabytých znalostí.";;"kmkpr" +"1226";"JJB249";"Úvod do studia českého jazyka I";"Schneiderová,S.";"Schneiderová,S.";"3";"3";"3";"5";"2";"3";"4";"3";"3";"2";"2";"1";"2";;;"kmkpr" +"1227";"JJB269";"Sociální kontext komunikace";"Vranka,M.";;"3";"4";"2";"4";"3";NULL;NULL;NULL;"2";"3";"2";"3";"3";;;"kmkpr" +"1228";"JJB406";"Tvorba a prostředky v mediální komunikaci";"Chudinová,E.";;"4";"2";"3";"3";"3";NULL;NULL;NULL;"2";"4";"4";"4";"5";;;"kmkpr" +"1229";"JJB407";"Bakalářský proseminář";"Rosenfeldová,J.";;"2";"3";"2";"2";"1";NULL;NULL;NULL;"4";"1";"3";"2";"2";;;"kmkpr" +"1230";"JLB033";"Němčina I";;"Faltýnová,R.";"5";"3";NULL;NULL;NULL;"5";"4";"5";"1";"5";"5";"3";"5";;;"cjp" +"1231";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"5";"4";"4";NULL;"4";NULL;NULL;NULL;"1";"5";"2";"4";"5";;;"kms" +"1232";"JJM080";"Zábava v televizním vysílání";"Kruml,M.";;"5";"1";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"1233";"JJM211";"Kvalitativní výzkum mediálních publik";;"Reifová,I.";"4";"3";NULL;NULL;NULL;"5";"5";"4";"1";"3";"5";"4";"4";;;"kms" +"1234";"JEM002";"Master´s Thesis Seminar II";;"Havránek,T.,Havránková,Z.,Mejstřík,M.";"4";"5";NULL;NULL;NULL;"4";"5";"3";"1";"4";"3";"4";"3";;;"ies" +"1235";"JJB226";"Strategie a finanční management marketingu I";"Majerik,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Náhled z praxe";;"kmkpr" +"1236";"JMM345";"Government in United States";"Sehnálková,J.";;"5";"3";"4";"5";"5";NULL;NULL;NULL;"1";"5";"3";"4";"4";;;"kas" +"1237";"JMM601";"U.S. and Human Rights";"Raška,F.";;"4";"2";"3";"5";"3";NULL;NULL;NULL;"1";"3";"4";"4";"3";;;"kas" +"1238";"JPM524";"Energy Security";"Holubcová,J.,Kučerová,I.";;"5";"3";"4";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"4";;;"kmv" +"1239";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"4";;;"cjp" +"1240";"JLB041";"Španělština I";;"Mlýnková,L.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"1241";"JEM027";"Monetary Economics";"Holub,T.,Malovaná,S.";"Břízová,P.,Hájek,J.,Holub,T.,Malovaná,S.";"5";"4";"5";"5";"5";"4";"5";"4";"1";"5";"4";"5";"5";"\"Nad březovým hájkem poletují holubi, krajinka jako malovaná.\" Tím chci říct, že složení vyučujících považuji za optimální a navrhuji jej zachovat. Jinak především samozřejmě vyzdvihuji doc. Holuba a jeho erudici, jsem velmi rád, že jsem se od něj mohl leccos zajímavého nového dozvědět.";"Přednáška a seminář těsně za sebou ve večerních hodinách nejsou ideální pro udržení pozornosti. Na druhou stranu chápu, že vyučující (a koneckonců i někteří studenti) to asi s prací mohou jinak těžko skloubit.";"ies" +"1242";"JJM208";"Mediální systémy a jejich komparace";"Miessler,J.";;"3";"5";"5";"4";"4";NULL;NULL;NULL;"2";"4";"4";"4";"4";"Nasazení vyučujícího a jeho snaha předat znalosti studentům.";"Vyučující změnil osnovu předmětu, aby byla více \"aktuální,\" ale potom zadával většinou k četbě texty cca 10 let staré, o jejichž aktuálnosti by se dalo diskutovat. Texty byly zadávány jako četba na další hodinu a až poté bylo dané téma rozebíráno na přednášce, bez předchozí znalosti kontextu byly texty někdy náročnější na pochopení (zejména četba v angličtině). Otázky v testech na četbu se mnohdy netýkaly samotného textu, ale byly zaměřeny na jiná (obecná) témata, která v tomto kurzu nebyla probírána (a nejsou probírána ani v ostatních kurzech na fsv). Pro skupinové prezentace byla zadávána velmi široká témata, která se obtížně dají zvládnout odprezentovat do 15 minut tak, aby bylo téma zpracováno kvalitně - priorita byla dávána dodržení časového limitu než kvalitě informací. Samotné skupinové prezentace v předmětu přednáškového typu nejsou tak vhodné jako např. na seminářích, kde je na ně více času a prostoru. Vyučující měl celkově vyšší nároky pro splnění kurzu, je třeba zmínit, že v posledním ročníku se studenti musí věnovat i ostatním předmětům a zejména pak psaní diplomové práce.";"kms" +"1243";"JJM200";"Diplomový seminář";;;"5";"5";NULL;NULL;NULL;"5";"5";"3";"1";"5";"5";"3";"5";;;"kms" +"1244";"JLB100";"Czech as a Foreign Language I";;"Frantesová,E.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"3";"4";"5";;;"cjp" +"1245";"JMM067";"Russia and Eurasia in World Politics";"Šír,J.";;"4";"3";"4";"4";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"krvs" +"1246";"JMMZ042";"Cohesion Policy of the EU in Central and East European Countries.";"Hauser,J.";;"5";"3";"4";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"krvs" +"1247";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"2";"5";"5";;;"kms" +"1248";"JJM320";"Propaganda a československá média po roce 1948";"Suk,P.";;"5";"1";"4";"5";"5";NULL;NULL;NULL;"1";"3";"3";"5";"5";;;"kms" +"1249";"JMMZ050";"Political Systems of East European Countries in the 20th Century";"Kubát,M.";;"2";"2";"4";"4";"3";NULL;NULL;NULL;"1";"4";"1";"3";"1";;"Lectures could be more (inter)active";"krvs" +"1250";"JMMZ331";"Qualitative methods in social sciences";"Weiss,T.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"3";"4";"4";"3";;;"kzs" +"1251";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Tričkový repertoár pana magistra";;"kms" +"1252";"JJM373";"Ethical Issues in Media, Business and Society";"Orhan,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"1253";"JJM324";"Teorie mediální komunikace";"Jirák,J.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"1254";"JPM689";"Conflict Studies";"Karásek,T.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kbs" +"1255";"JPM706";"Terrorism and Counterterrorism";"Bureš,O.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kbs" +"1256";"JJM372";"Consumer Behaviour";"Orhan,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"1257";"JJM371";"New Media and Entrepreneurship";"Orhan,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"1258";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"4";"5";"5";"2";"5";NULL;NULL;NULL;"1";"5";"5";"5";"4";"Líbily se mi názorné příklady do života, látka byla velmi zajímavě vysvětlena, když pan doktor něco vysvětloval, pochopila jsem to. Některé přednášené věci mě zaujaly natolik, že jsem se začala o obor zajímat více, celkově ve mně pan doktor vzbudil zájem o ekonomii.";"Příliš se mi však nelíbil přístup pana doktora vůči studentům, cítila jsem jakési opovržení nad naší generací (třeba jen můj subjektivní pocit), což lze do jisté míry pochopit. Pedagog by měl mít profesionální odstup, ale tenhle byl možná až moc velký.";"ies" +"1259";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Šafařík,P.";"5";"5";"5";"5";"5";"4";"5";"2";"1";"5";"3";"5";"5";"Výklad byl vždy veden poutavou formou, pan doktor si vždy dokázal udržet mojí pozornost. Vzájemná interakce mezi studenty a přednášejícím.";;"knrs" +"1260";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"4";"5";"Dokonalá strukturovanost, stručné, jasné, přehledné.";;"krvs" +"1261";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"1";"3";"3";"1";"1";NULL;NULL;NULL;"1";"1";"1";"1";"1";;"Osobně bych navrhovala změnu profesora, zvedla by se jak kvalita předmětu, tak kvalita našich znalostí a zájem o tento předmět";"ies" +"1262";"JSB007";"Dějiny sociologie I";"Šanderová,J.";"Šrám,K.";"5";"3";"5";"5";"5";"5";"5";"5";"3";"5";"5";"5";"5";;;"ks" +"1263";"JSB025";"Sociální problémy";"Frič,P.";;"4";"4";"4";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kvsp" +"1264";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Rössler,J.";"5";"5";"3";"4";"3";"5";"5";"5";"1";"5";"5";"5";"5";;;"ks" +"1265";"JSB530";"Úvod do sociologického myšlení";"Bureš,J.,Numerato,D.";"Wirthová,J.";NULL;NULL;"5";"5";"5";"5";"5";"5";NULL;NULL;NULL;NULL;NULL;;;"ks" +"1266";"JMB171";"Moderní dějiny Maďarska";"Irmanová,E.";;"3";"4";"3";"5";"3";NULL;NULL;NULL;"2";"3";"3";"4";"2";"Dokonalá strukturovanost, maximální až nadstandardní snaha vyjít studentům vstříc, prostor pro otázky.";"Mluvený projev. Paní doktorka hovoří krásně, leč příliš rychle, takřka nemožné vést si poznámky, student se snadno ztratil a vrátit se zpět bylo velice obtížné. Rovněž maďarská jména nejsou příliš známá, kdyby se psala pokaždé na tabuli, bylo by to skvělé.";"krvs" +"1267";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Buřičová,B.";NULL;NULL;"4";"4";"3";"3";"5";"4";NULL;NULL;NULL;NULL;NULL;;;"ks" +"1268";"JSB544";"Vybrané kapitoly středoškolské matematiky";;"Hendl,J.";NULL;NULL;NULL;NULL;NULL;"4";"4";"3";NULL;NULL;NULL;NULL;NULL;;;"ks" +"1269";"JJB226";"Strategie a finanční management marketingu I";"Majerik,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Klidný přístup vyučujícího, reálné setkání s oborem na příkladech z praxe";;"kmkpr" +"1270";"JJB406";"Tvorba a prostředky v mediální komunikaci";"Chudinová,E.";;"1";"3";"3";"2";"1";NULL;NULL;NULL;"3";"1";"1";"2";"1";;"Pouze sáhodlouhý výčet teoretických pojmů, nulové zapojení reálné praxe, strohé učení se nazpaměť věcí, které většina ze studentů už zná.";"kmkpr" +"1271";"JJM240";"Cultural studies";"Soukup,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"kms" +"1272";"JSB998";"Úvod do sociologie";"Soukup,P.";;"3";"1";"3";"5";"1";NULL;NULL;NULL;"2";"3";"2";"2";"2";"Přátelský přístup ke studentům, individuální přístup, celkově velice sympatické působení.";"Hodně velký důraz na prezentace, které obsahovaly chyby. Přišlo mi, že mi stačí zůstat doma a prezentace si přečíst, že tím ušetřím čas, protože kromě informací obsažených v prezentacích jsem se málokdy dozvěděla něco nového. Ačkoliv je pan doktor nesmírně sympatický a novátorský, mě tohle novátorství neoslovilo, ačkoliv mnoho dalších lidí ano. Postrádala jsem přesnější pokyny k vyplňování úkolu.";"ks" +"1273";"JJB226";"Strategie a finanční management marketingu I";"Majerik,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmkpr" +"1274";"JLB029";"Španělština odborná I";;"Mlýnková,L.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"1275";"JJM343";"Interkulturní komunikace";"Soukup,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Vyučující umí témata výborně a záživně podat, obsah kurzu je zajímavý, velmi často přiměje studenty k zamyšlení nad různými kulturně-společenskými jevy.";;"kms" +"1276";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"4";"4";"4";"5";"3";NULL;NULL;NULL;"2";"3";"1";"3";"1";"We have found out some practical information.";;"ies" +"1277";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"4";"1";NULL;NULL;NULL;"4";"5";"4";"1";"2";"4";"3";"5";;;"ies" +"1278";"JJJM191";"Media and the Children";"Zezulková,M.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";"I learnt how to do a research from scratch, gained knowledge about the relationship between children and media, practised my photoshop skills to make a research poster.";"Sometimes I wasn't sure if I had lecture because the schedule of the course was not clear enough.I think that a group of five is a bit too many since my group always had difficulties to compromise a time to meet outside class.";"kms" +"1279";"NMMA701";"Matematika 1";"Spurný,J.";"Honzík,P.";"4";"5";"5";"5";"3";"5";"5";"2";"1";"4";"5";"5";"3";"The teacher’s attitude.";;"ies" +"1280";"JLB003";"Angličtina pro ekonomy I";;"Poslušná,L.";"3";"1";NULL;NULL;NULL;"5";"5";"2";"1";"3";"3";"2";"2";"I enjoyed listening to the teacher, because she talked as if she had quite an english accent.";;"cjp" +"1281";"JJM234";"Media and Society: An Introduction";"Jirák,J.";;"3";"3";"3";"3";"3";NULL;NULL;NULL;"1";"3";"2";"3";"3";"I keep a habit to watch news everyday because there is a media news assignment every lecture. The lecturer always engaged us to the discussion and explained the controversial topics in the media industry.";"The requirement of the group report wasn't very clear as we didn't have an standard about the content or the format. It was a bit difficult because we did not know what the lecturer expect from us.";"kms" +"1282";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"2";NULL;NULL;NULL;"3";"3";"4";"3";"2";"2";"3";"5";"Pravidelné projekce.";"Uvádět i nízkorozpočtové filmy od menších studií.";"kz" +"1283";"JJM362";"History of media";;"Neuzil,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"The lecturer explained every topic in a very organised and interesting way. I gained a lot of background knowledge about the history of every aspects of media. I enjoyed all of the lessons.";;"kz" +"1284";"JSM026";"Klíčové otázky sociální antropologie";"Grygar,J.,Hrešanová,E.,Uherek,Z.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"2";"3";"3";"5";"4";"Diskuzní charakter a střídání přednášejících.";"Má-li to být přiblížení antropologie pro neantropology, měl by kurz obsahovat i nějaká znamější východiska minulosti antropologického myšlení, zejména zmínění důležitých autorů a jejich stěžejních myšlenek.";"ks" +"1285";"JLB009";"Angličtina pro žurnalisty I";;"Prošková,A.";"4";"3";NULL;NULL;NULL;"4";"5";"4";"1";"4";"3";"4";"4";"I learnt a lot of new vocabulary items for news reporting which will be very useful in my future career.";;"cjp" +"1286";"JSM554";"Diplomový seminář";;"Grygar,J.,Hájek,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"2";"3";"5";"5";"5";"Vzájemné diskutování dílčího postupu.";"Chtělo by to i něco k praktické části DP - práce s daty, analýza,... Možná bych také nemluvil o psaní úvodu práce, ale o teoretické části (teoretickém úvodu?).";"ks" +"1287";"JSM570";"Inovativní prezentace vědeckého poznání I";;"Spalová,B.";"5";"4";NULL;NULL;NULL;"5";"5";"4";"3";"3";"5";"3";"5";;;"ks" +"1288";"JSM572";"Sociologie organizací";"Čada,K.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"5";"5";;;"ks" +"1289";"JLB100";"Czech as a Foreign Language I";;"Mazúrková,B.";"4";"4";NULL;NULL;NULL;"4";"5";"5";"1";"4";"4";"4";"4";"The lecturer used different kinds of teaching methods to help us remember the Czech vocabulary items and grammar which I enjoyed very much. There was also a field trip to real farmer's market to let us communicate with local people in Czech, it was really interesting. Not only had I gained knowledge but I also knew more places in Prague.";"Other than the exercises in the textbook, more extra practises should have provided to prepare for mid-term test and final examinations.";"cjp" +"1290";"JLM011";"Angličtina pro veřejnou a sociální politiku I";;"Klírová,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"1291";"JSM103";"Academic Writing";;"Blokker,P.";"4";"3";NULL;NULL;NULL;"4";"4";"4";"2";"3";"3";"3";"3";;;"ks" +"1292";"JSM406";"Statistics in SPSS";;"Soukup,P.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"ks" +"1293";"JSM480";"Evaluation Research";;"Remr,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"4";;;"ks" +"1294";"JSM518";"Public Policy";"Potůček,M.,Vlčková,K.";;NULL;"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kvsp" +"1295";"JSM692";"Introduction to Social Research Methodology";"Remr,J.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ks" +"1296";"JSB004";"Sociální nerovnosti";"Šanderová,J.";;"4";"3";"5";"5";"1";NULL;NULL;NULL;"4";"4";"3";"4";"5";;;"ks" +"1297";"JSB031";"Bakalářský seminář I - diskuse projektů";;"Spalová,B.";"4";"3";NULL;NULL;NULL;"5";"5";"4";"1";"4";"5";"1";"4";;;"ks" +"1298";"JSB033";"Praktika z kvalitativního výzkumu";;"Marková Volejníčková,R.";"4";"4";NULL;NULL;NULL;"5";"5";"2";"1";"4";"5";"1";"4";;;"ks" +"1299";"NMMA703";"Matematika 3";"Zelený,M.";"Zelený,M.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"1300";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"5";"4";"2";"4";NULL;NULL;NULL;"1";"5";"4";"4";"2";"Oceňuji poznámky z přednášek, které nám byly poskytnuty.";"Nálepkování, přístup ke studentům - užívání zvonečku pro zahájení a ukončení hodiny. Přehnaná kontrola a opatření u ZK.";"ies" +"1301";"JLM011";"Angličtina pro veřejnou a sociální politiku I";;"Klírová,M.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Diskuze mezi studenty.";"Organizaci kurzu. Na počátku kurzu jsme nevěděli, že budeme psát English Journal. Bylo to velmi časové náročné s ostatními kurzy.";"cjp" +"1302";"JSB025";"Sociální problémy";"Frič,P.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"3";;;"kvsp" +"1303";"JSM516";"Sociální politika v perspektivě životního cyklu";"Dobiášová,K.,Kotrusová,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kvsp" +"1304";"JSM518";"Public Policy";"Potůček,M.,Vlčková,K.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;"Organizaci kurzu hned na počátku semestru.";"kvsp" +"1305";"JLB100";"Czech as a Foreign Language I";;"Nováková,K.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";"Separation of exercises in such a way that everyone with each other student has the opportunity to practice the language. language classes are the one when we can actually get to know each other.A lot depends on the book, but the teacher creates how course looks like and make us to don't want miss any class :)";;"cjp" +"1306";"JEM003";"Advanced Microeconomics";"Janda,K.,Melikhova,O.";"Janda,K.,Melikhova,O.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"1307";"JEM027";"Monetary Economics";"Holub,T.,Malovaná,S.";"Břízová,P.,Hájek,J.,Holub,T.,Malovaná,S.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"1308";"JEM037";"Financial Markets";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"1309";"JEM062";"Introductory Econometrics";"Kukačka,J.";"Kukačka,J.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"1310";"JLB104";"Czech for Chinese speaking students";;"Vaníčková,K.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"1311";"JLM063";"English for Chinese Speaking Students";;"Štěpánková,D.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"1312";"JPB592";"US Government and Politics";"Kotábová,V.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"It is important that the classes are run by someone who is not only interested in the US political system but also went there regularly. Thanks to this, he can better explain the nuances based on his own experience.";;"kp" +"1313";"JPM324";"Geography and Politics in Europe within Global Regionalism";"Doboš,B.,Riegl,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"The teacher's knowledge went beyond the facts, but he can succinctly explain some historical phenomenon, its causes and effects. He requires specific knowledge, but does not focus on the very small details, he just want us to understand the links between events.";;"kp" +"1314";"JJB284";"Firemní komunikace a kultura";"Poucha,T.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"1";"4";"5";"5";"5";"Oceňuji přístup profesora, který kladl důraz na praktické využití probírané látky, vše bylo navíc zábavnou a zajímavou formou prezentováno.";"Nic.";"kmkpr" +"1315";"JMM671";"Rebuilding Europe";;"Rovná,L.";"4";"3";NULL;NULL;NULL;"3";"3";"4";"3";"3";"3";"4";"3";"The opportunity to focus on one important world's problem and get to know it in all aspects. Opportunity to exchange views with students in Prague and with students in Paris and Geteborg.";"Due to the low knowledge of the mechanisms of work of EU bodies by students, the teacher should better take care of its clarification.";"kzs" +"1316";"JJB630";"Krizová komunikace";"Chudinová,E.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"2";"5";"3";"4";"4";"Získala jsem zajímavé teoretické znalosti, které lze využít i dále v jiných kurzech. Líbilo se mi, že nám paní profesorka poskytla k testu veškeré materiály a celkově byla její výuka i přístup velmi \"lidský\".";"Uvedla bych více praktických příkladů, například případ jisté krizové situace a procházeli by se možné postupy jak situaci vyřešit apod. tak, abychom si teoretické znalosti více spojili s praxi. Jelikož dnešní doba patří internetu, zahrnula bych do předmětu i řešení krizové komunikace v rámci community managementu na sociálních sítí, jistá aktuálnost využitých médií mi v kurzu chyběla.";"kmkpr" +"1317";"JJB406";"Tvorba a prostředky v mediální komunikaci";"Chudinová,E.";;"3";"4";"5";"5";"4";NULL;NULL;NULL;"2";"3";"3";"3";"3";"Oceňuji přístup profesorky, je velmi přívětivá. Zajímavé bylo i zadání seminární práce.";"Celkově se jednalo o velké zahlcování informacemi, kdy bylo těžké rozlišit, co je opravdu důležité a co nikoliv. Též mi chybí nějaké detailnější hodnocení práce, co se líbilo, co se nelíbilo, co by chtělo zlepšit.";"kmkpr" +"1318";"JPB593";"Political Economy of Regionalism";"Miková,I.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"4";"The teacher gave access to articles and chapters, presentations. He skillfully talked about what was the subject of the class on specific examples (theory + practice).";"presentations for students should be on the same level - biography for one student, comparison of theory for two (instead of biography for 2 and comparison for 2)";"kmv" +"1319";"JJB240";"Marketing a tvorba značky";"Průša,P.";;"3";"5";"4";"5";"4";NULL;NULL;NULL;"1";"3";"4";"4";"3";"Oceňuji množství praktických příkladů uvedených v hodině.";"Hodnocení není úplně jasné, hlavně co se například hodnocení skupinových prezentací týče. Jedna skupina naprosto očividně nesplnila zadání (místo analýzy dle Scotta Davise dělala rozbor komunikace) a přesto byla hodnocena nejlépe ze všech.";"kmkpr" +"1320";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"1";"2";"1";"1";"1";NULL;NULL;NULL;"3";"1";"2";"4";"3";"It teaches important IR theories";"The lecture style is not engaging and the material overlaps with other courses";"kmv" +"1321";"JPM595";"Arms Control and Disarmament";"Hynek,N.,Smetana,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"The pacing of assignments, the simulation game, and the guest lecturers";"Groups could spend more class time working together or could be required to coordinate outside class, but the pacing of peer-to-peer work and presentaitons in class felt a bit awkward";"kbs" +"1322";"JPM613";"Armed Forces and Society";"Kučera,T.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"The consistent pacing of material and the breakdown of class time kept the class engaging.";"Moodle is not always the most reliable platform for students to review their grades, though I don't know if something better exists.";"kbs" +"1323";"JPM705";"Human Security";"Hynek,N.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"4";"4";"5";"5";"4";"It covers important subject matter for this degree and for field work";"Greater coordination among lecturers";"kbs" +"1324";"JPM650";"Intelligence";"Bahenský,V.,Galeotti,M.";;"5";"3";"5";"3";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Very enlightening and engaging lectures";"Evaluation based on more assignments";"kbs" +"1325";"JPM707";"Peacekeeping and Peacebuilding";"Bureš,O.";;"4";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"4";"Great content. Learned lots to do with UN peacekeeping. Very interesting content. Extremely knowledgeable and hard working teacher. Enjoyed group project and presentation, found it useful. Enjoyed class discussions.";"Unattainable reading schedule to manage with 5 other classes. Final exam, although an interesting idea, is far to difficult for one person and should have been a group task. There was no guidance and previous examples were not given. Although standard for a regular essay, the word count should have been far less for this individual task as it is virtually impossible to drag out 3000 words worth of content for a task like this with minimal information and still provide quality content. Interesting task but in my opinion the conditions are far too difficult for people who have just started learning about peacekeeping and will likely be detrimental to grades.";"kbs" +"1326";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"5";"4";"4";"2";"4";NULL;NULL;NULL;"4";"5";"4";"3";"4";"The ability to expound on IR theories and apply them in contemporary cases in International Relations.";"The teacher's approach towards students while testing the knowledge of the course : It was a little too rigid.";"kmv" +"1327";"JPM306";"African Security";"Werkman,K.";;"5";"3";"4";"3";"4";NULL;NULL;NULL;"5";"3";"4";"3";"4";"It was the exact and precise touch on all the necessary flashpoints in African society.";"Africa is a continent yet diverse And should have been more comprehensive if Each region was covered extensively and broadly. Rather than just narrowly.";"kbs" +"1328";"JPM561";"Regional Security Studies";"Karásek,T.,Klosek,K.";"Karásek,T.,Klosek,K.";"4";"3";"5";"4";"4";"4";"5";"5";"1";"5";"4";"4";"4";"Critically Examining everything and comprehensive approach to the course.";"Nothing it was absolutely Worth the time.";"kbs" +"1329";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"3";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"5";"4";"4";"The lectures materials were simple to understand even though they were structurally tougher than expected.";"Nothing it was perfect.";"kmv" +"1330";"JPM689";"Conflict Studies";"Karásek,T.";;"5";"2";"4";"5";"5";NULL;NULL;NULL;"1";"4";"5";"4";"3";"The ability of the course to Cover aspects of COnflicts in brief , yet Good enough.";"Nothing it was Great.";"kbs" +"1331";"JPM708";"Ethics and Violence";"Karásek,T.,Kučera,T.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"3";"5";"The discussion, The course created room and enabled students to share their opinions and ideas, conflicting in most cases these ideas, however invaluable. Was a great experience.";"Nothing it was Perfect.";"kbs" +"1332";"JMB065";"Úvod do mezinárodního a evropského práva";"Šlosarčík,I.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Přednášky byly velice zajímavé, oceňuji především vysvětlování látky pomocí příkladů z praxe.";;"kzs" +"1333";"JPM701";"European and Transatlantic Security";"Kazharski,A.";;"4";"3";"4";"5";"3";NULL;NULL;NULL;"1";"3";"3";"4";"4";"reading materials were interesting";"discussions could be planned for the second part of the lesson and in the first part it would be nice if teacher gave us more deeper explanations and some hints as discussions were a bit 'chaotic'";"kbs" +"1334";"JPM306";"African Security";"Werkman,K.";;"3";"2";"2";"5";"2";NULL;NULL;NULL;"1";"4";"2";"4";"4";"Good literature.";"The structure of the lectures.";"kbs" +"1335";"JPM727";"Orchestration in Global Governance";;"Abbott,K.,Parízek,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kmv" +"1336";"JPM595";"Arms Control and Disarmament";"Hynek,N.,Smetana,M.";;"4";"2";"4";"5";"3";NULL;NULL;NULL;"1";"4";"4";"4";"4";"The country studies and the simulation at the end.";"Better structuring of the lectures.";"kbs" +"1337";"JLB019";"Francouzština odborná I";;"Bosáková,L.";"2";"2";NULL;NULL;NULL;"2";"4";"2";"1";"1";"2";"2";"1";;"Do not speak in Czech during the course. It is a advanced level, the class should be entirely in French";"cjp" +"1338";"JPM650";"Intelligence";"Bahenský,V.,Galeotti,M.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"3";"3";"4";"4";;;"kbs" +"1339";"JPM694";"Strategic Studies";"Hynek,N.,Ludvík,J.,Smetana,M.";;"4";"3";"4";"5";"3";NULL;NULL;NULL;"1";"4";"4";"4";"4";"Good literature.";"The weekly's, I didnt feel like giving feedback on great literary pieces is a good thing, this should be done differently. for example a bigger paper, or weekly applications of concepts on contemporary conflicts/ situations.";"kbs" +"1340";"JLM001";"Academic English I";;"Cotte,P.";"3";"1";NULL;NULL;NULL;"4";"5";"4";"1";"4";"4";"4";"3";;;"cjp" +"1341";"JPM706";"Terrorism and Counterterrorism";"Bureš,O.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"It was one of the best classes ever in my life. The lecturer was so professional, well-prepared and his knowledge is remarkable. The readings were interesting and helpful to understand the whole picture of the course. The evaluation of the students was fair and professional.";"The lecturer definitely needs more time to elaborate every thoughts. Moreover, I would give the lecturer more time, because of the debates. We had no time to discuss our opininon with the group and the lecturer, due to the time limit.";"kbs" +"1342";"JPM707";"Peacekeeping and Peacebuilding";"Bureš,O.";;NULL;NULL;"5";"5";"5";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kbs" +"1343";"JPM708";"Ethics and Violence";"Karásek,T.,Kučera,T.";;NULL;NULL;"3";"3";"3";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kbs" +"1344";"JPM526";"Justice and Reconciliation in Post-Conflict Societies";;"Werkman,K.";"3";"3";NULL;NULL;NULL;"2";"1";"3";"5";"4";"4";"4";"2";;"Communication with students is ridiculously poor. Professor NEVER replies to emails.";"kmv" +"1345";"JPM696";"Economic Warfare";"Ludvík,J.";;"4";"3";"4";"5";"3";NULL;NULL;NULL;"1";"4";"3";"4";"4";"Interesting case studies.";"Leave out the group discussions, waste of time. Spend more time on lecturing the students about different aspects of EC Warfare.";"kbs" +"1346";"JPM712";"Insurgency and Counterinsurgency";"Aslan,E.";;"3";"3";"4";"3";"3";NULL;NULL;NULL;"3";"4";"2";"4";"3";;;"kbs" +"1347";"JPM607";"International Negotiations";;"Parízek,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"5";"5";;;"kmv" +"1348";"JPM671";"Odborná stáž B";;;NULL;NULL;NULL;NULL;NULL;"5";"5";"5";NULL;NULL;NULL;NULL;NULL;;;"kmv" +"1349";"JLM006";"Angličtina pro politology II";;"Panešová,K.";NULL;NULL;NULL;NULL;NULL;"5";"5";"5";NULL;NULL;NULL;NULL;NULL;;;"cjp" +"1350";"JLB099";"Rozřazovací test z angličtiny";;"Panešová,K.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"1";"3";"3";"4";"5";;;"cjp" +"1351";"JJB143";"Žurnalistika a feminismus";"Krobová,T.,Osvaldová,B.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"3";"5";"2";"3";"5";"Nejvíc jsem si cenila přístupu přednášející k tématu - velká otevřenost, podněty k diskuzi. Zároveň se mi líbila i forma atestu - prezentace aktuality a napsání eseje, která měla velmi volné téma.";;"kz" +"1352";"JMB011";"Moderní dějiny Ruska";"Litera,B.,Pečenka,M.";"seminář nenavštěvován";"1";"5";"3";"1";"3";"3";"2";"3";"1";"3";"1";"3";"1";;"Neobjektivny pristup, nezmyselnost eseji, vysledok neodzrkadluje vedomosti, je to o nahode, pripadne nalade vyucujucich...keby sme chceli studovat historiu tak ideme na FF";"krvs" +"1353";"JJB237";"Právo a etika v marketingu";"Winter,F.";;"3";"2";"4";"4";"4";NULL;NULL;NULL;"1";"4";"2";"4";"4";"Přístup přednášejícího k tématu, názorné příklady, možnost debaty";"Zaktualizování příkladů k tématu, mnoho ukázaných reklam je docela starých. Sice pořád fungují jako příklady, ale ukázání něčeho současnějšího, nebo třeba porovnání současného se starším by bylo přínosnější.";"kmkpr" +"1354";"JJB406";"Tvorba a prostředky v mediální komunikaci";"Chudinová,E.";;"3";"2";"4";"4";"2";NULL;NULL;NULL;"2";"2";"2";"3";"3";;"Zpřístupnit prezentaci na začátku kurzu, jelikož to umožňuje studentovi více se věnovat přednášce samotné a zoufale neopisovat slidy a nestíhat ani výklad, ani opisování. Celá prezentace byla slovensky, což je přece jen cizí jazyk pro většinu studentů, při vnímání přednášky si člověk musel v hlavě překládat, což vstřebávání obsahu ještě zpomaluje. Zároveň výukový jazyk čeština uvedený v sylabu je zavádějící - i zadání zkoušky bylo slovensky. Nebyla by na škodu zpětná vazba k seminární praci, či alespoň bodové ohodnocení.";"kmkpr" +"1355";"JJB629";"Tiskový mluvčí - praxe a teorie";"Chudinová,E.";;"4";"2";"4";"4";"3";NULL;NULL;NULL;"2";"3";"4";"3";"4";"Kombinaci teorie a praxe - relativně mnoho prostoru pro nácvik dovedností.";"Více příkladů z českého či mezinárodního prostředí, velká část prezentace a obsahu přednášek uváděla slovenské prostředí (rozsáhlý popis legislativy, etymologie slova hovorca, praktické příklady v podstatě výhradně ze Slovenska). Pro porovnání je to sice pěkné, ale reálně nijak přínosné. Většina českých studentů se neorientuje ve slovenském prostředí, chyběly tedy české, bližší, příklady, či mezinárodně známé příklady (USA, Německo).";"kmkpr" +"1356";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"1357";"JMB036";"Moderní dějiny Běloruska";"Zilynskyj,B.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"krvs" +"1358";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"knrs" +"1359";"JMB037";"Moderní dějiny Polska";"Vykoukal,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"krvs" +"1360";"JMB402";"Úvod do společenských věd II";;"Hofmeisterová,K.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"krvs" +"1361";"JMB497";"Metodický úvod pro kombinované studium";"Kubát,M.";;"3";"4";"5";"3";"4";NULL;NULL;NULL;"1";"4";"2";"3";"4";;;"krvs" +"1362";"JJB231";"Dějiny a teorie public relations";"Halada,J.,Hejlová,D.";;"3";"5";"4";"4";"3";NULL;NULL;NULL;"1";"5";"1";"5";"4";"Dostatek doporučené literatury, ústní forma zkoušky, seminární práce s relativně volnou rukou, co se tématu týče.";"- v sylabu na začátku semestru uvést, kdy je teorie a kdy dějiny (příprava na přednášky byla náročná, nikdo nikdy netušil, co bude příště)- zadávat texty, literaturu, před přednáškou na dané téma (opět by byla snazší příprava na přednášku, kde se o tématu diskutovalo, ale v podstatě docházelo spíš k tipování než konstruktivní debatě)- nebo naplnit přednášky o teorii teorií (bez textů se špatně názorné příklady chápou, přednáška se pak jeví trochu zbytečná), v prezentaci teorie sice byla, ale projela se narychlo na konci přednášky, což se taky míjí účinkem - při zadávání seminárek udělat třeba tabulku v excelu, kde by přednášející schválili konkrétní témata, na seminárky bylo relativně málo času, pod tématy jsme si nedokázali moc představit, zadání seminárky ohledně voleb do PSP bylo osvětleno o dost později, než bylo zadáno druhým přednášejícím - studentům byly práce vraceny za nesplněné podmínky až v lednu, přestože se seminárky odevzdávaly ke konci listopadu, často to bylo pár dní před zkouškou, což je ve zkouškovém dost šibeniční častémata přednášek- přestože je německá propaganda z evropské historie PR v podstatě nejlépe popsaná, tak byla probrána minimálně , nebyly k ní studijní texty, pouze obrázky, navíc jsme se ji věnovali v rámci teoretických přednášek (chápu, že se to překrývá, ale stejně se jedná spíše o historii) - Napoleon i Richelieu jsou zajisté důležití, ale myslím, že právě oni se probírali velmi podrobně na úkor výkladu aktuálnější propagandy (nacistická, komunistická, nebo třeba čínská či v KLDR)";"kmkpr" +"1363";"JJB403";"Institucionální a vládní komunikace";"Shavit,A.,Soukeník,Š.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"- seminární práce s velmi konkrétním zadáním v u nás méně obvyklých formách (v krátkém čase, počítání slov, ne znaků)- strukturované přednášky, které obsáhly jak teorii, tak mnoho názorných příkladů - otevřený přístup přednášejících, možnost o všem diskutovat- zpětná vazba k seminárním pracím";"- pravděpodobně nejde o nedostatek tohoto kurzu, ale nám studentům v rámci žádného kurzu nejsou vysvětleny různé formy prací (position paper, case study atp.) a jejich náležitosti - asi to patří do bakalářského prosemináře, ale tam to prostě není, jejich znalost je v tomto kurzu zásadní, větší tolerance či počáteční vysvětlení formy by bylo velmi vítáno";"kmkpr" +"1364";"JEM004";"Advanced Macroeconomics";"Baxa,J.,Hromádková,E.";"Baxa,J.,Hromádková,E.,Šestořád,T.,Troch,T.,Žáček,J.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"4";"5";"4";"4";"The teachers are very very good, and their explanations are clear and concise. I appreciated the fact that we had the consultation before the exam. This course is very useful for anyone who intends to do macroeconomic research.";"The course is very extensive, and lectures comprised tremendous amount of information, that needed more than 1h 20 min of explanation, at least in my opinion. Attending lectures was very useful, and I only wish there was less material that had to be covered or more time spent on explanation of certain topics. Also, I missed more detailed feedback on the homeworks we were submitting. I would appreciate if the correct solution to the homework was posted online, or the most common mistakes were addressed during seminars.";"ies" +"1365";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"4";"2";"4";"5";"5";NULL;NULL;NULL;"2";"4";"3";"3";"4";;;"ies" +"1366";"JJM320";"Propaganda a československá média po roce 1948";"Suk,P.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Velmi zajímavý historický exkurz do metod propagandistického působení v době socialismu - poznatky jsou využitelné i pro dnešní dobu.";"Ocenil bych větší množství praktických příkladů, jak propaganda působila.";"kms" +"1367";"JJM324";"Teorie mediální komunikace";"Jirák,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Vhled do oblasti teorie mediálních studií, který považuji za nezbytný předpoklad pro studium dalších předmětů v rámci oboru.";"Ocenil bych možnost náhradního termínu průběžného testu v případě, kdy z objektivních důvodů nebylo možné jej v rámci semestrálních setkání absolvovat; je nicméně pravdou, že i při absenci bylo možné podmínky kurzu splnit.";"kms" +"1368";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"5";"2";"4";"5";"4";NULL;NULL;NULL;"1";"5";"5";"4";"5";"Samotnou koncepci kurzu - jeho zaměření považuji i z hlediska současné situace za velmi žádoucí a praktické.";"Byť byla v mnohých případech materie na vysvětlení složitá, ocenil bych jednodušší podání výkladu s četnějším využitím praktických příkladů.";"kms" +"1369";"JJM330";"Trendy současných českých médií";"Aust,O.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"3";"4";"5";"5";"Diskuze s velmi zajímavými osobnostmi, praktické zaměření kurzu.";"Nároky na absolvování kurzu by mohly být vyšší.";"kms" +"1370";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"4";"2";NULL;NULL;NULL;"4";"3";"4";"2";"4";"3";"4";"5";;;"ies" +"1371";"NMMA701";"Matematika 1";"Spurný,J.";"Honzík,P.";"4";"4";"4";"4";"4";"3";"3";"3";"1";"5";"4";"2";"4";;;"ies" +"1372";"JJM373";"Ethical Issues in Media, Business and Society";"Orhan,M.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";"Praktický přístup k probírané látce, interaktivita.";"Koncepci plnění studijních povinností (například esej + závěrečná písemná prezenční zkouška prověřující základní pochopení probírané látky, namísto e-zkouškových testů zaměřujících se na detailnější informace, které však bylo v zásadě možné dohledat).";"kms" +"1373";"JMMZ276";"Deutsche und Tschechen – nahe und ferne Nachbarn (von der Habsburgermonarchie bis zur europäischen Union)";"Zimmermann,V.";"Zimmermann,V.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"4";"3";"5";"5";"- PP-Präsentation, die wir am Ende des Kurses per Mail bekommen haben- Form der mündlichen Prüfung - sehr gut durchgeführt und ohne StressDer ganze Kurs war sehr interessant und hat auch Spaß gemacht. Der Stoff war sehr gut geglidert und wurde verständlich vermittelt. Vielen Dank!";;"knrs" +"1374";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";NULL;"5";"Jsem velmi ráda, že jsme dostávali tabulky s obtížnými slovy. Ušetřily mi čas. Děkuji za ně. Navíc jsem se díky nim na hodinu mohla připravit předem a během kurzu jsem si slova zopakovala. Také si moc vážím energie paní Kunzové a toho, že nás dokázala zapojit a přimět k činnosti i tak brzo ráno.";"Nemám připomínky. S kurzem jsem byla velmi spokojená.";"cjp" +"1375";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"5";"4";NULL;NULL;NULL;"4";"5";"5";"1";"5";"5";"4";"4";"Rozšíření slovní zásoby, společná práce v seminářích";;"cjp" +"1376";"JPM300";"Geopolitics of sovereignty, state failure and unrecognized states";"Riegl,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"4";"5";"Prof has a great music taste. Apart from that, there are lots of new information I gathered from the concepts of failed states and collapsed states and whatnot.";"Too much terminology! Although I don't see if it can really be avoided...";"kp" +"1377";"JPM599";"ON WAR I.";"Kofroň,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"This course is the main reason I signed up for Geopolitical Studies, and the professor has clearly delivered on it.!";"Sometimes the classes tend to get too dragging...but I guess that's because we guys don't prepare for the classes in advance sometimes!";"kp" +"1378";"JMBZ264";"Seminar zu den aktuellen Fragen";;"Renner,T.";"5";"3";NULL;NULL;NULL;"4";"5";"5";"1";"5";"5";"5";"5";"Zajímavá témata referátů, pro mě osobně byla nejvíc zajímavá politika, případně politické souvislosti s daným tématem.";"Možná by stálo za to, zaměřit se i na přípravu diskusí - může být víc vedena studenty, např. student, který připravuje referát bude mít za úkol připravit více otázek k tématu a pošle je už dopředu (společně se zdroji) ostatním kolegům. Po prezentaci referátu bude jeho úkolem diskusi vést, tedy pokládat otázky a vyzývat ostatní studenty k odpovědím, případně přidá další podněty do debaty. Zároveň bych debatu časově omezila, např. 30 minut. Poté by referující student shrnul výsledky debaty a mohla by následovat pětiminutová pauza. Poté by se přešlo k \"mini\" tématu, nebylo referátu na 5-10 minut, následnou debatu by také vedl prezentující student (stejným způsobem jako v předešlém případě), už by trvala pouze 15 minut. Vyučující by mohl méně zasahovat do diskuze a pobízet ostatní k účasti a místo toho by mohl být přímým účastníkem diskuze.";"knrs" +"1379";"JEB111";"Advanced Data Analysis in MS Excel";;"Poláková,N.,Polák,P.";"5";"3";NULL;NULL;NULL;"3";"3";"5";"1";"5";"5";"3";"5";"Praktický kurz, skvělé přednášky, ochotný přednášející. Nemám co bych vytknul";;"ies" +"1380";"JJM202";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"3";"3";"4";"2";"4";NULL;NULL;NULL;"1";"4";"2";"4";"3";"Historie filmového průmyslu a konglomerizace médií. Zajímavá doplňková četba.";"Aktuálnost, relevance k současným médiím a světu. Chápu, že se předmět jmenuje \"Vývoj a postavení médií v moderní společnosti\" a respektuji, že vznik novin, tisku a žurnalsitiky jsou zásadní témata, ale strávit polovinu kurzu v této oblasti (a do obskurních detailů se zabývat tiskařskými stroji životnímy příběhy Pulitzera a Hearsta) a například vznik a vývoj internetu (o sociálních sítích ani nemluvím) zmínit asi tak v 5 minutách, a to pouze skrze téma pokládání optických kabelů mi přijde dost smutné.Stejně tak téma propagandy by si zasloužilo zásadní update. Ano, klíčové období propagandy bylo za první a druhé světové války, ale vyvíjela se i později a vyvíjí se dodnes. Proto si myslím, že by bylo přinejmenším na místě, aby v přednáškách (nebo alespoň v doplňkové četbě) došlo i na vývoj propagandy po světových válkách. Je sice moc fajn vědět, že za Rakousko-Uherska se posílaly válečné pohlednice, ale pro pochopení dnešních médií a propagandy by bylo rozhodně mnohem lepší vědět například něco o americké nebo izraelské propagandě v druhé polovině století nebo nástupu propagandy v digitálních médiích (se kterou dnes tak obtížně bojujeme).Zkrátka si myslím, že by bylo na místě, aby si vyučující buď uvědomili, že už není rok 1999 a některé dříve zásadní věci, už v kontextu dnešního světa nejsou tak zásadní. Média dnešní \"moderní společnosti\" nejsou pouze tisk, televize a film. V opačném případě by se předmět mohl jmenovat třeba \"Vývoj a postavení médií do roku 1970\" nebo \"Vývoj a postavení médií ve společnosti I\" a pro zbytek by mohl vzniknout navazující kurz v druhém semestru.";"kms" +"1381";"JMBZ290";"Konversatorium zu den aktuellen Fragen";;"Renner,T.";"5";"3";NULL;NULL;NULL;"4";"5";"5";"1";"5";"5";"5";"5";"Vnímání a přijetí rozdílných pohledů a názorů k tématům, aktivní snaha vyučujícího o plynulou diskusi, pozitivní přístup, včasné zasílání zdrojů.";"Vzhledem k tomu, že se během semináře hodně diskutuje, na první hodině bych studenty nechala, aby se každý krátce přestavil a řekl něco o sobě. I když se možná znají od vidění, je to dobré vědět, s kým vlastně diskutuji (a hlavně jak se jmenuje). Na začátku každé hodiny bych přidala malý small talk - tak aby se nejlépe každý vyjádřil k nějaké jednoduché otázce a rozmluvil se - někdy je obtížné začít mluvit rovnou německy, k náročnějšímu tématu a ještě ke všemu se snažit \"diskutovat\". Ve chvíli, kdy atmosféra začne houstnout (tedy nikdo neodpovídá, neví co říct), dala bych buď krátkou pauzu, nebo přidala nový podnět do diskuze (např. na projektoru zobrazit nějaké video), nebo dala právě nějakou odlehčující otázku. Možná by stálo také za to, zaměřit se i na přípravu diskusí - může být víc vedena studenty, např. student, který připravuje referát bude mít za úkol připravit více otázek k tématu a pošle je už dopředu (společně se zdroji) ostatním kolegům. Po prezentaci referátu bude jeho úkolem diskusi vést, tedy pokládat otázky a vyzývat ostatní studenty k odpovědím, případně přidá další podněty do debaty. Zároveň bych debatu časově omezila, např. 20 minut. Poté by referující student shrnul výsledky debaty a mohla by následovat desetiminutová pauza. Pokud by studenti neměli další potřebu k diskuzi, vybídla bych je k tomu, aby např. ve dvou skupinách hledali argumenty pro různé názory týkající se daného tématu. Následně by mohli výsledky konzultovat ve dvojicích. Klidně bych byla pro, aby se formy diskusí během semestru střídaly, určitě jsem ale pro, aby referující student diskusi následně vedl.";"knrs" +"1382";"JJB334";"Zábava v médiích";"Kruml,M.";;"4";"2";"4";"5";"3";NULL;NULL;NULL;"1";"3";"1";"3";"3";;;"kms" +"1383";"JJB606";"Televize jako instituce";"Štoll,M.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kms" +"1384";"JMM103";"Seminář k současné hovorové ruštině";;"Smirnova,T.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Мне была очень полезна работа с видео роликами и то, что преподаватель очень часто обращалась к нам студентам и позволяла нам высказать наше мнение и использовать новый словарный запас. Кроме того я узнала о русской культуре на много больше чем я надеялась.";"У меня нет никаких претензий.";"krvs" +"1385";"JJB607";"Analýzy mediálních obsahů";"Křeček,J.";;"4";"4";"3";"4";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"1386";"JJB617";"Vybrané novinářské osobnosti 20. století";"Železný,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"2";"4";"5";;;"kms" +"1387";"JJB618";"Propagandistické působení médií v 1 polovině 20. století";"Suk,P.";;"4";"4";"3";"4";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kms" +"1388";"JSB003";"Oborová sociologie";"Numerato,D.";;"4";"4";"4";"5";"5";NULL;NULL;NULL;"1";"5";"3";"4";"5";;;"ks" +"1389";"JSB023";"Praktika z kvantitativního výzkumu I";;"Špaček,O.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"3";"5";;;"ks" +"1390";"JSB027";"Sociální politika jako společenská praxe";"Dobiášová,K.,Vlčková,K.";;"5";"5";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kvsp" +"1391";"JLB035";"Francouzština I";;"Bosáková,L.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"3";"5";"5";"4";"5";;;"cjp" +"1392";"JMM703";"Post-Soviet Central Eurasia";"Lídl,V.,Šír,J.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"4";"Information was picked and arranged in a comprehensive and understandable manner; useful readings";"The exam included a lot of questions which were not covered in literature or on lectures (for example, on geography); exam requirements should be elaborated at the beginning of the course.";"krvs" +"1393";"JMB497";"Metodický úvod pro kombinované studium";"Kubát,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"krvs" +"1394";"JMB499";"Současné metodologie";"Kubát,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"krvs" +"1395";"JMB523";"Mezinárodní aktuality I";"Fojtek,V.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kas" +"1396";"JMM130";"Ethno-Political Conflicts in the Caucasus";"Brisku,A.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Very well lead seminars; good course structure";"Nothing";"krvs" +"1397";"JJM371";"New Media and Entrepreneurship";"Orhan,M.";;"2";"2";"3";"5";"1";NULL;NULL;NULL;"3";"2";"2";"1";"1";"International interaction.";"I expected to learn more. We discussed the economical topics very briefly and we were not required to work with this content any further. It is nice when a course is not very demanding, but this time to me it felt like it wasn't very useful.";"kms" +"1398";"JSB998";"Úvod do sociologie";"Soukup,P.";;"4";"3";"4";"3";"4";NULL;NULL;NULL;"2";"3";"2";"4";"4";;;"ks" +"1399";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"4";"3";"3";"4";"3";NULL;NULL;NULL;"2";"5";"3";"5";"4";;;"kas" +"1400";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"4";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"1401";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"4";"2";"4";"5";"2";NULL;NULL;NULL;"1";"3";"3";"4";"3";;;"knrs" +"1402";"JMM283";"Splendor and Misery of Détente";"Fojtek,V.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kas" +"1403";"JMMZ109";"Comparison of Central European Political Systems";"Kubát,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"knrs" +"1404";"JPM300";"Geopolitics of sovereignty, state failure and unrecognized states";"Riegl,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"1405";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Kůželová,M.";"4";"5";"4";"5";"5";"5";"4";"5";"2";"5";"5";"5";"5";;;"krvs" +"1406";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Šafařík,P.";"5";"5";"5";"4";"5";"4";"5";"4";NULL;"5";"5";"5";"5";;;"knrs" +"1407";"JMB402";"Úvod do společenských věd II";;"Šafařík,P.";"4";"3";NULL;NULL;NULL;"4";"5";"5";"2";"4";"5";"3";"3";;;"krvs" +"1408";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"4";"3";"4";"4";"2";NULL;NULL;NULL;"2";"4";"4";"3";"3";;;"krvs" +"1409";"JLB035";"Francouzština I";;"Bosáková,L.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"1410";"JMMZ278";"System polityczny Republiki Czeskiej w perspektywie Europy Środkowej";"Kubát,M.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Chronologickou prezentaci historie. Kurz byl velice přínosný.";;"knrs" +"1411";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"1412";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"4";"4";"4";"3";"2";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"ies" +"1413";"JPM150";"Poloprezidentské režimy v postkomunistické Evropě";"Mlejnek,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Koncentrace na státy střední a východní Evropy";;"kp" +"1414";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"5";"4";"4";"5";"5";"5";"5";"5";"1";"4";"4";"4";NULL;"I liked that the seminars always began with a recap of the most important theory and formulas that were then used during the exercises.";"I would have appreciated if the lectures had a slightly slower pace. Mr Kudashvili is a good teacher but tends to speak quite fast which does not leave much time for the students to process the information he is sharing.";"ies" +"1415";"JEB026";"European Economic Integration";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"2";"4";"3";"5";"1";"2";"3";"2";"1";"3";"1";"3";"2";;"Vylepšení přednášek, pokrýt především jádro přednášky, aby člověk věděl, co si z hodiny především odnést. Předmět celkově zkrátit, a zaměřit se na potřebné informace. Někteří cvičící a jejich přístup ke studentům byl zbytečně příkrý a strohý. Především v průběhu prezentací.";"ies" +"1416";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"4";"5";"5";"4";"5";"5";"5";"5";"2";"4";"4";"4";NULL;;"Byla jsem velice zklamaná final testem. Jeho rozsah byl příliš dlouhý na to, aby se dal během dvou hodin vyplnit. Bylo by třeba buď snížit počet otázek (jak teoretických, tak početních), či zvýšit časovou dotaci, přinejlepším o celou hodinu. Jedině pak by bylo možné dopočítat příklady a místo snahy co nejrychleji zakroužkovat nějaké odpovědi v multiple choice otázkách se nad nimi skutečně moci zamyslet. Příklady v početní části měly taková zadání, že studenti trávili většinu testu tím, že ručně násobili a dělili. Výsledky z prvního kroku příkladu byly vždy potřeba k dalším výpočtům, takže dokud nebyla zcela dořešena první část, nebylo možné počítat další. Kvůli tomu nebylo možné se věnovat dalším příkladům, což mi přijde jako velká škoda, protože přestože by studenti byli schopni je vyřešit, nemohli svoje znalosti předvést (ne kvůli neznalosti, ale přílišné časové náročnosti řešení). V takovou chvíli nabývám dojmu, že test se místo prověření znalostí mikroekonomie snaží studenty co nejvíce potrápit a nedává jim možnost ukázat, co umí.";"ies" +"1417";"JJB617";"Vybrané novinářské osobnosti 20. století";"Železný,J.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"3";"3";"5";"5";"Výborný přístup vyučujícího, který je vždy skvěle připravený a ještě stíhá být zábavný. Navíc vždy vyjde vstříc a cokoliv ochotně vysvětlí.";"Ač to může být zvláštní, ocenila bych větší náročnost. Předmět mi přišel dost lehký na to, kolik za něj dostaneme kreditů.";"kms" +"1418";"JEB105";"Statistics";"Červinka,M.";"Nevrla,M.";"4";"5";"4";"5";"5";"5";"5";"5";"1";NULL;NULL;NULL;NULL;"Domácí úkoly byly časově velice náročné, ale myslím si, že jsou velice užitečnou přípravou na midterm a final test. Studentům by nejspíše pomohlo, kdyby na ně měli alespoň o pár dnů více času (např. 10 dní). S přednáškami a cvičeními jsem byla spokojená.";;"ies" +"1419";"JEB047";"Účetnictví II";"Kemény,I.";;"4";"2";"5";"5";"4";NULL;NULL;NULL;"1";"3";"4";"4";"5";"Na kurzu oceňuji, že po něm student získá základní přehled o účetnictví, účtování samotném i dopadu účetních položek na fungování a daně podniku.";"Na Účetnictví II by se hodilo mít více příkladů a nově probranou látku (neprocvičovat většinu hodin látku z předchozího kurzu).";"ies" +"1420";"JEM040";"Účetní a daňové poradenství";;"Kemény,I.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";"Předmět je velmi praktický, student získá přehled v této oblasti.";;"ies" +"1421";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"3";"4";"4";"4";"3";"3";"3";"2";"2";"3";"3";"3";"2";"Prednášky boli na vysokej úrovni a určite by som ponechal robenie rôznych experimentov na hodinách.";"Kurz zanechal napriek kvalitným prednáškam veľmi zlý dojem nakoľko mi prišiel hodnotiaci systém, ktorý v priebehu semestra (resp. pred skúškovým) zmenil pravidlá veľmi nespravodlivý. Bolo veľmi nespravodlivé že študent, ktorý sa počas semestra napriek iným študijným povinnostiam dokázal venovať poctivo aj micru a napísal tým pádom midterm pomerne slušne z toho nemal absolútne žiadnu výhodu, nakoľko študent ktorý Micro otvoril pár dni pred skúškou mohol byt rovnako ohodnotený, napriek tomu že sa na Midterm trebárs vôbec nenaucil a mal následne malo bodov. Preto do budúcna navrhujem, že aby takýto systém bol naozaj motivujúci, bolo by dobre “odmeniť” slušný výsledok z Midtermu nejakou toleranciou pri eventuálnom nesplnení nejakej hranice pri Finali. Cely zvláštne nastavený hodnotiaci systém vrhol na kurz veľmi zle svetlo a zanechal zlý dojem z mikroekonomie nakoľko sa rovnako ako ja veľa študentov cítilo asi nspravodlivo keď miesto známky C/B mali F lebo im o bod ušlo splniť časť zo záverečného testu. Mnohí študenti sa sťažovali aj na “zlomky” vo výsledkoch príkladov v teste, to však mi ako problém vôbec nepríde lebo neviem čo by inak študent dve hodiny na záverečnom teste robil.";"ies" +"1422";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"2";"4";"5";;;"kp" +"1423";"JEM137";"Real Estate Investment";"Jandík,T.,Streblov,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"This is definitely one of the best modules at IES. I deeply appreciate that it provides students with necessary theoretical concepts, as well as the practical side of real estate and illustration of how theory is connected to the real world. I also appreciate the friendly and helpful, as well as professional approach of the lecturers. This module, although demanding, will indeed have a substantial value added for any student.";"I would probably appreciate more examples of some financial models.";"ies" +"1424";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Líbí se mi, že jsem se dozvěděl spoustu zajímavostí z praxe typu \"jak se to obvykle dělá\", ocenil jsem informace o současných nebo minulých - ale netradičních a zajímavých - problémech a případech.";"Pan Pražák občas hodinou prolétl tak rychle, že nebylo možné stíhat sledovat jeho výklad a zároveň všechno, co bylo v jeho prezentaci :-(";"ies" +"1425";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"3";"1";NULL;NULL;NULL;"3";"4";"3";"2";"2";"2";"3";"3";"Oceňuji otevřenost k diskuzi a líbil se mi výběr aktuálních nebo kontroverzních témat.";"Rozhodně se mi nelíbila zaujatost některých přednášejících v některých otázkách, které jsou podle mého názoru natolik odborné, že výsledek jejich prezentování a informačního přívalu je tím negativně ovlivněn. Je samozřejmé, že každý - i přednášející - může (a měl by mít) vlastní názor a nemít důvod jej tajit před studenty, nicméně obhajovat se rádoby hlubší znalostí ve srovnání se studenty a argumentovat vyšším věkem nebo větší občanskou zapáleností považuji minimálně za scestné.";"ies" +"1426";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"5";"4";"5";"5";"5";"Ocenil jsem \"polopatičnost\" a postupnost, kterou přednášející pan Stráský aplikoval při vysvětlování jednotlivých kroků při výpočtu. Líbil se mi jeho celkový přístup a rozhodně mi kurz hodně dal.";"Čas konání. Od půl sedmé do osmi je na matematikou občas prostě příliš pozdě :-)";"ies" +"1427";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"3";"1";"4";"5";"1";"4";"5";"1";"1";"2";"1";"3";"5";"Interaktivitu během přednášek.";"Rád bych se dozvěděl hlubší detaily, na které bohužel už nezbyl čas.";"ies" +"1428";"JLB003";"Angličtina pro ekonomy I";;"Poslušná,L.";"4";"2";NULL;NULL;NULL;"5";"5";"5";"1";"2";"4";"4";"5";"Líbily se mi prezentace, zajímavé aktivity jako kvízy na mobilech (ačkoliv nerad držím pořád mobil v ruce) nebo slovní mapy a možnost slovního projevu během hodin.";"Spíše než gramatice bych se rád věnoval slovní zásobě z oblasti businessu. Rozhodně by, myslím, bylo také skvělé, kdyby se na hodinách více diskutovalo např. o současných tématech na poli businessu.";"cjp" +"1429";"JEB105";"Statistics";"Červinka,M.";"Červinka,M.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"1430";"NMMA701";"Matematika 1";"Spurný,J.";"Honzík,P.";"4";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"Superseminář.";"Nevím, co by se mělo dát zlepšovat. Současná podoba kurzu umožňuje, alespoň podle mého názoru, zvládnout zápočty i zkoušku.";"ies" +"1431";"JEM132";"Company Valuation";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"This is certainly one of the best modules at IES. I really liked the lectures, as they were interactive, explained the concepts well and helped you understand their relation to the real business world. I also like the variety of assignments, which definitely developed analytical skills in many different ways. I appreciate the lectures from Mr Novak, as well as from our guest lecturers and the professional and helpful approach of all of them. Honestly, more engaging and motivating modules like this at IES!";"Comparing to the lectures and the lecturers, communication and the approach of the teaching assistants is completely different experience. I can be an issue related to the course organisation rather than the teaching assistants themselves, however, when preparing an assignment that is divided into a few stages over the semester, one would expect to receive the information about the points earned, as well as some feedback. It also takes a few days to respond to our email. Besides, relying solely on other groups (and you) reviewing your model instead of being able to get an insight from professionals from this field is uncomparable. Also the whole concept of seminars does not have much value added, as we only did several easy examples (similar to the bachelor Financial Management course), which is far from the difficulty of the assignment itself. Textbook examples are often straightforward, whereas if we used more real data, the seminar would be useful if not for the assignment itself, then at least for us to review what we might have done better or what we did incorrectly.";"ies" +"1432";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"4";"4";"4";"3";"3";NULL;NULL;NULL;"1";"4";"3";"4";"4";"Rychlost zpracování výsledků zkoušky.";;"ies" +"1433";"JJM248";"Vývoj grafického designu a polygrafického zpracování periodik";"Slanec,J.";;"4";"3";"3";"4";"4";NULL;NULL;NULL;"1";"4";"3";"3";"4";"Ocenil bych přehlednou prezentaci, která byla vždy součástí výuky.";"Místy chyběl širší kontext (především o českých graficích, někdy i jednotlivých směrech).";"kz" +"1434";"JLB005";"Angličtina pro politology I";;"Stružková,I.";"5";"2";NULL;NULL;NULL;"4";"5";"5";"1";"3";"5";"4";"5";"Osobní přístup, dobře zpracované vyučovací materiály a atmosféru na hodinách.";;"cjp" +"1435";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Nádherné mapy, zajímavosti na každé přednášce jsem se dozvěděl něco nového a užitečného. Propojení světa mapy s realitou. Přístup pana doktora Romancova.";;"kp" +"1436";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"3";"3";"5";;;"cjp" +"1437";"JLB041";"Španělština I";;"Mlýnková,L.";"5";"4";NULL;NULL;NULL;"5";"3";"5";"1";"5";"5";"4";"5";;;"cjp" +"1438";"JPM719";"Diplomacy of the European Union";"Pajtinka,E.";;"1";"2";"1";"1";"2";NULL;NULL;NULL;"4";"2";"1";"2";"1";"I was very interested in the topic but disappointed. The course was completely unaccaptable for various reasons.";"The lectures consisted of the teacher reading out loud from his slides, he didn't talk freely and is English was not very good. He encouraged us to ask questions, but when we did he answered in a rude way as if we were stupid to ask. He entire attitude said \"I do not want to be here\".The professor did not respect the schedule of the classes at all, once he finished three hours earlier and made us write an essay instead (in the time he was supposed to teach us something), saying that if we write this essay, it will be taken in consideration in the final grade. After the exam, however, he affirmed to have said that it would be taken into consideration only if exceptionally good. One day he started teaching earlier than what he said the day before, so that many missed half an hour of class. Then he made us write a spontaneous test (again in the time he was supposed to teach), but those who had missed this half an hour obviously could not answer everything because they had missed parts of it. Also, this \"spontaneous test\" and essay were not mentioned in the syllabus.The exam itself was a total mess. The teacher did not show up himself, the exam was held together with a different exam. It started too late, the room was too small, people were talking the entire time, given that the professor was not there it was not possible to ask for clarifications, and so on. It ended with very strict grading, which I find unappropriate given the poor teaching. I hope for further students that this class will not be taught like this again.";"kmv" +"1439";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"3";"2";"3";"3";"1";NULL;NULL;NULL;"1";"4";"2";"3";"3";;;"krvs" +"1440";"JJM249";"Duchovní kořeny evropské kultury";"Halada,J.";;"3";"2";"4";"5";"3";NULL;NULL;NULL;"1";"3";"3";"4";"3";"Vyučující je hodný, výuka probíhá v uvolněné atmosféře.Ve výuce je ve velké míře zařazován kontext, který je k pochopení dané látky důležitý.";"Kurz jednoznačně potřebuje lépe vyvážit. Hodiny neodpovídaly sylabu, některé přednášky hovořily o jedné osobnosti, další o celých epochách.výrazně potřebuje vylepšit ústní zkouška, která probíhá skupinově. Vyučující nedával rovnoměrnou příležitost všem zkoušeným, známky byly podle mého názoru voleny zčásti náhodně. Celkově mám ze zkoušky výrazně negativní pocit.";"kz" +"1441";"JPB018";"Dějiny mezinárodních vztahů I";"Soukup,J.";;"4";"1";"5";"5";"4";NULL;NULL;NULL;"3";"4";"4";"5";"5";"Materiály ukazované v rámci přednášky, videa, mapy, fotky.";;"kmv" +"1442";"JMB402";"Úvod do společenských věd II";;"Pondělíček,J.";"3";"3";NULL;NULL;NULL;"5";"4";"5";"1";"4";"5";"3";"4";;;"krvs" +"1443";"JSB003";"Oborová sociologie";"Numerato,D.";;"4";"4";"4";"5";"3";NULL;NULL;NULL;"1";"4";"2";"4";"4";;;"ks" +"1444";"JEB026";"European Economic Integration";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"3";"3";"3";"4";"2";"3";"3";"1";"1";"2";"4";"5";"4";"Veľmi kvalitné skriptá z ktorých sa nedá len dobre naučiť na záverečnú skúšku ale aj dozvedieť sa viac o takom zaujímavom koncepte akou je zrovna EÚ z ekonomického hľadiska. Oceňujem aj používanie konkrétnych príkladov aby sa študentovi ľahšie pamätala teória, ktorá sa skrývala za rôznymi modelmi a štúdiami.";"Napriek kvalitným skriptám a zaujímavému obsahu, by som určite navrhol zmeniť spôsob prednášania. Myslím, že prečítať si to čo je v prezentáciách si vedia študenti aj sami (netvrdím, že by to priebežne s určitosťou robili), ale tých, ktorých téma predmetu naozaj veľmi zaujíma, by určite potešili aj nejaké diskusie, alebo iná forma pridanej hodnoty z chodenia na prednášky. Som názoru, že kapacita akou je práve pán prednášajúci, by určite týmto spôsobom odovzdal veľa zaujímavých informácii a týmto motivoval viac študentov sa zaujímať o túto tému. Veľmi ma bavilo pripraviť sa na skúšku, ale na prednášky som veľmi rýchlo prestal chodiť.";"ies" +"1445";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Kocian,J.";"4";"3";"5";"5";"5";"4";"5";"1";"2";"5";"2";"4";"5";;;"knrs" +"1446";"JSB033";"Praktika z kvalitativního výzkumu";;"Spalová,B.";"4";"4";NULL;NULL;NULL;"4";"5";"5";"1";"3";"5";"3";"4";;;"ks" +"1447";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Lizcová,Z.";"3";"3";"3";"4";"3";"4";"5";"2";"1";"4";"2";"4";"3";;;"krvs" +"1448";"JJM259";"Mediální systémy a jejich komparace";"Miessler,J.";;"3";"3";"3";"4";"3";NULL;NULL;NULL;"1";"2";"2";"3";"3";;"Více času na prezentace. Hlasitější a výraznější přednes vyučujícího.";"kz" +"1449";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"4";"2";"2";"5";"1";NULL;NULL;NULL;"1";"4";"1";"4";"3";;;"knrs" +"1450";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"3";"3";"2";"3";"1";NULL;NULL;NULL;"2";"4";"2";"4";"4";;;"kas" +"1451";"JPB020";"Úvod do mezinárodních vztahů";"Karlas,J.,Kučera,T.";;"3";"4";"4";"4";"3";NULL;NULL;NULL;"1";"4";"2";"3";"3";"Příkladná práce s moodlem. Všechny materiály a věci týkající se výuky byly na moodlu.";"Význam midtermtestů byl dosti mizivý. Možná více specifikovat heslo v slovníčku mezinárodních vztahů a co to obnáší, případně do toho studenty více uvádět a motivovat. Výuka byla téměř pouze teoretická a drobné sklouznutí k praktické části mezinárodních vztahů by určitě bylo přínosem.";"kmv" +"1452";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"2";"4";"2";"5";"5";;;"ks" +"1453";"JPB221";"Metodologický proseminář I";;"Komasová,S.,Parízek,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Práce s moodlem. Praktičnost kurzu, bez tohoto kurzu a znalostí získaných v něm bych obtížně plnil studijní povinnosti v ostatních kurzech.";;"kmv" +"1454";"JJM279";"Divadelní kritika";"Homolová Richtrová,N.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Otevřená diskuze, zajímavé texty a ukázky.";"Pokud možno trochu více konkrétních ukázek divadelních inscenací, aby nad nimi bylo možno ve třídě diskutovat. Možná částečně zkrátit debatu nad texty literární kritky a tím uvolnit čas pro několik, třeba krátkých, ukázek.";"kz" +"1455";"JJM356";"Metodologické přístupy ke studiu žurnalistiky";"Nečas,V.,Veselková,Z.";;"3";"3";"3";"4";"3";NULL;NULL;NULL;"3";"3";"4";"2";"3";"Oceňuji prezentace, k prověření praktických schopností poměrně dobře posloužily průběžné úkoly.";"Přednášky obsahovaly jen základy, které byly oproti literatuře vyžadované u zkoušky nedostatečné, některým jevům byla věnována přehnaná pozornost, na mnohé další se naopak nedostalo.Velmi dlouhou dobu trvalo opravení průběžných úkolů";"kz" +"1456";"JPB252";"České novověké dějiny I.";"Kučera,J.";;"4";"4";"4";"4";"3";NULL;NULL;NULL;"1";"3";"3";"5";"4";;;"kp" +"1457";"JPB256";"Politické systémy I";"Brunclík,M.,Jeřábek,M.,Kotábová,V.,Perottino,M.";;"5";"4";"5";"4";"4";NULL;NULL;NULL;"2";"5";"4";"5";"5";;;"kp" +"1458";"JJB221";"Ekonomická teorie marketingu";"Postler,M.";;"3";"3";"3";"5";"3";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kmkpr" +"1459";"JJB224";"Psychologie marketingové komunikace";"Vranka,M.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"3";"4";"4";"4";"4";;;"kmkpr" +"1460";"JJB237";"Právo a etika v marketingu";"Winter,F.";;"5";"4";"5";"5";"3";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmkpr" +"1461";"JJB135";"Filmový seminář I";;"Šobr,M.";"4";"1";NULL;NULL;NULL;NULL;NULL;NULL;"1";"3";"4";"3";"5";;;"kz" +"1462";"JJB269";"Sociální kontext komunikace";"Vranka,M.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"3";"4";"4";"5";"4";;;"kmkpr" +"1463";"JMB056";"Reflexe velkých debat v sociálních vědách ve filmu";;"Kozák,K.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"5";"5";;"Zapomněla jsem odeslat nápady na další filmy v e-mailu, tak připojuji zde:Izraelsko-palestinský konflikt: Omarhttps://www.csfd.cz/film/343116-omar/prehled/Čínský sen - úplně možná nezapadá do témat, ale je velmi zajímavý, ať už z urbanistického hlediska nebo kvůli absurditě, s kterou se v Číně setkáváme v mnoha aspektechhttps://www.jedensvet.cz/2017/filmy-a-z/33086-cinsky-sen";"kas" +"1464";"JJB401";"Komerční a nekomerční marketingová komunikace";"Báča,L.,Obluk,O.";;"5";"5";"4";"4";"4";NULL;NULL;NULL;"3";"5";"5";"5";"5";;;"kmkpr" +"1465";"JJB406";"Tvorba a prostředky v mediální komunikaci";"Chudinová,E.";;"4";"3";"3";"5";"3";NULL;NULL;NULL;"3";"3";"4";"4";"3";;;"kmkpr" +"1466";"JJB407";"Bakalářský proseminář";"Rosenfeldová,J.";;"4";"3";"4";"4";"5";NULL;NULL;NULL;"3";"5";"4";"4";"5";;;"kmkpr" +"1467";"JPB258";"Politické myšlení novověku I";"Franěk,J.,Kučera,J.";;"3";"3";"4";"3";"3";NULL;NULL;NULL;"2";"4";"2";"3";"4";;"Předem přesně upřesnit jak se zkouší, obecně více informovat studenty o průběhu zkoušení v tomto kurzu by bylo dobré. Případně pokud by mohli oba zkoušející vypisovat termíny v SISu, aby nedocházelo k obsazení a přelidnění zkušebního termínu u jednoho z nich. Termíny by se též mohli vypisovat od prvního zkušebního týdne.";"kp" +"1468";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"ks" +"1469";"JSB055";"Současná sociální antropologie";;"Grygar,J.,Hrešanová,E.";"5";"5";NULL;NULL;NULL;"4";"4";"5";"2";"4";"1";"4";"5";"- zajímavý výběr povinných textů- průběžné testy, asi přínosnější než psaní textů z četby a časově méně náročné- přednášky hostů";"- možná užší formulaci témat závěrečné ústní zkoušky";"ks" +"1470";"JJB298";"Marketingová komunikace malých a středních podniků";"Koudelková,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"5";"5";"Praktické pojetí";"Větší zaměření na oblast podnikání, obchodu, financí";"kmkpr" +"1471";"JJM224";"Politická ekonomie komunikace";"Vochocová,L.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"3";"5";"3";"5";"5";;;"kms" +"1472";"JSB133";"Zemědělství a rozvoj venkova (vybraná témata z rurální sociologie)";"Zagata,L.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"2";"4";"2";"5";"5";"- zajímavá akt. témata-";;"ks" +"1473";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"5";"4";"4";"4";"5";NULL;NULL;NULL;"1";"5";"5";"4";"5";"Praktičnost, možná více ukazovat věci jako Eklep, Newton a další databáze. To se studentům hodí nejvíce. Jinak jeden z nejpraktičtějších kurzů.";"Lepší komunikace mezi vyučujícími a studenty.";"kp" +"1474";"JPB569";"Workshop Politické a státní instituce v praxi";;"Brunclík,M.";"5";"2";NULL;NULL;NULL;"4";"4";"5";"2";"5";"5";"4";"5";;"Lépe specifikovat práci k zakončení";"kp" +"1475";"JSB517";"Hudební subkultury mládeže";"Oravcová,A.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"2";"4";"3";"5";"4";"- zajímavé texty";"- velká část některých hodin probíhala formou zkoušení z textu, ptaní se na pojmy, což lehce škodilo výkladu";"ks" +"1476";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"3";"4";"3";"2";"4";NULL;NULL;NULL;"1";"3";"2";"3";"3";"Střídaní vyučujících - přináší více pohledu";"Upravit náročnost záverečného zápočtu, na který příprava trvá déle než na jiné lépe ohodnocené předměty.";"ies" +"1477";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"5";"2";NULL;NULL;NULL;"4";"5";"5";"2";"5";"5";"5";"5";"Rozhodně je to předmet díky kterému se rozdíly ze středních škol zmenšují a studenti se zdánlivo horší matematikou tak mohou dohonit své spolužáky s lepší úrovní.";"Dr. Stráský ke kurzu přistupuje zodpovědne a vytváří skvělou atmosféru počas výuky. Problémem ale je, že mnoho je druhu zápisu se liší od těch vyučovaných na MatFyzu.";"ies" +"1478";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"3";"5";"4";"5";"5";"Dva termíny kurzu, místo jedného ako je uvedeno v SISu.";"nic";"kz" +"1479";"JLB011";"Němčina pro ekonomy nižší I";;"Faltýnová,R.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Rozhodne môj najlepší predmet v tomto semestri. O tomto kurze je často od starších žiakov počuť, že je nekvalitný, nič nové nenaučí a prístup pani Faltýnovej je zlý, ale to vôbec nie je pravda. Pani Faltýnová pristupuje ku kurzu zodpovedne, na hodiny je skvelo pripravená a zároveň veľmi nápomocná študentom. Využíva rôzne metódy učenia nových slovíčok a diskusií, čo z hodín robí vždy tie najzaujímavejšie v týždni.";"Problémom trošku je, že 2 hodiny naraz sú často dosť náročné v spojitosti s iným rozvrhom študentov.";"cjp" +"1480";"JMM034";"Obrazy a stereotypy Balkánu";"Šístek,F.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"5";"3";"2";"4";"This courses helped me a lot with getting better with understanding Czech, which was the reason for me to take a course in Czech language. Furthermore, the lectures gave me new clues and conceptual keys, extremely helpful to understand and analyse Balkan issues, concepts that were totally new to me. I personnally thank M. Šístek for having accepted me in his class and for his interesting and rich courses.";".";"krvs" +"1481";"JSB998";"Úvod do sociologie";"Soukup,P.";;"4";"2";"4";"5";"1";NULL;NULL;NULL;"2";"4";"2";"5";"5";"Přístup Dr. Soukupa, který své materiály zveřejňuje, čím umožní studentum IES, který se v Jinonicích neučí, učit se výhradně doma.";;"ks" +"1482";"JMM422";"Ethnic Issues and Territories in Eastern, East Central and Southeastern Europe";"Vykoukal,J.";;"2";"2";"3";"4";"1";NULL;NULL;NULL;"1";"1";"2";"3";"1";"This course was mainly (only) composed of raw statistics and very basic informations about the ethnic issues, that we should have studied. I was very disappointed because I only heard things I already new or that weren't analysed at all.";"Give a real analysis on the issues which are studied and stress the actual and current questions related to, instead of raw statistics taken from the book, which are useless without a critical and analytical approach.";"krvs" +"1483";"JSM026";"Klíčové otázky sociální antropologie";"Grygar,J.,Hrešanová,E.,Uherek,Z.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";"Kurz nabízí představení současných témat sociální antropologie a umožňuje tak nově příchozím studentům mgr studia zorientovat se v oboru. Přesto se však pro absolventy bc studia rozhodně nejedná o čisté opakování a naopak je zde velký prostor pro rozvíjení také jejich dovedností.";;"ks" +"1484";"JMMZ224";"Cultural Memory and Identity in the Balkans";"Asavei,M.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"5";"5";"5";"Ms Asavei's course was really interesting and providing good lectures and readings. The analysis of Balkan issues about memory and identity was complete, precise and original. I particularly appreciated the teacher's intellectual honesty, something that I consider as very valuable.";"The readings were sometimes tooo long to be read entirely and above all understood, maybe it would be possible to reduce the length of them to focus on the main points that must be remembered by the students. And it would be good to assure that the teacher gets the room keys easier :)";"krvs" +"1485";"JMMZ274";"Geschichte des Rassismus";"Barth,B.";;"3";"4";"5";"5";"4";NULL;NULL;NULL;"1";"3";"3";"5";"3";"The course was very rich, maybe too because some points were quickly overviewed. I appreciated it for helping me with German language, but finally haven't got so much knowledge from it, maybe also because of the language barrier. However, i acquired new important clues to understand modern racism.";"Reduce the number of themes to have them better mastered by the students.";"knrs" +"1486";"JSM027";"Urbánní antropologie";"Uherek,Z.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Jedná se o jeden z nejlepších kurzů, který jsem na ISS za 6 let absolvovala. Vyučující dokázal v naprosto šíleném čase (17:00-19:40) udržet pozornost studentů po celou dobu kurzu, seznámit je se základy této specializace a propojit je zároveň s klíčovými znalostmi celého oboru.";;"ks" +"1487";"JSB546";"Urban Change and Grassroots Movements";"Pixová,M.";;"5";"3";"4";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"My favourite course, especially because it helped me deciding about my choice of masters in urbanism. The approach and the methods were definitely great, and the amount of work really sustainable, in spite of sometimeq too long and too abstract readings. It really had the students understanding the new issues related to urbanism in the context of post-socialist society and inba globalized country. Thank you.";"Concentrate the readings on the main clues, even if the course explicited them well. Get more time for the personal assessments.";"ks" +"1488";"JSM578";"Anthropology of EU";"Uherek,Z.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";;"Kurz je podobně jako další kurzy vyučované v anglickém jazyce přizpůsoben náročností zahraničním studentům (včetně těch kteří v minulosti neabsolvovali jediný antropologický kurz).";"ks" +"1489";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"3";"4";"4";"4";"2";"5";"5";"5";"2";"1";"2";"3";"3";;"For some reason, assistant Ugur Gok disappeared for the second half of the semester. In my opinion, mr Gok was probably the best seminar instructor this school had and after his sudden departure, I felt no need to visit the seminars anymore. He was able to go through the topic without unnecessary explanation but managed to answer all questions and misunderstandings. His seminars were clear and fast and resulted in perfect knowledge of those students who paid attention. Losing mr Gok was possibly the worst thing that could have happened to Microeconomics 2 course.";"ies" +"1490";"NMMA703";"Matematika 3";"Zelený,M.";"Turčinová,H.";"5";"5";"5";"5";"5";"2";"2";"3";"1";"2";"4";"3";"4";;;"ies" +"1491";"JSM554";"Diplomový seminář";;"Grygar,J.,Hájek,M.";"5";"4";NULL;NULL;NULL;"4";"5";"5";"1";"4";"5";"4";"5";;;"ks" +"1492";"JJB006";"Současný český jazyk III";;"Svobodová,I.";"4";"4";NULL;NULL;NULL;"4";"5";"5";"2";"4";"5";"5";"4";"nejvíce oceňuji praktický přístup k výuce. Ten navrhuji zachovat a možná i rozšířit.";"Naopak bych lehce omezil teoretickou část výuky. Jevilo se mi, že mnohdy byl rozsah požadované a vykládané látky příliš rozsáhly.";"kms" +"1493";"JJB014";"Žurnalistická tvorba III - Časopisecká tvorba";"Osvaldová,B.";"Maňák,V.";"5";"3";"4";"4";"3";"4";"5";"3";"1";"4";"5";"5";"5";"Nejvíce oceňuji aktivní přístu vedoucích kurzu a jejich přístup ke studentům. Myslím, že právě takový vztah mezi vyučujícím a žákem je ideálním příkladem, ze kterého by mohli vycházet i ostatní.";"Cvičení ve formě redakčních porad mi občas přišla zbytečná a nepřinášela velký posun v práci na časopisu.";"kz" +"1494";"JPM607";"International Negotiations";;"Parízek,M.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"4";"4";;;"kmv" +"1495";"JPM727";"Orchestration in Global Governance";;"Abbott,K.,Parízek,M.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"5";"5";"There's something about the very structure of block courses that makes me learn more in shorter time and remember it better. Plus, it was not held in Jinonice!";;"kmv" +"1496";"JPM526";"Justice and Reconciliation in Post-Conflict Societies";;"Werkman,K.";"4";"3";NULL;NULL;NULL;"4";"3";"4";"3";"5";"5";"5";"5";"the movies were very interesting. Plus, good way of remembering things.";"It would be nice to receive comments on the diaries after submitting them, otherwise it is impossible to improve.Also, communication has been quite difficult. E-mails have not been answered or only after delay and after several attempts of receiving an answer.";"kmv" +"1497";"JPM561";"Regional Security Studies";"Karásek,T.,Klosek,K.";"Karásek,T.,Klosek,K.";"4";"5";"4";"5";"3";"4";"5";"4";"1";"5";"1";"5";"5";;;"kbs" +"1498";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmv" +"1499";"JPM694";"Strategic Studies";"Hynek,N.,Ludvík,J.,Smetana,M.";;"5";"5";"4";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kbs" +"1500";"JPM698";"Middle East Security";"Daniel,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kbs" +"1501";"JPM689";"Conflict Studies";"Karásek,T.";;"2";"5";"4";"4";"1";NULL;NULL;NULL;"2";"4";"2";"4";"1";;;"kbs" +"1502";"JPM706";"Terrorism and Counterterrorism";"Bureš,O.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kbs" +"1503";"JJB226";"Strategie a finanční management marketingu I";"Majerik,P.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"3";"5";"3";"5";"5";;;"kmkpr" +"1504";"JJB238";"Kritika marketingové komunikace";"Rosenfeldová,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"3";"5";"3";"5";"5";;;"kmkpr" +"1505";"JJB243";"Aktuální trendy a vývoj v oboru I.";"Hejlová,D.,Vranka,M.";"Hejlová,D.,Vranka,M.";"5";"1";"5";"5";"5";"5";"5";"5";"1";"5";"1";"5";"5";;;"kmkpr" +"1506";"JJB293";"Role výzkumů v politických a komerčních kampaních";;"Rosenfeldová,J.";"3";"2";NULL;NULL;NULL;"2";"5";"2";"4";"3";"3";"4";"2";;;"kmkpr" +"1507";"JJB298";"Marketingová komunikace malých a středních podniků";"Koudelková,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmkpr" +"1508";"JLB035";"Francouzština I";;"Bosáková,L.";"3";"1";NULL;NULL;NULL;"3";"5";"4";"1";"3";"3";"2";"4";;;"cjp" +"1509";"JMB011";"Moderní dějiny Ruska";"Litera,B.,Pečenka,M.";"seminář nenavštěvován";"5";"4";"5";"4";"5";"5";"5";"5";"2";"5";"4";"5";"5";;;"krvs" +"1510";"JSB007";"Dějiny sociologie I";"Šanderová,J.";"Tesárek,J.";"3";"3";"5";"4";"4";"5";"5";"5";"4";"5";"4";"5";"5";;;"ks" +"1511";"JSB025";"Sociální problémy";"Frič,P.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kvsp" +"1512";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Coufalová,L.,Svobodová,T.";"4";"3";"5";"5";"2";"3";"3";"5";"1";"5";"5";"5";"5";;"Dva cvičící na jednom semináři by měli stejně hodnotit. V úkolech mi jedna vytkla něco, co druhá pochválila!";"ks" +"1513";"JEM183";"Mathematical Methods in Macroeconomics";"Stráský,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"ies" +"1514";"JSB530";"Úvod do sociologického myšlení";"Bureš,J.,Numerato,D.";"Teichmanová,K.";"3";"3";"2";"3";"1";"3";"3";"5";"3";"3";"3";"3";"3";;;"ks" +"1515";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"5";"4";"5";"5";"5";"4";"5";"4";"2";"5";"4";"5";"5";;;"ies" +"1516";"JEM141";"Traditional and Alternative Risk Transfer in the Insurance Sector";"Pompella,M.,Teplý,P.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"3";"3";"5";;;"ies" +"1517";"JJB135";"Filmový seminář I";;"Šobr,M.";"4";"3";NULL;NULL;NULL;"5";"5";"3";"1";"3";"3";"3";"5";;;"kz" +"1518";"JEM181";"Data Science with R";"Herman,D.,Krištoufek,L.";"Herman,D.,Krištoufek,L.";"5";"4";"5";"5";"3";"5";"5";"3";"1";"5";"5";"5";"5";;;"ies" +"1519";"JLB013";"Němčina odborná I";;"Křenková,D.";"4";"4";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"4";"5";"Přístup vyučujícícho, různé typy cvičení a aktivit, možnost ovlivnit tématický okruh hodin";"Dvouhodinový kurz jazyka je velmi náročný, co se koncentrace týče. Velmi bych ocenila 5-10 min přestávku v polovině dvouhodinovky.";"cjp" +"1520";"JMM074";"Landmarks in 20th Century U.S. History and Their Interpretations";"Pondělíček,J.";;"3";"3";"3";"4";"3";NULL;NULL;NULL;"4";"4";"3";"4";"3";"jiný přístup k výkladu historie";"sylabus ani četba nebyly přístupné v Sisu, četba se často rozesílala například den či dva před hodinou, kdy na její přečtení již nebyl čas, reakce na emaily byla zdlouhavá, reakce na návrhy písemných prací byla delší než měsíc";"kas" +"1521";"JMM200";"Roots of American Music - Folklore, Blues, Jazz";"Calda,M.";;"4";"2";"3";"4";"2";NULL;NULL;NULL;"2";"4";"3";"4";"4";"oceňuji jeden z mála kurzů, který se věnuje kultuře, znalosti vyučujícího jsou velkolepé, je vidět, že dané téma ho opravdu velmi baví";"i když nevím jak přesně, bylo by dobré zlepšit interaktivnost . Kurz je zajímavý, ale težkose v hodinách udržuje pozornost";"kas" +"1522";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"3";"4";"4";"1";NULL;NULL;NULL;"1";"2";"2";"2";"1";;"Pan Kameníček by mohl občas použít mikrofón, když nechce mluvit hlasitě. Nicméně se snaží předmět dělat zajímavější, než by mohl být.";"ies" +"1523";"JMM271";"Metodologický seminář";;"Kýrová,L.";"3";"5";NULL;NULL;NULL;"3";"5";"3";"1";"3";"3";NULL;NULL;"přístup vyučujícího ke studentům";"je těžké udržet koncentraci, jelikož vyučující akorát předčítá text z prezentací, na to, že se jedná pouze o zápočtový kurz, jsou požadavky na něj celkem náročné, místo historického přehledu různých metodologických přístupů bych ocenila jejich praktické využití - nácvik. Zadat za výstupní práci literature review bez jejího nácviku nebo vyzkoušení v hodninách mi připadá hloupé. Ocenila bych nejprve nácvik v hodině. Ocenila bych také zpětnou vazbu na závěrečnou práci (zaslání zpět emailem během zkouškového). Je vidět, že vyučující je zaměřen na jedno téma a veškerá (i zdánlivě nesouvisející) témata jsou vykládána skrze toto jedno téma.";"krvs" +"1524";"JLB005";"Angličtina pro politology I";;"Panešová,K.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";;;"cjp" +"1525";"JPB269";"Současné problémy mezinárodních vztahů";"Knutelská,V.";;"3";"3";"3";"3";"2";NULL;NULL;NULL;"1";"2";"1";"3";"2";;"Hodnocení fact-checing. Je zde poměrně mnoho... rozporů.";"kmv" +"1526";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"3";"5";"5";"3";NULL;NULL;NULL;"1";"3";"3";"4";"5";"Pan Romancov má předmět skvěle zvládnutý, nejedná se pouze o monotónní výklad.";;"kp" +"1527";"JLB027";"Ruština odborná I - vyšší";;"Mistrová,V.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"1528";"JPB018";"Dějiny mezinárodních vztahů I";"Soukup,J.";;"5";"3";"5";"5";"3";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Pan Soukup dokáže zaujmout.";;"kmv" +"1529";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"5";"5";"5";"4";"5";NULL;NULL;NULL;"1";"5";"5";"5";"3";;;"kp" +"1530";"JPB020";"Úvod do mezinárodních vztahů";"Karlas,J.,Kučera,T.";;"3";"4";"4";"4";"5";NULL;NULL;NULL;"1";"5";"4";"5";"4";;;"kmv" +"1531";"JPB218";"Dějiny novověké Evropy I.";"Kučera,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"3";;;"kp" +"1532";"JMM277";"Historie a kultura";"Vykoukal,J.";"Kýrová,L.";"2";"5";"4";"5";"4";"3";"5";"3";"1";"3";"2";"3";"2";"přístup vyučujících";"Přednáška - vyučující působí dojmem, že se během svého výkladu ani nenadechne, nekonečný sled informací je těžké sledovat, jedná se vlastně o stejný předmět, který studenti IMS absolvovali již na bakalářském stupni, test by měl být vystaven více na vědomostech z hodin než na četběSeminář - Je vidět, že vyučující je zaměřen na jedno téma a veškerá (i zdánlivě nesouvisející) témata jsou vykládána skrze toto jedno téma.";"krvs" +"1533";"JPB221";"Metodologický proseminář I";;"Bahenský,V.,Kofroň,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Pan Bahenský se velice snaží, jen tak dále.";;"kmv" +"1534";"JJB040";"Kreativita v jazyce";"Šoltys,O.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"2";"4";"5";"5";"5";;;"kz" +"1535";"JPB252";"České novověké dějiny I.";"Kučera,J.";;"4";"5";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"5";"2";;;"kp" +"1536";"JJB144";"Kompaktní kurz";;"Freidingerová,T.";"2";"2";NULL;NULL;NULL;"2";"2";"1";"2";"1";"1";"2";"1";;;"kz" +"1537";"JJB135";"Filmový seminář I";;"Šobr,M.";"4";"1";NULL;NULL;NULL;"4";"3";"2";"2";"2";"2";"1";"5";;;"kz" +"1538";"JPB256";"Politické systémy I";"Brunclík,M.,Jeřábek,M.,Kotábová,V.,Perottino,M.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"4";;;"kp" +"1539";"JPB258";"Politické myšlení novověku I";"Franěk,J.,Kučera,J.";;"4";"5";"4";"5";"3";NULL;NULL;NULL;"1";"3";"3";"4";"3";;;"kp" +"1540";"JPB242";"Geografie vnitropolitických konfliktů";;"Doboš,B.,Riegl,M.";"4";"4";NULL;NULL;NULL;"5";"5";"4";"1";"5";"3";"4";"5";;;"kp" +"1541";"JPB569";"Workshop Politické a státní instituce v praxi";;"Brunclík,M.";"4";"1";NULL;NULL;NULL;"5";"5";"3";"1";"4";"2";"3";"5";;;"kp" +"1542";"JMM278";"Politika a mezinárodní vztahy";"Kubát,M.,Weiss,T.";"Bečka,J.";"4";"3";"4";"4";"4";"3";"4";"2";"1";"4";"3";"4";"4";;"Přednáška - sjednotit nějak koncepci kurzu, tím, že se střídají tři přednášející, chybí návaznost jednotlivých bloků, v testu se nacházely nejasné otázkySeminář - odpadal velmi často, nekolidoval s tématy z přednášek, debaty s vyučujícím byly sice zajímavé, ale nepřipadaly mi nijak přínosné ke složení zkoušky nebo k budoucím státnicím. Četba na seminář yla zasílána velmi pozdě (pokud vůbec) - někdy i v den hodiny";"krvs" +"1543";"JPB583";"Politický systém ČR II. (regionální a lokální úroveň)";"Hornek,J.,Jüptner,P.,Musilová,K.,Němcová,L.";;"4";"4";"4";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"1544";"JPB587";"Víceúrovňové vládnutí (stát, region, občan)";"Perottino,M.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"2";"4";"3";"5";"5";;;"kp" +"1545";"JSB004";"Sociální nerovnosti";"Šanderová,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"3";"4";"2";"3";"5";;;"ks" +"1546";"JSB601";"Veřejný sektor a veřejná správa";"Kohoutek,J.,Veselý,A.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kvsp" +"1547";"JSB537";"Analýza dat v SPSS";"Soukup,P.";"Oreský,J.";"5";"4";"5";"5";"3";"5";"5";"5";"3";"3";"5";"2";"4";;;"ks" +"1548";"JSB490";"Úvod do politické sociologie";"Císař,O.";;"4";"5";"5";"4";"5";NULL;NULL;NULL;"1";"4";"3";"3";"3";;;"ks" +"1549";"JSB522";"Sociální politika jako společenská praxe";"Dobiášová,K.,Kotrusová,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kvsp" +"1550";"JMM279";"Společnost a ekonomika";"Fiřtová,M.";"Fiřtová,M.";"4";"5";"4";"5";"5";"5";"5";"5";"1";"4";"3";"4";"4";"Přístup vyučujícího, zajímavá témata, dobrá propojenost přednášek se seminářem, dobré vysvětlení na teritoriální specializaci";"více počítat s tím, že pro studenty IMS, kteří absolvovali na bakalářském stupni kurz ekonomie s panem Kameníčkem, je tento kurz vlastně prvním kurzem týkajícím se ekonomie (hodiny pana Kameníčka jsou bohužel velmi nepřínosné) Proto je neěkdy těžší sledovat a rozumět všemu, co se na hodinách probírá, jelikož vyučující počítá s tím, že studenti již mají základy ekonomie díky hodinám s panem Kameníčkem";"kas" +"1551";"JPB218";"Dějiny novověké Evropy I.";"Kučera,J.";;"4";"1";"2";"2";"1";NULL;NULL;NULL;"2";"3";"2";"2";"4";;;"kp" +"1552";"JPB596";"Čínská zahraniční a bezpečnostní politika";"Karmazin,A.";;"5";"3";"4";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";"Oceňuji přípravu profesora Karmazína, tento předmět mi rozšířil znalosti a dozvěděli jsme se dostatek nových poznatků z čínské zahraniční a bezpečnostní politiky.";"Zapojení studentů do přednášky.";"kbs" +"1553";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"3";"4";"1";"1";"5";"1";"1";"1";"3";"5";"2";"5";"4";;"Zapojení studentů během přednášek. Ne je pouze shazovat, že nic neví o nové látce, kterou se teprve učí. Nepochopitelné zrušení seminářů.Komunikace s vyučujícím nebyla dostatečná.";"kp" +"1554";"JMMZ314";"Major Issues in Contemporary Public Debates in the U.S. I";"Sehnálková,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Rozhodně jeden z nejlepších a najzajímavějších povinných předmětů severoamerické specializace. skvělý přístup vyučujícícho ke studentům, zajímavé povinnosti ke splnění kurzu, zpětná vazba, povinná četba byla aktuální, tématická z různorodých zdrojů, věcná a přitom ne tak dlouhá, takže se dalo zvládnout vždy vše přečíst";"dobu a místo výuky :)";"kas" +"1555";"JMMZ313";"Government in United States";"Sehnálková,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"I přesto, že vyučující upozorňovala na \"nezáživnost\" kurzu, zdál se mi díky jejímu skvělému přístupu, různým přístupům výuky, různým zdrojům, interaktivnosti atd kurz velmi dobrý. I přesto, že je téma obtížné někdy na pochopení, díky vyučující byly hodiny zábavné a záživné a nakonec i pochopitelné :)";"doba a místo výuky :)";"kas" +"1556";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"3";"2";"2";"1";"2";NULL;NULL;NULL;"2";"5";"3";"4";"4";;"Navrhuji zkrátit dobu opravování testů. Předmět vyučují dva profesoři, tak nechápu, proč musí test opravovat pouze profesor Švec, který navíc naprosto nereaguje na emaily a také ignoruje dobu opravení. Také by mohlo být vypsáno více termínů.";"kp" +"1557";"JLB005";"Angličtina pro politology I";;"Panešová,K.";"5";"1";NULL;NULL;NULL;"5";"5";"2";"1";"2";"2";"2";"5";"Kurz odpovídal předpokladu sjednocování jazykových schopností studentů v prvním ročníku studia, tedy bych zachoval elementární jazykový rozovj v rámci živých konverzacích, tak jako jsem je zažíval v seminářích s Mgr. Panešovou.";"Zvýraznil bych před začátkem studia možnost absolování závěrečného testu ještě před začátkem semestru, čímž z předmětu odpadnou studenti, kteří naopak berou možnost rozvoje dalším, případně zdůraznit možnosti individuálních plnění pro takové studenty.";"cjp" +"1558";"JMMZ315";"U.S. Foreign Policy";"Raška,F.";;"2";"3";"2";"3";"1";NULL;NULL;NULL;"1";"2";"2";"2";"1";;"Kurz spočívá v povinném přečtení 2 knih, v hodinách se znalosti nijak neprohlubují, jen se předčítá, co bylo v četbě. Velmi nezáživné, nedá se udržet pozornost. Četba velmi dlouhá";"kas" +"1559";"JLB099";"Rozřazovací test z angličtiny";;"Panešová,K.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"5";"3";"3";"2";"5";;;"cjp" +"1560";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"V prvním ročníku jeden z nejzajímavějších předmětů - oceňuji určité vystřízivění v rámci začátku na té úrovni, kdy člověk zjistí, že politická geografie není jen o hlavních městech etc. - v podaní Dr. Romancova velmi vřele podáno.";;"kp" +"1561";"JPB018";"Dějiny mezinárodních vztahů I";"Soukup,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"3";"5";"5";"5";"5";"Přístup Dr. Soukupa je jedne z nejpříjemnějších na fakultě, jeho podání problematiky je též excelentní.";"Přesto, že se jedná o přednášky, tématiky těchto kurzů přímo vybízí k větší interakci.";"kmv" +"1562";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"3";"3";"3";"4";"4";NULL;NULL;NULL;"4";"4";"2";"3";"3";;;"kp" +"1563";"JPB269";"Současné problémy mezinárodních vztahů";"Knutelská,V.";;"2";"3";"2";"3";"1";NULL;NULL;NULL;"3";"4";"2";"2";"3";;;"kmv" +"1564";"JPM910";"The Nature and Function of the State";"Franěk,J.,Pettit,P.";;"3";"4";"4";"4";"2";NULL;NULL;NULL;"1";"3";"2";"2";"3";;;"kp" +"1565";"JPB020";"Úvod do mezinárodních vztahů";"Karlas,J.,Kučera,T.";;"3";"3";"4";"3";"3";NULL;NULL;NULL;"1";"4";"4";"4";"4";;"Připadal jsem si málo připraven na fakt, že celý přdemět bude velmi neexaktní a působí, že vyžaduje mnoho vlastní interpretace. Přitom závěrečný test, přestože je složen z vlastních otázek, mnoho prostoru pro vlastní vyjádření neposkytuje.";"kmv" +"1566";"JPB268";"Evropská integrace";"Plechanovová,B.";;"2";"2";"2";"1";"1";NULL;NULL;NULL;"4";"3";"3";"3";"3";;;"kmv" +"1567";"JPB258";"Politické myšlení novověku I";"Franěk,J.,Kučera,J.";;"2";"3";"3";"3";"1";NULL;NULL;NULL;"2";"2";"2";"2";"3";;;"kp" +"1568";"JPB228";"Mírové smlouvy a konference v mez. systému";"Jeřábek,M.";;"4";"4";"4";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kmv" +"1569";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"4";"2";"2";"3";NULL;NULL;NULL;"1";"3";"1";"2";"2";;;"ies" +"1570";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"3";"4";"4";"3";NULL;NULL;NULL;"1";"3";"3";"3";"3";;;"ies" +"1571";"JLB013";"Němčina odborná I";;"Křenková,D.";"4";"4";NULL;NULL;NULL;"5";"5";"4";"1";"3";"4";"4";"5";"Ze všeho nejvíc bych ocenil přístup paní doktorky vůči studentům. Je velice přátelská a ochotná poradit. Na každé hodině jsme si připravovali aktuality a vypracovali aktualitář. Byli jsme motivováni se předem připravovat. Během hodiny jsme ve dvojicích nebo ve skupinkách o tématech diskutovali.";;"cjp" +"1572";"JPB221";"Metodologický proseminář I";;"Mlejnek,J.,Valková,I.";"4";"3";NULL;NULL;NULL;"4";"3";"4";"1";"4";"5";"4";"4";;;"kmv" +"1573";"JPB252";"České novověké dějiny I.";"Kučera,J.";;"4";"2";"3";"2";"2";NULL;NULL;NULL;NULL;"3";"1";"5";"4";;;"kp" +"1574";"JPB256";"Politické systémy I";"Brunclík,M.,Jeřábek,M.,Kotábová,V.,Perottino,M.";;"5";"3";"5";"3";"4";NULL;NULL;NULL;"1";"5";"3";"5";"5";;"Existoval diametrální rozdíl mezi přístupy přednášejících, který například u mě vyústil v to, že jsem navštěvoval pouze přednášky jednoho z nich (přístup druhého/druhé (záměrně neuvádím) byl velmi demotivující.)";"kp" +"1575";"JJM080";"Zábava v televizním vysílání";"Kruml,M.";;"5";"2";"5";"5";"3";NULL;NULL;NULL;"2";"5";"1";"3";"5";;;"kms" +"1576";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"5";"5";"4";"3";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"V prvním ročníku rozhodně \"nejmateriálnější\" předmět, který působí i jako dobrá reklama politologickým studiím.";"Rozdílnost v přístupech třech přednášejících ústí i v rozdílný zájem o jednotlivá přednášená témata.";"kp" +"1577";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"5";"5";;;"cjp" +"1578";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"2";"4";"3";"1";"2";NULL;NULL;NULL;"4";"4";"1";"3";"5";;"Tento semestr se nekonaly semináře, tudíž postrádala interakci a nácvik dovedností. Výuka pana vyučujícího byla v 8 ráno, tudíž bylo obtížné se vždy účastnit. Pan doktor se nás snažil zastrašovat tím, že do moodlu napsal, že musíme umět všechnu literaturu, tedy i tu doporučenou. Nicméně do mailu napsal, že doporučuje i studium doporučené literatury. Připadalo mi to trochu navzájem vylučující. Postupem času zvyšuje obtížnost kurzu a využívá technických pomůcek jako např. mobilů a promítačů k tomu, aby studenta při zkoušce zbytečně znervózňoval. Jinak bych navrhoval zlepšit jeho politiku vůči studentům v tom, aby vypsal víc termínů než počet \"nutných termínů\", které jsou ve školním řádu, aby měli studenti dostatek prostoru a času se na zkoušku připravit. Také, aby vyučující bral v potaz, že mají studenti i jiné předměty ke splnění. Postupné navyšování obtížnosti testů mi připadá nelogické. Zároveň by mohl reagovat na maily.";"kp" +"1579";"JPB218";"Dějiny novověké Evropy I.";"Kučera,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"1580";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"2";"5";"5";;;"krvs" +"1581";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kp" +"1582";"JPB228";"Mírové smlouvy a konference v mez. systému";"Jeřábek,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmv" +"1583";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"3";"3";"4";"4";"5";"4";"4";"1";"1";"4";"3";"3";"3";;"Nebyly semináře. V minulých letech byly a studenti si je moc chválili. Rozhodně zavést semináře do výuky znovu!";"kp" +"1584";"JMB402";"Úvod do společenských věd II";;"Fiřtová,M.";"4";"4";NULL;NULL;NULL;"5";"4";"5";"1";"4";"4";"4";"5";;;"krvs" +"1585";"JPB260";"Politické myšlení novověku III";"Kučera,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Docenta Kučeru rozhodně baví to, co učí. Je to na něm vidět, dobře se poslouchá. Student pak na přednášky chodí rád, těší se na ně. Velké plus za jeho přístup.";;"kp" +"1586";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Papežová,K.";"5";"2";"5";"5";"5";"5";"5";"4";"1";"5";"5";"5";"5";;;"knrs" +"1587";"JPB268";"Evropská integrace";"Plechanovová,B.";;"1";"5";"1";"2";"1";NULL;NULL;NULL;"3";"3";"2";"2";NULL;;"Požadavky k rešerším říci prosím dříve, než odevzdáme první, dokonce i druhou rešerši.";"kmv" +"1588";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"2";"4";"3";"2";"3";NULL;NULL;NULL;"1";"3";"2";"2";"5";"Studenti si museli ve skupinkách připravit prezentace. Seznamovali se s aktuálními politickými ději a reagovali na ně.";"V seminářích nebyla skoro žádná interakce, semináře byly od toho, aby skupiny odprezentovaly svá témata. Však dále to nemělo moc přidanou hodnotu. Testy jsou vypracovány doktorem Švecem, který neopravil testy ve své lhůtě. Studenti, kteří absolvovali termín byli nuceni se zapsat na další termín, aniž by znali svojí známku. Stále čekáme na výsledky z 1. a 2. termínu. Je to jako čekání na Godota... Studenti, kteří musejí skládat test, který není koncipovaný na pochopených vědomostech a klíčových politických událostí zemí. Nýbrž se jedná spíše o pouhé memorování různých prezidentů, premiérů, stran apod. Studenti jsou nuceni si vše zapamatovat. Část testu, která je vyber z možností je 1-4 možností správně, což výrazně ztěžuje absolvování testu. Pro větší uplatnitelnost předmětu bych doporučoval cvičení místo seminářů, kde by se probíraly důležité části jednotlivých Ústav, politické a stranické systémy a na jednotlivé země aplikovat politické teorie.";"kp" +"1589";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Andrle,J.";"4";"2";"4";"4";"5";"4";"4";"4";"1";"4";"4";"4";"5";;;"krvs" +"1590";"JJM080";"Zábava v televizním vysílání";"Kruml,M.";;"5";"1";"5";"5";"4";NULL;NULL;NULL;"3";"4";"2";"3";"5";"Praktické ukázky, přístup vyučujícího";"Moc mi nevyhovoval večerní čas kurzu, ale to je asi individuální.";"kms" +"1591";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"3";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"2";"3";"3";;;"knrs" +"1592";"JPB269";"Současné problémy mezinárodních vztahů";"Knutelská,V.";;NULL;NULL;"5";"5";"5";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kmv" +"1593";"JPB589";"Seminář k politickému myšlení: 19. století";;"Novotný,J.";NULL;NULL;NULL;NULL;NULL;"5";"5";"5";NULL;NULL;NULL;NULL;NULL;;;"kp" +"1594";"JMB058";"Československá a česká zahraniční politika po r. 1989 I.";"Handl,V.,Kunštát,M.";;"4";"2";"4";"5";"4";NULL;NULL;NULL;"1";"4";"2";"4";"4";;;"knrs" +"1595";"JJM201";"Teorie masové a mediální komunikace";"Jirák,J.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"3";"4";"1";"4";"5";"Přístup vyučujícího, ústní formu zkoušení";;"kms" +"1596";"JJM214";"Čtení textů ke studiu médií - populární kultura";;"Reifová,I.";"4";"3";NULL;NULL;NULL;"5";"5";"3";"1";"4";"4";"3";"4";"Přístup vyučujícího, možnost projevit svůj názor";;"kms" +"1597";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kas" +"1598";"JMB171";"Moderní dějiny Maďarska";"Irmanová,E.";;"5";"2";"4";"5";"5";NULL;NULL;NULL;"1";"5";"2";"3";"4";;;"krvs" +"1599";"JPB594";"Realism in International Relations";"Odintsov,N.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kmv" +"1600";"JPB595";"Justice in Politics and International Relations";"Salamon,J.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kp" +"1601";"JMB173";"Vnitřní dějiny Balkánu do roku 1914";"Šesták,M.";;"3";"2";"2";"4";"2";NULL;NULL;NULL;"1";"2";"1";"2";"2";;;"krvs" +"1602";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"2";"4";"5";;;"ks" +"1603";"JLB005";"Angličtina pro politology I";;"Panešová,K.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Přístup vyučujícího";;"cjp" +"1604";"JPB268";"Evropská integrace";"Plechanovová,B.";;"1";"5";"1";"1";"1";NULL;NULL;NULL;"3";"3";"2";"3";"1";;"hodnocení testu a rešerší";"kmv" +"1605";"JPB558";"Výběrový seminář: Politická komunikace";"Váňa,T.";;"5";"2";"5";"5";"3";NULL;NULL;NULL;"2";"5";"2";"3";"5";;;"kp" +"1606";"JJB135";"Filmový seminář I";;"Šobr,M.";"4";"1";NULL;NULL;NULL;"3";"3";"3";"1";"2";"3";"3";"4";;;"kz" +"1607";"JPB569";"Workshop Politické a státní instituce v praxi";;"Brunclík,M.";"3";"2";NULL;NULL;NULL;"3";"4";"4";"1";"3";"2";"3";"3";;;"kp" +"1608";"JPM127";"Geopolitika a geostrategie 1815 - 1945";"Romancov,M.";;NULL;NULL;"4";"3";"3";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kp" +"1609";"JPB587";"Víceúrovňové vládnutí (stát, region, občan)";"Perottino,M.";;"3";"3";"4";"5";"3";NULL;NULL;NULL;"3";"4";"2";"3";"3";;;"kp" +"1610";"JPM160";"Česká komunální politika";"Jüptner,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"3";"5";"3";"4";"4";;;"kp" +"1611";"JPB592";"US Government and Politics";"Kotábová,V.";;"4";"3";"4";"4";"5";NULL;NULL;NULL;"1";"4";"2";"3";"5";;;"kp" +"1612";"JPB218";"Dějiny novověké Evropy I.";"Kučera,J.";;"5";"4";"5";"5";"3";NULL;NULL;NULL;"1";"4";"3";"4";"5";;;"kp" +"1613";"JPB227";"Politický system ČR";"Charvát,J.";;"5";"3";"4";"5";"3";NULL;NULL;NULL;"2";"5";"3";"3";"5";;;"kp" +"1614";"JSB490";"Úvod do politické sociologie";"Císař,O.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"3";"1";"4";"5";;;"ks" +"1615";"JSB522";"Sociální politika jako společenská praxe";"Dobiášová,K.,Kotrusová,M.";;"4";"3";"4";"5";"3";NULL;NULL;NULL;"1";"4";"2";"3";"4";;;"kvsp" +"1616";"JPM342";"Konflikty v demokracii a národní identita: teoretické problémy analýzy";"Říchová,B.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"2";"4";"3";"4";"4";;;"kp" +"1617";"JLB005";"Angličtina pro politology I";;"Panešová,K.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";"Oceňuji přátelský a shovívavý přístup pí Panešové.";;"cjp" +"1618";"JPM344";"Diplomní seminář II.";;"Brunclík,M.,Franěk,J.,Hroch,M.,Charvát,J.,Jüptner,P.,Kofroň,J.,Kotábová,V.,Krausz Hladká,M.,Kubátová,H.,Kučera,J.,Landovský,J.,Mlejnek,J.,Perottino,M.,Riegl,M.,Romancov,M.,Říchová,B.,Salamon,J.,Shavit,A.,Švec,K.";NULL;NULL;NULL;NULL;NULL;"3";"3";"3";NULL;NULL;NULL;NULL;NULL;;;"kp" +"1619";"JPM909";"Rousseau and Nationalism: On the Government of Poland";;"Franěk,J.,Kelly,C.";NULL;NULL;NULL;NULL;NULL;"5";"5";"5";NULL;NULL;NULL;NULL;NULL;;;"kp" +"1620";"JPB228";"Mírové smlouvy a konference v mez. systému";"Jeřábek,M.";;"5";"4";"4";"5";"5";NULL;NULL;NULL;"2";"5";"5";"4";"5";;;"kmv" +"1621";"JPM910";"The Nature and Function of the State";"Franěk,J.,Pettit,P.";;NULL;NULL;"4";"4";"5";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kp" +"1622";"JSB537";"Analýza dat v SPSS";"Soukup,P.";"Oreský,J.";"1";"5";"3";"3";"1";"4";"5";"5";"2";"1";"3";"1";"1";;;"ks" +"1623";"JPB242";"Geografie vnitropolitických konfliktů";;"Doboš,B.,Riegl,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"4";"5";;;"kp" +"1624";"JPB260";"Politické myšlení novověku III";"Kučera,J.";;"4";"5";"5";"5";"3";NULL;NULL;NULL;"1";"5";"2";"5";"5";;;"kp" +"1625";"JSB601";"Veřejný sektor a veřejná správa";"Kohoutek,J.,Veselý,A.";;"3";"3";"3";"4";"3";NULL;NULL;NULL;"1";"2";"1";"2";"3";;;"kvsp" +"1626";"JPB202";"Politické strany v Evropě";"Perottino,M.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Přednášky pana docenta Perottina jsou poutavé a vždy zajímavé.";;"kp" +"1627";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";NULL;"2";NULL;NULL;NULL;NULL;NULL;"1";NULL;"3";NULL;"3";;;"ies" +"1628";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"4";"5";"4";"2";"4";NULL;NULL;NULL;"1";"5";"4";"4";"4";;"komunikáciu so študentmi, rýchlejšie opravenie skúšok (nepremrhať 2 opravné termíny kvôli neopravenej skúške;) )";"kp" +"1629";"JPB263";"Bakalářský seminář II.";;"Brunclík,M.,Bureš,O.,Ditrych,O.,Franěk,J.,Gelnarová,J.,Hynek,N.,Charvát,J.,Jeřábek,M.,Jüptner,P.,Karásek,T.,Karlas,J.,Knutelská,V.,Kofroň,J.,Kotábová,V.,Krausz Hladká,M.,Kubátová,H.,Kučera,J.,Kučerová,I.,Landovský,J.,Ludvík,J.,Makariusová,R.,Mlejnek,J.,Pa";"5";"3";NULL;NULL;NULL;"5";"5";"1";"1";"5";"5";"3";"5";;;"kp" +"1630";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"5";"3";"4";"4";"3";NULL;NULL;NULL;"3";"4";"4";"4";"5";"Oceňuji obohacení kurzu o diskuze se studenty a ukázku dobových textů a obrázků. Vhodně doplňují výuku a navyšují interaktivnost celého předmětu.";"Jistě by bylo pro povinný kurz lepší zvolit lepší denní dobu, než jsou tři hodiny večer. Když už je nutné mít kurz 1x za 14 dní, určitě je lepší dopoledne či odpoledne.";"kp" +"1631";"JPB565";"Stáž v praxi";;"Kuľková,M.,Švec,K.";"5";"3";NULL;NULL;NULL;"2";"2";"1";"1";"5";"5";"3";"5";;"pri možnostiach výberu stáže dať študentom dopredu vedieť, čoho sa ich stáž v danej inštitúcii bude týkať, či administratívnych prác, projektoch atď. aby nemuseli robiť \"sekretárku\" na Ministerstve vnútra ČR a 100 hodín uzatvárať spisy.";"kp" +"1632";"JPM306";"African Security";"Werkman,K.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"4";"5";"Oceňuji, že kurz byl koncipován odlišně od ostatních kurzů a místo prezentace vyučujícího byl veden prakticky pouze formou diskuse mezi vyučující a studenty.";"Nic, Kurz mi vyhovoval v takové formě v jaké byl veden.";"kbs" +"1633";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"4";"5";"3";"2";"5";"3";"2";"5";"2";"4";"4";"5";"4";"Přednášky jsou velmi zajímavé a přínosné. Vzhledem k náročnosti přednášené látky až nečekaně poutavé.";;"kp" +"1634";"JPB227";"Politický system ČR";"Charvát,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"2";"4";"5";;;"kp" +"1635";"JJB135";"Filmový seminář I";;"Šobr,M.";"4";"1";NULL;NULL;NULL;"2";"3";"2";"1";"3";"1";"4";"5";;;"kz" +"1636";"JPM613";"Armed Forces and Society";"Kučera,T.";;"4";"5";"5";"5";"4";NULL;NULL;NULL;"1";"4";"5";"4";"4";"Oceňuji skupinovou esej, která mi pomohla vylepšit dovednosti, spolupráci atp. a také oxfordskou debatu, která rovněž přispěla ke zlepšení našich dovedností. Simulace puče byla velmi zábavná.";"Domácí úkol před simulací puče - počítání na mapách atp. byl až příliš složitý a myslím, že by bylo lepší si tyto úkoly více projet společně v hodině, než je dělat doma samostatně.";"kbs" +"1637";"JPB202";"Politické strany v Evropě";"Perottino,M.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"2";"4";"3";"4";"5";;;"kp" +"1638";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"4";"3";"5";"5";"3";NULL;NULL;NULL;"3";"3";"3";"4";"5";;;"kp" +"1639";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"1";"4";"2";"2";"1";NULL;NULL;NULL;"1";"2";"1";"2";"1";"The valuable lesson that not all courses will be understandable or fun, but must be completed.";"The attitude and overall outlook of the professor towards his students. He seems to be on a moral high stool above us. This takes the form of for example not accepting any questions during the lecture.Professor Kameníček is without a doubt a skilled academic, however his lectures are highly frowned upon by most students. He is unable to hear even with the microphone, his explanations are very bizarre and often misleading and confusing almost to the state that googling topics will get us a better understanding of them.";"ies" +"1640";"JPB242";"Geografie vnitropolitických konfliktů";;"Doboš,B.,Riegl,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"5";"5";"Semináře jsou rozhodně přínosné pro rozšíření probrané teorie";"Navrhuji zahrnout hodnocení úkolů do závěrečného hodnocení. Kurz by pak mohl být zakončen zkouškou a ne zápočtem.";"kp" +"1641";"JPB227";"Politický system ČR";"Charvát,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"1642";"JEM132";"Company Valuation";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"3";"4";"3";"3";"2";NULL;NULL;NULL;"1";"4";"3";"4";"2";;;"ies" +"1643";"JPM697";"Asia Security";"Kolmaš,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Kurz byl velice zajímavý, přinesl velké rozšíření znalostí, vyučující této látce velmi rozumí a umí to i podat. Kurz byl skvělý.";"Možná se více zabývat i jinými částmi Asie, i když chápu, že vzhledem k délce kurzu to není příliš možné.";"kbs" +"1644";"JPM701";"European and Transatlantic Security";"Kazharski,A.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Oceňuji diskuse a způsob jakým byly vedeny a také úkoly z četby, které jsme museli psát a pomohly nám tak pochopit více probíranou literaturu. Čekala jsem, že kurz nebude příliš zajímavý, vzhledem k tomu, že mě tato oblast příliš nezajímá, vyučující jej ovšem svými dovednosti dokázal přeměnit v jeden z nejlepších kurzů jaký jsem na univerzitě studovala.";"Nic, kurz je skvělý.";"kbs" +"1645";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"2";"4";"4";"3";"5";"1";"1";"1";"2";"4";"4";"5";"5";;"Nerušit semináře, které měly rozebírat se studenty danou literaturu. Rozhodně je to velký mínus pro celý kurz.";"kp" +"1646";"JPB258";"Politické myšlení novověku I";"Franěk,J.,Kučera,J.";;"4";"3";"5";"4";"3";NULL;NULL;NULL;"2";"4";"3";"4";"4";;"Přihlašování na zkoušky by v obojím případě mohlo být přes SIS, aby nedocházelo ke zmatkům, která část abecedy patří k jakému zkoušejícímu.";"kp" +"1647";"JPM708";"Ethics and Violence";"Karásek,T.,Kučera,T.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"5";"5";"Diskuze, velmi zajímavé a přesně ta oblast, kterou jsem si potřebovala procvičit. Semináře mě velmi bavily.";"Literaturu. Byla až příliš dlouhá - i když jsme na ni měli dva týdny tak 200 je i na magisterské studium až příliš a také často nebyla relevantní k diskuzi a i přesto, že jsem ji četla, jsem ji při diskuzi příliš nevyužila. Kromě toho se mi zbytek kurzu líbil.";"kbs" +"1648";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"3";"5";"3";"2";"3";NULL;NULL;NULL;"1";"4";"3";"3";"4";"Prezentace studentů.";"Hodnocení kroužkovacích testů, které trvá déle jak 14 dní by se rozhodně mohlo zlepšit. Studenti tak přicházejí o další termíny zkoušek.";"kp" +"1649";"JLB005";"Angličtina pro politology I";;"Panešová,K.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"1";"5";"5";"4";"5";"nejmilejší učitelka angličtiny, jakou jsem poznala :) je fakt skvělá";;"cjp" +"1650";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"4";"3";NULL;NULL;NULL;"4";"5";"3";"1";"3";"2";"2";"4";"Having the opportunity to speak in English on various topics.";"The course seems to be too focused on unimportant vocabulary, rather then focusing on expanding our conversational skills.";"cjp" +"1651";"JPB260";"Politické myšlení novověku III";"Kučera,J.";;"5";"3";"3";"2";"2";NULL;NULL;NULL;NULL;"2";"3";"3";"2";;;"kp" +"1652";"JPB268";"Evropská integrace";"Plechanovová,B.";;"2";"4";"3";"2";"1";NULL;NULL;NULL;"4";"2";"3";"3";"3";"Psaní rešerší, studentům to pomohlo se zorientovat v dané problematice";"Dát obecný koncept tomu, co je to rešerše, co má student psát. Hodnocení testů je netransparentní a škála 6/3/0 bodů absolutně nevyhovující. Nejlepší by bylo proměnit test na multiplechoice, kdy se těžkost testu zachová, opravování je jednodušší a transparentnost je zaručena.";"kmv" +"1653";"JPB227";"Politický system ČR";"Charvát,J.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;NULL;"4";"4";"4";"5";"přístup vyučujícího, na jeho přednášky jsem ráda chodila, i když jsem měla občas pocit, že jsme hned na začátku odbočili od tématu a už se k němu do konce přednášky nedostali :D neberu to jako mínus, zrovna byly volby, takže se i líbilo, že se bavíme i o něčem aktuálním :)";;"kp" +"1654";"JPB569";"Workshop Politické a státní instituce v praxi";;"Brunclík,M.";"3";"2";NULL;NULL;NULL;"2";"4";"2";"3";"3";"3";"2";"3";;;"kp" +"1655";"JPB229";"Regionální politické systémy: Skotsko, Wales";"Říchová,B.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"2";"4";"4";"4";"4";;;"kp" +"1656";"JPB596";"Čínská zahraniční a bezpečnostní politika";"Karmazin,A.";;"5";"4";"4";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kbs" +"1657";"JMB402";"Úvod do společenských věd II";;"Hofmeisterová,K.";"4";"4";NULL;NULL;NULL;"5";"4";"4";"1";"5";"4";"3";"4";"Learning about oral presentation.";;"krvs" +"1658";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"5";"4";"5";"5";"5";"5";"5";"5";"2";"5";"5";"5";"5";;;"kp" +"1659";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"4";"5";;;"ks" +"1660";"JMB156";"Greek Language I";"Maniati,E.";"Maniati,E.";"2";"2";"2";"4";"2";"2";"4";"2";"3";"1";"1";"1";"2";;"Punctuality with the professor.Better presentation of the course in SIS (not presenting it as 2 separate lectures)Presentation of the language. It seemed that the teacher didn´t quite know how to challenge the subject. Compared to English classes, Greek was mediocre at best.";"krvs" +"1661";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"5";"4";"4";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kas" +"1662";"JJM208";"Mediální systémy a jejich komparace";"Miessler,J.";;"4";"5";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";"Otevřený přístup vyučujícího, pana magistra Miesslera, vůči jakýmkoliv návrhům a podnětům od studentů a zároveň maximální možný \"servis\" pro studenty v ohledu přípravy studijních materiálů a požadavků kurzu.";;"kms" +"1663";"JJM229";"Vývoj televizního vysílání v českých zemích";"Štoll,M.";;"5";"2";"5";"5";"3";NULL;NULL;NULL;"2";"4";"3";"5";"5";"Velice zajímavé, předmět jsem si zapsal jako volitelný, byl jsem na všech přednáškách a byl to snad jediný předmět semestru, který mě opravdu bavil.";;"kms" +"1664";"JJM249";"Duchovní kořeny evropské kultury";"Halada,J.";;"2";"1";"3";"4";"1";NULL;NULL;NULL;"3";"1";"1";"2";"2";;"Nesouvisí se zákaldní náplní studia, navíc jsme prakticky totožný předmět absolvoval v bakalářském studiu (a již tehdy mi přišel zbytečný).";"kz" +"1665";"JJM210";"Kvantitativní obsahová analýza";;"Nečas,V.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Skvělý vyučující, pan doktor Nečas, jeho individuální přístup ke studentům a cenná pomoc při zpracování metodiky závěrečných prací.";;"kms" +"1666";"JJM355";"Teoretické koncepty ke studiu žurnalistiky I";"Jirků,J.,Němcová Tejkalová,A.";"Jirků,J.";"3";"4";"4";"5";"5";"5";"5";"5";"1";"3";"3";"5";"2";"Rozložení přednášky - základy + semináře - rozšiřující učivo.";;"kz" +"1667";"JJM251";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"2";"3";"3";"3";"3";NULL;NULL;NULL;"1";"3";"2";"3";"2";;"Kurz tak z 90 % opakoval látku, kterou jsme již absolvovali v prvním ročníku bakalářského studia a i absolventi jiných madiální bakalářských oborů se shodovali, že již tyto znalosti mají.";"kz" +"1668";"JJM252";"Specifika sportovní žurnalistiky";"Němcová Tejkalová,A.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"3";"4";"4";"4";"4";;"Některé pasáže byly až příliš teoretické a kopírovaly teoretické koncepty.";"kz" +"1669";"JJM356";"Metodologické přístupy ke studiu žurnalistiky";"Nečas,V.,Veselková,Z.";;"2";"2";"3";"4";"4";NULL;NULL;NULL;"3";"2";"3";"3";"3";"Studenti dostanou příležitost zpracovat tezi diplomové práce a následně k ní dostanou zpětnou vazbu.";"Zmatené zadávání průběžných úkolů a jejich hodnocení.";"kz" +"1670";"JJM217";"Čtení textů ke studiu médií - Nová média";;"Jirků,J.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"1";"5";"4";"5";"5";"Vstřícný přístup pana doktora Jirků ke studentům, adekvátní a na splnění časově flexibilní požadavky kurzu.";;"kms" +"1671";"JJM360";"Ekonomika v médiích";"Klimeš,D.";;"4";"2";"4";"4";"3";NULL;NULL;NULL;"1";"4";"4";"4";"4";"Vyučuje jej člověk z praxe, který navíc zve hosty, kteří taktéž v praxi působí.";"V teoretické části předmětu je vykládána teorie, která následně není při testu vyžadována. Náplň testu však musí studenti studovat sami.";"kz" +"1672";"JJM224";"Politická ekonomie komunikace";"Vochocová,L.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Skvělá vyučující, paní doktorka Vochocová, která v hodinách zprostředkovává obrovsky široké penzum informací a oborových souvislostí zábavnou a pochopitelnou formou.";;"kms" +"1673";"JLB035";"Francouzština I";;"Bosáková,L.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"1674";"JPM724";"Critical Approaches to International Politics and Security";;"Daniel,J.,Rychnovská,D.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"5";"4";"4";"3";"5";;;"kmv" +"1675";"JJM329";"Média a kultura";"Bednařík,P.,Jirák,J.";;"4";"3";"4";"5";"2";NULL;NULL;NULL;"1";"3";"3";"4";"3";;;"kms" +"1676";"JJM332";"Přirozený jazyk a média";"Podzimek,J.";;"2";"3";"3";"5";"4";NULL;NULL;NULL;"1";"2";"1";"4";"2";;;"kms" +"1677";"JJB611";"Česká média po roce 1990";"Jirák,J.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";"Zadání seminární práce v týmech.";;"kms" +"1678";"JJB612";"Média a životní styl";"Knapík,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Velmi poutavé a zajímavé vyprávění přednášejícího.";;"kms" +"1679";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"2";"2";"1";"2";NULL;NULL;NULL;"1";"2";"1";"2";"1";;;"ies" +"1680";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"5";"5";;;"cjp" +"1681";"JLB033";"Němčina I";;"Faltýnová,R.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"3";"5";;;"cjp" +"1682";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"3";"1";"3";"4";"1";NULL;NULL;NULL;"1";"2";"1";"3";"4";;;"krvs" +"1683";"JMB402";"Úvod do společenských věd II";;"Čapinská,B.";"3";"1";NULL;NULL;NULL;"4";"5";"3";"2";"3";"3";"2";"3";;;"krvs" +"1684";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Čížek,M.";"5";"5";"5";"5";"5";"3";"3";"5";"1";"5";"1";"5";"5";;;"knrs" +"1685";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Andrle,J.";"5";"4";"4";"4";"5";"2";"3";"3";"2";"5";"1";"5";"5";;;"krvs" +"1686";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"4";"2";"4";"5";"4";NULL;NULL;NULL;"1";"3";"2";"3";"4";;;"knrs" +"1687";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"5";"3";"4";"5";"4";NULL;NULL;NULL;"1";"5";"2";"5";"5";;;"kas" +"1688";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"2";"4";"1";"4";"5";;;"ks" +"1689";"JEB022";"Institutional Economics";"Kopečná,V.,Schwarz,J.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"3";"5";"5";"5";"5";;;"ies" +"1690";"JLB053";"Angličtina pro sociální vědy I";;"Gloverová,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"1691";"JEB026";"European Economic Integration";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"3";"4";"3";"4";"4";"3";"4";"4";"1";"5";"3";"3";"3";;;"ies" +"1692";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"5";"4";"5";"4";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"krvs" +"1693";"JEB120";"Financial Economics";"Žigraiová,D.";;"3";"5";"1";"2";"3";NULL;NULL;NULL;"1";"4";"4";"3";"2";;;"ies" +"1694";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ks" +"1695";"JMB091";"Religion, secularity and laicity in Europe (19th-21th centuries)";"Bauer,P.";;"5";"2";"5";"4";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kzs" +"1696";"JSB455";"Economic Sociology and European Capitalism";"Blokker,P.";;"3";"3";"5";"5";"5";NULL;NULL;NULL;"1";"3";"2";"3";"3";;;"ks" +"1697";"JMB402";"Úvod do společenských věd II";;"Karasová,N.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"5";"5";;;"krvs" +"1698";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Čížek,M.";"5";"4";"5";"5";"5";"4";"5";"5";"1";"5";"5";"5";"5";;;"knrs" +"1699";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Čížek,M.";"5";"4";"4";"4";"3";"4";"5";"5";"2";"5";"5";"5";"5";;;"krvs" +"1700";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"4";"5";"4";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kas" +"1701";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"4";"4";"4";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"knrs" +"1702";"JMB204";"Skotsko, Wales a Severní Irsko v kontextu moderních britských dějin";"Kasáková,Z.";;"4";"5";"4";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kzs" +"1703";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"1";"5";"1";"1";"1";NULL;NULL;NULL;"2";"1";"1";"1";"1";;;"ies" +"1704";"JJB613";"Úvod do studia nových médií";"Jirků,J.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"5";"5";"Praktické příklady k teoretickému výkladu.";;"kms" +"1705";"JJB625";"Manipulace v audiovizuálním sdělení";"Štoll,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"4";"5";"Zábavné příklady z praxe.";;"kms" +"1706";"JJM201";"Teorie masové a mediální komunikace";"Jirák,J.";;"4";"4";"4";"5";"2";NULL;NULL;NULL;"1";"5";"1";"5";"4";;;"kms" +"1707";"JLB005";"Angličtina pro politology I";;"Panešová,K.";"5";"4";NULL;NULL;NULL;"5";"5";"4";"1";"5";"4";NULL;"5";"diverzita aktivit, velmi dobrá organizace, vysvětlení látky, přístup ke studentům, efektivní výuka";;"cjp" +"1708";"JJB626";"Vybrané otázky mediálního vzdělávávání";"Wolák,R.";;"3";"3";"3";"3";"3";NULL;NULL;NULL;"2";"3";"3";"3";"2";;;"kms" +"1709";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"2";"5";"1";"5";"5";"komplexní, srozumitelný a poutavý výklad, využití technologií k demonstraci probíraných témat (ukázka indexů, map, animací prezentující daný jev)";"větší interaktivita";"kp" +"1710";"JJM202";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"3";"4";"3";"3";"4";NULL;NULL;NULL;"1";"4";"1";"4";"3";"Oceňuji přístup doktora Bednaříka. Jeho lehký humor, který se ale většinou nějak týkal tématu, mi zpříjemňoval přednášky. A jeho výklad mě bavit více než výklad doktora Cebeho.";"Myslím si, že forma testu s otevřenými otázkami nebyla úplně ideální a tento typ otázek by se mnohem více hodil jako ústní zkouška. Vlastně doteď nevím, co jsem do testu nenapsala nebo napsala špatně, že mi bylo strženo tolik bodů.";"kms" +"1711";"JPB020";"Úvod do mezinárodních vztahů";"Karlas,J.,Kučera,T.";;"3";"4";"3";"4";"2";NULL;NULL;NULL;"1";"5";"1";"5";"4";"kombinace prezentací a četby";"lepší výklad - příklady přesahující promítanou prezentaci, záživnější podání";"kmv" +"1712";"JPB221";"Metodologický proseminář I";;"Komasová,S.,Parízek,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"3";"5";"1";"5";"způsob vedení kurzu, prezentace látky, zadané úkoly, přístup ke studentům";;"kmv" +"1713";"JJM201";"Teorie masové a mediální komunikace";"Jirák,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"1714";"JSB998";"Úvod do sociologie";"Soukup,P.";;"4";"1";"5";"5";"2";NULL;NULL;NULL;"1";"3";"1";"2";"3";;"ztížit úroveň kurzu a zkoušky, jít víc do hloubky";"ks" +"1715";"JJM202";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"1716";"JJM204";"Výzkum médií I";"Křeček,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"1717";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"1";"3";"5";"3";"4";"Oceňuji různá cvičení na trénování slovní zásoby. Program hodin je celkově dobře udělaný, i přesto, že je výuka dlouhá, rychle utíká.";"Nemusí to být nutně, ale líbilo by se mi, když by se pracovalo s aktuálnějšími texty.";"cjp" +"1718";"JJM229";"Vývoj televizního vysílání v českých zemích";"Štoll,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"1719";"JJM231";"Mediální výchova v rodině";;"Šťastná,L.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kms" +"1720";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"4";"4";"4";"4";"5";NULL;NULL;NULL;"2";"5";"3";"5";"5";"interaktivita, komplexnost";"v části celostátní politiky větší koncepce, srozumitelnost";"kp" +"1721";"JJB600";"Úvod do studia médií I.";"Nečas,V.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"2";"5";"2";"5";"5";"četba, komplexnost, vedení přednášek a prezentace látky";;"kms" +"1722";"JJB601";"Rozbor právního prostředí v českých médiích";"Benda,J.";;"5";"4";"3";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"zadané úkoly. oceňuji získaný vhled do problematiky";"způsob prezentace - momentálně je velmi těžké udržet tempo přednášejícího vzhledem k faktu, že obsáhlé prezentace nejsou nikde dostupné. více vysvětlení, méně čtení prezentace";"kms" +"1723";"JJB605";"Novodobá historie médií";"Bednařík,P.,Končelík,J.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"5";"5";"četba, vedení přednášek, názorné ukázky doprovázené dobovými předměty, jednoduše vysvětlená látka.";"přehlednost prezentací. momentálně jsou přehlcené, špatně se v nich orientuje";"kms" +"1724";"JJB616";"Vybrané novinářské osobnosti 19. století";"Sekera,M.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"5";"1";"4";"4";"srozumitelný výklad látky";"větší ukázky a využití textů během výkladu";"kms" +"1725";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"4";"3";"4";"5";"3";NULL;NULL;NULL;"1";"3";"3";"4";"4";;;"ies" +"1726";"JJB620";"Mediální produkce - žurnalistická práce I.";"Vlasák,Z.";;"4";"2";"4";"5";"4";NULL;NULL;NULL;"1";"2";"4";"2";"4";"domácí práce, srozumitelné přednášky, výborná komunikace";;"kms" +"1727";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"5";"5";"5";"5";"5";;;"knrs" +"1728";"JJM204";"Výzkum médií I";"Křeček,J.";;"4";"2";"4";"5";"5";NULL;NULL;NULL;"2";"2";"4";"3";"4";"Oceňuji tu fázi výzkumu, kdy jsme sami měli porovnat své výsledky se spolužáky a sami podat reflexi toho, kde jsme udělali chybu. To pro mě bylo přínosné.";;"kms" +"1729";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"4";"4";"2";"2";"4";NULL;NULL;NULL;"1";"4";"1";"3";"1";"Zajímavá mikroekonomická a makroekonomická tématika. Látka je srozumitelná, naopak by mohla být probírána ještě více do hloubky + přidávat příklady z praxe. Navíc by se Úvod mohl vyučuvovat ZS i v LS (Ekonomie I. a II.), aby byla látka skutečně probrána důsledně s praktickým/reálným přesahem. Kurzu dávám dobré hodnocení, vědomostně mě velmi obohatil.";"Výměna přednášejícího je základem zlepšení předmětu Úvod do ekonomie.";"ies" +"1730";"JMMZ042";"Cohesion Policy of the EU in Central and East European Countries.";"Hauser,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"the quality of the information received, the pleasant atmosphere during the lectures and the discussions on current issues regarding the future of the cohesion policy, the future of EU..";"maybe to do something in order to apply the concepts, like group projects to apply for European structural funds";"krvs" +"1731";"JJM351";"Kritická analýza mediálních sdělení v českém periodickém tisku";;"Benda,J.";"5";"3";NULL;NULL;NULL;"4";"5";"5";"1";"5";"1";"4";"5";"Oceňuji koncept tohoto předmětu. Rozšířil mi obzory a přivedl mě k tomu, o některých tématech přemýšlet jinak a uvědomovat si, že každá mince má dvě strany.";;"kms" +"1732";"JLB039";"Ruština odborná I - nižší";;"Mistrová,V.";"5";"5";NULL;NULL;NULL;"5";"4";"5";"1";"5";"5";"5";"5";"Velmi oceňuji aktuálnost textů.";;"cjp" +"1733";"JMB068";"Komunistické vládnutí v Československu: prosazování, podoba a společenská reflexe (1945 až dodnes)";"Cuhra,J.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Díky složitosti problematiky let 1948–1989 by mohl být předmět rozdělen na dvě části a učen v ZS i LS, aby byl ještě přínosnější. Hodnotím předmět velice kladně, je škoda jeho malý rozsah a malá kapacita předmětu. Více podobných vyučujích...";;"krvs" +"1734";"JMM067";"Russia and Eurasia in World Politics";"Šír,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Oceňuji výběr témat.";;"krvs" +"1735";"JSB546";"Urban Change and Grassroots Movements";"Pixová,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"quality information and excursions that helped to have a better understanding of the issues discussed during the seminar";;"ks" +"1736";"JMM130";"Ethno-Political Conflicts in the Caucasus";"Brisku,A.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Velmi oceňuji důslednou zpětnou vazbu vyučujícího k psaným výstupům studentů.";;"krvs" +"1737";"JSM421";"Contemporary social theory";"Balon,J.";;"5";"2";"4";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";"interesting discussions every time";;"ks" +"1738";"JSM628";"European policies and practice towards ethnic minorities";"Bernard Thompson Mikes,A.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"We had really interesting presentations, we had to constantly offer mutual feedback, we had workshops on discrimination. A very exciting lecture that helped me to understand the problems faced by ethnic minorities and to think more clearly about solutions to improve the situation";;"kvsp" +"1739";"JPB263";"Bakalářský seminář II.";;"Brunclík,M.,Bureš,O.,Ditrych,O.,Franěk,J.,Gelnarová,J.,Hynek,N.,Charvát,J.,Jeřábek,M.,Jüptner,P.,Karásek,T.,Karlas,J.,Knutelská,V.,Kofroň,J.,Kotábová,V.,Krausz Hladká,M.,Kubátová,H.,Kučera,J.,Kučerová,I.,Landovský,J.,Ludvík,J.,Makariusová,R.,Mlejnek,J.,Pa";"3";"3";NULL;NULL;NULL;"5";"5";"5";"1";"3";"4";"4";"4";;;"kp" +"1740";"JPB202";"Politické strany v Evropě";"Perottino,M.";;"4";"2";"5";"5";"4";NULL;NULL;NULL;"3";"3";"3";"4";"5";"Přednáška věnovaná analýze voleb do Poslanecké sněmovny, možnost absolvovat zkoušku v předtermínu";"Rozšířit kurz i do druhého semestru, aby mohlo být probráno více evropských států";"kp" +"1741";"JPB569";"Workshop Politické a státní instituce v praxi";;"Brunclík,M.";"5";"1";NULL;NULL;NULL;"4";"5";"5";"2";"2";"2";"4";"5";"První kurz, který mi dal představu, čemu se dá po vystudování v našem oboru věnovat. Jako nejzajímavější hodnotím přednášky Martina Ayrera, Ondřeje Klapala a Jana Herzmanna.";"Určitě by bylo dobré pozvat na přednášku i někoho z aktivních politiků.";"kp" +"1742";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"3";"4";"4";"4";"4";"3";"3";"3";"2";"5";"3";"4";"4";"Přednášky dr. Švece jsou velmi zajímavé, látku dokáže jasně a srozumitelně vysvětlit.";"Vrátit kurz do podoby střídání přednášek a seminářů, abychom se nemuseli povinné literatuře věnovat pouze prostřednictvím samostudia. Navíc z důvodu nedostatku času ani neproběhl jediný plánovaný seminář na konci semestru. Zároveň by bylo dobré poskytnout studentům veškerou literaturu (včetně publikací od M. Nováka, G. Sartoriho, B. Říchové nebo M. Skovajsy), pokud ji máme ke zkoušce nastudovat.";"kp" +"1743";"JPB268";"Evropská integrace";"Plechanovová,B.";;"2";"4";"2";"2";"1";NULL;NULL;NULL;"4";"4";"2";"4";"2";;"Hodnotit rešerše v průběhu semestru, aby měli studenti možnost zjistit, v čem chybují a případně chybu neopakovali v dalších rešerších. Také nechápu hodnocení otázek ve zkouškovém testu (6, 3, nebo 0 bodů), stejně tak v rešerši (10, 6, 3, nebo 0 bodů). Podmínky pro absolvování kurzu jsou kvůli tomu velmi zmatené. Navíc paní docentka opravuje zkouškové testy velmi dlouho.";"kmv" +"1744";"JJB238";"Kritika marketingové komunikace";"Rosenfeldová,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"3";"5";"5";"Lidský přístup, přednášku Terezy Krobové, zveřejnění prezentací";;"kmkpr" +"1745";"JJB276";"Public relations v praxi";;"Hejlová,D.";"4";"3";NULL;NULL;NULL;"3";"4";"4";"1";"3";"4";"4";"4";"Přednášející, kteří jsou z praxe";"Zveřejnit prezentace";"kmkpr" +"1746";"JJB279";"Art marketing";"Ježková,T.";;"4";"4";"4";"5";"5";NULL;NULL;NULL;"3";"4";"1";"4";"5";"Živost, lidský přístup, přednášející";"Zveřejnění prezentací";"kmkpr" +"1747";"JLB005";"Angličtina pro politology I";;"Panešová,K.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"2";"4";"4";"4";"5";;;"cjp" +"1748";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"1749";"JPB018";"Dějiny mezinárodních vztahů I";"Soukup,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"3";"4";"4";"5";"5";;;"kmv" +"1750";"JPB020";"Úvod do mezinárodních vztahů";"Karlas,J.,Kučera,T.";;"2";"4";"2";"4";"1";NULL;NULL;NULL;"1";"3";"3";"3";"2";;;"kmv" +"1751";"JPB221";"Metodologický proseminář I";;"Bahenský,V.,Kofroň,J.";"4";"3";NULL;NULL;NULL;"3";"5";"5";"1";"5";"5";"5";"5";;"Kurz byl dobrý, ale chtělo by to, aby vyučující Bahenský měl více sebedůvěry a věčně se nám neomlouval, když jsme něco nepochopili, nebo když něco spletl.";"kmv" +"1752";"JPB252";"České novověké dějiny I.";"Kučera,J.";;"3";"3";"4";"4";"2";NULL;NULL;NULL;"1";"4";"2";"3";"2";;;"kp" +"1753";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"4";"4";"5";"3";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kp" +"1754";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"3";"5";"5";"3";NULL;NULL;NULL;"2";"5";"4";"2";"5";"Je super, že jsou přednášky na youtube - proto dávám 3 za přínos účasti na přednáškách, když shlédnu přednášku tam, tak už nemusím do Jinonic, což se hodí, když jsem nemocná, jsem u doktora, atd. Zavedla bych to i u jiných předmětů.";;"ks" +"1755";"JEM001";"Master´s Thesis Seminar I";;"Havránek,T.,Havránková,Z.,Mejstřík,M.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"3";"5";"1";"5";;;"ies" +"1756";"JEM027";"Monetary Economics";"Holub,T.,Malovaná,S.";"Břízová,P.,Hájek,J.,Holub,T.,Malovaná,S.";"4";"3";"5";"5";"3";"5";"5";"3";"1";"5";"4";"5";"4";"Interesting topicsShort but efficient homeworks";"Timing of Lectures - hard to focus 2 hours straight after 5PM - but understandableSeminar paper";"ies" +"1757";"JEM181";"Data Science with R";"Herman,D.,Krištoufek,L.";"Herman,D.,Krištoufek,L.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"Very knowledgeable Lecturer with great attitude!Large knowledge expansion in R coding DataCamp assignment HomeworksFinal projectNo Exam";"Create Data Science with R.2/ Advanced Data Science with R - so it is possible to go into more detail Create similar subject on Bachelor levelCreate third specialization on IES - Data Science - high demand for students, attractive for students (Include courses like Quantitative Finance, Applied Econometric etc.)";"ies" +"1758";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"3";"3";"3";"3";NULL;NULL;NULL;"1";"3";"2";"3";"2";;;"ies" +"1759";"JJM254";"Mediální tvorba";"Čásenský,R.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Praktické informace, skvělý přístup pedagoga, poutavá forma výuky, dobrá atmosféra";"Nevadilo by mi psát během semestru několik prací, které by nám vyučující okomentoval a mohli bychom se tak zlepšovat ve svém stylu psaní. Myslím, že konkrétně od tohoto profesora by konstruktivní kritika byla velmi přínosná.";"kz" +"1760";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"3";;;"kp" +"1761";"JSB012";"Úvod do empirického výzkumu ve společenských vědách";"Jeřábek,H.";"Přibáňová,T.";"4";"4";"5";"5";"4";"5";"4";"4";"1";"5";"4";"3";"4";;;"ks" +"1762";"JSB407";"Globální problémy životního prostředí a udržitelný rozvoj";"Drhová,Z.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"3";"3";"4";;;"kvsp" +"1763";"JSB513";"Úvod do akademické práce";"Höfer,K.,Mouralová,M.,Veselý,A.";;"4";"3";"5";"5";"3";NULL;NULL;NULL;"1";"3";"3";"3";"3";;;"kvsp" +"1764";"JEM034";"Corporate Finance";"Bychkova,O.,Malířová,T.";;"3";"2";"4";"4";"1";NULL;NULL;NULL;"1";"1";"1";"4";"2";"Fair MidtermStudents can pass even without taking the Final exam if enough pointsAvailability of materials";"Unnecessary long & time demanding homeworks - I mean 10+ problems for each week?? - I can learn the same in half of the timeSubject is all about computation - no theory about how Corporate finance differs from regular companies/ start-ups etc... Evaluation of HW and Midterm took very long time (more than month) - given that we have a HW every week with strict deadlines it is kind of unfairIt is shame that we have this subject as obligatory in Finance and Banking specialization when there is so many other much more interesting subjects available and we had to take this course as we need to have 5 courses fulfilled out of 5 available, at least for now. It is easy to pass if you spend enormous, unnecessary time on homeworks I stopped going to lectures and seminars after 2 weeks as there was no point going there - and NO the solution is not to not publishing materials from these, but rather change the structure so it is not all about computations and change the HW rules (3-5 problems/week would be enough)!!Quite disappointed, given the expectations ....";"ies" +"1765";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"3";"4";;;"ks" +"1766";"JSB025";"Sociální problémy";"Frič,P.";;"4";"4";"5";"4";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kvsp" +"1767";"JPB228";"Mírové smlouvy a konference v mez. systému";"Jeřábek,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";"Aktivní přístup ke studentům během přednášek. Studenti si vypracováním vlastních prací a distribucí ostatním studentům dělají kvalitní poznatky k danému tématu.";;"kmv" +"1768";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"3";"3";"4";"1";"3";NULL;NULL;NULL;"2";"4";"4";"4";"3";;"Přístup pana doktora Švece, směrem k odpovídání studentům na případné dotazy emailem, které jsou spjaty se studiem. Také výsledky testů by měly být zveřejněny do týdne, čekat na výsledky testů, které jsou ve formě kroužkování odpovědí (ABCD) přes 14 dní je tristní! Pro studenta je toto velice demotivující směrem ke studiu jako takovému, ale také k danému předmětu a problematice.";"kp" +"1769";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"2";"2";"2";"3";NULL;NULL;NULL;"1";"3";"3";"2";"3";;;"ies" +"1770";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"3";"5";"3";"3";"2";"4";"4";"4";"2";"4";"3";"3";"3";;;"ies" +"1771";"JSB012";"Úvod do empirického výzkumu ve společenských vědách";"Jeřábek,H.";"Přibáňová,T.";"4";"2";"4";"5";"1";"5";"5";"5";"1";"2";"4";"3";"4";;"Závěrečný test by měl obsahovat kvantitativní i kvalitativní část i pro obor PVP, vypovídací schopnost celkové známky na základě 6 krátkých otázek v ZT mi přijde nízká.";"ks" +"1772";"JSB028";"Informační gramotnost";"Tomandlová,V.";;"5";"1";"4";"5";"5";NULL;NULL;NULL;NULL;"4";"4";"3";"5";;;"kvsp" +"1773";"JSB998";"Úvod do sociologie";"Soukup,P.";;"3";"1";"5";"5";"1";NULL;NULL;NULL;NULL;"3";"3";"3";"4";;"Závěrečný test je absolutní fraška, která bohužel dělá z celého (jinak zajímavého) předmětu zbytečnou \"dávačku\". Předmět za 5 kreditů by měl být ukončen odpovídajícím způsobem, ne testem s možnostmi a,b,c,d, který lze vyplnit za 5 minut. Osobně jsem za celý semestr nebyl na jediné přednášce a na závěrečný test mi stačilo učit se cca 2,5 hodiny. Pokud jsou náklady na zavedení ústní zkoušky příliš vysoké, doporučuji do testu alespoň doplnit otevřené otázky, které zkoumají reálné pochopení problematiky sociologie.";"ks" +"1774";"JMM130";"Ethno-Political Conflicts in the Caucasus";"Brisku,A.";;"5";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"4";"3";"5";"The professor is very good. He takes into account our opinion and helps us with everything we need.";"Take into account the geopolitical aspect. It is very important in the region right now.";"krvs" +"1775";"JPM425";"Conflict & Cooperation in International River Basins";"Landovský,J.";;"5";"2";"4";"5";"3";NULL;NULL;NULL;"2";"3";"4";"3";"5";"It was a small group so we could all participate. You find people from different parts of the world and the teacher makes the classes quite dinamic.";;"kp" +"1776";"JSM527";"Metody analýzy a tvorby politik II.";"Veselý,A.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Režim kursu, časové uspořádání, přístup pedagoga i doktorandů výborné. Vše vyhovující zcela.";"Možná časová dotace cvičení - leckdy bych přivítala pro větší probrání témat.";"kvsp" +"1777";"JSM528";"Seminář k diplomové práci I.";;"Kohoutek,J.,Ochrana,F.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"5";"5";"5";"5";"5";"Rozsah tématu, přínos pro psaní DP velký.";"Bez připomínek";"kvsp" +"1778";"JSM641";"Sociální problémy";"Frič,P.";;"5";"5";"4";"5";"5";NULL;NULL;NULL;"1";"5";"4";"3";"4";;;"kvsp" +"1779";"JSM644";"Základy politologie";"Kotlas,P.";;"4";"3";"3";"5";"4";NULL;NULL;NULL;"1";"4";"3";"3";"4";;;"kvsp" +"1780";"JSM646";"Veřejná správa";"Ochrana,F.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kvsp" +"1781";"JSM647";"Manažerské metody ve veřejné a sociální politice";"Ochrana,F.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kvsp" +"1782";"JSM705";"Řízení kvality a performance management ve veřejné správě";"Plaček,M.";;"4";"4";"4";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kvsp" +"1783";"JLM006";"Angličtina pro politology II";;"Panešová,K.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"5";"5";"The professor's attitude to the students; the possibility to practise English a lot during the class; skills to express own opinion in English.";"The course could be conducted more often (twice a week) for better English training.";"cjp" +"1784";"JMM583";"Evropská energetická politika a energetická bezpečnost EU";"Fischer,J.";;"3";"4";"4";"5";"2";NULL;NULL;NULL;"2";"3";"4";"4";"3";"New knowledge about some aspects of states' energy policy.";"More information about strategies and concrete actions of the states and the EU in this area is necessary.";"kzs" +"1785";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"4";"5";"4";"4";"3";NULL;NULL;NULL;"1";"4";"4";"4";"4";"The wide spectrum of new information which is a basis for International relations professions.";"The provided information is very complicated for understanding so it could be improved through better explanation and presentation of information in the class.";"kmv" +"1786";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"5";"5";"5";"4";NULL;NULL;NULL;"1";"4";"5";"4";"5";"New very important skills of working in R.";"Everything is good.";"kmv" +"1787";"JPM690";"Liberalism in International Relations (TIR)";;"Karlas,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Different kinds of liberalism theory were presented in efficient manner. Literature provided for every kind sheds light on general context and presents separate case studies that helps to better understand the issue.";"Everything is good.";"kmv" +"1788";"JPM692";"Internal Security of the EU [ES]";"Hokovský,R.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"2";"4";"4";"4";"4";"New knowledge about strategies in internal security of the EU.";;"kmv" +"1789";"JPM719";"Diplomacy of the European Union";"Pajtinka,E.";;"3";"3";"3";"4";"3";NULL;NULL;NULL;"1";"3";"1";"2";"3";"That it was not conducted during the whole semester.";"Presenting information could be supplemented with case studies.";"kmv" +"1790";"JPM721";"Komparace ekonomik zemí EU";"Kučerová,I.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";"The comprehensive overview of the EU member states' economies.";"Everything is good.";"kmv" +"1791";"JPM727";"Orchestration in Global Governance";;"Abbott,K.,Parízek,M.";"5";"4";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"5";"4";"Presenting of a new concept in international relations.";"Everything is good.";"kmv" +"1792";"JSM103";"Academic Writing";;"Blokker,P.";"4";"3";NULL;NULL;NULL;"4";"4";"4";"2";"4";"4";"3";"4";"Recommendations on how to write own assignment.";"More training and practical exercises are needed.";"ks" +"1793";"JEB039";"International Trade";"Semerák,V.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"4";"3";"Professor is very patience , would not mind to spare his private time to help students to understand the materials.";;"ies" +"1794";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"1795";"JJB600";"Úvod do studia médií I.";"Nečas,V.";;"3";"5";"4";"4";"3";NULL;NULL;NULL;"3";"3";"3";"4";"3";;;"kms" +"1796";"JJB601";"Rozbor právního prostředí v českých médiích";"Benda,J.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kms" +"1797";"JJB605";"Novodobá historie médií";"Bednařík,P.,Končelík,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"1798";"JJB616";"Vybrané novinářské osobnosti 19. století";"Sekera,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kms" +"1799";"JJB620";"Mediální produkce - žurnalistická práce I.";"Vlasák,Z.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"1800";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"4";"2";"4";"4";"4";NULL;NULL;NULL;"1";"4";"1";"4";"3";;;"ies" +"1801";"NMMA701";"Matematika 1";"Spurný,J.";"Rondoš,J.";"5";"4";"5";"5";"5";"3";"4";"4";"1";"4";"5";"5";"5";;;"ies" +"1802";"JJM199";"Literární a knižní kritika";"Čeňková,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Oceňuji skvělý výběr hostů, možnost účasti na kulturních akcích apod., jelikož to kurz určitě ozvláštnilo a ještě zpříjemnilo.";"Nemám ke kurzu výtky.";"kz" +"1803";"JJM249";"Duchovní kořeny evropské kultury";"Halada,J.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"3";"4";"4";"Milý přístup vyučujícího, příjemnou atmosféru kurzu, snaha podat historický, kulturní, filozofický kontext zajímavou formou.";"Nemám ke kurzu výtky.";"kz" +"1804";"JSB998";"Úvod do sociologie";"Soukup,P.";;"3";"1";"5";"5";"2";NULL;NULL;NULL;"1";"3";"1";"3";"3";;;"ks" +"1805";"JJM355";"Teoretické koncepty ke studiu žurnalistiky I";"Jirků,J.,Němcová Tejkalová,A.";"Jirků,J.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"Ukázku teoretických konceptů na praktických příkladech, společné praktické vystoupení, milý přístup obou vyučujících.";"Nemám ke kurzu výtky";"kz" +"1806";"JLB003";"Angličtina pro ekonomy I";;"Gloverová,M.";"4";"3";NULL;NULL;NULL;"5";"5";"3";"1";"3";"4";"4";"4";;;"cjp" +"1807";"JJM251";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"4";"5";"4";"4";"4";NULL;NULL;NULL;"1";"4";"3";"5";"4";"Doplnění kontextu pro ostatní předměty";"Poměrně přísné hodnocení kurzu v poměru s náročností.";"kz" +"1808";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"4";"5";"1";"1";"1";"1";"1";"5";;;"kz" +"1809";"JEM001";"Master´s Thesis Seminar I";;"Havránek,T.,Havránková,Z.,Mejstřík,M.";"3";"3";NULL;NULL;NULL;"3";"3";"3";"1";"3";"3";"3";"3";;;"ies" +"1810";"JEB111";"Advanced Data Analysis in MS Excel";;"Poláková,N.,Polák,P.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"1811";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"4";"3";"3";"5";"1";"3";"4";"1";"1";"4";"3";"5";"5";;;"ies" +"1812";"JJM356";"Metodologické přístupy ke studiu žurnalistiky";"Nečas,V.,Veselková,Z.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"2";"5";"4";"5";"5";"Praktické ukázky, např. příklady konkrétních diplomových prací.";"Nemám ke kurzu výtky";"kz" +"1813";"JJB221";"Ekonomická teorie marketingu";"Postler,M.";;"3";"1";"3";"3";"1";NULL;NULL;NULL;"1";"1";"1";"1";"1";;;"kmkpr" +"1814";"JJM360";"Ekonomika v médiích";"Klimeš,D.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Výběr hostů, ukázka příkaldů z praxe, milý přístup vyučujícího.";"Nemám ke kurzu výtky";"kz" +"1815";"JEM034";"Corporate Finance";"Bychkova,O.,Malířová,T.";;"3";"3";"3";"3";"3";NULL;NULL;NULL;"3";"3";"3";"3";"4";;;"ies" +"1816";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"4";"3";NULL;NULL;NULL;"3";"4";"4";"2";"2";"3";"4";"4";;;"ies" +"1817";"JEM040";"Účetní a daňové poradenství";;"Kemény,I.";"3";"3";NULL;NULL;NULL;"3";"3";"3";"3";"3";"3";"3";"3";;;"ies" +"1818";"JEB120";"Financial Economics";"Žigraiová,D.";;"3";"3";"3";"3";"3";NULL;NULL;NULL;"2";"3";"3";"3";"3";;;"ies" +"1819";"JJM362";"History of media";;"Neuzil,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Milý přístup vyučujícího, možnost absolvování kurzu v anglickém jazyce i pro české studenty, zábavná forma, uplatnění praxe na tvorbě dokumentu. Jeden z nejlepších volitelných předmětů, které jsem zatím na FSV absolvovala.I have to appreciate the friendly approach of the teacher, the possibility of attending the course in English even for the Czech students, the entertaining form of the course, the practical part of the course - creating a documentary. I believe it is one of the best courses I have had on the FSV.";"Ke kurzu nemám žádné výtky.I have no objection to the course.";"kz" +"1820";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"4";"2";NULL;NULL;NULL;"4";"4";"3";"1";"3";"2";"3";"4";;;"ies" +"1821";"JJM363";"Czech-German-Jewish Literary Triangle";;"Peroutková,M.";"2";"3";NULL;NULL;NULL;"2";"2";"3";"1";"3";"2";"2";"2";"Možnost kurzu v anglickém jazyce, interakce se zahraničními studenty";"Kurz hodnotím jako příliš statický - hodiny probíhaly stále stejným způsobem, a to powerpointovou prezentací a pak diskuzí studentů o četbě. Zároveň poměrně vysoké nároky na studenty, vzhledem k tomu, že šlo o volitelný, v podstatě mimooborový, \"zájmový\" předmět - povinná četba knih každý týden.";"kz" +"1822";"JLM006";"Angličtina pro politology II";;"Panešová,K.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"5";"5";"Hluboce si vážím a děkuji za perfektní přístup vyučující paní magistře Kamile Panešové!";"Mohly by být dvouhodinovky";"cjp" +"1823";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"2";"5";"2";"4";"2";NULL;NULL;NULL;"2";"3";"1";"2";"1";"Nepovinnou docházku, účast na přednáškách byla totiž dost zbytečná.";"Prezentace vyučujících byly zmatené a nepřehledné. Povinnost číst zadané ukázky textů považuji obecně za dobré, jejich množství bylo ale dost kontraproduktivní. Výběr některých článků nevhodný, pro pochopení látky spíš matoucí než přínosný. Čas vyhrazený na zkoušku neúměrný požadavkům na počet slov. Velmi dlouhá doba opravení testu...";"kmv" +"1824";"JMM189";"Economic transformation in East Central and Southeastern Europe";"Trejbal,V.";;"3";"4";"5";"4";"4";NULL;NULL;NULL;"1";"5";"4";"4";"4";"I value most ability of the lecturer in explaining topics in a simple way.";"Lecturer could speak a little slower in order to improve understanding of what he says.";"krvs" +"1825";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"3";"4";"2";"5";"Both teachers were high quality - one is not even mad paying attention to someone so passionate about his subject";;"ies" +"1826";"JJB040";"Kreativita v jazyce";"Šoltys,O.";;"1";"4";"1";"1";"2";NULL;NULL;NULL;"3";"2";"2";"1";"1";"Předmět spočíval pouze v psaní textů, jejichž téma vyučující náhodně vymýšlel (například Vysoká Sněžka 1603). Samotné psaní a následné předčítání textů před ostatními bylo mnohdy zajímavé a zábavné. Za to ale lze poděkovat pouze nám studentům, kteří jsme texty tvořili. Vyučující texty de facto nehodnotil, nevznášel žádnou konstruktivní kritiku, a jeho výstupy byly zcela zbytečné.";"Všechno. \"Výklad\" pana Šoltyse je nesrozumitelný, nesouvislý a často nemá co dělat s tématem výuky. Pan Šoltys sám je na studenty často nepříjemný a někdy nechápe, co se mu snaží sdělit. Texty neumí nijak konstruktivně zhodnotit a známky udílí pravděpodobně dle sympatií.";"kz" +"1827";"JSB998";"Úvod do sociologie";"Soukup,P.";;"2";"1";"5";"4";"3";NULL;NULL;NULL;"3";"2";"1";"1";"5";"I liked the possibility of watching lectures online. I wish there was such option for math.";"It is not like I think teacher had anything to do with my disdain for the subject itself, its just I really do not like sociology in general - I took it only for easy credits. But there is one thing I despised and that was the normalization of deviant behavior by encouraging students to raise their hands if they ever smoke pot/stole something atd... It would be no problem to show the statistics and state the arguments for legalizing and so forth but to portray pathologic behavior patterns as not being worth among hiding I find quite disgusting.";"ks" +"1828";"JPM607";"International Negotiations";;"Parízek,M.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Neotřelý přístup, investovanou energii vyučujícího. Praktické informace, ke kterým se jinde příliš nedostaneme, propojení teorie s reálným světem. Perfektně funkční platformu moodlu a naprostou spolehlivost vyučujícího, lidský přístup, záživnost přednášek, zajímavé readingy.";"Myšlenka jednání výborná, provedení z časových důvodů celkem hektické, chtělo by lépe rozvrhnout. V prezentacích lépe popsat početní úkoly, před testem by si člověk rád prošel znovu, ale hotové grafy moc nenapověděly.";"kmv" +"1829";"JMM384";"Cold War in Documents 1945-1962";"Smetana,V.";;"3";"4";"5";"4";"5";NULL;NULL;NULL;"1";"5";"5";"5";"4";"The particular way with which it has been held, working directly on documents.";"Lecturer should give more time to students for preparing for presentations.";"kas" +"1830";"JMMZ109";"Comparison of Central European Political Systems";"Kubát,M.";;"3";"4";"4";"4";"3";NULL;NULL;NULL;"1";"5";NULL;"4";"4";"I value most ability of the lecturer in explaining topics in a simple way.";"None.";"knrs" +"1831";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"4";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Dokonale funkční zázemí moodlu, rychlou komunikaci s vyučujícím. Blbuvzdorné videa pro R. Možnost spustit testy kdykoliv, více pokusů pro početní úlohy. Snahu udělat pekelnou statistiku co nejvíce zábavnou. Zvládnutelná hranice pro absolvování.";"Druhá polovina kurzu dost hektická, objem učiva na úkor pochopení. Celkově kurz extrémně přínosný, často jsem ale měla pocit, že se rychlostí blesku každý týden snažíme dohnat to, co jsme měli postupně získávat během let na bakaláři. Slovíčkaření u testů.";"kmv" +"1832";"JLB003";"Angličtina pro ekonomy I";;"Poslušná,L.";"3";"2";NULL;NULL;NULL;"5";"5";"3";"1";"3";"2";"4";"5";"I liked discussions. Also the teacher was speaking much better English than on what I was used to on high school.";"I would definitely discard some presentation topics which are too broad. I would allow pupils to substitute the topic by anything they like (of course - the teacher would still be green-lighting every topic)";"cjp" +"1833";"JPM690";"Liberalism in International Relations (TIR)";;"Karlas,J.";"4";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"3";"5";"5";"Profesionální přístup vyučujícího, lidský přístup, spolehlivost, přehledné informace, možnost získávat body za aktivitu, mírné hodnocení testů, 14 denní frekvenci.";"Zasílané otázky k četbě často neseděly s tím, co se reálně probíralo na hodině.";"kmv" +"1834";"JEM179";"History and Methodology of Economics";"Baxa,J.,Paulus,M.";;"5";"4";"4";"4";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"A lot of high quality reading which one would not find by himself. And the most valuable thing - no pre-requisites even though the subject was ofter far in the macro territory. As a freshman who cannot take any of the economic courses because of the pre-requisites, I found this subject the only one that made me feel as attending university and not just a ninth year of a grammar school with extra hard math.";;"ies" +"1835";"JPB268";"Evropská integrace";"Plechanovová,B.";;"3";"5";"1";"1";"1";NULL;NULL;NULL;"3";"2";"2";"1";"1";"Rozhodně zachovat \"rešerše\", motivují studenta zabývat se obsahem kursu i během semestru, nikoliv jen ve zkouškovém období. Oceňuji, že oproti minulému roku došlo k dílčím zlepšení, mám však k nim dál jisté výhrady. Jak mají vypadat není ze zadání úplně jasné, to co je požadováno dle mého názoru nemá s rešerší příliš společného. Větší problém ale shledávám k nulové zpětné vazbě. Jelikož se hodnotí ad-hoc až po napsání zkoušky, nemá student absolutně žádnou představu do jaké míry jsou v pořádku a zda by při psaní těch následujících měl postupovat jinak.";"Přístup vyučujícího";"kmv" +"1836";"JPM260";"Vybrané problémy britské zahraniční politiky v 19. a 20. století, ES";"Soukup,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Přístup a přehled vyučujícího";"Více termínů na zkoušku";"kmv" +"1837";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";NULL;"5";;;"kp" +"1838";"JPB018";"Dějiny mezinárodních vztahů I";"Soukup,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"2";"4";"4";"4";"5";;;"kmv" +"1839";"JPB020";"Úvod do mezinárodních vztahů";"Karlas,J.,Kučera,T.";;"3";"5";"2";"4";"1";NULL;NULL;NULL;"1";"2";"2";"3";"3";;;"kmv" +"1840";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"3";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"2";"3";"5";"The lecture about the Czech relations to president office was especially good.";"I would not give platform to the Klinika guy.";"ies" +"1841";"JPB221";"Metodologický proseminář I";;"Střítecký,V.,Tesař,J.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"3";"4";"2";"4";;;"kmv" +"1842";"JMM674";"Maritime security: Geopolitics of the Indian and Pacific Oceans";"Hornát,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kas" +"1843";"JPB252";"České novověké dějiny I.";"Kučera,J.";;"2";"4";"2";"1";"2";NULL;NULL;NULL;"1";"3";"1";"1";"1";;;"kp" +"1844";"JPM425";"Conflict & Cooperation in International River Basins";"Landovský,J.";;"4";"1";"4";"5";"4";NULL;NULL;NULL;"3";"4";"4";"4";"3";;;"kp" +"1845";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"5";"3";"5";"3";"5";NULL;NULL;NULL;"1";"4";"5";"5";"5";;;"kp" +"1846";"JPM613";"Armed Forces and Society";"Kučera,T.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"5";"5";"5";"5";"5";;;"kbs" +"1847";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"2";"5";"5";"3";NULL;NULL;NULL;"1";"3";"3";"2";"4";;;"ks" +"1848";"JPM692";"Internal Security of the EU [ES]";"Hokovský,R.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kmv" +"1849";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"5";"3";NULL;NULL;NULL;"4";"4";"4";"1";"3";"3";"3";"3";;;"ies" +"1850";"JPM701";"Security and Defence Integration in Europe and Transatlantic Relations";"Kazharski,A.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"2";"4";"4";"4";"4";;;"kbs" +"1851";"JEB003";"Ekonomie I";"Fanta,N.,Kracík,J.,Švarcová,N.";"Fanta,N.,Kracík,J.,Švarcová,N.";"2";"5";"2";"4";"3";"4";"4";"2";"1";"3";"3";"2";"2";"It is probably good that we had to create academic paper.";"I would normalize the difficulty level with the English version of the course. The Principles of Economics are from what I have heard much easier and they do not even have to write the essay.";"ies" +"1852";"JJB006";"Současný český jazyk III";;"Svobodová,I.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kms" +"1853";"NMMA701";"Matematika 1";"Spurný,J.";"Skříšovský,E.";"5";"5";"4";"5";"5";"4";"4";"5";"2";"4";"5";"4";"3";"The fact that the subject is concentrating on mathematical analysis I find good because it purifies us, students, of the antic, high-school understanding of math as something which should be related to absolute values and in the end to something tangible in real life which is not the Western way of (mathematical) thinking about space.";"The benches in the bottom left are squeaking when under pressure.";"ies" +"1854";"JJB014";"Žurnalistická tvorba III - Časopisecká tvorba";"Osvaldová,B.";"Maňák,V.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"kz" +"1855";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"3";NULL;NULL;NULL;"3";"3";"5";"2";"5";"5";"5";"5";;;"kz" +"1856";"JLB013";"Němčina odborná I";;"Křenková,D.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"přístup a vstřícnost vyučující";;"cjp" +"1857";"JLB053";"Angličtina pro sociální vědy I";;"Gloverová,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"1858";"JJB154";"Introduction to photojournalism";;"Láb,F.,Štefaniková,S.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"The ability to see a whole range of work produced by other students";;"kz" +"1859";"JMB057";"Cultural Legacies and Developments in the Balkans: Modern and Traditional Entanglements";"Asavei,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"5";"4";;;"krvs" +"1860";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"5";"1";"4";"1";NULL;NULL;NULL;"5";;;"kz" +"1861";"JLB005";"Angličtina pro politology I";;"Panešová,K.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"1862";"JPB011";"Politická geografie I";"Romancov,M.";;"5";NULL;"5";"5";"5";NULL;NULL;NULL;"1";NULL;NULL;NULL;NULL;;;"kp" +"1863";"JPB018";"Dějiny mezinárodních vztahů I";"Soukup,J.";;"5";NULL;"5";"5";"4";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kmv" +"1864";"JPB020";"Úvod do mezinárodních vztahů";"Karlas,J.,Kučera,T.";;"3";"5";"5";"5";"3";NULL;NULL;NULL;NULL;"5";NULL;"5";NULL;;;"kmv" +"1865";"JPB221";"Metodologický proseminář I";;"Bahenský,V.,Kofroň,J.";"5";"4";NULL;NULL;NULL;"4";"5";"5";"1";"5";"5";"5";"5";;;"kmv" +"1866";"JPB252";"České novověké dějiny I.";"Kučera,J.";;"3";NULL;"5";"3";"2";NULL;NULL;NULL;NULL;"3";"1";"3";NULL;;;"kp" +"1867";"JPB269";"Současné problémy mezinárodních vztahů";"Knutelská,V.";;"4";"4";"4";"4";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmv" +"1868";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";NULL;;;"kp" +"1869";"JSB998";"Úvod do sociologie";"Soukup,P.";;"4";NULL;"5";"5";"3";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"ks" +"1870";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"3";"5";"5";"5";"5";"3";"3";"3";"1";"3";"3";"2";"3";"Positive atmosphere the lecturer provided";"It was too theoretical and abstract. Since it is mandatory course and there a lot of especially international students with no econometrics background, I believe providing more \"real to life\" examples would have helped to understand the material much better. The last lectures were great, as lecturer started to provide those examples. Moreover, the first seminar teacher was not good at all. He acted super arrogant and never explained properly, as if we should have known everything by ourselves.";"ies" +"1871";"JEM032";"Banking";"Mejstřík,M.,Pečená,M.,Teplý,P.";"Pečená,M.";"5";"3";"5";"5";"5";"3";"3";"3";"1";"4";"4";"5";"5";"Very good lecturers from real market. Very enjoyed every lecture";"Started too late";"ies" +"1872";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"3";"5";"3";"4";NULL;NULL;NULL;"1";"4";"2";"3";"1";"Zveřejňování materiálů na webu";;"ies" +"1873";"JLB005";"Angličtina pro politology I";;"Panešová,K.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"1874";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"2";"5";"5";"3";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kp" +"1875";"JPB018";"Dějiny mezinárodních vztahů I";"Soukup,J.";;"4";"2";"5";"5";"3";NULL;NULL;NULL;"3";"3";"3";"3";"5";;;"kmv" +"1876";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"1877";"JJB014";"Žurnalistická tvorba III - Časopisecká tvorba";"Osvaldová,B.";"Hájek,R.";"4";"3";"4";"4";"2";"4";"5";"3";"1";"4";"5";"4";"5";;;"kz" +"1878";"JMB499";"Současné metodologie";"Kubát,M.";;"3";"4";"2";"2";"4";NULL;NULL;NULL;"1";"4";"3";"3";"4";;;"krvs" +"1879";"JMB523";"Mezinárodní aktuality I";"Fojtek,V.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"kas" +"1880";"JMB503";"Soudobé české dějiny";"Kocian,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"krvs" +"1881";"JMB498";"Metodologie soudobých dějin";"Smetana,V.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"krvs" +"1882";"JJB170";"Počítačové zpracování foto a graf. design";"Slanec,J.";;"4";"2";"3";"5";"3";NULL;NULL;NULL;"1";"3";"4";"4";"5";;;"kz" +"1883";"JJM231";"Mediální výchova v rodině";;"Šťastná,L.";"4";"4";NULL;NULL;NULL;"4";"5";"5";"1";"4";"3";"4";"4";;;"kms" +"1884";"JJB009";"Úvod do psychologie";"Vranka,M.";;"4";"4";"4";"5";"4";NULL;NULL;NULL;"2";"5";"3";"4";"4";;;"kz" +"1885";"JLB019";"Francouzština odborná I";;"Bosáková,L.";"4";"3";NULL;NULL;NULL;"4";"5";"4";"1";"4";"3";"4";"4";"-poskytnutí materiálů z každé hodiny v sisu-jasné nároky a dobře připravené hodiny-externí hosté - rodilí mluvčí";;"cjp" +"1886";"JJB006";"Současný český jazyk III";;"Svobodová,I.";"5";"5";NULL;NULL;NULL;"4";"4";"5";"2";"3";"5";"3";"4";;;"kms" +"1887";"JJB003";"Dějiny masových médií III";"Bednařík,P.,Končelík,J.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kms" +"1888";"JJB002";"Dějiny masových médií II";"Sekera,M.";;"3";"2";"3";"2";"2";NULL;NULL;NULL;"4";"3";"2";"3";"5";;;"kms" +"1889";"JJB0111";"Journalism Ethics/Úvod do etiky žurnalistické práce";"Neuzil,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"5";"5";;;"kz" +"1890";"JLB100";"Czech as a Foreign Language I";;"Frantesová,E.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";NULL;NULL;"5";"help us learn Czech faster and understand the Czech";;"cjp" +"1891";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"5";"2";"5";"5";"3";NULL;NULL;NULL;"1";"5";"3";"4";"5";;;"ies" +"1892";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"4";"2";NULL;NULL;NULL;"5";"5";"5";"1";"3";"4";"4";"5";;;"ies" +"1893";"NMMA701";"Matematika 1";"Spurný,J.";"Rondoš,J.";"5";"5";"5";"5";"5";"5";"5";"5";"2";"5";"5";"5";NULL;;;"ies" +"1894";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"3";"4";"4";"5";;;"ies" +"1895";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"4";"3";"4";"5";"3";"3";"5";"3";"1";"4";"4";"4";"5";;;"ies" +"1896";"JLB003";"Angličtina pro ekonomy I";;"Poslušná,L.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"1897";"JLB035";"Francouzština I";;"Dundrová,M.";"2";"1";NULL;NULL;NULL;"3";"5";"2";"1";"1";"1";"2";"3";;;"cjp" +"1898";"JSB998";"Úvod do sociologie";"Soukup,P.";;NULL;"1";NULL;NULL;NULL;NULL;NULL;NULL;NULL;"2";NULL;NULL;NULL;;;"ks" +"1899";"JJB004";"Současný český jazyk I";;"Svobodová,I.";"3";"5";NULL;NULL;NULL;"5";"1";"3";"1";"3";"5";"3";"2";;"Velmi se mi nelíbilo, jak p. Svobodová byla velmi nepříjemná, chvílemi až zlá na studenty, zbytečně je schazovala, když něco nevěděli apod.";"kz" +"1900";"JJB010";"Základy filozofie a vzdělanosti";"Halada,J.";;"3";"3";"3";"5";"2";NULL;NULL;NULL;"3";"3";"1";"2";"3";;"Je mi líto, že by pan prof. Halada mohl udělat o mnoho zajímavější a hlavně nás o mnoho víc naučit. Jeho znalosti jsou neskutečné, pokud ale měli každou přednášku studenti referát, absolutně je nevyužil. Také bych uprednostnila, kdybychom se více věnovali filosofickým teoriím než tomu, kdy se jaky filosof narodil";"kz" +"1901";"JJB243";"Aktuální trendy a vývoj v oboru I.";"Hejlová,D.,Vranka,M.";"Hejlová,D.,Vranka,M.";"5";"1";"5";"5";"5";"5";"5";"5";"1";"2";"1";"4";"5";"Celkově kurz hodnotím jako jeden z mých nejoblíbenějších, bavil mě výběr zajímavých hostů";"Čas výuky, páteční poledne považuji za poměrně nešťastně zvolené, spousta mých spolužáků si kurz nezapisuje jen kvůli tomu, že je v pátek a byla by to jejich jediná hodina, navíc např. jezdí na víkendy domů atd., raději bych kurz přesunula např. na čtvrtek a nebo alespoň změnila čas na dřívější hodinu a ne v půlce dne";"kmkpr" +"1902";"JJB628";"Marketing módních značek - teorie";"Hejlová,D.,Koudelková,P.";;"3";"3";"5";"3";"5";NULL;NULL;NULL;"3";"4";"2";"4";"3";"Tento kurz byl jedním z důvodů, proč jsem se na obor hlásila, tudíž jsem se na něj velmi těšila a měla vysoké očekávání. Pozitivně hodnotím náplň přednášek, probíraná témata mě bavila a určitě jsem se dozvěděla něco nového, např. z oblasti udržitelnosti módního průmyslu. Bylo navíc evidentní, že přednášející módnímu průmyslu rozumí a má bohaté zkušenosti. Velmi kladně hodnotím návštěvu výstavy Manolo Blahnika a zajímavá byla i zkušenost s mystery shoppingem.";"Ačkoliv mě hodiny bavily, musím poznamenat, že bych ocenila větší zapojení marketingové stránky, protože o marketingu módních značek jako takovém jsem se až tolik nedozvěděla. Těšila jsem se také na zajímavé hosty, avšak bohužel nakonec kvůli nemocím atd. jsme měli hosta jen jednoho. S čím jsem však byla nejvíce nespokojená byla forma atestu, v SISu sice o jeho podmínkách něco stojí, nicméně konkrétní zadání (test a seminární práce) nám bylo řečeno až v 10. týdnu výuky. Každý máme spoustu dalších předmětů, na které máme jiné úkoly a všichni si nějak organizujeme svůj čas a dozvědět se zadání takto na poslední chvíli mi přijde nefér. Mluvím hlavně o zadání seminární práce, na kterou bylo potřeba nejdříve provést 3 rozhovory, což je časově poměrně náročné (hledat v hektickém vánočním období lidi, kteří mají čas si semnou jít povídat o módě, není zas tak jednoduché) a pokud by práce byla zadána už na začátku semestru, nebo alespoň bylo avizováno, že práce bude zadána až v ten a ten den, mohla bych si její zpracování lépe rozvrhnout a mnohem více si s prací pohrát. Což mě opravdu mrzí, protože jinak bylo téma a vůbec zadání s rozhovory pro mě velmi zajímavé. Také nutnost odevzdání práce v tištěné formě a to i s přepisem rozhovorů mi přijde jako dost paradoxní, vzhledem k množství papíru, které jsem na tisk musela použít a k tomu, že jedním z tématů práce byla právě udržitelnost a ekologie.";"kmkpr" +"1903";"JEB110";"Econometrics II";"Pertold-Gebicka,B.";"Chorna,O.,Malinská,B.,Pertold-Gebicka,B.,Pleticha,P.";"3";"4";"3";"3";"3";"5";"5";"5";"1";"3";"2";"2";"2";;;"ies" +"1904";"JEM062";"Introductory Econometrics";"Kukačka,J.";"Kukačka,J.";"5";"2";"5";"5";"5";"5";"5";"5";"1";"4";"4";"4";"5";;;"ies" +"1905";"JEM141";"Traditional and Alternative Risk Transfer in the Insurance Sector";"Pompella,M.,Teplý,P.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ies" +"1906";"JEM181";"Data Science with R";"Herman,D.,Krištoufek,L.";"Herman,D.,Krištoufek,L.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"1907";"JLB101";"Czech as a Foreign Language II";;"Mazúrková,B.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"5";;;"cjp" +"1908";"JJB630";"Krizová komunikace";"Chudinová,E.";;"3";"3";"3";"3";"4";NULL;NULL;NULL;"2";"5";"3";"4";"3";"Myslím, že mi kurz určitě přinesl poznatky z krizové komunikace a mám pocit, že jsem se něco naučila, což není samozřejmostí u každého kurzu. Zachovala bych také zadání seminárních prácí, tedy rozbor případové studie, ačkoliv bych tyto rozbory více prováděla i v hodinách, viz níže.";"Téma krizové komunikace mi upřímně nepřijde jako natolik obsáhlé, aby vystačilo na samostatný kurz. Během kurzu i během přípravy na test jsem měla pocit, že se točí stále pár věcí pořád dokolečka jen jinými slovy. Buď to bych tedy navrhovala náplň kurzu nějak více rozšířit a nebo věnovat třeba 2-3 přednášky krizové komunikace v jiném kurzu. Navrhuji také zapojení více případových studií, těch je, myslím, zrovna v krizové komunikaci mnoho, ať už špatných či dobrých, a bylo by zajímavé je společně na hodinách rozebrat (v hodině jsme se věnovali jen jedné slovenské kauze). Zbytečná mi připadala i hodina s hostem, bývalou slovenskou premiérkou, která nám měla něco říct ke krizové komunikaci z vlastních zkušeností, nicméně o KK nepadlo ani slovo a většinu času jen vedla monolog na různá politická témata.";"kmkpr" +"1909";"JJB0111";"Journalism Ethics/Úvod do etiky žurnalistické práce";"Neuzil,M.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"5";"5";"The approach of the lecturer";"More practical tasks during the courses";"kz" +"1910";"JJB148";"Audiovizual Interpreting the Reality";"Štoll,M.";;"2";"1";"3";"4";"2";NULL;NULL;NULL;"4";"2";"2";"3";"2";"Presenting concrete examples (movies) to emphasize the theory";"Teacher's English";"kz" +"1911";"JJM117";"Popular Culture";"Turnau,T.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Complex theories explained in simple words";"Some brief repetition before an exam";"kms" +"1912";"JJM242";"Comics as a Medium";"Hrdina,M.";;"3";"3";"3";"3";"2";NULL;NULL;NULL;"3";"3";"4";"4";"3";"Interesting topic";"The requirements for the assignments could be form more precisely";"kms" +"1913";"JJM234";"Media and Society: An Introduction";"Jirák,J.";;"3";"3";"3";"4";"1";NULL;NULL;NULL;"1";"1";"2";"2";"3";"The practical tasks";"Theoretical aspect could be improved";"kms" +"1914";"JJB606";"Televize jako instituce";"Štoll,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"1";"4";"5";;;"kms" +"1915";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"3";"3";"2";"1";"2";NULL;NULL;NULL;"1";"3";"2";"3";"3";;;"kp" +"1916";"JJB002";"Dějiny masových médií II";"Sekera,M.";;"2";"1";"4";"4";"3";NULL;NULL;NULL;"4";"2";"2";"3";"3";;;"kms" +"1917";"JJB003";"Dějiny masových médií III";"Bednařík,P.,Končelík,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"1";"4";"5";;;"kms" +"1918";"JJB006";"Současný český jazyk III";;"Svobodová,I.";"5";"3";NULL;NULL;NULL;"4";"5";"5";"1";"5";"5";"1";"4";;;"kms" +"1919";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"2";"4";"5";"5";"4";"Práce s dobovými dokumenty byla velmi zajímavá";"Z organizačního hlediska bych ocenila, kdyby přednášky probíhaly každý týden a byly kratší. Udržet pozornost tři hodiny v kuse (resp. s desetiminutovou přestávkou) je náročné a mám pocit, že si z přednášek tolik neodnáším, jako kdyby byly kratší a probíhaly častěji.";"kp" +"1920";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"4";"5";"5";"3";"4";"3";"3";"3";"2";"5";"4";"5";"4";"Přednášky byly zajímavé a dobře vedené. Jen je škoda, že jsme tento semestr nestihli semináře.";"Přidat publikace z povinné četby do Moodle. Je skvělé, že mnoho povinných i doporučených článků/kapitol z četby už tam je, ale některé stěžejní publikace je velmi složité sehnat, protože jsou v Jinonické knihovně neustále vypůjčené.";"kp" +"1921";"JPB258";"Politické myšlení novověku I";"Franěk,J.,Kučera,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Kurzy jsou velmi zajímavé a v podání obou vyučujících i mnohdy zábavné.";;"kp" +"1922";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"4";"5";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"5";"4";"Prezentace jako doplněk informací z přednášky";"Když skupina studentů odprezentuje špatně nebo nepodá důležité informace, vyučující by se měl snažit je doplnit. Je to hlavně chyba studentů, kteří prezentaci s vyučujícím předem nekonzultovali, přesto je důležité informace, které chyběly v prezentaci doplnit, vzhledem k tomu, že se z nich částečně skládá zkouška.";"kp" +"1923";"JPB269";"Současné problémy mezinárodních vztahů";"Knutelská,V.";;"4";"4";"4";"5";"2";NULL;NULL;NULL;"2";"2";"5";"3";"4";"Fact checkingy a Policy Brief jsou skvělým nácvikem akademické práce a schopnosti získané v kurzu budou jistě velmi nápomocné při psaní bakalářské práce";"Přednášky hostů, ačkoliv byly velmi zajímavé, mi ne vždy přišly přínosné a relevantní k obsahu kurzu. Přínos přednášek se lišila host od hosta.";"kmv" +"1924";"JPB578";"Classics of Political Thought";"Salamon,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Přednášky byly velmi zajímavé a skvěle podané. Průběžné čtení přednáškám dodávalo další rozměr, alespoň pro ty, kteří opravdu poctivě četli.";"Dvanáctistránková esej jako finální test je téměř nadlidský výkon, ale vzhledem k povaze a obsahu kurzu dává esejová forma asi největší smysl.";"kp" +"1925";"JPB592";"US Government and Politics";"Kotábová,V.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Důraz na diskusi o relevantních tématech a současném dění.";"Zdálo se mi, že ne všichni studenti pochopili formát tzv. \"digest of news\", které se odevzdávaly před zkouškou, což vedlo ke zmatkům. Pro příště by bylo dobré třeba na první hodinu přinést ukázkovou práci, nebo ji dát do SISu.";"kp" +"1926";"JPM909";"Rousseau and Nationalism: On the Government of Poland";;"Franěk,J.,Kelly,C.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Líbila se mi hloubka a detail probírané látky. V kontrastu s dalšími kurzy, které se snaží plošně pokrýt co nejvíce látky za cenu občasných generalizací, byl tento kurz příjemnou změnou. Zkouška formou eseje na předem konzultované téma mi také vyhovuje, nutí to studenty zamýšlet se nad problematikou místo toho, aby museli umět nazpaměť odříkat všechnu látku.";;"kp" +"1927";"JJM240";"Cultural studies";"Soukup,M.";;"4";"2";"4";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"3";;;"kms" +"1928";"JJM372";"Consumer Behaviour";"Orhan,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"kms" +"1929";"JMM128";"Prezentace v médiích";"Procházková,B.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Naprosto geniální kurz, za celé své studium jsem neměla praktičtější předmět. Předmět byl neuvěřitelně přínosný, studenti se dozvěděli, jak by měla vypadat prezentace, jak se chovat před publikem, potenciálně v televizi a rozhlase a vše jsme si mohli vyzkoušet, což bylo to nejlepší.";"Zahrnula bych i zpětnou vazbu k referátům, kurz by mohl být klidně dvou semestrální, abychom si mohli vše důkladně vyzkoušet a třeba dělat to samé v druhém semestru a sledovat pokrok studentů a posouvat je zase dál.";"kzs" +"1930";"JLB029";"Španělština odborná I";;"Mlýnková,L.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"4";"5";"Velmi energický kurz se snahou zapojit studenty, co nejvíce do výuky, i přes větší počet studentů na hodině. Střídání různých aktivit a zároveň velký důraz na prohloubení znalostí studentů. Nemám co dodat, neabsolvovala jsem lepší jazykový kurz než tento.";;"cjp" +"1931";"JMM027";"Contemporary Mediterranean";"Králová,K.,Mejstřík,M.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"2";"4";"4";"Oceňuji téma kurzu pokrývající teritoria, která se moc na IMS neučí. Zároveň je velmi pozitivní zvaní různých expertů na dané oblasti a témata do hodin.";"Jelikož na hodiny byla i četba a bavili jsme se o ní asi pouze na 2 hodinách z celého semestru, tak bych doporučila, buď se o dané četbě bavit vždy, navázat na ní (ale ne opakovat naprosto to, co bylo v četbě, což se párkrát na hodinách stalo). Student pak nevěděl, jestli na dané hodině po něm bude četba žádána, nebo jestli ji bude vyučující \"papouškovat\", nebo ji naopak ani nedotkne. V takové situaci se pak stane, že studenti četbu nečtou a ve chvíli, kdy ji jeden z vyučujících na hodinu vyžaduje a má hodinu na četbě postavenou, tak to zkříží plány všem. Tudíž bych doporučila sjednotit přístup všech vyučujících, včetně hostujících, sjednotit přístup k četbě.";"kzs" +"1932";"JPB227";"Politický system ČR";"Charvát,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kp" +"1933";"JPB589";"Seminář k politickému myšlení: 19. století";;"Novotný,J.";"4";"3";NULL;NULL;NULL;"4";"5";"3";"2";"4";"4";"4";"5";;;"kp" +"1934";"JPB227";"Politický system ČR";"Charvát,J.";;"5";"3";"5";"5";"3";NULL;NULL;NULL;"2";"5";"5";"5";"5";"Přístup pana Chartváta hodnotím velmi kladně. Reagoval obratem na e-maily a byl ochoten poradit a vyjít vstříc.";;"kp" +"1935";"JSM502";"Diplomový seminář I";;"Dobiášová,K.,Kotrusová,M.";"5";"2";NULL;NULL;NULL;"5";"5";"3";"1";"4";"5";"4";"4";;;"kvsp" +"1936";"JPB260";"Politické myšlení novověku III";"Kučera,J.";;"5";"5";"5";"4";"3";NULL;NULL;NULL;"2";"5";"4";"5";"5";;"Bylo by dobré, aby v SIS byl uveden konkrétní seznam stěžejních autorů a myslitelů, kterým se kurz věnuje.";"kp" +"1937";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"5";"5";"5";"1";"2";NULL;NULL;NULL;"2";"3";"3";"3";"1";;"Kurz je obsahově velice obtížný. Nerozumím, na jakém principu byly vybírány státy, které se do předmětů Komparace I., II. a III. zařadily. Jako zásadnější nebo zajímavější by byl politický systém např. Ruska, Švýcarska nebo Itálie, než zemí pobaltí nebo ekonomicky absolutně bezvýznamného Rumunska. Z hlediska přístupu vyučujících - testy jsou extrémně obtížné, trvá i několik týdnu, než je vyučující opraví (to pak způsobuje problém při zápisu na další termín), náhradní termíny, po uplynutí doby řádného zkouškového období, se nevypisují. Pan doktor Švec neodepisuje na e-maily a nesnaží se se studenty nijak komunikovat. Pan doktor Mlejnek má přístup dobrý, komunikuje a je nápomocný.";"kp" +"1938";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"4";"4";"3";"4";"4";NULL;NULL;NULL;"1";"4";"3";"4";"3";;;"ies" +"1939";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"kp" +"1940";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"4";;;"kp" +"1941";"JPB256";"Politické systémy I";"Brunclík,M.,Jeřábek,M.,Kotábová,V.,Perottino,M.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kp" +"1942";"JPB221";"Metodologický proseminář I";;"Komasová,S.,Parízek,M.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"1";"4";"5";"4";"5";;;"kmv" +"1943";"JPB218";"Dějiny novověké Evropy I.";"Kučera,J.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"5";"3";"4";"4";;;"kp" +"1944";"JPB252";"České novověké dějiny I.";"Kučera,J.";;"3";"3";"4";"4";"3";NULL;NULL;NULL;"1";"5";"3";"4";"4";;;"kp" +"1945";"JPB018";"Dějiny mezinárodních vztahů I";"Soukup,J.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"3";"4";"3";"4";"5";;;"kmv" +"1946";"JPB258";"Politické myšlení novověku I";"Franěk,J.,Kučera,J.";;"4";"5";"4";"4";"4";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kp" +"1947";"JJB009";"Úvod do psychologie";"Vranka,M.";;"3";"3";"4";"4";"3";NULL;NULL;NULL;"1";"3";"1";"2";"3";;;"kz" +"1948";"JJB014";"Žurnalistická tvorba III - Časopisecká tvorba";"Osvaldová,B.";"Maňák,V.";"5";"2";"5";"5";"4";"5";"5";"2";"1";"5";"5";"2";"5";;;"kz" +"1949";"JJB019";"Práce s agenturními informacemi";"Prázová,I.,Trunečková,L.";"Prázová,I.,Trunečková,L.";"3";"3";"4";"4";"5";"4";"4";"5";"1";"4";"4";"2";"4";;;"kz" +"1950";"JJB021";"Bakalářský seminář";;"Prázová,I.";"2";"1";NULL;NULL;NULL;"4";"4";"3";"1";"2";"1";"1";"3";;;"kz" +"1951";"JJB006";"Současný český jazyk III";;"Svobodová,I.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";"Myslím si, že celkově je koncept kurzu velmi dobrý. Oceňuji především neustálé procvičování gramatických jevů, které jsou mnohdy i po stém opakování problémem.";;"kms" +"1952";"JJB138";"Sportovní žurnalistika v televizi I";"Záruba,R.";"Záruba,R.";"5";"1";"5";"5";"5";"5";"5";"5";"1";"5";"5";"3";"5";;;"kz" +"1953";"JJB617";"Vybrané novinářské osobnosti 20. století";"Železný,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"1";"4";"5";;;"kms" +"1954";"JLB009";"Angličtina pro žurnalisty I";;"Prošková,A.";"4";"2";NULL;NULL;NULL;"3";"4";"2";"1";"4";"3";"1";"3";;;"cjp" +"1955";"JJB019";"Práce s agenturními informacemi";"Prázová,I.,Trunečková,L.";"Prázová,I.,Trunečková,L.";"4";"3";"5";"5";"5";"5";"5";"5";"1";"4";"4";"4";"4";"Procvičení vyhledávání nejrůznějších informací. Návštěva ČTK byla velmi zajímavá.";"Myslím, že pro psaní různých seminárních prací a esejů by bylo lepší, kdyby kurz byl již v prvním ročníku bakalářského studia.";"kz" +"1956";"JJB021";"Bakalářský seminář";;"Prázová,I.";"4";"3";NULL;NULL;NULL;"5";"4";"5";"1";"4";"4";"3";"4";"Oceňují cvičné napsání bakalářské teze a zpětnou vazbu.";;"kz" +"1957";"JMM047";"Právní a institucionální rámec evropské integrace.";"Šlosarčík,I.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"3";"5";"5";"Autentický výklad vyučujícího, který má s probíranou látku patřičné zkušenosti";;"kzs" +"1958";"JMM048";"European Union in International Affairs";"Weiss,T.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"3";"5";"3";"3";"Výuka v anglickém jazyce, vyučující";"Neřešit celý semestr jedno téma z možných uhlů, pokud nebylo ani zjištěno, zda studenty téma vůbec zajímá. Skupinová práce na prezentaci je v pořádku u vypracování dlouhé práce už je mnohem problematičtější - upravit.";"kzs" +"1959";"JMM271";"Metodologický seminář";;"Weiss,T.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"4";"4";"extra konanou přednášku, vyučujícího";"Doporučená změna by se týkala změny probíraného obsahu, což však není možné.";"krvs" +"1960";"JMM277";"Historie a kultura";"Vykoukal,J.";"Tomalová,E.";"3";"4";"5";"5";"5";"5";"5";"5";"1";"3";"3";"4";"1";"Vyučujícího";"Bohužel jsem zřejmě nepochopila podstatu látky tohoto kurzu";"krvs" +"1961";"JMM278";"Politika a mezinárodní vztahy";"Kubát,M.,Weiss,T.";"Juhás,T.";"4";"4";"5";"5";"5";"5";"5";"5";"1";"4";"4";"5";"4";"Celé trio vyučujících, které je dokonalé.";;"krvs" +"1962";"JMM279";"Společnost a ekonomika";"Fiřtová,M.";"Young,M.";"5";"5";"5";"5";"5";"4";"5";"5";"1";"5";"5";"5";"5";"Nejlepší kurz - vhodně zvolená látka, srozumitelně podaná ekonomie studentům sociálních věd a báječná přednášející. Tyto hodiny byly opravdu radost.";"Týká se semináře: Vyučující by neměl nechat jednoho neustále opilého studenta, aby kazil hodinu všem a prošel s tím (Young - Bezkočka)";"kas" +"1963";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"4";"5";"- the approach of the teacher, regular assignments on Moodle (which prompted continuous preparation which came in handy for the Final test), helpful videos with instructions";;"kmv" +"1964";"JPM706";"Terrorism and Counterterrorism";"Bureš,O.";;"5";"4";"5";"4";"5";NULL;NULL;NULL;"1";"4";"4";"5";"4";"Interesting readings (though there was quite a lot of them)";"Having 3 different country presentations per lesson (in week 10 and 11) was overwhelming";"kbs" +"1965";"JMM583";"Evropská energetická politika a energetická bezpečnost EU";"Fischer,J.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"2";"4";"4";"4";"4";"Námi zvolená témata, o kterých se diskutovalo. Projekt úspor - ačkoli jsem k němu zprvu byla skeptická, nakonec ho velmi oceňuji.";;"kzs" +"1966";"JLB005";"Angličtina pro politology I";;"Panešová,K.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"3";"5";;;"cjp" +"1967";"JPB218";"Dějiny novověké Evropy I.";"Kučera,J.";;"5";"3";"5";"4";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kp" +"1968";"JSB027";"Sociální politika jako společenská praxe";"Dobiášová,K.,Vlčková,K.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"velké plus přednáška profesora Potůčka";;"kvsp" +"1969";"JMB507";"Soudobé dějiny střední Evropy";"Vykoukal,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"profesionální přístup profesora, znalosti,";;"krvs" +"1970";"JMB509";"Soudobé dějiny jihovýchodní Evropy";"Tejchman,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"krvs" +"1971";"JJB226";"Strategie a finanční management marketingu I";"Majerik,P.";;"4";"3";"4";"4";"3";NULL;NULL;NULL;"2";"4";"3";"4";"3";;;"kmkpr" +"1972";"JJB231";"Dějiny a teorie public relations";"Halada,J.,Hejlová,D.";;"2";"4";"4";"2";"2";NULL;NULL;NULL;"1";"3";"2";"3";"3";;"Informovanost ohledně Moodle. Lepší přístup při zkoušení studentů ze strany doktorky Hejlové.";"kmkpr" +"1973";"JJB238";"Kritika marketingové komunikace";"Rosenfeldová,J.";;"4";"2";"4";"5";"3";NULL;NULL;NULL;"3";"4";"2";"4";"4";;;"kmkpr" +"1974";"JJB406";"Tvorba a prostředky v mediální komunikaci";"Chudinová,E.";;"2";"2";"2";"4";"1";NULL;NULL;NULL;"2";"2";"1";"2";"1";;;"kmkpr" +"1975";"JJB255";"Digitální komunikace";;"Klimeš,D.";"5";"4";NULL;NULL;NULL;"5";"5";"4";"2";"4";"4";"4";"5";;;"kmkpr" +"1976";"JPB263";"Bakalářský seminář II.";;"Brunclík,M.,Bureš,O.,Ditrych,O.,Franěk,J.,Gelnarová,J.,Hynek,N.,Charvát,J.,Jeřábek,M.,Jüptner,P.,Karásek,T.,Karlas,J.,Knutelská,V.,Kofroň,J.,Kotábová,V.,Krausz Hladká,M.,Kubátová,H.,Kučera,J.,Kučerová,I.,Landovský,J.,Ludvík,J.,Makariusová,R.,Mlejnek,J.,Pa";"5";"5";NULL;NULL;NULL;"5";"5";"5";"5";"5";"5";"5";"5";"Velice vstřícný a přátelský přístup mého vedoucího, pana doktora Jeřábka. Je velice nápomocný, výborně se s ním komunikuje. Motivuje mě k práci.";;"kp" +"1977";"JJB635";"Interkulturní marketing";"Rosenfeldová,J.";;"4";"2";"4";"5";"4";NULL;NULL;NULL;"3";"3";"2";"4";"4";;;"kmkpr" +"1978";"JPB268";"Evropská integrace";"Plechanovová,B.";;"1";"5";"1";"1";"1";NULL;NULL;NULL;"3";"3";"3";"2";"1";"Nic.";"Vlastně vše. Kurz, který je zvláště v současnosti velice aktuální, je bohužel pojat přímo katastrofálně. Vyučující není schopná kurz podat poutavou, nebo alespoň průměrně záživnou formou. Čte slidy z počítače a účast na přednáškách není ničím přínosná, jelikož přečíst příslušnou literaturu je zábavnější a je u něho vyšší pravděpodobnost, že si to člověk zapamatuje. Kurz nerozvíjí ani kritické smýšlení, ani v něm není umožněn prostor pro diskusi. Jedna návštěva Evropského parlamentu s průvodcem mi dala víc, než celý semestr tohoto kurzu.K vyučující - je s ní velmi špatná komunikace, neodepisuje na e-maily, způsob hodnocení testů je při nejmenším zvláštní (metoda 0/3/6, jedna z rešerší za 0, celý předmět nesplňen, ale neexistuje nikde žádný vzor toho, jak přesně by měla rešerše za plný počet bodů vypadat). Ačkoliv je na kurz přihlášeno třeba 100 studentů, počet míst na zkoušce odpovída např. 150, tzn. není vypsáno dostatek míst, aby všichni studenti měli 3 pokusy, na které mají nárok. Testy opravuje s velkým zpožděním, tudíž do dalšího vypsaného termínu student neví, jak dopadl a jestli se má případně na další termín přihlásit, jelikož je jich vypsán omezený počet a na další by se nemusel kvůli kapacitě vejít, nebo termín už není. Paní doktorka reagovala na návrh, že v situaci, kdy ona nestíha opravovat, se studentni automaticky přihlásí na další vypsaný termín (aby vůbec měli možnost dalšího pokusu) tak, že nebude ten předchozí (který dle pravidel již měl být dávno opraven) vůbec opravovat a studentovi tak jen propadne termín. Toto považuji za naprosto katastrofání přístup a pro mě nepochopitelný extrémně negativní postoj ke studentům. Je mi to velice líto, kurz zněl jako zajímavý a velice praktický ke studiu, ukázal se ovšem jen jako obrovské břímě.";"kmv" +"1979";"JJB240";"Marketing a tvorba značky";"Průša,P.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kmkpr" +"1980";"JJB403";"Institucionální a vládní komunikace";"Shavit,A.,Soukeník,Š.";;"4";"4";"5";"4";"3";NULL;NULL;NULL;"2";"3";"4";"4";"4";;;"kmkpr" +"1981";"JSM514";"Metody a techniky práce s informacemi";"Tomandlová,V.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";NULL;"5";;;"kvsp" +"1982";"JLM011";"Angličtina pro veřejnou a sociální politiku I";;"Klírová,M.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"1983";"JSM612";"Kriminalita a současná česká společnost";"Cejp,M.";;"5";"1";"4";"5";"3";NULL;NULL;NULL;"1";"5";"3";"3";"5";;;"kvsp" +"1984";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"2";"4";;;"kp" +"1985";"NMMA703";"Matematika 3";"Zelený,M.";"Johanis,M.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"Omlouvám se, omylem jsem odeslala hodnocení matematiky do Microeconomics II. Takže jen zopakuju, že kurz Matematika 3 byl perfektní, jak přednáška tak cvičení, nemám co vytknout, vyučující jsou výborní, látka mě baví a hodně mi to dalo.";"Možná dát kurz ještě trochu dříve, i když je to asi složité. Od čtyř odpoledne už pro mě byl problém vnímat, ale to je asi spíš subjektivní.";"ies" +"1986";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"2";"2";"3";"3";"2";"1";"1";"1";"3";"2";"2";"4";"2";;"Přístup pana Mgr. Nikoloze Kudashviliho. Tento vyučující s námi nejednal pěkně, údajně studenty na hodině i zesměšňoval, choval se arogantně. Bohužel jsem doteď neviděla svůj midterm, a nejspíš už nikdy neuvidím, vyučující nereagoval na emaily, neposkytoval informace, když nám dal termín, kdy se můžeme přijít na midterm podívat, odešel záhadně z budovy o půl hodiny dřív. Nerozumím, proč mám z midtermu jen asi polovinu bodů, když podle kontroly se správnými výsledky mi vycházela maximálně jedna chyba. Neříkám, že se stala chyba, možná jsem něco přehlídla, ale chtěla jsem test aspoň vidět, potom už jsem ztratila motivaci do předmětu něco dělat. Navíc kvůli agresivnímu a výbušnému chování pana vyučujícího k některým studentům a nemožnosti jej zastihnout nebo získat jakékoliv informace se z předmětu postupně stala spíše fraška a předmět vtipů. Chápu, že má nejspíš mnoho dalších povinností, ale připadá mi, že svoje povinnosti zanedbával a odmítal to přiznat, což bylo asi nejhorší. Jinak asi předmět nemusí být špatný, jenže na vyučujícím záleží strašně moc.";"ies" +"1987";"JJB231";"Dějiny a teorie public relations";"Halada,J.,Hejlová,D.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Moc se mi líbilo, že konečně byl na MKPR trochu náročnější kurz. Tím, že jsme tu četbu museli číst doopravdy a doopravdy jít ke zkoušce, ne si jen najít pár starých otázek z minulých roků a naučit se odpovědi, tak mi ten předmět opravdu něco dal. Myslím, že takový styl učení má mnohem větší smysl než se učit (řečeno s nadsázkou) definice marketingu, politického marketingu atd. a psát kroužkovací testy nebo se naučit nazpaměť náhodná jména a termíny bez opravdového pochopení. Učení na DTPR mě bavilo a dověděla jsem se hodně věcí i mimo marketing a PR. Výklad i doporučená četba byly zajímavé.";"Možná nebylo úplně fér, jak někteří poctivější a zodpovědnější studenti, kteří se na zkoušku připravovali, třeba neuspěli kvůli špatné otázce, ale někdo zase dostal A zadarmo, protože nezbyl čas. Mělo by to být objektivní a lépe naplánované.";"kmkpr" +"1988";"JPB596";"Čínská zahraniční a bezpečnostní politika";"Karmazin,A.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"4";"5";"Když výuka odpadla, byla poté po domluvě nahrazena. Což bylo za mě velké plus. Přišly mi přínosné různé ,,aktivity¨ ke konci hodiny, které se týkaly vždy probíraného tématu a celkový obsah a rozvržení předmětu bylo za mě velmi dobré.";"-";"kbs" +"1989";"JPB260";"Politické myšlení novověku III";"Kučera,J.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"5";;;"kp" +"1990";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"4";"5";"5";"3";"5";"5";"3";"5";"2";"5";"3";"5";"3";;;"kp" +"1991";"JPB229";"Regionální politické systémy: Skotsko, Wales";"Říchová,B.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"3";"5";"3";"3";"5";;;"kp" +"1992";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"4";"4";"5";"4";"4";NULL;NULL;NULL;"1";"5";"3";"4";"4";;;"kp" +"1993";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"5";"5";"5";"4";"4";NULL;NULL;NULL;"2";"5";"3";"3";"4";;;"kp" +"1994";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"5";"3";"5";"5";"3";NULL;NULL;NULL;"1";"3";"3";"2";"5";;;"ies" +"1995";"JJM080";"Zábava v televizním vysílání";"Kruml,M.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kms" +"1996";"JEM007";"Applied Microeconometrics";"Pertold-Gebicka,B.";"Pertold-Gebicka,B.,Rečková,D.";"4";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"4";"5";"the homework were very intresting";;"ies" +"1997";"JEM020";"Ethics and Economics";"Cahlík,T.";"Cahlík,T.";"4";"3";"5";"5";"5";"5";"5";"4";"1";"5";"3";"5";"5";;;"ies" +"1998";"JSM518";"Public Policy";"Potůček,M.,Vlčková,K.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kvsp" +"1999";"JSM692";"Introduction to Social Research Methodology";"Remr,J.";;"3";"2";"4";"5";"4";NULL;NULL;NULL;"1";"3";"3";"2";"3";;;"ks" +"2000";"JJM343";"Interkulturní komunikace";"Soukup,M.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"Enthusiasmus vyučujícího! A dále zajímavý, nemonotónní výklad. Toto je předmět, u kterého mi prostě (díkybohu) nešlo sedět a neposlouchat. :)";"Méně \"her\" / \"sociálních experimentů\" které zaberou velkou část přednášky, je škoda přicházet o zajímavý výklad.";"kms" +"2001";"JJB605";"Novodobá historie médií";"Bednařík,P.,Končelík,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Best lecturers ever!";;"kms" +"2002";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"3";"5";"5";"3";"5";"Prístup vyučující, snaha o interakci se studenty, jejich zapojení do výuky";;"kp" +"2003";"JJB620";"Mediální produkce - žurnalistická práce I.";"Vlasák,Z.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"2004";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"3";"5";"4";"2";"4";NULL;NULL;NULL;NULL;"3";"3";"4";"1";;"zkrácení času opravy testu minimálne na tretinu současné čekací doby !!!";"kp" +"2005";"JEM003";"Advanced Microeconomics";"Janda,K.,Melikhova,O.";"Janda,K.,Melikhova,O.";"4";"4";"4";"4";"3";"4";"5";"5";"1";"3";"3";"3";"4";"Semináře byly přínosné";;"ies" +"2006";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"5";"4";"5";"5";"5";"3";"4";"2";"2";"5";"4";"4";"5";"Dobré přednášky";"Prezentacím chybí struktura (alespoň outline na začátku prezentace by pomohl) což zbytečně zdržuje při učení a mate. Přísně hodnocený midterm (bylo by dobré kdyby byla kvantilová regrese lépe vysvětlena na semináři včetně upozornění na časté chyby).";"ies" +"2007";"JEM032";"Banking";"Mejstřík,M.,Pečená,M.,Teplý,P.";"Pečená,M.";"3";"4";"3";"3";"3";"4";"4";"4";"1";"4";"3";"4";"4";;"Prezentace.";"ies" +"2008";"JEM037";"Financial Markets";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"5";"4";"5";"5";"5";"4";"5";"4";"1";"4";"4";"4";"5";"Good lectures";;"ies" +"2009";"JEM181";"Data Science with R";"Herman,D.,Krištoufek,L.";"Herman,D.,Krištoufek,L.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"4";"5";;;"ies" +"2010";"JMM530";"European Comparative Politics and Society";"Rovná,L.";;"1";"1";"1";"3";"1";NULL;NULL;NULL;"1";"1";"1";"1";"1";;"The professor needs to actually teach class, rather than assign groups of students to talk about preselected topics each class. I feel as though I learned very little. Overall, I am very disappointed with this course.";"kzs" +"2011";"JMMZ331";"Qualitative methods in social sciences";"Weiss,T.";;"4";"2";"5";"5";"3";NULL;NULL;NULL;"1";"3";"3";"4";"3";"Taught a subject that most students find to be incredibly boring in a way that made class interesting and informative.";;"kzs" +"2012";"JJB226";"Strategie a finanční management marketingu I";"Majerik,P.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"3";"5";"4";"4";"5";"Přístup přednášejícího a příklady kreativity v marketingové komunikaci.";;"kmkpr" +"2013";"JMMZ333";"Transnational history of contemporary Europe";"Matějka,O.";;"2";"2";"1";"2";"1";NULL;NULL;NULL;"1";"1";"2";"2";"1";;"The professor needs to actually teach the class himself, rather than assign students to present about a preselected topic each class. The class should also be structured in a way that teaches students the related history (e.g. rather than just talk about the controversies of the French Revolution, it would also be prudent to talk about the causes and events leading up to the Revolution, or at least assign extra readings that address this). Moreover, the final exam was absurd; the book that over half of the test was based off of was never talked about in class, which is a shame for two reasons: 1) because the book is so stimulating and deserves to be considered, and 2) because we had no idea which handful of the thousands of subjects/events/historical figures mentioned in the book were going to be on the test. At the very least, a study guide that narrowed the field of possible test materials needs to be provided.";"kzs" +"2014";"JJB231";"Dějiny a teorie public relations";"Halada,J.,Hejlová,D.";;"2";"5";"1";"1";"1";NULL;NULL;NULL;"3";"3";"1";"1";"2";;"Zcela chyběla struktura kurzu i jednotlivých přednášek. Účast na přednáškách nebyla pro studenty přínosná, téměř v polovině přednášek chyběly faktické informace. Celý kurz byl zmatečný, stejně tak i jeho hodnocení. Jestliže je studentům sděleno, že se do výsledného hodnocení kurzu nepromítnou seminární práce, mělo by to být i dodrženo. Během zkoušky nnavíc nebyly předem známé okruhy. Bylo by tedy vhodné, alespoň rozdělit otázky na teoretické a historické, v lepším případě ověřit znalosti studentů testem.";"kmkpr" +"2015";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"3";"3";"3";"2";NULL;NULL;NULL;"1";"2";"1";"1";"2";;;"ies" +"2016";"JLB001";"Angličtina pro sociology I";;"Štěpánková,D.";"4";"1";NULL;NULL;NULL;"3";"5";"1";"1";"1";"2";"4";"4";;;"cjp" +"2017";"JSB051";"Úvod do sociální antropologie";"Uherek,Z.";"Hrešanová,E.";"4";"3";"5";"5";"3";"4";"4";"4";"2";"4";"2";"4";"4";;;"ks" +"2018";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Rybín,F.,Vlčková,A.";"5";"3";"5";"5";"5";"5";"5";"5";"2";"5";"5";"5";"5";;;"ks" +"2019";"JSB530";"Úvod do sociologického myšlení";"Bureš,J.,Numerato,D.";"Bureš,J.";"3";"2";"2";"5";"1";"4";"5";"3";"3";"3";"2";"4";"2";;;"ks" +"2020";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Hanzlík,P.";"2";"5";"3";"3";"1";"3";"5";"5";"1";"3";"3";"1";"1";;;"ks" +"2021";"JSB007";"Dějiny sociologie I";"Šanderová,J.";"Bureš,J.";"2";"2";"3";"3";"4";"4";"5";"5";"4";"3";"2";"4";"4";;;"ks" +"2022";"JSB025";"Sociální problémy";"Frič,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kvsp" +"2023";"JJB237";"Právo a etika v marketingu";"Winter,F.";;"4";"2";"5";"5";"4";NULL;NULL;NULL;"1";"3";"3";"4";"5";"Přístup přednášejícího, příklady z praxe.";;"kmkpr" +"2024";"JJB240";"Marketing a tvorba značky";"Průša,P.";;"5";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"4";"3";"5";"Názornost a interaktivní přednášky.";;"kmkpr" +"2025";"JJB403";"Institucionální a vládní komunikace";"Shavit,A.,Soukeník,Š.";;"5";"4";"4";"4";"5";NULL;NULL;NULL;"3";"4";"3";"4";"5";"Strukturu kurzu, přístup přednášejících.";"Byla použita upravená stupnice hodnocení (viz Podmínky zakončení předmětu: Výkon studenta/ky bude uveden v procentech (0-100 %) a dle nich stanovení klasifikace: 0-55 % = F, 56-62 % = E, 63-71 % = D, 72-80 %=C, 81-93 %=B a 94% a více=A.)";"kmkpr" +"2026";"JJB255";"Digitální komunikace";;"Klimeš,D.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"5";"5";"Velmi dobrý výběr hostů.";;"kmkpr" +"2027";"JJB243";"Aktuální trendy a vývoj v oboru I.";"Hejlová,D.,Vranka,M.";"Hejlová,D.,Vranka,M.";"4";"2";"4";"4";"4";"4";"4";"4";"3";"4";"1";"4";"4";;;"kmkpr" +"2028";"JJB268";"Sportovní marketing";"Šesták,Z.";;"1";"3";"1";"1";"1";NULL;NULL;NULL;"3";"1";"1";"1";"1";;"Je třeba zlepšit přístup přednášejícího ke studentkám, jeho projev a vystupování.";"kmkpr" +"2029";"JPB258";"Politické myšlení novověku I";"Franěk,J.,Kučera,J.";;"4";"3";"3";"4";"4";NULL;NULL;NULL;"1";"2";"2";"4";"4";;;"kp" +"2030";"JPB268";"Evropská integrace";"Plechanovová,B.";;"2";"5";"3";"2";"2";NULL;NULL;NULL;"4";"4";"2";"3";"2";;"Rozhodně by se dala zlepšit komunikace v souvislosti se zkouškami. Myslím také, že kapacita pro 30 lidí na zkoušku v aule je zbytečně omezující.";"kmv" +"2031";"JPB569";"Workshop Politické a státní instituce v praxi";;"Brunclík,M.";"4";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Výběr hostů byl - až na výjimky - velice zajímavý a odpovídal různým možnostem, které studenti politologie v budoucnu mají. U tohoto kurzu jistě ani nevadilo jeho zařazení do podvečerních hodin. I přístup pana doktora Brunclíka byl profesionální, oprava seminárních prací proběhla velmi rychle.";;"kp" +"2032";"JPB596";"Čínská zahraniční a bezpečnostní politika";"Karmazin,A.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Kurz byl velmi zajímavě strukturovaný - od teoretických souvislostí přes historii až po konkrétní ukázky čínské politiky a současné problémy. Přístup pana Karmazina byl vždy vysoce profesionální. Za mě jeden z nejlepších kurzů na FSV vůbec.";;"kbs" +"2033";"JJM248";"Vývoj grafického designu a polygrafického zpracování periodik";"Slanec,J.";;"3";"2";"3";"5";"3";NULL;NULL;NULL;"1";"2";"1";"3";"2";"flexibilní přístup učitele, materiály ke studiu k dispozici";"zlepšit strukturu, informací bylo hodně a bylo náročné se v nich občas vyznat";"kz" +"2034";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"4";"5";"skvělé informace z praxe, názory a zkušenosti profesionála, nejlepší předmět!";"Je velký rozdíl mezi tím, co bylo na kurzu odpřednášeno a tím z čeho bychom pak v rámci mediální tvorby měli skládat státnice. Myslím, že teorie, ze které bych měla státnicovat, nebyla odpřednášena, za to bylo odpřednášeno spousta zkušeností, které se hodí. Doporučuji přehodnotit sylabus předmětu.";"kms" +"2035";"JJM249";"Duchovní kořeny evropské kultury";"Halada,J.";;"4";"4";"5";"5";"2";NULL;NULL;NULL;"1";"4";"3";"3";"3";"přístup vyučujícícho";"zlepšit strukturu, nedá se říct, že by přednášky nějakou měly...";"kz" +"2036";"JJM320";"Propaganda a československá média po roce 1948";"Suk,P.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"5";"5";;;"kms" +"2037";"JJM324";"Teorie mediální komunikace";"Jirák,J.";;"3";"4";"5";"5";"3";NULL;NULL;NULL;"1";"4";"2";"2";"3";"zisk bodů za jednotlivé části (testy, anotace)";;"kms" +"2038";"JLB041";"Španělština I";;"Mlýnková,L.";"4";"2";NULL;NULL;NULL;"5";"5";"5";"1";"3";"3";"3";"3";"The teacher is very patient with the students and helps them understand new concepts easily.";"The course should be taught only in Spanish and not in Czech. It's rather demanding to keep up with the book explaining Spanish grammar only in Czech.";"cjp" +"2039";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"2";"4";"3";"4";"2";NULL;NULL;NULL;"1";"3";"3";"2";"2";;"pokud to jde, zlepšit přednes vyučujícího a taky více konkretizovat výklad";"kms" +"2040";"JPM607";"International Negotiations";;"Parízek,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Offers a fair introduction to the complexity of the reality of negotiations.";"Since the assigning on countries is purely random, there needs to be a way to get the smaller nations to interact as many students with countries with weaker influence in the WTO simply do not participate in the negotiations and fall back on the final result of the negotiations being conducted by the bigger countries.";"kmv" +"2041";"JJM373";"Ethical Issues in Media, Business and Society";"Orhan,M.";;"3";"3";"5";"5";"2";NULL;NULL;NULL;"1";"3";"3";"2";"3";"quizzes";;"kms" +"2042";"JJM355";"Teoretické koncepty ke studiu žurnalistiky I";"Jirků,J.,Němcová Tejkalová,A.";"Jirků,J.";"4";"4";"5";"5";"5";"5";"5";"5";"1";"4";"3";"3";"3";"prezentace na seminářích, naskenované dokumenty v sylabu";;"kz" +"2043";"JPM719";"Diplomacy of the European Union";"Pajtinka,E.";;"2";"2";"3";"3";"2";NULL;NULL;NULL;"4";"2";"2";"3";"2";"It was fairly easy.";"The professor did not seem comfortable teaching in English and was sometimes not able to comprehend some questions raised by the students. Also, the classes were regularly shortened, with long unnecessary pauses of about half an hour between different topics. The course was rather unorganized and the content covered could have been limited to fewer lectures than allocated to the block course.";"kmv" +"2044";"JMB408";"Moderní dějiny Ruska";"Litera,B.,Novák,P.,Pečenka,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"3";"5";"5";;;"krvs" +"2045";"JJM251";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"4";"5";"5";"3";"5";NULL;NULL;NULL;"1";"5";"3";"4";"4";;"sehnat literaturu v českém jazyce, popř. naskenovat do sylabu";"kz" +"2046";"JMB249";"Seminář k dějinám středo- a jihovýchodní Evropy";;"Balla,P.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"2";"5";"3";"5";"5";;;"krvs" +"2047";"JMB409";"Moderní dějiny středo- a jihovýchodní Evropy-dcera JMB013";"Balla,P.,Švec,L.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"3";"5";"5";;;"krvs" +"2048";"JJM356";"Metodologické přístupy ke studiu žurnalistiky";"Nečas,V.,Veselková,Z.";;"4";"3";"5";"3";"4";NULL;NULL;NULL;"2";"4";"4";"4";"4";"průběžné úkoly";;"kz" +"2049";"JMB410";"Moderní dějiny západní Evropy";"Matějka,O.,Váška,J.";;"5";"4";"3";"5";"4";NULL;NULL;NULL;"3";"5";"3";"5";"5";;;"kzs" +"2050";"JMB204";"Skotsko, Wales a Severní Irsko v kontextu moderních britských dějin";"Kasáková,Z.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"2";"4";"5";;;"kzs" +"2051";"JMB414";"Seminář k aktualitám I";;"Hofmeisterová,K.";"4";"3";NULL;NULL;NULL;"4";"5";"3";"2";"3";"3";"1";"3";;;"krvs" +"2052";"JPM727";"Orchestration in Global Governance";;"Abbott,K.,Parízek,M.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"The Guest lecturer was very interesting and shared some great new concepts.";;"kmv" +"2053";"JJM254";"Mediální tvorba";"Čásenský,R.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"3";"5";"5";"skvělé přednášky p. Čásenského, spousta zajímavých informací, zkušeností z praxe, které nikde nenajdete!";"doporučovala bych dát nějak dohromady reálnou náplň přednášek a náplň přednášek podle sylabu. Mám obavu, že sylabus předpokládal, že se dozvím zcela něco jiného než jsem se dozvěděla. A zřejmě budu mít problém u státnic.... protože jsem načerpala spoustu skvělých zkušeností z praxe, které bych nikde jinde nenašla, ale teorie, která bude na státnicích přednášena nebyla...";"kz" +"2054";"JJM360";"Ekonomika v médiích";"Klimeš,D.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"1";"3";"3";"3";"5";"Přístup vyučujícího a skvělé a zajímavé přednášky pozvaných hostů";;"kz" +"2055";"JJB135";"Filmový seminář I";;"Šobr,M.";"4";NULL;NULL;NULL;NULL;"3";"3";"3";"1";NULL;NULL;NULL;"5";"Bohužel tento semestr mě zklamal výběr filmů.";;"kz" +"2056";"JSB522";"Sociální politika jako společenská praxe";"Dobiášová,K.,Kotrusová,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Kurz byl výborný. Nejlepší kurz, který jsem v zimním semestru navštěvoval. Skvěle rozšiřoval znalosti z letního kurzu a byl výborně koncipován. Moc se mi líbily exkurze v terénu, které byly k tématu, přednášky byly zajímavé a paní Jitka je rozhodně nejlepší cvičící, kterého jsme letos měli. Kurz měl i naprosto logickou strukturu a podle mě by mohl sloužit jako dobrý příklad pro ostatní kurzy. Obě přednášející mají také velmi dobré pedagogické vlastnosti a na přednášky byla radost chodit.";"Nic mě nenapadá. Snad jen, že by mohlo víc takhle dobře sestavených kurzů.";"kvsp" +"2057";"JPM711";"Issues in Russian and Eurasian Security";"Aslan,E.";;"3";"4";"2";"3";"1";NULL;NULL;NULL;"2";"4";"2";"4";"2";;"10 hodin z 11 je o Kavkaze, Rusku se nevěnovala žádná pozornost, tohle zaměření mi v roce 2017 přijde mimo.. stejně tak mi osobně nevyhovuje systém kdy je hlavní osou hodin systém studentských prezentací, poslouchat někoho, kdo si to načetl večer předem místo lektora, který v místě dělal osobně výzkum, pro mě nemá žádnou přidanou hodnotu";"kbs" +"2058";"JPM650";"Intelligence";"Bahenský,V.,Galeotti,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"kbs" +"2059";"JPM698";"Middle East Security";"Daniel,J.";;"5";"4";"4";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";NULL;"vynikajici readingy a komplexni zamereni na ruzne rozmery bezpecnosti vcetne prikladu z praxe";"neni nutne se kazdych 30 vterin ptat, jestli maji studenti nejaky dotaz, zvlast kdyz temer nikdy nemaji :)";"kbs" +"2060";"JPM706";"Terrorism and Counterterrorism";"Bureš,O.";;"4";"5";"4";"4";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";;"mozna az zbytecny fokus na detaily";"kbs" +"2061";"JJM363";"Czech-German-Jewish Literary Triangle";;"Peroutková,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Wonderful";;"kz" +"2062";"JMM121";"Central European Cinema";;"Duta,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"4";"Intimate knowledge of the films that a non-native speaker would not normally understand";"Communications with the teacher";"krvs" +"2063";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"5";"5";"4";"1";"3";"3";"4";"5";"Úvodní přednášky.";"Osobní zápis rozdělit časově podle abecedy, aby při něm člověk neztratil mládí. Fronty byly opravdu převeliké.";"kz" +"2064";"JPM127";"Geopolitika a geostrategie 1815 - 1945";"Romancov,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"3";"2";"3";"5";"Poskytnuté mapy.";"Doporučil bych aktualizovat test.";"kp" +"2065";"JPM160";"Česká komunální politika";"Jüptner,P.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Férový přístup vyučujícího.";;"kp" +"2066";"JPM348";"Nové přístupy k místní správě a přímá volba starostů";;"Jüptner,P.";"4";"3";NULL;NULL;NULL;"5";"5";"3";"1";"4";"5";"5";"5";;"Ocenil bych více úvodních přednášek vyučujícího a zvážil menší skupiny. Případně prezentoval pouze jednou.";"kp" +"2067";"JPM574";"Moderní strany a stranické systémy v Evropě";"Brunclík,M.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"3";"4";"4";"4";;;"kp" +"2068";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"4";"3";"3";"2";NULL;NULL;NULL;"1";"3";"2";"2";"2";;;"ies" +"2069";"JPM579";"Teorie politických stran";"Perottino,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"2070";"JLB001";"Angličtina pro sociology I";;"Klírová,M.";"4";"3";NULL;NULL;NULL;"4";"3";"4";"4";"3";"4";"3";"4";;;"cjp" +"2071";"JLB033";"Němčina I";;"Faltýnová,R.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"2";"5";;;"cjp" +"2072";"JPM344";"Diplomní seminář II.";;"Brunclík,M.,Franěk,J.,Hroch,M.,Charvát,J.,Jüptner,P.,Kofroň,J.,Kotábová,V.,Krausz Hladká,M.,Kubátová,H.,Kučera,J.,Landovský,J.,Mlejnek,J.,Perottino,M.,Riegl,M.,Romancov,M.,Říchová,B.,Salamon,J.,Shavit,A.,Švec,K.";"5";NULL;NULL;NULL;NULL;"5";"5";"5";NULL;"5";"5";"5";"5";;;"kp" +"2073";"JPM342";"Konflikty v demokracii a národní identita: teoretické problémy analýzy";"Říchová,B.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kp" +"2074";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"4";"4";"4";"4";"4";"4";"4";"4";"2";"4";"4";"3";"3";;;"ies" +"2075";"JMMZ188";"European Union in International Relations";"Weiss,T.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"4";"5";"Kurz má ohromný potenciál už svou náplní a rovněž díky osobě T. Weisse. Škoda pouze, že účastníků nakonec bylo skoro 30, což anonymizovalo případnou aktivitu jednotlivých studentů, na kterou se logicky ani dostat nemohlo.Rozhodně oceňuji přístup Tomáše Weisse a rovněž skladbu povinností k ukončení předmětu.Obecně jinak skvělý a přínosný kurz, který v tomto semestru neskončil u povrchu tématu, ale šel do jeho hloubky. To velmi chválím.Zajímavá rovněž byla národnostní pestrost našeho semináře, ale kvůli vytíženosti a počtu studentů potenciál do případných debat a polemik bohužel nebyl naplněn; vcelku logicky.";"Za mě kurz neokleštit pouze na země EaP. Zejména pak z důvodu, že většinu studentů tvořili studenti výměnných pobytů nejenom z Evropy, na které může být cílení jen na země EaP až moc podrobné a tím i náročné.Zvolit zaměření jednotlivých přednášek/seminářů po regionech, jak tomu bylo v dřívějších letech.Kurzu by určitě slušela forma 2/2, kdy by doc. Weiss mohl odborně uvést téma, aby se mohlo na semináři pokračovat jednotlivými prezentacemi či diskuzemi.";"kzs" +"2076";"JEM132";"Company Valuation";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"3";"5";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"2";"Practicle attitude of the lecturer, trying to explain the broader view on the topic of enterprise valuation";"Seminars were disaster, absolutely not well managed. The theoretical part from the lectures was not complemented by some well structured seminars with examples from practice. Homework assigned was very difficult and extremely time-consuming. The students had to study all materials by themselves, because nothing was explained or showed during the seminars. Also, during the case study, we didn´t receive any evaluation of the respective phases, therefore we did not know if we were going the right direction.The final test was also not fair - people who studied materials from lectures, seminars and who even did read the book did not have better chances to pass the final test than people, who did not study but only guessed during the final. Multiple choice questions as if they were just copied from totally different course, not done for the topics and problems we covered during the semester.";"ies" +"2077";"JPM700";"Space Security";"Doboš,B.";;"4";"3";"4";"3";"5";NULL;NULL;NULL;"2";"4";"2";"4";"5";"I truly enjoyed the variety of topics covered, ranging from more technical to political, normative, and legal aspects linked to space security. The lessons were very thorough and straight to the point.Audio-visual material was interesting as well.Readings were also interesting.";"The power point presentations could be improved, firstly from a visual point of view given that their were often just white background and black bulleted texts, and secondly by adding more information in offering students a valuable a solid base to use while revising for the exam.I would have preferred to have focused more on the security aspect of space security, with more emphasis and focus put on theories and policies. Also, the historical parts could be deepened. Maybe if not possible in class then by receiving suggested readings and compulsory ones. Indeed, despite having truly appreciated the availability given by the course to a large set of further readings, maybe the list could be expanded.Finally, I would have rather preferred to have as the main assignment for the course a personal essay to be written either on an argument/question given by the professor, or on something chosen by myself reflecting the topics covered in class.";"kbs" +"2078";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"4";"4";"5";"5";"5";"2";"4";"1";"2";"5";"5";"5";"5";"Great lecturer with enthusiasm for the subject, very motivating attitude, great explanations. Trying to always put respective problematics in a broader context. Not being too strict about theoretical part, focusing more on the practicle side of topics.";"Seminars were quite poor (exception was the first seminar leader). More practicle examples are needed to suplement the theoretical topics covered in lectures. In the midterm/final tests, there are plenty of practicle examples, but none was done or shown during the seminars!";"ies" +"2079";"JEM034";"Corporate Finance";"Bychkova,O.,Malířová,T.";;"3";"3";"4";"5";"3";NULL;NULL;NULL;"1";"4";"3";"3";"3";"Interesting topics covered";"The subject covers too many topics, which cannot be explained properly due to lack of time. Great potential of the course, very interesting problematics, but only very theoretical examples are covered. It is not really clear, how the issues are managed in practice, which is what students would be very interested in.";"ies" +"2080";"JEM003";"Advanced Microeconomics";"Janda,K.,Melikhova,O.";"Janda,K.,Melikhova,O.";"5";"4";"5";"5";"4";"4";"5";"4";"1";"4";"4";"4";"4";"Great lecturer with sense of humour. Good attitude to students. Very fair system of exams and final evaluation.";"Lecturer could write bigger letters on the board! Difficult to see!!!! Also, topics that were not covered in the classes still appeared in the exams, which is not very fair. If the lecturer does not have time to explain in the classes, how can we have time read many extra pages in a book to study extra topics?";"ies" +"2081";"JPM656";"Technology and warfare";"Kučera,T.";;"3";"4";"3";"3";"4";NULL;NULL;NULL;"1";"4";"2";"3";"4";"The readings were truly interesting. The Oxford style debate was a good exercise.Book review also a good exercise.Interesting audio-visual materia.";"I would improve the overall structure. Since the course also follows a clear historical line, this should be made much more clear since the start, already giving the students a general idea before then going deeper into specific historical events and all.Also, since the course deals with technology, maybe some time could be spent explaining precisely more technical aspects of certain weapons and inventions.Furthermore, despite the readings being truly interesting and the various authors cited in class helpful in understanding who the main thinkers are, more order must be made between the various different \"schools of thought\" for each argument/topic covered.";"kbs" +"2082";"JEM035";"Financial Markets Instruments I";"Dědek,O.,Poláková,N.,Polák,P.";"Dědek,O.,Poláková,N.,Polák,P.";"4";"4";"4";"5";"4";"2";"5";"1";NULL;"4";"4";"4";"4";;"Seminars poorly managed. No added value in attending the seminars - respective groups solving problems, but it is very difficult to follow the solutions, the groups are unable to explain the problems properly, sometimes even confuse the problems because they do not understand the topic themselves. The seminar leader tried to cover some issues from the lectures, which would be great but talks so fast that it is impossible to follow! It would be great, if he could take some extra time and really try to explain to the students and actually care if the audience is following or not!";"ies" +"2083";"JLB019";"Francouzština odborná I";;"Bosáková,L.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"1";"3";"5";"2";"5";"Přístup vyučující, zvaní frankofonních hostů do hodin, způsob evaluace a sledování videí.";"Nic.";"cjp" +"2084";"JSM514";"Metody a techniky práce s informacemi";"Tomandlová,V.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kvsp" +"2085";"JSM612";"Kriminalita a současná česká společnost";"Cejp,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kvsp" +"2086";"JLM011";"Angličtina pro veřejnou a sociální politiku I";;"Klírová,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"2087";"JLB033";"Němčina I";;"Křenková,D.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"2";"4";"Přístup vyučující, učební materiály, organizaci hodiny.";"Ocenila bych výběr témat bližší sociálním vědám než praktickému životu.";"cjp" +"2088";"JPB260";"Politické myšlení novověku III";"Kučera,J.";;"5";"4";"5";"4";"5";NULL;NULL;NULL;"1";"5";"3";"4";"5";"Vysokou erudici vyučujícího a jeho způsob výkladu.";"Upřesnění povinné literatury (doporučená četba na přednáškách se neshodovala se SISem).";"kp" +"2089";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"4";"5";"5";"4";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Sdílení četby k tématům doktora Mlejnka v readeru.";"Při testování nebyla vyvážena témata doktora Švece a Mlejnka, rovněž mi chyběly příspěvky k tématům doktora Švece v readeru.";"kp" +"2090";"JPB589";"Seminář k politickému myšlení: 19. století";;"Novotný,J.";"3";"3";NULL;NULL;NULL;"4";"5";"3";"1";"3";"4";"4";"3";;;"kp" +"2091";"JEB120";"Financial Economics";"Žigraiová,D.";;"2";"2";"1";"2";"2";NULL;NULL;NULL;"2";"3";"1";"2";"2";;;"ies" +"2092";"JMB515";"Německá otázka v mezinárodních vztazích a československé zahraniční politice";"Nigrin,T.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"4";"Způsob evaluace, strukturu a obsah přednášky.";"Z mého pohledu se zaměření předmětu dost překrývá s předmětem Soudobé dějiny německy mluvících zemí.";"knrs" +"2093";"JEM003";"Advanced Microeconomics";"Janda,K.,Melikhova,O.";"Janda,K.,Melikhova,O.";"1";"5";"2";"2";"1";"4";"4";"4";"1";"2";"2";"1";"1";;;"ies" +"2094";"JEM027";"Monetary Economics";"Holub,T.,Malovaná,S.";"Břízová,P.,Hájek,J.,Holub,T.,Malovaná,S.";"3";"4";"4";"4";"3";"4";"4";"2";"1";"4";"4";"4";"4";;;"ies" +"2095";"JEM037";"Financial Markets";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"3";"4";"4";"4";"3";"4";"4";"4";"1";"4";"4";"4";"4";;;"ies" +"2096";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"4";"2";"5";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"ies" +"2097";"JSM502";"Diplomový seminář I";;"Dobiášová,K.,Kotrusová,M.";"4";"4";NULL;NULL;NULL;"4";"4";"5";"1";"5";"5";"4";"4";;;"kvsp" +"2098";"JSM507";"Metody tvorby politik";"Veselý,A.";;"4";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"4";;;"kvsp" +"2099";"JPM118";"Výběrový seminář: Volby v USA";"Kotábová,V.";;"4";"2";"4";"4";"4";NULL;NULL;NULL;"1";"4";"2";"3";"4";"Zajímavost témat. Ústní prezentace studentů --> aktivita";;"kp" +"2100";"JPM146";"Přechody k demokracii v teorii a praxi I";"Mlejnek,J.";;"5";"3";"4";"4";"5";NULL;NULL;NULL;"1";"4";"4";"4";"4";"Možnost zlepšení průměru známkování díky ústní prezentaci. Zajímavosti od pana doktory.";;"kp" +"2101";"JPM150";"Poloprezidentské režimy v postkomunistické Evropě";"Mlejnek,J.";;"4";"3";"4";"4";"5";NULL;NULL;NULL;"1";"4";"3";"4";"4";"Hlavně zajímavosti pana doktora. Rozšíření povědomí o státech jako jsou Litva, Rumunsko ...";;"kp" +"2102";"JPM641";"Světový regionalismus";"Riegl,M.";;"4";"4";"4";"4";"5";NULL;NULL;NULL;"1";"5";"4";"4";"4";"Rozšíření geografických znalostí.";;"kp" +"2103";"JPM160";"Česká komunální politika";"Jüptner,P.";;"4";"5";"5";"4";"5";NULL;NULL;NULL;"2";"5";"5";"4";"4";"Nutná aktivita ve formě rozhovorů se zástupci municipalit.";;"kp" +"2104";"JPM342";"Konflikty v demokracii a národní identita: teoretické problémy analýzy";"Říchová,B.";;"3";"3";"4";"4";"4";NULL;NULL;NULL;"3";"4";"4";"4";"3";"Znalosti paní profesorky, která srozumitelně dokáže vysvětlit často nelehkou látku.";;"kp" +"2105";"JPM579";"Teorie politických stran";"Perottino,M.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"2";"4";"4";"4";"5";"Hlavně pana doktora. Jeho veselá nátura velmi zpříjemňuje probíranou látku. Aktivizace studentů ve formě úvodní diskuse o aktualitách.";;"kp" +"2106";"JPM639";"Problémy ústavního inženýrství";"Brunclík,M.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"2";"4";"4";"4";"4";"Nutnost prezentace, seminární práce a oponentury. Aktivizuje to studenty.";;"kp" +"2107";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"1";"5";"1";"1";"1";NULL;NULL;NULL;"1";"2";"2";"2";"1";;;"ies" +"2108";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;NULL;"4";"3";"3";"4";;;"kas" +"2109";"JMMZ318";"Mexican Politics, Economy and Society.";"Bernkopfová,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"5";"5";;;"kas" +"2110";"JMMZ327";"Knowledge Policies in Europe";"Young,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"5";"5";;;"kzs" +"2111";"JMM248";"Sociálně politický vývoj Irska";"Šlosarčík,I.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"4";"5";;;"kzs" +"2112";"JMMZ217";"Current Debates in British Politics and on the Constitution";"McLean,I.,Peterson,S.";"McLean,I.,Peterson,S.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"4";"4";"5";;;"kzs" +"2113";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"4";"2";"4";"4";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"ies" +"2114";"JMM273";"Diplomový seminář II";;"Kasáková,Z.";"3";"3";NULL;NULL;NULL;"2";"3";"2";"3";"2";"4";"2";"3";;;"krvs" +"2115";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"4";"3";NULL;NULL;NULL;"3";"2";"4";"1";"4";"3";"4";"4";;;"ies" +"2116";"JMM302";"Russia after 1991";"Svoboda,K.";;"4";"3";"3";"5";"3";NULL;NULL;NULL;"2";"5";"3";"3";"4";"Velmi dobře vybraná, kvalitní literatura k jednotlivým tématům v sisu. Možná by bylo přínosné více propojit četbu s diskuzí na přednáškách. Velmi dobrá přednáška na téma interpretace voleb v Rusku. Někdy velmi vhodné ilustrativní ukázky-např. projev Žirinovského.";"Více strukturovat přednášky ohledně hlavních témat a argumentačních bodů. Někdy se přednášky zamotávají a není jasné, co jsou hlavní závěry, teze či nastolené otázky. Občas opakování témat, např. kolektivní Putin na více hodinách-viz problém se strukturováním přednášek. Někdy je předpokládané povědomí o osobnostech, které jsou zmiňovány, poněkud nerealistické-hodně viditelné v případu přednášky na téma neformálních struktur v Rusku. Velké množství osob, které nejsou dostatečně uvedeny ani přednášejícím ani četbou na hodinu-možná vhodné se omezit na menší počet ilustrativních příkladů a ty rozebrat více do hloubky. Někdy asi trochu přílišné spoléhání na anekdotické důkazy/argumenty při výkladu.";"krvs" +"2117";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"4";"4";NULL;NULL;NULL;"4";"5";"4";"1";"5";"5";"4";"5";;;"ies" +"2118";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"4";"3";"4";"4";"3";"3";"4";"2";"1";"5";"5";"5";"5";;;"ies" +"2119";"JLB003";"Angličtina pro ekonomy I";;"Gloverová,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"5";;;"cjp" +"2120";"JLB041";"Španělština I";;"Mlýnková,L.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"4";;;"cjp" +"2121";"JSB998";"Úvod do sociologie";"Soukup,P.";;"3";"2";"4";"5";"3";NULL;NULL;NULL;"1";"3";"1";"2";"3";;"Hodnocení wikihesel, zpětná vazba od konzultanta, obzvlášť, pokud heslo není v pořádku";"ks" +"2122";"JPM052";"Mezinárodní ekonomické vztahy (MEV)";"Kučerová,I.";;"3";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"2";"5";"4";"The scale of knowledge and authors presented";"Could be more modern which means that instead of writing a final test we could write 2-3 papers on assigned topics.";"kmv" +"2123";"NMMA701";"Matematika 1";"Spurný,J.";"Rondoš,J.";"4";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"4";"3";;;"ies" +"2124";"JPM099";"Baltic regional cooperation and Russia";"Zájedová,I.";;"4";"2";"2";"5";"4";NULL;NULL;NULL;"3";"2";"4";"3";"4";"Visit to Estonian embassy";"Overall organization of the course, clarity of assessment, lectures";"kmv" +"2125";"JPM717";"Continental Philosophy and IR";;"Ditrych,O.";"5";"4";NULL;NULL;NULL;"4";"5";"5";"1";"5";"5";"5";"5";"Great selection of readings and organization of the course";;"kmv" +"2126";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Lectures and the use of moodle to make me sit and practice every week";;"kmv" +"2127";"JPM727";"Orchestration in Global Governance";;"Abbott,K.,Parízek,M.";"5";"4";NULL;NULL;NULL;"4";"5";"5";"1";"5";"5";"3";"4";"Learning about completely new approach";"Lectures were too long, maybe add more breaks";"kmv" +"2128";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"4";"4";"3";"3";"4";NULL;NULL;NULL;"1";"5";"4";"5";"4";;;"ies" +"2129";"JEM037";"Financial Markets";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"5";"3";"5";"4";"4";"4";"4";"5";"1";"4";"4";"4";"4";;;"ies" +"2130";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"4";"2";"1";"1";NULL;NULL;NULL;"1";"1";"2";"2";"1";;;"ies" +"2131";"JJB135";"Filmový seminář I";;"Šobr,M.";"3";"1";NULL;NULL;NULL;"4";"4";"4";"2";"4";"4";"3";"5";;;"kz" +"2132";"JSB003";"Oborová sociologie";"Numerato,D.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"4";;;"ks" +"2133";"JSB021";"Základy demografie";"Šídlo,L.";;"4";"4";"4";"3";"4";NULL;NULL;NULL;"2";"4";"5";"4";"4";"Kurz byl přínosný a rozšířil mi znalosti. Ocenila jsem i střídání přednášek a cvičení";"Zkouška byla příliš těžká, pak doktor tam vytahoval neskutečné detaily, například vědět kdo byl první docentem demografie na PřF UK mi nepřijde velice důležité. Obtížnost zkoušky lze vidět i ve výsledcích zkoušky, kdy nejlepší známkou bylo jedno C a jinak samé E. Příklady na vypočítání byly úplně jiné než to, co jsme dělali na cvičeních, proto bych třeba ocenila domácí úkoly s příklady jako v testu, aby se na to dalo lépe připravit. Další věc je učení se vzorečků nazpaměť. To mi přijde velice nepodstatné a navíc ani při statistice to po nás nikdo nechce, což je povinný předmět a ne pvp.";"ks" +"2134";"JSB010";"Současná sociologie";"Balon,J.";;"4";"5";"4";"4";"4";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"ks" +"2135";"JSB023";"Praktika z kvantitativního výzkumu I";;"Špaček,O.";"3";"4";NULL;NULL;NULL;"4";"4";"4";"1";"3";"3";"3";"3";;;"ks" +"2136";"JSB027";"Sociální politika jako společenská praxe";"Dobiášová,K.,Vlčková,K.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"4";"4";;;"kvsp" +"2137";"JSB407";"Globální problémy životního prostředí a udržitelný rozvoj";"Drhová,Z.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"3";"4";;;"kvsp" +"2138";"JSB517";"Hudební subkultury mládeže";"Oravcová,A.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"4";"5";;;"ks" +"2139";"JSB023";"Praktika z kvantitativního výzkumu I";;"Tuček,M.";"1";"2";NULL;NULL;NULL;"1";"1";"2";"4";"2";"3";"2";"2";;"Pan docent Tuček neumí diskutovat. Přerušuje vás a jede si to svoje bez toho, aniž aby vás poslouchal.Nejpřijde mi příliš přínosné týden co týden opravovat tři otázky, když se vyučujícímu nelíbí to, jak jste to podle něj opravili.Cvičení mi příliš nedali, pouze kritiku týden co týden, pochvala nikdy žádná.";"ks" +"2140";"JLB059";"Sociological Cinema";;"Blokker,P.,Štěpánková,D.";NULL;NULL;NULL;NULL;NULL;"4";"4";"4";NULL;NULL;NULL;NULL;NULL;;;"cjp" +"2141";"JSB010";"Současná sociologie";"Balon,J.";;"1";"2";"2";"2";"1";NULL;NULL;NULL;"2";"3";"1";"3";"2";;"Pan doktor nemá žádné prezentující schopnosti. Celou přednášku vede monotónním hlasem a s prezentací, ze které pokud chvíli neposloucháte, nemáte nic, jelikož pak doktor neumí členit text.Zpětnou vazbu na anotace odevzdávané v listopadu jsme dostali v půlce ledna, což mi rozhodně nepřijde v pořádku.Taky nebylo stanovené minimum na test, což bych změnila, protože se může stát, že budete muset opravovat buď recenzi nebo test, ale pokud pan doktor dodá výsledky recenzí o dva měsíce později, tak test asi opravovat nebudu moci.Byla bych pro vyměnění vyučujícího, někoho, koho to alespoň baví.Navíc se ve zkouškovém testu objevila látka, kterou jsme na přednáškách neprobírali. Možná pak doktor zapomněl, že dvě přednášky byly zrušeny a nenahrazeny.";"ks" +"2142";"JSB003";"Oborová sociologie";"Numerato,D.";;"4";"3";"4";"3";"4";NULL;NULL;NULL;"2";"4";NULL;NULL;"4";"+ Průběžné testy";"Průběžné testy z literatury - příliš těžké otázky na věci, které buď v textu nebyly podstatné nebo si je nikdo nepamatoval, i přes to, že ten text četl.Pan doktor mluvil často až příliš monotónně.";"ks" +"2143";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"4";"2";"5";"5";"5";NULL;NULL;NULL;"1";"3";"2";"2";"4";;;"ies" +"2144";"NMMA701";"Matematika 1";"Spurný,J.";"Rondoš,J.";"5";"5";"5";"5";"5";"4";"5";"5";"1";"5";"5";"5";"5";"Především velká ochota a odbornost pana profesora Spurného.";"Těžší zápočty, možná nějaký ten domácí úkol a více počítání testových příkladů na cvičeních.";"ies" +"2145";"JLB017";"Němčina pro ekonomy vyšší I";;"Faltýnová,R.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;"více psaného projevu";"cjp" +"2146";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"2";"5";"2";"2";"5";"Pan Soukup je velice poutavý přednášející.";"Poněkud zaujaté opravování wikihesel a nic nevypovídající test z poměrně zajímavého a dobře přednášeného předmětu.";"ks" +"2147";"JLB003";"Angličtina pro ekonomy I";;"Gloverová,M.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"1";"3";"3";"3";"5";"Velmi sympatická vyučující, poměrně uvolněná atmosféra a naučení se nějakých slovíček navíc.";"Asi nic, myslím si, že pro ty, co umí dobře anglicky je kurz spíš jakýsi socializační seminář, ale samozřejmě je velmi důležitý pro ty, co zrovna anglicky nekralují.";"cjp" +"2148";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"3";"1";"4";"5";"2";"3";"3";"1";"1";"5";"4";"4";"5";"Aplia";"Domnívám se, že by stačila číst jen učebnice plus výborná aplikace Aplia. Na přednáškách bych spíše uvítal nějaké zajímavosti. Pan doktor Janský by jistě radši vykládal něco ze svého zaměření a zval si různé hosty, než vykládal základní vztah nabídky a poptávky.";"ies" +"2149";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"4";"2";NULL;NULL;NULL;"5";"4";"3";"1";"3";"3";"3";"4";;;"ies" +"2150";"JEB044";"Financial Accounting";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Naprosto excelentní doktor Novák, kvalitní cvičící a myslím si, že díky tomuto předmětu se IES může měřit i s těmi nejlepšími univerzitami.";"Více bonus points- motivace pro všechny a postupně zvedat obtížnost získání bodů pro jednotlivé studenty.";"ies" +"2151";"JJB238";"Kritika marketingové komunikace";"Rosenfeldová,J.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"2";"4";"3";"4";"5";"Přednášky mě obecně bavily a bavila mě i příprava na zkoušku, látka tohoto typu, tzn. trochu dějin a trochu společenských věd je mi blízká, tudíž v podstatě za sebe nemám co vytknout";"možná bych ještě více některá témata provázala s marketingem, občas mi přišla až moc pouze dějepisná, např. kapitola s Karlem Marxemtaké výběr tématu práce bych posunula na pozdější datum, když jsem se snažila splnit zadání (tzn. vybrat si téma, které napojím na koncepty z přednášek) bylo to při začátku listopadu poměrně složité, protože v této době jsme měli probraný zatím hlavně historický vývoj KMK a až na pozdějších hodinách jsme probírali koncepty jako gender, greenwashing, culture jamming, apod., na které lze kritickou práci napojit spíše než na kritický přístup Frankfurtské školy";"kmkpr" +"2152";"JJB231";"Dějiny a teorie public relations";"Halada,J.,Hejlová,D.";;"3";"5";"4";"3";"3";NULL;NULL;NULL;"1";"5";"4";"5";"4";"Ačkoliv byl předmět v porovnání s jinými opravdu náročný, musím uznat, že po přípravě a následné zkoušce mám pocit, že opravdu disponuji komplexními teoretickými znalostmi PR.";"Upřesnila bych podmínky atestace na začátku kurzu: 2 seminární práce byly původně koncipovány pouze jako podmínka přistoupení ke zkoušce, nicméně nakonec byly součástí hodnocení. Dějinnou část kurzu bych více vztahovala k PR. Teoretickou část bych zase ještě více prokládala konkrétními příklady z praxe (case studies).";"kmkpr" +"2153";"JJB226";"Strategie a finanční management marketingu I";"Majerik,P.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"2";"4";"3";"4";"5";"Pozitivně hodnotím celou náplň kurzu, zatím jsem neabsolvovala žádný jiný kurz týkající se médií/mediálních agentur, tudíž pro mě byla většinou nová a užitečná. Vyhovoval mi přístup vyučujícího, zachovala bych také plusové body za docházku (motivující vzhledem k rannímu času kurzu) i formu atestu v podobě projektu a testu.";"Aktualizovat prezentaci, některé věci jsme v hodinách neprobírali. Změnila bych také název kurzu, jelikož podle mě úplně neodpovídá náplni.";"kmkpr" +"2154";"JJB255";"Digitální komunikace";;"Klimeš,D.";"3";"4";NULL;NULL;NULL;"4";"5";"3";"1";"3";"3";"3";"3";"Moc se mi líbila návštěva agentury Havas a také účast dvou zajímavých hostů z praxe. Také doporučuji zachovat diskuzi nad aktualitami na začátku každé hodiny.";"Rozhodně bych se kurz snažila více směřovat na aktuální digitální komunikaci, většina náplně se totiž soustředila hlavně na historický vývoj, respektive na předchůdce digitální komunikace. Také mi úplně nevyhovovalo zadání skupinového projektu od hosta, a to hlavně z časového hlediska, kdy jsme dostali na vypracování pouze týden, což je opravdu málo i vzhledem k tomu, že úkol měl být zpracován ve 12 lidech . Nelze se pak divit nižší úrovni zpracování.";"kmkpr" +"2155";"JJB600";"Úvod do studia médií I.";"Nečas,V.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;"3";"3";"3";"5";"5";;;"kms" +"2156";"JJB601";"Rozbor právního prostředí v českých médiích";"Benda,J.";;"1";"4";"1";"2";"1";NULL;NULL;NULL;"1";"2";"1";"1";"1";;;"kms" +"2157";"JJB605";"Novodobá historie médií";"Bednařík,P.,Končelík,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"4";"3";"5";"5";;;"kms" +"2158";"JJB616";"Vybrané novinářské osobnosti 19. století";"Sekera,M.";;"3";"2";"3";"4";"3";NULL;NULL;NULL;"1";"3";"2";"2";"3";;;"kms" +"2159";"JJB620";"Mediální produkce - žurnalistická práce I.";"Vlasák,Z.";;"4";"1";"4";"5";"4";NULL;NULL;NULL;"2";"3";"5";"3";"5";;;"kms" +"2160";"JJB135";"Filmový seminář I";;"Šobr,M.";"4";"2";NULL;NULL;NULL;"5";"5";"2";"1";"1";"5";"5";"5";;;"kz" +"2161";"JSB998";"Úvod do sociologie";"Soukup,P.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"ks" +"2162";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kas" +"2163";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Andrle,J.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"4";"5";"5";;;"krvs" +"2164";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Papežová,K.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"4";"5";"5";;;"knrs" +"2165";"JLB047";"Ruština obecná I";;"Mistrová,V.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"3";;;"cjp" +"2166";"JMB402";"Úvod do společenských věd II";;"Mertová,V.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";"Naprosto perfektní připravenost na zápočtový test, domácí úkoly opravdu k něčemu byly, empatie ze strany vyučující.";;"krvs" +"2167";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Kačmárová,P.";"5";"4";"3";"3";"5";"5";"5";"5";"1";"4";"3";"5";"5";"Seminář: vyučující se nás co nejvíc snažila naučit se spojovat si souvislosti a zadávala nám užitečné a zajímavé texty k přečtení.";;"knrs" +"2168";"JSB998";"Úvod do sociologie";"Soukup,P.";;"4";"2";"5";"5";"5";NULL;NULL;NULL;NULL;"3";"3";"4";"4";;;"ks" +"2169";"JLB033";"Němčina I";;"Faltýnová,R.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"3";"4";;;"cjp" +"2170";"JSB517";"Hudební subkultury mládeže";"Oravcová,A.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"ks" +"2171";"JSB131";"Velké empirické výzkumy ČR";"Tuček,M.";;"4";"3";"2";"5";"3";NULL;NULL;NULL;"2";"4";"2";"3";"3";;;"ks" +"2172";"JSB031";"Bakalářský seminář I - diskuse projektů";;"Angelovská,O.,Mouralová,M.";"3";"3";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"4";"5";;;"ks" +"2173";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"4";"4";"2";"4";"1";NULL;NULL;NULL;"1";"4";"3";"2";"3";;;"ies" +"2174";"JLB033";"Němčina I";;"Faltýnová,R.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"2175";"JSB007";"Dějiny sociologie I";"Šanderová,J.";"Orcígr,V.";"4";NULL;"4";"4";"3";"4";"5";"5";"3";"4";"3";"5";"4";;;"ks" +"2176";"JSB025";"Sociální problémy";"Frič,P.";;"5";"3";"5";"4";"4";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kvsp" +"2177";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Rybín,F.,Vlčková,A.";"5";"4";"5";"5";"4";"5";"5";"5";"2";"5";"5";"5";"5";;;"ks" +"2178";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"1";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"5";;;"ks" +"2179";"JMB242";"Balkans after 1989";"Hofmeisterová,K.,Kocián,J.,Králová,K.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"5";"4";"4";;;"krvs" +"2180";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"1";"3";"2";"1";"1";NULL;NULL;NULL;"1";"2";"1";"2";"1";;;"ies" +"2181";"JLB001";"Angličtina pro sociology I";;"Klírová,M.";"4";"3";NULL;NULL;NULL;"4";"5";"4";"2";"4";"5";"2";"4";;;"cjp" +"2182";"JSB007";"Dějiny sociologie I";"Šanderová,J.";"Veselský,M.";"3";"3";"3";"4";"3";"4";"5";"4";"2";"3";"2";"4";"4";;;"ks" +"2183";"JSB051";"Úvod do sociální antropologie";"Uherek,Z.";"Hrešanová,E.";"5";"3";"4";"5";"5";"5";"4";"4";"1";"5";"3";"5";"5";;;"ks" +"2184";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Obermajerová,K.";"4";"3";"4";"4";"5";"5";"5";"5";"1";"5";"5";"4";"5";;;"ks" +"2185";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Kouřílek,J.";"2";"4";"2";"3";"1";"4";"5";"5";"1";"3";"4";"2";"3";;;"ks" +"2186";"JJB600";"Úvod do studia médií I.";"Nečas,V.";;"3";"2";"5";"4";"4";NULL;NULL;NULL;"2";"4";"1";"4";"3";;;"kms" +"2187";"JJB601";"Rozbor právního prostředí v českých médiích";"Benda,J.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"5";"5";"5";"4";;;"kms" +"2188";"JJB605";"Novodobá historie médií";"Bednařík,P.,Končelík,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"2";"4";"5";;;"kms" +"2189";"JJB613";"Úvod do studia nových médií";"Jirků,J.";;"4";"3";"4";"4";"2";NULL;NULL;NULL;"3";"3";"3";"3";"2";;;"kms" +"2190";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"5";"1";"5";"5";"Články z novin jsou zajímavý přínos, je to zábavnější a záživnější, než padesát odborných knih, které stejně nikdo nestihne přečíst.";"Škoda toho, že se to učí takhle večer. Ohledně testu mi chybělo bodování jednotlivých otázek.";"kp" +"2191";"JEB044";"Financial Accounting";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"This is the most useful course I have taken at IES so far. Mr Novak's lectures were excellent. I enjoyed working with the case studies.";"I think there should have been significantly more time to complete the midterm, extra 10 or 15 minutes.The teaching assistants were supposed to provide us with answers to the home assignments. Unfortunately, that rarely happened. This meant that we never really learned much from these assignments because we did not know whether our answers were correct or not and why. I think it would be best to start each seminar by discussing the home assignment and then move on to other exercises concerning the case study.At times I found it difficult to complete the assignments without having the seminar on the given topic first. For example when it came to the topic Corporate Bonds, there were certain computations that we had not done during the lecture (we did not even know the formula for them) but had to perform in order to solve the assignment. I feel as though it would be nice to first read the case study, then discuss it at the seminar, do some exercises as practice, and then do the home assignment to see whether we understand everything correctly.";"ies" +"2192";"JPB596";"Čínská zahraniční a bezpečnostní politika";"Karmazin,A.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Moderní, zábavné a detailně podané téma, které zajíma na politologii snad každého. Výborný seminář!";;"kbs" +"2193";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";NULL;"5";"2";"2";NULL;NULL;NULL;"1";"3";"2";"3";"3";"Pan profesor má dobře sepsané přednášky.";"Bylo by dobré kdyby pan profesor mluvil hlasitěji a ochotněji odpovídal na dotazy";"ies" +"2194";"JEB044";"Financial Accounting";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"5";"5";;;"ies" +"2195";"JEB105";"Statistics";"Červinka,M.";"Smutná,Š.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"4";"4";"5";;;"ies" +"2196";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"4";"4";"4";"5";;;"ies" +"2197";"NMMA703";"Matematika 3";"Zelený,M.";"Zelený,M.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"2198";"JLB039";"Ruština odborná I - nižší";;"Mistrová,V.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"4";"5";;;"cjp" +"2199";"JLB027";"Ruština odborná I - vyšší";;"Mistrová,V.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"4";"5";;;"cjp" +"2200";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"5";"5";"5";"5";"5";"5";"5";"5";"2";"5";"4";"4";"4";;;"ies" +"2201";"JPM699";"Security and Technology";"Střítecký,V.";;"2";"2";"3";"4";"2";NULL;NULL;NULL;"3";"2";"3";"4";"3";"Learning about the 'dangers' of AI in relation to Social and political sciences";"The slides are 3 or so years old and could be updated with new cases etc. The lectures at one point ran a week behind on syllabus, so maybe better preparation by the lecturer";"kbs" +"2202";"JPM689";"Conflict Studies";"Karásek,T.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"4";"2";"5";"4";"Karasek is a perfect engaging lecturer and brings the dry material to life.";"An example essay question on SIS before the exam as the questions were quite complicated and many didn't know what to expect. Also, assigned reading per lecture would be appreciated";"kbs" +"2203";"JPM705";"Human Security";"Hynek,N.";;"3";"2";"4";"5";"5";NULL;NULL;NULL;"4";"4";"4";"3";"4";"I liked the idea of having 4 different lecturers, but in practise we only had two. Bruner was very good in general and at explaining potentially difficult law things to non-law students. The topics chosen were very interesting and often not on things covered in other courses.";"It would have been nice if all lecturers made a cohesive PowerPoint rather than writing on the board and sometimes failing to make a point. Also planning-wise of the classes, there is room for improvement.";"kbs" +"2204";"JEM032";"Banking";"Mejstřík,M.,Pečená,M.,Teplý,P.";"Pečená,M.";"5";"3";"4";"5";"3";"5";"5";"5";"1";"5";"1";"5";"5";;;"ies" +"2205";"JPM706";"Terrorism and Counterterrorism";"Bureš,O.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";"The lecturer was one of the best I've had at Charles. He knows how to engage students, cover the most important points and he also has assignments that make sense and are representative to the workload of the classes in general.";"The presentations didn't seem to be marked for the presentation as a whole, but only whether you answered the questions. Had I known this, I would have approached it differently. It would have been nice to have even longer questions so that the discussions could go deeper and we could cover more";"kbs" +"2206";"JJB004";"Současný český jazyk I";;"Svobodová,I.";"3";"4";NULL;NULL;NULL;"3";"2";"5";"1";"2";"4";"2";"3";;;"kz" +"2207";"JJB010";"Základy filozofie a vzdělanosti";"Halada,J.";;"4";"2";"4";"5";"3";NULL;NULL;NULL;"2";"4";"2";"3";"4";"Studentské referáty a následné diskuze o nich.";;"kz" +"2208";"JPM708";"Ethics and Violence";"Karásek,T.,Kučera,T.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";"I really enjoyed the debates (even though they weren't actually Oxford style debates). The topics covered were very interesting and gave me new insights/food for thought.";"I think there was too much assigned reading and it often wasn't necessary to read it before the classes (to take part in the classes). When the PhD student took over one of the lectures, t seemed as if she wasn't prepared (perhaps due to a last minute change), so maybe better planning for that?";"kbs" +"2209";"NMMA703";"Matematika 3";"Zelený,M.";"Bartoš,A.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"4";"5";"Na kurze oceňujem prístup pána docenta Zeleného ku svojim študentom, jeho ochotu vysvetliť každú nejasnosť a empatiu, že niektorým veciam študent nepochopí hneď. Pán prednášajúci vysvetľuje veľmi jasne a určite oceňujem snahu vybrať čo najzrozumiteľnejší dôkaz k vetám pokiaľ je to možné. Určite by som vyzdvihol aj vysokú kvalitu seminárov s pánom cvičiacim Bartošom, vďaka ktorému sa stalo počítanie relatívne jednoduchým, nakoľko sa nám nesnažil iba ukázať nejaký \"algoritmus\" ako vyriešiť príklad, ale aj čo je za tým.";;"ies" +"2210";"JJB012";"Žurnalistická tvorba I";"Osvaldová,B.";"Krobová,T.,Osvaldová,B.,Slanec,J.";"5";"2";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"kz" +"2211";"JJB015";"Česká literatura I";;"Čeňková,J.,Malý,R.";"3";"2";NULL;NULL;NULL;"4";"3";"2";"1";"2";"2";"2";"3";;;"kz" +"2212";"JJB017";"Grafický design a základy polygrafie I";"Slanec,J.";;"4";"1";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kz" +"2213";"JJB998";"Úvod do ekonomie";"Poljakov,N.";;"3";"2";"2";"3";"5";NULL;NULL;NULL;"4";"5";"3";"5";"4";;;"kz" +"2214";"JLB009";"Angličtina pro žurnalisty I";;"Prošková,A.";"3";"1";NULL;NULL;NULL;"4";"5";"4";"1";"4";"4";"4";"4";;;"cjp" +"2215";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"2";"5";"4";"3";NULL;NULL;NULL;"2";"5";"2";"4";"4";;;"ks" +"2216";"JEB047";"Účetnictví II";"Kemény,I.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Velmi kladně hodnotím přístup paní Kemény, která je velmi milá nejen v průběhu celého semestru, ale i v průběhu ústní zkoušky. Nevyvíjí na vás žádný tlak, je to spíše jako přátelské popovídání nad účetní závěrkou. Jako vyučující je naprosto skvělá, která svému oboru dokonale rozumí a jejímu výkladu neměl nikdo problémy porozumět.";;"ies" +"2217";"JEM020";"Ethics and Economics";"Cahlík,T.";"Cahlík,T.";"5";"2";"5";"5";"4";"5";"5";"3";"1";"5";"4";"5";"5";;;"ies" +"2218";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"4";"5";"3";"5";"1";NULL;NULL;NULL;"2";"5";"2";"4";"5";"The practical reading made each subject more related and was my favorite part of the reading";"Lectures can be more interactive rathen than just reading of the slider";"kmv" +"2219";"JSB455";"Economic Sociology and European Capitalism";"Blokker,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";"Dr. Blokker is amazing teacher.";;"ks" +"2220";"JJB401";"Komerční a nekomerční marketingová komunikace";"Báča,L.,Obluk,O.";;"4";"5";"5";"5";"3";NULL;NULL;NULL;"1";"5";"4";"5";"4";"Rozptyl učiva, vzájemné propojování politik a celkový background celé veřejné politiky. Je to takové proniknutí do problematiky. Také oceňuji webové stránky pana profesora Potůčka, kde student najde veškeré informace, podklady pro učení, odkazy, zajímavé články a v neposlední řadě \"příručku ke zkoušce\", která mně osobně byla nápomocná k tomu, abych se v tom kvantu informací zorientovala. Plus informace o hodnocení. Pozitivní je velké propojování s angličtinou (přednášky+terminologie)";"Podle mě je kurz organizačně hrozně komplikovaný. Jsou hodnoceny semináře, otázky, SP a zkouška. Něco se dělá skupinově, něco ne. Každý seminář jsme strávili zbytečně mnoho času vysvětlováním \"pravidel kurzu\", než abychom se věnovali veřejné politice. Kurz by také mohl reflektovat počet přihlášených studentů. Pokud jich je obvykle 50 nebo 60 a letos 30, je třeba to zohlednit např. v počtu skupin, v seminářích atd. Asi by to vyřešily dvě cesty, kde jedna by byla pro menší počet studentů a druhá pro vyšší.";"kmkpr" +"2221";"JSM518";"Public Policy";"Potůček,M.,Vlčková,K.";;"4";"5";"5";"5";"2";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Propojení anglické terminologie. Webové stránky prof. Potůčka se všemi informacemi. Opravdu velký vhled do VP!";"Kurz je z organizačního hlediska zbytečně komplikovaný. Vysvětlováním se tráví zbytečně mnoho času na začátku každého semináře - místo toho, abychom se věnovali veřejné politice. Taky bych zrušila seminární práci a nahradila ji např. projektem, který bude mít za úkol řešit nějakou palčivou otázku (VP problém). Může být zpracováno prezentací, videem nebo klidně i nějakým textem, ale bylo by fajn, kdyby si člověk mohl vybrat. Těch textů je (celkově za všechny předměty) neuvěřitelně moc a pak to studenty moc nebaví. Myslím, že takové řešení otázky kouření v restauracích pomocí projektu by bylo přínosnější.";"kvsp" +"2222";"JPM561";"Regional Security Studies";"Karásek,T.,Klosek,K.";"Karásek,T.,Klosek,K.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"3";"4";"New topics every other class and vey rich information about conflicts and history";"Type of questions in the tests sometimes very broad or exta specialized and also updating the reading material (and put them in order because it can be very confusing)Some conflicts were over mentioned and others were nit even included";"kbs" +"2223";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"5";"2";"The lecturer was always available and made sure sttudents understood everything inside/outside of the classroom And even used jokes ane candy";"Honestly, it’s as good as it gets ( in a good way)";"kmv" +"2224";"JPM698";"Middle East Security";"Daniel,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"The lecturer was very attentive and made every class valuable and interesting";"Updating the readings";"kbs" +"2225";"JJB605";"Novodobá historie médií";"Bednařík,P.,Končelík,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Najviac ocenujem pristup prednasajucich pana Bednarika a pana Koncelika. Nie je jednoduche vykladat latku tak, ze zaujme celu poslucharen, ale tymto svom panom sa to podarilo aj v neskorych odpolednych hodinach. Ocenujem aj to, ze nasi profesori nam na prednasky prinasali autenticke materialy pre lepsiu predstavivost ohladne vyucovanej latky. Seminarne prace boli ohodnotene velmi spravodlivo a vidno ze s tym dali vela prace, priebezne testy vzdy odpovedali predlozenym materialom.";"Nic.";"kms" +"2226";"JSM620";"Politologické aspekty tvorby politik: Veřejné politiky v kontextu politiky";"Novotný,V.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Praktický přínos. Propojení teorie s praxí.";"Navrhuji vyměnit skupinovou závěrečnou práci za práci jednotlivců.";"kvsp" +"2227";"JPM699";"Security and Technology";"Střítecký,V.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"I could not wait to go to class everytime it was very fun and reLly great topics and the lecturer made the course even greater";"Nothing it was great";"kbs" +"2228";"JMMZ149";"EU Institutions";"Šlosarčík,I.";;"1";"3";"1";"2";"3";NULL;NULL;NULL;"4";"2";"2";"2";"1";;"The quality of the course could definately be improved. I found it personally very chaotic and unstructered";"kzs" +"2229";"JJB601";"Rozbor právního prostředí v českých médiích";"Benda,J.";;"5";"5";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";"Ocenujem formu prednasok, stavbu zaverecneho testu a trpezlivost pri zadavani opravnych uloh.";"Hodilo by sa oddovodnenie nesplnenia ukolu. Praca nevyhovie ale student nevie konkretne preco.";"kms" +"2230";"JMMZ316";"Evolution of Sino-American Relations";"Sehnálková,J.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"2";"4";"4";"4";"5";;;"kas" +"2231";"JPM648";"Politics of Security in Northeast Asia";"Karásková,I.";;"3";"2";"4";"4";"5";NULL;NULL;NULL;"2";"4";"4";"4";"4";;;"kmv" +"2232";"JJB600";"Úvod do studia médií I.";"Nečas,V.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"Strukturu hodenotenia vyslednej znamky - priebezne testy + zaverecny test. Prakticke ukazky a velky rozsah vedomosti o tematike prednasajuceho.";"Nic.";"kms" +"2233";"JPM664";"Geopolitics of Great Powers: China";"Karásková,I.";;"4";"3";"4";"4";"5";NULL;NULL;NULL;"2";"4";"4";"4";"4";;;"kp" +"2234";"JPM692";"Internal Security of the EU [ES]";"Hokovský,R.";;"1";"3";"1";"2";"3";NULL;NULL;NULL;"4";"1";"1";"1";"1";;"Very chaotic and unstructured";"kmv" +"2235";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Hornát,J.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"krvs" +"2236";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"4";"3";"4";"5";"1";NULL;NULL;NULL;"1";"4";"4";"4";"3";;;"knrs" +"2237";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"5";"5";"4";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kas" +"2238";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"2";"5";"5";"3";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ks" +"2239";"JMB415";"Seminář k aktualitám II";;"Šír,J.";"4";"3";NULL;NULL;NULL;"5";"5";"4";"2";"4";"4";"4";"5";"Komentáře k monitoringům jsou skvělé a vedoucí semináře se dobře snažil vyvolávat diskuzi mezi studenty. Velká část studentů ovšem nepromluví a radši nechá mluvit ostatní, pokud nemusí.";"Každý ze studentů by mohl krátce prezentovat na svůj region každou hodinu. Tato pětiminutová prezentace by musela mít jasnou strukturu a také určitou úroveň.";"krvs" +"2240";"JLB100";"Czech as a Foreign Language I";;"Nováková,K.";"5";"2";NULL;NULL;NULL;"5";"5";"4";"1";"5";"3";"4";"3";;;"cjp" +"2241";"JEM020";"Ethics and Economics";"Cahlík,T.";"Cahlík,T.";"5";"4";"5";"5";"4";"5";"5";"4";"1";"5";"5";"5";"5";;;"ies" +"2242";"JEB120";"Financial Economics";"Žigraiová,D.";;"1";"3";"1";"2";"1";NULL;NULL;NULL;"3";"3";"3";"2";"1";;;"ies" +"2243";"JEB026";"European Economic Integration";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"5";"2";"5";"4";"3";"4";"4";"2";"1";"4";"4";"4";"5";;;"ies" +"2244";"JEB026";"European Economic Integration";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"2";"3";"3";"2";"2";"3";"4";"1";"2";"3";"3";"4";"3";;"Awfully long test evaluation - preterm was graded after more than 3 weeks.";"ies" +"2245";"JEB111";"Advanced Data Analysis in MS Excel";;"Poláková,N.,Polák,P.";"5";"2";NULL;NULL;NULL;"4";"5";"4";"1";"5";"5";"5";"5";"Excel is a widely used program and this course covers the basics really well. Also the Materials provided are very well made and easy to learn from.";"The \"advanced\" in the course name is a little misleading as this course explains everything from the basics.";"ies" +"2246";"JEM020";"Ethics and Economics";"Cahlík,T.";"Cahlík,T.";"5";"4";"5";"5";"5";"5";"5";"5";"5";"5";"4";"4";"4";;;"ies" +"2247";"JEM034";"Corporate Finance";"Bychkova,O.,Malířová,T.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"4";"4";"4";"4";"4";;;"ies" +"2248";"JJB617";"Vybrané novinářské osobnosti 20. století";"Železný,J.";;"3";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"kms" +"2249";"JSM103";"Academic Writing";;"Blokker,P.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"The way of teaching and approach of the lecturer have been truest useful for developing writing skills especially for non-native English students.";"Everything has been covered as it should be covered.";"ks" +"2250";"JSM692";"Introduction to Social Research Methodology";"Remr,J.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"5";"4";"3";"It's been pretty useful for those who wants to broaden and deepen her/his knowledge and her/his approaches related to further researches in terms of methodology especially, of quantitative ones.";;"ks" +"2251";"JSM406";"Statistics in SPSS";;"Soukup,P.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"4";"3";;;"ks" +"2252";"JSM477";"Sociology of Critique";"Blokker,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"The lecturer has known the drill and this has let the students understand better the critical sociological thinking. Even for the ones who are not that familiar with the topics which have been covered in the classroom, the lecturer's attitude and experience has made those students comprehend the topics.";;"ks" +"2253";"JSM578";"Anthropology of EU";"Uherek,Z.";;"4";"2";"5";"5";"3";NULL;NULL;NULL;"2";"4";"4";"3";"4";;;"ks" +"2254";"JSM421";"Contemporary social theory";"Balon,J.";;"4";"3";"3";"5";"2";NULL;NULL;NULL;"2";"4";"3";"4";"2";;;"ks" +"2255";"JEB044";"Financial Accounting";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"5";"5";"Je to jeden z mála kurzů, který naučí něco praktického. Přednášky velmi srozumitelné, dobrá interakce se studenty. Case studies podporují domácí přípravu.";;"ies" +"2256";"JEB105";"Statistics";"Červinka,M.";"Hanus,L.";"3";"4";"3";"3";"2";"3";"4";"5";"1";"4";"4";"3";"4";"Dobře procvičené úlohy ze seminářů";"Přednášky byly často přetaženy, příliš mnoho informací najednou, často byly dost nesrozumitelné. Také poslední dva úkoly byly moc blízko u sebe, navíc v době, kdy už jsou zkouškové předtermíny.";"ies" +"2257";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"2";"3";"2";"3";"1";"4";"4";"4";"3";"2";"2";"2";"3";;"Naprosto nesmyslný první termín zkoušky, v písemce byla obsažena i látka, která se nestihla procvičit - dělali jsme jí na cvičení hodinu před písemkou. Také zbytečně složité výpočty bez možnosti použít kalkulačku, kvůli zdlouhavému počítání složitých zlomků bylo téměř nemožné písemku stihnout.";"ies" +"2258";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"4";"2";"4";"4";"3";"3";"3";"3";"2";"3";"2";"3";"4";"Zajímavé přednášky, bohužel v pátek v 8 hodin ráno.";"Přednášky příliš brzy, při seminářích se často zbytečně dlouho opakovala látka přednášky a pak již nezbýval čas na příklady.";"ies" +"2259";"JMM047";"Právní a institucionální rámec evropské integrace.";"Šlosarčík,I.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"3";"5";"5";"pana Šlosarčíka";;"kzs" +"2260";"JMM048";"European Union in International Affairs";"Weiss,T.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"5";"5";;;"kzs" +"2261";"JJM234";"Media and Society: An Introduction";"Jirák,J.";;"4";"2";"5";"5";"5";NULL;NULL;NULL;"1";"3";"4";"5";"3";"The weekly work on media news which helped me to improve my critical analysis about what the news in general but also to be able to pick up what is important.";"Maybe more indications to know what the teacher wait for concerning the final group paper. The lenght wasn't clear and a clear example would have helped us to identify the needs.";"kms" +"2262";"JMM271";"Metodologický seminář";;"Weiss,T.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";;;"krvs" +"2263";"JMM277";"Historie a kultura";"Vykoukal,J.";"Tomalová,E.";"3";"3";"5";"5";"4";"4";"5";"3";"1";"3";"3";"3";"3";;;"krvs" +"2264";"JMM278";"Politika a mezinárodní vztahy";"Kubát,M.,Weiss,T.";"Juhás,T.";"2";"3";"4";"5";"2";"2";"2";"3";"1";"2";"2";"3";"3";;;"krvs" +"2265";"JMM663";"Europe in the French mind: a historical–civilizational point of view";"Bauer,P.";;"3";"2";"3";"5";"3";NULL;NULL;NULL;"3";"2";"2";"2";"3";;;"kzs" +"2266";"JPM708";"Ethics and Violence";"Karásek,T.,Kučera,T.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"4";"5";"The Oxford presentation was a very smart and efficient mean to motivate me to look for informations and the current world issue. It was a way to learn how to support a position at speaking.";"Maybe it can be interesting to provide few details about the background of some issues approached by students during the debates in order to favor the participation of all students. For me, as a 2nd year bachelor student, some debates after the presentations were very interesting to listen to but I wasn't able to take part in even if I had done the readings and tried to read news about the topics. I felt that I didn't have enough knowledge to answer the questions of my classmates.";"kbs" +"2267";"JMM027";"Contemporary Mediterranean";"Králová,K.,Mejstřík,M.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";;;"kzs" +"2268";"JMB057";"Cultural Legacies and Developments in the Balkans: Modern and Traditional Entanglements";"Asavei,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"The overall approach of the topic which links art and social representations in the Balkans.";"Maybe it can be interesting to give a global and short overview of each country during the first presentation including some economic data and as I said in the course I would have appreciate to see some film's extract.";"krvs" +"2269";"JMM629";"Hollywood/Europe: A Transnational Film Culture.";"Nowell,R.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"The fact that each session includes a time for debate about the readings and the screenings and that the explanations of the teacher is just a further of what he has tried to make arose from us.";"Maybe because it's a course about cinema which involves passionate people it can be interesting to organize extra meetings just to have debates about films for fun.";"kas" +"2270";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"1";"5";"2";"2";"2";NULL;NULL;NULL;"1";"2";"2";"2";"1";;;"kmv" +"2271";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmv" +"2272";"JPM658";"International Economic Relations";"Parízek,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmv" +"2273";"JPM690";"Liberalism in International Relations (TIR)";;"Karlas,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"5";;;"kmv" +"2274";"JPM689";"Conflict Studies";"Karásek,T.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kbs" +"2275";"JPM725";"Technology and Security: Contemporary Warfare in the 21st Century";;"Csernatoni,R.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kmv" +"2276";"JEB044";"Financial Accounting";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"3";"4";"4";"4";"4";NULL;NULL;NULL;"1";"5";"3";NULL;"4";"Aktivni zapojeni studentu v prubehu seminaru a motivovani bonusovyma bodama je dobry napad. Dale vyklad pana Novaka je vcelku kvalitni. S Lectures jsem byl spokojen.";"Urcite bych zlepsil cviceni, kde to bylo katastrofalni v nekterych pripadech (Barbora H.). Bylo to nudne, neprinosne a neuchopitelne. Z cviceni jsem si v 80% opravdu nic neodnesl.";"ies" +"2277";"JJB003";"Dějiny masových médií III";"Bednařík,P.,Končelík,J.";;"5";"4";"4";"5";"4";NULL;NULL;NULL;"2";"4";"4";"3";"5";"Skvělá a sehraná dvojice vyučujících, poutavé a zajímavé přednášky.";"Zařadila bych do testu i více vypisovacích otázek, kde student prokáže znalost souvislostí, principů. Může lépe prokázat, že tematice rozumí, chápe kontext a dokáže ji interpretovat. Já osobně například z tohoto důvodu oceňuji spíše ústní zkoušky, než ty písemné.";"kms" +"2278";"JJB006";"Současný český jazyk III";;"Svobodová,I.";"4";"4";NULL;NULL;NULL;"3";"4";"5";"2";"4";"4";"2";"4";"Dobře připravené a studentům poskytované materiály. Spoustu příkladů z praxe.";;"kms" +"2279";"JJB009";"Úvod do psychologie";"Vranka,M.";;"3";NULL;"4";"4";"4";NULL;NULL;NULL;"2";"4";"2";"4";"3";"Zajímavý přístup k látce, nešlo jen o obyčejné opakování základů z psychologie, jak ji známe z gymnázia, ale šlo o dobře a zajímavě zvoléná témata. Oceňuji provázanost s moodlem, poskytnuté prezentace i zajímavé materiály. Přečetla jsem všechny texty a oceňuji jejich výběr. Čtení bylo užitečné k testu, ale zároveň mě bavilo a obohatilo mě. Dobrý výběr studijních materiálů. Taky oceňuji možnost účastnit se experimentů a výzkumů, nejen že se jednalo o praktickou pomoc ke zkoušce v podobě bodů, ale sama účast ve studijích mi přišla přínosná.";;"kz" +"2280";"JJB002";"Dějiny masových médií II";"Sekera,M.";;"3";"3";"3";"3";"2";NULL;NULL;NULL;"3";"3";"1";"2";"3";"Oceňuji zajímavé exkurze do archivu a muzea. Zejména archiv ve Stromovce mi později posloužil jako zdroj informací k seminární práci a jsem ráda, že jsem tuto možnost díky předmětu objevila.";;"kms" +"2281";"JJB019";"Práce s agenturními informacemi";"Prázová,I.,Trunečková,L.";"Prázová,I.,Trunečková,L.";"4";"3";"4";"4";"5";"4";"4";"5";"1";"4";"5";"4";"4";"Zaměření na více aspektů problematiky zdrojů (s tím souvisí i několik vyučujících), předmět byl díky tomu petrý. Zejména oceňuji přednášky od novináře z praxe (moc se omlouvám, já jsem zapomněla jméno dotyčného), který nám ukázal jak pracovat s různými veřejně dostupnými zdroji, jak to v praxi funguje a vše demonstroval na aktuální situaci (rozkrývali jsme kauzu Čapí hnízdo).";;"kz" +"2282";"JJB009";"Úvod do psychologie";"Vranka,M.";;"3";"3";"2";"2";"2";NULL;NULL;NULL;"1";"4";"2";"3";"3";"Výborné doplňkové materiály.";"Interaktivitu předmětu.";"kz" +"2283";"JJB021";"Bakalářský seminář";;"Prázová,I.";"1";"1";NULL;NULL;NULL;"1";"3";"1";"1";"1";"1";"1";"1";"Rozumně nízká dotace hodin.";"Takřka vše.";"kz" +"2284";"JJB006";"Současný český jazyk III";;"Svobodová,I.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"4";"4";"Mnoho praktických cvičení, ve kterých se dobře trénovala teorie.";"Prakticky nic. Velmi dobré.";"kms" +"2285";"JJB003";"Dějiny masových médií III";"Bednařík,P.,Končelík,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"5";"5";"Vynikající pedagogové, výborná kombinace humoru, informací a souvislostí. Nemám co vytknout.";"Nic.";"kms" +"2286";"JJB014";"Žurnalistická tvorba III - Časopisecká tvorba";"Osvaldová,B.";"Hájek,R.";"5";"2";"4";"4";"2";"5";"5";"5";"1";"5";"5";"3";"5";"Vynikající duo pedagogů. Praktická část. Jestli opravdu předmět v tomto formátu končí, je to špatná zpráva pro UK IKSŽ KŽ.";"Méně poučené/zábavné přednášky. Cvičení a praktická práce velmi vítězila.";"kz" +"2287";"JMM029";"(Po)válečná společenství západního Balkánu po rozpadu Východního bloku";"Žíla,O.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"krvs" +"2288";"JMB171";"Moderní dějiny Maďarska";"Irmanová,E.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"krvs" +"2289";"JMB018";"Bakalářský seminář I";;"Kubát,M.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"2";"5";"5";"5";"5";;;"krvs" +"2290";"JMB011";"Moderní dějiny Ruska";"Litera,B.,Pečenka,M.";"seminář nenavštěvován";"4";"5";"5";"4";"5";"5";"5";"5";"1";"5";"5";"5";"4";;;"krvs" +"2291";"JMB317";"Greek Language III";;;"4";"2";"4";"5";"5";NULL;NULL;NULL;"3";"4";"4";"3";"4";;;"krvs" +"2292";"JMM067";"Russia and Eurasia in World Politics";"Šír,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"The professor was very good as was the material";;"krvs" +"2293";"JMM671";"Rebuilding Europe";;"Rovná,L.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"2";"3";"4";"4";"4";;;"kzs" +"2294";"JMMZ141";"Russian Language I";;"Shvedova,O.";"2";"3";NULL;NULL;NULL;"2";"5";"4";"1";"3";"2";"2";"1";;;"krvs" +"2295";"JMMZ331";"Qualitative methods in social sciences";"Weiss,T.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"2";"2";"3";"2";"2";;;"kzs" +"2296";"JPM687";"Astropolitics";"Doboš,B.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kp" +"2297";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"3";"3";"3";"1";"1";"1";"1";"5";;;"kz" +"2298";"JLB041";"Španělština I";;"Mlýnková,L.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"2299";"JPM607";"International Negotiations";;"Parízek,M.";"3";"3";NULL;NULL;NULL;"3";"3";"3";"3";"3";"3";"3";"3";;;"kmv" +"2300";"JPM689";"Conflict Studies";"Karásek,T.";;NULL;NULL;"5";"5";"5";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kbs" +"2301";"JPM719";"Diplomacy of the European Union";"Pajtinka,E.";;"1";"1";"1";"1";"1";NULL;NULL;NULL;"5";"1";"1";"1";"1";;;"kmv" +"2302";"JPM721";"Komparace ekonomik zemí EU";"Kučerová,I.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmv" +"2303";"JPM724";"Critical Approaches to International Politics and Security";;"Daniel,J.,Rychnovská,D.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kmv" +"2304";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"3";"2";"5";"5";"1";NULL;NULL;NULL;"1";"3";"3";"4";"3";;"Konzistentnost přednášeného materiálu s zápočtovým testem.";"ies" +"2305";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"3";"5";"2";"5";"5";"Volnost, neformálnost, přátelskost a otevřenost";;"ies" +"2306";"JLB003";"Angličtina pro ekonomy I";;"Gloverová,M.";"4";"3";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"3";"5";"Přístup paní Gloverové, která je velmi milá, snaživá a přátelská.";;"cjp" +"2307";"JLB029";"Španělština odborná I";;"Mlýnková,L.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Paní Mlýnková je nejlepší vyučující, co si student se zájmem o jazyky může přát. Energická, přátelská, nápomocná.";;"cjp" +"2308";"JSB998";"Úvod do sociologie";"Soukup,P.";;"3";"1";"3";"4";"1";NULL;NULL;NULL;"1";"3";"1";"3";"4";;;"ks" +"2309";"NMMA701";"Matematika 1";"Spurný,J.";"Skříšovský,E.";"5";"5";"5";"5";"5";"4";"5";"5";"2";"5";"5";"5";"5";;;"ies" +"2310";"JEB111";"Advanced Data Analysis in MS Excel";;"Poláková,N.,Polák,P.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;"Ne snad přímo v kurzu, ale chybí mi navazující výuka Excelových dovedností (rozuměj další prohloubení znalostí, jako např. Macros)";"ies" +"2311";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"4";"3";"3";"5";"1";"4";"5";"1";"1";"5";"4";"5";"5";;;"ies" +"2312";"JEM163";"Principles of Microeconomics";"Janský,P.";"Král,M.,Moravcová,H.,Palanský,M.";"2";"3";"5";"5";"4";"1";"2";"1";"1";"1";"3";"2";"1";;"The seminar instructor did not instruct. Instead, he solved problems on his own on the board with his back to the class with no explanation. I understand he is working on his PhD and has a full time job, but for a \"world-renowned\" economics department I was quite disappointed with the class seminar and felt it was the worst class I have ever taken in higher education. My experience with this course made me decide to strictly pursue a more political and philosophical approach to my studies, and I owe my course grade (which was one of the better ones of my cohort) solely to Youtube and ACDC Leadership Economics videos. The lecture was helpful, but due to work I chose not to attend every lecture. I wish the lecture had been mandatory and the seminar had been optional, as that is how valued each aspect of the class.";"ies" +"2313";"JMM530";"European Comparative Politics and Society";"Rovná,L.";;"3";"1";"2";"5";"1";NULL;NULL;NULL;"3";"2";"1";"2";"2";"In a heavy semester this class was not too challenging and did not require very much from me.";"The materials provided for the class are completely outdated and many are no longer relevant in political studies. In addition, we weren't very much taught by the professor - really every class was taught by a group of students who had to give a 20 minute presentation on a topic. The instructor did not teach much. The syllabus also stated that the course would be graded as a combination of participation, presentation, and essay, however some students found out it was only the essay that counted for our grade. I think that is a bit dishonest and the syllabus provided should but committed to in regards to the grading structure.";"kzs" +"2314";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"4";"4";"4";"4";"3";NULL;NULL;NULL;"3";"5";"4";"5";"4";"Dr. Ditrych is brilliant and his lectures were quite interesting and informative. The breadth of the course was overwhelming, but provided a great knowledge base from which to expand on in upcoming semesters.";"The reading expectations are unclear. I believe reading 200 pages a week for a single class is simply unacceptable for full-time students. I could not ascertain how much was required and how much was supplementary. I think that could be outlined better.";"kmv" +"2315";"JPM323";"Global Political Philosophy";"Salamon,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"The seminar and lecture together made for a fantastic course that was reminiscent of the types of courses I loved from my undergraduate institution. Dr. Salamon is excellent at demonstrating the importance of each political approach and its relation to the other approaches discussed. This was absolutely my favorite class to attend every week.";"No suggestions.";"kp" +"2316";"JPM324";"Geography and Politics in Europe within Global Regionalism";"Doboš,B.,Riegl,M.";;"4";"4";"4";"3";"4";NULL;NULL;NULL;"1";"5";"3";"4";"4";"It has provided me with a greater vocabulary and understanding for writing about geopolitics from various perspectives, as well as a greater historical knowledge.";"It is difficult to determine which course readings a truly required, and which are supplementary. It seemed to be suggested that all books be read over the course of the semester, but I think that is quite a but much for full time students taking 40+ credits. Truthfully, I did not read beyond the seminar reading as the rest was simply too much.";"kp" +"2317";"JEM179";"History and Methodology of Economics";"Baxa,J.,Paulus,M.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"5";"4";"I very much appreciated the first half of the course and the historical approach to the foundations of modern economics. As a student who is not focused on the economic aspect of my degree, this is very much the type of class I want to take. Professor Paulus is an excellent teacher and he knows how to engage his students and prompt debate and discussion (he was one of the few instructors I had who excelled at this.)";"No suggestions.";"ies" +"2318";"JPM699";"Security and Technology";"Střítecký,V.";;"2";"3";"3";"3";"3";NULL;NULL;NULL;"3";"4";"4";"2";"3";"The course really covers topics of utmost importance in nowadays societies. The group exercise was useful since it gave students the possibility to practice with new softwares and produce something different that the usual academic essays.";"The course needs to be rethought and restructured from the basics.A clear logic and structure needs to be better prepared, and fully expressed during the very first lesson.For instance, it would be better to have covered the more technical aspects of the course at the very beginning in a simple and direct way. This to give the students the basics to then understand the broader issues and consequences the covered technologies might have on society, democracy, national security, etc. The other lessons should have been more focused on several of such issues. I truly appreciated the focus on filter bubbles and related consequences, surveillances and related consequences, AI etc. But I would have liked it more to have explored philosophical, ethical, and sociological aspects as well. Precisely to get the full picture. The link with terrorism was truly fascinating, but here again the overall lesson lacked structure and a clear line of argumentation.Also, the tool to prepare the group presentations should have been given at the very start of the course, and not almost at its end.Finally, more readings should be assigned to students each week, presenting both side of the coin. Finally, despite the huge list of suggested readings, rather than alphabetically ordered, it should be thematically ordered.";"kbs" +"2319";"JPM191";"Geopolitics of Great Powers: Russia";"Baštář Leichtová,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"3";"4";"5";"5";;;"kp" +"2320";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"5";"4";"5";"4";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmv" +"2321";"JEM003";"Advanced Microeconomics";"Janda,K.,Melikhova,O.";"Janda,K.,Melikhova,O.";"2";"3";"2";"3";"1";"4";"4";"5";"1";"2";"3";"3";"4";"semináře";"poskytnout studijní materiály z přednášek. Nebo aspoň promítat definice na plátno, protože písmo vyučujícího se nedá přečíst.";"ies" +"2322";"JEM004";"Advanced Macroeconomics";"Baxa,J.,Hromádková,E.";"Baxa,J.,Hromádková,E.,Šestořád,T.,Troch,T.,Žáček,J.";"5";"4";"5";"5";"3";"5";"5";"5";"1";"5";"4";"5";"5";;;"ies" +"2323";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"1";"5";"5";"5";"5";;;"cjp" +"2324";"JMB091";"Religion, secularity and laicity in Europe (19th-21th centuries)";"Bauer,P.";;"2";"3";"1";"3";"1";NULL;NULL;NULL;"2";"1";"2";"2";"1";;;"kzs" +"2325";"JMB178";"U.S. in the 1960s and 1970s";"Raška,F.";;"2";"2";"1";"3";"1";NULL;NULL;NULL;"2";"2";"2";"2";"2";;;"kas" +"2326";"JSB051";"Úvod do sociální antropologie";"Uherek,Z.";"Hrešanová,E.";"4";"5";"5";"5";"5";"5";"5";"5";"1";"5";"4";"5";"5";;;"ks" +"2327";"JPB578";"Classics of Political Thought";"Salamon,J.";;"5";"4";"4";"4";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kp" +"2328";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Rössler,J.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ks" +"2329";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"4";"4";"5";"5";"4";"3";"5";"3";"1";"5";"3";"4";"5";;"v domácích úkolech bych dala více real life cvičení.";"ies" +"2330";"JSB530";"Úvod do sociologického myšlení";"Bureš,J.,Numerato,D.";"Wirthová,J.";"5";"3";"3";"5";"3";"5";"5";"5";"1";"5";"5";"5";"5";;;"ks" +"2331";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"1";"5";"1";"1";"1";NULL;NULL;NULL;"1";"3";"1";"3";"1";;;"ies" +"2332";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Záhlava,J.";"4";"5";"4";"4";"4";"4";"5";"5";"1";"5";"5";"5";"4";;;"ks" +"2333";"JJB601";"Rozbor právního prostředí v českých médiích";"Benda,J.";;"2";"4";"1";"2";"1";NULL;NULL;NULL;"1";"4";"1";"4";"3";;"Promítanou prezentaci a celkový vývýklad vyučujícího";"kms" +"2334";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"4";"3";"2";"1";NULL;NULL;NULL;"1";"2";"2";"2";"1";;;"ies" +"2335";"JLB019";"Francouzština odborná I";;"Bosáková,L.";"4";"3";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"4";"4";;;"cjp" +"2336";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"5";"5";;;"kp" +"2337";"JPB018";"Dějiny mezinárodních vztahů I";"Soukup,J.";;"4";"3";"5";"5";"3";NULL;NULL;NULL;"3";"4";"4";"4";"4";;;"kmv" +"2338";"JPB020";"Úvod do mezinárodních vztahů";"Karlas,J.,Kučera,T.";;"2";"5";"3";"3";"3";NULL;NULL;NULL;"1";"3";"2";"3";"3";;;"kmv" +"2339";"JPB221";"Metodologický proseminář I";;"Komasová,S.,Parízek,M.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"5";"4";;;"kmv" +"2340";"JPB252";"České novověké dějiny I.";"Kučera,J.";;"2";"2";"2";"2";"1";NULL;NULL;NULL;"1";"2";"2";"2";"2";;;"kp" +"2341";"JPB256";"Politické systémy I";"Brunclík,M.,Jeřábek,M.,Kotábová,V.,Perottino,M.";;"5";"4";"5";"4";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"2342";"JJB004";"Současný český jazyk I";;"Svobodová,I.";"3";"5";NULL;NULL;NULL;"3";"3";"5";"1";"5";"5";"4";"4";;;"kz" +"2343";"JJB010";"Základy filozofie a vzdělanosti";"Halada,J.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"3";"4";"3";"4";"5";;;"kz" +"2344";"JJB012";"Žurnalistická tvorba I";"Osvaldová,B.";"Trunečková,L.";"4";"5";"5";"3";"4";"5";"4";"5";"1";"5";"5";"5";"5";;;"kz" +"2345";"JJB015";"Česká literatura I";;"Čeňková,J.,Malý,R.";"5";"5";NULL;NULL;NULL;"5";"4";"4";"1";"4";"3";"4";"5";;;"kz" +"2346";"JLB053";"Angličtina pro sociální vědy I";;"Prošková,A.";"3";"2";NULL;NULL;NULL;"3";"4";"3";"1";"3";"3";"2";"2";;;"cjp" +"2347";"JSB998";"Úvod do sociologie";"Soukup,P.";;"4";"3";"4";"5";"3";NULL;NULL;NULL;"2";"4";"3";"4";"4";;;"ks" +"2348";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"3";"4";"5";"5";"3";NULL;NULL;NULL;"3";"4";"4";"4";"4";;;"kp" +"2349";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"3";"4";"5";"4";"3";NULL;NULL;NULL;"2";"4";"4";"4";"4";;"potreba absolvovať semináre, ktoré v tomto akademickom roku neboli súčasťou výuky";"kp" +"2350";"JPB258";"Politické myšlení novověku I";"Franěk,J.,Kučera,J.";;"3";"3";"3";"3";"3";NULL;NULL;NULL;"3";"3";"3";"3";"3";"ústnu skúšku";;"kp" +"2351";"JPB565";"Stáž v praxi";;"Kuľková,M.,Švec,K.";"5";"4";NULL;NULL;NULL;"4";"3";"4";"1";"5";"5";"5";"5";"možnosť uznávania predmetu, aj na základe toho, že stáž je vykonávaná mimo územia ČR";"prístup ku študentovi";"kp" +"2352";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"3";"3";"4";"4";"4";NULL;NULL;NULL;"3";"3";"3";"3";"3";;;"kp" +"2353";"JEM037";"Financial Markets";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"4";"2";"5";"4";"2";"4";"5";"5";"1";"5";"3";"5";"5";"organizace seminářů, diskuze na dané papery.";"možná navrhnout různorodější papery, ne mít 4 ze 6 na globální finanční krizi";"ies" +"2354";"JMB533";"Česká republika v integračních procesech";"Šlosarčík,I.";;"5";"4";"5";"4";"5";NULL;NULL;NULL;"1";"5";"4";"5";"4";;;"kzs" +"2355";"JMB534";"Evropská unie - vybrané problémy";"Mejstřík,M.";;"5";"5";"5";"4";"3";NULL;NULL;NULL;"2";"3";"4";"3";"3";;;"kzs" +"2356";"JMB535";"Bezpečnostní problémy současného světa";"Weiss,T.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kzs" +"2357";"JMB536";"Bakalářský seminář pro kombinovaný obor Teritoriální studia I";;"Vykoukal,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"3";"5";;;"krvs" +"2358";"JLB100";"Czech as a Foreign Language I";;"Frantesová,E.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"2";"5";"4";"5";"4";;;"cjp" +"2359";"JPM429";"Global terrorism (CS)";;"Makariusová,R.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kmv" +"2360";"JPM658";"International Economic Relations";"Parízek,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmv" +"2361";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"4";"5";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"5";"4";;;"kmv" +"2362";"JPM689";"Conflict Studies";"Karásek,T.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"5";"4";;"The fact that even if the class is based in lectures, it could be a little more practical and not that theoretical. But even though, I think it is interesting.";"kbs" +"2363";"JEM003";"Advanced Microeconomics";"Janda,K.,Melikhova,O.";"Janda,K.,Melikhova,O.";"1";"5";"2";"3";"1";"4";"4";"4";"1";"2";"2";"2";"2";;"Did not enjoy the course, material was very abstract and taught too abstract for me to understand. Need the theorems, but then need to give a couple examples with numbers and practicality with it.";"ies" +"2364";"JEM004";"Advanced Macroeconomics";"Baxa,J.,Hromádková,E.";"Baxa,J.,Hromádková,E.,Šestořád,T.,Troch,T.,Žáček,J.";"3";"5";"3";"4";"2";"3";"3";"2";"1";"2";"2";"2";"2";;"Give real examples or just examples with numbers so that that we can understand them. It was just here is a model and here is another model. Need to go in depth with each parameter and give examples of what changes with different scenarios.";"ies" +"2365";"JEM037";"Financial Markets";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"4";"4";"4";"5";"3";"4";"4";"4";"1";"4";"4";"4";"4";"Financial markets I enjoy and know some about through experience";"The readings were difficult to understand and follow and not all that interesting even for a native English speaker. They do add value but maybe more exciting of articles to read for those subjects.";"ies" +"2366";"JEM062";"Introductory Econometrics";"Kukačka,J.";"Kukačka,J.";"4";"4";"4";"5";"4";"5";"5";"5";"1";"4";"4";"4";"4";"Very good structure to the course. New what to expect and how each thing taught would be gone over.";"The lectures are too long and dry. Too many slides. Focus more on the subject, the idea of it, and then do an example. There is too much theoretical in the lecture for me and I loose interest. The computer lab seminars are good. Like going over the concepts, but again a little too much to cover in the time.";"ies" +"2367";"JLB100";"Czech as a Foreign Language I";;"Nováková,K.";"4";"5";NULL;NULL;NULL;"4";"5";"4";"1";"4";"4";"4";"4";"Learning basic Czech.";"Challenging course for someone who only really speaks English. A lot of people know another language or usually a Slavic language and so this is easy for them. Don't know how this can be improved except have a slower class and focuses on the necessities rather than the small details. Classes are too long. I don't have that kind of attention span.";"cjp" +"2368";"JLB013";"Němčina odborná I";;"Křenková,D.";"4";"3";NULL;NULL;NULL;"5";"5";"4";"1";"4";"3";"3";"5";;;"cjp" +"2369";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"4";"4";NULL;NULL;NULL;"5";"5";"4";"1";"3";"4";"3";"5";"Procvičování limit a derivací, které je přínosné pro kurz Matematika I. Zábavný přístup vyučujícího.";"Změnit čas výuky kurzu, tedy ne brzo ráno ani večer.";"ies" +"2370";"JSB998";"Úvod do sociologie";"Soukup,P.";;"4";"2";"5";"5";"2";NULL;NULL;NULL;"2";"3";"1";"3";"4";"Oceňuji nahraná videa z přednášek na Youtube a poutavý přístup vyučujícího.";;"ks" +"2371";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"3";"4";"5";"Získání základních znalostí z oboru práva. Vysvětlování problematiky na praktických situacích.";;"ies" +"2372";"NMMA701";"Matematika 1";"Spurný,J.";"Honzík,P.";"4";"5";"5";"4";"5";"2";"3";"4";"1";"5";"4";"5";"5";;"Více společně vypočítaných zkouškových příkladů.";"ies" +"2373";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"4";"1";NULL;NULL;NULL;"4";"4";"2";"2";"4";"2";"4";"4";;;"ies" +"2374";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"4";"4";"4";"4";"2";"4";"4";"3";"1";"5";"4";"4";"4";;"Větší přínos účasti na přednáškách. Většinou se toho stihne probrat jen zlomek látky a student si vše musí stejně doučit sám.";"ies" +"2375";"JLB003";"Angličtina pro ekonomy I";;"Gloverová,M.";"4";"3";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"4";"5";"Oceňuji lidský přístup paní Gloverové, angličtina s ní mě bavila.";;"cjp" +"2376";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"3";"3";"3";"1";"3";"2";"2";"5";"Možnost shlédnout aktuální filmy každý týden za minimální cenu.";;"kz" +"2377";"JLB011";"Němčina pro ekonomy nižší I";;"Faltýnová,R.";"4";"3";NULL;NULL;NULL;"4";"4";"4";"1";"3";"4";"4";"5";;"Méně domácích úkolů.";"cjp" +"2378";"JJB135";"Filmový seminář I";;"Šobr,M.";NULL;NULL;NULL;NULL;NULL;"4";"4";"3";NULL;NULL;NULL;NULL;NULL;;;"kz" +"2379";"JSB025";"Sociální problémy";"Frič,P.";;"4";"4";"4";"5";"3";NULL;NULL;NULL;"1";"4";"3";"5";"5";;;"kvsp" +"2380";"JJB226";"Strategie a finanční management marketingu I";"Majerik,P.";;"5";"3";"4";"4";"3";NULL;NULL;NULL;"1";"4";"3";"4";"5";;;"kmkpr" +"2381";"JJB284";"Firemní komunikace a kultura";"Poucha,T.";;"3";"4";"3";"2";"3";NULL;NULL;NULL;"1";"3";"2";"2";"2";"Zkušenosti vyučujícího, bylo zajímavé slyšet o problémech v konkrétních firmách a jakým způsobem se to řešilo";"Hodnocení výkonu studenta bylo velmi neprůhledné, během semestru jsme vůbec nevěděli jakou váhu má práce, jakou test a jakou aktivita. Nedozvěděli jsme se to ani při závěrečném hodnocení, nebylo mi to pořádně objasněno ani z emailové komunikace s vyučujícím. Možná by bylo vhodné na tom zapracovat a předem to stanovit na začátku semestru, aby poté nedocházelo k tomu, že většina studentů nechápe z jakého důvodu obdrželi takovou známku.";"kmkpr" +"2382";"JJB021";"Bakalářský seminář";;"Prázová,I.";"1";"1";NULL;NULL;NULL;"1";"2";"1";"2";"1";"1";"1";"1";"Kurz je ztráta času, nedozvěděl jsem se tady nic zásadního, co by mi při psaní bakalářské práce mohlo pomoct";;"kz" +"2383";"JJB066";"Rozhlas a televize ve světě";"Moravec,V.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"2";"3";"5";;;"kz" +"2384";"JJB067";"Mluvní a pohybová výchova I";;"Pavel,L.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"2";"5";"3";"5";;;"kz" +"2385";"JJB069";"Tvůrčí dílny I - televizní";"Lokšík,M.";;"5";"3";"4";"3";"3";NULL;NULL;NULL;"1";"4";"5";"4";"5";;;"kz" +"2386";"JJB083";"Editování zpravodajských relací";"Beneš,P.";;"1";"1";"3";"4";"2";NULL;NULL;NULL;"1";"1";"1";"1";"2";;;"kz" +"2387";"JJB086";"Managing Multimedia Projects";"Juřík,O.";;"3";"3";"4";"5";"3";NULL;NULL;NULL;"1";"2";"3";"2";"3";;;"kz" +"2388";"JLB045";"Angličtina pro marketing I";;"Stružková,I.";"3";"3";NULL;NULL;NULL;"4";"5";"5";"1";"4";"4";"3";"4";"Debatování nad probíraným tématem ve skupinkách/dvojicích";;"cjp" +"2389";"JEM163";"Principles of Microeconomics";"Janský,P.";"Král,M.,Moravcová,H.,Palanský,M.";"4";"2";"5";"5";"4";"3";"5";"3";"1";"5";"3";"3";"4";;;"ies" +"2390";"JEM179";"History and Methodology of Economics";"Baxa,J.,Paulus,M.";;"3";"5";"4";"4";"2";NULL;NULL;NULL;NULL;"3";"2";"2";"2";;;"ies" +"2391";"JLB003";"Angličtina pro ekonomy I";;"Gloverová,M.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"5";"5";"5";"5";"5";;;"cjp" +"2392";"JJB231";"Dějiny a teorie public relations";"Halada,J.,Hejlová,D.";;"4";"5";"4";"3";"3";NULL;NULL;NULL;"2";"4";"2";"4";"3";;"Vzhledem k odevzdání prací v listopadu mi přijde krajně nevhodné, aby se studentům vracely k přepracování v lednu týden před ústní zkouškou. V této době už se většina studentů připravuje na zkoušku a opravdu nemají čas přepisovat celou práci, nemluvě o tom, jakou paniku to vyvolalo napříč celým ročníkem. Věřím, že je v silách přednášejících opravit práce dříve, aby se studenti nemuseli do poslední chvíle stresovat zda jim nepřijde email o tom, že mají práci předělat.";"kmkpr" +"2393";"JLB104";"Czech for Chinese speaking students";;"Vaníčková,K.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"2394";"JPM323";"Global Political Philosophy";"Salamon,J.";;NULL;"5";"5";"5";"4";NULL;NULL;NULL;NULL;"3";"3";"3";"3";;;"kp" +"2395";"JJM208";"Mediální systémy a jejich komparace";"Miessler,J.";;"4";"3";"3";"4";"5";NULL;NULL;NULL;"2";"4";"5";"5";"5";"V kurzu jsem nejvíc ocenila průběžnou práci, možnost získat závěrečné hodnocení z mnoha dílčích úkolů a častou četbu.";"Zamyslela bych se nad strukturou výuky: není příliš mnoho času probrat zajímavá témata do hloubky, což je škoda.";"kms" +"2396";"JJM213";"Metody historického výzkumu";;"Bednařík,P.,Končelík,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"4";"5";"Oceňuji neocenitelnou zpětnou vazbu vzhledem k přípravě diplomové práce. Pro kohokoliv s historickým i jiným tématem jsou \"návody\" na zpracování tématu zkušeností, kterou jinde nezíská.";;"kms" +"2397";"JJM216";"Čtení textů ke studiu médií - česká média po roce 1945";;"Bednařík,P.,Končelík,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"3";"5";"5";"5";"5";;"Ocenila bych možná kratší vystoupení studentů a větší reflexi tématu od vyučujících nebo naopak chtít po studentech více hloubkové zpracování tématu. Probírané historické události jsou příliš zajímavé na to, aby se do nich neproniklo hlouběji.";"kms" +"2398";"JMMZ141";"Russian Language I";;"Shvedova,O.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"3";"5";;;"krvs" +"2399";"JSB003";"Oborová sociologie";"Numerato,D.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"ks" +"2400";"JSB027";"Sociální politika jako společenská praxe";"Dobiášová,K.,Vlčková,K.";;"4";"5";"3";"2";"3";NULL;NULL;NULL;"1";"4";"3";"3";"1";;;"kvsp" +"2401";"JSB028";"Informační gramotnost";"Tomandlová,V.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"1";"4";"5";"3";"5";;;"kvsp" +"2402";"JSB455";"Economic Sociology and European Capitalism";"Blokker,P.";;"3";"4";"4";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"ks" +"2403";"JSB490";"Úvod do politické sociologie";"Císař,O.";;"1";"5";"3";"3";"3";NULL;NULL;NULL;"1";"3";"3";"3";"1";;;"ks" +"2404";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Buřičová,B.";NULL;NULL;"1";"1";"1";"5";"5";"5";NULL;NULL;NULL;NULL;NULL;;;"ks" +"2405";"JSB537";"Analýza dat v SPSS";"Soukup,P.";"Oreský,J.";"1";"5";"2";"1";"1";"4";"4";"4";"3";"2";"1";"2";"1";;;"ks" +"2406";"JSB023";"Praktika z kvantitativního výzkumu I";;"Špaček,O.";"4";"4";NULL;NULL;NULL;"4";"3";"4";"2";"3";"4";"4";"4";;;"ks" +"2407";"JSB010";"Současná sociologie";"Balon,J.";;"3";"4";"4";"4";"2";NULL;NULL;NULL;"2";"3";"3";"2";"3";;;"ks" +"2408";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"2409";"JMB011";"Moderní dějiny Ruska";"Litera,B.,Pečenka,M.";"Kolenovská,D.";"5";"3";"5";"4";"5";"5";"5";"4";"2";"5";"5";"5";"5";;;"krvs" +"2410";"JMB415";"Seminář k aktualitám II";;"Young,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"5";"5";"Kurz je výborne navrhnutý a predovšetkým oceňujem, že na každej hodine dostal každý študent dostatok priestoru na vyjadrenie názoru. Za obrovské plus považujem znalosť vyučujúceho a jeho všeobecný prehľad, ktorý pomáhal formovať kurz ako celok.";;"krvs" +"2411";"JMB018";"Bakalářský seminář I";;"Emler,D.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"5";"5";"Oceňujem hlavne analytické schopnosti vyučujúceho a jeho podporu, ktorá pomáha formovať naše práce.";;"krvs" +"2412";"JMB013";"Moderní dějiny středo- a jihovýchodní Evropy";"Balla,P.,Švec,L.";"seminář nenavštěvován";"5";"4";"5";"5";"4";"5";"5";"4";"4";"5";"5";"5";"5";;;"krvs" +"2413";"JMB015";"Moderní dějiny západní Evropy";"Matějka,O.,Váška,J.";"seminář nenavštěvován";"5";"5";"4";"5";"5";"5";"4";"4";"2";"5";"4";"5";"4";;;"kzs" +"2414";"JMB037";"Moderní dějiny Polska";"Vykoukal,J.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Oceňujem hlavne celkový prehľad vyučujúceho a jeho výklad, ktorý je zrozumiteľný a pochopiteľný ako aj výber textov, ktoré pomáhajú rozšíriť preberané téma.";;"krvs" +"2415";"JLB033";"Němčina I";;"Faltýnová,R.";"4";"3";NULL;NULL;NULL;"4";"4";"5";"2";"4";"5";"5";"5";;;"cjp" +"2416";"JJB0111";"Journalism Ethics/Úvod do etiky žurnalistické práce";"Neuzil,M.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"5";"4";"5";;;"kz" +"2417";"JMB057";"Cultural Legacies and Developments in the Balkans: Modern and Traditional Entanglements";"Asavei,M.";;"3";"3";"5";"5";"5";NULL;NULL;NULL;"1";"2";"5";"4";"4";;;"krvs" +"2418";"JMB091";"Religion, secularity and laicity in Europe (19th-21th centuries)";"Bauer,P.";;"3";"3";"4";"4";"5";NULL;NULL;NULL;"1";"5";"5";"5";"3";;;"kzs" +"2419";"JMB178";"U.S. in the 1960s and 1970s";"Raška,F.";;"3";"3";"5";"5";"5";NULL;NULL;NULL;"1";"3";"4";"5";"5";;;"kas" +"2420";"JMM348";"American Literature 1900-1950";"Hanuš,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"3";"5";"5";"5";;;"kas" +"2421";"JMM027";"Contemporary Mediterranean";"Králová,K.,Mejstřík,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"3";"5";"5";"5";;;"kzs" +"2422";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Čížek,M.";"4";"4";"3";"3";"3";"5";"5";"5";NULL;"4";"4";"4";"4";;;"krvs" +"2423";"JJB600";"Úvod do studia médií I.";"Nečas,V.";;"4";"4";"5";"4";"5";NULL;NULL;NULL;"3";"4";"2";"4";"3";"Prezentace, srozumitelný výklad, zpestření výuky zajímavostmi.";"Více specifikovat, na jaké téma by se student u zkoušky měl zaměřit a tím pádem více připravit.";"kms" +"2424";"JJB601";"Rozbor právního prostředí v českých médiích";"Benda,J.";;"3";"4";"3";"2";"4";NULL;NULL;NULL;"1";"5";"4";"4";"2";"Propojení teoretických příkladů s praxí. Prezentace.";"Odůvodnění špatně vyhodnocených úkolů v průběhu roku. Vysvětlení, co na úkolu bylo špatně a jaké mělo být správné řešení. Zpřístupnit prezentace z přednášek studentům.";"kms" +"2425";"JJB605";"Novodobá historie médií";"Bednařík,P.,Končelík,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"4";"4";"Přátelský přístup ke studentům ze strany profesorů. Poutavá výuka, zábavné metody přednášení vysvětlované látky. Prezentace. Doplňování výkladu o různé věci (časopisy, lístky, ...), které sloužily k názornému příkladu.";"Čas přednášky u kombinovaného studia bych změnila - přednášku bych dala dříve než jako poslední ze všech přednášek konajících se v pátek. Předmět je velmi zajímavý a považuji za škodu, že se studenti už na konci celého náročného dne tolik nesoustředí. Dřívější vyhodnocení seminárních prací.";"kms" +"2426";"JJB616";"Vybrané novinářské osobnosti 19. století";"Sekera,M.";;"4";"2";"4";"4";"3";NULL;NULL;NULL;"1";"3";"3";"3";"5";"Přátelský přístup vyučujícího. Poutavé podání výkladu. Uvedení faktů do věcných souvislostí. Doplnění výkladu o názorné ukázky (např. fotograií osobností) na internetu.";"Zvýšit hlas přednášejícího.";"kms" +"2427";"JJB631";"Social Media: Strategy, Tactics and Analytics";"Audyová,P.";;"3";"3";"2";"4";"4";NULL;NULL;NULL;"2";"4";"4";"3";"4";"i learned some knowledge about social media promoting";;"kmkpr" +"2428";"JJB620";"Mediální produkce - žurnalistická práce I.";"Vlasák,Z.";;"4";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";"Humor a vtip přednášejícího. Uvedení názorných příkladů z praxe u právě projednávaného tématu. Zpětná vazba k odevzdaným úkolům. Pojetí kurzu jako workshopu. Prezentace.";"-";"kms" +"2429";"JJB236";"Komunikace s médii";"Schneiderová,S.";;"3";"2";"4";"4";"4";NULL;NULL;NULL;"2";"2";"4";"3";"4";;;"kmkpr" +"2430";"JJB238";"Kritika marketingové komunikace";"Rosenfeldová,J.";;"3";"4";"2";"3";"2";NULL;NULL;NULL;"3";"4";"1";"3";NULL;;;"kmkpr" +"2431";"JJB240";"Marketing a tvorba značky";"Průša,P.";;"4";"3";"4";"3";"4";NULL;NULL;NULL;"2";"4";"4";"4";"4";;"Aby nebyl od 8:00 ráno. Asi by to pomohlo i účasti.";"kmkpr" +"2432";"JLB100";"Czech as a Foreign Language I";;"Frantesová,E.";"5";"4";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"4";"5";"i love my tacher";;"cjp" +"2433";"JJB634";"Litigace PR";"Novák,L.,Štrégl,R.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";;"O něco málo větší rozsah & větší počet studentů – možná i dvojnásobně velká skupina by ještě nebyla na úkor kvality a více se zhodnotí práce.";"kmkpr" +"2434";"JLB104";"Czech for Chinese speaking students";;"Vaníčková,K.";"4";"2";NULL;NULL;NULL;"4";"4";"4";"3";"4";"4";"4";"4";"teacher is very kind and helpful";;"cjp" +"2435";"JJM360";"Ekonomika v médiích";"Klimeš,D.";;"4";"1";"5";"5";"4";NULL;NULL;NULL;"1";"4";"2";"4";"2";"Skvělé a zajímavé hosty.";"Nedávat deadline na odevzdání seminárky na 23. prosince :)";"kz" +"2436";"JSM692";"Introduction to Social Research Methodology";"Remr,J.";;"4";"4";"3";"5";"4";NULL;NULL;NULL;"2";"4";"4";"4";"4";;;"ks" +"2437";"JSB454";"Social Web: (Big) Data Mining";"Růžička,J.";;"4";"4";"3";"5";"4";NULL;NULL;NULL;"1";"4";"5";"2";"4";"Formát 4 × dopoledne za semestr.";"Přesunout ho z Jinonic někam do Prahy.";"ks" +"2438";"JLB033";"Němčina I";;"Faltýnová,R.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"5";"5";"4";"3";"5";"Příjemný přístup, podrobné vysvětlování neznámých slovíček jinými synonymy v Nj, práce ve skupinách";"Ocenila bych nějaké poslechové cvičení.";"cjp" +"2439";"JJB600";"Úvod do studia médií I.";"Nečas,V.";;"4";"4";"4";"4";"3";NULL;NULL;NULL;"1";"4";"4";"4";"4";"Oceňuji názornost, používání prezentací, srozumitelné vysvětlení látky.";"Možná by bylo lepší, kdyby studijní texty byly pouze v češtině.";"kms" +"2440";"JSB003";"Oborová sociologie";"Numerato,D.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"5";"5";"x";"x";"ks" +"2441";"JSB010";"Současná sociologie";"Balon,J.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Psaní písemných prací z doporučené literatury";"Obdržení včasné odezvy na odeslanou práci (hodnocení, výtky,...)";"ks" +"2442";"JJB601";"Rozbor právního prostředí v českých médiích";"Benda,J.";;"3";"4";"4";"3";"3";NULL;NULL;NULL;"1";"4";"4";"4";"4";"Oceňuji odborné znalosti vyučujícího.";"Možná by bylo lepší, kdyby prezentace z hodin byly studentům dostupné, a dále také to, kdyby látka byla probírána pomalejším tempem.";"kms" +"2443";"JJB605";"Novodobá historie médií";"Bednařík,P.,Končelík,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Oceňuji profesionální a zároveň lidský přístup vyučujících, jejich zápal pro věc, ale také schopnost předat vědomosti studentům. Hodiny byly zajímavé a zábavné.";"Nevím, asi nic.";"kms" +"2444";"JSB133";"Zemědělství a rozvoj venkova (vybraná témata z rurální sociologie)";"Zagata,L.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"2";"4";"4";"4";"5";"Nutnost přemýšlet nad souvislostmi ohledně venkova, které jsem brala za automatické.";"Jiná učebna, 2066 byla při počtu studentů v kurzu buď vydýchaná, nebo při větrání naopak příliš chladná, stav mezi nebylo možno regulovat.";"ks" +"2445";"JJB616";"Vybrané novinářské osobnosti 19. století";"Sekera,M.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"4";"Oceňuji profesionální a zároveň lidský přístup vyučujícího, jeho znalosti a zajímavý výklad.";"Nevím, asi nic.";"kms" +"2446";"JJB620";"Mediální produkce - žurnalistická práce I.";"Vlasák,Z.";;"4";"4";"4";"5";"5";NULL;NULL;NULL;"1";"4";"5";"4";"4";"Oceňuji profesionální a zároveň lidský přístup vyučujícího, jeho znalosti i schopnost tyto znalosti předat studentům. Oceňuji také rychlé vyhodnocení domácích úkolů včetně konstruktivních připomínek, které byly velmi přínosné.";"Možná by bylo lepší, kdyby zadávané úkoly byly stejně snadno splnitelné pro všechny studenty (např. udělat rozhovor muselo být pro studenty, kteří již v médiích pracují, mnohem snazší než pro ty, kteří nemají zkušenosti ani kontakty na zajímavé osobnosti).";"kms" +"2447";"JSB537";"Analýza dat v SPSS";"Soukup,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"3";"5";"5";"4";"5";"Shrnutí teoretických znalostí z předchozích předmětů na přednáškách, následná aplikace na navazujících cvičeních. Vstřícný přístup vyučujícího i cvičících.";"Na počátku zmatky, nestihla se procvičit celá látka. Nutnost doučování, aby student mohl složit státní zkoušky ze SPSS. Doučování jednorázové na 6 hodin (ve volném čase cvičících - děkuji moc) moc koncentrované a náročné na úplné vstřebání.";"ks" +"2448";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"4";"4";"4";"3";"3";NULL;NULL;NULL;"1";"5";"3";"3";"5";"Vhled do jiného oboru";"Pan profesor na přednáškách mluví vcelku potichu. Myslím, že by každý na přednášce ocenil, kdyby bylo pana přednášejícího lépe slyšet.";"ies" +"2449";"JPB018";"Dějiny mezinárodních vztahů I";"Soukup,J.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"3";"4";"3";"5";"5";"Jeden z nejlepších kurzů vůbec. Pan profesor je evidentně zapálený do přednášené látky, což jen příspívá k atmosféře přednášek, na které jsem chodil zřejmě nejraději ze všech.";;"kmv" +"2450";"JPB221";"Metodologický proseminář I";;"Střítecký,V.,Tesař,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Pan Tesař nasty skvěle uvedl do tohoto kurzu, který budeme absolvovat po celou dobu studia. Velice zajímavou formou jsem pobírali různá témata, která se týkají praktických věcí.";;"kmv" +"2451";"JPB020";"Úvod do mezinárodních vztahů";"Karlas,J.,Kučera,T.";;"3";"3";"3";"3";"4";NULL;NULL;NULL;"1";"5";"2";"4";"4";"Závěrečný test mi přijde jako správně koncipovaný. Pouze člověk, který se učil na zkoušku ji dokáže absolvovat.";"Jeden z vyučujících na mne nepůsobil nejlepším dojmem, který nedokážu naprosto přesně popsat. Nechci ho jmenovat, nicméně mi přišlo, že spíše přednášel svou prezentaci než látku. Chyběl mi podrobnější výklad a interpretace přednášené látky. Druhý učitel, který je zřejmě zkušenější toto zvádl velice dobře a dokázal zaujmout studenty.";"kmv" +"2452";"NMMA703";"Matematika 3";"Zelený,M.";"Bartoš,A.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"3";"4";"2";NULL;"Přístup vyučujících.";"Teorie ohledně Lebesguova integrálu byla nadbytečná, když pak nebyla vyžadována u zkoušky. Chyběl mi předmět Seminář matematické analýzy, bylo tudíž těžší se připravit na písemnou část zkoušky.";"ies" +"2453";"JLM001";"Academic English I";;"Cotte,P.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"5";"The approach of the lecturer.";"Spending less time with exercises and more time conversating or actually writing in class.";"cjp" +"2454";"JEB141";"Introduction to Market Design";"Gregor,M.,Kastl,J.";;"4";"3";"4";"4";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";"Oceňuji kompaktnost.";"Ideální by bylo, kdyby kurz proběhl o týden dříve, aby se nekryl s hlavním proudem zkoušek.";"ies" +"2455";"JPB256";"Politické systémy I";"Brunclík,M.,Jeřábek,M.,Kotábová,V.,Perottino,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"3";"4";"5";"Oceňuji, že každý systém vyučuje jiný člověk, který se specializuje na danou problematiku. Ačkoliv mne kurz zezačátku moc nebavil, nakonec jsem si uvědomil jeho důležitost a velice jsem si ho oblíbil a obě části kurzu jsem si velice užil.";;"kp" +"2456";"JEB136";"Topics in Industrial Organization";"Schwarz,J.";;"4";"4";"4";"5";"5";NULL;NULL;NULL;"1";"4";"3";"5";"5";"Setkávání s osobnostmi z oboru.";"Některá čtení byla zadávána jen týden před přednáškou, což při jejich délce a kolizi s midtermy nebylo ideální. Některá čtení byla příliš dlouhá (nad 25 stran už se mi zdá článek jako příliš dlouhý). To je umocněno tím, že student nemá žádnou volbu a články číst musí. Je škoda, že nelze třeba jeden článek \"vynechat\", protože ne všechna témata mě osobně přišla až tak zajímavá. Každý toto ale může vnímat jinak, proto bych zavedla možnost jeden článek jednoduše nečíst.";"ies" +"2457";"JPB269";"Současné problémy mezinárodních vztahů";"Knutelská,V.";;"3";"3";"3";"2";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";"Bylo zajímavé slyšet různé přednásky od hostů, kteří se zabývali různými tématy. Psaní fact-checkingu bylo zajímavé.";"Přišlo mi, že vzhledem k tomu, že na kurz chodí ti samí hosté paní profesorka není moc zainteresována do jejich projevu. Ocenil bych větší výklad k PolicyBriefu, či nastínění toho co se čeká od fact-checkingu. Některá témata viz. \"Porušuje polská vláda polskou Ústavu?\" mi přijdou irelevantní, jelikož se o tomto témetu přou ústavní právníci a pro studenta, který je na škole například první rok, je toto poměrně špatně uchopitelné.";"kmv" +"2458";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"5";"2";"5";"5";"4";"5";"5";"5";"1";"5";"4";"4";"5";"It was quite interesting to have first the seminar where we had everything explained in a simple way and without much context, and then have the lecture where we could understand the context. All three lecturers had good teaching skills and made the content of the course interesting and understandable.";"Lectures at 8AM. I can understand seminars this early - some people simply prefer to get up early. Unfortunately, that is not my case, thus their contribution was strictly limited.";"ies" +"2459";"JLB005";"Angličtina pro politology I";;"Panešová,K.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"3";"5";"Paní Panešová je absolutně nejlepší lektorka angličtiny, kterou jsem měl tu čest poznat. Jak po pedagogické, tak po stránce přístupu ke studentům je jedinečná.";;"cjp" +"2460";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Jeden z nejlepších kurzů, pan Romancov je naživo ještě lepší než na Twitteru. Velice si cením způsobu jakým přednáší látku studentům. Profesor Romancov ještě znásobil mou lásku k předmětům, které se zabývají geografií, geopolitikou apod.";"Kurz je od půl sedmé večer.";"kp" +"2461";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"The approach of Mrs Chytilová who made the content of the lectures very easy to understand and very interesting as well. I have always been more inclined to prefer macro, but her approach has made me reconsider my preferences.";"There has been a wide discussion about the exam setup. Numbers should not be too simple to calculate, but what we had in the preterm was probably the other side of the extreme. Making conclusions thereof is thus very complicated.I actually never attended the seminars on Thursdays at 8 as I always went to those on Tuesdays. I cannot understand the fact that Mr Gok stopped to teach at those seminars as I believe his teaching skills to be superior to those of his substitutes.";"ies" +"2462";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"3";"5";"5";"Naprosto jediněčný kurz. Všechny tří úrovně přináší naprosto jiný pohled na politiku.";;"kp" +"2463";"JPB252";"České novověké dějiny I.";"Kučera,J.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;"2";"4";"2";"4";"5";"Naprosto jedinečný přednes.";"Možná větší konceptualizaci přednášené látky, jelikož je těžké si odnést poznámky. Na druhou stranu je možná specifická povaha předmětu důvodem, proč je tento předmět tak zajímavý a zábavný.";"kp" +"2464";"JEB105";"Statistics";"Červinka,M.";"Nevrla,M.";"5";"5";"5";"5";"5";"4";"5";"5";"1";"5";"5";"5";"5";"I guess Mr Červinka's approach is something students just need to get used to. I took me two semesters worth of lectures, but when looking back, I appreaciate the work he is doing for us as students.I also liked the structure of the final written exam.";"The home assignments were sometimes a bit too complicated. Understand the reasoning behind grading just two of the exercises, but probably it might be better to have just 4 exercises that we need to compute instead of 6.I didn't properly understand the reasoning behind testing hypotheses and confidence intervals during the lectures and it all started to make sense only when I studied it before the exam. It might be good to slow down a little in this field.";"ies" +"2465";"JPB565";"Stáž v praxi";;"Kuľková,M.,Švec,K.";"5";"3";NULL;NULL;NULL;"4";"5";"5";"1";"4";"5";"4";"5";;;"kp" +"2466";"JMB409";"Moderní dějiny středo- a jihovýchodní Evropy-dcera JMB013";"Balla,P.,Švec,L.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Výborné přednášky, na které jsem rád docházel. Dozvěděl jsem se spoustu nového a zajímavého, mnohdy se i pobavil. Docházka na ně navíc opravdu významně pomáhá k úspěšnému absolvování kurzu. Pro mě asi nejlepší kurz tohoto zimního semestru :).";"Myslím, že by se v kurzu slušelo věnovat větší pozornost modernějším dějinám. Procesy národního uvědomění v Estonsku, Lotyšsku a Litvě jsou sice zajímavé, pro dnešní dobu se však mnohem více zdají být relevantní události po roce 1914. Absolventi kurzu odchází do třetího ročníku bez podrobnějšího vhledu do rozpadu Jugoslávie či historického vývoje na Ukrajině – obojí je přitom dnes žhavé téma. Zkoušení skrze eseje má leccos do sebe, ale nerozumím způsobu, jakým se zde provádí. Studentům pořádně na hodině není vysvětleno, jak se má esej napsat, aby uspěla. O nějakém nácviku jejího psaní ani nemůže být řeč. Na západních školách se sice takto testuje běžně, ale studenti jsou tam již ze střední školy s formátem eseje dobře obeznámeni a univerzita je v tomto směru dále rozvíjí! Troufám si tvrdit, že valná většina maturantů v ČR má jen minimální znalosti a zkušenosti s psaním esejí (první ročník na FSV na tom změní pramálo) – podle toho pak částečně vypadají i výsledky zkoušek.";"krvs" +"2467";"JLB005";"Angličtina pro politology I";;"Stružková,I.";"3";"2";NULL;NULL;NULL;"4";"3";"2";"1";"3";"3";"3";"2";;"Přijde mi, že 2 hodiny na výuku na cizího jazyka je dlouhá. Ztrácela jsem koncentraci na výklad.";"cjp" +"2468";"JPB020";"Úvod do mezinárodních vztahů";"Karlas,J.,Kučera,T.";;"2";"3";"2";"3";"1";NULL;NULL;NULL;"1";"4";"3";"2";"2";;;"kmv" +"2469";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"2470";"JPB252";"České novověké dějiny I.";"Kučera,J.";;"3";"4";"3";"3";"3";NULL;NULL;NULL;NULL;"3";"3";"3";"3";;;"kp" +"2471";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"1";"3";"1";"1";"1";NULL;NULL;NULL;"1";"2";"1";"1";"1";"Jediné věci, které byly při tomto kurzu správně provedeny, byla kontrola podvádění při závěrečném testu a netradiční pojetí některých ekonomických jevů. V podmínkách závěrečného testu by bylo používat poznámky nebo telefon velice obtížné. Netradičním pojetím myslím např. chování ekonomických subjektů v post-komunistických zemích.";"Vyměnit přednášejícího. Uvést ekonomické souvislosti do větších souvislostí historických, ale i územních. Úvod do Ekonomie by mohl být také více zaměř";"ies" +"2472";"JMB242";"Balkans after 1989";"Hofmeisterová,K.,Kocián,J.,Králová,K.";;"5";"3";"4";"5";"5";NULL;NULL;NULL;"1";"5";"2";"5";"5";"Měnící se hlavní lektoři při každém semináři. Tento způsob zajišťoval představení různých náhledů na danou problematiku. Také diskuse na konci semináře byly v drtivé většině případů velmi produktivní. V neposlední řadě jsem ocenil přístup lektorů, kteří se rádi podělili o své vlastní zkušenosti z daných oblastí a projevili opravdový zájem ve svých přednáškách.";"Nejsem si jist na kolik by to bylo možné, ale mně osobně by pomohlo, kdyby se mi dostalo reakcí ostatních studentů na mou prezentaci v rámci feedbacku, který byl každý týden posílán.";"krvs" +"2473";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"1";"4";"2";"1";"1";NULL;NULL;NULL;"1";"2";"1";"2";"1";;;"ies" +"2474";"JLM011";"Angličtina pro veřejnou a sociální politiku I";;"Klírová,M.";"5";"3";NULL;NULL;NULL;"4";"5";"4";"1";"4";"5";"4";"5";"speaking";;"cjp" +"2475";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"4";"2";"4";"5";"4";NULL;NULL;NULL;"1";"2";"1";"2";"4";;;"ies" +"2476";"JJB154";"Introduction to photojournalism";;"Láb,F.,Štefaniková,S.";"4";"4";NULL;NULL;NULL;"4";"3";"4";"1";"5";"4";"4";"4";;;"kz" +"2477";"JJM117";"Popular Culture";"Turnau,T.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kms" +"2478";"JJM234";"Media and Society: An Introduction";"Jirák,J.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kms" +"2479";"JJM239";"Media Sociology";"Miessler,J.";;"5";"4";"4";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"kms" +"2480";"JJM208";"Mediální systémy a jejich komparace";"Miessler,J.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";;;"kms" +"2481";"JJM211";"Kvalitativní výzkum mediálních publik";;"Reifová,I.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"1";"4";"5";"4";"5";;;"kms" +"2482";"JJM295";"Rozhlasový a televizní dokument";"Štoll,M.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"5";"3";"4";"5";;;"kz" +"2483";"JJM373";"Ethical Issues in Media, Business and Society";"Orhan,M.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"5";"5";;;"kms" +"2484";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"5";"4";"4";"4";"5";NULL;NULL;NULL;"2";"5";"2";"5";"5";"Přednášky jsou koncipovány tak, aby člověk porozuměl souvislostem, které se zdají jinak nepodstatné, ale ve výsledku mají zásadní dopady na svět.";"Pan doktor Kozák používá velmi, velmi, velmi často výplňkové \"errrr\". Pro mnohé studenty to ubírá na kvalitách přednášek. Dále by bylo lepší, kdyby byly prezentace aktuálnější. Některé z tabulek jsou z roku 2014, což není pro takovýto předmět ideální.";"kas" +"2485";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"3";"2";"3";"3";"1";NULL;NULL;NULL;"4";"3";"2";"1";NULL;;"občas to na mě působilo jako změť naprosto náhodných tabulek, grafů a obrázků";"kas" +"2486";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"4";"2";"5";"5";"1";NULL;NULL;NULL;"1";"1";"2";"3";"2";"přístup a pedagogické zkušenosti vyučujícího";;"krvs" +"2487";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"3";"4";"5";;;"cjp" +"2488";"JPB218";"Dějiny novověké Evropy I.";"Kučera,J.";;"5";"2";"4";"4";"4";NULL;NULL;NULL;"1";"5";"4";"4";"4";;;"kp" +"2489";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"4";"4";"4";"3";"4";NULL;NULL;NULL;"1";"4";"4";"4";"3";;"Na výsledek testu čekám již 11 dní - tudíž bych navrhovala zlepšit rychlost opravování.";"kp" +"2490";"JPB597";"Current Political Extremism";"Charvát,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kp" +"2491";"JLB019";"Francouzština odborná I";;"Bosáková,L.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"- aktivity v hodinach-prubezne testy- domaci ukoly";"Paradoxne vice ukolu (respektive prubeznych testu)";"cjp" +"2492";"JLB057";"Academic Writing for Bachelors";;"Goodall,A.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"3";"5";"5";"5";"5";"Ocenuji postupne projiti krok za krom jak pristupovat k psani akademickeho textu. Prijde mi, ze tento kurz by nemel byt pouze volitelnym predmetem, ale mel by byt povinne volitelny pro vsechny, co se chystaji psat bakalarskou/diplomovou praci.";"Nic";"cjp" +"2493";"JMB248";"Seminář k dějinám Ruska";;"Kolenovská,D.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Pristup vyucujici - naprosto perfektni, pomohla pochopit probiranou latku. Skvely feedback!";"Vicehodinova dotace :).";"krvs" +"2494";"JMB408";"Moderní dějiny Ruska";"Litera,B.,Novák,P.,Pečenka,M.";;"4";"5";"3";"3";"3";NULL;NULL;NULL;"1";"3";"3";"4";"3";;"Kurzu chybi jednotna ucelena skripta - i pres psani poznamek a dochazeni na prednasky nema student moznosti pristupu jedne nebo dvou publikace, kde by byla latka obsazena. V doporucene cetbe je zhruba 12 titulu, mnoho z nich zcela nesehnatelnych.";"krvs" +"2495";"JMB409";"Moderní dějiny středo- a jihovýchodní Evropy-dcera JMB013";"Balla,P.,Švec,L.";;"3";"5";"3";"2";"4";NULL;NULL;NULL;"1";"2";"2";"3";"3";;"Chybi kohorenteni a prakticka skripta.";"krvs" +"2496";"JJM080";"Zábava v televizním vysílání";"Kruml,M.";;"3";"1";"5";"4";"3";NULL;NULL;NULL;"2";"3";"2";"3";"3";"Nejpřínosnější byla odbornost kantora a jeho zapálení pro věc.";;"kms" +"2497";"JJM202";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"4";"4";"4";"4";"5";NULL;NULL;NULL;"1";"4";"3";"5";"4";;;"kms" +"2498";"JJM204";"Výzkum médií I";"Křeček,J.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;"2";"4";"5";"4";"4";;;"kms" +"2499";"JJM201";"Teorie masové a mediální komunikace";"Jirák,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"4";"3";"5";"5";;;"kms" +"2500";"JJM243";"Média a životní styl";"Knapík,J.";"Knapík,J.";"3";"4";"3";"3";"4";"3";"3";"3";"1";"3";"2";"2";"2";;;"kms" +"2501";"JJM295";"Rozhlasový a televizní dokument";"Štoll,M.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"2";"4";"3";"3";"4";;;"kz" +"2502";"JLB053";"Angličtina pro sociální vědy I";;"Prošková,A.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";;;"cjp" +"2503";"JJB600";"Úvod do studia médií I.";"Nečas,V.";;"4";"3";"5";"3";"5";NULL;NULL;NULL;"3";"5";"3";"4";"5";;;"kms" +"2504";"JJB601";"Rozbor právního prostředí v českých médiích";"Benda,J.";;"3";"3";"3";"3";"5";NULL;NULL;NULL;"1";"4";"4";"3";"4";;;"kms" +"2505";"JJB605";"Novodobá historie médií";"Bednařík,P.,Končelík,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kms" +"2506";"JJB616";"Vybrané novinářské osobnosti 19. století";"Sekera,M.";;"4";"3";"5";"3";"5";NULL;NULL;NULL;"1";"4";"3";"4";"3";;;"kms" +"2507";"JJB620";"Mediální produkce - žurnalistická práce I.";"Vlasák,Z.";;"4";"2";"3";"4";"5";NULL;NULL;NULL;"1";"3";"4";"4";"5";;;"kms" +"2508";"JJM254";"Mediální tvorba";"Čásenský,R.";;"5";"2";"4";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Předmět je doprovázen velkým množstvím příkladů z praxe.";"Vyučující mluví příliš potichu.";"kz" +"2509";"JJM251";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"4";"4";"4";"3";"4";NULL;NULL;NULL;"1";"5";"4";"4";"4";"Přehledné zadání studijní literatury, systém průběžných textů.";"Nesplnění sylabu - vyučující se na přednáškách příliš dlouho zdržovali úvodními kapitolami a poté již nestíhali probrat vše (včetně některých důležitých témat).";"kz" +"2510";"JJM355";"Teoretické koncepty ke studiu žurnalistiky I";"Jirků,J.,Němcová Tejkalová,A.";"Jirků,J.";"4";"4";"5";"4";"5";"5";"5";"5";"1";"5";"4";"4";"4";"Nejvíce oceňuji průběh seminářů, které vedou k jasnému pochopení probraných témat i zadaných textů. Užitečný je i společný praktický úkol.";"Přednášky jsou ve velkém tempu a studenti nemají čas si zapisovat a zároveň u toho pochopit probíranou látku.";"kz" +"2511";"JMM530";"European Comparative Politics and Society";"Rovná,L.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"2";"4";"3";"4";"5";;;"kzs" +"2512";"JMMZ331";"Qualitative methods in social sciences";"Weiss,T.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kzs" +"2513";"JMMZ332";"Culture and politics in Europe";"Tomalová,E.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kzs" +"2514";"JMMZ333";"Transnational history of contemporary Europe";"Matějka,O.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kzs" +"2515";"JMMZ334";"Current Challenges in Europe";"Mejstřík,M.,Tomalová,E.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"4";"5";"5";"5";;;"kzs" +"2516";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"2";"5";"5";"3";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kp" +"2517";"JSB010";"Současná sociologie";"Balon,J.";;"5";"3";"4";"5";"4";NULL;NULL;NULL;"2";"5";"4";"5";"5";;;"ks" +"2518";"JSB025";"Sociální problémy";"Frič,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kvsp" +"2519";"JSB031";"Bakalářský seminář I - diskuse projektů";;"Spalová,B.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"ks" +"2520";"JSB033";"Praktika z kvalitativního výzkumu";;"Spalová,B.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"3";"5";"4";"5";;;"ks" +"2521";"JSB490";"Úvod do politické sociologie";"Císař,O.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"ks" +"2522";"JSB534";"Introduction to Visual Sociology";"Wladyniak,L.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"3";"5";"3";"5";;;"ks" +"2523";"JSM612";"Kriminalita a současná česká společnost";"Cejp,M.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"5";"5";;;"kvsp" +"2524";"JSB004";"Sociální nerovnosti";"Šanderová,J.";;"3";"5";"3";"1";"3";NULL;NULL;NULL;"3";"3";"2";"2";"2";;;"ks" +"2525";"JSB537";"Analýza dat v SPSS";"Soukup,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"4";"3";"4";"4";"Oceňuji způsob přednášek pana Soukupa, přestože SPSS není jedním z mých oblíbených předmětů, na přednášky jsem vždy chodila ráda. Vlastně to byla jedna z mála přednáše nakterou sem chodila.A taky bych chtěla zmínit kluky ze cvičení, jsou výborní, skvělě dokáží vysvětlit věci probírané na přednáškách.";;"ks" +"2526";"JSB025";"Sociální problémy";"Frič,P.";;"3";"3";"3";"3";"3";NULL;NULL;NULL;"2";"3";"3";"3";"3";;;"kvsp" +"2527";"JMB410";"Moderní dějiny západní Evropy";"Matějka,O.,Váška,J.";;"1";"4";"3";"2";"1";NULL;NULL;NULL;"1";"1";"1";"3";"1";"nevím";"kurzu kdy nám v podstatě dali povinnou četbu a přesnáška byla uplně bez náplně. Za druhé mi vadila jejich neschopnost udělat vyrovnané testy při zavěrečných zkouškách, jeden termín s uplně očividnými daty kdy projdou skoro všichni termín další s iformacemi na které člověk nenarazí a projde málo kdo nemluvě o tématech esejí. (chápu, že ale toto je subjektivní). Co mi ale vadilo uplně ze všeho nejvíc, byla neomalenost při opravování naších testů. Když se plná třída snažila se soustředit na psaní Esejů páni si sedli do první lavice a smáli se na celou třídu jak si předříkavali naše odpovědi a bili se do hlavy. To mi opravdu přišlo jako něco, co by neudělal ani 15ti letý poberťák a velice mě to rozčílilo.";"kzs" +"2528";"JMB212";"Moderní dějiny Japonska";"Labus,D.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kas" +"2529";"JMB248";"Seminář k dějinám Ruska";;"Jasenčáková,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"5";"5";"Paní Jasenčákovou";;"krvs" +"2530";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"1";"3";"1";"1";"1";NULL;NULL;NULL;"1";"1";"1";"1";"1";;"Více se zaměřit na ekonomii z ekonomického hlediska. Kurz je pojmut z pohledu sociologického a pro mě jakožto studenta vyšlého z gymnázia nepřinesl vůbec nic nového, naopak jsem se dozvěděla méně, než na gymnáziu.";"ies" +"2531";"JLB005";"Angličtina pro politology I";;"Panešová,K.";"5";"2";NULL;NULL;NULL;"5";"5";"4";"2";"5";"4";"5";"5";;;"cjp" +"2532";"JJM249";"Duchovní kořeny evropské kultury";"Halada,J.";;"3";"2";"5";"5";"3";NULL;NULL;NULL;"1";"1";"1";"4";"3";;;"kz" +"2533";"JJM254";"Mediální tvorba";"Čásenský,R.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kz" +"2534";"JPM524";"Energy Security";"Holubcová,J.,Kučerová,I.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"2";"5";"4";"4";"5";;;"kmv" +"2535";"JPM561";"Regional Security Studies";"Karásek,T.,Klosek,K.";"Karásek,T.,Klosek,K.";"3";"4";"5";"5";"3";"5";"5";"3";"1";"3";"2";"4";"3";;;"kbs" +"2536";"JPM694";"Strategic Studies";"Hynek,N.,Ludvík,J.,Smetana,M.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"4";"3";"3";;;"kbs" +"2537";"JPM702";"NATO and EU in Crisis Management";"Karásek,T.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"4";"4";"4";"4";;;"kbs" +"2538";"JJB221";"Ekonomická teorie marketingu";"Postler,M.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"2";"4";"4";"Nejvíce oceňuji praktické příklady z marketingu k jednotlivým ekonomickým úkazům, díky kterým si student může spoustu věcí propojit a lépe pochopit.";"Pan profesor někdy vypráví až moc zdlouhavě, díky čemuž student často ztrácí nit a neví, o čem se vlastně hovoří. Stejně tak se stávalo, že za celé dvě hodiny jsme probrali pouze 2 slajdy z prezentace, na kterých nebylo téměř nic a přitom pan profesor pojmul mnohem širší pohled celé problematiky. Doporučení by tedy bylo aktualizovat a rozšířit studijní prezentaci.";"kmkpr" +"2539";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"5";"3";"3";"5";"4";NULL;NULL;NULL;"1";"3";"3";"3";"4";;;"kms" +"2540";"JJM324";"Teorie mediální komunikace";"Jirák,J.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kms" +"2541";"JJB224";"Psychologie marketingové komunikace";"Vranka,M.";;"5";"4";"5";"4";"5";NULL;NULL;NULL;"2";"5";"3";"4";"5";"I když to studenty může kolikrát štvát, myslím, že minitesty v průběhu semestru jsou skvělé. Studenta to donutí si přečíst materiály, ke kterým by se s největší pravděpodobností jinak nedostal, a které jsou opravdu zajímavé. Mě osobně bavilo je číst, zlepšila jsem se díky nim ve čtení v anglickém jazyce a navíc jsem si rozšířila obzory, co se psychologie (a nejen jí) týče.";;"kmkpr" +"2542";"JJB226";"Strategie a finanční management marketingu I";"Majerik,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"Opravdu oceňuji zkušenosti, o které se s námi pan profesor podělil. Jakožto člověk v oboru nám sdělil spoustu zajímavých poznatků, dal nám nespočet příkladů z praxe. Také pro mě bylo velkým přínosem zpracování briefu. Poznatky, které jsme se učili, jsem si díky tomu mohla vyzkoušet v praxi, urovnat si díky tomu informace a rozšířit obzory. Také oceňuji, že pan profesor po nás v hodinách požadoval brainstormingy, kdy jsme za pár minut měli vymyslet, jak bychom něco propagovali, což je pro marketing také nezbytné.";"Výtkou by bylo pozdní zadání briefu, na který jsme se vším všudy měli pouze týden. Stihnout se to dalo, s kolegyněmi jsme se ovšem shodly, že bychom ho propracovaly do mnohem větších detailů, pokud by bylo více času. Vzhledem k tomu, že ani jedna z nás nebyla z Prahy, za ten týden jsme totiž neměly moc šancí se setkat a své nápady pořádně komunikovat.";"kmkpr" +"2543";"JEM001";"Master´s Thesis Seminar I";;"Havránek,T.,Havránková,Z.,Mejstřík,M.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"1";"4";"2";"3";"4";;;"ies" +"2544";"JEM020";"Ethics and Economics";"Cahlík,T.";"Cahlík,T.";"3";"3";"5";"5";"4";"5";"5";"4";"1";"4";"4";"4";"4";;;"ies" +"2545";"JEM027";"Monetary Economics";"Holub,T.,Malovaná,S.";"Břízová,P.,Hájek,J.,Holub,T.,Malovaná,S.";"4";"3";"5";"5";"4";"5";"5";"4";"1";"3";"4";"4";"4";;;"ies" +"2546";"JEM035";"Financial Markets Instruments I";"Dědek,O.,Poláková,N.,Polák,P.";"Dědek,O.,Poláková,N.,Polák,P.";"4";"4";"4";"4";"3";"5";"5";"4";"1";"4";"4";"4";"3";;;"ies" +"2547";"JEM062";"Introductory Econometrics";"Kukačka,J.";"Kukačka,J.";"5";"2";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"2548";"JJB243";"Aktuální trendy a vývoj v oboru I.";"Hejlová,D.,Vranka,M.";"Hejlová,D.,Vranka,M.";"5";"3";"5";"5";"5";"5";"5";"5";"2";"5";"3";"5";"5";"Setkávání se s takovými lidmi a možnost pokládat jim naše otázky je opravdu úžasná. Je skvělé, že na naší škole takový předmět existuje, jsem za to moc vděčná. Studenti si setkáním s hosty rozšíří obzory, získají inspiraci a i motivaci něco podobného dokázat.";;"kmkpr" +"2549";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"4";"5";"4";"4";;;"ies" +"2550";"JJB249";"Úvod do studia českého jazyka I";"Schneiderová,S.";"Schneiderová,S.";"4";"4";"4";"5";"5";"4";"5";"5";"2";"3";"2";"2";"4";;;"kmkpr" +"2551";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"2";"4";"5";"Styl přednášení pana Soukupa mě jednoduše bavil. Do seriózní přednášky sem tam hodí nějaký vtip a dává praktické příklady z reality, díky čemuž si student informace lépe zapamatuje.";;"ks" +"2552";"JLB045";"Angličtina pro marketing I";;"Stružková,I.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"3";"5";"5";;;"cjp" +"2553";"JMB018";"Bakalářský seminář I";;"Pečenka,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"krvs" +"2554";"JJB407";"Bakalářský proseminář";"Rosenfeldová,J.";;"5";"4";"4";"4";"4";NULL;NULL;NULL;"3";"4";"5";"3";"5";;;"kmkpr" +"2555";"JJB269";"Sociální kontext komunikace";"Vranka,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"3";"4";"5";"Oceňuji debaty, které v hodinách pan Vranka inicioval na daná témata. Student je pak nucený se nad tématem více zamyslet.";;"kmkpr" +"2556";"JJB631";"Social Media: Strategy, Tactics and Analytics";"Audyová,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Jeden z nejlepší kurzů, co jsem měla možnost za dobu svého studia na MKPR absolvovat. Obrovský přínos vědomostí i praktické přípravy kampaně na sociálních sítích. Velmi oceňuji možnost pracovat na reálných případech, navíc s reálným feedbackem. Skvělé bylo, že předmět byl v angličtině. Kurz je poměrně dosti časově náročný, což ve výsledku ale nevadí, protože průběžné plnění úkolů během roku pak velmi pomůže při závěrečné zkoušce. Super výběr hostů, zejména přednáška Petra Václavíka byla skvělá a obrovsky přínosná. Doufám, že takovýchto předmětů bude stále více. Celková koncepce kurzu mi vyhovovala, přestože byla časově náročná.";"Příprava prezentací na hodiny je dobrá průprava na závěrečnou zkoušku, obsahově však prakticky nebylo možné shrnout do pěti požadovaných minut vše, co bývalo zadáno a co by bylo důležité. Možná zkusit nějak lépe strukturovat zadání, aby bylo jasné, co přesně musí zaznít a co případně mít jen zpracované. Zároveň bych uvítala klidně i obsáhlejší feedback na závěrečnou prezentaci. Například ještě více do hloubky, co se klientům/vyučíjícímu líbilo/nelíbilo + nějaká doporučení, z čeho se poučit. U závěrečné zkoušky by bylo také skvělé, kdybychom měli více času na prezentaci. Kvůli časovému presu nebylo možné dle mého názoru představit kampaň dostatečně do hloubky a i částečně kvůli tomu jsme pak nezvládli představit kampaň komplexně tak, jak jsme ji měli vymyšlenou. Pokud by bylo třeba možné zkoušku rozdělit na dva termíny a tím pádem získat více času na prezentaci, bylo by to skvělé.";"kmkpr" +"2557";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"3";"5";"4";"4";"5";"4";"3";"3";"4";"5";"4";"4";"2";;"Dochvilnost";"kp" +"2558";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"3";"4";"4";"1";"3";NULL;NULL;NULL;"4";"4";"3";"4";"2";;"Dochvilnost, délka opravování testů, hodnocení prezentací - Jak je možné, že člověk, který na semináře nechodil a který tedy ani žádnou prezentaci neměl, má z prezentace více bodů, než má značná část studentů? :) A jak je možné, že člověk, který ve své části prezentace jen řekl, že všechno již bylo zmíněno a nemá o čem povídat, má 19 nebo 20 bodů ze 20 možných?";"kp" +"2559";"JPB268";"Evropská integrace";"Plechanovová,B.";;"2";"4";"1";"1";"2";NULL;NULL;NULL;"4";"5";NULL;"5";"2";;"Styl přednášení; délka opravování testů; celková organizace kurzu (viz 0 bodů z rešerše = neúspěšný pokus) - navrhoval bych alespoň možnost opravy rešerše či zadání rešerše nové";"kmv" +"2560";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"4";"2";"1";"1";NULL;NULL;NULL;"1";"4";"1";"4";"2";;;"ies" +"2561";"JLB001";"Angličtina pro sociology I";;"Štěpánková,D.";"2";"1";NULL;NULL;NULL;"3";"2";"2";"1";"4";"4";"3";"2";;;"cjp" +"2562";"JLB033";"Němčina I";;"Křenková,D.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"3";"4";"1";"5";;;"cjp" +"2563";"JSB007";"Dějiny sociologie I";"Šanderová,J.";"Šrám,K.";"4";"2";"4";"5";"2";"5";"5";"5";"4";"5";"1";"4";"4";;;"ks" +"2564";"JSB025";"Sociální problémy";"Frič,P.";;"5";"4";"5";"5";"2";NULL;NULL;NULL;"2";"5";"5";"4";"5";;;"kvsp" +"2565";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Coufalová,L.,Svobodová,T.";"5";"4";"4";"5";"4";"4";"5";"5";"3";"5";"5";"4";"5";;;"ks" +"2566";"JSB530";"Úvod do sociologického myšlení";"Bureš,J.,Numerato,D.";"Teichmanová,K.";"1";"1";"3";"3";"1";"2";"1";"1";"1";"2";"2";"3";"2";;;"ks" +"2567";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Buřičová,B.";"2";"4";"1";"1";"3";"5";"5";"5";"1";"4";"4";"3";"2";;;"ks" +"2568";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"5";"5";"4";"2";"4";"3";"4";"5";;;"kz" +"2569";"JJB221";"Ekonomická teorie marketingu";"Postler,M.";;"4";"3";"3";"3";"3";NULL;NULL;NULL;"1";"2";"3";"4";"3";;;"kmkpr" +"2570";"JJB334";"Zábava v médiích";"Kruml,M.";;"4";"1";"4";"5";"3";NULL;NULL;NULL;"1";"4";"1";"4";"4";"Určitě bych zachovala promítání ukázek některých seriálů. Pomohlo mi to v orientaci.";;"kms" +"2571";"JJB237";"Právo a etika v marketingu";"Winter,F.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kmkpr" +"2572";"JJB269";"Sociální kontext komunikace";"Vranka,M.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"3";"4";"3";;;"kmkpr" +"2573";"JMB414";"Seminář k aktualitám I";;"Mazzali,F.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"3";"5";;;"krvs" +"2574";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"4";"4";"4";"4";NULL;NULL;NULL;"2";"4";"4";"4";"5";;;"ks" +"2575";"JJB224";"Psychologie marketingové komunikace";"Vranka,M.";;"5";"4";"4";"4";"5";NULL;NULL;NULL;"1";"5";"4";"4";"4";;;"kmkpr" +"2576";"JLB045";"Angličtina pro marketing I";;"Stružková,I.";"3";"3";NULL;NULL;NULL;"2";"3";"3";"1";"4";"3";"3";"3";;;"cjp" +"2577";"JJB606";"Televize jako instituce";"Štoll,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Zajímavé a vtipné přednášky. Oceňuji uvádění konkrétních příkladů a osobních zkušeností přednášejícího.";;"kms" +"2578";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"3";"2";"2";"2";NULL;NULL;NULL;"1";"2";"1";"2";"2";;;"ies" +"2579";"JMB402";"Úvod do společenských věd II";;"Jasenčáková,M.";"4";"1";NULL;NULL;NULL;"5";"5";"5";"2";"4";"5";"3";"4";;;"krvs" +"2580";"JJB607";"Analýzy mediálních obsahů";"Křeček,J.";;"4";"1";"4";"5";"4";NULL;NULL;NULL;"1";"3";"5";"4";"5";;;"kms" +"2581";"JMB036";"Moderní dějiny Běloruska";"Zilynskyj,B.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"krvs" +"2582";"JJB617";"Vybrané novinářské osobnosti 20. století";"Železný,J.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"kms" +"2583";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"2";"4";"4";"4";"5";;;"ks" +"2584";"JJB618";"Propagandistické působení médií v 1 polovině 20. století";"Suk,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kms" +"2585";"JMB212";"Moderní dějiny Japonska";"Labus,D.";;"5";"1";"4";"5";"5";NULL;NULL;NULL;"1";"5";"3";"4";"5";;;"kas" +"2586";"JMB248";"Seminář k dějinám Ruska";;"Novák,P.";"4";"2";NULL;NULL;NULL;"5";"5";"4";"1";"5";"4";"4";"5";"Během kurzu se člověk dozví mnoho zajímavých informací o zahraničních vztazích Ruska. Pan doktor se velice příjemný člověk a vyhází studentům vstříc. Člověk se nemusí bát na cokoliv zeptat.";;"krvs" +"2587";"JJM240";"Cultural studies";"Soukup,M.";;"3";"1";"3";"4";"4";NULL;NULL;NULL;"2";"3";"3";"2";"3";"it was a good introduction to the subject of cultural studies, with lots of examples";"the level of English of the professor wasn't very good so it wasn't so easy to go deeper into some subjects";"kms" +"2588";"JMB249";"Seminář k dějinám středo- a jihovýchodní Evropy";;"Lukešová,O.";"5";"3";NULL;NULL;NULL;"5";"4";"5";"1";"5";"5";"5";"5";"Informace, které jsme se dozvěděli na semináři se daly krásně využít u zkoušky ze SJVE. Díky testům na začátku hodin jsme si zopakovali, co jsme se v minulé hodině naučili. Oceňuji i cvičné testy místo čtení článků na další hodinu. Bylo to určitě o 100 % prospěšnější než čtení článků.";;"krvs" +"2589";"JMB057";"Cultural Legacies and Developments in the Balkans: Modern and Traditional Entanglements";"Asavei,M.";;"4";"4";"5";"4";"5";NULL;NULL;NULL;"1";"5";"4";"5";"4";"i really appreciate all the subjects we talked about in class, i learned a lot about the Balkans";"maybe less presentations from the students and more lessons made by the professor";"krvs" +"2590";"JMB408";"Moderní dějiny Ruska";"Litera,B.,Novák,P.,Pečenka,M.";;"5";"4";"5";"3";"5";NULL;NULL;NULL;"3";"5";"3";"4";"5";;;"krvs" +"2591";"JMMZ042";"Cohesion Policy of the EU in Central and East European Countries.";"Hauser,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"it was a really good class about Cohesion Policy, i learned a lot and our teacher had a great way to present things: it made me think about maybe working in this field in my future !";;"krvs" +"2592";"JMMZ094";"Introduction to History, Politics and Society of Eastern Europe";"Vykoukal,J.";;"4";"3";"4";"3";"4";NULL;NULL;NULL;"1";"4";"3";"3";"3";"the course was a good introduction for Eastern Europe, it was very general";"it was maybe too general and i think it would have been better with more exchange and discussion between the professor and the students";"krvs" +"2593";"JMMZ149";"EU Institutions";"Šlosarčík,I.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";"this course was very good : it wasn't only a general presentation about EU Institutions but we went deeper into some topics, everything was really interesting";;"kzs" +"2594";"JPM707";"Peacekeeping and Peacebuilding";"Bureš,O.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"2";"5";"3";"5";"5";"Interesting readings and movie + huge space for discussions";;"kbs" +"2595";"JPM699";"Security and Technology";"Střítecký,V.";;"3";"3";"5";"5";"3";NULL;NULL;NULL;"1";"4";"5";"3";"4";"Working with the PC program and interesting topics";;"kbs" +"2596";"JEM034";"Corporate Finance";"Bychkova,O.,Malířová,T.";;"5";"5";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"4";"4";;;"ies" +"2597";"JPM650";"Intelligence";"Bahenský,V.,Galeotti,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";;;"kbs" +"2598";"JEM037";"Financial Markets";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"4";"4";"5";"5";"4";"5";"5";"5";"2";"5";"5";"5";"5";;;"ies" +"2599";"JEM040";"Účetní a daňové poradenství";;"Kemény,I.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"5";"5";;;"ies" +"2600";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";NULL;NULL;"3";"3";"3";"3";"3";NULL;NULL;NULL;NULL;NULL;NULL;;;"ies" +"2601";"JLB035";"Francouzština I";;"Bosáková,L.";"5";"4";NULL;NULL;NULL;"4";"5";"5";"1";"4";"4";"4";"4";;;"cjp" +"2602";"JJM329";"Média a kultura";"Bednařík,P.,Jirák,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"1";"5";"5";;;"kms" +"2603";"JJM360";"Ekonomika v médiích";"Klimeš,D.";;"4";"2";"5";"5";"4";NULL;NULL;NULL;"1";"3";"1";"4";"3";"Vyučující je skvělý a studenti se setkají se zajímavými osobnostmi. Dostanou možnost se ptát lidí, se kterými by se jinak nemohli setkat.";;"kz" +"2604";"JJM330";"Trendy současných českých médií";"Aust,O.";;"4";"2";"5";"5";"4";NULL;NULL;NULL;"1";"4";"1";"5";"5";;;"kms" +"2605";"JJM260";"Novinářská etika v praxi";"Moravec,V.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kz" +"2606";"JPM260";"Vybrané problémy britské zahraniční politiky v 19. a 20. století, ES";"Soukup,J.";;"5";"3";"4";"5";"4";NULL;NULL;NULL;"3";"4";"3";"5";"4";;;"kmv" +"2607";"JJM331";"Výzkum médií II";"Vochocová,L.";;"3";"5";"3";"3";"1";NULL;NULL;NULL;"1";"3";"4";"2";"5";;;"kms" +"2608";"JJM360";"Ekonomika v médiích";"Klimeš,D.";;"3";"3";"4";"4";"3";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kz" +"2609";"JJM080";"Zábava v televizním vysílání";"Kruml,M.";;"3";"3";"4";"5";"5";NULL;NULL;NULL;"2";"3";"1";"4";"5";"Approach of the teacher";;"kms" +"2610";"JEB047";"Účetnictví II";"Kemény,I.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Irena je Bůh.";;"ies" +"2611";"JJM274";"Práce sportovního reportéra a komentátora";"Záruba,R.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kz" +"2612";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"3";"5";"4";"3";"4";NULL;NULL;NULL;"1";"4";"4";"3";"3";;;"kmv" +"2613";"JJM294";"Teorie a praxe rozhlasové a televizní moderace";"Moravec,V.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kz" +"2614";"JSM005";"Sociální struktura ČR: stav, vývoj, srovnání s EU";"Tuček,M.";;"3";"1";"2";"4";"2";NULL;NULL;NULL;"3";"2";"1";"2";"2";"Dobré je psaní krátkých textů, kde není nutné psát odborně, ale spíše si srovnáváme svůj pohled na dané téma.";"Přednášející málokdy dojde k nějakému jasnému závěru; je těžké určit, jaký cíl jeho přednášky mají. Je těžké jeho řeč sledovat.";"ks" +"2615";"JPM689";"Conflict Studies";"Karásek,T.";;"3";"3";"4";"4";"3";NULL;NULL;NULL;"1";"3";"3";"3";"3";;;"kbs" +"2616";"JEM003";"Advanced Microeconomics";"Janda,K.,Melikhova,O.";"Janda,K.,Melikhova,O.";"3";"5";"3";"3";"3";"3";"3";"3";"3";"4";"3";"4";NULL;;"Rozložit zkouškové pokusy na celé zkouškové!";"ies" +"2617";"JPM725";"Technology and Security: Contemporary Warfare in the 21st Century";;"Csernatoni,R.";"3";"3";NULL;NULL;NULL;"4";"4";"3";"1";"4";"3";"3";"3";;;"kmv" +"2618";"JJM204";"Výzkum médií I";"Křeček,J.";;"4";"3";"4";"4";"3";NULL;NULL;NULL;"2";"4";"5";"3";"4";"Learning a nww method of analysing media";"In the first few lessons i am not sure that we understood the professor, it took us few lessons till we ubderstood what is the teacher trying to explain us and what is our task";"kms" +"2619";"JEM004";"Advanced Macroeconomics";"Baxa,J.,Hromádková,E.";"Baxa,J.,Hromádková,E.,Šestořád,T.,Troch,T.,Žáček,J.";NULL;NULL;"3";"3";"3";"3";"3";"3";NULL;NULL;NULL;NULL;NULL;"Rozložit zkouškové termíny na celé zkouškové!!!";;"ies" +"2620";"JJM329";"Média a kultura";"Bednařík,P.,Jirák,J.";;"4";"2";"4";"4";"2";NULL;NULL;NULL;"2";"3";"2";"3";"3";;;"kms" +"2621";"JEM034";"Corporate Finance";"Bychkova,O.,Malířová,T.";;NULL;NULL;"1";"1";"1";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;"Dodržovat lhůty na opravu midtermu a úkolů. Přednášející midterm opravovala déle než měsíc. Hanba.";"ies" +"2622";"JJM330";"Trendy současných českých médií";"Aust,O.";;"4";"3";"4";"4";"3";NULL;NULL;NULL;"1";"4";"3";"4";"3";;;"kms" +"2623";"JJM331";"Výzkum médií II";"Vochocová,L.";;"4";"4";"4";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"3";;;"kms" +"2624";"JEM040";"Účetní a daňové poradenství";;"Kemény,I.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"5";"Přednášející je zlatá <3";;"ies" +"2625";"JSM421";"Contemporary social theory";"Balon,J.";;"3";"3";"2";"4";"2";NULL;NULL;NULL;"2";"3";"3";"4";"3";"Forma zkoušky - prezentace, test, krátká esej. Možnost udělat prezentaci na mnou vybrané téma.";"Velmi dlouho trvá sečtení výsledků, známku nemám ani měsíc po dodání všech náležitostí.";"ks" +"2626";"JJM243";"Média a životní styl";"Knapík,J.";"Knapík,J.";"3";"3";"2";"3";"3";"2";"3";"3";"1";"4";"1";"4";"2";"New interesting information";"Teachers speaking was too monotone, very hard to concentrate because of so many informations";"kms" +"2627";"JJM332";"Přirozený jazyk a média";"Podzimek,J.";;"3";"1";"3";"3";"3";NULL;NULL;NULL;"1";"3";"2";"3";"3";;;"kms" +"2628";"JSM559";"Kvalitativní výzkum: pokročilé a experimentální metody";"Grygar,J.,Spalová,B.";;"5";"3";"4";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"Super je interaktivní forma, možnost si na sobě zkusit metody analýzy i nějaká data sami vyvořit.";"nenapadá mě nic :)";"ks" +"2629";"JJM295";"Rozhlasový a televizní dokument";"Štoll,M.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;"2";"4";"2";"3";"5";"Interesting speaking of the teacher and good examples of documentaries";"The structure of ppt presentation, structure of this course";"kz" +"2630";"JSM570";"Inovativní prezentace vědeckého poznání I";;"Spalová,B.";"4";"4";NULL;NULL;NULL;"4";"5";"4";"2";"3";"5";"3";"4";"Představení různých metod, jakými více dostat vědecké poznání mezi lidi. Zajímaví hosté, chození na přednášky \"ven\"";"předem do sisu napsat, že je lepší si tento kurz nejprve odchodit a zapsat až následující rok. případně ho protáhnout na dvousemestrální - první semestr zakončen návrhem projektu, druhý semestr zakončen reálným výstupem.";"ks" +"2631";"JJM226";"Teorie účinků médií";"Nečas,V.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"Attitude of professor";;"kms" +"2632";"JJB617";"Vybrané novinářské osobnosti 20. století";"Železný,J.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"2";"5";"5";;;"kms" +"2633";"JJM202";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"3";"5";"3";"3";"2";NULL;NULL;NULL;"1";"3";"2";"3";"2";"Sence of humor";"It is very hard to pass this course, studying only from texts is hard- i would recommend that professors could explain more what exactly should we concentrate on during reading of those texts, they are long ang sometimes it is hard to realize what is the most important information";"kms" +"2634";"JJB625";"Manipulace v audiovizuálním sdělení";"Štoll,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"2635";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"3";"4";"4";"4";"2";NULL;NULL;NULL;"1";"3";"3";"4";"2";;;"kms" +"2636";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"5";"5";"4";"5";"3";NULL;NULL;NULL;"1";"3";"4";"4";"4";;;"kms" +"2637";"JJM320";"Propaganda a československá média po roce 1948";"Suk,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kms" +"2638";"JJM245";"Úvod do vizuální komunikace";"Průchová,A.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"2";"4";"5";"Velmi energickou vyučující, která předmět vyučuje svědomitě a záživně.";;"kz" +"2639";"JJM324";"Teorie mediální komunikace";"Jirák,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kms" +"2640";"JJM246";"Historie a estetika fotožurnalismu";"Lábová,A.,Štefaniková,S.";;"1";"1";"3";"2";"1";NULL;NULL;NULL;"3";"1";"2";"2";"3";;"Náplň kurzu vůbec neodpovídala avizovanému sylabu. Předmět působil poněkud roztříštěně, střídali se zde tři vyučující, přičemž každý měl zcela jinou představu o průběhu výuky. Požadavky k zakončení předmětu také neodpovídají uvedenému v sisu.";"kz" +"2641";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";"New movies";;"kz" +"2642";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"4";"4";"3";"5";"5";NULL;NULL;NULL;"1";"4";"5";"4";"3";;;"kms" +"2643";"JJM201";"Teorie masové a mediální komunikace";"Jirák,J.";;"3";"3";"4";"3";"4";NULL;NULL;NULL;"2";"3";"4";"5";"3";;;"kms" +"2644";"JPM127";"Geopolitika a geostrategie 1815 - 1945";"Romancov,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";"Strukturu přednášení.";;"kp" +"2645";"JPM052";"Mezinárodní ekonomické vztahy (MEV)";"Kučerová,I.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"5";"4";"lepšie som pochopila prepojenie ekonomických aspektov a udalostí s vývojom medzinárodných vzťahov i to, ako sa navzájom ovplyvňujú";;"kmv" +"2646";"JJM360";"Ekonomika v médiích";"Klimeš,D.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"2";"5";"4";"Energického vyučujícího s bohatými zkušenostmi a vstřícným chováním ke studentům.";;"kz" +"2647";"JJM356";"Metodologické přístupy ke studiu žurnalistiky";"Nečas,V.,Veselková,Z.";;"3";"3";"4";"4";"3";NULL;NULL;NULL;"2";"4";"5";"4";"3";;;"kz" +"2648";"JJM251";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"4";"5";"4";"4";"4";NULL;NULL;NULL;"1";"3";"2";"4";"4";;;"kz" +"2649";"JJM247";"Český stranický systém";"Just,P.";;"5";"2";"4";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kz" +"2650";"JJM249";"Duchovní kořeny evropské kultury";"Halada,J.";;"4";"2";"3";"5";"3";NULL;NULL;NULL;"1";"2";"1";"2";"4";;;"kz" +"2651";"JJM355";"Teoretické koncepty ke studiu žurnalistiky I";"Jirků,J.,Němcová Tejkalová,A.";"Jirků,J.";"5";"5";"3";"3";"3";"5";"5";"5";"1";"5";"4";"5";"5";;;"kz" +"2652";"JPM432";"European Public Space: Interest Representation and Public Debate, ES";"Knutelská,V.";;"5";"4";"4";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmv" +"2653";"JJM329";"Média a kultura";"Bednařík,P.,Jirák,J.";;"2";"2";"4";"4";"1";NULL;NULL;NULL;"1";"1";"3";"1";"1";"Domnívám se, že kurz není relevantní pro mediální studia. Říkáme si 3 hodiny ve kterém filmu vystupují novináři a jak jsou reprezentováni. Kurz ale neobsahuje žádnou metodologii ani definici výzkumné otázky. Nesrovnáváme ani reprezentaci s realitou. Je to prostě takové zajímavé a přijemné povídání o filmech s promítáním ukázek.Obsah kurzu odpovídá deskripci, je veden profesionálně, závěrečná práce je hodnocena odpovědně, nejsem si ale jistý významem.Obsah přednášek by se dle mého názoru hodil spíše do univerzity třetího věku. Je to prostě takové pěkné povídání.";;"kms" +"2654";"JSM016";"Sociology of Science and Scientific Knowledge";;"Maršálek,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Guests; research orientation on the course (as in the course having a research question)";;"ks" +"2655";"JPM306";"African Security";"Werkman,K.";;"1";"4";"4";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";"Vedení diskuzní formou";;"kbs" +"2656";"JSM020";"Seminář k aktuální veřejně politické problematice";;"Balon,J.,Císař,O.";"2";"1";NULL;NULL;NULL;"3";"4";"1";"1";"4";"2";"2";"2";"Orientaci na aktuální problematiku";"Přejmenovat kurs na \"Prezentace témat diplomových prací\"";"ks" +"2657";"JPM561";"Regional Security Studies";"Karásek,T.,Klosek,K.";"Karásek,T.,Klosek,K.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"4";"5";"5";"Testování formou 5ti testů. Zkoušená látka se tím rozdělí a není tolik práce na konci semestru.";;"kbs" +"2658";"JSM032";"Applied Social Research";"Remr,J.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ks" +"2659";"JPM706";"Comparative Counterterrorism";"Bureš,O.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Skupinové prezentace a diskuze v hodině.";"V rámci přednášky bych trochu zkrátila opakování fakt ze zadané četby a nechala více prostoru na diskuze, které se z časových důvodů nestihly rozvinout. Bylo by velmi zajímavé dozvědět se více o pohledech studentů z různých zemí a s různými zkušenostmi. Celkově se mi ale kurz líbil.";"kbs" +"2660";"JSM103";"Academic Writing";;"Blokker,P.";"3";"4";NULL;NULL;NULL;"3";"5";"1";"2";"2";"4";"2";"2";;"I would appreciate if the course focused more on analyzing actual pieces of writing by professionals and academics, rather then discussing principles of writing abstractly – that is of course important, but here it was often the case that the principle was discussed over and over with little added value, instead of looking at different possible applications of said principle.";"ks" +"2661";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"4";"4";"4";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kms" +"2662";"JSM572";"Sociologie organizací";"Čada,K.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"5";"5";"5";"5";"5";;;"ks" +"2663";"JSM554";"Diplomový seminář";;"Remr,J.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"ks" +"2664";"JJM259";"Mediální systémy a jejich komparace";"Miessler,J.";;"4";"3";"4";"4";"3";NULL;NULL;NULL;"2";"4";"3";"5";"4";"Dobrá komunikace s vyučujícím, motivace ke čtení průběžné literatury, kterou nám vyučující vždy včas poskytoval pomocí souborů sis.";"Zrušit zbytečné výhody a bonusy za to, že někdo prezentuje první nebo poslední. Kurz je určen pro studenty 2. ročníku magisterského studia a ne pro studenty prvního ročníku gymnázia. Náplň přednášek navic obsahovala přesně to, co jsme si před ní nastudovali kvůli testu, nepřinášela tak příliš nových informací ani přístupů.";"kz" +"2665";"JSM573";"Výzkumné kolokvium AVM I";;"Remr,J.";"1";"1";NULL;NULL;NULL;"1";"1";"1";"5";"1";"1";"1";"1";;;"ks" +"2666";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"4";"2";"4";"4";"5";NULL;NULL;NULL;"2";"4";"1";"3";"4";;;"ies" +"2667";"JSM642";"Metody práce s informacemi";"Tomandlová,V.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"4";"5";;;"kvsp" +"2668";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"4";"1";NULL;NULL;NULL;"4";"3";"4";"1";"2";"5";"3";"3";;;"ies" +"2669";"JSM641";"Sociální problémy";"Frič,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kvsp" +"2670";"JSM640";"Základy sociologie";"Paulíček,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kvsp" +"2671";"JLB003";"Angličtina pro ekonomy I";;"Poslušná,L.";"5";"4";NULL;NULL;NULL;"4";"5";"5";"2";"5";"4";"5";"5";;;"cjp" +"2672";"JSM523";"Sociální politika";"Angelovská,O.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kvsp" +"2673";"JSM522";"Veřejná ekonomie";"Kotherová,Z.";;"4";"5";"4";"4";"5";NULL;NULL;NULL;"1";"5";"5";"5";"4";;;"kvsp" +"2674";"JSM521";"Veřejná politika";"Chalupová,P.,Potůček,M.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kvsp" +"2675";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"4";"4";"4";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kms" +"2676";"JJM331";"Výzkum médií II";"Vochocová,L.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"2677";"JJM330";"Trendy současných českých médií";"Aust,O.";;"5";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kms" +"2678";"JJM330";"Trendy současných českých médií";"Aust,O.";;"4";"2";"5";"5";"5";NULL;NULL;NULL;"1";"3";"3";"5";"5";;;"kms" +"2679";"JJM373";"Ethical Issues in Media, Business and Society";"Orhan,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"2";"3";"5";"I appreciate the concept of the exams, the attitude of Mr. Orhan and overall experience coming with taking the class. I would gladly recommend this course to other students.";;"kms" +"2680";"JJM201";"Teorie masové a mediální komunikace";"Jirák,J.";;"3";"4";"4";"4";"2";NULL;NULL;NULL;"2";"3";"1";"3";"3";;;"kms" +"2681";"JJM202";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;NULL;NULL;"3";"3";"3";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kms" +"2682";"JJM208";"Mediální systémy a jejich komparace";"Miessler,J.";;"2";"3";"1";"2";"1";NULL;NULL;NULL;"1";"2";"1";"2";"1";;;"kms" +"2683";"JJM212";"Analýza politické komunikace";"Křeček,J.";;"4";"3";"5";"5";"3";NULL;NULL;NULL;"2";"4";"4";"3";"5";;;"kms" +"2684";"JJM214";"Čtení textů ke studiu médií - populární kultura";;"Reifová,I.";"3";"2";NULL;NULL;NULL;"4";"4";"4";"1";"2";"1";"4";"2";;;"kms" +"2685";"JJM202";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kms" +"2686";"JJM204";"Výzkum médií I";"Křeček,J.";;"3";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"3";;;"kms" +"2687";"JJM224";"Politická ekonomie komunikace";"Vochocová,L.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"5";"5";;;"kms" +"2688";"JJM343";"Interkulturní komunikace";"Soukup,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kms" +"2689";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"3";"4";"3";"5";"3";NULL;NULL;NULL;"1";"4";"1";"3";"3";;;"kms" +"2690";"JJM324";"Teorie mediální komunikace";"Jirák,J.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"1";"2";"5";;;"kms" +"2691";"JJM320";"Propaganda a československá média po roce 1948";"Suk,P.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"1";"2";"5";;;"kms" +"2692";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"3";"5";"4";"3";"3";NULL;NULL;NULL;"1";"4";"1";"3";"1";;;"kms" +"2693";"JJB618";"Propagandistické působení médií v 1 polovině 20. století";"Suk,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";NULL;"4";"3";;;"kms" +"2694";"JSM095";"Study of Political Mobilization and Social Movements";"Císař,O.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"4";"5";;;"ks" +"2695";"JSM103";"Academic Writing";;"Blokker,P.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"2";"5";;;"ks" +"2696";"JSM480";"Evaluation Research";;"Remr,J.";"4";"4";NULL;NULL;NULL;"5";"5";"3";"1";"4";"2";"3";"4";;;"ks" +"2697";"JEM003";"Advanced Microeconomics";"Janda,K.,Melikhova,O.";"Janda,K.,Melikhova,O.";"4";"3";"3";"4";"2";"5";"5";"5";"1";"2";"2";"2";"3";"The seminars were more helpful in understanding the subject.";"The lecture";"ies" +"2698";"JJB055";"Tvůrčí dílny tisk I - tvůrčí psaní";"Malý,R.,Novotný,D.";"Malý,R.,Novotný,D.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"3";"5";;;"kz" +"2699";"JJB629";"Tiskový mluvčí - praxe a teorie";"Chudinová,E.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmkpr" +"2700";"JJM280";"Filmová a televizní kritika";"Štoll,M.";;"4";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kz" +"2701";"JEM004";"Advanced Macroeconomics";"Baxa,J.,Hromádková,E.";"Baxa,J.,Hromádková,E.,Šestořád,T.,Troch,T.,Žáček,J.";"4";"4";"4";"4";"4";"4";"4";"4";"1";"2";"2";"2";"2";;;"ies" +"2702";"JJM200";"Diplomový seminář";;;"4";"4";NULL;NULL;NULL;"4";"4";"4";"1";"4";"4";"4";"4";;;"kms" +"2703";"JEM032";"Banking";"Mejstřík,M.,Pečená,M.,Teplý,P.";"Pečená,M.";"4";"2";"4";"4";"4";"4";"4";"4";"2";"4";"4";"4";"4";;;"ies" +"2704";"JPB011";"Politická geografie I";"Romancov,M.";;"5";NULL;"5";"5";"3";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kp" +"2705";"JEM037";"Financial Markets";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"4";"2";"5";"5";"4";"5";"5";"4";"2";"4";"4";"4";"4";;;"ies" +"2706";"JEM062";"Introductory Econometrics";"Kukačka,J.";"Kukačka,J.";"4";"3";"5";"5";"5";"5";"5";"5";"1";"3";"3";"3";"4";;;"ies" +"2707";"JJM363";"Czech-German-Jewish Literary Triangle";;"Peroutková,M.";"2";"2";NULL;NULL;NULL;"3";"4";"4";"1";"2";"2";"2";"2";;"Hodiny byly monotónní. Navrhovala bych oživit je různorodějšími aktivitami. Rovněž není možné přečíst jednu knihu za týden (z toho důvodu, že knihy a odborné články čteme i na jiné předměty).";"kz" +"2708";"JJM080";"Zábava v televizním vysílání";"Kruml,M.";;"5";"1";"5";"5";"3";NULL;NULL;NULL;"3";"5";"1";"1";"5";;;"kms" +"2709";"JJM201";"Teorie masové a mediální komunikace";"Jirák,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"3";"5";"1";"5";"5";;;"kms" +"2710";"JJM202";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"3";"4";"3";"2";"2";NULL;NULL;NULL;"1";"4";"1";"1";"3";;;"kms" +"2711";"JPM052";"Mezinárodní ekonomické vztahy (MEV)";"Kučerová,I.";;"4";"4";"3";"4";"3";NULL;NULL;NULL;"1";"4";"3";"4";"3";;;"kmv" +"2712";"JPM432";"European Public Space: Interest Representation and Public Debate, ES";"Knutelská,V.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kmv" +"2713";"JJM360";"Ekonomika v médiích";"Klimeš,D.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";;;"kz" +"2714";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"4";"5";;;"kmv" +"2715";"JJM204";"Výzkum médií I";"Křeček,J.";;"5";"3";"3";"3";"1";NULL;NULL;NULL;"3";"5";"5";"1";"5";;;"kms" +"2716";"JPM699";"Security and Technology";"Střítecký,V.";;"4";"4";"4";"5";"4";NULL;NULL;NULL;"1";"5";"4";"4";"4";;;"kbs" +"2717";"JJM199";"Literární a knižní kritika";"Čeňková,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kz" +"2718";"JJM249";"Duchovní kořeny evropské kultury";"Halada,J.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"3";"5";;;"kz" +"2719";"JJM224";"Politická ekonomie komunikace";"Vochocová,L.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"3";"5";"1";"5";"5";;;"kms" +"2720";"JPM719";"Diplomacy of the European Union";"Pajtinka,E.";;"1";"3";"2";"2";"4";NULL;NULL;NULL;"3";"4";"3";"3";"1";;"- stupnice hodnocení (která byla po stížnostech studentů změněna) neodpovídala fakultním doporučením a byla zbytečně přísná (známka C za 89-80 %)- jelikož se předmět učí v angličtině, měly by tomu odpovídat jazykové kompetence vyučujícího- hodnocení výuky skládající se z napsání eseje a až poté testu ve zkouškovém období se zdálo improvizované (nebylo předem ohlášeno, na esej měli studenti jen několik málo hodin)- bylo avizováno, že bude jen jediný termín pro zkoušku - velmi nepříjemná situace pro studenty s pracovními a jinými povinnostmi, se kterými se na magisterském studiu počítá (později byl přidán ještě druhý termín)";"kmv" +"2721";"JJM331";"Výzkum médií II";"Vochocová,L.";;"4";"4";"4";"5";"5";NULL;NULL;NULL;"3";"5";"5";"5";"4";;;"kms" +"2722";"JJM356";"Metodologické přístupy ke studiu žurnalistiky";"Nečas,V.,Veselková,Z.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"4";"4";;;"kz" +"2723";"JJM251";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"5";"4";;;"kz" +"2724";"JJM362";"History of media";;"Neuzil,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kz" +"2725";"JJM355";"Teoretické koncepty ke studiu žurnalistiky I";"Jirků,J.,Němcová Tejkalová,A.";"Jirků,J.";"4";"5";"5";"5";"5";"5";"5";"5";"1";"5";"4";"4";"4";"Prezentace během seminářů, praktické příklady během přednášek.";"Přijde mi, že počet kreditů neodpovídá vysoké náročnosti předmětu.";"kz" +"2726";"JPM727";"Orchestration in Global Governance";;"Abbott,K.,Parízek,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"5";;;"kmv" +"2727";"JJM201";"Teorie masové a mediální komunikace";"Jirák,J.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"3";"4";"3";"4";"3";;;"kms" +"2728";"JJM202";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"3";"4";"3";"3";"3";NULL;NULL;NULL;"2";"4";"2";"4";"2";;;"kms" +"2729";"JJM204";"Výzkum médií I";"Křeček,J.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"3";"4";"4";"3";"4";;;"kms" +"2730";"JJM229";"Vývoj televizního vysílání v českých zemích";"Štoll,M.";;"4";"2";"5";"5";"4";NULL;NULL;NULL;"3";"4";"2";"3";"5";;;"kms" +"2731";"JJM351";"Kritická analýza mediálních sdělení v českém periodickém tisku";;"Benda,J.";"3";"4";NULL;NULL;NULL;"4";"5";"4";"1";"4";"4";"3";"5";;;"kms" +"2732";"JPB018";"Dějiny mezinárodních vztahů I";"Soukup,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"3";"5";"5";"5";"5";;;"kmv" +"2733";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"4";"3";"3";"3";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kms" +"2734";"JJM320";"Propaganda a československá média po roce 1948";"Suk,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"2735";"JJM324";"Teorie mediální komunikace";"Jirák,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"4";;;"kms" +"2736";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"3";"4";"3";"4";"5";NULL;NULL;NULL;"1";"3";"4";"3";"3";;;"kms" +"2737";"JJM373";"Ethical Issues in Media, Business and Society";"Orhan,M.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"3";"5";;;"kms" +"2738";"JMMZ336";"History and Society in the Russian Cinema";;"Kolenovská,D.,Mazzali,F.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"krvs" +"2739";"JJM199";"Literární a knižní kritika";"Čeňková,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"Kurz mi poskytl možnost rozšířit si obzory v oblasti knižní produkce - rozhodně bych nepolevovala v nárocích. Zároveň se mi velice líbila možnost vybrat si jednu knihu a věnovat se jí do hloubky a poté o výsledku informovat ostatní studenty. Toto zcela jistě do kulturního zaměření patří a je to nenahraditelná zkušenost. Zcela jistě kurz doporučuji, skvělá práce.";;"kz" +"2740";"JJM355";"Teoretické koncepty ke studiu žurnalistiky I";"Jirků,J.,Němcová Tejkalová,A.";"Jirků,J.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"Velice se mi líbila kombinace přednášek a seminářů s tím, že semináře měly jasný účel a jednalo se v nich především o utřídění znalostí. Pokud prezentace studentů z nějakého důvodu nebyly srozumitelné, bylo vše dovysvětleno a to považuji za klíčové. Zcela jistě oceňuji i rozšíření znalostí v žurnalistické teorii.";;"kz" +"2741";"JJM329";"Média a kultura";"Bednařík,P.,Jirák,J.";;"4";"4";"5";"4";"4";NULL;NULL;NULL;"1";"3";"4";"4";"3";;;"kms" +"2742";"JJM330";"Trendy současných českých médií";"Aust,O.";;"3";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"3";"4";"3";;;"kms" +"2743";"JJM331";"Výzkum médií II";"Vochocová,L.";;"4";"4";"5";"4";"4";NULL;NULL;NULL;"2";"4";"5";"5";"3";;;"kms" +"2744";"JJM334";"Diplomový seminář";;;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"2745";"JMM039";"Západní Evropa a svět";"Tomalová,E.,Váška,J.";;"5";"3";"4";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kzs" +"2746";"JLB041";"Španělština I";;"Mlýnková,L.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"2";"3";"4";;;"cjp" +"2747";"JMMZ340";"Freedom of Speech";"Klvaňa,T.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"5";"4";"structure, required readings, in-class discussions";;"kas" +"2748";"JJB135";"Filmový seminář I";;"Šobr,M.";"4";"1";NULL;NULL;NULL;"4";"3";"5";"3";"4";"2";"4";"3";;;"kz" +"2749";"JPM099";"Baltic regional cooperation and Russia";"Zájedová,I.";;"4";"2";"5";"5";"4";NULL;NULL;NULL;"2";"4";"5";"3";"4";;;"kmv" +"2750";"JJM259";"Mediální systémy a jejich komparace";"Miessler,J.";;"2";"5";"4";"5";"1";NULL;NULL;NULL;"1";"3";"3";"3";"1";"Myslím, že největším problémem bylo, že nás kurz měl připravit na státnice, ale probrali jsme v něm věci, které se u státnic v červnu 2018 zatím neobjeví, protože se ještě bude zkoušet podle otázek pana Trampoty.";;"kz" +"2751";"JPM191";"Geopolitics of Great Powers: Russia";"Baštář Leichtová,M.";;"3";"1";"5";"5";"3";NULL;NULL;NULL;"1";"2";"2";"3";"3";;;"kp" +"2752";"JPM719";"Diplomacy of the European Union";"Pajtinka,E.";;"2";"3";"2";"2";"4";NULL;NULL;NULL;"4";"3";"2";"2";"2";;"The seminar was cancelled several times and the conditions of the course were changed during the semester. The lecturer made up some conditions on the last lecture and then he adjusted the conditions in the Sylabus (there was no essay as a condition for the exam and then this condition smoothly appeared there, when we, students, said that it was not written in Syllabus). Also, he cancelled few lectures.";"kmv" +"2753";"JPM721";"Komparace ekonomik zemí EU";"Kučerová,I.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"2";"5";"2";"5";"3";;;"kmv" +"2754";"JJM260";"Novinářská etika v praxi";"Moravec,V.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"5";"5";"5";"5";"5";"Pan doktor Moravec mě vždy ve svých předmětech dokázal zaujmout a motivovat k dalšímu studiu probírané oblasti. Velmi dobrý kurz.";"Jediné minus kurzu je to, že se v něm probírají velmi podobné věci, co jsme už jednou slyšeli na bakaláři.";"kz" +"2755";"JJB021";"Bakalářský seminář";;"Prázová,I.";"2";"2";NULL;NULL;NULL;"3";"3";"3";"1";"2";"1";"2";"2";;;"kz" +"2756";"JJM279";"Divadelní kritika";"Homolová Richtrová,N.";;"4";"2";"3";"5";"2";NULL;NULL;NULL;"1";"2";"2";"3";"2";;;"kz" +"2757";"JJB066";"Rozhlas a televize ve světě";"Moravec,V.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kz" +"2758";"JJM280";"Filmová a televizní kritika";"Štoll,M.";;"4";"4";"5";"5";"2";NULL;NULL;NULL;"1";"4";"3";"3";"3";;;"kz" +"2759";"JJM294";"Teorie a praxe rozhlasové a televizní moderace";"Moravec,V.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Skvělý a zábavný předmět, který se KONEČNĚ po záplavě teoretických předmětů orientuje také na praxi.";"nic";"kz" +"2760";"JJM264";"Diplomový seminář II.";;;"3";"4";"3";"3";"3";NULL;NULL;NULL;"1";"3";"3";"3";"4";;"K čemu je tenhle kurz? Vždyt konzultace diplomek by probíhaly i tak...";"kz" +"2761";"JJM208";"Mediální systémy a jejich komparace";"Miessler,J.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"3";"3";"5";;;"kms" +"2762";"JPM052";"Mezinárodní ekonomické vztahy (MEV)";"Kučerová,I.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"2";"3";"4";;;"kmv" +"2763";"JPM191";"Geopolitics of Great Powers: Russia";"Baštář Leichtová,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"3";"4";"5";;;"kp" +"2764";"JPM721";"Komparace ekonomik zemí EU";"Kučerová,I.";;"5";"3";"4";"5";"3";NULL;NULL;NULL;"2";"3";"3";"3";"5";;;"kmv" +"2765";"JJM226";"Teorie účinků médií";"Nečas,V.";;"4";"3";"4";"4";"3";NULL;NULL;NULL;"2";"3";"2";"3";"4";;;"kms" +"2766";"JEM003";"Advanced Microeconomics";"Janda,K.,Melikhova,O.";"Janda,K.,Melikhova,O.";"3";"3";"1";"4";"1";"4";"4";"4";"2";"2";"2";"3";"2";;;"ies" +"2767";"JEM004";"Advanced Macroeconomics";"Baxa,J.,Hromádková,E.";"Baxa,J.,Hromádková,E.,Šestořád,T.,Troch,T.,Žáček,J.";"2";"5";"3";"3";"4";"3";"3";"4";"2";"3";"3";"2";"3";;;"ies" +"2768";"JEM032";"Banking";"Mejstřík,M.,Pečená,M.,Teplý,P.";"Pečená,M.";"4";"3";"3";"4";"3";"4";"3";"3";"1";"4";"4";"4";"4";;"Oral exam - there is no reason, why it should have weight of 20% of the final grade. Grading of the oral exam appears to be random at best and undermines your progress throughtout the year. In current state, the maximum weight I would suggest is 5%, so in case you are missing just those few points, you can potentionally make up for it.";"ies" +"2769";"JEM034";"Corporate Finance";"Bychkova,O.,Malířová,T.";;"4";"3";"4";"4";"3";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"ies" +"2770";"JEM181";"Data Science with R";"Herman,D.,Krištoufek,L.";"Herman,D.,Krištoufek,L.";"4";"3";"4";"4";"4";"4";"4";"4";"1";"5";"5";"5";"5";;;"ies" +"2771";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"3";"4";"3";"3";"2";"2";"3";"3";"1";"3";"3";"3";"3";;;"ies" +"2772";"JPM700";"Space Security";"Doboš,B.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"2";"5";"5";;;"kbs" +"2773";"JPM699";"Security and Technology";"Střítecký,V.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kbs" +"2774";"JPM693";"Traditional and Critical Concepts in Security Studies";"Rychnovská,D.";;"2";"4";"5";"5";"3";NULL;NULL;NULL;"1";"3";"2";"5";"2";;;"kbs" +"2775";"JPM611";"Cyber Security";"Duračinská,Z.,Střítecký,V.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"5";"4";;;"kbs" +"2776";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"3";"3";"5";"4";"2";NULL;NULL;NULL;"1";"4";"5";"4";"3";;;"kmv" +"2777";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"2";"3";"3";"5";"1";NULL;NULL;NULL;"1";"1";"1";"4";"2";;;"kmv" +"2778";"JLB013";"Němčina odborná I";;"Křenková,D.";"4";"4";NULL;NULL;NULL;"4";"5";"4";"1";"4";"4";"4";"4";;"Myslím, že by bylo lepší, kdybychom probrali méně témat i gramatiky, ale zato hlouběji. (Viz např. předložky s druhým pádem - raději bych se naučila pořádně používat šest, než znát povrchně patnáct.)Vyplňování různých gramatických cvičení považuji za nezbytné při přípravě na určitý typ zkoušek (skládání certifikátů apod.), ale pro běžné naučení látky mi přijde poněkud neefektivní - neosvojím si to natolik, abych to byla schopna použít při mluvení.";"cjp" +"2779";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"3";"2";NULL;NULL;NULL;"2";"1";"2";"1";"2";"1";"1";"2";;"Povinné prezentace mi nepřišly jako dobrý nápad, lepší by bylo je udělat dobrovolné.";"cjp" +"2780";"JMM040";"Societal changes in Western European countries";"Bauer,P.";;"1";"1";"2";"2";"1";NULL;NULL;NULL;"1";"1";"1";"1";"1";"Tento předmět by vůbec neměl existovat.";"Navrhuji tento kurz zrušit a raději se věnovat něčemu hodnotnému. Za celý semestr jsem upřímně nepochopil o čem tento předmět je, k čemu spěje a proč ho máme.";"kzs" +"2781";"JMB250";"Seminář k dějinám západní Evropy";;"Váška,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Dobře vybraná a zajímavá témata každé hodiny.";;"kzs" +"2782";"JMB249";"Seminář k dějinám středo- a jihovýchodní Evropy";;"Lukešová,O.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"krvs" +"2783";"JJM343";"Interkulturní komunikace";"Soukup,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"3";"4";"4";"4";"5";;;"kms" +"2784";"JJM201";"Teorie masové a mediální komunikace";"Jirák,J.";;"4";"5";"4";"4";"4";NULL;NULL;NULL;"2";"5";"3";"5";"4";;"Jelikož témat ke zkoušce je mnoho a některá jsou poněkud náročnější, tak bych ocenila, kdyby byla na přednáškách odprezentována právě ta obtížnější témata.";"kms" +"2785";"JMB410";"Moderní dějiny západní Evropy";"Matějka,O.,Váška,J.";;"3";"3";"5";"5";"2";NULL;NULL;NULL;"3";"2";"1";"2";"3";;;"kzs" +"2786";"JMB408";"Moderní dějiny Ruska";"Litera,B.,Novák,P.,Pečenka,M.";;"4";"4";"4";"5";"4";NULL;NULL;NULL;"1";"4";"4";"5";"4";;;"krvs" +"2787";"JMB409";"Moderní dějiny středo- a jihovýchodní Evropy-dcera JMB013";"Balla,P.,Švec,L.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"5";"5";;;"krvs" +"2788";"JJM080";"Zábava v televizním vysílání";"Kruml,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kms" +"2789";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"3";"4";"3";"4";"2";NULL;NULL;NULL;"2";"3";"2";"3";"3";;;"kmv" +"2790";"JJM295";"Rozhlasový a televizní dokument";"Štoll,M.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kz" +"2791";"JJM204";"Výzkum médií I";"Křeček,J.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kms" +"2792";"JPM595";"Arms Control and Disarmament";"Hynek,N.,Smetana,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Struktura kurzu je dobře provedena, simulace na konci taktéž. Úroveň interakce mezi studenty a vyučujícími je obrovské plus.";;"kbs" +"2793";"JJM214";"Čtení textů ke studiu médií - populární kultura";;"Reifová,I.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kms" +"2794";"JPM694";"Strategic Studies";"Hynek,N.,Ludvík,J.,Smetana,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"kbs" +"2795";"JPM712";"Insurgency and Counterinsurgency";"Aslan,E.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"3";"5";"5";;;"kbs" +"2796";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"5";"5";"5";"3";NULL;NULL;NULL;"1";"5";"5";"3";"5";;;"kmv" +"2797";"JJM202";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"4";"5";"4";"4";"4";NULL;NULL;NULL;"1";"5";"3";"4";"4";;;"kms" +"2798";"JPB258";"Politické myšlení novověku I";"Franěk,J.,Kučera,J.";;"4";"3";"4";"5";"3";NULL;NULL;NULL;"2";"5";"4";"4";"4";"Oba vyučující přednášejí velmi poutavě, látku velmi dobře vysvětlí.";"Docent Kučera by se mohl více soustředit na výklad o dílech jednotlivých filozofů a méně na výklad historických souvislostí.";"kp" +"2799";"JJM249";"Duchovní kořeny evropské kultury";"Halada,J.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;"1";"4";"2";"4";"4";;;"kz" +"2800";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"4";"4";"3";"1";"1";NULL;NULL;NULL;"1";"5";"1";"3";"4";;;"ies" +"2801";"JPB269";"Současné problémy mezinárodních vztahů";"Knutelská,V.";;"3";"3";"4";"5";"2";NULL;NULL;NULL;"2";"3";"2";"5";"4";"Propojení teoretických a praktických znalostí v závěrečném policy briefu.";"Posunout začátek alespoň na půl desátou, studentů by na přednášky chodilo určitě více.";"kmv" +"2802";"JJM355";"Teoretické koncepty ke studiu žurnalistiky I";"Jirků,J.,Němcová Tejkalová,A.";"Jirků,J.";"4";"5";"4";"5";"5";"5";"5";"5";"1";"5";"3";"5";"5";"Praktické semináře";;"kz" +"2803";"JJM251";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"4";"5";"3";"3";"5";NULL;NULL;NULL;"1";"5";"2";"5";"5";"Video ukázky";"Klidně více praktických ukázek";"kz" +"2804";"JJM252";"Specifika sportovní žurnalistiky";"Němcová Tejkalová,A.";;"5";"3";"4";"5";"5";NULL;NULL;NULL;"1";"5";"3";"4";"5";"Praktické ukázky, videa, příklady...";"Klidně vyrazit někam do terénu (exkurze či něco podobného)";"kz" +"2805";"JSB007";"Dějiny sociologie I";"Šanderová,J.";"Šrám,K.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ks" +"2806";"JJM356";"Metodologické přístupy ke studiu žurnalistiky";"Nečas,V.,Veselková,Z.";;"5";"3";"5";"4";"5";NULL;NULL;NULL;"1";"4";"5";"4";"5";;;"kz" +"2807";"JJM360";"Ekonomika v médiích";"Klimeš,D.";;"4";"4";"4";"5";"3";NULL;NULL;NULL;"1";"5";"3";"4";"4";;;"kz" +"2808";"JSM516";"Sociální politika v perspektivě životního cyklu";"Dobiášová,K.,Kotrusová,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Nejvíce si cením toho, že jsme měli možnost slyšet přednášky hostů. Hosté byli velmi přínosnou součástí kurzu. Velmi mě těší přístup obou vyučujících. Velmi zajímavě zvolený obsah kurzu.";"Navrhovala bych více průběžných úkolů a méně skupinové práce. Je opravdu těžké domluvit se v tak velkých skupinách (3 a více), kolikrát to kazí výsledky ostatních, někteří se naopak \"vezou\". Stejně tak bych uvítala zatraktivění výuky například pomocí videí nebo praktických zkušeností. (návštěva institucí apod.)";"kvsp" +"2809";"JSB028";"Informační gramotnost";"Tomandlová,V.";;"3";"1";"3";"5";"5";NULL;NULL;NULL;"1";"3";"3";"3";"4";;;"kvsp" +"2810";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Hanzlík,P.";"3";"3";"2";"5";"1";"4";"5";"5";"1";"1";"1";"1";"2";;;"ks" +"2811";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Rössler,J.";NULL;"4";"3";"5";"3";"4";"4";"3";"1";"5";"2";"3";"4";;;"ks" +"2812";"JLB033";"Němčina I";;"Faltýnová,R.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"3";"3";"5";"5";;;"cjp" +"2813";"JSB025";"Sociální problémy";"Frič,P.";;"5";"3";"4";"4";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"kvsp" +"2814";"JPM118";"Výběrový seminář: Volby v USA";"Kotábová,V.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"2815";"JPM127";"Geopolitika a geostrategie 1815 - 1945";"Romancov,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"2816";"JSB530";"Úvod do sociologického myšlení";"Bureš,J.,Numerato,D.";"Bureš,J.";"4";"4";"4";"5";"1";"5";"5";"4";"1";"5";"5";"5";"5";;;"ks" +"2817";"JPM146";"Přechody k demokracii v teorii a praxi I";"Mlejnek,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"4";;;"kp" +"2818";"JPM150";"Poloprezidentské režimy v postkomunistické Evropě";"Mlejnek,J.";;"5";"3";"5";"5";"3";NULL;NULL;NULL;"1";"4";"3";"4";"3";;;"kp" +"2819";"JPM160";"Česká komunální politika";"Jüptner,P.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"3";"The interactive site of lectures, the need to talk to regional polititians to compose the seminar paper";"Less of the practical demans (a students needs to 1.write seminar paper, 2. present it in PP, 3. Write a test (a bit illogical test) 4. Pass oral exam";"kp" +"2820";"JEM001";"Master´s Thesis Seminar I";;"Havránek,T.,Havránková,Z.,Mejstřík,M.";"3";"4";NULL;NULL;NULL;"3";"4";"3";"1";"1";"3";"1";"1";;"Myslím si, že řada dlouhých e-mailů, které jsme během semestrů dostali, byla spíš kontraproduktivní. Při celkovém zahlcení školou, prací apod. tohle vede pouze k tomu, že to člověk začne po krátké době ignorovat. Je to trochu podobné jako \"overfitting the model\". Důraz na kvalitu prací je pochopitelný a jistě žádoucí, na druhou stranu pořád to není disertace a ne každý z magistrů bude chtít pokračovat na doktorát, takže jistá shovívavost je na místě. (Na druhou stranu uznávám, že během konzultací v průběhu semestru ji nakonec vyučující projevovali, především prof. Mejstřík. Nicméně například první komentáře k tezím působily docela demotivačně.)";"ies" +"2821";"JSM005";"Sociální struktura ČR: stav, vývoj, srovnání s EU";"Tuček,M.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"3";"5";"5";"Originalitu hodin plynoucí z inspirace aktuálním děním.";;"ks" +"2822";"JEM179";"History and Methodology of Economics";"Baxa,J.,Paulus,M.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Je to skvělý kurz a rozhodně bych se přimlouval za jeho zařazení do studijního plánu i pro magistry. Myslím, že kurzů jako je tento je na IES v posledních letech nedostatek a jsem rád, že jsem jej mohl alespoň během svého posledního roku studia absolvovat. Pohled na historický vývoj teorie i na některé alternativní přístupy, kterými se jinak na IES moc nezabýváme, mi přijde velmi poučný a zajímavý. Rozhodně kurz všem doporučuji, zejména pokud ještě nechodíte do práce a máte na studium dost času.";"Kurz je poměrně náročný v tom, že je třeba na každý týden přečíst docela velké množství anglicky psaných textů. Pro někoho, kdo není rodilý mluvčí ani příliš rychlý čtenář to v kombinaci s tím, že má další předměty a třeba i práci, poněkud ubírá tomuto kurzu na kouzlu a atraktivitě. Na druhou stranu chápu, že to zrovna u tohohle předmětu bez toho čtení úplně nejde. Nicméně dávám ke zvážení, zda přeci jen by nestačilo mít třeba na každou přednášku v průměru dva články místo tří.";"ies" +"2823";"JSM495";"Volební chování v teorii a aplikovaném výzkumu";"Fišer,J.,Prokop,D.";;NULL;NULL;"3";"3";"3";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;"Kurz se neotevřel";"Kurz se neotevřel";"ks" +"2824";"JLB037";"Italština I";;"Přívozníková,P.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"1";"5";"Individuální přístup. Ochotu a přístup vyučující.";"Rozdělit kurz na dva podle znalosti italštiny. Všichni by měli více času věnovat se zlepšování své úrovně. A navíc by za dvojí práci byla ohodnocena i vyučující.";"cjp" +"2825";"JPM146";"Přechody k demokracii v teorii a praxi I";"Mlejnek,J.";;"3";"3";"3";"4";"2";NULL;NULL;NULL;"1";"2";"2";"2";"3";"Oceňuji semináře, jejich formát je dobře udělaný. Co neoceňuji, je slabý vhled do politologických teorií přechodů režimů k demokracii. Ten je obsahově pouze úvodní, osobně jsem postrádal teorie autoritarismu a totalitarismu Linze a Brzezinskeho.";"Výše zmíněný vhled do teorií, více domácí přípravy - čtení textů. Rozebrali jsme si příliš malé množství autorů, chyběl mi (zmíněný) Brzezinky, Linz, ale i Arendt.";"kp" +"2826";"JJM204";"Výzkum médií I";"Křeček,J.";;"3";"3";"3";"3";"3";NULL;NULL;NULL;"1";"3";"4";"2";"3";;;"kms" +"2827";"JJM216";"Čtení textů ke studiu médií - česká média po roce 1945";;"Bednařík,P.,Končelík,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"2";"4";"3";"5";"5";;;"kms" +"2828";"JPM160";"Česká komunální politika";"Jüptner,P.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Oceňuji zapálenost pana doktora Juptnera (omlouvám se, že za tu nepřehlásku), toto nadšení vyvolalo v nejednom studentovi zájem o komunální politiku, která je i v očích studentů politologie vnímána jako nudná. Pan doktor Juptner dokázal, že to tak být nemusí a dokázal vzbudit zájem v komunální politice. Oceňuji důraz na povinnou literaturu, která pro mě byla přínosná. Taky jsem rád, že jsem se v tom musel trochu \"poplácat\", to vše ke zkouškám patří. Přednášky super, vyžadována aktivita. Pan doktor Juptner má super přístup.";"Myslím, že více prostoru by si zasloužila přednáška s teoretickou reflexí. Jinak vše super.";"kp" +"2829";"JJM243";"Média a životní styl";"Knapík,J.";"Knapík,J.";"4";"4";"3";"5";"5";"3";"5";"5";"1";"5";"3";"4";"4";;;"kms" +"2830";"JPM718";"Critical Perspectives on Violence";;"Ditrych,O.";"2";"5";NULL;NULL;NULL;"1";"1";"1";"2";"4";"4";"2";"2";;;"kmv" +"2831";"JSM477";"Sociology of Critique";"Blokker,P.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"3";"5";"5";"5";"5";;;"ks" +"2832";"JSM628";"European policies and practice towards ethnic minorities";"Bernard Thompson Mikes,A.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kvsp" +"2833";"JSM692";"Introduction to Social Research Methodology";"Remr,J.";;"2";"4";"1";"3";"2";NULL;NULL;NULL;"1";"1";"1";"3";"1";;;"ks" +"2834";"JPM595";"Arms Control and Disarmament";"Hynek,N.,Smetana,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Firstly, different approach to teaching which allowed to get deeper in the topic in terms of knowledge application. Secondly, guests´ insights from the real world provided very valuable information. Thirdly, use of \"technology\" such as Google Drive allowed for more effective work and would be great if more courses use it.";"The room - too small, little airCreation of some kind of arms overview table which would allow to remember also valuable information given by class-mates";"kbs" +"2835";"JEB105";"Statistics";"Červinka,M.";"Červinka,M.";"4";"4";"5";"4";"4";"3";"4";"3";"1";"4";"3";"2";"5";;;"ies" +"2836";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"4";"3";"5";"4";"2";"4";"4";"2";"1";"4";"4";"4";"5";;;"ies" +"2837";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"3";"1";"5";"5";"2";"5";"5";"2";"1";"1";"1";"3";"2";;"I found the difficulty level of the course very easy, it basically copied the scheme of Principles of Economics II, which is a prerequisite of the course and therefore all students should be familiar with its content. After discussing the content of the course with other students taking the Czech version of the subject, I believe that the English version is not sufficient in providing the basic knowledge of macroeconomics that a student who passed Macroeconomics I should have.";"ies" +"2838";"JJB268";"Sportovní marketing";"Šesták,Z.";;"1";"2";"1";"2";"1";NULL;NULL;NULL;"2";"2";"1";"2";"1";;"Hodně bizarní předmět, který v podání tohoto vyučujícího na akademické půdě nemá co dělat.";"kmkpr" +"2839";"JJB634";"Litigace PR";"Novák,L.,Štrégl,R.";;"5";NULL;"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";"Komorní předmět s vynikajícími lektory, kteří mají zájem o diskuze a dávají výborný a relevantní feedback. To, že se rádi podělí o insight z praxe je už jenom třešnička. Víc takových předmětů, prosím!";"Pět setkání bylo dost málo a některé věci se musely trošku uspěchávat.";"kmkpr" +"2840";"JJB237";"Právo a etika v marketingu";"Winter,F.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";"Pan Winter je na právo skutečný odborník. Jeho přednášky byly zajímavé a obohacující. Též jsem ocenila, že test byl formou eseje, kde jsme měli odhalit špatné znaky reklamy, dalo mi to mnohem víc než klasické testy.";;"kmkpr" +"2841";"JJB633";"Marketing Communications";"Zezulková,M.";;"3";"1";"4";"5";"2";NULL;NULL;NULL;"3";"2";"4";"3";"3";"Mezinárodní prostředí, videa jako výstup předmětu (je to něco jiného než všude jinde, byť hodně záleží, jestli chytnete schopný tým).";"Předmět jakoby nevěděl, co chce naučit. Oceňuju možnost spolurozhodovat o náplni kurzu, ale zde to byl extrém, kde se ta koncepce vymýšlela za chodu. Většina přizvaných řečníků byla nudná a ocenil bych diverzitu - 4/4 z jediné agentury není úplně ideální.";"kmkpr" +"2842";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"1";"4";"1";"1";"5";"Super předmět, kde můžeme vidět na velkém plátně ve velmi dobré kvalitě ty nejnovější snímky. Výstupem jsou dvě recenze na film, což není nijak velký požadavek. Filmy byly super, opravdu dobrý výběr.";;"kz" +"2843";"JJB224";"Psychologie marketingové komunikace";"Vranka,M.";;"4";"4";"5";"4";"3";NULL;NULL;NULL;"1";"5";"3";"5";"4";"Moc oceňuji provázanost psychologie a marketingu, informačně je to jeden z nejbohatších kurzů vůbec.";"Pan Vranka by měl učit nějaký menší povinně volitelný předmět, kde se dá reálně diskutovat; v takovém počtu se pokoušet o jakoukoliv interaktivitu bylo téměř k ničemu a je to ohromná škoda.";"kmkpr" +"2844";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"2";"4";"2";"1";"5";"Lektorské úvody";"rozšířit i směrem k veřejnosti mimo UK.";"kz" +"2845";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"3";"5";"3";"2";"2";NULL;NULL;NULL;"1";"3";"1";"4";"2";;"Více akcentovat světové souvislosti.";"kms" +"2846";"JJM320";"Propaganda a československá média po roce 1948";"Suk,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Vyučujícího, ukázky z praxe";;"kms" +"2847";"JJM324";"Teorie mediální komunikace";"Jirák,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"2";"4";"5";"Vyučujícího, poutavý výklad";;"kms" +"2848";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"2";"1";"1";"3";"2";NULL;NULL;NULL;"1";"3";"3";"2";"2";;"Vystupování vyučujícího - oživit přednes, udělat látku poutavější.";"kms" +"2849";"JJM373";"Ethical Issues in Media, Business and Society";"Orhan,M.";;"3";"3";"5";"5";"2";NULL;NULL;NULL;"1";"2";"5";"1";"3";"online forma, nutí studenta se aktivně účastnit.";"držet stejná pravidla od začátku do konce.";"kms" +"2850";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"3";"1";"1";"1";NULL;NULL;NULL;"1";"3";"1";"3";"1";;"Zlepšit přístup ke studentům. Potřeboval jsem z přednášky odejít o 15 minut dřív z důvodu návštěvy lékaře. Snažil jsem se provést to tak abych vyrušil co nejméně lidí, ale přesto byl můj odchod hlasitě komentován ze strany vyučujícího s nevybíravými poznámkami. Fakt, že při první přednášce vyučujicí zbaví své přednášky jedné z hlavnimch výhod přednášek a to osobní interakce tím, že zakáže studentům pokládat dotazy je dle mého názoru velká škoda a nezachrání to ani fakt, že se dle názoru vyučujícího se dotazují pouze lidé co na sebe chtějí upozornit a málokdy položí věcný dotaz. Celý kurz na mě udělal dojem, že si vyučující přišel odříkat svých 90 minut a cokoliv nad rámec není ochoten udělat. Tento dojem umocnil i hromadný email, který nám byl rozposlán vyučujícím v termínu, kdy jsme vypracovávali seminární práce a který oznamoval, že vyučující nehodlá odpovídat na žádné dotazy zaslané emailem. Užití alternativního systému samba místo klasického sisu hodnotím jako zbytečně zmatečný krok a fakt, že byly prezentace nacházející se v tomto systému odstraněny krátce před začátkem zkouškového období je z mého pohledu čistě záškodnický krok.";"ies" +"2851";"JEM003";"Advanced Microeconomics";"Janda,K.,Melikhova,O.";"Janda,K.,Melikhova,O.";"4";"3";"5";"5";"4";"5";"5";"4";"1";"3";"4";"3";"4";"Systém 4 testů během semestru, kdy se počítá vždy lepší výsledek z každých dvou.";"Možná občas lépe rozvrhnout čas a vysvětloat příklady pomaleji, v důsledku zběsilého opisování z tabule poté student nemá šanci některé příklady pochopit již na semináři.";"ies" +"2852";"JLB005";"Angličtina pro politology I";;"Panešová,K.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"5";"5";"Snahu o to, aby studenti měli co možná největší prostor ke komunikaci a mohli se anglicky bavit i mezi sebou.";"Feedback na úkoly, vím, že mám splněno, ale reakci jsem dostal pouze na 1-2.";"cjp" +"2853";"JLB099";"Rozřazovací test z angličtiny";;"Panešová,K.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"1";"3";NULL;NULL;"5";;;"cjp" +"2854";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"5";"4";"5";"4";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Pedagoga, hlavně jeho spravedlivý přístup ke všem. Dále i obsah vykládané látky. Ze začátku mi kurz přišel zbytečný v kontextu mého studia, ale přišel jsem na to, že se na něm vykládají zajímavé věci a člověk zjistí důležité souvislosti, které jsou v kombinaci s politologií velmi podstatné.";"Pan profesor byl belmi tichý a i když se snažil, občas to člověka uspávalo.";"ies" +"2855";"JSM527";"Metody analýzy a tvorby politik II.";"Veselý,A.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Vše.";"Pokud možno větší časová dotace na semináře... ale ne na úkor přednášek... ač je to asi nemožné, bylo by to fajn.";"kvsp" +"2856";"JSM528";"Seminář k diplomové práci I.";;"Kohoutek,J.,Ochrana,F.";"3";"2";NULL;NULL;NULL;"3";"4";"3";"1";"3";"3";"2";"3";;"Navrhuji navýšení požadavků na studenty v oblasti prokázaného seznámení s odbornou literaturou týkající se metodologie apod. - mohly by následovat diskuse na základě předepsané literatury, což by mohlo zkvalitnit přístup studentů a ve výsledku i samotné práce. Spíše povrchní prezentace všech pro všechny mají skoro minimální efekt.";"kvsp" +"2857";"JSM705";"Řízení kvality a performance management ve veřejné správě";"Plaček,M.";;"4";"2";"4";"5";"5";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kvsp" +"2858";"JLB001";"Angličtina pro sociology I";;"Klírová,M.";"2";"3";NULL;NULL;NULL;"3";"3";"2";"1";"2";"2";"2";"2";;"-měly by být průběžné testy, -na konci vůbec nevím co jsme vlastně brali--nic nám není vysvětleni, vše musíme pobrat sami- tak tam nemusím chodit";"cjp" +"2859";"JLB035";"Francouzština I";;"Bosáková,L.";"4";"4";NULL;NULL;NULL;"4";"5";"4";"1";"4";"3";"3";"4";"-přístup učitelky, její snaha všem pomoct a dát nám zápočet-možnost se poradit a získat vysětlení na konzultačních hodinách";"-možná víc vysvětlovat věci od základů- protože někteří jedinci co pozapomněli se oproti ostatním lepší nechytají";"cjp" +"2860";"JSB003";"Oborová sociologie";"Numerato,D.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"2";"4";"3";"4";"5";"-zajímavá témata-průběžné testy nebyla tolik náročné, otázky byly srozumitelné";"-možná více vyjasnit jaké odpovědi chce v závěrečném testu";"ks" +"2861";"JSB010";"Současná sociologie";"Balon,J.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"3";"4";"3";"4";"4";;;"ks" +"2862";"JSB023";"Praktika z kvantitativního výzkumu I";;"Tuček,M.";"2";"2";NULL;NULL;NULL;"2";"2";"1";"1";"3";"4";"3";"3";;"-přišlo mi že předmět je jen proto aby se udělal placený výzkum a ostatní se jen vezli-nelíbilo sem i že jsme museli řešit za vyučující problém s cvičeními a na začátku semestru někdo nevěděl kdy bude mít hodinu-také by pan vyučující mohl při hodnocení neměnit tolik názory";"ks" +"2863";"JSB055";"Současná sociální antropologie";;"Grygar,J.,Hrešanová,E.";"4";"4";NULL;NULL;NULL;"4";"2";"3";NULL;"4";"3";"4";"4";;"-zrušila bych namátkové průběžné testy z literatury- musím kvůli nim opakovat předmět-texty sem četla ale i tak jsem měla málo bodů- nevím co po mě chtěli- nelíbí sem i že musíme text pochopit tak jak ho vidí vyučující a když to vidíme jinak je to špatně-některé testy obsahovaly jen text na danou hodinu a na ty přechozí ne takže se nedalo nahnat body jinde když jsem špatně pochopil dané texty-docela podraz byl snížení minima na 33- nešla jsem do opravy protože by mi to nedalo na 35 ale kdybych věděla že bude stačit 33 šla bych- zbytečně tak opakuju-nahradila bych to třeba opět úkoly jako u Dějin atropologie";"ks" +"2864";"JSB311";"Antropologie náboženství";"Spalová,B.";;"4";"2";"2";"4";"1";NULL;NULL;NULL;"1";"4";"3";"3";"4";"-exkurze";"-přednášky byly o ničem a k splnění kursu celkem zbytečné-chybí mí písemné hodnocení za seminárky- bylo mi řečeno kolik mám bodů po zkoušce, ale ráda bych věděla proč";"ks" +"2865";"JSB537";"Analýza dat v SPSS";"Soukup,P.";;"4";"3";"4";"4";"3";NULL;NULL;NULL;"1";"4";"4";"3";"4";"-oceňuji možnost opravy průběžných testů";"-příště dřív zajisti cvičící";"ks" +"2866";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"5";"5";;;"kz" +"2867";"JSB027";"Sociální politika jako společenská praxe";"Dobiášová,K.,Vlčková,K.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"5";"5";"5";"5";"5";"individuální přístup a možná domluva, otevřená možnost konzultace v případných nejasnostech";;"kvsp" +"2868";"JSB031";"Bakalářský seminář I - diskuse projektů";;"Angelovská,O.,Mouralová,M.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"5";"5";"možnost zpětného ohodnocení i opakovaně - snaha vyřešit případné nedostatky, možnost individuálního přístupu, skvělá domluva se cvičícími v případě nejasností a problémů, diskuze s ostatními, velmi přínosný byly zpětné vazby i od spolužáků a zároveň čtení jiných prací, aktivní zájem o studenty";;"ks" +"2869";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"3";"3";"4";"5";"3";NULL;NULL;NULL;"1";"2";"4";"4";"2";"The teacher was really nice but I believe that this course is a bit weird.... Surely, we should do our assignments on our own but on the other hand you cannot blame the students if they get help from each other... I mean I frankly do not really care of this course because I know that I will not use it for my thesis, so frankly this was a bit of a time loss for me. and I dont think that threatening students to call them out on the act that they \"cheated\" is fair... I mean at the end of the day we do not always take classes because we want to, but because we simply have to and we just want to pass them with the best grade possible, hence the group work.....";;"kmv" +"2870";"JPM698";"Middle East Security";"Daniel,J.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"I really enjoyed the class, it really was the highlight of my semester. It was my topic of specialization during my bachelor, and I wrote about Hezbollah for my final thesis. However, I think it is a bit saddening and frustrating that not a lot of students (if none) read the required readings.... There was little to no participation during class (apart from the 2/3 students who actually read the readings), and I think that some readings were really challenging and deserved to be discussed. Maybe you could try to find a way of involving the students more by asking some questions about the readings that would help to understand the weekly topics or something?";;"kbs" +"2871";"JPM702";"NATO and EU in Crisis Management";"Karásek,T.";;"3";"3";"5";"5";"5";NULL;NULL;NULL;"1";"2";"3";"3";"3";"Good course but I believe the students should be more challenged, that questions should be asked etc because it does not feel that challenging to attend the classes as they merely look like auditorium lectures";;"kbs" +"2872";"JPM611";"Cyber Security";"Duračinská,Z.,Střítecký,V.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kbs" +"2873";"JPM650";"Intelligence";"Bahenský,V.,Galeotti,M.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Interactive last lesson.";"The second task was not logical, It was difficult and hard to do.";"kbs" +"2874";"JPM698";"Middle East Security";"Daniel,J.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"5";"5";"Host lecurers were great.";"The materials do not necessarily have to be long scientific articles, many news sites have interesting and challenging analyzes to keep up with the situation.";"kbs" +"2875";"JPM699";"Security and Technology";"Střítecký,V.";;"4";"3";"5";"4";"3";NULL;NULL;NULL;"1";"4";"3";"3";"4";;"Mr. Stritecky do not communicate with students.";"kbs" +"2876";"JEM032";"Banking";"Mejstřík,M.,Pečená,M.,Teplý,P.";"Pečená,M.";"4";"4";"4";"4";"3";"4";"4";"4";"1";"4";"3";"4";"4";;;"ies" +"2877";"JEM179";"History and Methodology of Economics";"Baxa,J.,Paulus,M.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"ies" +"2878";"JSM692";"Introduction to Social Research Methodology";"Remr,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ks" +"2879";"JMM128";"Prezentace v médiích";"Procházková,B.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"3";"5";"4";"5";"Praktické cvičení, zlepšení sebeprezentace, práce s trémou...";;"kzs" +"2880";"JMM530";"European Comparative Politics and Society";"Rovná,L.";;"3";"3";"4";"4";"3";NULL;NULL;NULL;"1";"4";"2";"4";"3";"I liked the topics discussed in class";"The texts did not have a similar length, so the length of presentations differed a lot.";"kzs" +"2881";"JMM673";"Promoting democracy abroad: the US and the EU in third countries";"Hornát,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"3";"4";"5";"I liked the texts, they were quite interesting and I also liked the topics of the lecture.";;"kas" +"2882";"JSB023";"Praktika z kvantitativního výzkumu I";;"Tuček,M.";"4";"4";NULL;NULL;NULL;"5";"5";"3";"1";"4";"5";"5";"5";;;"ks" +"2883";"JSB055";"Současná sociální antropologie";;"Grygar,J.,Hrešanová,E.";"3";"5";NULL;NULL;NULL;"5";"3";"5";"1";"4";"3";"4";"1";;"Systém přepadových testů, kterými byly průběžně testovány znalosti studentů, se mi u takového předmětu jeví jako zcela nevhodný, písemné úkoly jsou v takovém případě mnohem lepší variantou. Student má možnost si správnou odpověď v klidu rozmyslet a úkol odevzdat dle uvážení. Samotné přepadové testy mi na vysoké škole připadají úplně mimo, takový systém u mne naposledy probíhal na prvním stupni gymnázia, tudíž jej nepovažuji ani za vhodný ani za přínosný.";"ks" +"2884";"JMMZ152";"European Economic Integration";"Young,M.";;"4";"5";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"3";"I liked that the subject did not focus on the mathematics.";"The texts were sometimes too long and too complicated focusing on too many topic. I would appreciate if the texts focused, due to the complexity of subject, only on one key topic.";"kzs" +"2885";"JMMZ327";"Knowledge Policies in Europe";"Young,M.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"2";"4";"4";"4";"4";"I liked the discussions in the class and the form of the presentation which student had to have in the class.";"The length of the texts are sometimes too overwhelming.";"kzs" +"2886";"JSB311";"Antropologie náboženství";"Spalová,B.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"5";"4";;;"ks" +"2887";"JMB091";"Religion, secularity and laicity in Europe (19th-21th centuries)";"Bauer,P.";;"5";"3";"3";"5";"5";NULL;NULL;NULL;"1";"5";"2";"5";"5";;"The teaching method could be improved. The text, which is presented in the class could also be handed out as handouts, so that there is more time for discussion. I did not like the way of teaching that the teacher read his paper the whole time and the students had to listen and write every sentence down. I think it is more valuable if this could happen in a short introduction to the lessons or in form of text which have to be read before the lessons, so that there is more time for actual discussion.";"kzs" +"2888";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"3";"1";"4";"3";"Hodně příkladů z praxe, to bylo dobré.";"Občas se skákalo z jednoho tématu na druhý, což mátlo při učení.";"ies" +"2889";"JSB003";"Oborová sociologie";"Numerato,D.";;"2";"4";"5";"5";"5";NULL;NULL;NULL;"1";"3";"1";"5";"5";"Skvělý a srozumitelný výklad pana Dina Numerata.";"Možná zbytečně moc teorií například v oblasti rodiny. Pletou se pak jména, ale to asi k VŠ vzdělání patří, že se člověk učí i to, co si po testu už nebude zase pamatovat. :)";"ks" +"2890";"JSB023";"Praktika z kvantitativního výzkumu I";;"Tuček,M.";"2";"3";NULL;NULL;NULL;"1";"1";"1";"4";"5";"5";"4";"5";"Konečně kvantitativní výzkum v praxi. Určitě zachovat formát dotazování ve školách. Student musí do terénu, což možná už pak v práci dělat nebude, ale je dobré si to vyzkoušet - pak ví, co prožívají tazatelé.";"Vyučující nám dával rady, jak dotazník upravit, ale často nám poté, co jsme danou chybu opravili, řekl, ať to změníme tak, jak to bylo předtím. Dotazování mohlo probíhat již v průběhu semestru, na cvičeních už pak nebylo moc co řešit, dotazníky byly hotové dříve, než v prosinci. Ve zkouškovém období pak bylo náročné najít si čas a zařídit vyplňování dotazníků.";"ks" +"2891";"JSB027";"Sociální politika jako společenská praxe";"Dobiášová,K.,Vlčková,K.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"2";"5";"5";"Přednášející přednášejí srozumitelně a poutavě. Úkoly na semináře byly velmi přínosné.";"Možná by se hodilo více se zaměřit i na vypočítávání různých dávek, více se vyznat v systému českého soc. zabezpečení. Navíc seminář v terénu byl takový, že jsme šli pouze na návštěvu stacionáře, ale ocenila bych více, kdybychom se mohli zapojit (třeba povinná týdenní praxe, nebo tak něco).";"kvsp" +"2892";"JSB537";"Analýza dat v SPSS";"Soukup,P.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"2";"5";"5";"4";"5";"Přednášející přednášel srozumitelně a zábavně.";"Závěrečný test na teorii na 15 minut se nedal stihnout. Netestoval znalosti, ale rychlost (kterou netřeba testovat). Cvičení byla v pořádku, jeden cvičící však nebyl připraven a vůbec nám nedokázal vysvětlit spoustu věcí. Takže příště možná lépe připravený cvičící.";"ks" +"2893";"JSB543";"Digitální etnografie";;"Hrešanová,E.";"4";"5";NULL;NULL;NULL;"4";"5";"3";"1";"5";"5";"3";"5";"Sice náročný, ale velmi přínosný předmět co se týče rozšíření praktických dovedností. Dobrý vhled do etnografického výzkumu, který mě bavil provádět, protože jsem mohla zkoumat to, co mě opravdu zajímá a výsledky také byly zajímavé (nebyla to jen \"ta práce, kterou musím udělat do školy\", ale přinesla i výsledky, které byly přínosné nejen pro můj vlastní zájem).";"Výzkum by zabral mnohem více času, nedalo se stihnout vše, co by bylo ještě možné k hlubšímu výzkumu. Jak výzkum provádět jsme se dozvěděli až v průběhu kurzu (což je logické), ale první úkol (výzkumnou zprávu) jsme měli hodnocenou jako kdybychom už kurz měli za sebou a znali všechny náležitosti, které byly potřeba k psaní té zprávy. Ty jsme však poznali až v průběhu kurzu. Ta zpráva tedy byla hodnocena trochu příliš přísně. Brala bych akorát v případě, že by byl prerekvizitou nějaký kvalitativní výzkum.";"ks" +"2894";"JSB021";"Základy demografie";"Šídlo,L.";;"4";"5";"2";"3";"3";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Nabízí komplexní vhled od demografické situace u nás i ve světě.";"Test byl nesmyslně těžký. Při počítání chyběla data, snad aby se studenti nachytali. Navíc test obsahoval otázky, které se ptaly na detaily (např. jak se vyvíjel název sčítání lidu, domů, bytů), ale na důležité věci (třeba proč se u nás počty obyvatel vyvíjely tak, jak se vyvíjely) ne.";"ks" +"2895";"JPM689";"Conflict Studies";"Karásek,T.";;"4";"2";"5";"5";"3";NULL;NULL;NULL;"2";"3";"4";"5";"4";;;"kbs" +"2896";"JSB010";"Současná sociologie";"Balon,J.";;"3";"4";"2";"5";"3";NULL;NULL;NULL;"2";"2";"1";"3";"1";;"Teorie, které mluví o konceptech, o kterých člověka ani nenapadne přemýšlet a které jsou často nepochopitelné a pro běžného člověka moc k ničemu nejsou.";"ks" +"2897";"JPM689";"Conflict Studies";"Karásek,T.";;"4";"3";"5";"5";"3";NULL;NULL;NULL;"2";"2";"3";"4";"4";"- A decent introduction into \"conflict terminology\" and approaches of conflict management / resolution.- Great lecturer.";"I would welcome more real-life examples of particular approaches covered, to see how they perform in practice, not just in theory";"kbs" +"2898";"JPM725";"Technology and Security: Contemporary Warfare in the 21st Century";;"Csernatoni,R.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"5";"5";"- Great and knowledgeable lecturer with a friendly approach.- A wide range of topics with great relevance to current events and new developments- Interesting readings; I also appreciated the presentations that analyzed them in greater detail";"- Having classes every week instead of every fortnight, and going deeper into every topic, would perhaps be better- An introductory lecture focused on some theoretical introduction or underpinnings would have been useful, to help students approach the readings more critically";"kmv" +"2899";"JJB224";"Psychologie marketingové komunikace";"Vranka,M.";;NULL;NULL;"5";"5";"4";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kmkpr" +"2900";"JJB237";"Právo a etika v marketingu";"Winter,F.";;"3";NULL;"2";"2";"1";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kmkpr" +"2901";"JJB269";"Sociální kontext komunikace";"Vranka,M.";;NULL;NULL;"5";"5";"5";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kmkpr" +"2902";"JJB276";"Public relations v praxi";;"Hejlová,D.";NULL;NULL;NULL;NULL;NULL;"5";"5";"5";NULL;NULL;NULL;NULL;NULL;;;"kmkpr" +"2903";"JJB403";"Institucionální a vládní komunikace";"Shavit,A.,Soukeník,Š.";;NULL;NULL;"3";"1";"3";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kmkpr" +"2904";"JJB407";"Bakalářský proseminář";"Rosenfeldová,J.";;NULL;NULL;"3";"4";"4";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kmkpr" +"2905";"JJB629";"Tiskový mluvčí - praxe a teorie";"Chudinová,E.";;NULL;NULL;"2";"5";"4";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kmkpr" +"2906";"JJB632";"Strategická politická komunikace";"Petrová,B.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmkpr" +"2907";"JMB248";"Seminář k dějinám Ruska";;"Novák,P.";"4";"3";NULL;NULL;NULL;"4";"5";"5";"1";"5";"3";"5";"5";;;"krvs" +"2908";"JMB250";"Seminář k dějinám západní Evropy";;"Simbartlová,A.";"3";"2";NULL;NULL;NULL;"4";"4";"3";"1";"3";"3";"4";"4";"Zajímavá témata a texty, celková koncepce semináře je docela dobrá";;"kzs" +"2909";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"1";"4";"1";"1";"3";NULL;NULL;NULL;"1";"4";"1";"4";"1";;;"ies" +"2910";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"3";"1";"5";"5";"1";NULL;NULL;NULL;"1";"2";"3";"3";"4";;;"krvs" +"2911";"JMB402";"Úvod do společenských věd II";;"Pondělíček,J.";"3";"4";NULL;NULL;NULL;"5";"5";"5";"2";"4";"5";"4";"4";;;"krvs" +"2912";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"4";"4";"1";"4";"5";NULL;NULL;NULL;"1";"5";"1";"4";"4";;;"kas" +"2913";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"5";"2";NULL;NULL;NULL;"5";"5";"4";"1";"3";"4";"4";"5";"Velmi vstřícný přístup paní Kunzové, která ve všem byla nápomocná a chápavá.";;"cjp" +"2914";"JSB003";"Oborová sociologie";"Numerato,D.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"ks" +"2915";"JLB019";"Francouzština odborná I";;"Bosáková,L.";"5";"3";NULL;NULL;NULL;"4";"5";"3";"1";"3";"3";"3";"5";;;"cjp" +"2916";"JSB010";"Současná sociologie";"Balon,J.";;"4";"5";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"5";"5";;;"ks" +"2917";"JMB414";"Seminář k aktualitám I";;"Karasová,N.";"5";"3";NULL;NULL;NULL;"4";"5";"5";"2";"5";"4";"5";"5";"Přátelský a vstřícný přístup vyučující.";"Možná nechat o něco víc času na kratší seminárku (třeba do 2 týdnů od prezentace)";"krvs" +"2918";"JMB047";"Vybrané problémy mezinárodních konfliktů.";"Čížek,M.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"2";"5";"3";"5";"5";"Svérázný způsob přednášení, kdy člověk často pochopí ty reálné souvislosti (i když jsou třeba oficiálně předkládány jinak), poměrně zábavný seminář";"Kratší seminárku pro ty, kteří neprezentují o hodině (nestíhám :( )";"krvs" +"2919";"JMB065";"Úvod do mezinárodního a evropského práva";"Šlosarčík,I.";;"5";"2";"5";"4";"3";NULL;NULL;NULL;"3";"3";"2";"3";"5";;;"kzs" +"2920";"JMB250";"Seminář k dějinám západní Evropy";;"Mejstřík,M.";"4";"3";NULL;NULL;NULL;"4";"5";"4";"3";"5";"3";"4";"5";"Střídání různých stylů výkladu během kurzu (přednášení, referát, oponentura, aktuality, brainstorming,...) - lidi pak méně usínají, když se to střídá";;"kzs" +"2921";"JMB408";"Moderní dějiny Ruska";"Litera,B.,Novák,P.,Pečenka,M.";;"3";"5";"5";"4";"3";NULL;NULL;NULL;"2";"4";"2";"4";"3";"Hodnocení esejí je poměrně přísné. Dále také to, že body z midtermu se dají použít pouze při prvním pokusu, považuji za mínus, kdyby zůstávaly, a tak se nemusela při druhém a dalších pokusech psát i látka midtermu, bylo by to mnohem lepší.";"Uznat body z midtermu i v dalších pokusech testu";"krvs" +"2922";"JMB409";"Moderní dějiny středo- a jihovýchodní Evropy-dcera JMB013";"Balla,P.,Švec,L.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"2";"4";"4";"Oceňuji velmi zábavné přednášky doktora Baly.";;"krvs" +"2923";"JSB025";"Sociální problémy";"Frič,P.";;"5";"5";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";;"K závěrečné zkoušce by mohl být rozesílán rozpis s pořadím, ve kterém budou studenti zkoušeni, aby nemuseli na zkoušku čekat několik hodin.";"kvsp" +"2924";"JMB410";"Moderní dějiny západní Evropy";"Matějka,O.,Váška,J.";;"3";"4";"4";"4";"3";NULL;NULL;NULL;"3";"3";"2";"3";"3";"Zajímavavá témata";"Udělat přednášky trochu víc oživující";"kzs" +"2925";"JSB054";"Výzkumný seminář";;"Hrešanová,E.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Kurz byl z mého pohledu jeden z nejlepších a jeho organizace mi vyhovovala.";"Snad jenom tématu etiky by stačilo věnovat méně hodin.";"ks" +"2926";"JSB537";"Analýza dat v SPSS";"Soukup,P.";;"3";"5";"5";"5";"4";NULL;NULL;NULL;"2";"3";"5";"4";"3";"Velice pozitivně hodnotím oba cvičící, kteří se nám snažili maximálně pomáhat, vzhledem ke špatné celkové organizaci kurzu. Doučování před před státnicemi pro nás bylo velice přínosné. Velké díky tedy patří cvičícím, kteří nám věnují svůj drahocenný čas, abychom pokud možno všichni státnice úspěšně absolvovali.";"Organizaci kurzu. Problém na začátku semestru s chybějícími cvičícími a samozřejmě obsah učiva ke státnicím, jehož polovina se během kurzu vůbec neprobrala.Částečné překrývání se kurzu s jiným povinným kurzem.";"ks" +"2927";"JSB055";"Současná sociální antropologie";;"Grygar,J.,Hrešanová,E.";"4";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Zajímavé, přínosné a tématicky dobře zvolené přednášky. Jeden z nejzajímavějších kurzů.";"Lépe sestavit průběžné testy (zejména test o 3 otázkách, kdy se u uzavřené otázky při nesprávné odpovědi ztrácí 1/3 bodů).";"ks" +"2928";"JSB490";"Úvod do politické sociologie";"Císař,O.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ks" +"2929";"JSB023";"Praktika z kvantitativního výzkumu I";;"Tuček,M.";"3";"4";NULL;NULL;NULL;"4";"4";"4";"2";"3";"5";"4";"5";;;"ks" +"2930";"JJM080";"Zábava v televizním vysílání";"Kruml,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"2";"5";"3";"5";"5";;;"kms" +"2931";"JEB022";"Institutional Economics";"Kopečná,V.,Schwarz,J.";;"4";"3";"4";"4";"2";NULL;NULL;NULL;"2";"4";"3";"4";"4";;;"ies" +"2932";"JMM599";"Contemporary American Cinema";"Nowell,R.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kas" +"2933";"JMMZ315";"U.S. Foreign Policy";"Raška,F.";;"1";"3";"1";"4";"1";NULL;NULL;NULL;"1";"1";"1";"1";"1";;;"kas" +"2934";"JMMZ313";"Government in United States";"Sehnálková,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kas" +"2935";"JMMZ314";"Major Issues in Contemporary Public Debates in the U.S. I";"Sehnálková,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kas" +"2936";"JMM279";"Společnost a ekonomika";"Fiřtová,M.";"Fiřtová,M.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"kas" +"2937";"JMM271";"Metodologický seminář";;"Kýrová,L.";"3";"3";NULL;NULL;NULL;"3";"5";"3";"1";"3";"3";"3";"2";;;"krvs" +"2938";"JMM278";"Politika a mezinárodní vztahy";"Kubát,M.,Weiss,T.";"Bečka,J.";"5";"2";"4";"4";"5";"5";"5";"3";"1";"5";"4";"4";"3";;;"krvs" +"2939";"JJB040";"Kreativita v jazyce";"Šoltys,O.";;"1";"3";"1";"1";"1";NULL;NULL;NULL;"2";"1";"1";"1";"1";;;"kz" +"2940";"JJB049";"Počítačové zpracování fotografie a grafický design";;"Láb,F.,Štefaniková,S.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"2";"4";"4";"4";"5";;;"kz" +"2941";"JJB052";"Tvůrčí dílny FOTO I";"Lábová,A.";;"5";"3";"5";"4";"4";NULL;NULL;NULL;"1";"4";"5";"4";"5";;;"kz" +"2942";"JJB059";"Kritika v médiích - televizní";"Novotný,D.";;"5";"3";"5";"5";"3";NULL;NULL;NULL;"1";"3";"4";"3";"5";;;"kz" +"2943";"JJB144";"Kompaktní kurz";;"Freidingerová,T.";"2";"3";NULL;NULL;NULL;"3";"5";"1";"1";"2";"2";"2";"2";;;"kz" +"2944";"JJB170";"Počítačové zpracování foto a graf. design";"Slanec,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"4";"4";"3";"5";;;"kz" +"2945";"JJB037";"Kritika v médiích I";;;"3";"3";"4";"4";"2";NULL;NULL;NULL;"1";"3";"3";"3";"3";;;"kz" +"2946";"JEM163";"Principles of Microeconomics";"Janský,P.";"Král,M.,Moravcová,H.,Palanský,M.";"4";"2";"5";"5";"4";"3";"3";"4";"2";"4";"4";"4";"5";"Mankiw's textbook.";;"ies" +"2947";"JEM179";"History and Methodology of Economics";"Baxa,J.,Paulus,M.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Everything - system of teaching, readings, attitude of teacher. Perfect course!";;"ies" +"2948";"JMM530";"European Comparative Politics and Society";"Rovná,L.";;"3";"1";"5";"5";"3";NULL;NULL;NULL;"1";"2";"1";"4";"5";"Grade A and kindness of prof. Rovná.";;"kzs" +"2949";"JPB202";"Politické strany v Evropě";"Perottino,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;"Výkladu by prospělo oživit přednášky nějakou interakcí ze strany studentů. Strohé prezentace plné informací, které by si student mohl pročíst doma a následně probírat v hodině. Oživit prezentace o nějaká videa, obrázky...";"kp" +"2950";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"3";"4";"2";"2";"2";NULL;NULL;NULL;"2";"4";"2";"5";"5";;;"kmv" +"2951";"JPM324";"Geography and Politics in Europe within Global Regionalism";"Doboš,B.,Riegl,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kp" +"2952";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"3";"4";"3";"3";"3";NULL;NULL;NULL;"1";"4";"1";"5";"4";;"Přístup vyučujícího ve zkouškovém období";"kp" +"2953";"JPM689";"Conflict Studies";"Karásek,T.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Dr. Karásek is amazing, his lectures are - together with doc. Karlas' - the most well-prepared.";"Way too much reading. Most of it is useful, however it's about 1900 pages, which is impossible to go through responsibly. I think dividing the literature into \"obligatory\" and \"further reading\" would be nice, as well as connecting the individual texts to some thematic points.Another small issue - I found the lectures led by hosts much less interesting than lectures of Dr. Karásek (unfortunately I missed the Italian lecturer, so I only mean the rest). But that's just a detail, and others might see it differently.";"kbs" +"2954";"JPM429";"Global terrorism (CS)";;"Makariusová,R.";"3";"4";NULL;NULL;NULL;"3";"5";"3";"2";"4";"3";"4";"4";"Overall the readings were interesting and covered the topics well - which is good, since it's basically the only course on terrorism we have.";"a) I did not believe the older comments about time management, but they were true - not a single group managed to present the whole presentation during the given class, as we run out of time. I understand that every part of the lesson is important, however, for some people this may be important as they might be missing the next lesson, or something like that. b) 5000 words is way too much for an essay which is like 40 % of the grade - given that we don't usually know so much about the topic, it takes way too much effort to create such a long text. But on the bright side, the grading must have been reasonable, so I appreciate that : )";"kmv" +"2955";"JMB534";"Evropská unie - vybrané problémy";"Mejstřík,M.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"5";"3";"4";"3";;;"kzs" +"2956";"JEB026";"European Economic Integration";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"2";"3";"1";"2";"1";"1";"1";"1";"1";"5";"2";"5";"4";"After this course I was able to understand some things from micro- or macro- economics, that did not make much sense before. It was greatly connected with these courses. The materials (presentations) uploaded by the lecturer were perfect - very detailed, very well explained, and logical.";"Attitude of the teaching assistants and the lecturer to the teaching. The lecturer has very good slides, but his teaching skills are very poor, he was only reading the slides and attending the lecture was of no added value. The same with the seminars - TAs are complaining that we do not attend the seminars, but even when we do, they have nothing prepared, so after each pair conducts its presentation they send us home. Very disappointing.";"ies" +"2957";"JEB110";"Econometrics II";"Pertold-Gebicka,B.";"Chorna,O.,Malinská,B.,Pertold-Gebicka,B.,Pleticha,P.";"5";"2";"3";"5";"3";"5";"5";"5";"1";"5";"5";"5";"5";"Quality of the teaching assistant. After Econometrics I, I did not expect much from the quality of the seminars, but I must admit that these TAs were very well prepared, patient, and helpful. Good choice!Moreover, the book from which we are sourcing for the course is very understandable and makes the whole understanding of the course easier.";"I did not attended lectures to the end of course because there were a group of annoying students constantly asking questions about everything. I know it is important to ask when there is something unclear, but if the question is more complicated or not that much to the topic, I would prefer the teacher to answer it after the lecture and not to spend too much time on that. Then we could hardly ever make it to the end of the slides and learn it by ourselves. Moreover, the waiting time for getting to know any single result from either HWs, midterm, paper, or final exam is very long. I would prefer to know it sooner.";"ies" +"2958";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"3";"4";"5";"Oceňuji celý kurz jako takový, je velmi dobré, že studium na FSV takový kurz poskytuje.";"Upravit informace v SiSu";"kz" +"2959";"JSB003";"Oborová sociologie";"Numerato,D.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"1";"5";"5";;;"ks" +"2960";"JPB268";"Evropská integrace";"Plechanovová,B.";;"2";"3";"2";"2";"2";NULL;NULL;NULL;"3";"3";"3";"3";"3";;;"kmv" +"2961";"JSB004";"Sociální nerovnosti";"Šanderová,J.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"4";"4";"2";"5";"4";;;"ks" +"2962";"JLB005";"Angličtina pro politology I";;"Stružková,I.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Oceňuji zejména příjemný přístup ze strany vyučující, poskytuje cenné rady.";;"cjp" +"2963";"JSB010";"Současná sociologie";"Balon,J.";;"4";"4";"4";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Dobrovolnou možnost zpracovat a přednést prezentaci místo anotace a recenze";;"ks" +"2964";"JPB268";"Evropská integrace";"Plechanovová,B.";;"1";"5";"3";"1";"1";NULL;NULL;NULL;"4";"3";"2";"3";"1";;"Přístup k vypisování termínů, které jsou nedostatečné k počtu studentů a k tomu navíc prodleva opravování testů, která studentům znemožňuje se přihlásit na další termín, je strašná. Doba překračuje stanovených 7 pracovních dní a přesto paní docentka Plechanovová odmítá vypsat další termíny, přestože mnoho studentů právě kvůli jejímu pozdnímu opravování nedostane možnost ani dvou pokusů, natož tří.";"kmv" +"2965";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"4";"3";"4";"4";"5";NULL;NULL;NULL;"1";"4";"4";"4";"4";"Interaktivnost přednášek, která studenta nutí udržet pozornost";"Ocenila bych vetší informovanost, co se týče zadání seminárních prací. Pokud student nebyl na přednášce kde se téma zadávalo, tak poté nemá šanci ho nikde dohledat.";"kp" +"2966";"JLB001";"Angličtina pro sociology I";;"Klírová,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";"Dobrovolný úkol při jehož splnění odpadá jedno cvičení v testu. Úkoly celkově, nejlépe si na nich člověk zopakuje a naučí se probíranou látku.";"Nic mě nenapadá.";"cjp" +"2967";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"3";"3";"4";"2";"5";NULL;NULL;NULL;"1";"4";"4";"4";"4";;"Zejména komunikaci se studenty ze strany pana Švece, dost často neodpovídá na emaily.";"kp" +"2968";"JSB023";"Praktika z kvantitativního výzkumu I";;"Špaček,O.";"4";"5";NULL;NULL;NULL;"4";"2";"5";"1";"5";"5";"5";"5";;;"ks" +"2969";"JSB027";"Sociální politika jako společenská praxe";"Dobiášová,K.,Vlčková,K.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Prezentace na moodle (na přednášky jsem chodila, ale mít u ruky prezentaci mi usnadňovalo psaní výpisků, bez ní bych asi nestíhala)Skvělé vyučující (na cvičení jsem měla Mgr. Kamilku Vlčkovou, byla skvělá, je v oboru velmi vzdělaná a umí to předat studentům.)";;"kvsp" +"2970";"JLB013";"Němčina odborná I";;"Křenková,D.";"4";"2";NULL;NULL;NULL;"5";"5";"4";"1";"2";"3";"3";"3";"aktuality";;"cjp" +"2971";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"2";"2";"3";"1";NULL;NULL;NULL;"1";"3";"1";"2";"3";;;"ies" +"2972";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;NULL;NULL;"4";"3";"2";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"krvs" +"2973";"JMB402";"Úvod do společenských věd II";;"Šafařík,P.";"2";"3";NULL;NULL;NULL;"2";"2";"5";"2";"3";"4";"2";"2";;;"krvs" +"2974";"JJB040";"Kreativita v jazyce";"Šoltys,O.";;"3";"3";"3";"4";"4";NULL;NULL;NULL;"2";"3";"4";"2";"4";;;"kz" +"2975";"JMB208";"Dějiny státu a práva v německy mluvících zemích";"Mlsna,P.";;"1";"3";"2";"2";"1";NULL;NULL;NULL;"5";"4";"1";"3";"2";;;"knrs" +"2976";"JSM647";"Manažerské metody ve veřejné a sociální politice";"Ochrana,F.";;"3";"3";"4";"5";"2";NULL;NULL;NULL;"1";"2";"1";"1";"2";;;"kvsp" +"2977";"JSB003";"Oborová sociologie";"Numerato,D.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"2";"5";"5";"5";"5";"Průběžné testy byly dobré na procvičení probrané látky.";"Přišlo mi, že se posledních pár textů nevztahovalo až tolik k probírané látce.";"ks" +"2978";"JMB118";"Geografie německy mluvících zemí";"Baštová,P.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"knrs" +"2979";"JJB050";"Tvůrčí dílny tisk";"Kubík,J.,Osvaldová,B.";;"3";"3";"3";"3";"3";NULL;NULL;NULL;"2";"3";"3";"3";"3";;;"kz" +"2980";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"3";"5";"2";"4";NULL;NULL;NULL;"1";"4";"3";"4";"2";"Připravenost vyučujícího a propracované příklady, díky kterým lze daný problém lépe pochopit.";"Kurz by navštěvovalo jistě mnohem více studentů, kdyby si pan Kameníček odpustil některé nevhodné komentáře během výuky.";"ies" +"2981";"JMBZ200";"Bakalářský seminář pro česko-německá studia I.";;"Nigrin,T.";"4";"3";NULL;NULL;NULL;"4";"4";"4";"1";"3";"4";"3";"4";;;"knrs" +"2982";"JSB537";"Analýza dat v SPSS";"Soukup,P.";"Oreský,J.";"5";"5";"5";"5";"5";"5";"5";"5";"3";"5";"5";"5";"5";"Skvělí cvičící, kteří se snaží, abychom látku pochopili. Skvělý přednášející.";"Přijde mi opravdu nespravedlivé, že ve státnicích máme látku, která nám nebyla vyložena. Chápu, že státní zkouška musí být srovnatelná s předchozími ročníky, to je spravedlivé, ale předchozí ročníky mají výhodu, že jim byla vyložena celá látka. Navíc měli povoleno mít ke zkoušce syntax, zatímco my máme vše nosit v hlavě, to pro nás státnici činí těžší.";"ks" +"2983";"JMB208";"Dějiny státu a práva v německy mluvících zemích";"Mlsna,P.";;"3";"4";"3";"2";"3";NULL;NULL;NULL;"4";"4";"2";"4";"3";;;"knrs" +"2984";"JJB052";"Tvůrčí dílny FOTO I";"Lábová,A.";;"4";"4";"4";"5";"5";NULL;NULL;NULL;"1";"4";"5";"4";"5";;;"kz" +"2985";"JLB035";"Francouzština I";;"Bosáková,L.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"3";"4";;;"cjp" +"2986";"JMB218";"Německo a Rakousko po roce 1989";"Emler,D.,Kunštát,M.,Mlsna,P.,Nigrin,T.,Šafařík,P.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"3";"5";"5";"5";"4";;;"knrs" +"2987";"JSB998";"Úvod do sociologie";"Soukup,P.";;"3";"3";"5";"5";"1";NULL;NULL;NULL;"1";"3";"1";"3";"4";;;"ks" +"2988";"JLB035";"Francouzština I";;"Dundrová,M.";"5";NULL;NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";NULL;"5";;;"cjp" +"2989";"JMMZ274";"Geschichte des Rassismus";"Barth,B.";;"4";"4";"4";"3";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"knrs" +"2990";"JMBZ264";"Seminar zu den aktuellen Fragen";;"Renner,T.";"4";"4";NULL;NULL;NULL;"4";"5";"4";"1";"4";"1";"4";"4";;;"knrs" +"2991";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"2";"5";"5";"3";"3";"4";"4";"5";;;"kz" +"2992";"JSB010";"Současná sociologie";"Balon,J.";;"4";"4";"5";"4";"5";NULL;NULL;NULL;"2";"5";"4";"5";"5";"Sepsání recenze a anotace na námi zvolené dílo z doporučeného seznamu literatury. Je to zajímavé doplnění kurzu.";"Rychlejší opravování recenzí a anotací.";"ks" +"2993";"JSB003";"Oborová sociologie";"Numerato,D.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"5";"5";;;"ks" +"2994";"JMMZ276";"Deutsche und Tschechen – nahe und ferne Nachbarn (von der Habsburgermonarchie bis zur europäischen Union)";"Zimmermann,V.";"Zimmermann,V.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"knrs" +"2995";"JJB059";"Kritika v médiích - televizní";"Novotný,D.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kz" +"2996";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"4";"3";"4";"4";"5";NULL;NULL;NULL;"2";"4";"3";"5";"5";;"Testy by mohly být opravovány rychleji, ideálně před dalším termínem";"kp" +"2997";"JJB142";"Literatura faktu";"Halada,J.";;"3";"2";"4";"5";"4";NULL;NULL;NULL;"2";"3";"3";"4";"3";;;"kz" +"2998";"JMBZ290";"Konversatorium zu den aktuellen Fragen";;"Renner,T.";"4";"4";NULL;NULL;NULL;"4";"5";"4";"1";"4";"1";"4";"4";;;"knrs" +"2999";"JJB144";"Kompaktní kurz";;"Freidingerová,T.";"4";"4";NULL;NULL;NULL;"4";"5";"4";"1";"5";"3";"3";"5";;;"kz" +"3000";"JJB169";"Věda v médiích";"Kasík,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"4";"3";"4";"5";;;"kz" +"3001";"JLB001";"Angličtina pro sociology I";;"Klírová,M.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"4";"4";"Paní Klírová byla velice vstřícná, milá a lidská. Praktické úkoly mi pomohly látku velice rychle pochopit.";"Líbilo by se mi, kdyby byla hodina více konverzační.";"cjp" +"3002";"JMB497";"Metodický úvod pro kombinované studium";"Kubát,M.";;"4";"5";"5";"5";"5";NULL;NULL;NULL;"1";"3";"5";"4";"4";"That i passed";"The basement of clear and easy Electronic instructions what to really do";"krvs" +"3003";"JLB037";"Italština I";;"Přívozníková,P.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";"Skvělá vyučující, která se studentům věnovala i mimo rámec samotné výuky. Děkujeme.";;"cjp" +"3004";"JSB021";"Základy demografie";"Šídlo,L.";;"4";"5";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"3";"3";"Cvičení a bonusové body, které se přičítají k výsledku zkoušky.";"Závěrečný test byl příliš náročný. A praktická část na vypočítání standardizace, úmrtnosti atd byla úplně jiná než jsme se učili na cvičeních.";"ks" +"3005";"JLB033";"Němčina I";;"Křenková,D.";"4";"1";NULL;NULL;NULL;"5";"5";"4";"1";"3";"4";"4";"5";"Vyučující byla velice vstřícná, snažila se nám pomoci látku pochopit a povzbuzovala nás ke studiu.";"S kurzem jsem byla spokojena.";"cjp" +"3006";"JEB003";"Ekonomie I";"Fanta,N.,Kracík,J.,Švarcová,N.";"Fanta,N.,Kracík,J.,Švarcová,N.";"4";"3";"5";"5";"4";"2";"2";"3";"1";"4";"4";"4";"4";;;"ies" +"3007";"JJM201";"Teorie masové a mediální komunikace";"Jirák,J.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"2";"3";"4";"5";"5";;;"kms" +"3008";"JJB334";"Zábava v médiích";"Kruml,M.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"1";"5";"3";"4";"4";;;"kms" +"3009";"JSB028";"Informační gramotnost";"Tomandlová,V.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"2";"5";"Jedno dlouhé cvičení, na kterém jsme se naučili a dozvěděli vše potřebné k vypracování závěrečné práce.";"Nic mě nenapadá.";"kvsp" +"3010";"JJB600";"Úvod do studia médií I.";"Nečas,V.";;"3";"4";"4";"4";"5";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kms" +"3011";"JMB499";"Současné metodologie";"Kubát,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"2";"3";"4";"5";"it creates a general basement for understanding the classical theories";"Bief and complex electronic document of main neccesaries";"krvs" +"3012";"JEM020";"Ethics and Economics";"Cahlík,T.";"Cahlík,T.";"5";"3";"5";"5";"5";"5";"5";"3";"1";"5";"3";"5";"5";;;"ies" +"3013";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"3";"2";"1";"1";NULL;NULL;NULL;"1";"3";"3";"1";"2";;;"ies" +"3014";"JSB031";"Bakalářský seminář I - diskuse projektů";;"Numerato,D.";"4";"3";NULL;NULL;NULL;"5";"5";"4";"1";"3";"4";"3";"4";"Rady vyučujícího ohledně tvorby bakalářské práce.";;"ks" +"3015";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"5";"3";"5";"4";"4";NULL;NULL;NULL;"1";"5";"3";"4";"5";"Podle mne je to opravdu vynikajici ucitel i kdyz ma docela zvlastni humor. Priklady ktere pan doktor pouzival na prednaskach byly velmi prinosne pro pochopeni probirane latky. Diky tomuto kurzu vidim a chapu hodne souvislosti, ktere se tykaji ekonomie i v dalsich predmetech ktere jsem behem semestru mela.";;"ies" +"3016";"JSB055";"Současná sociální antropologie";;"Grygar,J.,Hrešanová,E.";"4";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"3";"4";"4";"Možnost vybrat si téma u zkoušky.";"Nedávat průběžné testy, ale úkoly. U úkolu (které jsme dostávali v kurzu Dějiny sociální antropologie) jsem vždy podle zadání věděla, na co se v textu zaměřit a bylo tak snazší rozpoznat důležité informace.";"ks" +"3017";"JJB600";"Úvod do studia médií I.";"Nečas,V.";;"3";"4";"4";"4";"4";NULL;NULL;NULL;"3";"3";"2";"3";"3";;;"kms" +"3018";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"4";"1";"4";"5";"4";NULL;NULL;NULL;"2";"4";"5";"5";"4";;;"krvs" +"3019";"JSB490";"Úvod do politické sociologie";"Císař,O.";;"4";"2";"5";"4";"3";NULL;NULL;NULL;"1";"3";"2";"4";"4";;;"ks" +"3020";"JJB600";"Úvod do studia médií I.";"Nečas,V.";;"4";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"1";"2";"3";;;"kms" +"3021";"JSB007";"Dějiny sociologie I";"Šanderová,J.";"Bureš,J.";"3";"3";"4";"4";"3";"5";"4";"3";"2";"3";"3";"3";"2";"Milý vyučující, prostor vyjádřit se, zeptat se.";"Abstrakty by se mohly psát z jednodušších knih (úryvků).";"ks" +"3022";"JJB601";"Rozbor právního prostředí v českých médiích";"Benda,J.";;"3";"4";"2";"2";"3";NULL;NULL;NULL;"1";"4";"3";"3";"3";;;"kms" +"3023";"JMB402";"Úvod do společenských věd II";;"Šafařík,P.";"4";"4";NULL;NULL;NULL;"4";"5";"5";"2";"4";"4";"4";"4";"Průběžné úkoly určitě byly přínosem pro rozšíření důležitých dovedností.";"Závěrečný test měl velmi matoucí a těžko pochopitelné zadání.";"krvs" +"3024";"JSM026";"Klíčové otázky sociální antropologie";"Grygar,J.,Hrešanová,E.,Uherek,Z.";;"2";"3";"3";"3";"3";NULL;NULL;NULL;"3";"3";"3";"3";"2";;"S kurzem příliš spokojený nejsem. Témata v Ingoldově knize, kterými jsme se zabývali, mi připadala neaktuální a následné studentské prezentace mi připadaly, až na některé výjimky, jako pouhé slovíčkaření. Kromě toho do teď nemáme opravené úkoly, které jsme odevzdávali na konci listopadu.";"ks" +"3025";"JJB601";"Rozbor právního prostředí v českých médiích";"Benda,J.";;"3";"4";"2";"3";"5";NULL;NULL;NULL;"1";"2";"1";"1";"2";;;"kms" +"3026";"JJB605";"Novodobá historie médií";"Bednařík,P.,Končelík,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"3027";"JJB616";"Vybrané novinářské osobnosti 19. století";"Sekera,M.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"2";"2";"4";;;"kms" +"3028";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Šafařík,P.";"5";"3";"5";"5";"5";"4";"5";"4";"1";"3";"3";"3";"4";"Skvěý pan Velek";;"knrs" +"3029";"JJB605";"Novodobá historie médií";"Bednařík,P.,Končelík,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"1";"4";"5";;;"kms" +"3030";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"3";"3";"5";"5";"3";NULL;NULL;NULL;"1";"2";"1";"4";"3";;;"ies" +"3031";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"2";"5";"3";"2";"2";"4";"4";"4";"2";"3";"3";"3";"3";;"Nedokážu pochopit dvě věci. 1) Jak můžete nechat odejít jedinýho vyučujícího který dokázal vše vysvětlit na popud nějakýho uraženýho studenta, kterému se nepovedl midterm. 2) Nejsme tak velkej institut na to, aby BYL TAKOVÝ PROBLÉM DOMLUVIT SE S OSTATNÍMI VYUČUJÍCÍMY A NAPLÁNOVAT ZKOUŠKY TAK ABY SE NEKRYLI POVINNÉ PŘEDMĚTY, todle prostě nedáva smysl, máte na nás dost vysoké nároky na to aby ste mohli dát aspoň trošku úsilý do plnění Vašich povinností a neztěžování už tak vyhroceného období zkoušek, a nemluvě o opravováni testů. Nepřijde mi že je od Vás fér opravit předchozí termín den před dalším termínem, obzvlášt když to zkombinujeme s tim co jsem napsal výše.";"ies" +"3032";"JJB620";"Mediální produkce - žurnalistická práce I.";"Vlasák,Z.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"5";"5";;;"kms" +"3033";"JJB616";"Vybrané novinářské osobnosti 19. století";"Sekera,M.";;"4";"1";"5";"5";"5";NULL;NULL;NULL;"1";"5";"1";"4";"4";;;"kms" +"3034";"JSB025";"Sociální problémy";"Frič,P.";;"3";"3";"5";"4";"4";NULL;NULL;NULL;"1";"3";"3";"3";"3";"Minieseje mi pomohly látku pochopit.";"Lépe látku před miniesejí vysvětlit. Přesně sdělit, co v minieseji vyžaduji a chci. Mohli bychom se vyhnout špatným známkám.";"kvsp" +"3035";"JSB311";"Antropologie náboženství";"Spalová,B.";;"5";"5";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"4";"5";"Exkurze do buddhistického centra do mešity.";"Kurz je velice časově náročný. Týdenní úkoly, exkurze, seminární práce. Předmět by si zasloužil více kreditů.";"ks" +"3036";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"4";"4";"5";"4";"5";NULL;NULL;NULL;"2";"4";"3";"4";"3";;;"ies" +"3037";"JJB620";"Mediální produkce - žurnalistická práce I.";"Vlasák,Z.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"4";;;"kms" +"3038";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"3";"5";"5";"5";"4";NULL;NULL;NULL;"1";"3";"2";"3";"5";;;"ies" +"3039";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"4";"1";NULL;NULL;NULL;"4";"4";"4";"2";"4";"3";"4";"4";;;"ies" +"3040";"JMB523";"Mezinárodní aktuality I";"Fojtek,V.";;"5";"3";"3";"3";"3";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Its complex attitude without strict learning only from books";"Maybe the complex Electronic materials";"kas" +"3041";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Kůželová,M.";"3";"4";"3";"4";"3";"5";"5";"5";"2";"5";"2";"4";"4";;;"krvs" +"3042";"JJB142";"Literatura faktu";"Halada,J.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";"Lidský přístup pana docenta ke studentům :)";;"kz" +"3043";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"2";"2";"5";"3";"5";;;"ies" +"3044";"JJB334";"Zábava v médiích";"Kruml,M.";;"4";"2";"4";"4";"2";NULL;NULL;NULL;"1";"4";"1";"3";"4";;;"kms" +"3045";"JJB606";"Televize jako instituce";"Štoll,M.";;"3";"3";"3";"4";"3";NULL;NULL;NULL;"1";"3";"2";"3";"3";;;"kms" +"3046";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"4";"2";"3";"3";"2";"5";"5";"5";"3";"5";"5";"5";"5";;;"ies" +"3047";"JEB026";"European Economic Integration";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"3";"4";"2";"4";"2";"3";"4";"4";"1";"5";"2";"5";"4";"Jasně strukturovaná závěrečná zkouška; prezentace, které mohou sloužit jako výtah ze skript.";"Angličtina přednášejícího a slečny cvičící. Zejména silný francouzský přízvuk byl překážkou v porozumění.";"ies" +"3048";"JJB607";"Analýzy mediálních obsahů";"Křeček,J.";;"3";"2";"4";"5";"5";NULL;NULL;NULL;"1";"3";"5";"3";"4";;;"kms" +"3049";"JEB141";"Introduction to Market Design";"Gregor,M.,Kastl,J.";;"3";"4";"4";"5";"2";NULL;NULL;NULL;"1";"2";"2";"3";"2";;;"ies" +"3050";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"3";"2";"1";"3";NULL;NULL;NULL;"1";"2";"1";"2";"2";;;"ies" +"3051";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"3";"2";"4";"5";"3";NULL;NULL;NULL;"1";"4";"4";"3";"2";"Vstřícný přístup";;"knrs" +"3052";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"3";"4";"3";"4";"4";NULL;NULL;NULL;"1";"4";"3";"4";"2";;;"ies" +"3053";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"4";"5";NULL;NULL;NULL;"3";"3";"2";"2";"2";"2";"2";"5";;;"ies" +"3054";"JLB003";"Angličtina pro ekonomy I";;"Poslušná,L.";"4";"3";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"3";"4";;;"cjp" +"3055";"JJB617";"Vybrané novinářské osobnosti 20. století";"Železný,J.";;"4";"3";"5";"3";"5";NULL;NULL;NULL;"1";"4";"2";"3";"5";;;"kms" +"3056";"JJB618";"Propagandistické působení médií v 1 polovině 20. století";"Suk,P.";;"5";"4";"4";"3";"5";NULL;NULL;NULL;"1";"5";"2";"5";"5";;;"kms" +"3057";"JEM032";"Banking";"Mejstřík,M.,Pečená,M.,Teplý,P.";"Pečená,M.";"5";"5";"5";"5";"4";"5";"5";"5";"1";"5";"5";"5";"4";;;"ies" +"3058";"JSB051";"Úvod do sociální antropologie";"Uherek,Z.";"Hrešanová,E.";"4";"2";"4";"4";"3";"4";"3";"5";"1";"4";"2";"4";"5";"Povinná četba na seminář byla velice zajímavá a zábavná.";;"ks" +"3059";"JLB001";"Angličtina pro sociology I";;"Štěpánková,D.";"2";"2";NULL;NULL;NULL;"2";"4";"1";"1";"3";"3";"4";"2";;;"cjp" +"3060";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kas" +"3061";"JSB537";"Analýza dat v SPSS";"Soukup,P.";"Oreský,J.";"4";"5";"5";"4";"3";"5";"5";"5";"4";"5";"5";"3";"5";"Velmi vstřícný přístup cvičících.";"Rozhodně rozvrhnutí probírané látky. Absence téměř poloviny všech cvičení a tak i probírané látky, kterou ovšem potřebujeme ke složení státní zkoušky, bylo trochu nespravedlivé.";"ks" +"3062";"JSB998";"Úvod do sociologie";"Soukup,P.";;"4";"3";"5";"3";"4";NULL;NULL;NULL;"2";"4";"3";"3";"3";;;"ks" +"3063";"JEM020";"Ethics and Economics";"Cahlík,T.";"Cahlík,T.";"5";"2";"5";"5";"4";"5";"5";"4";"1";"4";"5";"5";"5";;;"ies" +"3064";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"2";"5";"5";"3";NULL;NULL;NULL;"2";"4";"4";"4";"5";;;"ks" +"3065";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"4";"4";NULL;NULL;NULL;"2";"3";"3";"3";"2";"2";"3";"4";;;"ies" +"3066";"JSB025";"Sociální problémy";"Frič,P.";;"5";"3";"5";"3";"3";NULL;NULL;NULL;"2";"5";"4";"4";"5";;;"kvsp" +"3067";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"4";"1";NULL;NULL;NULL;"4";"5";"5";"2";"4";"1";"1";"4";;;"ies" +"3068";"NMMA701";"Matematika 1";"Spurný,J.";"Honzík,P.";"5";"5";"5";"5";"5";"4";"4";"4";"2";"5";"5";"4";"5";;;"ies" +"3069";"JJB606";"Televize jako instituce";"Štoll,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"3";"2";"2";"2";;;"kms" +"3070";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Přibáňová,T.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"Přístup vyučujícího přednášek byl skvělý. Na cvičení jsem vše hned pochopila a nebyl problém vypracovat správně úkol.";"Mohli bychom zrušit ,,článek\".";"ks" +"3071";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"4";"2";NULL;NULL;NULL;"4";"3";"2";"1";"3";"3";"3";"5";;;"ies" +"3072";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"4";"4";NULL;NULL;NULL;"3";"5";"2";"1";"2";"4";"4";"5";;;"ies" +"3073";"JSB033";"Praktika z kvalitativního výzkumu";;"Marková Volejníčková,R.";"4";"4";NULL;NULL;NULL;"4";"4";"5";"1";"5";"5";"3";"5";"Skupinová seminární práce, i když to je často více než obtížně, naučit se pracovat v týmu je přínosné.";"Rychlejší opravení práce a zapsání zápočtu do sisu.";"ks" +"3074";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"3";"2";NULL;NULL;NULL;"2";"3";"2";"1";"2";"3";"3";"4";;;"ies" +"3075";"JEB044";"Financial Accounting";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"4";"4";"4";"5";"3";NULL;NULL;NULL;"1";"5";"3";"4";"5";;;"ies" +"3076";"JEB105";"Statistics";"Červinka,M.";"Červinka,M.";"4";"4";"5";"4";"4";"5";"4";"5";"1";"5";"5";"5";"5";;;"ies" +"3077";"JSB023";"Praktika z kvantitativního výzkumu I";;"Špaček,O.";"4";"4";NULL;NULL;NULL;"4";"4";"5";"1";"5";"5";"3";"4";"Dvousemestrální práce.";"Lepší koordinace týmů.";"ks" +"3078";"JSB530";"Úvod do sociologického myšlení";"Bureš,J.,Numerato,D.";"Moskvina,Y.";"2";"3";"5";"5";"3";"4";"5";"3";"2";"3";"5";"3";"3";"Psani deniku a abstraktu";"Podle mne skupinova prace by nemela byt po nas vyzadovana behem zkouskeho obdobi.";"ks" +"3079";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"3";"3";"5";"5";"2";"3";"4";"2";"1";"3";"3";"2";"4";;;"ies" +"3080";"JEB110";"Econometrics 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ekonomie";"Kameníček,J.";;"1";"5";"2";"1";"1";NULL;NULL;NULL;"1";"2";"2";"1";"1";;;"ies" +"3087";"JJB003";"Dějiny masových médií III";"Bednařík,P.,Končelík,J.";;"4";"4";"4";"5";"4";NULL;NULL;NULL;"1";"4";"2";"5";"3";;;"kms" +"3088";"JJB170";"Počítačové zpracování foto a graf. design";"Slanec,J.";;"2";"2";"2";"2";"2";NULL;NULL;NULL;"1";"2";"3";"2";"3";;;"kz" +"3089";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Rössler,J.";"5";"5";"4";"5";"5";"5";"5";"5";"1";"5";"4";"5";"5";;;"ks" +"3090";"JLM001";"Academic English I";;"Cotte,P.";"3";"2";NULL;NULL;NULL;"4";"5";"4";"1";"3";"4";"3";"3";;"Maybe it was a little bit too easy, I wouln't Mrs. Cotte being a more strict with us.";"cjp" +"3091";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"4";"4";"5";"4";"5";NULL;NULL;NULL;"1";"5";"5";"5";"4";;;"ies" +"3092";"JJB006";"Současný český jazyk III";;"Svobodová,I.";"4";"5";NULL;NULL;NULL;"4";"5";"5";"3";"2";"5";"2";"3";;;"kms" +"3093";"NMMA703";"Matematika 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Pečenky nejen v rámci přednášek, ale i v průběhu zkoušky.";;"krvs" +"3180";"JSB998";"Úvod do sociologie";"Soukup,P.";;"4";"3";"4";"5";"3";NULL;NULL;NULL;"2";"4";"4";"4";"5";;;"ks" +"3181";"JPB020";"Úvod do mezinárodních vztahů";"Karlas,J.,Kučera,T.";;"3";"5";"3";"4";"3";NULL;NULL;NULL;"1";"3";"3";"3";"2";;;"kmv" +"3182";"JJB249";"Úvod do studia českého jazyka I";"Schneiderová,S.";"Schneiderová,S.";"3";"4";"4";"3";"1";"4";"3";"4";"3";"2";"3";"2";"3";;;"kmkpr" +"3183";"JJB601";"Rozbor právního prostředí v českých médiích";"Benda,J.";;"5";"5";"5";"4";"3";NULL;NULL;NULL;"1";"5";"2";"5";"5";;;"kms" +"3184";"JJB253";"Markething - online publikování a populární kultura I.";;"Maxa,M.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kmkpr" +"3185";"JJB269";"Sociální kontext komunikace";"Vranka,M.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"5";"5";;;"kmkpr" +"3186";"JJB401";"Komerční a nekomerční marketingová komunikace";"Báča,L.,Obluk,O.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmkpr" +"3187";"JJB407";"Bakalářský proseminář";"Rosenfeldová,J.";;"3";"3";"4";"4";"1";NULL;NULL;NULL;"4";"3";"1";"1";"3";;;"kmkpr" +"3188";"JMB249";"Seminář k dějinám středo- a jihovýchodní Evropy";;"Balla,P.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"krvs" +"3189";"JLB045";"Angličtina pro marketing I";;"Stružková,I.";"2";"4";NULL;NULL;NULL;"3";"3";"2";"2";"2";"2";"3";"3";;;"cjp" +"3190";"JSB998";"Úvod do sociologie";"Soukup,P.";;"4";"3";"5";"5";"2";NULL;NULL;NULL;"1";"5";"3";"4";"5";;;"ks" +"3191";"JMB408";"Moderní dějiny Ruska";"Litera,B.,Novák,P.,Pečenka,M.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Nejvíce oceňuji svérázný přístup dr. Pečenky";;"krvs" +"3192";"JMB410";"Moderní dějiny západní Evropy";"Matějka,O.,Váška,J.";;"3";"4";"4";"4";"1";NULL;NULL;NULL;"2";"2";"2";"2";"3";;;"kzs" +"3193";"JMB409";"Moderní dějiny středo- a jihovýchodní Evropy-dcera JMB013";"Balla,P.,Švec,L.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Suverénně nejlepší (mnou absolvovaný) předmět na IMS";;"krvs" +"3194";"JMM718";"Polština I";;"Sitarz,K.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";"Nejvíce oceňuji osobní přístup pana SitarzeVýhodou kurzu je vyučující, který je rodilým mluvčím";;"knrs" +"3195";"JJB017";"Grafický design a základy polygrafie I";"Slanec,J.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kz" +"3196";"JJB012";"Žurnalistická tvorba I";"Osvaldová,B.";"Krobová,T.,Osvaldová,B.,Slanec,J.";"5";"4";"4";"5";"5";"5";"5";"5";"1";"4";"5";"5";"5";;;"kz" +"3197";"NMMA703";"Matematika 3";"Zelený,M.";"Bartoš,A.";"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";;;"ies" +"3198";"JJB015";"Česká literatura I";;"Čeňková,J.,Malý,R.";"4";"3";NULL;NULL;NULL;"4";"4";"3";"1";"3";"3";"4";"4";;;"kz" +"3199";"JEB141";"Introduction to Market Design";"Gregor,M.,Kastl,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"This course was really interesting and I think it was one of the best that was offered to me during the last 3 years at the university. It will shown you another field of economics about which you might not even know.";;"ies" +"3200";"JJB004";"Současný český jazyk I";;"Svobodová,I.";"4";"3";NULL;NULL;NULL;"4";"3";"5";"2";"3";"4";"4";"4";;;"kz" +"3201";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"3";"3";"4";"2";NULL;NULL;NULL;"1";"3";"4";"2";"2";"Závěrečny test";"Hlasitější přednes, zmenšení kapacity, rozdělit na min. 3 skupiny (z množství studentů tohoto kurzu v roce 2017)";"ies" +"3202";"JEB026";"European Economic Integration";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"2";"4";"1";"3";"1";"4";"4";"4";NULL;"5";"5";"5";"2";"The topic of this course is very interesting and important to know. However, I do not see a point in attending course where the teacher just reads what is written in the presentation. I could do that on my own at home. And that is the reason why I do not value the course in general. I am almost sure that it is better to do not attend the lectures and spend the time studying the material on your own.";;"ies" +"3203";"JJB018";"Úvod do fotožurnalistiky";"Lábová,A.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"2";"4";"4";"5";"4";;;"kz" +"3204";"JJB135";"Filmový seminář I";;"Šobr,M.";"4";"1";NULL;NULL;NULL;"4";"4";"5";"1";"3";"4";"3";"4";;;"kz" +"3205";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"4";"4";"2";"1";"2";"2";"2";"5";;;"kz" +"3206";"JEB003";"Ekonomie I";"Fanta,N.,Kracík,J.,Švarcová,N.";"Fanta,N.,Kracík,J.,Švarcová,N.";"5";"4";"5";"5";"3";"1";"1";"2";"1";"5";"3";"5";"5";;;"ies" +"3207";"JLB001";"Angličtina pro sociology I";;"Klírová,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"5";;;"cjp" +"3208";"JJB618";"Propagandistické působení médií v 1 polovině 20. století";"Suk,P.";;"3";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"3";;"Některé informace, které vyučující prezentoval, nebyly pravdivé. Např. nepravdivé tvrzení že twitter syřanky Bany al-Abed byl hoax a že dívka je ve skutečnosti Egypťanka (to je tvrzení Bašára Assáda a ruských dezinformačních webů, dívka mezitím s rodinou uprchla do Turecka a její cesta byla mediálně dokumentována), nebo že Masaryk nemohl ani v nemoci abdikovat, protože Beneš by se kvůli věku nemohl stát prezidentem (naopak byla ČSR téměř jedinou zemí, kde věková hranice prezidentských kandidátů byla 35 a ne 40, právě proto, aby v případě Masarykovy smrti mohl nastoupit Beneš, kterému bylo 35 v roce 1919).";"kms" +"3209";"JLB035";"Francouzština I";;"Bosáková,L.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"4";;;"cjp" +"3210";"JLB001";"Angličtina pro sociology I";;"Klírová,M.";"4";"2";NULL;NULL;NULL;"4";"5";"4";"1";"4";"4";"4";"5";;;"cjp" +"3211";"JPB228";"Mírové smlouvy a konference v mez. systému";"Jeřábek,M.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Přístup doktora Jeřábka k výuce, aktivní zapojování studentů do diskuze a snaha o důkladné vysvětlení souvislostí.";;"kmv" +"3212";"JSB003";"Oborová sociologie";"Numerato,D.";;"5";"3";"5";"5";"3";NULL;NULL;NULL;"2";"4";"4";"4";"4";;;"ks" +"3213";"NMMA701";"Matematika 1";"Spurný,J.";"Skříšovský,E.";"5";"4";"5";"5";"4";"5";"5";"5";"1";"5";"4";"3";"5";;;"ies" +"3214";"JSB544";"Vybrané kapitoly středoškolské matematiky";;"Hendl,J.";"2";"3";NULL;NULL;NULL;"1";"2";"2";"2";"3";"2";"1";"1";;;"ks" +"3215";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"5";"2";"5";"5";"3";NULL;NULL;NULL;"2";"4";"2";"5";"4";;;"ies" +"3216";"JSB023";"Praktika z kvantitativního výzkumu I";;"Tuček,M.";"4";"2";NULL;NULL;NULL;"4";"3";"3";"3";"3";"4";"3";"4";;;"ks" +"3217";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"2";"2";"3";"2";"2";"4";"4";"4";"1";"2";"1";"3";"2";;;"ies" +"3218";"JSB530";"Úvod do sociologického myšlení";"Bureš,J.,Numerato,D.";"Bureš,J.";"4";"3";"3";"4";"1";"5";"5";"5";"1";"4";"4";"5";"4";;;"ks" +"3219";"NMMA701";"Matematika 1";"Spurný,J.";"Skříšovský,E.";"5";"5";"5";"5";"5";"4";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"3220";"JEB022";"Institutional Economics";"Kopečná,V.,Schwarz,J.";;"4";"2";"3";"5";"2";NULL;NULL;NULL;"1";"4";"2";"5";"4";;;"ies" +"3221";"JSB027";"Sociální politika jako společenská praxe";"Dobiášová,K.,Vlčková,K.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"4";"5";;;"kvsp" +"3222";"JEB044";"Financial Accounting";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"2";"4";"4";"2";"2";NULL;NULL;NULL;"1";"2";"2";"2";"2";;;"ies" +"3223";"JSB998";"Úvod do sociologie";"Soukup,P.";;"3";"1";"5";"5";"2";NULL;NULL;NULL;"2";"3";"3";"3";"2";;;"ks" +"3224";"JSB455";"Economic Sociology and European Capitalism";"Blokker,P.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"ks" +"3225";"JLB003";"Angličtina pro ekonomy I";;"Poslušná,L.";"4";"1";NULL;NULL;NULL;"4";"5";"1";"1";"1";"3";"1";"4";;;"cjp" +"3226";"JSB490";"Úvod do politické sociologie";"Císař,O.";;"4";"5";"5";"4";"5";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"ks" +"3227";"JEM020";"Ethics and Economics";"Cahlík,T.";"Cahlík,T.";"5";"4";"5";"5";"4";"5";"5";"2";"1";"5";"5";"5";"5";;;"ies" +"3228";"JPM910";"The Nature and Function of the State";"Franěk,J.,Pettit,P.";;"5";NULL;"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"3229";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"4";"3";NULL;NULL;NULL;"5";"5";"4";"2";"3";"2";"3";"5";;;"ies" +"3230";"JSB031";"Bakalářský seminář I - diskuse projektů";;"Angelovská,O.,Mouralová,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"3";"4";"3";"5";"Oceňuji skvělý přístup cvičících, nadšení a pochopení. Líbí se mi jejich touha udělat semináře ještě lepší, taky se nás někdo ptá na názor na výuku během semestru. Úkoly mě občas nebavily, ale aspoň mě donutily máknut na bakalářce a něco napsat. Taky jsem se naučila různé techniky, jak text zdokonalit. Děkuji oběma cvičícím.";;"ks" +"3231";"JSB537";"Analýza dat v SPSS";"Soukup,P.";"Oreský,J.";"4";"4";"5";"5";"4";"5";"5";"5";"3";"5";"5";"5";"5";"Oceňuji hlavně přístup cvičících (Oreský, Líbal), výborné vysvětlení a ochota dělat doučování, a to i o víkendu.";;"ks" +"3232";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Coufalová,L.,Svobodová,T.";"4";"3";"5";"5";"3";"5";"5";"5";"2";"4";"5";"5";"5";;;"ks" +"3233";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"4";"1";NULL;NULL;NULL;"4";"4";"3";"2";"3";"4";"3";"5";;;"ies" +"3234";"JSB051";"Úvod do sociální antropologie";"Uherek,Z.";"Hrešanová,E.";"3";"3";"4";"4";"2";"2";"2";"4";"2";"3";"3";"4";"3";;;"ks" +"3235";"JSB025";"Sociální problémy";"Frič,P.";;"4";"4";"4";"4";"3";NULL;NULL;NULL;"2";"4";"4";"4";"4";;;"kvsp" +"3236";"JSB007";"Dějiny sociologie I";"Šanderová,J.";"Orcígr,V.";"3";"5";"3";"3";"2";"5";"5";"4";"2";"4";"4";"4";"4";;;"ks" +"3237";"JJB600";"Úvod do studia médií I.";"Nečas,V.";;"4";"5";"5";"4";"3";NULL;NULL;NULL;"2";"2";"2";"3";"3";"Přednášky jsou velice přínosné a zajímavé, pan Nečas měl vždy zajímavé příklady v médiích.";"Nechci nic zlepšit, nicméně je kurz velice náročný, spousta teorie, testy se skládají hlavně z té teorie, která se bohužel k intenzivitě studia (kombinovaná forma) čte hlavně doma. Pracuji v mediální agentuře, ale souvislosti s praxí a tím, co jsme se učili na testy, si vůbec neuvědomuji.";"kms" +"3238";"JSB407";"Globální problémy životního prostředí a udržitelný rozvoj";"Drhová,Z.";;"4";"3";"4";"4";"3";NULL;NULL;NULL;"2";"4";"3";"3";"4";;"Doporučila bych trochu vylepšit komunikaci se studenty. Občas jsme nevěděli, co se po nás přesně chce.";"kvsp" +"3239";"JEB105";"Statistics";"Červinka,M.";"Hanus,L.";"5";"5";"4";"4";"3";"4";"5";"5";"1";"5";"3";"4";NULL;;;"ies" +"3240";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"2";"4";"2";"4";"5";"Osoba vyučujícího.";;"kp" +"3241";"NMMA703";"Matematika 3";"Zelený,M.";"Bartoš,A.";"5";"4";"5";"5";"5";"3";"3";"3";"1";"5";"3";"4";"5";;;"ies" +"3242";"JJB221";"Ekonomická teorie marketingu";"Postler,M.";;"2";"1";"3";"1";"3";NULL;NULL;NULL;NULL;"2";"1";"1";"1";;;"kmkpr" +"3243";"JJB066";"Rozhlas a televize ve světě";"Moravec,V.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"1";"5";"4";;;"kz" +"3244";"JSB010";"Současná sociologie";"Balon,J.";;"3";"4";"2";"3";"3";NULL;NULL;NULL;"1";"4";"2";"3";"3";"Oceňuji, jak velké má p.Balon znalosti. Jen by bylo potřeba je předat trochu záživněji. Super nápas s prezentacemi studentů.";"Zdá se, že p. Balona přednášení a výuka studentů obecně moc nebaví a je to vidět na průběhu přednášek i na postupně klesající účasti studentů.";"ks" +"3245";"JEB111";"Advanced Data Analysis in MS Excel";;"Poláková,N.,Polák,P.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"3";"5";"5";"3";"5";;;"ies" +"3246";"JJB067";"Mluvní a pohybová výchova I";;"Pavel,L.";"3";"1";NULL;NULL;NULL;"4";"3";"3";"1";"1";"4";"2";"3";"DObrou atmosféru v malých skupinkách";"Viz zpětná vazba odeslaná přímo kantorovi";"kz" +"3247";"JSB534";"Introduction to Visual Sociology";"Wladyniak,L.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"5";"3";"5";;;"ks" +"3248";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"3";"4";"1";"3";NULL;NULL;NULL;"2";"3";"4";"4";"1";;;"ies" +"3249";"JSB133";"Zemědělství a rozvoj venkova (vybraná témata z rurální sociologie)";"Zagata,L.";;"5";"3";"5";"4";"3";NULL;NULL;NULL;"1";"4";"3";"4";"5";;;"ks" +"3250";"JJB601";"Rozbor právního prostředí v českých médiích";"Benda,J.";;"5";"5";"3";"2";"3";NULL;NULL;NULL;"1";"5";"3";"5";"3";"Ač byl kurz a přednášky hodně o paragrafech, tak závěrečný test byl z teorie, která je určitě přínosná i v mediálním oboru.";"Vylepšila bych komunikaci mezi učitelem a studenty, dostávali jsme spoustu úkolů, které buď prošly nebo neprošly, nicméně jsme nikdy nedostali zpětnou vazbu, co bylo špatně. Při porovnávání s ostatními studenty mezi námi byli minimální rozdíly a přitom někdo prošel a někdo ne. Věřím, že to bylo spravedlivé, nicméně by bylo lepší to odůvodnit.";"kms" +"3251";"JMB173";"Vnitřní dějiny Balkánu do roku 1914";"Šesták,M.";;"5";"3";"4";"5";"5";NULL;NULL;NULL;"2";"5";"3";"5";"5";;;"krvs" +"3252";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"Interakce vyučujícího a studentů.";;"kp" +"3253";"JSB004";"Sociální nerovnosti";"Šanderová,J.";;"4";"4";"4";"5";"3";NULL;NULL;NULL;"2";"4";"2";"3";"4";;;"ks" +"3254";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"4";"2";"5";"4";"5";NULL;NULL;NULL;"1";"3";"4";"5";"5";;;"krvs" +"3255";"JSB517";"Hudební subkultury mládeže";"Oravcová,A.";;"3";"3";"4";"4";"3";NULL;NULL;NULL;"1";"4";"2";"2";"4";;;"ks" +"3256";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"1";"4";"1";"1";"1";NULL;NULL;NULL;"1";"3";"1";"3";"1";;"Vyučujícího.";"ies" +"3257";"JMB402";"Úvod do společenských věd II";;"Jasenčáková,M.";"4";"3";NULL;NULL;NULL;"4";"5";"5";"2";"4";"5";"3";"4";;;"krvs" +"3258";"JPB020";"Úvod do mezinárodních vztahů";"Karlas,J.,Kučera,T.";;"3";"3";"3";"5";"4";NULL;NULL;NULL;"2";"4";"2";"4";"4";;"Více práce s konkrétními příkldy.";"kmv" +"3259";"JJB605";"Novodobá historie médií";"Bednařík,P.,Končelík,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Přístup učitelů ke studentům, praktické ukázky...";"Nic";"kms" +"3260";"JMMZ141";"Russian Language I";;"Shvedova,O.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Having the course three times a week was very helpful as it gave us plenty of time to practice. I liked the focus on speaking and listening rather than only doing book work. The level of interaction was really high and helped us all to feel comfortable and improve. The instructor was always available to help us which was very valuable.";;"krvs" +"3261";"JLB001";"Angličtina pro sociology I";;"Klírová,M.";"4";"3";NULL;NULL;NULL;"3";"5";"3";"1";"3";"3";"3";"4";;"Vyučující trvalo opravování našich prací. Domníváme se, že je ani nečetla, protože nakonec nebyly opravené.";"cjp" +"3262";"JJB616";"Vybrané novinářské osobnosti 19. století";"Sekera,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"4";"4";"Ukázky dobový fotografií, videí...";"Nic";"kms" +"3263";"JJB618";"Propagandistické působení médií v 1 polovině 20. století";"Suk,P.";;"5";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";"Prezentace hodně formou fotografií...";"Nic";"kms" +"3264";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"1";"1";"1";"1";"5";;;"kz" +"3265";"JLB041";"Španělština I";;"Mlýnková,L.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"3";"5";;;"cjp" +"3266";"JMM130";"Ethno-Political Conflicts in the Caucasus";"Brisku,A.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"The course was very interesting and provided us with valuable insight on how to approach ethno-political conflict. The coursework pushed us to develop our critical thinking skills and to approach issues from a variety of perspectives. The instructor was always available to help and clearly cared a great deal about the subject.";;"krvs" +"3267";"JSB004";"Sociální nerovnosti";"Šanderová,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"3";"5";"2";"5";"5";;;"ks" +"3268";"JJB620";"Mediální produkce - žurnalistická práce I.";"Vlasák,Z.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"5";"5";"Praktické zkoušení, úkoly z praxe";"Nic";"kms" +"3269";"JSB537";"Analýza dat v SPSS";"Soukup,P.";"Oreský,J.";"4";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"4";"4";"Cvičící";"Dostupnost SPSSDostatek cvičících a jejich včasné zajištěníOhlášení látky potřebné ke státnicím s předstihem";"ks" +"3270";"JJB014";"Žurnalistická tvorba III - Časopisecká tvorba";"Osvaldová,B.";"Maňák,V.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"Časopisecká tvorba pod vedením pánů Hájka a Maňáka mě osobně (a mnohé mé kolegy) zatím o psaní a o současném novinářském provozu naučila nejvíce. Tento předmět byl bezkonkurenčně tím nejpřínosnějším v dosavadním studiu žurnalistiky. O žánrech jsme se, jejich přímou tvorbou, naučili mnohem více než v předešlých teoretických přednáškách v prvním ročníku. Vedení a velká porce času nám věnovaná od pedagogů byly obrovským přínosem. I teoretická část kurzu byla dobře podaná, tak, aby nás bavila a co nejvíc - motivovala. Tu motivaci a inspiraci bych ocenil asi nejvíce. Nevím, co mě čeká v dalších semestrech, ale s ohledem na dosavadní studium, kéž by takovýchto seminářů bylo více.";"Snad jen, aby jej nadále vyučovali pánové Hájek a Maňák, i když už to asi není reálné.";"kz" +"3271";"JSB007";"Dějiny sociologie I";"Šanderová,J.";"Bureš,J.";"3";"5";"3";"5";"3";"5";"5";"4";"2";"3";"4";"4";"4";;;"ks" +"3272";"JSM508";"Psaní diplomové práce";;"Dobiášová,K.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Doporučuji zachovat vedení kurzu distanční formou s možnými individuálními konzultacemi paní doktorky Dobiášové. Ona je skvělá, dokáže k psaní práce namotivovat a dát užitečné rady. Velmi užitečná je možnost vyzkoušet si obhajobu diplomové práce nanečisto. Děkuji za tento kurz.";"Nic, všechno je skvělé.";"kvsp" +"3273";"JSB025";"Sociální problémy";"Frič,P.";;"4";"4";"4";"3";"3";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kvsp" +"3274";"JSB311";"Antropologie náboženství";"Spalová,B.";;"4";"2";"5";"5";"3";NULL;NULL;NULL;"1";"4";"2";"4";"4";;"dřívější opravování úkolůjasnější interpretace zadání úkolůčas exkurzí";"ks" +"3275";"JJB009";"Úvod do psychologie";"Vranka,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"kz" +"3276";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"4";"4";"5";"5";"2";NULL;NULL;NULL;"1";"4";"2";"1";"4";;;"ies" +"3277";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"4";"3";NULL;NULL;NULL;"5";"4";"5";"1";"3";"3";"4";"5";;;"ies" +"3278";"JSB133";"Zemědělství a rozvoj venkova (vybraná témata z rurální sociologie)";"Zagata,L.";;"5";"3";"5";"5";"3";NULL;NULL;NULL;"1";"4";"1";"4";"5";;;"ks" +"3279";"JJB002";"Dějiny masových médií II";"Sekera,M.";;"2";"3";"4";"4";"4";NULL;NULL;NULL;"4";"2";"1";"2";"3";;;"kms" +"3280";"JJB006";"Současný český jazyk III";;"Svobodová,I.";"3";"2";NULL;NULL;NULL;"2";"3";"3";"3";"3";"4";"2";"3";;;"kms" +"3281";"JSB131";"Velké empirické výzkumy ČR";"Tuček,M.";;"4";"2";"4";"5";"4";NULL;NULL;NULL;"1";"5";"1";"4";"5";;;"ks" +"3282";"JMB533";"Česká republika v integračních procesech";"Šlosarčík,I.";;"5";"4";"5";"4";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";"Výklad pana profesora";;"kzs" +"3283";"JMM009";"Dokumenty amerických dějin I";;"Kýrová,L.";"3";"3";NULL;NULL;NULL;"5";"5";"4";"2";"3";"3";"3";"2";;;"kas" +"3284";"JJB009";"Úvod do psychologie";"Vranka,M.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"2";"5";"2";"3";"4";;;"kz" +"3285";"JJB003";"Dějiny masových médií III";"Bednařík,P.,Končelík,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Skvělý přístup vyučujících ke studentům, zábavné a zajímavé přednášky";;"kms" +"3286";"JLB003";"Angličtina pro ekonomy I";;"Poslušná,L.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"5";"5";;;"cjp" +"3287";"JJB006";"Současný český jazyk III";;"Svobodová,I.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"5";"5";"Ochotná vyučující a skvělý přístup ke studentům";;"kms" +"3288";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"1";"2";"2";"2";"1";"2";"2";"1";"2";"3";"2";"2";"2";;;"ies" +"3289";"JJB014";"Žurnalistická tvorba III - Časopisecká tvorba";"Osvaldová,B.";"Maňák,V.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"kz" +"3290";"JJB021";"Bakalářský seminář";;"Prázová,I.";"3";"1";NULL;NULL;NULL;"3";"5";"3";"1";"2";"3";"2";"3";;"Přijde mi zbytečné, aby studenti absolvovali kurz ve druhém ročníku. Než se dostanou do třetího, kde se bakalářské práce řeší nejvíce, vše zapomenou.";"kz" +"3291";"JSB031";"Bakalářský seminář I - diskuse projektů";;"Numerato,D.";"4";"3";NULL;NULL;NULL;"5";"5";"3";"1";"5";"5";"3";"4";;;"ks" +"3292";"JJM320";"Propaganda a československá média po roce 1948";"Suk,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kms" +"3293";"NMMA701";"Matematika 1";"Spurný,J.";"Rondoš,J.";"5";"5";"5";"5";"5";"2";"4";"3";"1";"5";"5";"4";"5";;;"ies" +"3294";"JJB019";"Práce s agenturními informacemi";"Prázová,I.,Trunečková,L.";"Prázová,I.,Trunečková,L.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"Ochotná vyučující s lidským přístupem ke studentům, zajímavé přednášky Jakuba Troníčka";;"kz" +"3295";"JJB014";"Žurnalistická tvorba III - Časopisecká tvorba";"Osvaldová,B.";"Maňák,V.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"Ochotní a v oboru zkušení vyučující, zajímavé přednášky";;"kz" +"3296";"JJB143";"Žurnalistika a feminismus";"Krobová,T.,Osvaldová,B.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"3";"5";"Skvělá vyučující, která umí posluchače zaujmout";;"kz" +"3297";"JJB019";"Práce s agenturními informacemi";"Prázová,I.,Trunečková,L.";"Prázová,I.,Trunečková,L.";"2";"2";"2";"4";"1";"3";"4";"2";"1";"4";"2";"4";"2";;;"kz" +"3298";"JJB169";"Věda v médiích";"Kasík,P.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"5";"Vyučující schopný studenty zaujmout";;"kz" +"3299";"JJB021";"Bakalářský seminář";;"Prázová,I.";"2";"2";NULL;NULL;NULL;"3";"4";"2";"1";"3";"4";"2";"3";;;"kz" +"3300";"JSB998";"Úvod do sociologie";"Soukup,P.";;"4";"4";"4";"4";"3";NULL;NULL;NULL;"1";"4";"3";"3";"4";;;"ks" +"3301";"JEB026";"European Economic Integration";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"2";"3";"2";"3";"1";"4";"4";"1";"1";"2";"2";"2";"1";;"Bylo by zcela jistě vhodnější, aby přednášky probíhaly jinou formou než pouhým předčítáním slidů “slovo od slova“. Počet slidů jednotlivých lekcí je neadekvátní, a vzhledem k velkému množství faktografických informací sestává učení na zkoušku v zásadě jen z memorování těchto údajů namísto chápání modelů a souvislostí, které bylo v kurzu několikrát zdůrazněno jako to podstatné. Je potřeba zde zmínit, že závěrečná zkouška nebyla příliš náročná, což ovšem nevypovídá o kvalitě kurzu jako takového. Přístupu cvičících, myslím, není příliš co vytknout.";"ies" +"3302";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"5";"4";"4";"2";"5";"5";"5";"5";"Pana přednášejícího!";;"kz" +"3303";"JJB243";"Aktuální trendy a vývoj v oboru I.";"Hejlová,D.,Vranka,M.";"Hejlová,D.,Vranka,M.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"3";"5";"5";;;"kmkpr" +"3304";"JJB135";"Filmový seminář I";;"Šobr,M.";"4";"3";NULL;NULL;NULL;"4";"4";"3";"1";"3";"4";"3";"5";"Oceňuji možnost dostat se na nové filmy a taky výběr těchto filmů";"Nic";"kz" +"3305";"JMM273";"Diplomový seminář II";;"Kasáková,Z.";"3";"5";NULL;NULL;NULL;"3";"4";"2";"2";"2";"3";"1";"2";"Samozřejmě je fajn, že předmět studenta nutí pracovat na své diplomové práce.";"Užitečné by bylo, aby studenti dostali příklad napsaného \"ideálního\" výzkumného rámce, abychom si mohli představit, co vlastně máme psát. Jinak se studenti stále hledají a pořádně netuší, jak má výzkumný rámec vypadat a jak má vypadat naplnění požadavků na výzkumný rámec.";"krvs" +"3306";"JEB044";"Financial Accounting";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"ies" +"3307";"JEB110";"Econometrics II";"Pertold-Gebicka,B.";"Chorna,O.,Malinská,B.,Pertold-Gebicka,B.,Pleticha,P.";"4";"4";"3";"4";"2";"4";"4";"3";"1";"5";"3";"4";"4";"- množství a formu úkolů";"- midterm: především formulaci otázek a hodnocení jednotlivých podotázek bez ohledu na předchozí odpověď";"ies" +"3308";"JSB998";"Úvod do sociologie";"Soukup,P.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"3";"2";"2";"3";"4";;;"ks" +"3309";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"4";"4";"4";"4";"3";NULL;NULL;NULL;"3";"3";"3";"4";"4";;;"kas" +"3310";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"3";"3";"5";"5";"3";NULL;NULL;NULL;"2";"3";"3";"3";"4";;;"knrs" +"3311";"JMB402";"Úvod do společenských věd II";;"Čapinská,B.";"4";"4";NULL;NULL;NULL;"4";"5";"5";"3";"5";"5";"4";"4";;;"krvs" +"3312";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Kocian,J.";"4";"5";"5";"5";"3";"4";"5";"5";"3";"4";"2";"4";"3";;;"knrs" +"3313";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Andrle,J.";"4";"5";"4";"4";"3";"4";"4";"4";"3";"3";"2";"4";"2";;;"krvs" +"3314";"JLB037";"Italština I";;"Přívozníková,P.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"The teacher was very very helpful and always willing to explain problems we encountered. Beginners were also accepted to the course and I thing they benefited from the course significantly.";"Create a separate seminar for beginer students which would complete the acquired level of Italian.";"cjp" +"3315";"JPB221";"Metodologický proseminář I";;"Komasová,S.,Parízek,M.";"5";"3";NULL;NULL;NULL;"5";"3";"3";"1";"4";"4";"4";"5";;;"kmv" +"3316";"JEB110";"Econometrics II";"Pertold-Gebicka,B.";"Chorna,O.,Malinská,B.,Pertold-Gebicka,B.,Pleticha,P.";"1";"4";"2";"4";"3";"5";"5";"3";"1";"4";"4";"2";"2";;"The biggest problem of the semester was that we have never managed to cover all the material during lectures and then seminar's teachers needed to catch up resulting in very poor quality of practical problems explanations. It would be very beneficial to adjust the amount of chapters to the time available. Also, I would appreciate more practical examples to see real applications as the theory was getting too abstract to understand.";"ies" +"3317";"JPB020";"Úvod do mezinárodních vztahů";"Karlas,J.,Kučera,T.";;"3";"5";"3";"3";"3";NULL;NULL;NULL;"3";"4";"4";"4";"1";;;"kmv" +"3318";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"3";"5";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kp" +"3319";"JPB018";"Dějiny mezinárodních vztahů I";"Soukup,J.";;"3";"4";"3";"4";"3";NULL;NULL;NULL;"2";"4";"4";"3";"3";;;"kmv" +"3320";"JPB252";"České novověké dějiny I.";"Kučera,J.";;"4";"3";"4";"3";"3";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kp" +"3321";"JPB256";"Politické systémy I";"Brunclík,M.,Jeřábek,M.,Kotábová,V.,Perottino,M.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"2";"4";"3";"4";"4";;;"kp" +"3322";"JEB026";"European Economic Integration";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"2";"3";"2";"4";"2";"2";"2";"2";"1";"4";"2";"2";"2";"Very important topics in today's European society";"Teacher only reading lecture slides does not make the lecture very interesting nor enriching. I do not critisćize him as there vere so many topics that it could not be manageable in 80 minutes otherwise. I would suggest to focus more on some topics only and explain/discuss them more broadly, on examples and more intuitivly.";"ies" +"3323";"JSB023";"Praktika z kvantitativního výzkumu I";;"Tuček,M.";"4";"3";NULL;NULL;NULL;"3";"3";"2";"2";"2";"5";"3";"4";;;"ks" +"3324";"JLB053";"Angličtina pro sociální vědy I";;"Gloverová,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"4";"5";"Paní Gloverová sama o sobě byla úžasná. Vstřícná, hodna, proste super";"Nic";"cjp" +"3325";"JLB005";"Angličtina pro politology I";;"Panešová,K.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";;;"cjp" +"3326";"JMB402";"Úvod do společenských věd II";;"Mertová,V.";"5";"3";NULL;NULL;NULL;"4";"5";"5";"1";"5";"5";"4";"5";"Procvičování věcí, co se nám budou hodit, naopak které se nám hodit moc nebudou tak se jimi zbytečně nezabývali";"Nic";"krvs" +"3327";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"3";"5";"2";"1";NULL;NULL;NULL;"1";"4";"2";"1";"1";"Psaní eseje";"Přístup vyučujícího nebo změna vyučujícího.";"ies" +"3328";"JLB001";"Angličtina pro sociology I";;"Klírová,M.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"3";"4";;;"cjp" +"3329";"JSB007";"Dějiny sociologie I";"Šanderová,J.";"Hanzal,P.";"4";"4";"5";"5";"4";"4";"3";"4";"3";"4";"4";"4";"4";;;"ks" +"3330";"JSB021";"Základy demografie";"Šídlo,L.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"2";"4";"4";"5";"5";;;"ks" +"3331";"JSB025";"Sociální problémy";"Frič,P.";;"3";"4";"5";"4";"3";NULL;NULL;NULL;"2";"3";"2";"3";"4";;;"kvsp" +"3332";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Rybín,F.,Vlčková,A.";"5";"4";"5";"5";"4";"4";"5";"5";"2";"5";"5";"5";"5";;;"ks" +"3333";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Buřičová,B.";"4";"4";"5";"3";"3";"4";"5";"5";"1";"4";"4";"4";"4";;;"ks" +"3334";"JSB544";"Vybrané kapitoly středoškolské matematiky";;"Hendl,J.";"3";"3";NULL;NULL;NULL;"5";"3";"4";"1";"3";"3";"3";"2";;;"ks" +"3335";"JMB065";"Úvod do mezinárodního a evropského práva";"Šlosarčík,I.";;"3";"4";"3";"4";"4";NULL;NULL;NULL;"1";"4";"3";"4";"3";;;"kzs" +"3336";"JMBZ289";"Central European Culture from the 19th Century to 1945";"Emler,D.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"knrs" +"3337";"JMB068";"Komunistické vládnutí v Československu: prosazování, podoba a společenská reflexe (1945 až dodnes)";"Cuhra,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Nejvíce oceňuji styl výuky. Z hodin jsem si toho hodně zapamatovala.";"Nic mě nenapadá, se vším jsem byla spokojená.";"krvs" +"3338";"JSB051";"Úvod do sociální antropologie";"Uherek,Z.";"Hrešanová,E.";"3";"5";"3";"4";"4";"4";"4";"4";"2";"4";"3";"3";"4";;;"ks" +"3339";"JSB311";"Antropologie náboženství";"Spalová,B.";;"5";"5";"5";"5";"4";NULL;NULL;NULL;"2";"4";"4";"3";"5";"Zajímavá témata, dobří vyučující i hostující přednášející, dobrý přístup a komunikace se studenty";;"ks" +"3340";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"4";"3";"1";"1";NULL;NULL;NULL;"1";"4";"1";"3";"2";;;"ies" +"3341";"JLB001";"Angličtina pro sociology I";;"Klírová,M.";"4";"2";NULL;NULL;NULL;"4";"5";"5";"1";"3";"4";"4";"5";;;"cjp" +"3342";"JSB007";"Dějiny sociologie I";"Šanderová,J.";"Hanzal,P.";"5";"4";"5";"5";"5";"5";"5";"5";"3";"5";"5";"5";"5";;;"ks" +"3343";"JSB025";"Sociální problémy";"Frič,P.";;"5";"4";"3";"4";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kvsp" +"3344";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Rybín,F.,Vlčková,A.";"5";"5";"4";"5";"5";"2";"5";"4";"1";"5";"3";"4";"5";;;"ks" +"3345";"JSB490";"Úvod do politické sociologie";"Císař,O.";;"3";"5";"4";"2";"4";NULL;NULL;NULL;"2";"4";"3";"3";"4";;"Pana vyučujícího naštvalo, že studenti nechodili na přednášky, což sice chápu, ale zároveň povinné to není, takže za to neměl nikoho penalizovat, což se dělo. Studenti, kteří šli na předtermín zkoušky dostali všichni A s tím, že \"Veselé vánoce\" a my někteří, kteří jsme se zrovna na předtermín nemohli dostavit, trpíme na tom, že teď rozdává F asi nehledě na výkon nebo jen proto, že o někom usoudí, že opisoval ze sdílených poznámek bez jediného důkazu.";"ks" +"3346";"JSB530";"Úvod do sociologického myšlení";"Bureš,J.,Numerato,D.";"Teichmanová,K.";"5";"2";NULL;"5";"5";"4";"5";"5";"1";"3";"4";"4";"3";;;"ks" +"3347";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Hanzlík,P.";"4";"4";"4";"4";"4";"5";"5";"5";"2";"4";"5";"3";"5";;;"ks" +"3348";"JSM578";"Anthropology of EU";"Uherek,Z.";;NULL;NULL;"4";"4";"4";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"ks" +"3349";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Buřičová,B.";"3";"5";"2";"4";"1";"5";"5";"5";"1";"5";"5";"5";"3";;;"ks" +"3350";"JSB544";"Vybrané kapitoly středoškolské matematiky";;"Hendl,J.";"3";"4";NULL;NULL;NULL;"2";"3";"3";"1";"5";"4";"3";"1";;;"ks" +"3351";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Coufalová,L.,Svobodová,T.";"5";"3";"5";"5";"4";"5";"5";"5";"1";"5";"5";"5";"5";;;"ks" +"3352";"JSB543";"Digitální etnografie";;"Hrešanová,E.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"5";;;"ks" +"3353";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Buřičová,B.";"3";"2";"3";"3";"3";"5";"5";"5";"1";"4";"4";"4";"4";;;"ks" +"3354";"JSB027";"Sociální politika jako společenská praxe";"Dobiášová,K.,Vlčková,K.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kvsp" +"3355";"JJB611";"Česká média po roce 1990";"Jirák,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"3";"5";"5";"5";"Celkové vedení kurzu, přístup ke studentům - náplň vyžadující účast v průběhu - průběžné testy + prezentace jako klíčová část hodnocení.";;"kms" +"3356";"JSB021";"Základy demografie";"Šídlo,L.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"3";"4";;;"ks" +"3357";"JJB612";"Média a životní styl";"Knapík,J.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"3";"3";"4";"3";"Výborný vyučující, vstřícný ke studentům, v testech se objevuje opravdu to, co je součástí přednášek - žádné \"chytáky\"";"Obsah kurzu se bohužel dost překrývá s předměty, které jsme plnili v loňských ročnících a tak už se nelze dozvědět mnoho nového, ve většině látky se jednalo o rekapitulaci již řečeného";"kms" +"3358";"JLB047";"Ruština obecná I";;"Mistrová,V.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"3";"5";;;"cjp" +"3359";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"4";"5";"3";"1";"2";"2";"2";"5";;;"kz" +"3360";"JSB131";"Velké empirické výzkumy ČR";"Tuček,M.";;"4";"2";"3";"4";"3";NULL;NULL;NULL;"3";"5";"2";"4";"5";;;"ks" +"3361";"JSB003";"Oborová sociologie";"Numerato,D.";;"4";"2";"5";"5";"5";NULL;NULL;NULL;"2";"4";"3";"5";"5";;;"ks" +"3362";"JSB004";"Sociální nerovnosti";"Šanderová,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"3";"4";"3";"5";"5";;;"ks" +"3363";"JJB607";"Analýzy mediálních obsahů";"Křeček,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Kurz je se v mnohém liší od většiny ostatních vyučovaných předmětů, je vyžadována vlastní a precizní práce, ale ne pouze formou seinárek - to je na VŠ příjemná změna";"Kurz opakuji, protože v loňském roce pro mne byly instrukce velmi obtížně pochopitelné - tudíž navrhuji zlepšit uvedení do předmětu tak, aby ho pochopila i část studentů, která se ve své praxi dosud s analýzami tohoto typu nesetkala";"kms" +"3364";"JSB010";"Současná sociologie";"Balon,J.";;"4";"3";"5";"5";"3";NULL;NULL;NULL;"3";"5";"3";"5";"4";;;"ks" +"3365";"JSB311";"Antropologie náboženství";"Spalová,B.";;"5";"5";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"4";"5";"Zachovala bych nadále 3 exkurze (dříve, myslím, bývala pouze 1) - rozšíření obzoru.";;"ks" +"3366";"JSB537";"Analýza dat v SPSS";"Soukup,P.";"Oreský,J.";"4";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"4";"5";"Cvičícího Oreského (přátelský přístup, pomoc při nesnázích, vycházel vstříc s požadavky, dobré vysvětlení látky).";"Nevyhovoval mi nový systém výuky SPSS, 1 semestr oproti dřívějším dvěma. Nedostatek času pro vyložení veškeré látky na přednášce i na cvičeních.";"ks" +"3367";"JMM629";"Hollywood/Europe: A Transnational Film Culture.";"Nowell,R.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kas" +"3368";"JSB055";"Současná sociální antropologie";;"Grygar,J.,Hrešanová,E.";"4";"4";NULL;NULL;NULL;"5";"4";"4";"1";"5";"3";"5";"4";;;"ks" +"3369";"JSB033";"Praktika z kvalitativního výzkumu";;"Spalová,B.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"4";"5";;;"ks" +"3370";"JSB031";"Bakalářský seminář I - diskuse projektů";;"Balon,J.";"4";"3";NULL;NULL;NULL;"4";"5";"4";"3";"4";"5";"3";"4";;;"ks" +"3371";"JJB625";"Manipulace v audiovizuálním sdělení";"Štoll,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Obsah předmětu se dost prolíná do praxe - aktuální a zajímavé.";;"kms" +"3372";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";NULL;NULL;NULL;NULL;"4";"4";"4";NULL;NULL;NULL;NULL;NULL;;;"kz" +"3373";"JJB613";"Úvod do studia nových médií";"Jirků,J.";;"4";"4";"3";"4";"5";NULL;NULL;NULL;"1";"4";"5";"4";"4";;;"kms" +"3374";"JJB226";"Strategie a finanční management marketingu I";"Majerik,P.";;NULL;NULL;"5";"5";"5";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kmkpr" +"3375";"JJB231";"Dějiny a teorie public relations";"Halada,J.,Hejlová,D.";;NULL;NULL;"5";"4";"5";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kmkpr" +"3376";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"5";"3";"5";"5";"4";"4";"4";"4";"2";"5";"5";"3";"5";;;"ies" +"3377";"JEM034";"Corporate Finance";"Bychkova,O.,Malířová,T.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"2";"4";"3";;;"ies" +"3378";"JEM184";"New Keynesian DSGE Modeling";"Maršál,A.,Svačina,D.";"Maršál,A.,Svačina,D.";"4";"3";"4";"5";"5";"4";"5";"5";"2";"5";"5";"5";"5";;;"ies" +"3379";"JEM183";"Mathematical Methods in Macroeconomics";"Stráský,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"ies" +"3380";"JLB041";"Španělština I";;"Mlýnková,L.";"5";"4";NULL;NULL;NULL;"4";"5";"5";"1";"4";"4";"4";"5";"Energický prístup vyučujúcej, vďaka ktorej sa mi podarilo mnohé vedomosti upevniť a zároveň zistiť, kde sú moje medzery z predošlého štúdia. Nebolo ťažké rozumieť jej výkladu a dve hodiny ubehli celkom rýchlo - vyučujúca dokázala udržať našu pozornosť celý čas aj v pondelok o 8. ráno, čo by na inom predmete zrejme nebolo možné. Týždenné emaily s popisom diania na hodine a materiálmi sú veľmi užitočné, pokiaľ niekto nebol na hodine alebo si nestihol poznačiť všetky domáce úlohy.";;"cjp" +"3381";"JJB600";"Úvod do studia médií I.";"Nečas,V.";;"5";"4";"5";"4";"4";NULL;NULL;NULL;"2";"4";"4";"4";"5";;;"kms" +"3382";"JJB601";"Rozbor právního prostředí v českých médiích";"Benda,J.";;"3";"4";"3";"3";"3";NULL;NULL;NULL;"1";"4";"3";"4";"3";;;"kms" +"3383";"JJB605";"Novodobá historie médií";"Bednařík,P.,Končelík,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"3384";"JJB616";"Vybrané novinářské osobnosti 19. století";"Sekera,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"3385";"JJB620";"Mediální produkce - žurnalistická práce I.";"Vlasák,Z.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kms" +"3386";"JMB079";"The Geography of North America";"Pitoňák,M.";;"5";"4";"4";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"The course provides a deeper look at the different regions of the North America. It made me realize the patterns which are present within the continent – even though I was subconsciously aware of them, I never had the separate thoughts interconnected. This course makes you think and understand the region, its culture and how the geography determines and affects people's behaviour or lifestyle. This was also possible thanks to the cultural references made by the lecturer (books, films, TV series) and being a geek I really appreciated them. I also liked the visually great presentations, they were the neatest I've ever seen.";;"kas" +"3387";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"3";"4";"2";"2";NULL;NULL;NULL;"1";"4";"2";"3";"2";;;"ies" +"3388";"JJB224";"Psychologie marketingové komunikace";"Vranka,M.";;"4";"4";"4";"5";"3";NULL;NULL;NULL;"2";"4";"2";"3";"4";;;"kmkpr" +"3389";"JMB197";"Kapitoly z moderních dějin Itálie";"Mejstřík,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"4";"5";"4";"5";"5";"Jeden z najlepších kurzov na IMS, vďaka ktorému som si zamilovala taliansku históriu. Veľká pomoc k skúške z Moderných dejín západnej Európy. Týždenné domáce úlohy pomáhajú upevniť si vedomosti a hľadaním a formulovaním odpovede sa človek priebežne učí na záverečnú skúšku. Skvelá bola aj návšteva talianskeho veľvyslanectva.";;"kzs" +"3390";"JJB226";"Strategie a finanční management marketingu I";"Majerik,P.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"2";"4";"5";;;"kmkpr" +"3391";"JLB037";"Italština I";;"Přívozníková,P.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"2";"5";;;"cjp" +"3392";"JJB231";"Dějiny a teorie public relations";"Halada,J.,Hejlová,D.";;"3";"5";"4";"4";"3";NULL;NULL;NULL;"1";"5";"2";"5";"4";;;"kmkpr" +"3393";"JJB235";"Proces tvorby v marketingové komunikaci";"Bezouška,M.";;"4";"2";"4";"5";"4";NULL;NULL;NULL;"2";"4";"3";"5";"4";;;"kmkpr" +"3394";"JLB053";"Angličtina pro sociální vědy I";;"Štěpánková,D.";"4";"1";NULL;NULL;NULL;"4";"5";"4";"1";"4";"3";"3";"5";;;"cjp" +"3395";"JJB255";"Digitální komunikace";;"Klimeš,D.";"5";"3";NULL;NULL;NULL;"4";"5";"3";"1";"2";"2";"3";"5";;;"kmkpr" +"3396";"JJB269";"Sociální kontext komunikace";"Vranka,M.";;"4";"4";"4";"4";"2";NULL;NULL;NULL;"1";"3";"2";"3";"4";;;"kmkpr" +"3397";"JJB401";"Komerční a nekomerční marketingová komunikace";"Báča,L.,Obluk,O.";;"5";"4";"5";"4";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kmkpr" +"3398";"JSB007";"Dějiny sociologie I";"Šanderová,J.";"Cuker,I.";"4";"3";"5";"5";"5";"5";"5";"5";"3";NULL;"4";"5";"4";;;"ks" +"3399";"JJB407";"Bakalářský proseminář";"Rosenfeldová,J.";;"3";"2";"4";"5";"2";NULL;NULL;NULL;"3";"3";"2";"3";"4";;;"kmkpr" +"3400";"JLB045";"Angličtina pro marketing I";;"Stružková,I.";"4";"4";NULL;NULL;NULL;"4";"5";"5";"1";"4";"3";"4";"4";;;"cjp" +"3401";"JJB628";"Marketing módních značek - teorie";"Hejlová,D.,Koudelková,P.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"2";"3";"4";;;"kmkpr" +"3402";"JSB025";"Sociální problémy";"Frič,P.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kvsp" +"3403";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Coufalová,L.,Svobodová,T.";"5";"4";"5";"5";"4";"5";"5";"5";"1";"5";"5";"5";"5";;;"ks" +"3404";"JSB530";"Úvod do sociologického myšlení";"Bureš,J.,Numerato,D.";"Wirthová,J.";"4";"5";"5";"5";"3";"5";"5";"4";"1";"4";"4";"4";"4";;;"ks" +"3405";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"2";"3";"2";"1";NULL;NULL;NULL;"1";"2";"1";"2";"1";;"Bohužel moc nechápu smysl nutnosti opakovat na navazujícím magisterském studiu tento předmět v případě, kdy měl student na bakalářském studiu horší známku než 2. Současně neshledávám tento předmět nijak zvlášť přínosný.";"ies" +"3406";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Buřičová,B.";"3";"3";"5";"5";"3";"5";"5";"5";"1";"3";"5";"3";"4";;;"ks" +"3407";"JLM011";"Angličtina pro veřejnou a sociální politiku I";;"Klírová,M.";"4";"3";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"4";"4";"Oceňuji snahu přednášejícího co nejvíce studentům přiblížit akademickou angličtinu tématy, které je baví. Hodně jsme diskutovali, učili se poslouchat jeden druhého a následně reagovat, čímž jsme se i přirozenou cestou učili slovní zásobu spojenou s veřejnou a sociální politikou.";"Občas jsem měla pocit, že jsem jednotlivá probíraná témata nestíhala vstřebat.";"cjp" +"3408";"JSB025";"Sociální problémy";"Frič,P.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";"Oceňuji průběh seminářů - tedy celosemestrální skupivnovou práci. Dále také účast hostů na přednáškách.";"Více do kurzu začlenit diskuzi k jednotlivým probíraným tématům.";"kvsp" +"3409";"JJB600";"Úvod do studia médií I.";"Nečas,V.";;"3";"5";"4";"4";"2";NULL;NULL;NULL;"2";"3";"2";"4";"2";;;"kms" +"3410";"JJB601";"Rozbor právního prostředí v českých médiích";"Benda,J.";;"4";"5";"3";"2";"5";NULL;NULL;NULL;"1";"4";"3";"3";"3";;;"kms" +"3411";"JJB605";"Novodobá historie médií";"Bednařík,P.,Končelík,J.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"4";"4";;;"kms" +"3412";"JJB606";"Televize jako instituce";"Štoll,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"všechno! Skvělý kurz, profesionálně vedený, zajímavé informace, užitečné a praktické souvislosti!";;"kms" +"3413";"JJB616";"Vybrané novinářské osobnosti 19. století";"Sekera,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"4";"5";"Super milé přednášky s konkrétními výklady a příklady.";;"kms" +"3414";"JJB618";"Propagandistické působení médií v 1 polovině 20. století";"Suk,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kms" +"3415";"JJB620";"Mediální produkce - žurnalistická práce I.";"Vlasák,Z.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Praktický předmět! Jsem moc ráda za práci vyučujícího, který nám posílal individuální zpětnou vazbu na naše práce!";;"kms" +"3416";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ies" +"3417";"JLB027";"Ruština odborná I - vyšší";;"Mistrová,V.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"2";"Velmi profesionální přístup, zajímavá současná témata - rozšíření znalostí z aktuální problematiky. Skvěle koncipované hodiny, jak gramatika, tak praktické mluvení, uvažování nad různými tématy.Bohužel mi předmět zabíral strašně času, což při práci bylo nereálné..";;"cjp" +"3418";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"3419";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"5";"5";;;"ies" +"3420";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"3421";"JEB044";"Financial Accounting";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"3";"4";"4";"3";"4";NULL;NULL;NULL;"2";"3";"2";"4";"3";;;"ies" +"3422";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";"Přístup přednášejícího.";;"ks" +"3423";"JSM406";"Statistics in SPSS";;"Soukup,P.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";NULL;;;"ks" +"3424";"JSM514";"Metody a techniky práce s informacemi";"Tomandlová,V.";;"4";"4";"5";"3";"4";NULL;NULL;NULL;"1";"3";"4";"3";"4";;;"kvsp" +"3425";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"2";"5";;"Možná by mohlo být právo vyučováno i v návaznosti na obor ekonomie a finance a to mnohem hlouběji než právní základdní znalosti, které by měl mít každý občan :) (samozřejmě jako ÚdsP 2)";"ies" +"3426";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"3";"1";NULL;NULL;NULL;"3";"2";"3";"1";"2";"2";"1";"3";;;"ies" +"3427";"JJM247";"Český stranický systém";"Just,P.";;"4";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Velice příjemný vyučující";;"kz" +"3428";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"5";"1";NULL;NULL;NULL;"5";"5";"3";"2";"3";"3";"3";"5";;;"ies" +"3429";"JSM516";"Sociální politika v perspektivě životního cyklu";"Dobiášová,K.,Kotrusová,M.";;"5";"3";"5";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";"Především účast hostů na přednáškách a také průbeh seminářů - skupinové práce.";"Více diskutovat o jednotlivých tématech probíraných na přednáškách.";"kvsp" +"3430";"JSM518";"Public Policy";"Potůček,M.,Vlčková,K.";;"4";"4";"5";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";"Semináře, průběžné práce.";"Přizpůsobit přednášky tak, aby odpovídaly závěrené zkoušce. Tedy více aplikovat teorie na příkladech a podobně.";"kvsp" +"3431";"JMB414";"Seminář k aktualitám I";;"El-Ahmadieh,J.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"4";"5";"5";"5";"5";;;"krvs" +"3432";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"3";"2";"3";"4";"2";"2";"3";"1";"1";"3";"3";"3";"3";;;"ies" +"3433";"JEB111";"Advanced Data Analysis in MS Excel";;"Poláková,N.,Polák,P.";"4";"3";NULL;NULL;NULL;"4";"4";"4";"2";"3";"4";"3";"4";;;"ies" +"3434";"JSM612";"Kriminalita a současná česká společnost";"Cejp,M.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";"Předmět byl zajímavý. Zkouška probíhala dle mého názoru skvělým způsobem.";"Možná více kriminalitu propojovat se sociálními problémy.";"kvsp" +"3435";"JJB607";"Analýzy mediálních obsahů";"Křeček,J.";;"4";"4";"5";"4";"5";NULL;NULL;NULL;"1";"5";"5";"4";"5";"Praktickou zkoušku během které jsme si analýzu reálně vyzkoušeli.";"Analýza pro mě byla úplnou novinkou a první úkol pro mě byl zcela nejasný a nevěděla jsem co dělát.";"kms" +"3436";"JLB003";"Angličtina pro ekonomy I";;"Poslušná,L.";"4";"1";NULL;NULL;NULL;"5";"4";"2";"1";"2";"2";"2";"3";;;"cjp" +"3437";"JJB617";"Vybrané novinářské osobnosti 20. století";"Železný,J.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"5";"4";"Na celém kurzu jsem nejvíce ocenila výborně schopnosti vyučujícího. Výklad byl zajímavý a skvěle daný do souvislostí";;"kms" +"3438";"NMMA701";"Matematika 1";"Spurný,J.";"Rondoš,J.";"4";"5";"4";"5";"5";"3";"4";"5";"2";"5";"4";"5";"4";;;"ies" +"3439";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"4";"4";"3";"3";NULL;NULL;NULL;"1";"3";"3";"3";"3";;;"ies" +"3440";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"3";"4";;;"krvs" +"3441";"JJB618";"Propagandistické působení médií v 1 polovině 20. století";"Suk,P.";;"4";"4";"4";"5";"4";NULL;NULL;NULL;"1";"5";"3";"5";"4";;;"kms" +"3442";"JMB402";"Úvod do společenských věd II";;"Kocián,J.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"5";"5";;;"krvs" +"3443";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Šafařík,P.";"5";"2";"5";"5";"5";"4";"5";"5";"1";"5";"3";"4";"5";;;"knrs" +"3444";"JJB606";"Televize jako instituce";"Štoll,M.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kms" +"3445";"JJB334";"Zábava v médiích";"Kruml,M.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";"Praktické ukázky související s výkladem";;"kms" +"3446";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Čížek,M.";"5";"4";"4";"4";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"krvs" +"3447";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"5";;;"knrs" +"3448";"JMB065";"Úvod do mezinárodního a evropského práva";"Šlosarčík,I.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"3";"5";"5";"5";"5";;;"kzs" +"3449";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"4";"5";"5";"5";"3";NULL;NULL;NULL;"2";"3";"3";"4";"3";;;"kas" +"3450";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"4";"5";"5";"3";NULL;NULL;NULL;"2";"4";"4";"3";"5";;;"ks" +"3451";"JSB010";"Současná sociologie";"Balon,J.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"2";"5";"1";"5";"5";"Líbila se mi možnost prezentací";;"ks" +"3452";"JSB133";"Zemědělství a rozvoj venkova (vybraná témata z rurální sociologie)";"Zagata,L.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"2";"5";"5";"Celkově se mi kurz líbil snad skoro nejvíce ze všech, co jsem měla tento semestr zapsané. Pan docent je velice schopným přednášejícím, srozumitelně předává znalosti, občas doplňuje grafickým příkladem, což je skvělé na zapamatování a pochopení daného problému, a má velmi milý přednes.";;"ks" +"3453";"JMB408";"Moderní dějiny Ruska";"Litera,B.,Novák,P.,Pečenka,M.";;"3";"5";"4";"1";"4";NULL;NULL;NULL;"1";"4";NULL;"5";"3";"Musím říct, že pan doktor Pečenka se ukázal být velmi dobrým vypravěčem. Narozdíl od předmětu Přehled moderních světových dějin bylo poznat, že ho látka mnohonásobně více zajímá a byla radost poslouchat jeho zapálení.";"Ačkoli mě přednášky i látka poměrně nadchly, byla jsem velmi zklamaná z hodnocení testů a esejí. Osobně si myslím, že pan Pečenka ani pan Litera nemají soudnost a neumí objektivně hodnotit studenty, kteří jsou na bakalářském oboru mezinárodní teritoriální studia a ne oboru rusistika se zaměření na ruské dějiny. Nepřijde mi normální, že při mém termínu prošlo pouze 6 studentů z 25 a to to hodnotím z pohledu jednoho z těch šťastlivců, co ten první termín udělal. Je to demotivující pro většinu studentů, jelikož Rusko není jediným teritoriem, kterým musíme projít. Krom toho se mi příčila arogance, se kterou k nám pan Pečenka během zkoušky přistupoval. Pokud se mu nelíbí, co mu píšem do esejí a testů, a všem dává F nebo E, tak by se měl zamyslet sám nad sebou, že to teda asi blbě vyučuje, a ne nám jenom dávat najevo, jaký jsme hlupáci.";"krvs" +"3454";"JJB135";"Filmový seminář I";;"Šobr,M.";"4";"1";NULL;NULL;NULL;"4";"4";"5";"2";"4";"2";"3";"5";"Dobré filmy :-)";;"kz" +"3455";"JJB004";"Současný český jazyk I";;"Svobodová,I.";"4";"5";NULL;NULL;NULL;"5";"4";"4";"1";"5";"5";"4";"5";"Náročnost.";"Více obsáhnout teorii.";"kz" +"3456";"JJB010";"Základy filozofie a vzdělanosti";"Halada,J.";;"4";"2";"5";"5";"4";NULL;NULL;NULL;"2";"4";"3";"4";"5";;;"kz" +"3457";"JJB012";"Žurnalistická tvorba I";"Osvaldová,B.";"Trunečka,O.";"4";"3";"5";"4";"4";"4";"5";"4";"2";"4";"3";"4";"5";;;"kz" +"3458";"JJB015";"Česká literatura I";;"Čeňková,J.,Malý,R.";"4";"3";NULL;NULL;NULL;"5";"4";"4";"2";"4";"3";"3";"4";;"Důraz na kvalitu prezentací studentů (jakoby občas mí spolužáci pouze přečetli zdařilou wikipedii)..";"kz" +"3459";"JJB998";"Úvod do ekonomie";"Poljakov,N.";;"4";"4";"4";"3";"4";NULL;NULL;NULL;"2";"4";"3";"4";"4";;;"kz" +"3460";"JLB019";"Francouzština odborná I";;"Bosáková,L.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"3";"5";"Stávající styl výuky.";"Zvýšit náročnost.";"cjp" +"3461";"JLB053";"Angličtina pro sociální vědy I";;"Prošková,A.";"4";"3";NULL;NULL;NULL;"5";"5";"4";"2";"4";"3";"4";"4";;"Dynamickou aktivitu vyučující (hodil by se stejně intenzivní přístup po celý semestr).";"cjp" +"3462";"JSB998";"Úvod do sociologie";"Soukup,P.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"2";"4";"3";"4";"5";"Přístup přednášejícího k látce i studentům.";;"ks" +"3463";"JMB409";"Moderní dějiny středo- a jihovýchodní Evropy-dcera JMB013";"Balla,P.,Švec,L.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"5";"5";"5";"5";"5";"Balla je nejlepší přednášející na IMS!";;"krvs" +"3464";"JMB250";"Seminář k dějinám západní Evropy";;"Synkule,M.";"2";"4";NULL;NULL;NULL;"1";"4";"1";"3";"3";NULL;"4";NULL;;"Chtěla jsem se do budoucna zaměřit na západní Evropu, od mého nástupu na IMS mám jasno, že to je teritorium, které mě zajímá nejvíce. Po absolvování tohoto kurzu ale nevím, jestli se mi na tuto katedru chce. A to ne protože by snad historie západní Evropy nenaplnila mé očekávání, naopak, když jsem se jí pak sama učila doma, tak mě to opravdu bavilo ze všech teritorií nejvíce. Přednášky pana Vášky a Matějky mi ale přišly opravdu hrozné. Chtěla jsem slyšet dějiny, které jsou i to, co je zkoušeno v testech, ale místo toho se na přednáškách pouze filosofovalo a na 90% látky vůbec nedošlo. Opravdu mi nepřijde v pořádku \"načtěte si všechno sami doma, a pak se s náma choďte na přednášky zamýšlet nad industrializací\". Přednášky, až ná pár témat v esejích, nebyly opravdu přínosné vůbec v ničem, což velmi rychle vypozorovala i většina mých spolužáků a účast se tak pohybovala kolem 5-20 lidí. Jak je asi možné, že na přednáškách pana Bally bylo vždycky plno? Ne, opravdu to není proto, že by celý ročník ujížděl na Maďarsku...";"kzs" +"3465";"JPB583";"Politický systém ČR II. (regionální a lokální úroveň)";"Hornek,J.,Jüptner,P.,Musilová,K.,Němcová,L.";;"4";"2";"3";"3";"3";NULL;NULL;NULL;"1";"3";"4";"4";"4";;;"kp" +"3466";"JMB047";"Vybrané problémy mezinárodních konfliktů.";"Čížek,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Jeden z nejlepších vypravěčů na IMS.";;"krvs" +"3467";"JSB543";"Digitální etnografie";;"Hrešanová,E.";"2";"4";NULL;NULL;NULL;"4";"4";"5";"1";"4";"5";"3";"2";;;"ks" +"3468";"JPB202";"Politické strany v Evropě";"Perottino,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"1";"4";"5";;;"kp" +"3469";"JPB227";"Politický system ČR";"Charvát,J.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"1";"3";"5";;;"kp" +"3470";"JPB565";"Stáž v praxi";;"Kuľková,M.,Švec,K.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";;;"kp" +"3471";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"4";"3";"4";"4";"3";NULL;NULL;NULL;"1";"3";"3";"4";"4";;;"kp" +"3472";"JLB047";"Ruština obecná I";;"Mistrová,V.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"3";"5";"Oceňuji, že kurz vychází z učebnice pro samouky, tudíž se lze na požadovanou minimální vstupní úroveň připravit samoučením. Rozhodně oceňuji přístup vyučující.";;"cjp" +"3473";"JSB004";"Sociální nerovnosti";"Šanderová,J.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"3";"4";"2";"5";"5";;;"ks" +"3474";"JSB021";"Základy demografie";"Šídlo,L.";;"3";"5";"3";"3";"2";NULL;NULL;NULL;"2";"2";"3";"4";"3";;"Určitě bych změnil formu závěrečného testu. Především teoretickou část testu, která jde možná až příliš moc do detailu. Některé otázky jsou spíše hlavolamem (slovíčkaření či rozhodnutí zda se jedná o rok 60 či 61) než nástrojem na ověření znalostí.";"ks" +"3475";"JSB031";"Bakalářský seminář I - diskuse projektů";;"Spalová,B.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"3";"5";"Oceňuji celkovou formu semináře. Prezentace částí bakalářské práce i oponování prací kolegů je velmi poučné. Je výborné mít zpětnou vazbu ke své práci od kolegů.";;"ks" +"3476";"JSB033";"Praktika z kvalitativního výzkumu";;"Spalová,B.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"1";"3";"3";"1";"4";;;"ks" +"3477";"JSB131";"Velké empirické výzkumy ČR";"Tuček,M.";;"2";"3";"3";"3";"3";NULL;NULL;NULL;"1";"2";"2";"2";"3";;;"ks" +"3478";"JSB490";"Úvod do politické sociologie";"Císař,O.";;"5";"3";"5";"4";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Výborné přednášky od výborného přednášejícího.";;"ks" +"3479";"JJB221";"Ekonomická teorie marketingu";"Postler,M.";;"4";"4";"5";"4";"4";NULL;NULL;NULL;"1";"4";"3";"3";"4";;;"kmkpr" +"3480";"JJB237";"Právo a etika v marketingu";"Winter,F.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kmkpr" +"3481";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kz" +"3482";"JJB226";"Strategie a finanční management marketingu I";"Majerik,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"5";"5";"5";"5";"5";;;"kmkpr" +"3483";"JJB231";"Dějiny a teorie public relations";"Halada,J.,Hejlová,D.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmkpr" +"3484";"JJB235";"Proces tvorby v marketingové komunikaci";"Bezouška,M.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmkpr" +"3485";"JJB237";"Právo a etika v marketingu";"Winter,F.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmkpr" +"3486";"JJB238";"Kritika marketingové komunikace";"Rosenfeldová,J.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"3";"5";"5";"5";"5";;;"kmkpr" +"3487";"JJB298";"Marketingová komunikace malých a středních podniků";"Koudelková,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmkpr" +"3488";"JJB401";"Komerční a nekomerční marketingová komunikace";"Báča,L.,Obluk,O.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmkpr" +"3489";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"3490";"JLB099";"Rozřazovací test z angličtiny";;"Kunzová,J.";"2";"5";NULL;NULL;NULL;"5";"5";"1";"1";"1";"1";"1";"1";;;"cjp" +"3491";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"5";"5";;;"cjp" +"3492";"JMB173";"Vnitřní dějiny Balkánu do roku 1914";"Šesták,M.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"krvs" +"3493";"JMB212";"Moderní dějiny Japonska";"Labus,D.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"2";"5";"5";;;"kas" +"3494";"JMB248";"Seminář k dějinám Ruska";;"Kolenovská,D.";NULL;"4";NULL;NULL;NULL;"5";"5";"4";"1";"5";"4";"5";"5";;;"krvs" +"3495";"JMB171";"Moderní dějiny Maďarska";"Irmanová,E.";;NULL;"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"krvs" +"3496";"JMB156";"Greek Language I";"Maniati,E.";"Maniati,E.";"4";"3";"4";"5";"5";"4";"5";"5";"2";"5";"3";"3";"5";;;"krvs" +"3497";"JLB053";"Angličtina pro sociální vědy I";;"Štěpánková,D.";"2";"1";NULL;NULL;NULL;"3";"4";"2";"1";"1";"1";"1";"2";;;"cjp" +"3498";"JSB051";"Úvod do sociální antropologie";"Uherek,Z.";"Hrešanová,E.";"3";"3";"5";"4";"2";"4";"3";"5";"1";"4";"4";"4";"4";;;"ks" +"3499";"JSB003";"Oborová sociologie";"Numerato,D.";;"4";"5";"5";"5";"1";NULL;NULL;NULL;"1";"4";"1";"3";"3";;;"ks" +"3500";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"4";"3";"5";"4";"3";"5";"5";"5";"1";"4";"4";"4";"5";"Cvičícího - pana Ugura Goka";"Nebrat nám v polovině semestru nejlepšího cvičícího IESu";"ies" +"3501";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Buřičová,B.";"2";"3";"1";"1";"1";"5";"5";"5";"1";"3";"5";"2";"2";;;"ks" +"3502";"JSB021";"Základy demografie";"Šídlo,L.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"2";"5";;;"ks" +"3503";"JSB023";"Praktika z kvantitativního výzkumu I";;"Špaček,O.";"3";"3";NULL;NULL;NULL;"2";"2";"3";"1";"3";"4";"2";"4";;;"ks" +"3504";"JSB033";"Praktika z kvalitativního výzkumu";;"Wladyniak,L.";"4";"1";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"1";"4";;;"ks" +"3505";"JLB033";"Němčina I";;"Faltýnová,R.";"4";"5";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"3";"2";;;"cjp" +"3506";"JSB490";"Úvod do politické sociologie";"Císař,O.";;"5";"5";"4";"4";"5";NULL;NULL;NULL;"1";"4";"2";"4";"4";;;"ks" +"3507";"JMB011";"Moderní dějiny Ruska";"Litera,B.,Pečenka,M.";"seminář nenavštěvován";"4";"5";"4";"3";"3";"4";"4";"2";"1";"4";"3";"4";"2";;;"krvs" +"3508";"JMB013";"Moderní dějiny středo- a jihovýchodní Evropy";"Balla,P.,Švec,L.";"seminář nenavštěvován";"4";"5";"4";"4";"4";"5";"4";"4";"1";"4";"3";"4";"2";;;"krvs" +"3509";"JMB018";"Bakalářský seminář I";;"Kubát,M.";"4";"2";NULL;NULL;NULL;"5";"5";"2";"1";"3";"4";"2";"4";;;"krvs" +"3510";"JJB403";"Institucionální a vládní komunikace";"Shavit,A.,Soukeník,Š.";;NULL;NULL;"4";"4";"4";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kmkpr" +"3511";"JMB414";"Seminář k aktualitám I";;"Mazzali,F.";"5";"4";NULL;NULL;NULL;"5";"4";"5";"1";"5";"4";"5";"5";;;"krvs" +"3512";"JJB600";"Úvod do studia médií I.";"Nečas,V.";;"5";"4";"4";"4";"4";NULL;NULL;NULL;"1";"5";"2";"5";"5";;;"kms" +"3513";"JJB601";"Rozbor právního prostředí v českých médiích";"Benda,J.";;"3";"4";"3";"3";"3";NULL;NULL;NULL;"1";"3";"5";"3";"2";;;"kms" +"3514";"JJB605";"Novodobá historie médií";"Bednařík,P.,Končelík,J.";;"5";"3";"4";"4";"5";NULL;NULL;NULL;"1";"4";"5";"4";"4";;;"kms" +"3515";"JJB616";"Vybrané novinářské osobnosti 19. století";"Sekera,M.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kms" +"3516";"JJB620";"Mediální produkce - žurnalistická práce I.";"Vlasák,Z.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kms" +"3517";"JLB005";"Angličtina pro politology I";;"Panešová,K.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Přístup lektora. Celkový přínos ohledně znalostí a hlavně slovní zásoby.";;"cjp" +"3518";"JSB490";"Úvod do politické sociologie";"Císař,O.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"5";"5";"5";"5";"5";"Přístup a znalost lektora. Způsob předávání znalostí a rozšíření spektra znalostí pro studenty.";;"ks" +"3519";"JSB522";"Sociální politika jako společenská praxe";"Dobiášová,K.,Kotrusová,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Jedná se o jeden z nejzajímavějších kurzů oboru. Celkový přínos hodnotím velice kladně, společně s přístupy všech lektorů.";"Procházka s bezdomovci v mírnějším termínu než v prosinci. Tuhá zima byla problémem. Jinak její přínos je nepochybný, jen si člověk z druhé části moc nepomatuje, protože se soustředil na tělesné potřeby tepla. :)";"kvsp" +"3520";"JPB228";"Mírové smlouvy a konference v mez. systému";"Jeřábek,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"4";"Přehled vyučujícího a jeho neskutečné spektrum znalostí.";"Menší zmatenost ohledně zadávání glos a organizace jejich prezentace";"kmv" +"3521";"JPB583";"Politický systém ČR II. (regionální a lokální úroveň)";"Hornek,J.,Jüptner,P.,Musilová,K.,Němcová,L.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Zajímavý přístup ohledně prezentace politického systému lokální úrovně pomocí případových studií. Tento přístup hodnotím kladně. Líbil se mi, díky tomu, že případové studie prezentovali samotní doktorandi byli informace přímo ze zdroje doplněné osobními pocity.";;"kp" +"3522";"JSB601";"Veřejný sektor a veřejná správa";"Kohoutek,J.,Veselý,A.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Velmi oceňuji přístup vyučujícího. Díky jeho znalostem a znalostem hostů, kteří byli přítomni na několika přednáškám se mi dostalo velkého spektra znalostí, kterých si vážím a vidím jejich význam pro můj život i další studium.";;"kvsp" +"3523";"JSB537";"Analýza dat v SPSS";"Soukup,P.";"Oreský,J.";"1";"1";"5";"5";"5";"3";"5";"5";"2";"4";"4";"4";"2";"Možnost seznámit se s programem SPSS";"Lepší organizaci. Podle mě se tento předmět hodí pro obor Politologie a veřejná politika. Jen mi přijde, že si odnášíme jen základní množství znalostí. Nehledě na organizaci předmětu, která ve mě ze začátku budila strach, že se mi předmět nepodaří absolvovat.";"ks" +"3524";"JSB025";"Sociální problémy";"Frič,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Přístup vyučujícího. Jeho dobrou náladu na každé přednášce, bylo vidět, že se o téma zajímá, má o něm velké spektrum znalostí a rád ho předává dál.";;"kvsp" +"3525";"JJM247";"Český stranický systém";"Just,P.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"1";"5";"1";"4";"5";;;"kz" +"3526";"JJM264";"Diplomový seminář II.";;;"3";"1";"3";"3";"3";NULL;NULL;NULL;"5";"1";"1";"1";"3";;;"kz" +"3527";"JJM273";"Sportovní žurnalistika ve světě";"Bosák,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"3";"4";"5";;;"kz" +"3528";"JJM354";"Dějiny populární hudby";"Halada,A.";;"3";"3";"5";"5";"3";NULL;NULL;NULL;"5";"3";"3";"3";"3";;;"kz" +"3529";"JJM269";"Tvůrčí dílny I. – komentář";;"Osvaldová,B.,Šídlo,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";NULL;"5";"5";;;"kz" +"3530";"JJM259";"Mediální systémy a jejich komparace";"Miessler,J.";;"4";"5";"3";"5";"4";NULL;NULL;NULL;"1";"4";"2";"4";"4";;"Bylo vidět, že pan doktor učí kurz poprvé, ale i tak byl kurz velmi slušný.";"kz" +"3531";"JJM260";"Novinářská etika v praxi";"Moravec,V.";;"4";"4";"5";"4";"4";NULL;NULL;NULL;"1";"4";"2";"5";"4";;"Kurz bych přesunul do modulu E pro studenty, kteří nestudovali bakaláře, protože prakticky kopíruje podobný kurz v bakalářském studiu.";"kz" +"3532";"JJB600";"Úvod do studia médií I.";"Nečas,V.";;"4";"5";"4";"5";"5";NULL;NULL;NULL;"2";"3";"4";"4";"3";"Dostupnost materiálu a veškerých informací k samostudiu.";;"kms" +"3533";"JJB601";"Rozbor právního prostředí v českých médiích";"Benda,J.";;"3";"4";"3";"3";"3";NULL;NULL;NULL;"1";"3";"3";"3";"2";;"Nedostupnost prezentací nebo potřebného materiálu.";"kms" +"3534";"JJB605";"Novodobá historie médií";"Bednařík,P.,Končelík,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Přednášející se vzájemně doplňují, což dodává přednášce větší impulz a lépe se to poslouchá.";;"kms" +"3535";"JJB616";"Vybrané novinářské osobnosti 19. století";"Sekera,M.";;"4";"2";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kms" +"3536";"JJB620";"Mediální produkce - žurnalistická práce I.";"Vlasák,Z.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"5";"5";;;"kms" +"3537";"JPM706";"Terrorism and Counterterrorism";"Bureš,O.";;"4";"4";"4";"3";"5";NULL;NULL;NULL;"1";"5";"1";"4";"5";"The fact that often the readings were offering different point of views was truly interesting.";"Sometimes in some lectures all the topics could not be covered due to lack fo time. Maybe better time-management could be improved.";"kbs" +"3538";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"2";"5";"3";"2";"5";;;"ies" +"3539";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"4";"2";NULL;NULL;NULL;"4";"4";"5";"2";"2";"1";"3";"5";"Tento kurz měl velice pozitivní efekt v oblasti pochopení praktických stránek studia a rozbor současných témat napomohl spojení si vysoce teoretických znalostí z jiných předmětů s děním ve světě.";;"ies" +"3540";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"3";"1";NULL;NULL;NULL;"5";"5";"2";"1";"2";"4";"5";"5";;;"ies" +"3541";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"3";"1";"3";"4";"1";"2";"2";"1";"2";"2";"4";"4";"4";;;"ies" +"3542";"JEM179";"History and Methodology of Economics";"Baxa,J.,Paulus,M.";;"5";"5";"4";"4";"5";NULL;NULL;NULL;"1";"5";"1";"5";"4";;;"ies" +"3543";"JLB003";"Angličtina pro ekonomy I";;"Poslušná,L.";"4";"1";NULL;NULL;NULL;"5";"5";"3";"1";"4";"5";"2";"5";;;"cjp" +"3544";"NMMA701";"Matematika 1";"Spurný,J.";"Rondoš,J.";"5";"5";"5";"5";"5";"4";"5";"5";"2";"5";"5";"5";"5";;;"ies" +"3545";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"3";"4";"3";"4";NULL;NULL;NULL;"1";"4";"2";"3";"3";;;"ies" +"3546";"JLB005";"Angličtina pro politology I";;"Panešová,K.";"5";"1";NULL;NULL;NULL;"4";"5";"4";"1";"2";"4";"2";"4";;;"cjp" +"3547";"JLB041";"Španělština I";;"Mlýnková,L.";"4";"3";NULL;NULL;NULL;"4";"5";"5";"1";"4";"4";"1";"4";;;"cjp" +"3548";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"2";"5";"4";"5";NULL;NULL;NULL;"1";"4";"2";"4";"5";;;"kp" +"3549";"JPB018";"Dějiny mezinárodních vztahů I";"Soukup,J.";;"4";"2";"4";"4";"5";NULL;NULL;NULL;"2";"4";"2";"3";"5";;;"kmv" +"3550";"JPB020";"Úvod do mezinárodních vztahů";"Karlas,J.,Kučera,T.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kmv" +"3551";"JPB221";"Metodologický proseminář I";;"Střítecký,V.,Tesař,J.";"4";"3";NULL;NULL;NULL;"4";"5";"5";"1";"4";"5";"4";"4";;;"kmv" +"3552";"JPB252";"České novověké dějiny I.";"Kučera,J.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"2";"4";"4";;;"kp" +"3553";"JPB256";"Politické systémy I";"Brunclík,M.,Jeřábek,M.,Kotábová,V.,Perottino,M.";;"4";"4";"4";"4";"5";NULL;NULL;NULL;"1";"5";"4";"4";"4";;;"kp" +"3554";"JPB258";"Politické myšlení novověku I";"Franěk,J.,Kučera,J.";;"3";"3";"3";"4";"3";NULL;NULL;NULL;"1";"3";"2";"2";"3";;;"kp" +"3555";"JPB269";"Současné problémy mezinárodních vztahů";"Knutelská,V.";;"5";"3";"4";"4";"5";NULL;NULL;NULL;"2";"4";"4";"4";"5";;;"kmv" +"3556";"JSB998";"Úvod do sociologie";"Soukup,P.";;"4";"3";"4";"4";"3";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"ks" +"3557";"JSB007";"Dějiny sociologie I";"Šanderová,J.";"Bureš,J.";"4";"3";"5";"5";"5";"5";"5";"5";"2";"4";"4";"4";"5";;;"ks" +"3558";"JSB025";"Sociální problémy";"Frič,P.";;"3";"3";"3";"4";"3";NULL;NULL;NULL;"1";"3";"3";"2";"3";;;"kvsp" +"3559";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Coufalová,L.,Svobodová,T.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ks" +"3560";"JSB530";"Úvod do sociologického myšlení";"Bureš,J.,Numerato,D.";"Moskvina,Y.";"4";"1";"4";"5";"3";"5";"5";"5";"4";"3";"3";"4";"4";;;"ks" +"3561";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Buřičová,B.";"3";"5";"3";"4";"4";"5";"5";"5";"1";"3";"3";"3";"3";;;"ks" +"3562";"JSB544";"Vybrané kapitoly středoškolské matematiky";;"Hendl,J.";"3";"5";NULL;NULL;NULL;"3";"3";"3";"1";"3";"3";"3";"3";;;"ks" +"3563";"JEM179";"History and Methodology of Economics";"Baxa,J.,Paulus,M.";;"3";"3";"4";"5";"3";NULL;NULL;NULL;"1";"3";"3";"3";"3";;;"ies" +"3564";"JEB003";"Ekonomie I";"Fanta,N.,Kracík,J.,Švarcová,N.";"Fanta,N.,Kracík,J.,Švarcová,N.";"4";"3";"4";"5";"4";"4";"4";"4";"1";"5";"4";"4";"4";;;"ies" +"3565";"JMM293";"The Special Relationship between the United States and Great Britain";"Raška,F.";;"3";"3";"2";"2";"3";NULL;NULL;NULL;"1";"3";"3";"3";"2";;;"kas" +"3566";"JMM601";"U.S. and Human Rights";"Raška,F.";;"3";"3";"2";"2";"3";NULL;NULL;NULL;"1";"3";"3";"3";"2";;;"kas" +"3567";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"4";"3";"4";"4";"5";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"ies" +"3568";"JMMZ205";"Race, Ethnicity, and Gender in American History and Literature";;"Janíčková,M.,Robbins,D.";"4";"3";NULL;NULL;NULL;"4";"4";"3";"3";"4";"4";"4";"5";;;"kas" +"3569";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"2";"3";"5";;;"ies" +"3570";"JSM578";"Anthropology of EU";"Uherek,Z.";;"2";"1";"2";"4";"1";NULL;NULL;NULL;"1";"2";"2";"2";"2";;;"ks" +"3571";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"4";"2";NULL;NULL;NULL;"4";"5";"5";"2";"3";NULL;"4";"4";;;"ies" +"3572";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"5";"2";NULL;NULL;NULL;"4";"5";"3";"2";"3";"3";"2";"5";;;"ies" +"3573";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"2";"5";NULL;"4";;;"ies" +"3574";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"5";"5";;;"ies" +"3575";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"3";"3";"4";"5";"3";"4";"4";"2";NULL;"4";"4";"5";"4";;;"ies" +"3576";"JLB003";"Angličtina pro ekonomy I";;"Gloverová,M.";"4";"3";NULL;NULL;NULL;"5";"5";"4";"1";"4";"3";"4";"5";;;"cjp" +"3577";"JLB003";"Angličtina pro ekonomy I";;"Poslušná,L.";NULL;NULL;NULL;NULL;NULL;"5";"5";"5";NULL;NULL;NULL;NULL;NULL;;;"cjp" +"3578";"JSB998";"Úvod do sociologie";"Soukup,P.";;NULL;NULL;"5";"5";"5";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"ks" +"3579";"NMMA701";"Matematika 1";"Spurný,J.";"Honzík,P.";NULL;NULL;"5";"5";"5";"2";"3";"5";NULL;NULL;NULL;NULL;NULL;;;"ies" +"3580";"NMMA701";"Matematika 1";"Spurný,J.";"Honzík,P.";"4";"5";"5";"4";"5";"4";"4";"4";"1";"5";"4";"3";"3";;;"ies" +"3581";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"5";"4";"5";"1";"4";"1";"2";"5";;;"kz" +"3582";"JJM360";"Ekonomika v médiích";"Klimeš,D.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"5";"2";"4";"4";;;"kz" +"3583";"JJM199";"Literární a knižní kritika";"Čeňková,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";NULL;NULL;NULL;"5";;;"kz" +"3584";"JJM248";"Vývoj grafického designu a polygrafického zpracování periodik";"Slanec,J.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";NULL;NULL;"4";;;"kz" +"3585";"JJM249";"Duchovní kořeny evropské kultury";"Halada,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"kz" +"3586";"JJM259";"Mediální systémy a jejich komparace";"Miessler,J.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kz" +"3587";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"3";"1";"1";"1";NULL;NULL;NULL;"1";"3";"1";"2";"2";"Oceňuji předem vypracované materiály k přednáškám přímo od pana Kameníčka. Tyto materiály jsou velmi užitečné a rozhodně by se měly zachovat.";"Obecně přístup ke kurzu. Přednášky byly nudné i přes jejich někdy zajímavý obsah. Z větší části to bylo způsobeno přístupem přednášejícího a hlavně jeho velmi monotónním a sníženým tónem hlasu.";"ies" +"3588";"JLB033";"Němčina I";;"Faltýnová,R.";"4";"3";NULL;NULL;NULL;"4";"5";"4";"1";"4";"4";"2";"5";"Oceňuji připravené materiály do výuky. Student se nemusí tahat s těžkou učebnicí, ale vždy se pracuje jen s pracovními listy, které jsou užitečné.";;"cjp" +"3589";"JLB053";"Angličtina pro sociální vědy I";;"Štěpánková,D.";"3";"3";NULL;NULL;NULL;"3";"3";"2";"1";"3";"2";"1";"2";;"Výuka by mohla probíhat aktivnějším tempem a stihlo by se toho probrat více.";"cjp" +"3590";"JSB007";"Dějiny sociologie I";"Šanderová,J.";"Hanzal,P.";"4";"2";"4";"4";"5";"2";"4";"2";"4";"3";"2";"2";"3";"Oceňuji připravené prezentace k výuce, o niž se může student při studiu opřít.";;"ks" +"3591";"JSB051";"Úvod do sociální antropologie";"Uherek,Z.";"Hrešanová,E.";"2";"1";"3";"4";"1";"4";"4";"5";"1";"3";"1";"4";"3";"Oceňuji, že je k tomuto kurzu veden seminář, který blíže a praktičtěji uvede studenta do témat sociální antropologie.";"Přednášky by měly být systematičtější, přehlednější a srozumitelnější. Na to, že se jednalo o úvod do sociální antropologie, to vyžadovalo spoustu znalostí a informací z oblasti sociální antropologie, které jsem jakožto student prvního ročníku a prvního semestru postrádala.";"ks" +"3592";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Coufalová,L.,Svobodová,T.";"3";"4";"4";"5";"2";"5";"5";"5";"1";"4";"4";"3";"4";"Oceňuji, že ke kurzu náleží seminář, který se zaměřuje na nácvik praktických dovedností.";;"ks" +"3593";"JJM226";"Teorie účinků médií";"Nečas,V.";;"5";"3";"4";"3";"3";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Velmi ocenuji nutnost cteni textu, ktere jsou pro studium tohoto oboru zcela zasadni. Texty jsou samozrejme v osnovach i ostanich predmetu na Medialnich studiich, ale ne vsude je jejich cteni \"vynuceno.\" Jedna se asi o nejkomplexnejsi a nejuzitecnejsi predmet. Ocenuji i tipy ke statnicim.";"Obsah prednasek se z vetsiny prekryva s obsahem textu, ktery meli vsichni precist. To mi prijde zbytecne a radeji bych obsah prednasky venovala doplneni dalsich autoru, zajimavostem nebo dalsim zdrojum";"kms" +"3594";"JSB530";"Úvod do sociologického myšlení";"Bureš,J.,Numerato,D.";"Wirthová,J.";"2";"2";"3";"4";"4";"5";"5";"2";"3";"3";"5";"3";"3";;"Kurz na mě trochu působil nepřipraveně. Působilo to, jako by postrádal cílový pedagogický záměr. Jako kdyby přednášející a cvičící nevěděli, co je cílem tohoto kurzu, natož aby to pak věděli studenti.";"ks" +"3595";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"4";"4";"4";"5";"5";NULL;NULL;NULL;"1";"4";"2";"3";"5";"Snahu vyučujících říct nám toho co nejvíce.";"Možná se více soustředit na určitou problematiku než přebíhat z jednoho tématu na druhé.";"ies" +"3596";"JJB221";"Ekonomická teorie marketingu";"Postler,M.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kmkpr" +"3597";"JJB334";"Zábava v médiích";"Kruml,M.";;"4";"3";"4";"5";"3";NULL;NULL;NULL;"1";"4";"2";"4";"4";;;"kms" +"3598";"JJB606";"Televize jako instituce";"Štoll,M.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kms" +"3599";"JJB607";"Analýzy mediálních obsahů";"Křeček,J.";;"4";"2";"4";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"3";;;"kms" +"3600";"JJB617";"Vybrané novinářské osobnosti 20. století";"Železný,J.";;"5";"3";"5";"3";"4";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kms" +"3601";"JJB618";"Propagandistické působení médií v 1 polovině 20. století";"Suk,P.";;"5";"5";"5";"4";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kms" +"3602";"JLB041";"Španělština I";;"Mlýnková,L.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"4";;;"cjp" +"3603";"JJB298";"Marketingová komunikace malých a středních podniků";"Koudelková,P.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kmkpr" +"3604";"JMB013";"Moderní dějiny středo- a jihovýchodní Evropy";"Balla,P.,Švec,L.";"seminář nenavštěvován";"4";"5";"3";"4";"4";"3";"3";"3";"1";"5";"1";"4";"3";;;"krvs" +"3605";"JMB018";"Bakalářský seminář I";;"Kubát,M.";"5";"2";NULL;NULL;NULL;"5";"5";"4";"1";"5";"4";"2";"4";;;"krvs" +"3606";"JMB415";"Seminář k aktualitám II";;"Karasová,N.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"3";"4";"5";;;"krvs" +"3607";"JJM216";"Čtení textů ke studiu médií - česká média po roce 1945";;"Bednařík,P.,Končelík,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"4";"5";"Pristup vyucujich a jejich vseznalost je uzasna.";"Dostat se pres Bendarika a Koncelika ke slovu je obcas obtizne. Oni samozrejme vedi strasne moc zajimavych pribehu, ale chybi prostor pro diskuzi";"kms" +"3608";"JMM183";"Současná východní Evropa I";"Lídl,V.,Šír,J.";;"4";"1";"5";"5";"5";NULL;NULL;NULL;"1";"3";"3";"3";"4";;;"krvs" +"3609";"JMM086";"Diplomový seminář II";;"Králová,K.,Svoboda,K.,Švec,L.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";;;"krvs" +"3610";"JJM202";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"3";"4";"3";"3";"3";NULL;NULL;NULL;"1";"4";"3";"4";"3";;"Cebe nepusti Bednarika moc ke slovu, coz je skoda, protoze Bednarik narozdil od cebeho vypada, ze ho vyuka studentu bavi, ze ho to neobtezuje a ze by nebyl v tu chvili radsi nekde jinde. Take by podle me bylo fajn transparentnejsi hodnoceni otevrenych otazek. Po preptani se na hodnoceni mi bylo receno, ze ac jsem na otazku odpovedela, nekdo napsal treba jeste neco navic, kvuli cemuz mi byly odecteny body. Hodnoceni je velmi prisne, takze i kdyz se ucite a vite odpoved na kazdou otazku, ziskat A ci B je kvuli zvlastnimu hodnoceni temer nemozne a celkem demotivujici. Myslim, ze je to zbytecne.";"kms" +"3611";"JJB018";"Úvod do fotožurnalistiky";"Lábová,A.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kz" +"3612";"JJB137";"Televizní publicistika";;"Kopa,O.";"5";"3";NULL;NULL;NULL;"4";"4";"5";"1";"5";"5";"5";"5";;;"kz" +"3613";"JJB611";"Česká média po roce 1990";"Jirák,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"kms" +"3614";"JJB612";"Média a životní styl";"Knapík,J.";;"5";"3";"4";"5";"5";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kms" +"3615";"JJB613";"Úvod do studia nových médií";"Jirků,J.";;"4";"5";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"4";;;"kms" +"3616";"JJB625";"Manipulace v audiovizuálním sdělení";"Štoll,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"3617";"JJB626";"Vybrané otázky mediálního vzdělávávání";"Wolák,R.";;"5";"2";"5";"4";"4";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kms" +"3618";"JPM664";"Geopolitics of Great Powers: China";"Karásková,I.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"3619";"JSB517";"Hudební subkultury mládeže";"Oravcová,A.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ks" +"3620";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"4";"2";"1";"4";NULL;NULL;NULL;"1";"4";"1";"3";"3";;;"ies" +"3621";"JLB019";"Francouzština odborná I";;"Bosáková,L.";"3";"3";NULL;NULL;NULL;"5";"4";"3";"1";"3";"3";"3";"3";;;"cjp" +"3622";"JEB141";"Introduction to Market Design";"Gregor,M.,Kastl,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";;;"ies" +"3623";"JLB053";"Angličtina pro sociální vědy I";;"Gloverová,M.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"3";"5";;;"cjp" +"3624";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"3";"1";"4";"5";"2";NULL;NULL;NULL;"1";"3";"4";"2";"3";;;"krvs" +"3625";"JEM183";"Mathematical Methods in Macroeconomics";"Stráský,J.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;"1";"3";"4";"3";"3";;;"ies" +"3626";"JMM271";"Metodologický seminář";;"Kýrová,L.";"3";"5";NULL;NULL;NULL;"4";"4";"2";"1";"2";"3";"2";"2";;;"krvs" +"3627";"JMM277";"Historie a kultura";"Vykoukal,J.";"Kýrová,L.";"2";"3";"5";"5";"2";"4";"4";"1";"1";"1";"1";"1";"2";;;"krvs" +"3628";"JMB402";"Úvod do společenských věd II";;"Šafařík,P.";"2";"4";NULL;NULL;NULL;"2";"3";"2";"2";"4";"5";"3";"2";;;"krvs" +"3629";"JMM278";"Politika a mezinárodní vztahy";"Kubát,M.,Weiss,T.";"Bečka,J.";"2";"3";"3";"3";"1";"5";"5";"4";"1";"1";"1";"1";"2";;;"krvs" +"3630";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Kocian,J.";"5";"1";"5";"5";"5";"4";"5";"4";"1";"5";"1";"4";"5";;;"knrs" +"3631";"JMM279";"Společnost a ekonomika";"Fiřtová,M.";"Fiřtová,M.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"kas" +"3632";"JMMZ314";"Major Issues in Contemporary Public Debates in the U.S. I";"Sehnálková,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kas" +"3633";"JMMZ313";"Government in United States";"Sehnálková,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kas" +"3634";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Andrle,J.";"3";"1";"3";"3";"4";"5";"3";"5";"2";"4";"1";"4";"4";;;"krvs" +"3635";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"3";"3";"3";"5";"2";NULL;NULL;NULL;"1";"4";"1";"2";"3";;;"knrs" +"3636";"JMB058";"Československá a česká zahraniční politika po r. 1989 I.";"Handl,V.,Kunštát,M.";;"5";"4";"4";"5";"5";NULL;NULL;NULL;"1";"5";"2";"4";"5";;;"knrs" +"3637";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"5";"4";"4";"5";"4";NULL;NULL;NULL;"2";"4";"4";"5";"5";;;"kas" +"3638";"JMB216";"Postsovětský prostor v 90. letech";"Lídl,V.,Šír,J.";;"5";"2";"4";"5";"4";NULL;NULL;NULL;"2";"5";"1";"3";"5";;;"krvs" +"3639";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"4";"5";;;"ks" +"3640";"JEB141";"Introduction to Market Design";"Gregor,M.,Kastl,J.";;"4";"1";"5";"5";"5";NULL;NULL;NULL;"1";"4";"1";"4";"5";;;"ies" +"3641";"JEM003";"Advanced Microeconomics";"Janda,K.,Melikhova,O.";"Janda,K.,Melikhova,O.";"4";"4";"4";"5";"2";"5";"5";"5";"1";"5";"5";"4";"5";;;"ies" +"3642";"JEM004";"Advanced Macroeconomics";"Baxa,J.,Hromádková,E.";"Baxa,J.,Hromádková,E.,Šestořád,T.,Troch,T.,Žáček,J.";"4";"5";"4";"5";"5";"4";"5";"5";"1";"4";"4";"4";"4";;;"ies" +"3643";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"5";"4";"5";"5";"5";"4";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"3644";"JEM027";"Monetary Economics";"Holub,T.,Malovaná,S.";"Břízová,P.,Hájek,J.,Holub,T.,Malovaná,S.";"5";"3";"5";"5";"5";"4";"5";"4";"1";"5";"3";"5";"5";;;"ies" +"3645";"JEM123";"Economics of Least Developed Countries";"Bauer,M.";"Bauer,M.";"5";"3";"5";"5";"5";"5";"5";"4";"1";"5";"4";"5";"5";;;"ies" +"3646";"JJB004";"Současný český jazyk I";;"Svobodová,I.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"4";;;"kz" +"3647";"JJB010";"Základy filozofie a vzdělanosti";"Halada,J.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"3";"4";"3";"3";;;"kz" +"3648";"JJB012";"Žurnalistická tvorba I";"Osvaldová,B.";"Trunečka,O.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"kz" +"3649";"JJB015";"Česká literatura I";;"Čeňková,J.,Malý,R.";"5";"2";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"4";"4";;;"kz" +"3650";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"5";"5";;;"kz" +"3651";"JJB998";"Úvod do ekonomie";"Poljakov,N.";;"4";"3";"5";"4";"5";NULL;NULL;NULL;"3";"4";"4";"4";"5";;;"kz" +"3652";"JLB009";"Angličtina pro žurnalisty I";;"Prošková,A.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"3653";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"2";"4";"4";"4";"5";;;"ks" +"3654";"JLB102";"Czech as a Foreign Language III";;"Nováková,K.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"3655";"JEB120";"Financial Economics";"Žigraiová,D.";;"2";"5";"1";"1";"1";NULL;NULL;NULL;"3";"2";"2";"2";"4";;"Many things could be improved in this subject. Firstly, I think the teacher has good knowledge of the subject, but is not a good teacher at all. I think the professor does not have any teaching skills and it needs huge improvements. I. personally, think that the two lectures of such an important subject should not be squeezed in the time of one hour and twenty minutes, which was the case several times during the semester. This cut number of lectures in the semester and there were several weeks when we did not have neither lectures not seminars, at all. The same goes for seminars and exercises. The professor has poor performances as a teacher (often \"talking\" to the white board and looking at the presentation and not at aula full of students). This might have been all better if the subject was not as important as it is and the exam is not easy at all. I think it would be easier if the teacher was teaching us and not the whiteboard and reading from the presentations.";"ies" +"3656";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"3";"1";"5";"5";;;"ies" +"3657";"JEB044";"Financial Accounting";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ies" +"3658";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"4";"3";NULL;NULL;NULL;"5";"4";"5";"1";"2";"5";"1";"4";;;"ies" +"3659";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"4";"2";"4";"5";"3";"2";"5";"2";"1";"3";"4";"4";"4";;;"ies" +"3660";"JEB111";"Advanced Data Analysis in MS Excel";;"Poláková,N.,Polák,P.";"5";"4";NULL;NULL;NULL;"4";"5";"5";"1";"5";"5";"4";"5";;;"ies" +"3661";"JLB003";"Angličtina pro ekonomy I";;"Gloverová,M.";"4";"1";NULL;NULL;NULL;"5";"5";"3";"1";"3";"3";NULL;"3";;;"cjp" +"3662";"JLB041";"Španělština I";;"Mlýnková,L.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";NULL;"5";;;"cjp" +"3663";"NMMA701";"Matematika 1";"Spurný,J.";"Skříšovský,E.";"4";"5";"3";"5";"4";"3";"5";"5";"1";"5";"4";"5";"4";;;"ies" +"3664";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"2";"4";"3";"3";"3";"3";"3";"4";"2";"4";"3";"4";"5";;"The teacher in the seminars should have been a bit more slower and better explaining formulas and the meaning of those formulas in their application. The homework are not substantial with the difficulty of the exercises done on seminars.";"ies" +"3665";"JEB136";"Topics in Industrial Organization";"Schwarz,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"3";"5";"5";"5";"5";"This course is one of the most valuable ones when it comes to understanding the way firms operate in the market. It gave me the best picture of the firms that might, potentially, become my workplace. The IES Programme should include more subjects like this.";;"ies" +"3666";"NMMA711";"Mathematics 1";"Bárta,T.";"Bárta,T.,Vlasák,V.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"The professor this year was amazing and he is the real professor. When I say this I mean that Mr. Barta has teaching skills that cannot be compared to the poor teaching skills of the professor Vlasak, which was horrible, again. Dr. Barta was doing his best to explain us mathematics and the usage of it. This university should keep the professors like Barta is.";;"ies" +"3667";"JEB105";"Statistics";"Červinka,M.";"Červinka,M.";"4";"5";"5";"5";"5";"5";"5";"5";"2";"4";"4";"4";"5";;;"ies" +"3668";"JPB579";"Bc. seminář Politologie a veřejná politika I";;"Kváča,V.,Mouralová,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kp" +"3669";"JSB407";"Globální problémy životního prostředí a udržitelný rozvoj";"Drhová,Z.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kvsp" +"3670";"JSB537";"Analýza dat v SPSS";"Soukup,P.";"Oreský,J.";"5";"4";"5";"5";"5";NULL;NULL;NULL;"3";"5";"5";"5";"5";;;"ks" +"3671";"JSB004";"Sociální nerovnosti";"Šanderová,J.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"ks" +"3672";"JSB025";"Sociální problémy";"Frič,P.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"4";"4";;;"kvsp" +"3673";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"3";"3";"2";"2";NULL;NULL;NULL;"1";"3";"2";"2";"3";;;"ies" +"3674";"JMB414";"Seminář k aktualitám I";;"Karasová,N.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"4";"5";"Oceňuji velice podrobnou a přínosnou zpětnou vazbu k seminární práci. Cvičící vyžadovala, abychom se její připomínky k SP pokusili zohlednit a abychom poslali opravenou verzi zpět. Vnímám pozitivně, jelikož to donutí se nad prací opravdu zamyslet a vylepšit ji, čímž se mohou zlepšit dovednosti v psaní seminárních prací. Cvičící také podrobně a s konstruktivní kritikou zhodnotila i ústní referáty.Také se mi velice líbil přístup ke studentům, který byl velice přátelský a vstřícný. Debaty byly uvolněné a zapojovali se téměř všichni. Struktura i organizace kurzu mi vyhovovala a myslím, že by měla být zachována.";"Možná by bylo dobré trochu zvýšit požadavky, zdálo se mi, že kurz byl poměrně nenáročný. Napadá mě třeba, že by každý student měl za úkol pravidelně, každý týden, prostřednictvím médií monitorovat dění v určitém regionu a udělat z toho nějaký výstup...měli jsme sice každý asi 3x za semestr aktualitu, ale nijak velkou přípravu to nevyžadovalo..";"krvs" +"3675";"JLB035";"Francouzština I";;"Bosáková,L.";"4";"3";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"3";"5";;;"cjp" +"3676";"JJB334";"Zábava v médiích";"Kruml,M.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"4";"1";"3";"5";;;"kms" +"3677";"JJB617";"Vybrané novinářské osobnosti 20. století";"Železný,J.";;"4";"1";"5";"3";"4";NULL;NULL;NULL;"1";"4";"1";"3";"5";;;"kms" +"3678";"JJB605";"Novodobá historie médií";"Bednařík,P.,Končelík,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"1";"4";"5";;;"kms" +"3679";"JMB047";"Vybrané problémy mezinárodních konfliktů.";"Čížek,M.";;"4";"1";"5";"5";"3";NULL;NULL;NULL;"1";"3";"4";"4";"5";"Oceňuji především přístup vyučujícího ke studentům a také to, že každou látku dovede poutavě, zajímavě a s humorem podat. Také mi vyhovovala forma závěrečné zkoušky.";"Někomu možná nemusí vyhovovat (i když mě osobně to nevadilo) až příliš časté vstupy pana doktora Čížka do referátů studentů. Každý referát pak trvá dost dlouho a jde to na úkor zajímavého výkladu vyučujícího. Samotným referujícím to třeba může také vadit (např. že ztrácejí notu atd., nestíhají referát přednést atd.).";"krvs" +"3680";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"1";NULL;"5";"5";"5";;;"kp" +"3681";"JPB018";"Dějiny mezinárodních vztahů I";"Soukup,J.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmv" +"3682";"JPM910";"The Nature and Function of the State";"Franěk,J.,Pettit,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"3683";"JPB595";"Justice in Politics and International Relations";"Salamon,J.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"3684";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"5";"5";"Cooperation";;"cjp" +"3685";"JPB578";"Classics of Political Thought";"Salamon,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"3686";"JPB258";"Politické myšlení novověku I";"Franěk,J.,Kučera,J.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"3687";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"4";"5";"5";"5";"3";NULL;NULL;NULL;"1";"4";"5";"5";"4";;;"kp" +"3688";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"kp" +"3689";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"4";"5";"4";"1";"4";"2";"4";"5";;;"kz" +"3690";"JJB221";"Ekonomická teorie marketingu";"Postler,M.";;"3";"3";"3";"5";"3";NULL;NULL;NULL;"1";"4";"2";"3";"4";;;"kmkpr" +"3691";"JJB224";"Psychologie marketingové komunikace";"Vranka,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kmkpr" +"3692";"JJB224";"Psychologie marketingové komunikace";"Vranka,M.";;"4";"4";"5";"5";"3";NULL;NULL;NULL;"2";"4";"2";"4";"4";;;"kmkpr" +"3693";"JJB237";"Právo a etika v marketingu";"Winter,F.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"2";"5";"5";;;"kmkpr" +"3694";"JJB237";"Právo a etika v marketingu";"Winter,F.";;"4";"2";"5";"5";"5";NULL;NULL;NULL;"2";"4";"2";"4";"5";;;"kmkpr" +"3695";"JJB255";"Digitální komunikace";;"Klimeš,D.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"1";"4";"2";"4";"5";;;"kmkpr" +"3696";"JJB243";"Aktuální trendy a vývoj v oboru I.";"Hejlová,D.,Vranka,M.";"Hejlová,D.,Vranka,M.";"4";"2";"5";"5";"5";"5";"5";"5";"1";"4";"2";"5";"5";;;"kmkpr" +"3697";"JJB249";"Úvod do studia českého jazyka I";"Schneiderová,S.";"Schneiderová,S.";"4";"4";"4";"4";"4";"4";"4";"4";"2";"4";"4";"3";"5";;;"kmkpr" +"3698";"JJB253";"Markething - online publikování a populární kultura I.";;"Maxa,M.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"3";"2";"5";"5";;;"kmkpr" +"3699";"JJB269";"Sociální kontext komunikace";"Vranka,M.";;"2";"4";"5";"5";"2";NULL;NULL;NULL;"2";"2";"1";"2";"2";;;"kmkpr" +"3700";"JPM910";"The Nature and Function of the State";"Franěk,J.,Pettit,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kp" +"3701";"JJB401";"Komerční a nekomerční marketingová komunikace";"Báča,L.,Obluk,O.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmkpr" +"3702";"JJB269";"Sociální kontext komunikace";"Vranka,M.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kmkpr" +"3703";"JJB407";"Bakalářský proseminář";"Rosenfeldová,J.";;"5";"3";"4";"5";"3";NULL;NULL;NULL;"4";"5";"5";"1";"5";;;"kmkpr" +"3704";"JJB401";"Komerční a nekomerční marketingová komunikace";"Báča,L.,Obluk,O.";;"4";"5";"4";"4";"5";NULL;NULL;NULL;"1";"5";"5";"4";"5";;;"kmkpr" +"3705";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";NULL;NULL;"5";"5";"5";"4";"5";"4";NULL;NULL;NULL;NULL;NULL;;;"ies" +"3706";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"2";"4";"4";;;"ks" +"3707";"JJB334";"Zábava v médiích";"Kruml,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"1";"5";"5";;;"kms" +"3708";"JMB058";"Československá a česká zahraniční politika po r. 1989 I.";"Handl,V.,Kunštát,M.";;"3";"2";"3";"4";"4";NULL;NULL;NULL;"1";"5";"2";"3";"4";"Besedy s dimplomaty či zaměstnanci MZV";;"knrs" +"3709";"JMB249";"Seminář k dějinám středo- a jihovýchodní Evropy";;"Lukešová,O.";"5";"2";NULL;NULL;NULL;"3";"4";"5";"1";"5";"3";"3";"5";"Oceňuji pravidelné testy a testy \"nanečisto\" na každou hodinu, jelikož to velmi pomůže v přípravě na záverečnou zkoušku.";"Dát studentům podrobnější zpětnou vazbu k seminárním pracím.";"krvs" +"3710";"JJB606";"Televize jako instituce";"Štoll,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kms" +"3711";"JMMZ205";"Race, Ethnicity, and Gender in American History and Literature";;"Janíčková,M.,Robbins,D.";"4";"2";NULL;NULL;NULL;"4";"4";"4";"1";"5";"4";"4";"4";;;"kas" +"3712";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"4";"3";"3";"3";"2";NULL;NULL;NULL;"1";"4";"2";"3";"3";;;"ies" +"3713";"JJB607";"Analýzy mediálních obsahů";"Křeček,J.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"3714";"JLB033";"Němčina I";;"Faltýnová,R.";"4";"3";NULL;NULL;NULL;"3";"3";"4";"1";"4";"4";"3";"4";;;"cjp" +"3715";"JJB617";"Vybrané novinářské osobnosti 20. století";"Železný,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"3716";"JLB099";"Rozřazovací test z angličtiny";;"Kunzová,J.";"5";"3";NULL;NULL;NULL;"3";"3";"3";"1";"3";"3";"3";"3";;;"cjp" +"3717";"JJB618";"Propagandistické působení médií v 1 polovině 20. století";"Suk,P.";;"4";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"3718";"JJB238";"Kritika marketingové komunikace";"Rosenfeldová,J.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"2";"4";"2";"4";"5";;;"kmkpr" +"3719";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"4";"5";;;"ks" +"3720";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kas" +"3721";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"4";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"krvs" +"3722";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Hornát,J.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"krvs" +"3723";"JMB402";"Úvod do společenských věd II";;"Jasenčáková,M.";"4";"3";NULL;NULL;NULL;"4";"5";"4";"2";"4";"4";"4";"4";;;"krvs" +"3724";"JJB284";"Firemní komunikace a kultura";"Poucha,T.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Jeden z nejlepších kurzů na MKPR. Nic bych neměnila.";;"kmkpr" +"3725";"JPM052";"Mezinárodní ekonomické vztahy (MEV)";"Kučerová,I.";;"3";"1";"1";"5";"3";NULL;NULL;NULL;"1";"3";"1";"2";"2";;;"kmv" +"3726";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"3";"4";;;"knrs" +"3727";"JPM607";"International Negotiations";;"Parízek,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"2";"4";"5";"5";"5";;;"kmv" +"3728";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Čížek,M.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"knrs" +"3729";"JPM697";"Asia Security";"Kolmaš,M.";;"5";"3";"4";"5";"4";NULL;NULL;NULL;"1";"5";"4";"4";"4";;;"kbs" +"3730";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"3";"4";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kmv" +"3731";"JJB351";"Tvorba videí";;"Mikulka,J.";"3";"5";NULL;NULL;NULL;"3";"5";"3";"1";"1";"2";"4";"3";;"Kurz byl časově mnohem náročnější, než kurzy za 6 kreditů, což mi nepřipadá adekvátní. Rozhodně má velký potenciál a mohl by být pro studenty velmi zajímavý, ale zatím je trochu nepropracovaný a chybí mu teoretická část. Místo tvoření videa každý týden a jeho následné kritizování by bylo vhodnější nejprve do hloubky probrat jednotlivá témata důležitá ke tvorbě videa, protože v současné podobě kurzu se bohužel nikdo moc nového nenaučil - natáčel ten, kdo už uměl natáčet, stříhal ten, kdo už stříhat uměl a celkový přínos kurzu je kromě příjemné atmosféry bohužel nulový.";"kmkpr" +"3732";"JPM702";"NATO and EU in Crisis Management";"Karásek,T.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"4";"3";"5";"5";;;"kbs" +"3733";"JMB250";"Seminář k dějinám západní Evropy";;"Simbartlová,A.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Líbilo se mi úplně vše - hlavně přístup cvičící ke studentům, struktura každé hodiny, připojení krátkého výkladu ke každému tématu (který byl navíc velmi zajímavý a srozumitelný), výběr textů...nemám asi nic, co bych vytkla. Pro mě doposud nejzajímavější a nejlepší seminář na IMS, jak co se týče tématu, tak přístupu cvičící. Oceňuji systém seminární práce a referát týkaly vždy jen jednoho textu, který jsme se měli snažit jasně a stručně analyzovat a vyložit.";"Není co vytknout";"kzs" +"3734";"JPM701";"European and Transatlantic Security";"Kazharski,A.";;"3";"4";"3";"5";"4";NULL;NULL;NULL;"2";"4";"2";"4";"3";;;"kbs" +"3735";"JPM699";"Security and Technology";"Střítecký,V.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kbs" +"3736";"JPM693";"Traditional and Critical Concepts in Security Studies";"Rychnovská,D.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kbs" +"3737";"JPM689";"Conflict Studies";"Karásek,T.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kbs" +"3738";"JPM656";"Technology and warfare";"Kučera,T.";;"5";"4";"4";"5";"4";NULL;NULL;NULL;"1";"4";"5";"5";"4";;;"kbs" +"3739";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"3";"4";"1";"5";"2";NULL;NULL;NULL;"2";"4";"3";"4";"3";;;"kmv" +"3740";"JPM561";"Regional Security Studies";"Karásek,T.,Klosek,K.";"Karásek,T.,Klosek,K.";"5";"3";"5";"5";"5";"3";"5";"5";"2";"5";"5";"5";"5";;;"kbs" +"3741";"JPM644";"Contemporary International Relations in East Asia";"Kolmaš,M.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"3";"5";"4";;;"kmv" +"3742";"JPM719";"Diplomacy of the European Union";"Pajtinka,E.";;"2";"4";"2";"1";"3";NULL;NULL;NULL;"1";"3";"1";"4";"2";;;"kmv" +"3743";"JPM727";"Orchestration in Global Governance";;"Abbott,K.,Parízek,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kmv" +"3744";"JPM721";"Komparace ekonomik zemí EU";"Kučerová,I.";;"1";"3";"1";"5";"2";NULL;NULL;NULL;"1";"2";"1";"3";"2";;;"kmv" +"3745";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"4";"3";"3";"3";NULL;NULL;NULL;"2";"2";"2";"3";"3";;;"ies" +"3746";"JSB007";"Dějiny sociologie I";"Šanderová,J.";"Orcígr,V.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ks" +"3747";"JSB025";"Sociální problémy";"Frič,P.";;"3";"3";"3";"3";"3";NULL;NULL;NULL;"1";"3";"3";"3";"3";;;"kvsp" +"3748";"JMB408";"Moderní dějiny Ruska";"Litera,B.,Novák,P.,Pečenka,M.";;"3";"5";"3";"1";"3";NULL;NULL;NULL;"1";"5";"4";"3";"3";"Přednášky pana doktora Pečenky. Také to, že při záverečné zkoušce nám u každého z jednotlivých témat esejí navrhnul osnovu, tedy nám sdělil, co by v eseji mělo být určitě zahrnuto (což byl asi jediný pozitivní aspekt jeho přístupu ke studentům na zkoušce).";"Přístup pana doktora Pečenky ke studentům během souborné zkoušky jsem nepochopila. Pro studenty je to naprosto demotivující a zbytečně je to znervózní. Před zkouškou studentům v podstatě vpálí, že nemají šanci uspět a celkově jsem z toho měla pocit, že se nám vlastně vysmívá a snaží se nás záměrně shodit.";"krvs" +"3749";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Obermajerová,K.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ks" +"3750";"JSB530";"Úvod do sociologického myšlení";"Bureš,J.,Numerato,D.";"Bureš,J.";"5";"5";"5";"5";"5";"5";"5";"5";"3";"5";"5";"5";"5";;;"ks" +"3751";"JJB631";"Social Media: Strategy, Tactics and Analytics";"Audyová,P.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kmkpr" +"3752";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Kouřílek,J.";"3";"3";"3";"3";"3";"3";"3";"3";"3";"3";"3";"3";"3";;;"ks" +"3753";"JJB635";"Interkulturní marketing";"Rosenfeldová,J.";;"4";"1";"5";"5";"5";NULL;NULL;NULL;"4";"3";"3";"4";"5";;;"kmkpr" +"3754";"JSB544";"Vybrané kapitoly středoškolské matematiky";;"Hendl,J.";"1";"2";NULL;NULL;NULL;"3";"3";"2";"1";"2";"2";"2";"3";;;"ks" +"3755";"JMMZ314";"Major Issues in Contemporary Public Debates in the U.S. I";"Sehnálková,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"4";"5";;;"kas" +"3756";"JMMZ313";"Government in United States";"Sehnálková,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kas" +"3757";"JMM279";"Společnost a ekonomika";"Fiřtová,M.";"Fiřtová,M.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"kas" +"3758";"JMM278";"Politika a mezinárodní vztahy";"Kubát,M.,Weiss,T.";"Bečka,J.";"4";"4";"5";"4";"2";"4";"5";"3";"1";"4";"3";"4";"4";;;"krvs" +"3759";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"5";"2";"1";"1";NULL;NULL;NULL;"1";"2";"1";"2";"1";;"Vyučujícího";"ies" +"3760";"JMM277";"Historie a kultura";"Vykoukal,J.";"Kýrová,L.";"3";"5";"1";"5";"1";"2";"4";"2";"1";"3";"3";"4";"1";;;"krvs" +"3761";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Náplň výuky a styl práce v hodinách i mimo ně";"Klidně by mohlo být ještě více konverzace na dané téma";"cjp" +"3762";"JLB019";"Francouzština odborná I";;"Bosáková,L.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Neustálé rozšiřování slovní zásoby";"Možná tak mně samotného :)";"cjp" +"3763";"JMM271";"Metodologický seminář";;"Kýrová,L.";"3";"2";NULL;NULL;NULL;"2";"5";"3";"1";"4";"4";"3";"3";;;"krvs" +"3764";"JMB402";"Úvod do společenských věd II";;"Jasenčáková,M.";"4";"3";NULL;NULL;NULL;"4";"5";"4";"2";"4";"5";"5";"5";"Nacvičování ústních prezentací a oponentur";"Školní počítače (:";"krvs" +"3765";"JMMZ315";"U.S. Foreign Policy";"Raška,F.";;"2";"1";"2";"5";"1";NULL;NULL;NULL;"1";"2";"1";"2";"2";;;"kas" +"3766";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ks" +"3767";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"Témata přednášek";;"kas" +"3768";"JMB409";"Moderní dějiny středo- a jihovýchodní Evropy-dcera JMB013";"Balla,P.,Švec,L.";;"4";"5";"5";"5";"4";NULL;NULL;NULL;"3";"5";"3";"3";"4";"Přednášky pana doktora Bally. Oceňuji i přístup ke studentům obou vyučujících. Oba vyučující byli při závěrečné zkoušce moc hodní.";"Myslím, že by bylo lepší, kdyby studenti mohli mít při psaní esejí při ruce poznámky. Při takovém množství látky je dobré mít něco, od čeho se lze odpíchnout. Někoho to může svádět jenom k přepisování faktů, pokud ale člověk ví, jak má vypadat esej, tak by to pro něj bylo určitě přínosné. Každý může mít někdy okno a přítomnost poznámek by tomuto mohla předejít. Myslím, že i poznámkami před očima lze napsat kvalitní esej.";"krvs" +"3769";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Šafařík,P.";"5";"3";"5";"5";"5";"5";"5";"5";"2";"5";"5";"5";"5";;;"knrs" +"3770";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Andrle,J.";"5";"3";"5";"5";"5";"5";"5";"5";"2";"5";"5";"5";"5";;;"krvs" +"3771";"JJB600";"Úvod do studia médií I.";"Nečas,V.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"3";"5";"5";"Přístup pan profesora.";"Nezlepšovala bych nic, byla jsem spokojená";"kms" +"3772";"JEM059";"Quantitative Finance I";"Baruník,J.,Vácha,L.";"Baruník,J.,Vácha,L.";"3";"5";"3";"5";"3";"4";"4";"3";"2";"4";"5";"3";"2";"Ač mě tento kurz za celý semestr nedokázal moc chytit, chtěl bych ocenit přístup a dovednosti přednášejícího doc. Baruníka. Jeho nadšení pro svůj předmět, které umí vložit i do svého přednášení, činilo přednášky daleko snesitelnější. Také oceňuji, že nám byly z jeho strany dány k dispozici kódy ve Wolframu, ze kterých bylo možné při dělání úkolů vycházet. Nakonec bych ocenil i relativní shovívavost u zkoušky.";"Kurz mě bohužel nedokázal jako celek nadchnout. Rozhodně jej z vlastní zkušenosti nedoporučuji si brát v kombinaci s relativně velkým vytížením dalšími předměty, případně prací. Asi nejhůře musím hodnotit přednášky dr. Váchy, který sice evidentně tématu rozumí, ale vykládá naprosto nezáživně, monotónně a tak trochu, že je to vlastně samozřejmost. Tento styl výkladu v kombinaci s tím, že jsem byl třeba unavený z práce vedl k tomu, že jsem z přednášky nic neměl a vlastně ve všech případech jeho přednášek jsem se pak už nedokázal donutit setrvat na seminář a že jsem pak i u zkoušky zjistil, že s jím přednášenými tématy mám největší problém.";"ies" +"3773";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"5";"5";"Srozumitelný styl výuky";;"knrs" +"3774";"JJB035";"Odvětvové zpravodajství - ekonomie";"Kameníček,J.";;"3";"4";"2";"2";"1";NULL;NULL;NULL;"1";"1";"3";"3";"2";"Oceňuji výběr témat svou vlastní skupinou studentů a jejich zajímavé postřehy. Protože nic dalšího kromě studentských prezentací a štiplavých poznámek vyučujícího se nekoná, dalším ročníkům přeji šťastnou ruku při výběru témat.";"Zprůhlednit hodnocení vyučujícího. Ačkoliv každý týden aktivně aktualizuje SAMBU a s nekritickým přístupem kriticky kamenuje studenty, tak se kromě výsledného verdiktu nikdo nedozví přesné bodové ohodnocení podle velmi podrobně zpracované tabulky v každotýdenním přehledu.";"kz" +"3775";"JMB178";"U.S. in the 1960s and 1970s";"Raška,F.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"4";"5";;;"kas" +"3776";"JMB410";"Moderní dějiny západní Evropy";"Matějka,O.,Váška,J.";;"3";"3";"3";"3";"2";NULL;NULL;NULL;"2";"4";"2";"3";"3";;"- Čas kurzu- Oprava midtermu trvala skoro měsíc";"kzs" +"3777";"JJB040";"Kreativita v jazyce";"Šoltys,O.";;"2";"1";"1";"2";"1";NULL;NULL;NULL;"1";"1";"2";"1";"2";"Navrhuju zachovat kurz zaměřený na rozvoj kreativity pro novináře bezpochyby podnětný. Některé z úkolů byly zajímavé a podnětné.";"Změnit vyučujícího.";"kz" +"3778";"JJB050";"Tvůrčí dílny tisk";"Kubík,J.,Osvaldová,B.";;NULL;NULL;"3";"3";"3";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kz" +"3779";"JJB052";"Tvůrčí dílny FOTO I";"Lábová,A.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"3";"5";"4";"5";"Jeden z nejlepších předmětů na škole. Lábová je skvělá a její poznámky jsou nejen adekvátně kritické, tak občas vtipné.";"Není co zlepšovat.";"kz" +"3780";"JJB059";"Kritika v médiích - televizní";"Novotný,D.";;"4";"3";"4";"5";"3";NULL;NULL;NULL;"3";"2";"4";"4";"5";"Psaní textů z týdne na týden včetně diskutování s ostatními studenty.";"Nic, je to ten lepší ze dvou praktických předmětů D. J. Novotného, kde texty rovnou rozebírá a nečeká na závěrečnou konzultaci, takže se člověk může posouvat průběžně. Nebo minimálně letos to tak možná i díky málému počtu studentů bylo.";"kz" +"3781";"JLB047";"Ruština obecná I";;"Mistrová,V.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Oceňuji přístup PhDr. Mistrové, který je velmi přátelský.";;"cjp" +"3782";"JJB167";"Moderování zpravodajských relací";;"Moravec,V.,Šobr,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"2";"5";"5";"5";"Jeden z nejlepších předmětů na škole. Rozhodně nechat cvičení na uměleckou interpretaci a úvodní hodinu s výkladem k práci s technikou.";"Víc se věnovat nonverbální komunaci. Možná nejlíp udělat dvouhodinovku a natáčet každý týden.";"kz" +"3783";"JPM611";"Cyber Security";"Duračinská,Z.,Střítecký,V.";;"2";"3";"2";"4";"1";NULL;NULL;NULL;"3";"2";"1";"2";"1";;"Less assumptions that students know the details of computing/networks/cyber attacks and more detailed break down of those aspects of cyber security. It would also be beneficial for the teacher to not just talk for 50 minutes about computers non-stop and instead encourage more interaction, speak with examples, and--again--make sure to break down the details without assuming that students know. Most students have a B.A. and have never touched this kind of material before.";"kbs" +"3784";"JJB169";"Věda v médiích";"Kasík,P.";;"4";"2";"3";"4";"1";NULL;NULL;NULL;"2";"3";"3";"3";"3";"Předmět samotný je skvělý nápad, jde určitě o problém, který je třeba diskutovat.";"Kasík trochu moc utíká od tématu, prakticky jsme stihli vždycky tak pětinu toho, co plánoval a pak se bavili o jiných věcech.";"kz" +"3785";"JPM650";"Intelligence";"Bahenský,V.,Galeotti,M.";;"5";"3";"5";"4";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kbs" +"3786";"JPM656";"Technology and warfare";"Kučera,T.";;"1";"3";"1";"3";"1";NULL;NULL;NULL;"1";"1";"1";"1";"1";;"The homework was unhelpful--long, unnecessary texts and dated videos made me not want to go to class to listen to the professor blandly talk. I also found much of the material completely useless. We spent far too long talking about very old systems of warfare (stirrups, longbows, etc.).";"kbs" +"3787";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"2";"3";"3";"1";NULL;NULL;NULL;"1";NULL;"2";"3";"3";;;"ies" +"3788";"JLB041";"Španělština I";;"Mlýnková,L.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"2";"5";;;"cjp" +"3789";"JMB178";"U.S. in the 1960s and 1970s";"Raška,F.";;"4";"2";"4";"5";"3";NULL;NULL;NULL;"1";"4";"2";"4";"4";;;"kas" +"3790";"JMB204";"Skotsko, Wales a Severní Irsko v kontextu moderních britských dějin";"Kasáková,Z.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"2";"4";"4";;;"kzs" +"3791";"JPM699";"Security and Technology";"Střítecký,V.";;"2";"3";"1";"3";"1";NULL;NULL;NULL;"3";"2";"3";"1";"1";"Learning NodeXL/Gephi";"We needed a full class on learning how to use NodeXL and Gephi. My group and I spent 3 hours trying to figure out how to use a function that is supposed to take 5mins when we did our group project. The lectures were extremely dull and a better teaching style is needed for students to actually want to come to class.";"kbs" +"3792";"JMBZ193";"American Media, Culture and Globalization";"Klvaňa,T.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"kas" +"3793";"JPM710";"Radicalization and Deradicalization";"Aslan,E.";;"1";"2";"1";"1";"1";NULL;NULL;NULL;"4";"1";"1";"3";"1";;"Professor Souleimanov is a terrible lecturer. He frequently showed up late, he never had lecture notes or an actual lesson prepared, he relied entirely on students to deliver content to the class, was frequently on his phone during class presentations, and he unprofessionally delivered grade feedback to students in front of the whole class. I am writing my thesis on radicalisation processes and would never recommend this class or this professor as a source of knowledge on the topic.";"kbs" +"3794";"JJB407";"Bakalářský proseminář";"Rosenfeldová,J.";;"3";"3";"3";"5";"3";NULL;NULL;NULL;"4";"3";"3";"3";"3";;;"kmkpr" +"3795";"JSB998";"Úvod do sociologie";"Soukup,P.";;"4";"3";"5";"5";"3";NULL;NULL;NULL;"1";"4";"2";"3";"4";;;"ks" +"3796";"JEM004";"Advanced Macroeconomics";"Baxa,J.,Hromádková,E.";"Baxa,J.,Hromádková,E.,Šestořád,T.,Troch,T.,Žáček,J.";"5";"4";"5";"5";"4";"5";"5";"5";"1";"4";"5";"5";"5";"Positive and friendly approach of the teachers, the fact that we learned in three ways (lecture, TS, ES) helped to understand the subject from different point of views";"the final exam could containt only the most important topic, otheriwise its too much information to contain ; the organization of the course in sis - I took some lessons twice because I followed my timeschedule in SIS";"ies" +"3797";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"4";"5";"5";"5";"4";"3";"3";"3";"1";"4";"5";"3";"3";;"Speaking as someone who wasn't studying undergraduate program at Charles University, it was too hard for me. I tried really hard and sacrified lots of my time and I never really succeeded (mostly homeworks); I attend the pre-training and it simply was not enough - possible consultation of homeworks, if someone got stuck. Furthermore, I dont think that the concept of changing leaders of seminars, is efficient. I attended all of them and still I would love to have notes (from all of them) sometimes it was too fast to follow up.";"ies" +"3798";"JEM034";"Corporate Finance";"Bychkova,O.,Malířová,T.";;"5";"3";"5";"4";"4";NULL;NULL;NULL;"1";"5";"3";"4";"5";"Even the course was focused on wide range, the concept of every topic was presented very efficiently and it wasnt that hard to understand, still I think I learned a lot.";"The midterm test consisted of some topics that were explained in the course aftewards (not before the test). The teacher was sometime late and than prolonged the course. Speaking as working student I wasnt pleased to by late to work then.";"ies" +"3799";"JEM035";"Financial Markets Instruments I";"Dědek,O.,Poláková,N.,Polák,P.";"Dědek,O.,Poláková,N.,Polák,P.";"3";"3";"3";"5";"2";"3";"4";"2";"1";"4";"2";"4";"3";"The topics covered are interesting.";"The organisation of the course seemed complicated to me from time to time. The topics are really interesting but the way of presenting is rather boring. Some midterm test is missing, or clearly stating the proportion of the test and oral exam on the final grade. The final exam seemed harder to me then examples covered during the course.";"ies" +"3800";"JEM040";"Účetní a daňové poradenství";;"Kemény,I.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"3";"5";"5";;;"ies" +"3801";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"4";"2";"1";"3";NULL;NULL;NULL;"1";"3";"2";"2";"2";;;"ies" +"3802";"JLB053";"Angličtina pro sociální vědy I";;"Gloverová,M.";"5";"2";NULL;NULL;NULL;"5";"5";"3";"1";"3";"2";"3";"4";;;"cjp" +"3803";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"4";"2";"5";"5";"5";"5";"5";"5";"1";"5";"4";"4";"5";;;"ies" +"3804";"JSB517";"Hudební subkultury mládeže";"Oravcová,A.";;"3";"3";"2";"2";"1";NULL;NULL;NULL;"2";"2";"1";"2";"2";;"Přístup kantorky ke studentům. Nevyhovují mi určitá oslovení. Zároveň doporučuji zaměřit pozornost i na jiné hudební žánry, než jsou hip-hop a rap, kurz mě tímto velmi zklamal.";"ks" +"3805";"JEM001";"Master´s Thesis Seminar I";;"Havránek,T.,Havránková,Z.,Mejstřík,M.";"4";"2";NULL;NULL;NULL;"5";"5";"3";"1";"3";"3";"3";"5";"Z môjho pohľadu je dobré, že je účasť povinná, tak isto je dobré, že sme museli už určitú časť práce povinne odovzdávať. Prinútilo ma to sadnúť si nad moju prácu skôr, než by som to asi spravila za iných okolností. Spätne musím uznať, že je dobré aj načasovanie odovzdávania prvého draftu do 31.12. Mala som síce náročnejší semester s veľa predmetmi, a preto som pred Vianocami mala dosť starostí aj s midtermami a predtermínmi a inými úkolmi, ale cez skúškové by na to čas nebol vôbec.";"Viem, že mám spolužiakov, ktorí na diplomovej práci začali pracovať už skutočne skoro a preto boli pre nich semináre užitočnejšie - mali možnosť sa spýtať na čo potrebovali. Stále nás je ale určite viac, ktorí sme sa k písaniu skutočne dostali až v druhej polke semestra a preto na prvých dvoch seminároch som viac-menej nevyužila túto možnosť a nebolo čo konzultovať. Odovzdala som proposal, oznámila, že mám už zohnané dáta, ale inak pre mňa účasť na nich nemala nijaký prínos. Nie je to teda ani tak návrh na zlepšenie, pretože zrušenie povinnej účasti nepovažujem za adekvátne riešenie, niektorí ľudia by boli ukrátení o konzultácie a my ostatní by sme neboli aspoň psychologicky prinútení našu diplomku odkladať. Len skôr taký postreh. Myslím si, že povinná účasť bude prínosnejšia v Master´s Thesis Seminar II";"ies" +"3806";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"4";"4";"5";"5";"5";"3";"5";"3";"1";"4";"3";"4";"5";"Pán Baruník je veľmi kvalitný prednášajúci - podal nám učivo veľmi pochopiteľne a na skúšku sa mi učilo ľahšie s kvalitnými poznámkami, ktoré som vďaka nemu mala. Taktiež ma milo prekvapil Martin Hronec ako cvičiaci.";"Bohužiaľ pán Nevrla, napriek jeho evidentne kvalitným vedomostiam, ich z môjho pohľadu nevedel nám študentom podať a po jeho cvičení som na semináre prestala chodiť.";"ies" +"3807";"JMB497";"Metodický úvod pro kombinované studium";"Kubát,M.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"krvs" +"3808";"JEB120";"Financial Economics";"Žigraiová,D.";;"4";"4";"2";"3";"3";NULL;NULL;NULL;"3";"3";"4";"5";"5";;;"ies" +"3809";"JMB499";"Současné metodologie";"Kubát,M.";;"5";"4";"5";"5";"3";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"krvs" +"3810";"NMMA711";"Mathematics 1";"Bárta,T.";"Bárta,T.,Vlasák,V.";"5";"5";"5";"5";"5";"4";"4";"5";"1";"5";"4";"5";"5";;;"ies" +"3811";"JMB523";"Mezinárodní aktuality I";"Fojtek,V.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kas" +"3812";"JEB026";"European Economic Integration";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"3";"4";"2";"5";"1";"3";"3";"1";"1";"5";"3";"5";"5";;"Doporučila bych zlepšit úroveň přednášek, aby měly pro studenty větší přidanou hodnotou. To samé platí o seminářích, kterých se studenti téměř nezúčastňují a prezentace jsou tak předváděny před nulovým publikem.";"ies" +"3813";"JEB110";"Econometrics II";"Pertold-Gebicka,B.";"Chorna,O.,Malinská,B.,Pertold-Gebicka,B.,Pleticha,P.";"3";"3";"2";"3";"4";"5";"5";"5";"1";"3";"4";"3";"4";"Velmi oceňuji vstřícný přístup všech cvičících (zejména pak slečny Chorné, která ve vstřícnosti skutečně vynikala a snažila se nám před závěrečným testem poskytnout veškerou pomoc), kteří celý semestr suplovali za paní doktorku Gebickou ve výkladu toho, co se nestihlo probrat na přednášce (čímž ovšem, samozřejmě, utrpěla kvalita \"seminářů\" jako takových, jelikož na probrání úloh rázem zbývalo méně času). U pana Pletichy jsou pak elektronická řešení jeho seminářů super nadstandard.";"Přednášky paní doktorky Gebické byly, bohužel, poměrně špatné kvality. Často se zdála nepřipravená, ztrácela se i v jednodušším výkladu a nestíhala vše odpřednášet, přičemž látku přeskakovala a v dalších hodinách se k ní už nevracela, tedy její nastudování zůstalo na studentech samotných. V momentě, kdy studentům něco nebylo jasné a paní doktorka slíbila, že to do prezentací doplní, stejně se tak nestalo (příkladem je odvození funkcí Tobit modelu). Přestože navázat na výborný kurz pana docenta Krištoufka není jistě lehké, čekala jsem od tohoto kurzu výrazně víc a jsem z něj velmi rozpačitá.";"ies" +"3814";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"4";"1";"2";"2";NULL;NULL;NULL;"1";"1";"1";"2";"1";;;"ies" +"3815";"JJB135";"Filmový seminář I";;"Šobr,M.";"4";"2";NULL;NULL;NULL;"4";"5";"4";"1";"3";"3";"4";"5";;;"kz" +"3816";"JLB005";"Angličtina pro politology I";;"Panešová,K.";"4";"1";NULL;NULL;NULL;"5";"5";"2";"1";"2";"4";"4";"4";;;"cjp" +"3817";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"3";"4";"5";"5";NULL;NULL;NULL;"1";"4";"5";"4";"4";;;"kp" +"3818";"JPB018";"Dějiny mezinárodních vztahů I";"Soukup,J.";;"4";"2";"4";"5";"2";NULL;NULL;NULL;"2";"3";"4";"3";"5";;;"kmv" +"3819";"JPB221";"Metodologický proseminář I";;"Bahenský,V.,Kofroň,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";;;"kmv" +"3820";"JPB252";"České novověké dějiny I.";"Kučera,J.";;"3";"3";"3";"2";"2";NULL;NULL;NULL;"2";"2";"1";"2";"3";;;"kp" +"3821";"JPB256";"Politické systémy I";"Brunclík,M.,Jeřábek,M.,Kotábová,V.,Perottino,M.";;"4";"4";"4";"5";"3";NULL;NULL;NULL;"1";"5";"3";"4";"5";;;"kp" +"3822";"JJM259";"Mediální systémy a jejich komparace";"Miessler,J.";;"1";"2";"1";"1";"1";NULL;NULL;NULL;"2";"2";"1";"2";"1";;;"kz" +"3823";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"4";"4";"4";"3";"4";NULL;NULL;NULL;"1";"5";"3";"4";"4";;;"kp" +"3824";"JEB136";"Topics in Industrial Organization";"Schwarz,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"3";"4";"1";"4";"4";;;"ies" +"3825";"JEB141";"Introduction to Market Design";"Gregor,M.,Kastl,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";"Kurz byl skvělý! Pan profesor Kastl je výborný přednášející, s nímž byla radost strávit tři půldny. Látka probíraná v kurzu je sice teoretická a založená na algoritmech a větách, ale byla výborně předvedena na konkrétních příkladech a velmi zábavným způsobem. Celkově beru tento kurz jako jeden z absolutně nejlepších, které byla možnost v rámci bakalářského studia absolvovat.";;"ies" +"3826";"JMB065";"Úvod do mezinárodního a evropského práva";"Šlosarčík,I.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"3";"5";"5";"5";"5";;;"kzs" +"3827";"JMB249";"Seminář k dějinám středo- a jihovýchodní Evropy";;"Balla,P.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"krvs" +"3828";"JMB408";"Moderní dějiny Ruska";"Litera,B.,Novák,P.,Pečenka,M.";;"5";"4";"4";"4";"5";NULL;NULL;NULL;"2";"4";"5";"5";"4";;;"krvs" +"3829";"JLB011";"Němčina pro ekonomy nižší I";;"Faltýnová,R.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"3";"2";"5";"Paní inženýrka Faltýnová je výborná pedagožka! Oceňuji zejména její přípravu na hodiny, její přístup ke studentům (jen snad občas zbytečně trošku tvrdší k \"horším\" studentům) a obecně náplň hodin, které byly nabity informacemi, rychle odsýpaly a skutečně stály za to. Po zkušenosti s anglickým jazykem s paní magistrou Gloverovou tedy velká pochvala i dalšímu jazykovému kurzu! Díky za něj.";;"cjp" +"3830";"JLB059";"Sociological Cinema";;"Blokker,P.,Štěpánková,D.";"4";"2";NULL;NULL;NULL;"3";"3";"3";"1";"3";"3";"2";"4";;;"cjp" +"3831";"JLB001";"Angličtina pro sociology I";;"Klírová,M.";"4";"4";NULL;NULL;NULL;"4";"4";"4";"1";"4";"4";"4";"5";"Vlastní aktivitu v podobě Language Journal a celkově aktivity v hodině i na doma.";"Domácí přípravy bylo občas příliš mnoho nebo byla poměrně náročná";"cjp" +"3832";"JSB003";"Oborová sociologie";"Numerato,D.";;"4";"3";"4";"3";"2";NULL;NULL;NULL;"1";"4";"2";"4";"3";;;"ks" +"3833";"JSB010";"Současná sociologie";"Balon,J.";;"4";"3";"3";"3";"3";NULL;NULL;NULL;"2";"5";"2";"4";"5";;;"ks" +"3834";"JSB023";"Praktika z kvantitativního výzkumu I";;"Tuček,M.";"2";"3";NULL;NULL;NULL;"3";"2";"1";"1";"2";"3";"1";"3";;;"ks" +"3835";"JJB618";"Propagandistické působení médií v 1 polovině 20. století";"Suk,P.";;"4";"4";"4";"3";"4";NULL;NULL;NULL;"1";"4";"1";"4";"5";;;"kms" +"3836";"JJB617";"Vybrané novinářské osobnosti 20. století";"Železný,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";"Nejvíce oceňuji kantora Železného, jeho přístup i životní filosofii.";"Více Železných";"kms" +"3837";"JJB607";"Analýzy mediálních obsahů";"Křeček,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"3";"4";;;"kms" +"3838";"JJB606";"Televize jako instituce";"Štoll,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kms" +"3839";"JJB334";"Zábava v médiích";"Kruml,M.";;"5";"2";"5";"4";"3";NULL;NULL;NULL;"1";"3";"2";"4";"5";;;"kms" +"3840";"JSB537";"Analýza dat v SPSS";"Soukup,P.";;"3";"5";"4";"4";"4";NULL;NULL;NULL;"4";"5";"4";"4";"5";;"Letošní provedení kurzu bylo špatné - nebyli zajištěni cvičící";"ks" +"3841";"JSB490";"Úvod do politické sociologie";"Císař,O.";;NULL;NULL;"5";"4";"2";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"ks" +"3842";"JSB311";"Antropologie náboženství";"Spalová,B.";;"4";"5";"5";"5";"4";NULL;NULL;NULL;"1";"4";"5";"3";"5";"Rozmanitost studijních povinností - úkoly, terénní zápisky, seminárka";;"ks" +"3843";"JSB055";"Současná sociální antropologie";;"Grygar,J.,Hrešanová,E.";"5";"5";NULL;NULL;NULL;"5";"4";"3";"1";"4";"2";"4";"5";;;"ks" +"3844";"JSB054";"Výzkumný seminář";;"Hrešanová,E.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"2";"4";"5";;;"ks" +"3845";"JSB033";"Praktika z kvalitativního výzkumu";;"Spalová,B.";"4";"5";NULL;NULL;NULL;"5";"5";"5";"1";"3";"5";"3";"5";;;"ks" +"3846";"JJM259";"Mediální systémy a jejich komparace";"Miessler,J.";;"2";"3";"2";"4";"2";NULL;NULL;NULL;"1";"2";"2";"3";"2";"Recenze knihy byla dobrý nápad, přidal bych u tohoto úkolu na jeho důležitosti. Komunikace s kantorem byla dobrá, v tom bych viděl asi největší pozitivum tohoto předmětu.";"Lepší organizace a systém kurzu. Kurz byl značně nepromyšlený a z toho důvodu byl chaotický. Velký problém byl s prezentacemi, které takřka nesplnily svůj účel. Ostatně celý kurz vůbec nesplnil mé představy o výuce mediálních systémů.";"kz" +"3847";"JJM360";"Ekonomika v médiích";"Klimeš,D.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";"Zajímaví hosté, dobrá tematika předmětu. Osoba učitele byla velmi důležitá, doktor Klimeš umí svým výkladem zaujmout.";"Větší probírání teoretických východisek. Kniha Základy ekonomie byla dobře zvolená, ale uvítal bych větší zapojení informací z dané publikace přímo na hodinách.";"kz" +"3848";"JMB409";"Moderní dějiny středo- a jihovýchodní Evropy-dcera JMB013";"Balla,P.,Švec,L.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"4";;;"krvs" +"3849";"JMB410";"Moderní dějiny západní Evropy";"Matějka,O.,Váška,J.";;"2";"4";"3";"3";"3";NULL;NULL;NULL;"3";"3";"3";"3";"3";;;"kzs" +"3850";"JJM273";"Sportovní žurnalistika ve světě";"Bosák,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"3";"5";"5";"5";"5";"Velice zajímavý předmět, velkou roli hrála osoba vyučujícího. Mnoho informací bylo přímo z praxe, což bylo velmi přínosné.";"Specifikace závěrečného úkolu by mohla být detailnější.";"kz" +"3851";"JEB141";"Introduction to Market Design";"Gregor,M.,Kastl,J.";;"3";"3";"4";"4";"3";NULL;NULL;NULL;"1";"4";"2";"2";"3";;;"ies" +"3852";"JMM128";"Prezentace v médiích";"Procházková,B.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kzs" +"3853";"JEM037";"Financial Markets";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"2";"5";"2";"3";"1";"5";"5";"5";"3";"1";"2";"1";"2";;;"ies" +"3854";"JEM199";"Financial Crisis and Risk Management";"Horváth,R.,Opatrný,M.,TSOMOCOS,D.";;"3";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"2";"5";"3";;;"ies" +"3855";"JJM274";"Práce sportovního reportéra a komentátora";"Záruba,R.";;"5";"4";"4";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Vzhledem k ostatním předmětům se jednalo o náročný kurz založený na praktických dovednostech, to je vynikající. Průběh kurzu bych zachoval takový, jaký je.";;"kz" +"3856";"JJM275";"Tvůrčí dílny I (sportovní)";;"Trunečka,O.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Domluva vyučujícího se studenty byla klíčem k celému kurzu. Rozhodně zachovat a uzpůsobit každý jeden semestr vždy požadavkům všech zúčastněných.";;"kz" +"3857";"JJM290";"Tvůrčí dílny I – rozhlas a televize";;"Maršík,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Pro studenty modulu E velmi dobře vedený kurz. Příliš bych neměnil včetně vyučujícího, doktora Maršíka, se kterým se vždy dobře spolupracovalo.";;"kz" +"3858";"JJM264";"Diplomový seminář II.";;;"4";"4";"3";"3";"3";NULL;NULL;NULL;"2";"3";"3";"3";"3";"nehodnotím - předmět je individualizovaný a nemá smysl jej hodnotit";;"kz" +"3859";"JLB035";"Francouzština I";;"Dundrová,M.";"3";"4";NULL;NULL;NULL;"5";"5";"3";"1";"3";"3";"4";"4";"Oceňuji přístup vyučujících a náplň kurzu.";"Problémem je, že úroveň studentů znalosti druhých jazyků je velmi rozdílná. Patřím mezi studenty horší a kurz je pro mě náročný. A nestíhám ostatní dohnat. Což není problém kurzu, spíše celkového přístupu k výuce jazyků na FSV.";"cjp" +"3860";"JMB410";"Moderní dějiny západní Evropy";"Matějka,O.,Váška,J.";;"3";"3";"4";"5";"2";NULL;NULL;NULL;"2";"3";"3";"4";"3";;"Přednášky byly skvělý, ale v testu se pak objevovaly věci, které s přednáškami nesouvisely. Moc času se strávilo na industrializaci, když se v rámci západní Evropy mohlo dojít k jiným tématům. Chybí opora jako je v Rusku, aby student věděl, co se od něj u zkoušky očekává.Hodnotit studenty podle psaní eseje mi nepřijde jako vhodný způsob pro prověření jeho znalostí. Spousta studentů mého ročníku se připravovala a pak neuspěla právě díky eseji.";"kzs" +"3861";"JMB408";"Moderní dějiny Ruska";"Litera,B.,Novák,P.,Pečenka,M.";;"4";"4";"5";"4";"5";NULL;NULL;NULL;"2";"5";"4";"5";"4";"Vyučující! Oceňuji oporu, díky které víme, co v testu očekávat. Výpis pojmů a požadavků v jako jediném ze tří teritorií je férový. Rychlá oprava výsledků.";"Nelíbí se mi systém hodnocení. Když se mi podaří midterm a nechám si ho uznat, tak mi platí pouze jednou. To mi přijde zbytečné. Hodnocení na základě esejí mi nepřijde adekvátní prověření znalostí. To jsou věci, které by se daly zlepšit. Nevím, jestli je hodnocení esejí tak kritické nebo studenti neumí eseje psát, ale někde je chyba, která by se měla napravit. Výsledky mluví za vše.";"krvs" +"3862";"JJB226";"Strategie a finanční management marketingu I";"Majerik,P.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"2";"4";"4";"4";"5";"Skvělý přístup vyučujícího, přátelská komunikace";"Nic";"kmkpr" +"3863";"JJB231";"Dějiny a teorie public relations";"Halada,J.,Hejlová,D.";;"3";"4";"5";"2";"4";NULL;NULL;NULL;"2";"4";"4";"4";"4";"Vyučujících si vážím, výklad byl zajímavý a přínosný.";"Dost mě zklamala závěrečná zkouška, kdy studenti ve skupinách, které šly mezi prvními byli hodnoceni přísněji a v závěru zkoušení- kvůli tomu, že jeden z vyučujících pospíchal - zkoušející ohodnotili několik posledních skupin pouze na základě seminárních prací a zkouška samotná neproběhla. Tito studenti pak pochopitelně odcházeli ze zkoušky s o mnoho lepším výsledkem. Myslím si, že by se mělo měřit stejným metrem a zkoušející by se měli za všech okolností snažit být objektivní. Osobně se necítím být nijak dotčena, můj výsledek byl dobrý, nicméně nesouhlasím s tímto přístupem a myslím si, že by se podobné situace určitě neměly opakovat. Není to fér vůči studentům, kteří také mají excelentní seminární práce, ale u zkoušky si pak vytáhnou tězší otázku a mají kvůli tomu horší výsledek.";"kmkpr" +"3864";"JMB409";"Moderní dějiny středo- a jihovýchodní Evropy-dcera JMB013";"Balla,P.,Švec,L.";;"4";"5";"5";"4";"5";NULL;NULL;NULL;"2";"5";"4";"5";"4";"Vyučující. Náplň kurzu. Přednášky odpovídaly tématům v závěrečném testu a byly přínosné. Včasné oznámení výsledků.";"Lepší oznamování výsledků. Co se týče midtermu, závěrečných testů a esejí. Mnohdy jsme nevěděli, kolik máme bodů, co jsme pokazili a co musíme příště napravit. Chybí opora, díky které bychom věděli, co v testu očekávat za pojmy a osobnosti. Nějaký výpis požadavků. Systém esejí není dobré hodnocení znalostí.";"krvs" +"3865";"JMB249";"Seminář k dějinám středo- a jihovýchodní Evropy";;"Balla,P.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"5";"5";"Vyhovovalo mi všechno, jak rozbor textů, tak pokus o diskuzi a přednesení vlastních postřehů, hodnocení prezentací a hodnocení závěrečné práce, výběr témat.";"Možná výběr některých textů.";"krvs" +"3866";"JMB250";"Seminář k dějinám západní Evropy";;"Synkule,M.";"3";"1";NULL;NULL;NULL;"2";"3";"3";"4";"3";"3";"4";"4";"Výběr témat byl vhodný a zajímavý.";"Přístup ke studentům a k závěrečným pracím. Odpadávání hodin. Výběr textů.";"kzs" +"3867";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Témata, způsob výuky.";"Zadané prezentace - možná zkusit jinou formou.";"cjp" +"3868";"JMB414";"Seminář k aktualitám I";;"Synkule,M.";"4";"2";NULL;NULL;NULL;"4";"3";"5";"3";"5";"5";"5";"5";"Systém, styl hodiny, rozvržení práci, možnost výběru různých témat.";"Esej - 10 stránek možná hodně, vzhledem k tomu, že je to esej na aktuální problém.Možnost volného výběrů témat byla někdy přítěží, například při psaní esejí, kde by se hodila určité navedení ze strany vyučujícího. Ale to naopak může někdo brát jako pozitivum.";"krvs" +"3869";"JEB105";"Statistics";"Červinka,M.";"Červinka,M.";"5";"4";"4";"5";"4";"5";"5";"5";"1";"5";"5";"4";"5";;;"ies" +"3870";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"4";"4";"5";"5";"4";"3";"4";"2";"2";"3";"5";"4";"3";;;"ies" +"3871";"JEB111";"Advanced Data Analysis in MS Excel";;"Poláková,N.,Polák,P.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"3872";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"3";"3";"5";"5";"4";"5";"5";"4";"1";"4";"3";"4";"3";;;"ies" +"3873";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"5";"3";"5";"4";"5";NULL;NULL;NULL;"1";"5";"4";"4";"4";;;"ies" +"3874";"JLB053";"Angličtina pro sociální vědy I";;"Štěpánková,D.";"3";"3";NULL;NULL;NULL;"3";"3";"3";"1";"2";"2";"2";"1";;;"cjp" +"3875";"JSB007";"Dějiny sociologie I";"Šanderová,J.";"Kotík,M.";"4";"5";"5";"5";"5";"5";"4";"4";"3";"5";"5";"5";"5";;;"ks" +"3876";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Coufalová,L.,Svobodová,T.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ks" +"3877";"JSB530";"Úvod do sociologického myšlení";"Bureš,J.,Numerato,D.";"Moskvina,Y.";"5";"3";"4";"5";"4";"4";"4";"5";"1";"5";"5";"5";"5";;;"ks" +"3878";"JSB025";"Sociální problémy";"Frič,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"4";;;"kvsp" +"3879";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Hanzlík,P.";"3";"4";"3";"4";"3";"5";"5";"5";"1";"4";"4";"3";"4";;;"ks" +"3880";"JSB544";"Vybrané kapitoly středoškolské matematiky";;"Hendl,J.";"2";"4";NULL;NULL;NULL;"3";"4";"3";"1";"3";"3";"2";"2";;;"ks" +"3881";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"3";"4";"2";"1";"2";"1";"1";"1";"2";"3";"1";"3";"2";;"Rozhodně lepší komunikace vyučujícího (dr. Švece) se studenty. Na žádný z emailů, které jsem dr. Švecovi adresoval, neodpověděl. Ve zkouškovém období to dokonce měla být jediná možnost, jak si domluvit konzultační hodiny. Žádné semináře se nekonaly.";"kp" +"3882";"JLB005";"Angličtina pro politology I";;"Stružková,I.";"5";"3";NULL;NULL;NULL;"4";"5";"4";"1";"4";"4";"3";"4";"Rozhodně přístup Mgr. Ivy Stružkové. Velmi vstřícná a milá vyučující.";;"cjp" +"3883";"JEB022";"Institutional Economics";"Kopečná,V.,Schwarz,J.";;"5";"4";"5";"5";"3";NULL;NULL;NULL;"2";"4";"2";"5";"4";;;"ies" +"3884";"JEB026";"European Economic Integration";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"3";"3";"3";"2";"1";"1";"1";"1";"2";"2";"1";"2";"2";;"Velmi nejasné hodnocení prezentacií. Na přednášce jsou většinou jen předčítány prezentace. Příliš mnoho pozornosti věnováno historii, která pak ale není zkoušena. 3 termíny ve zkouškovém období je dost málo, zvláště když jsou všechny vypsány v rámci dvou týdnů a výsledek předposledního termínu je oznámen ani ne 48 hodin před posledním pokusem...";"ies" +"3885";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"4";"4";"5";"5";"1";NULL;NULL;NULL;"1";"4";"2";"5";"3";"Bylo vidět, že oba přednášející jsou do svého oboru zapálení a opravdu se nám to snažili přiblížit, i když to často nebyla zábavná témata. Dávali praktické příklady.";;"ies" +"3886";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"1";"2";NULL;NULL;NULL;"1";"2";"1";"2";"1";"1";"1";"1";"asi čas, který tomu všichni ostatní kromě pana Vyhnánka věnovali";"O tomto předmětu se mluví jako o kreditech zadarmo, ale rozhodně tomu tak není. Člověk si tam musí odsedět 50% (což je naprosto pochopitelné) a vlastně mu to nic nepřinese. Řekla bych, že přínosná bylo možná tak první hodina. Na většinu se pak pak Vyhnánek nedostavil, a když ano, tak se rozjel hodinový monolog na nějaké úplně nesmyslné téma. Bylo to většinou docela utrpení. Asi částečně kladně by se dalo hodnotit, že jsme museli napsat esej, brala jsem to jako trénink na důležitější předměty.";"ies" +"3887";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"4";"3";NULL;NULL;NULL;"4";"5";"4";"2";"5";"5";"5";"5";"Přednášející se nám snažil některé věci vysvětlit opravdu polopaticky, když viděl, že se vůbec nechytáme. Také bylo vidět, že se snaží dělat hodinu zábavnou.";;"ies" +"3888";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"4";"2";"5";"5";"2";"2";"4";"1";"2";"5";"5";"5";"5";"Přednášející se vždy snažil podat nám látku co nejpochopitelněji.";"Semináře (kromě těch od Mgr. Palanského) byly utrpením. Angličtina opravdu na nízké úrovni, hlavně výslovnost, někdy bylo až těžké rozumět (např. německému přízvuku).";"ies" +"3889";"JLB003";"Angličtina pro ekonomy I";;"Gloverová,M.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"1";"5";"5";"5";"5";"Mgr. Gloverová je opravdu vynikající vyučující, hodiny byly vždy zábavné, interaktivní, často jsme dostávali prostor se vyjádřit k daným tématům. Úkoly, co nám zadávala, mi nepřišly jako zbytečné.";;"cjp" +"3890";"JLB041";"Španělština I";;"Mlýnková,L.";"4";"1";NULL;NULL;NULL;"5";"4";"3";"1";"3";"4";"4";"4";"Entusiasmus vyučující.";"Někdy mi přišlo, že jdeme hodně rychle, že spousta lidí se třeba na novou gramatiku moc nechytalo.";"cjp" +"3891";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"2";"5";"5";"1";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ks" +"3892";"NMMA701";"Matematika 1";"Spurný,J.";"Honzík,P.";"4";"5";"5";"5";"5";"1";"1";"3";"1";"5";"5";"5";"5";"Velmi mi vyhovoval styl vedení přednášek pana profesora Spurného. Snažil se nám všechno co nejvíce přiblížit, vždy dával prostor pro dotazy. Předmět sám o sobě je extrémně náročný, tak bylo opravdu příjemné, že alespoň vyučující byl vstřícný.";"Myslím, že většina lidí si dost stěžovala na své učitele ze seminářů. Z mého pohledu to byl vcelku promrhaný čas, protože jsem v seminářích ničemu neporozuměla a někdy bylo opravdu těžké se vyučujícího na něco ptát, protože jsme si potom všichni připadali hloupě.";"ies" +"3893";"JMB414";"Seminář k aktualitám I";;"Šír,J.";"2";"4";NULL;NULL;NULL;"2";"3";"3";"1";"3";"4";"3";"2";;;"krvs" +"3894";"JMB011";"Moderní dějiny Ruska";"Litera,B.,Pečenka,M.";"Novák,P.";"4";"3";"4";"3";"4";"3";"4";"3";"2";"4";"2";"3";"4";;;"krvs" +"3895";"JPB227";"Politický system ČR";"Charvát,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"4";"Přístup vyučujícího.";;"kp" +"3896";"JMB013";"Moderní dějiny středo- a jihovýchodní Evropy";"Balla,P.,Švec,L.";"Lukešová,O.";"4";"4";"4";"4";"4";"5";"5";"5";"2";"4";"2";"3";"4";;;"krvs" +"3897";"JMB015";"Moderní dějiny západní Evropy";"Matějka,O.,Váška,J.";"Váška,J.";"4";"3";"3";"5";"4";"5";"5";"5";"2";"5";"3";"4";"4";;;"kzs" +"3898";"JSB051";"Úvod do sociální antropologie";"Uherek,Z.";"Hrešanová,E.";"5";"2";"4";"5";"4";"4";"5";"4";"1";"5";"1";"4";"5";;;"ks" +"3899";"JMD018";"Metodologie sociálních věd";"Aslan,E.";;"5";"3";"4";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"krvs" +"3900";"JMD017";"Teorie a praxe akademické práce";;"Weiss,T.";"4";"3";NULL;NULL;NULL;"4";"5";"5";"1";"5";"3";"3";"4";;;"kzs" +"3901";"JEB141";"Introduction to Market Design";"Gregor,M.,Kastl,J.";;"5";"1";"5";"4";"5";NULL;NULL;NULL;"1";"4";"4";"3";"5";;;"ies" +"3902";"JEM001";"Master´s Thesis Seminar I";;"Havránek,T.,Havránková,Z.,Mejstřík,M.";"5";"5";NULL;NULL;NULL;"4";"4";"4";"1";"5";"5";"5";"4";;;"ies" +"3903";"JEM037";"Financial Markets";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"4";"3";"5";"5";"4";"4";"5";"4";"1";"4";"4";"4";"4";;;"ies" +"3904";"JEM137";"Real Estate Investment";"Jandík,T.,Streblov,P.";;"3";"5";"4";"5";"3";NULL;NULL;NULL;"1";NULL;"4";"3";"3";;;"ies" +"3905";"JEM199";"Financial Crisis and Risk Management";"Horváth,R.,Opatrný,M.,TSOMOCOS,D.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"3";"3";"3";"4";;;"ies" +"3906";"JMM143";"Economy and Politics in the 20th Century Eastern Europe";"Svoboda,K.";;"4";"2";"4";"4";"4";NULL;NULL;NULL;"3";"4";"1";"4";"4";"The teacher presented a clear overview of the timeframe and focused a lot on explaining why people thought what they thought at that time.";"The exam does not completely correspond to the information of the lectures and readings.";"krvs" +"3907";"JMM189";"Economic transformation in East Central and Southeastern Europe";"Trejbal,V.";;"2";"3";"2";"1";"3";NULL;NULL;NULL;"5";"3";"2";"3";"2";"The teacher clearly explained economic mechanisms that were important during the economic transformation. The links with economic theory were interesting.";"The teacher often seemed unprepared and uninterested.";"krvs" +"3908";"JMM663";"Europe in the French mind: a historical–civilizational point of view";"Bauer,P.";;"3";"3";"2";"5";"3";NULL;NULL;NULL;"2";"3";"4";"2";"3";"The teacher showed an interesting new approach to study historical thought. Great attitude of teacher towards students.";"The explaining of the teacher was sometimes difficult to understand.";"kzs" +"3909";"JMMZ042";"Cohesion Policy of the EU in Central and East European Countries.";"Hauser,J.";;"4";"2";"4";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";"Interesting, original subject. Personal experience of the teacher with the subject was valuable.";"Lectures were sometimes too similar. Some material covered twice in practice.";"krvs" +"3910";"JMMZ050";"Political Systems of East European Countries in the 20th Century";"Kubát,M.";;"4";"2";"4";"4";"4";NULL;NULL;NULL;"1";"4";"2";"5";"4";"Teacher covered a lot of distance and seemed very well-prepared.";"Information sometimes quite descriptive, little analytical.";"krvs" +"3911";"JPB260";"Politické myšlení novověku III";"Kučera,J.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"3912";"JPB227";"Politický system ČR";"Charvát,J.";;"5";"3";"5";"5";"3";NULL;NULL;NULL;"2";"4";"4";"5";"5";"Oceňuji přístup vyučujícího ke studentům i formu přednášení. Oceňuji snahu vyučujícího navádět studenty k pochopení souvislostí, vedení diskuzí v průběhu přednášek.";;"kp" +"3913";"JJB004";"Současný český jazyk I";;"Svobodová,I.";"4";"3";NULL;NULL;NULL;"2";"1";"2";"1";"3";"3";"2";"2";;;"kz" +"3914";"JJB010";"Základy filozofie a vzdělanosti";"Halada,J.";;"2";"5";"4";"5";"2";NULL;NULL;NULL;"1";"3";"2";"4";"3";;;"kz" +"3915";"JJB012";"Žurnalistická tvorba I";"Osvaldová,B.";"Krobová,T.,Osvaldová,B.,Slanec,J.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"kz" +"3916";"JJB015";"Česká literatura I";;"Čeňková,J.,Malý,R.";"4";"3";NULL;NULL;NULL;"2";"2";"3";"1";"3";"4";"3";"3";;;"kz" +"3917";"JJB017";"Grafický design a základy polygrafie I";"Slanec,J.";;"3";"2";"4";"5";"2";NULL;NULL;NULL;"1";"3";"4";"2";"4";;;"kz" +"3918";"JJB018";"Úvod do fotožurnalistiky";"Lábová,A.";;"4";"2";"4";"5";"4";NULL;NULL;NULL;"2";"5";"3";"5";"5";;;"kz" +"3919";"JJB998";"Úvod do ekonomie";"Poljakov,N.";;"5";"4";"5";"4";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kz" +"3920";"JLB009";"Angličtina pro žurnalisty I";;"Prošková,A.";"4";"4";NULL;NULL;NULL;"5";"5";"4";"1";"5";"5";"4";"5";;;"cjp" +"3921";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"3";"2";"2";"2";NULL;NULL;NULL;"1";"3";"3";"4";"3";;;"ies" +"3922";"JLB005";"Angličtina pro politology I";;"Panešová,K.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"3923";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"4";"5";"5";"5";"4";NULL;NULL;NULL;"1";"5";NULL;"4";"3";;;"kp" +"3924";"JSB012";"Úvod do empirického výzkumu ve společenských vědách";"Jeřábek,H.";"Přibáňová,T.";"5";"2";"5";"5";"2";"5";"5";"5";"1";"5";"5";"5";"5";;;"ks" +"3925";"JSB513";"Úvod do akademické práce";"Höfer,K.,Mouralová,M.,Veselý,A.";;"3";"2";"4";"5";"5";NULL;NULL;NULL;"1";"3";"3";"3";"4";;;"kvsp" +"3926";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"2";"5";"5";"2";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ks" +"3927";"JJB240";"Marketing a tvorba značky";"Průša,P.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmkpr" +"3928";"JLB099";"Rozřazovací test z angličtiny";;"Panešová,K.";NULL;NULL;NULL;NULL;NULL;"5";"5";"5";NULL;NULL;NULL;NULL;NULL;;;"cjp" +"3929";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"3";"4";"3";"3";"3";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kmv" +"3930";"JPM430";"Marxism in International Relations (TIR)";;"Střítecký,V.";"5";"3";NULL;NULL;NULL;"5";"2";"5";"1";"5";"5";"5";"5";;;"kmv" +"3931";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"3";"4";"3";"3";;;"kmv" +"3932";"JPM658";"International Economic Relations";"Parízek,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmv" +"3933";"JPM689";"Conflict Studies";"Karásek,T.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kbs" +"3934";"JPM725";"Technology and Security: Contemporary Warfare in the 21st Century";;"Csernatoni,R.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kmv" +"3935";"JSB537";"Analýza dat v SPSS";"Soukup,P.";"Oreský,J.";"2";"5";"4";"4";"2";"5";"5";"5";"3";"5";"5";"5";"3";"Nejvíce oceňuji práci cvičících, kteří si na nás udělali čas a vysvětlili nám kompletní látku z předmětu statistika II, který byl zrušen.";"Mít cvičící již od samého začátku, ať nemusíme vše dohánět.Kurz by neměl zahrnovat látku ze statistiky II. Je velice obtížné za jedno odpoledne pochopit látku za jeden semestr a za pár týdnů z ní dělat státnice.";"ks" +"3936";"JSB010";"Současná sociologie";"Balon,J.";;"4";"4";"4";"4";"5";NULL;NULL;NULL;"1";"5";"3";"4";"4";;;"ks" +"3937";"JSB023";"Praktika z kvantitativního výzkumu I";;"Špaček,O.";"2";"4";NULL;NULL;NULL;"2";"3";"4";"3";"4";"5";"4";"2";"zajímavá témata výzkumů";;"ks" +"3938";"NMMA703";"Matematika 3";"Zelený,M.";"Johanis,M.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"3939";"JEB110";"Econometrics II";"Pertold-Gebicka,B.";"Chorna,O.,Malinská,B.,Pertold-Gebicka,B.,Pleticha,P.";"3";"4";"2";"2";"2";"3";"3";"3";"1";"3";"3";"3";"3";;;"ies" +"3940";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"5";"2";"5";"5";"4";"5";"5";"4";"1";"5";"5";"5";"5";;;"ies" +"3941";"JEB136";"Topics in Industrial Organization";"Schwarz,J.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"2";"5";"3";"5";"4";;"Velmi náročné (dlouhá čtení, midterm, final) na poměrně nízké kreditové ohodnocení.";"ies" +"3942";"JEB105";"Statistics";"Červinka,M.";"Červinka,M.";"5";"4";"5";"5";"5";"5";"5";"5";NULL;"5";"5";"5";"5";;;"ies" +"3943";"JEM020";"Ethics and Economics";"Cahlík,T.";"Cahlík,T.";"4";"3";"4";"5";"4";"4";"5";"3";"1";"4";"3";"4";"4";;;"ies" +"3944";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"2";"4";"2";"4";"4";;;"kz" +"3945";"JEB141";"Introduction to Market Design";"Gregor,M.,Kastl,J.";;"3";"4";"4";"2";"3";NULL;NULL;NULL;"2";"4";"4";"3";"4";;;"ies" +"3946";"JPM185";"Evropská integrace - teorie a příklady, ES";"Jeřábek,M.";;"2";"3";"2";"3";"3";NULL;NULL;NULL;"3";"4";"2";"3";"1";"The content - I find theories very interesting";"I dont think the evaluation from the side of the teacher was very objective. During the oral exam, he did not let the students speak and was constantly jumping from one student to another. From my 3 page long preparation I didnt say almost anything - the teacher was constantly interrupting my speech, digging for an answer to one very specific question, which I couldnt respond according to his expectation. I ended up with a bad mark, without a chance to show everything else I knew about the topic. I was disappointed by the oral examination, the attitude of the teacher and the unfair grade I got based on very narrow-minded evaluation.";"kmv" +"3947";"JPM052";"Mezinárodní ekonomické vztahy (MEV)";"Kučerová,I.";;"3";"4";"3";"5";"5";NULL;NULL;NULL;"1";"2";"2";"1";"4";"Obsah by mohol byť veľmi zaujímavý - témy aj základné myšlienky.";"V súčasnosti je to trochu \"nalejvárna\". Veľa autorov, publikácii, kategorizácii - ale nedozvedáme sa širší kontext. Možno by bolo dobré zredukovať záber a ísť viac do hĺbky vybraných prístupov.";"kmv" +"3948";"JJB600";"Úvod do studia médií I.";"Nečas,V.";;"4";"4";"4";"4";"2";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kms" +"3949";"JJB601";"Rozbor právního prostředí v českých médiích";"Benda,J.";;"1";"5";"3";"1";"1";NULL;NULL;NULL;"1";"3";"4";"3";"1";;;"kms" +"3950";"JJB605";"Novodobá historie médií";"Bednařík,P.,Končelík,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Zachování profesorské dvojice, je naprosto skvělá!";;"kms" +"3951";"JEB026";"European Economic Integration";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"2";"3";NULL;"4";"1";"3";"3";"3";NULL;"3";"2";"3";"2";;"Zkrátit délku prezentací, probírat méně věcí a víc důkladně.";"ies" +"3952";"JEM199";"Financial Crisis and Risk Management";"Horváth,R.,Opatrný,M.,TSOMOCOS,D.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ies" +"3953";"NMMA703";"Matematika 3";"Zelený,M.";"Zelený,M.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"4";"5";;;"ies" +"3954";"JJB021";"Bakalářský seminář";;"Prázová,I.";"3";"2";NULL;NULL;NULL;"3";"4";"4";"1";"4";"4";"2";"3";;;"kz" +"3955";"JJB143";"Žurnalistika a feminismus";"Krobová,T.,Osvaldová,B.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"3";"4";"3";"4";"5";;;"kz" +"3956";"JJB014";"Žurnalistická tvorba III - Časopisecká tvorba";"Osvaldová,B.";"Maňák,V.";"5";"4";"5";"5";"4";"5";"5";"5";"1";"5";"5";"5";"5";"Asi nejlepší kurz jaký jsme zatím na bakalářské žurnalistice měla. Takhle přesně si představuji praktickou vyuku žurnalistiky. Skvělý a profesionální přístup vyučujících, důkladná, pečlivá práce s texty, dostatek zpětné vazby a nápadů, jak texty posunout dál. Zároveň dostatek prostoru pro autory samotné a jejich nápady. Tvorba Erga, spravování sociiálních sítí, redakční porady jsou zároveň kreativní, tvůrčí, ale i naučné. Oceňuji osobní a přátelský přístup vyučujících a dobrou komunikaci.";;"kz" +"3957";"JEB044";"Financial Accounting";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";"The lectures were terrific. Financial Accounting is the only course where I visited lectures not because I needed to make notes, but because I really enjoyed Jiri Novak's approach (he encouraged students to participate in discussions actively). He really loves the subject and his lectures were just top quality.";"The overall quality of the seminars can be improved. Most of them were less interesting and driving against the background of the lecture before. Next, there are almost no relevant materials for preparation for midterm and final exam on the page of the course.";"ies" +"3958";"JEB105";"Statistics";"Červinka,M.";"Hanus,L.";"3";"5";"1";"5";"1";"3";"4";"3";"1";"5";"5";"5";"4";"The home assignments were very quality and very useful. The midterm and the final exam (especially its structure) were well-prepared. Visually, the course was very good. Overall, I liked Michal Cervinka's creative and thorough approach in many aspects. Sarlota Smutna's seminars were very quality.";"The lectures were useless for me. They did not add anything to what is written in the textbook. Sometimes the material was presented even worse than it is presented in the textbook (for example, Rao-Cramer theorem). It seemed to me that the lecturer does not like the subject or does not have good teaching skills. And assuming that the lecturer does not like the subject, we can further assume that he does not really feel the subject. That is, he does not know well which of the topics/theorems/proofs are important for students (take a look at Statistics 110 Harvard course on youtube and you will understand me). And by the way, even the choice of the main textbook of the course also seems questionable to me. It does a good job at explaining later topics (point estimation, confidence interval, hypotheses testing), but it overcomplicated things at earlier stages (given that we are students of Economics and Finance, not Mathematical Statistics).";"ies" +"3959";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"3";"4";"2";"5";"1";"2";"4";"1";"1";"2";"1";"2";"2";"I liked Sarlota Smutna's seminars. She was working hard clearing the mess of irrelevant information and presenting the material to students so that they can grasp at least something.";"There was a lot of really boring and irrelevant information on the lectures, which were not very driving. The overall quality of the seminars (except Sarlota Smutna's seminars) could be improved, too. There was no reason to visit lectures or seminars. Even the main textbook of the course was really bad at presenting the material.";"ies" +"3960";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"3";"2";"3";"4";"1";"3";"4";"2";"1";"3";"1";"3";"3";"When I was doing the home assignment with GDP, I felt like I was doing something valuable. The main textbook of the course is perfect.";;"ies" +"3961";"NMMA703";"Matematika 3";"Zelený,M.";"Zelený,M.";"5";"4";"4";"5";"4";"4";"5";"4";"1";"5";"5";"5";"5";;;"ies" +"3962";"JSB003";"Oborová sociologie";"Numerato,D.";;"3";"4";"5";"5";"2";NULL;NULL;NULL;"1";"3";"1";"3";"3";;;"ks" +"3963";"JSB537";"Analýza dat v SPSS";"Soukup,P.";;"2";"5";"4";"4";"1";NULL;NULL;NULL;"4";"2";"2";"1";"1";;;"ks" +"3964";"JSB534";"Introduction to Visual Sociology";"Wladyniak,L.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"5";"3";"5";"I really enjoyed this course. Whole course was rather refreshing and I also learned quite alot.";;"ks" +"3965";"JSB023";"Praktika z kvantitativního výzkumu I";;"Špaček,O.";"3";"4";NULL;NULL;NULL;"3";"4";"5";"3";"1";"2";"3";"3";;;"ks" +"3966";"JSB517";"Hudební subkultury mládeže";"Oravcová,A.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"3";"5";"Oceňuji svěží přístup k sociologii. Pro mě jeden z nejlepších kurzů za tento semestr.";;"ks" +"3967";"JPB256";"Politické systémy I";"Brunclík,M.,Jeřábek,M.,Kotábová,V.,Perottino,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"1";"5";"5";;;"kp" +"3968";"JPB258";"Politické myšlení novověku I";"Franěk,J.,Kučera,J.";;"3";"5";"5";"3";"5";NULL;NULL;NULL;"4";"5";"1";"1";"1";;;"kp" +"3969";"JPB252";"České novověké dějiny I.";"Kučera,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"1";"3";"5";;;"kp" +"3970";"JPB221";"Metodologický proseminář I";;"Komasová,S.,Parízek,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kmv" +"3971";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"1";"5";"5";"1";"3";NULL;NULL;NULL;"1";"3";"2";"5";"1";;;"kp" +"3972";"JPB020";"Úvod do mezinárodních vztahů";"Karlas,J.,Kučera,T.";;"1";"5";"1";"3";"1";NULL;NULL;NULL;"1";"5";"1";"1";"1";;;"kmv" +"3973";"JPB018";"Dějiny mezinárodních vztahů I";"Soukup,J.";;"5";"1";"5";"5";"1";NULL;NULL;NULL;"2";"3";"1";"1";"5";;;"kmv" +"3974";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"1";"5";"5";;;"kp" +"3975";"JLB099";"Rozřazovací test z angličtiny";;"Stružková,I.";"3";"3";NULL;NULL;NULL;"3";"3";"5";"1";"1";"5";"1";"5";;;"cjp" +"3976";"JLB005";"Angličtina pro politology I";;"Panešová,K.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"2";"5";"2";"3";"5";;;"cjp" +"3977";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"1";"5";"1";"1";"1";NULL;NULL;NULL;"1";"1";"1";"1";"1";;;"ies" +"3978";"JEM059";"Quantitative Finance I";"Baruník,J.,Vácha,L.";"Baruník,J.,Vácha,L.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"I have learned to use and to understand new models and approaches to predict financial markets, and why all methods fails in real-life.";;"ies" +"3979";"JEM062";"Introductory Econometrics";"Kukačka,J.";"Kukačka,J.";"4";"5";"5";"5";"5";"5";"5";"5";"1";"4";"4";"4";"5";"As a statistician, we normally use different appraches to solve models and how to use data for usually hard to explain models. Here I worked from a very practical point of view, so I had to go on the data like a practical one who really want s to submit data, that are compareable with underlying theory instead of just saying why the model is the best and outnumbers theory.";;"ies" +"3980";"JLB059";"Sociological Cinema";;"Blokker,P.,Štěpánková,D.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"5";"5";"I really liked the mix of documentaries and classic films.";"I would probably conduct the discussion a bit differently. Less direct description and more in depth comparsion and analyzing.";"cjp" +"3981";"JEM181";"Data Science with R";"Herman,D.,Krištoufek,L.";"Herman,D.,Krištoufek,L.";"4";"4";"5";"4";"5";"5";"5";"5";"1";"4";"5";"5";"5";"i learned to use new methods to predict and classify data. It is also demanded for jobs and PhD positions, so it is very useful later on. The lecturer also made several examples. On the other hand I have to say hat the Bonus points are not those in data camp, but in his lectures where students have only 5 minutes time to run quite difficult tasks.";;"ies" +"3982";"JSB003";"Oborová sociologie";"Numerato,D.";;"4";"4";"4";"5";"4";NULL;NULL;NULL;"2";"5";"3";"5";"5";;;"ks" +"3983";"JSB004";"Sociální nerovnosti";"Šanderová,J.";;"4";"2";"4";"5";"5";NULL;NULL;NULL;"3";"3";"1";"4";"4";;;"ks" +"3984";"JSB003";"Oborová sociologie";"Numerato,D.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Velice se mi líbila selekce témat a jejich propojení s mnoha různými teoriemi, což nám pomáhá i v mnoha dalších předmětech. Dále bych vyzdvihla přítup Dina Numerata, neboť je na něm vidět zapálení pro to, co vyučuje a touha předat to studentům. Také je s ním rychlá a srozumitelná emailová komunikace.";"Asi by bylo dobré umožnit opravu průběžných testů, protože potom ti, kteří se na test rozhodnou nejít proto, že se nestíhají naučit, jsou ve výhodě oproti těm, kteří zodpovědně jdou na původní termín.";"ks" +"3985";"JSB454";"Social Web: (Big) Data Mining";"Růžička,J.";;"4";"5";"3";"4";"4";NULL;NULL;NULL;"1";"5";"5";"5";"2";"The most valuable was that I have learned a new programming language and how to use basic functions in Linux. Also scraping data is in demand for jobs and definitely useful. Also, Selenium and beautiful soup will be still helpful, also for other projects.";"For beginners, this course was quite too difficult. Only a very few people have gotten some results, most quitted a long time before. Some slides with working code snippets would be extremely useful for beginners.";"ks" +"3986";"JSB010";"Současná sociologie";"Balon,J.";;"3";"4";"4";"5";"4";NULL;NULL;NULL;"2";"5";"2";"4";"5";"Prezentace studentů, přijde mi to jako dobrý koncept.";;"ks" +"3987";"JSB055";"Současná sociální antropologie";;"Grygar,J.,Hrešanová,E.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"3";"5";"5";"Testy na povinnou literaturu, je to velký motivační prvek.";;"ks" +"3988";"JSB131";"Velké empirické výzkumy ČR";"Tuček,M.";;"3";"3";"4";"5";"5";NULL;NULL;NULL;"3";"3";"3";"3";"4";;;"ks" +"3989";"JSB033";"Praktika z kvalitativního výzkumu";;"Marková Volejníčková,R.";"4";"5";NULL;NULL;NULL;"4";"5";"5";"1";"4";"5";"3";"5";;;"ks" +"3990";"JSB027";"Sociální politika jako společenská praxe";"Dobiášová,K.,Vlčková,K.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Sociální politika mě z celého studia baví nejvíc, proto mě těší, že je výuka vedena velmi kvalitně. Přednášky od všech vyučujících a cvičích jsou poutavé i navzdory tomu, že jsou delší. Člověk si z přednášky dost odnese a zapamatuje, což velmi cením.";"V tomto kurzu mi oproti Úvodu do sociální politiky přišly méně přínosné semináře, což ale nebylo vinou cvičících. Spíše jich bylo málo, neboť čas jim vyhrazený byl namíchán s exkurzemi v terénu, které považuji za velmi přínosné a možná by tak bylo lepší dát v tomto kurzu prostor pouze jim.";"kvsp" +"3991";"JSB031";"Bakalářský seminář I - diskuse projektů";;"Numerato,D.";"5";"4";NULL;NULL;NULL;"4";"5";"5";"2";"4";"5";"4";"5";"Prezentace projektů a rozborů jednotlivých částí BP";;"ks" +"3992";"JSB537";"Analýza dat v SPSS";"Soukup,P.";;"3";"5";"5";"4";"3";NULL;NULL;NULL;"4";"4";"5";"5";"3";"Nejvíce jsem v kurzu oceňovala přístup cvičícího Jana Oreského, který pro nás dělal náhradní cvičení i ve svém volném čase a bez něhož by pravděpodobně neúspěšnost studentů v kurzu byla mnohem vyšší. Témata vysvětluje rychle, a přesto srozumitelně.";"Organizaci výuky a koncepci státnic. Analýza dat v SPSS byla (a bude) kvůli těmto faktorům velmi stresující. Nelze se totiž spoléhat na konsesus mezi cvičícím a studenty, že se semestr vynechané látky naučí za 5 hodin o víkendu.";"ks" +"3993";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"3";"3";"3";"1";NULL;NULL;NULL;"1";"2";"2";"2";"2";;;"ies" +"3994";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"5";"3";NULL;NULL;NULL;"4";"5";"4";"1";"5";"5";NULL;"5";;;"cjp" +"3995";"JLB019";"Francouzština odborná I";;"Bosáková,L.";"5";"2";NULL;NULL;NULL;"4";"5";"4";"2";"4";"4";"4";"4";;;"cjp" +"3996";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"5";"4";"4";"5";"5";NULL;NULL;NULL;"3";"5";"4";"5";"5";;;"kas" +"3997";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"4";"3";"4";"5";;;"ks" +"3998";"JLB005";"Angličtina pro politology I";;"Panešová,K.";"4";"4";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"2";"4";"Vstřícnost Mgr. Panešové. Na e-maily reaguje rychle, ochotně, na hodinách byla vždy velmi milá a panovala tam velmi příjemná atmosféra.";"Méně domácích úkolů, více se soustředit na procvičování gramatiky a slovní zásoby.";"cjp" +"3999";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"4";"4";"5";"5";"5";"4";"5";"3";"1";"4";"4";"3";"4";"- přístup, cíl na pochopení hlavních myšlenek a nebazírování v testech na psaní definic a odvozování vzorců- strukturu předmětu - úkoly a projekt, pomohou pochopit aplikaci jednotlivých modelů v praxi";"Kvalitu seminářů, možná větší interaktivitu nebo detailnější ukazované studované příklady. Dost možná je to moje chyba a nepřipravenost na výuku, ale občas jsem měla pocit, že jsem si ze semináře neodnesla více než bylo napsáno v handoutu. Zároveň by bylo super mít nějakou konkrétnější \"case study\", jak správně vést empirický projekt po praktické stránce, co a jak modelovat a testovat, na co si dát pozor. Samozřejmě všechno to vychází ze seminářů a přednášek a teoreticky bych to po absolvování kurzu měla zvládat, ale tohle by mi přišlo jako pěkný celistvější pohled na věc.";"ies" +"4000";"JEM181";"Data Science with R";"Herman,D.,Krištoufek,L.";"Herman,D.,Krištoufek,L.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"3";"5";"předmět je velmi praktický, zabývá se mnoha tématy napříč celým spektrem data science + velmi oceňuji možnost využívat interaktivní kurzy na datacampu";;"ies" +"4001";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"1";"4";"1";"1";"1";NULL;NULL;NULL;"1";"1";"1";"1";"1";"Bohužel není co ocenit.";"Změnit vyučujícího, najít někoho, kdo má zájem pomáhat studentům a ne jim za každou cenu přidělávat potíže (např. neodpovídáním na dotazy, smazáním prezentací před zkouškovým obdobím).";"ies" +"4002";"JSB003";"Oborová sociologie";"Numerato,D.";;"5";"3";"4";"5";"4";NULL;NULL;NULL;"2";"5";"3";"3";"5";"Tento kurz byl jeden z nejlepších v tomto semestru. Zajímavá náplň, kvalitní přednášející.";"Možná by bylo zajímavé udělat semináře podobně jako na kurzu Úvod do Sociologického myšlení.";"ks" +"4003";"JLB001";"Angličtina pro sociology I";;"Klírová,M.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Přístup vyučující.";"Více motivovat studenty ke konverzaci.";"cjp" +"4004";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Coufalová,L.,Svobodová,T.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"Milé cvičící a moc milého a ochotného vyučujícího.";"Nic.";"ks" +"4005";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"4";"3";"2";"3";NULL;NULL;NULL;"1";"5";"3";"5";"4";"Souhrn učiva je věcný a student má větší povědomí o souvislostech v ekonomickém dění.";"1) Otevřené otázky v písemné zkoušce mi přijdou poměrně subjektivně hodnotitelné a časově náročné.2) Vyučující při přednáškách není příliš slyšet, což snižuje jeho atraktivitu přednesu.3) Vyučující by měl interagovat se studenty a také pravidlo, že se nikdo nesmí hlásit považuji za nepochopitelné.";"ies" +"4006";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Buřičová,B.";"3";"5";"2";"2";"2";"5";"5";"5";"1";"3";"3";"3";"3";"Semináře byly moc nápomocné.";"Celkovou organizaci, přístup ke studentům jako k sociologům, ne jako je studentům matematiky.";"ks" +"4007";"JSB544";"Vybrané kapitoly středoškolské matematiky";;"Hendl,J.";"2";"3";NULL;NULL;NULL;"2";"2";"1";"1";"1";"1";"1";"1";"Nic.";"Probírat učivo z přednášek ZLM, ne opakovat funkce z gymnázia. Většina z nás si tento kurz zapsala jako doplňující k hlavní přednášce, ovšem učili jsme se úplně něco jiného.";"ks" +"4008";"JLB019";"Francouzština odborná I";;"Bosáková,L.";"5";"2";NULL;NULL;NULL;"4";"5";"5";"1";"4";"4";"3";"5";;;"cjp" +"4009";"JLB001";"Angličtina pro sociology I";;"Klírová,M.";"4";"5";NULL;NULL;NULL;"4";"5";"5";"1";"4";"4";"3";"3";"Učitelka byla hrozně milá a super. Oceňuii její přístup a vstříctnost. Kurz pro mě byl ale poměrně dost obtížný (mnoho akademické nové slovní zásoby a v zadání úkolů jsem se někda ztrácela a nechápala je, vím, že to nezní moc konstruktivně, ale myslím, že by kurz by mohl začínat lehčeji.";"Trošku zjednodušit zadání, zmenšit obtížnost o menší stupeň.";"cjp" +"4010";"JMB414";"Seminář k aktualitám I";;"Šír,J.";"4";"4";NULL;NULL;NULL;"4";"5";"5";"1";"5";"5";"5";"5";"Pan doktor Šír má velmi ochotný a milý přístup ke studentům a jeho hodiny byly velmi zajímavé a přínosné. Mezi povinnosti patří sepsat 2 referáty o 3 stranách a poslat každý týden aktualitu na zvolenou oblast, což je ve výsledku prospěšné pro zjištění více informací o teritoriu, co nás zajímá.";;"krvs" +"4011";"JMB097";"Moderní dějiny pobaltských zemí";"Švec,L.";;"5";"3";"4";"5";"4";NULL;NULL;NULL;"1";"5";"5";"4";"5";"Na panu docentovi je vidět, že má tuhle oblast rád a svoje nadšení předává i studentům. Na konci předmětu jsme psali práci, která porovnávala zdroje, co jsme použili v referátu, což je fajn jako nácvik pro psaní dalších větších prací.";;"krvs" +"4012";"JEB105";"Statistics";"Červinka,M.";"Nevrla,M.";"5";"5";"3";"4";"4";"3";"5";"5";"1";"4";"4";"5";"4";;;"ies" +"4013";"JSB007";"Dějiny sociologie I";"Šanderová,J.";"Cuker,I.";"5";"4";"5";"5";"5";"5";"5";"5";"3";"5";"5";"5";"5";"Přístup jak cvičících, tak přednášející byl přívětivý, přednášky záživné a obohacující. Pociťuji přínos v hlubokém rozboru děl, konkretizování myšlenek autorů a utváření nového náhledu/postoje na danou věc.";"Při vytváření abstraktů bych zadala konkrétní osnovu, na co se máme v rozebíraných textech zaměřit. Texty byly příliš dlouhé a vyskytovalo se zde nadměrně informací, které se těžko daly obsáhnout do jedné stránky.";"ks" +"4014";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"4";"4";"4";"5";"4";"5";"5";"4";"1";"5";"4";"4";"4";;;"ies" +"4015";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"4";"3";"5";"5";"4";"4";"5";"3";"1";"4";"4";"4";"4";;;"ies" +"4016";"JSB537";"Analýza dat v SPSS";"Soukup,P.";;NULL;"3";"5";"4";"4";NULL;NULL;NULL;"1";"5";"5";"4";"5";"Ač byl pro mne tento předmět nejvíc obávaný, ve výsledku byl jedním z nejpřínosnějších a nejsnadnějších. Přednášky jsou rozhodně přínosné, ale jejich přínosnost se, alespoň pro mne, ukázala až na seminářích. Nejvíce oceňuji cvičící, díky kterým bylo možné tímto kurzem vůbec projít. Věnovali se nám rozhodně více než museli, mnohdy ve svém volném čase.";"Je nutné předmět mnohem lépe organizovat. Chápu, že byl letos výjimečně problém sehnat cvičící, ale jak je možné, že byl zrušen celý kurz Statistika II, učivo z něj nebylo přesunuto do jiného, ale studenti tyto znalosti musí mít, aby byli schopni udělat státnice. Našemu ročníku už cvičení, která neproběhla nikdo nenahradí, ale prosím, aby byl tento problém vyřešen pro budoucí studenty.";"ks" +"4017";"JJB221";"Ekonomická teorie marketingu";"Postler,M.";;"3";"1";"2";"3";"2";NULL;NULL;NULL;"1";"3";"2";"2";"2";;;"kmkpr" +"4018";"JMB248";"Seminář k dějinám Ruska";;"Kolenovská,D.";"5";"2";NULL;NULL;NULL;"4";"4";"5";"2";"5";"5";"5";"5";"Seminář s paní doktorkou Kolenovskou mi dost pomohl u závěrečné zkoušky, nejen kvůli probírané látce, ale také díky jejímu zhodnocení mé seminárky. Snažila jsem se pak ty chyby pamatovat a znovu je neudělat u zkoušky.";;"krvs" +"4019";"NMMA703";"Matematika 3";"Zelený,M.";"Zelený,M.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"4020";"JSB007";"Dějiny sociologie I";"Šanderová,J.";"Tesárek,J.";"5";"5";"5";"5";"4";"5";"5";"5";"3";"4";"3";"4";"4";"Dozvídání se o dějinnách sociologie je určitě důležité. Semináře i přednášky byli zajímavé, shrutí a rozebrání textu na základě teorie a přednášky v seminářích by mohlo být možná trošku lepší.";"Čtení starých sociologických textů jako třeba Comta nemuselo být tak dlouhé, mohli jsme mít třeba kratší část jen pro představu. Některé texty se četli moc dlouho a špatně.";"ks" +"4021";"JSB025";"Sociální problémy";"Frič,P.";;"2";"4";"4";"2";"4";NULL;NULL;NULL;"2";"4";"3";"4";"3";;"1) Nedostatečná informovanost studentů. (Kam a kdy se odevzdává zadaná práce.)2) Při psaní esejí přílišná kritika. 3) Studenti se nevyznají v tom, co po nich vyučující vlastně chce.4) Student se má dozvědět známku své práce před konečnou ústní zkouškou. 5) Zkouška by měla být ve psané podobě, dvě otázky nevypovídají o znalosti studenta.";"kvsp" +"4022";"JMB250";"Seminář k dějinám západní Evropy";;"Mejstřík,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"2";"5";"3";"4";"5";"Pan doktor Mejstřík je velmi milý a ochotný a semináře byly moc zajímavé. O Itálii (a Španělsku) se toho člověk na běžných hodinách západní Evropy moc nedoví, takže bylo super dozvědět se něco víc.";;"kzs" +"4023";"JEB003";"Ekonomie I";"Fanta,N.,Kracík,J.,Švarcová,N.";"Fanta,N.,Kracík,J.,Švarcová,N.";"4";"4";"3";"5";"4";"3";"4";"3";"1";"4";"3";"4";"3";;;"ies" +"4024";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"4";"2";"5";"4";"3";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"ies" +"4025";"JMB408";"Moderní dějiny Ruska";"Litera,B.,Novák,P.,Pečenka,M.";;"4";"5";"4";"3";"5";NULL;NULL;NULL;"1";"5";"3";"3";"4";"Pro ty, co mají rádi Rusko, je to fajn předmět. Dokud nepřijde zkouška, tam se to pak trochu komplikuje, protože obecně psaní esejí ve druháku často působí problém. Nicméně, ať už se zdají témata jakákoliv, dost jsem ocenila to, že jsme před zkouškou dostali papír s několika body, kterých se držet při psaní. Což je na druhou stranu docela fér a u jiných teritorií to takhle sepsané není.";;"krvs" +"4026";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"5";"2";NULL;NULL;NULL;"4";"4";"3";"2";"4";"3";"4";"4";;;"ies" +"4027";"JSB051";"Úvod do sociální antropologie";"Uherek,Z.";"Hrešanová,E.";"5";"4";"5";"5";"4";"5";"5";"5";"1";"4";"4";"5";"5";"Texty na semináře byli vážně super, ty bych určitě zachovala.";"Asi jsem čekla, že po kurzu budu vědět víc ze sociální antropologie z teorie, trochu";"ks" +"4028";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"5";"5";;;"ies" +"4029";"JLB003";"Angličtina pro ekonomy I";;"Poslušná,L.";"4";"3";NULL;NULL;NULL;"5";"5";"4";"1";"5";"5";"5";"5";;;"cjp" +"4030";"JSB998";"Úvod do sociologie";"Soukup,P.";;NULL;NULL;"5";"5";"3";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"ks" +"4031";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Rybín,F.,Vlčková,A.";"5";"5";"5";"5";"4";"5";"5";"4";"1";"5";"5";"4";"4";"Přístup kantora byl velmi přívětivý.";"Dostupnost materiálu, lepší textový souhrn učiva. Zabývat se kvalitativním i kvantitativním výzkumem v podání jednoho přednášejícího, max dvou.";"ks" +"4032";"NMMA701";"Matematika 1";"Spurný,J.";"Skříšovský,E.";"4";"5";"5";"4";"5";"4";"5";"4";"2";"4";"4";"4";"3";;;"ies" +"4033";"JMB409";"Moderní dějiny středo- a jihovýchodní Evropy-dcera JMB013";"Balla,P.,Švec,L.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"4";"5";;;"krvs" +"4034";"JSB530";"Úvod do sociologického myšlení";"Bureš,J.,Numerato,D.";"Bureš,J.";"5";"4";"3";"5";"3";"5";"5";"5";"1";"4";"5";"5";"5";"Líbil se mi přítup a nadšení cvičícího.";;"ks" +"4035";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Buřičová,B.";"3";"5";"4";"5";"5";"5";"5";"5";"1";"5";"5";"5";"4";;"Přednášející učivo vysvětloval příliš komplikovaně z pohledu studentů jako ,,lajků\".";"ks" +"4036";"JSB490";"Úvod do politické sociologie";"Císař,O.";;"3";"4";"3";"5";"4";NULL;NULL;NULL;"1";"2";"2";"2";"2";;;"ks" +"4037";"JSB544";"Vybrané kapitoly středoškolské matematiky";;"Hendl,J.";"5";"4";NULL;NULL;NULL;"3";"3";"5";"1";"4";"5";"4";"4";"Malý počet studentů ve třídě zlepšil celkový dojem.";;"ks" +"4038";"JMB410";"Moderní dějiny západní Evropy";"Matějka,O.,Váška,J.";;"5";"4";"4";"4";"4";NULL;NULL;NULL;"3";"5";"3";"5";"5";"Přednášky mají velmi zajímavá témata, obtížnost kurzu je hlavně kvůli testu, kde na rozdíl od ostatních teritorií není vždy úplně odhadnutelné, co očekávat a jak se připravovat, vzhledem k množství povinné literatury.";;"kzs" +"4039";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"3";"3";"2";"2";"3";NULL;NULL;NULL;"1";"3";"1";"2";"2";;;"kms" +"4040";"JJM320";"Propaganda a československá média po roce 1948";"Suk,P.";;"3";"3";"3";"3";"4";NULL;NULL;NULL;"1";"5";"1";"4";"5";;;"kms" +"4041";"JJM324";"Teorie mediální komunikace";"Jirák,J.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"3";"1";"2";"3";;;"kms" +"4042";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"4";"2";"3";"4";"4";NULL;NULL;NULL;"1";"4";"2";"2";"4";;;"kms" +"4043";"JJM373";"Ethical Issues in Media, Business and Society";"Orhan,M.";;"5";"1";"4";"5";"3";NULL;NULL;NULL;"1";"4";"2";"4";"5";"Doporučuji pokračovat v aplikování přístupu profesora, který smýšlí moderněji o tom, jak by měla probíhat výuka. Jeho předmět je praktický, využitelný a způsob výuky včetně komunikace je zábavný.";;"kms" +"4044";"JLB037";"Italština I";;"Přívozníková,P.";"4";"3";NULL;NULL;NULL;"5";"5";"4";"1";"5";"5";"4";"5";;;"cjp" +"4045";"JPM526";"Justice and Reconciliation in Post-Conflict Societies";;"Werkman,K.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";NULL;"5";"5";;;"kmv" +"4046";"JPM719";"Diplomacy of the European Union";"Pajtinka,E.";;"3";"3";"3";"3";"3";NULL;NULL;NULL;"4";"2";"1";"3";"3";;;"kmv" +"4047";"JLB001";"Angličtina pro sociology I";;"Klírová,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Efektivní systém kurzu.";;"cjp" +"4048";"JLB047";"Ruština obecná I";;"Mistrová,V.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"4";"Systém výuky.";;"cjp" +"4049";"JSB004";"Sociální nerovnosti";"Šanderová,J.";;"4";"3";"5";"5";"3";NULL;NULL;NULL;"2";"4";"4";"4";"4";;;"ks" +"4050";"JSB031";"Bakalářský seminář I - diskuse projektů";;"Numerato,D.";"5";"4";NULL;NULL;NULL;"5";"5";"4";"1";"5";"5";"5";"5";;;"ks" +"4051";"JSB131";"Velké empirické výzkumy ČR";"Tuček,M.";;"3";"2";"2";"4";"1";NULL;NULL;NULL;"1";"2";"2";"2";"2";;;"ks" +"4052";"JSB133";"Zemědělství a rozvoj venkova (vybraná témata z rurální sociologie)";"Zagata,L.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"ks" +"4053";"JSB537";"Analýza dat v SPSS";"Soukup,P.";;"4";"4";"5";"5";"3";NULL;NULL;NULL;"1";"4";"5";"4";"4";;;"ks" +"4054";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"3";"4";"4";"5";"2";NULL;NULL;NULL;NULL;"4";"5";"5";"4";;;"kmv" +"4055";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;NULL;"3";"4";"5";"2";NULL;NULL;NULL;NULL;"3";"5";"5";"4";;"The final test, given that it is the only mark given for the entire course, could have more questions; it could look the same a the final test for IR program students, who have an extra section";"kmv" +"4056";"JPM656";"Technology and warfare";"Kučera,T.";;"4";"3";"3";"4";"3";NULL;NULL;NULL;"1";"4";"3";"5";"4";"Very interesting topics; sub-weekly Moodle assignments; well-structured course; many opportunities to gain \"extra credit\"; attempts to engage students in discussions during class";"Perhaps the lecturing style";"kbs" +"4057";"JPM052";"Mezinárodní ekonomické vztahy (MEV)";"Kučerová,I.";;"4";"2";"5";"4";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Příjemná vyučující, dobrá atmosféra na hodinách";;"kmv" +"4058";"JLB019";"Francouzština odborná I";;"Bosáková,L.";"5";"3";NULL;NULL;NULL;"5";"5";"3";"1";"4";"3";"2";"5";"Interaktivnost studentů a vyučujícího.";;"cjp" +"4059";"JMM047";"Právní a institucionální rámec evropské integrace.";"Šlosarčík,I.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kzs" +"4060";"JMM048";"European Union in International Affairs";"Weiss,T.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Přístup vyučujícího.";;"kzs" +"4061";"JMM248";"Sociálně politický vývoj Irska";"Šlosarčík,I.";;"5";"3";"5";"4";"5";NULL;NULL;NULL;"1";"5";"3";"4";"5";"Přístup vyučujícího.";;"kzs" +"4062";"JMM271";"Metodologický seminář";;"Weiss,T.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"3";"5";"Přístup vyučujícího.";;"krvs" +"4063";"JMM277";"Historie a kultura";"Vykoukal,J.";"Tomalová,E.";"5";"3";"5";"5";"4";"5";"5";"5";"1";"5";"4";"5";"5";"Přístup vyučujících.";;"krvs" +"4064";"JMM278";"Politika a mezinárodní vztahy";"Kubát,M.,Weiss,T.";"Juhás,T.";"5";"4";"5";"5";"5";"4";"5";"5";"1";"5";"3";"4";"5";"Přístup vyučujících.";;"krvs" +"4065";"JLB033";"Němčina I";;"Faltýnová,R.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Možnost získání bonusových bodů do závěrečného testu během semestru";;"cjp" +"4066";"JPB218";"Dějiny novověké Evropy I.";"Kučera,J.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"3";"3";"4";;;"kp" +"4067";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"5";"5";;;"kp" +"4068";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"4";"4";"4";"4";"5";"4";"4";"4";"1";"5";"3";"3";"4";;;"kp" +"4069";"JMM279";"Společnost a ekonomika";"Fiřtová,M.";"Young,M.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"3";"4";"5";"Přístup vyučujících.";;"kas" +"4070";"JPB258";"Politické myšlení novověku I";"Franěk,J.,Kučera,J.";;"4";"4";"3";"3";"3";NULL;NULL;NULL;"2";"5";"3";"3";"4";;;"kp" +"4071";"JPB268";"Evropská integrace";"Plechanovová,B.";;"2";"5";"2";"2";"1";NULL;NULL;NULL;"3";"4";"3";"3";"2";;;"kmv" +"4072";"JPB269";"Současné problémy mezinárodních vztahů";"Knutelská,V.";;"4";"3";"4";"5";"3";NULL;NULL;NULL;"1";"2";"5";"2";"4";;;"kmv" +"4073";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"2";"3";"2";"2";"1";NULL;NULL;NULL;"1";"1";"1";"1";"1";;;"kp" +"4074";"JLB100";"Czech as a Foreign Language I";;"Frantesová,E.";"5";"3";NULL;NULL;NULL;"5";"4";"5";"1";"4";"4";"4";"5";;;"cjp" +"4075";"JMM422";"Ethnic Issues and Territories in Eastern, East Central and Southeastern Europe";"Vykoukal,J.";;"3";"4";"4";"4";"2";NULL;NULL;NULL;NULL;"3";"2";"3";"3";;;"krvs" +"4076";"JMMZ109";"Comparison of Central European Political Systems";"Kubát,M.";;"3";"2";"3";"2";"4";NULL;NULL;NULL;"1";"4";"2";"4";"4";;;"knrs" +"4077";"JMMZ113";"Central European Culture History I.";"Emler,D.";;"4";"2";"4";"4";"4";NULL;NULL;NULL;"4";"5";"4";"5";"5";;;"knrs" +"4078";"JMMZ336";"History and Society in the Russian Cinema";;"Kolenovská,D.,Mazzali,F.";"3";"2";NULL;NULL;NULL;"3";"2";"2";"2";"3";"2";"3";"3";;;"krvs" +"4079";"JMMZ136";"Language of the Region B I.";;;NULL;NULL;NULL;NULL;NULL;"3";"3";"3";NULL;NULL;NULL;NULL;NULL;;;"knrs" +"4080";"JPB020";"Úvod do mezinárodních vztahů";"Karlas,J.,Kučera,T.";;"4";"3";"3";"4";"4";NULL;NULL;NULL;"1";"4";"2";"4";"3";"Zajímavý systém průběžných testů a celková práce vyučujících s aplikací Moodle, ocenil jsem možnost získávat body průběžně a využití této platformy. Přednáška věnující se životnímu prostředí v mezinárodních vztazích mi určitě mnoho věcí objasnila – zachoval bych ji.";"Kurz mi v některých oblastech přišel možná až příliš zjednodušující, respektive jen velmi zběžně řešící některá zajímavá témata, jimž by měly být vyhrazeny odlišné kurzy (např. problematika bezpečnostních studií, státu...). Naopak jsem čekal více teorie a způsobů nahlížení na mezinárodní vztahy (realismus, liberalismus, kritické teorie...).";"kmv" +"4081";"JMB414";"Seminář k aktualitám I";;"Lídl,V.";"4";"4";NULL;NULL;NULL;"5";"5";"2";"2";"2";"5";"3";"3";"Weekly reports nutí k pravidelné práci s informačními zdroji, sledování světového dění a také pomáhají s nácvikem psaní. Podobně účelné se zdají být i referáty, byť někdy jejich kvalita očividně pokulhávala (což samozřejmě není potíž vyučujícího). VELMI jsem ocenil hodiny vedené externisty – přinesly spoustu nových a zajímavých informací, novou perspektivu a celkově kurz ozvláštnily. Děkuji za ně!";"V porovnání s jinými semináři k aktualitám (dle informací z doslechu) se tento zdál jako velmi náročný na samostatnou práci, která někdy možná až přesahovala hranice účelnosti (závěrečná dlouhá seminární práce). Celková přinosnost seminářů je pak samozřejmě odvislá od připravenosti a aktivity všech účastníků, kterou se ne vždy podařilo mobilizovat.";"krvs" +"4082";"JMB408";"Moderní dějiny Ruska";"Litera,B.,Novák,P.,Pečenka,M.";;"5";"4";"5";"4";"5";NULL;NULL;NULL;"2";"5";"3";"5";"5";"Přednášky byly skutečně zajímavé, zábavné a přínosné, chodil jsem na ně rád – jak na ty Dr. Pečenky, tak na ty Dr. Litery. Podobně i literatura dle SISu měla mnoho do sebe.";"Myslím, že by se v kurzu slušelo věnovat větší pozornost sovětským dějinám. Zkoušení skrze eseje sice má leccos do sebe, ale nerozumím způsobu, jakým se provádí. Studentům pořádně nikdo nevysvětlí, jak se má esej napsat, aby uspěla (mluvení do duše od Dr. Pečenky 5 minut před začátkem psaní nepočítám). O nějakém nácviku jejího psaní ani nemůže být řeč. Na západních školách se sice takto testuje běžně, ale studenti jsou tam již ze střední školy s formátem eseje dobře obeznámeni a univerzita je v tomto směru dále rozvíjí! Troufám si tvrdit, že valná většina maturantů v ČR má jen minimální znalosti a zkušenosti s psaním esejí (první ročník na FSV na tom změní pramálo) – podle toho pak částečně vypadají i výsledky zkoušek.";"krvs" +"4083";"JLB033";"Němčina I";;"Faltýnová,R.";"4";"4";NULL;NULL;NULL;"4";"5";"5";"1";"5";"4";"4";"5";"Velmi vstřícný přístup paní inženýrky, oceňuji i důraz na gramatiku.";"V některých hodinách se zbytečně mnoho času tráví nad jedním příliš komplikovaným cvičením, jehož vysvětlování trvá velmi dlouho a studenti si stejně často neví rady, jak vlastně postupovat. Vše se pak zbytečně protahuje. V jednoduchosti je síla. :)";"cjp" +"4084";"JMB178";"U.S. in the 1960s and 1970s";"Raška,F.";;"3";"3";"3";"5";"3";NULL;NULL;NULL;"1";"3";"3";"3";"2";"The lecturer is overall nice towards the students. I appreciate being able to pass this course on the basis of my term paper and not an exam.";"The lecturer presents a lot of his personal opinions as facts or correct interpretations. Despite often asking others to speak up, he does not seem very open about hearing out other opinions. The assigned reading is often extremely long.";"kas" +"4085";"JMB250";"Seminář k dějinám západní Evropy";;"Váška,J.";"4";"4";NULL;NULL;NULL;"5";"5";"4";"1";"4";"3";"4";"4";"Na semináři panuje přátelská atmosféra a Dr. Váška je vůči studentům velmi vstřícný a nápomocný. Některá povinná četba je zajímavá.";"Koncept semináře do značné míry stojí a padá na připravenosti a aktivitě všech jeho účastníků, kterou vyučující jen obtížně ovlivní. Ani pomyslný bič v podobě pravidelných testů na povinnou četbu se nezdál být příliš nápomocný.";"kzs" +"4086";"JMB248";"Seminář k dějinám Ruska";;"Novák,P.";"4";"3";NULL;NULL;NULL;"5";"5";"4";"1";"5";"3";"5";"4";"Dr. Novák je vůči studentům vstřícný, semináře jsou vedeny v klidném duchu a leckterá témata jsou opravdu zajímavá.";"Seminář se možná až příliš soustředí na dobu carskou a zanedbává moderní Rusko.";"krvs" +"4087";"JMB410";"Moderní dějiny západní Evropy";"Matějka,O.,Váška,J.";;"3";"3";"4";"5";"3";NULL;NULL;NULL;"1";"3";"3";"4";"2";"Velmi zajímavá povinná literatura (Blanning a Judt, méně již Jackson) a některá témata (např. syndrom Vichy).";"Kurz často kladl velký důraz na témata velmi obecná, což mi nepřišlo tolik přínosné. Mnohá z probraných témat jsme do značné míry řešili na jiných kurzech (industrializace – PMSD, periodizace dějin – úvod do historie, antropocén – úvod do teritoriálních studií/teorie ve společenských vědách...). Od tohoto předmětu bych čekal podrobnější rozbor dějin jednotlivých států a následnou syntézu na širší úrovni, dějiny regionu jako celku. Srovnám-li dějiny ZE s dějinami SJVE (oboje v zimním semestru), tak si studenti z přednášek odnáší zhruba podobnou úroveň znalostí o vývoji Velké Británie jako o vývoji v Polsku či Maďarsku, zato o dějinách Litvy se dozvědí více než o celém Beneluxu dohromady. Vzhledem k tomu, že v západní Evropě se skutečně cca od 18. století do druhé světové války doopravdy \"lámaly dějiny\" světa, přijde mi to nedostačující.Drasticky podceněná ve výuce je také druhá polovina 20. století. Není-li čas, přijde mi právě toto období jako důležitější než témata 18./19. století. Od účasti na přednáškách spoustu studentů nejspíše odradí i to, že midterm i faktografický test ve zkouškovém období nemá s přednáškami společného téměř nic.";"kzs" +"4088";"JLB027";"Ruština odborná I - vyšší";;"Mistrová,V.";"4";"4";NULL;NULL;NULL;"5";"5";"4";"1";"5";"5";"4";"4";;;"cjp" +"4089";"JMM130";"Ethno-Political Conflicts in the Caucasus";"Brisku,A.";;"5";"5";"5";"5";"3";NULL;NULL;NULL;"1";"5";"5";"5";"4";;;"krvs" +"4090";"JMM067";"Russia and Eurasia in World Politics";"Šír,J.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Dozvěděla jsem se mnoho málo známých ale zásadních informací, za které jsem vděčná. Oceňuji, že jsme mluvili o aktuálních otázkách a zabývali jsme se jimi do hloubky místo toho, abychom je zkoušeli za každou cenu spojt s politologickou teorií. Také jsem ráda, že byly probírané problémy zasazeny do globálního politického kontextu a do souvislostí s mezinárodním právem.";"Možná, že bychom mohli probírat i nějaké téma, které by nám umožnilo lépe a nezaujatě pochopit, proč je ruská zahraniční politika taková, jaká je.";"krvs" +"4091";"JMM277";"Historie a kultura";"Vykoukal,J.";"Kolenovská,D.";"3";"3";"5";"5";"4";"4";"5";"2";"1";"2";"2";"5";"3";"Děkuji za přehled významných teorií (přednáška) a jejich konkrétních příkladů (seminář). Obojí mi pomohlo pochopit témata, která jsme probírali. Současně si ale myslím, že současné teorie a příklady by měly získat výrazně více prostoru než ty z minulého století nebo ještě vzdálenější minulosti. Toto hodnocení se vztahuje jak na přednášku tak na seminář.";;"krvs" +"4092";"JSM103";"Academic Writing";;"Blokker,P.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"the skills of writing paper";;"ks" +"4093";"JSM406";"Statistics in SPSS";;"Soukup,P.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"ks" +"4094";"JSM518";"Public Policy";"Potůček,M.,Vlčková,K.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kvsp" +"4095";"JSM480";"Evaluation Research";;"Remr,J.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";;;"ks" +"4096";"JMB497";"Metodický úvod pro kombinované studium";"Kubát,M.";;"4";"3";"3";"3";"3";NULL;NULL;NULL;"1";"2";"4";"3";"3";;;"krvs" +"4097";"JMB498";"Metodologie soudobých dějin";"Smetana,V.";;"4";"3";"4";"4";"3";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"krvs" +"4098";"JMB523";"Mezinárodní aktuality I";"Fojtek,V.";;"5";"2";"5";"5";"3";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kas" +"4099";"JJB631";"Social Media: Strategy, Tactics and Analytics";"Audyová,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kmkpr" +"4100";"JEB110";"Econometrics II";"Pertold-Gebicka,B.";"Chorna,O.,Malinská,B.,Pertold-Gebicka,B.,Pleticha,P.";"3";"4";"3";"3";"3";"4";"5";"4";"1";"5";"4";"5";"4";;;"ies" +"4101";"JJB040";"Kreativita v jazyce";"Šoltys,O.";;"3";"2";"1";"2";"1";NULL;NULL;NULL;"2";"1";"1";"1";"1";;;"kz" +"4102";"JPM127";"Geopolitika a geostrategie 1815 - 1945";"Romancov,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"4";"5";;;"kp" +"4103";"JPM344";"Diplomní seminář II.";;"Brunclík,M.,Franěk,J.,Hroch,M.,Charvát,J.,Jüptner,P.,Kofroň,J.,Kotábová,V.,Krausz Hladká,M.,Kubátová,H.,Kučera,J.,Landovský,J.,Mlejnek,J.,Perottino,M.,Riegl,M.,Romancov,M.,Říchová,B.,Salamon,J.,Shavit,A.,Švec,K.";NULL;NULL;NULL;NULL;NULL;"5";"5";"5";NULL;NULL;NULL;NULL;NULL;;;"kp" +"4104";"JPM348";"Nové přístupy k místní správě a přímá volba starostů";;"Jüptner,P.";"4";"3";NULL;NULL;NULL;"4";"5";"3";"2";"4";"5";"4";"4";;;"kp" +"4105";"JPM641";"Světový regionalismus";"Riegl,M.";;"5";"5";"5";"4";"5";NULL;NULL;NULL;"1";"5";"4";"3";"3";;;"kp" +"4106";"JEM003";"Advanced Microeconomics";"Janda,K.,Melikhova,O.";"Janda,K.,Melikhova,O.";"3";"4";"2";"5";"3";"4";"5";"4";"1";"3";"3";"2";"2";;;"ies" +"4107";"JLB100";"Czech as a Foreign Language I";;"Frantesová,E.";"4";"1";NULL;NULL;NULL;"5";"5";"5";"1";"2";"3";"4";"5";"students' participation are good, dialogue practice in class";"would be better if the vocabulary plays a big role, i think the grammatical practice is good, but we still don't know much czech words in daily life.";"cjp" +"4108";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"4";"3";"4";"4";"4";"3";"2";"2";"2";"5";"4";"4";"4";;;"ies" +"4109";"JEM037";"Financial Markets";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"4";"1";"4";"4";"4";"2";"3";"3";"2";"5";"4";"5";"4";;;"ies" +"4110";"JEM062";"Introductory Econometrics";"Kukačka,J.";"Kukačka,J.";"5";"2";"5";"5";"5";"5";"5";"4";"1";"4";"5";"5";"5";"The materials were always uploaded right after the seminars. All the teaching materials and the lecture notes were very organized and easy to understand. Lecturer was very passionate about the course and students liked it. I was able to achieve broad idea of econometrics. I hope the lecturer teaches more courses related to econometrics";"Using R instead of Gretl in class would be better for students, because in advanced econometrics R is used. More mathematics in the course would be nice (proof of some important theorems or equations)";"ies" +"4111";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"4";"4";"4";"4";"5";NULL;NULL;NULL;"2";"5";"5";"4";"5";;;"kmv" +"4112";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"3";"5";"5";"5";"2";NULL;NULL;NULL;"1";"2";"5";"1";"2";;;"kmv" +"4113";"JPM658";"International Economic Relations";"Parízek,M.";;"4";"4";"4";"4";"5";NULL;NULL;NULL;"2";"5";"4";"5";"5";;;"kmv" +"4114";"JPM717";"Continental Philosophy and IR";;"Ditrych,O.";"3";"5";NULL;NULL;NULL;"3";"4";"5";"1";"1";"1";"1";"2";;;"kmv" +"4115";"JPM719";"Diplomacy of the European Union";"Pajtinka,E.";;"3";"3";"2";"3";"4";NULL;NULL;NULL;"2";"4";"4";"4";"2";;;"kmv" +"4116";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"3";"1";"1";"1";"3";"3";"2";"5";;;"kz" +"4117";"JPM150";"Poloprezidentské režimy v postkomunistické Evropě";"Mlejnek,J.";;"4";"4";"4";"5";"3";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"4118";"JPM160";"Česká komunální politika";"Jüptner,P.";;"5";"5";"4";"4";"3";NULL;NULL;NULL;"3";"5";"5";"5";"5";;;"kp" +"4119";"JPM342";"Konflikty v demokracii a národní identita: teoretické problémy analýzy";"Říchová,B.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kp" +"4120";"JPM348";"Nové přístupy k místní správě a přímá volba starostů";;"Jüptner,P.";"4";"3";NULL;NULL;NULL;"4";"5";"5";"2";"5";"4";"5";"4";;;"kp" +"4121";"JEM003";"Advanced Microeconomics";"Janda,K.,Melikhova,O.";"Janda,K.,Melikhova,O.";"5";"4";"4";"5";"4";"5";"4";"5";"1";"3";"4";"3";"4";"More understanding in economics";"Teacher's writing in blackboard could be more big";"ies" +"4122";"JPM574";"Moderní strany a stranické systémy v Evropě";"Brunclík,M.";;"4";"3";"4";"5";"3";NULL;NULL;NULL;"1";"5";"5";"5";"4";;;"kp" +"4123";"JEM163";"Principles of Microeconomics";"Janský,P.";"Král,M.,Moravcová,H.,Palanský,M.";"4";"3";"5";"5";"4";"5";"3";"5";"1";"4";"5";"5";"5";"The course is great,the seminar is helpful for better understanding";;"ies" +"4124";"JEM027";"Monetary Economics";"Holub,T.,Malovaná,S.";"Břízová,P.,Hájek,J.,Holub,T.,Malovaná,S.";"3";"5";"3";"4";"4";"3";"4";"3";"1";"3";"4";"4";"3";;;"ies" +"4125";"JEM037";"Financial Markets";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"5";"3";"4";"4";"5";"4";"5";"5";"1";"4";"4";"4";"4";;;"ies" +"4126";"JLB104";"Czech for Chinese speaking students";;"Vaníčková,K.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"5";;;"cjp" +"4127";"JLM063";"English for Chinese Speaking Students";;"Štěpánková,D.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"5";"5";;;"cjp" +"4128";"JEM059";"Quantitative Finance I";"Baruník,J.,Vácha,L.";"Baruník,J.,Vácha,L.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"4";"5";;;"ies" +"4129";"JLM006";"Angličtina pro politology II";;"Panešová,K.";"5";"1";NULL;NULL;NULL;"4";"5";"5";"2";"3";NULL;NULL;NULL;;;"cjp" +"4130";"JMM143";"Economy and Politics in the 20th Century Eastern Europe";"Svoboda,K.";;"2";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"krvs" +"4131";"JEM163";"Principles of Microeconomics";"Janský,P.";"Král,M.,Moravcová,H.,Palanský,M.";"5";"3";"5";"5";"4";"5";"5";"4";"1";"4";"4";"4";"5";"Lecture and seminar teachers are very good!";;"ies" +"4132";"JMM590";"EU in its Southeastern Neighbourhood: Politics of Expectations";"Najšlová,L.";;"4";"4";"5";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kzs" +"4133";"JLB100";"Czech as a Foreign Language I";;"Frantesová,E.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"4134";"JMMZ141";"Russian Language I";;"Shvedova,O.";"4";"4";NULL;NULL;NULL;"4";"4";"4";"1";"4";"3";"4";"4";;;"krvs" +"4135";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"3";"3";"3";"4";"3";NULL;NULL;NULL;"1";"4";"2";"5";"4";;;"kmv" +"4136";"JMMZ042";"Cohesion Policy of the EU in Central and East European Countries.";"Hauser,J.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"krvs" +"4137";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmv" +"4138";"JEM004";"Advanced Macroeconomics";"Baxa,J.,Hromádková,E.";"Baxa,J.,Hromádková,E.,Šestořád,T.,Troch,T.,Žáček,J.";"4";"4";"5";"5";"4";"4";"5";"4";"1";"4";"5";"4";"5";;;"ies" +"4139";"JPM700";"Space Security";"Doboš,B.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kbs" +"4140";"JMMZ019";"M.A. Thesis Seminar I (IMESS)";;"Vykoukal,J.";"4";"3";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"4";"4";;;"krvs" +"4141";"JEB105";"Statistics";"Červinka,M.";"Hanus,L.";"5";"4";"3";"5";"3";"5";"5";"5";"1";"5";"5";"5";"5";"I consider the course to be very useful for my future studies, testing hypotheses and confidence intervals are topics that i find practical and enjoyable. I must say the professor was much better in explaining than the last year and I want to thank him for the effort.";"The topics covered in the subject are often not as difficult as they seem to be after the lesson covering them. Sometimes I had no idea what are we talking about and after watching statistics videos on youtube, I realized the topics are clear and understandable. I would suggest before every new topic, such as central limit theorem, point estimation, confidence intervals and testing hypotheses, to firstly spend 5-10 minutes just explaining the intuition behind the concepts so the students would get broad idea what is the topic about. Then explaining the theory behind it. It would be much more easier to understand. It is good to say super simple examples which show how the concept works.";"ies" +"4142";"JEM179";"History and Methodology of Economics";"Baxa,J.,Paulus,M.";;"4";"5";"3";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"3";;;"ies" +"4143";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"4";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"4144";"JMM074";"Landmarks in 20th Century U.S. History and Their Interpretations";"Pondělíček,J.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"2";"4";"5";"5";"4";;;"kas" +"4145";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;"Kdyby tak slo vice hodin....";"kms" +"4146";"JPM052";"Mezinárodní ekonomické vztahy (MEV)";"Kučerová,I.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"2";"5";"3";;;"kmv" +"4147";"JEM020";"Ethics and Economics";"Cahlík,T.";"Cahlík,T.";"4";"3";"4";"4";"4";"4";"4";"4";"1";"3";"3";"3";"3";;;"ies" +"4148";"JMM278";"Politika a mezinárodní vztahy";"Kubát,M.,Weiss,T.";"Bečka,J.";"3";"2";"5";"4";"2";"3";"4";"3";"2";"3";"1";"1";"2";;;"krvs" +"4149";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Vyjimecny pedagog";;"kms" +"4150";"JPM430";"Marxism in International Relations (TIR)";;"Střítecký,V.";"3";"2";NULL;NULL;NULL;"4";"4";"4";"1";"4";"3";"4";"3";;;"kmv" +"4151";"JEB003";"Ekonomie I";"Fanta,N.,Kracík,J.,Švarcová,N.";"Fanta,N.,Kracík,J.,Švarcová,N.";"4";"4";"4";"4";"4";"4";"4";"5";"1";"4";"4";"4";"4";;;"ies" +"4152";"JEM037";"Financial Markets";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"4";"4";"5";"3";"5";"4";"4";"3";"1";"4";"3";"4";"4";;;"ies" +"4153";"JSB454";"Social Web: (Big) Data Mining";"Růžička,J.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"4";"5";;;"ks" +"4154";"JSM005";"Sociální struktura ČR: stav, vývoj, srovnání s EU";"Tuček,M.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"1";"5";"4";;;"ks" +"4155";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"5";"3";;;"kmv" +"4156";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"3";"5";"2";"4";"3";NULL;NULL;NULL;"4";"2";"2";"1";"3";;"The lectures content and presentations in terms of clarity and organisation.";"kmv" +"4157";"JEM040";"Účetní a daňové poradenství";;"Kemény,I.";"4";"3";NULL;NULL;NULL;"3";"4";"3";"2";"4";"5";"4";"4";;;"ies" +"4158";"JMM279";"Společnost a ekonomika";"Fiřtová,M.";"Fiřtová,M.";"4";"4";"4";"5";"5";"4";"5";"5";"1";"4";"4";"4";"4";;;"kas" +"4159";"JLB104";"Czech for Chinese speaking students";;"Vaníčková,K.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"5";"5";"The atmosphere is very good and I learned a lot useful Czech language and culture from the class which is very important for foreign students to get used to the local life.";;"cjp" +"4160";"JJM200";"Diplomový seminář";;;"3";"3";NULL;NULL;NULL;"3";"3";"3";"1";"3";"3";"3";"3";;;"kms" +"4161";"JSM554";"Diplomový seminář";;"Remr,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"3";"5";;;"ks" +"4162";"JEM199";"Financial Crisis and Risk Management";"Horváth,R.,Opatrný,M.,TSOMOCOS,D.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"ies" +"4163";"JPM689";"Conflict Studies";"Karásek,T.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kbs" +"4164";"JEM003";"Advanced Microeconomics";"Janda,K.,Melikhova,O.";"Janda,K.,Melikhova,O.";"1";"5";"1";"5";"3";"5";"5";"5";"3";"1";"1";"1";"1";"The course has too much unnecessary theoretical concepts that are often outdated and have no practical implications for an academic career since it is far too narrow.";"The course has no real life, practical implications.";"ies" +"4165";"JPM724";"Critical Approaches to International Politics and Security";;"Daniel,J.,Rychnovská,D.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"3";;;"kmv" +"4166";"JMM674";"Maritime security: Geopolitics of the Indian and Pacific Oceans";"Hornát,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kas" +"4167";"JPM671";"Odborná stáž B";;;NULL;NULL;NULL;NULL;NULL;"3";"3";"3";NULL;NULL;NULL;NULL;NULL;;;"kmv" +"4168";"JPM719";"Diplomacy of the European Union";"Pajtinka,E.";;NULL;NULL;"3";"3";"3";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kmv" +"4169";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"5";"5";;;"kmv" +"4170";"JSB025";"Sociální problémy";"Frič,P.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kvsp" +"4171";"JMM086";"Diplomový seminář II";;"Králová,K.,Svoboda,K.,Švec,L.";"5";"3";NULL;NULL;NULL;"4";"5";"5";"1";"3";"3";"3";"5";;;"krvs" +"4172";"JPM658";"International Economic Relations";"Parízek,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kmv" +"4173";"JMM183";"Současná východní Evropa I";"Lídl,V.,Šír,J.";;"5";"4";"4";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"5";;;"krvs" +"4174";"JPM721";"Komparace ekonomik zemí EU";"Kučerová,I.";;"4";NULL;"4";"4";"2";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kmv" +"4175";"JSM502";"Diplomový seminář I";;"Dobiášová,K.,Kotrusová,M.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kvsp" +"4176";"JMM189";"Economic transformation in East Central and Southeastern Europe";"Trejbal,V.";;"3";"3";"4";"4";"3";NULL;NULL;NULL;"3";"3";"3";"3";"3";;;"krvs" +"4177";"JSM527";"Metody analýzy a tvorby politik II.";"Veselý,A.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";"Skvělé jsou konkrétní příklady ke každému bloku jak v přednáškách, tak v rámci materiálů ke cvičením, široký kontext, odkazy na další literaturu.";;"kvsp" +"4178";"JPM689";"Conflict Studies";"Karásek,T.";;"4";"4";"4";"5";"4";NULL;NULL;NULL;"1";"5";"4";"4";"4";;;"kbs" +"4179";"JSM507";"Metody tvorby politik";"Veselý,A.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kvsp" +"4180";"JMM718";"Polština I";;"Sitarz,K.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"3";"4";"2";"5";;;"knrs" +"4181";"JPM717";"Continental Philosophy and IR";;"Ditrych,O.";"4";"5";NULL;NULL;NULL;"4";"5";"4";"3";"3";"3";"3";"3";;;"kmv" +"4182";"JSM518";"Public Policy";"Potůček,M.,Vlčková,K.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kvsp" +"4183";"JPM718";"Critical Perspectives on Violence";;"Ditrych,O.";"5";"4";NULL;NULL;NULL;"4";"5";"4";"2";"4";"4";"4";"3";;;"kmv" +"4184";"JSM528";"Seminář k diplomové práci I.";;"Kohoutek,J.,Ochrana,F.";"4";"3";NULL;NULL;NULL;"4";"5";"4";"1";"3";"4";"3";"4";;;"kvsp" +"4185";"JJM329";"Média a kultura";"Bednařík,P.,Jirák,J.";;"4";"3";"5";"5";"3";NULL;NULL;NULL;"1";"2";"2";"4";"3";"Kurz byl velmi zábavný a zajímavý, konkrétní příklady";"Kurz byl sice velmi zábavný, ale chyběla teorie a pointa";"kms" +"4186";"JSM620";"Politologické aspekty tvorby politik: Veřejné politiky v kontextu politiky";"Novotný,V.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kvsp" +"4187";"JSM644";"Základy politologie";"Kotlas,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;NULL;"5";"4";"5";"5";;;"kvsp" +"4188";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"2";"3";"4";;;"kms" +"4189";"JSM646";"Veřejná správa";"Ochrana,F.";;"4";"4";"4";"4";"3";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kvsp" +"4190";"JSM647";"Manažerské metody ve veřejné a sociální politice";"Ochrana,F.";;"4";"5";"4";"4";"3";NULL;NULL;NULL;NULL;NULL;NULL;NULL;"4";;;"kvsp" +"4191";"JJM199";"Literární a knižní kritika";"Čeňková,J.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";"Líbil se mi výběr knih, ze kterých jsme si mohli vybírat, o kterých budeme psát knižní kritiku. Zajímaví byli rovněž přednášky hostů.";"Uvítala bych větší množství požadovaných textů, aby si člověk literární kritiku více nacvičil.";"kz" +"4192";"JEM181";"Data Science with R";"Herman,D.,Krištoufek,L.";"Herman,D.,Krištoufek,L.";"5";"3";"5";"5";"3";"5";"5";"3";"1";"5";"5";"4";"5";;;"ies" +"4193";"JLB047";"Ruština obecná I";;"Mistrová,V.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Přístup paní doktorky Mistrové je naprosto skvělý, je strašně milá, umí výtečně rusky a na těch hodinách děláme věci, které se hodí, není to jen odříkávání gramatiky.";;"cjp" +"4194";"JJM330";"Trendy současných českých médií";"Aust,O.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"3";"2";"4";"4";"hosté";"pan Aust sliboval materiály, které odeslal až v den zkoušky.";"kms" +"4195";"JLM063";"English for Chinese Speaking Students";;"Štěpánková,D.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"5";"5";;;"cjp" +"4196";"JEM034";"Corporate Finance";"Bychkova,O.,Malířová,T.";;"2";"3";"2";"1";"1";NULL;NULL;NULL;"3";"3";"3";"3";"1";;;"ies" +"4197";"JJM331";"Výzkum médií II";"Vochocová,L.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"2";"4";"5";"5";"4";;;"kms" +"4198";"JJM320";"Propaganda a československá média po roce 1948";"Suk,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"kms" +"4199";"JJM332";"Přirozený jazyk a média";"Podzimek,J.";;"3";"3";"4";"4";"4";NULL;NULL;NULL;"1";"3";"2";"2";"3";;;"kms" +"4200";"JJM356";"Metodologické přístupy ke studiu žurnalistiky";"Nečas,V.,Veselková,Z.";;"3";"4";"3";"4";"3";NULL;NULL;NULL;"1";"4";"3";"4";"3";"Užitečné mi přišly průběžné úkoly.";;"kz" +"4201";"JLB019";"Francouzština odborná I";;"Bosáková,L.";"3";"3";NULL;NULL;NULL;"4";"4";"4";"1";"4";"4";"4";"4";;"Možná méně videí";"cjp" +"4202";"JMM097";"Etnické a národnostní problémy střední a jihovýchodní Evropy";"Vykoukal,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Skvělý kurz pro prohloubení znalostí o etnických menšinách, myslím, že jsme se dozvěděli úplně vše.";;"krvs" +"4203";"JJM324";"Teorie mediální komunikace";"Jirák,J.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"3";"3";"3";"4";;;"kms" +"4204";"JMM273";"Diplomový seminář II";;"Kasáková,Z.";"4";"3";NULL;NULL;NULL;"4";"4";"3";"1";"2";"5";"4";"5";;;"krvs" +"4205";"JJM329";"Média a kultura";"Bednařík,P.,Jirák,J.";;"3";"3";"3";"3";"2";NULL;NULL;NULL;"1";"2";"1";"2";"3";;;"kms" +"4206";"NMMA703";"Matematika 3";"Zelený,M.";"Zelený,M.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";NULL;NULL;"5";"Pan docent Zelený je skvělý vyučující. Cením si jeho pečlivosti, důkladnosti zápisu důkazů, férového přístupu a milého vystupování. Kurz byl skvěle organizovaný, všechny materiály a potřebné informace byly dobře dostupné.";;"ies" +"4207";"JMM279";"Společnost a ekonomika";"Fiřtová,M.";"Svoboda,K.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"Paní doktorka Fiřtová je nejlepší. Konečně jeden kurz ekonomie, který mě bavil. Není to jen nudné čtení grafů a podivné povídání o jestřábech a hrdličkách a tom, že všichni kradou (Nechápu, jak může pořád Kameníček učit). Byli jsme plně zaujati výkladem a vše bylo dobře vysvětleno i pro ekonomické laiky. Četba byla zajímavá a rozhodně užitečná. Prostě skvělý";"Škoda, že tento předmět nemá 12 přednášek a klidně stejný počet seminářů, některé věci by bylo lepší probrat ještě více podrobně.";"kas" +"4208";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"3";"3";"3";"4";;;"kms" +"4209";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"3";"2";"3";"1";NULL;NULL;NULL;"1";"4";"2";"3";"2";;;"ies" +"4210";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"4";"4";"5";"4";;;"kmv" +"4211";"JJM330";"Trendy současných českých médií";"Aust,O.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";"Vyučujícího";;"kms" +"4212";"JPM717";"Continental Philosophy and IR";;"Ditrych,O.";"5";"4";NULL;NULL;NULL;"4";"4";"4";"2";"5";"4";"4";"5";;;"kmv" +"4213";"JJM373";"Ethical Issues in Media, Business and Society";"Orhan,M.";;"3";"4";"3";"3";"2";NULL;NULL;NULL;"1";"2";"2";"2";"2";;;"kms" +"4214";"JJM224";"Politická ekonomie komunikace";"Vochocová,L.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"3";"3";"2";"4";"4";;;"kms" +"4215";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"4";"5";"4";"4";"1";NULL;NULL;NULL;"1";"4";"4";"4";"1";;;"kmv" +"4216";"JPM260";"Vybrané problémy britské zahraniční politiky v 19. a 20. století, ES";"Soukup,J.";;NULL;NULL;"5";"4";"2";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kmv" +"4217";"JPM664";"Geopolitics of Great Powers: China";"Karásková,I.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"3";"4";"4";"4";"4";;;"kp" +"4218";"JJM331";"Výzkum médií II";"Vochocová,L.";;"3";"4";"3";"5";"3";NULL;NULL;NULL;"1";"4";"3";"3";"3";;;"kms" +"4219";"JJM259";"Mediální systémy a jejich komparace";"Miessler,J.";;"3";"4";"5";"5";"1";NULL;NULL;NULL;"1";"2";"2";"2";"2";"Oceňuji přístup vyučujícího i jasně daná pravidla pro absolvování tohoto kurzu. Tím, že byla pravidla jasně daná, každý student věděl, jak lze dosáhnout absolvování tohoto předmětu.";"Na druhou stranu postrádám smysl tohoto kurzu. Jen další teoretický předmět, který sice rozvíjí kritické myšlení, ale nijak nepomůže budoucím žurnalistům k větší odbornosti. Předmět dobrý pro mediální analytiky, ne pro budoucí novináři, kteří potřebují znát co nejvíce věcí z oblasti ekonomie, politologie, historie, politické geografie, kultury, literatury apod.";"kz" +"4220";"JJM259";"Mediální systémy a jejich komparace";"Miessler,J.";;"3";"5";"3";"4";"4";NULL;NULL;NULL;"1";"4";"3";"4";"3";"Líbilo se mi představení mediálních systémů jednotlivých zemí.";"Přišlo mi zbytečné přednášet opětovně o smyslu probíraných textů. Vzhledem k tomu, že jsme texty stejně museli číst kvůli pravidelným testům, mi přišlo, že se to pouze znovu opakuje. Navíc mi nepřišlo šikovné uspořádání prezentací. Dala bych na každou hodinu menší počet prezentací s tím, že by se s nimi mohlo začít již od druhé hodiny. Nelíbil se mi ani důraz na časové omezení prezentací. Mediální systémy jsou tak komplexní záležitostí, že patnáct minut mi přišlo mnohdy jako příliš krátký čas..";"kz" +"4221";"JJM332";"Přirozený jazyk a média";"Podzimek,J.";;"2";"2";"2";"2";"2";NULL;NULL;NULL;"1";"2";"1";"3";"2";;;"kms" +"4222";"JJM260";"Novinářská etika v praxi";"Moravec,V.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Děkuji za pana doktora Moravce, za jeho přístup ke studentům a k přednáškám. Takovéto předměty prosím na fakultě zachovat.";"Byl jsem pouze zklamán návštěvou vrchní státní zástupkyně paní Bradáčové, která z návštěvy udělala ne příliš záživnou přednášku. Očekával jsem větší možnost diskuze.";"kz" +"4223";"JPM264";"Bezpečnostní politika ČR";"Kučera,T.,Ludvík,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"4";"5";"Hostující přednášející z oboru; prostor pro diskuzi";;"kbs" +"4224";"JJM360";"Ekonomika v médiích";"Klimeš,D.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"4";"5";"Oceňuji znalosti a odbornost hostů, kteří na některých přednáškách přednášeli.";"Eseje bych neprocházel s úplně každým, myslím si, že to není nutné znát témata všech studentů, kteří předmět absolvují.";"kz" +"4225";"JJM360";"Ekonomika v médiích";"Klimeš,D.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"4";"4";"Přišli mi zajímaví hosté a jejich přednášky.";"Poslední dvě hodiny se souhrnem témat esejí mi přišly poněkud zdlouhavé. Místo představování jednotlivých esejů (jejichž témata jsme si stejně mohli přečíst v tabulce) bych spíše uvítala další výklad o ekonomii v žurnalistice či dalšího hosta.";"kz" +"4226";"JEM002";"Master´s Thesis Seminar II";;"Havránek,T.,Havránková,Z.,Mejstřík,M.";"5";"2";NULL;NULL;NULL;"5";"5";"2";"1";"2";"2";"2";"5";;;"ies" +"4227";"JPM323";"Global Political Philosophy";"Salamon,J.";;"5";"5";"4";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"4";;;"kp" +"4228";"JJM279";"Divadelní kritika";"Homolová Richtrová,N.";;"4";"4";"4";"5";"4";NULL;NULL;NULL;"2";"3";"5";"4";"4";"Líbily se mi rozbory divadelních záznamů i návštěva divadla.";"Možná bych ocenila ještě více rozborů divadelních záznamů.";"kz" +"4229";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"5";"4";"5";"4";"4";NULL;NULL;NULL;"1";"5";"3";"4";"4";;;"kms" +"4230";"JJM273";"Sportovní žurnalistika ve světě";"Bosák,J.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"2";"4";"4";"5";"5";"Přístup pana Bosáka, atmosféru na hodinách, závěrečný úkol.";"Možná ještě více podrobněji projít jednotlivé země z hlediska sportovního vysílání. Myslím, že to bylo probráno dobře, ale ocenil bych ještě větší podrobnosti.";"kz" +"4231";"JJM320";"Propaganda a československá média po roce 1948";"Suk,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"3";;;"kms" +"4232";"JJM324";"Teorie mediální komunikace";"Jirák,J.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kms" +"4233";"JJM274";"Práce sportovního reportéra a komentátora";"Záruba,R.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Jednotlivá cvičení, strukturu přednášek, přístup pana Záruby.";"Nic.";"kz" +"4234";"JLM011";"Angličtina pro veřejnou a sociální politiku I";;"Klírová,M.";"4";"4";NULL;NULL;NULL;"5";"5";"4";"1";"5";"5";"4";"4";"Nejlepší jsou diskuze v malých skupinkách a možnost skupinové prezentace v angličtině i s následující zpětnou vazbou.";"Vzhledem k velké náročnosti na pozornost jsou dvě hodiny bez přestávky příliš dlouhé, takže i pro zlepšení aktivity studentů by bylo lepší udělat vždy uprostřed alespoň krátkou pauzu.";"cjp" +"4235";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"2";"1";"2";"5";"1";NULL;NULL;NULL;"1";"2";"3";"3";"3";;;"kms" +"4236";"JJM275";"Tvůrčí dílny I (sportovní)";;"Trunečka,O.";NULL;NULL;NULL;NULL;NULL;"5";"5";"4";NULL;NULL;NULL;NULL;NULL;"Oceňuji přístup pana Trunečky. Oceňuji, že jsme se na struktuře semestru mohli podílet my sami a stanovili jsme si nějaké cíle apod. Oceňuji také hosty, kteří přišli - pan Kasík a pan Mádl.";"Nic mě nenapadá.";"kz" +"4237";"JJM329";"Média a kultura";"Bednařík,P.,Jirák,J.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kms" +"4238";"JJM330";"Trendy současných českých médií";"Aust,O.";;"4";"2";"5";"3";"4";NULL;NULL;NULL;"3";"4";"4";"5";"5";;;"kms" +"4239";"JPM429";"Global terrorism (CS)";;"Makariusová,R.";"5";"3";NULL;NULL;NULL;"4";"5";"4";"3";"5";"4";"4";"5";"Líbilo se mi, že část hodiny byla ponechána přímo studentským prezentacím, ale zároveň měla svou (obecnější) prezentaci i vyučující. Zároveň součástí byla diskuze, které se mohli zúčastnit všichni, a největším přínosem byli cizinci či studenti z Erasmu, kteří měli třeba vlastní zkušenosti (např. student z Pákistánu).";"Určitě bych dala studentské prezentace dříve než debatu, protože se nikdy nestíhala dokončit prezentace. Vyučující by též měla studenty utnout, když už mluví zbytečně dlouho (my byli první a snažili jsme se mít max 5 min na studenta, zatímco v pozdějších prezentacích někteří mluvili i 10 a více minut)";"kmv" +"4240";"JJM280";"Filmová a televizní kritika";"Štoll,M.";;"2";"3";"3";"3";"3";NULL;NULL;NULL;"1";"3";"4";"3";"3";"Přišel mi užitečný filmový slovníček. Pojmy ze světa filmu pro mne byly často nové a užitečné.";"Líbilo se mi, kdyby se na hodinách četli i nějaké zahraniční texty. Texty mi nepřišly příliš aktuální a přišlo by mi zajímavé, kdybychom četli i nějaké americké texty (například z Hollywoodu 30. a 70. let) nebo i z dalších zemí. Celkové zaměření na československou kinematografii mi přišlo příliš omezené. Navíc se mi nelíbilo, že se na hodinách objevovaly práce bez souhlasu autorů. Myslím tím diplomovou práci s gramatickými chybami i krátký film o mladých lidech na vesnici (na název už si nevzpomínám). Myslím si, že k demonstrování těchto chyb se daly využívat i jiné, veřejně dostupné práce.";"kz" +"4241";"JJM332";"Přirozený jazyk a média";"Podzimek,J.";;"4";"1";"4";"5";"3";NULL;NULL;NULL;"4";"3";"3";"2";"3";;;"kms" +"4242";"JPM727";"Orchestration in Global Governance";;"Abbott,K.,Parízek,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"5";;;"kmv" +"4243";"JJM373";"Ethical Issues in Media, Business and Society";"Orhan,M.";;"3";"3";"3";"4";"3";NULL;NULL;NULL;"2";"3";"2";"2";"2";;;"kms" +"4244";"JJM264";"Diplomový seminář II.";;;"1";"3";"3";"3";"1";NULL;NULL;NULL;"5";"1";"1";"1";"1";"Nic.";"Nerozumím významu tohoto předmětu. Proč nelze zapsat spíše v letním semestru, neznám nikoho, kdo začínal psát diplomku už v zimním semestru. Každý má v zimním semestru ještě plno jiných povinností - přednášky, zkoušky, literatura, seminární práce, eseje apod. Jde tady jen o zápočet, který je zapsán na základě toho, že vedoucí obdrží už v lednu/únoru pár napsaných stránek. Tento předmět pro mě absolutně postřádá smysl.";"kz" +"4245";"JPM725";"Technology and Security: Contemporary Warfare in the 21st Century";;"Csernatoni,R.";"5";"4";NULL;NULL;NULL;"4";"5";"5";"1";"4";"4";"5";"5";"Discussions; critical reflection of theories and concepts; relevant, current, and interesting topics";"Either extent the length/number of lectures or cut the student presentations";"kmv" +"4246";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"2";"5";"3";"2";"3";NULL;NULL;NULL;"1";"3";"3";"2";"2";;;"kms" +"4247";"JSM103";"Academic Writing";;"Blokker,P.";"1";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"5";"5";"Positive approach of the Lecturer";"Smaller class would be more sutable";"ks" +"4248";"JJM320";"Propaganda a československá média po roce 1948";"Suk,P.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"4249";"JPM526";"Justice and Reconciliation in Post-Conflict Societies";;"Werkman,K.";"5";"3";NULL;NULL;NULL;"3";"3";"4";"3";"5";"3";"4";"5";"Skvělé bylo promítnutí filmu/dokumentu, a následná diskuze... v některých momentech však diskuze hodně vázla. Moc se mi líbila forma úkolů, kdy se měl sepsat vlastní názor, nápad, myšlenka, pocit k danému případu, takže i když jsem se to bála říct otevřeně na kurzu, mohla jsem to pak shrnout ve week diary.";"Bylo mi líto vyučující, že měla starosti s dětmi, do toho se studenty, a pak spoustu čtení během ledna. Možná zkrátit závěrečnou esej. Případně udělat jiný formát (např. v jiném předmětu se udělala prezentace a k tomu na 5-8 stran souhrn textu k prezentaci - taková kratší esej, která však musela být provázána s prezentací).";"kmv" +"4250";"JJM324";"Teorie mediální komunikace";"Jirák,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"4251";"JPM692";"Internal Security of the EU [ES]";"Hokovský,R.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"4";"4";"Detailed information from the person with deep insight";"I think more lectures would help students";"kmv" +"4252";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"4";"4";"4";"3";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kms" +"4253";"JJM373";"Ethical Issues in Media, Business and Society";"Orhan,M.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"4254";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"3";"3";"3";"4";;;"kms" +"4255";"JMM143";"Economy and Politics in the 20th Century Eastern Europe";"Svoboda,K.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"4";"Great detailed knowledge of each topic and authentic attitude";"More active involvement of students";"krvs" +"4256";"JJM320";"Propaganda a československá média po roce 1948";"Suk,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kms" +"4257";"JPM185";"Evropská integrace - teorie a příklady, ES";"Jeřábek,M.";;"3";"4";"4";"4";"3";NULL;NULL;NULL;NULL;"4";"4";"3";"4";;;"kmv" +"4258";"JPM052";"Mezinárodní ekonomické vztahy (MEV)";"Kučerová,I.";;"5";NULL;"4";"4";"2";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kmv" +"4259";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"5";"5";"4";"4";"2";NULL;NULL;NULL;"2";"5";"4";"4";"5";;;"kmv" +"4260";"JLB003";"Angličtina pro ekonomy I";;"Gloverová,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"2";"4";"4";"4";"5";;;"cjp" +"4261";"NMMA701";"Matematika 1";"Spurný,J.";"Skříšovský,E.";"4";"5";"4";"4";"4";"4";"4";"5";"1";"5";"5";"4";"4";;;"ies" +"4262";"JSM518";"Public Policy";"Potůček,M.,Vlčková,K.";;"4";"5";"3";"3";"2";NULL;NULL;NULL;"1";"4";"4";"5";"4";;;"kvsp" +"4263";"JPM644";"Contemporary International Relations in East Asia";"Kolmaš,M.";;"5";"5";"4";"4";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Kurz byl velmi zajímavý.";"Vadilo mi, že když vyučující položil otázku, a viděl, že nikdo dlouho neodpovídá, že tedy neodpověděl on sám.";"kmv" +"4264";"JSM612";"Kriminalita a současná česká společnost";"Cejp,M.";;"3";"1";"4";"3";"3";NULL;NULL;NULL;"1";"3";"1";"2";"4";;;"kvsp" +"4265";"JJB004";"Současný český jazyk I";;"Svobodová,I.";"5";"5";NULL;NULL;NULL;"5";"4";"5";"2";"5";"5";"4";"4";"Výuka gramatických jevů, které reálně využijeme v praxi";"Možná výuku trošku zvolni, zpomalit, výklad byl často hodně odborný a rychlý. Před zkouškou bych ocenila ještě rozsáhlejší opakovací materiály s výsledky.";"kz" +"4266";"JSM620";"Politologické aspekty tvorby politik: Veřejné politiky v kontextu politiky";"Novotný,V.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"3";"4";"5";"4";"4";;;"kvsp" +"4267";"JPM689";"Conflict Studies";"Karásek,T.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Pan Karásek je skvělý přednášející a má bohaté znalosti. Hosté kurzu měli též velmi zajímavá témata, ovšem jejich prezentace byly trochu chaotické. Avšak bylo to zajímavé oživení, takže si myslím, že jeden či dva hosté by tam vždy měli být.";"Nic.";"kbs" +"4268";"JJB017";"Grafický design a základy polygrafie I";"Slanec,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"3";"2";"3";"4";"Korektury textů a příjemný přístup vyučujícího";"Na začátku mě překvapilo, že je kurz moc teoretický, bez praxe. Ta ale přichází v navazujícím předmětu, takže je to vlastně v pořádku.";"kz" +"4269";"JPM721";"Komparace ekonomik zemí EU";"Kučerová,I.";;"5";"4";"4";"5";"5";NULL;NULL;NULL;"3";"4";"3";"5";"5";"Líbí se mi, že je není potřeba nic dělat v průběhu semestru, protože to míváme většinou spoustu jiných úkolů, esejí a dalších povinností. Tuto formu bych určitě zachovala.";"Nevím.";"kmv" +"4270";"JJM080";"Zábava v televizním vysílání";"Kruml,M.";;"3";"3";"5";"5";"3";NULL;NULL;NULL;"2";"3";"1";"2";"4";"Kurz byl zábavný, chytlavý";;"kms" +"4271";"JJM201";"Teorie masové a mediální komunikace";"Jirák,J.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"2";"4";"1";"5";"4";"Přístup pana profesora byl jako vždy velice profesionální a fundovaný, to se mi moc líbilo. Jeho přednášky se poslouchají samy.";"Otázek ke zkoušce bylo poměrně hodně, možná by neškodilo pár ubrat.";"kms" +"4272";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"5";"3";"4";"5";"5";NULL;NULL;NULL;"1";"4";"2";"5";"4";;;"kms" +"4273";"JJM320";"Propaganda a československá média po roce 1948";"Suk,P.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"5";"1";"3";"4";;;"kms" +"4274";"JJM202";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"4";"5";"5";"5";"4";NULL;NULL;NULL;"1";"4";"1";"4";"4";"Výuka dějin je důležitá z hlediska pochopení současnosti, rozhodně mi absolvování toho předmětu přijde v tomto směru přínosné.";"Hodnocení testů bylo velice přísné, možná by sálo za to před testem alespoň rámcově říct, jakým stylem a v jakém rozsahu odpovídat. Zdálo se mi, že k otázkám vyučující chtěli vědět více, než z otázek samotných vyplývalo.";"kms" +"4275";"JLB041";"Španělština I";;"Mlýnková,L.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"The experience and energy of the lector.";"Everything was allright...";"cjp" +"4276";"JJM324";"Teorie mediální komunikace";"Jirák,J.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"1";"5";"5";;;"kms" +"4277";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"4";"4";;;"kms" +"4278";"JJM373";"Ethical Issues in Media, Business and Society";"Orhan,M.";;"3";"3";"2";"2";"1";NULL;NULL;NULL;"1";"4";"5";"5";"4";;;"kms" +"4279";"JJM204";"Výzkum médií I";"Křeček,J.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"2";"4";"Příjemný a ochotný přístup vyučujícího, jasně dané náležitosti ke splnění předmětu";"Nic mě nenapadá";"kms" +"4280";"JMM039";"Západní Evropa a svět";"Tomalová,E.,Váška,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"The debate on the current situation and the extensive knowledge of the professor.";"Everything was allright....";"kzs" +"4281";"JJM254";"Mediální tvorba";"Čásenský,R.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Příběhy z praxe, pan Čásenský se skvěle poslouchá a byl to nejpříjemnější předmět tohoto semestru.";;"kz" +"4282";"JEB141";"Introduction to Market Design";"Gregor,M.,Kastl,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"3";"5";"Prakticky prinos";;"ies" +"4283";"JEM199";"Financial Crisis and Risk Management";"Horváth,R.,Opatrný,M.,TSOMOCOS,D.";;"4";"4";"3";"4";"4";NULL;NULL;NULL;"1";"4";"2";"4";"4";;;"ies" +"4284";"JJM348";"Competencies and Skill for International Academia";"Zezulková,M.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"3";"5";"5";"5";"5";"Přístup a ochota lektorky, její zkušenosti a nadhled";;"kms" +"4285";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"4";"4";"5";"3";"4";NULL;NULL;NULL;"1";"3";"3";"3";"4";;;"kms" +"4286";"JPM706";"Terrorism and Counterterrorism";"Bureš,O.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Přístup kantora, jeho široké spektrum znalostí. Nutnost studia odborné literatury k tématu. Časté diskuze.";"Pan docent je velmi profesionální a udržuje si odstup od jakéhokoliv subjektivity ze své strany, která by mohla výuku nebo studenty nějak ovlivnit. Na druhou stranu bych jeho názory velice rád slyšel.";"kbs" +"4287";"JJM320";"Propaganda a československá média po roce 1948";"Suk,P.";;"4";"3";"5";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kms" +"4288";"JJM249";"Duchovní kořeny evropské kultury";"Halada,J.";;"2";"3";"2";"2";"2";NULL;NULL;NULL;"1";"1";"1";"2";"2";;;"kz" +"4289";"JJM199";"Literární a knižní kritika";"Čeňková,J.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"2";"5";"4";"3";"4";;;"kz" +"4290";"JJM340";"Tvůrčí dílny – tvůrčí psaní I";"Novotný,D.";"Novotný,D.";NULL;"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"3";"5";;;"kz" +"4291";"JJM324";"Teorie mediální komunikace";"Jirák,J.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"3";"3";"4";"4";;;"kms" +"4292";"JJM297";"Novinář jako politický aktér";"Charvát,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"3";"5";"4";"4";"5";;;"kz" +"4293";"JJM246";"Historie a estetika fotožurnalismu";"Lábová,A.,Štefaniková,S.";;"2";"2";"4";"4";"1";NULL;NULL;NULL;"3";"1";"1";"1";"2";;"Na kurz jsem se velmi těšila, ale bohužel mě zklamal. Po absolvování fotografického zaměření na bakaláři jsem očekávala rozšíření znalostí a je velká škoda, že k němu nedošlo. Bavily by mě skutečné přednášky s teoretickým obsahem obdobně jako na bakaláři během předmětu o historii fotožurnalistiky. Diskuze nad fotografy a jejich dílem, nad odbornými texty či fotografickým pokrytím současných událostí.";"kz" +"4294";"JEB105";"Statistics";"Červinka,M.";"Smutná,Š.";"4";"5";"4";"4";"5";"4";"4";"5";"1";"4";"4";"4";"4";;;"ies" +"4295";"JJM248";"Vývoj grafického designu a polygrafického zpracování periodik";"Slanec,J.";;"3";"3";"3";"3";"2";NULL;NULL;NULL;"1";"3";"1";"1";"2";;;"kz" +"4296";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"2";"3";"4";"3";"1";NULL;NULL;NULL;"1";"2";"2";"2";"3";;;"kms" +"4297";"JJM199";"Literární a knižní kritika";"Čeňková,J.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"3";"5";;;"kz" +"4298";"JPM613";"Armed Forces and Society";"Kučera,T.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"3";"3";"4";;;"kbs" +"4299";"JJM373";"Ethical Issues in Media, Business and Society";"Orhan,M.";;"3";"3";"4";"4";"2";NULL;NULL;NULL;"1";"2";"3";"3";"2";;;"kms" +"4300";"JJM356";"Metodologické přístupy ke studiu žurnalistiky";"Nečas,V.,Veselková,Z.";;"3";"5";"4";"2";"4";NULL;NULL;NULL;"2";"3";"4";"3";"3";"Je skvělé, že prezentace byly průběžně nahrávány do ISu. Jediný předmět, díky kterému jsem si neupsala ruku. Tento přístup je velice vstřícný ke studentům, toho jsem si cenila.";"Kritiku seminárních prací by mohl vyučující demonstrovat na pracích z předchozího akademického roku, ne veřejně srážet sebevědomí přítomným. (Hodnocení e-mailem či soukromě s možností reakce studenta a zpětné vazby je samozřejmě v pořádku.)";"kz" +"4301";"JPM656";"Technology and warfare";"Kučera,T.";;"3";"4";"4";"4";"3";NULL;NULL;NULL;"1";"3";"2";"2";"3";;;"kbs" +"4302";"JPM702";"NATO and EU in Crisis Management";"Karásek,T.";;"4";"3";"5";"5";"3";NULL;NULL;NULL;"1";"3";"2";"4";"5";;;"kbs" +"4303";"JPM707";"Peacekeeping and Peacebuilding";"Bureš,O.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Vhled do problematiky nejen jednotlivých PKOs, ale zároveň do problémů celé UN a obtížnost jejich řešení. Literatura nabízející širokou škálu pohledů na věc. Diskuze.";;"kbs" +"4304";"JPM706";"Terrorism and Counterterrorism";"Bureš,O.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kbs" +"4305";"JSM518";"Public Policy";"Potůček,M.,Vlčková,K.";;"2";"5";"4";"4";"2";NULL;NULL;NULL;"2";"3";"2";"4";"3";;"Méně povinností, kurz je příliš náročný na začátek magisterského studia a to hlavně kvůli nesnadné orientaci v povinnostech a angličtině při přednáškách. Lepší by byl kurz samostatně pro české studenty a pro studenty studující v angličtině spolu se studenty z erasmu také zvláštní kruz. Základy veřejné politiky by bylo lepší na začátku se učit v českém a jazyce a teprve poté (v letním semestru například) anglicky. Také kvůli složité koordinaci českých a anglicky studujících studentů na seminářích vznikal občas zmatek - například při utváření skupin na seminářích či opravě úkolů (otázek), kdy se na výsledky čekalo poměrně dlouho.";"kvsp" +"4306";"JPM707";"Peacekeeping and Peacebuilding";"Bureš,O.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"4";"4";;;"kbs" +"4307";"JMM086";"Diplomový seminář II";;"Králová,K.,Svoboda,K.,Švec,L.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";;;"krvs" +"4308";"JPM712";"Insurgency and Counterinsurgency";"Aslan,E.";;"5";"4";"4";"4";"3";NULL;NULL;NULL;"3";"4";"3";"4";"5";;;"kbs" +"4309";"JEB120";"Financial Economics";"Žigraiová,D.";;"3";"4";"2";"4";"2";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"ies" +"4310";"JMM189";"Economic transformation in East Central and Southeastern Europe";"Trejbal,V.";;"2";"3";"2";"2";"2";NULL;NULL;NULL;"3";"3";"1";"3";"2";;;"krvs" +"4311";"JJM245";"Úvod do vizuální komunikace";"Průchová,A.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Skvělé pedagogické vedení, ze kterého čiší nadšení pro obor. Do přednášek bylo zakomponováno mnoho i z dalších oborů, tak aby si student mohl látku kurzu propojit i s tím, co jeho samotného zajímá. Skutečně mě předmět nadchl. Díky!";;"kz" +"4312";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Výborný přístup kantora a jeho zjevná motivace.";;"kmv" +"4313";"JJM355";"Teoretické koncepty ke studiu žurnalistiky I";"Jirků,J.,Němcová Tejkalová,A.";"Jirků,J.";"4";"4";"3";"4";"2";"5";"5";"5";"1";"5";"3";"5";"4";"Především semináře, ty byly skutečně přínosné a v malé skupině došlu i na diskuze.";;"kz" +"4314";"JEM037";"Financial Markets";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"4";"4";"5";"5";"5";"4";"4";"4";"1";"5";"5";"5";"4";;;"ies" +"4315";"JJM247";"Český stranický systém";"Just,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"2";"4";"5";"Oceňuji, že se předmět soustředil i na polistopadový vývoj a aktuální politickou situaci.";;"kz" +"4316";"JEM001";"Master´s Thesis Seminar I";;"Havránek,T.,Havránková,Z.,Mejstřík,M.";"4";"4";NULL;NULL;NULL;"4";"4";"3";"1";"3";"2";"3";"3";;;"ies" +"4317";"JEM017";"Business Cycles Theory";"Baxa,J.,Kučera,A.,Vácha,L.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"4";"5";;;"ies" +"4318";"JMMZ019";"M.A. Thesis Seminar I (IMESS)";;"Vykoukal,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"krvs" +"4319";"JMMZ042";"Cohesion Policy of the EU in Central and East European Countries.";"Hauser,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"krvs" +"4320";"JMMZ152";"European Economic Integration";"Young,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kzs" +"4321";"JLB101";"Czech as a Foreign Language II";;"Mazúrková,B.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"4322";"JEM162";"Energy Markets & Economics";"Elms,N.,Valíčková,P.";;"3";"3";"3";"4";"2";NULL;NULL;NULL;"1";"4";"3";"4";"3";;;"ies" +"4323";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"3";"4";"2";"4";"1";NULL;NULL;NULL;"1";"3";"2";"3";"3";;;"kms" +"4324";"JEM002";"Master´s Thesis Seminar II";;"Havránek,T.,Havránková,Z.,Mejstřík,M.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"3";"3";"5";"individuální konzultace";"rozdělení rozpisu na půlhodinové úseky v rámci jednoho semináře (všichni přijdou v 5 a pak hodinu a půl čekají)";"ies" +"4325";"JJM329";"Média a kultura";"Bednařík,P.,Jirák,J.";;"2";"4";"3";"4";"2";NULL;NULL;NULL;"1";"1";"3";"1";"2";;;"kms" +"4326";"JEM053";"Stochastické procesy v ekonomii I";;;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"ies" +"4327";"JJM330";"Trendy současných českých médií";"Aust,O.";;"4";"3";"3";"3";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kms" +"4328";"JJM331";"Výzkum médií II";"Vochocová,L.";;"4";"4";"5";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kms" +"4329";"JJM332";"Přirozený jazyk a média";"Podzimek,J.";;"3";"3";"2";"3";"1";NULL;NULL;NULL;"1";"3";"3";"3";"3";;;"kms" +"4330";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"4";"3";"4";"5";"3";NULL;NULL;NULL;"1";"3";"5";"4";"4";;;"ies" +"4331";"JEM002";"Master´s Thesis Seminar II";;"Havránek,T.,Havránková,Z.,Mejstřík,M.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"5";"Valuable remarks of Mr. Havranek.";"It would be great if students got an email also with detailed explanations of what students should do to submit thesis (to bring printed copy to the secretary, not to forget to sign it, on which page, etc.).";"ies" +"4332";"JMM271";"Metodologický seminář";;"Matějka,O.";"4";"4";NULL;NULL;NULL;"5";"5";"4";"3";"5";"5";"4";"5";;;"krvs" +"4333";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"2";"4";NULL;NULL;NULL;"3";"3";"2";"3";"2";"2";"2";"2";;;"ies" +"4334";"JMMZ217";"Current Debates in British Politics and on the Constitution";"McLean,I.,Peterson,S.";"McLean,I.,Peterson,S.";"5";"3";"5";"5";"4";"5";"5";"4";"1";"5";"3";"5";"5";;;"kzs" +"4335";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"3";"5";"5";"5";"3";"2";"4";"1";"3";"3";"1";"4";"4";;;"ies" +"4336";"JLB003";"Angličtina pro ekonomy I";;"Gloverová,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"3";"5";;;"cjp" +"4337";"JSB998";"Úvod do sociologie";"Soukup,P.";;"2";"2";"4";"4";"1";NULL;NULL;NULL;"3";"2";"1";"1";"1";;;"ks" +"4338";"JMM278";"Politika a mezinárodní vztahy";"Kubát,M.,Weiss,T.";"Mejstřík,M.";"4";"4";"4";"4";"4";"5";"5";"5";"1";"4";"3";"4";"3";"Na kurzu nejvíc oceňuji seminář s dr. Mejstříkem, který byl velmi přínosný a založený především na diskuzích o aktuálních tématech.";;"krvs" +"4339";"NMMA701";"Matematika 1";"Spurný,J.";"Skříšovský,E.";"3";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"3";"5";;;"ies" +"4340";"JMM714";"Deutsche und deutschsprachige Literatur als Spigel der Wndel des 20. Jhs. (UPK)";;;"4";"4";"4";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"knrs" +"4341";"JMM710";"Europa Środkowa i Środkowowschodnia w dobie wspólczesnej (UPK)";;;"5";"4";"5";"5";"4";NULL;NULL;NULL;"3";"4";"4";"4";"5";"pana profesora, je skvělý";;"knrs" +"4342";"JMM713";"Deutsch-polnische Beziehungen 1815-1989 UPK)";;;"3";"4";"3";"3";"4";NULL;NULL;NULL;"2";"4";"3";"5";"4";;;"knrs" +"4343";"JJM249";"Duchovní kořeny evropské kultury";"Halada,J.";;"3";"3";"3";"3";"1";NULL;NULL;NULL;"1";"2";"1";"3";"3";;;"kz" +"4344";"JMM712";"Polen und Deutschland im vereinigten Europa (1990-2013) (UPK)";;;"4";"3";"4";"5";"5";NULL;NULL;NULL;"3";"4";"4";"4";"4";;;"knrs" +"4345";"JMM279";"Společnost a ekonomika";"Fiřtová,M.";"Young,M.";"5";"5";"5";"5";"5";"5";"5";"4";"1";"5";"3";"3";"4";"Na kurzu oceňuji především výklad dr. Fiřtové, která o ekonomických tématech dokáže vykládat velmi zajímavě i pro ty, kteří nemají v oboru mnoho příliš znalostí. Dr. Young pak v semináři navázal a dovysvětlil případné nejasnosti.";;"kas" +"4346";"JJM355";"Teoretické koncepty ke studiu žurnalistiky I";"Jirků,J.,Němcová Tejkalová,A.";"Jirků,J.";"5";"4";"5";"5";"4";"5";"3";"4";"2";"4";"3";"4";"4";;;"kz" +"4347";"JMM067";"Russia and Eurasia in World Politics";"Šír,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"very informative and analytical, greatly contextualized";"none";"krvs" +"4348";"JJM252";"Specifika sportovní žurnalistiky";"Němcová Tejkalová,A.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kz" +"4349";"JJM356";"Metodologické přístupy ke studiu žurnalistiky";"Nečas,V.,Veselková,Z.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"2";"4";"3";"4";"4";;;"kz" +"4350";"JJM294";"Teorie a praxe rozhlasové a televizní moderace";"Moravec,V.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"4";"5";;;"kz" +"4351";"JJM251";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"3";"3";"3";"2";"1";NULL;NULL;NULL;"2";"2";"1";"2";"2";;;"kz" +"4352";"JMM039";"Západní Evropa a svět";"Tomalová,E.,Váška,J.";;"5";"4";"5";"4";"4";NULL;NULL;NULL;"3";"4";"4";"5";"5";;;"kzs" +"4353";"JMM530";"European Comparative Politics and Society";"Rovná,L.";;"3";"3";"4";"4";"2";NULL;NULL;NULL;"3";"2";"3";"4";"3";"Na kurzu oceňuji hlavně prof. Rovnou a její zkušenosti.";"Obsah kurzu je pro studenty, kteří absolvovali bakalářský program na IMS, něčím, co už slyšeli nesčetněkrát. Opakování se sice hodí, ale přínos celého kurzu nepovažuju za příliš velký.";"kzs" +"4354";"JPM641";"Světový regionalismus";"Riegl,M.";;"3";"4";"2";"3";"3";NULL;NULL;NULL;"1";"4";"2";"3";"3";;;"kp" +"4355";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"4";"5";;;"kmv" +"4356";"JPM348";"Nové přístupy k místní správě a přímá volba starostů";;"Jüptner,P.";"4";"4";NULL;NULL;NULL;"4";"4";"5";"1";"5";"5";"4";"4";;;"kp" +"4357";"JPM127";"Geopolitika a geostrategie 1815 - 1945";"Romancov,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"2";"4";"5";;;"kp" +"4358";"JPM561";"Regional Security Studies";"Karásek,T.,Klosek,K.";"Karásek,T.,Klosek,K.";"4";"4";"4";"4";"3";"3";"3";"4";"1";"4";"4";"4";"5";;;"kbs" +"4359";"JPM598";"Grand Strategies";"Ditrych,O.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"3";"5";;;"kbs" +"4360";"JPM613";"Armed Forces and Society";"Kučera,T.";;"4";"4";"5";"4";"5";NULL;NULL;NULL;"2";"5";"5";"4";"5";;;"kbs" +"4361";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"3";"5";;;"kmv" +"4362";"JPM701";"European and Transatlantic Security";"Kazharski,A.";;"4";"3";"4";"5";"3";NULL;NULL;NULL;"3";"3";"4";"4";"5";;;"kbs" +"4363";"JJM330";"Trendy současných českých médií";"Aust,O.";;"5";"3";"5";"4";"5";NULL;NULL;NULL;"5";"5";"5";"5";"5";"Praxi";;"kms" +"4364";"JJM080";"Zábava v televizním vysílání";"Kruml,M.";;"4";"1";"4";"5";"4";NULL;NULL;NULL;"2";"3";"1";"4";"5";;;"kms" +"4365";"JJM117";"Popular Culture";"Turnau,T.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"2";"4";"4";;;"kms" +"4366";"JJM201";"Teorie masové a mediální komunikace";"Jirák,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"2";"5";"5";;;"kms" +"4367";"JJM202";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"3";"3";"3";"3";"3";NULL;NULL;NULL;"1";"3";"1";"4";"4";;;"kms" +"4368";"JJM204";"Výzkum médií I";"Křeček,J.";;"3";"2";"3";"4";"3";NULL;NULL;NULL;"1";"2";"5";"4";"4";;;"kms" +"4369";"JJM208";"Mediální systémy a jejich komparace";"Miessler,J.";;"4";"5";"4";"4";"3";NULL;NULL;NULL;"1";"4";"3";"4";"4";;"méně prezentací, více vlastní výuky";"kms" +"4370";"JJM224";"Politická ekonomie komunikace";"Vochocová,L.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"1";"5";"5";;;"kms" +"4371";"JJM226";"Teorie účinků médií";"Nečas,V.";;"5";"3";"5";"4";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kms" +"4372";"JJM229";"Vývoj televizního vysílání v českých zemích";"Štoll,M.";;"4";"2";"5";"5";"4";NULL;NULL;NULL;"2";"4";"1";"4";"5";;;"kms" +"4373";"JJM243";"Média a životní styl";"Knapík,J.";"Knapík,J.";"4";"3";"4";"5";"2";"4";"5";"3";"1";"4";"1";"3";"4";;;"kms" +"4374";"JJM210";"Kvantitativní obsahová analýza";;"Nečas,V.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"2";"4";"5";"4";"4";;;"kms" +"4375";"JJM295";"Rozhlasový a televizní dokument";"Štoll,M.";;"4";"2";"5";"5";"3";NULL;NULL;NULL;"2";"3";"1";"3";"5";;;"kz" +"4376";"JJM343";"Interkulturní komunikace";"Soukup,M.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"3";"5";"2";"5";"5";;;"kms" +"4377";"JPM641";"Světový regionalismus";"Riegl,M.";;"5";"3";"4";"3";"3";NULL;NULL;NULL;"1";"4";"4";"5";"3";;;"kp" +"4378";"JJM214";"Čtení textů ke studiu médií - populární kultura";;"Reifová,I.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"5";"4";;;"kms" +"4379";"JPM345";"Diplomní seminář III.";;"Brunclík,M.,Franěk,J.,Hroch,M.,Charvát,J.,Jüptner,P.,Kofroň,J.,Kotábová,V.,Krausz Hladká,M.,Kubátová,H.,Kučera,J.,Landovský,J.,Mlejnek,J.,Perottino,M.,Riegl,M.,Romancov,M.,Říchová,B.,Salamon,J.,Shavit,A.,Švec,K.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";;;"kp" +"4380";"JJM295";"Rozhlasový a televizní dokument";"Štoll,M.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"2";"4";"4";"4";"5";;;"kz" +"4381";"JPM118";"Výběrový seminář: Volby v USA";"Kotábová,V.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"4";"5";"Bohaté diskuze a relativně volný prostor při výběru témat k prezentacím.";"-";"kp" +"4382";"JLB100";"Czech as a Foreign Language I";;"Frantesová,E.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"3";"3";"5";;;"cjp" +"4383";"JPM561";"Regional Security Studies";"Karásek,T.,Klosek,K.";"Karásek,T.,Klosek,K.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"kbs" +"4384";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"3";"3";"3";"2";"2";NULL;NULL;NULL;"1";"2";"4";"1";"2";;;"kmv" +"4385";"JPM693";"Traditional and Critical Concepts in Security Studies";"Rychnovská,D.";;"5";"3";"4";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kbs" +"4386";"JPM689";"Conflict Studies";"Karásek,T.";;"5";"3";"5";"4";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kbs" +"4387";"JEB044";"Financial Accounting";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"3";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"2";;;"ies" +"4388";"JEB105";"Statistics";"Červinka,M.";"Hanus,L.";"4";"5";"5";"5";"5";"5";"5";"5";"1";"4";"3";"3";"5";;;"ies" +"4389";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"4";"3";"4";"4";"1";"3";"3";"1";"1";"3";"3";"3";"5";;;"ies" +"4390";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"3";"1";"1";"1";"5";;;"kz" +"4391";"JJM214";"Čtení textů ke studiu médií - populární kultura";;"Reifová,I.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"2";"4";"5";"5";"5";;;"kms" +"4392";"NMMA703";"Matematika 3";"Zelený,M.";"Zelený,M.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"4393";"JJM226";"Teorie účinků médií";"Nečas,V.";;"5";"4";"5";"4";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kms" +"4394";"JLB019";"Francouzština odborná I";;"Bosáková,L.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"4395";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"3";"4";"3";"3";"2";NULL;NULL;NULL;"1";"4";"5";"5";"4";;;"kmv" +"4396";"JPM052";"Mezinárodní ekonomické vztahy (MEV)";"Kučerová,I.";;"4";"4";"4";"4";"5";NULL;NULL;NULL;"1";"3";NULL;"4";"4";"Šíři probrané látky.";"Výklad se chvílemi nedá stíhat, a než aby člověk více přemýšlel nad probíraným tématem a snažil se třeba interagovat s vyučujícím, tak se soustředí na to, aby si vše stihl zapsat. Ubral bych tedy na rychlosti výkladu a přidal o něco více interakce/diskuse.";"kmv" +"4397";"JMM703";"Post-Soviet Central Eurasia";"Lídl,V.,Šír,J.";;"4";"3";"4";"4";"5";NULL;NULL;NULL;"1";"4";"3";"4";"4";;"Setkala jsem se se dvěma problémy během kurzu. První se týká nedorozumění ohledně prezentace, kvůli kterému jsem zbytečně psala jednu práci na víc. Druhý se týká zkoušky - byly v ni otázky které nejsou v literatuře a ani nebyly probírané na přednáškách. Týká se to geografii Střední Asie, o které jsme mluvili jen v geopolitickém kontextu. Neměla jsem s tim problém jenom protože mám znalosti z jiné fakulty, jinak zkoušku asi bych neudělala.";"krvs" +"4398";"JPM260";"Vybrané problémy britské zahraniční politiky v 19. a 20. století, ES";"Soukup,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"Interakci se studenty, probírání souvislostí, nadšení vyučujícího do probírané látky a snahu probudit toto nadšení i ve studentech.";;"kmv" +"4399";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"5";"5";"5";"3";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmv" +"4400";"JSM527";"Metody analýzy a tvorby politik II.";"Veselý,A.";;"5";"5";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"4";"4";;;"kvsp" +"4401";"JMM505";"Moderní dějiny středo- a jihovýchodní Evropy";"Balla,P.,Švec,L.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Řekla bych že tento kurz je bázi pro jakékoliv další studium v oboru BAS. Dozvíte spoustu informácií, beze kterych nelze studium na oboru představit. Zvlášť bych chtěla ocenit pristup pana doktora Bally - jeho přednášky jsou úchvatné a zajímavé.";"Přesto že celkově hodnotím kurz jako vynikající, stále nerozumím proč kurz je pouze za 1 kredit, když je natolik náročný. Pro studenta mgr. je to ještě více méně přijatelné, ale myslím si že studenti bc.studia by měli být hodně frustrované takovým množstvím kreditů ve srovnání s obrovskou náročností kurzu.";"krvs" +"4402";"JPM690";"Liberalism in International Relations (TIR)";;"Karlas,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"5";"5";;;"kmv" +"4403";"JPM644";"Contemporary International Relations in East Asia";"Kolmaš,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"5";"5";;;"kmv" +"4404";"JPM689";"Conflict Studies";"Karásek,T.";;"5";"5";"5";"4";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kbs" +"4405";"JSM528";"Seminář k diplomové práci I.";;"Kohoutek,J.,Ochrana,F.";"3";"2";NULL;NULL;NULL;"3";"3";"3";"1";"2";"3";"2";"3";;;"kvsp" +"4406";"JPM725";"Technology and Security: Contemporary Warfare in the 21st Century";;"Csernatoni,R.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Celý kurz se mi velmi líbil. Oceňuji na něm zejména jeho moderní pojetí a připravenost vyučující, která navíc vhodně propojovala teorii s praktickými situacemi. Rovněž doporučená četba nebyla příliš obecná a skutečně nabízela studentům prohloubení znalostí v dané problematice.";;"kmv" +"4407";"JMM034";"Obrazy a stereotypy Balkánu";"Šístek,F.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"5";"5";;;"krvs" +"4408";"JSB012";"Úvod do empirického výzkumu ve společenských vědách";"Jeřábek,H.";"Přibáňová,T.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ks" +"4409";"JJM080";"Zábava v televizním vysílání";"Kruml,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"2";"5";"5";;;"kms" +"4410";"JJM201";"Teorie masové a mediální komunikace";"Jirák,J.";;"4";"5";"5";"5";"5";NULL;NULL;NULL;"2";"5";"2";"5";"4";;;"kms" +"4411";"JJM202";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kms" +"4412";"JJM204";"Výzkum médií I";"Křeček,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"3";"4";;;"kms" +"4413";"JJM229";"Vývoj televizního vysílání v českých zemích";"Štoll,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"5";"5";"3";"4";"5";;;"kms" +"4414";"JJM254";"Mediální tvorba";"Čásenský,R.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"4";"5";;;"kz" +"4415";"JJM199";"Literární a knižní kritika";"Čeňková,J.";;"3";"3";"5";"5";"5";NULL;NULL;NULL;"2";"4";"3";"5";"5";;;"kz" +"4416";"JJM249";"Duchovní kořeny evropské kultury";"Halada,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kz" +"4417";"JJM355";"Teoretické koncepty ke studiu žurnalistiky I";"Jirků,J.,Němcová Tejkalová,A.";"Jirků,J.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"kz" +"4418";"JJM251";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"5";"5";"3";"3";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kz" +"4419";"JJM356";"Metodologické přístupy ke studiu žurnalistiky";"Nečas,V.,Veselková,Z.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kz" +"4420";"JJM360";"Ekonomika v médiích";"Klimeš,D.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kz" +"4421";"JEM166";"Master´s Thesis Seminar - IEPS";;"Benáček,V.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"presentations about the state of the individual theses. Therefore it was possible to see individual progress";;"ies" +"4422";"JEM179";"History and Methodology of Economics";"Baxa,J.,Paulus,M.";;"4";"5";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"4";"3";;;"ies" +"4423";"JEB039";"International Trade";"Semerák,V.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ies" +"4424";"JEB022";"Institutional Economics";"Kopečná,V.,Schwarz,J.";;"4";"3";"2";"5";"3";NULL;NULL;NULL;"2";"3";"4";"4";"3";;;"ies" +"4425";"JEM062";"Introductory Econometrics";"Kukačka,J.";"Kukačka,J.";"4";"4";"4";"5";"3";"4";"5";"4";"1";"4";"4";"4";"4";;;"ies" +"4426";"JPM607";"International Negotiations";;"Parízek,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kmv" +"4427";"JEB044";"Financial Accounting";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ies" +"4428";"JSM406";"Statistics in SPSS";;"Soukup,P.";"4";"1";NULL;NULL;NULL;"4";"5";"3";"1";"4";"4";"4";"5";;;"ks" +"4429";"JEB120";"Financial Economics";"Žigraiová,D.";;"2";"4";"2";"2";"2";NULL;NULL;NULL;"4";"5";"5";"5";"5";;;"ies" +"4430";"JEB141";"Introduction to Market Design";"Gregor,M.,Kastl,J.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"3";;;"ies" +"4431";"JEM020";"Ethics and Economics";"Cahlík,T.";"Cahlík,T.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"4";;;"ies" +"4432";"JSM421";"Contemporary social theory";"Balon,J.";;"1";"1";"2";"4";"1";NULL;NULL;NULL;"1";"1";"2";"3";"3";;;"ks" +"4433";"JEM182";"Economics of Innovation";"Sidorkin,O.,Srholec,M.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ies" +"4434";"NMMA711";"Mathematics 1";"Bárta,T.";"Bárta,T.,Vlasák,V.";"4";"5";"5";"5";"5";"3";"5";"3";"1";"3";"4";"5";"1";;;"ies" +"4435";"JEB105";"Statistics";"Červinka,M.";"Červinka,M.";"4";"5";"3";"3";"3";"4";"4";"4";"1";"4";"4";"5";"4";;"Zapojit data do výuky. Názornější vysvětlování, např. více grafů, animací atd.";"ies" +"4436";"JJM202";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"2";"4";"1";"2";"1";NULL;NULL;NULL;"1";"2";"1";"2";"2";;;"kms" +"4437";"JJM247";"Český stranický systém";"Just,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"3";"5";"Oceňuji způsob zkoušky psaným testem.";;"kz" +"4438";"JJM259";"Mediální systémy a jejich komparace";"Miessler,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"3";"5";"Oceńuji prezentace, které jsme si museli připravovat a psaní eseje na závěr.";"Myslím, že když jsme psali na začátku hodiny krátký test z četby, nemuseli jsme jí tolik dopodrobna probírat na hodině. Také je škoda, že náš ročník ještě bude mít státnice v původní podobě, tudíž nenavazuje předmět na státnicové otázky.";"kz" +"4439";"JJM260";"Novinářská etika v praxi";"Moravec,V.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"3";"3";"4";"5";"Líbily se mi konkrétní příklady z praxe a psaní esejů i forma zkoušky rozhovorem nad čtenými texty.";"myslím, že by bylo přínosné mít více hostů na hodině.";"kz" +"4440";"JJM288";"Proměny žurnalistiky v éře konvergence médií";"Jirků,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"3";"3";"3";"5";"Způsob prezentací, které jsme si museli připravit.";"Větší provázanost se státnicovými otázkami.";"kz" +"4441";"JJM248";"Vývoj grafického designu a polygrafického zpracování periodik";"Slanec,J.";;"3";"2";"3";"4";"3";NULL;NULL;NULL;"1";"4";"1";"4";"3";;;"kz" +"4442";"JJM291";"Tvůrčí dílny I – tisk";;"Matyášová,J.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"1";"3";"3";"1";"5";"Nácvik kratších textů.";"Více hostů na hodině.";"kz" +"4443";"JJM340";"Tvůrčí dílny – tvůrčí psaní I";"Novotný,D.";"Novotný,D.";"5";"2";"5";"5";"5";"5";"5";"5";"1";"4";"5";"3";"5";"Různé způsoby přístupů k textům, nácvik různých forem psaní povídek i návrhů struktur románů";"Detailněji rozebrat studentské práce a možná nepsat na každou hodinu jednu práci, ale mít na to více času.";"kz" +"4444";"JJM363";"Czech-German-Jewish Literary Triangle";;"Peroutková,M.";"3";"3";NULL;NULL;NULL;"3";"5";"3";"1";"3";"3";"3";"3";"Oceňuji detailně připravené prezentace od vyučujícího.";"Raději bych se více věnovala analýze textů než probírání života autorů.";"kz" +"4445";"JJM264";"Diplomový seminář II.";;;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"3";"3";"3";"5";;;"kz" +"4446";"JJM249";"Duchovní kořeny evropské kultury";"Halada,J.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"5";"1";"1";"4";;;"kz" +"4447";"JJM355";"Teoretické koncepty ke studiu žurnalistiky I";"Jirků,J.,Němcová Tejkalová,A.";"Jirků,J.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"kz" +"4448";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"4";"5";"přístup paní profesorky";;"cjp" +"4449";"JJM251";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"1";"5";"5";;;"kz" +"4450";"JMM079";"Hospodářský a sociální systém NMZ I";"Mlsna,P.";;"4";"4";"5";"4";"5";NULL;NULL;NULL;"3";"4";"4";"4";"4";;;"knrs" +"4451";"JMM083";"Deutsche und mitteleuropäische Geschichte im 20. Jh.";"Barth,B.";;"4";"4";"5";"4";"5";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"knrs" +"4452";"JJM356";"Metodologické přístupy ke studiu žurnalistiky";"Nečas,V.,Veselková,Z.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"5";"3";"4";;;"kz" +"4453";"JMM271";"Metodologický seminář";;"Šmidrkal,V.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"4";"5";;;"krvs" +"4454";"JJM254";"Mediální tvorba";"Čásenský,R.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"3";"3";"5";"5";;;"kz" +"4455";"JMM277";"Historie a kultura";"Vykoukal,J.";"Šmidrkal,V.";"4";"4";"5";"5";"5";"5";"5";"4";"1";"4";"4";"4";"4";;;"krvs" +"4456";"JMM278";"Politika a mezinárodní vztahy";"Kubát,M.,Weiss,T.";"Eberle,J.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"4";"4";"4";"4";;;"krvs" +"4457";"JMM279";"Společnost a ekonomika";"Fiřtová,M.";"Kačmárová,P.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"4";"5";"5";;;"kas" +"4458";"JJM259";"Mediální systémy a jejich komparace";"Miessler,J.";;"4";"4";"4";"5";"4";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kz" +"4459";"JMMZ306";"Deutschland und Zentraleuropa aktuell I";;"Göttmann,A.";"4";"4";NULL;NULL;NULL;"4";"4";"4";"2";"4";"4";"4";"4";;;"knrs" +"4460";"JMMZ324";"Sprachwerkstatt Deutsch. Schreiben, Lesen und Diskutieren fürs Studium I";;"Göttmann,A.";"4";"3";NULL;NULL;NULL;"4";"5";"4";"1";"4";"3";"4";"4";;;"knrs" +"4461";"JJM363";"Czech-German-Jewish Literary Triangle";;"Peroutková,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"3";"3";"1";"4";;;"kz" +"4462";"JJM324";"Teorie mediální komunikace";"Jirák,J.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kms" +"4463";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kms" +"4464";"JJM329";"Média a kultura";"Bednařík,P.,Jirák,J.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"3";"4";"4";"4";;;"kms" +"4465";"JJM330";"Trendy současných českých médií";"Aust,O.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;"1";"4";"3";"3";"4";;;"kms" +"4466";"JJM373";"Ethical Issues in Media, Business and Society";"Orhan,M.";;"4";"3";"4";"4";"3";NULL;NULL;NULL;"1";"3";"3";"3";"3";;;"kms" +"4467";"JJM246";"Historie a estetika fotožurnalismu";"Lábová,A.,Štefaniková,S.";;"2";"1";"5";"5";"1";NULL;NULL;NULL;"3";"1";"1";"2";"4";"Vyučující.";"Je velká škoda, že tento předmět s tak skvělým zaměřením nedosáhl ani špetky mého očekávání. Těšila jsem se na kurz, který rozšíří mé znalosti z historie fotožurnalismu, provede estetickými možnostmi reportážní fotky a zaujme odborným výkladem skvělé vyučující. Bohužel ale pouštění pořadů z archivu ČT tato slibná a dle mého názoru realizovatelná očekávání opravdu nesplnila. Jsem nadšená, že je na tomto oboru možné studovat fotografii a byla bych ráda, kdyby to tak i zůstalo. A myslím, že je v kompetenci stávajících vyučujících takový předmět vést zajímavě a hodnotně. Prozatím tomu tak bylo.";"kz" +"4468";"JJM245";"Úvod do vizuální komunikace";"Průchová,A.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Skvělá vyučující, která svým entusiasmem a odborností dovedla tento již sám o sobě zajímavý kurz k dokonalosti.";;"kz" +"4469";"JMM530";"European Comparative Politics and Society";"Rovná,L.";;"3";"3";"3";"2";"3";NULL;NULL;NULL;"1";"2";"3";"3";"2";;;"kzs" +"4470";"JMMZ331";"Qualitative methods in social sciences";"Weiss,T.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kzs" +"4471";"JMMZ332";"Culture and politics in Europe";"Tomalová,E.";;"4";"3";"4";"4";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kzs" +"4472";"JMMZ333";"Transnational history of contemporary Europe";"Matějka,O.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kzs" +"4473";"JLB100";"Czech as a Foreign Language I";;"Frantesová,E.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"4";"5";;;"cjp" +"4474";"JMMZ334";"Current Challenges in Europe";"Mejstřík,M.,Tomalová,E.";;"4";"3";"4";"4";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kzs" +"4475";"JSB534";"Introduction to Visual Sociology";"Wladyniak,L.";;"4";"2";"4";"5";"4";NULL;NULL;NULL;"1";"3";"4";"4";"3";;;"ks" +"4476";"JEM004";"Advanced Macroeconomics";"Baxa,J.,Hromádková,E.";"Baxa,J.,Hromádková,E.,Šestořád,T.,Troch,T.,Žáček,J.";"5";"4";"5";"5";"4";"2";"4";"5";"1";"4";"3";"4";"4";"- kvalita poskytnutych materialu- rychle odpovedi vyucujicich, ochota- konecne smysluplne vyuziti moodle";"- pedagogicke schopnosti a anglictina cvicicich- na seminarich obcas chybi jisty nadhled, zasazeni prikladu do souvislosti";"ies" +"4477";"JEM034";"Corporate Finance";"Bychkova,O.,Malířová,T.";;"2";"3";"3";"4";"1";NULL;NULL;NULL;"2";"1";"1";"1";"1";"- kvalita dostupnych materialu v sis";"- ucast na prednaskach i seminarich bezcenna - pouhe opakovani zakladu nebo dosazovani do vzorecku- latka neni koherentni, roztristena od bondu pres analyzu investic az po ocenovani akcii/opci, bez souvislosti- kurz jde pouze po povrchu spousty temat, malokdy jde do hloubky (a kdyz uz ano, chybi srozumitelny vyklad)- vyucovat v mgr. predmetu diskontovani cash-flow je zoufale, drtiva vetsina latky jsou zaklady vyucovane jiz na bc. predmetech- mnozstvi povinnosti a jejich terminy naprosto neodpovidaji kreditovemu ohodnoceni- hodnoceni a opravovani ukolu/midtermu/zaverecneho testu trva neprimerene dlouho k povinnostem a terminum pro studenty (cekaci doba az dva mesice)- nejasna hranice pro spolupraci u domacich ukolu- celkove nevidim smysl pro takovyto predmet na mgr. urovni, latka odpovida priblizne 2. rocniku bc., predmety jako Financial Markets nebo Financial Markets Instruments cast latky prekryvaji, avsak snazi se o hlubsi porozumeni daneho tematu a jsou podstatne obsahlejsi";"ies" +"4478";"JEM035";"Financial Markets Instruments I";"Dědek,O.,Poláková,N.,Polák,P.";"Dědek,O.,Poláková,N.,Polák,P.";"5";"3";"3";"5";"5";"4";"5";"4";"1";"5";"4";"5";"5";"- fundovanost prednasejiciho- dostupna skripta p. Dedka a materialy obecne";"- obcas srozumitelnejsi vyklad prednasejiciho";"ies" +"4479";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"2";"4";"2";"4";"4";;;"kmv" +"4480";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"3";"4";"3";"4";"2";NULL;NULL;NULL;"2";"3";"2";"3";"3";;;"kmv" +"4481";"JPM689";"Conflict Studies";"Karásek,T.";;"5";"3";"5";"4";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kbs" +"4482";"JEM037";"Financial Markets";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"4";"3";"4";"5";"4";"3";"4";"5";"1";"4";"3";"4";"4";"- zajimave reseni seminaru a souvisejicich povinnosti";;"ies" +"4483";"JPM724";"Critical Approaches to International Politics and Security";;"Daniel,J.,Rychnovská,D.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kmv" +"4484";"JPM561";"Regional Security Studies";"Karásek,T.,Klosek,K.";"Karásek,T.,Klosek,K.";"4";"3";"5";"5";"4";"5";"5";"4";"1";"4";"4";"4";"4";;;"kbs" +"4485";"JPM052";"Mezinárodní ekonomické vztahy (MEV)";"Kučerová,I.";;"4";NULL;"5";"5";"5";NULL;NULL;NULL;"1";"2";"3";"5";NULL;;"Přednášet pomaleji nebo poskytnout studentům přístup k prezentacím. Přednášky jsou zajimavé a dobře strukturované, ale človek by musel absolvovat kurz těsnopisu, aby to vše stihl zapsat.";"kmv" +"4486";"JPM716";"The Geopolitics of Defence Industry and Arms Trade";"Kopečný,T.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kp" +"4487";"JPM701";"European and Transatlantic Security";"Kazharski,A.";;"4";"3";"4";"5";"3";NULL;NULL;NULL;"2";"3";"3";"4";"4";;;"kbs" +"4488";"JPM430";"Marxism in International Relations (TIR)";;"Střítecký,V.";"3";"3";NULL;NULL;NULL;"5";"4";"3";"1";"2";"2";"4";"3";;;"kmv" +"4489";"JPM697";"Asia Security";"Kolmaš,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kbs" +"4490";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"3";"5";"4";"3";;;"kmv" +"4491";"JSB998";"Úvod do sociologie";"Soukup,P.";;"3";"1";"4";"5";"1";NULL;NULL;NULL;"1";"2";"2";"2";"4";"Krátký test s rychlým vyhodnocením.";"I když člověk dostane plný počet bodů z testu, tak stejně nemá šanci dostat za A, pokud nezíská body za wikiheslo.";"ks" +"4492";"NMMA701";"Matematika 1";"Spurný,J.";"Rondoš,J.";"4";"5";"5";"5";"5";"4";"5";"5";"1";"5";"5";"4";"5";"Obtížnost.";"Zvýšit kreditové ohodnocení.";"ies" +"4493";"JLB047";"Ruština obecná I";;"Mistrová,V.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"3";"5";"Snaha vyučující seznámit nás s aktuálními tématy srozumitelnou formou.";"Zajistit srovnatelnější vstupní úroveň studentů, aby to mělo pro všechny přítomné větší přínos. Dbala bych na výsledky vstupního testu.";"cjp" +"4494";"JLB011";"Němčina pro ekonomy nižší I";;"Faltýnová,R.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"5";"Zaujala mě přednáška o Deutsche Börse. Určitě by mohla proběhnout nějaká další!";"Více diskutovat o různých aktuálních tématech.";"cjp" +"4495";"JLB003";"Angličtina pro ekonomy I";;"Gloverová,M.";"4";"4";NULL;NULL;NULL;"5";"5";"3";"1";"4";"4";"4";"5";"Milý a ochotný přístup vyučující. Slovíčka se vztahují k látce probírané na kurzu Principles of economics.";"Neučit se definice slovíček.";"cjp" +"4496";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"3";"3";"2";"1";"4";"1";"2";"5";"Výběr aktuálních filmů.";"Snažit se vybírat různorodější žánry filmů.";"kz" +"4497";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"1";"5";"1";"1";"2";NULL;NULL;NULL;"1";"4";"1";"3";"1";"Pedagog se nechoval profesionálně, jeho vystupování bylo nadřazené a nepříjemné. Nedokázal dodržovat, to co řekl, nedodržoval termíny, které si sám stanovil, měl zmatky v pravidlech.";"změnit vyučujícího";"kms" +"4498";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"5";"4";"5";"5";"4";"3";"4";"2";"1";"4";"3";"4";"5";"Aplii, díky které je možnost si látku procvičit a naučit během semestru.";"Zvýšit efektivitu cvičení.";"ies" +"4499";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"4";"2";"3";"5";"5";NULL;NULL;NULL;"1";"5";"1";"3";"5";;;"kms" +"4500";"JJM373";"Ethical Issues in Media, Business and Society";"Orhan,M.";;"2";"3";"4";"5";"1";NULL;NULL;NULL;"1";"1";"2";"1";"2";;;"kms" +"4501";"JJM324";"Teorie mediální komunikace";"Jirák,J.";;"3";"4";"5";"3";"4";NULL;NULL;NULL;"1";"5";"1";"4";"4";;;"kms" +"4502";"JJM320";"Propaganda a československá média po roce 1948";"Suk,P.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";NULL;"1";"5";"5";;;"kms" +"4503";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"4";"2";"4";"4";"1";"4";"4";"4";"1";"3";"2";"3";"3";;;"ies" +"4504";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"4";"3";NULL;NULL;NULL;"3";"4";"4";"1";"3";"4";"4";"5";"Procvičování příkladů, aktivní počítání.";"Možná zmenšit počet dotazů \"co s tím\" a raději procvičit více příkladů.";"ies" +"4505";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"4";"1";NULL;NULL;NULL;"3";"4";"3";"1";"4";"2";"3";"5";"Probírání aktuálních a různorodých témat.";"Dát dopředu vědět, o čem se bude zhruba v kurzu diskutovat, aby byl člověk v obraze.";"ies" +"4506";"JEB136";"Topics in Industrial Organization";"Schwarz,J.";;"3";"1";"5";"3";"4";NULL;NULL;NULL;"2";"4";"2";"3";"3";"An opportunity to meet guests/ professionals from many areas of business.";;"ies" +"4507";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"3";"4";"4";"3";"2";"4";"5";"5";"1";"3";"3";"4";"3";"Zajímavé byly experimenty, které paní doc. PhDr. Julie Chytilová Ph.D. občas dělala. Oceňuji i příklady ze světa podložené studiema. Prospěšné byly i příklady na přednáškách.";"Poprosil bych paní doc. PhDr. Julie Chytilová Ph.D. aby nemazala tabuli hned co dopíše příklad. K mému úžasu to dělal i přestože měla několik dalších tabulí volných.";"ies" +"4508";"NMMA703";"Matematika 3";"Zelený,M.";"Johanis,M.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"Oceňuji snahu pana doc. RNDr. Miroslav Zelený Ph.D. ve vysvětlování složitějších konceptů. Občas na okamžik zabrousí do historie nebo poví nějký krátký příběh spojený s danou větou anebo člověkem. Přednášky jsou hodnotné, cvičení také. Navíc bych ocenil, že konečně mám pocit, že obtížnost příkladů z cvičení zhruba odpovídá zkouškovým.";;"ies" +"4509";"JMM530";"European Comparative Politics and Society";"Rovná,L.";;"4";"3";"3";"4";"3";NULL;NULL;NULL;"1";"4";"3";"4";"3";"It covered a lot of content";"It’s to general";"kzs" +"4510";"JMMZ333";"Transnational history of contemporary Europe";"Matějka,O.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"2";"4";"4";"4";"4";"Different approach towards history";"Fail to connect classes.";"kzs" +"4511";"JMMZ334";"Current Challenges in Europe";"Mejstřík,M.,Tomalová,E.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"4";"The experts from all the topics";;"kzs" +"4512";"JMMZ331";"Qualitative methods in social sciences";"Weiss,T.";;"4";"3";"5";"5";"3";NULL;NULL;NULL;"1";"4";"5";"5";"4";;;"kzs" +"4513";"JMMZ332";"Culture and politics in Europe";"Tomalová,E.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"4";"Experts in the topics";;"kzs" +"4514";"JMM530";"European Comparative Politics and Society";"Rovná,L.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Interactive discussion on the topics of comparative politics";;"kzs" +"4515";"JMMZ331";"Qualitative methods in social sciences";"Weiss,T.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kzs" +"4516";"JMMZ332";"Culture and politics in Europe";"Tomalová,E.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kzs" +"4517";"JMMZ333";"Transnational history of contemporary Europe";"Matějka,O.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kzs" +"4518";"JMMZ334";"Current Challenges in Europe";"Mejstřík,M.,Tomalová,E.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kzs" +"4519";"NMMA703";"Matematika 3";"Zelený,M.";"Zelený,M.";"5";"5";"5";"5";"4";"5";"5";"5";"1";"4";"1";"2";"3";;;"ies" +"4520";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"4";"5";"5";"4";"3";NULL;NULL;NULL;"1";"5";"3";"3";"4";;;"kms" +"4521";"JJM324";"Teorie mediální komunikace";"Jirák,J.";;"4";"5";"5";"3";"4";NULL;NULL;NULL;"1";"4";"4";"5";"4";;;"kms" +"4522";"JJM320";"Propaganda a československá média po roce 1948";"Suk,P.";;"5";"4";"5";"5";"3";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kms" +"4523";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kms" +"4524";"JJM373";"Ethical Issues in Media, Business and Society";"Orhan,M.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kms" +"4525";"JEM184";"New Keynesian DSGE Modeling";"Maršál,A.,Svačina,D.";"Maršál,A.,Svačina,D.";"4";"3";"4";"5";"5";"4";"5";"5";"1";"3";"4";"4";"4";"Systém domácích úkolů + final který je postavený právě na těchto úkolech.";"Poskytnout nějaké prezentace nebo materiály jiné než používané učebnice.";"ies" +"4526";"JJB019";"Práce s agenturními informacemi";"Prázová,I.,Trunečková,L.";"Prázová,I.,Trunečková,L.";"2";"4";"1";"2";"1";"1";"2";"1";"1";"2";"2";"2";"2";"Pana Troníčka z Rozhlasu.";"Vše. Jde o naprosto zastaralý předmět, který má navíc nesmyslně obtížné známkování. Skutečná zbytečnost.";"kz" +"4527";"JJB002";"Dějiny masových médií II";"Sekera,M.";;"1";"1";"1";"2";"1";NULL;NULL;NULL;"4";"1";"1";"1";"1";"Dvě poměrně zajímavé exkurze.";"Prakticky vše. Přednášející nechodil na přednášky, ty často odpadaly, anebo byly exkurze. Získat tak snadno kredity by mi nevadilo. V tomto případě však ano, a to zejména proto, že jde o státnicový předmět. Srovnání s nesmírně obtížnými a nespravedlivými Dějinami 1 a po všech stránkách vynikajícími Dějinami 3 je donebevolající.";"kms" +"4528";"JJB143";"Žurnalistika a feminismus";"Krobová,T.,Osvaldová,B.";;"3";"1";"4";"5";"3";NULL;NULL;NULL;"3";"4";"1";"3";"3";"Otevřeného a velmi liberálního přístupu vyučujícího.";"Především to, aby šlo skutečně o ŽURNALISTIKU a feminismus. O femu jsme se toho dozvěděli dostatek, o žurnalistice a jejímu vztahu k feminismu však nikoliv. Aby propříště nedošlo k matení studentstva, navrhuji změnu názvu předmětu na MARKETING a feminismus, popřípadě REKLAMA a feminismus.";"kz" +"4529";"JMB414";"Seminář k aktualitám I";;"Karasová,N.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"3";"5";"+ přístup kantorky, atmosféra umožňující věcnou debatu mezi studenty, obsah";;"krvs" +"4530";"JMB058";"Československá a česká zahraniční politika po r. 1989 I.";"Handl,V.,Kunštát,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"+ přístup kantora, obsah, práce ve skupinách, debaty s politiky z ministerstva, velvyslanectví atd.";;"knrs" +"4531";"JMB249";"Seminář k dějinám středo- a jihovýchodní Evropy";;"Lukešová,O.";"4";"4";NULL;NULL;NULL;"3";"4";"5";"2";"4";"3";"5";"4";"+ krátké testy na začátku hodiny místo povinné četby (dobrá příprava k závěrečné zkoušce z MD SJVE)";;"krvs" +"4532";"JMB250";"Seminář k dějinám západní Evropy";;"Simbartlová,A.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"1";"5";"4";"3";"5";"+ přístup kantorky, rozšíření obzoru o migraci v ZE";;"kzs" +"4533";"JMB408";"Moderní dějiny Ruska";"Litera,B.,Novák,P.,Pečenka,M.";;"4";"5";"4";"3";"5";NULL;NULL;NULL;"3";"5";"3";"5";"3";;;"krvs" +"4534";"JMB409";"Moderní dějiny středo- a jihovýchodní Evropy-dcera JMB013";"Balla,P.,Švec,L.";;"4";"5";"5";"5";"4";NULL;NULL;NULL;"3";"5";"3";"5";"3";;;"krvs" +"4535";"JMB410";"Moderní dějiny západní Evropy";"Matějka,O.,Váška,J.";;"5";"4";"4";"5";"4";NULL;NULL;NULL;"3";"5";"3";"5";"4";;;"kzs" +"4536";"JEM001";"Master´s Thesis Seminar I";;"Havránek,T.,Havránková,Z.,Mejstřík,M.";"5";"3";NULL;NULL;NULL;"4";"5";"2";"1";"3";"4";"3";"5";"Feedback. Jako jeden z mála předmětů si může být člověk jistý tím, že to opravdu někdo četl a feedback dává hlavu a patu.";;"ies" +"4537";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"5";"4";"5";"5";"5";"4";"4";"3";"1";"4";"4";"3";"5";"Přednášky. A cvika dělaná v markdownu/okomentovaném R kodu.";;"ies" +"4538";"JEM020";"Ethics and Economics";"Cahlík,T.";"Cahlík,T.";"4";"2";"3";"3";"2";"3";"3";"3";"1";"3";"2";"2";"3";"Strukturu přednášek + zajimavých čtení/videí.";"Přístup vyučujícího - aneb dostat 10/10 za úkol, který jsem neodevzdal celý mi přišlo dost pod úroveň IES.";"ies" +"4539";"JEM032";"Banking";"Mejstřík,M.,Pečená,M.,Teplý,P.";"Pečená,M.";"3";"2";"3";"4";"2";"3";"3";"3";"1";"2";"3";"3";"3";;"Změnit projekt. Spíš by se hodil do company valuation. Balance sheet, analyza spolecnosti etc.. samotny uverovy navrh tam mel jenom jednu malou podcast. A taky neposilat k hodnoceni projekty, ktere jsou vylozene opsane. Ja jsem nevedel, co k tomu napsat a nebylo tam v podstate nic k hodnoceni.";"ies" +"4540";"JLB102";"Czech as a Foreign Language III";;"Nováková,K.";"5";"2";NULL;NULL;NULL;"4";"4";"4";"1";"4";"4";"4";"4";;;"cjp" +"4541";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"4";"4";"3";"4";"4";NULL;NULL;NULL;"3";"4";"1";"3";"4";;;"kmv" +"4542";"JPM407";"Feminism in International Relations (TIR)";;"Plechanovová,B.";"4";"3";NULL;NULL;NULL;"3";"4";"4";"3";"4";"3";"4";"4";;;"kmv" +"4543";"JPM689";"Conflict Studies";"Karásek,T.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"2";"4";"4";;;"kbs" +"4544";"JPM718";"Critical Perspectives on Violence";;"Ditrych,O.";"4";"4";NULL;NULL;NULL;"4";"4";"4";"1";"4";"4";"4";"4";;;"kmv" +"4545";"JPM683";"Jiná jazyková znalost I";;;"4";"3";NULL;NULL;NULL;"3";"3";"3";"1";"3";"3";"3";"3";;;"kmv" +"4546";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"5";"3";"5";"5";"5";"5";"5";"5";"3";"5";"5";"5";"5";"knowledge of the tutors";"-";"ies" +"4547";"JLB003";"Angličtina pro ekonomy I";;"Poslušná,L.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"learning experience";"-";"cjp" +"4548";"JEM020";"Ethics and Economics";"Cahlík,T.";"Cahlík,T.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"4549";"NMMA711";"Mathematics 1";"Bárta,T.";"Bárta,T.,Vlasák,V.";"5";"4";"5";"5";"5";"3";"3";"5";"1";"5";"5";"5";"5";;;"ies" +"4550";"JEM003";"Advanced Microeconomics";"Janda,K.,Melikhova,O.";"Janda,K.,Melikhova,O.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"4";"4";"4";"4";;;"ies" +"4551";"JEM004";"Advanced Macroeconomics";"Baxa,J.,Hromádková,E.";"Baxa,J.,Hromádková,E.,Šestořád,T.,Troch,T.,Žáček,J.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"4";"4";"4";;;"ies" +"4552";"JEM162";"Energy Markets & Economics";"Elms,N.,Valíčková,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"4";;;"ies" +"4553";"JEM003";"Advanced Microeconomics";"Janda,K.,Melikhova,O.";"Janda,K.,Melikhova,O.";"5";"4";"5";"5";"4";"5";"5";"5";"1";"5";"5";"4";"5";;;"ies" +"4554";"JEM004";"Advanced Macroeconomics";"Baxa,J.,Hromádková,E.";"Baxa,J.,Hromádková,E.,Šestořád,T.,Troch,T.,Žáček,J.";"5";"5";"5";"5";"4";"5";"5";"5";"1";"5";"5";"5";"5";"Skvělá komunikace se studenty, vstřícný přístup, jeden z mála předmětů na ies umožňující dialog mezi studenty a vyučujícími.";;"ies" +"4555";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"5";"5";"5";"5";"5";"3";"3";"4";"1";"5";"5";"5";"5";"Opravdu velký přínos účasti na přednáškách, vše bylo výborně vysvětleno.";;"ies" +"4556";"JEM035";"Financial Markets Instruments I";"Dědek,O.,Poláková,N.,Polák,P.";"Dědek,O.,Poláková,N.,Polák,P.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"4557";"JEM040";"Účetní a daňové poradenství";;"Kemény,I.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"4";"5";;;"ies" +"4558";"JPM127";"Geopolitika a geostrategie 1815 - 1945";"Romancov,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"4";"5";;;"kp" +"4559";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"1";"1";"1";"1";"5";;;"kz" +"4560";"JJM080";"Zábava v televizním vysílání";"Kruml,M.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kms" +"4561";"JJM201";"Teorie masové a mediální komunikace";"Jirák,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kms" +"4562";"JJM202";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"3";"5";"3";"3";"5";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kms" +"4563";"JJM204";"Výzkum médií I";"Křeček,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"4564";"JJM226";"Teorie účinků médií";"Nečas,V.";;"5";"3";"4";"4";"3";NULL;NULL;NULL;"2";"5";"4";"5";"5";;;"kms" +"4565";"JJM343";"Interkulturní komunikace";"Soukup,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kms" +"4566";"JEB110";"Econometrics II";"Pertold-Gebicka,B.";"Chorna,O.,Malinská,B.,Pertold-Gebicka,B.,Pleticha,P.";"2";"4";"3";"3";"3";"3";"4";"3";"2";"4";"3";"3";"2";"1) Empirical project was very beneficial, as well as the homeworks which force students to work more consistently throughout the semester. Deadline for project in examination period was also good since students could chose when do they want to work on the project.2) Ms Malinska's seminars were interesting and her explanations were quite well, but the content could be more connected to content of lectures. Mr Pleticha explains the concepts quite well and has good attitude towards students and well prepared materials (mail with explanation of frequent errors, extensive seminar solutions). I also appreciate Ms Chorna for the good attitude towards students (it was very useful that we got notes to seminar solutions.Dr. Pertold-Gebicka is for sure a true expert in the field and knows a lot about it. Content of the course itself is very interesting, but the teaching needs change. Overall I learned a lot during the semester, but I believe that it is more because of my independent work than because of the lectures and seminars - attendance was not very beneficial and it was usually better to go through the materials alone.";"1) Course organizational side was not good. I was mostly frustrated by the course because of this. We had to wait for pre-term final exam results for around 2 weeks and knew the results only a few days before the first term in January (which could be problematic for some students who needed to retake the exam - not my case). When I compare this with Econometrics I, which had an exam of similar length, we got results one or two days after the test.Also, I waited for project results for almost 3 full weeks after the deadline. I understand that the project is quite extensive, but I believe that 2 weeks should be more than enough, given the facts that students chose to sumbit it at different dates, majority of students chose to do the project in pairs, and also some students chose to not work on the project at all.2) Course teaching needs to change. Dr. Pertold-Gebicka sometimes had trouble to answer questions (especially in the beginning of the course; I have to note that this issue got much better as we were getting towards the end of the course). Sometimes, we spent too much time on mathematical derivations (often because Dr. Pertold-Gebicka seemed not very well prepared for them), and due to this we didn't have a lot of time to go through some important topics (for example seasonality) and also to devote more time to understanding of important concepts.Also, I think that it does not make much sense to devote a full lecture to course organisation and empirical project - it would be more than enough to put the slides devoted to project into SIS.3) It would be good to have solution for Ms Malinska's part of the seminars. Ms Chorna is sometimes very nervous between the seminars and it is not necessary, because I believe that she understands the concepts well and isn't afraid to admit her rare mistakes - more confidence would make the seminars excellent.";"ies" +"4567";"JEB044";"Financial Accounting";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"4";"5";"5";"4";"5";NULL;NULL;NULL;"2";"4";"5";"4";"4";;;"ies" +"4568";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"4";"4";"5";"5";"4";"3";"3";"3";"2";"4";"4";"4";"4";;;"ies" +"4569";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"4";"3";"4";"3";"3";"4";"4";"3";"2";"4";"3";"4";"4";;;"ies" +"4570";"JEM181";"Data Science with R";"Herman,D.,Krištoufek,L.";"Herman,D.,Krištoufek,L.";"4";"4";"5";"5";"4";"5";"5";"4";"1";"5";"5";"4";"5";;;"ies" +"4571";"JMB065";"Úvod do mezinárodního a evropského práva";"Šlosarčík,I.";;"4";"3";"3";"4";"3";NULL;NULL;NULL;"3";"4";"1";"3";"4";;;"kzs" +"4572";"JMB207";"Hospodářské dějiny německy mluvících zemí";"Mlsna,P.";;"3";"4";"4";"2";"2";NULL;NULL;NULL;"4";"4";"2";"4";"3";;;"knrs" +"4573";"JMB218";"Německo a Rakousko po roce 1989";"Emler,D.,Kunštát,M.,Mlsna,P.,Nigrin,T.,Šafařík,P.";;"4";"4";"4";"5";"4";NULL;NULL;NULL;"2";"4";"2";"4";"4";;;"knrs" +"4574";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"4";"3";"5";"2";"4";NULL;NULL;NULL;"1";"5";"3";"5";"4";;;"kms" +"4575";"JJM320";"Propaganda a československá média po roce 1948";"Suk,P.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"2";"5";"4";;;"kms" +"4576";"JJM324";"Teorie mediální komunikace";"Jirák,J.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kms" +"4577";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kms" +"4578";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"5";"3";"5";"1";"4";"3";"2";"5";;;"kz" +"4579";"JJM188";"Kvalitativní výzkum mediálních obsahů";;"Vochocová,L.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kms" +"4580";"JJM080";"Zábava v televizním vysílání";"Kruml,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Přístup vyučujícího a pojetí hodin, kdy je výklad doplňován projekcí relevantních ukázek.";;"kms" +"4581";"JJM204";"Výzkum médií I";"Křeček,J.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"Přístup vyučujícího, forma společného řešení úkolů.";"Občas panoval trochu zmatek v tom, na jakých pravidlech jsme se v hodině domluvili.";"kms" +"4582";"JLB013";"Němčina odborná I";;"Křenková,D.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"2";"5";;;"cjp" +"4583";"JJM201";"Teorie masové a mediální komunikace";"Jirák,J.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Vřelý a lidský přístup vyučujícího.";"Okruhy k ústní zkoušce by se měly více prolínat s obsahem hodin. Některé okruhy jsou velmi komplexní a pro studenty může být obtížné se na zkoušku správně připravit, neboť neví, zda téma správně uchopili.";"kms" +"4584";"JMM273";"Diplomový seminář II";;"Bečka,J.";"5";"2";NULL;NULL;NULL;"4";"5";"3";NULL;"2";"2";"2";"4";;;"krvs" +"4585";"JMMZ319";"Government and Politics in Canada";"Fiřtová,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"3";"5";"5";;;"kas" +"4586";"JJM202";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"3";"5";"3";"3";"3";NULL;NULL;NULL;"1";"5";"3";"5";"3";"Přednášky byly zajímavé a oba přednášející vtipní. Oceňuji interaktivitu v podobě grafů, ilustrací či projekcí videí.";"Hodnocení testů je absurdně přísné. S ohledem na to, jak obsahově titěrné jsou některé otázky či kolik má člověk na vypracování testu času, se občas nelze ubránit dojmu, že plný počet bodů by pánové nedali ani sami sobě. Je víc než v pořádku být přísný, ale mělo by to být také nějak relevantní. Když student odpoví na všechny otázky správně, ale má pak jen něco málo přes polovinu bodů, je to dost frustrující a demotivující.";"kms" +"4587";"JMMZ315";"U.S. Foreign Policy";"Raška,F.";;"5";"4";"4";"5";"3";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kas" +"4588";"JMM277";"Historie a kultura";"Vykoukal,J.";"Kýrová,L.";"4";"4";"5";"5";"5";"5";"5";"5";"1";"4";"3";"2";"4";;;"krvs" +"4589";"JJM117";"Popular Culture";"Turnau,T.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"4";"Great attitude, structure, they way it's organised.";;"kms" +"4590";"JJM343";"Interkulturní komunikace";"Soukup,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Pravděpodobně ten nejlepší kurz, jaký jsem za čtyři roky na vysoké škole absolvoval. Vyučující dokáže velmi umně a zajímavě propojovat teorii s praxí, dokáže studenty vtáhnout a každá přednáška je strhujícím vystoupením, do kterého se mohou zapojit i samotní studenti formou různých pokusů či diskuzí. Kromě nových poznatků si odnáším i řadu vtipů, tipů na čtení a velkou inspiraci. Takhle nějak by podle mě mělo vypadat moderní vyučování 21. století.";"Bez připomínek.";"kms" +"4591";"JJB003";"Dějiny masových médií III";"Bednařík,P.,Končelík,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"3";"5";"5";;;"kms" +"4592";"JSB454";"Social Web: (Big) Data Mining";"Růžička,J.";;"3";"5";"4";"5";"3";NULL;NULL;NULL;"1";"3";"3";"3";"3";;;"ks" +"4593";"JSM032";"Applied Social Research";"Remr,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ks" +"4594";"JSM312";"Electoral, Market, Media and Social Research: Paul Lazarsfeld's Methodology";"Jeřábek,H.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"3";"3";;"This course overlaps with other cources.";"ks" +"4595";"JSM554";"Diplomový seminář";;"Remr,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"ks" +"4596";"JSM480";"Evaluation Research";;"Remr,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"ks" +"4597";"JPM689";"Conflict Studies";"Karásek,T.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kbs" +"4598";"JPM693";"Traditional and Critical Concepts in Security Studies";"Rychnovská,D.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"4";;;"kbs" +"4599";"JPM699";"Security and Technology";"Střítecký,V.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"5";"4";"4";;;"kbs" +"4600";"JPM701";"European and Transatlantic Security";"Kazharski,A.";;"4";"4";"3";"4";"3";NULL;NULL;NULL;"1";"3";"3";"4";"4";;;"kbs" +"4601";"JPM716";"The Geopolitics of Defence Industry and Arms Trade";"Kopečný,T.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"The course offers practical insights into the defence industry. I valued that the lecturer is directly from the industry and was willing to share many interesting things. One sees what is the industry actually about. The visit to the companies was a very good experience.";"The readings should be assigned in a way that everyone reads them before class so we can discuss them better.";"kp" +"4602";"JLB001";"Angličtina pro sociology I";;"Klírová,M.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Velmi oceňuji odborný slovník a tématiku probíranou na hodinách.";"Možná více skupinových úkolů v hodinách.";"cjp" +"4603";"JLB047";"Ruština obecná I";;"Mistrová,V.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Nejvíce oceňuji přístup pí. PhDr. Mistrové a především její individualitu ke každému studentu zvláště.";"Na kurzu nevidím žádná vylepšení.";"cjp" +"4604";"JLB099";"Rozřazovací test z angličtiny";;"Klírová,M.";"3";"5";NULL;NULL;NULL;"3";"3";"3";"1";"1";"1";"1";"3";"Jendá se pouze o rozřazovací test.";"Myslím si, že by bylo vhodně do budoucích let nastavit angličtinu jako povinnou pro všechny bez rozřazovacího testu, např. po všechny tři roky od \"jednoduché\" angličtiny (1. ročník), přes přípravný kurz (2. ročník), až k povinné angličtině pro sociology (3. ročník).";"cjp" +"4605";"JSB031";"Bakalářský seminář I - diskuse projektů";;"Spalová,B.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"3";"5";"5";"5";"Oceňuji debatu o projektech a bakalářských pracích, která nám pomůže v úpravách práce.";"Nic.";"ks" +"4606";"JSB066";"Historický seminař";"Křen,J.";;"3";"3";"3";"3";"3";NULL;NULL;NULL;"3";"3";"3";"3";"3";"Kurz se nekonal.";"Kurz se nekonal";"ks" +"4607";"JSB033";"Praktika z kvalitativního výzkumu";;"Marková Volejníčková,R.";"3";"2";NULL;NULL;NULL;"3";"4";"2";"1";"2";"3";"3";"3";"Oceňuji praxi v kvalitativním výzkumu.";"Myslím, že se vyučující zaměřovala více na teorii (ve velkém množství a málo vysvětlenou). Poté se nám špatně pracovalo a vše jsme museli sami dohledávat pro pochopení.";"ks" +"4608";"JSB131";"Velké empirické výzkumy ČR";"Tuček,M.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"2";"4";"5";"Oceňuji přínos z českého prostředí empirického výzkumu.";"Možná více zajímavých příkladů, které by mohly velmi zaujmout studenty.";"ks" +"4609";"JSB133";"Zemědělství a rozvoj venkova (vybraná témata z rurální sociologie)";"Zagata,L.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Oceňuji schopnosti vyučujícího. Kurz byl poučný a zábavný.";"Nic";"ks" +"4610";"JSB543";"Digitální etnografie";;"Hrešanová,E.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Kurz byl velice zábavný. Opravdu se zaměřil na praxi v oblasti netnografie, což jsem velmi ocenila. Dnes je tato věda velmi významná. Také oceňuji i teoretické znalosti, které nám byly dostatečně vysvětleny.";"Nic. Vše bylo perfektní.";"ks" +"4611";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"1";"3";"2";"4";"1";NULL;NULL;NULL;"1";"1";"2";"1";"1";;"Zmenit ho z povin. na volitelny :)";"kms" +"4612";"JJM373";"Ethical Issues in Media, Business and Society";"Orhan,M.";;"1";"1";"4";"5";"1";NULL;NULL;NULL;"1";"1";"1";"1";"1";"Its very easy to pass + the teacher is fun";"I cant imagine a situation when I will be needing this course in Media studies, its mostly about corporate ethics (which you will probably know anyway after you pass an onboarding in any corporate company)";"kms" +"4613";"JJM355";"Teoretické koncepty ke studiu žurnalistiky I";"Jirků,J.,Němcová Tejkalová,A.";"Jirků,J.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"kz" +"4614";"JJM251";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"5";"4";;;"kz" +"4615";"JJM356";"Metodologické přístupy ke studiu žurnalistiky";"Nečas,V.,Veselková,Z.";;"3";"2";"5";"5";"3";NULL;NULL;NULL;"1";"4";"4";"4";"3";;;"kz" +"4616";"JJM259";"Mediální systémy a jejich komparace";"Miessler,J.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kz" +"4617";"JJM360";"Ekonomika v médiích";"Klimeš,D.";;"5";"4";"4";"5";"4";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kz" +"4618";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"2";NULL;NULL;NULL;"4";"3";"4";"2";"5";"3";"2";"5";;;"kz" +"4619";"JJB224";"Psychologie marketingové komunikace";"Vranka,M.";;"5";"4";"5";"3";"4";NULL;NULL;NULL;"2";"4";"4";"4";"5";;;"kmkpr" +"4620";"JMM074";"Landmarks in 20th Century U.S. History and Their Interpretations";"Pondělíček,J.";;"2";"3";"3";"2";"2";NULL;NULL;NULL;"3";"2";"2";"3";"1";"It´s time schedule.";"It should state its goals more clearly. I do not think that historical interpretation of events should be a compulsory course - we are not historians. The assigned readings were long, sent to us late and usually not very interesting. I was uncomfortable with discussions A x B, it felt forced. Responsivness of the lecturer. Everything was late - from the assigned readings to grades. I do really not mind late, people are busy, but You should not promise something You can not uphold.";"kas" +"4621";"JLB033";"Němčina I";;"Faltýnová,R.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"5";"4";"4";"4";"5";;;"cjp" +"4622";"JMB068";"Komunistické vládnutí v Československu: prosazování, podoba a společenská reflexe (1945 až dodnes)";"Cuhra,J.";;"4";"2";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"4";"5";;;"krvs" +"4623";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Kouřílek,J.";"4";"4";NULL;NULL;NULL;"4";"5";"5";"1";"4";"4";"2";"3";"Semináře a propočítávání úkolů bylo důležité.";;"ks" +"4624";"JJJM191";"Media and the Children";"Zezulková,M.";;"5";"3";"4";"5";"5";NULL;NULL;NULL;"1";"3";"5";"4";"5";"the teacher and the experience with the children";"a more detailed guideline for the poster";"kms" +"4625";"JJM240";"Cultural studies";"Soukup,M.";;"4";"3";"3";"4";"5";NULL;NULL;NULL;"3";"3";"2";"4";"4";"various topics";"the language";"kms" +"4626";"JJM242";"Comics as a Medium";"Hrdina,M.";;"4";"2";"4";"4";"4";NULL;NULL;NULL;"3";"4";"4";"3";"4";"the creation of our own comics";"a more detailed guideline for the essay";"kms" +"4627";"JJM371";"New Media and Entrepreneurship";"Orhan,M.";;"3";"3";"5";"5";"3";NULL;NULL;NULL;"1";"2";"2";"3";"2";;;"kms" +"4628";"JMM348";"American Literature 1900-1950";"Hanuš,J.";;"4";"3";"4";"4";"5";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kas" +"4629";"JMM629";"Hollywood/Europe: A Transnational Film Culture.";"Nowell,R.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kas" +"4630";"JPM711";"Issues in Russian and Eurasian Security";"Aslan,E.";;"2";"4";"2";"4";"3";NULL;NULL;NULL;"1";"2";"2";"2";"2";;"It would be good if the course provides basic lectures together with slides on Russian and Eurasian security issues, especially for students like me who have no basic knowledge on the whole region.";"kbs" +"4631";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"1";"3";"2";"3";"2";NULL;NULL;NULL;"2";"2";"2";"2";"1";;"The course was changed to 8am instead of the agreed time slot during registration of classes, it would be good if the situation was managed better.";"kmv" +"4632";"JPM693";"Traditional and Critical Concepts in Security Studies";"Rychnovská,D.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"The professor puts in a lot of effort to make her classes interesting and makes sure that the students learn something from the course. While the weekly assignments are tough to follow through, they did help me to understand the readings better and come to class with a broad sense of what the professor was going to teach. I enjoyed her lessons a lot. I personally like the \"hands-on\" sessions because we got to stand up and move around, which really helped to keep my mind awake and hence not forget what I have learned from the classes.";"-nil-";"kbs" +"4633";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"5";"5";"5";"3";NULL;NULL;NULL;"1";"4";"4";"4";"4";"The professor was really helpful in assuring the students not to be afraid of the numbers. Throughout the course, he was always constantly making sure that we are keeping up with the syllabus and tried to make the difficult concepts easy to understand.";"-nil-";"kmv" +"4634";"JPM127";"Geopolitika a geostrategie 1815 - 1945";"Romancov,M.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"2";"4";"4";"4";"5";"pristup vyucujiciho";;"kp" +"4635";"JPM342";"Konflikty v demokracii a národní identita: teoretické problémy analýzy";"Říchová,B.";;"2";"3";"4";"2";"2";NULL;NULL;NULL;"2";"3";"3";"4";"2";;"vyklad obsahu kurzu";"kp" +"4636";"JJB600";"Úvod do studia médií I.";"Nečas,V.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"5";"5";;;"kms" +"4637";"JJB601";"Rozbor právního prostředí v českých médiích";"Benda,J.";;"4";"5";"5";"4";"5";NULL;NULL;NULL;"1";"5";"5";"5";"4";;;"kms" +"4638";"JJB605";"Novodobá historie médií";"Bednařík,P.,Končelík,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kms" +"4639";"JJB616";"Vybrané novinářské osobnosti 19. století";"Sekera,M.";;"5";"3";"5";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kms" +"4640";"JJB620";"Mediální produkce - žurnalistická práce I.";"Vlasák,Z.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"4641";"JLB035";"Francouzština I";;"Dundrová,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"3";"5";;;"cjp" +"4642";"JPM052";"Mezinárodní ekonomické vztahy (MEV)";"Kučerová,I.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmv" +"4643";"JPM260";"Vybrané problémy britské zahraniční politiky v 19. a 20. století, ES";"Soukup,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmv" +"4644";"JPM689";"Conflict Studies";"Karásek,T.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kbs" +"4645";"JPM719";"Diplomacy of the European Union";"Pajtinka,E.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;"3";"4";"4";"4";"4";;;"kmv" +"4646";"JPM725";"Technology and Security: Contemporary Warfare in the 21st Century";;"Csernatoni,R.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kmv" +"4647";"JJM208";"Mediální systémy a jejich komparace";"Miessler,J.";;"4";"4";"4";"5";"4";NULL;NULL;NULL;"2";"3";"5";"5";"3";"Vyučujícího přístup hodnotím velmi kladně - pečlivý, vždy vše včas, opravy skoro okamžitě atd. Také povinná četba má smysl (v rámci tohoto relativně úzce zaměřeného kurzu byla možná dejme tomu trochu monotónní, resp. dokázala bych si představit zajímavější čtení), každopádně např. v jiných kurzech by se také nadmíru hodila a není tam (resp. není vyžadována na každou hodinu, není testována - takže si ji přečte jen málokdo...) - přeci jen je četba jednotlivých autorů základ pro studium humanitních věd...";"prezentace pěti lidí omezená striktně na 15 minut byl podle mého vcelku nezvládnutelný úkol";"kms" +"4648";"JJB334";"Zábava v médiích";"Kruml,M.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"1";"3";"3";"3";"5";;;"kms" +"4649";"JJB606";"Televize jako instituce";"Štoll,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kms" +"4650";"JJB607";"Analýzy mediálních obsahů";"Křeček,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"4651";"JJB617";"Vybrané novinářské osobnosti 20. století";"Železný,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"kms" +"4652";"JJB618";"Propagandistické působení médií v 1 polovině 20. století";"Suk,P.";;"5";"4";"4";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kms" +"4653";"JPM324";"Geography and Politics in Europe within Global Regionalism";"Doboš,B.,Riegl,M.";;"4";"4";"4";"5";"5";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kp" +"4654";"JMM530";"European Comparative Politics and Society";"Rovná,L.";;"3";"2";"2";"5";"1";NULL;NULL;NULL;"1";"2";"1";"2";"3";;;"kzs" +"4655";"JEM179";"History and Methodology of Economics";"Baxa,J.,Paulus,M.";;"4";"5";"4";"5";"4";NULL;NULL;NULL;"1";"5";"4";"5";"3";;;"ies" +"4656";"JEM163";"Principles of Microeconomics";"Janský,P.";"Král,M.,Moravcová,H.,Palanský,M.";"4";"4";"2";"3";"1";"2";"4";"2";"1";"3";"2";"2";"2";;;"ies" +"4657";"JMM034";"Obrazy a stereotypy Balkánu";"Šístek,F.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"2";"5";"5";;;"krvs" +"4658";"JMM091";"Koncepce a interpretace balkánských dějin";"Šístek,F.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"3";"5";"2";"5";"5";;"Jeden semestr je málo.";"krvs" +"4659";"JMM121";"Central European Cinema";;"Duta,M.";"3";"2";NULL;NULL;NULL;"3";"5";"4";"1";"3";"1";"2";"3";;;"krvs" +"4660";"JMM271";"Metodologický seminář";;"Šír,J.";"4";"3";NULL;NULL;NULL;"3";"4";"5";"1";"5";"4";"3";"5";;"Pomoc s rozpočtem v grantové žádosti by se hodila.";"krvs" +"4661";"JMM277";"Historie a kultura";"Vykoukal,J.";"Vykoukal,J.";"4";"3";"4";"5";"4";"4";"5";"5";"1";"5";"2";"5";"5";"Přehled, přesah do současnosti.";"Zdůraznit, co máme umět. Co je nejdůležitější vědět, co si zapamatovat nazpaměť, a co se naopak snažit pochopit.";"krvs" +"4662";"JMM278";"Politika a mezinárodní vztahy";"Kubát,M.,Weiss,T.";"Kocián,J.";"3";"4";"4";"4";"2";"5";"5";"5";"2";"2";"1";"2";"3";;"Kurz opakuje hodně z Úvodu do politiky. To by nemusel. Seminář byl užitečnější než přednášky. Témata přednášek jsou v pořádku, ale myslím, že by mohli jít vyučující ještě hlouběji.";"krvs" +"4663";"JMM279";"Společnost a ekonomika";"Fiřtová,M.";"Svoboda,K.";"3";"4";"5";"5";"5";"4";"5";"4";"2";"3";"1";"4";"2";;"Zdůraznit, co je potřeba pochopit, a naopak co je potřeba se naučit nazpaměť (např. definice). Přednáška o Hayeku a Keynesovi byla podle mě trochu moc komplikovaná.";"kas" +"4664";"JJB018";"Úvod do fotožurnalistiky";"Lábová,A.";;"3";"1";"3";"4";"2";NULL;NULL;NULL;"2";"2";"2";"4";"3";;;"kz" +"4665";"JEB044";"Financial Accounting";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Skvělé přednášky, rozhodně se vyplatí chodit. Samotné prezentace jsou opravdu kvalitní a samotné stačí jako materiál pro studium.";;"ies" +"4666";"JJM356";"Metodologické přístupy ke studiu žurnalistiky";"Nečas,V.,Veselková,Z.";;"2";"3";"4";"3";"3";NULL;NULL;NULL;"2";"3";"4";"3";"3";;;"kz" +"4667";"JJM259";"Mediální systémy a jejich komparace";"Miessler,J.";;"3";"4";"4";"4";"4";NULL;NULL;NULL;"1";"3";"3";"3";"3";;;"kz" +"4668";"JJM264";"Diplomový seminář II.";;;"2";"2";"4";"4";"3";NULL;NULL;NULL;"3";"2";"2";"2";"2";;;"kz" +"4669";"JJM260";"Novinářská etika v praxi";"Moravec,V.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";;;"kz" +"4670";"JJM360";"Ekonomika v médiích";"Klimeš,D.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"3";"3";"3";"4";;;"kz" +"4671";"JJM290";"Tvůrčí dílny I – rozhlas a televize";;"Lokšík,M.";"4";"4";NULL;NULL;NULL;"4";"4";"4";"1";"4";"4";"4";"4";;;"kz" +"4672";"JJM294";"Teorie a praxe rozhlasové a televizní moderace";"Moravec,V.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kz" +"4673";"JJM354";"Dějiny populární hudby";"Halada,A.";;"3";"3";"4";"5";"3";NULL;NULL;NULL;"3";"3";"3";"3";"3";;;"kz" +"4674";"JLB037";"Italština I";;"Přívozníková,P.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Nejvíce oceňuji přístup paní Přívozníkové. Je velmi milá a ochotná pomoci i ve svém volném čase. Mockrát děkuji za super kurz, který mě hodně naučil.";;"cjp" +"4675";"JSB004";"Sociální nerovnosti";"Šanderová,J.";;"5";"4";"5";"5";"3";NULL;NULL;NULL;"2";"5";"3";"5";"5";"Paní Šanderovou a její výklad.";;"ks" +"4676";"JSB031";"Bakalářský seminář I - diskuse projektů";;"Balon,J.";"4";"3";NULL;NULL;NULL;"5";"5";"3";"1";"3";"5";"3";"5";;;"ks" +"4677";"JEB022";"Institutional Economics";"Kopečná,V.,Schwarz,J.";;"3";"4";"1";"1";"1";NULL;NULL;NULL;"2";"1";"1";"1";"1";;;"ies" +"4678";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"3";"3";"3";"3";"3";"3";"3";"3";"3";"3";"3";"3";"3";;;"ies" +"4679";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"3";"5";"4";"5";"5";"Paní doktorka je velmi milá, její výklad je poutavý a zajímavý. Také se mi líbily dobové texty, které jsme v průběhu semestru četli, stejně tak dobové plakáty a obrázky ukazované na přednáškách. Pro mě nejlepší kurz v tomto semestru.";"Výuka formou přednáškových bloků mi vyhovovala, bylo by však lepší ji přesunout na dřívější dobu, ne v 17 hodin večer.";"kp" +"4680";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"3";"3";"3";"3";"3";"3";"3";"3";"3";"3";"3";"3";"3";;;"ies" +"4681";"JEB120";"Financial Economics";"Žigraiová,D.";;"1";"3";"1";"3";"1";NULL;NULL;NULL;"3";"1";"1";"1";"1";;;"ies" +"4682";"JEM032";"Banking";"Mejstřík,M.,Pečená,M.,Teplý,P.";"Pečená,M.";"5";"3";"4";"5";"3";"5";"5";"3";"1";"5";"3";"5";"5";;;"ies" +"4683";"JEM137";"Real Estate Investment";"Jandík,T.,Streblov,P.";;"5";"4";"4";"5";"5";NULL;NULL;NULL;"1";"4";"5";"5";"5";;;"ies" +"4684";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"3";"2";"3";"4";"1";NULL;NULL;NULL;"3";"2";"1";"2";"3";"To, že se vyučující snaží vyjít studentům vstříc s opakovací hodinou, předtermínem před Vánoci nebo posunutím přednášky do větší učebny.";"Opravování zkoušek tak, aby byly známé výsledky ještě před následujícím termínem!!";"kmv" +"4685";"JEM037";"Financial Markets";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"5";"2";"5";"5";"4";"4";"5";"5";"1";"5";"3";"5";"5";;;"ies" +"4686";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"5";"3";"5";"Interakce na moodle, videoinstrukce, jak co dělat, to bylo super.";"Nepsat testy online, člověku stačí ctrl+f a pokud není úplný idiot, tak projde celým kurzem.";"kmv" +"4687";"JPM689";"Conflict Studies";"Karásek,T.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"2";"1";"2";"4";"Kurz byl veden zodpovědně a pokrýval zajímavá témata, která by se vešla do několika zváštních kurzů, takže možná zvážit jeho rozšíření na dva semestry.";"Kurz pokrýval témata, která by se vešla do několika zváštních kurzů, takže možná zvážit jeho rozšíření na dva semestry. Přijde mi, že by se hodilo přidat seminární práci, zkouška na 2 otevřené otázky je dost málo... A opravovat rychleji!!";"kbs" +"4688";"JMM128";"Prezentace v médiích";"Procházková,B.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"3";"5";"2";"5";"Tahle novinka ve volitelných předmětech byla určitě správnou volbou. Je to asi nejvíce praktický kurz ze všech dosud absolvovaných na bakaláři i magistru. Velkým přínosem je vyučující Bára, která aktivně řadu let působí v médiích, což je velká přidaná hodnota kurzu. Ač je to člověku někdy nepříjemné, překonává sám sebe při nácviku nejrůznějších projevů a prezentací v různých typech médií (televize, rádio, konference...), které probíhají za velmi reálných podmínek (odpovídající oblečení, nahrávání na kameru, audio záznam...). Zpětně se vystoupení analyzuje, hledají se chyby, ale také přednosti. Opravdu zajímavá zkušenost a poznatky z kurzu, které se neztratí ani v profesním ani v osobním životě.";"Jedině snad, aby nezačínal v 8h ráno :-)";"kzs" +"4689";"JSM502";"Diplomový seminář I";;"Dobiášová,K.,Kotrusová,M.";"3";"2";NULL;NULL;NULL;"3";"4";"2";"1";"2";"3";"3";"3";;"Bakalářský seminář mi přišel lepší... Vždy se vysvětlil jeden okruh (metodologie, teoretická část, výzkumné cíle) a následoval úkol. Diplomový seminář byl trochu o ničem...byl hodně obecný a chybělo mi právě rozdělení na okruhy, které by se postupně zpracovávaly. Člověk byl spíš domotivovaný";"kvsp" +"4690";"JSM507";"Metody tvorby politik";"Veselý,A.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Tenhle kurz byl skvělý, hrozně mě bavil. Jednoznačně největší plus je přístup vyučujícího, který je dle mého názoru naprosto bezkonkurenční. Je velmi lidský, ale zároveň velký vzor a autorita. Mám pocit, že v tomhle kurzu se toho člověk naučí asi nejvíc za celé studium. Moc se mi líbí styl výuky - nejdříve teorie, která je ovšem podána tak, aby jí člověk pochopil. Následuje praktická část a práce na jednotlivých projektech. Zpracování závěrečné práce je poměrně náročné, ale přínosné.";"Rozsah výuky 3h v kuse je vcelku náročný na soustředění, navíc když ihned navazuje další povinný předmět. Dala bych více prostoru brainstormingu a diskusi nad jednotlivými projekty již na začátku kurzu, aby jednotlivé skupinky mohly pracovat s návrhy ostatních studentů.";"kvsp" +"4691";"JSM612";"Kriminalita a současná česká společnost";"Cejp,M.";;"4";"1";"4";"5";"3";NULL;NULL;NULL;"1";"3";"3";"4";"4";"Zajímavý výklad";;"kvsp" +"4692";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"4";"5";"5";"2";"2";"2";"2";"5";"Je to skvělý relax";;"kz" +"4693";"JJB620";"Mediální produkce - žurnalistická práce I.";"Vlasák,Z.";;"5";"2";"4";"4";"5";NULL;NULL;NULL;"1";"4";"5";"4";"5";"Velice jsem ocenila domácí úkoly - vypracovávání nejrůznějších žurnalistických žánrů, skvělá byla také zpětná reakce od pana přednášejícího na domácí úkoly.";"Více teoretických poznatků na přednáškách.";"kms" +"4694";"JJB616";"Vybrané novinářské osobnosti 19. století";"Sekera,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"2";"4";"5";"Zajímavé a poutavé přednášky.";"Kurz se mi líbil, nenapadá mě co zlepšit.";"kms" +"4695";"JJB605";"Novodobá historie médií";"Bednařík,P.,Končelík,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Naprosto skvělé přednášky, neuvěřitelně zajímavé a poutavé. Duo pánů vyučujících je naprosto skvělé.";"Kurz je naprosto skvělý, není co zlepšovat.";"kms" +"4696";"JJB601";"Rozbor právního prostředí v českých médiích";"Benda,J.";;"4";"4";"5";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";"Ocenila jsem prezentace přednášejícího a domácí úkoly, které procvičily znalosti z přednášky.";"Přednášky by mohly být zajímavěji podané.";"kms" +"4697";"JJB600";"Úvod do studia médií I.";"Nečas,V.";;"4";"5";"4";"4";"4";NULL;NULL;NULL;"2";"4";"4";"4";"4";"Průběžné testy, které ověřovaly znalosti ze zadané literatury.";"Více zapojit studenty do přednášené materie.";"kms" +"4698";"JJB170";"Počítačové zpracování foto a graf. design";"Slanec,J.";;"4";"1";"4";"4";"2";NULL;NULL;NULL;"1";"2";"3";"2";"5";"Pro mě odpočinkový kurz, na kterém jsem si mohla procvičit grafické schopnosti, které již mám a realizovat vlastní projekt, který mě bavil. Výuka porbíhá formou konzultací, tudíž jsem se přímo z hodin asi nenaučila nic, ale zdokonalila jsem se v používání programu a efektivitě práce.";"Pro studenty, kteří nemají s grafikou zkušenosti musel být asi kurz matoucí, proto by bylo možná vhodné mu dát nějakou struktura a alespoň nastínit, co nezkušení studenti mohou vytvářet a co se mohou naučit, aby se na to poté mohli zaměřit.";"kz" +"4699";"JJB224";"Psychologie marketingové komunikace";"Vranka,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"Velmi se mi líbí systém bleskových testů, které na jednu stranu sice mohou být nepříjmné a stresující, ale je to systém, který nás myslím opravdu donutí se na každou hodinu připravovat, přečíst si texty a zároveň i zaručí dobrou docházku. Také se mi líbí možnost se dobrovolně účastnit praktických psychologických výzkumů a za aktivitu navíc tak získat body.";"Během učení na závěrečný test jsem se učila spíše teoretické věci a definici různých pojmů a poté jsem měla problém je v testu aplikovat při otázkách na využití v marketingu. Možná je to pouze chyba na mé straně, ale uvítala bych více marketingových příkladů (mnoho jich samozřejmě bylo, ale u některých efektů jsem nevěděla jaký příklad v testu uvést).";"kmkpr" +"4700";"JJB231";"Dějiny a teorie public relations";"Halada,J.,Hejlová,D.";;"3";"4";"2";"2";"1";NULL;NULL;NULL;"2";"3";"3";"4";"2";"Některé hodiny byly zábavné, ale bohužel ne příliš informačně přínosné. Líbilo se mi psaní dvou seminárních prací, protože si myslím, že témata byla vhodně zvolená.";"Minusem prací bylo pozdní hodnocení a zahrnutí do výsledné známky, i když tomu tak původně být nemělo. Zároveň si člověk příliš neodnesl z přednášek, vše jsem si musela až sama nastudovat před zkouškou, od které jsme nevěděli co očekávat, a tudíž co vše nastudovat. Zároveň mi zkouška přišla hodně o štestí na otázku, jelikož byly nevyvážené (někdy jen vysvětlení pojmu, jindy vysvětlení problému a nutnost znát i mnoho jmen pro úplné zodpovězení) a navíc mnohdy až příliš odborně položené.";"kmkpr" +"4701";"JJB237";"Právo a etika v marketingu";"Winter,F.";;"4";"2";"5";"5";"2";NULL;NULL;NULL;"1";"3";"3";"4";"5";"Velmi se mi líbily příklady uváděné na hodinách a celkově styl přednášek pana Wintera.";"Mrzí mě trochu nejednoznačné hodnocení, kdy známky jsou pravděpodobně rozdávány náhodou a není možné se na svůj test přijít podívat a zjistit co bylo špatně a proč jsem tudíž dostala horší známku. (mrzí mě to zejména z pohledu člověka, který se snaží o dobrý studijní průměr a i když ne o moc, tak o stupeň horší známka jej posune)";"kmkpr" +"4702";"JJB238";"Kritika marketingové komunikace";"Rosenfeldová,J.";;"5";"4";"4";"5";"5";NULL;NULL;NULL;"3";"5";"5";"5";"5";"Rozhodně můj neoblíbenější kurz tento semestr. Velmi zajímavá témata a i zajímavé podání na přednáškách. Seminární práce s širokým tématem, která mě bavila psát a i příjemná zkouška.";"Aby tolik neodpadával a aby Mgr. Krobová měla více přednášek - ty byly velmi dobré.";"kmkpr" +"4703";"JJB243";"Aktuální trendy a vývoj v oboru I.";"Hejlová,D.,Vranka,M.";"Hejlová,D.,Vranka,M.";"4";"1";"4";"4";"5";"5";"5";"5";"2";"4";"4";"4";"5";"Pestrost zvolených hostů a příprava otázky předem.";;"kmkpr" +"4704";"JJB249";"Úvod do studia českého jazyka I";"Schneiderová,S.";"Schneiderová,S.";"2";"5";"2";"3";"2";"3";"3";"3";"1";"3";"2";"2";"2";"Aktuality a studentské prezentace. Poskytování materiálů i s řešením.";"Nepochopila jsem látku, ale na druhou stranu nevím, jak více by měla být vysvětlena. Bylo uvedeno několik příkladů, ale prinipy příliš ne, zejména zpětně je nebylo možné z materiálů odvodit. Stále nechápu jak se na tento předmět učit. Test mi přišel velmi obtížný a místy i poměrně zákeřný...o tom ale myslím svědčí i celkové výsledky, kdy většina lidí má špatné známky.";"kmkpr" +"4705";"JMB497";"Metodický úvod pro kombinované studium";"Kubát,M.";;"5";"4";"5";"4";"5";NULL;NULL;NULL;"5";"4";"4";"4";"4";"Nelze co vytknout. Jasně vysvětlené požadavky vyučujícího a problematika. Strukturovaně předané informace během přednášky.";"-";"krvs" +"4706";"JMB516";"Kultura a umění ve 20. století";"Pelánová,A.";;"3";"3";"4";"2";"1";NULL;NULL;NULL;"1";"3";"1";"3";"2";"-";"Více praktický přístup - tj. ne teorie ale vice \"donuti\" student, aby navštívili místa, galerie, výstavy a podali z nich např. zprávuInformace - nedozvěděl jsem se kdy bude přednáška";"knrs" +"4707";"JLB005";"Angličtina pro politology I";;"Panešová,K.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"4708";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"4709";"JPB018";"Dějiny mezinárodních vztahů I";"Soukup,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmv" +"4710";"JMB499";"Současné metodologie";"Kubát,M.";;"4";"4";"5";"4";"5";NULL;NULL;NULL;"5";"4";"4";"4";"5";"-";"-";"krvs" +"4711";"JPB020";"Úvod do mezinárodních vztahů";"Karlas,J.,Kučera,T.";;"5";"3";"4";"5";"4";NULL;NULL;NULL;"1";"5";"5";"4";"5";;;"kmv" +"4712";"JMB523";"Mezinárodní aktuality I";"Fojtek,V.";;"4";"2";"3";"4";"5";NULL;NULL;NULL;"1";"3";"3";"4";"5";"-";"-";"kas" +"4713";"JPB221";"Metodologický proseminář I";;"Mlejnek,J.,Valková,I.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kmv" +"4714";"JSM502";"Diplomový seminář I";;"Dobiášová,K.,Kotrusová,M.";"5";"2";NULL;NULL;NULL;"4";"5";"4";"1";"1";"2";"3";"4";"Velmi oceňuji vstřícný přístup vedoucích semináře.";;"kvsp" +"4715";"JJM247";"Český stranický systém";"Just,P.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"2";"4";"3";"4";"4";;;"kz" +"4716";"JJM249";"Duchovní kořeny evropské kultury";"Halada,J.";;"4";"2";"5";"5";"2";NULL;NULL;NULL;"1";"2";"2";"3";"3";;;"kz" +"4717";"JSM507";"Metody tvorby politik";"Veselý,A.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Přístup přednášejícího - ochota naslouchat a bavit se se studenty, přizpůsobovat hodiny apod. Dobrá nálada!";;"kvsp" +"4718";"JJM260";"Novinářská etika v praxi";"Moravec,V.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"navrhuji zachovat především pana doktora Moravce, který je jedním z mála pedagogů na našem institutu, kteří se snaží ve studentech vzbudit nějaké nadšení ke studiu, samostatný zájem a pochopení pro úlohu novináře v současné společnosti";;"kz" +"4719";"JSM514";"Metody a techniky práce s informacemi";"Tomandlová,V.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"3";"4";"Seznam databází (hlavně těch českých - jak knihy tak časopisy, před kurzem jsem používala pouze zahraniční) a zaměření se na citační formy.";;"kvsp" +"4720";"JJM251";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"3";"4";"3";"4";"2";NULL;NULL;NULL;"1";"2";"1";"1";"2";;;"kz" +"4721";"JJM355";"Teoretické koncepty ke studiu žurnalistiky I";"Jirků,J.,Němcová Tejkalová,A.";"Jirků,J.";"4";"4";"3";"3";"3";"4";"5";"4";"1";"2";"2";"2";"2";;"tento předmět nemá pro studenty, kteří nehodlají skončit na akademické půdě jako teoretici médií, žádný smysl. přesah do žurnalistické praxe je nulový.";"kz" +"4722";"JEB009";"Makroekonomie I";"Hlaváček,M.,Kolcunová,D.";"Hlaváček,M.,Kolcunová,D.";"4";"4";"4";"4";"2";"4";"4";"4";"2";"3";"1";"3";"5";"Aktivita na seminářích, jenom dva domácí úkoly. To bylo super. Sice zase přematematizovaný jako vždycky a ten závěrečnej test byla těžká tipovačka, protože se na to nedalo připravit ani ze cvik, učebnice nebo přednášek, ale aspoň tady není to kritérium 50% z finálního testu. Kdyby bylo, tak by se ten předmět asi nedal udělat.";"Jednodušší final? Dávat body i za postupy a nejenom za výsledky.";"ies" +"4723";"JPB268";"Evropská integrace";"Plechanovová,B.";;"2";"4";"2";"1";"1";NULL;NULL;NULL;"3";"3";"1";"2";"1";;;"kmv" +"4724";"JPB260";"Politické myšlení novověku III";"Kučera,J.";;"5";"3";"5";"4";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kp" +"4725";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"3";"4";"3";"4";"4";"4";"4";"4";"1";"3";"2";"3";"3";;;"kp" +"4726";"JEB105";"Statistics";"Červinka,M.";"Hanus,L.";"4";"5";"4";"5";"5";"4";"4";"4";"1";"5";"4";"4";"4";"ústní zkouška byla fajn. Kdybyste to ale neučil Vy, tak by to bylo peklo. Midterm byl taky super.";"Úkoly se hodnotili mírněji než minulý rok, stejně je to ale strašný stres, když člověk může dostat 0 nebo 4. V závěrečném testu bych ocenil, aby první cvičení, kdy má člověk napsat dvě definice, obsahovalo jenom definice, které máme v prezentaci označeny jako definice.";"ies" +"4727";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"4";"3";"3";"3";"3";NULL;NULL;NULL;"1";"3";"3";"3";"3";;;"kp" +"4728";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"4";"4";"3";"3";"3";NULL;NULL;NULL;"1";"3";"4";"2";"3";;;"kp" +"4729";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"1";"4";"3";"1";"1";"4";"4";"3";"2";"3";"2";"2";"1";"Prakticky nic. Byla to hrůza. Aspoň byly tenhle rok srozumitelný úkoly a midterm taky šel.";"Úplně všechno. Člověk celý semestr maká a pak mu o bod unikne jedna část finalu kvůli tomu, že místo počítání člověk upravuje nechutný zlomek pod odmocninou. Jasně, dneska bych to už zaokrouhlil, ale z matiky jsme všichni nacvičení upravovat do úplnýho upravení, takže to fakt žralo spoustu času. První termín nedalo víc jak 50% lidí. Osobně podle sisu tipuju tak něco k 60%. To už není chyba studentů, ale zadání. Smutný je, že termíny byly jenom 4, takže pro hodně lidí to byl fakt velkej problém jít na další, protože světe div se, všechny termíny až na jeden se PŘEKRÝVALY se statistikou nebo matikou. Tohle je naprosto tristní, že si garant předmětu, který je zároveň garantem studia nedovede pohlídat, aby se termíny nekryly. Zejména v případě, kdy se udělá takhle těžkej předtermín. Byl to naprostý plivanec do tváře a výsměch veškerý práci během semestru. Ještě víc mě rozpálilo, že těsně před předtermínem přišel e-mail, který měnil způsob klasifikace. Najednou nám bylo řečeno, že na výsledku midtermu nezáleží, pokud jsme ho napsali špatně. Tento samotný krok považuju za šťastný, ale je to naprostý výsměch studentům, když si nás pak několik bylo stěžovat a na otázku zda lze změnit požadavek 50% z obou částí nebo vypsat další termíny nám bylo řečeno, že to nejde, protože se to stanovilo na začátku. Způsob klasifikace ale změnit šlo. Zaslechl jsem zprávy, že ke změně klasifikace došlo na základě nějaké stížnosti na midterm. V zásadě lze tedy měnit vše, ale musí se chtít. Přesně tento přístup, kdy bylo odmítnuto vypsat další termíny, ale se změnou klasifikace nebyl problém, mi přišel jako ukázka naprosté AROGANCE MOCI a OPOVRHOVÁNÍ STUDENTY. Teď něco k teorii finalu. Obsah přednášek ani učebnice naprosto nedostačoval k přípravě na teoretickou část finalu, zejména na otevřené otázky, které byly o level náročnosti výše než je učebnice a během přednášek se nic takového neprobíralo. Kurz byl prostě hrůza a jestli mi něco znechutilo IES, tak tento kurz se vším všudy.";"ies" +"4730";"JSM502";"Diplomový seminář I";;"Dobiášová,K.,Kotrusová,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";NULL;NULL;"diplomový seminář jako povinný předmět jsem velice ocenil. Za celý semestr se postupně probralo to, co je v diplomové práci povinnou a důležitou součástí. Díky diplomovému semináři jsem na diplomové práci začal postupně pracovat a díky zpětné vazbě od vyučujících a také kolegů z kurzu, jsem měl možnost ověřit si, jestli postupuji správně. Vyučující považuji za odborníky v dané oblasti a jejich vedení během kurzu mi hodně pomohlo se v problematice zorientovat.";;"kvsp" +"4731";"JJB226";"Strategie a finanční management marketingu I";"Majerik,P.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kmkpr" +"4732";"JJB276";"Public relations v praxi";;"Hejlová,D.";"3";"3";NULL;NULL;NULL;"4";"4";"2";"1";"2";"3";"2";"2";;"hodnocení úkolů během přednášek zpravidla zabralo půlku výuky a nemělo téměř žádný přínos, jelikož na vypracování podrobnější strategie by bylo potřeba více času; zadání se u jednotlivých týmů opakovalo, takže se u prezentace nudili jak studenti, tak samotní přednášející; úkoly navíc přestaly být v půlce semestru zadávány, což přineslo ještě větší zmatek a nikdo nevěděl, jaké jsou podmínky pro splnění předmětu; do této chvíle (téměř měsíc po odevzdání závěrečného úkolu) studenti neobdrželi výsledné hodnocení/známku za tento kurz–> navrhuji předem nastavit podmínky pro splnění kurzu a neměnit je v průběhu semestru; upravit způsob zadávání a hodnocení průběžných úkolů; informovat mezi sebou jednotlivé přednášející, aby se sdělované informace neopakovaly";"kmkpr" +"4733";"JPB268";"Evropská integrace";"Plechanovová,B.";;"1";"5";"1";"1";"1";NULL;NULL;NULL;"4";"2";"1";"2";"1";"Upřímně mě nenapadá vůbec nic.";"Vyučujícího, přístup k přednáškám, forma závěrečného testu...";"kmv" +"4734";"JPM717";"Continental Philosophy and IR";;"Ditrych,O.";"4";"2";NULL;NULL;NULL;"5";"5";"5";"1";"3";"4";"4";"3";"- Having the students prepare a summary and a critique of the readings for each lesson- Dr. Ditrych's teaching skills";"- I did not find the texts interesting - I felt like the authors discussed topics with little added value, just for the sake of being \"philosophers\". I did not see how they impacted the study of IR";"kmv" +"4735";"JPM699";"Security and Technology";"Střítecký,V.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"2";"4";"5";"4";"4";"- I loved the group presentation assignment (topological analysis of a conspiration theory on social networks), and learning to work with NodeXL / Gephi- The whole concept of the subject - learning about artificial intelligence and deep neural networks, and about how they impact security and IR - was super super cool and very forward-looking. I applaud the faculty for that!";"- I was bummed we had to skip the lesson on digital surveillance- I found the course to be a bit chaotic. Especially the first half, when the lecturer was presenting. Despite his skills and knowledge, I felt like we were covering the same topic every lesson for the first five weeks, and I honestly do not remember anything from them. On the other hand, I remember a lot from the lecturer's presentation on ISIS propaganda on Twitter (we could have seen more of that!) and the student presentations.- I'd just recommend revamping the first theoretical half of the course a bit, to make the presentations a bit more diversified and have them connect with the practical part more seamlessly- I understand the professor has a lot of work to do, but if he replied to our e-mails (sooner, or at all), that would be really helpful";"kbs" +"4736";"JEB141";"Introduction to Market Design";"Gregor,M.,Kastl,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"4";"5";;;"ies" +"4737";"JEM001";"Master´s Thesis Seminar I";;"Havránek,T.,Havránková,Z.,Mejstřík,M.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"3";"4";"3";"5";;;"ies" +"4738";"JEM007";"Applied Microeconometrics";"Pertold-Gebicka,B.";"Pertold-Gebicka,B.,Rečková,D.";"5";"3";"5";"5";"5";"5";"5";"4";"1";"4";"5";"5";"5";;;"ies" +"4739";"JEM032";"Banking";"Mejstřík,M.,Pečená,M.,Teplý,P.";"Pečená,M.";"4";"4";"4";"5";"3";"4";"5";"3";"1";"3";"4";"4";"4";;;"ies" +"4740";"JEM034";"Corporate Finance";"Bychkova,O.,Malířová,T.";;"3";"4";"4";"4";"3";NULL;NULL;NULL;"2";"2";"2";"2";"2";;;"ies" +"4741";"JEM040";"Účetní a daňové poradenství";;"Kemény,I.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"2";"4";"5";"3";"5";;;"ies" +"4742";"JEM181";"Data Science with R";"Herman,D.,Krištoufek,L.";"Herman,D.,Krištoufek,L.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"4";"5";;;"ies" +"4743";"JEM199";"Financial Crisis and Risk Management";"Horváth,R.,Opatrný,M.,TSOMOCOS,D.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"3";"4";"5";"4";;;"ies" +"4744";"JEB009";"Makroekonomie I";"Hlaváček,M.,Kolcunová,D.";"Hlaváček,M.,Kolcunová,D.";"4";"3";"4";"5";"3";"4";"5";"5";"3";"3";"4";"4";"4";;;"ies" +"4745";"JJB003";"Dějiny masových médií III";"Bednařík,P.,Končelík,J.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"Celkově je kurz skvěle koncipován. Velký přínos má nejen v pochopení a naučení se důležitých oborových informací, ale někdy také v lepším vysvětlení důležitých historických událostí dvacátého století. Práce na seminární práci byla velmi náročná, ale zajímavá. Oceňuji i konkrétní hodnocení každé práce.";"Nic.";"kms" +"4746";"JLB053";"Angličtina pro sociální vědy I";;"Prošková,A.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";"Propojování gramatiky s novými slovíčky, nejen z žurnalistické angličtiny, která se nám budou hodit. Zároveň to, že se pořád mluví anglicky.";"-";"cjp" +"4747";"JMM368";"Maďarsko po roce 1989";"Irmanová,E.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"krvs" +"4748";"JMM390";"Slovensko po roce 1989";;"Irmanová,E.";NULL;NULL;NULL;NULL;NULL;"4";"4";"4";NULL;NULL;NULL;NULL;NULL;;;"krvs" +"4749";"JPM160";"Česká komunální politika";"Jüptner,P.";;NULL;NULL;"4";"4";"4";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kp" +"4750";"JPM260";"Vybrané problémy britské zahraniční politiky v 19. a 20. století, ES";"Soukup,J.";;"5";NULL;"5";"5";"5";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kmv" +"4751";"JPM342";"Konflikty v demokracii a národní identita: teoretické problémy analýzy";"Říchová,B.";;"4";NULL;"4";"4";"4";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kp" +"4752";"JPM344";"Diplomní seminář II.";;"Brunclík,M.,Franěk,J.,Hroch,M.,Charvát,J.,Jüptner,P.,Kofroň,J.,Kotábová,V.,Krausz Hladká,M.,Kubátová,H.,Kučera,J.,Landovský,J.,Mlejnek,J.,Perottino,M.,Riegl,M.,Romancov,M.,Říchová,B.,Salamon,J.,Shavit,A.,Švec,K.";NULL;NULL;NULL;NULL;NULL;"4";"4";"4";NULL;NULL;NULL;NULL;NULL;;;"kp" +"4753";"JPM641";"Světový regionalismus";"Riegl,M.";;NULL;NULL;"5";"5";"5";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kp" +"4754";"JJB143";"Žurnalistika a feminismus";"Krobová,T.,Osvaldová,B.";;"5";"5";"5";"5";"4";NULL;NULL;NULL;"1";"4";"5";"4";"5";"Že neklade největší důraz na testy, ale na práce, které studenti odevzdávají.";"-";"kz" +"4755";"JJB009";"Úvod do psychologie";"Vranka,M.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";"Možnost získat bonusové body účastí ve výzkumech.";;"kz" +"4756";"JEB022";"Institutional Economics";"Kopečná,V.,Schwarz,J.";;"3";"3";"4";"5";"3";NULL;NULL;NULL;"1";"4";"2";"4";"4";;;"ies" +"4757";"JEB044";"Financial Accounting";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ies" +"4758";"JEB105";"Statistics";"Červinka,M.";"Smutná,Š.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"4759";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"4";"4";"4";"5";"3";"5";"5";"4";"1";"3";"4";"4";"4";;;"ies" +"4760";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"4";"5";;;"ies" +"4761";"NMMA703";"Matematika 3";"Zelený,M.";"Turčinová,H.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"4762";"JLB102";"Czech as a Foreign Language III";;"Nováková,K.";"4";"4";NULL;NULL;NULL;"4";"4";"4";"1";"5";"5";"4";"5";;;"cjp" +"4763";"JMM302";"Russia after 1991";"Svoboda,K.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"krvs" +"4764";"JMMZ084";"Eastern Europe Today II";;"Lídl,V.,Šír,J.";"2";"3";NULL;NULL;NULL;"3";"4";"2";"1";"3";"2";"3";"3";;;"krvs" +"4765";"JMMZ336";"History and Society in the Russian Cinema";;"Kolenovská,D.,Mazzali,F.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"3";"5";"5";;;"krvs" +"4766";"JMMZ095";"M.A. Thesis Seminar for BECES I";;"Vykoukal,J.";"3";"2";NULL;NULL;NULL;"5";"5";"3";"1";"2";"3";"3";"3";;;"krvs" +"4767";"JPB268";"Evropská integrace";"Plechanovová,B.";;"2";"4";"2";"3";"1";NULL;NULL;NULL;"3";"3";"3";"3";"2";;"Psaní esejí bych zachoval, ale bylo by lepší, kdyby byly opraveny již v průběhu semestru a student věděl jak dobře je napsal. Dosud jsou opravovány jen pokud student napíše test na požadovaný počet bodů.. i když písemný test napíše na plný počet bodů a přidělenou esej k testu měl špatnou, tak mu není dán zápočet.. vyučující testy opravuje velice dlouho a studentům je tak znemožněno absolvování více termínů (během opravování mu zkrátka propadnou).. osobně si myslím, že nároky vyučující jsou přemrštěné.. i přes zodpovězení všech otázek mnoha studenty, jí odpovědi připadají nedostačující a hodnotí je menším počtem bodů, což ve výsledku znamenalo neúspěšnost v testu (nutno podotknout, že mnoho studentů mělo z testu dobrý pocit a byli si jistí, že 100% splní minimální množství bodů).. Pokud chce vyučující zachovat náročnost, mohla by to nahradit například prezentacemi na hodinách nebo podobnými nástroji.";"kmv" +"4768";"JMB065";"Úvod do mezinárodního a evropského práva";"Šlosarčík,I.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"3";"5";"2";"5";"5";"Velice oceňuji vybraná témata, která vyučující dokáže zajímavě podat, i samotný přístup vyučujícího. Líbila se mi také celková struktura kurzu. Závěrečná zkouška může být ale kvůli možnosti více správných odpovědí u testu poněkud náročná.";;"kzs" +"4769";"JMM040";"Societal changes in Western European countries";"Bauer,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"The comprehensive overview of topics such as modernity, migration or the evolution of identity.";"Everything was alright...";"kzs" +"4770";"JMB250";"Seminář k dějinám západní Evropy";;"Simbartlová,A.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"1";"5";"4";"4";"5";"Velice oceňuji výběr textů, který byl připraven na každou hodinu k danému tématu. Texty byly zajímavé a rozhodně velice přínosné. Tento seminář nám zároveň umožnil udělat si celkový obrázek o otázce migrace, jak z historického hlediska, tak z pohledu jednotlivých politik a států. Paní Simbartlová byla velice příjemná, dokázala nám se vším poradit a zároveň odpovědět na naše dotazy. Určitě tento seminář doporučuji!";;"kzs" +"4771";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"4";"3";"4";"3";"4";NULL;NULL;NULL;"2";"4";"3";"4";"4";"kombinace přednášek a prezentací studentů";"rychlejší opravování testů doktora Švece";"kp" +"4772";"JPB202";"Politické strany v Evropě";"Perottino,M.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"2";"4";"3";"4";"5";;;"kp" +"4773";"JMB408";"Moderní dějiny Ruska";"Litera,B.,Novák,P.,Pečenka,M.";;"3";"5";"3";"3";"4";NULL;NULL;NULL;"1";"5";"3";"5";"4";;;"krvs" +"4774";"JMB409";"Moderní dějiny středo- a jihovýchodní Evropy-dcera JMB013";"Balla,P.,Švec,L.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"2";"5";"3";"5";"5";;;"krvs" +"4775";"JMB410";"Moderní dějiny západní Evropy";"Matějka,O.,Váška,J.";;"3";"5";"2";"5";"2";NULL;NULL;NULL;"3";"5";"3";"5";"4";;;"kzs" +"4776";"JMB414";"Seminář k aktualitám I";;"Synkule,M.";"3";"4";NULL;NULL;NULL;"3";"3";"2";"4";"1";"3";"1";"2";"Oceňuji debaty o aktuálním dění a také debaty na závěr prezentací, protože ve skupině deseti lidí se musejí zapojit i ti tišší lidé, aby vůbec nějaká debata probíhala.";"Přesně nevím, co mělo být přínosem tohoto kurzu. Možná by to chtělo seminář lépe strukturovat, například na začátku představit odkud čerpat informace o aktuálním dění, co je relevantní a co ne, nebo představit nejaktuálnější témata, kterým bychom se třeba v prezentacích mohli během semestru věnovat. Zároveň by to chtělo lépe definovat, jaká témata prezentací jsme si měli vybírat, protože některá byla možná až moc aktuální (třeba několik dnů) a nemohli jsme tedy získat pohled na problematiku z časového odstupu a například předpokládat důsledky či budoucí vývoj. Vyučující by se mohl také během semináře více angažovat (nebyla vidět žádná promyšlenost ani cíl kurzu) a lépe si kurz naplánovat, protože kvůli jeho zahraničním cestám výuka často odpadávala.";"krvs" +"4777";"JPM127";"Geopolitika a geostrategie 1815 - 1945";"Romancov,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"4778";"JPM146";"Přechody k demokracii v teorii a praxi I";"Mlejnek,J.";;"5";"3";"4";"4";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"4779";"JPM150";"Poloprezidentské režimy v postkomunistické Evropě";"Mlejnek,J.";;"3";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kp" +"4780";"JPM574";"Moderní strany a stranické systémy v Evropě";"Brunclík,M.";;"4";"2";"4";"5";"3";NULL;NULL;NULL;"1";"3";"3";"3";"4";;;"kp" +"4781";"JPM579";"Teorie politických stran";"Perottino,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kp" +"4782";"JPM639";"Problémy ústavního inženýrství";"Brunclík,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"4783";"JPM653";"Politika a média";"Švec,K.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kp" +"4784";"JPB202";"Politické strany v Evropě";"Perottino,M.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"2";"4";"3";"4";"5";"Struktura kurzu je zcela vyhovující. Nabízí užitečné vědomosti z historie a fungování prominentních evropských politických subjektů. Na začátku přednášek jsou navíc diskutovány aktuální společenské i politické události.";"Dostali jsme zákaz jíst fíkus :-(";"kp" +"4785";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"3";"4";"2";"2";"3";NULL;NULL;NULL;"2";"3";"1";"2";"3";;;"kmv" +"4786";"JPM052";"Mezinárodní ekonomické vztahy (MEV)";"Kučerová,I.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kmv" +"4787";"JPM429";"Global terrorism (CS)";;"Makariusová,R.";"4";"3";NULL;NULL;NULL;"4";"5";"3";"1";"3";"1";"4";"4";;;"kmv" +"4788";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"3";"4";"4";"4";"5";"Kurz je nesmírně zajímavý a přednes doktorky Gelnarové poutavý. Navrhuji zachovat strukturu přednášek i volbu dobových textů.";;"kp" +"4789";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"4";"3";"4";"5";"3";NULL;NULL;NULL;"1";"5";"5";"3";"4";;;"kmv" +"4790";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"4";"4";"4";"4";"4";"1";"1";"1";"1";"4";"4";"4";"5";"Přednášky doktora Švece jsou celkem dobře připravené a studentům je poskytnuto mnoho užitečných vědomostí ohledně vývoje politické vědy.";"Bylo by vhodno stihnout látku na přednáškách probrat včas, aby studenti nebyli ochuzeni o závěrečné semináře a aby se nemusely přednášky konat až téměř do Vánoc.";"kp" +"4791";"JPM690";"Liberalism in International Relations (TIR)";;"Karlas,J.";"4";"3";NULL;NULL;NULL;"3";"5";"3";"1";"5";"4";"4";"5";;;"kmv" +"4792";"JPM689";"Conflict Studies";"Karásek,T.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"2";"4";"5";;;"kbs" +"4793";"JPM705";"Human Security";"Hynek,N.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"3";"5";"5";"5";"5";;;"kbs" +"4794";"JEM004";"Advanced Macroeconomics";"Baxa,J.,Hromádková,E.";"Baxa,J.,Hromádková,E.,Šestořád,T.,Troch,T.,Žáček,J.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"4";"5";"5";"Homeworks, although it took quite time to do them, they helped with the learning process and at the end the preparation for final took less time.";;"ies" +"4795";"JLB100";"Czech as a Foreign Language I";;"Frantesová,E.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"The professor was always very helpful, and very engaged with the students. It was clear she was passionate about teaching Czech, and she always helped us to succeed and not to feel discouraged. Taking Czech as a Foreign Language was a very positive experience.";;"cjp" +"4796";"JMM530";"European Comparative Politics and Society";"Rovná,L.";;"2";"2";"2";"3";"1";NULL;NULL;NULL;"1";"3";"1";"2";"2";;;"kzs" +"4797";"JMMZ331";"Qualitative methods in social sciences";"Weiss,T.";;"3";"2";"4";"4";"3";NULL;NULL;NULL;"1";"2";"2";"3";"2";;;"kzs" +"4798";"JMMZ333";"Transnational history of contemporary Europe";"Matějka,O.";;"2";"3";"2";"4";"2";NULL;NULL;NULL;"1";"3";"2";"2";"1";;;"kzs" +"4799";"JMMZ332";"Culture and politics in Europe";"Tomalová,E.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kzs" +"4800";"JPB258";"Politické myšlení novověku I";"Franěk,J.,Kučera,J.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"2";"4";"4";"4";"4";"Oceňuji probíraná témata i osobnosti.";;"kp" +"4801";"JMMZ334";"Current Challenges in Europe";"Mejstřík,M.,Tomalová,E.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kzs" +"4802";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"4";"4";"4";"3";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";"Přednášky byly vždy kvalitní a poutavé. Díky seminářům si studenti mohli procvičit rétorické schopnosti, přípravu prezentací i práci v kolektivu.";"Chtělo by to zvýšit tempo opravy testů. Pro pedagoga by mělo být prioritou, aby student znal výsledek svého snažení co nejdříve.";"kp" +"4803";"JSB031";"Bakalářský seminář I - diskuse projektů";;"Balon,J.";"4";"3";NULL;NULL;NULL;"5";"5";"4";"1";"5";"5";"5";"5";;;"ks" +"4804";"JSB033";"Praktika z kvalitativního výzkumu";;"Marková Volejníčková,R.";"5";"4";NULL;NULL;NULL;"5";"5";"4";"1";"5";"5";"5";"5";;;"ks" +"4805";"JSB454";"Social Web: (Big) Data Mining";"Růžička,J.";;"4";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"3";"4";;;"ks" +"4806";"JSB455";"Economic Sociology and European Capitalism";"Blokker,P.";;"4";"4";"5";"5";"3";NULL;NULL;NULL;"1";"4";"3";"3";"4";;;"ks" +"4807";"JSB534";"Introduction to Visual Sociology";"Wladyniak,L.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"ks" +"4808";"JPB565";"Stáž v praxi";;"Kuľková,M.,Švec,K.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";"Stáž v praxi představuje skvělou příležitost vyzkoušet si práci v některé z nabízených institucí, poznat sympatické kolegy a načerpat profesní zkušenosti v životopisu nepostradatelné.";"Podle mě není nutné, aby lhůta na odevzdání formulářů byla 14 dnů po skončení stáže. Osobně bych preferoval jako deadline konec zkouškového období.";"kp" +"4809";"JPB593";"Political Economy of Regionalism";"Miková,I.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";"Téma kurzu je rozhodně zajímavé a užitečné pro spoustu budoucích politologů. Výuka v anglickém jazyce navíc zajišťuje možnost účasti zahraničním studentům, díky čemuž je studentský kolektiv patřičně multikulturní.";"Magistra Miková by se neměla nechat znervózňovat nefungujícím projektorem. Technické závady by měla ignorovat a pokračovat v přednášce.";"kmv" +"4810";"JPB268";"Evropská integrace";"Plechanovová,B.";;"3";"5";"4";"3";"4";NULL;NULL;NULL;"3";"4";"4";"4";"3";"Kurz nabízí vhodné souhrnné znalosti evropské integrace, vývoje evropských společenství i institucí.";"Navrhuji hodnotit rešerši bez ohledu na to, zda student zkoušku splnil, či nikoli. Vhodná je také zpětná vazba k rešerši a možnost její opravy.Doporučuji hodnocení odpovědí v testu na bodové škále 0-6, nikoli pouze 0/3/6.Doporučuji zvýšit kapacitu každého termínu zkoušky. Myslím, že 30 lidí je celkem málo.Testy by měly být vyhodnoceny co nejrychleji, rozhodně před konáním dalšího termínu zkoušky. Je-li to problém (třeba i z důvodu velkého množství testů), navrhuji změnu formy testu na kroužkovací (uzavřené) otázky.";"kmv" +"4811";"JLB027";"Ruština odborná I - vyšší";;"Mistrová,V.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";NULL;NULL;NULL;"5";;;"cjp" +"4812";"JMM278";"Politika a mezinárodní vztahy";"Kubát,M.,Weiss,T.";"Kocián,J.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"4";"4";"3";"4";;;"krvs" +"4813";"JMM279";"Společnost a ekonomika";"Fiřtová,M.";"Svoboda,K.";"5";"4";"5";"5";"5";"5";"5";"5";"2";"5";"5";"5";"5";;;"kas" +"4814";"JLB033";"Němčina I";;"Faltýnová,R.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";NULL;"5";;;"cjp" +"4815";"JMM054";"Koncepce a interpretace ruských a východoevropských dějin";"Kolenovská,D.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"4";"5";;;"krvs" +"4816";"JEM001";"Master´s Thesis Seminar I";;"Havránek,T.,Havránková,Z.,Mejstřík,M.";"4";"3";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"4";"5";;;"ies" +"4817";"JPB227";"Politický system ČR";"Charvát,J.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"1";"4";"2";"5";"4";"přístup profesora";;"kp" +"4818";"JPB558";"Výběrový seminář: Politická komunikace";"Váňa,T.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"4";"5";;;"kp" +"4819";"JPB597";"Current Political Extremism";"Charvát,J.";;"5";"3";"5";"5";"3";NULL;NULL;NULL;"1";"3";"3";"3";"4";;;"kp" +"4820";"JPB260";"Politické myšlení novověku III";"Kučera,J.";;"2";"4";"2";"4";"2";NULL;NULL;NULL;"1";"2";"2";"3";"2";;;"kp" +"4821";"JPB263";"Bakalářský seminář II.";;"Brunclík,M.,Bureš,O.,Ditrych,O.,Franěk,J.,Gelnarová,J.,Hynek,N.,Charvát,J.,Jeřábek,M.,Jüptner,P.,Karásek,T.,Karlas,J.,Knutelská,V.,Kofroň,J.,Kotábová,V.,Krausz Hladká,M.,Kubátová,H.,Kučera,J.,Kučerová,I.,Landovský,J.,Ludvík,J.,Makariusová,R.,Mlejnek,J.,Pa";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;"nelze hodnotit obecne, kurzy jsou velice individulani dle vedouciho prace";;"kp" +"4822";"JPB565";"Stáž v praxi";;"Kuľková,M.,Švec,K.";"5";"3";NULL;NULL;NULL;"3";"3";"5";"1";"5";"5";"3";"5";"velice individualni dle vyberu staze, nelze obecne hodnotit";;"kp" +"4823";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"4";"2";"2";"1";NULL;NULL;NULL;"2";"3";"2";"3";"1";;;"ies" +"4824";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";NULL;"5";"4";"4";"5";;;"cjp" +"4825";"JJB131";"Praktický fotožurnalismus";;"Láb,F.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"5";"5";"I learned a lot of new things concerning analog photography and it was a very lovely experience to work with such erudite and enthusiastic lecturer";;"kz" +"4826";"JJB135";"Filmový seminář I";;"Šobr,M.";NULL;NULL;NULL;NULL;NULL;"3";"4";"4";NULL;NULL;NULL;NULL;NULL;;;"kz" +"4827";"JLB019";"Francouzština odborná I";;"Bosáková,L.";"4";"2";NULL;NULL;NULL;"4";"5";"4";"1";"5";"2";"5";"4";;;"cjp" +"4828";"JEM020";"Ethics and Economics";"Cahlík,T.";"Cahlík,T.";"4";"4";"4";"4";"4";"4";"4";"4";"3";"4";"2";"5";"5";;;"ies" +"4829";"JJB631";"Social Media: Strategy, Tactics and Analytics";"Audyová,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmkpr" +"4830";"JJB633";"Marketing Communications";"Zezulková,M.";;"4";"3";"3";"3";"3";NULL;NULL;NULL;"3";"1";"1";"1";"4";;;"kmkpr" +"4831";"JJM233";"Intercultural Communication Management";"Lütke Notarp,U.";;"3";"3";"4";"5";"4";NULL;NULL;NULL;"1";"3";"3";"3";"4";;;"kms" +"4832";"JJM373";"Ethical Issues in Media, Business and Society";"Orhan,M.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"3";"4";"4";"5";;;"kms" +"4833";"JLB003";"Angličtina pro ekonomy I";;"Gloverová,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"4834";"JJB133";"DTP";;"Slanec,J.";"4";"2";NULL;NULL;NULL;"4";"5";"3";"1";"2";"4";"3";"4";;;"kz" +"4835";"JJB231";"Dějiny a teorie public relations";"Halada,J.,Hejlová,D.";;"4";"4";"5";"4";"3";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmkpr" +"4836";"JJB235";"Proces tvorby v marketingové komunikaci";"Bezouška,M.";;"3";NULL;"4";"4";"2";NULL;NULL;NULL;NULL;"2";"2";"2";"3";;;"kmkpr" +"4837";"JJB237";"Právo a etika v marketingu";"Winter,F.";;"4";"1";"4";"5";"2";NULL;NULL;NULL;"2";"3";"2";"3";"4";;;"kmkpr" +"4838";"JJB279";"Art marketing";"Ježková,T.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"4";"3";"4";"5";;;"kmkpr" +"4839";"JMB409";"Moderní dějiny středo- a jihovýchodní Evropy-dcera JMB013";"Balla,P.,Švec,L.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"krvs" +"4840";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"4841";"JLB019";"Francouzština odborná I";;"Bosáková,L.";"5";"4";NULL;NULL;NULL;"4";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"4842";"JMB414";"Seminář k aktualitám I";;"Cotte,P.";"4";"3";NULL;NULL;NULL;"4";"5";"5";"1";"4";"5";"5";"5";;;"krvs" 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And although I understand the idea was to put us in a context in which we face the dilemmas UN commanders face, I believe it didn't allow us to use a lot of the material. In my opinion the description of a new crisis in country \"X\" and how would you react if you were to establish the PKO would be more interesting. We could use lectures about financing, equipment, troop contributions, gender inclusion, chapters, mandates, regional Organization support.";"kbs" +"4884";"JJB004";"Současný český jazyk I";;"Svobodová,I.";"5";"4";NULL;NULL;NULL;"3";"3";"5";"1";"5";"5";"4";"4";;;"kz" +"4885";"JJB010";"Základy filozofie a vzdělanosti";"Halada,J.";;"4";"1";"5";"3";"2";NULL;NULL;NULL;"1";"4";"1";"3";"2";;;"kz" +"4886";"JJB012";"Žurnalistická tvorba I";"Osvaldová,B.";"Krobová,T.,Osvaldová,B.,Slanec,J.";"5";"3";"5";"5";"5";"5";"5";"5";"1";NULL;"5";"5";"5";;;"kz" +"4887";"JJB015";"Česká literatura I";;"Čeňková,J.,Malý,R.";"5";"3";NULL;NULL;NULL;"5";"4";"4";"1";"4";"3";"4";"4";;;"kz" +"4888";"JJB017";"Grafický design a základy polygrafie I";"Slanec,J.";;"5";"2";"5";"4";"4";NULL;NULL;NULL;"1";"5";"5";"3";"3";;;"kz" +"4889";"JEB110";"Econometrics II";"Pertold-Gebicka,B.";"Chorna,O.,Malinská,B.,Pertold-Gebicka,B.,Pleticha,P.";"3";"4";"3";"5";"3";"4";"5";"3";"1";"3";"2";"3";"2";;;"ies" +"4890";"JJB600";"Úvod do studia médií I.";"Nečas,V.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kms" +"4891";"JJB018";"Úvod do fotožurnalistiky";"Lábová,A.";;"5";"1";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kz" +"4892";"JJB998";"Úvod do ekonomie";"Poljakov,N.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kz" +"4893";"JJB601";"Rozbor právního prostředí v českých médiích";"Benda,J.";;"4";"2";"4";"4";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Líbí se mi domácí úkoly, kde se dá ověřit znalost tématu";"Větší zpětnou vazbu ohledně úkolů, aby student věděl, co zlepšit, co se povedlo apod.";"kms" +"4894";"JLB053";"Angličtina pro sociální vědy I";;"Prošková,A.";"5";"3";NULL;NULL;NULL;"4";"4";"5";"1";"3";"3";"3";"4";;;"cjp" +"4895";"JEB026";"European Economic Integration";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"4";"4";"4";"3";"2";"3";"3";"1";"1";"4";"1";"3";"4";;;"ies" +"4896";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"4";;;"ks" +"4897";"JJB600";"Úvod do studia médií I.";"Nečas,V.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"2";"4";"4";"4";"4";;;"kms" +"4898";"JJB601";"Rozbor právního prostředí v českých médiích";"Benda,J.";;"3";"4";"3";"3";"3";NULL;NULL;NULL;"1";"4";"4";"4";"3";;;"kms" +"4899";"JJB605";"Novodobá historie médií";"Bednařík,P.,Končelík,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"4900";"JJB616";"Vybrané novinářské osobnosti 19. století";"Sekera,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kms" +"4901";"JJB620";"Mediální produkce - žurnalistická práce I.";"Vlasák,Z.";;"4";"2";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kms" +"4902";"JJM295";"Rozhlasový a televizní dokument";"Štoll,M.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";"zábavná forma, praktické ukázky";;"kz" +"4903";"JJM080";"Zábava v televizním vysílání";"Kruml,M.";;"5";"1";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"5";"praktické ukázky";;"kms" +"4904";"JMMZ340";"Freedom of Speech";"Klvaňa,T.";;"5";"2";"4";"5";"5";NULL;NULL;NULL;"2";"5";"5";"4";"5";;;"kas" +"4905";"JJM201";"Teorie masové a mediální komunikace";"Jirák,J.";;"4";"4";"5";"4";"4";NULL;NULL;NULL;"2";"5";"4";"5";"4";;;"kms" +"4906";"JPB227";"Politický system ČR";"Charvát,J.";;"5";"2";"5";"5";"3";NULL;NULL;NULL;"2";"3";"3";"3";"5";;;"kp" +"4907";"JJM202";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"3";"5";"3";"3";"3";NULL;NULL;NULL;"2";"4";"4";"4";"3";;"forma výuky";"kms" +"4908";"JPB597";"Current Political Extremism";"Charvát,J.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"2";"4";"3";"4";"5";;;"kp" +"4909";"JPB594";"Realism in International Relations";"Odintsov,N.";;"4";"4";"2";"2";"3";NULL;NULL;NULL;"2";"4";"3";"4";"3";;;"kmv" +"4910";"JPB592";"US Government and Politics";"Kotábová,V.";;"3";"4";"3";"2";"3";NULL;NULL;NULL;"1";"3";"2";"3";"3";;;"kp" +"4911";"JPB569";"Workshop Politické a státní instituce v praxi";;"Brunclík,M.";"5";"1";NULL;NULL;NULL;"4";"4";"5";"2";"3";"4";"3";"5";;;"kp" +"4912";"JJM229";"Vývoj televizního vysílání v českých zemích";"Štoll,M.";;"3";"3";"4";"4";"4";NULL;NULL;NULL;"2";"4";"3";"4";"4";;;"kms" +"4913";"JJM226";"Teorie účinků médií";"Nečas,V.";;"5";"4";"4";"4";"4";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kms" +"4914";"JPB260";"Politické myšlení novověku III";"Kučera,J.";;"4";"5";"3";"3";"5";NULL;NULL;NULL;"1";"5";"2";"4";"5";;;"kp" +"4915";"JJM204";"Výzkum médií I";"Křeček,J.";;"3";"1";"3";"3";"2";NULL;NULL;NULL;"2";"3";"4";"2";"3";;"forma výuky, zmatený přístup,";"kms" +"4916";"JJM224";"Politická ekonomie komunikace";"Vochocová,L.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";"komunikativní účast při hodině, možnost debaty, vhodné příkladné situace, přístup profesora";;"kms" +"4917";"JEM002";"Master´s Thesis Seminar II";;"Havránek,T.,Havránková,Z.,Mejstřík,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"4918";"JJB240";"Marketing a tvorba značky";"Průša,P.";;"3";"4";"2";"4";"3";NULL;NULL;NULL;"2";"3";"3";"3";"2";"Pan vyučující postupuje velmi strukturovaně a nechává mezi studenty probíhat debaty na dobrá témata.";"Trochu to působilo jakoby si vyučující přečetl pár knih, z nich vybral pár bodů a ty pak jednoduše předříkával studentům. Navíc je celý kurz o analýze značky (a vesměs jen velkých značek), o tvorbě nové značky, což by se nám hodně hodilo, bylo řečeno jen minimum.";"kmkpr" +"4919";"JJM201";"Teorie masové a mediální komunikace";"Jirák,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"3";"3";"4";"3";"3";"The form, practise, practise, practise.";;"kms" +"4920";"JJB401";"Komerční a nekomerční marketingová komunikace";"Báča,L.,Obluk,O.";;"5";"4";"5";"4";"5";NULL;NULL;NULL;"2";"5";"4";"5";"5";"Přednášející jsou skvělí, mají obrovský přehled a učí marketingově myslet a ne jen marketing dělat. A vynikající je i systém zkoušek jako projektů, kde se přednášející nebojí vyhazovat lidi, kteří se na to úplně vykašlali.";"Pokud to nebyl jenom pokus o znervóznění, tak bych poprosil věnovat pozornost prezentaci zkoušených a ne SMS.";"kmkpr" +"4921";"JJM202";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"4";"4";"3";"4";"4";NULL;NULL;NULL;"1";"5";"4";"5";"3";;"The understanding of texts is different - as results of our course demonstrate, students did not understand the text the same way the teachers did (some deeper explenation of what is important or specifying the question would help).";"kms" +"4922";"JJM204";"Výzkum médií I";"Křeček,J.";;"2";"2";"2";"3";"2";NULL;NULL;NULL;"3";"2";"3";"2";"2";;"Some deeper explanation or further information regarding the topic during our classes will be appreciated.";"kms" +"4923";"JJM214";"Čtení textů ke studiu médií - populární kultura";;"Reifová,I.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"3";"4";"4";"4";"4";;;"kms" +"4924";"JJM224";"Politická ekonomie komunikace";"Vochocová,L.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"3";"5";"5";"5";"5";;;"kms" +"4925";"JJM234";"Media and Society: An Introduction";"Jirák,J.";;NULL;NULL;"5";"5";"5";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;"Note: I evaluated Mass and Media Communication Theory as this one (made a mistake).";;"kms" +"4926";"JLB029";"Španělština odborná I";;"Mlýnková,L.";"4";"3";NULL;NULL;NULL;"4";"5";"5";"1";"5";"5";"4";"5";"Velkým pozitivem je interaktivní a sympatický přístup vyučující, který je v porovnání s některými jinými jazykovými lektory na Univerzitě opravdu kvalitní. Dobrá je i struktura výuky následující gramatiku z učebnici. Přínosné jsou rozhodně i prezentace studentů podporující jazykové dovednosti jak přednášejícího, tak posluchačů, což se též týká dílčích domácích úkolů.";"Možným vylepšením by mohla být skupinová práce studentů na nějakém projektu / prezentaci, při níž by si kromě rozvíjení jazykových dovedností procvičili i schopnost spolupráce, čímž by mimo jiné došlo by k většímu sociálnímu propojení mezi studenty ústící v možnou spolupráci jak v rámci předměty, tak i po jeho dokončení.";"cjp" +"4927";"JJM080";"Zábava v televizním vysílání";"Kruml,M.";;"4";"3";"5";"5";"2";NULL;NULL;NULL;"2";"4";"2";"3";"4";;;"kms" +"4928";"JJM117";"Popular Culture";"Turnau,T.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"kms" +"4929";"JJM245";"Úvod do vizuální komunikace";"Průchová,A.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kz" +"4930";"JJM249";"Duchovní kořeny evropské kultury";"Halada,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"2";"4";"5";;;"kz" +"4931";"JPM146";"Přechody k demokracii v teorii a praxi I";"Mlejnek,J.";;"4";"3";"3";"5";"2";NULL;NULL;NULL;"1";"5";"4";"5";"4";"Prvním pozitivem kurzu je vhodně propojená teorie s praxí, kdy jsou nejdříve přestaveny teorie, které jsou později aplikovaný v jednotlivých příkladech. Druhý pozitivem je nápadité pojetí seminárních prezentací, především jejich rozmanitost. Akorát v rámci prezentací by se hodilo klást větší důraz na jejich propojení s tématem kurzu, tedy přechodů k demokracii.Třetím pozitivem je poskytnutí výukových prezentací pro studentu, což umožňuje zacílit jejich soustředění během přednášek k dané látce více tím, že si nemusí urputně psát zápisky, a mohou tak více diskutovat.";"Prvním nedostatkem kurzu by mohl být přílišný čas věnovaný jednotlivým příkladů tranzice. Studenti si sice takto více zapamatují a více se ponoří do daného příkladu, avšak finální podíl času věnovaný jednomu příkladu je opravdu obrovský, a za dané období by se tak dalo probrat více přechodů k demokracii.Druhým nedostatkem kurzu by mohla být ne-interaktivní přístup vyučujícího. Větší aktivnější zapojení studentů do výuky ze strany vyučujícího během přednášek by jistě bylo k užitku celého kurzu.Třetím nedostatkem kurzu by mohla být nevyužitá příležitost možnosti diskuze ze strany studentů a věnování času více interaktivnějším aktivitám či diskutování dalších příkladů vyplývající z poskytnutí výukových prezentací pro studenty. Jedná se tak o situaci odvozenou z druhého nedostatku.";"kp" +"4932";"JJM251";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"4";"5";"4";"4";"2";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kz" +"4933";"JPM160";"Česká komunální politika";"Jüptner,P.";;"5";"5";"4";"5";"4";NULL;NULL;NULL;"2";"5";"5";"5";"5";"Prvním velkým pozitivem je důraz na participaci a aktivitu studentů skrze podmínky splnění kurzu zahrnující vykonání rozhovoru, představení prezentace a vypracování seminární práce.Druhý velkým pozitivem je důraz na interakci se studenty v průběhu přednášek jak rámci klasických lekcí, tak i v průběhu těch suplovaných, čímž doceňuji práci i Karolíny Musilové a Jakuba Hornka.Třetím velkým pozitivem je zaměření na to, zda studenti látku pochopili projevující se častějším praktickým opakováním a diskusemi v závěrečných hodinách.";"Nedostatkem kurzu by mohla být absence možnosti poskytnutí výukových prezentací studentům pomáhající lepšímu pochopení a uchopení látky vyúsťující v lepší připravenost studentů jak na závěrečné diskuse, tak na finální zkoušku.Jinak kurz považuji za jeden z těch nejpřínosnějších, kterými jsem v průběhu studia na IPS prošel, především díky stylu a struktuře výuky.";"kp" +"4934";"JPM342";"Konflikty v demokracii a národní identita: teoretické problémy analýzy";"Říchová,B.";;"3";"3";"4";"4";"4";NULL;NULL;NULL;"3";"4";"4";"4";"4";"Prvním pozitivem kurzu je výrazné propojení teorie a praxe, kdy u každé představené teorie bylo zmíněno několik příkladů, přičemž vždy minimálně jeden byl rozebrán podrobněji.Druhým pozitivem kurzu je poskytnutí učebních prezentací studentům, což mohlo ušetřit čas studentům při zpracování seminární práce, který by byl v opačném případě mrhán.";"Prvním nedostatkem kurzu by pravděpodobně byla jeho nejasná organizace, kdy většina studentů si nebyla jista přesnými podmínkami splnění kurzu a také termíny jednotlivých přednášek.Druhým nedostatkem kurzu by mohla být menší míra interakce v průběhu výuky, kdy sice vyučující pokládala otázka a i odpovědí se dočkala, ale osobně považuji tento rozsah za pouze základní a určitě by se dalo v tomto směru nějak více studenty zapojit do výuky.";"kp" +"4935";"JPM574";"Moderní strany a stranické systémy v Evropě";"Brunclík,M.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"5";"4";"4";"5";"Pozitivem kurzu je jeho strukturalizace a aplikovaní probíraných teorií na konkretní příklady.";"Nedostatek kurzu vidím v nevyužití moderních technologií k vizualizaci probírané látky. Například díky shrnujícím heslům a použití obrázků v powerpointových prezentací si studenti mohou látku lépe vyobrazit a následně ji pochopit. Pozdější poskytnutí těchto prezentací studentům by mohlo ušetřit čas v rámci přednášek a vytvořil by se tak prostor diskusi či více interaktivnější výuku. Dále by v rámci větší participace studentů nebyla na škodu zahrnutí nějaké skupinové aktivity, například společný projekt či prezentace.";"kp" +"4936";"JPM579";"Teorie politických stran";"Perottino,M.";;"4";"4";"4";"5";"5";NULL;NULL;NULL;"2";"5";"4";"5";"5";"Prvním pozitivem kurzu je interaktivní přístup vyučujícího s důrazem na zapojení studentů do učebního procesu, jak rámci klasických přednášek, tak i v průběhu hodin vedených doktorandy, čímž doceňuji práci i Jakuba Staubera a Daniela Šárovce.Druhým pozitivem je silné propojení teorie s praxí projevující se aplikováním klasických teorií na aktuální příklady.";"Nedostatkem kurzu by mohla být absence možnosti poskytnutí výukových prezentací studentům pomáhající lepšímu pochopení a uchopení látky vyúsťující v lepší připravenost studentů jak na časté diskuse, čímž by se jejich obsah rozhodně obohatil, tak i na finální zkoušku.";"kp" +"4937";"JPM639";"Problémy ústavního inženýrství";"Brunclík,M.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"5";"4";"4";"4";"Prvním pozitivem kurzu je aplikovaní probíraných teorií na konkretní příklady.Druhým pozitivem je věnování většiny kurzu studentským prezentacím a následným diskusím.";"Prvním nedostatek kurzu vidím v nevyužití moderních technologií k vizualizaci probírané látky. Například díky shrnujícím heslům a použití obrázků v powerpointových prezentací si studenti mohou látku lépe vyobrazit a následně ji pochopit. Pozdější poskytnutí těchto prezentací studentům by mohlo ušetřit čas v rámci přednášek a vytvořil by se tak prostor diskusi či více interaktivnější výuku.Druhý nedostatek kurzu by mohlo být zbytečně velké množství prezentací, otázkou je, zda by nebyla lepší skupinová práce utvářející větší prostor pro následnou diskusi a podporující dovednosti spolupráce.";"kp" +"4938";"JPM641";"Světový regionalismus";"Riegl,M.";;"4";"5";"5";"3";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";"Prvním pozitivem kurzu je interaktivní přístup vyučujícího s důrazem na zapojení studentů do učebního procesu.Druhým pozitivem kurzu je jeho strukturalizace a v konkretních příkladech využití probíraných teorií.Dále jsou velkým pozitivem obecný důraz na diskusi a podpora kauzálního přemýšlení studentů. Mezi pozitiva také řadím tempo přednášek, které se sice mohlo zdát rychlejší, avšak mně osobně vyhovovalo.";"Nedostatkem kurzu by mohla být absence možnosti poskytnutí výukových prezentací studentům pomáhající lepšímu pochopení a uchopení látky vyúsťující v lepší připravenost studentů jak na časté diskuse, čímž by se jejich obsah rozhodně obohatil, tak i na finální zkoušku.";"kp" +"4939";"JPM653";"Politika a média";"Švec,K.";;"2";"1";"4";"4";"4";NULL;NULL;NULL;"1";"3";"2";"2";"4";"Přínosem kurzu je rozhodně jeho zaměření a způsob výuky. Dobrým konceptem jsou týdenní prezentace studentů s aktualitami, avšak zde by se hodil nějaký jasně daný rámec, který by umožňoval větší “vědecký” přínos z daných prezentací. Pozitivně hodnotím i lekci věnovanou psaní komentáře a možnost exkurze do ČT.";"Nedostatek kurzu spatřuji v jeho nejasném přínosu v rámci oboru. Zároveň kreditové ohodnocení kurzu by mohlo být sníženo anebo lépe jeho náplň by se mohla stát více sofistikovanou s větším důrazem například na participaci studentů či jejich práci mimo výuku.";"kp" +"4940";"JJM354";"Dějiny populární hudby";"Halada,A.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"5";"3";"2";"4";"5";;;"kz" +"4941";"JJM242";"Comics as a Medium";"Hrdina,M.";;"3";"4";"3";"4";"3";NULL;NULL;NULL;"1";"4";"5";"2";"3";;;"kms" +"4942";"JJM240";"Cultural studies";"Soukup,M.";;"3";"3";"2";"5";"4";NULL;NULL;NULL;"2";"4";"2";"2";"3";;;"kms" +"4943";"JJM201";"Teorie masové a mediální komunikace";"Jirák,J.";;"4";"4";"5";"5";"3";NULL;NULL;NULL;"2";"5";"2";"5";"4";;;"kms" +"4944";"JEB009";"Makroekonomie I";"Hlaváček,M.,Kolcunová,D.";"Hlaváček,M.,Kolcunová,D.";"4";"3";"5";"5";"3";"3";"3";"4";"1";"2";"2";"3";"3";"Rozšíření výkladu o praxi v ČNB";"Příklady a přednášky se moc logicky nepojily, méně modelů a naopak více současných a budoucích problémů.";"ies" +"4945";"JEB022";"Institutional Economics";"Kopečná,V.,Schwarz,J.";;"2";NULL;"3";"3";"1";NULL;NULL;NULL;"1";NULL;NULL;NULL;"3";;"Zdůraznit, kolik čtení to obnáší; Přednášky byly aspoň z počátku zdlouhavé čtení slidů a pak jsem přestal chodit";"ies" +"4946";"JEB044";"Financial Accounting";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"4";"5";"Energic presentation by Mr. Novák and Mrs.Kolouchová. Homeworks took just the right amount of time and effort and kept us better prepared and solving problems on our own.";"Better communication of what will be examined. Especially in the midterm. First seminars were very discouraging to attend so I believe many people stopped going because of that. I felt very tired after the lecture as it required a lot of concentration - I would probably come to the seminar if it wasnt right after the lecture.";"ies" +"4947";"JMM248";"Sociálně politický vývoj Irska";"Šlosarčík,I.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"2";"4";"3";"4";"4";"The skills of the lecturer - well structured classes, interesting topics. The fact, that the lecturer decided to teach the class despite low number of students.";;"kzs" +"4948";"JMM271";"Metodologický seminář";;"Kýrová,L.";"1";"4";NULL;NULL;NULL;"1";"3";"2";"2";"3";"4";"3";"2";"Broad range of approaches introduced. Guest lectures.";"I dislike \"outsourcing\" of the job of a lecturer. Half of the class, a discussion about the readings, was supposed to be led by students which often resulted in confusion, prolonged awkward silence and a \"contest\" who´ll chicken out first a will say something. It was uncomfortable. The other half, the lecturer often read thru powerpoint slides, word by word, without properly explaining it, let alone suggesting which approach is best for cerain types of research, adding to the confusion. In the end, I´ve learned definitions of several methodological approaches, but I do not know what to do with it. The readings. I respect the fact, that the lecturer specializes in this field, but she basically turned a course about methodology into a Native American Studies.";"krvs" +"4949";"JEB105";"Statistics";"Červinka,M.";"Smutná,Š.";"4";"5";"3";"5";"5";"5";"5";"5";"1";"4";"4";"4";"4";"Seminars were great";"Proofs of theorems were really badly visible, never use red colour while writing on white table please. Also if the proofs were in the presentation, you would have a lot more time to explain actually important things, not if it was meant to be i or j index.I wonder if we really need to waste slides with theorems that do not have names, are never used and are never tested (luckily). Seminar problems were behind homeworks - about one week, which required a lot of unintended self study (I think)";"ies" +"4950";"JPM260";"Vybrané problémy britské zahraniční politiky v 19. a 20. století, ES";"Soukup,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"3";"5";"3";"5";"5";;;"kmv" +"4951";"JPM429";"Global terrorism (CS)";;"Makariusová,R.";"4";"5";NULL;NULL;NULL;"3";"5";"5";"2";"3";"4";"2";"4";"Timemanagement hodin nebyl příliš dobře naplánován, problém s časovou kapacitou byl velký";;"kmv" +"4952";"JPM644";"Contemporary International Relations in East Asia";"Kolmaš,M.";;"4";"4";"4";"4";"5";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kmv" +"4953";"JPM721";"Komparace ekonomik zemí EU";"Kučerová,I.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"2";"5";"4";;;"kmv" +"4954";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"3";"4";"3";"4";"3";"5";"3";"5";"1";"3";"4";"3";"4";"Examples from real world - marketing, mosquito nets, ...";"Lectures became boring and the teacher seemed not really interested. Exam was testing microeconomics but you had to spend most of time calculating fractions. Please let us know the results of homeworks and tests sooner.";"ies" +"4955";"JSM527";"Metody analýzy a tvorby politik II.";"Veselý,A.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"kombinaci přednášené teorie a k ní tematicky navazující zadání jednotlivých cvičení";;"kvsp" +"4956";"JSM528";"Seminář k diplomové práci I.";;"Kohoutek,J.,Ochrana,F.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"řadu praktických podnětů , jak přistupovat k akademickému psaní prací";;"kvsp" +"4957";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"4";"3";"3";"3";NULL;NULL;NULL;"1";"3";NULL;"3";"2";;;"ies" +"4958";"JEM141";"Traditional and Alternative Risk Transfer in the Insurance Sector";"Pompella,M.,Teplý,P.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"2";"4";"5";;;"ies" +"4959";"JEB110";"Econometrics II";"Pertold-Gebicka,B.";"Chorna,O.,Malinská,B.,Pertold-Gebicka,B.,Pleticha,P.";"3";"4";"3";"4";"2";"3";"3";"4";"1";"4";"3";"2";NULL;"Oceňuji: Nejvíce na celém kurzu se mi líbil přístup a styl výuky cvičícího Petra Pletichy. Navrhuji: Myslím si, že shrnutí z posledních seminářů od A.Chorna bylo velmi prospěšné - na semináři když člověk dává pozor a snaží se přemýšlet nad danými úkoly (hlavně v R), tak si nestihne daný úkol zapsat.";"Více příkladů na přednáškách. Rychlejší opravu finalu, aby student věděl, zda zkoušku musí opakovat s dostatečným předstihem před dalším termínem zkoušky a taky rychlejší zapsání známky do SISu, která uklidní studenta do další části zkouškového.";"ies" +"4960";"JMM278";"Politika a mezinárodní vztahy";"Kubát,M.,Weiss,T.";"Bečka,J.";"4";"4";"5";"4";"5";"5";"5";"4";"2";"5";"3";"5";"5";"The variety of topics, interdisciplinary approach.";"The readings - not publically accessible and I seriously doubt many people read any of it.";"krvs" +"4961";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"4";"4";;;"krvs" +"4962";"JMB402";"Úvod do společenských věd II";;"Šafařík,P.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"krvs" +"4963";"JSM642";"Metody práce s informacemi";"Tomandlová,V.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"přednášející podrobně a srozumitelně seznámila studenty jak pracovat se zdroji psaní akademických prací";;"kvsp" +"4964";"NMMA703";"Matematika 3";"Zelený,M.";"Bartoš,A.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"4";"5";"4";"5";"Předmět je vypilovaný, profesionální výuka.";;"ies" +"4965";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Šafařík,P.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"knrs" +"4966";"JJM199";"Literární a knižní kritika";"Čeňková,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";"Nove vedomosti o autoroch a knihach, ktore som dovtedy nepoznala, a doplnujuce poznatky o tych znamejsich";;"kz" +"4967";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"knrs" +"4968";"JMMZ314";"Major Issues in Contemporary Public Debates in the U.S. I";"Sehnálková,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";"Great atmosphere at the lectures. Broad range of topics covered. Reasonable requirements. Interesting readings, mostly. Approach of the lecturer.";"None, why change something that works perfectly?";"kas" +"4969";"JSM644";"Základy politologie";"Kotlas,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"velice poutavý způsob přednášení s informačním přesahem daleko nad rámec politologie jako předmětu";;"kvsp" +"4970";"JMB037";"Moderní dějiny Polska";"Vykoukal,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"krvs" +"4971";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ks" +"4972";"JMMZ313";"Government in United States";"Sehnálková,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"5";"5";"Well-structured classes. Reasonable requirements. Approach of the lecturer.";"The final test requires a punishing amount of hand writing. Also lined papers would help a lot.";"kas" +"4973";"JJM248";"Vývoj grafického designu a polygrafického zpracování periodik";"Slanec,J.";;"3";"2";"4";"3";"3";NULL;NULL;NULL;"2";"3";"2";"3";"3";;"Powepoint Presentation of the lecturer :)";"kz" +"4974";"JMMZ315";"U.S. Foreign Policy";"Raška,F.";;"5";"3";"4";"5";"5";NULL;NULL;NULL;"2";"5";"4";"5";"5";"Focus on the interagency process of the US National security. Reasonable requirements.";"Less students, in class this big, it is not easy to engage in a balanced discussion. 5-6 people talk all every time, others just sit.";"kas" +"4975";"JSM646";"Veřejná správa";"Ochrana,F.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"přístup vyučujícího a rozsah předaných informací";;"kvsp" +"4976";"JJB620";"Mediální produkce - žurnalistická práce I.";"Vlasák,Z.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"2";"5";"Líbilo se mi propojení s příklady z praxe a i samotná koncepce kurzu";;"kms" +"4977";"JJM249";"Duchovní kořeny evropské kultury";"Halada,J.";;"4";"1";"4";"4";"3";NULL;NULL;NULL;"1";"4";"2";"3";"3";"Nove vedomosti o filozofii a jej predstaviteloch";;"kz" +"4978";"JSM647";"Manažerské metody ve veřejné a sociální politice";"Ochrana,F.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"přístup vyučujícího, kdy srozumitelně vysvětluje velice složitou problematiku";;"kvsp" +"4979";"JMMZ318";"Mexican Politics, Economy and Society.";"Bernkopfová,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Broad range of topics covered. The approach of a lecturer.";"Cut down the readings a bit. When it is 80-100p a week, it´s a killer and not even dedicated students manage it.";"kas" +"4980";"JSM705";"Řízení kvality a performance management ve veřejné správě";"Plaček,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"přístup vyučujícího, který vyžaduje zpětnou vazbu, čímž přednášenou problematiku přibližuje praxi";;"kvsp" +"4981";"JJM355";"Teoretické koncepty ke studiu žurnalistiky I";"Jirků,J.,Němcová Tejkalová,A.";"Jirků,J.";"5";"4";"5";"5";"4";"5";"5";"5";"1";"4";"4";"5";"5";"Vysvetlenie jednotlivych pristupov k studiu zurnalistiky, prezentacie, ktore sme si pripravovali a doplnanie prednasok so seminarmi";;"kz" +"4982";"JEM034";"Corporate Finance";"Bychkova,O.,Malířová,T.";;"3";"3";"3";"5";"2";NULL;NULL;NULL;"1";"3";"4";"2";NULL;"Teacher was very kind and always tried to help. HW and Seminar Solutions posted in SIS were very helpful - the extra work was appreciated.";"I would like to see more theory and discussion as a background for practical examples";"ies" +"4983";"JJM251";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"4";"4";"3";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kz" +"4984";"JEM132";"Company Valuation";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Nejvíce oceňuju ucelenost a komplexnost tohoto kurzu, který nejen že studenta dokáže naučit studenta probíranou látku, ale také mu pomůže pochopit a přemýšlet o souvislostech. Konkrétně se mi hodně líbil úkol na identifikaci společností.";"Lepší přehled o průběžných bodech. Více času na Final, kde jsou otázky koncipované, tak aby se nad nimi člověk zamyslel - tak ať na to student dostane dostatek času (zkušenost z 1. termínu).";"ies" +"4985";"JJM356";"Metodologické přístupy ke studiu žurnalistiky";"Nečas,V.,Veselková,Z.";;"3";"3";"4";"3";"3";NULL;NULL;NULL;"2";"3";"4";"4";"3";;;"kz" +"4986";"JJM254";"Mediální tvorba";"Čásenský,R.";;"4";"2";"4";"5";"4";NULL;NULL;NULL;"1";"3";"3";"4";"4";;;"kz" +"4987";"JJM279";"Divadelní kritika";"Homolová Richtrová,N.";;"4";"2";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"5";"4";"Prakticke ukazky a navstevu divadla :)";;"kz" +"4988";"JJM362";"History of media";;"Neuzil,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Final presentation and communicating in English";;"kz" +"4989";"JJB255";"Digitální komunikace";;"Klimeš,D.";"4";"2";NULL;NULL;NULL;"5";"5";"3";"2";"3";"3";"3";"5";"Velmi mě bavily aktuality na začátku hodiny, považuji tyto diskuze za přínosné.";"Přestože se jedná o digitální marketing, tak je dle mého kurz již lehce zastaralý a nevěnuje se aktuálním trendům. Zároveň mi v kurzu chyběla praxe jak s digitálními nástroji pracovat, například SEO či PPC.";"kmkpr" +"4990";"JJB401";"Komerční a nekomerční marketingová komunikace";"Báča,L.,Obluk,O.";;"4";"5";"4";"4";"3";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Přestože jsem se toho hodně obávala, velmi mě bavil závěrečný úkol a podoba zkoušky. Obecně se spíše straním kreativních strategií, ale tento úkol mě velmi bavil, dobře jsme spolupracovaly ve skupince a vytvořily něco, na co jsem opravdu hrdá.";"Informace na přednáškách jsou takové, že pro prváka můžou být nesrozumitelné a pro druháka jdou už zase nudné, jelikož se opakují. Chtělo by to asi něco mezi tím, obecně mě témata přednášek tolik nezaujala.";"kmkpr" +"4991";"JJB630";"Krizová komunikace";"Chudinová,E.";;"3";"2";"3";"3";"2";NULL;NULL;NULL;"3";"2";"1";"1";"4";"Oceňuji, že nám byly zaslány prezentace a tudíž jsme měli materiály, ze kterých bylo možno se připravovat.";"Mrzelo mě, že termín průběžného testu nebyl oznámen již na začátku semestru, ale až týden před testem, což mi značně zkomplikovalo mé naplánované mimoškolní aktivity.";"kmkpr" +"4992";"JJB276";"Public relations v praxi";;"Hejlová,D.";"4";"2";NULL;NULL;NULL;"4";"4";"2";"1";"2";"2";"2";"4";"Oceňuji, že kurzem pricházely různé významné osobnosti z českého PR. Nápad s prací ve skupinkách byl také dobrý, ale nedotažený.";"Reálně zadávat úkoly. Fakticky jsme měli pouze dva, vyučující na následujících hodinách o nich ale často nic nevěděli a netušili, co s námi mají kontrolovat a jak je hodnotit. Celkově externisti nebyli obeznámeni s tím jak má kurz fungovat, což ůsobilo velké zmatky. Také bylo příslíbeno sdílení prezentací, ale nestalo se tak bohužel.";"kmkpr" +"4993";"JJB293";"Role výzkumů v politických a komerčních kampaních";;"Rosenfeldová,J.";"3";"1";NULL;NULL;NULL;"5";"2";"3";"3";"2";"2";"2";"4";"Pokud se již hodina konala tak byla zajímavá a zábavná.";"Hodiny často odpadaly, vzhledem k výuce externistů byl velký zmatek v tématech a především v hodnocení předmětu.";"kmkpr" +"4994";"JJB628";"Marketing módních značek - teorie";"Hejlová,D.,Koudelková,P.";;"3";"4";"4";"3";"3";NULL;NULL;NULL;"2";"2";"2";"2";"3";"Bavil mě mystery shopping, ale očekávala bych možná odlišné zpracování výsledků. Potěšila mě také návštěva výstavy na Kampě. Líbilo se mi i téma seminární práce, které ovšem mohlo být specifikováno dříve.";"Od kurzu jsem měla pravděpodobně úplně jiná očekávání, myslela jsem, že se naučím obecně jaké hlavní techniky se používají v oblasti módy a na co je třeba myslet v těchto kampaních, plus rozebrání konkrétních příkladů a značek. Ve výsledku si ale přímo z hodin příliš nepamatuji bohužel. Mrzelo mě pozdní zadání tématu seminární práce, zejména vzhledem k rozsahu a časové náročnosti a také oznámení testu pouze týden dopředu.";"kmkpr" +"4995";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"4996";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kz" +"4997";"JLB003";"Angličtina pro ekonomy I";;"Gloverová,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"4998";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ks" +"4999";"NMMA701";"Matematika 1";"Spurný,J.";"Rondoš,J.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"5000";"JJB135";"Filmový seminář I";;"Šobr,M.";"4";"1";NULL;NULL;NULL;"4";"3";"4";"1";"3";"2";"3";"4";;;"kz" +"5001";"JSM554";"Diplomový seminář";;"Remr,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Prezentování metod a diskuze, možnost zapojení všech";;"ks" +"5002";"JPB569";"Workshop Politické a státní instituce v praxi";;"Brunclík,M.";"4";"2";NULL;NULL;NULL;"5";"5";"4";"1";"4";"3";"4";"5";"Předmět mi přišel velmi praktický. Je dobré vidět, jak se můžeme s touto školou jednou uplatnit.Většina hostů byla zajímavá.";;"kp" +"5003";"JSB407";"Globální problémy životního prostředí a udržitelný rozvoj";"Drhová,Z.";;"4";"4";"4";"4";"5";NULL;NULL;NULL;"1";"4";"3";"3";"4";"Líbí se mi, že kurz nabízel něco trochu jiného, než tradiční politologie a sociologie. Práce na prezentacích a portfoliu byla zajímavá a přínosná.";;"kvsp" +"5004";"JSB490";"Úvod do politické sociologie";"Císař,O.";;"3";"5";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"3";;"Možná bych nebyl tak striktní ve vyřazování studentů prvních ročníků z kurzu. Přišlo mi to, jako dobrý doplněk k Úvodu do sociologie, kdy bylo možné propojit oborové souvislosti. Já jsem například již mnoho věcí ze sociologie zapomněl (jsem politolog) a musel jsem si je znova oživovat. Chápu však, že by bylo asi kontraproduktivní mít oba předměty v jednom semestru. Navíc sociologové možná měly nějaké další předměty, které jim pomohly se pohybovat více v souvislostech, takže je můj komentář možná zbytečný.";"ks" +"5005";"JPB587";"Víceúrovňové vládnutí (stát, region, občan)";"Perottino,M.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"2";"4";"3";"4";"4";"Velmi se mi líbilo, jak s námi docent Perottino na začátku přednášek diskutoval aktuální dění na politické scéně. Přednášky byly zajímavé a to i ty, které nevedl přímo docent Perottino.";;"kp" +"5006";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"1";"3";"1";"1";"1";NULL;NULL;NULL;"1";"1";"1";"1";"1";;;"kmv" +"5007";"JPM598";"Grand Strategies";"Ditrych,O.";;"1";NULL;"1";"1";"1";NULL;NULL;NULL;"1";"1";"1";"1";"1";;;"kbs" +"5008";"JPM561";"Regional Security Studies";"Karásek,T.,Klosek,K.";"Karásek,T.,Klosek,K.";"1";"3";"1";"1";"1";"1";"1";"1";"1";"1";"1";"1";"1";;;"kbs" +"5009";"JPM613";"Armed Forces and Society";"Kučera,T.";;"1";"1";"1";"1";"1";NULL;NULL;NULL;"1";"1";"1";"1";"1";;;"kbs" +"5010";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"1";"1";"1";"1";"1";NULL;NULL;NULL;"1";"1";"1";"1";"1";;;"kmv" +"5011";"JPM701";"European and Transatlantic Security";"Kazharski,A.";;"1";"5";"1";"1";"1";NULL;NULL;NULL;"1";"1";"1";"1";"1";;;"kbs" +"5012";"JPM118";"Výběrový seminář: Volby v USA";"Kotábová,V.";;NULL;"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"5013";"JPM127";"Geopolitika a geostrategie 1815 - 1945";"Romancov,M.";;"5";"3";"4";"4";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"5014";"JPM146";"Přechody k demokracii v teorii a praxi I";"Mlejnek,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"5015";"JPM342";"Konflikty v demokracii a národní identita: teoretické problémy analýzy";"Říchová,B.";;"1";"4";"3";"3";"3";NULL;NULL;NULL;"4";"4";"4";"4";"3";;;"kp" +"5016";"JPM579";"Teorie politických stran";"Perottino,M.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"5017";"JPM639";"Problémy ústavního inženýrství";"Brunclík,M.";;"5";"5";"4";"4";"3";NULL;NULL;NULL;"1";"3";"3";"3";"4";;;"kp" +"5018";"JJB284";"Firemní komunikace a kultura";"Poucha,T.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"5";"5";"Za mě nějvíce přínosný a zábavný povinně volitelný předmět. Pan Poucha je špičkou v oboru a i tu nejnudnější prezentaci dokáže přednášet zábavnou formou. Při hodnocení je relativně mírný, ale stále férový. Zasloužené 3 kredity.";;"kmkpr" +"5019";"JJM188";"Kvalitativní výzkum mediálních obsahů";;"Vochocová,L.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Tento kurz mi přišel rozhodně jako jeden z nejlepších. Vyhovoval mi menší počet účastníků, prakticky každý měl možnost zapojit se do diskuze, analýzy byly prováděné na konkrétních případech. Velký přínos podle mě měl kurz především pro ty, co již začali psát své diplomové práce. Vyučující se vždy snažila poradit a odnesl jsem si díky tomu spoustu věcných a trefných poznámek. Kurz bych tedy určitě doporučil.";;"kms" +"5020";"JEM163";"Principles of Microeconomics";"Janský,P.";"Král,M.,Moravcová,H.,Palanský,M.";"4";"3";"4";"5";"5";"5";"5";"4";"1";"4";"4";"5";"4";;;"ies" +"5021";"JEM179";"History and Methodology of Economics";"Baxa,J.,Paulus,M.";;"4";"4";"4";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"3";;;"ies" +"5022";"JLB104";"Czech for Chinese speaking students";;"Vaníčková,K.";"5";"2";NULL;NULL;NULL;"4";"5";"4";"2";"5";"5";"5";"5";;;"cjp" +"5023";"JLM063";"English for Chinese Speaking Students";;"Štěpánková,D.";"4";"2";NULL;NULL;NULL;"4";"5";"4";"1";"4";"4";"4";"4";;;"cjp" +"5024";"JPM648";"Politics of Security in Northeast Asia";"Karásková,I.";;"5";"3";"4";"5";"4";NULL;NULL;NULL;"2";"4";"3";"5";"5";;;"kmv" +"5025";"JPM664";"Geopolitics of Great Powers: China";"Karásková,I.";;"5";"3";"4";"5";"4";NULL;NULL;NULL;"2";"4";"4";"3";"5";;;"kp" +"5026";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kmv" +"5027";"JPM323";"Global Political Philosophy";"Salamon,J.";;"5";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kp" +"5028";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"4";"3";"2";"3";NULL;NULL;NULL;"1";"3";"1";"2";"2";;"Mluvit více nahlas";"ies" +"5029";"JPB256";"Politické systémy I";"Brunclík,M.,Jeřábek,M.,Kotábová,V.,Perottino,M.";;"4";"3";"4";"3";"5";NULL;NULL;NULL;"3";"4";"2";"3";"5";"Nespočetné zajímavosti";;"kp" +"5030";"JSB012";"Úvod do empirického výzkumu ve společenských vědách";"Jeřábek,H.";"Přibáňová,T.";"4";"3";"5";"5";"4";"4";"5";"4";"2";"4";"3";"4";"4";;;"ks" +"5031";"JSB025";"Sociální problémy";"Frič,P.";;"3";"5";"5";"5";"4";NULL;NULL;NULL;"1";"3";"2";"3";"3";;;"kvsp" +"5032";"JSB028";"Informační gramotnost";"Tomandlová,V.";;"5";"2";"4";"5";"5";NULL;NULL;NULL;"1";"3";"1";"2";"4";;;"kvsp" +"5033";"JSB407";"Globální problémy životního prostředí a udržitelný rozvoj";"Drhová,Z.";;"5";"3";"5";"5";"3";NULL;NULL;NULL;"2";"4";"2";"2";"4";;;"kvsp" +"5034";"JSB513";"Úvod do akademické práce";"Höfer,K.,Mouralová,M.,Veselý,A.";;"4";"3";"4";"3";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kvsp" +"5035";"JEB039";"International Trade";"Semerák,V.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"4";"5";"During the course I studied new approaches in analyzing international trade. Input-output tables, the general equilibrium model will be usefull for my future studies and career. The course also provides better understanding of effects of trade policies.";;"ies" +"5036";"JEB044";"Financial Accounting";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"5";"5";;"Home assigniments were much simpler than midterm and final tests, assignments contained only multiple choice questions. I would like to practice more in solving difficult open-ended questions with calculations, because they help in understanding concepts form lectures.";"ies" +"5037";"JJM329";"Média a kultura";"Bednařík,P.,Jirák,J.";;"4";"3";"5";"5";"3";NULL;NULL;NULL;"1";"2";"3";"3";"4";;;"kms" +"5038";"JJM330";"Trendy současných českých médií";"Aust,O.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";;;"kms" +"5039";"JJM331";"Výzkum médií II";"Vochocová,L.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"5040";"JJM332";"Přirozený jazyk a média";"Podzimek,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"5041";"JJM334";"Diplomový seminář";;;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"3";"3";"3";"5";;;"kms" +"5042";"JEM123";"Economics of Least Developed Countries";"Bauer,M.";"Bauer,M.";"4";"2";"4";"4";"3";"4";"4";"4";"1";"4";"3";"4";"4";;;"ies" +"5043";"JEM181";"Data Science with R";"Herman,D.,Krištoufek,L.";"Herman,D.,Krištoufek,L.";"4";"3";"5";"5";"4";"5";"5";"5";"1";"4";"5";"5";"5";;;"ies" +"5044";"JMB503";"Soudobé české dějiny";"Kocian,J.";;"3";"2";"3";"5";"3";NULL;NULL;NULL;"1";"3";"3";"3";"4";;;"krvs" +"5045";"JMB508";"Soudobé dějiny východní Evropy";"Kolenovská,D.,Svoboda,K.";;"4";"5";"4";"4";"3";NULL;NULL;NULL;"1";"5";"4";"4";"4";;;"krvs" +"5046";"JMM674";"Maritime security: Geopolitics of the Indian and Pacific Oceans";"Hornát,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kas" +"5047";"JMB509";"Soudobé dějiny jihovýchodní Evropy";"Tejchman,M.";;"4";"4";"4";"5";"3";NULL;NULL;NULL;"1";"4";"3";"3";"4";;;"krvs" +"5048";"JMMZ315";"U.S. Foreign Policy";"Raška,F.";;"2";"2";"1";"2";"1";NULL;NULL;NULL;"2";"1";"1";"1";"1";"So far the worst mandatory class within the SAS. During the classes the teacher only repeats the mandatory texts which were quite uninteresting. It seems that the teacher likes to listen to himself. I took very little amount of knoledge from the class. I expected that I will learn more pratical aspects and evolution and current issues of the the US foreign policy. The requirements (15pgs long essay and short 5 min presentation) were laughable, during the oral exam I had a feeling the teacher did not read my essay.";"change teacher. there needs to be emphasis on current and practical aspects of the foreign policy. The texts and the classes were focused purely on the actors. the mandatory readings are too much of a central point of the class.";"kas" +"5049";"JEM040";"Účetní a daňové poradenství";;"Kemény,I.";"4";"4";NULL;NULL;NULL;"4";"4";"4";NULL;"4";"4";"4";"4";;;"ies" +"5050";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kp" +"5051";"JPM146";"Přechody k demokracii v teorii a praxi I";"Mlejnek,J.";;"4";"4";"4";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"4";;;"kp" +"5052";"JPM150";"Poloprezidentské režimy v postkomunistické Evropě";"Mlejnek,J.";;"5";"4";"4";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kp" +"5053";"JPM160";"Česká komunální politika";"Jüptner,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kp" +"5054";"JPM342";"Konflikty v demokracii a národní identita: teoretické problémy analýzy";"Říchová,B.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"3";"4";"4";"4";"4";;;"kp" +"5055";"JPM579";"Teorie politických stran";"Perottino,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";;;"kp" +"5056";"JPM653";"Politika a média";"Švec,K.";;"4";"2";"4";"4";"3";NULL;NULL;NULL;"1";"3";"3";"3";"5";;;"kp" +"5057";"JMMZ339";"Populism in the U.S.";"Klvaňa,T.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"I liked the form of the final exam";"there was perhaps too much attention to Plato, Machiavelli and Hobbs. I could see spending less time on them and focus even more on the current issues of the US populism, maybe compare it with Europe/World. Other than that, the Class was really good";"kas" +"5058";"JMMZ340";"Freedom of Speech";"Klvaňa,T.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"I liked the book by Garton Ash which has been the central point of the class in combination with the websites. I also enjoyed the way how the final test was conducted.";"The book was great, but i could imagine to use some other sources as well.";"kas" +"5059";"JLB033";"Němčina I";;"Faltýnová,R.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"5";"5";;;"cjp" +"5060";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"4";"2";"1";"1";NULL;NULL;NULL;"1";"2";"2";"2";"1";;;"ies" +"5061";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"4";"1";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"3";;;"krvs" +"5062";"JMB402";"Úvod do společenských věd II";;"Šafařík,P.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"4";"4";;;"krvs" +"5063";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Šafařík,P.";"5";"1";"5";"5";"5";"4";"5";"5";"1";"5";"4";"5";"5";;;"knrs" +"5064";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Janeček,P.";"5";"1";"5";"5";"5";"5";"5";"5";"1";"5";"4";"5";"5";"Skvělé semináře s panem doktorem Janečkem, který probíranou látku dokázal vždy zajímavým způsobem podat.";;"krvs" +"5065";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"4";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"1";"4";"4";;;"knrs" +"5066";"JMB036";"Moderní dějiny Běloruska";"Zilynskyj,B.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Pan doktor Zilinskyj je opravdovou kapacitou ve svém oboru a o daném tématu dokázal zajímavě vyprávět. Přínosné byly také časté přesahy do dějin jiných států (Ukrajina, Litva, Polsko, Rusko).";;"krvs" +"5067";"JJM208";"Mediální systémy a jejich komparace";"Miessler,J.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"3";"5";"4";"4";"4";;;"kms" +"5068";"JJM210";"Kvantitativní obsahová analýza";;"Nečas,V.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"5";"5";"4";"3";"5";;;"kms" +"5069";"JJM226";"Teorie účinků médií";"Nečas,V.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kms" +"5070";"JJM229";"Vývoj televizního vysílání v českých zemích";"Štoll,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"5";"5";;;"kms" +"5071";"JJM295";"Rozhlasový a televizní dokument";"Štoll,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"3";"4";"5";;;"kz" +"5072";"JJM200";"Diplomový seminář";;;"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"3";"5";;;"kms" +"5073";"JJM290";"Tvůrčí dílny I – rozhlas a televize";;"Lokšík,M.";"2";"1";NULL;NULL;NULL;"1";"2";"1";"1";"3";"3";"3";"2";;;"kz" +"5074";"JJM259";"Mediální systémy a jejich komparace";"Miessler,J.";;"2";"3";"2";"3";"1";NULL;NULL;NULL;"1";"2";NULL;"1";"1";;"- zahrnout do učiva něco jiného, než to co si můžeme přečíst doma";"kz" +"5075";"JJM254";"Mediální tvorba";"Čásenský,R.";;"4";"2";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kz" +"5076";"JJM356";"Metodologické přístupy ke studiu žurnalistiky";"Nečas,V.,Veselková,Z.";;"2";"4";"3";"2";"2";NULL;NULL;NULL;"1";"2";"3";"2";"3";;;"kz" +"5077";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"4";"1";"5";"5";"3";NULL;NULL;NULL;"1";"4";"2";"5";"5";;;"ies" +"5078";"JJM248";"Vývoj grafického designu a polygrafického zpracování periodik";"Slanec,J.";;"2";"3";"2";"3";"1";NULL;NULL;NULL;"1";"3";"1";"3";"2";;;"kz" +"5079";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"3";"2";NULL;NULL;NULL;"3";"2";"2";"2";"2";"1";"3";"4";"Diskuze vedené panem Benem, Kopcem a Josefem byly fakticky přínosné a zábavné. Myslím, že by měli dostávat více prostoru. Umí vést a řídit diskuzi tak, aby i studenti se dostali ke slovu.";;"ies" +"5080";"JJM251";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"3";"4";"3";"3";"3";NULL;NULL;NULL;"1";"3";"1";"3";"3";;;"kz" +"5081";"JEB026";"European Economic Integration";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"3";"3";"1";"3";"1";"1";"2";"1";"2";"4";"3";"3";"3";;;"ies" +"5082";"JEB047";"Účetnictví II";"Kemény,I.";;"3";"3";"3";"5";"2";NULL;NULL;NULL;"4";"3";"1";"3";"4";;;"ies" +"5083";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"3";"2";NULL;NULL;NULL;"5";"5";"2";"2";"3";"4";"3";"5";;;"ies" +"5084";"JEB110";"Econometrics II";"Pertold-Gebicka,B.";"Chorna,O.,Malinská,B.,Pertold-Gebicka,B.,Pleticha,P.";"3";"3";"2";"4";"2";"5";"3";"5";"1";"3";"3";"3";"3";;;"ies" +"5085";"JEB111";"Advanced Data Analysis in MS Excel";;"Poláková,N.,Polák,P.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"5";"5";"Předmět jsem si užíval s každou hodinou více. Moje znalosti Excelu se rapidně zlepšily a předmět doporučuji každému. Klidně i v prvním ročníku. Pan Polák je skvělým přednášejícim, tedy alespoň na Excelu, předmětu rozumí a výklad místy i pobaví. Hledáte-li tedy prakticky zaměřený předmět, kde se Excel naučíte od základů, jděte do toho.";;"ies" +"5086";"JEB111";"Advanced Data Analysis in MS Excel";;"Poláková,N.,Polák,P.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"5";"5";;;"ies" +"5087";"JEM020";"Ethics and Economics";"Cahlík,T.";"Cahlík,T.";"3";"2";"4";"5";"1";"5";"5";"1";"1";"3";"3";"1";"1";;;"ies" +"5088";"JJB334";"Zábava v médiích";"Kruml,M.";;"3";"2";"5";"4";"4";NULL;NULL;NULL;"2";"2";"2";"1";"1";;;"kms" +"5089";"NMMA701";"Matematika 1";"Spurný,J.";"Rondoš,J.";"5";"5";"5";"5";"5";"1";"4";"1";"2";"5";"5";"5";"5";"Pan Spurný má skvělé přednášky. Velmi prostudentsky orientované. Doporučuji se aktivně zapojovat již během semestru na přednáškách a cvičeních. Zásadním způsobem usnadní zkouškové období.";;"ies" +"5090";"JJB021";"Bakalářský seminář";;"Prázová,I.";"1";"2";NULL;NULL;NULL;"3";"4";"1";"1";"1";"1";"2";"1";;;"kz" +"5091";"JSM521";"Veřejná politika";"Chalupová,P.,Potůček,M.";;"3";"4";"5";"3";"4";NULL;NULL;NULL;"1";"3";"3";"2";"4";"Líbily se mi diskuze v rámci kurzu na aktuální témata, jinak je hodně nutná domácí příprava a kurz samotný je spíš o úvodu do problematiky. Pan profesor Potiůček však přednáší velmi poutavě a zajímavě, studentni jsou zapojeni do přednášek, ptá se na jejich názory, to se mi moc líbilo.";"Kurz je hodně teoretický. Odevzdání seminární práce by nemělo být vázané na připuštění ke zkoušce. Pokud má student téma náročné na zíksání informací z různých institucí, psaní práce to značně prodlouží a je škoda jít kvůli tomu až na posledí termín, obzvlášť, když ne všechny termíny studentovi časově vyhovují, vzhledem k pracovním povinnostem. Také se mi nelíbil přistup konzultantky seminární práce, která požadovala citovat ve formátu závorkách v textu. protože citování ve formátu poznámek pod čarou se jí nelíbí.";"kvsp" +"5092";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"1";"2";"2";"1";"5";;;"kz" +"5093";"JLB041";"Španělština I";;"Mlýnková,L.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";;;"cjp" +"5094";"JEM034";"Corporate Finance";"Bychkova,O.,Malířová,T.";;"5";"3";"4";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ies" +"5095";"JPB583";"Politický systém ČR II. (regionální a lokální úroveň)";"Hornek,J.,Jüptner,P.,Musilová,K.,Němcová,L.";;"5";"4";"4";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"5096";"JPM648";"Politics of Security in Northeast Asia";"Karásková,I.";;"4";"2";"4";"5";"3";NULL;NULL;NULL;"3";"3";"4";"4";"4";;;"kmv" +"5097";"JEM062";"Introductory Econometrics";"Kukačka,J.";"Kukačka,J.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"5098";"JLB101";"Czech as a Foreign Language II";;"Mazúrková,B.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"5099";"JSB490";"Úvod do politické sociologie";"Císař,O.";;"3";"5";"5";"3";"3";NULL;NULL;NULL;"1";"3";"3";"3";"3";;"V ramci kurzu nekteri studenti meli moznost pripravit si prezentace spojene s danou temou prednasky. Bohuzel prednasejici nikdy nikoho nenechal dokoncit prezentace, protoze vzdy je prerusil a pokracoval s vlastnim vykladem. Taktez dane prezentace nebyli nikde k dispozici, i kdyz na ne, podle slov prednasejiciho navazuje zkouska (kdyz prezentujici nemeli moznost prezentovat vsechny slajdy, divak se nema ke vsem informacim jak dostat). Asi by melo dojit ke zmene konceptu prednasek, vyklad prednasejiciho byl prinosem, takze prezentace studentu nejsou treba.";"ks" +"5100";"JMMZ143";"Russian Language III";;"Shvedova,O.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"krvs" +"5101";"JPM191";"Geopolitics of Great Powers: Russia";"Baštář Leichtová,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"5102";"JEM137";"Real Estate Investment";"Jandík,T.,Streblov,P.";;"2";"4";"2";"5";"2";NULL;NULL;NULL;"2";"2";"2";"2";"2";;;"ies" +"5103";"JEB105";"Statistics";"Červinka,M.";"Hanus,L.";"3";"4";"3";"5";"3";"2";"4";"4";"1";"4";"4";"3";"3";;;"ies" +"5104";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"2";"4";"2";"3";"1";"4";"3";"2";"1";"3";"2";"3";"2";;;"ies" +"5105";"NMMA703";"Matematika 3";"Zelený,M.";"Johanis,M.";"4";"4";"5";"5";"4";"5";"3";"5";"1";"4";"4";"3";"4";;;"ies" +"5106";"JMM039";"Západní Evropa a svět";"Tomalová,E.,Váška,J.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"2";"4";"3";"4";"4";;;"kzs" +"5107";"JMM279";"Společnost a ekonomika";"Fiřtová,M.";"Young,M.";"5";"4";"5";"5";"5";"5";"4";"5";"1";"5";"5";"3";"4";;;"kas" +"5108";"JSB513";"Úvod do akademické práce";"Höfer,K.,Mouralová,M.,Veselý,A.";;"4";"4";"5";"5";"3";NULL;NULL;NULL;"2";"3";"4";"2";"3";;"Stejna situace jako u uvodu do statistiky pro studenty PVP. Podle slov prednasejiciho by studenti oboru PVP meli mit vlastni cviceni, protoze nejsou od nas vyzadovane takove naroky jako od studentu oboru Sociologie. Bohuzel pro nedosatek cvicicich nastala situace, kdy pocet sociologu na cviceni pro PVP byl 2-3x vyssi (plus studenti kteri maji statnice ze statistiky) nez studenti PVP. Dane ucebny nejsou ani kapacitne na to uzpusobene, Takze se slo tempem pro ne, co rekl i cvicici na zacatku kurzu, ze na to nebude brat ohled, ze je to cviceni pro PVP. Stejne tak se k nam nedostali o tom, ze existuje FB skupina pro dany kurz, kam se sdileji informace. a bylo nam oznameno jen tak mimochodem nekdy kolem zacatku prosince.";"kvsp" +"5109";"JMM128";"Prezentace v médiích";"Procházková,B.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"2";"4";;;"kzs" +"5110";"JMM277";"Historie a kultura";"Vykoukal,J.";"Bauer,P.";"3";"4";"3";"3";"2";"3";"3";"2";"1";"3";"3";"4";"3";;;"krvs" +"5111";"JMM629";"Hollywood/Europe: A Transnational Film Culture.";"Nowell,R.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"3";"5";"It was one of the best courses I have ever had so far,... Professionally prepared, totally interesting, I have learnt a lot, seen a lot, started to think differently about certain issues. Richard was always willing to help, he a very smart guy. Really great, thanks for this.";"Two things - first, the papers which sometimes gave me a little bit of trouble because they were quite time demanding. 3 papers per semester seem to be a little bit too much, especially if this is not the obligatory course. However, I didn´t mind writing them in the end, because it gave me a great amount of new information, it´s just maybe that 2 would be more okay I guess. Then, just a little recommendation for Richard - keep in mind that not every student is cool with the British accent, especially when speaking so fast!";"kas" +"5112";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"2";"1";"1";"2";NULL;NULL;NULL;"1";"3";"2";"3";"2";;;"ies" +"5113";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"5114";"JLB033";"Němčina I";;"Křenková,D.";"4";"3";NULL;NULL;NULL;"4";"5";"4";"1";"3";"3";"3";"4";;;"cjp" +"5115";"JMB402";"Úvod do společenských věd II";;"Fiřtová,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"krvs" +"5116";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Šafařík,P.";NULL;NULL;"4";"4";"3";"4";"5";"5";NULL;NULL;NULL;NULL;NULL;;;"knrs" +"5117";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Kůželová,M.";NULL;NULL;"4";"4";"4";"3";"3";"3";NULL;NULL;NULL;NULL;NULL;;;"krvs" +"5118";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;NULL;NULL;"4";"5";"3";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"knrs" +"5119";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;NULL;NULL;"4";"5";"5";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kas" +"5120";"JMM040";"Societal changes in Western European countries";"Bauer,P.";;"2";"3";"2";"4";"1";NULL;NULL;NULL;"2";"2";"1";"2";"1";;"Frontal way of teaching which made the course not just uninteresting, but boring. The information given were chaotic, lot of names, no clear link between what students are studying and what they were doing in the course.";"kzs" +"5121";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Buřičová,B.";"3";"4";"2";"2";"1";"5";"5";"5";"1";"3";"3";"2";"3";"Oceňuji práci a ochotu cvičících.";"Její celkovou organizaci, chod zkoušek byl zmatený.";"ks" +"5122";"JSB028";"Informační gramotnost";"Tomandlová,V.";;"4";"1";"3";"3";"5";NULL;NULL;NULL;"1";"4";"5";"4";"5";"Cením si dovedností, které jsem měla možnost alespoň z části otestovat.";"nic mě nenapdadá";"kvsp" +"5123";"JMM663";"Europe in the French mind: a historical–civilizational point of view";"Bauer,P.";;"2";"3";"2";"3";"2";NULL;NULL;NULL;"2";"2";"2";"2";"2";;;"kzs" +"5124";"JSB530";"Úvod do sociologického myšlení";"Bureš,J.,Numerato,D.";"Wirthová,J.";"4";"4";"4";"5";"3";"5";"5";"4";"2";"3";"4";"4";"4";"Oceňuji přístup vyučujících i cvičících jak k výuce, tak i ke studentům. Navrhuji zachovat dosavadní deníkové záznamy.";"Možná občas scházelo dostatečné vysvětlení ohledně úkolů, avšak nejednalo se o ni zásadního, co by se následně nevyjasnilo.";"ks" +"5125";"JMM200";"Roots of American Music - Folklore, Blues, Jazz";"Calda,M.";;"4";"2";"5";"5";"3";NULL;NULL;NULL;"1";"5";"3";"2";"5";;;"kas" +"5126";"JMM530";"European Comparative Politics and Society";"Rovná,L.";;"3";"3";"5";"5";"3";NULL;NULL;NULL;"1";"4";"2";"4";"4";;;"kzs" +"5127";"JMM271";"Metodologický seminář";;"Matějka,O.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"3";"4";;;"krvs" +"5128";"JSB007";"Dějiny sociologie I";"Šanderová,J.";"Cuker,I.";"4";"2";"4";"4";"5";"4";"5";"4";"3";"4";"4";"4";"5";;;"ks" +"5129";"JLB053";"Angličtina pro sociální vědy I";;"Štěpánková,D.";"4";"2";NULL;NULL;NULL;"3";"4";"4";"1";"3";"3";"1";"4";;"Čas výuky.";"cjp" +"5130";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"4";"3";"2";"2";NULL;NULL;NULL;"1";"5";"3";"3";"4";;"Komunikaci vyučujícího se studenty.";"ies" +"5131";"JSB544";"Vybrané kapitoly středoškolské matematiky";;"Hendl,J.";"2";"3";NULL;NULL;NULL;"3";"1";"3";"1";"2";"2";"1";"1";;;"ks" +"5132";"JSM642";"Metody práce s informacemi";"Tomandlová,V.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"3";"5";"4";"5";;;"kvsp" +"5133";"JSM640";"Základy sociologie";"Paulíček,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";"Diskuze na aktuální témata během přednášek. Vstřícný přístup přednášejícího ke studentům.";;"kvsp" +"5134";"JJM117";"Popular Culture";"Turnau,T.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"5";"5";;;"kms" +"5135";"JSM522";"Veřejná ekonomie";"Kotherová,Z.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"4";"5";;;"kvsp" +"5136";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"2";"4";"3";"4";"5";;;"ies" +"5137";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"4";"2";NULL;NULL;NULL;"4";"4";"5";"1";"4";"4";"4";"4";;;"ies" +"5138";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"4";"3";"4";"5";"3";"4";"5";"4";"2";"4";"4";"4";"5";;;"ies" +"5139";"JJM242";"Comics as a Medium";"Hrdina,M.";;"4";"2";"3";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"3";;;"kms" +"5140";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"2";NULL;NULL;NULL;"4";"4";"4";"1";"3";"3";"3";"5";;;"kz" +"5141";"JJB221";"Ekonomická teorie marketingu";"Postler,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kmkpr" +"5142";"JLB003";"Angličtina pro ekonomy I";;"Poslušná,L.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"5143";"JMM273";"Diplomový seminář II";;"Králová,K.,Svoboda,K.,Švec,L.";"4";"4";NULL;NULL;NULL;"4";"5";"4";"1";"3";"4";"4";"4";;;"krvs" +"5144";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"ks" +"5145";"NMMA701";"Matematika 1";"Spurný,J.";"Honzík,P.";"5";"5";"5";"5";"5";"4";"3";"5";"1";"4";"5";"5";"4";;;"ies" +"5146";"JPM052";"Mezinárodní ekonomické vztahy (MEV)";"Kučerová,I.";;"3";"5";"4";"5";"3";NULL;NULL;NULL;"2";"3";"1";"4";"1";;"The course was tough to overcome but it needed to be done as it is compulsory. However, I believe that if students of international relations need to pass a course on international economic relations, it could be done much more interesting. This course is rather about memorizing (years, treaties, systems, divisions, etc.) than learning to understand functionning of international economic relations. It would have been more valuable for me personally if this course was more about actual happening- actual cooperations, treaties, economic sanctions, transatlantic economic relations, EU-USA cooperation, EU member states-China/Russia cooperation. There are many ways to grasp the topic.";"kmv" +"5147";"JMM279";"Společnost a ekonomika";"Fiřtová,M.";"Svoboda,K.";"4";"5";"5";"5";"5";"4";"5";"4";"1";"4";"3";"5";"4";"Oceňuji zajímavý výběr témat jak na přednášce, tak na semináři (RES)";;"kas" +"5148";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"4";"2";"2";"2";NULL;NULL;NULL;"1";"3";"3";"3";"3";;"Pan profesor by mohl začít používat mikrofon, je těžké slyšet někoho kdo mluví potichu v hale kde je 100 lidí";"ies" +"5149";"JSM421";"Contemporary social theory";"Balon,J.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"2";"4";"4";"4";"4";;;"ks" +"5150";"JJM360";"Ekonomika v médiích";"Klimeš,D.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kz" +"5151";"JSM527";"Metody analýzy a tvorby politik II.";"Veselý,A.";;"5";"5";"5";"4";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kvsp" +"5152";"JJM208";"Mediální systémy a jejich komparace";"Miessler,J.";;"4";"1";"4";"5";"3";NULL;NULL;NULL;"3";"3";"3";"4";"4";;;"kms" +"5153";"JPB227";"Politický system ČR";"Charvát,J.";;"3";"1";"3";"5";"1";NULL;NULL;NULL;"1";"4";"1";"3";"3";;;"kp" +"5154";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"4";"4";"4";"4";"4";"3";"3";"3";"1";"5";"2";"5";"5";;"Preferoval bych opětovné zavedení seminářů.";"kp" +"5155";"JPB260";"Politické myšlení novověku III";"Kučera,J.";;"2";"4";"2";"3";"1";NULL;NULL;NULL;"3";"3";"1";"3";"2";;"Velice mi nevyhovuje neexistující, respektive neaktuální sylabus.";"kp" +"5156";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"5";"2";"3";"4";;"- Lepší hodnocení prezentací (téěř plný počet bodů za prezentaci prý dostali i ti, kteří reálně ani neprezetovali)- Nějakým způsobem zlepšit test, kdy nebude třeba jen vědět informace, ale také pochopit souvislosti (otázky typu kdo a kdy byl prezidentem/premiérem mi přijdou nesmyslné. Smutné je pak zejména to, když se tyto informace dají lehce vyhledat na telefonu, což někteří při testu praktikují)";"kp" +"5157";"JPB268";"Evropská integrace";"Plechanovová,B.";;"2";"3";"3";"3";"1";NULL;NULL;NULL;"4";"2";"1";"3";"1";;;"kmv" +"5158";"JPB578";"Classics of Political Thought";"Salamon,J.";;"5";"1";"4";"5";"5";NULL;NULL;NULL;"1";"5";"2";"3";"5";;;"kp" +"5159";"JPB593";"Political Economy of Regionalism";"Miková,I.";;"2";"3";"3";"5";"3";NULL;NULL;NULL;"1";"3";"1";"1";"2";;;"kmv" +"5160";"JJM343";"Interkulturní komunikace";"Soukup,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"3";"4";"3";"4";"5";"Děkuji za zábavné, energické a poučné začátky dlouhé čtvrteční výuky! Velice mě bavily praktické úkoly, možnost vyjádřit svůj názor, zachytit svou oblast zájmu v závěrečné eseji i určitá tajemnost a svéráz celého výkladu. Je vidět, že pan docent je vyučující na svém místě. Předmět chválím a doporučuji, kudy to jen jde. :)";"Nejsem si jistý, zdali je to ke zlepšení nebo ne (resp. jsem vcelku přesvědčený, že jde o záměr), ale při některých praktických úkolech v hodině bylo zpočátku podnícení aktivity prakticky nulové - např. u předávání čokolády nebo u evropských hodnot - a aktivita působila dost rozpačitě pro obě strany. Obávám se, že to je pouze lidským faktorem, ale je možné, že by malá změna, povzbuzení nebo impuls k vytvoření diskuze stačil k větší aktivitě.Také by se hodilo na jedné z posledních přednášek připomenout nutnost odevzdat esej - osobně jsem o tom věděl, ale vím, že ta zmínka padla na začátku kurzu a to je poměrně dlouhá doba.Ocenil bych navazující předmět, který bude více konkrétní - tento předmět považuji za určité nahlédnutí pod pokličku, snahu o uvědomění si problematiky interkulturní komunikace, ale možnost navštěvovat předmět s komplexnějším obsáhnutím rozdílů mezi jednotlivými kulturami by se mi jakožto absolventovi marketingové komunikace a PR líbilo velice a byl bych schopný tyto poznatky použít i v praxi.";"kms" +"5161";"JSM026";"Klíčové otázky sociální antropologie";"Grygar,J.,Hrešanová,E.,Uherek,Z.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"4";"5";"Zpracování vlastní úvahy na dané téma před samotným kurzem – umožnilo to krystalizovat myšlenky a tak i snadné (a občas i vášnivé) zapojení do debaty s ostatními kolegy v rámci samotného semináře.";"Ačkoli se mi líbil formát - tedy každá hodina věnovaná jedné kapitole/úvaze z Tima Ingolda, bylo by dobré ke kurzu vypsat více současné literatury zabývající se daným tématem. Občas byly v literatuře pouze texty, které nechají nahlédnout do debat před 20 až 30 lety, ale ne do současného diskurzu.";"ks" +"5162";"JJM243";"Média a životní styl";"Knapík,J.";"Knapík,J.";"4";"4";"4";"5";"5";"4";"5";"4";"2";"5";"2";"5";"3";"Je vidět, že pan docent je do tématu velice zapálený, jeho znalosti a přehled jsou úctyhodné! Také oceňuji férový přístup, zejména z hlediska závěrečné eseje a otevřenosti jiným než vypsaným tématům. Co se týče kurzu jako takového, líbí se mi záběr, který kurz má - obsahem jsou velmi cenné znalosti, které často střední školy v dějepisných hodinách nestíhají pokrýt a/nebo jsou natolik specificky zaměřené, že by bylo třeba se probrat velkým množstvím literatury. Navíc jde o období naší historie, které známe pouze z vyprávění prarodičů a rodičů a které je velice důležité pro uvědomění si kvalit tehdejších a dnešních poměrů, zvláště s ohledem na nálady společnosti v dnešní době.";"Zapálenost přednášejícího a záběr kurzu s sebou bohužel přináší velice náročné přednášky, při kterých si stíhají zapisovat jen ti nejrychlejší a bylo znát, že postupem času řady přítomných studentů prořídly. Myslím, že by bylo dobré některým tématům (především těm nesouvisejícím s médii) ubrat na rozsahu přednášek. Jsem si jistý, že by to naopak přineslo pozitivnější přístup k přednáškám.Podobný dojem mám i z podmínek atestu - fyzické i psychické vypětí při intenzivních přednáškách a následné zjištění potřeby 10 normostranové eseje znamenaly odliv zapsaných studentů, neboť v daný den měla velká část z nás předměty od půl 10 do 5 odpoledne bez možnosti oběda. Jsem si jistý, že zmírnění počtu normostran i přesunutí mimo čas oběda by tomuto předmětu prospělo.";"kms" +"5163";"JJB334";"Zábava v médiích";"Kruml,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"1";"5";"5";"Nazorne ukazky, zkusenosti z praxe.";;"kms" +"5164";"JSM421";"Contemporary social theory";"Balon,J.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"2";"4";"3";"4";"4";;;"ks" +"5165";"JJB618";"Propagandistické působení médií v 1 polovině 20. století";"Suk,P.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"1";"5";"5";"Fotografie, obrazky.";"Poskytnout aspon nejake pisemne podklady k predmetu - kdyz si clovek pise, co prednasejici rika, nestiha zaznamenavat to, co se zrovna vyskytuje v prezentacich, tudiz student nemuze mit i pri plne koncentraci kompletni obsah prednasky.";"kms" +"5166";"JJM080";"Zábava v televizním vysílání";"Kruml,M.";;"4";"1";"4";"5";"4";NULL;NULL;NULL;"3";"4";"2";"4";"4";"Kurz navazuje i na jiné předměty, např. na Vývoj a postavení médií v moderní společnosti. To je jedině dobře, přináší totiž další souvislosti k danému učivu. Opět je úctyhodný zápal vyučujícího, na přednáškách je velice znát, že se v daném oboru orientuje a je znalý. Perfektní je bohatý počet ukázek, pohled \"za oponu\" i široké žánrové zastoupení.";"V průběhu kurzu mi vadily zejména dvě věci: jednoduše řečeno ne všechny ukázky byly \"mým šálkem kávy\" a myslím si, že u témat jako jsou estrády není třeba pouštět takové množství materiálu. S tím se váže i skutečnost, že často nebylo nahrávkám (zejména oněm estrádám) vůbec rozumět. Je mi jasné, že to je způsobeno stářím některých ukázek, ale bylo by dobré s tím počítat a udělat určitou zvukovou předpřípravu. Stávalo se mi, že jsem z toho důvodu nebyl schopný udržet pozornost - mluvící \"hlava\", které není rozumět, mi nepřišla nikterak zajímavá.";"kms" +"5167";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"ks" +"5168";"JPM716";"The Geopolitics of Defence Industry and Arms Trade";"Kopečný,T.";;"3";"3";"5";"4";"4";NULL;NULL;NULL;"1";"3";"3";"3";"4";"political outlooks on arms industry";"slides and visual ways of teaching";"kp" +"5169";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";;;"kas" +"5170";"JSM406";"Statistics in SPSS";;"Soukup,P.";"1";"5";NULL;NULL;NULL;"1";"1";"2";"3";"1";"3";"3";"1";;"lecture slides didn't correspond with hw which was confusing, not explained well , course outline of work changed through out the semester";"ks" +"5171";"JSB534";"Introduction to Visual Sociology";"Wladyniak,L.";;"4";"2";"4";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";"very interesting great teacher clear outlines of course , good atmosphere for learning , good english speaking teacher and g";;"ks" +"5172";"JSM477";"Sociology of Critique";"Blokker,P.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"4";"the most interesting and challenging course i had with a great teacher";;"ks" +"5173";"JJM234";"Media and Society: An Introduction";"Jirák,J.";;"3";"5";"5";"5";"5";NULL;NULL;NULL;"3";"3";"4";"4";"3";"Občas to bylo utrpení, ale jsem rád za nutnost odevzdávat pravidelné týdenní novinky ze světa médií! Následné debaty mi u spousty jiných předmětů alespoň trochu chyběly.";"Obávám se, že to kurz jako takový nezlepší, ale celkovým zklamáním byli zahraniční studenti.Některým nebylo vůbec rozumět - a jsem rád, že byla snaha je na to upozorňovat i ze strany vyučujícího - zdaleka největším problémem byla spolupráce s nimi na závěrečném projektu. Zadání projektu bylo dle mého možná moc vágní, ale určitě by nezachránilo nedodržování termínů, neúčast na společných schůzkách, práce dle jiného než domluveného zadání a další problémy, které celkově snížily můj dojem z jinak dobrého a hlavně aktuálního předmětu. A to nelze říci, že bych týmové projekty neměl rád - praktické projekty mi na tomto oboru vysloveně chybí. S tím souvisí i skutečnost, že by vyučující měl více zasahovat do složení týmů, nejen co do počtu různých národností, ale i pohlaví nebo třeba studovaných oborů. Takto se výběr členů týmů omezil prakticky jen na okolní přísedící.Stejně tak mi někdy přišly \"otravné\" stále se opakující zprávy. Např. kauza MeToo zabrala minimálně 4 týdny za sebou a to samozřejmě zejména v době, kdy už toho pro každého muselo být hodně, nehledě na závažnost tématu. Opět se však jedná o lidský faktor, který s předmětem jako takovým má pramálo společného.";"kms" +"5174";"JPM611";"Cyber Security";"Duračinská,Z.,Střítecký,V.";;"4";"4";"4";"5";"4";NULL;NULL;NULL;"1";"3";"3";"4";"4";"The Professor was always available and tried her best in making students understand. The Professor showed the geopolitical implication of cyber threats and attacks, which is therefore linked with the field of international security.";"A clear structure of the entire course to be complemented with each class structure might better help students to understand the purpose of the class and put all classes abdn topics learned together.";"kbs" +"5175";"JJM329";"Média a kultura";"Bednařík,P.,Jirák,J.";;"4";"2";"4";"5";"3";NULL;NULL;NULL;"1";"2";"2";"3";"3";;;"kms" +"5176";"JJM330";"Trendy současných českých médií";"Aust,O.";;"4";"2";"5";"4";"5";NULL;NULL;NULL;"1";"4";"3";"5";"5";;;"kms" +"5177";"JJM331";"Výzkum médií II";"Vochocová,L.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"5178";"JJM332";"Přirozený jazyk a média";"Podzimek,J.";;"2";"2";"2";"4";"2";NULL;NULL;NULL;"1";"2";"2";"2";"3";;;"kms" +"5179";"JPM650";"Intelligence";"Bahenský,V.,Galeotti,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";"Each class provided practical exercises that required students' interaction and suggestion to solve intelligence related issues.";"Before to develop the assignment for the class, it might be useful to practice this in class or see some example.";"kbs" +"5180";"JSM020";"Seminář k aktuální veřejně politické problematice";;"Balon,J.,Císař,O.";"4";"2";NULL;NULL;NULL;"4";"5";"4";"2";"4";"4";"4";"5";;;"ks" +"5181";"JPM696";"Economic Warfare";"Ludvík,J.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;"I am not sure that having groups to write conclusions is the most effective way to learn; sometimes students are not engaged and do not contribute enough to the group or people disagree on how and what to write. The idea of the group is good, yet maybe it is only for discussion.";"kbs" +"5182";"JSM095";"Study of Political Mobilization and Social Movements";"Císař,O.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"2";"5";"5";"5";"4";;;"ks" +"5183";"JSM103";"Academic Writing";;"Blokker,P.";"4";"4";NULL;NULL;NULL;"4";"5";"3";"2";"3";"3";"3";"4";;;"ks" +"5184";"JSM480";"Evaluation Research";;"Remr,J.";"4";"4";NULL;NULL;NULL;"4";"5";"4";"2";"3";"3";"3";"3";;;"ks" +"5185";"JSM554";"Diplomový seminář";;"Tuček,M.";"3";"2";NULL;NULL;NULL;"3";"4";"3";"1";"3";"4";"3";"4";;;"ks" +"5186";"JPM699";"Security and Technology";"Střítecký,V.";;"4";"4";"4";"4";"3";NULL;NULL;NULL;"1";"4";"5";"3";"4";"The work we had to develop for the social network analysis was very valuable as we learned how to use computer programs like NodeXL and gephi and apply these to hashtags. It was a great and interesting project that help students to develop analyses and hypotheses on social networks in twitter based on hashtag.";"It might be useful to learn how to use the programs before the last week of classes. Students need more time to learn how to use efficiently the programs and see how they work.";"kbs" +"5187";"JJM249";"Duchovní kořeny evropské kultury";"Halada,J.";;"1";"2";"3";"3";"1";NULL;NULL;NULL;"1";"1";"1";"1";"1";"Přijde mi, že filosofie je stále se opakující už od gymnázia, předmět mi přišel dost zbytečný, se studiem to nemá nic společného. Kdyby se alespoň řešili nějaké otázky spojené s mediálními issues nebo žurnalistikou..";;"kz" +"5188";"JJM117";"Popular Culture";"Turnau,T.";;"4";"5";"5";"5";"5";NULL;NULL;NULL;"2";"5";"2";"5";"4";"Lidskost přednášejícího je neuvěřitelná a takřka individuální, ovšem přesto kolektivní přístup ke studentům si zasluhuje pochvalu. Ještě neuvěřitelnější je schopnost přednášejícího donutit mě pochopit koncepty, kterými se předmět zabývá. Zcela jistě jsem pro zachování pomocné ruky při zpracovávání pravidelných otázek na základě povinné literatury, bez které by bylo splnění úkolu a pochopení literatury prakticky nemožné. Stejně tak by nebylo možné pochopit látku bez povinné eseje, testu a doprovodných materiálů. Výborná je možnost předtermínového odevzdání eseje a poskytnutí zpětné vazby.";"Tento předmět je pro filosofií nepolíbené studenty brutální. Výklad na základě prezentací je čas od času zmatečný, rychlý a pochopení je někdy absolutně nemožné. Literatura je tak obtížná, že jsem ji byl schopný číst pouze v nočních hodinách, abych se nenutil ji pochopit slovo od slova. Poté, co jsem dopsal test, jsem půl hodiny nebyl schopný nad čímkoli přemýšlet. Ovšem jestli bych chtěl něco změnit? Myslím, že koncept předmětu je tak zaběhlý a vykrystalizovaný, že jakákoli změna by byla kontraproduktivní. Protože ať už je předmět sebeobtížnější, jeho známkování je férové, logické a přiměřené. To se o jiných předmětech (a zvlášť povinných) říci opravdu nedá.";"kms" +"5189";"JPM700";"Space Security";"Doboš,B.";;"5";"3";"4";"4";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"The project was really interesting. It was law and security related. It was a complete new topic for me.";;"kbs" +"5190";"JJM355";"Teoretické koncepty ke studiu žurnalistiky I";"Jirků,J.,Němcová Tejkalová,A.";"Jirků,J.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"Spojení praxe s teorií.";;"kz" +"5191";"JJM251";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"3";"5";"1";"1";"1";NULL;NULL;NULL;"1";"3";"1";"2";"3";;"Nepatlat se na jednom místě. Nevím, k čemu nám bylo, že jsme věděli, že otec pro svého syna vyhrál noviny v kartách... Kdyby byly třeba ukázky dobových novin, co se psalo, ne jen hrubý nástin toho, kdo co vlastnil a kdo co koupil. k čemu?";"kz" +"5192";"JJM252";"Specifika sportovní žurnalistiky";"Němcová Tejkalová,A.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Diskusi nad tématy";;"kz" +"5193";"JJM356";"Metodologické přístupy ke studiu žurnalistiky";"Nečas,V.,Veselková,Z.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"4";"4";"4";"4";;;"kz" +"5194";"JJM360";"Ekonomika v médiích";"Klimeš,D.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Hosté, kteří nám přednášeli.";;"kz" +"5195";"JPM697";"Asia Security";"Kolmaš,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Lectures has very interesting topics and the lecturer was very knowledgeable about them. Overall very good course, recommend.";;"kbs" +"5196";"JPM698";"Middle East Security";"Daniel,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"One of the best courses I've had. Lectures were very interesting; the teacher is very knowledgeable about the Middle East and occasional guest lecturers were brought more points of view to discussed issues. Another good thing about the course were the readings - interesting articles from and about the Middle East. Cannot recommend enough.";;"kbs" +"5197";"JJM204";"Výzkum médií I";"Křeček,J.";;"3";"4";"3";"4";"5";NULL;NULL;NULL;"3";"4";"4";"4";"4";"Určitě jsem pro zachování praktických úkolů, test reliability je perfektním způsobem, jak otestovat pochopení látky studenty. Pokud by studenti věděli dříve, o čem onen výzkum je, jistě bych ocenil i možnost zapojit se do procesu tvorby a řešení kódovací knihy. Jenže jak popisuji v další odpovědi, má to svá úskalí.";"I přes mé jinak pozitivní hodnocení je kurz velice problematický. Problémy se odehrávají na celkem třech úrovních:1) Přednášející tak nějak předpokládá, že studenti ví, o co jde. Žádná ukázka na první/druhé přednášce, žádné vysvětlení souvislostí. Pouze okamžitý \"skok\" do teorie, který není moc související s praxí. Až do přibližně páté přednášky 99 % studentů nevědělo, o čem předmět skutečně je a \"co je to to kódování\".2) Přednášející zároveň předpokládá, že se absolutně nezkušení studenti dopídí správného řešení. Toto řešení je zcela kruciální pro splnění celého předmětu. Často dochází ke zmatkům, které se přenášejí i do prvního řádného úkolu. Ještě štěstí, že tento test reliability je.3) Nehledě na body 1) a 2) přichází přednášející na další přednášky se zcela odlišným řešením oproti tomu, které studenti na přednášce předtím spolu s přednášejícím (resp. pod jeho vedením) vymyslí. To bych zcela pochopil, pokud by se tak dělo s předchozím avízem a přednášející by studenty k předchozímu řešení nevedl. Takto se minimální vodění za ruku drasticky a hlavně zcela náhodně obrací.Výklad i způsob vedení přednášejícího k řešení problémů je zmatečný. To se však dá omluvit, nepovažoval bych to za kruciální nedostatek.";"kms" +"5198";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"2";"3";"4";"4";"2";NULL;NULL;NULL;"4";"4";"2";"4";"3";;;"kp" +"5199";"JPB592";"US Government and Politics";"Kotábová,V.";;"3";"3";"2";"2";"3";NULL;NULL;NULL;"1";"3";"2";"2";"3";;;"kp" +"5200";"JPB258";"Politické myšlení novověku I";"Franěk,J.,Kučera,J.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"2";"4";"5";"4";"4";;;"kp" +"5201";"JPB020";"Úvod do mezinárodních vztahů";"Karlas,J.,Kučera,T.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";;;"kmv" +"5202";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"1";"4";"1";"2";"1";NULL;NULL;NULL;"3";"1";"1";"1";"1";;"One of the worst courses on the faculty. The lectures had absolutely no added value what so ever, since they were so boring and very hard to follow. If the student wanted to catch up, the readings were obscenely long and too many.I don't understand why has this course remained in this state since evaluations from students from the previous years are similarly disappointed and frustrated.";"kmv" +"5203";"JJM201";"Teorie masové a mediální komunikace";"Jirák,J.";;"4";"4";"5";"5";"3";NULL;NULL;NULL;"3";"4";"2";"4";"4";"Přednášky jsou poutavé, obsažné a logicky vedené. Obsah souvisí i s dalšími předměty, jak povinnými, tak nepovinnými. Průběžný test je definitivně důležitým pro dosažení dobrého výsledku - je dobře, že je zahrnut do celkového hodnocení. Hodnocení je férové a střízlivé, což jiné předměty mohou jedině závidět.";"Problémem kurzu je jeho rozsáhlost. Přednášky zabírají přibližně 1/3 všech okruhů, které studenti mají mít nastudované ke zkoušce, což je zanechává před problémem, zdali je to, co se učili, opravdu to, co chce zkoušející slyšet. Některá témata jsou samozřejmě triviální, ale některá by bylo dobré zmínit alespoň velmi okrajově, aby bylo jasné, co je v nich pro zkoušejícího stěžejní. Zároveň je třeba dodat, že průběžný test v polovině semestru pokrývá právě tu přibližnou 1/3 všech okruhů z přednášek, tudíž by se dalo očekávat, že prostor k rozšíření okruhů zmíněných na přednáškách je. Jiným řešením by bylo předmět rozdělit na dva různé - ostatně masová a mediální komunikace jsou natolik široké oblasti, že je jeden předmět o 80 minutách týdně nemůže za semestr pokrýt.";"kms" +"5204";"JMM599";"Contemporary American Cinema";"Nowell,R.";;"3";"5";"4";"5";"5";NULL;NULL;NULL;"1";"5";"5";"4";"3";"This class let students have new point of view of watching movies";;"kas" +"5205";"NMMA711";"Mathematics 1";"Bárta,T.";"Bárta,T.,Vlasák,V.";"3";"5";"3";"5";"5";"2";"5";"2";"1";"4";"3";"2";"1";;;"ies" +"5206";"NMMA713";"Introductory Mathematics";;"Vlasák,V.";"2";"3";NULL;NULL;NULL;"3";"4";"5";"1";"2";"2";"2";"2";;;"ies" +"5207";"JPM561";"Regional Security Studies";"Karásek,T.,Klosek,K.";"Karásek,T.,Klosek,K.";"2";"3";"3";"3";"1";"3";"2";"2";"2";"2";"1";"2";"1";;"Very disappointed with this course. Lectures looked very promising, however, they covered only very basic list of conflicts without any deeper discussion. Students can find lists of conflicts on wikipedia. Regarding the \"seminars\", I don't understand why was the test so long. We spent half of the semester writing tests, without any chance of discussing to topic. Also, if the student had any questions about the reading, he had no chance of getting explanation before or after the test. Instead, it would be better if students had 60 minutes at the most to finish the test and the rest of the time could be used to discuss readings and to have real seminar.";"kbs" +"5208";"JJM202";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"3";"4";"4";"3";"3";NULL;NULL;NULL;"2";"4";"2";"5";"4";"Tento předmět je ve skutečnosti takový mediální dějepis: na zájemce jsou připravena zajímavá fakta a techničtěji orientovaní studenti si užijí některé z přednášek o technickém pokroku. Vše doplňuje literatura, kterou buď studenti již znají nebo ji musí obstarat - slouží hlavně k přípravě na průběžné testy. Na druhou stranu je tento mix dostatečně vyvážený a tak by to mělo být.";"Nepřijde mi zcela zřejmé, co se po studentech v testech vlastně chce. Hodnocení patrně probíhá stylem \"nenapsal jste to špatně, ale někdo jiný to napsal lépe.\" Strhnout 1 bod ze 3bodové otázky jen za to, že student nenapíše zdánlivě nepotřebný detail je přemrštěné a irelevantní. Ruku na srdce, kdybych neznal některé otázky z průběžných testů předem, jistě bych předmět neudělal. A přitom by testy ani na první pohled nemusely být tak obtížné.Na druhou stranu je zde časové či prostorové omezení: průběžné testy jsou omezené na 30 minut. Za 30 minut není možné napsat vyčerpávající odpověď na 6 otázek. A to nebyl problém popsat odpověďmi celé 3 strany A4. Studenti se tak báli, aby vůbec stihli odpovědět na všechny otázky.Závěrečný test je zase nepochopitelně omezený místem: 10 otázek vč. odpovědí se má vejít na 1 list A4. Opět, není problém napsat vyčerpávající odpověď, pokud by nebylo omezení místem. A i když možnost získat další papír byla, pak tu máme časové omezení (60 minut, pokud se nepletu), které test složený z 10 poměrně širokých otázek měl.Ať tak nebo tak, zkoušející by se měli rozhodnout, co vlastně chtějí: zdali chtějí mít méně opravování (tomu odpovídají časově a prostorově omezené testy) nebo chtějí všechno opravovat pod drobnohledem (tomu odpovídá zevrubnost a přísnost hodnocení). Vlastně jsem si jistý, že to neví ani oni sami. Možná to něco napovídá o dlouhodobě nastaveném konceptu hodnocení předmětu. Možná to jen přibližuje předmět na úroveň středoškolského dějepisu. Pak by ale ono známkování mělo být na základě většího množství bodů a ne \"ušmudlaných\" 3-6. A to proti průběžným testům nic nemám, ba naopak: pokud na ně někdo nemá vůli přijít a/nebo se alespoň trochu připravit, ten předmět si nezaslouží splnit.";"kms" +"5209";"JJM330";"Trendy současných českých médií";"Aust,O.";;"3";"3";"5";"5";"3";NULL;NULL;NULL;"1";"2";"2";"4";"3";;;"kms" +"5210";"JJM329";"Média a kultura";"Bednařík,P.,Jirák,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"5211";"JJM331";"Výzkum médií II";"Vochocová,L.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kms" +"5212";"JEM027";"Monetary Economics";"Holub,T.,Malovaná,S.";"Břízová,P.,Hájek,J.,Holub,T.,Malovaná,S.";"5";"3";"4";"5";"5";"4";"5";"4";"1";"5";"2";"4";"5";;;"ies" +"5213";"JJM332";"Přirozený jazyk a média";"Podzimek,J.";;"3";"2";"3";"3";"3";NULL;NULL;NULL;"1";"3";"3";"4";"4";;;"kms" +"5214";"JJB600";"Úvod do studia médií I.";"Nečas,V.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"5215";"JJB601";"Rozbor právního prostředí v českých médiích";"Benda,J.";;"2";"4";"1";"2";"2";NULL;NULL;NULL;"1";"2";"2";"3";"2";;;"kms" +"5216";"JPM598";"Grand Strategies";"Ditrych,O.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;"Very good course, the only thing I would change is the compulsory attendance for the first class + paper if the student missed the first class, since students are able to choose which courses to attend in the first week and the schedule can still change.";"kbs" +"5217";"JJB605";"Novodobá historie médií";"Bednařík,P.,Končelík,J.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kms" +"5218";"JJB616";"Vybrané novinářské osobnosti 19. století";"Sekera,M.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"5";"2";"4";"5";;;"kms" +"5219";"JJB620";"Mediální produkce - žurnalistická práce I.";"Vlasák,Z.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"5220";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"3";"5";"5";"5";"5";NULL;NULL;NULL;"1";"3";"5";"3";"3";"The teacher was very enthusiastic which elevated the course.";"Calculations were too abstracts for me. When using tables and numbers in the R program, the results did not have any meaning for me and I only followed steps, that the teacher showed me. If there was any practical usage of these tables, numbers and results, the course would have more of an impact.";"kmv" +"5221";"JJM212";"Analýza politické komunikace";"Křeček,J.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"3";"4";"5";"4";"5";;;"kms" +"5222";"JEM181";"Data Science with R";"Herman,D.,Krištoufek,L.";"Herman,D.,Krištoufek,L.";"5";"4";"5";"5";"4";"5";"5";"4";"1";"5";"5";"5";"5";"DataCamp access and the home assignments there are invaluable for gradual learning of the R language via frequent interactive homeworks.The structure of the course is great, as well as the lecturer. An emphasis on practical use of the R language is welcome.";"After finishing the DataCamp assignments already, the regular two homeworks seemed unnecessarily long - especially the second one. Also the final project was rather time demanding, although a great experience with the real world data analysis.";"ies" +"5223";"JJM229";"Vývoj televizního vysílání v českých zemích";"Štoll,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"3";"5";"4";"4";"5";;;"kms" +"5224";"JJM373";"Ethical Issues in Media, Business and Society";"Orhan,M.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"5225";"JJM226";"Teorie účinků médií";"Nečas,V.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kms" +"5226";"JPB260";"Politické myšlení novověku III";"Kučera,J.";;"4";"5";"3";"4";"2";NULL;NULL;NULL;"1";"4";"1";"2";"4";;;"kp" +"5227";"JPB263";"Bakalářský seminář II.";;"Brunclík,M.,Bureš,O.,Ditrych,O.,Franěk,J.,Gelnarová,J.,Hynek,N.,Charvát,J.,Jeřábek,M.,Jüptner,P.,Karásek,T.,Karlas,J.,Knutelská,V.,Kofroň,J.,Kotábová,V.,Krausz Hladká,M.,Kubátová,H.,Kučera,J.,Kučerová,I.,Landovský,J.,Ludvík,J.,Makariusová,R.,Mlejnek,J.,Pa";"4";"3";NULL;NULL;NULL;"5";"4";"5";"1";"5";"5";"3";"5";;;"kp" +"5228";"JEM179";"History and Methodology of Economics";"Baxa,J.,Paulus,M.";;"4";"4";"5";"4";"5";NULL;NULL;NULL;"1";"4";"3";"4";"3";;;"ies" +"5229";"JEM199";"Financial Crisis and Risk Management";"Horváth,R.,Opatrný,M.,TSOMOCOS,D.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"3";;;"ies" +"5230";"NMMA713";"Introductory Mathematics";;"Vlasák,V.";"4";"2";NULL;NULL;NULL;"5";"5";"4";"2";"3";"4";"4";"5";;;"ies" +"5231";"JPB268";"Evropská integrace";"Plechanovová,B.";;"1";"5";"2";"2";"2";NULL;NULL;NULL;"3";"1";"1";"1";"1";;;"kmv" +"5232";"JPB596";"Čínská zahraniční a bezpečnostní politika";"Karmazin,A.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kbs" +"5233";"JPB229";"Regionální politické systémy: Skotsko, Wales";"Říchová,B.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kp" +"5234";"JPB242";"Geografie vnitropolitických konfliktů";;"Doboš,B.,Riegl,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kp" +"5235";"JLB039";"Ruština odborná I - nižší";;"Mistrová,V.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"4";"Přístup vyučující";;"cjp" +"5236";"JSB027";"Sociální politika jako společenská praxe";"Dobiášová,K.,Vlčková,K.";;"3";"3";"3";"3";"3";NULL;NULL;NULL;"1";"4";"3";"4";NULL;"Praktické vycházky v rámci cvičení";;"kvsp" +"5237";"JEB105";"Statistics";"Červinka,M.";"Hanus,L.";"5";"5";"5";"5";"4";"4";"5";"5";"1";"5";"3";"5";"5";"Kurz je velmi dobře strukturovaný. Cvičení jsou spíše zaměřená na méně běžné a komplikovanjější úlohy, než které se pak objeví v testu, studenta tedy poměrně dobře připraví. Systém úkolů pomůže látku lépe pochopit běhěm semestru. Zkouškové testy jsou výborně vymyšlené, hlavně se mi líbí možnost vybrat si jednodušší variantu úlohy (za méně bodů), pokud si student neví rady s tou těžší.";"Občas jsem měl pocit, že se rigorózní matematice věnovala až příliš velká pozornost - hlavně důkazům. Nicméně kurz má velkou časovou dotaci, takže je to celkem pochopitelné.";"ies" +"5238";"JEB009";"Makroekonomie I";"Hlaváček,M.,Kolcunová,D.";"Hlaváček,M.,Kolcunová,D.";"3";"4";"3";"4";"3";"4";"4";"4";"1";"3";"3";"3";"3";;;"ies" +"5239";"JEB105";"Statistics";"Červinka,M.";"Smutná,Š.";"4";"4";"4";"4";"5";"4";"4";"4";"1";"4";"4";"4";"4";;;"ies" +"5240";"JEB022";"Institutional Economics";"Kopečná,V.,Schwarz,J.";;"4";"3";"3";"4";"3";NULL;NULL;NULL;"2";"4";"4";"3";"4";;;"ies" +"5241";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"2";NULL;NULL;NULL;"5";"5";"4";"1";"5";"5";"5";"4";"Komentáře k filmům a zmínění hlubšího kontextu";;"kz" +"5242";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"5";"5";"5";"5";"5";"Obrovský přehled vyučujícího";;"kp" +"5243";"JPB221";"Metodologický proseminář I";;"Střítecký,V.,Tesař,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Přístup pana Tesaře, snaha udělat hodiny zajímavější a interaktivnější.";;"kmv" +"5244";"JPB018";"Dějiny mezinárodních vztahů I";"Soukup,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Velký přehled vyučujícího, občas trochu uhne od hlavního tématu k větším detailům, ale dostane se k věcem, které by se člověk jinak nedozvěděl, zvlášť v otázce Britského impéria, což já osobně velmi oceňuji.";;"kmv" +"5245";"JPB252";"České novověké dějiny I.";"Kučera,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"4";"4";"Šíři vykládaných informací a historické vložky o kultuře a společnosti.";"Občas pan docent trochu skáče, dostane se tak k zajímavým věcem, ale hůř se pak věci dávají do souvislostí. Také někdy méně osobní averze k některým historickým událostem/osobnostem.";"kp" +"5246";"JPB020";"Úvod do mezinárodních vztahů";"Karlas,J.,Kučera,T.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Témata, snaha o průběžné otázky a interaktivitu";;"kmv" +"5247";"JJB334";"Zábava v médiích";"Kruml,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"1";"3";"5";;;"kms" +"5248";"JJB600";"Úvod do studia médií I.";"Nečas,V.";;"5";"5";"5";"4";"4";NULL;NULL;NULL;"2";"5";"3";"5";"5";;;"kms" +"5249";"JJB601";"Rozbor právního prostředí v českých médiích";"Benda,J.";;"4";"5";"4";"3";"4";NULL;NULL;NULL;"1";"5";"3";"5";"5";;"u úkolů informovat studenty, proč jejich úkol nevyhověl zadání - alespoň stručně";"kms" +"5250";"JJB618";"Propagandistické působení médií v 1 polovině 20. století";"Suk,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"4";"5";"zajímavé informace podané zajímavou formou";;"kms" +"5251";"JJB006";"Současný český jazyk III";;"Svobodová,I.";"4";"4";NULL;NULL;NULL;"4";"5";"5";"2";"5";"5";"5";"5";;;"kms" +"5252";"JJB014";"Žurnalistická tvorba III - Časopisecká tvorba";"Osvaldová,B.";"Maňák,V.";"4";"5";"5";"5";"1";"5";"5";"5";"1";"5";"5";"5";"5";;;"kz" +"5253";"JJB019";"Práce s agenturními informacemi";"Prázová,I.,Trunečková,L.";"Prázová,I.,Trunečková,L.";"1";"1";"3";"5";"1";"4";"5";"1";"1";"2";"2";"1";"1";;;"kz" +"5254";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"4";"3";"4";"4";"3";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kmv" +"5255";"JPM052";"Mezinárodní ekonomické vztahy (MEV)";"Kučerová,I.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"3";"3";"3";;;"kmv" +"5256";"JPM191";"Geopolitics of Great Powers: Russia";"Baštář Leichtová,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"2";"4";"4";"4";"5";"Interactive structure of the classes.";;"kp" +"5257";"JPM430";"Marxism in International Relations (TIR)";;"Střítecký,V.";"4";"2";NULL;NULL;NULL;"4";"5";"4";"1";"3";"3";"3";"4";;;"kmv" +"5258";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"4";"4";"5";"5";"3";NULL;NULL;NULL;"1";"3";"4";"3";"3";;;"kmv" +"5259";"JJJM191";"Media and the Children";"Zezulková,M.";;"5";"2";"5";"5";"3";NULL;NULL;NULL;"2";"4";"5";"4";"5";"The whole project experience, from interviewing the participant to analysing and finalising the poster.";;"kms" +"5260";"JJB017";"Grafický design a základy polygrafie I";"Slanec,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";"V grafické tvorbě jsem nováček, tudíž teorie byla pro mě velmi zajímavá. Úplně nejvíce jsem nicméně ocenil praktické korektury textů, to by si pomalu zasloužilo vlastní předmět.";"Možná praktické vsuvky a cvičení, ale vzhledem k počtu studentů se to realizuje obtížně (navíc na to je zaměřený navazující předmět). Za mě tudíž spokojenost.";"kz" +"5261";"JJM372";"Consumer Behaviour";"Orhan,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kms" +"5262";"JMM599";"Contemporary American Cinema";"Nowell,R.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kas" +"5263";"JMM629";"Hollywood/Europe: A Transnational Film Culture.";"Nowell,R.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kas" +"5264";"JMM671";"Rebuilding Europe";;"Rovná,L.";"3";"2";NULL;NULL;NULL;"3";"3";"4";"3";"4";"2";"4";"4";;;"kzs" +"5265";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"Positive approach of the lecturer and teaching assistants, their academic rigour and their will to explain and to help understand the concepts. The materials for the course are great source of knowledge and include references for external sources as well. During the seminars, practical examples were used. The course provides solid base for future research work and the lecturer always stressed the real-life application of the methods, e.g. in academic papers. Both lectures and seminars are definitely worth attending and make the course even more interesting.";"I would set the deadline for the final project before Christmas/right after Christmas, i.e. before the exam period. It would help students to familiarize with the methods sooner when working on the projects which would improve their understanding and the outcome of the final exam.";"ies" +"5266";"JEM004";"Advanced Macroeconomics";"Baxa,J.,Hromádková,E.";"Baxa,J.,Hromádková,E.,Šestořád,T.,Troch,T.,Žáček,J.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"4";"4";"4";"5";"Lecturers' approach which made the course really interesting, even though the topics presented are rather difficult. They explained the concepts clearly, the materials provided were sufficient to fully understand the topic and there was no need to search for additional materials.";;"ies" +"5267";"JEM181";"Data Science with R";"Herman,D.,Krištoufek,L.";"Herman,D.,Krištoufek,L.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"This course provides great overview of a wide range of methods, such as regressions and machine learning, which can serve as a base for individual studying. DataCamp assignments were very useful since they familiarize students with the topic during the semester. The final project was great in order to conclude the whole course and was a chance to further develop students' knowledge. Overall the course is one of the most useful courses at IES since many students use R for their graduate theses and it is also demanded skill at the job market.";"Sometimes there was too much to cover during the lecture (regarding the code). However I understand that even this is already a short version and it is better to hear the things during the lecture and then check it by yourself.";"ies" +"5268";"JEM037";"Financial Markets";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"It was one of the most interesting courses I have had and it provided a great overview of the financial markets from many perspectives. I think this is a valuable course for anyone interested in career in finance. The lectures were interesting with many real-life examples (academic papers) which made it even more intriguing. I also appreciate how the seminars are lead.";;"ies" +"5269";"JEM137";"Real Estate Investment";"Jandík,T.,Streblov,P.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"The course connects general finance knowledge and sets it into more practical setting. Both lecturers are experienced professionals and good teachers. That brings many benefits and makes the course really interesting. Moreover the lecturers are willing to help and discuss any concerns, which I really appreciate. The final project is a great way to apply the knowledge in practice.";"I would set more dates for the final, maybe one pre-term before Christmas if it was possible.";"ies" +"5270";"JEM035";"Financial Markets Instruments I";"Dědek,O.,Poláková,N.,Polák,P.";"Dědek,O.,Poláková,N.,Polák,P.";"4";"4";"4";"4";"3";"5";"5";"3";"1";"4";"4";"5";"4";"The course is very usefull for students interested in finance. There is a lot to cover, however the lectures are presented in a nice and consice way. The materials are more than sufficient, there are many useful exercises and materials at lecturer's personal website.";;"ies" +"5271";"JJB003";"Dějiny masových médií III";"Bednařík,P.,Končelík,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Přednášky obou vyučujících byly velmi zajímavé a zábavné. Oceňuji také zpětnou vazbu, kterou jsme dostali k seminární práci.";;"kms" +"5272";"JJB131";"Praktický fotožurnalismus";;"Láb,F.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"2";"4";"5";"3";"5";;;"kz" +"5273";"JMB250";"Seminář k dějinám západní Evropy";;"Simbartlová,A.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"5";"5";"Kurz o migraci a integraci v západní Evropě byl velmi zajímavý a poučný. Oceňuji znalosti vyučující, její pečlivou přípravu na každou hodinu.";;"kzs" +"5274";"JMM027";"Contemporary Mediterranean";"Králová,K.,Mejstřík,M.";;"4";"2";"5";"5";"4";NULL;NULL;NULL;"1";"3";"3";"4";"3";;;"kzs" +"5275";"JPM407";"Feminism in International Relations (TIR)";;"Plechanovová,B.";"3";"3";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"4";"3";;;"kmv" +"5276";"JPM613";"Armed Forces and Society";"Kučera,T.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"5";"5";;;"kbs" +"5277";"JJB600";"Úvod do studia médií I.";"Nečas,V.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"2";"5";"3";"5";"5";;;"kms" +"5278";"JJB601";"Rozbor právního prostředí v českých médiích";"Benda,J.";;"3";"2";"3";"2";"1";NULL;NULL;NULL;"1";"3";"1";"3";"3";;;"kms" +"5279";"JJB605";"Novodobá historie médií";"Bednařík,P.,Končelík,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kms" +"5280";"JJB616";"Vybrané novinářské osobnosti 19. století";"Sekera,M.";;"3";"2";"5";"5";"2";NULL;NULL;NULL;"1";"2";"2";"2";"3";;;"kms" +"5281";"JJB620";"Mediální produkce - žurnalistická práce I.";"Vlasák,Z.";;"4";"1";"3";"5";"3";NULL;NULL;NULL;"2";"3";"4";"2";"3";;;"kms" +"5282";"JEB022";"Institutional Economics";"Kopečná,V.,Schwarz,J.";;"3";"2";"4";"4";"2";NULL;NULL;NULL;"1";"2";"3";"4";"3";;;"ies" +"5283";"JEB026";"European Economic Integration";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"3";"3";"2";"2";"1";"1";"2";"1";"1";"3";"2";"4";"3";;"The attitude of teaching assistants during seminars. Grading of seminar presentation was by my opinion very subjective and depended on personal opinion of the teaching assistant not on the quality of presentation or given criteria.";"ies" +"5284";"JLB027";"Ruština odborná I - vyšší";;"Mistrová,V.";"5";"1";NULL;NULL;NULL;"5";"3";"3";"1";"3";"4";"5";"5";;;"cjp" +"5285";"JMM130";"Ethno-Political Conflicts in the Caucasus";"Brisku,A.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"5";"5";"5";;;"krvs" +"5286";"JEM003";"Advanced Microeconomics";"Janda,K.,Melikhova,O.";"Janda,K.,Melikhova,O.";"5";"4";"5";"4";"4";"2";"2";"2";"1";"5";"3";"4";"5";;;"ies" +"5287";"JMM629";"Hollywood/Europe: A Transnational Film Culture.";"Nowell,R.";;"4";"4";"4";"4";"5";NULL;NULL;NULL;"1";"3";"4";"5";"3";;;"kas" +"5288";"JEB110";"Econometrics II";"Pertold-Gebicka,B.";"Chorna,O.,Malinská,B.,Pertold-Gebicka,B.,Pleticha,P.";"2";"5";"1";"2";"2";"2";"2";"2";"1";"4";"4";"2";"2";;"Time management of the lecture. During few lectures lecturer did not manage to cover all topics she wanted to because of lack of time. Problem is she never go back and explain it on other lecture. She just skip it. Lectures were quite confusing and very often lecture and seminar claimed completely different statements.";"ies" +"5289";"JMM271";"Metodologický seminář";;"Šír,J.";"4";"3";NULL;NULL;NULL;"5";"5";"4";"1";"5";"4";"5";"4";"Děkuji za přínosné studijní texty a také za živou formu výuky, kdy se vyučující střídavě ptal studentů a vykládal.";"Některé texty by mohly být trochu stručnější a kratší.";"krvs" +"5290";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"5";"4";"4";"5";"3";"3";"3";"2";"2";"5";"4";"5";"5";;"I would appreciate a better structure of lecture' s presentations. Sometimes I would like to read some basic table of content in each presentation or more visible main terms and it's comments. I had serious problems to identify which sentences follow the previous ideas and which are new terms.";"ies" +"5291";"JJB154";"Introduction to photojournalism";;"Láb,F.,Štefaniková,S.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";"Teachers are carefully listening to the students needs, spend a lot of time reviewing their works and that is really valuable when it comes to progress.";"Some theoric aspects probably should have been deepened (especially post production), but that's a detail.";"kz" +"5292";"JJM233";"Intercultural Communication Management";"Lütke Notarp,U.";;"4";"1";"4";"4";"3";NULL;NULL;NULL;"1";"3";"2";"4";"3";"Double approach to the subject : a theoretical one and a practical one through games that really helped understanding the concepts";"A lot of times spent on games and group-works that were not that valuable in terms of knowledges. The result is that we weren't through a lot of things exposed in the booklet that looked pretty interesting, and that's disappointing.";"kms" +"5293";"JMM278";"Politika a mezinárodní vztahy";"Kubát,M.,Weiss,T.";"Kocián,J.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"Skvělý výběr aktuálních témat! A to jak na přednáškách, tak na seminářích. Děkuji za ně.";"Seminář byl velmi přínosný, ale tak náročný, že jsem jen málokdy měla čas na dost důkladnou přípravu. V některých případech bych byla ráda za méně široká témata referátů (např. téma extremismus v sovětském Rusku i v Ruské federaci). Na druhou stranu jsem vděčná za široký přehled, který jsem díky semináři získala, takže uznávám, že u některých témat (např. organizovaný zločin) by zjednodušování kurzu uškodilo.";"krvs" +"5294";"JEM035";"Financial Markets Instruments I";"Dědek,O.,Poláková,N.,Polák,P.";"Dědek,O.,Poláková,N.,Polák,P.";"3";"3";"3";"2";"4";"2";"3";"2";"2";"3";"2";"2";"4";;"I would like to hear comments on problems solved during the seminars by the seminar instructor instead of solution presented by a group of students. From my point of view this approach is not valuable for me.";"ies" +"5295";"JJM199";"Literární a knižní kritika";"Čeňková,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kz" +"5296";"JEB136";"Topics in Industrial Organization";"Schwarz,J.";;"4";"4";"4";"4";"2";NULL;NULL;NULL;"1";"3";"3";"3";"4";;;"ies" +"5297";"JEB141";"Introduction to Market Design";"Gregor,M.,Kastl,J.";;"4";"3";"2";"2";"2";NULL;NULL;NULL;"1";"2";"4";"4";"3";;;"ies" +"5298";"JJM245";"Úvod do vizuální komunikace";"Průchová,A.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kz" +"5299";"JJM246";"Historie a estetika fotožurnalismu";"Lábová,A.,Štefaniková,S.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"4";"5";"4";"5";;;"kz" +"5300";"JEM040";"Účetní a daňové poradenství";;"Kemény,I.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"3";"4";"4";;"I would like to recommend to spend more time with the solution of problems during the seminars. Possibly write down all steps of solution on the white board. That is the only thing that I would like to change, sum of all it is very valuable subject with great lecturer.";"ies" +"5301";"JJM249";"Duchovní kořeny evropské kultury";"Halada,J.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"5";NULL;"5";"4";;;"kz" +"5302";"JJM355";"Teoretické koncepty ke studiu žurnalistiky I";"Jirků,J.,Němcová Tejkalová,A.";"Jirků,J.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"4";"5";"5";;;"kz" +"5303";"JJM251";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";NULL;"5";"4";;;"kz" +"5304";"JJB221";"Ekonomická teorie marketingu";"Postler,M.";;"4";"2";"4";"5";"4";NULL;NULL;NULL;"1";"4";"4";"5";"5";;;"kmkpr" +"5305";"JJM356";"Metodologické přístupy ke studiu žurnalistiky";"Nečas,V.,Veselková,Z.";;"4";"5";"5";"5";"4";NULL;NULL;NULL;"2";"5";"5";"5";"4";;;"kz" +"5306";"NMMA703";"Matematika 3";"Zelený,M.";"Turčinová,H.";"4";"4";"5";"5";"5";"3";"5";"5";"1";"4";"4";"4";"4";;;"ies" +"5307";"JMM279";"Společnost a ekonomika";"Fiřtová,M.";"Svoboda,K.";"5";"5";"5";"5";"5";"4";"4";"4";"1";"5";"4";"5";"5";"Už samotná témata přednášek byla velmi aktuální a vyučující je navíc dokázala skvěle propojovat se současností. Z hodin jsem odcházela (nepřeháním) fascinovaná novými souvislostmi. Velmi jsem oceňovala i to, že vyučující pracovala s aktuálními a přesnými daty a interpretovala grafy přímo před námi. Myslím si, že buducí studenti to ocení také.";"V semináři bych si přála více pracovat s aktuálními daty a státní statistikou. Zjistila jsem, že statistiky Rosstatu pro mě nejsou vždy srozumitelné.";"kas" +"5308";"JLB013";"Němčina odborná I";;"Křenková,D.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"3";"3";"3";"5";;;"cjp" +"5309";"JMB175";"Moderní dějiny Chorvatska";"Šesták,M.";;"5";"3";"4";"5";"4";NULL;NULL;NULL;"3";"4";"3";"3";"4";;;"krvs" +"5310";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Šafařík,P.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"4";"4";"4";"5";;;"knrs" +"5311";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"4";"3";"4";"2";"3";NULL;NULL;NULL;"1";"3";"3";"3";"4";;;"ies" +"5312";"JLB019";"Francouzština odborná I";;"Bosáková,L.";"4";"4";NULL;NULL;NULL;"5";"5";"4";"1";"5";"2";"3";"4";;;"cjp" +"5313";"JMM074";"Landmarks in 20th Century U.S. History and Their Interpretations";"Pondělíček,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";"I appreciated the teaching style - debating about the given topic and trying to find out what were or are the approaches to the given topic, rather than just learn facts about the given topic.";"I liked the course overall. I would send the readings to the participants earlier (at least 3 days ahead).";"kas" +"5314";"JMM273";"Diplomový seminář II";;"Bečka,J.";"3";"1";NULL;NULL;NULL;"3";"4";"1";"1";"2";"1";"1";"3";;;"krvs" +"5315";"JMMZ319";"Government and Politics in Canada";"Fiřtová,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"The course focused on Canda, one of the countries our specialization is focused on. It was taught very well and attitude of Ms.Fiřtová was great. She was always prepared and she taught us important things which gave me a better understanding of the whole area of North America. I enjoyed this course and liked every aspect of it.";;"kas" +"5316";"JEB022";"Institutional Economics";"Kopečná,V.,Schwarz,J.";;"3";"2";"3";"4";"1";NULL;NULL;NULL;NULL;"4";"4";NULL;NULL;"Some readings werr interrsting";;"ies" +"5317";"JMB414";"Seminář k aktualitám I";;"El-Ahmadieh,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"3";"5";"5";"5";"5";;;"krvs" +"5318";"JMB250";"Seminář k dějinám západní Evropy";;"Simbartlová,A.";"3";"2";NULL;NULL;NULL;"2";"4";"3";"1";"2";"1";"3";"1";;;"kzs" +"5319";"JMB249";"Seminář k dějinám středo- a jihovýchodní Evropy";;"Žíla,O.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"krvs" +"5320";"JJB224";"Psychologie marketingové komunikace";"Vranka,M.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";"Dobré byly průběžné testy, člověk si zopakoval látku i v průběhu semestru.";"Nic mě nenapadá.";"kmkpr" +"5321";"JEB105";"Statistics";"Červinka,M.";"Červinka,M.";"3";"5";"3";"3";"4";"4";"4";"4";"1";"4";"3";"2";"3";;;"ies" +"5322";"JMB065";"Úvod do mezinárodního a evropského práva";"Šlosarčík,I.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"3";"5";"5";;;"kzs" +"5323";"JLB013";"Němčina odborná I";;"Křenková,D.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"5";"5";;;"cjp" +"5324";"JJB238";"Kritika marketingové komunikace";"Rosenfeldová,J.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"3";"4";"4";"4";"5";"Líbili se mi praktické přednášky hostů.";"Některá látka by mohla byýt probírána více do podrobna.";"kmkpr" +"5325";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"4";"3";"4";"4";"3";"3";"4";"2";"3";"4";"3";"4";"4";;;"ies" +"5326";"JMB047";"Vybrané problémy mezinárodních konfliktů.";"Čížek,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"krvs" +"5327";"JJB284";"Firemní komunikace a kultura";"Poucha,T.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"Líbili se mi praktické příklady.";"Nic mě nenapadá.";"kmkpr" +"5328";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"3";"3";"4";"4";"3";"3";"4";"3";"2";"3";"2";"3";"3";;;"ies" +"5329";"JMB056";"Reflexe velkých debat v sociálních vědách ve filmu";;"Kozák,K.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kas" +"5330";"NMMA703";"Matematika 3";"Zelený,M.";"Bartoš,A.";"4";"5";"5";"5";"4";"5";"5";"4";"1";"4";"4";"3";"4";;;"ies" +"5331";"JMB013";"Moderní dějiny středo- a jihovýchodní Evropy";"Balla,P.,Švec,L.";"seminář nenavštěvován";"5";"5";"5";"5";"5";"5";"5";"5";"3";"5";"1";"5";"5";;;"krvs" +"5332";"JJB009";"Úvod do psychologie";"Vranka,M.";;"5";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"3";"2";"4";;;"kz" +"5333";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"3";"5";"Možnost za málo peněz shlédnout nové filmy. Možnost vybrat si ze dvou časů promítání.";"Placení probíhá směšným způsobem, heslo dostáváme na utržené papírky o velikosti 2x2 cm. Celé bych to udělala elektronicky. Učebna není moc pohodlná, zvlášť když je na programu dvě a půl hodiny dlouhý film, ale s tím se nedá nic dělat.";"kz" +"5334";"JPB011";"Politická geografie I";"Romancov,M.";;"4";"4";"4";"4";"3";NULL;NULL;NULL;"2";"4";"3";"3";"4";;;"kp" +"5335";"JJB633";"Marketing Communications";"Zezulková,M.";;"2";"2";"2";"3";"1";NULL;NULL;NULL;"3";"1";"1";"1";"2";;;"kmkpr" +"5336";"JJB002";"Dějiny masových médií II";"Sekera,M.";;"2";"1";"4";"4";"2";NULL;NULL;NULL;"4";"3";"3";"2";"2";"návštěvu knihovny národního muzea";;"kms" +"5337";"JMB065";"Úvod do mezinárodního a evropského práva";"Šlosarčík,I.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"3";"2";"4";"5";;;"kzs" +"5338";"JPB221";"Metodologický proseminář I";;"Střítecký,V.,Tesař,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";;;"kmv" +"5339";"JPB242";"Geografie vnitropolitických konfliktů";;"Doboš,B.,Riegl,M.";"3";"5";NULL;NULL;NULL;"3";"4";"3";"2";"4";"2";"3";"3";;;"kp" +"5340";"JJB003";"Dějiny masových médií III";"Bednařík,P.,Končelík,J.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"5341";"JPB018";"Dějiny mezinárodních vztahů I";"Soukup,J.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"2";"3";"2";"2";"5";;;"kmv" +"5342";"JJB406";"Tvorba a prostředky v mediální komunikaci";"Chudinová,E.";;"4";"4";"4";"5";"5";NULL;NULL;NULL;"2";"5";"5";"4";"5";"Přívětivý přístup učitele.";"Občas byla jazyková bariéra.";"kmkpr" +"5343";"JJB006";"Současný český jazyk III";;"Svobodová,I.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"5";"5";;;"kms" +"5344";"JSB004";"Sociální nerovnosti";"Šanderová,J.";;"4";"3";"3";"4";"2";NULL;NULL;NULL;"1";"4";"2";"4";"4";;;"ks" +"5345";"JJB401";"Komerční a nekomerční marketingová komunikace";"Báča,L.,Obluk,O.";;"5";"3";"5";"5";"3";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Spojení s praxí, vyučující z praxe.";;"kmkpr" +"5346";"JJB009";"Úvod do psychologie";"Vranka,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"4";"3";"4";"5";;;"kz" +"5347";"JJM360";"Ekonomika v médiích";"Klimeš,D.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"Oceňuji přednášky lidí z praxe.";"Nic bych nevytkla.";"kz" +"5348";"JJB014";"Žurnalistická tvorba III - Časopisecká tvorba";"Osvaldová,B.";"Maňák,V.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"vše - je to jeden z nejlepších a nejpřínosnějších předmětů";;"kz" +"5349";"JJB628";"Marketing módních značek - teorie";"Hejlová,D.,Koudelková,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"Oceňuji neovyklovou výuku formou výstav a praktických úkolů.";"Více přednášek lidí u praxe.";"kmkpr" +"5350";"JJB019";"Práce s agenturními informacemi";"Prázová,I.,Trunečková,L.";"Prázová,I.,Trunečková,L.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"4";"5";"4";"5";;;"kz" +"5351";"JJB021";"Bakalářský seminář";;"Prázová,I.";"3";"3";NULL;NULL;NULL;"4";"5";"1";"1";"3";"3";"2";"3";;;"kz" +"5352";"JJB052";"Tvůrčí dílny FOTO I";"Lábová,A.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"4";"5";;;"kz" +"5353";"JJB143";"Žurnalistika a feminismus";"Krobová,T.,Osvaldová,B.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kz" +"5354";"JLB039";"Ruština odborná I - nižší";;"Mistrová,V.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";;;"cjp" +"5355";"JSB998";"Úvod do sociologie";"Soukup,P.";;"4";"2";"5";"5";"4";NULL;NULL;NULL;"2";"3";"2";"4";"4";;;"ks" +"5356";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"3";"5";"3";"1";"3";"2";"1";"5";;;"kz" +"5357";"JMB078";"Seminář k současné hovorové ruštině";;"Smirnova,T.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"5";;;"krvs" +"5358";"JJB221";"Ekonomická teorie marketingu";"Postler,M.";;"4";"1";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"5";"5";"historky z totáče";;"kmkpr" +"5359";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"5";;;"knrs" +"5360";"JLB053";"Angličtina pro sociální vědy I";;"Gloverová,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"4";"5";;;"cjp" +"5361";"JJB237";"Právo a etika v marketingu";"Winter,F.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";;;"kmkpr" +"5362";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"4";"3";"4";"4";"3";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"krvs" +"5363";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"3";"5";"5";"3";NULL;NULL;NULL;"3";"3";"3";"3";"4";;;"ks" +"5364";"JMB402";"Úvod do společenských věd II";;"Pondělíček,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"5";"5";;;"krvs" +"5365";"JJB407";"Bakalářský proseminář";"Rosenfeldová,J.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"4";"3";"4";"3";"4";;;"kmkpr" +"5366";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Papežová,K.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"4";"4";"5";;;"knrs" +"5367";"JMB068";"Komunistické vládnutí v Československu: prosazování, podoba a společenská reflexe (1945 až dodnes)";"Cuhra,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"4";"5";;;"krvs" +"5368";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Lizcová,Z.";"4";"4";"4";"4";"4";"5";"5";"5";"1";"4";NULL;"4";"4";;;"krvs" +"5369";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"3";"2";"2";"2";NULL;NULL;NULL;"1";"2";"2";"3";"2";;;"ies" +"5370";"JPM127";"Geopolitika a geostrategie 1815 - 1945";"Romancov,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Přístup a znalosti vyučujícího, prezentace a práce s mapami.";"Dostupnost map ze SISu.";"kp" +"5371";"JMB533";"Česká republika v integračních procesech";"Šlosarčík,I.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;NULL;"5";"5";"5";"5";"Dobře rozvržená přednáška, ověření znalostí pomocí nového typu seminárních prací";;"kzs" +"5372";"JMB507";"Soudobé dějiny střední Evropy";"Vykoukal,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"přístup vyučujícího";;"krvs" +"5373";"JMB534";"Evropská unie - vybrané problémy";"Mejstřík,M.";;"2";"5";"1";"2";"2";NULL;NULL;NULL;NULL;NULL;"1";"1";"3";;"Bylo by vhodné dát k dispozici studijní materiály, díky nimž potom bude možné složit zkoušku, v současné době jsou nedostačující. Také by bylo dobré vědět dopředu, jakým způsobem bude zkouška koncipována a co od nás vyučující vlastně očekává a jak jeho očekáváním vyhovět.";"kzs" +"5374";"JMB508";"Soudobé dějiny východní Evropy";"Kolenovská,D.,Svoboda,K.";;"5";"5";"4";"4";"4";NULL;NULL;NULL;"1";"5";"4";"4";"4";"přístup vyučujícího";;"krvs" +"5375";"JMB509";"Soudobé dějiny jihovýchodní Evropy";"Tejchman,M.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"přístup vyučujícího";;"krvs" +"5376";"JPB268";"Evropská integrace";"Plechanovová,B.";;"1";"5";"1";"1";"2";NULL;NULL;NULL;"3";"4";"3";"3";"1";;"systém hodnocení je velice nepřehledný, velice mne zarazilo, že vyučující i při překročení termínu opravy testu nedovoluje zapsat se na další termín (podle jejího vyjádření, pokud by student absolvoval druhý termín v momentě, kdy předchozí nemá opravený, dostal by automaticky z prvního termínu F), vyučující také vypisuje nedostatečný počet termínů s nelogickými daty zápisu a odhlášení a na opakované prosby studentů o vypsání dalších reaguje velice arogantně.";"kmv" +"5377";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";;;"cjp" +"5378";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"krvs" +"5379";"JMB402";"Úvod do společenských věd II";;"Fiřtová,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";;;"krvs" +"5380";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"2";"2";"2";"2";"2";NULL;NULL;NULL;"2";"2";"2";"4";"3";;;"knrs" +"5381";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"ks" +"5382";"JMB536";"Bakalářský seminář pro kombinovaný obor Teritoriální studia I";;"Vykoukal,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";NULL;"5";"5";"5";"5";;;"krvs" +"5383";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kas" +"5384";"JMB535";"Bezpečnostní problémy současného světa";"Weiss,T.";;"5";"4";"5";"4";"5";NULL;NULL;NULL;NULL;"5";"4";"5";NULL;;;"kzs" +"5385";"JMB207";"Hospodářské dějiny německy mluvících zemí";"Mlsna,P.";;"4";NULL;"5";"5";"5";NULL;NULL;NULL;"3";"4";"3";"5";"5";;;"knrs" +"5386";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"3";"3";"4";"4";"3";NULL;NULL;NULL;"2";"4";"3";"4";"4";;"Vyučující velice dlouho opravuje testy";"kp" +"5387";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"4";"4";"4";"4";"3";"1";"1";"1";"2";"4";"4";"4";"4";;"nechápu, proč vyučující zrušil doprovodné semináře, které kolegové, kteří se jich účastnili v minulých letech, chválili";"kp" +"5388";"JEB110";"Econometrics II";"Pertold-Gebicka,B.";"Chorna,O.,Malinská,B.,Pertold-Gebicka,B.,Pleticha,P.";"3";"5";"3";"4";"3";"3";"4";"3";"1";"4";"4";"4";NULL;;;"ies" +"5389";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"5";"4";"5";"4";"4";NULL;NULL;NULL;"3";"5";"3";"5";"5";;;"kp" +"5390";"JPB269";"Současné problémy mezinárodních vztahů";"Knutelská,V.";;"4";"3";"4";"4";"3";NULL;NULL;NULL;"2";"4";"5";"4";"5";"férový přístup vyučující - přísná, ale na všechny stejně";;"kmv" +"5391";"JSM005";"Sociální struktura ČR: stav, vývoj, srovnání s EU";"Tuček,M.";;"2";"2";"3";"3";"2";NULL;NULL;NULL;"2";"1";"1";"1";"2";;"písanie prác bez zdrojov nie je v poriadku, rovnako ako \"orálny sylabus\" kde nie je jasné čo sa bude počas semestra robiť a čítať, ale hovorí sa to vždy na predchádzajúcej hodine.";"ks" +"5392";"JEB035";"Advanced Statistics";"Křehlík,T.,Víšek,J.";"Křehlík,T.,Víšek,J.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"4";"4";"5";"I am very happy I took this course and had the chance to meet Prof. Víšek. He is very kind and knowledgeable. Seminars with Dr. Křehlík were also great.";;"ies" +"5393";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"5";"3";"1";"2";NULL;NULL;NULL;"1";"2";"1";"1";"1";;;"ies" +"5394";"JMB414";"Seminář k aktualitám I";;"Cotte,P.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"krvs" +"5395";"JSM020";"Seminář k aktuální veřejně politické problematice";;"Balon,J.,Císař,O.";"4";"4";NULL;NULL;NULL;"4";"3";"4";"2";"4";"4";"2";"5";"čítanie a kritické hodnotenie textov bolo náročné ale prínosné. Oceňujem tiež dynamiku prednášok a energiu prednášajúceho.";"možno by som navrhla neuznať tie prezentácie, ktoré (po vysvetlení ako majú a ako nemajú vyzerať) nespĺňali kritéria. Nech ostatní študenti nestrácajú čas bezobsažnými prezentáciami alebo takými ktoré kopírujú obsahy zadaných textov.Nechať viac priestoru na diskusiu študentom, ktorí mali za úlohu prečítať si texty.";"ks" +"5396";"JLB013";"Němčina odborná I";;"Křenková,D.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"4";"5";;;"cjp" +"5397";"JEM199";"Financial Crisis and Risk Management";"Horváth,R.,Opatrný,M.,TSOMOCOS,D.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"4";"5";"It was a very nice course with a great lecturer and interesting case studies.";;"ies" +"5398";"JLB053";"Angličtina pro sociální vědy I";;"Gloverová,M.";"5";"2";NULL;NULL;NULL;"5";"5";"4";"1";"3";"3";"3";"4";;;"cjp" +"5399";"JMMZ274";"Geschichte des Rassismus";"Barth,B.";;"4";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"knrs" +"5400";"JMBZ197";"Sprachwerkstatt Deutsch. Schreiben, Lesen und Diskutieren fürs Studium I";;"Göttmann,A.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"5";"5";;;"knrs" +"5401";"JJM200";"Diplomový seminář";;;"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kms" +"5402";"JMBZ289";"Central European Culture from the 19th Century to 1945";"Emler,D.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"3";"3";"3";"4";;;"knrs" +"5403";"JMB118";"Geografie německy mluvících zemí";"Baštová,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"knrs" +"5404";"JMB065";"Úvod do mezinárodního a evropského práva";"Šlosarčík,I.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"5";"5";;;"kzs" +"5405";"JJM329";"Média a kultura";"Bednařík,P.,Jirák,J.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"3";"4";;;"kms" +"5406";"JJM330";"Trendy současných českých médií";"Aust,O.";;"3";"3";"3";"3";"3";NULL;NULL;NULL;"1";"2";"2";"3";"3";;;"kms" +"5407";"JSM095";"Study of Political Mobilization and Social Movements";"Císař,O.";;"4";"4";"4";"3";"4";NULL;NULL;NULL;"2";"4";"4";"2";"4";"Čítanie a kritické hodnotenie textov bolo náročné ale prínosné. Oceňujem aj dynamiku prednášok.";"neuznať zlé prezentácie. nechať viac priestoru na diskusiu študentom o textoch ktoré čítali.";"ks" +"5408";"JJM259";"Mediální systémy a jejich komparace";"Miessler,J.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kz" +"5409";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"3";"2";"3";"3";"2";NULL;NULL;NULL;"1";"2";"3";"2";"3";;;"krvs" +"5410";"JJM360";"Ekonomika v médiích";"Klimeš,D.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kz" +"5411";"JJM331";"Výzkum médií II";"Vochocová,L.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";"Přístup vyučující a následná komunikace během zkouškového období + rychlost hodnocení.";;"kms" +"5412";"JSM421";"Contemporary social theory";"Balon,J.";;"1";"4";"1";"2";"1";NULL;NULL;NULL;"2";"3";"2";"1";"1";"Samotné texty v kurze. Akurát by bolo dobré môcť o ich obsahu viac debatovať. Chýbal mi tu seminár.";"1. Nenechať prednášať študentov, pokiaľ je jasné že si prezentáciu buď nepripravili alebo majú minimálne schopnosti niečo dať ostatným. 2. Rozprávať sa na prednáškach aj o textoch ktoré sme mali čítať.3. urobiť hodinu dynamickejšiu, zapájať študentov4. zvážiť či prezentácia na takej úrovni ako bola uznávaná tento semester je v náročnosti na jej naplnenie na rovnakej úrovni ako ozdrojovoaná záverečná esej s obsahom 4000 slov.";"ks" +"5413";"JLB029";"Španělština odborná I";;"Mlýnková,L.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Samotná osoba paní Mlýnkové je pro mě zosobněním toho nejpříjemnějšího, co španělština u nás nabízí. Navíc je to největší odbornice, kterou jsem za své studium jazyka potkal.";"Nic.";"cjp" +"5414";"JMB402";"Úvod do společenských věd II";;"Čapinská,B.";"4";"4";NULL;NULL;NULL;"4";"4";"5";"2";"4";"5";"3";"5";;;"krvs" +"5415";"JJM334";"Diplomový seminář";;;"3";"2";"3";"3";"3";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kms" +"5416";"JJM269";"Tvůrčí dílny I. – komentář";;"Osvaldová,B.,Šídlo,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"5";"5";"pana Šídla :-)";;"kz" +"5417";"JSM020";"Seminář k aktuální veřejně politické problematice";;"Balon,J.,Císař,O.";"4";"1";NULL;NULL;NULL;"4";"5";"5";"1";"4";"3";"4";"4";;;"ks" +"5418";"JJM208";"Mediální systémy a jejich komparace";"Miessler,J.";;"3";"3";"3";"5";"2";NULL;NULL;NULL;"1";"4";"4";"4";"4";"Rozhodně oceňuji systematičnost. Přehledná bibliografie, přehledné prezentace.";"Jsem ráda, že už jsme se o zpětné vazbě bavili přímo na přednášce, to opravdu nikdo jiný nedělá. Navrhovala bych přeci jen omezit testy z literatury třeba na polovinu (určitě ne zrušit), bylo by ale víc prostoru v hodinách, které už tak jsou relativně krátké.";"kms" +"5419";"JSM566";"Výzkumné kolokvium SVP I";;"Numerato,D.";"3";"3";NULL;NULL;NULL;"4";"3";"3";"4";"4";"4";"5";"3";;;"ks" +"5420";"JMB013";"Moderní dějiny středo- a jihovýchodní Evropy";"Balla,P.,Švec,L.";"seminář nenavštěvován";"5";"4";"5";"5";"4";"5";"5";"5";"2";"5";"2";"5";"4";;;"krvs" +"5421";"JLB041";"Španělština I";;"Mlýnková,L.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"5";"5";;;"cjp" +"5422";"JJM212";"Analýza politické komunikace";"Křeček,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"5";"5";"Velmi oceňuji opravdu seminární přístup. Nezřídka se stává, že i v takovém malém počtu studentů semináře vypadají jako přednášky. Tady měl každý prostor se vyjádřit a zapojit.";;"kms" +"5423";"JLB053";"Angličtina pro sociální vědy I";;"Prošková,A.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"The teacher's approach to me was very encouraging.";;"cjp" +"5424";"JLM006";"Angličtina pro politology II";;"Panešová,K.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"5425";"JMB212";"Moderní dějiny Japonska";"Labus,D.";;"5";"1";"4";"5";"3";NULL;NULL;NULL;"2";"4";"1";"2";"4";;;"kas" +"5426";"JLB013";"Němčina odborná I";;"Křenková,D.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"4";"4";;;"cjp" +"5427";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"2";"4";"2";"3";"1";NULL;NULL;NULL;"1";"3";"1";"2";"2";;;"kmv" +"5428";"JJM213";"Metody historického výzkumu";;"Bednařík,P.,Končelík,J.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Seminář je perfektní pro všechny, kteří už mají v hlavě diplomovou práci a státnice. Od vyučujících se člověk dozví spoustu tipů a doporučení, jak to všechno zvládnout, a navíc se vtipem.";;"kms" +"5429";"JPM185";"Evropská integrace - teorie a příklady, ES";"Jeřábek,M.";;"3";"3";"4";"5";"4";NULL;NULL;NULL;"1";"3";"3";"3";"2";;;"kmv" +"5430";"JMB414";"Seminář k aktualitám I";;"Cotte,P.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";;;"krvs" +"5431";"JPM260";"Vybrané problémy britské zahraniční politiky v 19. a 20. století, ES";"Soukup,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"3";"5";"5";;;"kmv" +"5432";"JJM214";"Čtení textů ke studiu médií - populární kultura";;"Reifová,I.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"5";"4";"Každý týden se čte ne zcela jednoduchý text, většinou v angličtině, už proto je předmět přínosný.";"Rozumím, že je to součástí studia, ale netrávila bych tolik času otázkami na \"způsoby čtení\", ale více bych se věnovala samotnému obsahu textů.";"kms" +"5433";"JMB018";"Bakalářský seminář I";;"Tůma,O.";"3";"1";NULL;NULL;NULL;"5";"5";"3";"3";"1";"2";"1";NULL;;;"krvs" +"5434";"JPM607";"International Negotiations";;"Parízek,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"5";;;"kmv" +"5435";"JPM692";"Internal Security of the EU [ES]";"Hokovský,R.";;"5";"5";"5";"2";"3";NULL;NULL;NULL;"3";"5";"5";"3";"3";;;"kmv" +"5436";"JPB268";"Evropská integrace";"Plechanovová,B.";;"3";"4";"3";"2";"4";NULL;NULL;NULL;"3";"3";"3";"2";"4";"Rešerše (ovšem s výraznými změnami popsanými v návrzích na zlepšení kurzu)";"Rešerše: Koncept rešerší má samozřejmě smysl a určitě bych ho zachoval, osobně však vidím několik nedostatků. Za rešerši je velmi krátký čas a je zadávána přes víkend, kdy je knihovna zavřená (navrhuji prodloužit alespoň na 7 dní). Ze tří rešerší je hodnocena pouze jedna a to až po zkoušce. Na dvě rešerše se student vůbec nedozví žádnou zpětnou vazbu a také ani neví, která rešerše mu byla hodnocena a proč dostal tolik bodů, kolik dostal bodů (navrhuji krátkou písemnou zpětnou vazbu na každou rešerši, hodnocení rešerší v průběhu semestru, která ušetří čas ve zkouškovém období a zároveň dá studentovi představu o tom, kolik bodů potřebuje z testu získat).Zkouškové období: Osobně jsem problémy ve zkouškovém období neměl, ale vím, že problémy s vypisováním termínů a jejich kapacitou z loňského roku přetrvávají. Termíny lze jistě rozepsat i takovým způsobem, aby mezi nimi bylo vždy 7 pracovních dní, během který má vyučující povinnost zkoušku opravit (toto je samozřejmě také nutno ze strany vyučující dodržet). Nenastane tak situace, že jde student na druhý termín, když nemá první ještě opravený. Na konci semestru je také nutno přesně deklarovat, kolik termínů bude vypsáno a zdali budou náhradní a za jakých podmínek je nutno absolvovat. Tuto informaci jsem postrádal, pokud byla někomu ze strany vyučující sdělena, musí počítat s tím, že se nedostane ke každému a je nutno informovat všechny (např. přes e-mail, SIS, moodle). Co se týče termínů opravování zkoušek, je nutno je dodržovat. Od studentů jsou termíny vyžadovány taktéž a jejich nesplnění vede k penalizaci. Samotnou zkoušku lze také drobně upravit (např. za ní uvést pár klíčových slov, o kterých má student psát - vyučující se pak vyhne čtení několika stránkových odpovědí na jiné téma a student ví, co je od něj očekáváno). Jestliže je opravování zkoušek časové náročné, což bezpochyby je, nabízí se také možnost nahradit některé otevřené otázky sérií multiple choice otázek (klidně 5 možných odpovědí s možností všech či žádné správné odpovědí). Komunikace: Mnoha studentům se nedostalo odpovědi na jejich e-maily s dotazy. Je jistě časově náročné na všechny odpovídat a existují tu konzultační hodiny, nabízí se však také možnost nastavit kurz a informovat o něm tak, aby tyto dotazy vůbec nemusely vznikat. Pak pouze stačí studenty odkazovat na informace uvedené v moodlu či SISu. Přednášky: Budoucí studenti by jistě ocenili případovou studii na unijní legislativu a její implementaci začleněnou do přednášek.";"kmv" +"5437";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"3";"3";"3";"4";;;"kmv" +"5438";"JJM229";"Vývoj televizního vysílání v českých zemích";"Štoll,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"1";"5";"5";;;"kms" +"5439";"JMB218";"Německo a Rakousko po roce 1989";"Emler,D.,Kunštát,M.,Mlsna,P.,Nigrin,T.,Šafařík,P.";;"3";"3";"3";"4";"1";NULL;NULL;NULL;"2";"2";"1";"2";"2";;;"knrs" +"5440";"JJM254";"Mediální tvorba";"Čásenský,R.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kz" +"5441";"JPM690";"Liberalism in International Relations (TIR)";;"Karlas,J.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"4";;;"kmv" +"5442";"JJM247";"Český stranický systém";"Just,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"4";"5";;;"kz" +"5443";"JJM259";"Mediální systémy a jejich komparace";"Miessler,J.";;"3";"4";"2";"2";"3";NULL;NULL;NULL;"1";"3";"4";"4";"3";"Systém bodování je dobrý, průběžné testy z přečtených textů jsou přínosné a motivující. Oceňuju možnost projevit se v různých aspektech práce (prezentace, recenze, uvažování o přečtených textech)";"Není potřeba rozebírat dopodrobna text, který byl zadaný k přečtení jako domácí úkol";"kz" +"5444";"JJB279";"Art marketing";"Ježková,T.";;"3";"4";"4";"5";"4";NULL;NULL;NULL;"1";"2";"1";"2";"3";;;"kmkpr" +"5445";"JJB284";"Firemní komunikace a kultura";"Poucha,T.";;"1";"3";"2";"3";"2";NULL;NULL;NULL;"1";"2";"1";"2";"1";;;"kmkpr" +"5446";"JJM355";"Teoretické koncepty ke studiu žurnalistiky I";"Jirků,J.,Němcová Tejkalová,A.";"Jirků,J.";"3";"4";"5";"5";"3";"5";"3";"5";"1";"2";"2";"3";"3";;;"kz" +"5447";"JJM260";"Novinářská etika v praxi";"Moravec,V.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kz" +"5448";"JJM199";"Literární a knižní kritika";"Čeňková,J.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"5";"5";;;"kz" +"5449";"JJM360";"Ekonomika v médiích";"Klimeš,D.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kz" +"5450";"JEB044";"Financial Accounting";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"4";"4";"5";"5";"3";NULL;NULL;NULL;"2";"5";"4";"4";"4";"Lecturer’s good ability to catch students’ attention";"I didnt find seminars helpful. Case studies do not match the questions asked during the exams. The biggest problem about the course is that it takes so much time to correct the exams. Eg i have been waiting for my mudterm results for 17 days. Also I do not like that there were just three dates for exam, i’d appreciate more & maybe a pre-term";"ies" +"5451";"JJM273";"Sportovní žurnalistika ve světě";"Bosák,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"3";"4";"2";"3";"5";;;"kz" +"5452";"JMM384";"Cold War in Documents 1945-1962";"Smetana,V.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kas" +"5453";"JJM356";"Metodologické přístupy ke studiu žurnalistiky";"Nečas,V.,Veselková,Z.";;"4";"4";"4";"5";"3";NULL;NULL;NULL;"1";"3";"3";"3";"4";;"Nerozumím, proč se tento kurz vyučuje až na magisterském oboru, když by byl mnohem více potřebný na bakalářském stupni před psaním první diplomové práce. Tehdy jsme si museli metody nastudovat sami a nikdo nám nevysvětloval, jak má vypadat teze nebo co je to kódovací kniha. Teď mi tento kurz už přijde zbytečný, protože díky samostudiu už stejně z předchozího stupně vzdělání víme, jak práce psát.";"kz" +"5454";"JJM274";"Práce sportovního reportéra a komentátora";"Záruba,R.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kz" +"5455";"JJB633";"Marketing Communications";"Zezulková,M.";;"3";"3";"4";"3";"4";NULL;NULL;NULL;"2";"2";"2";"4";"3";;;"kmkpr" +"5456";"JJM275";"Tvůrčí dílny I (sportovní)";;"Trunečka,O.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"5";"5";;;"kz" +"5457";"JJM264";"Diplomový seminář II.";;;"2";"3";"3";"3";"1";NULL;NULL;NULL;"1";"1";"1";"1";"1";;;"kz" +"5458";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"1";"1";"1";"1";"5";;;"kz" +"5459";"JJB635";"Interkulturní marketing";"Rosenfeldová,J.";;"3";"2";"4";"4";"3";NULL;NULL;NULL;"4";"2";"1";"2";"3";;;"kmkpr" +"5460";"JPB558";"Výběrový seminář: Politická komunikace";"Váňa,T.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"2";"4";"3";"2";"5";"Velmi dobrý výklad látky, která na IPS zcela chybí. Propojení teoretické stránky s praktickými ukázkami. Současné nastavení zkoušky.";"Malá kapacita kurzu vybízí k diskuzi a zapojení studentů do průběhu semináře. K tomu bohužel v průběhu výuky kurzu bohužel prakticky nedocházelo. Jedná se tedy spíše o přednášku s nepovinnou účastí.";"kp" +"5461";"JLB013";"Němčina odborná I";;"Křenková,D.";"4";"3";NULL;NULL;NULL;"4";"5";"4";"1";"4";"2";"4";"5";;;"cjp" +"5462";"JJM251";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Přednášející umí látku podat zajímavě a v souvislostech.";;"kz" +"5463";"JJM260";"Novinářská etika v praxi";"Moravec,V.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";"Zajímavé přednášky";;"kz" +"5464";"JJM200";"Diplomový seminář";;;"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kms" +"5465";"JJM360";"Ekonomika v médiích";"Klimeš,D.";;"4";"4";"4";"5";"4";NULL;NULL;NULL;"1";"4";"5";"4";"4";;;"kz" +"5466";"JPB001";"Bakalářský seminář I.";;"Brunclík,M.,Bureš,O.,Ditrych,O.,Franěk,J.,Gelnarová,J.,Hynek,N.,Charvát,J.,Jeřábek,M.,Jüptner,P.,Karásek,T.,Karlas,J.,Knutelská,V.,Kofroň,J.,Kotábová,V.,Krausz Hladká,M.,Kubátová,H.,Kučera,J.,Kučera,T.,Kučerová,I.,Landovský,J.,Ludvík,J.,Makariusová,R.,Mle";"4";"3";NULL;NULL;NULL;"5";"5";"5";"3";"3";"3";"3";"4";;"První, informativní hodina, která byla uvedena v rozvrhu, buď odpadla, nebo o jejím zrušení nebyl nikdo zpraven. Jel jsem zbytečně do školy a nedozvěděl jsem se, co jsem potřeboval.";"kp" +"5467";"JPM198";"Contemporary Latin America";"Krausz Hladká,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kp" +"5468";"JJM355";"Teoretické koncepty ke studiu žurnalistiky I";"Jirků,J.,Němcová Tejkalová,A.";"Jirků,J.";"4";"5";"3";"4";"4";"5";"5";"5";"1";"3";"4";"3";"4";"Semináře dovysvětlily to, co jsme mnohdy díky rychlému tempu na přednáškách a komplikovanosti látky ne vždy zcela pochopili.";"Množství povinné literatury je podle mě zbytečně vysoké.";"kz" +"5469";"JJM290";"Tvůrčí dílny I – rozhlas a televize";;"Maršík,J.";"4";"3";NULL;NULL;NULL;"4";"5";"5";"1";"4";"5";"3";"5";;;"kz" +"5470";"JMM601";"U.S. and Human Rights";"Raška,F.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"The teacher's approach to me was very encouraging.";;"kas" +"5471";"JPB268";"Evropská integrace";"Plechanovová,B.";;"1";"5";"1";"1";"1";NULL;NULL;NULL;"3";"2";"1";"3";"1";;"-výsledky testů se člověk dozví až tak po čtrnácti dnech od napsání testu- přestože se během semestru psaly tři rešerše výsledky z nich se nemáte šanci dozvědět do té doby, než zjistíte, že jste předmět neudělali, což je pěkně na nic- bylo by lepší rešerše hodnotit průběžně a dostat zpětnou vazbu, abychom věděli, co zlepšit příště, co bylo uděláno dobře/špatně- hodnocení testů je také záhada, i když člověk popíše čtyři strany, je dost veliká šance, že předmět neudělá (to si pak člověk říká, jestli hodnocení neprobíhá nějakým náhodným losem nebo co)- přednášky jsou hodně \"vyčerpávající\"- i když se jedná o zajímavé a stále poměrně aktuální téma, člověk musí neustále hledat sílu na to, jak udržet pozornost...";"kmv" +"5472";"JLB053";"Angličtina pro sociální vědy I";;"Gloverová,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"5473";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"3";"4";"3";"2";"1";"3";"1";"1";"3";"2";"1";"2";"1";"ocenuji články v povinné literatuře";"Kurz by se měl více zaměřit na literaturu, která je ke kurzu vyžadována, a dále ji rozvíjet, aby student skutečně chápal souvislosti a význam, z tohoto důvodu se domnívám, že vyřazení seminářů byla veliká chyba, která znehodnotila kurz. Dále by se kurz měl zaměřit více na detail jednotlivých témat, aby nebyla tolik abstraktní a nic neříkající";"kp" +"5474";"JJM291";"Tvůrčí dílny I – tisk";;"Matyášová,J.";"5";"5";NULL;NULL;NULL;"4";"4";"5";"1";"4";"4";"5";"5";"Velmi prakrické úkoly";;"kz" +"5475";"JJM249";"Duchovní kořeny evropské kultury";"Halada,J.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";NULL;"3";NULL;NULL;;"Kurz téměř zcela kopíruje podobný předmět z bakalářského stupně, nerozumím, proč je vůbec do učebního plánu zařazen. Každý z nás si v nějakém stádiu studia filozofií prošel, takže základy už všichni v této době máme.";"kz" +"5476";"JJM340";"Tvůrčí dílny – tvůrčí psaní I";"Malý,R.";"Malý,R.";"4";"4";"3";"5";"5";"3";"5";"5";"2";"4";"5";"4";"5";;;"kz" +"5477";"JPB020";"Úvod do mezinárodních vztahů";"Karlas,J.,Kučera,T.";;"2";"4";"2";"2";"1";NULL;NULL;NULL;"2";"2";"2";"2";"2";;;"kmv" +"5478";"JEB047";"Účetnictví II";"Kemény,I.";;"3";"2";"3";"5";"3";NULL;NULL;NULL;"4";"2";"3";"4";"3";"-";"I would just suggest to change the time of this course. Friday afternoon doesnt really worth it";"ies" +"5479";"JPB229";"Regionální politické systémy: Skotsko, Wales";"Říchová,B.";;"4";"3";"4";"3";"3";NULL;NULL;NULL;"2";"4";"3";"3";"4";;"Paní vyučující byla občas bezdůvodně nepříjemná. Z tohoto důvodu jsem pak ztratil motivaci docházet na hodiny.";"kp" +"5480";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"3";"4";"4";"2";"5";NULL;NULL;NULL;"1";"4";"3";"3";"5";;;"kms" +"5481";"JPB268";"Evropská integrace";"Plechanovová,B.";;"2";"3";"2";"1";"1";NULL;NULL;NULL;"4";"2";"2";"3";"2";;;"kmv" +"5482";"JPB258";"Politické myšlení novověku I";"Franěk,J.,Kučera,J.";;"2";"1";"2";"4";"1";NULL;NULL;NULL;"2";"1";"1";"1";"1";;"Kurz by měl být zaměřen více na politické myšlení a nikoliv jen přelet přes významné osoby od antiky až do 14. století. Kurz aktuálně studentům nemohl dát představu o tehdejší mentalitě či vlivech, které by vedly k určitému politickému myšlení. Spíše jen vypíchl některé osobnosti a řekl k nim, kdo to byli, nikoliv, co udělali (vyjimkou je Aristoteles a Platon)";"kp" +"5483";"JJM320";"Propaganda a československá média po roce 1948";"Suk,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Vědomosti vyučujícího.";;"kms" +"5484";"JPB268";"Evropská integrace";"Plechanovová,B.";;"2";"5";"2";"2";"1";NULL;NULL;NULL;"2";"3";"2";"2";"1";;"Kurz je až zbytečně těžký a přístup paní vyučující je velmi špatný.";"kmv" +"5485";"JJM373";"Ethical Issues in Media, Business and Society";"Orhan,M.";;"3";"1";"5";"5";"5";NULL;NULL;NULL;"1";"2";"2";"2";"3";;;"kms" +"5486";"JJB231";"Dějiny a teorie public relations";"Halada,J.,Hejlová,D.";;"4";"3";"3";"4";"3";NULL;NULL;NULL;"1";"4";"1";"3";"4";"Velmi podrobné hodnocení seminárních prací studentů. Ústní zkouška.";"Na začátku kurzu bylo avizováno, že seminární práce nebudou zohledněny při zhodnocení studenta, že budou složit pouze k připuštění ke zkoušce. Nakonec byly seminární práce při hodnocení studenta zohledněny. Seminární práce byly taktéž hodnoceny těsně před zkouškou, přestože termín odevzdání byl několik týdnů před koncem semestru. Studenti jsou pak nuceni přepisovat seminární práce ve zkouškovém období, kdy musí plnit jiné zkoušky. Termín odevzdání seminárních prací není nutné mít ve stejný den. Seminární práce mohou být zadány již na začátku semestru. Nevyváženost otázek na ústní zkoušce - někdy jsou odpovědí tři slova z prezentace, někdy tři odstavce z odborné literatury.";"kmkpr" +"5487";"JJM340";"Tvůrčí dílny – tvůrčí psaní I";"Novotný,D.";"Novotný,D.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"4";"5";"4";"5";"Okamžitý feedback a individuální přístup.";;"kz" +"5488";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"4";"4";"4";"4";"2";"4";"4";"2";"1";"4";"3";"4";"4";;"Přidat semináře. (Nebo alespoň nerušit semináře existující).";"kp" +"5489";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"5";"4";"5";"4";"5";NULL;NULL;NULL;"2";"5";"4";"4";"5";"Navrhuji zachovat složení kurzu takové, jaké je.";"Pan Švec, ačkoli chápu jeho vytížení v České televizi, by mohl věnovat více času studentům. Dosud mi nebyla zapsána známka za předmět, i když jsem zkoušku splnil (musím totiž počkat na to, až mi bude vyhodnocena seminární práce).";"kp" +"5490";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"3";"4";"5";"5";"3";"4";"3";"4";"1";"3";"3";"4";"1";"Choices of seminars throughout the week!";"I do not like the condition of not using calculations on the exam. I spend a lot of time with calculations which could be easily done by elementary school student (provided by a lot of time) here we had 15 pages and 2 hours";"ies" +"5491";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"3";"2";"4";"4";"3";NULL;NULL;NULL;"1";"3";"1";"1";"2";;"Kurz nabízí pouze obecný přehled, nikoliv znalosti, které by se student měl naučit na UNI. Dále bych rád poznamenal, že studentské prezentace byly prázdné a zbytečné, kurz se místo nich mohl více zaměřit na detail jednotlivých systémů, který nebyl dostatečně pokryt";"kp" +"5492";"JJM279";"Divadelní kritika";"Homolová Richtrová,N.";;"3";"3";"4";"5";"4";NULL;NULL;NULL;NULL;"2";"4";"3";"3";"Paní Richtrová se nás opravdu snažila nadchnout pro divadlo, dokonce nás vzala na představení do Stavovského divadla.";"Nemám pocit, že bych se v kurzu příliš naučila, jak se kriticky dívat na divadlo. Chybělo mi hlubší vysvětlení nějakých základů recenzování v tomto oboru, které se podle mě nedá úplně naučit čtením starých recenzí z 19. století.";"kz" +"5493";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"4";"3";"4";"3";"3";NULL;NULL;NULL;"1";"4";"3";"3";"4";"Oceňuji závěrečný test zaměřený na probranou látku a zadanou literaturu. Obtížností byl vyvážený, při i jen troše snahy se dal zvládnout, na \"jedničku\" však bylo potřeba věnovat studiu dostatek času.";"Čas na průběžný test. 16 minut na 4 otevřené otázky bylo málo. Rozumím účelu, aby se student dokázal vyjádřit stručně a jasně, ovšem zarovnat čas na 20 minut by neuškodilo. Celý zbytek hodiny byl nakonec zaměřený na výuku o tiskových baronech, konkrétně Pulitzerovi. Výklad by určitě snesl čtyřminutové zkrácení.";"kms" +"5494";"JJM247";"Český stranický systém";"Just,P.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"2";"4";"5";;"Přednášku doplnit o prezentaci např.s volebními výsledky či podobou vládních koalic.";"kz" +"5495";"JJB003";"Dějiny masových médií III";"Bednařík,P.,Končelík,J.";;"5";"3";"5";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kms" +"5496";"JJB142";"Literatura faktu";"Halada,J.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"2";"4";;;"kz" +"5497";"JEB141";"Introduction to Market Design";"Gregor,M.,Kastl,J.";;"5";"1";"5";"5";"1";NULL;NULL;NULL;"1";"1";"1";"1";"5";"The final exam was very easy.";"The lectures were too simple to be valuable as far as attendance goes.";"ies" +"5498";"JPB592";"US Government and Politics";"Kotábová,V.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"4";"4";"4";"4";;;"kp" +"5499";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"4";"4";"5";"1";"1";"1";"2";"5";"-";"Switch it to different time";"kz" +"5500";"JPB578";"Classics of Political Thought";"Salamon,J.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"2";"4";"4";"4";"4";"přístup přednášejícího";"napsat 12 stránek v hodině jako test mi přijde jako zbytečné týrání mojí chudinky ruky :(";"kp" +"5501";"JJB055";"Tvůrčí dílny tisk I - tvůrčí psaní";"Malý,R.,Novotný,D.";"Malý,R.,Novotný,D.";"4";"2";"4";"5";"3";"4";"5";"3";"1";"2";"3";"4";"4";;"Mnohem přísnější hodnocení. Škola nás má učit psát, ne nás chválit. Pokud by mě vyučující na hodině veřejně urazil a moji špatnou práci roztrhal, bude to mít mnohem větší efekt (možná si popláču, ale mám motivaci se zlepšit). Mít ke každému dvě poznámky s tím, že všichni se trefili do žánru, to není moc přínosné – pouze pro studenty kreditově. Je lepší se ztrapnit na škole než v práci, kam pak můžete nastoupit v naivním domnění, že 'umíte psát', protože na škole jsem měl 'áčko' z dílen. Chápu, tento názor bude velice ojedinělý, ale škola tu není proto, aby se nám líbila, ale aby nás učila – je to jediná úloha fakulty. A tento úkol fakulta neplní a zcela selhává. Učíme se za pochodu a křiku v práci a fakulta hází jen byrokratické klacky pod nohy a nic neučí, jen plýtvá naším časem...";"kz" +"5502";"JPB260";"Politické myšlení novověku III";"Kučera,J.";;"4";"3";"4";"4";"5";NULL;NULL;NULL;"1";"4";"1";"3";"3";"JP";"Větší zaměření na klíčové filozofy, kteří jsou součástí okruhů u státnic. Více filozofie, méně dějepisu.";"kp" +"5503";"JEB141";"Introduction to Market Design";"Gregor,M.,Kastl,J.";;"3";"3";"3";"5";"2";NULL;NULL;NULL;"1";"3";"4";"2";"3";"Compact course";"I find the course a little bit boring";"ies" +"5504";"JEM020";"Ethics and Economics";"Cahlík,T.";"Cahlík,T.";"5";"2";"4";"5";"1";"4";"5";"1";"1";"1";"1";"1";"5";"The course gave incentives to think about deep ethical subjects and therefore had great potential for being interesting to people who are into moral philosophy.";"There was no feedback given to the assignments or to the final essay (or almost none). Even though the teacher tried, there was not much discussion at the lectures and I felt like the full potential of the course's topics remained unexplored. The subject could be way more interesting than it is now. I would appreciate it if the lectures encouraged more discussion, perhaps in smaller groups.";"ies" +"5505";"JPB269";"Současné problémy mezinárodních vztahů";"Knutelská,V.";;"2";"1";"2";"4";"1";NULL;NULL;NULL;"2";"1";"3";"1";"2";"ocenoval jsem především praktický nácvik akademických dovedností";"Domnívám se, že by se kurz měl více zaměřit na psaní policy briefů/paperů, kterých by se za semestr mělo odevzdat více, aby studenti skutečně věděli, jak je napsat. Jednotlivé přednášky by se měli tomuto tématu ve vymezeném čase věnovat. Zároven bych navrhl, aby jednotlivé práce byly odevzávány až po přednášce na ono téma, aby studenti měli důvod přijít.Fact-checkingy se mi jevily poněkud zbytečné";"kmv" +"5506";"JJM320";"Propaganda a československá média po roce 1948";"Suk,P.";;"4";"2";"3";"4";"4";NULL;NULL;NULL;"1";"3";"2";"3";"4";"Kurz byl zajímavý zejména proto, že na čtyřicet let totalitního režimu by se nemělo zapomínat a je potřeba objektivně vyzdvihovat, v čem režim ovlivňoval veřejné mínění.";"Kurz by se mohl více zaměřit na jednotlivé aspekty propagandy, detailněji se věnovat jen vybraným událostem. Domnívám se, že naše generace ještě stále má dobrou představu o minulém režimu a není tedy potřeba se tolik zaměřovat na obecné souvislosti.";"kms" +"5507";"JEB111";"Advanced Data Analysis in MS Excel";;"Poláková,N.,Polák,P.";"5";"2";NULL;NULL;NULL;"4";"3";"5";"3";"5";"5";"5";"5";"-";"-";"ies" +"5508";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"3";"3";"5";"4";"3";"2";"4";"4";"3";"4";"2";"4";"3";"-";"The time of the course";"ies" +"5509";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"2";"3";"1";"5";"5";;;"kp" +"5510";"JPB263";"Bakalářský seminář II.";;"Brunclík,M.,Bureš,O.,Ditrych,O.,Franěk,J.,Gelnarová,J.,Hynek,N.,Charvát,J.,Jeřábek,M.,Jüptner,P.,Karásek,T.,Karlas,J.,Knutelská,V.,Kofroň,J.,Kotábová,V.,Krausz Hladká,M.,Kubátová,H.,Kučera,J.,Kučerová,I.,Landovský,J.,Ludvík,J.,Makariusová,R.,Mlejnek,J.,Pa";NULL;NULL;NULL;NULL;NULL;"2";"4";"1";NULL;NULL;NULL;NULL;NULL;;"čekala bych trochu podporu nebo navedení k nějaké literatuře či jiným zdrojům popřípadě třeba radu, jak dané téma uchopit lépe, ale kde nic tu nic :/";"kp" +"5511";"JEB003";"Ekonomie I";"Fanta,N.,Kracík,J.,Švarcová,N.";"Fanta,N.,Kracík,J.,Švarcová,N.";"2";"3";"5";"5";"4";"2";"4";"3";"1";"4";"4";"4";"4";"Styl přednášek a domácích úkolů";"Lepší výklad a vedení během cvičení";"ies" +"5512";"JPB588";"Seminář k politickému myšlení: Antika";;"Franěk,J.";"4";"1";NULL;NULL;NULL;"3";"4";"3";"1";"4";"2";"4";"5";"velice ocenuji nutnost četby";"místo Gorgia bych možná více ocenil některou z knih Ústavy.";"kp" +"5513";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"3";"3";"3";"3";"3";NULL;NULL;NULL;"4";"3";"3";"3";"3";;;"kp" +"5514";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"3";"5";"3";"2";"5";"3";"2";"2";"1";"3";"2";"3";"2";;;"kp" +"5515";"JEM032";"Banking";"Mejstřík,M.,Pečená,M.,Teplý,P.";"Pečená,M.";"4";"3";"4";"5";"2";"5";"5";"1";"1";"2";"2";"2";"5";"Most of the syllabus covered topics we have already covered in other courses so the midterm and final exams were relatively easy to pass. The lectures are not bad, even though they are not the most attention grabbing thing either and they are not necessary.";"The credit appraisal is long and tiresome and there was no real coverage of the forecasting skills necessary. In the end it was just copying reports from the company's financial statements and making up some numbers in order to calculate annuity.";"ies" +"5516";"JPB256";"Politické systémy I";"Brunclík,M.,Jeřábek,M.,Kotábová,V.,Perottino,M.";;"4";"1";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"kp" +"5517";"JEB039";"International Trade";"Semerák,V.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Skupinové úkoly (většinou mezinárodní spolupráce)";"Pravidelně v semináři najít alespoň chvíli na diskuzi ohledně úkolů (často na to nezbyl čas).";"ies" +"5518";"JPB258";"Politické myšlení novověku I";"Franěk,J.,Kučera,J.";;"1";"5";"1";"1";"3";NULL;NULL;NULL;"3";"1";"1";"1";"1";;;"kp" +"5519";"JPB268";"Evropská integrace";"Plechanovová,B.";;"2";"4";"1";"2";"1";NULL;NULL;NULL;"4";"1";"2";"2";"2";;;"kmv" +"5520";"JPB268";"Evropská integrace";"Plechanovová,B.";;"1";"5";"2";"1";"1";NULL;NULL;NULL;"3";"3";"2";"2";"1";;;"kmv" +"5521";"JSB998";"Úvod do sociologie";"Soukup,P.";;"3";"1";"4";"5";"1";NULL;NULL;NULL;"1";"1";"1";"1";"4";;;"ks" +"5522";"JPB269";"Současné problémy mezinárodních vztahů";"Knutelská,V.";;"4";"2";"4";"4";"5";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kmv" +"5523";"JMB178";"U.S. in the 1960s and 1970s";"Raška,F.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kas" +"5524";"JPB558";"Výběrový seminář: Politická komunikace";"Váňa,T.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"4";"5";;;"kp" +"5525";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"3";"5";"přínosy z praxe obou přednášejících";;"ies" +"5526";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"1";"5";"1";"1";"3";NULL;NULL;NULL;"2";"1";"1";"1";"1";;;"kp" +"5527";"JJM324";"Teorie mediální komunikace";"Jirák,J.";;"4";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"4";"4";"Oceňuji systém bodování a hodnocení založený i na účasti na přednáškách. Je to dobrý způsob motivace a vzhledem k tomu, že se jednalo o první hodinu zaměřenou čistě na teorii, měl tento způsob rozhodně pozitivní dopad na celkovou docházku. Navíc výklad vyučujícího určitě přispěl k pochopení zadaných materiálů.";;"kms" +"5528";"JPB597";"Current Political Extremism";"Charvát,J.";;"5";"2";"4";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"5529";"JJB334";"Zábava v médiích";"Kruml,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Ukázky z vybraných pořadů.";;"kms" +"5530";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"1";"2";"3";"4";"5";;;"kz" +"5531";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"3";"2";NULL;NULL;NULL;"4";"3";"3";"1";"2";"2";"3";"4";;;"ies" +"5532";"JJB221";"Ekonomická teorie marketingu";"Postler,M.";;NULL;NULL;"5";"5";"5";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kmkpr" +"5533";"JJB224";"Psychologie marketingové komunikace";"Vranka,M.";;"4";"5";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";;;"kmkpr" +"5534";"JJB606";"Televize jako instituce";"Štoll,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Struktura kurzu rozdělená na pohled na televizi ze 3 různých perspektiv.";;"kms" +"5535";"JJB237";"Právo a etika v marketingu";"Winter,F.";;"5";NULL;"5";"5";"5";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kmkpr" +"5536";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"4";"2";NULL;NULL;NULL;"3";"5";"4";"1";"4";"4";"4";"5";;;"ies" +"5537";"JJB249";"Úvod do studia českého jazyka I";"Schneiderová,S.";"Schneiderová,S.";NULL;NULL;"5";"5";"5";"5";"5";"5";NULL;NULL;NULL;NULL;NULL;;;"kmkpr" +"5538";"JJB021";"Bakalářský seminář";;"Prázová,I.";"3";"4";NULL;NULL;NULL;"3";"4";"1";"1";"2";"1";"1";"1";;;"kz" +"5539";"JJB255";"Digitální komunikace";;"Klimeš,D.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"5";"5";;;"kmkpr" +"5540";"JJB269";"Sociální kontext komunikace";"Vranka,M.";;NULL;NULL;"5";"5";"5";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kmkpr" +"5541";"JLB003";"Angličtina pro ekonomy I";;"Poslušná,L.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"1";"5";"4";"4";"5";;;"cjp" +"5542";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"2";"2";"2";"4";"1";NULL;NULL;NULL;"1";"4";"5";"5";"2";"Oceňuji závěrečnou seminární práci. Ta určitě nejvíce pomohla k pochopení a přenesení odvykládané teorie do praxe. Bez povinnosti vypracovat tuto esej by výklad byl pouze změtí předčítaných slidů.";"Navrhuji rozhodně se zaměřit na zlepšení výkladu pana Podzimka. Monotónnost s jakou předčítal to, co jsme na tabuli viděli všichni, učinila z předmětu pouhou povinnost. První přednáška probíhala v den parlamentních voleb, kdy jsme byli předchozí večer svědky velké debaty lídrů několika stran. Určitě bych tedy ocenil, kdyby se předmět Argumentace a přesvědčování v médiích zaměřil na argumentaci a přesvědčování v médiích. Myslím, že debata a celé předvolební období mohlo posloužit k praktickým ukázkám vykládané teorie.";"kms" +"5543";"JJB406";"Tvorba a prostředky v mediální komunikaci";"Chudinová,E.";;NULL;NULL;"4";"5";"3";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kmkpr" +"5544";"JLB019";"Francouzština odborná I";;"Bosáková,L.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"4";"5";;;"cjp" +"5545";"JSB998";"Úvod do sociologie";"Soukup,P.";;"3";"2";"3";"2";"1";NULL;NULL;NULL;"1";"2";"1";"2";"3";;;"ks" +"5546";"NMMA701";"Matematika 1";"Spurný,J.";"Honzík,P.";"5";"5";"5";"5";"4";"3";"3";"4";"1";"5";"5";"5";"5";;;"ies" +"5547";"JEM034";"Corporate Finance";"Bychkova,O.,Malířová,T.";;"4";"2";"5";"1";"2";NULL;NULL;NULL;"5";"3";"2";"2";"5";"The study materials are of excellent quality, especially the midterm and final reviews. The home assignments were numerous but relatively easy and they made it much easier to prepare for the exams.";"The teacher routinely came 15 or 20 minutes late to the lectures, as well as to the midterm and final exam. No explanation or apology was given for this. We got the results from the midterm test after five weeks (!!!).";"ies" +"5548";"JJB607";"Analýzy mediálních obsahů";"Křeček,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"5549";"JEB141";"Introduction to Market Design";"Gregor,M.,Kastl,J.";;"4";"3";"4";"5";"3";NULL;NULL;NULL;"1";"3";"3";"3";"4";;;"ies" +"5550";"JJB617";"Vybrané novinářské osobnosti 20. století";"Železný,J.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"To, že každý z účastníků kurzu měl připravit krátkou charekteristiku vybrané novinářské osobnosti a představit ji ostatním. Probrali jsme poměrně hodně zajímavých osobností a zjistili o nich to podstatné.";;"kms" +"5551";"JEM001";"Master´s Thesis Seminar I";;"Havránek,T.,Havránková,Z.,Mejstřík,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"3";"3";"3";"3";;;"ies" +"5552";"JJM373";"Ethical Issues in Media, Business and Society";"Orhan,M.";;"5";"1";"4";"4";"1";NULL;NULL;NULL;"1";"4";"2";"5";"2";"Oceňuji přístup vyučujícího jak k látce, tak i studentům. Byl k dispozici v podstatě kdykoli bylo potřeba. Také oceňuji praktické ukázky toho, o čem byly přednášky.";"Nepouštět si film. Vzhledem ke třem setkáním během celého semestru se mi nezdá adekvátní jedno z těchto setkání věnovat promítání filmu. Přínosu filmu rozumím, ale kdo by měl zájem, podívá se na něj doma sám.";"kms" +"5553";"JJB135";"Filmový seminář I";;"Šobr,M.";"3";"2";NULL;NULL;NULL;"3";"3";"3";"1";"3";"3";"3";"3";;;"kz" +"5554";"JJB181";"Současná blízkovýchodní arabská média";"Veselý,J.";;"4";"5";"4";"5";"5";NULL;NULL;NULL;"1";"4";"2";"5";"4";;"Na to, že se jedná o volitelný kurz za 2 kredity jsou požadavky na splnění předmětu neúměrně vysoké - to většinu studentů odrazuje.";"kz" +"5555";"JJB618";"Propagandistické působení médií v 1 polovině 20. století";"Suk,P.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Zajímavosti o hercích a osobnostech z období první republiky a protektorátu + souvislosti a podobné rysy nacistické a sovětské propagandy.";;"kms" +"5556";"JEM162";"Energy Markets & Economics";"Elms,N.,Valíčková,P.";;"3";"3";"3";"3";"3";NULL;NULL;NULL;"3";"3";"3";"3";"3";;;"ies" +"5557";"JPB260";"Politické myšlení novověku III";"Kučera,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";NULL;"4";"5";;;"kp" +"5558";"JJB086";"Managing Multimedia Projects";"Juřík,O.";;"4";"5";"5";"4";"5";NULL;NULL;NULL;"1";"5";"5";"4";"4";;"Zbytečně moc úkolů, které se nakonec nijak nevyužily (kromě loga) - zachovat méně úkolů, ne skupinové a ještě individuální na každý den.";"kz" +"5559";"JPB242";"Geografie vnitropolitických konfliktů";;"Doboš,B.,Riegl,M.";"3";"5";NULL;NULL;NULL;"2";"4";"2";"1";"5";NULL;"4";"2";;"Studenti by určitě ocenili srozumitelnější přednášení prezentací a celkové zlepšení kvality prezentací. Suchý výčet pojmů a jmen na jednotlivých slidech byl téměř zbytečný. Navíc kvůli rychlosti přednášení se to velmi často nedalo stihnout zapsat.";"kp" +"5560";"JEM037";"Financial Markets";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"5";"2";"5";"5";"2";"4";"5";"4";"1";"3";"3";"3";"5";"There were only three seminars but they were well structured and relatively interesting. The final exam was quite easy.";;"ies" +"5561";"JJB067";"Mluvní a pohybová výchova I";;"Pavel,L.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";;;"kz" +"5562";"JPB260";"Politické myšlení novověku III";"Kučera,J.";;"4";"2";"3";"3";"3";NULL;NULL;NULL;"2";"5";"1";"4";"4";"Pan docent má svérázný, ale spravedlivý, způsob zkoušení. Líbí se mi styl jeho přednášení.";;"kp" +"5563";"JEM132";"Company Valuation";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"4";"The lectures by Mr. Novak are fantastic. I would recommend going to them even if you didn't take the course.";"The seminars and guest lectures were a waste of time. We either did things that were already covered by the lectures or we did calculations which were not well presented (although I found \"Yes? No? Maybe? Don't care?\" quite entertaining). The guest lectures were very vague and didn't really give insight as to what the people were doing for a living. The project was really bad. It was split into five phases which built on one another but we got no feedback on the individual phases so if you made a mistake in phase two, phase five would give you silly numbers. The requested tasks were not clear and there was no help from the side of the seminars. We were not supposed to consult the project with other groups but from what I gathered, other groups didn't really understand the problems either.";"ies" +"5564";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;NULL;"5";"5";"5";"5";"Nejvíce si vážím úsměvu doktorky Gelnarové a jejího vřelého přístupu ke studentům. Dále oceňuji samotný styl přednášení, kdy důraz nebyl kladen na suchý výčet faktů a dat, ale na pochopení dobových souvislostí a společnosti samotné.";;"kp" +"5565";"JJM329";"Média a kultura";"Bednařík,P.,Jirák,J.";;"3";"2";"4";"5";"4";NULL;NULL;NULL;"1";"2";"3";"3";"3";;;"kms" +"5566";"JJM330";"Trendy současných českých médií";"Aust,O.";;"4";"3";"4";"4";"5";NULL;NULL;NULL;"1";"4";"2";"3";"3";;;"kms" +"5567";"JJM080";"Zábava v televizním vysílání";"Kruml,M.";;"2";"2";"4";"5";"3";NULL;NULL;NULL;"1";"2";"1";"1";"3";;;"kms" +"5568";"JJB135";"Filmový seminář I";;"Šobr,M.";"4";"1";NULL;NULL;NULL;"4";"3";"3";"1";"3";"3";"3";"5";;;"kz" +"5569";"JJM331";"Výzkum médií II";"Vochocová,L.";;"4";"4";"4";"5";"3";NULL;NULL;NULL;"1";"5";"4";"5";"4";;;"kms" +"5570";"JJM208";"Mediální systémy a jejich komparace";"Miessler,J.";;"3";"3";"5";"4";"3";NULL;NULL;NULL;"2";"3";"1";"2";"3";;;"kms" +"5571";"JJM217";"Čtení textů ke studiu médií - Nová média";;"Jirků,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"5";"5";"Možnost vybrání si textů podle svého zájmu, zároveň ale i rozšíření si znalostí z oblasti zájmu ostatních studentů. Velice oceňuji přístup profesora ke studentům i celkově k předmětu.";"S kurzem jsem byla spokojená. Spíše než návrh na zlepšení kurzu by bylo vhodnější ze strany studentů zlepšit přístup ke studiu obecně. S tím ale bohužel profesor nemůže nic zásadně udělat... Na škole je celkem dost \"zbytečných\" předmětů, na které je chodit povinností. Pak je tu pár takových, ze kterých si studenti mohou doopravdy něco odnést i do praxe, na ty ale nechodí, jelikož účast není povinná....";"kms" +"5572";"JMM039";"Západní Evropa a svět";"Tomalová,E.,Váška,J.";;"4";"3";"5";"3";"4";NULL;NULL;NULL;"1";"4";"3";"3";"4";;"Dílčí hodnocení by mělo být pro studenty více transparentní, aby pak měli jasno, jak si v průběhu semestru vedou.";"kzs" +"5573";"JJM332";"Přirozený jazyk a média";"Podzimek,J.";;"3";"2";"3";"4";"3";NULL;NULL;NULL;"1";"3";"3";"4";"4";;;"kms" +"5574";"JJB083";"Editování zpravodajských relací";"Beneš,P.";;"3";"4";"3";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"3";;"Těžko učit práci s Octopem, když je školní Octopus víceméně prázdný. Buď vyučující může zkusit zprostředkovat exkurzi přímo v ČT/ČRo nebo ten školní víc propracovat a rozšířit, nasimulovat konkrétní reportáže v bodových scénářích atd.";"kz" +"5575";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"3";"3";"3";"4";"3";NULL;NULL;NULL;"1";"3";"3";"3";"3";;;"kms" +"5576";"JMM128";"Prezentace v médiích";"Procházková,B.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"3";"5";"Velmi si cením praktických dovedností, které jsme během kurzu měli možnost získat, a rovněž i velmi osobní a vstřícný přístup přednášející. Nebylo by na škodu rozložit předmět do obou semestrů, aby tak měl ještě větší přínos v oblasti nácviku dovedností.";;"kzs" +"5577";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"3";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"3";;;"kms" +"5578";"JJB069";"Tvůrčí dílny I - televizní";"Lokšík,M.";;"4";"5";"4";"4";"4";NULL;NULL;NULL;"1";"4";"5";"4";"5";;;"kz" +"5579";"JJM373";"Ethical Issues in Media, Business and Society";"Orhan,M.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"3";"4";"4";"3";"3";;;"kms" +"5580";"JJB066";"Rozhlas a televize ve světě";"Moravec,V.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kz" +"5581";"JJM324";"Teorie mediální komunikace";"Jirák,J.";;"3";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"3";"3";"3";;;"kms" +"5582";"JLB053";"Angličtina pro sociální vědy I";;"Gloverová,M.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Nejvíce oceňuji individuální a otevřený přístup Mrs. Gloverové.";"nic";"cjp" +"5583";"JLB013";"Němčina odborná I";;"Křenková,D.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"1";"4";"3";"3";"5";;;"cjp" +"5584";"JJB334";"Zábava v médiích";"Kruml,M.";;NULL;NULL;"5";"5";"4";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kms" +"5585";"JMB410";"Moderní dějiny západní Evropy";"Matějka,O.,Váška,J.";;"3";"4";"5";"4";"4";NULL;NULL;NULL;"3";"4";"2";"5";"4";"Zaujetí pro věc (profesorů) a jejich smysl pro humor :)";"Dvě oddělené přednášky na sebe nenavazovaly (chápu, že každý prof. se věnuje jiným tématům, ale i tak) a to občas mohlo studenty mást a dezorientovat v probíraném období. A také zrušení možnosti \"kompenzační eseje\" těsně před zkouškovým období?? Obzvlášť když jsme byli na počátku ZS informováni vyučujícími o možnosti těchto kompenzačních prací a tedy to některým dávalo naděje při neúspěchu u midtermu";"kzs" +"5586";"JSM502";"Diplomový seminář I";;"Dobiášová,K.,Kotrusová,M.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"5";;;"kvsp" +"5587";"JSM507";"Metody tvorby politik";"Veselý,A.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kvsp" +"5588";"JMM271";"Metodologický seminář";;"Matějka,O.";"4";"2";NULL;NULL;NULL;"5";"5";"4";"1";"3";"3";"4";"4";;;"krvs" +"5589";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"3";"3";"3";"3";"2";NULL;NULL;NULL;"3";"3";"1";"3";"4";"Oproti očekávání byl závěrečný test férový a zvládnutelný";"Rozčísnout monotónní styl přednášek";"kmv" +"5590";"JMM279";"Společnost a ekonomika";"Fiřtová,M.";"Young,M.";"5";"4";"5";"5";"5";"5";"5";"4";"1";"5";"3";"5";"5";;;"kas" +"5591";"JSM528";"Seminář k diplomové práci I.";;"Kohoutek,J.,Ochrana,F.";"4";"3";NULL;NULL;NULL;"5";"5";"4";"1";"3";"4";"4";"5";;;"kvsp" +"5592";"JMM348";"American Literature 1900-1950";"Hanuš,J.";;"4";"2";"5";"4";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kas" +"5593";"JMMZ083";"Eastern Europe Today I";;"Lídl,V.,Šír,J.";"4";"4";NULL;NULL;NULL;"4";"4";"4";"1";"4";"5";"5";"4";"The personal research I had to put into the subjects I researched allowed me to expand upon my learning of those subjects.";"Professors: Having a more structured student participation aspect to it would be most beneficial. There was not much classroom atmosphere to the class. Sometimes, there was. Most times, there was not. Most students were disengaged because they were not required to pay attention, nor did they find interest in doing so. Requiring more student engagement would improve the quality of the atmosphere and contribute to more enjoyment overall.";"krvs" +"5594";"JMB409";"Moderní dějiny středo- a jihovýchodní Evropy-dcera JMB013";"Balla,P.,Švec,L.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"2";"4";"2";"5";"4";;"Jednodušší zkoušku..";"krvs" +"5595";"JMB408";"Moderní dějiny Ruska";"Litera,B.,Novák,P.,Pečenka,M.";;"3";"5";"4";"2";"4";NULL;NULL;NULL;"1";"4";"4";"4";"1";;"Možnost splnit zkoušku z Ruska (bez týdnů spánkové deprimace a depresí) je jako přežít Velkou čistku na vyšším postu v SSSR.";"krvs" +"5596";"JMM040";"Societal changes in Western European countries";"Bauer,P.";;"2";"5";"2";"3";"3";NULL;NULL;NULL;"1";"3";"3";"2";"2";;;"kzs" +"5597";"JPM697";"Asia Security";"Kolmaš,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kbs" +"5598";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"3";"4";;;"kmv" +"5599";"JPM598";"Grand Strategies";"Ditrych,O.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kbs" +"5600";"JMM277";"Historie a kultura";"Vykoukal,J.";"Bauer,P.";"3";"4";"4";"5";"2";"3";"3";"2";"1";"4";"2";"2";"3";;;"krvs" +"5601";"JMB414";"Seminář k aktualitám I";;"Andrle,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"krvs" +"5602";"JPM561";"Regional Security Studies";"Karásek,T.,Klosek,K.";"Karásek,T.,Klosek,K.";"4";"4";"4";"3";"1";"2";"3";"1";"1";"5";"3";"4";"5";"Celý kurz je de facto samostudium s témeř nulovým přínosem přednášek. Ne že by vyučující nebyl kvalitní, ale vzhledem k formátu předmětu byl výklad extrémně povrchní, což je u magisterského oboru zarážející. Semináře neexistuji, místo nich co druhý týden testy, které krom pár jednotlivců všichni stihli napsat během prvních 40 minut, takže se za semestr promarnilo 5x40 minut času. Kurz je tedy spíše na úrovni bakalářského úvodu do.. a ne magisterského předmětu.";;"kbs" +"5603";"JMB250";"Seminář k dějinám západní Evropy";;"Synkule,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"3";"5";"4";"4";"5";;;"kzs" +"5604";"JMM278";"Politika a mezinárodní vztahy";"Kubát,M.,Weiss,T.";"Mejstřík,M.";"4";"3";"4";"5";"4";"5";"5";"5";"1";"2";"4";"4";"4";;;"krvs" +"5605";"JMB249";"Seminář k dějinám středo- a jihovýchodní Evropy";;"Zilynskyj,B.";"4";"1";NULL;NULL;NULL;"4";"5";"4";"1";"4";"4";"4";"4";;;"krvs" +"5606";"JLB041";"Španělština I";;"Mlýnková,L.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"5607";"JMM530";"European Comparative Politics and Society";"Rovná,L.";;"3";"2";"4";"5";"2";NULL;NULL;NULL;"1";"2";"2";"3";"3";;;"kzs" +"5608";"JMMZ313";"Government in United States";"Sehnálková,J.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"5";"5";"5";"5";"5";;;"kas" +"5609";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmv" +"5610";"JMMZ136";"Language of the Region B I.";;;"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"The only German approach was very nice. It was so difficult, but I knew it was the better approach to learning a language. Goethe Institute is where I was enrolled, by the way.";;"knrs" +"5611";"JPM690";"Liberalism in International Relations (TIR)";;"Karlas,J.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kmv" +"5612";"JJM355";"Teoretické koncepty ke studiu žurnalistiky I";"Jirků,J.,Němcová Tejkalová,A.";"Jirků,J.";"4";"3";"5";"5";"5";"5";"5";"5";"1";"4";"3";"4";"4";;;"kz" +"5613";"JLM011";"Angličtina pro veřejnou a sociální politiku I";;"Klírová,M.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Great teacher, I learned new words related on Public and Social Policy";;"cjp" +"5614";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";;;"ies" +"5615";"JSM103";"Academic Writing";;"Blokker,P.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"2";"5";"4";"5";"4";"Excellent professor with good methodology!";;"ks" +"5616";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"3";"2";NULL;NULL;NULL;"4";"4";"4";"1";"3";"4";"4";"4";;;"ies" +"5617";"JSM406";"Statistics in SPSS";;"Soukup,P.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"2";"4";"5";"5";"5";"New knowledge with Spps software and how I could apply it in Social Sciences.";;"ks" +"5618";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"5619";"JMMZ132";"Germany and Austria and the Visegrad Countries";;;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"4";"4";"5";"5";"The professor cared about the subject, and he made the learning environment comfortable and engaging. He was very easy to approach with struggles from the class material. He did a great job providing further clarity on the subject matter. The chosen subject material was also very suitable to the subject of the class.";"Just clearer student objectives from the beginning of enrollment into the course, not our first meeting. It would help a student like me who typically has to wait until early November (in the fall semester) to start on the class expectations- large papers. A quicker meeting in early October would address these concerns.";"knrs" +"5620";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"5";"3";"4";"5";"4";"1";"4";"1";"1";"5";"5";"5";"5";;;"ies" +"5621";"JJM320";"Propaganda a československá média po roce 1948";"Suk,P.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"3";"2";"4";"4";;;"kms" +"5622";"JSM480";"Evaluation Research";;"Remr,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"5";"4";"Professor Jiri has a deeo knowledge about evaluation research. Great Professor";;"ks" +"5623";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"3";"3";"4";"4";"3";NULL;NULL;NULL;"1";"4";"2";"4";"4";;;"kmv" +"5624";"JSM518";"Public Policy";"Potůček,M.,Vlčková,K.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"5";"5";"5";"5";"5";"Professor Potucek is a master of Public Policy. It is a pleasure to have classes with him";"Seminar time could be improve the quality of the discussion";"kvsp" +"5625";"JLB003";"Angličtina pro ekonomy I";;"Poslušná,L.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"5626";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"3";"3";"2";"5";;;"kz" +"5627";"JSB998";"Úvod do sociologie";"Soukup,P.";;"4";"3";"5";"5";"3";NULL;NULL;NULL;"1";"5";"4";"5";"4";;;"ks" +"5628";"JPM561";"Regional Security Studies";"Karásek,T.,Klosek,K.";"Karásek,T.,Klosek,K.";"5";"4";"4";"5";"4";"4";"5";"4";"1";"4";"4";"4";"5";;;"kbs" +"5629";"JSM692";"Introduction to Social Research Methodology";"Remr,J.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"ks" +"5630";"NMMA701";"Matematika 1";"Spurný,J.";"Honzík,P.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"5631";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"4";"3";"5";"5";"3";NULL;NULL;NULL;"1";"4";"5";"3";"4";;;"kmv" +"5632";"JPM689";"Conflict Studies";"Karásek,T.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"3";"4";"5";;;"kbs" +"5633";"JJM324";"Teorie mediální komunikace";"Jirák,J.";;"3";"3";"4";"5";"3";NULL;NULL;NULL;"1";"3";"4";"3";"3";;;"kms" +"5634";"JPM710";"Radicalization and Deradicalization";"Aslan,E.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"4";"5";;;"kbs" +"5635";"JMMZ263";"Master Thesis Seminar for CECS I.";;;"5";"1";"5";"5";"4";NULL;NULL;NULL;"1";"3";"3";"3";"3";;;"knrs" +"5636";"JMMZ265";"Methodology of Comparative Studies and Academic Writing";;;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"This course was wonderful in addressing the actual Master's Thesis beginning stages. Everyone should take this course.";"This course should be offered in the 2nd semester of a two year degree, not later or earlier. This would allow students an appropriate amount of time to consider their own Master's Thesis over summer. Without the course, students are still expected to think about their topic over the summer. However, they (including myself) had no idea where to begin. The course would put them in a more suitable position to structure their thought process, not running the risk of feeling rushed on time in the 2nd year of the program.";"knrs" +"5637";"JEM032";"Banking";"Mejstřík,M.,Pečená,M.,Teplý,P.";"Pečená,M.";"4";"3";"3";"3";"1";"4";"5";"4";"3";"4";"3";"4";"4";;"Z prezentací o 80 slidech se skoro nedá učit a množství různých obrázků je v tomto případě na škodu.";"ies" +"5638";"JJM080";"Zábava v televizním vysílání";"Kruml,M.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"kms" +"5639";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";NULL;NULL;NULL;;;"ies" +"5640";"JJM224";"Politická ekonomie komunikace";"Vochocová,L.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kms" +"5641";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"5";"2";"3";"5";"4";NULL;NULL;NULL;"2";"4";"3";"5";"5";;;"kms" +"5642";"JJM251";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"5";"3";"4";"4";;;"kz" +"5643";"JJM259";"Mediální systémy a jejich komparace";"Miessler,J.";;"4";"3";"5";"5";"2";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kz" +"5644";"JEM003";"Advanced Microeconomics";"Janda,K.,Melikhova,O.";"Janda,K.,Melikhova,O.";"4";"4";"3";"3";"1";"5";"5";"4";"2";"4";"4";"3";"3";"Rozdělení látky do více testů během semestru je velice dobrý nápad.";;"ies" +"5645";"JJM343";"Interkulturní komunikace";"Soukup,M.";;"5";"3";"5";"4";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"5646";"JLB001";"Angličtina pro sociology I";;"Štěpánková,D.";NULL;"1";NULL;NULL;NULL;"4";"5";"1";"1";"1";"1";"1";"1";"Sociologická témata. Paní magistra je velmi sympatická a má dobrou angličtinu (proto je škoda, že se toho nevyužije pořádně).";"Náročnost - ne nutně kvantitou učiva, spíš obsahově. Kurz byl spíše středoškolský.";"cjp" +"5647";"JJM373";"Ethical Issues in Media, Business and Society";"Orhan,M.";;"2";"2";"3";"2";"3";NULL;NULL;NULL;"1";"3";"4";"2";"2";;;"kms" +"5648";"JEM040";"Účetní a daňové poradenství";;"Kemény,I.";"5";"2";NULL;NULL;NULL;"4";"5";"5";"2";"5";"5";"5";"5";;;"ies" +"5649";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"4";"4";"5";"5";"4";"3";"4";"3";"2";"4";"4";"4";"3";;"Bylo by dobré, kdyby byly úkoly odevzdané před midtermem také opraveny před midtermem. Stávalo se, že v úkolu byly chyby, které pak většina opakovala i v midtermu.";"ies" +"5650";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Buřičová,B.";NULL;NULL;"2";"2";"1";"4";"5";"4";"1";"2";"2";"1";NULL;;"Příliš mnoho širokých témat na málo prostoru. Každé téma se jen lehce naťukne, na cvičení se k tomu udělají dva nebo tři příklady. Z toho se nedá pochopit ani co to probíráme, natož jak to funguje a jak se to vztahuje k sociologii. Ano, jsou to „Základy logiky a matematiky“, ale místo základů z toho byl akorát zmatek. Nebyl vůbec čas si látku vysvětlit/procvičit, a tak jediné, o co šlo, bylo bez hlubšího porozumění látce se naučit dosadit čísla tak, jak to bylo v prezentaci. Nejvíc to asi odnesli cvičící, kteří měli za úkol, co nejrychleji a nejjednodušeji vysvětlit obsáhlou látku.";"ks" +"5651";"JSM020";"Seminář k aktuální veřejně politické problematice";;"Balon,J.,Císař,O.";"3";"1";NULL;NULL;NULL;"3";"4";"2";"1";"4";"1";"2";"3";;"Původně jsem si představovala, že se budeme, dle názvu věnovat aktuálním politickým otázkám a ne tomu, že budeme prezentovat své diplomové projekty někde na škále od ochrany pejsků až po csr. Přínosem je, že se můžeme dozvědět něco, co by nás normálně vůbec nezajímalo.";"ks" +"5652";"JMB197";"Kapitoly z moderních dějin Itálie";"Mejstřík,M.";;"5";"3";"4";"5";"5";NULL;NULL;NULL;"1";"5";NULL;"4";"5";;;"kzs" +"5653";"JEM035";"Financial Markets Instruments I";"Dědek,O.,Poláková,N.,Polák,P.";"Dědek,O.,Poláková,N.,Polák,P.";"4";"3";"4";"5";"3";"4";"5";"2";"2";"4";"4";"4";"4";;;"ies" +"5654";"JLB035";"Francouzština I";;"Dundrová,M.";"5";"4";NULL;NULL;NULL;"4";"5";"5";"2";"5";"5";"5";"5";;;"cjp" +"5655";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;NULL;"5";NULL;"5";"4";;;"kp" +"5656";"JEM004";"Advanced Macroeconomics";"Baxa,J.,Hromádková,E.";"Baxa,J.,Hromádková,E.,Šestořád,T.,Troch,T.,Žáček,J.";"5";"5";"5";"5";"5";"5";"5";"5";"2";"4";"4";"4";"5";"Předmět, který baví díky učitelům.";;"ies" +"5657";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"4";"3";"3";"2";"1";NULL;NULL;NULL;"1";"4";"1";"4";"3";"Zajímavé propojení historie a vývoje médií pro ty, kteří výuku na tohle téma zatím neabsolvovali.";"Asi to nejde, ale chtělo by to i něco praktického. Těžko se udržuje pozornost při náloži takového množství teorie.";"kms" +"5658";"JMB414";"Seminář k aktualitám I";;"Lídl,V.";"3";"5";NULL;NULL;NULL;"3";"2";"4";"2";"4";"5";"4";"3";"Zbytečně moc požadavků - 10 normostran seminárky, 11 reportů, recenze, seznam zdrojů, 2 referáty. To jsou požadavky pomalu na 2 standardní semináře.Reporty jsou jinak výborná věc, student se naučí region poznat, uvažovat o něm, pochopí souvislosti... Oproti tomu seminárka je potom naprosto zbytečná - shrnutí situace v regionu např. na XY normostran dává mnohem větší smysl.Hodnocení zdrojů je také super, 2 referáty jsou zbytečné, mnohem lepší by bylo referátovou dotaci zkrátit a na její úkor např. dodat více ucelujících souvislostí v jednotlivých regionech, které chyběly. Tím se dostávám k začátku hodin, který fungoval tak, že každý z nás řekl několik vět jako aktualitu k regionu. To mi přišlo úplně zbytečné a nikdo to moc neposlouchal. Mnohem lepší by bylo, kdyby si na každou hodinu JEDEN student připravil ucelení toho, co se v jeho regionu děje důležitého - to může potom doplnit učitel a každý z nás by získal obraz o tom co je důležité v regionech těch druhých.Tzn. seminář dobrý, ale mohlo by se postupovat ještě efektivněji místo mnoha vlastně potom už zbytečných požadavků.";"Viz. co navrhuji zachovat...";"krvs" +"5659";"JPB228";"Mírové smlouvy a konference v mez. systému";"Jeřábek,M.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";NULL;"3";"5";;;"kmv" +"5660";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"1";"5";"2";"5";"5";;;"ies" +"5661";"JPB242";"Geografie vnitropolitických konfliktů";;"Doboš,B.,Riegl,M.";"4";"4";NULL;NULL;NULL;"5";"5";"4";"1";"3";"1";"3";"4";;;"kp" +"5662";"JSM027";"Urbánní antropologie";"Uherek,Z.";;"4";"2";"5";"3";"5";NULL;NULL;NULL;"1";"3";NULL;"3";"4";"Zajímavé i pro studenty neantropologického zaměření.";"Komunikace vyučujícího přes email.";"ks" +"5663";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"5";"4";"5";"5";"4";"5";"5";"4";NULL;"5";NULL;"5";"5";;;"kp" +"5664";"JLB003";"Angličtina pro ekonomy I";;"Gloverová,M.";"4";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"5665";"JPB260";"Politické myšlení novověku III";"Kučera,J.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;NULL;"5";NULL;"5";"4";;;"kp" +"5666";"NMMA701";"Matematika 1";"Spurný,J.";"Honzík,P.";"5";"5";"5";"5";"5";"2";"4";"3";"1";"5";"5";"5";"5";;;"ies" +"5667";"JSM095";"Study of Political Mobilization and Social Movements";"Císař,O.";;"5";"5";"5";"4";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"docenta Císaře považuji za nejschopnějšího pedagoga oboru Veřejnosti a politiky, vyhovuje mi práce a příprava v průběhu roku na position paperech a hodina založená na kvalitní diskuzi.";;"ks" +"5668";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"5";"4";"5";"4";"5";NULL;NULL;NULL;"1";"5";NULL;"5";"5";;;"kp" +"5669";"JPB268";"Evropská integrace";"Plechanovová,B.";;"3";"5";"3";"3";"2";NULL;NULL;NULL;NULL;"5";NULL;"5";"3";;;"kmv" +"5670";"JPB269";"Současné problémy mezinárodních vztahů";"Knutelská,V.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;NULL;"4";NULL;"5";"5";;;"kmv" +"5671";"JSM421";"Contemporary social theory";"Balon,J.";;"3";"1";"3";"5";"1";NULL;NULL;NULL;"1";"4";"3";"5";"3";"Líbí se mi jak je koncipovaná povinná četba - srovnání různých pohledů na různá témata, stejně tak mi vyhovuje i koncepce zkoušky, pokud by ovšem nebyla prezentována jako \"nemusíte se bát, že by někdo zkoušku neudělal\" - motivace tak trochu klesá.";"Přijde mi, že poměr výkonu a časové náročnosti při prezentaci a seminární práci, jsou velmi nerovnoměrné, byť jsou považované za rovnocenné. Výkony některých studentů při prezentaci byly tristní a evidentně to nikoho nezaráželo. Předmět mě začal bavit, až když jsem se ponořila do povinné četby, nicméně přednášky za moc nestály.";"ks" +"5672";"JMB047";"Vybrané problémy mezinárodních konfliktů.";"Čížek,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"krvs" +"5673";"JMB204";"Skotsko, Wales a Severní Irsko v kontextu moderních britských dějin";"Kasáková,Z.";;"4";"3";"3";"5";"4";NULL;NULL;NULL;"2";"3";"2";"4";"4";;;"kzs" +"5674";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;NULL;"4";NULL;"5";"4";;;"kp" +"5675";"JMB248";"Seminář k dějinám Ruska";;"Litera,B.";"4";"3";NULL;NULL;NULL;"4";"5";"3";"3";"4";"3";"4";"5";;;"krvs" +"5676";"JPB221";"Metodologický proseminář I";;"Komasová,S.,Parízek,M.";"4";"3";NULL;NULL;NULL;"4";"4";"4";"1";"3";"4";"4";"5";;;"kmv" +"5677";"JMB249";"Seminář k dějinám středo- a jihovýchodní Evropy";;"Lukešová,O.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Kurz opravdu pomohl ke zkoušce k SJVE. Student se zde naučí značnou část toho, co na zkoušce bude potřebovat, kurz je zajímavý, testy na začátku hodin jsou dobrou motivací, můžu jen doporučit, seminář mi opravdu pomohl a hodně mne naučil.";;"krvs" +"5678";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"2";"4";"2";"2";"2";NULL;NULL;NULL;"1";"3";"3";"2";"2";;;"kp" +"5679";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"5";;;"kp" +"5680";"JPB256";"Politické systémy I";"Brunclík,M.,Jeřábek,M.,Kotábová,V.,Perottino,M.";;"4";"2";"5";"4";"5";NULL;NULL;NULL;"3";"4";"3";"3";"4";;;"kp" +"5681";"JJB143";"Žurnalistika a feminismus";"Krobová,T.,Osvaldová,B.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Přístup Terezy Krobové, výběr aktuálních témat.";;"kz" +"5682";"JPB018";"Dějiny mezinárodních vztahů I";"Soukup,J.";;"5";"1";"5";"5";"4";NULL;NULL;NULL;"4";"3";"3";"3";"5";;;"kmv" +"5683";"JJB253";"Markething - online publikování a populární kultura I.";;"Maxa,M.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"4";"5";"Skvělý přístup přednášejícího, zajímavá a aktuální témata";;"kmkpr" +"5684";"JPB252";"České novověké dějiny I.";"Kučera,J.";;"3";"4";"2";"2";"2";NULL;NULL;NULL;"1";"1";"1";"1";"1";;;"kp" +"5685";"JJB351";"Tvorba videí";;"Mikulka,J.";"4";"4";NULL;NULL;NULL;"4";"4";"5";"1";"2";"5";"3";"4";"Dobrá forma a relevance zpětné vazby, zábavné přednášky";"Bylo by fajn se zamyslet na načasováním kurzu v rámci semestru. Předmět patří mezi časově úplně nejnáročnější, každý týden natáčení zabralo minimálně dvě celá odpoledne, což značně komplikoval fakt, že většinu videí jsme točili o víkendech během adventu, kdy bylo snad nejtěžší najít si čas. Právě ten časový press pak způsoboval, že ta videa nemohla být stoprocentní. Pokaždé stříhal ten, kdo to uměl, aby se ušetřil čas, takže ostatní se to vlastně nenaučili pořádně. :)";"kmkpr" +"5686";"JPM613";"Armed Forces and Society";"Kučera,T.";;"3";"4";"5";"5";"3";NULL;NULL;NULL;"1";"2";"2";"2";"4";"The crisis game is interesting";"Please if you can cancel the online homework. It takes minimum 3 or 4 hours.";"kbs" +"5687";"JSM477";"Sociology of Critique";"Blokker,P.";;"4";"5";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;"Myslím, že 3 hodiny v kuse jsou příliš náročné a nelze se tak dlouho soustředit na probírané téma - mnohé věci jsou také vysvětlovány příliš dopodrobna, zdlouhavě a polopatě.";"ks" +"5688";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"4";"5";"3";"5";"The teacher has a sense of humor and I have never felt bored.As for the course, it was interesting to discover how simplicity is so much complicated on a computer.";"To practice more on paper rather than online. We can understand the material better if we practice through small exercises in class.To work on the psychology of the course. Because at first, I had fear as its math and it is computerized but when I have practiced with a friend of mine, I have found it very easy. I would prefer that the teacher will inform the students about R in a positive way or neutral.";"kmv" +"5689";"JSM578";"Anthropology of EU";"Uherek,Z.";;"4";"5";"5";"4";"4";NULL;NULL;NULL;"1";"5";"3";"4";"5";;;"ks" +"5690";"JJB635";"Interkulturní marketing";"Rosenfeldová,J.";;"4";"3";"5";"5";"3";NULL;NULL;NULL;"4";"3";"3";"3";"3";;;"kmkpr" +"5691";"JPM693";"Traditional and Critical Concepts in Security Studies";"Rychnovská,D.";;"5";"5";"5";"5";"4";NULL;NULL;NULL;"1";"4";"5";"4";"5";;"Please, participation in class must be graded.The questions of online quizzes seem very hard and tricky so I would prefer that Mrs. Dagmar would simplify the questions.The Feedbacks are always online instead of communicating them face to face and this a major problem in most of the courses. When everything is online, the result is always a misunderstanding, stress and less effort for the future. So I suggest consultations in most of the courses, the same as Ethics and Violence and Security and Technology.";"kbs" +"5692";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"2";"4";"4";"4";"4";NULL;NULL;NULL;"2";"5";"3";"5";"5";"A general overview of theory and helping to identify what I am interested in specializing on";"Time- yes it is a bit silly, but the 11:00 AM time slot was much better than the 8:00 AM, both for the students and the lectures";"kmv" +"5693";"JJM320";"Propaganda a československá média po roce 1948";"Suk,P.";;"3";"4";"1";"2";"3";NULL;NULL;NULL;"1";"3";"1";"4";"2";;"Poskytnutí prezentace, menší arogance přednášejícího.";"kms" +"5694";"JPM052";"Mezinárodní ekonomické vztahy (MEV)";"Kučerová,I.";;"4";"2";"2";"2";"3";NULL;NULL;NULL;"3";"3";"2";"3";"3";;;"kmv" +"5695";"JPM099";"Baltic regional cooperation and Russia";"Zájedová,I.";;"2";"1";"2";"4";"4";NULL;NULL;NULL;"2";"2";"4";"3";"2";"The professor had connections in the diplomatic sphere that made for an excellent visit to the Estonian embassy";"Professor could have been better prepared for lectures, three hours was unnecessarily long for the course, professor talked over some students without letting conversation to flow";"kmv" +"5696";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Just overall an exceptional course taught by an excellent teacher.";;"kmv" +"5697";"JSB007";"Dějiny sociologie I";"Šanderová,J.";"Šrám,K.";"5";"3";"5";"5";"4";"5";"5";"5";"4";"5";"4";"5";"5";;;"ks" +"5698";"JPM717";"Continental Philosophy and IR";;"Ditrych,O.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Professor Dytrich was excellent in prompting conversation and though among students, allowing for mistakes, and correcting them without being judgmental. Took some very difficult subject matter and made it understandable. Excellent course.";;"kmv" +"5699";"JPM699";"Security and Technology";"Střítecký,V.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"First. I really appreciate the simplicity of the readings of the articles provided by Dr. Vit (less is more). I have enjoyed attending the classes and reading most of the articles to the point that I have finished them before the deadline.Second, I also appreciate the methodology of the course and especially the essay part: we were free to choose the topic we want.Third, the group presentation and participation in the class have overall 40% which gives the chance for a student to improve his/her grade.";"More discussions in class.";"kbs" +"5700";"JSB530";"Úvod do sociologického myšlení";"Bureš,J.,Numerato,D.";"Bureš,J.";"2";NULL;"3";"3";"2";"3";"2";"1";"2";"2";"2";"2";"1";"Vedoucí semináře chodil na hodiny nepřipravený. Každý týden jsme odevzdávali deníkové záznamy, ale od vyučujícího se nám nedostávalo žádné zpětné vazby. Úkoly opravoval s několikatýdenním zpožděním a tím celý deník naprosto ztrácel na významu. Pokud vyučující na seminář nemá čas, neměl by ho vést. Kurz mi nic nepřinesl.";;"ks" +"5701";"JJM324";"Teorie mediální komunikace";"Jirák,J.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kms" +"5702";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"5";"3";"4";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"kms" +"5703";"JJM373";"Ethical Issues in Media, Business and Society";"Orhan,M.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"5";"5";;;"kms" +"5704";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kz" +"5705";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Coufalová,L.,Svobodová,T.";NULL;"5";"4";"5";"4";"5";"5";"5";NULL;"5";"4";"5";"5";;;"ks" +"5706";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"4";"4";"3";"3";"4";"3";"4";"3";"2";"4";"4";"4";"4";;;"kp" +"5707";"JPM708";"Ethics and Violence";"Karásek,T.,Kučera,T.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"5";"5";"The freedom to choose the essay we want.I appreciate the consultation part as a condition before writing the essay.I also appreciate that Moodle doesn't exist in the Ethics and Violence class so there is more interaction between student and his teacher.Objective teacher and experienced";"I would prefer that Dr. Thomas Karasek will assess the presentations and the essays at the same time. We had a second teacher who has demonstrated a little of subjectivity. She cares about knowledge more than the outcomes of the discussions in class and she couldn't control the class during the presentations. Since it is about ethics, my recommendations are to encourage the students to maintain an ethical behavior in negotiations example: one of my classmates laughed at me when I was arguing him and he uses the word \"fuck\" all the time which do not demonstrate an environment of ethics neither peace.So please, ethics in communication.";"kbs" +"5708";"JLB100";"Czech as a Foreign Language I";;"Mazúrková,B.";"5";"2";NULL;NULL;NULL;"4";"4";"5";"2";"5";"1";"5";"5";"Learning in a kind courese atmosphere the basics of czech grammar and conversation. The teacher was also very eager to let us try the new-learned language in practice on the street. The atmosphere was open, fair and motivating.";"I would have liked to learn more grammar.";"cjp" +"5709";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"5";"4";"4";"4";"4";NULL;NULL;NULL;"2";"5";"4";"4";"5";;;"kp" +"5710";"JPM910";"The Nature and Function of the State";"Franěk,J.,Pettit,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"5711";"JJM216";"Čtení textů ke studiu médií - česká média po roce 1945";;"Bednařík,P.,Končelík,J.";"4";"3";NULL;NULL;NULL;"3";"4";"5";"1";"4";"4";"5";"5";;;"kms" +"5712";"JMMZ050";"Political Systems of East European Countries in the 20th Century";"Kubát,M.";;"3";"2";"2";"1";"4";NULL;NULL;NULL;"1";"5";"1";"5";"2";"I value the fact, that the teacher gave us lots of information and connected the single lectures and discussed topics with each other. At the end I was well informed about the topic and can use this knowledge as a basis for future research.";"The participants weren't integrated or activated during the course. The general approach of the teacher was overtly confrontative and the pace was way the fast, regarding the early time of the course. Therefore most of the participants lost the track or simply were not at all motivated for the topics.";"krvs" +"5713";"JJM320";"Propaganda a československá média po roce 1948";"Suk,P.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"2";"3";"2";"3";"4";;;"kms" +"5714";"JJM346";"Sémiotická analýza";;"Podzimek,J.";"4";"4";NULL;NULL;NULL;"4";"4";"4";"1";"5";"5";"5";"5";;;"kms" +"5715";"JJB135";"Filmový seminář I";;"Šobr,M.";"4";"1";NULL;NULL;NULL;"3";"3";"1";"1";"2";"3";"1";"4";;;"kz" +"5716";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"4";"2";"4";"5";"4";NULL;NULL;NULL;"2";"3";"3";"4";"5";;;"kp" +"5717";"JPB229";"Regionální politické systémy: Skotsko, Wales";"Říchová,B.";;"5";"4";"5";"4";"5";NULL;NULL;NULL;"2";"5";"3";"4";"5";;;"kp" +"5718";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"5";"4";"5";"3";"5";"4";"3";"5";"2";"5";"4";"5";"4";;;"kp" +"5719";"JPM694";"Strategic Studies";"Hynek,N.,Ludvík,J.,Smetana,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"New concepts in strategic studies that are appliedThe teaching method with 2 teachers was enjoyable and encouraged more interaction.The conference with Dr. Pollack.The class is very comfortable and my colleagues were very ethical in negotiations and not the same as my colleagues in Ethics and Violence who demonstrated hatred after communication in class.The readings are interesting although it is always stressful to read online. But the selection is diverse.I have enjoyed the class.";"I just recommend more diverse readings from thinkers from the Arab world and thinkers from the west who might oppose the leadership or politics of their countries.";"kbs" +"5720";"JMMZ276";"Deutsche und Tschechen – nahe und ferne Nachbarn (von der Habsburgermonarchie bis zur europäischen Union)";"Zimmermann,V.";"Zimmermann,V.";"5";"1";"5";"5";"5";"5";"5";"5";"1";"5";"1";"5";"5";"I highly the value the positive and friendly attitude of the teacher. He was enthusiatic about his topic and gave us lots of space to discuss or to pose our own questions. He understood very well to break topics down, so all participants (which had different levels of language understanding) could follow his explanations.";"Eventually there was to much content to put it all together, so our teacher had to jump over certain things or had to summarize spontaneous. This could be modified.";"knrs" +"5721";"JPB260";"Politické myšlení novověku III";"Kučera,J.";;"4";"3";"5";"4";"5";NULL;NULL;NULL;"1";"4";"3";"4";"3";;;"kp" +"5722";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"4";"4";"4";"4";"5";NULL;NULL;NULL;"1";"5";"3";"4";"4";;;"kp" +"5723";"JPB596";"Čínská zahraniční a bezpečnostní politika";"Karmazin,A.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";NULL;"5";;;"kbs" +"5724";"JPM910";"The Nature and Function of the State";"Franěk,J.,Pettit,P.";;"3";"1";"5";"5";"3";NULL;NULL;NULL;"1";"4";"1";"3";"3";;;"kp" +"5725";"JPB592";"US Government and Politics";"Kotábová,V.";;"4";"2";"3";"4";"4";NULL;NULL;NULL;"1";"3";"3";"4";"5";;;"kp" +"5726";"JPB268";"Evropská integrace";"Plechanovová,B.";;"3";"4";"1";"3";"2";NULL;NULL;NULL;"4";"4";"4";"4";"2";;"Témata rešerší by neměla být zveřejňována pět dnů před odevzdáním. Ze tří rešerší vyučující opravuje pouze jednu, hodnocení by si určitě zasloužila celá práce studentů a ne pouze náhodně vybraná část. Termíny přednášek byly často měněny, termín jedné přednášky byl ohlášen tři hodiny před jejím začátkem. Hodnocení jednotlivých otázek v testu není spravedlivé při bodování 0/3/6.";"kmv" +"5727";"JMMZ302";"Unter dem Mantel des internationalen Minderheitenschutzes. Nationalitätenpolitik in Ostmitteleuropa in der Zwischenkriegszeit";"Kučera,J.";;"5";"3";"4";"5";"5";NULL;NULL;NULL;"2";"5";"3";"4";"5";"The teacher gave us lots of reading materials for our preparation. We learned how to critically read and review different sources, like constitutions and law codes. In general there was a good balance between sources and texts of scientific research. We got a broad overview and detailed insight into the topic. The teacher understood the topic very well and could elaborate. The atmosphere was friendly and open.";"We should have focused more on the prepared texts. Unfortunately sometimes our teacher repeaded himself over the span of different course sessions.";"knrs" +"5728";"JPM185";"Evropská integrace - teorie a příklady, ES";"Jeřábek,M.";;"4";"3";"4";"5";"3";NULL;NULL;NULL;"4";"5";"5";"5";"5";;;"kmv" +"5729";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmv" +"5730";"JPM721";"Komparace ekonomik zemí EU";"Kučerová,I.";;"4";"3";"3";"3";"1";NULL;NULL;NULL;"2";"3";"3";"4";"4";;;"kmv" +"5731";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"2";"5";"2";"2";"1";NULL;NULL;NULL;"2";"3";"3";"3";"1";;;"kmv" +"5732";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"4";"5";"5";"4";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kms" +"5733";"JJM320";"Propaganda a československá média po roce 1948";"Suk,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"1";"5";"5";;;"kms" +"5734";"JJM324";"Teorie mediální komunikace";"Jirák,J.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"1";"4";"4";;;"kms" +"5735";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"4";;;"kms" +"5736";"JMB069";"Transatlantic Security Cooperation";"Weiss,T.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;"1";"4";"2";"4";"4";;;"kzs" +"5737";"JJM373";"Ethical Issues in Media, Business and Society";"Orhan,M.";;"2";"3";"3";"3";"3";NULL;NULL;NULL;"1";"2";"2";"2";"3";;;"kms" +"5738";"JMB250";"Seminář k dějinám západní Evropy";;"Váška,J.";"4";"4";NULL;NULL;NULL;"4";"4";"5";"1";"5";"3";"4";"3";;;"kzs" +"5739";"JMB408";"Moderní dějiny Ruska";"Litera,B.,Novák,P.,Pečenka,M.";;"2";"5";"3";"1";"2";NULL;NULL;NULL;NULL;"4";"5";"3";"2";;;"krvs" +"5740";"JMB410";"Moderní dějiny západní Evropy";"Matějka,O.,Váška,J.";;"4";"3";"4";"4";"3";NULL;NULL;NULL;"2";"5";"4";"4";"4";;;"kzs" +"5741";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"3";"3";NULL;NULL;NULL;"4";"5";"4";"1";"5";"4";"2";"3";;;"cjp" +"5742";"JLB104";"Czech for Chinese speaking students";;"Vaníčková,K.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"5743";"JMB409";"Moderní dějiny středo- a jihovýchodní Evropy-dcera JMB013";"Balla,P.,Švec,L.";;"4";"4";"4";"5";"3";NULL;NULL;NULL;"1";"4";"5";"4";"4";;;"krvs" +"5744";"JLM063";"English for Chinese Speaking Students";;"Štěpánková,D.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"5745";"JLB099";"Rozřazovací test z angličtiny";;"Kunzová,J.";NULL;NULL;NULL;NULL;NULL;"5";"5";"5";NULL;NULL;NULL;NULL;NULL;;;"cjp" +"5746";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmv" +"5747";"JPM664";"Geopolitics of Great Powers: China";"Karásková,I.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"5748";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"1";"3";"1";"3";"1";NULL;NULL;NULL;"2";"1";"1";"1";"1";"Nic";"Jiný učitel";"ies" +"5749";"JEM003";"Advanced Microeconomics";"Janda,K.,Melikhova,O.";"Janda,K.,Melikhova,O.";"3";"3";"3";"4";"3";"4";"4";"4";"2";"3";"1";"1";"3";"The seminars made us understand the topic of lectures very well.";"I would maybe expect based also on the experience from the other two compulsory courses: Advanced Econometrics and Advanced Macroeconomics that the course should provide us with some more recent progresses in the field of Microeconomics.";"ies" +"5750";"JEM004";"Advanced Macroeconomics";"Baxa,J.,Hromádková,E.";"Baxa,J.,Hromádková,E.,Šestořád,T.,Troch,T.,Žáček,J.";"4";"4";"4";"5";"4";"5";"5";"3";"2";"4";"3";"5";"4";"A very friendly attitude of both lecturers and seminar leaders.";"Personally I found the added value of empirical seminars as very small.";"ies" +"5751";"JSM005";"Sociální struktura ČR: stav, vývoj, srovnání s EU";"Tuček,M.";;"3";"2";"3";"3";"3";NULL;NULL;NULL;"1";"2";"2";"2";"3";"Psaní úvah, nicméně doporučuji zpřesnit zadání a očekávání vyuřujícího, jakým stylem si úvahu představuje.";"I přes krátké úvahy bych příště nechala velkou semestrální práci v rámci zkoušky.";"ks" +"5752";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"4";"4";"5";"5";"4";"4";"4";"4";"2";"5";"4";"4";"4";"Friendly attitude of Mr Barunik to students and his positive thinking made one enjoy the lectures!";"The output (or notes) from the seminars were not always in the same form (e.g. html format) and this made revisions more complicated.";"ies" +"5753";"JSB025";"Sociální problémy";"Frič,P.";;"4";"4";"4";"3";"5";NULL;NULL;NULL;"2";"4";"2";"4";"3";;;"kvsp" +"5754";"JSM031";"Analytické metody výzkumu pro mgr.";"Jeřábek,H.";"Daneš,D.";"4";"4";"4";"5";"3";"3";"4";"5";"1";"5";"4";"5";"5";"Semináře a přednášky externistů.";"Některé přednášky se doplňovaly s tématy seminářů, ale zapracovala bych na propojení obou částí kurzu. Bylo by dobré se na přednáškách dozvědět teorii na semináři probíraných metod. Taky mi přišla naprosto zbytečné psát recenzi na metodu užitou v nějakém článku (který jsme si mohli vybrat sami). Buďto zadejte nějaké články, které bude opravdu přínosné přečíst a psát na ně recenzi, nebo změňte zadání.";"ks" +"5755";"JEM181";"Data Science with R";"Herman,D.,Krištoufek,L.";"Herman,D.,Krištoufek,L.";"3";"4";"4";"4";"3";"3";"4";"4";"1";"3";"4";"4";"4";"Free access to DataCamp courses was a great thing.";"Maybe it was just my laziness but generally I felt that the engagement of students during the lectures was not very often (with the exception of Bonus questions). Maybe some bits of DataCamp assignments could be done during the lecture time as well to increase attention of the students.";"ies" +"5756";"JSB051";"Úvod do sociální antropologie";"Uherek,Z.";"Hrešanová,E.";"4";"3";"4";"5";"5";"4";"4";"4";"2";"5";"3";"5";"5";;;"ks" +"5757";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"4";"5";"Férové podmínky plnění předmětu.";"Nic mě nenapadá.";"ies" +"5758";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Buřičová,B.";"4";"2";"3";"3";"2";"5";"5";"5";"2";"3";"4";"2";"5";;;"ks" +"5759";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Rössler,J.";"5";"3";"3";"4";"3";"5";"5";"5";"1";"5";"5";"5";"5";;;"ks" +"5760";"JJB618";"Propagandistické působení médií v 1 polovině 20. století";"Suk,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Zajímavé informace, poutavé vyprávění, názornost.";"Možná by bylo dobré do budoucna zpřístupnit některé materiály studentům.";"kms" +"5761";"JJB620";"Mediální produkce - žurnalistická práce I.";"Vlasák,Z.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Praktický nácvik psaní, trefné připomínky vyučujícího k odevzdaným úkolům.";"Nic mě nenapadá. :)";"kms" +"5762";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"2";"5";"4";"2";"5";"4";"1";"1";"4";"4";"2";"3";"4";;"Náplní jeden z nejdůležitějších kurzů bakalářské politologie, který je ale bohužel z valné většiny redukován na domácí četbu. Přednášky se vztahují jenom k malé části látky, semináře letos nebyly vůbec. Kurz má ohromný rozsah, ale témata neprobírá do hloubky - chybí reflexe četby. Student se tak převážně učí pojmy z různých teorií, aniž by byl schopen tyto pojmy pochopit a vysvětlit v šiřším kontextu. Navíc naprosto neadekvátní dotace kreditů vzhledem k obtížnosti kurzu. A nakonec - psaní seminárních prací bez podnětné zpětné vazby (takové, která se týká i obsahu a argumentace, nejen splnění paginace) studentovi nic nedává. V horším případě jej utvrdí v jeho chybách.";"kp" +"5763";"JJB626";"Vybrané otázky mediálního vzdělávávání";"Wolák,R.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Diskuzi, kterou jsme o mediálním vzdělávání vedli.";"Nic mě nenapadá.";"kms" +"5764";"JLB013";"Němčina odborná I";;"Křenková,D.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Přístup vyučující, komunikace výhradně v německém jazyce, materiály a další doporučení k zdokonalení jazyka i mimo seminář.";"Nic mě nenapadá.";"cjp" +"5765";"JLB053";"Angličtina pro sociální vědy I";;"Gloverová,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Komunikace až na výjimky výhradně v anglickém jazyce, prokládání výuky různými aktivitami, hrami. Z hodin jsem odcházela odpočinutá, moc mě to bavilo. A řekla bych, že jsem se v angličtině docela zlepšila, byť jsem nevěřila, že se k jejímu studiu na VŠ ještě dostanu.Vyučující je výborná pedagožka. Celkově jsem s úrovní výuky jazyka na FSV UK velmi spokojená.";"Nic mě nenapadá.";"cjp" +"5766";"JSM480";"Evaluation Research";;"Remr,J.";NULL;NULL;NULL;NULL;NULL;"3";"4";"1";NULL;NULL;NULL;NULL;NULL;;"I eventually stopped showing up for lectures, because I had a feeling we were discussing still all the same theoretical explanation why is evaluation important and never learned anything about how is evaluation actually done. I would appreciate more methodology, not just very theoretical and repeating stuff.";"ks" +"5767";"JMB065";"Úvod do mezinárodního a evropského práva";"Šlosarčík,I.";;"5";"3";"5";"4";"5";NULL;NULL;NULL;"1";"5";"3";"4";"5";;;"kzs" +"5768";"JMB208";"Dějiny státu a práva v německy mluvících zemích";"Mlsna,P.";;"4";"5";"5";"5";"4";NULL;NULL;NULL;"4";"5";"3";"5";"5";"Vyučující je odborník na téma předmětu, myslím, že přednášky, kterých bohužel ve výsledku nebylo tolik, byly velmi dobře připravené.";"Mrzí mě, že z velké části přednášky odpadaly. Samotná zkouška je pak o dost náročnější, když se ani rámcově některá témata neotevřou.";"knrs" +"5769";"JJM211";"Kvalitativní výzkum mediálních publik";;"Reifová,I.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"5";"Přístup lektora a strukttura kurzu";"Někdy příliš dlouhé povídání o teorii";"kms" +"5770";"JJM224";"Politická ekonomie komunikace";"Vochocová,L.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"5";"5";"3";"4";"5";"Vhled do politické participace na soc. sítích (velmi zajímavé)";"nic.";"kms" +"5771";"JPB578";"Classics of Political Thought";"Salamon,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kp" +"5772";"JJM117";"Popular Culture";"Turnau,T.";;"3";"3";"3";"3";"3";NULL;NULL;NULL;"3";"3";"3";"3";"3";;;"kms" +"5773";"JMBZ200";"Bakalářský seminář pro česko-německá studia I.";;"Nigrin,T.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"knrs" +"5774";"JPB597";"Current Political Extremism";"Charvát,J.";;"4";"2";"3";"4";"3";NULL;NULL;NULL;"1";"5";"3";"5";"5";;"Byl bych pro navrácení výuky tohoto předmětu do českého jazyka. Kurz vychází z knihy dostupné pouze v češtině, týká se mj. specifického tématu českého extremismu a měl jsem pocit, že přednášení v angličtině vyučujícího limitovalo.";"kp" +"5775";"JJM372";"Consumer Behaviour";"Orhan,M.";;"3";"5";"4";"4";"4";NULL;NULL;NULL;"2";"4";"4";"4";"3";;"Zbytečně vysoká obtížnost.";"kms" +"5776";"JMBZ289";"Central European Culture from the 19th Century to 1945";"Emler,D.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"Předmět probíhal v anglickém jazyce. Oceňuji pozvání vyučujícího ze zahraničí.";"Nic mě nenapadá.";"knrs" +"5777";"JJM295";"Rozhlasový a televizní dokument";"Štoll,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"Přístup a struktura předmětu velmi dobrá.";;"kz" +"5778";"JMBZ290";"Konversatorium zu den aktuellen Fragen";;"Renner,T.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Otázky, které během semináře zaznívaly ze strany vyučujícího, má skutečně velký rozhled.";"Nic mě nenapadá.";"knrs" +"5779";"JJM234";"Media and Society: An Introduction";"Jirák,J.";;"3";"3";"5";"5";"5";NULL;NULL;NULL;"2";"3";"3";"3";"3";;;"kms" +"5780";"JMMZ274";"Geschichte des Rassismus";"Barth,B.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Oceňuji přístup pana profesora, nejdřív jsem se bála, že nebudu stíhat výuku v němčině a nakonec to dopadlo dobře.";"Nic mě nenapadá.";"knrs" +"5781";"JJB066";"Rozhlas a televize ve světě";"Moravec,V.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"2";"4";"2";"4";"4";"Přístup pana Moravce ke studentům, zajímavé přednášky";"Upřímně mi přijde závěrečná práce na 10 stran zbytečně obsáhlá. Celkově si myslím, že by se měly zkoušet spíše praktické věci než dlouhé seminárky.";"kz" +"5782";"JPB227";"Politický system ČR";"Charvát,J.";;"4";"2";"4";"3";"4";NULL;NULL;NULL;NULL;"4";"3";"5";"5";;"Psaní seminárních prací bez podnětné zpětné vazby (tj. týkající se nejen formálních požadavků jako zdrojů, ale též obsahu a argumentace) je dle mého názoru zbytečná práce, která nevede k rozvoji schopností studenta. V horším případě se utvrdí ve svých chybách. Psaní seminárek obecně považuji za dobrou příležitost zlepšit své psaní, ale mrzí mě, jak častý je přístup, kdy je seminárka požadována v zásadě proto, aby student neměl kredity \"moc zalevno\".";"kp" +"5783";"JJB067";"Mluvní a pohybová výchova I";;"Pavel,L.";"3";"2";NULL;NULL;NULL;"3";"3";"4";"1";"4";"5";"3";"4";;"Ideálně ještě zmenšit počet studentů na výuce. Přidat více cviků, ke kterým se jinak nedostaneme.";"kz" +"5784";"JPM695";"War Studies";"Hays II,G.";;"3";"4";"4";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";"Good discussion overall, good reactions from the teacher to nurture discussion.";"The final paper is way too complicated - it was almost as long as some of my friends' bachelor theses. Also maybe the discussions should be more moderated. It would be better to focus more on crucial points of the literature.";"kbs" +"5785";"JJB071";"Tvůrčí dílny I - rozhlasové";"Maršík,J.";"Lovaš,K.,Lucký,J.";"3";"2";"3";"3";"3";"3";"3";"3";"3";"3";"3";"3";"3";;;"kz" +"5786";"JJB083";"Editování zpravodajských relací";"Beneš,P.";;"1";"1";"2";"2";"2";NULL;NULL;NULL;"1";"1";"1";"1";"1";;;"kz" +"5787";"JLB009";"Angličtina pro žurnalisty I";;"Prošková,A.";NULL;NULL;NULL;NULL;NULL;"5";"5";"5";NULL;NULL;NULL;NULL;NULL;;;"cjp" +"5788";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"3";"4";"3";"3";"2";NULL;NULL;NULL;"1";"3";"1";"5";"3";;;"kmv" +"5789";"JPM430";"Marxism in International Relations (TIR)";;"Střítecký,V.";"3";"2";NULL;NULL;NULL;"5";"5";"3";"1";"5";"4";"4";"3";;;"kmv" +"5790";"JMB118";"Geografie německy mluvících zemí";"Baštová,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"knrs" +"5791";"JPM329";"Making of Modern Asia";"Krausz Hladká,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";"Skvela a zajimava prezentace, pochopeni souvislosti, velmi me to bavilo.";;"kp" +"5792";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;NULL;NULL;"4";"5";"2";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;"Chtěl bych poděkovat za zpětnou vazbu k eseji. Považuji psaní těchto prací za dobrý způsob, jak může student zlepšit své psaní, bohužel ale v mnoha kurzech nedostávají studenti zpětnou vazbu k obsahu a argumentaci v práci, ale pouze k formálním požadavkům. Čili vážím si toho, že i přes velké množství studentů v kurzu práce podrobně hodnotíte.";;"ies" +"5793";"JPM644";"Contemporary International Relations in East Asia";"Kolmaš,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Jeden z nejlepsich predmetu behem semestru, interaktivni prednasky, zajimavy vyklad, souteze.";;"kmv" +"5794";"JPM719";"Diplomacy of the European Union";"Pajtinka,E.";;"2";"3";"3";"3";"2";NULL;NULL;NULL;"3";"3";"2";"3";"3";"Blokova vyuka byla obcas zkracovana a zpusob prezentace nebyl az tak nm zajimavy.";"Mene textu v prezentaci, vic prolinat konrketnimi priklady EU diplomacke.";"kmv" +"5795";"JPM910";"The Nature and Function of the State";"Franěk,J.,Pettit,P.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;"Je 6. 2. 2018, více než tři měsíce po ukončení kurzu. A přesto ani já, ani kolegové, kteří byli na kurzu se mnou, nemáme zapsán zápočet v SISu. Rozumím, že výsledky nelze zapisovat v listopadu, ale přijde mi zcela opodstatněné očekávat, že nejpozději do konce ledna by se to dalo stihnout.";"kp" +"5796";"JPM687";"Astropolitics";"Doboš,B.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"5797";"JPB593";"Political Economy of Regionalism";"Miková,I.";;"3";"5";"3";"5";"3";NULL;NULL;NULL;"2";"4";"4";"3";"3";;"Povinná literatura ke kurzu mi přišla velmi obtížná. Spoléhala na seznámení s čtenáře s komplexní terminologií a teoriemi, které začátečníci v oboru politické ekonomie, kam, myslím, většina studentů patři, nemají. Zároveň je literatury opravdu hodně, což spolu s požadavky docházky, prezentace a testu dělá tento kurz jedním z nejtěžších, se kterým jsem se během bakalářského studia setkal. Přišlo by mi hodnotnější, kdyby studentské prezentace místo občas příliš podrobného doplňování přednášek, se vázaly k jednotlivým článkům z povinné četby. Jednak by tím přednášející mohl četbu shrnout pro ostatní a zároveň by to umožnilo četbu reflektovat na přednáškách (v současnosti četba s kurzem vlastně moc nesouvisí - dokud nepřijde test).";"kmv" +"5798";"JLB033";"Němčina I";;"Faltýnová,R.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"5799";"JEM132";"Company Valuation";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"5";"4";;;"ies" +"5800";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"2";"4";"2";"2";"1";NULL;NULL;NULL;"2";"2";"3";"3";"1";;"Je naprosto nepřípustné, aby vyučující opravoval zkoušku přes dva kalendářní týdny.";"kp" +"5801";"JPB260";"Politické myšlení novověku III";"Kučera,J.";;"3";"4";"4";"4";"4";NULL;NULL;NULL;"2";"4";"1";"4";"3";"Zapálenost vyučujícího pro předmět.";"Vyučující by měl vypisovat termíny zkoušek do SIS.";"kp" +"5802";"JMB513";"Soudobé dějiny Dálného východu";"Sýkora,J.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"3";"3";"3";"3";;;"kas" +"5803";"JMB528";"Současné problémy německy mluvících zemí";"Nigrin,T.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"3";"4";;;"knrs" +"5804";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"2";"4";"2";"2";"2";"2";"2";"1";"2";"3";"3";"4";"2";;"Vyučující vůbec neodpovídá na emaily.";"kp" +"5805";"JMB530";"Současná Severní Amerika";"Calda,M.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"4";;;"kas" +"5806";"JMB529";"Současná západní Evropa";"Rovná,L.,Váška,J.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kzs" +"5807";"JMB065";"Úvod do mezinárodního a evropského práva";"Šlosarčík,I.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"3";"5";"2";"5";"5";"Zapálenost vyučujícího, umí skvěle a poutavě vysvětlit probíranou látku.";"Bylo by dobré podpořit výklad nějakými prezentacemi.";"kzs" +"5808";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"1";"4";"5";;;"ies" +"5809";"JPB001";"Bakalářský seminář I.";;"Brunclík,M.,Bureš,O.,Ditrych,O.,Franěk,J.,Gelnarová,J.,Hynek,N.,Charvát,J.,Jeřábek,M.,Jüptner,P.,Karásek,T.,Karlas,J.,Knutelská,V.,Kofroň,J.,Kotábová,V.,Krausz Hladká,M.,Kubátová,H.,Kučera,J.,Kučera,T.,Kučerová,I.,Landovský,J.,Ludvík,J.,Makariusová,R.,Mle";NULL;NULL;NULL;NULL;NULL;"3";"3";"3";NULL;NULL;NULL;NULL;NULL;;"Vyučující nevypisují skoro žádná témata k bakalářským pracem. Student si musí téma zbytečně vymýšlet sám, přičemž k tomu mu nepomůže vůbec žádný vyučující a celé to vede pouze k frustraci studenta.";"kp" +"5810";"JLB005";"Angličtina pro politology I";;"Panešová,K.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"5";"5";;;"cjp" +"5811";"JJM259";"Mediální systémy a jejich komparace";"Miessler,J.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kz" +"5812";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"5813";"JPB018";"Dějiny mezinárodních vztahů I";"Soukup,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kmv" +"5814";"JJM260";"Novinářská etika v praxi";"Moravec,V.";;"4";"3";"5";"4";"4";NULL;NULL;NULL;"1";NULL;"3";"5";"5";;"Kurz je naprosto totožný s žurnalistickou etikou na bakalářském stupni žurnalistiky. Tudíž bych předmět vypsala buď pouze pro nově příchozí, tedy pro modul E, nebo jej rozdělila na dva, jeden pro nově příchozí a jeden pro studenty, kteří absolvovali žurnalistiku na bakaláři.";"kz" +"5815";"JPB020";"Úvod do mezinárodních vztahů";"Karlas,J.,Kučera,T.";;"4";"3";"4";"4";"3";NULL;NULL;NULL;"1";"4";"3";"4";"3";;;"kmv" +"5816";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"3";"4";"3";"4";"4";;;"kp" +"5817";"JPB221";"Metodologický proseminář I";;"Mlejnek,J.,Valková,I.";"5";"2";NULL;NULL;NULL;"5";"5";"4";"1";"5";"5";"5";"5";;;"kmv" +"5818";"JPB252";"České novověké dějiny I.";"Kučera,J.";;"4";"3";"5";"4";"4";NULL;NULL;NULL;"1";"4";"3";"3";"3";;;"kp" +"5819";"JJM279";"Divadelní kritika";"Homolová Richtrová,N.";;"3";"2";"3";"4";"1";NULL;NULL;NULL;"2";"2";"2";"2";"2";;;"kz" +"5820";"JJM280";"Filmová a televizní kritika";"Štoll,M.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"2";"4";"4";"4";"4";;;"kz" +"5821";"JPB269";"Současné problémy mezinárodních vztahů";"Knutelská,V.";;"5";"4";"4";"4";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmv" +"5822";"JJM264";"Diplomový seminář II.";;;"2";"1";"5";"5";"1";NULL;NULL;NULL;"1";"1";"1";"1";"1";;;"kz" +"5823";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"5824";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";;;"ks" +"5825";"JLB013";"Němčina odborná I";;"Křenková,D.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"5";;;"cjp" +"5826";"JPM146";"Přechody k demokracii v teorii a praxi I";"Mlejnek,J.";;"2";"4";"3";"3";"2";NULL;NULL;NULL;"1";"4";"4";"4";"3";;;"kp" +"5827";"JPM160";"Česká komunální politika";"Jüptner,P.";;"4";"5";"5";"4";"4";NULL;NULL;NULL;"2";"4";"5";"5";"4";;;"kp" +"5828";"JPM342";"Konflikty v demokracii a národní identita: teoretické problémy analýzy";"Říchová,B.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"4";"5";"5";"5";"5";"Zakončení kurzu pouze seminární prací. Pokud jsou na práci kladeny odpovídající nároky, považuji tento způsob za přínosnější, než zakončení kurzu zkouškou.";;"kp" +"5829";"JPM579";"Teorie politických stran";"Perottino,M.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"2";"4";"3";"4";"5";;;"kp" +"5830";"JPM639";"Problémy ústavního inženýrství";"Brunclík,M.";;"4";"2";"4";"5";"2";NULL;NULL;NULL;"2";"4";"5";"4";"4";;;"kp" +"5831";"JPM653";"Politika a média";"Švec,K.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"1";"3";"4";"4";"5";;;"kp" +"5832";"JJB293";"Role výzkumů v politických a komerčních kampaních";;"Rosenfeldová,J.";"3";"3";NULL;NULL;NULL;"4";"4";"4";"4";"3";"4";"3";"3";;;"kmkpr" +"5833";"JLB001";"Angličtina pro sociology I";;"Klírová,M.";"5";"4";NULL;NULL;NULL;"4";"4";"4";"1";"4";"3";"3";"4";;;"cjp" +"5834";"JSB537";"Analýza dat v SPSS";"Soukup,P.";"Oreský,J.";"3";"5";"4";"4";"4";"3";"3";"3";"3";"3";"2";"1";"2";;"Asi by mělo být více cvičení - příprava ke státnicím chybí";"ks" +"5835";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"3";"3";"3";"5";;;"ies" +"5836";"JJM248";"Vývoj grafického designu a polygrafického zpracování periodik";"Slanec,J.";;"4";"2";"3";"4";"2";NULL;NULL;NULL;"1";"4";"1";"3";"3";;;"kz" +"5837";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"3";"5";"3";"5";;;"ies" +"5838";"NMMA701";"Matematika 1";"Spurný,J.";"Rondoš,J.";"5";"5";"5";"5";"5";"1";"1";"1";"1";"5";"5";"2";"3";;"Vyučující Rondoš byl absolutně nechopný cokoli vysvětlit, konstatování \"protože to tak je....., aby to vycházelo....\" a podobné se stávaly už bonmoty";"ies" +"5839";"JJM249";"Duchovní kořeny evropské kultury";"Halada,J.";;"2";"2";"2";"3";"1";NULL;NULL;NULL;"1";"2";"1";"1";"2";;;"kz" +"5840";"JJM355";"Teoretické koncepty ke studiu žurnalistiky I";"Jirků,J.,Němcová Tejkalová,A.";"Jirků,J.";"4";"4";"4";"4";"4";"5";"5";"5";"1";"4";"3";"4";"4";;;"kz" +"5841";"JJM251";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"2";"5";"3";"2";"4";NULL;NULL;NULL;"1";"4";"1";"4";"3";;;"kz" +"5842";"JJM356";"Metodologické přístupy ke studiu žurnalistiky";"Nečas,V.,Veselková,Z.";;"5";"2";"4";"4";"4";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"kz" +"5843";"JJM254";"Mediální tvorba";"Čásenský,R.";;"4";"1";"4";"5";"3";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kz" +"5844";"JJM360";"Ekonomika v médiích";"Klimeš,D.";;"4";"2";"4";"5";"3";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kz" +"5845";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"4";"4";"4";"5";;;"ies" +"5846";"JLB003";"Angličtina pro ekonomy I";;"Poslušná,L.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"5";;;"cjp" +"5847";"JJM363";"Czech-German-Jewish Literary Triangle";;"Peroutková,M.";"4";"2";NULL;NULL;NULL;"4";"5";"3";"1";"3";"3";"1";"3";;;"kz" +"5848";"JSB998";"Úvod do sociologie";"Soukup,P.";;"3";"1";"5";"5";"5";NULL;NULL;NULL;"1";"1";"1";"1";"5";;;"ks" +"5849";"JLB019";"Francouzština odborná I";;"Bosáková,L.";"5";"3";NULL;NULL;NULL;"4";"5";"5";"1";"5";"4";"4";"5";;;"cjp" +"5850";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"4";"3";"5";"4";"5";"5";"4";"5";"1";"5";"3";"4";"5";;;"kp" +"5851";"JPM699";"Security and Technology";"Střítecký,V.";;"3";"2";"3";"5";"2";NULL;NULL;NULL;"2";"3";"3";"2";"3";"The presentations on a conspiracy theory on Twitter was exciting, fun, and really interesting to learn about the influence of echo chambers by studying those. It was cool to learn how to use NodeXL and Gephi for social network analysis, which will be great for future analysis in jobs.";"I feel as if the integration of how technology has influenced security and international relations was not touched upon much. The concepts of machine learning and AI are fascinating, but we did not learn much on how those have begun to impact security or their potential impact on security whether it is private, public, national, or international security.";"kbs" +"5852";"JLB009";"Angličtina pro žurnalisty I";;"Prošková,A.";"4";"3";NULL;NULL;NULL;"4";"5";"5";"1";"4";"5";"4";"4";;;"cjp" +"5853";"JPB260";"Politické myšlení novověku III";"Kučera,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kp" +"5854";"JMM673";"Promoting democracy abroad: the US and the EU in third countries";"Hornát,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Professor was very helpful and cooperative. Always replied to emails and provided feedback.";;"kas" +"5855";"JPM191";"Geopolitics of Great Powers: Russia";"Baštář Leichtová,M.";;"3";"3";"3";"5";"3";NULL;NULL;NULL;"3";"3";"3";"3";"2";;"Professor seemed unaware of many facts that influenced the course, especially for those who were not familiar with history of Russia. In addition, although claiming to be neutral, professor expressed her obvious bias position. Some sessions looked like CNN documentaries.";"kp" +"5856";"JMD017";"Teorie a praxe akademické práce";;"Weiss,T.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"5";"5";"doc. Weisse a jeho přístup, zajímavou četbu";"zařadit kurz hned na začátku studia";"kzs" +"5857";"JMD018";"Metodologie sociálních věd";"Aslan,E.";;"4";"3";"3";"3";"4";NULL;NULL;NULL;"2";"2";"4";"4";"3";;"Vyučující často pouze kritizoval český akademický svět a vychvaloval zahraniční. Není to moc velká motivace pro začínající doktorandy. Naše projekty velmi kritizoval, ale nenabízel možnosti, jak to zlepšit. Většina četby se týkala studí založených na mnoha rozhovorech, vyučující nezohledňoval, že vyučuje i mnoho studentů moderních dějin, kteří nemohou s rozhovory úplně počítat. Mělo by být také vyjasněno dopředu, jestli je kurz česky nebo anglicky, aby si studenti nepřipravili česky prezentaci a na místě museli mluvit anglicky.";"krvs" +"5858";"JPM716";"The Geopolitics of Defence Industry and Arms Trade";"Kopečný,T.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"3";"5";"5";"5";"5";"It was very interesting and informative. The professor has a great attitude and explains everything very well.";;"kp" +"5859";"JEM163";"Principles of Microeconomics";"Janský,P.";"Král,M.,Moravcová,H.,Palanský,M.";"3";"4";"4";"5";"4";"3";"3";"4";"1";"5";"5";"5";"5";"It expands my knowledge horizon to another deeper and meaningful level.";"There are many students in the class. Thus, if the number could be reduced to some degree, it would be more effective.";"ies" +"5860";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"5";"1";"5";"4";"1";NULL;NULL;NULL;"1";"1";"1";"1";"5";"Nevím, nechodil jsem.";"Nevím, nenavštěvoval jsem.";"ies" +"5861";"JEM179";"History and Methodology of Economics";"Baxa,J.,Paulus,M.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"The professor's knowledge on this subject is beyond my expectations. Simply amazing!";"Sometimes, I feel like, despite of being the best teacher, the professor speaks too quickly. It is hard to follow sometimes.";"ies" +"5862";"JLB005";"Angličtina pro politology I";;"Panešová,K.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";NULL;"5";"Připravenost paní přednášející Kamily Panešové. Byla velice dobře připravená na každou lekci.";"Nic.";"cjp" +"5863";"JPB202";"Politické strany v Evropě";"Perottino,M.";;"5";"3";"5";"5";"2";NULL;NULL;NULL;"1";"3";"2";"4";"5";;;"kp" +"5864";"JMM530";"European Comparative Politics and Society";"Rovná,L.";;"3";"3";"2";"5";"3";NULL;NULL;NULL;"3";"3";"2";"2";"2";"The presentation part is pretty useful.";"The teaching skill of the professor.";"kzs" +"5865";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"1";"3";"1";"3";"1";NULL;NULL;NULL;"3";"3";"2";"2";"1";"If I have to be honest, I feel like didn't learn much from this course.";"Teaching skills of the professors";"kmv" +"5866";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"4";"4";"3";"4";NULL;NULL;NULL;"2";"3";"5";"3";"3";;;"ies" +"5867";"JPM323";"Global Political Philosophy";"Salamon,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Professor's teaching styles and reading materials.";"It is perfect already.";"kp" +"5868";"JPM324";"Geography and Politics in Europe within Global Regionalism";"Doboš,B.,Riegl,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"The professor's knowledge on subject matter is mind-blowing. Very knowledgable professor!";"It is perfect.";"kp" +"5869";"JSM646";"Veřejná správa";"Ochrana,F.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"4";"5";;;"kvsp" +"5870";"JSM647";"Manažerské metody ve veřejné a sociální politice";"Ochrana,F.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"5";"5";;;"kvsp" +"5871";"JSM528";"Seminář k diplomové práci I.";;"Kohoutek,J.,Ochrana,F.";"4";"3";NULL;NULL;NULL;"4";"4";"3";"1";"3";"4";"3";"4";;;"kvsp" +"5872";"JMM368";"Maďarsko po roce 1989";"Irmanová,E.";;"3";"2";"3";"4";"2";NULL;NULL;NULL;"1";"3";"2";"3";"3";;;"krvs" +"5873";"JPM641";"Světový regionalismus";"Riegl,M.";;"5";"5";"5";"4";"5";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kp" +"5874";"JSM527";"Metody analýzy a tvorby politik II.";"Veselý,A.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"5";"5";"5";;;"kvsp" +"5875";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"5";"5";;;"cjp" +"5876";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;NULL;"5";"4";"5";"5";;;"krvs" +"5877";"JMB402";"Úvod do společenských věd II";;"Juhás,T.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"krvs" +"5878";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Šafařík,P.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"knrs" +"5879";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Kůželová,M.";"5";"4";"5";"5";"5";"5";"5";"5";NULL;"5";"5";"5";"5";;;"krvs" +"5880";"JEB026";"European Economic Integration";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"3";"2";"2";"3";"2";"3";"3";"2";"1";"4";"3";"5";"5";;"Lectures could je more interactive, even at cost of less material being covered during them";"ies" +"5881";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"5";"5";"5";"5";"5";;;"knrs" +"5882";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ies" +"5883";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;NULL;"5";"5";"5";"5";;;"kas" +"5884";"JEM017";"Business Cycles Theory";"Baxa,J.,Kučera,A.,Vácha,L.";;"5";"5";"4";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Homework :) And feedback on them";;"ies" +"5885";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;NULL;"5";"5";"5";"5";;;"ks" +"5886";"JEM132";"Company Valuation";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Interactive lecture style";"I would appreciate much more feedback on home assignments and case study, targeting what should be improved";"ies" +"5887";"JMB414";"Seminář k aktualitám I";;"Cotte,P.";"5";"2";NULL;NULL;NULL;"5";"5";"4";"2";"4";"4";"4";"5";;;"krvs" +"5888";"JMB249";"Seminář k dějinám středo- a jihovýchodní Evropy";;"Žíla,O.";"5";"2";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"5";"5";;;"krvs" +"5889";"JMB410";"Moderní dějiny západní Evropy";"Matějka,O.,Váška,J.";;"3";"3";"5";"4";"2";NULL;NULL;NULL;"3";"2";"2";"3";"4";;"Probíraná témata sice byla zajímavá, ale v konečném testu se objevovalo něco úplně jiného. V momentě, kdy se půlku hodin probírá kolonialismus, ale v závěrečném testu padne otázka na Itálii 20. let 20. století, je trochu bezúčelné přednášky vůbec navštěvovat. Bohužel jsme se na přednáškách nedozvěděli obecnou historii zemí ZE, což je trochu škoda, protože by nám přednášející určitě mohl nastínit více - čím se státy liší, v čem jsou si podobné. To, že jsme se o historii Irska jako takové dozvěděli až z poznámek před testem, ale hlavně že víme rok objevení telefonu...je prostě velké zmaření potenciálu takovéto zajímavé oblasti.";"kzs" +"5890";"JJB067";"Mluvní a pohybová výchova I";;"Pavel,L.";"3";"1";NULL;NULL;NULL;"3";"5";"4";"1";"3";"4";"2";"3";;;"kz" +"5891";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"3";"1";"1";"1";NULL;NULL;NULL;"1";"2";"1";"1";"2";;;"ies" +"5892";"JPM690";"Liberalism in International Relations (TIR)";;"Karlas,J.";"4";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"4";;;"kmv" +"5893";"JPM607";"International Negotiations";;"Parízek,M.";"4";"2";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"5";"4";;;"kmv" +"5894";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Rössler,J.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"4";"5";"5";;;"ks" +"5895";"JPM721";"Komparace ekonomik zemí EU";"Kučerová,I.";;"3";"3";"3";"5";"4";NULL;NULL;NULL;"1";"4";"4";"5";"4";;;"kmv" +"5896";"JSB007";"Dějiny sociologie I";"Šanderová,J.";"Kotík,M.";"4";"2";"5";"5";"4";"2";"1";"3";"3";"4";"3";"5";"5";;;"ks" +"5897";"JJB069";"Tvůrčí dílny I - televizní";"Lokšík,M.";;"4";"2";"5";"5";"5";NULL;NULL;NULL;"1";"3";"5";"4";"5";;;"kz" +"5898";"JSB025";"Sociální problémy";"Frič,P.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kvsp" +"5899";"JJB071";"Tvůrčí dílny I - rozhlasové";"Maršík,J.";"Lovaš,K.,Lucký,J.";"4";"3";"3";"5";"5";"3";"5";"5";"1";"3";"4";"4";"4";;;"kz" +"5900";"JSB530";"Úvod do sociologického myšlení";"Bureš,J.,Numerato,D.";"Bureš,J.";"5";"3";"5";"5";"2";"5";"5";"4";"1";"2";"5";"3";"5";;;"ks" +"5901";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Kouřílek,J.";"3";"4";NULL;NULL;NULL;"5";"5";"5";"1";"3";"4";"1";"3";;;"ks" +"5902";"JLB053";"Angličtina pro sociální vědy I";;"Štěpánková,D.";"4";"2";NULL;NULL;NULL;"4";"5";"5";"1";"4";"4";"4";"5";;;"cjp" +"5903";"JMB409";"Moderní dějiny středo- a jihovýchodní Evropy-dcera JMB013";"Balla,P.,Švec,L.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"Přístup obou vyučujících byl výborný. Oceňuju, že v testech se opravdu objevovaly jen pojmy, se kterými jsme se setkali na přednáškách.";;"krvs" +"5904";"JMB408";"Moderní dějiny Ruska";"Litera,B.,Novák,P.,Pečenka,M.";;"5";"5";"5";"3";"4";NULL;NULL;NULL;"2";"5";"4";"5";"4";;;"krvs" +"5905";"JMB250";"Seminář k dějinám západní Evropy";;"Váška,J.";"4";"4";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"5";"4";;;"kzs" +"5906";"JLB029";"Španělština odborná I";;"Mlýnková,L.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"4";"5";;;"cjp" +"5907";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"4";"4";"5";"3";"5";NULL;NULL;NULL;"1";"5";"2";"3";"3";"Výklad profesora - velmi inspirující a naučný";"Vylepšit možnost přípravy na zkoušku - lepší guidance.Vylepšit příležitosti pro studenty, kteří na některou hodinu nemohou.";"kms" +"5908";"JJM320";"Propaganda a československá média po roce 1948";"Suk,P.";;"3";"2";"3";"3";"5";NULL;NULL;NULL;"1";"4";"2";"3";"3";"Výklad - skvělé";"Vylepšit možnost přípravy na zkoušku - lepší guidance.Vylepšit příležitosti pro studenty, kteří na některou hodinu nemohou.Více vysvětlit dobové souvislosti a jít více do hloubky u důležitých postav.";"kms" +"5909";"JJM324";"Teorie mediální komunikace";"Jirák,J.";;"3";"4";"5";"5";"5";NULL;NULL;NULL;"1";"2";"2";"3";"2";;"Vylepšit možnost přípravy na zkoušku - lepší guidance.Vylepšit příležitosti pro studenty, kteří na některou hodinu nemohou.Více vysvětlit dobové souvislosti a jít více do hloubky u důležitých postav.";"kms" +"5910";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"3";"2";"4";"5";"5";NULL;NULL;NULL;"1";"4";"5";"4";"5";"Způsob hodnocení a průběžnou práci.";;"kms" +"5911";"JJM373";"Ethical Issues in Media, Business and Society";"Orhan,M.";;"4";"1";"4";"5";"1";NULL;NULL;NULL;"1";"2";"2";"1";"5";;;"kms" +"5912";"JJM231";"Mediální výchova v rodině";;"Šťastná,L.";"5";"4";NULL;NULL;NULL;"5";"5";"4";"1";"5";"5";"5";"5";"Oceňuji přístup vyučujícího.";;"kms" +"5913";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kz" +"5914";"JJM202";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"4";"5";"5";"4";"5";NULL;NULL;NULL;"2";"5";"1";"5";"5";"Zajímavou zkušeností bylo střídání dvou vyučujících. Nejvíce oceňuji jejich zápal a způsob výkladu látky.";"Získat v kurzu výbornou (jedničku) je nadlidský úkon. Pokud to není účel, doporučila bych změnit podmínky, aby získání Áčka bylo reálné.";"kms" +"5915";"JJB135";"Filmový seminář I";;"Šobr,M.";"4";"1";NULL;NULL;NULL;"4";"4";"3";"2";"4";"1";"1";"5";"Kurz jako takový je skvělým zpestřením běžné výuky. Také oceňuji úvodní komentář, který prozradí více nejen o daném filmu, ale i o dalších aktuálních filmech, proto pomáhá lépe se orientovat v současné filmové tvorbě.";"Ocenila bych, kdyby byl seznam promítaných filmů k dispozici předem.";"kz" +"5916";"JJB221";"Ekonomická teorie marketingu";"Postler,M.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"2";"4";"5";"Velmi oceňuji to, že se kurz zaměřuje přímo na marketing a neobsahuje rozsáhlé teoretické úvody do ekonomie.";;"kmkpr" +"5917";"JSM521";"Veřejná politika";"Chalupová,P.,Potůček,M.";;"5";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";"Vstřícnost a lidskost vyučující";;"kvsp" +"5918";"JJB224";"Psychologie marketingové komunikace";"Vranka,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Kurz mi přišel velmi zajímavý, zejména proto, že se zabýval velmi aktuálními poznatky z tohoto oboru a jeho obsahem nebyla pouhá teorie, ale také velké množství přesahů do praxe.";;"kmkpr" +"5919";"JSM522";"Veřejná ekonomie";"Kotherová,Z.";;"3";"5";"4";"3";"4";NULL;NULL;NULL;"1";"3";"4";"4";"3";;"vyšší míru tolerance ohledně zadaných prací";"kvsp" +"5920";"JJB226";"Strategie a finanční management marketingu I";"Majerik,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"3";"5";"4";"5";"5";"Kurz byl skvělým obohacením běžné výuky, přínosem je zejména pohled na tvorbu kampaní ze stránky strategického plánování.";;"kmkpr" +"5921";"JJB004";"Současný český jazyk I";;"Svobodová,I.";"4";"5";NULL;NULL;NULL;"3";"2";"5";"2";"5";"5";"3";"2";"Opravdu nás donutili naučit se problematiku.";;"kz" +"5922";"JSM523";"Sociální politika";"Angelovská,O.";;"5";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";"lidský přístup vyučující";;"kvsp" +"5923";"JJB010";"Základy filozofie a vzdělanosti";"Halada,J.";;"4";"2";"5";"5";"2";NULL;NULL;NULL;"3";"1";"2";"5";"4";;;"kz" +"5924";"JJB237";"Právo a etika v marketingu";"Winter,F.";;"5";"3";"5";"4";"5";NULL;NULL;NULL;"2";"5";"2";"5";"5";"Velmi oceňuji schopnost pedagoga podat téma práva takto zajímavou a přístupnou formou. Teorie byla doplňována velkým množstvím přesahů do praxe.";;"kmkpr" +"5925";"JJB012";"Žurnalistická tvorba I";"Osvaldová,B.";"Krobová,T.,Osvaldová,B.,Slanec,J.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"kz" +"5926";"JSM640";"Základy sociologie";"Paulíček,M.";;"4";NULL;"4";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kvsp" +"5927";"JJB015";"Česká literatura I";;"Čeňková,J.,Malý,R.";"3";"3";NULL;NULL;NULL;"4";"4";"1";"1";"4";"3";"4";"3";;;"kz" +"5928";"JJB017";"Grafický design a základy polygrafie I";"Slanec,J.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kz" +"5929";"JJM229";"Vývoj televizního vysílání v českých zemích";"Štoll,M.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"2";"5";"2";"5";"5";"Oceňuji přístup a výklad vyučujícího.";;"kms" +"5930";"JSM641";"Sociální problémy";"Frič,P.";;"4";"4";"3";"4";"4";NULL;NULL;NULL;"1";"4";"2";"3";"3";;;"kvsp" +"5931";"JJB018";"Úvod do fotožurnalistiky";"Lábová,A.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"3";"5";"4";"5";"5";;;"kz" +"5932";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"5";"5";;;"kz" +"5933";"JJB253";"Markething - online publikování a populární kultura I.";;"Maxa,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"5";"5";"Obsahem výuky jsou velmi zajímavá, aktuální a důležitá témata. Oceňuji zejména to, že je zde velký prostor dáván studentům, kteří díky tomu získají zkušenosti s veřejným prezentováním své práce.";;"kmkpr" +"5934";"JSM642";"Metody práce s informacemi";"Tomandlová,V.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"1";"3";"4";"3";"4";;;"kvsp" +"5935";"JJB998";"Úvod do ekonomie";"Poljakov,N.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"3";"5";"5";"5";"5";"Zbytečně jsme se neučili vzorce, ale učili jsem se látku, kterou můžeme využít v praxi.";;"kz" +"5936";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"3";"2";"3";"2";NULL;NULL;NULL;"1";"4";"2";"2";"2";"Formát písemného testu je vyhovující.";"Hodnocení esejí je pochybné, stejně jako pochybné metody výuky. Také by bylo při přednáškách dobré nešeptat do mikrofonu.";"ies" +"5937";"JLB053";"Angličtina pro sociální vědy I";;"Prošková,A.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"5";"4";"Elán a nadšení pro předmět vyučujícího.";;"cjp" +"5938";"JJB255";"Digitální komunikace";;"Klimeš,D.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Celkově velmi dobře pojatý kurz, na teoretickou část výuky navazovaly přednášky hostů, kteří studentům předávali své zkušenosti z praxe. Výuka byla interaktivní, velký prostor je věnován studentům a společným diskuzím.";;"kmkpr" +"5939";"JSB998";"Úvod do sociologie";"Soukup,P.";;"4";"3";"5";"5";"3";NULL;NULL;NULL;"3";"4";"3";"4";"4";;;"ks" +"5940";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"5";"Nepovinná docházka.";"Víceméně nic.";"cjp" +"5941";"JJM249";"Duchovní kořeny evropské kultury";"Halada,J.";;"3";"1";"4";"5";"2";NULL;NULL;NULL;"2";"4";"1";"2";"3";"Rozsáhlé znalosti pana docenta Halady jsou v oblasti filosofie značné, zůstává ale především u klasických osobností";"chce to větší napojení na moderní filosofii";"kz" +"5942";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"krvs" +"5943";"JJB269";"Sociální kontext komunikace";"Vranka,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Celkově hodnotím kurz jako velmi zajímavý, téma komunikace je zde probíráno obecně, proto si myslím, že bude skvělým úvodem k předmětům zabývajícím se přímo marketingovou komunikací.";"Ocenila bych více přesahů do marketingu, chápu, že předmět je teoretickým úvodem, ale myslím si, že větší propojenost s oborem ho udělá o to zajímavějším.";"kmkpr" +"5944";"JMB402";"Úvod do společenských věd II";;"Hrušková,T.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"2";"4";"3";"3";"5";;;"krvs" +"5945";"JEB022";"Institutional Economics";"Kopečná,V.,Schwarz,J.";;"3";"3";"3";"2";"2";NULL;NULL;NULL;"1";"2";"1";"2";"2";"The openess to debate in class.";"Increasing the contextualization of the subject.";"ies" +"5946";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Papežová,K.";"5";"3";"5";"5";"5";"5";"5";"5";"2";"5";"4";"4";"5";;;"knrs" +"5947";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Čížek,M.";"5";"3";"5";"5";"5";"5";"5";"5";"2";"5";"5";"3";"5";;;"krvs" +"5948";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"2";"4";"4";"4";"5";;;"knrs" +"5949";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"1";"4";"3";"3";"2";"3";"3";"2";"3";"1";"1";"1";"2";"The work of the teacher giving the lecture.";"Increasing the contextualization of the subject and giving it a more practical utility.";"ies" +"5950";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"2";"4";"4";"4";"5";;;"kas" +"5951";"JSB998";"Úvod do sociologie";"Soukup,P.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"2";"4";"4";"4";"4";;;"ks" +"5952";"JEB136";"Topics in Industrial Organization";"Schwarz,J.";;"2";"3";"2";"2";"1";NULL;NULL;NULL;"2";"2";"1";"1";"2";"The seminars with people from different industries.";"Increasing the participation of the student in the subject.";"ies" +"5953";"JEM020";"Ethics and Economics";"Cahlík,T.";"Cahlík,T.";"4";"2";"4";"5";"4";"4";"5";"2";"2";"4";"2";"2";"3";"The work of the teacher giving the lecture and his attitude towards the students.";"Increasing the contextualization of the course.";"ies" +"5954";"JJM355";"Teoretické koncepty ke studiu žurnalistiky I";"Jirků,J.,Němcová Tejkalová,A.";"Jirků,J.";"3";"4";"4";"5";"1";"5";"5";"4";NULL;"2";"4";"1";"2";"velmi oceňuji semináře s panem profesorem Jirků, jeho způsob práce se studenty a zasazení probírané látky z přednášek";"Méně sociologie, méně teorie, obsah přednášek by se měl zredukovat, jinak je docházka na ně spíš z povinnosti";"kz" +"5955";"JJB401";"Komerční a nekomerční marketingová komunikace";"Báča,L.,Obluk,O.";;"5";"5";"5";"4";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"Výuka byla velmi praktická, zajímavá, velmi oceňuji zahrnutí oblasti nekomerčního marketingu.";;"kmkpr" +"5956";"JJM372";"Consumer Behaviour";"Orhan,M.";;"4";"2";"4";"5";"5";NULL;NULL;NULL;"2";"3";"3";"3";"3";"The attitude of the teacher and the opportunities given to the students to participate in class.";"The implantation of essay driven test.";"kms" +"5957";"JJB406";"Tvorba a prostředky v mediální komunikaci";"Chudinová,E.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"2";"4";"3";"4";"4";"Oceňuji zahrnutí problematiky médií do studijního plánu tohoto oboru.";"Chybělo mi propojení mediální tvorby a marketingové komunikace, takto pojatý kurz by zapadal spíše do oboru žurnalistiky.";"kmkpr" +"5958";"JJB407";"Bakalářský proseminář";"Rosenfeldová,J.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"3";"3";"5";"3";"5";"Velmi oceňuji osobní přístup profesora a detailní zpětnou vazbu k odevzdaným pracím.";;"kmkpr" +"5959";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"4";"3";"2";"3";NULL;NULL;NULL;"1";"4";"2";"4";"3";"Oceňuji celkem praktická témata.";"Dle mého názoru by měl být kurz pouze volitelný.";"ies" +"5960";"JJM356";"Metodologické přístupy ke studiu žurnalistiky";"Nečas,V.,Veselková,Z.";;"2";"4";"4";"5";"1";NULL;NULL;NULL;NULL;"1";"2";"1";"1";"Předmět bych sloučil s diplomovým seminářem.";"Přednáška \"jak napsat odbornou práci\" je podobně smrtelná jako bakalářský seminář, většinu z toho jsme ale už někdy dělali nebo probírali. Psaní povinných anotací článků považuji za ztrátu času. Zastávám názor, že se metodologie nedá naučit jinak, než jí reálně používat. Jinak je to čisté utrpení. Navíc máme diplomový seminář.";"kz" +"5961";"JLB001";"Angličtina pro sociology I";;"Klírová,M.";"4";"4";NULL;NULL;NULL;"4";"5";"5";"1";"5";"4";"3";"4";"Oceňuji probíraná témata a rozšiřování slovní zásoby, která se vztahuje k sociologii.";"Některé úkoly by nemusely být tak náročné.";"cjp" +"5962";"JLB039";"Ruština odborná I - nižší";;"Mistrová,V.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"3";"4";"4";"Oceňuji střídání výuky gramatiky se čtením aktuálních článků a textů.";"Pracovat pomaleji.";"cjp" +"5963";"JJM259";"Mediální systémy a jejich komparace";"Miessler,J.";;"3";"3";"4";"4";"3";NULL;NULL;NULL;"2";"3";"2";"3";"3";"Témata prezentací";"Mediální systémy se točí především kolem knih od Hallina, Manciniho, Sieberta a spol. Ostatní z nich přímo nebo nepřímo vychází. Z toho vzniká pocit, že se člověk učí jedno a to samé dokola. Výběr z povinné četby byl sice benevolentní, ale přečíst si všechno v podstatě nemožné. Navíc vybrané práce jen navazují na předchozí autory, takže se to brzy začne zajídat. Dohromady s ostatními teoretickými předměty vzniká pocit \"jsem vůbec na žurnalistice?\".";"kz" +"5964";"JMB402";"Úvod do společenských věd II";;"Čapinská,B.";"4";"3";NULL;NULL;NULL;"2";"4";"2";"1";"3";"3";"2";"3";;"bylo by dobré sjednotit požadavky cvičících";"krvs" +"5965";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"5";"3";"4";"5";"1";NULL;NULL;NULL;"1";"2";"1";"1";"4";"milý přístup vyučující";;"knrs" +"5966";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Čížek,M.";"4";"5";"5";"4";"3";"4";"3";"2";"1";"5";"2";"4";"5";;;"knrs" +"5967";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Lizcová,Z.";"5";"5";"5";"5";"5";"4";"5";"4";"2";"5";"3";"5";"5";;;"krvs" +"5968";"JLB099";"Rozřazovací test z angličtiny";;"Kunzová,J.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";NULL;"5";;;"cjp" +"5969";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"4";"3";"2";"3";NULL;NULL;NULL;"1";"3";"3";"3";"2";;;"ies" +"5970";"JJM208";"Mediální systémy a jejich komparace";"Miessler,J.";;"4";"4";"4";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"1";"testy každou hodinu, donutí to si něco o literatuře zjistit";"Nenutit diskuzi za každou cenu, recenze knihy byla zbytečná práce navíc";"kms" +"5971";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"4";"4";"4";"4";"3";NULL;NULL;NULL;"1";"3";"4";"2";"5";;;"kp" +"5972";"JLB013";"Němčina odborná I";;"Křenková,D.";"5";"4";NULL;NULL;NULL;"5";"5";"4";"1";NULL;NULL;NULL;"4";;;"cjp" +"5973";"JJM213";"Metody historického výzkumu";;"Bednařík,P.,Končelík,J.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"5";"5";"Přístup vyučujících, upřímná konstruktivní kritika prezentovaných návrhů DP";"Více se zaměřit na konkrétní metody (to nám asi měl přinést výzkum médií, ale...)";"kms" +"5974";"JJM216";"Čtení textů ke studiu médií - česká média po roce 1945";;"Bednařík,P.,Končelík,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"5";"5";"dobře zvolené historické momenty k analýze, osobní zkušenosti vyučujících, vzpomínání na mládí, diskuse nad aktuální politikou";"nutit prezentující, aby měli více audiovizuálních materiálů - je to pak atraktivnější";"kms" +"5975";"JJM200";"Diplomový seminář";;;"1";"3";NULL;NULL;NULL;"3";"3";"1";"5";"1";"1";"1";"1";;"Uniká mi smysl tohoto předmětu. Tak jako tak DP musíme napsat, proč k tomu je tento předmět?";"kms" +"5976";"JLB001";"Angličtina pro sociology I";;"Klírová,M.";"4";"2";NULL;NULL;NULL;"4";"3";"4";"1";"3";"4";"4";"5";;"We often waited long for feedback";"cjp" +"5977";"JEB026";"European Economic Integration";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"Debatz,L.,Hedbávný,P.,Khymych,O.,Komárek,L.,Macháček,V.";"3";"3";"3";"3";"2";"1";"1";"1";"1";"4";"3";"3";"4";"obsah kurzu, bližšie oboznámenie s EUškoda, že na IES nie je viac európsky zameraných predmetov na túto tému";"Nečítať celé prezentácie študentom, ale nechať iba dôležité body a zvyšok vysvetliť - potom by na prednášky chodilo viac študentov. Nevidím jediný zmysel seminárov. Je fajn, že je povinné naštudovať si danú tému a tak sa aj viac naučiť, ale celý zmysel sa stráca z toho, že semináre sú venované iba prezentovaniu prezentácií. Navyše každý vyučujúci semináru má iné kritéria na hodnotenie prezentácií, čo znevýhodňuje študentov. Výsledky z prezentácie prišli o mesiac, takže relevantnosť hodnotenia je dosť nízka. To súvisí aj s tým, že väčšina študentov dostala nízke hodnotenie takmer (napriek tomu, že príprave venovali veľa času) s nijakým komentárom, resp. argumenty, prečo sme dostali málo bodov boli miestami až smiešne a nie hodné IESu. Osobne prístup a vyučujúcich zo semináru a hodnotenie prezentácií hodnotím veľmi negatívne.";"ies" +"5978";"JSB007";"Dějiny sociologie I";"Šanderová,J.";"Hanzal,P.";"4";"3";"4";"4";"4";"3";"3";"4";"3";"4";"4";"4";"4";;;"ks" +"5979";"JSB025";"Sociální problémy";"Frič,P.";;"3";"4";"3";"3";"4";NULL;NULL;NULL;"2";"4";"3";"3";"2";;;"kvsp" +"5980";"JSB530";"Úvod do sociologického myšlení";"Bureš,J.,Numerato,D.";"Bureš,J.";"4";"2";"4";"4";"3";"5";"5";"5";"1";"4";"4";"4";"5";;;"ks" +"5981";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Hanzlík,P.";"3";"3";"3";"3";"3";"3";"4";"4";"1";"3";"3";"3";"3";;;"ks" +"5982";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Rybín,F.,Vlčková,A.";"3";"4";"4";"4";"4";"3";"4";"4";"1";"4";"2";"4";"3";;;"ks" +"5983";"JSB051";"Úvod do sociální antropologie";"Uherek,Z.";"Hrešanová,E.";"3";"3";"3";"3";"3";"3";"3";"4";"1";"4";"3";"4";"4";;;"ks" +"5984";"JEM003";"Advanced Microeconomics";"Janda,K.,Melikhova,O.";"Janda,K.,Melikhova,O.";"1";"2";"1";"3";"1";"2";"3";"1";"1";"1";"1";"1";"1";"No homeworks, no midterm, at least I did not have to lose my time as the content of the subject is completely useless";"This course definitely should not be mandatory, as only a very few students can really use at least something from what we covered in their future praxis. Please let our school be modern and praxis-oriented.";"ies" +"5985";"JJM188";"Kvalitativní výzkum mediálních obsahů";;"Vochocová,L.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Přístup profesorky, její ochotu přizpůsobit se přáním studentů a uzpůsobit tomu svůj plán výuky. Velmi si cením jejího přátelského přístupu a ochoty pomoci za každé situace.";;"kms" +"5986";"JJM208";"Mediální systémy a jejich komparace";"Miessler,J.";;"3";"5";"3";"4";"5";NULL;NULL;NULL;"1";"4";"3";"3";"3";;;"kms" +"5987";"JJM240";"Cultural studies";"Soukup,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kms" +"5988";"JJM216";"Čtení textů ke studiu médií - česká média po roce 1945";;"Bednařík,P.,Končelík,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";"Kurz dokáže spojit příjemné s užitečným. Při výuce se toho student nejen hodně dozví, ale také se zasměje a odchází s dobrou náladou do zbytku dne.";;"kms" +"5989";"JJM371";"New Media and Entrepreneurship";"Orhan,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"5990";"JJM240";"Cultural studies";"Soukup,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"5991";"JLB100";"Czech as a Foreign Language I";;"Frantesová,E.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"5992";"JJM340";"Tvůrčí dílny – tvůrčí psaní I";"Novotný,D.";"Novotný,D.";"5";"2";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"Podpora kreativity, umělecká konzultace, profesor má akademický přístup, hodnotí kladně každý nápad.";;"kz" +"5993";"JSM103";"Academic Writing";;"Blokker,P.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"5";"5";;;"ks" +"5994";"JJM200";"Diplomový seminář";;;"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kms" +"5995";"JLM006";"Angličtina pro politology II";;"Panešová,K.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"5";"5";"Především oceňuji velmi pozitivní přístup paní Mgr. Panešové! Cvičení celkově bylo celkově velmi užitečné a pomohlo mi prohloubit nezbytné znalosti v oboru.";;"cjp" +"5996";"JJM329";"Média a kultura";"Bednařík,P.,Jirák,J.";;"5";"3";"5";"5";"3";NULL;NULL;NULL;"1";"3";NULL;"3";"5";"Vstřícný přístup vyučujících.";;"kms" +"5997";"JJM332";"Přirozený jazyk a média";"Podzimek,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";NULL;"5";"5";;;"kms" +"5998";"JJM331";"Výzkum médií II";"Vochocová,L.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Způsob vedení kurzu (debata, zapojování studentů) a zejména přednášející PhDr. Lenku Vochocovou, Ph.D. - poskytuje dobrou zpětnou vazbu k odevzdaným projektům/seminárním pracím, komunikuje se studenty v souvislosti s požadavky ke zkoušce a studijní literaturou, celkově vstřícný přístup ke studentům.";;"kms" +"5999";"JSM518";"Public Policy";"Potůček,M.,Vlčková,K.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kvsp" +"6000";"JSM694";"Diploma Seminar I";;"Frič,P.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kvsp" +"6001";"JEB110";"Econometrics II";"Pertold-Gebicka,B.";"Chorna,O.,Malinská,B.,Pertold-Gebicka,B.,Pleticha,P.";"4";"4";"3";"4";"5";"4";"5";"5";"1";"4";"4";"4";"4";"rozšírenie znalostí z ekonometrie, prístup vyučujúcich";"hodiny boli občas trošku zmätočné, pretože sa preskakovali slidy, chyby v prezentáciach. veľmi dlho (3 týždne) sme čakali na hodnotenie projektu.";"ies" +"6002";"JJM373";"Ethical Issues in Media, Business and Society";"Orhan,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";NULL;"4";"5";"Zaměření kurzu na praktické aspekty jako je obchodní etika, jež jsou mezioborové a neomezují se jen na oblast mediálních studií. Velmi vstřícný přístup přednášejícího ke studentům, možnost navštěvovat kurz v angličtině i v rámci kombinovaného studia.";;"kms" +"6003";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"4";"4";"4";"4";"5";NULL;NULL;NULL;"1";"5";NULL;"5";"4";"Poskytnutí veškeré požadované literatury ke zkoušce vyučujícím.";;"kms" +"6004";"JJB221";"Ekonomická teorie marketingu";"Postler,M.";;"4";"2";"4";"5";"4";NULL;NULL;NULL;"1";"4";"4";"3";"5";;;"kmkpr" +"6005";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"5";"3";"5";"5";"5";"2";"3";"2";"2";"4";"4";"4";"5";"Great lecturer, practical homeoworks";"Seminars - the teachers were not that good and the maretials provided very usually out of the scope of what we really did on lectures and what we should learn";"ies" +"6006";"JEM037";"Financial Markets";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"4";"3";"4";"4";"3";"4";"5";"5";"2";"5";"4";"5";"5";"Interesting topics, structure of the seminars, not a huuge overload of work as at most of the other courses";"Maybe too much information in each lecture, so it is hard to learn everything profoundly at the end";"ies" +"6007";"JEM132";"Company Valuation";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"1";"4";"4";"3";"4";NULL;NULL;NULL;"2";"2";"2";"3";"1";"Lectures where the topics were explained in depth, so we had a broader picture and we were able to understand why are we using certain methods, what is the purpose of them";"Unfortunately, this course turned out to be quite annoying. The first half of the lectures were as I described above, but during the following lectures we were going into detail that much that we ended up faar away from the original topic, and at the end we covered like one fifth of the whole presentation. Next, the seminars - I think the intention was good and Mr. Novák wanted us to see various people from the praxis, but at the end the seminars were disorganized and we did not learn alomost nothing there At the same time, there was a HUUUUGE OVERLOAD of work at this course. On the lectures, we learned the theory, but then nobody showed us the practical things and we had to spend hours and hours with 5 phases of the project, learning everything ourselves, which was super hard sometimes. Then the project counted for 20% of our grade, which is almost nothing regarding the time we spent with it. In addition to that, we had 2 homeworks, and especially the second one was almost impossible to complete at the end of the semester with all the pre-terms, homeworks, and, of course, a phase 4 to do for our project. And at the end, we did not even present our project, but a project of someone else, and it was again SUPER ANNOYING to try to evaluate work of someone else when we dont know the correct answers at all, and it takes sooo much time to go through the Excel, the ppt etc. And this stupid presentation was evaluated by 10%, the same as half of our hard work at the project!! And finally, this course ended with an amazing test that had nothing to do with what we covered during the lectures. Studying all the course materials was almost equal to coming to the test and just guessing. So, PLEASE, try to change the structure of this course, because it has a potential to be a good one. Hire one competent person for seminars, and show students the parts of valuation in the praxis, for example on some existing company, other than the one the students are supposed to evaluate. Get rid of the homeworks so that students can focus on the project, and let students present their work at the end. And please, test the students from what they were really able to learn at the course.";"ies" +"6008";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmv" +"6009";"JPM052";"Mezinárodní ekonomické vztahy (MEV)";"Kučerová,I.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmv" +"6010";"JPM690";"Liberalism in International Relations (TIR)";;"Karlas,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kmv" +"6011";"JPM719";"Diplomacy of the European Union";"Pajtinka,E.";;"2";"3";"2";"2";"2";NULL;NULL;NULL;"4";"2";"2";"2";"2";;"Výuka často odpadávala a změna podmínek ke splnění zkoušky v polovině semestru.";"kmv" +"6012";"JPM146";"Přechody k demokracii v teorii a praxi I";"Mlejnek,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"6013";"JPM150";"Poloprezidentské režimy v postkomunistické Evropě";"Mlejnek,J.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"6014";"JPM574";"Moderní strany a stranické systémy v Evropě";"Brunclík,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"6015";"JPM639";"Problémy ústavního inženýrství";"Brunclík,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"6016";"JPM579";"Teorie politických stran";"Perottino,M.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"6017";"JPM670";"Odborná stáž A";;;"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kmv" +"6018";"JPM260";"Vybrané problémy britské zahraniční politiky v 19. a 20. století, ES";"Soukup,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmv" +"6019";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"2";"3";"5";"5";"2";"4";"5";"4";"1";"4";"4";"5";"5";;;"ies" +"6020";"JPB218";"Dějiny novověké Evropy I.";"Kučera,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kp" +"6021";"JPB227";"Politický system ČR";"Charvát,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"4";"5";;;"kp" +"6022";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"Kvalitní přednášky, zkouška sice těžká, ale vyučující se neptá na žádné zákeřnosti.";"Nic";"kp" +"6023";"JEM181";"Data Science with R";"Herman,D.,Krištoufek,L.";"Herman,D.,Krištoufek,L.";"4";"4";"4";"5";"4";"4";"5";"4";"1";"5";"5";"5";"5";"The final project on a topic we select ourselves, fair homeworks, interesting topics covered";"Less is sometimes more - the code was sometimes too long and too complicated to follow, to understand profondly";"ies" +"6024";"JLB035";"Francouzština I";;"Bosáková,L.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"6025";"JJB035";"Odvětvové zpravodajství - ekonomie";"Kameníček,J.";;"1";"5";"1";"1";"1";NULL;NULL;NULL;"1";"3";"3";"2";"1";"Čas výuky. Po obědě to bylo ideální.";"Vyučujícího, sylabus a vyučujícího. Ano, změna je tak nutná, že ji musím zdůraznit dvakrát.";"kz" +"6026";"JJB040";"Kreativita v jazyce";"Šoltys,O.";;"4";"2";"3";"4";"3";NULL;NULL;NULL;"1";"1";"2";"2";"5";"Vyučujícího. Tolik pozitivní energie si neodnáším ani z dvouhodinové lekce jógy a tu si navíc musím platit. Pokud se něco panu Šoltysovi ještě daří, je to zlepšování nálady všem účastníkům hodin. A sebevědomí. Tolik pochval za jednu hodinu jsem nenasbírala za celé doživotní studium. Velmi povzbudivé.";;"kz" +"6027";"JJB052";"Tvůrčí dílny FOTO I";"Lábová,A.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"5";"5";"Pokud je něco, co drží žurnalistiku na FSV nad vodou, je to duo doktorka Lábová a docent Láb. A kterýkoli předmět u nich. Z mého pohledu se jedná o jedny z mála vyučujících, kteří spojují zkušenosti z praxe (protože je vidět, že opravdu nějaké mají) s výukou. Jejich kritika je konstruktivní a sdělují ji studentům lidskou a slušnou formou. Jako dospělí lidé dospělým lidem. Jejich přístup ke studentům je neuvěřitelně vstřícný a sám o sobě nutí studenty úkoly na cvičení vypracovávat.";"Rolety na oknech, aby fotografie na projektoru lépe vynikly. Nebo pořídit lepší projektor.";"kz" +"6028";"JJB059";"Kritika v médiích - televizní";"Novotný,D.";;"5";"3";"4";"5";"3";NULL;NULL;NULL;"2";"3";"3";"3";"5";;;"kz" +"6029";"JPM191";"Geopolitics of Great Powers: Russia";"Baštář Leichtová,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"The lectures were interesting, easy to follow. Seminars included active discussions on highly relevant topics.";;"kp" +"6030";"JPM690";"Liberalism in International Relations (TIR)";;"Karlas,J.";"4";"4";NULL;NULL;NULL;"4";"4";"4";"1";"4";"4";"4";"3";;;"kmv" +"6031";"JPM708";"Ethics and Violence";"Karásek,T.,Kučera,T.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";NULL;NULL;NULL;NULL;"The course is challenging in the best possible way. I really enjoyed the debates.";;"kbs" +"6032";"JPM724";"Critical Approaches to International Politics and Security";;"Daniel,J.,Rychnovská,D.";"4";"4";NULL;NULL;NULL;"4";"5";"4";"1";"4";"4";"4";"4";;;"kmv" +"6033";"JPM725";"Technology and Security: Contemporary Warfare in the 21st Century";;"Csernatoni,R.";"3";"4";NULL;NULL;NULL;"4";"4";"3";"1";"4";"4";"4";"3";;;"kmv" +"6034";"JPM727";"Orchestration in Global Governance";;"Abbott,K.,Parízek,M.";"4";"2";NULL;NULL;NULL;"5";"5";"5";"1";"3";"4";"4";"5";;;"kmv" +"6035";"JMM339";"American National Security Policy";"Raška,F.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"expand my knowledge";"nothing";"kas" +"6036";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"3";"expand my knowledge";"nothing";"kmv" +"6037";"JPM191";"Geopolitics of Great Powers: Russia";"Baštář Leichtová,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"expand my knowledge";"nothing";"kp" +"6038";"JPM602";"Masterś Thesis Seminar I.";;"Kofroň,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"1";"expand my knowledge";"nothing";"kp" +"6039";"JPM620";"Geopolitical Thought";"Kofroň,J.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"3";"expanding my knowledge";"nothing";"kp" +"6040";"JPM329";"Making of Modern Asia";"Krausz Hladká,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"expand my knowledge";"nothing";"kp" +"6041";"JPM599";"ON WAR I.";"Kofroň,J.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"2";"expand my knowledge";"nothing";"kp" +"6042";"JPM425";"Conflict & Cooperation in International River Basins";"Landovský,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"expand my knowledge";"nothing";"kp" +"6043";"JLB013";"Němčina odborná I";;"Křenková,D.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Paní Křenková má skvělou přípravu na hodiny, které jsou vedeny opravdu zábavně a pokaždé jinak. Měli jsme možnost hodně diskutovat. Obohatila jsem svou slovní zásobu o odborné výrazy. Za mě super!";"Nic mě nenapadá.";"cjp" +"6044";"JLB013";"Němčina odborná I";;"Křenková,D.";"5";"4";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"3";"4";"Velmi milá, profesionální a kompetentní vyučující se kterou je radost se němčinu učit. Snažila se pro nás hodiny vždy udělat zábavnější, což velmi oceňuji.";;"cjp" +"6045";"JPM611";"Cyber Security";"Duračinská,Z.,Střítecký,V.";;"3";"3";"3";"5";"4";NULL;NULL;NULL;"1";"4";"3";"3";"4";;;"kbs" +"6046";"JPM694";"Strategic Studies";"Hynek,N.,Ludvík,J.,Smetana,M.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"5";"5";;"Kurz byl velmi zajímavý, jediné, s čím jsem měl trochu problém byl rozdílný přístup k některým studentům. Během semestru jsme měli povolené dvakrát odevzdat úkol o týden později. Já jsem se svou známkou spokojený a věřím, že odpovídá mému výkonu v kurzu, jen mě trochu zarazilo, že někteří studenti opakovaně odevzdali úkol později a nejenže nebyli z kurzu vyloučení, jak je avizováno v syllabu, ale dostali dokonce nejlepší známku. Obecně byl kurz velmi zajímavý a rozhodně prospěšný, jediné, co mi tedy vadilo, byl rozdílný přístup.";"kbs" +"6047";"JPM701";"European and Transatlantic Security";"Kazharski,A.";;"3";"3";"2";"4";"2";NULL;NULL;NULL;"1";"2";"2";"3";"2";;"Once I witnessed a student who did not fulfill one of the tasks we were required to submit before every class. The student failed to do so. nevertheless, the teacher told him that it is okay and that he does not have to write the assignement. I find this very unfair because I submitted all the tasks in time. Moreover, what I found very ridiculous was the fact that the teacher of the course European and Transatlantic Security did not know that Malta is a state.";"kbs" +"6048";"JJB040";"Kreativita v jazyce";"Šoltys,O.";;"1";"5";"1";"4";"1";NULL;NULL;NULL;"1";"1";"2";"1";"1";;"Kurz se zabývá tvorbou absurdních a nesmyslných textů. Nepřináší žádné oborové vědomosti, a co se rozvoje kreativity týče, ten tam sice je, ale nijak přínosný. Dal by se zlepšit výběr témat ke zpracování, vyučující by měl dbát na lepší vysvětlení studentům, a obecně na zatraktivnění teoretické části předmětu.";"kz" +"6049";"JPB228";"Mírové smlouvy a konference v mez. systému";"Jeřábek,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmv" +"6050";"JPB256";"Politické systémy I";"Brunclík,M.,Jeřábek,M.,Kotábová,V.,Perottino,M.";;"4";"4";"4";"5";"5";NULL;NULL;NULL;"4";"4";"5";"4";"5";;;"kp" +"6051";"JJB169";"Věda v médiích";"Kasík,P.";;"4";"1";"5";"5";"5";NULL;NULL;NULL;"2";"5";"2";"5";"5";"Oceňuji přístup vyučujícího ke studentům a využití aktuálních témat, jeho znalostí z praxe a zapojení témat, která si vybírají sami studenti. Tématika je vysvětlena velmi dobře, srozumitelně a zábavně.";;"kz" +"6052";"JPB258";"Politické myšlení novověku I";"Franěk,J.,Kučera,J.";;"3";"4";"4";"5";"5";NULL;NULL;NULL;"2";"4";"5";"5";"4";;;"kp" +"6053";"JPM703";"Czech Security Policy";"Kučera,T.,Ludvík,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Velmi zajímavý kurz, který nám přiblížil bezpečnost ČR z \"tradičního\", avšak i moderního pohledu. Přednášející většinou více než zajímaví!";;"kbs" +"6054";"JJB037";"Kritika v médiích I";;;"3";"1";"3";"4";"2";NULL;NULL;NULL;"1";"3";"2";"2";"2";"Na kurzu oceňuji především rychlé psaní krátkých recenzí přímo na hodinách.";"Kurz sestává především z referátů o autorech a novinářích, které si studenti připraví, doplňovaných vyučujícím. Nedá se říct, že by to byl zrovna nejatraktivnější způsob výuky, vyučující by měl dát přednost vlastnímu výkladu v interaktivní formě.";"kz" +"6055";"JJB628";"Marketing módních značek - teorie";"Hejlová,D.,Koudelková,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Paní Hejlová je výborná vyučující, která velmi dobře rozumí tématu, a dokáže studenty úžasně vtáhnout do výkladu. Používá praktické metody, exkurze, experimenty, výuka je velmi interaktivní. Zároveň volí témata přednášek i s ohledem na přání studentů, hodiny často probíhají i ve formě diskuze nad daným problémem. Vynikající předmět.";"Hodiny s druhou vyučující mi již nepřišly tolik přínosné, neodnášela jsem si nové informace a v porovnání s ostatními hodinami působily velmi nezáživně.";"kmkpr" +"6056";"JEM199";"Financial Crisis and Risk Management";"Horváth,R.,Opatrný,M.,TSOMOCOS,D.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;"1";"3";"3";"4";"4";;;"ies" +"6057";"JJB298";"Marketingová komunikace malých a středních podniků";"Koudelková,P.";;NULL;NULL;"3";"4";"3";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kmkpr" +"6058";"JJB403";"Institucionální a vládní komunikace";"Shavit,A.,Soukeník,Š.";;NULL;NULL;"4";"4";"4";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kmkpr" +"6059";"JEM003";"Advanced Microeconomics";"Janda,K.,Melikhova,O.";"Janda,K.,Melikhova,O.";"3";"4";"4";"5";"3";"4";"4";"3";"1";"3";"4";"3";"3";"pan profesor má dobrý humor a zároveň dokáže dobře probírat látku tak, aby to šlo pochopit.";"Přišla mi nepoměrná náročnosti jednotlivých zkoušek. První byla pro toho, kdo nestudovat bakaláře na IES šokující a odstrašující. Což je asi dobře pro motivaci. Na druhou stranu třetí zkouška byla velmi jednoduchá obtížností. Mám jednu velkou výhradu k opravování druhé zkoušky. Nechtěl bych rozepírat toho, co opravuje, může se stát. Ale chtěl bych poukázat na to, že když opravené testy neodpovídají realitě, velmi to podkopává důvěru ve školu a bylo to pro mne ten týden do náhledu velmi stresující. Běhěm náhledů druhého testu hodně studentů odlišně vylepšilo známku, což na důvěře a průhlednosti taky nepřidává. Ale stane se, chápu, že ty delší příklady je složitější hodnotit, když navíc studenti tak škrábou :) Každopádně děkuji, semináře byly dobře udělané a testy opraveny taky dobře celkově i rychle.";"ies" +"6060";"JPM712";"Insurgency and Counterinsurgency";"Aslan,E.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"Very interesting course! Inititally I was not very interested in the topic of (CO)IN but the teacher made the lessons very valuable and he has a very broad knowledge about this topic which was amazing.";;"kbs" +"6061";"JLB033";"Němčina I";;"Faltýnová,R.";"4";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"V kurzu mi vyhovoval systém výuky a příprava na zkoušku.";"Možná bych rozšířila slovní zásobu, kterou musíme umět.";"cjp" +"6062";"JMB118";"Geografie německy mluvících zemí";"Baštová,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Na kurzu se mi líbilo úplně všechno, hodiny mě bavily a nic bych neměnila.";"Nic bych nevylepšovala.";"knrs" +"6063";"JMB197";"Kapitoly z moderních dějin Itálie";"Mejstřík,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Líbil se mi styl výkladu vyučujícího, díky kterému jsem si látku z přednášek bezproblémově zapamatovala. Bylo vidět, že vyučující je v dané oblasti skutečným odborníkem. Hodiny nikdy nebyly nudné, vždy jsem na ně ráda chodila.";"Nic bych neměnila, všechno mi naprosto vyhovovalo.";"kzs" +"6064";"JMB204";"Skotsko, Wales a Severní Irsko v kontextu moderních britských dějin";"Kasáková,Z.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Ocenuji styl výkladu paní doktorky Kasákové.";"Nic bych nevylepšovala, vše mi vyhovovalo.";"kzs" +"6065";"JMB533";"Česká republika v integračních procesech";"Šlosarčík,I.";;"5";"4";"5";"4";"5";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kzs" +"6066";"JMB534";"Evropská unie - vybrané problémy";"Mejstřík,M.";;"5";"5";"5";"5";"4";NULL;NULL;NULL;"5";"4";"4";"4";"5";;;"kzs" +"6067";"JMB535";"Bezpečnostní problémy současného světa";"Weiss,T.";;"5";"4";"5";"4";"5";NULL;NULL;NULL;"4";"5";"4";"4";"5";;;"kzs" +"6068";"JMB242";"Balkans after 1989";"Hofmeisterová,K.,Kocián,J.,Králová,K.";;"3";"4";"4";"5";"5";NULL;NULL;NULL;"1";"5";"5";"3";"3";"Navrhovala bych zachovat formu zkoušky a psaní esejí.";"Asi bych zrušila psaní reportů z každé hodiny, což mi přijde celkem nadbytečné.";"krvs" +"6069";"JMB536";"Bakalářský seminář pro kombinovaný obor Teritoriální studia I";;"Vykoukal,J.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"5";"4";"5";"4";"4";;;"krvs" +"6070";"JMB216";"Postsovětský prostor v 90. letech";"Lídl,V.,Šír,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Na kurzu nejvíce oceňuji jak pana Šíra tak pana Lídla, a jejich styl přednášek.";"Nic bych nevylepšovala, vše mi vyhovovalo.";"krvs" +"6071";"JPM699";"Security and Technology";"Střítecký,V.";;"5";"3";"5";"5";"3";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kbs" +"6072";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";;;"cjp" +"6073";"JMB218";"Německo a Rakousko po roce 1989";"Emler,D.,Kunštát,M.,Mlsna,P.,Nigrin,T.,Šafařík,P.";;"1";"5";"2";"1";"2";NULL;NULL;NULL;"3";"3";"3";"3";"1";"Nic mě nenapadá, vše bych změnila.";"Navrhuji zrušit systém, že na každé přednášce je jiný vyučující.";"knrs" +"6074";"JMB402";"Úvod do společenských věd II";;"Karasová,N.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"3";"5";"4";"5";;;"krvs" +"6075";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"4";"4";"4";"2";"2";NULL;NULL;NULL;"1";"2";"1";"3";"4";;;"ies" +"6076";"JLB033";"Němčina I";;"Faltýnová,R.";"4";"4";NULL;NULL;NULL;"4";"3";"4";"1";"4";"4";"2";"4";;;"cjp" +"6077";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Kačmárová,P.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"knrs" +"6078";"JLB053";"Angličtina pro sociální vědy I";;"Gloverová,M.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";;;"cjp" +"6079";"JPB587";"Víceúrovňové vládnutí (stát, region, občan)";"Perottino,M.";;"4";"2";"4";"4";"5";NULL;NULL;NULL;"3";"5";"4";"4";"5";;;"kp" +"6080";"JSB012";"Úvod do empirického výzkumu ve společenských vědách";"Jeřábek,H.";"Přibáňová,T.";"4";"4";"4";"4";"3";"5";"5";"4";"1";"4";"3";"4";"4";;;"ks" +"6081";"JSB513";"Úvod do akademické práce";"Höfer,K.,Mouralová,M.,Veselý,A.";;"3";"1";"5";"5";"4";NULL;NULL;NULL;"1";"3";"3";"3";"3";;;"kvsp" +"6082";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"1";"2";"3";"5";;;"kz" +"6083";"JSB998";"Úvod do sociologie";"Soukup,P.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"3";;;"ks" +"6084";"JJB221";"Ekonomická teorie marketingu";"Postler,M.";;"4";"3";"3";"4";"2";NULL;NULL;NULL;"1";"4";"3";"4";"5";;;"kmkpr" +"6085";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"4";"5";;;"ks" +"6086";"JJB004";"Současný český jazyk I";;"Svobodová,I.";"3";"4";NULL;NULL;NULL;"3";"2";"3";NULL;"3";"4";"3";"3";"Lingvistická cvičení";;"kz" +"6087";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"5";"4";"4";"2";"2";"2";"2";"5";;;"kz" +"6088";"JJB143";"Žurnalistika a feminismus";"Krobová,T.,Osvaldová,B.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"3";"5";"3";"4";"5";"Skvělou vyučující, rozebírání aktualit a feministické porno.";;"kz" +"6089";"JMB408";"Moderní dějiny Ruska";"Litera,B.,Novák,P.,Pečenka,M.";;"1";"5";"3";"3";"2";NULL;NULL;NULL;NULL;"1";"1";"2";"1";;;"krvs" +"6090";"JJB231";"Dějiny a teorie public relations";"Halada,J.,Hejlová,D.";;"4";"4";"5";"4";"3";NULL;NULL;NULL;"2";"5";"4";"5";"4";;;"kmkpr" +"6091";"JJB238";"Kritika marketingové komunikace";"Rosenfeldová,J.";;"4";"4";"4";"5";"2";NULL;NULL;NULL;"2";"5";"4";"5";"3";;;"kmkpr" +"6092";"JJB255";"Digitální komunikace";;"Klimeš,D.";"4";"3";NULL;NULL;NULL;"5";"5";"4";"2";"4";"3";"4";"5";;;"kmkpr" +"6093";"JJB628";"Marketing módních značek - teorie";"Hejlová,D.,Koudelková,P.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"2";"5";"4";"4";"5";"Výstava Manolo Blahnik";;"kmkpr" +"6094";"JJB401";"Komerční a nekomerční marketingová komunikace";"Báča,L.,Obluk,O.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"4";;;"kmkpr" +"6095";"JJB403";"Institucionální a vládní komunikace";"Shavit,A.,Soukeník,Š.";;"4";"4";"4";"5";"4";NULL;NULL;NULL;"2";"5";"4";"5";"4";;;"kmkpr" +"6096";"JJB406";"Tvorba a prostředky v mediální komunikaci";"Chudinová,E.";;"3";"3";"4";"4";"3";NULL;NULL;NULL;"2";"3";"3";"3";"3";;;"kmkpr" +"6097";"JJB632";"Strategická politická komunikace";"Petrová,B.";;"5";"4";"5";"4";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmkpr" +"6098";"JSM103";"Academic Writing";;"Blokker,P.";"4";"3";NULL;NULL;NULL;"4";"3";"4";"1";"3";"4";"2";"3";;;"ks" +"6099";"JSM477";"Sociology of Critique";"Blokker,P.";;"3";"4";"4";"4";"4";NULL;NULL;NULL;"1";"3";"4";"4";"5";;;"ks" +"6100";"JJB611";"Česká média po roce 1990";"Jirák,J.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;"1";"5";"2";"4";"5";;;"kms" +"6101";"JSM480";"Evaluation Research";;"Remr,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";"5";"5";;;"ks" +"6102";"JJB612";"Média a životní styl";"Knapík,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"6103";"JJB613";"Úvod do studia nových médií";"Jirků,J.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"6104";"JJM329";"Média a kultura";"Bednařík,P.,Jirák,J.";;"3";"3";"3";"3";"3";NULL;NULL;NULL;"3";"3";"3";"3";"3";;;"kms" +"6105";"JSM518";"Public Policy";"Potůček,M.,Vlčková,K.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kvsp" +"6106";"JJM330";"Trendy současných českých médií";"Aust,O.";;"3";"3";"3";"3";"3";NULL;NULL;NULL;"3";"3";"3";"3";"3";;;"kms" +"6107";"JJB625";"Manipulace v audiovizuálním sdělení";"Štoll,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"6108";"JJB626";"Vybrané otázky mediálního vzdělávávání";"Wolák,R.";;"4";"2";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kms" +"6109";"JJM331";"Výzkum médií II";"Vochocová,L.";;"3";"3";"3";"3";"3";NULL;NULL;NULL;"1";"3";"3";"3";"3";;;"kms" +"6110";"JSM628";"European policies and practice towards ethnic minorities";"Bernard Thompson Mikes,A.";;"3";"3";"3";"4";"4";NULL;NULL;NULL;"3";"4";"4";"5";"5";;;"kvsp" +"6111";"JLB045";"Angličtina pro marketing I";;"Stružková,I.";"4";"4";NULL;NULL;NULL;"3";"5";"5";"1";"5";"5";"5";"4";;;"cjp" +"6112";"JSM694";"Diploma Seminar I";;"Frič,P.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"3";"4";"4";"5";;;"kvsp" +"6113";"JJM332";"Přirozený jazyk a média";"Podzimek,J.";;"3";"3";"4";"4";"3";NULL;NULL;NULL;"1";"4";"3";"3";"4";;;"kms" +"6114";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kms" +"6115";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"5";"2";"5";"3";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ies" +"6116";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"4";"3";"5";"5";"5";"3";"3";"3";"1";"4";"4";"4";"4";"přednášky jsou velmi užitečné pro pochopení logiky, šetří čas na učení.";"je to skvělý kurz. Jen zkouška mi trošku přišla ošemetná v tom, že se podobala těm z minulých let, výsledek pak záleží na tom do jaké míry si studenti projdou otázky z minulých let. Bez tohoto je zkouška podstatně těžší. Možná místo úsilí na HWKs věnovat energii vytvoření nových otázek.";"ies" +"6117";"JLB053";"Angličtina pro sociální vědy I";;"Gloverová,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"5";"5";;;"cjp" +"6118";"JEM034";"Corporate Finance";"Bychkova,O.,Malířová,T.";;"3";"4";"3";"3";"3";NULL;NULL;NULL;"3";"3";"3";"3";"3";"fine presentations focusing on relevant things only.";"I would somehow seperate or the seminars, that were not so useful.";"ies" +"6119";"JEM035";"Financial Markets Instruments I";"Dědek,O.,Poláková,N.,Polák,P.";"Dědek,O.,Poláková,N.,Polák,P.";"3";"3";"3";"4";"3";"3";"3";"2";"1";"4";"3";"3";"4";"lectures";"i would provide the students with the information that later exam terms are more difficult. Alternatively, I would make the same level.Second, last year I was confused that FMI1 is a prerequsite for FMI2, which seems logical from the numbering.";"ies" +"6120";"JEM037";"Financial Markets";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"4";"3";"5";"5";"4";"4";"3";"4";"1";"4";"3";"5";"5";"fine lectures, the structure of the seminars is good as well. Thank You";"Perhaps, do not be so kind when evaluating the student presentations :)";"ies" +"6121";"JMM277";"Historie a kultura";"Vykoukal,J.";"Bauer,P.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"krvs" +"6122";"JMM278";"Politika a mezinárodní vztahy";"Kubát,M.,Weiss,T.";"Mejstřík,M.";NULL;NULL;"5";"5";"5";"5";"5";"5";NULL;NULL;NULL;NULL;NULL;;;"krvs" +"6123";"JMM530";"European Comparative Politics and Society";"Rovná,L.";;NULL;NULL;"5";"5";"3";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"kzs" +"6124";"JMM271";"Metodologický seminář";;"Matějka,O.";NULL;NULL;NULL;NULL;NULL;"5";"5";"5";NULL;NULL;NULL;NULL;NULL;;;"krvs" +"6125";"JSM692";"Introduction to Social Research Methodology";"Remr,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"ks" +"6126";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"4";"3";;"Dvě přednášky za sebou jednou za dva týdny byly úmorné, radši bych chodila každý týden na jednu.";"kp" +"6127";"JPM052";"Mezinárodní ekonomické vztahy (MEV)";"Kučerová,I.";;"3";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kmv" +"6128";"JSM578";"Anthropology of EU";"Uherek,Z.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"ks" +"6129";"JPM429";"Global terrorism (CS)";;"Makariusová,R.";"4";"4";NULL;NULL;NULL;"4";"4";"4";"1";"4";"4";"4";"4";;;"kmv" +"6130";"JPM689";"Conflict Studies";"Karásek,T.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kbs" +"6131";"JSM421";"Contemporary social theory";"Balon,J.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"4";;;"ks" +"6132";"JPM670";"Odborná stáž A";;;"3";"3";NULL;NULL;NULL;"3";"3";"3";"1";"3";"3";"3";"3";;;"kmv" +"6133";"JPM671";"Odborná stáž B";;;"3";"3";NULL;NULL;NULL;"3";"3";"3";"1";"3";"3";"3";"3";;;"kmv" +"6134";"JSM103";"Academic Writing";;"Blokker,P.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"4";;;"ks" +"6135";"JSM406";"Statistics in SPSS";;"Soukup,P.";"4";"3";NULL;NULL;NULL;"4";"5";"4";"1";"5";"5";"4";"4";;;"ks" +"6136";"JLB035";"Francouzština I";;"Bosáková,L.";"4";"3";NULL;NULL;NULL;"4";"4";"3";"1";"3";"3";"3";"4";;;"cjp" +"6137";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"2";"4";"2";"2";"1";NULL;NULL;NULL;"2";"3";"3";"3";"2";;;"kmv" +"6138";"JPM052";"Mezinárodní ekonomické vztahy (MEV)";"Kučerová,I.";;"4";"4";"5";"4";"5";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kmv" +"6139";"JPM429";"Global terrorism (CS)";;"Makariusová,R.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"5";"5";;;"kmv" +"6140";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"3";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kmv" +"6141";"JPM690";"Liberalism in International Relations (TIR)";;"Karlas,J.";"4";"4";NULL;NULL;NULL;"4";"5";"4";"1";"4";"4";"4";"5";;;"kmv" +"6142";"JJM329";"Média a kultura";"Bednařík,P.,Jirák,J.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"3";"2";"2";"3";;"Asi by bylo dobré trochu rozšířit přednášku tématicky, ne jen na stereotypy v mediích, ale co tyto stereotypy vytvořilo a jaké důsledky mají tyto stereotypy pro media a vnímání médií.";"kms" +"6143";"JPM689";"Conflict Studies";"Karásek,T.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kbs" +"6144";"JEB003";"Ekonomie I";"Fanta,N.,Kracík,J.,Švarcová,N.";"Fanta,N.,Kracík,J.,Švarcová,N.";"4";"4";"4";"5";"5";"4";"5";"4";"1";"5";"4";"4";"4";;;"ies" +"6145";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"4";"3";"4";"4";"3";NULL;NULL;NULL;"1";"5";"4";"4";"4";;;"ies" +"6146";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"ies" +"6147";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"5";"5";"4";"1";"3";"3";"3";"5";;;"kz" +"6148";"JLB005";"Angličtina pro politology I";;"Panešová,K.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"6149";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"5";"2";NULL;NULL;NULL;"4";"4";"3";"2";"4";"3";"4";"5";;;"ies" +"6150";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"1";"4";"2";"2";"2";NULL;NULL;NULL;"3";"2";"2";"2";"2";;;"kp" +"6151";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"4";"4";NULL;NULL;NULL;"5";"5";"3";"3";"4";"4";"3";"4";;;"ies" +"6152";"JSB012";"Úvod do empirického výzkumu ve společenských vědách";"Jeřábek,H.";"Přibáňová,T.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"4";"5";"5";"5";;;"ks" +"6153";"JSB513";"Úvod do akademické práce";"Höfer,K.,Mouralová,M.,Veselý,A.";;"2";"3";"3";"2";"2";NULL;NULL;NULL;"1";"3";"3";"3";"3";;;"kvsp" +"6154";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"3";"3";"3";"1";"3";"3";"3";"5";;;"kz" +"6155";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"5";"5";"5";"5";"5";;;"ks" +"6156";"JLB003";"Angličtina pro ekonomy I";;"Poslušná,L.";"5";"4";NULL;NULL;NULL;"5";"5";"3";"1";"4";"5";"4";"5";;;"cjp" +"6157";"JSB998";"Úvod do sociologie";"Soukup,P.";;"4";"2";"5";"4";"2";NULL;NULL;NULL;"1";"3";"4";"3";"4";;;"ks" +"6158";"NMMA701";"Matematika 1";"Spurný,J.";"Honzík,P.";"4";"5";"5";"5";"5";"5";"5";"5";"1";"4";"5";"4";"4";;;"ies" +"6159";"JJM330";"Trendy současných českých médií";"Aust,O.";;"4";"2";"4";"3";"4";NULL;NULL;NULL;"1";"3";"3";"4";"3";;;"kms" +"6160";"JEM003";"Advanced Microeconomics";"Janda,K.,Melikhova,O.";"Janda,K.,Melikhova,O.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"6161";"JJM331";"Výzkum médií II";"Vochocová,L.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"3";"4";"Kurz je dobře koncipován, a v rámci studií dává smysl. Nabízí teoretický průřez jedním z oborových větví ve kterých je možné se dál specializovat.";;"kms" +"6162";"JJM332";"Přirozený jazyk a média";"Podzimek,J.";;"3";"3";"3";"4";"3";NULL;NULL;NULL;"1";"2";"1";"1";"3";;"Jedná se o kurz, který se v rámci tří semestrů studia více méně opakuje již potřetí. Teorie jazyka je sice důležitá, ale v tomto kurzu naprosto schází ta druhá část kterou kurz implikuje a to je část praktická. Během přednášek se vyučující opíral převážně o teorii jazyka, avšak nějaké reálné příklady, se kterými by mohli studenti pracovat chyběly. Z teorie není jak vyvodit jak jsou tento kurz a vědomosti z něj aplikovatelné v praxi.";"kms" +"6163";"JEM004";"Advanced Macroeconomics";"Baxa,J.,Hromádková,E.";"Baxa,J.,Hromádková,E.,Šestořád,T.,Troch,T.,Žáček,J.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"6164";"JJM080";"Zábava v televizním vysílání";"Kruml,M.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"4";"2";"4";"5";;;"kms" +"6165";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"6166";"JEM037";"Financial Markets";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"6167";"JEM034";"Corporate Finance";"Bychkova,O.,Malířová,T.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ies" +"6168";"JEM035";"Financial Markets Instruments I";"Dědek,O.,Poláková,N.,Polák,P.";"Dědek,O.,Poláková,N.,Polák,P.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"6169";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"5";"4";"4";"5";"4";NULL;NULL;NULL;"1";"4";"5";"4";"4";;;"kms" +"6170";"JJM320";"Propaganda a československá média po roce 1948";"Suk,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";;;"kms" +"6171";"JJM324";"Teorie mediální komunikace";"Jirák,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"kms" +"6172";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"4";"4";"4";"5";"5";NULL;NULL;NULL;"1";"4";"5";"4";"4";;;"kms" +"6173";"JJM259";"Mediální systémy a jejich komparace";"Miessler,J.";;"2";"1";"3";"4";"1";NULL;NULL;NULL;"1";"2";"1";"3";"2";;"Přístup k celému kurzu. Podle mě ho rozhodně nelze pojmout stylem, jako to bylo letos - tedy že studenti dostanou domů za úkol přečíst si nějaké texty, které se následně na další hodině rozebírají. To je absolutně zbytečné, stačí je buď rozebrat nebo si je přečíst.";"kz" +"6174";"JJB224";"Psychologie marketingové komunikace";"Vranka,M.";;"3";"4";"3";"5";"2";NULL;NULL;NULL;"1";"3";"1";"4";"3";;;"kmkpr" +"6175";"JJB231";"Dějiny a teorie public relations";"Halada,J.,Hejlová,D.";;"4";"4";"4";"4";"2";NULL;NULL;NULL;"2";"5";"1";"4";"5";;;"kmkpr" +"6176";"JJM260";"Novinářská etika v praxi";"Moravec,V.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"4";"5";"Vyučujícího a jeho praktický přístup k celému kurzu, kdy se na přednáškách k jednotlivým probíraným tématům píšou průběžné eseje a drobné texty.";;"kz" +"6177";"JJB004";"Současný český jazyk I";;"Svobodová,I.";"4";"4";NULL;NULL;NULL;"5";"4";"5";"1";"5";"5";"4";NULL;;;"kz" +"6178";"JJB012";"Žurnalistická tvorba I";"Osvaldová,B.";"Trunečková,L.";"5";"3";"5";"5";"5";"5";"5";"5";"2";"5";"4";"5";NULL;;;"kz" +"6179";"JJB015";"Česká literatura I";;"Čeňková,J.,Malý,R.";"4";"2";NULL;NULL;NULL;"5";"5";"4";"1";"3";"1";"2";NULL;;;"kz" +"6180";"JPM711";"Issues in Russian and Eurasian Security";"Aslan,E.";;"3";"3";"3";"3";"3";NULL;NULL;NULL;"3";"3";"3";"3";"2";"The professor has a lot of expertise on some of the subject matter, and the readings are interesting.";"Too much time is spent on theoretical issues, which doesn't give enough time for case studies which most of the students want to deal with. Most of the theoretical work on terrorism and war is dealt with in other courses. The student presentations sometimes seem redundant.";"kbs" +"6181";"JJB998";"Úvod do ekonomie";"Poljakov,N.";;"5";"2";"5";"3";"5";NULL;NULL;NULL;"1";"5";"2";"3";NULL;;;"kz" +"6182";"JLB009";"Angličtina pro žurnalisty I";;"Prošková,A.";"4";"2";NULL;NULL;NULL;"4";"5";"5";"1";"4";"5";"4";NULL;;;"cjp" +"6183";"JMMZ141";"Russian Language I";;"Shvedova,O.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"The professor is great, she's really helpful and her teaching methods, with focuses on speaking and listening, help with a difficult language!";;"krvs" +"6184";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"1";"2";"2";"3";"2";NULL;NULL;NULL;"2";"3";"2";"3";"1";"Some of the readings are essential.";"I don't think it should be required for masters students, it seems like material that any international relations student will already have experience in. There is a lot of reading on the syllabus that isn't dealt with at all in class. And the professors aren't the best at answering questions or explaining how the theories are relevant to practical workings of international relations, often they'd give a half-answer and say \"It's just a theory.\"";"kmv" +"6185";"JPM561";"Regional Security Studies";"Karásek,T.,Klosek,K.";"Karásek,T.,Klosek,K.";"4";"3";"5";"4";"4";"5";"4";"4";"1";"4";"4";"4";"4";"The lecturers do a great job at explaining a large region in a limited amount of time.";"It's kind of difficult to get into anything in depth covering such large areas in a single class, but it was great for overviews of regional dynamics.";"kbs" +"6186";"JJM360";"Ekonomika v médiích";"Klimeš,D.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"2";"5";"5";"Aby mezi studenty přišli lidé z praxe, jako třeba národní koordinátor digitální agendy Ondřej Malý nebo investigativní novinářka Pavla Holcová. To bylo super.";;"kz" +"6187";"JJM273";"Sportovní žurnalistika ve světě";"Bosák,J.";;"5";"1";"5";"5";"4";NULL;NULL;NULL;"3";"4";"1";"4";"5";"Už samo o sobě to, že předmět vyučuje Jaromír Bosák, je zárukou dobře poslouchatelné přednášky, přestože se sám vyučující ani o žádnou ucelenou přednášku nesnaží. Jeho slova se ze sportovního televizního prostředí se ale velmi dobře poslouchají.";"Možná by to chtělo přidat více praktických věcí, které by souvisely s probíranými tématy.";"kz" +"6188";"JMB414";"Seminář k aktualitám I";;"Hofmeisterová,K.";"4";"3";NULL;NULL;NULL;"4";"5";"4";"2";"4";"4";"5";"5";;;"krvs" +"6189";"JMB069";"Transatlantic Security Cooperation";"Weiss,T.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"2";"3";"3";"3";"4";;;"kzs" +"6190";"JMB250";"Seminář k dějinám západní Evropy";;"Simbartlová,A.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"prístup vyučujúcej, debaty ohľadne témat";;"kzs" +"6191";"JMMZ339";"Populism in the U.S.";"Klvaňa,T.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"very good teaching skills of the lecturer, clear interpretation, interesting readings which were always discussed in the class, discussions";;"kas" +"6192";"JJM274";"Práce sportovního reportéra a komentátora";"Záruba,R.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"3";"5";"3";"5";"Praktická cvičení ve studiu, stříhání a zpracovávání vlastní reportáže, pravidelná příprava vlastního zpravodajského textu.";;"kz" +"6193";"JJM280";"Filmová a televizní kritika";"Štoll,M.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"3";"4";"3";"4";"4";;"Možná by bylo dobré zkusit napsat nějakou kritiku už během semestru a ne jen na konci kurzu.";"kz" +"6194";"JMB410";"Moderní dějiny západní Evropy";"Matějka,O.,Váška,J.";;"4";"5";"4";"4";"4";NULL;NULL;NULL;"2";"4";"5";"5";"4";;;"kzs" +"6195";"JEM035";"Financial Markets Instruments I";"Dědek,O.,Poláková,N.,Polák,P.";"Dědek,O.,Poláková,N.,Polák,P.";"3";"3";"4";"3";"1";"5";"5";"4";"2";"4";"5";"4";"4";"Petra Polaka.";"Ustni zkousku.";"ies" +"6196";"JMB409";"Moderní dějiny středo- a jihovýchodní Evropy-dcera JMB013";"Balla,P.,Švec,L.";;"4";"5";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"4";;;"krvs" +"6197";"JMB408";"Moderní dějiny Ruska";"Litera,B.,Novák,P.,Pečenka,M.";;"4";"4";"4";"3";"4";NULL;NULL;NULL;"2";"4";"4";"4";"4";;;"krvs" +"6198";"JEB141";"Introduction to Market Design";"Gregor,M.,Kastl,J.";;"3";"2";"4";"5";"3";NULL;NULL;NULL;"1";"2";"3";"2";"3";"casovou narocnost";;"ies" +"6199";"JJM294";"Teorie a praxe rozhlasové a televizní moderace";"Moravec,V.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"1";"5";"3";"5";"Pravidelná praktická cvičení ve studiu.";"Při praktických cvičeních by bylo do budoucna lepší se více zaměřit na to, jak studenti zvládají improvizovaný text (improvizované živé vystoupení).";"kz" +"6200";"JEM003";"Advanced Microeconomics";"Janda,K.,Melikhova,O.";"Janda,K.,Melikhova,O.";"5";"5";"3";"3";"2";"4";"5";"5";"2";"5";"5";"3";"5";"Oksanu.";;"ies" +"6201";"JEM004";"Advanced Macroeconomics";"Baxa,J.,Hromádková,E.";"Baxa,J.,Hromádková,E.,Šestořád,T.,Troch,T.,Žáček,J.";"5";"5";"5";"5";"5";"5";"5";"5";"2";"5";"5";"5";"5";"Vse bylo fenomenalni.";;"ies" +"6202";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"3";"3";"5";"5";"3";"3";"3";"1";"2";"4";"2";"3";"3";"Pana Barunika.";"Na to, jak jsem mel ekonometrii vzdy rad, tak jsem byl tezce zklaman. Prednasky byly dobre, ale cviceni mi nic nedavala. Zato midterm a pisemka byly docela tezke. Takze tezko pozitivne hodnotit.";"ies" +"6203";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"3";"4";"3";"3";"2";NULL;NULL;NULL;"1";"3";"1";"4";"2";;;"kms" +"6204";"JJM320";"Propaganda a československá média po roce 1948";"Suk,P.";;"2";"2";"3";"2";"2";NULL;NULL;NULL;"1";"4";"1";"4";"3";;;"kms" +"6205";"JJM324";"Teorie mediální komunikace";"Jirák,J.";;"4";"4";"5";"5";"2";NULL;NULL;NULL;"2";"4";"1";"4";"3";;;"kms" +"6206";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"2";"3";"5";"4";"2";NULL;NULL;NULL;"1";"2";"3";"2";"1";"The professor is great, he makes a boring topic really funny";"The class is more about learning how to use a computer program than learning about any of the mathematics behind it. Anybody can type in commands into a computer program and not understand a thing about what it actually means.";"kmv" +"6207";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"3";"2";"2";"4";"2";NULL;NULL;NULL;"1";"3";"3";"3";"3";;;"kms" +"6208";"JPM693";"Traditional and Critical Concepts in Security Studies";"Rychnovská,D.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";"Dagmar is the only professor I've had at Charles who has even tried to make the classes interactive and make students actually think rather than just talk at them with a powerpoint. She's a great lecturer and the class topics are important for students in this field to think about.";;"kbs" +"6209";"JPM150";"Poloprezidentské režimy v postkomunistické Evropě";"Mlejnek,J.";;"3";"4";"2";"4";"2";NULL;NULL;NULL;"1";"4";"1";"4";"2";;;"kp" +"6210";"JPM160";"Česká komunální politika";"Jüptner,P.";;"4";"5";"5";"5";"4";NULL;NULL;NULL;"2";"4";"2";"4";"3";;;"kp" +"6211";"JJM080";"Zábava v televizním vysílání";"Kruml,M.";;"5";"2";"4";"4";"5";NULL;NULL;NULL;"1";"5";"3";"3";"5";"Super praktický předmět, spousta zajímavých ukázek";"Možná se věnovat podrobněji jenom určitému období nebo určité zemi (nebo třeba kurz rozdělit do dvou semestrů), takhle se to všechno prolétlo jen tak letem světem...";"kms" +"6212";"JPM639";"Problémy ústavního inženýrství";"Brunclík,M.";;"3";"3";"3";"4";"2";NULL;NULL;NULL;"2";"4";"1";"4";"3";;;"kp" +"6213";"JPM699";"Security and Technology";"Střítecký,V.";;"4";"3";"4";"4";"3";NULL;NULL;NULL;"1";"3";"4";"4";"4";"The subject matter is really interesting, the lecturer is great too.";"We never really get that deep into any of the subject matter, it only seems like surface level material, maybe because of the difficulty in understanding some aspects of the information without an academic background in it.";"kbs" +"6214";"JJM208";"Mediální systémy a jejich komparace";"Miessler,J.";;"2";"4";"3";"4";"3";NULL;NULL;NULL;"1";"4";"3";"4";"3";;"Určitě by bylo super se věnovat i jiným autorům a modelům než jen Hallinovi a Mancinimu";"kms" +"6215";"JPM653";"Politika a média";"Švec,K.";;"4";"2";"4";"4";"4";NULL;NULL;NULL;"1";"4";"3";"3";"4";;;"kp" +"6216";"JJM211";"Kvalitativní výzkum mediálních publik";;"Reifová,I.";"4";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"4";;;"kms" +"6217";"JJM295";"Rozhlasový a televizní dokument";"Štoll,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Martin Štoll opět výborný!!";"Nerozděloval bych hodiny na jenom \"teoretické\" a jenom \"ukázkové\", více bych je prolínal";"kz" +"6218";"JJM200";"Diplomový seminář";;;"5";"5";NULL;NULL;NULL;"5";"5";"5";"5";"5";"5";"5";"5";;;"kms" +"6219";"JLB057";"Academic Writing for Bachelors";;"Goodall,A.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Feedback";;"cjp" +"6220";"JMM277";"Historie a kultura";"Vykoukal,J.";"Bauer,P.";"4";"4";"5";"5";"4";"2";"2";"2";"1";"4";"3";"3";"3";"Oceňuji především přednášky doc. Vykoukala.";"Navrhuji změnu semináře. Dr. Bauer jej pojmul jako další přednášku, během které mluvil většinu času on, následně dal velmi krátký prostor člověku s prezentací a poté opět pokračoval ve svém výkladu. Uvítala bych větší prostor pro diskuze a jejich podněcování, nikoliv snahu je co nejrychleji ukončit.";"krvs" +"6221";"JMM040";"Societal changes in Western European countries";"Bauer,P.";;"2";"3";"2";"2";"2";NULL;NULL;NULL;"3";"3";"1";"3";"1";;"Pokud už musí být kurz povinný pro studenty ZES, navrhovala bych ho přesunout do letního semestru, jelikož tématicky pokrývá úplně to stejné, co seminář k předmětu Historie a kultura (navíc vychází ze stejných textů). Samotný formát kurzu by také potřeboval změnu, - to, že vyučující sedí za stolem, diktuje text připravený v počítači a nabádá studenty, aby si hlavně všechno pořádně zapsali, mi nepřijde úplně šťastné. Sice tak neprobíhala každá hodina, ale párkrát k tomu došlo. Dále by bylo dobré, kdyby dr. Bauer dal větší prostor studentům. Každý měl sice za úkol přednést prezentaci dle vlastního výběru, která, jak bylo na začátku avizováno, měla mít asi patnáct minut. V praxi ale byl na prezentace vyhrazen pouze krátký čas ke konci hodiny, ne vždy dostačující. Prezentace pak často zůstaly bez jakékoliv odezvy ze strany vyučujícího.";"kzs" +"6222";"JEM166";"Master´s Thesis Seminar - IEPS";;"Benáček,V.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Very helpful instructors; you enter the class thinking you have an idea of how to write a thesis but you leave realizing a lot of changes need to be made and that makes you a better student";"Perhaps find a way to deal with students abroad better. Lots of information was emailed to me but I discovered random information from consulting my classmates as well that I missed from being abroad and not in the classroom. Other than that, no changes, it was structured really well";"ies" +"6223";"JEM003";"Advanced Microeconomics";"Janda,K.,Melikhova,O.";"Janda,K.,Melikhova,O.";"5";"3";"4";"5";"4";"5";"5";"5";"1";"5";"4";"4";"5";"All professors are very patient and nice to explain questions.And the seminar is very clear and comprehensive.";"Sometimes I can't follow the lecture very well.";"ies" +"6224";"JEM037";"Financial Markets";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"4";"The whole course contains different parts which can improve my comprehensive knowledge and empirical skills.All the professor are very excellent and conscientious.";"Sometimes I can't follow the seminar very well.";"ies" +"6225";"JEM032";"Banking";"Mejstřík,M.,Pečená,M.,Teplý,P.";"Pečená,M.";"5";"3";"4";"5";"5";"5";"5";"5";"1";"5";"5";"5";"4";"The whole course contains presentation and discussion which improve the empirical skills and comprehensive knowledge.";;"ies" +"6226";"JEM062";"Introductory Econometrics";"Kukačka,J.";"Kukačka,J.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"The professor is very excellent and conscientious.He is patient and good at giving a clear and good explanation about questions.";;"ies" +"6227";"JEM141";"Traditional and Alternative Risk Transfer in the Insurance Sector";"Pompella,M.,Teplý,P.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"4";"5";"This course can provide me one new perspective on Alternative Risk transfer.And it does not spend too many time but can give some key points about Insurance Sector.";;"ies" +"6228";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"5";"2";"5";"5";"4";"5";"5";"4";"1";"5";"2";"4";"5";;;"kp" +"6229";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"5";"4";"5";"5";"5";"4";"5";"5";"1";"4";"4";"5";"4";"All of the teachers are excellent and conscientious.";;"ies" +"6230";"JEM004";"Advanced Macroeconomics";"Baxa,J.,Hromádková,E.";"Baxa,J.,Hromádková,E.,Šestořád,T.,Troch,T.,Žáček,J.";"5";"4";"5";"5";"5";"4";"4";"4";"1";"4";"4";"4";"4";"This course can improve some empirical skills and comprehensive skills.";;"ies" +"6231";"JPB256";"Politické systémy I";"Brunclík,M.,Jeřábek,M.,Kotábová,V.,Perottino,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"6232";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"4";"4";"5";"3";"2";NULL;NULL;NULL;"1";"4";"3";"4";"3";"Strukturu dělení na tři bloky";"Lepší komunikace vyučujících";"kp" +"6233";"JLB005";"Angličtina pro politology I";;"Stružková,I.";"5";"3";NULL;NULL;NULL;"4";"5";"5";"1";"3";"2";"5";"5";"Nejlepší na kurzu je Mrs Stružková a její styl výuky. Líbí se mi články a cvičení, které na hodinu připravuje.";"Aby netrval 2 hodiny!!!! Je nemožné se tak dlouho soustředit!";"cjp" +"6234";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"3";"3";"4";"4";"2";NULL;NULL;NULL;"3";"4";"2";"5";"3";"Pitvání dobových textů je zajímavé, originální. Člověka to úplně vtáhne do dané doby.";"Záživnost kurzuMírnější hodnocení u zkoušky";"kp" +"6235";"JJB611";"Česká média po roce 1990";"Jirák,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"3";"3";"3";"4";"S kurzem jsem byla spokojena.";"-";"kms" +"6236";"JJB612";"Média a životní styl";"Knapík,J.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"3";"3";"3";"4";"-";"-";"kms" +"6237";"JJB613";"Úvod do studia nových médií";"Jirků,J.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"3";"3";"3";"4";"-";"-";"kms" +"6238";"JPM711";"Issues in Russian and Eurasian Security";"Aslan,E.";;"3";"3";"4";"4";"3";NULL;NULL;NULL;"1";"3";"2";"2";"3";;;"kbs" +"6239";"JJB625";"Manipulace v audiovizuálním sdělení";"Štoll,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Všechno :)";"Proč něco spravovat, když to není rozbité? :)";"kms" +"6240";"JPB258";"Politické myšlení novověku I";"Franěk,J.,Kučera,J.";;"4";"3";"3";"3";"1";NULL;NULL;NULL;"2";"3";"2";"2";"3";"profesora, co přednášel o antice";"profesora, co přednášel o křesťanství";"kp" +"6241";"JPM613";"Armed Forces and Society";"Kučera,T.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kbs" +"6242";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"3";"5";"3";"5";"2";NULL;NULL;NULL;"1";"3";"4";"4";"4";;;"kmv" +"6243";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"2";"3";"3";"3";"1";NULL;NULL;NULL;"1";"4";"2";"4";"3";;"Komunikaci s žáky - profesor vůbec neodpovídá na seminární práci a nezapisuje známkyZpůsob výuky - přítomnost na hodině byla spíš ztrátou času";"kp" +"6244";"JPM696";"Economic Warfare";"Ludvík,J.";;"3";"3";"4";"5";"5";NULL;NULL;NULL;"1";"3";"3";"3";"3";;;"kbs" +"6245";"JPB596";"Čínská zahraniční a bezpečnostní politika";"Karmazin,A.";;"5";"3";"4";"5";"4";NULL;NULL;NULL;"2";"4";"4";"4";"5";"Vyučující je super";"Povinná literatura - články v angličtině - jsou někdy příliš dlouhé a složité (když ke zkoušce stačí spíš pochopit souvislosti obecně, hlavní myšlenky a rozdíly, a ne znát všechno dopodrobna!), je to příliš náročné na čtení";"kbs" +"6246";"JMB248";"Seminář k dějinám Ruska";;"Kolenovská,D.";"3";"3";NULL;NULL;NULL;"3";"5";"3";"2";"2";"3";"2";"3";"Konzultácie s vyučujúcou ohľadom záverečnej skúšky. Dobre mienené rady pomohli lepšie pochopiť chyby v eseji a načrtli, ako by mala esej vyzerať a čo treba vylepšiť. Taktiež konštruktívne poznámky k seminárnym prácam a ochota pomôcť s hľadaním zdrojov.";"Neprerušovať prezentácie študentov - doplňujúce informácie sú síce zaujímavé, veľmi často však vyučujúca odbieha od pôvodnej témy. To v konečnom dôsledku mätie poslucháčov a vedie k ich nesústredenosti. Zároveň to oberá referenta o čas vyčlenený na jeho prezentáciu.";"krvs" +"6247";"JMMZ050";"Political Systems of East European Countries in the 20th Century";"Kubát,M.";;"5";"2";"4";"4";"5";NULL;NULL;NULL;NULL;"5";"3";"3";"4";"Good panorama of the constitutional history of the region, qualitative explanations despite the fact that the lack of time obliged the teacher to go very fast through a lot of case";"Some cases missing because of the lack of time. It would be better in my opinion to choose a limited number a country and go through their whole history instead of going through all of them but ignoring some periods (like we did for Hungary for example). It is pretty frustrating.";"krvs" +"6248";"JMMZ083";"Eastern Europe Today I";;"Lídl,V.,Šír,J.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"3";NULL;"4";"Good topics going through the whole area in different fieldsA lot of room was left to students' debates and interventions";"I just had some problems concerning the final paper. Instructions were not very clear, especially concerning the methodology. As a foreign student, I used my methodology of an \"academic paper\" that was not the one expected by the professor : a clear explanation of the expectations would have been better.";"krvs" +"6249";"JMM079";"Hospodářský a sociální systém NMZ I";"Mlsna,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"3";"5";"5";"5";"5";;;"knrs" +"6250";"JMB410";"Moderní dějiny západní Evropy";"Matějka,O.,Váška,J.";;"2";"3";"2";"2";"2";NULL;NULL;NULL;"4";"2";"3";"3";"2";;"Tento kurz celkom nenapĺňal moje pôvodné očakávania. Vyučujúci kvôli svojmu rétorickému štýlu majú problém upútať a udržať si pozornosť študentov. I keď je iné poňatie problematiky zaujímavé, zišla by sa jasnejšia a menej chaotická osnova predmetu. Buď preberať iba teoretické koncepty a interpretácie diania v západnej Európe alebo prejsť chronologicky vývoj v jednotlivých štátoch (ako tomu je pri dejinách Ruska alebo SJVE). Prípadne to skombinovať do systému, kedy by jeden vyučujúci mal teóriu a druhý chronológiu. Nevenovať 3 prednášky priemyselnej revolúcii, ale myslieť na to, že treba prebrať dianie až do 90. rokov, nie iba do druhej svetovej - jednoducho, zamerať sa na to, na čo je kladený dôraz v záverečnom teste. Ten je náhodným zhlukom pojmov a mien, ktoré má študent poznať (zrejme z čítania povinnej literatúry, pretože v obsahu prednášok sa vyskytujú zriedka). Zároveň mne a ďalším študentom prišlo nevhodné a neprofesionálne správanie vyučujúcich pri opravovaní testov v miestnosti, kde študenti práve písali eseje. Uznávam, že mnohé z odpovedí boli pravdepodobne veľmi vtipné. No výsmechom a zabávaním sa na nesprávnych odpovediach prítomných študentov nielen vyrušovali pri písaní, ale hlavne demotivovali a ponižovali.";"kzs" +"6251";"JSM521";"Veřejná politika";"Chalupová,P.,Potůček,M.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"3";"5";"3";"5";"5";"kvalitní, náročný předmět";"větší propracovanost konzultací seminárních práci";"kvsp" +"6252";"JSM644";"Základy politologie";"Kotlas,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kvsp" +"6253";"JSM646";"Veřejná správa";"Ochrana,F.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kvsp" +"6254";"JSM647";"Manažerské metody ve veřejné a sociální politice";"Ochrana,F.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"jednoduše výborný";;"kvsp" +"6255";"JSM705";"Řízení kvality a performance management ve veřejné správě";"Plaček,M.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"5";"5";"5";"4";;;"kvsp" +"6256";"JSM528";"Seminář k diplomové práci I.";;"Kohoutek,J.,Ochrana,F.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kvsp" +"6257";"JSM527";"Metody analýzy a tvorby politik II.";"Veselý,A.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"výborný přístup ke studentům a propracováná zpětná vazba";;"kvsp" +"6258";"JEM001";"Master´s Thesis Seminar I";;"Havránek,T.,Havránková,Z.,Mejstřík,M.";"3";"3";NULL;NULL;NULL;"3";"3";"3";"1";"3";"3";"3";"3";"That I was forced to write first 15 pages of master thesis.";;"ies" +"6259";"JEM017";"Business Cycles Theory";"Baxa,J.,Kučera,A.,Vácha,L.";;"3";"4";"4";"5";"2";NULL;NULL;NULL;"1";"3";"4";"2";"3";"Friendly environment";;"ies" +"6260";"JMB079";"The Geography of North America";"Pitoňák,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Systém hodnocení je velice dobře nastaven.";;"kas" +"6261";"JMB047";"Vybrané problémy mezinárodních konfliktů.";"Čížek,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"krvs" +"6262";"JEM034";"Corporate Finance";"Bychkova,O.,Malířová,T.";;"4";"3";"3";"2";"2";NULL;NULL;NULL;"4";"5";"4";"5";"4";"Good materials (solutions to seminars and homeworks)";"Mentioning more real business information instead of only exercises. Grade midterm earlier.";"ies" +"6263";"JPM909";"Rousseau and Nationalism: On the Government of Poland";;"Franěk,J.,Kelly,C.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"2";"3";"2";"5";;;"kp" +"6264";"JPB254";"Politická věda ve 20. století: geneze a proměny oboru I.";"Švec,K.";"Švec,K.";"5";"5";"5";"5";"4";"5";"5";"4";"1";"5";"4";"4";"5";;"Mrzí mě, že letos nebyly semináře. Myslím, že je třeba určité texty z povinné četby s někým probrat.";"kp" +"6265";"JEM137";"Real Estate Investment";"Jandík,T.,Streblov,P.";;"3";"3";"3";"3";"2";NULL;NULL;NULL;"2";"3";"4";"4";"2";;;"ies" +"6266";"JEM199";"Financial Crisis and Risk Management";"Horváth,R.,Opatrný,M.,TSOMOCOS,D.";;"3";"2";"3";"3";"4";NULL;NULL;NULL;"1";"3";"2";"4";"2";;"The lecturer often asked a question and then did not clearly answer it.";"ies" +"6267";"JMB408";"Moderní dějiny Ruska";"Litera,B.,Novák,P.,Pečenka,M.";;"5";"4";"4";"3";"5";NULL;NULL;NULL;"2";"5";"4";"4";"4";;"Kým prednášky boli zábavné a zaujímavé, záverečná skúška bola pre mnohých traumou. Kým test sa pri poriadnej snahe a istom šťastí dá zvládnuť, esej mnohým navodzuje pocit, že nevedia písať - a aby uspeli, musia úplne zmeniť svoj dlhoročný štýl. Napriek tomu, že sú poskytnuté rady, ako postupovať pri písaní, takmer nikto nevie, či jeho práca bude spĺňať kritéria a predstavu vyučujúcich. Možno je úspech iba náhodný a prístup vyučujúceho pri opravovaní sa mení pri každej eseji.";"krvs" +"6268";"JJB135";"Filmový seminář I";;"Šobr,M.";"3";"1";NULL;NULL;NULL;"5";"5";"1";"2";"1";"1";NULL;NULL;;"Výber filmov";"kz" +"6269";"JPB227";"Politický system ČR";"Charvát,J.";;"3";"2";"4";"2";"2";NULL;NULL;NULL;"1";"3";"1";"3";"2";;"Zišla by sa lepšia organizovanosť kurzu a jeho obsahu.";"kp" +"6270";"JJB224";"Psychologie marketingové komunikace";"Vranka,M.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmkpr" +"6271";"JJB231";"Dějiny a teorie public relations";"Halada,J.,Hejlová,D.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmkpr" +"6272";"JJB255";"Digitální komunikace";;"Klimeš,D.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kmkpr" +"6273";"JJB403";"Institucionální a vládní komunikace";"Shavit,A.,Soukeník,Š.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"4";;"Spôsob hodnotenia – mierne nevyvážené, že dvojstranová názorová esej mala takmer rovnaký význam v rámci hodnotenia ako seminárna práca o rozsahu 4000 slov. Tiež by som ocenila obsiahlejší feedback k prácam.";"kmkpr" +"6274";"JPB260";"Politické myšlení novověku III";"Kučera,J.";;"5";"5";"5";NULL;"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kp" +"6275";"JJB406";"Tvorba a prostředky v mediální komunikaci";"Chudinová,E.";;"3";"3";"5";"5";"3";NULL;NULL;NULL;"1";"4";"4";"4";"4";;"Ocenila by som feedback k seminárnej práci.";"kmkpr" +"6276";"JJB630";"Krizová komunikace";"Chudinová,E.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"4";"5";"Páčilo sa mi vlastné prezentovanie prípadovej štúdie konkrétnej krízovej komunikácie";;"kmkpr" +"6277";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"1";"4";"3";"3";"2";NULL;NULL;NULL;"1";"3";"2";"2";"1";;"Lepšia informovanosť študentov či lepší prehľad kurzu napr. aké sú seminárne témy na daný semester, deadliny odovzdania a kedy môžme čakať výsledok práce.";"kp" +"6278";"JJB628";"Marketing módních značek - teorie";"Hejlová,D.,Koudelková,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Páčila sa mi návšteva výstavy, tiež veľmi oceňujem účasť odborníkov na prednáškach a samotný obsah predmetu, ktorý bol veľmi zaujímavý.";;"kmkpr" +"6279";"JMM277";"Historie a kultura";"Vykoukal,J.";"Kýrová,L.";"1";"3";"5";"5";"1";"1";"1";"1";"1";"1";"1";"1";"1";"Nějak jsem ani nepochopila, proč nám zase tohle cpou. Člověk si myslel, že si to odkroutil už na bakaláři, ale ono se to nacpe už i na magistr. Upřímně jsem mluvila s několika lidmi a ti říkali, že už zase nechtějí opakovat Úvod do historie, takže na magistr půjdou jinam.Je to naprosto zbytečný předmět.";"zrušit ho?";"krvs" +"6280";"JEM001";"Master´s Thesis Seminar I";;"Havránek,T.,Havránková,Z.,Mejstřík,M.";"3";"2";NULL;NULL;NULL;"4";"4";"2";"1";"2";"2";"2";"2";;;"ies" +"6281";"JPB593";"Political Economy of Regionalism";"Miková,I.";;"4";"4";"3";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"4";;;"kmv" +"6282";"JMM271";"Metodologický seminář";;"Kýrová,L.";"3";"2";NULL;NULL;NULL;"2";"4";"1";"1";"1";"1";"1";"1";"Oceňuji to, co slyboval, bohužel to zůstalo jen u sylabu.";"Záhadným způsobem se vyučující z metodologického semináře povedlo udělat seminář o Indiánech a to téměř z každé hodiny. Nic jiného se neřešilo. Bohužel ani pak nějaké ty \"metodologické\" informace nebyly podány nejlépe, protože vyučující pouze předčítala, co měla napsané v prezentacích. Navíc se jednalo o předmět za 3 kredity, ale když porovnám požadavky, tak mi to zabíralo asi stejně času jako dva předměty za 6 kreditů. Na výhrady vyučující nereagovala s tím, že v USA by četby bylo mnohem více. Buď ať dají za předmět víc kreditů nebo ať vyučující ubere na náročnosti. Hlavně by asi spousta lidí ocenila, kdyby nemuseli pořád číst 40 stránkové texty o indiánech.";"krvs" +"6283";"JEM027";"Monetary Economics";"Holub,T.,Malovaná,S.";"Břízová,P.,Hájek,J.,Holub,T.,Malovaná,S.";"3";"3";"5";"5";"4";"4";"4";"4";"1";"5";"2";"5";"4";;;"ies" +"6284";"JMM278";"Politika a mezinárodní vztahy";"Kubát,M.,Weiss,T.";"Bečka,J.";"3";"2";"5";"5";"1";"5";"5";"5";"1";"3";"3";"3";"3";;"Nějak mám pocit, že už jsme to všechno jednou slyšeli na bakaláři.";"krvs" +"6285";"JEM032";"Banking";"Mejstřík,M.,Pečená,M.,Teplý,P.";"Pečená,M.";"3";"3";"3";"3";"3";"3";"3";"3";"1";"4";"3";"4";"3";;;"ies" +"6286";"JEM034";"Corporate Finance";"Bychkova,O.,Malířová,T.";;"2";"3";"3";"2";"2";NULL;NULL;NULL;"2";"2";"2";"2";"2";;;"ies" +"6287";"JPB597";"Current Political Extremism";"Charvát,J.";;"4";"3";"4";"5";"3";NULL;NULL;NULL;"1";"4";"1";"5";"4";;;"kp" +"6288";"JEM037";"Financial Markets";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"3";"3";"4";"4";"4";"4";"5";"3";"2";"3";"3";"3";"3";;;"ies" +"6289";"JEM059";"Quantitative Finance I";"Baruník,J.,Vácha,L.";"Baruník,J.,Vácha,L.";"5";"4";"5";"5";"5";"5";"5";"5";"2";"5";"5";"5";"5";;;"ies" +"6290";"JMM279";"Společnost a ekonomika";"Fiřtová,M.";"Fiřtová,M.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"Ze společného základu to byl jediný předmět, který za něco stál. Paní Fiřtová umí látku podat, aby ji dokázali pochopit všichni. I když byla látka pro mne obtížná, na hodiny jsem se těšila, protože vždy stály za to. I četba byla vybrána tak, aby dokázala zaujmout. Jen je velká škoda, že máme pouze ekonomické základy od pana Kameníčka z bakaláře, takže vlastně nemáme vůbec žádné, takže nám musela vysvětlovat naprosté základy nebo jsme si je pak museli dohledávat. Což je trochu kontraproduktivní. Nejlepší by bylo, kdyby paní Fiřtová přebrala ekonomii na bakaláři.";;"kas" +"6291";"JEM179";"History and Methodology of Economics";"Baxa,J.,Paulus,M.";;"3";"3";"4";"4";"3";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"ies" +"6292";"JMMZ316";"Evolution of Sino-American Relations";"Sehnálková,J.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Nejlepší PVP, co v rámci IMS vůbec je! Vyučujicí má skvělé znalosti na dané téma a umí je podat.";;"kas" +"6293";"JMMZ315";"U.S. Foreign Policy";"Raška,F.";;"3";"3";"3";"4";"2";NULL;NULL;NULL;"1";"3";"2";"3";"3";"Témata, která jsou probírána.";"Bohužel způsob, jakým je kurz vyučován není nejlepší. Na každou hodinu přečíst 40 stránek velmi složitého textu, plného zkratek, který člověk čte několik hodin a stejnak si nepamatuje, co bylo na předchozí stránce není moc efektivní. A následné přeříkání obsahu té četby vyučujícím na hodině na efektivitě moc nepřidá. Pokud by vyučující poslal kratší texty a na hodině podal ty složitější informace, bylo by to podstatně přínosnější.";"kas" +"6294";"JMM674";"Maritime security: Geopolitics of the Indian and Pacific Oceans";"Hornát,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"Jeden v znejlepších PVP na IMS. Rozhodně bych na předmětu nic nevypsala, jen bych o něm více informovala mezi studenty, zasloužil by si větší účast.";"Bylo by možné přidat třeba pozici EU k problematice.";"kas" +"6295";"JMM074";"Landmarks in 20th Century U.S. History and Their Interpretations";"Pondělíček,J.";;"3";"3";"2";"4";"2";NULL;NULL;NULL;"3";"3";"4";"4";"4";"Líbí se mi formát semináře a má to velký potenciál.";"Bohužel seminář byl trochu potopen přístupem vyučujícího. Text, který byl na danou hodinu několikrát poslal večer před hodinou, která byla následující den dopoledne. Některá témata byla takové, že k nim nikdo neměl moc, co říct. Do konce semestru nebyl doplněn sylabus.";"kas" +"6296";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"3";"2";"3";"4";"4";NULL;NULL;NULL;"2";"4";"2";"3";"3";;;"krvs" +"6297";"JSB998";"Úvod do sociologie";"Soukup,P.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"2";"4";"3";"3";"4";;;"ks" +"6298";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"2";"3";"3";"3";"4";;;"kas" +"6299";"JMB056";"Reflexe velkých debat v sociálních vědách ve filmu";;"Kozák,K.";"5";"2";NULL;NULL;NULL;"4";"4";"4";"2";"4";"3";"4";"4";;;"kas" +"6300";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"3";"2";"4";"4";"3";NULL;NULL;NULL;"2";"3";"2";"3";"2";;;"knrs" +"6301";"JMMZ314";"Major Issues in Contemporary Public Debates in the U.S. I";"Sehnálková,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Naprosto skvělé! K paní Sehnálkové by měli někteří kolegové pořádat exkurze, aby viděli, jak vést předmět, který sice podát spoustu znalostí, ale zároveň studenty baví. Stejně tak s výběrem povinné četby, která byla vždy k tématu, podala základní informace a zajímavě doplnila, co se pak řešilo na hodině. Stejně tak práce, které se psaly na předmět byly výborně vybrány a měly nějaký přínos.";;"kas" +"6302";"JMB402";"Úvod do společenských věd II";;"Fiřtová,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"2";"4";"5";"4";"5";;;"krvs" +"6303";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Hornát,J.";"2";"3";"4";"4";"4";"5";"4";"4";"2";"3";"2";"2";"2";;;"krvs" +"6304";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Kačmárová,P.";"2";"3";"4";"3";"3";"5";"5";"4";"2";"2";"2";"3";"2";;;"knrs" +"6305";"JMMZ313";"Government in United States";"Sehnálková,J.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Jedná se o velmi náročný předmět, který byl vyučován od půl sedmé večer. Přesto jsme všichni na něj chodili rádi, protože paní Sehnálková umí informace podat, takovým způsobem, že jí všichni i v sedm večer věnují pozornost podstatně více než na většině jiných přednášek.";"Myslím si, že by si předmět zasloužil druhou část, protože spousta věcí by chtěla probrat více do hloubky.";"kas" +"6306";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"3";"4";"4";"3";"4";NULL;NULL;NULL;"1";"3";"4";"4";"4";;;"kms" +"6307";"JJM324";"Teorie mediální komunikace";"Jirák,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"2";"4";;;"kms" +"6308";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"3";"3";"5";"5";"1";NULL;NULL;NULL;"1";"3";"1";"1";"2";;"Bohužel pořád moc netuším o čem předmět byl.";"kms" +"6309";"JJM320";"Propaganda a československá média po roce 1948";"Suk,P.";;"3";"3";"4";"4";"4";NULL;NULL;NULL;"1";"3";"3";"3";"1";;;"kms" +"6310";"JJM373";"Ethical Issues in Media, Business and Society";"Orhan,M.";;"5";"1";"5";"5";"1";NULL;NULL;NULL;"1";"3";"2";"2";"4";;;"kms" +"6311";"JMB065";"Úvod do mezinárodního a evropského práva";"Šlosarčík,I.";;"4";"1";"4";"4";"4";NULL;NULL;NULL;"3";"4";"4";"4";"4";;;"kzs" +"6312";"JMBZ289";"Central European Culture from the 19th Century to 1945";"Emler,D.";;"4";"1";"5";"5";"5";NULL;NULL;NULL;"2";"4";"4";"4";"4";;"Kurz by mel být v cestine.";"knrs" +"6313";"JMB409";"Moderní dějiny středo- a jihovýchodní Evropy-dcera JMB013";"Balla,P.,Švec,L.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"3";"5";"5";"5";"5";;;"krvs" +"6314";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"1";"2";"2";"1";"2";"2";"1";"2";"2";"1";"1";"2";"1";;;"ies" +"6315";"JEB105";"Statistics";"Červinka,M.";"Smutná,Š.";"4";"4";"3";"4";"2";"3";"4";"4";"1";"4";"3";"4";"5";;;"ies" +"6316";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"1";"3";"3";"5";"3";"3";"3";"3";"1";"1";"1";"1";"1";;;"ies" +"6317";"JEM181";"Data Science with R";"Herman,D.,Krištoufek,L.";"Herman,D.,Krištoufek,L.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"Thank you so much for this fantastic course! The covered topics were all interesting and the possibility to request more DataCamp exercises was also great. It would be nice to have more courses like this at IES dealing with data science and coding, perhaps also using Python.";;"ies" +"6318";"NMMA703";"Matematika 3";"Zelený,M.";"Zelený,M.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"6319";"JEB044";"Financial Accounting";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ies" +"6320";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"4";"4";"3";"4";NULL;NULL;NULL;"1";"3";"3";"2";NULL;;;"ies" +"6321";"JLB005";"Angličtina pro politology I";;"Panešová,K.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"4";"4";;;"cjp" +"6322";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"4";"4";"5";"4";"5";NULL;NULL;NULL;"3";"4";"3";"4";"3";;"Rychleji opravovat testy";"kp" +"6323";"JPB227";"Politický system ČR";"Charvát,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;NULL;"5";"4";"5";"5";;;"kp" +"6324";"JPB229";"Regionální politické systémy: Skotsko, Wales";"Říchová,B.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;NULL;"4";"4";"4";"5";;;"kp" +"6325";"JPB260";"Politické myšlení novověku III";"Kučera,J.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;NULL;"4";"3";"4";"4";;;"kp" +"6326";"JPB597";"Current Political Extremism";"Charvát,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;NULL;"5";"4";"5";"5";;;"kp" +"6327";"JLB035";"Francouzština I";;"Bosáková,L.";"4";"4";NULL;NULL;NULL;"4";"4";"3";"1";"3";"3";"2";"3";;;"cjp" +"6328";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Čížek,M.";"3";NULL;"2";"2";"2";"2";"2";"3";"1";"4";"4";"3";"2";;;"krvs" +"6329";"JMB402";"Úvod do společenských věd II";;"Fiřtová,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";"Velice se mi líbila možnost vyzkoušet si úkoly, které byly následně i opraveny s komentářem, takže se šlo velmi přehledně k chybám vrátit. Vyučující byla výborná, dokázala nám otázky vysvětlit, byla důsledná a výklad byl poutavý, články, které jsme zpracovávali, byly pečlivě a zajímavě vybírány.";"Často pro mě bylo zadání úkolu nejasné, protože se jednalo pro mě o nové téma (nebyl to pouze můj případ), úkol jsem nesplnila zcela správně a byl vysvětlen na hodině až po odevzdání. Kdybychom si spolu prošli zadání předem, byla bych raději.";"krvs" +"6330";"JEB141";"Introduction to Market Design";"Gregor,M.,Kastl,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Velmi zajímavé téma užívané v praxi. Skvěle a zajímavě vysvětlované.";;"ies" +"6331";"JMB118";"Geografie německy mluvících zemí";"Baštová,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Musím říci, že za tento semestr se jednalo o nejlepší předmět. Paní Baštová byla vždy připravená na hodiny, má k dispozici vždy aktuální údaje, byla k nám velmi vstřícná a musím ocenit, jakým způsobem vymyslela prezentace studentů, které po její kontrole, jsou vždycky velmi dobře zpracované a navíc se člověk s vybraným tématem osobně seznámí hlouběji. Moc Děkuji za skvělé přednášky.";;"knrs" +"6332";"JEM017";"Business Cycles Theory";"Baxa,J.,Kučera,A.,Vácha,L.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Velké množství úkolů je sice časově velmi náročné, ale skvěle doplní přednášenou látku. Během přednášek, které probíhají v malém počtu studentů, je možné o problémech diskutovat. Ocenila jsem rovněž přiměřenou toleranci u dodržování termínů na odevzdávání úkolů - podstatné je, zda byl úkol udělán a zda něco naučil, není rozhodující, zda byl úkol odevzdán na minutu přesně (a nekvalitně) nebo o pár hodin později";;"ies" +"6333";"JJB169";"Věda v médiích";"Kasík,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"3";"5";"Pohodový a milý přístup vyučujícího, který svým přístupem ke studentům/studentkám a přednášecím stylem dokáže zaujmout a skutečně něco hodnotného předat. Náplň hodin perfektní a znalosti rozšiřující jen v tom nejlepším smyslu.";"Nenapadá mě jediná věc.";"kz" +"6334";"JEM123";"Economics of Least Developed Countries";"Bauer,M.";"Bauer,M.";"5";"3";"5";"5";"5";"4";"5";"5";"1";"5";"4";"5";"5";"Již tak velmi zajímavému předmětu přispívají i osobní zkušenosti přednášejícího. Ocenila jsem rovněž popisované výzkumy/články, které poskytnout dobrou představu o postavení výzkumu a o jeho překážkách.";;"ies" +"6335";"JLB033";"Němčina I";;"Faltýnová,R.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"1";"5";;;"cjp" +"6336";"JLM006";"Angličtina pro politology II";;"Panešová,K.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"5";"5";"Nejpřínosnější byla debata na téma vstupu Turecka do EU. Myslím, že ta přinesla nejvíce nových zkušeností.";;"cjp" +"6337";"JEB141";"Introduction to Market Design";"Gregor,M.,Kastl,J.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"ies" +"6338";"JEM020";"Ethics and Economics";"Cahlík,T.";"Cahlík,T.";"5";NULL;"4";"5";"3";"4";"5";"4";"1";"4";NULL;"4";"5";;;"ies" +"6339";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"4";"4";"3";"4";"4";NULL;NULL;NULL;"2";"4";"1";"5";"5";;;"kmv" +"6340";"JLB009";"Angličtina pro žurnalisty I";;"Prošková,A.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"1";"5";"3";"3";"4";"Prezentace a weekly news.";"Více diskutovat.";"cjp" +"6341";"JPM407";"Feminism in International Relations (TIR)";;"Plechanovová,B.";"5";"4";NULL;NULL;NULL;"4";"4";"3";"3";"5";"3";"4";"5";;;"kmv" +"6342";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"3";"5";"3";"4";;;"kmv" +"6343";"JEM181";"Data Science with R";"Herman,D.,Krištoufek,L.";"Herman,D.,Krištoufek,L.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"6344";"JPM689";"Conflict Studies";"Karásek,T.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kbs" +"6345";"JPM718";"Critical Perspectives on Violence";;"Ditrych,O.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"4";;;"kmv" +"6346";"JEM162";"Energy Markets & Economics";"Elms,N.,Valíčková,P.";;"4";"4";"4";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"4";"I appreciated the final group term paper on energy market of one country. Nevertheless the required length of the paper was quite extensive - credit points awarded for this subject could be re-examined. Total time devoted to this subject was higher than for other subjects that are awarded by 6 credits.";"I would prefer differently created final exam. There were simply too many questions that were not that difficult, however there was not enough time to answer them. And I believe my time management was not the biggest problem (given quite low mean and median scores). Some of the questions were quite interesting - it was not only about simple reciting facts from lectures, we had to think about the problems and our opinions - and I did not have enough time to form my opinions. I believe I would be able to answer correctly much more given more time. The point of the exam should not be in examining speed-writting but in checking knowledge of students and their ability to think.";"ies" +"6347";"JEM199";"Financial Crisis and Risk Management";"Horváth,R.,Opatrný,M.,TSOMOCOS,D.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"I appreciated the provided case studies - the insight into the problems was really thorough.";;"ies" +"6348";"JMBZ228";"Úvod do společenských věd I.";"Kubát,M.";;"4";"1";"5";"5";"2";NULL;NULL;NULL;"1";"3";"2";"4";"2";;;"knrs" +"6349";"JMB197";"Kapitoly z moderních dějin Itálie";"Mejstřík,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"5";"5";"Velmi oceňuji, že četba byla na každou hodinu ke stažení v moodle.";;"kzs" +"6350";"JPM693";"Traditional and Critical Concepts in Security Studies";"Rychnovská,D.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Nejvíce si cením přístupu vyučující, a to především toho, že nás do výuky aktivně zapojovala. Díky tomu bylo látku jednodušší pochopit. Líbilo se mi i rozvržení hodiny a aktivity, které hodiny zpříjemňovaly. Za mě šlo o jeden z nejlepších předmětů vyučovaných u nás.";;"kbs" +"6351";"JJB050";"Tvůrčí dílny tisk";"Kubík,J.,Osvaldová,B.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kz" +"6352";"JPM656";"Seminar in Security Concepts";"Kučera,T.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"5";"5";"5";"5";"5";"Pan Kučera má velkou spoustu znalostí, kterou v hodinách upotřebí. Líbí se mi způsob, jakým hodiny pojal, možnost diskuze se studenty ohledně palčivých témat téhle doby byly zajímavé a hodinu výrazně oživily. Ne v každém semináři mají studenti možnost se o těchto věcech bavit, proto si vážím toho, že jsem tento seminář mohla navštěvovat.";;"kbs" +"6353";"JJB055";"Tvůrčí dílny tisk I - tvůrčí psaní";"Malý,R.,Novotný,D.";"Malý,R.,Novotný,D.";"4";"3";"5";"5";"5";"5";"5";"5";"2";"4";"5";"3";"5";"Různorodost v tématech, volnost v možnosti uchopení témat, lidský a zároveň profesionální přístup vyučujícího - odborníka, upřímná snaha studenty a studentky zaujmout, motivovat, kriticky hodnotit ale nesoudit.";"Lépe formulovat zpětnou vazbu, někdy nebylo zcela zřejmé, co přesně se vyučující snaží sdělit.";"kz" +"6354";"JJB037";"Kritika v médiích I";;;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"3";"4";"Pohodový ale zároveň profesionální přístup vyučujícího.";"Možná by bylo přínsnější občas zkrátit délku referátů a více o věcech hovořit.";"kz" +"6355";"JJM318";"Kapitoly z dějin médií";"Cebe,J.";;"3";"4";"4";"4";"4";NULL;NULL;NULL;"1";NULL;"4";"3";"3";;;"kms" +"6356";"JLB041";"Španělština I";;"Mlýnková,L.";"3";"3";NULL;NULL;NULL;"3";"5";"3";"1";"2";"2";"2";"3";;;"cjp" +"6357";"JPB578";"Classics of Political Thought";"Salamon,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";;;"kp" +"6358";"JJM320";"Propaganda a československá média po roce 1948";"Suk,P.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kms" +"6359";"JJM324";"Teorie mediální komunikace";"Jirák,J.";;"3";"5";"4";"4";"3";NULL;NULL;NULL;"1";"4";"3";"3";"2";;;"kms" +"6360";"JJM325";"Argumentace a přesvědčování v médiích";"Podzimek,J.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"3";"4";;;"kms" +"6361";"JJM373";"Ethical Issues in Media, Business and Society";"Orhan,M.";;"2";"3";"3";"3";"3";NULL;NULL;NULL;"1";"2";"2";"2";"1";;;"kms" +"6362";"JMB248";"Seminář k dějinám Ruska";;"Jasenčáková,M.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"5";"5";"Skvely pristup vedouci seminare Mgr. Jasencakove, ktera se nas snazila, co nejlepe pripravit na zaverecnou zkousku a zaroven umela hodiny pripravit zajimave a davala dostatecny prostor i nasim dotazum a zajmum.";;"krvs" +"6363";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"5";"4";"5";"5";"5";"3";"5";"5";"1";"5";"5";"4";"5";;"Some seminar leaders should speak louder and slower otherwise it might be hard to keep up with seminar";"ies" +"6364";"JMB408";"Moderní dějiny Ruska";"Litera,B.,Novák,P.,Pečenka,M.";;"3";"5";"4";"4";"4";NULL;NULL;NULL;"2";"5";"4";"4";"3";;"Co se tyce pozadavku na uspesne zakonceni predmetu, jedna se dle meho nazoru o zatim nejtezsi predmet oboru MTS. Spatna bilance uspesnosti vypovida neco i o narocich vedoucich predmetu.";"krvs" +"6365";"JEM034";"Corporate Finance";"Bychkova,O.,Malířová,T.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"5";"5";"Homeworks, as they were the key to study regularly and obtain valuable points.";"Sometimes the plan for lecture and seminar was too ambitious. Teacher wanted to cover more than was possible in time we had for lecture and seminar.";"ies" +"6366";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"4";"3";"3";"3";"3";NULL;NULL;NULL;"1";"3";"2";"4";"4";;;"ies" +"6367";"JEM035";"Financial Markets Instruments I";"Dědek,O.,Poláková,N.,Polák,P.";"Dědek,O.,Poláková,N.,Polák,P.";"4";"5";"4";"4";"4";"5";"5";"5";"1";"5";"4";"4";"4";;;"ies" +"6368";"JEM040";"Účetní a daňové poradenství";;"Kemény,I.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"5";"5";"The lecturer was amazing, her drive and enthusiasm about the subject was very inspiring and positive. It was pleassure to come to lectures.";;"ies" +"6369";"JMM673";"Promoting democracy abroad: the US and the EU in third countries";"Hornát,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kas" +"6370";"JMM671";"Rebuilding Europe";;"Rovná,L.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kzs" +"6371";"JPB202";"Politické strany v Evropě";"Perottino,M.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kp" +"6372";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"4";"4";"4";"5";"3";NULL;NULL;NULL;"3";"4";"4";"4";"4";;;"kp" +"6373";"JPB258";"Politické myšlení novověku I";"Franěk,J.,Kučera,J.";;"2";"5";"2";"2";"1";NULL;NULL;NULL;"2";"3";"2";"1";"1";;;"kp" +"6374";"JPB268";"Evropská integrace";"Plechanovová,B.";;"1";"5";"2";"2";"1";NULL;NULL;NULL;"4";"3";"3";"3";"2";;;"kmv" +"6375";"JPB269";"Současné problémy mezinárodních vztahů";"Knutelská,V.";;"3";"4";"3";"3";"3";NULL;NULL;NULL;"1";"1";"3";"2";"2";;;"kmv" +"6376";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"3";"4";"4";"4";"3";NULL;NULL;NULL;"1";"4";"4";"4";"3";;;"kp" +"6377";"JPB595";"Justice in Politics and International Relations";"Salamon,J.";;"3";"4";"3";"4";"1";NULL;NULL;NULL;"1";"2";"3";"3";"3";;;"kp" +"6378";"JPB597";"Current Political Extremism";"Charvát,J.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"2";"4";"4";"3";"4";;;"kp" +"6379";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"4";"4";"5";"2";"4";"4";"4";"5";;;"kz" +"6380";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"4";"3";"3";"4";"5";NULL;NULL;NULL;"5";"5";"4";"5";"5";;;"krvs" +"6381";"JMMZ274";"Geschichte des Rassismus";"Barth,B.";;"3";"3";"4";"4";"3";NULL;NULL;NULL;"5";"4";"3";"4";"4";;;"knrs" +"6382";"JEB044";"Financial Accounting";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"5";"5";"4";"5";"4";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"ies" +"6383";"JEB047";"Účetnictví II";"Kemény,I.";;"4";"4";"4";"4";"3";NULL;NULL;NULL;"3";"5";"4";"4";"4";;;"ies" +"6384";"JEB105";"Statistics";"Červinka,M.";"Nevrla,M.";"5";"5";"5";"4";"5";"4";"5";"4";"1";"5";"5";"5";"4";;;"ies" +"6385";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"5";"4";"5";"5";"4";"5";"5";"4";"1";"5";"5";"5";"5";;;"ies" +"6386";"NMMA703";"Matematika 3";"Zelený,M.";"Turčinová,H.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"6387";"JEB111";"Advanced Data Analysis in MS Excel";;"Poláková,N.,Polák,P.";"5";"4";NULL;NULL;NULL;"4";"4";"4";"1";"4";"5";"4";"5";;;"ies" +"6388";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"4";"5";"5";"4";"4";"3";"3";"3";"1";"4";"5";"4";"3";;;"ies" +"6389";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"5";"4";"5";;;"kmv" +"6390";"JPM694";"Strategic Studies";"Hynek,N.,Ludvík,J.,Smetana,M.";;"5";"3";"4";"5";"4";NULL;NULL;NULL;"1";"5";"3";"4";"5";;;"kbs" +"6391";"JPM696";"Economic Warfare";"Ludvík,J.";;"5";"3";"4";"5";"4";NULL;NULL;NULL;"1";"5";"3";"4";"5";;"The group conclusions entail the danger that the burden between students is shared unequally. Maybe each students should write his or her own short conclusion.";"kbs" +"6392";"JPM701";"European and Transatlantic Security";"Kazharski,A.";;"4";"4";"4";"5";"4";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"kbs" +"6393";"JPM702";"NATO and EU in Crisis Management";"Karásek,T.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"2";"4";"4";"4";"4";;;"kbs" +"6394";"JSM016";"Sociology of Science and Scientific Knowledge";;"Maršálek,J.";"3";"4";NULL;NULL;NULL;"3";"2";"3";"1";"4";"3";"3";"3";"interesting lectors";;"ks" +"6395";"JSM020";"Seminář k aktuální veřejně politické problematice";;"Balon,J.,Císař,O.";NULL;NULL;NULL;NULL;NULL;"1";"5";"1";NULL;NULL;NULL;NULL;NULL;;;"ks" +"6396";"JSM027";"Urbánní antropologie";"Uherek,Z.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"2";"3";"4";;;"ks" +"6397";"JSM032";"Applied Social Research";"Remr,J.";;NULL;NULL;"5";"3";"3";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;;"ks" +"6398";"JSM095";"Study of Political Mobilization and Social Movements";"Císař,O.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"3";"3";"5";;;"ks" +"6399";"JSM554";"Diplomový seminář";;"Tuček,M.";NULL;NULL;NULL;NULL;NULL;"4";"5";"3";NULL;NULL;NULL;NULL;NULL;;;"ks" +"6400";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"4";"4";"3";"3";"2";NULL;NULL;NULL;"1";"3";"2";"2";"3";;;"kmv" +"6401";"JLB102";"Czech as a Foreign Language III";;"Nováková,K.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"1";"3";"4";"4";"4";;;"cjp" +"6402";"JMM121";"Central European Cinema";;"Duta,M.";"4";"3";NULL;NULL;NULL;"4";"4";"4";"3";"3";"3";"3";"4";;;"krvs" +"6403";"JMMZ084";"Eastern Europe Today II";;"Lídl,V.,Šír,J.";"3";"3";NULL;NULL;NULL;"2";"2";"2";"1";"3";"3";"3";"2";;;"krvs" +"6404";"JMMZ095";"M.A. Thesis Seminar for BECES I";;"Vykoukal,J.";"4";"3";NULL;NULL;NULL;"4";"4";"4";"1";"3";"3";"4";"4";;;"krvs" +"6405";"JMM279";"Společnost a ekonomika";"Fiřtová,M.";"Svoboda,K.";"5";"4";"5";"5";"5";"5";"5";"5";"5";"5";"5";"5";"5";"Nejvíce oceňuji energičnost přednášející (i vedoucího semináře) během hodin. Tak jak to dělají oni, by to měli dělat i ostatní vyučující. Když je totiž zapálený vyučující do svého předmětu, je větší šance, že to nadchne i studenty. To se v případě Ekonomiky a společnosti stalo a myslím, že nejen u mě. Za mě jeden z nejlepších předmětu na IMS vůbec. Děkuji :)";"Já bych byla klidně pro navýšení počtu přednášek i seminářů na dvanáct a dvanáct. Se současným počtem (šest a šest) mi přišlo, že jsme vše jen tak trochu nakousli. V případě semináře by bylo příště vhodnější nebýt v té velké aule.";"kas" +"6406";"JMM271";"Metodologický seminář";;"Šír,J.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Oceňuji přístup pana Šíra k výuce předmětu, stejně jako ke studentům. Ačkoliv byl někdy k prezentujícímu referátu třeba trochu kritický, myslím, že vždy to byla oprávněná kritika. Navíc si myslím, že kritika může pomoci člověku velice pomoci k sebezlepšení. Když jsem navíc měla problém se zpracováním referátu, pohotově mi odpověděl a poradil. Obsahově mi kurz také vyhovoval. Nebudu popírat, že to nebylo tolik zajímavé, ale rozhodně potřebné a jsem za kurz ráda. Děkuji.";;"krvs" +"6407";"JMM189";"Economic transformation in East Central and Southeastern Europe";"Trejbal,V.";;"3";"1";"4";"2";"1";NULL;NULL;NULL;"5";"3";"2";"3";"1";;"Myslím, že by nebylo špatné věnovat více času jednotlivým případům transformujících se zemí, tj. konkrétně probrat průběh v jednotlivých zemích. Referáty by pak toto doplnily.";"krvs" +"6408";"JMM277";"Historie a kultura";"Vykoukal,J.";"Vykoukal,J.";"4";"4";"4";"5";"5";"4";"5";"5";"1";"4";"3";"5";"4";"Myslím, že předmět byl velmi zajímavý a rozhodně pro nás důležitý. Po jeho absolvování mi tak nějak přijde, že mi spousta věcí do sebe zapadá. :)";"Myslím, že by byl dobrý krátký komentář k seminární práci. Student s ní tráví relativně dost času, tak by si zasloužil vědět, co má zlepšit pro příště.";"krvs" +"6409";"JMM278";"Politika a mezinárodní vztahy";"Kubát,M.,Weiss,T.";"Kocián,J.";"3";"4";"4";"2";"4";"5";"5";"5";"1";"2";"3";"4";"3";"Seminář s panem Kociánem byl jeden z nejlepších. Forma, jakou vedl seminář vedl, byla nejvíc super, protože plně reflektovala obsah přednášek. Asi jako na jediném jsme opravdu aplikovali teorie z přednášek na praxi z našeho regionu. Děkuji! :)";"Vynechala bych politiku pana Kubáta, kterou jsme absolvovali během bakalářského studia. Víc času bych věnovala tomu, co nám chtěli sdělit pan Weiss a Šlosarčík, tj. mezinárodním vztahů, bezpečnostním studiím, státu jako aktéra MV apod...";"krvs" +"6410";"JMMZ050";"Political Systems of East European Countries in the 20th Century";"Kubát,M.";;"3";"2";"5";"3";"1";NULL;NULL;NULL;"1";"2";"3";"4";"4";"Mám ráda přístup pana Kubáta. Vše podá jasně, srozumitelně.";"Myslím, že když je to povinný kurz, tak by mohl být obtížnější.";"krvs" +"6411";"JMM091";"Koncepce a interpretace balkánských dějin";"Šístek,F.";;"5";"4";"4";"5";"5";NULL;NULL;NULL;"2";"5";"4";"4";"5";"Četba, kterou pan Šístek vybíral, byla velmi zajímavá a rozhodně důležitá pro studenty našeho oboru/regionu. Děkujeme.";"Myslím, že by bylo dobré poslat studentům zpětné hodnocení seminární práce. Ráda bych věděla, v čem se příště zlepšit.";"krvs" +"6412";"JLB019";"Francouzština odborná I";;"Bosáková,L.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";"Energii vyučující, její ochotu pomáhat studentům a reflektovat požadavku studentů na to, co procvičovat.";"Možná méně videí, nebo ne tak dlouhých a více slovíček na rozšíření slovní zásoby.";"cjp" +"6413";"JMM097";"Etnické a národnostní problémy střední a jihovýchodní Evropy";"Vykoukal,J.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"Forma vedení tohoto kurzu byla super. Obecně se mi kurz hodně líbil. Děkuji.";;"krvs" +"6414";"JEM027";"Monetary Economics";"Holub,T.,Malovaná,S.";"Břízová,P.,Hájek,J.,Holub,T.,Malovaná,S.";"4";"4";"4";"4";"4";"4";"4";"3";"1";"3";"4";"4";"4";;;"ies" +"6415";"JMM674";"Maritime security: Geopolitics of the Indian and Pacific Oceans";"Hornát,J.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"2";"3";"4";"4";"4";;;"kas" +"6416";"JMMZ316";"Evolution of Sino-American Relations";"Sehnálková,J.";;"4";"2";"5";"5";"4";NULL;NULL;NULL;"1";"3";"4";"4";"5";;;"kas" +"6417";"JPM524";"Energy Security";"Holubcová,J.,Kučerová,I.";;"4";"2";"4";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kmv" +"6418";"JEM027";"Monetary Economics";"Holub,T.,Malovaná,S.";"Břízová,P.,Hájek,J.,Holub,T.,Malovaná,S.";"4";"4";"4";"4";"4";"3";"3";"3";"1";"4";"4";"4";"4";;;"ies" +"6419";"JMMZ316";"Evolution of Sino-American Relations";"Sehnálková,J.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kas" +"6420";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"3";"3";"2";"3";"2";NULL;NULL;NULL;"2";"4";"4";"4";"4";;;"kmv" +"6421";"JPM524";"Energy Security";"Holubcová,J.,Kučerová,I.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kmv" +"6422";"JEM040";"Účetní a daňové poradenství";;"Kemény,I.";"4";"2";NULL;NULL;NULL;"4";"5";"4";"1";"4";"5";"4";"4";;;"ies" +"6423";"JEM003";"Advanced Microeconomics";"Janda,K.,Melikhova,O.";"Janda,K.,Melikhova,O.";"3";"3";"3";"4";"2";"4";"5";"4";"1";"2";"3";"2";"3";;;"ies" +"6424";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"5";"4";"5";"5";"5";"2";"4";"2";"1";"4";"5";"5";"5";"písemné testy nevyžadovaly znalost složitých vzorců nazpaměť, ale naopak ověřovaly pochopení podstaty probírané látky v celkových souvislostech; kurz poskytuje především přehled nejpoužívanějších metod ekonometrie a vysvětluje, jak se mezi nimi orientovat";;"ies" +"6425";"JMB529";"Současná západní Evropa";"Rovná,L.,Váška,J.";;"5";NULL;"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kzs" +"6426";"JEM004";"Advanced Macroeconomics";"Baxa,J.,Hromádková,E.";"Baxa,J.,Hromádková,E.,Šestořád,T.,Troch,T.,Žáček,J.";"5";"5";"5";"5";"4";"4";"5";"5";"1";"5";"5";"5";"5";"empirické semináře vhodně propojují teoretické znalosti a reálná data";"podíl otázek testů vztahujících se k empirickým seminářům je dle mého názoru nadměrný - zvlášť v porovnání s rozsahem přednáškových materiálů pak dochází k tomu, že jsou testovány znalosti na pomezí ekonometrie (což samo o sobě nemusí být špatně, ale bylo by vhodné na to náležitě upozornit, neb jinak student očekává otázky především makroekonomické)";"ies" +"6427";"JMB530";"Současná Severní Amerika";"Calda,M.";;"5";NULL;"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kas" +"6428";"JMB534";"Evropská unie - vybrané problémy";"Mejstřík,M.";;"5";NULL;"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kzs" +"6429";"JEM032";"Banking";"Mejstřík,M.,Pečená,M.,Teplý,P.";"Pečená,M.";"4";"4";"4";"4";"3";"5";"5";"4";"2";"5";"4";"5";"4";;"závěrečná ústní zkouška je zcela neadekvátní vzhledem k celkovému množství bodů, které se při ní rozděluje; často i na základě jediné otázky (která nevyžaduje složitější odpověď a která navíc může vycházet přímo z písemného testu) dochází k prakticky náhodnému udělení bodů, které představují celých 20% možného maxima předmětu (což vyniká např. v porovnání s povinným projektem, na němž studenti stráví několik dní práce a získají za něj nejvýše 15% bodů); řešením může být značné snížení bodů rozdělovaných u ústní zkoušky (tak, aby toto odpovídalo její současné úrovni), nebo naopak přehodnocení jejího pojetí (a tedy dostatečný prostor pro kvalifikovanou diskuzi o tématu, na základě které může dojít k objektivnímu ohodnocení znalostí studenta)";"ies" +"6430";"JSB007";"Dějiny sociologie I";"Šanderová,J.";"Hanzal,P.";"4";"3";"5";"5";"5";"4";"5";"5";"3";"5";"4";"5";"5";"Oceňuji podrobný výklad";;"ks" +"6431";"JLB099";"Rozřazovací test z angličtiny";;"Klírová,M.";"2";"5";NULL;NULL;NULL;"5";"5";"5";"1";"1";"3";"1";"2";;;"cjp" +"6432";"JMM271";"Metodologický seminář";;"Šmidrkal,V.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"krvs" +"6433";"JMM277";"Historie a kultura";"Vykoukal,J.";"Šmidrkal,V.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"krvs" +"6434";"JMM279";"Společnost a ekonomika";"Fiřtová,M.";"Kačmárová,P.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"kas" +"6435";"JMMZ306";"Deutschland und Zentraleuropa aktuell I";;"Göttmann,A.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"knrs" +"6436";"JMM083";"Deutsche und mitteleuropäische Geschichte im 20. Jh.";"Barth,B.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"knrs" +"6437";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"4";"4";"4";"5";"5";NULL;NULL;NULL;"1";"3";"2";"5";"3";;;"ies" +"6438";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"3";"2";NULL;NULL;NULL;"3";"4";"4";"2";"2";"2";"2";"2";;;"ies" +"6439";"JEM199";"Financial Crisis and Risk Management";"Horváth,R.,Opatrný,M.,TSOMOCOS,D.";;"5";"4";"4";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"ies" +"6440";"JLB100";"Czech as a Foreign Language I";;"Frantesová,E.";"4";"4";NULL;NULL;NULL;"4";"4";"5";"3";"5";"4";"5";"5";;;"cjp" +"6441";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"4";"3";"4";"5";"2";"3";"4";"3";"1";"4";"4";"4";"4";;;"ies" +"6442";"JEM020";"Ethics and Economics";"Cahlík,T.";"Cahlík,T.";"5";"4";"4";"5";"4";"4";"4";"2";"1";"5";"4";"5";"5";;;"ies" +"6443";"JLB003";"Angličtina pro ekonomy I";;"Gloverová,M.";"3";"2";NULL;NULL;NULL;"5";"5";"1";"1";"1";"3";"2";"2";;;"cjp" +"6444";"JJB231";"Dějiny a teorie public relations";"Halada,J.,Hejlová,D.";;"4";"4";"4";"4";"1";NULL;NULL;NULL;"1";"4";"4";"3";"3";"Přednášky mě bavily, zejména výklad doktorky Hejlové se dobře poslouchá.Dějiny PR: látka byla na přednáškách probrána v plné míře a bylo možné se z přednášek připravit na zkoušku, docent Halada byl nápomocen ve všech ohledech.";"Teorie PR: nebyly probrány všechny aspekty a z pouhé účasti na přednáškách nebylo možné zkoušku zdárně složit - to by ovšem nebyl problém, pokud by byly ke zkoušce dostupné okruhy, ty nám ovšem poskytnuty nebyly. Na zkoušce nebylo možné si připravit odpovědi dopředu a následně byla žákům vytýkána nedostatečná strukturovanost a promyšlenost odpovědí - to ale bez předchozí přípravy není příliš možné, když na otázku odpovídáte hned po jejím obdržení.Seminární práce: I když se práce odevzdávaly již 24. 11., zpráva o přijetí, či nepřijetí eseje ke zkoušce byla podána nejdříve týden před prvním termínem, to samé se opakovalo i před druhým a třetím datem zkoušky; domnívám se, že lhůta pro opravení prací je 5 pracovních dní, ne 6 týdnů a více.";"kmkpr" +"6445";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"ks" +"6446";"NMMA701";"Matematika 1";"Spurný,J.";"Rondoš,J.";"3";"5";"5";"5";"4";"4";"5";"4";"1";"4";"5";"2";"3";;;"ies" +"6447";"JJM329";"Média a kultura";"Bednařík,P.,Jirák,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";"Praktické ukázky";"Asi nic";"kms" +"6448";"JJM330";"Trendy současných českých médií";"Aust,O.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Hosti byli skvělí";"Nic";"kms" +"6449";"JEM179";"History and Methodology of Economics";"Baxa,J.,Paulus,M.";;"5";"5";"4";"4";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ies" +"6450";"JJM331";"Výzkum médií II";"Vochocová,L.";;"4";"5";"5";"5";"5";NULL;NULL;NULL;"2";"4";"4";"4";"5";"Praktickou část";"Diskuzi k praktické části - odevzdání dříve, shrnutí nejčastějších chyb na přednášce";"kms" +"6451";"JJM332";"Přirozený jazyk a média";"Podzimek,J.";;"3";"4";"2";"3";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;"Přednes přednášejícího, zapojení studentů do diskuze";"kms" +"6452";"JMM189";"Economic transformation in East Central and Southeastern Europe";"Trejbal,V.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"3";"5";"3";"4";"3";;;"krvs" +"6453";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"4";"3";NULL;NULL;NULL;"5";"4";"4";"1";"3";"4";"2";"5";;;"cjp" +"6454";"JMMZ050";"Political Systems of East European Countries in the 20th Century";"Kubát,M.";;"5";"4";"4";"4";"5";NULL;NULL;NULL;"1";"5";"4";"4";"4";"Explaining the political system in Central and Eastern Europe in a very clear way.";"If the teacher can provide more explanation on some particular system or politician, it would be more helpful.";"krvs" +"6455";"JPB229";"Regionální politické systémy: Skotsko, Wales";"Říchová,B.";;"4";"4";"4";"4";"5";NULL;NULL;NULL;"1";"5";"3";"3";"4";;;"kp" +"6456";"JPB569";"Workshop Politické a státní instituce v praxi";;"Brunclík,M.";"5";"2";NULL;NULL;NULL;"4";"4";"4";"2";"5";"2";"4";"5";;;"kp" +"6457";"JPB018";"Dějiny mezinárodních vztahů I";"Soukup,J.";;"4";"2";"5";"5";"3";NULL;NULL;NULL;"2";"2";"3";"3";"5";;;"kmv" +"6458";"JPB269";"Současné problémy mezinárodních vztahů";"Knutelská,V.";;"3";"3";"3";"3";"3";NULL;NULL;NULL;"2";"4";"4";"5";"4";;;"kmv" +"6459";"JMMZ094";"Introduction to History, Politics and Society of Eastern Europe";"Vykoukal,J.";;"5";"5";"4";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"4";;;"krvs" +"6460";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"4";"4";"4";"1";"4";NULL;NULL;NULL;"2";"4";"3";"3";"3";;"Přístup dr. Švece - velmi těžké až nemožné pana doktora kontaktovat. Na žádný z mých emailů neodpověděl, ani na ty, které se týkaly hodnocení práce.S dr. Mlejnkem nebyl žádný problém.";"kp" +"6461";"JJB235";"Proces tvorby v marketingové komunikaci";"Bezouška,M.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"2";"3";"3";"3";"4";;;"kmkpr" +"6462";"JMB037";"Moderní dějiny Polska";"Vykoukal,J.";;"4";"2";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"3";"5";;;"krvs" +"6463";"JJB237";"Právo a etika v marketingu";"Winter,F.";;"5";"3";"4";"5";"3";NULL;NULL;NULL;"1";"4";"3";"3";"5";;;"kmkpr" +"6464";"JMB402";"Úvod do společenských věd II";;"Karasová,N.";"4";"3";NULL;NULL;NULL;"5";"5";"4";"1";"4";"5";"2";"4";;;"krvs" +"6465";"JMB056";"Reflexe velkých debat v sociálních vědách ve filmu";;"Kozák,K.";"4";"3";NULL;NULL;NULL;"5";"5";"4";"1";"5";"5";"4";"5";;;"kas" +"6466";"JJB238";"Kritika marketingové komunikace";"Rosenfeldová,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"4";"4";"4";"5";;;"kmkpr" +"6467";"JMB212";"Moderní dějiny Japonska";"Labus,D.";;"4";"2";"4";"2";"3";NULL;NULL;NULL;"2";"4";"2";"3";"5";;;"kas" +"6468";"JJB243";"Aktuální trendy a vývoj v oboru I.";"Hejlová,D.,Vranka,M.";"Hejlová,D.,Vranka,M.";"5";"2";"5";"5";"5";"5";"5";"5";"1";"4";"3";"4";"5";"Různorodost hostů je velkým přínosem, je velmi zajímavé vidět rozmanitost oboru a možnosti uplatnění v praxi.";;"kmkpr" +"6469";"JJB293";"Role výzkumů v politických a komerčních kampaních";;"Rosenfeldová,J.";"4";"3";NULL;NULL;NULL;"5";"4";"4";"2";"4";"3";"4";"4";"Skvělí hosté, forma seminářů - založeny na dialogu a interakci se studenty, zajímavé a velmi aktuální case studies";"Menší zmatek ohledně zakončení kurzu";"kmkpr" +"6470";"JJB279";"Art marketing";"Ježková,T.";;NULL;NULL;"5";"5";"5";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;"Výuka byla formou workshopů s hosty působícími v kulturní sféře, je to skvělé oživení přednášky; velmi pozitivně také hodnotím přístup vyučující i možnost nepsat seminární práci, ale odpřednášet ji na hodině formou prezentace.Kurz mě velmi bavil a ocenila bych více takových kurzů.";;"kmkpr" +"6471";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"2";"5";"3";"4";"3";NULL;NULL;NULL;"4";"4";"2";"3";"2";;"Přednášky bývaly zbytečně večer a kvůli délce přednáškového bloku bylo velmi těžké se soustředit po celou dobu.";"kp" +"6472";"JJB401";"Komerční a nekomerční marketingová komunikace";"Báča,L.,Obluk,O.";;"5";"4";"4";"4";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Zakončení kurzu formou vypracování projektu";;"kmkpr" +"6473";"JJB401";"Komerční a nekomerční marketingová komunikace";"Báča,L.,Obluk,O.";;"5";"4";"5";"5";"3";NULL;NULL;NULL;"1";"3";"5";"4";"5";"Zkoušku formou tvorby kampaně - lepší zúročení znalostí než v ostatních kurzech. Skvělí přednášející.";"Víc než jeden popis kompletní kampaně";"kmkpr" +"6474";"JJB403";"Institucionální a vládní komunikace";"Shavit,A.,Soukeník,Š.";;"4";"4";"4";"4";"5";NULL;NULL;NULL;"1";"4";"4";"4";"4";"Velmi pozitivně hodnotím přednášky pod vedením Marcely Voženílkové a Štěpána Soukeníka";"Celkový dojem z kurzu kazí nepříliš vstřícné chování Anny Shavit, které se můj vkus občas hraničilo s nezdvořilostí.";"kmkpr" +"6475";"JMB197";"Kapitoly z moderních dějin Itálie";"Mejstřík,M.";;"4";"3";"4";"5";"3";NULL;NULL;NULL;"2";"4";"2";"3";"5";"Kvalitní přednášky, dohledávání dalších informací prostřednictvím domácích úkolů.";;"kzs" +"6476";"JJM360";"Ekonomika v médiích";"Klimeš,D.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"3";"4";"5";"přednášejícího, hosty, diskuzi";;"kz" +"6477";"JPB258";"Politické myšlení novověku I";"Franěk,J.,Kučera,J.";;"2";"4";"2";"3";"3";NULL;NULL;NULL;"2";"3";"3";"3";"3";;;"kp" +"6478";"JJB630";"Krizová komunikace";"Chudinová,E.";;"5";"3";"4";"5";"3";NULL;NULL;NULL;"1";"5";"4";"4";"4";;;"kmkpr" +"6479";"JJM354";"Dějiny populární hudby";"Halada,A.";;"4";"1";"4";"5";"3";NULL;NULL;NULL;"4";"4";"1";"2";"4";"přednášejícího";;"kz" +"6480";"JMB212";"Moderní dějiny Japonska";"Labus,D.";;"3";"4";"3";"3";"3";NULL;NULL;NULL;"2";"4";"2";"3";"3";;;"kas" +"6481";"JJM290";"Tvůrčí dílny I – rozhlas a televize";;"Maršík,J.";"4";"3";NULL;NULL;NULL;NULL;NULL;"4";"1";"4";"5";"4";"4";"přístup pana Maršíka, technické minimum";"- přístup, komunikaci a organizaci pana Lokšíka: neumí jasně a stručně sdělit, co se od studentů očekává a v jakém časovém horizontu, nedokáže efektivně vysvětlit vše potřebné těm, kteří jsou v kontaktu s technikou poprvé, a ke studentům s touhou po nějaké struktuře přistupuje jako k potížistům, přičemž sám výuku zdržuje zbytečným vykecáváním- koncept výuky: zimní semestr je příliš nabitý, v důsledku čehož nemůže (také díky přístupu pana Lokšíka) být věnován dostatečný prostor osvojení si všech dovedností potřebných k práci s technikou, což se pak odráží i na výsledku";"kz" +"6482";"JMB249";"Seminář k dějinám středo- a jihovýchodní Evropy";;"Zilynskyj,B.";"5";"2";NULL;NULL;NULL;"5";"5";"4";"2";"3";"3";"4";"5";"Diskuze nad referáty studentů, dodatečný výklad cvičícího.";"Vysvětlení některých klíčových pojmů z oblasti náboženství. Probrat méně zemí podrobněji.";"krvs" +"6483";"JMM283";"Splendor and Misery of Détente";"Fojtek,V.";;"4";"4";"4";"5";"4";NULL;NULL;NULL;"2";"4";"3";"4";"4";"It provides many information and backgrounds (especially video material) about the Cold War situation in the Eastern countries.";;"kas" +"6484";"JMB250";"Seminář k dějinám západní Evropy";;"Synkule,M.";"4";"3";NULL;NULL;NULL;"4";"5";"4";"1";"4";"3";"3";"4";"Diskuze nad referáty studentů, povinnou četbou i celými tématem.";;"kzs" +"6485";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"2";"4";"4";"3";"4";;;"kp" +"6486";"JPB229";"Regionální politické systémy: Skotsko, Wales";"Říchová,B.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"4";"5";;;"kp" +"6487";"JPB260";"Politické myšlení novověku III";"Kučera,J.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kp" +"6488";"JPB597";"Current Political Extremism";"Charvát,J.";;"5";"2";"4";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"6489";"JMM384";"Cold War in Documents 1945-1962";"Smetana,V.";;"4";"4";"4";"5";"4";NULL;NULL;NULL;"2";"4";"4";"4";"4";;;"kas" +"6490";"JMB408";"Moderní dějiny Ruska";"Litera,B.,Novák,P.,Pečenka,M.";;"4";"5";"4";"4";"5";NULL;NULL;NULL;"1";"4";"2";"3";"4";"Přehledné chronologické přednášky informující o vnitřní situaci Ruska i o jeho pozici v mezinárodních vztazích.";"Přílišný důraz na některá z mého pohledu pro obor nepříliš důležitá témata, jako rolnická a nevolnická otázka v Rusku";"krvs" +"6491";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"3";"3";"3";"3";"3";NULL;NULL;NULL;"1";"3";"3";"3";"3";;"Course shall be divided into both terms. For one term, there is a huge amount of information.";"kp" +"6492";"JSB998";"Úvod do sociologie";"Soukup,P.";;"4";"2";"5";"4";"4";NULL;NULL;NULL;"2";"3";"3";"4";"4";;;"ks" +"6493";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"4";"2";"4";"5";"3";NULL;NULL;NULL;"2";"3";"3";"4";"3";;;"knrs" +"6494";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Čížek,M.";"5";"3";"4";"4";NULL;"4";"4";"4";"1";"4";"5";"4";"5";;;"krvs" +"6495";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"3";"4";"3";"2";"3";NULL;NULL;NULL;"2";"3";"3";"4";"3";;;"ies" +"6496";"JPB268";"Evropská integrace";"Plechanovová,B.";;"2";"3";"2";"2";"2";NULL;NULL;NULL;"4";"3";"3";"3";"3";;"Kurz má veľmi zložité hodnotenie a čiastočne nespravodlivé. Je nespravodlivé aby študenti, čo napíšu test na plný počet bodov neprešli z rešerše. Je naozaj potrebné toto hodnotenie upraviť";"kmv" +"6497";"JJM264";"Diplomový seminář II.";;;NULL;NULL;"3";"3";"3";NULL;NULL;NULL;NULL;NULL;NULL;NULL;NULL;;"organizaci (týká se i DS I) a komunikaci směrem ke studentů: díky změnám v rozvrhu nebylo jasné, zda vůbec seminář probíhá, kde, s kým a co je podmínkou jeho ukončení";"kz" +"6498";"JMB409";"Moderní dějiny středo- a jihovýchodní Evropy-dcera JMB013";"Balla,P.,Švec,L.";;"5";"5";"5";"5";"4";NULL;NULL;NULL;"2";"4";"3";"3";"5";"Přehledné chronologické přednášky. Vysvětlování vztahu některých historických událostí k současnosti.";;"krvs" +"6499";"JMB410";"Moderní dějiny západní Evropy";"Matějka,O.,Váška,J.";;"3";"5";"4";"4";"3";NULL;NULL;NULL;"1";"3";"3";"4";"4";"Úvod do některých témat ovlivňujících historické i současné dění v zemích západní Evropy. Důraz na souvislosti velkých témat (průmyslová revoluce, kolonialismus) v jednotlivých zemích regionu.";"Větší důraz na chronologický výklad dějin, který byl upozaďován a důraz byl kladen spíše na historická společenská témata. Chápu, že to mělo vést studenty k četbě povinné a doporučené literatury a schvaluji to, ale přesto by alespoň stručný výklad pomohl k lepší orientaci v dějinách západní Evropy.";"kzs" +"6500";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"2";"1";"5";"5";"3";"1";"1";"5";;;"kz" +"6501";"JJB221";"Ekonomická teorie marketingu";"Postler,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"5";"5";"5";"5";"5";;;"kmkpr" +"6502";"JJB224";"Psychologie marketingové komunikace";"Vranka,M.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"2";"4";"5";"5";"4";;;"kmkpr" +"6503";"JJB237";"Právo a etika v marketingu";"Winter,F.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"3";"5";"5";"5";"5";;;"kmkpr" +"6504";"JJB240";"Marketing a tvorba značky";"Průša,P.";;"3";"4";"3";"4";"4";NULL;NULL;NULL;"2";"4";"4";"4";"3";;;"kmkpr" +"6505";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"3";"3";"4";"3";"3";NULL;NULL;NULL;"1";"3";"3";"3";"4";;;"kp" +"6506";"JJB249";"Úvod do studia českého jazyka I";"Schneiderová,S.";"Schneiderová,S.";"5";"4";"5";"5";"5";"5";"5";"5";"3";"5";"4";"1";"4";;;"kmkpr" +"6507";"JPB595";"Justice in Politics and International Relations";"Salamon,J.";;"3";"3";"3";"3";"2";NULL;NULL;NULL;"1";"3";"3";"3";"3";;;"kp" +"6508";"JJB269";"Sociální kontext komunikace";"Vranka,M.";;"3";"2";"5";"5";"1";NULL;NULL;NULL;"2";"1";"1";"1";"1";;;"kmkpr" +"6509";"JJB406";"Tvorba a prostředky v mediální komunikaci";"Chudinová,E.";;"1";"3";"5";"3";"2";NULL;NULL;NULL;"2";"1";"1";"1";"1";;;"kmkpr" +"6510";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"2";"4";"4";"4";"5";"Prístup pedagóga";;"ks" +"6511";"JJB407";"Bakalářský proseminář";"Rosenfeldová,J.";;"1";"1";"4";"1";"1";NULL;NULL;NULL;"4";"3";"4";"1";"1";;;"kmkpr" +"6512";"NMMA711";"Mathematics 1";"Bárta,T.";"Bárta,T.,Vlasák,V.";"4";"5";"5";"5";"5";"1";"2";"2";"1";"2";"3";"3";"1";"Mr Barta is a great teacher, who can clearly explain the material, patient about pretty simple questions asked and always have few ways to explain to you. Homeworks was a great idea for this subject, also the difficulty of them was just perfect to cover the concept and show how to approach different topics";"There is a huge amount that has to be learnt by heart, credits awarded definitely doesn`t represent the time that should be spent on studying.";"ies" +"6513";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"3";"3";"3";"3";"3";NULL;NULL;NULL;"2";"3";"3";"4";"4";;;"kmv" +"6514";"JPM561";"Regional Security Studies";"Karásek,T.,Klosek,K.";"Karásek,T.,Klosek,K.";"5";"4";"5";"5";"4";"5";"5";"5";"1";"4";"4";"4";"5";"The teaching method.";"Maybe South America should be included.";"kbs" +"6515";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";"I've learned how to use R Studio, which would is literally the only skill i posses now as a IR student. Something marketable.";"Tom And Jerry ( closing slide ) at the end should be replaced by some other cartoon each time, and so keep the students attention, what would happen next. :)) It is very hard to present the materials of this subject as interesting, but the professor manages somehow. Maybe more up to date examples and comparison to again catch the attention of the students. Funny always works";"kmv" +"6516";"JJM371";"New Media and Entrepreneurship";"Orhan,M.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Professor`s attitude to his subject and students is great as it helps to create discussions, relevant examples and cases were provided all the time to understand not just how it works in theory but also in practice. Great approach, great subject, great professor!";;"kms" +"6517";"JEB141";"Introduction to Market Design";"Gregor,M.,Kastl,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Theoretical part might seem a little bit confusing, however, relevant examples provided by the professor always cleared it out how and where it works.";"Would love to cover more examples and places where this theory is applied";"ies" +"6518";"JPM693";"Traditional and Critical Concepts in Security Studies";"Rychnovská,D.";;"3";"4";"3";"4";"4";NULL;NULL;NULL;"2";"3";"3";"4";"3";;;"kbs" +"6519";"JPM699";"Security and Technology";"Střítecký,V.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"2";"4";"4";"4";"4";;;"kbs" +"6520";"JPM701";"European and Transatlantic Security";"Kazharski,A.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kbs" +"6521";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"4";"4";"5";"5";"5";"5";"5";"5";"1";"4";"4";"4";"3";"Nikoloz is an amazing teacher who can answer all questions and explain the material well, however, sometimes talks the way too fast";;"ies" +"6522";"JSM628";"European policies and practice towards ethnic minorities";"Bernard Thompson Mikes,A.";;"2";"3";"4";"3";"2";NULL;NULL;NULL;"2";"3";"3";"3";"1";"Interesting discussion on the rare occasion";"Organisation of classes should be more effective and cancellations should be facilitated by another teacher";"kvsp" +"6523";"JJM233";"Intercultural Communication Management";"Lütke Notarp,U.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";"The practical exercises were very helpful to understand theory. It was also very nice to exchange with student from other countries about their culture.";"I think it could be better if we had true tables ti take notes. But it is not that bad with the chairs.";"kms" +"6524";"JMM027";"Contemporary Mediterranean";"Králová,K.,Mejstřík,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"The subjects we dealt with were interesting.";"Maybe the lecturers could give a bit more contextualization before the different presentations of students.";"kzs" +"6525";"JPB597";"Current Political Extremism";"Charvát,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"The structure of the course was very clear and easy to learn.";;"kp" +"6526";"JMMZ224";"Cultural Memory and Identity in the Balkans";"Asavei,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"krvs" +"6527";"JMMZ109";"Comparison of Central European Political Systems";"Kubát,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"The topics covered were interesting.";;"knrs" +"6528";"JSM642";"Metody práce s informacemi";"Tomandlová,V.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"3";"5";;;"kvsp" +"6529";"JSM641";"Sociální problémy";"Frič,P.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kvsp" +"6530";"JSM640";"Základy sociologie";"Paulíček,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kvsp" +"6531";"JSM523";"Sociální politika";"Angelovská,O.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"4";"5";;;"kvsp" +"6532";"JSM522";"Veřejná ekonomie";"Kotherová,Z.";;"4";"5";"4";"4";"5";NULL;NULL;NULL;"1";"4";"4";"5";"4";;;"kvsp" +"6533";"JSM521";"Veřejná politika";"Chalupová,P.,Potůček,M.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"kvsp" +"6534";"JSB010";"Současná sociologie";"Balon,J.";;"4";"3";"4";"4";"3";NULL;NULL;NULL;"2";"5";"3";"4";"4";"Četba celé knihy místo týdenních textů mi přijde dobrá, i proto, že je to něco jiného než v ostatních kurzech.Stejně tak bych zachoval psaní recenze, další věc, co si v ostatních kurzech nevyzkoušíme.Z nějakého důvodu mě bavila přednáška na Bourdieho, ale nedokážu říct, proč, nebo čím se lišila od ostatních, které mě třeba tolik nebavily.";"Je tu ten problém, že lidi nechodí na přednášky. To vím, že řešíte jak Vy, tak ostatní vyučující ve většině kurzů, ale co s tím? To, co jsem viděl v některých ostatních kurzech, jako je zavádění povinné nebo bodované docházky, mi přijde jako léčení příznaků místo hledání jádra problému. Tohle vlastně není moc užitečný komentář.";"ks" +"6535";"JJM320";"Propaganda a československá média po roce 1948";"Suk,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kms" +"6536";"JLB033";"Němčina I";;"Faltýnová,R.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"4";"3";"4";;;"cjp" +"6537";"JEB136";"Topics in Industrial Organization";"Schwarz,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Great approach, one of the best subjects taken during studies! Coming lecturers were really relevant to the papers we read";;"ies" +"6538";"JSB055";"Současná sociální antropologie";;"Grygar,J.,Hrešanová,E.";"4";"4";NULL;NULL;NULL;"4";"4";"5";"1";"4";"3";"4";"4";"Hodně textů, které jsme četli, byly zábavné i informativní. Rabinow, boxování, kohoutí zápasy i třeba obchod s orgány byly super texty.Hostující přednášející Jitka Jeníková byla úžasná, a přišlo mi, že rozumí antropologickému myšlení, ozvlášť na to, že antropoložka není. Takových hostů není nikdy dost!";"Já upřímně nejsem moc fanoušek těch průběžných testů. Nepřijde mi, že by moje výsledky z nich moc odpovídaly tomu, jak jsem který text pochopil. Navíc mi to přijde takové středoškolské a docela zbytečně stresující. Chápu, že jde o to, abychom se učili z hodiny na hodinu, ale přijde mi, že tohle není ideální. Třeba ty texty, které jsme psali v Dějinách sociální antropologie sice zabraly víc času jak nám napsat, tak asi i Vám opravit, ale něco takového by pro mě určitě bylo lepší. Třeba, že bychom psali každý druhý týden nějaký text/reakci/shrnutí/..., takhle byste toho neměli tolik na opravování každý týden, a my bychom se museli připravovat minimálně na každou druhou hodinu. To mi zní víc fajn a je to i kreativnější než psát test.Hostující přednášející Pavel Himl mi nepřišel ani moc zábavný, ani tak přínosný (kromě příběhu jak vzniklo \"hokus pokus\").Četl jsem v rámci přípravy na zkoušku text \"The Manager and His Powers\" od Johna Lawa a přišel mi jako krátké, výstižné a srozumitelné vysvětlení o co jde v materiálním obratu. Zvážil bych jestli ho nezařadit do kurzu třeba místo textu o paní Liv, který sice byl takový lidštější, ale o dost méně srozumitelný a podstatu materiálního obratu si z něj člověk musel víc dolovat.";"ks" +"6539";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kp" +"6540";"JJB143";"Žurnalistika a feminismus";"Krobová,T.,Osvaldová,B.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kz" +"6541";"JEB044";"Financial Accounting";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"3";"5";"4";"4";"3";NULL;NULL;NULL;"1";"4";"2";"2";"2";"Discussions were nice, however, some students need to speak up as I couldn`t hear part of their answers";"There is a huge lack of practice, homeworks are not that relevant to the exams, only two seminars were helpful - midterm and final revisions, others - again nothing relevant was discussed.";"ies" +"6542";"JPB202";"Politické strany v Evropě";"Perottino,M.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;"Aby vyucujici vcas hodnotil testy";"kp" +"6543";"JPB221";"Metodologický proseminář I";;"Střítecký,V.,Tesař,J.";"5";"5";NULL;NULL;NULL;"4";"5";"5";"1";"5";"5";"5";"5";;;"kmv" +"6544";"JPB229";"Regionální politické systémy: Skotsko, Wales";"Říchová,B.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"2";"4";"3";"4";"5";;;"kp" +"6545";"JPB258";"Politické myšlení novověku I";"Franěk,J.,Kučera,J.";;"4";"5";"4";"4";"3";NULL;NULL;NULL;"1";"4";"3";"3";"4";;;"kp" +"6546";"JPB262";"Politické systémy střední Evropy";"Mlejnek,J.,Švec,K.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;"aby vyucujici vcas hodnotil testy";"kp" +"6547";"JPB268";"Evropská integrace";"Plechanovová,B.";;"3";"4";"2";"2";"1";NULL;NULL;NULL;"4";"4";"4";"4";"3";;;"kmv" +"6548";"JPB269";"Současné problémy mezinárodních vztahů";"Knutelská,V.";;"4";"3";"2";"2";"2";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kmv" +"6549";"JPB558";"Výběrový seminář: Politická komunikace";"Váňa,T.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kp" +"6550";"JEB035";"Advanced Statistics";"Křehlík,T.,Víšek,J.";"Křehlík,T.,Víšek,J.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"6551";"JEB120";"Financial Economics";"Žigraiová,D.";;"4";"2";"4";"5";"5";NULL;NULL;NULL;"1";"3";"4";"4";"4";;"Lectures could be less monotonous, we were just going through slides word by word... I would welcome things like real life examples, videos etc.";"ies" +"6552";"JEB044";"Financial Accounting";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"5";"4";"5";"4";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Lectures were excellent";"Try to ignore hypermotivated students who already have 10 activity points. Create more exam questions, so we can go through them on the seminars after midterm. And lastly don't use problems we have not seen before in the midterm (the tax problem) as most students have limited analytical thinking and can't deal with something they have never seen before.";"ies" +"6553";"JEB110";"Econometrics II";"Pertold-Gebicka,B.";"Chorna,O.,Malinská,B.,Pertold-Gebicka,B.,Pleticha,P.";"3";"5";"4";"4";"4";"4";"5";"3";"2";"4";"4";"4";"4";;"Lecturer was good but she spend way too much time at marginal problems and then there was no time left for important ones, so I would welcome better time managment, maybe review lecture slides. Ms.Malinska could provide solutions to the seminars.";"ies" +"6554";"JPM700";"Space Security";"Doboš,B.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"kbs" +"6555";"JPM650";"Intelligence";"Bahenský,V.,Galeotti,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";"I heard this was the 'raddest course available'. It was true. Mark Galeotti is a great showman and a knowledgeable tutor. I always looked forward to his lectures, enjoyed them, and took a lot from them.";"I think the requirement for a final paper in the middle of the semester is not neccessary. We have quite a lot to do during the semester and this is extra workload that could well be saved for the exam period.";"kbs" +"6556";"JPM701";"European and Transatlantic Security";"Kazharski,A.";;"3";"4";"3";"5";"3";NULL;NULL;NULL;"1";"4";"1";"4";"3";"The readings were, most of the time, very interesting, the lecturer had great knowledge of the matter, and was very kind.";"The course was a bit disorganized, it seemed to me. The whole structure - 3hour seminars roughly every two weeks, on FRIDAYS - didn't seem very well thought-out to me. Mr. Kazharski was visibly tired most of the time, and so were we. The seminars consisted mainly of discussions on the literature, which was pretty good the few times that the discussion actually got going, but kind of awkward other times, when me or my colleauges did not have much to say to the readings. I would appreciate a more structured and active approach from the lecturer, giving a bit more backbone to the dicussions and providing us with some firm material during the lectures. Student participation is all the rage today, but I don't mind being simply taught from time to time, especially if the lecturer HAS interesting stuff to say, as Mr. Kazharski certainly does.We were required to read and write quite a lot for every class, which is absolutely reasonable. Since we were supposed to hand our work sufficiently in advance of the class so that the lecturer could read it, it was slightly disheartening then to hear him remark that he has not yet read it, during the class. I understand that he has a lot to do and that it is not easy to read and meaningfully react to all our papers, but then the requirements could be set up in other way so that the lecturer is not overwhelmed and we do not feel like we're working in vain (although the reading and writing is needed for the subsequent class discussions).The final paper seemed a bit unreasonably ambitious to me, given the somewhat loose way the whole course went.";"kbs" +"6557";"JMMZ141";"Russian Language I";;"Shvedova,O.";"4";"5";NULL;NULL;NULL;"5";"4";"4";"1";"3";"4";"4";"4";"i love the intense structure of the course";"it is overall too difficulte for basic level students, and the pace is too fast";"krvs" +"6558";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"4";"5";"4";"5";"4";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kmv" +"6559";"JSM018";"Economic Sociology and European Capitalism for MA";"Blokker,P.";;"3";"3";"5";"4";"4";NULL;NULL;NULL;"1";"3";"2";"2";"3";;;"ks" +"6560";"JLM006";"Angličtina pro politology II";;"Panešová,K.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"3";"5";"5";"5";"5";;;"cjp" +"6561";"JSM095";"Study of Political Mobilization and Social Movements";"Císař,O.";;"4";"4";"3";"2";"3";NULL;NULL;NULL;"1";"4";"3";"2";"2";;;"ks" +"6562";"JPM683";"Jiná jazyková znalost I";;;"5";"1";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kmv" +"6563";"JPM561";"Regional Security Studies";"Karásek,T.,Klosek,K.";"Karásek,T.,Klosek,K.";"3";"4";"4";"4";"4";"4";"4";"4";"1";"4";"1";"3";"4";;"The tests were often based on remembering unimportant details and were designed to try and trick us. I don't think that's a right way to test somebody's knowledge in university or anywhere else. This is not supposed to be a pub quiz.";"kbs" +"6564";"JPM191";"Geopolitics of Great Powers: Russia";"Baštář Leichtová,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"6565";"JSM421";"Contemporary social theory";"Balon,J.";;"2";"2";"2";"4";"2";NULL;NULL;NULL;"2";"3";"2";"3";"1";;;"ks" +"6566";"JSM578";"Anthropology of EU";"Uherek,Z.";;"3";"2";"3";"5";"3";NULL;NULL;NULL;"1";"3";"2";"2";"2";;;"ks" +"6567";"JPM727";"Orchestration in Global Governance";;"Abbott,K.,Parízek,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kmv" +"6568";"JPM725";"Technology and Security: Contemporary Warfare in the 21st Century";;"Csernatoni,R.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kmv" +"6569";"JSM692";"Introduction to Social Research Methodology";"Remr,J.";;"3";"3";"5";"5";"3";NULL;NULL;NULL;"2";"2";"3";"2";"3";;;"ks" +"6570";"JPM689";"Conflict Studies";"Karásek,T.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kbs" +"6571";"JPM430";"Marxism in International Relations (TIR)";;"Střítecký,V.";"5";"5";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kmv" +"6572";"JEM168";"European Economic Integration";"Komárek,L.";"Hedbávný,P.";"2";"4";"2";"3";"1";"2";"2";"2";"1";"2";"1";"2";"1";"nothing";;"ies" +"6573";"JPM526";"Justice and Reconciliation in Post-Conflict Societies";;"Werkman,K.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kmv" +"6574";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmv" +"6575";"JPM658";"International Economic Relations";"Parízek,M.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmv" +"6576";"JMM027";"Contemporary Mediterranean";"Králová,K.,Mejstřík,M.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";"space for student presentations therefore avoiding excessive lecturing timepractice of writing policy brief";"class debate, this is often up to the students, however perhaps could be better guided";"kzs" +"6577";"JMM048";"European Union in International Affairs";"Weiss,T.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";"well structured and clearly explainedspace for student presentation which avoids excessive lecture times";"more space for student debate";"kzs" +"6578";"JPM694";"Strategic Studies";"Hynek,N.,Ludvík,J.,Smetana,M.";;"3";"3";"4";"5";"3";NULL;NULL;NULL;"1";"3";"3";"3";"3";"I enjoyed the lectures on psychology and on the strategic implications of nuclear weapons, and also the lecture on 19th of October.I cannot say anything against the lecturers, perhaps with the exception of Mr. Ludvík's English, as he could use a bit broader and more fitting vocabulary and could work on being more confident and speaking more fluently, but I understand that's easier said than done.";"I am afraid the course did not leave much trace in my memory. It was not extraordinarily difficult, nor extraordinarily interesting. Often it felt very, very basic, which is understandable, I suppose, given its place in the curriculum, but it would not hurt to make it a bit more dense. I enjoyed the lectures on psychology and on the strategy of nuclear weapons, other times I was often just kinda bored.The room was too small (albeit with a nice view) and the furniture was probably designd by a sadist. It must have been hard to come up with something so user-unfriendly. That is not a fault of the course nor the teachers, however, of course.";"kbs" +"6579";"JLB013";"Němčina odborná I";;"Křenková,D.";"4";"5";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"5";"Precvičovanie posluchu, čítania, hovorenia aj gramatiky, hodiny dobre štrukturované, každý dostal priestor. Zdieľaná emailová schránka s cvičeniami nielen z hodiny ale aj doplňujúcimi na precvičenie gramatiky boli veľmi nápomocné. Prístup magistry Křenkovej k študentom bol vždy nápomocný a otvorený.";"Kurz bol zameraný na nemecké a rakúske reálie, politický systém apod. Ak má byť kurz zameraný všetkým špecializáciam, tak iné ako Česko-nemecké štúdia boli po obsahovej stránke znevýhodnené";"cjp" +"6580";"JPM696";"Economic Warfare";"Ludvík,J.";;"4";"4";"3";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";"While constructing scenarios and role-playing in Strategic studies, I sometimes felt like we were just kids playing and it has little educational value, because we just do not know enough. This was mitigated in the EW by better feedback from the teachers and better structured tasks. I liked the literature and the cases on which we worked in the groups, as I learned a lot of new stuff from that.";"The constant shifts in the working groups make-up were a bit annoying. Communication from the teachers about what we need to do and how do they rate what we have done could be faster and, more importantly, more systematic. All of this got way better towards the end of the course. I felt like the lectures could have been more dense, with less fruitless discussions among students for the sake of student participation. For example, after some 30 minutes of intense debate, we found out that rich and powerful states tend to win wars. I kind of suspected that even before.I felt very uncomfortable with rating my colleagues and I think this kind of corporate snitching does not have its place in the university. I have once experienced a case of extreme freeriding, damaging to the whole group, in a different course, but I'm not sure this is the right way to go. Or, to put it differently, I'm going to rate my colleagues with straight As, unless they are complete assholes.";"kbs" +"6581";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"3";"2";"2";"1";NULL;NULL;NULL;"1";"2";"2";"2";"1";;;"ies" +"6582";"JMBZ193";"American Media, Culture and Globalization";"Klvaňa,T.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Give us the global content regarding media, culture related to U.S., not just from EU prospective or US prospective";;"kas" +"6583";"JLM011";"Angličtina pro veřejnou a sociální politiku I";;"Klírová,M.";"5";"3";NULL;NULL;NULL;"4";"5";"5";"1";"4";"5";"4";"4";;;"cjp" +"6584";"JSB515";"Vysokoškolská vzdělávací politika";"Vlk,A.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kvsp" +"6585";"JSM514";"Metody a techniky práce s informacemi";"Tomandlová,V.";;"3";"2";"4";"4";"4";NULL;NULL;NULL;"1";"3";"4";"3";"4";;;"kvsp" +"6586";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"3";"4";"4";"4";"1";NULL;NULL;NULL;"2";"3";"2";"5";"4";;;"kmv" +"6587";"JPM052";"Mezinárodní ekonomické vztahy (MEV)";"Kučerová,I.";;"4";"2";"4";"4";"2";NULL;NULL;NULL;"1";"3";"3";"2";"4";;;"kmv" +"6588";"JSM516";"Sociální politika v perspektivě životního cyklu";"Dobiášová,K.,Kotrusová,M.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"5";"4";"5";"4";;;"kvsp" +"6589";"JPM127";"Geopolitika a geostrategie 1815 - 1945";"Romancov,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kp" +"6590";"JPM260";"Vybrané problémy britské zahraniční politiky v 19. a 20. století, ES";"Soukup,J.";;"4";"2";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kmv" +"6591";"JSM518";"Public Policy";"Potůček,M.,Vlčková,K.";;"3";"5";"4";"3";"2";NULL;NULL;NULL;"1";"4";"3";"3";"3";;;"kvsp" +"6592";"JPM430";"Marxism in International Relations (TIR)";;"Střítecký,V.";"3";"2";NULL;NULL;NULL;"3";"4";"4";"1";"2";"3";"3";"4";;;"kmv" +"6593";"JPM607";"International Negotiations";;"Parízek,M.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"4";"5";;;"kmv" +"6594";"JSM612";"Kriminalita a současná česká společnost";"Cejp,M.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"5";;;"kvsp" +"6595";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"3";"4";"5";"5";"2";NULL;NULL;NULL;"2";"3";"3";"2";"4";;;"kmv" +"6596";"JMBZ193";"American Media, Culture and Globalization";"Klvaňa,T.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";"Vazila jsem si predevsim ruznorodosti hodin, ktere byly casto koncipovane v jinem stylu. Hodne prinosna prace s ruznymi informacnimi zdroji (knihy, filmy, videa...) a nasledne diskuze.";;"kas" +"6597";"JPM644";"Contemporary International Relations in East Asia";"Kolmaš,M.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kmv" +"6598";"JJB221";"Ekonomická teorie marketingu";"Postler,M.";;"4";"3";"4";"3";"5";NULL;NULL;NULL;"1";"5";"2";"5";"5";;;"kmkpr" +"6599";"JJB231";"Dějiny a teorie public relations";"Halada,J.,Hejlová,D.";;"4";"5";"4";"4";"4";NULL;NULL;NULL;"1";"5";"3";"4";"4";;;"kmkpr" +"6600";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"4";"3";"4";"4";"1";NULL;NULL;NULL;"2";"4";"1";"3";"5";;;"kp" +"6601";"JPM526";"Justice and Reconciliation in Post-Conflict Societies";;"Werkman,K.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"3";"5";"5";;;"kmv" +"6602";"JPM689";"Conflict Studies";"Karásek,T.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kbs" +"6603";"JPM429";"Global terrorism (CS)";;"Makariusová,R.";"4";"4";NULL;NULL;NULL;"4";"5";"4";"1";"4";"3";"4";"4";;;"kmv" +"6604";"JPM052";"Mezinárodní ekonomické vztahy (MEV)";"Kučerová,I.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kmv" +"6605";"JMM074";"Landmarks in 20th Century U.S. History and Their Interpretations";"Pondělíček,J.";;"5";"4";"5";"4";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";"Dobre moderované diskusie, každý dostal priestor, zaujímavé témy. Koncept rozdelenia triedy na dve skupiny diskutujúce kontroverzné landmarks US history je nápomocný na precvičenie argumentácie aj pochopenie súvislostí. Projekt k seminárke zaslaný v strede semestra je dobrý donucovaní spôsob ako študentov prinútiť zamyslieť sa nad seminárkou skôr.";"Feedback k seminárke a teda povinnosť ísť k skúške by mohol byť poslaný skôr, študenti tak nevedia či sa pripravovať na obhajobu alebo nie.";"kas" +"6606";"JMB516";"Kultura a umění ve 20. století";"Pelánová,A.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"knrs" +"6607";"JMB515";"Německá otázka v mezinárodních vztazích a československé zahraniční politice";"Nigrin,T.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"knrs" +"6608";"JMB513";"Soudobé dějiny Dálného východu";"Sýkora,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kas" +"6609";"JMB409";"Moderní dějiny středo- a jihovýchodní Evropy-dcera JMB013";"Balla,P.,Švec,L.";;"3";"5";"4";"5";"4";NULL;NULL;NULL;"2";"5";"1";"4";"2";;;"krvs" +"6610";"JEB039";"International Trade";"Semerák,V.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"ies" +"6611";"JMM273";"Diplomový seminář II";;"Bečka,J.";"5";"3";NULL;NULL;NULL;"3";"3";"3";"1";"3";"3";"3";"4";;;"krvs" +"6612";"JMM279";"Společnost a ekonomika";"Fiřtová,M.";"Fiřtová,M.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"Dobre vybrané texty, zaujímavé a relevanentné. Prednášky podložené grafmi a videami.";"Preberali sa v podstate dve protichodné myšlienky ale na vybraných textoch bol vidieť bias proti liberalizmu a voľnému trhu. Bolo by zaujímavé čítať aj nejaký kontroverznejší text obhajujúci liberalizáciu obchodu či laissez faire.";"kas" +"6613";"NMMA703";"Matematika 3";"Zelený,M.";"Johanis,M.";"5";"5";"5";"4";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"6614";"JMM673";"Promoting democracy abroad: the US and the EU in third countries";"Hornát,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Zaujímavý koncept eseje o demokracii, človek sa vyhrá s myšlienkami. Texty pokryli ako moralizačné tak aj praktické aspekty promovania demokracie, dobrý balance medzi teóriou a empirickými príkladmi.";;"kas" +"6615";"JJB631";"Social Media: Strategy, Tactics and Analytics";"Audyová,P.";;"4";"2";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"3";"4";"The guest lecturers were definitely a big plus for the course since they were people that were working in this sphere and could give advice and tips from their personal experience";"The workload of the course could be increased for sure since the essay topics were very interesting but the required length and depth of the essays were not that long or demanding a lot of work. In other words, I would not have minded having had bigger assignments for interesting essay topics that we were provided as homework";"kmkpr" +"6616";"JJM117";"Popular Culture";"Turnau,T.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"3";"2";"3";"2";;;"kms" +"6617";"JJM240";"Cultural studies";"Soukup,M.";;"3";"2";"3";"5";"3";NULL;NULL;NULL;"1";"2";"2";"2";"4";"I really appreciated that we could choose and formulate the topic of the final essay ourselves, which made it way more interesting and pleasant to write the paper";"There could be less information about all the people known for discovering and exploring certain things (like photography, globalization, cultural diversity) and more interesting discussions created and encouraged about those topics";"kms" +"6618";"JJM372";"Consumer Behaviour";"Orhan,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"The way the whole course was explained was very interesting, the examples, the discussions were very interesting as well. Safe to say one of the best courses that I took during my exchange";"The lectures could be longer since the information that is given during them is so interesting time flies quite quickly. I feel like this course could be expanded for sure.";"kms" +"6619";"JMM629";"Hollywood/Europe: A Transnational Film Culture.";"Nowell,R.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"The discussions about films made me get used to looking at the movies from a lot of different perspectives, pay attention to detail. The course was very interesting.";"-";"kas" +"6620";"JMM048";"European Union in International Affairs";"Weiss,T.";;"4";"2";"3";"4";"3";NULL;NULL;NULL;"1";"3";"2";"4";"4";;;"kzs" +"6621";"JMM673";"Promoting democracy abroad: the US and the EU in third countries";"Hornát,J.";;"4";"2";"5";"5";"4";NULL;NULL;NULL;"1";"3";"2";"4";"4";;;"kas" +"6622";"JEB120";"Financial Economics";"Žigraiová,D.";;"1";"4";"1";"1";"1";NULL;NULL;NULL;"4";"3";"1";"5";"5";;"The teacher is terrible. Visiting lectures was just senseless.";"ies" +"6623";"JPM561";"Regional Security Studies";"Karásek,T.,Klosek,K.";"Karásek,T.,Klosek,K.";"2";"4";"2";"3";"2";"2";"3";"2";"1";"1";"3";NULL;"2";;"It would be better to limit the number of people who can attend this course and thereby make discussions during the course possible. The lecture was just an overview and actually did not help me to improve my knowledge, also because it was only based on studying for the tests shortly before and after that not working with the studied knowledge anymore.";"kbs" +"6624";"JLM001";"Academic English I";;"Cotte,P.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"3";"5";"Ms Cotte is the best! Always positive and full of energy to teach!";;"cjp" +"6625";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"4";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"5";"5";"The good humor and passion of Michal";"Can't think of anything right now";"kmv" +"6626";"JPM696";"Economic Warfare";"Ludvík,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"2";"5";"4";"4";"4";"I find the method of group conclusions giving the student a broader understanding of the topic. The reason being that in most cases we would often find similarities in different countries policies, saying that, this method gives a more depth understanding in the modus operandi of international politics.";"Some changes in schedule would be good. Some seminars or lectures this semester were cancelled because of national holidays";"kbs" +"6627";"JPM561";"Regional Security Studies";"Karásek,T.,Klosek,K.";"Karásek,T.,Klosek,K.";"4";"4";"4";"5";"4";NULL;NULL;NULL;"1";"5";"3";"4";"4";;;"kbs" +"6628";"JPM693";"Traditional and Critical Concepts in Security Studies";"Rychnovská,D.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"The course gives a broader understanding to International Politics and its dynamics. Dagmar, was very good at initiating instructive debates in class between students and always smiles (which for me is very important)";"Can't think of anything";"kbs" +"6629";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"4";"4";NULL;NULL;NULL;"4";"5";"5";"1";"5";"4";"3";"4";;;"cjp" +"6630";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"3";"3";"5";"4";"3";"4";"5";"4";"1";"3";"3";"3";"3";"Pan Polák dokázal velmi dobře vysvětlit jednotlivé příklady a zároveň poukazoval na různé souvislosti, které nám mohli pomoci k lepšímu pochopení látky. Škoda, že seminář s ním nebyl častěji. S paní Chytilovou jsme několikrát dělali nějaký experiment, na kterém jsme se aktivně podíleli. Přišlo mi to zajímavé, ale na druhou stranu jsme s tím vždy strávili příliš mnoho času, protože to vždy trvalo (než se všichni připojili, hlasovali, atd.). Tento čas mohl být ve finále využit mnohem lépe- například k tomu, abychom nemuseli probírat některé příklady do final testu na posledním semináři, který končil deset minut před začátkem onoho testu!";"Paní Chytilová i jednotliví cvičící odvedli svou práci poměrně dobře a v tomto smyslu bych jim nic nevytýkal. Ovšem během semestru se objevilo nezvykle mnoho různých situací a nesrovnalostí, které posouvají můj dojem z kurzu špatným směrem. Například dnes jsem psal final test a v jednom příkladu byl překlep, který měnil jeho zadání. Po dotazech nám bylo řečeno, že i když někdo příklad špatně (přitom vlastně správně) pochopil, bude mu to uznáno- což je sice chválihodné, ale na druhou stranu musím dodat, že takové chyby se objevovaly velmi často a napříč všemi materiály. Další věc- čekání na výsledky z midtermu bylo velmi dlouhé (což mne sice netěší, ale neberu to jako problém) a opět bylo vše provázeno různými nesrovnalostmi, protože došlo k úpravám v hodnocení kvůli nešťastně zadanému příkladu atd. Na jednu stranu jsem rád, že došlo k řešení situace a studenti nebyli jednorázově odmítnuti, ovšem myslím si, že jádro problému bylo především v zadání a tento problém tudíž vůbec nemusel nastat. Další zvláštností byla událost ohledně Ugura. Uznávám, že na konzultaci před midtermem nevěděl z hlavy některé důležité vzorečky a tudíž to byl spíše neformální rozhovor s praktickými radami. Ovšem na cvičeních jsme s ním byli spokojení- soustředil na předvedení a vysvětlení postupu a na jakékoliv dotazy normálně odpovídal, takže z jeho hodin jsem si vlastně odnášel velmi mnoho praktických dovedností. Tudíž bych chtěl říci, že mne jeho konec poměrně překvapil a pokud byl odvolán pouze na základě nespokojenosti některých studentů (kteří k němu často ani na cvičení nechodili) a tato situace nebyla nějak hlouběji zkoumána někým kompetentním, tak to bylo velmi neprofesionální. U cvičení bych ještě zmínil fakt, že paní Jonášová se velmi soustředí na opakování teorie na začátku hodiny a problém je ten, že potom velmi často nestíhá dopočítat všechny příklady. Souvisí to s tím, co jsem napsal k Ugurovi- občas je lepší vlastními slovy popsat postup, než zopakovat celý teoretický úvod. Nakonec bych ještě zmínil fakt, že termíny final testů se kryly s nejobtížnějšími zkouškami (z matematiky a statistiky), což rozhodně není dobré řešení.";"ies" +"6631";"JMB248";"Seminář k dějinám Ruska";;"Jasenčáková,M.";"4";"3";NULL;NULL;NULL;"4";"5";"5";"2";"5";"3";"4";"5";"Přístup vyučující ke studentům";;"krvs" +"6632";"JMB249";"Seminář k dějinám středo- a jihovýchodní Evropy";;"Zilynskyj,B.";"4";"3";NULL;NULL;NULL;"5";"5";"3";"1";"3";"4";"4";"5";;;"krvs" +"6633";"JMB410";"Moderní dějiny západní Evropy";"Matějka,O.,Váška,J.";;"3";"5";"3";"3";"1";NULL;NULL;NULL;NULL;"3";"3";"2";"3";;;"kzs" +"6634";"JMB408";"Moderní dějiny Ruska";"Litera,B.,Novák,P.,Pečenka,M.";;"5";"5";"4";"5";"4";NULL;NULL;NULL;"2";"5";"4";"5";"5";;;"krvs" +"6635";"JMB409";"Moderní dějiny středo- a jihovýchodní Evropy-dcera JMB013";"Balla,P.,Švec,L.";;"5";"5";"4";"5";"5";NULL;NULL;NULL;"2";"5";"4";"4";"4";;;"krvs" +"6636";"JPB558";"Výběrový seminář: Politická komunikace";"Váňa,T.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"6637";"JPB579";"Bc. seminář Politologie a veřejná politika I";;"Kváča,V.,Mouralová,M.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"5";"5";;;"kp" +"6638";"JSB004";"Sociální nerovnosti";"Šanderová,J.";;"4";"4";"4";"5";"3";NULL;NULL;NULL;"3";"4";"4";"4";"5";;;"ks" +"6639";"JSB073";"Veřejné politiky v trojdimenzionálním pojetí politiky";"Novotný,V.";;"3";"5";"4";"5";"3";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kvsp" +"6640";"JSB407";"Globální problémy životního prostředí a udržitelný rozvoj";"Drhová,Z.";;"3";"5";"4";"5";"3";NULL;NULL;NULL;"2";"3";"3";"3";"4";;;"kvsp" +"6641";"JEM034";"Corporate Finance";"Bychkova,O.,Malířová,T.";;"3";"3";"3";"4";"2";NULL;NULL;NULL;"1";"4";"4";"3";"4";;;"ies" +"6642";"JEM040";"Účetní a daňové poradenství";;"Kemény,I.";"3";"2";NULL;NULL;NULL;"3";"4";"3";"1";"3";"4";"3";"3";;;"ies" +"6643";"JEM132";"Company Valuation";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"ies" +"6644";"JEM137";"Real Estate Investment";"Jandík,T.,Streblov,P.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"2";"5";"5";"4";"5";;;"ies" +"6645";"JEM162";"Energy Markets & Economics";"Elms,N.,Valíčková,P.";;"3";"3";"3";"4";"2";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"ies" +"6646";"JEB136";"Topics in Industrial Organization";"Schwarz,J.";;"3";"3";"3";"4";"5";NULL;NULL;NULL;"2";"3";"1";"3";"3";;;"ies" +"6647";"JPM561";"Regional Security Studies";"Karásek,T.,Klosek,K.";"Karásek,T.,Klosek,K.";"3";"4";"3";"5";"1";"3";"5";"3";"1";"2";"2";"2";"2";;;"kbs" +"6648";"JPM598";"Grand Strategies";"Ditrych,O.";;"4";"2";"3";"5";"3";NULL;NULL;NULL;"1";"3";"3";"3";"2";;;"kbs" +"6649";"JPM703";"Czech Security Policy";"Kučera,T.,Ludvík,J.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"4";"3";"5";"3";;;"kbs" +"6650";"JSM707";"Networks in Political Sciences, Management and Administration";"Schneider,V.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"4";"5";;;"kvsp" +"6651";"JPB227";"Politický system ČR";"Charvát,J.";;"5";"1";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";"Pan dr. Charvát je vynikající přednášející.";"Tomuto kurzu podle mého chybí pokračování v letním semestru. Dalo by se v některých věcech jít více do hloubky, např. se více zabývat ústavou.";"kp" +"6652";"JEB111";"Advanced Data Analysis in MS Excel";;"Poláková,N.,Polák,P.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"3";"5";"Oceňuji přístup pana Poláka. Se vším ochotně poradí a je na něm vidět, že ho tento kurz baví.";;"ies" +"6653";"JEB044";"Financial Accounting";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"3";"4";"5";"4";"4";NULL;NULL;NULL;"2";"4";"3";"4";"3";"Pan Novák se snaží častými otázkami probouzet aktivitu studentů, což beru jako pozitivní a užitečný fakt.";"Přednášející a cvičící odvedli svou práci a v tomto směru bych jim nic nevytýkal. Na začátku tohoto kurzu jsem z něho měl dobrý dojem a myslím si, že mezi studenty obecně panovala dobrá atmosféra. Zlom přišel s midtermem, který nebyl podle představ většiny studentů. Já osobně jsem se na něj poměrně dobře připravil a vzorové otázky jsem bez větších problému uměl zodpovědět. Nakonec jsem získal nadprůměrný počet bodů, ale upřímně řečeno, během testu mi přišlo, že jsem se asi učil na něco jiného. Pan Novák se sice následně situací zabýval a pořád řešil, zda se nám ta či ona otázka zdála nepřiměřená atd. Já si ovšem nemyslím, že by to bylo konkrétními otázkami, jednoduše nám to přišlo poměrně těžké. A věřte, že se mi do teď ještě nestalo, že by úplně každý student, včetně těch nejlepších, říkal, že se mu něco zdálo těžké a zvláštní. V obtížnosti midtermu ale pořád nebyl, alespoň tedy pro mne, zásadní problém. Ten přišel ve chvíli, kdy jsme od pana Nováka obdrželi odpověď k midtermu : Podle nás bylo vše v pořádku, asi byste se měli začít více učit. V tu chvíli se můj zodpovědný přístup vytratil. Napomohl tomu také fakt, že opravení midtermu (i když má pan Novák asi osm asistentů) trvalo velmi dlouhou dobu. Čekání na výsledky neberu jako zásadní problém, ale například pan Červinka, který má na studenty poměrně vysoké nároky, nám opravil midterm do druhého dne(!), což od něho beru jako určité vyjádření respektu vůči studentům. Naopak pan Novák, který nám ještě explicitně zdůrazňoval, že má na nás vysoké požadavky, na mne působil opačným dojmem. Nechci tím říci, že by byl neochotný či nepříjemný (byl naopak velmi milý), ale myslím si, že zkrátka ke kurzu přistupoval vlastním způsobem, s vlastními pravidly a bez ohledu na věci okolo- a to je podle mne důvod, proč na mne jeho jednání ve finále zapůsobilo tak, jak jsem zde popsal.";"ies" +"6654";"JPB597";"Current Political Extremism";"Charvát,J.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"4";"4";"5";"Very good course, 88/100 students would recommend. Dr. Charvat is a great lecturer.";;"kp" +"6655";"JJB235";"Proces tvorby v marketingové komunikaci";"Bezouška,M.";;"4";"2";"4";"4";"3";NULL;NULL;NULL;"1";"4";"3";"3";"4";;;"kmkpr" +"6656";"JJB401";"Komerční a nekomerční marketingová komunikace";"Báča,L.,Obluk,O.";;"5";"3";"5";"4";"4";NULL;NULL;NULL;"1";"5";"3";"4";"5";;;"kmkpr" +"6657";"JJB009";"Úvod do psychologie";"Vranka,M.";;"3";"3";"3";"4";"3";NULL;NULL;NULL;"2";"2";"2";"2";"3";"Vědecké výzkumy";;"kz" +"6658";"JEM062";"Introductory Econometrics";"Kukačka,J.";"Kukačka,J.";"2";"5";"4";"4";"3";"3";"5";"3";"1";"3";"3";"3";"4";;;"ies" +"6659";"JEM166";"Master´s Thesis Seminar - IEPS";;"Benáček,V.";"4";"3";NULL;NULL;NULL;"4";"4";"3";"1";"4";"4";"4";"4";;;"ies" +"6660";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"1";"4";"1";"1";"1";NULL;NULL;NULL;"2";"3";"3";"3";"1";"1) Grading style should be changed.2) More class discussions should be encourage. 3) Classroom activities should be counted for grades.";;"kmv" +"6661";"JPM611";"Cyber Security";"Duračinská,Z.,Střítecký,V.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";"The attitude of our lecturer was outstanding. I really appreciated the interactive classes.";;"kbs" +"6662";"JPM324";"Geography and Politics in Europe within Global Regionalism";"Doboš,B.,Riegl,M.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";"1) Lecture slides should be provided, otherwise students cannot concentrate on the lecture because students have to write down the lecture notes.";;"kp" +"6663";"JSM657";"Welfare State, Political Parties, and the Policy Process";"Novotný,V.";;"2";"2";"2";"3";"2";NULL;NULL;NULL;"1";"2";"2";"2";"2";;;"kvsp" +"6664";"JPM650";"Intelligence";"Bahenský,V.,Galeotti,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";"The lecturer was always well-prepared. The knowledge and experience of the lecturer were essential to this course.";;"kbs" +"6665";"JPM699";"Security and Technology";"Střítecký,V.";;"3";"4";"3";"4";"4";NULL;NULL;NULL;"2";"4";"4";"3";"3";;"The lecturer was often late and unprepared, therefore I would suggest to change the lecturer or the starting time of the course. The lecturer should update his/her presentation, because it seemed obsolete. The lecturer should provide some examples in terms of essay topics.";"kbs" +"6666";"JMMZ340";"Freedom of Speech";"Klvaňa,T.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"3";"5";"The personality and the approach of the head of the course.";"Probably the bigger difficulty of final exams.";"kas" +"6667";"NMMA703";"Matematika 3";"Zelený,M.";"Turčinová,H.";"5";"5";"5";"5";"4";"4";"5";"5";"1";"4";"5";"4";"4";;;"ies" +"6668";"JPM700";"Space Security";"Doboš,B.";;"4";"4";"4";"4";"5";NULL;NULL;NULL;"1";"4";"4";"4";"5";"I had no background knowledge about the topic, but due to the preparedness of the lecturer I studied a lot. I appreciated the contribution of the guest lecturers.";"I would definitiely reconsider the group work task.";"kbs" +"6669";"JMM599";"Contemporary American Cinema";"Nowell,R.";;"5";"4";"4";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kas" +"6670";"JPM694";"Strategic Studies";"Hynek,N.,Ludvík,J.,Smetana,M.";;"2";"4";"2";"3";"2";NULL;NULL;NULL;"1";"1";"2";"2";"1";;;"kbs" +"6671";"JPM712";"Insurgency and Counterinsurgency";"Aslan,E.";;"5";"4";"5";"4";"4";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kbs" +"6672";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"4";"5";"4";"4";"4";NULL;NULL;NULL;"2";"3";"3";"4";"3";;;"kmv" +"6673";"JPM260";"Vybrané problémy britské zahraniční politiky v 19. a 20. století, ES";"Soukup,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kmv" +"6674";"JLM006";"Angličtina pro politology II";;"Panešová,K.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"2";"4";"5";"5";"5";;;"cjp" +"6675";"JMB498";"Metodologie soudobých dějin";"Smetana,V.";;NULL;"4";"3";"1";"3";NULL;NULL;NULL;"1";"3";"1";"2";"1";;"The lecturer does not obviously understand that critique can be communicated in a humanly way. His one-on-one communication was entirely inappropriate; he yelled at me like if I were a little helpless child and made every effort to make me feel like an idiot. He obviously had a bad day but that is no excuse. Although I have not spent my entire professional career in academia like himself, I am only two years younger, I study while working full time at a demanding job, have my own accomplishments. I am 42 years old. The lecturer’s demeanor is totally unacceptable to me.";"krvs" +"6676";"JPM727";"Orchestration in Global Governance";;"Abbott,K.,Parízek,M.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"5";;;"kmv" +"6677";"JEB023";"Úvod do studia práva";"Pražák,P.,Wintr,J.";;"5";"2";"5";"5";"2";NULL;NULL;NULL;"1";"3";"2";"3";"4";;;"ies" +"6678";"JEB055";"Seminář k aktualitám I";;"Vyhnánek,T.";"3";"1";NULL;NULL;NULL;"4";"4";"1";"1";"1";"1";"2";"3";;;"ies" +"6679";"JEB058";"Seminář matematické analýzy I";;"Stráský,J.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"4";;;"ies" +"6680";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"5";"3";"5";"5";"3";"4";"5";"2";"1";"4";"3";"4";"5";;;"ies" +"6681";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"5";"5";"4";"1";"2";"2";"1";"5";;;"kz" +"6682";"JLB003";"Angličtina pro ekonomy I";;"Gloverová,M.";"5";"2";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"5";"5";;;"cjp" +"6683";"JLB035";"Francouzština I";;"Dundrová,M.";"4";"2";NULL;NULL;NULL;"5";"5";"5";"1";"4";"3";"4";"4";;;"cjp" +"6684";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"2";"5";"5";"2";NULL;NULL;NULL;"1";"4";"2";"3";"5";;;"ks" +"6685";"NMMA701";"Matematika 1";"Spurný,J.";"Honzík,P.";"5";"5";"5";"5";"5";"3";"4";"5";"1";"5";"5";"4";"4";;;"ies" +"6686";"JEM059";"Quantitative Finance I";"Baruník,J.,Vácha,L.";"Baruník,J.,Vácha,L.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"6687";"JMM279";"Společnost a ekonomika";"Fiřtová,M.";"Fiřtová,M.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"4";"5";"5";"Nejlepší ze všech tří společných předmětů, jak přednáška tak seminář. Seminář jako jediný opravdu rozvíjel látku probíranou v hodinách. Všechna témata byla aktuální, zajímavá, dobře zvolená. Požadavky vyšší než v ostatních kurzech, nicméně tomu na konci odpovídal i přínos předmětu.";"Bylo by lepší mít předmět roztáhnout na dva semestry, aby mohlo být pokryto víc temát a do větší hloubky.";"kas" +"6688";"JMMZ314";"Major Issues in Contemporary Public Debates in the U.S. I";"Sehnálková,J.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Rozhodně jeden z nejlepších předmětů, ideální formát zkoušky, zajímavá témata, texty také k věci a přínosné. Kdyby bylo celé studium pojato podobnou formou, byla by úroveň o mnoho výš. Skvělý nápad s facebokovou skupinou! Spolu s předmětem government to bylo největší rozšíření znalostí v severoamerické specializaci.";"Pro každé téma by se hodilo více prostoru, aby se mohlo jít více do hloubky. Hodiny s guest lecturers nebyly moc přínosné. Oceňuji snahu, ale sama vyučující témata rozhodně zvládla pokrýt lépe.";"kas" +"6689";"JMMZ313";"Government in United States";"Sehnálková,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";"spolu s major issues výborně vedený kurz, vhodně zvolená témata, skvělý přístup vyučující.";"některá témata (soudní systém např.) mohla jít více do hloubky, občas \"pouze\" kopírovala témata, která jsme se (pokračující z bakaláře) učili na státnice (aniž by nám je tehdy někdo vysvětlil), magisterský kurz mžná mohl být trochu podrobnější.";"kas" +"6690";"JMMZ315";"U.S. Foreign Policy";"Raška,F.";;"1";"1";"1";"3";"1";NULL;NULL;NULL;"1";"1";"3";"1";"1";;"Nejhorší ze všech předmětů, nulový přínos, po několika úvodních hodinách již probíhal formou desetiminutové promluvy vyučujícího k zadaný textům a zbytek byly prezentace ostatních studentů na zcela nahodilá témata, předmět naprsoto selhal v tom, co od něj bylo očekáváno - vysvětlit historické základy am. FP, dnešní situaci, z čeho americká FP vychází atp., clekově naprosto zbytečný kurz, nově získané informace žádné. Doporučuji zvážit změnu vyučujícího, např. dr.Hornáta. Nulový komentář k písemné práci, ústní zkouška zhruba dvě minuty, taktéž žádná zpětná vazba, student se nutně ptá, k čemu tu seminárí práci psal, a zda ji někdo vůbec četl.";"kas" +"6691";"JMM278";"Politika a mezinárodní vztahy";"Kubát,M.,Weiss,T.";"Bečka,J.";"3";"3";"4";"4";"3";"3";"5";"3";"3";"3";"3";"3";"2";"Přednášky celkem zajímavé, ale podobný předmět byl již v bakalářském studiu. Některá témata šla pojmout více aktuálně a celkově mohla být více propracovaná. Seminář pojatý velmi laxně, žádná struktura, spíše náhodná diskuse a s tétatem předmětu ne tak úplně souvisel. Vyučující semináře měl ke studentům výborný a vstřícný přístup, hodiny by mi však vyhovovaly více strukturované. Texty na semináře většinou velmi zajímavé.";"Přednáčky do značné míry pouze kopírovaly již probraná témata na bakaláři, byla sice zajíamvá, nicméně celkově je předmět jeden velký shluk témat \"kdo-ví-podle-jakého-klíče\" vybraných témat, obzvláště přednášky docenta weisse, byť zajímavé, zabáraly takovou šíři, že byly vlastně zbytečné.seminář byl taktéž vlastně zbytečný, naprosto nerozvíjel témata probíraná v hodinách, bez obsahu a struktury, víceméně pouze diskuzní kroužek,, což nemusí být špatné, nicméně celkový přínos semináře (např. v porovnání se seminářem k ekonomice) byl velmi malý. chybou bylo, že nebyla vedena docházka a účast na semináři tomum odpovídala, což mělo vliv i na diskuzi.";"krvs" +"6692";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"1";"5";"1";"1";"1";"3";"3";"3";"3";"1";"1";"1";"1";"Nejvíce ocenuji, že většina cvičení je stažená z internetu. U většiny jiných kurzů bych to sice považoval za známku toho, že chce daný vyučující pouze ušetřit práci, kteoru by musel na vymýšlení vlastních příkladů vynaložit. U tohoto kurzu to však považuji za jeho nejsilnější stránku, jelikož v těch několika málo příkladech, které vyučující sami vymysleli, bylo takové množství chyb, že každý rozumný člověk musí uznat, že je kopírování seminářů z Mankiwa jediná možnost, jak dát studentům alespon nějaké studijní materiály, ve kterých nebývá moc chyb. Nechci tím však říct, že by semináře byly nějak přínosně (jedinou výjimkou byl pan magistr Brož), často jsem pak při učení zjistil, že je postup chybný, tak jsem skončil u toho, že jsem danou látku učil z internetu, jelikož tam se dané chyby nevyskytovaly.";"Na kurzu se dá asi zlepšit úplně všechno, nejvíce bych rozhodně ocenil, kdyby pan Kudashvili jednal se studenty s respektem. Já, i mnoho bých spolužáků, máme bohužel opačné zkušenosti (detaily napíši až u hodnocení macra II, jelikož by mě pan Kudashvili mohl poznat a zhoršit mi prospěch v dalším semestru).";"ies" +"6693";"JPB268";"Evropská integrace";"Plechanovová,B.";;"2";"5";"3";"1";"2";NULL;NULL;NULL;"4";"3";"2";"3";"1";;"zrychlit opravu testů a myslím, že by na ně mělo bylt více času protože požadované množství textu se dá za hodinu času napsat jen velmi těžce";"kmv" +"6694";"JLB029";"Španělština odborná I";;"Mlýnková,L.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"3";"5";;;"cjp" +"6695";"JMM674";"Maritime security: Geopolitics of the Indian and Pacific Oceans";"Hornát,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"- it is an excellent course to broaden your knowledge, because the topic is oftentimes not discussed elsewhere- I gained very useful geopolitical knowledge about the region- great feedback";"- to have time for more discussion";"kas" +"6696";"JMM277";"Historie a kultura";"Vykoukal,J.";"Kýrová,L.";"3";"3";"3";"5";"3";"2";"4";"1";"1";"3";"3";"4";"3";"přednášky doc.Vykoukala dobré, zajímavé (leč vedeny smrtícím tempem, což nicméně studenti z bakaláře již vědí:)), občas se témata pro navazující bakaláře opakovala, celkově to však nebylo špatné.";"Špatný seminář. Dokrotka Kýrová je bezpochyby velmi znalá, orientuje se v tématu, a vždy byla velmi ochotná a schopná vše vysvětlit. Nicméně seminář probíhal velmi volně, bez struktury, víceméně pouze diskuze o textech, často rozpačité mlčení, diskuze mnohdy k ničemu nevedla, zpětně viděný přínos semináře velmi malý.";"krvs" +"6697";"JMM271";"Metodologický seminář";;"Kýrová,L.";"3";"3";NULL;NULL;NULL;"3";"4";"3";"1";"3";"3";"3";"3";;"V kurzu byly celkem velké nároky na splnění, ale předmět je hodnocen pouze zápočtem";"krvs" +"6698";"JMM703";"Post-Soviet Central Eurasia";"Lídl,V.,Šír,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"-very interesting topic, which is oftentimes neglected-great approach of the teacher-interesting strucutre of the course (host lecturers, movie etc)";"--one introductory lesson might have been useful, especially for students without specialisation in east europe";"krvs" +"6699";"JMM277";"Historie a kultura";"Vykoukal,J.";"Kýrová,L.";"3";"3";"4";"4";"3";"3";"4";"4";"1";"3";"3";"4";"3";;;"krvs" +"6700";"JMM279";"Společnost a ekonomika";"Fiřtová,M.";"Fiřtová,M.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"3";"5";"5";;;"kas" +"6701";"JMM271";"Metodologický seminář";;"Kýrová,L.";"1";"4";NULL;NULL;NULL;"1";"5";"1";"1";"1";"3";"2";"1";"doktorka kýrová vemi ochotná vysvětlit jakékoliv dotazy, milá a vstřícná, bezpochyby odborník v tématu. občas byly debaty vedené studenty dobré.skvělá hodina s guest lecture mladou slečnou, naopak nudný antropolog.";"Po Foreign Policy nejhorší předmět, debaty vedené studenty občas byly přínosem pouze pro daonu dvojici. Kdžy došlo na doktorku Kýrovou, obrovské, podrobné prezentace, které navíc paní doktorka četla, načež do dvou minut všichni bohužel ztratili pozornost. Všem bylo jasné, že je doktorka Kýrová odborník a vždy byla schopná řípadné dotazy vysvětlit, nicméně hodina jako takové vedená byla velmi špatná, nudná, víceméně bez přínosu, protože není možné udržet pozornost u rychlým tempem předčítaných obrovských slidů powerpointu. k závěrečné práci jsme nedostali jediný komentář, stejně tak jako u semináře k historii a kultuře - k čemu je takovou práci psát, když student nedostane jakoukoliv zpětnou vazbu? pro kolonku v sisu?";"krvs" +"6702";"JSB455";"Economic Sociology and European Capitalism";"Blokker,P.";;"2";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"2";"4";"5";;;"ks" +"6703";"JMMZ314";"Major Issues in Contemporary Public Debates in the U.S. I";"Sehnálková,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kas" +"6704";"JPM710";"Radicalization and Deradicalization";"Aslan,E.";;"3";"4";"2";"2";"2";NULL;NULL;NULL;"1";"3";"1";"3";"2";"-reasearch of the prof - very intersting";"-prof doesnt really teach, students do presentations for every class and oftentimes they just repeat the articles you were supposed to read";"kbs" +"6705";"JMMZ313";"Government in United States";"Sehnálková,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"5";"5";"3";"5";"5";;;"kas" +"6706";"JSM103";"Academic Writing";;"Blokker,P.";"3";"3";NULL;NULL;NULL;"5";"5";"3";"1";"3";"2";"5";"4";;;"ks" +"6707";"JSM480";"Evaluation Research";;"Remr,J.";"3";"4";NULL;NULL;NULL;"3";"5";"1";"1";"4";"2";"4";"3";;;"ks" +"6708";"JPM721";"Komparace ekonomik zemí EU";"Kučerová,I.";;"1";"1";"3";"3";"1";NULL;NULL;NULL;"1";"1";"1";"1";"2";;"the class is called comparison, there were no criteria how we compare the economies - there were just random facts about the countries -often not updated";"kmv" +"6709";"JMM673";"Promoting democracy abroad: the US and the EU in third countries";"Hornát,J.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"5";"ne vždycky jsme stihli v hodině vše, co bylo naplánované, to byla asi celkově škoda :)";;"kas" +"6710";"JMMZ315";"U.S. Foreign Policy";"Raška,F.";;"3";"2";"4";"4";"2";NULL;NULL;NULL;"1";"3";"3";"3";"4";;;"kas" +"6711";"JMMZ339";"Populism in the U.S.";"Klvaňa,T.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"kas" +"6712";"JJM080";"Zábava v televizním vysílání";"Kruml,M.";;"5";"2";"4";"5";"4";NULL;NULL;NULL;"1";"4";"2";"3";"5";"jednoduché a zábavné :)";;"kms" +"6713";"JJM201";"Teorie masové a mediální komunikace";"Jirák,J.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kms" +"6714";"JMM074";"Landmarks in 20th Century U.S. History and Their Interpretations";"Pondělíček,J.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"3";"4";"4";"4";"4";"velmi zajímavý úhel pohledu předmětu, probírat různé interpretace je určitě důležité a celkově to hodnotím jako přínosné, i při zpracovávání prací do jiných předmětů. vyučující byl schopen vést zajímavým způsobem diskuze, atmosféra na hodinách příjemná a podmětná.";"organizační schopnosti vyučujícího,obrovské prodlevy mezi reakcemi vyučujícího. k závěrečné práci žádný komentář, opakující se téma, proč ty práce píšeme?..";"kas" +"6715";"JJM202";"Vývoj a postavení médií v moderní společnosti";"Bednařík,P.,Cebe,J.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kms" +"6716";"JJM204";"Výzkum médií I";"Křeček,J.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"4";"5";"4";"4";;;"kms" +"6717";"JPB268";"Evropská integrace";"Plechanovová,B.";;"1";"5";"1";"1";"1";NULL;NULL;NULL;"3";"1";"1";"1";"1";;;"kmv" +"6718";"JEB105";"Statistics";"Červinka,M.";"Červinka,M.";"3";"5";"3";"5";"2";"3";"5";"3";"1";"3";"2";"3";"3";"Za pozitivní pouvažuji, že jsou prezentace zčásti stažené z internetu a zčásti přepsaná učebnice. Eliminuje se tím nešvar z jiných předmětů, a sice neuvěřitelné množství chyb ve studijních materiálech.";"Tento předmět se mi hodnotí velice těžko. Pan doktor Červinka problematice rozumí, avšak pro mě osobně přednášky nijakou přidanou hodnotu neměly. Důkazy pan doktor přepisuje z papíru na tabuli a i přesto se v nich vyskytuje značné množství chyb. Jak je možné, že věci, které my mámě umět na zkoušku zpaměti on zpaměti neumí? Pomíjím tedy to, že jemu stačí se nazpaměť naučit těch několik málo důkazů, které jsou na konkrétní hodině přednášeny. U několika vět pan doktor působil velice nesebejistým dojmem a občas také omylem dokázal jinou větu, než původně zamýšlel.";"ies" +"6719";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"1";"3";"1";"1";"1";"4";"5";"2";"1";"1";"1";"1";"1";"Doporučil bych pravděpodobně zachovat ty studijní materiály, které obsahují méně, než tři chyby na stránku.";"Z mého pohledu doposud nejméně kvalitní kurz na IES. Prezentace i cvičení obsahovaly velkou řadu chyb, stejně tak, jako závěrečné testy, jejichž obtížnost netkvěla v úrovni mikroekonomie, ale pouze v mechanických zdlouhavých početních operací. Jedním z faktorů byl pravděpodobně poněkud nesmyslný zákaz kalkulaček. Před vstupem na IES jsem netušil, že se budu učit takové množství poměrně pokročilé matematické teotie, abych nakonec skončil u toho, že obsah nějakého z kurzů - a v tomto případě tedy poměrně zásadního - matematicky nesedí. Můj závěrečný test, stejně, jako většina testů dalších spolužáků, ztroskotal pouze na tom, že jsem v tak velkém množství triviálních matematických operací, jako násobení víceciferných čísel, udělal chyby, což způsobovala hlavně absence kalkulaček, protože se domnívám, že většina studentů, kteří prošli i první matematikou (natož tedy třemi, jako většina z nás, co měla zápsáno Microeconomics II) tyto operace obecně ovládá. Pokud tedy vedení IES chce po studentech absolvovat kurzy podobné kvality, navrhoval bych, aby se minimalizovala jejich matematická znalost - pak se, snad, nebude stávat, že budou schopni vyvrátit každé zadání/řešení příkladů, případně teorii na přednášce. Zároveň se domnívám, že za všechny chyby kurzu zodpovídají učitelé/asistenti, vzhledem k tomu, že věda mikroekonomie sama o sobě matematicky funguje, čili jakékoliv nesoulady vznikaly pouze nekompetencí lidí zodpovědných za kurz. Jedním zásadním zlepšením by tedy mělo být plné využití matematických znalostí studentů v rámci kurzu, bylo by dobré používat matematiku v plném rozsahu tak, jak to máme naučené z náročných kurzů na mff, ale, prosím, správně.";"ies" +"6720";"NMMA703";"Matematika 3";"Zelený,M.";"Bartoš,A.";"5";"5";"5";"5";"4";"3";"5";"3";"1";"3";"1";"1";"4";"Tento kurz se mi hodnotí opravdu velice těžko. Pan docent Zelený je kvalitní pedagog, který problematice rozumí nepoměrně více, než jakýkoliv vyučující na IESu, kterého jsem měl zatím za své studium možnost potkat. Přijde mi to trochu smutné, že jsem studentem IESu, avšak jediný kurz, který je zajištován jinou fakultou, je zvládnut nepoměrně lépe než jakýkoliv z povinných kurzů na IESu (tím nechci říci, že by ty volitelné byly lepší, ale zatím jsem jich moc neměl). Kurz je tedy dobrý, ale nevidím v něm žádný přínos pro mé ekonomické znalosti.";"Opravdu nevím, v čem mi kurz Matematika III prohloubil znalosti ekonomie. Přijde mi naprosto absurdní, že na jedné straně říkáme, že se na IESu učí matematizovaná ekonomie, učíme se relativně pokročilou matematickou teorii a počítéme těžké příklady, ale na druhé straně existují předměty jako Micro nebo Macro, které ve své současné podobě jsou v kontextu takto pokročilé matiky naprosto absurdní. Jsem si jistý, že kdyby pan docent Zelený navštívil cvičení z Macra, kde se aplikuje následující postup, tak by asi dostal infarkt: máme rovnici A*B=C*D, aplikujeme logaritmus: log(A*B)=log(C*D), následně využíváme vlastností logaritmu: logA+logB=logC+logD, následně před každý člen napíšeme deltu (rozdíl): delta*logA+delta*logB=delta*logC+delta*logD, následně škrtneme logaritmy a dostáváme: deltaA+deltaB=deltaC+deltaD. Kdyby se z tohoto postupu pan docent vzpamatoval, tak ještě doporučuji, aby se šel podívat na nějaké semináře nebo přednášky kurzu Microeconomics II. Například používání Lagrange při počítání extrémů je všechno, jen ne matematicky správně. I \"teorie\" Micra II je naprosto matematicky nekorektní. Například na úvodní přednášce nám paní docentka Chytilová zadefinovala production function i s vlastnostmi, jaké musí production function vždy mít. Když jsem pak počítal na semináři. v domácím úkole anebo i v testu úlohy právě na production function, tak drtivá většina funkcí vůbec produkční funckí nebyly, protože nesplňovaly dané vlastnosti. Při mých dotazech na cvičící, proč dané produkční funkce předpoklady nesplňují, se mi akorát dostalo údivu cvičících, kteří vůbec nevěděli, že produkční funkce nějaké předpoklady vůbec má.";"ies" +"6721";"JEB039";"International Trade";"Semerák,V.";;"4";"4";"3";"5";"3";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"ies" +"6722";"JEM179";"History and Methodology of Economics";"Baxa,J.,Paulus,M.";;"5";"5";"5";"5";"3";NULL;NULL;NULL;"1";"5";"5";"5";"3";;;"ies" +"6723";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"1";"3";"1";"2";"1";"1";"1";"1";"2";"1";"1";"1";"1";"Nikdy se mi to v hodnocení ještě nestalo, ale ani po usilovném přemýšlení jsem na žádné pozitivum tohoto kurzu nepřišel.";"Z mého pohledu je Micro II zdaleka nejtristnější kurz ne IESu (což není nijak lehké v konkurenci tak absurdních předmětů jako je Macro I v podání pana Kudashviliho). Ve studijních materiálech se vyskytuje neuvěřitelné množství chyb. Nijak jsem se tyto chyby nesnažil aktivně hledat, avšak napočítal jsem jich zatím okolo devadesáti. Pokud by chtělo vedení vidět nějakou část těchto materálů, ve kterých je snad více věcí špatně než správně, tak doporučuji za teorii první přednášku a za počítání seminář pana Poláka k problematice dvou továren. První přednáška mi přijde jako dobré shrnutí absurdity celého kurzu, která je umocněna ještě kontextem pokročilosti matematického aparátu, který nabýváme v kurzech matematiky na matfyzu. Paní docentka Chytilová nám právě na úvodní přednášce zadefinovala production function i s vlastnostmi, jaké musí production function vždy mít. Když jsem pak počítal na semináři, v domácím úkole anebo i v testu úlohy právě na production function, tak drtivá většina funkcí vůbec produkční funkcí nebyly, protože nesplňovaly dané vlastnosti (stejný problém byl i s cost a supply functions). Při mých dotazech na cvičící, proč dané produkční funkce předpoklady nesplňují, se mi akorát dostalo údivu cvičících, kteří vůbec nevěděli, že produkční funkce nějaké předpoklady vůbec má. Kapitola sama pro sebe je dnes již legendární konzultace s panem Ugurem. Nejsmutnější mi přijde, že jsem si myslel, že se jednalo pouze o selhání jednotlivce a že ostatní členové pedagogického týmu mají v problematice hluboké znalosti. Nyní jsem však přesvědčen, že pan Ugur měl znalosti mikroekonomie srovnatelné s ostatními cvičícími. Snad jediné pozitivum tohoto kurzu je jeho umění překvapovat: Když si již všichni studenti na konci kurzu mysleli, že jsou připraveni na všechno, tak přišel final test. Opravdu doporučuji vedení si tyto testy vyžádat a projít si jejich řešení, ke kterému měli studenti v časovém limitu bez kalkulaček dospět. K tomuto testu se váže mé nejkonkrétnější doporučení na zlepšení tohoto kurzu: nenechávejte pana Poláka připravovat počítací část. Na jedné straně pan Polák měl, pokud se nemýlím, na starosti dvě cvičení, ve kterých má v oficiálním řešení v SISu tak obrovské množství chyb, že jsem přesvědčen, že kdyby takovéto řešení odevzdal, když by final psal on, tak ani neprojde (ve druhé části cvičení na oligopoly sice chybu neměl žádnou, ale jen z toho důvodů, že na vypracování výsledků patrně rezignoval). Na druhé straně zadává testy, které jsou početně velice náchylné na chyby. Opravdu nevím, jaký má smysl, že z dvouhodinového testu mikroekonomie strávím 80 minut sčítáním zlomků a čtyřciferných čísel. Dále se v testech také opakovaně vyskytovalo zadání, ve kterém jsme ve finálním kroku měli počítat kubickou rovnici, jejímž řešením žádné číslo, které by se dalo \"uhodnout\" nebylo. Pokud by existoval povinný kurz Micra III, tak bych se, poučen zkušeností z kurzu Micra II, na final připravoval pouze trénováním sčítání a násobení čtyřciferných zlomků. Závěrem bych ještě rád dodal, že pan Polák hodnotí testy velice subjektivně a hlavně nekonzistentně. Nekonzistnentě nemyslím pouze tak, že by hodnotil každému studentovi testy jinak, to dělá také, ale hlavně v tom, že strhává body za to, co on sám má uvedeno v oficiálním řešení v SISu.";"ies" +"6724";"JEB105";"Statistics";"Červinka,M.";"Červinka,M.";"3";"5";"3";"5";"2";"4";"5";"5";"1";"4";"3";"4";NULL;"Určitě oceňuji aktivní přístup pana doktora Červinky, zejména jeho velkou responzivnost na emailové dotazy k teorii i příkladům ve zkouškovém období. Nebýt předmětů jako Macroeconomics I s panem magistrem Kudashvilim, málem bych solidní jednání pana doktora považoval za samozřejmost a běžnou profesionalitu, takto ji chci obzvlášť vyzdvihnout. Rovněž ústní zkoušení probíhalo v příjemné atmosféře.Dále oceňuji výběr příkladů ve cvičeních, kde se opravdu typově nacházel alespoň jeden příklad od snad každého druhu příkladu ve zkouškových písemkách, jinými slovy, jednalo se o velmi dobrý reprezentativní vzorek. Semináře tedy hodnotím jako velmi přínosné. Jen bych v tomto bodě opět (jako již třetím semestrem) zkritizoval formu hodnocení, ačkoliv reklamuji stejné chyby (nevím, jestli mohu někomu nedoporučit povinný předmět; kde přesně je rozdíl znalostí a dovedností), tyto zůstávají a přibývají další. Když tedy hodnotím \"přístup cvičícího\" jako \"velký\", předpokládám, že tím říkám, že přístup pana doktora byl dobrý.";"Upřímně řečeno jsem chtěl kurzu Statistics dát spíše špatné hodnocení, nicméně porovnání s ostatními předměty tohoto semestru mě donutilo dát lepší bodové hodnocení. Nicméně objektivně: zejména je třeba zlepšit přednášení důkazů. Rozumím tomu, že 70 důkazů je hodně na odpřednesení, ale i pro nás je to hodně k naučení, a nemyslím si, že součástí domácí přípravy je i dohledávání důkazů, které se má člověk učit. Vzhledem k tomu, že důkazy nejsou v prezentacích a jiných zveřejněných kurzových materiálech (nehledá-li člověk paralelně ve třech učebnicích a na internetu - každý je jinde), měly by být odpředneseny, popřípadě, není-li čas je všechny odpřednést, pak by některé další věty mohly být oficiálně bez důkazu. U některých vět pak byla dokázáno z důkazu například jen a), přestože bylo zkoušeno i b) a c). Obecně i samotný přednes důkazů by mohl být kvalitnější, aby člověk při učení nemusel odpřednesenou formu důkazu zpochybňovat a ověřovat z více zdrojů. Z části i díky nedostatkům v přednesu důkazů (definice si člověk stejně musí projít sám, chápu, že nejsou primárním obsahem přednášek), jsem u přednášek nenašel velkou přidanou hodnotu.Poněkud nepříjemné bylo časové rozvržení úkolů, kde poslední dva úkoly vyšly na poslední dva týdny semestru, totéž se stalo v Microeconomics II i Macroeconomics I (kde se tedy jednalo o jeden úkol), v součtu tedy bylo ve dvou týdnech 5 úkolů, do čehož se člověk snažil stihnout nějaké předtermíny.";"ies" +"6725";"JJB224";"Psychologie marketingové komunikace";"Vranka,M.";;"4";"3";"3";"4";"4";NULL;NULL;NULL;"1";"5";"2";"4";"4";;;"kmkpr" +"6726";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"2";"4";"2";"3";"3";NULL;NULL;NULL;"1";"2";"1";"2";"1";;;"kmv" +"6727";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"4";"4";"4";"4";"3";NULL;NULL;NULL;"1";"3";"3";"3";"3";;;"ies" +"6728";"JPM658";"International Economic Relations";"Parízek,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmv" +"6729";"JLB001";"Angličtina pro sociology I";;"Štěpánková,D.";"3";"2";NULL;NULL;NULL;"3";"3";"3";"1";"2";"2";"2";"2";;;"cjp" +"6730";"JSB007";"Dějiny sociologie I";"Šanderová,J.";"Bureš,J.";"5";"3";"5";"5";"5";"5";"5";"5";"2";"4";"5";"5";"4";;;"ks" +"6731";"JPM689";"Conflict Studies";"Karásek,T.";;"2";"4";"3";"3";"3";NULL;NULL;NULL;"1";"1";"1";"2";"2";;;"kbs" +"6732";"JSB051";"Úvod do sociální antropologie";"Uherek,Z.";"Hrešanová,E.";"4";"3";"5";"5";"3";"5";"5";"4";"2";"4";"4";"5";"5";"Výběr textů, které se diskutovaly při seminářích byl podle mě zajímavý a přínosný a i samotné diskuze byly povedené.";;"ks" +"6733";"JEM027";"Monetary Economics";"Holub,T.,Malovaná,S.";"Břízová,P.,Hájek,J.,Holub,T.,Malovaná,S.";"1";"4";"2";"3";"2";"4";"4";"4";"1";"2";"2";"2";"2";;;"ies" +"6734";"JLM006";"Angličtina pro politology II";;"Panešová,K.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Very positive and especially kind attitude of the techer. Course significantly enhanced and enriched my knowldge and ability to work and prepare in english language.";;"cjp" +"6735";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Rybín,F.,Vlčková,A.";"5";"4";"5";"5";"4";"5";"5";"5";"1";"5";"5";"5";"5";;;"ks" +"6736";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"3";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"2";"5";"5";;;"kmv" +"6737";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"3";"4";"3";"4";"3";NULL;NULL;NULL;"1";"5";"4";"5";"3";;"To suite the grading of final papers in a better way";"kmv" +"6738";"JSB530";"Úvod do sociologického myšlení";"Bureš,J.,Numerato,D.";"Bureš,J.";"4";"4";"4";"4";"4";"5";"5";"5";"1";"4";"4";"4";"3";;;"ks" +"6739";"JPM260";"Vybrané problémy britské zahraniční politiky v 19. a 20. století, ES";"Soukup,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";"The relaxed atmosphere of the whole class every week. Lecture was never boring, always enriched with interesting facts and the teacher's attitude was very positive.";;"kmv" +"6740";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"2";NULL;NULL;NULL;"4";"4";"3";"1";"3";"3";"3";"5";;;"kz" +"6741";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Kouřílek,J.";"3";"4";"3";"5";"3";"4";"5";"3";"2";"3";"4";"1";"1";;;"ks" +"6742";"JPM429";"Global terrorism (CS)";;"Makariusová,R.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Global Terrorism was my favourite seminar of the whole Winter semester. The attitude was brilliant, the in-class work was always interesting, this seminar significantly enriched my knowledge in field of terrorism. Whole seminar was led in a very interesting way.";;"kmv" +"6743";"JPM561";"Regional Security Studies";"Karásek,T.,Klosek,K.";"Karásek,T.,Klosek,K.";"5";"3";"5";"5";"5";"5";"5";"5";"1";"5";"4";"5";"5";;;"kbs" +"6744";"JSB544";"Vybrané kapitoly středoškolské matematiky";;"Hendl,J.";"2";"4";NULL;NULL;NULL;"4";"5";"2";"1";"3";"2";"1";"1";;;"ks" +"6745";"JPM690";"Liberalism in International Relations (TIR)";;"Karlas,J.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"I value especially the techer's attitude towards preparing for the class. Mr. Karlas always sent out before the seminar a kind of hand-out to prepare for the class which was always filled with intersting discussions. Mr. Karlas was also very helpful in discussing the topic for essay.";;"kmv" +"6746";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"4";;;"kmv" +"6747";"JSB998";"Úvod do sociologie";"Soukup,P.";;"3";"2";"4";"5";"1";NULL;NULL;NULL;"1";"1";"2";"2";"3";;;"ks" +"6748";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"3";"2";"2";"3";"4";NULL;NULL;NULL;"1";"3";"3";"2";"2";;;"kp" +"6749";"JPB258";"Politické myšlení novověku I";"Franěk,J.,Kučera,J.";;"4";"5";"3";"3";"2";NULL;NULL;NULL;"2";"3";"2";"4";"4";;;"kp" +"6750";"JPM698";"Middle East Security";"Daniel,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"4";;;"kbs" +"6751";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"kp" +"6752";"JPB252";"České novověké dějiny I.";"Kučera,J.";;"3";"2";"2";"2";"2";NULL;NULL;NULL;"1";"2";"2";"2";"3";;;"kp" +"6753";"JPB221";"Metodologický proseminář I";;"Bahenský,V.,Kofroň,J.";"3";"3";NULL;NULL;NULL;"3";"4";"4";"1";"3";"4";"3";"3";;;"kmv" +"6754";"JPB018";"Dějiny mezinárodních vztahů I";"Soukup,J.";;"5";"2";"5";"5";"3";NULL;NULL;NULL;"1";"3";"3";"4";"5";;;"kmv" +"6755";"JPM699";"Security and Technology";"Střítecký,V.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kbs" +"6756";"JPB020";"Úvod do mezinárodních vztahů";"Karlas,J.,Kučera,T.";;"3";"3";"2";"3";"1";NULL;NULL;NULL;"1";"3";"2";"3";"3";;;"kmv" +"6757";"JEM062";"Introductory Econometrics";"Kukačka,J.";"Kukačka,J.";"4";"4";"4";"4";"4";"4";"4";"4";"1";"5";"5";"5";"4";;;"ies" +"6758";"JEM123";"Economics of Least Developed Countries";"Bauer,M.";"Bauer,M.";"2";"3";"2";"1";"5";"2";"1";"5";"1";"2";"2";"2";"1";;;"ies" +"6759";"JEM162";"Energy Markets & Economics";"Elms,N.,Valíčková,P.";;"5";"5";"4";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"ies" +"6760";"JPM700";"Space Security";"Doboš,B.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kbs" +"6761";"JPM660";"Internship";;;"5";"1";NULL;NULL;NULL;"5";"5";"3";"1";"3";"3";"3";"5";;;"kmv" +"6762";"JPB227";"Politický system ČR";"Charvát,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;NULL;"5";"3";"5";"5";;;"kp" +"6763";"JPB268";"Evropská integrace";"Plechanovová,B.";;"1";"5";"2";"1";"1";NULL;NULL;NULL;NULL;"4";"1";"4";NULL;"Obsah kurzu byl celkem široký.";"Určitě hodnocení rešerší a testů. Hodnocení 6/3/0 resp. 10/6/3/0 mi přijde absurdní a naprosto nereflektuje jemné nuance v kvalitě práce. Obzvlášť když přihlédneme k faktu, že se jedná o otevřené otázky.";"kmv" +"6764";"JSB025";"Sociální problémy";"Frič,P.";;"5";"4";"4";"3";"4";NULL;NULL;NULL;"1";"5";"3";"5";"5";;"Rozbor konkrétních sociálních problémů, ne pouze okrajové zmínění.";"kvsp" +"6765";"JSB529";"Úvod do empirického výzkumu pro sociology";"Hájek,M.,Jeřábek,H.";"Rybín,F.,Vlčková,A.";"5";"4";"5";"5";"5";"3";"5";"5";"1";"5";"5";"5";"5";"Vše v pořádku.";;"ks" +"6766";"JSB530";"Úvod do sociologického myšlení";"Bureš,J.,Numerato,D.";"Moskvina,Y.";"2";"3";"3";"4";"2";"2";"3";"3";"2";"2";"3";"4";"2";"Prostor k diskuzi, k vyjádření svého názoru.";"Více teoretického podložení, méně úkolů a deníků.";"ks" +"6767";"JSB536";"Základy logiky a matematiky";"Hendl,J.";"Buřičová,B.";"4";"4";"2";"3";"4";"4";"5";"5";"1";"4";"3";"3";"3";;"Výklad přednášejícího.";"ks" +"6768";"JSB027";"Sociální politika jako společenská praxe";"Dobiášová,K.,Vlčková,K.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"2";"5";"5";"5";"5";;;"kvsp" +"6769";"JMB011";"Moderní dějiny Ruska";"Litera,B.,Pečenka,M.";"seminář nenavštěvován";"5";"5";"5";"2";"5";"4";"3";"5";"1";"5";"4";"5";"4";;;"krvs" +"6770";"JPB596";"Čínská zahraniční a bezpečnostní politika";"Karmazin,A.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"3";"5";"4";"5";"5";;;"kbs" +"6771";"JPB268";"Evropská integrace";"Plechanovová,B.";;"2";"5";"2";"2";"3";NULL;NULL;NULL;"2";"3";"2";"3";"1";;;"kmv" +"6772";"JJB224";"Psychologie marketingové komunikace";"Vranka,M.";;"4";"3";"4";"5";"4";NULL;NULL;NULL;"1";"5";"1";"3";"3";"Spoluúčast na výzkumech.";"Praktická cvičení. Využití metod v praxi.";"kmkpr" +"6773";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"3";"3";"4";"4";"2";"4";"4";"3";"1";"2";"2";"3";"3";;;"ies" +"6774";"NMMA703";"Matematika 3";"Zelený,M.";"Johanis,M.";"5";"5";"5";"5";"3";"3";"4";"5";"1";"5";"5";"4";"5";;;"ies" +"6775";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"3";"2";"3";"3";"1";"3";"3";"2";"2";"3";"3";"4";"3";;;"ies" +"6776";"JEB136";"Topics in Industrial Organization";"Schwarz,J.";;"4";"1";"3";"3";"3";NULL;NULL;NULL;"2";"4";"1";"4";"3";;;"ies" +"6777";"JEB044";"Financial Accounting";"Astakhov,A.,Cazachevici,A.,Čornanič,A.,Hildebrandt,B.,Chalupka,R.,Kolouchová,P.,Novák,J.,Polyák,O.,Wang,Y.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"4";;;"ies" +"6778";"JEM005";"Advanced Econometrics";"Baruník,J.,Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"Hronec,M.,Kraicová,L.,Kurka,J.,Nevrla,M.";"5";"4";"5";"5";"5";"2";"3";"3";"1";"5";"4";"5";"5";"The lectures were really great! Everything was well and clearly explained and I especially appreciated the approach Dr. Barunik has to his students.";"I think the seminars were not very good. The seminar leaders (except for Ms. Kraicova) seemed not ready to teach us. Some of them were too nervous about teaching, some of them about not having enough time to cover everything... But most importantly, I did not like the fact that we almost never worked with real data, but we were always generating random numbers and then working with them. Again this does not concern Ms. Kraicova whose seminars were great - well prepared, clearly explained and extensively commented in R - thank you!";"ies" +"6779";"JMB208";"Dějiny státu a práva v německy mluvících zemích";"Mlsna,P.";;"3";"4";"5";"5";"4";NULL;NULL;NULL;"4";"5";"1";"4";NULL;;;"knrs" +"6780";"JJB012";"Žurnalistická tvorba I";"Osvaldová,B.";"Trunečka,O.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"4";"5";"5";NULL;;;"kz" +"6781";"JJB998";"Úvod do ekonomie";"Poljakov,N.";;"4";"3";"3";"3";"5";NULL;NULL;NULL;"2";"4";"5";"5";NULL;;;"kz" +"6782";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"4";"3";"4";"4";"3";NULL;NULL;NULL;"1";"3";NULL;"2";NULL;;;"krvs" +"6783";"JLB033";"Němčina I";;"Křenková,D.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"5";NULL;"5";;;"cjp" +"6784";"JJB004";"Současný český jazyk I";;"Svobodová,I.";"3";"5";NULL;NULL;NULL;"5";"2";"5";"2";"5";"4";"4";NULL;;;"kz" +"6785";"JJB010";"Základy filozofie a vzdělanosti";"Halada,J.";;"3";"3";"5";"5";"1";NULL;NULL;NULL;"2";NULL;NULL;NULL;NULL;;;"kz" +"6786";"JPB225";"Československý politický systém I";"Gelnarová,J.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"2";"4";"3";"4";"4";;;"kp" +"6787";"JPB269";"Současné problémy mezinárodních vztahů";"Knutelská,V.";;"3";"1";"3";"3";"1";NULL;NULL;NULL;NULL;"4";"4";"1";"3";;;"kmv" +"6788";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"3";"5";;;"cjp" +"6789";"JPB268";"Evropská integrace";"Plechanovová,B.";;"2";"5";"2";"2";"1";NULL;NULL;NULL;"2";"4";"3";"3";"2";;;"kmv" +"6790";"JMB056";"Reflexe velkých debat v sociálních vědách ve filmu";;"Kozák,K.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"5";"3";"5";"5";;;"kas" +"6791";"JPB242";"Geografie vnitropolitických konfliktů";;"Doboš,B.,Riegl,M.";"3";"4";NULL;NULL;NULL;"3";"2";"2";"1";"3";"1";"2";"2";;;"kp" +"6792";"JSB998";"Úvod do sociologie";"Soukup,P.";;"5";"2";"5";"5";"4";NULL;NULL;NULL;"2";"4";"4";"4";"5";;;"ks" +"6793";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"5";"1";"1";"1";NULL;NULL;NULL;"2";"3";NULL;"2";"2";;;"ies" +"6794";"JJB009";"Úvod do psychologie";"Vranka,M.";;"3";"3";"5";"3";"2";NULL;NULL;NULL;"2";"3";"2";"3";"2";;;"kz" +"6795";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"4";"2";"5";"5";"3";NULL;NULL;NULL;NULL;"3";NULL;"3";"4";;;"krvs" +"6796";"JJB019";"Práce s agenturními informacemi";"Prázová,I.,Trunečková,L.";"Prázová,I.,Trunečková,L.";"3";"4";"5";"4";"4";"5";"4";"5";"1";"3";"3";"3";"2";;;"kz" +"6797";"JMB402";"Úvod do společenských věd II";;"Papežová,K.";"4";"4";NULL;NULL;NULL;"5";"5";"4";"1";"2";"3";NULL;"4";;;"krvs" +"6798";"JJB066";"Rozhlas a televize ve světě";"Moravec,V.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kz" +"6799";"JJB067";"Mluvní a pohybová výchova I";;"Pavel,L.";"4";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"3";"4";;;"kz" +"6800";"JJB069";"Tvůrčí dílny I - televizní";"Lokšík,M.";;"3";"4";"5";"4";"4";NULL;NULL;NULL;"1";"5";"5";"5";"3";;;"kz" +"6801";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Čížek,M.";"3";"5";"5";"5";"5";"5";"5";"5";"2";"5";"5";"5";"3";;;"knrs" +"6802";"JJB071";"Tvůrčí dílny I - rozhlasové";"Maršík,J.";"Lovaš,K.,Lucký,J.";"4";"4";"5";"3";"4";"5";"3";"4";"2";"4";"4";"4";"3";;;"kz" +"6803";"JJB083";"Editování zpravodajských relací";"Beneš,P.";;"2";"2";"4";"3";"1";NULL;NULL;NULL;"1";"2";"2";"2";"1";;;"kz" +"6804";"JJB169";"Věda v médiích";"Kasík,P.";;"3";"3";"5";"4";"2";NULL;NULL;NULL;"2";"3";"2";"2";"3";;;"kz" +"6805";"JJB173";"Rozhlasová publicistika pro děti a mládež";;"Maršík,J.";"4";"3";NULL;NULL;NULL;"5";"4";"4";"1";"4";"4";"4";"4";;;"kz" +"6806";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"3";"5";NULL;"4";"5";;;"kas" +"6807";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Čížek,M.";"4";"5";"4";"4";"4";"5";"5";"5";NULL;"5";NULL;"5";"4";;;"krvs" +"6808";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"3";"2";"4";"5";"3";NULL;NULL;NULL;NULL;"3";NULL;"2";"2";;;"knrs" +"6809";"JMM074";"Landmarks in 20th Century U.S. History and Their Interpretations";"Pondělíček,J.";;"3";"3";"4";"4";"4";NULL;NULL;NULL;"3";"4";"4";"4";"3";;"I didn't get much evaluation of the paper. I can't improve in the future if nobody gives me feedback";"kas" +"6810";"JMM271";"Metodologický seminář";;"Kýrová,L.";"3";"4";NULL;NULL;NULL;"3";"4";"2";"1";"3";"4";"4";"3";"Last two classes were very good. When we had to apply the knowledge we were taught during the classes.";"Less talking, more applying of the methods. The texts are very long and we don't have time to read all of them properly.";"krvs" +"6811";"JMM277";"Historie a kultura";"Vykoukal,J.";"Kýrová,L.";"4";"4";"5";"5";"5";"3";"4";"4";"1";"4";"4";"4";"4";"Zajímavé přednášky";"Občas opravdu dlouhé texty";"krvs" +"6812";"JMM278";"Politika a mezinárodní vztahy";"Kubát,M.,Weiss,T.";"Bečka,J.";"4";"4";"5";"5";"5";"4";"4";"4";"1";"4";"4";"4";"4";;"Texty mi přišly zbytečné";"krvs" +"6813";"JMM279";"Společnost a ekonomika";"Fiřtová,M.";"Fiřtová,M.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"Všechno, zajímavá témata, zajímavé přednášky";;"kas" +"6814";"JMMZ314";"Major Issues in Contemporary Public Debates in the U.S. I";"Sehnálková,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kas" +"6815";"JMMZ313";"Government in United States";"Sehnálková,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kas" +"6816";"JMMZ315";"U.S. Foreign Policy";"Raška,F.";;"3";"2";"3";"3";"3";NULL;NULL;NULL;"1";"2";"3";"3";"3";;"It would be good if we didn't talk all the time only about things we read in the books. And the books are very difficult for people who don't live in the United States and don't have the knowledge of this subject.";"kas" +"6817";"JEB101";"Principles of Economics I";"Janský,P.";"Godar,S.,Král,M.,Moravcová,H.,Palanský,M.";"3";"3";"4";"5";"3";"5";"5";"5";"1";"5";"5";"4";"3";;;"ies" +"6818";"JMM599";"Contemporary American Cinema";"Nowell,R.";;"4";"2";"5";"5";"5";NULL;NULL;NULL;"1";"3";"5";"5";"5";"The course expands your general knowledge of movie-making and helps you think more attentively and critically when watching and type of film.";"The only thing that I didn't like about this course was that it ended at 20:00, and by the middle of the class I was tired and couldn't focus properly.";"kas" +"6819";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"5";NULL;NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Najviac oceňujem prístup vyučujúcej pani Jany Kunzovej. Taktiež sa mi páčili rôzne aktivity, ktorým sme sa venovali počas hodín. Celkovo hodnitím kurz veľmi veľmi pozitívne.";;"cjp" +"6820";"JJM329";"Média a kultura";"Bednařík,P.,Jirák,J.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"3";"2";"4";"4";;;"kms" +"6821";"JJM331";"Výzkum médií II";"Vochocová,L.";;"4";"5";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"3";"4";;;"kms" +"6822";"JJM332";"Přirozený jazyk a média";"Podzimek,J.";;"3";"2";"3";"5";"4";NULL;NULL;NULL;"1";"2";"2";"2";"3";;;"kms" +"6823";"JMB402";"Úvod do společenských věd II";;"Mertová,V.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"4";"Veľmi oceňujem prístup vyučujúcej pani Mertovej. Hodiny boli veľmi zaujímavé a bola ochotná pomáhať aj mimo hodín. Celkovo hodnotím kurz veľmi pozitivne.";;"krvs" +"6824";"JJM334";"Diplomový seminář";;;"3";"3";"3";"3";"1";NULL;NULL;NULL;"2";"3";"5";"3";"3";;;"kms" +"6825";"NMMA711";"Mathematics 1";"Bárta,T.";"Bárta,T.,Vlasák,V.";"4";"4";"4";"5";"5";"3";"5";"3";"1";"4";"4";"4";"2";"This course teaches the topics of mathematics through theorems and extensive details, which is a depth of math that I think really allows you to understand every aspect of the problems you are solving.";"I am not sure if just taking high school math or the SATs is enough prior knowledge to take this course. I, for instance, was seeing most of the material in Math 1 for the first time, and I had to teach myself most of it in a short period of time. Also, at least for me, because of this, the class moved quite fast and there weren't any helpful resources to help me understand better. I realize that for some students in the class this course was not difficult to adapt to, because they had extensive prior knowledge in the topics of Math 1 from high school, but I think that because of those students, the whole class was expected to be on their level. In addition to this, while the theorems are helpful to know in the process of learning the subject and how to solve the problems, I think that memorizing 52 theorems and some proofs for the oral part of the exam is a bit difficult to do and also somewhat unnecessary. I understand that they are a vital part of the course, but I think the pressure of knowing them all at once for the exam is too much. Instead of this, I would suggest that there be theorem quizzes throughout the year, because it would encourage students to know them better little by little, instead of learning them all at once at the end of the semester (which is what most students did anyway). As for the homework, while I did appreciate the weekly homework we had because they allowed us to obtain more points towards the exam, I actually thought that it wasn't enough practice. This might sound strange coming from a student, but I actually think that more than just one homework assignment per week would be even more beneficial.";"ies" +"6826";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"3";"5";"I really liked this course, sincerely it was for the first time in my life, that I foud studying statistics interesting and kind of easy. My knowledge of the topic broadened a lot. The preparation of the course was amazing, all the presentations and videos on moodle were unusually elaborated and helped a lot with understandig. I really appreciate the work of the lecturer, I personally think many others could learn from him. Very engaging classes also oriented on practical skills, good job! Thank you, Michal.";"I think there were not any problems with this course, as there was enough possibilities how to gain points throughout the course. It required working quite a lot during the semester, but I much prefer that than only one exam at the end. Michal was always very helpful and willing to explain any problem that we had multiple times. I really enjoyed all classes, once again, thank you Michal.";"kmv" +"6827";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"3";"2";"3";"4";"3";NULL;NULL;NULL;"1";"3";"2";"3";"3";;"It would be good, if there would some kind of continuous work during the semester. The lectures alone does not seem to be enough for such important subject.";"kmv" +"6828";"JLB035";"Francouzština I";;"Dundrová,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"5";"Velmi přívětivý přístup paní Dundrové, různé styly výuky";;"cjp" +"6829";"JPM561";"Regional Security Studies";"Karásek,T.,Klosek,K.";"Karásek,T.,Klosek,K.";"4";"4";"4";"5";"4";"3";"5";"5";"1";"4";"4";"5";"4";;"The amount of reading was sometimes too high. e.g. 5 articles per week are two times more than in other subjects. This in combination with the fact, that for the test it did not really matter whether one read the articles at all or not, leads me to skip few whole reading blocs.";"kbs" +"6830";"JPM595";"Arms Control and Disarmament";"Hynek,N.,Smetana,M.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"The simulation game was based on a great amount of interactive work which helped me to develop my independent academic skills, which, I believe, will be very useful in future. The fact, that we had an opportunity to have a class with professional in the field of disarmament was also very valuable.";;"kbs" +"6831";"JMB178";"U.S. in the 1960s and 1970s";"Raška,F.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Super prednášajúci.";;"kas" +"6832";"JJB231";"Dějiny a teorie public relations";"Halada,J.,Hejlová,D.";;"4";"4";"4";"3";"4";NULL;NULL;NULL;"1";"5";"3";"5";"5";"- Kurz, kde jsem se opravdu něco naučil - jak historické souvislosti, tak teoretické znalosti včetně nejvýznamnějších teoretiků a praktiků. K teoretickým znalostem jsem tak vždy měl i kontext historický. - Skvělé využití moodle pro prezentace a průběžnou četbu, student se tak neztratí i když zmešká hodiny. - Patří mezi jedno procento předmětů, kde získáme od vyučujících reflexi na seminární práce od obou vyučujících. Podněty byly rozsáhlé a konkrétní, student tak ví, co do příště má zlepšit.- Obtížnost zkoušek donutí studenta učit se skutečně do hloubky (kurz \"Dějiny a teorie marketingové komunikace\" by mohl být koncipován obdobně vzhledem k tomu, že se oba jednají o státnicové předměty)";"- Pozdní hodnocení seminárních prací: odevzdání v půlce listopadu, podnět k přepracování až během zkouškového období, navíc nesystematické a postupné. Student je neustále v nejistotě, pokud je studentovi v horším případě seminární práce vrácena, nemá možnost si předem smysluplně naplánovat čas na učení a přepracování seminární práce.- Vysoce neobjektivní zkouška: vyučující první skupiny studentů dusí, postupně zjistí, že jim dochází čas a vyžadují méně a méně informací. Ve více případěch dokonce došlo k naprosto nepřípustnému ohodnocení poslední skupiny stupněm A pouze na základě seminárních prací a bez zkoušení, jelikož zkoušející již neměli čas.";"kmkpr" +"6833";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Everything. The approach towards us, students without mathematical knowledge was ideal. The continuous work and presentation really helped me to develop my skills.";"There could a small part (maybe like a voluntary homework) where students could get develop their knowledge in how to better present statistics in their papers (e.g. academic writing for statistics in social sciences).";"kmv" +"6834";"JJB403";"Institucionální a vládní komunikace";"Shavit,A.,Soukeník,Š.";;"4";"4";"4";"5";"5";NULL;NULL;NULL;"1";"3";"4";"4";"4";"- koncepce kurzu - strukturované přednášky a přesně časově vymezené seminární práce, vysoká obtížnost kurzu (k získání výborného výsledku)- přístup vyučujících ke studentům - velmi dobrá, pohotová e-mailová komunikace";"- Zadaná literatura a diskuze nad ní pouze na první a druhé přednášce - myslím si, že zrovna u takto interdisciplinárního předmětu je (i vzhledem k jeho požadavkům) zadávání literatury a diskuze nad ní klíčové k pochopení různých konceptů. - Krátký komentář k prvnímu position paperu, žádný komentář k seminární práci, pouze známka - zaběhlý standart MKPR";"kmkpr" +"6835";"JPM693";"Traditional and Critical Concepts in Security Studies";"Rychnovská,D.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"The continuous weekly work and interactive classes.";"There could be a smaller room than 1034, it would help with getting everybody to participate more in the class.";"kbs" +"6836";"JPM694";"Strategic Studies";"Hynek,N.,Ludvík,J.,Smetana,M.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kbs" +"6837";"JSB537";"Analýza dat v SPSS";"Soukup,P.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"3";"4";;"Ten problém, že nejsou cvičící, ale to asi víte.Ve státnicích jsou i věci, které se po změnách počtu semestrů statistických předmětů už neproberou.";"ks" +"6838";"JSB534";"Introduction to Visual Sociology";"Wladyniak,L.";;"4";"4";"4";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";"Projekt/foto-esej mi přišel jako nejlpší část kurzu. Je to kreativní, člověk musí někam do terénu, pracovat v týmu a zároveň pak i použít teoretické znalosti při psaní.Výlet na Karlovo náměstí byl taky super, možná jen jsme si mohli dát sraz rovnou tam, časově to bylo celkem těsné. Následující prezentace a diskuze fotek také stála za to.Četba celkem v pohodě, většinou nenadchne ani neurazí. Text od Marisol Clark-Ibáňez o photo-elicitation s dětmi byl super.";"Prezentace jednotlivých skupin o četbě byla často docela nudné. Nevím, jak moc s tím můžete udělat Vy, ale třeba říct, ať lidi jenom nerekapitulují ten text, nebo že si tu prezentaci můžou udělat volnější by možná mohlo pomoct.";"ks" +"6839";"JJB135";"Filmový seminář I";;"Šobr,M.";"5";"1";NULL;NULL;NULL;"5";"5";"5";"1";"4";"1";"4";"5";;;"kz" +"6840";"JMB414";"Seminář k aktualitám I";;"Young,M.";"5";"3";NULL;NULL;NULL;"4";"5";"5";"1";"5";"4";"5";"5";;;"krvs" +"6841";"JMB047";"Vybrané problémy mezinárodních konfliktů.";"Čížek,M.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"krvs" +"6842";"JMB249";"Seminář k dějinám středo- a jihovýchodní Evropy";;"Lukešová,O.";"4";"4";NULL;NULL;NULL;"4";"4";"5";"1";"5";"4";"5";"5";;;"krvs" +"6843";"JMB250";"Seminář k dějinám západní Evropy";;"Váška,J.";"3";"5";NULL;NULL;NULL;"3";"4";"1";"1";"1";"3";"2";"1";;;"kzs" +"6844";"JMB408";"Moderní dějiny Ruska";"Litera,B.,Novák,P.,Pečenka,M.";;"3";"5";"3";"2";"2";NULL;NULL;NULL;"2";"4";"2";"3";"3";;;"krvs" +"6845";"JSB517";"Hudební subkultury mládeže";"Oravcová,A.";;"4";"4";"4";"5";"4";NULL;NULL;NULL;"2";"4";"4";"4";"4";"Je fajn, že jsme psali konspekt jen na každý druhý text a člověk si tak mohl vybrat aspoň trochu, co ho zajímá.Supervolné téma na psaní závěrečné eseje.Pro mě nejzajímavější části kurzu byly ty, kde se spojovalo téma, které jsme zrovna barli s Vaší zkušeností - jak jste se v práci psotavila proti nepěkným příspěvkům na firemním facebooku, co o Vás předpokládají muži, když jste žena bv backstagi na koncertě a tak.";"Ta četba byla upřímně celkem nudná. Chápu, že ze začátku je dobrý dát si nějakou teorii a pochopit ty základy, jak studium subkultur vzniklo a tak. Ale přišlo mi, že i pozdější texty byly takové \"zaseklé\" v koukání na všechno skrz pracující třídu Británie 60. let. Existuje spoustu zajímavých a bizarních subkultur, o kterých by se tam podle mě dal hodit zábavnější text, a na něm se pak třeba bavit o nějakém obecnějším tématu.Moc jsem nebyl moudrý z toho, jak má vypadat závěrečná práce. Přišlo mi, že zadání, které bylo v moodlu, to co jste mi řekla v hodině, a to co jsem pochopil z vašeho feedbacku k mé práci, byly celkem odlišné obrazy té práce. To jaké práce vyžadují různí vyučující se výrazně liší mezi předměty, tak by možná stálo za to napsat do Moodlu ještě jednoznačněji, i když Vám to může třeba připadat jako pro debily. Určitě byste pak dostala i kvalitnější práce.";"ks" +"6846";"JMB409";"Moderní dějiny středo- a jihovýchodní Evropy-dcera JMB013";"Balla,P.,Švec,L.";;"4";"4";"4";"4";"4";NULL;NULL;NULL;"3";"5";"3";"5";"4";;;"krvs" +"6847";"JMB410";"Moderní dějiny západní Evropy";"Matějka,O.,Váška,J.";;"1";"3";"3";"3";"1";NULL;NULL;NULL;"3";"2";"1";"3";"2";;;"kzs" +"6848";"JMB013";"Moderní dějiny středo- a jihovýchodní Evropy";"Balla,P.,Švec,L.";"Balla,P.";"5";"4";"5";"5";"5";"5";"5";"5";"2";"5";"4";"5";"5";"Přístup vyučujících, forma přednášky kombinované s prezentací";;"krvs" +"6849";"JMB015";"Moderní dějiny západní Evropy";"Matějka,O.,Váška,J.";"seminář nenavštěvován";"4";"4";"3";"4";"3";"4";"5";"4";"2";"4";"4";"5";"5";;;"kzs" +"6850";"JMB069";"Transatlantic Security Cooperation";"Weiss,T.";;"4";"4";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"5";"4";;"I would suggest giving a bit more time for the exam. It's not that difficult, but 60 minutes for writing a 600 words essay was not enough for us to really think about what we want to say.";"kzs" +"6851";"JSB311";"Antropologie náboženství";"Spalová,B.";;"5";"4";"4";"5";"4";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Výlety, od teď mi ostatní antropoligcké předměty, kde si čteme jak ta antropologie nejde dělat jen z univerzity, a musí se do terénu, a přitom jenom sedíme na univerzitě, přijdou nenápadité a trošku pokrytecké.Psaní textů kreativnější něž jenom shrnutí. Shrnout se dá i text, který člověk vůbec nevnímá a nepochopí, jenom přepapouškuje. Ale když na něj nějak musí reagovat, tak to je úplně jiný mód myšlení.Pan host kognitivní antropolog Kaše byl super zajímavý v textech i na přednášce.Nejpřínosnější a nejzábavnější texty: V moci ďábla, Mluvící pes, muslimové v evropě.";"To pozdní opravování textů no, mě osobně to nevadí a jsme radši za slovní feedback než jenom moc neříkající body. Na druhou stranu třeba druhé terénní poznámky jsme psali bez toho, abychom měli feedback z prvních, což asi taky není ideální.Když píšeme texty během semestru, z toho dvakrát i terénní poznámky, tak mi přijde ta závěrečná seminárka trochu zbytečná. Na druhou stranu nevím, o čem bychom se pak bavili na zkoušce, a zase to lidi pošle do toho terénu, tak možná ji klidně nechte.Já fakt nemám rád ten strukturalismus, ale to je asi spíš taková moje osobní věc.Úkol 10 byl oproti předchozím 9 takové trošku nijaké shrnutí (jak ty texty, tak to co jsme o nich měli psát). Ale zase jsem z něj měl dost bodů, takže...Pro mě nejlepší předmět semestru, a to bych od náboženství nečekal, takže <3.";"ks" +"6852";"JSB023";"Praktika z kvantitativního výzkumu I";;"Tuček,M.";"4";"4";NULL;NULL;NULL;"4";"4";"4";"1";"4";"4";"4";"4";;;"ks" +"6853";"JEM004";"Advanced Macroeconomics";"Baxa,J.,Hromádková,E.";"Baxa,J.,Hromádková,E.,Šestořád,T.,Troch,T.,Žáček,J.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";"The lectures were amazing, fun and interesting - one of the best ones I had at IES! Everything was very well explained and Ms. Hromadkova always made sure we understood the theory. I also liked how everything from previous lesson was repeated and summarized at the beginning of lecture. Additionaly, I am glad that empirical seminars were included so that we could see how the theoretical stuff we leared is used in real life.Concerning the computational seminars (I had Jan Žáček as the seminar leader), they were also great, everything was clear and concise, he really understands it well.";"I would prefer if the assignments were done in groups of 3 people instead of 2 (because of time it took to complete them), but at the same time I realize that it was better to have them in groups of 2 because we learned more by doing them, which was especially useful in the final exam :)";"ies" +"6854";"JJM288";"Proměny žurnalistiky v éře konvergence médií";"Jirků,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";;;"kz" +"6855";"JPB268";"Evropská integrace";"Plechanovová,B.";;"2";"2";"3";"2";"2";NULL;NULL;NULL;"3";"3";"2";"3";"2";;"Aby se výsledky rešerší dozvídali studenti průbežně a dříve než po výsledcích závěrečné zkoušky. Informace k rešerším by měli být podány dříve než po termínu odevzdání poslední řešerše. (např. povolenné zdroje)";"kmv" +"6856";"JJB238";"Kritika marketingové komunikace";"Rosenfeldová,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"3";"5";"3";"5";"5";"- Zadávání průběžné literatury a prezentace dostupné v SISu- vysoká obtížnost a návaznost na kurz \"Dějiny a teorie PR\" - student zde dále získává znalosti z různých filozofických směrů a rozvíjí své kritické myšlení- přednášející přednáší velmi srozumitelně a zajímavě- jeden z mála kurzů mkpr, kde vyučující poskytne detailní hodnocení seminární práce";;"kmkpr" +"6857";"JPB594";"Realism in International Relations";"Odintsov,N.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmv" +"6858";"JPB011";"Politická geografie I";"Romancov,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"kp" +"6859";"JPB018";"Dějiny mezinárodních vztahů I";"Soukup,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"2";"4";"2";"4";"5";;;"kmv" +"6860";"JPB020";"Úvod do mezinárodních vztahů";"Karlas,J.,Kučera,T.";;"3";"4";"4";"4";"4";NULL;NULL;NULL;"1";"4";"3";"3";"4";;;"kmv" +"6861";"JPB221";"Metodologický proseminář I";;"Komasová,S.,Parízek,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"5";;;"kmv" +"6862";"JPB252";"České novověké dějiny I.";"Kučera,J.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"6863";"JPB256";"Politické systémy I";"Brunclík,M.,Jeřábek,M.,Kotábová,V.,Perottino,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"3";"5";"4";"4";"5";;;"kp" +"6864";"JPB567";"Analýza politiky";"Jüptner,P.,Riegl,M.,Švec,K.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"2";"5";"3";"3";"4";;;"kp" +"6865";"JJB607";"Analýzy mediálních obsahů";"Křeček,J.";;"2";"2";"2";"3";"2";NULL;NULL;NULL;"1";"1";"4";"1";"2";;;"kms" +"6866";"JMM530";"European Comparative Politics and Society";"Rovná,L.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kzs" +"6867";"JMM337";"Major Issues in Contemporary Public Debates in the U.S. I";"Sehnálková,J.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kas" +"6868";"JMMZ149";"EU Institutions";"Šlosarčík,I.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kzs" +"6869";"JMM025";"Putin´s Russia";"Veselý,L.";;"5";"1";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"4";;;"krvs" +"6870";"JPM052";"Mezinárodní ekonomické vztahy (MEV)";"Kučerová,I.";;"3";"3";"3";"1";"2";NULL;NULL;NULL;"1";"1";"1";"2";"1";;"Attitude of the lecturer - she may not realize it, but she looks really annoyed by the fact that she must teach. Moreover, her lectures are the same for many, many years, which is also telling - and not only this - the test questions are repeating over the years... Because of this no one really needs to attend the lectures or read the literature (which is often outdated - some websites are not available anymore, some materials in the list of compulsory reading, e.g. some IOs documents - were already replaced by newer versions, but the teacher does not care). You can pass just from the materials available on social media. It's a pity, because the lecturer knows apparently a lot about the subject matter - yet, she is not able to transfer her knowledge, not even talking about motivating the students to dig deeper into it. I suggest a study/research holiday abroad - to get new incentives - or consider a new occupation, since this is not good for anyone.";"kmv" +"6871";"JLB047";"Ruština obecná I";;"Mistrová,V.";"4";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"The cultural and political context - and the diversity of activities involved. I also appreciated the attitude of the teacher as she was able to approach everyone in an individual manner and work with a group with very different levels of knowledge with great professionalism.";;"cjp" +"6872";"JLB053";"Angličtina pro sociální vědy I";;"Gloverová,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"cjp" +"6873";"JMB118";"Geografie německy mluvících zemí";"Baštová,P.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"5";"5";"5";"5";"5";;;"knrs" +"6874";"JMBZ197";"Sprachwerkstatt Deutsch. Schreiben, Lesen und Diskutieren fürs Studium I";;"Göttmann,A.";"5";"1";NULL;NULL;NULL;"5";"5";"4";"1";"5";"5";"5";"5";;;"knrs" +"6875";"JMBZ264";"Seminar zu den aktuellen Fragen";;"Renner,T.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"knrs" +"6876";"JMBZ289";"Central European Culture from the 19th Century to 1945";"Emler,D.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";;;"knrs" +"6877";"JMMZ279";"Demokratie in Deutschland im 20. Jahrhundert";"Barth,B.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"knrs" +"6878";"JEB998";"Úvod do ekonomie";"Kameníček,J.";;"2";"3";"2";"3";"2";NULL;NULL;NULL;"1";"3";"3";"3";"4";;;"ies" +"6879";"JLB035";"Francouzština I";;"Bosáková,L.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"4";"3";"3";"5";;;"cjp" +"6880";"JMB401";"Úvod do společenských věd I";"Kubát,M.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"4";"5";;;"krvs" +"6881";"JMB402";"Úvod do společenských věd II";;"Papežová,K.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"2";"4";"4";"3";"5";;;"krvs" +"6882";"JMB403";"Dvě století střední Evropy I";"Velek,L.";"Kocian,J.";"4";"3";"5";"5";"4";"4";"5";"3";"2";"4";"3";"4";"4";;;"knrs" +"6883";"JMB405";"Přehled moderních světových dějin I";"Pečenka,M.,Smetana,V.";"Hornát,J.";"3";"3";"3";"4";"3";"5";"5";"5";"2";"4";"3";"4";"4";;;"krvs" +"6884";"JMB023";"Dějiny evropského myšlení";"Zelená,A.";;"3";"2";"3";"5";"3";NULL;NULL;NULL;"1";"3";"3";"3";"3";;;"knrs" +"6885";"JMB113";"Úvod do teritoriálních studií";"Kozák,K.";;"3";"4";"2";"5";"4";NULL;NULL;NULL;"3";"4";"4";"4";"4";;;"kas" +"6886";"JSB998";"Úvod do sociologie";"Soukup,P.";;"4";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"3";"3";"5";;;"ks" +"6887";"JPB268";"Evropská integrace";"Plechanovová,B.";;"1";"5";"2";"2";"1";NULL;NULL;NULL;"3";"2";"2";"2";"1";;"Projev v přednáškách";"kmv" +"6888";"JPM699";"Security and Technology";"Střítecký,V.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kbs" +"6889";"JPM052";"Mezinárodní ekonomické vztahy (MEV)";"Kučerová,I.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kmv" +"6890";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kmv" +"6891";"JPM561";"Regional Security Studies";"Karásek,T.,Klosek,K.";"Karásek,T.,Klosek,K.";"4";"4";"4";"5";"3";"3";"3";"3";"1";"3";"4";"4";"4";;;"kbs" +"6892";"JPM598";"Grand Strategies";"Ditrych,O.";;"4";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"kbs" +"6893";"JPM430";"Marxism in International Relations (TIR)";;"Střítecký,V.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";;;"kmv" +"6894";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"2";"4";"3";"3";"3";NULL;NULL;NULL;"2";"3";"3";"2";"3";;;"kmv" +"6895";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"3";"4";"3";"3";"3";NULL;NULL;NULL;"2";"3";"3";"2";"3";;;"kmv" +"6896";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"4";"5";"4";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kmv" +"6897";"JPM561";"Regional Security Studies";"Karásek,T.,Klosek,K.";"Karásek,T.,Klosek,K.";"5";"4";"5";"5";"5";"5";"5";"5";"1";"5";"4";"5";"5";;;"kbs" +"6898";"JPM699";"Security and Technology";"Střítecký,V.";;"4";"4";"5";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"kbs" +"6899";"JPM628";"Researching International Politics: Quantitative Methods";"Parízek,M.";;"4";"5";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"4";"3";;;"kmv" +"6900";"JPM698";"Middle East Security";"Daniel,J.";;"4";"5";"5";"5";"5";NULL;NULL;NULL;"1";"4";"4";"5";"5";;;"kbs" +"6901";"JPM660";"Internship";;;NULL;NULL;NULL;NULL;NULL;"5";"5";"5";NULL;NULL;NULL;NULL;NULL;;;"kmv" +"6902";"JJM117";"Popular Culture";"Turnau,T.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"4";"5";"I really appreciated the effort of the professor to include materials other than texts in the class. Also, the content learned is very new to me, which was quite fun.";"Perhaps the discussion of current popular pieces of work using the theories taught in class should be included. It will help us better apply the theories in our assessment of the works of popular culture.";"kms" +"6903";"JMM302";"Russia after 1991";"Svoboda,K.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Oceňuji skvělý výběr témat. Jsem ráda, že jsme mluvili o hospodářství posledních let SSSR, ale také jsem vděčná, že jsme se mnohem více věnovali současnému Rusku. Kromě toho mi vyhovuje, že vyučující vysvětluje souvislosti, ale zároveň dává studentům možnost, aby se ptali nebo komentovali jeho výklad. Nebyla jsem zvyklá na to, že mohou studenti dostat během hodin tolik prostoru, ale v příštím semestru se pokusím tuto možost lépe využít.";"Navrhuji častější práci s konkrétními statistikami. Obecné charakteristiky současného Ruska byly velmi zajímavé, ale občas mi chyběla data, která by je dokládala.";"krvs" +"6904";"JJM233";"Intercultural Communication Management";"Lütke Notarp,U.";;"3";"3";"3";"4";"3";NULL;NULL;NULL;"1";"3";"3";"3";"3";"The content of the class is useful for helping us students understand why certain people from certain cultures act the way they do.";"The group discussions are too frequent and therefore becomes repetitive. I would recommend the professor to keep group discussions or actings to a maximum of 2 or 3 times throughout the semester.";"kms" +"6905";"JJM239";"Media Sociology";"Miessler,J.";;"5";"4";"4";"4";"4";NULL;NULL;NULL;"1";"5";"4";"4";"5";"I do not have such courses back in my home country and as such, I really gained a lot of media-related sociological knowledge this past semester. As a communications student, I am very sure that this knowledge will come in handy in the near future.";"A guideline for the project would be greatly appreciated. The style and content of the project were not specified and so the presentations sometimes fail to cover some important content. Perhaps an evaluation rubric could be sent to the students before the start of the presentation weeks.";"kms" +"6906";"JMMZ339";"Populism in the U.S.";"Klvaňa,T.";;"5";"4";"4";"4";"4";NULL;NULL;NULL;"1";"5";"4";"4";"4";"Being exposed to the wealth of knowledge. Every class is worthy of attendance because something new can be learned every class. The skills to analyze texts are particularly useful and universal which value adds the class.";"Useful links to information on the content covered in class or the history of US politics can be included in the course syllabus for students without any background in US politics.";"kas" +"6907";"JPM043";"Theories of International Relations MV (TIR)";"Ditrych,O.,Plechanovová,B.";;"2";"4";"2";"2";"1";NULL;NULL;NULL;"3";"2";"2";"4";"1";;"The sheer amount of content covered in class is too much. Having a very long list of readings makes things worse because one cannot adequately master all the theories taught. Perhaps the professors should remove 3 types of theories so they have more time to better explain the remaining theories. Students will thus be better able to grasp and learn the theories instead of merely skimming over the numerous readings and not retaining vital information.";"kmv" +"6908";"JMM583";"Evropská energetická politika a energetická bezpečnost EU";"Fischer,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Oceňuji předevšm to, že jsem se díky předmětu naučila lépe chápat aktuální dění, dávat si ho do souvislosti s cenami, se záměry různých podniků a institucí. Také děkuji za obecný přehled o energii a energetické politice.";"Nic. Byla jsem velmi spokojená.";"kzs" +"6909";"JLB027";"Ruština odborná I - vyšší";;"Mistrová,V.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Vstricny a lidsky pristup, zajimava a aktualni temata.Byla probirana dulezita gramatika a slovni zasoba. Skvely kurz.";;"cjp" +"6910";"JMB415";"Seminář k aktualitám II";;"Cotte,P.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"I really enjoyed the helpful and enthusiastic attitude of Ms. Cotte. We went through many interesting topics that broadened our horizons.";;"krvs" +"6911";"JPM561";"Regional Security Studies";"Karásek,T.,Klosek,K.";"Karásek,T.,Klosek,K.";"4";"5";"5";"4";"3";"5";"4";"4";"1";"5";"4";"5";"4";"Overall, I liked this course, even though it was rather difficult. I liked the classes because of the lecturer, whose discourse was very interesting and entertaining. However, the clasese covered more general topics that I sometimes already knew and did have only a small contribution on studying the readings. I liked that the readings provided wide range of knowledge even though they were sometimes very hard to understand.";"The readings seemed very useful to me in order to gain knowledge, although some of them were very difficult to understand and even though I tended to dedicate more and more time to read and study them, it did not reflect on the results of my tests. In general, I consider the tests very difficult. I think it is a great form of an evaluation and also a possibility to gain points throughout the course, but sometimes the questions were obviously intentended to be such hard and I am no sure if this should be their objective over the knowledge gained. I also did not like at all that there were not any feedback on the results of the tests. I would learn much more if I could have seen my correct and wrong answers. I would prefer to get the correct answers after each test.";"kbs" +"6912";"JMB248";"Seminář k dějinám Ruska";;"Novák,P.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"Celkova forma seminare mi vyhovovala, cetba byla zajimava a obohacujici, nasledne (leckdy opravdu zaludne) dotazy vyucujiciho donutily cloveka se skutecne zamyslet a davat veci do souvislosti.";;"krvs" +"6913";"JMB216";"Postsovětský prostor v 90. letech";"Lídl,V.,Šír,J.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;"1";"5";"2";"5";"4";"Velice zajímavý předmět, oba pedagogové mají široký rozsah znalostí";"Kurz je zakončen napsáním akademické recenze na danou doporučenou literaturu. Osobně pro mě byl problém s tím, že většinu znalostí o regionu jsem získal až v rámci tohoto kurzu. Vzhledem k tomu, že akademická recenze už vyžaduje i širší osobní znalost na dané téma, kterou bohužel předmět v modelu 1 přednáška, jeden stát nemůže poskytnout. Domnívám se tedy, že kritika odborné práce pak po faktické stránce nemohla být alespoň z mé strany přiliš kvalitní.";"krvs" +"6914";"JSB004";"Sociální nerovnosti";"Šanderová,J.";;"4";"2";"5";"5";"2";NULL;NULL;NULL;"4";"4";"3";"4";"5";;;"ks" +"6915";"JSB027";"Sociální politika jako společenská praxe";"Dobiášová,K.,Vlčková,K.";;"5";"4";"5";"4";"3";NULL;NULL;NULL;"1";"4";"3";"4";"5";;;"kvsp" +"6916";"JMB097";"Moderní dějiny pobaltských zemí";"Švec,L.";;"5";"3";"5";"4";"4";NULL;NULL;NULL;"1";"4";"1";"5";"3";"Docent Švec se zaujetím přednáší a občas mluví i o osobních zkušenostech z regionu.";;"krvs" +"6917";"JMB408";"Moderní dějiny Ruska";"Litera,B.,Novák,P.,Pečenka,M.";;"3";"5";"4";"2";"5";NULL;NULL;NULL;"1";"5";"1";"5";"3";;;"krvs" +"6918";"JSB028";"Informační gramotnost";"Tomandlová,V.";;"4";"1";"5";"5";"5";NULL;NULL;NULL;"1";"3";"3";"3";"4";;;"kvsp" +"6919";"JSB133";"Zemědělství a rozvoj venkova (vybraná témata z rurální sociologie)";"Zagata,L.";;"5";"3";"5";"5";"4";NULL;NULL;NULL;"1";"5";"3";"5";"5";;;"ks" +"6920";"JSB407";"Globální problémy životního prostředí a udržitelný rozvoj";"Drhová,Z.";;"4";"3";"4";"3";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";;;"kvsp" +"6921";"JJB143";"Žurnalistika a feminismus";"Krobová,T.,Osvaldová,B.";;"3";"1";"5";"5";"2";NULL;NULL;NULL;"3";"2";"1";"2";"3";"Mgr. Krobová přednáší se zaujetím, tématu se zjevně vášnivě věnuje a hodně o něm ví. Cením její otevřenost a přístup.";"Kurz je bohužel příliš povrchní. Ve výsledku bylo tématem spíše povrchní bodové shrnutí souvislostí. Mgr. Krobová spíše předpokládá, že studenti netuší, kdo a co je Foucalt nebo Simone de Beauvoir a čas, který by tak mohl být věnován hlubším genderovým tématům, stráví na takřka maturitní látce.";"kz" +"6922";"JMB249";"Seminář k dějinám středo- a jihovýchodní Evropy";;"Zilynskyj,B.";"4";"1";NULL;NULL;NULL;"4";"5";"3";"2";"5";"3";"4";"4";"Originalni a zajimave tema seminare. Velice mily a vstricny pristup vyucujiciho.";"Vyucujici byl benevolentni mozna az prilis.";"krvs" +"6923";"JJB167";"Moderování zpravodajských relací";;"Moravec,V.,Šobr,M.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"2";"3";"5";"2";"5";;;"kz" +"6924";"JEM001";"Master´s Thesis Seminar I";;"Havránek,T.,Havránková,Z.,Mejstřík,M.";"3";"4";NULL;NULL;NULL;"3";"4";"3";"1";"2";"3";"1";"4";;;"ies" +"6925";"JEM037";"Financial Markets";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"Brož,V.,Čovan,M.,Kočenda,E.,Moravcová,M.";"4";"3";"4";"5";"4";"4";"5";"4";"1";"4";"4";"4";"4";;;"ies" +"6926";"JEM184";"New Keynesian DSGE Modeling";"Maršál,A.,Svačina,D.";"Maršál,A.,Svačina,D.";"4";"4";"3";"5";"5";"3";"5";"5";"1";"5";"5";"5";"3";;"Navrhuji i v pozdějších stádiích zapojovat studenty do výuky a také dávat k dispozici více materiálů z přednášek, jelikož se často stane, že student něco nepochytí.";"ies" +"6927";"JLB037";"Italština I";;"Přívozníková,P.";"5";"3";NULL;NULL;NULL;"4";"5";"5";"1";"3";"3";"1";"5";"Oceňuji lidský a aktivní přístup vyučující, každotýdenní úkoly, které studenty donutí něco opravdu dělat, zajímavá témata přednášek.";"Navrhuji během hodin více mluvit v italštině a \"donutit\" studenty mluvit také - jediný takový způsob je podle mě učitelem nějakým způsobem moderovaná debata mezi všemi.";"cjp" +"6928";"JPM146";"Přechody k demokracii v teorii a praxi I";"Mlejnek,J.";;"2";"4";"1";"2";"3";NULL;NULL;NULL;"1";"3";"2";"3";"2";"Náplň kurzu, tedy zaměření na oblasti přechodů k demokracii, jimž v bakalářském studiu nebyla věnována pozornost.";"Změnila bych koncepci seminářů, ve kterých měl student formou prezentace zpracovat knihu. Motivací studentů mělo být bodové zvýhodnění v závěrečném testu. Závěrečný test se ze třetiny skládal z otázek ze semináře, jejichž obsah dle mého náhoru nebyl v porovnání s obsahem znalostí z kurzu podstatný. Domnívám se, že by závěrečný test měl hodnotit oborové znalosti, nikoliv znalost náhodných informací obsažených v jedné ze zpracovaných knih. Podklady k nim navzdory účasti v hodinách navíc nebyly dostačující. Seminární prezentace mi připadají velice přínosné, ale pouze za předpokladu, že v reakci na ně student obdrží relevantní zpětnou vazbu (ta bohužel absentovala úplně).";"kp" +"6929";"JPM698";"Middle East Security";"Daniel,J.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;"1";"3";"3";"5";"5";"I really liked this course, I appreciate also the guest lectures, even though that some of them were not as interesting as others. I also liked that there were mid-term and final exams and not only final essay. I really appreciate the approach of the lecturer as it was very interesting and entertaining.";"I was a little bit disappointed that the subject matter of this course was kind of basic and general. I hoped that this course will provide rather deepening of basic knowledge of the Middle East, that I think majority of the class already had. I would like to spend less time on history facts and focus on current realities of the region. However, at the end there were very interesting poits of view provided in this course too.";"kbs" +"6930";"JPM641";"Světový regionalismus";"Riegl,M.";;"5";"4";"5";"4";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Faktografické informaceŠiroký geopolitický kontextAnglické pojmoslovíPropojování faktografie s praxí a současným děním.";"Rozšířit kurz do více semestrů. Infomace byly tak nahuštěny, že by kurz zasloužil větší prostor. Navíc jako jeden z mála v magisterském programu politologii postihuje oblast současné geopolitiky.";"kp" +"6931";"JJB224";"Psychologie marketingové komunikace";"Vranka,M.";;"4";"4";"4";"4";"5";NULL;NULL;NULL;"1";"4";"3";"4";"5";;"Systém losování studentů pro náhodné testy by z mého pohledu mohl být spravedlivější - osobně jsem za celý semestr nebyl vybrán ani jednou, někteří jedinci naopak psali test nejméně 3x. Pokud už v současnosti vše náhodně generuje program, mohl by například studentům přiřazovat rozdílné koeficienty podle toho, kolikrát již byli vytaženi. Nutnost přípravy na každou přednášku by zůstala zachována, testy by se ale rozdělily mezi větší množství studentů.";"kmkpr" +"6932";"JLM006";"Angličtina pro politology II";;"Panešová,K.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"2";"5";"5";"4";"5";"Procvičování praktických dovedností. Čtení textů s aktuální tematikou. Aktivní účast v hodinách.";"Ocenila bych více požadavků na domácí přípravu, například v psaní odborných textů, čímž by student mohl zlepšit své schopnosti.";"cjp" +"6933";"JPM342";"Konflikty v demokracii a národní identita: teoretické problémy analýzy";"Říchová,B.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"2";"4";"4";"4";"5";"Obecně náplň kurzu, kterou považuji za vysoce přínosnou pro oblast studia.Hodnocení kurzu formou seminární práce, na kterou student obdrží relevantní zpětnou vazbu.";"Požadavky na domácí přípravu, četbu textů apod. a zároveň i na znalosti z celého kurzu.";"kp" +"6934";"JJB226";"Strategie a finanční management marketingu I";"Majerik,P.";;"4";"3";"3";"5";"4";NULL;NULL;NULL;"1";"4";"2";"4";"4";;;"kmkpr" +"6935";"JJB235";"Proces tvorby v marketingové komunikaci";"Bezouška,M.";;"4";"2";"3";"4";"3";NULL;NULL;NULL;"2";"3";"3";"3";"4";;;"kmkpr" +"6936";"JPM579";"Teorie politických stran";"Perottino,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"4";"5";"5";"5";"Osobní přístup kantora, neustálé propojování teorie s praxí, motivace k zjišťování informací před hodinou";"Místnost, ve které přednášky probíhají";"kp" +"6937";"JJB238";"Kritika marketingové komunikace";"Rosenfeldová,J.";;"4";"4";"4";"5";"4";NULL;NULL;NULL;"3";"3";"3";"4";"5";;;"kmkpr" +"6938";"JJB243";"Aktuální trendy a vývoj v oboru I.";"Hejlová,D.,Vranka,M.";"Hejlová,D.,Vranka,M.";"4";"1";"4";"4";"5";"4";"4";"5";"1";"3";"2";"4";"4";;;"kmkpr" +"6939";"JJB255";"Digitální komunikace";;"Klimeš,D.";"3";"1";NULL;NULL;NULL;"4";"4";"3";"1";"3";"3";"3";"3";;;"kmkpr" +"6940";"JJB276";"Public relations v praxi";;"Hejlová,D.";"4";"3";NULL;NULL;NULL;"3";"3";"5";"1";"4";"2";"4";"5";;;"kmkpr" +"6941";"JJB401";"Komerční a nekomerční marketingová komunikace";"Báča,L.,Obluk,O.";;"3";"3";"3";"3";"3";NULL;NULL;NULL;"2";"3";"4";"3";"4";;"Vylepšit hodnocení závěrečných prací. Většina z nás nad nimi strávila mnoho hodin, čemuž z mého pohledu neodpovídal závěrečný feedback. Vzhledem k velkým oborovým zkušenostem obou vyučujících jsem očekával praktičtější a konkrétnější hodnocení.";"kmkpr" +"6942";"JJB629";"Tiskový mluvčí - praxe a teorie";"Chudinová,E.";;"3";"2";"3";"4";"3";NULL;NULL;NULL;"1";"2";"3";"3";"3";;;"kmkpr" +"6943";"JJB231";"Dějiny a teorie public relations";"Halada,J.,Hejlová,D.";;"3";"4";"4";"3";"4";NULL;NULL;NULL;"1";"2";"3";"3";"4";;"Posílat závěrečné práce k přepracování až během ledna nepovažuji za férové vůči studentům, zvlášť, když deadline na jejich odevzdání byl již během listopadu. Chápu, že opravit takové množství textů zabere čas, nicméně věřím, že je v silách profesorů zvládnout tento úkol ještě do začátku zkouškového, tak aby studenti měli na úpravu dostatek času.";"kmkpr" +"6944";"JPM639";"Problémy ústavního inženýrství";"Brunclík,M.";;"3";"4";"3";"5";"3";NULL;NULL;NULL;"1";"4";"4";"4";"3";"Náplň studia zasahující do oblasti justice.";"Velkou propast mezi nároky na seminární prezentace (trvající cca 5-10 minut) a seminární práce (rozsah 8-10 normostran). Seminární práce mně osobně výrazně posloužila k nácviku dodností, ale zejména časová náročnost na jejím zpracování byla nepoměrná s přípravou prezentací.";"kp" +"6945";"JJB268";"Sportovní marketing";"Šesták,Z.";;"2";"3";"2";"3";"3";NULL;NULL;NULL;"1";"1";"1";"1";"2";"Zachovat kontakt s praxí v podobě zvaní oborových hostů na přednášky.";"Jakkoliv má pan magistr Šesták v oblasti sportu neoddiskutovatelnou praxi, nejsem si po absolvování kurzu jistý, že je vhodným vyučujícím pro tento předmět. Z mého pohledu bohužel na žádné přednášce nezazněly žádné hodnotné informace: témat se sice probralo hodně, všechna ale velmi povrchně. V jednu chvíli se mluvilo o sponzoringu, za chvíli o dopingu. Kurzu chyběla jakákoliv smysluplná struktura. Co vlastně mělo být obsahem předmětu bohužel nevím doteď. Navrhoval bych věnovat se pouze několika tématům (např. eventy či hodnota sportovce) a rozebírat je více do hloubky a organizovaněji, naopak úplně vypustit témata jako krizová komunikace, která máme podrobně nastudovaná z jiných předmětů.";"kmkpr" +"6946";"JMM277";"Historie a kultura";"Vykoukal,J.";"Kýrová,L.";"3";"3";"4";"5";"3";"3";"5";"2";"1";"2";"2";"3";"2";;"Velmi mi chybí zpětná vazba k odevzdané seminární práci. V případě, že k práci nedostanu komentář, silně tím pro mě klesá přínos z jejího psaní";"krvs" +"6947";"JMM074";"Landmarks in 20th Century U.S. History and Their Interpretations";"Pondělíček,J.";;"4";"4";"4";"3";"4";NULL;NULL;NULL;"2";"5";"5";"5";"4";;"Komunikační dovednosti vyučujícího...";"kas" +"6948";"JPM429";"Global terrorism (CS)";;"Makariusová,R.";"4";"4";NULL;NULL;NULL;"4";"4";"4";"2";"4";"3";"3";"3";"The way of testing - combination of continuous testing and essay writing is efficient. Also the space for discussions.";"-feedback after the essay-more academic discussion - less 'how do you feel' discussions";"kmv" +"6949";"JPM689";"Conflict Studies";"Karásek,T.";;"3";"3";"4";"4";"4";NULL;NULL;NULL;"1";"3";"3";"3";"3";"The legal aspects of the issues.";"The way of testing - maybe essay writing during the semester would be more beneficial - and pleasant for the teacher to read than the hand writing.";"kbs" +"6950";"JPM725";"Technology and Security: Contemporary Warfare in the 21st Century";;"Csernatoni,R.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"5";"5";"5";"5";"The amazing feedback provided after the essay submission.The overall quality and attitude of the lecturer - I am glad that our University is attractive for such skilled people.";"Perhaps a better structure would be to give the compulsory readings and have the lecture by the teacher, next week/lecture have the presentation on the topic by the students, so that they could only concentrate on complementary or for them interesting aspects, otherwise it often led to unnecessary repeating. I assume is the idea was to complement what was not said during the presentation, but it leads to confusions. Then again - start the topic by the teacher, followed by the debate based on the compulsory readings, next lecture presentation on the topic by students, etc. Or the discussion after presentation. Also, it's good not to interrupt the students during the presentations too much (which would be again possible with a different structure and would lead to less frustration :).";"kmv" +"6951";"JPM727";"Orchestration in Global Governance";;"Abbott,K.,Parízek,M.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"4";"4";"The opportunity to have a course with such a capacity.The willingness to discuss and answer many questions.The overall friendly approach of both lecturers.The structure of the course - testing: first concentrating on one theoretical concept and then its practical applications.The option to concentrate on one's area of interest in applying the concept.";"Well, obviously and unfortunately necessarily - it was too short, so it's quite hard to tell. Also, Dr Parízek is one of the best at the Faculty which makes it hard to come up with something to complain about.";"kmv" +"6952";"JMB019";"Bakalářský seminář II";;"Tůma,O.";"2";"1";NULL;NULL;NULL;"3";"4";"1";"1";"1";"1";"1";"2";"možnost absolvovat distančně, ideálně kdyby to šlo v obou semestrech";"trochu byrokratické je dávání zápočtu až po odevzdání BP, nikoliv po přednesení exposé - když si student během semestru rozmyslí, že práci bude obhajovat až později v dalším semestru, nemá nárok na zpětný zápočet a musí předmět jako kdyby opakovat. Pro lidi aspirující na stipendium by to mohl být problém kvůli prospěchu.";"krvs" +"6953";"JMB415";"Seminář k aktualitám II";;"Mazzali,F.";"5";"2";NULL;NULL;NULL;"5";"5";"5";"1";"4";"4";"5";"5";"Francesca is great and her approach and attitude to students are fantastic. Especially appreciated is that she doesn't demand students to write long essays and prefers in-class presentations and debates.";;"krvs" +"6954";"JJB224";"Psychologie marketingové komunikace";"Vranka,M.";;"2";"2";"4";"5";"1";NULL;NULL;NULL;"1";"2";"1";"4";"2";;"Testy na začátku hodin jsou sice vybírány na základě náhody, ale nereflektují skutečnou přípravu všech studentů - pouze vybraných. Navíc to, že může být student vybrán opakovaně, je sice dobré z důvodu motivace se připravovat, ale není to férové vůči studentům, kteří pak mohou mít třeba dvakrát záporné body a jiní, byť se třeba rovněž nepřipraví, o žádné body nepřijdou. Navrhuji při opakovaném vybrání raději body neodebírat v případě špatné odpovědi (aby to bylo fér), ale v případě správné odpovědi body přidávat, aby byla zachována motivace.";"kmkpr" +"6955";"JJB226";"Strategie a finanční management marketingu I";"Majerik,P.";;"4";"2";"5";"5";"4";NULL;NULL;NULL;"2";"5";"1";"5";"5";"Atestace formou praktické prezentace; odborný výklad";"začátek v 8 ráno není příliš motivující pro dojíždějící studenty";"kmkpr" +"6956";"JJB228";"Komerční marketing";"Báča,L.,Obluk,O.";;"4";"2";"5";"4";"4";NULL;NULL;NULL;"2";"4";"1";"3";"4";"přednášky od odborníků";;"kmkpr" +"6957";"JJB231";"Dějiny a teorie public relations";"Halada,J.,Hejlová,D.";;"4";"5";"5";"4";"4";NULL;NULL;NULL;"2";"4";"1";"5";"5";"Odborné přednášky, praktická cvičení na přednáškách";"-dvě seminární práce - proč? Chápu, že oba vyučující učí něco jiného, ale chtít dvě seminární práce (navíc s odevzdáním tentýž den) je možná až zbytečně náročné. Chápal bych dvě práce, kdyby jedna se odevzdávala v listopadu a druhá v prosinci. Například. Ale takto je to obrovský stres pro studenty.- ústní zkoušení - skvělé, prověří skutečné znalosti studenta, ale proč se chodí po trojicích? Může to zvyšovat trému studentů a především to studentům nedává takovou šanci vyniknout. Ano, šetří to čas, když v kurzu je 60 studentů, ale když chtějí mít vyučující takovéto podmínky, tak by jim měli přizpůsobit i formu zkoušení. Proč se například nevypíše seznam nebo nestanoví se dané pořadí studentů ke zkoušce? Ti pak musí jak blbci čekat 4 hodiny na chodbě, než na ně přijde řada. A když jdou na řadu v až jako jedni z posledních, platí daň za to, že ani zkoušející už to příliš nebaví a jejich přístup je (podle vyprávění studentů, kteří šli na začátku) odlišný - podmínky tedy nejsou férové pro všechny, v mém případě se vůbec nebral zřetel na seznam literatury a známka byla (poměrně přísně) udělena za to, že si student vytáhne otázku, která mu zrovna nesedne. Ok, je to pech, ale možnost vylepšení klasifikace například přečtenou literaturou mi nebyla ani dána.- přednášky doc. Halady nejsou příliš relevantní k oboru a zhruba tak první jeho 4 jsou zajímavé, ale prakticky zbytečné k závěrečné zkoušce. Středověká a novověká historie není ve zkoušení prakticky vůbec zahrnuta. Dále je škoda, že neexistuje přesný sylabus a pořadí přednášek, takže studenti neví, kdo zrovna bude přednášet.";"kmkpr" +"6958";"JJB235";"Proces tvorby v marketingové komunikaci";"Bezouška,M.";;"4";"1";"4";"4";"3";NULL;NULL;NULL;"3";"3";"1";"3";"3";"pohodový přístup vyučujícího ke studentům";"jasně stanovit podmínky atestace v sylabu nebo je oznámit na začátku semestru; nezadávat závěrečnou práci až koncem semestru- proč je nutné chodit na závěrečnou ústní zkoušku, když se na ní student stejně jen dozví hodnocení na základě odevzdané práce a prakticky není zkoušen? A zkoušení jsou jen někteří? Neměli by buď být zkoušení všichni, nebo nikdo?";"kmkpr" +"6959";"JJB236";"Komunikace s médii";"Schneiderová,S.";;"3";"1";"4";"5";"2";NULL;NULL;NULL;"2";"2";"4";"3";"3";"Praktická cvičení";"obsah výuky se příliš kryl s PVP předmětem \"Kultura mluveného projevu\", v přednáškách se studenti nedozvěděli příliš nového";"kmkpr" +"6960";"JJB237";"Právo a etika v marketingu";"Winter,F.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"2";"5";"5";"test formou praktické eseje zkoumající pochopení látky";;"kmkpr" +"6961";"JJB238";"Kritika marketingové komunikace";"Rosenfeldová,J.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"3";"5";"2";"5";"5";"zajímavé a odborné přednášky, vtipné exkurzy a příklady, přednáška hosta, doporučené filmy - celkově zajímavá výuka; prezentace dostupné studentům ke stažení; hodnocení formou posudku práce; ústní zkoušení a pořadí studentů; facebooková skupina pro komunikaci";;"kmkpr" +"6962";"JJB268";"Sportovní marketing";"Šesták,Z.";;"5";"2";"4";"5";"4";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Zajímavé téma, propojení zájmů a praxe, odborné znalosti a zkušenosti vyučujícího, kontakty a zajímaví hosté, neortodoxní přístup k výuce, rozšiřování obecných znalostí";"komunikace vyučujícího přímo se studenty - přes SIS by mělo být snadné posílat hromadné maily či vypisovat termíny zkoušek, a ne ponechávat informace na předání studentům";"kmkpr" +"6963";"JJB229";"Nekomerční marketing";;;"3";"3";"4";"4";"4";NULL;NULL;NULL;"1";"4";"3";"4";"4";"psaní kreativního briefu";"bylo by vhodné oznámit formu/deadline atestace již na začátku semestru a ne na poslední hodině";"kmkpr" +"6964";"JPM314";"Theories of International Relations";"Ditrych,O.,Plechanovová,B.";;"2";"5";"4";"4";"1";NULL;NULL;NULL;"1";"2";"1";"1";"3";"Je pochopitelné, že teoretické základy studia jsou jeho nutnou součástí, přesto mi forma tohoto kurzu naprosto nevyhovovala. Kurz byl sice velmi obsáhlý, ale přínos přednášek mi připadal naprosto minimální, vzhledem k tomu, že látka byla podle mého názoru vykládána tím nejvíce komplikovaným způsobem. Je poměrně jasné, že odborné texty mohou být složitější na pochopení, ale nevidím jediný důvod, proč by přednášky následně nemohly látku předat o něco jednoznačnějším a snáze pochopitelným způsobem. Vyučující sice poskytli své prezentace, což bylo užitečné, avšak poznat z nich, co je a není důležité, bylo i po účasti na přednáškách prakticky nemožné, vzhledem k tomu, že informace buď byla příliš kusá nebo naopak tak obsáhlá, že nešlo poznat, co si vlastně vybrat.";"Absence jakékoli možnosti získat body v průběhu kurzu, mi připadá opravdu kritická. Hodnotit výkon studentů pouze na základě jednoho závěrečného testu mi přijde neadekvátní k množství a náročnosti probrané látky. Přednášky bohužel nebyly příliš přínosné pro pochopení látky. Vše bylo vykládáno nadmíru složitým způsobem a s mnoha detaily a odbočkami, které nakonec nebyly vůbec podstatné. Přišlo by mi mnohem přínosnější zjednodušit a shrnout vše podstatné z textů právě na přednáškách. Navíc velmi rychlý výklad na přednáškách situaci vůbec neusnadňoval. Účast na přednáškách mi rozhodně nepomohla lépe zvládnout požadované učivo a celkově je úroveň získaných znalostí z tohoto kurzu pro mne velmi nízká.";"kmv" +"6965";"JLB007";"Angličtina pro IMS I";;"Kunzová,J.";"5";"3";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"4";"5";;;"cjp" +"6966";"JLB027";"Ruština odborná I - vyšší";;"Mistrová,V.";"4";"4";NULL;NULL;NULL;"5";"5";"4";"1";"4";"4";"3";"5";;;"cjp" +"6967";"JMB414";"Seminář k aktualitám I";;"Šír,J.";"5";"3";NULL;NULL;NULL;"5";"5";"5";"2";"5";"4";"5";"5";;;"krvs" +"6968";"JMB097";"Moderní dějiny pobaltských zemí";"Švec,L.";;"4";"2";"4";"5";"4";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"krvs" +"6969";"JMB248";"Seminář k dějinám Ruska";;"Jasenčáková,M.";"4";"2";NULL;NULL;NULL;"4";"5";"4";"2";"4";"3";"4";"4";;;"krvs" +"6970";"JMB250";"Seminář k dějinám západní Evropy";;"Synkule,M.";"4";"2";NULL;NULL;NULL;"5";"5";"4";"2";"4";"4";"4";"5";;;"kzs" +"6971";"JMB408";"Moderní dějiny Ruska";"Litera,B.,Novák,P.,Pečenka,M.";;"4";"3";"4";"4";"3";NULL;NULL;NULL;"1";"4";"4";"4";"4";;;"krvs" +"6972";"JMB409";"Moderní dějiny středo- a jihovýchodní Evropy-dcera JMB013";"Balla,P.,Švec,L.";;"4";"5";"4";"5";"3";NULL;NULL;NULL;"1";"4";"4";"4";"5";;;"krvs" +"6973";"JEB105";"Statistics";"Červinka,M.";"Smutná,Š.";"2";"4";"1";"2";"2";"5";"5";"5";"1";"4";"4";"3";"1";"Domácí úkoly, ačkoliv systém hodnocení je extrémně demotivační, studentům zabírají úkoly mnoho hodin a vyučující dokážou ohodnotit jen třetinu?";"Přednášky. Pan Červinka se vše snaží dělat mnohonásobně komplikovanější než to ve skutečnosti je. Až při samostatném učení na zkoušku mi předmět přišel zajímavý a vlastně pochopitelný.";"ies" +"6974";"JMB410";"Moderní dějiny západní Evropy";"Matějka,O.,Váška,J.";;"4";"3";"4";"5";"3";NULL;NULL;NULL;"1";"4";"4";"4";NULL;;;"kzs" +"6975";"JMBZ193";"American Media, Culture and Globalization";"Klvaňa,T.";;"5";"2";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"4";"5";;;"kas" +"6976";"NMMA703";"Matematika 3";"Zelený,M.";"Zelený,M.";"5";"5";"5";"5";"5";"5";"5";"5";"1";"5";"5";"5";"5";;;"ies" +"6977";"JEB114";"Macroeconomics I";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"Brož,V.,Horváth,R.,Kudashvili,N.,Mareš,J.";"1";"1";"1";"1";"1";"1";"1";"1";"3";"2";"2";"2";"1";;;"ies" +"6978";"JEB108";"Microeconomics II";"Čechová,K.,Chytilová,J.,Jonášová,J.,Polák,P.";"Chytilová,J.,Jonášová,J.,Smutná,Š.";"3";"3";"5";"5";"4";"3";"3";"4";"1";"2";"1";"3";"3";;;"ies" +"6979";"JEB111";"Advanced Data Analysis in MS Excel";;"Poláková,N.,Polák,P.";"5";"4";NULL;NULL;NULL;"5";"5";"5";"1";"3";"4";"2";"4";;;"ies" +"6980";"JPM611";"Cyber Security";"Duračinská,Z.,Střítecký,V.";;"3";"4";"3";"5";"5";NULL;NULL;NULL;"2";"4";"3";"4";"4";"That a field specialist with detailed knowledge of the area has been brought in to teach the students. The explanations were covered with detailed real-life examples and case studies helping to see the practical side and existing challenges in the field.";"The lecturer attempted to cover everything relevant and sometimes moved too fast making the lecture difficult to follow. Also understandable as we were the first group the lecturer had ever taught, so hopefully this will change in time. Perhaps prioritising the topics and selecting out relevant information would be helpful to make the course more suitable for students with soft sciences background.";"kbs" +"6981";"JPM696";"Economic Warfare";"Ludvík,J.";;"3";"2";"1";"2";"1";NULL;NULL;NULL;"4";"3";"2";"3";"2";"Group work and the themes we used to understand the importance of economy as a tool in warfare.";"The course was completely self taught - students had to learn the case studies and take conclusions, which is a regular approach, however, instead of receiving feedback and taking conclusions, students were then divided into groups to make their own conclusions and were not assessed once again. The teacher claimed he let the algorithms choose who needs to present someone else's text whereas in reality it seems the teacher attempted making some people uncomfortable. Although at this level students should be able to and also are able to learn individually, lack supervision and assistance from the professor was strongly felt throughout the course. The feedback received from the weekly papers was also minimal with one-two word comments for the whole assignments. It was one of the courses i was most eager to learn about beforehand and most disappointing to have been to.";"kbs" +"6982";"JPM699";"Security and Technology";"Střítecký,V.";;"2";"3";"2";"4";"1";NULL;NULL;NULL;"3";"2";"2";"3";"2";"Learning about the impact of social media in our daily decisions and artificial intelligence and its development.";"Teaching style. Firstly, we were told that students are expected to hold a presentation and it should be prepared in an early stage although what is expected of us was not clearly explained and the basic info regarding the group work was only given at the very last minute. The lectures were discussing about conspiracies but were lacking content and were impossible to follow. The lectures did not match with readings and whilst readings were often well chosen, the following classes were poorly articulated, slow and of questionable value.";"kbs" +"6983";"JPM700";"Space Security";"Doboš,B.";;"5";"4";"5";"5";"5";NULL;NULL;NULL;"3";"4";"3";"5";"5";"The teacher was engaging and visibly interested in the area, which always makes the students more involved. The classes were taught with enthusiasm and some of the guest lectures with field specialists gave more insight to the challenges in the subject area.";"Towards the end of the course some of the topics became repetitive, and one of the guest lectures was very poor and extremely boring.";"kbs" +"6984";"JPM656";"Technology and warfare";"Kučera,T.";;"4";"4";"2";"2";"3";NULL;NULL;NULL;"2";"4";"4";"3";"4";"A lot of topics covered, interesting to write a book review.";"Lack of feedback on the content of the assessments despite having bi-weekly and time-comsuming homework tasks. Some of the videos and materials added in the tasks were not relevant and the homework was poorly set up with constantly re-occurring bugs etc.";"kbs" +"6985";"JPM699";"Security and Technology";"Střítecký,V.";;"3";"3";"4";"5";"3";NULL;NULL;NULL;"1";"3";"4";"2";"4";"I found the work at the group presentation very useful in the end, even though it seemed very hard at first. I also liked the feedback we got after the presentation but I would like to get the evaluation earlier, on time when I still remember the whole presentation as it went.";"I found the classes little bit missing some sort of connection between each other. I hoped we would be focusing more on the topic of ISIS on the social media, which seemed so interesting to me but we did not at the end. I think we spent too much time on AI during the course, it is a very difficult topic to understand on the technical basis, that I would prefer to focus more on maybe effects of the automatization or the fourth reolution on society or something similar. In general, unfortunatelly, this course was not very engaging however I think it has the potential if we did not get stuck on the same things all the time. Studying real examples from the social media was very interesting. However, I did not see any connection to broader IR reality and IR field as such.";"kbs" +"6986";"JPM300";"Geopolitics of sovereignty, state failure and unrecognized states";"Riegl,M.";;"5";"3";"5";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";"Professor was very knowledgeable and good at explaining complex concepts.";;"kp" +"6987";"JPM648";"Politics of Security in Northeast Asia";"Karásková,I.";;"4";"3";"4";"5";"5";NULL;NULL;NULL;"2";"4";"4";"4";"2";;;"kmv" +"6988";"JPM604";"Geopolitics and Geostrategy I.";"Romancov,M.";;"5";"4";"5";"4";"5";NULL;NULL;NULL;"1";"5";"5";"5";"5";;;"kp" +"6989";"JPM661";"Foreign Policy Analysis: The Neoclassical Realist Approach";"Morgado Albino,N.";;"3";"4";"4";"5";"5";NULL;NULL;NULL;"1";"5";"5";"5";"3";;"Professor was slow to notify students of their grades.";"kmv" +"6990";"JPM599";"ON WAR I.";"Kofroň,J.";;"4";"4";"3";"3";"4";NULL;NULL;NULL;"1";"3";"3";"4";"4";;;"kp" +"6991";"JPM620";"Geopolitical Thought";"Kofroň,J.";;"3";"4";"3";"3";"3";NULL;NULL;NULL;"1";"4";"3";"4";"3";;;"kp" +"6992";"JPM602";"Masterś Thesis Seminar I.";;"Kofroň,J.";"3";"3";NULL;NULL;NULL;"3";"3";"2";"1";"2";"2";"3";"3";;;"kp" +"6993";"JJM260";"Novinářská etika v praxi";"Moravec,V.";;"5";"5";"5";"5";"5";NULL;NULL;NULL;"1";"5";"4";"5";"5";"Tlak ze strany vyucujicicho - eseje, pestovani vlastniho nazoru, dochazka.";"Vetsi ucebnu?";"kz" +"6994";"JJM264";"Diplomový seminář II.";;;"2";"3";"3";"3";"3";NULL;NULL;NULL;"1";"1";"1";"2";"3";;"Mel by byt oba semestry, ne pouze v zimnim, studenti diplomovou praci odevzdavaji v letnim i v zimnim semestru.";"kz" +"6995";"JJM360";"Ekonomika v médiích";"Klimeš,D.";;"5";"4";"5";"5";"4";NULL;NULL;NULL;"2";"5";"3";"5";"5";"Hosty a jejich prednasky.";;"kz" +"6996";"JJM354";"Dějiny populární hudby";"Halada,A.";;"3";"3";"5";"5";"1";NULL;NULL;NULL;"5";"1";"3";"3";"5";"Kurz mel velky potencial, zabavne a prinosne prednasky - bohuzel pan Halada onemocnel a kurz se pak uz nekonal, coz me mrzi,tema bylo dobre a na hodiny jsem se tesila.";"Aby prednasky probihaly.";"kz" +"6997";"JJM340";"Tvůrčí dílny – tvůrčí psaní I";"Novotný,D.";"Novotný,D.";"5";"5";"5";"5";"5";"5";"4";"5";"1";"5";"5";"4";"5";"Ja kurz absolvovala s panem docentem Malym - jako i jine jeho predmety byl velmi prinosny.";;"kz" diff --git a/04_pandas/auxiliary/importing_code_from_giants.PNG b/04_pandas/auxiliary/importing_code_from_giants.PNG new file mode 100644 index 0000000..dd2dc2f Binary files /dev/null and b/04_pandas/auxiliary/importing_code_from_giants.PNG differ diff --git a/04_pandas/auxiliary/mc_escher_print gallery.png b/04_pandas/auxiliary/mc_escher_print gallery.png new file mode 100644 index 0000000..43d0206 Binary files /dev/null and b/04_pandas/auxiliary/mc_escher_print gallery.png differ diff --git a/04_pandas/auxiliary/survey_questions_2010.pdf b/04_pandas/auxiliary/survey_questions_2010.pdf new file mode 100644 index 0000000..271aef58 Binary files /dev/null and b/04_pandas/auxiliary/survey_questions_2010.pdf differ diff --git a/05_AdvancedPandas/adv_pandas.ipynb b/05_AdvancedPandas/adv_pandas.ipynb new file mode 100644 index 0000000..afbcae5 --- /dev/null +++ b/05_AdvancedPandas/adv_pandas.ipynb @@ -0,0 +1,31086 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "import pandas as pd\n", + "import zipfile\n", + "import numpy as np\n", + "\n", + "import datetime\n", + "\n", + "idx = pd.IndexSlice\n", + "\n", + "plotconfig = {\n", + " 'style':'.',\n", + " 'grid':True,\n", + " 'markersize':5,\n", + " 'figsize':(20,6)\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "with zipfile.ZipFile(\"data/covid.zip\") as z:\n", + " with z.open(\"Covid data/CovidDeaths.csv\") as f: \n", + " covid =pd.read_csv(f,index_col=['iso_code','date'], parse_dates=['date'], date_parser=lambda d: pd.to_datetime(d, format=\"%d-%m-%y\"))\n", + "\n", + " country_columns = ['continent','location','population']\n", + " countries = covid.groupby('iso_code').apply(lambda g: g.iloc[0][country_columns])\n", + " \n", + " countries = countries[countries.apply(lambda row: len(row.name) == 3,axis=1)]\n", + " countries.continent = countries.continent.astype('category')\n", + "\n", + " keep_covid_columns = ['new_cases','new_deaths','icu_patients','hosp_patients']\n", + "\n", + " covid = covid[keep_covid_columns]\n", + " covid = covid[covid.apply(lambda row: len(row.name[0]) == 3,axis=1)]\n", + "\n", + " covid = covid.sort_index()\n", + "\n", + " covid = covid.reset_index()\n", + "\n", + "countries = countries" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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continentlocationpopulation
iso_code
ABWNorth AmericaAruba106536.0
AFGAsiaAfghanistan40099462.0
AGOAfricaAngola34503774.0
AIANorth AmericaAnguilla15753.0
ALBEuropeAlbania2854710.0
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iso_codedatenew_casesnew_deathsicu_patientshosp_patients
0ABW2020-03-132.0NaNNaNNaN
1ABW2020-03-140.0NaNNaNNaN
2ABW2020-03-150.0NaNNaNNaN
3ABW2020-03-160.0NaNNaNNaN
4ABW2020-03-171.0NaNNaNNaN
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" + ], + "text/plain": [ + " iso_code date new_cases new_deaths icu_patients hosp_patients\n", + "0 ABW 2020-03-13 2.0 NaN NaN NaN\n", + "1 ABW 2020-03-14 0.0 NaN NaN NaN\n", + "2 ABW 2020-03-15 0.0 NaN NaN NaN\n", + "3 ABW 2020-03-16 0.0 NaN NaN NaN\n", + "4 ABW 2020-03-17 1.0 NaN NaN NaN" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "covid.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'CZE' in covid['iso_code'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665954985981 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "czech_cases = covid.loc[covid['iso_code'] == 'CZE'].set_index('date')\n", + "slovak_cases = covid.loc[covid['iso_code'] == 'SVK'].set_index('date')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### Args / Kwargs" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665951549434 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'style': '.', 'grid': True, 'markersize': 5, 'figsize': (20, 6)}" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plotconfig" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "czech_cases['new_cases'].plot(**plotconfig)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "## Indexing data\n", + "### Using `loc` - selecting based on index labels" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DatetimeIndex(['2020-03-01', '2020-03-02', '2020-03-03', '2020-03-04',\n", + " '2020-03-05', '2020-03-06', '2020-03-07', '2020-03-08',\n", + " '2020-03-09', '2020-03-10',\n", + " ...\n", + " '2022-08-29', '2022-08-30', '2022-08-31', '2022-09-01',\n", + " '2022-09-02', '2022-09-03', '2022-09-04', '2022-09-05',\n", + " '2022-09-06', '2022-09-07'],\n", + " dtype='datetime64[ns]', name='date', length=921, freq=None)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "czech_cases.index" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "datetime.date(2020, 3, 1)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "datetime.date(year = 2020, month = 3, day =1)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "iso_code CZE\n", + "new_cases 14149.0\n", + "new_deaths 112.0\n", + "icu_patients 749.0\n", + "hosp_patients 4310.0\n", + "Name: 2020-12-24 00:00:00, dtype: object" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "czech_cases.loc[datetime.datetime(year = 2020, month = 12, day =24)]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665951600065 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "iso_code CZE\n", + "new_cases 14149.0\n", + "new_deaths 112.0\n", + "icu_patients 749.0\n", + "hosp_patients 4310.0\n", + "Name: 2020-12-24 00:00:00, dtype: object" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "czech_cases.loc['2020-12-24']" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665951760007 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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iso_codenew_casesnew_deathsicu_patientshosp_patients
date
2020-10-07CZE5338.035.0346.01785.0
2020-10-08CZE5397.040.0358.01956.0
2020-10-09CZE8617.036.0389.02149.0
2020-10-13CZE8326.055.0452.02692.0
2020-10-14CZE9543.066.0478.02990.0
..................
2022-04-04CZE6579.032.0164.01866.0
2022-04-05CZE6729.029.0167.01833.0
2022-04-06CZE5305.032.0165.01740.0
2022-04-11CZE6155.037.0133.01515.0
2022-07-26CZE7241.030.090.01250.0
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210 rows × 5 columns

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" + ], + "text/plain": [ + " iso_code new_cases new_deaths icu_patients hosp_patients\n", + "date \n", + "2020-10-07 CZE 5338.0 35.0 346.0 1785.0\n", + "2020-10-08 CZE 5397.0 40.0 358.0 1956.0\n", + "2020-10-09 CZE 8617.0 36.0 389.0 2149.0\n", + "2020-10-13 CZE 8326.0 55.0 452.0 2692.0\n", + "2020-10-14 CZE 9543.0 66.0 478.0 2990.0\n", + "... ... ... ... ... ...\n", + "2022-04-04 CZE 6579.0 32.0 164.0 1866.0\n", + "2022-04-05 CZE 6729.0 29.0 167.0 1833.0\n", + "2022-04-06 CZE 5305.0 32.0 165.0 1740.0\n", + "2022-04-11 CZE 6155.0 37.0 133.0 1515.0\n", + "2022-07-26 CZE 7241.0 30.0 90.0 1250.0\n", + "\n", + "[210 rows x 5 columns]" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "czech_cases[(czech_cases['new_cases'] >= 5000) & (czech_cases['new_cases'] < 15000)]" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665951809009 + }, + "jupyter": { + 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = czech_cases.plot(color = 'lightgrey',label='other values',legend=True,**plotconfig)\n", + "czech_cases.loc[(czech_cases['new_cases'] >= 5000) & (czech_cases['new_cases'] < 15000), 'new_cases'].plot(ax=ax,label='Values between 500 and 750',legend=True,**plotconfig)\n", + "czech_cases.loc[czech_cases.index.weekday == 6, 'new_cases'].plot(ax=ax,label='Sunday',legend=True,**plotconfig)\n", + "czech_cases.loc[czech_cases.index.weekday == 5,'new_cases'].plot(ax=ax,label='Saturday',legend=True,**plotconfig)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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iso_codedatenew_casesnew_deathsicu_patientshosp_patients
0ABW2020-03-132.0NaNNaNNaN
1ABW2020-03-140.0NaNNaNNaN
2ABW2020-03-150.0NaNNaNNaN
3ABW2020-03-160.0NaNNaNNaN
4ABW2020-03-171.0NaNNaNNaN
.....................
201112ZWE2022-09-0311.00.0NaNNaN
201113ZWE2022-09-048.00.0NaNNaN
201114ZWE2022-09-056.00.0NaNNaN
201115ZWE2022-09-0613.00.0NaNNaN
201116ZWE2022-09-0743.00.0NaNNaN
\n", + "

201117 rows × 6 columns

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" + ], + "text/plain": [ + " iso_code date new_cases new_deaths icu_patients hosp_patients\n", + "0 ABW 2020-03-13 2.0 NaN NaN NaN\n", + "1 ABW 2020-03-14 0.0 NaN NaN NaN\n", + "2 ABW 2020-03-15 0.0 NaN NaN NaN\n", + "3 ABW 2020-03-16 0.0 NaN NaN NaN\n", + "4 ABW 2020-03-17 1.0 NaN NaN NaN\n", + "... ... ... ... ... ... ...\n", + "201112 ZWE 2022-09-03 11.0 0.0 NaN NaN\n", + "201113 ZWE 2022-09-04 8.0 0.0 NaN NaN\n", + "201114 ZWE 2022-09-05 6.0 0.0 NaN NaN\n", + "201115 ZWE 2022-09-06 13.0 0.0 NaN NaN\n", + "201116 ZWE 2022-09-07 43.0 0.0 NaN NaN\n", + "\n", + "[201117 rows x 6 columns]" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "covid" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "CSSR = covid.loc[covid['iso_code'].isin(['SVK','CZE'])] " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "## `MultiIndex`" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "CSSR = CSSR.set_index(['iso_code','date']) " + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665952122480 + }, + "jupyter": { + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "new_cases 14149.0\n", + "new_deaths 112.0\n", + "icu_patients 749.0\n", + "hosp_patients 4310.0\n", + "Name: (CZE, 2020-12-24 00:00:00), dtype: float64" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CSSR.loc[('CZE','2020-12-24')]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "if slicing or multi-selecting use `idx = pd.IndexSlice`" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DatetimeIndex(['2020-03-01', '2020-03-02', '2020-03-03', '2020-03-04',\n", + " '2020-03-05', '2020-03-06', '2020-03-07', '2020-03-08',\n", + " '2020-03-09', '2020-03-10',\n", + " ...\n", + " '2022-08-29', '2022-08-30', '2022-08-31', '2022-09-01',\n", + " '2022-09-02', '2022-09-03', '2022-09-04', '2022-09-05',\n", + " '2022-09-06', '2022-09-07'],\n", + " dtype='datetime64[ns]', name='date', length=1837, freq=None)" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CSSR.index.get_level_values('date')" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665952570874 + }, + "jupyter": { + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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new_casesnew_deathsicu_patientshosp_patients
iso_codedate
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2020-12-254402.083.0769.04386.0
2020-12-262706.091.0806.04651.0
2020-12-273030.094.0846.04966.0
SVK2020-12-247354.046.0190.01947.0
2020-12-255064.00.0197.02037.0
2020-12-261182.00.0202.02121.0
2020-12-271086.041.0218.02238.0
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" + ], + "text/plain": [ + " new_cases new_deaths icu_patients hosp_patients\n", + "iso_code date \n", + "CZE 2020-12-24 14149.0 112.0 749.0 4310.0\n", + " 2020-12-25 4402.0 83.0 769.0 4386.0\n", + " 2020-12-26 2706.0 91.0 806.0 4651.0\n", + " 2020-12-27 3030.0 94.0 846.0 4966.0\n", + "SVK 2020-12-24 7354.0 46.0 190.0 1947.0\n", + " 2020-12-25 5064.0 0.0 197.0 2037.0\n", + " 2020-12-26 1182.0 0.0 202.0 2121.0\n", + " 2020-12-27 1086.0 41.0 218.0 2238.0" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "czechoslovak_christmas = CSSR.loc[pd.IndexSlice[['CZE','SVK'],'2020-12-24':'2020-12-27'],:] #\n", + "czechoslovak_christmas" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* alternatively use notation below with `slice()`" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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new_casesnew_deathsicu_patientshosp_patients
iso_codedate
CZE2020-03-013.0NaNNaNNaN
2020-03-020.0NaNNaNNaN
2020-03-032.0NaNNaNNaN
2020-03-043.0NaNNaNNaN
2020-03-054.0NaNNaNNaN
..................
SVK2022-09-030.00.024.0364.0
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2022-09-060.00.0NaNNaN
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" + ], + "text/plain": [ + " new_cases new_deaths icu_patients hosp_patients\n", + "iso_code date \n", + "CZE 2020-03-01 3.0 NaN NaN NaN\n", + " 2020-03-02 0.0 NaN NaN NaN\n", + " 2020-03-03 2.0 NaN NaN NaN\n", + " 2020-03-04 3.0 NaN NaN NaN\n", + " 2020-03-05 4.0 NaN NaN NaN\n", + "... ... ... ... ...\n", + "SVK 2022-09-03 0.0 0.0 24.0 364.0\n", + " 2022-09-04 0.0 0.0 26.0 380.0\n", + " 2022-09-05 0.0 0.0 NaN NaN\n", + " 2022-09-06 0.0 0.0 NaN NaN\n", + " 2022-09-07 0.0 0.0 NaN NaN\n", + "\n", + "[1837 rows x 4 columns]" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CSSR.loc[(['CZE','SVK'],slice(None))]" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "MultiIndex([(1, 'a'),\n", + " (1, '2')],\n", + " )" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# you can create custom multiindex, not only set it up using set_index \n", + "pd.MultiIndex.from_arrays([[1,1],['a','2']])" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['CZE', 'CZE', 'CZE', 'CZE', 'CZE', 'CZE', 'CZE', 'CZE', 'CZE', 'CZE',\n", + " ...\n", + " 'SVK', 'SVK', 'SVK', 'SVK', 'SVK', 'SVK', 'SVK', 'SVK', 'SVK', 'SVK'],\n", + " dtype='object', name='iso_code', length=1837)" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get specific level from multiindex\n", + "CSSR.index.get_level_values(level = 'iso_code') # or level = 0" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datenew_casesnew_deathsicu_patientshosp_patients
iso_code
CZE2020-03-013.0NaNNaNNaN
CZE2020-03-020.0NaNNaNNaN
CZE2020-03-032.0NaNNaNNaN
CZE2020-03-043.0NaNNaNNaN
CZE2020-03-054.0NaNNaNNaN
..................
SVK2022-09-030.00.024.0364.0
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1837 rows × 5 columns

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" + ], + "text/plain": [ + " date new_cases new_deaths icu_patients hosp_patients\n", + "iso_code \n", + "CZE 2020-03-01 3.0 NaN NaN NaN\n", + "CZE 2020-03-02 0.0 NaN NaN NaN\n", + "CZE 2020-03-03 2.0 NaN NaN NaN\n", + "CZE 2020-03-04 3.0 NaN NaN NaN\n", + "CZE 2020-03-05 4.0 NaN NaN NaN\n", + "... ... ... ... ... ...\n", + "SVK 2022-09-03 0.0 0.0 24.0 364.0\n", + "SVK 2022-09-04 0.0 0.0 26.0 380.0\n", + "SVK 2022-09-05 0.0 0.0 NaN NaN\n", + "SVK 2022-09-06 0.0 0.0 NaN NaN\n", + "SVK 2022-09-07 0.0 0.0 NaN NaN\n", + "\n", + "[1837 rows x 5 columns]" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# .reset_index enables reseting only specific level\n", + "CSSR.reset_index(level = 'date')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "## Reshaping and pivoting\n", + "\n", + "https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html\n", + "\n", + "### Reshape `pd.Series` into `pd.DataFrame`: `.unstack`" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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iso_codeCZESVK
date
2020-12-2414149.07354.0
2020-12-254402.05064.0
2020-12-262706.01182.0
2020-12-273030.01086.0
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" + ], + "text/plain": [ + "iso_code CZE SVK\n", + "date \n", + "2020-12-24 14149.0 7354.0\n", + "2020-12-25 4402.0 5064.0\n", + "2020-12-26 2706.0 1182.0\n", + "2020-12-27 3030.0 1086.0" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "czechoslovak_christmas['new_cases'].unstack(level = 'iso_code')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### Stack `pd.DataFrame` to `pd.Series`\n" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665952586728 + }, + "jupyter": { + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "iso_code date \n", + "CZE 2020-03-01 new_cases 3.0\n", + " 2020-03-02 new_cases 0.0\n", + " 2020-03-03 new_cases 2.0\n", + " 2020-03-04 new_cases 3.0\n", + " 2020-03-05 new_cases 4.0\n", + " ... \n", + "SVK 2022-09-05 new_deaths 0.0\n", + " 2022-09-06 new_cases 0.0\n", + " new_deaths 0.0\n", + " 2022-09-07 new_cases 0.0\n", + " new_deaths 0.0\n", + "Length: 7248, dtype: float64" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CSSR.stack()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### melting -> long format" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [], + "source": [ + "CSSR = CSSR.reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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variablevalue
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1iso_codeCZE
2iso_codeCZE
3iso_codeCZE
4iso_codeCZE
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" + ], + "text/plain": [ + " variable value\n", + "0 iso_code CZE\n", + "1 iso_code CZE\n", + "2 iso_code CZE\n", + "3 iso_code CZE\n", + "4 iso_code CZE" + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CSSR.melt().head()" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['iso_code', 'date', 'new_cases', 'new_deaths', 'icu_patients',\n", + " 'hosp_patients'], dtype=object)" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CSSR.melt()['variable'].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "## Applying functions\n", + "\n", + "#### Aggregation\n", + "- decreasing dimensionality" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665952730689 + }, + "jupyter": { + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Martin Hronec\\AppData\\Local\\Temp\\ipykernel_27088\\1289622617.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n", + " czech_cases.mean()\n" + ] + }, + { + "data": { + "text/plain": [ + "new_cases 4415.203482\n", + "new_deaths 46.125141\n", + "icu_patients 368.860927\n", + "hosp_patients 2229.001098\n", + "dtype: float64" + ] + }, + "execution_count": 117, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "czech_cases.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665952760899 + }, + "jupyter": { + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "iso_code CZE\n", + "new_cases 0.0\n", + "new_deaths 0.0\n", + "icu_patients 3.0\n", + "hosp_patients 2.0\n", + "dtype: object" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "czech_cases.min()" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665952769140 + }, + "jupyter": { + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "iso_code CZECZECZECZECZECZECZECZECZECZECZECZECZECZECZEC...\n", + "new_cases 4057572.0\n", + "new_deaths 40913.0\n", + "icu_patients 334188.0\n", + "hosp_patients 2030620.0\n", + "dtype: object" + ] + }, + "execution_count": 119, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "czech_cases.sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### Transforming\n", + "* preserves dimensionality and shape" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [], + "source": [ + "czech_cases = czech_cases.set_index('iso_code', append = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " new_cases new_deaths icu_patients hosp_patients\n", + "date iso_code \n", + "2020-03-01 CZE -0.627210 NaN NaN NaN\n", + "2020-03-02 CZE -0.627637 NaN NaN NaN\n", + "2020-03-03 CZE -0.627352 NaN NaN NaN\n", + "2020-03-04 CZE -0.627210 NaN NaN NaN\n", + "2020-03-05 CZE -0.627068 NaN NaN NaN\n", + "... ... ... ... ...\n", + "2022-09-03 CZE -0.573760 -0.691793 -0.675474 -0.616278\n", + "2022-09-04 CZE -0.602191 -0.676115 -0.679711 -0.607474\n", + "2022-09-05 CZE -0.235151 -0.629081 -0.660645 -0.565751\n", + "2022-09-06 CZE -0.278223 -0.535013 -0.664882 -0.562688\n", + "2022-09-07 CZE -0.302532 -0.503657 -0.673356 -0.585272\n", + "\n", + "[921 rows x 4 columns]" + ] + }, + "execution_count": 134, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "czech_cases.apply(lambda series: (series - np.mean(series)) / np.std(series))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "## Group By\n", + "\n", + "**Split-Apply-Combine Logic**\n", + "\n", + "https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html\n", + "\n", + "* Splitting the data into groups based on some criteria.\n", + "* Applying a function to each group independently.\n", + "* Combining the results into a data structure.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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continentlocationpopulation
iso_code
ABWNorth AmericaAruba106536.0
AFGAsiaAfghanistan40099462.0
AGOAfricaAngola34503774.0
AIANorth AmericaAnguilla15753.0
ALBEuropeAlbania2854710.0
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" + ], + "text/plain": [ + " continent location population\n", + "iso_code \n", + "ABW North America Aruba 106536.0\n", + "AFG Asia Afghanistan 40099462.0\n", + "AGO Africa Angola 34503774.0\n", + "AIA North America Anguilla 15753.0\n", + "ALB Europe Albania 2854710.0" + ] + }, + "execution_count": 136, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "countries.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [], + "source": [ + "covid =covid.merge(countries, how = 'left', on = 'iso_code')" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665955110620 + }, + "jupyter": { + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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new_casesnew_deathsicu_patientshosp_patientspopulation
continent
Africa9.00.0124.04954.013461888.0
Asia224.02.0154.01197.019196465.0
Europe314.04.0113.0646.05403021.0
North America4.00.01086.06212.0190338.0
Oceania0.00.047.0323.0218764.0
South America531.013.0734.0577.017797737.0
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" + ], + "text/plain": [ + " new_cases new_deaths icu_patients hosp_patients population\n", + "continent \n", + "Africa 9.0 0.0 124.0 4954.0 13461888.0\n", + "Asia 224.0 2.0 154.0 1197.0 19196465.0\n", + "Europe 314.0 4.0 113.0 646.0 5403021.0\n", + "North America 4.0 0.0 1086.0 6212.0 190338.0\n", + "Oceania 0.0 0.0 47.0 323.0 218764.0\n", + "South America 531.0 13.0 734.0 577.0 17797737.0" + ] + }, + "execution_count": 147, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "covid.groupby('continent').median()" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [], + "source": [ + "g = covid.groupby(['continent', 'date'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys([('Africa', Timestamp('2020-02-07 00:00:00')), ('Africa', Timestamp('2020-02-08 00:00:00')), ('Africa', Timestamp('2020-02-09 00:00:00')), ('Africa', 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170525, 171888, 174585, 175503, 181359, 184593,\n", + " 185494, 198432, 199336, 200238],\n", + " dtype='int64'), Int64Index([ 1860, 12086, 13939, 15740, 28505, 29412, 34097, 35013,\n", + " 35924, 36831, 39985, 47323, 51020, 52878, 53779, 56587,\n", + " 62759, 66079, 67906, 68811, 69708, 70615, 94149, 102003,\n", + " 102921, 111209, 113947, 118518, 123657, 124565, 126373, 127266,\n", + " 129132, 130937, 131860, 153552, 155381, 156304, 159581, 162312,\n", + " 165010, 165895, 170526, 171889, 174586, 175504, 181360, 184594,\n", + " 185495, 198433, 199337, 200239],\n", + " dtype='int64'), Int64Index([ 1861, 12087, 13940, 15741, 28506, 29413, 34098, 35014,\n", + " 35925, 36832, 39986, 47324, 51021, 52879, 53780, 56588,\n", + " 62760, 66080, 67907, 68812, 69709, 70616, 94150, 102004,\n", + " 102922, 111210, 113948, 118519, 123658, 124566, 126374, 127267,\n", + " 129133, 130938, 131861, 153553, 155382, 156305, 159582, 162313,\n", + " 165011, 165896, 170527, 171890, 174587, 175505, 181361, 184595,\n", + " 185496, 198434, 199338, 200240],\n", + " dtype='int64'), Int64Index([ 1862, 12088, 13941, 15742, 28507, 29414, 34099, 35015,\n", + " 35926, 36833, 39987, 47325, 51022, 52880, 53781, 56589,\n", + " 62761, 66081, 67908, 68813, 69710, 70617, 94151, 102005,\n", + " 102923, 111211, 113949, 118520, 123659, 124567, 126375, 127268,\n", + " 129134, 130939, 131862, 153554, 155383, 156306, 159583, 162314,\n", + " 165012, 165897, 170528, 171891, 174588, 175506, 181362, 184596,\n", + " 185497, 198435, 199339, 200241],\n", + " dtype='int64'), Int64Index([ 1863, 12089, 13942, 15743, 28508, 29415, 34100, 35016,\n", + " 35927, 36834, 39988, 47326, 51023, 52881, 53782, 56590,\n", + " 62762, 66082, 67909, 68814, 69711, 70618, 94152, 102006,\n", + " 102924, 111212, 113950, 118521, 123660, 124568, 126376, 127269,\n", + " 129135, 130940, 131863, 153555, 155384, 156307, 159584, 162315,\n", + " 165013, 165898, 170529, 171892, 174589, 175507, 181363, 184597,\n", + " 185498, 198436, 199340, 200242],\n", + " dtype='int64'), Int64Index([ 1864, 12090, 13943, 15744, 28509, 29416, 34101, 35017,\n", + " 35928, 36835, 39989, 47327, 51024, 52882, 53783, 56591,\n", + " 62763, 66083, 67910, 68815, 69712, 70619, 94153, 102007,\n", + " 102925, 111213, 113951, 118522, 123661, 124569, 126377, 127270,\n", + " 129136, 130941, 131864, 153556, 155385, 156308, 159585, 162316,\n", + " 165014, 165899, 170530, 171893, 174590, 175508, 181364, 184598,\n", + " 185499, 198437, 199341, 200243],\n", + " dtype='int64'), Int64Index([ 1865, 12091, 13944, 15745, 28510, 29417, 34102, 35018,\n", + " 35929, 36836, 39990, 47328, 51025, 52883, 53784, 56592,\n", + " 62764, 66084, 67911, 68816, 69713, 70620, 94154, 102008,\n", + " 102926, 111214, 113952, 118523, 123662, 124570, 126378, 127271,\n", + " 129137, 130942, 131865, 153557, 155386, 156309, 159586, 162317,\n", + " 165015, 165900, 170531, 171894, 174591, 175509, 181365, 184599,\n", + " 185500, 198438, 199342, 200244],\n", + " dtype='int64'), Int64Index([ 1866, 12092, 13945, 15746, 28511, 29418, 34103, 35019,\n", + " 35930, 36837, 39991, 47329, 51026, 52884, 53785, 56593,\n", + " 62765, 66085, 67912, 68817, 69714, 70621, 94155, 102009,\n", + " 102927, 111215, 113953, 118524, 123663, 124571, 126379, 127272,\n", + " 129138, 130943, 131866, 153558, 155387, 156310, 159587, 162318,\n", + " 165016, 165901, 170532, 171895, 174592, 175510, 181366, 184600,\n", + " 185501, 198439, 199343, 200245],\n", + " dtype='int64'), Int64Index([ 1867, 12093, 13946, 15747, 28512, 29419, 34104, 35020,\n", + " 35931, 36838, 39992, 47330, 51027, 52885, 53786, 56594,\n", + " 62766, 66086, 67913, 68818, 69715, 70622, 94156, 102010,\n", + " 102928, 111216, 113954, 118525, 123664, 124572, 126380, 127273,\n", + " 129139, 130944, 131867, 153559, 155388, 156311, 159588, 162319,\n", + " 165017, 165902, 170533, 171896, 174593, 175511, 181367, 184601,\n", + " 185502, 198440, 199344, 200246],\n", + " dtype='int64'), Int64Index([ 1868, 12094, 13947, 15748, 28513, 29420, 34105, 35021,\n", + " 35932, 36839, 39993, 47331, 51028, 52886, 53787, 56595,\n", + " 62767, 66087, 67914, 68819, 69716, 70623, 94157, 102011,\n", + " 102929, 111217, 113955, 118526, 123665, 124573, 126381, 127274,\n", + " 129140, 130945, 131868, 153560, 155389, 156312, 159589, 162320,\n", + " 165018, 165903, 170534, 171897, 174594, 175512, 181368, 184602,\n", + " 185503, 198441, 199345, 200247],\n", + " dtype='int64'), Int64Index([ 1869, 12095, 13948, 15749, 28514, 29421, 34106, 35022,\n", + " 35933, 36840, 39994, 47332, 51029, 52887, 53788, 56596,\n", + " 62768, 66088, 67915, 68820, 69717, 70624, 94158, 102012,\n", + " 102930, 111218, 113956, 118527, 123666, 124574, 126382, 127275,\n", + " 129141, 130946, 131869, 153561, 155390, 156313, 159590, 162321,\n", + " 165019, 165904, 170535, 171898, 174595, 175513, 181369, 184603,\n", + " 185504, 198442, 199346, 200248],\n", + " dtype='int64'), Int64Index([ 1870, 12096, 13949, 15750, 28515, 29422, 34107, 35023,\n", + " 35934, 36841, 39995, 47333, 51030, 52888, 53789, 56597,\n", + " 62769, 66089, 67916, 68821, 69718, 70625, 94159, 102013,\n", + " 102931, 111219, 113957, 118528, 123667, 124575, 126383, 127276,\n", + " 129142, 130947, 131870, 153562, 155391, 156314, 159591, 162322,\n", + " 165020, 165905, 170536, 171899, 174596, 175514, 181370, 184604,\n", + " 185505, 198443, 199347, 200249],\n", + " dtype='int64'), Int64Index([ 1871, 12097, 13950, 15751, 28516, 29423, 34108, 35024,\n", + " 35935, 36842, 39996, 47334, 51031, 52889, 53790, 56598,\n", + " 62770, 66090, 67917, 68822, 69719, 70626, 94160, 102014,\n", + " 102932, 111220, 113958, 118529, 123668, 124576, 126384, 127277,\n", + " 129143, 130948, 131871, 153563, 155392, 156315, 159592, 162323,\n", + " 165021, 165906, 170537, 171900, 174597, 175515, 181371, 184605,\n", + " 185506, 198444, 199348, 200250],\n", + " dtype='int64'), Int64Index([ 1872, 12098, 13951, 15752, 28517, 29424, 34109, 35025,\n", + " 35936, 36843, 39997, 47335, 51032, 52890, 53791, 56599,\n", + " 62771, 66091, 67918, 68823, 69720, 70627, 94161, 102015,\n", + " 102933, 111221, 113959, 118530, 123669, 124577, 126385, 127278,\n", + " 129144, 130949, 131872, 153564, 155393, 156316, 159593, 162324,\n", + " 165022, 165907, 170538, 171901, 174598, 175516, 181372, 184606,\n", + " 185507, 198445, 199349, 200251],\n", + " dtype='int64'), Int64Index([ 1873, 12099, 13952, 15753, 28518, 29425, 34110, 35026,\n", + " 35937, 36844, 39998, 47336, 51033, 52891, 53792, 56600,\n", + " 62772, 66092, 67919, 68824, 69721, 70628, 94162, 102016,\n", + " 102934, 111222, 113960, 118531, 123670, 124578, 126386, 127279,\n", + " 129145, 130950, 131873, 153565, 155394, 156317, 159594, 162325,\n", + " 165023, 165908, 170539, 171902, 174599, 175517, 181373, 184607,\n", + " 185508, 198446, 199350, 200252],\n", + " dtype='int64'), Int64Index([ 1874, 12100, 13953, 15754, 28519, 29426, 34111, 35027,\n", + " 35938, 36845, 39999, 47337, 51034, 52892, 53793, 56601,\n", + " 62773, 66093, 67920, 68825, 69722, 70629, 94163, 102017,\n", + " 102935, 111223, 113961, 118532, 123671, 124579, 126387, 127280,\n", + " 129146, 130951, 131874, 153566, 155395, 156318, 159595, 162326,\n", + " 165024, 165909, 170540, 171903, 174600, 175518, 181374, 184608,\n", + " 185509, 198447, 199351, 200253],\n", + " dtype='int64'), Int64Index([ 1875, 12101, 13954, 15755, 28520, 29427, 34112, 35028,\n", + " 35939, 36846, 40000, 47338, 51035, 52893, 53794, 56602,\n", + " 62774, 66094, 67921, 68826, 69723, 70630, 94164, 102018,\n", + " 102936, 111224, 113962, 118533, 123672, 124580, 126388, 127281,\n", + " 129147, 130952, 131875, 153567, 155396, 156319, 159596, 162327,\n", + " 165025, 165910, 170541, 171904, 174601, 175519, 181375, 184609,\n", + " 185510, 198448, 199352, 200254],\n", + " dtype='int64'), Int64Index([ 1876, 12102, 13955, 15756, 28521, 29428, 34113, 35029,\n", + " 35940, 36847, 40001, 47339, 51036, 52894, 53795, 56603,\n", + " 62775, 66095, 67922, 68827, 69724, 70631, 94165, 102019,\n", + " 102937, 111225, 113963, 118534, 123673, 124581, 126389, 127282,\n", + " 129148, 130953, 131876, 153568, 155397, 156320, 159597, 162328,\n", + " 165026, 165911, 170542, 171905, 174602, 175520, 181376, 184610,\n", + " 185511, 198449, 199353, 200255],\n", + " dtype='int64'), Int64Index([ 1877, 12103, 13956, 15757, 28522, 29429, 34114, 35030,\n", + " 35941, 36848, 39100, 40002, 47340, 51037, 52895, 53796,\n", + " 56604, 62776, 66096, 67923, 68828, 69725, 70632, 94166,\n", + " 102020, 102938, 111226, 113964, 118535, 123674, 124582, 126390,\n", + " 127283, 129149, 130954, 131877, 153569, 155398, 156321, 159598,\n", + " 162329, 165027, 165912, 170543, 171906, 174603, 175521, 181377,\n", + " 184611, 185512, 198450, 199354, 200256],\n", + " dtype='int64'), Int64Index([ 1878, 12104, 13957, 15758, 28523, 29430, 34115, 35031,\n", + " 35942, 36849, 39101, 40003, 47341, 51038, 52896, 53797,\n", + " 56605, 62777, 66097, 67924, 68829, 69726, 70633, 94167,\n", + " 102021, 102939, 111227, 113965, 118536, 123675, 124583, 126391,\n", + " 127284, 129150, 130955, 131878, 153570, 155399, 156322, 159599,\n", + " 162330, 165028, 165913, 170544, 171907, 174604, 175522, 181378,\n", + " 184612, 185513, 198451, 199355, 200257],\n", + " dtype='int64'), Int64Index([ 1879, 12105, 13958, 15759, 28524, 29431, 34116, 35032,\n", + " 35943, 36850, 39102, 40004, 47342, 51039, 52897, 53798,\n", + " 56606, 62778, 66098, 67925, 68830, 69727, 70634, 94168,\n", + " 102022, 102940, 111228, 113966, 118537, 123676, 124584, 126392,\n", + " 127285, 129151, 130956, 131879, 153571, 155400, 156323, 159600,\n", + " 162331, 165029, 165914, 170545, 171908, 174605, 175523, 181379,\n", + " 184613, 185514, 198452, 199356, 200258],\n", + " dtype='int64'), Int64Index([ 1880, 12106, 13959, 15760, 28525, 29432, 34117, 35033,\n", + " 35944, 36851, 39103, 40005, 47343, 51040, 52898, 53799,\n", + " 56607, 62779, 66099, 67926, 68831, 69728, 70635, 94169,\n", + " 102023, 102941, 111229, 113967, 118538, 123677, 124585, 126393,\n", + " 127286, 129152, 130957, 131880, 153572, 155401, 156324, 159601,\n", + " 162332, 165030, 165915, 170546, 171909, 174606, 175524, 181380,\n", + " 184614, 185515, 198453, 199357, 200259],\n", + " dtype='int64'), Int64Index([ 1881, 12107, 13960, 15761, 28526, 29433, 34118, 35034,\n", + " 35945, 36852, 39104, 40006, 47344, 51041, 52899, 53800,\n", + " 56608, 62780, 66100, 67927, 68832, 69729, 70636, 94170,\n", + " 102024, 102942, 111230, 113968, 118539, 123678, 124586, 126394,\n", + " 127287, 129153, 130958, 131881, 153573, 155402, 156325, 159602,\n", + " 162333, 165031, 165916, 170547, 171910, 174607, 175525, 181381,\n", + " 184615, 185516, 198454, 199358, 200260],\n", + " dtype='int64'), Int64Index([ 1882, 12108, 13961, 15762, 28527, 29434, 34119, 35035,\n", + " 35946, 36853, 39105, 40007, 47345, 51042, 52900, 53801,\n", + " 56609, 62781, 66101, 67928, 68833, 69730, 70637, 94171,\n", + " 102025, 102943, 111231, 113969, 118540, 123679, 124587, 126395,\n", + " 127288, 129154, 130959, 131882, 153574, 155403, 156326, 159603,\n", + " 162334, 165032, 165917, 170548, 171911, 174608, 175526, 181382,\n", + " 184616, 185517, 198455, 199359, 200261],\n", + " dtype='int64'), Int64Index([ 1883, 12109, 13962, 15763, 28528, 29435, 34120, 35036,\n", + " 35947, 36854, 39106, 40008, 47346, 51043, 52901, 53802,\n", + " 56610, 62782, 66102, 67929, 68834, 69731, 70638, 94172,\n", + " 102026, 102944, 111232, 113970, 118541, 123680, 124588, 126396,\n", + " 127289, 129155, 130960, 131883, 153575, 155404, 156327, 159604,\n", + " 162335, 165033, 165918, 170549, 171912, 174609, 175527, 181383,\n", + " 184617, 185518, 198456, 199360, 200262],\n", + " dtype='int64'), Int64Index([ 1884, 12110, 13963, 15764, 28529, 29436, 34121, 35037,\n", + " 35948, 36855, 39107, 40009, 47347, 51044, 52902, 53803,\n", + " 56611, 62783, 66103, 67930, 68835, 69732, 70639, 94173,\n", + " 102027, 102945, 111233, 113971, 118542, 123681, 124589, 126397,\n", + " 127290, 129156, 130961, 131884, 153576, 155405, 156328, 159605,\n", + " 162336, 165034, 165919, 170550, 171913, 174610, 175528, 181384,\n", + " 184618, 185519, 198457, 199361, 200263],\n", + " dtype='int64'), Int64Index([ 1885, 12111, 13964, 15765, 28530, 29437, 34122, 35038,\n", + " 35949, 36856, 39108, 40010, 47348, 51045, 52903, 53804,\n", + " 56612, 62784, 66104, 67931, 68836, 69733, 70640, 94174,\n", + " 102028, 102946, 111234, 113972, 118543, 123682, 124590, 126398,\n", + " 127291, 129157, 130962, 131885, 153577, 155406, 156329, 159606,\n", + " 162337, 165035, 165920, 170551, 171914, 174611, 175529, 181385,\n", + " 184619, 185520, 198458, 199362, 200264],\n", + " dtype='int64'), Int64Index([ 1886, 12112, 13965, 15766, 28531, 29438, 34123, 35039,\n", + " 35950, 36857, 39109, 40011, 47349, 51046, 52904, 53805,\n", + " 56613, 62785, 66105, 67932, 68837, 69734, 70641, 94175,\n", + " 102029, 102947, 111235, 113973, 118544, 123683, 124591, 126399,\n", + " 127292, 129158, 130963, 131886, 153578, 155407, 156330, 159607,\n", + " 162338, 165036, 165921, 170552, 171915, 174612, 175530, 181386,\n", + " 184620, 185521, 198459, 199363, 200265],\n", + " dtype='int64'), Int64Index([ 1887, 12113, 13966, 15767, 28532, 29439, 34124, 35040,\n", + " 35951, 36858, 39110, 40012, 47350, 51047, 52905, 53806,\n", + " 56614, 62786, 66106, 67933, 68838, 69735, 70642, 94176,\n", + " 102030, 102948, 111236, 113974, 118545, 123684, 124592, 126400,\n", + " 127293, 129159, 130964, 131887, 153579, 155408, 156331, 159608,\n", + " 162339, 165037, 165922, 170553, 171916, 174613, 175531, 181387,\n", + " 184621, 185522, 198460, 199364, 200266],\n", + " dtype='int64'), Int64Index([ 1888, 12114, 13967, 15768, 28533, 29440, 34125, 35041,\n", + " 35952, 36859, 39111, 40013, 47351, 51048, 52906, 53807,\n", + " 56615, 62787, 66107, 67934, 68839, 69736, 70643, 94177,\n", + " 102031, 102949, 111237, 113975, 118546, 123685, 124593, 126401,\n", + " 127294, 129160, 130965, 131888, 153580, 155409, 156332, 159609,\n", + " 162340, 165038, 165923, 170554, 171917, 174614, 175532, 181388,\n", + " 184622, 185523, 198461, 199365, 200267],\n", + " dtype='int64'), Int64Index([ 1889, 12115, 13968, 15769, 28534, 29441, 34126, 35042,\n", + " 35953, 36860, 39112, 40014, 47352, 51049, 52907, 53808,\n", + " 56616, 62788, 66108, 67935, 68840, 69737, 70644, 94178,\n", + " 102032, 102950, 111238, 113976, 118547, 123686, 124594, 126402,\n", + " 127295, 129161, 130966, 131889, 153581, 155410, 156333, 159610,\n", + " 162341, 165039, 165924, 170555, 171918, 174615, 175533, 181389,\n", + " 184623, 185524, 198462, 199366, 200268],\n", + " dtype='int64'), Int64Index([ 1890, 12116, 13969, 15770, 28535, 29442, 34127, 35043,\n", + " 35954, 36861, 39113, 40015, 47353, 51050, 52908, 53809,\n", + " 56617, 62789, 66109, 67936, 68841, 69738, 70645, 94179,\n", + " 102033, 102951, 106580, 111239, 113977, 118548, 123687, 124595,\n", + " 126403, 127296, 129162, 130967, 131890, 153582, 155411, 156334,\n", + " 159611, 162342, 165040, 165925, 170556, 171919, 174616, 175534,\n", + " 181390, 184624, 185525, 198463, 199367, 200269],\n", + " dtype='int64'), Int64Index([ 1891, 12117, 13970, 15771, 28536, 29443, 34128, 35044,\n", + " 35955, 36862, 39114, 40016, 47354, 51051, 52909, 53810,\n", + " 56618, 62790, 66110, 67937, 68842, 69739, 70646, 94180,\n", + " 102034, 102952, 106581, 111240, 113978, 118549, 123688, 124596,\n", + " 126404, 127297, 129163, 130968, 131891, 153583, 155412, 156335,\n", + " 159612, 162343, 165041, 165926, 170557, 171920, 174617, 175535,\n", + " 181391, 184625, 185526, 198464, 199368, 200270],\n", + " dtype='int64'), Int64Index([ 1892, 12118, 13971, 15772, 28537, 29444, 34129, 35045,\n", + " 35956, 36863, 39115, 40017, 47355, 51052, 52910, 53811,\n", + " 56619, 62791, 66111, 67938, 68843, 69740, 70647, 94181,\n", + " 102035, 102953, 106582, 111241, 113979, 118550, 123689, 124597,\n", + " 126405, 127298, 129164, 130969, 131892, 153584, 155413, 156336,\n", + " 159613, 162344, 165042, 165927, 170558, 171921, 174618, 175536,\n", + " 181392, 184626, 185527, 198465, 199369, 200271],\n", + " dtype='int64'), Int64Index([ 1893, 12119, 13972, 15773, 28538, 29445, 34130, 35046,\n", + " 35957, 36864, 39116, 40018, 47356, 51053, 52911, 53812,\n", + " 56620, 62792, 66112, 67939, 68844, 69741, 70648, 94182,\n", + " 102036, 102954, 106583, 111242, 113980, 118551, 123690, 124598,\n", + " 126406, 127299, 129165, 130970, 131893, 153585, 155414, 156337,\n", + " 159614, 162345, 165043, 165928, 170559, 171922, 174619, 175537,\n", + " 181393, 184627, 185528, 198466, 199370, 200272],\n", + " dtype='int64'), Int64Index([ 1894, 12120, 13973, 15774, 28539, 29446, 34131, 35047,\n", + " 35958, 36865, 39117, 40019, 47357, 51054, 52912, 53813,\n", + " 56621, 62793, 66113, 67940, 68845, 69742, 70649, 94183,\n", + " 102037, 102955, 106584, 111243, 113981, 118552, 123691, 124599,\n", + " 126407, 127300, 129166, 130971, 131894, 153586, 155415, 156338,\n", + " 159615, 162346, 165044, 165929, 170560, 171923, 174620, 175538,\n", + " 181394, 184628, 185529, 198467, 199371, 200273],\n", + " dtype='int64'), Int64Index([ 1895, 12121, 13974, 15775, 28540, 29447, 34132, 35048,\n", + " 35959, 36866, 39118, 40020, 47358, 51055, 52913, 53814,\n", + " 56622, 62794, 66114, 67941, 68846, 69743, 70650, 94184,\n", + " 102038, 102956, 106585, 111244, 113982, 118553, 123692, 124600,\n", + " 126408, 127301, 129167, 130972, 131895, 153587, 155416, 156339,\n", + " 159616, 162347, 165045, 165930, 170561, 171924, 174621, 175539,\n", + " 181395, 184629, 185530, 198468, 199372, 200274],\n", + " dtype='int64'), Int64Index([ 1896, 12122, 13975, 15776, 28541, 29448, 34133, 35049,\n", + " 35960, 36867, 39119, 40021, 47359, 51056, 52914, 53815,\n", + " 56623, 62795, 66115, 67942, 68847, 69744, 70651, 94185,\n", + " 102039, 102957, 106586, 111245, 113983, 118554, 123693, 124601,\n", + " 126409, 127302, 129168, 130973, 131896, 153588, 155417, 156340,\n", + " 159617, 162348, 165046, 165931, 170562, 171925, 174622, 175540,\n", + " 181396, 184630, 185531, 198469, 199373, 200275],\n", + " dtype='int64'), Int64Index([ 1897, 12123, 13976, 15777, 28542, 29449, 34134, 35050,\n", + " 35961, 36868, 39120, 40022, 47360, 51057, 52915, 53816,\n", + " 56624, 62796, 66116, 67943, 68848, 69745, 70652, 94186,\n", + " 102040, 102958, 106587, 111246, 113984, 118555, 123694, 124602,\n", + " 126410, 127303, 129169, 130974, 131897, 153589, 155418, 156341,\n", + " 159618, 162349, 165047, 165932, 170563, 171926, 174623, 175541,\n", + " 181397, 184631, 185532, 198470, 199374, 200276],\n", + " dtype='int64'), Int64Index([ 1898, 12124, 13977, 15778, 28543, 29450, 34135, 35051,\n", + " 35962, 36869, 39121, 40023, 47361, 51058, 52916, 53817,\n", + " 56625, 62797, 66117, 67944, 68849, 69746, 70653, 94187,\n", + " 102041, 102959, 106588, 111247, 113985, 118556, 123695, 124603,\n", + " 126411, 127304, 129170, 130975, 131898, 153590, 155419, 156342,\n", + " 159619, 162350, 165048, 165933, 170564, 171927, 174624, 175542,\n", + " 181398, 184632, 185533, 198471, 199375, 200277],\n", + " dtype='int64'), Int64Index([ 1899, 12125, 13978, 15779, 28544, 29451, 34136, 35052,\n", + " 35963, 36870, 39122, 40024, 47362, 51059, 52917, 53818,\n", + " 56626, 62798, 66118, 67945, 68850, 69747, 70654, 94188,\n", + " 102042, 102960, 106589, 111248, 113986, 118557, 123696, 124604,\n", + " 126412, 127305, 129171, 130976, 131899, 153591, 155420, 156343,\n", + " 159620, 162351, 165049, 165934, 170565, 171928, 174625, 175543,\n", + " 181399, 184633, 185534, 198472, 199376, 200278],\n", + " dtype='int64'), Int64Index([ 1900, 12126, 13979, 15780, 28545, 29452, 34137, 35053,\n", + " 35964, 36871, 39123, 40025, 47363, 51060, 52918, 53819,\n", + " 56627, 62799, 66119, 67946, 68851, 69748, 70655, 94189,\n", + " 102043, 102961, 106590, 111249, 113987, 118558, 123697, 124605,\n", + " 126413, 127306, 129172, 130977, 131900, 153592, 155421, 156344,\n", + " 159621, 162352, 165050, 165935, 170566, 171929, 174626, 175544,\n", + " 181400, 184634, 185535, 198473, 199377, 200279],\n", + " dtype='int64'), Int64Index([ 1901, 12127, 13980, 15781, 28546, 29453, 34138, 35054,\n", + " 35965, 36872, 39124, 40026, 47364, 51061, 52919, 53820,\n", + " 56628, 62800, 66120, 67947, 68852, 69749, 70656, 94190,\n", + " 102044, 102962, 106591, 111250, 113988, 118559, 123698, 124606,\n", + " 126414, 127307, 129173, 130978, 131901, 153593, 155422, 156345,\n", + " 159622, 162353, 165051, 165936, 170567, 171930, 174627, 175545,\n", + " 181401, 184635, 185536, 198474, 199378, 200280],\n", + " dtype='int64'), Int64Index([ 1902, 12128, 13981, 15782, 28547, 29454, 34139, 35055,\n", + " 35966, 36873, 39125, 40027, 47365, 51062, 52920, 53821,\n", + " 56629, 62801, 66121, 67948, 68853, 69750, 70657, 94191,\n", + " 102045, 102963, 106592, 111251, 113989, 118560, 123699, 124607,\n", + " 126415, 127308, 129174, 130979, 131902, 153594, 155423, 156346,\n", + " 159623, 162354, 165052, 165937, 170568, 171931, 174628, 175546,\n", + " 181402, 184636, 185537, 198475, 199379, 200281],\n", + " dtype='int64'), Int64Index([ 1903, 12129, 13982, 15783, 28548, 29455, 34140, 35056,\n", + " 35967, 36874, 39126, 40028, 47366, 51063, 52921, 53822,\n", + " 56630, 62802, 66122, 67949, 68854, 69751, 70658, 94192,\n", + " 102046, 102964, 106593, 111252, 113990, 118561, 123700, 124608,\n", + " 126416, 127309, 129175, 130980, 131903, 153595, 155424, 156347,\n", + " 159624, 162355, 165053, 165938, 170569, 171932, 174629, 175547,\n", + " 181403, 184637, 185538, 198476, 199380, 200282],\n", + " dtype='int64'), Int64Index([ 1904, 12130, 13983, 15784, 28549, 29456, 34141, 35057,\n", + " 35968, 36875, 39127, 40029, 47367, 51064, 52922, 53823,\n", + " 56631, 62803, 66123, 67950, 68855, 69752, 70659, 94193,\n", + " 102047, 102965, 106594, 111253, 113991, 118562, 123701, 124609,\n", + " 126417, 127310, 129176, 130981, 131904, 153596, 155425, 156348,\n", + " 159625, 162356, 165054, 165939, 170570, 171933, 174630, 175548,\n", + " 181404, 184638, 185539, 198477, 199381, 200283],\n", + " dtype='int64'), Int64Index([ 1905, 12131, 13984, 15785, 28550, 29457, 34142, 35058,\n", + " 35969, 36876, 39128, 40030, 47368, 51065, 52923, 53824,\n", + " 56632, 62804, 66124, 67951, 68856, 69753, 70660, 94194,\n", + " 102048, 102966, 106595, 111254, 113992, 118563, 123702, 124610,\n", + " 126418, 127311, 129177, 130982, 131905, 153597, 155426, 156349,\n", + " 159626, 162357, 165055, 165940, 170571, 171934, 174631, 175549,\n", + " 181405, 184639, 185540, 198478, 199382, 200284],\n", + " dtype='int64'), Int64Index([ 1906, 12132, 13985, 15786, 28551, 29458, 34143, 35059,\n", + " 35970, 36877, 39129, 40031, 47369, 51066, 52924, 53825,\n", + " 56633, 62805, 66125, 67952, 68857, 69754, 70661, 94195,\n", + " 102049, 102967, 106596, 111255, 113993, 118564, 123703, 124611,\n", + " 126419, 127312, 129178, 130983, 131906, 153598, 155427, 156350,\n", + " 159627, 162358, 165056, 165941, 170572, 171935, 174632, 175550,\n", + " 181406, 184640, 185541, 198479, 199383, 200285],\n", + " dtype='int64'), Int64Index([ 1907, 12133, 13986, 15787, 28552, 29459, 34144, 35060,\n", + " 35971, 36878, 39130, 40032, 47370, 51067, 52925, 53826,\n", + " 56634, 62806, 66126, 67953, 68858, 69755, 70662, 94196,\n", + " 102050, 102968, 106597, 111256, 113994, 118565, 123704, 124612,\n", + " 126420, 127313, 129179, 130984, 131907, 153599, 155428, 156351,\n", + " 159628, 162359, 165057, 165942, 170573, 171936, 174633, 175551,\n", + " 181407, 184641, 185542, 198480, 199384, 200286],\n", + " dtype='int64'), Int64Index([ 1908, 12134, 13987, 15788, 28553, 29460, 34145, 35061,\n", + " 35972, 36879, 39131, 40033, 47371, 51068, 52926, 53827,\n", + " 56635, 62807, 66127, 67954, 68859, 69756, 70663, 94197,\n", + " 102051, 102969, 106598, 111257, 113995, 118566, 123705, 124613,\n", + " 126421, 127314, 129180, 130985, 131908, 153600, 155429, 156352,\n", + " 159629, 162360, 165058, 165943, 170574, 171937, 174634, 175552,\n", + " 181408, 184642, 185543, 198481, 199385, 200287],\n", + " dtype='int64'), Int64Index([ 1909, 12135, 13988, 15789, 28554, 29461, 34146, 35062,\n", + " 35973, 36880, 39132, 40034, 47372, 51069, 52927, 53828,\n", + " 56636, 62808, 66128, 67955, 68860, 69757, 70664, 94198,\n", + " 102052, 102970, 106599, 111258, 113996, 118567, 123706, 124614,\n", + " 126422, 127315, 129181, 130986, 131909, 153601, 155430, 156353,\n", + " 159630, 162361, 165059, 165944, 170575, 171938, 174635, 175553,\n", + " 181409, 184643, 185544, 198482, 199386, 200288],\n", + " dtype='int64'), Int64Index([ 1910, 12136, 13989, 15790, 28555, 29462, 34147, 35063,\n", + " 35974, 36881, 39133, 40035, 47373, 51070, 52928, 53829,\n", + " 56637, 62809, 66129, 67956, 68861, 69758, 70665, 94199,\n", + " 102053, 102971, 106600, 111259, 113997, 118568, 123707, 124615,\n", + " 126423, 127316, 129182, 130987, 131910, 153602, 155431, 156354,\n", + " 159631, 162362, 165060, 165945, 170576, 171939, 174636, 175554,\n", + " 181410, 184644, 185545, 198483, 199387, 200289],\n", + " dtype='int64'), Int64Index([ 1911, 12137, 13990, 15791, 28556, 29463, 34148, 35064,\n", + " 35975, 36882, 39134, 40036, 47374, 51071, 52929, 53830,\n", + " 56638, 62810, 66130, 67957, 68862, 69759, 70666, 94200,\n", + " 102054, 102972, 106601, 111260, 113998, 118569, 123708, 124616,\n", + " 126424, 127317, 129183, 130988, 131911, 153603, 155432, 156355,\n", + " 159632, 162363, 165061, 165946, 170577, 171940, 174637, 175555,\n", + " 181411, 184645, 185546, 198484, 199388, 200290],\n", + " dtype='int64'), Int64Index([ 1912, 12138, 13991, 15792, 28557, 29464, 34149, 35065,\n", + " 35976, 36883, 39135, 40037, 47375, 51072, 52930, 53831,\n", + " 56639, 62811, 66131, 67958, 68863, 69760, 70667, 94201,\n", + " 102055, 102973, 106602, 111261, 113999, 118570, 123709, 124617,\n", + " 126425, 127318, 129184, 130989, 131912, 153604, 155433, 156356,\n", + " 159633, 162364, 165062, 165947, 170578, 171941, 174638, 175556,\n", + " 181412, 184646, 185547, 198485, 199389, 200291],\n", + " dtype='int64'), Int64Index([ 1913, 12139, 13992, 15793, 28558, 29465, 34150, 35066,\n", + " 35977, 36884, 39136, 40038, 47376, 51073, 52931, 53832,\n", + " 56640, 62812, 66132, 67959, 68864, 69761, 70668, 94202,\n", + " 102056, 102974, 106603, 111262, 114000, 118571, 123710, 124618,\n", + " 126426, 127319, 129185, 130990, 131913, 153605, 155434, 156357,\n", + " 159634, 162365, 165063, 165948, 170579, 171942, 174639, 175557,\n", + " 181413, 184647, 185548, 198486, 199390, 200292],\n", + " dtype='int64'), Int64Index([ 1914, 12140, 13993, 15794, 28559, 29466, 34151, 35067,\n", + " 35978, 36885, 39137, 40039, 47377, 51074, 52932, 53833,\n", + " 56641, 62813, 66133, 67960, 68865, 69762, 70669, 94203,\n", + " 102057, 102975, 106604, 111263, 114001, 118572, 123711, 124619,\n", + " 126427, 127320, 129186, 130991, 131914, 153606, 155435, 156358,\n", + " 159635, 162366, 165064, 165949, 170580, 171943, 174640, 175558,\n", + " 181414, 184648, 185549, 198487, 199391, 200293],\n", + " dtype='int64'), Int64Index([ 1915, 12141, 13994, 15795, 28560, 29467, 34152, 35068,\n", + " 35979, 36886, 39138, 40040, 47378, 51075, 52933, 53834,\n", + " 56642, 62814, 66134, 67961, 68866, 69763, 70670, 94204,\n", + " 102058, 102976, 106605, 111264, 114002, 118573, 123712, 124620,\n", + " 126428, 127321, 129187, 130992, 131915, 153607, 155436, 156359,\n", + " 159636, 162367, 165065, 165950, 170581, 171944, 174641, 175559,\n", + " 181415, 184649, 185550, 198488, 199392, 200294],\n", + " dtype='int64'), Int64Index([ 1916, 12142, 13995, 15796, 28561, 29468, 34153, 35069,\n", + " 35980, 36887, 39139, 40041, 47379, 51076, 52934, 53835,\n", + " 56643, 62815, 66135, 67962, 68867, 69764, 70671, 94205,\n", + " 102059, 102977, 106606, 111265, 114003, 118574, 123713, 124621,\n", + " 126429, 127322, 129188, 130993, 131916, 153608, 155437, 156360,\n", + " 159637, 162368, 165066, 165951, 170582, 171945, 174642, 175560,\n", + " 181416, 184650, 185551, 198489, 199393, 200295],\n", + " dtype='int64'), Int64Index([ 1917, 12143, 13996, 15797, 28562, 29469, 34154, 35070,\n", + " 35981, 36888, 39140, 40042, 47380, 51077, 52935, 53836,\n", + " 56644, 62816, 66136, 67963, 68868, 69765, 70672, 94206,\n", + " 102060, 102978, 106607, 111266, 114004, 118575, 123714, 124622,\n", + " 126430, 127323, 129189, 130994, 131917, 153609, 155438, 156361,\n", + " 159638, 162369, 165067, 165952, 170583, 171946, 174643, 175561,\n", + " 181417, 184651, 185552, 198490, 199394, 200296],\n", + " dtype='int64'), Int64Index([ 1918, 12144, 13997, 15798, 28563, 29470, 34155, 35071,\n", + " 35982, 36889, 39141, 40043, 47381, 51078, 52936, 53837,\n", + " 56645, 62817, 66137, 67964, 68869, 69766, 70673, 94207,\n", + " 102061, 102979, 106608, 111267, 114005, 118576, 123715, 124623,\n", + " 126431, 127324, 129190, 130995, 131918, 153610, 155439, 156362,\n", + " 159639, 162370, 165068, 165953, 170584, 171947, 174644, 175562,\n", + " 181418, 184652, 185553, 198491, 199395, 200297],\n", + " dtype='int64'), Int64Index([ 1919, 12145, 13998, 15799, 28564, 29471, 34156, 35072,\n", + " 35983, 36890, 39142, 40044, 47382, 51079, 52937, 53838,\n", + " 56646, 62818, 66138, 67965, 68870, 69767, 70674, 94208,\n", + " 102062, 102980, 106609, 111268, 114006, 118577, 123716, 124624,\n", + " 126432, 127325, 129191, 130996, 131919, 153611, 155440, 156363,\n", + " 159640, 162371, 165069, 165954, 170585, 171948, 174645, 175563,\n", + " 181419, 184653, 185554, 198492, 199396, 200298],\n", + " dtype='int64'), Int64Index([ 1920, 12146, 13999, 15800, 28565, 29472, 34157, 35073,\n", + " 35984, 36891, 39143, 40045, 47383, 51080, 52938, 53839,\n", + " 56647, 62819, 66139, 67966, 68871, 69768, 70675, 94209,\n", + " 102063, 102981, 106610, 111269, 114007, 118578, 123717, 124625,\n", + " 126433, 127326, 129192, 130997, 131920, 153612, 155441, 156364,\n", + " 159641, 162372, 165070, 165955, 170586, 171949, 174646, 175564,\n", + " 181420, 184654, 185555, 198493, 199397, 200299],\n", + " dtype='int64'), Int64Index([ 1921, 12147, 14000, 15801, 28566, 29473, 34158, 35074,\n", + " 35985, 36892, 39144, 40046, 47384, 51081, 52939, 53840,\n", + " 56648, 62820, 66140, 67967, 68872, 69769, 70676, 94210,\n", + " 102064, 102982, 106611, 111270, 114008, 118579, 123718, 124626,\n", + " 126434, 127327, 129193, 130998, 131921, 153613, 155442, 156365,\n", + " 159642, 162373, 165071, 165956, 170587, 171950, 174647, 175565,\n", + " 181421, 184655, 185556, 198494, 199398, 200300],\n", + " dtype='int64'), Int64Index([ 1922, 12148, 14001, 15802, 28567, 29474, 34159, 35075,\n", + " 35986, 36893, 39145, 40047, 47385, 51082, 52940, 53841,\n", + " 56649, 62821, 66141, 67968, 68873, 69770, 70677, 94211,\n", + " 102065, 102983, 106612, 111271, 114009, 118580, 123719, 124627,\n", + " 126435, 127328, 129194, 130999, 131922, 153614, 155443, 156366,\n", + " 159643, 162374, 165072, 165957, 170588, 171951, 174648, 175566,\n", + " 181422, 184656, 185557, 198495, 199399, 200301],\n", + " dtype='int64'), Int64Index([ 1923, 12149, 14002, 15803, 28568, 29475, 34160, 35076,\n", + " 35987, 36894, 39146, 40048, 47386, 51083, 52941, 53842,\n", + " 56650, 62822, 66142, 67969, 68874, 69771, 70678, 94212,\n", + " 102066, 102984, 106613, 111272, 114010, 118581, 123720, 124628,\n", + " 126436, 127329, 129195, 131000, 131923, 153615, 155444, 156367,\n", + " 159644, 162375, 165073, 165958, 170589, 171952, 174649, 175567,\n", + " 181423, 184657, 185558, 198496, 199400, 200302],\n", + " dtype='int64'), Int64Index([ 1924, 12150, 14003, 15804, 28569, 29476, 34161, 35077,\n", + " 35988, 36895, 39147, 40049, 47387, 51084, 52942, 53843,\n", + " 56651, 62823, 66143, 67970, 68875, 69772, 70679, 94213,\n", + " 102067, 102985, 106614, 111273, 114011, 118582, 123721, 124629,\n", + " 126437, 127330, 129196, 131001, 131924, 153616, 155445, 156368,\n", + " 159645, 162376, 165074, 165959, 170590, 171953, 174650, 175568,\n", + " 181424, 184658, 185559, 198497, 199401, 200303],\n", + " dtype='int64'), Int64Index([ 1925, 12151, 14004, 15805, 28570, 29477, 34162, 35078,\n", + " 35989, 36896, 39148, 40050, 47388, 51085, 52943, 53844,\n", + " 56652, 62824, 66144, 67971, 68876, 69773, 70680, 94214,\n", + " 102068, 102986, 106615, 111274, 114012, 118583, 123722, 124630,\n", + " 126438, 127331, 129197, 131002, 131925, 153617, 155446, 156369,\n", + " 159646, 162377, 165075, 165960, 170591, 171954, 174651, 175569,\n", + " 181425, 184659, 185560, 198498, 199402, 200304],\n", + " dtype='int64'), Int64Index([ 1926, 12152, 14005, 15806, 28571, 29478, 34163, 35079,\n", + " 35990, 36897, 39149, 40051, 47389, 51086, 52944, 53845,\n", + " 56653, 62825, 66145, 67972, 68877, 69774, 70681, 94215,\n", + " 102069, 102987, 106616, 111275, 114013, 118584, 123723, 124631,\n", + " 126439, 127332, 129198, 131003, 131926, 153618, 155447, 156370,\n", + " 159647, 162378, 165076, 165961, 170592, 171955, 174652, 175570,\n", + " 181426, 184660, 185561, 198499, 199403, 200305],\n", + " dtype='int64'), Int64Index([ 1927, 12153, 14006, 15807, 28572, 29479, 34164, 35080,\n", + " 35991, 36898, 39150, 40052, 47390, 51087, 52945, 53846,\n", + " 56654, 62826, 66146, 67973, 68878, 69775, 70682, 94216,\n", + " 102070, 102988, 106617, 111276, 114014, 118585, 123724, 124632,\n", + " 126440, 127333, 129199, 131004, 131927, 153619, 155448, 156371,\n", + " 159648, 162379, 165077, 165962, 170593, 171956, 174653, 175571,\n", + " 181427, 184661, 185562, 198500, 199404, 200306],\n", + " dtype='int64'), Int64Index([ 1928, 12154, 14007, 15808, 28573, 29480, 34165, 35081,\n", + " 35992, 36899, 39151, 40053, 47391, 51088, 52946, 53847,\n", + " 56655, 62827, 66147, 67974, 68879, 69776, 70683, 94217,\n", + " 102071, 102989, 106618, 111277, 114015, 118586, 123725, 124633,\n", + " 126441, 127334, 129200, 131005, 131928, 153620, 155449, 156372,\n", + " 159649, 162380, 165078, 165963, 170594, 171957, 174654, 175572,\n", + " 181428, 184662, 185563, 198501, 199405, 200307],\n", + " dtype='int64'), Int64Index([ 1929, 12155, 14008, 15809, 28574, 29481, 34166, 35082,\n", + " 35993, 36900, 39152, 40054, 47392, 51089, 52947, 53848,\n", + " 56656, 62828, 66148, 67975, 68880, 69777, 70684, 94218,\n", + " 102072, 102990, 106619, 111278, 114016, 118587, 123726, 124634,\n", + " 126442, 127335, 129201, 131006, 131929, 153621, 155450, 156373,\n", + " 159650, 162381, 165079, 165964, 170595, 171958, 174655, 175573,\n", + " 181429, 184663, 185564, 198502, 199406, 200308],\n", + " dtype='int64'), Int64Index([ 1930, 12156, 14009, 15810, 28575, 29482, 34167, 35083,\n", + " 35994, 36901, 39153, 40055, 47393, 51090, 52948, 53849,\n", + " 56657, 62829, 66149, 67976, 68881, 69778, 70685, 94219,\n", + " 102073, 102991, 106620, 111279, 114017, 118588, 123727, 124635,\n", + " 126443, 127336, 129202, 131007, 131930, 153622, 155451, 156374,\n", + " 159651, 162382, 165080, 165965, 170596, 171959, 174656, 175574,\n", + " 181430, 184664, 185565, 198503, 199407, 200309],\n", + " dtype='int64'), Int64Index([ 1931, 12157, 14010, 15811, 28576, 29483, 34168, 35084,\n", + " 35995, 36902, 39154, 40056, 47394, 51091, 52949, 53850,\n", + " 56658, 62830, 66150, 67977, 68882, 69779, 70686, 94220,\n", + " 102074, 102992, 106621, 111280, 114018, 118589, 123728, 124636,\n", + " 126444, 127337, 129203, 131008, 131931, 153623, 155452, 156375,\n", + " 159652, 162383, 165081, 165966, 170597, 171960, 174657, 175575,\n", + " 181431, 184665, 185566, 198504, 199408, 200310],\n", + " dtype='int64'), Int64Index([ 1932, 12158, 14011, 15812, 28577, 29484, 34169, 35085,\n", + " 35996, 36903, 39155, 40057, 47395, 51092, 52950, 53851,\n", + " 56659, 62831, 66151, 67978, 68883, 69780, 70687, 94221,\n", + " 102075, 102993, 106622, 111281, 114019, 118590, 123729, 124637,\n", + " 126445, 127338, 129204, 131009, 131932, 153624, 155453, 156376,\n", + " 159653, 162384, 165082, 165967, 170598, 171961, 174658, 175576,\n", + " 181432, 184666, 185567, 198505, 199409, 200311],\n", + " dtype='int64'), Int64Index([ 1933, 12159, 14012, 15813, 28578, 29485, 34170, 35086,\n", + " 35997, 36904, 39156, 40058, 47396, 51093, 52951, 53852,\n", + " 56660, 62832, 66152, 67979, 68884, 69781, 70688, 94222,\n", + " 102076, 102994, 106623, 111282, 114020, 118591, 123730, 124638,\n", + " 126446, 127339, 129205, 131010, 131933, 153625, 155454, 156377,\n", + " 159654, 162385, 165083, 165968, 170599, 171962, 174659, 175577,\n", + " 181433, 184667, 185568, 198506, 199410, 200312],\n", + " dtype='int64'), Int64Index([ 1934, 12160, 14013, 15814, 28579, 29486, 34171, 35087,\n", + " 35998, 36905, 39157, 40059, 47397, 51094, 52952, 53853,\n", + " 56661, 62833, 66153, 67980, 68885, 69782, 70689, 94223,\n", + " 102077, 102995, 106624, 111283, 114021, 118592, 123731, 124639,\n", + " 126447, 127340, 129206, 131011, 131934, 153626, 155455, 156378,\n", + " 159655, 162386, 165084, 165969, 170600, 171963, 174660, 175578,\n", + " 181434, 184668, 185569, 198507, 199411, 200313],\n", + " dtype='int64'), Int64Index([ 1935, 12161, 14014, 15815, 28580, 29487, 34172, 35088,\n", + " 35999, 36906, 39158, 40060, 47398, 51095, 52953, 53854,\n", + " 56662, 62834, 66154, 67981, 68886, 69783, 70690, 94224,\n", + " 102078, 102996, 106625, 111284, 114022, 118593, 123732, 124640,\n", + " 126448, 127341, 129207, 131012, 131935, 153627, 155456, 156379,\n", + " 159656, 162387, 165085, 165970, 170601, 171964, 174661, 175579,\n", + " 181435, 184669, 185570, 198508, 199412, 200314],\n", + " dtype='int64'), Int64Index([ 1936, 12162, 14015, 15816, 28581, 29488, 34173, 35089,\n", + " 36000, 36907, 39159, 40061, 47399, 51096, 52954, 53855,\n", + " 56663, 62835, 66155, 67982, 68887, 69784, 70691, 94225,\n", + " 102079, 102997, 106626, 111285, 114023, 118594, 123733, 124641,\n", + " 126449, 127342, 129208, 131013, 131936, 153628, 155457, 156380,\n", + " 159657, 162388, 165086, 165971, 170602, 171965, 174662, 175580,\n", + " 181436, 184670, 185571, 198509, 199413, 200315],\n", + " dtype='int64'), Int64Index([ 1937, 12163, 14016, 15817, 28582, 29489, 34174, 35090,\n", + " 36001, 36908, 39160, 40062, 47400, 51097, 52955, 53856,\n", + " 56664, 62836, 66156, 67983, 68888, 69785, 70692, 94226,\n", + " 102080, 102998, 106627, 111286, 114024, 118595, 123734, 124642,\n", + " 126450, 127343, 129209, 131014, 131937, 153629, 155458, 156381,\n", + " 159658, 162389, 165087, 165972, 170603, 171966, 174663, 175581,\n", + " 181437, 184671, 185572, 198510, 199414, 200316],\n", + " dtype='int64'), Int64Index([ 1938, 12164, 14017, 15818, 28583, 29490, 34175, 35091,\n", + " 36002, 36909, 39161, 40063, 47401, 51098, 52956, 53857,\n", + " 56665, 62837, 66157, 67984, 68889, 69786, 70693, 94227,\n", + " 102081, 102999, 106628, 111287, 114025, 118596, 123735, 124643,\n", + " 126451, 127344, 129210, 131015, 131938, 153630, 155459, 156382,\n", + " 159659, 162390, 165088, 165973, 170604, 171967, 174664, 175582,\n", + " 181438, 184672, 185573, 198511, 199415, 200317],\n", + " dtype='int64'), Int64Index([ 1939, 12165, 14018, 15819, 28584, 29491, 34176, 35092,\n", + " 36003, 36910, 39162, 40064, 47402, 51099, 52957, 53858,\n", + " 56666, 62838, 66158, 67985, 68890, 69787, 70694, 94228,\n", + " 102082, 103000, 106629, 111288, 114026, 118597, 123736, 124644,\n", + " 126452, 127345, 129211, 131016, 131939, 153631, 155460, 156383,\n", + " 159660, 162391, 165089, 165974, 170605, 171968, 174665, 175583,\n", + " 181439, 184673, 185574, 198512, 199416, 200318],\n", + " dtype='int64'), Int64Index([ 1940, 12166, 14019, 15820, 28585, 29492, 34177, 35093,\n", + " 36004, 36911, 39163, 40065, 47403, 51100, 52958, 53859,\n", + " 56667, 62839, 66159, 67986, 68891, 69788, 70695, 94229,\n", + " 102083, 103001, 106630, 111289, 114027, 118598, 123737, 124645,\n", + " 126453, 127346, 129212, 131017, 131940, 153632, 155461, 156384,\n", + " 159661, 162392, 165090, 165975, 170606, 171969, 174666, 175584,\n", + " 181440, 184674, 185575, 198513, 199417, 200319],\n", + " dtype='int64'), Int64Index([ 1941, 12167, 14020, 15821, 28586, 29493, 34178, 35094,\n", + " 36005, 36912, 39164, 40066, 47404, 51101, 52959, 53860,\n", + " 56668, 62840, 66160, 67987, 68892, 69789, 70696, 94230,\n", + " 102084, 103002, 106631, 111290, 114028, 118599, 123738, 124646,\n", + " 126454, 127347, 129213, 131018, 131941, 153633, 155462, 156385,\n", + " 159662, 162393, 165091, 165976, 170607, 171970, 174667, 175585,\n", + " 181441, 184675, 185576, 198514, 199418, 200320],\n", + " dtype='int64'), Int64Index([ 1942, 12168, 14021, 15822, 28587, 29494, 34179, 35095,\n", + " 36006, 36913, 39165, 40067, 47405, 51102, 52960, 53861,\n", + " 56669, 62841, 66161, 67988, 68893, 69790, 70697, 94231,\n", + " 102085, 103003, 106632, 111291, 114029, 118600, 123739, 124647,\n", + " 126455, 127348, 129214, 131019, 131942, 153634, 155463, 156386,\n", + " 159663, 162394, 165092, 165977, 170608, 171971, 174668, 175586,\n", + " 181442, 184676, 185577, 198515, 199419, 200321],\n", + " dtype='int64'), Int64Index([ 1943, 12169, 14022, 15823, 28588, 29495, 34180, 35096,\n", + " 36007, 36914, 39166, 40068, 47406, 51103, 52961, 53862,\n", + " 56670, 62842, 66162, 67989, 68894, 69791, 70698, 94232,\n", + " 102086, 103004, 106633, 111292, 114030, 118601, 123740, 124648,\n", + " 126456, 127349, 129215, 131020, 131943, 153635, 155464, 156387,\n", + " 159664, 162395, 165093, 165978, 170609, 171972, 174669, 175587,\n", + " 181443, 184677, 185578, 198516, 199420, 200322],\n", + " dtype='int64'), Int64Index([ 1944, 12170, 14023, 15824, 28589, 29496, 34181, 35097,\n", + " 36008, 36915, 39167, 40069, 47407, 51104, 52962, 53863,\n", + " 56671, 62843, 66163, 67990, 68895, 69792, 70699, 94233,\n", + " 102087, 103005, 106634, 111293, 114031, 118602, 123741, 124649,\n", + " 126457, 127350, 129216, 131021, 131944, 153636, 155465, 156388,\n", + " 159665, 162396, 165094, 165979, 170610, 171973, 174670, 175588,\n", + " 181444, 184678, 185579, 198517, 199421, 200323],\n", + " dtype='int64'), Int64Index([ 1945, 12171, 14024, 15825, 28590, 29497, 34182, 35098,\n", + " 36009, 36916, 39168, 40070, 47408, 51105, 52963, 53864,\n", + " 56672, 62844, 66164, 67991, 68896, 69793, 70700, 94234,\n", + " 102088, 103006, 106635, 111294, 114032, 118603, 123742, 124650,\n", + " 126458, 127351, 129217, 131022, 131945, 153637, 155466, 156389,\n", + " 159666, 162397, 165095, 165980, 170611, 171974, 174671, 175589,\n", + " 181445, 184679, 185580, 198518, 199422, 200324],\n", + " dtype='int64'), Int64Index([ 1946, 12172, 14025, 15826, 28591, 29498, 34183, 35099,\n", + " 36010, 36917, 39169, 40071, 47409, 51106, 52964, 53865,\n", + " 56673, 62845, 66165, 67992, 68897, 69794, 70701, 94235,\n", + " 102089, 103007, 106636, 111295, 114033, 118604, 123743, 124651,\n", + " 126459, 127352, 129218, 131023, 131946, 153638, 155467, 156390,\n", + " 159667, 162398, 165096, 165981, 170612, 171975, 174672, 175590,\n", + " 181446, 184680, 185581, 198519, 199423, 200325],\n", + " dtype='int64'), Int64Index([ 1947, 12173, 14026, 15827, 28592, 29499, 34184, 35100,\n", + " 36011, 36918, 39170, 40072, 47410, 51107, 52965, 53866,\n", + " 56674, 62846, 66166, 67993, 68898, 69795, 70702, 94236,\n", + " 102090, 103008, 106637, 111296, 114034, 118605, 123744, 124652,\n", + " 126460, 127353, 129219, 131024, 131947, 153639, 155468, 156391,\n", + " 159668, 162399, 165097, 165982, 170613, 171976, 174673, 175591,\n", + " 181447, 184681, 185582, 198520, 199424, 200326],\n", + " dtype='int64'), Int64Index([ 1948, 12174, 14027, 15828, 28593, 29500, 34185, 35101,\n", + " 36012, 36919, 39171, 40073, 47411, 51108, 52966, 53867,\n", + " 56675, 62847, 66167, 67994, 68899, 69796, 70703, 94237,\n", + " 102091, 103009, 106638, 111297, 114035, 118606, 123745, 124653,\n", + " 126461, 127354, 129220, 131025, 131948, 153640, 155469, 156392,\n", + " 159669, 162400, 165098, 165983, 170614, 171977, 174674, 175592,\n", + " 181448, 184682, 185583, 198521, 199425, 200327],\n", + " dtype='int64'), Int64Index([ 1949, 12175, 14028, 15829, 28594, 29501, 34186, 35102,\n", + " 36013, 36920, 39172, 40074, 47412, 51109, 52967, 53868,\n", + " 56676, 62848, 66168, 67995, 68900, 69797, 70704, 94238,\n", + " 102092, 103010, 106639, 111298, 114036, 118607, 123746, 124654,\n", + " 126462, 127355, 129221, 131026, 131949, 153641, 155470, 156393,\n", + " 159670, 162401, 165099, 165984, 170615, 171978, 174675, 175593,\n", + " 181449, 184683, 185584, 198522, 199426, 200328],\n", + " dtype='int64'), Int64Index([ 1950, 12176, 14029, 15830, 28595, 29502, 34187, 35103,\n", + " 36014, 36921, 39173, 40075, 47413, 51110, 52968, 53869,\n", + " 56677, 62849, 66169, 67996, 68901, 69798, 70705, 94239,\n", + " 102093, 103011, 106640, 111299, 114037, 118608, 123747, 124655,\n", + " 126463, 127356, 129222, 131027, 131950, 153642, 155471, 156394,\n", + " 159671, 162402, 165100, 165985, 170616, 171979, 174676, 175594,\n", + " 181450, 184684, 185585, 198523, 199427, 200329],\n", + " dtype='int64'), Int64Index([ 1951, 12177, 14030, 15831, 28596, 29503, 34188, 35104,\n", + " 36015, 36922, 39174, 40076, 47414, 51111, 52969, 53870,\n", + " 56678, 62850, 66170, 67997, 68902, 69799, 70706, 94240,\n", + " 102094, 103012, 106641, 111300, 114038, 118609, 123748, 124656,\n", + " 126464, 127357, 129223, 131028, 131951, 153643, 155472, 156395,\n", + " 159672, 162403, 165101, 165986, 170617, 171980, 174677, 175595,\n", + " 181451, 184685, 185586, 198524, 199428, 200330],\n", + " dtype='int64'), Int64Index([ 1952, 12178, 14031, 15832, 28597, 29504, 34189, 35105,\n", + " 36016, 36923, 39175, 40077, 47415, 51112, 52970, 53871,\n", + " 56679, 62851, 66171, 67998, 68903, 69800, 70707, 94241,\n", + " 102095, 103013, 106642, 111301, 114039, 118610, 123749, 124657,\n", + " 126465, 127358, 129224, 131029, 131952, 153644, 155473, 156396,\n", + " 159673, 162404, 165102, 165987, 170618, 171981, 174678, 175596,\n", + " 181452, 184686, 185587, 198525, 199429, 200331],\n", + " dtype='int64'), Int64Index([ 1953, 12179, 14032, 15833, 28598, 29505, 34190, 35106,\n", + " 36017, 36924, 39176, 40078, 47416, 51113, 52971, 53872,\n", + " 56680, 62852, 66172, 67999, 68904, 69801, 70708, 94242,\n", + " 102096, 103014, 106643, 111302, 114040, 118611, 123750, 124658,\n", + " 126466, 127359, 129225, 131030, 131953, 153645, 155474, 156397,\n", + " 159674, 162405, 165103, 165988, 170619, 171982, 174679, 175597,\n", + " 181453, 184687, 185588, 198526, 199430, 200332],\n", + " dtype='int64'), Int64Index([ 1954, 12180, 14033, 15834, 28599, 29506, 34191, 35107,\n", + " 36018, 36925, 39177, 40079, 47417, 51114, 52972, 53873,\n", + " 56681, 62853, 66173, 68000, 68905, 69802, 70709, 94243,\n", + " 102097, 103015, 106644, 111303, 114041, 118612, 123751, 124659,\n", + " 126467, 127360, 129226, 131031, 131954, 153646, 155475, 156398,\n", + " 159675, 162406, 165104, 165989, 170620, 171983, 174680, 175598,\n", + " 181454, 184688, 185589, 198527, 199431, 200333],\n", + " dtype='int64'), Int64Index([ 1955, 12181, 14034, 15835, 28600, 29507, 34192, 35108,\n", + " 36019, 36926, 39178, 40080, 47418, 51115, 52973, 53874,\n", + " 56682, 62854, 66174, 68001, 68906, 69803, 70710, 94244,\n", + " 102098, 103016, 106645, 111304, 114042, 118613, 123752, 124660,\n", + " 126468, 127361, 129227, 131032, 131955, 153647, 155476, 156399,\n", + " 159676, 162407, 165105, 165990, 170621, 171984, 174681, 175599,\n", + " 181455, 184689, 185590, 198528, 199432, 200334],\n", + " dtype='int64'), Int64Index([ 1956, 12182, 14035, 15836, 28601, 29508, 34193, 35109,\n", + " 36020, 36927, 39179, 40081, 47419, 51116, 52974, 53875,\n", + " 56683, 62855, 66175, 68002, 68907, 69804, 70711, 94245,\n", + " 102099, 103017, 106646, 111305, 114043, 118614, 123753, 124661,\n", + " 126469, 127362, 129228, 131033, 131956, 153648, 155477, 156400,\n", + " 159677, 162408, 165106, 165991, 170622, 171985, 174682, 175600,\n", + " 181456, 184690, 185591, 198529, 199433, 200335],\n", + " dtype='int64'), Int64Index([ 1957, 12183, 14036, 15837, 28602, 29509, 34194, 35110,\n", + " 36021, 36928, 39180, 40082, 47420, 51117, 52975, 53876,\n", + " 56684, 62856, 66176, 68003, 68908, 69805, 70712, 94246,\n", + " 102100, 103018, 106647, 111306, 114044, 118615, 123754, 124662,\n", + " 126470, 127363, 129229, 131034, 131957, 153649, 155478, 156401,\n", + " 159678, 162409, 165107, 165992, 170623, 171986, 174683, 175601,\n", + " 181457, 184691, 185592, 198530, 199434, 200336],\n", + " dtype='int64'), Int64Index([ 1958, 12184, 14037, 15838, 28603, 29510, 34195, 35111,\n", + " 36022, 36929, 39181, 40083, 47421, 51118, 52976, 53877,\n", + " 56685, 62857, 66177, 68004, 68909, 69806, 70713, 94247,\n", + " 102101, 103019, 106648, 111307, 114045, 118616, 123755, 124663,\n", + " 126471, 127364, 129230, 131035, 131958, 153650, 155479, 156402,\n", + " 159679, 162410, 165108, 165993, 170624, 171987, 174684, 175602,\n", + " 181458, 184692, 185593, 198531, 199435, 200337],\n", + " dtype='int64'), Int64Index([ 1959, 12185, 14038, 15839, 28604, 29511, 34196, 35112,\n", + " 36023, 36930, 39182, 40084, 47422, 51119, 52977, 53878,\n", + " 56686, 62858, 66178, 68005, 68910, 69807, 70714, 94248,\n", + " 102102, 103020, 106649, 111308, 114046, 118617, 123756, 124664,\n", + " 126472, 127365, 129231, 131036, 131959, 153651, 155480, 156403,\n", + " 159680, 162411, 165109, 165994, 170625, 171988, 174685, 175603,\n", + " 181459, 184693, 185594, 198532, 199436, 200338],\n", + " dtype='int64'), Int64Index([ 1960, 12186, 14039, 15840, 28605, 29512, 34197, 35113,\n", + " 36024, 36931, 39183, 40085, 47423, 51120, 52978, 53879,\n", + " 56687, 62859, 66179, 68006, 68911, 69808, 70715, 94249,\n", + " 102103, 103021, 106650, 111309, 114047, 118618, 123757, 124665,\n", + " 126473, 127366, 129232, 131037, 131960, 153652, 155481, 156404,\n", + " 159681, 162412, 165110, 165995, 170626, 171989, 174686, 175604,\n", + " 181460, 184694, 185595, 198533, 199437, 200339],\n", + " dtype='int64'), Int64Index([ 1961, 12187, 14040, 15841, 28606, 29513, 34198, 35114,\n", + " 36025, 36932, 39184, 40086, 47424, 51121, 52979, 53880,\n", + " 56688, 62860, 66180, 68007, 68912, 69809, 70716, 94250,\n", + " 102104, 103022, 106651, 111310, 114048, 118619, 123758, 124666,\n", + " 126474, 127367, 129233, 131038, 131961, 153653, 155482, 156405,\n", + " 159682, 162413, 165111, 165996, 170627, 171990, 174687, 175605,\n", + " 181461, 184695, 185596, 198534, 199438, 200340],\n", + " dtype='int64'), Int64Index([ 1962, 12188, 14041, 15842, 28607, 29514, 34199, 35115,\n", + " 36026, 36933, 39185, 40087, 47425, 51122, 52980, 53881,\n", + " 56689, 62861, 66181, 68008, 68913, 69810, 70717, 94251,\n", + " 102105, 103023, 106652, 111311, 114049, 118620, 123759, 124667,\n", + " 126475, 127368, 129234, 131039, 131962, 153654, 155483, 156406,\n", + " 159683, 162414, 165112, 165997, 170628, 171991, 174688, 175606,\n", + " 181462, 184696, 185597, 198535, 199439, 200341],\n", + " dtype='int64'), Int64Index([ 1963, 12189, 14042, 15843, 28608, 29515, 34200, 35116,\n", + " 36027, 36934, 39186, 40088, 47426, 51123, 52981, 53882,\n", + " 56690, 62862, 66182, 68009, 68914, 69811, 70718, 94252,\n", + " 102106, 103024, 106653, 111312, 114050, 118621, 123760, 124668,\n", + " 126476, 127369, 129235, 131040, 131963, 153655, 155484, 156407,\n", + " 159684, 162415, 165113, 165998, 170629, 171992, 174689, 175607,\n", + " 181463, 184697, 185598, 198536, 199440, 200342],\n", + " dtype='int64'), Int64Index([ 1964, 12190, 14043, 15844, 28609, 29516, 34201, 35117,\n", + " 36028, 36935, 39187, 40089, 47427, 51124, 52982, 53883,\n", + " 56691, 62863, 66183, 68010, 68915, 69812, 70719, 94253,\n", + " 102107, 103025, 106654, 111313, 114051, 118622, 123761, 124669,\n", + " 126477, 127370, 129236, 131041, 131964, 153656, 155485, 156408,\n", + " 159685, 162416, 165114, 165999, 170630, 171993, 174690, 175608,\n", + " 181464, 184698, 185599, 198537, 199441, 200343],\n", + " dtype='int64'), Int64Index([ 1965, 12191, 14044, 15845, 28610, 29517, 34202, 35118,\n", + " 36029, 36936, 39188, 40090, 47428, 51125, 52983, 53884,\n", + " 56692, 62864, 66184, 68011, 68916, 69813, 70720, 94254,\n", + " 102108, 103026, 106655, 111314, 114052, 118623, 123762, 124670,\n", + " 126478, 127371, 129237, 131042, 131965, 153657, 155486, 156409,\n", + " 159686, 162417, 165115, 166000, 170631, 171994, 174691, 175609,\n", + " 181465, 184699, 185600, 198538, 199442, 200344],\n", + " dtype='int64'), Int64Index([ 1966, 12192, 14045, 15846, 28611, 29518, 34203, 35119,\n", + " 36030, 36937, 39189, 40091, 47429, 51126, 52984, 53885,\n", + " 56693, 62865, 66185, 68012, 68917, 69814, 70721, 94255,\n", + " 102109, 103027, 106656, 111315, 114053, 118624, 123763, 124671,\n", + " 126479, 127372, 129238, 131043, 131966, 153658, 155487, 156410,\n", + " 159687, 162418, 165116, 166001, 170632, 171995, 174692, 175610,\n", + " 181466, 184700, 185601, 198539, 199443, 200345],\n", + " dtype='int64'), Int64Index([ 1967, 12193, 14046, 15847, 28612, 29519, 34204, 35120,\n", + " 36031, 36938, 39190, 40092, 47430, 51127, 52985, 53886,\n", + " 56694, 62866, 66186, 68013, 68918, 69815, 70722, 94256,\n", + " 102110, 103028, 106657, 111316, 114054, 118625, 123764, 124672,\n", + " 126480, 127373, 129239, 131044, 131967, 153659, 155488, 156411,\n", + " 159688, 162419, 165117, 166002, 170633, 171996, 174693, 175611,\n", + " 181467, 184701, 185602, 198540, 199444, 200346],\n", + " dtype='int64'), Int64Index([ 1968, 12194, 14047, 15848, 28613, 29520, 34205, 35121,\n", + " 36032, 36939, 39191, 40093, 47431, 51128, 52986, 53887,\n", + " 56695, 62867, 66187, 68014, 68919, 69816, 70723, 94257,\n", + " 102111, 103029, 106658, 111317, 114055, 118626, 123765, 124673,\n", + " 126481, 127374, 129240, 131045, 131968, 153660, 155489, 156412,\n", + " 159689, 162420, 165118, 166003, 170634, 171997, 174694, 175612,\n", + " 181468, 184702, 185603, 198541, 199445, 200347],\n", + " dtype='int64'), Int64Index([ 1969, 12195, 14048, 15849, 28614, 29521, 34206, 35122,\n", + " 36033, 36940, 39192, 40094, 47432, 51129, 52987, 53888,\n", + " 56696, 62868, 66188, 68015, 68920, 69817, 70724, 94258,\n", + " 102112, 103030, 106659, 111318, 114056, 118627, 123766, 124674,\n", + " 126482, 127375, 129241, 131046, 131969, 153661, 155490, 156413,\n", + " 159690, 162421, 165119, 166004, 170635, 171998, 174695, 175613,\n", + " 181469, 184703, 185604, 198542, 199446, 200348],\n", + " dtype='int64'), Int64Index([ 1970, 12196, 14049, 15850, 28615, 29522, 34207, 35123,\n", + " 36034, 36941, 39193, 40095, 47433, 51130, 52988, 53889,\n", + " 56697, 62869, 66189, 68016, 68921, 69818, 70725, 94259,\n", + " 102113, 103031, 106660, 111319, 114057, 118628, 123767, 124675,\n", + " 126483, 127376, 129242, 131047, 131970, 153662, 155491, 156414,\n", + " 159691, 162422, 165120, 166005, 170636, 171999, 174696, 175614,\n", + " 181470, 184704, 185605, 198543, 199447, 200349],\n", + " dtype='int64'), Int64Index([ 1971, 12197, 14050, 15851, 28616, 29523, 34208, 35124,\n", + " 36035, 36942, 39194, 40096, 47434, 51131, 52989, 53890,\n", + " 56698, 62870, 66190, 68017, 68922, 69819, 70726, 94260,\n", + " 102114, 103032, 106661, 111320, 114058, 118629, 123768, 124676,\n", + " 126484, 127377, 129243, 131048, 131971, 153663, 155492, 156415,\n", + " 159692, 162423, 165121, 166006, 170637, 172000, 174697, 175615,\n", + " 181471, 184705, 185606, 198544, 199448, 200350],\n", + " dtype='int64'), Int64Index([ 1972, 12198, 14051, 15852, 28617, 29524, 34209, 35125,\n", + " 36036, 36943, 39195, 40097, 47435, 51132, 52990, 53891,\n", + " 56699, 62871, 66191, 68018, 68923, 69820, 70727, 94261,\n", + " 102115, 103033, 106662, 111321, 114059, 118630, 123769, 124677,\n", + " 126485, 127378, 129244, 131049, 131972, 153664, 155493, 156416,\n", + " 159693, 162424, 165122, 166007, 170638, 172001, 174698, 175616,\n", + " 181472, 184706, 185607, 198545, 199449, 200351],\n", + " dtype='int64'), Int64Index([ 1973, 12199, 14052, 15853, 28618, 29525, 34210, 35126,\n", + " 36037, 36944, 39196, 40098, 47436, 51133, 52991, 53892,\n", + " 56700, 62872, 66192, 68019, 68924, 69821, 70728, 94262,\n", + " 102116, 103034, 106663, 111322, 114060, 118631, 123770, 124678,\n", + " 126486, 127379, 129245, 131050, 131973, 153665, 155494, 156417,\n", + " 159694, 162425, 165123, 166008, 170639, 172002, 174699, 175617,\n", + " 181473, 184707, 185608, 198546, 199450, 200352],\n", + " dtype='int64'), Int64Index([ 1974, 12200, 14053, 15854, 28619, 29526, 34211, 35127,\n", + " 36038, 36945, 39197, 40099, 47437, 51134, 52992, 53893,\n", + " 56701, 62873, 66193, 68020, 68925, 69822, 70729, 94263,\n", + " 102117, 103035, 106664, 111323, 114061, 118632, 123771, 124679,\n", + " 126487, 127380, 129246, 131051, 131974, 153666, 155495, 156418,\n", + " 159695, 162426, 165124, 166009, 170640, 172003, 174700, 175618,\n", + " 181474, 184708, 185609, 198547, 199451, 200353],\n", + " dtype='int64'), Int64Index([ 1975, 12201, 14054, 15855, 28620, 29527, 34212, 35128,\n", + " 36039, 36946, 39198, 40100, 47438, 51135, 52993, 53894,\n", + " 56702, 62874, 66194, 68021, 68926, 69823, 70730, 94264,\n", + " 102118, 103036, 106665, 111324, 114062, 118633, 123772, 124680,\n", + " 126488, 127381, 129247, 131052, 131975, 153667, 155496, 156419,\n", + " 159696, 162427, 165125, 166010, 170641, 172004, 174701, 175619,\n", + " 181475, 184709, 185610, 198548, 199452, 200354],\n", + " dtype='int64'), Int64Index([ 1976, 12202, 14055, 15856, 28621, 29528, 34213, 35129,\n", + " 36040, 36947, 39199, 40101, 47439, 51136, 52994, 53895,\n", + " 56703, 62875, 66195, 68022, 68927, 69824, 70731, 94265,\n", + " 102119, 103037, 106666, 111325, 114063, 118634, 123773, 124681,\n", + " 126489, 127382, 129248, 131053, 131976, 153668, 155497, 156420,\n", + " 159697, 162428, 165126, 166011, 170642, 172005, 174702, 175620,\n", + " 181476, 184710, 185611, 198549, 199453, 200355],\n", + " dtype='int64'), Int64Index([ 1977, 12203, 14056, 15857, 28622, 29529, 34214, 35130,\n", + " 36041, 36948, 39200, 40102, 47440, 51137, 52995, 53896,\n", + " 56704, 62876, 66196, 68023, 68928, 69825, 70732, 94266,\n", + " 102120, 103038, 106667, 111326, 114064, 118635, 123774, 124682,\n", + " 126490, 127383, 129249, 131054, 131977, 153669, 155498, 156421,\n", + " 159698, 162429, 165127, 166012, 170643, 172006, 174703, 175621,\n", + " 181477, 184711, 185612, 198550, 199454, 200356],\n", + " dtype='int64'), Int64Index([ 1978, 12204, 14057, 15858, 28623, 29530, 34215, 35131,\n", + " 36042, 36949, 39201, 40103, 47441, 51138, 52996, 53897,\n", + " 56705, 62877, 66197, 68024, 68929, 69826, 70733, 94267,\n", + " 102121, 103039, 106668, 111327, 114065, 118636, 123775, 124683,\n", + " 126491, 127384, 129250, 131055, 131978, 153670, 155499, 156422,\n", + " 159699, 162430, 165128, 166013, 170644, 172007, 174704, 175622,\n", + " 181478, 184712, 185613, 198551, 199455, 200357],\n", + " dtype='int64'), Int64Index([ 1979, 12205, 14058, 15859, 28624, 29531, 34216, 35132,\n", + " 36043, 36950, 39202, 40104, 47442, 51139, 52997, 53898,\n", + " 56706, 62878, 66198, 68025, 68930, 69827, 70734, 94268,\n", + " 102122, 103040, 106669, 111328, 114066, 118637, 123776, 124684,\n", + " 126492, 127385, 129251, 131056, 131979, 153671, 155500, 156423,\n", + " 159700, 162431, 165129, 166014, 170645, 172008, 174705, 175623,\n", + " 181479, 184713, 185614, 198552, 199456, 200358],\n", + " dtype='int64'), Int64Index([ 1980, 12206, 14059, 15860, 28625, 29532, 34217, 35133,\n", + " 36044, 36951, 39203, 40105, 47443, 51140, 52998, 53899,\n", + " 56707, 62879, 66199, 68026, 68931, 69828, 70735, 94269,\n", + " 102123, 103041, 106670, 111329, 114067, 118638, 123777, 124685,\n", + " 126493, 127386, 129252, 131057, 131980, 153672, 155501, 156424,\n", + " 159701, 162432, 165130, 166015, 170646, 172009, 174706, 175624,\n", + " 181480, 184714, 185615, 198553, 199457, 200359],\n", + " dtype='int64'), Int64Index([ 1981, 12207, 14060, 15861, 28626, 29533, 34218, 35134,\n", + " 36045, 36952, 39204, 40106, 47444, 51141, 52999, 53900,\n", + " 56708, 62880, 66200, 68027, 68932, 69829, 70736, 94270,\n", + " 102124, 103042, 106671, 111330, 114068, 118639, 123778, 124686,\n", + " 126494, 127387, 129253, 131058, 131981, 153673, 155502, 156425,\n", + " 159702, 162433, 165131, 166016, 170647, 172010, 174707, 175625,\n", + " 181481, 184715, 185616, 198554, 199458, 200360],\n", + " dtype='int64'), Int64Index([ 1982, 12208, 14061, 15862, 28627, 29534, 34219, 35135,\n", + " 36046, 36953, 39205, 40107, 47445, 51142, 53000, 53901,\n", + " 56709, 62881, 66201, 68028, 68933, 69830, 70737, 94271,\n", + " 102125, 103043, 106672, 111331, 114069, 118640, 123779, 124687,\n", + " 126495, 127388, 129254, 131059, 131982, 153674, 155503, 156426,\n", + " 159703, 162434, 165132, 166017, 170648, 172011, 174708, 175626,\n", + " 181482, 184716, 185617, 198555, 199459, 200361],\n", + " dtype='int64'), Int64Index([ 1983, 12209, 14062, 15863, 28628, 29535, 34220, 35136,\n", + " 36047, 36954, 39206, 40108, 47446, 51143, 53001, 53902,\n", + " 56710, 62882, 66202, 68029, 68934, 69831, 70738, 94272,\n", + " 102126, 103044, 106673, 111332, 114070, 118641, 123780, 124688,\n", + " 126496, 127389, 129255, 131060, 131983, 153675, 155504, 156427,\n", + " 159704, 162435, 165133, 166018, 170649, 172012, 174709, 175627,\n", + " 181483, 184717, 185618, 198556, 199460, 200362],\n", + " dtype='int64'), Int64Index([ 1984, 12210, 14063, 15864, 28629, 29536, 34221, 35137,\n", + " 36048, 36955, 39207, 40109, 47447, 51144, 53002, 53903,\n", + " 56711, 62883, 66203, 68030, 68935, 69832, 70739, 94273,\n", + " 102127, 103045, 106674, 111333, 114071, 118642, 123781, 124689,\n", + " 126497, 127390, 129256, 131061, 131984, 153676, 155505, 156428,\n", + " 159705, 162436, 165134, 166019, 170650, 172013, 174710, 175628,\n", + " 181484, 184718, 185619, 198557, 199461, 200363],\n", + " dtype='int64'), Int64Index([ 1985, 12211, 14064, 15865, 28630, 29537, 34222, 35138,\n", + " 36049, 36956, 39208, 40110, 47448, 51145, 53003, 53904,\n", + " 56712, 62884, 66204, 68031, 68936, 69833, 70740, 94274,\n", + " 102128, 103046, 106675, 111334, 114072, 118643, 123782, 124690,\n", + " 126498, 127391, 129257, 131062, 131985, 153677, 155506, 156429,\n", + " 159706, 162437, 165135, 166020, 170651, 172014, 174711, 175629,\n", + " 181485, 184719, 185620, 198558, 199462, 200364],\n", + " dtype='int64'), Int64Index([ 1986, 12212, 14065, 15866, 28631, 29538, 34223, 35139,\n", + " 36050, 36957, 39209, 40111, 47449, 51146, 53004, 53905,\n", + " 56713, 62885, 66205, 68032, 68937, 69834, 70741, 94275,\n", + " 102129, 103047, 106676, 111335, 114073, 118644, 123783, 124691,\n", + " 126499, 127392, 129258, 131063, 131986, 153678, 155507, 156430,\n", + " 159707, 162438, 165136, 166021, 170652, 172015, 174712, 175630,\n", + " 181486, 184720, 185621, 198559, 199463, 200365],\n", + " dtype='int64'), Int64Index([ 1987, 12213, 14066, 15867, 28632, 29539, 34224, 35140,\n", + " 36051, 36958, 39210, 40112, 47450, 51147, 53005, 53906,\n", + " 56714, 62886, 66206, 68033, 68938, 69835, 70742, 94276,\n", + " 102130, 103048, 106677, 111336, 114074, 118645, 123784, 124692,\n", + " 126500, 127393, 129259, 131064, 131987, 153679, 155508, 156431,\n", + " 159708, 162439, 165137, 166022, 170653, 172016, 174713, 175631,\n", + " 181487, 184721, 185622, 198560, 199464, 200366],\n", + " dtype='int64'), Int64Index([ 1988, 12214, 14067, 15868, 28633, 29540, 34225, 35141,\n", + " 36052, 36959, 39211, 40113, 47451, 51148, 53006, 53907,\n", + " 56715, 62887, 66207, 68034, 68939, 69836, 70743, 94277,\n", + " 102131, 103049, 106678, 111337, 114075, 118646, 123785, 124693,\n", + " 126501, 127394, 129260, 131065, 131988, 153680, 155509, 156432,\n", + " 159709, 162440, 165138, 166023, 170654, 172017, 174714, 175632,\n", + " 181488, 184722, 185623, 198561, 199465, 200367],\n", + " dtype='int64'), Int64Index([ 1989, 12215, 14068, 15869, 28634, 29541, 34226, 35142,\n", + " 36053, 36960, 39212, 40114, 47452, 51149, 53007, 53908,\n", + " 56716, 62888, 66208, 68035, 68940, 69837, 70744, 94278,\n", + " 102132, 103050, 106679, 111338, 114076, 118647, 123786, 124694,\n", + " 126502, 127395, 129261, 131066, 131989, 153681, 155510, 156433,\n", + " 159710, 162441, 165139, 166024, 170655, 172018, 174715, 175633,\n", + " 181489, 184723, 185624, 198562, 199466, 200368],\n", + " dtype='int64'), Int64Index([ 1990, 12216, 14069, 15870, 28635, 29542, 34227, 35143,\n", + " 36054, 36961, 39213, 40115, 47453, 51150, 53008, 53909,\n", + " 56717, 62889, 66209, 68036, 68941, 69838, 70745, 94279,\n", + " 102133, 103051, 106680, 111339, 114077, 118648, 123787, 124695,\n", + " 126503, 127396, 129262, 131067, 131990, 153682, 155511, 156434,\n", + " 159711, 162442, 165140, 166025, 170656, 172019, 174716, 175634,\n", + " 181490, 184724, 185625, 198563, 199467, 200369],\n", + " dtype='int64'), Int64Index([ 1991, 12217, 14070, 15871, 28636, 29543, 34228, 35144,\n", + " 36055, 36962, 39214, 40116, 47454, 51151, 53009, 53910,\n", + " 56718, 62890, 66210, 68037, 68942, 69839, 70746, 94280,\n", + " 102134, 103052, 106681, 111340, 114078, 118649, 123788, 124696,\n", + " 126504, 127397, 129263, 131068, 131991, 153683, 155512, 156435,\n", + " 159712, 162443, 165141, 166026, 170657, 172020, 174717, 175635,\n", + " 181491, 184725, 185626, 198564, 199468, 200370],\n", + " dtype='int64'), Int64Index([ 1992, 12218, 14071, 15872, 28637, 29544, 34229, 35145,\n", + " 36056, 36963, 39215, 40117, 47455, 51152, 53010, 53911,\n", + " 56719, 62891, 66211, 68038, 68943, 69840, 70747, 94281,\n", + " 102135, 103053, 106682, 111341, 114079, 118650, 123789, 124697,\n", + " 126505, 127398, 129264, 131069, 131992, 153684, 155513, 156436,\n", + " 159713, 162444, 165142, 166027, 170658, 172021, 174718, 175636,\n", + " 181492, 184726, 185627, 198565, 199469, 200371],\n", + " dtype='int64'), Int64Index([ 1993, 12219, 14072, 15873, 28638, 29545, 34230, 35146,\n", + " 36057, 36964, 39216, 40118, 47456, 51153, 53011, 53912,\n", + " 56720, 62892, 66212, 68039, 68944, 69841, 70748, 94282,\n", + " 102136, 103054, 106683, 111342, 114080, 118651, 123790, 124698,\n", + " 126506, 127399, 129265, 131070, 131993, 153685, 155514, 156437,\n", + " 159714, 162445, 165143, 166028, 170659, 172022, 174719, 175637,\n", + " 181493, 184727, 185628, 198566, 199470, 200372],\n", + " dtype='int64'), Int64Index([ 1994, 12220, 14073, 15874, 28639, 29546, 34231, 35147,\n", + " 36058, 36965, 39217, 40119, 47457, 51154, 53012, 53913,\n", + " 56721, 62893, 66213, 68040, 68945, 69842, 70749, 94283,\n", + " 102137, 103055, 106684, 111343, 114081, 118652, 123791, 124699,\n", + " 126507, 127400, 129266, 131071, 131994, 153686, 155515, 156438,\n", + " 159715, 162446, 165144, 166029, 170660, 172023, 174720, 175638,\n", + " 181494, 184728, 185629, 198567, 199471, 200373],\n", + " dtype='int64'), Int64Index([ 1995, 12221, 14074, 15875, 28640, 29547, 34232, 35148,\n", + " 36059, 36966, 39218, 40120, 47458, 51155, 53013, 53914,\n", + " 56722, 62894, 66214, 68041, 68946, 69843, 70750, 94284,\n", + " 102138, 103056, 106685, 111344, 114082, 118653, 123792, 124700,\n", + " 126508, 127401, 129267, 131072, 131995, 153687, 155516, 156439,\n", + " 159716, 162447, 165145, 166030, 170661, 172024, 174721, 175639,\n", + " 181495, 184729, 185630, 198568, 199472, 200374],\n", + " dtype='int64'), Int64Index([ 1996, 12222, 14075, 15876, 28641, 29548, 34233, 35149,\n", + " 36060, 36967, 39219, 40121, 47459, 51156, 53014, 53915,\n", + " 56723, 62895, 66215, 68042, 68947, 69844, 70751, 94285,\n", + " 102139, 103057, 106686, 111345, 114083, 118654, 123793, 124701,\n", + " 126509, 127402, 129268, 131073, 131996, 153688, 155517, 156440,\n", + " 159717, 162448, 165146, 166031, 170662, 172025, 174722, 175640,\n", + " 181496, 184730, 185631, 198569, 199473, 200375],\n", + " dtype='int64'), Int64Index([ 1997, 12223, 14076, 15877, 28642, 29549, 34234, 35150,\n", + " 36061, 36968, 39220, 40122, 47460, 51157, 53015, 53916,\n", + " 56724, 62896, 66216, 68043, 68948, 69845, 70752, 94286,\n", + " 102140, 103058, 106687, 111346, 114084, 118655, 123794, 124702,\n", + " 126510, 127403, 129269, 131074, 131997, 153689, 155518, 156441,\n", + " 159718, 162449, 165147, 166032, 170663, 172026, 174723, 175641,\n", + " 181497, 184731, 185632, 198570, 199474, 200376],\n", + " dtype='int64'), Int64Index([ 1998, 12224, 14077, 15878, 28643, 29550, 34235, 35151,\n", + " 36062, 36969, 39221, 40123, 47461, 51158, 53016, 53917,\n", + " 56725, 62897, 66217, 68044, 68949, 69846, 70753, 94287,\n", + " 102141, 103059, 106688, 111347, 114085, 118656, 123795, 124703,\n", + " 126511, 127404, 129270, 131075, 131998, 153690, 155519, 156442,\n", + " 159719, 162450, 165148, 166033, 170664, 172027, 174724, 175642,\n", + " 181498, 184732, 185633, 198571, 199475, 200377],\n", + " dtype='int64'), Int64Index([ 1999, 12225, 14078, 15879, 28644, 29551, 34236, 35152,\n", + " 36063, 36970, 39222, 40124, 47462, 51159, 53017, 53918,\n", + " 56726, 62898, 66218, 68045, 68950, 69847, 70754, 94288,\n", + " 102142, 103060, 106689, 111348, 114086, 118657, 123796, 124704,\n", + " 126512, 127405, 129271, 131076, 131999, 153691, 155520, 156443,\n", + " 159720, 162451, 165149, 166034, 170665, 172028, 174725, 175643,\n", + " 181499, 184733, 185634, 198572, 199476, 200378],\n", + " dtype='int64'), Int64Index([ 2000, 12226, 14079, 15880, 28645, 29552, 34237, 35153,\n", + " 36064, 36971, 39223, 40125, 47463, 51160, 53018, 53919,\n", + " 56727, 62899, 66219, 68046, 68951, 69848, 70755, 94289,\n", + " 102143, 103061, 106690, 111349, 114087, 118658, 123797, 124705,\n", + " 126513, 127406, 129272, 131077, 132000, 153692, 155521, 156444,\n", + " 159721, 162452, 165150, 166035, 170666, 172029, 174726, 175644,\n", + " 181500, 184734, 185635, 198573, 199477, 200379],\n", + " dtype='int64'), Int64Index([ 2001, 12227, 14080, 15881, 28646, 29553, 34238, 35154,\n", + " 36065, 36972, 39224, 40126, 47464, 51161, 53019, 53920,\n", + " 56728, 62900, 66220, 68047, 68952, 69849, 70756, 94290,\n", + " 102144, 103062, 106691, 111350, 114088, 118659, 123798, 124706,\n", + " 126514, 127407, 129273, 131078, 132001, 153693, 155522, 156445,\n", + " 159722, 162453, 165151, 166036, 170667, 172030, 174727, 175645,\n", + " 181501, 184735, 185636, 198574, 199478, 200380],\n", + " dtype='int64'), Int64Index([ 2002, 12228, 14081, 15882, 28647, 29554, 34239, 35155,\n", + " 36066, 36973, 39225, 40127, 47465, 51162, 53020, 53921,\n", + " 56729, 62901, 66221, 68048, 68953, 69850, 70757, 94291,\n", + " 102145, 103063, 106692, 111351, 114089, 118660, 123799, 124707,\n", + " 126515, 127408, 129274, 131079, 132002, 153694, 155523, 156446,\n", + " 159723, 162454, 165152, 166037, 170668, 172031, 174728, 175646,\n", + " 181502, 184736, 185637, 198575, 199479, 200381],\n", + " dtype='int64'), Int64Index([ 2003, 12229, 14082, 15883, 28648, 29555, 34240, 35156,\n", + " 36067, 36974, 39226, 40128, 47466, 51163, 53021, 53922,\n", + " 56730, 62902, 66222, 68049, 68954, 69851, 70758, 94292,\n", + " 102146, 103064, 106693, 111352, 114090, 118661, 123800, 124708,\n", + " 126516, 127409, 129275, 131080, 132003, 153695, 155524, 156447,\n", + " 159724, 162455, 165153, 166038, 170669, 172032, 174729, 175647,\n", + " 181503, 184737, 185638, 198576, 199480, 200382],\n", + " dtype='int64'), Int64Index([ 2004, 12230, 14083, 15884, 28649, 29556, 34241, 35157,\n", + " 36068, 36975, 39227, 40129, 47467, 51164, 53022, 53923,\n", + " 56731, 62903, 66223, 68050, 68955, 69852, 70759, 94293,\n", + " 102147, 103065, 106694, 111353, 114091, 118662, 123801, 124709,\n", + " 126517, 127410, 129276, 131081, 132004, 153696, 155525, 156448,\n", + " 159725, 162456, 165154, 166039, 170670, 172033, 174730, 175648,\n", + " 181504, 184738, 185639, 198577, 199481, 200383],\n", + " dtype='int64'), Int64Index([ 2005, 12231, 14084, 15885, 28650, 29557, 34242, 35158,\n", + " 36069, 36976, 39228, 40130, 47468, 51165, 53023, 53924,\n", + " 56732, 62904, 66224, 68051, 68956, 69853, 70760, 94294,\n", + " 102148, 103066, 106695, 111354, 114092, 118663, 123802, 124710,\n", + " 126518, 127411, 129277, 131082, 132005, 153697, 155526, 156449,\n", + " 159726, 162457, 165155, 166040, 170671, 172034, 174731, 175649,\n", + " 181505, 184739, 185640, 198578, 199482, 200384],\n", + " dtype='int64'), Int64Index([ 2006, 12232, 14085, 15886, 28651, 29558, 34243, 35159,\n", + " 36070, 36977, 39229, 40131, 47469, 51166, 53024, 53925,\n", + " 56733, 62905, 66225, 68052, 68957, 69854, 70761, 94295,\n", + " 102149, 103067, 106696, 111355, 114093, 118664, 123803, 124711,\n", + " 126519, 127412, 129278, 131083, 132006, 153698, 155527, 156450,\n", + " 159727, 162458, 165156, 166041, 170672, 172035, 174732, 175650,\n", + " 181506, 184740, 185641, 198579, 199483, 200385],\n", + " dtype='int64'), Int64Index([ 2007, 12233, 14086, 15887, 28652, 29559, 34244, 35160,\n", + " 36071, 36978, 39230, 40132, 47470, 51167, 53025, 53926,\n", + " 56734, 62906, 66226, 68053, 68958, 69855, 70762, 94296,\n", + " 102150, 103068, 106697, 111356, 114094, 118665, 123804, 124712,\n", + " 126520, 127413, 129279, 131084, 132007, 153699, 155528, 156451,\n", + " 158141, 159728, 162459, 165157, 166042, 170673, 172036, 174733,\n", + " 175651, 181507, 184741, 185642, 198580, 199484, 200386],\n", + " dtype='int64'), Int64Index([ 2008, 12234, 14087, 15888, 28653, 29560, 34245, 35161,\n", + " 36072, 36979, 39231, 40133, 47471, 51168, 53026, 53927,\n", + " 56735, 62907, 66227, 68054, 68959, 69856, 70763, 94297,\n", + " 102151, 103069, 106698, 111357, 114095, 118666, 123805, 124713,\n", + " 126521, 127414, 129280, 131085, 132008, 153700, 155529, 156452,\n", + " 158142, 159729, 162460, 165158, 166043, 170674, 172037, 174734,\n", + " 175652, 181508, 184742, 185643, 198581, 199485, 200387],\n", + " dtype='int64'), Int64Index([ 2009, 12235, 14088, 15889, 28654, 29561, 34246, 35162,\n", + " 36073, 36980, 39232, 40134, 47472, 51169, 53027, 53928,\n", + " 56736, 62908, 66228, 68055, 68960, 69857, 70764, 94298,\n", + " 102152, 103070, 106699, 111358, 114096, 118667, 123806, 124714,\n", + " 126522, 127415, 129281, 131086, 132009, 153701, 155530, 156453,\n", + " 158143, 159730, 162461, 165159, 166044, 170675, 172038, 174735,\n", + " 175653, 181509, 184743, 185644, 198582, 199486, 200388],\n", + " dtype='int64'), Int64Index([ 2010, 12236, 14089, 15890, 28655, 29562, 34247, 35163,\n", + " 36074, 36981, 39233, 40135, 47473, 51170, 53028, 53929,\n", + " 56737, 62909, 66229, 68056, 68961, 69858, 70765, 94299,\n", + " 102153, 103071, 106700, 111359, 114097, 118668, 123807, 124715,\n", + " 126523, 127416, 129282, 131087, 132010, 153702, 155531, 156454,\n", + " 158144, 159731, 162462, 165160, 166045, 170676, 172039, 174736,\n", + " 175654, 181510, 184744, 185645, 198583, 199487, 200389],\n", + " dtype='int64'), Int64Index([ 2011, 12237, 14090, 15891, 28656, 29563, 34248, 35164,\n", + " 36075, 36982, 39234, 40136, 47474, 51171, 53029, 53930,\n", + " 56738, 62910, 66230, 68057, 68962, 69859, 70766, 94300,\n", + " 102154, 103072, 106701, 111360, 114098, 118669, 123808, 124716,\n", + " 126524, 127417, 129283, 131088, 132011, 153703, 155532, 156455,\n", + " 158145, 159732, 162463, 165161, 166046, 170677, 172040, 174737,\n", + " 175655, 181511, 184745, 185646, 198584, 199488, 200390],\n", + " dtype='int64'), Int64Index([ 2012, 12238, 14091, 15892, 28657, 29564, 34249, 35165,\n", + " 36076, 36983, 39235, 40137, 47475, 51172, 53030, 53931,\n", + " 56739, 62911, 66231, 68058, 68963, 69860, 70767, 94301,\n", + " 102155, 103073, 106702, 111361, 114099, 118670, 123809, 124717,\n", + " 126525, 127418, 129284, 131089, 132012, 153704, 155533, 156456,\n", + " 158146, 159733, 162464, 165162, 166047, 170678, 172041, 174738,\n", + " 175656, 181512, 184746, 185647, 198585, 199489, 200391],\n", + " dtype='int64'), Int64Index([ 2013, 12239, 14092, 15893, 28658, 29565, 34250, 35166,\n", + " 36077, 36984, 39236, 40138, 47476, 51173, 53031, 53932,\n", + " 56740, 62912, 66232, 68059, 68964, 69861, 70768, 94302,\n", + " 102156, 103074, 106703, 111362, 114100, 118671, 123810, 124718,\n", + " 126526, 127419, 129285, 131090, 132013, 153705, 155534, 156457,\n", + " 158147, 159734, 162465, 165163, 166048, 170679, 172042, 174739,\n", + " 175657, 181513, 184747, 185648, 198586, 199490, 200392],\n", + " dtype='int64'), Int64Index([ 2014, 12240, 14093, 15894, 28659, 29566, 34251, 35167,\n", + " 36078, 36985, 39237, 40139, 47477, 51174, 53032, 53933,\n", + " 56741, 62913, 66233, 68060, 68965, 69862, 70769, 94303,\n", + " 102157, 103075, 106704, 111363, 114101, 118672, 123811, 124719,\n", + " 126527, 127420, 129286, 131091, 132014, 153706, 155535, 156458,\n", + " 158148, 159735, 162466, 165164, 166049, 170680, 172043, 174740,\n", + " 175658, 181514, 184748, 185649, 198587, 199491, 200393],\n", + " dtype='int64'), Int64Index([ 2015, 12241, 14094, 15895, 28660, 29567, 34252, 35168,\n", + " 36079, 36986, 39238, 40140, 47478, 51175, 53033, 53934,\n", + " 56742, 62914, 66234, 68061, 68966, 69863, 70770, 94304,\n", + " 102158, 103076, 106705, 111364, 114102, 118673, 123812, 124720,\n", + " 126528, 127421, 129287, 131092, 132015, 153707, 155536, 156459,\n", + " 158149, 159736, 162467, 165165, 166050, 170681, 172044, 174741,\n", + " 175659, 181515, 184749, 185650, 198588, 199492, 200394],\n", + " dtype='int64'), Int64Index([ 2016, 12242, 14095, 15896, 28661, 29568, 34253, 35169,\n", + " 36080, 36987, 39239, 40141, 47479, 51176, 53034, 53935,\n", + " 56743, 62915, 66235, 68062, 68967, 69864, 70771, 94305,\n", + " 102159, 103077, 106706, 111365, 114103, 118674, 123813, 124721,\n", + " 126529, 127422, 129288, 131093, 132016, 153708, 155537, 156460,\n", + " 158150, 159737, 162468, 165166, 166051, 170682, 172045, 174742,\n", + " 175660, 181516, 184750, 185651, 198589, 199493, 200395],\n", + " dtype='int64'), Int64Index([ 2017, 12243, 14096, 15897, 28662, 29569, 34254, 35170,\n", + " 36081, 36988, 39240, 40142, 47480, 51177, 53035, 53936,\n", + " 56744, 62916, 66236, 68063, 68968, 69865, 70772, 94306,\n", + " 102160, 103078, 106707, 111366, 114104, 118675, 123814, 124722,\n", + " 126530, 127423, 129289, 131094, 132017, 153709, 155538, 156461,\n", + " 158151, 159738, 162469, 165167, 166052, 170683, 172046, 174743,\n", + " 175661, 181517, 184751, 185652, 198590, 199494, 200396],\n", + " dtype='int64'), Int64Index([ 2018, 12244, 14097, 15898, 28663, 29570, 34255, 35171,\n", + " 36082, 36989, 39241, 40143, 47481, 51178, 53036, 53937,\n", + " 56745, 62917, 66237, 68064, 68969, 69866, 70773, 94307,\n", + " 102161, 103079, 106708, 111367, 114105, 118676, 123815, 124723,\n", + " 126531, 127424, 129290, 131095, 132018, 153710, 155539, 156462,\n", + " 158152, 159739, 162470, 165168, 166053, 170684, 172047, 174744,\n", + " 175662, 181518, 184752, 185653, 198591, 199495, 200397],\n", + " dtype='int64'), Int64Index([ 2019, 12245, 14098, 15899, 28664, 29571, 34256, 35172,\n", + " 36083, 36990, 39242, 40144, 47482, 51179, 53037, 53938,\n", + " 56746, 62918, 66238, 68065, 68970, 69867, 70774, 94308,\n", + " 102162, 103080, 106709, 111368, 114106, 118677, 123816, 124724,\n", + " 126532, 127425, 129291, 131096, 132019, 153711, 155540, 156463,\n", + " 158153, 159740, 162471, 165169, 166054, 170685, 172048, 174745,\n", + " 175663, 181519, 184753, 185654, 198592, 199496, 200398],\n", + " dtype='int64'), Int64Index([ 2020, 12246, 14099, 15900, 28665, 29572, 34257, 35173,\n", + " 36084, 36991, 39243, 40145, 47483, 51180, 53038, 53939,\n", + " 56747, 62919, 66239, 68066, 68971, 69868, 70775, 94309,\n", + " 102163, 103081, 106710, 111369, 114107, 118678, 123817, 124725,\n", + " 126533, 127426, 129292, 131097, 132020, 153712, 155541, 156464,\n", + " 158154, 159741, 162472, 165170, 166055, 170686, 172049, 174746,\n", + " 175664, 181520, 184754, 185655, 198593, 199497, 200399],\n", + " dtype='int64'), Int64Index([ 2021, 12247, 14100, 15901, 28666, 29573, 34258, 35174,\n", + " 36085, 36992, 39244, 40146, 47484, 51181, 53039, 53940,\n", + " 56748, 62920, 66240, 68067, 68972, 69869, 70776, 94310,\n", + " 102164, 103082, 106711, 111370, 114108, 118679, 123818, 124726,\n", + " 126534, 127427, 129293, 131098, 132021, 153713, 155542, 156465,\n", + " 158155, 159742, 162473, 165171, 166056, 170687, 172050, 174747,\n", + " 175665, 181521, 184755, 185656, 198594, 199498, 200400],\n", + " dtype='int64'), Int64Index([ 2022, 12248, 14101, 15902, 28667, 29574, 34259, 35175,\n", + " 36086, 36993, 39245, 40147, 47485, 51182, 53040, 53941,\n", + " 56749, 62921, 66241, 68068, 68973, 69870, 70777, 94311,\n", + " 102165, 103083, 106712, 111371, 114109, 118680, 123819, 124727,\n", + " 126535, 127428, 129294, 131099, 132022, 153714, 155543, 156466,\n", + " 158156, 159743, 162474, 165172, 166057, 170688, 172051, 174748,\n", + " 175666, 181522, 184756, 185657, 198595, 199499, 200401],\n", + " dtype='int64'), Int64Index([ 2023, 12249, 14102, 15903, 28668, 29575, 34260, 35176,\n", + " 36087, 36994, 39246, 40148, 47486, 51183, 53041, 53942,\n", + " 56750, 62922, 66242, 68069, 68974, 69871, 70778, 94312,\n", + " 102166, 103084, 106713, 111372, 114110, 118681, 123820, 124728,\n", + " 126536, 127429, 129295, 131100, 132023, 153715, 155544, 156467,\n", + " 158157, 159744, 162475, 165173, 166058, 170689, 172052, 174749,\n", + " 175667, 181523, 184757, 185658, 198596, 199500, 200402],\n", + " dtype='int64'), Int64Index([ 2024, 12250, 14103, 15904, 28669, 29576, 34261, 35177,\n", + " 36088, 36995, 39247, 40149, 47487, 51184, 53042, 53943,\n", + " 56751, 62923, 66243, 68070, 68975, 69872, 70779, 94313,\n", + " 102167, 103085, 106714, 111373, 114111, 118682, 123821, 124729,\n", + " 126537, 127430, 129296, 131101, 132024, 153716, 155545, 156468,\n", + " 158158, 159745, 162476, 165174, 166059, 170690, 172053, 174750,\n", + " 175668, 181524, 184758, 185659, 198597, 199501, 200403],\n", + " dtype='int64'), Int64Index([ 2025, 12251, 14104, 15905, 28670, 29577, 34262, 35178,\n", + " 36089, 36996, 39248, 40150, 47488, 51185, 53043, 53944,\n", + " 56752, 62924, 66244, 68071, 68976, 69873, 70780, 94314,\n", + " 102168, 103086, 106715, 111374, 114112, 118683, 123822, 124730,\n", + " 126538, 127431, 129297, 131102, 132025, 153717, 155546, 156469,\n", + " 158159, 159746, 162477, 165175, 166060, 170691, 172054, 174751,\n", + " 175669, 181525, 184759, 185660, 198598, 199502, 200404],\n", + " dtype='int64'), Int64Index([ 2026, 12252, 14105, 15906, 28671, 29578, 34263, 35179,\n", + " 36090, 36997, 39249, 40151, 47489, 51186, 53044, 53945,\n", + " 56753, 62925, 66245, 68072, 68977, 69874, 70781, 94315,\n", + " 102169, 103087, 106716, 111375, 114113, 118684, 123823, 124731,\n", + " 126539, 127432, 129298, 131103, 132026, 153718, 155547, 156470,\n", + " 158160, 159747, 162478, 165176, 166061, 170692, 172055, 174752,\n", + " 175670, 181526, 184760, 185661, 198599, 199503, 200405],\n", + " dtype='int64'), Int64Index([ 2027, 12253, 14106, 15907, 28672, 29579, 34264, 35180,\n", + " 36091, 36998, 39250, 40152, 47490, 51187, 53045, 53946,\n", + " 56754, 62926, 66246, 68073, 68978, 69875, 70782, 94316,\n", + " 102170, 103088, 106717, 111376, 114114, 118685, 123824, 124732,\n", + " 126540, 127433, 129299, 131104, 132027, 153719, 155548, 156471,\n", + " 158161, 159748, 162479, 165177, 166062, 170693, 172056, 174753,\n", + " 175671, 181527, 184761, 185662, 198600, 199504, 200406],\n", + " dtype='int64'), Int64Index([ 2028, 12254, 14107, 15908, 28673, 29580, 34265, 35181,\n", + " 36092, 36999, 39251, 40153, 47491, 51188, 53046, 53947,\n", + " 56755, 62927, 66247, 68074, 68979, 69876, 70783, 94317,\n", + " 102171, 103089, 106718, 111377, 114115, 118686, 123825, 124733,\n", + " 126541, 127434, 129300, 131105, 132028, 153720, 155549, 156472,\n", + " 158162, 159749, 162480, 165178, 166063, 170694, 172057, 174754,\n", + " 175672, 181528, 184762, 185663, 198601, 199505, 200407],\n", + " dtype='int64'), Int64Index([ 2029, 12255, 14108, 15909, 28674, 29581, 34266, 35182,\n", + " 36093, 37000, 39252, 40154, 47492, 51189, 53047, 53948,\n", + " 56756, 62928, 66248, 68075, 68980, 69877, 70784, 94318,\n", + " 102172, 103090, 106719, 111378, 114116, 118687, 123826, 124734,\n", + " 126542, 127435, 129301, 131106, 132029, 153721, 155550, 156473,\n", + " 158163, 159750, 162481, 165179, 166064, 170695, 172058, 174755,\n", + " 175673, 181529, 184763, 185664, 198602, 199506, 200408],\n", + " dtype='int64'), Int64Index([ 2030, 12256, 14109, 15910, 28675, 29582, 34267, 35183,\n", + " 36094, 37001, 39253, 40155, 47493, 51190, 53048, 53949,\n", + " 56757, 62929, 66249, 68076, 68981, 69878, 70785, 94319,\n", + " 102173, 103091, 106720, 111379, 114117, 118688, 123827, 124735,\n", + " 126543, 127436, 129302, 131107, 132030, 153722, 155551, 156474,\n", + " 158164, 159751, 162482, 165180, 166065, 170696, 172059, 174756,\n", + " 175674, 181530, 184764, 185665, 198603, 199507, 200409],\n", + " dtype='int64'), Int64Index([ 2031, 12257, 14110, 15911, 28676, 29583, 34268, 35184,\n", + " 36095, 37002, 39254, 40156, 47494, 51191, 53049, 53950,\n", + " 56758, 62930, 66250, 68077, 68982, 69879, 70786, 94320,\n", + " 102174, 103092, 106721, 111380, 114118, 118689, 123828, 124736,\n", + " 126544, 127437, 129303, 131108, 132031, 153723, 155552, 156475,\n", + " 158165, 159752, 162483, 165181, 166066, 170697, 172060, 174757,\n", + " 175675, 181531, 184765, 185666, 198604, 199508, 200410],\n", + " dtype='int64'), Int64Index([ 2032, 12258, 14111, 15912, 28677, 29584, 34269, 35185,\n", + " 36096, 37003, 39255, 40157, 47495, 51192, 53050, 53951,\n", + " 56759, 62931, 66251, 68078, 68983, 69880, 70787, 94321,\n", + " 102175, 103093, 106722, 111381, 114119, 118690, 123829, 124737,\n", + " 126545, 127438, 129304, 131109, 132032, 153724, 155553, 156476,\n", + " 158166, 159753, 162484, 165182, 166067, 170698, 172061, 174758,\n", + " 175676, 181532, 184766, 185667, 198605, 199509, 200411],\n", + " dtype='int64'), Int64Index([ 2033, 12259, 14112, 15913, 28678, 29585, 34270, 35186,\n", + " 36097, 37004, 39256, 40158, 47496, 51193, 53051, 53952,\n", + " 56760, 62932, 66252, 68079, 68984, 69881, 70788, 94322,\n", + " 102176, 103094, 106723, 111382, 114120, 118691, 123830, 124738,\n", + " 126546, 127439, 129305, 131110, 132033, 153725, 155554, 156477,\n", + " 158167, 159754, 162485, 165183, 166068, 170699, 172062, 174759,\n", + " 175677, 181533, 184767, 185668, 198606, 199510, 200412],\n", + " dtype='int64'), Int64Index([ 2034, 12260, 14113, 15914, 28679, 29586, 34271, 35187,\n", + " 36098, 37005, 39257, 40159, 47497, 51194, 53052, 53953,\n", + " 56761, 62933, 66253, 68080, 68985, 69882, 70789, 94323,\n", + " 102177, 103095, 106724, 111383, 114121, 118692, 123831, 124739,\n", + " 126547, 127440, 129306, 131111, 132034, 153726, 155555, 156478,\n", + " 158168, 159755, 162486, 165184, 166069, 170700, 172063, 174760,\n", + " 175678, 181534, 184768, 185669, 198607, 199511, 200413],\n", + " dtype='int64'), Int64Index([ 2035, 12261, 14114, 15915, 28680, 29587, 34272, 35188,\n", + " 36099, 37006, 39258, 40160, 47498, 51195, 53053, 53954,\n", + " 56762, 62934, 66254, 68081, 68986, 69883, 70790, 94324,\n", + " 102178, 103096, 106725, 111384, 114122, 118693, 123832, 124740,\n", + " 126548, 127441, 129307, 131112, 132035, 153727, 155556, 156479,\n", + " 158169, 159756, 162487, 165185, 166070, 170701, 172064, 174761,\n", + " 175679, 181535, 184769, 185670, 198608, 199512, 200414],\n", + " dtype='int64'), Int64Index([ 2036, 12262, 14115, 15916, 28681, 29588, 34273, 35189,\n", + " 36100, 37007, 39259, 40161, 47499, 51196, 53054, 53955,\n", + " 56763, 62935, 66255, 68082, 68987, 69884, 70791, 94325,\n", + " 102179, 103097, 106726, 111385, 114123, 118694, 123833, 124741,\n", + " 126549, 127442, 129308, 131113, 132036, 153728, 155557, 156480,\n", + " 158170, 159757, 162488, 165186, 166071, 170702, 172065, 174762,\n", + " 175680, 181536, 184770, 185671, 198609, 199513, 200415],\n", + " dtype='int64'), Int64Index([ 2037, 12263, 14116, 15917, 28682, 29589, 34274, 35190,\n", + " 36101, 37008, 39260, 40162, 47500, 51197, 53055, 53956,\n", + " 56764, 62936, 66256, 68083, 68988, 69885, 70792, 94326,\n", + " 102180, 103098, 106727, 111386, 114124, 118695, 123834, 124742,\n", + " 126550, 127443, 129309, 131114, 132037, 153729, 155558, 156481,\n", + " 158171, 159758, 162489, 165187, 166072, 170703, 172066, 174763,\n", + " 175681, 181537, 184771, 185672, 198610, 199514, 200416],\n", + " dtype='int64'), Int64Index([ 2038, 12264, 14117, 15918, 28683, 29590, 34275, 35191,\n", + " 36102, 37009, 39261, 40163, 47501, 51198, 53056, 53957,\n", + " 56765, 62937, 66257, 68084, 68989, 69886, 70793, 94327,\n", + " 102181, 103099, 106728, 111387, 114125, 118696, 123835, 124743,\n", + " 126551, 127444, 129310, 131115, 132038, 153730, 155559, 156482,\n", + " 158172, 159759, 162490, 165188, 166073, 170704, 172067, 174764,\n", + " 175682, 181538, 184772, 185673, 198611, 199515, 200417],\n", + " dtype='int64'), Int64Index([ 2039, 12265, 14118, 15919, 28684, 29591, 34276, 35192,\n", + " 36103, 37010, 39262, 40164, 47502, 51199, 53057, 53958,\n", + " 56766, 62938, 66258, 68085, 68990, 69887, 70794, 94328,\n", + " 102182, 103100, 106729, 111388, 114126, 118697, 123836, 124744,\n", + " 126552, 127445, 129311, 131116, 132039, 153731, 155560, 156483,\n", + " 158173, 159760, 162491, 165189, 166074, 170705, 172068, 174765,\n", + " 175683, 181539, 184773, 185674, 198612, 199516, 200418],\n", + " dtype='int64'), Int64Index([ 2040, 12266, 14119, 15920, 28685, 29592, 34277, 35193,\n", + " 36104, 37011, 39263, 40165, 47503, 51200, 53058, 53959,\n", + " 56767, 62939, 66259, 68086, 68991, 69888, 70795, 94329,\n", + " 102183, 103101, 106730, 111389, 114127, 118698, 123837, 124745,\n", + " 126553, 127446, 129312, 131117, 132040, 153732, 155561, 156484,\n", + " 158174, 159761, 162492, 165190, 166075, 170706, 172069, 174766,\n", + " 175684, 181540, 184774, 185675, 198613, 199517, 200419],\n", + " dtype='int64'), Int64Index([ 2041, 12267, 14120, 15921, 28686, 29593, 34278, 35194,\n", + " 36105, 37012, 39264, 40166, 47504, 51201, 53059, 53960,\n", + " 56768, 62940, 66260, 68087, 68992, 69889, 70796, 94330,\n", + " 102184, 103102, 106731, 111390, 114128, 118699, 123838, 124746,\n", + " 126554, 127447, 129313, 131118, 132041, 153733, 155562, 156485,\n", + " 158175, 159762, 162493, 165191, 166076, 170707, 172070, 174767,\n", + " 175685, 181541, 184775, 185676, 198614, 199518, 200420],\n", + " dtype='int64'), Int64Index([ 2042, 12268, 14121, 15922, 28687, 29594, 34279, 35195,\n", + " 36106, 37013, 39265, 40167, 47505, 51202, 53060, 53961,\n", + " 56769, 62941, 66261, 68088, 68993, 69890, 70797, 94331,\n", + " 102185, 103103, 106732, 111391, 114129, 118700, 123839, 124747,\n", + " 126555, 127448, 129314, 131119, 132042, 153734, 155563, 156486,\n", + " 158176, 159763, 162494, 165192, 166077, 170708, 172071, 174768,\n", + " 175686, 181542, 184776, 185677, 198615, 199519, 200421],\n", + " dtype='int64'), Int64Index([ 2043, 12269, 14122, 15923, 28688, 29595, 34280, 35196,\n", + " 36107, 37014, 39266, 40168, 47506, 51203, 53061, 53962,\n", + " 56770, 62942, 66262, 68089, 68994, 69891, 70798, 94332,\n", + " 102186, 103104, 106733, 111392, 114130, 118701, 123840, 124748,\n", + " 126556, 127449, 129315, 131120, 132043, 153735, 155564, 156487,\n", + " 158177, 159764, 162495, 165193, 166078, 170709, 172072, 174769,\n", + " 175687, 181543, 184777, 185678, 198616, 199520, 200422],\n", + " dtype='int64'), Int64Index([ 2044, 12270, 14123, 15924, 28689, 29596, 34281, 35197,\n", + " 36108, 37015, 39267, 40169, 47507, 51204, 53062, 53963,\n", + " 56771, 62943, 66263, 68090, 68995, 69892, 70799, 94333,\n", + " 102187, 103105, 106734, 111393, 114131, 118702, 123841, 124749,\n", + " 126557, 127450, 129316, 131121, 132044, 153736, 155565, 156488,\n", + " 158178, 159765, 162496, 165194, 166079, 170710, 172073, 174770,\n", + " 175688, 181544, 184778, 185679, 198617, 199521, 200423],\n", + " dtype='int64'), Int64Index([ 2045, 12271, 14124, 15925, 28690, 29597, 34282, 35198,\n", + " 36109, 37016, 39268, 40170, 47508, 51205, 53063, 53964,\n", + " 56772, 62944, 66264, 68091, 68996, 69893, 70800, 94334,\n", + " 102188, 103106, 106735, 111394, 114132, 118703, 123842, 124750,\n", + " 126558, 127451, 129317, 131122, 132045, 153737, 155566, 156489,\n", + " 158179, 159766, 162497, 165195, 166080, 170711, 172074, 174771,\n", + " 175689, 181545, 184779, 185680, 198618, 199522, 200424],\n", + " dtype='int64'), Int64Index([ 2046, 12272, 14125, 15926, 28691, 29598, 34283, 35199,\n", + " 36110, 37017, 39269, 40171, 47509, 51206, 53064, 53965,\n", + " 56773, 62945, 66265, 68092, 68997, 69894, 70801, 94335,\n", + " 102189, 103107, 106736, 111395, 114133, 118704, 123843, 124751,\n", + " 126559, 127452, 129318, 131123, 132046, 153738, 155567, 156490,\n", + " 158180, 159767, 162498, 165196, 166081, 170712, 172075, 174772,\n", + " 175690, 181546, 184780, 185681, 198619, 199523, 200425],\n", + " dtype='int64'), Int64Index([ 2047, 12273, 14126, 15927, 28692, 29599, 34284, 35200,\n", + " 36111, 37018, 39270, 40172, 47510, 51207, 53065, 53966,\n", + " 56774, 62946, 66266, 68093, 68998, 69895, 70802, 94336,\n", + " 102190, 103108, 106737, 111396, 114134, 118705, 123844, 124752,\n", + " 126560, 127453, 129319, 131124, 132047, 153739, 155568, 156491,\n", + " 158181, 159768, 162499, 165197, 166082, 170713, 172076, 174773,\n", + " 175691, 181547, 184781, 185682, 198620, 199524, 200426],\n", + " dtype='int64'), Int64Index([ 2048, 12274, 14127, 15928, 28693, 29600, 34285, 35201,\n", + " 36112, 37019, 39271, 40173, 47511, 51208, 53066, 53967,\n", + " 56775, 62947, 66267, 68094, 68999, 69896, 70803, 94337,\n", + " 102191, 103109, 106738, 111397, 114135, 118706, 123845, 124753,\n", + " 126561, 127454, 129320, 131125, 132048, 153740, 155569, 156492,\n", + " 158182, 159769, 162500, 165198, 166083, 170714, 172077, 174774,\n", + " 175692, 181548, 184782, 185683, 198621, 199525, 200427],\n", + " dtype='int64'), Int64Index([ 2049, 12275, 14128, 15929, 28694, 29601, 34286, 35202,\n", + " 36113, 37020, 39272, 40174, 47512, 51209, 53067, 53968,\n", + " 56776, 62948, 66268, 68095, 69000, 69897, 70804, 94338,\n", + " 102192, 103110, 106739, 111398, 114136, 118707, 123846, 124754,\n", + " 126562, 127455, 129321, 131126, 132049, 153741, 155570, 156493,\n", + " 158183, 159770, 162501, 165199, 166084, 170715, 172078, 174775,\n", + " 175693, 181549, 184783, 185684, 198622, 199526, 200428],\n", + " dtype='int64'), Int64Index([ 2050, 12276, 14129, 15930, 28695, 29602, 34287, 35203,\n", + " 36114, 37021, 39273, 40175, 47513, 51210, 53068, 53969,\n", + " 56777, 62949, 66269, 68096, 69001, 69898, 70805, 94339,\n", + " 102193, 103111, 106740, 111399, 114137, 118708, 123847, 124755,\n", + " 126563, 127456, 129322, 131127, 132050, 153742, 155571, 156494,\n", + " 158184, 159771, 162502, 165200, 166085, 170716, 172079, 174776,\n", + " 175694, 181550, 184784, 185685, 198623, 199527, 200429],\n", + " dtype='int64'), Int64Index([ 2051, 12277, 14130, 15931, 28696, 29603, 34288, 35204,\n", + " 36115, 37022, 39274, 40176, 47514, 51211, 53069, 53970,\n", + " 56778, 62950, 66270, 68097, 69002, 69899, 70806, 94340,\n", + " 102194, 103112, 106741, 111400, 114138, 118709, 123848, 124756,\n", + " 126564, 127457, 129323, 131128, 132051, 153743, 155572, 156495,\n", + " 158185, 159772, 162503, 165201, 166086, 170717, 172080, 174777,\n", + " 175695, 181551, 184785, 185686, 198624, 199528, 200430],\n", + " dtype='int64'), Int64Index([ 2052, 12278, 14131, 15932, 28697, 29604, 34289, 35205,\n", + " 36116, 37023, 39275, 40177, 47515, 51212, 53070, 53971,\n", + " 56779, 62951, 66271, 68098, 69003, 69900, 70807, 94341,\n", + " 102195, 103113, 106742, 111401, 114139, 118710, 123849, 124757,\n", + " 126565, 127458, 129324, 131129, 132052, 153744, 155573, 156496,\n", + " 158186, 159773, 162504, 165202, 166087, 170718, 172081, 174778,\n", + " 175696, 181552, 184786, 185687, 198625, 199529, 200431],\n", + " dtype='int64'), Int64Index([ 2053, 12279, 14132, 15933, 28698, 29605, 34290, 35206,\n", + " 36117, 37024, 39276, 40178, 47516, 51213, 53071, 53972,\n", + " 56780, 62952, 66272, 68099, 69004, 69901, 70808, 94342,\n", + " 102196, 103114, 106743, 111402, 114140, 118711, 123850, 124758,\n", + " 126566, 127459, 129325, 131130, 132053, 153745, 155574, 156497,\n", + " 158187, 159774, 162505, 165203, 166088, 170719, 172082, 174779,\n", + " 175697, 181553, 184787, 185688, 198626, 199530, 200432],\n", + " dtype='int64'), Int64Index([ 2054, 12280, 14133, 15934, 28699, 29606, 34291, 35207,\n", + " 36118, 37025, 39277, 40179, 47517, 51214, 53072, 53973,\n", + " 56781, 62953, 66273, 68100, 69005, 69902, 70809, 94343,\n", + " 102197, 103115, 106744, 111403, 114141, 118712, 123851, 124759,\n", + " 126567, 127460, 129326, 131131, 132054, 153746, 155575, 156498,\n", + " 158188, 159775, 162506, 165204, 166089, 170720, 172083, 174780,\n", + " 175698, 181554, 184788, 185689, 198627, 199531, 200433],\n", + " dtype='int64'), Int64Index([ 2055, 12281, 14134, 15935, 28700, 29607, 34292, 35208,\n", + " 36119, 37026, 39278, 40180, 47518, 51215, 53073, 53974,\n", + " 56782, 62954, 66274, 68101, 69006, 69903, 70810, 94344,\n", + " 102198, 103116, 106745, 111404, 114142, 118713, 123852, 124760,\n", + " 126568, 127461, 129327, 131132, 132055, 153747, 155576, 156499,\n", + " 158189, 159776, 162507, 165205, 166090, 170721, 172084, 174781,\n", + " 175699, 181555, 184789, 185690, 198628, 199532, 200434],\n", + " dtype='int64'), Int64Index([ 2056, 12282, 14135, 15936, 28701, 29608, 34293, 35209,\n", + " 36120, 37027, 39279, 40181, 47519, 51216, 53074, 53975,\n", + " 56783, 62955, 66275, 68102, 69007, 69904, 70811, 94345,\n", + " 102199, 103117, 106746, 111405, 114143, 118714, 123853, 124761,\n", + " 126569, 127462, 129328, 131133, 132056, 153748, 155577, 156500,\n", + " 158190, 159777, 162508, 165206, 166091, 170722, 172085, 174782,\n", + " 175700, 181556, 184790, 185691, 198629, 199533, 200435],\n", + " dtype='int64'), Int64Index([ 2057, 12283, 14136, 15937, 28702, 29609, 34294, 35210,\n", + " 36121, 37028, 39280, 40182, 47520, 51217, 53075, 53976,\n", + " 56784, 62956, 66276, 68103, 69008, 69905, 70812, 94346,\n", + " 102200, 103118, 106747, 111406, 114144, 118715, 123854, 124762,\n", + " 126570, 127463, 129329, 131134, 132057, 153749, 155578, 156501,\n", + " 158191, 159778, 162509, 165207, 166092, 170723, 172086, 174783,\n", + " 175701, 181557, 184791, 185692, 198630, 199534, 200436],\n", + " dtype='int64'), Int64Index([ 2058, 12284, 14137, 15938, 28703, 29610, 34295, 35211,\n", + " 36122, 37029, 39281, 40183, 47521, 51218, 53076, 53977,\n", + " 56785, 62957, 66277, 68104, 69009, 69906, 70813, 94347,\n", + " 102201, 103119, 106748, 111407, 114145, 118716, 123855, 124763,\n", + " 126571, 127464, 129330, 131135, 132058, 153750, 155579, 156502,\n", + " 158192, 159779, 162510, 165208, 166093, 170724, 172087, 174784,\n", + " 175702, 181558, 184792, 185693, 198631, 199535, 200437],\n", + " dtype='int64'), Int64Index([ 2059, 12285, 14138, 15939, 28704, 29611, 34296, 35212,\n", + " 36123, 37030, 39282, 40184, 47522, 51219, 53077, 53978,\n", + " 56786, 62958, 66278, 68105, 69010, 69907, 70814, 94348,\n", + " 102202, 103120, 106749, 111408, 114146, 118717, 123856, 124764,\n", + " 126572, 127465, 129331, 131136, 132059, 153751, 155580, 156503,\n", + " 158193, 159780, 162511, 165209, 166094, 170725, 172088, 174785,\n", + " 175703, 181559, 184793, 185694, 198632, 199536, 200438],\n", + " dtype='int64'), Int64Index([ 2060, 12286, 14139, 15940, 28705, 29612, 34297, 35213,\n", + " 36124, 37031, 39283, 40185, 47523, 51220, 53078, 53979,\n", + " 56787, 62959, 66279, 68106, 69011, 69908, 70815, 94349,\n", + " 102203, 103121, 106750, 111409, 114147, 118718, 123857, 124765,\n", + " 126573, 127466, 129332, 131137, 132060, 153752, 155581, 156504,\n", + " 158194, 159781, 162512, 165210, 166095, 170726, 172089, 174786,\n", + " 175704, 181560, 184794, 185695, 198633, 199537, 200439],\n", + " dtype='int64'), Int64Index([ 2061, 12287, 14140, 15941, 28706, 29613, 34298, 35214,\n", + " 36125, 37032, 39284, 40186, 47524, 51221, 53079, 53980,\n", + " 56788, 62960, 66280, 68107, 69012, 69909, 70816, 94350,\n", + " 102204, 103122, 106751, 111410, 114148, 118719, 123858, 124766,\n", + " 126574, 127467, 129333, 131138, 132061, 153753, 155582, 156505,\n", + " 158195, 159782, 162513, 165211, 166096, 170727, 172090, 174787,\n", + " 175705, 181561, 184795, 185696, 198634, 199538, 200440],\n", + " dtype='int64'), Int64Index([ 2062, 12288, 14141, 15942, 28707, 29614, 34299, 35215,\n", + " 36126, 37033, 39285, 40187, 47525, 51222, 53080, 53981,\n", + " 56789, 62961, 66281, 68108, 69013, 69910, 70817, 94351,\n", + " 102205, 103123, 106752, 111411, 114149, 118720, 123859, 124767,\n", + " 126575, 127468, 129334, 131139, 132062, 153754, 155583, 156506,\n", + " 158196, 159783, 162514, 165212, 166097, 170728, 172091, 174788,\n", + " 175706, 181562, 184796, 185697, 198635, 199539, 200441],\n", + " dtype='int64'), Int64Index([ 2063, 12289, 14142, 15943, 28708, 29615, 34300, 35216,\n", + " 36127, 37034, 39286, 40188, 47526, 51223, 53081, 53982,\n", + " 56790, 62962, 66282, 68109, 69014, 69911, 70818, 94352,\n", + " 102206, 103124, 106753, 111412, 114150, 118721, 123860, 124768,\n", + " 126576, 127469, 129335, 131140, 132063, 153755, 155584, 156507,\n", + " 158197, 159784, 162515, 165213, 166098, 170729, 172092, 174789,\n", + " 175707, 181563, 184797, 185698, 198636, 199540, 200442],\n", + " dtype='int64'), Int64Index([ 2064, 12290, 14143, 15944, 28709, 29616, 34301, 35217,\n", + " 36128, 37035, 39287, 40189, 47527, 51224, 53082, 53983,\n", + " 56791, 62963, 66283, 68110, 69015, 69912, 70819, 94353,\n", + " 102207, 103125, 106754, 111413, 114151, 118722, 123861, 124769,\n", + " 126577, 127470, 129336, 131141, 132064, 153756, 155585, 156508,\n", + " 158198, 159785, 162516, 165214, 166099, 170730, 172093, 174790,\n", + " 175708, 181564, 184798, 185699, 198637, 199541, 200443],\n", + " dtype='int64'), Int64Index([ 2065, 12291, 14144, 15945, 28710, 29617, 34302, 35218,\n", + " 36129, 37036, 39288, 40190, 47528, 51225, 53083, 53984,\n", + " 56792, 62964, 66284, 68111, 69016, 69913, 70820, 94354,\n", + " 102208, 103126, 106755, 111414, 114152, 118723, 123862, 124770,\n", + " 126578, 127471, 129337, 131142, 132065, 153757, 155586, 156509,\n", + " 158199, 159786, 162517, 165215, 166100, 170731, 172094, 174791,\n", + " 175709, 181565, 184799, 185700, 198638, 199542, 200444],\n", + " dtype='int64'), Int64Index([ 2066, 12292, 14145, 15946, 28711, 29618, 34303, 35219,\n", + " 36130, 37037, 39289, 40191, 47529, 51226, 53084, 53985,\n", + " 56793, 62965, 66285, 68112, 69017, 69914, 70821, 94355,\n", + " 102209, 103127, 106756, 111415, 114153, 118724, 123863, 124771,\n", + " 126579, 127472, 129338, 131143, 132066, 153758, 155587, 156510,\n", + " 158200, 159787, 162518, 165216, 166101, 170732, 172095, 174792,\n", + " 175710, 181566, 184800, 185701, 198639, 199543, 200445],\n", + " dtype='int64'), Int64Index([ 2067, 12293, 14146, 15947, 28712, 29619, 34304, 35220,\n", + " 36131, 37038, 39290, 40192, 47530, 51227, 53085, 53986,\n", + " 56794, 62966, 66286, 68113, 69018, 69915, 70822, 94356,\n", + " 102210, 103128, 106757, 111416, 114154, 118725, 123864, 124772,\n", + " 126580, 127473, 129339, 131144, 132067, 153759, 155588, 156511,\n", + " 158201, 159788, 162519, 165217, 166102, 170733, 172096, 174793,\n", + " 175711, 181567, 184801, 185702, 198640, 199544, 200446],\n", + " dtype='int64'), Int64Index([ 2068, 12294, 14147, 15948, 28713, 29620, 34305, 35221,\n", + " 36132, 37039, 39291, 40193, 47531, 51228, 53086, 53987,\n", + " 56795, 62967, 66287, 68114, 69019, 69916, 70823, 94357,\n", + " 102211, 103129, 106758, 111417, 114155, 118726, 123865, 124773,\n", + " 126581, 127474, 129340, 131145, 132068, 153760, 155589, 156512,\n", + " 158202, 159789, 162520, 165218, 166103, 170734, 172097, 174794,\n", + " 175712, 181568, 184802, 185703, 198641, 199545, 200447],\n", + " dtype='int64'), Int64Index([ 2069, 12295, 14148, 15949, 28714, 29621, 34306, 35222,\n", + " 36133, 37040, 39292, 40194, 47532, 51229, 53087, 53988,\n", + " 56796, 62968, 66288, 68115, 69020, 69917, 70824, 94358,\n", + " 102212, 103130, 106759, 111418, 114156, 118727, 123866, 124774,\n", + " 126582, 127475, 129341, 131146, 132069, 153761, 155590, 156513,\n", + " 158203, 159790, 162521, 165219, 166104, 170735, 172098, 174795,\n", + " 175713, 181569, 184803, 185704, 198642, 199546, 200448],\n", + " dtype='int64'), Int64Index([ 2070, 12296, 14149, 15950, 28715, 29622, 34307, 35223,\n", + " 36134, 37041, 39293, 40195, 47533, 51230, 53088, 53989,\n", + " 56797, 62969, 66289, 68116, 69021, 69918, 70825, 94359,\n", + " 102213, 103131, 106760, 111419, 114157, 118728, 123867, 124775,\n", + " 126583, 127476, 129342, 131147, 132070, 153762, 155591, 156514,\n", + " 158204, 159791, 162522, 165220, 166105, 170736, 172099, 174796,\n", + " 175714, 181570, 184804, 185705, 198643, 199547, 200449],\n", + " dtype='int64'), Int64Index([ 2071, 12297, 14150, 15951, 28716, 29623, 34308, 35224,\n", + " 36135, 37042, 39294, 40196, 47534, 51231, 53089, 53990,\n", + " 56798, 62970, 66290, 68117, 69022, 69919, 70826, 94360,\n", + " 102214, 103132, 106761, 111420, 114158, 118729, 123868, 124776,\n", + " 126584, 127477, 129343, 131148, 132071, 153763, 155592, 156515,\n", + " 158205, 159792, 162523, 165221, 166106, 170737, 172100, 174797,\n", + " 175715, 181571, 184805, 185706, 198644, 199548, 200450],\n", + " dtype='int64'), Int64Index([ 2072, 12298, 14151, 15952, 28717, 29624, 34309, 35225,\n", + " 36136, 37043, 39295, 40197, 47535, 51232, 53090, 53991,\n", + " 56799, 62971, 66291, 68118, 69023, 69920, 70827, 94361,\n", + " 102215, 103133, 106762, 111421, 114159, 118730, 123869, 124777,\n", + " 126585, 127478, 129344, 131149, 132072, 153764, 155593, 156516,\n", + " 158206, 159793, 162524, 165222, 166107, 170738, 172101, 174798,\n", + " 175716, 181572, 184806, 185707, 198645, 199549, 200451],\n", + " dtype='int64'), Int64Index([ 2073, 12299, 14152, 15953, 28718, 29625, 34310, 35226,\n", + " 36137, 37044, 39296, 40198, 47536, 51233, 53091, 53992,\n", + " 56800, 62972, 66292, 68119, 69024, 69921, 70828, 94362,\n", + " 102216, 103134, 106763, 111422, 114160, 118731, 123870, 124778,\n", + " 126586, 127479, 129345, 131150, 132073, 153765, 155594, 156517,\n", + " 158207, 159794, 162525, 165223, 166108, 170739, 172102, 174799,\n", + " 175717, 181573, 184807, 185708, 198646, 199550, 200452],\n", + " dtype='int64'), Int64Index([ 2074, 12300, 14153, 15954, 28719, 29626, 34311, 35227,\n", + " 36138, 37045, 39297, 40199, 47537, 51234, 53092, 53993,\n", + " 56801, 62973, 66293, 68120, 69025, 69922, 70829, 94363,\n", + " 102217, 103135, 106764, 111423, 114161, 118732, 123871, 124779,\n", + " 126587, 127480, 129346, 131151, 132074, 153766, 155595, 156518,\n", + " 158208, 159795, 162526, 165224, 166109, 170740, 172103, 174800,\n", + " 175718, 181574, 184808, 185709, 198647, 199551, 200453],\n", + " dtype='int64'), Int64Index([ 2075, 12301, 14154, 15955, 28720, 29627, 34312, 35228,\n", + " 36139, 37046, 39298, 40200, 47538, 51235, 53093, 53994,\n", + " 56802, 62974, 66294, 68121, 69026, 69923, 70830, 94364,\n", + " 102218, 103136, 106765, 111424, 114162, 118733, 123872, 124780,\n", + " 126588, 127481, 129347, 131152, 132075, 153767, 155596, 156519,\n", + " 158209, 159796, 162527, 165225, 166110, 170741, 172104, 174801,\n", + " 175719, 181575, 184809, 185710, 198648, 199552, 200454],\n", + " dtype='int64'), Int64Index([ 2076, 12302, 14155, 15956, 28721, 29628, 34313, 35229,\n", + " 36140, 37047, 39299, 40201, 47539, 51236, 53094, 53995,\n", + " 56803, 62975, 66295, 68122, 69027, 69924, 70831, 94365,\n", + " 102219, 103137, 106766, 111425, 114163, 118734, 123873, 124781,\n", + " 126589, 127482, 129348, 131153, 132076, 153768, 155597, 156520,\n", + " 158210, 159797, 162528, 165226, 166111, 170742, 172105, 174802,\n", + " 175720, 181576, 184810, 185711, 198649, 199553, 200455],\n", + " dtype='int64'), Int64Index([ 2077, 12303, 14156, 15957, 28722, 29629, 34314, 35230,\n", + " 36141, 37048, 39300, 40202, 47540, 51237, 53095, 53996,\n", + " 56804, 62976, 66296, 68123, 69028, 69925, 70832, 94366,\n", + " 102220, 103138, 106767, 111426, 114164, 118735, 123874, 124782,\n", + " 126590, 127483, 129349, 131154, 132077, 153769, 155598, 156521,\n", + " 158211, 159798, 162529, 165227, 166112, 170743, 172106, 174803,\n", + " 175721, 181577, 184811, 185712, 198650, 199554, 200456],\n", + " dtype='int64'), Int64Index([ 2078, 12304, 14157, 15958, 28723, 29630, 34315, 35231,\n", + " 36142, 37049, 39301, 40203, 47541, 51238, 53096, 53997,\n", + " 56805, 62977, 66297, 68124, 69029, 69926, 70833, 94367,\n", + " 102221, 103139, 106768, 111427, 114165, 118736, 123875, 124783,\n", + " 126591, 127484, 129350, 131155, 132078, 153770, 155599, 156522,\n", + " 158212, 159799, 162530, 165228, 166113, 170744, 172107, 174804,\n", + " 175722, 181578, 184812, 185713, 198651, 199555, 200457],\n", + " dtype='int64'), Int64Index([ 2079, 12305, 14158, 15959, 28724, 29631, 34316, 35232,\n", + " 36143, 37050, 39302, 40204, 47542, 51239, 53097, 53998,\n", + " 56806, 62978, 66298, 68125, 69030, 69927, 70834, 94368,\n", + " 102222, 103140, 106769, 111428, 114166, 118737, 123876, 124784,\n", + " 126592, 127485, 129351, 131156, 132079, 153771, 155600, 156523,\n", + " 158213, 159800, 162531, 165229, 166114, 170745, 172108, 174805,\n", + " 175723, 181579, 184813, 185714, 198652, 199556, 200458],\n", + " dtype='int64'), Int64Index([ 2080, 12306, 14159, 15960, 28725, 29632, 34317, 35233,\n", + " 36144, 37051, 39303, 40205, 47543, 51240, 53098, 53999,\n", + " 56807, 62979, 66299, 68126, 69031, 69928, 70835, 94369,\n", + " 102223, 103141, 106770, 111429, 114167, 118738, 123877, 124785,\n", + " 126593, 127486, 129352, 131157, 132080, 153772, 155601, 156524,\n", + " 158214, 159801, 162532, 165230, 166115, 170746, 172109, 174806,\n", + " 175724, 181580, 184814, 185715, 198653, 199557, 200459],\n", + " dtype='int64'), Int64Index([ 2081, 12307, 14160, 15961, 28726, 29633, 34318, 35234,\n", + " 36145, 37052, 39304, 40206, 47544, 51241, 53099, 54000,\n", + " 56808, 62980, 66300, 68127, 69032, 69929, 70836, 94370,\n", + " 102224, 103142, 106771, 111430, 114168, 118739, 123878, 124786,\n", + " 126594, 127487, 129353, 131158, 132081, 153773, 155602, 156525,\n", + " 158215, 159802, 162533, 165231, 166116, 170747, 172110, 174807,\n", + " 175725, 181581, 184815, 185716, 198654, 199558, 200460],\n", + " dtype='int64'), Int64Index([ 2082, 12308, 14161, 15962, 28727, 29634, 34319, 35235,\n", + " 36146, 37053, 39305, 40207, 47545, 51242, 53100, 54001,\n", + " 56809, 62981, 66301, 68128, 69033, 69930, 70837, 94371,\n", + " 102225, 103143, 106772, 111431, 114169, 118740, 123879, 124787,\n", + " 126595, 127488, 129354, 131159, 132082, 153774, 155603, 156526,\n", + " 158216, 159803, 162534, 165232, 166117, 170748, 172111, 174808,\n", + " 175726, 181582, 184816, 185717, 198655, 199559, 200461],\n", + " dtype='int64'), Int64Index([ 2083, 12309, 14162, 15963, 28728, 29635, 34320, 35236,\n", + " 36147, 37054, 39306, 40208, 47546, 51243, 53101, 54002,\n", + " 56810, 62982, 66302, 68129, 69034, 69931, 70838, 94372,\n", + " 102226, 103144, 106773, 111432, 114170, 118741, 123880, 124788,\n", + " 126596, 127489, 129355, 131160, 132083, 153775, 155604, 156527,\n", + " 158217, 159804, 162535, 165233, 166118, 170749, 172112, 174809,\n", + " 175727, 181583, 184817, 185718, 198656, 199560, 200462],\n", + " dtype='int64'), Int64Index([ 2084, 12310, 14163, 15964, 28729, 29636, 34321, 35237,\n", + " 36148, 37055, 39307, 40209, 47547, 51244, 53102, 54003,\n", + " 56811, 62983, 66303, 68130, 69035, 69932, 70839, 94373,\n", + " 102227, 103145, 106774, 111433, 114171, 118742, 123881, 124789,\n", + " 126597, 127490, 129356, 131161, 132084, 153776, 155605, 156528,\n", + " 158218, 159805, 162536, 165234, 166119, 170750, 172113, 174810,\n", + " 175728, 181584, 184818, 185719, 198657, 199561, 200463],\n", + " dtype='int64'), Int64Index([ 2085, 12311, 14164, 15965, 28730, 29637, 34322, 35238,\n", + " 36149, 37056, 39308, 40210, 47548, 51245, 53103, 54004,\n", + " 56812, 62984, 66304, 68131, 69036, 69933, 70840, 94374,\n", + " 102228, 103146, 106775, 111434, 114172, 118743, 123882, 124790,\n", + " 126598, 127491, 129357, 131162, 132085, 153777, 155606, 156529,\n", + " 158219, 159806, 162537, 165235, 166120, 170751, 172114, 174811,\n", + " 175729, 181585, 184819, 185720, 198658, 199562, 200464],\n", + " dtype='int64'), Int64Index([ 2086, 12312, 14165, 15966, 28731, 29638, 34323, 35239,\n", + " 36150, 37057, 39309, 40211, 47549, 51246, 53104, 54005,\n", + " 56813, 62985, 66305, 68132, 69037, 69934, 70841, 94375,\n", + " 102229, 103147, 106776, 111435, 114173, 118744, 123883, 124791,\n", + " 126599, 127492, 129358, 131163, 132086, 153778, 155607, 156530,\n", + " 158220, 159807, 162538, 165236, 166121, 170752, 172115, 174812,\n", + " 175730, 181586, 184820, 185721, 198659, 199563, 200465],\n", + " dtype='int64'), Int64Index([ 2087, 12313, 14166, 15967, 28732, 29639, 34324, 35240,\n", + " 36151, 37058, 39310, 40212, 47550, 51247, 53105, 54006,\n", + " 56814, 62986, 66306, 68133, 69038, 69935, 70842, 94376,\n", + " 102230, 103148, 106777, 111436, 114174, 118745, 123884, 124792,\n", + " 126600, 127493, 129359, 131164, 132087, 153779, 155608, 156531,\n", + " 158221, 159808, 162539, 165237, 166122, 170753, 172116, 174813,\n", + " 175731, 181587, 184821, 185722, 198660, 199564, 200466],\n", + " dtype='int64'), Int64Index([ 2088, 12314, 14167, 15968, 28733, 29640, 34325, 35241,\n", + " 36152, 37059, 39311, 40213, 47551, 51248, 53106, 54007,\n", + " 56815, 62987, 66307, 68134, 69039, 69936, 70843, 94377,\n", + " 102231, 103149, 106778, 111437, 114175, 118746, 123885, 124793,\n", + " 126601, 127494, 129360, 131165, 132088, 153780, 155609, 156532,\n", + " 158222, 159809, 162540, 165238, 166123, 170754, 172117, 174814,\n", + " 175732, 181588, 184822, 185723, 198661, 199565, 200467],\n", + " dtype='int64'), Int64Index([ 2089, 12315, 14168, 15969, 28734, 29641, 34326, 35242,\n", + " 36153, 37060, 39312, 40214, 47552, 51249, 53107, 54008,\n", + " 56816, 62988, 66308, 68135, 69040, 69937, 70844, 94378,\n", + " 102232, 103150, 106779, 111438, 114176, 118747, 123886, 124794,\n", + " 126602, 127495, 129361, 131166, 132089, 153781, 155610, 156533,\n", + " 158223, 159810, 162541, 165239, 166124, 170755, 172118, 174815,\n", + " 175733, 181589, 184823, 185724, 198662, 199566, 200468],\n", + " dtype='int64'), Int64Index([ 2090, 12316, 14169, 15970, 28735, 29642, 34327, 35243,\n", + " 36154, 37061, 39313, 40215, 47553, 51250, 53108, 54009,\n", + " 56817, 62989, 66309, 68136, 69041, 69938, 70845, 94379,\n", + " 102233, 103151, 106780, 111439, 114177, 118748, 123887, 124795,\n", + " 126603, 127496, 129362, 131167, 132090, 153782, 155611, 156534,\n", + " 158224, 159811, 162542, 165240, 166125, 170756, 172119, 174816,\n", + " 175734, 181590, 184824, 185725, 198663, 199567, 200469],\n", + " dtype='int64'), Int64Index([ 2091, 12317, 14170, 15971, 28736, 29643, 34328, 35244,\n", + " 36155, 37062, 39314, 40216, 47554, 51251, 53109, 54010,\n", + " 56818, 62990, 66310, 68137, 69042, 69939, 70846, 94380,\n", + " 102234, 103152, 106781, 111440, 114178, 118749, 123888, 124796,\n", + " 126604, 127497, 129363, 131168, 132091, 153783, 155612, 156535,\n", + " 158225, 159812, 162543, 165241, 166126, 170757, 172120, 174817,\n", + " 175735, 181591, 184825, 185726, 198664, 199568, 200470],\n", + " dtype='int64'), Int64Index([ 2092, 12318, 14171, 15972, 28737, 29644, 34329, 35245,\n", + " 36156, 37063, 39315, 40217, 47555, 51252, 53110, 54011,\n", + " 56819, 62991, 66311, 68138, 69043, 69940, 70847, 94381,\n", + " 102235, 103153, 106782, 111441, 114179, 118750, 123889, 124797,\n", + " 126605, 127498, 129364, 131169, 132092, 153784, 155613, 156536,\n", + " 158226, 159813, 162544, 165242, 166127, 170758, 172121, 174818,\n", + " 175736, 181592, 184826, 185727, 198665, 199569, 200471],\n", + " dtype='int64'), Int64Index([ 2093, 12319, 14172, 15973, 28738, 29645, 34330, 35246,\n", + " 36157, 37064, 39316, 40218, 47556, 51253, 53111, 54012,\n", + " 56820, 62992, 66312, 68139, 69044, 69941, 70848, 94382,\n", + " 102236, 103154, 106783, 111442, 114180, 118751, 123890, 124798,\n", + " 126606, 127499, 129365, 131170, 132093, 153785, 155614, 156537,\n", + " 158227, 159814, 162545, 165243, 166128, 170759, 172122, 174819,\n", + " 175737, 181593, 184827, 185728, 198666, 199570, 200472],\n", + " dtype='int64'), Int64Index([ 2094, 12320, 14173, 15974, 28739, 29646, 34331, 35247,\n", + " 36158, 37065, 39317, 40219, 47557, 51254, 53112, 54013,\n", + " 56821, 62993, 66313, 68140, 69045, 69942, 70849, 94383,\n", + " 102237, 103155, 106784, 111443, 114181, 118752, 123891, 124799,\n", + " 126607, 127500, 129366, 131171, 132094, 153786, 155615, 156538,\n", + " 158228, 159815, 162546, 165244, 166129, 170760, 172123, 174820,\n", + " 175738, 181594, 184828, 185729, 198667, 199571, 200473],\n", + " dtype='int64'), Int64Index([ 2095, 12321, 14174, 15975, 28740, 29647, 34332, 35248,\n", + " 36159, 37066, 39318, 40220, 47558, 51255, 53113, 54014,\n", + " 56822, 62994, 66314, 68141, 69046, 69943, 70850, 94384,\n", + " 102238, 103156, 106785, 111444, 114182, 118753, 123892, 124800,\n", + " 126608, 127501, 129367, 131172, 132095, 153787, 155616, 156539,\n", + " 158229, 159816, 162547, 165245, 166130, 170761, 172124, 174821,\n", + " 175739, 181595, 184829, 185730, 198668, 199572, 200474],\n", + " dtype='int64'), Int64Index([ 2096, 12322, 14175, 15976, 28741, 29648, 34333, 35249,\n", + " 36160, 37067, 39319, 40221, 47559, 51256, 53114, 54015,\n", + " 56823, 62995, 66315, 68142, 69047, 69944, 70851, 94385,\n", + " 102239, 103157, 106786, 111445, 114183, 118754, 123893, 124801,\n", + " 126609, 127502, 129368, 131173, 132096, 153788, 155617, 156540,\n", + " 158230, 159817, 162548, 165246, 166131, 170762, 172125, 174822,\n", + " 175740, 181596, 184830, 185731, 198669, 199573, 200475],\n", + " dtype='int64'), Int64Index([ 2097, 12323, 14176, 15977, 28742, 29649, 34334, 35250,\n", + " 36161, 37068, 39320, 40222, 47560, 51257, 53115, 54016,\n", + " 56824, 62996, 66316, 68143, 69048, 69945, 70852, 94386,\n", + " 102240, 103158, 106787, 111446, 114184, 118755, 123894, 124802,\n", + " 126610, 127503, 129369, 131174, 132097, 153789, 155618, 156541,\n", + " 158231, 159818, 162549, 165247, 166132, 170763, 172126, 174823,\n", + " 175741, 181597, 184831, 185732, 198670, 199574, 200476],\n", + " dtype='int64'), Int64Index([ 2098, 12324, 14177, 15978, 28743, 29650, 34335, 35251,\n", + " 36162, 37069, 39321, 40223, 47561, 51258, 53116, 54017,\n", + " 56825, 62997, 66317, 68144, 69049, 69946, 70853, 94387,\n", + " 102241, 103159, 106788, 111447, 114185, 118756, 123895, 124803,\n", + " 126611, 127504, 129370, 131175, 132098, 153790, 155619, 156542,\n", + " 158232, 159819, 162550, 165248, 166133, 170764, 172127, 174824,\n", + " 175742, 181598, 184832, 185733, 198671, 199575, 200477],\n", + " dtype='int64'), Int64Index([ 2099, 12325, 14178, 15979, 28744, 29651, 34336, 35252,\n", + " 36163, 37070, 39322, 40224, 47562, 51259, 53117, 54018,\n", + " 56826, 62998, 66318, 68145, 69050, 69947, 70854, 94388,\n", + " 102242, 103160, 106789, 111448, 114186, 118757, 123896, 124804,\n", + " 126612, 127505, 129371, 131176, 132099, 153791, 155620, 156543,\n", + " 158233, 159820, 162551, 165249, 166134, 170765, 172128, 174825,\n", + " 175743, 181599, 184833, 185734, 198672, 199576, 200478],\n", + " dtype='int64'), Int64Index([ 2100, 12326, 14179, 15980, 28745, 29652, 34337, 35253,\n", + " 36164, 37071, 39323, 40225, 47563, 51260, 53118, 54019,\n", + " 56827, 62999, 66319, 68146, 69051, 69948, 70855, 94389,\n", + " 102243, 103161, 106790, 111449, 114187, 118758, 123897, 124805,\n", + " 126613, 127506, 129372, 131177, 132100, 153792, 155621, 156544,\n", + " 158234, 159821, 162552, 165250, 166135, 170766, 172129, 174826,\n", + " 175744, 181600, 184834, 185735, 198673, 199577, 200479],\n", + " dtype='int64'), Int64Index([ 2101, 12327, 14180, 15981, 28746, 29653, 34338, 35254,\n", + " 36165, 37072, 39324, 40226, 47564, 51261, 53119, 54020,\n", + " 56828, 63000, 66320, 68147, 69052, 69949, 70856, 94390,\n", + " 102244, 103162, 106791, 111450, 114188, 118759, 123898, 124806,\n", + " 126614, 127507, 129373, 131178, 132101, 153793, 155622, 156545,\n", + " 158235, 159822, 162553, 165251, 166136, 170767, 172130, 174827,\n", + " 175745, 181601, 184835, 185736, 198674, 199578, 200480],\n", + " dtype='int64'), Int64Index([ 2102, 12328, 14181, 15982, 28747, 29654, 34339, 35255,\n", + " 36166, 37073, 39325, 40227, 47565, 51262, 53120, 54021,\n", + " 56829, 63001, 66321, 68148, 69053, 69950, 70857, 94391,\n", + " 102245, 103163, 106792, 111451, 114189, 118760, 123899, 124807,\n", + " 126615, 127508, 129374, 131179, 132102, 153794, 155623, 156546,\n", + " 158236, 159823, 162554, 165252, 166137, 170768, 172131, 174828,\n", + " 175746, 181602, 184836, 185737, 198675, 199579, 200481],\n", + " dtype='int64'), Int64Index([ 2103, 12329, 14182, 15983, 28748, 29655, 34340, 35256,\n", + " 36167, 37074, 39326, 40228, 47566, 51263, 53121, 54022,\n", + " 56830, 63002, 66322, 68149, 69054, 69951, 70858, 94392,\n", + " 102246, 103164, 106793, 111452, 114190, 118761, 123900, 124808,\n", + " 126616, 127509, 129375, 131180, 132103, 153795, 155624, 156547,\n", + " 158237, 159824, 162555, 165253, 166138, 170769, 172132, 174829,\n", + " 175747, 181603, 184837, 185738, 198676, 199580, 200482],\n", + " dtype='int64'), Int64Index([ 2104, 12330, 14183, 15984, 28749, 29656, 34341, 35257,\n", + " 36168, 37075, 39327, 40229, 47567, 51264, 53122, 54023,\n", + " 56831, 63003, 66323, 68150, 69055, 69952, 70859, 94393,\n", + " 102247, 103165, 106794, 111453, 114191, 118762, 123901, 124809,\n", + " 126617, 127510, 129376, 131181, 132104, 153796, 155625, 156548,\n", + " 158238, 159825, 162556, 165254, 166139, 170770, 172133, 174830,\n", + " 175748, 181604, 184838, 185739, 198677, 199581, 200483],\n", + " dtype='int64'), Int64Index([ 2105, 12331, 14184, 15985, 28750, 29657, 34342, 35258,\n", + " 36169, 37076, 39328, 40230, 47568, 51265, 53123, 54024,\n", + " 56832, 63004, 66324, 68151, 69056, 69953, 70860, 94394,\n", + " 102248, 103166, 106795, 111454, 114192, 118763, 123902, 124810,\n", + " 126618, 127511, 129377, 131182, 132105, 153797, 155626, 156549,\n", + " 158239, 159826, 162557, 165255, 166140, 170771, 172134, 174831,\n", + " 175749, 181605, 184839, 185740, 198678, 199582, 200484],\n", + " dtype='int64'), Int64Index([ 2106, 12332, 14185, 15986, 28751, 29658, 34343, 35259,\n", + " 36170, 37077, 39329, 40231, 47569, 51266, 53124, 54025,\n", + " 56833, 63005, 66325, 68152, 69057, 69954, 70861, 94395,\n", + " 102249, 103167, 106796, 111455, 114193, 118764, 123903, 124811,\n", + " 126619, 127512, 129378, 131183, 132106, 153798, 155627, 156550,\n", + " 158240, 159827, 162558, 165256, 166141, 170772, 172135, 174832,\n", + " 175750, 181606, 184840, 185741, 198679, 199583, 200485],\n", + " dtype='int64'), Int64Index([ 2107, 12333, 14186, 15987, 28752, 29659, 34344, 35260,\n", + " 36171, 37078, 39330, 40232, 47570, 51267, 53125, 54026,\n", + " 56834, 63006, 66326, 68153, 69058, 69955, 70862, 94396,\n", + " 102250, 103168, 106797, 111456, 114194, 118765, 123904, 124812,\n", + " 126620, 127513, 129379, 131184, 132107, 153799, 155628, 156551,\n", + " 158241, 159828, 162559, 165257, 166142, 170773, 172136, 174833,\n", + " 175751, 181607, 184841, 185742, 198680, 199584, 200486],\n", + " dtype='int64'), Int64Index([ 2108, 12334, 14187, 15988, 28753, 29660, 34345, 35261,\n", + " 36172, 37079, 39331, 40233, 47571, 51268, 53126, 54027,\n", + " 56835, 63007, 66327, 68154, 69059, 69956, 70863, 94397,\n", + " 102251, 103169, 106798, 111457, 114195, 118766, 123905, 124813,\n", + " 126621, 127514, 129380, 131185, 132108, 153800, 155629, 156552,\n", + " 158242, 159829, 162560, 165258, 166143, 170774, 172137, 174834,\n", + " 175752, 181608, 184842, 185743, 198681, 199585, 200487],\n", + " dtype='int64'), Int64Index([ 2109, 12335, 14188, 15989, 28754, 29661, 34346, 35262,\n", + " 36173, 37080, 39332, 40234, 47572, 51269, 53127, 54028,\n", + " 56836, 63008, 66328, 68155, 69060, 69957, 70864, 94398,\n", + " 102252, 103170, 106799, 111458, 114196, 118767, 123906, 124814,\n", + " 126622, 127515, 129381, 131186, 132109, 153801, 155630, 156553,\n", + " 158243, 159830, 162561, 165259, 166144, 170775, 172138, 174835,\n", + " 175753, 181609, 184843, 185744, 198682, 199586, 200488],\n", + " dtype='int64'), Int64Index([ 2110, 12336, 14189, 15990, 28755, 29662, 34347, 35263,\n", + " 36174, 37081, 39333, 40235, 47573, 51270, 53128, 54029,\n", + " 56837, 63009, 66329, 68156, 69061, 69958, 70865, 94399,\n", + " 102253, 103171, 106800, 111459, 114197, 118768, 123907, 124815,\n", + " 126623, 127516, 129382, 131187, 132110, 153802, 155631, 156554,\n", + " 158244, 159831, 162562, 165260, 166145, 170776, 172139, 174836,\n", + " 175754, 181610, 184844, 185745, 198683, 199587, 200489],\n", + " dtype='int64'), Int64Index([ 2111, 12337, 14190, 15991, 28756, 29663, 34348, 35264,\n", + " 36175, 37082, 39334, 40236, 47574, 51271, 53129, 54030,\n", + " 56838, 63010, 66330, 68157, 69062, 69959, 70866, 94400,\n", + " 102254, 103172, 106801, 111460, 114198, 118769, 123908, 124816,\n", + " 126624, 127517, 129383, 131188, 132111, 153803, 155632, 156555,\n", + " 158245, 159832, 162563, 165261, 166146, 170777, 172140, 174837,\n", + " 175755, 181611, 184845, 185746, 198684, 199588, 200490],\n", + " dtype='int64'), Int64Index([ 2112, 12338, 14191, 15992, 28757, 29664, 34349, 35265,\n", + " 36176, 37083, 39335, 40237, 47575, 51272, 53130, 54031,\n", + " 56839, 63011, 66331, 68158, 69063, 69960, 70867, 94401,\n", + " 102255, 103173, 106802, 111461, 114199, 118770, 123909, 124817,\n", + " 126625, 127518, 129384, 131189, 132112, 153804, 155633, 156556,\n", + " 158246, 159833, 162564, 165262, 166147, 170778, 172141, 174838,\n", + " 175756, 181612, 184846, 185747, 198685, 199589, 200491],\n", + " dtype='int64'), Int64Index([ 2113, 12339, 14192, 15993, 28758, 29665, 34350, 35266,\n", + " 36177, 37084, 39336, 40238, 47576, 51273, 53131, 54032,\n", + " 56840, 63012, 66332, 68159, 69064, 69961, 70868, 94402,\n", + " 102256, 103174, 106803, 111462, 114200, 118771, 123910, 124818,\n", + " 126626, 127519, 129385, 131190, 132113, 153805, 155634, 156557,\n", + " 158247, 159834, 162565, 165263, 166148, 170779, 172142, 174839,\n", + " 175757, 181613, 184847, 185748, 198686, 199590, 200492],\n", + " dtype='int64'), Int64Index([ 2114, 12340, 14193, 15994, 28759, 29666, 34351, 35267,\n", + " 36178, 37085, 39337, 40239, 47577, 51274, 53132, 54033,\n", + " 56841, 63013, 66333, 68160, 69065, 69962, 70869, 94403,\n", + " 102257, 103175, 106804, 111463, 114201, 118772, 123911, 124819,\n", + " 126627, 127520, 129386, 131191, 132114, 153806, 155635, 156558,\n", + " 158248, 159835, 162566, 165264, 166149, 170780, 172143, 174840,\n", + " 175758, 181614, 184848, 185749, 198687, 199591, 200493],\n", + " dtype='int64'), Int64Index([ 2115, 12341, 14194, 15995, 28760, 29667, 34352, 35268,\n", + " 36179, 37086, 39338, 40240, 47578, 51275, 53133, 54034,\n", + " 56842, 63014, 66334, 68161, 69066, 69963, 70870, 94404,\n", + " 102258, 103176, 106805, 111464, 114202, 118773, 123912, 124820,\n", + " 126628, 127521, 129387, 131192, 132115, 153807, 155636, 156559,\n", + " 158249, 159836, 162567, 165265, 166150, 170781, 172144, 174841,\n", + " 175759, 181615, 184849, 185750, 198688, 199592, 200494],\n", + " dtype='int64'), Int64Index([ 2116, 12342, 14195, 15996, 28761, 29668, 34353, 35269,\n", + " 36180, 37087, 39339, 40241, 47579, 51276, 53134, 54035,\n", + " 56843, 63015, 66335, 68162, 69067, 69964, 70871, 94405,\n", + " 102259, 103177, 106806, 111465, 114203, 118774, 123913, 124821,\n", + " 126629, 127522, 129388, 131193, 132116, 153808, 155637, 156560,\n", + " 158250, 159837, 162568, 165266, 166151, 170782, 172145, 174842,\n", + " 175760, 181616, 184850, 185751, 198689, 199593, 200495],\n", + " dtype='int64'), Int64Index([ 2117, 12343, 14196, 15997, 28762, 29669, 34354, 35270,\n", + " 36181, 37088, 39340, 40242, 47580, 51277, 53135, 54036,\n", + " 56844, 63016, 66336, 68163, 69068, 69965, 70872, 94406,\n", + " 102260, 103178, 106807, 111466, 114204, 118775, 123914, 124822,\n", + " 126630, 127523, 129389, 131194, 132117, 153809, 155638, 156561,\n", + " 158251, 159838, 162569, 165267, 166152, 170783, 172146, 174843,\n", + " 175761, 181617, 184851, 185752, 198690, 199594, 200496],\n", + " dtype='int64'), Int64Index([ 2118, 12344, 14197, 15998, 28763, 29670, 34355, 35271,\n", + " 36182, 37089, 39341, 40243, 47581, 51278, 53136, 54037,\n", + " 56845, 63017, 66337, 68164, 69069, 69966, 70873, 94407,\n", + " 102261, 103179, 106808, 111467, 114205, 118776, 123915, 124823,\n", + " 126631, 127524, 129390, 131195, 132118, 153810, 155639, 156562,\n", + " 158252, 159839, 162570, 165268, 166153, 170784, 172147, 174844,\n", + " 175762, 181618, 184852, 185753, 198691, 199595, 200497],\n", + " dtype='int64'), Int64Index([ 2119, 12345, 14198, 15999, 28764, 29671, 34356, 35272,\n", + " 36183, 37090, 39342, 40244, 47582, 51279, 53137, 54038,\n", + " 56846, 63018, 66338, 68165, 69070, 69967, 70874, 94408,\n", + " 102262, 103180, 106809, 111468, 114206, 118777, 123916, 124824,\n", + " 126632, 127525, 129391, 131196, 132119, 153811, 155640, 156563,\n", + " 158253, 159840, 162571, 165269, 166154, 170785, 172148, 174845,\n", + " 175763, 181619, 184853, 185754, 198692, 199596, 200498],\n", + " dtype='int64'), Int64Index([ 2120, 12346, 14199, 16000, 28765, 29672, 34357, 35273,\n", + " 36184, 37091, 39343, 40245, 47583, 51280, 53138, 54039,\n", + " 56847, 63019, 66339, 68166, 69071, 69968, 70875, 94409,\n", + " 102263, 103181, 106810, 111469, 114207, 118778, 123917, 124825,\n", + " 126633, 127526, 129392, 131197, 132120, 153812, 155641, 156564,\n", + " 158254, 159841, 162572, 165270, 166155, 170786, 172149, 174846,\n", + " 175764, 181620, 184854, 185755, 198693, 199597, 200499],\n", + " dtype='int64'), Int64Index([ 2121, 12347, 14200, 16001, 28766, 29673, 34358, 35274,\n", + " 36185, 37092, 39344, 40246, 47584, 51281, 53139, 54040,\n", + " 56848, 63020, 66340, 68167, 69072, 69969, 70876, 94410,\n", + " 102264, 103182, 106811, 111470, 114208, 118779, 123918, 124826,\n", + " 126634, 127527, 129393, 131198, 132121, 153813, 155642, 156565,\n", + " 158255, 159842, 162573, 165271, 166156, 170787, 172150, 174847,\n", + " 175765, 181621, 184855, 185756, 198694, 199598, 200500],\n", + " dtype='int64'), Int64Index([ 2122, 12348, 14201, 16002, 28767, 29674, 34359, 35275,\n", + " 36186, 37093, 39345, 40247, 47585, 51282, 53140, 54041,\n", + " 56849, 63021, 66341, 68168, 69073, 69970, 70877, 94411,\n", + " 102265, 103183, 106812, 111471, 114209, 118780, 123919, 124827,\n", + " 126635, 127528, 129394, 131199, 132122, 153814, 155643, 156566,\n", + " 158256, 159843, 162574, 165272, 166157, 170788, 172151, 174848,\n", + " 175766, 181622, 184856, 185757, 198695, 199599, 200501],\n", + " dtype='int64'), Int64Index([ 2123, 12349, 14202, 16003, 28768, 29675, 34360, 35276,\n", + " 36187, 37094, 39346, 40248, 47586, 51283, 53141, 54042,\n", + " 56850, 63022, 66342, 68169, 69074, 69971, 70878, 94412,\n", + " 102266, 103184, 106813, 111472, 114210, 118781, 123920, 124828,\n", + " 126636, 127529, 129395, 131200, 132123, 153815, 155644, 156567,\n", + " 158257, 159844, 162575, 165273, 166158, 170789, 172152, 174849,\n", + " 175767, 181623, 184857, 185758, 198696, 199600, 200502],\n", + " dtype='int64'), Int64Index([ 2124, 12350, 14203, 16004, 28769, 29676, 34361, 35277,\n", + " 36188, 37095, 39347, 40249, 47587, 51284, 53142, 54043,\n", + " 56851, 63023, 66343, 68170, 69075, 69972, 70879, 94413,\n", + " 102267, 103185, 106814, 111473, 114211, 118782, 123921, 124829,\n", + " 126637, 127530, 129396, 131201, 132124, 153816, 155645, 156568,\n", + " 158258, 159845, 162576, 165274, 166159, 170790, 172153, 174850,\n", + " 175768, 181624, 184858, 185759, 198697, 199601, 200503],\n", + " dtype='int64'), Int64Index([ 2125, 12351, 14204, 16005, 28770, 29677, 34362, 35278,\n", + " 36189, 37096, 39348, 40250, 47588, 51285, 53143, 54044,\n", + " 56852, 63024, 66344, 68171, 69076, 69973, 70880, 94414,\n", + " 102268, 103186, 106815, 111474, 114212, 118783, 123922, 124830,\n", + " 126638, 127531, 129397, 131202, 132125, 153817, 155646, 156569,\n", + " 158259, 159846, 162577, 165275, 166160, 170791, 172154, 174851,\n", + " 175769, 181625, 184859, 185760, 198698, 199602, 200504],\n", + " dtype='int64'), Int64Index([ 2126, 12352, 14205, 16006, 28771, 29678, 34363, 35279,\n", + " 36190, 37097, 39349, 40251, 47589, 51286, 53144, 54045,\n", + " 56853, 63025, 66345, 68172, 69077, 69974, 70881, 94415,\n", + " 102269, 103187, 106816, 111475, 114213, 118784, 123923, 124831,\n", + " 126639, 127532, 129398, 131203, 132126, 153818, 155647, 156570,\n", + " 158260, 159847, 162578, 165276, 166161, 170792, 172155, 174852,\n", + " 175770, 181626, 184860, 185761, 198699, 199603, 200505],\n", + " dtype='int64'), Int64Index([ 2127, 12353, 14206, 16007, 28772, 29679, 34364, 35280,\n", + " 36191, 37098, 39350, 40252, 47590, 51287, 53145, 54046,\n", + " 56854, 63026, 66346, 68173, 69078, 69975, 70882, 94416,\n", + " 102270, 103188, 106817, 111476, 114214, 118785, 123924, 124832,\n", + " 126640, 127533, 129399, 131204, 132127, 153819, 155648, 156571,\n", + " 158261, 159848, 162579, 165277, 166162, 170793, 172156, 174853,\n", + " 175771, 181627, 184861, 185762, 198700, 199604, 200506],\n", + " dtype='int64'), Int64Index([ 2128, 12354, 14207, 16008, 28773, 29680, 34365, 35281,\n", + " 36192, 37099, 39351, 40253, 47591, 51288, 53146, 54047,\n", + " 56855, 63027, 66347, 68174, 69079, 69976, 70883, 94417,\n", + " 102271, 103189, 106818, 111477, 114215, 118786, 123925, 124833,\n", + " 126641, 127534, 129400, 131205, 132128, 153820, 155649, 156572,\n", + " 158262, 159849, 162580, 165278, 166163, 170794, 172157, 174854,\n", + " 175772, 181628, 184862, 185763, 198701, 199605, 200507],\n", + " dtype='int64'), Int64Index([ 2129, 12355, 14208, 16009, 28774, 29681, 34366, 35282,\n", + " 36193, 37100, 39352, 40254, 47592, 51289, 53147, 54048,\n", + " 56856, 63028, 66348, 68175, 69080, 69977, 70884, 94418,\n", + " 102272, 103190, 106819, 111478, 114216, 118787, 123926, 124834,\n", + " 126642, 127535, 129401, 131206, 132129, 153821, 155650, 156573,\n", + " 158263, 159850, 162581, 165279, 166164, 170795, 172158, 174855,\n", + " 175773, 181629, 184863, 185764, 198702, 199606, 200508],\n", + " dtype='int64'), Int64Index([ 2130, 12356, 14209, 16010, 28775, 29682, 34367, 35283,\n", + " 36194, 37101, 39353, 40255, 47593, 51290, 53148, 54049,\n", + " 56857, 63029, 66349, 68176, 69081, 69978, 70885, 94419,\n", + " 102273, 103191, 106820, 111479, 114217, 118788, 123927, 124835,\n", + " 126643, 127536, 129402, 131207, 132130, 153822, 155651, 156574,\n", + " 158264, 159851, 162582, 165280, 166165, 170796, 172159, 174856,\n", + " 175774, 181630, 184864, 185765, 198703, 199607, 200509],\n", + " dtype='int64'), Int64Index([ 2131, 12357, 14210, 16011, 28776, 29683, 34368, 35284,\n", + " 36195, 37102, 39354, 40256, 47594, 51291, 53149, 54050,\n", + " 56858, 63030, 66350, 68177, 69082, 69979, 70886, 94420,\n", + " 102274, 103192, 106821, 111480, 114218, 118789, 123928, 124836,\n", + " 126644, 127537, 129403, 131208, 132131, 153823, 155652, 156575,\n", + " 158265, 159852, 162583, 165281, 166166, 170797, 172160, 174857,\n", + " 175775, 181631, 184865, 185766, 198704, 199608, 200510],\n", + " dtype='int64'), Int64Index([ 2132, 12358, 14211, 16012, 28777, 29684, 34369, 35285,\n", + " 36196, 37103, 39355, 40257, 47595, 51292, 53150, 54051,\n", + " 56859, 63031, 66351, 68178, 69083, 69980, 70887, 94421,\n", + " 102275, 103193, 106822, 111481, 114219, 118790, 123929, 124837,\n", + " 126645, 127538, 129404, 131209, 132132, 153824, 155653, 156576,\n", + " 158266, 159853, 162584, 165282, 166167, 170798, 172161, 174858,\n", + " 175776, 181632, 184866, 185767, 198705, 199609, 200511],\n", + " dtype='int64'), Int64Index([ 2133, 12359, 14212, 16013, 28778, 29685, 34370, 35286,\n", + " 36197, 37104, 39356, 40258, 47596, 51293, 53151, 54052,\n", + " 56860, 63032, 66352, 68179, 69084, 69981, 70888, 94422,\n", + " 102276, 103194, 106823, 111482, 114220, 118791, 123930, 124838,\n", + " 126646, 127539, 129405, 131210, 132133, 153825, 155654, 156577,\n", + " 158267, 159854, 162585, 165283, 166168, 170799, 172162, 174859,\n", + " 175777, 181633, 184867, 185768, 198706, 199610, 200512],\n", + " dtype='int64'), Int64Index([ 2134, 12360, 14213, 16014, 28779, 29686, 34371, 35287,\n", + " 36198, 37105, 39357, 40259, 47597, 51294, 53152, 54053,\n", + " 56861, 63033, 66353, 68180, 69085, 69982, 70889, 94423,\n", + " 102277, 103195, 106824, 111483, 114221, 118792, 123931, 124839,\n", + " 126647, 127540, 129406, 131211, 132134, 153826, 155655, 156578,\n", + " 158268, 159855, 162586, 165284, 166169, 170800, 172163, 174860,\n", + " 175778, 181634, 184868, 185769, 198707, 199611, 200513],\n", + " dtype='int64'), Int64Index([ 2135, 12361, 14214, 16015, 28780, 29687, 34372, 35288,\n", + " 36199, 37106, 39358, 40260, 47598, 51295, 53153, 54054,\n", + " 56862, 63034, 66354, 68181, 69086, 69983, 70890, 94424,\n", + " 102278, 103196, 106825, 111484, 114222, 118793, 123932, 124840,\n", + " 126648, 127541, 129407, 131212, 132135, 153827, 155656, 156579,\n", + " 158269, 159856, 162587, 165285, 166170, 170801, 172164, 174861,\n", + " 175779, 181635, 184869, 185770, 198708, 199612, 200514],\n", + " dtype='int64'), Int64Index([ 2136, 12362, 14215, 16016, 28781, 29688, 34373, 35289,\n", + " 36200, 37107, 39359, 40261, 47599, 51296, 53154, 54055,\n", + " 56863, 63035, 66355, 68182, 69087, 69984, 70891, 94425,\n", + " 102279, 103197, 106826, 111485, 114223, 118794, 123933, 124841,\n", + " 126649, 127542, 129408, 131213, 132136, 153828, 155657, 156580,\n", + " 158270, 159857, 162588, 165286, 166171, 170802, 172165, 174862,\n", + " 175780, 181636, 184870, 185771, 198709, 199613, 200515],\n", + " dtype='int64'), Int64Index([ 2137, 12363, 14216, 16017, 28782, 29689, 34374, 35290,\n", + " 36201, 37108, 39360, 40262, 47600, 51297, 53155, 54056,\n", + " 56864, 63036, 66356, 68183, 69088, 69985, 70892, 94426,\n", + " 102280, 103198, 106827, 111486, 114224, 118795, 123934, 124842,\n", + " 126650, 127543, 129409, 131214, 132137, 153829, 155658, 156581,\n", + " 158271, 159858, 162589, 165287, 166172, 170803, 172166, 174863,\n", + " 175781, 181637, 184871, 185772, 198710, 199614, 200516],\n", + " dtype='int64'), Int64Index([ 2138, 12364, 14217, 16018, 28783, 29690, 34375, 35291,\n", + " 36202, 37109, 39361, 40263, 47601, 51298, 53156, 54057,\n", + " 56865, 63037, 66357, 68184, 69089, 69986, 70893, 94427,\n", + " 102281, 103199, 106828, 111487, 114225, 118796, 123935, 124843,\n", + " 126651, 127544, 129410, 131215, 132138, 153830, 155659, 156582,\n", + " 158272, 159859, 162590, 165288, 166173, 170804, 172167, 174864,\n", + " 175782, 181638, 184872, 185773, 198711, 199615, 200517],\n", + " dtype='int64'), Int64Index([ 2139, 12365, 14218, 16019, 28784, 29691, 34376, 35292,\n", + " 36203, 37110, 39362, 40264, 47602, 51299, 53157, 54058,\n", + " 56866, 63038, 66358, 68185, 69090, 69987, 70894, 94428,\n", + " 102282, 103200, 106829, 111488, 114226, 118797, 123936, 124844,\n", + " 126652, 127545, 129411, 131216, 132139, 153831, 155660, 156583,\n", + " 158273, 159860, 162591, 165289, 166174, 170805, 172168, 174865,\n", + " 175783, 181639, 184873, 185774, 198712, 199616, 200518],\n", + " dtype='int64'), Int64Index([ 2140, 12366, 14219, 16020, 28785, 29692, 34377, 35293,\n", + " 36204, 37111, 39363, 40265, 47603, 51300, 53158, 54059,\n", + " 56867, 63039, 66359, 68186, 69091, 69988, 70895, 94429,\n", + " 102283, 103201, 106830, 111489, 114227, 118798, 123937, 124845,\n", + " 126653, 127546, 129412, 131217, 132140, 153832, 155661, 156584,\n", + " 158274, 159861, 162592, 165290, 166175, 170806, 172169, 174866,\n", + " 175784, 181640, 184874, 185775, 198713, 199617, 200519],\n", + " dtype='int64'), Int64Index([ 2141, 12367, 14220, 16021, 28786, 29693, 34378, 35294,\n", + " 36205, 37112, 39364, 40266, 47604, 51301, 53159, 54060,\n", + " 56868, 63040, 66360, 68187, 69092, 69989, 70896, 94430,\n", + " 102284, 103202, 106831, 111490, 114228, 118799, 123938, 124846,\n", + " 126654, 127547, 129413, 131218, 132141, 153833, 155662, 156585,\n", + " 158275, 159862, 162593, 165291, 166176, 170807, 172170, 174867,\n", + " 175785, 181641, 184875, 185776, 198714, 199618, 200520],\n", + " dtype='int64'), Int64Index([ 2142, 12368, 14221, 16022, 28787, 29694, 34379, 35295,\n", + " 36206, 37113, 39365, 40267, 47605, 51302, 53160, 54061,\n", + " 56869, 63041, 66361, 68188, 69093, 69990, 70897, 94431,\n", + " 102285, 103203, 106832, 111491, 114229, 118800, 123939, 124847,\n", + " 126655, 127548, 129414, 131219, 132142, 153834, 155663, 156586,\n", + " 158276, 159863, 162594, 165292, 166177, 170808, 172171, 174868,\n", + " 175786, 181642, 184876, 185777, 198715, 199619, 200521],\n", + " dtype='int64'), Int64Index([ 2143, 12369, 14222, 16023, 28788, 29695, 34380, 35296,\n", + " 36207, 37114, 39366, 40268, 47606, 51303, 53161, 54062,\n", + " 56870, 63042, 66362, 68189, 69094, 69991, 70898, 94432,\n", + " 102286, 103204, 106833, 111492, 114230, 118801, 123940, 124848,\n", + " 126656, 127549, 129415, 131220, 132143, 153835, 155664, 156587,\n", + " 158277, 159864, 162595, 165293, 166178, 170809, 172172, 174869,\n", + " 175787, 181643, 184877, 185778, 198716, 199620, 200522],\n", + " dtype='int64'), Int64Index([ 2144, 12370, 14223, 16024, 28789, 29696, 34381, 35297,\n", + " 36208, 37115, 39367, 40269, 47607, 51304, 53162, 54063,\n", + " 56871, 63043, 66363, 68190, 69095, 69992, 70899, 94433,\n", + " 102287, 103205, 106834, 111493, 114231, 118802, 123941, 124849,\n", + " 126657, 127550, 129416, 131221, 132144, 153836, 155665, 156588,\n", + " 158278, 159865, 162596, 165294, 166179, 170810, 172173, 174870,\n", + " 175788, 181644, 184878, 185779, 198717, 199621, 200523],\n", + " dtype='int64'), Int64Index([ 2145, 12371, 14224, 16025, 28790, 29697, 34382, 35298,\n", + " 36209, 37116, 39368, 40270, 47608, 51305, 53163, 54064,\n", + " 56872, 63044, 66364, 68191, 69096, 69993, 70900, 94434,\n", + " 102288, 103206, 106835, 111494, 114232, 118803, 123942, 124850,\n", + " 126658, 127551, 129417, 131222, 132145, 153837, 155666, 156589,\n", + " 158279, 159866, 162597, 165295, 166180, 170811, 172174, 174871,\n", + " 175789, 181645, 184879, 185780, 198718, 199622, 200524],\n", + " dtype='int64'), Int64Index([ 2146, 12372, 14225, 16026, 28791, 29698, 34383, 35299,\n", + " 36210, 37117, 39369, 40271, 47609, 51306, 53164, 54065,\n", + " 56873, 63045, 66365, 68192, 69097, 69994, 70901, 94435,\n", + " 102289, 103207, 106836, 111495, 114233, 118804, 123943, 124851,\n", + " 126659, 127552, 129418, 131223, 132146, 153838, 155667, 156590,\n", + " 158280, 159867, 162598, 165296, 166181, 170812, 172175, 174872,\n", + " 175790, 181646, 184880, 185781, 198719, 199623, 200525],\n", + " dtype='int64'), Int64Index([ 2147, 12373, 14226, 16027, 28792, 29699, 34384, 35300,\n", + " 36211, 37118, 39370, 40272, 47610, 51307, 53165, 54066,\n", + " 56874, 63046, 66366, 68193, 69098, 69995, 70902, 94436,\n", + " 102290, 103208, 106837, 111496, 114234, 118805, 123944, 124852,\n", + " 126660, 127553, 129419, 131224, 132147, 153839, 155668, 156591,\n", + " 158281, 159868, 162599, 165297, 166182, 170813, 172176, 174873,\n", + " 175791, 181647, 184881, 185782, 198720, 199624, 200526],\n", + " dtype='int64'), Int64Index([ 2148, 12374, 14227, 16028, 28793, 29700, 34385, 35301,\n", + " 36212, 37119, 39371, 40273, 47611, 51308, 53166, 54067,\n", + " 56875, 63047, 66367, 68194, 69099, 69996, 70903, 94437,\n", + " 102291, 103209, 106838, 111497, 114235, 118806, 123945, 124853,\n", + " 126661, 127554, 129420, 131225, 132148, 153840, 155669, 156592,\n", + " 158282, 159869, 162600, 165298, 166183, 170814, 172177, 174874,\n", + " 175792, 181648, 184882, 185783, 198721, 199625, 200527],\n", + " dtype='int64'), Int64Index([ 2149, 12375, 14228, 16029, 28794, 29701, 34386, 35302,\n", + " 36213, 37120, 39372, 40274, 47612, 51309, 53167, 54068,\n", + " 56876, 63048, 66368, 68195, 69100, 69997, 70904, 94438,\n", + " 102292, 103210, 106839, 111498, 114236, 118807, 123946, 124854,\n", + " 126662, 127555, 129421, 131226, 132149, 153841, 155670, 156593,\n", + " 158283, 159870, 162601, 165299, 166184, 170815, 172178, 174875,\n", + " 175793, 181649, 184883, 185784, 198722, 199626, 200528],\n", + " dtype='int64'), Int64Index([ 2150, 12376, 14229, 16030, 28795, 29702, 34387, 35303,\n", + " 36214, 37121, 39373, 40275, 47613, 51310, 53168, 54069,\n", + " 56877, 63049, 66369, 68196, 69101, 69998, 70905, 94439,\n", + " 102293, 103211, 106840, 111499, 114237, 118808, 123947, 124855,\n", + " 126663, 127556, 129422, 131227, 132150, 153842, 155671, 156594,\n", + " 158284, 159871, 162602, 165300, 166185, 170816, 172179, 174876,\n", + " 175794, 181650, 184884, 185785, 198723, 199627, 200529],\n", + " dtype='int64'), Int64Index([ 2151, 12377, 14230, 16031, 28796, 29703, 34388, 35304,\n", + " 36215, 37122, 39374, 40276, 47614, 51311, 53169, 54070,\n", + " 56878, 63050, 66370, 68197, 69102, 69999, 70906, 94440,\n", + " 102294, 103212, 106841, 111500, 114238, 118809, 123948, 124856,\n", + " 126664, 127557, 129423, 131228, 132151, 153843, 155672, 156595,\n", + " 158285, 159872, 162603, 165301, 166186, 170817, 172180, 174877,\n", + " 175795, 181651, 184885, 185786, 198724, 199628, 200530],\n", + " dtype='int64'), Int64Index([ 2152, 12378, 14231, 16032, 28797, 29704, 34389, 35305,\n", + " 36216, 37123, 39375, 40277, 47615, 51312, 53170, 54071,\n", + " 56879, 63051, 66371, 68198, 69103, 70000, 70907, 94441,\n", + " 102295, 103213, 106842, 111501, 114239, 118810, 123949, 124857,\n", + " 126665, 127558, 129424, 131229, 132152, 153844, 155673, 156596,\n", + " 158286, 159873, 162604, 165302, 166187, 170818, 172181, 174878,\n", + " 175796, 181652, 184886, 185787, 198725, 199629, 200531],\n", + " dtype='int64'), Int64Index([ 2153, 12379, 14232, 16033, 28798, 29705, 34390, 35306,\n", + " 36217, 37124, 39376, 40278, 47616, 51313, 53171, 54072,\n", + " 56880, 63052, 66372, 68199, 69104, 70001, 70908, 94442,\n", + " 102296, 103214, 106843, 111502, 114240, 118811, 123950, 124858,\n", + " 126666, 127559, 129425, 131230, 132153, 153845, 155674, 156597,\n", + " 158287, 159874, 162605, 165303, 166188, 170819, 172182, 174879,\n", + " 175797, 181653, 184887, 185788, 198726, 199630, 200532],\n", + " dtype='int64'), Int64Index([ 2154, 12380, 14233, 16034, 28799, 29706, 34391, 35307,\n", + " 36218, 37125, 39377, 40279, 47617, 51314, 53172, 54073,\n", + " 56881, 63053, 66373, 68200, 69105, 70002, 70909, 94443,\n", + " 102297, 103215, 106844, 111503, 114241, 118812, 123951, 124859,\n", + " 126667, 127560, 129426, 131231, 132154, 153846, 155675, 156598,\n", + " 158288, 159875, 162606, 165304, 166189, 170820, 172183, 174880,\n", + " 175798, 181654, 184888, 185789, 198727, 199631, 200533],\n", + " dtype='int64'), Int64Index([ 2155, 12381, 14234, 16035, 28800, 29707, 34392, 35308,\n", + " 36219, 37126, 39378, 40280, 47618, 51315, 53173, 54074,\n", + " 56882, 63054, 66374, 68201, 69106, 70003, 70910, 94444,\n", + " 102298, 103216, 106845, 111504, 114242, 118813, 123952, 124860,\n", + " 126668, 127561, 129427, 131232, 132155, 153847, 155676, 156599,\n", + " 158289, 159876, 162607, 165305, 166190, 170821, 172184, 174881,\n", + " 175799, 181655, 184889, 185790, 198728, 199632, 200534],\n", + " dtype='int64'), Int64Index([ 2156, 12382, 14235, 16036, 28801, 29708, 34393, 35309,\n", + " 36220, 37127, 39379, 40281, 47619, 51316, 53174, 54075,\n", + " 56883, 63055, 66375, 68202, 69107, 70004, 70911, 94445,\n", + " 102299, 103217, 106846, 111505, 114243, 118814, 123953, 124861,\n", + " 126669, 127562, 129428, 131233, 132156, 153848, 155677, 156600,\n", + " 158290, 159877, 162608, 165306, 166191, 170822, 172185, 174882,\n", + " 175800, 181656, 184890, 185791, 198729, 199633, 200535],\n", + " dtype='int64'), Int64Index([ 2157, 12383, 14236, 16037, 28802, 29709, 34394, 35310,\n", + " 36221, 37128, 39380, 40282, 47620, 51317, 53175, 54076,\n", + " 56884, 63056, 66376, 68203, 69108, 70005, 70912, 94446,\n", + " 102300, 103218, 106847, 111506, 114244, 118815, 123954, 124862,\n", + " 126670, 127563, 129429, 131234, 132157, 153849, 155678, 156601,\n", + " 158291, 159878, 162609, 165307, 166192, 170823, 172186, 174883,\n", + " 175801, 181657, 184891, 185792, 198730, 199634, 200536],\n", + " dtype='int64'), Int64Index([ 2158, 12384, 14237, 16038, 28803, 29710, 34395, 35311,\n", + " 36222, 37129, 39381, 40283, 47621, 51318, 53176, 54077,\n", + " 56885, 63057, 66377, 68204, 69109, 70006, 70913, 94447,\n", + " 102301, 103219, 106848, 111507, 114245, 118816, 123955, 124863,\n", + " 126671, 127564, 129430, 131235, 132158, 153850, 155679, 156602,\n", + " 158292, 159879, 162610, 165308, 166193, 170824, 172187, 174884,\n", + " 175802, 181658, 184892, 185793, 198731, 199635, 200537],\n", + " dtype='int64'), Int64Index([ 2159, 12385, 14238, 16039, 28804, 29711, 34396, 35312,\n", + " 36223, 37130, 39382, 40284, 47622, 51319, 53177, 54078,\n", + " 56886, 63058, 66378, 68205, 69110, 70007, 70914, 94448,\n", + " 102302, 103220, 106849, 111508, 114246, 118817, 123956, 124864,\n", + " 126672, 127565, 129431, 131236, 132159, 153851, 155680, 156603,\n", + " 158293, 159880, 162611, 165309, 166194, 170825, 172188, 174885,\n", + " 175803, 181659, 184893, 185794, 198732, 199636, 200538],\n", + " dtype='int64'), Int64Index([ 2160, 12386, 14239, 16040, 28805, 29712, 34397, 35313,\n", + " 36224, 37131, 39383, 40285, 47623, 51320, 53178, 54079,\n", + " 56887, 63059, 66379, 68206, 69111, 70008, 70915, 94449,\n", + " 102303, 103221, 106850, 111509, 114247, 118818, 123957, 124865,\n", + " 126673, 127566, 129432, 131237, 132160, 153852, 155681, 156604,\n", + " 158294, 159881, 162612, 165310, 166195, 170826, 172189, 174886,\n", + " 175804, 181660, 184894, 185795, 198733, 199637, 200539],\n", + " dtype='int64'), Int64Index([ 2161, 12387, 14240, 16041, 28806, 29713, 34398, 35314,\n", + " 36225, 37132, 39384, 40286, 47624, 51321, 53179, 54080,\n", + " 56888, 63060, 66380, 68207, 69112, 70009, 70916, 94450,\n", + " 102304, 103222, 106851, 111510, 114248, 118819, 123958, 124866,\n", + " 126674, 127567, 129433, 131238, 132161, 153853, 155682, 156605,\n", + " 158295, 159882, 162613, 165311, 166196, 170827, 172190, 174887,\n", + " 175805, 181661, 184895, 185796, 198734, 199638, 200540],\n", + " dtype='int64'), Int64Index([ 2162, 12388, 14241, 16042, 28807, 29714, 34399, 35315,\n", + " 36226, 37133, 39385, 40287, 47625, 51322, 53180, 54081,\n", + " 56889, 63061, 66381, 68208, 69113, 70010, 70917, 94451,\n", + " 102305, 103223, 106852, 111511, 114249, 118820, 123959, 124867,\n", + " 126675, 127568, 129434, 131239, 132162, 153854, 155683, 156606,\n", + " 158296, 159883, 162614, 165312, 166197, 170828, 172191, 174888,\n", + " 175806, 181662, 184896, 185797, 198735, 199639, 200541],\n", + " dtype='int64'), Int64Index([ 2163, 12389, 14242, 16043, 28808, 29715, 34400, 35316,\n", + " 36227, 37134, 39386, 40288, 47626, 51323, 53181, 54082,\n", + " 56890, 63062, 66382, 68209, 69114, 70011, 70918, 94452,\n", + " 102306, 103224, 106853, 111512, 114250, 118821, 123960, 124868,\n", + " 126676, 127569, 129435, 131240, 132163, 153855, 155684, 156607,\n", + " 158297, 159884, 162615, 165313, 166198, 170829, 172192, 174889,\n", + " 175807, 181663, 184897, 185798, 198736, 199640, 200542],\n", + " dtype='int64'), Int64Index([ 2164, 12390, 14243, 16044, 28809, 29716, 34401, 35317,\n", + " 36228, 37135, 39387, 40289, 47627, 51324, 53182, 54083,\n", + " 56891, 63063, 66383, 68210, 69115, 70012, 70919, 94453,\n", + " 102307, 103225, 106854, 111513, 114251, 118822, 123961, 124869,\n", + " 126677, 127570, 129436, 131241, 132164, 153856, 155685, 156608,\n", + " 158298, 159885, 162616, 165314, 166199, 170830, 172193, 174890,\n", + " 175808, 181664, 184898, 185799, 198737, 199641, 200543],\n", + " dtype='int64'), Int64Index([ 2165, 12391, 14244, 16045, 28810, 29717, 34402, 35318,\n", + " 36229, 37136, 39388, 40290, 47628, 51325, 53183, 54084,\n", + " 56892, 63064, 66384, 68211, 69116, 70013, 70920, 94454,\n", + " 102308, 103226, 106855, 111514, 114252, 118823, 123962, 124870,\n", + " 126678, 127571, 129437, 131242, 132165, 153857, 155686, 156609,\n", + " 158299, 159886, 162617, 165315, 166200, 170831, 172194, 174891,\n", + " 175809, 181665, 184899, 185800, 198738, 199642, 200544],\n", + " dtype='int64'), Int64Index([ 2166, 12392, 14245, 16046, 28811, 29718, 34403, 35319,\n", + " 36230, 37137, 39389, 40291, 47629, 51326, 53184, 54085,\n", + " 56893, 63065, 66385, 68212, 69117, 70014, 70921, 94455,\n", + " 102309, 103227, 106856, 111515, 114253, 118824, 123963, 124871,\n", + " 126679, 127572, 129438, 131243, 132166, 153858, 155687, 156610,\n", + " 158300, 159887, 162618, 165316, 166201, 170832, 172195, 174892,\n", + " 175810, 181666, 184900, 185801, 198739, 199643, 200545],\n", + " dtype='int64'), Int64Index([ 2167, 12393, 14246, 16047, 28812, 29719, 34404, 35320,\n", + " 36231, 37138, 39390, 40292, 47630, 51327, 53185, 54086,\n", + " 56894, 63066, 66386, 68213, 69118, 70015, 70922, 94456,\n", + " 102310, 103228, 106857, 111516, 114254, 118825, 123964, 124872,\n", + " 126680, 127573, 129439, 131244, 132167, 153859, 155688, 156611,\n", + " 158301, 159888, 162619, 165317, 166202, 170833, 172196, 174893,\n", + " 175811, 181667, 184901, 185802, 198740, 199644, 200546],\n", + " dtype='int64'), Int64Index([ 2168, 12394, 14247, 16048, 28813, 29720, 34405, 35321,\n", + " 36232, 37139, 39391, 40293, 47631, 51328, 53186, 54087,\n", + " 56895, 63067, 66387, 68214, 69119, 70016, 70923, 94457,\n", + " 102311, 103229, 106858, 111517, 114255, 118826, 123965, 124873,\n", + " 126681, 127574, 129440, 131245, 132168, 153860, 155689, 156612,\n", + " 158302, 159889, 162620, 165318, 166203, 170834, 172197, 174894,\n", + " 175812, 181668, 184902, 185803, 198741, 199645, 200547],\n", + " dtype='int64'), Int64Index([ 2169, 12395, 14248, 16049, 28814, 29721, 34406, 35322,\n", + " 36233, 37140, 39392, 40294, 47632, 51329, 53187, 54088,\n", + " 56896, 63068, 66388, 68215, 69120, 70017, 70924, 94458,\n", + " 102312, 103230, 106859, 111518, 114256, 118827, 123966, 124874,\n", + " 126682, 127575, 129441, 131246, 132169, 153861, 155690, 156613,\n", + " 158303, 159890, 162621, 165319, 166204, 170835, 172198, 174895,\n", + " 175813, 181669, 184903, 185804, 198742, 199646, 200548],\n", + " dtype='int64'), Int64Index([ 2170, 12396, 14249, 16050, 28815, 29722, 34407, 35323,\n", + " 36234, 37141, 39393, 40295, 47633, 51330, 53188, 54089,\n", + " 56897, 63069, 66389, 68216, 69121, 70018, 70925, 94459,\n", + " 102313, 103231, 106860, 111519, 114257, 118828, 123967, 124875,\n", + " 126683, 127576, 129442, 131247, 132170, 153862, 155691, 156614,\n", + " 158304, 159891, 162622, 165320, 166205, 170836, 172199, 174896,\n", + " 175814, 181670, 184904, 185805, 198743, 199647, 200549],\n", + " dtype='int64'), Int64Index([ 2171, 12397, 14250, 16051, 28816, 29723, 34408, 35324,\n", + " 36235, 37142, 39394, 40296, 47634, 51331, 53189, 54090,\n", + " 56898, 63070, 66390, 68217, 69122, 70019, 70926, 94460,\n", + " 102314, 103232, 106861, 111520, 114258, 118829, 123968, 124876,\n", + " 126684, 127577, 129443, 131248, 132171, 153863, 155692, 156615,\n", + " 158305, 159892, 162623, 165321, 166206, 170837, 172200, 174897,\n", + " 175815, 181671, 184905, 185806, 198744, 199648, 200550],\n", + " dtype='int64'), Int64Index([ 2172, 12398, 14251, 16052, 28817, 29724, 34409, 35325,\n", + " 36236, 37143, 39395, 40297, 47635, 51332, 53190, 54091,\n", + " 56899, 63071, 66391, 68218, 69123, 70020, 70927, 94461,\n", + " 102315, 103233, 106862, 111521, 114259, 118830, 123969, 124877,\n", + " 126685, 127578, 129444, 131249, 132172, 153864, 155693, 156616,\n", + " 158306, 159893, 162624, 165322, 166207, 170838, 172201, 174898,\n", + " 175816, 181672, 184906, 185807, 198745, 199649, 200551],\n", + " dtype='int64'), Int64Index([ 2173, 12399, 14252, 16053, 28818, 29725, 34410, 35326,\n", + " 36237, 37144, 39396, 40298, 47636, 51333, 53191, 54092,\n", + " 56900, 63072, 66392, 68219, 69124, 70021, 70928, 94462,\n", + " 102316, 103234, 106863, 111522, 114260, 118831, 123970, 124878,\n", + " 126686, 127579, 129445, 131250, 132173, 153865, 155694, 156617,\n", + " 158307, 159894, 162625, 165323, 166208, 170839, 172202, 174899,\n", + " 175817, 181673, 184907, 185808, 198746, 199650, 200552],\n", + " dtype='int64'), Int64Index([ 2174, 12400, 14253, 16054, 28819, 29726, 34411, 35327,\n", + " 36238, 37145, 39397, 40299, 47637, 51334, 53192, 54093,\n", + " 56901, 63073, 66393, 68220, 69125, 70022, 70929, 94463,\n", + " 102317, 103235, 106864, 111523, 114261, 118832, 123971, 124879,\n", + " 126687, 127580, 129446, 131251, 132174, 153866, 155695, 156618,\n", + " 158308, 159895, 162626, 165324, 166209, 170840, 172203, 174900,\n", + " 175818, 181674, 184908, 185809, 198747, 199651, 200553],\n", + " dtype='int64'), Int64Index([ 2175, 12401, 14254, 16055, 28820, 29727, 34412, 35328,\n", + " 36239, 37146, 39398, 40300, 47638, 51335, 53193, 54094,\n", + " 56902, 63074, 66394, 68221, 69126, 70023, 70930, 94464,\n", + " 102318, 103236, 106865, 111524, 114262, 118833, 123972, 124880,\n", + " 126688, 127581, 129447, 131252, 132175, 153867, 155696, 156619,\n", + " 158309, 159896, 162627, 165325, 166210, 170841, 172204, 174901,\n", + " 175819, 181675, 184909, 185810, 198748, 199652, 200554],\n", + " dtype='int64'), Int64Index([ 2176, 12402, 14255, 16056, 28821, 29728, 34413, 35329,\n", + " 36240, 37147, 39399, 40301, 47639, 51336, 53194, 54095,\n", + " 56903, 63075, 66395, 68222, 69127, 70024, 70931, 94465,\n", + " 102319, 103237, 106866, 111525, 114263, 118834, 123973, 124881,\n", + " 126689, 127582, 129448, 131253, 132176, 153868, 155697, 156620,\n", + " 158310, 159897, 162628, 165326, 166211, 170842, 172205, 174902,\n", + " 175820, 181676, 184910, 185811, 198749, 199653, 200555],\n", + " dtype='int64'), Int64Index([ 2177, 12403, 14256, 16057, 28822, 29729, 34414, 35330,\n", + " 36241, 37148, 39400, 40302, 47640, 51337, 53195, 54096,\n", + " 56904, 63076, 66396, 68223, 69128, 70025, 70932, 94466,\n", + " 102320, 103238, 106867, 111526, 114264, 118835, 123974, 124882,\n", + " 126690, 127583, 129449, 131254, 132177, 153869, 155698, 156621,\n", + " 158311, 159898, 162629, 165327, 166212, 170843, 172206, 174903,\n", + " 175821, 181677, 184911, 185812, 198750, 199654, 200556],\n", + " dtype='int64'), Int64Index([ 2178, 12404, 14257, 16058, 28823, 29730, 34415, 35331,\n", + " 36242, 37149, 39401, 40303, 47641, 51338, 53196, 54097,\n", + " 56905, 63077, 66397, 68224, 69129, 70026, 70933, 94467,\n", + " 102321, 103239, 106868, 111527, 114265, 118836, 123975, 124883,\n", + " 126691, 127584, 129450, 131255, 132178, 153870, 155699, 156622,\n", + " 158312, 159899, 162630, 165328, 166213, 170844, 172207, 174904,\n", + " 175822, 181678, 184912, 185813, 198751, 199655, 200557],\n", + " dtype='int64'), Int64Index([ 2179, 12405, 14258, 16059, 28824, 29731, 34416, 35332,\n", + " 36243, 37150, 39402, 40304, 47642, 51339, 53197, 54098,\n", + " 56906, 63078, 66398, 68225, 69130, 70027, 70934, 94468,\n", + " 102322, 103240, 106869, 111528, 114266, 118837, 123976, 124884,\n", + " 126692, 127585, 129451, 131256, 132179, 153871, 155700, 156623,\n", + " 158313, 159900, 162631, 165329, 166214, 170845, 172208, 174905,\n", + " 175823, 181679, 184913, 185814, 198752, 199656, 200558],\n", + " dtype='int64'), Int64Index([ 2180, 12406, 14259, 16060, 28825, 29732, 34417, 35333,\n", + " 36244, 37151, 39403, 40305, 47643, 51340, 53198, 54099,\n", + " 56907, 63079, 66399, 68226, 69131, 70028, 70935, 94469,\n", + " 102323, 103241, 106870, 111529, 114267, 118838, 123977, 124885,\n", + " 126693, 127586, 129452, 131257, 132180, 153872, 155701, 156624,\n", + " 158314, 159901, 162632, 165330, 166215, 170846, 172209, 174906,\n", + " 175824, 181680, 184914, 185815, 198753, 199657, 200559],\n", + " dtype='int64'), Int64Index([ 2181, 12407, 14260, 16061, 28826, 29733, 34418, 35334,\n", + " 36245, 37152, 39404, 40306, 47644, 51341, 53199, 54100,\n", + " 56908, 63080, 66400, 68227, 69132, 70029, 70936, 94470,\n", + " 102324, 103242, 106871, 111530, 114268, 118839, 123978, 124886,\n", + " 126694, 127587, 129453, 131258, 132181, 153873, 155702, 156625,\n", + " 158315, 159902, 162633, 165331, 166216, 170847, 172210, 174907,\n", + " 175825, 181681, 184915, 185816, 198754, 199658, 200560],\n", + " dtype='int64'), Int64Index([ 2182, 12408, 14261, 16062, 28827, 29734, 34419, 35335,\n", + " 36246, 37153, 39405, 40307, 47645, 51342, 53200, 54101,\n", + " 56909, 63081, 66401, 68228, 69133, 70030, 70937, 94471,\n", + " 102325, 103243, 106872, 111531, 114269, 118840, 123979, 124887,\n", + " 126695, 127588, 129454, 131259, 132182, 153874, 155703, 156626,\n", + " 158316, 159903, 162634, 165332, 166217, 170848, 172211, 174908,\n", + " 175826, 181682, 184916, 185817, 198755, 199659, 200561],\n", + " dtype='int64'), Int64Index([ 2183, 12409, 14262, 16063, 28828, 29735, 34420, 35336,\n", + " 36247, 37154, 39406, 40308, 47646, 51343, 53201, 54102,\n", + " 56910, 63082, 66402, 68229, 69134, 70031, 70938, 94472,\n", + " 102326, 103244, 106873, 111532, 114270, 118841, 123980, 124888,\n", + " 126696, 127589, 129455, 131260, 132183, 153875, 155704, 156627,\n", + " 158317, 159904, 162635, 165333, 166218, 170849, 172212, 174909,\n", + " 175827, 181683, 184917, 185818, 198756, 199660, 200562],\n", + " dtype='int64'), Int64Index([ 2184, 12410, 14263, 16064, 28829, 29736, 34421, 35337,\n", + " 36248, 37155, 39407, 40309, 47647, 51344, 53202, 54103,\n", + " 56911, 63083, 66403, 68230, 69135, 70032, 70939, 94473,\n", + " 102327, 103245, 106874, 111533, 114271, 118842, 123981, 124889,\n", + " 126697, 127590, 129456, 131261, 132184, 153876, 155705, 156628,\n", + " 158318, 159905, 162636, 165334, 166219, 170850, 172213, 174910,\n", + " 175828, 181684, 184918, 185819, 198757, 199661, 200563],\n", + " dtype='int64'), Int64Index([ 2185, 12411, 14264, 16065, 28830, 29737, 34422, 35338,\n", + " 36249, 37156, 39408, 40310, 47648, 51345, 53203, 54104,\n", + " 56912, 63084, 66404, 68231, 69136, 70033, 70940, 94474,\n", + " 102328, 103246, 106875, 111534, 114272, 118843, 123982, 124890,\n", + " 126698, 127591, 129457, 131262, 132185, 153877, 155706, 156629,\n", + " 158319, 159906, 162637, 165335, 166220, 170851, 172214, 174911,\n", + " 175829, 181685, 184919, 185820, 198758, 199662, 200564],\n", + " dtype='int64'), Int64Index([ 2186, 12412, 14265, 16066, 28831, 29738, 34423, 35339,\n", + " 36250, 37157, 39409, 40311, 47649, 51346, 53204, 54105,\n", + " 56913, 63085, 66405, 68232, 69137, 70034, 70941, 94475,\n", + " 102329, 103247, 106876, 111535, 114273, 118844, 123983, 124891,\n", + " 126699, 127592, 129458, 131263, 132186, 153878, 155707, 156630,\n", + " 158320, 159907, 162638, 165336, 166221, 170852, 172215, 174912,\n", + " 175830, 181686, 184920, 185821, 198759, 199663, 200565],\n", + " dtype='int64'), Int64Index([ 2187, 12413, 14266, 16067, 28832, 29739, 34424, 35340,\n", + " 36251, 37158, 39410, 40312, 47650, 51347, 53205, 54106,\n", + " 56914, 63086, 66406, 68233, 69138, 70035, 70942, 94476,\n", + " 102330, 103248, 106877, 111536, 114274, 118845, 123984, 124892,\n", + " 126700, 127593, 129459, 131264, 132187, 153879, 155708, 156631,\n", + " 158321, 159908, 162639, 165337, 166222, 170853, 172216, 174913,\n", + " 175831, 181687, 184921, 185822, 198760, 199664, 200566],\n", + " dtype='int64'), Int64Index([ 2188, 12414, 14267, 16068, 28833, 29740, 34425, 35341,\n", + " 36252, 37159, 39411, 40313, 47651, 51348, 53206, 54107,\n", + " 56915, 63087, 66407, 68234, 69139, 70036, 70943, 94477,\n", + " 102331, 103249, 106878, 111537, 114275, 118846, 123985, 124893,\n", + " 126701, 127594, 129460, 131265, 132188, 153880, 155709, 156632,\n", + " 158322, 159909, 162640, 165338, 166223, 170854, 172217, 174914,\n", + " 175832, 181688, 184922, 185823, 198761, 199665, 200567],\n", + " dtype='int64'), Int64Index([ 2189, 12415, 14268, 16069, 28834, 29741, 34426, 35342,\n", + " 36253, 37160, 39412, 40314, 47652, 51349, 53207, 54108,\n", + " 56916, 63088, 66408, 68235, 69140, 70037, 70944, 94478,\n", + " 102332, 103250, 106879, 111538, 114276, 118847, 123986, 124894,\n", + " 126702, 127595, 129461, 131266, 132189, 153881, 155710, 156633,\n", + " 158323, 159910, 162641, 165339, 166224, 170855, 172218, 174915,\n", + " 175833, 181689, 184923, 185824, 198762, 199666, 200568],\n", + " dtype='int64'), Int64Index([ 2190, 12416, 14269, 16070, 28835, 29742, 34427, 35343,\n", + " 36254, 37161, 39413, 40315, 47653, 51350, 53208, 54109,\n", + " 56917, 63089, 66409, 68236, 69141, 70038, 70945, 94479,\n", + " 102333, 103251, 106880, 111539, 114277, 118848, 123987, 124895,\n", + " 126703, 127596, 129462, 131267, 132190, 153882, 155711, 156634,\n", + " 158324, 159911, 162642, 165340, 166225, 170856, 172219, 174916,\n", + " 175834, 181690, 184924, 185825, 198763, 199667, 200569],\n", + " dtype='int64'), Int64Index([ 2191, 12417, 14270, 16071, 28836, 29743, 34428, 35344,\n", + " 36255, 37162, 39414, 40316, 47654, 51351, 53209, 54110,\n", + " 56918, 63090, 66410, 68237, 69142, 70039, 70946, 94480,\n", + " 102334, 103252, 106881, 111540, 114278, 118849, 123988, 124896,\n", + " 126704, 127597, 129463, 131268, 132191, 153883, 155712, 156635,\n", + " 158325, 159912, 162643, 165341, 166226, 170857, 172220, 174917,\n", + " 175835, 181691, 184925, 185826, 198764, 199668, 200570],\n", + " dtype='int64'), Int64Index([ 2192, 12418, 14271, 16072, 28837, 29744, 34429, 35345,\n", + " 36256, 37163, 39415, 40317, 47655, 51352, 53210, 54111,\n", + " 56919, 63091, 66411, 68238, 69143, 70040, 70947, 94481,\n", + " 102335, 103253, 106882, 111541, 114279, 118850, 123989, 124897,\n", + " 126705, 127598, 129464, 131269, 132192, 153884, 155713, 156636,\n", + " 158326, 159913, 162644, 165342, 166227, 170858, 172221, 174918,\n", + " 175836, 181692, 184926, 185827, 198765, 199669, 200571],\n", + " dtype='int64'), Int64Index([ 2193, 12419, 14272, 16073, 28838, 29745, 34430, 35346,\n", + " 36257, 37164, 39416, 40318, 47656, 51353, 53211, 54112,\n", + " 56920, 63092, 66412, 68239, 69144, 70041, 70948, 94482,\n", + " 102336, 103254, 106883, 111542, 114280, 118851, 123990, 124898,\n", + " 126706, 127599, 129465, 131270, 132193, 153885, 155714, 156637,\n", + " 158327, 159914, 162645, 165343, 166228, 170859, 172222, 174919,\n", + " 175837, 181693, 184927, 185828, 198766, 199670, 200572],\n", + " dtype='int64'), Int64Index([ 2194, 12420, 14273, 16074, 28839, 29746, 34431, 35347,\n", + " 36258, 37165, 39417, 40319, 47657, 51354, 53212, 54113,\n", + " 56921, 63093, 66413, 68240, 69145, 70042, 70949, 94483,\n", + " 102337, 103255, 106884, 111543, 114281, 118852, 123991, 124899,\n", + " 126707, 127600, 129466, 131271, 132194, 153886, 155715, 156638,\n", + " 158328, 159915, 162646, 165344, 166229, 170860, 172223, 174920,\n", + " 175838, 181694, 184928, 185829, 198767, 199671, 200573],\n", + " dtype='int64'), Int64Index([ 2195, 12421, 14274, 16075, 28840, 29747, 34432, 35348,\n", + " 36259, 37166, 39418, 40320, 47658, 51355, 53213, 54114,\n", + " 56922, 63094, 66414, 68241, 69146, 70043, 70950, 94484,\n", + " 102338, 103256, 106885, 111544, 114282, 118853, 123992, 124900,\n", + " 126708, 127601, 129467, 131272, 132195, 153887, 155716, 156639,\n", + " 158329, 159916, 162647, 165345, 166230, 170861, 172224, 174921,\n", + " 175839, 181695, 184929, 185830, 198768, 199672, 200574],\n", + " dtype='int64'), Int64Index([ 2196, 12422, 14275, 16076, 28841, 29748, 34433, 35349,\n", + " 36260, 37167, 39419, 40321, 47659, 51356, 53214, 54115,\n", + " 56923, 63095, 66415, 68242, 69147, 70044, 70951, 94485,\n", + " 102339, 103257, 106886, 111545, 114283, 118854, 123993, 124901,\n", + " 126709, 127602, 129468, 131273, 132196, 153888, 155717, 156640,\n", + " 158330, 159917, 162648, 165346, 166231, 170862, 172225, 174922,\n", + " 175840, 181696, 184930, 185831, 198769, 199673, 200575],\n", + " dtype='int64'), Int64Index([ 2197, 12423, 14276, 16077, 28842, 29749, 34434, 35350,\n", + " 36261, 37168, 39420, 40322, 47660, 51357, 53215, 54116,\n", + " 56924, 63096, 66416, 68243, 69148, 70045, 70952, 94486,\n", + " 102340, 103258, 106887, 111546, 114284, 118855, 123994, 124902,\n", + " 126710, 127603, 129469, 131274, 132197, 153889, 155718, 156641,\n", + " 158331, 159918, 162649, 165347, 166232, 170863, 172226, 174923,\n", + " 175841, 181697, 184931, 185832, 198770, 199674, 200576],\n", + " dtype='int64'), Int64Index([ 2198, 12424, 14277, 16078, 28843, 29750, 34435, 35351,\n", + " 36262, 37169, 39421, 40323, 47661, 51358, 53216, 54117,\n", + " 56925, 63097, 66417, 68244, 69149, 70046, 70953, 94487,\n", + " 102341, 103259, 106888, 111547, 114285, 118856, 123995, 124903,\n", + " 126711, 127604, 129470, 131275, 132198, 153890, 155719, 156642,\n", + " 158332, 159919, 162650, 165348, 166233, 170864, 172227, 174924,\n", + " 175842, 181698, 184932, 185833, 198771, 199675, 200577],\n", + " dtype='int64'), Int64Index([ 2199, 12425, 14278, 16079, 28844, 29751, 34436, 35352,\n", + " 36263, 37170, 39422, 40324, 47662, 51359, 53217, 54118,\n", + " 56926, 63098, 66418, 68245, 69150, 70047, 70954, 94488,\n", + " 102342, 103260, 106889, 111548, 114286, 118857, 123996, 124904,\n", + " 126712, 127605, 129471, 131276, 132199, 153891, 155720, 156643,\n", + " 158333, 159920, 162651, 165349, 166234, 170865, 172228, 174925,\n", + " 175843, 181699, 184933, 185834, 198772, 199676, 200578],\n", + " dtype='int64'), Int64Index([ 2200, 12426, 14279, 16080, 28845, 29752, 34437, 35353,\n", + " 36264, 37171, 39423, 40325, 47663, 51360, 53218, 54119,\n", + " 56927, 63099, 66419, 68246, 69151, 70048, 70955, 94489,\n", + " 102343, 103261, 106890, 111549, 114287, 118858, 123997, 124905,\n", + " 126713, 127606, 129472, 131277, 132200, 153892, 155721, 156644,\n", + " 158334, 159921, 162652, 165350, 166235, 170866, 172229, 174926,\n", + " 175844, 181700, 184934, 185835, 198773, 199677, 200579],\n", + " dtype='int64'), Int64Index([ 2201, 12427, 14280, 16081, 28846, 29753, 34438, 35354,\n", + " 36265, 37172, 39424, 40326, 47664, 51361, 53219, 54120,\n", + " 56928, 63100, 66420, 68247, 69152, 70049, 70956, 94490,\n", + " 102344, 103262, 106891, 111550, 114288, 118859, 123998, 124906,\n", + " 126714, 127607, 129473, 131278, 132201, 153893, 155722, 156645,\n", + " 158335, 159922, 162653, 165351, 166236, 170867, 172230, 174927,\n", + " 175845, 181701, 184935, 185836, 198774, 199678, 200580],\n", + " dtype='int64'), Int64Index([ 2202, 12428, 14281, 16082, 28847, 29754, 34439, 35355,\n", + " 36266, 37173, 39425, 40327, 47665, 51362, 53220, 54121,\n", + " 56929, 63101, 66421, 68248, 69153, 70050, 70957, 94491,\n", + " 102345, 103263, 106892, 111551, 114289, 118860, 123999, 124907,\n", + " 126715, 127608, 129474, 131279, 132202, 153894, 155723, 156646,\n", + " 158336, 159923, 162654, 165352, 166237, 170868, 172231, 174928,\n", + " 175846, 181702, 184936, 185837, 198775, 199679, 200581],\n", + " dtype='int64'), Int64Index([ 2203, 12429, 14282, 16083, 28848, 29755, 34440, 35356,\n", + " 36267, 37174, 39426, 40328, 47666, 51363, 53221, 54122,\n", + " 56930, 63102, 66422, 68249, 69154, 70051, 70958, 94492,\n", + " 102346, 103264, 106893, 111552, 114290, 118861, 124000, 124908,\n", + " 126716, 127609, 129475, 131280, 132203, 153895, 155724, 156647,\n", + " 158337, 159924, 162655, 165353, 166238, 170869, 172232, 174929,\n", + " 175847, 181703, 184937, 185838, 198776, 199680, 200582],\n", + " dtype='int64'), Int64Index([ 2204, 12430, 14283, 16084, 28849, 29756, 34441, 35357,\n", + " 36268, 37175, 39427, 40329, 47667, 51364, 53222, 54123,\n", + " 56931, 63103, 66423, 68250, 69155, 70052, 70959, 94493,\n", + " 102347, 103265, 106894, 111553, 114291, 118862, 124001, 124909,\n", + " 126717, 127610, 129476, 131281, 132204, 153896, 155725, 156648,\n", + " 158338, 159925, 162656, 165354, 166239, 170870, 172233, 174930,\n", + " 175848, 181704, 184938, 185839, 198777, 199681, 200583],\n", + " dtype='int64'), Int64Index([ 2205, 12431, 14284, 16085, 28850, 29757, 34442, 35358,\n", + " 36269, 37176, 39428, 40330, 47668, 51365, 53223, 54124,\n", + " 56932, 63104, 66424, 68251, 69156, 70053, 70960, 94494,\n", + " 102348, 103266, 106895, 111554, 114292, 118863, 124002, 124910,\n", + " 126718, 127611, 129477, 131282, 132205, 153897, 155726, 156649,\n", + " 158339, 159926, 162657, 165355, 166240, 170871, 172234, 174931,\n", + " 175849, 181705, 184939, 185840, 198778, 199682, 200584],\n", + " dtype='int64'), Int64Index([ 2206, 12432, 14285, 16086, 28851, 29758, 34443, 35359,\n", + " 36270, 37177, 39429, 40331, 47669, 51366, 53224, 54125,\n", + " 56933, 63105, 66425, 68252, 69157, 70054, 70961, 94495,\n", + " 102349, 103267, 106896, 111555, 114293, 118864, 124003, 124911,\n", + " 126719, 127612, 129478, 131283, 132206, 153898, 155727, 156650,\n", + " 158340, 159927, 162658, 165356, 166241, 170872, 172235, 174932,\n", + " 175850, 181706, 184940, 185841, 198779, 199683, 200585],\n", + " dtype='int64'), Int64Index([ 2207, 12433, 14286, 16087, 28852, 29759, 34444, 35360,\n", + " 36271, 37178, 39430, 40332, 47670, 51367, 53225, 54126,\n", + " 56934, 63106, 66426, 68253, 69158, 70055, 70962, 94496,\n", + " 102350, 103268, 106897, 111556, 114294, 118865, 124004, 124912,\n", + " 126720, 127613, 129479, 131284, 132207, 153899, 155728, 156651,\n", + " 158341, 159928, 162659, 165357, 166242, 170873, 172236, 174933,\n", + " 175851, 181707, 184941, 185842, 198780, 199684, 200586],\n", + " dtype='int64'), Int64Index([ 2208, 12434, 14287, 16088, 28853, 29760, 34445, 35361,\n", + " 36272, 37179, 39431, 40333, 47671, 51368, 53226, 54127,\n", + " 56935, 63107, 66427, 68254, 69159, 70056, 70963, 94497,\n", + " 102351, 103269, 106898, 111557, 114295, 118866, 124005, 124913,\n", + " 126721, 127614, 129480, 131285, 132208, 153900, 155729, 156652,\n", + " 158342, 159929, 162660, 165358, 166243, 170874, 172237, 174934,\n", + " 175852, 181708, 184942, 185843, 198781, 199685, 200587],\n", + " dtype='int64'), Int64Index([ 2209, 12435, 14288, 16089, 28854, 29761, 34446, 35362,\n", + " 36273, 37180, 39432, 40334, 47672, 51369, 53227, 54128,\n", + " 56936, 63108, 66428, 68255, 69160, 70057, 70964, 94498,\n", + " 102352, 103270, 106899, 111558, 114296, 118867, 124006, 124914,\n", + " 126722, 127615, 129481, 131286, 132209, 153901, 155730, 156653,\n", + " 158343, 159930, 162661, 165359, 166244, 170875, 172238, 174935,\n", + " 175853, 181709, 184943, 185844, 198782, 199686, 200588],\n", + " dtype='int64'), Int64Index([ 2210, 12436, 14289, 16090, 28855, 29762, 34447, 35363,\n", + " 36274, 37181, 39433, 40335, 47673, 51370, 53228, 54129,\n", + " 56937, 63109, 66429, 68256, 69161, 70058, 70965, 94499,\n", + " 102353, 103271, 106900, 111559, 114297, 118868, 124007, 124915,\n", + " 126723, 127616, 129482, 131287, 132210, 153902, 155731, 156654,\n", + " 158344, 159931, 162662, 165360, 166245, 170876, 172239, 174936,\n", + " 175854, 181710, 184944, 185845, 198783, 199687, 200589],\n", + " dtype='int64'), Int64Index([ 2211, 12437, 14290, 16091, 28856, 29763, 34448, 35364,\n", + " 36275, 37182, 39434, 40336, 47674, 51371, 53229, 54130,\n", + " 56938, 63110, 66430, 68257, 69162, 70059, 70966, 94500,\n", + " 102354, 103272, 106901, 111560, 114298, 118869, 124008, 124916,\n", + " 126724, 127617, 129483, 131288, 132211, 153903, 155732, 156655,\n", + " 158345, 159932, 162663, 165361, 166246, 170877, 172240, 174937,\n", + " 175855, 181711, 184945, 185846, 198784, 199688, 200590],\n", + " dtype='int64'), Int64Index([ 2212, 12438, 14291, 16092, 28857, 29764, 34449, 35365,\n", + " 36276, 37183, 39435, 40337, 47675, 51372, 53230, 54131,\n", + " 56939, 63111, 66431, 68258, 69163, 70060, 70967, 94501,\n", + " 102355, 103273, 106902, 111561, 114299, 118870, 124009, 124917,\n", + " 126725, 127618, 129484, 131289, 132212, 153904, 155733, 156656,\n", + " 158346, 159933, 162664, 165362, 166247, 170878, 172241, 174938,\n", + " 175856, 181712, 184946, 185847, 198785, 199689, 200591],\n", + " dtype='int64'), Int64Index([ 2213, 12439, 14292, 16093, 28858, 29765, 34450, 35366,\n", + " 36277, 37184, 39436, 40338, 47676, 51373, 53231, 54132,\n", + " 56940, 63112, 66432, 68259, 69164, 70061, 70968, 94502,\n", + " 102356, 103274, 106903, 111562, 114300, 118871, 124010, 124918,\n", + " 126726, 127619, 129485, 131290, 132213, 153905, 155734, 156657,\n", + " 158347, 159934, 162665, 165363, 166248, 170879, 172242, 174939,\n", + " 175857, 181713, 184947, 185848, 198786, 199690, 200592],\n", + " dtype='int64'), Int64Index([ 2214, 12440, 14293, 16094, 28859, 29766, 34451, 35367,\n", + " 36278, 37185, 39437, 40339, 47677, 51374, 53232, 54133,\n", + " 56941, 63113, 66433, 68260, 69165, 70062, 70969, 94503,\n", + " 102357, 103275, 106904, 111563, 114301, 118872, 124011, 124919,\n", + " 126727, 127620, 129486, 131291, 132214, 153906, 155735, 156658,\n", + " 158348, 159935, 162666, 165364, 166249, 170880, 172243, 174940,\n", + " 175858, 181714, 184948, 185849, 198787, 199691, 200593],\n", + " dtype='int64'), Int64Index([ 2215, 12441, 14294, 16095, 28860, 29767, 34452, 35368,\n", + " 36279, 37186, 39438, 40340, 47678, 51375, 53233, 54134,\n", + " 56942, 63114, 66434, 68261, 69166, 70063, 70970, 94504,\n", + " 102358, 103276, 106905, 111564, 114302, 118873, 124012, 124920,\n", + " 126728, 127621, 129487, 131292, 132215, 153907, 155736, 156659,\n", + " 158349, 159936, 162667, 165365, 166250, 170881, 172244, 174941,\n", + " 175859, 181715, 184949, 185850, 198788, 199692, 200594],\n", + " dtype='int64'), Int64Index([ 2216, 12442, 14295, 16096, 28861, 29768, 34453, 35369,\n", + " 36280, 37187, 39439, 40341, 47679, 51376, 53234, 54135,\n", + " 56943, 63115, 66435, 68262, 69167, 70064, 70971, 94505,\n", + " 102359, 103277, 106906, 111565, 114303, 118874, 124013, 124921,\n", + " 126729, 127622, 129488, 131293, 132216, 153908, 155737, 156660,\n", + " 158350, 159937, 162668, 165366, 166251, 170882, 172245, 174942,\n", + " 175860, 181716, 184950, 185851, 198789, 199693, 200595],\n", + " dtype='int64'), Int64Index([ 2217, 12443, 14296, 16097, 28862, 29769, 34454, 35370,\n", + " 36281, 37188, 39440, 40342, 47680, 51377, 53235, 54136,\n", + " 56944, 63116, 66436, 68263, 69168, 70065, 70972, 94506,\n", + " 102360, 103278, 106907, 111566, 114304, 118875, 124014, 124922,\n", + " 126730, 127623, 129489, 131294, 132217, 153909, 155738, 156661,\n", + " 158351, 159938, 162669, 165367, 166252, 170883, 172246, 174943,\n", + " 175861, 181717, 184951, 185852, 198790, 199694, 200596],\n", + " dtype='int64'), Int64Index([ 2218, 12444, 14297, 16098, 28863, 29770, 34455, 35371,\n", + " 36282, 37189, 39441, 40343, 47681, 51378, 53236, 54137,\n", + " 56945, 63117, 66437, 68264, 69169, 70066, 70973, 94507,\n", + " 102361, 103279, 106908, 111567, 114305, 118876, 124015, 124923,\n", + " 126731, 127624, 129490, 131295, 132218, 153910, 155739, 156662,\n", + " 158352, 159939, 162670, 165368, 166253, 170884, 172247, 174944,\n", + " 175862, 181718, 184952, 185853, 198791, 199695, 200597],\n", + " dtype='int64'), Int64Index([ 2219, 12445, 14298, 16099, 28864, 29771, 34456, 35372,\n", + " 36283, 37190, 39442, 40344, 47682, 51379, 53237, 54138,\n", + " 56946, 63118, 66438, 68265, 69170, 70067, 70974, 94508,\n", + " 102362, 103280, 106909, 111568, 114306, 118877, 124016, 124924,\n", + " 126732, 127625, 129491, 131296, 132219, 153911, 155740, 156663,\n", + " 158353, 159940, 162671, 165369, 166254, 170885, 172248, 174945,\n", + " 175863, 181719, 184953, 185854, 198792, 199696, 200598],\n", + " dtype='int64'), Int64Index([ 2220, 12446, 14299, 16100, 28865, 29772, 34457, 35373,\n", + " 36284, 37191, 39443, 40345, 47683, 51380, 53238, 54139,\n", + " 56947, 63119, 66439, 68266, 69171, 70068, 70975, 94509,\n", + " 102363, 103281, 106910, 111569, 114307, 118878, 124017, 124925,\n", + " 126733, 127626, 129492, 131297, 132220, 153912, 155741, 156664,\n", + " 158354, 159941, 162672, 165370, 166255, 170886, 172249, 174946,\n", + " 175864, 181720, 184954, 185855, 198793, 199697, 200599],\n", + " dtype='int64'), Int64Index([ 2221, 12447, 14300, 16101, 28866, 29773, 34458, 35374,\n", + " 36285, 37192, 39444, 40346, 47684, 51381, 53239, 54140,\n", + " 56948, 63120, 66440, 68267, 69172, 70069, 70976, 94510,\n", + " 102364, 103282, 106911, 111570, 114308, 118879, 124018, 124926,\n", + " 126734, 127627, 129493, 131298, 132221, 153913, 155742, 156665,\n", + " 158355, 159942, 162673, 165371, 166256, 170887, 172250, 174947,\n", + " 175865, 181721, 184955, 185856, 198794, 199698, 200600],\n", + " dtype='int64'), Int64Index([ 2222, 12448, 14301, 16102, 28867, 29774, 34459, 35375,\n", + " 36286, 37193, 39445, 40347, 47685, 51382, 53240, 54141,\n", + " 56949, 63121, 66441, 68268, 69173, 70070, 70977, 94511,\n", + " 102365, 103283, 106912, 111571, 114309, 118880, 124019, 124927,\n", + " 126735, 127628, 129494, 131299, 132222, 153914, 155743, 156666,\n", + " 158356, 159943, 162674, 165372, 166257, 170888, 172251, 174948,\n", + " 175866, 181722, 184956, 185857, 198795, 199699, 200601],\n", + " dtype='int64'), Int64Index([ 2223, 12449, 14302, 16103, 28868, 29775, 34460, 35376,\n", + " 36287, 37194, 39446, 40348, 47686, 51383, 53241, 54142,\n", + " 56950, 63122, 66442, 68269, 69174, 70071, 70978, 94512,\n", + " 102366, 103284, 106913, 111572, 114310, 118881, 124020, 124928,\n", + " 126736, 127629, 129495, 131300, 132223, 153915, 155744, 156667,\n", + " 158357, 159944, 162675, 165373, 166258, 170889, 172252, 174949,\n", + " 175867, 181723, 184957, 185858, 198796, 199700, 200602],\n", + " dtype='int64'), Int64Index([ 2224, 12450, 14303, 16104, 28869, 29776, 34461, 35377,\n", + " 36288, 37195, 39447, 40349, 47687, 51384, 53242, 54143,\n", + " 56951, 63123, 66443, 68270, 69175, 70072, 70979, 94513,\n", + " 102367, 103285, 106914, 111573, 114311, 118882, 124021, 124929,\n", + " 126737, 127630, 129496, 131301, 132224, 153916, 155745, 156668,\n", + " 158358, 159945, 162676, 165374, 166259, 170890, 172253, 174950,\n", + " 175868, 181724, 184958, 185859, 198797, 199701, 200603],\n", + " dtype='int64'), Int64Index([ 2225, 12451, 14304, 16105, 28870, 29777, 34462, 35378,\n", + " 36289, 37196, 39448, 40350, 47688, 51385, 53243, 54144,\n", + " 56952, 63124, 66444, 68271, 69176, 70073, 70980, 94514,\n", + " 102368, 103286, 106915, 111574, 114312, 118883, 124022, 124930,\n", + " 126738, 127631, 129497, 131302, 132225, 153917, 155746, 156669,\n", + " 158359, 159946, 162677, 165375, 166260, 170891, 172254, 174951,\n", + " 175869, 181725, 184959, 185860, 198798, 199702, 200604],\n", + " dtype='int64'), Int64Index([ 2226, 12452, 14305, 16106, 28871, 29778, 34463, 35379,\n", + " 36290, 37197, 39449, 40351, 47689, 51386, 53244, 54145,\n", + " 56953, 63125, 66445, 68272, 69177, 70074, 70981, 94515,\n", + " 102369, 103287, 106916, 111575, 114313, 118884, 124023, 124931,\n", + " 126739, 127632, 129498, 131303, 132226, 153918, 155747, 156670,\n", + " 158360, 159947, 162678, 165376, 166261, 170892, 172255, 174952,\n", + " 175870, 181726, 184960, 185861, 198799, 199703, 200605],\n", + " dtype='int64'), Int64Index([ 2227, 12453, 14306, 16107, 28872, 29779, 34464, 35380,\n", + " 36291, 37198, 39450, 40352, 47690, 51387, 53245, 54146,\n", + " 56954, 63126, 66446, 68273, 69178, 70075, 70982, 94516,\n", + " 102370, 103288, 106917, 111576, 114314, 118885, 124024, 124932,\n", + " 126740, 127633, 129499, 131304, 132227, 153919, 155748, 156671,\n", + " 158361, 159948, 162679, 165377, 166262, 170893, 172256, 174953,\n", + " 175871, 181727, 184961, 185862, 198800, 199704, 200606],\n", + " dtype='int64'), Int64Index([ 2228, 12454, 14307, 16108, 28873, 29780, 34465, 35381,\n", + " 36292, 37199, 39451, 40353, 47691, 51388, 53246, 54147,\n", + " 56955, 63127, 66447, 68274, 69179, 70076, 70983, 94517,\n", + " 102371, 103289, 106918, 111577, 114315, 118886, 124025, 124933,\n", + " 126741, 127634, 129500, 131305, 132228, 153920, 155749, 156672,\n", + " 158362, 159949, 162680, 165378, 166263, 170894, 172257, 174954,\n", + " 175872, 181728, 184962, 185863, 198801, 199705, 200607],\n", + " dtype='int64'), Int64Index([ 2229, 12455, 14308, 16109, 28874, 29781, 34466, 35382,\n", + " 36293, 37200, 39452, 40354, 47692, 51389, 53247, 54148,\n", + " 56956, 63128, 66448, 68275, 69180, 70077, 70984, 94518,\n", + " 102372, 103290, 106919, 111578, 114316, 118887, 124026, 124934,\n", + " 126742, 127635, 129501, 131306, 132229, 153921, 155750, 156673,\n", + " 158363, 159950, 162681, 165379, 166264, 170895, 172258, 174955,\n", + " 175873, 181729, 184963, 185864, 198802, 199706, 200608],\n", + " dtype='int64'), Int64Index([ 2230, 12456, 14309, 16110, 28875, 29782, 34467, 35383,\n", + " 36294, 37201, 39453, 40355, 47693, 51390, 53248, 54149,\n", + " 56957, 63129, 66449, 68276, 69181, 70078, 70985, 94519,\n", + " 102373, 103291, 106920, 111579, 114317, 118888, 124027, 124935,\n", + " 126743, 127636, 129502, 131307, 132230, 153922, 155751, 156674,\n", + " 158364, 159951, 162682, 165380, 166265, 170896, 172259, 174956,\n", + " 175874, 181730, 184964, 185865, 198803, 199707, 200609],\n", + " dtype='int64'), Int64Index([ 2231, 12457, 14310, 16111, 28876, 29783, 34468, 35384,\n", + " 36295, 37202, 39454, 40356, 47694, 51391, 53249, 54150,\n", + " 56958, 63130, 66450, 68277, 69182, 70079, 70986, 94520,\n", + " 102374, 103292, 106921, 111580, 114318, 118889, 124028, 124936,\n", + " 126744, 127637, 129503, 131308, 132231, 153923, 155752, 156675,\n", + " 158365, 159952, 162683, 165381, 166266, 170897, 172260, 174957,\n", + " 175875, 181731, 184965, 185866, 198804, 199708, 200610],\n", + " dtype='int64'), Int64Index([ 2232, 12458, 14311, 16112, 28877, 29784, 34469, 35385,\n", + " 36296, 37203, 39455, 40357, 47695, 51392, 53250, 54151,\n", + " 56959, 63131, 66451, 68278, 69183, 70080, 70987, 94521,\n", + " 102375, 103293, 106922, 111581, 114319, 118890, 124029, 124937,\n", + " 126745, 127638, 129504, 131309, 132232, 153924, 155753, 156676,\n", + " 158366, 159953, 162684, 165382, 166267, 170898, 172261, 174958,\n", + " 175876, 181732, 184966, 185867, 198805, 199709, 200611],\n", + " dtype='int64'), Int64Index([ 2233, 12459, 14312, 16113, 28878, 29785, 34470, 35386,\n", + " 36297, 37204, 39456, 40358, 47696, 51393, 53251, 54152,\n", + " 56960, 63132, 66452, 68279, 69184, 70081, 70988, 94522,\n", + " 102376, 103294, 106923, 111582, 114320, 118891, 124030, 124938,\n", + " 126746, 127639, 129505, 131310, 132233, 153925, 155754, 156677,\n", + " 158367, 159954, 162685, 165383, 166268, 170899, 172262, 174959,\n", + " 175877, 181733, 184967, 185868, 198806, 199710, 200612],\n", + " dtype='int64'), Int64Index([ 2234, 12460, 14313, 16114, 28879, 29786, 34471, 35387,\n", + " 36298, 37205, 39457, 40359, 47697, 51394, 53252, 54153,\n", + " 56961, 63133, 66453, 68280, 69185, 70082, 70989, 94523,\n", + " 102377, 103295, 106924, 111583, 114321, 118892, 124031, 124939,\n", + " 126747, 127640, 129506, 131311, 132234, 153926, 155755, 156678,\n", + " 158368, 159955, 162686, 165384, 166269, 170900, 172263, 174960,\n", + " 175878, 181734, 184968, 185869, 198807, 199711, 200613],\n", + " dtype='int64'), Int64Index([ 2235, 12461, 14314, 16115, 28880, 29787, 34472, 35388,\n", + " 36299, 37206, 39458, 40360, 47698, 51395, 53253, 54154,\n", + " 56962, 63134, 66454, 68281, 69186, 70083, 70990, 94524,\n", + " 102378, 103296, 106925, 111584, 114322, 118893, 124032, 124940,\n", + " 126748, 127641, 129507, 131312, 132235, 153927, 155756, 156679,\n", + " 158369, 159956, 162687, 165385, 166270, 170901, 172264, 174961,\n", + " 175879, 181735, 184969, 185870, 198808, 199712, 200614],\n", + " dtype='int64'), Int64Index([ 2236, 12462, 14315, 16116, 28881, 29788, 34473, 35389,\n", + " 36300, 37207, 39459, 40361, 47699, 51396, 53254, 54155,\n", + " 56963, 63135, 66455, 68282, 69187, 70084, 70991, 94525,\n", + " 102379, 103297, 106926, 111585, 114323, 118894, 124033, 124941,\n", + " 126749, 127642, 129508, 131313, 132236, 153928, 155757, 156680,\n", + " 158370, 159957, 162688, 165386, 166271, 170902, 172265, 174962,\n", + " 175880, 181736, 184970, 185871, 198809, 199713, 200615],\n", + " dtype='int64'), Int64Index([ 2237, 12463, 14316, 16117, 28882, 29789, 34474, 35390,\n", + " 36301, 37208, 39460, 40362, 47700, 51397, 53255, 54156,\n", + " 56964, 63136, 66456, 68283, 69188, 70085, 70992, 94526,\n", + " 102380, 103298, 106927, 111586, 114324, 118895, 124034, 124942,\n", + " 126750, 127643, 129509, 131314, 132237, 153929, 155758, 156681,\n", + " 158371, 159958, 162689, 165387, 166272, 170903, 172266, 174963,\n", + " 175881, 181737, 184971, 185872, 198810, 199714, 200616],\n", + " dtype='int64'), Int64Index([ 2238, 12464, 14317, 16118, 28883, 29790, 34475, 35391,\n", + " 36302, 37209, 39461, 40363, 47701, 51398, 53256, 54157,\n", + " 56965, 63137, 66457, 68284, 69189, 70086, 70993, 94527,\n", + " 102381, 103299, 106928, 111587, 114325, 118896, 124035, 124943,\n", + " 126751, 127644, 129510, 131315, 132238, 153930, 155759, 156682,\n", + " 158372, 159959, 162690, 165388, 166273, 170904, 172267, 174964,\n", + " 175882, 181738, 184972, 185873, 198811, 199715, 200617],\n", + " dtype='int64'), Int64Index([ 2239, 12465, 14318, 16119, 28884, 29791, 34476, 35392,\n", + " 36303, 37210, 39462, 40364, 47702, 51399, 53257, 54158,\n", + " 56966, 63138, 66458, 68285, 69190, 70087, 70994, 94528,\n", + " 102382, 103300, 106929, 111588, 114326, 118897, 124036, 124944,\n", + " 126752, 127645, 129511, 131316, 132239, 153931, 155760, 156683,\n", + " 158373, 159960, 162691, 165389, 166274, 170905, 172268, 174965,\n", + " 175883, 181739, 184973, 185874, 198812, 199716, 200618],\n", + " dtype='int64'), Int64Index([ 2240, 12466, 14319, 16120, 28885, 29792, 34477, 35393,\n", + " 36304, 37211, 39463, 40365, 47703, 51400, 53258, 54159,\n", + " 56967, 63139, 66459, 68286, 69191, 70088, 70995, 94529,\n", + " 102383, 103301, 106930, 111589, 114327, 118898, 124037, 124945,\n", + " 126753, 127646, 129512, 131317, 132240, 153932, 155761, 156684,\n", + " 158374, 159961, 162692, 165390, 166275, 170906, 172269, 174966,\n", + " 175884, 181740, 184974, 185875, 198813, 199717, 200619],\n", + " dtype='int64'), Int64Index([ 2241, 12467, 14320, 16121, 28886, 29793, 34478, 35394,\n", + " 36305, 37212, 39464, 40366, 47704, 51401, 53259, 54160,\n", + " 56968, 63140, 66460, 68287, 69192, 70089, 70996, 94530,\n", + " 102384, 103302, 106931, 111590, 114328, 118899, 124038, 124946,\n", + " 126754, 127647, 129513, 131318, 132241, 153933, 155762, 156685,\n", + " 158375, 159962, 162693, 165391, 166276, 170907, 172270, 174967,\n", + " 175885, 181741, 184975, 185876, 198814, 199718, 200620],\n", + " dtype='int64'), Int64Index([ 2242, 12468, 14321, 16122, 28887, 29794, 34479, 35395,\n", + " 36306, 37213, 39465, 40367, 47705, 51402, 53260, 54161,\n", + " 56969, 63141, 66461, 68288, 69193, 70090, 70997, 94531,\n", + " 102385, 103303, 106932, 111591, 114329, 118900, 124039, 124947,\n", + " 126755, 127648, 129514, 131319, 132242, 153934, 155763, 156686,\n", + " 158376, 159963, 162694, 165392, 166277, 170908, 172271, 174968,\n", + " 175886, 181742, 184976, 185877, 198815, 199719, 200621],\n", + " dtype='int64'), Int64Index([ 2243, 12469, 14322, 16123, 28888, 29795, 34480, 35396,\n", + " 36307, 37214, 39466, 40368, 47706, 51403, 53261, 54162,\n", + " 56970, 63142, 66462, 68289, 69194, 70091, 70998, 94532,\n", + " 102386, 103304, 106933, 111592, 114330, 118901, 124040, 124948,\n", + " 126756, 127649, 129515, 131320, 132243, 153935, 155764, 156687,\n", + " 158377, 159964, 162695, 165393, 166278, 170909, 172272, 174969,\n", + " 175887, 181743, 184977, 185878, 198816, 199720, 200622],\n", + " dtype='int64'), Int64Index([ 2244, 12470, 14323, 16124, 28889, 29796, 34481, 35397,\n", + " 36308, 37215, 39467, 40369, 47707, 51404, 53262, 54163,\n", + " 56971, 63143, 66463, 68290, 69195, 70092, 70999, 94533,\n", + " 102387, 103305, 106934, 111593, 114331, 118902, 124041, 124949,\n", + " 126757, 127650, 129516, 131321, 132244, 153936, 155765, 156688,\n", + " 158378, 159965, 162696, 165394, 166279, 170910, 172273, 174970,\n", + " 175888, 181744, 184978, 185879, 198817, 199721, 200623],\n", + " dtype='int64'), Int64Index([ 2245, 12471, 14324, 16125, 28890, 29797, 34482, 35398,\n", + " 36309, 37216, 39468, 40370, 47708, 51405, 53263, 54164,\n", + " 56972, 63144, 66464, 68291, 69196, 70093, 71000, 94534,\n", + " 102388, 103306, 106935, 111594, 114332, 118903, 124042, 124950,\n", + " 126758, 127651, 129517, 131322, 132245, 153937, 155766, 156689,\n", + " 158379, 159966, 162697, 165395, 166280, 170911, 172274, 174971,\n", + " 175889, 181745, 184979, 185880, 198818, 199722, 200624],\n", + " dtype='int64'), Int64Index([ 2246, 12472, 14325, 16126, 28891, 29798, 34483, 35399,\n", + " 36310, 37217, 39469, 40371, 47709, 51406, 53264, 54165,\n", + " 56973, 63145, 66465, 68292, 69197, 70094, 71001, 94535,\n", + " 102389, 103307, 106936, 111595, 114333, 118904, 124043, 124951,\n", + " 126759, 127652, 129518, 131323, 132246, 153938, 155767, 156690,\n", + " 158380, 159967, 162698, 165396, 166281, 170912, 172275, 174972,\n", + " 175890, 181746, 184980, 185881, 198819, 199723, 200625],\n", + " dtype='int64'), Int64Index([ 2247, 12473, 14326, 16127, 28892, 29799, 34484, 35400,\n", + " 36311, 37218, 39470, 40372, 47710, 51407, 53265, 54166,\n", + " 56974, 63146, 66466, 68293, 69198, 70095, 71002, 94536,\n", + " 102390, 103308, 106937, 111596, 114334, 118905, 124044, 124952,\n", + " 126760, 127653, 129519, 131324, 132247, 153939, 155768, 156691,\n", + " 158381, 159968, 162699, 165397, 166282, 170913, 172276, 174973,\n", + " 175891, 181747, 184981, 185882, 198820, 199724, 200626],\n", + " dtype='int64'), Int64Index([ 2248, 12474, 14327, 16128, 28893, 29800, 34485, 35401,\n", + " 36312, 37219, 39471, 40373, 47711, 51408, 53266, 54167,\n", + " 56975, 63147, 66467, 68294, 69199, 70096, 71003, 94537,\n", + " 102391, 103309, 106938, 111597, 114335, 118906, 124045, 124953,\n", + " 126761, 127654, 129520, 131325, 132248, 153940, 155769, 156692,\n", + " 158382, 159969, 162700, 165398, 166283, 170914, 172277, 174974,\n", + " 175892, 181748, 184982, 185883, 198821, 199725, 200627],\n", + " dtype='int64'), Int64Index([ 2249, 12475, 14328, 16129, 28894, 29801, 34486, 35402,\n", + " 36313, 37220, 39472, 40374, 47712, 51409, 53267, 54168,\n", + " 56976, 63148, 66468, 68295, 69200, 70097, 71004, 94538,\n", + " 102392, 103310, 106939, 111598, 114336, 118907, 124046, 124954,\n", + " 126762, 127655, 129521, 131326, 132249, 153941, 155770, 156693,\n", + " 158383, 159970, 162701, 165399, 166284, 170915, 172278, 174975,\n", + " 175893, 181749, 184983, 185884, 198822, 199726, 200628],\n", + " dtype='int64'), Int64Index([ 2250, 12476, 14329, 16130, 28895, 29802, 34487, 35403,\n", + " 36314, 37221, 39473, 40375, 47713, 51410, 53268, 54169,\n", + " 56977, 63149, 66469, 68296, 69201, 70098, 71005, 94539,\n", + " 102393, 103311, 106940, 111599, 114337, 118908, 124047, 124955,\n", + " 126763, 127656, 129522, 131327, 132250, 153942, 155771, 156694,\n", + " 158384, 159971, 162702, 165400, 166285, 170916, 172279, 174976,\n", + " 175894, 181750, 184984, 185885, 198823, 199727, 200629],\n", + " dtype='int64'), Int64Index([ 2251, 12477, 14330, 16131, 28896, 29803, 34488, 35404,\n", + " 36315, 37222, 39474, 40376, 47714, 51411, 53269, 54170,\n", + " 56978, 63150, 66470, 68297, 69202, 70099, 71006, 94540,\n", + " 102394, 103312, 106941, 111600, 114338, 118909, 124048, 124956,\n", + " 126764, 127657, 129523, 131328, 132251, 153943, 155772, 156695,\n", + " 158385, 159972, 162703, 165401, 166286, 170917, 172280, 174977,\n", + " 175895, 181751, 184985, 185886, 198824, 199728, 200630],\n", + " dtype='int64'), Int64Index([ 2252, 12478, 14331, 16132, 28897, 29804, 34489, 35405,\n", + " 36316, 37223, 39475, 40377, 47715, 51412, 53270, 54171,\n", + " 56979, 63151, 66471, 68298, 69203, 70100, 71007, 94541,\n", + " 102395, 103313, 106942, 111601, 114339, 118910, 124049, 124957,\n", + " 126765, 127658, 129524, 131329, 132252, 153944, 155773, 156696,\n", + " 158386, 159973, 162704, 165402, 166287, 170918, 172281, 174978,\n", + " 175896, 181752, 184986, 185887, 198825, 199729, 200631],\n", + " dtype='int64'), Int64Index([ 2253, 12479, 14332, 16133, 28898, 29805, 34490, 35406,\n", + " 36317, 37224, 39476, 40378, 47716, 51413, 53271, 54172,\n", + " 56980, 63152, 66472, 68299, 69204, 70101, 71008, 94542,\n", + " 102396, 103314, 106943, 111602, 114340, 118911, 124050, 124958,\n", + " 126766, 127659, 129525, 131330, 132253, 153945, 155774, 156697,\n", + " 158387, 159974, 162705, 165403, 166288, 170919, 172282, 174979,\n", + " 175897, 181753, 184987, 185888, 198826, 199730, 200632],\n", + " dtype='int64'), Int64Index([ 2254, 12480, 14333, 16134, 28899, 29806, 34491, 35407,\n", + " 36318, 37225, 39477, 40379, 47717, 51414, 53272, 54173,\n", + " 56981, 63153, 66473, 68300, 69205, 70102, 71009, 94543,\n", + " 102397, 103315, 106944, 111603, 114341, 118912, 124051, 124959,\n", + " 126767, 127660, 129526, 131331, 132254, 153946, 155775, 156698,\n", + " 158388, 159975, 162706, 165404, 166289, 170920, 172283, 174980,\n", + " 175898, 181754, 184988, 185889, 198827, 199731, 200633],\n", + " dtype='int64'), Int64Index([ 2255, 12481, 14334, 16135, 28900, 29807, 34492, 35408,\n", + " 36319, 37226, 39478, 40380, 47718, 51415, 53273, 54174,\n", + " 56982, 63154, 66474, 68301, 69206, 70103, 71010, 94544,\n", + " 102398, 103316, 106945, 111604, 114342, 118913, 124052, 124960,\n", + " 126768, 127661, 129527, 131332, 132255, 153947, 155776, 156699,\n", + " 158389, 159976, 162707, 165405, 166290, 170921, 172284, 174981,\n", + " 175899, 181755, 184989, 185890, 198828, 199732, 200634],\n", + " dtype='int64'), Int64Index([ 2256, 12482, 14335, 16136, 28901, 29808, 34493, 35409,\n", + " 36320, 37227, 39479, 40381, 47719, 51416, 53274, 54175,\n", + " 56983, 63155, 66475, 68302, 69207, 70104, 71011, 94545,\n", + " 102399, 103317, 106946, 111605, 114343, 118914, 124053, 124961,\n", + " 126769, 127662, 129528, 131333, 132256, 153948, 155777, 156700,\n", + " 158390, 159977, 162708, 165406, 166291, 170922, 172285, 174982,\n", + " 175900, 181756, 184990, 185891, 198829, 199733, 200635],\n", + " dtype='int64'), Int64Index([ 2257, 12483, 14336, 16137, 28902, 29809, 34494, 35410,\n", + " 36321, 37228, 39480, 40382, 47720, 51417, 53275, 54176,\n", + " 56984, 63156, 66476, 68303, 69208, 70105, 71012, 94546,\n", + " 102400, 103318, 106947, 111606, 114344, 118915, 124054, 124962,\n", + " 126770, 127663, 129529, 131334, 132257, 153949, 155778, 156701,\n", + " 158391, 159978, 162709, 165407, 166292, 170923, 172286, 174983,\n", + " 175901, 181757, 184991, 185892, 198830, 199734, 200636],\n", + " dtype='int64'), Int64Index([ 2258, 12484, 14337, 16138, 28903, 29810, 34495, 35411,\n", + " 36322, 37229, 39481, 40383, 47721, 51418, 53276, 54177,\n", + " 56985, 63157, 66477, 68304, 69209, 70106, 71013, 94547,\n", + " 102401, 103319, 106948, 111607, 114345, 118916, 124055, 124963,\n", + " 126771, 127664, 129530, 131335, 132258, 153950, 155779, 156702,\n", + " 158392, 159979, 162710, 165408, 166293, 170924, 172287, 174984,\n", + " 175902, 181758, 184992, 185893, 198831, 199735, 200637],\n", + " dtype='int64'), Int64Index([ 2259, 12485, 14338, 16139, 28904, 29811, 34496, 35412,\n", + " 36323, 37230, 39482, 40384, 47722, 51419, 53277, 54178,\n", + " 56986, 63158, 66478, 68305, 69210, 70107, 71014, 94548,\n", + " 102402, 103320, 106949, 111608, 114346, 118917, 124056, 124964,\n", + " 126772, 127665, 129531, 131336, 132259, 153951, 155780, 156703,\n", + " 158393, 159980, 162711, 165409, 166294, 170925, 172288, 174985,\n", + " 175903, 181759, 184993, 185894, 198832, 199736, 200638],\n", + " dtype='int64'), Int64Index([ 2260, 12486, 14339, 16140, 28905, 29812, 34497, 35413,\n", + " 36324, 37231, 39483, 40385, 47723, 51420, 53278, 54179,\n", + " 56987, 63159, 66479, 68306, 69211, 70108, 71015, 94549,\n", + " 102403, 103321, 106950, 111609, 114347, 118918, 124057, 124965,\n", + " 126773, 127666, 129532, 131337, 132260, 153952, 155781, 156704,\n", + " 158394, 159981, 162712, 165410, 166295, 170926, 172289, 174986,\n", + " 175904, 181760, 184994, 185895, 198833, 199737, 200639],\n", + " dtype='int64'), Int64Index([ 2261, 12487, 14340, 16141, 28906, 29813, 34498, 35414,\n", + " 36325, 37232, 39484, 40386, 47724, 51421, 53279, 54180,\n", + " 56988, 63160, 66480, 68307, 69212, 70109, 71016, 94550,\n", + " 102404, 103322, 106951, 111610, 114348, 118919, 124058, 124966,\n", + " 126774, 127667, 129533, 131338, 132261, 153953, 155782, 156705,\n", + " 158395, 159982, 162713, 165411, 166296, 170927, 172290, 174987,\n", + " 175905, 181761, 184995, 185896, 198834, 199738, 200640],\n", + " dtype='int64'), Int64Index([ 2262, 12488, 14341, 16142, 28907, 29814, 34499, 35415,\n", + " 36326, 37233, 39485, 40387, 47725, 51422, 53280, 54181,\n", + " 56989, 63161, 66481, 68308, 69213, 70110, 71017, 94551,\n", + " 102405, 103323, 106952, 111611, 114349, 118920, 124059, 124967,\n", + " 126775, 127668, 129534, 131339, 132262, 153954, 155783, 156706,\n", + " 158396, 159983, 162714, 165412, 166297, 170928, 172291, 174988,\n", + " 175906, 181762, 184996, 185897, 198835, 199739, 200641],\n", + " dtype='int64'), Int64Index([ 2263, 12489, 14342, 16143, 28908, 29815, 34500, 35416,\n", + " 36327, 37234, 39486, 40388, 47726, 51423, 53281, 54182,\n", + " 56990, 63162, 66482, 68309, 69214, 70111, 71018, 94552,\n", + " 102406, 103324, 106953, 111612, 114350, 118921, 124060, 124968,\n", + " 126776, 127669, 129535, 131340, 132263, 153955, 155784, 156707,\n", + " 158397, 159984, 162715, 165413, 166298, 170929, 172292, 174989,\n", + " 175907, 181763, 184997, 185898, 198836, 199740, 200642],\n", + " dtype='int64'), Int64Index([ 2264, 12490, 14343, 16144, 28909, 29816, 34501, 35417,\n", + " 36328, 37235, 39487, 40389, 47727, 51424, 53282, 54183,\n", + " 56991, 63163, 66483, 68310, 69215, 70112, 71019, 94553,\n", + " 102407, 103325, 106954, 111613, 114351, 118922, 124061, 124969,\n", + " 126777, 127670, 129536, 131341, 132264, 153956, 155785, 156708,\n", + " 158398, 159985, 162716, 165414, 166299, 170930, 172293, 174990,\n", + " 175908, 181764, 184998, 185899, 198837, 199741, 200643],\n", + " dtype='int64'), Int64Index([ 2265, 12491, 14344, 16145, 28910, 29817, 34502, 35418,\n", + " 36329, 37236, 39488, 40390, 47728, 51425, 53283, 54184,\n", + " 56992, 63164, 66484, 68311, 69216, 70113, 71020, 94554,\n", + " 102408, 103326, 106955, 111614, 114352, 118923, 124062, 124970,\n", + " 126778, 127671, 129537, 131342, 132265, 153957, 155786, 156709,\n", + " 158399, 159986, 162717, 165415, 166300, 170931, 172294, 174991,\n", + " 175909, 181765, 184999, 185900, 198838, 199742, 200644],\n", + " dtype='int64'), Int64Index([ 2266, 12492, 14345, 16146, 28911, 29818, 34503, 35419,\n", + " 36330, 37237, 39489, 40391, 47729, 51426, 53284, 54185,\n", + " 56993, 63165, 66485, 68312, 69217, 70114, 71021, 94555,\n", + " 102409, 103327, 106956, 111615, 114353, 118924, 124063, 124971,\n", + " 126779, 127672, 129538, 131343, 132266, 153958, 155787, 156710,\n", + " 158400, 159987, 162718, 165416, 166301, 170932, 172295, 174992,\n", + " 175910, 181766, 185000, 185901, 198839, 199743, 200645],\n", + " dtype='int64'), Int64Index([ 2267, 12493, 14346, 16147, 28912, 29819, 34504, 35420,\n", + " 36331, 37238, 39490, 40392, 47730, 51427, 53285, 54186,\n", + " 56994, 63166, 66486, 68313, 69218, 70115, 71022, 94556,\n", + " 102410, 103328, 106957, 111616, 114354, 118925, 124064, 124972,\n", + " 126780, 127673, 129539, 131344, 132267, 153959, 155788, 156711,\n", + " 158401, 159988, 162719, 165417, 166302, 170933, 172296, 174993,\n", + " 175911, 181767, 185001, 185902, 198840, 199744, 200646],\n", + " dtype='int64'), Int64Index([ 2268, 12494, 14347, 16148, 28913, 29820, 34505, 35421,\n", + " 36332, 37239, 39491, 40393, 47731, 51428, 53286, 54187,\n", + " 56995, 63167, 66487, 68314, 69219, 70116, 71023, 94557,\n", + " 102411, 103329, 106958, 111617, 114355, 118926, 124065, 124973,\n", + " 126781, 127674, 129540, 131345, 132268, 153960, 155789, 156712,\n", + " 158402, 159989, 162720, 165418, 166303, 170934, 172297, 174994,\n", + " 175912, 181768, 185002, 185903, 198841, 199745, 200647],\n", + " dtype='int64'), Int64Index([ 2269, 12495, 14348, 16149, 28914, 29821, 34506, 35422,\n", + " 36333, 37240, 39492, 40394, 47732, 51429, 53287, 54188,\n", + " 56996, 63168, 66488, 68315, 69220, 70117, 71024, 94558,\n", + " 102412, 103330, 106959, 111618, 114356, 118927, 124066, 124974,\n", + " 126782, 127675, 129541, 131346, 132269, 153961, 155790, 156713,\n", + " 158403, 159990, 162721, 165419, 166304, 170935, 172298, 174995,\n", + " 175913, 181769, 185003, 185904, 198842, 199746, 200648],\n", + " dtype='int64'), Int64Index([ 2270, 12496, 14349, 16150, 28915, 29822, 34507, 35423,\n", + " 36334, 37241, 39493, 40395, 47733, 51430, 53288, 54189,\n", + " 56997, 63169, 66489, 68316, 69221, 70118, 71025, 94559,\n", + " 102413, 103331, 106960, 111619, 114357, 118928, 124067, 124975,\n", + " 126783, 127676, 129542, 131347, 132270, 153962, 155791, 156714,\n", + " 158404, 159991, 162722, 165420, 166305, 170936, 172299, 174996,\n", + " 175914, 181770, 185004, 185905, 198843, 199747, 200649],\n", + " dtype='int64'), Int64Index([ 2271, 12497, 14350, 16151, 28916, 29823, 34508, 35424,\n", + " 36335, 37242, 39494, 40396, 47734, 51431, 53289, 54190,\n", + " 56998, 63170, 66490, 68317, 69222, 70119, 71026, 94560,\n", + " 102414, 103332, 106961, 111620, 114358, 118929, 124068, 124976,\n", + " 126784, 127677, 129543, 131348, 132271, 153963, 155792, 156715,\n", + " 158405, 159992, 162723, 165421, 166306, 170937, 172300, 174997,\n", + " 175915, 181771, 185005, 185906, 198844, 199748, 200650],\n", + " dtype='int64'), Int64Index([ 2272, 12498, 14351, 16152, 28917, 29824, 34509, 35425,\n", + " 36336, 37243, 39495, 40397, 47735, 51432, 53290, 54191,\n", + " 56999, 63171, 66491, 68318, 69223, 70120, 71027, 94561,\n", + " 102415, 103333, 106962, 111621, 114359, 118930, 124069, 124977,\n", + " 126785, 127678, 129544, 131349, 132272, 153964, 155793, 156716,\n", + " 158406, 159993, 162724, 165422, 166307, 170938, 172301, 174998,\n", + " 175916, 181772, 185006, 185907, 198845, 199749, 200651],\n", + " dtype='int64'), Int64Index([ 2273, 12499, 14352, 16153, 28918, 29825, 34510, 35426,\n", + " 36337, 37244, 39496, 40398, 47736, 51433, 53291, 54192,\n", + " 57000, 63172, 66492, 68319, 69224, 70121, 71028, 94562,\n", + " 102416, 103334, 106963, 111622, 114360, 118931, 124070, 124978,\n", + " 126786, 127679, 129545, 131350, 132273, 153965, 155794, 156717,\n", + " 158407, 159994, 162725, 165423, 166308, 170939, 172302, 174999,\n", + " 175917, 181773, 185007, 185908, 198846, 199750, 200652],\n", + " dtype='int64'), Int64Index([ 2274, 12500, 14353, 16154, 28919, 29826, 34511, 35427,\n", + " 36338, 37245, 39497, 40399, 47737, 51434, 53292, 54193,\n", + " 57001, 63173, 66493, 68320, 69225, 70122, 71029, 94563,\n", + " 102417, 103335, 106964, 111623, 114361, 118932, 124071, 124979,\n", + " 126787, 127680, 129546, 131351, 132274, 153966, 155795, 156718,\n", + " 158408, 159995, 162726, 165424, 166309, 170940, 172303, 175000,\n", + " 175918, 181774, 185008, 185909, 198847, 199751, 200653],\n", + " dtype='int64'), Int64Index([ 2275, 12501, 14354, 16155, 28920, 29827, 34512, 35428,\n", + " 36339, 37246, 39498, 40400, 47738, 51435, 53293, 54194,\n", + " 57002, 63174, 66494, 68321, 69226, 70123, 71030, 94564,\n", + " 102418, 103336, 106965, 111624, 114362, 118933, 124072, 124980,\n", + " 126788, 127681, 129547, 131352, 132275, 153967, 155796, 156719,\n", + " 158409, 159996, 162727, 165425, 166310, 170941, 172304, 175001,\n", + " 175919, 181775, 185009, 185910, 198848, 199752, 200654],\n", + " dtype='int64'), Int64Index([ 2276, 12502, 14355, 16156, 28921, 29828, 34513, 35429,\n", + " 36340, 37247, 39499, 40401, 47739, 51436, 53294, 54195,\n", + " 57003, 63175, 66495, 68322, 69227, 70124, 71031, 94565,\n", + " 102419, 103337, 106966, 111625, 114363, 118934, 124073, 124981,\n", + " 126789, 127682, 129548, 131353, 132276, 153968, 155797, 156720,\n", + " 158410, 159997, 162728, 165426, 166311, 170942, 172305, 175002,\n", + " 175920, 181776, 185010, 185911, 198849, 199753, 200655],\n", + " dtype='int64'), Int64Index([ 2277, 12503, 14356, 16157, 28922, 29829, 34514, 35430,\n", + " 36341, 37248, 39500, 40402, 47740, 51437, 53295, 54196,\n", + " 57004, 63176, 66496, 68323, 69228, 70125, 71032, 94566,\n", + " 102420, 103338, 106967, 111626, 114364, 118935, 124074, 124982,\n", + " 126790, 127683, 129549, 131354, 132277, 153969, 155798, 156721,\n", + " 158411, 159998, 162729, 165427, 166312, 170943, 172306, 175003,\n", + " 175921, 181777, 185011, 185912, 198850, 199754, 200656],\n", + " dtype='int64'), Int64Index([ 2278, 12504, 14357, 16158, 28923, 29830, 34515, 35431,\n", + " 36342, 37249, 39501, 40403, 47741, 51438, 53296, 54197,\n", + " 57005, 63177, 66497, 68324, 69229, 70126, 71033, 94567,\n", + " 102421, 103339, 106968, 111627, 114365, 118936, 124075, 124983,\n", + " 126791, 127684, 129550, 131355, 132278, 153970, 155799, 156722,\n", + " 158412, 159999, 162730, 165428, 166313, 170944, 172307, 175004,\n", + " 175922, 181778, 185012, 185913, 198851, 199755, 200657],\n", + " dtype='int64'), Int64Index([ 2279, 12505, 14358, 16159, 28924, 29831, 34516, 35432,\n", + " 36343, 37250, 39502, 40404, 47742, 51439, 53297, 54198,\n", + " 57006, 63178, 66498, 68325, 69230, 70127, 71034, 94568,\n", + " 102422, 103340, 106969, 111628, 114366, 118937, 124076, 124984,\n", + " 126792, 127685, 129551, 131356, 132279, 153971, 155800, 156723,\n", + " 158413, 160000, 162731, 165429, 166314, 170945, 172308, 175005,\n", + " 175923, 181779, 185013, 185914, 198852, 199756, 200658],\n", + " dtype='int64'), Int64Index([ 2280, 12506, 14359, 16160, 28925, 29832, 34517, 35433,\n", + " 36344, 37251, 39503, 40405, 47743, 51440, 53298, 54199,\n", + " 57007, 63179, 66499, 68326, 69231, 70128, 71035, 94569,\n", + " 102423, 103341, 106970, 111629, 114367, 118938, 124077, 124985,\n", + " 126793, 127686, 129552, 131357, 132280, 153972, 155801, 156724,\n", + " 158414, 160001, 162732, 165430, 166315, 170946, 172309, 175006,\n", + " 175924, 181780, 185014, 185915, 198853, 199757, 200659],\n", + " dtype='int64'), Int64Index([ 2281, 12507, 14360, 16161, 28926, 29833, 34518, 35434,\n", + " 36345, 37252, 39504, 40406, 47744, 51441, 53299, 54200,\n", + " 57008, 63180, 66500, 68327, 69232, 70129, 71036, 94570,\n", + " 102424, 103342, 106971, 111630, 114368, 118939, 124078, 124986,\n", + " 126794, 127687, 129553, 131358, 132281, 153973, 155802, 156725,\n", + " 158415, 160002, 162733, 165431, 166316, 170947, 172310, 175007,\n", + " 175925, 181781, 185015, 185916, 198854, 199758, 200660],\n", + " dtype='int64'), Int64Index([ 2282, 12508, 14361, 16162, 28927, 29834, 34519, 35435,\n", + " 36346, 37253, 39505, 40407, 47745, 51442, 53300, 54201,\n", + " 57009, 63181, 66501, 68328, 69233, 70130, 71037, 94571,\n", + " 102425, 103343, 106972, 111631, 114369, 118940, 124079, 124987,\n", + " 126795, 127688, 129554, 131359, 132282, 153974, 155803, 156726,\n", + " 158416, 160003, 162734, 165432, 166317, 170948, 172311, 175008,\n", + " 175926, 181782, 185016, 185917, 198855, 199759, 200661],\n", + " dtype='int64'), Int64Index([ 2283, 12509, 14362, 16163, 28928, 29835, 34520, 35436,\n", + " 36347, 37254, 39506, 40408, 47746, 51443, 53301, 54202,\n", + " 57010, 63182, 66502, 68329, 69234, 70131, 71038, 94572,\n", + " 102426, 103344, 106973, 111632, 114370, 118941, 124080, 124988,\n", + " 126796, 127689, 129555, 131360, 132283, 153975, 155804, 156727,\n", + " 158417, 160004, 162735, 165433, 166318, 170949, 172312, 175009,\n", + " 175927, 181783, 185017, 185918, 198856, 199760, 200662],\n", + " dtype='int64'), Int64Index([ 2284, 12510, 14363, 16164, 28929, 29836, 34521, 35437,\n", + " 36348, 37255, 39507, 40409, 47747, 51444, 53302, 54203,\n", + " 57011, 63183, 66503, 68330, 69235, 70132, 71039, 94573,\n", + " 102427, 103345, 106974, 111633, 114371, 118942, 124081, 124989,\n", + " 126797, 127690, 129556, 131361, 132284, 153976, 155805, 156728,\n", + " 158418, 160005, 162736, 165434, 166319, 170950, 172313, 175010,\n", + " 175928, 181784, 185018, 185919, 198857, 199761, 200663],\n", + " dtype='int64'), Int64Index([ 2285, 12511, 14364, 16165, 28930, 29837, 34522, 35438,\n", + " 36349, 37256, 39508, 40410, 47748, 51445, 53303, 54204,\n", + " 57012, 63184, 66504, 68331, 69236, 70133, 71040, 94574,\n", + " 102428, 103346, 106975, 111634, 114372, 118943, 124082, 124990,\n", + " 126798, 127691, 129557, 131362, 132285, 153977, 155806, 156729,\n", + " 158419, 160006, 162737, 165435, 166320, 170951, 172314, 175011,\n", + " 175929, 181785, 185019, 185920, 198858, 199762, 200664],\n", + " dtype='int64'), Int64Index([ 2286, 12512, 14365, 16166, 28931, 29838, 34523, 35439,\n", + " 36350, 37257, 39509, 40411, 47749, 51446, 53304, 54205,\n", + " 57013, 63185, 66505, 68332, 69237, 70134, 71041, 94575,\n", + " 102429, 103347, 106976, 111635, 114373, 118944, 124083, 124991,\n", + " 126799, 127692, 129558, 131363, 132286, 153978, 155807, 156730,\n", + " 158420, 160007, 162738, 165436, 166321, 170952, 172315, 175012,\n", + " 175930, 181786, 185020, 185921, 198859, 199763, 200665],\n", + " dtype='int64'), Int64Index([ 2287, 12513, 14366, 16167, 28932, 29839, 34524, 35440,\n", + " 36351, 37258, 39510, 40412, 47750, 51447, 53305, 54206,\n", + " 57014, 63186, 66506, 68333, 69238, 70135, 71042, 94576,\n", + " 102430, 103348, 106977, 111636, 114374, 118945, 124084, 124992,\n", + " 126800, 127693, 129559, 131364, 132287, 153979, 155808, 156731,\n", + " 158421, 160008, 162739, 165437, 166322, 170953, 172316, 175013,\n", + " 175931, 181787, 185021, 185922, 198860, 199764, 200666],\n", + " dtype='int64'), Int64Index([ 2288, 12514, 14367, 16168, 28933, 29840, 34525, 35441,\n", + " 36352, 37259, 39511, 40413, 47751, 51448, 53306, 54207,\n", + " 57015, 63187, 66507, 68334, 69239, 70136, 71043, 94577,\n", + " 102431, 103349, 106978, 111637, 114375, 118946, 124085, 124993,\n", + " 126801, 127694, 129560, 131365, 132288, 153980, 155809, 156732,\n", + " 158422, 160009, 162740, 165438, 166323, 170954, 172317, 175014,\n", + " 175932, 181788, 185022, 185923, 198861, 199765, 200667],\n", + " dtype='int64'), Int64Index([ 2289, 12515, 14368, 16169, 28934, 29841, 34526, 35442,\n", + " 36353, 37260, 39512, 40414, 47752, 51449, 53307, 54208,\n", + " 57016, 63188, 66508, 68335, 69240, 70137, 71044, 94578,\n", + " 102432, 103350, 106979, 111638, 114376, 118947, 124086, 124994,\n", + " 126802, 127695, 129561, 131366, 132289, 153981, 155810, 156733,\n", + " 158423, 160010, 162741, 165439, 166324, 170955, 172318, 175015,\n", + " 175933, 181789, 185023, 185924, 198862, 199766, 200668],\n", + " dtype='int64'), Int64Index([ 2290, 12516, 14369, 16170, 28935, 29842, 34527, 35443,\n", + " 36354, 37261, 39513, 40415, 47753, 51450, 53308, 54209,\n", + " 57017, 63189, 66509, 68336, 69241, 70138, 71045, 94579,\n", + " 102433, 103351, 106980, 111639, 114377, 118948, 124087, 124995,\n", + " 126803, 127696, 129562, 131367, 132290, 153982, 155811, 156734,\n", + " 158424, 160011, 162742, 165440, 166325, 170956, 172319, 175016,\n", + " 175934, 181790, 185024, 185925, 198863, 199767, 200669],\n", + " dtype='int64'), Int64Index([ 2291, 12517, 14370, 16171, 28936, 29843, 34528, 35444,\n", + " 36355, 37262, 39514, 40416, 47754, 51451, 53309, 54210,\n", + " 57018, 63190, 66510, 68337, 69242, 70139, 71046, 94580,\n", + " 102434, 103352, 106981, 111640, 114378, 118949, 124088, 124996,\n", + " 126804, 127697, 129563, 131368, 132291, 153983, 155812, 156735,\n", + " 158425, 160012, 162743, 165441, 166326, 170957, 172320, 175017,\n", + " 175935, 181791, 185025, 185926, 198864, 199768, 200670],\n", + " dtype='int64'), Int64Index([ 2292, 12518, 14371, 16172, 28937, 29844, 34529, 35445,\n", + " 36356, 37263, 39515, 40417, 47755, 51452, 53310, 54211,\n", + " 57019, 63191, 66511, 68338, 69243, 70140, 71047, 94581,\n", + " 102435, 103353, 106982, 111641, 114379, 118950, 124089, 124997,\n", + " 126805, 127698, 129564, 131369, 132292, 153984, 155813, 156736,\n", + " 158426, 160013, 162744, 165442, 166327, 170958, 172321, 175018,\n", + " 175936, 181792, 185026, 185927, 198865, 199769, 200671],\n", + " dtype='int64'), Int64Index([ 2293, 12519, 14372, 16173, 28938, 29845, 34530, 35446,\n", + " 36357, 37264, 39516, 40418, 47756, 51453, 53311, 54212,\n", + " 57020, 63192, 66512, 68339, 69244, 70141, 71048, 94582,\n", + " 102436, 103354, 106983, 111642, 114380, 118951, 124090, 124998,\n", + " 126806, 127699, 129565, 131370, 132293, 153985, 155814, 156737,\n", + " 158427, 160014, 162745, 165443, 166328, 170959, 172322, 175019,\n", + " 175937, 181793, 185027, 185928, 198866, 199770, 200672],\n", + " dtype='int64'), Int64Index([ 2294, 12520, 14373, 16174, 28939, 29846, 34531, 35447,\n", + " 36358, 37265, 39517, 40419, 47757, 51454, 53312, 54213,\n", + " 57021, 63193, 66513, 68340, 69245, 70142, 71049, 94583,\n", + " 102437, 103355, 106984, 111643, 114381, 118952, 124091, 124999,\n", + " 126807, 127700, 129566, 131371, 132294, 153986, 155815, 156738,\n", + " 158428, 160015, 162746, 165444, 166329, 170960, 172323, 175020,\n", + " 175938, 181794, 185028, 185929, 198867, 199771, 200673],\n", + " dtype='int64'), Int64Index([ 2295, 12521, 14374, 16175, 28940, 29847, 34532, 35448,\n", + " 36359, 37266, 39518, 40420, 47758, 51455, 53313, 54214,\n", + " 57022, 63194, 66514, 68341, 69246, 70143, 71050, 94584,\n", + " 102438, 103356, 106985, 111644, 114382, 118953, 124092, 125000,\n", + " 126808, 127701, 129567, 131372, 132295, 153987, 155816, 156739,\n", + " 158429, 160016, 162747, 165445, 166330, 170961, 172324, 175021,\n", + " 175939, 181795, 185029, 185930, 198868, 199772, 200674],\n", + " dtype='int64'), Int64Index([ 2296, 12522, 14375, 16176, 28941, 29848, 34533, 35449,\n", + " 36360, 37267, 39519, 40421, 47759, 51456, 53314, 54215,\n", + " 57023, 63195, 66515, 68342, 69247, 70144, 71051, 94585,\n", + " 102439, 103357, 106986, 111645, 114383, 118954, 124093, 125001,\n", + " 126809, 127702, 129568, 131373, 132296, 153988, 155817, 156740,\n", + " 158430, 160017, 162748, 165446, 166331, 170962, 172325, 175022,\n", + " 175940, 181796, 185030, 185931, 198869, 199773, 200675],\n", + " dtype='int64'), Int64Index([ 2297, 12523, 14376, 16177, 28942, 29849, 34534, 35450,\n", + " 36361, 37268, 39520, 40422, 47760, 51457, 53315, 54216,\n", + " 57024, 63196, 66516, 68343, 69248, 70145, 71052, 94586,\n", + " 102440, 103358, 106987, 111646, 114384, 118955, 124094, 125002,\n", + " 126810, 127703, 129569, 131374, 132297, 153989, 155818, 156741,\n", + " 158431, 160018, 162749, 165447, 166332, 170963, 172326, 175023,\n", + " 175941, 181797, 185031, 185932, 198870, 199774, 200676],\n", + " dtype='int64'), Int64Index([ 2298, 12524, 14377, 16178, 28943, 29850, 34535, 35451,\n", + " 36362, 37269, 39521, 40423, 47761, 51458, 53316, 54217,\n", + " 57025, 63197, 66517, 68344, 69249, 70146, 71053, 94587,\n", + " 102441, 103359, 106988, 111647, 114385, 118956, 124095, 125003,\n", + " 126811, 127704, 129570, 131375, 132298, 153990, 155819, 156742,\n", + " 158432, 160019, 162750, 165448, 166333, 170964, 172327, 175024,\n", + " 175942, 181798, 185032, 185933, 198871, 199775, 200677],\n", + " dtype='int64'), Int64Index([ 2299, 12525, 14378, 16179, 28944, 29851, 34536, 35452,\n", + " 36363, 37270, 39522, 40424, 47762, 51459, 53317, 54218,\n", + " 57026, 63198, 66518, 68345, 69250, 70147, 71054, 94588,\n", + " 102442, 103360, 106989, 111648, 114386, 118957, 124096, 125004,\n", + " 126812, 127705, 129571, 131376, 132299, 153991, 155820, 156743,\n", + " 158433, 160020, 162751, 165449, 166334, 170965, 172328, 175025,\n", + " 175943, 181799, 185033, 185934, 198872, 199776, 200678],\n", + " dtype='int64'), Int64Index([ 2300, 12526, 14379, 16180, 28945, 29852, 34537, 35453,\n", + " 36364, 37271, 39523, 40425, 47763, 51460, 53318, 54219,\n", + " 57027, 63199, 66519, 68346, 69251, 70148, 71055, 94589,\n", + " 102443, 103361, 106990, 111649, 114387, 118958, 124097, 125005,\n", + " 126813, 127706, 129572, 131377, 132300, 153992, 155821, 156744,\n", + " 158434, 160021, 162752, 165450, 166335, 170966, 172329, 175026,\n", + " 175944, 181800, 185034, 185935, 198873, 199777, 200679],\n", + " dtype='int64'), Int64Index([ 2301, 12527, 14380, 16181, 28946, 29853, 34538, 35454,\n", + " 36365, 37272, 39524, 40426, 47764, 51461, 53319, 54220,\n", + " 57028, 63200, 66520, 68347, 69252, 70149, 71056, 94590,\n", + " 102444, 103362, 106991, 111650, 114388, 118959, 124098, 125006,\n", + " 126814, 127707, 129573, 131378, 132301, 153993, 155822, 156745,\n", + " 158435, 160022, 162753, 165451, 166336, 170967, 172330, 175027,\n", + " 175945, 181801, 185035, 185936, 198874, 199778, 200680],\n", + " dtype='int64'), Int64Index([ 2302, 12528, 14381, 16182, 28947, 29854, 34539, 35455,\n", + " 36366, 37273, 39525, 40427, 47765, 51462, 53320, 54221,\n", + " 57029, 63201, 66521, 68348, 69253, 70150, 71057, 94591,\n", + " 102445, 103363, 106992, 111651, 114389, 118960, 124099, 125007,\n", + " 126815, 127708, 129574, 131379, 132302, 153994, 155823, 156746,\n", + " 158436, 160023, 162754, 165452, 166337, 170968, 172331, 175028,\n", + " 175946, 181802, 185036, 185937, 198875, 199779, 200681],\n", + " dtype='int64'), Int64Index([ 2303, 12529, 14382, 16183, 28948, 29855, 34540, 35456,\n", + " 36367, 37274, 39526, 40428, 47766, 51463, 53321, 54222,\n", + " 57030, 63202, 66522, 68349, 69254, 70151, 71058, 94592,\n", + " 102446, 103364, 106993, 111652, 114390, 118961, 124100, 125008,\n", + " 126816, 127709, 129575, 131380, 132303, 153995, 155824, 156747,\n", + " 158437, 160024, 162755, 165453, 166338, 170969, 172332, 175029,\n", + " 175947, 181803, 185037, 185938, 198876, 199780, 200682],\n", + " dtype='int64'), Int64Index([ 2304, 12530, 14383, 16184, 28949, 29856, 34541, 35457,\n", + " 36368, 37275, 39527, 40429, 47767, 51464, 53322, 54223,\n", + " 57031, 63203, 66523, 68350, 69255, 70152, 71059, 94593,\n", + " 102447, 103365, 106994, 111653, 114391, 118962, 124101, 125009,\n", + " 126817, 127710, 129576, 131381, 132304, 153996, 155825, 156748,\n", + " 158438, 160025, 162756, 165454, 166339, 170970, 172333, 175030,\n", + " 175948, 181804, 185038, 185939, 198877, 199781, 200683],\n", + " dtype='int64'), Int64Index([ 2305, 12531, 14384, 16185, 28950, 29857, 34542, 35458,\n", + " 36369, 37276, 39528, 40430, 47768, 51465, 53323, 54224,\n", + " 57032, 63204, 66524, 68351, 69256, 70153, 71060, 94594,\n", + " 102448, 103366, 106995, 111654, 114392, 118963, 124102, 125010,\n", + " 126818, 127711, 129577, 131382, 132305, 153997, 155826, 156749,\n", + " 158439, 160026, 162757, 165455, 166340, 170971, 172334, 175031,\n", + " 175949, 181805, 185039, 185940, 198878, 199782, 200684],\n", + " dtype='int64'), Int64Index([ 2306, 12532, 14385, 16186, 28951, 29858, 34543, 35459,\n", + " 36370, 37277, 39529, 40431, 47769, 51466, 53324, 54225,\n", + " 57033, 63205, 66525, 68352, 69257, 70154, 71061, 94595,\n", + " 102449, 103367, 106996, 111655, 114393, 118964, 124103, 125011,\n", + " 126819, 127712, 129578, 131383, 132306, 153998, 155827, 156750,\n", + " 158440, 160027, 162758, 165456, 166341, 170972, 172335, 175032,\n", + " 175950, 181806, 185040, 185941, 198879, 199783, 200685],\n", + " dtype='int64'), Int64Index([ 2307, 12533, 14386, 16187, 28952, 29859, 34544, 35460,\n", + " 36371, 37278, 39530, 40432, 47770, 51467, 53325, 54226,\n", + " 57034, 63206, 66526, 68353, 69258, 70155, 71062, 94596,\n", + " 102450, 103368, 106997, 111656, 114394, 118965, 124104, 125012,\n", + " 126820, 127713, 129579, 131384, 132307, 153999, 155828, 156751,\n", + " 158441, 160028, 162759, 165457, 166342, 170973, 172336, 175033,\n", + " 175951, 181807, 185041, 185942, 198880, 199784, 200686],\n", + " dtype='int64'), Int64Index([ 2308, 12534, 14387, 16188, 28953, 29860, 34545, 35461,\n", + " 36372, 37279, 39531, 40433, 47771, 51468, 53326, 54227,\n", + " 57035, 63207, 66527, 68354, 69259, 70156, 71063, 94597,\n", + " 102451, 103369, 106998, 111657, 114395, 118966, 124105, 125013,\n", + " 126821, 127714, 129580, 131385, 132308, 154000, 155829, 156752,\n", + " 158442, 160029, 162760, 165458, 166343, 170974, 172337, 175034,\n", + " 175952, 181808, 185042, 185943, 198881, 199785, 200687],\n", + " dtype='int64'), Int64Index([ 2309, 12535, 14388, 16189, 28954, 29861, 34546, 35462,\n", + " 36373, 37280, 39532, 40434, 47772, 51469, 53327, 54228,\n", + " 57036, 63208, 66528, 68355, 69260, 70157, 71064, 94598,\n", + " 102452, 103370, 106999, 111658, 114396, 118967, 124106, 125014,\n", + " 126822, 127715, 129581, 131386, 132309, 154001, 155830, 156753,\n", + " 158443, 160030, 162761, 165459, 166344, 170975, 172338, 175035,\n", + " 175953, 181809, 185043, 185944, 198882, 199786, 200688],\n", + " dtype='int64'), Int64Index([ 2310, 12536, 14389, 16190, 28955, 29862, 34547, 35463,\n", + " 36374, 37281, 39533, 40435, 47773, 51470, 53328, 54229,\n", + " 57037, 63209, 66529, 68356, 69261, 70158, 71065, 94599,\n", + " 102453, 103371, 107000, 111659, 114397, 118968, 124107, 125015,\n", + " 126823, 127716, 129582, 131387, 132310, 154002, 155831, 156754,\n", + " 158444, 160031, 162762, 165460, 166345, 170976, 172339, 175036,\n", + " 175954, 181810, 185044, 185945, 198883, 199787, 200689],\n", + " dtype='int64'), Int64Index([ 2311, 12537, 14390, 16191, 28956, 29863, 34548, 35464,\n", + " 36375, 37282, 39534, 40436, 47774, 51471, 53329, 54230,\n", + " 57038, 63210, 66530, 68357, 69262, 70159, 71066, 94600,\n", + " 102454, 103372, 107001, 111660, 114398, 118969, 124108, 125016,\n", + " 126824, 127717, 129583, 131388, 132311, 154003, 155832, 156755,\n", + " 158445, 160032, 162763, 165461, 166346, 170977, 172340, 175037,\n", + " 175955, 181811, 185045, 185946, 198884, 199788, 200690],\n", + " dtype='int64'), Int64Index([ 2312, 12538, 14391, 16192, 28957, 29864, 34549, 35465,\n", + " 36376, 37283, 39535, 40437, 47775, 51472, 53330, 54231,\n", + " 57039, 63211, 66531, 68358, 69263, 70160, 71067, 94601,\n", + " 102455, 103373, 107002, 111661, 114399, 118970, 124109, 125017,\n", + " 126825, 127718, 129584, 131389, 132312, 154004, 155833, 156756,\n", + " 158446, 160033, 162764, 165462, 166347, 170978, 172341, 175038,\n", + " 175956, 181812, 185046, 185947, 198885, 199789, 200691],\n", + " dtype='int64'), Int64Index([ 2313, 12539, 14392, 16193, 28958, 29865, 34550, 35466,\n", + " 36377, 37284, 39536, 40438, 47776, 51473, 53331, 54232,\n", + " 57040, 63212, 66532, 68359, 69264, 70161, 71068, 94602,\n", + " 102456, 103374, 107003, 111662, 114400, 118971, 124110, 125018,\n", + " 126826, 127719, 129585, 131390, 132313, 154005, 155834, 156757,\n", + " 158447, 160034, 162765, 165463, 166348, 170979, 172342, 175039,\n", + " 175957, 181813, 185047, 185948, 198886, 199790, 200692],\n", + " dtype='int64'), Int64Index([ 2314, 12540, 14393, 16194, 28959, 29866, 34551, 35467,\n", + " 36378, 37285, 39537, 40439, 47777, 51474, 53332, 54233,\n", + " 57041, 63213, 66533, 68360, 69265, 70162, 71069, 94603,\n", + " 102457, 103375, 107004, 111663, 114401, 118972, 124111, 125019,\n", + " 126827, 127720, 129586, 131391, 132314, 154006, 155835, 156758,\n", + " 158448, 160035, 162766, 165464, 166349, 170980, 172343, 175040,\n", + " 175958, 181814, 185048, 185949, 198887, 199791, 200693],\n", + " dtype='int64'), Int64Index([ 2315, 12541, 14394, 16195, 28960, 29867, 34552, 35468,\n", + " 36379, 37286, 39538, 40440, 47778, 51475, 53333, 54234,\n", + " 57042, 63214, 66534, 68361, 69266, 70163, 71070, 94604,\n", + " 102458, 103376, 107005, 111664, 114402, 118973, 124112, 125020,\n", + " 126828, 127721, 129587, 131392, 132315, 154007, 155836, 156759,\n", + " 158449, 160036, 162767, 165465, 166350, 170981, 172344, 175041,\n", + " 175959, 181815, 185049, 185950, 198888, 199792, 200694],\n", + " dtype='int64'), Int64Index([ 2316, 12542, 14395, 16196, 28961, 29868, 34553, 35469,\n", + " 36380, 37287, 39539, 40441, 47779, 51476, 53334, 54235,\n", + " 57043, 63215, 66535, 68362, 69267, 70164, 71071, 94605,\n", + " 102459, 103377, 107006, 111665, 114403, 118974, 124113, 125021,\n", + " 126829, 127722, 129588, 131393, 132316, 154008, 155837, 156760,\n", + " 158450, 160037, 162768, 165466, 166351, 170982, 172345, 175042,\n", + " 175960, 181816, 185050, 185951, 198889, 199793, 200695],\n", + " dtype='int64'), Int64Index([ 2317, 12543, 14396, 16197, 28962, 29869, 34554, 35470,\n", + " 36381, 37288, 39540, 40442, 47780, 51477, 53335, 54236,\n", + " 57044, 63216, 66536, 68363, 69268, 70165, 71072, 94606,\n", + " 102460, 103378, 107007, 111666, 114404, 118975, 124114, 125022,\n", + " 126830, 127723, 129589, 131394, 132317, 154009, 155838, 156761,\n", + " 158451, 160038, 162769, 165467, 166352, 170983, 172346, 175043,\n", + " 175961, 181817, 185051, 185952, 198890, 199794, 200696],\n", + " dtype='int64'), Int64Index([ 2318, 12544, 14397, 16198, 28963, 29870, 34555, 35471,\n", + " 36382, 37289, 39541, 40443, 47781, 51478, 53336, 54237,\n", + " 57045, 63217, 66537, 68364, 69269, 70166, 71073, 94607,\n", + " 102461, 103379, 107008, 111667, 114405, 118976, 124115, 125023,\n", + " 126831, 127724, 129590, 131395, 132318, 154010, 155839, 156762,\n", + " 158452, 160039, 162770, 165468, 166353, 170984, 172347, 175044,\n", + " 175962, 181818, 185052, 185953, 198891, 199795, 200697],\n", + " dtype='int64'), Int64Index([ 2319, 12545, 14398, 16199, 28964, 29871, 34556, 35472,\n", + " 36383, 37290, 39542, 40444, 47782, 51479, 53337, 54238,\n", + " 57046, 63218, 66538, 68365, 69270, 70167, 71074, 94608,\n", + " 102462, 103380, 107009, 111668, 114406, 118977, 124116, 125024,\n", + " 126832, 127725, 129591, 131396, 132319, 154011, 155840, 156763,\n", + " 158453, 160040, 162771, 165469, 166354, 170985, 172348, 175045,\n", + " 175963, 181819, 185053, 185954, 198892, 199796, 200698],\n", + " dtype='int64'), Int64Index([ 2320, 12546, 14399, 16200, 28965, 29872, 34557, 35473,\n", + " 36384, 37291, 39543, 40445, 47783, 51480, 53338, 54239,\n", + " 57047, 63219, 66539, 68366, 69271, 70168, 71075, 94609,\n", + " 102463, 103381, 107010, 111669, 114407, 118978, 124117, 125025,\n", + " 126833, 127726, 129592, 131397, 132320, 154012, 155841, 156764,\n", + " 158454, 160041, 162772, 165470, 166355, 170986, 172349, 175046,\n", + " 175964, 181820, 185054, 185955, 198893, 199797, 200699],\n", + " dtype='int64'), Int64Index([ 2321, 12547, 14400, 16201, 28966, 29873, 34558, 35474,\n", + " 36385, 37292, 39544, 40446, 47784, 51481, 53339, 54240,\n", + " 57048, 63220, 66540, 68367, 69272, 70169, 71076, 94610,\n", + " 102464, 103382, 107011, 111670, 114408, 118979, 124118, 125026,\n", + " 126834, 127727, 129593, 131398, 132321, 154013, 155842, 156765,\n", + " 158455, 160042, 162773, 165471, 166356, 170987, 172350, 175047,\n", + " 175965, 181821, 185055, 185956, 198894, 199798, 200700],\n", + " dtype='int64'), Int64Index([ 2322, 12548, 14401, 16202, 28967, 29874, 34559, 35475,\n", + " 36386, 37293, 39545, 40447, 47785, 51482, 53340, 54241,\n", + " 57049, 63221, 66541, 68368, 69273, 70170, 71077, 94611,\n", + " 102465, 103383, 107012, 111671, 114409, 118980, 124119, 125027,\n", + " 126835, 127728, 129594, 131399, 132322, 154014, 155843, 156766,\n", + " 158456, 160043, 162774, 165472, 166357, 170988, 172351, 175048,\n", + " 175966, 181822, 185056, 185957, 198895, 199799, 200701],\n", + " dtype='int64'), Int64Index([ 2323, 12549, 14402, 16203, 28968, 29875, 34560, 35476,\n", + " 36387, 37294, 39546, 40448, 47786, 51483, 53341, 54242,\n", + " 57050, 63222, 66542, 68369, 69274, 70171, 71078, 94612,\n", + " 102466, 103384, 107013, 111672, 114410, 118981, 124120, 125028,\n", + " 126836, 127729, 129595, 131400, 132323, 154015, 155844, 156767,\n", + " 158457, 160044, 162775, 165473, 166358, 170989, 172352, 175049,\n", + " 175967, 181823, 185057, 185958, 198896, 199800, 200702],\n", + " dtype='int64'), Int64Index([ 2324, 12550, 14403, 16204, 28969, 29876, 34561, 35477,\n", + " 36388, 37295, 39547, 40449, 47787, 51484, 53342, 54243,\n", + " 57051, 63223, 66543, 68370, 69275, 70172, 71079, 94613,\n", + " 102467, 103385, 107014, 111673, 114411, 118982, 124121, 125029,\n", + " 126837, 127730, 129596, 131401, 132324, 154016, 155845, 156768,\n", + " 158458, 160045, 162776, 165474, 166359, 170990, 172353, 175050,\n", + " 175968, 181824, 185058, 185959, 198897, 199801, 200703],\n", + " dtype='int64'), Int64Index([ 2325, 12551, 14404, 16205, 28970, 29877, 34562, 35478,\n", + " 36389, 37296, 39548, 40450, 47788, 51485, 53343, 54244,\n", + " 57052, 63224, 66544, 68371, 69276, 70173, 71080, 94614,\n", + " 102468, 103386, 107015, 111674, 114412, 118983, 124122, 125030,\n", + " 126838, 127731, 129597, 131402, 132325, 154017, 155846, 156769,\n", + " 158459, 160046, 162777, 165475, 166360, 170991, 172354, 175051,\n", + " 175969, 181825, 185059, 185960, 198898, 199802, 200704],\n", + " dtype='int64'), Int64Index([ 2326, 12552, 14405, 16206, 28971, 29878, 34563, 35479,\n", + " 36390, 37297, 39549, 40451, 47789, 51486, 53344, 54245,\n", + " 57053, 63225, 66545, 68372, 69277, 70174, 71081, 94615,\n", + " 102469, 103387, 107016, 111675, 114413, 118984, 124123, 125031,\n", + " 126839, 127732, 129598, 131403, 132326, 154018, 155847, 156770,\n", + " 158460, 160047, 162778, 165476, 166361, 170992, 172355, 175052,\n", + " 175970, 181826, 185060, 185961, 198899, 199803, 200705],\n", + " dtype='int64'), Int64Index([ 2327, 12553, 14406, 16207, 28972, 29879, 34564, 35480,\n", + " 36391, 37298, 39550, 40452, 47790, 51487, 53345, 54246,\n", + " 57054, 63226, 66546, 68373, 69278, 70175, 71082, 94616,\n", + " 102470, 103388, 107017, 111676, 114414, 118985, 124124, 125032,\n", + " 126840, 127733, 129599, 131404, 132327, 154019, 155848, 156771,\n", + " 158461, 160048, 162779, 165477, 166362, 170993, 172356, 175053,\n", + " 175971, 181827, 185061, 185962, 198900, 199804, 200706],\n", + " dtype='int64'), Int64Index([ 2328, 12554, 14407, 16208, 28973, 29880, 34565, 35481,\n", + " 36392, 37299, 39551, 40453, 47791, 51488, 53346, 54247,\n", + " 57055, 63227, 66547, 68374, 69279, 70176, 71083, 94617,\n", + " 102471, 103389, 107018, 111677, 114415, 118986, 124125, 125033,\n", + " 126841, 127734, 129600, 131405, 132328, 154020, 155849, 156772,\n", + " 158462, 160049, 162780, 165478, 166363, 170994, 172357, 175054,\n", + " 175972, 181828, 185062, 185963, 198901, 199805, 200707],\n", + " dtype='int64'), Int64Index([ 2329, 12555, 14408, 16209, 28974, 29881, 34566, 35482,\n", + " 36393, 37300, 39552, 40454, 47792, 51489, 53347, 54248,\n", + " 57056, 63228, 66548, 68375, 69280, 70177, 71084, 94618,\n", + " 102472, 103390, 107019, 111678, 114416, 118987, 124126, 125034,\n", + " 126842, 127735, 129601, 131406, 132329, 154021, 155850, 156773,\n", + " 158463, 160050, 162781, 165479, 166364, 170995, 172358, 175055,\n", + " 175973, 181829, 185063, 185964, 198902, 199806, 200708],\n", + " dtype='int64'), Int64Index([ 2330, 12556, 14409, 16210, 28975, 29882, 34567, 35483,\n", + " 36394, 37301, 39553, 40455, 47793, 51490, 53348, 54249,\n", + " 57057, 63229, 66549, 68376, 69281, 70178, 71085, 94619,\n", + " 102473, 103391, 107020, 111679, 114417, 118988, 124127, 125035,\n", + " 126843, 127736, 129602, 131407, 132330, 154022, 155851, 156774,\n", + " 158464, 160051, 162782, 165480, 166365, 170996, 172359, 175056,\n", + " 175974, 181830, 185064, 185965, 198903, 199807, 200709],\n", + " dtype='int64'), Int64Index([ 2331, 12557, 14410, 16211, 28976, 29883, 34568, 35484,\n", + " 36395, 37302, 39554, 40456, 47794, 51491, 53349, 54250,\n", + " 57058, 63230, 66550, 68377, 69282, 70179, 71086, 94620,\n", + " 102474, 103392, 107021, 111680, 114418, 118989, 124128, 125036,\n", + " 126844, 127737, 129603, 131408, 132331, 154023, 155852, 156775,\n", + " 158465, 160052, 162783, 165481, 166366, 170997, 172360, 175057,\n", + " 175975, 181831, 185065, 185966, 198904, 199808, 200710],\n", + " dtype='int64'), Int64Index([ 2332, 12558, 14411, 16212, 28977, 29884, 34569, 35485,\n", + " 36396, 37303, 39555, 40457, 47795, 51492, 53350, 54251,\n", + " 57059, 63231, 66551, 68378, 69283, 70180, 71087, 94621,\n", + " 102475, 103393, 107022, 111681, 114419, 118990, 124129, 125037,\n", + " 126845, 127738, 129604, 131409, 132332, 154024, 155853, 156776,\n", + " 158466, 160053, 162784, 165482, 166367, 170998, 172361, 175058,\n", + " 175976, 181832, 185066, 185967, 198905, 199809, 200711],\n", + " dtype='int64'), Int64Index([ 2333, 12559, 14412, 16213, 28978, 29885, 34570, 35486,\n", + " 36397, 37304, 39556, 40458, 47796, 51493, 53351, 54252,\n", + " 57060, 63232, 66552, 68379, 69284, 70181, 71088, 94622,\n", + " 102476, 103394, 107023, 111682, 114420, 118991, 124130, 125038,\n", + " 126846, 127739, 129605, 131410, 132333, 154025, 155854, 156777,\n", + " 158467, 160054, 162785, 165483, 166368, 170999, 172362, 175059,\n", + " 175977, 181833, 185067, 185968, 198906, 199810, 200712],\n", + " dtype='int64'), Int64Index([ 2334, 12560, 14413, 16214, 28979, 29886, 34571, 35487,\n", + " 36398, 37305, 39557, 40459, 47797, 51494, 53352, 54253,\n", + " 57061, 63233, 66553, 68380, 69285, 70182, 71089, 94623,\n", + " 102477, 103395, 107024, 111683, 114421, 118992, 124131, 125039,\n", + " 126847, 127740, 129606, 131411, 132334, 154026, 155855, 156778,\n", + " 158468, 160055, 162786, 165484, 166369, 171000, 172363, 175060,\n", + " 175978, 181834, 185068, 185969, 198907, 199811, 200713],\n", + " dtype='int64'), Int64Index([ 2335, 12561, 14414, 16215, 28980, 29887, 34572, 35488,\n", + " 36399, 37306, 39558, 40460, 47798, 51495, 53353, 54254,\n", + " 57062, 63234, 66554, 68381, 69286, 70183, 71090, 94624,\n", + " 102478, 103396, 107025, 111684, 114422, 118993, 124132, 125040,\n", + " 126848, 127741, 129607, 131412, 132335, 154027, 155856, 156779,\n", + " 158469, 160056, 162787, 165485, 166370, 171001, 172364, 175061,\n", + " 175979, 181835, 185069, 185970, 198908, 199812, 200714],\n", + " dtype='int64'), Int64Index([ 2336, 12562, 14415, 16216, 28981, 29888, 34573, 35489,\n", + " 36400, 37307, 39559, 40461, 47799, 51496, 53354, 54255,\n", + " 57063, 63235, 66555, 68382, 69287, 70184, 71091, 94625,\n", + " 102479, 103397, 107026, 111685, 114423, 118994, 124133, 125041,\n", + " 126849, 127742, 129608, 131413, 132336, 154028, 155857, 156780,\n", + " 158470, 160057, 162788, 165486, 166371, 171002, 172365, 175062,\n", + " 175980, 181836, 185070, 185971, 198909, 199813, 200715],\n", + " dtype='int64'), Int64Index([ 2337, 12563, 14416, 16217, 28982, 29889, 34574, 35490,\n", + " 36401, 37308, 39560, 40462, 47800, 51497, 53355, 54256,\n", + " 57064, 63236, 66556, 68383, 69288, 70185, 71092, 94626,\n", + " 102480, 103398, 107027, 111686, 114424, 118995, 124134, 125042,\n", + " 126850, 127743, 129609, 131414, 132337, 154029, 155858, 156781,\n", + " 158471, 160058, 162789, 165487, 166372, 171003, 172366, 175063,\n", + " 175981, 181837, 185071, 185972, 198910, 199814, 200716],\n", + " dtype='int64'), Int64Index([ 2338, 12564, 14417, 16218, 28983, 29890, 34575, 35491,\n", + " 36402, 37309, 39561, 40463, 47801, 51498, 53356, 54257,\n", + " 57065, 63237, 66557, 68384, 69289, 70186, 71093, 94627,\n", + " 102481, 103399, 107028, 111687, 114425, 118996, 124135, 125043,\n", + " 126851, 127744, 129610, 131415, 132338, 154030, 155859, 156782,\n", + " 158472, 160059, 162790, 165488, 166373, 171004, 172367, 175064,\n", + " 175982, 181838, 185072, 185973, 198911, 199815, 200717],\n", + " dtype='int64'), Int64Index([ 2339, 12565, 14418, 16219, 28984, 29891, 34576, 35492,\n", + " 36403, 37310, 39562, 40464, 47802, 51499, 53357, 54258,\n", + " 57066, 63238, 66558, 68385, 69290, 70187, 71094, 94628,\n", + " 102482, 103400, 107029, 111688, 114426, 118997, 124136, 125044,\n", + " 126852, 127745, 129611, 131416, 132339, 154031, 155860, 156783,\n", + " 158473, 160060, 162791, 165489, 166374, 171005, 172368, 175065,\n", + " 175983, 181839, 185073, 185974, 198912, 199816, 200718],\n", + " dtype='int64'), Int64Index([ 2340, 12566, 14419, 16220, 28985, 29892, 34577, 35493,\n", + " 36404, 37311, 39563, 40465, 47803, 51500, 53358, 54259,\n", + " 57067, 63239, 66559, 68386, 69291, 70188, 71095, 94629,\n", + " 102483, 103401, 107030, 111689, 114427, 118998, 124137, 125045,\n", + " 126853, 127746, 129612, 131417, 132340, 154032, 155861, 156784,\n", + " 158474, 160061, 162792, 165490, 166375, 171006, 172369, 175066,\n", + " 175984, 181840, 185074, 185975, 198913, 199817, 200719],\n", + " dtype='int64'), Int64Index([ 2341, 12567, 14420, 16221, 28986, 29893, 34578, 35494,\n", + " 36405, 37312, 39564, 40466, 47804, 51501, 53359, 54260,\n", + " 57068, 63240, 66560, 68387, 69292, 70189, 71096, 94630,\n", + " 102484, 103402, 107031, 111690, 114428, 118999, 124138, 125046,\n", + " 126854, 127747, 129613, 131418, 132341, 154033, 155862, 156785,\n", + " 158475, 160062, 162793, 165491, 166376, 171007, 172370, 175067,\n", + " 175985, 181841, 185075, 185976, 198914, 199818, 200720],\n", + " dtype='int64'), Int64Index([ 2342, 12568, 14421, 16222, 28987, 29894, 34579, 35495,\n", + " 36406, 37313, 39565, 40467, 47805, 51502, 53360, 54261,\n", + " 57069, 63241, 66561, 68388, 69293, 70190, 71097, 94631,\n", + " 102485, 103403, 107032, 111691, 114429, 119000, 124139, 125047,\n", + " 126855, 127748, 129614, 131419, 132342, 154034, 155863, 156786,\n", + " 158476, 160063, 162794, 165492, 166377, 171008, 172371, 175068,\n", + " 175986, 181842, 185076, 185977, 198915, 199819, 200721],\n", + " dtype='int64'), Int64Index([ 2343, 12569, 14422, 16223, 28988, 29895, 34580, 35496,\n", + " 36407, 37314, 39566, 40468, 47806, 51503, 53361, 54262,\n", + " 57070, 63242, 66562, 68389, 69294, 70191, 71098, 94632,\n", + " 102486, 103404, 107033, 111692, 114430, 119001, 124140, 125048,\n", + " 126856, 127749, 129615, 131420, 132343, 154035, 155864, 156787,\n", + " 158477, 160064, 162795, 165493, 166378, 171009, 172372, 175069,\n", + " 175987, 181843, 185077, 185978, 198916, 199820, 200722],\n", + " dtype='int64'), Int64Index([ 2344, 12570, 14423, 16224, 28989, 29896, 34581, 35497,\n", + " 36408, 37315, 39567, 40469, 47807, 51504, 53362, 54263,\n", + " 57071, 63243, 66563, 68390, 69295, 70192, 71099, 94633,\n", + " 102487, 103405, 107034, 111693, 114431, 119002, 124141, 125049,\n", + " 126857, 127750, 129616, 131421, 132344, 154036, 155865, 156788,\n", + " 158478, 160065, 162796, 165494, 166379, 171010, 172373, 175070,\n", + " 175988, 181844, 185078, 185979, 198917, 199821, 200723],\n", + " dtype='int64'), Int64Index([ 2345, 12571, 14424, 16225, 28990, 29897, 34582, 35498,\n", + " 36409, 37316, 39568, 40470, 47808, 51505, 53363, 54264,\n", + " 57072, 63244, 66564, 68391, 69296, 70193, 71100, 94634,\n", + " 102488, 103406, 107035, 111694, 114432, 119003, 124142, 125050,\n", + " 126858, 127751, 129617, 131422, 132345, 154037, 155866, 156789,\n", + " 158479, 160066, 162797, 165495, 166380, 171011, 172374, 175071,\n", + " 175989, 181845, 185079, 185980, 198918, 199822, 200724],\n", + " dtype='int64'), Int64Index([ 2346, 12572, 14425, 16226, 28991, 29898, 34583, 35499,\n", + " 36410, 37317, 39569, 40471, 47809, 51506, 53364, 54265,\n", + " 57073, 63245, 66565, 68392, 69297, 70194, 71101, 94635,\n", + " 102489, 103407, 107036, 111695, 114433, 119004, 124143, 125051,\n", + " 126859, 127752, 129618, 131423, 132346, 154038, 155867, 156790,\n", + " 158480, 160067, 162798, 165496, 166381, 171012, 172375, 175072,\n", + " 175990, 181846, 185080, 185981, 198919, 199823, 200725],\n", + " dtype='int64'), Int64Index([ 2347, 12573, 14426, 16227, 28992, 29899, 34584, 35500,\n", + " 36411, 37318, 39570, 40472, 47810, 51507, 53365, 54266,\n", + " 57074, 63246, 66566, 68393, 69298, 70195, 71102, 94636,\n", + " 102490, 103408, 107037, 111696, 114434, 119005, 124144, 125052,\n", + " 126860, 127753, 129619, 131424, 132347, 154039, 155868, 156791,\n", + " 158481, 160068, 162799, 165497, 166382, 171013, 172376, 175073,\n", + " 175991, 181847, 185081, 185982, 198920, 199824, 200726],\n", + " dtype='int64'), Int64Index([ 2348, 12574, 14427, 16228, 28993, 29900, 34585, 35501,\n", + " 36412, 37319, 39571, 40473, 47811, 51508, 53366, 54267,\n", + " 57075, 63247, 66567, 68394, 69299, 70196, 71103, 94637,\n", + " 102491, 103409, 107038, 111697, 114435, 119006, 124145, 125053,\n", + " 126861, 127754, 129620, 131425, 132348, 154040, 155869, 156792,\n", + " 158482, 160069, 162800, 165498, 166383, 171014, 172377, 175074,\n", + " 175992, 181848, 185082, 185983, 198921, 199825, 200727],\n", + " dtype='int64'), Int64Index([ 2349, 12575, 14428, 16229, 28994, 29901, 34586, 35502,\n", + " 36413, 37320, 39572, 40474, 47812, 51509, 53367, 54268,\n", + " 57076, 63248, 66568, 68395, 69300, 70197, 71104, 94638,\n", + " 102492, 103410, 107039, 111698, 114436, 119007, 124146, 125054,\n", + " 126862, 127755, 129621, 131426, 132349, 154041, 155870, 156793,\n", + " 158483, 160070, 162801, 165499, 166384, 171015, 172378, 175075,\n", + " 175993, 181849, 185083, 185984, 198922, 199826, 200728],\n", + " dtype='int64'), Int64Index([ 2350, 12576, 14429, 16230, 28995, 29902, 34587, 35503,\n", + " 36414, 37321, 39573, 40475, 47813, 51510, 53368, 54269,\n", + " 57077, 63249, 66569, 68396, 69301, 70198, 71105, 94639,\n", + " 102493, 103411, 107040, 111699, 114437, 119008, 124147, 125055,\n", + " 126863, 127756, 129622, 131427, 132350, 154042, 155871, 156794,\n", + " 158484, 160071, 162802, 165500, 166385, 171016, 172379, 175076,\n", + " 175994, 181850, 185084, 185985, 198923, 199827, 200729],\n", + " dtype='int64'), Int64Index([ 2351, 12577, 14430, 16231, 28996, 29903, 34588, 35504,\n", + " 36415, 37322, 39574, 40476, 47814, 51511, 53369, 54270,\n", + " 57078, 63250, 66570, 68397, 69302, 70199, 71106, 94640,\n", + " 102494, 103412, 107041, 111700, 114438, 119009, 124148, 125056,\n", + " 126864, 127757, 129623, 131428, 132351, 154043, 155872, 156795,\n", + " 158485, 160072, 162803, 165501, 166386, 171017, 172380, 175077,\n", + " 175995, 181851, 185085, 185986, 198924, 199828, 200730],\n", + " dtype='int64'), Int64Index([ 2352, 12578, 14431, 16232, 28997, 29904, 34589, 35505,\n", + " 36416, 37323, 39575, 40477, 47815, 51512, 53370, 54271,\n", + " 57079, 63251, 66571, 68398, 69303, 70200, 71107, 94641,\n", + " 102495, 103413, 107042, 111701, 114439, 119010, 124149, 125057,\n", + " 126865, 127758, 129624, 131429, 132352, 154044, 155873, 156796,\n", + " 158486, 160073, 162804, 165502, 166387, 171018, 172381, 175078,\n", + " 175996, 181852, 185086, 185987, 198925, 199829, 200731],\n", + " dtype='int64'), Int64Index([ 2353, 12579, 14432, 16233, 28998, 29905, 34590, 35506,\n", + " 36417, 37324, 39576, 40478, 47816, 51513, 53371, 54272,\n", + " 57080, 63252, 66572, 68399, 69304, 70201, 71108, 94642,\n", + " 102496, 103414, 107043, 111702, 114440, 119011, 124150, 125058,\n", + " 126866, 127759, 129625, 131430, 132353, 154045, 155874, 156797,\n", + " 158487, 160074, 162805, 165503, 166388, 171019, 172382, 175079,\n", + " 175997, 181853, 185087, 185988, 198926, 199830, 200732],\n", + " dtype='int64'), Int64Index([ 2354, 12580, 14433, 16234, 28999, 29906, 34591, 35507,\n", + " 36418, 37325, 39577, 40479, 47817, 51514, 53372, 54273,\n", + " 57081, 63253, 66573, 68400, 69305, 70202, 71109, 94643,\n", + " 102497, 103415, 107044, 111703, 114441, 119012, 124151, 125059,\n", + " 126867, 127760, 129626, 131431, 132354, 154046, 155875, 156798,\n", + " 158488, 160075, 162806, 165504, 166389, 171020, 172383, 175080,\n", + " 175998, 181854, 185088, 185989, 198927, 199831, 200733],\n", + " dtype='int64'), Int64Index([ 2355, 12581, 14434, 16235, 29000, 29907, 34592, 35508,\n", + " 36419, 37326, 39578, 40480, 47818, 51515, 53373, 54274,\n", + " 57082, 63254, 66574, 68401, 69306, 70203, 71110, 94644,\n", + " 102498, 103416, 107045, 111704, 114442, 119013, 124152, 125060,\n", + " 126868, 127761, 129627, 131432, 132355, 154047, 155876, 156799,\n", + " 158489, 160076, 162807, 165505, 166390, 171021, 172384, 175081,\n", + " 175999, 181855, 185089, 185990, 198928, 199832, 200734],\n", + " dtype='int64'), Int64Index([ 2356, 12582, 14435, 16236, 29001, 29908, 34593, 35509,\n", + " 36420, 37327, 39579, 40481, 47819, 51516, 53374, 54275,\n", + " 57083, 63255, 66575, 68402, 69307, 70204, 71111, 94645,\n", + " 102499, 103417, 107046, 111705, 114443, 119014, 124153, 125061,\n", + " 126869, 127762, 129628, 131433, 132356, 154048, 155877, 156800,\n", + " 158490, 160077, 162808, 165506, 166391, 171022, 172385, 175082,\n", + " 176000, 181856, 185090, 185991, 198929, 199833, 200735],\n", + " dtype='int64'), Int64Index([ 2357, 12583, 14436, 16237, 29002, 29909, 34594, 35510,\n", + " 36421, 37328, 39580, 40482, 47820, 51517, 53375, 54276,\n", + " 57084, 63256, 66576, 68403, 69308, 70205, 71112, 94646,\n", + " 102500, 103418, 107047, 111706, 114444, 119015, 124154, 125062,\n", + " 126870, 127763, 129629, 131434, 132357, 154049, 155878, 156801,\n", + " 158491, 160078, 162809, 165507, 166392, 171023, 172386, 175083,\n", + " 176001, 181857, 185091, 185992, 198930, 199834, 200736],\n", + " dtype='int64'), Int64Index([ 2358, 12584, 14437, 16238, 29003, 29910, 34595, 35511,\n", + " 36422, 37329, 39581, 40483, 47821, 51518, 53376, 54277,\n", + " 57085, 63257, 66577, 68404, 69309, 70206, 71113, 94647,\n", + " 102501, 103419, 107048, 111707, 114445, 119016, 124155, 125063,\n", + " 126871, 127764, 129630, 131435, 132358, 154050, 155879, 156802,\n", + " 158492, 160079, 162810, 165508, 166393, 171024, 172387, 175084,\n", + " 176002, 181858, 185092, 185993, 198931, 199835, 200737],\n", + " dtype='int64'), Int64Index([ 2359, 12585, 14438, 16239, 29004, 29911, 34596, 35512,\n", + " 36423, 37330, 39582, 40484, 47822, 51519, 53377, 54278,\n", + " 57086, 63258, 66578, 68405, 69310, 70207, 71114, 94648,\n", + " 102502, 103420, 107049, 111708, 114446, 119017, 124156, 125064,\n", + " 126872, 127765, 129631, 131436, 132359, 154051, 155880, 156803,\n", + " 158493, 160080, 162811, 165509, 166394, 171025, 172388, 175085,\n", + " 176003, 181859, 185093, 185994, 198932, 199836, 200738],\n", + " dtype='int64'), Int64Index([ 2360, 12586, 14439, 16240, 29005, 29912, 34597, 35513,\n", + " 36424, 37331, 39583, 40485, 47823, 51520, 53378, 54279,\n", + " 57087, 63259, 66579, 68406, 69311, 70208, 71115, 94649,\n", + " 102503, 103421, 107050, 111709, 114447, 119018, 124157, 125065,\n", + " 126873, 127766, 129632, 131437, 132360, 154052, 155881, 156804,\n", + " 158494, 160081, 162812, 165510, 166395, 171026, 172389, 175086,\n", + " 176004, 181860, 185094, 185995, 198933, 199837, 200739],\n", + " dtype='int64'), Int64Index([ 2361, 12587, 14440, 16241, 29006, 29913, 34598, 35514,\n", + " 36425, 37332, 39584, 40486, 47824, 51521, 53379, 54280,\n", + " 57088, 63260, 66580, 68407, 69312, 70209, 71116, 94650,\n", + " 102504, 103422, 107051, 111710, 114448, 119019, 124158, 125066,\n", + " 126874, 127767, 129633, 131438, 132361, 154053, 155882, 156805,\n", + " 158495, 160082, 162813, 165511, 166396, 171027, 172390, 175087,\n", + " 176005, 181861, 185095, 185996, 198934, 199838, 200740],\n", + " dtype='int64'), Int64Index([ 2362, 12588, 14441, 16242, 29007, 29914, 34599, 35515,\n", + " 36426, 37333, 39585, 40487, 47825, 51522, 53380, 54281,\n", + " 57089, 63261, 66581, 68408, 69313, 70210, 71117, 94651,\n", + " 102505, 103423, 107052, 111711, 114449, 119020, 124159, 125067,\n", + " 126875, 127768, 129634, 131439, 132362, 154054, 155883, 156806,\n", + " 158496, 160083, 162814, 165512, 166397, 171028, 172391, 175088,\n", + " 176006, 181862, 185096, 185997, 198935, 199839, 200741],\n", + " dtype='int64'), Int64Index([ 2363, 12589, 14442, 16243, 29008, 29915, 34600, 35516,\n", + " 36427, 37334, 39586, 40488, 47826, 51523, 53381, 54282,\n", + " 57090, 63262, 66582, 68409, 69314, 70211, 71118, 94652,\n", + " 102506, 103424, 107053, 111712, 114450, 119021, 124160, 125068,\n", + " 126876, 127769, 129635, 131440, 132363, 154055, 155884, 156807,\n", + " 158497, 160084, 162815, 165513, 166398, 171029, 172392, 175089,\n", + " 176007, 181863, 185097, 185998, 198936, 199840, 200742],\n", + " dtype='int64'), Int64Index([ 2364, 12590, 14443, 16244, 29009, 29916, 34601, 35517,\n", + " 36428, 37335, 39587, 40489, 47827, 51524, 53382, 54283,\n", + " 57091, 63263, 66583, 68410, 69315, 70212, 71119, 94653,\n", + " 102507, 103425, 107054, 111713, 114451, 119022, 124161, 125069,\n", + " 126877, 127770, 129636, 131441, 132364, 154056, 155885, 156808,\n", + " 158498, 160085, 162816, 165514, 166399, 171030, 172393, 175090,\n", + " 176008, 181864, 185098, 185999, 198937, 199841, 200743],\n", + " dtype='int64'), Int64Index([ 2365, 12591, 14444, 16245, 29010, 29917, 34602, 35518,\n", + " 36429, 37336, 39588, 40490, 47828, 51525, 53383, 54284,\n", + " 57092, 63264, 66584, 68411, 69316, 70213, 71120, 94654,\n", + " 102508, 103426, 107055, 111714, 114452, 119023, 124162, 125070,\n", + " 126878, 127771, 129637, 131442, 132365, 154057, 155886, 156809,\n", + " 158499, 160086, 162817, 165515, 166400, 171031, 172394, 175091,\n", + " 176009, 181865, 185099, 186000, 198938, 199842, 200744],\n", + " dtype='int64'), Int64Index([ 2366, 12592, 14445, 16246, 29011, 29918, 34603, 35519,\n", + " 36430, 37337, 39589, 40491, 47829, 51526, 53384, 54285,\n", + " 57093, 63265, 66585, 68412, 69317, 70214, 71121, 94655,\n", + " 102509, 103427, 107056, 111715, 114453, 119024, 124163, 125071,\n", + " 126879, 127772, 129638, 131443, 132366, 154058, 155887, 156810,\n", + " 158500, 160087, 162818, 165516, 166401, 171032, 172395, 175092,\n", + " 176010, 181866, 185100, 186001, 198939, 199843, 200745],\n", + " dtype='int64'), Int64Index([ 2367, 12593, 14446, 16247, 29012, 29919, 34604, 35520,\n", + " 36431, 37338, 39590, 40492, 47830, 51527, 53385, 54286,\n", + " 57094, 63266, 66586, 68413, 69318, 70215, 71122, 94656,\n", + " 102510, 103428, 107057, 111716, 114454, 119025, 124164, 125072,\n", + " 126880, 127773, 129639, 131444, 132367, 154059, 155888, 156811,\n", + " 158501, 160088, 162819, 165517, 166402, 171033, 172396, 175093,\n", + " 176011, 181867, 185101, 186002, 198940, 199844, 200746],\n", + " dtype='int64'), Int64Index([ 2368, 12594, 14447, 16248, 29013, 29920, 34605, 35521,\n", + " 36432, 37339, 39591, 40493, 47831, 51528, 53386, 54287,\n", + " 57095, 63267, 66587, 68414, 69319, 70216, 71123, 94657,\n", + " 102511, 103429, 107058, 111717, 114455, 119026, 124165, 125073,\n", + " 126881, 127774, 129640, 131445, 132368, 154060, 155889, 156812,\n", + " 158502, 160089, 162820, 165518, 166403, 171034, 172397, 175094,\n", + " 176012, 181868, 185102, 186003, 198941, 199845, 200747],\n", + " dtype='int64'), Int64Index([ 2369, 12595, 14448, 16249, 29014, 29921, 34606, 35522,\n", + " 36433, 37340, 39592, 40494, 47832, 51529, 53387, 54288,\n", + " 57096, 63268, 66588, 68415, 69320, 70217, 71124, 94658,\n", + " 102512, 103430, 107059, 111718, 114456, 119027, 124166, 125074,\n", + " 126882, 127775, 129641, 131446, 132369, 154061, 155890, 156813,\n", + " 158503, 160090, 162821, 165519, 166404, 171035, 172398, 175095,\n", + " 176013, 181869, 185103, 186004, 198942, 199846, 200748],\n", + " dtype='int64'), Int64Index([ 2370, 12596, 14449, 16250, 29015, 29922, 34607, 35523,\n", + " 36434, 37341, 39593, 40495, 47833, 51530, 53388, 54289,\n", + " 57097, 63269, 66589, 68416, 69321, 70218, 71125, 94659,\n", + " 102513, 103431, 107060, 111719, 114457, 119028, 124167, 125075,\n", + " 126883, 127776, 129642, 131447, 132370, 154062, 155891, 156814,\n", + " 158504, 160091, 162822, 165520, 166405, 171036, 172399, 175096,\n", + " 176014, 181870, 185104, 186005, 198943, 199847, 200749],\n", + " dtype='int64'), Int64Index([ 2371, 12597, 14450, 16251, 29016, 29923, 34608, 35524,\n", + " 36435, 37342, 39594, 40496, 47834, 51531, 53389, 54290,\n", + " 57098, 63270, 66590, 68417, 69322, 70219, 71126, 94660,\n", + " 102514, 103432, 107061, 111720, 114458, 119029, 124168, 125076,\n", + " 126884, 127777, 129643, 131448, 132371, 154063, 155892, 156815,\n", + " 158505, 160092, 162823, 165521, 166406, 171037, 172400, 175097,\n", + " 176015, 181871, 185105, 186006, 198944, 199848, 200750],\n", + " dtype='int64'), Int64Index([ 2372, 12598, 14451, 16252, 29017, 29924, 34609, 35525,\n", + " 36436, 37343, 39595, 40497, 47835, 51532, 53390, 54291,\n", + " 57099, 63271, 66591, 68418, 69323, 70220, 71127, 94661,\n", + " 102515, 103433, 107062, 111721, 114459, 119030, 124169, 125077,\n", + " 126885, 127778, 129644, 131449, 132372, 154064, 155893, 156816,\n", + " 158506, 160093, 162824, 165522, 166407, 171038, 172401, 175098,\n", + " 176016, 181872, 185106, 186007, 198945, 199849, 200751],\n", + " dtype='int64'), Int64Index([ 2373, 12599, 14452, 16253, 29018, 29925, 34610, 35526,\n", + " 36437, 37344, 39596, 40498, 47836, 51533, 53391, 54292,\n", + " 57100, 63272, 66592, 68419, 69324, 70221, 71128, 94662,\n", + " 102516, 103434, 107063, 111722, 114460, 119031, 124170, 125078,\n", + " 126886, 127779, 129645, 131450, 132373, 154065, 155894, 156817,\n", + " 158507, 160094, 162825, 165523, 166408, 171039, 172402, 175099,\n", + " 176017, 181873, 185107, 186008, 198946, 199850, 200752],\n", + " dtype='int64'), Int64Index([ 2374, 12600, 14453, 16254, 29019, 29926, 34611, 35527,\n", + " 36438, 37345, 39597, 40499, 47837, 51534, 53392, 54293,\n", + " 57101, 63273, 66593, 68420, 69325, 70222, 71129, 94663,\n", + " 102517, 103435, 107064, 111723, 114461, 119032, 124171, 125079,\n", + " 126887, 127780, 129646, 131451, 132374, 154066, 155895, 156818,\n", + " 158508, 160095, 162826, 165524, 166409, 171040, 172403, 175100,\n", + " 176018, 181874, 185108, 186009, 198947, 199851, 200753],\n", + " dtype='int64'), Int64Index([ 2375, 12601, 14454, 16255, 29020, 29927, 34612, 35528,\n", + " 36439, 37346, 39598, 40500, 47838, 51535, 53393, 54294,\n", + " 57102, 63274, 66594, 68421, 69326, 70223, 71130, 94664,\n", + " 102518, 103436, 107065, 111724, 114462, 119033, 124172, 125080,\n", + " 126888, 127781, 129647, 131452, 132375, 154067, 155896, 156819,\n", + " 158509, 160096, 162827, 165525, 166410, 171041, 172404, 175101,\n", + " 176019, 181875, 185109, 186010, 198948, 199852, 200754],\n", + " dtype='int64'), Int64Index([ 2376, 12602, 14455, 16256, 29021, 29928, 34613, 35529,\n", + " 36440, 37347, 39599, 40501, 47839, 51536, 53394, 54295,\n", + " 57103, 63275, 66595, 68422, 69327, 70224, 71131, 94665,\n", + " 102519, 103437, 107066, 111725, 114463, 119034, 124173, 125081,\n", + " 126889, 127782, 129648, 131453, 132376, 154068, 155897, 156820,\n", + " 158510, 160097, 162828, 165526, 166411, 171042, 172405, 175102,\n", + " 176020, 181876, 185110, 186011, 198949, 199853, 200755],\n", + " dtype='int64'), Int64Index([ 2377, 12603, 14456, 16257, 29022, 29929, 34614, 35530,\n", + " 36441, 37348, 39600, 40502, 47840, 51537, 53395, 54296,\n", + " 57104, 63276, 66596, 68423, 69328, 70225, 71132, 94666,\n", + " 102520, 103438, 107067, 111726, 114464, 119035, 124174, 125082,\n", + " 126890, 127783, 129649, 131454, 132377, 154069, 155898, 156821,\n", + " 158511, 160098, 162829, 165527, 166412, 171043, 172406, 175103,\n", + " 176021, 181877, 185111, 186012, 198950, 199854, 200756],\n", + " dtype='int64'), Int64Index([ 2378, 12604, 14457, 16258, 29023, 29930, 34615, 35531,\n", + " 36442, 37349, 39601, 40503, 47841, 51538, 53396, 54297,\n", + " 57105, 63277, 66597, 68424, 69329, 70226, 71133, 94667,\n", + " 102521, 103439, 107068, 111727, 114465, 119036, 124175, 125083,\n", + " 126891, 127784, 129650, 131455, 132378, 154070, 155899, 156822,\n", + " 158512, 160099, 162830, 165528, 166413, 171044, 172407, 175104,\n", + " 176022, 181878, 185112, 186013, 198951, 199855, 200757],\n", + " dtype='int64'), Int64Index([ 2379, 12605, 14458, 16259, 29024, 29931, 34616, 35532,\n", + " 36443, 37350, 39602, 40504, 47842, 51539, 53397, 54298,\n", + " 57106, 63278, 66598, 68425, 69330, 70227, 71134, 94668,\n", + " 102522, 103440, 107069, 111728, 114466, 119037, 124176, 125084,\n", + " 126892, 127785, 129651, 131456, 132379, 154071, 155900, 156823,\n", + " 158513, 160100, 162831, 165529, 166414, 171045, 172408, 175105,\n", + " 176023, 181879, 185113, 186014, 198952, 199856, 200758],\n", + " dtype='int64'), Int64Index([ 2380, 12606, 14459, 16260, 29025, 29932, 34617, 35533,\n", + " 36444, 37351, 39603, 40505, 47843, 51540, 53398, 54299,\n", + " 57107, 63279, 66599, 68426, 69331, 70228, 71135, 94669,\n", + " 102523, 103441, 107070, 111729, 114467, 119038, 124177, 125085,\n", + " 126893, 127786, 129652, 131457, 132380, 154072, 155901, 156824,\n", + " 158514, 160101, 162832, 165530, 166415, 171046, 172409, 175106,\n", + " 176024, 181880, 185114, 186015, 198953, 199857, 200759],\n", + " dtype='int64'), Int64Index([ 2381, 12607, 14460, 16261, 29026, 29933, 34618, 35534,\n", + " 36445, 37352, 39604, 40506, 47844, 51541, 53399, 54300,\n", + " 57108, 63280, 66600, 68427, 69332, 70229, 71136, 94670,\n", + " 102524, 103442, 107071, 111730, 114468, 119039, 124178, 125086,\n", + " 126894, 127787, 129653, 131458, 132381, 154073, 155902, 156825,\n", + " 158515, 160102, 162833, 165531, 166416, 171047, 172410, 175107,\n", + " 176025, 181881, 185115, 186016, 198954, 199858, 200760],\n", + " dtype='int64'), Int64Index([ 2382, 12608, 14461, 16262, 29027, 29934, 34619, 35535,\n", + " 36446, 37353, 39605, 40507, 47845, 51542, 53400, 54301,\n", + " 57109, 63281, 66601, 68428, 69333, 70230, 71137, 94671,\n", + " 102525, 103443, 107072, 111731, 114469, 119040, 124179, 125087,\n", + " 126895, 127788, 129654, 131459, 132382, 154074, 155903, 156826,\n", + " 158516, 160103, 162834, 165532, 166417, 171048, 172411, 175108,\n", + " 176026, 181882, 185116, 186017, 198955, 199859, 200761],\n", + " dtype='int64'), Int64Index([ 2383, 12609, 14462, 16263, 29028, 29935, 34620, 35536,\n", + " 36447, 37354, 39606, 40508, 47846, 51543, 53401, 54302,\n", + " 57110, 63282, 66602, 68429, 69334, 70231, 71138, 94672,\n", + " 102526, 103444, 107073, 111732, 114470, 119041, 124180, 125088,\n", + " 126896, 127789, 129655, 131460, 132383, 154075, 155904, 156827,\n", + " 158517, 160104, 162835, 165533, 166418, 171049, 172412, 175109,\n", + " 176027, 181883, 185117, 186018, 198956, 199860, 200762],\n", + " dtype='int64'), Int64Index([ 2384, 12610, 14463, 16264, 29029, 29936, 34621, 35537,\n", + " 36448, 37355, 39607, 40509, 47847, 51544, 53402, 54303,\n", + " 57111, 63283, 66603, 68430, 69335, 70232, 71139, 94673,\n", + " 102527, 103445, 107074, 111733, 114471, 119042, 124181, 125089,\n", + " 126897, 127790, 129656, 131461, 132384, 154076, 155905, 156828,\n", + " 158518, 160105, 162836, 165534, 166419, 171050, 172413, 175110,\n", + " 176028, 181884, 185118, 186019, 198957, 199861, 200763],\n", + " dtype='int64'), Int64Index([ 2385, 12611, 14464, 16265, 29030, 29937, 34622, 35538,\n", + " 36449, 37356, 39608, 40510, 47848, 51545, 53403, 54304,\n", + " 57112, 63284, 66604, 68431, 69336, 70233, 71140, 94674,\n", + " 102528, 103446, 107075, 111734, 114472, 119043, 124182, 125090,\n", + " 126898, 127791, 129657, 131462, 132385, 154077, 155906, 156829,\n", + " 158519, 160106, 162837, 165535, 166420, 171051, 172414, 175111,\n", + " 176029, 181885, 185119, 186020, 198958, 199862, 200764],\n", + " dtype='int64'), Int64Index([ 2386, 12612, 14465, 16266, 29031, 29938, 34623, 35539,\n", + " 36450, 37357, 39609, 40511, 47849, 51546, 53404, 54305,\n", + " 57113, 63285, 66605, 68432, 69337, 70234, 71141, 94675,\n", + " 102529, 103447, 107076, 111735, 114473, 119044, 124183, 125091,\n", + " 126899, 127792, 129658, 131463, 132386, 154078, 155907, 156830,\n", + " 158520, 160107, 162838, 165536, 166421, 171052, 172415, 175112,\n", + " 176030, 181886, 185120, 186021, 198959, 199863, 200765],\n", + " dtype='int64'), Int64Index([ 2387, 12613, 14466, 16267, 29032, 29939, 34624, 35540,\n", + " 36451, 37358, 39610, 40512, 47850, 51547, 53405, 54306,\n", + " 57114, 63286, 66606, 68433, 69338, 70235, 71142, 94676,\n", + " 102530, 103448, 107077, 111736, 114474, 119045, 124184, 125092,\n", + " 126900, 127793, 129659, 131464, 132387, 154079, 155908, 156831,\n", + " 158521, 160108, 162839, 165537, 166422, 171053, 172416, 175113,\n", + " 176031, 181887, 185121, 186022, 198960, 199864, 200766],\n", + " dtype='int64'), Int64Index([ 2388, 12614, 14467, 16268, 29033, 29940, 34625, 35541,\n", + " 36452, 37359, 39611, 40513, 47851, 51548, 53406, 54307,\n", + " 57115, 63287, 66607, 68434, 69339, 70236, 71143, 94677,\n", + " 102531, 103449, 107078, 111737, 114475, 119046, 124185, 125093,\n", + " 126901, 127794, 129660, 131465, 132388, 154080, 155909, 156832,\n", + " 158522, 160109, 162840, 165538, 166423, 171054, 172417, 175114,\n", + " 176032, 181888, 185122, 186023, 198961, 199865, 200767],\n", + " dtype='int64'), Int64Index([ 2389, 12615, 14468, 16269, 29034, 29941, 34626, 35542,\n", + " 36453, 37360, 39612, 40514, 47852, 51549, 53407, 54308,\n", + " 57116, 63288, 66608, 68435, 69340, 70237, 71144, 94678,\n", + " 102532, 103450, 107079, 111738, 114476, 119047, 124186, 125094,\n", + " 126902, 127795, 129661, 131466, 132389, 154081, 155910, 156833,\n", + " 158523, 160110, 162841, 165539, 166424, 171055, 172418, 175115,\n", + " 176033, 181889, 185123, 186024, 198962, 199866, 200768],\n", + " dtype='int64'), Int64Index([ 2390, 12616, 14469, 16270, 29035, 29942, 34627, 35543,\n", + " 36454, 37361, 39613, 40515, 47853, 51550, 53408, 54309,\n", + " 57117, 63289, 66609, 68436, 69341, 70238, 71145, 94679,\n", + " 102533, 103451, 107080, 111739, 114477, 119048, 124187, 125095,\n", + " 126903, 127796, 129662, 131467, 132390, 154082, 155911, 156834,\n", + " 158524, 160111, 162842, 165540, 166425, 171056, 172419, 175116,\n", + " 176034, 181890, 185124, 186025, 198963, 199867, 200769],\n", + " dtype='int64'), Int64Index([ 2391, 12617, 14470, 16271, 29036, 29943, 34628, 35544,\n", + " 36455, 37362, 39614, 40516, 47854, 51551, 53409, 54310,\n", + " 57118, 63290, 66610, 68437, 69342, 70239, 71146, 94680,\n", + " 102534, 103452, 107081, 111740, 114478, 119049, 124188, 125096,\n", + " 126904, 127797, 129663, 131468, 132391, 154083, 155912, 156835,\n", + " 158525, 160112, 162843, 165541, 166426, 171057, 172420, 175117,\n", + " 176035, 181891, 185125, 186026, 198964, 199868, 200770],\n", + " dtype='int64'), Int64Index([ 2392, 12618, 14471, 16272, 29037, 29944, 34629, 35545,\n", + " 36456, 37363, 39615, 40517, 47855, 51552, 53410, 54311,\n", + " 57119, 63291, 66611, 68438, 69343, 70240, 71147, 94681,\n", + " 102535, 103453, 107082, 111741, 114479, 119050, 124189, 125097,\n", + " 126905, 127798, 129664, 131469, 132392, 154084, 155913, 156836,\n", + " 158526, 160113, 162844, 165542, 166427, 171058, 172421, 175118,\n", + " 176036, 181892, 185126, 186027, 198965, 199869, 200771],\n", + " dtype='int64'), Int64Index([ 2393, 12619, 14472, 16273, 29038, 29945, 34630, 35546,\n", + " 36457, 37364, 39616, 40518, 47856, 51553, 53411, 54312,\n", + " 57120, 63292, 66612, 68439, 69344, 70241, 71148, 94682,\n", + " 102536, 103454, 107083, 111742, 114480, 119051, 124190, 125098,\n", + " 126906, 127799, 129665, 131470, 132393, 154085, 155914, 156837,\n", + " 158527, 160114, 162845, 165543, 166428, 171059, 172422, 175119,\n", + " 176037, 181893, 185127, 186028, 198966, 199870, 200772],\n", + " dtype='int64'), Int64Index([ 2394, 12620, 14473, 16274, 29039, 29946, 34631, 35547,\n", + " 36458, 37365, 39617, 40519, 47857, 51554, 53412, 54313,\n", + " 57121, 63293, 66613, 68440, 69345, 70242, 71149, 94683,\n", + " 102537, 103455, 107084, 111743, 114481, 119052, 124191, 125099,\n", + " 126907, 127800, 129666, 131471, 132394, 154086, 155915, 156838,\n", + " 158528, 160115, 162846, 165544, 166429, 171060, 172423, 175120,\n", + " 176038, 181894, 185128, 186029, 198967, 199871, 200773],\n", + " dtype='int64'), Int64Index([ 2395, 12621, 14474, 16275, 29040, 29947, 34632, 35548,\n", + " 36459, 37366, 39618, 40520, 47858, 51555, 53413, 54314,\n", + " 57122, 63294, 66614, 68441, 69346, 70243, 71150, 94684,\n", + " 102538, 103456, 107085, 111744, 114482, 119053, 124192, 125100,\n", + " 126908, 127801, 129667, 131472, 132395, 154087, 155916, 156839,\n", + " 158529, 160116, 162847, 165545, 166430, 171061, 172424, 175121,\n", + " 176039, 181895, 185129, 186030, 198968, 199872, 200774],\n", + " dtype='int64'), Int64Index([ 2396, 12622, 14475, 16276, 29041, 29948, 34633, 35549,\n", + " 36460, 37367, 39619, 40521, 47859, 51556, 53414, 54315,\n", + " 57123, 63295, 66615, 68442, 69347, 70244, 71151, 94685,\n", + " 102539, 103457, 107086, 111745, 114483, 119054, 124193, 125101,\n", + " 126909, 127802, 129668, 131473, 132396, 154088, 155917, 156840,\n", + " 158530, 160117, 162848, 165546, 166431, 171062, 172425, 175122,\n", + " 176040, 181896, 185130, 186031, 198969, 199873, 200775],\n", + " dtype='int64'), Int64Index([ 2397, 12623, 14476, 16277, 29042, 29949, 34634, 35550,\n", + " 36461, 37368, 39620, 40522, 47860, 51557, 53415, 54316,\n", + " 57124, 63296, 66616, 68443, 69348, 70245, 71152, 94686,\n", + " 102540, 103458, 107087, 111746, 114484, 119055, 124194, 125102,\n", + " 126910, 127803, 129669, 131474, 132397, 154089, 155918, 156841,\n", + " 158531, 160118, 162849, 165547, 166432, 171063, 172426, 175123,\n", + " 176041, 181897, 185131, 186032, 198970, 199874, 200776],\n", + " dtype='int64'), Int64Index([ 2398, 12624, 14477, 16278, 29043, 29950, 34635, 35551,\n", + " 36462, 37369, 39621, 40523, 47861, 51558, 53416, 54317,\n", + " 57125, 63297, 66617, 68444, 69349, 70246, 71153, 94687,\n", + " 102541, 103459, 107088, 111747, 114485, 119056, 124195, 125103,\n", + " 126911, 127804, 129670, 131475, 132398, 154090, 155919, 156842,\n", + " 158532, 160119, 162850, 165548, 166433, 171064, 172427, 175124,\n", + " 176042, 181898, 185132, 186033, 198971, 199875, 200777],\n", + " dtype='int64'), Int64Index([ 2399, 12625, 14478, 16279, 29044, 29951, 34636, 35552,\n", + " 36463, 37370, 39622, 40524, 47862, 51559, 53417, 54318,\n", + " 57126, 63298, 66618, 68445, 69350, 70247, 71154, 94688,\n", + " 102542, 103460, 107089, 111748, 114486, 119057, 124196, 125104,\n", + " 126912, 127805, 129671, 131476, 132399, 154091, 155920, 156843,\n", + " 158533, 160120, 162851, 165549, 166434, 171065, 172428, 175125,\n", + " 176043, 181899, 185133, 186034, 198972, 199876, 200778],\n", + " dtype='int64'), Int64Index([ 2400, 12626, 14479, 16280, 29045, 29952, 34637, 35553,\n", + " 36464, 37371, 39623, 40525, 47863, 51560, 53418, 54319,\n", + " 57127, 63299, 66619, 68446, 69351, 70248, 71155, 94689,\n", + " 102543, 103461, 107090, 111749, 114487, 119058, 124197, 125105,\n", + " 126913, 127806, 129672, 131477, 132400, 154092, 155921, 156844,\n", + " 158534, 160121, 162852, 165550, 166435, 171066, 172429, 175126,\n", + " 176044, 181900, 185134, 186035, 198973, 199877, 200779],\n", + " dtype='int64'), Int64Index([ 2401, 12627, 14480, 16281, 29046, 29953, 34638, 35554,\n", + " 36465, 37372, 39624, 40526, 47864, 51561, 53419, 54320,\n", + " 57128, 63300, 66620, 68447, 69352, 70249, 71156, 94690,\n", + " 102544, 103462, 107091, 111750, 114488, 119059, 124198, 125106,\n", + " 126914, 127807, 129673, 131478, 132401, 154093, 155922, 156845,\n", + " 158535, 160122, 162853, 165551, 166436, 171067, 172430, 175127,\n", + " 176045, 181901, 185135, 186036, 198974, 199878, 200780],\n", + " dtype='int64'), Int64Index([ 2402, 12628, 14481, 16282, 29047, 29954, 34639, 35555,\n", + " 36466, 37373, 39625, 40527, 47865, 51562, 53420, 54321,\n", + " 57129, 63301, 66621, 68448, 69353, 70250, 71157, 94691,\n", + " 102545, 103463, 107092, 111751, 114489, 119060, 124199, 125107,\n", + " 126915, 127808, 129674, 131479, 132402, 154094, 155923, 156846,\n", + " 158536, 160123, 162854, 165552, 166437, 171068, 172431, 175128,\n", + " 176046, 181902, 185136, 186037, 198975, 199879, 200781],\n", + " dtype='int64'), Int64Index([ 2403, 12629, 14482, 16283, 29048, 29955, 34640, 35556,\n", + " 36467, 37374, 39626, 40528, 47866, 51563, 53421, 54322,\n", + " 57130, 63302, 66622, 68449, 69354, 70251, 71158, 94692,\n", + " 102546, 103464, 107093, 111752, 114490, 119061, 124200, 125108,\n", + " 126916, 127809, 129675, 131480, 132403, 154095, 155924, 156847,\n", + " 158537, 160124, 162855, 165553, 166438, 171069, 172432, 175129,\n", + " 176047, 181903, 185137, 186038, 198976, 199880, 200782],\n", + " dtype='int64'), Int64Index([ 2404, 12630, 14483, 16284, 29049, 29956, 34641, 35557,\n", + " 36468, 37375, 39627, 40529, 47867, 51564, 53422, 54323,\n", + " 57131, 63303, 66623, 68450, 69355, 70252, 71159, 94693,\n", + " 102547, 103465, 107094, 111753, 114491, 119062, 124201, 125109,\n", + " 126917, 127810, 129676, 131481, 132404, 154096, 155925, 156848,\n", + " 158538, 160125, 162856, 165554, 166439, 171070, 172433, 175130,\n", + " 176048, 181904, 185138, 186039, 198977, 199881, 200783],\n", + " dtype='int64'), Int64Index([ 2405, 12631, 14484, 16285, 29050, 29957, 34642, 35558,\n", + " 36469, 37376, 39628, 40530, 47868, 51565, 53423, 54324,\n", + " 57132, 63304, 66624, 68451, 69356, 70253, 71160, 94694,\n", + " 102548, 103466, 107095, 111754, 114492, 119063, 124202, 125110,\n", + " 126918, 127811, 129677, 131482, 132405, 154097, 155926, 156849,\n", + " 158539, 160126, 162857, 165555, 166440, 171071, 172434, 175131,\n", + " 176049, 181905, 185139, 186040, 198978, 199882, 200784],\n", + " dtype='int64'), Int64Index([ 2406, 12632, 14485, 16286, 29051, 29958, 34643, 35559,\n", + " 36470, 37377, 39629, 40531, 47869, 51566, 53424, 54325,\n", + " 57133, 63305, 66625, 68452, 69357, 70254, 71161, 94695,\n", + " 102549, 103467, 107096, 111755, 114493, 119064, 124203, 125111,\n", + " 126919, 127812, 129678, 131483, 132406, 154098, 155927, 156850,\n", + " 158540, 160127, 162858, 165556, 166441, 171072, 172435, 175132,\n", + " 176050, 181906, 185140, 186041, 198979, 199883, 200785],\n", + " dtype='int64'), Int64Index([ 2407, 12633, 14486, 16287, 29052, 29959, 34644, 35560,\n", + " 36471, 37378, 39630, 40532, 47870, 51567, 53425, 54326,\n", + " 57134, 63306, 66626, 68453, 69358, 70255, 71162, 94696,\n", + " 102550, 103468, 107097, 111756, 114494, 119065, 124204, 125112,\n", + " 126920, 127813, 129679, 131484, 132407, 154099, 155928, 156851,\n", + " 158541, 160128, 162859, 165557, 166442, 171073, 172436, 175133,\n", + " 176051, 181907, 185141, 186042, 198980, 199884, 200786],\n", + " dtype='int64'), Int64Index([ 2408, 12634, 14487, 16288, 29053, 29960, 34645, 35561,\n", + " 36472, 37379, 39631, 40533, 47871, 51568, 53426, 54327,\n", + " 57135, 63307, 66627, 68454, 69359, 70256, 71163, 94697,\n", + " 102551, 103469, 107098, 111757, 114495, 119066, 124205, 125113,\n", + " 126921, 127814, 129680, 131485, 132408, 154100, 155929, 156852,\n", + " 158542, 160129, 162860, 165558, 166443, 171074, 172437, 175134,\n", + " 176052, 181908, 185142, 186043, 198981, 199885, 200787],\n", + " dtype='int64'), Int64Index([ 2409, 12635, 14488, 16289, 29054, 29961, 34646, 35562,\n", + " 36473, 37380, 39632, 40534, 47872, 51569, 53427, 54328,\n", + " 57136, 63308, 66628, 68455, 69360, 70257, 71164, 94698,\n", + " 102552, 103470, 107099, 111758, 114496, 119067, 124206, 125114,\n", + " 126922, 127815, 129681, 131486, 132409, 154101, 155930, 156853,\n", + " 158543, 160130, 162861, 165559, 166444, 171075, 172438, 175135,\n", + " 176053, 181909, 185143, 186044, 198982, 199886, 200788],\n", + " dtype='int64'), Int64Index([ 2410, 12636, 14489, 16290, 29055, 29962, 34647, 35563,\n", + " 36474, 37381, 39633, 40535, 47873, 51570, 53428, 54329,\n", + " 57137, 63309, 66629, 68456, 69361, 70258, 71165, 94699,\n", + " 102553, 103471, 107100, 111759, 114497, 119068, 124207, 125115,\n", + " 126923, 127816, 129682, 131487, 132410, 154102, 155931, 156854,\n", + " 158544, 160131, 162862, 165560, 166445, 171076, 172439, 175136,\n", + " 176054, 181910, 185144, 186045, 198983, 199887, 200789],\n", + " dtype='int64'), Int64Index([ 2411, 12637, 14490, 16291, 29056, 29963, 34648, 35564,\n", + " 36475, 37382, 39634, 40536, 47874, 51571, 53429, 54330,\n", + " 57138, 63310, 66630, 68457, 69362, 70259, 71166, 94700,\n", + " 102554, 103472, 107101, 111760, 114498, 119069, 124208, 125116,\n", + " 126924, 127817, 129683, 131488, 132411, 154103, 155932, 156855,\n", + " 158545, 160132, 162863, 165561, 166446, 171077, 172440, 175137,\n", + " 176055, 181911, 185145, 186046, 198984, 199888, 200790],\n", + " dtype='int64'), Int64Index([ 2412, 12638, 14491, 16292, 29057, 29964, 34649, 35565,\n", + " 36476, 37383, 39635, 40537, 47875, 51572, 53430, 54331,\n", + " 57139, 63311, 66631, 68458, 69363, 70260, 71167, 94701,\n", + " 102555, 103473, 107102, 111761, 114499, 119070, 124209, 125117,\n", + " 126925, 127818, 129684, 131489, 132412, 154104, 155933, 156856,\n", + " 158546, 160133, 162864, 165562, 166447, 171078, 172441, 175138,\n", + " 176056, 181912, 185146, 186047, 198985, 199889, 200791],\n", + " dtype='int64'), Int64Index([ 2413, 12639, 14492, 16293, 29058, 29965, 34650, 35566,\n", + " 36477, 37384, 39636, 40538, 47876, 51573, 53431, 54332,\n", + " 57140, 63312, 66632, 68459, 69364, 70261, 71168, 94702,\n", + " 102556, 103474, 107103, 111762, 114500, 119071, 124210, 125118,\n", + " 126926, 127819, 129685, 131490, 132413, 154105, 155934, 156857,\n", + " 158547, 160134, 162865, 165563, 166448, 171079, 172442, 175139,\n", + " 176057, 181913, 185147, 186048, 198986, 199890, 200792],\n", + " dtype='int64'), Int64Index([ 2414, 12640, 14493, 16294, 29059, 29966, 34651, 35567,\n", + " 36478, 37385, 39637, 40539, 47877, 51574, 53432, 54333,\n", + " 57141, 63313, 66633, 68460, 69365, 70262, 71169, 94703,\n", + " 102557, 103475, 107104, 111763, 114501, 119072, 124211, 125119,\n", + " 126927, 127820, 129686, 131491, 132414, 154106, 155935, 156858,\n", + " 158548, 160135, 162866, 165564, 166449, 171080, 172443, 175140,\n", + " 176058, 181914, 185148, 186049, 198987, 199891, 200793],\n", + " dtype='int64'), Int64Index([ 2415, 12641, 14494, 16295, 29060, 29967, 34652, 35568,\n", + " 36479, 37386, 39638, 40540, 47878, 51575, 53433, 54334,\n", + " 57142, 63314, 66634, 68461, 69366, 70263, 71170, 94704,\n", + " 102558, 103476, 107105, 111764, 114502, 119073, 124212, 125120,\n", + " 126928, 127821, 129687, 131492, 132415, 154107, 155936, 156859,\n", + " 158549, 160136, 162867, 165565, 166450, 171081, 172444, 175141,\n", + " 176059, 181915, 185149, 186050, 198988, 199892, 200794],\n", + " dtype='int64'), Int64Index([ 2416, 12642, 14495, 16296, 29061, 29968, 34653, 35569,\n", + " 36480, 37387, 39639, 40541, 47879, 51576, 53434, 54335,\n", + " 57143, 63315, 66635, 68462, 69367, 70264, 71171, 94705,\n", + " 102559, 103477, 107106, 111765, 114503, 119074, 124213, 125121,\n", + " 126929, 127822, 129688, 131493, 132416, 154108, 155937, 156860,\n", + " 158550, 160137, 162868, 165566, 166451, 171082, 172445, 175142,\n", + " 176060, 181916, 185150, 186051, 198989, 199893, 200795],\n", + " dtype='int64'), Int64Index([ 2417, 12643, 14496, 16297, 29062, 29969, 34654, 35570,\n", + " 36481, 37388, 39640, 40542, 47880, 51577, 53435, 54336,\n", + " 57144, 63316, 66636, 68463, 69368, 70265, 71172, 94706,\n", + " 102560, 103478, 107107, 111766, 114504, 119075, 124214, 125122,\n", + " 126930, 127823, 129689, 131494, 132417, 154109, 155938, 156861,\n", + " 158551, 160138, 162869, 165567, 166452, 171083, 172446, 175143,\n", + " 176061, 181917, 185151, 186052, 198990, 199894, 200796],\n", + " dtype='int64'), Int64Index([ 2418, 12644, 14497, 16298, 29063, 29970, 34655, 35571,\n", + " 36482, 37389, 39641, 40543, 47881, 51578, 53436, 54337,\n", + " 57145, 63317, 66637, 68464, 69369, 70266, 71173, 94707,\n", + " 102561, 103479, 107108, 111767, 114505, 119076, 124215, 125123,\n", + " 126931, 127824, 129690, 131495, 132418, 154110, 155939, 156862,\n", + " 158552, 160139, 162870, 165568, 166453, 171084, 172447, 175144,\n", + " 176062, 181918, 185152, 186053, 198991, 199895, 200797],\n", + " dtype='int64'), Int64Index([ 2419, 12645, 14498, 16299, 29064, 29971, 34656, 35572,\n", + " 36483, 37390, 39642, 40544, 47882, 51579, 53437, 54338,\n", + " 57146, 63318, 66638, 68465, 69370, 70267, 71174, 94708,\n", + " 102562, 103480, 107109, 111768, 114506, 119077, 124216, 125124,\n", + " 126932, 127825, 129691, 131496, 132419, 154111, 155940, 156863,\n", + " 158553, 160140, 162871, 165569, 166454, 171085, 172448, 175145,\n", + " 176063, 181919, 185153, 186054, 198992, 199896, 200798],\n", + " dtype='int64'), Int64Index([ 2420, 12646, 14499, 16300, 29065, 29972, 34657, 35573,\n", + " 36484, 37391, 39643, 40545, 47883, 51580, 53438, 54339,\n", + " 57147, 63319, 66639, 68466, 69371, 70268, 71175, 94709,\n", + " 102563, 103481, 107110, 111769, 114507, 119078, 124217, 125125,\n", + " 126933, 127826, 129692, 131497, 132420, 154112, 155941, 156864,\n", + " 158554, 160141, 162872, 165570, 166455, 171086, 172449, 175146,\n", + " 176064, 181920, 185154, 186055, 198993, 199897, 200799],\n", + " dtype='int64'), Int64Index([ 2421, 12647, 14500, 16301, 29066, 29973, 34658, 35574,\n", + " 36485, 37392, 39644, 40546, 47884, 51581, 53439, 54340,\n", + " 57148, 63320, 66640, 68467, 69372, 70269, 71176, 94710,\n", + " 102564, 103482, 107111, 111770, 114508, 119079, 124218, 125126,\n", + " 126934, 127827, 129693, 131498, 132421, 154113, 155942, 156865,\n", + " 158555, 160142, 162873, 165571, 166456, 171087, 172450, 175147,\n", + " 176065, 181921, 185155, 186056, 198994, 199898, 200800],\n", + " dtype='int64'), Int64Index([ 2422, 12648, 14501, 16302, 29067, 29974, 34659, 35575,\n", + " 36486, 37393, 39645, 40547, 47885, 51582, 53440, 54341,\n", + " 57149, 63321, 66641, 68468, 69373, 70270, 71177, 94711,\n", + " 102565, 103483, 107112, 111771, 114509, 119080, 124219, 125127,\n", + " 126935, 127828, 129694, 131499, 132422, 154114, 155943, 156866,\n", + " 158556, 160143, 162874, 165572, 166457, 171088, 172451, 175148,\n", + " 176066, 181922, 185156, 186057, 198995, 199899, 200801],\n", + " dtype='int64'), Int64Index([ 2423, 12649, 14502, 16303, 29068, 29975, 34660, 35576,\n", + " 36487, 37394, 39646, 40548, 47886, 51583, 53441, 54342,\n", + " 57150, 63322, 66642, 68469, 69374, 70271, 71178, 94712,\n", + " 102566, 103484, 107113, 111772, 114510, 119081, 124220, 125128,\n", + " 126936, 127829, 129695, 131500, 132423, 154115, 155944, 156867,\n", + " 158557, 160144, 162875, 165573, 166458, 171089, 172452, 175149,\n", + " 176067, 181923, 185157, 186058, 198996, 199900, 200802],\n", + " dtype='int64'), Int64Index([ 2424, 12650, 14503, 16304, 29069, 29976, 34661, 35577,\n", + " 36488, 37395, 39647, 40549, 47887, 51584, 53442, 54343,\n", + " 57151, 63323, 66643, 68470, 69375, 70272, 71179, 94713,\n", + " 102567, 103485, 107114, 111773, 114511, 119082, 124221, 125129,\n", + " 126937, 127830, 129696, 131501, 132424, 154116, 155945, 156868,\n", + " 158558, 160145, 162876, 165574, 166459, 171090, 172453, 175150,\n", + " 176068, 181924, 185158, 186059, 198997, 199901, 200803],\n", + " dtype='int64'), Int64Index([ 2425, 12651, 14504, 16305, 29070, 29977, 34662, 35578,\n", + " 36489, 37396, 39648, 40550, 47888, 51585, 53443, 54344,\n", + " 57152, 63324, 66644, 68471, 69376, 70273, 71180, 94714,\n", + " 102568, 103486, 107115, 111774, 114512, 119083, 124222, 125130,\n", + " 126938, 127831, 129697, 131502, 132425, 154117, 155946, 156869,\n", + " 158559, 160146, 162877, 165575, 166460, 171091, 172454, 175151,\n", + " 176069, 181925, 185159, 186060, 198998, 199902, 200804],\n", + " dtype='int64'), Int64Index([ 2426, 12652, 14505, 16306, 29071, 29978, 34663, 35579,\n", + " 36490, 37397, 39649, 40551, 47889, 51586, 53444, 54345,\n", + " 57153, 63325, 66645, 68472, 69377, 70274, 71181, 94715,\n", + " 102569, 103487, 107116, 111775, 114513, 119084, 124223, 125131,\n", + " 126939, 127832, 129698, 131503, 132426, 154118, 155947, 156870,\n", + " 158560, 160147, 162878, 165576, 166461, 171092, 172455, 175152,\n", + " 176070, 181926, 185160, 186061, 198999, 199903, 200805],\n", + " dtype='int64'), Int64Index([ 2427, 12653, 14506, 16307, 29072, 29979, 34664, 35580,\n", + " 36491, 37398, 39650, 40552, 47890, 51587, 53445, 54346,\n", + " 57154, 63326, 66646, 68473, 69378, 70275, 71182, 94716,\n", + " 102570, 103488, 107117, 111776, 114514, 119085, 124224, 125132,\n", + " 126940, 127833, 129699, 131504, 132427, 154119, 155948, 156871,\n", + " 158561, 160148, 162879, 165577, 166462, 171093, 172456, 175153,\n", + " 176071, 181927, 185161, 186062, 199000, 199904, 200806],\n", + " dtype='int64'), Int64Index([ 2428, 12654, 14507, 16308, 29073, 29980, 34665, 35581,\n", + " 36492, 37399, 39651, 40553, 47891, 51588, 53446, 54347,\n", + " 57155, 63327, 66647, 68474, 69379, 70276, 71183, 94717,\n", + " 102571, 103489, 107118, 111777, 114515, 119086, 124225, 125133,\n", + " 126941, 127834, 129700, 131505, 132428, 154120, 155949, 156872,\n", + " 158562, 160149, 162880, 165578, 166463, 171094, 172457, 175154,\n", + " 176072, 181928, 185162, 186063, 199001, 199905, 200807],\n", + " dtype='int64'), Int64Index([ 2429, 12655, 14508, 16309, 29074, 29981, 34666, 35582,\n", + " 36493, 37400, 39652, 40554, 47892, 51589, 53447, 54348,\n", + " 57156, 63328, 66648, 68475, 69380, 70277, 71184, 94718,\n", + " 102572, 103490, 107119, 111778, 114516, 119087, 124226, 125134,\n", + " 126942, 127835, 129701, 131506, 132429, 154121, 155950, 156873,\n", + " 158563, 160150, 162881, 165579, 166464, 171095, 172458, 175155,\n", + " 176073, 181929, 185163, 186064, 199002, 199906, 200808],\n", + " dtype='int64'), Int64Index([ 2430, 12656, 14509, 16310, 29075, 29982, 34667, 35583,\n", + " 36494, 37401, 39653, 40555, 47893, 51590, 53448, 54349,\n", + " 57157, 63329, 66649, 68476, 69381, 70278, 71185, 94719,\n", + " 102573, 103491, 107120, 111779, 114517, 119088, 124227, 125135,\n", + " 126943, 127836, 129702, 131507, 132430, 154122, 155951, 156874,\n", + " 158564, 160151, 162882, 165580, 166465, 171096, 172459, 175156,\n", + " 176074, 181930, 185164, 186065, 199003, 199907, 200809],\n", + " dtype='int64'), Int64Index([ 2431, 12657, 14510, 16311, 29076, 29983, 34668, 35584,\n", + " 36495, 37402, 39654, 40556, 47894, 51591, 53449, 54350,\n", + " 57158, 63330, 66650, 68477, 69382, 70279, 71186, 94720,\n", + " 102574, 103492, 107121, 111780, 114518, 119089, 124228, 125136,\n", + " 126944, 127837, 129703, 131508, 132431, 154123, 155952, 156875,\n", + " 158565, 160152, 162883, 165581, 166466, 171097, 172460, 175157,\n", + " 176075, 181931, 185165, 186066, 199004, 199908, 200810],\n", + " dtype='int64'), Int64Index([ 2432, 12658, 14511, 16312, 29077, 29984, 34669, 35585,\n", + " 36496, 37403, 39655, 40557, 47895, 51592, 53450, 54351,\n", + " 57159, 63331, 66651, 68478, 69383, 70280, 71187, 94721,\n", + " 102575, 103493, 107122, 111781, 114519, 119090, 124229, 125137,\n", + " 126945, 127838, 129704, 131509, 132432, 154124, 155953, 156876,\n", + " 158566, 160153, 162884, 165582, 166467, 171098, 172461, 175158,\n", + " 176076, 181932, 185166, 186067, 199005, 199909, 200811],\n", + " dtype='int64'), Int64Index([ 2433, 12659, 14512, 16313, 29078, 29985, 34670, 35586,\n", + " 36497, 37404, 39656, 40558, 47896, 51593, 53451, 54352,\n", + " 57160, 63332, 66652, 68479, 69384, 70281, 71188, 94722,\n", + " 102576, 103494, 107123, 111782, 114520, 119091, 124230, 125138,\n", + " 126946, 127839, 129705, 131510, 132433, 154125, 155954, 156877,\n", + " 158567, 160154, 162885, 165583, 166468, 171099, 172462, 175159,\n", + " 176077, 181933, 185167, 186068, 199006, 199910, 200812],\n", + " dtype='int64'), Int64Index([ 2434, 12660, 14513, 16314, 29079, 29986, 34671, 35587,\n", + " 36498, 37405, 39657, 40559, 47897, 51594, 53452, 54353,\n", + " 57161, 63333, 66653, 68480, 69385, 70282, 71189, 94723,\n", + " 102577, 103495, 107124, 111783, 114521, 119092, 124231, 125139,\n", + " 126947, 127840, 129706, 131511, 132434, 154126, 155955, 156878,\n", + " 158568, 160155, 162886, 165584, 166469, 171100, 172463, 175160,\n", + " 176078, 181934, 185168, 186069, 199007, 199911, 200813],\n", + " dtype='int64'), Int64Index([ 2435, 12661, 14514, 16315, 29080, 29987, 34672, 35588,\n", + " 36499, 37406, 39658, 40560, 47898, 51595, 53453, 54354,\n", + " 57162, 63334, 66654, 68481, 69386, 70283, 71190, 94724,\n", + " 102578, 103496, 107125, 111784, 114522, 119093, 124232, 125140,\n", + " 126948, 127841, 129707, 131512, 132435, 154127, 155956, 156879,\n", + " 158569, 160156, 162887, 165585, 166470, 171101, 172464, 175161,\n", + " 176079, 181935, 185169, 186070, 199008, 199912, 200814],\n", + " dtype='int64'), Int64Index([ 2436, 12662, 14515, 16316, 29081, 29988, 34673, 35589,\n", + " 36500, 37407, 39659, 40561, 47899, 51596, 53454, 54355,\n", + " 57163, 63335, 66655, 68482, 69387, 70284, 71191, 94725,\n", + " 102579, 103497, 107126, 111785, 114523, 119094, 124233, 125141,\n", + " 126949, 127842, 129708, 131513, 132436, 154128, 155957, 156880,\n", + " 158570, 160157, 162888, 165586, 166471, 171102, 172465, 175162,\n", + " 176080, 181936, 185170, 186071, 199009, 199913, 200815],\n", + " dtype='int64'), Int64Index([ 2437, 12663, 14516, 16317, 29082, 29989, 34674, 35590,\n", + " 36501, 37408, 39660, 40562, 47900, 51597, 53455, 54356,\n", + " 57164, 63336, 66656, 68483, 69388, 70285, 71192, 94726,\n", + " 102580, 103498, 107127, 111786, 114524, 119095, 124234, 125142,\n", + " 126950, 127843, 129709, 131514, 132437, 154129, 155958, 156881,\n", + " 158571, 160158, 162889, 165587, 166472, 171103, 172466, 175163,\n", + " 176081, 181937, 185171, 186072, 199010, 199914, 200816],\n", + " dtype='int64'), Int64Index([ 2438, 12664, 14517, 16318, 29083, 29990, 34675, 35591,\n", + " 36502, 37409, 39661, 40563, 47901, 51598, 53456, 54357,\n", + " 57165, 63337, 66657, 68484, 69389, 70286, 71193, 94727,\n", + " 102581, 103499, 107128, 111787, 114525, 119096, 124235, 125143,\n", + " 126951, 127844, 129710, 131515, 132438, 154130, 155959, 156882,\n", + " 158572, 160159, 162890, 165588, 166473, 171104, 172467, 175164,\n", + " 176082, 181938, 185172, 186073, 199011, 199915, 200817],\n", + " dtype='int64'), Int64Index([ 2439, 12665, 14518, 16319, 29084, 29991, 34676, 35592,\n", + " 36503, 37410, 39662, 40564, 47902, 51599, 53457, 54358,\n", + " 57166, 63338, 66658, 68485, 69390, 70287, 71194, 94728,\n", + " 102582, 103500, 107129, 111788, 114526, 119097, 124236, 125144,\n", + " 126952, 127845, 129711, 131516, 132439, 154131, 155960, 156883,\n", + " 158573, 160160, 162891, 165589, 166474, 171105, 172468, 175165,\n", + " 176083, 181939, 185173, 186074, 199012, 199916, 200818],\n", + " dtype='int64'), Int64Index([ 2440, 12666, 14519, 16320, 29085, 29992, 34677, 35593,\n", + " 36504, 37411, 39663, 40565, 47903, 51600, 53458, 54359,\n", + " 57167, 63339, 66659, 68486, 69391, 70288, 71195, 94729,\n", + " 102583, 103501, 107130, 111789, 114527, 119098, 124237, 125145,\n", + " 126953, 127846, 129712, 131517, 132440, 154132, 155961, 156884,\n", + " 158574, 160161, 162892, 165590, 166475, 171106, 172469, 175166,\n", + " 176084, 181940, 185174, 186075, 199013, 199917, 200819],\n", + " dtype='int64'), Int64Index([ 2441, 12667, 14520, 16321, 29086, 29993, 34678, 35594,\n", + " 36505, 37412, 39664, 40566, 47904, 51601, 53459, 54360,\n", + " 57168, 63340, 66660, 68487, 69392, 70289, 71196, 94730,\n", + " 102584, 103502, 107131, 111790, 114528, 119099, 124238, 125146,\n", + " 126954, 127847, 129713, 131518, 132441, 154133, 155962, 156885,\n", + " 158575, 160162, 162893, 165591, 166476, 171107, 172470, 175167,\n", + " 176085, 181941, 185175, 186076, 199014, 199918, 200820],\n", + " dtype='int64'), Int64Index([ 2442, 12668, 14521, 16322, 29087, 29994, 34679, 35595,\n", + " 36506, 37413, 39665, 40567, 47905, 51602, 53460, 54361,\n", + " 57169, 63341, 66661, 68488, 69393, 70290, 71197, 94731,\n", + " 102585, 103503, 107132, 111791, 114529, 119100, 124239, 125147,\n", + " 126955, 127848, 129714, 131519, 132442, 154134, 155963, 156886,\n", + " 158576, 160163, 162894, 165592, 166477, 171108, 172471, 175168,\n", + " 176086, 181942, 185176, 186077, 199015, 199919, 200821],\n", + " dtype='int64'), Int64Index([ 2443, 12669, 14522, 16323, 29088, 29995, 34680, 35596,\n", + " 36507, 37414, 39666, 40568, 47906, 51603, 53461, 54362,\n", + " 57170, 63342, 66662, 68489, 69394, 70291, 71198, 94732,\n", + " 102586, 103504, 107133, 111792, 114530, 119101, 124240, 125148,\n", + " 126956, 127849, 129715, 131520, 132443, 154135, 155964, 156887,\n", + " 158577, 160164, 162895, 165593, 166478, 171109, 172472, 175169,\n", + " 176087, 181943, 185177, 186078, 199016, 199920, 200822],\n", + " dtype='int64'), Int64Index([ 2444, 12670, 14523, 16324, 29089, 29996, 34681, 35597,\n", + " 36508, 37415, 39667, 40569, 47907, 51604, 53462, 54363,\n", + " 57171, 63343, 66663, 68490, 69395, 70292, 71199, 94733,\n", + " 102587, 103505, 107134, 111793, 114531, 119102, 124241, 125149,\n", + " 126957, 127850, 129716, 131521, 132444, 154136, 155965, 156888,\n", + " 158578, 160165, 162896, 165594, 166479, 171110, 172473, 175170,\n", + " 176088, 181944, 185178, 186079, 199017, 199921, 200823],\n", + " dtype='int64'), Int64Index([ 2445, 12671, 14524, 16325, 29090, 29997, 34682, 35598,\n", + " 36509, 37416, 39668, 40570, 47908, 51605, 53463, 54364,\n", + " 57172, 63344, 66664, 68491, 69396, 70293, 71200, 94734,\n", + " 102588, 103506, 107135, 111794, 114532, 119103, 124242, 125150,\n", + " 126958, 127851, 129717, 131522, 132445, 154137, 155966, 156889,\n", + " 158579, 160166, 162897, 165595, 166480, 171111, 172474, 175171,\n", + " 176089, 181945, 185179, 186080, 199018, 199922, 200824],\n", + " dtype='int64'), Int64Index([ 2446, 12672, 14525, 16326, 29091, 29998, 34683, 35599,\n", + " 36510, 37417, 39669, 40571, 47909, 51606, 53464, 54365,\n", + " 57173, 63345, 66665, 68492, 69397, 70294, 71201, 94735,\n", + " 102589, 103507, 107136, 111795, 114533, 119104, 124243, 125151,\n", + " 126959, 127852, 129718, 131523, 132446, 154138, 155967, 156890,\n", + " 158580, 160167, 162898, 165596, 166481, 171112, 172475, 175172,\n", + " 176090, 181946, 185180, 186081, 199019, 199923, 200825],\n", + " dtype='int64'), Int64Index([ 2447, 12673, 14526, 16327, 29092, 29999, 34684, 35600,\n", + " 36511, 37418, 39670, 40572, 47910, 51607, 53465, 54366,\n", + " 57174, 63346, 66666, 68493, 69398, 70295, 71202, 94736,\n", + " 102590, 103508, 107137, 111796, 114534, 119105, 124244, 125152,\n", + " 126960, 127853, 129719, 131524, 132447, 154139, 155968, 156891,\n", + " 158581, 160168, 162899, 165597, 166482, 171113, 172476, 175173,\n", + " 176091, 181947, 185181, 186082, 199020, 199924, 200826],\n", + " dtype='int64'), Int64Index([ 2448, 12674, 14527, 16328, 29093, 30000, 34685, 35601,\n", + " 36512, 37419, 39671, 40573, 47911, 51608, 53466, 54367,\n", + " 57175, 63347, 66667, 68494, 69399, 70296, 71203, 94737,\n", + " 102591, 103509, 107138, 111797, 114535, 119106, 124245, 125153,\n", + " 126961, 127854, 129720, 131525, 132448, 154140, 155969, 156892,\n", + " 158582, 160169, 162900, 165598, 166483, 171114, 172477, 175174,\n", + " 176092, 181948, 185182, 186083, 199021, 199925, 200827],\n", + " dtype='int64'), Int64Index([ 2449, 12675, 14528, 16329, 29094, 30001, 34686, 35602,\n", + " 36513, 37420, 39672, 40574, 47912, 51609, 53467, 54368,\n", + " 57176, 63348, 66668, 68495, 69400, 70297, 71204, 94738,\n", + " 102592, 103510, 107139, 111798, 114536, 119107, 124246, 125154,\n", + " 126962, 127855, 129721, 131526, 132449, 154141, 155970, 156893,\n", + " 158583, 160170, 162901, 165599, 166484, 171115, 172478, 175175,\n", + " 176093, 181949, 185183, 186084, 199022, 199926, 200828],\n", + " dtype='int64'), Int64Index([ 2450, 12676, 14529, 16330, 29095, 30002, 34687, 35603,\n", + " 36514, 37421, 39673, 40575, 47913, 51610, 53468, 54369,\n", + " 57177, 63349, 66669, 68496, 69401, 70298, 71205, 94739,\n", + " 102593, 103511, 107140, 111799, 114537, 119108, 124247, 125155,\n", + " 126963, 127856, 129722, 131527, 132450, 154142, 155971, 156894,\n", + " 158584, 160171, 162902, 165600, 166485, 171116, 172479, 175176,\n", + " 176094, 181950, 185184, 186085, 199023, 199927, 200829],\n", + " dtype='int64'), Int64Index([ 2451, 12677, 14530, 16331, 29096, 30003, 34688, 35604,\n", + " 36515, 37422, 39674, 40576, 47914, 51611, 53469, 54370,\n", + " 57178, 63350, 66670, 68497, 69402, 70299, 71206, 94740,\n", + " 102594, 103512, 107141, 111800, 114538, 119109, 124248, 125156,\n", + " 126964, 127857, 129723, 131528, 132451, 154143, 155972, 156895,\n", + " 158585, 160172, 162903, 165601, 166486, 171117, 172480, 175177,\n", + " 176095, 181951, 185185, 186086, 199024, 199928, 200830],\n", + " dtype='int64'), Int64Index([ 2452, 12678, 14531, 16332, 29097, 30004, 34689, 35605,\n", + " 36516, 37423, 39675, 40577, 47915, 51612, 53470, 54371,\n", + " 57179, 63351, 66671, 68498, 69403, 70300, 71207, 94741,\n", + " 102595, 103513, 107142, 111801, 114539, 119110, 124249, 125157,\n", + " 126965, 127858, 129724, 131529, 132452, 154144, 155973, 156896,\n", + " 158586, 160173, 162904, 165602, 166487, 171118, 172481, 175178,\n", + " 176096, 181952, 185186, 186087, 199025, 199929, 200831],\n", + " dtype='int64'), Int64Index([ 2453, 12679, 14532, 16333, 29098, 30005, 34690, 35606,\n", + " 36517, 37424, 39676, 40578, 47916, 51613, 53471, 54372,\n", + " 57180, 63352, 66672, 68499, 69404, 70301, 71208, 94742,\n", + " 102596, 103514, 107143, 111802, 114540, 119111, 124250, 125158,\n", + " 126966, 127859, 129725, 131530, 132453, 154145, 155974, 156897,\n", + " 158587, 160174, 162905, 165603, 166488, 171119, 172482, 175179,\n", + " 176097, 181953, 185187, 186088, 199026, 199930, 200832],\n", + " dtype='int64'), Int64Index([ 2454, 12680, 14533, 16334, 29099, 30006, 34691, 35607,\n", + " 36518, 37425, 39677, 40579, 47917, 51614, 53472, 54373,\n", + " 57181, 63353, 66673, 68500, 69405, 70302, 71209, 94743,\n", + " 102597, 103515, 107144, 111803, 114541, 119112, 124251, 125159,\n", + " 126967, 127860, 129726, 131531, 132454, 154146, 155975, 156898,\n", + " 158588, 160175, 162906, 165604, 166489, 171120, 172483, 175180,\n", + " 176098, 181954, 185188, 186089, 199027, 199931, 200833],\n", + " dtype='int64'), Int64Index([ 2455, 12681, 14534, 16335, 29100, 30007, 34692, 35608,\n", + " 36519, 37426, 39678, 40580, 47918, 51615, 53473, 54374,\n", + " 57182, 63354, 66674, 68501, 69406, 70303, 71210, 94744,\n", + " 102598, 103516, 107145, 111804, 114542, 119113, 124252, 125160,\n", + " 126968, 127861, 129727, 131532, 132455, 154147, 155976, 156899,\n", + " 158589, 160176, 162907, 165605, 166490, 171121, 172484, 175181,\n", + " 176099, 181955, 185189, 186090, 199028, 199932, 200834],\n", + " dtype='int64'), Int64Index([ 2456, 12682, 14535, 16336, 29101, 30008, 34693, 35609,\n", + " 36520, 37427, 39679, 40581, 47919, 51616, 53474, 54375,\n", + " 57183, 63355, 66675, 68502, 69407, 70304, 71211, 94745,\n", + " 102599, 103517, 107146, 111805, 114543, 119114, 124253, 125161,\n", + " 126969, 127862, 129728, 131533, 132456, 154148, 155977, 156900,\n", + " 158590, 160177, 162908, 165606, 166491, 171122, 172485, 175182,\n", + " 176100, 181956, 185190, 186091, 199029, 199933, 200835],\n", + " dtype='int64'), Int64Index([ 2457, 12683, 14536, 16337, 29102, 30009, 34694, 35610,\n", + " 36521, 37428, 39680, 40582, 47920, 51617, 53475, 54376,\n", + " 57184, 63356, 66676, 68503, 69408, 70305, 71212, 94746,\n", + " 102600, 103518, 107147, 111806, 114544, 119115, 124254, 125162,\n", + " 126970, 127863, 129729, 131534, 132457, 154149, 155978, 156901,\n", + " 158591, 160178, 162909, 165607, 166492, 171123, 172486, 175183,\n", + " 176101, 181957, 185191, 186092, 199030, 199934, 200836],\n", + " dtype='int64'), Int64Index([ 2458, 12684, 14537, 16338, 29103, 30010, 34695, 35611,\n", + " 36522, 37429, 39681, 40583, 47921, 51618, 53476, 54377,\n", + " 57185, 63357, 66677, 68504, 69409, 70306, 71213, 94747,\n", + " 102601, 103519, 107148, 111807, 114545, 119116, 124255, 125163,\n", + " 126971, 127864, 129730, 131535, 132458, 154150, 155979, 156902,\n", + " 158592, 160179, 162910, 165608, 166493, 171124, 172487, 175184,\n", + " 176102, 181958, 185192, 186093, 199031, 199935, 200837],\n", + " dtype='int64'), Int64Index([ 2459, 12685, 14538, 16339, 29104, 30011, 34696, 35612,\n", + " 36523, 37430, 39682, 40584, 47922, 51619, 53477, 54378,\n", + " 57186, 63358, 66678, 68505, 69410, 70307, 71214, 94748,\n", + " 102602, 103520, 107149, 111808, 114546, 119117, 124256, 125164,\n", + " 126972, 127865, 129731, 131536, 132459, 154151, 155980, 156903,\n", + " 158593, 160180, 162911, 165609, 166494, 171125, 172488, 175185,\n", + " 176103, 181959, 185193, 186094, 199032, 199936, 200838],\n", + " dtype='int64'), Int64Index([ 2460, 12686, 14539, 16340, 29105, 30012, 34697, 35613,\n", + " 36524, 37431, 39683, 40585, 47923, 51620, 53478, 54379,\n", + " 57187, 63359, 66679, 68506, 69411, 70308, 71215, 94749,\n", + " 102603, 103521, 107150, 111809, 114547, 119118, 124257, 125165,\n", + " 126973, 127866, 129732, 131537, 132460, 154152, 155981, 156904,\n", + " 158594, 160181, 162912, 165610, 166495, 171126, 172489, 175186,\n", + " 176104, 181960, 185194, 186095, 199033, 199937, 200839],\n", + " dtype='int64'), Int64Index([ 2461, 12687, 14540, 16341, 29106, 30013, 34698, 35614,\n", + " 36525, 37432, 39684, 40586, 47924, 51621, 53479, 54380,\n", + " 57188, 63360, 66680, 68507, 69412, 70309, 71216, 94750,\n", + " 102604, 103522, 107151, 111810, 114548, 119119, 124258, 125166,\n", + " 126974, 127867, 129733, 131538, 132461, 154153, 155982, 156905,\n", + " 158595, 160182, 162913, 165611, 166496, 171127, 172490, 175187,\n", + " 176105, 181961, 185195, 186096, 199034, 199938, 200840],\n", + " dtype='int64'), Int64Index([ 2462, 12688, 14541, 16342, 29107, 30014, 34699, 35615,\n", + " 36526, 37433, 39685, 40587, 47925, 51622, 53480, 54381,\n", + " 57189, 63361, 66681, 68508, 69413, 70310, 71217, 94751,\n", + " 102605, 103523, 107152, 111811, 114549, 119120, 124259, 125167,\n", + " 126975, 127868, 129734, 131539, 132462, 154154, 155983, 156906,\n", + " 158596, 160183, 162914, 165612, 166497, 171128, 172491, 175188,\n", + " 176106, 181962, 185196, 186097, 199035, 199939, 200841],\n", + " dtype='int64'), Int64Index([ 2463, 12689, 14542, 16343, 29108, 30015, 34700, 35616,\n", + " 36527, 37434, 39686, 40588, 47926, 51623, 53481, 54382,\n", + " 57190, 63362, 66682, 68509, 69414, 70311, 71218, 94752,\n", + " 102606, 103524, 107153, 111812, 114550, 119121, 124260, 125168,\n", + " 126976, 127869, 129735, 131540, 132463, 154155, 155984, 156907,\n", + " 158597, 160184, 162915, 165613, 166498, 171129, 172492, 175189,\n", + " 176107, 181963, 185197, 186098, 199036, 199940, 200842],\n", + " dtype='int64'), Int64Index([ 2464, 12690, 14543, 16344, 29109, 30016, 34701, 35617,\n", + " 36528, 37435, 39687, 40589, 47927, 51624, 53482, 54383,\n", + " 57191, 63363, 66683, 68510, 69415, 70312, 71219, 94753,\n", + " 102607, 103525, 107154, 111813, 114551, 119122, 124261, 125169,\n", + " 126977, 127870, 129736, 131541, 132464, 154156, 155985, 156908,\n", + " 158598, 160185, 162916, 165614, 166499, 171130, 172493, 175190,\n", + " 176108, 181964, 185198, 186099, 199037, 199941, 200843],\n", + " dtype='int64'), Int64Index([ 2465, 12691, 14544, 16345, 29110, 30017, 34702, 35618,\n", + " 36529, 37436, 39688, 40590, 47928, 51625, 53483, 54384,\n", + " 57192, 63364, 66684, 68511, 69416, 70313, 71220, 94754,\n", + " 102608, 103526, 107155, 111814, 114552, 119123, 124262, 125170,\n", + " 126978, 127871, 129737, 131542, 132465, 154157, 155986, 156909,\n", + " 158599, 160186, 162917, 165615, 166500, 171131, 172494, 175191,\n", + " 176109, 181965, 185199, 186100, 199038, 199942, 200844],\n", + " dtype='int64'), Int64Index([ 2466, 12692, 14545, 16346, 29111, 30018, 34703, 35619,\n", + " 36530, 37437, 39689, 40591, 47929, 51626, 53484, 54385,\n", + " 57193, 63365, 66685, 68512, 69417, 70314, 71221, 94755,\n", + " 102609, 103527, 107156, 111815, 114553, 119124, 124263, 125171,\n", + " 126979, 127872, 129738, 131543, 132466, 154158, 155987, 156910,\n", + " 158600, 160187, 162918, 165616, 166501, 171132, 172495, 175192,\n", + " 176110, 181966, 185200, 186101, 199039, 199943, 200845],\n", + " dtype='int64'), Int64Index([ 2467, 12693, 14546, 16347, 29112, 30019, 34704, 35620,\n", + " 36531, 37438, 39690, 40592, 47930, 51627, 53485, 54386,\n", + " 57194, 63366, 66686, 68513, 69418, 70315, 71222, 94756,\n", + " 102610, 103528, 107157, 111816, 114554, 119125, 124264, 125172,\n", + " 126980, 127873, 129739, 131544, 132467, 154159, 155988, 156911,\n", + " 158601, 160188, 162919, 165617, 166502, 171133, 172496, 175193,\n", + " 176111, 181967, 185201, 186102, 199040, 199944, 200846],\n", + " dtype='int64'), Int64Index([ 2468, 12694, 14547, 16348, 29113, 30020, 34705, 35621,\n", + " 36532, 37439, 39691, 40593, 47931, 51628, 53486, 54387,\n", + " 57195, 63367, 66687, 68514, 69419, 70316, 71223, 94757,\n", + " 102611, 103529, 107158, 111817, 114555, 119126, 124265, 125173,\n", + " 126981, 127874, 129740, 131545, 132468, 154160, 155989, 156912,\n", + " 158602, 160189, 162920, 165618, 166503, 171134, 172497, 175194,\n", + " 176112, 181968, 185202, 186103, 199041, 199945, 200847],\n", + " dtype='int64'), Int64Index([ 2469, 12695, 14548, 16349, 29114, 30021, 34706, 35622,\n", + " 36533, 37440, 39692, 40594, 47932, 51629, 53487, 54388,\n", + " 57196, 63368, 66688, 68515, 69420, 70317, 71224, 94758,\n", + " 102612, 103530, 107159, 111818, 114556, 119127, 124266, 125174,\n", + " 126982, 127875, 129741, 131546, 132469, 154161, 155990, 156913,\n", + " 158603, 160190, 162921, 165619, 166504, 171135, 172498, 175195,\n", + " 176113, 181969, 185203, 186104, 199042, 199946, 200848],\n", + " dtype='int64'), Int64Index([ 2470, 12696, 14549, 16350, 29115, 30022, 34707, 35623,\n", + " 36534, 37441, 39693, 40595, 47933, 51630, 53488, 54389,\n", + " 57197, 63369, 66689, 68516, 69421, 70318, 71225, 94759,\n", + " 102613, 103531, 107160, 111819, 114557, 119128, 124267, 125175,\n", + " 126983, 127876, 129742, 131547, 132470, 154162, 155991, 156914,\n", + " 158604, 160191, 162922, 165620, 166505, 171136, 172499, 175196,\n", + " 176114, 181970, 185204, 186105, 199043, 199947, 200849],\n", + " dtype='int64'), Int64Index([ 2471, 12697, 14550, 16351, 29116, 30023, 34708, 35624,\n", + " 36535, 37442, 39694, 40596, 47934, 51631, 53489, 54390,\n", + " 57198, 63370, 66690, 68517, 69422, 70319, 71226, 94760,\n", + " 102614, 103532, 107161, 111820, 114558, 119129, 124268, 125176,\n", + " 126984, 127877, 129743, 131548, 132471, 154163, 155992, 156915,\n", + " 158605, 160192, 162923, 165621, 166506, 171137, 172500, 175197,\n", + " 176115, 181971, 185205, 186106, 199044, 199948, 200850],\n", + " dtype='int64'), Int64Index([ 2472, 12698, 14551, 16352, 29117, 30024, 34709, 35625,\n", + " 36536, 37443, 39695, 40597, 47935, 51632, 53490, 54391,\n", + " 57199, 63371, 66691, 68518, 69423, 70320, 71227, 94761,\n", + " 102615, 103533, 107162, 111821, 114559, 119130, 124269, 125177,\n", + " 126985, 127878, 129744, 131549, 132472, 154164, 155993, 156916,\n", + " 158606, 160193, 162924, 165622, 166507, 171138, 172501, 175198,\n", + " 176116, 181972, 185206, 186107, 199045, 199949, 200851],\n", + " dtype='int64'), Int64Index([ 2473, 12699, 14552, 16353, 29118, 30025, 34710, 35626,\n", + " 36537, 37444, 39696, 40598, 47936, 51633, 53491, 54392,\n", + " 57200, 63372, 66692, 68519, 69424, 70321, 71228, 94762,\n", + " 102616, 103534, 107163, 111822, 114560, 119131, 124270, 125178,\n", + " 126986, 127879, 129745, 131550, 132473, 154165, 155994, 156917,\n", + " 158607, 160194, 162925, 165623, 166508, 171139, 172502, 175199,\n", + " 176117, 181973, 185207, 186108, 199046, 199950, 200852],\n", + " dtype='int64'), Int64Index([ 2474, 12700, 14553, 16354, 29119, 30026, 34711, 35627,\n", + " 36538, 37445, 39697, 40599, 47937, 51634, 53492, 54393,\n", + " 57201, 63373, 66693, 68520, 69425, 70322, 71229, 94763,\n", + " 102617, 103535, 107164, 111823, 114561, 119132, 124271, 125179,\n", + " 126987, 127880, 129746, 131551, 132474, 154166, 155995, 156918,\n", + " 158608, 160195, 162926, 165624, 166509, 171140, 172503, 175200,\n", + " 176118, 181974, 185208, 186109, 199047, 199951, 200853],\n", + " dtype='int64'), Int64Index([ 2475, 12701, 14554, 16355, 29120, 30027, 34712, 35628,\n", + " 36539, 37446, 39698, 40600, 47938, 51635, 53493, 54394,\n", + " 57202, 63374, 66694, 68521, 69426, 70323, 71230, 94764,\n", + " 102618, 103536, 107165, 111824, 114562, 119133, 124272, 125180,\n", + " 126988, 127881, 129747, 131552, 132475, 154167, 155996, 156919,\n", + " 158609, 160196, 162927, 165625, 166510, 171141, 172504, 175201,\n", + " 176119, 181975, 185209, 186110, 199048, 199952, 200854],\n", + " dtype='int64'), Int64Index([ 2476, 12702, 14555, 16356, 29121, 30028, 34713, 35629,\n", + " 36540, 37447, 39699, 40601, 47939, 51636, 53494, 54395,\n", + " 57203, 63375, 66695, 68522, 69427, 70324, 71231, 94765,\n", + " 102619, 103537, 107166, 111825, 114563, 119134, 124273, 125181,\n", + " 126989, 127882, 129748, 131553, 132476, 154168, 155997, 156920,\n", + " 158610, 160197, 162928, 165626, 166511, 171142, 172505, 175202,\n", + " 176120, 181976, 185210, 186111, 199049, 199953, 200855],\n", + " dtype='int64'), Int64Index([ 2477, 12703, 14556, 16357, 29122, 30029, 34714, 35630,\n", + " 36541, 37448, 39700, 40602, 47940, 51637, 53495, 54396,\n", + " 57204, 63376, 66696, 68523, 69428, 70325, 71232, 94766,\n", + " 102620, 103538, 107167, 111826, 114564, 119135, 124274, 125182,\n", + " 126990, 127883, 129749, 131554, 132477, 154169, 155998, 156921,\n", + " 158611, 160198, 162929, 165627, 166512, 171143, 172506, 175203,\n", + " 176121, 181977, 185211, 186112, 199050, 199954, 200856],\n", + " dtype='int64'), Int64Index([ 2478, 12704, 14557, 16358, 29123, 30030, 34715, 35631,\n", + " 36542, 37449, 39701, 40603, 47941, 51638, 53496, 54397,\n", + " 57205, 63377, 66697, 68524, 69429, 70326, 71233, 94767,\n", + " 102621, 103539, 107168, 111827, 114565, 119136, 124275, 125183,\n", + " 126991, 127884, 129750, 131555, 132478, 154170, 155999, 156922,\n", + " 158612, 160199, 162930, 165628, 166513, 171144, 172507, 175204,\n", + " 176122, 181978, 185212, 186113, 199051, 199955, 200857],\n", + " dtype='int64'), Int64Index([ 2479, 12705, 14558, 16359, 29124, 30031, 34716, 35632,\n", + " 36543, 37450, 39702, 40604, 47942, 51639, 53497, 54398,\n", + " 57206, 63378, 66698, 68525, 69430, 70327, 71234, 94768,\n", + " 102622, 103540, 107169, 111828, 114566, 119137, 124276, 125184,\n", + " 126992, 127885, 129751, 131556, 132479, 154171, 156000, 156923,\n", + " 158613, 160200, 162931, 165629, 166514, 171145, 172508, 175205,\n", + " 176123, 181979, 185213, 186114, 199052, 199956, 200858],\n", + " dtype='int64'), Int64Index([ 2480, 12706, 14559, 16360, 29125, 30032, 34717, 35633,\n", + " 36544, 37451, 39703, 40605, 47943, 51640, 53498, 54399,\n", + " 57207, 63379, 66699, 68526, 69431, 70328, 71235, 94769,\n", + " 102623, 103541, 107170, 111829, 114567, 119138, 124277, 125185,\n", + " 126993, 127886, 129752, 131557, 132480, 154172, 156001, 156924,\n", + " 158614, 160201, 162932, 165630, 166515, 171146, 172509, 175206,\n", + " 176124, 181980, 185214, 186115, 199053, 199957, 200859],\n", + " dtype='int64'), Int64Index([ 2481, 12707, 14560, 16361, 29126, 30033, 34718, 35634,\n", + " 36545, 37452, 39704, 40606, 47944, 51641, 53499, 54400,\n", + " 57208, 63380, 66700, 68527, 69432, 70329, 71236, 94770,\n", + " 102624, 103542, 107171, 111830, 114568, 119139, 124278, 125186,\n", + " 126994, 127887, 129753, 131558, 132481, 154173, 156002, 156925,\n", + " 158615, 160202, 162933, 165631, 166516, 171147, 172510, 175207,\n", + " 176125, 181981, 185215, 186116, 199054, 199958, 200860],\n", + " dtype='int64'), Int64Index([ 2482, 12708, 14561, 16362, 29127, 30034, 34719, 35635,\n", + " 36546, 37453, 39705, 40607, 47945, 51642, 53500, 54401,\n", + " 57209, 63381, 66701, 68528, 69433, 70330, 71237, 94771,\n", + " 102625, 103543, 107172, 111831, 114569, 119140, 124279, 125187,\n", + " 126995, 127888, 129754, 131559, 132482, 154174, 156003, 156926,\n", + " 158616, 160203, 162934, 165632, 166517, 171148, 172511, 175208,\n", + " 176126, 181982, 185216, 186117, 199055, 199959, 200861],\n", + " dtype='int64'), Int64Index([ 2483, 12709, 14562, 16363, 29128, 30035, 34720, 35636,\n", + " 36547, 37454, 39706, 40608, 47946, 51643, 53501, 54402,\n", + " 57210, 63382, 66702, 68529, 69434, 70331, 71238, 94772,\n", + " 102626, 103544, 107173, 111832, 114570, 119141, 124280, 125188,\n", + " 126996, 127889, 129755, 131560, 132483, 154175, 156004, 156927,\n", + " 158617, 160204, 162935, 165633, 166518, 171149, 172512, 175209,\n", + " 176127, 181983, 185217, 186118, 199056, 199960, 200862],\n", + " dtype='int64'), Int64Index([ 2484, 12710, 14563, 16364, 29129, 30036, 34721, 35637,\n", + " 36548, 37455, 39707, 40609, 47947, 51644, 53502, 54403,\n", + " 57211, 63383, 66703, 68530, 69435, 70332, 71239, 94773,\n", + " 102627, 103545, 107174, 111833, 114571, 119142, 124281, 125189,\n", + " 126997, 127890, 129756, 131561, 132484, 154176, 156005, 156928,\n", + " 158618, 160205, 162936, 165634, 166519, 171150, 172513, 175210,\n", + " 176128, 181984, 185218, 186119, 199057, 199961, 200863],\n", + " dtype='int64'), Int64Index([ 2485, 12711, 14564, 16365, 29130, 30037, 34722, 35638,\n", + " 36549, 37456, 39708, 40610, 47948, 51645, 53503, 54404,\n", + " 57212, 63384, 66704, 68531, 69436, 70333, 71240, 94774,\n", + " 102628, 103546, 107175, 111834, 114572, 119143, 124282, 125190,\n", + " 126998, 127891, 129757, 131562, 132485, 154177, 156006, 156929,\n", + " 158619, 160206, 162937, 165635, 166520, 171151, 172514, 175211,\n", + " 176129, 181985, 185219, 186120, 199058, 199962, 200864],\n", + " dtype='int64'), Int64Index([ 2486, 12712, 14565, 16366, 29131, 30038, 34723, 35639,\n", + " 36550, 37457, 39709, 40611, 47949, 51646, 53504, 54405,\n", + " 57213, 63385, 66705, 68532, 69437, 70334, 71241, 94775,\n", + " 102629, 103547, 107176, 111835, 114573, 119144, 124283, 125191,\n", + " 126999, 127892, 129758, 131563, 132486, 154178, 156007, 156930,\n", + " 158620, 160207, 162938, 165636, 166521, 171152, 172515, 175212,\n", + " 176130, 181986, 185220, 186121, 199059, 199963, 200865],\n", + " dtype='int64'), Int64Index([ 2487, 12713, 14566, 16367, 29132, 30039, 34724, 35640,\n", + " 36551, 37458, 39710, 40612, 47950, 51647, 53505, 54406,\n", + " 57214, 63386, 66706, 68533, 69438, 70335, 71242, 94776,\n", + " 102630, 103548, 107177, 111836, 114574, 119145, 124284, 125192,\n", + " 127000, 127893, 129759, 131564, 132487, 154179, 156008, 156931,\n", + " 158621, 160208, 162939, 165637, 166522, 171153, 172516, 175213,\n", + " 176131, 181987, 185221, 186122, 199060, 199964, 200866],\n", + " dtype='int64'), Int64Index([ 2488, 12714, 14567, 16368, 29133, 30040, 34725, 35641,\n", + " 36552, 37459, 39711, 40613, 47951, 51648, 53506, 54407,\n", + " 57215, 63387, 66707, 68534, 69439, 70336, 71243, 94777,\n", + " 102631, 103549, 107178, 111837, 114575, 119146, 124285, 125193,\n", + " 127001, 127894, 129760, 131565, 132488, 154180, 156009, 156932,\n", + " 158622, 160209, 162940, 165638, 166523, 171154, 172517, 175214,\n", + " 176132, 181988, 185222, 186123, 199061, 199965, 200867],\n", + " dtype='int64'), Int64Index([ 2489, 12715, 14568, 16369, 29134, 30041, 34726, 35642,\n", + " 36553, 37460, 39712, 40614, 47952, 51649, 53507, 54408,\n", + " 57216, 63388, 66708, 68535, 69440, 70337, 71244, 94778,\n", + " 102632, 103550, 107179, 111838, 114576, 119147, 124286, 125194,\n", + " 127002, 127895, 129761, 131566, 132489, 154181, 156010, 156933,\n", + " 158623, 160210, 162941, 165639, 166524, 171155, 172518, 175215,\n", + " 176133, 181989, 185223, 186124, 199062, 199966, 200868],\n", + " dtype='int64'), Int64Index([ 2490, 12716, 14569, 16370, 29135, 30042, 34727, 35643,\n", + " 36554, 37461, 39713, 40615, 47953, 51650, 53508, 54409,\n", + " 57217, 63389, 66709, 68536, 69441, 70338, 71245, 94779,\n", + " 102633, 103551, 107180, 111839, 114577, 119148, 124287, 125195,\n", + " 127003, 127896, 129762, 131567, 132490, 154182, 156011, 156934,\n", + " 158624, 160211, 162942, 165640, 166525, 171156, 172519, 175216,\n", + " 176134, 181990, 185224, 186125, 199063, 199967, 200869],\n", + " dtype='int64'), Int64Index([ 2491, 12717, 14570, 16371, 29136, 30043, 34728, 35644,\n", + " 36555, 37462, 39714, 40616, 47954, 51651, 53509, 54410,\n", + " 57218, 63390, 66710, 68537, 69442, 70339, 71246, 94780,\n", + " 102634, 103552, 107181, 111840, 114578, 119149, 124288, 125196,\n", + " 127004, 127897, 129763, 131568, 132491, 154183, 156012, 156935,\n", + " 158625, 160212, 162943, 165641, 166526, 171157, 172520, 175217,\n", + " 176135, 181991, 185225, 186126, 199064, 199968, 200870],\n", + " dtype='int64'), Int64Index([ 2492, 12718, 14571, 16372, 29137, 30044, 34729, 35645,\n", + " 36556, 37463, 39715, 40617, 47955, 51652, 53510, 54411,\n", + " 57219, 63391, 66711, 68538, 69443, 70340, 71247, 94781,\n", + " 102635, 103553, 107182, 111841, 114579, 119150, 124289, 125197,\n", + " 127005, 127898, 129764, 131569, 132492, 154184, 156013, 156936,\n", + " 158626, 160213, 162944, 165642, 166527, 171158, 172521, 175218,\n", + " 176136, 181992, 185226, 186127, 199065, 199969, 200871],\n", + " dtype='int64'), Int64Index([ 2493, 12719, 14572, 16373, 29138, 30045, 34730, 35646,\n", + " 36557, 37464, 39716, 40618, 47956, 51653, 53511, 54412,\n", + " 57220, 63392, 66712, 68539, 69444, 70341, 71248, 94782,\n", + " 102636, 103554, 107183, 111842, 114580, 119151, 124290, 125198,\n", + " 127006, 127899, 129765, 131570, 132493, 154185, 156014, 156937,\n", + " 158627, 160214, 162945, 165643, 166528, 171159, 172522, 175219,\n", + " 176137, 181993, 185227, 186128, 199066, 199970, 200872],\n", + " dtype='int64'), Int64Index([ 2494, 12720, 14573, 16374, 29139, 30046, 34731, 35647,\n", + " 36558, 37465, 39717, 40619, 47957, 51654, 53512, 54413,\n", + " 57221, 63393, 66713, 68540, 69445, 70342, 71249, 94783,\n", + " 102637, 103555, 107184, 111843, 114581, 119152, 124291, 125199,\n", + " 127007, 127900, 129766, 131571, 132494, 154186, 156015, 156938,\n", + " 158628, 160215, 162946, 165644, 166529, 171160, 172523, 175220,\n", + " 176138, 181994, 185228, 186129, 199067, 199971, 200873],\n", + " dtype='int64'), Int64Index([ 2495, 12721, 14574, 16375, 29140, 30047, 34732, 35648,\n", + " 36559, 37466, 39718, 40620, 47958, 51655, 53513, 54414,\n", + " 57222, 63394, 66714, 68541, 69446, 70343, 71250, 94784,\n", + " 102638, 103556, 107185, 111844, 114582, 119153, 124292, 125200,\n", + " 127008, 127901, 129767, 131572, 132495, 154187, 156016, 156939,\n", + " 158629, 160216, 162947, 165645, 166530, 171161, 172524, 175221,\n", + " 176139, 181995, 185229, 186130, 199068, 199972, 200874],\n", + " dtype='int64'), Int64Index([ 2496, 12722, 14575, 16376, 29141, 30048, 34733, 35649,\n", + " 36560, 37467, 39719, 40621, 47959, 51656, 53514, 54415,\n", + " 57223, 63395, 66715, 68542, 69447, 70344, 71251, 94785,\n", + " 102639, 103557, 107186, 111845, 114583, 119154, 124293, 125201,\n", + " 127009, 127902, 129768, 131573, 132496, 154188, 156017, 156940,\n", + " 158630, 160217, 162948, 165646, 166531, 171162, 172525, 175222,\n", + " 176140, 181996, 185230, 186131, 199069, 199973, 200875],\n", + " dtype='int64'), Int64Index([ 2497, 12723, 14576, 16377, 29142, 30049, 34734, 35650,\n", + " 36561, 37468, 39720, 40622, 47960, 51657, 53515, 54416,\n", + " 57224, 63396, 66716, 68543, 69448, 70345, 71252, 94786,\n", + " 102640, 103558, 107187, 111846, 114584, 119155, 124294, 125202,\n", + " 127010, 127903, 129769, 131574, 132497, 154189, 156018, 156941,\n", + " 158631, 160218, 162949, 165647, 166532, 171163, 172526, 175223,\n", + " 176141, 181997, 185231, 186132, 199070, 199974, 200876],\n", + " dtype='int64'), Int64Index([ 2498, 12724, 14577, 16378, 29143, 30050, 34735, 35651,\n", + " 36562, 37469, 39721, 40623, 47961, 51658, 53516, 54417,\n", + " 57225, 63397, 66717, 68544, 69449, 70346, 71253, 94787,\n", + " 102641, 103559, 107188, 111847, 114585, 119156, 124295, 125203,\n", + " 127011, 127904, 129770, 131575, 132498, 154190, 156019, 156942,\n", + " 158632, 160219, 162950, 165648, 166533, 171164, 172527, 175224,\n", + " 176142, 181998, 185232, 186133, 199071, 199975, 200877],\n", + " dtype='int64'), Int64Index([ 2499, 12725, 14578, 16379, 29144, 30051, 34736, 35652,\n", + " 36563, 37470, 39722, 40624, 47962, 51659, 53517, 54418,\n", + " 57226, 63398, 66718, 68545, 69450, 70347, 71254, 94788,\n", + " 102642, 103560, 107189, 111848, 114586, 119157, 124296, 125204,\n", + " 127012, 127905, 129771, 131576, 132499, 154191, 156020, 156943,\n", + " 158633, 160220, 162951, 165649, 166534, 171165, 172528, 175225,\n", + " 176143, 181999, 185233, 186134, 199072, 199976, 200878],\n", + " dtype='int64'), Int64Index([ 2500, 12726, 14579, 16380, 29145, 30052, 34737, 35653,\n", + " 36564, 37471, 39723, 40625, 47963, 51660, 53518, 54419,\n", + " 57227, 63399, 66719, 68546, 69451, 70348, 71255, 94789,\n", + " 102643, 103561, 107190, 111849, 114587, 119158, 124297, 125205,\n", + " 127013, 127906, 129772, 131577, 132500, 154192, 156021, 156944,\n", + " 158634, 160221, 162952, 165650, 166535, 171166, 172529, 175226,\n", + " 176144, 182000, 185234, 186135, 199073, 199977, 200879],\n", + " dtype='int64'), Int64Index([ 2501, 12727, 14580, 16381, 29146, 30053, 34738, 35654,\n", + " 36565, 37472, 39724, 40626, 47964, 51661, 53519, 54420,\n", + " 57228, 63400, 66720, 68547, 69452, 70349, 71256, 94790,\n", + " 102644, 103562, 107191, 111850, 114588, 119159, 124298, 125206,\n", + " 127014, 127907, 129773, 131578, 132501, 154193, 156022, 156945,\n", + " 158635, 160222, 162953, 165651, 166536, 171167, 172530, 175227,\n", + " 176145, 182001, 185235, 186136, 199074, 199978, 200880],\n", + " dtype='int64'), Int64Index([ 2502, 12728, 14581, 16382, 29147, 30054, 34739, 35655,\n", + " 36566, 37473, 39725, 40627, 47965, 51662, 53520, 54421,\n", + " 57229, 63401, 66721, 68548, 69453, 70350, 71257, 94791,\n", + " 102645, 103563, 107192, 111851, 114589, 119160, 124299, 125207,\n", + " 127015, 127908, 129774, 131579, 132502, 154194, 156023, 156946,\n", + " 158636, 160223, 162954, 165652, 166537, 171168, 172531, 175228,\n", + " 176146, 182002, 185236, 186137, 199075, 199979, 200881],\n", + " dtype='int64'), Int64Index([ 2503, 12729, 14582, 16383, 29148, 30055, 34740, 35656,\n", + " 36567, 37474, 39726, 40628, 47966, 51663, 53521, 54422,\n", + " 57230, 63402, 66722, 68549, 69454, 70351, 71258, 94792,\n", + " 102646, 103564, 107193, 111852, 114590, 119161, 124300, 125208,\n", + " 127016, 127909, 129775, 131580, 132503, 154195, 156024, 156947,\n", + " 158637, 160224, 162955, 165653, 166538, 171169, 172532, 175229,\n", + " 176147, 182003, 185237, 186138, 199076, 199980, 200882],\n", + " dtype='int64'), Int64Index([ 2504, 12730, 14583, 16384, 29149, 30056, 34741, 35657,\n", + " 36568, 37475, 39727, 40629, 47967, 51664, 53522, 54423,\n", + " 57231, 63403, 66723, 68550, 69455, 70352, 71259, 94793,\n", + " 102647, 103565, 107194, 111853, 114591, 119162, 124301, 125209,\n", + " 127017, 127910, 129776, 131581, 132504, 154196, 156025, 156948,\n", + " 158638, 160225, 162956, 165654, 166539, 171170, 172533, 175230,\n", + " 176148, 182004, 185238, 186139, 199077, 199981, 200883],\n", + " dtype='int64'), Int64Index([ 2505, 12731, 14584, 16385, 29150, 30057, 34742, 35658,\n", + " 36569, 37476, 39728, 40630, 47968, 51665, 53523, 54424,\n", + " 57232, 63404, 66724, 68551, 69456, 70353, 71260, 94794,\n", + " 102648, 103566, 107195, 111854, 114592, 119163, 124302, 125210,\n", + " 127018, 127911, 129777, 131582, 132505, 154197, 156026, 156949,\n", + " 158639, 160226, 162957, 165655, 166540, 171171, 172534, 175231,\n", + " 176149, 182005, 185239, 186140, 199078, 199982, 200884],\n", + " dtype='int64'), Int64Index([ 2506, 12732, 14585, 16386, 29151, 30058, 34743, 35659,\n", + " 36570, 37477, 39729, 40631, 47969, 51666, 53524, 54425,\n", + " 57233, 63405, 66725, 68552, 69457, 70354, 71261, 94795,\n", + " 102649, 103567, 107196, 111855, 114593, 119164, 124303, 125211,\n", + " 127019, 127912, 129778, 131583, 132506, 154198, 156027, 156950,\n", + " 158640, 160227, 162958, 165656, 166541, 171172, 172535, 175232,\n", + " 176150, 182006, 185240, 186141, 199079, 199983, 200885],\n", + " dtype='int64'), Int64Index([ 2507, 12733, 14586, 16387, 29152, 30059, 34744, 35660,\n", + " 36571, 37478, 39730, 40632, 47970, 51667, 53525, 54426,\n", + " 57234, 63406, 66726, 68553, 69458, 70355, 71262, 94796,\n", + " 102650, 103568, 107197, 111856, 114594, 119165, 124304, 125212,\n", + " 127020, 127913, 129779, 131584, 132507, 154199, 156028, 156951,\n", + " 158641, 160228, 162959, 165657, 166542, 171173, 172536, 175233,\n", + " 176151, 182007, 185241, 186142, 199080, 199984, 200886],\n", + " dtype='int64'), Int64Index([ 2508, 12734, 14587, 16388, 29153, 30060, 34745, 35661,\n", + " 36572, 37479, 39731, 40633, 47971, 51668, 53526, 54427,\n", + " 57235, 63407, 66727, 68554, 69459, 70356, 71263, 94797,\n", + " 102651, 103569, 107198, 111857, 114595, 119166, 124305, 125213,\n", + " 127021, 127914, 129780, 131585, 132508, 154200, 156029, 156952,\n", + " 158642, 160229, 162960, 165658, 166543, 171174, 172537, 175234,\n", + " 176152, 182008, 185242, 186143, 199081, 199985, 200887],\n", + " dtype='int64'), Int64Index([ 2509, 12735, 14588, 16389, 29154, 30061, 34746, 35662,\n", + " 36573, 37480, 39732, 40634, 47972, 51669, 53527, 54428,\n", + " 57236, 63408, 66728, 68555, 69460, 70357, 71264, 94798,\n", + " 102652, 103570, 107199, 111858, 114596, 119167, 124306, 125214,\n", + " 127022, 127915, 129781, 131586, 132509, 154201, 156030, 156953,\n", + " 158643, 160230, 162961, 165659, 166544, 171175, 172538, 175235,\n", + " 176153, 182009, 185243, 186144, 199082, 199986, 200888],\n", + " dtype='int64'), Int64Index([ 2510, 12736, 14589, 16390, 29155, 30062, 34747, 35663,\n", + " 36574, 37481, 39733, 40635, 47973, 51670, 53528, 54429,\n", + " 57237, 63409, 66729, 68556, 69461, 70358, 71265, 94799,\n", + " 102653, 103571, 107200, 111859, 114597, 119168, 124307, 125215,\n", + " 127023, 127916, 129782, 131587, 132510, 154202, 156031, 156954,\n", + " 158644, 160231, 162962, 165660, 166545, 171176, 172539, 175236,\n", + " 176154, 182010, 185244, 186145, 199083, 199987, 200889],\n", + " dtype='int64'), Int64Index([ 2511, 12737, 14590, 16391, 29156, 30063, 34748, 35664,\n", + " 36575, 37482, 39734, 40636, 47974, 51671, 53529, 54430,\n", + " 57238, 63410, 66730, 68557, 69462, 70359, 71266, 94800,\n", + " 102654, 103572, 107201, 111860, 114598, 119169, 124308, 125216,\n", + " 127024, 127917, 129783, 131588, 132511, 154203, 156032, 156955,\n", + " 158645, 160232, 162963, 165661, 166546, 171177, 172540, 175237,\n", + " 176155, 182011, 185245, 186146, 199084, 199988, 200890],\n", + " dtype='int64'), Int64Index([ 2512, 12738, 14591, 16392, 29157, 30064, 34749, 35665,\n", + " 36576, 37483, 39735, 40637, 47975, 51672, 53530, 54431,\n", + " 57239, 63411, 66731, 68558, 69463, 70360, 71267, 94801,\n", + " 102655, 103573, 107202, 111861, 114599, 119170, 124309, 125217,\n", + " 127025, 127918, 129784, 131589, 132512, 154204, 156033, 156956,\n", + " 158646, 160233, 162964, 165662, 166547, 171178, 172541, 175238,\n", + " 176156, 182012, 185246, 186147, 199085, 199989, 200891],\n", + " dtype='int64'), Int64Index([ 2513, 12739, 14592, 16393, 29158, 30065, 34750, 35666,\n", + " 36577, 37484, 39736, 40638, 47976, 51673, 53531, 54432,\n", + " 57240, 63412, 66732, 68559, 69464, 70361, 71268, 94802,\n", + " 102656, 103574, 107203, 111862, 114600, 119171, 124310, 125218,\n", + " 127026, 127919, 129785, 131590, 132513, 154205, 156034, 156957,\n", + " 158647, 160234, 162965, 165663, 166548, 171179, 172542, 175239,\n", + " 176157, 182013, 185247, 186148, 199086, 199990, 200892],\n", + " dtype='int64'), Int64Index([ 2514, 12740, 14593, 16394, 29159, 30066, 34751, 35667,\n", + " 36578, 37485, 39737, 40639, 47977, 51674, 53532, 54433,\n", + " 57241, 63413, 66733, 68560, 69465, 70362, 71269, 94803,\n", + " 102657, 103575, 107204, 111863, 114601, 119172, 124311, 125219,\n", + " 127027, 127920, 129786, 131591, 132514, 154206, 156035, 156958,\n", + " 158648, 160235, 162966, 165664, 166549, 171180, 172543, 175240,\n", + " 176158, 182014, 185248, 186149, 199087, 199991, 200893],\n", + " dtype='int64'), Int64Index([ 2515, 12741, 14594, 16395, 29160, 30067, 34752, 35668,\n", + " 36579, 37486, 39738, 40640, 47978, 51675, 53533, 54434,\n", + " 57242, 63414, 66734, 68561, 69466, 70363, 71270, 94804,\n", + " 102658, 103576, 107205, 111864, 114602, 119173, 124312, 125220,\n", + " 127028, 127921, 129787, 131592, 132515, 154207, 156036, 156959,\n", + " 158649, 160236, 162967, 165665, 166550, 171181, 172544, 175241,\n", + " 176159, 182015, 185249, 186150, 199088, 199992, 200894],\n", + " dtype='int64'), Int64Index([ 2516, 12742, 14595, 16396, 29161, 30068, 34753, 35669,\n", + " 36580, 37487, 39739, 40641, 47979, 51676, 53534, 54435,\n", + " 57243, 63415, 66735, 68562, 69467, 70364, 71271, 94805,\n", + " 102659, 103577, 107206, 111865, 114603, 119174, 124313, 125221,\n", + " 127029, 127922, 129788, 131593, 132516, 154208, 156037, 156960,\n", + " 158650, 160237, 162968, 165666, 166551, 171182, 172545, 175242,\n", + " 176160, 182016, 185250, 186151, 199089, 199993, 200895],\n", + " dtype='int64'), Int64Index([ 2517, 12743, 14596, 16397, 29162, 30069, 34754, 35670,\n", + " 36581, 37488, 39740, 40642, 47980, 51677, 53535, 54436,\n", + " 57244, 63416, 66736, 68563, 69468, 70365, 71272, 94806,\n", + " 102660, 103578, 107207, 111866, 114604, 119175, 124314, 125222,\n", + " 127030, 127923, 129789, 131594, 132517, 154209, 156038, 156961,\n", + " 158651, 160238, 162969, 165667, 166552, 171183, 172546, 175243,\n", + " 176161, 182017, 185251, 186152, 199090, 199994, 200896],\n", + " dtype='int64'), Int64Index([ 2518, 12744, 14597, 16398, 29163, 30070, 34755, 35671,\n", + " 36582, 37489, 39741, 40643, 47981, 51678, 53536, 54437,\n", + " 57245, 63417, 66737, 68564, 69469, 70366, 71273, 94807,\n", + " 102661, 103579, 107208, 111867, 114605, 119176, 124315, 125223,\n", + " 127031, 127924, 129790, 131595, 132518, 154210, 156039, 156962,\n", + " 158652, 160239, 162970, 165668, 166553, 171184, 172547, 175244,\n", + " 176162, 182018, 185252, 186153, 199091, 199995, 200897],\n", + " dtype='int64'), Int64Index([ 2519, 12745, 14598, 16399, 29164, 30071, 34756, 35672,\n", + " 36583, 37490, 39742, 40644, 47982, 51679, 53537, 54438,\n", + " 57246, 63418, 66738, 68565, 69470, 70367, 71274, 94808,\n", + " 102662, 103580, 107209, 111868, 114606, 119177, 124316, 125224,\n", + " 127032, 127925, 129791, 131596, 132519, 154211, 156040, 156963,\n", + " 158653, 160240, 162971, 165669, 166554, 171185, 172548, 175245,\n", + " 176163, 182019, 185253, 186154, 199092, 199996, 200898],\n", + " dtype='int64'), Int64Index([ 2520, 12746, 14599, 16400, 29165, 30072, 34757, 35673,\n", + " 36584, 37491, 39743, 40645, 47983, 51680, 53538, 54439,\n", + " 57247, 63419, 66739, 68566, 69471, 70368, 71275, 94809,\n", + " 102663, 103581, 107210, 111869, 114607, 119178, 124317, 125225,\n", + " 127033, 127926, 129792, 131597, 132520, 154212, 156041, 156964,\n", + " 158654, 160241, 162972, 165670, 166555, 171186, 172549, 175246,\n", + " 176164, 182020, 185254, 186155, 199093, 199997, 200899],\n", + " dtype='int64'), Int64Index([ 2521, 12747, 14600, 16401, 29166, 30073, 34758, 35674,\n", + " 36585, 37492, 39744, 40646, 47984, 51681, 53539, 54440,\n", + " 57248, 63420, 66740, 68567, 69472, 70369, 71276, 94810,\n", + " 102664, 103582, 107211, 111870, 114608, 119179, 124318, 125226,\n", + " 127034, 127927, 129793, 131598, 132521, 154213, 156042, 156965,\n", + " 158655, 160242, 162973, 165671, 166556, 171187, 172550, 175247,\n", + " 176165, 182021, 185255, 186156, 199094, 199998, 200900],\n", + " dtype='int64'), Int64Index([ 2522, 12748, 14601, 16402, 29167, 30074, 34759, 35675,\n", + " 36586, 37493, 39745, 40647, 47985, 51682, 53540, 54441,\n", + " 57249, 63421, 66741, 68568, 69473, 70370, 71277, 94811,\n", + " 102665, 103583, 107212, 111871, 114609, 119180, 124319, 125227,\n", + " 127035, 127928, 129794, 131599, 132522, 154214, 156043, 156966,\n", + " 158656, 160243, 162974, 165672, 166557, 171188, 172551, 175248,\n", + " 176166, 182022, 185256, 186157, 199095, 199999, 200901],\n", + " dtype='int64'), Int64Index([ 2523, 12749, 14602, 16403, 29168, 30075, 34760, 35676,\n", + " 36587, 37494, 39746, 40648, 47986, 51683, 53541, 54442,\n", + " 57250, 63422, 66742, 68569, 69474, 70371, 71278, 94812,\n", + " 102666, 103584, 107213, 111872, 114610, 119181, 124320, 125228,\n", + " 127036, 127929, 129795, 131600, 132523, 154215, 156044, 156967,\n", + " 158657, 160244, 162975, 165673, 166558, 171189, 172552, 175249,\n", + " 176167, 182023, 185257, 186158, 199096, 200000, 200902],\n", + " dtype='int64'), Int64Index([ 2524, 12750, 14603, 16404, 29169, 30076, 34761, 35677,\n", + " 36588, 37495, 39747, 40649, 47987, 51684, 53542, 54443,\n", + " 57251, 63423, 66743, 68570, 69475, 70372, 71279, 94813,\n", + " 102667, 103585, 107214, 111873, 114611, 119182, 124321, 125229,\n", + " 127037, 127930, 129796, 131601, 132524, 154216, 156045, 156968,\n", + " 158658, 160245, 162976, 165674, 166559, 171190, 172553, 175250,\n", + " 176168, 182024, 185258, 186159, 199097, 200001, 200903],\n", + " dtype='int64'), Int64Index([ 2525, 12751, 14604, 16405, 29170, 30077, 34762, 35678,\n", + " 36589, 37496, 39748, 40650, 47988, 51685, 53543, 54444,\n", + " 57252, 63424, 66744, 68571, 69476, 70373, 71280, 94814,\n", + " 102668, 103586, 107215, 111874, 114612, 119183, 124322, 125230,\n", + " 127038, 127931, 129797, 131602, 132525, 154217, 156046, 156969,\n", + " 158659, 160246, 162977, 165675, 166560, 171191, 172554, 175251,\n", + " 176169, 182025, 185259, 186160, 199098, 200002, 200904],\n", + " dtype='int64'), Int64Index([ 2526, 12752, 14605, 16406, 29171, 30078, 34763, 35679,\n", + " 36590, 37497, 39749, 40651, 47989, 51686, 53544, 54445,\n", + " 57253, 63425, 66745, 68572, 69477, 70374, 71281, 94815,\n", + " 102669, 103587, 107216, 111875, 114613, 119184, 124323, 125231,\n", + " 127039, 127932, 129798, 131603, 132526, 154218, 156047, 156970,\n", + " 158660, 160247, 162978, 165676, 166561, 171192, 172555, 175252,\n", + " 176170, 182026, 185260, 186161, 199099, 200003, 200905],\n", + " dtype='int64'), Int64Index([ 2527, 12753, 14606, 16407, 29172, 30079, 34764, 35680,\n", + " 36591, 37498, 39750, 40652, 47990, 51687, 53545, 54446,\n", + " 57254, 63426, 66746, 68573, 69478, 70375, 71282, 94816,\n", + " 102670, 103588, 107217, 111876, 114614, 119185, 124324, 125232,\n", + " 127040, 127933, 129799, 131604, 132527, 154219, 156048, 156971,\n", + " 158661, 160248, 162979, 165677, 166562, 171193, 172556, 175253,\n", + " 176171, 182027, 185261, 186162, 199100, 200004, 200906],\n", + " dtype='int64'), Int64Index([ 2528, 12754, 14607, 16408, 29173, 30080, 34765, 35681,\n", + " 36592, 37499, 39751, 40653, 47991, 51688, 53546, 54447,\n", + " 57255, 63427, 66747, 68574, 69479, 70376, 71283, 94817,\n", + " 102671, 103589, 107218, 111877, 114615, 119186, 124325, 125233,\n", + " 127041, 127934, 129800, 131605, 132528, 154220, 156049, 156972,\n", + " 158662, 160249, 162980, 165678, 166563, 171194, 172557, 175254,\n", + " 176172, 182028, 185262, 186163, 199101, 200005, 200907],\n", + " dtype='int64'), Int64Index([ 2529, 12755, 14608, 16409, 29174, 30081, 34766, 35682,\n", + " 36593, 37500, 39752, 40654, 47992, 51689, 53547, 54448,\n", + " 57256, 63428, 66748, 68575, 69480, 70377, 71284, 94818,\n", + " 102672, 103590, 107219, 111878, 114616, 119187, 124326, 125234,\n", + " 127042, 127935, 129801, 131606, 132529, 154221, 156050, 156973,\n", + " 158663, 160250, 162981, 165679, 166564, 171195, 172558, 175255,\n", + " 176173, 182029, 185263, 186164, 199102, 200006, 200908],\n", + " dtype='int64'), Int64Index([ 2530, 12756, 14609, 16410, 29175, 30082, 34767, 35683,\n", + " 36594, 37501, 39753, 40655, 47993, 51690, 53548, 54449,\n", + " 57257, 63429, 66749, 68576, 69481, 70378, 71285, 94819,\n", + " 102673, 103591, 107220, 111879, 114617, 119188, 124327, 125235,\n", + " 127043, 127936, 129802, 131607, 132530, 154222, 156051, 156974,\n", + " 158664, 160251, 162982, 165680, 166565, 171196, 172559, 175256,\n", + " 176174, 182030, 185264, 186165, 199103, 200007, 200909],\n", + " dtype='int64'), Int64Index([ 2531, 12757, 14610, 16411, 29176, 30083, 34768, 35684,\n", + " 36595, 37502, 39754, 40656, 47994, 51691, 53549, 54450,\n", + " 57258, 63430, 66750, 68577, 69482, 70379, 71286, 94820,\n", + " 102674, 103592, 107221, 111880, 114618, 119189, 124328, 125236,\n", + " 127044, 127937, 129803, 131608, 132531, 154223, 156052, 156975,\n", + " 158665, 160252, 162983, 165681, 166566, 171197, 172560, 175257,\n", + " 176175, 182031, 185265, 186166, 199104, 200008, 200910],\n", + " dtype='int64'), Int64Index([ 2532, 12758, 14611, 16412, 29177, 30084, 34769, 35685,\n", + " 36596, 37503, 39755, 40657, 47995, 51692, 53550, 54451,\n", + " 57259, 63431, 66751, 68578, 69483, 70380, 71287, 94821,\n", + " 102675, 103593, 107222, 111881, 114619, 119190, 124329, 125237,\n", + " 127045, 127938, 129804, 131609, 132532, 154224, 156053, 156976,\n", + " 158666, 160253, 162984, 165682, 166567, 171198, 172561, 175258,\n", + " 176176, 182032, 185266, 186167, 199105, 200009, 200911],\n", + " dtype='int64'), Int64Index([ 2533, 12759, 14612, 16413, 29178, 30085, 34770, 35686,\n", + " 36597, 37504, 39756, 40658, 47996, 51693, 53551, 54452,\n", + " 57260, 63432, 66752, 68579, 69484, 70381, 71288, 94822,\n", + " 102676, 103594, 107223, 111882, 114620, 119191, 124330, 125238,\n", + " 127046, 127939, 129805, 131610, 132533, 154225, 156054, 156977,\n", + " 158667, 160254, 162985, 165683, 166568, 171199, 172562, 175259,\n", + " 176177, 182033, 185267, 186168, 199106, 200010, 200912],\n", + " dtype='int64'), Int64Index([ 2534, 12760, 14613, 16414, 29179, 30086, 34771, 35687,\n", + " 36598, 37505, 39757, 40659, 47997, 51694, 53552, 54453,\n", + " 57261, 63433, 66753, 68580, 69485, 70382, 71289, 94823,\n", + " 102677, 103595, 107224, 111883, 114621, 119192, 124331, 125239,\n", + " 127047, 127940, 129806, 131611, 132534, 154226, 156055, 156978,\n", + " 158668, 160255, 162986, 165684, 166569, 171200, 172563, 175260,\n", + " 176178, 182034, 185268, 186169, 199107, 200011, 200913],\n", + " dtype='int64'), Int64Index([ 2535, 12761, 14614, 16415, 29180, 30087, 34772, 35688,\n", + " 36599, 37506, 39758, 40660, 47998, 51695, 53553, 54454,\n", + " 57262, 63434, 66754, 68581, 69486, 70383, 71290, 94824,\n", + " 102678, 103596, 107225, 111884, 114622, 119193, 124332, 125240,\n", + " 127048, 127941, 129807, 131612, 132535, 154227, 156056, 156979,\n", + " 158669, 160256, 162987, 165685, 166570, 171201, 172564, 175261,\n", + " 176179, 182035, 185269, 186170, 199108, 200012, 200914],\n", + " dtype='int64'), Int64Index([ 2536, 12762, 14615, 16416, 29181, 30088, 34773, 35689,\n", + " 36600, 37507, 39759, 40661, 47999, 51696, 53554, 54455,\n", + " 57263, 63435, 66755, 68582, 69487, 70384, 71291, 94825,\n", + " 102679, 103597, 107226, 111885, 114623, 119194, 124333, 125241,\n", + " 127049, 127942, 129808, 131613, 132536, 154228, 156057, 156980,\n", + " 158670, 160257, 162988, 165686, 166571, 171202, 172565, 175262,\n", + " 176180, 182036, 185270, 186171, 199109, 200013, 200915],\n", + " dtype='int64'), Int64Index([ 2537, 12763, 14616, 16417, 29182, 30089, 34774, 35690,\n", + " 36601, 37508, 39760, 40662, 48000, 51697, 53555, 54456,\n", + " 57264, 63436, 66756, 68583, 69488, 70385, 71292, 94826,\n", + " 102680, 103598, 107227, 111886, 114624, 119195, 124334, 125242,\n", + " 127050, 127943, 129809, 131614, 132537, 154229, 156058, 156981,\n", + " 158671, 160258, 162989, 165687, 166572, 171203, 172566, 175263,\n", + " 176181, 182037, 185271, 186172, 199110, 200014, 200916],\n", + " dtype='int64'), Int64Index([ 2538, 12764, 14617, 16418, 29183, 30090, 34775, 35691,\n", + " 36602, 37509, 39761, 40663, 48001, 51698, 53556, 54457,\n", + " 57265, 63437, 66757, 68584, 69489, 70386, 71293, 94827,\n", + " 102681, 103599, 107228, 111887, 114625, 119196, 124335, 125243,\n", + " 127051, 127944, 129810, 131615, 132538, 154230, 156059, 156982,\n", + " 158672, 160259, 162990, 165688, 166573, 171204, 172567, 175264,\n", + " 176182, 182038, 185272, 186173, 199111, 200015, 200917],\n", + " dtype='int64'), Int64Index([ 2539, 12765, 14618, 16419, 29184, 30091, 34776, 35692,\n", + " 36603, 37510, 39762, 40664, 48002, 51699, 53557, 54458,\n", + " 57266, 63438, 66758, 68585, 69490, 70387, 71294, 94828,\n", + " 102682, 103600, 107229, 111888, 114626, 119197, 124336, 125244,\n", + " 127052, 127945, 129811, 131616, 132539, 154231, 156060, 156983,\n", + " 158673, 160260, 162991, 165689, 166574, 171205, 172568, 175265,\n", + " 176183, 182039, 185273, 186174, 199112, 200016, 200918],\n", + " dtype='int64'), Int64Index([ 2540, 12766, 14619, 16420, 29185, 30092, 34777, 35693,\n", + " 36604, 37511, 39763, 40665, 48003, 51700, 53558, 54459,\n", + " 57267, 63439, 66759, 68586, 69491, 70388, 71295, 94829,\n", + " 102683, 103601, 107230, 111889, 114627, 119198, 124337, 125245,\n", + " 127053, 127946, 129812, 131617, 132540, 154232, 156061, 156984,\n", + " 158674, 160261, 162992, 165690, 166575, 171206, 172569, 175266,\n", + " 176184, 182040, 185274, 186175, 199113, 200017, 200919],\n", + " dtype='int64'), Int64Index([ 2541, 12767, 14620, 16421, 29186, 30093, 34778, 35694,\n", + " 36605, 37512, 39764, 40666, 48004, 51701, 53559, 54460,\n", + " 57268, 63440, 66760, 68587, 69492, 70389, 71296, 94830,\n", + " 102684, 103602, 107231, 111890, 114628, 119199, 124338, 125246,\n", + " 127054, 127947, 129813, 131618, 132541, 154233, 156062, 156985,\n", + " 158675, 160262, 162993, 165691, 166576, 171207, 172570, 175267,\n", + " 176185, 182041, 185275, 186176, 199114, 200018, 200920],\n", + " dtype='int64'), Int64Index([ 2542, 12768, 14621, 16422, 29187, 30094, 34779, 35695,\n", + " 36606, 37513, 39765, 40667, 48005, 51702, 53560, 54461,\n", + " 57269, 63441, 66761, 68588, 69493, 70390, 71297, 94831,\n", + " 102685, 103603, 107232, 111891, 114629, 119200, 124339, 125247,\n", + " 127055, 127948, 129814, 131619, 132542, 154234, 156063, 156986,\n", + " 158676, 160263, 162994, 165692, 166577, 171208, 172571, 175268,\n", + " 176186, 182042, 185276, 186177, 199115, 200019, 200921],\n", + " dtype='int64'), Int64Index([ 2543, 12769, 14622, 16423, 29188, 30095, 34780, 35696,\n", + " 36607, 37514, 39766, 40668, 48006, 51703, 53561, 54462,\n", + " 57270, 63442, 66762, 68589, 69494, 70391, 71298, 94832,\n", + " 102686, 103604, 107233, 111892, 114630, 119201, 124340, 125248,\n", + " 127056, 127949, 129815, 131620, 132543, 154235, 156064, 156987,\n", + " 158677, 160264, 162995, 165693, 166578, 171209, 172572, 175269,\n", + " 176187, 182043, 185277, 186178, 199116, 200020, 200922],\n", + " dtype='int64'), Int64Index([ 2544, 12770, 14623, 16424, 29189, 30096, 34781, 35697,\n", + " 36608, 37515, 39767, 40669, 48007, 51704, 53562, 54463,\n", + " 57271, 63443, 66763, 68590, 69495, 70392, 71299, 94833,\n", + " 102687, 103605, 107234, 111893, 114631, 119202, 124341, 125249,\n", + " 127057, 127950, 129816, 131621, 132544, 154236, 156065, 156988,\n", + " 158678, 160265, 162996, 165694, 166579, 171210, 172573, 175270,\n", + " 176188, 182044, 185278, 186179, 199117, 200021, 200923],\n", + " dtype='int64'), Int64Index([ 2545, 12771, 14624, 16425, 29190, 30097, 34782, 35698,\n", + " 36609, 37516, 39768, 40670, 48008, 51705, 53563, 54464,\n", + " 57272, 63444, 66764, 68591, 69496, 70393, 71300, 94834,\n", + " 102688, 103606, 107235, 111894, 114632, 119203, 124342, 125250,\n", + " 127058, 127951, 129817, 131622, 132545, 154237, 156066, 156989,\n", + " 158679, 160266, 162997, 165695, 166580, 171211, 172574, 175271,\n", + " 176189, 182045, 185279, 186180, 199118, 200022, 200924],\n", + " dtype='int64'), Int64Index([ 2546, 12772, 14625, 16426, 29191, 30098, 34783, 35699,\n", + " 36610, 37517, 39769, 40671, 48009, 51706, 53564, 54465,\n", + " 57273, 63445, 66765, 68592, 69497, 70394, 71301, 94835,\n", + " 102689, 103607, 107236, 111895, 114633, 119204, 124343, 125251,\n", + " 127059, 127952, 129818, 131623, 132546, 154238, 156067, 156990,\n", + " 158680, 160267, 162998, 165696, 166581, 171212, 172575, 175272,\n", + " 176190, 182046, 185280, 186181, 199119, 200023, 200925],\n", + " dtype='int64'), Int64Index([ 2547, 12773, 14626, 16427, 29192, 30099, 34784, 35700,\n", + " 36611, 37518, 39770, 40672, 48010, 51707, 53565, 54466,\n", + " 57274, 63446, 66766, 68593, 69498, 70395, 71302, 94836,\n", + " 102690, 103608, 107237, 111896, 114634, 119205, 124344, 125252,\n", + " 127060, 127953, 129819, 131624, 132547, 154239, 156068, 156991,\n", + " 158681, 160268, 162999, 165697, 166582, 171213, 172576, 175273,\n", + " 176191, 182047, 185281, 186182, 199120, 200024, 200926],\n", + " dtype='int64'), Int64Index([ 2548, 12774, 14627, 16428, 29193, 30100, 34785, 35701,\n", + " 36612, 37519, 39771, 40673, 48011, 51708, 53566, 54467,\n", + " 57275, 63447, 66767, 68594, 69499, 70396, 71303, 94837,\n", + " 102691, 103609, 107238, 111897, 114635, 119206, 124345, 125253,\n", + " 127061, 127954, 129820, 131625, 132548, 154240, 156069, 156992,\n", + " 158682, 160269, 163000, 165698, 166583, 171214, 172577, 175274,\n", + " 176192, 182048, 185282, 186183, 199121, 200025, 200927],\n", + " dtype='int64'), Int64Index([ 2549, 12775, 14628, 16429, 29194, 30101, 34786, 35702,\n", + " 36613, 37520, 39772, 40674, 48012, 51709, 53567, 54468,\n", + " 57276, 63448, 66768, 68595, 69500, 70397, 71304, 94838,\n", + " 102692, 103610, 107239, 111898, 114636, 119207, 124346, 125254,\n", + " 127062, 127955, 129821, 131626, 132549, 154241, 156070, 156993,\n", + " 158683, 160270, 163001, 165699, 166584, 171215, 172578, 175275,\n", + " 176193, 182049, 185283, 186184, 199122, 200026, 200928],\n", + " dtype='int64'), Int64Index([ 2550, 12776, 14629, 16430, 29195, 30102, 34787, 35703,\n", + " 36614, 37521, 39773, 40675, 48013, 51710, 53568, 54469,\n", + " 57277, 63449, 66769, 68596, 69501, 70398, 71305, 94839,\n", + " 102693, 103611, 107240, 111899, 114637, 119208, 124347, 125255,\n", + " 127063, 127956, 129822, 131627, 132550, 154242, 156071, 156994,\n", + " 158684, 160271, 163002, 165700, 166585, 171216, 172579, 175276,\n", + " 176194, 182050, 185284, 186185, 199123, 200027, 200929],\n", + " dtype='int64'), Int64Index([ 2551, 12777, 14630, 16431, 29196, 30103, 34788, 35704,\n", + " 36615, 37522, 39774, 40676, 48014, 51711, 53569, 54470,\n", + " 57278, 63450, 66770, 68597, 69502, 70399, 71306, 94840,\n", + " 102694, 103612, 107241, 111900, 114638, 119209, 124348, 125256,\n", + " 127064, 127957, 129823, 131628, 132551, 154243, 156072, 156995,\n", + " 158685, 160272, 163003, 165701, 166586, 171217, 172580, 175277,\n", + " 176195, 182051, 185285, 186186, 199124, 200028, 200930],\n", + " dtype='int64'), Int64Index([ 2552, 12778, 14631, 16432, 29197, 30104, 34789, 35705,\n", + " 36616, 37523, 39775, 40677, 48015, 51712, 53570, 54471,\n", + " 57279, 63451, 66771, 68598, 69503, 70400, 71307, 94841,\n", + " 102695, 103613, 107242, 111901, 114639, 119210, 124349, 125257,\n", + " 127065, 127958, 129824, 131629, 132552, 154244, 156073, 156996,\n", + " 158686, 160273, 163004, 165702, 166587, 171218, 172581, 175278,\n", + " 176196, 182052, 185286, 186187, 199125, 200029, 200931],\n", + " dtype='int64'), Int64Index([ 2553, 12779, 14632, 16433, 29198, 30105, 34790, 35706,\n", + " 36617, 37524, 39776, 40678, 48016, 51713, 53571, 54472,\n", + " 57280, 63452, 66772, 68599, 69504, 70401, 71308, 94842,\n", + " 102696, 103614, 107243, 111902, 114640, 119211, 124350, 125258,\n", + " 127066, 127959, 129825, 131630, 132553, 154245, 156074, 156997,\n", + " 158687, 160274, 163005, 165703, 166588, 171219, 172582, 175279,\n", + " 176197, 182053, 185287, 186188, 199126, 200030, 200932],\n", + " dtype='int64'), Int64Index([ 2554, 12780, 14633, 16434, 29199, 30106, 34791, 35707,\n", + " 36618, 37525, 39777, 40679, 48017, 51714, 53572, 54473,\n", + " 57281, 63453, 66773, 68600, 69505, 70402, 71309, 94843,\n", + " 102697, 103615, 107244, 111903, 114641, 119212, 124351, 125259,\n", + " 127067, 127960, 129826, 131631, 132554, 154246, 156075, 156998,\n", + " 158688, 160275, 163006, 165704, 166589, 171220, 172583, 175280,\n", + " 176198, 182054, 185288, 186189, 199127, 200031, 200933],\n", + " dtype='int64'), Int64Index([ 2555, 12781, 14634, 16435, 29200, 30107, 34792, 35708,\n", + " 36619, 37526, 39778, 40680, 48018, 51715, 53573, 54474,\n", + " 57282, 63454, 66774, 68601, 69506, 70403, 71310, 94844,\n", + " 102698, 103616, 107245, 111904, 114642, 119213, 124352, 125260,\n", + " 127068, 127961, 129827, 131632, 132555, 154247, 156076, 156999,\n", + " 158689, 160276, 163007, 165705, 166590, 171221, 172584, 175281,\n", + " 176199, 182055, 185289, 186190, 199128, 200032, 200934],\n", + " dtype='int64'), Int64Index([ 2556, 12782, 14635, 16436, 29201, 30108, 34793, 35709,\n", + " 36620, 37527, 39779, 40681, 48019, 51716, 53574, 54475,\n", + " 57283, 63455, 66775, 68602, 69507, 70404, 71311, 94845,\n", + " 102699, 103617, 107246, 111905, 114643, 119214, 124353, 125261,\n", + " 127069, 127962, 129828, 131633, 132556, 154248, 156077, 157000,\n", + " 158690, 160277, 163008, 165706, 166591, 171222, 172585, 175282,\n", + " 176200, 182056, 185290, 186191, 199129, 200033, 200935],\n", + " dtype='int64'), Int64Index([ 2557, 12783, 14636, 16437, 29202, 30109, 34794, 35710,\n", + " 36621, 37528, 39780, 40682, 48020, 51717, 53575, 54476,\n", + " 57284, 63456, 66776, 68603, 69508, 70405, 71312, 94846,\n", + " 102700, 103618, 107247, 111906, 114644, 119215, 124354, 125262,\n", + " 127070, 127963, 129829, 131634, 132557, 154249, 156078, 157001,\n", + " 158691, 160278, 163009, 165707, 166592, 171223, 172586, 175283,\n", + " 176201, 182057, 185291, 186192, 199130, 200034, 200936],\n", + " dtype='int64'), Int64Index([ 2558, 12784, 14637, 16438, 29203, 30110, 34795, 35711,\n", + " 36622, 37529, 39781, 40683, 48021, 51718, 53576, 54477,\n", + " 57285, 63457, 66777, 68604, 69509, 70406, 71313, 94847,\n", + " 102701, 103619, 107248, 111907, 114645, 119216, 124355, 125263,\n", + " 127071, 127964, 129830, 131635, 132558, 154250, 156079, 157002,\n", + " 158692, 160279, 163010, 165708, 166593, 171224, 172587, 175284,\n", + " 176202, 182058, 185292, 186193, 199131, 200035, 200937],\n", + " dtype='int64'), Int64Index([ 2559, 12785, 14638, 16439, 29204, 30111, 34796, 35712,\n", + " 36623, 37530, 39782, 40684, 48022, 51719, 53577, 54478,\n", + " 57286, 63458, 66778, 68605, 69510, 70407, 71314, 94848,\n", + " 102702, 103620, 107249, 111908, 114646, 119217, 124356, 125264,\n", + " 127072, 127965, 129831, 131636, 132559, 154251, 156080, 157003,\n", + " 158693, 160280, 163011, 165709, 166594, 171225, 172588, 175285,\n", + " 176203, 182059, 185293, 186194, 199132, 200036, 200938],\n", + " dtype='int64'), Int64Index([ 2560, 12786, 14639, 16440, 29205, 30112, 34797, 35713,\n", + " 36624, 37531, 39783, 40685, 48023, 51720, 53578, 54479,\n", + " 57287, 63459, 66779, 68606, 69511, 70408, 71315, 94849,\n", + " 102703, 103621, 107250, 111909, 114647, 119218, 124357, 125265,\n", + " 127073, 127966, 129832, 131637, 132560, 154252, 156081, 157004,\n", + " 158694, 160281, 163012, 165710, 166595, 171226, 172589, 175286,\n", + " 176204, 182060, 185294, 186195, 199133, 200037, 200939],\n", + " dtype='int64'), Int64Index([ 2561, 12787, 14640, 16441, 29206, 30113, 34798, 35714,\n", + " 36625, 37532, 39784, 40686, 48024, 51721, 53579, 54480,\n", + " 57288, 63460, 66780, 68607, 69512, 70409, 71316, 94850,\n", + " 102704, 103622, 107251, 111910, 114648, 119219, 124358, 125266,\n", + " 127074, 127967, 129833, 131638, 132561, 154253, 156082, 157005,\n", + " 158695, 160282, 163013, 165711, 166596, 171227, 172590, 175287,\n", + " 176205, 182061, 185295, 186196, 199134, 200038, 200940],\n", + " dtype='int64'), Int64Index([ 2562, 12788, 14641, 16442, 29207, 30114, 34799, 35715,\n", + " 36626, 37533, 39785, 40687, 48025, 51722, 53580, 54481,\n", + " 57289, 63461, 66781, 68608, 69513, 70410, 71317, 94851,\n", + " 102705, 103623, 107252, 111911, 114649, 119220, 124359, 125267,\n", + " 127075, 127968, 129834, 131639, 132562, 154254, 156083, 157006,\n", + " 158696, 160283, 163014, 165712, 166597, 171228, 172591, 175288,\n", + " 176206, 182062, 185296, 186197, 199135, 200039, 200941],\n", + " dtype='int64'), Int64Index([ 2563, 12789, 14642, 16443, 29208, 30115, 34800, 35716,\n", + " 36627, 37534, 39786, 40688, 48026, 51723, 53581, 54482,\n", + " 57290, 63462, 66782, 68609, 69514, 70411, 71318, 94852,\n", + " 102706, 103624, 107253, 111912, 114650, 119221, 124360, 125268,\n", + " 127076, 127969, 129835, 131640, 132563, 154255, 156084, 157007,\n", + " 158697, 160284, 163015, 165713, 166598, 171229, 172592, 175289,\n", + " 176207, 182063, 185297, 186198, 199136, 200040, 200942],\n", + " dtype='int64'), Int64Index([ 2564, 12790, 14643, 16444, 29209, 30116, 34801, 35717,\n", + " 36628, 37535, 39787, 40689, 48027, 51724, 53582, 54483,\n", + " 57291, 63463, 66783, 68610, 69515, 70412, 71319, 94853,\n", + " 102707, 103625, 107254, 111913, 114651, 119222, 124361, 125269,\n", + " 127077, 127970, 129836, 131641, 132564, 154256, 156085, 157008,\n", + " 158698, 160285, 163016, 165714, 166599, 171230, 172593, 175290,\n", + " 176208, 182064, 185298, 186199, 199137, 200041, 200943],\n", + " dtype='int64'), Int64Index([ 2565, 12791, 14644, 16445, 29210, 30117, 34802, 35718,\n", + " 36629, 37536, 39788, 40690, 48028, 51725, 53583, 54484,\n", + " 57292, 63464, 66784, 68611, 69516, 70413, 71320, 94854,\n", + " 102708, 103626, 107255, 111914, 114652, 119223, 124362, 125270,\n", + " 127078, 127971, 129837, 131642, 132565, 154257, 156086, 157009,\n", + " 158699, 160286, 163017, 165715, 166600, 171231, 172594, 175291,\n", + " 176209, 182065, 185299, 186200, 199138, 200042, 200944],\n", + " dtype='int64'), Int64Index([ 2566, 12792, 14645, 16446, 29211, 30118, 34803, 35719,\n", + " 36630, 37537, 39789, 40691, 48029, 51726, 53584, 54485,\n", + " 57293, 63465, 66785, 68612, 69517, 70414, 71321, 94855,\n", + " 102709, 103627, 107256, 111915, 114653, 119224, 124363, 125271,\n", + " 127079, 127972, 129838, 131643, 132566, 154258, 156087, 157010,\n", + " 158700, 160287, 163018, 165716, 166601, 171232, 172595, 175292,\n", + " 176210, 182066, 185300, 186201, 199139, 200043, 200945],\n", + " dtype='int64'), Int64Index([ 2567, 12793, 14646, 16447, 29212, 30119, 34804, 35720,\n", + " 36631, 37538, 39790, 40692, 48030, 51727, 53585, 54486,\n", + " 57294, 63466, 66786, 68613, 69518, 70415, 71322, 94856,\n", + " 102710, 103628, 107257, 111916, 114654, 119225, 124364, 125272,\n", + " 127080, 127973, 129839, 131644, 132567, 154259, 156088, 157011,\n", + " 158701, 160288, 163019, 165717, 166602, 171233, 172596, 175293,\n", + " 176211, 182067, 185301, 186202, 199140, 200044, 200946],\n", + " dtype='int64'), Int64Index([ 2568, 12794, 14647, 16448, 29213, 30120, 34805, 35721,\n", + " 36632, 37539, 39791, 40693, 48031, 51728, 53586, 54487,\n", + " 57295, 63467, 66787, 68614, 69519, 70416, 71323, 94857,\n", + " 102711, 103629, 107258, 111917, 114655, 119226, 124365, 125273,\n", + " 127081, 127974, 129840, 131645, 132568, 154260, 156089, 157012,\n", + " 158702, 160289, 163020, 165718, 166603, 171234, 172597, 175294,\n", + " 176212, 182068, 185302, 186203, 199141, 200045, 200947],\n", + " dtype='int64'), Int64Index([ 2569, 12795, 14648, 16449, 29214, 30121, 34806, 35722,\n", + " 36633, 37540, 39792, 40694, 48032, 51729, 53587, 54488,\n", + " 57296, 63468, 66788, 68615, 69520, 70417, 71324, 94858,\n", + " 102712, 103630, 107259, 111918, 114656, 119227, 124366, 125274,\n", + " 127082, 127975, 129841, 131646, 132569, 154261, 156090, 157013,\n", + " 158703, 160290, 163021, 165719, 166604, 171235, 172598, 175295,\n", + " 176213, 182069, 185303, 186204, 199142, 200046, 200948],\n", + " dtype='int64'), Int64Index([ 2570, 12796, 14649, 16450, 29215, 30122, 34807, 35723,\n", + " 36634, 37541, 39793, 40695, 48033, 51730, 53588, 54489,\n", + " 57297, 63469, 66789, 68616, 69521, 70418, 71325, 94859,\n", + " 102713, 103631, 107260, 111919, 114657, 119228, 124367, 125275,\n", + " 127083, 127976, 129842, 131647, 132570, 154262, 156091, 157014,\n", + " 158704, 160291, 163022, 165720, 166605, 171236, 172599, 175296,\n", + " 176214, 182070, 185304, 186205, 199143, 200047, 200949],\n", + " dtype='int64'), Int64Index([ 2571, 12797, 14650, 16451, 29216, 30123, 34808, 35724,\n", + " 36635, 37542, 39794, 40696, 48034, 51731, 53589, 54490,\n", + " 57298, 63470, 66790, 68617, 69522, 70419, 71326, 94860,\n", + " 102714, 103632, 107261, 111920, 114658, 119229, 124368, 125276,\n", + " 127084, 127977, 129843, 131648, 132571, 154263, 156092, 157015,\n", + " 158705, 160292, 163023, 165721, 166606, 171237, 172600, 175297,\n", + " 176215, 182071, 185305, 186206, 199144, 200048, 200950],\n", + " dtype='int64'), Int64Index([ 2572, 12798, 14651, 16452, 29217, 30124, 34809, 35725,\n", + " 36636, 37543, 39795, 40697, 48035, 51732, 53590, 54491,\n", + " 57299, 63471, 66791, 68618, 69523, 70420, 71327, 94861,\n", + " 102715, 103633, 107262, 111921, 114659, 119230, 124369, 125277,\n", + " 127085, 127978, 129844, 131649, 132572, 154264, 156093, 157016,\n", + " 158706, 160293, 163024, 165722, 166607, 171238, 172601, 175298,\n", + " 176216, 182072, 185306, 186207, 199145, 200049, 200951],\n", + " dtype='int64'), Int64Index([ 2573, 12799, 14652, 16453, 29218, 30125, 34810, 35726,\n", + " 36637, 37544, 39796, 40698, 48036, 51733, 53591, 54492,\n", + " 57300, 63472, 66792, 68619, 69524, 70421, 71328, 94862,\n", + " 102716, 103634, 107263, 111922, 114660, 119231, 124370, 125278,\n", + " 127086, 127979, 129845, 131650, 132573, 154265, 156094, 157017,\n", + " 158707, 160294, 163025, 165723, 166608, 171239, 172602, 175299,\n", + " 176217, 182073, 185307, 186208, 199146, 200050, 200952],\n", + " dtype='int64'), Int64Index([ 2574, 12800, 14653, 16454, 29219, 30126, 34811, 35727,\n", + " 36638, 37545, 39797, 40699, 48037, 51734, 53592, 54493,\n", + " 57301, 63473, 66793, 68620, 69525, 70422, 71329, 94863,\n", + " 102717, 103635, 107264, 111923, 114661, 119232, 124371, 125279,\n", + " 127087, 127980, 129846, 131651, 132574, 154266, 156095, 157018,\n", + " 158708, 160295, 163026, 165724, 166609, 171240, 172603, 175300,\n", + " 176218, 182074, 185308, 186209, 199147, 200051, 200953],\n", + " dtype='int64'), Int64Index([ 2575, 12801, 14654, 16455, 29220, 30127, 34812, 35728,\n", + " 36639, 37546, 39798, 40700, 48038, 51735, 53593, 54494,\n", + " 57302, 63474, 66794, 68621, 69526, 70423, 71330, 94864,\n", + " 102718, 103636, 107265, 111924, 114662, 119233, 124372, 125280,\n", + " 127088, 127981, 129847, 131652, 132575, 154267, 156096, 157019,\n", + " 158709, 160296, 163027, 165725, 166610, 171241, 172604, 175301,\n", + " 176219, 182075, 185309, 186210, 199148, 200052, 200954],\n", + " dtype='int64'), Int64Index([ 2576, 12802, 14655, 16456, 29221, 30128, 34813, 35729,\n", + " 36640, 37547, 39799, 40701, 48039, 51736, 53594, 54495,\n", + " 57303, 63475, 66795, 68622, 69527, 70424, 71331, 94865,\n", + " 102719, 103637, 107266, 111925, 114663, 119234, 124373, 125281,\n", + " 127089, 127982, 129848, 131653, 132576, 154268, 156097, 157020,\n", + " 158710, 160297, 163028, 165726, 166611, 171242, 172605, 175302,\n", + " 176220, 182076, 185310, 186211, 199149, 200053, 200955],\n", + " dtype='int64'), Int64Index([ 2577, 12803, 14656, 16457, 29222, 30129, 34814, 35730,\n", + " 36641, 37548, 39800, 40702, 48040, 51737, 53595, 54496,\n", + " 57304, 63476, 66796, 68623, 69528, 70425, 71332, 94866,\n", + " 102720, 103638, 107267, 111926, 114664, 119235, 124374, 125282,\n", + " 127090, 127983, 129849, 131654, 132577, 154269, 156098, 157021,\n", + " 158711, 160298, 163029, 165727, 166612, 171243, 172606, 175303,\n", + " 176221, 182077, 185311, 186212, 199150, 200054, 200956],\n", + " dtype='int64'), Int64Index([ 2578, 12804, 14657, 16458, 29223, 30130, 34815, 35731,\n", + " 36642, 37549, 39801, 40703, 48041, 51738, 53596, 54497,\n", + " 57305, 63477, 66797, 68624, 69529, 70426, 71333, 94867,\n", + " 102721, 103639, 107268, 111927, 114665, 119236, 124375, 125283,\n", + " 127091, 127984, 129850, 131655, 132578, 154270, 156099, 157022,\n", + " 158712, 160299, 163030, 165728, 166613, 171244, 172607, 175304,\n", + " 176222, 182078, 185312, 186213, 199151, 200055, 200957],\n", + " dtype='int64'), Int64Index([ 2579, 12805, 14658, 16459, 29224, 30131, 34816, 35732,\n", + " 36643, 37550, 39802, 40704, 48042, 51739, 53597, 54498,\n", + " 57306, 63478, 66798, 68625, 69530, 70427, 71334, 94868,\n", + " 102722, 103640, 107269, 111928, 114666, 119237, 124376, 125284,\n", + " 127092, 127985, 129851, 131656, 132579, 154271, 156100, 157023,\n", + " 158713, 160300, 163031, 165729, 166614, 171245, 172608, 175305,\n", + " 176223, 182079, 185313, 186214, 199152, 200056, 200958],\n", + " dtype='int64'), Int64Index([ 2580, 12806, 14659, 16460, 29225, 30132, 34817, 35733,\n", + " 36644, 37551, 39803, 40705, 48043, 51740, 53598, 54499,\n", + " 57307, 63479, 66799, 68626, 69531, 70428, 71335, 94869,\n", + " 102723, 103641, 107270, 111929, 114667, 119238, 124377, 125285,\n", + " 127093, 127986, 129852, 131657, 132580, 154272, 156101, 157024,\n", + " 158714, 160301, 163032, 165730, 166615, 171246, 172609, 175306,\n", + " 176224, 182080, 185314, 186215, 199153, 200057, 200959],\n", + " dtype='int64'), Int64Index([ 2581, 12807, 14660, 16461, 29226, 30133, 34818, 35734,\n", + " 36645, 37552, 39804, 40706, 48044, 51741, 53599, 54500,\n", + " 57308, 63480, 66800, 68627, 69532, 70429, 71336, 94870,\n", + " 102724, 103642, 107271, 111930, 114668, 119239, 124378, 125286,\n", + " 127094, 127987, 129853, 131658, 132581, 154273, 156102, 157025,\n", + " 158715, 160302, 163033, 165731, 166616, 171247, 172610, 175307,\n", + " 176225, 182081, 185315, 186216, 199154, 200058, 200960],\n", + " dtype='int64'), Int64Index([ 2582, 12808, 14661, 16462, 29227, 30134, 34819, 35735,\n", + " 36646, 37553, 39805, 40707, 48045, 51742, 53600, 54501,\n", + " 57309, 63481, 66801, 68628, 69533, 70430, 71337, 94871,\n", + " 102725, 103643, 107272, 111931, 114669, 119240, 124379, 125287,\n", + " 127095, 127988, 129854, 131659, 132582, 154274, 156103, 157026,\n", + " 158716, 160303, 163034, 165732, 166617, 171248, 172611, 175308,\n", + " 176226, 182082, 185316, 186217, 199155, 200059, 200961],\n", + " dtype='int64'), Int64Index([ 2583, 12809, 14662, 16463, 29228, 30135, 34820, 35736,\n", + " 36647, 37554, 39806, 40708, 48046, 51743, 53601, 54502,\n", + " 57310, 63482, 66802, 68629, 69534, 70431, 71338, 94872,\n", + " 102726, 103644, 107273, 111932, 114670, 119241, 124380, 125288,\n", + " 127096, 127989, 129855, 131660, 132583, 154275, 156104, 157027,\n", + " 158717, 160304, 163035, 165733, 166618, 171249, 172612, 175309,\n", + " 176227, 182083, 185317, 186218, 199156, 200060, 200962],\n", + " dtype='int64'), Int64Index([ 2584, 12810, 14663, 16464, 29229, 30136, 34821, 35737,\n", + " 36648, 37555, 39807, 40709, 48047, 51744, 53602, 54503,\n", + " 57311, 63483, 66803, 68630, 69535, 70432, 71339, 94873,\n", + " 102727, 103645, 107274, 111933, 114671, 119242, 124381, 125289,\n", + " 127097, 127990, 129856, 131661, 132584, 154276, 156105, 157028,\n", + " 158718, 160305, 163036, 165734, 166619, 171250, 172613, 175310,\n", + " 176228, 182084, 185318, 186219, 199157, 200061, 200963],\n", + " dtype='int64'), Int64Index([ 2585, 12811, 14664, 16465, 29230, 30137, 34822, 35738,\n", + " 36649, 37556, 39808, 40710, 48048, 51745, 53603, 54504,\n", + " 57312, 63484, 66804, 68631, 69536, 70433, 71340, 94874,\n", + " 102728, 103646, 107275, 111934, 114672, 119243, 124382, 125290,\n", + " 127098, 127991, 129857, 131662, 132585, 154277, 156106, 157029,\n", + " 158719, 160306, 163037, 165735, 166620, 171251, 172614, 175311,\n", + " 176229, 182085, 185319, 186220, 199158, 200062, 200964],\n", + " dtype='int64'), Int64Index([ 2586, 12812, 14665, 16466, 29231, 30138, 34823, 35739,\n", + " 36650, 37557, 39809, 40711, 48049, 51746, 53604, 54505,\n", + " 57313, 63485, 66805, 68632, 69537, 70434, 71341, 94875,\n", + " 102729, 103647, 107276, 111935, 114673, 119244, 124383, 125291,\n", + " 127099, 127992, 129858, 131663, 132586, 154278, 156107, 157030,\n", + " 158720, 160307, 163038, 165736, 166621, 171252, 172615, 175312,\n", + " 176230, 182086, 185320, 186221, 199159, 200063, 200965],\n", + " dtype='int64'), Int64Index([ 2587, 12813, 14666, 16467, 29232, 30139, 34824, 35740,\n", + " 36651, 37558, 39810, 40712, 48050, 51747, 53605, 54506,\n", + " 57314, 63486, 66806, 68633, 69538, 70435, 71342, 94876,\n", + " 102730, 103648, 107277, 111936, 114674, 119245, 124384, 125292,\n", + " 127100, 127993, 129859, 131664, 132587, 154279, 156108, 157031,\n", + " 158721, 160308, 163039, 165737, 166622, 171253, 172616, 175313,\n", + " 176231, 182087, 185321, 186222, 199160, 200064, 200966],\n", + " dtype='int64'), Int64Index([ 2588, 12814, 14667, 16468, 29233, 30140, 34825, 35741,\n", + " 36652, 37559, 39811, 40713, 48051, 51748, 53606, 54507,\n", + " 57315, 63487, 66807, 68634, 69539, 70436, 71343, 94877,\n", + " 102731, 103649, 107278, 111937, 114675, 119246, 124385, 125293,\n", + " 127101, 127994, 129860, 131665, 132588, 154280, 156109, 157032,\n", + " 158722, 160309, 163040, 165738, 166623, 171254, 172617, 175314,\n", + " 176232, 182088, 185322, 186223, 199161, 200065, 200967],\n", + " dtype='int64'), Int64Index([ 2589, 12815, 14668, 16469, 29234, 30141, 34826, 35742,\n", + " 36653, 37560, 39812, 40714, 48052, 51749, 53607, 54508,\n", + " 57316, 63488, 66808, 68635, 69540, 70437, 71344, 94878,\n", + " 102732, 103650, 107279, 111938, 114676, 119247, 124386, 125294,\n", + " 127102, 127995, 129861, 131666, 132589, 154281, 156110, 157033,\n", + " 158723, 160310, 163041, 165739, 166624, 171255, 172618, 175315,\n", + " 176233, 182089, 185323, 186224, 199162, 200066, 200968],\n", + " dtype='int64'), Int64Index([ 2590, 12816, 14669, 16470, 29235, 30142, 34827, 35743,\n", + " 36654, 37561, 39813, 40715, 48053, 51750, 53608, 54509,\n", + " 57317, 63489, 66809, 68636, 69541, 70438, 71345, 94879,\n", + " 102733, 103651, 107280, 111939, 114677, 119248, 124387, 125295,\n", + " 127103, 127996, 129862, 131667, 132590, 154282, 156111, 157034,\n", + " 158724, 160311, 163042, 165740, 166625, 171256, 172619, 175316,\n", + " 176234, 182090, 185324, 186225, 199163, 200067, 200969],\n", + " dtype='int64'), Int64Index([ 2591, 12817, 14670, 16471, 29236, 30143, 34828, 35744,\n", + " 36655, 37562, 39814, 40716, 48054, 51751, 53609, 54510,\n", + " 57318, 63490, 66810, 68637, 69542, 70439, 71346, 94880,\n", + " 102734, 103652, 107281, 111940, 114678, 119249, 124388, 125296,\n", + " 127104, 127997, 129863, 131668, 132591, 154283, 156112, 157035,\n", + " 158725, 160312, 163043, 165741, 166626, 171257, 172620, 175317,\n", + " 176235, 182091, 185325, 186226, 199164, 200068, 200970],\n", + " dtype='int64'), Int64Index([ 2592, 12818, 14671, 16472, 29237, 30144, 34829, 35745,\n", + " 36656, 37563, 39815, 40717, 48055, 51752, 53610, 54511,\n", + " 57319, 63491, 66811, 68638, 69543, 70440, 71347, 94881,\n", + " 102735, 103653, 107282, 111941, 114679, 119250, 124389, 125297,\n", + " 127105, 127998, 129864, 131669, 132592, 154284, 156113, 157036,\n", + " 158726, 160313, 163044, 165742, 166627, 171258, 172621, 175318,\n", + " 176236, 182092, 185326, 186227, 199165, 200069, 200971],\n", + " dtype='int64'), Int64Index([ 2593, 12819, 14672, 16473, 29238, 30145, 34830, 35746,\n", + " 36657, 37564, 39816, 40718, 48056, 51753, 53611, 54512,\n", + " 57320, 63492, 66812, 68639, 69544, 70441, 71348, 94882,\n", + " 102736, 103654, 107283, 111942, 114680, 119251, 124390, 125298,\n", + " 127106, 127999, 129865, 131670, 132593, 154285, 156114, 157037,\n", + " 158727, 160314, 163045, 165743, 166628, 171259, 172622, 175319,\n", + " 176237, 182093, 185327, 186228, 199166, 200070, 200972],\n", + " dtype='int64'), Int64Index([ 2594, 12820, 14673, 16474, 29239, 30146, 34831, 35747,\n", + " 36658, 37565, 39817, 40719, 48057, 51754, 53612, 54513,\n", + " 57321, 63493, 66813, 68640, 69545, 70442, 71349, 94883,\n", + " 102737, 103655, 107284, 111943, 114681, 119252, 124391, 125299,\n", + " 127107, 128000, 129866, 131671, 132594, 154286, 156115, 157038,\n", + " 158728, 160315, 163046, 165744, 166629, 171260, 172623, 175320,\n", + " 176238, 182094, 185328, 186229, 199167, 200071, 200973],\n", + " dtype='int64'), Int64Index([ 2595, 12821, 14674, 16475, 29240, 30147, 34832, 35748,\n", + " 36659, 37566, 39818, 40720, 48058, 51755, 53613, 54514,\n", + " 57322, 63494, 66814, 68641, 69546, 70443, 71350, 94884,\n", + " 102738, 103656, 107285, 111944, 114682, 119253, 124392, 125300,\n", + " 127108, 128001, 129867, 131672, 132595, 154287, 156116, 157039,\n", + " 158729, 160316, 163047, 165745, 166630, 171261, 172624, 175321,\n", + " 176239, 182095, 185329, 186230, 199168, 200072, 200974],\n", + " dtype='int64'), Int64Index([ 2596, 12822, 14675, 16476, 29241, 30148, 34833, 35749,\n", + " 36660, 37567, 39819, 40721, 48059, 51756, 53614, 54515,\n", + " 57323, 63495, 66815, 68642, 69547, 70444, 71351, 94885,\n", + " 102739, 103657, 107286, 111945, 114683, 119254, 124393, 125301,\n", + " 127109, 128002, 129868, 131673, 132596, 154288, 156117, 157040,\n", + " 158730, 160317, 163048, 165746, 166631, 171262, 172625, 175322,\n", + " 176240, 182096, 185330, 186231, 199169, 200073, 200975],\n", + " dtype='int64'), Int64Index([ 2597, 12823, 14676, 16477, 29242, 30149, 34834, 35750,\n", + " 36661, 37568, 39820, 40722, 48060, 51757, 53615, 54516,\n", + " 54657, 57324, 63496, 66816, 68643, 69548, 70445, 71352,\n", + " 94886, 102740, 103658, 107287, 111946, 114684, 119255, 124394,\n", + " 125302, 127110, 128003, 129869, 131674, 132597, 154289, 156118,\n", + " 157041, 158731, 160318, 163049, 165747, 166632, 171263, 172626,\n", + " 175323, 176241, 182097, 185331, 186232, 199170, 200074, 200976],\n", + " dtype='int64'), Int64Index([ 2598, 12824, 14677, 16478, 29243, 30150, 34835, 35751,\n", + " 36662, 37569, 39821, 40723, 48061, 51758, 53616, 54517,\n", + " 57325, 63497, 66817, 68644, 69549, 70446, 71353, 94887,\n", + " 102741, 103659, 107288, 111947, 114685, 119256, 124395, 125303,\n", + " 127111, 128004, 129870, 131675, 132598, 154290, 156119, 157042,\n", + " 158732, 160319, 163050, 165748, 166633, 171264, 172627, 175324,\n", + " 176242, 182098, 185332, 186233, 199171, 200075, 200977],\n", + " dtype='int64'), Int64Index([ 2599, 12825, 14678, 16479, 29244, 30151, 34836, 35752,\n", + " 36663, 37570, 39822, 40724, 48062, 51759, 53617, 54518,\n", + " 57326, 63498, 66818, 68645, 69550, 70447, 71354, 94888,\n", + " 102742, 103660, 107289, 111948, 114686, 119257, 124396, 125304,\n", + " 127112, 128005, 129871, 131676, 132599, 154291, 156120, 157043,\n", + " 158733, 160320, 163051, 165749, 166634, 171265, 172628, 175325,\n", + " 176243, 182099, 185333, 186234, 199172, 200076, 200978],\n", + " dtype='int64'), Int64Index([ 2600, 12826, 14679, 16480, 29245, 30152, 34837, 35753,\n", + " 36664, 37571, 39823, 40725, 48063, 51760, 53618, 54519,\n", + " 57327, 63499, 66819, 68646, 69551, 70448, 71355, 94889,\n", + " 102743, 103661, 107290, 111949, 114687, 119258, 124397, 125305,\n", + " 127113, 128006, 129872, 131677, 132600, 154292, 156121, 157044,\n", + " 158734, 160321, 163052, 165750, 166635, 171266, 172629, 175326,\n", + " 176244, 182100, 185334, 186235, 199173, 200077, 200979],\n", + " dtype='int64'), Int64Index([ 2601, 12827, 14680, 16481, 29246, 30153, 34838, 35754,\n", + " 36665, 37572, 39824, 40726, 48064, 51761, 53619, 54520,\n", + " 57328, 63500, 66820, 68647, 69552, 70449, 71356, 94890,\n", + " 102744, 103662, 107291, 111950, 114688, 119259, 124398, 125306,\n", + " 127114, 128007, 129873, 131678, 132601, 154293, 156122, 157045,\n", + " 158735, 160322, 163053, 165751, 166636, 171267, 172630, 175327,\n", + " 176245, 182101, 185335, 186236, 199174, 200078, 200980],\n", + " dtype='int64'), Int64Index([ 2602, 12828, 14681, 16482, 29247, 30154, 34839, 35755,\n", + " 36666, 37573, 39825, 40727, 48065, 51762, 53620, 54521,\n", + " 57329, 63501, 66821, 68648, 69553, 70450, 71357, 94891,\n", + " 102745, 103663, 107292, 111951, 114689, 119260, 124399, 125307,\n", + " 127115, 128008, 129874, 131679, 132602, 154294, 156123, 157046,\n", + " 158736, 160323, 163054, 165752, 166637, 171268, 172631, 175328,\n", + " 176246, 182102, 185336, 186237, 199175, 200079, 200981],\n", + " dtype='int64'), Int64Index([ 2603, 12829, 14682, 16483, 29248, 30155, 34840, 35756,\n", + " 36667, 37574, 39826, 40728, 48066, 51763, 53621, 54522,\n", + " 57330, 63502, 66822, 68649, 69554, 70451, 71358, 94892,\n", + " 102746, 103664, 107293, 111952, 114690, 119261, 124400, 125308,\n", + " 127116, 128009, 129875, 131680, 132603, 154295, 156124, 157047,\n", + " 158737, 160324, 163055, 165753, 166638, 171269, 172632, 175329,\n", + " 176247, 182103, 185337, 186238, 199176, 200080, 200982],\n", + " dtype='int64'), Int64Index([ 2604, 12830, 14683, 16484, 29249, 30156, 34841, 35757,\n", + " 36668, 37575, 39827, 40729, 48067, 51764, 53622, 54523,\n", + " 57331, 63503, 66823, 68650, 69555, 70452, 71359, 94893,\n", + " 102747, 103665, 107294, 111953, 114691, 119262, 124401, 125309,\n", + " 127117, 128010, 129876, 131681, 132604, 154296, 156125, 157048,\n", + " 158738, 160325, 163056, 165754, 166639, 171270, 172633, 175330,\n", + " 176248, 182104, 185338, 186239, 199177, 200081, 200983],\n", + " dtype='int64'), Int64Index([ 2605, 12831, 14684, 16485, 29250, 30157, 34842, 35758,\n", + " 36669, 37576, 39828, 40730, 48068, 51765, 53623, 54524,\n", + " 57332, 63504, 66824, 68651, 69556, 70453, 71360, 94894,\n", + " 102748, 103666, 107295, 111954, 114692, 119263, 124402, 125310,\n", + " 127118, 128011, 129877, 131682, 132605, 154297, 156126, 157049,\n", + " 158739, 160326, 163057, 165755, 166640, 171271, 172634, 175331,\n", + " 176249, 182105, 185339, 186240, 199178, 200082, 200984],\n", + " dtype='int64'), Int64Index([ 2606, 12832, 14685, 16486, 29251, 30158, 34843, 35759,\n", + " 36670, 37577, 39829, 40731, 48069, 51766, 53624, 54525,\n", + " 57333, 63505, 66825, 68652, 69557, 70454, 71361, 94895,\n", + " 102749, 103667, 107296, 111955, 114693, 119264, 124403, 125311,\n", + " 127119, 128012, 129878, 131683, 132606, 154298, 156127, 157050,\n", + " 158740, 160327, 163058, 165756, 166641, 171272, 172635, 175332,\n", + " 176250, 182106, 185340, 186241, 199179, 200083, 200985],\n", + " dtype='int64'), Int64Index([ 2607, 12833, 14686, 16487, 29252, 30159, 34844, 35760,\n", + " 36671, 37578, 39830, 40732, 48070, 51767, 53625, 54526,\n", + " 57334, 63506, 66826, 68653, 69558, 70455, 71362, 94896,\n", + " 102750, 103668, 107297, 111956, 114694, 119265, 124404, 125312,\n", + " 127120, 128013, 129879, 131684, 132607, 154299, 156128, 157051,\n", + " 158741, 160328, 163059, 165757, 166642, 171273, 172636, 175333,\n", + " 176251, 182107, 185341, 186242, 199180, 200084, 200986],\n", + " dtype='int64'), Int64Index([ 2608, 12834, 14687, 16488, 29253, 30160, 34845, 35761,\n", + " 36672, 37579, 39831, 40733, 48071, 51768, 53626, 54527,\n", + " 57335, 63507, 66827, 68654, 69559, 70456, 71363, 94897,\n", + " 102751, 103669, 107298, 111957, 114695, 119266, 124405, 125313,\n", + " 127121, 128014, 129880, 131685, 132608, 154300, 156129, 157052,\n", + " 158742, 160329, 163060, 165758, 166643, 171274, 172637, 175334,\n", + " 176252, 182108, 185342, 186243, 199181, 200085, 200987],\n", + " dtype='int64'), Int64Index([ 2609, 12835, 14688, 16489, 29254, 30161, 34846, 35762,\n", + " 36673, 37580, 39832, 40734, 48072, 51769, 53627, 54528,\n", + " 57336, 63508, 66828, 68655, 69560, 70457, 71364, 94898,\n", + " 102752, 103670, 107299, 111958, 114696, 119267, 124406, 125314,\n", + " 127122, 128015, 129881, 131686, 132609, 154301, 156130, 157053,\n", + " 158743, 160330, 163061, 165759, 166644, 171275, 172638, 175335,\n", + " 176253, 182109, 185343, 186244, 199182, 200086, 200988],\n", + " dtype='int64'), Int64Index([ 2610, 12836, 14689, 16490, 29255, 30162, 34847, 35763,\n", + " 36674, 37581, 39833, 40735, 48073, 51770, 53628, 54529,\n", + " 57337, 63509, 66829, 68656, 69561, 70458, 71365, 94899,\n", + " 102753, 103671, 107300, 111959, 114697, 119268, 124407, 125315,\n", + " 127123, 128016, 129882, 131687, 132610, 154302, 156131, 157054,\n", + " 158744, 160331, 163062, 165760, 166645, 171276, 172639, 175336,\n", + " 176254, 182110, 185344, 186245, 199183, 200087, 200989],\n", + " dtype='int64'), Int64Index([ 2611, 12837, 14690, 16491, 29256, 30163, 34848, 35764,\n", + " 36675, 37582, 39834, 40736, 48074, 51771, 53629, 54530,\n", + " 57338, 63510, 66830, 68657, 69562, 70459, 71366, 94900,\n", + " 102754, 103672, 107301, 111960, 114698, 119269, 124408, 125316,\n", + " 127124, 128017, 129883, 131688, 132611, 154303, 156132, 157055,\n", + " 158745, 160332, 163063, 165761, 166646, 171277, 172640, 175337,\n", + " 176255, 182111, 185345, 186246, 199184, 200088, 200990],\n", + " dtype='int64'), Int64Index([ 2612, 12838, 14691, 16492, 29257, 30164, 34849, 35765,\n", + " 36676, 37583, 39835, 40737, 48075, 51772, 53630, 54531,\n", + " 57339, 63511, 66831, 68658, 69563, 70460, 71367, 94901,\n", + " 102755, 103673, 107302, 111961, 114699, 119270, 124409, 125317,\n", + " 127125, 128018, 129884, 131689, 132612, 154304, 156133, 157056,\n", + " 158746, 160333, 163064, 165762, 166647, 171278, 172641, 175338,\n", + " 176256, 182112, 185346, 186247, 199185, 200089, 200991],\n", + " dtype='int64'), Int64Index([ 2613, 12839, 14692, 16493, 29258, 30165, 34850, 35766,\n", + " 36677, 37584, 39836, 40738, 48076, 51773, 53631, 54532,\n", + " 57340, 63512, 66832, 68659, 69564, 70461, 71368, 94902,\n", + " 102756, 103674, 107303, 111962, 114700, 119271, 124410, 125318,\n", + " 127126, 128019, 129885, 131690, 132613, 154305, 156134, 157057,\n", + " 158747, 160334, 163065, 165763, 166648, 171279, 172642, 175339,\n", + " 176257, 182113, 185347, 186248, 199186, 200090, 200992],\n", + " dtype='int64'), Int64Index([ 2614, 12840, 14693, 16494, 29259, 30166, 34851, 35767,\n", + " 36678, 37585, 39837, 40739, 48077, 51774, 53632, 54533,\n", + " 57341, 63513, 66833, 68660, 69565, 70462, 71369, 94903,\n", + " 102757, 103675, 107304, 111963, 114701, 119272, 124411, 125319,\n", + " 127127, 128020, 129886, 131691, 132614, 154306, 156135, 157058,\n", + " 158748, 160335, 163066, 165764, 166649, 171280, 172643, 175340,\n", + " 176258, 182114, 185348, 186249, 199187, 200091, 200993],\n", + " dtype='int64'), Int64Index([ 2615, 12841, 14694, 16495, 29260, 30167, 34852, 35768,\n", + " 36679, 37586, 39838, 40740, 48078, 51775, 53633, 54534,\n", + " 57342, 63514, 66834, 68661, 69566, 70463, 71370, 94904,\n", + " 102758, 103676, 107305, 111964, 114702, 119273, 124412, 125320,\n", + " 127128, 128021, 129887, 131692, 132615, 154307, 156136, 157059,\n", + " 158749, 160336, 163067, 165765, 166650, 171281, 172644, 175341,\n", + " 176259, 182115, 185349, 186250, 199188, 200092, 200994],\n", + " dtype='int64'), Int64Index([ 2616, 12842, 14695, 16496, 29261, 30168, 34853, 35769,\n", + " 36680, 37587, 39839, 40741, 48079, 51776, 53634, 54535,\n", + " 57343, 63515, 66835, 68662, 69567, 70464, 71371, 94905,\n", + " 102759, 103677, 107306, 111965, 114703, 119274, 124413, 125321,\n", + " 127129, 128022, 129888, 131693, 132616, 154308, 156137, 157060,\n", + " 158750, 160337, 163068, 165766, 166651, 171282, 172645, 175342,\n", + " 176260, 182116, 185350, 186251, 199189, 200093, 200995],\n", + " dtype='int64'), Int64Index([ 2617, 12843, 14696, 16497, 29262, 30169, 34854, 35770,\n", + " 36681, 37588, 39840, 40742, 48080, 51777, 53635, 54536,\n", + " 57344, 63516, 66836, 68663, 69568, 70465, 71372, 94906,\n", + " 102760, 103678, 107307, 111966, 114704, 119275, 124414, 125322,\n", + " 127130, 128023, 129889, 131694, 132617, 154309, 156138, 157061,\n", + " 158751, 160338, 163069, 165767, 166652, 171283, 172646, 175343,\n", + " 176261, 182117, 185351, 186252, 199190, 200094, 200996],\n", + " dtype='int64'), Int64Index([ 2618, 12844, 14697, 16498, 29263, 30170, 34855, 35771,\n", + " 36682, 37589, 39841, 40743, 48081, 51778, 53636, 54537,\n", + " 57345, 63517, 66837, 68664, 69569, 70466, 71373, 94907,\n", + " 102761, 103679, 107308, 111967, 114705, 119276, 124415, 125323,\n", + " 127131, 128024, 129890, 131695, 132618, 154310, 156139, 157062,\n", + " 158752, 160339, 163070, 165768, 166653, 171284, 172647, 175344,\n", + " 176262, 182118, 185352, 186253, 199191, 200095, 200997],\n", + " dtype='int64'), Int64Index([ 2619, 12845, 14698, 16499, 29264, 30171, 34856, 35772,\n", + " 36683, 37590, 39842, 40744, 48082, 51779, 53637, 54538,\n", + " 57346, 63518, 66838, 68665, 69570, 70467, 71374, 94908,\n", + " 102762, 103680, 107309, 111968, 114706, 119277, 124416, 125324,\n", + " 127132, 128025, 129891, 131696, 132619, 154311, 156140, 157063,\n", + " 158753, 160340, 163071, 165769, 166654, 171285, 172648, 175345,\n", + " 176263, 182119, 185353, 186254, 199192, 200096, 200998],\n", + " dtype='int64'), Int64Index([ 2620, 12846, 14699, 16500, 29265, 30172, 34857, 35773,\n", + " 36684, 37591, 39843, 40745, 48083, 51780, 53638, 54539,\n", + " 57347, 63519, 66839, 68666, 69571, 70468, 71375, 94909,\n", + " 102763, 103681, 107310, 111969, 114707, 119278, 124417, 125325,\n", + " 127133, 128026, 129892, 131697, 132620, 154312, 156141, 157064,\n", + " 158754, 160341, 163072, 165770, 166655, 171286, 172649, 175346,\n", + " 176264, 182120, 185354, 186255, 199193, 200097, 200999],\n", + " dtype='int64'), Int64Index([ 2621, 12847, 14700, 16501, 29266, 30173, 34858, 35774,\n", + " 36685, 37592, 39844, 40746, 48084, 51781, 53639, 54540,\n", + " 57348, 63520, 66840, 68667, 69572, 70469, 71376, 94910,\n", + " 102764, 103682, 107311, 111970, 114708, 119279, 124418, 125326,\n", + " 127134, 128027, 129893, 131698, 132621, 154313, 156142, 157065,\n", + " 158755, 160342, 163073, 165771, 166656, 171287, 172650, 175347,\n", + " 176265, 182121, 185355, 186256, 199194, 200098, 201000],\n", + " dtype='int64'), Int64Index([ 2622, 12848, 14701, 16502, 29267, 30174, 34859, 35775,\n", + " 36686, 37593, 39845, 40747, 48085, 51782, 53640, 54541,\n", + " 57349, 63521, 66841, 68668, 69573, 70470, 71377, 94911,\n", + " 102765, 103683, 107312, 111971, 114709, 119280, 124419, 125327,\n", + " 127135, 128028, 129894, 131699, 132622, 154314, 156143, 157066,\n", + " 158756, 160343, 163074, 165772, 166657, 171288, 172651, 175348,\n", + " 176266, 182122, 185356, 186257, 199195, 200099, 201001],\n", + " dtype='int64'), Int64Index([ 2623, 12849, 14702, 16503, 29268, 30175, 34860, 35776,\n", + " 36687, 37594, 39846, 40748, 48086, 51783, 53641, 54542,\n", + " 57350, 63522, 66842, 68669, 69574, 70471, 71378, 94912,\n", + " 102766, 103684, 107313, 111972, 114710, 119281, 124420, 125328,\n", + " 127136, 128029, 129895, 131700, 132623, 154315, 156144, 157067,\n", + " 158757, 160344, 163075, 165773, 166658, 171289, 172652, 175349,\n", + " 176267, 182123, 185357, 186258, 199196, 200100, 201002],\n", + " dtype='int64'), Int64Index([ 2624, 12850, 14703, 16504, 29269, 30176, 34861, 35777,\n", + " 36688, 37595, 39847, 40749, 48087, 51784, 53642, 54543,\n", + " 57351, 63523, 66843, 68670, 69575, 70472, 71379, 94913,\n", + " 102767, 103685, 107314, 111973, 114711, 119282, 124421, 125329,\n", + " 127137, 128030, 129896, 131701, 132624, 154316, 156145, 157068,\n", + " 158758, 160345, 163076, 165774, 166659, 171290, 172653, 175350,\n", + " 176268, 182124, 185358, 186259, 199197, 200101, 201003],\n", + " dtype='int64'), Int64Index([ 2625, 12851, 14704, 16505, 29270, 30177, 34862, 35778,\n", + " 36689, 37596, 39848, 40750, 48088, 51785, 53643, 54544,\n", + " 57352, 63524, 66844, 68671, 69576, 70473, 71380, 94914,\n", + " 102768, 103686, 107315, 111974, 114712, 119283, 124422, 125330,\n", + " 127138, 128031, 129897, 131702, 132625, 154317, 156146, 157069,\n", + " 158759, 160346, 163077, 165775, 166660, 171291, 172654, 175351,\n", + " 176269, 182125, 185359, 186260, 199198, 200102, 201004],\n", + " dtype='int64'), Int64Index([ 2626, 12852, 14705, 16506, 29271, 30178, 34863, 35779,\n", + " 36690, 37597, 39849, 40751, 48089, 51786, 53644, 54545,\n", + " 57353, 63525, 66845, 68672, 69577, 70474, 71381, 94915,\n", + " 102769, 103687, 107316, 111975, 114713, 119284, 124423, 125331,\n", + " 127139, 128032, 129898, 131703, 132626, 154318, 156147, 157070,\n", + " 158760, 160347, 163078, 165776, 166661, 171292, 172655, 175352,\n", + " 176270, 182126, 185360, 186261, 199199, 200103, 201005],\n", + " dtype='int64'), Int64Index([ 2627, 12853, 14706, 16507, 29272, 30179, 34864, 35780,\n", + " 36691, 37598, 39850, 40752, 48090, 51787, 53645, 54546,\n", + " 57354, 63526, 66846, 68673, 69578, 70475, 71382, 94916,\n", + " 102770, 103688, 107317, 111976, 114714, 119285, 124424, 125332,\n", + " 127140, 128033, 129899, 131704, 132627, 154319, 156148, 157071,\n", + " 158761, 160348, 163079, 165777, 166662, 171293, 172656, 175353,\n", + " 176271, 182127, 185361, 186262, 199200, 200104, 201006],\n", + " dtype='int64'), Int64Index([ 2628, 12854, 14707, 16508, 29273, 30180, 34865, 35781,\n", + " 36692, 37599, 39851, 40753, 48091, 51788, 53646, 54547,\n", + " 57355, 63527, 66847, 68674, 69579, 70476, 71383, 94917,\n", + " 102771, 103689, 107318, 111977, 114715, 119286, 124425, 125333,\n", + " 127141, 128034, 129900, 131705, 132628, 154320, 156149, 157072,\n", + " 158762, 160349, 163080, 165778, 166663, 171294, 172657, 175354,\n", + " 176272, 182128, 185362, 186263, 199201, 200105, 201007],\n", + " dtype='int64'), Int64Index([ 2629, 12855, 14708, 16509, 29274, 30181, 34866, 35782,\n", + " 36693, 37600, 39852, 40754, 48092, 51789, 53647, 54548,\n", + " 57356, 63528, 66848, 68675, 69580, 70477, 71384, 94918,\n", + " 102772, 103690, 107319, 111978, 114716, 119287, 124426, 125334,\n", + " 127142, 128035, 129901, 131706, 132629, 154321, 156150, 157073,\n", + " 158763, 160350, 163081, 165779, 166664, 171295, 172658, 175355,\n", + " 176273, 182129, 185363, 186264, 199202, 200106, 201008],\n", + " dtype='int64'), Int64Index([ 2630, 12856, 14709, 16510, 29275, 30182, 34867, 35783,\n", + " 36694, 37601, 39853, 40755, 48093, 51790, 53648, 54549,\n", + " 57357, 63529, 66849, 68676, 69581, 70478, 71385, 94919,\n", + " 102773, 103691, 107320, 111979, 114717, 119288, 124427, 125335,\n", + " 127143, 128036, 129902, 131707, 132630, 154322, 156151, 157074,\n", + " 158764, 160351, 163082, 165780, 166665, 171296, 172659, 175356,\n", + " 176274, 182130, 185364, 186265, 199203, 200107, 201009],\n", + " dtype='int64'), Int64Index([ 2631, 12857, 14710, 16511, 29276, 30183, 34868, 35784,\n", + " 36695, 37602, 39854, 40756, 48094, 51791, 53649, 54550,\n", + " 57358, 63530, 66850, 68677, 69582, 70479, 71386, 94920,\n", + " 102774, 103692, 107321, 111980, 114718, 119289, 124428, 125336,\n", + " 127144, 128037, 129903, 131708, 132631, 154323, 156152, 157075,\n", + " 158765, 160352, 163083, 165781, 166666, 171297, 172660, 175357,\n", + " 176275, 182131, 185365, 186266, 199204, 200108, 201010],\n", + " dtype='int64'), Int64Index([ 2632, 12858, 14711, 16512, 29277, 30184, 34869, 35785,\n", + " 36696, 37603, 39855, 40757, 48095, 51792, 53650, 54551,\n", + " 57359, 63531, 66851, 68678, 69583, 70480, 71387, 94921,\n", + " 102775, 103693, 107322, 111981, 114719, 119290, 124429, 125337,\n", + " 127145, 128038, 129904, 131709, 132632, 154324, 156153, 157076,\n", + " 158766, 160353, 163084, 165782, 166667, 171298, 172661, 175358,\n", + " 176276, 182132, 185366, 186267, 199205, 200109, 201011],\n", + " dtype='int64'), Int64Index([ 2633, 12859, 14712, 16513, 29278, 30185, 34870, 35786,\n", + " 36697, 37604, 39856, 40758, 48096, 51793, 53651, 54552,\n", + " 57360, 63532, 66852, 68679, 69584, 70481, 71388, 94922,\n", + " 102776, 103694, 107323, 111982, 114720, 119291, 124430, 125338,\n", + " 127146, 128039, 129905, 131710, 132633, 154325, 156154, 157077,\n", + " 158767, 160354, 163085, 165783, 166668, 171299, 172662, 175359,\n", + " 176277, 182133, 185367, 186268, 199206, 200110, 201012],\n", + " dtype='int64'), Int64Index([ 2634, 12860, 14713, 16514, 29279, 30186, 34871, 35787,\n", + " 36698, 37605, 39857, 40759, 48097, 51794, 53652, 54553,\n", + " 57361, 63533, 66853, 68680, 69585, 70482, 71389, 94923,\n", + " 102777, 103695, 107324, 111983, 114721, 119292, 124431, 125339,\n", + " 127147, 128040, 129906, 131711, 132634, 154326, 156155, 157078,\n", + " 158768, 160355, 163086, 165784, 166669, 171300, 172663, 175360,\n", + " 176278, 182134, 185368, 186269, 199207, 200111, 201013],\n", + " dtype='int64'), Int64Index([ 2635, 12861, 14714, 16515, 29280, 30187, 34872, 35788,\n", + " 36699, 37606, 39858, 40760, 48098, 51795, 53653, 54554,\n", + " 57362, 63534, 66854, 68681, 69586, 70483, 71390, 94924,\n", + " 102778, 103696, 107325, 111984, 114722, 119293, 124432, 125340,\n", + " 127148, 128041, 129907, 131712, 132635, 154327, 156156, 157079,\n", + " 158769, 160356, 163087, 165785, 166670, 171301, 172664, 175361,\n", + " 176279, 182135, 185369, 186270, 199208, 200112, 201014],\n", + " dtype='int64'), Int64Index([ 2636, 12862, 14715, 16516, 29281, 30188, 34873, 35789,\n", + " 36700, 37607, 39859, 40761, 48099, 51796, 53654, 54555,\n", + " 57363, 63535, 66855, 68682, 69587, 70484, 71391, 94925,\n", + " 102779, 103697, 107326, 111985, 114723, 119294, 124433, 125341,\n", + " 127149, 128042, 129908, 131713, 132636, 154328, 156157, 157080,\n", + " 158770, 160357, 163088, 165786, 166671, 171302, 172665, 175362,\n", + " 176280, 182136, 185370, 186271, 199209, 200113, 201015],\n", + " dtype='int64'), Int64Index([ 2637, 12863, 14716, 16517, 29282, 30189, 34874, 35790,\n", + " 36701, 37608, 39860, 40762, 48100, 51797, 53655, 54556,\n", + " 57364, 63536, 66856, 68683, 69588, 70485, 71392, 94926,\n", + " 102780, 103698, 107327, 111986, 114724, 119295, 124434, 125342,\n", + " 127150, 128043, 129909, 131714, 132637, 154329, 156158, 157081,\n", + " 158771, 160358, 163089, 165787, 166672, 171303, 172666, 175363,\n", + " 176281, 182137, 185371, 186272, 199210, 200114, 201016],\n", + " dtype='int64'), Int64Index([ 2638, 12864, 14717, 16518, 29283, 30190, 34875, 35791,\n", + " 36702, 37609, 39861, 40763, 48101, 51798, 53656, 54557,\n", + " 57365, 63537, 66857, 68684, 69589, 70486, 71393, 94927,\n", + " 102781, 103699, 107328, 111987, 114725, 119296, 124435, 125343,\n", + " 127151, 128044, 129910, 131715, 132638, 154330, 156159, 157082,\n", + " 158772, 160359, 163090, 165788, 166673, 171304, 172667, 175364,\n", + " 176282, 182138, 185372, 186273, 199211, 200115, 201017],\n", + " dtype='int64'), Int64Index([ 2639, 12865, 14718, 16519, 29284, 30191, 34876, 35792,\n", + " 36703, 37610, 39862, 40764, 48102, 51799, 53657, 54558,\n", + " 57366, 63538, 66858, 68685, 69590, 70487, 71394, 94928,\n", + " 102782, 103700, 107329, 111988, 114726, 119297, 124436, 125344,\n", + " 127152, 128045, 129911, 131716, 132639, 154331, 156160, 157083,\n", + " 158773, 160360, 163091, 165789, 166674, 171305, 172668, 175365,\n", + " 176283, 182139, 185373, 186274, 199212, 200116, 201018],\n", + " dtype='int64'), Int64Index([ 2640, 12866, 14719, 16520, 29285, 30192, 34877, 35793,\n", + " 36704, 37611, 39863, 40765, 48103, 51800, 53658, 54559,\n", + " 57367, 63539, 66859, 68686, 69591, 70488, 71395, 94929,\n", + " 102783, 103701, 107330, 111989, 114727, 119298, 124437, 125345,\n", + " 127153, 128046, 129912, 131717, 132640, 154332, 156161, 157084,\n", + " 158774, 160361, 163092, 165790, 166675, 171306, 172669, 175366,\n", + " 176284, 182140, 185374, 186275, 199213, 200117, 201019],\n", + " dtype='int64'), Int64Index([ 2641, 12867, 14720, 16521, 29286, 30193, 34878, 35794,\n", + " 36705, 37612, 39864, 40766, 48104, 51801, 53659, 54560,\n", + " 57368, 63540, 66860, 68687, 69592, 70489, 71396, 94930,\n", + " 102784, 103702, 107331, 111990, 114728, 119299, 124438, 125346,\n", + " 127154, 128047, 129913, 131718, 132641, 154333, 156162, 157085,\n", + " 158775, 160362, 163093, 165791, 166676, 171307, 172670, 175367,\n", + " 176285, 182141, 185375, 186276, 199214, 200118, 201020],\n", + " dtype='int64'), Int64Index([ 2642, 12868, 14721, 16522, 29287, 30194, 34879, 35795,\n", + " 36706, 37613, 39865, 40767, 48105, 51802, 53660, 54561,\n", + " 57369, 63541, 66861, 68688, 69593, 70490, 71397, 94931,\n", + " 102785, 103703, 107332, 111991, 114729, 119300, 124439, 125347,\n", + " 127155, 128048, 129914, 131719, 132642, 154334, 156163, 157086,\n", + " 158776, 160363, 163094, 165792, 166677, 171308, 172671, 175368,\n", + " 176286, 182142, 185376, 186277, 199215, 200119, 201021],\n", + " dtype='int64'), Int64Index([ 2643, 12869, 14722, 16523, 29288, 30195, 34880, 35796,\n", + " 36707, 37614, 39866, 40768, 48106, 51803, 53661, 54562,\n", + " 57370, 63542, 66862, 68689, 69594, 70491, 71398, 94932,\n", + " 102786, 103704, 107333, 111992, 114730, 119301, 124440, 125348,\n", + " 127156, 128049, 129915, 131720, 132643, 154335, 156164, 157087,\n", + " 158777, 160364, 163095, 165793, 166678, 171309, 172672, 175369,\n", + " 176287, 182143, 185377, 186278, 199216, 200120, 201022],\n", + " dtype='int64'), Int64Index([ 2644, 12870, 14723, 16524, 29289, 30196, 34881, 35797,\n", + " 36708, 37615, 39867, 40769, 48107, 51804, 53662, 54563,\n", + " 57371, 63543, 66863, 68690, 69595, 70492, 71399, 94933,\n", + " 102787, 103705, 107334, 111993, 114731, 119302, 124441, 125349,\n", + " 127157, 128050, 129916, 131721, 132644, 154336, 156165, 157088,\n", + " 158778, 160365, 163096, 165794, 166679, 171310, 172673, 175370,\n", + " 176288, 182144, 185378, 186279, 199217, 200121, 201023],\n", + " dtype='int64'), Int64Index([ 2645, 12871, 14724, 16525, 29290, 30197, 34882, 35798,\n", + " 36709, 37616, 39868, 40770, 48108, 51805, 53663, 54564,\n", + " 57372, 63544, 66864, 68691, 69596, 70493, 71400, 94934,\n", + " 102788, 103706, 107335, 111994, 114732, 119303, 124442, 125350,\n", + " 127158, 128051, 129917, 131722, 132645, 154337, 156166, 157089,\n", + " 158779, 160366, 163097, 165795, 166680, 171311, 172674, 175371,\n", + " 176289, 182145, 185379, 186280, 199218, 200122, 201024],\n", + " dtype='int64'), Int64Index([ 2646, 12872, 14725, 16526, 29291, 30198, 34883, 35799,\n", + " 36710, 37617, 39869, 40771, 48109, 51806, 53664, 54565,\n", + " 57373, 63545, 66865, 68692, 69597, 70494, 71401, 94935,\n", + " 102789, 103707, 107336, 111995, 114733, 119304, 124443, 125351,\n", + " 127159, 128052, 129918, 131723, 132646, 154338, 156167, 157090,\n", + " 158780, 160367, 163098, 165796, 166681, 171312, 172675, 175372,\n", + " 176290, 182146, 185380, 186281, 199219, 200123, 201025],\n", + " dtype='int64'), Int64Index([ 2647, 12873, 14726, 16527, 29292, 30199, 34884, 35800,\n", + " 36711, 37618, 39870, 40772, 48110, 51807, 53665, 54566,\n", + " 57374, 63546, 66866, 68693, 69598, 70495, 71402, 94936,\n", + " 102790, 103708, 107337, 111996, 114734, 119305, 124444, 125352,\n", + " 127160, 128053, 129919, 131724, 132647, 154339, 156168, 157091,\n", + " 158781, 160368, 163099, 165797, 166682, 171313, 172676, 175373,\n", + " 176291, 182147, 185381, 186282, 199220, 200124, 201026],\n", + " dtype='int64'), Int64Index([ 2648, 12874, 14727, 16528, 29293, 30200, 34885, 35801,\n", + " 36712, 37619, 39871, 40773, 48111, 51808, 53666, 54567,\n", + " 57375, 63547, 66867, 68694, 69599, 70496, 71403, 94937,\n", + " 102791, 103709, 107338, 111997, 114735, 119306, 124445, 125353,\n", + " 127161, 128054, 129920, 131725, 132648, 154340, 156169, 157092,\n", + " 158782, 160369, 163100, 165798, 166683, 171314, 172677, 175374,\n", + " 176292, 182148, 185382, 186283, 199221, 200125, 201027],\n", + " dtype='int64'), Int64Index([ 2649, 12875, 14728, 16529, 29294, 30201, 34886, 35802,\n", + " 36713, 37620, 39872, 40774, 48112, 51809, 53667, 54568,\n", + " 57376, 63548, 66868, 68695, 69600, 70497, 71404, 94938,\n", + " 102792, 103710, 107339, 111998, 114736, 119307, 124446, 125354,\n", + " 127162, 128055, 129921, 131726, 132649, 154341, 156170, 157093,\n", + " 158783, 160370, 163101, 165799, 166684, 171315, 172678, 175375,\n", + " 176293, 182149, 185383, 186284, 199222, 200126, 201028],\n", + " dtype='int64'), Int64Index([ 2650, 12876, 14729, 16530, 29295, 30202, 34887, 35803,\n", + " 36714, 37621, 39873, 40775, 48113, 51810, 53668, 54569,\n", + " 57377, 63549, 66869, 68696, 69601, 70498, 71405, 94939,\n", + " 102793, 103711, 107340, 111999, 114737, 119308, 124447, 125355,\n", + " 127163, 128056, 129922, 131727, 132650, 154342, 156171, 157094,\n", + " 158784, 160371, 163102, 165800, 166685, 171316, 172679, 175376,\n", + " 176294, 182150, 185384, 186285, 199223, 200127, 201029],\n", + " dtype='int64'), Int64Index([ 2651, 12877, 14730, 16531, 29296, 30203, 34888, 35804,\n", + " 36715, 37622, 39874, 40776, 48114, 51811, 53669, 54570,\n", + " 57378, 63550, 66870, 68697, 69602, 70499, 71406, 94940,\n", + " 102794, 103712, 107341, 112000, 114738, 119309, 124448, 125356,\n", + " 127164, 128057, 129923, 131728, 132651, 154343, 156172, 157095,\n", + " 158785, 160372, 163103, 165801, 166686, 171317, 172680, 175377,\n", + " 176295, 182151, 185385, 186286, 199224, 200128, 201030],\n", + " dtype='int64'), Int64Index([ 2652, 12878, 14731, 16532, 29297, 30204, 34889, 35805,\n", + " 36716, 37623, 39875, 40777, 48115, 51812, 53670, 54571,\n", + " 57379, 63551, 66871, 68698, 69603, 70500, 71407, 94941,\n", + " 102795, 103713, 107342, 112001, 114739, 119310, 124449, 125357,\n", + " 127165, 128058, 129924, 131729, 132652, 154344, 156173, 157096,\n", + " 158786, 160373, 163104, 165802, 166687, 171318, 172681, 175378,\n", + " 176296, 182152, 185386, 186287, 199225, 200129, 201031],\n", + " dtype='int64'), Int64Index([ 2653, 12879, 14732, 16533, 29298, 30205, 34890, 35806,\n", + " 36717, 37624, 39876, 40778, 48116, 51813, 53671, 54572,\n", + " 57380, 63552, 66872, 68699, 69604, 70501, 71408, 94942,\n", + " 102796, 103714, 107343, 112002, 114740, 119311, 124450, 125358,\n", + " 127166, 128059, 129925, 131730, 132653, 154345, 156174, 157097,\n", + " 158787, 160374, 163105, 165803, 166688, 171319, 172682, 175379,\n", + " 176297, 182153, 185387, 186288, 199226, 200130, 201032],\n", + " dtype='int64'), Int64Index([ 2654, 12880, 14733, 16534, 29299, 30206, 34891, 35807,\n", + " 36718, 37625, 39877, 40779, 48117, 51814, 53672, 54573,\n", + " 57381, 63553, 66873, 68700, 69605, 70502, 71409, 94943,\n", + " 102797, 103715, 107344, 112003, 114741, 119312, 124451, 125359,\n", + " 127167, 128060, 129926, 131731, 132654, 154346, 156175, 157098,\n", + " 158788, 160375, 163106, 165804, 166689, 171320, 172683, 175380,\n", + " 176298, 182154, 185388, 186289, 199227, 200131, 201033],\n", + " dtype='int64'), Int64Index([ 2655, 12881, 14734, 16535, 29300, 30207, 34892, 35808,\n", + " 36719, 37626, 39878, 40780, 48118, 51815, 53673, 54574,\n", + " 57382, 63554, 66874, 68701, 69606, 70503, 71410, 94944,\n", + " 102798, 103716, 107345, 112004, 114742, 119313, 124452, 125360,\n", + " 127168, 128061, 129927, 131732, 132655, 154347, 156176, 157099,\n", + " 158789, 160376, 163107, 165805, 166690, 171321, 172684, 175381,\n", + " 176299, 182155, 185389, 186290, 199228, 200132, 201034],\n", + " dtype='int64'), Int64Index([ 2656, 12882, 14735, 16536, 29301, 30208, 34893, 35809,\n", + " 36720, 37627, 39879, 40781, 48119, 51816, 53674, 54575,\n", + " 57383, 63555, 66875, 68702, 69607, 70504, 71411, 94945,\n", + " 102799, 103717, 107346, 112005, 114743, 119314, 124453, 125361,\n", + " 127169, 128062, 129928, 131733, 132656, 154348, 156177, 157100,\n", + " 158790, 160377, 163108, 165806, 166691, 171322, 172685, 175382,\n", + " 176300, 182156, 185390, 186291, 199229, 200133, 201035],\n", + " dtype='int64'), Int64Index([ 2657, 12883, 14736, 16537, 29302, 30209, 34894, 35810,\n", + " 36721, 37628, 39880, 40782, 48120, 51817, 53675, 54576,\n", + " 57384, 63556, 66876, 68703, 69608, 70505, 71412, 94946,\n", + " 102800, 103718, 107347, 112006, 114744, 119315, 124454, 125362,\n", + " 127170, 128063, 129929, 131734, 132657, 154349, 156178, 157101,\n", + " 158791, 160378, 163109, 165807, 166692, 171323, 172686, 175383,\n", + " 176301, 182157, 185391, 186292, 199230, 200134, 201036],\n", + " dtype='int64'), Int64Index([ 2658, 12884, 14737, 16538, 29303, 30210, 34895, 35811,\n", + " 36722, 37629, 39881, 40783, 48121, 51818, 53676, 54577,\n", + " 57385, 63557, 66877, 68704, 69609, 70506, 71413, 94947,\n", + " 102801, 103719, 107348, 112007, 114745, 119316, 124455, 125363,\n", + " 127171, 128064, 129930, 131735, 132658, 154350, 156179, 157102,\n", + " 158792, 160379, 163110, 165808, 166693, 171324, 172687, 175384,\n", + " 176302, 182158, 185392, 186293, 199231, 200135, 201037],\n", + " dtype='int64'), Int64Index([ 2659, 12885, 14738, 16539, 29304, 30211, 34896, 35812,\n", + " 36723, 37630, 39882, 40784, 48122, 51819, 53677, 54578,\n", + " 57386, 63558, 66878, 68705, 69610, 70507, 71414, 94948,\n", + " 102802, 103720, 107349, 112008, 114746, 119317, 124456, 125364,\n", + " 127172, 128065, 129931, 131736, 132659, 154351, 156180, 157103,\n", + " 158793, 160380, 163111, 165809, 166694, 171325, 172688, 175385,\n", + " 176303, 182159, 185393, 186294, 199232, 200136, 201038],\n", + " dtype='int64'), Int64Index([ 2660, 12886, 14739, 16540, 29305, 30212, 34897, 35813,\n", + " 36724, 37631, 39883, 40785, 48123, 51820, 53678, 54579,\n", + " 57387, 63559, 66879, 68706, 69611, 70508, 71415, 94949,\n", + " 102803, 103721, 107350, 112009, 114747, 119318, 124457, 125365,\n", + " 127173, 128066, 129932, 131737, 132660, 154352, 156181, 157104,\n", + " 158794, 160381, 163112, 165810, 166695, 171326, 172689, 175386,\n", + " 176304, 182160, 185394, 186295, 199233, 200137, 201039],\n", + " dtype='int64'), Int64Index([ 2661, 12887, 14740, 16541, 29306, 30213, 34898, 35814,\n", + " 36725, 37632, 39884, 40786, 48124, 51821, 53679, 54580,\n", + " 57388, 63560, 66880, 68707, 69612, 70509, 71416, 94950,\n", + " 102804, 103722, 107351, 112010, 114748, 119319, 124458, 125366,\n", + " 127174, 128067, 129933, 131738, 132661, 154353, 156182, 157105,\n", + " 158795, 160382, 163113, 165811, 166696, 171327, 172690, 175387,\n", + " 176305, 182161, 185395, 186296, 199234, 200138, 201040],\n", + " dtype='int64'), Int64Index([ 2662, 12888, 14741, 16542, 29307, 30214, 34899, 35815,\n", + " 36726, 37633, 39885, 40787, 48125, 51822, 53680, 54581,\n", + " 57389, 63561, 66881, 68708, 69613, 70510, 71417, 94951,\n", + " 102805, 103723, 107352, 112011, 114749, 119320, 124459, 125367,\n", + " 127175, 128068, 129934, 131739, 132662, 154354, 156183, 157106,\n", + " 158796, 160383, 163114, 165812, 166697, 171328, 172691, 175388,\n", + " 176306, 182162, 185396, 186297, 199235, 200139, 201041],\n", + " dtype='int64'), Int64Index([ 2663, 12889, 14742, 16543, 29308, 30215, 34900, 35816,\n", + " 36727, 37634, 39886, 40788, 48126, 51823, 53681, 54582,\n", + " 57390, 63562, 66882, 68709, 69614, 70511, 71418, 94952,\n", + " 102806, 103724, 107353, 112012, 114750, 119321, 124460, 125368,\n", + " 127176, 128069, 129935, 131740, 132663, 154355, 156184, 157107,\n", + " 158797, 160384, 163115, 165813, 166698, 171329, 172692, 175389,\n", + " 176307, 182163, 185397, 186298, 199236, 200140, 201042],\n", + " dtype='int64'), Int64Index([ 2664, 12890, 14743, 16544, 29309, 30216, 34901, 35817,\n", + " 36728, 37635, 39887, 40789, 48127, 51824, 53682, 54583,\n", + " 57391, 63563, 66883, 68710, 69615, 70512, 71419, 94953,\n", + " 102807, 103725, 107354, 112013, 114751, 119322, 124461, 125369,\n", + " 127177, 128070, 129936, 131741, 132664, 154356, 156185, 157108,\n", + " 158798, 160385, 163116, 165814, 166699, 171330, 172693, 175390,\n", + " 176308, 182164, 185398, 186299, 199237, 200141, 201043],\n", + " dtype='int64'), Int64Index([ 2665, 12891, 14744, 16545, 29310, 30217, 34902, 35818,\n", + " 36729, 37636, 39888, 40790, 48128, 51825, 53683, 54584,\n", + " 57392, 63564, 66884, 68711, 69616, 70513, 71420, 94954,\n", + " 102808, 103726, 107355, 112014, 114752, 119323, 124462, 125370,\n", + " 127178, 128071, 129937, 131742, 132665, 154357, 156186, 157109,\n", + " 158799, 160386, 163117, 165815, 166700, 171331, 172694, 175391,\n", + " 176309, 182165, 185399, 186300, 199238, 200142, 201044],\n", + " dtype='int64'), Int64Index([ 2666, 12892, 14745, 16546, 29311, 30218, 34903, 35819,\n", + " 36730, 37637, 39889, 40791, 48129, 51826, 53684, 54585,\n", + " 57393, 63565, 66885, 68712, 69617, 70514, 71421, 94955,\n", + " 102809, 103727, 107356, 112015, 114753, 119324, 124463, 125371,\n", + " 127179, 128072, 129938, 131743, 132666, 154358, 156187, 157110,\n", + " 158800, 160387, 163118, 165816, 166701, 171332, 172695, 175392,\n", + " 176310, 182166, 185400, 186301, 199239, 200143, 201045],\n", + " dtype='int64'), Int64Index([ 2667, 12893, 14746, 16547, 29312, 30219, 34904, 35820,\n", + " 36731, 37638, 39890, 40792, 48130, 51827, 53685, 54586,\n", + " 57394, 63566, 66886, 68713, 69618, 70515, 71422, 94956,\n", + " 102810, 103728, 107357, 112016, 114754, 119325, 124464, 125372,\n", + " 127180, 128073, 129939, 131744, 132667, 154359, 156188, 157111,\n", + " 158801, 160388, 163119, 165817, 166702, 171333, 172696, 175393,\n", + " 176311, 182167, 185401, 186302, 199240, 200144, 201046],\n", + " dtype='int64'), Int64Index([ 2668, 12894, 14747, 16548, 29313, 30220, 34905, 35821,\n", + " 36732, 37639, 39891, 40793, 48131, 51828, 53686, 54587,\n", + " 57395, 63567, 66887, 68714, 69619, 70516, 71423, 94957,\n", + " 102811, 103729, 107358, 112017, 114755, 119326, 124465, 125373,\n", + " 127181, 128074, 129940, 131745, 132668, 154360, 156189, 157112,\n", + " 158802, 160389, 163120, 165818, 166703, 171334, 172697, 175394,\n", + " 176312, 182168, 185402, 186303, 199241, 200145, 201047],\n", + " dtype='int64'), Int64Index([ 2669, 12895, 14748, 16549, 29314, 30221, 34906, 35822,\n", + " 36733, 37640, 39892, 40794, 48132, 51829, 53687, 54588,\n", + " 57396, 63568, 66888, 68715, 69620, 70517, 71424, 94958,\n", + " 102812, 103730, 107359, 112018, 114756, 119327, 124466, 125374,\n", + " 127182, 128075, 129941, 131746, 132669, 154361, 156190, 157113,\n", + " 158803, 160390, 163121, 165819, 166704, 171335, 172698, 175395,\n", + " 176313, 182169, 185403, 186304, 199242, 200146, 201048],\n", + " dtype='int64'), Int64Index([ 2670, 12896, 14749, 16550, 29315, 30222, 34907, 35823,\n", + " 36734, 37641, 39893, 40795, 48133, 51830, 53688, 54589,\n", + " 57397, 63569, 66889, 68716, 69621, 70518, 71425, 94959,\n", + " 102813, 103731, 107360, 112019, 114757, 119328, 124467, 125375,\n", + " 127183, 128076, 129942, 131747, 132670, 154362, 156191, 157114,\n", + " 158804, 160391, 163122, 165820, 166705, 171336, 172699, 175396,\n", + " 176314, 182170, 185404, 186305, 199243, 200147, 201049],\n", + " dtype='int64'), Int64Index([ 2671, 12897, 14750, 16551, 29316, 30223, 34908, 35824,\n", + " 36735, 37642, 39894, 40796, 48134, 51831, 53689, 54590,\n", + " 57398, 63570, 66890, 68717, 69622, 70519, 71426, 94960,\n", + " 102814, 103732, 107361, 112020, 114758, 119329, 124468, 125376,\n", + " 127184, 128077, 129943, 131748, 132671, 154363, 156192, 157115,\n", + " 158805, 160392, 163123, 165821, 166706, 171337, 172700, 175397,\n", + " 176315, 182171, 185405, 186306, 199244, 200148, 201050],\n", + " dtype='int64'), Int64Index([ 2672, 12898, 14751, 16552, 29317, 30224, 34909, 35825,\n", + " 36736, 37643, 39895, 40797, 48135, 51832, 53690, 54591,\n", + " 57399, 63571, 66891, 68718, 69623, 70520, 71427, 94961,\n", + " 102815, 103733, 107362, 112021, 114759, 119330, 124469, 125377,\n", + " 127185, 128078, 129944, 131749, 132672, 154364, 156193, 157116,\n", + " 158806, 160393, 163124, 165822, 166707, 171338, 172701, 175398,\n", + " 176316, 182172, 185406, 186307, 199245, 200149, 201051],\n", + " dtype='int64'), Int64Index([ 2673, 12899, 14752, 16553, 29318, 30225, 34910, 35826,\n", + " 36737, 37644, 39896, 40798, 48136, 51833, 53691, 54592,\n", + " 57400, 63572, 66892, 68719, 69624, 70521, 71428, 94962,\n", + " 102816, 103734, 107363, 112022, 114760, 119331, 124470, 125378,\n", + " 127186, 128079, 129945, 131750, 132673, 154365, 156194, 157117,\n", + " 158807, 160394, 163125, 165823, 166708, 171339, 172702, 175399,\n", + " 176317, 182173, 185407, 186308, 199246, 200150, 201052],\n", + " dtype='int64'), Int64Index([ 2674, 12900, 14753, 16554, 29319, 30226, 34911, 35827,\n", + " 36738, 37645, 39897, 40799, 48137, 51834, 53692, 54593,\n", + " 57401, 63573, 66893, 68720, 69625, 70522, 71429, 94963,\n", + " 102817, 103735, 107364, 112023, 114761, 119332, 124471, 125379,\n", + " 127187, 128080, 129946, 131751, 132674, 154366, 156195, 157118,\n", + " 158808, 160395, 163126, 165824, 166709, 171340, 172703, 175400,\n", + " 176318, 182174, 185408, 186309, 199247, 200151, 201053],\n", + " dtype='int64'), Int64Index([ 2675, 12901, 14754, 16555, 29320, 30227, 34912, 35828,\n", + " 36739, 37646, 39898, 40800, 48138, 51835, 53693, 54594,\n", + " 57402, 63574, 66894, 68721, 69626, 70523, 71430, 94964,\n", + " 102818, 103736, 107365, 112024, 114762, 119333, 124472, 125380,\n", + " 127188, 128081, 129947, 131752, 132675, 154367, 156196, 157119,\n", + " 158809, 160396, 163127, 165825, 166710, 171341, 172704, 175401,\n", + " 176319, 182175, 185409, 186310, 199248, 200152, 201054],\n", + " dtype='int64'), Int64Index([ 2676, 12902, 14755, 16556, 29321, 30228, 34913, 35829,\n", + " 36740, 37647, 39899, 40801, 48139, 51836, 53694, 54595,\n", + " 57403, 63575, 66895, 68722, 69627, 70524, 71431, 94965,\n", + " 102819, 103737, 107366, 112025, 114763, 119334, 124473, 125381,\n", + " 127189, 128082, 129948, 131753, 132676, 154368, 156197, 157120,\n", + " 158810, 160397, 163128, 165826, 166711, 171342, 172705, 175402,\n", + " 176320, 182176, 185410, 186311, 199249, 200153, 201055],\n", + " dtype='int64'), Int64Index([ 2677, 12903, 14756, 16557, 29322, 30229, 34914, 35830,\n", + " 36741, 37648, 39900, 40802, 48140, 51837, 53695, 54596,\n", + " 57404, 63576, 66896, 68723, 69628, 70525, 71432, 94966,\n", + " 102820, 103738, 107367, 112026, 114764, 119335, 124474, 125382,\n", + " 127190, 128083, 129949, 131754, 132677, 154369, 156198, 157121,\n", + " 158811, 160398, 163129, 165827, 166712, 171343, 172706, 175403,\n", + " 176321, 182177, 185411, 186312, 199250, 200154, 201056],\n", + " dtype='int64'), Int64Index([ 2678, 12904, 14757, 16558, 29323, 30230, 34915, 35831,\n", + " 36742, 37649, 39901, 40803, 48141, 51838, 53696, 54597,\n", + " 57405, 63577, 66897, 68724, 69629, 70526, 71433, 94967,\n", + " 102821, 103739, 107368, 112027, 114765, 119336, 124475, 125383,\n", + " 127191, 128084, 129950, 131755, 132678, 154370, 156199, 157122,\n", + " 158812, 160399, 163130, 165828, 166713, 171344, 172707, 175404,\n", + " 176322, 182178, 185412, 186313, 199251, 200155, 201057],\n", + " dtype='int64'), Int64Index([ 2679, 12905, 14758, 16559, 29324, 30231, 34916, 35832,\n", + " 36743, 37650, 39902, 40804, 48142, 51839, 53697, 54598,\n", + " 57406, 63578, 66898, 68725, 69630, 70527, 71434, 94968,\n", + " 102822, 103740, 107369, 112028, 114766, 119337, 124476, 125384,\n", + " 127192, 128085, 129951, 131756, 132679, 154371, 156200, 157123,\n", + " 158813, 160400, 163131, 165829, 166714, 171345, 172708, 175405,\n", + " 176323, 182179, 185413, 186314, 199252, 200156, 201058],\n", + " dtype='int64'), Int64Index([ 2680, 12906, 14759, 16560, 29325, 30232, 34917, 35833,\n", + " 36744, 37651, 39903, 40805, 48143, 51840, 53698, 54599,\n", + " 57407, 63579, 66899, 68726, 69631, 70528, 71435, 94969,\n", + " 102823, 103741, 107370, 112029, 114767, 119338, 124477, 125385,\n", + " 127193, 128086, 129952, 131757, 132680, 154372, 156201, 157124,\n", + " 158814, 160401, 163132, 165830, 166715, 171346, 172709, 175406,\n", + " 176324, 182180, 185414, 186315, 199253, 200157, 201059],\n", + " dtype='int64'), Int64Index([ 2681, 12907, 14760, 16561, 29326, 30233, 34918, 35834,\n", + " 36745, 37652, 39904, 40806, 48144, 51841, 53699, 54600,\n", + " 57408, 63580, 66900, 68727, 69632, 70529, 71436, 94970,\n", + " 102824, 103742, 107371, 112030, 114768, 119339, 124478, 125386,\n", + " 127194, 128087, 129953, 131758, 132681, 154373, 156202, 157125,\n", + " 158815, 160402, 163133, 165831, 166716, 171347, 172710, 175407,\n", + " 176325, 182181, 185415, 186316, 199254, 200158, 201060],\n", + " dtype='int64'), Int64Index([ 2682, 12908, 14761, 16562, 29327, 30234, 34919, 35835,\n", + " 36746, 37653, 39905, 40807, 48145, 51842, 53700, 54601,\n", + " 57409, 63581, 66901, 68728, 69633, 70530, 71437, 94971,\n", + " 102825, 103743, 107372, 112031, 114769, 119340, 124479, 125387,\n", + " 127195, 128088, 129954, 131759, 132682, 154374, 156203, 157126,\n", + " 158816, 160403, 163134, 165832, 166717, 171348, 172711, 175408,\n", + " 176326, 182182, 185416, 186317, 199255, 200159, 201061],\n", + " dtype='int64'), Int64Index([ 2683, 12909, 14762, 16563, 29328, 30235, 34920, 35836,\n", + " 36747, 37654, 39906, 40808, 48146, 51843, 53701, 54602,\n", + " 57410, 63582, 66902, 68729, 69634, 70531, 71438, 94972,\n", + " 102826, 103744, 107373, 112032, 114770, 119341, 124480, 125388,\n", + " 127196, 128089, 129955, 131760, 132683, 154375, 156204, 157127,\n", + " 158817, 160404, 163135, 165833, 166718, 171349, 172712, 175409,\n", + " 176327, 182183, 185417, 186318, 199256, 200160, 201062],\n", + " dtype='int64'), Int64Index([ 2684, 12910, 14763, 16564, 29329, 30236, 34921, 35837,\n", + " 36748, 37655, 39907, 40809, 48147, 51844, 53702, 54603,\n", + " 57411, 63583, 66903, 68730, 69635, 70532, 71439, 94973,\n", + " 102827, 103745, 107374, 112033, 114771, 119342, 124481, 125389,\n", + " 127197, 128090, 129956, 131761, 132684, 154376, 156205, 157128,\n", + " 158818, 160405, 163136, 165834, 166719, 171350, 172713, 175410,\n", + " 176328, 182184, 185418, 186319, 199257, 200161, 201063],\n", + " dtype='int64'), Int64Index([ 2685, 12911, 14764, 16565, 29330, 30237, 34922, 35838,\n", + " 36749, 37656, 39908, 40810, 48148, 51845, 53703, 54604,\n", + " 57412, 63584, 66904, 68731, 69636, 70533, 71440, 94974,\n", + " 102828, 103746, 107375, 112034, 114772, 119343, 124482, 125390,\n", + " 127198, 128091, 129957, 131762, 132685, 154377, 156206, 157129,\n", + " 158819, 160406, 163137, 165835, 166720, 171351, 172714, 175411,\n", + " 176329, 182185, 185419, 186320, 199258, 200162, 201064],\n", + " dtype='int64'), Int64Index([ 2686, 12912, 14765, 16566, 29331, 30238, 34923, 35839,\n", + " 36750, 37657, 39909, 40811, 48149, 51846, 53704, 54605,\n", + " 57413, 63585, 66905, 68732, 69637, 70534, 71441, 94975,\n", + " 102829, 103747, 107376, 112035, 114773, 119344, 124483, 125391,\n", + " 127199, 128092, 129958, 131763, 132686, 154378, 156207, 157130,\n", + " 158820, 160407, 163138, 165836, 166721, 171352, 172715, 175412,\n", + " 176330, 182186, 185420, 186321, 199259, 200163, 201065],\n", + " dtype='int64'), Int64Index([ 2687, 12913, 14766, 16567, 29332, 30239, 34924, 35840,\n", + " 36751, 37658, 39910, 40812, 48150, 51847, 53705, 54606,\n", + " 57414, 63586, 66906, 68733, 69638, 70535, 71442, 94976,\n", + " 102830, 103748, 107377, 112036, 114774, 119345, 124484, 125392,\n", + " 127200, 128093, 129959, 131764, 132687, 154379, 156208, 157131,\n", + " 158821, 160408, 163139, 165837, 166722, 171353, 172716, 175413,\n", + " 176331, 182187, 185421, 186322, 199260, 200164, 201066],\n", + " dtype='int64'), Int64Index([ 2688, 12914, 14767, 16568, 29333, 30240, 34925, 35841,\n", + " 36752, 37659, 39911, 40813, 48151, 51848, 53706, 54607,\n", + " 57415, 63587, 66907, 68734, 69639, 70536, 71443, 94977,\n", + " 102831, 103749, 107378, 112037, 114775, 119346, 124485, 125393,\n", + " 127201, 128094, 129960, 131765, 132688, 154380, 156209, 157132,\n", + " 158822, 160409, 163140, 165838, 166723, 171354, 172717, 175414,\n", + " 176332, 182188, 185422, 186323, 199261, 200165, 201067],\n", + " dtype='int64'), Int64Index([ 2689, 12915, 14768, 16569, 29334, 30241, 34926, 35842,\n", + " 36753, 37660, 39912, 40814, 48152, 51849, 53707, 54608,\n", + " 57416, 63588, 66908, 68735, 69640, 70537, 71444, 94978,\n", + " 102832, 103750, 107379, 112038, 114776, 119347, 124486, 125394,\n", + " 127202, 128095, 129961, 131766, 132689, 154381, 156210, 157133,\n", + " 158823, 160410, 163141, 165839, 166724, 171355, 172718, 175415,\n", + " 176333, 182189, 185423, 186324, 199262, 200166, 201068],\n", + " dtype='int64'), Int64Index([ 2690, 12916, 14769, 16570, 29335, 30242, 34927, 35843,\n", + " 36754, 37661, 39913, 40815, 48153, 51850, 53708, 54609,\n", + " 57417, 63589, 66909, 68736, 69641, 70538, 71445, 94979,\n", + " 102833, 103751, 107380, 112039, 114777, 119348, 124487, 125395,\n", + " 127203, 128096, 129962, 131767, 132690, 154382, 156211, 157134,\n", + " 158824, 160411, 163142, 165840, 166725, 171356, 172719, 175416,\n", + " 176334, 182190, 185424, 186325, 199263, 200167, 201069],\n", + " dtype='int64'), Int64Index([ 2691, 12917, 14770, 16571, 29336, 30243, 34928, 35844,\n", + " 36755, 37662, 39914, 40816, 48154, 51851, 53709, 54610,\n", + " 57418, 63590, 66910, 68737, 69642, 70539, 71446, 94980,\n", + " 102834, 103752, 107381, 112040, 114778, 119349, 124488, 125396,\n", + " 127204, 128097, 129963, 131768, 132691, 154383, 156212, 157135,\n", + " 158825, 160412, 163143, 165841, 166726, 171357, 172720, 175417,\n", + " 176335, 182191, 185425, 186326, 199264, 200168, 201070],\n", + " dtype='int64'), Int64Index([ 2692, 12918, 14771, 16572, 29337, 30244, 34929, 35845,\n", + " 36756, 37663, 39915, 40817, 48155, 51852, 53710, 54611,\n", + " 57419, 63591, 66911, 68738, 69643, 70540, 71447, 94981,\n", + " 102835, 103753, 107382, 112041, 114779, 119350, 124489, 125397,\n", + " 127205, 128098, 129964, 131769, 132692, 154384, 156213, 157136,\n", + " 158826, 160413, 163144, 165842, 166727, 171358, 172721, 175418,\n", + " 176336, 182192, 185426, 186327, 199265, 200169, 201071],\n", + " dtype='int64'), Int64Index([ 2693, 12919, 14772, 16573, 29338, 30245, 34930, 35846,\n", + " 36757, 37664, 39916, 40818, 48156, 51853, 53711, 54612,\n", + " 57420, 63592, 66912, 68739, 69644, 70541, 71448, 94982,\n", + " 102836, 103754, 107383, 112042, 114780, 119351, 124490, 125398,\n", + " 127206, 128099, 129965, 131770, 132693, 154385, 156214, 157137,\n", + " 158827, 160414, 163145, 165843, 166728, 171359, 172722, 175419,\n", + " 176337, 182193, 185427, 186328, 199266, 200170, 201072],\n", + " dtype='int64'), Int64Index([ 2694, 12920, 14773, 16574, 29339, 30246, 34931, 35847,\n", + " 36758, 37665, 39917, 40819, 48157, 51854, 53712, 54613,\n", + " 57421, 63593, 66913, 68740, 69645, 70542, 71449, 94983,\n", + " 102837, 103755, 107384, 112043, 114781, 119352, 124491, 125399,\n", + " 127207, 128100, 129966, 131771, 132694, 154386, 156215, 157138,\n", + " 158828, 160415, 163146, 165844, 166729, 171360, 172723, 175420,\n", + " 176338, 182194, 185428, 186329, 199267, 200171, 201073],\n", + " dtype='int64'), Int64Index([ 2695, 12921, 14774, 16575, 29340, 30247, 34932, 35848,\n", + " 36759, 37666, 39918, 40820, 48158, 51855, 53713, 54614,\n", + " 57422, 63594, 66914, 68741, 69646, 70543, 71450, 94984,\n", + " 102838, 103756, 107385, 112044, 114782, 119353, 124492, 125400,\n", + " 127208, 128101, 129967, 131772, 132695, 154387, 156216, 157139,\n", + " 158829, 160416, 163147, 165845, 166730, 171361, 172724, 175421,\n", + " 176339, 182195, 185429, 186330, 199268, 200172, 201074],\n", + " dtype='int64'), Int64Index([ 2696, 12922, 14775, 16576, 29341, 30248, 34933, 35849,\n", + " 36760, 37667, 39919, 40821, 48159, 51856, 53714, 54615,\n", + " 57423, 63595, 66915, 68742, 69647, 70544, 71451, 94985,\n", + " 102839, 103757, 107386, 112045, 114783, 119354, 124493, 125401,\n", + " 127209, 128102, 129968, 131773, 132696, 154388, 156217, 157140,\n", + " 158830, 160417, 163148, 165846, 166731, 171362, 172725, 175422,\n", + " 176340, 182196, 185430, 186331, 199269, 200173, 201075],\n", + " dtype='int64'), Int64Index([ 2697, 12923, 14776, 16577, 29342, 30249, 34934, 35850,\n", + " 36761, 37668, 39920, 40822, 48160, 51857, 53715, 54616,\n", + " 57424, 63596, 66916, 68743, 69648, 70545, 71452, 94986,\n", + " 102840, 103758, 107387, 112046, 114784, 119355, 124494, 125402,\n", + " 127210, 128103, 129969, 131774, 132697, 154389, 156218, 157141,\n", + " 158831, 160418, 163149, 165847, 166732, 171363, 172726, 175423,\n", + " 176341, 182197, 185431, 186332, 199270, 200174, 201076],\n", + " dtype='int64'), Int64Index([ 2698, 12924, 14777, 16578, 29343, 30250, 34935, 35851,\n", + " 36762, 37669, 39921, 40823, 48161, 51858, 53716, 54617,\n", + " 57425, 63597, 66917, 68744, 69649, 70546, 71453, 94987,\n", + " 102841, 103759, 107388, 112047, 114785, 119356, 124495, 125403,\n", + " 127211, 128104, 129970, 131775, 132698, 154390, 156219, 157142,\n", + " 158832, 160419, 163150, 165848, 166733, 171364, 172727, 175424,\n", + " 176342, 182198, 185432, 186333, 199271, 200175, 201077],\n", + " dtype='int64'), Int64Index([ 2699, 12925, 14778, 16579, 29344, 30251, 34936, 35852,\n", + " 36763, 37670, 39922, 40824, 48162, 51859, 53717, 54618,\n", + " 57426, 63598, 66918, 68745, 69650, 70547, 71454, 94988,\n", + " 102842, 103760, 107389, 112048, 114786, 119357, 124496, 125404,\n", + " 127212, 128105, 129971, 131776, 132699, 154391, 156220, 157143,\n", + " 158833, 160420, 163151, 165849, 166734, 171365, 172728, 175425,\n", + " 176343, 182199, 185433, 186334, 199272, 200176, 201078],\n", + " dtype='int64'), Int64Index([ 2700, 12926, 14779, 16580, 29345, 30252, 34937, 35853,\n", + " 36764, 37671, 39923, 40825, 48163, 51860, 53718, 54619,\n", + " 57427, 63599, 66919, 68746, 69651, 70548, 71455, 94989,\n", + " 102843, 103761, 107390, 112049, 114787, 119358, 124497, 125405,\n", + " 127213, 128106, 129972, 131777, 132700, 154392, 156221, 157144,\n", + " 158834, 160421, 163152, 165850, 166735, 171366, 172729, 175426,\n", + " 176344, 182200, 185434, 186335, 199273, 200177, 201079],\n", + " dtype='int64'), Int64Index([ 2701, 12927, 14780, 16581, 29346, 30253, 34938, 35854,\n", + " 36765, 37672, 39924, 40826, 48164, 51861, 53719, 54620,\n", + " 57428, 63600, 66920, 68747, 69652, 70549, 71456, 94990,\n", + " 102844, 103762, 107391, 112050, 114788, 119359, 124498, 125406,\n", + " 127214, 128107, 129973, 131778, 132701, 154393, 156222, 157145,\n", + " 158835, 160422, 163153, 165851, 166736, 171367, 172730, 175427,\n", + " 176345, 182201, 185435, 186336, 199274, 200178, 201080],\n", + " dtype='int64'), Int64Index([ 2702, 12928, 14781, 16582, 29347, 30254, 34939, 35855,\n", + " 36766, 37673, 39925, 40827, 48165, 51862, 53720, 54621,\n", + " 57429, 63601, 66921, 68748, 69653, 70550, 71457, 94991,\n", + " 102845, 103763, 107392, 112051, 114789, 119360, 124499, 125407,\n", + " 127215, 128108, 129974, 131779, 132702, 154394, 156223, 157146,\n", + " 158836, 160423, 163154, 165852, 166737, 171368, 172731, 175428,\n", + " 176346, 182202, 185436, 186337, 199275, 200179, 201081],\n", + " dtype='int64'), Int64Index([ 2703, 12929, 14782, 16583, 29348, 30255, 34940, 35856,\n", + " 36767, 37674, 39926, 40828, 48166, 51863, 53721, 54622,\n", + " 57430, 63602, 66922, 68749, 69654, 70551, 71458, 94992,\n", + " 102846, 103764, 107393, 112052, 114790, 119361, 124500, 125408,\n", + " 127216, 128109, 129975, 131780, 132703, 154395, 156224, 157147,\n", + " 158837, 160424, 163155, 165853, 166738, 171369, 172732, 175429,\n", + " 176347, 182203, 185437, 186338, 199276, 200180, 201082],\n", + " dtype='int64'), Int64Index([ 2704, 12930, 14783, 16584, 29349, 30256, 34941, 35857,\n", + " 36768, 37675, 39927, 40829, 48167, 51864, 53722, 54623,\n", + " 57431, 63603, 66923, 68750, 69655, 70552, 71459, 94993,\n", + " 102847, 103765, 107394, 112053, 114791, 119362, 124501, 125409,\n", + " 127217, 128110, 129976, 131781, 132704, 154396, 156225, 157148,\n", + " 158838, 160425, 163156, 165854, 166739, 171370, 172733, 175430,\n", + " 176348, 182204, 185438, 186339, 199277, 200181, 201083],\n", + " dtype='int64'), Int64Index([ 2705, 12931, 14784, 16585, 29350, 30257, 34942, 35858,\n", + " 36769, 37676, 39928, 40830, 48168, 51865, 53723, 54624,\n", + " 57432, 63604, 66924, 68751, 69656, 70553, 71460, 94994,\n", + " 102848, 103766, 107395, 112054, 114792, 119363, 124502, 125410,\n", + " 127218, 128111, 129977, 131782, 132705, 154397, 156226, 157149,\n", + " 158839, 160426, 163157, 165855, 166740, 171371, 172734, 175431,\n", + " 176349, 182205, 185439, 186340, 199278, 200182, 201084],\n", + " dtype='int64'), Int64Index([ 2706, 12932, 14785, 16586, 29351, 30258, 34943, 35859,\n", + " 36770, 37677, 39929, 40831, 48169, 51866, 53724, 54625,\n", + " 57433, 63605, 66925, 68752, 69657, 70554, 71461, 94995,\n", + " 102849, 103767, 107396, 112055, 114793, 119364, 124503, 125411,\n", + " 127219, 128112, 129978, 131783, 132706, 154398, 156227, 157150,\n", + " 158840, 160427, 163158, 165856, 166741, 171372, 172735, 175432,\n", + " 176350, 182206, 185440, 186341, 199279, 200183, 201085],\n", + " dtype='int64'), Int64Index([ 2707, 12933, 14786, 16587, 29352, 30259, 34944, 35860,\n", + " 36771, 37678, 39930, 40832, 48170, 51867, 53725, 54626,\n", + " 57434, 63606, 66926, 68753, 69658, 70555, 71462, 94996,\n", + " 102850, 103768, 107397, 112056, 114794, 119365, 124504, 125412,\n", + " 127220, 128113, 129979, 131784, 132707, 154399, 156228, 157151,\n", + " 158841, 160428, 163159, 165857, 166742, 171373, 172736, 175433,\n", + " 176351, 182207, 185441, 186342, 199280, 200184, 201086],\n", + " dtype='int64'), Int64Index([ 2708, 12934, 14787, 16588, 29353, 30260, 34945, 35861,\n", + " 36772, 37679, 39931, 40833, 48171, 51868, 53726, 54627,\n", + " 57435, 63607, 66927, 68754, 69659, 70556, 71463, 94997,\n", + " 102851, 103769, 107398, 112057, 114795, 119366, 124505, 125413,\n", + " 127221, 128114, 129980, 131785, 132708, 154400, 156229, 157152,\n", + " 158842, 160429, 163160, 165858, 166743, 171374, 172737, 175434,\n", + " 176352, 182208, 185442, 186343, 199281, 200185, 201087],\n", + " dtype='int64'), Int64Index([ 2709, 12935, 14788, 16589, 29354, 30261, 34946, 35862,\n", + " 36773, 37680, 39932, 40834, 48172, 51869, 53727, 54628,\n", + " 57436, 63608, 66928, 68755, 69660, 70557, 71464, 94998,\n", + " 102852, 103770, 107399, 112058, 114796, 119367, 124506, 125414,\n", + " 127222, 128115, 129981, 131786, 132709, 154401, 156230, 157153,\n", + " 158843, 160430, 163161, 165859, 166744, 171375, 172738, 175435,\n", + " 176353, 182209, 185443, 186344, 199282, 200186, 201088],\n", + " dtype='int64'), Int64Index([ 2710, 12936, 14789, 16590, 29355, 30262, 34947, 35863,\n", + " 36774, 37681, 39933, 40835, 48173, 51870, 53728, 54629,\n", + " 57437, 63609, 66929, 68756, 69661, 70558, 71465, 94999,\n", + " 102853, 103771, 107400, 112059, 114797, 119368, 124507, 125415,\n", + " 127223, 128116, 129982, 131787, 132710, 154402, 156231, 157154,\n", + " 158844, 160431, 163162, 165860, 166745, 171376, 172739, 175436,\n", + " 176354, 182210, 185444, 186345, 199283, 200187, 201089],\n", + " dtype='int64'), Int64Index([ 2711, 12937, 14790, 16591, 29356, 30263, 34948, 35864,\n", + " 36775, 37682, 39934, 40836, 48174, 51871, 53729, 54630,\n", + " 57438, 63610, 66930, 68757, 69662, 70559, 71466, 95000,\n", + " 102854, 103772, 107401, 112060, 114798, 119369, 124508, 125416,\n", + " 127224, 128117, 129983, 131788, 132711, 154403, 156232, 157155,\n", + " 158845, 160432, 163163, 165861, 166746, 171377, 172740, 175437,\n", + " 176355, 182211, 185445, 186346, 199284, 200188, 201090],\n", + " dtype='int64'), Int64Index([ 2712, 12938, 14791, 16592, 29357, 30264, 34949, 35865,\n", + " 36776, 37683, 39935, 40837, 48175, 51872, 53730, 54631,\n", + " 57439, 63611, 66931, 68758, 69663, 70560, 71467, 95001,\n", + " 102855, 103773, 107402, 112061, 114799, 119370, 124509, 125417,\n", + " 127225, 128118, 129984, 131789, 132712, 154404, 156233, 157156,\n", + " 158846, 160433, 163164, 165862, 166747, 171378, 172741, 175438,\n", + " 176356, 182212, 185446, 186347, 199285, 200189, 201091],\n", + " dtype='int64'), Int64Index([ 2713, 12939, 14792, 16593, 29358, 30265, 34950, 35866,\n", + " 36777, 37684, 39936, 40838, 48176, 51873, 53731, 54632,\n", + " 57440, 63612, 66932, 68759, 69664, 70561, 71468, 95002,\n", + " 102856, 103774, 107403, 112062, 114800, 119371, 124510, 125418,\n", + " 127226, 128119, 129985, 131790, 132713, 154405, 156234, 157157,\n", + " 158847, 160434, 163165, 165863, 166748, 171379, 172742, 175439,\n", + " 176357, 182213, 185447, 186348, 199286, 200190, 201092],\n", + " dtype='int64'), Int64Index([ 2714, 12940, 14793, 16594, 29359, 30266, 34951, 35867,\n", + " 36778, 37685, 39937, 40839, 48177, 51874, 53732, 54633,\n", + " 57441, 63613, 66933, 68760, 69665, 70562, 71469, 95003,\n", + " 102857, 103775, 107404, 112063, 114801, 119372, 124511, 125419,\n", + " 127227, 128120, 129986, 131791, 132714, 154406, 156235, 157158,\n", + " 158848, 160435, 163166, 165864, 166749, 171380, 172743, 175440,\n", + " 176358, 182214, 185448, 186349, 199287, 200191, 201093],\n", + " dtype='int64'), Int64Index([ 2715, 12941, 14794, 16595, 29360, 30267, 34952, 35868,\n", + " 36779, 37686, 39938, 40840, 48178, 51875, 53733, 54634,\n", + " 57442, 63614, 66934, 68761, 69666, 70563, 71470, 95004,\n", + " 102858, 103776, 107405, 112064, 114802, 119373, 124512, 125420,\n", + " 127228, 128121, 129987, 131792, 132715, 154407, 156236, 157159,\n", + " 158849, 160436, 163167, 165865, 166750, 171381, 172744, 175441,\n", + " 176359, 182215, 185449, 186350, 199288, 200192, 201094],\n", + " dtype='int64'), Int64Index([ 2716, 12942, 14795, 16596, 29361, 30268, 34953, 35869,\n", + " 36780, 37687, 39939, 40841, 48179, 51876, 53734, 54635,\n", + " 57443, 63615, 66935, 68762, 69667, 70564, 71471, 95005,\n", + " 102859, 103777, 107406, 112065, 114803, 119374, 124513, 125421,\n", + " 127229, 128122, 129988, 131793, 132716, 154408, 156237, 157160,\n", + " 158850, 160437, 163168, 165866, 166751, 171382, 172745, 175442,\n", + " 176360, 182216, 185450, 186351, 199289, 200193, 201095],\n", + " dtype='int64'), Int64Index([ 2717, 12943, 14796, 16597, 29362, 30269, 34954, 35870,\n", + " 36781, 37688, 39940, 40842, 48180, 51877, 53735, 54636,\n", + " 57444, 63616, 66936, 68763, 69668, 70565, 71472, 95006,\n", + " 102860, 103778, 107407, 112066, 114804, 119375, 124514, 125422,\n", + " 127230, 128123, 129989, 131794, 132717, 154409, 156238, 157161,\n", + " 158851, 160438, 163169, 165867, 166752, 171383, 172746, 175443,\n", + " 176361, 182217, 185451, 186352, 199290, 200194, 201096],\n", + " dtype='int64'), Int64Index([ 2718, 12944, 14797, 16598, 29363, 30270, 34955, 35871,\n", + " 36782, 37689, 39941, 40843, 48181, 51878, 53736, 54637,\n", + " 57445, 63617, 66937, 68764, 69669, 70566, 71473, 95007,\n", + " 102861, 103779, 107408, 112067, 114805, 119376, 124515, 125423,\n", + " 127231, 128124, 129990, 131795, 132718, 154410, 156239, 157162,\n", + " 158852, 160439, 163170, 165868, 166753, 171384, 172747, 175444,\n", + " 176362, 182218, 185452, 186353, 199291, 200195, 201097],\n", + " dtype='int64'), Int64Index([ 2719, 12945, 14798, 16599, 29364, 30271, 34956, 35872,\n", + " 36783, 37690, 39942, 40844, 48182, 51879, 53737, 54638,\n", + " 57446, 63618, 66938, 68765, 69670, 70567, 71474, 95008,\n", + " 102862, 103780, 107409, 112068, 114806, 119377, 124516, 125424,\n", + " 127232, 128125, 129991, 131796, 132719, 154411, 156240, 157163,\n", + " 158853, 160440, 163171, 165869, 166754, 171385, 172748, 175445,\n", + " 176363, 182219, 185453, 186354, 199292, 200196, 201098],\n", + " dtype='int64'), Int64Index([ 2720, 12946, 14799, 16600, 29365, 30272, 34957, 35873,\n", + " 36784, 37691, 39943, 40845, 48183, 51880, 53738, 54639,\n", + " 57447, 63619, 66939, 68766, 69671, 70568, 71475, 95009,\n", + " 102863, 103781, 107410, 112069, 114807, 119378, 124517, 125425,\n", + " 127233, 128126, 129992, 131797, 132720, 154412, 156241, 157164,\n", + " 158854, 160441, 163172, 165870, 166755, 171386, 172749, 175446,\n", + " 176364, 182220, 185454, 186355, 199293, 200197, 201099],\n", + " dtype='int64'), Int64Index([ 2721, 12947, 14800, 16601, 29366, 30273, 34958, 35874,\n", + " 36785, 37692, 39944, 40846, 48184, 51881, 53739, 54640,\n", + " 57448, 63620, 66940, 68767, 69672, 70569, 71476, 95010,\n", + " 102864, 103782, 107411, 112070, 114808, 119379, 124518, 125426,\n", + " 127234, 128127, 129993, 131798, 132721, 154413, 156242, 157165,\n", + " 158855, 160442, 163173, 165871, 166756, 171387, 172750, 175447,\n", + " 176365, 182221, 185455, 186356, 199294, 200198, 201100],\n", + " dtype='int64'), Int64Index([ 2722, 12948, 14801, 16602, 29367, 30274, 34959, 35875,\n", + " 36786, 37693, 39945, 40847, 48185, 51882, 53740, 54641,\n", + " 57449, 63621, 66941, 68768, 69673, 70570, 71477, 95011,\n", + " 102865, 103783, 107412, 112071, 114809, 119380, 124519, 125427,\n", + " 127235, 128128, 129994, 131799, 132722, 154414, 156243, 157166,\n", + " 158856, 160443, 163174, 165872, 166757, 171388, 172751, 175448,\n", + " 176366, 182222, 185456, 186357, 199295, 200199, 201101],\n", + " dtype='int64'), Int64Index([ 2723, 12949, 14802, 16603, 29368, 30275, 34960, 35876,\n", + " 36787, 37694, 39946, 40848, 48186, 51883, 53741, 54642,\n", + " 57450, 63622, 66942, 68769, 69674, 70571, 71478, 95012,\n", + " 102866, 103784, 107413, 112072, 114810, 119381, 124520, 125428,\n", + " 127236, 128129, 129995, 131800, 132723, 154415, 156244, 157167,\n", + " 158857, 160444, 163175, 165873, 166758, 171389, 172752, 175449,\n", + " 176367, 182223, 185457, 186358, 199296, 200200, 201102],\n", + " dtype='int64'), Int64Index([ 2724, 12950, 14803, 16604, 29369, 30276, 34961, 35877,\n", + " 36788, 37695, 39947, 40849, 48187, 51884, 53742, 54643,\n", + " 57451, 63623, 66943, 68770, 69675, 70572, 71479, 95013,\n", + " 102867, 103785, 107414, 112073, 114811, 119382, 124521, 125429,\n", + " 127237, 128130, 129996, 131801, 132724, 154416, 156245, 157168,\n", + " 158858, 160445, 163176, 165874, 166759, 171390, 172753, 175450,\n", + " 176368, 182224, 185458, 186359, 199297, 200201, 201103],\n", + " dtype='int64'), Int64Index([ 2725, 12951, 14804, 16605, 29370, 30277, 34962, 35878,\n", + " 36789, 37696, 39948, 40850, 48188, 51885, 53743, 54644,\n", + " 57452, 63624, 66944, 68771, 69676, 70573, 71480, 95014,\n", + " 102868, 103786, 107415, 112074, 114812, 119383, 124522, 125430,\n", + " 127238, 128131, 129997, 131802, 132725, 154417, 156246, 157169,\n", + " 158859, 160446, 163177, 165875, 166760, 171391, 172754, 175451,\n", + " 176369, 182225, 185459, 186360, 199298, 200202, 201104],\n", + " dtype='int64'), Int64Index([ 2726, 12952, 14805, 16606, 29371, 30278, 34963, 35879,\n", + " 36790, 37697, 39949, 40851, 48189, 51886, 53744, 54645,\n", + " 57453, 63625, 66945, 68772, 69677, 70574, 71481, 95015,\n", + " 102869, 103787, 107416, 112075, 114813, 119384, 124523, 125431,\n", + " 127239, 128132, 129998, 131803, 132726, 154418, 156247, 157170,\n", + " 158860, 160447, 163178, 165876, 166761, 171392, 172755, 175452,\n", + " 176370, 182226, 185460, 186361, 199299, 200203, 201105],\n", + " dtype='int64'), Int64Index([ 2727, 12953, 14806, 16607, 29372, 30279, 34964, 35880,\n", + " 36791, 37698, 39950, 40852, 48190, 51887, 53745, 54646,\n", + " 57454, 63626, 66946, 68773, 69678, 70575, 71482, 95016,\n", + " 102870, 103788, 107417, 112076, 114814, 119385, 124524, 125432,\n", + " 127240, 128133, 129999, 131804, 132727, 154419, 156248, 157171,\n", + " 158861, 160448, 163179, 165877, 166762, 171393, 172756, 175453,\n", + " 176371, 182227, 185461, 186362, 199300, 200204, 201106],\n", + " dtype='int64'), Int64Index([ 2728, 12954, 14807, 16608, 29373, 30280, 34965, 35881,\n", + " 36792, 37699, 39951, 40853, 48191, 51888, 53746, 54647,\n", + " 57455, 63627, 66947, 68774, 69679, 70576, 71483, 95017,\n", + " 102871, 103789, 107418, 112077, 114815, 119386, 124525, 125433,\n", + " 127241, 128134, 130000, 131805, 132728, 154420, 156249, 157172,\n", + " 158862, 160449, 163180, 165878, 166763, 171394, 172757, 175454,\n", + " 176372, 182228, 185462, 186363, 199301, 200205, 201107],\n", + " dtype='int64'), Int64Index([ 2729, 12955, 14808, 16609, 29374, 30281, 34966, 35882,\n", + " 36793, 37700, 39952, 40854, 48192, 51889, 53747, 54648,\n", + " 57456, 63628, 66948, 68775, 69680, 70577, 71484, 95018,\n", + " 102872, 103790, 107419, 112078, 114816, 119387, 124526, 125434,\n", + " 127242, 128135, 130001, 131806, 132729, 154421, 156250, 157173,\n", + " 158863, 160450, 163181, 165879, 166764, 171395, 172758, 175455,\n", + " 176373, 182229, 185463, 186364, 199302, 200206, 201108],\n", + " dtype='int64'), Int64Index([ 2730, 12956, 14809, 16610, 29375, 30282, 34967, 35883,\n", + " 36794, 37701, 39953, 40855, 48193, 51890, 53748, 54649,\n", + " 57457, 63629, 66949, 68776, 69681, 70578, 71485, 95019,\n", + " 102873, 103791, 107420, 112079, 114817, 119388, 124527, 125435,\n", + " 127243, 128136, 130002, 131807, 132730, 154422, 156251, 157174,\n", + " 158864, 160451, 163182, 165880, 166765, 171396, 172759, 175456,\n", + " 176374, 182230, 185464, 186365, 199303, 200207, 201109],\n", + " dtype='int64'), Int64Index([ 2731, 12957, 14810, 16611, 29376, 30283, 34968, 35884,\n", + " 36795, 37702, 39954, 40856, 48194, 51891, 53749, 54650,\n", + " 57458, 63630, 66950, 68777, 69682, 70579, 71486, 95020,\n", + " 102874, 103792, 107421, 112080, 114818, 119389, 124528, 125436,\n", + " 127244, 128137, 130003, 131808, 132731, 154423, 156252, 157175,\n", + " 158865, 160452, 163183, 165881, 166766, 171397, 172760, 175457,\n", + " 176375, 182231, 185465, 186366, 199304, 200208, 201110],\n", + " dtype='int64'), Int64Index([ 2732, 12958, 14811, 16612, 29377, 30284, 34969, 35885,\n", + " 36796, 37703, 39955, 40857, 48195, 51892, 53750, 54651,\n", + " 57459, 63631, 66951, 68778, 69683, 70580, 71487, 95021,\n", + " 102875, 103793, 107422, 112081, 114819, 119390, 124529, 125437,\n", + " 127245, 128138, 130004, 131809, 132732, 154424, 156253, 157176,\n", + " 158866, 160453, 163184, 165882, 166767, 171398, 172761, 175458,\n", + " 176376, 182232, 185466, 186367, 199305, 200209, 201111],\n", + " dtype='int64'), Int64Index([ 2733, 12959, 14812, 16613, 29378, 30285, 34970, 35886,\n", + " 36797, 37704, 39956, 40858, 48196, 51893, 53751, 54652,\n", + " 57460, 63632, 66952, 68779, 69684, 70581, 71488, 95022,\n", + " 102876, 103794, 107423, 112082, 114820, 119391, 124530, 125438,\n", + " 127246, 128139, 130005, 131810, 132733, 154425, 156254, 157177,\n", + " 158867, 160454, 163185, 165883, 166768, 171399, 172762, 175459,\n", + " 176377, 182233, 185467, 186368, 199306, 200210, 201112],\n", + " dtype='int64'), Int64Index([ 2734, 12960, 14813, 16614, 29379, 30286, 34971, 35887,\n", + " 36798, 37705, 39957, 40859, 48197, 51894, 53752, 54653,\n", + " 57461, 63633, 66953, 68780, 69685, 70582, 71489, 95023,\n", + " 102877, 103795, 107424, 112083, 114821, 119392, 124531, 125439,\n", + " 127247, 128140, 130006, 131811, 132734, 154426, 156255, 157178,\n", + " 158868, 160455, 163186, 165884, 166769, 171400, 172763, 175460,\n", + " 176378, 182234, 185468, 186369, 199307, 200211, 201113],\n", + " dtype='int64'), Int64Index([ 2735, 12961, 14814, 16615, 29380, 30287, 34972, 35888,\n", + " 36799, 37706, 39958, 40860, 48198, 51895, 53753, 54654,\n", + " 57462, 63634, 66954, 68781, 69686, 70583, 71490, 95024,\n", + " 102878, 103796, 107425, 112084, 114822, 119393, 124532, 125440,\n", + " 127248, 128141, 130007, 131812, 132735, 154427, 156256, 157179,\n", + " 158869, 160456, 163187, 165885, 166770, 171401, 172764, 175461,\n", + " 176379, 182235, 185469, 186370, 199308, 200212, 201114],\n", + " dtype='int64'), Int64Index([ 2736, 12962, 14815, 16616, 29381, 30288, 34973, 35889,\n", + " 36800, 37707, 39959, 40861, 48199, 51896, 53754, 54655,\n", + " 57463, 63635, 66955, 68782, 69687, 70584, 71491, 95025,\n", + " 102879, 103797, 107426, 112085, 114823, 119394, 124533, 125441,\n", + " 127249, 128142, 130008, 131813, 132736, 154428, 156257, 157180,\n", + " 158870, 160457, 163188, 165886, 166771, 171402, 172765, 175462,\n", + " 176380, 182236, 185470, 186371, 199309, 200213, 201115],\n", + " dtype='int64'), Int64Index([ 2737, 12963, 14816, 16617, 29382, 30289, 34974, 35890,\n", + " 36801, 37708, 39960, 40862, 48200, 51897, 53755, 54656,\n", + " 57464, 63636, 66956, 68783, 69688, 70585, 71492, 95026,\n", + " 102880, 103798, 107427, 112086, 114824, 119395, 124534, 125442,\n", + " 127250, 128143, 130009, 131814, 132737, 154429, 156258, 157181,\n", + " 158871, 160458, 163189, 165887, 166772, 171403, 172766, 175463,\n", + " 176381, 182237, 185471, 186372, 199310, 200214, 201116],\n", + " dtype='int64'), Int64Index([176382], dtype='int64'), Int64Index([176383], dtype='int64'), Int64Index([176384], dtype='int64'), Int64Index([176385], dtype='int64'), Int64Index([176386], dtype='int64'), Int64Index([176387], dtype='int64'), Int64Index([176388], dtype='int64'), Int64Index([176389], dtype='int64'), Int64Index([176390], dtype='int64'), Int64Index([176391], dtype='int64'), Int64Index([176392], dtype='int64'), Int64Index([176393], dtype='int64'), Int64Index([176394, 183600], dtype='int64'), Int64Index([176395, 183601], dtype='int64'), Int64Index([176396, 183602], dtype='int64'), Int64Index([176397, 183603], dtype='int64'), Int64Index([176398, 183604], dtype='int64'), Int64Index([176399, 183605], dtype='int64'), Int64Index([33104, 92242, 98261, 110183, 176400, 183606], dtype='int64'), Int64Index([33105, 76900, 92243, 98262, 110184, 157182, 176401, 183607,\n", + " 194512],\n", + " dtype='int64'), Int64Index([33106, 76901, 92244, 98263, 110185, 128144, 157183, 176402, 183608,\n", + " 194513],\n", + " dtype='int64'), Int64Index([ 33107, 76902, 92245, 98264, 110186, 128145, 135758, 157184,\n", + " 176403, 183609, 194514],\n", + " dtype='int64'), Int64Index([ 33108, 76903, 92246, 98265, 110187, 128146, 135759, 157185,\n", + " 176404, 183610, 194515],\n", + " dtype='int64'), Int64Index([ 33109, 76904, 92247, 95931, 98266, 105625, 110188, 128147,\n", + " 135760, 157186, 176405, 183611, 194516],\n", + " dtype='int64'), Int64Index([ 33110, 76905, 92248, 95932, 98267, 105626, 110189, 128148,\n", + " 135761, 157187, 176406, 183612, 194517],\n", + " dtype='int64'), Int64Index([ 5478, 33111, 76906, 92249, 95933, 98268, 105627, 110190,\n", + " 128149, 135762, 157188, 176407, 183613, 194518],\n", + " dtype='int64'), Int64Index([ 5479, 33112, 76907, 83339, 92250, 95934, 98269, 105628,\n", + " 110191, 128150, 135763, 142221, 157189, 176408, 183614, 194519],\n", + " dtype='int64'), Int64Index([ 5480, 33113, 76908, 83340, 92251, 95935, 98270, 105629,\n", + " 110192, 128151, 135764, 142222, 157190, 176409, 183615, 194520],\n", + " dtype='int64'), Int64Index([ 5481, 33114, 76909, 83341, 92252, 95936, 98271, 105630,\n", + " 110193, 128152, 135765, 142223, 157191, 176410, 183616, 194521],\n", + " dtype='int64'), Int64Index([ 5482, 33115, 76910, 83342, 92253, 95937, 98272, 105631,\n", + " 110194, 128153, 135766, 142224, 157192, 176411, 183617, 194522],\n", + " dtype='int64'), Int64Index([ 5483, 7412, 33116, 76911, 83343, 92254, 95938, 98273,\n", + " 105632, 110195, 128154, 135767, 142225, 157193, 176412, 183618,\n", + " 194523],\n", + " dtype='int64'), Int64Index([ 5484, 7413, 33117, 76912, 83344, 92255, 95939, 98274,\n", + " 105633, 110196, 128155, 135768, 142226, 157194, 176413, 183619,\n", + " 194524],\n", + " dtype='int64'), Int64Index([ 5485, 7414, 33118, 76913, 83345, 92256, 95940, 98275,\n", + " 105634, 110197, 128156, 135769, 142227, 157195, 176414, 183620,\n", + " 194525],\n", + " dtype='int64'), Int64Index([ 5486, 7415, 33119, 76914, 83346, 92257, 95941, 98276,\n", + " 105635, 110198, 128157, 135770, 142228, 157196, 176415, 183621,\n", + " 194526],\n", + " dtype='int64'), Int64Index([ 5487, 7416, 33120, 76915, 83347, 92258, 95942, 98277,\n", + " 105636, 110199, 128158, 135771, 142229, 157197, 176416, 183622,\n", + " 194527],\n", + " dtype='int64'), Int64Index([ 5488, 7417, 33121, 76916, 83348, 92259, 95943, 98278,\n", + " 105637, 110200, 128159, 135772, 142230, 157198, 176417, 183623,\n", + " 194528],\n", + " dtype='int64'), Int64Index([ 5489, 7418, 33122, 76917, 83349, 92260, 95944, 98279,\n", + " 105638, 110201, 128160, 135773, 142231, 157199, 176418, 183624,\n", + " 194529],\n", + " dtype='int64'), Int64Index([ 5490, 7419, 33123, 76918, 83350, 92261, 95945, 98280,\n", + " 105639, 110202, 128161, 135774, 142232, 157200, 176419, 183625,\n", + " 194530],\n", + " dtype='int64'), Int64Index([ 5491, 7420, 33124, 76919, 83351, 92262, 95946, 98281,\n", + " 105640, 110203, 128162, 135775, 142233, 157201, 176420, 183626,\n", + " 194531],\n", + " dtype='int64'), Int64Index([ 5492, 7421, 33125, 76920, 83352, 92263, 95947, 98282,\n", + " 105641, 110204, 128163, 135776, 142234, 157202, 176421, 183627,\n", + " 194532],\n", + " dtype='int64'), Int64Index([ 5493, 7422, 33126, 76921, 83353, 92264, 95948, 98283,\n", + " 105642, 110205, 128164, 135777, 142235, 157203, 176422, 183628,\n", + " 194533],\n", + " dtype='int64'), Int64Index([ 5494, 7423, 33127, 76922, 83354, 92265, 95949, 98284,\n", + " 105643, 110206, 128165, 135778, 142236, 157204, 176423, 183629,\n", + " 194534],\n", + " dtype='int64'), Int64Index([ 5495, 7424, 33128, 76923, 83355, 92266, 95950, 98285,\n", + " 105644, 110207, 128166, 135779, 142237, 157205, 176424, 183630,\n", + " 194535],\n", + " dtype='int64'), Int64Index([ 5496, 7425, 33129, 76924, 83356, 92267, 95951, 98286,\n", + " 105645, 110208, 128167, 135780, 142238, 157206, 176425, 183631,\n", + " 194536],\n", + " dtype='int64'), Int64Index([ 5497, 7426, 33130, 76925, 83357, 92268, 95952, 98287,\n", + " 105646, 110209, 128168, 135781, 142239, 157207, 176426, 183632,\n", + " 194537],\n", + " dtype='int64'), Int64Index([ 5498, 7427, 33131, 76926, 83358, 92269, 95953, 98288,\n", + " 105647, 110210, 128169, 135782, 142240, 157208, 176427, 183633,\n", + " 194538],\n", + " dtype='int64'), Int64Index([ 5499, 7428, 33132, 76927, 83359, 85213, 92270, 95954,\n", + " 98289, 105648, 110211, 128170, 135783, 142241, 157209, 176428,\n", + " 183634, 194539],\n", + " dtype='int64'), Int64Index([ 5500, 7429, 33133, 76928, 83360, 85214, 87995, 92271,\n", + " 95955, 98290, 105649, 110212, 128171, 135784, 142242, 157210,\n", + " 176429, 183635, 194540],\n", + " dtype='int64'), Int64Index([ 5501, 7430, 33134, 76929, 83361, 85215, 87996, 92272,\n", + " 95956, 98291, 101046, 105650, 110213, 128172, 135785, 142243,\n", + " 157211, 176430, 183636, 194541],\n", + " dtype='int64'), Int64Index([ 5502, 7431, 33135, 76930, 83362, 85216, 87997, 92273,\n", + " 95957, 98292, 101047, 105651, 110214, 128173, 135786, 142244,\n", + " 157212, 176431, 183637, 194542],\n", + " dtype='int64'), Int64Index([ 5503, 7432, 33136, 76931, 83363, 85217, 87998, 92274,\n", + " 95958, 98293, 101048, 105652, 110215, 128174, 135787, 142245,\n", + " 157213, 176432, 183638, 194543],\n", + " dtype='int64'), Int64Index([ 909, 5504, 7433, 18446, 33137, 76932, 83364, 85218,\n", + " 86145, 87999, 92275, 95959, 98294, 99221, 101049, 105653,\n", + " 110216, 128175, 135788, 138098, 142246, 157214, 176433, 183639,\n", + " 194544],\n", + " dtype='int64'), Int64Index([ 910, 5505, 7434, 18447, 33138, 76933, 83365, 85219,\n", + " 86146, 88000, 92276, 95960, 98295, 99222, 101050, 105654,\n", + " 110217, 128176, 135789, 138099, 139025, 142247, 157215, 176434,\n", + " 183640, 194545],\n", + " dtype='int64'), Int64Index([ 911, 5506, 7435, 18448, 33139, 64589, 76934, 83366,\n", + " 85220, 86147, 88001, 92277, 95961, 98296, 99223, 101051,\n", + " 105655, 110218, 128177, 135790, 138100, 139026, 142248, 157216,\n", + " 176435, 183641, 194546],\n", + " dtype='int64'), Int64Index([ 912, 5507, 7436, 18449, 33140, 64590, 76935, 83367,\n", + " 85221, 86148, 88002, 92278, 95962, 98297, 99224, 101052,\n", + " 105656, 110219, 128178, 135791, 138101, 139027, 142249, 157217,\n", + " 176436, 183642, 194547],\n", + " dtype='int64'), Int64Index([ 913, 5508, 7437, 18450, 33141, 64591, 76936, 83368,\n", + " 85222, 86149, 88003, 92279, 95963, 98298, 99225, 101053,\n", + " 105657, 110220, 128179, 135792, 138102, 139028, 142250, 157218,\n", + " 176437, 183643, 194548],\n", + " dtype='int64'), Int64Index([ 914, 5509, 7438, 18451, 33142, 64592, 76937, 83369,\n", + " 85223, 86150, 88004, 92280, 95964, 98299, 99226, 101054,\n", + " 105658, 110221, 128180, 135793, 138103, 139029, 142251, 150723,\n", + " 157219, 176438, 183644, 194549],\n", + " dtype='int64'), Int64Index([ 915, 5510, 7439, 11152, 18452, 33143, 64593, 76938,\n", + " 83370, 85224, 86151, 88005, 92281, 95965, 98300, 99227,\n", + " 101055, 105659, 110222, 128181, 135794, 138104, 139030, 142252,\n", + " 150724, 157220, 176439, 183645, 194550],\n", + " dtype='int64'), Int64Index([ 916, 5511, 7440, 11153, 18453, 33144, 64594, 76939,\n", + " 81517, 83371, 85225, 86152, 88006, 92282, 95966, 98301,\n", + " 99228, 101056, 105660, 110223, 128182, 135795, 138105, 139031,\n", + " 142253, 150725, 154430, 157221, 176440, 183646, 194551],\n", + " dtype='int64'), Int64Index([ 917, 5512, 7441, 11154, 18454, 33145, 64595, 76940,\n", + " 81518, 83372, 85226, 86153, 88007, 91323, 92283, 95967,\n", + " 98302, 99229, 101057, 105661, 110224, 128183, 135796, 138106,\n", + " 139032, 142254, 150726, 154431, 157222, 176441, 183647, 194552],\n", + " dtype='int64'), Int64Index([ 918, 5513, 7442, 11155, 18455, 33146, 64596, 76941,\n", + " 81519, 83373, 85227, 86154, 88008, 91324, 92284, 95968,\n", + " 98303, 99230, 101058, 105662, 110225, 128184, 135797, 138107,\n", + " 139033, 142255, 150727, 154432, 157223, 176442, 183648, 194553],\n", + " dtype='int64'), Int64Index([ 919, 5514, 7443, 11156, 18456, 33147, 64597, 76942,\n", + " 81520, 83374, 85228, 86155, 88009, 91325, 92285, 95969,\n", + " 98304, 99231, 101059, 105663, 110226, 128185, 135798, 138108,\n", + " 139034, 142256, 148897, 150728, 154433, 157224, 176443, 183649,\n", + " 194554],\n", + " dtype='int64'), Int64Index([ 920, 5515, 7444, 11157, 18457, 27575, 33148, 64598,\n", + " 76943, 81521, 83375, 85229, 86156, 88010, 91326, 92286,\n", + " 95970, 98305, 99232, 101060, 105664, 110227, 128186, 135799,\n", + " 138109, 139035, 142257, 148898, 150729, 154434, 157225, 176444,\n", + " 183650, 194555],\n", + " dtype='int64'), Int64Index([ 921, 5516, 7445, 11158, 18458, 27576, 33149, 64599,\n", + " 76944, 81522, 83376, 85230, 86157, 88011, 91327, 92287,\n", + " 95971, 98306, 99233, 101061, 105665, 110228, 128187, 135800,\n", + " 138110, 139036, 142258, 148899, 150730, 154435, 157226, 176445,\n", + " 183651, 194556],\n", + " dtype='int64'), Int64Index([ 922, 5517, 7446, 11159, 16618, 18459, 27577, 33150,\n", + " 64600, 76945, 81523, 83377, 85231, 86158, 88012, 91328,\n", + " 92288, 95972, 98307, 99234, 101062, 105666, 110229, 114825,\n", + " 128188, 135801, 138111, 139037, 142259, 148900, 150731, 154436,\n", + " 157227, 176446, 183652, 194557],\n", + " dtype='int64'), Int64Index([ 923, 5518, 7447, 11160, 16619, 18460, 26662, 27578,\n", + " 33151, 64601, 76946, 81524, 83378, 85232, 86159, 88013,\n", + " 91329, 92289, 95973, 98308, 99235, 101063, 105667, 110230,\n", + " 114826, 128189, 135802, 138112, 139038, 142260, 148901, 150732,\n", + " 154437, 157228, 176447, 183653, 194558],\n", + " dtype='int64'), Int64Index([ 924, 5519, 7448, 11161, 16620, 18461, 26663, 27579,\n", + " 33152, 64602, 76947, 81525, 83379, 85233, 86160, 88014,\n", + " 91330, 92290, 95974, 98309, 99236, 101064, 105668, 110231,\n", + " 114827, 122113, 128190, 135803, 138113, 139039, 142261, 148902,\n", + " 150733, 154438, 157229, 176448, 183654, 194559],\n", + " dtype='int64'), Int64Index([ 925, 5520, 7449, 11162, 16621, 18462, 26664, 27580,\n", + " 33153, 64603, 76948, 81526, 83380, 85234, 86161, 88015,\n", + " 91331, 92291, 95975, 98310, 99237, 101065, 105669, 110232,\n", + " 114828, 122114, 128191, 135804, 138114, 139040, 142262, 148903,\n", + " 150734, 154439, 157230, 176449, 182238, 183655, 194560],\n", + " dtype='int64'), Int64Index([ 926, 5521, 7450, 11163, 16622, 18463, 26665, 27581,\n", + " 33154, 64604, 76949, 81527, 83381, 85235, 86162, 88016,\n", + " 91332, 92292, 95976, 98311, 99238, 101066, 105670, 110233,\n", + " 114829, 122115, 128192, 135805, 138115, 139041, 142263, 148904,\n", + " 150735, 154440, 157231, 176450, 182239, 183656, 194561],\n", + " dtype='int64'), Int64Index([ 927, 5522, 7451, 11164, 16623, 18464, 26666, 27582,\n", + " 33155, 64605, 76950, 81528, 83382, 85236, 86163, 88017,\n", + " 91333, 92293, 93202, 95977, 98312, 99239, 101067, 105671,\n", + " 110234, 114830, 122116, 128193, 135806, 138116, 139042, 142264,\n", + " 148905, 150736, 154441, 157232, 176451, 182240, 183657, 194562],\n", + " dtype='int64'), Int64Index([ 928, 5523, 7452, 11165, 16624, 18465, 26667, 27583,\n", + " 33156, 64606, 76951, 81529, 83383, 85237, 86164, 88018,\n", + " 91334, 92294, 93203, 95978, 98313, 99240, 101068, 105672,\n", + " 110235, 114831, 122117, 128194, 135807, 138117, 139043, 142265,\n", + " 148906, 150737, 154442, 157233, 176452, 182241, 183658, 194563],\n", + " dtype='int64'), Int64Index([ 929, 5524, 7453, 11166, 16625, 18466, 26668, 27584,\n", + " 33157, 64607, 76952, 81530, 83384, 85238, 86165, 88019,\n", + " 91335, 92295, 93204, 95979, 98314, 99241, 101069, 105673,\n", + " 110236, 114832, 122118, 128195, 135808, 138118, 139044, 142266,\n", + " 148907, 150738, 154443, 157234, 176453, 182242, 183659, 189161,\n", + " 194564],\n", + " dtype='int64'), Int64Index([ 930, 5525, 7454, 11167, 16626, 18467, 26669, 27585,\n", + " 33158, 64608, 76953, 81531, 83385, 85239, 86166, 88020,\n", + " 91336, 92296, 93205, 95980, 98315, 99242, 101070, 105674,\n", + " 110237, 114833, 122119, 128196, 135809, 138119, 139045, 142267,\n", + " 148908, 150739, 154444, 157235, 176454, 182243, 183660, 189162,\n", + " 194565],\n", + " dtype='int64'), Int64Index([ 931, 5526, 7455, 11168, 16627, 18468, 26670, 27586,\n", + " 33159, 64609, 76954, 81532, 83386, 85240, 86167, 88021,\n", + " 91337, 92297, 93206, 95981, 98316, 99243, 101071, 105675,\n", + " 110238, 114834, 122120, 128197, 135810, 138120, 139046, 142268,\n", + " 148909, 150740, 154445, 157236, 176455, 182244, 183661, 189163,\n", + " 194566],\n", + " dtype='int64'), Int64Index([ 932, 5527, 7456, 11169, 16628, 18469, 26671, 27587,\n", + " 33160, 64610, 76955, 81533, 83387, 85241, 86168, 88022,\n", + " 91338, 92298, 93207, 95027, 95982, 98317, 99244, 101072,\n", + " 105676, 110239, 114835, 122121, 128198, 135811, 138121, 139047,\n", + " 142269, 148910, 150741, 154446, 157237, 176456, 182245, 183662,\n", + " 189164, 194567],\n", + " dtype='int64'), Int64Index([ 933, 5528, 7457, 11170, 16629, 18470, 26672, 27588,\n", + " 33161, 64611, 76956, 81534, 83388, 85242, 86169, 88023,\n", + " 91339, 92299, 93208, 95028, 95983, 98318, 99245, 101073,\n", + " 105677, 110240, 114836, 122122, 128199, 135812, 138122, 139048,\n", + " 142270, 148911, 150742, 154447, 157238, 176457, 182246, 183663,\n", + " 189165, 194568],\n", + " dtype='int64'), Int64Index([ 934, 5529, 7458, 11171, 16630, 18471, 26673, 27589,\n", + " 33162, 64612, 76957, 81535, 83389, 85243, 86170, 88024,\n", + " 91340, 92300, 93209, 95029, 95984, 98319, 99246, 101074,\n", + " 105678, 110241, 114837, 122123, 128200, 135813, 138123, 139049,\n", + " 142271, 148912, 150743, 154448, 157239, 176458, 182247, 183664,\n", + " 189166, 194569],\n", + " dtype='int64'), Int64Index([ 935, 5530, 7459, 11172, 16631, 18472, 26674, 27590,\n", + " 33163, 64613, 76958, 81536, 83390, 85244, 86171, 88025,\n", + " 91341, 92301, 93210, 95030, 95985, 98320, 99247, 101075,\n", + " 105679, 110242, 114838, 122124, 128201, 135814, 138124, 139050,\n", + " 142272, 148913, 150744, 154449, 157240, 176459, 182248, 183665,\n", + " 189167, 194570],\n", + " dtype='int64'), Int64Index([ 936, 5531, 7460, 11173, 16632, 18473, 26675, 27591,\n", + " 33164, 64614, 76959, 81537, 83391, 85245, 86172, 88026,\n", + " 91342, 92302, 93211, 95031, 95986, 98321, 99248, 101076,\n", + " 105680, 110243, 114839, 122125, 128202, 135815, 138125, 139051,\n", + " 142273, 148914, 150745, 154450, 157241, 172767, 176460, 179006,\n", + " 182249, 183666, 189168, 194571],\n", + " dtype='int64'), Int64Index([ 937, 5532, 7461, 11174, 16633, 18474, 26676, 27592,\n", + " 33165, 64615, 76960, 81538, 83392, 85246, 86173, 88027,\n", + " 91343, 92303, 93212, 95032, 95987, 98322, 99249, 101077,\n", + " 105681, 110244, 114840, 122126, 128203, 135816, 138126, 139052,\n", + " 142274, 148915, 150746, 154451, 157242, 172768, 176461, 179007,\n", + " 182250, 183667, 189169, 194572],\n", + " dtype='int64'), Int64Index([ 938, 5533, 7462, 11175, 16634, 18475, 26677, 27593,\n", + " 33166, 64616, 76961, 81539, 83393, 85247, 86174, 88028,\n", + " 91344, 92304, 93213, 95033, 95988, 98323, 99250, 100148,\n", + " 101078, 105682, 110245, 114841, 122127, 128204, 135817, 138127,\n", + " 139053, 142275, 148916, 150747, 154452, 157243, 172769, 176462,\n", + " 179008, 182251, 183668, 189170, 194573],\n", + " dtype='int64'), Int64Index([ 939, 5534, 7463, 11176, 16635, 18476, 26678, 27594,\n", + " 33167, 64617, 76962, 81540, 83394, 85248, 86175, 88029,\n", + " 91345, 92305, 93214, 95034, 95989, 98324, 99251, 100149,\n", + " 101079, 105683, 110246, 114842, 122128, 128205, 135818, 138128,\n", + " 139054, 142276, 148917, 150748, 154453, 157244, 172770, 176463,\n", + " 179009, 182252, 183669, 189171, 194574],\n", + " dtype='int64'), Int64Index([ 940, 5535, 7464, 11177, 16636, 18477, 26679, 27595,\n", + " 33168, 64618, 76963, 81541, 83395, 85249, 86176, 88030,\n", + " 91346, 92306, 93215, 95035, 95990, 98325, 99252, 100150,\n", + " 101080, 105684, 110247, 114843, 122129, 128206, 135819, 138129,\n", + " 139055, 142277, 148918, 150749, 154454, 157245, 172771, 176464,\n", + " 179010, 182253, 183670, 189172, 194575],\n", + " dtype='int64'), Int64Index([ 941, 5536, 7465, 11178, 16637, 18478, 26680, 27596,\n", + " 33169, 64619, 76964, 81542, 83396, 85250, 86177, 88031,\n", + " 91347, 92307, 93216, 95036, 95991, 98326, 99253, 100151,\n", + " 101081, 105685, 110248, 114844, 120313, 122130, 128207, 135820,\n", + " 138130, 139056, 142278, 148919, 150750, 154455, 157246, 172772,\n", + " 176465, 179011, 182254, 183671, 189173, 194576],\n", + " dtype='int64'), Int64Index([ 942, 5537, 7466, 11179, 16638, 18479, 26681, 27597,\n", + " 33170, 64620, 76965, 81543, 83397, 85251, 86178, 88032,\n", + " 91348, 92308, 93217, 95037, 95992, 98327, 99254, 100152,\n", + " 101082, 105686, 110249, 114845, 120314, 122131, 128208, 135821,\n", + " 138131, 139057, 142279, 148920, 150751, 154456, 157247, 172773,\n", + " 176466, 179012, 182255, 183672, 189174, 194577],\n", + " dtype='int64'), Int64Index([ 943, 5538, 7467, 11180, 16639, 18480, 26682, 27598,\n", + " 33171, 64621, 76966, 81544, 83398, 85252, 86179, 88033,\n", + " 91349, 92309, 93218, 95038, 95993, 98328, 99255, 100153,\n", + " 101083, 105687, 110250, 114846, 120315, 122132, 128209, 135822,\n", + " 138132, 139058, 142280, 148921, 150752, 154457, 157248, 172774,\n", + " 176467, 179013, 182256, 183673, 189175, 194578],\n", + " dtype='int64'), Int64Index([ 944, 5539, 7468, 11181, 16640, 18481, 26683, 27599,\n", + " 33172, 64622, 76967, 81545, 83399, 85253, 86180, 88034,\n", + " 91350, 92310, 93219, 95039, 95994, 98329, 99256, 100154,\n", + " 101084, 105688, 110251, 114847, 120316, 122133, 128210, 135823,\n", + " 138133, 139059, 142281, 148922, 150753, 154458, 157249, 172775,\n", + " 176468, 179014, 182257, 183674, 189176, 194579],\n", + " dtype='int64'), Int64Index([ 945, 5540, 7469, 11182, 16641, 18482, 26684, 27600,\n", + " 33173, 64623, 76968, 81546, 83400, 85254, 86181, 88035,\n", + " 91351, 92311, 93220, 95040, 95995, 98330, 99257, 100155,\n", + " 101085, 105689, 110252, 114848, 120317, 122134, 128211, 135824,\n", + " 138134, 139060, 142282, 148923, 150754, 154459, 157250, 172776,\n", + " 176469, 179015, 182258, 183675, 189177, 194580],\n", + " dtype='int64'), Int64Index([ 946, 5541, 7470, 11183, 16642, 18483, 26685, 27601,\n", + " 33174, 64624, 76969, 81547, 83401, 85255, 86182, 88036,\n", + " 91352, 92312, 93221, 95041, 95996, 98331, 99258, 100156,\n", + " 101086, 105690, 110253, 114849, 120318, 122135, 128212, 135825,\n", + " 138135, 139061, 142283, 148924, 150755, 154460, 157251, 172777,\n", + " 176470, 179016, 182259, 183676, 189178, 194581],\n", + " dtype='int64'), Int64Index([ 947, 5542, 7471, 11184, 16643, 18484, 26686, 27602,\n", + " 33175, 64625, 76970, 81548, 83402, 85256, 86183, 88037,\n", + " 91353, 92313, 93222, 95042, 95997, 98332, 99259, 100157,\n", + " 101087, 105691, 110254, 114850, 120319, 122136, 128213, 135826,\n", + " 138136, 139062, 142284, 146215, 148925, 150756, 154461, 157252,\n", + " 172778, 176471, 179017, 182260, 183677, 189179, 194582],\n", + " dtype='int64'), Int64Index([ 948, 5543, 7472, 11185, 16644, 18485, 26687, 27603,\n", + " 33176, 64626, 76971, 81549, 83403, 85257, 86184, 88038,\n", + " 91354, 92314, 93223, 95043, 95998, 98333, 99260, 100158,\n", + " 101088, 105692, 110255, 114851, 120320, 122137, 128214, 135827,\n", + " 138137, 139063, 142285, 146216, 148926, 150757, 154462, 157253,\n", + " 172779, 176472, 179018, 182261, 183678, 189180, 194583],\n", + " dtype='int64'), Int64Index([ 949, 5544, 7473, 11186, 16645, 18486, 26688, 27604,\n", + " 33177, 64627, 76972, 81550, 83404, 85258, 86185, 88039,\n", + " 91355, 92315, 93224, 95044, 95999, 98334, 99261, 100159,\n", + " 101089, 105693, 110256, 114852, 120321, 122138, 128215, 135828,\n", + " 138138, 139064, 142286, 146217, 148927, 150758, 154463, 157254,\n", + " 172780, 176473, 179019, 182262, 183679, 189181, 194584],\n", + " dtype='int64'), Int64Index([ 950, 5545, 7474, 11187, 16646, 18487, 26689, 27605,\n", + " 33178, 64628, 76973, 81551, 83405, 85259, 86186, 88040,\n", + " 91356, 92316, 93225, 95045, 96000, 98335, 99262, 100160,\n", + " 101090, 105694, 110257, 114853, 120322, 122139, 128216, 135829,\n", + " 138139, 139065, 142287, 146218, 148928, 150759, 154464, 157255,\n", + " 172781, 176474, 179020, 182263, 183680, 189182, 194585],\n", + " dtype='int64'), Int64Index([ 951, 5546, 7475, 11188, 16647, 18488, 26690, 27606,\n", + " 33179, 64629, 76974, 81552, 83406, 85260, 86187, 88041,\n", + " 91357, 92317, 93226, 95046, 96001, 98336, 99263, 100161,\n", + " 101091, 105695, 110258, 114854, 120323, 122140, 128217, 135830,\n", + " 138140, 139066, 142288, 146219, 148929, 150760, 154465, 157256,\n", + " 172782, 176475, 179021, 182264, 183681, 189183, 194586],\n", + " dtype='int64'), Int64Index([ 952, 5547, 7476, 11189, 16648, 18489, 26691, 27607,\n", + " 33180, 64630, 76975, 81553, 83407, 85261, 86188, 88042,\n", + " 91358, 92318, 93227, 95047, 96002, 98337, 99264, 100162,\n", + " 101092, 105696, 110259, 114855, 120324, 122141, 128218, 135831,\n", + " 138141, 139067, 142289, 146220, 148930, 150761, 154466, 157257,\n", + " 172783, 176476, 179022, 182265, 183682, 189184, 194587],\n", + " dtype='int64'), Int64Index([ 953, 5548, 7477, 11190, 16649, 18490, 26692, 27608,\n", + " 33181, 64631, 76976, 81554, 83408, 85262, 86189, 88043,\n", + " 91359, 92319, 93228, 95048, 96003, 98338, 99265, 100163,\n", + " 101093, 105697, 110260, 114856, 120325, 122142, 128219, 135832,\n", + " 138142, 139068, 142290, 146221, 148931, 150762, 154467, 157258,\n", + " 172784, 176477, 179023, 182266, 183683, 189185, 194588],\n", + " dtype='int64'), Int64Index([ 954, 5549, 7478, 11191, 16650, 18491, 26693, 27609,\n", + " 33182, 64632, 76977, 81555, 83409, 85263, 86190, 88044,\n", + " 91360, 92320, 93229, 95049, 96004, 98339, 99266, 100164,\n", + " 101094, 105698, 110261, 114857, 120326, 122143, 128220, 135833,\n", + " 138143, 139069, 142291, 146222, 148932, 150763, 154468, 157259,\n", + " 172785, 176478, 179024, 182267, 183684, 189186, 194589],\n", + " dtype='int64'), Int64Index([ 955, 5550, 7479, 11192, 16651, 18492, 26694, 27610,\n", + " 33183, 64633, 76978, 81556, 83410, 85264, 86191, 88045,\n", + " 91361, 92321, 93230, 95050, 96005, 98340, 99267, 100165,\n", + " 101095, 105699, 110262, 114858, 120327, 122144, 128221, 135834,\n", + " 138144, 139070, 142292, 146223, 148933, 150764, 154469, 157260,\n", + " 172786, 176479, 179025, 182268, 183685, 189187, 194590, 197486],\n", + " dtype='int64'), Int64Index([ 956, 5551, 7480, 11193, 16652, 18493, 26695, 27611,\n", + " 33184, 64634, 76979, 81557, 83411, 85265, 86192, 88046,\n", + " 91362, 92322, 93231, 95051, 96006, 98341, 99268, 100166,\n", + " 101096, 105700, 110263, 114859, 120328, 122145, 128222, 135835,\n", + " 138145, 139071, 142293, 146224, 148934, 150765, 154470, 157261,\n", + " 172787, 176480, 179026, 182269, 183686, 189188, 194591, 197487],\n", + " dtype='int64'), Int64Index([ 957, 5552, 7481, 11194, 16653, 18494, 26696, 27612,\n", + " 33185, 64635, 76980, 81558, 83412, 85266, 86193, 88047,\n", + " 91363, 92323, 93232, 95052, 96007, 98342, 99269, 100167,\n", + " 101097, 105701, 110264, 114860, 120329, 122146, 128223, 135836,\n", + " 138146, 139072, 142294, 146225, 148935, 150766, 154471, 157262,\n", + " 172788, 176481, 179027, 182270, 183687, 189189, 194592, 197488],\n", + " dtype='int64'), Int64Index([ 958, 5553, 7482, 11195, 16654, 18495, 26697, 27613,\n", + " 33186, 64636, 76981, 81559, 83413, 85267, 86194, 88048,\n", + " 91364, 92324, 93233, 95053, 96008, 98343, 99270, 100168,\n", + " 101098, 105702, 110265, 114861, 120330, 122147, 128224, 135837,\n", + " 138147, 139073, 142295, 146226, 148936, 150767, 154472, 157263,\n", + " 172789, 176482, 179028, 182271, 183688, 189190, 194593, 197489],\n", + " dtype='int64'), Int64Index([ 959, 5554, 7483, 11196, 16655, 18496, 26698, 27614,\n", + " 33187, 64637, 76982, 81560, 83414, 85268, 86195, 88049,\n", + " 91365, 92325, 93234, 95054, 96009, 98344, 99271, 100169,\n", + " 101099, 105703, 110266, 114862, 120331, 122148, 128225, 135838,\n", + " 138148, 139074, 142296, 146227, 148937, 150768, 154473, 157264,\n", + " 172790, 176483, 179029, 182272, 183689, 189191, 194594, 197490],\n", + " dtype='int64'), Int64Index([ 960, 5555, 7484, 11197, 16656, 18497, 26699, 27615,\n", + " 33188, 64638, 76983, 81561, 83415, 85269, 86196, 88050,\n", + " 91366, 92326, 93235, 95055, 96010, 98345, 99272, 100170,\n", + " 101100, 105704, 110267, 114863, 120332, 122149, 128226, 135839,\n", + " 138149, 139075, 142297, 146228, 148938, 150769, 154474, 157265,\n", + " 172791, 176484, 179030, 182273, 183690, 189192, 194595, 197491],\n", + " dtype='int64'), Int64Index([ 961, 5556, 7485, 11198, 16657, 18498, 26700, 27616,\n", + " 33189, 64639, 76984, 81562, 83416, 85270, 86197, 88051,\n", + " 91367, 92327, 93236, 95056, 96011, 98346, 99273, 100171,\n", + " 101101, 105705, 110268, 114864, 120333, 122150, 128227, 135840,\n", + " 138150, 139076, 142298, 146229, 148939, 150770, 154475, 157266,\n", + " 172792, 176485, 179031, 182274, 183691, 189193, 194596, 197492],\n", + " dtype='int64'), Int64Index([ 962, 5557, 7486, 11199, 16658, 18499, 26701, 27617,\n", + " 33190, 64640, 76985, 81563, 83417, 85271, 86198, 88052,\n", + " 91368, 92328, 93237, 95057, 96012, 98347, 99274, 100172,\n", + " 101102, 105706, 110269, 114865, 120334, 122151, 128228, 135841,\n", + " 138151, 139077, 142299, 146230, 148940, 150771, 154476, 157267,\n", + " 172793, 176486, 179032, 182275, 183692, 189194, 194597, 197493],\n", + " dtype='int64'), Int64Index([ 963, 5558, 7487, 11200, 16659, 18500, 26702, 27618,\n", + " 33191, 64641, 76986, 81564, 83418, 85272, 86199, 88053,\n", + " 91369, 92329, 93238, 95058, 96013, 98348, 99275, 100173,\n", + " 101103, 105707, 110270, 114866, 120335, 122152, 128229, 135842,\n", + " 138152, 139078, 142300, 146231, 148941, 150772, 154477, 157268,\n", + " 172794, 176487, 179033, 182276, 183693, 189195, 194598, 197494],\n", + " dtype='int64'), Int64Index([ 964, 5559, 7488, 11201, 16660, 18501, 26703, 27619,\n", + " 33192, 64642, 76987, 81565, 83419, 85273, 86200, 88054,\n", + " 91370, 92330, 93239, 95059, 96014, 98349, 99276, 100174,\n", + " 101104, 105708, 110271, 114867, 120336, 122153, 128230, 135843,\n", + " 138153, 139079, 142301, 146232, 148942, 150773, 154478, 157269,\n", + " 172795, 176488, 179034, 182277, 183694, 189196, 194599, 197495],\n", + " dtype='int64'), Int64Index([ 965, 5560, 7489, 11202, 16661, 18502, 26704, 27620,\n", + " 33193, 64643, 76988, 81566, 83420, 85274, 86201, 88055,\n", + " 91371, 92331, 93240, 95060, 96015, 98350, 99277, 100175,\n", + " 101105, 105709, 110272, 114868, 120337, 122154, 128231, 135844,\n", + " 138154, 139080, 142302, 146233, 148943, 150774, 154479, 157270,\n", + " 172796, 176489, 179035, 182278, 183695, 189197, 194600, 197496],\n", + " dtype='int64'), Int64Index([ 966, 5561, 7490, 11203, 16662, 18503, 26705, 27621,\n", + " 33194, 64644, 76989, 81567, 83421, 85275, 86202, 88056,\n", + " 91372, 92332, 93241, 95061, 96016, 98351, 99278, 100176,\n", + " 101106, 105710, 110273, 114869, 120338, 122155, 128232, 135845,\n", + " 138155, 139081, 142303, 146234, 148944, 150775, 154480, 157271,\n", + " 172797, 176490, 179036, 182279, 183696, 189198, 194601, 197497],\n", + " dtype='int64'), Int64Index([ 967, 5562, 7491, 11204, 16663, 18504, 26706, 27622,\n", + " 33195, 64645, 76990, 81568, 83422, 85276, 86203, 88057,\n", + " 91373, 92333, 93242, 95062, 96017, 98352, 99279, 100177,\n", + " 101107, 105711, 110274, 114870, 120339, 122156, 128233, 135846,\n", + " 138156, 139082, 142304, 146235, 148945, 150776, 154481, 157272,\n", + " 172798, 176491, 179037, 182280, 183697, 189199, 194602, 197498],\n", + " dtype='int64'), Int64Index([ 968, 5563, 7492, 11205, 16664, 18505, 26707, 27623,\n", + " 33196, 64646, 76991, 81569, 83423, 85277, 86204, 88058,\n", + " 91374, 92334, 93243, 95063, 96018, 98353, 99280, 100178,\n", + " 101108, 105712, 110275, 114871, 120340, 122157, 128234, 135847,\n", + " 138157, 139083, 142305, 146236, 148946, 150777, 154482, 157273,\n", + " 172799, 176492, 179038, 182281, 183698, 189200, 194603, 197499],\n", + " dtype='int64'), Int64Index([ 969, 5564, 7493, 11206, 16665, 18506, 26708, 27624,\n", + " 33197, 64647, 76992, 81570, 83424, 85278, 86205, 88059,\n", + " 91375, 92335, 93244, 95064, 96019, 98354, 99281, 100179,\n", + " 101109, 105713, 110276, 114872, 120341, 122158, 128235, 135848,\n", + " 138158, 139084, 142306, 146237, 148947, 150778, 154483, 157274,\n", + " 172800, 176493, 179039, 182282, 183699, 189201, 194604, 197500],\n", + " dtype='int64'), Int64Index([ 970, 5565, 7494, 11207, 16666, 18507, 26709, 27625,\n", + " 33198, 64648, 76993, 81571, 83425, 85279, 86206, 88060,\n", + " 91376, 92336, 93245, 95065, 96020, 98355, 99282, 100180,\n", + " 101110, 105714, 110277, 114873, 120342, 122159, 128236, 135849,\n", + " 138159, 139085, 142307, 146238, 148948, 150779, 154484, 157275,\n", + " 172801, 176494, 179040, 182283, 183700, 189202, 194605, 197501],\n", + " dtype='int64'), Int64Index([ 971, 5566, 7495, 11208, 16667, 18508, 26710, 27626,\n", + " 33199, 64649, 76994, 81572, 83426, 85280, 86207, 88061,\n", + " 91377, 92337, 93246, 95066, 96021, 98356, 99283, 100181,\n", + " 101111, 105715, 110278, 114874, 120343, 122160, 128237, 135850,\n", + " 138160, 139086, 142308, 146239, 148949, 150780, 154485, 157276,\n", + " 172802, 176495, 179041, 182284, 183701, 189203, 194606, 197502],\n", + " dtype='int64'), Int64Index([ 972, 5567, 7496, 11209, 16668, 18509, 26711, 27627,\n", + " 33200, 64650, 76995, 81573, 83427, 85281, 86208, 88062,\n", + " 91378, 92338, 93247, 95067, 96022, 98357, 99284, 100182,\n", + " 101112, 105716, 110279, 114875, 120344, 122161, 128238, 135851,\n", + " 138161, 139087, 142309, 146240, 148950, 150781, 154486, 157277,\n", + " 172803, 176496, 179042, 182285, 183702, 189204, 194607, 197503],\n", + " dtype='int64'), Int64Index([ 973, 5568, 7497, 11210, 16669, 18510, 26712, 27628,\n", + " 33201, 64651, 76996, 81574, 83428, 85282, 86209, 88063,\n", + " 91379, 92339, 93248, 95068, 96023, 98358, 99285, 100183,\n", + " 101113, 105717, 110280, 114876, 120345, 122162, 128239, 135852,\n", + " 138162, 139088, 142310, 146241, 148951, 150782, 154487, 157278,\n", + " 172804, 176497, 179043, 182286, 183703, 189205, 194608, 197504],\n", + " dtype='int64'), Int64Index([ 974, 5569, 7498, 11211, 16670, 18511, 26713, 27629,\n", + " 33202, 64652, 76997, 81575, 83429, 85283, 86210, 88064,\n", + " 91380, 92340, 93249, 95069, 96024, 98359, 99286, 100184,\n", + " 101114, 105718, 110281, 114877, 120346, 122163, 128240, 135853,\n", + " 138163, 139089, 142311, 146242, 148952, 150783, 154488, 157279,\n", + " 172805, 176498, 179044, 182287, 183704, 189206, 194609, 197505],\n", + " dtype='int64'), Int64Index([ 975, 5570, 7499, 11212, 16671, 18512, 26714, 27630,\n", + " 33203, 64653, 76998, 81576, 83430, 85284, 86211, 88065,\n", + " 91381, 92341, 93250, 95070, 96025, 98360, 99287, 100185,\n", + " 101115, 105719, 110282, 114878, 120347, 122164, 128241, 135854,\n", + " 138164, 139090, 142312, 146243, 148953, 150784, 154489, 157280,\n", + " 172806, 176499, 179045, 182288, 183705, 189207, 194610, 197506],\n", + " dtype='int64'), Int64Index([ 976, 5571, 7500, 11213, 16672, 18513, 26715, 27631,\n", + " 33204, 64654, 76999, 81577, 83431, 85285, 86212, 88066,\n", + " 91382, 92342, 93251, 95071, 96026, 98361, 99288, 100186,\n", + " 101116, 105720, 110283, 114879, 120348, 122165, 128242, 135855,\n", + " 138165, 139091, 142313, 146244, 148954, 150785, 154490, 157281,\n", + " 172807, 176500, 177360, 179046, 182289, 183706, 189208, 194611,\n", + " 197507],\n", + " dtype='int64'), Int64Index([ 977, 5572, 7501, 11214, 16673, 18514, 26716, 27632,\n", + " 33205, 64655, 77000, 81578, 83432, 85286, 86213, 88067,\n", + " 91383, 92343, 93252, 95072, 96027, 98362, 99289, 100187,\n", + " 101117, 105721, 110284, 114880, 120349, 122166, 128243, 135856,\n", + " 138166, 139092, 142314, 146245, 148955, 150786, 154491, 157282,\n", + " 172808, 176501, 177361, 179047, 182290, 183707, 189209, 194612,\n", + " 197508],\n", + " dtype='int64'), Int64Index([ 978, 5573, 7502, 11215, 16674, 18515, 26717, 27633,\n", + " 33206, 64656, 77001, 81579, 83433, 85287, 86214, 88068,\n", + " 91384, 92344, 93253, 95073, 96028, 98363, 99290, 100188,\n", + " 101118, 105722, 110285, 114881, 120350, 122167, 128244, 135857,\n", + " 138167, 139093, 142315, 146246, 148956, 150787, 154492, 157283,\n", + " 172809, 176502, 177362, 179048, 182291, 183708, 189210, 194613,\n", + " 197509],\n", + " dtype='int64'), Int64Index([ 979, 5574, 7503, 11216, 16675, 18516, 26718, 27634,\n", + " 33207, 64657, 77002, 81580, 83434, 85288, 86215, 88069,\n", + " 91385, 92345, 93254, 95074, 96029, 98364, 99291, 100189,\n", + " 101119, 105723, 110286, 114882, 120351, 122168, 128245, 135858,\n", + " 138168, 139094, 142316, 146247, 148957, 150788, 154493, 157284,\n", + " 172810, 176503, 177363, 179049, 182292, 183709, 189211, 194614,\n", + " 197510],\n", + " dtype='int64'), Int64Index([ 980, 5575, 7504, 11217, 16676, 18517, 26719, 27635,\n", + " 33208, 64658, 77003, 81581, 83435, 85289, 86216, 88070,\n", + " 91386, 92346, 93255, 95075, 96030, 98365, 99292, 100190,\n", + " 101120, 105724, 110287, 114883, 120352, 122169, 128246, 135859,\n", + " 138169, 139095, 142317, 146248, 148958, 150789, 154494, 157285,\n", + " 172811, 176504, 177364, 179050, 182293, 183710, 189212, 194615,\n", + " 197511],\n", + " dtype='int64'), Int64Index([ 981, 5576, 7505, 11218, 16677, 18518, 26720, 27636,\n", + " 33209, 64659, 77004, 81582, 83436, 85290, 86217, 88071,\n", + " 91387, 92347, 93256, 95076, 96031, 98366, 99293, 100191,\n", + " 101121, 105725, 110288, 114884, 120353, 122170, 128247, 135860,\n", + " 138170, 139096, 142318, 146249, 148959, 150790, 154495, 157286,\n", + " 172812, 176505, 177365, 179051, 182294, 183711, 189213, 194616,\n", + " 197512],\n", + " dtype='int64'), Int64Index([ 982, 5577, 7506, 11219, 16678, 18519, 26721, 27637,\n", + " 33210, 64660, 77005, 81583, 83437, 85291, 86218, 88072,\n", + " 91388, 92348, 93257, 95077, 96032, 98367, 99294, 100192,\n", + " 101122, 105726, 110289, 114885, 120354, 122171, 128248, 135861,\n", + " 138171, 139097, 142319, 146250, 148960, 150791, 154496, 157287,\n", + " 172813, 176506, 177366, 179052, 182295, 183712, 189214, 194617,\n", + " 197513],\n", + " dtype='int64'), Int64Index([ 983, 5578, 7507, 11220, 16679, 18520, 26722, 27638,\n", + " 33211, 64661, 77006, 81584, 83438, 85292, 86219, 88073,\n", + " 91389, 92349, 93258, 95078, 96033, 98368, 99295, 100193,\n", + " 101123, 105727, 110290, 114886, 120355, 122172, 128249, 135862,\n", + " 138172, 139098, 142320, 146251, 148961, 150792, 154497, 157288,\n", + " 172814, 176507, 177367, 179053, 182296, 183713, 189215, 194618,\n", + " 197514],\n", + " dtype='int64'), Int64Index([ 984, 5579, 7508, 11221, 16680, 18521, 26723, 27639,\n", + " 33212, 64662, 77007, 81585, 83439, 85293, 86220, 88074,\n", + " 91390, 92350, 93259, 95079, 96034, 98369, 99296, 100194,\n", + " 101124, 105728, 110291, 114887, 120356, 122173, 128250, 135863,\n", + " 138173, 139099, 142321, 146252, 148962, 150793, 154498, 157289,\n", + " 172815, 176508, 177368, 179054, 182297, 183714, 189216, 194619,\n", + " 197515],\n", + " dtype='int64'), Int64Index([ 985, 5580, 7509, 11222, 16681, 18522, 26724, 27640,\n", + " 33213, 64663, 77008, 81586, 83440, 85294, 86221, 88075,\n", + " 91391, 92351, 93260, 95080, 96035, 98370, 99297, 100195,\n", + " 101125, 105729, 110292, 114888, 120357, 122174, 128251, 135864,\n", + " 138174, 139100, 142322, 146253, 148963, 150794, 154499, 157290,\n", + " 172816, 176509, 177369, 179055, 182298, 183715, 189217, 194620,\n", + " 197516],\n", + " dtype='int64'), Int64Index([ 986, 5581, 7510, 11223, 16682, 18523, 26725, 27641,\n", + " 33214, 64664, 77009, 81587, 83441, 85295, 86222, 88076,\n", + " 91392, 92352, 93261, 95081, 96036, 98371, 99298, 100196,\n", + " 101126, 105730, 110293, 114889, 120358, 122175, 128252, 135865,\n", + " 138175, 139101, 142323, 146254, 148964, 150795, 154500, 157291,\n", + " 172817, 176510, 177370, 179056, 182299, 183716, 189218, 194621,\n", + " 197517],\n", + " dtype='int64'), Int64Index([ 987, 5582, 7511, 11224, 16683, 18524, 26726, 27642,\n", + " 33215, 64665, 77010, 81588, 83442, 85296, 86223, 88077,\n", + " 91393, 92353, 93262, 95082, 96037, 98372, 99299, 100197,\n", + " 101127, 105731, 110294, 114890, 120359, 122176, 128253, 135866,\n", + " 138176, 139102, 142324, 146255, 148965, 150796, 154501, 157292,\n", + " 172818, 176511, 177371, 179057, 182300, 183717, 189219, 194622,\n", + " 197518],\n", + " dtype='int64'), Int64Index([ 988, 5583, 7512, 11225, 16684, 18525, 26727, 27643,\n", + " 33216, 64666, 77011, 81589, 83443, 85297, 86224, 88078,\n", + " 91394, 92354, 93263, 95083, 96038, 98373, 99300, 100198,\n", + " 101128, 105732, 110295, 114891, 120360, 122177, 128254, 135867,\n", + " 138177, 139103, 142325, 146256, 148966, 150797, 154502, 157293,\n", + " 172819, 176512, 177372, 179058, 182301, 183718, 189220, 194623,\n", + " 197519],\n", + " dtype='int64'), Int64Index([ 989, 5584, 7513, 11226, 16685, 18526, 26728, 27644,\n", + " 33217, 64667, 77012, 81590, 83444, 85298, 86225, 88079,\n", + " 91395, 92355, 93264, 95084, 96039, 98374, 99301, 100199,\n", + " 101129, 105733, 110296, 114892, 120361, 122178, 128255, 135868,\n", + " 138178, 139104, 142326, 146257, 148967, 150798, 154503, 157294,\n", + " 172820, 176513, 177373, 179059, 182302, 183719, 189221, 194624,\n", + " 197520],\n", + " dtype='int64'), Int64Index([ 990, 5585, 7514, 11227, 16686, 18527, 26729, 27645,\n", + " 33218, 64668, 77013, 81591, 83445, 85299, 86226, 88080,\n", + " 91396, 92356, 93265, 95085, 96040, 98375, 99302, 100200,\n", + " 101130, 105734, 110297, 114893, 120362, 122179, 128256, 135869,\n", + " 138179, 139105, 142327, 146258, 148968, 150799, 154504, 157295,\n", + " 172821, 176514, 177374, 179060, 182303, 183720, 189222, 194625,\n", + " 197521],\n", + " dtype='int64'), Int64Index([ 991, 5586, 7515, 11228, 16687, 18528, 26730, 27646,\n", + " 33219, 64669, 77014, 81592, 83446, 85300, 86227, 88081,\n", + " 91397, 92357, 93266, 95086, 96041, 98376, 99303, 100201,\n", + " 101131, 105735, 110298, 114894, 120363, 122180, 128257, 135870,\n", + " 138180, 139106, 142328, 146259, 148969, 150800, 154505, 157296,\n", + " 172822, 176515, 177375, 179061, 182304, 183721, 189223, 194626,\n", + " 197522],\n", + " dtype='int64'), Int64Index([ 992, 5587, 7516, 11229, 16688, 18529, 26731, 27647,\n", + " 33220, 64670, 77015, 81593, 83447, 85301, 86228, 88082,\n", + " 91398, 92358, 93267, 95087, 96042, 98377, 99304, 100202,\n", + " 101132, 105736, 110299, 114895, 120364, 122181, 128258, 135871,\n", + " 138181, 139107, 142329, 146260, 148970, 150801, 154506, 157297,\n", + " 172823, 176516, 177376, 179062, 182305, 183722, 189224, 194627,\n", + " 197523],\n", + " dtype='int64'), Int64Index([ 993, 5588, 7517, 11230, 16689, 18530, 26732, 27648,\n", + " 33221, 64671, 77016, 81594, 83448, 85302, 86229, 88083,\n", + " 91399, 92359, 93268, 95088, 96043, 98378, 99305, 100203,\n", + " 101133, 105737, 110300, 114896, 120365, 122182, 128259, 135872,\n", + " 138182, 139108, 142330, 146261, 148971, 150802, 154507, 157298,\n", + " 172824, 176517, 177377, 179063, 182306, 183723, 189225, 194628,\n", + " 197524],\n", + " dtype='int64'), Int64Index([ 994, 5589, 7518, 11231, 16690, 18531, 26733, 27649,\n", + " 33222, 64672, 77017, 81595, 83449, 85303, 86230, 88084,\n", + " 91400, 92360, 93269, 95089, 96044, 98379, 99306, 100204,\n", + " 101134, 105738, 110301, 114897, 120366, 122183, 128260, 135873,\n", + " 138183, 139109, 142331, 146262, 148972, 150803, 154508, 157299,\n", + " 172825, 176518, 177378, 179064, 182307, 183724, 189226, 194629,\n", + " 197525],\n", + " dtype='int64'), Int64Index([ 995, 5590, 7519, 11232, 16691, 18532, 26734, 27650,\n", + " 33223, 64673, 77018, 81596, 83450, 85304, 86231, 88085,\n", + " 91401, 92361, 93270, 95090, 96045, 98380, 99307, 100205,\n", + " 101135, 105739, 110302, 114898, 120367, 122184, 128261, 135874,\n", + " 138184, 139110, 142332, 146263, 148973, 150804, 154509, 157300,\n", + " 172826, 176519, 177379, 179065, 182308, 183725, 189227, 194630,\n", + " 197526],\n", + " dtype='int64'), Int64Index([ 996, 5591, 7520, 11233, 16692, 18533, 26735, 27651,\n", + " 33224, 64674, 77019, 81597, 83451, 85305, 86232, 88086,\n", + " 91402, 92362, 93271, 95091, 96046, 98381, 99308, 100206,\n", + " 101136, 105740, 110303, 114899, 120368, 122185, 128262, 135875,\n", + " 138185, 139111, 142333, 146264, 148974, 150805, 154510, 157301,\n", + " 172827, 176520, 177380, 179066, 182309, 183726, 189228, 194631,\n", + " 197527],\n", + " dtype='int64'), Int64Index([ 997, 5592, 7521, 11234, 16693, 18534, 26736, 27652,\n", + " 33225, 64675, 77020, 81598, 83452, 85306, 86233, 88087,\n", + " 91403, 92363, 93272, 95092, 96047, 98382, 99309, 100207,\n", + " 101137, 105741, 110304, 114900, 120369, 122186, 128263, 135876,\n", + " 138186, 139112, 142334, 146265, 148975, 150806, 154511, 157302,\n", + " 172828, 176521, 177381, 179067, 182310, 183727, 189229, 194632,\n", + " 197528],\n", + " dtype='int64'), Int64Index([ 998, 5593, 7522, 11235, 16694, 18535, 26737, 27653,\n", + " 33226, 64676, 77021, 81599, 83453, 85307, 86234, 88088,\n", + " 91404, 92364, 93273, 95093, 96048, 98383, 99310, 100208,\n", + " 101138, 105742, 110305, 114901, 120370, 122187, 128264, 135877,\n", + " 138187, 139113, 142335, 146266, 148976, 150807, 154512, 157303,\n", + " 172829, 176522, 177382, 179068, 182311, 183728, 189230, 194633,\n", + " 197529],\n", + " dtype='int64'), Int64Index([ 999, 5594, 7523, 11236, 16695, 18536, 26738, 27654,\n", + " 33227, 64677, 77022, 81600, 83454, 85308, 86235, 88089,\n", + " 91405, 92365, 93274, 95094, 96049, 98384, 99311, 100209,\n", + " 101139, 105743, 110306, 114902, 120371, 122188, 128265, 135878,\n", + " 138188, 139114, 142336, 146267, 148977, 150808, 154513, 157304,\n", + " 172830, 176523, 177383, 179069, 182312, 183729, 189231, 194634,\n", + " 197530],\n", + " dtype='int64'), Int64Index([ 1000, 5595, 7524, 11237, 16696, 18537, 26739, 27655,\n", + " 33228, 64678, 77023, 81601, 83455, 85309, 86236, 88090,\n", + " 91406, 92366, 93275, 95095, 96050, 98385, 99312, 100210,\n", + " 101140, 105744, 110307, 114903, 120372, 122189, 128266, 135879,\n", + " 138189, 139115, 142337, 146268, 148978, 150809, 154514, 157305,\n", + " 172831, 176524, 177384, 179070, 182313, 183730, 189232, 194635,\n", + " 197531],\n", + " dtype='int64'), Int64Index([ 1001, 5596, 7525, 11238, 16697, 18538, 26740, 27656,\n", + " 33229, 64679, 77024, 81602, 83456, 85310, 86237, 88091,\n", + " 91407, 92367, 93276, 95096, 96051, 98386, 99313, 100211,\n", + " 101141, 105745, 110308, 114904, 120373, 122190, 128267, 135880,\n", + " 138190, 139116, 142338, 146269, 148979, 150810, 154515, 157306,\n", + " 172832, 176525, 177385, 179071, 182314, 183731, 189233, 194636,\n", + " 197532],\n", + " dtype='int64'), Int64Index([ 1002, 5597, 7526, 11239, 16698, 18539, 26741, 27657,\n", + " 33230, 64680, 77025, 81603, 83457, 85311, 86238, 88092,\n", + " 91408, 92368, 93277, 95097, 96052, 98387, 99314, 100212,\n", + " 101142, 105746, 110309, 114905, 120374, 122191, 128268, 135881,\n", + " 138191, 139117, 142339, 146270, 148980, 150811, 154516, 157307,\n", + " 172833, 176526, 177386, 179072, 182315, 183732, 189234, 194637,\n", + " 197533],\n", + " dtype='int64'), Int64Index([ 1003, 5598, 7527, 11240, 16699, 18540, 26742, 27658,\n", + " 33231, 64681, 77026, 81604, 83458, 85312, 86239, 88093,\n", + " 91409, 92369, 93278, 95098, 96053, 98388, 99315, 100213,\n", + " 101143, 105747, 110310, 114906, 120375, 122192, 128269, 135882,\n", + " 138192, 139118, 142340, 146271, 148981, 150812, 154517, 157308,\n", + " 172834, 176527, 177387, 179073, 182316, 183733, 189235, 194638,\n", + " 197534],\n", + " dtype='int64'), Int64Index([ 1004, 5599, 7528, 11241, 16700, 18541, 26743, 27659,\n", + " 33232, 64682, 77027, 81605, 83459, 85313, 86240, 88094,\n", + " 91410, 92370, 93279, 95099, 96054, 98389, 99316, 100214,\n", + " 101144, 105748, 110311, 114907, 120376, 122193, 128270, 135883,\n", + " 138193, 139119, 142341, 146272, 148982, 150813, 154518, 157309,\n", + " 172835, 176528, 177388, 179074, 182317, 183734, 189236, 194639,\n", + " 197535],\n", + " dtype='int64'), Int64Index([ 1005, 5600, 7529, 11242, 16701, 18542, 26744, 27660,\n", + " 33233, 64683, 77028, 81606, 83460, 85314, 86241, 88095,\n", + " 91411, 92371, 93280, 95100, 96055, 98390, 99317, 100215,\n", + " 101145, 105749, 110312, 114908, 120377, 122194, 128271, 135884,\n", + " 138194, 139120, 142342, 146273, 148983, 150814, 154519, 157310,\n", + " 172836, 176529, 177389, 179075, 182318, 183735, 189237, 194640,\n", + " 197536],\n", + " dtype='int64'), Int64Index([ 1006, 5601, 7530, 11243, 16702, 18543, 26745, 27661,\n", + " 33234, 64684, 77029, 81607, 83461, 85315, 86242, 88096,\n", + " 91412, 92372, 93281, 95101, 96056, 98391, 99318, 100216,\n", + " 101146, 105750, 110313, 114909, 120378, 122195, 128272, 135885,\n", + " 138195, 139121, 142343, 146274, 148984, 150815, 154520, 157311,\n", + " 172837, 176530, 177390, 179076, 182319, 183736, 189238, 194641,\n", + " 197537],\n", + " dtype='int64'), Int64Index([ 1007, 5602, 7531, 11244, 16703, 18544, 26746, 27662,\n", + " 33235, 64685, 77030, 81608, 83462, 85316, 86243, 88097,\n", + " 91413, 92373, 93282, 95102, 96057, 98392, 99319, 100217,\n", + " 101147, 105751, 110314, 114910, 120379, 122196, 128273, 135886,\n", + " 138196, 139122, 142344, 146275, 148985, 150816, 154521, 157312,\n", + " 172838, 176531, 177391, 179077, 182320, 183737, 189239, 194642,\n", + " 197538],\n", + " dtype='int64'), Int64Index([ 1008, 5603, 7532, 11245, 16704, 18545, 26747, 27663,\n", + " 33236, 64686, 77031, 81609, 83463, 85317, 86244, 88098,\n", + " 91414, 92374, 93283, 95103, 96058, 98393, 99320, 100218,\n", + " 101148, 105752, 110315, 114911, 120380, 122197, 128274, 135887,\n", + " 138197, 139123, 142345, 146276, 148986, 150817, 154522, 157313,\n", + " 172839, 176532, 177392, 179078, 182321, 183738, 189240, 194643,\n", + " 197539],\n", + " dtype='int64'), Int64Index([ 1009, 5604, 7533, 11246, 16705, 18546, 26748, 27664,\n", + " 33237, 64687, 77032, 81610, 83464, 85318, 86245, 88099,\n", + " 91415, 92375, 93284, 95104, 96059, 98394, 99321, 100219,\n", + " 101149, 105753, 110316, 114912, 120381, 122198, 128275, 135888,\n", + " 138198, 139124, 142346, 146277, 148987, 150818, 154523, 157314,\n", + " 172840, 176533, 177393, 179079, 182322, 183739, 189241, 194644,\n", + " 197540],\n", + " dtype='int64'), Int64Index([ 1010, 5605, 7534, 11247, 16706, 18547, 26749, 27665,\n", + " 33238, 64688, 77033, 81611, 83465, 85319, 86246, 88100,\n", + " 91416, 92376, 93285, 95105, 96060, 98395, 99322, 100220,\n", + " 101150, 105754, 110317, 114913, 120382, 122199, 128276, 135889,\n", + " 138199, 139125, 142347, 146278, 148988, 150819, 154524, 157315,\n", + " 172841, 176534, 177394, 179080, 182323, 183740, 189242, 194645,\n", + " 197541],\n", + " dtype='int64'), Int64Index([ 1011, 5606, 7535, 11248, 16707, 18548, 26750, 27666,\n", + " 33239, 64689, 77034, 81612, 83466, 85320, 86247, 88101,\n", + " 91417, 92377, 93286, 95106, 96061, 98396, 99323, 100221,\n", + " 101151, 105755, 110318, 114914, 120383, 122200, 128277, 135890,\n", + " 138200, 139126, 142348, 146279, 148989, 150820, 154525, 157316,\n", + " 172842, 176535, 177395, 179081, 182324, 183741, 189243, 194646,\n", + " 197542],\n", + " dtype='int64'), Int64Index([ 1012, 5607, 7536, 11249, 16708, 18549, 26751, 27667,\n", + " 33240, 64690, 77035, 81613, 83467, 85321, 86248, 88102,\n", + " 91418, 92378, 93287, 95107, 96062, 98397, 99324, 100222,\n", + " 101152, 105756, 110319, 114915, 120384, 122201, 128278, 135891,\n", + " 138201, 139127, 142349, 146280, 148990, 150821, 154526, 157317,\n", + " 172843, 176536, 177396, 179082, 182325, 183742, 189244, 194647,\n", + " 197543],\n", + " dtype='int64'), Int64Index([ 1013, 5608, 7537, 11250, 16709, 18550, 26752, 27668,\n", + " 33241, 64691, 77036, 81614, 83468, 85322, 86249, 88103,\n", + " 91419, 92379, 93288, 95108, 96063, 98398, 99325, 100223,\n", + " 101153, 105757, 110320, 114916, 120385, 122202, 128279, 135892,\n", + " 138202, 139128, 142350, 146281, 148991, 150822, 154527, 157318,\n", + " 172844, 176537, 177397, 179083, 182326, 183743, 189245, 194648,\n", + " 197544],\n", + " dtype='int64'), Int64Index([ 1014, 5609, 7538, 11251, 16710, 18551, 26753, 27669,\n", + " 33242, 64692, 77037, 81615, 83469, 85323, 86250, 88104,\n", + " 91420, 92380, 93289, 95109, 96064, 98399, 99326, 100224,\n", + " 101154, 105758, 110321, 114917, 120386, 122203, 128280, 135893,\n", + " 138203, 139129, 142351, 146282, 148992, 150823, 154528, 157319,\n", + " 172845, 176538, 177398, 179084, 182327, 183744, 189246, 194649,\n", + " 197545],\n", + " dtype='int64'), Int64Index([ 1015, 5610, 7539, 11252, 16711, 18552, 26754, 27670,\n", + " 33243, 64693, 77038, 81616, 83470, 85324, 86251, 88105,\n", + " 91421, 92381, 93290, 95110, 96065, 98400, 99327, 100225,\n", + " 101155, 105759, 110322, 114918, 120387, 122204, 128281, 135894,\n", + " 138204, 139130, 142352, 146283, 148993, 150824, 154529, 157320,\n", + " 172846, 176539, 177399, 179085, 182328, 183745, 189247, 194650,\n", + " 197546],\n", + " dtype='int64'), Int64Index([ 1016, 5611, 7540, 11253, 16712, 18553, 26755, 27671,\n", + " 33244, 64694, 77039, 81617, 83471, 85325, 86252, 88106,\n", + " 91422, 92382, 93291, 95111, 96066, 98401, 99328, 100226,\n", + " 101156, 105760, 110323, 114919, 120388, 122205, 128282, 135895,\n", + " 138205, 139131, 142353, 146284, 148994, 150825, 154530, 157321,\n", + " 172847, 176540, 177400, 179086, 182329, 183746, 189248, 194651,\n", + " 197547],\n", + " dtype='int64'), Int64Index([ 1017, 5612, 7541, 11254, 16713, 18554, 26756, 27672,\n", + " 33245, 64695, 77040, 81618, 83472, 85326, 86253, 88107,\n", + " 91423, 92383, 93292, 95112, 96067, 98402, 99329, 100227,\n", + " 101157, 105761, 110324, 114920, 120389, 122206, 128283, 135896,\n", + " 138206, 139132, 142354, 146285, 148995, 150826, 154531, 157322,\n", + " 172848, 176541, 177401, 179087, 182330, 183747, 189249, 194652,\n", + " 197548],\n", + " dtype='int64'), Int64Index([ 1018, 5613, 7542, 11255, 16714, 18555, 26757, 27673,\n", + " 33246, 64696, 77041, 81619, 83473, 85327, 86254, 88108,\n", + " 91424, 92384, 93293, 95113, 96068, 98403, 99330, 100228,\n", + " 101158, 105762, 110325, 114921, 120390, 122207, 128284, 135897,\n", + " 138207, 139133, 142355, 146286, 148996, 150827, 154532, 157323,\n", + " 172849, 176542, 177402, 179088, 182331, 183748, 189250, 194653,\n", + " 197549],\n", + " dtype='int64'), Int64Index([ 1019, 5614, 7543, 11256, 16715, 18556, 26758, 27674,\n", + " 33247, 64697, 77042, 81620, 83474, 85328, 86255, 88109,\n", + " 91425, 92385, 93294, 95114, 96069, 98404, 99331, 100229,\n", + " 101159, 105763, 110326, 114922, 120391, 122208, 128285, 135898,\n", + " 138208, 139134, 142356, 146287, 148997, 150828, 154533, 157324,\n", + " 172850, 176543, 177403, 179089, 182332, 183749, 189251, 194654,\n", + " 197550],\n", + " dtype='int64'), Int64Index([ 1020, 5615, 7544, 11257, 16716, 18557, 26759, 27675,\n", + " 33248, 64698, 77043, 81621, 83475, 85329, 86256, 88110,\n", + " 91426, 92386, 93295, 95115, 96070, 98405, 99332, 100230,\n", + " 101160, 105764, 110327, 114923, 120392, 122209, 128286, 135899,\n", + " 138209, 139135, 142357, 146288, 148998, 150829, 154534, 157325,\n", + " 172851, 176544, 177404, 179090, 182333, 183750, 189252, 194655,\n", + " 197551],\n", + " dtype='int64'), Int64Index([ 1021, 5616, 7545, 11258, 16717, 18558, 26760, 27676,\n", + " 33249, 64699, 77044, 81622, 83476, 85330, 86257, 88111,\n", + " 91427, 92387, 93296, 95116, 96071, 98406, 99333, 100231,\n", + " 101161, 105765, 110328, 114924, 120393, 122210, 128287, 135900,\n", + " 138210, 139136, 142358, 146289, 148999, 150830, 154535, 157326,\n", + " 172852, 176545, 177405, 179091, 182334, 183751, 189253, 194656,\n", + " 197552],\n", + " dtype='int64'), Int64Index([ 1022, 5617, 7546, 11259, 16718, 18559, 26761, 27677,\n", + " 33250, 64700, 77045, 81623, 83477, 85331, 86258, 88112,\n", + " 91428, 92388, 93297, 95117, 96072, 98407, 99334, 100232,\n", + " 101162, 105766, 110329, 114925, 120394, 122211, 128288, 135901,\n", + " 138211, 139137, 142359, 146290, 149000, 150831, 154536, 157327,\n", + " 172853, 176546, 177406, 179092, 182335, 183752, 189254, 194657,\n", + " 197553],\n", + " dtype='int64'), Int64Index([ 1023, 5618, 7547, 11260, 16719, 18560, 26762, 27678,\n", + " 33251, 64701, 77046, 81624, 83478, 85332, 86259, 88113,\n", + " 91429, 92389, 93298, 95118, 96073, 98408, 99335, 100233,\n", + " 101163, 105767, 110330, 114926, 120395, 122212, 128289, 135902,\n", + " 138212, 139138, 142360, 146291, 149001, 150832, 154537, 157328,\n", + " 172854, 176547, 177407, 179093, 182336, 183753, 189255, 194658,\n", + " 197554],\n", + " dtype='int64'), Int64Index([ 1024, 5619, 7548, 11261, 16720, 18561, 26763, 27679,\n", + " 33252, 64702, 77047, 81625, 83479, 85333, 86260, 88114,\n", + " 91430, 92390, 93299, 95119, 96074, 98409, 99336, 100234,\n", + " 101164, 105768, 110331, 114927, 120396, 122213, 128290, 135903,\n", + " 138213, 139139, 142361, 146292, 149002, 150833, 154538, 157329,\n", + " 172855, 176548, 177408, 179094, 182337, 183754, 189256, 194659,\n", + " 197555],\n", + " dtype='int64'), Int64Index([ 1025, 5620, 7549, 11262, 16721, 18562, 26764, 27680,\n", + " 33253, 64703, 77048, 81626, 83480, 85334, 86261, 88115,\n", + " 91431, 92391, 93300, 95120, 96075, 98410, 99337, 100235,\n", + " 101165, 105769, 110332, 114928, 120397, 122214, 128291, 135904,\n", + " 138214, 139140, 142362, 146293, 149003, 150834, 154539, 157330,\n", + " 172856, 176549, 177409, 179095, 182338, 183755, 189257, 194660,\n", + " 197556],\n", + " dtype='int64'), Int64Index([ 1026, 5621, 7550, 11263, 16722, 18563, 26765, 27681,\n", + " 33254, 64704, 77049, 81627, 83481, 85335, 86262, 88116,\n", + " 91432, 92392, 93301, 95121, 96076, 98411, 99338, 100236,\n", + " 101166, 105770, 110333, 114929, 120398, 122215, 128292, 135905,\n", + " 138215, 139141, 142363, 146294, 149004, 150835, 154540, 157331,\n", + " 172857, 176550, 177410, 179096, 182339, 183756, 189258, 194661,\n", + " 197557],\n", + " dtype='int64'), Int64Index([ 1027, 5622, 7551, 11264, 16723, 18564, 26766, 27682,\n", + " 33255, 64705, 77050, 81628, 83482, 85336, 86263, 88117,\n", + " 91433, 92393, 93302, 95122, 96077, 98412, 99339, 100237,\n", + " 101167, 105771, 110334, 114930, 120399, 122216, 128293, 135906,\n", + " 138216, 139142, 142364, 146295, 149005, 150836, 154541, 157332,\n", + " 172858, 176551, 177411, 179097, 182340, 183757, 189259, 194662,\n", + " 197558],\n", + " dtype='int64'), Int64Index([ 1028, 5623, 7552, 11265, 16724, 18565, 26767, 27683,\n", + " 33256, 64706, 77051, 81629, 83483, 85337, 86264, 88118,\n", + " 91434, 92394, 93303, 95123, 96078, 98413, 99340, 100238,\n", + " 101168, 105772, 110335, 114931, 120400, 122217, 128294, 135907,\n", + " 138217, 139143, 142365, 146296, 149006, 150837, 154542, 157333,\n", + " 172859, 176552, 177412, 179098, 182341, 183758, 189260, 194663,\n", + " 197559],\n", + " dtype='int64'), Int64Index([ 1029, 5624, 7553, 11266, 16725, 18566, 26768, 27684,\n", + " 33257, 64707, 77052, 81630, 83484, 85338, 86265, 88119,\n", + " 91435, 92395, 93304, 95124, 96079, 98414, 99341, 100239,\n", + " 101169, 105773, 110336, 114932, 120401, 122218, 128295, 135908,\n", + " 138218, 139144, 142366, 146297, 149007, 150838, 154543, 157334,\n", + " 172860, 176553, 177413, 179099, 182342, 183759, 189261, 194664,\n", + " 197560],\n", + " dtype='int64'), Int64Index([ 1030, 5625, 7554, 11267, 16726, 18567, 26769, 27685,\n", + " 33258, 64708, 77053, 81631, 83485, 85339, 86266, 88120,\n", + " 91436, 92396, 93305, 95125, 96080, 98415, 99342, 100240,\n", + " 101170, 105774, 110337, 114933, 120402, 122219, 128296, 135909,\n", + " 138219, 139145, 142367, 146298, 149008, 150839, 154544, 157335,\n", + " 172861, 176554, 177414, 179100, 182343, 183760, 189262, 194665,\n", + " 197561],\n", + " dtype='int64'), Int64Index([ 1031, 5626, 7555, 11268, 16727, 18568, 26770, 27686,\n", + " 33259, 64709, 77054, 81632, 83486, 85340, 86267, 88121,\n", + " 91437, 92397, 93306, 95126, 96081, 98416, 99343, 100241,\n", + " 101171, 105775, 110338, 114934, 120403, 122220, 128297, 135910,\n", + " 138220, 139146, 142368, 146299, 149009, 150840, 154545, 157336,\n", + " 172862, 176555, 177415, 179101, 182344, 183761, 189263, 194666,\n", + " 197562],\n", + " dtype='int64'), Int64Index([ 1032, 5627, 7556, 11269, 16728, 18569, 26771, 27687,\n", + " 33260, 64710, 77055, 81633, 83487, 85341, 86268, 88122,\n", + " 91438, 92398, 93307, 95127, 96082, 98417, 99344, 100242,\n", + " 101172, 105776, 110339, 114935, 120404, 122221, 128298, 135911,\n", + " 138221, 139147, 142369, 146300, 149010, 150841, 154546, 157337,\n", + " 172863, 176556, 177416, 179102, 182345, 183762, 189264, 194667,\n", + " 197563],\n", + " dtype='int64'), Int64Index([ 1033, 5628, 7557, 11270, 16729, 18570, 26772, 27688,\n", + " 33261, 64711, 77056, 81634, 83488, 85342, 86269, 88123,\n", + " 91439, 92399, 93308, 95128, 96083, 98418, 99345, 100243,\n", + " 101173, 105777, 110340, 114936, 120405, 122222, 128299, 135912,\n", + " 138222, 139148, 142370, 146301, 149011, 150842, 154547, 157338,\n", + " 172864, 176557, 177417, 179103, 182346, 183763, 189265, 194668,\n", + " 197564],\n", + " dtype='int64'), Int64Index([ 1034, 5629, 7558, 11271, 16730, 18571, 26773, 27689,\n", + " 33262, 64712, 77057, 81635, 83489, 85343, 86270, 88124,\n", + " 91440, 92400, 93309, 95129, 96084, 98419, 99346, 100244,\n", + " 101174, 105778, 110341, 114937, 120406, 122223, 128300, 135913,\n", + " 138223, 139149, 142371, 146302, 149012, 150843, 154548, 157339,\n", + " 172865, 176558, 177418, 179104, 182347, 183764, 189266, 194669,\n", + " 197565],\n", + " dtype='int64'), Int64Index([ 1035, 5630, 7559, 11272, 16731, 18572, 26774, 27690,\n", + " 33263, 64713, 77058, 81636, 83490, 85344, 86271, 88125,\n", + " 91441, 92401, 93310, 95130, 96085, 98420, 99347, 100245,\n", + " 101175, 105779, 110342, 114938, 120407, 122224, 128301, 135914,\n", + " 138224, 139150, 142372, 146303, 149013, 150844, 154549, 157340,\n", + " 172866, 176559, 177419, 179105, 182348, 183765, 189267, 194670,\n", + " 197566],\n", + " dtype='int64'), Int64Index([ 1036, 5631, 7560, 11273, 16732, 18573, 26775, 27691,\n", + " 33264, 64714, 77059, 81637, 83491, 85345, 86272, 88126,\n", + " 91442, 92402, 93311, 95131, 96086, 98421, 99348, 100246,\n", + " 101176, 105780, 110343, 114939, 120408, 122225, 128302, 135915,\n", + " 138225, 139151, 142373, 146304, 149014, 150845, 154550, 157341,\n", + " 172867, 176560, 177420, 179106, 182349, 183766, 189268, 194671,\n", + " 197567],\n", + " dtype='int64'), Int64Index([ 1037, 5632, 7561, 11274, 16733, 18574, 26776, 27692,\n", + " 33265, 64715, 77060, 81638, 83492, 85346, 86273, 88127,\n", + " 91443, 92403, 93312, 95132, 96087, 98422, 99349, 100247,\n", + " 101177, 105781, 110344, 114940, 120409, 122226, 128303, 135916,\n", + " 138226, 139152, 142374, 146305, 149015, 150846, 154551, 157342,\n", + " 172868, 176561, 177421, 179107, 182350, 183767, 189269, 194672,\n", + " 197568],\n", + " dtype='int64'), Int64Index([ 1038, 5633, 7562, 11275, 16734, 18575, 26777, 27693,\n", + " 33266, 64716, 77061, 81639, 83493, 85347, 86274, 88128,\n", + " 91444, 92404, 93313, 95133, 96088, 98423, 99350, 100248,\n", + " 101178, 105782, 110345, 114941, 120410, 122227, 128304, 135917,\n", + " 138227, 139153, 142375, 146306, 149016, 150847, 154552, 157343,\n", + " 172869, 176562, 177422, 179108, 182351, 183768, 189270, 194673,\n", + " 197569],\n", + " dtype='int64'), Int64Index([ 1039, 5634, 7563, 11276, 16735, 18576, 26778, 27694,\n", + " 33267, 64717, 77062, 81640, 83494, 85348, 86275, 88129,\n", + " 91445, 92405, 93314, 95134, 96089, 98424, 99351, 100249,\n", + " 101179, 105783, 110346, 114942, 120411, 122228, 128305, 135918,\n", + " 138228, 139154, 142376, 146307, 149017, 150848, 154553, 157344,\n", + " 172870, 176563, 177423, 179109, 182352, 183769, 189271, 194674,\n", + " 197570],\n", + " dtype='int64'), Int64Index([ 1040, 5635, 7564, 11277, 16736, 18577, 26779, 27695,\n", + " 33268, 64718, 77063, 81641, 83495, 85349, 86276, 88130,\n", + " 91446, 92406, 93315, 95135, 96090, 98425, 99352, 100250,\n", + " 101180, 105784, 110347, 114943, 120412, 122229, 128306, 135919,\n", + " 138229, 139155, 142377, 146308, 149018, 150849, 154554, 157345,\n", + " 172871, 176564, 177424, 179110, 182353, 183770, 189272, 194675,\n", + " 197571],\n", + " dtype='int64'), Int64Index([ 1041, 5636, 7565, 11278, 16737, 18578, 26780, 27696,\n", + " 33269, 64719, 77064, 81642, 83496, 85350, 86277, 88131,\n", + " 91447, 92407, 93316, 95136, 96091, 98426, 99353, 100251,\n", + " 101181, 105785, 110348, 114944, 120413, 122230, 128307, 135920,\n", + " 138230, 139156, 142378, 146309, 149019, 150850, 154555, 157346,\n", + " 172872, 176565, 177425, 179111, 182354, 183771, 189273, 194676,\n", + " 197572],\n", + " dtype='int64'), Int64Index([ 1042, 5637, 7566, 11279, 16738, 18579, 26781, 27697,\n", + " 33270, 64720, 77065, 81643, 83497, 85351, 86278, 88132,\n", + " 91448, 92408, 93317, 95137, 96092, 98427, 99354, 100252,\n", + " 101182, 105786, 110349, 114945, 120414, 122231, 128308, 135921,\n", + " 138231, 139157, 142379, 146310, 149020, 150851, 154556, 157347,\n", + " 172873, 176566, 177426, 179112, 182355, 183772, 189274, 194677,\n", + " 197573],\n", + " dtype='int64'), Int64Index([ 1043, 5638, 7567, 11280, 16739, 18580, 26782, 27698,\n", + " 33271, 64721, 77066, 81644, 83498, 85352, 86279, 88133,\n", + " 91449, 92409, 93318, 95138, 96093, 98428, 99355, 100253,\n", + " 101183, 105787, 110350, 114946, 120415, 122232, 128309, 135922,\n", + " 138232, 139158, 142380, 146311, 149021, 150852, 154557, 157348,\n", + " 172874, 176567, 177427, 179113, 182356, 183773, 189275, 194678,\n", + " 197574],\n", + " dtype='int64'), Int64Index([ 1044, 5639, 7568, 11281, 16740, 18581, 26783, 27699,\n", + " 33272, 64722, 77067, 81645, 83499, 85353, 86280, 88134,\n", + " 91450, 92410, 93319, 95139, 96094, 98429, 99356, 100254,\n", + " 101184, 105788, 110351, 114947, 120416, 122233, 128310, 135923,\n", + " 138233, 139159, 142381, 146312, 149022, 150853, 154558, 157349,\n", + " 172875, 176568, 177428, 179114, 182357, 183774, 189276, 194679,\n", + " 197575],\n", + " dtype='int64'), Int64Index([ 1045, 5640, 7569, 11282, 16741, 18582, 26784, 27700,\n", + " 33273, 64723, 77068, 81646, 83500, 85354, 86281, 88135,\n", + " 91451, 92411, 93320, 95140, 96095, 98430, 99357, 100255,\n", + " 101185, 105789, 110352, 114948, 120417, 122234, 128311, 135924,\n", + " 138234, 139160, 142382, 146313, 149023, 150854, 154559, 157350,\n", + " 172876, 176569, 177429, 179115, 182358, 183775, 189277, 194680,\n", + " 197576],\n", + " dtype='int64'), Int64Index([ 1046, 5641, 7570, 11283, 16742, 18583, 26785, 27701,\n", + " 33274, 64724, 77069, 81647, 83501, 85355, 86282, 88136,\n", + " 91452, 92412, 93321, 95141, 96096, 98431, 99358, 100256,\n", + " 101186, 105790, 110353, 114949, 120418, 122235, 128312, 135925,\n", + " 138235, 139161, 142383, 146314, 149024, 150855, 154560, 157351,\n", + " 172877, 176570, 177430, 179116, 182359, 183776, 189278, 194681,\n", + " 197577],\n", + " dtype='int64'), Int64Index([ 1047, 5642, 7571, 11284, 16743, 18584, 26786, 27702,\n", + " 33275, 64725, 77070, 81648, 83502, 85356, 86283, 88137,\n", + " 91453, 92413, 93322, 95142, 96097, 98432, 99359, 100257,\n", + " 101187, 105791, 110354, 114950, 120419, 122236, 128313, 135926,\n", + " 138236, 139162, 142384, 146315, 149025, 150856, 154561, 157352,\n", + " 172878, 176571, 177431, 179117, 182360, 183777, 189279, 194682,\n", + " 197578],\n", + " dtype='int64'), Int64Index([ 1048, 5643, 7572, 11285, 16744, 18585, 26787, 27703,\n", + " 33276, 64726, 77071, 81649, 83503, 85357, 86284, 88138,\n", + " 91454, 92414, 93323, 95143, 96098, 98433, 99360, 100258,\n", + " 101188, 105792, 110355, 114951, 120420, 122237, 128314, 135927,\n", + " 138237, 139163, 142385, 146316, 149026, 150857, 154562, 157353,\n", + " 172879, 176572, 177432, 179118, 182361, 183778, 189280, 194683,\n", + " 197579],\n", + " dtype='int64'), Int64Index([ 1049, 5644, 7573, 11286, 16745, 18586, 26788, 27704,\n", + " 33277, 64727, 77072, 81650, 83504, 85358, 86285, 88139,\n", + " 91455, 92415, 93324, 95144, 96099, 98434, 99361, 100259,\n", + " 101189, 105793, 110356, 114952, 120421, 122238, 128315, 135928,\n", + " 138238, 139164, 142386, 146317, 149027, 150858, 154563, 157354,\n", + " 172880, 176573, 177433, 179119, 182362, 183779, 189281, 194684,\n", + " 197580],\n", + " dtype='int64'), Int64Index([ 1050, 5645, 7574, 11287, 16746, 18587, 26789, 27705,\n", + " 33278, 64728, 77073, 81651, 83505, 85359, 86286, 88140,\n", + " 91456, 92416, 93325, 95145, 96100, 98435, 99362, 100260,\n", + " 101190, 105794, 110357, 114953, 120422, 122239, 128316, 135929,\n", + " 138239, 139165, 142387, 146318, 149028, 150859, 154564, 157355,\n", + " 172881, 176574, 177434, 179120, 182363, 183780, 189282, 194685,\n", + " 197581],\n", + " dtype='int64'), Int64Index([ 1051, 5646, 7575, 11288, 16747, 18588, 26790, 27706,\n", + " 33279, 64729, 77074, 81652, 83506, 85360, 86287, 88141,\n", + " 91457, 92417, 93326, 95146, 96101, 98436, 99363, 100261,\n", + " 101191, 105795, 110358, 114954, 120423, 122240, 128317, 135930,\n", + " 138240, 139166, 142388, 146319, 149029, 150860, 154565, 157356,\n", + " 172882, 176575, 177435, 179121, 182364, 183781, 189283, 194686,\n", + " 197582],\n", + " dtype='int64'), Int64Index([ 1052, 5647, 7576, 11289, 16748, 18589, 26791, 27707,\n", + " 33280, 64730, 77075, 81653, 83507, 85361, 86288, 88142,\n", + " 91458, 92418, 93327, 95147, 96102, 98437, 99364, 100262,\n", + " 101192, 105796, 110359, 114955, 120424, 122241, 128318, 135931,\n", + " 138241, 139167, 142389, 146320, 149030, 150861, 154566, 157357,\n", + " 172883, 176576, 177436, 179122, 182365, 183782, 189284, 194687,\n", + " 197583],\n", + " dtype='int64'), Int64Index([ 1053, 5648, 7577, 11290, 16749, 18590, 26792, 27708,\n", + " 33281, 64731, 77076, 81654, 83508, 85362, 86289, 88143,\n", + " 91459, 92419, 93328, 95148, 96103, 98438, 99365, 100263,\n", + " 101193, 105797, 110360, 114956, 120425, 122242, 128319, 135932,\n", + " 138242, 139168, 142390, 146321, 149031, 150862, 154567, 157358,\n", + " 172884, 176577, 177437, 179123, 182366, 183783, 189285, 194688,\n", + " 197584],\n", + " dtype='int64'), Int64Index([ 1054, 5649, 7578, 11291, 16750, 18591, 26793, 27709,\n", + " 33282, 64732, 77077, 81655, 83509, 85363, 86290, 88144,\n", + " 91460, 92420, 93329, 95149, 96104, 98439, 99366, 100264,\n", + " 101194, 105798, 110361, 114957, 120426, 122243, 128320, 135933,\n", + " 138243, 139169, 142391, 146322, 149032, 150863, 154568, 157359,\n", + " 172885, 176578, 177438, 179124, 182367, 183784, 189286, 194689,\n", + " 197585],\n", + " dtype='int64'), Int64Index([ 1055, 5650, 7579, 11292, 16751, 18592, 26794, 27710,\n", + " 33283, 64733, 77078, 81656, 83510, 85364, 86291, 88145,\n", + " 91461, 92421, 93330, 95150, 96105, 98440, 99367, 100265,\n", + " 101195, 105799, 110362, 114958, 120427, 122244, 128321, 135934,\n", + " 138244, 139170, 142392, 146323, 149033, 150864, 154569, 157360,\n", + " 172886, 176579, 177439, 179125, 182368, 183785, 189287, 194690,\n", + " 197586],\n", + " dtype='int64'), Int64Index([ 1056, 5651, 7580, 11293, 16752, 18593, 26795, 27711,\n", + " 33284, 64734, 77079, 81657, 83511, 85365, 86292, 88146,\n", + " 91462, 92422, 93331, 95151, 96106, 98441, 99368, 100266,\n", + " 101196, 105800, 110363, 114959, 120428, 122245, 128322, 135935,\n", + " 138245, 139171, 142393, 146324, 149034, 150865, 154570, 157361,\n", + " 172887, 176580, 177440, 179126, 182369, 183786, 189288, 194691,\n", + " 197587],\n", + " dtype='int64'), Int64Index([ 1057, 5652, 7581, 11294, 16753, 18594, 26796, 27712,\n", + " 33285, 64735, 77080, 81658, 83512, 85366, 86293, 88147,\n", + " 91463, 92423, 93332, 95152, 96107, 98442, 99369, 100267,\n", + " 101197, 105801, 110364, 114960, 120429, 122246, 128323, 135936,\n", + " 138246, 139172, 142394, 146325, 149035, 150866, 154571, 157362,\n", + " 172888, 176581, 177441, 179127, 182370, 183787, 189289, 194692,\n", + " 197588],\n", + " dtype='int64'), Int64Index([ 1058, 5653, 7582, 11295, 16754, 18595, 26797, 27713,\n", + " 33286, 64736, 77081, 81659, 83513, 85367, 86294, 88148,\n", + " 91464, 92424, 93333, 95153, 96108, 98443, 99370, 100268,\n", + " 101198, 105802, 110365, 114961, 120430, 122247, 128324, 135937,\n", + " 138247, 139173, 142395, 146326, 149036, 150867, 154572, 157363,\n", + " 172889, 176582, 177442, 179128, 182371, 183788, 189290, 194693,\n", + " 197589],\n", + " dtype='int64'), Int64Index([ 1059, 5654, 7583, 11296, 16755, 18596, 26798, 27714,\n", + " 33287, 64737, 77082, 81660, 83514, 85368, 86295, 88149,\n", + " 91465, 92425, 93334, 95154, 96109, 98444, 99371, 100269,\n", + " 101199, 105803, 110366, 114962, 120431, 122248, 128325, 135938,\n", + " 138248, 139174, 142396, 146327, 149037, 150868, 154573, 157364,\n", + " 172890, 176583, 177443, 179129, 182372, 183789, 189291, 194694,\n", + " 197590],\n", + " dtype='int64'), Int64Index([ 1060, 5655, 7584, 11297, 16756, 18597, 26799, 27715,\n", + " 33288, 64738, 77083, 81661, 83515, 85369, 86296, 88150,\n", + " 91466, 92426, 93335, 95155, 96110, 98445, 99372, 100270,\n", + " 101200, 105804, 110367, 114963, 120432, 122249, 128326, 135939,\n", + " 138249, 139175, 142397, 146328, 149038, 150869, 154574, 157365,\n", + " 172891, 176584, 177444, 179130, 182373, 183790, 189292, 194695,\n", + " 197591],\n", + " dtype='int64'), Int64Index([ 1061, 5656, 7585, 11298, 16757, 18598, 26800, 27716,\n", + " 33289, 64739, 77084, 81662, 83516, 85370, 86297, 88151,\n", + " 91467, 92427, 93336, 95156, 96111, 98446, 99373, 100271,\n", + " 101201, 105805, 110368, 114964, 120433, 122250, 128327, 135940,\n", + " 138250, 139176, 142398, 146329, 149039, 150870, 154575, 157366,\n", + " 172892, 176585, 177445, 179131, 182374, 183791, 189293, 194696,\n", + " 197592],\n", + " dtype='int64'), Int64Index([ 1062, 5657, 7586, 11299, 16758, 18599, 26801, 27717,\n", + " 33290, 64740, 77085, 81663, 83517, 85371, 86298, 88152,\n", + " 91468, 92428, 93337, 95157, 96112, 98447, 99374, 100272,\n", + " 101202, 105806, 110369, 114965, 120434, 122251, 128328, 135941,\n", + " 138251, 139177, 142399, 146330, 149040, 150871, 154576, 157367,\n", + " 172893, 176586, 177446, 179132, 182375, 183792, 189294, 194697,\n", + " 197593],\n", + " dtype='int64'), Int64Index([ 1063, 5658, 7587, 11300, 16759, 18600, 26802, 27718,\n", + " 33291, 64741, 77086, 81664, 83518, 85372, 86299, 88153,\n", + " 91469, 92429, 93338, 95158, 96113, 98448, 99375, 100273,\n", + " 101203, 105807, 110370, 114966, 120435, 122252, 128329, 135942,\n", + " 138252, 139178, 142400, 146331, 149041, 150872, 154577, 157368,\n", + " 172894, 176587, 177447, 179133, 182376, 183793, 189295, 194698,\n", + " 197594],\n", + " dtype='int64'), Int64Index([ 1064, 5659, 7588, 11301, 16760, 18601, 26803, 27719,\n", + " 33292, 64742, 77087, 81665, 83519, 85373, 86300, 88154,\n", + " 91470, 92430, 93339, 95159, 96114, 98449, 99376, 100274,\n", + " 101204, 105808, 110371, 114967, 120436, 122253, 128330, 135943,\n", + " 138253, 139179, 142401, 146332, 149042, 150873, 154578, 157369,\n", + " 172895, 176588, 177448, 179134, 182377, 183794, 189296, 194699,\n", + " 197595],\n", + " dtype='int64'), Int64Index([ 1065, 5660, 7589, 11302, 16761, 18602, 26804, 27720,\n", + " 33293, 64743, 77088, 81666, 83520, 85374, 86301, 88155,\n", + " 91471, 92431, 93340, 95160, 96115, 98450, 99377, 100275,\n", + " 101205, 105809, 110372, 114968, 120437, 122254, 128331, 135944,\n", + " 138254, 139180, 142402, 146333, 149043, 150874, 154579, 157370,\n", + " 172896, 176589, 177449, 179135, 182378, 183795, 189297, 194700,\n", + " 197596],\n", + " dtype='int64'), Int64Index([ 1066, 5661, 7590, 11303, 16762, 18603, 26805, 27721,\n", + " 33294, 64744, 77089, 81667, 83521, 85375, 86302, 88156,\n", + " 91472, 92432, 93341, 95161, 96116, 98451, 99378, 100276,\n", + " 101206, 105810, 110373, 114969, 120438, 122255, 128332, 135945,\n", + " 138255, 139181, 142403, 146334, 149044, 150875, 154580, 157371,\n", + " 172897, 176590, 177450, 179136, 182379, 183796, 189298, 194701,\n", + " 197597],\n", + " dtype='int64'), Int64Index([ 1067, 5662, 7591, 11304, 16763, 18604, 26806, 27722,\n", + " 33295, 64745, 77090, 81668, 83522, 85376, 86303, 88157,\n", + " 91473, 92433, 93342, 95162, 96117, 98452, 99379, 100277,\n", + " 101207, 105811, 110374, 114970, 120439, 122256, 128333, 135946,\n", + " 138256, 139182, 142404, 146335, 149045, 150876, 154581, 157372,\n", + " 172898, 176591, 177451, 179137, 182380, 183797, 189299, 194702,\n", + " 197598],\n", + " dtype='int64'), Int64Index([ 1068, 5663, 7592, 11305, 16764, 18605, 26807, 27723,\n", + " 33296, 64746, 77091, 81669, 83523, 85377, 86304, 88158,\n", + " 91474, 92434, 93343, 95163, 96118, 98453, 99380, 100278,\n", + " 101208, 105812, 110375, 114971, 120440, 122257, 128334, 135947,\n", + " 138257, 139183, 142405, 146336, 149046, 150877, 154582, 157373,\n", + " 172899, 176592, 177452, 179138, 182381, 183798, 189300, 194703,\n", + " 197599],\n", + " dtype='int64'), Int64Index([ 1069, 5664, 7593, 11306, 16765, 18606, 26808, 27724,\n", + " 33297, 64747, 77092, 81670, 83524, 85378, 86305, 88159,\n", + " 91475, 92435, 93344, 95164, 96119, 98454, 99381, 100279,\n", + " 101209, 105813, 110376, 114972, 120441, 122258, 128335, 135948,\n", + " 138258, 139184, 142406, 146337, 149047, 150878, 154583, 157374,\n", + " 172900, 176593, 177453, 179139, 182382, 183799, 189301, 194704,\n", + " 197600],\n", + " dtype='int64'), Int64Index([ 1070, 5665, 7594, 11307, 16766, 18607, 26809, 27725,\n", + " 33298, 64748, 77093, 81671, 83525, 85379, 86306, 88160,\n", + " 91476, 92436, 93345, 95165, 96120, 98455, 99382, 100280,\n", + " 101210, 105814, 110377, 114973, 120442, 122259, 128336, 135949,\n", + " 138259, 139185, 142407, 146338, 149048, 150879, 154584, 157375,\n", + " 172901, 176594, 177454, 179140, 182383, 183800, 189302, 194705,\n", + " 197601],\n", + " dtype='int64'), Int64Index([ 1071, 5666, 7595, 11308, 16767, 18608, 26810, 27726,\n", + " 33299, 64749, 77094, 81672, 83526, 85380, 86307, 88161,\n", + " 91477, 92437, 93346, 95166, 96121, 98456, 99383, 100281,\n", + " 101211, 105815, 110378, 114974, 120443, 122260, 128337, 135950,\n", + " 138260, 139186, 142408, 146339, 149049, 150880, 154585, 157376,\n", + " 172902, 176595, 177455, 179141, 182384, 183801, 189303, 194706,\n", + " 197602],\n", + " dtype='int64'), Int64Index([ 1072, 5667, 7596, 11309, 16768, 18609, 26811, 27727,\n", + " 33300, 64750, 77095, 81673, 83527, 85381, 86308, 88162,\n", + " 91478, 92438, 93347, 95167, 96122, 98457, 99384, 100282,\n", + " 101212, 105816, 110379, 114975, 120444, 122261, 128338, 135951,\n", + " 138261, 139187, 142409, 146340, 149050, 150881, 154586, 157377,\n", + " 172903, 176596, 177456, 179142, 182385, 183802, 189304, 194707,\n", + " 197603],\n", + " dtype='int64'), Int64Index([ 1073, 5668, 7597, 11310, 16769, 18610, 26812, 27728,\n", + " 33301, 64751, 77096, 81674, 83528, 85382, 86309, 88163,\n", + " 91479, 92439, 93348, 95168, 96123, 98458, 99385, 100283,\n", + " 101213, 105817, 110380, 114976, 120445, 122262, 128339, 135952,\n", + " 138262, 139188, 142410, 146341, 149051, 150882, 154587, 157378,\n", + " 172904, 176597, 177457, 179143, 182386, 183803, 189305, 194708,\n", + " 197604],\n", + " dtype='int64'), Int64Index([ 1074, 5669, 7598, 11311, 16770, 18611, 26813, 27729,\n", + " 33302, 64752, 77097, 81675, 83529, 85383, 86310, 88164,\n", + " 91480, 92440, 93349, 95169, 96124, 98459, 99386, 100284,\n", + " 101214, 105818, 110381, 114977, 120446, 122263, 128340, 135953,\n", + " 138263, 139189, 142411, 146342, 149052, 150883, 154588, 157379,\n", + " 172905, 176598, 177458, 179144, 182387, 183804, 189306, 194709,\n", + " 197605],\n", + " dtype='int64'), Int64Index([ 1075, 5670, 7599, 11312, 16771, 18612, 26814, 27730,\n", + " 33303, 64753, 77098, 81676, 83530, 85384, 86311, 88165,\n", + " 91481, 92441, 93350, 95170, 96125, 98460, 99387, 100285,\n", + " 101215, 105819, 110382, 114978, 120447, 122264, 128341, 135954,\n", + " 138264, 139190, 142412, 146343, 149053, 150884, 154589, 157380,\n", + " 172906, 176599, 177459, 179145, 182388, 183805, 189307, 194710,\n", + " 197606],\n", + " dtype='int64'), Int64Index([ 1076, 5671, 7600, 11313, 16772, 18613, 26815, 27731,\n", + " 33304, 64754, 77099, 81677, 83531, 85385, 86312, 88166,\n", + " 91482, 92442, 93351, 95171, 96126, 98461, 99388, 100286,\n", + " 101216, 105820, 110383, 114979, 120448, 122265, 128342, 135955,\n", + " 138265, 139191, 142413, 146344, 149054, 150885, 154590, 157381,\n", + " 172907, 176600, 177460, 179146, 182389, 183806, 189308, 194711,\n", + " 197607],\n", + " dtype='int64'), Int64Index([ 1077, 5672, 7601, 11314, 16773, 18614, 26816, 27732,\n", + " 33305, 64755, 77100, 81678, 83532, 85386, 86313, 88167,\n", + " 91483, 92443, 93352, 95172, 96127, 98462, 99389, 100287,\n", + " 101217, 105821, 110384, 114980, 120449, 122266, 128343, 135956,\n", + " 138266, 139192, 142414, 146345, 149055, 150886, 154591, 157382,\n", + " 172908, 176601, 177461, 179147, 182390, 183807, 189309, 194712,\n", + " 197608],\n", + " dtype='int64'), Int64Index([ 1078, 5673, 7602, 11315, 16774, 18615, 26817, 27733,\n", + " 33306, 64756, 77101, 81679, 83533, 85387, 86314, 88168,\n", + " 91484, 92444, 93353, 95173, 96128, 98463, 99390, 100288,\n", + " 101218, 105822, 110385, 114981, 120450, 122267, 128344, 135957,\n", + " 138267, 139193, 142415, 146346, 149056, 150887, 154592, 157383,\n", + " 172909, 176602, 177462, 179148, 182391, 183808, 189310, 194713,\n", + " 197609],\n", + " dtype='int64'), Int64Index([ 1079, 5674, 7603, 11316, 16775, 18616, 26818, 27734,\n", + " 33307, 64757, 77102, 81680, 83534, 85388, 86315, 88169,\n", + " 91485, 92445, 93354, 95174, 96129, 98464, 99391, 100289,\n", + " 101219, 105823, 110386, 114982, 120451, 122268, 128345, 135958,\n", + " 138268, 139194, 142416, 146347, 149057, 150888, 154593, 157384,\n", + " 172910, 176603, 177463, 179149, 182392, 183809, 189311, 194714,\n", + " 197610],\n", + " dtype='int64'), Int64Index([ 1080, 5675, 7604, 11317, 16776, 18617, 26819, 27735,\n", + " 33308, 64758, 77103, 81681, 83535, 85389, 86316, 88170,\n", + " 91486, 92446, 93355, 95175, 96130, 98465, 99392, 100290,\n", + " 101220, 105824, 110387, 114983, 120452, 122269, 128346, 135959,\n", + " 138269, 139195, 142417, 146348, 149058, 150889, 154594, 157385,\n", + " 172911, 176604, 177464, 179150, 182393, 183810, 189312, 194715,\n", + " 197611],\n", + " dtype='int64'), Int64Index([ 1081, 5676, 7605, 11318, 16777, 18618, 26820, 27736,\n", + " 33309, 64759, 77104, 81682, 83536, 85390, 86317, 88171,\n", + " 91487, 92447, 93356, 95176, 96131, 98466, 99393, 100291,\n", + " 101221, 105825, 110388, 114984, 120453, 122270, 128347, 135960,\n", + " 138270, 139196, 142418, 146349, 149059, 150890, 154595, 157386,\n", + " 172912, 176605, 177465, 179151, 182394, 183811, 189313, 194716,\n", + " 197612],\n", + " dtype='int64'), Int64Index([ 1082, 5677, 7606, 11319, 16778, 18619, 26821, 27737,\n", + " 33310, 64760, 77105, 81683, 83537, 85391, 86318, 88172,\n", + " 91488, 92448, 93357, 95177, 96132, 98467, 99394, 100292,\n", + " 101222, 105826, 110389, 114985, 120454, 122271, 128348, 135961,\n", + " 138271, 139197, 142419, 146350, 149060, 150891, 154596, 157387,\n", + " 172913, 176606, 177466, 179152, 182395, 183812, 189314, 194717,\n", + " 197613],\n", + " dtype='int64'), Int64Index([ 1083, 5678, 7607, 11320, 16779, 18620, 26822, 27738,\n", + " 33311, 64761, 77106, 81684, 83538, 85392, 86319, 88173,\n", + " 91489, 92449, 93358, 95178, 96133, 98468, 99395, 100293,\n", + " 101223, 105827, 110390, 114986, 120455, 122272, 128349, 135962,\n", + " 138272, 139198, 142420, 146351, 149061, 150892, 154597, 157388,\n", + " 172914, 176607, 177467, 179153, 182396, 183813, 189315, 194718,\n", + " 197614],\n", + " dtype='int64'), Int64Index([ 1084, 5679, 7608, 11321, 16780, 18621, 26823, 27739,\n", + " 33312, 64762, 77107, 81685, 83539, 85393, 86320, 88174,\n", + " 91490, 92450, 93359, 95179, 96134, 98469, 99396, 100294,\n", + " 101224, 105828, 110391, 114987, 120456, 122273, 128350, 135963,\n", + " 138273, 139199, 142421, 146352, 149062, 150893, 154598, 157389,\n", + " 172915, 176608, 177468, 179154, 182397, 183814, 189316, 194719,\n", + " 197615],\n", + " dtype='int64'), Int64Index([ 1085, 5680, 7609, 11322, 16781, 18622, 26824, 27740,\n", + " 33313, 64763, 77108, 81686, 83540, 85394, 86321, 88175,\n", + " 91491, 92451, 93360, 95180, 96135, 98470, 99397, 100295,\n", + " 101225, 105829, 110392, 114988, 120457, 122274, 128351, 135964,\n", + " 138274, 139200, 142422, 146353, 149063, 150894, 154599, 157390,\n", + " 172916, 176609, 177469, 179155, 182398, 183815, 189317, 194720,\n", + " 197616],\n", + " dtype='int64'), Int64Index([ 1086, 5681, 7610, 11323, 16782, 18623, 26825, 27741,\n", + " 33314, 64764, 77109, 81687, 83541, 85395, 86322, 88176,\n", + " 91492, 92452, 93361, 95181, 96136, 98471, 99398, 100296,\n", + " 101226, 105830, 110393, 114989, 120458, 122275, 128352, 135965,\n", + " 138275, 139201, 142423, 146354, 149064, 150895, 154600, 157391,\n", + " 172917, 176610, 177470, 179156, 182399, 183816, 189318, 194721,\n", + " 197617],\n", + " dtype='int64'), Int64Index([ 1087, 5682, 7611, 11324, 16783, 18624, 26826, 27742,\n", + " 33315, 64765, 77110, 81688, 83542, 85396, 86323, 88177,\n", + " 91493, 92453, 93362, 95182, 96137, 98472, 99399, 100297,\n", + " 101227, 105831, 110394, 114990, 120459, 122276, 128353, 135966,\n", + " 138276, 139202, 142424, 146355, 149065, 150896, 154601, 157392,\n", + " 172918, 176611, 177471, 179157, 182400, 183817, 189319, 194722,\n", + " 197618],\n", + " dtype='int64'), Int64Index([ 1088, 5683, 7612, 11325, 16784, 18625, 26827, 27743,\n", + " 33316, 64766, 77111, 81689, 83543, 85397, 86324, 88178,\n", + " 91494, 92454, 93363, 95183, 96138, 98473, 99400, 100298,\n", + " 101228, 105832, 110395, 114991, 120460, 122277, 128354, 135967,\n", + " 138277, 139203, 142425, 146356, 149066, 150897, 154602, 157393,\n", + " 172919, 176612, 177472, 179158, 182401, 183818, 189320, 194723,\n", + " 197619],\n", + " dtype='int64'), Int64Index([ 1089, 5684, 7613, 11326, 16785, 18626, 26828, 27744,\n", + " 33317, 64767, 77112, 81690, 83544, 85398, 86325, 88179,\n", + " 91495, 92455, 93364, 95184, 96139, 98474, 99401, 100299,\n", + " 101229, 105833, 110396, 114992, 120461, 122278, 128355, 135968,\n", + " 138278, 139204, 142426, 146357, 149067, 150898, 154603, 157394,\n", + " 172920, 176613, 177473, 179159, 182402, 183819, 189321, 194724,\n", + " 197620],\n", + " dtype='int64'), Int64Index([ 1090, 5685, 7614, 11327, 16786, 18627, 26829, 27745,\n", + " 33318, 64768, 77113, 81691, 83545, 85399, 86326, 88180,\n", + " 91496, 92456, 93365, 95185, 96140, 98475, 99402, 100300,\n", + " 101230, 105834, 110397, 114993, 120462, 122279, 128356, 135969,\n", + " 138279, 139205, 142427, 146358, 149068, 150899, 154604, 157395,\n", + " 172921, 176614, 177474, 179160, 182403, 183820, 189322, 194725,\n", + " 197621],\n", + " dtype='int64'), Int64Index([ 1091, 5686, 7615, 11328, 16787, 18628, 26830, 27746,\n", + " 33319, 64769, 77114, 81692, 83546, 85400, 86327, 88181,\n", + " 91497, 92457, 93366, 95186, 96141, 98476, 99403, 100301,\n", + " 101231, 105835, 110398, 114994, 120463, 122280, 128357, 135970,\n", + " 138280, 139206, 142428, 146359, 149069, 150900, 154605, 157396,\n", + " 172922, 176615, 177475, 179161, 182404, 183821, 189323, 194726,\n", + " 197622],\n", + " dtype='int64'), Int64Index([ 1092, 5687, 7616, 11329, 16788, 18629, 26831, 27747,\n", + " 33320, 64770, 77115, 81693, 83547, 85401, 86328, 88182,\n", + " 91498, 92458, 93367, 95187, 96142, 98477, 99404, 100302,\n", + " 101232, 105836, 110399, 114995, 120464, 122281, 128358, 135971,\n", + " 138281, 139207, 142429, 146360, 149070, 150901, 154606, 157397,\n", + " 172923, 176616, 177476, 179162, 182405, 183822, 189324, 194727,\n", + " 197623],\n", + " dtype='int64'), Int64Index([ 1093, 5688, 7617, 11330, 16789, 18630, 26832, 27748,\n", + " 33321, 64771, 77116, 81694, 83548, 85402, 86329, 88183,\n", + " 91499, 92459, 93368, 95188, 96143, 98478, 99405, 100303,\n", + " 101233, 105837, 110400, 114996, 120465, 122282, 128359, 135972,\n", + " 138282, 139208, 142430, 146361, 149071, 150902, 154607, 157398,\n", + " 172924, 176617, 177477, 179163, 182406, 183823, 189325, 194728,\n", + " 197624],\n", + " dtype='int64'), Int64Index([ 1094, 5689, 7618, 11331, 16790, 18631, 26833, 27749,\n", + " 33322, 64772, 77117, 81695, 83549, 85403, 86330, 88184,\n", + " 91500, 92460, 93369, 95189, 96144, 98479, 99406, 100304,\n", + " 101234, 105838, 110401, 114997, 120466, 122283, 128360, 135973,\n", + " 138283, 139209, 142431, 146362, 149072, 150903, 154608, 157399,\n", + " 172925, 176618, 177478, 179164, 182407, 183824, 189326, 194729,\n", + " 197625],\n", + " dtype='int64'), Int64Index([ 1095, 5690, 7619, 11332, 16791, 18632, 26834, 27750,\n", + " 33323, 64773, 77118, 81696, 83550, 85404, 86331, 88185,\n", + " 91501, 92461, 93370, 95190, 96145, 98480, 99407, 100305,\n", + " 101235, 105839, 110402, 114998, 120467, 122284, 128361, 135974,\n", + " 138284, 139210, 142432, 146363, 149073, 150904, 154609, 157400,\n", + " 172926, 176619, 177479, 179165, 182408, 183825, 189327, 194730,\n", + " 197626],\n", + " dtype='int64'), Int64Index([ 1096, 5691, 7620, 11333, 16792, 18633, 26835, 27751,\n", + " 33324, 64774, 77119, 81697, 83551, 85405, 86332, 88186,\n", + " 91502, 92462, 93371, 95191, 96146, 98481, 99408, 100306,\n", + " 101236, 105840, 110403, 114999, 120468, 122285, 128362, 135975,\n", + " 138285, 139211, 142433, 146364, 149074, 150905, 154610, 157401,\n", + " 172927, 176620, 177480, 179166, 182409, 183826, 189328, 194731,\n", + " 197627],\n", + " dtype='int64'), Int64Index([ 1097, 5692, 7621, 11334, 16793, 18634, 26836, 27752,\n", + " 33325, 64775, 77120, 81698, 83552, 85406, 86333, 88187,\n", + " 91503, 92463, 93372, 95192, 96147, 98482, 99409, 100307,\n", + " 101237, 105841, 110404, 115000, 120469, 122286, 128363, 135976,\n", + " 138286, 139212, 142434, 146365, 149075, 150906, 154611, 157402,\n", + " 172928, 176621, 177481, 179167, 182410, 183827, 189329, 194732,\n", + " 197628],\n", + " dtype='int64'), Int64Index([ 1098, 5693, 7622, 11335, 16794, 18635, 26837, 27753,\n", + " 33326, 64776, 77121, 81699, 83553, 85407, 86334, 88188,\n", + " 91504, 92464, 93373, 95193, 96148, 98483, 99410, 100308,\n", + " 101238, 105842, 110405, 115001, 120470, 122287, 128364, 135977,\n", + " 138287, 139213, 142435, 146366, 149076, 150907, 154612, 157403,\n", + " 172929, 176622, 177482, 179168, 182411, 183828, 189330, 194733,\n", + " 197629],\n", + " dtype='int64'), Int64Index([ 1099, 5694, 7623, 11336, 16795, 18636, 26838, 27754,\n", + " 33327, 64777, 77122, 81700, 83554, 85408, 86335, 88189,\n", + " 91505, 92465, 93374, 95194, 96149, 98484, 99411, 100309,\n", + " 101239, 105843, 110406, 115002, 120471, 122288, 128365, 135978,\n", + " 138288, 139214, 142436, 146367, 149077, 150908, 154613, 157404,\n", + " 172930, 176623, 177483, 179169, 182412, 183829, 189331, 194734,\n", + " 197630],\n", + " dtype='int64'), Int64Index([ 1100, 5695, 7624, 11337, 16796, 18637, 26839, 27755,\n", + " 33328, 64778, 77123, 81701, 83555, 85409, 86336, 88190,\n", + " 91506, 92466, 93375, 95195, 96150, 98485, 99412, 100310,\n", + " 101240, 105844, 110407, 115003, 120472, 122289, 128366, 135979,\n", + " 138289, 139215, 142437, 146368, 149078, 150909, 154614, 157405,\n", + " 172931, 176624, 177484, 179170, 182413, 183830, 189332, 194735,\n", + " 197631],\n", + " dtype='int64'), Int64Index([ 1101, 5696, 7625, 11338, 16797, 18638, 26840, 27756,\n", + " 33329, 64779, 77124, 81702, 83556, 85410, 86337, 88191,\n", + " 91507, 92467, 93376, 95196, 96151, 98486, 99413, 100311,\n", + " 101241, 105845, 110408, 115004, 120473, 122290, 128367, 135980,\n", + " 138290, 139216, 142438, 146369, 149079, 150910, 154615, 157406,\n", + " 172932, 176625, 177485, 179171, 182414, 183831, 189333, 194736,\n", + " 197632],\n", + " dtype='int64'), Int64Index([ 1102, 5697, 7626, 11339, 16798, 18639, 26841, 27757,\n", + " 33330, 64780, 77125, 81703, 83557, 85411, 86338, 88192,\n", + " 91508, 92468, 93377, 95197, 96152, 98487, 99414, 100312,\n", + " 101242, 105846, 110409, 115005, 120474, 122291, 128368, 135981,\n", + " 138291, 139217, 142439, 146370, 149080, 150911, 154616, 157407,\n", + " 172933, 176626, 177486, 179172, 182415, 183832, 189334, 194737,\n", + " 197633],\n", + " dtype='int64'), Int64Index([ 1103, 5698, 7627, 11340, 16799, 18640, 26842, 27758,\n", + " 33331, 64781, 77126, 81704, 83558, 85412, 86339, 88193,\n", + " 91509, 92469, 93378, 95198, 96153, 98488, 99415, 100313,\n", + " 101243, 105847, 110410, 115006, 120475, 122292, 128369, 135982,\n", + " 138292, 139218, 142440, 146371, 149081, 150912, 154617, 157408,\n", + " 172934, 176627, 177487, 179173, 182416, 183833, 189335, 194738,\n", + " 197634],\n", + " dtype='int64'), Int64Index([ 1104, 5699, 7628, 11341, 16800, 18641, 26843, 27759,\n", + " 33332, 64782, 77127, 81705, 83559, 85413, 86340, 88194,\n", + " 91510, 92470, 93379, 95199, 96154, 98489, 99416, 100314,\n", + " 101244, 105848, 110411, 115007, 120476, 122293, 128370, 135983,\n", + " 138293, 139219, 142441, 146372, 149082, 150913, 154618, 157409,\n", + " 172935, 176628, 177488, 179174, 182417, 183834, 189336, 194739,\n", + " 197635],\n", + " dtype='int64'), Int64Index([ 1105, 5700, 7629, 11342, 16801, 18642, 26844, 27760,\n", + " 33333, 64783, 77128, 81706, 83560, 85414, 86341, 88195,\n", + " 91511, 92471, 93380, 95200, 96155, 98490, 99417, 100315,\n", + " 101245, 105849, 110412, 115008, 120477, 122294, 128371, 135984,\n", + " 138294, 139220, 142442, 146373, 149083, 150914, 154619, 157410,\n", + " 172936, 176629, 177489, 179175, 182418, 183835, 189337, 194740,\n", + " 197636],\n", + " dtype='int64'), Int64Index([ 1106, 5701, 7630, 11343, 16802, 18643, 26845, 27761,\n", + " 33334, 64784, 77129, 81707, 83561, 85415, 86342, 88196,\n", + " 91512, 92472, 93381, 95201, 96156, 98491, 99418, 100316,\n", + " 101246, 105850, 110413, 115009, 120478, 122295, 128372, 135985,\n", + " 138295, 139221, 142443, 146374, 149084, 150915, 154620, 157411,\n", + " 172937, 176630, 177490, 179176, 182419, 183836, 189338, 194741,\n", + " 197637],\n", + " dtype='int64'), Int64Index([ 1107, 5702, 7631, 11344, 16803, 18644, 26846, 27762,\n", + " 33335, 64785, 77130, 81708, 83562, 85416, 86343, 88197,\n", + " 91513, 92473, 93382, 95202, 96157, 98492, 99419, 100317,\n", + " 101247, 105851, 110414, 115010, 120479, 122296, 128373, 135986,\n", + " 138296, 139222, 142444, 146375, 149085, 150916, 154621, 157412,\n", + " 172938, 176631, 177491, 179177, 182420, 183837, 189339, 194742,\n", + " 197638],\n", + " dtype='int64'), Int64Index([ 1108, 5703, 7632, 11345, 16804, 18645, 26847, 27763,\n", + " 33336, 64786, 77131, 81709, 83563, 85417, 86344, 88198,\n", + " 91514, 92474, 93383, 95203, 96158, 98493, 99420, 100318,\n", + " 101248, 105852, 110415, 115011, 120480, 122297, 128374, 135987,\n", + " 138297, 139223, 142445, 146376, 149086, 150917, 154622, 157413,\n", + " 172939, 176632, 177492, 179178, 182421, 183838, 189340, 194743,\n", + " 197639],\n", + " dtype='int64'), Int64Index([ 1109, 5704, 7633, 11346, 16805, 18646, 26848, 27764,\n", + " 33337, 64787, 77132, 81710, 83564, 85418, 86345, 88199,\n", + " 91515, 92475, 93384, 95204, 96159, 98494, 99421, 100319,\n", + " 101249, 105853, 110416, 115012, 120481, 122298, 128375, 135988,\n", + " 138298, 139224, 142446, 146377, 149087, 150918, 154623, 157414,\n", + " 172940, 176633, 177493, 179179, 182422, 183839, 189341, 194744,\n", + " 197640],\n", + " dtype='int64'), Int64Index([ 1110, 5705, 7634, 11347, 16806, 18647, 26849, 27765,\n", + " 33338, 64788, 77133, 81711, 83565, 85419, 86346, 88200,\n", + " 91516, 92476, 93385, 95205, 96160, 98495, 99422, 100320,\n", + " 101250, 105854, 110417, 115013, 120482, 122299, 128376, 135989,\n", + " 138299, 139225, 142447, 146378, 149088, 150919, 154624, 157415,\n", + " 172941, 176634, 177494, 179180, 182423, 183840, 189342, 194745,\n", + " 197641],\n", + " dtype='int64'), Int64Index([ 1111, 5706, 7635, 11348, 16807, 18648, 26850, 27766,\n", + " 33339, 64789, 77134, 81712, 83566, 85420, 86347, 88201,\n", + " 91517, 92477, 93386, 95206, 96161, 98496, 99423, 100321,\n", + " 101251, 105855, 110418, 115014, 120483, 122300, 128377, 135990,\n", + " 138300, 139226, 142448, 146379, 149089, 150920, 154625, 157416,\n", + " 172942, 176635, 177495, 179181, 182424, 183841, 189343, 194746,\n", + " 197642],\n", + " dtype='int64'), Int64Index([ 1112, 5707, 7636, 11349, 16808, 18649, 26851, 27767,\n", + " 33340, 64790, 77135, 81713, 83567, 85421, 86348, 88202,\n", + " 91518, 92478, 93387, 95207, 96162, 98497, 99424, 100322,\n", + " 101252, 105856, 110419, 115015, 120484, 122301, 128378, 135991,\n", + " 138301, 139227, 142449, 146380, 149090, 150921, 154626, 157417,\n", + " 172943, 176636, 177496, 179182, 182425, 183842, 189344, 194747,\n", + " 197643],\n", + " dtype='int64'), Int64Index([ 1113, 5708, 7637, 11350, 16809, 18650, 26852, 27768,\n", + " 33341, 64791, 77136, 81714, 83568, 85422, 86349, 88203,\n", + " 91519, 92479, 93388, 95208, 96163, 98498, 99425, 100323,\n", + " 101253, 105857, 110420, 115016, 120485, 122302, 128379, 135992,\n", + " 138302, 139228, 142450, 146381, 149091, 150922, 154627, 157418,\n", + " 172944, 176637, 177497, 179183, 182426, 183843, 189345, 194748,\n", + " 197644],\n", + " dtype='int64'), Int64Index([ 1114, 5709, 7638, 11351, 16810, 18651, 26853, 27769,\n", + " 33342, 64792, 77137, 81715, 83569, 85423, 86350, 88204,\n", + " 91520, 92480, 93389, 95209, 96164, 98499, 99426, 100324,\n", + " 101254, 105858, 110421, 115017, 120486, 122303, 128380, 135993,\n", + " 138303, 139229, 142451, 146382, 149092, 150923, 154628, 157419,\n", + " 172945, 176638, 177498, 179184, 182427, 183844, 189346, 194749,\n", + " 197645],\n", + " dtype='int64'), Int64Index([ 1115, 5710, 7639, 11352, 16811, 18652, 26854, 27770,\n", + " 33343, 64793, 77138, 81716, 83570, 85424, 86351, 88205,\n", + " 91521, 92481, 93390, 95210, 96165, 98500, 99427, 100325,\n", + " 101255, 105859, 110422, 115018, 120487, 122304, 128381, 135994,\n", + " 138304, 139230, 142452, 146383, 149093, 150924, 154629, 157420,\n", + " 172946, 176639, 177499, 179185, 182428, 183845, 189347, 194750,\n", + " 197646],\n", + " dtype='int64'), Int64Index([ 1116, 5711, 7640, 11353, 16812, 18653, 26855, 27771,\n", + " 33344, 64794, 77139, 81717, 83571, 85425, 86352, 88206,\n", + " 91522, 92482, 93391, 95211, 96166, 98501, 99428, 100326,\n", + " 101256, 105860, 110423, 115019, 120488, 122305, 128382, 135995,\n", + " 138305, 139231, 142453, 146384, 149094, 150925, 154630, 157421,\n", + " 172947, 176640, 177500, 179186, 182429, 183846, 189348, 194751,\n", + " 197647],\n", + " dtype='int64'), Int64Index([ 1117, 5712, 7641, 11354, 16813, 18654, 26856, 27772,\n", + " 33345, 64795, 77140, 81718, 83572, 85426, 86353, 88207,\n", + " 91523, 92483, 93392, 95212, 96167, 98502, 99429, 100327,\n", + " 101257, 105861, 110424, 115020, 120489, 122306, 128383, 135996,\n", + " 138306, 139232, 142454, 146385, 149095, 150926, 154631, 157422,\n", + " 172948, 176641, 177501, 179187, 182430, 183847, 189349, 194752,\n", + " 197648],\n", + " dtype='int64'), Int64Index([ 1118, 5713, 7642, 11355, 16814, 18655, 26857, 27773,\n", + " 33346, 64796, 77141, 81719, 83573, 85427, 86354, 88208,\n", + " 91524, 92484, 93393, 95213, 96168, 98503, 99430, 100328,\n", + " 101258, 105862, 110425, 115021, 120490, 122307, 128384, 135997,\n", + " 138307, 139233, 142455, 146386, 149096, 150927, 154632, 157423,\n", + " 172949, 176642, 177502, 179188, 182431, 183848, 189350, 194753,\n", + " 197649],\n", + " dtype='int64'), Int64Index([ 1119, 5714, 7643, 11356, 16815, 18656, 26858, 27774,\n", + " 33347, 64797, 77142, 81720, 83574, 85428, 86355, 88209,\n", + " 91525, 92485, 93394, 95214, 96169, 98504, 99431, 100329,\n", + " 101259, 105863, 110426, 115022, 120491, 122308, 128385, 135998,\n", + " 138308, 139234, 142456, 146387, 149097, 150928, 154633, 157424,\n", + " 172950, 176643, 177503, 179189, 182432, 183849, 189351, 194754,\n", + " 197650],\n", + " dtype='int64'), Int64Index([ 1120, 5715, 7644, 11357, 16816, 18657, 26859, 27775,\n", + " 33348, 64798, 77143, 81721, 83575, 85429, 86356, 88210,\n", + " 91526, 92486, 93395, 95215, 96170, 98505, 99432, 100330,\n", + " 101260, 105864, 110427, 115023, 120492, 122309, 128386, 135999,\n", + " 138309, 139235, 142457, 146388, 149098, 150929, 154634, 157425,\n", + " 172951, 176644, 177504, 179190, 182433, 183850, 189352, 194755,\n", + " 197651],\n", + " dtype='int64'), Int64Index([ 1121, 5716, 7645, 11358, 16817, 18658, 26860, 27776,\n", + " 33349, 64799, 77144, 81722, 83576, 85430, 86357, 88211,\n", + " 91527, 92487, 93396, 95216, 96171, 98506, 99433, 100331,\n", + " 101261, 105865, 110428, 115024, 120493, 122310, 128387, 136000,\n", + " 138310, 139236, 142458, 146389, 149099, 150930, 154635, 157426,\n", + " 172952, 176645, 177505, 179191, 182434, 183851, 189353, 194756,\n", + " 197652],\n", + " dtype='int64'), Int64Index([ 1122, 5717, 7646, 11359, 16818, 18659, 26861, 27777,\n", + " 33350, 64800, 77145, 81723, 83577, 85431, 86358, 88212,\n", + " 91528, 92488, 93397, 95217, 96172, 98507, 99434, 100332,\n", + " 101262, 105866, 110429, 115025, 120494, 122311, 128388, 136001,\n", + " 138311, 139237, 142459, 146390, 149100, 150931, 154636, 157427,\n", + " 172953, 176646, 177506, 179192, 182435, 183852, 189354, 194757,\n", + " 197653],\n", + " dtype='int64'), Int64Index([ 1123, 5718, 7647, 11360, 16819, 18660, 26862, 27778,\n", + " 33351, 64801, 77146, 81724, 83578, 85432, 86359, 88213,\n", + " 91529, 92489, 93398, 95218, 96173, 98508, 99435, 100333,\n", + " 101263, 105867, 110430, 115026, 120495, 122312, 128389, 136002,\n", + " 138312, 139238, 142460, 146391, 149101, 150932, 154637, 157428,\n", + " 172954, 176647, 177507, 179193, 182436, 183853, 189355, 194758,\n", + " 197654],\n", + " dtype='int64'), Int64Index([ 1124, 5719, 7648, 11361, 16820, 18661, 26863, 27779,\n", + " 33352, 64802, 77147, 81725, 83579, 85433, 86360, 88214,\n", + " 91530, 92490, 93399, 95219, 96174, 98509, 99436, 100334,\n", + " 101264, 105868, 110431, 115027, 120496, 122313, 128390, 136003,\n", + " 138313, 139239, 142461, 146392, 149102, 150933, 154638, 157429,\n", + " 172955, 176648, 177508, 179194, 182437, 183854, 189356, 194759,\n", + " 197655],\n", + " dtype='int64'), Int64Index([ 1125, 5720, 7649, 11362, 16821, 18662, 26864, 27780,\n", + " 33353, 64803, 77148, 81726, 83580, 85434, 86361, 88215,\n", + " 91531, 92491, 93400, 95220, 96175, 98510, 99437, 100335,\n", + " 101265, 105869, 110432, 115028, 120497, 122314, 128391, 136004,\n", + " 138314, 139240, 142462, 146393, 149103, 150934, 154639, 157430,\n", + " 172956, 176649, 177509, 179195, 182438, 183855, 189357, 194760,\n", + " 197656],\n", + " dtype='int64'), Int64Index([ 1126, 5721, 7650, 11363, 16822, 18663, 26865, 27781,\n", + " 33354, 64804, 77149, 81727, 83581, 85435, 86362, 88216,\n", + " 91532, 92492, 93401, 95221, 96176, 98511, 99438, 100336,\n", + " 101266, 105870, 110433, 115029, 120498, 122315, 128392, 136005,\n", + " 138315, 139241, 142463, 146394, 149104, 150935, 154640, 157431,\n", + " 172957, 176650, 177510, 179196, 182439, 183856, 189358, 194761,\n", + " 197657],\n", + " dtype='int64'), Int64Index([ 1127, 5722, 7651, 11364, 16823, 18664, 26866, 27782,\n", + " 33355, 64805, 77150, 81728, 83582, 85436, 86363, 88217,\n", + " 91533, 92493, 93402, 95222, 96177, 98512, 99439, 100337,\n", + " 101267, 105871, 110434, 115030, 120499, 122316, 128393, 136006,\n", + " 138316, 139242, 142464, 146395, 149105, 150936, 154641, 157432,\n", + " 172958, 176651, 177511, 179197, 182440, 183857, 189359, 194762,\n", + " 197658],\n", + " dtype='int64'), Int64Index([ 1128, 5723, 7652, 11365, 16824, 18665, 26867, 27783,\n", + " 33356, 64806, 77151, 81729, 83583, 85437, 86364, 88218,\n", + " 91534, 92494, 93403, 95223, 96178, 98513, 99440, 100338,\n", + " 101268, 105872, 110435, 115031, 120500, 122317, 128394, 136007,\n", + " 138317, 139243, 142465, 146396, 149106, 150937, 154642, 157433,\n", + " 172959, 176652, 177512, 179198, 182441, 183858, 189360, 194763,\n", + " 197659],\n", + " dtype='int64'), Int64Index([ 1129, 5724, 7653, 11366, 16825, 18666, 26868, 27784,\n", + " 33357, 64807, 77152, 81730, 83584, 85438, 86365, 88219,\n", + " 91535, 92495, 93404, 95224, 96179, 98514, 99441, 100339,\n", + " 101269, 105873, 110436, 115032, 120501, 122318, 128395, 136008,\n", + " 138318, 139244, 142466, 146397, 149107, 150938, 154643, 157434,\n", + " 172960, 176653, 177513, 179199, 182442, 183859, 189361, 194764,\n", + " 197660],\n", + " dtype='int64'), Int64Index([ 1130, 5725, 7654, 11367, 16826, 18667, 26869, 27785,\n", + " 33358, 64808, 77153, 81731, 83585, 85439, 86366, 88220,\n", + " 91536, 92496, 93405, 95225, 96180, 98515, 99442, 100340,\n", + " 101270, 105874, 110437, 115033, 120502, 122319, 128396, 136009,\n", + " 138319, 139245, 142467, 146398, 149108, 150939, 154644, 157435,\n", + " 172961, 176654, 177514, 179200, 182443, 183860, 189362, 194765,\n", + " 197661],\n", + " dtype='int64'), Int64Index([ 1131, 5726, 7655, 11368, 16827, 18668, 26870, 27786,\n", + " 33359, 64809, 77154, 81732, 83586, 85440, 86367, 88221,\n", + " 91537, 92497, 93406, 95226, 96181, 98516, 99443, 100341,\n", + " 101271, 105875, 110438, 115034, 120503, 122320, 128397, 136010,\n", + " 138320, 139246, 142468, 146399, 149109, 150940, 154645, 157436,\n", + " 172962, 176655, 177515, 179201, 182444, 183861, 189363, 194766,\n", + " 197662],\n", + " dtype='int64'), Int64Index([ 1132, 5727, 7656, 11369, 16828, 18669, 26871, 27787,\n", + " 33360, 64810, 77155, 81733, 83587, 85441, 86368, 88222,\n", + " 91538, 92498, 93407, 95227, 96182, 98517, 99444, 100342,\n", + " 101272, 105876, 110439, 115035, 120504, 122321, 128398, 136011,\n", + " 138321, 139247, 142469, 146400, 149110, 150941, 154646, 157437,\n", + " 172963, 176656, 177516, 179202, 182445, 183862, 189364, 194767,\n", + " 197663],\n", + " dtype='int64'), Int64Index([ 1133, 5728, 7657, 11370, 16829, 18670, 26872, 27788,\n", + " 33361, 64811, 77156, 81734, 83588, 85442, 86369, 88223,\n", + " 91539, 92499, 93408, 95228, 96183, 98518, 99445, 100343,\n", + " 101273, 105877, 110440, 115036, 120505, 122322, 128399, 136012,\n", + " 138322, 139248, 142470, 146401, 149111, 150942, 154647, 157438,\n", + " 172964, 176657, 177517, 179203, 182446, 183863, 189365, 194768,\n", + " 197664],\n", + " dtype='int64'), Int64Index([ 1134, 5729, 7658, 11371, 16830, 18671, 26873, 27789,\n", + " 33362, 64812, 77157, 81735, 83589, 85443, 86370, 88224,\n", + " 91540, 92500, 93409, 95229, 96184, 98519, 99446, 100344,\n", + " 101274, 105878, 110441, 115037, 120506, 122323, 128400, 136013,\n", + " 138323, 139249, 142471, 146402, 149112, 150943, 154648, 157439,\n", + " 172965, 176658, 177518, 179204, 182447, 183864, 189366, 194769,\n", + " 197665],\n", + " dtype='int64'), Int64Index([ 1135, 5730, 7659, 11372, 16831, 18672, 26874, 27790,\n", + " 33363, 64813, 77158, 81736, 83590, 85444, 86371, 88225,\n", + " 91541, 92501, 93410, 95230, 96185, 98520, 99447, 100345,\n", + " 101275, 105879, 110442, 115038, 120507, 122324, 128401, 136014,\n", + " 138324, 139250, 142472, 146403, 149113, 150944, 154649, 157440,\n", + " 172966, 176659, 177519, 179205, 182448, 183865, 189367, 194770,\n", + " 197666],\n", + " dtype='int64'), Int64Index([ 1136, 5731, 7660, 11373, 16832, 18673, 26875, 27791,\n", + " 33364, 64814, 77159, 81737, 83591, 85445, 86372, 88226,\n", + " 91542, 92502, 93411, 95231, 96186, 98521, 99448, 100346,\n", + " 101276, 105880, 110443, 115039, 120508, 122325, 128402, 136015,\n", + " 138325, 139251, 142473, 146404, 149114, 150945, 154650, 157441,\n", + " 172967, 176660, 177520, 179206, 182449, 183866, 189368, 194771,\n", + " 197667],\n", + " dtype='int64'), Int64Index([ 1137, 5732, 7661, 11374, 16833, 18674, 26876, 27792,\n", + " 33365, 64815, 77160, 81738, 83592, 85446, 86373, 88227,\n", + " 91543, 92503, 93412, 95232, 96187, 98522, 99449, 100347,\n", + " 101277, 105881, 110444, 115040, 120509, 122326, 128403, 136016,\n", + " 138326, 139252, 142474, 146405, 149115, 150946, 154651, 157442,\n", + " 172968, 176661, 177521, 179207, 182450, 183867, 189369, 194772,\n", + " 197668],\n", + " dtype='int64'), Int64Index([ 1138, 5733, 7662, 11375, 16834, 18675, 26877, 27793,\n", + " 33366, 64816, 77161, 81739, 83593, 85447, 86374, 88228,\n", + " 91544, 92504, 93413, 95233, 96188, 98523, 99450, 100348,\n", + " 101278, 105882, 110445, 115041, 120510, 122327, 128404, 136017,\n", + " 138327, 139253, 142475, 146406, 149116, 150947, 154652, 157443,\n", + " 172969, 176662, 177522, 179208, 182451, 183868, 189370, 194773,\n", + " 197669],\n", + " dtype='int64'), Int64Index([ 1139, 5734, 7663, 11376, 16835, 18676, 26878, 27794,\n", + " 33367, 64817, 77162, 81740, 83594, 85448, 86375, 88229,\n", + " 91545, 92505, 93414, 95234, 96189, 98524, 99451, 100349,\n", + " 101279, 105883, 110446, 115042, 120511, 122328, 128405, 136018,\n", + " 138328, 139254, 142476, 146407, 149117, 150948, 154653, 157444,\n", + " 172970, 176663, 177523, 179209, 182452, 183869, 189371, 194774,\n", + " 197670],\n", + " dtype='int64'), Int64Index([ 1140, 5735, 7664, 11377, 16836, 18677, 26879, 27795,\n", + " 33368, 64818, 77163, 81741, 83595, 85449, 86376, 88230,\n", + " 91546, 92506, 93415, 95235, 96190, 98525, 99452, 100350,\n", + " 101280, 105884, 110447, 115043, 120512, 122329, 128406, 136019,\n", + " 138329, 139255, 142477, 146408, 149118, 150949, 154654, 157445,\n", + " 172971, 176664, 177524, 179210, 182453, 183870, 189372, 194775,\n", + " 197671],\n", + " dtype='int64'), Int64Index([ 1141, 5736, 7665, 11378, 16837, 18678, 26880, 27796,\n", + " 33369, 64819, 77164, 81742, 83596, 85450, 86377, 88231,\n", + " 91547, 92507, 93416, 95236, 96191, 98526, 99453, 100351,\n", + " 101281, 105885, 110448, 115044, 120513, 122330, 128407, 136020,\n", + " 138330, 139256, 142478, 146409, 149119, 150950, 154655, 157446,\n", + " 172972, 176665, 177525, 179211, 182454, 183871, 189373, 194776,\n", + " 197672],\n", + " dtype='int64'), Int64Index([ 1142, 5737, 7666, 11379, 16838, 18679, 26881, 27797,\n", + " 33370, 64820, 77165, 81743, 83597, 85451, 86378, 88232,\n", + " 91548, 92508, 93417, 95237, 96192, 98527, 99454, 100352,\n", + " 101282, 105886, 110449, 115045, 120514, 122331, 128408, 136021,\n", + " 138331, 139257, 142479, 146410, 149120, 150951, 154656, 157447,\n", + " 172973, 176666, 177526, 179212, 182455, 183872, 189374, 194777,\n", + " 197673],\n", + " dtype='int64'), Int64Index([ 1143, 5738, 7667, 11380, 16839, 18680, 26882, 27798,\n", + " 33371, 64821, 77166, 81744, 83598, 85452, 86379, 88233,\n", + " 91549, 92509, 93418, 95238, 96193, 98528, 99455, 100353,\n", + " 101283, 105887, 110450, 115046, 120515, 122332, 128409, 136022,\n", + " 138332, 139258, 142480, 146411, 149121, 150952, 154657, 157448,\n", + " 172974, 176667, 177527, 179213, 182456, 183873, 189375, 194778,\n", + " 197674],\n", + " dtype='int64'), Int64Index([ 1144, 5739, 7668, 11381, 16840, 18681, 26883, 27799,\n", + " 33372, 64822, 77167, 81745, 83599, 85453, 86380, 88234,\n", + " 91550, 92510, 93419, 95239, 96194, 98529, 99456, 100354,\n", + " 101284, 105888, 110451, 115047, 120516, 122333, 128410, 136023,\n", + " 138333, 139259, 142481, 146412, 149122, 150953, 154658, 157449,\n", + " 172975, 176668, 177528, 179214, 182457, 183874, 189376, 194779,\n", + " 197675],\n", + " dtype='int64'), Int64Index([ 1145, 5740, 7669, 11382, 16841, 18682, 26884, 27800,\n", + " 33373, 64823, 77168, 81746, 83600, 85454, 86381, 88235,\n", + " 91551, 92511, 93420, 95240, 96195, 98530, 99457, 100355,\n", + " 101285, 105889, 110452, 115048, 120517, 122334, 128411, 136024,\n", + " 138334, 139260, 142482, 146413, 149123, 150954, 154659, 157450,\n", + " 172976, 176669, 177529, 179215, 182458, 183875, 189377, 194780,\n", + " 197676],\n", + " dtype='int64'), Int64Index([ 1146, 5741, 7670, 11383, 16842, 18683, 26885, 27801,\n", + " 33374, 64824, 77169, 81747, 83601, 85455, 86382, 88236,\n", + " 91552, 92512, 93421, 95241, 96196, 98531, 99458, 100356,\n", + " 101286, 105890, 110453, 115049, 120518, 122335, 128412, 136025,\n", + " 138335, 139261, 142483, 146414, 149124, 150955, 154660, 157451,\n", + " 172977, 176670, 177530, 179216, 182459, 183876, 189378, 194781,\n", + " 197677],\n", + " dtype='int64'), Int64Index([ 1147, 5742, 7671, 11384, 16843, 18684, 26886, 27802,\n", + " 33375, 64825, 77170, 81748, 83602, 85456, 86383, 88237,\n", + " 91553, 92513, 93422, 95242, 96197, 98532, 99459, 100357,\n", + " 101287, 105891, 110454, 115050, 120519, 122336, 128413, 136026,\n", + " 138336, 139262, 142484, 146415, 149125, 150956, 154661, 157452,\n", + " 172978, 176671, 177531, 179217, 182460, 183877, 189379, 194782,\n", + " 197678],\n", + " dtype='int64'), Int64Index([ 1148, 5743, 7672, 11385, 16844, 18685, 26887, 27803,\n", + " 33376, 64826, 77171, 81749, 83603, 85457, 86384, 88238,\n", + " 91554, 92514, 93423, 95243, 96198, 98533, 99460, 100358,\n", + " 101288, 105892, 110455, 115051, 120520, 122337, 128414, 136027,\n", + " 138337, 139263, 142485, 146416, 149126, 150957, 154662, 157453,\n", + " 172979, 176672, 177532, 179218, 182461, 183878, 189380, 194783,\n", + " 197679],\n", + " dtype='int64'), Int64Index([ 1149, 5744, 7673, 11386, 16845, 18686, 26888, 27804,\n", + " 33377, 64827, 77172, 81750, 83604, 85458, 86385, 88239,\n", + " 91555, 92515, 93424, 95244, 96199, 98534, 99461, 100359,\n", + " 101289, 105893, 110456, 115052, 120521, 122338, 128415, 136028,\n", + " 138338, 139264, 142486, 146417, 149127, 150958, 154663, 157454,\n", + " 172980, 176673, 177533, 179219, 182462, 183879, 189381, 194784,\n", + " 197680],\n", + " dtype='int64'), Int64Index([ 1150, 5745, 7674, 11387, 16846, 18687, 26889, 27805,\n", + " 33378, 64828, 77173, 81751, 83605, 85459, 86386, 88240,\n", + " 91556, 92516, 93425, 95245, 96200, 98535, 99462, 100360,\n", + " 101290, 105894, 110457, 115053, 120522, 122339, 128416, 136029,\n", + " 138339, 139265, 142487, 146418, 149128, 150959, 154664, 157455,\n", + " 172981, 176674, 177534, 179220, 182463, 183880, 189382, 194785,\n", + " 197681],\n", + " dtype='int64'), Int64Index([ 1151, 5746, 7675, 11388, 16847, 18688, 26890, 27806,\n", + " 33379, 64829, 77174, 81752, 83606, 85460, 86387, 88241,\n", + " 91557, 92517, 93426, 95246, 96201, 98536, 99463, 100361,\n", + " 101291, 105895, 110458, 115054, 120523, 122340, 128417, 136030,\n", + " 138340, 139266, 142488, 146419, 149129, 150960, 154665, 157456,\n", + " 172982, 176675, 177535, 179221, 182464, 183881, 189383, 194786,\n", + " 197682],\n", + " dtype='int64'), Int64Index([ 1152, 5747, 7676, 11389, 16848, 18689, 26891, 27807,\n", + " 33380, 64830, 77175, 81753, 83607, 85461, 86388, 88242,\n", + " 91558, 92518, 93427, 95247, 96202, 98537, 99464, 100362,\n", + " 101292, 105896, 110459, 115055, 120524, 122341, 128418, 136031,\n", + " 138341, 139267, 142489, 146420, 149130, 150961, 154666, 157457,\n", + " 172983, 176676, 177536, 179222, 182465, 183882, 189384, 194787,\n", + " 197683],\n", + " dtype='int64'), Int64Index([ 1153, 5748, 7677, 11390, 16849, 18690, 26892, 27808,\n", + " 33381, 64831, 77176, 81754, 83608, 85462, 86389, 88243,\n", + " 91559, 92519, 93428, 95248, 96203, 98538, 99465, 100363,\n", + " 101293, 105897, 110460, 115056, 120525, 122342, 128419, 136032,\n", + " 138342, 139268, 142490, 146421, 149131, 150962, 154667, 157458,\n", + " 172984, 176677, 177537, 179223, 182466, 183883, 189385, 194788,\n", + " 197684],\n", + " dtype='int64'), Int64Index([ 1154, 5749, 7678, 11391, 16850, 18691, 26893, 27809,\n", + " 33382, 64832, 77177, 81755, 83609, 85463, 86390, 88244,\n", + " 91560, 92520, 93429, 95249, 96204, 98539, 99466, 100364,\n", + " 101294, 105898, 110461, 115057, 120526, 122343, 128420, 136033,\n", + " 138343, 139269, 142491, 146422, 149132, 150963, 154668, 157459,\n", + " 172985, 176678, 177538, 179224, 182467, 183884, 189386, 194789,\n", + " 197685],\n", + " dtype='int64'), Int64Index([ 1155, 5750, 7679, 11392, 16851, 18692, 26894, 27810,\n", + " 33383, 64833, 77178, 81756, 83610, 85464, 86391, 88245,\n", + " 91561, 92521, 93430, 95250, 96205, 98540, 99467, 100365,\n", + " 101295, 105899, 110462, 115058, 120527, 122344, 128421, 136034,\n", + " 138344, 139270, 142492, 146423, 149133, 150964, 154669, 157460,\n", + " 172986, 176679, 177539, 179225, 182468, 183885, 189387, 194790,\n", + " 197686],\n", + " dtype='int64'), Int64Index([ 1156, 5751, 7680, 11393, 16852, 18693, 26895, 27811,\n", + " 33384, 64834, 77179, 81757, 83611, 85465, 86392, 88246,\n", + " 91562, 92522, 93431, 95251, 96206, 98541, 99468, 100366,\n", + " 101296, 105900, 110463, 115059, 120528, 122345, 128422, 136035,\n", + " 138345, 139271, 142493, 146424, 149134, 150965, 154670, 157461,\n", + " 172987, 176680, 177540, 179226, 182469, 183886, 189388, 194791,\n", + " 197687],\n", + " dtype='int64'), Int64Index([ 1157, 5752, 7681, 11394, 16853, 18694, 26896, 27812,\n", + " 33385, 64835, 77180, 81758, 83612, 85466, 86393, 88247,\n", + " 91563, 92523, 93432, 95252, 96207, 98542, 99469, 100367,\n", + " 101297, 105901, 110464, 115060, 120529, 122346, 128423, 136036,\n", + " 138346, 139272, 142494, 146425, 149135, 150966, 154671, 157462,\n", + " 172988, 176681, 177541, 179227, 182470, 183887, 189389, 194792,\n", + " 197688],\n", + " dtype='int64'), Int64Index([ 1158, 5753, 7682, 11395, 16854, 18695, 26897, 27813,\n", + " 33386, 64836, 77181, 81759, 83613, 85467, 86394, 88248,\n", + " 91564, 92524, 93433, 95253, 96208, 98543, 99470, 100368,\n", + " 101298, 105902, 110465, 115061, 120530, 122347, 128424, 136037,\n", + " 138347, 139273, 142495, 146426, 149136, 150967, 154672, 157463,\n", + " 172989, 176682, 177542, 179228, 182471, 183888, 189390, 194793,\n", + " 197689],\n", + " dtype='int64'), Int64Index([ 1159, 5754, 7683, 11396, 16855, 18696, 26898, 27814,\n", + " 33387, 64837, 77182, 81760, 83614, 85468, 86395, 88249,\n", + " 91565, 92525, 93434, 95254, 96209, 98544, 99471, 100369,\n", + " 101299, 105903, 110466, 115062, 120531, 122348, 128425, 136038,\n", + " 138348, 139274, 142496, 146427, 149137, 150968, 154673, 157464,\n", + " 172990, 176683, 177543, 179229, 182472, 183889, 189391, 194794,\n", + " 197690],\n", + " dtype='int64'), Int64Index([ 1160, 5755, 7684, 11397, 16856, 18697, 26899, 27815,\n", + " 33388, 64838, 77183, 81761, 83615, 85469, 86396, 88250,\n", + " 91566, 92526, 93435, 95255, 96210, 98545, 99472, 100370,\n", + " 101300, 105904, 110467, 115063, 120532, 122349, 128426, 136039,\n", + " 138349, 139275, 142497, 146428, 149138, 150969, 154674, 157465,\n", + " 172991, 176684, 177544, 179230, 182473, 183890, 189392, 194795,\n", + " 197691],\n", + " dtype='int64'), Int64Index([ 1161, 5756, 7685, 11398, 16857, 18698, 26900, 27816,\n", + " 33389, 64839, 77184, 81762, 83616, 85470, 86397, 88251,\n", + " 91567, 92527, 93436, 95256, 96211, 98546, 99473, 100371,\n", + " 101301, 105905, 110468, 115064, 120533, 122350, 128427, 136040,\n", + " 138350, 139276, 142498, 146429, 149139, 150970, 154675, 157466,\n", + " 172992, 176685, 177545, 179231, 182474, 183891, 189393, 194796,\n", + " 197692],\n", + " dtype='int64'), Int64Index([ 1162, 5757, 7686, 11399, 16858, 18699, 26901, 27817,\n", + " 33390, 64840, 77185, 81763, 83617, 85471, 86398, 88252,\n", + " 91568, 92528, 93437, 95257, 96212, 98547, 99474, 100372,\n", + " 101302, 105906, 110469, 115065, 120534, 122351, 128428, 136041,\n", + " 138351, 139277, 142499, 146430, 149140, 150971, 154676, 157467,\n", + " 172993, 176686, 177546, 179232, 182475, 183892, 189394, 194797,\n", + " 197693],\n", + " dtype='int64'), Int64Index([ 1163, 5758, 7687, 11400, 16859, 18700, 26902, 27818,\n", + " 33391, 64841, 77186, 81764, 83618, 85472, 86399, 88253,\n", + " 91569, 92529, 93438, 95258, 96213, 98548, 99475, 100373,\n", + " 101303, 105907, 110470, 115066, 120535, 122352, 128429, 136042,\n", + " 138352, 139278, 142500, 146431, 149141, 150972, 154677, 157468,\n", + " 172994, 176687, 177547, 179233, 182476, 183893, 189395, 194798,\n", + " 197694],\n", + " dtype='int64'), Int64Index([ 1164, 5759, 7688, 11401, 16860, 18701, 26903, 27819,\n", + " 33392, 64842, 77187, 81765, 83619, 85473, 86400, 88254,\n", + " 91570, 92530, 93439, 95259, 96214, 98549, 99476, 100374,\n", + " 101304, 105908, 110471, 115067, 120536, 122353, 128430, 136043,\n", + " 138353, 139279, 142501, 146432, 149142, 150973, 154678, 157469,\n", + " 172995, 176688, 177548, 179234, 182477, 183894, 189396, 194799,\n", + " 197695],\n", + " dtype='int64'), Int64Index([ 1165, 5760, 7689, 11402, 16861, 18702, 26904, 27820,\n", + " 33393, 64843, 77188, 81766, 83620, 85474, 86401, 88255,\n", + " 91571, 92531, 93440, 95260, 96215, 98550, 99477, 100375,\n", + " 101305, 105909, 110472, 115068, 120537, 122354, 128431, 136044,\n", + " 138354, 139280, 142502, 146433, 149143, 150974, 154679, 157470,\n", + " 172996, 176689, 177549, 179235, 182478, 183895, 189397, 194800,\n", + " 197696],\n", + " dtype='int64'), Int64Index([ 1166, 5761, 7690, 11403, 16862, 18703, 26905, 27821,\n", + " 33394, 64844, 77189, 81767, 83621, 85475, 86402, 88256,\n", + " 91572, 92532, 93441, 95261, 96216, 98551, 99478, 100376,\n", + " 101306, 105910, 110473, 115069, 120538, 122355, 128432, 136045,\n", + " 138355, 139281, 142503, 146434, 149144, 150975, 154680, 157471,\n", + " 172997, 176690, 177550, 179236, 182479, 183896, 189398, 194801,\n", + " 197697],\n", + " dtype='int64'), Int64Index([ 1167, 5762, 7691, 11404, 16863, 18704, 26906, 27822,\n", + " 33395, 64845, 77190, 81768, 83622, 85476, 86403, 88257,\n", + " 91573, 92533, 93442, 95262, 96217, 98552, 99479, 100377,\n", + " 101307, 105911, 110474, 115070, 120539, 122356, 128433, 136046,\n", + " 138356, 139282, 142504, 146435, 149145, 150976, 154681, 157472,\n", + " 172998, 176691, 177551, 179237, 182480, 183897, 189399, 194802,\n", + " 197698],\n", + " dtype='int64'), Int64Index([ 1168, 5763, 7692, 11405, 16864, 18705, 26907, 27823,\n", + " 33396, 64846, 77191, 81769, 83623, 85477, 86404, 88258,\n", + " 91574, 92534, 93443, 95263, 96218, 98553, 99480, 100378,\n", + " 101308, 105912, 110475, 115071, 120540, 122357, 128434, 136047,\n", + " 138357, 139283, 142505, 146436, 149146, 150977, 154682, 157473,\n", + " 172999, 176692, 177552, 179238, 182481, 183898, 189400, 194803,\n", + " 197699],\n", + " dtype='int64'), Int64Index([ 1169, 5764, 7693, 11406, 16865, 18706, 26908, 27824,\n", + " 33397, 64847, 77192, 81770, 83624, 85478, 86405, 88259,\n", + " 91575, 92535, 93444, 95264, 96219, 98554, 99481, 100379,\n", + " 101309, 105913, 110476, 115072, 120541, 122358, 128435, 136048,\n", + " 138358, 139284, 142506, 146437, 149147, 150978, 154683, 157474,\n", + " 173000, 176693, 177553, 179239, 182482, 183899, 189401, 194804,\n", + " 197700],\n", + " dtype='int64'), Int64Index([ 1170, 5765, 7694, 11407, 16866, 18707, 26909, 27825,\n", + " 33398, 64848, 77193, 81771, 83625, 85479, 86406, 88260,\n", + " 91576, 92536, 93445, 95265, 96220, 98555, 99482, 100380,\n", + " 101310, 105914, 110477, 115073, 120542, 122359, 128436, 136049,\n", + " 138359, 139285, 142507, 146438, 149148, 150979, 154684, 157475,\n", + " 173001, 176694, 177554, 179240, 182483, 183900, 189402, 194805,\n", + " 197701],\n", + " dtype='int64'), Int64Index([ 1171, 5766, 7695, 11408, 16867, 18708, 26910, 27826,\n", + " 33399, 64849, 77194, 81772, 83626, 85480, 86407, 88261,\n", + " 91577, 92537, 93446, 95266, 96221, 98556, 99483, 100381,\n", + " 101311, 105915, 110478, 115074, 120543, 122360, 128437, 136050,\n", + " 138360, 139286, 142508, 146439, 149149, 150980, 154685, 157476,\n", + " 173002, 176695, 177555, 179241, 182484, 183901, 189403, 194806,\n", + " 197702],\n", + " dtype='int64'), Int64Index([ 1172, 5767, 7696, 11409, 16868, 18709, 26911, 27827,\n", + " 33400, 64850, 77195, 81773, 83627, 85481, 86408, 88262,\n", + " 91578, 92538, 93447, 95267, 96222, 98557, 99484, 100382,\n", + " 101312, 105916, 110479, 115075, 120544, 122361, 128438, 136051,\n", + " 138361, 139287, 142509, 146440, 149150, 150981, 154686, 157477,\n", + " 173003, 176696, 177556, 179242, 182485, 183902, 189404, 194807,\n", + " 197703],\n", + " dtype='int64'), Int64Index([ 1173, 5768, 7697, 11410, 16869, 18710, 26912, 27828,\n", + " 33401, 64851, 77196, 81774, 83628, 85482, 86409, 88263,\n", + " 91579, 92539, 93448, 95268, 96223, 98558, 99485, 100383,\n", + " 101313, 105917, 110480, 115076, 120545, 122362, 128439, 136052,\n", + " 138362, 139288, 142510, 146441, 149151, 150982, 154687, 157478,\n", + " 173004, 176697, 177557, 179243, 182486, 183903, 189405, 194808,\n", + " 197704],\n", + " dtype='int64'), Int64Index([ 1174, 5769, 7698, 11411, 16870, 18711, 26913, 27829,\n", + " 33402, 64852, 77197, 81775, 83629, 85483, 86410, 88264,\n", + " 91580, 92540, 93449, 95269, 96224, 98559, 99486, 100384,\n", + " 101314, 105918, 110481, 115077, 120546, 122363, 128440, 136053,\n", + " 138363, 139289, 142511, 146442, 149152, 150983, 154688, 157479,\n", + " 173005, 176698, 177558, 179244, 182487, 183904, 189406, 194809,\n", + " 197705],\n", + " dtype='int64'), Int64Index([ 1175, 5770, 7699, 11412, 16871, 18712, 26914, 27830,\n", + " 33403, 64853, 77198, 81776, 83630, 85484, 86411, 88265,\n", + " 91581, 92541, 93450, 95270, 96225, 98560, 99487, 100385,\n", + " 101315, 105919, 110482, 115078, 120547, 122364, 128441, 136054,\n", + " 138364, 139290, 142512, 146443, 149153, 150984, 154689, 157480,\n", + " 173006, 176699, 177559, 179245, 182488, 183905, 189407, 194810,\n", + " 197706],\n", + " dtype='int64'), Int64Index([ 1176, 5771, 7700, 11413, 16872, 18713, 26915, 27831,\n", + " 33404, 64854, 77199, 81777, 83631, 85485, 86412, 88266,\n", + " 91582, 92542, 93451, 95271, 96226, 98561, 99488, 100386,\n", + " 101316, 105920, 110483, 115079, 120548, 122365, 128442, 136055,\n", + " 138365, 139291, 142513, 146444, 149154, 150985, 154690, 157481,\n", + " 173007, 176700, 177560, 179246, 182489, 183906, 189408, 194811,\n", + " 197707],\n", + " dtype='int64'), Int64Index([ 1177, 5772, 7701, 11414, 16873, 18714, 26916, 27832,\n", + " 33405, 64855, 77200, 81778, 83632, 85486, 86413, 88267,\n", + " 91583, 92543, 93452, 95272, 96227, 98562, 99489, 100387,\n", + " 101317, 105921, 110484, 115080, 120549, 122366, 128443, 136056,\n", + " 138366, 139292, 142514, 146445, 149155, 150986, 154691, 157482,\n", + " 173008, 176701, 177561, 179247, 182490, 183907, 189409, 194812,\n", + " 197708],\n", + " dtype='int64'), Int64Index([ 1178, 5773, 7702, 11415, 16874, 18715, 26917, 27833,\n", + " 33406, 64856, 77201, 81779, 83633, 85487, 86414, 88268,\n", + " 91584, 92544, 93453, 95273, 96228, 98563, 99490, 100388,\n", + " 101318, 105922, 110485, 115081, 120550, 122367, 128444, 136057,\n", + " 138367, 139293, 142515, 146446, 149156, 150987, 154692, 157483,\n", + " 173009, 176702, 177562, 179248, 182491, 183908, 189410, 194813,\n", + " 197709],\n", + " dtype='int64'), Int64Index([ 1179, 5774, 7703, 11416, 16875, 18716, 26918, 27834,\n", + " 33407, 64857, 77202, 81780, 83634, 85488, 86415, 88269,\n", + " 91585, 92545, 93454, 95274, 96229, 98564, 99491, 100389,\n", + " 101319, 105923, 110486, 115082, 120551, 122368, 128445, 136058,\n", + " 138368, 139294, 142516, 146447, 149157, 150988, 154693, 157484,\n", + " 173010, 176703, 177563, 179249, 182492, 183909, 189411, 194814,\n", + " 197710],\n", + " dtype='int64'), Int64Index([ 1180, 5775, 7704, 11417, 16876, 18717, 26919, 27835,\n", + " 33408, 64858, 77203, 81781, 83635, 85489, 86416, 88270,\n", + " 91586, 92546, 93455, 95275, 96230, 98565, 99492, 100390,\n", + " 101320, 105924, 110487, 115083, 120552, 122369, 128446, 136059,\n", + " 138369, 139295, 142517, 146448, 149158, 150989, 154694, 157485,\n", + " 173011, 176704, 177564, 179250, 182493, 183910, 189412, 194815,\n", + " 197711],\n", + " dtype='int64'), Int64Index([ 1181, 5776, 7705, 11418, 16877, 18718, 26920, 27836,\n", + " 33409, 64859, 77204, 81782, 83636, 85490, 86417, 88271,\n", + " 91587, 92547, 93456, 95276, 96231, 98566, 99493, 100391,\n", + " 101321, 105925, 110488, 115084, 120553, 122370, 128447, 136060,\n", + " 138370, 139296, 142518, 146449, 149159, 150990, 154695, 157486,\n", + " 173012, 176705, 177565, 179251, 182494, 183911, 189413, 194816,\n", + " 197712],\n", + " dtype='int64'), Int64Index([ 1182, 5777, 7706, 11419, 16878, 18719, 26921, 27837,\n", + " 33410, 64860, 77205, 81783, 83637, 85491, 86418, 88272,\n", + " 91588, 92548, 93457, 95277, 96232, 98567, 99494, 100392,\n", + " 101322, 105926, 110489, 115085, 120554, 122371, 128448, 136061,\n", + " 138371, 139297, 142519, 146450, 149160, 150991, 154696, 157487,\n", + " 173013, 176706, 177566, 179252, 182495, 183912, 189414, 194817,\n", + " 197713],\n", + " dtype='int64'), Int64Index([ 1183, 5778, 7707, 11420, 16879, 18720, 26922, 27838,\n", + " 33411, 64861, 77206, 81784, 83638, 85492, 86419, 88273,\n", + " 91589, 92549, 93458, 95278, 96233, 98568, 99495, 100393,\n", + " 101323, 105927, 110490, 115086, 120555, 122372, 128449, 136062,\n", + " 138372, 139298, 142520, 146451, 149161, 150992, 154697, 157488,\n", + " 173014, 176707, 177567, 179253, 182496, 183913, 189415, 194818,\n", + " 197714],\n", + " dtype='int64'), Int64Index([ 1184, 5779, 7708, 11421, 16880, 18721, 26923, 27839,\n", + " 33412, 64862, 77207, 81785, 83639, 85493, 86420, 88274,\n", + " 91590, 92550, 93459, 95279, 96234, 98569, 99496, 100394,\n", + " 101324, 105928, 110491, 115087, 120556, 122373, 128450, 136063,\n", + " 138373, 139299, 142521, 146452, 149162, 150993, 154698, 157489,\n", + " 173015, 176708, 177568, 179254, 182497, 183914, 189416, 194819,\n", + " 197715],\n", + " dtype='int64'), Int64Index([ 1185, 5780, 7709, 11422, 16881, 18722, 26924, 27840,\n", + " 33413, 64863, 77208, 81786, 83640, 85494, 86421, 88275,\n", + " 91591, 92551, 93460, 95280, 96235, 98570, 99497, 100395,\n", + " 101325, 105929, 110492, 115088, 120557, 122374, 128451, 136064,\n", + " 138374, 139300, 142522, 146453, 149163, 150994, 154699, 157490,\n", + " 173016, 176709, 177569, 179255, 182498, 183915, 189417, 194820,\n", + " 197716],\n", + " dtype='int64'), Int64Index([ 1186, 5781, 7710, 11423, 16882, 18723, 26925, 27841,\n", + " 33414, 64864, 77209, 81787, 83641, 85495, 86422, 88276,\n", + " 91592, 92552, 93461, 95281, 96236, 98571, 99498, 100396,\n", + " 101326, 105930, 110493, 115089, 120558, 122375, 128452, 136065,\n", + " 138375, 139301, 142523, 146454, 149164, 150995, 154700, 157491,\n", + " 173017, 176710, 177570, 179256, 182499, 183916, 189418, 194821,\n", + " 197717],\n", + " dtype='int64'), Int64Index([ 1187, 5782, 7711, 11424, 16883, 18724, 26926, 27842,\n", + " 33415, 64865, 77210, 81788, 83642, 85496, 86423, 88277,\n", + " 91593, 92553, 93462, 95282, 96237, 98572, 99499, 100397,\n", + " 101327, 105931, 110494, 115090, 120559, 122376, 128453, 136066,\n", + " 138376, 139302, 142524, 146455, 149165, 150996, 154701, 157492,\n", + " 173018, 176711, 177571, 179257, 182500, 183917, 189419, 194822,\n", + " 197718],\n", + " dtype='int64'), Int64Index([ 1188, 5783, 7712, 11425, 16884, 18725, 26927, 27843,\n", + " 33416, 64866, 77211, 81789, 83643, 85497, 86424, 88278,\n", + " 91594, 92554, 93463, 95283, 96238, 98573, 99500, 100398,\n", + " 101328, 105932, 110495, 115091, 120560, 122377, 128454, 136067,\n", + " 138377, 139303, 142525, 146456, 149166, 150997, 154702, 157493,\n", + " 173019, 176712, 177572, 179258, 182501, 183918, 189420, 194823,\n", + " 197719],\n", + " dtype='int64'), Int64Index([ 1189, 5784, 7713, 11426, 16885, 18726, 26928, 27844,\n", + " 33417, 64867, 77212, 81790, 83644, 85498, 86425, 88279,\n", + " 91595, 92555, 93464, 95284, 96239, 98574, 99501, 100399,\n", + " 101329, 105933, 110496, 115092, 120561, 122378, 128455, 136068,\n", + " 138378, 139304, 142526, 146457, 149167, 150998, 154703, 157494,\n", + " 173020, 176713, 177573, 179259, 182502, 183919, 189421, 194824,\n", + " 197720],\n", + " dtype='int64'), Int64Index([ 1190, 5785, 7714, 11427, 16886, 18727, 26929, 27845,\n", + " 33418, 64868, 77213, 81791, 83645, 85499, 86426, 88280,\n", + " 91596, 92556, 93465, 95285, 96240, 98575, 99502, 100400,\n", + " 101330, 105934, 110497, 115093, 120562, 122379, 128456, 136069,\n", + " 138379, 139305, 142527, 146458, 149168, 150999, 154704, 157495,\n", + " 173021, 176714, 177574, 179260, 182503, 183920, 189422, 194825,\n", + " 197721],\n", + " dtype='int64'), Int64Index([ 1191, 5786, 7715, 11428, 16887, 18728, 26930, 27846,\n", + " 33419, 64869, 77214, 81792, 83646, 85500, 86427, 88281,\n", + " 91597, 92557, 93466, 95286, 96241, 98576, 99503, 100401,\n", + " 101331, 105935, 110498, 115094, 120563, 122380, 128457, 136070,\n", + " 138380, 139306, 142528, 146459, 149169, 151000, 154705, 157496,\n", + " 173022, 176715, 177575, 179261, 182504, 183921, 189423, 194826,\n", + " 197722],\n", + " dtype='int64'), Int64Index([ 1192, 5787, 7716, 11429, 16888, 18729, 26931, 27847,\n", + " 33420, 64870, 77215, 81793, 83647, 85501, 86428, 88282,\n", + " 91598, 92558, 93467, 95287, 96242, 98577, 99504, 100402,\n", + " 101332, 105936, 110499, 115095, 120564, 122381, 128458, 136071,\n", + " 138381, 139307, 142529, 146460, 149170, 151001, 154706, 157497,\n", + " 173023, 176716, 177576, 179262, 182505, 183922, 189424, 194827,\n", + " 197723],\n", + " dtype='int64'), Int64Index([ 1193, 5788, 7717, 11430, 16889, 18730, 26932, 27848,\n", + " 33421, 64871, 77216, 81794, 83648, 85502, 86429, 88283,\n", + " 91599, 92559, 93468, 95288, 96243, 98578, 99505, 100403,\n", + " 101333, 105937, 110500, 115096, 120565, 122382, 128459, 136072,\n", + " 138382, 139308, 142530, 146461, 149171, 151002, 154707, 157498,\n", + " 173024, 176717, 177577, 179263, 182506, 183923, 189425, 194828,\n", + " 197724],\n", + " dtype='int64'), Int64Index([ 1194, 5789, 7718, 11431, 16890, 18731, 26933, 27849,\n", + " 33422, 64872, 77217, 81795, 83649, 85503, 86430, 88284,\n", + " 91600, 92560, 93469, 95289, 96244, 98579, 99506, 100404,\n", + " 101334, 105938, 110501, 115097, 120566, 122383, 128460, 136073,\n", + " 138383, 139309, 142531, 146462, 149172, 151003, 154708, 157499,\n", + " 173025, 176718, 177578, 179264, 182507, 183924, 189426, 194829,\n", + " 197725],\n", + " dtype='int64'), Int64Index([ 1195, 5790, 7719, 11432, 16891, 18732, 26934, 27850,\n", + " 33423, 64873, 77218, 81796, 83650, 85504, 86431, 88285,\n", + " 91601, 92561, 93470, 95290, 96245, 98580, 99507, 100405,\n", + " 101335, 105939, 110502, 115098, 120567, 122384, 128461, 136074,\n", + " 138384, 139310, 142532, 146463, 149173, 151004, 154709, 157500,\n", + " 173026, 176719, 177579, 179265, 182508, 183925, 189427, 194830,\n", + " 197726],\n", + " dtype='int64'), Int64Index([ 1196, 5791, 7720, 11433, 16892, 18733, 26935, 27851,\n", + " 33424, 64874, 77219, 81797, 83651, 85505, 86432, 88286,\n", + " 91602, 92562, 93471, 95291, 96246, 98581, 99508, 100406,\n", + " 101336, 105940, 110503, 115099, 120568, 122385, 128462, 136075,\n", + " 138385, 139311, 142533, 146464, 149174, 151005, 154710, 157501,\n", + " 173027, 176720, 177580, 179266, 182509, 183926, 189428, 194831,\n", + " 197727],\n", + " dtype='int64'), Int64Index([ 1197, 5792, 7721, 11434, 16893, 18734, 26936, 27852,\n", + " 33425, 64875, 77220, 81798, 83652, 85506, 86433, 88287,\n", + " 91603, 92563, 93472, 95292, 96247, 98582, 99509, 100407,\n", + " 101337, 105941, 110504, 115100, 120569, 122386, 128463, 136076,\n", + " 138386, 139312, 142534, 146465, 149175, 151006, 154711, 157502,\n", + " 173028, 176721, 177581, 179267, 182510, 183927, 189429, 194832,\n", + " 197728],\n", + " dtype='int64'), Int64Index([ 1198, 5793, 7722, 11435, 16894, 18735, 26937, 27853,\n", + " 33426, 64876, 77221, 81799, 83653, 85507, 86434, 88288,\n", + " 91604, 92564, 93473, 95293, 96248, 98583, 99510, 100408,\n", + " 101338, 105942, 110505, 115101, 120570, 122387, 128464, 136077,\n", + " 138387, 139313, 142535, 146466, 149176, 151007, 154712, 157503,\n", + " 173029, 176722, 177582, 179268, 182511, 183928, 189430, 194833,\n", + " 197729],\n", + " dtype='int64'), Int64Index([ 1199, 5794, 7723, 11436, 16895, 18736, 26938, 27854,\n", + " 33427, 64877, 77222, 81800, 83654, 85508, 86435, 88289,\n", + " 91605, 92565, 93474, 95294, 96249, 98584, 99511, 100409,\n", + " 101339, 105943, 110506, 115102, 120571, 122388, 128465, 136078,\n", + " 138388, 139314, 142536, 146467, 149177, 151008, 154713, 157504,\n", + " 173030, 176723, 177583, 179269, 182512, 183929, 189431, 194834,\n", + " 197730],\n", + " dtype='int64'), Int64Index([ 1200, 5795, 7724, 11437, 16896, 18737, 26939, 27855,\n", + " 33428, 64878, 77223, 81801, 83655, 85509, 86436, 88290,\n", + " 91606, 92566, 93475, 95295, 96250, 98585, 99512, 100410,\n", + " 101340, 105944, 110507, 115103, 120572, 122389, 128466, 136079,\n", + " 138389, 139315, 142537, 146468, 149178, 151009, 154714, 157505,\n", + " 173031, 176724, 177584, 179270, 182513, 183930, 189432, 194835,\n", + " 197731],\n", + " dtype='int64'), Int64Index([ 1201, 5796, 7725, 11438, 16897, 18738, 26940, 27856,\n", + " 33429, 64879, 77224, 81802, 83656, 85510, 86437, 88291,\n", + " 91607, 92567, 93476, 95296, 96251, 98586, 99513, 100411,\n", + " 101341, 105945, 110508, 115104, 120573, 122390, 128467, 136080,\n", + " 138390, 139316, 142538, 146469, 149179, 151010, 154715, 157506,\n", + " 173032, 176725, 177585, 179271, 182514, 183931, 189433, 194836,\n", + " 197732],\n", + " dtype='int64'), Int64Index([ 1202, 5797, 7726, 11439, 16898, 18739, 26941, 27857,\n", + " 33430, 64880, 77225, 81803, 83657, 85511, 86438, 88292,\n", + " 91608, 92568, 93477, 95297, 96252, 98587, 99514, 100412,\n", + " 101342, 105946, 110509, 115105, 120574, 122391, 128468, 136081,\n", + " 138391, 139317, 142539, 146470, 149180, 151011, 154716, 157507,\n", + " 173033, 176726, 177586, 179272, 182515, 183932, 189434, 194837,\n", + " 197733],\n", + " dtype='int64'), Int64Index([ 1203, 5798, 7727, 11440, 16899, 18740, 26942, 27858,\n", + " 33431, 64881, 77226, 81804, 83658, 85512, 86439, 88293,\n", + " 91609, 92569, 93478, 95298, 96253, 98588, 99515, 100413,\n", + " 101343, 105947, 110510, 115106, 120575, 122392, 128469, 136082,\n", + " 138392, 139318, 142540, 146471, 149181, 151012, 154717, 157508,\n", + " 173034, 176727, 177587, 179273, 182516, 183933, 189435, 194838,\n", + " 197734],\n", + " dtype='int64'), Int64Index([ 1204, 5799, 7728, 11441, 16900, 18741, 26943, 27859,\n", + " 33432, 64882, 77227, 81805, 83659, 85513, 86440, 88294,\n", + " 91610, 92570, 93479, 95299, 96254, 98589, 99516, 100414,\n", + " 101344, 105948, 110511, 115107, 120576, 122393, 128470, 136083,\n", + " 138393, 139319, 142541, 146472, 149182, 151013, 154718, 157509,\n", + " 173035, 176728, 177588, 179274, 182517, 183934, 189436, 194839,\n", + " 197735],\n", + " dtype='int64'), Int64Index([ 1205, 5800, 7729, 11442, 16901, 18742, 26944, 27860,\n", + " 33433, 64883, 77228, 81806, 83660, 85514, 86441, 88295,\n", + " 91611, 92571, 93480, 95300, 96255, 98590, 99517, 100415,\n", + " 101345, 105949, 110512, 115108, 120577, 122394, 128471, 136084,\n", + " 138394, 139320, 142542, 146473, 149183, 151014, 154719, 157510,\n", + " 173036, 176729, 177589, 179275, 182518, 183935, 189437, 194840,\n", + " 197736],\n", + " dtype='int64'), Int64Index([ 1206, 5801, 7730, 11443, 16902, 18743, 26945, 27861,\n", + " 33434, 64884, 77229, 81807, 83661, 85515, 86442, 88296,\n", + " 91612, 92572, 93481, 95301, 96256, 98591, 99518, 100416,\n", + " 101346, 105950, 110513, 115109, 120578, 122395, 128472, 136085,\n", + " 138395, 139321, 142543, 146474, 149184, 151015, 154720, 157511,\n", + " 173037, 176730, 177590, 179276, 182519, 183936, 189438, 194841,\n", + " 197737],\n", + " dtype='int64'), Int64Index([ 1207, 5802, 7731, 11444, 16903, 18744, 26946, 27862,\n", + " 33435, 64885, 77230, 81808, 83662, 85516, 86443, 88297,\n", + " 91613, 92573, 93482, 95302, 96257, 98592, 99519, 100417,\n", + " 101347, 105951, 110514, 115110, 120579, 122396, 128473, 136086,\n", + " 138396, 139322, 142544, 146475, 149185, 151016, 154721, 157512,\n", + " 173038, 176731, 177591, 179277, 182520, 183937, 189439, 194842,\n", + " 197738],\n", + " dtype='int64'), Int64Index([ 1208, 5803, 7732, 11445, 16904, 18745, 26947, 27863,\n", + " 33436, 64886, 77231, 81809, 83663, 85517, 86444, 88298,\n", + " 91614, 92574, 93483, 95303, 96258, 98593, 99520, 100418,\n", + " 101348, 105952, 110515, 115111, 120580, 122397, 128474, 136087,\n", + " 138397, 139323, 142545, 146476, 149186, 151017, 154722, 157513,\n", + " 173039, 176732, 177592, 179278, 182521, 183938, 189440, 194843,\n", + " 197739],\n", + " dtype='int64'), Int64Index([ 1209, 5804, 7733, 11446, 16905, 18746, 26948, 27864,\n", + " 33437, 64887, 77232, 81810, 83664, 85518, 86445, 88299,\n", + " 91615, 92575, 93484, 95304, 96259, 98594, 99521, 100419,\n", + " 101349, 105953, 110516, 115112, 120581, 122398, 128475, 136088,\n", + " 138398, 139324, 142546, 146477, 149187, 151018, 154723, 157514,\n", + " 173040, 176733, 177593, 179279, 182522, 183939, 189441, 194844,\n", + " 197740],\n", + " dtype='int64'), Int64Index([ 1210, 5805, 7734, 11447, 16906, 18747, 26949, 27865,\n", + " 33438, 64888, 77233, 81811, 83665, 85519, 86446, 88300,\n", + " 91616, 92576, 93485, 95305, 96260, 98595, 99522, 100420,\n", + " 101350, 105954, 110517, 115113, 120582, 122399, 128476, 136089,\n", + " 138399, 139325, 142547, 146478, 149188, 151019, 154724, 157515,\n", + " 173041, 176734, 177594, 179280, 182523, 183940, 189442, 194845,\n", + " 197741],\n", + " dtype='int64'), Int64Index([ 1211, 5806, 7735, 11448, 16907, 18748, 26950, 27866,\n", + " 33439, 64889, 77234, 81812, 83666, 85520, 86447, 88301,\n", + " 91617, 92577, 93486, 95306, 96261, 98596, 99523, 100421,\n", + " 101351, 105955, 110518, 115114, 120583, 122400, 128477, 136090,\n", + " 138400, 139326, 142548, 146479, 149189, 151020, 154725, 157516,\n", + " 173042, 176735, 177595, 179281, 182524, 183941, 189443, 194846,\n", + " 197742],\n", + " dtype='int64'), Int64Index([ 1212, 5807, 7736, 11449, 16908, 18749, 26951, 27867,\n", + " 33440, 64890, 77235, 81813, 83667, 85521, 86448, 88302,\n", + " 91618, 92578, 93487, 95307, 96262, 98597, 99524, 100422,\n", + " 101352, 105956, 110519, 115115, 120584, 122401, 128478, 136091,\n", + " 138401, 139327, 142549, 146480, 149190, 151021, 154726, 157517,\n", + " 173043, 176736, 177596, 179282, 182525, 183942, 189444, 194847,\n", + " 197743],\n", + " dtype='int64'), Int64Index([ 1213, 5808, 7737, 11450, 16909, 18750, 26952, 27868,\n", + " 33441, 64891, 77236, 81814, 83668, 85522, 86449, 88303,\n", + " 91619, 92579, 93488, 95308, 96263, 98598, 99525, 100423,\n", + " 101353, 105957, 110520, 115116, 120585, 122402, 128479, 136092,\n", + " 138402, 139328, 142550, 146481, 149191, 151022, 154727, 157518,\n", + " 173044, 176737, 177597, 179283, 182526, 183943, 189445, 194848,\n", + " 197744],\n", + " dtype='int64'), Int64Index([ 1214, 5809, 7738, 11451, 16910, 18751, 26953, 27869,\n", + " 33442, 64892, 77237, 81815, 83669, 85523, 86450, 88304,\n", + " 91620, 92580, 93489, 95309, 96264, 98599, 99526, 100424,\n", + " 101354, 105958, 110521, 115117, 120586, 122403, 128480, 136093,\n", + " 138403, 139329, 142551, 146482, 149192, 151023, 154728, 157519,\n", + " 173045, 176738, 177598, 179284, 182527, 183944, 189446, 194849,\n", + " 197745],\n", + " dtype='int64'), Int64Index([ 1215, 5810, 7739, 11452, 16911, 18752, 26954, 27870,\n", + " 33443, 64893, 77238, 81816, 83670, 85524, 86451, 88305,\n", + " 91621, 92581, 93490, 95310, 96265, 98600, 99527, 100425,\n", + " 101355, 105959, 110522, 115118, 120587, 122404, 128481, 136094,\n", + " 138404, 139330, 142552, 146483, 149193, 151024, 154729, 157520,\n", + " 173046, 176739, 177599, 179285, 182528, 183945, 189447, 194850,\n", + " 197746],\n", + " dtype='int64'), Int64Index([ 1216, 5811, 7740, 11453, 16912, 18753, 26955, 27871,\n", + " 33444, 64894, 77239, 81817, 83671, 85525, 86452, 88306,\n", + " 91622, 92582, 93491, 95311, 96266, 98601, 99528, 100426,\n", + " 101356, 105960, 110523, 115119, 120588, 122405, 128482, 136095,\n", + " 138405, 139331, 142553, 146484, 149194, 151025, 154730, 157521,\n", + " 173047, 176740, 177600, 179286, 182529, 183946, 189448, 194851,\n", + " 197747],\n", + " dtype='int64'), Int64Index([ 1217, 5812, 7741, 11454, 16913, 18754, 26956, 27872,\n", + " 33445, 64895, 77240, 81818, 83672, 85526, 86453, 88307,\n", + " 91623, 92583, 93492, 95312, 96267, 98602, 99529, 100427,\n", + " 101357, 105961, 110524, 115120, 120589, 122406, 128483, 136096,\n", + " 138406, 139332, 142554, 146485, 149195, 151026, 154731, 157522,\n", + " 173048, 176741, 177601, 179287, 182530, 183947, 189449, 194852,\n", + " 197748],\n", + " dtype='int64'), Int64Index([ 1218, 5813, 7742, 11455, 16914, 18755, 26957, 27873,\n", + " 33446, 64896, 77241, 81819, 83673, 85527, 86454, 88308,\n", + " 91624, 92584, 93493, 95313, 96268, 98603, 99530, 100428,\n", + " 101358, 105962, 110525, 115121, 120590, 122407, 128484, 136097,\n", + " 138407, 139333, 142555, 146486, 149196, 151027, 154732, 157523,\n", + " 173049, 176742, 177602, 179288, 182531, 183948, 189450, 194853,\n", + " 197749],\n", + " dtype='int64'), Int64Index([ 1219, 5814, 7743, 11456, 16915, 18756, 26958, 27874,\n", + " 33447, 64897, 77242, 81820, 83674, 85528, 86455, 88309,\n", + " 91625, 92585, 93494, 95314, 96269, 98604, 99531, 100429,\n", + " 101359, 105963, 110526, 115122, 120591, 122408, 128485, 136098,\n", + " 138408, 139334, 142556, 146487, 149197, 151028, 154733, 157524,\n", + " 173050, 176743, 177603, 179289, 182532, 183949, 189451, 194854,\n", + " 197750],\n", + " dtype='int64'), Int64Index([ 1220, 5815, 7744, 11457, 16916, 18757, 26959, 27875,\n", + " 33448, 64898, 77243, 81821, 83675, 85529, 86456, 88310,\n", + " 91626, 92586, 93495, 95315, 96270, 98605, 99532, 100430,\n", + " 101360, 105964, 110527, 115123, 120592, 122409, 128486, 136099,\n", + " 138409, 139335, 142557, 146488, 149198, 151029, 154734, 157525,\n", + " 173051, 176744, 177604, 179290, 182533, 183950, 189452, 194855,\n", + " 197751],\n", + " dtype='int64'), Int64Index([ 1221, 5816, 7745, 11458, 16917, 18758, 26960, 27876,\n", + " 33449, 64899, 77244, 81822, 83676, 85530, 86457, 88311,\n", + " 91627, 92587, 93496, 95316, 96271, 98606, 99533, 100431,\n", + " 101361, 105965, 110528, 115124, 120593, 122410, 128487, 136100,\n", + " 138410, 139336, 142558, 146489, 149199, 151030, 154735, 157526,\n", + " 173052, 176745, 177605, 179291, 182534, 183951, 189453, 194856,\n", + " 197752],\n", + " dtype='int64'), Int64Index([ 1222, 5817, 7746, 11459, 16918, 18759, 26961, 27877,\n", + " 33450, 64900, 77245, 81823, 83677, 85531, 86458, 88312,\n", + " 91628, 92588, 93497, 95317, 96272, 98607, 99534, 100432,\n", + " 101362, 105966, 110529, 115125, 120594, 122411, 128488, 136101,\n", + " 138411, 139337, 142559, 146490, 149200, 151031, 154736, 157527,\n", + " 173053, 176746, 177606, 179292, 182535, 183952, 189454, 194857,\n", + " 197753],\n", + " dtype='int64'), Int64Index([ 1223, 5818, 7747, 11460, 16919, 18760, 26962, 27878,\n", + " 33451, 64901, 77246, 81824, 83678, 85532, 86459, 88313,\n", + " 91629, 92589, 93498, 95318, 96273, 98608, 99535, 100433,\n", + " 101363, 105967, 110530, 115126, 120595, 122412, 128489, 136102,\n", + " 138412, 139338, 142560, 146491, 149201, 151032, 154737, 157528,\n", + " 173054, 176747, 177607, 179293, 182536, 183953, 189455, 194858,\n", + " 197754],\n", + " dtype='int64'), Int64Index([ 1224, 5819, 7748, 11461, 16920, 18761, 26963, 27879,\n", + " 33452, 64902, 77247, 81825, 83679, 85533, 86460, 88314,\n", + " 91630, 92590, 93499, 95319, 96274, 98609, 99536, 100434,\n", + " 101364, 105968, 110531, 115127, 120596, 122413, 128490, 136103,\n", + " 138413, 139339, 142561, 146492, 149202, 151033, 154738, 157529,\n", + " 173055, 176748, 177608, 179294, 182537, 183954, 189456, 194859,\n", + " 197755],\n", + " dtype='int64'), Int64Index([ 1225, 5820, 7749, 11462, 16921, 18762, 26964, 27880,\n", + " 33453, 64903, 77248, 81826, 83680, 85534, 86461, 88315,\n", + " 91631, 92591, 93500, 95320, 96275, 98610, 99537, 100435,\n", + " 101365, 105969, 110532, 115128, 120597, 122414, 128491, 136104,\n", + " 138414, 139340, 142562, 146493, 149203, 151034, 154739, 157530,\n", + " 173056, 176749, 177609, 179295, 182538, 183955, 189457, 194860,\n", + " 197756],\n", + " dtype='int64'), Int64Index([ 1226, 5821, 7750, 11463, 16922, 18763, 26965, 27881,\n", + " 33454, 64904, 77249, 81827, 83681, 85535, 86462, 88316,\n", + " 91632, 92592, 93501, 95321, 96276, 98611, 99538, 100436,\n", + " 101366, 105970, 110533, 115129, 120598, 122415, 128492, 136105,\n", + " 138415, 139341, 142563, 146494, 149204, 151035, 154740, 157531,\n", + " 173057, 176750, 177610, 179296, 182539, 183956, 189458, 194861,\n", + " 197757],\n", + " dtype='int64'), Int64Index([ 1227, 5822, 7751, 11464, 16923, 18764, 26966, 27882,\n", + " 33455, 64905, 77250, 81828, 83682, 85536, 86463, 88317,\n", + " 91633, 92593, 93502, 95322, 96277, 98612, 99539, 100437,\n", + " 101367, 105971, 110534, 115130, 120599, 122416, 128493, 136106,\n", + " 138416, 139342, 142564, 146495, 149205, 151036, 154741, 157532,\n", + " 173058, 176751, 177611, 179297, 182540, 183957, 189459, 194862,\n", + " 197758],\n", + " dtype='int64'), Int64Index([ 1228, 5823, 7752, 11465, 16924, 18765, 26967, 27883,\n", + " 33456, 64906, 77251, 81829, 83683, 85537, 86464, 88318,\n", + " 91634, 92594, 93503, 95323, 96278, 98613, 99540, 100438,\n", + " 101368, 105972, 110535, 115131, 120600, 122417, 128494, 136107,\n", + " 138417, 139343, 142565, 146496, 149206, 151037, 154742, 157533,\n", + " 173059, 176752, 177612, 179298, 182541, 183958, 189460, 194863,\n", + " 197759],\n", + " dtype='int64'), Int64Index([ 1229, 5824, 7753, 11466, 16925, 18766, 26968, 27884,\n", + " 33457, 64907, 77252, 81830, 83684, 85538, 86465, 88319,\n", + " 91635, 92595, 93504, 95324, 96279, 98614, 99541, 100439,\n", + " 101369, 105973, 110536, 115132, 120601, 122418, 128495, 136108,\n", + " 138418, 139344, 142566, 146497, 149207, 151038, 154743, 157534,\n", + " 173060, 176753, 177613, 179299, 182542, 183959, 189461, 194864,\n", + " 197760],\n", + " dtype='int64'), Int64Index([ 1230, 5825, 7754, 11467, 16926, 18767, 26969, 27885,\n", + " 33458, 64908, 77253, 81831, 83685, 85539, 86466, 88320,\n", + " 91636, 92596, 93505, 95325, 96280, 98615, 99542, 100440,\n", + " 101370, 105974, 110537, 115133, 120602, 122419, 128496, 136109,\n", + " 138419, 139345, 142567, 146498, 149208, 151039, 154744, 157535,\n", + " 173061, 176754, 177614, 179300, 182543, 183960, 189462, 194865,\n", + " 197761],\n", + " dtype='int64'), Int64Index([ 1231, 5826, 7755, 11468, 16927, 18768, 26970, 27886,\n", + " 33459, 64909, 77254, 81832, 83686, 85540, 86467, 88321,\n", + " 91637, 92597, 93506, 95326, 96281, 98616, 99543, 100441,\n", + " 101371, 105975, 110538, 115134, 120603, 122420, 128497, 136110,\n", + " 138420, 139346, 142568, 146499, 149209, 151040, 154745, 157536,\n", + " 173062, 176755, 177615, 179301, 182544, 183961, 189463, 194866,\n", + " 197762],\n", + " dtype='int64'), Int64Index([ 1232, 5827, 7756, 11469, 16928, 18769, 26971, 27887,\n", + " 33460, 64910, 77255, 81833, 83687, 85541, 86468, 88322,\n", + " 91638, 92598, 93507, 95327, 96282, 98617, 99544, 100442,\n", + " 101372, 105976, 110539, 115135, 120604, 122421, 128498, 136111,\n", + " 138421, 139347, 142569, 146500, 149210, 151041, 154746, 157537,\n", + " 173063, 176756, 177616, 179302, 182545, 183962, 189464, 194867,\n", + " 197763],\n", + " dtype='int64'), Int64Index([ 1233, 5828, 7757, 11470, 16929, 18770, 26972, 27888,\n", + " 33461, 64911, 77256, 81834, 83688, 85542, 86469, 88323,\n", + " 91639, 92599, 93508, 95328, 96283, 98618, 99545, 100443,\n", + " 101373, 105977, 110540, 115136, 120605, 122422, 128499, 136112,\n", + " 138422, 139348, 142570, 146501, 149211, 151042, 154747, 157538,\n", + " 173064, 176757, 177617, 179303, 182546, 183963, 189465, 194868,\n", + " 197764],\n", + " dtype='int64'), Int64Index([ 1234, 5829, 7758, 11471, 16930, 18771, 26973, 27889,\n", + " 33462, 64912, 77257, 81835, 83689, 85543, 86470, 88324,\n", + " 91640, 92600, 93509, 95329, 96284, 98619, 99546, 100444,\n", + " 101374, 105978, 110541, 115137, 120606, 122423, 128500, 136113,\n", + " 138423, 139349, 142571, 146502, 149212, 151043, 154748, 157539,\n", + " 173065, 176758, 177618, 179304, 182547, 183964, 189466, 194869,\n", + " 197765],\n", + " dtype='int64'), Int64Index([ 1235, 5830, 7759, 11472, 16931, 18772, 26974, 27890,\n", + " 33463, 64913, 77258, 81836, 83690, 85544, 86471, 88325,\n", + " 91641, 92601, 93510, 95330, 96285, 98620, 99547, 100445,\n", + " 101375, 105979, 110542, 115138, 120607, 122424, 128501, 136114,\n", + " 138424, 139350, 142572, 146503, 149213, 151044, 154749, 157540,\n", + " 173066, 176759, 177619, 179305, 182548, 183965, 189467, 194870,\n", + " 197766],\n", + " dtype='int64'), Int64Index([ 1236, 5831, 7760, 11473, 16932, 18773, 26975, 27891,\n", + " 33464, 64914, 77259, 81837, 83691, 85545, 86472, 88326,\n", + " 91642, 92602, 93511, 95331, 96286, 98621, 99548, 100446,\n", + " 101376, 105980, 110543, 115139, 120608, 122425, 128502, 136115,\n", + " 138425, 139351, 142573, 146504, 149214, 151045, 154750, 157541,\n", + " 173067, 176760, 177620, 179306, 182549, 183966, 189468, 194871,\n", + " 197767],\n", + " dtype='int64'), Int64Index([ 1237, 5832, 7761, 11474, 16933, 18774, 26976, 27892,\n", + " 33465, 64915, 77260, 81838, 83692, 85546, 86473, 88327,\n", + " 91643, 92603, 93512, 95332, 96287, 98622, 99549, 100447,\n", + " 101377, 105981, 110544, 115140, 120609, 122426, 128503, 136116,\n", + " 138426, 139352, 142574, 146505, 149215, 151046, 154751, 157542,\n", + " 173068, 176761, 177621, 179307, 182550, 183967, 189469, 194872,\n", + " 197768],\n", + " dtype='int64'), Int64Index([ 1238, 5833, 7762, 11475, 16934, 18775, 26977, 27893,\n", + " 33466, 64916, 77261, 81839, 83693, 85547, 86474, 88328,\n", + " 91644, 92604, 93513, 95333, 96288, 98623, 99550, 100448,\n", + " 101378, 105982, 110545, 115141, 120610, 122427, 128504, 136117,\n", + " 138427, 139353, 142575, 146506, 149216, 151047, 154752, 157543,\n", + " 173069, 176762, 177622, 179308, 182551, 183968, 189470, 194873,\n", + " 197769],\n", + " dtype='int64'), Int64Index([ 1239, 5834, 7763, 11476, 16935, 18776, 26978, 27894,\n", + " 33467, 64917, 77262, 81840, 83694, 85548, 86475, 88329,\n", + " 91645, 92605, 93514, 95334, 96289, 98624, 99551, 100449,\n", + " 101379, 105983, 110546, 115142, 120611, 122428, 128505, 136118,\n", + " 138428, 139354, 142576, 146507, 149217, 151048, 154753, 157544,\n", + " 173070, 176763, 177623, 179309, 182552, 183969, 189471, 194874,\n", + " 197770],\n", + " dtype='int64'), Int64Index([ 1240, 5835, 7764, 11477, 16936, 18777, 26979, 27895,\n", + " 33468, 64918, 77263, 81841, 83695, 85549, 86476, 88330,\n", + " 91646, 92606, 93515, 95335, 96290, 98625, 99552, 100450,\n", + " 101380, 105984, 110547, 115143, 120612, 122429, 128506, 136119,\n", + " 138429, 139355, 142577, 146508, 149218, 151049, 154754, 157545,\n", + " 173071, 176764, 177624, 179310, 182553, 183970, 189472, 194875,\n", + " 197771],\n", + " dtype='int64'), Int64Index([ 1241, 5836, 7765, 11478, 16937, 18778, 26980, 27896,\n", + " 33469, 64919, 77264, 81842, 83696, 85550, 86477, 88331,\n", + " 91647, 92607, 93516, 95336, 96291, 98626, 99553, 100451,\n", + " 101381, 105985, 110548, 115144, 120613, 122430, 128507, 136120,\n", + " 138430, 139356, 142578, 146509, 149219, 151050, 154755, 157546,\n", + " 173072, 176765, 177625, 179311, 182554, 183971, 189473, 194876,\n", + " 197772],\n", + " dtype='int64'), Int64Index([ 1242, 5837, 7766, 11479, 16938, 18779, 26981, 27897,\n", + " 33470, 64920, 77265, 81843, 83697, 85551, 86478, 88332,\n", + " 91648, 92608, 93517, 95337, 96292, 98627, 99554, 100452,\n", + " 101382, 105986, 110549, 115145, 120614, 122431, 128508, 136121,\n", + " 138431, 139357, 142579, 146510, 149220, 151051, 154756, 157547,\n", + " 173073, 176766, 177626, 179312, 182555, 183972, 189474, 194877,\n", + " 197773],\n", + " dtype='int64'), Int64Index([ 1243, 5838, 7767, 11480, 16939, 18780, 26982, 27898,\n", + " 33471, 64921, 77266, 81844, 83698, 85552, 86479, 88333,\n", + " 91649, 92609, 93518, 95338, 96293, 98628, 99555, 100453,\n", + " 101383, 105987, 110550, 115146, 120615, 122432, 128509, 136122,\n", + " 138432, 139358, 142580, 146511, 149221, 151052, 154757, 157548,\n", + " 173074, 176767, 177627, 179313, 182556, 183973, 189475, 194878,\n", + " 197774],\n", + " dtype='int64'), Int64Index([ 1244, 5839, 7768, 11481, 16940, 18781, 26983, 27899,\n", + " 33472, 64922, 77267, 81845, 83699, 85553, 86480, 88334,\n", + " 91650, 92610, 93519, 95339, 96294, 98629, 99556, 100454,\n", + " 101384, 105988, 110551, 115147, 120616, 122433, 128510, 136123,\n", + " 138433, 139359, 142581, 146512, 149222, 151053, 154758, 157549,\n", + " 173075, 176768, 177628, 179314, 182557, 183974, 189476, 194879,\n", + " 197775],\n", + " dtype='int64'), Int64Index([ 1245, 5840, 7769, 11482, 16941, 18782, 26984, 27900,\n", + " 33473, 64923, 77268, 81846, 83700, 85554, 86481, 88335,\n", + " 91651, 92611, 93520, 95340, 96295, 98630, 99557, 100455,\n", + " 101385, 105989, 110552, 115148, 120617, 122434, 128511, 136124,\n", + " 138434, 139360, 142582, 146513, 149223, 151054, 154759, 157550,\n", + " 173076, 176769, 177629, 179315, 182558, 183975, 189477, 194880,\n", + " 197776],\n", + " dtype='int64'), Int64Index([ 1246, 5841, 7770, 11483, 16942, 18783, 26985, 27901,\n", + " 33474, 64924, 77269, 81847, 83701, 85555, 86482, 88336,\n", + " 91652, 92612, 93521, 95341, 96296, 98631, 99558, 100456,\n", + " 101386, 105990, 110553, 115149, 120618, 122435, 128512, 136125,\n", + " 138435, 139361, 142583, 146514, 149224, 151055, 154760, 157551,\n", + " 173077, 176770, 177630, 179316, 182559, 183976, 189478, 194881,\n", + " 197777],\n", + " dtype='int64'), Int64Index([ 1247, 5842, 7771, 11484, 16943, 18784, 26986, 27902,\n", + " 33475, 64925, 77270, 81848, 83702, 85556, 86483, 88337,\n", + " 91653, 92613, 93522, 95342, 96297, 98632, 99559, 100457,\n", + " 101387, 105991, 110554, 115150, 120619, 122436, 128513, 136126,\n", + " 138436, 139362, 142584, 146515, 149225, 151056, 154761, 157552,\n", + " 173078, 176771, 177631, 179317, 182560, 183977, 189479, 194882,\n", + " 197778],\n", + " dtype='int64'), Int64Index([ 1248, 5843, 7772, 11485, 16944, 18785, 26987, 27903,\n", + " 33476, 64926, 77271, 81849, 83703, 85557, 86484, 88338,\n", + " 91654, 92614, 93523, 95343, 96298, 98633, 99560, 100458,\n", + " 101388, 105992, 110555, 115151, 120620, 122437, 128514, 136127,\n", + " 138437, 139363, 142585, 146516, 149226, 151057, 154762, 157553,\n", + " 173079, 176772, 177632, 179318, 182561, 183978, 189480, 194883,\n", + " 197779],\n", + " dtype='int64'), Int64Index([ 1249, 5844, 7773, 11486, 16945, 18786, 26988, 27904,\n", + " 33477, 64927, 77272, 81850, 83704, 85558, 86485, 88339,\n", + " 91655, 92615, 93524, 95344, 96299, 98634, 99561, 100459,\n", + " 101389, 105993, 110556, 115152, 120621, 122438, 128515, 136128,\n", + " 138438, 139364, 142586, 146517, 149227, 151058, 154763, 157554,\n", + " 173080, 176773, 177633, 179319, 182562, 183979, 189481, 194884,\n", + " 197780],\n", + " dtype='int64'), Int64Index([ 1250, 5845, 7774, 11487, 16946, 18787, 26989, 27905,\n", + " 33478, 64928, 77273, 81851, 83705, 85559, 86486, 88340,\n", + " 91656, 92616, 93525, 95345, 96300, 98635, 99562, 100460,\n", + " 101390, 105994, 110557, 115153, 120622, 122439, 128516, 136129,\n", + " 138439, 139365, 142587, 146518, 149228, 151059, 154764, 157555,\n", + " 173081, 176774, 177634, 179320, 182563, 183980, 189482, 194885,\n", + " 197781],\n", + " dtype='int64'), Int64Index([ 1251, 5846, 7775, 11488, 16947, 18788, 26990, 27906,\n", + " 33479, 64929, 77274, 81852, 83706, 85560, 86487, 88341,\n", + " 91657, 92617, 93526, 95346, 96301, 98636, 99563, 100461,\n", + " 101391, 105995, 110558, 115154, 120623, 122440, 128517, 136130,\n", + " 138440, 139366, 142588, 146519, 149229, 151060, 154765, 157556,\n", + " 173082, 176775, 177635, 179321, 182564, 183981, 189483, 194886,\n", + " 197782],\n", + " dtype='int64'), Int64Index([ 1252, 5847, 7776, 11489, 16948, 18789, 26991, 27907,\n", + " 33480, 64930, 77275, 81853, 83707, 85561, 86488, 88342,\n", + " 91658, 92618, 93527, 95347, 96302, 98637, 99564, 100462,\n", + " 101392, 105996, 110559, 115155, 120624, 122441, 128518, 136131,\n", + " 138441, 139367, 142589, 146520, 149230, 151061, 154766, 157557,\n", + " 173083, 176776, 177636, 179322, 182565, 183982, 189484, 194887,\n", + " 197783],\n", + " dtype='int64'), Int64Index([ 1253, 5848, 7777, 11490, 16949, 18790, 26992, 27908,\n", + " 33481, 64931, 77276, 81854, 83708, 85562, 86489, 88343,\n", + " 91659, 92619, 93528, 95348, 96303, 98638, 99565, 100463,\n", + " 101393, 105997, 110560, 115156, 120625, 122442, 128519, 136132,\n", + " 138442, 139368, 142590, 146521, 149231, 151062, 154767, 157558,\n", + " 173084, 176777, 177637, 179323, 182566, 183983, 189485, 194888,\n", + " 197784],\n", + " dtype='int64'), Int64Index([ 1254, 5849, 7778, 11491, 16950, 18791, 26993, 27909,\n", + " 33482, 64932, 77277, 81855, 83709, 85563, 86490, 88344,\n", + " 91660, 92620, 93529, 95349, 96304, 98639, 99566, 100464,\n", + " 101394, 105998, 110561, 115157, 120626, 122443, 128520, 136133,\n", + " 138443, 139369, 142591, 146522, 149232, 151063, 154768, 157559,\n", + " 173085, 176778, 177638, 179324, 182567, 183984, 189486, 194889,\n", + " 197785],\n", + " dtype='int64'), Int64Index([ 1255, 5850, 7779, 11492, 16951, 18792, 26994, 27910,\n", + " 33483, 64933, 77278, 81856, 83710, 85564, 86491, 88345,\n", + " 91661, 92621, 93530, 95350, 96305, 98640, 99567, 100465,\n", + " 101395, 105999, 110562, 115158, 120627, 122444, 128521, 136134,\n", + " 138444, 139370, 142592, 146523, 149233, 151064, 154769, 157560,\n", + " 173086, 176779, 177639, 179325, 182568, 183985, 189487, 194890,\n", + " 197786],\n", + " dtype='int64'), Int64Index([ 1256, 5851, 7780, 11493, 16952, 18793, 26995, 27911,\n", + " 33484, 64934, 77279, 81857, 83711, 85565, 86492, 88346,\n", + " 91662, 92622, 93531, 95351, 96306, 98641, 99568, 100466,\n", + " 101396, 106000, 110563, 115159, 120628, 122445, 128522, 136135,\n", + " 138445, 139371, 142593, 146524, 149234, 151065, 154770, 157561,\n", + " 173087, 176780, 177640, 179326, 182569, 183986, 189488, 194891,\n", + " 197787],\n", + " dtype='int64'), Int64Index([ 1257, 5852, 7781, 11494, 16953, 18794, 26996, 27912,\n", + " 33485, 64935, 77280, 81858, 83712, 85566, 86493, 88347,\n", + " 91663, 92623, 93532, 95352, 96307, 98642, 99569, 100467,\n", + " 101397, 106001, 110564, 115160, 120629, 122446, 128523, 136136,\n", + " 138446, 139372, 142594, 146525, 149235, 151066, 154771, 157562,\n", + " 173088, 176781, 177641, 179327, 182570, 183987, 189489, 194892,\n", + " 197788],\n", + " dtype='int64'), Int64Index([ 1258, 5853, 7782, 11495, 16954, 18795, 26997, 27913,\n", + " 33486, 64936, 77281, 81859, 83713, 85567, 86494, 88348,\n", + " 91664, 92624, 93533, 95353, 96308, 98643, 99570, 100468,\n", + " 101398, 106002, 110565, 115161, 120630, 122447, 128524, 136137,\n", + " 138447, 139373, 142595, 146526, 149236, 151067, 154772, 157563,\n", + " 173089, 176782, 177642, 179328, 182571, 183988, 189490, 194893,\n", + " 197789],\n", + " dtype='int64'), Int64Index([ 1259, 5854, 7783, 11496, 16955, 18796, 26998, 27914,\n", + " 33487, 64937, 77282, 81860, 83714, 85568, 86495, 88349,\n", + " 91665, 92625, 93534, 95354, 96309, 98644, 99571, 100469,\n", + " 101399, 106003, 110566, 115162, 120631, 122448, 128525, 136138,\n", + " 138448, 139374, 142596, 146527, 149237, 151068, 154773, 157564,\n", + " 173090, 176783, 177643, 179329, 182572, 183989, 189491, 194894,\n", + " 197790],\n", + " dtype='int64'), Int64Index([ 1260, 5855, 7784, 11497, 16956, 18797, 26999, 27915,\n", + " 33488, 64938, 77283, 81861, 83715, 85569, 86496, 88350,\n", + " 91666, 92626, 93535, 95355, 96310, 98645, 99572, 100470,\n", + " 101400, 106004, 110567, 115163, 120632, 122449, 128526, 136139,\n", + " 138449, 139375, 142597, 146528, 149238, 151069, 154774, 157565,\n", + " 173091, 176784, 177644, 179330, 182573, 183990, 189492, 194895,\n", + " 197791],\n", + " dtype='int64'), Int64Index([ 1261, 5856, 7785, 11498, 16957, 18798, 27000, 27916,\n", + " 33489, 64939, 77284, 81862, 83716, 85570, 86497, 88351,\n", + " 91667, 92627, 93536, 95356, 96311, 98646, 99573, 100471,\n", + " 101401, 106005, 110568, 115164, 120633, 122450, 128527, 136140,\n", + " 138450, 139376, 142598, 146529, 149239, 151070, 154775, 157566,\n", + " 173092, 176785, 177645, 179331, 182574, 183991, 189493, 194896,\n", + " 197792],\n", + " dtype='int64'), Int64Index([ 1262, 5857, 7786, 11499, 16958, 18799, 27001, 27917,\n", + " 33490, 64940, 77285, 81863, 83717, 85571, 86498, 88352,\n", + " 91668, 92628, 93537, 95357, 96312, 98647, 99574, 100472,\n", + " 101402, 106006, 110569, 115165, 120634, 122451, 128528, 136141,\n", + " 138451, 139377, 142599, 146530, 149240, 151071, 154776, 157567,\n", + " 173093, 176786, 177646, 179332, 182575, 183992, 189494, 194897,\n", + " 197793],\n", + " dtype='int64'), Int64Index([ 1263, 5858, 7787, 11500, 16959, 18800, 27002, 27918,\n", + " 33491, 64941, 77286, 81864, 83718, 85572, 86499, 88353,\n", + " 91669, 92629, 93538, 95358, 96313, 98648, 99575, 100473,\n", + " 101403, 106007, 110570, 115166, 120635, 122452, 128529, 136142,\n", + " 138452, 139378, 142600, 146531, 149241, 151072, 154777, 157568,\n", + " 173094, 176787, 177647, 179333, 182576, 183993, 189495, 194898,\n", + " 197794],\n", + " dtype='int64'), Int64Index([ 1264, 5859, 7788, 11501, 16960, 18801, 27003, 27919,\n", + " 33492, 64942, 77287, 81865, 83719, 85573, 86500, 88354,\n", + " 91670, 92630, 93539, 95359, 96314, 98649, 99576, 100474,\n", + " 101404, 106008, 110571, 115167, 120636, 122453, 128530, 136143,\n", + " 138453, 139379, 142601, 146532, 149242, 151073, 154778, 157569,\n", + " 173095, 176788, 177648, 179334, 182577, 183994, 189496, 194899,\n", + " 197795],\n", + " dtype='int64'), Int64Index([ 1265, 5860, 7789, 11502, 16961, 18802, 27004, 27920,\n", + " 33493, 64943, 77288, 81866, 83720, 85574, 86501, 88355,\n", + " 91671, 92631, 93540, 95360, 96315, 98650, 99577, 100475,\n", + " 101405, 106009, 110572, 115168, 120637, 122454, 128531, 136144,\n", + " 138454, 139380, 142602, 146533, 149243, 151074, 154779, 157570,\n", + " 173096, 176789, 177649, 179335, 182578, 183995, 189497, 194900,\n", + " 197796],\n", + " dtype='int64'), Int64Index([ 1266, 5861, 7790, 11503, 16962, 18803, 27005, 27921,\n", + " 33494, 64944, 77289, 81867, 83721, 85575, 86502, 88356,\n", + " 91672, 92632, 93541, 95361, 96316, 98651, 99578, 100476,\n", + " 101406, 106010, 110573, 115169, 120638, 122455, 128532, 136145,\n", + " 138455, 139381, 142603, 146534, 149244, 151075, 154780, 157571,\n", + " 173097, 176790, 177650, 179336, 182579, 183996, 189498, 194901,\n", + " 197797],\n", + " dtype='int64'), Int64Index([ 1267, 5862, 7791, 11504, 16963, 18804, 27006, 27922,\n", + " 33495, 64945, 77290, 81868, 83722, 85576, 86503, 88357,\n", + " 91673, 92633, 93542, 95362, 96317, 98652, 99579, 100477,\n", + " 101407, 106011, 110574, 115170, 120639, 122456, 128533, 136146,\n", + " 138456, 139382, 142604, 146535, 149245, 151076, 154781, 157572,\n", + " 173098, 176791, 177651, 179337, 182580, 183997, 189499, 194902,\n", + " 197798],\n", + " dtype='int64'), Int64Index([ 1268, 5863, 7792, 11505, 16964, 18805, 27007, 27923,\n", + " 33496, 64946, 77291, 81869, 83723, 85577, 86504, 88358,\n", + " 91674, 92634, 93543, 95363, 96318, 98653, 99580, 100478,\n", + " 101408, 106012, 110575, 115171, 120640, 122457, 128534, 136147,\n", + " 138457, 139383, 142605, 146536, 149246, 151077, 154782, 157573,\n", + " 173099, 176792, 177652, 179338, 182581, 183998, 189500, 194903,\n", + " 197799],\n", + " dtype='int64'), Int64Index([ 1269, 5864, 7793, 11506, 16965, 18806, 27008, 27924,\n", + " 33497, 64947, 77292, 81870, 83724, 85578, 86505, 88359,\n", + " 91675, 92635, 93544, 95364, 96319, 98654, 99581, 100479,\n", + " 101409, 106013, 110576, 115172, 120641, 122458, 128535, 136148,\n", + " 138458, 139384, 142606, 146537, 149247, 151078, 154783, 157574,\n", + " 173100, 176793, 177653, 179339, 182582, 183999, 189501, 194904,\n", + " 197800],\n", + " dtype='int64'), Int64Index([ 1270, 5865, 7794, 11507, 16966, 18807, 27009, 27925,\n", + " 33498, 64948, 77293, 81871, 83725, 85579, 86506, 88360,\n", + " 91676, 92636, 93545, 95365, 96320, 98655, 99582, 100480,\n", + " 101410, 106014, 110577, 115173, 120642, 122459, 128536, 136149,\n", + " 138459, 139385, 142607, 146538, 149248, 151079, 154784, 157575,\n", + " 173101, 176794, 177654, 179340, 182583, 184000, 189502, 194905,\n", + " 197801],\n", + " dtype='int64'), Int64Index([ 1271, 5866, 7795, 11508, 16967, 18808, 27010, 27926,\n", + " 33499, 64949, 77294, 81872, 83726, 85580, 86507, 88361,\n", + " 91677, 92637, 93546, 95366, 96321, 98656, 99583, 100481,\n", + " 101411, 106015, 110578, 115174, 120643, 122460, 128537, 136150,\n", + " 138460, 139386, 142608, 146539, 149249, 151080, 154785, 157576,\n", + " 173102, 176795, 177655, 179341, 182584, 184001, 189503, 194906,\n", + " 197802],\n", + " dtype='int64'), Int64Index([ 1272, 5867, 7796, 11509, 16968, 18809, 27011, 27927,\n", + " 33500, 64950, 77295, 81873, 83727, 85581, 86508, 88362,\n", + " 91678, 92638, 93547, 95367, 96322, 98657, 99584, 100482,\n", + " 101412, 106016, 110579, 115175, 120644, 122461, 128538, 136151,\n", + " 138461, 139387, 142609, 146540, 149250, 151081, 154786, 157577,\n", + " 173103, 176796, 177656, 179342, 182585, 184002, 189504, 194907,\n", + " 197803],\n", + " dtype='int64'), Int64Index([ 1273, 5868, 7797, 11510, 16969, 18810, 27012, 27928,\n", + " 33501, 64951, 77296, 81874, 83728, 85582, 86509, 88363,\n", + " 91679, 92639, 93548, 95368, 96323, 98658, 99585, 100483,\n", + " 101413, 106017, 110580, 115176, 120645, 122462, 128539, 136152,\n", + " 138462, 139388, 142610, 146541, 149251, 151082, 154787, 157578,\n", + " 173104, 176797, 177657, 179343, 182586, 184003, 189505, 194908,\n", + " 197804],\n", + " dtype='int64'), Int64Index([ 1274, 5869, 7798, 11511, 16970, 18811, 27013, 27929,\n", + " 33502, 64952, 77297, 81875, 83729, 85583, 86510, 88364,\n", + " 91680, 92640, 93549, 95369, 96324, 98659, 99586, 100484,\n", + " 101414, 106018, 110581, 115177, 120646, 122463, 128540, 136153,\n", + " 138463, 139389, 142611, 146542, 149252, 151083, 154788, 157579,\n", + " 173105, 176798, 177658, 179344, 182587, 184004, 189506, 194909,\n", + " 197805],\n", + " dtype='int64'), Int64Index([ 1275, 5870, 7799, 11512, 16971, 18812, 27014, 27930,\n", + " 33503, 64953, 77298, 81876, 83730, 85584, 86511, 88365,\n", + " 91681, 92641, 93550, 95370, 96325, 98660, 99587, 100485,\n", + " 101415, 106019, 110582, 115178, 120647, 122464, 128541, 136154,\n", + " 138464, 139390, 142612, 146543, 149253, 151084, 154789, 157580,\n", + " 173106, 176799, 177659, 179345, 182588, 184005, 189507, 194910,\n", + " 197806],\n", + " dtype='int64'), Int64Index([ 1276, 5871, 7800, 11513, 16972, 18813, 27015, 27931,\n", + " 33504, 64954, 77299, 81877, 83731, 85585, 86512, 88366,\n", + " 91682, 92642, 93551, 95371, 96326, 98661, 99588, 100486,\n", + " 101416, 106020, 110583, 115179, 120648, 122465, 128542, 136155,\n", + " 138465, 139391, 142613, 146544, 149254, 151085, 154790, 157581,\n", + " 173107, 176800, 177660, 179346, 182589, 184006, 189508, 194911,\n", + " 197807],\n", + " dtype='int64'), Int64Index([ 1277, 5872, 7801, 11514, 16973, 18814, 27016, 27932,\n", + " 33505, 64955, 77300, 81878, 83732, 85586, 86513, 88367,\n", + " 91683, 92643, 93552, 95372, 96327, 98662, 99589, 100487,\n", + " 101417, 106021, 110584, 115180, 120649, 122466, 128543, 136156,\n", + " 138466, 139392, 142614, 146545, 149255, 151086, 154791, 157582,\n", + " 173108, 176801, 177661, 179347, 182590, 184007, 189509, 194912,\n", + " 197808],\n", + " dtype='int64'), Int64Index([ 1278, 5873, 7802, 11515, 16974, 18815, 27017, 27933,\n", + " 33506, 64956, 77301, 81879, 83733, 85587, 86514, 88368,\n", + " 91684, 92644, 93553, 95373, 96328, 98663, 99590, 100488,\n", + " 101418, 106022, 110585, 115181, 120650, 122467, 128544, 136157,\n", + " 138467, 139393, 142615, 146546, 149256, 151087, 154792, 157583,\n", + " 173109, 176802, 177662, 179348, 182591, 184008, 189510, 194913,\n", + " 197809],\n", + " dtype='int64'), Int64Index([ 1279, 5874, 7803, 11516, 16975, 18816, 27018, 27934,\n", + " 33507, 64957, 77302, 81880, 83734, 85588, 86515, 88369,\n", + " 91685, 92645, 93554, 95374, 96329, 98664, 99591, 100489,\n", + " 101419, 106023, 110586, 115182, 120651, 122468, 128545, 136158,\n", + " 138468, 139394, 142616, 146547, 149257, 151088, 154793, 157584,\n", + " 173110, 176803, 177663, 178634, 179349, 182592, 184009, 189511,\n", + " 194914, 197810],\n", + " dtype='int64'), Int64Index([ 1280, 5875, 7804, 11517, 16976, 18817, 27019, 27935,\n", + " 33508, 64958, 77303, 81881, 83735, 85589, 86516, 88370,\n", + " 91686, 92646, 93555, 95375, 96330, 98665, 99592, 100490,\n", + " 101420, 106024, 110587, 115183, 120652, 122469, 128546, 136159,\n", + " 138469, 139395, 142617, 146548, 149258, 151089, 154794, 157585,\n", + " 173111, 176804, 177664, 178635, 179350, 182593, 184010, 189512,\n", + " 194915, 197811],\n", + " dtype='int64'), Int64Index([ 1281, 5876, 7805, 11518, 16977, 18818, 27020, 27936,\n", + " 33509, 64959, 77304, 81882, 83736, 85590, 86517, 88371,\n", + " 91687, 92647, 93556, 95376, 96331, 98666, 99593, 100491,\n", + " 101421, 106025, 110588, 115184, 120653, 122470, 128547, 136160,\n", + " 138470, 139396, 142618, 146549, 149259, 151090, 154795, 157586,\n", + " 173112, 176805, 177665, 178636, 179351, 182594, 184011, 189513,\n", + " 194916, 197812],\n", + " dtype='int64'), Int64Index([ 1282, 5877, 7806, 11519, 16978, 18819, 27021, 27937,\n", + " 33510, 64960, 77305, 81883, 83737, 85591, 86518, 88372,\n", + " 91688, 92648, 93557, 95377, 96332, 98667, 99594, 100492,\n", + " 101422, 106026, 110589, 115185, 120654, 122471, 128548, 136161,\n", + " 138471, 139397, 142619, 146550, 149260, 151091, 154796, 157587,\n", + " 173113, 176806, 177666, 178637, 179352, 182595, 184012, 189514,\n", + " 194917, 197813],\n", + " dtype='int64'), Int64Index([ 1283, 5878, 7807, 11520, 16979, 18820, 27022, 27938,\n", + " 33511, 64961, 77306, 81884, 83738, 85592, 86519, 88373,\n", + " 91689, 92649, 93558, 95378, 96333, 98668, 99595, 100493,\n", + " 101423, 106027, 110590, 115186, 120655, 122472, 128549, 136162,\n", + " 138472, 139398, 142620, 146551, 149261, 151092, 154797, 157588,\n", + " 173114, 176807, 177667, 178638, 179353, 182596, 184013, 189515,\n", + " 194918, 197814],\n", + " dtype='int64'), Int64Index([ 1284, 5879, 7808, 11521, 16980, 18821, 27023, 27939,\n", + " 33512, 64962, 77307, 81885, 83739, 85593, 86520, 88374,\n", + " 91690, 92650, 93559, 95379, 96334, 98669, 99596, 100494,\n", + " 101424, 106028, 110591, 115187, 120656, 122473, 128550, 136163,\n", + " 138473, 139399, 142621, 146552, 149262, 151093, 154798, 157589,\n", + " 173115, 176808, 177668, 178639, 179354, 182597, 184014, 189516,\n", + " 194919, 197815],\n", + " dtype='int64'), Int64Index([ 1285, 5880, 7809, 11522, 16981, 18822, 27024, 27940,\n", + " 33513, 64963, 77308, 81886, 83740, 85594, 86521, 88375,\n", + " 91691, 92651, 93560, 95380, 96335, 98670, 99597, 100495,\n", + " 101425, 106029, 110592, 115188, 120657, 122474, 128551, 136164,\n", + " 138474, 139400, 142622, 146553, 149263, 151094, 154799, 157590,\n", + " 173116, 176809, 177669, 178640, 179355, 182598, 184015, 189517,\n", + " 194920, 197816],\n", + " dtype='int64'), Int64Index([ 1286, 5881, 7810, 11523, 16982, 18823, 27025, 27941,\n", + " 33514, 64964, 77309, 81887, 83741, 85595, 86522, 88376,\n", + " 91692, 92652, 93561, 95381, 96336, 98671, 99598, 100496,\n", + " 101426, 106030, 110593, 115189, 120658, 122475, 128552, 136165,\n", + " 138475, 139401, 142623, 146554, 149264, 151095, 154800, 157591,\n", + " 173117, 176810, 177670, 178641, 179356, 182599, 184016, 189518,\n", + " 194921, 197817],\n", + " dtype='int64'), Int64Index([ 1287, 5882, 7811, 11524, 16983, 18824, 27026, 27942,\n", + " 33515, 64965, 77310, 81888, 83742, 85596, 86523, 88377,\n", + " 91693, 92653, 93562, 95382, 96337, 98672, 99599, 100497,\n", + " 101427, 106031, 110594, 115190, 120659, 122476, 128553, 136166,\n", + " 138476, 139402, 142624, 146555, 149265, 151096, 154801, 157592,\n", + " 173118, 176811, 177671, 178642, 179357, 182600, 184017, 189519,\n", + " 194922, 197818],\n", + " dtype='int64'), Int64Index([ 1288, 5883, 7812, 11525, 16984, 18825, 27027, 27943,\n", + " 33516, 64966, 77311, 81889, 83743, 85597, 86524, 88378,\n", + " 91694, 92654, 93563, 95383, 96338, 98673, 99600, 100498,\n", + " 101428, 106032, 110595, 115191, 120660, 122477, 128554, 136167,\n", + " 138477, 139403, 142625, 146556, 149266, 151097, 154802, 157593,\n", + " 173119, 176812, 177672, 178643, 179358, 182601, 184018, 189520,\n", + " 194923, 197819],\n", + " dtype='int64'), Int64Index([ 1289, 5884, 7813, 11526, 16985, 18826, 27028, 27944,\n", + " 33517, 64967, 77312, 81890, 83744, 85598, 86525, 88379,\n", + " 91695, 92655, 93564, 95384, 96339, 98674, 99601, 100499,\n", + " 101429, 106033, 110596, 115192, 120661, 122478, 128555, 136168,\n", + " 138478, 139404, 142626, 146557, 149267, 151098, 154803, 157594,\n", + " 173120, 176813, 177673, 178644, 179359, 182602, 184019, 189521,\n", + " 194924, 197820],\n", + " dtype='int64'), Int64Index([ 1290, 5885, 7814, 11527, 16986, 18827, 27029, 27945,\n", + " 33518, 64968, 77313, 81891, 83745, 85599, 86526, 88380,\n", + " 91696, 92656, 93565, 95385, 96340, 98675, 99602, 100500,\n", + " 101430, 106034, 110597, 115193, 120662, 122479, 128556, 136169,\n", + " 138479, 139405, 142627, 146558, 149268, 151099, 154804, 157595,\n", + " 173121, 176814, 177674, 178645, 179360, 182603, 184020, 189522,\n", + " 194925, 197821],\n", + " dtype='int64'), Int64Index([ 1291, 5886, 7815, 11528, 16987, 18828, 27030, 27946,\n", + " 33519, 64969, 77314, 81892, 83746, 85600, 86527, 88381,\n", + " 91697, 92657, 93566, 95386, 96341, 98676, 99603, 100501,\n", + " 101431, 106035, 110598, 115194, 120663, 122480, 128557, 136170,\n", + " 138480, 139406, 142628, 146559, 149269, 151100, 154805, 157596,\n", + " 173122, 176815, 177675, 178646, 179361, 182604, 184021, 189523,\n", + " 194926, 197822],\n", + " dtype='int64'), Int64Index([ 1292, 5887, 7816, 11529, 16988, 18829, 27031, 27947,\n", + " 33520, 64970, 77315, 81893, 83747, 85601, 86528, 88382,\n", + " 91698, 92658, 93567, 95387, 96342, 98677, 99604, 100502,\n", + " 101432, 106036, 110599, 115195, 120664, 122481, 128558, 136171,\n", + " 138481, 139407, 142629, 146560, 149270, 151101, 154806, 157597,\n", + " 173123, 176816, 177676, 178647, 179362, 182605, 184022, 189524,\n", + " 194927, 197823],\n", + " dtype='int64'), Int64Index([ 1293, 5888, 7817, 11530, 16989, 18830, 27032, 27948,\n", + " 33521, 64971, 77316, 81894, 83748, 85602, 86529, 88383,\n", + " 91699, 92659, 93568, 95388, 96343, 98678, 99605, 100503,\n", + " 101433, 106037, 110600, 115196, 120665, 122482, 128559, 136172,\n", + " 138482, 139408, 142630, 146561, 149271, 151102, 154807, 157598,\n", + " 173124, 176817, 177677, 178648, 179363, 182606, 184023, 189525,\n", + " 194928, 197824],\n", + " dtype='int64'), Int64Index([ 1294, 5889, 7818, 11531, 16990, 18831, 27033, 27949,\n", + " 33522, 64972, 77317, 81895, 83749, 85603, 86530, 88384,\n", + " 91700, 92660, 93569, 95389, 96344, 98679, 99606, 100504,\n", + " 101434, 106038, 110601, 115197, 120666, 122483, 128560, 136173,\n", + " 138483, 139409, 142631, 146562, 149272, 151103, 154808, 157599,\n", + " 173125, 176818, 177678, 178649, 179364, 182607, 184024, 189526,\n", + " 194929, 197825],\n", + " dtype='int64'), Int64Index([ 1295, 5890, 7819, 11532, 16991, 18832, 27034, 27950,\n", + " 33523, 64973, 77318, 81896, 83750, 85604, 86531, 88385,\n", + " 91701, 92661, 93570, 95390, 96345, 98680, 99607, 100505,\n", + " 101435, 106039, 110602, 115198, 120667, 122484, 128561, 136174,\n", + " 138484, 139410, 142632, 146563, 149273, 151104, 154809, 157600,\n", + " 173126, 176819, 177679, 178650, 179365, 182608, 184025, 189527,\n", + " 194930, 197826],\n", + " dtype='int64'), Int64Index([ 1296, 5891, 7820, 11533, 16992, 18833, 27035, 27951,\n", + " 33524, 64974, 77319, 81897, 83751, 85605, 86532, 88386,\n", + " 91702, 92662, 93571, 95391, 96346, 98681, 99608, 100506,\n", + " 101436, 106040, 110603, 115199, 120668, 122485, 128562, 136175,\n", + " 138485, 139411, 142633, 146564, 149274, 151105, 154810, 157601,\n", + " 173127, 176820, 177680, 178651, 179366, 182609, 184026, 189528,\n", + " 194931, 197827],\n", + " dtype='int64'), Int64Index([ 1297, 5892, 7821, 11534, 16993, 18834, 27036, 27952,\n", + " 33525, 64975, 77320, 81898, 83752, 85606, 86533, 88387,\n", + " 91703, 92663, 93572, 95392, 96347, 98682, 99609, 100507,\n", + " 101437, 106041, 110604, 115200, 120669, 122486, 128563, 136176,\n", + " 138486, 139412, 142634, 146565, 149275, 151106, 154811, 157602,\n", + " 173128, 176821, 177681, 178652, 179367, 182610, 184027, 189529,\n", + " 194932, 197828],\n", + " dtype='int64'), Int64Index([ 1298, 5893, 7822, 11535, 16994, 18835, 27037, 27953,\n", + " 33526, 64976, 77321, 81899, 83753, 85607, 86534, 88388,\n", + " 91704, 92664, 93573, 95393, 96348, 98683, 99610, 100508,\n", + " 101438, 106042, 110605, 115201, 120670, 122487, 128564, 136177,\n", + " 138487, 139413, 142635, 146566, 149276, 151107, 154812, 157603,\n", + " 173129, 176822, 177682, 178653, 179368, 182611, 184028, 189530,\n", + " 194933, 197829],\n", + " dtype='int64'), Int64Index([ 1299, 5894, 7823, 11536, 16995, 18836, 27038, 27954,\n", + " 33527, 64977, 77322, 81900, 83754, 85608, 86535, 88389,\n", + " 91705, 92665, 93574, 95394, 96349, 98684, 99611, 100509,\n", + " 101439, 106043, 110606, 115202, 120671, 122488, 128565, 136178,\n", + " 138488, 139414, 142636, 146567, 149277, 151108, 154813, 157604,\n", + " 173130, 176823, 177683, 178654, 179369, 182612, 184029, 189531,\n", + " 194934, 197830],\n", + " dtype='int64'), Int64Index([ 1300, 5895, 7824, 11537, 16996, 18837, 27039, 27955,\n", + " 33528, 64978, 77323, 81901, 83755, 85609, 86536, 88390,\n", + " 91706, 92666, 93575, 95395, 96350, 98685, 99612, 100510,\n", + " 101440, 106044, 110607, 115203, 120672, 122489, 128566, 136179,\n", + " 138489, 139415, 142637, 146568, 149278, 151109, 154814, 157605,\n", + " 173131, 176824, 177684, 178655, 179370, 182613, 184030, 189532,\n", + " 194935, 197831],\n", + " dtype='int64'), Int64Index([ 1301, 5896, 7825, 11538, 16997, 18838, 27040, 27956,\n", + " 33529, 64979, 77324, 81902, 83756, 85610, 86537, 88391,\n", + " 91707, 92667, 93576, 95396, 96351, 98686, 99613, 100511,\n", + " 101441, 106045, 110608, 115204, 120673, 122490, 128567, 136180,\n", + " 138490, 139416, 142638, 146569, 149279, 151110, 154815, 157606,\n", + " 173132, 176825, 177685, 178656, 179371, 182614, 184031, 189533,\n", + " 194936, 197832],\n", + " dtype='int64'), Int64Index([ 1302, 5897, 7826, 11539, 16998, 18839, 27041, 27957,\n", + " 33530, 64980, 77325, 81903, 83757, 85611, 86538, 88392,\n", + " 91708, 92668, 93577, 95397, 96352, 98687, 99614, 100512,\n", + " 101442, 106046, 110609, 115205, 120674, 122491, 128568, 136181,\n", + " 138491, 139417, 142639, 146570, 149280, 151111, 154816, 157607,\n", + " 173133, 176826, 177686, 178657, 179372, 182615, 184032, 189534,\n", + " 194937, 197833],\n", + " dtype='int64'), Int64Index([ 1303, 5898, 7827, 11540, 16999, 18840, 27042, 27958,\n", + " 33531, 64981, 77326, 81904, 83758, 85612, 86539, 88393,\n", + " 91709, 92669, 93578, 95398, 96353, 98688, 99615, 100513,\n", + " 101443, 106047, 110610, 115206, 120675, 122492, 128569, 136182,\n", + " 138492, 139418, 142640, 146571, 149281, 151112, 154817, 157608,\n", + " 173134, 176827, 177687, 178658, 179373, 182616, 184033, 189535,\n", + " 194938, 197834],\n", + " dtype='int64'), Int64Index([ 1304, 5899, 7828, 11541, 17000, 18841, 27043, 27959,\n", + " 33532, 64982, 77327, 81905, 83759, 85613, 86540, 88394,\n", + " 91710, 92670, 93579, 95399, 96354, 98689, 99616, 100514,\n", + " 101444, 106048, 110611, 115207, 120676, 122493, 128570, 136183,\n", + " 138493, 139419, 142641, 146572, 149282, 151113, 154818, 157609,\n", + " 173135, 176828, 177688, 178659, 179374, 182617, 184034, 189536,\n", + " 194939, 197835],\n", + " dtype='int64'), Int64Index([ 1305, 5900, 7829, 11542, 17001, 18842, 27044, 27960,\n", + " 33533, 64983, 77328, 81906, 83760, 85614, 86541, 88395,\n", + " 91711, 92671, 93580, 95400, 96355, 98690, 99617, 100515,\n", + " 101445, 106049, 110612, 115208, 120677, 122494, 128571, 136184,\n", + " 138494, 139420, 142642, 146573, 149283, 151114, 154819, 157610,\n", + " 173136, 176829, 177689, 178660, 179375, 182618, 184035, 189537,\n", + " 194940, 197836],\n", + " dtype='int64'), Int64Index([ 1306, 5901, 7830, 11543, 17002, 18843, 27045, 27961,\n", + " 33534, 64984, 77329, 81907, 83761, 85615, 86542, 88396,\n", + " 91712, 92672, 93581, 95401, 96356, 98691, 99618, 100516,\n", + " 101446, 106050, 110613, 115209, 120678, 122495, 128572, 136185,\n", + " 138495, 139421, 142643, 146574, 149284, 151115, 154820, 157611,\n", + " 173137, 176830, 177690, 178661, 179376, 182619, 184036, 189538,\n", + " 194941, 197837],\n", + " dtype='int64'), Int64Index([ 1307, 5902, 7831, 11544, 17003, 18844, 27046, 27962,\n", + " 33535, 64985, 77330, 81908, 83762, 85616, 86543, 88397,\n", + " 91713, 92673, 93582, 95402, 96357, 98692, 99619, 100517,\n", + " 101447, 106051, 110614, 115210, 120679, 122496, 128573, 136186,\n", + " 138496, 139422, 142644, 146575, 149285, 151116, 154821, 157612,\n", + " 173138, 176831, 177691, 178662, 179377, 182620, 184037, 189539,\n", + " 194942, 197838],\n", + " dtype='int64'), Int64Index([ 1308, 5903, 7832, 11545, 17004, 18845, 27047, 27963,\n", + " 33536, 64986, 77331, 81909, 83763, 85617, 86544, 88398,\n", + " 91714, 92674, 93583, 95403, 96358, 98693, 99620, 100518,\n", + " 101448, 106052, 110615, 115211, 120680, 122497, 128574, 136187,\n", + " 138497, 139423, 142645, 146576, 149286, 151117, 154822, 157613,\n", + " 173139, 176832, 177692, 178663, 179378, 182621, 184038, 189540,\n", + " 194943, 197839],\n", + " dtype='int64'), Int64Index([ 1309, 5904, 7833, 11546, 17005, 18846, 27048, 27964,\n", + " 33537, 64987, 77332, 81910, 83764, 85618, 86545, 88399,\n", + " 91715, 92675, 93584, 95404, 96359, 98694, 99621, 100519,\n", + " 101449, 106053, 110616, 115212, 120681, 122498, 128575, 136188,\n", + " 138498, 139424, 142646, 146577, 149287, 151118, 154823, 157614,\n", + " 173140, 176833, 177693, 178664, 179379, 182622, 184039, 189541,\n", + " 194944, 197840],\n", + " dtype='int64'), Int64Index([ 1310, 5905, 7834, 11547, 17006, 18847, 27049, 27965,\n", + " 33538, 64988, 77333, 81911, 83765, 85619, 86546, 88400,\n", + " 91716, 92676, 93585, 95405, 96360, 98695, 99622, 100520,\n", + " 101450, 106054, 110617, 115213, 120682, 122499, 128576, 136189,\n", + " 138499, 139425, 142647, 146578, 149288, 151119, 154824, 157615,\n", + " 173141, 176834, 177694, 178665, 179380, 182623, 184040, 189542,\n", + " 194945, 197841],\n", + " dtype='int64'), Int64Index([ 1311, 5906, 7835, 11548, 17007, 18848, 27050, 27966,\n", + " 33539, 64989, 77334, 81912, 83766, 85620, 86547, 88401,\n", + " 91717, 92677, 93586, 95406, 96361, 98696, 99623, 100521,\n", + " 101451, 106055, 110618, 115214, 120683, 122500, 128577, 136190,\n", + " 138500, 139426, 142648, 146579, 149289, 151120, 154825, 157616,\n", + " 173142, 176835, 177695, 178666, 179381, 182624, 184041, 189543,\n", + " 194946, 197842],\n", + " dtype='int64'), Int64Index([ 1312, 5907, 7836, 11549, 17008, 18849, 27051, 27967,\n", + " 33540, 64990, 77335, 81913, 83767, 85621, 86548, 88402,\n", + " 91718, 92678, 93587, 95407, 96362, 98697, 99624, 100522,\n", + " 101452, 106056, 110619, 115215, 120684, 122501, 128578, 136191,\n", + " 138501, 139427, 142649, 146580, 149290, 151121, 154826, 157617,\n", + " 173143, 176836, 177696, 178667, 179382, 182625, 184042, 189544,\n", + " 194947, 197843],\n", + " dtype='int64'), Int64Index([ 1313, 5908, 7837, 11550, 17009, 18850, 27052, 27968,\n", + " 33541, 64991, 77336, 81914, 83768, 85622, 86549, 88403,\n", + " 91719, 92679, 93588, 95408, 96363, 98698, 99625, 100523,\n", + " 101453, 106057, 110620, 115216, 120685, 122502, 128579, 136192,\n", + " 138502, 139428, 142650, 146581, 149291, 151122, 154827, 157618,\n", + " 173144, 176837, 177697, 178668, 179383, 182626, 184043, 189545,\n", + " 194948, 197844],\n", + " dtype='int64'), Int64Index([ 1314, 5909, 7838, 11551, 17010, 18851, 27053, 27969,\n", + " 33542, 64992, 77337, 81915, 83769, 85623, 86550, 88404,\n", + " 91720, 92680, 93589, 95409, 96364, 98699, 99626, 100524,\n", + " 101454, 106058, 110621, 115217, 120686, 122503, 128580, 136193,\n", + " 138503, 139429, 142651, 146582, 149292, 151123, 154828, 157619,\n", + " 173145, 176838, 177698, 178669, 179384, 182627, 184044, 189546,\n", + " 194949, 197845],\n", + " dtype='int64'), Int64Index([ 1315, 5910, 7839, 11552, 17011, 18852, 27054, 27970,\n", + " 33543, 64993, 77338, 81916, 83770, 85624, 86551, 88405,\n", + " 91721, 92681, 93590, 95410, 96365, 98700, 99627, 100525,\n", + " 101455, 106059, 110622, 115218, 120687, 122504, 128581, 136194,\n", + " 138504, 139430, 142652, 146583, 149293, 151124, 154829, 157620,\n", + " 173146, 176839, 177699, 178670, 179385, 182628, 184045, 189547,\n", + " 194950, 197846],\n", + " dtype='int64'), Int64Index([ 1316, 5911, 7840, 11553, 17012, 18853, 27055, 27971,\n", + " 33544, 64994, 77339, 81917, 83771, 85625, 86552, 88406,\n", + " 91722, 92682, 93591, 95411, 96366, 98701, 99628, 100526,\n", + " 101456, 106060, 110623, 115219, 120688, 122505, 128582, 136195,\n", + " 138505, 139431, 142653, 146584, 149294, 151125, 154830, 157621,\n", + " 173147, 176840, 177700, 178671, 179386, 182629, 184046, 189548,\n", + " 194951, 197847],\n", + " dtype='int64'), Int64Index([ 1317, 5912, 7841, 11554, 17013, 18854, 27056, 27972,\n", + " 33545, 64995, 77340, 81918, 83772, 85626, 86553, 88407,\n", + " 91723, 92683, 93592, 95412, 96367, 98702, 99629, 100527,\n", + " 101457, 106061, 110624, 115220, 120689, 122506, 128583, 136196,\n", + " 138506, 139432, 142654, 146585, 149295, 151126, 154831, 157622,\n", + " 173148, 176841, 177701, 178672, 179387, 182630, 184047, 189549,\n", + " 194952, 197848],\n", + " dtype='int64'), Int64Index([ 1318, 5913, 7842, 11555, 17014, 18855, 27057, 27973,\n", + " 33546, 64996, 77341, 81919, 83773, 85627, 86554, 88408,\n", + " 91724, 92684, 93593, 95413, 96368, 98703, 99630, 100528,\n", + " 101458, 106062, 110625, 115221, 120690, 122507, 128584, 136197,\n", + " 138507, 139433, 142655, 146586, 149296, 151127, 154832, 157623,\n", + " 173149, 176842, 177702, 178673, 179388, 182631, 184048, 189550,\n", + " 194953, 197849],\n", + " dtype='int64'), Int64Index([ 1319, 5914, 7843, 11556, 17015, 18856, 27058, 27974,\n", + " 33547, 64997, 77342, 81920, 83774, 85628, 86555, 88409,\n", + " 91725, 92685, 93594, 95414, 96369, 98704, 99631, 100529,\n", + " 101459, 106063, 110626, 115222, 120691, 122508, 128585, 136198,\n", + " 138508, 139434, 142656, 146587, 149297, 151128, 154833, 157624,\n", + " 173150, 176843, 177703, 178674, 179389, 182632, 184049, 189551,\n", + " 194954, 197850],\n", + " dtype='int64'), Int64Index([ 1320, 5915, 7844, 11557, 17016, 18857, 27059, 27975,\n", + " 33548, 64998, 77343, 81921, 83775, 85629, 86556, 88410,\n", + " 91726, 92686, 93595, 95415, 96370, 98705, 99632, 100530,\n", + " 101460, 106064, 110627, 115223, 120692, 122509, 128586, 136199,\n", + " 138509, 139435, 142657, 146588, 149298, 151129, 154834, 157625,\n", + " 173151, 176844, 177704, 178675, 179390, 182633, 184050, 189552,\n", + " 194955, 197851],\n", + " dtype='int64'), Int64Index([ 1321, 5916, 7845, 11558, 17017, 18858, 27060, 27976,\n", + " 33549, 64999, 77344, 81922, 83776, 85630, 86557, 88411,\n", + " 91727, 92687, 93596, 95416, 96371, 98706, 99633, 100531,\n", + " 101461, 106065, 110628, 115224, 120693, 122510, 128587, 136200,\n", + " 138510, 139436, 142658, 146589, 149299, 151130, 154835, 157626,\n", + " 173152, 176845, 177705, 178676, 179391, 182634, 184051, 189553,\n", + " 194956, 197852],\n", + " dtype='int64'), Int64Index([ 1322, 5917, 7846, 11559, 17018, 18859, 27061, 27977,\n", + " 33550, 65000, 77345, 81923, 83777, 85631, 86558, 88412,\n", + " 91728, 92688, 93597, 95417, 96372, 98707, 99634, 100532,\n", + " 101462, 106066, 110629, 115225, 120694, 122511, 128588, 136201,\n", + " 138511, 139437, 142659, 146590, 149300, 151131, 154836, 157627,\n", + " 173153, 176846, 177706, 178677, 179392, 182635, 184052, 189554,\n", + " 194957, 197853],\n", + " dtype='int64'), Int64Index([ 1323, 5918, 7847, 11560, 17019, 18860, 27062, 27978,\n", + " 33551, 65001, 77346, 81924, 83778, 85632, 86559, 88413,\n", + " 91729, 92689, 93598, 95418, 96373, 98708, 99635, 100533,\n", + " 101463, 106067, 110630, 115226, 120695, 122512, 128589, 136202,\n", + " 138512, 139438, 142660, 146591, 149301, 151132, 154837, 157628,\n", + " 173154, 176847, 177707, 178678, 179393, 182636, 184053, 189555,\n", + " 194958, 197854],\n", + " dtype='int64'), Int64Index([ 1324, 5919, 7848, 11561, 17020, 18861, 27063, 27979,\n", + " 33552, 65002, 77347, 81925, 83779, 85633, 86560, 88414,\n", + " 91730, 92690, 93599, 95419, 96374, 98709, 99636, 100534,\n", + " 101464, 106068, 110631, 115227, 120696, 122513, 128590, 136203,\n", + " 138513, 139439, 142661, 146592, 149302, 151133, 154838, 157629,\n", + " 173155, 176848, 177708, 178679, 179394, 182637, 184054, 189556,\n", + " 194959, 197855],\n", + " dtype='int64'), Int64Index([ 1325, 5920, 7849, 11562, 17021, 18862, 27064, 27980,\n", + " 33553, 65003, 77348, 81926, 83780, 85634, 86561, 88415,\n", + " 91731, 92691, 93600, 95420, 96375, 98710, 99637, 100535,\n", + " 101465, 106069, 110632, 115228, 120697, 122514, 128591, 136204,\n", + " 138514, 139440, 142662, 146593, 149303, 151134, 154839, 157630,\n", + " 173156, 176849, 177709, 178680, 179395, 182638, 184055, 189557,\n", + " 194960, 197856],\n", + " dtype='int64'), Int64Index([ 1326, 5921, 7850, 11563, 17022, 18863, 27065, 27981,\n", + " 33554, 65004, 77349, 81927, 83781, 85635, 86562, 88416,\n", + " 91732, 92692, 93601, 95421, 96376, 98711, 99638, 100536,\n", + " 101466, 106070, 110633, 115229, 120698, 122515, 128592, 136205,\n", + " 138515, 139441, 142663, 146594, 149304, 151135, 154840, 157631,\n", + " 173157, 176850, 177710, 178681, 179396, 182639, 184056, 189558,\n", + " 194961, 197857],\n", + " dtype='int64'), Int64Index([ 1327, 5922, 7851, 11564, 17023, 18864, 27066, 27982,\n", + " 33555, 65005, 77350, 81928, 83782, 85636, 86563, 88417,\n", + " 91733, 92693, 93602, 95422, 96377, 98712, 99639, 100537,\n", + " 101467, 106071, 110634, 115230, 120699, 122516, 128593, 136206,\n", + " 138516, 139442, 142664, 146595, 149305, 151136, 154841, 157632,\n", + " 173158, 176851, 177711, 178682, 179397, 182640, 184057, 189559,\n", + " 194962, 197858],\n", + " dtype='int64'), Int64Index([ 1328, 5923, 7852, 11565, 17024, 18865, 27067, 27983,\n", + " 33556, 65006, 77351, 81929, 83783, 85637, 86564, 88418,\n", + " 91734, 92694, 93603, 95423, 96378, 98713, 99640, 100538,\n", + " 101468, 106072, 110635, 115231, 120700, 122517, 128594, 136207,\n", + " 138517, 139443, 142665, 146596, 149306, 151137, 154842, 157633,\n", + " 173159, 176852, 177712, 178683, 179398, 182641, 184058, 189560,\n", + " 194963, 197859],\n", + " dtype='int64'), Int64Index([ 1329, 5924, 7853, 11566, 17025, 18866, 27068, 27984,\n", + " 33557, 65007, 77352, 81930, 83784, 85638, 86565, 88419,\n", + " 91735, 92695, 93604, 95424, 96379, 98714, 99641, 100539,\n", + " 101469, 106073, 110636, 115232, 120701, 122518, 128595, 136208,\n", + " 138518, 139444, 142666, 146597, 149307, 151138, 154843, 157634,\n", + " 173160, 176853, 177713, 178684, 179399, 182642, 184059, 189561,\n", + " 194964, 197860],\n", + " dtype='int64'), Int64Index([ 1330, 5925, 7854, 11567, 17026, 18867, 27069, 27985,\n", + " 33558, 65008, 77353, 81931, 83785, 85639, 86566, 88420,\n", + " 91736, 92696, 93605, 95425, 96380, 98715, 99642, 100540,\n", + " 101470, 106074, 110637, 115233, 120702, 122519, 128596, 136209,\n", + " 138519, 139445, 142667, 146598, 149308, 151139, 154844, 157635,\n", + " 173161, 176854, 177714, 178685, 179400, 182643, 184060, 189562,\n", + " 194965, 197861],\n", + " dtype='int64'), Int64Index([ 1331, 5926, 7855, 11568, 17027, 18868, 27070, 27986,\n", + " 33559, 65009, 77354, 81932, 83786, 85640, 86567, 88421,\n", + " 91737, 92697, 93606, 95426, 96381, 98716, 99643, 100541,\n", + " 101471, 106075, 110638, 115234, 120703, 122520, 128597, 136210,\n", + " 138520, 139446, 142668, 146599, 149309, 151140, 154845, 157636,\n", + " 173162, 176855, 177715, 178686, 179401, 182644, 184061, 189563,\n", + " 194966, 197862],\n", + " dtype='int64'), Int64Index([ 1332, 5927, 7856, 11569, 17028, 18869, 27071, 27987,\n", + " 33560, 65010, 77355, 81933, 83787, 85641, 86568, 88422,\n", + " 91738, 92698, 93607, 95427, 96382, 98717, 99644, 100542,\n", + " 101472, 106076, 110639, 115235, 120704, 122521, 128598, 136211,\n", + " 138521, 139447, 142669, 146600, 149310, 151141, 154846, 157637,\n", + " 173163, 176856, 177716, 178687, 179402, 182645, 184062, 189564,\n", + " 194967, 197863],\n", + " dtype='int64'), Int64Index([ 1333, 5928, 7857, 11570, 17029, 18870, 27072, 27988,\n", + " 33561, 65011, 77356, 81934, 83788, 85642, 86569, 88423,\n", + " 91739, 92699, 93608, 95428, 96383, 98718, 99645, 100543,\n", + " 101473, 106077, 110640, 115236, 120705, 122522, 128599, 136212,\n", + " 138522, 139448, 142670, 146601, 149311, 151142, 154847, 157638,\n", + " 173164, 176857, 177717, 178688, 179403, 182646, 184063, 189565,\n", + " 194968, 197864],\n", + " dtype='int64'), Int64Index([ 1334, 5929, 7858, 11571, 17030, 18871, 27073, 27989,\n", + " 33562, 65012, 77357, 81935, 83789, 85643, 86570, 88424,\n", + " 91740, 92700, 93609, 95429, 96384, 98719, 99646, 100544,\n", + " 101474, 106078, 110641, 115237, 120706, 122523, 128600, 136213,\n", + " 138523, 139449, 142671, 146602, 149312, 151143, 154848, 157639,\n", + " 173165, 176858, 177718, 178689, 179404, 182647, 184064, 189566,\n", + " 194969, 197865],\n", + " dtype='int64'), Int64Index([ 1335, 5930, 7859, 11572, 17031, 18872, 27074, 27990,\n", + " 33563, 65013, 77358, 81936, 83790, 85644, 86571, 88425,\n", + " 91741, 92701, 93610, 95430, 96385, 98720, 99647, 100545,\n", + " 101475, 106079, 110642, 115238, 120707, 122524, 128601, 136214,\n", + " 138524, 139450, 142672, 146603, 149313, 151144, 154849, 157640,\n", + " 173166, 176859, 177719, 178690, 179405, 182648, 184065, 189567,\n", + " 194970, 197866],\n", + " dtype='int64'), Int64Index([ 1336, 5931, 7860, 11573, 17032, 18873, 27075, 27991,\n", + " 33564, 65014, 77359, 81937, 83791, 85645, 86572, 88426,\n", + " 91742, 92702, 93611, 95431, 96386, 98721, 99648, 100546,\n", + " 101476, 106080, 110643, 115239, 120708, 122525, 128602, 136215,\n", + " 138525, 139451, 142673, 146604, 149314, 151145, 154850, 157641,\n", + " 173167, 176860, 177720, 178691, 179406, 182649, 184066, 189568,\n", + " 194971, 197867],\n", + " dtype='int64'), Int64Index([ 1337, 5932, 7861, 11574, 17033, 18874, 27076, 27992,\n", + " 33565, 65015, 77360, 81938, 83792, 85646, 86573, 88427,\n", + " 91743, 92703, 93612, 95432, 96387, 98722, 99649, 100547,\n", + " 101477, 106081, 110644, 115240, 120709, 122526, 128603, 136216,\n", + " 138526, 139452, 142674, 146605, 149315, 151146, 154851, 157642,\n", + " 173168, 176861, 177721, 178692, 179407, 182650, 184067, 189569,\n", + " 194972, 197868],\n", + " dtype='int64'), Int64Index([ 1338, 5933, 7862, 11575, 17034, 18875, 27077, 27993,\n", + " 33566, 65016, 77361, 81939, 83793, 85647, 86574, 88428,\n", + " 91744, 92704, 93613, 95433, 96388, 98723, 99650, 100548,\n", + " 101478, 106082, 110645, 115241, 120710, 122527, 128604, 136217,\n", + " 138527, 139453, 142675, 146606, 149316, 151147, 154852, 157643,\n", + " 173169, 176862, 177722, 178693, 179408, 182651, 184068, 189570,\n", + " 194973, 197869],\n", + " dtype='int64'), Int64Index([ 1339, 5934, 7863, 11576, 17035, 18876, 27078, 27994,\n", + " 33567, 65017, 77362, 81940, 83794, 85648, 86575, 88429,\n", + " 91745, 92705, 93614, 95434, 96389, 98724, 99651, 100549,\n", + " 101479, 106083, 110646, 115242, 120711, 122528, 128605, 136218,\n", + " 138528, 139454, 142676, 146607, 149317, 151148, 154853, 157644,\n", + " 173170, 176863, 177723, 178694, 179409, 182652, 184069, 189571,\n", + " 194974, 197870],\n", + " dtype='int64'), Int64Index([ 1340, 5935, 7864, 11577, 17036, 18877, 27079, 27995,\n", + " 33568, 65018, 77363, 81941, 83795, 85649, 86576, 88430,\n", + " 91746, 92706, 93615, 95435, 96390, 98725, 99652, 100550,\n", + " 101480, 106084, 110647, 115243, 120712, 122529, 128606, 136219,\n", + " 138529, 139455, 142677, 146608, 149318, 151149, 154854, 157645,\n", + " 173171, 176864, 177724, 178695, 179410, 182653, 184070, 189572,\n", + " 194975, 197871],\n", + " dtype='int64'), Int64Index([ 1341, 5936, 7865, 11578, 17037, 18878, 27080, 27996,\n", + " 33569, 65019, 77364, 81942, 83796, 85650, 86577, 88431,\n", + " 91747, 92707, 93616, 95436, 96391, 98726, 99653, 100551,\n", + " 101481, 106085, 110648, 115244, 120713, 122530, 128607, 136220,\n", + " 138530, 139456, 142678, 146609, 149319, 151150, 154855, 157646,\n", + " 173172, 176865, 177725, 178696, 179411, 182654, 184071, 189573,\n", + " 194976, 197872],\n", + " dtype='int64'), Int64Index([ 1342, 5937, 7866, 11579, 17038, 18879, 27081, 27997,\n", + " 33570, 65020, 77365, 81943, 83797, 85651, 86578, 88432,\n", + " 91748, 92708, 93617, 95437, 96392, 98727, 99654, 100552,\n", + " 101482, 106086, 110649, 115245, 120714, 122531, 128608, 136221,\n", + " 138531, 139457, 142679, 146610, 149320, 151151, 154856, 157647,\n", + " 173173, 176866, 177726, 178697, 179412, 182655, 184072, 189574,\n", + " 194977, 197873],\n", + " dtype='int64'), Int64Index([ 1343, 5938, 7867, 11580, 17039, 18880, 27082, 27998,\n", + " 33571, 65021, 77366, 81944, 83798, 85652, 86579, 88433,\n", + " 91749, 92709, 93618, 95438, 96393, 98728, 99655, 100553,\n", + " 101483, 106087, 110650, 115246, 120715, 122532, 128609, 136222,\n", + " 138532, 139458, 142680, 146611, 149321, 151152, 154857, 157648,\n", + " 173174, 176867, 177727, 178698, 179413, 182656, 184073, 189575,\n", + " 194978, 197874],\n", + " dtype='int64'), Int64Index([ 1344, 5939, 7868, 11581, 17040, 18881, 27083, 27999,\n", + " 33572, 65022, 77367, 81945, 83799, 85653, 86580, 88434,\n", + " 91750, 92710, 93619, 95439, 96394, 98729, 99656, 100554,\n", + " 101484, 106088, 110651, 115247, 120716, 122533, 128610, 136223,\n", + " 138533, 139459, 142681, 146612, 149322, 151153, 154858, 157649,\n", + " 173175, 176868, 177728, 178699, 179414, 182657, 184074, 189576,\n", + " 194979, 197875],\n", + " dtype='int64'), Int64Index([ 1345, 5940, 7869, 11582, 17041, 18882, 27084, 28000,\n", + " 33573, 65023, 77368, 81946, 83800, 85654, 86581, 88435,\n", + " 91751, 92711, 93620, 95440, 96395, 98730, 99657, 100555,\n", + " 101485, 106089, 110652, 115248, 120717, 122534, 128611, 136224,\n", + " 138534, 139460, 142682, 146613, 149323, 151154, 154859, 157650,\n", + " 173176, 176869, 177729, 178700, 179415, 182658, 184075, 189577,\n", + " 194980, 197876],\n", + " dtype='int64'), Int64Index([ 1346, 5941, 7870, 11583, 17042, 18883, 27085, 28001,\n", + " 33574, 65024, 77369, 81947, 83801, 85655, 86582, 88436,\n", + " 91752, 92712, 93621, 95441, 96396, 98731, 99658, 100556,\n", + " 101486, 106090, 110653, 115249, 120718, 122535, 128612, 136225,\n", + " 138535, 139461, 142683, 146614, 149324, 151155, 154860, 157651,\n", + " 173177, 176870, 177730, 178701, 179416, 182659, 184076, 189578,\n", + " 194981, 197877],\n", + " dtype='int64'), Int64Index([ 1347, 5942, 7871, 11584, 17043, 18884, 27086, 28002,\n", + " 33575, 65025, 77370, 81948, 83802, 85656, 86583, 88437,\n", + " 91753, 92713, 93622, 95442, 96397, 98732, 99659, 100557,\n", + " 101487, 106091, 110654, 115250, 120719, 122536, 128613, 136226,\n", + " 138536, 139462, 142684, 146615, 149325, 151156, 154861, 157652,\n", + " 173178, 176871, 177731, 178702, 179417, 182660, 184077, 189579,\n", + " 194982, 197878],\n", + " dtype='int64'), Int64Index([ 1348, 5943, 7872, 11585, 17044, 18885, 27087, 28003,\n", + " 33576, 65026, 77371, 81949, 83803, 85657, 86584, 88438,\n", + " 91754, 92714, 93623, 95443, 96398, 98733, 99660, 100558,\n", + " 101488, 106092, 110655, 115251, 120720, 122537, 128614, 136227,\n", + " 138537, 139463, 142685, 146616, 149326, 151157, 154862, 157653,\n", + " 173179, 176872, 177732, 178703, 179418, 182661, 184078, 189580,\n", + " 194983, 197879],\n", + " dtype='int64'), Int64Index([ 1349, 5944, 7873, 11586, 17045, 18886, 27088, 28004,\n", + " 33577, 65027, 77372, 81950, 83804, 85658, 86585, 88439,\n", + " 91755, 92715, 93624, 95444, 96399, 98734, 99661, 100559,\n", + " 101489, 106093, 110656, 115252, 120721, 122538, 128615, 136228,\n", + " 138538, 139464, 142686, 146617, 149327, 151158, 154863, 157654,\n", + " 173180, 176873, 177733, 178704, 179419, 182662, 184079, 189581,\n", + " 194984, 197880],\n", + " dtype='int64'), Int64Index([ 1350, 5945, 7874, 11587, 17046, 18887, 27089, 28005,\n", + " 33578, 65028, 77373, 81951, 83805, 85659, 86586, 88440,\n", + " 91756, 92716, 93625, 95445, 96400, 98735, 99662, 100560,\n", + " 101490, 106094, 110657, 115253, 120722, 122539, 128616, 136229,\n", + " 138539, 139465, 142687, 146618, 149328, 151159, 154864, 157655,\n", + " 173181, 176874, 177734, 178705, 179420, 182663, 184080, 189582,\n", + " 194985, 197881],\n", + " dtype='int64'), Int64Index([ 1351, 5946, 7875, 11588, 17047, 18888, 27090, 28006,\n", + " 33579, 65029, 77374, 81952, 83806, 85660, 86587, 88441,\n", + " 91757, 92717, 93626, 95446, 96401, 98736, 99663, 100561,\n", + " 101491, 106095, 110658, 115254, 120723, 122540, 128617, 136230,\n", + " 138540, 139466, 142688, 146619, 149329, 151160, 154865, 157656,\n", + " 173182, 176875, 177735, 178706, 179421, 182664, 184081, 189583,\n", + " 194986, 197882],\n", + " dtype='int64'), Int64Index([ 1352, 5947, 7876, 11589, 17048, 18889, 27091, 28007,\n", + " 33580, 65030, 77375, 81953, 83807, 85661, 86588, 88442,\n", + " 91758, 92718, 93627, 95447, 96402, 98737, 99664, 100562,\n", + " 101492, 106096, 110659, 115255, 120724, 122541, 128618, 136231,\n", + " 138541, 139467, 142689, 146620, 149330, 151161, 154866, 157657,\n", + " 173183, 176876, 177736, 178707, 179422, 182665, 184082, 189584,\n", + " 194987, 197883],\n", + " dtype='int64'), Int64Index([ 1353, 5948, 7877, 11590, 17049, 18890, 27092, 28008,\n", + " 33581, 65031, 77376, 81954, 83808, 85662, 86589, 88443,\n", + " 91759, 92719, 93628, 95448, 96403, 98738, 99665, 100563,\n", + " 101493, 106097, 110660, 115256, 120725, 122542, 128619, 136232,\n", + " 138542, 139468, 142690, 146621, 149331, 151162, 154867, 157658,\n", + " 173184, 176877, 177737, 178708, 179423, 182666, 184083, 189585,\n", + " 194988, 197884],\n", + " dtype='int64'), Int64Index([ 1354, 5949, 7878, 11591, 17050, 18891, 27093, 28009,\n", + " 33582, 65032, 77377, 81955, 83809, 85663, 86590, 88444,\n", + " 91760, 92720, 93629, 95449, 96404, 98739, 99666, 100564,\n", + " 101494, 106098, 110661, 115257, 120726, 122543, 128620, 136233,\n", + " 138543, 139469, 142691, 146622, 149332, 151163, 154868, 157659,\n", + " 173185, 176878, 177738, 178709, 179424, 182667, 184084, 189586,\n", + " 194989, 197885],\n", + " dtype='int64'), Int64Index([ 1355, 5950, 7879, 11592, 17051, 18892, 27094, 28010,\n", + " 33583, 65033, 77378, 81956, 83810, 85664, 86591, 88445,\n", + " 91761, 92721, 93630, 95450, 96405, 98740, 99667, 100565,\n", + " 101495, 106099, 110662, 115258, 120727, 122544, 128621, 136234,\n", + " 138544, 139470, 142692, 146623, 149333, 151164, 154869, 157660,\n", + " 173186, 176879, 177739, 178710, 179425, 182668, 184085, 189587,\n", + " 194990, 197886],\n", + " dtype='int64'), Int64Index([ 1356, 5951, 7880, 11593, 17052, 18893, 27095, 28011,\n", + " 33584, 65034, 77379, 81957, 83811, 85665, 86592, 88446,\n", + " 91762, 92722, 93631, 95451, 96406, 98741, 99668, 100566,\n", + " 101496, 106100, 110663, 115259, 120728, 122545, 128622, 136235,\n", + " 138545, 139471, 142693, 146624, 149334, 151165, 154870, 157661,\n", + " 173187, 176880, 177740, 178711, 179426, 182669, 184086, 189588,\n", + " 194991, 197887],\n", + " dtype='int64'), Int64Index([ 1357, 5952, 7881, 11594, 17053, 18894, 27096, 28012,\n", + " 33585, 65035, 77380, 81958, 83812, 85666, 86593, 88447,\n", + " 91763, 92723, 93632, 95452, 96407, 98742, 99669, 100567,\n", + " 101497, 106101, 110664, 115260, 120729, 122546, 128623, 136236,\n", + " 138546, 139472, 142694, 146625, 149335, 151166, 154871, 157662,\n", + " 173188, 176881, 177741, 178712, 179427, 182670, 184087, 189589,\n", + " 194992, 197888],\n", + " dtype='int64'), Int64Index([ 1358, 5953, 7882, 11595, 17054, 18895, 27097, 28013,\n", + " 33586, 65036, 77381, 81959, 83813, 85667, 86594, 88448,\n", + " 91764, 92724, 93633, 95453, 96408, 98743, 99670, 100568,\n", + " 101498, 106102, 110665, 115261, 120730, 122547, 128624, 136237,\n", + " 138547, 139473, 142695, 146626, 149336, 151167, 154872, 157663,\n", + " 173189, 176882, 177742, 178713, 179428, 182671, 184088, 189590,\n", + " 194993, 197889],\n", + " dtype='int64'), Int64Index([ 1359, 5954, 7883, 11596, 17055, 18896, 27098, 28014,\n", + " 33587, 65037, 77382, 81960, 83814, 85668, 86595, 88449,\n", + " 91765, 92725, 93634, 95454, 96409, 98744, 99671, 100569,\n", + " 101499, 106103, 110666, 115262, 120731, 122548, 128625, 136238,\n", + " 138548, 139474, 142696, 146627, 149337, 151168, 154873, 157664,\n", + " 173190, 176883, 177743, 178714, 179429, 182672, 184089, 189591,\n", + " 194994, 197890],\n", + " dtype='int64'), Int64Index([ 1360, 5955, 7884, 11597, 17056, 18897, 27099, 28015,\n", + " 33588, 65038, 77383, 81961, 83815, 85669, 86596, 88450,\n", + " 91766, 92726, 93635, 95455, 96410, 98745, 99672, 100570,\n", + " 101500, 106104, 110667, 115263, 120732, 122549, 128626, 136239,\n", + " 138549, 139475, 142697, 146628, 149338, 151169, 154874, 157665,\n", + " 173191, 176884, 177744, 178715, 179430, 182673, 184090, 189592,\n", + " 194995, 197891],\n", + " dtype='int64'), Int64Index([ 1361, 5956, 7885, 11598, 17057, 18898, 27100, 28016,\n", + " 33589, 65039, 77384, 81962, 83816, 85670, 86597, 88451,\n", + " 91767, 92727, 93636, 95456, 96411, 98746, 99673, 100571,\n", + " 101501, 106105, 110668, 115264, 120733, 122550, 128627, 136240,\n", + " 138550, 139476, 142698, 146629, 149339, 151170, 154875, 157666,\n", + " 173192, 176885, 177745, 178716, 179431, 182674, 184091, 189593,\n", + " 194996, 197892],\n", + " dtype='int64'), Int64Index([ 1362, 5957, 7886, 11599, 17058, 18899, 27101, 28017,\n", + " 33590, 65040, 77385, 81963, 83817, 85671, 86598, 88452,\n", + " 91768, 92728, 93637, 95457, 96412, 98747, 99674, 100572,\n", + " 101502, 106106, 110669, 115265, 120734, 122551, 128628, 136241,\n", + " 138551, 139477, 142699, 146630, 149340, 151171, 154876, 157667,\n", + " 173193, 176886, 177746, 178717, 179432, 182675, 184092, 189594,\n", + " 194997, 197893],\n", + " dtype='int64'), Int64Index([ 1363, 5958, 7887, 11600, 17059, 18900, 27102, 28018,\n", + " 33591, 65041, 77386, 81964, 83818, 85672, 86599, 88453,\n", + " 91769, 92729, 93638, 95458, 96413, 98748, 99675, 100573,\n", + " 101503, 106107, 110670, 115266, 120735, 122552, 128629, 136242,\n", + " 138552, 139478, 142700, 146631, 149341, 151172, 154877, 157668,\n", + " 173194, 176887, 177747, 178718, 179433, 182676, 184093, 189595,\n", + " 194998, 197894],\n", + " dtype='int64'), Int64Index([ 1364, 5959, 7888, 11601, 17060, 18901, 27103, 28019,\n", + " 33592, 65042, 77387, 81965, 83819, 85673, 86600, 88454,\n", + " 91770, 92730, 93639, 95459, 96414, 98749, 99676, 100574,\n", + " 101504, 106108, 110671, 115267, 120736, 122553, 128630, 136243,\n", + " 138553, 139479, 142701, 146632, 149342, 151173, 154878, 157669,\n", + " 173195, 176888, 177748, 178719, 179434, 182677, 184094, 189596,\n", + " 194999, 197895],\n", + " dtype='int64'), Int64Index([ 1365, 5960, 7889, 11602, 17061, 18902, 27104, 28020,\n", + " 33593, 65043, 77388, 81966, 83820, 85674, 86601, 88455,\n", + " 91771, 92731, 93640, 95460, 96415, 98750, 99677, 100575,\n", + " 101505, 106109, 110672, 115268, 120737, 122554, 128631, 136244,\n", + " 138554, 139480, 142702, 146633, 149343, 151174, 154879, 157670,\n", + " 173196, 176889, 177749, 178720, 179435, 182678, 184095, 189597,\n", + " 195000, 197896],\n", + " dtype='int64'), Int64Index([ 1366, 5961, 7890, 11603, 17062, 18903, 27105, 28021,\n", + " 33594, 65044, 77389, 81967, 83821, 85675, 86602, 88456,\n", + " 91772, 92732, 93641, 95461, 96416, 98751, 99678, 100576,\n", + " 101506, 106110, 110673, 115269, 120738, 122555, 128632, 136245,\n", + " 138555, 139481, 142703, 146634, 149344, 151175, 154880, 157671,\n", + " 173197, 176890, 177750, 178721, 179436, 182679, 184096, 189598,\n", + " 195001, 197897],\n", + " dtype='int64'), Int64Index([ 1367, 5962, 7891, 11604, 17063, 18904, 27106, 28022,\n", + " 33595, 65045, 77390, 81968, 83822, 85676, 86603, 88457,\n", + " 91773, 92733, 93642, 95462, 96417, 98752, 99679, 100577,\n", + " 101507, 106111, 110674, 115270, 120739, 122556, 128633, 136246,\n", + " 138556, 139482, 142704, 146635, 149345, 151176, 154881, 157672,\n", + " 173198, 176891, 177751, 178722, 179437, 182680, 184097, 189599,\n", + " 195002, 197898],\n", + " dtype='int64'), Int64Index([ 1368, 5963, 7892, 11605, 17064, 18905, 27107, 28023,\n", + " 33596, 65046, 77391, 81969, 83823, 85677, 86604, 88458,\n", + " 91774, 92734, 93643, 95463, 96418, 98753, 99680, 100578,\n", + " 101508, 106112, 110675, 115271, 120740, 122557, 128634, 136247,\n", + " 138557, 139483, 142705, 146636, 149346, 151177, 154882, 157673,\n", + " 173199, 176892, 177752, 178723, 179438, 182681, 184098, 189600,\n", + " 195003, 197899],\n", + " dtype='int64'), Int64Index([ 1369, 5964, 7893, 11606, 17065, 18906, 27108, 28024,\n", + " 33597, 65047, 77392, 81970, 83824, 85678, 86605, 88459,\n", + " 91775, 92735, 93644, 95464, 96419, 98754, 99681, 100579,\n", + " 101509, 106113, 110676, 115272, 120741, 122558, 128635, 136248,\n", + " 138558, 139484, 142706, 146637, 149347, 151178, 154883, 157674,\n", + " 173200, 176893, 177753, 178724, 179439, 182682, 184099, 189601,\n", + " 195004, 197900],\n", + " dtype='int64'), Int64Index([ 1370, 5965, 7894, 11607, 17066, 18907, 27109, 28025,\n", + " 33598, 65048, 77393, 81971, 83825, 85679, 86606, 88460,\n", + " 91776, 92736, 93645, 95465, 96420, 98755, 99682, 100580,\n", + " 101510, 106114, 110677, 115273, 120742, 122559, 128636, 136249,\n", + " 138559, 139485, 142707, 146638, 149348, 151179, 154884, 157675,\n", + " 173201, 176894, 177754, 178725, 179440, 182683, 184100, 189602,\n", + " 195005, 197901],\n", + " dtype='int64'), Int64Index([ 1371, 5966, 7895, 11608, 17067, 18908, 27110, 28026,\n", + " 33599, 65049, 77394, 81972, 83826, 85680, 86607, 88461,\n", + " 91777, 92737, 93646, 95466, 96421, 98756, 99683, 100581,\n", + " 101511, 106115, 110678, 115274, 120743, 122560, 128637, 136250,\n", + " 138560, 139486, 142708, 146639, 149349, 151180, 154885, 157676,\n", + " 173202, 176895, 177755, 178726, 179441, 182684, 184101, 189603,\n", + " 195006, 197902],\n", + " dtype='int64'), Int64Index([ 1372, 5967, 7896, 11609, 17068, 18909, 27111, 28027,\n", + " 33600, 65050, 77395, 81973, 83827, 85681, 86608, 88462,\n", + " 91778, 92738, 93647, 95467, 96422, 98757, 99684, 100582,\n", + " 101512, 106116, 110679, 115275, 120744, 122561, 128638, 136251,\n", + " 138561, 139487, 142709, 146640, 149350, 151181, 154886, 157677,\n", + " 173203, 176896, 177756, 178727, 179442, 182685, 184102, 189604,\n", + " 195007, 197903],\n", + " dtype='int64'), Int64Index([ 1373, 5968, 7897, 11610, 17069, 18910, 27112, 28028,\n", + " 33601, 65051, 77396, 81974, 83828, 85682, 86609, 88463,\n", + " 91779, 92739, 93648, 95468, 96423, 98758, 99685, 100583,\n", + " 101513, 106117, 110680, 115276, 120745, 122562, 128639, 136252,\n", + " 138562, 139488, 142710, 146641, 149351, 151182, 154887, 157678,\n", + " 173204, 176897, 177757, 178728, 179443, 182686, 184103, 189605,\n", + " 195008, 197904],\n", + " dtype='int64'), Int64Index([ 1374, 5969, 7898, 11611, 17070, 18911, 27113, 28029,\n", + " 33602, 65052, 77397, 81975, 83829, 85683, 86610, 88464,\n", + " 91780, 92740, 93649, 95469, 96424, 98759, 99686, 100584,\n", + " 101514, 106118, 110681, 115277, 120746, 122563, 128640, 136253,\n", + " 138563, 139489, 142711, 146642, 149352, 151183, 154888, 157679,\n", + " 173205, 176898, 177758, 178729, 179444, 182687, 184104, 189606,\n", + " 195009, 197905],\n", + " dtype='int64'), Int64Index([ 1375, 5970, 7899, 11612, 17071, 18912, 27114, 28030,\n", + " 33603, 65053, 77398, 81976, 83830, 85684, 86611, 88465,\n", + " 91781, 92741, 93650, 95470, 96425, 98760, 99687, 100585,\n", + " 101515, 106119, 110682, 115278, 120747, 122564, 128641, 136254,\n", + " 138564, 139490, 142712, 146643, 149353, 151184, 154889, 157680,\n", + " 173206, 176899, 177759, 178730, 179445, 182688, 184105, 189607,\n", + " 195010, 197906],\n", + " dtype='int64'), Int64Index([ 1376, 5971, 7900, 11613, 17072, 18913, 27115, 28031,\n", + " 33604, 65054, 77399, 81977, 83831, 85685, 86612, 88466,\n", + " 91782, 92742, 93651, 95471, 96426, 98761, 99688, 100586,\n", + " 101516, 106120, 110683, 115279, 120748, 122565, 128642, 136255,\n", + " 138565, 139491, 142713, 146644, 149354, 151185, 154890, 157681,\n", + " 173207, 176900, 177760, 178731, 179446, 182689, 184106, 189608,\n", + " 195011, 197907],\n", + " dtype='int64'), Int64Index([ 1377, 5972, 7901, 11614, 17073, 18914, 27116, 28032,\n", + " 33605, 65055, 77400, 81978, 83832, 85686, 86613, 88467,\n", + " 91783, 92743, 93652, 95472, 96427, 98762, 99689, 100587,\n", + " 101517, 106121, 110684, 115280, 120749, 122566, 128643, 136256,\n", + " 138566, 139492, 142714, 146645, 149355, 151186, 154891, 157682,\n", + " 173208, 176901, 177761, 178732, 179447, 182690, 184107, 189609,\n", + " 195012, 197908],\n", + " dtype='int64'), Int64Index([ 1378, 5973, 7902, 11615, 17074, 18915, 27117, 28033,\n", + " 33606, 65056, 77401, 81979, 83833, 85687, 86614, 88468,\n", + " 91784, 92744, 93653, 95473, 96428, 98763, 99690, 100588,\n", + " 101518, 106122, 110685, 115281, 120750, 122567, 128644, 136257,\n", + " 138567, 139493, 142715, 146646, 149356, 151187, 154892, 157683,\n", + " 173209, 176902, 177762, 178733, 179448, 182691, 184108, 189610,\n", + " 195013, 197909],\n", + " dtype='int64'), Int64Index([ 1379, 5974, 7903, 11616, 17075, 18916, 27118, 28034,\n", + " 33607, 65057, 77402, 81980, 83834, 85688, 86615, 88469,\n", + " 91785, 92745, 93654, 95474, 96429, 98764, 99691, 100589,\n", + " 101519, 106123, 110686, 115282, 120751, 122568, 128645, 136258,\n", + " 138568, 139494, 142716, 146647, 149357, 151188, 154893, 157684,\n", + " 173210, 176903, 177763, 178734, 179449, 182692, 184109, 189611,\n", + " 195014, 197910],\n", + " dtype='int64'), Int64Index([ 1380, 5975, 7904, 11617, 17076, 18917, 27119, 28035,\n", + " 33608, 65058, 77403, 81981, 83835, 85689, 86616, 88470,\n", + " 91786, 92746, 93655, 95475, 96430, 98765, 99692, 100590,\n", + " 101520, 106124, 110687, 115283, 120752, 122569, 128646, 136259,\n", + " 138569, 139495, 142717, 146648, 149358, 151189, 154894, 157685,\n", + " 173211, 176904, 177764, 178735, 179450, 182693, 184110, 189612,\n", + " 195015, 197911],\n", + " dtype='int64'), Int64Index([ 1381, 5976, 7905, 11618, 17077, 18918, 27120, 28036,\n", + " 33609, 65059, 77404, 81982, 83836, 85690, 86617, 88471,\n", + " 91787, 92747, 93656, 95476, 96431, 98766, 99693, 100591,\n", + " 101521, 106125, 110688, 115284, 120753, 122570, 128647, 136260,\n", + " 138570, 139496, 142718, 146649, 149359, 151190, 154895, 157686,\n", + " 173212, 176905, 177765, 178736, 179451, 182694, 184111, 189613,\n", + " 195016, 197912],\n", + " dtype='int64'), Int64Index([ 1382, 5977, 7906, 11619, 17078, 18919, 27121, 28037,\n", + " 33610, 65060, 77405, 81983, 83837, 85691, 86618, 88472,\n", + " 91788, 92748, 93657, 95477, 96432, 98767, 99694, 100592,\n", + " 101522, 106126, 110689, 115285, 120754, 122571, 128648, 136261,\n", + " 138571, 139497, 142719, 146650, 149360, 151191, 154896, 157687,\n", + " 173213, 176906, 177766, 178737, 179452, 182695, 184112, 189614,\n", + " 195017, 197913],\n", + " dtype='int64'), Int64Index([ 1383, 5978, 7907, 11620, 17079, 18920, 27122, 28038,\n", + " 33611, 65061, 77406, 81984, 83838, 85692, 86619, 88473,\n", + " 91789, 92749, 93658, 95478, 96433, 98768, 99695, 100593,\n", + " 101523, 106127, 110690, 115286, 120755, 122572, 128649, 136262,\n", + " 138572, 139498, 142720, 146651, 149361, 151192, 154897, 157688,\n", + " 173214, 176907, 177767, 178738, 179453, 182696, 184113, 189615,\n", + " 195018, 197914],\n", + " dtype='int64'), Int64Index([ 1384, 5979, 7908, 11621, 17080, 18921, 27123, 28039,\n", + " 33612, 65062, 77407, 81985, 83839, 85693, 86620, 88474,\n", + " 91790, 92750, 93659, 95479, 96434, 98769, 99696, 100594,\n", + " 101524, 106128, 110691, 115287, 120756, 122573, 128650, 136263,\n", + " 138573, 139499, 142721, 146652, 149362, 151193, 154898, 157689,\n", + " 173215, 176908, 177768, 178739, 179454, 182697, 184114, 189616,\n", + " 195019, 197915],\n", + " dtype='int64'), Int64Index([ 1385, 5980, 7909, 11622, 17081, 18922, 27124, 28040,\n", + " 33613, 65063, 77408, 81986, 83840, 85694, 86621, 88475,\n", + " 91791, 92751, 93660, 95480, 96435, 98770, 99697, 100595,\n", + " 101525, 106129, 110692, 115288, 120757, 122574, 128651, 136264,\n", + " 138574, 139500, 142722, 146653, 149363, 151194, 154899, 157690,\n", + " 173216, 176909, 177769, 178740, 179455, 182698, 184115, 189617,\n", + " 195020, 197916],\n", + " dtype='int64'), Int64Index([ 1386, 5981, 7910, 11623, 17082, 18923, 27125, 28041,\n", + " 33614, 65064, 77409, 81987, 83841, 85695, 86622, 88476,\n", + " 91792, 92752, 93661, 95481, 96436, 98771, 99698, 100596,\n", + " 101526, 106130, 110693, 115289, 120758, 122575, 128652, 136265,\n", + " 138575, 139501, 142723, 146654, 149364, 151195, 154900, 157691,\n", + " 173217, 176910, 177770, 178741, 179456, 182699, 184116, 189618,\n", + " 195021, 197917],\n", + " dtype='int64'), Int64Index([ 1387, 5982, 7911, 11624, 17083, 18924, 27126, 28042,\n", + " 33615, 65065, 77410, 81988, 83842, 85696, 86623, 88477,\n", + " 91793, 92753, 93662, 95482, 96437, 98772, 99699, 100597,\n", + " 101527, 106131, 110694, 115290, 120759, 122576, 128653, 136266,\n", + " 138576, 139502, 142724, 146655, 149365, 151196, 154901, 157692,\n", + " 173218, 176911, 177771, 178742, 179457, 182700, 184117, 189619,\n", + " 195022, 197918],\n", + " dtype='int64'), Int64Index([ 1388, 5983, 7912, 11625, 17084, 18925, 27127, 28043,\n", + " 33616, 65066, 77411, 81989, 83843, 85697, 86624, 88478,\n", + " 91794, 92754, 93663, 95483, 96438, 98773, 99700, 100598,\n", + " 101528, 106132, 110695, 115291, 120760, 122577, 128654, 136267,\n", + " 138577, 139503, 142725, 146656, 149366, 151197, 154902, 157693,\n", + " 173219, 176912, 177772, 178743, 179458, 182701, 184118, 189620,\n", + " 195023, 197919],\n", + " dtype='int64'), Int64Index([ 1389, 5984, 7913, 11626, 17085, 18926, 27128, 28044,\n", + " 33617, 65067, 77412, 81990, 83844, 85698, 86625, 88479,\n", + " 91795, 92755, 93664, 95484, 96439, 98774, 99701, 100599,\n", + " 101529, 106133, 110696, 115292, 120761, 122578, 128655, 136268,\n", + " 138578, 139504, 142726, 146657, 149367, 151198, 154903, 157694,\n", + " 173220, 176913, 177773, 178744, 179459, 182702, 184119, 189621,\n", + " 195024, 197920],\n", + " dtype='int64'), Int64Index([ 1390, 5985, 7914, 11627, 17086, 18927, 27129, 28045,\n", + " 33618, 65068, 77413, 81991, 83845, 85699, 86626, 88480,\n", + " 91796, 92756, 93665, 95485, 96440, 98775, 99702, 100600,\n", + " 101530, 106134, 110697, 115293, 120762, 122579, 128656, 136269,\n", + " 138579, 139505, 142727, 146658, 149368, 151199, 154904, 157695,\n", + " 173221, 176914, 177774, 178745, 179460, 182703, 184120, 189622,\n", + " 195025, 197921],\n", + " dtype='int64'), Int64Index([ 1391, 5986, 7915, 11628, 17087, 18928, 27130, 28046,\n", + " 33619, 65069, 77414, 81992, 83846, 85700, 86627, 88481,\n", + " 91797, 92757, 93666, 95486, 96441, 98776, 99703, 100601,\n", + " 101531, 106135, 110698, 115294, 120763, 122580, 128657, 136270,\n", + " 138580, 139506, 142728, 146659, 149369, 151200, 154905, 157696,\n", + " 173222, 176915, 177775, 178746, 179461, 182704, 184121, 189623,\n", + " 195026, 197922],\n", + " dtype='int64'), Int64Index([ 1392, 5987, 7916, 11629, 17088, 18929, 27131, 28047,\n", + " 33620, 65070, 77415, 81993, 83847, 85701, 86628, 88482,\n", + " 91798, 92758, 93667, 95487, 96442, 98777, 99704, 100602,\n", + " 101532, 106136, 110699, 115295, 120764, 122581, 128658, 136271,\n", + " 138581, 139507, 142729, 146660, 149370, 151201, 154906, 157697,\n", + " 173223, 176916, 177776, 178747, 179462, 182705, 184122, 189624,\n", + " 195027, 197923],\n", + " dtype='int64'), Int64Index([ 1393, 5988, 7917, 11630, 17089, 18930, 27132, 28048,\n", + " 33621, 65071, 77416, 81994, 83848, 85702, 86629, 88483,\n", + " 91799, 92759, 93668, 95488, 96443, 98778, 99705, 100603,\n", + " 101533, 106137, 110700, 115296, 120765, 122582, 128659, 136272,\n", + " 138582, 139508, 142730, 146661, 149371, 151202, 154907, 157698,\n", + " 173224, 176917, 177777, 178748, 179463, 182706, 184123, 189625,\n", + " 195028, 197924],\n", + " dtype='int64'), Int64Index([ 1394, 5989, 7918, 11631, 17090, 18931, 27133, 28049,\n", + " 33622, 65072, 77417, 81995, 83849, 85703, 86630, 88484,\n", + " 91800, 92760, 93669, 95489, 96444, 98779, 99706, 100604,\n", + " 101534, 106138, 110701, 115297, 120766, 122583, 128660, 136273,\n", + " 138583, 139509, 142731, 146662, 149372, 151203, 154908, 157699,\n", + " 173225, 176918, 177778, 178749, 179464, 182707, 184124, 189626,\n", + " 195029, 197925],\n", + " dtype='int64'), Int64Index([ 1395, 5990, 7919, 11632, 17091, 18932, 27134, 28050,\n", + " 33623, 65073, 77418, 81996, 83850, 85704, 86631, 88485,\n", + " 91801, 92761, 93670, 95490, 96445, 98780, 99707, 100605,\n", + " 101535, 106139, 110702, 115298, 120767, 122584, 128661, 136274,\n", + " 138584, 139510, 142732, 146663, 149373, 151204, 154909, 157700,\n", + " 173226, 176919, 177779, 178750, 179465, 182708, 184125, 189627,\n", + " 195030, 197926],\n", + " dtype='int64'), Int64Index([ 1396, 5991, 7920, 11633, 17092, 18933, 27135, 28051,\n", + " 33624, 65074, 77419, 81997, 83851, 85705, 86632, 88486,\n", + " 91802, 92762, 93671, 95491, 96446, 98781, 99708, 100606,\n", + " 101536, 106140, 110703, 115299, 120768, 122585, 128662, 136275,\n", + " 138585, 139511, 142733, 146664, 149374, 151205, 154910, 157701,\n", + " 173227, 176920, 177780, 178751, 179466, 182709, 184126, 189628,\n", + " 195031, 197927],\n", + " dtype='int64'), Int64Index([ 1397, 5992, 7921, 11634, 17093, 18934, 27136, 28052,\n", + " 33625, 65075, 77420, 81998, 83852, 85706, 86633, 88487,\n", + " 91803, 92763, 93672, 95492, 96447, 98782, 99709, 100607,\n", + " 101537, 106141, 110704, 115300, 120769, 122586, 128663, 136276,\n", + " 138586, 139512, 142734, 146665, 149375, 151206, 154911, 157702,\n", + " 173228, 176921, 177781, 178752, 179467, 182710, 184127, 189629,\n", + " 195032, 197928],\n", + " dtype='int64'), Int64Index([ 1398, 5993, 7922, 11635, 17094, 18935, 27137, 28053,\n", + " 33626, 65076, 77421, 81999, 83853, 85707, 86634, 88488,\n", + " 91804, 92764, 93673, 95493, 96448, 98783, 99710, 100608,\n", + " 101538, 106142, 110705, 115301, 120770, 122587, 128664, 136277,\n", + " 138587, 139513, 142735, 146666, 149376, 151207, 154912, 157703,\n", + " 173229, 176922, 177782, 178753, 179468, 182711, 184128, 189630,\n", + " 195033, 197929],\n", + " dtype='int64'), Int64Index([ 1399, 5994, 7923, 11636, 17095, 18936, 27138, 28054,\n", + " 33627, 65077, 77422, 82000, 83854, 85708, 86635, 88489,\n", + " 91805, 92765, 93674, 95494, 96449, 98784, 99711, 100609,\n", + " 101539, 106143, 110706, 115302, 120771, 122588, 128665, 136278,\n", + " 138588, 139514, 142736, 146667, 149377, 151208, 154913, 157704,\n", + " 173230, 176923, 177783, 178754, 179469, 182712, 184129, 189631,\n", + " 195034, 197930],\n", + " dtype='int64'), Int64Index([ 1400, 5995, 7924, 11637, 17096, 18937, 27139, 28055,\n", + " 33628, 65078, 77423, 82001, 83855, 85709, 86636, 88490,\n", + " 91806, 92766, 93675, 95495, 96450, 98785, 99712, 100610,\n", + " 101540, 106144, 110707, 115303, 120772, 122589, 128666, 136279,\n", + " 138589, 139515, 142737, 146668, 149378, 151209, 154914, 157705,\n", + " 173231, 176924, 177784, 178755, 179470, 182713, 184130, 189632,\n", + " 195035, 197931],\n", + " dtype='int64'), Int64Index([ 1401, 5996, 7925, 11638, 17097, 18938, 27140, 28056,\n", + " 33629, 65079, 77424, 82002, 83856, 85710, 86637, 88491,\n", + " 91807, 92767, 93676, 95496, 96451, 98786, 99713, 100611,\n", + " 101541, 106145, 110708, 115304, 120773, 122590, 128667, 136280,\n", + " 138590, 139516, 142738, 146669, 149379, 151210, 154915, 157706,\n", + " 173232, 176925, 177785, 178756, 179471, 182714, 184131, 189633,\n", + " 195036, 197932],\n", + " dtype='int64'), Int64Index([ 1402, 5997, 7926, 11639, 17098, 18939, 27141, 28057,\n", + " 33630, 65080, 77425, 82003, 83857, 85711, 86638, 88492,\n", + " 91808, 92768, 93677, 95497, 96452, 98787, 99714, 100612,\n", + " 101542, 106146, 110709, 115305, 120774, 122591, 128668, 136281,\n", + " 138591, 139517, 142739, 146670, 149380, 151211, 154916, 157707,\n", + " 173233, 176926, 177786, 178757, 179472, 182715, 184132, 189634,\n", + " 195037, 197933],\n", + " dtype='int64'), Int64Index([ 1403, 5998, 7927, 11640, 17099, 18940, 27142, 28058,\n", + " 33631, 65081, 77426, 82004, 83858, 85712, 86639, 88493,\n", + " 91809, 92769, 93678, 95498, 96453, 98788, 99715, 100613,\n", + " 101543, 106147, 110710, 115306, 120775, 122592, 128669, 136282,\n", + " 138592, 139518, 142740, 146671, 149381, 151212, 154917, 157708,\n", + " 173234, 176927, 177787, 178758, 179473, 182716, 184133, 189635,\n", + " 195038, 197934],\n", + " dtype='int64'), Int64Index([ 1404, 5999, 7928, 11641, 17100, 18941, 27143, 28059,\n", + " 33632, 65082, 77427, 82005, 83859, 85713, 86640, 88494,\n", + " 91810, 92770, 93679, 95499, 96454, 98789, 99716, 100614,\n", + " 101544, 106148, 110711, 115307, 120776, 122593, 128670, 136283,\n", + " 138593, 139519, 142741, 146672, 149382, 151213, 154918, 157709,\n", + " 173235, 176928, 177788, 178759, 179474, 182717, 184134, 189636,\n", + " 195039, 197935],\n", + " dtype='int64'), Int64Index([ 1405, 6000, 7929, 11642, 17101, 18942, 27144, 28060,\n", + " 33633, 65083, 77428, 82006, 83860, 85714, 86641, 88495,\n", + " 91811, 92771, 93680, 95500, 96455, 98790, 99717, 100615,\n", + " 101545, 106149, 110712, 115308, 120777, 122594, 128671, 136284,\n", + " 138594, 139520, 142742, 146673, 149383, 151214, 154919, 157710,\n", + " 173236, 176929, 177789, 178760, 179475, 182718, 184135, 189637,\n", + " 195040, 197936],\n", + " dtype='int64'), Int64Index([ 1406, 6001, 7930, 11643, 17102, 18943, 27145, 28061,\n", + " 33634, 65084, 77429, 82007, 83861, 85715, 86642, 88496,\n", + " 91812, 92772, 93681, 95501, 96456, 98791, 99718, 100616,\n", + " 101546, 106150, 110713, 115309, 120778, 122595, 128672, 136285,\n", + " 138595, 139521, 142743, 146674, 149384, 151215, 154920, 157711,\n", + " 173237, 176930, 177790, 178761, 179476, 182719, 184136, 189638,\n", + " 195041, 197937],\n", + " dtype='int64'), Int64Index([ 1407, 6002, 7931, 11644, 17103, 18944, 27146, 28062,\n", + " 33635, 65085, 77430, 82008, 83862, 85716, 86643, 88497,\n", + " 91813, 92773, 93682, 95502, 96457, 98792, 99719, 100617,\n", + " 101547, 106151, 110714, 115310, 120779, 122596, 128673, 136286,\n", + " 138596, 139522, 142744, 146675, 149385, 151216, 154921, 157712,\n", + " 173238, 176931, 177791, 178762, 179477, 182720, 184137, 189639,\n", + " 195042, 197938],\n", + " dtype='int64'), Int64Index([ 1408, 6003, 7932, 11645, 17104, 18945, 27147, 28063,\n", + " 33636, 65086, 77431, 82009, 83863, 85717, 86644, 88498,\n", + " 91814, 92774, 93683, 95503, 96458, 98793, 99720, 100618,\n", + " 101548, 106152, 110715, 115311, 120780, 122597, 128674, 136287,\n", + " 138597, 139523, 142745, 146676, 149386, 151217, 154922, 157713,\n", + " 173239, 176932, 177792, 178763, 179478, 182721, 184138, 189640,\n", + " 195043, 197939],\n", + " dtype='int64'), Int64Index([ 1409, 6004, 7933, 11646, 17105, 18946, 27148, 28064,\n", + " 33637, 65087, 77432, 82010, 83864, 85718, 86645, 88499,\n", + " 91815, 92775, 93684, 95504, 96459, 98794, 99721, 100619,\n", + " 101549, 106153, 110716, 115312, 120781, 122598, 128675, 136288,\n", + " 138598, 139524, 142746, 146677, 149387, 151218, 154923, 157714,\n", + " 173240, 176933, 177793, 178764, 179479, 182722, 184139, 189641,\n", + " 195044, 197940],\n", + " dtype='int64'), Int64Index([ 1410, 6005, 7934, 11647, 17106, 18947, 27149, 28065,\n", + " 33638, 65088, 77433, 82011, 83865, 85719, 86646, 88500,\n", + " 91816, 92776, 93685, 95505, 96460, 98795, 99722, 100620,\n", + " 101550, 106154, 110717, 115313, 120782, 122599, 128676, 136289,\n", + " 138599, 139525, 142747, 146678, 149388, 151219, 154924, 157715,\n", + " 173241, 176934, 177794, 178765, 179480, 182723, 184140, 189642,\n", + " 195045, 197941],\n", + " dtype='int64'), Int64Index([ 1411, 6006, 7935, 11648, 17107, 18948, 27150, 28066,\n", + " 33639, 65089, 77434, 82012, 83866, 85720, 86647, 88501,\n", + " 91817, 92777, 93686, 95506, 96461, 98796, 99723, 100621,\n", + " 101551, 106155, 110718, 115314, 120783, 122600, 128677, 136290,\n", + " 138600, 139526, 142748, 146679, 149389, 151220, 154925, 157716,\n", + " 173242, 176935, 177795, 178766, 179481, 182724, 184141, 189643,\n", + " 195046, 197942],\n", + " dtype='int64'), Int64Index([ 1412, 6007, 7936, 11649, 17108, 18949, 27151, 28067,\n", + " 33640, 65090, 77435, 82013, 83867, 85721, 86648, 88502,\n", + " 91818, 92778, 93687, 95507, 96462, 98797, 99724, 100622,\n", + " 101552, 106156, 110719, 115315, 120784, 122601, 128678, 136291,\n", + " 138601, 139527, 142749, 146680, 149390, 151221, 154926, 157717,\n", + " 173243, 176936, 177796, 178767, 179482, 182725, 184142, 189644,\n", + " 195047, 197943],\n", + " dtype='int64'), Int64Index([ 1413, 6008, 7937, 11650, 17109, 18950, 27152, 28068,\n", + " 33641, 65091, 77436, 82014, 83868, 85722, 86649, 88503,\n", + " 91819, 92779, 93688, 95508, 96463, 98798, 99725, 100623,\n", + " 101553, 106157, 110720, 115316, 120785, 122602, 128679, 136292,\n", + " 138602, 139528, 142750, 146681, 149391, 151222, 154927, 157718,\n", + " 173244, 176937, 177797, 178768, 179483, 182726, 184143, 189645,\n", + " 195048, 197944],\n", + " dtype='int64'), Int64Index([ 1414, 6009, 7938, 11651, 17110, 18951, 27153, 28069,\n", + " 33642, 65092, 77437, 82015, 83869, 85723, 86650, 88504,\n", + " 91820, 92780, 93689, 95509, 96464, 98799, 99726, 100624,\n", + " 101554, 106158, 110721, 115317, 120786, 122603, 128680, 136293,\n", + " 138603, 139529, 142751, 146682, 149392, 151223, 154928, 157719,\n", + " 173245, 176938, 177798, 178769, 179484, 182727, 184144, 189646,\n", + " 195049, 197945],\n", + " dtype='int64'), Int64Index([ 1415, 6010, 7939, 11652, 17111, 18952, 27154, 28070,\n", + " 33643, 65093, 77438, 82016, 83870, 85724, 86651, 88505,\n", + " 91821, 92781, 93690, 95510, 96465, 98800, 99727, 100625,\n", + " 101555, 106159, 110722, 115318, 120787, 122604, 128681, 136294,\n", + " 138604, 139530, 142752, 146683, 149393, 151224, 154929, 157720,\n", + " 173246, 176939, 177799, 178770, 179485, 182728, 184145, 189647,\n", + " 195050, 197946],\n", + " dtype='int64'), Int64Index([ 1416, 6011, 7940, 11653, 17112, 18953, 27155, 28071,\n", + " 33644, 65094, 77439, 82017, 83871, 85725, 86652, 88506,\n", + " 91822, 92782, 93691, 95511, 96466, 98801, 99728, 100626,\n", + " 101556, 106160, 110723, 115319, 120788, 122605, 128682, 136295,\n", + " 138605, 139531, 142753, 146684, 149394, 151225, 154930, 157721,\n", + " 173247, 176940, 177800, 178771, 179486, 182729, 184146, 189648,\n", + " 195051, 197947],\n", + " dtype='int64'), Int64Index([ 1417, 6012, 7941, 11654, 17113, 18954, 27156, 28072,\n", + " 33645, 65095, 77440, 82018, 83872, 85726, 86653, 88507,\n", + " 91823, 92783, 93692, 95512, 96467, 98802, 99729, 100627,\n", + " 101557, 106161, 110724, 115320, 120789, 122606, 128683, 136296,\n", + " 138606, 139532, 142754, 146685, 149395, 151226, 154931, 157722,\n", + " 173248, 176941, 177801, 178772, 179487, 182730, 184147, 189649,\n", + " 195052, 197948],\n", + " dtype='int64'), Int64Index([ 1418, 6013, 7942, 11655, 17114, 18955, 27157, 28073,\n", + " 33646, 65096, 77441, 82019, 83873, 85727, 86654, 88508,\n", + " 91824, 92784, 93693, 95513, 96468, 98803, 99730, 100628,\n", + " 101558, 106162, 110725, 115321, 120790, 122607, 128684, 136297,\n", + " 138607, 139533, 142755, 146686, 149396, 151227, 154932, 157723,\n", + " 173249, 176942, 177802, 178773, 179488, 182731, 184148, 189650,\n", + " 195053, 197949],\n", + " dtype='int64'), Int64Index([ 1419, 6014, 7943, 11656, 17115, 18956, 27158, 28074,\n", + " 33647, 65097, 77442, 82020, 83874, 85728, 86655, 88509,\n", + " 91825, 92785, 93694, 95514, 96469, 98804, 99731, 100629,\n", + " 101559, 106163, 110726, 115322, 120791, 122608, 128685, 136298,\n", + " 138608, 139534, 142756, 146687, 149397, 151228, 154933, 157724,\n", + " 173250, 176943, 177803, 178774, 179489, 182732, 184149, 189651,\n", + " 195054, 197950],\n", + " dtype='int64'), Int64Index([ 1420, 6015, 7944, 11657, 17116, 18957, 27159, 28075,\n", + " 33648, 65098, 77443, 82021, 83875, 85729, 86656, 88510,\n", + " 91826, 92786, 93695, 95515, 96470, 98805, 99732, 100630,\n", + " 101560, 106164, 110727, 115323, 120792, 122609, 128686, 136299,\n", + " 138609, 139535, 142757, 146688, 149398, 151229, 154934, 157725,\n", + " 173251, 176944, 177804, 178775, 179490, 182733, 184150, 189652,\n", + " 195055, 197951],\n", + " dtype='int64'), Int64Index([ 1421, 6016, 7945, 11658, 17117, 18958, 27160, 28076,\n", + " 33649, 65099, 77444, 82022, 83876, 85730, 86657, 88511,\n", + " 91827, 92787, 93696, 95516, 96471, 98806, 99733, 100631,\n", + " 101561, 106165, 110728, 115324, 120793, 122610, 128687, 136300,\n", + " 138610, 139536, 142758, 146689, 149399, 151230, 154935, 157726,\n", + " 173252, 176945, 177805, 178776, 179491, 182734, 184151, 189653,\n", + " 195056, 197952],\n", + " dtype='int64'), Int64Index([ 1422, 6017, 7946, 11659, 17118, 18959, 27161, 28077,\n", + " 33650, 65100, 77445, 82023, 83877, 85731, 86658, 88512,\n", + " 91828, 92788, 93697, 95517, 96472, 98807, 99734, 100632,\n", + " 101562, 106166, 110729, 115325, 120794, 122611, 128688, 136301,\n", + " 138611, 139537, 142759, 146690, 149400, 151231, 154936, 157727,\n", + " 173253, 176946, 177806, 178777, 179492, 182735, 184152, 189654,\n", + " 195057, 197953],\n", + " dtype='int64'), Int64Index([ 1423, 6018, 7947, 11660, 17119, 18960, 27162, 28078,\n", + " 33651, 65101, 77446, 82024, 83878, 85732, 86659, 88513,\n", + " 91829, 92789, 93698, 95518, 96473, 98808, 99735, 100633,\n", + " 101563, 106167, 110730, 115326, 120795, 122612, 128689, 136302,\n", + " 138612, 139538, 142760, 146691, 149401, 151232, 154937, 157728,\n", + " 173254, 176947, 177807, 178778, 179493, 182736, 184153, 189655,\n", + " 195058, 197954],\n", + " dtype='int64'), Int64Index([ 1424, 6019, 7948, 11661, 17120, 18961, 27163, 28079,\n", + " 33652, 65102, 77447, 82025, 83879, 85733, 86660, 88514,\n", + " 91830, 92790, 93699, 95519, 96474, 98809, 99736, 100634,\n", + " 101564, 106168, 110731, 115327, 120796, 122613, 128690, 136303,\n", + " 138613, 139539, 142761, 146692, 149402, 151233, 154938, 157729,\n", + " 173255, 176948, 177808, 178779, 179494, 182737, 184154, 189656,\n", + " 195059, 197955],\n", + " dtype='int64'), Int64Index([ 1425, 6020, 7949, 11662, 17121, 18962, 27164, 28080,\n", + " 33653, 65103, 77448, 82026, 83880, 85734, 86661, 88515,\n", + " 91831, 92791, 93700, 95520, 96475, 98810, 99737, 100635,\n", + " 101565, 106169, 110732, 115328, 120797, 122614, 128691, 136304,\n", + " 138614, 139540, 142762, 146693, 149403, 151234, 154939, 157730,\n", + " 173256, 176949, 177809, 178780, 179495, 182738, 184155, 189657,\n", + " 195060, 197956],\n", + " dtype='int64'), Int64Index([ 1426, 6021, 7950, 11663, 17122, 18963, 27165, 28081,\n", + " 33654, 65104, 77449, 82027, 83881, 85735, 86662, 88516,\n", + " 91832, 92792, 93701, 95521, 96476, 98811, 99738, 100636,\n", + " 101566, 106170, 110733, 115329, 120798, 122615, 128692, 136305,\n", + " 138615, 139541, 142763, 146694, 149404, 151235, 154940, 157731,\n", + " 173257, 176950, 177810, 178781, 179496, 182739, 184156, 189658,\n", + " 195061, 197957],\n", + " dtype='int64'), Int64Index([ 1427, 6022, 7951, 11664, 17123, 18964, 27166, 28082,\n", + " 33655, 65105, 77450, 82028, 83882, 85736, 86663, 88517,\n", + " 91833, 92793, 93702, 95522, 96477, 98812, 99739, 100637,\n", + " 101567, 106171, 110734, 115330, 120799, 122616, 128693, 136306,\n", + " 138616, 139542, 142764, 146695, 149405, 151236, 154941, 157732,\n", + " 173258, 176951, 177811, 178782, 179497, 182740, 184157, 189659,\n", + " 195062, 197958],\n", + " dtype='int64'), Int64Index([ 1428, 6023, 7952, 11665, 17124, 18965, 27167, 28083,\n", + " 33656, 65106, 77451, 82029, 83883, 85737, 86664, 88518,\n", + " 91834, 92794, 93703, 95523, 96478, 98813, 99740, 100638,\n", + " 101568, 106172, 110735, 115331, 120800, 122617, 128694, 136307,\n", + " 138617, 139543, 142765, 146696, 149406, 151237, 154942, 157733,\n", + " 173259, 176952, 177812, 178783, 179498, 182741, 184158, 189660,\n", + " 195063, 197959],\n", + " dtype='int64'), Int64Index([ 1429, 6024, 7953, 11666, 17125, 18966, 27168, 28084,\n", + " 33657, 65107, 77452, 82030, 83884, 85738, 86665, 88519,\n", + " 91835, 92795, 93704, 95524, 96479, 98814, 99741, 100639,\n", + " 101569, 106173, 110736, 115332, 120801, 122618, 128695, 136308,\n", + " 138618, 139544, 142766, 146697, 149407, 151238, 154943, 157734,\n", + " 173260, 176953, 177813, 178784, 179499, 182742, 184159, 189661,\n", + " 195064, 197960],\n", + " dtype='int64'), Int64Index([ 1430, 6025, 7954, 11667, 17126, 18967, 27169, 28085,\n", + " 33658, 65108, 77453, 82031, 83885, 85739, 86666, 88520,\n", + " 91836, 92796, 93705, 95525, 96480, 98815, 99742, 100640,\n", + " 101570, 106174, 110737, 115333, 120802, 122619, 128696, 136309,\n", + " 138619, 139545, 142767, 146698, 149408, 151239, 154944, 157735,\n", + " 173261, 176954, 177814, 178785, 179500, 182743, 184160, 189662,\n", + " 195065, 197961],\n", + " dtype='int64'), Int64Index([ 1431, 6026, 7955, 11668, 17127, 18968, 27170, 28086,\n", + " 33659, 65109, 77454, 82032, 83886, 85740, 86667, 88521,\n", + " 91837, 92797, 93706, 95526, 96481, 98816, 99743, 100641,\n", + " 101571, 106175, 110738, 115334, 120803, 122620, 128697, 136310,\n", + " 138620, 139546, 142768, 146699, 149409, 151240, 154945, 157736,\n", + " 173262, 176955, 177815, 178786, 179501, 182744, 184161, 189663,\n", + " 195066, 197962],\n", + " dtype='int64'), Int64Index([ 1432, 6027, 7956, 11669, 17128, 18969, 27171, 28087,\n", + " 33660, 65110, 77455, 82033, 83887, 85741, 86668, 88522,\n", + " 91838, 92798, 93707, 95527, 96482, 98817, 99744, 100642,\n", + " 101572, 106176, 110739, 115335, 120804, 122621, 128698, 136311,\n", + " 138621, 139547, 142769, 146700, 149410, 151241, 154946, 157737,\n", + " 173263, 176956, 177816, 178787, 179502, 182745, 184162, 189664,\n", + " 195067, 197963],\n", + " dtype='int64'), Int64Index([ 1433, 6028, 7957, 11670, 17129, 18970, 27172, 28088,\n", + " 33661, 65111, 77456, 82034, 83888, 85742, 86669, 88523,\n", + " 91839, 92799, 93708, 95528, 96483, 98818, 99745, 100643,\n", + " 101573, 106177, 110740, 115336, 120805, 122622, 128699, 136312,\n", + " 138622, 139548, 142770, 146701, 149411, 151242, 154947, 157738,\n", + " 173264, 176957, 177817, 178788, 179503, 182746, 184163, 189665,\n", + " 195068, 197964],\n", + " dtype='int64'), Int64Index([ 1434, 6029, 7958, 11671, 17130, 18971, 27173, 28089,\n", + " 33662, 65112, 77457, 82035, 83889, 85743, 86670, 88524,\n", + " 91840, 92800, 93709, 95529, 96484, 98819, 99746, 100644,\n", + " 101574, 106178, 110741, 115337, 120806, 122623, 128700, 136313,\n", + " 138623, 139549, 142771, 146702, 149412, 151243, 154948, 157739,\n", + " 173265, 176958, 177818, 178789, 179504, 182747, 184164, 189666,\n", + " 195069, 197965],\n", + " dtype='int64'), Int64Index([ 1435, 6030, 7959, 11672, 17131, 18972, 27174, 28090,\n", + " 33663, 65113, 77458, 82036, 83890, 85744, 86671, 88525,\n", + " 91841, 92801, 93710, 95530, 96485, 98820, 99747, 100645,\n", + " 101575, 106179, 110742, 115338, 120807, 122624, 128701, 136314,\n", + " 138624, 139550, 142772, 146703, 149413, 151244, 154949, 157740,\n", + " 173266, 176959, 177819, 178790, 179505, 182748, 184165, 189667,\n", + " 195070, 197966],\n", + " dtype='int64'), Int64Index([ 1436, 6031, 7960, 11673, 17132, 18973, 27175, 28091,\n", + " 33664, 65114, 77459, 82037, 83891, 85745, 86672, 88526,\n", + " 91842, 92802, 93711, 95531, 96486, 98821, 99748, 100646,\n", + " 101576, 106180, 110743, 115339, 120808, 122625, 128702, 136315,\n", + " 138625, 139551, 142773, 146704, 149414, 151245, 154950, 157741,\n", + " 173267, 176960, 177820, 178791, 179506, 182749, 184166, 189668,\n", + " 195071, 197967],\n", + " dtype='int64'), Int64Index([ 1437, 6032, 7961, 11674, 17133, 18974, 27176, 28092,\n", + " 33665, 65115, 77460, 82038, 83892, 85746, 86673, 88527,\n", + " 91843, 92803, 93712, 95532, 96487, 98822, 99749, 100647,\n", + " 101577, 106181, 110744, 115340, 120809, 122626, 128703, 136316,\n", + " 138626, 139552, 142774, 146705, 149415, 151246, 154951, 157742,\n", + " 173268, 176961, 177821, 178792, 179507, 182750, 184167, 189669,\n", + " 195072, 197968],\n", + " dtype='int64'), Int64Index([ 1438, 6033, 7962, 11675, 17134, 18975, 27177, 28093,\n", + " 33666, 65116, 77461, 82039, 83893, 85747, 86674, 88528,\n", + " 91844, 92804, 93713, 95533, 96488, 98823, 99750, 100648,\n", + " 101578, 106182, 110745, 115341, 120810, 122627, 128704, 136317,\n", + " 138627, 139553, 142775, 146706, 149416, 151247, 154952, 157743,\n", + " 173269, 176962, 177822, 178793, 179508, 182751, 184168, 189670,\n", + " 195073, 197969],\n", + " dtype='int64'), Int64Index([ 1439, 6034, 7963, 11676, 17135, 18976, 27178, 28094,\n", + " 33667, 65117, 77462, 82040, 83894, 85748, 86675, 88529,\n", + " 91845, 92805, 93714, 95534, 96489, 98824, 99751, 100649,\n", + " 101579, 106183, 110746, 115342, 120811, 122628, 128705, 136318,\n", + " 138628, 139554, 142776, 146707, 149417, 151248, 154953, 157744,\n", + " 173270, 176963, 177823, 178794, 179509, 182752, 184169, 189671,\n", + " 195074, 197970],\n", + " dtype='int64'), Int64Index([ 1440, 6035, 7964, 11677, 17136, 18977, 27179, 28095,\n", + " 33668, 65118, 77463, 82041, 83895, 85749, 86676, 88530,\n", + " 91846, 92806, 93715, 95535, 96490, 98825, 99752, 100650,\n", + " 101580, 106184, 110747, 115343, 120812, 122629, 128706, 136319,\n", + " 138629, 139555, 142777, 146708, 149418, 151249, 154954, 157745,\n", + " 173271, 176964, 177824, 178795, 179510, 182753, 184170, 189672,\n", + " 195075, 197971],\n", + " dtype='int64'), Int64Index([ 1441, 6036, 7965, 11678, 17137, 18978, 27180, 28096,\n", + " 33669, 65119, 77464, 82042, 83896, 85750, 86677, 88531,\n", + " 91847, 92807, 93716, 95536, 96491, 98826, 99753, 100651,\n", + " 101581, 106185, 110748, 115344, 120813, 122630, 128707, 136320,\n", + " 138630, 139556, 142778, 146709, 149419, 151250, 154955, 157746,\n", + " 173272, 176965, 177825, 178796, 179511, 182754, 184171, 189673,\n", + " 195076, 197972],\n", + " dtype='int64'), Int64Index([ 1442, 6037, 7966, 11679, 17138, 18979, 27181, 28097,\n", + " 33670, 65120, 77465, 82043, 83897, 85751, 86678, 88532,\n", + " 91848, 92808, 93717, 95537, 96492, 98827, 99754, 100652,\n", + " 101582, 106186, 110749, 115345, 120814, 122631, 128708, 136321,\n", + " 138631, 139557, 142779, 146710, 149420, 151251, 154956, 157747,\n", + " 173273, 176966, 177826, 178797, 179512, 182755, 184172, 189674,\n", + " 195077, 197973],\n", + " dtype='int64'), Int64Index([ 1443, 6038, 7967, 11680, 17139, 18980, 27182, 28098,\n", + " 33671, 65121, 77466, 82044, 83898, 85752, 86679, 88533,\n", + " 91849, 92809, 93718, 95538, 96493, 98828, 99755, 100653,\n", + " 101583, 106187, 110750, 115346, 120815, 122632, 128709, 136322,\n", + " 138632, 139558, 142780, 146711, 149421, 151252, 154957, 157748,\n", + " 173274, 176967, 177827, 178798, 179513, 182756, 184173, 189675,\n", + " 195078, 197974],\n", + " dtype='int64'), Int64Index([ 1444, 6039, 7968, 11681, 17140, 18981, 27183, 28099,\n", + " 33672, 65122, 77467, 82045, 83899, 85753, 86680, 88534,\n", + " 91850, 92810, 93719, 95539, 96494, 98829, 99756, 100654,\n", + " 101584, 106188, 110751, 115347, 120816, 122633, 128710, 136323,\n", + " 138633, 139559, 142781, 146712, 149422, 151253, 154958, 157749,\n", + " 173275, 176968, 177828, 178799, 179514, 182757, 184174, 189676,\n", + " 195079, 197975],\n", + " dtype='int64'), Int64Index([ 1445, 6040, 7969, 11682, 17141, 18982, 27184, 28100,\n", + " 33673, 65123, 77468, 82046, 83900, 85754, 86681, 88535,\n", + " 91851, 92811, 93720, 95540, 96495, 98830, 99757, 100655,\n", + " 101585, 106189, 110752, 115348, 120817, 122634, 128711, 136324,\n", + " 138634, 139560, 142782, 146713, 149423, 151254, 154959, 157750,\n", + " 173276, 176969, 177829, 178800, 179515, 182758, 184175, 189677,\n", + " 195080, 197976],\n", + " dtype='int64'), Int64Index([ 1446, 6041, 7970, 11683, 17142, 18983, 27185, 28101,\n", + " 33674, 65124, 77469, 82047, 83901, 85755, 86682, 88536,\n", + " 91852, 92812, 93721, 95541, 96496, 98831, 99758, 100656,\n", + " 101586, 106190, 110753, 115349, 120818, 122635, 128712, 136325,\n", + " 138635, 139561, 142783, 146714, 149424, 151255, 154960, 157751,\n", + " 173277, 176970, 177830, 178801, 179516, 182759, 184176, 189678,\n", + " 195081, 197977],\n", + " dtype='int64'), Int64Index([ 1447, 6042, 7971, 11684, 17143, 18984, 27186, 28102,\n", + " 33675, 65125, 77470, 82048, 83902, 85756, 86683, 88537,\n", + " 91853, 92813, 93722, 95542, 96497, 98832, 99759, 100657,\n", + " 101587, 106191, 110754, 115350, 120819, 122636, 128713, 136326,\n", + " 138636, 139562, 142784, 146715, 149425, 151256, 154961, 157752,\n", + " 173278, 176971, 177831, 178802, 179517, 182760, 184177, 189679,\n", + " 195082, 197978],\n", + " dtype='int64'), Int64Index([ 1448, 6043, 7972, 11685, 17144, 18985, 27187, 28103,\n", + " 33676, 65126, 77471, 82049, 83903, 85757, 86684, 88538,\n", + " 91854, 92814, 93723, 95543, 96498, 98833, 99760, 100658,\n", + " 101588, 106192, 110755, 115351, 120820, 122637, 128714, 136327,\n", + " 138637, 139563, 142785, 146716, 149426, 151257, 154962, 157753,\n", + " 173279, 176972, 177832, 178803, 179518, 182761, 184178, 189680,\n", + " 195083, 197979],\n", + " dtype='int64'), Int64Index([ 1449, 6044, 7973, 11686, 17145, 18986, 27188, 28104,\n", + " 33677, 65127, 77472, 82050, 83904, 85758, 86685, 88539,\n", + " 91855, 92815, 93724, 95544, 96499, 98834, 99761, 100659,\n", + " 101589, 106193, 110756, 115352, 120821, 122638, 128715, 136328,\n", + " 138638, 139564, 142786, 146717, 149427, 151258, 154963, 157754,\n", + " 173280, 176973, 177833, 178804, 179519, 182762, 184179, 189681,\n", + " 195084, 197980],\n", + " dtype='int64'), Int64Index([ 1450, 6045, 7974, 11687, 17146, 18987, 27189, 28105,\n", + " 33678, 65128, 77473, 82051, 83905, 85759, 86686, 88540,\n", + " 91856, 92816, 93725, 95545, 96500, 98835, 99762, 100660,\n", + " 101590, 106194, 110757, 115353, 120822, 122639, 128716, 136329,\n", + " 138639, 139565, 142787, 146718, 149428, 151259, 154964, 157755,\n", + " 173281, 176974, 177834, 178805, 179520, 182763, 184180, 189682,\n", + " 195085, 197981],\n", + " dtype='int64'), Int64Index([ 1451, 6046, 7975, 11688, 17147, 18988, 27190, 28106,\n", + " 33679, 65129, 77474, 82052, 83906, 85760, 86687, 88541,\n", + " 91857, 92817, 93726, 95546, 96501, 98836, 99763, 100661,\n", + " 101591, 106195, 110758, 115354, 120823, 122640, 128717, 136330,\n", + " 138640, 139566, 142788, 146719, 149429, 151260, 154965, 157756,\n", + " 173282, 176975, 177835, 178806, 179521, 182764, 184181, 189683,\n", + " 195086, 197982],\n", + " dtype='int64'), Int64Index([ 1452, 6047, 7976, 11689, 17148, 18989, 27191, 28107,\n", + " 33680, 65130, 77475, 82053, 83907, 85761, 86688, 88542,\n", + " 91858, 92818, 93727, 95547, 96502, 98837, 99764, 100662,\n", + " 101592, 106196, 110759, 115355, 120824, 122641, 128718, 136331,\n", + " 138641, 139567, 142789, 146720, 149430, 151261, 154966, 157757,\n", + " 173283, 176976, 177836, 178807, 179522, 182765, 184182, 189684,\n", + " 195087, 197983],\n", + " dtype='int64'), Int64Index([ 1453, 6048, 7977, 11690, 17149, 18990, 27192, 28108,\n", + " 33681, 65131, 77476, 82054, 83908, 85762, 86689, 88543,\n", + " 91859, 92819, 93728, 95548, 96503, 98838, 99765, 100663,\n", + " 101593, 106197, 110760, 115356, 120825, 122642, 128719, 136332,\n", + " 138642, 139568, 142790, 146721, 149431, 151262, 154967, 157758,\n", + " 173284, 176977, 177837, 178808, 179523, 182766, 184183, 189685,\n", + " 195088, 197984],\n", + " dtype='int64'), Int64Index([ 1454, 6049, 7978, 11691, 17150, 18991, 27193, 28109,\n", + " 33682, 65132, 77477, 82055, 83909, 85763, 86690, 88544,\n", + " 91860, 92820, 93729, 95549, 96504, 98839, 99766, 100664,\n", + " 101594, 106198, 110761, 115357, 120826, 122643, 128720, 136333,\n", + " 138643, 139569, 142791, 146722, 149432, 151263, 154968, 157759,\n", + " 173285, 176978, 177838, 178809, 179524, 182767, 184184, 189686,\n", + " 195089, 197985],\n", + " dtype='int64'), Int64Index([ 1455, 6050, 7979, 11692, 17151, 18992, 27194, 28110,\n", + " 33683, 65133, 77478, 82056, 83910, 85764, 86691, 88545,\n", + " 91861, 92821, 93730, 95550, 96505, 98840, 99767, 100665,\n", + " 101595, 106199, 110762, 115358, 120827, 122644, 128721, 136334,\n", + " 138644, 139570, 142792, 146723, 149433, 151264, 154969, 157760,\n", + " 173286, 176979, 177839, 178810, 179525, 182768, 184185, 189687,\n", + " 195090, 197986],\n", + " dtype='int64'), Int64Index([ 1456, 6051, 7980, 11693, 17152, 18993, 27195, 28111,\n", + " 33684, 65134, 77479, 82057, 83911, 85765, 86692, 88546,\n", + " 91862, 92822, 93731, 95551, 96506, 98841, 99768, 100666,\n", + " 101596, 106200, 110763, 115359, 120828, 122645, 128722, 136335,\n", + " 138645, 139571, 142793, 146724, 149434, 151265, 154970, 157761,\n", + " 173287, 176980, 177840, 178811, 179526, 182769, 184186, 189688,\n", + " 195091, 197987],\n", + " dtype='int64'), Int64Index([ 1457, 6052, 7981, 11694, 17153, 18994, 27196, 28112,\n", + " 33685, 65135, 77480, 82058, 83912, 85766, 86693, 88547,\n", + " 91863, 92823, 93732, 95552, 96507, 98842, 99769, 100667,\n", + " 101597, 106201, 110764, 115360, 120829, 122646, 128723, 136336,\n", + " 138646, 139572, 142794, 146725, 149435, 151266, 154971, 157762,\n", + " 173288, 176981, 177841, 178812, 179527, 182770, 184187, 189689,\n", + " 195092, 197988],\n", + " dtype='int64'), Int64Index([ 1458, 6053, 7982, 11695, 17154, 18995, 27197, 28113,\n", + " 33686, 65136, 77481, 82059, 83913, 85767, 86694, 88548,\n", + " 91864, 92824, 93733, 95553, 96508, 98843, 99770, 100668,\n", + " 101598, 106202, 110765, 115361, 120830, 122647, 128724, 136337,\n", + " 138647, 139573, 142795, 146726, 149436, 151267, 154972, 157763,\n", + " 173289, 176982, 177842, 178813, 179528, 182771, 184188, 189690,\n", + " 195093, 197989],\n", + " dtype='int64'), Int64Index([ 1459, 6054, 7983, 11696, 17155, 18996, 27198, 28114,\n", + " 33687, 65137, 77482, 82060, 83914, 85768, 86695, 88549,\n", + " 91865, 92825, 93734, 95554, 96509, 98844, 99771, 100669,\n", + " 101599, 106203, 110766, 115362, 120831, 122648, 128725, 136338,\n", + " 138648, 139574, 142796, 146727, 149437, 151268, 154973, 157764,\n", + " 173290, 176983, 177843, 178814, 179529, 182772, 184189, 189691,\n", + " 195094, 197990],\n", + " dtype='int64'), Int64Index([ 1460, 6055, 7984, 11697, 17156, 18997, 27199, 28115,\n", + " 33688, 65138, 77483, 82061, 83915, 85769, 86696, 88550,\n", + " 91866, 92826, 93735, 95555, 96510, 98845, 99772, 100670,\n", + " 101600, 106204, 110767, 115363, 120832, 122649, 128726, 136339,\n", + " 138649, 139575, 142797, 146728, 149438, 151269, 154974, 157765,\n", + " 173291, 176984, 177844, 178815, 179530, 182773, 184190, 189692,\n", + " 195095, 197991],\n", + " dtype='int64'), Int64Index([ 1461, 6056, 7985, 11698, 17157, 18998, 27200, 28116,\n", + " 33689, 65139, 77484, 82062, 83916, 85770, 86697, 88551,\n", + " 91867, 92827, 93736, 95556, 96511, 98846, 99773, 100671,\n", + " 101601, 106205, 110768, 115364, 120833, 122650, 128727, 136340,\n", + " 138650, 139576, 142798, 146729, 149439, 151270, 154975, 157766,\n", + " 173292, 176985, 177845, 178816, 179531, 182774, 184191, 189693,\n", + " 195096, 197992],\n", + " dtype='int64'), Int64Index([ 1462, 6057, 7986, 11699, 17158, 18999, 27201, 28117,\n", + " 33690, 65140, 77485, 82063, 83917, 85771, 86698, 88552,\n", + " 91868, 92828, 93737, 95557, 96512, 98847, 99774, 100672,\n", + " 101602, 106206, 110769, 115365, 120834, 122651, 128728, 136341,\n", + " 138651, 139577, 142799, 146730, 149440, 151271, 154976, 157767,\n", + " 173293, 176986, 177846, 178817, 179532, 182775, 184192, 189694,\n", + " 195097, 197993],\n", + " dtype='int64'), Int64Index([ 1463, 6058, 7987, 11700, 17159, 19000, 27202, 28118,\n", + " 33691, 65141, 77486, 82064, 83918, 85772, 86699, 88553,\n", + " 91869, 92829, 93738, 95558, 96513, 98848, 99775, 100673,\n", + " 101603, 106207, 110770, 115366, 120835, 122652, 128729, 136342,\n", + " 138652, 139578, 142800, 146731, 149441, 151272, 154977, 157768,\n", + " 173294, 176987, 177847, 178818, 179533, 182776, 184193, 189695,\n", + " 195098, 197994],\n", + " dtype='int64'), Int64Index([ 1464, 6059, 7988, 11701, 17160, 19001, 27203, 28119,\n", + " 33692, 65142, 77487, 82065, 83919, 85773, 86700, 88554,\n", + " 91870, 92830, 93739, 95559, 96514, 98849, 99776, 100674,\n", + " 101604, 106208, 110771, 115367, 120836, 122653, 128730, 136343,\n", + " 138653, 139579, 142801, 146732, 149442, 151273, 154978, 157769,\n", + " 173295, 176988, 177848, 178819, 179534, 182777, 184194, 189696,\n", + " 195099, 197995],\n", + " dtype='int64'), Int64Index([ 1465, 6060, 7989, 11702, 17161, 19002, 27204, 28120,\n", + " 33693, 65143, 77488, 82066, 83920, 85774, 86701, 88555,\n", + " 91871, 92831, 93740, 95560, 96515, 98850, 99777, 100675,\n", + " 101605, 106209, 110772, 115368, 120837, 122654, 128731, 136344,\n", + " 138654, 139580, 142802, 146733, 149443, 151274, 154979, 157770,\n", + " 173296, 176989, 177849, 178820, 179535, 182778, 184195, 189697,\n", + " 195100, 197996],\n", + " dtype='int64'), Int64Index([ 1466, 6061, 7990, 11703, 17162, 19003, 27205, 28121,\n", + " 33694, 65144, 77489, 82067, 83921, 85775, 86702, 88556,\n", + " 91872, 92832, 93741, 95561, 96516, 98851, 99778, 100676,\n", + " 101606, 106210, 110773, 115369, 120838, 122655, 128732, 136345,\n", + " 138655, 139581, 142803, 146734, 149444, 151275, 154980, 157771,\n", + " 173297, 176990, 177850, 178821, 179536, 182779, 184196, 189698,\n", + " 195101, 197997],\n", + " dtype='int64'), Int64Index([ 1467, 6062, 7991, 11704, 17163, 19004, 27206, 28122,\n", + " 33695, 65145, 77490, 82068, 83922, 85776, 86703, 88557,\n", + " 91873, 92833, 93742, 95562, 96517, 98852, 99779, 100677,\n", + " 101607, 106211, 110774, 115370, 120839, 122656, 128733, 136346,\n", + " 138656, 139582, 142804, 146735, 149445, 151276, 154981, 157772,\n", + " 173298, 176991, 177851, 178822, 179537, 182780, 184197, 189699,\n", + " 195102, 197998],\n", + " dtype='int64'), Int64Index([ 1468, 6063, 7992, 11705, 17164, 19005, 27207, 28123,\n", + " 33696, 65146, 77491, 82069, 83923, 85777, 86704, 88558,\n", + " 91874, 92834, 93743, 95563, 96518, 98853, 99780, 100678,\n", + " 101608, 106212, 110775, 115371, 120840, 122657, 128734, 136347,\n", + " 138657, 139583, 142805, 146736, 149446, 151277, 154982, 157773,\n", + " 173299, 176992, 177852, 178823, 179538, 182781, 184198, 189700,\n", + " 195103, 197999],\n", + " dtype='int64'), Int64Index([ 1469, 6064, 7993, 11706, 17165, 19006, 27208, 28124,\n", + " 33697, 65147, 77492, 82070, 83924, 85778, 86705, 88559,\n", + " 91875, 92835, 93744, 95564, 96519, 98854, 99781, 100679,\n", + " 101609, 106213, 110776, 115372, 120841, 122658, 128735, 136348,\n", + " 138658, 139584, 142806, 146737, 149447, 151278, 154983, 157774,\n", + " 173300, 176993, 177853, 178824, 179539, 182782, 184199, 189701,\n", + " 195104, 198000],\n", + " dtype='int64'), Int64Index([ 1470, 6065, 7994, 11707, 17166, 19007, 27209, 28125,\n", + " 33698, 65148, 77493, 82071, 83925, 85779, 86706, 88560,\n", + " 91876, 92836, 93745, 95565, 96520, 98855, 99782, 100680,\n", + " 101610, 106214, 110777, 115373, 120842, 122659, 128736, 136349,\n", + " 138659, 139585, 142807, 146738, 149448, 151279, 154984, 157775,\n", + " 173301, 176994, 177854, 178825, 179540, 182783, 184200, 189702,\n", + " 195105, 198001],\n", + " dtype='int64'), Int64Index([ 1471, 6066, 7995, 11708, 17167, 19008, 27210, 28126,\n", + " 33699, 65149, 77494, 82072, 83926, 85780, 86707, 88561,\n", + " 91877, 92837, 93746, 95566, 96521, 98856, 99783, 100681,\n", + " 101611, 106215, 110778, 115374, 120843, 122660, 128737, 136350,\n", + " 138660, 139586, 142808, 146739, 149449, 151280, 154985, 157776,\n", + " 173302, 176995, 177855, 178826, 179541, 182784, 184201, 189703,\n", + " 195106, 198002],\n", + " dtype='int64'), Int64Index([ 1472, 6067, 7996, 11709, 17168, 19009, 27211, 28127,\n", + " 33700, 65150, 77495, 82073, 83927, 85781, 86708, 88562,\n", + " 91878, 92838, 93747, 95567, 96522, 98857, 99784, 100682,\n", + " 101612, 106216, 110779, 115375, 120844, 122661, 128738, 136351,\n", + " 138661, 139587, 142809, 146740, 149450, 151281, 154986, 157777,\n", + " 173303, 176996, 177856, 178827, 179542, 182785, 184202, 189704,\n", + " 195107, 198003],\n", + " dtype='int64'), Int64Index([ 1473, 6068, 7997, 11710, 17169, 19010, 27212, 28128,\n", + " 33701, 65151, 77496, 82074, 83928, 85782, 86709, 88563,\n", + " 91879, 92839, 93748, 95568, 96523, 98858, 99785, 100683,\n", + " 101613, 106217, 110780, 115376, 120845, 122662, 128739, 136352,\n", + " 138662, 139588, 142810, 146741, 149451, 151282, 154987, 157778,\n", + " 173304, 176997, 177857, 178828, 179543, 182786, 184203, 189705,\n", + " 195108, 198004],\n", + " dtype='int64'), Int64Index([ 1474, 6069, 7998, 11711, 17170, 19011, 27213, 28129,\n", + " 33702, 65152, 77497, 82075, 83929, 85783, 86710, 88564,\n", + " 91880, 92840, 93749, 95569, 96524, 98859, 99786, 100684,\n", + " 101614, 106218, 110781, 115377, 120846, 122663, 128740, 136353,\n", + " 138663, 139589, 142811, 146742, 149452, 151283, 154988, 157779,\n", + " 173305, 176998, 177858, 178829, 179544, 182787, 184204, 189706,\n", + " 195109, 198005],\n", + " dtype='int64'), Int64Index([ 1475, 6070, 7999, 11712, 17171, 19012, 27214, 28130,\n", + " 33703, 65153, 77498, 82076, 83930, 85784, 86711, 88565,\n", + " 91881, 92841, 93750, 95570, 96525, 98860, 99787, 100685,\n", + " 101615, 106219, 110782, 115378, 120847, 122664, 128741, 136354,\n", + " 138664, 139590, 142812, 146743, 149453, 151284, 154989, 157780,\n", + " 173306, 176999, 177859, 178830, 179545, 182788, 184205, 189707,\n", + " 195110, 198006],\n", + " dtype='int64'), Int64Index([ 1476, 6071, 8000, 11713, 17172, 19013, 27215, 28131,\n", + " 33704, 65154, 77499, 82077, 83931, 85785, 86712, 88566,\n", + " 91882, 92842, 93751, 95571, 96526, 98861, 99788, 100686,\n", + " 101616, 106220, 110783, 115379, 120848, 122665, 128742, 136355,\n", + " 138665, 139591, 142813, 146744, 149454, 151285, 154990, 157781,\n", + " 173307, 177000, 177860, 178831, 179546, 182789, 184206, 189708,\n", + " 195111, 198007],\n", + " dtype='int64'), Int64Index([ 1477, 6072, 8001, 11714, 17173, 19014, 27216, 28132,\n", + " 33705, 65155, 77500, 82078, 83932, 85786, 86713, 88567,\n", + " 91883, 92843, 93752, 95572, 96527, 98862, 99789, 100687,\n", + " 101617, 106221, 110784, 115380, 120849, 122666, 128743, 136356,\n", + " 138666, 139592, 142814, 146745, 149455, 151286, 154991, 157782,\n", + " 173308, 177001, 177861, 178832, 179547, 182790, 184207, 189709,\n", + " 195112, 198008],\n", + " dtype='int64'), Int64Index([ 1478, 6073, 8002, 11715, 17174, 19015, 27217, 28133,\n", + " 33706, 65156, 77501, 82079, 83933, 85787, 86714, 88568,\n", + " 91884, 92844, 93753, 95573, 96528, 98863, 99790, 100688,\n", + " 101618, 106222, 110785, 115381, 120850, 122667, 128744, 136357,\n", + " 138667, 139593, 142815, 146746, 149456, 151287, 154992, 157783,\n", + " 173309, 177002, 177862, 178833, 179548, 182791, 184208, 189710,\n", + " 195113, 198009],\n", + " dtype='int64'), Int64Index([ 1479, 6074, 8003, 11716, 17175, 19016, 27218, 28134,\n", + " 33707, 65157, 77502, 82080, 83934, 85788, 86715, 88569,\n", + " 91885, 92845, 93754, 95574, 96529, 98864, 99791, 100689,\n", + " 101619, 106223, 110786, 115382, 120851, 122668, 128745, 136358,\n", + " 138668, 139594, 142816, 146747, 149457, 151288, 154993, 157784,\n", + " 173310, 177003, 177863, 178834, 179549, 182792, 184209, 189711,\n", + " 195114, 198010],\n", + " dtype='int64'), Int64Index([ 1480, 6075, 8004, 11717, 17176, 19017, 27219, 28135,\n", + " 33708, 65158, 77503, 82081, 83935, 85789, 86716, 88570,\n", + " 91886, 92846, 93755, 95575, 96530, 98865, 99792, 100690,\n", + " 101620, 106224, 110787, 115383, 120852, 122669, 128746, 136359,\n", + " 138669, 139595, 142817, 146748, 149458, 151289, 154994, 157785,\n", + " 173311, 177004, 177864, 178835, 179550, 182793, 184210, 189712,\n", + " 195115, 198011],\n", + " dtype='int64'), Int64Index([ 1481, 6076, 8005, 11718, 17177, 19018, 27220, 28136,\n", + " 33709, 65159, 77504, 82082, 83936, 85790, 86717, 88571,\n", + " 91887, 92847, 93756, 95576, 96531, 98866, 99793, 100691,\n", + " 101621, 106225, 110788, 115384, 120853, 122670, 128747, 136360,\n", + " 138670, 139596, 142818, 146749, 149459, 151290, 154995, 157786,\n", + " 173312, 177005, 177865, 178836, 179551, 182794, 184211, 189713,\n", + " 195116, 198012],\n", + " dtype='int64'), Int64Index([ 1482, 6077, 8006, 11719, 17178, 19019, 27221, 28137,\n", + " 33710, 65160, 77505, 82083, 83937, 85791, 86718, 88572,\n", + " 91888, 92848, 93757, 95577, 96532, 98867, 99794, 100692,\n", + " 101622, 106226, 110789, 115385, 120854, 122671, 128748, 136361,\n", + " 138671, 139597, 142819, 146750, 149460, 151291, 154996, 157787,\n", + " 173313, 177006, 177866, 178837, 179552, 182795, 184212, 189714,\n", + " 195117, 198013],\n", + " dtype='int64'), Int64Index([ 1483, 6078, 8007, 11720, 17179, 19020, 27222, 28138,\n", + " 33711, 65161, 77506, 82084, 83938, 85792, 86719, 88573,\n", + " 91889, 92849, 93758, 95578, 96533, 98868, 99795, 100693,\n", + " 101623, 106227, 110790, 115386, 120855, 122672, 128749, 136362,\n", + " 138672, 139598, 142820, 146751, 149461, 151292, 154997, 157788,\n", + " 173314, 177007, 177867, 178838, 179553, 182796, 184213, 189715,\n", + " 195118, 198014],\n", + " dtype='int64'), Int64Index([ 1484, 6079, 8008, 11721, 17180, 19021, 27223, 28139,\n", + " 33712, 65162, 77507, 82085, 83939, 85793, 86720, 88574,\n", + " 91890, 92850, 93759, 95579, 96534, 98869, 99796, 100694,\n", + " 101624, 106228, 110791, 115387, 120856, 122673, 128750, 136363,\n", + " 138673, 139599, 142821, 146752, 149462, 151293, 154998, 157789,\n", + " 173315, 177008, 177868, 178839, 179554, 182797, 184214, 189716,\n", + " 195119, 198015],\n", + " dtype='int64'), Int64Index([ 1485, 6080, 8009, 11722, 17181, 19022, 27224, 28140,\n", + " 33713, 65163, 77508, 82086, 83940, 85794, 86721, 88575,\n", + " 91891, 92851, 93760, 95580, 96535, 98870, 99797, 100695,\n", + " 101625, 106229, 110792, 115388, 120857, 122674, 128751, 136364,\n", + " 138674, 139600, 142822, 146753, 149463, 151294, 154999, 157790,\n", + " 173316, 177009, 177869, 178840, 179555, 182798, 184215, 189717,\n", + " 195120, 198016],\n", + " dtype='int64'), Int64Index([ 1486, 6081, 8010, 11723, 17182, 19023, 27225, 28141,\n", + " 33714, 65164, 77509, 82087, 83941, 85795, 86722, 88576,\n", + " 91892, 92852, 93761, 95581, 96536, 98871, 99798, 100696,\n", + " 101626, 106230, 110793, 115389, 120858, 122675, 128752, 136365,\n", + " 138675, 139601, 142823, 146754, 149464, 151295, 155000, 157791,\n", + " 173317, 177010, 177870, 178841, 179556, 182799, 184216, 189718,\n", + " 195121, 198017],\n", + " dtype='int64'), Int64Index([ 1487, 6082, 8011, 11724, 17183, 19024, 27226, 28142,\n", + " 33715, 65165, 77510, 82088, 83942, 85796, 86723, 88577,\n", + " 91893, 92853, 93762, 95582, 96537, 98872, 99799, 100697,\n", + " 101627, 106231, 110794, 115390, 120859, 122676, 128753, 136366,\n", + " 138676, 139602, 142824, 146755, 149465, 151296, 155001, 157792,\n", + " 173318, 177011, 177871, 178842, 179557, 182800, 184217, 189719,\n", + " 195122, 198018],\n", + " dtype='int64'), Int64Index([ 1488, 6083, 8012, 11725, 17184, 19025, 27227, 28143,\n", + " 33716, 65166, 77511, 82089, 83943, 85797, 86724, 88578,\n", + " 91894, 92854, 93763, 95583, 96538, 98873, 99800, 100698,\n", + " 101628, 106232, 110795, 115391, 120860, 122677, 128754, 136367,\n", + " 138677, 139603, 142825, 146756, 149466, 151297, 155002, 157793,\n", + " 173319, 177012, 177872, 178843, 179558, 182801, 184218, 189720,\n", + " 195123, 198019],\n", + " dtype='int64'), Int64Index([ 1489, 6084, 8013, 11726, 17185, 19026, 27228, 28144,\n", + " 33717, 65167, 77512, 82090, 83944, 85798, 86725, 88579,\n", + " 91895, 92855, 93764, 95584, 96539, 98874, 99801, 100699,\n", + " 101629, 106233, 110796, 115392, 120861, 122678, 128755, 136368,\n", + " 138678, 139604, 142826, 146757, 149467, 151298, 155003, 157794,\n", + " 173320, 177013, 177873, 178844, 179559, 182802, 184219, 189721,\n", + " 195124, 198020],\n", + " dtype='int64'), Int64Index([ 1490, 6085, 8014, 11727, 17186, 19027, 27229, 28145,\n", + " 33718, 65168, 77513, 82091, 83945, 85799, 86726, 88580,\n", + " 91896, 92856, 93765, 95585, 96540, 98875, 99802, 100700,\n", + " 101630, 106234, 110797, 115393, 120862, 122679, 128756, 136369,\n", + " 138679, 139605, 142827, 146758, 149468, 151299, 155004, 157795,\n", + " 173321, 177014, 177874, 178845, 179560, 182803, 184220, 189722,\n", + " 195125, 198021],\n", + " dtype='int64'), Int64Index([ 1491, 6086, 8015, 11728, 17187, 19028, 27230, 28146,\n", + " 33719, 65169, 77514, 82092, 83946, 85800, 86727, 88581,\n", + " 91897, 92857, 93766, 95586, 96541, 98876, 99803, 100701,\n", + " 101631, 106235, 110798, 115394, 120863, 122680, 128757, 136370,\n", + " 138680, 139606, 142828, 146759, 149469, 151300, 155005, 157796,\n", + " 173322, 177015, 177875, 178846, 179561, 182804, 184221, 189723,\n", + " 195126, 198022],\n", + " dtype='int64'), Int64Index([ 1492, 6087, 8016, 11729, 17188, 19029, 27231, 28147,\n", + " 33720, 65170, 77515, 82093, 83947, 85801, 86728, 88582,\n", + " 91898, 92858, 93767, 95587, 96542, 98877, 99804, 100702,\n", + " 101632, 106236, 110799, 115395, 120864, 122681, 128758, 136371,\n", + " 138681, 139607, 142829, 146760, 149470, 151301, 155006, 157797,\n", + " 173323, 177016, 177876, 178847, 179562, 182805, 184222, 189724,\n", + " 195127, 198023],\n", + " dtype='int64'), Int64Index([ 1493, 6088, 8017, 11730, 17189, 19030, 27232, 28148,\n", + " 33721, 65171, 77516, 82094, 83948, 85802, 86729, 88583,\n", + " 91899, 92859, 93768, 95588, 96543, 98878, 99805, 100703,\n", + " 101633, 106237, 110800, 115396, 120865, 122682, 128759, 136372,\n", + " 138682, 139608, 142830, 146761, 149471, 151302, 155007, 157798,\n", + " 173324, 177017, 177877, 178848, 179563, 182806, 184223, 189725,\n", + " 195128, 198024],\n", + " dtype='int64'), Int64Index([ 1494, 6089, 8018, 11731, 17190, 19031, 27233, 28149,\n", + " 33722, 65172, 77517, 82095, 83949, 85803, 86730, 88584,\n", + " 91900, 92860, 93769, 95589, 96544, 98879, 99806, 100704,\n", + " 101634, 106238, 110801, 115397, 120866, 122683, 128760, 136373,\n", + " 138683, 139609, 142831, 146762, 149472, 151303, 155008, 157799,\n", + " 173325, 177018, 177878, 178849, 179564, 182807, 184224, 189726,\n", + " 195129, 198025],\n", + " dtype='int64'), Int64Index([ 1495, 6090, 8019, 11732, 17191, 19032, 27234, 28150,\n", + " 33723, 65173, 77518, 82096, 83950, 85804, 86731, 88585,\n", + " 91901, 92861, 93770, 95590, 96545, 98880, 99807, 100705,\n", + " 101635, 106239, 110802, 115398, 120867, 122684, 128761, 136374,\n", + " 138684, 139610, 142832, 146763, 149473, 151304, 155009, 157800,\n", + " 173326, 177019, 177879, 178850, 179565, 182808, 184225, 189727,\n", + " 195130, 198026],\n", + " dtype='int64'), Int64Index([ 1496, 6091, 8020, 11733, 17192, 19033, 27235, 28151,\n", + " 33724, 65174, 77519, 82097, 83951, 85805, 86732, 88586,\n", + " 91902, 92862, 93771, 95591, 96546, 98881, 99808, 100706,\n", + " 101636, 106240, 110803, 115399, 120868, 122685, 128762, 136375,\n", + " 138685, 139611, 142833, 146764, 149474, 151305, 155010, 157801,\n", + " 173327, 177020, 177880, 178851, 179566, 182809, 184226, 189728,\n", + " 195131, 198027],\n", + " dtype='int64'), Int64Index([ 1497, 6092, 8021, 11734, 17193, 19034, 27236, 28152,\n", + " 33725, 65175, 77520, 82098, 83952, 85806, 86733, 88587,\n", + " 91903, 92863, 93772, 95592, 96547, 98882, 99809, 100707,\n", + " 101637, 106241, 110804, 115400, 120869, 122686, 128763, 136376,\n", + " 138686, 139612, 142834, 146765, 149475, 151306, 155011, 157802,\n", + " 173328, 177021, 177881, 178852, 179567, 182810, 184227, 189729,\n", + " 195132, 198028],\n", + " dtype='int64'), Int64Index([ 1498, 6093, 8022, 11735, 17194, 19035, 27237, 28153,\n", + " 33726, 65176, 77521, 82099, 83953, 85807, 86734, 88588,\n", + " 91904, 92864, 93773, 95593, 96548, 98883, 99810, 100708,\n", + " 101638, 106242, 110805, 115401, 120870, 122687, 128764, 136377,\n", + " 138687, 139613, 142835, 146766, 149476, 151307, 155012, 157803,\n", + " 173329, 177022, 177882, 178853, 179568, 182811, 184228, 189730,\n", + " 195133, 198029],\n", + " dtype='int64'), Int64Index([ 1499, 6094, 8023, 11736, 17195, 19036, 27238, 28154,\n", + " 33727, 65177, 77522, 82100, 83954, 85808, 86735, 88589,\n", + " 91905, 92865, 93774, 95594, 96549, 98884, 99811, 100709,\n", + " 101639, 106243, 110806, 115402, 120871, 122688, 128765, 136378,\n", + " 138688, 139614, 142836, 146767, 149477, 151308, 155013, 157804,\n", + " 173330, 177023, 177883, 178854, 179569, 182812, 184229, 189731,\n", + " 195134, 198030],\n", + " dtype='int64'), Int64Index([ 1500, 6095, 8024, 11737, 17196, 19037, 27239, 28155,\n", + " 33728, 65178, 77523, 82101, 83955, 85809, 86736, 88590,\n", + " 91906, 92866, 93775, 95595, 96550, 98885, 99812, 100710,\n", + " 101640, 106244, 110807, 115403, 120872, 122689, 128766, 136379,\n", + " 138689, 139615, 142837, 146768, 149478, 151309, 155014, 157805,\n", + " 173331, 177024, 177884, 178855, 179570, 182813, 184230, 189732,\n", + " 195135, 198031],\n", + " dtype='int64'), Int64Index([ 1501, 6096, 8025, 11738, 17197, 19038, 27240, 28156,\n", + " 33729, 65179, 77524, 82102, 83956, 85810, 86737, 88591,\n", + " 91907, 92867, 93776, 95596, 96551, 98886, 99813, 100711,\n", + " 101641, 106245, 110808, 115404, 120873, 122690, 128767, 136380,\n", + " 138690, 139616, 142838, 146769, 149479, 151310, 155015, 157806,\n", + " 173332, 177025, 177885, 178856, 179571, 182814, 184231, 189733,\n", + " 195136, 198032],\n", + " dtype='int64'), Int64Index([ 1502, 6097, 8026, 11739, 17198, 19039, 27241, 28157,\n", + " 33730, 65180, 77525, 82103, 83957, 85811, 86738, 88592,\n", + " 91908, 92868, 93777, 95597, 96552, 98887, 99814, 100712,\n", + " 101642, 106246, 110809, 115405, 120874, 122691, 128768, 136381,\n", + " 138691, 139617, 142839, 146770, 149480, 151311, 155016, 157807,\n", + " 173333, 177026, 177886, 178857, 179572, 182815, 184232, 189734,\n", + " 195137, 198033],\n", + " dtype='int64'), Int64Index([ 1503, 6098, 8027, 11740, 17199, 19040, 27242, 28158,\n", + " 33731, 65181, 77526, 82104, 83958, 85812, 86739, 88593,\n", + " 91909, 92869, 93778, 95598, 96553, 98888, 99815, 100713,\n", + " 101643, 106247, 110810, 115406, 120875, 122692, 128769, 136382,\n", + " 138692, 139618, 142840, 146771, 149481, 151312, 155017, 157808,\n", + " 173334, 177027, 177887, 178858, 179573, 182816, 184233, 189735,\n", + " 195138, 198034],\n", + " dtype='int64'), Int64Index([ 1504, 6099, 8028, 11741, 17200, 19041, 27243, 28159,\n", + " 33732, 65182, 77527, 82105, 83959, 85813, 86740, 88594,\n", + " 91910, 92870, 93779, 95599, 96554, 98889, 99816, 100714,\n", + " 101644, 106248, 110811, 115407, 120876, 122693, 128770, 136383,\n", + " 138693, 139619, 142841, 146772, 149482, 151313, 155018, 157809,\n", + " 173335, 177028, 177888, 178859, 179574, 182817, 184234, 189736,\n", + " 195139, 198035],\n", + " dtype='int64'), Int64Index([ 1505, 6100, 8029, 11742, 17201, 19042, 27244, 28160,\n", + " 33733, 65183, 77528, 82106, 83960, 85814, 86741, 88595,\n", + " 91911, 92871, 93780, 95600, 96555, 98890, 99817, 100715,\n", + " 101645, 106249, 110812, 115408, 120877, 122694, 128771, 136384,\n", + " 138694, 139620, 142842, 146773, 149483, 151314, 155019, 157810,\n", + " 173336, 177029, 177889, 178860, 179575, 182818, 184235, 189737,\n", + " 195140, 198036],\n", + " dtype='int64'), Int64Index([ 1506, 6101, 8030, 11743, 17202, 19043, 27245, 28161,\n", + " 33734, 65184, 77529, 82107, 83961, 85815, 86742, 88596,\n", + " 91912, 92872, 93781, 95601, 96556, 98891, 99818, 100716,\n", + " 101646, 106250, 110813, 115409, 120878, 122695, 128772, 136385,\n", + " 138695, 139621, 142843, 146774, 149484, 151315, 155020, 157811,\n", + " 173337, 177030, 177890, 178861, 179576, 182819, 184236, 189738,\n", + " 195141, 198037],\n", + " dtype='int64'), Int64Index([ 1507, 6102, 8031, 11744, 17203, 19044, 27246, 28162,\n", + " 33735, 65185, 77530, 82108, 83962, 85816, 86743, 88597,\n", + " 91913, 92873, 93782, 95602, 96557, 98892, 99819, 100717,\n", + " 101647, 106251, 110814, 115410, 120879, 122696, 128773, 136386,\n", + " 138696, 139622, 142844, 146775, 149485, 151316, 155021, 157812,\n", + " 173338, 177031, 177891, 178862, 179577, 182820, 184237, 189739,\n", + " 195142, 198038],\n", + " dtype='int64'), Int64Index([ 1508, 6103, 8032, 11745, 17204, 19045, 27247, 28163,\n", + " 33736, 65186, 77531, 82109, 83963, 85817, 86744, 88598,\n", + " 91914, 92874, 93783, 95603, 96558, 98893, 99820, 100718,\n", + " 101648, 106252, 110815, 115411, 120880, 122697, 128774, 136387,\n", + " 138697, 139623, 142845, 146776, 149486, 151317, 155022, 157813,\n", + " 173339, 177032, 177892, 178863, 179578, 182821, 184238, 189740,\n", + " 195143, 198039],\n", + " dtype='int64'), Int64Index([ 1509, 6104, 8033, 11746, 17205, 19046, 27248, 28164,\n", + " 33737, 65187, 77532, 82110, 83964, 85818, 86745, 88599,\n", + " 91915, 92875, 93784, 95604, 96559, 98894, 99821, 100719,\n", + " 101649, 106253, 110816, 115412, 120881, 122698, 128775, 136388,\n", + " 138698, 139624, 142846, 146777, 149487, 151318, 155023, 157814,\n", + " 173340, 177033, 177893, 178864, 179579, 182822, 184239, 189741,\n", + " 195144, 198040],\n", + " dtype='int64'), Int64Index([ 1510, 6105, 8034, 11747, 17206, 19047, 27249, 28165,\n", + " 33738, 65188, 77533, 82111, 83965, 85819, 86746, 88600,\n", + " 91916, 92876, 93785, 95605, 96560, 98895, 99822, 100720,\n", + " 101650, 106254, 110817, 115413, 120882, 122699, 128776, 136389,\n", + " 138699, 139625, 142847, 146778, 149488, 151319, 155024, 157815,\n", + " 173341, 177034, 177894, 178865, 179580, 182823, 184240, 189742,\n", + " 195145, 198041],\n", + " dtype='int64'), Int64Index([ 1511, 6106, 8035, 11748, 17207, 19048, 27250, 28166,\n", + " 33739, 65189, 77534, 82112, 83966, 85820, 86747, 88601,\n", + " 91917, 92877, 93786, 95606, 96561, 98896, 99823, 100721,\n", + " 101651, 106255, 110818, 115414, 120883, 122700, 128777, 136390,\n", + " 138700, 139626, 142848, 146779, 149489, 151320, 155025, 157816,\n", + " 173342, 177035, 177895, 178866, 179581, 182824, 184241, 189743,\n", + " 195146, 198042],\n", + " dtype='int64'), Int64Index([ 1512, 6107, 8036, 11749, 17208, 19049, 27251, 28167,\n", + " 33740, 65190, 77535, 82113, 83967, 85821, 86748, 88602,\n", + " 91918, 92878, 93787, 95607, 96562, 98897, 99824, 100722,\n", + " 101652, 106256, 110819, 115415, 120884, 122701, 128778, 136391,\n", + " 138701, 139627, 142849, 146780, 149490, 151321, 155026, 157817,\n", + " 173343, 177036, 177896, 178867, 179582, 182825, 184242, 189744,\n", + " 195147, 198043],\n", + " dtype='int64'), Int64Index([ 1513, 6108, 8037, 11750, 17209, 19050, 27252, 28168,\n", + " 33741, 65191, 77536, 82114, 83968, 85822, 86749, 88603,\n", + " 91919, 92879, 93788, 95608, 96563, 98898, 99825, 100723,\n", + " 101653, 106257, 110820, 115416, 120885, 122702, 128779, 136392,\n", + " 138702, 139628, 142850, 146781, 149491, 151322, 155027, 157818,\n", + " 173344, 177037, 177897, 178868, 179583, 182826, 184243, 189745,\n", + " 195148, 198044],\n", + " dtype='int64'), Int64Index([ 1514, 6109, 8038, 11751, 17210, 19051, 27253, 28169,\n", + " 33742, 65192, 77537, 82115, 83969, 85823, 86750, 88604,\n", + " 91920, 92880, 93789, 95609, 96564, 98899, 99826, 100724,\n", + " 101654, 106258, 110821, 115417, 120886, 122703, 128780, 136393,\n", + " 138703, 139629, 142851, 146782, 149492, 151323, 155028, 157819,\n", + " 173345, 177038, 177898, 178869, 179584, 182827, 184244, 189746,\n", + " 195149, 198045],\n", + " dtype='int64'), Int64Index([ 1515, 6110, 8039, 11752, 17211, 19052, 27254, 28170,\n", + " 33743, 65193, 77538, 82116, 83970, 85824, 86751, 88605,\n", + " 91921, 92881, 93790, 95610, 96565, 98900, 99827, 100725,\n", + " 101655, 106259, 110822, 115418, 120887, 122704, 128781, 136394,\n", + " 138704, 139630, 142852, 146783, 149493, 151324, 155029, 157820,\n", + " 173346, 177039, 177899, 178870, 179585, 182828, 184245, 189747,\n", + " 195150, 198046],\n", + " dtype='int64'), Int64Index([ 1516, 6111, 8040, 11753, 17212, 19053, 27255, 28171,\n", + " 33744, 65194, 77539, 82117, 83971, 85825, 86752, 88606,\n", + " 91922, 92882, 93791, 95611, 96566, 98901, 99828, 100726,\n", + " 101656, 106260, 110823, 115419, 120888, 122705, 128782, 136395,\n", + " 138705, 139631, 142853, 146784, 149494, 151325, 155030, 157821,\n", + " 173347, 177040, 177900, 178871, 179586, 182829, 184246, 189748,\n", + " 195151, 198047],\n", + " dtype='int64'), Int64Index([ 1517, 6112, 8041, 11754, 17213, 19054, 27256, 28172,\n", + " 33745, 65195, 77540, 82118, 83972, 85826, 86753, 88607,\n", + " 91923, 92883, 93792, 95612, 96567, 98902, 99829, 100727,\n", + " 101657, 106261, 110824, 115420, 120889, 122706, 128783, 136396,\n", + " 138706, 139632, 142854, 146785, 149495, 151326, 155031, 157822,\n", + " 173348, 177041, 177901, 178872, 179587, 182830, 184247, 189749,\n", + " 195152, 198048],\n", + " dtype='int64'), Int64Index([ 1518, 6113, 8042, 11755, 17214, 19055, 27257, 28173,\n", + " 33746, 65196, 77541, 82119, 83973, 85827, 86754, 88608,\n", + " 91924, 92884, 93793, 95613, 96568, 98903, 99830, 100728,\n", + " 101658, 106262, 110825, 115421, 120890, 122707, 128784, 136397,\n", + " 138707, 139633, 142855, 146786, 149496, 151327, 155032, 157823,\n", + " 173349, 177042, 177902, 178873, 179588, 182831, 184248, 189750,\n", + " 195153, 198049],\n", + " dtype='int64'), Int64Index([ 1519, 6114, 8043, 11756, 17215, 19056, 27258, 28174,\n", + " 33747, 65197, 77542, 82120, 83974, 85828, 86755, 88609,\n", + " 91925, 92885, 93794, 95614, 96569, 98904, 99831, 100729,\n", + " 101659, 106263, 110826, 115422, 120891, 122708, 128785, 136398,\n", + " 138708, 139634, 142856, 146787, 149497, 151328, 155033, 157824,\n", + " 173350, 177043, 177903, 178874, 179589, 182832, 184249, 189751,\n", + " 195154, 198050],\n", + " dtype='int64'), Int64Index([ 1520, 6115, 8044, 11757, 17216, 19057, 27259, 28175,\n", + " 33748, 65198, 77543, 82121, 83975, 85829, 86756, 88610,\n", + " 91926, 92886, 93795, 95615, 96570, 98905, 99832, 100730,\n", + " 101660, 106264, 110827, 115423, 120892, 122709, 128786, 136399,\n", + " 138709, 139635, 142857, 146788, 149498, 151329, 155034, 157825,\n", + " 173351, 177044, 177904, 178875, 179590, 182833, 184250, 189752,\n", + " 195155, 198051],\n", + " dtype='int64'), Int64Index([ 1521, 6116, 8045, 11758, 17217, 19058, 27260, 28176,\n", + " 33749, 65199, 77544, 82122, 83976, 85830, 86757, 88611,\n", + " 91927, 92887, 93796, 95616, 96571, 98906, 99833, 100731,\n", + " 101661, 106265, 110828, 115424, 120893, 122710, 128787, 136400,\n", + " 138710, 139636, 142858, 146789, 149499, 151330, 155035, 157826,\n", + " 173352, 177045, 177905, 178876, 179591, 182834, 184251, 189753,\n", + " 195156, 198052],\n", + " dtype='int64'), Int64Index([ 1522, 6117, 8046, 11759, 17218, 19059, 27261, 28177,\n", + " 33750, 65200, 77545, 82123, 83977, 85831, 86758, 88612,\n", + " 91928, 92888, 93797, 95617, 96572, 98907, 99834, 100732,\n", + " 101662, 106266, 110829, 115425, 120894, 122711, 128788, 136401,\n", + " 138711, 139637, 142859, 146790, 149500, 151331, 155036, 157827,\n", + " 173353, 177046, 177906, 178877, 179592, 182835, 184252, 189754,\n", + " 195157, 198053],\n", + " dtype='int64'), Int64Index([ 1523, 6118, 8047, 11760, 17219, 19060, 27262, 28178,\n", + " 33751, 65201, 77546, 82124, 83978, 85832, 86759, 88613,\n", + " 91929, 92889, 93798, 95618, 96573, 98908, 99835, 100733,\n", + " 101663, 106267, 110830, 115426, 120895, 122712, 128789, 136402,\n", + " 138712, 139638, 142860, 146791, 149501, 151332, 155037, 157828,\n", + " 173354, 177047, 177907, 178878, 179593, 182836, 184253, 189755,\n", + " 195158, 198054],\n", + " dtype='int64'), Int64Index([ 1524, 6119, 8048, 11761, 17220, 19061, 27263, 28179,\n", + " 33752, 65202, 77547, 82125, 83979, 85833, 86760, 88614,\n", + " 91930, 92890, 93799, 95619, 96574, 98909, 99836, 100734,\n", + " 101664, 106268, 110831, 115427, 120896, 122713, 128790, 136403,\n", + " 138713, 139639, 142861, 146792, 149502, 151333, 155038, 157829,\n", + " 173355, 177048, 177908, 178879, 179594, 182837, 184254, 189756,\n", + " 195159, 198055],\n", + " dtype='int64'), Int64Index([ 1525, 6120, 8049, 11762, 17221, 19062, 27264, 28180,\n", + " 33753, 65203, 77548, 82126, 83980, 85834, 86761, 88615,\n", + " 91931, 92891, 93800, 95620, 96575, 98910, 99837, 100735,\n", + " 101665, 106269, 110832, 115428, 120897, 122714, 128791, 136404,\n", + " 138714, 139640, 142862, 146793, 149503, 151334, 155039, 157830,\n", + " 173356, 177049, 177909, 178880, 179595, 182838, 184255, 189757,\n", + " 195160, 198056],\n", + " dtype='int64'), Int64Index([ 1526, 6121, 8050, 11763, 17222, 19063, 27265, 28181,\n", + " 33754, 65204, 77549, 82127, 83981, 85835, 86762, 88616,\n", + " 91932, 92892, 93801, 95621, 96576, 98911, 99838, 100736,\n", + " 101666, 106270, 110833, 115429, 120898, 122715, 128792, 136405,\n", + " 138715, 139641, 142863, 146794, 149504, 151335, 155040, 157831,\n", + " 173357, 177050, 177910, 178881, 179596, 182839, 184256, 189758,\n", + " 195161, 198057],\n", + " dtype='int64'), Int64Index([ 1527, 6122, 8051, 11764, 17223, 19064, 27266, 28182,\n", + " 33755, 65205, 77550, 82128, 83982, 85836, 86763, 88617,\n", + " 91933, 92893, 93802, 95622, 96577, 98912, 99839, 100737,\n", + " 101667, 106271, 110834, 115430, 120899, 122716, 128793, 136406,\n", + " 138716, 139642, 142864, 146795, 149505, 151336, 155041, 157832,\n", + " 173358, 177051, 177911, 178882, 179597, 182840, 184257, 189759,\n", + " 195162, 198058],\n", + " dtype='int64'), Int64Index([ 1528, 6123, 8052, 11765, 17224, 19065, 27267, 28183,\n", + " 33756, 65206, 77551, 82129, 83983, 85837, 86764, 88618,\n", + " 91934, 92894, 93803, 95623, 96578, 98913, 99840, 100738,\n", + " 101668, 106272, 110835, 115431, 120900, 122717, 128794, 136407,\n", + " 138717, 139643, 142865, 146796, 149506, 151337, 155042, 157833,\n", + " 173359, 177052, 177912, 178883, 179598, 182841, 184258, 189760,\n", + " 195163, 198059],\n", + " dtype='int64'), Int64Index([ 1529, 6124, 8053, 11766, 17225, 19066, 27268, 28184,\n", + " 33757, 65207, 77552, 82130, 83984, 85838, 86765, 88619,\n", + " 91935, 92895, 93804, 95624, 96579, 98914, 99841, 100739,\n", + " 101669, 106273, 110836, 115432, 120901, 122718, 128795, 136408,\n", + " 138718, 139644, 142866, 146797, 149507, 151338, 155043, 157834,\n", + " 173360, 177053, 177913, 178884, 179599, 182842, 184259, 189761,\n", + " 195164, 198060],\n", + " dtype='int64'), Int64Index([ 1530, 6125, 8054, 11767, 17226, 19067, 27269, 28185,\n", + " 33758, 65208, 77553, 82131, 83985, 85839, 86766, 88620,\n", + " 91936, 92896, 93805, 95625, 96580, 98915, 99842, 100740,\n", + " 101670, 106274, 110837, 115433, 120902, 122719, 128796, 136409,\n", + " 138719, 139645, 142867, 146798, 149508, 151339, 155044, 157835,\n", + " 173361, 177054, 177914, 178885, 179600, 182843, 184260, 189762,\n", + " 195165, 198061],\n", + " dtype='int64'), Int64Index([ 1531, 6126, 8055, 11768, 17227, 19068, 27270, 28186,\n", + " 33759, 65209, 77554, 82132, 83986, 85840, 86767, 88621,\n", + " 91937, 92897, 93806, 95626, 96581, 98916, 99843, 100741,\n", + " 101671, 106275, 110838, 115434, 120903, 122720, 128797, 136410,\n", + " 138720, 139646, 142868, 146799, 149509, 151340, 155045, 157836,\n", + " 173362, 177055, 177915, 178886, 179601, 182844, 184261, 189763,\n", + " 195166, 198062],\n", + " dtype='int64'), Int64Index([ 1532, 6127, 8056, 11769, 17228, 19069, 27271, 28187,\n", + " 33760, 65210, 77555, 82133, 83987, 85841, 86768, 88622,\n", + " 91938, 92898, 93807, 95627, 96582, 98917, 99844, 100742,\n", + " 101672, 106276, 110839, 115435, 120904, 122721, 128798, 136411,\n", + " 138721, 139647, 142869, 146800, 149510, 151341, 155046, 157837,\n", + " 173363, 177056, 177916, 178887, 179602, 182845, 184262, 189764,\n", + " 195167, 198063],\n", + " dtype='int64'), Int64Index([ 1533, 6128, 8057, 11770, 17229, 19070, 27272, 28188,\n", + " 33761, 65211, 77556, 82134, 83988, 85842, 86769, 88623,\n", + " 91939, 92899, 93808, 95628, 96583, 98918, 99845, 100743,\n", + " 101673, 106277, 110840, 115436, 120905, 122722, 128799, 136412,\n", + " 138722, 139648, 142870, 146801, 149511, 151342, 155047, 157838,\n", + " 173364, 177057, 177917, 178888, 179603, 182846, 184263, 189765,\n", + " 195168, 198064],\n", + " dtype='int64'), Int64Index([ 1534, 6129, 8058, 11771, 17230, 19071, 27273, 28189,\n", + " 33762, 65212, 77557, 82135, 83989, 85843, 86770, 88624,\n", + " 91940, 92900, 93809, 95629, 96584, 98919, 99846, 100744,\n", + " 101674, 106278, 110841, 115437, 120906, 122723, 128800, 136413,\n", + " 138723, 139649, 142871, 146802, 149512, 151343, 155048, 157839,\n", + " 173365, 177058, 177918, 178889, 179604, 182847, 184264, 189766,\n", + " 195169, 198065],\n", + " dtype='int64'), Int64Index([ 1535, 6130, 8059, 11772, 17231, 19072, 27274, 28190,\n", + " 33763, 65213, 77558, 82136, 83990, 85844, 86771, 88625,\n", + " 91941, 92901, 93810, 95630, 96585, 98920, 99847, 100745,\n", + " 101675, 106279, 110842, 115438, 120907, 122724, 128801, 136414,\n", + " 138724, 139650, 142872, 146803, 149513, 151344, 155049, 157840,\n", + " 173366, 177059, 177919, 178890, 179605, 182848, 184265, 189767,\n", + " 195170, 198066],\n", + " dtype='int64'), Int64Index([ 1536, 6131, 8060, 11773, 17232, 19073, 27275, 28191,\n", + " 33764, 65214, 77559, 82137, 83991, 85845, 86772, 88626,\n", + " 91942, 92902, 93811, 95631, 96586, 98921, 99848, 100746,\n", + " 101676, 106280, 110843, 115439, 120908, 122725, 128802, 136415,\n", + " 138725, 139651, 142873, 146804, 149514, 151345, 155050, 157841,\n", + " 173367, 177060, 177920, 178891, 179606, 182849, 184266, 189768,\n", + " 195171, 198067],\n", + " dtype='int64'), Int64Index([ 1537, 6132, 8061, 11774, 17233, 19074, 27276, 28192,\n", + " 33765, 65215, 77560, 82138, 83992, 85846, 86773, 88627,\n", + " 91943, 92903, 93812, 95632, 96587, 98922, 99849, 100747,\n", + " 101677, 106281, 110844, 115440, 120909, 122726, 128803, 136416,\n", + " 138726, 139652, 142874, 146805, 149515, 151346, 155051, 157842,\n", + " 173368, 177061, 177921, 178892, 179607, 182850, 184267, 189769,\n", + " 195172, 198068],\n", + " dtype='int64'), Int64Index([ 1538, 6133, 8062, 11775, 17234, 19075, 27277, 28193,\n", + " 33766, 65216, 77561, 82139, 83993, 85847, 86774, 88628,\n", + " 91944, 92904, 93813, 95633, 96588, 98923, 99850, 100748,\n", + " 101678, 106282, 110845, 115441, 120910, 122727, 128804, 136417,\n", + " 138727, 139653, 142875, 146806, 149516, 151347, 155052, 157843,\n", + " 173369, 177062, 177922, 178893, 179608, 182851, 184268, 189770,\n", + " 195173, 198069],\n", + " dtype='int64'), Int64Index([ 1539, 6134, 8063, 11776, 17235, 19076, 27278, 28194,\n", + " 33767, 65217, 77562, 82140, 83994, 85848, 86775, 88629,\n", + " 91945, 92905, 93814, 95634, 96589, 98924, 99851, 100749,\n", + " 101679, 106283, 110846, 115442, 120911, 122728, 128805, 136418,\n", + " 138728, 139654, 142876, 146807, 149517, 151348, 155053, 157844,\n", + " 173370, 177063, 177923, 178894, 179609, 182852, 184269, 189771,\n", + " 195174, 198070],\n", + " dtype='int64'), Int64Index([ 1540, 6135, 8064, 11777, 17236, 19077, 27279, 28195,\n", + " 33768, 65218, 77563, 82141, 83995, 85849, 86776, 88630,\n", + " 91946, 92906, 93815, 95635, 96590, 98925, 99852, 100750,\n", + " 101680, 106284, 110847, 115443, 120912, 122729, 128806, 136419,\n", + " 138729, 139655, 142877, 146808, 149518, 151349, 155054, 157845,\n", + " 173371, 177064, 177924, 178895, 179610, 182853, 184270, 189772,\n", + " 195175, 198071],\n", + " dtype='int64'), Int64Index([ 1541, 6136, 8065, 11778, 17237, 19078, 27280, 28196,\n", + " 33769, 65219, 77564, 82142, 83996, 85850, 86777, 88631,\n", + " 91947, 92907, 93816, 95636, 96591, 98926, 99853, 100751,\n", + " 101681, 106285, 110848, 115444, 120913, 122730, 128807, 136420,\n", + " 138730, 139656, 142878, 146809, 149519, 151350, 155055, 157846,\n", + " 173372, 177065, 177925, 178896, 179611, 182854, 184271, 189773,\n", + " 195176, 198072],\n", + " dtype='int64'), Int64Index([ 1542, 6137, 8066, 11779, 17238, 19079, 27281, 28197,\n", + " 33770, 65220, 77565, 82143, 83997, 85851, 86778, 88632,\n", + " 91948, 92908, 93817, 95637, 96592, 98927, 99854, 100752,\n", + " 101682, 106286, 110849, 115445, 120914, 122731, 128808, 136421,\n", + " 138731, 139657, 142879, 146810, 149520, 151351, 155056, 157847,\n", + " 173373, 177066, 177926, 178897, 179612, 182855, 184272, 189774,\n", + " 195177, 198073],\n", + " dtype='int64'), Int64Index([ 1543, 6138, 8067, 11780, 17239, 19080, 27282, 28198,\n", + " 33771, 65221, 77566, 82144, 83998, 85852, 86779, 88633,\n", + " 91949, 92909, 93818, 95638, 96593, 98928, 99855, 100753,\n", + " 101683, 106287, 110850, 115446, 120915, 122732, 128809, 136422,\n", + " 138732, 139658, 142880, 146811, 149521, 151352, 155057, 157848,\n", + " 173374, 177067, 177927, 178898, 179613, 182856, 184273, 189775,\n", + " 195178, 198074],\n", + " dtype='int64'), Int64Index([ 1544, 6139, 8068, 11781, 17240, 19081, 27283, 28199,\n", + " 33772, 65222, 77567, 82145, 83999, 85853, 86780, 88634,\n", + " 91950, 92910, 93819, 95639, 96594, 98929, 99856, 100754,\n", + " 101684, 106288, 110851, 115447, 120916, 122733, 128810, 136423,\n", + " 138733, 139659, 142881, 146812, 149522, 151353, 155058, 157849,\n", + " 173375, 177068, 177928, 178899, 179614, 182857, 184274, 189776,\n", + " 195179, 198075],\n", + " dtype='int64'), Int64Index([ 1545, 6140, 8069, 11782, 17241, 19082, 27284, 28200,\n", + " 33773, 65223, 77568, 82146, 84000, 85854, 86781, 88635,\n", + " 91951, 92911, 93820, 95640, 96595, 98930, 99857, 100755,\n", + " 101685, 106289, 110852, 115448, 120917, 122734, 128811, 136424,\n", + " 138734, 139660, 142882, 146813, 149523, 151354, 155059, 157850,\n", + " 173376, 177069, 177929, 178900, 179615, 182858, 184275, 189777,\n", + " 195180, 198076],\n", + " dtype='int64'), Int64Index([ 1546, 6141, 8070, 11783, 17242, 19083, 27285, 28201,\n", + " 33774, 65224, 77569, 82147, 84001, 85855, 86782, 88636,\n", + " 91952, 92912, 93821, 95641, 96596, 98931, 99858, 100756,\n", + " 101686, 106290, 110853, 115449, 120918, 122735, 128812, 136425,\n", + " 138735, 139661, 142883, 146814, 149524, 151355, 155060, 157851,\n", + " 173377, 177070, 177930, 178901, 179616, 182859, 184276, 189778,\n", + " 195181, 198077],\n", + " dtype='int64'), Int64Index([ 1547, 6142, 8071, 11784, 17243, 19084, 27286, 28202,\n", + " 33775, 65225, 77570, 82148, 84002, 85856, 86783, 88637,\n", + " 91953, 92913, 93822, 95642, 96597, 98932, 99859, 100757,\n", + " 101687, 106291, 110854, 115450, 120919, 122736, 128813, 136426,\n", + " 138736, 139662, 142884, 146815, 149525, 151356, 155061, 157852,\n", + " 173378, 177071, 177931, 178902, 179617, 182860, 184277, 189779,\n", + " 195182, 198078],\n", + " dtype='int64'), Int64Index([ 1548, 6143, 8072, 11785, 17244, 19085, 27287, 28203,\n", + " 33776, 65226, 77571, 82149, 84003, 85857, 86784, 88638,\n", + " 91954, 92914, 93823, 95643, 96598, 98933, 99860, 100758,\n", + " 101688, 106292, 110855, 115451, 120920, 122737, 128814, 136427,\n", + " 138737, 139663, 142885, 146816, 149526, 151357, 155062, 157853,\n", + " 173379, 177072, 177932, 178903, 179618, 182861, 184278, 189780,\n", + " 195183, 198079],\n", + " dtype='int64'), Int64Index([ 1549, 6144, 8073, 11786, 17245, 19086, 27288, 28204,\n", + " 33777, 65227, 77572, 82150, 84004, 85858, 86785, 88639,\n", + " 91955, 92915, 93824, 95644, 96599, 98934, 99861, 100759,\n", + " 101689, 106293, 110856, 115452, 120921, 122738, 128815, 136428,\n", + " 138738, 139664, 142886, 146817, 149527, 151358, 155063, 157854,\n", + " 173380, 177073, 177933, 178904, 179619, 182862, 184279, 189781,\n", + " 195184, 198080],\n", + " dtype='int64'), Int64Index([ 1550, 6145, 8074, 11787, 17246, 19087, 27289, 28205,\n", + " 33778, 65228, 77573, 82151, 84005, 85859, 86786, 88640,\n", + " 91956, 92916, 93825, 95645, 96600, 98935, 99862, 100760,\n", + " 101690, 106294, 110857, 115453, 120922, 122739, 128816, 136429,\n", + " 138739, 139665, 142887, 146818, 149528, 151359, 155064, 157855,\n", + " 173381, 177074, 177934, 178905, 179620, 182863, 184280, 189782,\n", + " 195185, 198081],\n", + " dtype='int64'), Int64Index([ 1551, 6146, 8075, 11788, 17247, 19088, 27290, 28206,\n", + " 33779, 65229, 77574, 82152, 84006, 85860, 86787, 88641,\n", + " 91957, 92917, 93826, 95646, 96601, 98936, 99863, 100761,\n", + " 101691, 106295, 110858, 115454, 120923, 122740, 128817, 136430,\n", + " 138740, 139666, 142888, 146819, 149529, 151360, 155065, 157856,\n", + " 173382, 177075, 177935, 178906, 179621, 182864, 184281, 189783,\n", + " 195186, 198082],\n", + " dtype='int64'), Int64Index([ 1552, 6147, 8076, 11789, 17248, 19089, 27291, 28207,\n", + " 33780, 65230, 77575, 82153, 84007, 85861, 86788, 88642,\n", + " 91958, 92918, 93827, 95647, 96602, 98937, 99864, 100762,\n", + " 101692, 106296, 110859, 115455, 120924, 122741, 128818, 136431,\n", + " 138741, 139667, 142889, 146820, 149530, 151361, 155066, 157857,\n", + " 173383, 177076, 177936, 178907, 179622, 182865, 184282, 189784,\n", + " 195187, 198083],\n", + " dtype='int64'), Int64Index([ 1553, 6148, 8077, 11790, 17249, 19090, 27292, 28208,\n", + " 33781, 65231, 77576, 82154, 84008, 85862, 86789, 88643,\n", + " 91959, 92919, 93828, 95648, 96603, 98938, 99865, 100763,\n", + " 101693, 106297, 110860, 115456, 120925, 122742, 128819, 136432,\n", + " 138742, 139668, 142890, 146821, 149531, 151362, 155067, 157858,\n", + " 173384, 177077, 177937, 178908, 179623, 182866, 184283, 189785,\n", + " 195188, 198084],\n", + " dtype='int64'), Int64Index([ 1554, 6149, 8078, 11791, 17250, 19091, 27293, 28209,\n", + " 33782, 65232, 77577, 82155, 84009, 85863, 86790, 88644,\n", + " 91960, 92920, 93829, 95649, 96604, 98939, 99866, 100764,\n", + " 101694, 106298, 110861, 115457, 120926, 122743, 128820, 136433,\n", + " 138743, 139669, 142891, 146822, 149532, 151363, 155068, 157859,\n", + " 173385, 177078, 177938, 178909, 179624, 182867, 184284, 189786,\n", + " 195189, 198085],\n", + " dtype='int64'), Int64Index([ 1555, 6150, 8079, 11792, 17251, 19092, 27294, 28210,\n", + " 33783, 65233, 77578, 82156, 84010, 85864, 86791, 88645,\n", + " 91961, 92921, 93830, 95650, 96605, 98940, 99867, 100765,\n", + " 101695, 106299, 110862, 115458, 120927, 122744, 128821, 136434,\n", + " 138744, 139670, 142892, 146823, 149533, 151364, 155069, 157860,\n", + " 173386, 177079, 177939, 178910, 179625, 182868, 184285, 189787,\n", + " 195190, 198086],\n", + " dtype='int64'), Int64Index([ 1556, 6151, 8080, 11793, 17252, 19093, 27295, 28211,\n", + " 33784, 65234, 77579, 82157, 84011, 85865, 86792, 88646,\n", + " 91962, 92922, 93831, 95651, 96606, 98941, 99868, 100766,\n", + " 101696, 106300, 110863, 115459, 120928, 122745, 128822, 136435,\n", + " 138745, 139671, 142893, 146824, 149534, 151365, 155070, 157861,\n", + " 173387, 177080, 177940, 178911, 179626, 182869, 184286, 189788,\n", + " 195191, 198087],\n", + " dtype='int64'), Int64Index([ 1557, 6152, 8081, 11794, 17253, 19094, 27296, 28212,\n", + " 33785, 65235, 77580, 82158, 84012, 85866, 86793, 88647,\n", + " 91963, 92923, 93832, 95652, 96607, 98942, 99869, 100767,\n", + " 101697, 106301, 110864, 115460, 120929, 122746, 128823, 136436,\n", + " 138746, 139672, 142894, 146825, 149535, 151366, 155071, 157862,\n", + " 173388, 177081, 177941, 178912, 179627, 182870, 184287, 189789,\n", + " 195192, 198088],\n", + " dtype='int64'), Int64Index([ 1558, 6153, 8082, 11795, 17254, 19095, 27297, 28213,\n", + " 33786, 65236, 77581, 82159, 84013, 85867, 86794, 88648,\n", + " 91964, 92924, 93833, 95653, 96608, 98943, 99870, 100768,\n", + " 101698, 106302, 110865, 115461, 120930, 122747, 128824, 136437,\n", + " 138747, 139673, 142895, 146826, 149536, 151367, 155072, 157863,\n", + " 173389, 177082, 177942, 178913, 179628, 182871, 184288, 189790,\n", + " 195193, 198089],\n", + " dtype='int64'), Int64Index([ 1559, 6154, 8083, 11796, 17255, 19096, 27298, 28214,\n", + " 33787, 65237, 77582, 82160, 84014, 85868, 86795, 88649,\n", + " 91965, 92925, 93834, 95654, 96609, 98944, 99871, 100769,\n", + " 101699, 106303, 110866, 115462, 120931, 122748, 128825, 136438,\n", + " 138748, 139674, 142896, 146827, 149537, 151368, 155073, 157864,\n", + " 173390, 177083, 177943, 178914, 179629, 182872, 184289, 189791,\n", + " 195194, 198090],\n", + " dtype='int64'), Int64Index([ 1560, 6155, 8084, 11797, 17256, 19097, 27299, 28215,\n", + " 33788, 65238, 77583, 82161, 84015, 85869, 86796, 88650,\n", + " 91966, 92926, 93835, 95655, 96610, 98945, 99872, 100770,\n", + " 101700, 106304, 110867, 115463, 120932, 122749, 128826, 136439,\n", + " 138749, 139675, 142897, 146828, 149538, 151369, 155074, 157865,\n", + " 173391, 177084, 177944, 178915, 179630, 182873, 184290, 189792,\n", + " 195195, 198091],\n", + " dtype='int64'), Int64Index([ 1561, 6156, 8085, 11798, 17257, 19098, 27300, 28216,\n", + " 33789, 65239, 77584, 82162, 84016, 85870, 86797, 88651,\n", + " 91967, 92927, 93836, 95656, 96611, 98946, 99873, 100771,\n", + " 101701, 106305, 110868, 115464, 120933, 122750, 128827, 136440,\n", + " 138750, 139676, 142898, 146829, 149539, 151370, 155075, 157866,\n", + " 173392, 177085, 177945, 178916, 179631, 182874, 184291, 189793,\n", + " 195196, 198092],\n", + " dtype='int64'), Int64Index([ 1562, 6157, 8086, 11799, 17258, 19099, 27301, 28217,\n", + " 33790, 65240, 77585, 82163, 84017, 85871, 86798, 88652,\n", + " 91968, 92928, 93837, 95657, 96612, 98947, 99874, 100772,\n", + " 101702, 106306, 110869, 115465, 120934, 122751, 128828, 136441,\n", + " 138751, 139677, 142899, 146830, 149540, 151371, 155076, 157867,\n", + " 173393, 177086, 177946, 178917, 179632, 182875, 184292, 189794,\n", + " 195197, 198093],\n", + " dtype='int64'), Int64Index([ 1563, 6158, 8087, 11800, 17259, 19100, 27302, 28218,\n", + " 33791, 65241, 77586, 82164, 84018, 85872, 86799, 88653,\n", + " 91969, 92929, 93838, 95658, 96613, 98948, 99875, 100773,\n", + " 101703, 106307, 110870, 115466, 120935, 122752, 128829, 136442,\n", + " 138752, 139678, 142900, 146831, 149541, 151372, 155077, 157868,\n", + " 173394, 177087, 177947, 178918, 179633, 182876, 184293, 189795,\n", + " 195198, 198094],\n", + " dtype='int64'), Int64Index([ 1564, 6159, 8088, 11801, 17260, 19101, 27303, 28219,\n", + " 33792, 65242, 77587, 82165, 84019, 85873, 86800, 88654,\n", + " 91970, 92930, 93839, 95659, 96614, 98949, 99876, 100774,\n", + " 101704, 106308, 110871, 115467, 120936, 122753, 128830, 136443,\n", + " 138753, 139679, 142901, 146832, 149542, 151373, 155078, 157869,\n", + " 173395, 177088, 177948, 178919, 179634, 182877, 184294, 189796,\n", + " 195199, 198095],\n", + " dtype='int64'), Int64Index([ 1565, 6160, 8089, 11802, 17261, 19102, 27304, 28220,\n", + " 33793, 65243, 77588, 82166, 84020, 85874, 86801, 88655,\n", + " 91971, 92931, 93840, 95660, 96615, 98950, 99877, 100775,\n", + " 101705, 106309, 110872, 115468, 120937, 122754, 128831, 136444,\n", + " 138754, 139680, 142902, 146833, 149543, 151374, 155079, 157870,\n", + " 173396, 177089, 177949, 178920, 179635, 182878, 184295, 189797,\n", + " 195200, 198096],\n", + " dtype='int64'), Int64Index([ 1566, 6161, 8090, 11803, 17262, 19103, 27305, 28221,\n", + " 33794, 65244, 77589, 82167, 84021, 85875, 86802, 88656,\n", + " 91972, 92932, 93841, 95661, 96616, 98951, 99878, 100776,\n", + " 101706, 106310, 110873, 115469, 120938, 122755, 128832, 136445,\n", + " 138755, 139681, 142903, 146834, 149544, 151375, 155080, 157871,\n", + " 173397, 177090, 177950, 178921, 179636, 182879, 184296, 189798,\n", + " 195201, 198097],\n", + " dtype='int64'), Int64Index([ 1567, 6162, 8091, 11804, 17263, 19104, 27306, 28222,\n", + " 33795, 65245, 77590, 82168, 84022, 85876, 86803, 88657,\n", + " 91973, 92933, 93842, 95662, 96617, 98952, 99879, 100777,\n", + " 101707, 106311, 110874, 115470, 120939, 122756, 128833, 136446,\n", + " 138756, 139682, 142904, 146835, 149545, 151376, 155081, 157872,\n", + " 173398, 177091, 177951, 178922, 179637, 182880, 184297, 189799,\n", + " 195202, 198098],\n", + " dtype='int64'), Int64Index([ 1568, 6163, 8092, 11805, 17264, 19105, 27307, 28223,\n", + " 33796, 65246, 77591, 82169, 84023, 85877, 86804, 88658,\n", + " 91974, 92934, 93843, 95663, 96618, 98953, 99880, 100778,\n", + " 101708, 106312, 110875, 115471, 120940, 122757, 128834, 136447,\n", + " 138757, 139683, 142905, 146836, 149546, 151377, 155082, 157873,\n", + " 173399, 177092, 177952, 178923, 179638, 182881, 184298, 189800,\n", + " 195203, 198099],\n", + " dtype='int64'), Int64Index([ 1569, 6164, 8093, 11806, 17265, 19106, 27308, 28224,\n", + " 33797, 65247, 77592, 82170, 84024, 85878, 86805, 88659,\n", + " 91975, 92935, 93844, 95664, 96619, 98954, 99881, 100779,\n", + " 101709, 106313, 110876, 115472, 120941, 122758, 128835, 136448,\n", + " 138758, 139684, 142906, 146837, 149547, 151378, 155083, 157874,\n", + " 173400, 177093, 177953, 178924, 179639, 182882, 184299, 189801,\n", + " 195204, 198100],\n", + " dtype='int64'), Int64Index([ 1570, 6165, 8094, 11807, 17266, 19107, 27309, 28225,\n", + " 33798, 65248, 77593, 82171, 84025, 85879, 86806, 88660,\n", + " 91976, 92936, 93845, 95665, 96620, 98955, 99882, 100780,\n", + " 101710, 106314, 110877, 115473, 120942, 122759, 128836, 136449,\n", + " 138759, 139685, 142907, 146838, 149548, 151379, 155084, 157875,\n", + " 173401, 177094, 177954, 178925, 179640, 182883, 184300, 189802,\n", + " 195205, 198101],\n", + " dtype='int64'), Int64Index([ 1571, 6166, 8095, 11808, 17267, 19108, 27310, 28226,\n", + " 33799, 65249, 77594, 82172, 84026, 85880, 86807, 88661,\n", + " 91977, 92937, 93846, 95666, 96621, 98956, 99883, 100781,\n", + " 101711, 106315, 110878, 115474, 120943, 122760, 128837, 136450,\n", + " 138760, 139686, 142908, 146839, 149549, 151380, 155085, 157876,\n", + " 173402, 177095, 177955, 178926, 179641, 182884, 184301, 189803,\n", + " 195206, 198102],\n", + " dtype='int64'), Int64Index([ 1572, 6167, 8096, 11809, 17268, 19109, 27311, 28227,\n", + " 33800, 65250, 77595, 82173, 84027, 85881, 86808, 88662,\n", + " 91978, 92938, 93847, 95667, 96622, 98957, 99884, 100782,\n", + " 101712, 106316, 110879, 115475, 120944, 122761, 128838, 136451,\n", + " 138761, 139687, 142909, 146840, 149550, 151381, 155086, 157877,\n", + " 173403, 177096, 177956, 178927, 179642, 182885, 184302, 189804,\n", + " 195207, 198103],\n", + " dtype='int64'), Int64Index([ 1573, 6168, 8097, 11810, 17269, 19110, 27312, 28228,\n", + " 33801, 65251, 77596, 82174, 84028, 85882, 86809, 88663,\n", + " 91979, 92939, 93848, 95668, 96623, 98958, 99885, 100783,\n", + " 101713, 106317, 110880, 115476, 120945, 122762, 128839, 136452,\n", + " 138762, 139688, 142910, 146841, 149551, 151382, 155087, 157878,\n", + " 173404, 177097, 177957, 178928, 179643, 182886, 184303, 189805,\n", + " 195208, 198104],\n", + " dtype='int64'), Int64Index([ 1574, 6169, 8098, 11811, 17270, 19111, 27313, 28229,\n", + " 33802, 65252, 77597, 82175, 84029, 85883, 86810, 88664,\n", + " 91980, 92940, 93849, 95669, 96624, 98959, 99886, 100784,\n", + " 101714, 106318, 110881, 115477, 120946, 122763, 128840, 136453,\n", + " 138763, 139689, 142911, 146842, 149552, 151383, 155088, 157879,\n", + " 173405, 177098, 177958, 178929, 179644, 182887, 184304, 189806,\n", + " 195209, 198105],\n", + " dtype='int64'), Int64Index([ 1575, 6170, 8099, 11812, 17271, 19112, 27314, 28230,\n", + " 33803, 65253, 77598, 82176, 84030, 85884, 86811, 88665,\n", + " 91981, 92941, 93850, 95670, 96625, 98960, 99887, 100785,\n", + " 101715, 106319, 110882, 115478, 120947, 122764, 128841, 136454,\n", + " 138764, 139690, 142912, 146843, 149553, 151384, 155089, 157880,\n", + " 173406, 177099, 177959, 178930, 179645, 182888, 184305, 189807,\n", + " 195210, 198106],\n", + " dtype='int64'), Int64Index([ 1576, 6171, 8100, 11813, 17272, 19113, 27315, 28231,\n", + " 33804, 65254, 77599, 82177, 84031, 85885, 86812, 88666,\n", + " 91982, 92942, 93851, 95671, 96626, 98961, 99888, 100786,\n", + " 101716, 106320, 110883, 115479, 120948, 122765, 128842, 136455,\n", + " 138765, 139691, 142913, 146844, 149554, 151385, 155090, 157881,\n", + " 173407, 177100, 177960, 178931, 179646, 182889, 184306, 189808,\n", + " 195211, 198107],\n", + " dtype='int64'), Int64Index([ 1577, 6172, 8101, 11814, 17273, 19114, 27316, 28232,\n", + " 33805, 65255, 77600, 82178, 84032, 85886, 86813, 88667,\n", + " 91983, 92943, 93852, 95672, 96627, 98962, 99889, 100787,\n", + " 101717, 106321, 110884, 115480, 120949, 122766, 128843, 136456,\n", + " 138766, 139692, 142914, 146845, 149555, 151386, 155091, 157882,\n", + " 173408, 177101, 177961, 178932, 179647, 182890, 184307, 189809,\n", + " 195212, 198108],\n", + " dtype='int64'), Int64Index([ 1578, 6173, 8102, 11815, 17274, 19115, 27317, 28233,\n", + " 33806, 65256, 77601, 82179, 84033, 85887, 86814, 88668,\n", + " 91984, 92944, 93853, 95673, 96628, 98963, 99890, 100788,\n", + " 101718, 106322, 110885, 115481, 120950, 122767, 128844, 136457,\n", + " 138767, 139693, 142915, 146846, 149556, 151387, 155092, 157883,\n", + " 173409, 177102, 177962, 178933, 179648, 182891, 184308, 189810,\n", + " 195213, 198109],\n", + " dtype='int64'), Int64Index([ 1579, 6174, 8103, 11816, 17275, 19116, 27318, 28234,\n", + " 33807, 65257, 77602, 82180, 84034, 85888, 86815, 88669,\n", + " 91985, 92945, 93854, 95674, 96629, 98964, 99891, 100789,\n", + " 101719, 106323, 110886, 115482, 120951, 122768, 128845, 136458,\n", + " 138768, 139694, 142916, 146847, 149557, 151388, 155093, 157884,\n", + " 173410, 177103, 177963, 178934, 179649, 182892, 184309, 189811,\n", + " 195214, 198110],\n", + " dtype='int64'), Int64Index([ 1580, 6175, 8104, 11817, 17276, 19117, 27319, 28235,\n", + " 33808, 65258, 77603, 82181, 84035, 85889, 86816, 88670,\n", + " 91986, 92946, 93855, 95675, 96630, 98965, 99892, 100790,\n", + " 101720, 106324, 110887, 115483, 120952, 122769, 128846, 136459,\n", + " 138769, 139695, 142917, 146848, 149558, 151389, 155094, 157885,\n", + " 173411, 177104, 177964, 178935, 179650, 182893, 184310, 189812,\n", + " 195215, 198111],\n", + " dtype='int64'), Int64Index([ 1581, 6176, 8105, 11818, 17277, 19118, 27320, 28236,\n", + " 33809, 65259, 77604, 82182, 84036, 85890, 86817, 88671,\n", + " 91987, 92947, 93856, 95676, 96631, 98966, 99893, 100791,\n", + " 101721, 106325, 110888, 115484, 120953, 122770, 128847, 136460,\n", + " 138770, 139696, 142918, 146849, 149559, 151390, 155095, 157886,\n", + " 173412, 177105, 177965, 178936, 179651, 182894, 184311, 189813,\n", + " 195216, 198112],\n", + " dtype='int64'), Int64Index([ 1582, 6177, 8106, 11819, 17278, 19119, 27321, 28237,\n", + " 33810, 65260, 77605, 82183, 84037, 85891, 86818, 88672,\n", + " 91988, 92948, 93857, 95677, 96632, 98967, 99894, 100792,\n", + " 101722, 106326, 110889, 115485, 120954, 122771, 128848, 136461,\n", + " 138771, 139697, 142919, 146850, 149560, 151391, 155096, 157887,\n", + " 173413, 177106, 177966, 178937, 179652, 182895, 184312, 189814,\n", + " 195217, 198113],\n", + " dtype='int64'), Int64Index([ 1583, 6178, 8107, 11820, 17279, 19120, 27322, 28238,\n", + " 33811, 65261, 77606, 82184, 84038, 85892, 86819, 88673,\n", + " 91989, 92949, 93858, 95678, 96633, 98968, 99895, 100793,\n", + " 101723, 106327, 110890, 115486, 120955, 122772, 128849, 136462,\n", + " 138772, 139698, 142920, 146851, 149561, 151392, 155097, 157888,\n", + " 173414, 177107, 177967, 178938, 179653, 182896, 184313, 189815,\n", + " 195218, 198114],\n", + " dtype='int64'), Int64Index([ 1584, 6179, 8108, 11821, 17280, 19121, 27323, 28239,\n", + " 33812, 65262, 77607, 82185, 84039, 85893, 86820, 88674,\n", + " 91990, 92950, 93859, 95679, 96634, 98969, 99896, 100794,\n", + " 101724, 106328, 110891, 115487, 120956, 122773, 128850, 136463,\n", + " 138773, 139699, 142921, 146852, 149562, 151393, 155098, 157889,\n", + " 173415, 177108, 177968, 178939, 179654, 182897, 184314, 189816,\n", + " 195219, 198115],\n", + " dtype='int64'), Int64Index([ 1585, 6180, 8109, 11822, 17281, 19122, 27324, 28240,\n", + " 33813, 65263, 77608, 82186, 84040, 85894, 86821, 88675,\n", + " 91991, 92951, 93860, 95680, 96635, 98970, 99897, 100795,\n", + " 101725, 106329, 110892, 115488, 120957, 122774, 128851, 136464,\n", + " 138774, 139700, 142922, 146853, 149563, 151394, 155099, 157890,\n", + " 173416, 177109, 177969, 178940, 179655, 182898, 184315, 189817,\n", + " 195220, 198116],\n", + " dtype='int64'), Int64Index([ 1586, 6181, 8110, 11823, 17282, 19123, 27325, 28241,\n", + " 33814, 65264, 77609, 82187, 84041, 85895, 86822, 88676,\n", + " 91992, 92952, 93861, 95681, 96636, 98971, 99898, 100796,\n", + " 101726, 106330, 110893, 115489, 120958, 122775, 128852, 136465,\n", + " 138775, 139701, 142923, 146854, 149564, 151395, 155100, 157891,\n", + " 173417, 177110, 177970, 178941, 179656, 182899, 184316, 189818,\n", + " 195221, 198117],\n", + " dtype='int64'), Int64Index([ 1587, 6182, 8111, 11824, 17283, 19124, 27326, 28242,\n", + " 33815, 65265, 77610, 82188, 84042, 85896, 86823, 88677,\n", + " 91993, 92953, 93862, 95682, 96637, 98972, 99899, 100797,\n", + " 101727, 106331, 110894, 115490, 120959, 122776, 128853, 136466,\n", + " 138776, 139702, 142924, 146855, 149565, 151396, 155101, 157892,\n", + " 173418, 177111, 177971, 178942, 179657, 182900, 184317, 189819,\n", + " 195222, 198118],\n", + " dtype='int64'), Int64Index([ 1588, 6183, 8112, 11825, 17284, 19125, 27327, 28243,\n", + " 33816, 65266, 77611, 82189, 84043, 85897, 86824, 88678,\n", + " 91994, 92954, 93863, 95683, 96638, 98973, 99900, 100798,\n", + " 101728, 106332, 110895, 115491, 120960, 122777, 128854, 136467,\n", + " 138777, 139703, 142925, 146856, 149566, 151397, 155102, 157893,\n", + " 173419, 177112, 177972, 178943, 179658, 182901, 184318, 189820,\n", + " 195223, 198119],\n", + " dtype='int64'), Int64Index([ 1589, 6184, 8113, 11826, 17285, 19126, 27328, 28244,\n", + " 33817, 65267, 77612, 82190, 84044, 85898, 86825, 88679,\n", + " 91995, 92955, 93864, 95684, 96639, 98974, 99901, 100799,\n", + " 101729, 106333, 110896, 115492, 120961, 122778, 128855, 136468,\n", + " 138778, 139704, 142926, 146857, 149567, 151398, 155103, 157894,\n", + " 173420, 177113, 177973, 178944, 179659, 182902, 184319, 189821,\n", + " 195224, 198120],\n", + " dtype='int64'), Int64Index([ 1590, 6185, 8114, 11827, 17286, 19127, 27329, 28245,\n", + " 33818, 65268, 77613, 82191, 84045, 85899, 86826, 88680,\n", + " 91996, 92956, 93865, 95685, 96640, 98975, 99902, 100800,\n", + " 101730, 106334, 110897, 115493, 120962, 122779, 128856, 136469,\n", + " 138779, 139705, 142927, 146858, 149568, 151399, 155104, 157895,\n", + " 173421, 177114, 177974, 178945, 179660, 182903, 184320, 189822,\n", + " 195225, 198121],\n", + " dtype='int64'), Int64Index([ 1591, 6186, 8115, 11828, 17287, 19128, 27330, 28246,\n", + " 33819, 65269, 77614, 82192, 84046, 85900, 86827, 88681,\n", + " 91997, 92957, 93866, 95686, 96641, 98976, 99903, 100801,\n", + " 101731, 106335, 110898, 115494, 120963, 122780, 128857, 136470,\n", + " 138780, 139706, 142928, 146859, 149569, 151400, 155105, 157896,\n", + " 173422, 177115, 177975, 178946, 179661, 182904, 184321, 189823,\n", + " 195226, 198122],\n", + " dtype='int64'), Int64Index([ 1592, 6187, 8116, 11829, 17288, 19129, 27331, 28247,\n", + " 33820, 65270, 77615, 82193, 84047, 85901, 86828, 88682,\n", + " 91998, 92958, 93867, 95687, 96642, 98977, 99904, 100802,\n", + " 101732, 106336, 110899, 115495, 120964, 122781, 128858, 136471,\n", + " 138781, 139707, 142929, 146860, 149570, 151401, 155106, 157897,\n", + " 173423, 177116, 177976, 178947, 179662, 182905, 184322, 189824,\n", + " 195227, 198123],\n", + " dtype='int64'), Int64Index([ 1593, 6188, 8117, 11830, 17289, 19130, 27332, 28248,\n", + " 33821, 65271, 77616, 82194, 84048, 85902, 86829, 88683,\n", + " 91999, 92959, 93868, 95688, 96643, 98978, 99905, 100803,\n", + " 101733, 106337, 110900, 115496, 120965, 122782, 128859, 136472,\n", + " 138782, 139708, 142930, 146861, 149571, 151402, 155107, 157898,\n", + " 173424, 177117, 177977, 178948, 179663, 182906, 184323, 189825,\n", + " 195228, 198124],\n", + " dtype='int64'), Int64Index([ 1594, 6189, 8118, 11831, 17290, 19131, 27333, 28249,\n", + " 33822, 65272, 77617, 82195, 84049, 85903, 86830, 88684,\n", + " 92000, 92960, 93869, 95689, 96644, 98979, 99906, 100804,\n", + " 101734, 106338, 110901, 115497, 120966, 122783, 128860, 136473,\n", + " 138783, 139709, 142931, 146862, 149572, 151403, 155108, 157899,\n", + " 173425, 177118, 177978, 178949, 179664, 182907, 184324, 189826,\n", + " 195229, 198125],\n", + " dtype='int64'), Int64Index([ 1595, 6190, 8119, 11832, 17291, 19132, 27334, 28250,\n", + " 33823, 65273, 77618, 82196, 84050, 85904, 86831, 88685,\n", + " 92001, 92961, 93870, 95690, 96645, 98980, 99907, 100805,\n", + " 101735, 106339, 110902, 115498, 120967, 122784, 128861, 136474,\n", + " 138784, 139710, 142932, 146863, 149573, 151404, 155109, 157900,\n", + " 173426, 177119, 177979, 178950, 179665, 182908, 184325, 189827,\n", + " 195230, 198126],\n", + " dtype='int64'), Int64Index([ 1596, 6191, 8120, 11833, 17292, 19133, 27335, 28251,\n", + " 33824, 65274, 77619, 82197, 84051, 85905, 86832, 88686,\n", + " 92002, 92962, 93871, 95691, 96646, 98981, 99908, 100806,\n", + " 101736, 106340, 110903, 115499, 120968, 122785, 128862, 136475,\n", + " 138785, 139711, 142933, 146864, 149574, 151405, 155110, 157901,\n", + " 173427, 177120, 177980, 178951, 179666, 182909, 184326, 189828,\n", + " 195231, 198127],\n", + " dtype='int64'), Int64Index([ 1597, 6192, 8121, 11834, 17293, 19134, 27336, 28252,\n", + " 33825, 65275, 77620, 82198, 84052, 85906, 86833, 88687,\n", + " 92003, 92963, 93872, 95692, 96647, 98982, 99909, 100807,\n", + " 101737, 106341, 110904, 115500, 120969, 122786, 128863, 136476,\n", + " 138786, 139712, 142934, 146865, 149575, 151406, 155111, 157902,\n", + " 173428, 177121, 177981, 178952, 179667, 182910, 184327, 189829,\n", + " 195232, 198128],\n", + " dtype='int64'), Int64Index([ 1598, 6193, 8122, 11835, 17294, 19135, 27337, 28253,\n", + " 33826, 65276, 77621, 82199, 84053, 85907, 86834, 88688,\n", + " 92004, 92964, 93873, 95693, 96648, 98983, 99910, 100808,\n", + " 101738, 106342, 110905, 115501, 120970, 122787, 128864, 136477,\n", + " 138787, 139713, 142935, 146866, 149576, 151407, 155112, 157903,\n", + " 173429, 177122, 177982, 178953, 179668, 182911, 184328, 189830,\n", + " 195233, 198129],\n", + " dtype='int64'), Int64Index([ 1599, 6194, 8123, 11836, 17295, 19136, 27338, 28254,\n", + " 33827, 65277, 77622, 82200, 84054, 85908, 86835, 88689,\n", + " 92005, 92965, 93874, 95694, 96649, 98984, 99911, 100809,\n", + " 101739, 106343, 110906, 115502, 120971, 122788, 128865, 136478,\n", + " 138788, 139714, 142936, 146867, 149577, 151408, 155113, 157904,\n", + " 173430, 177123, 177983, 178954, 179669, 182912, 184329, 189831,\n", + " 195234, 198130],\n", + " dtype='int64'), Int64Index([ 1600, 6195, 8124, 11837, 17296, 19137, 27339, 28255,\n", + " 33828, 65278, 77623, 82201, 84055, 85909, 86836, 88690,\n", + " 92006, 92966, 93875, 95695, 96650, 98985, 99912, 100810,\n", + " 101740, 106344, 110907, 115503, 120972, 122789, 128866, 136479,\n", + " 138789, 139715, 142937, 146868, 149578, 151409, 155114, 157905,\n", + " 173431, 177124, 177984, 178955, 179670, 182913, 184330, 189832,\n", + " 195235, 198131],\n", + " dtype='int64'), Int64Index([ 1601, 6196, 8125, 11838, 17297, 19138, 27340, 28256,\n", + " 33829, 65279, 77624, 82202, 84056, 85910, 86837, 88691,\n", + " 92007, 92967, 93876, 95696, 96651, 98986, 99913, 100811,\n", + " 101741, 106345, 110908, 115504, 120973, 122790, 128867, 136480,\n", + " 138790, 139716, 142938, 146869, 149579, 151410, 155115, 157906,\n", + " 173432, 177125, 177985, 178956, 179671, 182914, 184331, 189833,\n", + " 195236, 198132],\n", + " dtype='int64'), Int64Index([ 1602, 6197, 8126, 11839, 17298, 19139, 27341, 28257,\n", + " 33830, 65280, 77625, 82203, 84057, 85911, 86838, 88692,\n", + " 92008, 92968, 93877, 95697, 96652, 98987, 99914, 100812,\n", + " 101742, 106346, 110909, 115505, 120974, 122791, 128868, 136481,\n", + " 138791, 139717, 142939, 146870, 149580, 151411, 155116, 157907,\n", + " 173433, 177126, 177986, 178957, 179672, 182915, 184332, 189834,\n", + " 195237, 198133],\n", + " dtype='int64'), Int64Index([ 1603, 6198, 8127, 11840, 17299, 19140, 27342, 28258,\n", + " 33831, 65281, 77626, 82204, 84058, 85912, 86839, 88693,\n", + " 92009, 92969, 93878, 95698, 96653, 98988, 99915, 100813,\n", + " 101743, 106347, 110910, 115506, 120975, 122792, 128869, 136482,\n", + " 138792, 139718, 142940, 146871, 149581, 151412, 155117, 157908,\n", + " 173434, 177127, 177987, 178958, 179673, 182916, 184333, 189835,\n", + " 195238, 198134],\n", + " dtype='int64'), Int64Index([ 1604, 6199, 8128, 11841, 17300, 19141, 27343, 28259,\n", + " 33832, 65282, 77627, 82205, 84059, 85913, 86840, 88694,\n", + " 92010, 92970, 93879, 95699, 96654, 98989, 99916, 100814,\n", + " 101744, 106348, 110911, 115507, 120976, 122793, 128870, 136483,\n", + " 138793, 139719, 142941, 146872, 149582, 151413, 155118, 157909,\n", + " 173435, 177128, 177988, 178959, 179674, 182917, 184334, 189836,\n", + " 195239, 198135],\n", + " dtype='int64'), Int64Index([ 1605, 6200, 8129, 11842, 17301, 19142, 27344, 28260,\n", + " 33833, 65283, 77628, 82206, 84060, 85914, 86841, 88695,\n", + " 92011, 92971, 93880, 95700, 96655, 98990, 99917, 100815,\n", + " 101745, 106349, 110912, 115508, 120977, 122794, 128871, 136484,\n", + " 138794, 139720, 142942, 146873, 149583, 151414, 155119, 157910,\n", + " 173436, 177129, 177989, 178960, 179675, 182918, 184335, 189837,\n", + " 195240, 198136],\n", + " dtype='int64'), Int64Index([ 1606, 6201, 8130, 11843, 17302, 19143, 27345, 28261,\n", + " 33834, 65284, 77629, 82207, 84061, 85915, 86842, 88696,\n", + " 92012, 92972, 93881, 95701, 96656, 98991, 99918, 100816,\n", + " 101746, 106350, 110913, 115509, 120978, 122795, 128872, 136485,\n", + " 138795, 139721, 142943, 146874, 149584, 151415, 155120, 157911,\n", + " 173437, 177130, 177990, 178961, 179676, 182919, 184336, 189838,\n", + " 195241, 198137],\n", + " dtype='int64'), Int64Index([ 1607, 6202, 8131, 11844, 17303, 19144, 27346, 28262,\n", + " 33835, 65285, 77630, 82208, 84062, 85916, 86843, 88697,\n", + " 92013, 92973, 93882, 95702, 96657, 98992, 99919, 100817,\n", + " 101747, 106351, 110914, 115510, 120979, 122796, 128873, 136486,\n", + " 138796, 139722, 142944, 146875, 149585, 151416, 155121, 157912,\n", + " 173438, 177131, 177991, 178962, 179677, 182920, 184337, 189839,\n", + " 195242, 198138],\n", + " dtype='int64'), Int64Index([ 1608, 6203, 8132, 11845, 17304, 19145, 27347, 28263,\n", + " 33836, 65286, 77631, 82209, 84063, 85917, 86844, 88698,\n", + " 92014, 92974, 93883, 95703, 96658, 98993, 99920, 100818,\n", + " 101748, 106352, 110915, 115511, 120980, 122797, 128874, 136487,\n", + " 138797, 139723, 142945, 146876, 149586, 151417, 155122, 157913,\n", + " 173439, 177132, 177992, 178963, 179678, 182921, 184338, 189840,\n", + " 195243, 198139],\n", + " dtype='int64'), Int64Index([ 1609, 6204, 8133, 11846, 17305, 19146, 27348, 28264,\n", + " 33837, 65287, 77632, 82210, 84064, 85918, 86845, 88699,\n", + " 92015, 92975, 93884, 95704, 96659, 98994, 99921, 100819,\n", + " 101749, 106353, 110916, 115512, 120981, 122798, 128875, 136488,\n", + " 138798, 139724, 142946, 146877, 149587, 151418, 155123, 157914,\n", + " 173440, 177133, 177993, 178964, 179679, 182922, 184339, 189841,\n", + " 195244, 198140],\n", + " dtype='int64'), Int64Index([ 1610, 6205, 8134, 11847, 17306, 19147, 27349, 28265,\n", + " 33838, 65288, 77633, 82211, 84065, 85919, 86846, 88700,\n", + " 92016, 92976, 93885, 95705, 96660, 98995, 99922, 100820,\n", + " 101750, 106354, 110917, 115513, 120982, 122799, 128876, 136489,\n", + " 138799, 139725, 142947, 146878, 149588, 151419, 155124, 157915,\n", + " 173441, 177134, 177994, 178965, 179680, 182923, 184340, 189842,\n", + " 195245, 198141],\n", + " dtype='int64'), Int64Index([ 1611, 6206, 8135, 11848, 17307, 19148, 27350, 28266,\n", + " 33839, 65289, 77634, 82212, 84066, 85920, 86847, 88701,\n", + " 92017, 92977, 93886, 95706, 96661, 98996, 99923, 100821,\n", + " 101751, 106355, 110918, 115514, 120983, 122800, 128877, 136490,\n", + " 138800, 139726, 142948, 146879, 149589, 151420, 155125, 157916,\n", + " 173442, 177135, 177995, 178966, 179681, 182924, 184341, 189843,\n", + " 195246, 198142],\n", + " dtype='int64'), Int64Index([ 1612, 6207, 8136, 11849, 17308, 19149, 27351, 28267,\n", + " 33840, 65290, 77635, 82213, 84067, 85921, 86848, 88702,\n", + " 92018, 92978, 93887, 95707, 96662, 98997, 99924, 100822,\n", + " 101752, 106356, 110919, 115515, 120984, 122801, 128878, 136491,\n", + " 138801, 139727, 142949, 146880, 149590, 151421, 155126, 157917,\n", + " 173443, 177136, 177996, 178967, 179682, 182925, 184342, 189844,\n", + " 195247, 198143],\n", + " dtype='int64'), Int64Index([ 1613, 6208, 8137, 11850, 17309, 19150, 27352, 28268,\n", + " 33841, 65291, 77636, 82214, 84068, 85922, 86849, 88703,\n", + " 92019, 92979, 93888, 95708, 96663, 98998, 99925, 100823,\n", + " 101753, 106357, 110920, 115516, 120985, 122802, 128879, 136492,\n", + " 138802, 139728, 142950, 146881, 149591, 151422, 155127, 157918,\n", + " 173444, 177137, 177997, 178968, 179683, 182926, 184343, 189845,\n", + " 195248, 198144],\n", + " dtype='int64'), Int64Index([ 1614, 6209, 8138, 11851, 17310, 19151, 27353, 28269,\n", + " 33842, 65292, 77637, 82215, 84069, 85923, 86850, 88704,\n", + " 92020, 92980, 93889, 95709, 96664, 98999, 99926, 100824,\n", + " 101754, 106358, 110921, 115517, 120986, 122803, 128880, 136493,\n", + " 138803, 139729, 142951, 146882, 149592, 151423, 155128, 157919,\n", + " 173445, 177138, 177998, 178969, 179684, 182927, 184344, 189846,\n", + " 195249, 198145],\n", + " dtype='int64'), Int64Index([ 1615, 6210, 8139, 11852, 17311, 19152, 27354, 28270,\n", + " 33843, 65293, 77638, 82216, 84070, 85924, 86851, 88705,\n", + " 92021, 92981, 93890, 95710, 96665, 99000, 99927, 100825,\n", + " 101755, 106359, 110922, 115518, 120987, 122804, 128881, 136494,\n", + " 138804, 139730, 142952, 146883, 149593, 151424, 155129, 157920,\n", + " 173446, 177139, 177999, 178970, 179685, 182928, 184345, 189847,\n", + " 195250, 198146],\n", + " dtype='int64'), Int64Index([ 1616, 6211, 8140, 11853, 17312, 19153, 27355, 28271,\n", + " 33844, 65294, 77639, 82217, 84071, 85925, 86852, 88706,\n", + " 92022, 92982, 93891, 95711, 96666, 99001, 99928, 100826,\n", + " 101756, 106360, 110923, 115519, 120988, 122805, 128882, 136495,\n", + " 138805, 139731, 142953, 146884, 149594, 151425, 155130, 157921,\n", + " 173447, 177140, 178000, 178971, 179686, 182929, 184346, 189848,\n", + " 195251, 198147],\n", + " dtype='int64'), Int64Index([ 1617, 6212, 8141, 11854, 17313, 19154, 27356, 28272,\n", + " 33845, 65295, 77640, 82218, 84072, 85926, 86853, 88707,\n", + " 92023, 92983, 93892, 95712, 96667, 99002, 99929, 100827,\n", + " 101757, 106361, 110924, 115520, 120989, 122806, 128883, 136496,\n", + " 138806, 139732, 142954, 146885, 149595, 151426, 155131, 157922,\n", + " 173448, 177141, 178001, 178972, 179687, 182930, 184347, 189849,\n", + " 195252, 198148],\n", + " dtype='int64'), Int64Index([ 1618, 6213, 8142, 11855, 17314, 19155, 27357, 28273,\n", + " 33846, 65296, 77641, 82219, 84073, 85927, 86854, 88708,\n", + " 92024, 92984, 93893, 95713, 96668, 99003, 99930, 100828,\n", + " 101758, 106362, 110925, 115521, 120990, 122807, 128884, 136497,\n", + " 138807, 139733, 142955, 146886, 149596, 151427, 155132, 157923,\n", + " 173449, 177142, 178002, 178973, 179688, 182931, 184348, 189850,\n", + " 195253, 198149],\n", + " dtype='int64'), Int64Index([ 1619, 6214, 8143, 11856, 17315, 19156, 27358, 28274,\n", + " 33847, 65297, 77642, 82220, 84074, 85928, 86855, 88709,\n", + " 92025, 92985, 93894, 95714, 96669, 99004, 99931, 100829,\n", + " 101759, 106363, 110926, 115522, 120991, 122808, 128885, 136498,\n", + " 138808, 139734, 142956, 146887, 149597, 151428, 155133, 157924,\n", + " 173450, 177143, 178003, 178974, 179689, 182932, 184349, 189851,\n", + " 195254, 198150],\n", + " dtype='int64'), Int64Index([ 1620, 6215, 8144, 11857, 17316, 19157, 27359, 28275,\n", + " 33848, 65298, 77643, 82221, 84075, 85929, 86856, 88710,\n", + " 92026, 92986, 93895, 95715, 96670, 99005, 99932, 100830,\n", + " 101760, 106364, 110927, 115523, 120992, 122809, 128886, 136499,\n", + " 138809, 139735, 142957, 146888, 149598, 151429, 155134, 157925,\n", + " 173451, 177144, 178004, 178975, 179690, 182933, 184350, 189852,\n", + " 195255, 198151],\n", + " dtype='int64'), Int64Index([ 1621, 6216, 8145, 11858, 17317, 19158, 27360, 28276,\n", + " 33849, 65299, 77644, 82222, 84076, 85930, 86857, 88711,\n", + " 92027, 92987, 93896, 95716, 96671, 99006, 99933, 100831,\n", + " 101761, 106365, 110928, 115524, 120993, 122810, 128887, 136500,\n", + " 138810, 139736, 142958, 146889, 149599, 151430, 155135, 157926,\n", + " 173452, 177145, 178005, 178976, 179691, 182934, 184351, 189853,\n", + " 195256, 198152],\n", + " dtype='int64'), Int64Index([ 1622, 6217, 8146, 11859, 17318, 19159, 27361, 28277,\n", + " 33850, 65300, 77645, 82223, 84077, 85931, 86858, 88712,\n", + " 92028, 92988, 93897, 95717, 96672, 99007, 99934, 100832,\n", + " 101762, 106366, 110929, 115525, 120994, 122811, 128888, 136501,\n", + " 138811, 139737, 142959, 146890, 149600, 151431, 155136, 157927,\n", + " 173453, 177146, 178006, 178977, 179692, 182935, 184352, 189854,\n", + " 195257, 198153],\n", + " dtype='int64'), Int64Index([ 1623, 6218, 8147, 11860, 17319, 19160, 27362, 28278,\n", + " 33851, 65301, 77646, 82224, 84078, 85932, 86859, 88713,\n", + " 92029, 92989, 93898, 95718, 96673, 99008, 99935, 100833,\n", + " 101763, 106367, 110930, 115526, 120995, 122812, 128889, 136502,\n", + " 138812, 139738, 142960, 146891, 149601, 151432, 155137, 157928,\n", + " 173454, 177147, 178007, 178978, 179693, 182936, 184353, 189855,\n", + " 195258, 198154],\n", + " dtype='int64'), Int64Index([ 1624, 6219, 8148, 11861, 17320, 19161, 27363, 28279,\n", + " 33852, 65302, 77647, 82225, 84079, 85933, 86860, 88714,\n", + " 92030, 92990, 93899, 95719, 96674, 99009, 99936, 100834,\n", + " 101764, 106368, 110931, 115527, 120996, 122813, 128890, 136503,\n", + " 138813, 139739, 142961, 146892, 149602, 151433, 155138, 157929,\n", + " 173455, 177148, 178008, 178979, 179694, 182937, 184354, 189856,\n", + " 195259, 198155],\n", + " dtype='int64'), Int64Index([ 1625, 6220, 8149, 11862, 17321, 19162, 27364, 28280,\n", + " 33853, 65303, 77648, 82226, 84080, 85934, 86861, 88715,\n", + " 92031, 92991, 93900, 95720, 96675, 99010, 99937, 100835,\n", + " 101765, 106369, 110932, 115528, 120997, 122814, 128891, 136504,\n", + " 138814, 139740, 142962, 146893, 149603, 151434, 155139, 157930,\n", + " 173456, 177149, 178009, 178980, 179695, 182938, 184355, 189857,\n", + " 195260, 198156],\n", + " dtype='int64'), Int64Index([ 1626, 6221, 8150, 11863, 17322, 19163, 27365, 28281,\n", + " 33854, 65304, 77649, 82227, 84081, 85935, 86862, 88716,\n", + " 92032, 92992, 93901, 95721, 96676, 99011, 99938, 100836,\n", + " 101766, 106370, 110933, 115529, 120998, 122815, 128892, 136505,\n", + " 138815, 139741, 142963, 146894, 149604, 151435, 155140, 157931,\n", + " 173457, 177150, 178010, 178981, 179696, 182939, 184356, 189858,\n", + " 195261, 198157],\n", + " dtype='int64'), Int64Index([ 1627, 6222, 8151, 11864, 17323, 19164, 27366, 28282,\n", + " 33855, 65305, 77650, 82228, 84082, 85936, 86863, 88717,\n", + " 92033, 92993, 93902, 95722, 96677, 99012, 99939, 100837,\n", + " 101767, 106371, 110934, 115530, 120999, 122816, 128893, 136506,\n", + " 138816, 139742, 142964, 146895, 149605, 151436, 155141, 157932,\n", + " 173458, 177151, 178011, 178982, 179697, 182940, 184357, 189859,\n", + " 195262, 198158],\n", + " dtype='int64'), Int64Index([ 1628, 6223, 8152, 11865, 17324, 19165, 27367, 28283,\n", + " 33856, 65306, 77651, 82229, 84083, 85937, 86864, 88718,\n", + " 92034, 92994, 93903, 95723, 96678, 99013, 99940, 100838,\n", + " 101768, 106372, 110935, 115531, 121000, 122817, 128894, 136507,\n", + " 138817, 139743, 142965, 146896, 149606, 151437, 155142, 157933,\n", + " 173459, 177152, 178012, 178983, 179698, 182941, 184358, 189860,\n", + " 195263, 198159],\n", + " dtype='int64'), Int64Index([ 1629, 6224, 8153, 11866, 17325, 19166, 27368, 28284,\n", + " 33857, 65307, 77652, 82230, 84084, 85938, 86865, 88719,\n", + " 92035, 92995, 93904, 95724, 96679, 99014, 99941, 100839,\n", + " 101769, 106373, 110936, 115532, 121001, 122818, 128895, 136508,\n", + " 138818, 139744, 142966, 146897, 149607, 151438, 155143, 157934,\n", + " 173460, 177153, 178013, 178984, 179699, 182942, 184359, 189861,\n", + " 195264, 198160],\n", + " dtype='int64'), Int64Index([ 1630, 6225, 8154, 11867, 17326, 19167, 27369, 28285,\n", + " 33858, 65308, 77653, 82231, 84085, 85939, 86866, 88720,\n", + " 92036, 92996, 93905, 95725, 96680, 99015, 99942, 100840,\n", + " 101770, 106374, 110937, 115533, 121002, 122819, 128896, 136509,\n", + " 138819, 139745, 142967, 146898, 149608, 151439, 155144, 157935,\n", + " 173461, 177154, 178014, 178985, 179700, 182943, 184360, 189862,\n", + " 195265, 198161],\n", + " dtype='int64'), Int64Index([ 1631, 6226, 8155, 11868, 17327, 19168, 27370, 28286,\n", + " 33859, 65309, 77654, 82232, 84086, 85940, 86867, 88721,\n", + " 92037, 92997, 93906, 95726, 96681, 99016, 99943, 100841,\n", + " 101771, 106375, 110938, 115534, 121003, 122820, 128897, 136510,\n", + " 138820, 139746, 142968, 146899, 149609, 151440, 155145, 157936,\n", + " 173462, 177155, 178015, 178986, 179701, 182944, 184361, 189863,\n", + " 195266, 198162],\n", + " dtype='int64'), Int64Index([ 1632, 6227, 8156, 11869, 17328, 19169, 27371, 28287,\n", + " 33860, 65310, 77655, 82233, 84087, 85941, 86868, 88722,\n", + " 92038, 92998, 93907, 95727, 96682, 99017, 99944, 100842,\n", + " 101772, 106376, 110939, 115535, 121004, 122821, 128898, 136511,\n", + " 138821, 139747, 142969, 146900, 149610, 151441, 155146, 157937,\n", + " 173463, 177156, 178016, 178987, 179702, 182945, 184362, 189864,\n", + " 195267, 198163],\n", + " dtype='int64'), Int64Index([ 1633, 6228, 8157, 11870, 17329, 19170, 27372, 28288,\n", + " 33861, 65311, 77656, 82234, 84088, 85942, 86869, 88723,\n", + " 92039, 92999, 93908, 95728, 96683, 99018, 99945, 100843,\n", + " 101773, 106377, 110940, 115536, 121005, 122822, 128899, 136512,\n", + " 138822, 139748, 142970, 146901, 149611, 151442, 155147, 157938,\n", + " 173464, 177157, 178017, 178988, 179703, 182946, 184363, 189865,\n", + " 195268, 198164],\n", + " dtype='int64'), Int64Index([ 1634, 6229, 8158, 11871, 17330, 19171, 27373, 28289,\n", + " 33862, 65312, 77657, 82235, 84089, 85943, 86870, 88724,\n", + " 92040, 93000, 93909, 95729, 96684, 99019, 99946, 100844,\n", + " 101774, 106378, 110941, 115537, 121006, 122823, 128900, 136513,\n", + " 138823, 139749, 142971, 146902, 149612, 151443, 155148, 157939,\n", + " 173465, 177158, 178018, 178989, 179704, 182947, 184364, 189866,\n", + " 195269, 198165],\n", + " dtype='int64'), Int64Index([ 1635, 6230, 8159, 11872, 17331, 19172, 27374, 28290,\n", + " 33863, 65313, 77658, 82236, 84090, 85944, 86871, 88725,\n", + " 92041, 93001, 93910, 95730, 96685, 99020, 99947, 100845,\n", + " 101775, 106379, 110942, 115538, 121007, 122824, 128901, 136514,\n", + " 138824, 139750, 142972, 146903, 149613, 151444, 155149, 157940,\n", + " 173466, 177159, 178019, 178990, 179705, 182948, 184365, 189867,\n", + " 195270, 198166],\n", + " dtype='int64'), Int64Index([ 1636, 6231, 8160, 11873, 17332, 19173, 27375, 28291,\n", + " 33864, 65314, 77659, 82237, 84091, 85945, 86872, 88726,\n", + " 92042, 93002, 93911, 95731, 96686, 99021, 99948, 100846,\n", + " 101776, 106380, 110943, 115539, 121008, 122825, 128902, 136515,\n", + " 138825, 139751, 142973, 146904, 149614, 151445, 155150, 157941,\n", + " 173467, 177160, 178020, 178991, 179706, 182949, 184366, 189868,\n", + " 195271, 198167],\n", + " dtype='int64'), Int64Index([ 1637, 6232, 8161, 11874, 17333, 19174, 27376, 28292,\n", + " 33865, 65315, 77660, 82238, 84092, 85946, 86873, 88727,\n", + " 92043, 93003, 93912, 95732, 96687, 99022, 99949, 100847,\n", + " 101777, 106381, 110944, 115540, 121009, 122826, 128903, 136516,\n", + " 138826, 139752, 142974, 146905, 149615, 151446, 155151, 157942,\n", + " 173468, 177161, 178021, 178992, 179707, 182950, 184367, 189869,\n", + " 195272, 198168],\n", + " dtype='int64'), Int64Index([ 1638, 6233, 8162, 11875, 17334, 19175, 27377, 28293,\n", + " 33866, 65316, 77661, 82239, 84093, 85947, 86874, 88728,\n", + " 92044, 93004, 93913, 95733, 96688, 99023, 99950, 100848,\n", + " 101778, 106382, 110945, 115541, 121010, 122827, 128904, 136517,\n", + " 138827, 139753, 142975, 146906, 149616, 151447, 155152, 157943,\n", + " 173469, 177162, 178022, 178993, 179708, 182951, 184368, 189870,\n", + " 195273, 198169],\n", + " dtype='int64'), Int64Index([ 1639, 6234, 8163, 11876, 17335, 19176, 27378, 28294,\n", + " 33867, 65317, 77662, 82240, 84094, 85948, 86875, 88729,\n", + " 92045, 93005, 93914, 95734, 96689, 99024, 99951, 100849,\n", + " 101779, 106383, 110946, 115542, 121011, 122828, 128905, 136518,\n", + " 138828, 139754, 142976, 146907, 149617, 151448, 155153, 157944,\n", + " 173470, 177163, 178023, 178994, 179709, 182952, 184369, 189871,\n", + " 195274, 198170],\n", + " dtype='int64'), Int64Index([ 1640, 6235, 8164, 11877, 17336, 19177, 27379, 28295,\n", + " 33868, 65318, 77663, 82241, 84095, 85949, 86876, 88730,\n", + " 92046, 93006, 93915, 95735, 96690, 99025, 99952, 100850,\n", + " 101780, 106384, 110947, 115543, 121012, 122829, 128906, 136519,\n", + " 138829, 139755, 142977, 146908, 149618, 151449, 155154, 157945,\n", + " 173471, 177164, 178024, 178995, 179710, 182953, 184370, 189872,\n", + " 195275, 198171],\n", + " dtype='int64'), Int64Index([ 1641, 6236, 8165, 11878, 17337, 19178, 27380, 28296,\n", + " 33869, 65319, 77664, 82242, 84096, 85950, 86877, 88731,\n", + " 92047, 93007, 93916, 95736, 96691, 99026, 99953, 100851,\n", + " 101781, 106385, 110948, 115544, 121013, 122830, 128907, 136520,\n", + " 138830, 139756, 142978, 146909, 149619, 151450, 155155, 157946,\n", + " 173472, 177165, 178025, 178996, 179711, 182954, 184371, 189873,\n", + " 195276, 198172],\n", + " dtype='int64'), Int64Index([ 1642, 6237, 8166, 11879, 17338, 19179, 27381, 28297,\n", + " 33870, 65320, 77665, 82243, 84097, 85951, 86878, 88732,\n", + " 92048, 93008, 93917, 95737, 96692, 99027, 99954, 100852,\n", + " 101782, 106386, 110949, 115545, 121014, 122831, 128908, 136521,\n", + " 138831, 139757, 142979, 146910, 149620, 151451, 155156, 157947,\n", + " 173473, 177166, 178026, 178997, 179712, 182955, 184372, 189874,\n", + " 195277, 198173],\n", + " dtype='int64'), Int64Index([ 1643, 6238, 8167, 11880, 17339, 19180, 27382, 28298,\n", + " 33871, 65321, 77666, 82244, 84098, 85952, 86879, 88733,\n", + " 92049, 93009, 93918, 95738, 96693, 99028, 99955, 100853,\n", + " 101783, 106387, 110950, 115546, 121015, 122832, 128909, 136522,\n", + " 138832, 139758, 142980, 146911, 149621, 151452, 155157, 157948,\n", + " 173474, 177167, 178027, 178998, 179713, 182956, 184373, 189875,\n", + " 195278, 198174],\n", + " dtype='int64'), Int64Index([ 1644, 6239, 8168, 11881, 17340, 19181, 27383, 28299,\n", + " 33872, 65322, 77667, 82245, 84099, 85953, 86880, 88734,\n", + " 92050, 93010, 93919, 95739, 96694, 99029, 99956, 100854,\n", + " 101784, 106388, 110951, 115547, 121016, 122833, 128910, 136523,\n", + " 138833, 139759, 142981, 146912, 149622, 151453, 155158, 157949,\n", + " 173475, 177168, 178028, 178999, 179714, 182957, 184374, 189876,\n", + " 195279, 198175],\n", + " dtype='int64'), Int64Index([ 1645, 6240, 8169, 11882, 17341, 19182, 27384, 28300,\n", + " 33873, 65323, 77668, 82246, 84100, 85954, 86881, 88735,\n", + " 92051, 93011, 93920, 95740, 96695, 99030, 99957, 100855,\n", + " 101785, 106389, 110952, 115548, 121017, 122834, 128911, 136524,\n", + " 138834, 139760, 142982, 146913, 149623, 151454, 155159, 157950,\n", + " 173476, 177169, 178029, 179000, 179715, 182958, 184375, 189877,\n", + " 195280, 198176],\n", + " dtype='int64'), Int64Index([ 1646, 6241, 8170, 11883, 17342, 19183, 27385, 28301,\n", + " 33874, 65324, 77669, 82247, 84101, 85955, 86882, 88736,\n", + " 92052, 93012, 93921, 95741, 96696, 99031, 99958, 100856,\n", + " 101786, 106390, 110953, 115549, 121018, 122835, 128912, 136525,\n", + " 138835, 139761, 142983, 146914, 149624, 151455, 155160, 157951,\n", + " 173477, 177170, 178030, 179001, 179716, 182959, 184376, 189878,\n", + " 195281, 198177],\n", + " dtype='int64'), Int64Index([ 1647, 6242, 8171, 11884, 17343, 19184, 27386, 28302,\n", + " 33875, 65325, 77670, 82248, 84102, 85956, 86883, 88737,\n", + " 92053, 93013, 93922, 95742, 96697, 99032, 99959, 100857,\n", + " 101787, 106391, 110954, 115550, 121019, 122836, 128913, 136526,\n", + " 138836, 139762, 142984, 146915, 149625, 151456, 155161, 157952,\n", + " 173478, 177171, 178031, 179002, 179717, 182960, 184377, 189879,\n", + " 195282, 198178],\n", + " dtype='int64'), Int64Index([ 1648, 6243, 8172, 11885, 17344, 19185, 27387, 28303,\n", + " 33876, 65326, 77671, 82249, 84103, 85957, 86884, 88738,\n", + " 92054, 93014, 93923, 95743, 96698, 99033, 99960, 100858,\n", + " 101788, 106392, 110955, 115551, 121020, 122837, 128914, 136527,\n", + " 138837, 139763, 142985, 146916, 149626, 151457, 155162, 157953,\n", + " 173479, 177172, 178032, 179003, 179718, 182961, 184378, 189880,\n", + " 195283, 198179],\n", + " dtype='int64'), Int64Index([ 1649, 6244, 8173, 11886, 17345, 19186, 27388, 28304,\n", + " 33877, 65327, 77672, 82250, 84104, 85958, 86885, 88739,\n", + " 92055, 93015, 93924, 95744, 96699, 99034, 99961, 100859,\n", + " 101789, 106393, 110956, 115552, 121021, 122838, 128915, 136528,\n", + " 138838, 139764, 142986, 146917, 149627, 151458, 155163, 157954,\n", + " 173480, 177173, 178033, 179004, 179719, 182962, 184379, 189881,\n", + " 195284, 198180],\n", + " dtype='int64'), Int64Index([ 1650, 6245, 8174, 11887, 17346, 19187, 27389, 28305,\n", + " 33878, 65328, 77673, 82251, 84105, 85959, 86886, 88740,\n", + " 92056, 93016, 93925, 95745, 96700, 99035, 99962, 100860,\n", + " 101790, 106394, 110957, 115553, 121022, 122839, 128916, 136529,\n", + " 138839, 139765, 142987, 146918, 149628, 151459, 155164, 157955,\n", + " 173481, 177174, 178034, 179005, 179720, 182963, 184380, 189882,\n", + " 195285, 198181],\n", + " dtype='int64'), Int64Index([ 1651, 6246, 8175, 11888, 17347, 19188, 27390, 28306,\n", + " 33879, 65329, 77674, 82252, 84106, 85960, 86887, 88741,\n", + " 92057, 93017, 93926, 95746, 96701, 99036, 99963, 100861,\n", + " 101791, 106395, 110958, 115554, 121023, 122840, 128917, 136530,\n", + " 138840, 139766, 142988, 146919, 149629, 151460, 155165, 157956,\n", + " 173482, 177175, 178035, 179721, 182964, 184381, 189883, 195286,\n", + " 198182],\n", + " dtype='int64'), Int64Index([ 1652, 6247, 8176, 11889, 17348, 19189, 27391, 28307,\n", + " 33880, 65330, 77675, 82253, 84107, 85961, 86888, 88742,\n", + " 92058, 93018, 93927, 95747, 96702, 99037, 99964, 100862,\n", + " 101792, 106396, 110959, 115555, 121024, 122841, 128918, 136531,\n", + " 138841, 139767, 142989, 146920, 149630, 151461, 155166, 157957,\n", + " 173483, 177176, 178036, 179722, 182965, 184382, 189884, 195287,\n", + " 198183],\n", + " dtype='int64'), Int64Index([ 1653, 6248, 8177, 11890, 17349, 19190, 27392, 28308,\n", + " 33881, 65331, 77676, 82254, 84108, 85962, 86889, 88743,\n", + " 92059, 93019, 93928, 95748, 96703, 99038, 99965, 100863,\n", + " 101793, 106397, 110960, 115556, 121025, 122842, 128919, 136532,\n", + " 138842, 139768, 142990, 146921, 149631, 151462, 155167, 157958,\n", + " 173484, 177177, 178037, 179723, 182966, 184383, 189885, 195288,\n", + " 198184],\n", + " dtype='int64'), Int64Index([ 1654, 6249, 8178, 11891, 17350, 19191, 27393, 28309,\n", + " 33882, 65332, 77677, 82255, 84109, 85963, 86890, 88744,\n", + " 92060, 93020, 93929, 95749, 96704, 99039, 99966, 100864,\n", + " 101794, 106398, 110961, 115557, 121026, 122843, 128920, 136533,\n", + " 138843, 139769, 142991, 146922, 149632, 151463, 155168, 157959,\n", + " 173485, 177178, 178038, 179724, 182967, 184384, 189886, 195289,\n", + " 198185],\n", + " dtype='int64'), Int64Index([ 1655, 6250, 8179, 11892, 17351, 19192, 27394, 28310,\n", + " 33883, 65333, 77678, 82256, 84110, 85964, 86891, 88745,\n", + " 92061, 93021, 93930, 95750, 96705, 99040, 99967, 100865,\n", + " 101795, 106399, 110962, 115558, 121027, 122844, 128921, 136534,\n", + " 138844, 139770, 142992, 146923, 149633, 151464, 155169, 157960,\n", + " 173486, 177179, 178039, 179725, 182968, 184385, 189887, 195290,\n", + " 198186],\n", + " dtype='int64'), Int64Index([ 1656, 6251, 8180, 11893, 17352, 19193, 27395, 28311,\n", + " 33884, 65334, 77679, 82257, 84111, 85965, 86892, 88746,\n", + " 92062, 93022, 93931, 95751, 96706, 99041, 99968, 100866,\n", + " 101796, 106400, 110963, 115559, 121028, 122845, 128922, 136535,\n", + " 138845, 139771, 142993, 146924, 149634, 151465, 155170, 157961,\n", + " 173487, 177180, 178040, 179726, 182969, 184386, 189888, 195291,\n", + " 198187],\n", + " dtype='int64'), Int64Index([ 1657, 6252, 8181, 11894, 17353, 19194, 27396, 28312,\n", + " 33885, 65335, 77680, 82258, 84112, 85966, 86893, 88747,\n", + " 92063, 93023, 93932, 95752, 96707, 99042, 99969, 100867,\n", + " 101797, 106401, 110964, 115560, 121029, 122846, 128923, 136536,\n", + " 138846, 139772, 142994, 146925, 149635, 151466, 155171, 157962,\n", + " 173488, 177181, 178041, 179727, 182970, 184387, 189889, 195292,\n", + " 198188],\n", + " dtype='int64'), Int64Index([ 1658, 6253, 8182, 11895, 17354, 19195, 27397, 28313,\n", + " 33886, 65336, 77681, 82259, 84113, 85967, 86894, 88748,\n", + " 92064, 93024, 93933, 95753, 96708, 99043, 99970, 100868,\n", + " 101798, 106402, 110965, 115561, 121030, 122847, 128924, 136537,\n", + " 138847, 139773, 142995, 146926, 149636, 151467, 155172, 157963,\n", + " 173489, 177182, 178042, 179728, 182971, 184388, 189890, 195293,\n", + " 198189],\n", + " dtype='int64'), Int64Index([ 1659, 6254, 8183, 11896, 17355, 19196, 27398, 28314,\n", + " 33887, 65337, 77682, 82260, 84114, 85968, 86895, 88749,\n", + " 92065, 93025, 93934, 95754, 96709, 99044, 99971, 100869,\n", + " 101799, 106403, 110966, 115562, 121031, 122848, 128925, 136538,\n", + " 138848, 139774, 142996, 146927, 149637, 151468, 155173, 157964,\n", + " 173490, 177183, 178043, 179729, 182972, 184389, 189891, 195294,\n", + " 198190],\n", + " dtype='int64'), Int64Index([ 1660, 6255, 8184, 11897, 17356, 19197, 27399, 28315,\n", + " 33888, 65338, 77683, 82261, 84115, 85969, 86896, 88750,\n", + " 92066, 93026, 93935, 95755, 96710, 99045, 99972, 100870,\n", + " 101800, 106404, 110967, 115563, 121032, 122849, 128926, 136539,\n", + " 138849, 139775, 142997, 146928, 149638, 151469, 155174, 157965,\n", + " 173491, 177184, 178044, 179730, 182973, 184390, 189892, 195295,\n", + " 198191],\n", + " dtype='int64'), Int64Index([ 1661, 6256, 8185, 11898, 17357, 19198, 27400, 28316,\n", + " 33889, 65339, 77684, 82262, 84116, 85970, 86897, 88751,\n", + " 92067, 93027, 93936, 95756, 96711, 99046, 99973, 100871,\n", + " 101801, 106405, 110968, 115564, 121033, 122850, 128927, 136540,\n", + " 138850, 139776, 142998, 146929, 149639, 151470, 155175, 157966,\n", + " 173492, 177185, 178045, 179731, 182974, 184391, 189893, 195296,\n", + " 198192],\n", + " dtype='int64'), Int64Index([ 1662, 6257, 8186, 11899, 17358, 19199, 27401, 28317,\n", + " 33890, 65340, 77685, 82263, 84117, 85971, 86898, 88752,\n", + " 92068, 93028, 93937, 95757, 96712, 99047, 99974, 100872,\n", + " 101802, 106406, 110969, 115565, 121034, 122851, 128928, 136541,\n", + " 138851, 139777, 142999, 146930, 149640, 151471, 155176, 157967,\n", + " 173493, 177186, 178046, 179732, 182975, 184392, 189894, 195297,\n", + " 198193],\n", + " dtype='int64'), Int64Index([ 1663, 6258, 8187, 11900, 17359, 19200, 27402, 28318,\n", + " 33891, 65341, 77686, 82264, 84118, 85972, 86899, 88753,\n", + " 92069, 93029, 93938, 95758, 96713, 99048, 99975, 100873,\n", + " 101803, 106407, 110970, 115566, 121035, 122852, 128929, 136542,\n", + " 138852, 139778, 143000, 146931, 149641, 151472, 155177, 157968,\n", + " 173494, 177187, 178047, 179733, 182976, 184393, 189895, 195298,\n", + " 198194],\n", + " dtype='int64'), Int64Index([ 1664, 6259, 8188, 11901, 17360, 19201, 27403, 28319,\n", + " 33892, 65342, 77687, 82265, 84119, 85973, 86900, 88754,\n", + " 92070, 93030, 93939, 95759, 96714, 99049, 99976, 100874,\n", + " 101804, 106408, 110971, 115567, 121036, 122853, 128930, 136543,\n", + " 138853, 139779, 143001, 146932, 149642, 151473, 155178, 157969,\n", + " 173495, 177188, 178048, 179734, 182977, 184394, 189896, 195299,\n", + " 198195],\n", + " dtype='int64'), Int64Index([ 1665, 6260, 8189, 11902, 17361, 19202, 27404, 28320,\n", + " 33893, 65343, 77688, 82266, 84120, 85974, 86901, 88755,\n", + " 92071, 93031, 93940, 95760, 96715, 99050, 99977, 100875,\n", + " 101805, 106409, 110972, 115568, 121037, 122854, 128931, 136544,\n", + " 138854, 139780, 143002, 146933, 149643, 151474, 155179, 157970,\n", + " 173496, 177189, 178049, 179735, 182978, 184395, 189897, 195300,\n", + " 198196],\n", + " dtype='int64'), Int64Index([ 1666, 6261, 8190, 11903, 17362, 19203, 27405, 28321,\n", + " 33894, 65344, 77689, 82267, 84121, 85975, 86902, 88756,\n", + " 92072, 93032, 93941, 95761, 96716, 99051, 99978, 100876,\n", + " 101806, 106410, 110973, 115569, 121038, 122855, 128932, 136545,\n", + " 138855, 139781, 143003, 146934, 149644, 151475, 155180, 157971,\n", + " 173497, 177190, 178050, 179736, 182979, 184396, 189898, 195301,\n", + " 198197],\n", + " dtype='int64'), Int64Index([ 1667, 6262, 8191, 11904, 17363, 19204, 27406, 28322,\n", + " 33895, 65345, 77690, 82268, 84122, 85976, 86903, 88757,\n", + " 92073, 93033, 93942, 95762, 96717, 99052, 99979, 100877,\n", + " 101807, 106411, 110974, 115570, 121039, 122856, 128933, 136546,\n", + " 138856, 139782, 143004, 146935, 149645, 151476, 155181, 157972,\n", + " 173498, 177191, 178051, 179737, 182980, 184397, 189899, 195302,\n", + " 198198],\n", + " dtype='int64'), Int64Index([ 1668, 6263, 8192, 11905, 17364, 19205, 27407, 28323,\n", + " 33896, 65346, 77691, 82269, 84123, 85977, 86904, 88758,\n", + " 92074, 93034, 93943, 95763, 96718, 99053, 99980, 100878,\n", + " 101808, 106412, 110975, 115571, 121040, 122857, 128934, 136547,\n", + " 138857, 139783, 143005, 146936, 149646, 151477, 155182, 157973,\n", + " 173499, 177192, 178052, 179738, 182981, 184398, 189900, 195303,\n", + " 198199],\n", + " dtype='int64'), Int64Index([ 1669, 6264, 8193, 11906, 17365, 19206, 27408, 28324,\n", + " 33897, 65347, 77692, 82270, 84124, 85978, 86905, 88759,\n", + " 92075, 93035, 93944, 95764, 96719, 99054, 99981, 100879,\n", + " 101809, 106413, 110976, 115572, 121041, 122858, 128935, 136548,\n", + " 138858, 139784, 143006, 146937, 149647, 151478, 155183, 157974,\n", + " 173500, 177193, 178053, 179739, 182982, 184399, 189901, 195304,\n", + " 198200],\n", + " dtype='int64'), Int64Index([ 1670, 6265, 8194, 11907, 17366, 19207, 27409, 28325,\n", + " 33898, 65348, 77693, 82271, 84125, 85979, 86906, 88760,\n", + " 92076, 93036, 93945, 95765, 96720, 99055, 99982, 100880,\n", + " 101810, 106414, 110977, 115573, 121042, 122859, 128936, 136549,\n", + " 138859, 139785, 143007, 146938, 149648, 151479, 155184, 157975,\n", + " 173501, 177194, 178054, 179740, 182983, 184400, 189902, 195305,\n", + " 198201],\n", + " dtype='int64'), Int64Index([ 1671, 6266, 8195, 11908, 17367, 19208, 27410, 28326,\n", + " 33899, 65349, 77694, 82272, 84126, 85980, 86907, 88761,\n", + " 92077, 93037, 93946, 95766, 96721, 99056, 99983, 100881,\n", + " 101811, 106415, 110978, 115574, 121043, 122860, 128937, 136550,\n", + " 138860, 139786, 143008, 146939, 149649, 151480, 155185, 157976,\n", + " 173502, 177195, 178055, 179741, 182984, 184401, 189903, 195306,\n", + " 198202],\n", + " dtype='int64'), Int64Index([ 1672, 6267, 8196, 11909, 17368, 19209, 27411, 28327,\n", + " 33900, 65350, 77695, 82273, 84127, 85981, 86908, 88762,\n", + " 92078, 93038, 93947, 95767, 96722, 99057, 99984, 100882,\n", + " 101812, 106416, 110979, 115575, 121044, 122861, 128938, 136551,\n", + " 138861, 139787, 143009, 146940, 149650, 151481, 155186, 157977,\n", + " 173503, 177196, 178056, 179742, 182985, 184402, 189904, 195307,\n", + " 198203],\n", + " dtype='int64'), Int64Index([ 1673, 6268, 8197, 11910, 17369, 19210, 27412, 28328,\n", + " 33901, 65351, 77696, 82274, 84128, 85982, 86909, 88763,\n", + " 92079, 93039, 93948, 95768, 96723, 99058, 99985, 100883,\n", + " 101813, 106417, 110980, 115576, 121045, 122862, 128939, 136552,\n", + " 138862, 139788, 143010, 146941, 149651, 151482, 155187, 157978,\n", + " 173504, 177197, 178057, 179743, 182986, 184403, 189905, 195308,\n", + " 198204],\n", + " dtype='int64'), Int64Index([ 1674, 6269, 8198, 11911, 17370, 19211, 27413, 28329,\n", + " 33902, 65352, 77697, 82275, 84129, 85983, 86910, 88764,\n", + " 92080, 93040, 93949, 95769, 96724, 99059, 99986, 100884,\n", + " 101814, 106418, 110981, 115577, 121046, 122863, 128940, 136553,\n", + " 138863, 139789, 143011, 146942, 149652, 151483, 155188, 157979,\n", + " 173505, 177198, 178058, 179744, 182987, 184404, 189906, 195309,\n", + " 198205],\n", + " dtype='int64'), Int64Index([ 1675, 6270, 8199, 11912, 17371, 19212, 27414, 28330,\n", + " 33903, 65353, 77698, 82276, 84130, 85984, 86911, 88765,\n", + " 92081, 93041, 93950, 95770, 96725, 99060, 99987, 100885,\n", + " 101815, 106419, 110982, 115578, 121047, 122864, 128941, 136554,\n", + " 138864, 139790, 143012, 146943, 149653, 151484, 155189, 157980,\n", + " 173506, 177199, 178059, 179745, 182988, 184405, 189907, 195310,\n", + " 198206],\n", + " dtype='int64'), Int64Index([ 1676, 6271, 8200, 11913, 17372, 19213, 27415, 28331,\n", + " 33904, 65354, 77699, 82277, 84131, 85985, 86912, 88766,\n", + " 92082, 93042, 93951, 95771, 96726, 99061, 99988, 100886,\n", + " 101816, 106420, 110983, 115579, 121048, 122865, 128942, 136555,\n", + " 138865, 139791, 143013, 149654, 151485, 155190, 157981, 173507,\n", + " 177200, 178060, 179746, 182989, 184406, 189908, 195311, 198207],\n", + " dtype='int64'), Int64Index([ 1677, 6272, 8201, 11914, 17373, 19214, 27416, 28332,\n", + " 33905, 65355, 77700, 82278, 84132, 85986, 86913, 88767,\n", + " 92083, 93043, 93952, 95772, 96727, 99062, 99989, 100887,\n", + " 101817, 106421, 110984, 115580, 121049, 122866, 128943, 136556,\n", + " 138866, 139792, 143014, 149655, 151486, 155191, 157982, 173508,\n", + " 177201, 178061, 179747, 182990, 184407, 189909, 195312, 198208],\n", + " dtype='int64'), Int64Index([ 1678, 6273, 8202, 11915, 17374, 19215, 27417, 28333,\n", + " 33906, 65356, 77701, 82279, 84133, 85987, 86914, 88768,\n", + " 92084, 93044, 93953, 95773, 96728, 99063, 99990, 100888,\n", + " 101818, 106422, 110985, 115581, 121050, 122867, 128944, 136557,\n", + " 138867, 139793, 143015, 149656, 151487, 155192, 157983, 173509,\n", + " 177202, 178062, 179748, 182991, 184408, 189910, 195313, 198209],\n", + " dtype='int64'), Int64Index([ 1679, 6274, 8203, 11916, 17375, 19216, 27418, 28334,\n", + " 33907, 65357, 77702, 82280, 84134, 85988, 86915, 88769,\n", + " 92085, 93045, 93954, 95774, 96729, 99064, 99991, 100889,\n", + " 101819, 106423, 110986, 115582, 121051, 122868, 128945, 136558,\n", + " 138868, 139794, 143016, 149657, 151488, 155193, 157984, 173510,\n", + " 177203, 178063, 179749, 182992, 184409, 189911, 195314, 198210],\n", + " dtype='int64'), Int64Index([ 1680, 6275, 8204, 11917, 17376, 19217, 27419, 28335,\n", + " 33908, 65358, 77703, 82281, 84135, 85989, 86916, 88770,\n", + " 92086, 93046, 93955, 95775, 96730, 99065, 99992, 100890,\n", + " 101820, 106424, 110987, 115583, 121052, 122869, 128946, 136559,\n", + " 138869, 139795, 143017, 149658, 151489, 155194, 157985, 173511,\n", + " 177204, 178064, 179750, 182993, 184410, 189912, 195315, 198211],\n", + " dtype='int64'), Int64Index([ 1681, 6276, 8205, 11918, 17377, 19218, 27420, 28336,\n", + " 33909, 65359, 77704, 82282, 84136, 85990, 86917, 88771,\n", + " 92087, 93047, 93956, 95776, 96731, 99066, 99993, 100891,\n", + " 101821, 106425, 110988, 115584, 121053, 122870, 128947, 136560,\n", + " 138870, 139796, 143018, 149659, 151490, 155195, 157986, 173512,\n", + " 177205, 178065, 179751, 182994, 184411, 189913, 195316, 198212],\n", + " dtype='int64'), Int64Index([ 1682, 6277, 8206, 11919, 17378, 19219, 27421, 28337,\n", + " 33910, 65360, 77705, 82283, 84137, 85991, 86918, 88772,\n", + " 92088, 93048, 93957, 95777, 96732, 99067, 99994, 100892,\n", + " 101822, 106426, 110989, 115585, 121054, 122871, 128948, 136561,\n", + " 138871, 139797, 143019, 149660, 151491, 155196, 157987, 173513,\n", + " 177206, 178066, 179752, 182995, 184412, 189914, 195317, 198213],\n", + " dtype='int64'), Int64Index([ 1683, 6278, 8207, 11920, 17379, 19220, 27422, 28338,\n", + " 33911, 65361, 77706, 82284, 84138, 85992, 86919, 88773,\n", + " 92089, 93049, 93958, 95778, 96733, 99068, 99995, 100893,\n", + " 101823, 106427, 110990, 115586, 121055, 122872, 128949, 136562,\n", + " 138872, 139798, 143020, 149661, 151492, 155197, 157988, 173514,\n", + " 177207, 178067, 179753, 182996, 184413, 189915, 195318, 198214],\n", + " dtype='int64'), Int64Index([ 1684, 6279, 8208, 11921, 17380, 19221, 27423, 28339,\n", + " 33912, 65362, 77707, 82285, 84139, 85993, 86920, 88774,\n", + " 92090, 93050, 93959, 95779, 96734, 99069, 99996, 100894,\n", + " 101824, 106428, 110991, 115587, 121056, 122873, 128950, 136563,\n", + " 138873, 139799, 143021, 149662, 151493, 155198, 157989, 173515,\n", + " 177208, 178068, 179754, 182997, 184414, 189916, 195319, 198215],\n", + " dtype='int64'), Int64Index([ 1685, 6280, 8209, 11922, 17381, 19222, 27424, 28340,\n", + " 33913, 65363, 77708, 82286, 84140, 85994, 86921, 88775,\n", + " 92091, 93051, 93960, 95780, 96735, 99070, 99997, 100895,\n", + " 101825, 106429, 110992, 115588, 121057, 122874, 128951, 136564,\n", + " 138874, 139800, 143022, 149663, 151494, 155199, 157990, 173516,\n", + " 177209, 178069, 179755, 182998, 184415, 189917, 195320, 198216],\n", + " dtype='int64'), Int64Index([ 1686, 6281, 8210, 11923, 17382, 19223, 27425, 28341,\n", + " 33914, 65364, 77709, 82287, 84141, 85995, 86922, 88776,\n", + " 92092, 93052, 93961, 95781, 96736, 99071, 99998, 100896,\n", + " 101826, 106430, 110993, 115589, 121058, 122875, 128952, 136565,\n", + " 138875, 139801, 143023, 149664, 151495, 155200, 157991, 173517,\n", + " 177210, 178070, 179756, 182999, 184416, 189918, 195321, 198217],\n", + " dtype='int64'), Int64Index([ 1687, 6282, 8211, 11924, 17383, 19224, 27426, 28342,\n", + " 33915, 65365, 77710, 82288, 84142, 85996, 86923, 88777,\n", + " 92093, 93053, 93962, 95782, 96737, 99072, 99999, 100897,\n", + " 101827, 106431, 110994, 115590, 121059, 122876, 128953, 136566,\n", + " 138876, 139802, 143024, 149665, 151496, 155201, 157992, 173518,\n", + " 177211, 178071, 179757, 183000, 184417, 189919, 195322, 198218],\n", + " dtype='int64'), Int64Index([ 1688, 6283, 8212, 11925, 17384, 19225, 27427, 28343,\n", + " 33916, 65366, 77711, 82289, 84143, 85997, 86924, 88778,\n", + " 92094, 93054, 93963, 95783, 96738, 99073, 100000, 100898,\n", + " 101828, 106432, 110995, 115591, 121060, 122877, 128954, 136567,\n", + " 138877, 139803, 143025, 149666, 151497, 155202, 157993, 173519,\n", + " 177212, 178072, 179758, 183001, 184418, 189920, 195323, 198219],\n", + " dtype='int64'), Int64Index([ 1689, 6284, 8213, 11926, 17385, 19226, 27428, 28344,\n", + " 33917, 65367, 77712, 82290, 84144, 85998, 86925, 88779,\n", + " 92095, 93055, 93964, 95784, 96739, 99074, 100001, 100899,\n", + " 101829, 106433, 110996, 115592, 121061, 122878, 128955, 136568,\n", + " 138878, 139804, 143026, 149667, 151498, 155203, 157994, 173520,\n", + " 177213, 178073, 179759, 183002, 184419, 189921, 195324, 198220],\n", + " dtype='int64'), Int64Index([ 1690, 6285, 8214, 11927, 17386, 19227, 27429, 28345,\n", + " 33918, 65368, 77713, 82291, 84145, 85999, 86926, 88780,\n", + " 92096, 93056, 93965, 95785, 96740, 99075, 100002, 100900,\n", + " 101830, 106434, 110997, 115593, 121062, 122879, 128956, 136569,\n", + " 138879, 139805, 143027, 149668, 151499, 155204, 157995, 173521,\n", + " 177214, 178074, 179760, 183003, 184420, 189922, 195325, 198221],\n", + " dtype='int64'), Int64Index([ 1691, 6286, 8215, 11928, 17387, 19228, 27430, 28346,\n", + " 33919, 65369, 77714, 82292, 84146, 86000, 86927, 88781,\n", + " 92097, 93057, 93966, 95786, 96741, 99076, 100003, 100901,\n", + " 101831, 106435, 110998, 115594, 121063, 122880, 128957, 136570,\n", + " 138880, 139806, 143028, 149669, 151500, 155205, 157996, 173522,\n", + " 177215, 178075, 179761, 183004, 184421, 189923, 195326, 198222],\n", + " dtype='int64'), Int64Index([ 1692, 6287, 8216, 11929, 17388, 19229, 27431, 28347,\n", + " 33920, 65370, 77715, 82293, 84147, 86001, 86928, 88782,\n", + " 92098, 93058, 93967, 95787, 96742, 99077, 100004, 100902,\n", + " 101832, 106436, 110999, 115595, 121064, 122881, 128958, 136571,\n", + " 138881, 139807, 143029, 149670, 151501, 155206, 157997, 173523,\n", + " 177216, 178076, 179762, 183005, 184422, 189924, 195327, 198223],\n", + " dtype='int64'), Int64Index([ 1693, 6288, 8217, 11930, 17389, 19230, 27432, 28348,\n", + " 33921, 65371, 77716, 82294, 84148, 86002, 86929, 88783,\n", + " 92099, 93059, 93968, 95788, 96743, 99078, 100005, 100903,\n", + " 101833, 106437, 111000, 115596, 121065, 122882, 128959, 136572,\n", + " 138882, 139808, 143030, 149671, 151502, 155207, 157998, 173524,\n", + " 177217, 178077, 179763, 183006, 184423, 189925, 195328, 198224],\n", + " dtype='int64'), Int64Index([ 1694, 6289, 8218, 11931, 17390, 19231, 27433, 28349,\n", + " 33922, 65372, 77717, 82295, 84149, 86003, 86930, 88784,\n", + " 92100, 93060, 93969, 95789, 96744, 99079, 100006, 100904,\n", + " 101834, 106438, 111001, 115597, 121066, 122883, 128960, 136573,\n", + " 138883, 139809, 143031, 149672, 151503, 155208, 157999, 173525,\n", + " 177218, 178078, 179764, 183007, 184424, 189926, 195329, 198225],\n", + " dtype='int64'), Int64Index([ 1695, 6290, 8219, 11932, 17391, 19232, 27434, 28350,\n", + " 33923, 65373, 77718, 82296, 84150, 86004, 86931, 88785,\n", + " 92101, 93061, 93970, 95790, 96745, 99080, 100007, 100905,\n", + " 101835, 106439, 111002, 115598, 121067, 122884, 128961, 136574,\n", + " 138884, 139810, 143032, 149673, 151504, 155209, 158000, 173526,\n", + " 177219, 178079, 179765, 183008, 184425, 189927, 195330, 198226],\n", + " dtype='int64'), Int64Index([ 1696, 6291, 8220, 11933, 17392, 19233, 27435, 28351,\n", + " 33924, 65374, 77719, 82297, 84151, 86005, 86932, 88786,\n", + " 92102, 93062, 93971, 95791, 96746, 99081, 100008, 100906,\n", + " 101836, 106440, 111003, 115599, 121068, 122885, 128962, 136575,\n", + " 138885, 139811, 143033, 149674, 151505, 155210, 158001, 173527,\n", + " 177220, 178080, 179766, 183009, 184426, 189928, 195331, 198227],\n", + " dtype='int64'), Int64Index([ 1697, 6292, 8221, 11934, 17393, 19234, 27436, 28352,\n", + " 33925, 65375, 77720, 82298, 84152, 86006, 86933, 88787,\n", + " 92103, 93063, 93972, 95792, 96747, 99082, 100009, 100907,\n", + " 101837, 106441, 111004, 115600, 121069, 122886, 128963, 136576,\n", + " 138886, 139812, 143034, 149675, 151506, 155211, 158002, 173528,\n", + " 177221, 178081, 179767, 183010, 184427, 189929, 195332, 198228],\n", + " dtype='int64'), Int64Index([ 1698, 6293, 8222, 11935, 17394, 19235, 27437, 28353,\n", + " 33926, 65376, 77721, 82299, 84153, 86007, 86934, 88788,\n", + " 92104, 93064, 93973, 95793, 96748, 99083, 100010, 100908,\n", + " 101838, 106442, 111005, 115601, 121070, 122887, 128964, 136577,\n", + " 138887, 139813, 143035, 149676, 151507, 155212, 158003, 173529,\n", + " 177222, 178082, 179768, 183011, 184428, 189930, 195333, 198229],\n", + " dtype='int64'), Int64Index([ 1699, 6294, 8223, 11936, 17395, 19236, 27438, 28354,\n", + " 33927, 65377, 77722, 82300, 84154, 86008, 86935, 88789,\n", + " 92105, 93065, 93974, 95794, 96749, 99084, 100011, 100909,\n", + " 101839, 106443, 111006, 115602, 121071, 122888, 128965, 136578,\n", + " 138888, 139814, 143036, 149677, 151508, 155213, 158004, 173530,\n", + " 177223, 178083, 179769, 183012, 184429, 189931, 195334, 198230],\n", + " dtype='int64'), Int64Index([ 1700, 6295, 8224, 11937, 17396, 19237, 27439, 28355,\n", + " 33928, 65378, 77723, 82301, 84155, 86009, 86936, 88790,\n", + " 92106, 93066, 93975, 95795, 96750, 99085, 100012, 100910,\n", + " 101840, 106444, 111007, 115603, 121072, 122889, 128966, 136579,\n", + " 138889, 139815, 143037, 149678, 151509, 155214, 158005, 173531,\n", + " 177224, 178084, 179770, 183013, 184430, 189932, 195335, 198231],\n", + " dtype='int64'), Int64Index([ 1701, 6296, 8225, 11938, 17397, 19238, 27440, 28356,\n", + " 33929, 65379, 77724, 82302, 84156, 86010, 86937, 88791,\n", + " 92107, 93067, 93976, 95796, 96751, 99086, 100013, 100911,\n", + " 101841, 106445, 111008, 115604, 121073, 122890, 128967, 136580,\n", + " 138890, 139816, 143038, 149679, 151510, 155215, 158006, 173532,\n", + " 177225, 178085, 179771, 183014, 184431, 189933, 195336, 198232],\n", + " dtype='int64'), Int64Index([ 1702, 6297, 8226, 11939, 17398, 19239, 27441, 28357,\n", + " 33930, 65380, 77725, 82303, 84157, 86011, 86938, 88792,\n", + " 92108, 93068, 93977, 95797, 96752, 99087, 100014, 100912,\n", + " 101842, 106446, 111009, 115605, 121074, 122891, 128968, 136581,\n", + " 138891, 139817, 143039, 149680, 151511, 155216, 158007, 173533,\n", + " 177226, 178086, 179772, 183015, 184432, 189934, 195337, 198233],\n", + " dtype='int64'), Int64Index([ 1703, 6298, 8227, 11940, 17399, 19240, 27442, 28358,\n", + " 33931, 65381, 77726, 82304, 84158, 86012, 86939, 88793,\n", + " 92109, 93069, 93978, 95798, 96753, 99088, 100015, 100913,\n", + " 101843, 106447, 111010, 115606, 121075, 122892, 128969, 136582,\n", + " 138892, 139818, 143040, 149681, 151512, 155217, 158008, 173534,\n", + " 177227, 178087, 179773, 183016, 184433, 189935, 195338, 198234],\n", + " dtype='int64'), Int64Index([ 1704, 6299, 8228, 11941, 17400, 19241, 27443, 28359,\n", + " 33932, 65382, 77727, 82305, 84159, 86013, 86940, 88794,\n", + " 92110, 93070, 93979, 95799, 96754, 99089, 100016, 100914,\n", + " 101844, 106448, 111011, 115607, 121076, 122893, 128970, 136583,\n", + " 138893, 139819, 143041, 149682, 151513, 155218, 158009, 173535,\n", + " 177228, 178088, 179774, 183017, 184434, 189936, 195339, 198235],\n", + " dtype='int64'), Int64Index([ 1705, 6300, 8229, 11942, 17401, 19242, 27444, 28360,\n", + " 33933, 65383, 77728, 82306, 84160, 86014, 86941, 88795,\n", + " 92111, 93071, 93980, 95800, 96755, 99090, 100017, 100915,\n", + " 101845, 106449, 111012, 115608, 121077, 122894, 128971, 136584,\n", + " 138894, 139820, 143042, 149683, 151514, 155219, 158010, 173536,\n", + " 177229, 178089, 179775, 183018, 184435, 189937, 195340, 198236],\n", + " dtype='int64'), Int64Index([ 1706, 6301, 8230, 11943, 17402, 19243, 27445, 28361,\n", + " 33934, 65384, 77729, 82307, 84161, 86015, 86942, 88796,\n", + " 92112, 93072, 93981, 95801, 96756, 99091, 100018, 100916,\n", + " 101846, 106450, 111013, 115609, 121078, 122895, 128972, 136585,\n", + " 138895, 139821, 143043, 149684, 151515, 155220, 158011, 173537,\n", + " 177230, 178090, 179776, 183019, 184436, 189938, 195341, 198237],\n", + " dtype='int64'), Int64Index([ 1707, 6302, 8231, 11944, 17403, 19244, 27446, 28362,\n", + " 33935, 65385, 77730, 82308, 84162, 86016, 86943, 88797,\n", + " 92113, 93073, 93982, 95802, 96757, 99092, 100019, 100917,\n", + " 101847, 106451, 111014, 115610, 121079, 122896, 128973, 136586,\n", + " 138896, 139822, 143044, 149685, 151516, 155221, 158012, 173538,\n", + " 177231, 178091, 179777, 183020, 184437, 189939, 195342, 198238],\n", + " dtype='int64'), Int64Index([ 1708, 6303, 8232, 11945, 17404, 19245, 27447, 28363,\n", + " 33936, 65386, 77731, 82309, 84163, 86017, 86944, 88798,\n", + " 92114, 93074, 93983, 95803, 96758, 99093, 100020, 100918,\n", + " 101848, 106452, 111015, 115611, 121080, 122897, 128974, 136587,\n", + " 138897, 139823, 143045, 149686, 151517, 155222, 158013, 173539,\n", + " 177232, 178092, 179778, 183021, 184438, 189940, 195343, 198239],\n", + " dtype='int64'), Int64Index([ 1709, 6304, 8233, 11946, 17405, 19246, 27448, 28364,\n", + " 33937, 65387, 77732, 82310, 84164, 86018, 86945, 88799,\n", + " 92115, 93075, 93984, 95804, 96759, 99094, 100021, 100919,\n", + " 101849, 106453, 111016, 115612, 121081, 122898, 128975, 136588,\n", + " 138898, 139824, 143046, 149687, 151518, 155223, 158014, 173540,\n", + " 177233, 178093, 179779, 183022, 184439, 189941, 195344, 198240],\n", + " dtype='int64'), Int64Index([ 1710, 6305, 8234, 11947, 17406, 19247, 27449, 28365,\n", + " 33938, 65388, 77733, 82311, 84165, 86019, 86946, 88800,\n", + " 92116, 93076, 93985, 95805, 96760, 99095, 100022, 100920,\n", + " 101850, 106454, 111017, 115613, 121082, 122899, 128976, 136589,\n", + " 138899, 139825, 143047, 149688, 151519, 155224, 158015, 173541,\n", + " 177234, 178094, 179780, 183023, 184440, 189942, 195345, 198241],\n", + " dtype='int64'), Int64Index([ 1711, 6306, 8235, 11948, 17407, 19248, 27450, 28366,\n", + " 33939, 65389, 77734, 82312, 84166, 86020, 86947, 88801,\n", + " 92117, 93077, 93986, 95806, 96761, 99096, 100023, 100921,\n", + " 101851, 106455, 111018, 115614, 121083, 122900, 128977, 136590,\n", + " 138900, 139826, 143048, 149689, 151520, 155225, 158016, 173542,\n", + " 177235, 178095, 179781, 183024, 184441, 189943, 195346, 198242],\n", + " dtype='int64'), Int64Index([ 1712, 6307, 8236, 11949, 17408, 19249, 27451, 28367,\n", + " 33940, 65390, 77735, 82313, 84167, 86021, 86948, 88802,\n", + " 92118, 93078, 93987, 95807, 96762, 99097, 100024, 100922,\n", + " 101852, 106456, 111019, 115615, 121084, 122901, 128978, 136591,\n", + " 138901, 139827, 143049, 149690, 151521, 155226, 158017, 173543,\n", + " 177236, 178096, 179782, 183025, 184442, 189944, 195347, 198243],\n", + " dtype='int64'), Int64Index([ 1713, 6308, 8237, 11950, 17409, 19250, 27452, 28368,\n", + " 33941, 65391, 77736, 82314, 84168, 86022, 86949, 88803,\n", + " 92119, 93079, 93988, 95808, 96763, 99098, 100025, 100923,\n", + " 101853, 106457, 111020, 115616, 121085, 122902, 128979, 136592,\n", + " 138902, 139828, 143050, 149691, 151522, 155227, 158018, 173544,\n", + " 177237, 178097, 179783, 183026, 184443, 189945, 195348, 198244],\n", + " dtype='int64'), Int64Index([ 1714, 6309, 8238, 11951, 17410, 19251, 27453, 28369,\n", + " 33942, 65392, 77737, 82315, 84169, 86023, 86950, 88804,\n", + " 92120, 93080, 93989, 95809, 96764, 99099, 100026, 100924,\n", + " 101854, 106458, 111021, 115617, 121086, 122903, 128980, 136593,\n", + " 138903, 139829, 143051, 149692, 151523, 155228, 158019, 173545,\n", + " 177238, 178098, 179784, 183027, 184444, 189946, 195349, 198245],\n", + " dtype='int64'), Int64Index([ 1715, 6310, 8239, 11952, 17411, 19252, 27454, 28370,\n", + " 33943, 65393, 77738, 82316, 84170, 86024, 86951, 88805,\n", + " 92121, 93081, 93990, 95810, 96765, 99100, 100027, 100925,\n", + " 101855, 106459, 111022, 115618, 121087, 122904, 128981, 136594,\n", + " 138904, 139830, 143052, 149693, 151524, 155229, 158020, 173546,\n", + " 177239, 178099, 179785, 183028, 184445, 189947, 195350, 198246],\n", + " dtype='int64'), Int64Index([ 1716, 6311, 8240, 11953, 17412, 19253, 27455, 28371,\n", + " 33944, 65394, 77739, 82317, 84171, 86025, 86952, 88806,\n", + " 92122, 93082, 93991, 95811, 96766, 99101, 100028, 100926,\n", + " 101856, 106460, 111023, 115619, 121088, 122905, 128982, 136595,\n", + " 138905, 139831, 143053, 149694, 151525, 155230, 158021, 173547,\n", + " 177240, 178100, 179786, 183029, 184446, 189948, 195351, 198247],\n", + " dtype='int64'), Int64Index([ 1717, 6312, 8241, 11954, 17413, 19254, 27456, 28372,\n", + " 33945, 65395, 77740, 82318, 84172, 86026, 86953, 88807,\n", + " 92123, 93083, 93992, 95812, 96767, 99102, 100029, 100927,\n", + " 101857, 106461, 111024, 115620, 121089, 122906, 128983, 136596,\n", + " 138906, 139832, 143054, 149695, 151526, 155231, 158022, 173548,\n", + " 177241, 178101, 179787, 183030, 184447, 189949, 195352, 198248],\n", + " dtype='int64'), Int64Index([ 1718, 6313, 8242, 11955, 17414, 19255, 27457, 28373,\n", + " 33946, 65396, 77741, 82319, 84173, 86027, 86954, 88808,\n", + " 92124, 93084, 93993, 95813, 96768, 99103, 100030, 100928,\n", + " 101858, 106462, 111025, 115621, 121090, 122907, 128984, 136597,\n", + " 138907, 139833, 143055, 149696, 151527, 155232, 158023, 173549,\n", + " 177242, 178102, 179788, 183031, 184448, 189950, 195353, 198249],\n", + " dtype='int64'), Int64Index([ 1719, 6314, 8243, 11956, 17415, 19256, 27458, 28374,\n", + " 33947, 65397, 77742, 82320, 84174, 86028, 86955, 88809,\n", + " 92125, 93085, 93994, 95814, 96769, 99104, 100031, 100929,\n", + " 101859, 106463, 111026, 115622, 121091, 122908, 128985, 136598,\n", + " 138908, 139834, 143056, 146944, 149697, 151528, 155233, 158024,\n", + " 173550, 177243, 178103, 179789, 183032, 184449, 189951, 195354,\n", + " 198250],\n", + " dtype='int64'), Int64Index([ 1720, 6315, 8244, 11957, 17416, 19257, 27459, 28375,\n", + " 33948, 65398, 77743, 82321, 84175, 86029, 86956, 88810,\n", + " 92126, 93086, 93995, 95815, 96770, 99105, 100032, 100930,\n", + " 101860, 106464, 111027, 115623, 121092, 122909, 128986, 136599,\n", + " 138909, 139835, 143057, 146945, 149698, 151529, 155234, 158025,\n", + " 173551, 177244, 178104, 179790, 183033, 184450, 189952, 195355,\n", + " 198251],\n", + " dtype='int64'), Int64Index([ 1721, 6316, 8245, 11958, 17417, 19258, 27460, 28376,\n", + " 33949, 65399, 77744, 82322, 84176, 86030, 86957, 88811,\n", + " 92127, 93087, 93996, 95816, 96771, 99106, 100033, 100931,\n", + " 101861, 106465, 111028, 115624, 121093, 122910, 128987, 136600,\n", + " 138910, 139836, 143058, 146946, 149699, 151530, 155235, 158026,\n", + " 173552, 177245, 178105, 179791, 183034, 184451, 189953, 195356,\n", + " 198252],\n", + " dtype='int64'), Int64Index([ 1722, 6317, 8246, 11959, 17418, 19259, 27461, 28377,\n", + " 33950, 65400, 77745, 82323, 84177, 86031, 86958, 88812,\n", + " 92128, 93088, 93997, 95817, 96772, 99107, 100034, 100932,\n", + " 101862, 106466, 111029, 115625, 121094, 122911, 128988, 136601,\n", + " 138911, 139837, 143059, 146947, 149700, 151531, 155236, 158027,\n", + " 173553, 177246, 178106, 179792, 183035, 184452, 189954, 195357,\n", + " 198253],\n", + " dtype='int64'), Int64Index([ 1723, 6318, 8247, 11960, 17419, 19260, 27462, 28378,\n", + " 33951, 65401, 77746, 82324, 84178, 86032, 86959, 88813,\n", + " 92129, 93089, 93998, 95818, 96773, 99108, 100035, 100933,\n", + " 101863, 106467, 111030, 115626, 121095, 122912, 128989, 136602,\n", + " 138912, 139838, 143060, 146948, 149701, 151532, 155237, 158028,\n", + " 173554, 177247, 178107, 179793, 183036, 184453, 189955, 195358,\n", + " 198254],\n", + " dtype='int64'), Int64Index([ 1724, 6319, 8248, 11961, 17420, 19261, 27463, 28379,\n", + " 33952, 65402, 77747, 82325, 84179, 86033, 86960, 88814,\n", + " 92130, 93090, 93999, 95819, 96774, 99109, 100036, 100934,\n", + " 101864, 106468, 111031, 115627, 121096, 122913, 128990, 136603,\n", + " 138913, 139839, 143061, 146949, 149702, 151533, 155238, 158029,\n", + " 173555, 177248, 178108, 179794, 183037, 184454, 189956, 195359,\n", + " 198255],\n", + " dtype='int64'), Int64Index([ 1725, 6320, 8249, 11962, 17421, 19262, 27464, 28380,\n", + " 33953, 65403, 77748, 82326, 84180, 86034, 86961, 88815,\n", + " 92131, 93091, 94000, 95820, 96775, 99110, 100037, 100935,\n", + " 101865, 106469, 111032, 115628, 121097, 122914, 128991, 136604,\n", + " 138914, 139840, 143062, 146950, 149703, 151534, 155239, 158030,\n", + " 173556, 177249, 178109, 179795, 183038, 184455, 189957, 195360,\n", + " 198256],\n", + " dtype='int64'), Int64Index([ 1726, 6321, 8250, 11963, 17422, 19263, 27465, 28381,\n", + " 33954, 65404, 77749, 82327, 84181, 86035, 86962, 88816,\n", + " 92132, 93092, 94001, 95821, 96776, 99111, 100038, 100936,\n", + " 101866, 106470, 111033, 115629, 121098, 122915, 128992, 136605,\n", + " 138915, 139841, 143063, 146951, 149704, 151535, 155240, 158031,\n", + " 173557, 177250, 178110, 179796, 183039, 184456, 189958, 195361,\n", + " 198257],\n", + " dtype='int64'), Int64Index([ 1727, 6322, 8251, 11964, 17423, 19264, 27466, 28382,\n", + " 33955, 65405, 77750, 82328, 84182, 86036, 86963, 88817,\n", + " 92133, 93093, 94002, 95822, 96777, 99112, 100039, 100937,\n", + " 101867, 106471, 111034, 115630, 121099, 122916, 128993, 136606,\n", + " 138916, 139842, 143064, 146952, 149705, 151536, 155241, 158032,\n", + " 173558, 177251, 178111, 179797, 183040, 184457, 189959, 195362,\n", + " 198258],\n", + " dtype='int64'), Int64Index([ 1728, 6323, 8252, 11965, 17424, 19265, 27467, 28383,\n", + " 33956, 65406, 77751, 82329, 84183, 86037, 86964, 88818,\n", + " 92134, 93094, 94003, 95823, 96778, 99113, 100040, 100938,\n", + " 101868, 106472, 111035, 115631, 121100, 122917, 128994, 136607,\n", + " 138917, 139843, 143065, 146953, 149706, 151537, 155242, 158033,\n", + " 173559, 177252, 178112, 179798, 183041, 184458, 189960, 195363,\n", + " 198259],\n", + " dtype='int64'), Int64Index([ 1729, 6324, 8253, 11966, 17425, 19266, 27468, 28384,\n", + " 33957, 65407, 77752, 82330, 84184, 86038, 86965, 88819,\n", + " 92135, 93095, 94004, 95824, 96779, 99114, 100041, 100939,\n", + " 101869, 106473, 111036, 115632, 121101, 122918, 128995, 136608,\n", + " 138918, 139844, 143066, 146954, 149707, 151538, 155243, 158034,\n", + " 173560, 177253, 178113, 179799, 183042, 184459, 189961, 195364,\n", + " 198260],\n", + " dtype='int64'), Int64Index([ 1730, 6325, 8254, 11967, 17426, 19267, 27469, 28385,\n", + " 33958, 65408, 77753, 82331, 84185, 86039, 86966, 88820,\n", + " 92136, 93096, 94005, 95825, 96780, 99115, 100042, 100940,\n", + " 101870, 106474, 111037, 115633, 121102, 122919, 128996, 136609,\n", + " 138919, 139845, 143067, 146955, 149708, 151539, 155244, 158035,\n", + " 173561, 177254, 178114, 179800, 183043, 184460, 189962, 195365,\n", + " 198261],\n", + " dtype='int64'), Int64Index([ 1731, 6326, 8255, 11968, 17427, 19268, 27470, 28386,\n", + " 33959, 65409, 77754, 82332, 84186, 86040, 86967, 88821,\n", + " 92137, 93097, 94006, 95826, 96781, 99116, 100043, 100941,\n", + " 101871, 106475, 111038, 115634, 121103, 122920, 128997, 136610,\n", + " 138920, 139846, 143068, 146956, 149709, 151540, 155245, 158036,\n", + " 173562, 177255, 178115, 179801, 183044, 184461, 189963, 195366,\n", + " 198262],\n", + " dtype='int64'), Int64Index([ 1732, 6327, 8256, 11969, 17428, 19269, 27471, 28387,\n", + " 33960, 65410, 77755, 82333, 84187, 86041, 86968, 88822,\n", + " 92138, 93098, 94007, 95827, 96782, 99117, 100044, 100942,\n", + " 101872, 106476, 111039, 115635, 121104, 122921, 128998, 136611,\n", + " 138921, 139847, 143069, 146957, 149710, 151541, 155246, 158037,\n", + " 173563, 177256, 178116, 179802, 183045, 184462, 189964, 195367,\n", + " 198263],\n", + " dtype='int64'), Int64Index([ 1733, 6328, 8257, 11970, 17429, 19270, 27472, 28388,\n", + " 33961, 65411, 77756, 82334, 84188, 86042, 86969, 88823,\n", + " 92139, 93099, 94008, 95828, 96783, 99118, 100045, 100943,\n", + " 101873, 106477, 111040, 115636, 121105, 122922, 128999, 136612,\n", + " 138922, 139848, 143070, 146958, 149711, 151542, 155247, 158038,\n", + " 173564, 177257, 178117, 179803, 183046, 184463, 189965, 195368,\n", + " 198264],\n", + " dtype='int64'), Int64Index([ 1734, 6329, 8258, 11971, 17430, 19271, 27473, 28389,\n", + " 33962, 65412, 77757, 82335, 84189, 86043, 86970, 88824,\n", + " 92140, 93100, 94009, 95829, 96784, 99119, 100046, 100944,\n", + " 101874, 106478, 111041, 115637, 121106, 122923, 129000, 136613,\n", + " 138923, 139849, 143071, 146959, 149712, 151543, 155248, 158039,\n", + " 173565, 177258, 178118, 179804, 183047, 184464, 189966, 195369,\n", + " 198265],\n", + " dtype='int64'), Int64Index([ 1735, 6330, 8259, 11972, 17431, 19272, 27474, 28390,\n", + " 33963, 65413, 77758, 82336, 84190, 86044, 86971, 88825,\n", + " 92141, 93101, 94010, 95830, 96785, 99120, 100047, 100945,\n", + " 101875, 106479, 111042, 115638, 121107, 122924, 129001, 136614,\n", + " 138924, 139850, 143072, 146960, 149713, 151544, 155249, 158040,\n", + " 173566, 177259, 178119, 179805, 183048, 184465, 189967, 195370,\n", + " 198266],\n", + " dtype='int64'), Int64Index([ 1736, 6331, 8260, 11973, 17432, 19273, 27475, 28391,\n", + " 33964, 65414, 77759, 82337, 84191, 86045, 86972, 88826,\n", + " 92142, 93102, 94011, 95831, 96786, 99121, 100048, 100946,\n", + " 101876, 106480, 111043, 115639, 121108, 122925, 129002, 136615,\n", + " 138925, 139851, 143073, 146961, 149714, 151545, 155250, 158041,\n", + " 173567, 177260, 178120, 179806, 183049, 184466, 189968, 195371,\n", + " 198267],\n", + " dtype='int64'), Int64Index([ 1737, 6332, 8261, 11974, 17433, 19274, 27476, 28392,\n", + " 33965, 65415, 77760, 82338, 84192, 86046, 86973, 88827,\n", + " 92143, 93103, 94012, 95832, 96787, 99122, 100049, 100947,\n", + " 101877, 106481, 111044, 115640, 121109, 122926, 129003, 136616,\n", + " 138926, 139852, 143074, 146962, 149715, 151546, 155251, 158042,\n", + " 173568, 177261, 178121, 179807, 183050, 184467, 189969, 195372,\n", + " 198268],\n", + " dtype='int64'), Int64Index([ 1738, 6333, 8262, 11975, 17434, 19275, 27477, 28393,\n", + " 33966, 65416, 77761, 82339, 84193, 86047, 86974, 88828,\n", + " 92144, 93104, 94013, 95833, 96788, 99123, 100050, 100948,\n", + " 101878, 106482, 111045, 115641, 121110, 122927, 129004, 136617,\n", + " 138927, 139853, 143075, 146963, 149716, 151547, 155252, 158043,\n", + " 173569, 177262, 178122, 179808, 183051, 184468, 189970, 195373,\n", + " 198269],\n", + " dtype='int64'), Int64Index([ 1739, 6334, 8263, 11976, 17435, 19276, 27478, 28394,\n", + " 33967, 65417, 77762, 82340, 84194, 86048, 86975, 88829,\n", + " 92145, 93105, 94014, 95834, 96789, 99124, 100051, 100949,\n", + " 101879, 106483, 111046, 115642, 121111, 122928, 129005, 136618,\n", + " 138928, 139854, 143076, 146964, 149717, 151548, 155253, 158044,\n", + " 173570, 177263, 178123, 179809, 183052, 184469, 189971, 195374,\n", + " 198270],\n", + " dtype='int64'), Int64Index([ 1740, 6335, 8264, 11977, 17436, 19277, 27479, 28395,\n", + " 33968, 65418, 77763, 82341, 84195, 86049, 86976, 88830,\n", + " 92146, 93106, 94015, 95835, 96790, 99125, 100052, 100950,\n", + " 101880, 106484, 111047, 115643, 121112, 122929, 129006, 136619,\n", + " 138929, 139855, 143077, 146965, 149718, 151549, 155254, 158045,\n", + " 173571, 177264, 178124, 179810, 183053, 184470, 189972, 195375,\n", + " 198271],\n", + " dtype='int64'), Int64Index([ 1741, 6336, 8265, 11978, 17437, 19278, 27480, 28396,\n", + " 33969, 65419, 77764, 82342, 84196, 86050, 86977, 88831,\n", + " 92147, 93107, 94016, 95836, 96791, 99126, 100053, 100951,\n", + " 101881, 106485, 111048, 115644, 121113, 122930, 129007, 136620,\n", + " 138930, 139856, 143078, 146966, 149719, 151550, 155255, 158046,\n", + " 173572, 177265, 178125, 179811, 183054, 184471, 189973, 195376,\n", + " 198272],\n", + " dtype='int64'), Int64Index([ 1742, 6337, 8266, 11979, 17438, 19279, 27481, 28397,\n", + " 33970, 65420, 77765, 82343, 84197, 86051, 86978, 88832,\n", + " 92148, 93108, 94017, 95837, 96792, 99127, 100054, 100952,\n", + " 101882, 106486, 111049, 115645, 121114, 122931, 129008, 136621,\n", + " 138931, 139857, 143079, 146967, 149720, 151551, 155256, 158047,\n", + " 173573, 177266, 178126, 179812, 183055, 184472, 189974, 195377,\n", + " 198273],\n", + " dtype='int64'), Int64Index([ 1743, 6338, 8267, 11980, 17439, 19280, 27482, 28398,\n", + " 33971, 65421, 77766, 82344, 84198, 86052, 86979, 88833,\n", + " 92149, 93109, 94018, 95838, 96793, 99128, 100055, 100953,\n", + " 101883, 106487, 111050, 115646, 121115, 122932, 129009, 136622,\n", + " 138932, 139858, 143080, 146968, 149721, 151552, 155257, 158048,\n", + " 173574, 177267, 178127, 179813, 183056, 184473, 189975, 195378,\n", + " 198274],\n", + " dtype='int64'), Int64Index([ 1744, 6339, 8268, 11981, 17440, 19281, 27483, 28399,\n", + " 33972, 65422, 77767, 82345, 84199, 86053, 86980, 88834,\n", + " 92150, 93110, 94019, 95839, 96794, 99129, 100056, 100954,\n", + " 101884, 106488, 111051, 115647, 121116, 122933, 129010, 136623,\n", + " 138933, 139859, 143081, 146969, 149722, 151553, 155258, 158049,\n", + " 173575, 177268, 178128, 179814, 183057, 184474, 189976, 195379,\n", + " 198275],\n", + " dtype='int64'), Int64Index([ 1745, 6340, 8269, 11982, 17441, 19282, 27484, 28400,\n", + " 33973, 65423, 77768, 82346, 84200, 86054, 86981, 88835,\n", + " 92151, 93111, 94020, 95840, 96795, 99130, 100057, 100955,\n", + " 101885, 106489, 111052, 115648, 121117, 122934, 129011, 136624,\n", + " 138934, 139860, 143082, 146970, 149723, 151554, 155259, 158050,\n", + " 173576, 177269, 178129, 179815, 183058, 184475, 189977, 195380,\n", + " 198276],\n", + " dtype='int64'), Int64Index([ 1746, 6341, 8270, 11983, 17442, 19283, 27485, 28401,\n", + " 33974, 65424, 77769, 82347, 84201, 86055, 86982, 88836,\n", + " 92152, 93112, 94021, 95841, 96796, 99131, 100058, 100956,\n", + " 101886, 106490, 111053, 115649, 121118, 122935, 129012, 136625,\n", + " 138935, 139861, 143083, 146971, 149724, 151555, 155260, 158051,\n", + " 173577, 177270, 178130, 179816, 183059, 184476, 189978, 195381,\n", + " 198277],\n", + " dtype='int64'), Int64Index([ 1747, 6342, 8271, 11984, 17443, 19284, 27486, 28402,\n", + " 33975, 65425, 77770, 82348, 84202, 86056, 86983, 88837,\n", + " 92153, 93113, 94022, 95842, 96797, 99132, 100059, 100957,\n", + " 101887, 106491, 111054, 115650, 121119, 122936, 129013, 136626,\n", + " 138936, 139862, 143084, 146972, 149725, 151556, 155261, 158052,\n", + " 173578, 177271, 178131, 179817, 183060, 184477, 189979, 195382,\n", + " 198278],\n", + " dtype='int64'), Int64Index([ 1748, 6343, 8272, 11985, 17444, 19285, 27487, 28403,\n", + " 33976, 65426, 77771, 82349, 84203, 86057, 86984, 88838,\n", + " 92154, 93114, 94023, 95843, 96798, 99133, 100060, 100958,\n", + " 101888, 106492, 111055, 115651, 121120, 122937, 129014, 136627,\n", + " 138937, 139863, 143085, 146973, 149726, 151557, 155262, 158053,\n", + " 173579, 177272, 178132, 179818, 183061, 184478, 189980, 195383,\n", + " 198279],\n", + " dtype='int64'), Int64Index([ 1749, 6344, 8273, 11986, 17445, 19286, 27488, 28404,\n", + " 33977, 65427, 77772, 82350, 84204, 86058, 86985, 88839,\n", + " 92155, 93115, 94024, 95844, 96799, 99134, 100061, 100959,\n", + " 101889, 106493, 111056, 115652, 121121, 122938, 129015, 136628,\n", + " 138938, 139864, 143086, 146974, 149727, 151558, 155263, 158054,\n", + " 173580, 177273, 178133, 179819, 183062, 184479, 189981, 195384,\n", + " 198280],\n", + " dtype='int64'), Int64Index([ 1750, 6345, 8274, 11987, 17446, 19287, 27489, 28405,\n", + " 33978, 65428, 77773, 82351, 84205, 86059, 86986, 88840,\n", + " 92156, 93116, 94025, 95845, 96800, 99135, 100062, 100960,\n", + " 101890, 106494, 111057, 115653, 121122, 122939, 129016, 136629,\n", + " 138939, 139865, 143087, 146975, 149728, 151559, 155264, 158055,\n", + " 173581, 177274, 178134, 179820, 183063, 184480, 189982, 195385,\n", + " 198281],\n", + " dtype='int64'), Int64Index([ 1751, 6346, 8275, 11988, 17447, 19288, 27490, 28406,\n", + " 33979, 65429, 77774, 82352, 84206, 86060, 86987, 88841,\n", + " 92157, 93117, 94026, 95846, 96801, 99136, 100063, 100961,\n", + " 101891, 106495, 111058, 115654, 121123, 122940, 129017, 136630,\n", + " 138940, 139866, 143088, 146976, 149729, 151560, 155265, 158056,\n", + " 173582, 177275, 178135, 179821, 183064, 184481, 189983, 195386,\n", + " 198282],\n", + " dtype='int64'), Int64Index([ 1752, 6347, 8276, 11989, 17448, 19289, 27491, 28407,\n", + " 33980, 65430, 77775, 82353, 84207, 86061, 86988, 88842,\n", + " 92158, 93118, 94027, 95847, 96802, 99137, 100064, 100962,\n", + " 101892, 106496, 111059, 115655, 121124, 122941, 129018, 136631,\n", + " 138941, 139867, 143089, 146977, 149730, 151561, 155266, 158057,\n", + " 173583, 177276, 178136, 179822, 183065, 184482, 189984, 195387,\n", + " 198283],\n", + " dtype='int64'), Int64Index([ 1753, 6348, 8277, 11990, 17449, 19290, 27492, 28408,\n", + " 33981, 65431, 77776, 82354, 84208, 86062, 86989, 88843,\n", + " 92159, 93119, 94028, 95848, 96803, 99138, 100065, 100963,\n", + " 101893, 106497, 111060, 115656, 121125, 122942, 129019, 136632,\n", + " 138942, 139868, 143090, 146978, 149731, 151562, 155267, 158058,\n", + " 173584, 177277, 178137, 179823, 183066, 184483, 189985, 195388,\n", + " 198284],\n", + " dtype='int64'), Int64Index([ 1754, 6349, 8278, 11991, 17450, 19291, 27493, 28409,\n", + " 33982, 65432, 77777, 82355, 84209, 86063, 86990, 88844,\n", + " 92160, 93120, 94029, 95849, 96804, 99139, 100066, 100964,\n", + " 101894, 106498, 111061, 115657, 121126, 122943, 129020, 136633,\n", + " 138943, 139869, 143091, 146979, 149732, 151563, 155268, 158059,\n", + " 173585, 177278, 178138, 179824, 183067, 184484, 189986, 195389,\n", + " 198285],\n", + " dtype='int64'), Int64Index([ 1755, 6350, 8279, 11992, 17451, 19292, 27494, 28410,\n", + " 33983, 65433, 77778, 82356, 84210, 86064, 86991, 88845,\n", + " 92161, 93121, 94030, 95850, 96805, 99140, 100067, 100965,\n", + " 101895, 106499, 111062, 115658, 121127, 122944, 129021, 136634,\n", + " 138944, 139870, 143092, 146980, 149733, 151564, 155269, 158060,\n", + " 173586, 177279, 178139, 179825, 183068, 184485, 189987, 195390,\n", + " 198286],\n", + " dtype='int64'), Int64Index([ 1756, 6351, 8280, 11993, 17452, 19293, 27495, 28411,\n", + " 33984, 65434, 77779, 82357, 84211, 86065, 86992, 88846,\n", + " 92162, 93122, 94031, 95851, 96806, 99141, 100068, 100966,\n", + " 101896, 106500, 111063, 115659, 121128, 122945, 129022, 136635,\n", + " 138945, 139871, 143093, 146981, 149734, 151565, 155270, 158061,\n", + " 173587, 177280, 178140, 179826, 183069, 184486, 189988, 195391,\n", + " 198287],\n", + " dtype='int64'), Int64Index([ 1757, 6352, 8281, 11994, 17453, 19294, 27496, 28412,\n", + " 33985, 65435, 77780, 82358, 84212, 86066, 86993, 88847,\n", + " 92163, 93123, 94032, 95852, 96807, 99142, 100069, 100967,\n", + " 101897, 106501, 111064, 115660, 121129, 122946, 129023, 136636,\n", + " 138946, 139872, 143094, 146982, 149735, 151566, 155271, 158062,\n", + " 173588, 177281, 178141, 179827, 183070, 184487, 189989, 195392,\n", + " 198288],\n", + " dtype='int64'), Int64Index([ 1758, 6353, 8282, 11995, 17454, 19295, 27497, 28413,\n", + " 33986, 65436, 77781, 82359, 84213, 86067, 86994, 88848,\n", + " 92164, 93124, 94033, 95853, 96808, 99143, 100070, 100968,\n", + " 101898, 106502, 111065, 115661, 121130, 122947, 129024, 136637,\n", + " 138947, 139873, 143095, 146983, 149736, 151567, 155272, 158063,\n", + " 173589, 177282, 178142, 179828, 183071, 184488, 189990, 195393,\n", + " 198289],\n", + " dtype='int64'), Int64Index([ 1759, 6354, 8283, 11996, 17455, 19296, 27498, 28414,\n", + " 33987, 65437, 77782, 82360, 84214, 86068, 86995, 88849,\n", + " 92165, 93125, 94034, 95854, 96809, 99144, 100071, 100969,\n", + " 101899, 106503, 111066, 115662, 121131, 122948, 129025, 136638,\n", + " 138948, 139874, 143096, 146984, 149737, 151568, 155273, 158064,\n", + " 173590, 177283, 178143, 179829, 183072, 184489, 189991, 195394,\n", + " 198290],\n", + " dtype='int64'), Int64Index([ 1760, 6355, 8284, 11997, 17456, 19297, 27499, 28415,\n", + " 33988, 65438, 77783, 82361, 84215, 86069, 86996, 88850,\n", + " 92166, 93126, 94035, 95855, 96810, 99145, 100072, 100970,\n", + " 101900, 106504, 111067, 115663, 121132, 122949, 129026, 136639,\n", + " 138949, 139875, 143097, 146985, 149738, 151569, 155274, 158065,\n", + " 173591, 177284, 178144, 179830, 183073, 184490, 189992, 195395,\n", + " 198291],\n", + " dtype='int64'), Int64Index([ 1761, 6356, 8285, 11998, 17457, 19298, 27500, 28416,\n", + " 33989, 65439, 77784, 82362, 84216, 86070, 86997, 88851,\n", + " 92167, 93127, 94036, 95856, 96811, 99146, 100073, 100971,\n", + " 101901, 106505, 111068, 115664, 121133, 122950, 129027, 136640,\n", + " 138950, 139876, 143098, 146986, 149739, 151570, 155275, 158066,\n", + " 173592, 177285, 178145, 179831, 183074, 184491, 189993, 195396,\n", + " 198292],\n", + " dtype='int64'), Int64Index([ 1762, 6357, 8286, 11999, 17458, 19299, 27501, 28417,\n", + " 33990, 65440, 77785, 82363, 84217, 86071, 86998, 88852,\n", + " 92168, 93128, 94037, 95857, 96812, 99147, 100074, 100972,\n", + " 101902, 106506, 111069, 115665, 121134, 122951, 129028, 136641,\n", + " 138951, 139877, 143099, 146987, 149740, 151571, 155276, 158067,\n", + " 173593, 177286, 178146, 179832, 183075, 184492, 189994, 195397,\n", + " 198293],\n", + " dtype='int64'), Int64Index([ 1763, 6358, 8287, 12000, 17459, 19300, 27502, 28418,\n", + " 33991, 65441, 77786, 82364, 84218, 86072, 86999, 88853,\n", + " 92169, 93129, 94038, 95858, 96813, 99148, 100075, 100973,\n", + " 101903, 106507, 111070, 115666, 121135, 122952, 129029, 136642,\n", + " 138952, 139878, 143100, 146988, 149741, 151572, 155277, 158068,\n", + " 173594, 177287, 178147, 179833, 183076, 184493, 189995, 195398,\n", + " 198294],\n", + " dtype='int64'), Int64Index([ 1764, 6359, 8288, 12001, 17460, 19301, 27503, 28419,\n", + " 33992, 65442, 77787, 82365, 84219, 86073, 87000, 88854,\n", + " 92170, 93130, 94039, 95859, 96814, 99149, 100076, 100974,\n", + " 101904, 106508, 111071, 115667, 121136, 122953, 129030, 136643,\n", + " 138953, 139879, 143101, 146989, 149742, 151573, 155278, 158069,\n", + " 173595, 177288, 178148, 179834, 183077, 184494, 189996, 195399,\n", + " 198295],\n", + " dtype='int64'), Int64Index([ 1765, 6360, 8289, 12002, 17461, 19302, 27504, 28420,\n", + " 33993, 65443, 77788, 82366, 84220, 86074, 87001, 88855,\n", + " 92171, 93131, 94040, 95860, 96815, 99150, 100077, 100975,\n", + " 101905, 106509, 111072, 115668, 121137, 122954, 129031, 136644,\n", + " 138954, 139880, 143102, 146990, 149743, 151574, 155279, 158070,\n", + " 173596, 177289, 178149, 179835, 183078, 184495, 189997, 195400,\n", + " 198296],\n", + " dtype='int64'), Int64Index([ 1766, 6361, 8290, 12003, 17462, 19303, 27505, 28421,\n", + " 33994, 65444, 77789, 82367, 84221, 86075, 87002, 88856,\n", + " 92172, 93132, 94041, 95861, 96816, 99151, 100078, 100976,\n", + " 101906, 106510, 111073, 115669, 121138, 122955, 129032, 136645,\n", + " 138955, 139881, 143103, 146991, 149744, 151575, 155280, 158071,\n", + " 173597, 177290, 178150, 179836, 183079, 184496, 189998, 195401,\n", + " 198297],\n", + " dtype='int64'), Int64Index([ 1767, 6362, 8291, 12004, 17463, 19304, 27506, 28422,\n", + " 33995, 65445, 77790, 82368, 84222, 86076, 87003, 88857,\n", + " 92173, 93133, 94042, 95862, 96817, 99152, 100079, 100977,\n", + " 101907, 106511, 111074, 115670, 121139, 122956, 129033, 136646,\n", + " 138956, 139882, 143104, 146992, 149745, 151576, 155281, 158072,\n", + " 173598, 177291, 178151, 179837, 183080, 184497, 189999, 195402,\n", + " 198298],\n", + " dtype='int64'), Int64Index([ 1768, 6363, 8292, 12005, 17464, 19305, 27507, 28423,\n", + " 33996, 65446, 77791, 82369, 84223, 86077, 87004, 88858,\n", + " 92174, 93134, 94043, 95863, 96818, 99153, 100080, 100978,\n", + " 101908, 106512, 111075, 115671, 121140, 122957, 129034, 136647,\n", + " 138957, 139883, 143105, 146993, 149746, 151577, 155282, 158073,\n", + " 173599, 177292, 178152, 179838, 183081, 184498, 190000, 195403,\n", + " 198299],\n", + " dtype='int64'), Int64Index([ 1769, 6364, 8293, 12006, 17465, 19306, 27508, 28424,\n", + " 33997, 65447, 77792, 82370, 84224, 86078, 87005, 88859,\n", + " 92175, 93135, 94044, 95864, 96819, 99154, 100081, 100979,\n", + " 101909, 106513, 111076, 115672, 121141, 122958, 129035, 136648,\n", + " 138958, 139884, 143106, 146994, 149747, 151578, 155283, 158074,\n", + " 173600, 177293, 178153, 179839, 183082, 184499, 190001, 195404,\n", + " 198300],\n", + " dtype='int64'), Int64Index([ 1770, 6365, 8294, 12007, 17466, 19307, 27509, 28425,\n", + " 33998, 65448, 77793, 82371, 84225, 86079, 87006, 88860,\n", + " 92176, 93136, 94045, 95865, 96820, 99155, 100082, 100980,\n", + " 101910, 106514, 111077, 115673, 121142, 122959, 129036, 136649,\n", + " 138959, 139885, 143107, 146995, 149748, 151579, 155284, 158075,\n", + " 173601, 177294, 178154, 179840, 183083, 184500, 190002, 195405,\n", + " 198301],\n", + " dtype='int64'), Int64Index([ 1771, 6366, 8295, 12008, 17467, 19308, 27510, 28426,\n", + " 33999, 65449, 77794, 82372, 84226, 86080, 87007, 88861,\n", + " 92177, 93137, 94046, 95866, 96821, 99156, 100083, 100981,\n", + " 101911, 106515, 111078, 115674, 121143, 122960, 129037, 136650,\n", + " 138960, 139886, 143108, 146996, 149749, 151580, 155285, 158076,\n", + " 173602, 177295, 178155, 179841, 183084, 184501, 190003, 195406,\n", + " 198302],\n", + " dtype='int64'), Int64Index([ 1772, 6367, 8296, 12009, 17468, 19309, 27511, 28427,\n", + " 34000, 65450, 77795, 82373, 84227, 86081, 87008, 88862,\n", + " 92178, 93138, 94047, 95867, 96822, 99157, 100084, 100982,\n", + " 101912, 106516, 111079, 115675, 121144, 122961, 129038, 136651,\n", + " 138961, 139887, 143109, 146997, 149750, 151581, 155286, 158077,\n", + " 173603, 177296, 178156, 179842, 183085, 184502, 190004, 195407,\n", + " 198303],\n", + " dtype='int64'), Int64Index([ 1773, 6368, 8297, 12010, 17469, 19310, 27512, 28428,\n", + " 34001, 65451, 77796, 82374, 84228, 86082, 87009, 88863,\n", + " 92179, 93139, 94048, 95868, 96823, 99158, 100085, 100983,\n", + " 101913, 106517, 111080, 115676, 121145, 122962, 129039, 136652,\n", + " 138962, 139888, 143110, 146998, 149751, 151582, 155287, 158078,\n", + " 173604, 177297, 178157, 179843, 183086, 184503, 190005, 195408,\n", + " 198304],\n", + " dtype='int64'), Int64Index([ 1774, 6369, 8298, 12011, 17470, 19311, 27513, 28429,\n", + " 34002, 65452, 77797, 82375, 84229, 86083, 87010, 88864,\n", + " 92180, 93140, 94049, 95869, 96824, 99159, 100086, 100984,\n", + " 101914, 106518, 111081, 115677, 121146, 122963, 129040, 136653,\n", + " 138963, 139889, 143111, 146999, 149752, 151583, 155288, 158079,\n", + " 173605, 177298, 178158, 179844, 183087, 184504, 190006, 195409,\n", + " 198305],\n", + " dtype='int64'), Int64Index([ 1775, 6370, 8299, 12012, 17471, 19312, 27514, 28430,\n", + " 34003, 65453, 77798, 82376, 84230, 86084, 87011, 88865,\n", + " 92181, 93141, 94050, 95870, 96825, 99160, 100087, 100985,\n", + " 101915, 106519, 111082, 115678, 121147, 122964, 129041, 136654,\n", + " 138964, 139890, 143112, 147000, 149753, 151584, 155289, 158080,\n", + " 173606, 177299, 178159, 179845, 183088, 184505, 190007, 195410,\n", + " 198306],\n", + " dtype='int64'), Int64Index([ 1776, 6371, 8300, 12013, 17472, 19313, 27515, 28431,\n", + " 34004, 65454, 77799, 82377, 84231, 86085, 87012, 88866,\n", + " 92182, 93142, 94051, 95871, 96826, 99161, 100088, 100986,\n", + " 101916, 106520, 111083, 115679, 121148, 122965, 129042, 136655,\n", + " 138965, 139891, 143113, 147001, 149754, 151585, 155290, 158081,\n", + " 173607, 177300, 178160, 179846, 183089, 184506, 190008, 195411,\n", + " 198307],\n", + " dtype='int64'), Int64Index([ 1777, 6372, 8301, 12014, 17473, 19314, 27516, 28432,\n", + " 34005, 65455, 77800, 82378, 84232, 86086, 87013, 88867,\n", + " 92183, 93143, 94052, 95872, 96827, 99162, 100089, 100987,\n", + " 101917, 106521, 111084, 115680, 121149, 122966, 129043, 136656,\n", + " 138966, 139892, 143114, 147002, 149755, 151586, 155291, 158082,\n", + " 173608, 177301, 178161, 179847, 183090, 184507, 190009, 195412,\n", + " 198308],\n", + " dtype='int64'), Int64Index([ 1778, 6373, 8302, 12015, 17474, 19315, 27517, 28433,\n", + " 34006, 65456, 77801, 82379, 84233, 86087, 87014, 88868,\n", + " 92184, 93144, 94053, 95873, 96828, 99163, 100090, 100988,\n", + " 101918, 106522, 111085, 115681, 121150, 122967, 129044, 136657,\n", + " 138967, 139893, 143115, 147003, 149756, 151587, 155292, 158083,\n", + " 173609, 177302, 178162, 179848, 183091, 184508, 190010, 195413,\n", + " 198309],\n", + " dtype='int64'), Int64Index([ 1779, 6374, 8303, 12016, 17475, 19316, 27518, 28434,\n", + " 34007, 65457, 77802, 82380, 84234, 86088, 87015, 88869,\n", + " 92185, 93145, 94054, 95874, 96829, 99164, 100091, 100989,\n", + " 101919, 106523, 111086, 115682, 121151, 122968, 129045, 136658,\n", + " 138968, 139894, 143116, 147004, 149757, 151588, 155293, 158084,\n", + " 173610, 177303, 178163, 179849, 183092, 184509, 190011, 195414,\n", + " 198310],\n", + " dtype='int64'), Int64Index([ 1780, 6375, 8304, 12017, 17476, 19317, 27519, 28435,\n", + " 34008, 65458, 77803, 82381, 84235, 86089, 87016, 88870,\n", + " 92186, 93146, 94055, 95875, 96830, 99165, 100092, 100990,\n", + " 101920, 106524, 111087, 115683, 121152, 122969, 129046, 136659,\n", + " 138969, 139895, 143117, 147005, 149758, 151589, 155294, 158085,\n", + " 173611, 177304, 178164, 179850, 183093, 184510, 190012, 195415,\n", + " 198311],\n", + " dtype='int64'), Int64Index([ 1781, 6376, 8305, 12018, 17477, 19318, 27520, 28436,\n", + " 34009, 65459, 77804, 82382, 84236, 86090, 87017, 88871,\n", + " 92187, 93147, 94056, 95876, 96831, 99166, 100093, 100991,\n", + " 101921, 106525, 111088, 115684, 121153, 122970, 129047, 136660,\n", + " 138970, 139896, 143118, 147006, 149759, 151590, 155295, 158086,\n", + " 173612, 177305, 178165, 179851, 183094, 184511, 190013, 195416,\n", + " 198312],\n", + " dtype='int64'), Int64Index([ 1782, 6377, 8306, 12019, 17478, 19319, 27521, 28437,\n", + " 34010, 65460, 77805, 82383, 84237, 86091, 87018, 88872,\n", + " 92188, 93148, 94057, 95877, 96832, 99167, 100094, 100992,\n", + " 101922, 106526, 111089, 115685, 121154, 122971, 129048, 136661,\n", + " 138971, 139897, 143119, 147007, 149760, 151591, 155296, 158087,\n", + " 173613, 177306, 178166, 179852, 183095, 184512, 190014, 195417,\n", + " 198313],\n", + " dtype='int64'), Int64Index([ 1783, 6378, 8307, 12020, 17479, 19320, 27522, 28438,\n", + " 34011, 65461, 77806, 82384, 84238, 86092, 87019, 88873,\n", + " 92189, 93149, 94058, 95878, 96833, 99168, 100095, 100993,\n", + " 101923, 106527, 111090, 115686, 121155, 122972, 129049, 136662,\n", + " 138972, 139898, 143120, 147008, 149761, 151592, 155297, 158088,\n", + " 173614, 177307, 178167, 179853, 183096, 184513, 190015, 195418,\n", + " 198314],\n", + " dtype='int64'), Int64Index([ 1784, 6379, 8308, 12021, 17480, 19321, 27523, 28439,\n", + " 34012, 65462, 77807, 82385, 84239, 86093, 87020, 88874,\n", + " 92190, 93150, 94059, 95879, 96834, 99169, 100096, 100994,\n", + " 101924, 106528, 111091, 115687, 121156, 122973, 129050, 136663,\n", + " 138973, 139899, 143121, 147009, 149762, 151593, 155298, 158089,\n", + " 173615, 177308, 178168, 179854, 183097, 184514, 190016, 195419,\n", + " 198315],\n", + " dtype='int64'), Int64Index([ 1785, 6380, 8309, 12022, 17481, 19322, 27524, 28440,\n", + " 34013, 65463, 77808, 82386, 84240, 86094, 87021, 88875,\n", + " 92191, 93151, 94060, 95880, 96835, 99170, 100097, 100995,\n", + " 101925, 106529, 111092, 115688, 121157, 122974, 129051, 136664,\n", + " 138974, 139900, 143122, 147010, 149763, 151594, 155299, 158090,\n", + " 173616, 177309, 178169, 179855, 183098, 184515, 190017, 195420,\n", + " 198316],\n", + " dtype='int64'), Int64Index([ 1786, 6381, 8310, 12023, 17482, 19323, 27525, 28441,\n", + " 34014, 65464, 77809, 82387, 84241, 86095, 87022, 88876,\n", + " 92192, 93152, 94061, 95881, 96836, 99171, 100098, 100996,\n", + " 101926, 106530, 111093, 115689, 121158, 122975, 129052, 136665,\n", + " 138975, 139901, 143123, 147011, 149764, 151595, 155300, 158091,\n", + " 173617, 177310, 178170, 179856, 183099, 184516, 190018, 195421,\n", + " 198317],\n", + " dtype='int64'), Int64Index([ 1787, 6382, 8311, 12024, 17483, 19324, 27526, 28442,\n", + " 34015, 65465, 77810, 82388, 84242, 86096, 87023, 88877,\n", + " 92193, 93153, 94062, 95882, 96837, 99172, 100099, 100997,\n", + " 101927, 106531, 111094, 115690, 121159, 122976, 129053, 136666,\n", + " 138976, 139902, 143124, 147012, 149765, 151596, 155301, 158092,\n", + " 173618, 177311, 178171, 179857, 183100, 184517, 190019, 195422,\n", + " 198318],\n", + " dtype='int64'), Int64Index([ 1788, 6383, 8312, 12025, 17484, 19325, 27527, 28443,\n", + " 34016, 65466, 77811, 82389, 84243, 86097, 87024, 88878,\n", + " 92194, 93154, 94063, 95883, 96838, 99173, 100100, 100998,\n", + " 101928, 106532, 111095, 115691, 121160, 122977, 129054, 136667,\n", + " 138977, 139903, 143125, 147013, 149766, 151597, 155302, 158093,\n", + " 173619, 177312, 178172, 179858, 183101, 184518, 190020, 195423,\n", + " 198319],\n", + " dtype='int64'), Int64Index([ 1789, 6384, 8313, 12026, 17485, 19326, 27528, 28444,\n", + " 34017, 65467, 77812, 82390, 84244, 86098, 87025, 88879,\n", + " 92195, 93155, 94064, 95884, 96839, 99174, 100101, 100999,\n", + " 101929, 106533, 111096, 115692, 121161, 122978, 129055, 136668,\n", + " 138978, 139904, 143126, 147014, 149767, 151598, 155303, 158094,\n", + " 173620, 177313, 178173, 179859, 183102, 184519, 190021, 195424,\n", + " 198320],\n", + " dtype='int64'), Int64Index([ 1790, 6385, 8314, 12027, 17486, 19327, 27529, 28445,\n", + " 34018, 65468, 77813, 82391, 84245, 86099, 87026, 88880,\n", + " 92196, 93156, 94065, 95885, 96840, 99175, 100102, 101000,\n", + " 101930, 106534, 111097, 115693, 121162, 122979, 129056, 136669,\n", + " 138979, 139905, 143127, 147015, 149768, 151599, 155304, 158095,\n", + " 173621, 177314, 178174, 179860, 183103, 184520, 190022, 195425,\n", + " 198321],\n", + " dtype='int64'), Int64Index([ 1791, 6386, 8315, 12028, 17487, 19328, 27530, 28446,\n", + " 34019, 65469, 77814, 82392, 84246, 86100, 87027, 88881,\n", + " 92197, 93157, 94066, 95886, 96841, 99176, 100103, 101001,\n", + " 101931, 106535, 111098, 115694, 121163, 122980, 129057, 136670,\n", + " 138980, 139906, 143128, 147016, 149769, 151600, 155305, 158096,\n", + " 173622, 177315, 178175, 179861, 183104, 184521, 190023, 195426,\n", + " 198322],\n", + " dtype='int64'), Int64Index([ 1792, 6387, 8316, 12029, 17488, 19329, 27531, 28447,\n", + " 34020, 65470, 77815, 82393, 84247, 86101, 87028, 88882,\n", + " 92198, 93158, 94067, 95887, 96842, 99177, 100104, 101002,\n", + " 101932, 106536, 111099, 115695, 121164, 122981, 129058, 136671,\n", + " 138981, 139907, 143129, 147017, 149770, 151601, 155306, 158097,\n", + " 173623, 177316, 178176, 179862, 183105, 184522, 190024, 195427,\n", + " 198323],\n", + " dtype='int64'), Int64Index([ 1793, 6388, 8317, 12030, 17489, 19330, 27532, 28448,\n", + " 34021, 65471, 77816, 82394, 84248, 86102, 87029, 88883,\n", + " 92199, 93159, 94068, 95888, 96843, 99178, 100105, 101003,\n", + " 101933, 106537, 111100, 115696, 121165, 122982, 129059, 136672,\n", + " 138982, 139908, 143130, 147018, 149771, 151602, 155307, 158098,\n", + " 173624, 177317, 178177, 179863, 183106, 184523, 190025, 195428,\n", + " 198324],\n", + " dtype='int64'), Int64Index([ 1794, 6389, 8318, 12031, 17490, 19331, 27533, 28449,\n", + " 34022, 65472, 77817, 82395, 84249, 86103, 87030, 88884,\n", + " 92200, 93160, 94069, 95889, 96844, 99179, 100106, 101004,\n", + " 101934, 106538, 111101, 115697, 121166, 122983, 129060, 136673,\n", + " 138983, 139909, 143131, 147019, 149772, 151603, 155308, 158099,\n", + " 173625, 177318, 178178, 179864, 183107, 184524, 190026, 195429,\n", + " 198325],\n", + " dtype='int64'), Int64Index([ 1795, 6390, 8319, 12032, 17491, 19332, 27534, 28450,\n", + " 34023, 65473, 77818, 82396, 84250, 86104, 87031, 88885,\n", + " 92201, 93161, 94070, 95890, 96845, 99180, 100107, 101005,\n", + " 101935, 106539, 111102, 115698, 121167, 122984, 129061, 136674,\n", + " 138984, 139910, 143132, 147020, 149773, 151604, 155309, 158100,\n", + " 173626, 177319, 178179, 179865, 183108, 184525, 190027, 195430,\n", + " 198326],\n", + " dtype='int64'), Int64Index([ 1796, 6391, 8320, 12033, 17492, 19333, 27535, 28451,\n", + " 34024, 65474, 77819, 82397, 84251, 86105, 87032, 88886,\n", + " 92202, 93162, 94071, 95891, 96846, 99181, 100108, 101006,\n", + " 101936, 106540, 111103, 115699, 121168, 122985, 129062, 136675,\n", + " 138985, 139911, 143133, 147021, 149774, 151605, 155310, 158101,\n", + " 173627, 177320, 178180, 179866, 183109, 184526, 190028, 195431,\n", + " 198327],\n", + " dtype='int64'), Int64Index([ 1797, 6392, 8321, 12034, 17493, 19334, 27536, 28452,\n", + " 34025, 65475, 77820, 82398, 84252, 86106, 87033, 88887,\n", + " 92203, 93163, 94072, 95892, 96847, 99182, 100109, 101007,\n", + " 101937, 106541, 111104, 115700, 121169, 122986, 129063, 136676,\n", + " 138986, 139912, 143134, 147022, 149775, 151606, 155311, 158102,\n", + " 173628, 177321, 178181, 179867, 183110, 184527, 190029, 195432,\n", + " 198328],\n", + " dtype='int64'), Int64Index([ 1798, 6393, 8322, 12035, 17494, 19335, 27537, 28453,\n", + " 34026, 65476, 77821, 82399, 84253, 86107, 87034, 88888,\n", + " 92204, 93164, 94073, 95893, 96848, 99183, 100110, 101008,\n", + " 101938, 106542, 111105, 115701, 121170, 122987, 129064, 136677,\n", + " 138987, 139913, 143135, 147023, 149776, 151607, 155312, 158103,\n", + " 173629, 177322, 178182, 179868, 183111, 184528, 190030, 195433,\n", + " 198329],\n", + " dtype='int64'), Int64Index([ 1799, 6394, 8323, 12036, 17495, 19336, 27538, 28454,\n", + " 34027, 65477, 77822, 82400, 84254, 86108, 87035, 88889,\n", + " 92205, 93165, 94074, 95894, 96849, 99184, 100111, 101009,\n", + " 101939, 106543, 111106, 115702, 121171, 122988, 129065, 136678,\n", + " 138988, 139914, 143136, 147024, 149777, 151608, 155313, 158104,\n", + " 173630, 177323, 178183, 179869, 183112, 184529, 190031, 195434,\n", + " 198330],\n", + " dtype='int64'), Int64Index([ 1800, 6395, 8324, 12037, 17496, 19337, 27539, 28455,\n", + " 34028, 65478, 77823, 82401, 84255, 86109, 87036, 88890,\n", + " 92206, 93166, 94075, 95895, 96850, 99185, 100112, 101010,\n", + " 101940, 106544, 111107, 115703, 121172, 122989, 129066, 136679,\n", + " 138989, 139915, 143137, 147025, 149778, 151609, 155314, 158105,\n", + " 173631, 177324, 178184, 179870, 183113, 184530, 190032, 195435,\n", + " 198331],\n", + " dtype='int64'), Int64Index([ 1801, 6396, 8325, 12038, 17497, 19338, 27540, 28456,\n", + " 34029, 65479, 77824, 82402, 84256, 86110, 87037, 88891,\n", + " 92207, 93167, 94076, 95896, 96851, 99186, 100113, 101011,\n", + " 101941, 106545, 111108, 115704, 121173, 122990, 129067, 136680,\n", + " 138990, 139916, 143138, 147026, 149779, 151610, 155315, 158106,\n", + " 173632, 177325, 178185, 179871, 183114, 184531, 190033, 195436,\n", + " 198332],\n", + " dtype='int64'), Int64Index([ 1802, 6397, 8326, 12039, 17498, 19339, 27541, 28457,\n", + " 34030, 65480, 77825, 82403, 84257, 86111, 87038, 88892,\n", + " 92208, 93168, 94077, 95897, 96852, 99187, 100114, 101012,\n", + " 101942, 106546, 111109, 115705, 121174, 122991, 129068, 136681,\n", + " 138991, 139917, 143139, 147027, 149780, 151611, 155316, 158107,\n", + " 173633, 177326, 178186, 179872, 183115, 184532, 190034, 195437,\n", + " 198333],\n", + " dtype='int64'), Int64Index([ 1803, 6398, 8327, 12040, 17499, 19340, 27542, 28458,\n", + " 34031, 65481, 77826, 82404, 84258, 86112, 87039, 88893,\n", + " 92209, 93169, 94078, 95898, 96853, 99188, 100115, 101013,\n", + " 101943, 106547, 111110, 115706, 121175, 122992, 129069, 136682,\n", + " 138992, 139918, 143140, 147028, 149781, 151612, 155317, 158108,\n", + " 173634, 177327, 178187, 179873, 183116, 184533, 190035, 195438,\n", + " 198334],\n", + " dtype='int64'), Int64Index([ 1804, 6399, 8328, 12041, 17500, 19341, 27543, 28459,\n", + " 34032, 65482, 77827, 82405, 84259, 86113, 87040, 88894,\n", + " 92210, 93170, 94079, 95899, 96854, 99189, 100116, 101014,\n", + " 101944, 106548, 111111, 115707, 121176, 122993, 129070, 136683,\n", + " 138993, 139919, 143141, 147029, 149782, 151613, 155318, 158109,\n", + " 173635, 177328, 178188, 179874, 183117, 184534, 190036, 195439,\n", + " 198335],\n", + " dtype='int64'), Int64Index([ 1805, 6400, 8329, 12042, 17501, 19342, 27544, 28460,\n", + " 34033, 65483, 77828, 82406, 84260, 86114, 87041, 88895,\n", + " 92211, 93171, 94080, 95900, 96855, 99190, 100117, 101015,\n", + " 101945, 106549, 111112, 115708, 121177, 122994, 129071, 136684,\n", + " 138994, 139920, 143142, 147030, 149783, 151614, 155319, 158110,\n", + " 173636, 177329, 178189, 179875, 183118, 184535, 190037, 195440,\n", + " 198336],\n", + " dtype='int64'), Int64Index([ 1806, 6401, 8330, 12043, 17502, 19343, 27545, 28461,\n", + " 34034, 65484, 77829, 82407, 84261, 86115, 87042, 88896,\n", + " 92212, 93172, 94081, 95901, 96856, 99191, 100118, 101016,\n", + " 101946, 106550, 111113, 115709, 121178, 122995, 129072, 136685,\n", + " 138995, 139921, 143143, 147031, 149784, 151615, 155320, 158111,\n", + " 173637, 177330, 178190, 179876, 183119, 184536, 190038, 195441,\n", + " 198337],\n", + " dtype='int64'), Int64Index([ 1807, 6402, 8331, 12044, 17503, 19344, 27546, 28462,\n", + " 34035, 65485, 77830, 82408, 84262, 86116, 87043, 88897,\n", + " 92213, 93173, 94082, 95902, 96857, 99192, 100119, 101017,\n", + " 101947, 106551, 111114, 115710, 121179, 122996, 129073, 136686,\n", + " 138996, 139922, 143144, 147032, 149785, 151616, 155321, 158112,\n", + " 173638, 177331, 178191, 179877, 183120, 184537, 190039, 195442,\n", + " 198338],\n", + " dtype='int64'), Int64Index([ 1808, 6403, 8332, 12045, 17504, 19345, 27547, 28463,\n", + " 34036, 65486, 77831, 82409, 84263, 86117, 87044, 88898,\n", + " 92214, 93174, 94083, 95903, 96858, 99193, 100120, 101018,\n", + " 101948, 106552, 111115, 115711, 121180, 122997, 129074, 136687,\n", + " 138997, 139923, 143145, 147033, 149786, 151617, 155322, 158113,\n", + " 173639, 177332, 178192, 179878, 183121, 184538, 190040, 195443,\n", + " 198339],\n", + " dtype='int64'), Int64Index([ 1809, 6404, 8333, 12046, 17505, 19346, 27548, 28464,\n", + " 34037, 65487, 77832, 82410, 84264, 86118, 87045, 88899,\n", + " 92215, 93175, 94084, 95904, 96859, 99194, 100121, 101019,\n", + " 101949, 106553, 111116, 115712, 121181, 122998, 129075, 136688,\n", + " 138998, 139924, 143146, 147034, 149787, 151618, 155323, 158114,\n", + " 173640, 177333, 178193, 179879, 183122, 184539, 190041, 195444,\n", + " 198340],\n", + " dtype='int64'), Int64Index([ 1810, 6405, 8334, 12047, 17506, 19347, 27549, 28465,\n", + " 34038, 65488, 77833, 82411, 84265, 86119, 87046, 88900,\n", + " 92216, 93176, 94085, 95905, 96860, 99195, 100122, 101020,\n", + " 101950, 106554, 111117, 115713, 121182, 122999, 129076, 136689,\n", + " 138999, 139925, 143147, 147035, 149788, 151619, 155324, 158115,\n", + " 173641, 177334, 178194, 179880, 183123, 184540, 190042, 195445,\n", + " 198341],\n", + " dtype='int64'), Int64Index([ 1811, 6406, 8335, 12048, 17507, 19348, 27550, 28466,\n", + " 34039, 65489, 77834, 82412, 84266, 86120, 87047, 88901,\n", + " 92217, 93177, 94086, 95906, 96861, 99196, 100123, 101021,\n", + " 101951, 106555, 111118, 115714, 121183, 123000, 129077, 136690,\n", + " 139000, 139926, 143148, 147036, 149789, 151620, 155325, 158116,\n", + " 173642, 177335, 178195, 179881, 183124, 184541, 190043, 195446,\n", + " 198342],\n", + " dtype='int64'), Int64Index([ 1812, 6407, 8336, 12049, 17508, 19349, 27551, 28467,\n", + " 34040, 65490, 77835, 82413, 84267, 86121, 87048, 88902,\n", + " 92218, 93178, 94087, 95907, 96862, 99197, 100124, 101022,\n", + " 101952, 106556, 111119, 115715, 121184, 123001, 129078, 136691,\n", + " 139001, 139927, 143149, 147037, 149790, 151621, 155326, 158117,\n", + " 173643, 177336, 178196, 179882, 183125, 184542, 190044, 195447,\n", + " 198343],\n", + " dtype='int64'), Int64Index([ 1813, 6408, 8337, 12050, 17509, 19350, 27552, 28468,\n", + " 34041, 65491, 77836, 82414, 84268, 86122, 87049, 88903,\n", + " 92219, 93179, 94088, 95908, 96863, 99198, 100125, 101023,\n", + " 101953, 106557, 111120, 115716, 121185, 123002, 129079, 136692,\n", + " 139002, 139928, 143150, 147038, 149791, 151622, 155327, 158118,\n", + " 173644, 177337, 178197, 179883, 183126, 184543, 190045, 195448,\n", + " 198344],\n", + " dtype='int64'), Int64Index([ 1814, 6409, 8338, 12051, 17510, 19351, 27553, 28469,\n", + " 34042, 65492, 77837, 82415, 84269, 86123, 87050, 88904,\n", + " 92220, 93180, 94089, 95909, 96864, 99199, 100126, 101024,\n", + " 101954, 106558, 111121, 115717, 121186, 123003, 129080, 136693,\n", + " 139003, 139929, 143151, 147039, 149792, 151623, 155328, 158119,\n", + " 173645, 177338, 178198, 179884, 183127, 184544, 190046, 195449,\n", + " 198345],\n", + " dtype='int64'), Int64Index([ 1815, 6410, 8339, 12052, 17511, 19352, 27554, 28470,\n", + " 34043, 65493, 77838, 82416, 84270, 86124, 87051, 88905,\n", + " 92221, 93181, 94090, 95910, 96865, 99200, 100127, 101025,\n", + " 101955, 106559, 111122, 115718, 121187, 123004, 129081, 136694,\n", + " 139004, 139930, 143152, 147040, 149793, 151624, 155329, 158120,\n", + " 173646, 177339, 178199, 179885, 183128, 184545, 190047, 195450,\n", + " 198346],\n", + " dtype='int64'), Int64Index([ 1816, 6411, 8340, 12053, 17512, 19353, 27555, 28471,\n", + " 34044, 65494, 77839, 82417, 84271, 86125, 87052, 88906,\n", + " 92222, 93182, 94091, 95911, 96866, 99201, 100128, 101026,\n", + " 101956, 106560, 111123, 115719, 121188, 123005, 129082, 136695,\n", + " 139005, 139931, 143153, 147041, 149794, 151625, 155330, 158121,\n", + " 173647, 177340, 178200, 179886, 183129, 184546, 190048, 195451,\n", + " 198347],\n", + " dtype='int64'), Int64Index([ 1817, 6412, 8341, 12054, 17513, 19354, 27556, 28472,\n", + " 34045, 65495, 77840, 82418, 84272, 86126, 87053, 88907,\n", + " 92223, 93183, 94092, 95912, 96867, 99202, 100129, 101027,\n", + " 101957, 106561, 111124, 115720, 121189, 123006, 129083, 136696,\n", + " 139006, 139932, 143154, 147042, 149795, 151626, 155331, 158122,\n", + " 173648, 177341, 178201, 179887, 183130, 184547, 190049, 195452,\n", + " 198348],\n", + " dtype='int64'), Int64Index([ 1818, 6413, 8342, 12055, 17514, 19355, 27557, 28473,\n", + " 34046, 65496, 77841, 82419, 84273, 86127, 87054, 88908,\n", + " 92224, 93184, 94093, 95913, 96868, 99203, 100130, 101028,\n", + " 101958, 106562, 111125, 115721, 121190, 123007, 129084, 136697,\n", + " 139007, 139933, 143155, 147043, 149796, 151627, 155332, 158123,\n", + " 173649, 177342, 178202, 179888, 183131, 184548, 190050, 195453,\n", + " 198349],\n", + " dtype='int64'), Int64Index([ 1819, 6414, 8343, 12056, 17515, 19356, 27558, 28474,\n", + " 34047, 65497, 77842, 82420, 84274, 86128, 87055, 88909,\n", + " 92225, 93185, 94094, 95914, 96869, 99204, 100131, 101029,\n", + " 101959, 106563, 111126, 115722, 121191, 123008, 129085, 136698,\n", + " 139008, 139934, 143156, 147044, 149797, 151628, 155333, 158124,\n", + " 173650, 177343, 178203, 179889, 183132, 184549, 190051, 195454,\n", + " 198350],\n", + " dtype='int64'), Int64Index([ 1820, 6415, 8344, 12057, 17516, 19357, 27559, 28475,\n", + " 34048, 65498, 77843, 82421, 84275, 86129, 87056, 88910,\n", + " 92226, 93186, 94095, 95915, 96870, 99205, 100132, 101030,\n", + " 101960, 106564, 111127, 115723, 121192, 123009, 129086, 136699,\n", + " 139009, 139935, 143157, 147045, 149798, 151629, 155334, 158125,\n", + " 173651, 177344, 178204, 179890, 183133, 184550, 190052, 195455,\n", + " 198351],\n", + " dtype='int64'), Int64Index([ 1821, 6416, 8345, 12058, 17517, 19358, 27560, 28476,\n", + " 34049, 65499, 77844, 82422, 84276, 86130, 87057, 88911,\n", + " 92227, 93187, 94096, 95916, 96871, 99206, 100133, 101031,\n", + " 101961, 106565, 111128, 115724, 121193, 123010, 129087, 136700,\n", + " 139010, 139936, 143158, 147046, 149799, 151630, 155335, 158126,\n", + " 173652, 177345, 178205, 179891, 183134, 184551, 190053, 195456,\n", + " 198352],\n", + " dtype='int64'), Int64Index([ 1822, 6417, 8346, 12059, 17518, 19359, 27561, 28477,\n", + " 34050, 65500, 77845, 82423, 84277, 86131, 87058, 88912,\n", + " 92228, 93188, 94097, 95917, 96872, 99207, 100134, 101032,\n", + " 101962, 106566, 111129, 115725, 121194, 123011, 129088, 136701,\n", + " 139011, 139937, 143159, 147047, 149800, 151631, 155336, 158127,\n", + " 173653, 177346, 178206, 179892, 183135, 184552, 190054, 195457,\n", + " 198353],\n", + " dtype='int64'), Int64Index([ 1823, 6418, 8347, 12060, 17519, 19360, 27562, 28478,\n", + " 34051, 65501, 77846, 82424, 84278, 86132, 87059, 88913,\n", + " 92229, 93189, 94098, 95918, 96873, 99208, 100135, 101033,\n", + " 101963, 106567, 111130, 115726, 121195, 123012, 129089, 136702,\n", + " 139012, 139938, 143160, 147048, 149801, 151632, 155337, 158128,\n", + " 173654, 177347, 178207, 179893, 183136, 184553, 190055, 195458,\n", + " 198354],\n", + " dtype='int64'), Int64Index([ 1824, 6419, 8348, 12061, 17520, 19361, 27563, 28479,\n", + " 34052, 65502, 77847, 82425, 84279, 86133, 87060, 88914,\n", + " 92230, 93190, 94099, 95919, 96874, 99209, 100136, 101034,\n", + " 101964, 106568, 111131, 115727, 121196, 123013, 129090, 136703,\n", + " 139013, 139939, 143161, 147049, 149802, 151633, 155338, 158129,\n", + " 173655, 177348, 178208, 179894, 183137, 184554, 190056, 195459,\n", + " 198355],\n", + " dtype='int64'), Int64Index([ 1825, 6420, 8349, 12062, 17521, 19362, 27564, 28480,\n", + " 34053, 65503, 77848, 82426, 84280, 86134, 87061, 88915,\n", + " 92231, 93191, 94100, 95920, 96875, 99210, 100137, 101035,\n", + " 101965, 106569, 111132, 115728, 121197, 123014, 129091, 136704,\n", + " 139014, 139940, 143162, 147050, 149803, 151634, 155339, 158130,\n", + " 173656, 177349, 178209, 179895, 183138, 184555, 190057, 195460,\n", + " 198356],\n", + " dtype='int64'), Int64Index([ 1826, 6421, 8350, 12063, 17522, 19363, 27565, 28481,\n", + " 34054, 65504, 77849, 82427, 84281, 86135, 87062, 88916,\n", + " 92232, 93192, 94101, 95921, 96876, 99211, 100138, 101036,\n", + " 101966, 106570, 111133, 115729, 121198, 123015, 129092, 136705,\n", + " 139015, 139941, 143163, 147051, 149804, 151635, 155340, 158131,\n", + " 173657, 177350, 178210, 179896, 183139, 184556, 190058, 195461,\n", + " 198357],\n", + " dtype='int64'), Int64Index([ 1827, 6422, 8351, 12064, 17523, 19364, 27566, 28482,\n", + " 34055, 65505, 77850, 82428, 84282, 86136, 87063, 88917,\n", + " 92233, 93193, 94102, 95922, 96877, 99212, 100139, 101037,\n", + " 101967, 106571, 111134, 115730, 121199, 123016, 129093, 136706,\n", + " 139016, 139942, 143164, 147052, 149805, 151636, 155341, 158132,\n", + " 173658, 177351, 178211, 179897, 183140, 184557, 190059, 195462,\n", + " 198358],\n", + " dtype='int64'), Int64Index([ 1828, 6423, 8352, 12065, 17524, 19365, 27567, 28483,\n", + " 34056, 65506, 77851, 82429, 84283, 86137, 87064, 88918,\n", + " 92234, 93194, 94103, 95923, 96878, 99213, 100140, 101038,\n", + " 101968, 106572, 111135, 115731, 121200, 123017, 129094, 136707,\n", + " 139017, 139943, 143165, 147053, 149806, 151637, 155342, 158133,\n", + " 173659, 177352, 178212, 179898, 183141, 184558, 190060, 195463,\n", + " 198359],\n", + " dtype='int64'), Int64Index([ 1829, 6424, 8353, 12066, 17525, 19366, 27568, 28484,\n", + " 34057, 65507, 77852, 82430, 84284, 86138, 87065, 88919,\n", + " 92235, 93195, 94104, 95924, 96879, 99214, 100141, 101039,\n", + " 101969, 106573, 111136, 115732, 121201, 123018, 129095, 136708,\n", + " 139018, 139944, 143166, 147054, 149807, 151638, 155343, 158134,\n", + " 173660, 177353, 178213, 179899, 183142, 184559, 190061, 195464,\n", + " 198360],\n", + " dtype='int64'), Int64Index([ 1830, 6425, 8354, 12067, 17526, 19367, 27569, 28485,\n", + " 34058, 65508, 77853, 82431, 84285, 86139, 87066, 88920,\n", + " 92236, 93196, 94105, 95925, 96880, 99215, 100142, 101040,\n", + " 101970, 106574, 111137, 115733, 121202, 123019, 129096, 136709,\n", + " 139019, 139945, 143167, 147055, 149808, 151639, 155344, 158135,\n", + " 173661, 177354, 178214, 179900, 183143, 184560, 190062, 195465,\n", + " 198361],\n", + " dtype='int64'), Int64Index([ 1831, 6426, 8355, 12068, 17527, 19368, 27570, 28486,\n", + " 34059, 65509, 77854, 82432, 84286, 86140, 87067, 88921,\n", + " 92237, 93197, 94106, 95926, 96881, 99216, 100143, 101041,\n", + " 101971, 106575, 111138, 115734, 121203, 123020, 129097, 136710,\n", + " 139020, 139946, 143168, 147056, 149809, 151640, 155345, 158136,\n", + " 173662, 177355, 178215, 179901, 183144, 184561, 190063, 195466,\n", + " 198362],\n", + " dtype='int64'), Int64Index([ 1832, 6427, 8356, 12069, 17528, 19369, 27571, 28487,\n", + " 34060, 65510, 77855, 82433, 84287, 86141, 87068, 88922,\n", + " 92238, 93198, 94107, 95927, 96882, 99217, 100144, 101042,\n", + " 101972, 106576, 111139, 115735, 121204, 123021, 129098, 136711,\n", + " 139021, 139947, 143169, 147057, 149810, 151641, 155346, 158137,\n", + " 173663, 177356, 178216, 179902, 183145, 184562, 190064, 195467,\n", + " 198363],\n", + " dtype='int64'), Int64Index([ 1833, 6428, 8357, 12070, 17529, 19370, 27572, 28488,\n", + " 34061, 65511, 77856, 82434, 84288, 86142, 87069, 88923,\n", + " 92239, 93199, 94108, 95928, 96883, 99218, 100145, 101043,\n", + " 101973, 106577, 111140, 115736, 121205, 123022, 129099, 136712,\n", + " 139022, 139948, 143170, 147058, 149811, 151642, 155347, 158138,\n", + " 173664, 177357, 178217, 179903, 183146, 184563, 190065, 195468,\n", + " 198364],\n", + " dtype='int64'), Int64Index([ 1834, 6429, 8358, 12071, 17530, 19371, 27573, 28489,\n", + " 34062, 65512, 77857, 82435, 84289, 86143, 87070, 88924,\n", + " 92240, 93200, 94109, 95929, 96884, 99219, 100146, 101044,\n", + " 101974, 106578, 111141, 115737, 121206, 123023, 129100, 136713,\n", + " 139023, 139949, 143171, 147059, 149812, 151643, 155348, 158139,\n", + " 173665, 177358, 178218, 179904, 183147, 184564, 190066, 195469,\n", + " 198365],\n", + " dtype='int64'), Int64Index([ 1835, 6430, 8359, 12072, 17531, 19372, 27574, 28490,\n", + " 34063, 65513, 77858, 82436, 84290, 86144, 87071, 88925,\n", + " 92241, 93201, 94110, 95930, 96885, 99220, 100147, 101045,\n", + " 101975, 106579, 111142, 115738, 121207, 123024, 129101, 136714,\n", + " 139024, 139950, 143172, 147060, 149813, 151644, 155349, 158140,\n", + " 173666, 177359, 178219, 179905, 183148, 184565, 190067, 195470,\n", + " 198366],\n", + " dtype='int64'), Int64Index([88926], dtype='int64'), Int64Index([55608, 71493, 109258], dtype='int64'), Int64Index([55609, 71494, 109259], dtype='int64'), Int64Index([60258], dtype='int64'), Int64Index([60259], dtype='int64'), Int64Index([60260], dtype='int64'), Int64Index([46341, 60261], dtype='int64'), Int64Index([46342, 60262], dtype='int64'), Int64Index([46343, 57465, 60263], dtype='int64'), Int64Index([46344, 57466, 60264, 63637], dtype='int64'), Int64Index([46345, 57467, 60265, 63638, 88927, 152570], dtype='int64'), Int64Index([46346, 54658, 57468, 60266, 63639, 88928, 152571, 169546], dtype='int64'), Int64Index([46347, 49101, 54659, 57469, 60267, 63640, 88929, 152572, 168597,\n", + " 169547],\n", + " dtype='int64'), Int64Index([46348, 49102, 54660, 57470, 60268, 63641, 88930, 152573, 168598,\n", + " 169548],\n", + " dtype='int64'), Int64Index([12964, 46349, 49103, 54661, 57471, 60269, 63642, 88931, 152574,\n", + " 168599, 169549],\n", + " dtype='int64'), Int64Index([ 12965, 46350, 49104, 54662, 55610, 57472, 60270, 63643,\n", + " 88932, 152575, 168600, 169550],\n", + " dtype='int64'), Int64Index([ 12966, 46351, 49105, 54663, 55611, 57473, 60271, 63644,\n", + " 88933, 152576, 168601, 169551],\n", + " dtype='int64'), Int64Index([ 12967, 46352, 49106, 54664, 55612, 57474, 60272, 63645,\n", + " 88934, 152577, 168602, 169552],\n", + " dtype='int64'), Int64Index([ 12968, 46353, 49107, 54665, 55613, 57475, 60273, 63646,\n", + " 88935, 152578, 168603, 169553],\n", + " dtype='int64'), Int64Index([ 12969, 46354, 49108, 54666, 55614, 57476, 60274, 63647,\n", + " 88936, 152579, 168604, 169554],\n", + " dtype='int64'), Int64Index([ 12970, 46355, 49109, 54667, 55615, 57477, 60275, 63648,\n", + " 88937, 152580, 168605, 169555],\n", + " dtype='int64'), Int64Index([ 12971, 46356, 49110, 54668, 55616, 57478, 60276, 63649,\n", + " 88938, 152581, 168606, 169556],\n", + " dtype='int64'), Int64Index([ 12972, 46357, 49111, 54669, 55617, 57479, 60277, 63650,\n", + " 71495, 88939, 109260, 134832, 152582, 168607, 169557],\n", + " dtype='int64'), Int64Index([ 12973, 46358, 49112, 54670, 55618, 57480, 60278, 63651,\n", + " 88940, 152583, 168608, 169558],\n", + " dtype='int64'), Int64Index([ 12974, 46359, 49113, 54671, 55619, 57481, 60279, 63652,\n", + " 88941, 152584, 168609, 169559],\n", + " dtype='int64'), Int64Index([ 12975, 46360, 49114, 54672, 55620, 57482, 60280, 63653,\n", + " 88942, 152585, 168610, 169560],\n", + " dtype='int64'), Int64Index([ 12976, 46361, 49115, 54673, 55621, 57483, 60281, 63654,\n", + " 88943, 152586, 168611, 169561],\n", + " dtype='int64'), Int64Index([ 12977, 46362, 49116, 54674, 55622, 57484, 60282, 63655,\n", + " 88944, 152587, 168612, 169562],\n", + " dtype='int64'), Int64Index([ 12978, 46363, 49117, 54675, 55623, 57485, 60283, 63656,\n", + " 88945, 152588, 168613, 169563],\n", + " dtype='int64'), Int64Index([ 12979, 46364, 49118, 54676, 55624, 57486, 60284, 63657,\n", + " 88946, 152589, 168614, 169564],\n", + " dtype='int64'), Int64Index([ 12980, 46365, 49119, 54677, 55625, 57487, 60285, 63658,\n", + " 88947, 152590, 168615, 169565],\n", + " dtype='int64'), Int64Index([ 12981, 46366, 49120, 54678, 55626, 57488, 60286, 63659,\n", + " 88948, 152591, 168616, 169566],\n", + " dtype='int64'), Int64Index([ 12982, 46367, 49121, 54679, 55627, 57489, 60287, 63660,\n", + " 88949, 152592, 168617, 169567],\n", + " dtype='int64'), Int64Index([ 12983, 46368, 49122, 54680, 55628, 57490, 60288, 63661,\n", + " 88950, 152593, 168618, 169568],\n", + " dtype='int64'), Int64Index([ 12984, 46369, 49123, 54681, 55629, 57491, 60289, 63662,\n", + " 88951, 108331, 152594, 168619, 169569],\n", + " dtype='int64'), Int64Index([ 3632, 10225, 12985, 31249, 46370, 49124, 54682, 55630,\n", + " 57492, 60290, 63663, 78770, 88952, 108332, 152595, 168620,\n", + " 169570],\n", + " dtype='int64'), Int64Index([ 3633, 10226, 12986, 31250, 46371, 49125, 54683, 55631,\n", + " 57493, 60291, 63664, 71496, 78771, 88953, 108333, 117574,\n", + " 134833, 151645, 152596, 164076, 168621, 169571],\n", + " dtype='int64'), Int64Index([ 3634, 10227, 12987, 31251, 46372, 49126, 54684, 55632,\n", + " 57494, 60292, 63665, 71497, 78772, 88954, 108334, 117575,\n", + " 133907, 134834, 151646, 152597, 164077, 168622, 169572],\n", + " dtype='int64'), Int64Index([ 3635, 10228, 12988, 21196, 31252, 46373, 49127, 54685,\n", + " 55633, 57495, 60293, 63666, 71498, 78773, 87072, 88955,\n", + " 108335, 117576, 133908, 134835, 151647, 152598, 164078, 168623,\n", + " 169573],\n", + " dtype='int64'), Int64Index([ 3636, 10229, 12989, 21197, 31253, 46374, 49128, 54686,\n", + " 55634, 57496, 60294, 63667, 71499, 78774, 84291, 87073,\n", + " 88956, 108336, 109261, 112087, 117577, 133909, 134836, 151648,\n", + " 152599, 161362, 164079, 168624, 169574],\n", + " dtype='int64'), Int64Index([ 3637, 10230, 12990, 21198, 31254, 45420, 46375, 49129,\n", + " 54687, 55635, 57497, 60295, 63668, 71500, 78775, 84292,\n", + " 87074, 88957, 108337, 109262, 112088, 117578, 133910, 134837,\n", + " 147061, 151649, 152600, 161363, 164080, 168625, 169575],\n", + " dtype='int64'), Int64Index([ 3638, 4558, 10231, 12991, 21199, 31255, 45421, 46376,\n", + " 49130, 54688, 55636, 57498, 60296, 63669, 71501, 78776,\n", + " 84293, 87075, 88958, 108338, 109263, 112089, 117579, 133911,\n", + " 134838, 147062, 151650, 152601, 161364, 164081, 168626, 169576],\n", + " dtype='int64'), Int64Index([ 3639, 4559, 10232, 12992, 21200, 31256, 45422, 46377,\n", + " 49131, 54689, 55637, 57499, 60297, 63670, 71502, 78777,\n", + " 80598, 84294, 87076, 88959, 108339, 109264, 112090, 117580,\n", + " 133912, 134839, 147063, 151651, 152602, 161365, 164082, 168627,\n", + " 169577, 186373],\n", + " dtype='int64'), Int64Index([ 3640, 4560, 10233, 12993, 21201, 31257, 45423, 46378,\n", + " 49132, 54690, 55638, 57500, 60298, 61216, 63671, 66957,\n", + " 71503, 78778, 80599, 84295, 87077, 88960, 104707, 108340,\n", + " 109265, 112091, 117581, 133913, 134840, 144457, 147064, 151652,\n", + " 152603, 161366, 164083, 168628, 169578, 186374],\n", + " dtype='int64'), Int64Index([ 3641, 4561, 10234, 12994, 20279, 21202, 31258, 45424,\n", + " 46379, 49133, 54691, 55639, 57501, 60299, 61217, 63672,\n", + " 66958, 71504, 78779, 80600, 84296, 87078, 88961, 104708,\n", + " 108341, 109266, 112092, 117582, 119396, 133914, 134841, 144458,\n", + " 147065, 151653, 152604, 161367, 164084, 168629, 169579, 186375],\n", + " dtype='int64'), Int64Index([ 3642, 4562, 10235, 12995, 20280, 21203, 31259, 45425,\n", + " 46380, 49134, 54692, 55640, 57502, 60300, 61218, 63673,\n", + " 66959, 71505, 78780, 80601, 84297, 87079, 88962, 104709,\n", + " 108342, 109267, 112093, 117583, 119397, 133915, 134842, 144459,\n", + " 147066, 151654, 152605, 161368, 164085, 167681, 168630, 169580,\n", + " 186376, 190068],\n", + " dtype='int64'), Int64Index([ 3643, 4563, 10236, 12996, 20281, 21204, 31260, 45426,\n", + " 46381, 49135, 54693, 55641, 57503, 60301, 61219, 63674,\n", + " 66960, 71506, 78781, 80602, 84298, 87080, 88963, 104710,\n", + " 108343, 109268, 112094, 117584, 119398, 133916, 134843, 144460,\n", + " 147067, 151655, 152606, 161369, 164086, 167682, 168631, 169581,\n", + " 186377, 190069],\n", + " dtype='int64'), Int64Index([ 3644, 4564, 10237, 12997, 17532, 20282, 21205, 31261,\n", + " 44506, 45427, 46382, 49136, 54694, 55642, 57504, 60302,\n", + " 61220, 63675, 66961, 71507, 78782, 80603, 84299, 87081,\n", + " 88964, 104711, 108344, 109269, 112095, 113009, 117585, 119399,\n", + " 133917, 134844, 144461, 147068, 151656, 152607, 161370, 164087,\n", + " 167683, 168632, 169582, 186378, 190070],\n", + " dtype='int64'), Int64Index([ 3645, 4565, 10238, 12998, 17533, 20283, 21206, 31262,\n", + " 44507, 45428, 46383, 49137, 54695, 55643, 57505, 60303,\n", + " 61221, 63676, 66962, 71508, 78783, 80604, 84300, 87082,\n", + " 88965, 104712, 108345, 109270, 112096, 113010, 117586, 119400,\n", + " 133918, 134845, 144462, 147069, 151657, 152608, 161371, 164088,\n", + " 167684, 168633, 169583, 186379, 190071],\n", + " dtype='int64'), Int64Index([ 3646, 4566, 10239, 12999, 17534, 20284, 21207, 31263,\n", + " 44508, 45429, 46384, 49138, 54696, 55644, 57506, 60304,\n", + " 61222, 63677, 66963, 71509, 78784, 80605, 84301, 87083,\n", + " 88966, 104713, 108346, 109271, 112097, 113011, 117587, 119401,\n", + " 133919, 134846, 144463, 147070, 151658, 152609, 161372, 164089,\n", + " 167685, 168634, 169584, 186380, 190072],\n", + " dtype='int64'), Int64Index([ 3647, 4567, 10240, 13000, 17535, 20285, 21208, 31264,\n", + " 44509, 45430, 46385, 49139, 54697, 55645, 57507, 60305,\n", + " 61223, 63678, 66964, 71510, 78785, 80606, 84302, 87084,\n", + " 88967, 104714, 108347, 109272, 112098, 113012, 117588, 119402,\n", + " 133920, 134847, 144464, 147071, 151659, 152610, 161373, 164090,\n", + " 167686, 168635, 169585, 186381, 190073],\n", + " dtype='int64'), Int64Index([ 3648, 4568, 10241, 13001, 17536, 20286, 21209, 31265,\n", + " 44510, 45431, 46386, 49140, 54698, 55646, 57508, 60306,\n", + " 61224, 63679, 66965, 71511, 78786, 80607, 84303, 87085,\n", + " 88968, 104715, 108348, 109273, 112099, 113013, 117589, 119403,\n", + " 133921, 134848, 144465, 147072, 151660, 152611, 161374, 164091,\n", + " 167687, 168636, 169586, 186382, 190074],\n", + " dtype='int64'), Int64Index([ 3649, 4569, 10242, 13002, 17537, 20287, 21210, 31266,\n", + " 44511, 45432, 46387, 49141, 54699, 55647, 57509, 60307,\n", + " 61225, 63680, 66966, 71512, 78787, 80608, 84304, 87086,\n", + " 88969, 104716, 108349, 109274, 112100, 113014, 117590, 119404,\n", + " 133922, 134849, 144466, 147073, 151661, 152612, 161375, 164092,\n", + " 167688, 168637, 169587, 186383, 190075],\n", + " dtype='int64'), Int64Index([ 3650, 4570, 10243, 13003, 17538, 20288, 21211, 31267,\n", + " 44512, 45433, 46388, 49142, 54700, 55648, 57510, 60308,\n", + " 61226, 63681, 66967, 71513, 78788, 80609, 84305, 87087,\n", + " 88970, 104717, 108350, 109275, 112101, 113015, 117591, 119405,\n", + " 133923, 134850, 144467, 147074, 151662, 152613, 161376, 164093,\n", + " 167689, 168638, 169588, 186384, 190076],\n", + " dtype='int64'), Int64Index([ 3651, 4571, 10244, 13004, 17539, 20289, 21212, 31268,\n", + " 44513, 45434, 46389, 49143, 54701, 55649, 57511, 60309,\n", + " 61227, 63682, 66968, 71514, 78789, 80610, 84306, 87088,\n", + " 88971, 104718, 108351, 109276, 112102, 113016, 117592, 119406,\n", + " 133924, 134851, 144468, 147075, 151663, 152614, 161377, 164094,\n", + " 167690, 168639, 169589, 186385, 190077],\n", + " dtype='int64'), Int64Index([ 3652, 4572, 10245, 13005, 17540, 20290, 21213, 31269,\n", + " 44514, 45435, 46390, 49144, 54702, 55650, 57512, 60310,\n", + " 61228, 63683, 66969, 71515, 78790, 80611, 84307, 87089,\n", + " 88972, 104719, 108352, 109277, 112103, 113017, 117593, 119407,\n", + " 133925, 134852, 144469, 147076, 151664, 152615, 161378, 164095,\n", + " 167691, 168640, 169590, 186386, 190078],\n", + " dtype='int64'), Int64Index([ 3653, 4573, 10246, 13006, 17541, 20291, 21214, 31270,\n", + " 44515, 45436, 46391, 49145, 54703, 55651, 57513, 60311,\n", + " 61229, 63684, 66970, 71516, 78791, 80612, 84308, 87090,\n", + " 88973, 104720, 108353, 109278, 112104, 113018, 117594, 119408,\n", + " 121208, 133926, 134853, 144470, 147077, 151665, 152616, 161379,\n", + " 164096, 167692, 168641, 169591, 186387, 190079],\n", + " dtype='int64'), Int64Index([ 3654, 4574, 10247, 13007, 17542, 20292, 21215, 31271,\n", + " 44516, 45437, 46392, 49146, 54704, 55652, 57514, 60312,\n", + " 61230, 63685, 66971, 71517, 78792, 80613, 84309, 87091,\n", + " 88974, 104721, 108354, 109279, 112105, 113019, 117595, 119409,\n", + " 121209, 133927, 134854, 144471, 147078, 151666, 152617, 161380,\n", + " 164097, 167693, 168642, 169592, 186388, 190080],\n", + " dtype='int64'), Int64Index([ 3655, 4575, 10248, 13008, 17543, 20293, 21216, 31272,\n", + " 44517, 45438, 46393, 49147, 54705, 55653, 57515, 60313,\n", + " 61231, 63686, 66972, 71518, 78793, 80614, 84310, 87092,\n", + " 88975, 104722, 107428, 108355, 109280, 112106, 113020, 117596,\n", + " 119410, 121210, 133928, 134855, 144472, 147079, 151667, 152618,\n", + " 161381, 164098, 167694, 168643, 169593, 186389, 190081],\n", + " dtype='int64'), Int64Index([ 3656, 4576, 10249, 13009, 17544, 20294, 21217, 31273,\n", + " 44518, 45439, 46394, 49148, 54706, 55654, 57516, 60314,\n", + " 61232, 63687, 66973, 71519, 78794, 80615, 82437, 84311,\n", + " 87093, 88976, 104723, 107429, 108356, 109281, 112107, 113021,\n", + " 117597, 119411, 121211, 133929, 134856, 144473, 147080, 151668,\n", + " 152619, 161382, 164099, 167695, 168644, 169594, 186390, 190082],\n", + " dtype='int64'), Int64Index([ 3657, 4577, 10250, 13010, 17545, 20295, 21218, 31274,\n", + " 44519, 45440, 46395, 49149, 54707, 55655, 57517, 60315,\n", + " 61233, 63688, 66974, 71520, 78795, 80616, 82438, 84312,\n", + " 87094, 88977, 104724, 107430, 108357, 109282, 112108, 113022,\n", + " 117598, 119412, 121212, 133930, 134857, 144474, 147081, 151669,\n", + " 152620, 161383, 164100, 167696, 168645, 169595, 186391, 190083],\n", + " dtype='int64'), Int64Index([ 3658, 4578, 10251, 13011, 17546, 20296, 21219, 31275,\n", + " 44520, 45441, 46396, 49150, 54708, 55656, 57518, 60316,\n", + " 61234, 63689, 66975, 71521, 78796, 80617, 82439, 84313,\n", + " 87095, 88978, 104725, 107431, 108358, 109283, 112109, 113023,\n", + " 117599, 119413, 121213, 133931, 134858, 144475, 147082, 151670,\n", + " 152621, 161384, 164101, 167697, 168646, 169596, 186392, 190084],\n", + " dtype='int64'), Int64Index([ 3659, 4579, 10252, 13012, 17547, 20297, 21220, 31276,\n", + " 44521, 45442, 46397, 49151, 54709, 55657, 57519, 60317,\n", + " 61235, 63690, 66976, 71522, 78797, 80618, 82440, 84314,\n", + " 87096, 88979, 104726, 107432, 108359, 109284, 112110, 113024,\n", + " 117600, 119414, 121214, 133932, 134859, 144476, 147083, 151671,\n", + " 152622, 161385, 164102, 167698, 168647, 169597, 186393, 190085],\n", + " dtype='int64'), Int64Index([ 3660, 4580, 10253, 13013, 17548, 20298, 21221, 31277,\n", + " 44522, 45443, 46398, 49152, 54710, 55658, 57520, 60318,\n", + " 61236, 63691, 66977, 71523, 78798, 80619, 82441, 84315,\n", + " 87097, 88980, 104727, 107433, 108360, 109285, 112111, 113025,\n", + " 117601, 119415, 121215, 133933, 134860, 144477, 147084, 151672,\n", + " 152623, 161386, 164103, 167699, 168648, 169598, 186394, 190086],\n", + " dtype='int64'), Int64Index([ 3661, 4581, 10254, 13014, 17549, 20299, 21222, 31278,\n", + " 44523, 45444, 46399, 49153, 54711, 55659, 57521, 60319,\n", + " 61237, 63692, 66978, 71524, 78799, 80620, 82442, 84316,\n", + " 87098, 88981, 104728, 107434, 108361, 109286, 112112, 113026,\n", + " 117602, 119416, 121216, 133934, 134861, 144478, 147085, 151673,\n", + " 152624, 161387, 164104, 167700, 168649, 169599, 186395, 190087],\n", + " dtype='int64'), Int64Index([ 3662, 4582, 10255, 13015, 17550, 20300, 21223, 31279,\n", + " 44524, 45445, 46400, 49154, 54712, 55660, 57522, 60320,\n", + " 61238, 63693, 66979, 71525, 78800, 80621, 82443, 84317,\n", + " 87099, 88982, 104729, 107435, 108362, 109287, 112113, 113027,\n", + " 117603, 119417, 121217, 133935, 134862, 144479, 147086, 151674,\n", + " 152625, 161388, 164105, 167701, 168650, 169600, 186396, 190088],\n", + " dtype='int64'), Int64Index([ 3663, 4583, 10256, 13016, 17551, 20301, 21224, 31280,\n", + " 44525, 45446, 46401, 49155, 54713, 55661, 57523, 60321,\n", + " 61239, 63694, 66980, 71526, 78801, 80622, 82444, 84318,\n", + " 87100, 88983, 104730, 107436, 108363, 109288, 112114, 113028,\n", + " 117604, 119418, 121218, 133936, 134863, 144480, 147087, 151675,\n", + " 152626, 161389, 164106, 167702, 168651, 169601, 186397, 190089],\n", + " dtype='int64'), Int64Index([ 3664, 4584, 10257, 13017, 17552, 20302, 21225, 31281,\n", + " 44526, 45447, 46402, 49156, 54714, 55662, 57524, 60322,\n", + " 61240, 63695, 66981, 71527, 78802, 80623, 82445, 84319,\n", + " 87101, 88984, 104731, 107437, 108364, 109289, 112115, 113029,\n", + " 117605, 119419, 121219, 133937, 134864, 144481, 147088, 151676,\n", + " 152627, 161390, 164107, 167703, 168652, 169602, 186398, 190090],\n", + " dtype='int64'), Int64Index([ 3665, 4585, 10258, 13018, 17553, 20303, 21226, 31282,\n", + " 44527, 45448, 46403, 49157, 54715, 55663, 57525, 60323,\n", + " 61241, 63696, 66982, 71528, 78803, 80624, 82446, 84320,\n", + " 87102, 88985, 104732, 107438, 108365, 109290, 112116, 113030,\n", + " 117606, 119420, 121220, 133938, 134865, 144482, 147089, 151677,\n", + " 152628, 161391, 164108, 167704, 168653, 169603, 186399, 190091],\n", + " dtype='int64'), Int64Index([ 3666, 4586, 10259, 13019, 17554, 20304, 21227, 31283,\n", + " 44528, 45449, 46404, 49158, 54716, 55664, 57526, 60324,\n", + " 61242, 63697, 66983, 71529, 78804, 80625, 82447, 84321,\n", + " 87103, 88986, 104733, 107439, 108366, 109291, 112117, 113031,\n", + " 117607, 119421, 121221, 133939, 134866, 144483, 147090, 151678,\n", + " 152629, 161392, 164109, 167705, 168654, 169604, 186400, 190092],\n", + " dtype='int64'), Int64Index([ 3667, 4587, 10260, 13020, 17555, 20305, 21228, 31284,\n", + " 44529, 45450, 46405, 49159, 54717, 55665, 57527, 60325,\n", + " 61243, 63698, 66984, 71530, 78805, 80626, 82448, 84322,\n", + " 87104, 88987, 104734, 107440, 108367, 109292, 112118, 113032,\n", + " 117608, 119422, 121222, 133940, 134867, 144484, 147091, 151679,\n", + " 152630, 161393, 164110, 167706, 168655, 169605, 186401, 190093],\n", + " dtype='int64'), Int64Index([ 3668, 4588, 10261, 13021, 17556, 20306, 21229, 31285,\n", + " 44530, 45451, 46406, 49160, 54718, 55666, 57528, 60326,\n", + " 61244, 63699, 66985, 71531, 78806, 80627, 82449, 84323,\n", + " 87105, 88988, 104735, 107441, 108368, 109293, 112119, 113033,\n", + " 117609, 119423, 121223, 133941, 134868, 144485, 147092, 151680,\n", + " 152631, 161394, 164111, 167707, 168656, 169606, 186402, 190094],\n", + " dtype='int64'), Int64Index([ 3669, 4589, 10262, 13022, 17557, 20307, 21230, 31286,\n", + " 44531, 45452, 46407, 49161, 54719, 55667, 57529, 60327,\n", + " 61245, 63700, 66986, 71532, 78807, 80628, 82450, 84324,\n", + " 87106, 88989, 104736, 107442, 108369, 109294, 112120, 113034,\n", + " 117610, 119424, 121224, 133942, 134869, 144486, 147093, 151681,\n", + " 152632, 161395, 164112, 167708, 168657, 169607, 186403, 190095],\n", + " dtype='int64'), Int64Index([ 3670, 4590, 10263, 13023, 17558, 20308, 21231, 31287,\n", + " 44532, 45453, 46408, 49162, 54720, 55668, 57530, 60328,\n", + " 61246, 63701, 66987, 71533, 78808, 80629, 82451, 84325,\n", + " 87107, 88990, 104737, 107443, 108370, 109295, 112121, 113035,\n", + " 117611, 119425, 121225, 133943, 134870, 144487, 147094, 151682,\n", + " 152633, 161396, 164113, 167709, 168658, 169608, 186404, 190096],\n", + " dtype='int64'), Int64Index([ 3671, 4591, 10264, 13024, 17559, 20309, 21232, 31288,\n", + " 44533, 45454, 46409, 49163, 54721, 55669, 57531, 60329,\n", + " 61247, 63702, 66988, 71534, 78809, 80630, 82452, 84326,\n", + " 87108, 88991, 104738, 107444, 108371, 109296, 112122, 113036,\n", + " 117612, 119426, 121226, 133944, 134871, 144488, 147095, 151683,\n", + " 152634, 161397, 164114, 167710, 168659, 169609, 186405, 190097],\n", + " dtype='int64'), Int64Index([ 3672, 4592, 10265, 13025, 17560, 20310, 21233, 31289,\n", + " 44534, 45455, 46410, 49164, 54722, 55670, 57532, 60330,\n", + " 61248, 63703, 66989, 71535, 78810, 80631, 82453, 84327,\n", + " 87109, 88992, 104739, 107445, 108372, 109297, 112123, 113037,\n", + " 117613, 119427, 121227, 133945, 134872, 144489, 147096, 151684,\n", + " 152635, 161398, 164115, 167711, 168660, 169610, 186406, 190098],\n", + " dtype='int64'), Int64Index([ 3673, 4593, 10266, 13026, 17561, 20311, 21234, 31290,\n", + " 44535, 45456, 46411, 49165, 54723, 55671, 57533, 60331,\n", + " 61249, 63704, 66990, 71536, 78811, 80632, 82454, 84328,\n", + " 87110, 88993, 104740, 107446, 108373, 109298, 112124, 113038,\n", + " 117614, 119428, 121228, 133946, 134873, 144490, 147097, 151685,\n", + " 152636, 161399, 164116, 167712, 168661, 169611, 186407, 190099],\n", + " dtype='int64'), Int64Index([ 3674, 4594, 10267, 13027, 17562, 20312, 21235, 31291,\n", + " 44536, 45457, 46412, 49166, 54724, 55672, 57534, 60332,\n", + " 61250, 63705, 66991, 71537, 78812, 80633, 82455, 84329,\n", + " 87111, 88994, 104741, 107447, 108374, 109299, 112125, 113039,\n", + " 117615, 119429, 121229, 133947, 134874, 144491, 147098, 151686,\n", + " 152637, 161400, 164117, 167713, 168662, 169612, 186408, 190100],\n", + " dtype='int64'), Int64Index([ 3675, 4595, 10268, 13028, 17563, 20313, 21236, 31292,\n", + " 44537, 45458, 46413, 49167, 54725, 55673, 57535, 60333,\n", + " 61251, 63706, 66992, 71538, 78813, 80634, 82456, 84330,\n", + " 87112, 88995, 104742, 107448, 108375, 109300, 112126, 113040,\n", + " 117616, 119430, 121230, 133948, 134875, 144492, 147099, 151687,\n", + " 152638, 161401, 164118, 167714, 168663, 169613, 186409, 190101],\n", + " dtype='int64'), Int64Index([ 3676, 4596, 10269, 13029, 17564, 20314, 21237, 31293,\n", + " 44538, 45459, 46414, 49168, 54726, 55674, 57536, 60334,\n", + " 61252, 63707, 66993, 71539, 78814, 80635, 82457, 84331,\n", + " 87113, 88996, 104743, 107449, 108376, 109301, 112127, 113041,\n", + " 117617, 119431, 121231, 133949, 134876, 144493, 147100, 151688,\n", + " 152639, 161402, 164119, 167715, 168664, 169614, 186410, 190102],\n", + " dtype='int64'), Int64Index([ 3677, 4597, 10270, 13030, 17565, 20315, 21238, 31294,\n", + " 44539, 45460, 46415, 49169, 54727, 55675, 57537, 60335,\n", + " 61253, 63708, 66994, 71540, 78815, 80636, 82458, 84332,\n", + " 87114, 88997, 104744, 107450, 108377, 109302, 112128, 113042,\n", + " 117618, 119432, 121232, 133950, 134877, 144494, 147101, 151689,\n", + " 152640, 161403, 164120, 167716, 168665, 169615, 186411, 190103],\n", + " dtype='int64'), Int64Index([ 3678, 4598, 10271, 13031, 17566, 20316, 21239, 31295,\n", + " 44540, 45461, 46416, 49170, 54728, 55676, 57538, 60336,\n", + " 61254, 63709, 66995, 71541, 78816, 80637, 82459, 84333,\n", + " 87115, 88998, 104745, 107451, 108378, 109303, 112129, 113043,\n", + " 117619, 119433, 121233, 133951, 134878, 144495, 147102, 151690,\n", + " 152641, 161404, 164121, 167717, 168666, 169616, 186412, 190104],\n", + " dtype='int64'), Int64Index([ 3679, 4599, 10272, 13032, 17567, 20317, 21240, 31296,\n", + " 44541, 45462, 46417, 49171, 54729, 55677, 57539, 60337,\n", + " 61255, 63710, 66996, 71542, 78817, 80638, 82460, 84334,\n", + " 87116, 88999, 104746, 107452, 108379, 109304, 112130, 113044,\n", + " 117620, 119434, 121234, 133952, 134879, 144496, 147103, 151691,\n", + " 152642, 161405, 164122, 167718, 168667, 169617, 186413, 190105],\n", + " dtype='int64'), Int64Index([ 3680, 4600, 10273, 13033, 17568, 20318, 21241, 31297,\n", + " 44542, 45463, 46418, 49172, 54730, 55678, 57540, 60338,\n", + " 61256, 63711, 66997, 71543, 78818, 80639, 82461, 84335,\n", + " 87117, 89000, 104747, 107453, 108380, 109305, 112131, 113045,\n", + " 117621, 119435, 121235, 133953, 134880, 144497, 147104, 151692,\n", + " 152643, 161406, 164123, 167719, 168668, 169618, 186414, 190106],\n", + " dtype='int64'), Int64Index([ 3681, 4601, 10274, 13034, 17569, 20319, 21242, 31298,\n", + " 44543, 45464, 46419, 49173, 54731, 55679, 57541, 60339,\n", + " 61257, 63712, 66998, 71544, 78819, 80640, 82462, 84336,\n", + " 87118, 89001, 104748, 107454, 108381, 109306, 112132, 113046,\n", + " 117622, 119436, 121236, 133954, 134881, 144498, 147105, 151693,\n", + " 152644, 161407, 164124, 167720, 168669, 169619, 186415, 190107],\n", + " dtype='int64'), Int64Index([ 3682, 4602, 10275, 13035, 17570, 20320, 21243, 31299,\n", + " 44544, 45465, 46420, 49174, 54732, 55680, 57542, 60340,\n", + " 61258, 63713, 66999, 71545, 78820, 80641, 82463, 84337,\n", + " 87119, 89002, 104749, 107455, 108382, 109307, 112133, 113047,\n", + " 117623, 119437, 121237, 133955, 134882, 144499, 147106, 151694,\n", + " 152645, 161408, 164125, 167721, 168670, 169620, 186416, 190108],\n", + " dtype='int64'), Int64Index([ 3683, 4603, 10276, 13036, 17571, 20321, 21244, 31300,\n", + " 44545, 45466, 46421, 49175, 54733, 55681, 57543, 60341,\n", + " 61259, 63714, 67000, 71546, 78821, 80642, 82464, 84338,\n", + " 87120, 89003, 104750, 107456, 108383, 109308, 112134, 113048,\n", + " 117624, 119438, 121238, 133956, 134883, 144500, 147107, 151695,\n", + " 152646, 161409, 164126, 167722, 168671, 169621, 186417, 190109],\n", + " dtype='int64'), Int64Index([ 3684, 4604, 10277, 13037, 17572, 20322, 21245, 31301,\n", + " 44546, 45467, 46422, 49176, 54734, 55682, 57544, 60342,\n", + " 61260, 63715, 67001, 71547, 78822, 80643, 82465, 84339,\n", + " 87121, 89004, 104751, 107457, 108384, 109309, 112135, 113049,\n", + " 117625, 119439, 121239, 133957, 134884, 144501, 147108, 151696,\n", + " 152647, 161410, 164127, 167723, 168672, 169622, 186418, 190110],\n", + " dtype='int64'), Int64Index([ 3685, 4605, 10278, 13038, 17573, 20323, 21246, 31302,\n", + " 44547, 45468, 46423, 49177, 54735, 55683, 57545, 60343,\n", + " 61261, 63716, 67002, 71548, 78823, 80644, 82466, 84340,\n", + " 87122, 89005, 104752, 107458, 108385, 109310, 112136, 113050,\n", + " 117626, 119440, 121240, 133958, 134885, 144502, 147109, 151697,\n", + " 152648, 161411, 164128, 167724, 168673, 169623, 186419, 190111],\n", + " dtype='int64'), Int64Index([ 3686, 4606, 10279, 13039, 17574, 20324, 21247, 31303,\n", + " 44548, 45469, 46424, 49178, 54736, 55684, 57546, 60344,\n", + " 61262, 63717, 67003, 71549, 78824, 80645, 82467, 84341,\n", + " 87123, 89006, 104753, 107459, 108386, 109311, 112137, 113051,\n", + " 117627, 119441, 121241, 133959, 134886, 144503, 147110, 151698,\n", + " 152649, 161412, 164129, 167725, 168674, 169624, 186420, 190112],\n", + " dtype='int64'), Int64Index([ 3687, 4607, 10280, 13040, 17575, 20325, 21248, 31304,\n", + " 44549, 45470, 46425, 49179, 54737, 55685, 57547, 60345,\n", + " 61263, 63718, 67004, 71550, 78825, 80646, 82468, 84342,\n", + " 87124, 89007, 104754, 107460, 108387, 109312, 112138, 113052,\n", + " 117628, 119442, 121242, 133960, 134887, 144504, 147111, 151699,\n", + " 152650, 161413, 164130, 167726, 168675, 169625, 186421, 190113],\n", + " dtype='int64'), Int64Index([ 3688, 4608, 10281, 13041, 17576, 20326, 21249, 31305,\n", + " 44550, 45471, 46426, 49180, 54738, 55686, 57548, 60346,\n", + " 61264, 63719, 67005, 71551, 78826, 80647, 82469, 84343,\n", + " 87125, 89008, 104755, 107461, 108388, 109313, 112139, 113053,\n", + " 117629, 119443, 121243, 133961, 134888, 144505, 147112, 151700,\n", + " 152651, 161414, 164131, 167727, 168676, 169626, 186422, 190114],\n", + " dtype='int64'), Int64Index([ 3689, 4609, 10282, 13042, 17577, 20327, 21250, 31306,\n", + " 44551, 45472, 46427, 49181, 54739, 55687, 57549, 60347,\n", + " 61265, 63720, 67006, 71552, 78827, 80648, 82470, 84344,\n", + " 87126, 89009, 104756, 107462, 108389, 109314, 112140, 113054,\n", + " 117630, 119444, 121244, 133962, 134889, 144506, 147113, 151701,\n", + " 152652, 161415, 164132, 167728, 168677, 169627, 186423, 190115],\n", + " dtype='int64'), Int64Index([ 3690, 4610, 10283, 13043, 17578, 20328, 21251, 31307,\n", + " 44552, 45473, 46428, 49182, 54740, 55688, 57550, 60348,\n", + " 61266, 63721, 67007, 71553, 78828, 80649, 82471, 84345,\n", + " 87127, 89010, 104757, 107463, 108390, 109315, 112141, 113055,\n", + " 117631, 119445, 121245, 133963, 134890, 144507, 147114, 151702,\n", + " 152653, 161416, 164133, 167729, 168678, 169628, 186424, 190116],\n", + " dtype='int64'), Int64Index([ 3691, 4611, 10284, 13044, 17579, 20329, 21252, 31308,\n", + " 44553, 45474, 46429, 49183, 54741, 55689, 57551, 60349,\n", + " 61267, 63722, 67008, 71554, 78829, 80650, 82472, 84346,\n", + " 87128, 89011, 104758, 107464, 108391, 109316, 112142, 113056,\n", + " 117632, 119446, 121246, 133964, 134891, 144508, 147115, 151703,\n", + " 152654, 161417, 164134, 167730, 168679, 169629, 186425, 190117],\n", + " dtype='int64'), Int64Index([ 3692, 4612, 10285, 13045, 17580, 20330, 21253, 31309,\n", + " 44554, 45475, 46430, 49184, 54742, 55690, 57552, 60350,\n", + " 61268, 63723, 67009, 71555, 78830, 80651, 82473, 84347,\n", + " 87129, 89012, 104759, 107465, 108392, 109317, 112143, 113057,\n", + " 117633, 119447, 121247, 133965, 134892, 144509, 147116, 151704,\n", + " 152655, 161418, 164135, 167731, 168680, 169630, 186426, 190118],\n", + " dtype='int64'), Int64Index([ 3693, 4613, 10286, 13046, 17581, 20331, 21254, 31310,\n", + " 44555, 45476, 46431, 49185, 54743, 55691, 57553, 60351,\n", + " 61269, 63724, 67010, 71556, 78831, 80652, 82474, 84348,\n", + " 87130, 89013, 104760, 107466, 108393, 109318, 112144, 113058,\n", + " 117634, 119448, 121248, 133966, 134893, 144510, 147117, 151705,\n", + " 152656, 161419, 164136, 167732, 168681, 169631, 186427, 190119],\n", + " dtype='int64'), Int64Index([ 3694, 4614, 10287, 13047, 17582, 20332, 21255, 31311,\n", + " 44556, 45477, 46432, 49186, 54744, 55692, 57554, 60352,\n", + " 61270, 63725, 67011, 71557, 78832, 80653, 82475, 84349,\n", + " 87131, 89014, 104761, 107467, 108394, 109319, 112145, 113059,\n", + " 117635, 119449, 121249, 133967, 134894, 144511, 147118, 151706,\n", + " 152657, 161420, 164137, 167733, 168682, 169632, 186428, 190120],\n", + " dtype='int64'), Int64Index([ 3695, 4615, 10288, 13048, 17583, 20333, 21256, 31312,\n", + " 44557, 45478, 46433, 49187, 54745, 55693, 57555, 60353,\n", + " 61271, 63726, 67012, 71558, 78833, 80654, 82476, 84350,\n", + " 87132, 89015, 104762, 107468, 108395, 109320, 112146, 113060,\n", + " 117636, 119450, 121250, 133968, 134895, 144512, 147119, 151707,\n", + " 152658, 161421, 164138, 167734, 168683, 169633, 186429, 190121],\n", + " dtype='int64'), Int64Index([ 3696, 4616, 10289, 13049, 17584, 20334, 21257, 31313,\n", + " 44558, 45479, 46434, 49188, 54746, 55694, 57556, 60354,\n", + " 61272, 63727, 67013, 71559, 78834, 80655, 82477, 84351,\n", + " 87133, 89016, 104763, 107469, 108396, 109321, 112147, 113061,\n", + " 117637, 119451, 121251, 133969, 134896, 144513, 147120, 151708,\n", + " 152659, 161422, 164139, 167735, 168684, 169634, 186430, 190122],\n", + " dtype='int64'), Int64Index([ 3697, 4617, 10290, 13050, 17585, 20335, 21258, 31314,\n", + " 44559, 45480, 46435, 49189, 54747, 55695, 57557, 60355,\n", + " 61273, 63728, 67014, 71560, 78835, 80656, 82478, 84352,\n", + " 87134, 89017, 104764, 107470, 108397, 109322, 112148, 113062,\n", + " 117638, 119452, 121252, 133970, 134897, 144514, 147121, 151709,\n", + " 152660, 161423, 164140, 167736, 168685, 169635, 186431, 190123],\n", + " dtype='int64'), Int64Index([ 3698, 4618, 10291, 13051, 17586, 20336, 21259, 31315,\n", + " 44560, 45481, 46436, 49190, 54748, 55696, 57558, 60356,\n", + " 61274, 63729, 67015, 71561, 78836, 80657, 82479, 84353,\n", + " 87135, 89018, 104765, 107471, 108398, 109323, 112149, 113063,\n", + " 117639, 119453, 121253, 133971, 134898, 144515, 147122, 151710,\n", + " 152661, 161424, 164141, 167737, 168686, 169636, 186432, 190124],\n", + " dtype='int64'), Int64Index([ 3699, 4619, 10292, 13052, 17587, 20337, 21260, 31316,\n", + " 44561, 45482, 46437, 49191, 54749, 55697, 57559, 60357,\n", + " 61275, 63730, 67016, 71562, 78837, 80658, 82480, 84354,\n", + " 87136, 89019, 104766, 107472, 108399, 109324, 112150, 113064,\n", + " 117640, 119454, 121254, 133972, 134899, 144516, 147123, 151711,\n", + " 152662, 161425, 164142, 167738, 168687, 169637, 186433, 190125],\n", + " dtype='int64'), Int64Index([ 3700, 4620, 10293, 13053, 17588, 20338, 21261, 31317,\n", + " 44562, 45483, 46438, 49192, 54750, 55698, 57560, 60358,\n", + " 61276, 63731, 67017, 71563, 78838, 80659, 82481, 84355,\n", + " 87137, 89020, 104767, 107473, 108400, 109325, 112151, 113065,\n", + " 117641, 119455, 121255, 133973, 134900, 144517, 147124, 151712,\n", + " 152663, 161426, 164143, 167739, 168688, 169638, 186434, 190126],\n", + " dtype='int64'), Int64Index([ 3701, 4621, 10294, 13054, 17589, 20339, 21262, 31318,\n", + " 44563, 45484, 46439, 49193, 54751, 55699, 57561, 60359,\n", + " 61277, 63732, 67018, 71564, 78839, 80660, 82482, 84356,\n", + " 87138, 89021, 104768, 107474, 108401, 109326, 112152, 113066,\n", + " 117642, 119456, 121256, 133974, 134901, 144518, 147125, 151713,\n", + " 152664, 161427, 164144, 167740, 168689, 169639, 186435, 190127],\n", + " dtype='int64'), Int64Index([ 3702, 4622, 10295, 13055, 17590, 20340, 21263, 31319,\n", + " 44564, 45485, 46440, 49194, 54752, 55700, 57562, 60360,\n", + " 61278, 63733, 67019, 71565, 78840, 80661, 82483, 84357,\n", + " 87139, 89022, 104769, 107475, 108402, 109327, 112153, 113067,\n", + " 117643, 119457, 121257, 133975, 134902, 144519, 147126, 151714,\n", + " 152665, 161428, 164145, 167741, 168690, 169640, 186436, 190128],\n", + " dtype='int64'), Int64Index([ 3703, 4623, 10296, 13056, 17591, 20341, 21264, 31320,\n", + " 44565, 45486, 46441, 49195, 54753, 55701, 57563, 60361,\n", + " 61279, 63734, 67020, 71566, 78841, 80662, 82484, 84358,\n", + " 87140, 89023, 104770, 107476, 108403, 109328, 112154, 113068,\n", + " 117644, 119458, 121258, 133976, 134903, 144520, 147127, 151715,\n", + " 152666, 161429, 164146, 167742, 168691, 169641, 186437, 190129],\n", + " dtype='int64'), Int64Index([ 3704, 4624, 10297, 13057, 17592, 20342, 21265, 31321,\n", + " 44566, 45487, 46442, 49196, 54754, 55702, 57564, 60362,\n", + " 61280, 63735, 67021, 71567, 78842, 80663, 82485, 84359,\n", + " 87141, 89024, 104771, 107477, 108404, 109329, 112155, 113069,\n", + " 117645, 119459, 121259, 133977, 134904, 144521, 147128, 151716,\n", + " 152667, 161430, 164147, 167743, 168692, 169642, 186438, 190130],\n", + " dtype='int64'), Int64Index([ 3705, 4625, 10298, 13058, 17593, 20343, 21266, 31322,\n", + " 44567, 45488, 46443, 49197, 54755, 55703, 57565, 60363,\n", + " 61281, 63736, 67022, 71568, 78843, 80664, 82486, 84360,\n", + " 87142, 89025, 104772, 107478, 108405, 109330, 112156, 113070,\n", + " 117646, 119460, 121260, 133978, 134905, 144522, 147129, 151717,\n", + " 152668, 161431, 164148, 167744, 168693, 169643, 186439, 190131],\n", + " dtype='int64'), Int64Index([ 3706, 4626, 10299, 13059, 17594, 20344, 21267, 31323,\n", + " 44568, 45489, 46444, 49198, 54756, 55704, 57566, 60364,\n", + " 61282, 63737, 67023, 71569, 78844, 80665, 82487, 84361,\n", + " 87143, 89026, 104773, 107479, 108406, 109331, 112157, 113071,\n", + " 117647, 119461, 121261, 133979, 134906, 144523, 147130, 151718,\n", + " 152669, 161432, 164149, 167745, 168694, 169644, 186440, 190132],\n", + " dtype='int64'), Int64Index([ 3707, 4627, 10300, 13060, 17595, 20345, 21268, 31324,\n", + " 44569, 45490, 46445, 49199, 54757, 55705, 57567, 60365,\n", + " 61283, 63738, 67024, 71570, 78845, 80666, 82488, 84362,\n", + " 87144, 89027, 104774, 107480, 108407, 109332, 112158, 113072,\n", + " 117648, 119462, 121262, 133980, 134907, 144524, 147131, 151719,\n", + " 152670, 161433, 164150, 167746, 168695, 169645, 186441, 190133],\n", + " dtype='int64'), Int64Index([ 3708, 4628, 10301, 13061, 17596, 20346, 21269, 31325,\n", + " 44570, 45491, 46446, 49200, 54758, 55706, 57568, 60366,\n", + " 61284, 63739, 67025, 71571, 78846, 80667, 82489, 84363,\n", + " 87145, 89028, 104775, 107481, 108408, 109333, 112159, 113073,\n", + " 117649, 119463, 121263, 133981, 134908, 144525, 147132, 151720,\n", + " 152671, 161434, 164151, 167747, 168696, 169646, 186442, 190134],\n", + " dtype='int64'), Int64Index([ 3709, 4629, 10302, 13062, 17597, 20347, 21270, 31326,\n", + " 44571, 45492, 46447, 49201, 54759, 55707, 57569, 60367,\n", + " 61285, 63740, 67026, 71572, 78847, 80668, 82490, 84364,\n", + " 87146, 89029, 104776, 107482, 108409, 109334, 112160, 113074,\n", + " 117650, 119464, 121264, 133982, 134909, 144526, 147133, 151721,\n", + " 152672, 161435, 164152, 167748, 168697, 169647, 186443, 190135],\n", + " dtype='int64'), Int64Index([ 3710, 4630, 10303, 13063, 17598, 20348, 21271, 31327,\n", + " 44572, 45493, 46448, 49202, 54760, 55708, 57570, 60368,\n", + " 61286, 63741, 67027, 71573, 78848, 80669, 82491, 84365,\n", + " 87147, 89030, 104777, 107483, 108410, 109335, 112161, 113075,\n", + " 117651, 119465, 121265, 133983, 134910, 144527, 147134, 151722,\n", + " 152673, 161436, 164153, 167749, 168698, 169648, 186444, 190136],\n", + " dtype='int64'), Int64Index([ 3711, 4631, 10304, 13064, 17599, 20349, 21272, 31328,\n", + " 44573, 45494, 46449, 49203, 54761, 55709, 57571, 60369,\n", + " 61287, 63742, 67028, 71574, 78849, 80670, 82492, 84366,\n", + " 87148, 89031, 104778, 107484, 108411, 109336, 112162, 113076,\n", + " 117652, 119466, 121266, 133984, 134911, 144528, 147135, 151723,\n", + " 152674, 161437, 164154, 167750, 168699, 169649, 186445, 190137],\n", + " dtype='int64'), Int64Index([ 3712, 4632, 10305, 13065, 17600, 20350, 21273, 31329,\n", + " 44574, 45495, 46450, 49204, 54762, 55710, 57572, 60370,\n", + " 61288, 63743, 67029, 71575, 78850, 80671, 82493, 84367,\n", + " 87149, 89032, 104779, 107485, 108412, 109337, 112163, 113077,\n", + " 117653, 119467, 121267, 133985, 134912, 144529, 147136, 151724,\n", + " 152675, 161438, 164155, 167751, 168700, 169650, 186446, 190138],\n", + " dtype='int64'), Int64Index([ 3713, 4633, 10306, 13066, 17601, 20351, 21274, 31330,\n", + " 44575, 45496, 46451, 49205, 54763, 55711, 57573, 60371,\n", + " 61289, 63744, 67030, 71576, 78851, 80672, 82494, 84368,\n", + " 87150, 89033, 104780, 107486, 108413, 109338, 112164, 113078,\n", + " 117654, 119468, 121268, 133986, 134913, 144530, 147137, 151725,\n", + " 152676, 161439, 164156, 167752, 168701, 169651, 186447, 190139],\n", + " dtype='int64'), Int64Index([ 3714, 4634, 10307, 13067, 17602, 20352, 21275, 31331,\n", + " 44576, 45497, 46452, 49206, 54764, 55712, 57574, 60372,\n", + " 61290, 63745, 67031, 71577, 78852, 80673, 82495, 84369,\n", + " 87151, 89034, 104781, 107487, 108414, 109339, 112165, 113079,\n", + " 117655, 119469, 121269, 133987, 134914, 144531, 147138, 151726,\n", + " 152677, 161440, 164157, 167753, 168702, 169652, 186448, 190140],\n", + " dtype='int64'), Int64Index([ 3715, 4635, 10308, 13068, 17603, 20353, 21276, 31332,\n", + " 44577, 45498, 46453, 49207, 54765, 55713, 57575, 60373,\n", + " 61291, 63746, 67032, 71578, 78853, 80674, 82496, 84370,\n", + " 87152, 89035, 104782, 107488, 108415, 109340, 112166, 113080,\n", + " 117656, 119470, 121270, 133988, 134915, 144532, 147139, 151727,\n", + " 152678, 161441, 164158, 167754, 168703, 169653, 186449, 190141],\n", + " dtype='int64'), Int64Index([ 3716, 4636, 10309, 13069, 17604, 20354, 21277, 31333,\n", + " 44578, 45499, 46454, 49208, 54766, 55714, 57576, 60374,\n", + " 61292, 63747, 67033, 71579, 78854, 80675, 82497, 84371,\n", + " 87153, 89036, 104783, 107489, 108416, 109341, 112167, 113081,\n", + " 117657, 119471, 121271, 133989, 134916, 144533, 147140, 151728,\n", + " 152679, 161442, 164159, 167755, 168704, 169654, 186450, 190142],\n", + " dtype='int64'), Int64Index([ 3717, 4637, 10310, 13070, 17605, 20355, 21278, 31334,\n", + " 44579, 45500, 46455, 49209, 54767, 55715, 57577, 60375,\n", + " 61293, 63748, 67034, 71580, 78855, 80676, 82498, 84372,\n", + " 87154, 89037, 104784, 107490, 108417, 109342, 112168, 113082,\n", + " 117658, 119472, 121272, 133990, 134917, 144534, 147141, 151729,\n", + " 152680, 161443, 164160, 167756, 168705, 169655, 186451, 190143],\n", + " dtype='int64'), Int64Index([ 3718, 4638, 10311, 13071, 17606, 20356, 21279, 31335,\n", + " 44580, 45501, 46456, 49210, 54768, 55716, 57578, 60376,\n", + " 61294, 63749, 67035, 71581, 78856, 80677, 82499, 84373,\n", + " 87155, 89038, 104785, 107491, 108418, 109343, 112169, 113083,\n", + " 117659, 119473, 121273, 133991, 134918, 144535, 147142, 151730,\n", + " 152681, 161444, 164161, 167757, 168706, 169656, 186452, 190144],\n", + " dtype='int64'), Int64Index([ 3719, 4639, 10312, 13072, 17607, 20357, 21280, 31336,\n", + " 44581, 45502, 46457, 49211, 54769, 55717, 57579, 60377,\n", + " 61295, 63750, 67036, 71582, 78857, 80678, 82500, 84374,\n", + " 87156, 89039, 104786, 107492, 108419, 109344, 112170, 113084,\n", + " 117660, 119474, 121274, 133992, 134919, 144536, 147143, 151731,\n", + " 152682, 161445, 164162, 167758, 168707, 169657, 186453, 190145],\n", + " dtype='int64'), Int64Index([ 3720, 4640, 10313, 13073, 17608, 20358, 21281, 31337,\n", + " 44582, 45503, 46458, 49212, 54770, 55718, 57580, 60378,\n", + " 61296, 63751, 67037, 71583, 78858, 80679, 82501, 84375,\n", + " 87157, 89040, 104787, 107493, 108420, 109345, 112171, 113085,\n", + " 117661, 119475, 121275, 133993, 134920, 144537, 147144, 151732,\n", + " 152683, 161446, 164163, 167759, 168708, 169658, 186454, 190146],\n", + " dtype='int64'), Int64Index([ 3721, 4641, 10314, 13074, 17609, 20359, 21282, 31338,\n", + " 44583, 45504, 46459, 49213, 54771, 55719, 57581, 60379,\n", + " 61297, 63752, 67038, 71584, 78859, 80680, 82502, 84376,\n", + " 87158, 89041, 104788, 107494, 108421, 109346, 112172, 113086,\n", + " 117662, 119476, 121276, 133994, 134921, 144538, 147145, 151733,\n", + " 152684, 161447, 164164, 167760, 168709, 169659, 186455, 190147],\n", + " dtype='int64'), Int64Index([ 3722, 4642, 10315, 13075, 17610, 20360, 21283, 31339,\n", + " 44584, 45505, 46460, 49214, 54772, 55720, 57582, 60380,\n", + " 61298, 63753, 67039, 71585, 78860, 80681, 82503, 84377,\n", + " 87159, 89042, 104789, 107495, 108422, 109347, 112173, 113087,\n", + " 117663, 119477, 121277, 133995, 134922, 144539, 147146, 151734,\n", + " 152685, 161448, 164165, 167761, 168710, 169660, 186456, 190148],\n", + " dtype='int64'), Int64Index([ 3723, 4643, 10316, 13076, 17611, 20361, 21284, 31340,\n", + " 44585, 45506, 46461, 49215, 54773, 55721, 57583, 60381,\n", + " 61299, 63754, 67040, 71586, 78861, 80682, 82504, 84378,\n", + " 87160, 89043, 104790, 107496, 108423, 109348, 112174, 113088,\n", + " 117664, 119478, 121278, 133996, 134923, 144540, 147147, 151735,\n", + " 152686, 161449, 164166, 167762, 168711, 169661, 186457, 190149],\n", + " dtype='int64'), Int64Index([ 3724, 4644, 10317, 13077, 17612, 20362, 21285, 31341,\n", + " 44586, 45507, 46462, 49216, 54774, 55722, 57584, 60382,\n", + " 61300, 63755, 67041, 71587, 78862, 80683, 82505, 84379,\n", + " 87161, 89044, 104791, 107497, 108424, 109349, 112175, 113089,\n", + " 117665, 119479, 121279, 133997, 134924, 144541, 147148, 151736,\n", + " 152687, 161450, 164167, 167763, 168712, 169662, 186458, 190150],\n", + " dtype='int64'), Int64Index([ 3725, 4645, 10318, 13078, 17613, 20363, 21286, 31342,\n", + " 44587, 45508, 46463, 49217, 54775, 55723, 57585, 60383,\n", + " 61301, 63756, 67042, 71588, 78863, 80684, 82506, 84380,\n", + " 87162, 89045, 104792, 107498, 108425, 109350, 112176, 113090,\n", + " 117666, 119480, 121280, 133998, 134925, 144542, 147149, 151737,\n", + " 152688, 161451, 164168, 167764, 168713, 169663, 186459, 190151],\n", + " dtype='int64'), Int64Index([ 3726, 4646, 10319, 13079, 17614, 20364, 21287, 31343,\n", + " 44588, 45509, 46464, 49218, 54776, 55724, 57586, 60384,\n", + " 61302, 63757, 67043, 71589, 78864, 80685, 82507, 84381,\n", + " 87163, 89046, 104793, 107499, 108426, 109351, 112177, 113091,\n", + " 117667, 119481, 121281, 133999, 134926, 144543, 147150, 151738,\n", + " 152689, 161452, 164169, 167765, 168714, 169664, 186460, 190152],\n", + " dtype='int64'), Int64Index([ 3727, 4647, 10320, 13080, 17615, 20365, 21288, 31344,\n", + " 44589, 45510, 46465, 49219, 54777, 55725, 57587, 60385,\n", + " 61303, 63758, 67044, 71590, 78865, 80686, 82508, 84382,\n", + " 87164, 89047, 104794, 107500, 108427, 109352, 112178, 113092,\n", + " 117668, 119482, 121282, 134000, 134927, 144544, 147151, 151739,\n", + " 152690, 161453, 164170, 167766, 168715, 169665, 186461, 190153],\n", + " dtype='int64'), Int64Index([ 3728, 4648, 10321, 13081, 17616, 20366, 21289, 31345,\n", + " 44590, 45511, 46466, 49220, 54778, 55726, 57588, 60386,\n", + " 61304, 63759, 67045, 71591, 78866, 80687, 82509, 84383,\n", + " 87165, 89048, 104795, 107501, 108428, 109353, 112179, 113093,\n", + " 117669, 119483, 121283, 134001, 134928, 144545, 147152, 151740,\n", + " 152691, 161454, 164171, 167767, 168716, 169666, 186462, 190154],\n", + " dtype='int64'), Int64Index([ 3729, 4649, 10322, 13082, 17617, 20367, 21290, 31346,\n", + " 44591, 45512, 46467, 49221, 54779, 55727, 57589, 60387,\n", + " 61305, 63760, 67046, 71592, 78867, 80688, 82510, 84384,\n", + " 87166, 89049, 104796, 107502, 108429, 109354, 112180, 113094,\n", + " 117670, 119484, 121284, 134002, 134929, 144546, 147153, 151741,\n", + " 152692, 161455, 164172, 167768, 168717, 169667, 186463, 190155],\n", + " dtype='int64'), Int64Index([ 3730, 4650, 10323, 13083, 17618, 20368, 21291, 31347,\n", + " 44592, 45513, 46468, 49222, 54780, 55728, 57590, 60388,\n", + " 61306, 63761, 67047, 71593, 78868, 80689, 82511, 84385,\n", + " 87167, 89050, 104797, 107503, 108430, 109355, 112181, 113095,\n", + " 117671, 119485, 121285, 134003, 134930, 144547, 147154, 151742,\n", + " 152693, 161456, 164173, 167769, 168718, 169668, 186464, 190156],\n", + " dtype='int64'), Int64Index([ 3731, 4651, 10324, 13084, 17619, 20369, 21292, 31348,\n", + " 44593, 45514, 46469, 49223, 54781, 55729, 57591, 60389,\n", + " 61307, 63762, 67048, 71594, 78869, 80690, 82512, 84386,\n", + " 87168, 89051, 104798, 107504, 108431, 109356, 112182, 113096,\n", + " 117672, 119486, 121286, 134004, 134931, 144548, 147155, 151743,\n", + " 152694, 161457, 164174, 167770, 168719, 169669, 186465, 190157],\n", + " dtype='int64'), Int64Index([ 3732, 4652, 10325, 13085, 17620, 20370, 21293, 31349,\n", + " 44594, 45515, 46470, 49224, 54782, 55730, 57592, 60390,\n", + " 61308, 63763, 67049, 71595, 78870, 80691, 82513, 84387,\n", + " 87169, 89052, 104799, 107505, 108432, 109357, 112183, 113097,\n", + " 117673, 119487, 121287, 134005, 134932, 144549, 147156, 151744,\n", + " 152695, 161458, 164175, 167771, 168720, 169670, 186466, 190158],\n", + " dtype='int64'), Int64Index([ 3733, 4653, 10326, 13086, 17621, 20371, 21294, 31350,\n", + " 44595, 45516, 46471, 49225, 54783, 55731, 57593, 60391,\n", + " 61309, 63764, 67050, 71596, 78871, 80692, 82514, 84388,\n", + " 87170, 89053, 104800, 107506, 108433, 109358, 112184, 113098,\n", + " 117674, 119488, 121288, 134006, 134933, 144550, 147157, 151745,\n", + " 152696, 161459, 164176, 167772, 168721, 169671, 186467, 190159],\n", + " dtype='int64'), Int64Index([ 3734, 4654, 10327, 13087, 17622, 20372, 21295, 31351,\n", + " 44596, 45517, 46472, 49226, 54784, 55732, 57594, 60392,\n", + " 61310, 63765, 67051, 71597, 78872, 80693, 82515, 84389,\n", + " 87171, 89054, 104801, 107507, 108434, 109359, 112185, 113099,\n", + " 117675, 119489, 121289, 134007, 134934, 144551, 147158, 151746,\n", + " 152697, 161460, 164177, 167773, 168722, 169672, 186468, 190160],\n", + " dtype='int64'), Int64Index([ 3735, 4655, 10328, 13088, 17623, 20373, 21296, 31352,\n", + " 44597, 45518, 46473, 49227, 54785, 55733, 57595, 60393,\n", + " 61311, 63766, 67052, 71598, 78873, 80694, 82516, 84390,\n", + " 87172, 89055, 104802, 107508, 108435, 109360, 112186, 113100,\n", + " 117676, 119490, 121290, 134008, 134935, 144552, 147159, 151747,\n", + " 152698, 161461, 164178, 167774, 168723, 169673, 186469, 190161],\n", + " dtype='int64'), Int64Index([ 3736, 4656, 10329, 13089, 17624, 20374, 21297, 31353,\n", + " 44598, 45519, 46474, 49228, 54786, 55734, 57596, 60394,\n", + " 61312, 63767, 67053, 71599, 78874, 80695, 82517, 84391,\n", + " 87173, 89056, 104803, 107509, 108436, 109361, 112187, 113101,\n", + " 117677, 119491, 121291, 134009, 134936, 144553, 147160, 151748,\n", + " 152699, 161462, 164179, 167775, 168724, 169674, 186470, 190162],\n", + " dtype='int64'), Int64Index([ 3737, 4657, 10330, 13090, 17625, 20375, 21298, 31354,\n", + " 44599, 45520, 46475, 49229, 54787, 55735, 57597, 60395,\n", + " 61313, 63768, 67054, 71600, 78875, 80696, 82518, 84392,\n", + " 87174, 89057, 104804, 107510, 108437, 109362, 112188, 113102,\n", + " 117678, 119492, 121292, 134010, 134937, 144554, 147161, 151749,\n", + " 152700, 161463, 164180, 167776, 168725, 169675, 186471, 190163],\n", + " dtype='int64'), Int64Index([ 3738, 4658, 10331, 13091, 17626, 20376, 21299, 31355,\n", + " 44600, 45521, 46476, 49230, 54788, 55736, 57598, 60396,\n", + " 61314, 63769, 67055, 71601, 78876, 80697, 82519, 84393,\n", + " 87175, 89058, 104805, 107511, 108438, 109363, 112189, 113103,\n", + " 117679, 119493, 121293, 134011, 134938, 144555, 147162, 151750,\n", + " 152701, 161464, 164181, 167777, 168726, 169676, 186472, 190164],\n", + " dtype='int64'), Int64Index([ 3739, 4659, 10332, 13092, 17627, 20377, 21300, 31356,\n", + " 44601, 45522, 46477, 49231, 54789, 55737, 57599, 60397,\n", + " 61315, 63770, 67056, 71602, 78877, 80698, 82520, 84394,\n", + " 87176, 89059, 104806, 107512, 108439, 109364, 112190, 113104,\n", + " 117680, 119494, 121294, 134012, 134939, 144556, 147163, 151751,\n", + " 152702, 161465, 164182, 167778, 168727, 169677, 186473, 190165],\n", + " dtype='int64'), Int64Index([ 3740, 4660, 10333, 13093, 17628, 20378, 21301, 31357,\n", + " 44602, 45523, 46478, 49232, 54790, 55738, 57600, 60398,\n", + " 61316, 63771, 67057, 71603, 78878, 80699, 82521, 84395,\n", + " 87177, 89060, 104807, 107513, 108440, 109365, 112191, 113105,\n", + " 117681, 119495, 121295, 134013, 134940, 144557, 147164, 151752,\n", + " 152703, 161466, 164183, 167779, 168728, 169678, 186474, 190166],\n", + " dtype='int64'), Int64Index([ 3741, 4661, 10334, 13094, 17629, 20379, 21302, 31358,\n", + " 44603, 45524, 46479, 49233, 54791, 55739, 57601, 60399,\n", + " 61317, 63772, 67058, 71604, 78879, 80700, 82522, 84396,\n", + " 87178, 89061, 104808, 107514, 108441, 109366, 112192, 113106,\n", + " 117682, 119496, 121296, 134014, 134941, 144558, 147165, 151753,\n", + " 152704, 161467, 164184, 167780, 168729, 169679, 186475, 190167],\n", + " dtype='int64'), Int64Index([ 3742, 4662, 10335, 13095, 17630, 20380, 21303, 31359,\n", + " 44604, 45525, 46480, 49234, 54792, 55740, 57602, 60400,\n", + " 61318, 63773, 67059, 71605, 78880, 80701, 82523, 84397,\n", + " 87179, 89062, 104809, 107515, 108442, 109367, 112193, 113107,\n", + " 117683, 119497, 121297, 134015, 134942, 144559, 147166, 151754,\n", + " 152705, 161468, 164185, 167781, 168730, 169680, 186476, 190168],\n", + " dtype='int64'), Int64Index([ 3743, 4663, 10336, 13096, 17631, 20381, 21304, 31360,\n", + " 44605, 45526, 46481, 49235, 54793, 55741, 57603, 60401,\n", + " 61319, 63774, 67060, 71606, 78881, 80702, 82524, 84398,\n", + " 87180, 89063, 104810, 107516, 108443, 109368, 112194, 113108,\n", + " 117684, 119498, 121298, 134016, 134943, 144560, 147167, 151755,\n", + " 152706, 161469, 164186, 167782, 168731, 169681, 186477, 190169],\n", + " dtype='int64'), Int64Index([ 3744, 4664, 10337, 13097, 17632, 20382, 21305, 31361,\n", + " 44606, 45527, 46482, 49236, 54794, 55742, 57604, 60402,\n", + " 61320, 63775, 67061, 71607, 78882, 80703, 82525, 84399,\n", + " 87181, 89064, 104811, 107517, 108444, 109369, 112195, 113109,\n", + " 117685, 119499, 121299, 134017, 134944, 144561, 147168, 151756,\n", + " 152707, 161470, 164187, 167783, 168732, 169682, 186478, 190170],\n", + " dtype='int64'), Int64Index([ 3745, 4665, 10338, 13098, 17633, 20383, 21306, 31362,\n", + " 44607, 45528, 46483, 49237, 54795, 55743, 57605, 60403,\n", + " 61321, 63776, 67062, 71608, 78883, 80704, 82526, 84400,\n", + " 87182, 89065, 104812, 107518, 108445, 109370, 112196, 113110,\n", + " 117686, 119500, 121300, 134018, 134945, 144562, 147169, 151757,\n", + " 152708, 161471, 164188, 167784, 168733, 169683, 186479, 190171],\n", + " dtype='int64'), Int64Index([ 3746, 4666, 10339, 13099, 17634, 20384, 21307, 31363,\n", + " 44608, 45529, 46484, 49238, 54796, 55744, 57606, 60404,\n", + " 61322, 63777, 67063, 71609, 78884, 80705, 82527, 84401,\n", + " 87183, 89066, 104813, 107519, 108446, 109371, 112197, 113111,\n", + " 117687, 119501, 121301, 134019, 134946, 144563, 147170, 151758,\n", + " 152709, 161472, 164189, 167785, 168734, 169684, 186480, 190172],\n", + " dtype='int64'), Int64Index([ 3747, 4667, 10340, 13100, 17635, 20385, 21308, 31364,\n", + " 44609, 45530, 46485, 49239, 54797, 55745, 57607, 60405,\n", + " 61323, 63778, 67064, 71610, 78885, 80706, 82528, 84402,\n", + " 87184, 89067, 104814, 107520, 108447, 109372, 112198, 113112,\n", + " 117688, 119502, 121302, 134020, 134947, 144564, 147171, 151759,\n", + " 152710, 161473, 164190, 167786, 168735, 169685, 186481, 190173],\n", + " dtype='int64'), Int64Index([ 3748, 4668, 10341, 13101, 17636, 20386, 21309, 31365,\n", + " 44610, 45531, 46486, 49240, 54798, 55746, 57608, 60406,\n", + " 61324, 63779, 67065, 71611, 78886, 80707, 82529, 84403,\n", + " 87185, 89068, 104815, 107521, 108448, 109373, 112199, 113113,\n", + " 117689, 119503, 121303, 134021, 134948, 144565, 147172, 151760,\n", + " 152711, 161474, 164191, 167787, 168736, 169686, 186482, 190174],\n", + " dtype='int64'), Int64Index([ 3749, 4669, 10342, 13102, 17637, 20387, 21310, 31366,\n", + " 44611, 45532, 46487, 49241, 54799, 55747, 57609, 60407,\n", + " 61325, 63780, 67066, 71612, 78887, 80708, 82530, 84404,\n", + " 87186, 89069, 104816, 107522, 108449, 109374, 112200, 113114,\n", + " 117690, 119504, 121304, 134022, 134949, 144566, 147173, 151761,\n", + " 152712, 161475, 164192, 167788, 168737, 169687, 186483, 190175],\n", + " dtype='int64'), Int64Index([ 3750, 4670, 10343, 13103, 17638, 20388, 21311, 31367,\n", + " 44612, 45533, 46488, 49242, 54800, 55748, 57610, 60408,\n", + " 61326, 63781, 67067, 71613, 78888, 80709, 82531, 84405,\n", + " 87187, 89070, 104817, 107523, 108450, 109375, 112201, 113115,\n", + " 117691, 119505, 121305, 134023, 134950, 144567, 147174, 151762,\n", + " 152713, 161476, 164193, 167789, 168738, 169688, 186484, 190176],\n", + " dtype='int64'), Int64Index([ 3751, 4671, 10344, 13104, 17639, 20389, 21312, 31368,\n", + " 44613, 45534, 46489, 49243, 54801, 55749, 57611, 60409,\n", + " 61327, 63782, 67068, 71614, 78889, 80710, 82532, 84406,\n", + " 87188, 89071, 104818, 107524, 108451, 109376, 112202, 113116,\n", + " 117692, 119506, 121306, 134024, 134951, 144568, 147175, 151763,\n", + " 152714, 161477, 164194, 167790, 168739, 169689, 186485, 190177],\n", + " dtype='int64'), Int64Index([ 3752, 4672, 10345, 13105, 17640, 20390, 21313, 31369,\n", + " 44614, 45535, 46490, 49244, 54802, 55750, 57612, 60410,\n", + " 61328, 63783, 67069, 71615, 78890, 80711, 82533, 84407,\n", + " 87189, 89072, 104819, 107525, 108452, 109377, 112203, 113117,\n", + " 117693, 119507, 121307, 134025, 134952, 144569, 147176, 151764,\n", + " 152715, 161478, 164195, 167791, 168740, 169690, 186486, 190178],\n", + " dtype='int64'), Int64Index([ 3753, 4673, 10346, 13106, 17641, 20391, 21314, 31370,\n", + " 44615, 45536, 46491, 49245, 54803, 55751, 57613, 60411,\n", + " 61329, 63784, 67070, 71616, 78891, 80712, 82534, 84408,\n", + " 87190, 89073, 104820, 107526, 108453, 109378, 112204, 113118,\n", + " 117694, 119508, 121308, 134026, 134953, 144570, 147177, 151765,\n", + " 152716, 161479, 164196, 167792, 168741, 169691, 186487, 190179],\n", + " dtype='int64'), Int64Index([ 3754, 4674, 10347, 13107, 17642, 20392, 21315, 31371,\n", + " 44616, 45537, 46492, 49246, 54804, 55752, 57614, 60412,\n", + " 61330, 63785, 67071, 71617, 78892, 80713, 82535, 84409,\n", + " 87191, 89074, 104821, 107527, 108454, 109379, 112205, 113119,\n", + " 117695, 119509, 121309, 134027, 134954, 144571, 147178, 151766,\n", + " 152717, 161480, 164197, 167793, 168742, 169692, 186488, 190180],\n", + " dtype='int64'), Int64Index([ 3755, 4675, 10348, 13108, 17643, 20393, 21316, 31372,\n", + " 44617, 45538, 46493, 49247, 54805, 55753, 57615, 60413,\n", + " 61331, 63786, 67072, 71618, 78893, 80714, 82536, 84410,\n", + " 87192, 89075, 104822, 107528, 108455, 109380, 112206, 113120,\n", + " 117696, 119510, 121310, 134028, 134955, 144572, 147179, 151767,\n", + " 152718, 161481, 164198, 167794, 168743, 169693, 186489, 190181],\n", + " dtype='int64'), Int64Index([ 3756, 4676, 10349, 13109, 17644, 20394, 21317, 31373,\n", + " 44618, 45539, 46494, 49248, 54806, 55754, 57616, 60414,\n", + " 61332, 63787, 67073, 71619, 78894, 80715, 82537, 84411,\n", + " 87193, 89076, 104823, 107529, 108456, 109381, 112207, 113121,\n", + " 117697, 119511, 121311, 134029, 134956, 144573, 147180, 151768,\n", + " 152719, 161482, 164199, 167795, 168744, 169694, 186490, 190182],\n", + " dtype='int64'), Int64Index([ 3757, 4677, 10350, 13110, 17645, 20395, 21318, 31374,\n", + " 44619, 45540, 46495, 49249, 54807, 55755, 57617, 60415,\n", + " 61333, 63788, 67074, 71620, 78895, 80716, 82538, 84412,\n", + " 87194, 89077, 104824, 107530, 108457, 109382, 112208, 113122,\n", + " 117698, 119512, 121312, 134030, 134957, 144574, 147181, 151769,\n", + " 152720, 161483, 164200, 167796, 168745, 169695, 186491, 190183],\n", + " dtype='int64'), Int64Index([ 3758, 4678, 10351, 13111, 17646, 20396, 21319, 31375,\n", + " 44620, 45541, 46496, 49250, 54808, 55756, 57618, 60416,\n", + " 61334, 63789, 67075, 71621, 78896, 80717, 82539, 84413,\n", + " 87195, 89078, 104825, 107531, 108458, 109383, 112209, 113123,\n", + " 117699, 119513, 121313, 134031, 134958, 144575, 147182, 151770,\n", + " 152721, 161484, 164201, 167797, 168746, 169696, 186492, 190184],\n", + " dtype='int64'), Int64Index([ 3759, 4679, 10352, 13112, 17647, 20397, 21320, 31376,\n", + " 44621, 45542, 46497, 49251, 54809, 55757, 57619, 60417,\n", + " 61335, 63790, 67076, 71622, 78897, 80718, 82540, 84414,\n", + " 87196, 89079, 104826, 107532, 108459, 109384, 112210, 113124,\n", + " 117700, 119514, 121314, 134032, 134959, 144576, 147183, 151771,\n", + " 152722, 161485, 164202, 167798, 168747, 169697, 186493, 190185],\n", + " dtype='int64'), Int64Index([ 3760, 4680, 10353, 13113, 17648, 20398, 21321, 31377,\n", + " 44622, 45543, 46498, 49252, 54810, 55758, 57620, 60418,\n", + " 61336, 63791, 67077, 71623, 78898, 80719, 82541, 84415,\n", + " 87197, 89080, 104827, 107533, 108460, 109385, 112211, 113125,\n", + " 117701, 119515, 121315, 134033, 134960, 144577, 147184, 151772,\n", + " 152723, 161486, 164203, 167799, 168748, 169698, 186494, 190186],\n", + " dtype='int64'), Int64Index([ 3761, 4681, 10354, 13114, 17649, 20399, 21322, 31378,\n", + " 44623, 45544, 46499, 49253, 54811, 55759, 57621, 60419,\n", + " 61337, 63792, 67078, 71624, 78899, 80720, 82542, 84416,\n", + " 87198, 89081, 104828, 107534, 108461, 109386, 112212, 113126,\n", + " 117702, 119516, 121316, 134034, 134961, 144578, 147185, 151773,\n", + " 152724, 161487, 164204, 167800, 168749, 169699, 186495, 190187],\n", + " dtype='int64'), Int64Index([ 3762, 4682, 10355, 13115, 17650, 20400, 21323, 31379,\n", + " 44624, 45545, 46500, 49254, 54812, 55760, 57622, 60420,\n", + " 61338, 63793, 67079, 71625, 78900, 80721, 82543, 84417,\n", + " 87199, 89082, 104829, 107535, 108462, 109387, 112213, 113127,\n", + " 117703, 119517, 121317, 134035, 134962, 144579, 147186, 151774,\n", + " 152725, 161488, 164205, 167801, 168750, 169700, 186496, 190188],\n", + " dtype='int64'), Int64Index([ 3763, 4683, 10356, 13116, 17651, 20401, 21324, 31380,\n", + " 44625, 45546, 46501, 49255, 54813, 55761, 57623, 60421,\n", + " 61339, 63794, 67080, 71626, 78901, 80722, 82544, 84418,\n", + " 87200, 89083, 104830, 107536, 108463, 109388, 112214, 113128,\n", + " 117704, 119518, 121318, 134036, 134963, 144580, 147187, 151775,\n", + " 152726, 161489, 164206, 167802, 168751, 169701, 186497, 190189],\n", + " dtype='int64'), Int64Index([ 3764, 4684, 10357, 13117, 17652, 20402, 21325, 31381,\n", + " 44626, 45547, 46502, 49256, 54814, 55762, 57624, 60422,\n", + " 61340, 63795, 67081, 71627, 78902, 80723, 82545, 84419,\n", + " 87201, 89084, 104831, 107537, 108464, 109389, 112215, 113129,\n", + " 117705, 119519, 121319, 134037, 134964, 144581, 147188, 151776,\n", + " 152727, 161490, 164207, 167803, 168752, 169702, 186498, 190190],\n", + " dtype='int64'), Int64Index([ 3765, 4685, 10358, 13118, 17653, 20403, 21326, 31382,\n", + " 44627, 45548, 46503, 49257, 54815, 55763, 57625, 60423,\n", + " 61341, 63796, 67082, 71628, 78903, 80724, 82546, 84420,\n", + " 87202, 89085, 104832, 107538, 108465, 109390, 112216, 113130,\n", + " 117706, 119520, 121320, 134038, 134965, 144582, 147189, 151777,\n", + " 152728, 161491, 164208, 167804, 168753, 169703, 186499, 190191],\n", + " dtype='int64'), Int64Index([ 3766, 4686, 10359, 13119, 17654, 20404, 21327, 31383,\n", + " 44628, 45549, 46504, 49258, 54816, 55764, 57626, 60424,\n", + " 61342, 63797, 67083, 71629, 78904, 80725, 82547, 84421,\n", + " 87203, 89086, 104833, 107539, 108466, 109391, 112217, 113131,\n", + " 117707, 119521, 121321, 134039, 134966, 144583, 147190, 151778,\n", + " 152729, 161492, 164209, 167805, 168754, 169704, 186500, 190192],\n", + " dtype='int64'), Int64Index([ 3767, 4687, 10360, 13120, 17655, 20405, 21328, 31384,\n", + " 44629, 45550, 46505, 49259, 54817, 55765, 57627, 60425,\n", + " 61343, 63798, 67084, 71630, 78905, 80726, 82548, 84422,\n", + " 87204, 89087, 104834, 107540, 108467, 109392, 112218, 113132,\n", + " 117708, 119522, 121322, 134040, 134967, 144584, 147191, 151779,\n", + " 152730, 161493, 164210, 167806, 168755, 169705, 186501, 190193],\n", + " dtype='int64'), Int64Index([ 3768, 4688, 10361, 13121, 17656, 20406, 21329, 31385,\n", + " 44630, 45551, 46506, 49260, 54818, 55766, 57628, 60426,\n", + " 61344, 63799, 67085, 71631, 78906, 80727, 82549, 84423,\n", + " 87205, 89088, 104835, 107541, 108468, 109393, 112219, 113133,\n", + " 117709, 119523, 121323, 134041, 134968, 144585, 147192, 151780,\n", + " 152731, 161494, 164211, 167807, 168756, 169706, 186502, 190194],\n", + " dtype='int64'), Int64Index([ 3769, 4689, 10362, 13122, 17657, 20407, 21330, 31386,\n", + " 44631, 45552, 46507, 49261, 54819, 55767, 57629, 60427,\n", + " 61345, 63800, 67086, 71632, 78907, 80728, 82550, 84424,\n", + " 87206, 89089, 104836, 107542, 108469, 109394, 112220, 113134,\n", + " 117710, 119524, 121324, 134042, 134969, 144586, 147193, 151781,\n", + " 152732, 161495, 164212, 167808, 168757, 169707, 186503, 190195],\n", + " dtype='int64'), Int64Index([ 3770, 4690, 10363, 13123, 17658, 20408, 21331, 31387,\n", + " 44632, 45553, 46508, 49262, 54820, 55768, 57630, 60428,\n", + " 61346, 63801, 67087, 71633, 78908, 80729, 82551, 84425,\n", + " 87207, 89090, 104837, 107543, 108470, 109395, 112221, 113135,\n", + " 117711, 119525, 121325, 134043, 134970, 144587, 147194, 151782,\n", + " 152733, 161496, 164213, 167809, 168758, 169708, 186504, 190196],\n", + " dtype='int64'), Int64Index([ 3771, 4691, 10364, 13124, 17659, 20409, 21332, 31388,\n", + " 44633, 45554, 46509, 49263, 54821, 55769, 57631, 60429,\n", + " 61347, 63802, 67088, 71634, 78909, 80730, 82552, 84426,\n", + " 87208, 89091, 104838, 107544, 108471, 109396, 112222, 113136,\n", + " 117712, 119526, 121326, 134044, 134971, 144588, 147195, 151783,\n", + " 152734, 161497, 164214, 167810, 168759, 169709, 186505, 190197],\n", + " dtype='int64'), Int64Index([ 3772, 4692, 10365, 13125, 17660, 20410, 21333, 31389,\n", + " 44634, 45555, 46510, 49264, 54822, 55770, 57632, 60430,\n", + " 61348, 63803, 67089, 71635, 78910, 80731, 82553, 84427,\n", + " 87209, 89092, 104839, 107545, 108472, 109397, 112223, 113137,\n", + " 117713, 119527, 121327, 134045, 134972, 144589, 147196, 151784,\n", + " 152735, 161498, 164215, 167811, 168760, 169710, 186506, 190198],\n", + " dtype='int64'), Int64Index([ 3773, 4693, 10366, 13126, 17661, 20411, 21334, 31390,\n", + " 44635, 45556, 46511, 49265, 54823, 55771, 57633, 60431,\n", + " 61349, 63804, 67090, 71636, 78911, 80732, 82554, 84428,\n", + " 87210, 89093, 104840, 107546, 108473, 109398, 112224, 113138,\n", + " 117714, 119528, 121328, 134046, 134973, 144590, 147197, 151785,\n", + " 152736, 161499, 164216, 167812, 168761, 169711, 186507, 190199],\n", + " dtype='int64'), Int64Index([ 3774, 4694, 10367, 13127, 17662, 20412, 21335, 31391,\n", + " 44636, 45557, 46512, 49266, 54824, 55772, 57634, 60432,\n", + " 61350, 63805, 67091, 71637, 78912, 80733, 82555, 84429,\n", + " 87211, 89094, 104841, 107547, 108474, 109399, 112225, 113139,\n", + " 117715, 119529, 121329, 134047, 134974, 144591, 147198, 151786,\n", + " 152737, 161500, 164217, 167813, 168762, 169712, 186508, 190200],\n", + " dtype='int64'), Int64Index([ 3775, 4695, 10368, 13128, 17663, 20413, 21336, 31392,\n", + " 44637, 45558, 46513, 49267, 54825, 55773, 57635, 60433,\n", + " 61351, 63806, 67092, 71638, 78913, 80734, 82556, 84430,\n", + " 87212, 89095, 104842, 107548, 108475, 109400, 112226, 113140,\n", + " 117716, 119530, 121330, 134048, 134975, 144592, 147199, 151787,\n", + " 152738, 161501, 164218, 167814, 168763, 169713, 186509, 190201],\n", + " dtype='int64'), Int64Index([ 3776, 4696, 10369, 13129, 17664, 20414, 21337, 31393,\n", + " 44638, 45559, 46514, 49268, 54826, 55774, 57636, 60434,\n", + " 61352, 63807, 67093, 71639, 78914, 80735, 82557, 84431,\n", + " 87213, 89096, 104843, 107549, 108476, 109401, 112227, 113141,\n", + " 117717, 119531, 121331, 134049, 134976, 144593, 147200, 151788,\n", + " 152739, 161502, 164219, 167815, 168764, 169714, 186510, 190202],\n", + " dtype='int64'), Int64Index([ 3777, 4697, 10370, 13130, 17665, 20415, 21338, 31394,\n", + " 44639, 45560, 46515, 49269, 54827, 55775, 57637, 60435,\n", + " 61353, 63808, 67094, 71640, 78915, 80736, 82558, 84432,\n", + " 87214, 89097, 104844, 107550, 108477, 109402, 112228, 113142,\n", + " 117718, 119532, 121332, 134050, 134977, 144594, 147201, 151789,\n", + " 152740, 161503, 164220, 167816, 168765, 169715, 186511, 190203],\n", + " dtype='int64'), Int64Index([ 3778, 4698, 10371, 13131, 17666, 20416, 21339, 31395,\n", + " 44640, 45561, 46516, 49270, 54828, 55776, 57638, 60436,\n", + " 61354, 63809, 67095, 71641, 78916, 80737, 82559, 84433,\n", + " 87215, 89098, 104845, 107551, 108478, 109403, 112229, 113143,\n", + " 117719, 119533, 121333, 134051, 134978, 144595, 147202, 151790,\n", + " 152741, 161504, 164221, 167817, 168766, 169716, 186512, 190204],\n", + " dtype='int64'), Int64Index([ 3779, 4699, 10372, 13132, 17667, 20417, 21340, 31396,\n", + " 44641, 45562, 46517, 49271, 54829, 55777, 57639, 60437,\n", + " 61355, 63810, 67096, 71642, 78917, 80738, 82560, 84434,\n", + " 87216, 89099, 104846, 107552, 108479, 109404, 112230, 113144,\n", + " 117720, 119534, 121334, 134052, 134979, 144596, 147203, 151791,\n", + " 152742, 161505, 164222, 167818, 168767, 169717, 186513, 190205],\n", + " dtype='int64'), Int64Index([ 3780, 4700, 10373, 13133, 17668, 20418, 21341, 31397,\n", + " 44642, 45563, 46518, 49272, 54830, 55778, 57640, 60438,\n", + " 61356, 63811, 67097, 71643, 78918, 80739, 82561, 84435,\n", + " 87217, 89100, 104847, 107553, 108480, 109405, 112231, 113145,\n", + " 117721, 119535, 121335, 134053, 134980, 144597, 147204, 151792,\n", + " 152743, 161506, 164223, 167819, 168768, 169718, 186514, 190206],\n", + " dtype='int64'), Int64Index([ 3781, 4701, 10374, 13134, 17669, 20419, 21342, 31398,\n", + " 44643, 45564, 46519, 49273, 54831, 55779, 57641, 60439,\n", + " 61357, 63812, 67098, 71644, 78919, 80740, 82562, 84436,\n", + " 87218, 89101, 104848, 107554, 108481, 109406, 112232, 113146,\n", + " 117722, 119536, 121336, 134054, 134981, 144598, 147205, 151793,\n", + " 152744, 161507, 164224, 167820, 168769, 169719, 186515, 190207],\n", + " dtype='int64'), Int64Index([ 3782, 4702, 10375, 13135, 17670, 20420, 21343, 31399,\n", + " 44644, 45565, 46520, 49274, 54832, 55780, 57642, 60440,\n", + " 61358, 63813, 67099, 71645, 78920, 80741, 82563, 84437,\n", + " 87219, 89102, 104849, 107555, 108482, 109407, 112233, 113147,\n", + " 117723, 119537, 121337, 134055, 134982, 144599, 147206, 151794,\n", + " 152745, 161508, 164225, 167821, 168770, 169720, 186516, 190208],\n", + " dtype='int64'), Int64Index([ 3783, 4703, 10376, 13136, 17671, 20421, 21344, 31400,\n", + " 44645, 45566, 46521, 49275, 54833, 55781, 57643, 60441,\n", + " 61359, 63814, 67100, 71646, 78921, 80742, 82564, 84438,\n", + " 87220, 89103, 104850, 107556, 108483, 109408, 112234, 113148,\n", + " 117724, 119538, 121338, 134056, 134983, 144600, 147207, 151795,\n", + " 152746, 161509, 164226, 167822, 168771, 169721, 186517, 190209],\n", + " dtype='int64'), Int64Index([ 3784, 4704, 10377, 13137, 17672, 20422, 21345, 31401,\n", + " 44646, 45567, 46522, 49276, 54834, 55782, 57644, 60442,\n", + " 61360, 63815, 67101, 71647, 78922, 80743, 82565, 84439,\n", + " 87221, 89104, 104851, 107557, 108484, 109409, 112235, 113149,\n", + " 117725, 119539, 121339, 134057, 134984, 144601, 147208, 151796,\n", + " 152747, 161510, 164227, 167823, 168772, 169722, 186518, 190210],\n", + " dtype='int64'), Int64Index([ 3785, 4705, 10378, 13138, 17673, 20423, 21346, 31402,\n", + " 44647, 45568, 46523, 49277, 54835, 55783, 57645, 60443,\n", + " 61361, 63816, 67102, 71648, 78923, 80744, 82566, 84440,\n", + " 87222, 89105, 104852, 107558, 108485, 109410, 112236, 113150,\n", + " 117726, 119540, 121340, 134058, 134985, 144602, 147209, 151797,\n", + " 152748, 161511, 164228, 167824, 168773, 169723, 186519, 190211],\n", + " dtype='int64'), Int64Index([ 3786, 4706, 10379, 13139, 17674, 20424, 21347, 31403,\n", + " 44648, 45569, 46524, 49278, 54836, 55784, 57646, 60444,\n", + " 61362, 63817, 67103, 71649, 78924, 80745, 82567, 84441,\n", + " 87223, 89106, 104853, 107559, 108486, 109411, 112237, 113151,\n", + " 117727, 119541, 121341, 134059, 134986, 144603, 147210, 151798,\n", + " 152749, 161512, 164229, 167825, 168774, 169724, 186520, 190212],\n", + " dtype='int64'), Int64Index([ 3787, 4707, 10380, 13140, 17675, 20425, 21348, 31404,\n", + " 44649, 45570, 46525, 49279, 54837, 55785, 57647, 60445,\n", + " 61363, 63818, 67104, 71650, 78925, 80746, 82568, 84442,\n", + " 87224, 89107, 104854, 107560, 108487, 109412, 112238, 113152,\n", + " 117728, 119542, 121342, 134060, 134987, 144604, 147211, 151799,\n", + " 152750, 161513, 164230, 167826, 168775, 169725, 186521, 190213],\n", + " dtype='int64'), Int64Index([ 3788, 4708, 10381, 13141, 17676, 20426, 21349, 31405,\n", + " 44650, 45571, 46526, 49280, 54838, 55786, 57648, 60446,\n", + " 61364, 63819, 67105, 71651, 78926, 80747, 82569, 84443,\n", + " 87225, 89108, 104855, 107561, 108488, 109413, 112239, 113153,\n", + " 117729, 119543, 121343, 134061, 134988, 144605, 147212, 151800,\n", + " 152751, 161514, 164231, 167827, 168776, 169726, 186522, 190214],\n", + " dtype='int64'), Int64Index([ 3789, 4709, 10382, 13142, 17677, 20427, 21350, 31406,\n", + " 44651, 45572, 46527, 49281, 54839, 55787, 57649, 60447,\n", + " 61365, 63820, 67106, 71652, 78927, 80748, 82570, 84444,\n", + " 87226, 89109, 104856, 107562, 108489, 109414, 112240, 113154,\n", + " 117730, 119544, 121344, 134062, 134989, 144606, 147213, 151801,\n", + " 152752, 161515, 164232, 167828, 168777, 169727, 186523, 190215],\n", + " dtype='int64'), Int64Index([ 3790, 4710, 10383, 13143, 17678, 20428, 21351, 31407,\n", + " 44652, 45573, 46528, 49282, 54840, 55788, 57650, 60448,\n", + " 61366, 63821, 67107, 71653, 78928, 80749, 82571, 84445,\n", + " 87227, 89110, 104857, 107563, 108490, 109415, 112241, 113155,\n", + " 117731, 119545, 121345, 134063, 134990, 144607, 147214, 151802,\n", + " 152753, 161516, 164233, 167829, 168778, 169728, 186524, 190216],\n", + " dtype='int64'), Int64Index([ 3791, 4711, 10384, 13144, 17679, 20429, 21352, 31408,\n", + " 44653, 45574, 46529, 49283, 54841, 55789, 57651, 60449,\n", + " 61367, 63822, 67108, 71654, 78929, 80750, 82572, 84446,\n", + " 87228, 89111, 104858, 107564, 108491, 109416, 112242, 113156,\n", + " 117732, 119546, 121346, 134064, 134991, 144608, 147215, 151803,\n", + " 152754, 161517, 164234, 167830, 168779, 169729, 186525, 190217],\n", + " dtype='int64'), Int64Index([ 3792, 4712, 10385, 13145, 17680, 20430, 21353, 31409,\n", + " 44654, 45575, 46530, 49284, 54842, 55790, 57652, 60450,\n", + " 61368, 63823, 67109, 71655, 78930, 80751, 82573, 84447,\n", + " 87229, 89112, 104859, 107565, 108492, 109417, 112243, 113157,\n", + " 117733, 119547, 121347, 134065, 134992, 144609, 147216, 151804,\n", + " 152755, 161518, 164235, 167831, 168780, 169730, 186526, 190218],\n", + " dtype='int64'), Int64Index([ 3793, 4713, 10386, 13146, 17681, 20431, 21354, 31410,\n", + " 44655, 45576, 46531, 49285, 54843, 55791, 57653, 60451,\n", + " 61369, 63824, 67110, 71656, 78931, 80752, 82574, 84448,\n", + " 87230, 89113, 104860, 107566, 108493, 109418, 112244, 113158,\n", + " 117734, 119548, 121348, 134066, 134993, 144610, 147217, 151805,\n", + " 152756, 161519, 164236, 167832, 168781, 169731, 186527, 190219],\n", + " dtype='int64'), Int64Index([ 3794, 4714, 10387, 13147, 17682, 20432, 21355, 31411,\n", + " 44656, 45577, 46532, 49286, 54844, 55792, 57654, 60452,\n", + " 61370, 63825, 67111, 71657, 78932, 80753, 82575, 84449,\n", + " 87231, 89114, 104861, 107567, 108494, 109419, 112245, 113159,\n", + " 117735, 119549, 121349, 134067, 134994, 144611, 147218, 151806,\n", + " 152757, 161520, 164237, 167833, 168782, 169732, 186528, 190220],\n", + " dtype='int64'), Int64Index([ 3795, 4715, 10388, 13148, 17683, 20433, 21356, 31412,\n", + " 44657, 45578, 46533, 49287, 54845, 55793, 57655, 60453,\n", + " 61371, 63826, 67112, 71658, 78933, 80754, 82576, 84450,\n", + " 87232, 89115, 104862, 107568, 108495, 109420, 112246, 113160,\n", + " 117736, 119550, 121350, 134068, 134995, 144612, 147219, 151807,\n", + " 152758, 161521, 164238, 167834, 168783, 169733, 186529, 190221],\n", + " dtype='int64'), Int64Index([ 3796, 4716, 10389, 13149, 17684, 20434, 21357, 31413,\n", + " 44658, 45579, 46534, 49288, 54846, 55794, 57656, 60454,\n", + " 61372, 63827, 67113, 71659, 78934, 80755, 82577, 84451,\n", + " 87233, 89116, 104863, 107569, 108496, 109421, 112247, 113161,\n", + " 117737, 119551, 121351, 134069, 134996, 144613, 147220, 151808,\n", + " 152759, 161522, 164239, 167835, 168784, 169734, 186530, 190222],\n", + " dtype='int64'), Int64Index([ 3797, 4717, 10390, 13150, 17685, 20435, 21358, 31414,\n", + " 44659, 45580, 46535, 49289, 54847, 55795, 57657, 60455,\n", + " 61373, 63828, 67114, 71660, 78935, 80756, 82578, 84452,\n", + " 87234, 89117, 104864, 107570, 108497, 109422, 112248, 113162,\n", + " 117738, 119552, 121352, 134070, 134997, 144614, 147221, 151809,\n", + " 152760, 161523, 164240, 167836, 168785, 169735, 186531, 190223],\n", + " dtype='int64'), Int64Index([ 3798, 4718, 10391, 13151, 17686, 20436, 21359, 31415,\n", + " 44660, 45581, 46536, 49290, 54848, 55796, 57658, 60456,\n", + " 61374, 63829, 67115, 71661, 78936, 80757, 82579, 84453,\n", + " 87235, 89118, 104865, 107571, 108498, 109423, 112249, 113163,\n", + " 117739, 119553, 121353, 134071, 134998, 144615, 147222, 151810,\n", + " 152761, 161524, 164241, 167837, 168786, 169736, 186532, 190224],\n", + " dtype='int64'), Int64Index([ 3799, 4719, 10392, 13152, 17687, 20437, 21360, 31416,\n", + " 44661, 45582, 46537, 49291, 54849, 55797, 57659, 60457,\n", + " 61375, 63830, 67116, 71662, 78937, 80758, 82580, 84454,\n", + " 87236, 89119, 104866, 107572, 108499, 109424, 112250, 113164,\n", + " 117740, 119554, 121354, 134072, 134999, 144616, 147223, 151811,\n", + " 152762, 161525, 164242, 167838, 168787, 169737, 186533, 190225],\n", + " dtype='int64'), Int64Index([ 3800, 4720, 10393, 13153, 17688, 20438, 21361, 31417,\n", + " 44662, 45583, 46538, 49292, 54850, 55798, 57660, 60458,\n", + " 61376, 63831, 67117, 71663, 78938, 80759, 82581, 84455,\n", + " 87237, 89120, 104867, 107573, 108500, 109425, 112251, 113165,\n", + " 117741, 119555, 121355, 134073, 135000, 144617, 147224, 151812,\n", + " 152763, 161526, 164243, 167839, 168788, 169738, 186534, 190226],\n", + " dtype='int64'), Int64Index([ 3801, 4721, 10394, 13154, 17689, 20439, 21362, 31418,\n", + " 44663, 45584, 46539, 49293, 54851, 55799, 57661, 60459,\n", + " 61377, 63832, 67118, 71664, 78939, 80760, 82582, 84456,\n", + " 87238, 89121, 104868, 107574, 108501, 109426, 112252, 113166,\n", + " 117742, 119556, 121356, 134074, 135001, 144618, 147225, 151813,\n", + " 152764, 161527, 164244, 167840, 168789, 169739, 186535, 190227],\n", + " dtype='int64'), Int64Index([ 3802, 4722, 10395, 13155, 17690, 20440, 21363, 31419,\n", + " 44664, 45585, 46540, 49294, 54852, 55800, 57662, 60460,\n", + " 61378, 63833, 67119, 71665, 78940, 80761, 82583, 84457,\n", + " 87239, 89122, 104869, 107575, 108502, 109427, 112253, 113167,\n", + " 117743, 119557, 121357, 134075, 135002, 144619, 147226, 151814,\n", + " 152765, 161528, 164245, 167841, 168790, 169740, 186536, 190228],\n", + " dtype='int64'), Int64Index([ 3803, 4723, 10396, 13156, 17691, 20441, 21364, 31420,\n", + " 44665, 45586, 46541, 49295, 54853, 55801, 57663, 60461,\n", + " 61379, 63834, 67120, 71666, 78941, 80762, 82584, 84458,\n", + " 87240, 89123, 104870, 107576, 108503, 109428, 112254, 113168,\n", + " 117744, 119558, 121358, 134076, 135003, 144620, 147227, 151815,\n", + " 152766, 161529, 164246, 167842, 168791, 169741, 186537, 190229],\n", + " dtype='int64'), Int64Index([ 3804, 4724, 10397, 13157, 17692, 20442, 21365, 31421,\n", + " 44666, 45587, 46542, 49296, 54854, 55802, 57664, 60462,\n", + " 61380, 63835, 67121, 71667, 78942, 80763, 82585, 84459,\n", + " 87241, 89124, 104871, 107577, 108504, 109429, 112255, 113169,\n", + " 117745, 119559, 121359, 134077, 135004, 144621, 147228, 151816,\n", + " 152767, 161530, 164247, 167843, 168792, 169742, 186538, 190230],\n", + " dtype='int64'), Int64Index([ 3805, 4725, 10398, 13158, 17693, 20443, 21366, 31422,\n", + " 44667, 45588, 46543, 49297, 54855, 55803, 57665, 60463,\n", + " 61381, 63836, 67122, 71668, 78943, 80764, 82586, 84460,\n", + " 87242, 89125, 104872, 107578, 108505, 109430, 112256, 113170,\n", + " 117746, 119560, 121360, 134078, 135005, 144622, 147229, 151817,\n", + " 152768, 161531, 164248, 167844, 168793, 169743, 186539, 190231],\n", + " dtype='int64'), Int64Index([ 3806, 4726, 10399, 13159, 17694, 20444, 21367, 31423,\n", + " 44668, 45589, 46544, 49298, 54856, 55804, 57666, 60464,\n", + " 61382, 63837, 67123, 71669, 78944, 80765, 82587, 84461,\n", + " 87243, 89126, 104873, 107579, 108506, 109431, 112257, 113171,\n", + " 117747, 119561, 121361, 134079, 135006, 144623, 147230, 151818,\n", + " 152769, 161532, 164249, 167845, 168794, 169744, 186540, 190232],\n", + " dtype='int64'), Int64Index([ 3807, 4727, 10400, 13160, 17695, 20445, 21368, 31424,\n", + " 44669, 45590, 46545, 49299, 54857, 55805, 57667, 60465,\n", + " 61383, 63838, 67124, 71670, 78945, 80766, 82588, 84462,\n", + " 87244, 89127, 104874, 107580, 108507, 109432, 112258, 113172,\n", + " 117748, 119562, 121362, 134080, 135007, 144624, 147231, 151819,\n", + " 152770, 161533, 164250, 167846, 168795, 169745, 186541, 190233],\n", + " dtype='int64'), Int64Index([ 3808, 4728, 10401, 13161, 17696, 20446, 21369, 31425,\n", + " 44670, 45591, 46546, 49300, 54858, 55806, 57668, 60466,\n", + " 61384, 63839, 67125, 71671, 78946, 80767, 82589, 84463,\n", + " 87245, 89128, 104875, 107581, 108508, 109433, 112259, 113173,\n", + " 117749, 119563, 121363, 134081, 135008, 144625, 147232, 151820,\n", + " 152771, 161534, 164251, 167847, 168796, 169746, 186542, 190234],\n", + " dtype='int64'), Int64Index([ 3809, 4729, 10402, 13162, 17697, 20447, 21370, 31426,\n", + " 44671, 45592, 46547, 49301, 54859, 55807, 57669, 60467,\n", + " 61385, 63840, 67126, 71672, 78947, 80768, 82590, 84464,\n", + " 87246, 89129, 104876, 107582, 108509, 109434, 112260, 113174,\n", + " 117750, 119564, 121364, 134082, 135009, 144626, 147233, 151821,\n", + " 152772, 161535, 164252, 167848, 168797, 169747, 186543, 190235],\n", + " dtype='int64'), Int64Index([ 3810, 4730, 10403, 13163, 17698, 20448, 21371, 31427,\n", + " 44672, 45593, 46548, 49302, 54860, 55808, 57670, 60468,\n", + " 61386, 63841, 67127, 71673, 78948, 80769, 82591, 84465,\n", + " 87247, 89130, 104877, 107583, 108510, 109435, 112261, 113175,\n", + " 117751, 119565, 121365, 134083, 135010, 144627, 147234, 151822,\n", + " 152773, 161536, 164253, 167849, 168798, 169748, 186544, 190236],\n", + " dtype='int64'), Int64Index([ 3811, 4731, 10404, 13164, 17699, 20449, 21372, 31428,\n", + " 44673, 45594, 46549, 49303, 54861, 55809, 57671, 60469,\n", + " 61387, 63842, 67128, 71674, 78949, 80770, 82592, 84466,\n", + " 87248, 89131, 104878, 107584, 108511, 109436, 112262, 113176,\n", + " 117752, 119566, 121366, 134084, 135011, 144628, 147235, 151823,\n", + " 152774, 161537, 164254, 167850, 168799, 169749, 186545, 190237],\n", + " dtype='int64'), Int64Index([ 3812, 4732, 10405, 13165, 17700, 20450, 21373, 31429,\n", + " 44674, 45595, 46550, 49304, 54862, 55810, 57672, 60470,\n", + " 61388, 63843, 67129, 71675, 78950, 80771, 82593, 84467,\n", + " 87249, 89132, 104879, 107585, 108512, 109437, 112263, 113177,\n", + " 117753, 119567, 121367, 134085, 135012, 144629, 147236, 151824,\n", + " 152775, 161538, 164255, 167851, 168800, 169750, 186546, 190238],\n", + " dtype='int64'), Int64Index([ 3813, 4733, 10406, 13166, 17701, 20451, 21374, 31430,\n", + " 44675, 45596, 46551, 49305, 54863, 55811, 57673, 60471,\n", + " 61389, 63844, 67130, 71676, 78951, 80772, 82594, 84468,\n", + " 87250, 89133, 104880, 107586, 108513, 109438, 112264, 113178,\n", + " 117754, 119568, 121368, 134086, 135013, 144630, 147237, 151825,\n", + " 152776, 161539, 164256, 167852, 168801, 169751, 186547, 190239],\n", + " dtype='int64'), Int64Index([ 3814, 4734, 10407, 13167, 17702, 20452, 21375, 31431,\n", + " 44676, 45597, 46552, 49306, 54864, 55812, 57674, 60472,\n", + " 61390, 63845, 67131, 71677, 78952, 80773, 82595, 84469,\n", + " 87251, 89134, 104881, 107587, 108514, 109439, 112265, 113179,\n", + " 117755, 119569, 121369, 134087, 135014, 144631, 147238, 151826,\n", + " 152777, 161540, 164257, 167853, 168802, 169752, 186548, 190240],\n", + " dtype='int64'), Int64Index([ 3815, 4735, 10408, 13168, 17703, 20453, 21376, 31432,\n", + " 44677, 45598, 46553, 49307, 54865, 55813, 57675, 60473,\n", + " 61391, 63846, 67132, 71678, 78953, 80774, 82596, 84470,\n", + " 87252, 89135, 104882, 107588, 108515, 109440, 112266, 113180,\n", + " 117756, 119570, 121370, 134088, 135015, 144632, 147239, 151827,\n", + " 152778, 161541, 164258, 167854, 168803, 169753, 186549, 190241],\n", + " dtype='int64'), Int64Index([ 3816, 4736, 10409, 13169, 17704, 20454, 21377, 31433,\n", + " 44678, 45599, 46554, 49308, 54866, 55814, 57676, 60474,\n", + " 61392, 63847, 67133, 71679, 78954, 80775, 82597, 84471,\n", + " 87253, 89136, 104883, 107589, 108516, 109441, 112267, 113181,\n", + " 117757, 119571, 121371, 134089, 135016, 144633, 147240, 151828,\n", + " 152779, 161542, 164259, 167855, 168804, 169754, 186550, 190242],\n", + " dtype='int64'), Int64Index([ 3817, 4737, 10410, 13170, 17705, 20455, 21378, 31434,\n", + " 44679, 45600, 46555, 49309, 54867, 55815, 57677, 60475,\n", + " 61393, 63848, 67134, 71680, 78955, 80776, 82598, 84472,\n", + " 87254, 89137, 104884, 107590, 108517, 109442, 112268, 113182,\n", + " 117758, 119572, 121372, 134090, 135017, 144634, 147241, 151829,\n", + " 152780, 161543, 164260, 167856, 168805, 169755, 186551, 190243],\n", + " dtype='int64'), Int64Index([ 3818, 4738, 10411, 13171, 17706, 20456, 21379, 31435,\n", + " 44680, 45601, 46556, 49310, 54868, 55816, 57678, 60476,\n", + " 61394, 63849, 67135, 71681, 78956, 80777, 82599, 84473,\n", + " 87255, 89138, 104885, 107591, 108518, 109443, 112269, 113183,\n", + " 117759, 119573, 121373, 134091, 135018, 144635, 147242, 151830,\n", + " 152781, 161544, 164261, 167857, 168806, 169756, 186552, 190244],\n", + " dtype='int64'), Int64Index([ 3819, 4739, 10412, 13172, 17707, 20457, 21380, 31436,\n", + " 44681, 45602, 46557, 49311, 54869, 55817, 57679, 60477,\n", + " 61395, 63850, 67136, 71682, 78957, 80778, 82600, 84474,\n", + " 87256, 89139, 104886, 107592, 108519, 109444, 112270, 113184,\n", + " 117760, 119574, 121374, 134092, 135019, 144636, 147243, 151831,\n", + " 152782, 161545, 164262, 167858, 168807, 169757, 186553, 190245],\n", + " dtype='int64'), Int64Index([ 3820, 4740, 10413, 13173, 17708, 20458, 21381, 31437,\n", + " 44682, 45603, 46558, 49312, 54870, 55818, 57680, 60478,\n", + " 61396, 63851, 67137, 71683, 78958, 80779, 82601, 84475,\n", + " 87257, 89140, 104887, 107593, 108520, 109445, 112271, 113185,\n", + " 117761, 119575, 121375, 134093, 135020, 144637, 147244, 151832,\n", + " 152783, 161546, 164263, 167859, 168808, 169758, 186554, 190246],\n", + " dtype='int64'), Int64Index([ 3821, 4741, 10414, 13174, 17709, 20459, 21382, 31438,\n", + " 44683, 45604, 46559, 49313, 54871, 55819, 57681, 60479,\n", + " 61397, 63852, 67138, 71684, 78959, 80780, 82602, 84476,\n", + " 87258, 89141, 104888, 107594, 108521, 109446, 112272, 113186,\n", + " 117762, 119576, 121376, 134094, 135021, 144638, 147245, 151833,\n", + " 152784, 161547, 164264, 167860, 168809, 169759, 186555, 190247],\n", + " dtype='int64'), Int64Index([ 3822, 4742, 10415, 13175, 17710, 20460, 21383, 31439,\n", + " 44684, 45605, 46560, 49314, 54872, 55820, 57682, 60480,\n", + " 61398, 63853, 67139, 71685, 78960, 80781, 82603, 84477,\n", + " 87259, 89142, 104889, 107595, 108522, 109447, 112273, 113187,\n", + " 117763, 119577, 121377, 134095, 135022, 144639, 147246, 151834,\n", + " 152785, 161548, 164265, 167861, 168810, 169760, 186556, 190248],\n", + " dtype='int64'), Int64Index([ 3823, 4743, 10416, 13176, 17711, 20461, 21384, 31440,\n", + " 44685, 45606, 46561, 49315, 54873, 55821, 57683, 60481,\n", + " 61399, 63854, 67140, 71686, 78961, 80782, 82604, 84478,\n", + " 87260, 89143, 104890, 107596, 108523, 109448, 112274, 113188,\n", + " 117764, 119578, 121378, 134096, 135023, 144640, 147247, 151835,\n", + " 152786, 161549, 164266, 167862, 168811, 169761, 186557, 190249],\n", + " dtype='int64'), Int64Index([ 3824, 4744, 10417, 13177, 17712, 20462, 21385, 31441,\n", + " 44686, 45607, 46562, 49316, 54874, 55822, 57684, 60482,\n", + " 61400, 63855, 67141, 71687, 78962, 80783, 82605, 84479,\n", + " 87261, 89144, 104891, 107597, 108524, 109449, 112275, 113189,\n", + " 117765, 119579, 121379, 134097, 135024, 144641, 147248, 151836,\n", + " 152787, 161550, 164267, 167863, 168812, 169762, 186558, 190250],\n", + " dtype='int64'), Int64Index([ 3825, 4745, 10418, 13178, 17713, 20463, 21386, 31442,\n", + " 44687, 45608, 46563, 49317, 54875, 55823, 57685, 60483,\n", + " 61401, 63856, 67142, 71688, 78963, 80784, 82606, 84480,\n", + " 87262, 89145, 104892, 107598, 108525, 109450, 112276, 113190,\n", + " 117766, 119580, 121380, 134098, 135025, 144642, 147249, 151837,\n", + " 152788, 161551, 164268, 167864, 168813, 169763, 186559, 190251],\n", + " dtype='int64'), Int64Index([ 3826, 4746, 10419, 13179, 17714, 20464, 21387, 31443,\n", + " 44688, 45609, 46564, 49318, 54876, 55824, 57686, 60484,\n", + " 61402, 63857, 67143, 71689, 78964, 80785, 82607, 84481,\n", + " 87263, 89146, 104893, 107599, 108526, 109451, 112277, 113191,\n", + " 117767, 119581, 121381, 134099, 135026, 144643, 147250, 151838,\n", + " 152789, 161552, 164269, 167865, 168814, 169764, 186560, 190252],\n", + " dtype='int64'), Int64Index([ 3827, 4747, 10420, 13180, 17715, 20465, 21388, 31444,\n", + " 44689, 45610, 46565, 49319, 54877, 55825, 57687, 60485,\n", + " 61403, 63858, 67144, 71690, 78965, 80786, 82608, 84482,\n", + " 87264, 89147, 104894, 107600, 108527, 109452, 112278, 113192,\n", + " 117768, 119582, 121382, 134100, 135027, 144644, 147251, 151839,\n", + " 152790, 161553, 164270, 167866, 168815, 169765, 186561, 190253],\n", + " dtype='int64'), Int64Index([ 3828, 4748, 10421, 13181, 17716, 20466, 21389, 31445,\n", + " 44690, 45611, 46566, 49320, 54878, 55826, 57688, 60486,\n", + " 61404, 63859, 67145, 71691, 78966, 80787, 82609, 84483,\n", + " 87265, 89148, 104895, 107601, 108528, 109453, 112279, 113193,\n", + " 117769, 119583, 121383, 134101, 135028, 144645, 147252, 151840,\n", + " 152791, 161554, 164271, 167867, 168816, 169766, 186562, 190254],\n", + " dtype='int64'), Int64Index([ 3829, 4749, 10422, 13182, 17717, 20467, 21390, 31446,\n", + " 44691, 45612, 46567, 49321, 54879, 55827, 57689, 60487,\n", + " 61405, 63860, 67146, 71692, 78967, 80788, 82610, 84484,\n", + " 87266, 89149, 104896, 107602, 108529, 109454, 112280, 113194,\n", + " 117770, 119584, 121384, 134102, 135029, 144646, 147253, 151841,\n", + " 152792, 161555, 164272, 167868, 168817, 169767, 186563, 190255],\n", + " dtype='int64'), Int64Index([ 3830, 4750, 10423, 13183, 17718, 20468, 21391, 31447,\n", + " 44692, 45613, 46568, 49322, 54880, 55828, 57690, 60488,\n", + " 61406, 63861, 67147, 71693, 78968, 80789, 82611, 84485,\n", + " 87267, 89150, 104897, 107603, 108530, 109455, 112281, 113195,\n", + " 117771, 119585, 121385, 134103, 135030, 144647, 147254, 151842,\n", + " 152793, 161556, 164273, 167869, 168818, 169768, 186564, 190256],\n", + " dtype='int64'), Int64Index([ 3831, 4751, 10424, 13184, 17719, 20469, 21392, 31448,\n", + " 44693, 45614, 46569, 49323, 54881, 55829, 57691, 60489,\n", + " 61407, 63862, 67148, 71694, 78969, 80790, 82612, 84486,\n", + " 87268, 89151, 104898, 107604, 108531, 109456, 112282, 113196,\n", + " 117772, 119586, 121386, 134104, 135031, 144648, 147255, 151843,\n", + " 152794, 161557, 164274, 167870, 168819, 169769, 186565, 190257],\n", + " dtype='int64'), Int64Index([ 3832, 4752, 10425, 13185, 17720, 20470, 21393, 31449,\n", + " 44694, 45615, 46570, 49324, 54882, 55830, 57692, 60490,\n", + " 61408, 63863, 67149, 71695, 78970, 80791, 82613, 84487,\n", + " 87269, 89152, 104899, 107605, 108532, 109457, 112283, 113197,\n", + " 117773, 119587, 121387, 134105, 135032, 144649, 147256, 151844,\n", + " 152795, 161558, 164275, 167871, 168820, 169770, 186566, 190258],\n", + " dtype='int64'), Int64Index([ 3833, 4753, 10426, 13186, 17721, 20471, 21394, 31450,\n", + " 44695, 45616, 46571, 49325, 54883, 55831, 57693, 60491,\n", + " 61409, 63864, 67150, 71696, 78971, 80792, 82614, 84488,\n", + " 87270, 89153, 104900, 107606, 108533, 109458, 112284, 113198,\n", + " 117774, 119588, 121388, 134106, 135033, 144650, 147257, 151845,\n", + " 152796, 161559, 164276, 167872, 168821, 169771, 186567, 190259],\n", + " dtype='int64'), Int64Index([ 3834, 4754, 10427, 13187, 17722, 20472, 21395, 31451,\n", + " 44696, 45617, 46572, 49326, 54884, 55832, 57694, 60492,\n", + " 61410, 63865, 67151, 71697, 78972, 80793, 82615, 84489,\n", + " 87271, 89154, 104901, 107607, 108534, 109459, 112285, 113199,\n", + " 117775, 119589, 121389, 134107, 135034, 144651, 147258, 151846,\n", + " 152797, 161560, 164277, 167873, 168822, 169772, 186568, 190260],\n", + " dtype='int64'), Int64Index([ 3835, 4755, 10428, 13188, 17723, 20473, 21396, 31452,\n", + " 44697, 45618, 46573, 49327, 54885, 55833, 57695, 60493,\n", + " 61411, 63866, 67152, 71698, 78973, 80794, 82616, 84490,\n", + " 87272, 89155, 104902, 107608, 108535, 109460, 112286, 113200,\n", + " 117776, 119590, 121390, 134108, 135035, 144652, 147259, 151847,\n", + " 152798, 161561, 164278, 167874, 168823, 169773, 186569, 190261],\n", + " dtype='int64'), Int64Index([ 3836, 4756, 10429, 13189, 17724, 20474, 21397, 31453,\n", + " 44698, 45619, 46574, 49328, 54886, 55834, 57696, 60494,\n", + " 61412, 63867, 67153, 71699, 78974, 80795, 82617, 84491,\n", + " 87273, 89156, 104903, 107609, 108536, 109461, 112287, 113201,\n", + " 117777, 119591, 121391, 134109, 135036, 144653, 147260, 151848,\n", + " 152799, 161562, 164279, 167875, 168824, 169774, 186570, 190262],\n", + " dtype='int64'), Int64Index([ 3837, 4757, 10430, 13190, 17725, 20475, 21398, 31454,\n", + " 44699, 45620, 46575, 49329, 54887, 55835, 57697, 60495,\n", + " 61413, 63868, 67154, 71700, 78975, 80796, 82618, 84492,\n", + " 87274, 89157, 104904, 107610, 108537, 109462, 112288, 113202,\n", + " 117778, 119592, 121392, 134110, 135037, 144654, 147261, 151849,\n", + " 152800, 161563, 164280, 167876, 168825, 169775, 186571, 190263],\n", + " dtype='int64'), Int64Index([ 3838, 4758, 10431, 13191, 17726, 20476, 21399, 31455,\n", + " 44700, 45621, 46576, 49330, 54888, 55836, 57698, 60496,\n", + " 61414, 63869, 67155, 71701, 78976, 80797, 82619, 84493,\n", + " 87275, 89158, 104905, 107611, 108538, 109463, 112289, 113203,\n", + " 117779, 119593, 121393, 134111, 135038, 144655, 147262, 151850,\n", + " 152801, 161564, 164281, 167877, 168826, 169776, 186572, 190264],\n", + " dtype='int64'), Int64Index([ 3839, 4759, 10432, 13192, 17727, 20477, 21400, 31456,\n", + " 44701, 45622, 46577, 49331, 54889, 55837, 57699, 60497,\n", + " 61415, 63870, 67156, 71702, 78977, 80798, 82620, 84494,\n", + " 87276, 89159, 104906, 107612, 108539, 109464, 112290, 113204,\n", + " 117780, 119594, 121394, 134112, 135039, 144656, 147263, 151851,\n", + " 152802, 161565, 164282, 167878, 168827, 169777, 186573, 190265],\n", + " dtype='int64'), Int64Index([ 3840, 4760, 10433, 13193, 17728, 20478, 21401, 31457,\n", + " 44702, 45623, 46578, 49332, 54890, 55838, 57700, 60498,\n", + " 61416, 63871, 67157, 71703, 78978, 80799, 82621, 84495,\n", + " 87277, 89160, 104907, 107613, 108540, 109465, 112291, 113205,\n", + " 117781, 119595, 121395, 134113, 135040, 144657, 147264, 151852,\n", + " 152803, 161566, 164283, 167879, 168828, 169778, 186574, 190266],\n", + " dtype='int64'), Int64Index([ 3841, 4761, 10434, 13194, 17729, 20479, 21402, 31458,\n", + " 44703, 45624, 46579, 49333, 54891, 55839, 57701, 60499,\n", + " 61417, 63872, 67158, 71704, 78979, 80800, 82622, 84496,\n", + " 87278, 89161, 104908, 107614, 108541, 109466, 112292, 113206,\n", + " 117782, 119596, 121396, 134114, 135041, 144658, 147265, 151853,\n", + " 152804, 161567, 164284, 167880, 168829, 169779, 186575, 190267],\n", + " dtype='int64'), Int64Index([ 3842, 4762, 10435, 13195, 17730, 20480, 21403, 31459,\n", + " 44704, 45625, 46580, 49334, 54892, 55840, 57702, 60500,\n", + " 61418, 63873, 67159, 71705, 78980, 80801, 82623, 84497,\n", + " 87279, 89162, 104909, 107615, 108542, 109467, 112293, 113207,\n", + " 117783, 119597, 121397, 134115, 135042, 144659, 147266, 151854,\n", + " 152805, 161568, 164285, 167881, 168830, 169780, 186576, 190268],\n", + " dtype='int64'), Int64Index([ 3843, 4763, 10436, 13196, 17731, 20481, 21404, 31460,\n", + " 44705, 45626, 46581, 49335, 54893, 55841, 57703, 60501,\n", + " 61419, 63874, 67160, 71706, 78981, 80802, 82624, 84498,\n", + " 87280, 89163, 104910, 107616, 108543, 109468, 112294, 113208,\n", + " 117784, 119598, 121398, 134116, 135043, 144660, 147267, 151855,\n", + " 152806, 161569, 164286, 167882, 168831, 169781, 186577, 190269],\n", + " dtype='int64'), Int64Index([ 3844, 4764, 10437, 13197, 17732, 20482, 21405, 31461,\n", + " 44706, 45627, 46582, 49336, 54894, 55842, 57704, 60502,\n", + " 61420, 63875, 67161, 71707, 78982, 80803, 82625, 84499,\n", + " 87281, 89164, 104911, 107617, 108544, 109469, 112295, 113209,\n", + " 117785, 119599, 121399, 134117, 135044, 144661, 147268, 151856,\n", + " 152807, 161570, 164287, 167883, 168832, 169782, 186578, 190270],\n", + " dtype='int64'), Int64Index([ 3845, 4765, 10438, 13198, 17733, 20483, 21406, 31462,\n", + " 44707, 45628, 46583, 49337, 54895, 55843, 57705, 60503,\n", + " 61421, 63876, 67162, 71708, 78983, 80804, 82626, 84500,\n", + " 87282, 89165, 104912, 107618, 108545, 109470, 112296, 113210,\n", + " 117786, 119600, 121400, 134118, 135045, 144662, 147269, 151857,\n", + " 152808, 161571, 164288, 167884, 168833, 169783, 186579, 190271],\n", + " dtype='int64'), Int64Index([ 3846, 4766, 10439, 13199, 17734, 20484, 21407, 31463,\n", + " 44708, 45629, 46584, 49338, 54896, 55844, 57706, 60504,\n", + " 61422, 63877, 67163, 71709, 78984, 80805, 82627, 84501,\n", + " 87283, 89166, 104913, 107619, 108546, 109471, 112297, 113211,\n", + " 117787, 119601, 121401, 134119, 135046, 144663, 147270, 151858,\n", + " 152809, 161572, 164289, 167885, 168834, 169784, 186580, 190272],\n", + " dtype='int64'), Int64Index([ 3847, 4767, 10440, 13200, 17735, 20485, 21408, 31464,\n", + " 44709, 45630, 46585, 49339, 54897, 55845, 57707, 60505,\n", + " 61423, 63878, 67164, 71710, 78985, 80806, 82628, 84502,\n", + " 87284, 89167, 104914, 107620, 108547, 109472, 112298, 113212,\n", + " 117788, 119602, 121402, 134120, 135047, 144664, 147271, 151859,\n", + " 152810, 161573, 164290, 167886, 168835, 169785, 186581, 190273],\n", + " dtype='int64'), Int64Index([ 3848, 4768, 10441, 13201, 17736, 20486, 21409, 31465,\n", + " 44710, 45631, 46586, 49340, 54898, 55846, 57708, 60506,\n", + " 61424, 63879, 67165, 71711, 78986, 80807, 82629, 84503,\n", + " 87285, 89168, 104915, 107621, 108548, 109473, 112299, 113213,\n", + " 117789, 119603, 121403, 134121, 135048, 144665, 147272, 151860,\n", + " 152811, 161574, 164291, 167887, 168836, 169786, 186582, 190274],\n", + " dtype='int64'), Int64Index([ 3849, 4769, 10442, 13202, 17737, 20487, 21410, 31466,\n", + " 44711, 45632, 46587, 49341, 54899, 55847, 57709, 60507,\n", + " 61425, 63880, 67166, 71712, 78987, 80808, 82630, 84504,\n", + " 87286, 89169, 104916, 107622, 108549, 109474, 112300, 113214,\n", + " 117790, 119604, 121404, 134122, 135049, 144666, 147273, 151861,\n", + " 152812, 161575, 164292, 167888, 168837, 169787, 186583, 190275],\n", + " dtype='int64'), Int64Index([ 3850, 4770, 10443, 13203, 17738, 20488, 21411, 31467,\n", + " 44712, 45633, 46588, 49342, 54900, 55848, 57710, 60508,\n", + " 61426, 63881, 67167, 71713, 78988, 80809, 82631, 84505,\n", + " 87287, 89170, 104917, 107623, 108550, 109475, 112301, 113215,\n", + " 117791, 119605, 121405, 134123, 135050, 144667, 147274, 151862,\n", + " 152813, 161576, 164293, 167889, 168838, 169788, 186584, 190276],\n", + " dtype='int64'), Int64Index([ 3851, 4771, 10444, 13204, 17739, 20489, 21412, 31468,\n", + " 44713, 45634, 46589, 49343, 54901, 55849, 57711, 60509,\n", + " 61427, 63882, 67168, 71714, 78989, 80810, 82632, 84506,\n", + " 87288, 89171, 104918, 107624, 108551, 109476, 112302, 113216,\n", + " 117792, 119606, 121406, 134124, 135051, 144668, 147275, 151863,\n", + " 152814, 161577, 164294, 167890, 168839, 169789, 186585, 190277],\n", + " dtype='int64'), Int64Index([ 3852, 4772, 10445, 13205, 17740, 20490, 21413, 31469,\n", + " 44714, 45635, 46590, 49344, 54902, 55850, 57712, 60510,\n", + " 61428, 63883, 67169, 71715, 78990, 80811, 82633, 84507,\n", + " 87289, 89172, 104919, 107625, 108552, 109477, 112303, 113217,\n", + " 117793, 119607, 121407, 134125, 135052, 144669, 147276, 151864,\n", + " 152815, 161578, 164295, 167891, 168840, 169790, 186586, 190278],\n", + " dtype='int64'), Int64Index([ 3853, 4773, 10446, 13206, 17741, 20491, 21414, 31470,\n", + " 44715, 45636, 46591, 49345, 54903, 55851, 57713, 60511,\n", + " 61429, 63884, 67170, 71716, 78991, 80812, 82634, 84508,\n", + " 87290, 89173, 104920, 107626, 108553, 109478, 112304, 113218,\n", + " 117794, 119608, 121408, 134126, 135053, 144670, 147277, 151865,\n", + " 152816, 161579, 164296, 167892, 168841, 169791, 186587, 190279],\n", + " dtype='int64'), Int64Index([ 3854, 4774, 10447, 13207, 17742, 20492, 21415, 31471,\n", + " 44716, 45637, 46592, 49346, 54904, 55852, 57714, 60512,\n", + " 61430, 63885, 67171, 71717, 78992, 80813, 82635, 84509,\n", + " 87291, 89174, 104921, 107627, 108554, 109479, 112305, 113219,\n", + " 117795, 119609, 121409, 134127, 135054, 144671, 147278, 151866,\n", + " 152817, 161580, 164297, 167893, 168842, 169792, 186588, 190280],\n", + " dtype='int64'), Int64Index([ 3855, 4775, 10448, 13208, 17743, 20493, 21416, 31472,\n", + " 44717, 45638, 46593, 49347, 54905, 55853, 57715, 60513,\n", + " 61431, 63886, 67172, 71718, 78993, 80814, 82636, 84510,\n", + " 87292, 89175, 104922, 107628, 108555, 109480, 112306, 113220,\n", + " 117796, 119610, 121410, 134128, 135055, 144672, 147279, 151867,\n", + " 152818, 161581, 164298, 167894, 168843, 169793, 186589, 190281],\n", + " dtype='int64'), Int64Index([ 3856, 4776, 10449, 13209, 17744, 20494, 21417, 31473,\n", + " 44718, 45639, 46594, 49348, 54906, 55854, 57716, 60514,\n", + " 61432, 63887, 67173, 71719, 78994, 80815, 82637, 84511,\n", + " 87293, 89176, 104923, 107629, 108556, 109481, 112307, 113221,\n", + " 117797, 119611, 121411, 134129, 135056, 144673, 147280, 151868,\n", + " 152819, 161582, 164299, 167895, 168844, 169794, 186590, 190282],\n", + " dtype='int64'), Int64Index([ 3857, 4777, 10450, 13210, 17745, 20495, 21418, 31474,\n", + " 44719, 45640, 46595, 49349, 54907, 55855, 57717, 60515,\n", + " 61433, 63888, 67174, 71720, 78995, 80816, 82638, 84512,\n", + " 87294, 89177, 104924, 107630, 108557, 109482, 112308, 113222,\n", + " 117798, 119612, 121412, 134130, 135057, 144674, 147281, 151869,\n", + " 152820, 161583, 164300, 167896, 168845, 169795, 186591, 190283],\n", + " dtype='int64'), Int64Index([ 3858, 4778, 10451, 13211, 17746, 20496, 21419, 31475,\n", + " 44720, 45641, 46596, 49350, 54908, 55856, 57718, 60516,\n", + " 61434, 63889, 67175, 71721, 78996, 80817, 82639, 84513,\n", + " 87295, 89178, 104925, 107631, 108558, 109483, 112309, 113223,\n", + " 117799, 119613, 121413, 134131, 135058, 144675, 147282, 151870,\n", + " 152821, 161584, 164301, 167897, 168846, 169796, 186592, 190284],\n", + " dtype='int64'), Int64Index([ 3859, 4779, 10452, 13212, 17747, 20497, 21420, 31476,\n", + " 44721, 45642, 46597, 49351, 54909, 55857, 57719, 60517,\n", + " 61435, 63890, 67176, 71722, 78997, 80818, 82640, 84514,\n", + " 87296, 89179, 104926, 107632, 108559, 109484, 112310, 113224,\n", + " 117800, 119614, 121414, 134132, 135059, 144676, 147283, 151871,\n", + " 152822, 161585, 164302, 167898, 168847, 169797, 186593, 190285],\n", + " dtype='int64'), Int64Index([ 3860, 4780, 10453, 13213, 17748, 20498, 21421, 31477,\n", + " 44722, 45643, 46598, 49352, 54910, 55858, 57720, 60518,\n", + " 61436, 63891, 67177, 71723, 78998, 80819, 82641, 84515,\n", + " 87297, 89180, 104927, 107633, 108560, 109485, 112311, 113225,\n", + " 117801, 119615, 121415, 134133, 135060, 144677, 147284, 151872,\n", + " 152823, 161586, 164303, 167899, 168848, 169798, 186594, 190286],\n", + " dtype='int64'), Int64Index([ 3861, 4781, 10454, 13214, 17749, 20499, 21422, 31478,\n", + " 44723, 45644, 46599, 49353, 54911, 55859, 57721, 60519,\n", + " 61437, 63892, 67178, 71724, 78999, 80820, 82642, 84516,\n", + " 87298, 89181, 104928, 107634, 108561, 109486, 112312, 113226,\n", + " 117802, 119616, 121416, 134134, 135061, 144678, 147285, 151873,\n", + " 152824, 161587, 164304, 167900, 168849, 169799, 186595, 190287],\n", + " dtype='int64'), Int64Index([ 3862, 4782, 10455, 13215, 17750, 20500, 21423, 31479,\n", + " 44724, 45645, 46600, 49354, 54912, 55860, 57722, 60520,\n", + " 61438, 63893, 67179, 71725, 79000, 80821, 82643, 84517,\n", + " 87299, 89182, 104929, 107635, 108562, 109487, 112313, 113227,\n", + " 117803, 119617, 121417, 134135, 135062, 144679, 147286, 151874,\n", + " 152825, 161588, 164305, 167901, 168850, 169800, 186596, 190288],\n", + " dtype='int64'), Int64Index([ 3863, 4783, 10456, 13216, 17751, 20501, 21424, 31480,\n", + " 44725, 45646, 46601, 49355, 54913, 55861, 57723, 60521,\n", + " 61439, 63894, 67180, 71726, 79001, 80822, 82644, 84518,\n", + " 87300, 89183, 104930, 107636, 108563, 109488, 112314, 113228,\n", + " 117804, 119618, 121418, 134136, 135063, 144680, 147287, 151875,\n", + " 152826, 161589, 164306, 167902, 168851, 169801, 186597, 190289],\n", + " dtype='int64'), Int64Index([ 3864, 4784, 10457, 13217, 17752, 20502, 21425, 31481,\n", + " 44726, 45647, 46602, 49356, 54914, 55862, 57724, 60522,\n", + " 61440, 63895, 67181, 71727, 79002, 80823, 82645, 84519,\n", + " 87301, 89184, 104931, 107637, 108564, 109489, 112315, 113229,\n", + " 117805, 119619, 121419, 134137, 135064, 144681, 147288, 151876,\n", + " 152827, 161590, 164307, 167903, 168852, 169802, 186598, 190290],\n", + " dtype='int64'), Int64Index([ 3865, 4785, 10458, 13218, 17753, 20503, 21426, 31482,\n", + " 44727, 45648, 46603, 49357, 54915, 55863, 57725, 60523,\n", + " 61441, 63896, 67182, 71728, 79003, 80824, 82646, 84520,\n", + " 87302, 89185, 104932, 107638, 108565, 109490, 112316, 113230,\n", + " 117806, 119620, 121420, 134138, 135065, 144682, 147289, 151877,\n", + " 152828, 161591, 164308, 167904, 168853, 169803, 186599, 190291],\n", + " dtype='int64'), Int64Index([ 3866, 4786, 10459, 13219, 17754, 20504, 21427, 31483,\n", + " 44728, 45649, 46604, 49358, 54916, 55864, 57726, 60524,\n", + " 61442, 63897, 67183, 71729, 79004, 80825, 82647, 84521,\n", + " 87303, 89186, 104933, 107639, 108566, 109491, 112317, 113231,\n", + " 117807, 119621, 121421, 134139, 135066, 144683, 147290, 151878,\n", + " 152829, 161592, 164309, 167905, 168854, 169804, 186600, 190292],\n", + " dtype='int64'), Int64Index([ 3867, 4787, 10460, 13220, 17755, 20505, 21428, 31484,\n", + " 44729, 45650, 46605, 49359, 54917, 55865, 57727, 60525,\n", + " 61443, 63898, 67184, 71730, 79005, 80826, 82648, 84522,\n", + " 87304, 89187, 104934, 107640, 108567, 109492, 112318, 113232,\n", + " 117808, 119622, 121422, 134140, 135067, 144684, 147291, 151879,\n", + " 152830, 161593, 164310, 167906, 168855, 169805, 186601, 190293],\n", + " dtype='int64'), Int64Index([ 3868, 4788, 10461, 13221, 17756, 20506, 21429, 31485,\n", + " 44730, 45651, 46606, 49360, 54918, 55866, 57728, 60526,\n", + " 61444, 63899, 67185, 71731, 79006, 80827, 82649, 84523,\n", + " 87305, 89188, 104935, 107641, 108568, 109493, 112319, 113233,\n", + " 117809, 119623, 121423, 134141, 135068, 144685, 147292, 151880,\n", + " 152831, 161594, 164311, 167907, 168856, 169806, 186602, 190294],\n", + " dtype='int64'), Int64Index([ 3869, 4789, 10462, 13222, 17757, 20507, 21430, 31486,\n", + " 44731, 45652, 46607, 49361, 54919, 55867, 57729, 60527,\n", + " 61445, 63900, 67186, 71732, 79007, 80828, 82650, 84524,\n", + " 87306, 89189, 104936, 107642, 108569, 109494, 112320, 113234,\n", + " 117810, 119624, 121424, 134142, 135069, 144686, 147293, 151881,\n", + " 152832, 161595, 164312, 167908, 168857, 169807, 186603, 190295],\n", + " dtype='int64'), Int64Index([ 3870, 4790, 10463, 13223, 17758, 20508, 21431, 31487,\n", + " 44732, 45653, 46608, 49362, 54920, 55868, 57730, 60528,\n", + " 61446, 63901, 67187, 71733, 79008, 80829, 82651, 84525,\n", + " 87307, 89190, 104937, 107643, 108570, 109495, 112321, 113235,\n", + " 117811, 119625, 121425, 134143, 135070, 144687, 147294, 151882,\n", + " 152833, 161596, 164313, 167909, 168858, 169808, 186604, 190296],\n", + " dtype='int64'), Int64Index([ 3871, 4791, 10464, 13224, 17759, 20509, 21432, 31488,\n", + " 44733, 45654, 46609, 49363, 54921, 55869, 57731, 60529,\n", + " 61447, 63902, 67188, 71734, 79009, 80830, 82652, 84526,\n", + " 87308, 89191, 104938, 107644, 108571, 109496, 112322, 113236,\n", + " 117812, 119626, 121426, 134144, 135071, 144688, 147295, 151883,\n", + " 152834, 161597, 164314, 167910, 168859, 169809, 186605, 190297],\n", + " dtype='int64'), Int64Index([ 3872, 4792, 10465, 13225, 17760, 20510, 21433, 31489,\n", + " 44734, 45655, 46610, 49364, 54922, 55870, 57732, 60530,\n", + " 61448, 63903, 67189, 71735, 79010, 80831, 82653, 84527,\n", + " 87309, 89192, 104939, 107645, 108572, 109497, 112323, 113237,\n", + " 117813, 119627, 121427, 134145, 135072, 144689, 147296, 151884,\n", + " 152835, 161598, 164315, 167911, 168860, 169810, 186606, 190298],\n", + " dtype='int64'), Int64Index([ 3873, 4793, 10466, 13226, 17761, 20511, 21434, 31490,\n", + " 44735, 45656, 46611, 49365, 54923, 55871, 57733, 60531,\n", + " 61449, 63904, 67190, 71736, 79011, 80832, 82654, 84528,\n", + " 87310, 89193, 104940, 107646, 108573, 109498, 112324, 113238,\n", + " 117814, 119628, 121428, 134146, 135073, 144690, 147297, 151885,\n", + " 152836, 161599, 164316, 167912, 168861, 169811, 186607, 190299],\n", + " dtype='int64'), Int64Index([ 3874, 4794, 10467, 13227, 17762, 20512, 21435, 31491,\n", + " 44736, 45657, 46612, 49366, 54924, 55872, 57734, 60532,\n", + " 61450, 63905, 67191, 71737, 79012, 80833, 82655, 84529,\n", + " 87311, 89194, 104941, 107647, 108574, 109499, 112325, 113239,\n", + " 117815, 119629, 121429, 134147, 135074, 144691, 147298, 151886,\n", + " 152837, 161600, 164317, 167913, 168862, 169812, 186608, 190300],\n", + " dtype='int64'), Int64Index([ 3875, 4795, 10468, 13228, 17763, 20513, 21436, 31492,\n", + " 44737, 45658, 46613, 49367, 54925, 55873, 57735, 60533,\n", + " 61451, 63906, 67192, 71738, 79013, 80834, 82656, 84530,\n", + " 87312, 89195, 104942, 107648, 108575, 109500, 112326, 113240,\n", + " 117816, 119630, 121430, 134148, 135075, 144692, 147299, 151887,\n", + " 152838, 161601, 164318, 167914, 168863, 169813, 186609, 190301],\n", + " dtype='int64'), Int64Index([ 3876, 4796, 10469, 13229, 17764, 20514, 21437, 31493,\n", + " 44738, 45659, 46614, 49368, 54926, 55874, 57736, 60534,\n", + " 61452, 63907, 67193, 71739, 79014, 80835, 82657, 84531,\n", + " 87313, 89196, 104943, 107649, 108576, 109501, 112327, 113241,\n", + " 117817, 119631, 121431, 134149, 135076, 144693, 147300, 151888,\n", + " 152839, 161602, 164319, 167915, 168864, 169814, 186610, 190302],\n", + " dtype='int64'), Int64Index([ 3877, 4797, 10470, 13230, 17765, 20515, 21438, 31494,\n", + " 44739, 45660, 46615, 49369, 54927, 55875, 57737, 60535,\n", + " 61453, 63908, 67194, 71740, 79015, 80836, 82658, 84532,\n", + " 87314, 89197, 104944, 107650, 108577, 109502, 112328, 113242,\n", + " 117818, 119632, 121432, 134150, 135077, 144694, 147301, 151889,\n", + " 152840, 161603, 164320, 167916, 168865, 169815, 186611, 190303],\n", + " dtype='int64'), Int64Index([ 3878, 4798, 10471, 13231, 17766, 20516, 21439, 31495,\n", + " 44740, 45661, 46616, 49370, 54928, 55876, 57738, 60536,\n", + " 61454, 63909, 67195, 71741, 79016, 80837, 82659, 84533,\n", + " 87315, 89198, 104945, 107651, 108578, 109503, 112329, 113243,\n", + " 117819, 119633, 121433, 134151, 135078, 144695, 147302, 151890,\n", + " 152841, 161604, 164321, 167917, 168866, 169816, 186612, 190304],\n", + " dtype='int64'), Int64Index([ 3879, 4799, 10472, 13232, 17767, 20517, 21440, 31496,\n", + " 44741, 45662, 46617, 49371, 54929, 55877, 57739, 60537,\n", + " 61455, 63910, 67196, 71742, 79017, 80838, 82660, 84534,\n", + " 87316, 89199, 104946, 107652, 108579, 109504, 112330, 113244,\n", + " 117820, 119634, 121434, 134152, 135079, 144696, 147303, 151891,\n", + " 152842, 161605, 164322, 167918, 168867, 169817, 186613, 190305],\n", + " dtype='int64'), Int64Index([ 3880, 4800, 10473, 13233, 17768, 20518, 21441, 31497,\n", + " 44742, 45663, 46618, 49372, 54930, 55878, 57740, 60538,\n", + " 61456, 63911, 67197, 71743, 79018, 80839, 82661, 84535,\n", + " 87317, 89200, 104947, 107653, 108580, 109505, 112331, 113245,\n", + " 117821, 119635, 121435, 134153, 135080, 144697, 147304, 151892,\n", + " 152843, 161606, 164323, 167919, 168868, 169818, 186614, 190306],\n", + " dtype='int64'), Int64Index([ 3881, 4801, 10474, 13234, 17769, 20519, 21442, 31498,\n", + " 44743, 45664, 46619, 49373, 54931, 55879, 57741, 60539,\n", + " 61457, 63912, 67198, 71744, 79019, 80840, 82662, 84536,\n", + " 87318, 89201, 104948, 107654, 108581, 109506, 112332, 113246,\n", + " 117822, 119636, 121436, 134154, 135081, 144698, 147305, 151893,\n", + " 152844, 161607, 164324, 167920, 168869, 169819, 186615, 190307],\n", + " dtype='int64'), Int64Index([ 3882, 4802, 10475, 13235, 17770, 20520, 21443, 31499,\n", + " 44744, 45665, 46620, 49374, 54932, 55880, 57742, 60540,\n", + " 61458, 63913, 67199, 71745, 79020, 80841, 82663, 84537,\n", + " 87319, 89202, 104949, 107655, 108582, 109507, 112333, 113247,\n", + " 117823, 119637, 121437, 134155, 135082, 144699, 147306, 151894,\n", + " 152845, 161608, 164325, 167921, 168870, 169820, 186616, 190308],\n", + " dtype='int64'), Int64Index([ 3883, 4803, 10476, 13236, 17771, 20521, 21444, 31500,\n", + " 44745, 45666, 46621, 49375, 54933, 55881, 57743, 60541,\n", + " 61459, 63914, 67200, 71746, 79021, 80842, 82664, 84538,\n", + " 87320, 89203, 104950, 107656, 108583, 109508, 112334, 113248,\n", + " 117824, 119638, 121438, 134156, 135083, 144700, 147307, 151895,\n", + " 152846, 161609, 164326, 167922, 168871, 169821, 186617, 190309],\n", + " dtype='int64'), Int64Index([ 3884, 4804, 10477, 13237, 17772, 20522, 21445, 31501,\n", + " 44746, 45667, 46622, 49376, 54934, 55882, 57744, 60542,\n", + " 61460, 63915, 67201, 71747, 79022, 80843, 82665, 84539,\n", + " 87321, 89204, 104951, 107657, 108584, 109509, 112335, 113249,\n", + " 117825, 119639, 121439, 134157, 135084, 144701, 147308, 151896,\n", + " 152847, 161610, 164327, 167923, 168872, 169822, 186618, 190310],\n", + " dtype='int64'), Int64Index([ 3885, 4805, 10478, 13238, 17773, 20523, 21446, 31502,\n", + " 44747, 45668, 46623, 49377, 54935, 55883, 57745, 60543,\n", + " 61461, 63916, 67202, 71748, 79023, 80844, 82666, 84540,\n", + " 87322, 89205, 104952, 107658, 108585, 109510, 112336, 113250,\n", + " 117826, 119640, 121440, 134158, 135085, 144702, 147309, 151897,\n", + " 152848, 161611, 164328, 167924, 168873, 169823, 186619, 190311],\n", + " dtype='int64'), Int64Index([ 3886, 4806, 10479, 13239, 17774, 20524, 21447, 31503,\n", + " 44748, 45669, 46624, 49378, 54936, 55884, 57746, 60544,\n", + " 61462, 63917, 67203, 71749, 79024, 80845, 82667, 84541,\n", + " 87323, 89206, 104953, 107659, 108586, 109511, 112337, 113251,\n", + " 117827, 119641, 121441, 134159, 135086, 144703, 147310, 151898,\n", + " 152849, 161612, 164329, 167925, 168874, 169824, 186620, 190312],\n", + " dtype='int64'), Int64Index([ 3887, 4807, 10480, 13240, 17775, 20525, 21448, 31504,\n", + " 44749, 45670, 46625, 49379, 54937, 55885, 57747, 60545,\n", + " 61463, 63918, 67204, 71750, 79025, 80846, 82668, 84542,\n", + " 87324, 89207, 104954, 107660, 108587, 109512, 112338, 113252,\n", + " 117828, 119642, 121442, 134160, 135087, 144704, 147311, 151899,\n", + " 152850, 161613, 164330, 167926, 168875, 169825, 186621, 190313],\n", + " dtype='int64'), Int64Index([ 3888, 4808, 10481, 13241, 17776, 20526, 21449, 31505,\n", + " 44750, 45671, 46626, 49380, 54938, 55886, 57748, 60546,\n", + " 61464, 63919, 67205, 71751, 79026, 80847, 82669, 84543,\n", + " 87325, 89208, 104955, 107661, 108588, 109513, 112339, 113253,\n", + " 117829, 119643, 121443, 134161, 135088, 144705, 147312, 151900,\n", + " 152851, 161614, 164331, 167927, 168876, 169826, 186622, 190314],\n", + " dtype='int64'), Int64Index([ 3889, 4809, 10482, 13242, 17777, 20527, 21450, 31506,\n", + " 44751, 45672, 46627, 49381, 54939, 55887, 57749, 60547,\n", + " 61465, 63920, 67206, 71752, 79027, 80848, 82670, 84544,\n", + " 87326, 89209, 104956, 107662, 108589, 109514, 112340, 113254,\n", + " 117830, 119644, 121444, 134162, 135089, 144706, 147313, 151901,\n", + " 152852, 161615, 164332, 167928, 168877, 169827, 186623, 190315],\n", + " dtype='int64'), Int64Index([ 3890, 4810, 10483, 13243, 17778, 20528, 21451, 31507,\n", + " 44752, 45673, 46628, 49382, 54940, 55888, 57750, 60548,\n", + " 61466, 63921, 67207, 71753, 79028, 80849, 82671, 84545,\n", + " 87327, 89210, 104957, 107663, 108590, 109515, 112341, 113255,\n", + " 117831, 119645, 121445, 134163, 135090, 144707, 147314, 151902,\n", + " 152853, 161616, 164333, 167929, 168878, 169828, 186624, 190316],\n", + " dtype='int64'), Int64Index([ 3891, 4811, 10484, 13244, 17779, 20529, 21452, 31508,\n", + " 44753, 45674, 46629, 49383, 54941, 55889, 57751, 60549,\n", + " 61467, 63922, 67208, 71754, 79029, 80850, 82672, 84546,\n", + " 87328, 89211, 104958, 107664, 108591, 109516, 112342, 113256,\n", + " 117832, 119646, 121446, 134164, 135091, 144708, 147315, 151903,\n", + " 152854, 161617, 164334, 167930, 168879, 169829, 186625, 190317],\n", + " dtype='int64'), Int64Index([ 3892, 4812, 10485, 13245, 17780, 20530, 21453, 31509,\n", + " 44754, 45675, 46630, 49384, 54942, 55890, 57752, 60550,\n", + " 61468, 63923, 67209, 71755, 79030, 80851, 82673, 84547,\n", + " 87329, 89212, 104959, 107665, 108592, 109517, 112343, 113257,\n", + " 117833, 119647, 121447, 134165, 135092, 144709, 147316, 151904,\n", + " 152855, 161618, 164335, 167931, 168880, 169830, 186626, 190318],\n", + " dtype='int64'), Int64Index([ 3893, 4813, 10486, 13246, 17781, 20531, 21454, 31510,\n", + " 44755, 45676, 46631, 49385, 54943, 55891, 57753, 60551,\n", + " 61469, 63924, 67210, 71756, 79031, 80852, 82674, 84548,\n", + " 87330, 89213, 104960, 107666, 108593, 109518, 112344, 113258,\n", + " 117834, 119648, 121448, 134166, 135093, 144710, 147317, 151905,\n", + " 152856, 161619, 164336, 167932, 168881, 169831, 186627, 190319],\n", + " dtype='int64'), Int64Index([ 3894, 4814, 10487, 13247, 17782, 20532, 21455, 31511,\n", + " 44756, 45677, 46632, 49386, 54944, 55892, 57754, 60552,\n", + " 61470, 63925, 67211, 71757, 79032, 80853, 82675, 84549,\n", + " 87331, 89214, 104961, 107667, 108594, 109519, 112345, 113259,\n", + " 117835, 119649, 121449, 134167, 135094, 144711, 147318, 151906,\n", + " 152857, 161620, 164337, 167933, 168882, 169832, 186628, 190320],\n", + " dtype='int64'), Int64Index([ 3895, 4815, 10488, 13248, 17783, 20533, 21456, 31512,\n", + " 44757, 45678, 46633, 49387, 54945, 55893, 57755, 60553,\n", + " 61471, 63926, 67212, 71758, 79033, 80854, 82676, 84550,\n", + " 87332, 89215, 104962, 107668, 108595, 109520, 112346, 113260,\n", + " 117836, 119650, 121450, 134168, 135095, 144712, 147319, 151907,\n", + " 152858, 161621, 164338, 167934, 168883, 169833, 186629, 190321],\n", + " dtype='int64'), Int64Index([ 3896, 4816, 10489, 13249, 17784, 20534, 21457, 31513,\n", + " 44758, 45679, 46634, 49388, 54946, 55894, 57756, 60554,\n", + " 61472, 63927, 67213, 71759, 79034, 80855, 82677, 84551,\n", + " 87333, 89216, 104963, 107669, 108596, 109521, 112347, 113261,\n", + " 117837, 119651, 121451, 134169, 135096, 144713, 147320, 151908,\n", + " 152859, 161622, 164339, 167935, 168884, 169834, 186630, 190322],\n", + " dtype='int64'), Int64Index([ 3897, 4817, 10490, 13250, 17785, 20535, 21458, 31514,\n", + " 44759, 45680, 46635, 49389, 54947, 55895, 57757, 60555,\n", + " 61473, 63928, 67214, 71760, 79035, 80856, 82678, 84552,\n", + " 87334, 89217, 104964, 107670, 108597, 109522, 112348, 113262,\n", + " 117838, 119652, 121452, 134170, 135097, 144714, 147321, 151909,\n", + " 152860, 161623, 164340, 167936, 168885, 169835, 186631, 190323],\n", + " dtype='int64'), Int64Index([ 3898, 4818, 10491, 13251, 17786, 20536, 21459, 31515,\n", + " 44760, 45681, 46636, 49390, 54948, 55896, 57758, 60556,\n", + " 61474, 63929, 67215, 71761, 79036, 80857, 82679, 84553,\n", + " 87335, 89218, 104965, 107671, 108598, 109523, 112349, 113263,\n", + " 117839, 119653, 121453, 134171, 135098, 144715, 147322, 151910,\n", + " 152861, 161624, 164341, 167937, 168886, 169836, 186632, 190324],\n", + " dtype='int64'), Int64Index([ 3899, 4819, 10492, 13252, 17787, 20537, 21460, 31516,\n", + " 44761, 45682, 46637, 49391, 54949, 55897, 57759, 60557,\n", + " 61475, 63930, 67216, 71762, 79037, 80858, 82680, 84554,\n", + " 87336, 89219, 104966, 107672, 108599, 109524, 112350, 113264,\n", + " 117840, 119654, 121454, 134172, 135099, 144716, 147323, 151911,\n", + " 152862, 161625, 164342, 167938, 168887, 169837, 186633, 190325],\n", + " dtype='int64'), Int64Index([ 3900, 4820, 10493, 13253, 17788, 20538, 21461, 31517,\n", + " 44762, 45683, 46638, 49392, 54950, 55898, 57760, 60558,\n", + " 61476, 63931, 67217, 71763, 79038, 80859, 82681, 84555,\n", + " 87337, 89220, 104967, 107673, 108600, 109525, 112351, 113265,\n", + " 117841, 119655, 121455, 134173, 135100, 144717, 147324, 151912,\n", + " 152863, 161626, 164343, 167939, 168888, 169838, 186634, 190326],\n", + " dtype='int64'), Int64Index([ 3901, 4821, 10494, 13254, 17789, 20539, 21462, 31518,\n", + " 44763, 45684, 46639, 49393, 54951, 55899, 57761, 60559,\n", + " 61477, 63932, 67218, 71764, 79039, 80860, 82682, 84556,\n", + " 87338, 89221, 104968, 107674, 108601, 109526, 112352, 113266,\n", + " 117842, 119656, 121456, 134174, 135101, 144718, 147325, 151913,\n", + " 152864, 161627, 164344, 167940, 168889, 169839, 186635, 190327],\n", + " dtype='int64'), Int64Index([ 3902, 4822, 10495, 13255, 17790, 20540, 21463, 31519,\n", + " 44764, 45685, 46640, 49394, 54952, 55900, 57762, 60560,\n", + " 61478, 63933, 67219, 71765, 79040, 80861, 82683, 84557,\n", + " 87339, 89222, 104969, 107675, 108602, 109527, 112353, 113267,\n", + " 117843, 119657, 121457, 134175, 135102, 144719, 147326, 151914,\n", + " 152865, 161628, 164345, 167941, 168890, 169840, 186636, 190328],\n", + " dtype='int64'), Int64Index([ 3903, 4823, 10496, 13256, 17791, 20541, 21464, 31520,\n", + " 44765, 45686, 46641, 49395, 54953, 55901, 57763, 60561,\n", + " 61479, 63934, 67220, 71766, 79041, 80862, 82684, 84558,\n", + " 87340, 89223, 104970, 107676, 108603, 109528, 112354, 113268,\n", + " 117844, 119658, 121458, 134176, 135103, 144720, 147327, 151915,\n", + " 152866, 161629, 164346, 167942, 168891, 169841, 186637, 190329],\n", + " dtype='int64'), Int64Index([ 3904, 4824, 10497, 13257, 17792, 20542, 21465, 31521,\n", + " 44766, 45687, 46642, 49396, 54954, 55902, 57764, 60562,\n", + " 61480, 63935, 67221, 71767, 79042, 80863, 82685, 84559,\n", + " 87341, 89224, 104971, 107677, 108604, 109529, 112355, 113269,\n", + " 117845, 119659, 121459, 134177, 135104, 144721, 147328, 151916,\n", + " 152867, 161630, 164347, 167943, 168892, 169842, 186638, 190330],\n", + " dtype='int64'), Int64Index([ 3905, 4825, 10498, 13258, 17793, 20543, 21466, 31522,\n", + " 44767, 45688, 46643, 49397, 54955, 55903, 57765, 60563,\n", + " 61481, 63936, 67222, 71768, 79043, 80864, 82686, 84560,\n", + " 87342, 89225, 104972, 107678, 108605, 109530, 112356, 113270,\n", + " 117846, 119660, 121460, 134178, 135105, 144722, 147329, 151917,\n", + " 152868, 161631, 164348, 167944, 168893, 169843, 186639, 190331],\n", + " dtype='int64'), Int64Index([ 3906, 4826, 10499, 13259, 17794, 20544, 21467, 31523,\n", + " 44768, 45689, 46644, 49398, 54956, 55904, 57766, 60564,\n", + " 61482, 63937, 67223, 71769, 79044, 80865, 82687, 84561,\n", + " 87343, 89226, 104973, 107679, 108606, 109531, 112357, 113271,\n", + " 117847, 119661, 121461, 134179, 135106, 144723, 147330, 151918,\n", + " 152869, 161632, 164349, 167945, 168894, 169844, 186640, 190332],\n", + " dtype='int64'), Int64Index([ 3907, 4827, 10500, 13260, 17795, 20545, 21468, 31524,\n", + " 44769, 45690, 46645, 49399, 54957, 55905, 57767, 60565,\n", + " 61483, 63938, 67224, 71770, 79045, 80866, 82688, 84562,\n", + " 87344, 89227, 104974, 107680, 108607, 109532, 112358, 113272,\n", + " 117848, 119662, 121462, 134180, 135107, 144724, 147331, 151919,\n", + " 152870, 161633, 164350, 167946, 168895, 169845, 186641, 190333],\n", + " dtype='int64'), Int64Index([ 3908, 4828, 10501, 13261, 17796, 20546, 21469, 31525,\n", + " 44770, 45691, 46646, 49400, 54958, 55906, 57768, 60566,\n", + " 61484, 63939, 67225, 71771, 79046, 80867, 82689, 84563,\n", + " 87345, 89228, 104975, 107681, 108608, 109533, 112359, 113273,\n", + " 117849, 119663, 121463, 134181, 135108, 144725, 147332, 151920,\n", + " 152871, 161634, 164351, 167947, 168896, 169846, 186642, 190334],\n", + " dtype='int64'), Int64Index([ 3909, 4829, 10502, 13262, 17797, 20547, 21470, 31526,\n", + " 44771, 45692, 46647, 49401, 54959, 55907, 57769, 60567,\n", + " 61485, 63940, 67226, 71772, 79047, 80868, 82690, 84564,\n", + " 87346, 89229, 104976, 107682, 108609, 109534, 112360, 113274,\n", + " 117850, 119664, 121464, 134182, 135109, 144726, 147333, 151921,\n", + " 152872, 161635, 164352, 167948, 168897, 169847, 186643, 190335],\n", + " dtype='int64'), Int64Index([ 3910, 4830, 10503, 13263, 17798, 20548, 21471, 31527,\n", + " 44772, 45693, 46648, 49402, 54960, 55908, 57770, 60568,\n", + " 61486, 63941, 67227, 71773, 79048, 80869, 82691, 84565,\n", + " 87347, 89230, 104977, 107683, 108610, 109535, 112361, 113275,\n", + " 117851, 119665, 121465, 134183, 135110, 144727, 147334, 151922,\n", + " 152873, 161636, 164353, 167949, 168898, 169848, 186644, 190336],\n", + " dtype='int64'), Int64Index([ 3911, 4831, 10504, 13264, 17799, 20549, 21472, 31528,\n", + " 44773, 45694, 46649, 49403, 54961, 55909, 57771, 60569,\n", + " 61487, 63942, 67228, 71774, 79049, 80870, 82692, 84566,\n", + " 87348, 89231, 104978, 107684, 108611, 109536, 112362, 113276,\n", + " 117852, 119666, 121466, 134184, 135111, 144728, 147335, 151923,\n", + " 152874, 161637, 164354, 167950, 168899, 169849, 186645, 190337],\n", + " dtype='int64'), Int64Index([ 3912, 4832, 10505, 13265, 17800, 20550, 21473, 31529,\n", + " 44774, 45695, 46650, 49404, 54962, 55910, 57772, 60570,\n", + " 61488, 63943, 67229, 71775, 79050, 80871, 82693, 84567,\n", + " 87349, 89232, 104979, 107685, 108612, 109537, 112363, 113277,\n", + " 117853, 119667, 121467, 134185, 135112, 144729, 147336, 151924,\n", + " 152875, 161638, 164355, 167951, 168900, 169850, 186646, 190338],\n", + " dtype='int64'), Int64Index([ 3913, 4833, 10506, 13266, 17801, 20551, 21474, 31530,\n", + " 44775, 45696, 46651, 49405, 54963, 55911, 57773, 60571,\n", + " 61489, 63944, 67230, 71776, 79051, 80872, 82694, 84568,\n", + " 87350, 89233, 104980, 107686, 108613, 109538, 112364, 113278,\n", + " 117854, 119668, 121468, 134186, 135113, 144730, 147337, 151925,\n", + " 152876, 161639, 164356, 167952, 168901, 169851, 186647, 190339],\n", + " dtype='int64'), Int64Index([ 3914, 4834, 10507, 13267, 17802, 20552, 21475, 31531,\n", + " 44776, 45697, 46652, 49406, 54964, 55912, 57774, 60572,\n", + " 61490, 63945, 67231, 71777, 79052, 80873, 82695, 84569,\n", + " 87351, 89234, 104981, 107687, 108614, 109539, 112365, 113279,\n", + " 117855, 119669, 121469, 134187, 135114, 144731, 147338, 151926,\n", + " 152877, 161640, 164357, 167953, 168902, 169852, 186648, 190340],\n", + " dtype='int64'), Int64Index([ 3915, 4835, 10508, 13268, 17803, 20553, 21476, 31532,\n", + " 44777, 45698, 46653, 49407, 54965, 55913, 57775, 60573,\n", + " 61491, 63946, 67232, 71778, 79053, 80874, 82696, 84570,\n", + " 87352, 89235, 104982, 107688, 108615, 109540, 112366, 113280,\n", + " 117856, 119670, 121470, 134188, 135115, 144732, 147339, 151927,\n", + " 152878, 161641, 164358, 167954, 168903, 169853, 186649, 190341],\n", + " dtype='int64'), Int64Index([ 3916, 4836, 10509, 13269, 17804, 20554, 21477, 31533,\n", + " 44778, 45699, 46654, 49408, 54966, 55914, 57776, 60574,\n", + " 61492, 63947, 67233, 71779, 79054, 80875, 82697, 84571,\n", + " 87353, 89236, 104983, 107689, 108616, 109541, 112367, 113281,\n", + " 117857, 119671, 121471, 134189, 135116, 144733, 147340, 151928,\n", + " 152879, 161642, 164359, 167955, 168904, 169854, 186650, 190342],\n", + " dtype='int64'), Int64Index([ 3917, 4837, 10510, 13270, 17805, 20555, 21478, 31534,\n", + " 44779, 45700, 46655, 49409, 54967, 55915, 57777, 60575,\n", + " 61493, 63948, 67234, 71780, 79055, 80876, 82698, 84572,\n", + " 87354, 89237, 104984, 107690, 108617, 109542, 112368, 113282,\n", + " 117858, 119672, 121472, 134190, 135117, 144734, 147341, 151929,\n", + " 152880, 161643, 164360, 167956, 168905, 169855, 186651, 190343],\n", + " dtype='int64'), Int64Index([ 3918, 4838, 10511, 13271, 17806, 20556, 21479, 31535,\n", + " 44780, 45701, 46656, 49410, 54968, 55916, 57778, 60576,\n", + " 61494, 63949, 67235, 71781, 79056, 80877, 82699, 84573,\n", + " 87355, 89238, 104985, 107691, 108618, 109543, 112369, 113283,\n", + " 117859, 119673, 121473, 134191, 135118, 144735, 147342, 151930,\n", + " 152881, 161644, 164361, 167957, 168906, 169856, 186652, 190344],\n", + " dtype='int64'), Int64Index([ 3919, 4839, 10512, 13272, 17807, 20557, 21480, 31536,\n", + " 44781, 45702, 46657, 49411, 54969, 55917, 57779, 60577,\n", + " 61495, 63950, 67236, 71782, 79057, 80878, 82700, 84574,\n", + " 87356, 89239, 104986, 107692, 108619, 109544, 112370, 113284,\n", + " 117860, 119674, 121474, 134192, 135119, 144736, 147343, 151931,\n", + " 152882, 161645, 164362, 167958, 168907, 169857, 186653, 190345],\n", + " dtype='int64'), Int64Index([ 3920, 4840, 10513, 13273, 17808, 20558, 21481, 31537,\n", + " 44782, 45703, 46658, 49412, 54970, 55918, 57780, 60578,\n", + " 61496, 63951, 67237, 71783, 79058, 80879, 82701, 84575,\n", + " 87357, 89240, 104987, 107693, 108620, 109545, 112371, 113285,\n", + " 117861, 119675, 121475, 134193, 135120, 144737, 147344, 151932,\n", + " 152883, 161646, 164363, 167959, 168908, 169858, 186654, 190346],\n", + " dtype='int64'), Int64Index([ 3921, 4841, 10514, 13274, 17809, 20559, 21482, 31538,\n", + " 44783, 45704, 46659, 49413, 54971, 55919, 57781, 60579,\n", + " 61497, 63952, 67238, 71784, 79059, 80880, 82702, 84576,\n", + " 87358, 89241, 104988, 107694, 108621, 109546, 112372, 113286,\n", + " 117862, 119676, 121476, 134194, 135121, 144738, 147345, 151933,\n", + " 152884, 161647, 164364, 167960, 168909, 169859, 186655, 190347],\n", + " dtype='int64'), Int64Index([ 3922, 4842, 10515, 13275, 17810, 20560, 21483, 31539,\n", + " 44784, 45705, 46660, 49414, 54972, 55920, 57782, 60580,\n", + " 61498, 63953, 67239, 71785, 79060, 80881, 82703, 84577,\n", + " 87359, 89242, 104989, 107695, 108622, 109547, 112373, 113287,\n", + " 117863, 119677, 121477, 134195, 135122, 144739, 147346, 151934,\n", + " 152885, 161648, 164365, 167961, 168910, 169860, 186656, 190348],\n", + " dtype='int64'), Int64Index([ 3923, 4843, 10516, 13276, 17811, 20561, 21484, 31540,\n", + " 44785, 45706, 46661, 49415, 54973, 55921, 57783, 60581,\n", + " 61499, 63954, 67240, 71786, 79061, 80882, 82704, 84578,\n", + " 87360, 89243, 104990, 107696, 108623, 109548, 112374, 113288,\n", + " 117864, 119678, 121478, 134196, 135123, 144740, 147347, 151935,\n", + " 152886, 161649, 164366, 167962, 168911, 169861, 186657, 190349],\n", + " dtype='int64'), Int64Index([ 3924, 4844, 10517, 13277, 17812, 20562, 21485, 31541,\n", + " 44786, 45707, 46662, 49416, 54974, 55922, 57784, 60582,\n", + " 61500, 63955, 67241, 71787, 79062, 80883, 82705, 84579,\n", + " 87361, 89244, 104991, 107697, 108624, 109549, 112375, 113289,\n", + " 117865, 119679, 121479, 134197, 135124, 144741, 147348, 151936,\n", + " 152887, 161650, 164367, 167963, 168912, 169862, 186658, 190350],\n", + " dtype='int64'), Int64Index([ 3925, 4845, 10518, 13278, 17813, 20563, 21486, 31542,\n", + " 44787, 45708, 46663, 49417, 54975, 55923, 57785, 60583,\n", + " 61501, 63956, 67242, 71788, 79063, 80884, 82706, 84580,\n", + " 87362, 89245, 104992, 107698, 108625, 109550, 112376, 113290,\n", + " 117866, 119680, 121480, 134198, 135125, 144742, 147349, 151937,\n", + " 152888, 161651, 164368, 167964, 168913, 169863, 186659, 190351],\n", + " dtype='int64'), Int64Index([ 3926, 4846, 10519, 13279, 17814, 20564, 21487, 31543,\n", + " 44788, 45709, 46664, 49418, 54976, 55924, 57786, 60584,\n", + " 61502, 63957, 67243, 71789, 79064, 80885, 82707, 84581,\n", + " 87363, 89246, 104993, 107699, 108626, 109551, 112377, 113291,\n", + " 117867, 119681, 121481, 134199, 135126, 144743, 147350, 151938,\n", + " 152889, 161652, 164369, 167965, 168914, 169864, 186660, 190352],\n", + " dtype='int64'), Int64Index([ 3927, 4847, 10520, 13280, 17815, 20565, 21488, 31544,\n", + " 44789, 45710, 46665, 49419, 54977, 55925, 57787, 60585,\n", + " 61503, 63958, 67244, 71790, 79065, 80886, 82708, 84582,\n", + " 87364, 89247, 104994, 107700, 108627, 109552, 112378, 113292,\n", + " 117868, 119682, 121482, 134200, 135127, 144744, 147351, 151939,\n", + " 152890, 161653, 164370, 167966, 168915, 169865, 186661, 190353],\n", + " dtype='int64'), Int64Index([ 3928, 4848, 10521, 13281, 17816, 20566, 21489, 31545,\n", + " 44790, 45711, 46666, 49420, 54978, 55926, 57788, 60586,\n", + " 61504, 63959, 67245, 71791, 79066, 80887, 82709, 84583,\n", + " 87365, 89248, 104995, 107701, 108628, 109553, 112379, 113293,\n", + " 117869, 119683, 121483, 134201, 135128, 144745, 147352, 151940,\n", + " 152891, 161654, 164371, 167967, 168916, 169866, 186662, 190354],\n", + " dtype='int64'), Int64Index([ 3929, 4849, 10522, 13282, 17817, 20567, 21490, 31546,\n", + " 44791, 45712, 46667, 49421, 54979, 55927, 57789, 60587,\n", + " 61505, 63960, 67246, 71792, 79067, 80888, 82710, 84584,\n", + " 87366, 89249, 104996, 107702, 108629, 109554, 112380, 113294,\n", + " 117870, 119684, 121484, 134202, 135129, 144746, 147353, 151941,\n", + " 152892, 161655, 164372, 167968, 168917, 169867, 186663, 190355],\n", + " dtype='int64'), Int64Index([ 3930, 4850, 10523, 13283, 17818, 20568, 21491, 31547,\n", + " 44792, 45713, 46668, 49422, 54980, 55928, 57790, 60588,\n", + " 61506, 63961, 67247, 71793, 79068, 80889, 82711, 84585,\n", + " 87367, 89250, 104997, 107703, 108630, 109555, 112381, 113295,\n", + " 117871, 119685, 121485, 134203, 135130, 144747, 147354, 151942,\n", + " 152893, 161656, 164373, 167969, 168918, 169868, 186664, 190356],\n", + " dtype='int64'), Int64Index([ 3931, 4851, 10524, 13284, 17819, 20569, 21492, 31548,\n", + " 44793, 45714, 46669, 49423, 54981, 55929, 57791, 60589,\n", + " 61507, 63962, 67248, 71794, 79069, 80890, 82712, 84586,\n", + " 87368, 89251, 104998, 107704, 108631, 109556, 112382, 113296,\n", + " 117872, 119686, 121486, 134204, 135131, 144748, 147355, 151943,\n", + " 152894, 161657, 164374, 167970, 168919, 169869, 186665, 190357],\n", + " dtype='int64'), Int64Index([ 3932, 4852, 10525, 13285, 17820, 20570, 21493, 31549,\n", + " 44794, 45715, 46670, 49424, 54982, 55930, 57792, 60590,\n", + " 61508, 63963, 67249, 71795, 79070, 80891, 82713, 84587,\n", + " 87369, 89252, 104999, 107705, 108632, 109557, 112383, 113297,\n", + " 117873, 119687, 121487, 134205, 135132, 144749, 147356, 151944,\n", + " 152895, 161658, 164375, 167971, 168920, 169870, 186666, 190358],\n", + " dtype='int64'), Int64Index([ 3933, 4853, 10526, 13286, 17821, 20571, 21494, 31550,\n", + " 44795, 45716, 46671, 49425, 54983, 55931, 57793, 60591,\n", + " 61509, 63964, 67250, 71796, 79071, 80892, 82714, 84588,\n", + " 87370, 89253, 105000, 107706, 108633, 109558, 112384, 113298,\n", + " 117874, 119688, 121488, 134206, 135133, 144750, 147357, 151945,\n", + " 152896, 161659, 164376, 167972, 168921, 169871, 186667, 190359],\n", + " dtype='int64'), Int64Index([ 3934, 4854, 10527, 13287, 17822, 20572, 21495, 31551,\n", + " 44796, 45717, 46672, 49426, 54984, 55932, 57794, 60592,\n", + " 61510, 63965, 67251, 71797, 79072, 80893, 82715, 84589,\n", + " 87371, 89254, 105001, 107707, 108634, 109559, 112385, 113299,\n", + " 117875, 119689, 121489, 134207, 135134, 144751, 147358, 151946,\n", + " 152897, 161660, 164377, 167973, 168922, 169872, 186668, 190360],\n", + " dtype='int64'), Int64Index([ 3935, 4855, 10528, 13288, 17823, 20573, 21496, 31552,\n", + " 44797, 45718, 46673, 49427, 54985, 55933, 57795, 60593,\n", + " 61511, 63966, 67252, 71798, 79073, 80894, 82716, 84590,\n", + " 87372, 89255, 105002, 107708, 108635, 109560, 112386, 113300,\n", + " 117876, 119690, 121490, 134208, 135135, 144752, 147359, 151947,\n", + " 152898, 161661, 164378, 167974, 168923, 169873, 186669, 190361],\n", + " dtype='int64'), Int64Index([ 3936, 4856, 10529, 13289, 17824, 20574, 21497, 31553,\n", + " 44798, 45719, 46674, 49428, 54986, 55934, 57796, 60594,\n", + " 61512, 63967, 67253, 71799, 79074, 80895, 82717, 84591,\n", + " 87373, 89256, 105003, 107709, 108636, 109561, 112387, 113301,\n", + " 117877, 119691, 121491, 134209, 135136, 144753, 147360, 151948,\n", + " 152899, 161662, 164379, 167975, 168924, 169874, 186670, 190362],\n", + " dtype='int64'), Int64Index([ 3937, 4857, 10530, 13290, 17825, 20575, 21498, 31554,\n", + " 44799, 45720, 46675, 49429, 54987, 55935, 57797, 60595,\n", + " 61513, 63968, 67254, 71800, 79075, 80896, 82718, 84592,\n", + " 87374, 89257, 105004, 107710, 108637, 109562, 112388, 113302,\n", + " 117878, 119692, 121492, 134210, 135137, 144754, 147361, 151949,\n", + " 152900, 161663, 164380, 167976, 168925, 169875, 186671, 190363],\n", + " dtype='int64'), Int64Index([ 3938, 4858, 10531, 13291, 17826, 20576, 21499, 31555,\n", + " 44800, 45721, 46676, 49430, 54988, 55936, 57798, 60596,\n", + " 61514, 63969, 67255, 71801, 79076, 80897, 82719, 84593,\n", + " 87375, 89258, 105005, 107711, 108638, 109563, 112389, 113303,\n", + " 117879, 119693, 121493, 134211, 135138, 144755, 147362, 151950,\n", + " 152901, 161664, 164381, 167977, 168926, 169876, 186672, 190364],\n", + " dtype='int64'), Int64Index([ 3939, 4859, 10532, 13292, 17827, 20577, 21500, 31556,\n", + " 44801, 45722, 46677, 49431, 54989, 55937, 57799, 60597,\n", + " 61515, 63970, 67256, 71802, 79077, 80898, 82720, 84594,\n", + " 87376, 89259, 105006, 107712, 108639, 109564, 112390, 113304,\n", + " 117880, 119694, 121494, 134212, 135139, 144756, 147363, 151951,\n", + " 152902, 161665, 164382, 167978, 168927, 169877, 186673, 190365],\n", + " dtype='int64'), Int64Index([ 3940, 4860, 10533, 13293, 17828, 20578, 21501, 31557,\n", + " 44802, 45723, 46678, 49432, 54990, 55938, 57800, 60598,\n", + " 61516, 63971, 67257, 71803, 79078, 80899, 82721, 84595,\n", + " 87377, 89260, 105007, 107713, 108640, 109565, 112391, 113305,\n", + " 117881, 119695, 121495, 134213, 135140, 144757, 147364, 151952,\n", + " 152903, 161666, 164383, 167979, 168928, 169878, 186674, 190366],\n", + " dtype='int64'), Int64Index([ 3941, 4861, 10534, 13294, 17829, 20579, 21502, 31558,\n", + " 44803, 45724, 46679, 49433, 54991, 55939, 57801, 60599,\n", + " 61517, 63972, 67258, 71804, 79079, 80900, 82722, 84596,\n", + " 87378, 89261, 105008, 107714, 108641, 109566, 112392, 113306,\n", + " 117882, 119696, 121496, 134214, 135141, 144758, 147365, 151953,\n", + " 152904, 161667, 164384, 167980, 168929, 169879, 186675, 190367],\n", + " dtype='int64'), Int64Index([ 3942, 4862, 10535, 13295, 17830, 20580, 21503, 31559,\n", + " 44804, 45725, 46680, 49434, 54992, 55940, 57802, 60600,\n", + " 61518, 63973, 67259, 71805, 79080, 80901, 82723, 84597,\n", + " 87379, 89262, 105009, 107715, 108642, 109567, 112393, 113307,\n", + " 117883, 119697, 121497, 134215, 135142, 144759, 147366, 151954,\n", + " 152905, 161668, 164385, 167981, 168930, 169880, 186676, 190368],\n", + " dtype='int64'), Int64Index([ 3943, 4863, 10536, 13296, 17831, 20581, 21504, 31560,\n", + " 44805, 45726, 46681, 49435, 54993, 55941, 57803, 60601,\n", + " 61519, 63974, 67260, 71806, 79081, 80902, 82724, 84598,\n", + " 87380, 89263, 105010, 107716, 108643, 109568, 112394, 113308,\n", + " 117884, 119698, 121498, 134216, 135143, 144760, 147367, 151955,\n", + " 152906, 161669, 164386, 167982, 168931, 169881, 186677, 190369],\n", + " dtype='int64'), Int64Index([ 3944, 4864, 10537, 13297, 17832, 20582, 21505, 31561,\n", + " 44806, 45727, 46682, 49436, 54994, 55942, 57804, 60602,\n", + " 61520, 63975, 67261, 71807, 79082, 80903, 82725, 84599,\n", + " 87381, 89264, 105011, 107717, 108644, 109569, 112395, 113309,\n", + " 117885, 119699, 121499, 134217, 135144, 144761, 147368, 151956,\n", + " 152907, 161670, 164387, 167983, 168932, 169882, 186678, 190370],\n", + " dtype='int64'), Int64Index([ 3945, 4865, 10538, 13298, 17833, 20583, 21506, 31562,\n", + " 44807, 45728, 46683, 49437, 54995, 55943, 57805, 60603,\n", + " 61521, 63976, 67262, 71808, 79083, 80904, 82726, 84600,\n", + " 87382, 89265, 105012, 107718, 108645, 109570, 112396, 113310,\n", + " 117886, 119700, 121500, 134218, 135145, 144762, 147369, 151957,\n", + " 152908, 161671, 164388, 167984, 168933, 169883, 186679, 190371],\n", + " dtype='int64'), Int64Index([ 3946, 4866, 10539, 13299, 17834, 20584, 21507, 31563,\n", + " 44808, 45729, 46684, 49438, 54996, 55944, 57806, 60604,\n", + " 61522, 63977, 67263, 71809, 79084, 80905, 82727, 84601,\n", + " 87383, 89266, 105013, 107719, 108646, 109571, 112397, 113311,\n", + " 117887, 119701, 121501, 134219, 135146, 144763, 147370, 151958,\n", + " 152909, 161672, 164389, 167985, 168934, 169884, 186680, 190372],\n", + " dtype='int64'), Int64Index([ 3947, 4867, 10540, 13300, 17835, 20585, 21508, 31564,\n", + " 44809, 45730, 46685, 49439, 54997, 55945, 57807, 60605,\n", + " 61523, 63978, 67264, 71810, 79085, 80906, 82728, 84602,\n", + " 87384, 89267, 105014, 107720, 108647, 109572, 112398, 113312,\n", + " 117888, 119702, 121502, 134220, 135147, 144764, 147371, 151959,\n", + " 152910, 161673, 164390, 167986, 168935, 169885, 186681, 190373],\n", + " dtype='int64'), Int64Index([ 3948, 4868, 10541, 13301, 17836, 20586, 21509, 31565,\n", + " 44810, 45731, 46686, 49440, 54998, 55946, 57808, 60606,\n", + " 61524, 63979, 67265, 71811, 79086, 80907, 82729, 84603,\n", + " 87385, 89268, 105015, 107721, 108648, 109573, 112399, 113313,\n", + " 117889, 119703, 121503, 134221, 135148, 144765, 147372, 151960,\n", + " 152911, 161674, 164391, 167987, 168936, 169886, 186682, 190374],\n", + " dtype='int64'), Int64Index([ 3949, 4869, 10542, 13302, 17837, 20587, 21510, 31566,\n", + " 44811, 45732, 46687, 49441, 54999, 55947, 57809, 60607,\n", + " 61525, 63980, 67266, 71812, 79087, 80908, 82730, 84604,\n", + " 87386, 89269, 105016, 107722, 108649, 109574, 112400, 113314,\n", + " 117890, 119704, 121504, 134222, 135149, 144766, 147373, 151961,\n", + " 152912, 161675, 164392, 167988, 168937, 169887, 186683, 190375],\n", + " dtype='int64'), Int64Index([ 3950, 4870, 10543, 13303, 17838, 20588, 21511, 31567,\n", + " 44812, 45733, 46688, 49442, 55000, 55948, 57810, 60608,\n", + " 61526, 63981, 67267, 71813, 79088, 80909, 82731, 84605,\n", + " 87387, 89270, 105017, 107723, 108650, 109575, 112401, 113315,\n", + " 117891, 119705, 121505, 134223, 135150, 144767, 147374, 151962,\n", + " 152913, 161676, 164393, 167989, 168938, 169888, 186684, 190376],\n", + " dtype='int64'), Int64Index([ 3951, 4871, 10544, 13304, 17839, 20589, 21512, 31568,\n", + " 44813, 45734, 46689, 49443, 55001, 55949, 57811, 60609,\n", + " 61527, 63982, 67268, 71814, 79089, 80910, 82732, 84606,\n", + " 87388, 89271, 105018, 107724, 108651, 109576, 112402, 113316,\n", + " 117892, 119706, 121506, 134224, 135151, 144768, 147375, 151963,\n", + " 152914, 161677, 164394, 167990, 168939, 169889, 186685, 190377],\n", + " dtype='int64'), Int64Index([ 3952, 4872, 10545, 13305, 17840, 20590, 21513, 31569,\n", + " 44814, 45735, 46690, 49444, 55002, 55950, 57812, 60610,\n", + " 61528, 63983, 67269, 71815, 79090, 80911, 82733, 84607,\n", + " 87389, 89272, 105019, 107725, 108652, 109577, 112403, 113317,\n", + " 117893, 119707, 121507, 134225, 135152, 144769, 147376, 151964,\n", + " 152915, 161678, 164395, 167991, 168940, 169890, 186686, 190378],\n", + " dtype='int64'), Int64Index([ 3953, 4873, 10546, 13306, 17841, 20591, 21514, 31570,\n", + " 44815, 45736, 46691, 49445, 55003, 55951, 57813, 60611,\n", + " 61529, 63984, 67270, 71816, 79091, 80912, 82734, 84608,\n", + " 87390, 89273, 105020, 107726, 108653, 109578, 112404, 113318,\n", + " 117894, 119708, 121508, 134226, 135153, 144770, 147377, 151965,\n", + " 152916, 161679, 164396, 167992, 168941, 169891, 186687, 190379],\n", + " dtype='int64'), Int64Index([ 3954, 4874, 10547, 13307, 17842, 20592, 21515, 31571,\n", + " 44816, 45737, 46692, 49446, 55004, 55952, 57814, 60612,\n", + " 61530, 63985, 67271, 71817, 79092, 80913, 82735, 84609,\n", + " 87391, 89274, 105021, 107727, 108654, 109579, 112405, 113319,\n", + " 117895, 119709, 121509, 134227, 135154, 144771, 147378, 151966,\n", + " 152917, 161680, 164397, 167993, 168942, 169892, 186688, 190380],\n", + " dtype='int64'), Int64Index([ 3955, 4875, 10548, 13308, 17843, 20593, 21516, 31572,\n", + " 44817, 45738, 46693, 49447, 55005, 55953, 57815, 60613,\n", + " 61531, 63986, 67272, 71818, 79093, 80914, 82736, 84610,\n", + " 87392, 89275, 105022, 107728, 108655, 109580, 112406, 113320,\n", + " 117896, 119710, 121510, 134228, 135155, 144772, 147379, 151967,\n", + " 152918, 161681, 164398, 167994, 168943, 169893, 186689, 190381],\n", + " dtype='int64'), Int64Index([ 3956, 4876, 10549, 13309, 17844, 20594, 21517, 31573,\n", + " 44818, 45739, 46694, 49448, 55006, 55954, 57816, 60614,\n", + " 61532, 63987, 67273, 71819, 79094, 80915, 82737, 84611,\n", + " 87393, 89276, 105023, 107729, 108656, 109581, 112407, 113321,\n", + " 117897, 119711, 121511, 134229, 135156, 144773, 147380, 151968,\n", + " 152919, 161682, 164399, 167995, 168944, 169894, 186690, 190382],\n", + " dtype='int64'), Int64Index([ 3957, 4877, 10550, 13310, 17845, 20595, 21518, 31574,\n", + " 44819, 45740, 46695, 49449, 55007, 55955, 57817, 60615,\n", + " 61533, 63988, 67274, 71820, 79095, 80916, 82738, 84612,\n", + " 87394, 89277, 105024, 107730, 108657, 109582, 112408, 113322,\n", + " 117898, 119712, 121512, 134230, 135157, 144774, 147381, 151969,\n", + " 152920, 161683, 164400, 167996, 168945, 169895, 186691, 190383],\n", + " dtype='int64'), Int64Index([ 3958, 4878, 10551, 13311, 17846, 20596, 21519, 31575,\n", + " 44820, 45741, 46696, 49450, 55008, 55956, 57818, 60616,\n", + " 61534, 63989, 67275, 71821, 79096, 80917, 82739, 84613,\n", + " 87395, 89278, 105025, 107731, 108658, 109583, 112409, 113323,\n", + " 117899, 119713, 121513, 134231, 135158, 144775, 147382, 151970,\n", + " 152921, 161684, 164401, 167997, 168946, 169896, 186692, 190384],\n", + " dtype='int64'), Int64Index([ 3959, 4879, 10552, 13312, 17847, 20597, 21520, 31576,\n", + " 44821, 45742, 46697, 49451, 55009, 55957, 57819, 60617,\n", + " 61535, 63990, 67276, 71822, 79097, 80918, 82740, 84614,\n", + " 87396, 89279, 105026, 107732, 108659, 109584, 112410, 113324,\n", + " 117900, 119714, 121514, 134232, 135159, 144776, 147383, 151971,\n", + " 152922, 161685, 164402, 167998, 168947, 169897, 186693, 190385],\n", + " dtype='int64'), Int64Index([ 3960, 4880, 10553, 13313, 17848, 20598, 21521, 31577,\n", + " 44822, 45743, 46698, 49452, 55010, 55958, 57820, 60618,\n", + " 61536, 63991, 67277, 71823, 79098, 80919, 82741, 84615,\n", + " 87397, 89280, 105027, 107733, 108660, 109585, 112411, 113325,\n", + " 117901, 119715, 121515, 134233, 135160, 144777, 147384, 151972,\n", + " 152923, 161686, 164403, 167999, 168948, 169898, 186694, 190386],\n", + " dtype='int64'), Int64Index([ 3961, 4881, 10554, 13314, 17849, 20599, 21522, 31578,\n", + " 44823, 45744, 46699, 49453, 55011, 55959, 57821, 60619,\n", + " 61537, 63992, 67278, 71824, 79099, 80920, 82742, 84616,\n", + " 87398, 89281, 105028, 107734, 108661, 109586, 112412, 113326,\n", + " 117902, 119716, 121516, 134234, 135161, 144778, 147385, 151973,\n", + " 152924, 161687, 164404, 168000, 168949, 169899, 186695, 190387],\n", + " dtype='int64'), Int64Index([ 3962, 4882, 10555, 13315, 17850, 20600, 21523, 31579,\n", + " 44824, 45745, 46700, 49454, 55012, 55960, 57822, 60620,\n", + " 61538, 63993, 67279, 71825, 79100, 80921, 82743, 84617,\n", + " 87399, 89282, 105029, 107735, 108662, 109587, 112413, 113327,\n", + " 117903, 119717, 121517, 134235, 135162, 144779, 147386, 151974,\n", + " 152925, 161688, 164405, 168001, 168950, 169900, 186696, 190388],\n", + " dtype='int64'), Int64Index([ 3963, 4883, 10556, 13316, 17851, 20601, 21524, 31580,\n", + " 44825, 45746, 46701, 49455, 55013, 55961, 57823, 60621,\n", + " 61539, 63994, 67280, 71826, 79101, 80922, 82744, 84618,\n", + " 87400, 89283, 105030, 107736, 108663, 109588, 112414, 113328,\n", + " 117904, 119718, 121518, 134236, 135163, 144780, 147387, 151975,\n", + " 152926, 161689, 164406, 168002, 168951, 169901, 186697, 190389],\n", + " dtype='int64'), Int64Index([ 3964, 4884, 10557, 13317, 17852, 20602, 21525, 31581,\n", + " 44826, 45747, 46702, 49456, 55014, 55962, 57824, 60622,\n", + " 61540, 63995, 67281, 71827, 79102, 80923, 82745, 84619,\n", + " 87401, 89284, 105031, 107737, 108664, 109589, 112415, 113329,\n", + " 117905, 119719, 121519, 134237, 135164, 144781, 147388, 151976,\n", + " 152927, 161690, 164407, 168003, 168952, 169902, 186698, 190390],\n", + " dtype='int64'), Int64Index([ 3965, 4885, 10558, 13318, 17853, 20603, 21526, 31582,\n", + " 44827, 45748, 46703, 49457, 55015, 55963, 57825, 60623,\n", + " 61541, 63996, 67282, 71828, 79103, 80924, 82746, 84620,\n", + " 87402, 89285, 105032, 107738, 108665, 109590, 112416, 113330,\n", + " 117906, 119720, 121520, 134238, 135165, 144782, 147389, 151977,\n", + " 152928, 161691, 164408, 168004, 168953, 169903, 186699, 190391],\n", + " dtype='int64'), Int64Index([ 3966, 4886, 10559, 13319, 17854, 20604, 21527, 31583,\n", + " 44828, 45749, 46704, 49458, 55016, 55964, 57826, 60624,\n", + " 61542, 63997, 65514, 67283, 71829, 79104, 80925, 82747,\n", + " 84621, 87403, 89286, 105033, 107739, 108666, 109591, 112417,\n", + " 113331, 117907, 119721, 121521, 134239, 135166, 144783, 147390,\n", + " 151978, 152929, 161692, 164409, 168005, 168954, 169904, 186700,\n", + " 190392],\n", + " dtype='int64'), Int64Index([ 3967, 4887, 10560, 13320, 17855, 20605, 21528, 31584,\n", + " 44829, 45750, 46705, 49459, 55017, 55965, 57827, 60625,\n", + " 61543, 63998, 65515, 67284, 71830, 79105, 80926, 82748,\n", + " 84622, 87404, 89287, 105034, 107740, 108667, 109592, 112418,\n", + " 113332, 117908, 119722, 121522, 134240, 135167, 144784, 147391,\n", + " 151979, 152930, 161693, 164410, 168006, 168955, 169905, 186701,\n", + " 190393],\n", + " dtype='int64'), Int64Index([ 3968, 4888, 10561, 13321, 17856, 20606, 21529, 31585,\n", + " 44830, 45751, 46706, 49460, 55018, 55966, 57828, 60626,\n", + " 61544, 63999, 65516, 67285, 71831, 79106, 80927, 82749,\n", + " 84623, 87405, 89288, 105035, 107741, 108668, 109593, 112419,\n", + " 113333, 117909, 119723, 121523, 134241, 135168, 144785, 147392,\n", + " 151980, 152931, 161694, 164411, 168007, 168956, 169906, 186702,\n", + " 190394],\n", + " dtype='int64'), Int64Index([ 3969, 4889, 10562, 13322, 17857, 20607, 21530, 31586,\n", + " 44831, 45752, 46707, 49461, 55019, 55967, 57829, 60627,\n", + " 61545, 64000, 65517, 67286, 71832, 79107, 80928, 82750,\n", + " 84624, 87406, 89289, 105036, 107742, 108669, 109594, 112420,\n", + " 113334, 117910, 119724, 121524, 134242, 135169, 144786, 147393,\n", + " 151981, 152932, 161695, 164412, 168008, 168957, 169907, 186703,\n", + " 190395],\n", + " dtype='int64'), Int64Index([ 3970, 4890, 10563, 13323, 17858, 20608, 21531, 31587,\n", + " 44832, 45753, 46708, 49462, 55020, 55968, 57830, 60628,\n", + " 61546, 64001, 65518, 67287, 71833, 79108, 80929, 82751,\n", + " 84625, 87407, 89290, 105037, 107743, 108670, 109595, 112421,\n", + " 113335, 117911, 119725, 121525, 134243, 135170, 144787, 147394,\n", + " 151982, 152933, 161696, 164413, 168009, 168958, 169908, 186704,\n", + " 190396],\n", + " dtype='int64'), Int64Index([ 3971, 4891, 10564, 13324, 17859, 20609, 21532, 31588,\n", + " 44833, 45754, 46709, 49463, 55021, 55969, 57831, 60629,\n", + " 61547, 64002, 65519, 67288, 71834, 79109, 80930, 82752,\n", + " 84626, 87408, 89291, 105038, 107744, 108671, 109596, 112422,\n", + " 113336, 117912, 119726, 121526, 134244, 135171, 144788, 147395,\n", + " 151983, 152934, 161697, 164414, 168010, 168959, 169909, 186705,\n", + " 190397],\n", + " dtype='int64'), Int64Index([ 3972, 4892, 10565, 13325, 17860, 20610, 21533, 31589,\n", + " 44834, 45755, 46710, 49464, 55022, 55970, 57832, 60630,\n", + " 61548, 64003, 65520, 67289, 71835, 79110, 80931, 82753,\n", + " 84627, 87409, 89292, 105039, 107745, 108672, 109597, 112423,\n", + " 113337, 117913, 119727, 121527, 134245, 135172, 144789, 147396,\n", + " 151984, 152935, 161698, 164415, 168011, 168960, 169910, 186706,\n", + " 190398],\n", + " dtype='int64'), Int64Index([ 3973, 4893, 10566, 13326, 17861, 20611, 21534, 31590,\n", + " 44835, 45756, 46711, 49465, 55023, 55971, 57833, 60631,\n", + " 61549, 64004, 65521, 67290, 71836, 79111, 80932, 82754,\n", + " 84628, 87410, 89293, 105040, 107746, 108673, 109598, 112424,\n", + " 113338, 117914, 119728, 121528, 134246, 135173, 144790, 147397,\n", + " 151985, 152936, 161699, 164416, 168012, 168961, 169911, 186707,\n", + " 190399],\n", + " dtype='int64'), Int64Index([ 3974, 4894, 10567, 13327, 17862, 20612, 21535, 31591,\n", + " 44836, 45757, 46712, 49466, 55024, 55972, 57834, 60632,\n", + " 61550, 64005, 65522, 67291, 71837, 79112, 80933, 82755,\n", + " 84629, 87411, 89294, 105041, 107747, 108674, 109599, 112425,\n", + " 113339, 117915, 119729, 121529, 134247, 135174, 144791, 147398,\n", + " 151986, 152937, 161700, 164417, 168013, 168962, 169912, 186708,\n", + " 190400],\n", + " dtype='int64'), Int64Index([ 3975, 4895, 10568, 13328, 17863, 20613, 21536, 31592,\n", + " 44837, 45758, 46713, 49467, 55025, 55973, 57835, 60633,\n", + " 61551, 64006, 65523, 67292, 71838, 79113, 80934, 82756,\n", + " 84630, 87412, 89295, 105042, 107748, 108675, 109600, 112426,\n", + " 113340, 117916, 119730, 121530, 134248, 135175, 144792, 147399,\n", + " 151987, 152938, 161701, 164418, 168014, 168963, 169913, 186709,\n", + " 190401],\n", + " dtype='int64'), Int64Index([ 3976, 4896, 10569, 13329, 17864, 20614, 21537, 31593,\n", + " 44838, 45759, 46714, 49468, 55026, 55974, 57836, 60634,\n", + " 61552, 64007, 65524, 67293, 71839, 79114, 80935, 82757,\n", + " 84631, 87413, 89296, 105043, 107749, 108676, 109601, 112427,\n", + " 113341, 117917, 119731, 121531, 134249, 135176, 144793, 147400,\n", + " 151988, 152939, 161702, 164419, 168015, 168964, 169914, 186710,\n", + " 190402],\n", + " dtype='int64'), Int64Index([ 3977, 4897, 10570, 13330, 17865, 20615, 21538, 31594,\n", + " 44839, 45760, 46715, 49469, 55027, 55975, 57837, 60635,\n", + " 61553, 64008, 65525, 67294, 71840, 79115, 80936, 82758,\n", + " 84632, 87414, 89297, 105044, 107750, 108677, 109602, 112428,\n", + " 113342, 117918, 119732, 121532, 134250, 135177, 144794, 147401,\n", + " 151989, 152940, 161703, 164420, 168016, 168965, 169915, 186711,\n", + " 190403],\n", + " dtype='int64'), Int64Index([ 3978, 4898, 10571, 13331, 17866, 20616, 21539, 31595,\n", + " 44840, 45761, 46716, 49470, 55028, 55976, 57838, 60636,\n", + " 61554, 64009, 65526, 67295, 71841, 79116, 80937, 82759,\n", + " 84633, 87415, 89298, 105045, 107751, 108678, 109603, 112429,\n", + " 113343, 117919, 119733, 121533, 134251, 135178, 144795, 147402,\n", + " 151990, 152941, 161704, 164421, 168017, 168966, 169916, 186712,\n", + " 190404],\n", + " dtype='int64'), Int64Index([ 3979, 4899, 10572, 13332, 17867, 20617, 21540, 31596,\n", + " 44841, 45762, 46717, 49471, 55029, 55977, 57839, 60637,\n", + " 61555, 64010, 65527, 67296, 71842, 79117, 80938, 82760,\n", + " 84634, 87416, 89299, 105046, 107752, 108679, 109604, 112430,\n", + " 113344, 117920, 119734, 121534, 134252, 135179, 144796, 147403,\n", + " 151991, 152942, 161705, 164422, 168018, 168967, 169917, 186713,\n", + " 190405],\n", + " dtype='int64'), Int64Index([ 3980, 4900, 10573, 13333, 17868, 20618, 21541, 31597,\n", + " 44842, 45763, 46718, 49472, 55030, 55978, 57840, 60638,\n", + " 61556, 64011, 65528, 67297, 71843, 79118, 80939, 82761,\n", + " 84635, 87417, 89300, 105047, 107753, 108680, 109605, 112431,\n", + " 113345, 117921, 119735, 121535, 134253, 135180, 144797, 147404,\n", + " 151992, 152943, 161706, 164423, 168019, 168968, 169918, 186714,\n", + " 190406],\n", + " dtype='int64'), Int64Index([ 3981, 4901, 10574, 13334, 17869, 20619, 21542, 31598,\n", + " 44843, 45764, 46719, 49473, 55031, 55979, 57841, 60639,\n", + " 61557, 64012, 65529, 67298, 71844, 79119, 80940, 82762,\n", + " 84636, 87418, 89301, 105048, 107754, 108681, 109606, 112432,\n", + " 113346, 117922, 119736, 121536, 134254, 135181, 144798, 147405,\n", + " 151993, 152944, 161707, 164424, 168020, 168969, 169919, 186715,\n", + " 190407],\n", + " dtype='int64'), Int64Index([ 3982, 4902, 10575, 13335, 17870, 20620, 21543, 31599,\n", + " 44844, 45765, 46720, 49474, 55032, 55980, 57842, 60640,\n", + " 61558, 64013, 65530, 67299, 71845, 79120, 80941, 82763,\n", + " 84637, 87419, 89302, 105049, 107755, 108682, 109607, 112433,\n", + " 113347, 117923, 119737, 121537, 134255, 135182, 144799, 147406,\n", + " 151994, 152945, 161708, 164425, 168021, 168970, 169920, 186716,\n", + " 190408],\n", + " dtype='int64'), Int64Index([ 3983, 4903, 10576, 13336, 17871, 20621, 21544, 31600,\n", + " 44845, 45766, 46721, 49475, 55033, 55981, 57843, 60641,\n", + " 61559, 64014, 65531, 67300, 71846, 79121, 80942, 82764,\n", + " 84638, 87420, 89303, 105050, 107756, 108683, 109608, 112434,\n", + " 113348, 117924, 119738, 121538, 134256, 135183, 144800, 147407,\n", + " 151995, 152946, 161709, 164426, 168022, 168971, 169921, 186717,\n", + " 190409],\n", + " dtype='int64'), Int64Index([ 3984, 4904, 10577, 13337, 17872, 20622, 21545, 31601,\n", + " 44846, 45767, 46722, 49476, 55034, 55982, 57844, 60642,\n", + " 61560, 64015, 65532, 67301, 71847, 79122, 80943, 82765,\n", + " 84639, 87421, 89304, 105051, 107757, 108684, 109609, 112435,\n", + " 113349, 117925, 119739, 121539, 134257, 135184, 144801, 147408,\n", + " 151996, 152947, 161710, 164427, 168023, 168972, 169922, 186718,\n", + " 190410],\n", + " dtype='int64'), Int64Index([ 3985, 4905, 10578, 13338, 17873, 20623, 21546, 31602,\n", + " 44847, 45768, 46723, 49477, 55035, 55983, 57845, 60643,\n", + " 61561, 64016, 65533, 67302, 71848, 79123, 80944, 82766,\n", + " 84640, 87422, 89305, 105052, 107758, 108685, 109610, 112436,\n", + " 113350, 117926, 119740, 121540, 134258, 135185, 144802, 147409,\n", + " 151997, 152948, 161711, 164428, 168024, 168973, 169923, 186719,\n", + " 190411],\n", + " dtype='int64'), Int64Index([ 3986, 4906, 10579, 13339, 17874, 20624, 21547, 31603,\n", + " 44848, 45769, 46724, 49478, 55036, 55984, 57846, 60644,\n", + " 61562, 64017, 65534, 67303, 71849, 79124, 80945, 82767,\n", + " 84641, 87423, 89306, 105053, 107759, 108686, 109611, 112437,\n", + " 113351, 117927, 119741, 121541, 134259, 135186, 144803, 147410,\n", + " 151998, 152949, 161712, 164429, 168025, 168974, 169924, 186720,\n", + " 190412],\n", + " dtype='int64'), Int64Index([ 3987, 4907, 10580, 13340, 17875, 20625, 21548, 31604,\n", + " 44849, 45770, 46725, 49479, 55037, 55985, 57847, 60645,\n", + " 61563, 64018, 65535, 67304, 71850, 79125, 80946, 82768,\n", + " 84642, 87424, 89307, 105054, 107760, 108687, 109612, 112438,\n", + " 113352, 117928, 119742, 121542, 134260, 135187, 144804, 147411,\n", + " 151999, 152950, 161713, 164430, 168026, 168975, 169925, 186721,\n", + " 190413],\n", + " dtype='int64'), Int64Index([ 3988, 4908, 10581, 13341, 17876, 20626, 21549, 31605,\n", + " 44850, 45771, 46726, 49480, 55038, 55986, 57848, 60646,\n", + " 61564, 64019, 65536, 67305, 71851, 79126, 80947, 82769,\n", + " 84643, 87425, 89308, 105055, 107761, 108688, 109613, 112439,\n", + " 113353, 117929, 119743, 121543, 134261, 135188, 144805, 147412,\n", + " 152000, 152951, 161714, 164431, 168027, 168976, 169926, 186722,\n", + " 190414],\n", + " dtype='int64'), Int64Index([ 3989, 4909, 10582, 13342, 17877, 20627, 21550, 31606,\n", + " 44851, 45772, 46727, 49481, 55039, 55987, 57849, 60647,\n", + " 61565, 64020, 65537, 67306, 71852, 79127, 80948, 82770,\n", + " 84644, 87426, 89309, 105056, 107762, 108689, 109614, 112440,\n", + " 113354, 117930, 119744, 121544, 134262, 135189, 144806, 147413,\n", + " 152001, 152952, 161715, 164432, 168028, 168977, 169927, 186723,\n", + " 190415],\n", + " dtype='int64'), Int64Index([ 3990, 4910, 10583, 13343, 17878, 20628, 21551, 31607,\n", + " 44852, 45773, 46728, 49482, 55040, 55988, 57850, 60648,\n", + " 61566, 64021, 65538, 67307, 71853, 79128, 80949, 82771,\n", + " 84645, 87427, 89310, 105057, 107763, 108690, 109615, 112441,\n", + " 113355, 117931, 119745, 121545, 134263, 135190, 144807, 147414,\n", + " 152002, 152953, 161716, 164433, 168029, 168978, 169928, 186724,\n", + " 190416],\n", + " dtype='int64'), Int64Index([ 3991, 4911, 10584, 13344, 17879, 20629, 21552, 31608,\n", + " 44853, 45774, 46729, 49483, 55041, 55989, 57851, 60649,\n", + " 61567, 64022, 65539, 67308, 71854, 79129, 80950, 82772,\n", + " 84646, 87428, 89311, 105058, 107764, 108691, 109616, 112442,\n", + " 113356, 117932, 119746, 121546, 134264, 135191, 144808, 147415,\n", + " 152003, 152954, 161717, 164434, 168030, 168979, 169929, 186725,\n", + " 190417],\n", + " dtype='int64'), Int64Index([ 3992, 4912, 10585, 13345, 17880, 20630, 21553, 31609,\n", + " 44854, 45775, 46730, 49484, 55042, 55990, 57852, 60650,\n", + " 61568, 64023, 65540, 67309, 71855, 79130, 80951, 82773,\n", + " 84647, 87429, 89312, 105059, 107765, 108692, 109617, 112443,\n", + " 113357, 117933, 119747, 121547, 134265, 135192, 144809, 147416,\n", + " 152004, 152955, 161718, 164435, 168031, 168980, 169930, 186726,\n", + " 190418],\n", + " dtype='int64'), Int64Index([ 3993, 4913, 10586, 13346, 17881, 20631, 21554, 31610,\n", + " 44855, 45776, 46731, 49485, 55043, 55991, 57853, 60651,\n", + " 61569, 64024, 65541, 67310, 71856, 79131, 80952, 82774,\n", + " 84648, 87430, 89313, 105060, 107766, 108693, 109618, 112444,\n", + " 113358, 117934, 119748, 121548, 134266, 135193, 144810, 147417,\n", + " 152005, 152956, 161719, 164436, 168032, 168981, 169931, 186727,\n", + " 190419],\n", + " dtype='int64'), Int64Index([ 3994, 4914, 10587, 13347, 17882, 20632, 21555, 31611,\n", + " 44856, 45777, 46732, 49486, 55044, 55992, 57854, 60652,\n", + " 61570, 64025, 65542, 67311, 71857, 79132, 80953, 82775,\n", + " 84649, 87431, 89314, 105061, 107767, 108694, 109619, 112445,\n", + " 113359, 117935, 119749, 121549, 134267, 135194, 144811, 147418,\n", + " 152006, 152957, 161720, 164437, 168033, 168982, 169932, 186728,\n", + " 190420],\n", + " dtype='int64'), Int64Index([ 3995, 4915, 10588, 13348, 17883, 20633, 21556, 31612,\n", + " 44857, 45778, 46733, 49487, 55045, 55993, 57855, 60653,\n", + " 61571, 64026, 65543, 67312, 71858, 79133, 80954, 82776,\n", + " 84650, 87432, 89315, 105062, 107768, 108695, 109620, 112446,\n", + " 113360, 117936, 119750, 121550, 134268, 135195, 144812, 147419,\n", + " 152007, 152958, 161721, 164438, 168034, 168983, 169933, 186729,\n", + " 190421],\n", + " dtype='int64'), Int64Index([ 3996, 4916, 10589, 13349, 17884, 20634, 21557, 31613,\n", + " 44858, 45779, 46734, 49488, 55046, 55994, 57856, 60654,\n", + " 61572, 64027, 65544, 67313, 71859, 79134, 80955, 82777,\n", + " 84651, 87433, 89316, 105063, 107769, 108696, 109621, 112447,\n", + " 113361, 117937, 119751, 121551, 134269, 135196, 144813, 147420,\n", + " 152008, 152959, 161722, 164439, 168035, 168984, 169934, 186730,\n", + " 190422],\n", + " dtype='int64'), Int64Index([ 3997, 4917, 10590, 13350, 17885, 20635, 21558, 31614,\n", + " 44859, 45780, 46735, 49489, 55047, 55995, 57857, 60655,\n", + " 61573, 64028, 65545, 67314, 71860, 79135, 80956, 82778,\n", + " 84652, 87434, 89317, 105064, 107770, 108697, 109622, 112448,\n", + " 113362, 117938, 119752, 121552, 134270, 135197, 144814, 147421,\n", + " 152009, 152960, 161723, 164440, 168036, 168985, 169935, 186731,\n", + " 190423],\n", + " dtype='int64'), Int64Index([ 3998, 4918, 10591, 13351, 17886, 20636, 21559, 31615,\n", + " 44860, 45781, 46736, 49490, 55048, 55996, 57858, 60656,\n", + " 61574, 64029, 65546, 67315, 71861, 79136, 80957, 82779,\n", + " 84653, 87435, 89318, 105065, 107771, 108698, 109623, 112449,\n", + " 113363, 117939, 119753, 121553, 134271, 135198, 144815, 147422,\n", + " 152010, 152961, 161724, 164441, 168037, 168986, 169936, 186732,\n", + " 190424],\n", + " dtype='int64'), Int64Index([ 3999, 4919, 10592, 13352, 17887, 20637, 21560, 31616,\n", + " 44861, 45782, 46737, 49491, 55049, 55997, 57859, 60657,\n", + " 61575, 64030, 65547, 67316, 71862, 79137, 80958, 82780,\n", + " 84654, 87436, 89319, 105066, 107772, 108699, 109624, 112450,\n", + " 113364, 117940, 119754, 121554, 134272, 135199, 144816, 147423,\n", + " 152011, 152962, 161725, 164442, 168038, 168987, 169937, 186733,\n", + " 190425],\n", + " dtype='int64'), Int64Index([ 4000, 4920, 10593, 13353, 17888, 20638, 21561, 31617,\n", + " 44862, 45783, 46738, 49492, 55050, 55998, 57860, 60658,\n", + " 61576, 64031, 65548, 67317, 71863, 79138, 80959, 82781,\n", + " 84655, 87437, 89320, 105067, 107773, 108700, 109625, 112451,\n", + " 113365, 117941, 119755, 121555, 134273, 135200, 144817, 147424,\n", + " 152012, 152963, 161726, 164443, 168039, 168988, 169938, 186734,\n", + " 190426],\n", + " dtype='int64'), Int64Index([ 4001, 4921, 10594, 13354, 17889, 20639, 21562, 31618,\n", + " 44863, 45784, 46739, 49493, 55051, 55999, 57861, 60659,\n", + " 61577, 64032, 65549, 67318, 71864, 79139, 80960, 82782,\n", + " 84656, 87438, 89321, 105068, 107774, 108701, 109626, 112452,\n", + " 113366, 117942, 119756, 121556, 134274, 135201, 144818, 147425,\n", + " 152013, 152964, 161727, 164444, 168040, 168989, 169939, 186735,\n", + " 190427],\n", + " dtype='int64'), Int64Index([ 4002, 4922, 10595, 13355, 17890, 20640, 21563, 31619,\n", + " 44864, 45785, 46740, 49494, 55052, 56000, 57862, 60660,\n", + " 61578, 64033, 65550, 67319, 71865, 79140, 80961, 82783,\n", + " 84657, 87439, 89322, 105069, 107775, 108702, 109627, 112453,\n", + " 113367, 117943, 119757, 121557, 134275, 135202, 144819, 147426,\n", + " 152014, 152965, 161728, 164445, 168041, 168990, 169940, 186736,\n", + " 190428],\n", + " dtype='int64'), Int64Index([ 4003, 4923, 10596, 13356, 17891, 20641, 21564, 31620,\n", + " 44865, 45786, 46741, 49495, 55053, 56001, 57863, 60661,\n", + " 61579, 64034, 65551, 67320, 71866, 79141, 80962, 82784,\n", + " 84658, 87440, 89323, 105070, 107776, 108703, 109628, 112454,\n", + " 113368, 117944, 119758, 121558, 134276, 135203, 144820, 147427,\n", + " 152015, 152966, 161729, 164446, 168042, 168991, 169941, 186737,\n", + " 190429],\n", + " dtype='int64'), Int64Index([ 4004, 4924, 10597, 13357, 17892, 20642, 21565, 31621,\n", + " 44866, 45787, 46742, 49496, 55054, 56002, 57864, 60662,\n", + " 61580, 64035, 65552, 67321, 71867, 79142, 80963, 82785,\n", + " 84659, 87441, 89324, 105071, 107777, 108704, 109629, 112455,\n", + " 113369, 117945, 119759, 121559, 134277, 135204, 144821, 147428,\n", + " 152016, 152967, 161730, 164447, 168043, 168992, 169942, 186738,\n", + " 190430],\n", + " dtype='int64'), Int64Index([ 4005, 4925, 10598, 13358, 17893, 20643, 21566, 31622,\n", + " 44867, 45788, 46743, 49497, 55055, 56003, 57865, 60663,\n", + " 61581, 64036, 65553, 67322, 71868, 79143, 80964, 82786,\n", + " 84660, 87442, 89325, 105072, 107778, 108705, 109630, 112456,\n", + " 113370, 117946, 119760, 121560, 134278, 135205, 144822, 147429,\n", + " 152017, 152968, 161731, 164448, 168044, 168993, 169943, 186739,\n", + " 190431],\n", + " dtype='int64'), Int64Index([ 4006, 4926, 10599, 13359, 17894, 20644, 21567, 31623,\n", + " 44868, 45789, 46744, 49498, 55056, 56004, 57866, 60664,\n", + " 61582, 64037, 65554, 67323, 71869, 79144, 80965, 82787,\n", + " 84661, 87443, 89326, 105073, 107779, 108706, 109631, 112457,\n", + " 113371, 117947, 119761, 121561, 134279, 135206, 144823, 147430,\n", + " 152018, 152969, 161732, 164449, 168045, 168994, 169944, 186740,\n", + " 190432],\n", + " dtype='int64'), Int64Index([ 4007, 4927, 10600, 13360, 17895, 20645, 21568, 31624,\n", + " 44869, 45790, 46745, 49499, 55057, 56005, 57867, 60665,\n", + " 61583, 64038, 65555, 67324, 71870, 79145, 80966, 82788,\n", + " 84662, 87444, 89327, 105074, 107780, 108707, 109632, 112458,\n", + " 113372, 117948, 119762, 121562, 134280, 135207, 144824, 147431,\n", + " 152019, 152970, 161733, 164450, 168046, 168995, 169945, 186741,\n", + " 190433],\n", + " dtype='int64'), Int64Index([ 4008, 4928, 10601, 13361, 17896, 20646, 21569, 31625,\n", + " 44870, 45791, 46746, 49500, 55058, 56006, 57868, 60666,\n", + " 61584, 64039, 65556, 67325, 71871, 79146, 80967, 82789,\n", + " 84663, 87445, 89328, 105075, 107781, 108708, 109633, 112459,\n", + " 113373, 117949, 119763, 121563, 134281, 135208, 144825, 147432,\n", + " 152020, 152971, 161734, 164451, 168047, 168996, 169946, 186742,\n", + " 190434],\n", + " dtype='int64'), Int64Index([ 4009, 4929, 10602, 13362, 17897, 20647, 21570, 31626,\n", + " 44871, 45792, 46747, 49501, 55059, 56007, 57869, 60667,\n", + " 61585, 64040, 65557, 67326, 71872, 79147, 80968, 82790,\n", + " 84664, 87446, 89329, 105076, 107782, 108709, 109634, 112460,\n", + " 113374, 117950, 119764, 121564, 134282, 135209, 144826, 147433,\n", + " 152021, 152972, 161735, 164452, 168048, 168997, 169947, 186743,\n", + " 190435],\n", + " dtype='int64'), Int64Index([ 4010, 4930, 10603, 13363, 17898, 20648, 21571, 31627,\n", + " 44872, 45793, 46748, 49502, 55060, 56008, 57870, 60668,\n", + " 61586, 64041, 65558, 67327, 71873, 79148, 80969, 82791,\n", + " 84665, 87447, 89330, 105077, 107783, 108710, 109635, 112461,\n", + " 113375, 117951, 119765, 121565, 134283, 135210, 144827, 147434,\n", + " 152022, 152973, 161736, 164453, 168049, 168998, 169948, 186744,\n", + " 190436],\n", + " dtype='int64'), Int64Index([ 4011, 4931, 10604, 13364, 17899, 20649, 21572, 31628,\n", + " 44873, 45794, 46749, 49503, 55061, 56009, 57871, 60669,\n", + " 61587, 64042, 65559, 67328, 71874, 79149, 80970, 82792,\n", + " 84666, 87448, 89331, 105078, 107784, 108711, 109636, 112462,\n", + " 113376, 117952, 119766, 121566, 134284, 135211, 144828, 147435,\n", + " 152023, 152974, 161737, 164454, 168050, 168999, 169949, 186745,\n", + " 190437],\n", + " dtype='int64'), Int64Index([ 4012, 4932, 10605, 13365, 17900, 20650, 21573, 31629,\n", + " 44874, 45795, 46750, 49504, 55062, 56010, 57872, 60670,\n", + " 61588, 64043, 65560, 67329, 71875, 79150, 80971, 82793,\n", + " 84667, 87449, 89332, 105079, 107785, 108712, 109637, 112463,\n", + " 113377, 117953, 119767, 121567, 134285, 135212, 144829, 147436,\n", + " 152024, 152975, 161738, 164455, 168051, 169000, 169950, 186746,\n", + " 190438],\n", + " dtype='int64'), Int64Index([ 4013, 4933, 10606, 13366, 17901, 20651, 21574, 31630,\n", + " 44875, 45796, 46751, 49505, 55063, 56011, 57873, 60671,\n", + " 61589, 64044, 65561, 67330, 71876, 79151, 80972, 82794,\n", + " 84668, 87450, 89333, 105080, 107786, 108713, 109638, 112464,\n", + " 113378, 117954, 119768, 121568, 134286, 135213, 144830, 147437,\n", + " 152025, 152976, 161739, 164456, 168052, 169001, 169951, 186747,\n", + " 190439],\n", + " dtype='int64'), Int64Index([ 4014, 4934, 10607, 13367, 17902, 20652, 21575, 31631,\n", + " 44876, 45797, 46752, 49506, 55064, 56012, 57874, 60672,\n", + " 61590, 64045, 65562, 67331, 71877, 79152, 80973, 82795,\n", + " 84669, 87451, 89334, 105081, 107787, 108714, 109639, 112465,\n", + " 113379, 117955, 119769, 121569, 134287, 135214, 144831, 147438,\n", + " 152026, 152977, 161740, 164457, 168053, 169002, 169952, 186748,\n", + " 190440],\n", + " dtype='int64'), Int64Index([ 4015, 4935, 10608, 13368, 17903, 20653, 21576, 31632,\n", + " 44877, 45798, 46753, 49507, 55065, 56013, 57875, 60673,\n", + " 61591, 64046, 65563, 67332, 71878, 79153, 80974, 82796,\n", + " 84670, 87452, 89335, 90790, 105082, 107788, 108715, 109640,\n", + " 112466, 113380, 117956, 119770, 121570, 134288, 135215, 144832,\n", + " 147439, 152027, 152978, 161741, 164458, 168054, 169003, 169953,\n", + " 186749, 190441],\n", + " dtype='int64'), Int64Index([ 4016, 4936, 10609, 13369, 17904, 20654, 21577, 31633,\n", + " 44878, 45799, 46754, 49508, 55066, 56014, 57876, 60674,\n", + " 61592, 64047, 65564, 67333, 71879, 79154, 80975, 82797,\n", + " 84671, 87453, 89336, 90791, 105083, 107789, 108716, 109641,\n", + " 112467, 113381, 117957, 119771, 121571, 134289, 135216, 144833,\n", + " 147440, 152028, 152979, 161742, 164459, 168055, 169004, 169954,\n", + " 186750, 190442],\n", + " dtype='int64'), Int64Index([ 4017, 4937, 10610, 13370, 17905, 20655, 21578, 31634,\n", + " 44879, 45800, 46755, 49509, 55067, 56015, 57877, 60675,\n", + " 61593, 64048, 65565, 67334, 71880, 79155, 80976, 82798,\n", + " 84672, 87454, 89337, 90792, 105084, 107790, 108717, 109642,\n", + " 112468, 113382, 117958, 119772, 121572, 134290, 135217, 144834,\n", + " 147441, 152029, 152980, 161743, 164460, 168056, 169005, 169955,\n", + " 186751, 190443],\n", + " dtype='int64'), Int64Index([ 4018, 4938, 10611, 13371, 17906, 20656, 21579, 31635,\n", + " 44880, 45801, 46756, 49510, 55068, 56016, 57878, 60676,\n", + " 61594, 64049, 65566, 67335, 71881, 79156, 80977, 82799,\n", + " 84673, 87455, 89338, 90793, 105085, 107791, 108718, 109643,\n", + " 112469, 113383, 117959, 119773, 121573, 134291, 135218, 144835,\n", + " 147442, 152030, 152981, 161744, 164461, 168057, 169006, 169956,\n", + " 186752, 190444],\n", + " dtype='int64'), Int64Index([ 4019, 4939, 10612, 13372, 17907, 20657, 21580, 31636,\n", + " 44881, 45802, 46757, 49511, 55069, 56017, 57879, 60677,\n", + " 61595, 64050, 65567, 67336, 71882, 79157, 80978, 82800,\n", + " 84674, 87456, 89339, 90794, 105086, 107792, 108719, 109644,\n", + " 112470, 113384, 117960, 119774, 121574, 134292, 135219, 144836,\n", + " 147443, 152031, 152982, 161745, 164462, 168058, 169007, 169957,\n", + " 186753, 190445],\n", + " dtype='int64'), Int64Index([ 4020, 4940, 10613, 13373, 17908, 20658, 21581, 31637,\n", + " 44882, 45803, 46758, 49512, 55070, 56018, 57880, 60678,\n", + " 61596, 64051, 65568, 67337, 71883, 79158, 80979, 82801,\n", + " 84675, 87457, 89340, 90795, 105087, 107793, 108720, 109645,\n", + " 112471, 113385, 117961, 119775, 121575, 134293, 135220, 144837,\n", + " 147444, 152032, 152983, 161746, 164463, 168059, 169008, 169958,\n", + " 186754, 190446],\n", + " dtype='int64'), Int64Index([ 4021, 4941, 10614, 13374, 17909, 20659, 21582, 31638,\n", + " 44883, 45804, 46759, 49513, 55071, 56019, 57881, 60679,\n", + " 61597, 64052, 65569, 67338, 71884, 79159, 80980, 82802,\n", + " 84676, 87458, 89341, 90796, 105088, 107794, 108721, 109646,\n", + " 112472, 113386, 117962, 119776, 121576, 134294, 135221, 144838,\n", + " 147445, 152033, 152984, 161747, 164464, 168060, 169009, 169959,\n", + " 186755, 190447],\n", + " dtype='int64'), Int64Index([ 4022, 4942, 10615, 13375, 17910, 20660, 21583, 31639,\n", + " 44884, 45805, 46760, 49514, 55072, 56020, 57882, 60680,\n", + " 61598, 64053, 65570, 67339, 71885, 79160, 80981, 82803,\n", + " 84677, 87459, 89342, 90797, 105089, 107795, 108722, 109647,\n", + " 112473, 113387, 117963, 119777, 121577, 134295, 135222, 144839,\n", + " 147446, 152034, 152985, 161748, 164465, 168061, 169010, 169960,\n", + " 186756, 190448],\n", + " dtype='int64'), Int64Index([ 4023, 4943, 10616, 13376, 17911, 20661, 21584, 31640,\n", + " 44885, 45806, 46761, 49515, 55073, 56021, 57883, 60681,\n", + " 61599, 64054, 65571, 67340, 71886, 79161, 80982, 82804,\n", + " 84678, 87460, 89343, 90798, 105090, 107796, 108723, 109648,\n", + " 112474, 113388, 117964, 119778, 121578, 134296, 135223, 144840,\n", + " 147447, 152035, 152986, 161749, 164466, 168062, 169011, 169961,\n", + " 186757, 190449],\n", + " dtype='int64'), Int64Index([ 4024, 4944, 10617, 13377, 17912, 20662, 21585, 31641,\n", + " 44886, 45807, 46762, 49516, 55074, 56022, 57884, 60682,\n", + " 61600, 64055, 65572, 67341, 71887, 79162, 80983, 82805,\n", + " 84679, 87461, 89344, 90799, 105091, 107797, 108724, 109649,\n", + " 112475, 113389, 117965, 119779, 121579, 134297, 135224, 144841,\n", + " 147448, 152036, 152987, 161750, 164467, 168063, 169012, 169962,\n", + " 186758, 190450],\n", + " dtype='int64'), Int64Index([ 4025, 4945, 10618, 13378, 17913, 20663, 21586, 31642,\n", + " 44887, 45808, 46763, 49517, 55075, 56023, 57885, 60683,\n", + " 61601, 64056, 65573, 67342, 71888, 79163, 80984, 82806,\n", + " 84680, 87462, 89345, 90800, 105092, 107798, 108725, 109650,\n", + " 112476, 113390, 117966, 119780, 121580, 134298, 135225, 144842,\n", + " 147449, 152037, 152988, 161751, 164468, 168064, 169013, 169963,\n", + " 186759, 190451],\n", + " dtype='int64'), Int64Index([ 4026, 4946, 10619, 13379, 17914, 20664, 21587, 31643,\n", + " 44888, 45809, 46764, 49518, 55076, 56024, 57886, 60684,\n", + " 61602, 64057, 65574, 67343, 71889, 79164, 80985, 82807,\n", + " 84681, 87463, 89346, 90801, 105093, 107799, 108726, 109651,\n", + " 112477, 113391, 117967, 119781, 121581, 134299, 135226, 144843,\n", + " 147450, 152038, 152989, 161752, 164469, 168065, 169014, 169964,\n", + " 186760, 190452],\n", + " dtype='int64'), Int64Index([ 4027, 4947, 10620, 13380, 17915, 20665, 21588, 31644,\n", + " 44889, 45810, 46765, 49519, 55077, 56025, 57887, 60685,\n", + " 61603, 64058, 65575, 67344, 71890, 79165, 80986, 82808,\n", + " 84682, 87464, 89347, 90802, 105094, 107800, 108727, 109652,\n", + " 112478, 113392, 117968, 119782, 121582, 134300, 135227, 144844,\n", + " 147451, 152039, 152990, 161753, 164470, 168066, 169015, 169965,\n", + " 186761, 190453],\n", + " dtype='int64'), Int64Index([ 4028, 4948, 10621, 13381, 17916, 20666, 21589, 31645,\n", + " 44890, 45811, 46766, 49520, 55078, 56026, 57888, 60686,\n", + " 61604, 64059, 65576, 67345, 71891, 79166, 80987, 82809,\n", + " 84683, 87465, 89348, 90803, 105095, 107801, 108728, 109653,\n", + " 112479, 113393, 117969, 119783, 121583, 134301, 135228, 144845,\n", + " 147452, 152040, 152991, 161754, 164471, 168067, 169016, 169966,\n", + " 186762, 190454],\n", + " dtype='int64'), Int64Index([ 4029, 4949, 10622, 13382, 17917, 20667, 21590, 31646,\n", + " 44891, 45812, 46767, 49521, 55079, 56027, 57889, 60687,\n", + " 61605, 64060, 65577, 67346, 71892, 79167, 80988, 82810,\n", + " 84684, 87466, 89349, 90804, 105096, 107802, 108729, 109654,\n", + " 112480, 113394, 117970, 119784, 121584, 134302, 135229, 144846,\n", + " 147453, 152041, 152992, 161755, 164472, 168068, 169017, 169967,\n", + " 186763, 190455],\n", + " dtype='int64'), Int64Index([ 4030, 4950, 10623, 13383, 17918, 20668, 21591, 31647,\n", + " 44892, 45813, 46768, 49522, 55080, 56028, 57890, 60688,\n", + " 61606, 64061, 65578, 67347, 71893, 79168, 80989, 82811,\n", + " 84685, 87467, 89350, 90805, 105097, 107803, 108730, 109655,\n", + " 112481, 113395, 117971, 119785, 121585, 134303, 135230, 144847,\n", + " 147454, 152042, 152993, 161756, 164473, 168069, 169018, 169968,\n", + " 186764, 190456],\n", + " dtype='int64'), Int64Index([ 4031, 4951, 10624, 13384, 17919, 20669, 21592, 31648,\n", + " 44893, 45814, 46769, 49523, 55081, 56029, 57891, 60689,\n", + " 61607, 64062, 65579, 67348, 71894, 79169, 80990, 82812,\n", + " 84686, 87468, 89351, 90806, 105098, 107804, 108731, 109656,\n", + " 112482, 113396, 117972, 119786, 121586, 134304, 135231, 144848,\n", + " 147455, 152043, 152994, 161757, 164474, 168070, 169019, 169969,\n", + " 186765, 190457],\n", + " dtype='int64'), Int64Index([ 4032, 4952, 10625, 13385, 17920, 20670, 21593, 31649,\n", + " 44894, 45815, 46770, 49524, 55082, 56030, 57892, 60690,\n", + " 61608, 64063, 65580, 67349, 71895, 79170, 80991, 82813,\n", + " 84687, 87469, 89352, 90807, 105099, 107805, 108732, 109657,\n", + " 112483, 113397, 117973, 119787, 121587, 134305, 135232, 144849,\n", + " 147456, 152044, 152995, 161758, 164475, 168071, 169020, 169970,\n", + " 186766, 190458],\n", + " dtype='int64'), Int64Index([ 4033, 4953, 10626, 13386, 17921, 20671, 21594, 31650,\n", + " 44895, 45816, 46771, 49525, 55083, 56031, 57893, 60691,\n", + " 61609, 64064, 65581, 67350, 71896, 79171, 80992, 82814,\n", + " 84688, 87470, 89353, 90808, 105100, 107806, 108733, 109658,\n", + " 112484, 113398, 117974, 119788, 121588, 134306, 135233, 144850,\n", + " 147457, 152045, 152996, 161759, 164476, 168072, 169021, 169971,\n", + " 186767, 190459],\n", + " dtype='int64'), Int64Index([ 4034, 4954, 10627, 13387, 17922, 20672, 21595, 31651,\n", + " 44896, 45817, 46772, 49526, 55084, 56032, 57894, 60692,\n", + " 61610, 64065, 65582, 67351, 71897, 79172, 80993, 82815,\n", + " 84689, 87471, 89354, 90809, 105101, 107807, 108734, 109659,\n", + " 112485, 113399, 117975, 119789, 121589, 134307, 135234, 144851,\n", + " 147458, 152046, 152997, 161760, 164477, 168073, 169022, 169972,\n", + " 186768, 190460],\n", + " dtype='int64'), Int64Index([ 4035, 4955, 10628, 13388, 17923, 20673, 21596, 31652,\n", + " 44897, 45818, 46773, 49527, 55085, 56033, 57895, 60693,\n", + " 61611, 64066, 65583, 67352, 71898, 79173, 80994, 82816,\n", + " 84690, 87472, 89355, 90810, 105102, 107808, 108735, 109660,\n", + " 112486, 113400, 117976, 119790, 121590, 134308, 135235, 144852,\n", + " 147459, 152047, 152998, 161761, 164478, 168074, 169023, 169973,\n", + " 186769, 190461],\n", + " dtype='int64'), Int64Index([ 4036, 4956, 10629, 13389, 17924, 20674, 21597, 31653,\n", + " 44898, 45819, 46774, 49528, 55086, 56034, 57896, 60694,\n", + " 61612, 64067, 65584, 67353, 71899, 79174, 80995, 82817,\n", + " 84691, 87473, 89356, 90811, 105103, 107809, 108736, 109661,\n", + " 112487, 113401, 117977, 119791, 121591, 134309, 135236, 144853,\n", + " 147460, 152048, 152999, 161762, 164479, 168075, 169024, 169974,\n", + " 186770, 190462],\n", + " dtype='int64'), Int64Index([ 4037, 4957, 10630, 13390, 17925, 20675, 21598, 31654,\n", + " 44899, 45820, 46775, 49529, 55087, 56035, 57897, 60695,\n", + " 61613, 64068, 65585, 67354, 71900, 79175, 80996, 82818,\n", + " 84692, 87474, 89357, 90812, 105104, 107810, 108737, 109662,\n", + " 112488, 113402, 117978, 119792, 121592, 134310, 135237, 144854,\n", + " 147461, 152049, 153000, 161763, 164480, 168076, 169025, 169975,\n", + " 186771, 190463],\n", + " dtype='int64'), Int64Index([ 4038, 4958, 10631, 13391, 17926, 20676, 21599, 31655,\n", + " 44900, 45821, 46776, 49530, 55088, 56036, 57898, 60696,\n", + " 61614, 64069, 65586, 67355, 71901, 79176, 80997, 82819,\n", + " 84693, 87475, 89358, 90813, 105105, 107811, 108738, 109663,\n", + " 112489, 113403, 117979, 119793, 121593, 134311, 135238, 144855,\n", + " 147462, 152050, 153001, 161764, 164481, 168077, 169026, 169976,\n", + " 186772, 190464],\n", + " dtype='int64'), Int64Index([ 4039, 4959, 10632, 13392, 17927, 20677, 21600, 31656,\n", + " 44901, 45822, 46777, 49531, 55089, 56037, 57899, 60697,\n", + " 61615, 64070, 65587, 67356, 71902, 79177, 80998, 82820,\n", + " 84694, 87476, 89359, 90814, 105106, 107812, 108739, 109664,\n", + " 112490, 113404, 117980, 119794, 121594, 134312, 135239, 144856,\n", + " 147463, 152051, 153002, 161765, 164482, 168078, 169027, 169977,\n", + " 186773, 190465],\n", + " dtype='int64'), Int64Index([ 4040, 4960, 10633, 13393, 17928, 20678, 21601, 31657,\n", + " 44902, 45823, 46778, 49532, 55090, 56038, 57900, 60698,\n", + " 61616, 64071, 65588, 67357, 71903, 79178, 80999, 82821,\n", + " 84695, 87477, 89360, 90815, 105107, 107813, 108740, 109665,\n", + " 112491, 113405, 117981, 119795, 121595, 134313, 135240, 144857,\n", + " 147464, 152052, 153003, 161766, 164483, 168079, 169028, 169978,\n", + " 186774, 190466],\n", + " dtype='int64'), Int64Index([ 4041, 4961, 10634, 13394, 17929, 20679, 21602, 31658,\n", + " 44903, 45824, 46779, 49533, 55091, 56039, 57901, 60699,\n", + " 61617, 64072, 65589, 67358, 71904, 79179, 81000, 82822,\n", + " 84696, 87478, 89361, 90816, 105108, 107814, 108741, 109666,\n", + " 112492, 113406, 117982, 119796, 121596, 134314, 135241, 144858,\n", + " 147465, 152053, 153004, 161767, 164484, 168080, 169029, 169979,\n", + " 186775, 190467],\n", + " dtype='int64'), Int64Index([ 4042, 4962, 10635, 13395, 17930, 20680, 21603, 31659,\n", + " 44904, 45825, 46780, 49534, 55092, 56040, 57902, 60700,\n", + " 61618, 64073, 65590, 67359, 71905, 79180, 81001, 82823,\n", + " 84697, 87479, 89362, 90817, 105109, 107815, 108742, 109667,\n", + " 112493, 113407, 117983, 119797, 121597, 134315, 135242, 144859,\n", + " 147466, 152054, 153005, 161768, 164485, 168081, 169030, 169980,\n", + " 186776, 190468],\n", + " dtype='int64'), Int64Index([ 4043, 4963, 10636, 13396, 17931, 20681, 21604, 31660,\n", + " 44905, 45826, 46781, 49535, 55093, 56041, 57903, 60701,\n", + " 61619, 64074, 65591, 67360, 71906, 79181, 81002, 82824,\n", + " 84698, 87480, 89363, 90818, 105110, 107816, 108743, 109668,\n", + " 112494, 113408, 117984, 119798, 121598, 134316, 135243, 144860,\n", + " 147467, 152055, 153006, 161769, 164486, 168082, 169031, 169981,\n", + " 186777, 190469],\n", + " dtype='int64'), Int64Index([ 4044, 4964, 10637, 13397, 17932, 20682, 21605, 31661,\n", + " 44906, 45827, 46782, 49536, 55094, 56042, 57904, 60702,\n", + " 61620, 64075, 65592, 67361, 71907, 79182, 81003, 82825,\n", + " 84699, 87481, 89364, 90819, 105111, 107817, 108744, 109669,\n", + " 112495, 113409, 117985, 119799, 121599, 134317, 135244, 144861,\n", + " 147468, 152056, 153007, 161770, 164487, 168083, 169032, 169982,\n", + " 186778, 190470],\n", + " dtype='int64'), Int64Index([ 4045, 4965, 10638, 13398, 17933, 20683, 21606, 31662,\n", + " 44907, 45828, 46783, 49537, 55095, 56043, 57905, 60703,\n", + " 61621, 64076, 65593, 67362, 71908, 79183, 81004, 82826,\n", + " 84700, 87482, 89365, 90820, 105112, 107818, 108745, 109670,\n", + " 112496, 113410, 117986, 119800, 121600, 134318, 135245, 144862,\n", + " 147469, 152057, 153008, 161771, 164488, 168084, 169033, 169983,\n", + " 186779, 190471],\n", + " dtype='int64'), Int64Index([ 4046, 4966, 10639, 13399, 17934, 20684, 21607, 31663,\n", + " 44908, 45829, 46784, 49538, 55096, 56044, 57906, 60704,\n", + " 61622, 64077, 65594, 67363, 71909, 79184, 81005, 82827,\n", + " 84701, 87483, 89366, 90821, 105113, 107819, 108746, 109671,\n", + " 112497, 113411, 117987, 119801, 121601, 134319, 135246, 144863,\n", + " 147470, 152058, 153009, 161772, 164489, 168085, 169034, 169984,\n", + " 186780, 190472],\n", + " dtype='int64'), Int64Index([ 4047, 4967, 10640, 13400, 17935, 20685, 21608, 31664,\n", + " 44909, 45830, 46785, 49539, 55097, 56045, 57907, 60705,\n", + " 61623, 64078, 65595, 67364, 71910, 79185, 81006, 82828,\n", + " 84702, 87484, 89367, 90822, 105114, 107820, 108747, 109672,\n", + " 112498, 113412, 117988, 119802, 121602, 134320, 135247, 144864,\n", + " 147471, 152059, 153010, 161773, 164490, 168086, 169035, 169985,\n", + " 186781, 190473],\n", + " dtype='int64'), Int64Index([ 4048, 4968, 10641, 13401, 17936, 20686, 21609, 31665,\n", + " 44910, 45831, 46786, 49540, 55098, 56046, 57908, 60706,\n", + " 61624, 64079, 65596, 67365, 71911, 79186, 81007, 82829,\n", + " 84703, 87485, 89368, 90823, 105115, 107821, 108748, 109673,\n", + " 112499, 113413, 117989, 119803, 121603, 134321, 135248, 144865,\n", + " 147472, 152060, 153011, 161774, 164491, 168087, 169036, 169986,\n", + " 186782, 190474],\n", + " dtype='int64'), Int64Index([ 4049, 4969, 10642, 13402, 17937, 20687, 21610, 31666,\n", + " 44911, 45832, 46787, 49541, 55099, 56047, 57909, 60707,\n", + " 61625, 64080, 65597, 67366, 71912, 79187, 81008, 82830,\n", + " 84704, 87486, 89369, 90824, 105116, 107822, 108749, 109674,\n", + " 112500, 113414, 117990, 119804, 121604, 134322, 135249, 144866,\n", + " 147473, 152061, 153012, 161775, 164492, 168088, 169037, 169987,\n", + " 186783, 190475],\n", + " dtype='int64'), Int64Index([ 4050, 4970, 10643, 13403, 17938, 20688, 21611, 31667,\n", + " 44912, 45833, 46788, 49542, 55100, 56048, 57910, 60708,\n", + " 61626, 64081, 65598, 67367, 71913, 79188, 81009, 82831,\n", + " 84705, 87487, 89370, 90825, 105117, 107823, 108750, 109675,\n", + " 112501, 113415, 117991, 119805, 121605, 134323, 135250, 144867,\n", + " 147474, 152062, 153013, 161776, 164493, 168089, 169038, 169988,\n", + " 186784, 190476],\n", + " dtype='int64'), Int64Index([ 4051, 4971, 10644, 13404, 17939, 20689, 21612, 31668,\n", + " 44913, 45834, 46789, 49543, 55101, 56049, 57911, 60709,\n", + " 61627, 64082, 65599, 67368, 71914, 79189, 81010, 82832,\n", + " 84706, 87488, 89371, 90826, 105118, 107824, 108751, 109676,\n", + " 112502, 113416, 117992, 119806, 121606, 134324, 135251, 144868,\n", + " 147475, 152063, 153014, 161777, 164494, 168090, 169039, 169989,\n", + " 186785, 190477],\n", + " dtype='int64'), Int64Index([ 4052, 4972, 10645, 13405, 17940, 20690, 21613, 31669,\n", + " 44914, 45835, 46790, 49544, 55102, 56050, 57912, 60710,\n", + " 61628, 64083, 65600, 67369, 71915, 79190, 81011, 82833,\n", + " 84707, 87489, 89372, 90827, 105119, 107825, 108752, 109677,\n", + " 112503, 113417, 117993, 119807, 121607, 134325, 135252, 144869,\n", + " 147476, 152064, 153015, 161778, 164495, 168091, 169040, 169990,\n", + " 186786, 190478],\n", + " dtype='int64'), Int64Index([ 4053, 4973, 10646, 13406, 17941, 20691, 21614, 31670,\n", + " 44915, 45836, 46791, 49545, 55103, 56051, 57913, 60711,\n", + " 61629, 64084, 65601, 67370, 71916, 79191, 81012, 82834,\n", + " 84708, 87490, 89373, 90828, 105120, 107826, 108753, 109678,\n", + " 112504, 113418, 117994, 119808, 121608, 134326, 135253, 144870,\n", + " 147477, 152065, 153016, 161779, 164496, 168092, 169041, 169991,\n", + " 186787, 190479],\n", + " dtype='int64'), Int64Index([ 4054, 4974, 10647, 13407, 17942, 20692, 21615, 31671,\n", + " 44916, 45837, 46792, 49546, 55104, 56052, 57914, 60712,\n", + " 61630, 64085, 65602, 67371, 71917, 79192, 81013, 82835,\n", + " 84709, 87491, 89374, 90829, 105121, 107827, 108754, 109679,\n", + " 112505, 113419, 117995, 119809, 121609, 134327, 135254, 144871,\n", + " 147478, 152066, 153017, 161780, 164497, 168093, 169042, 169992,\n", + " 186788, 190480],\n", + " dtype='int64'), Int64Index([ 4055, 4975, 10648, 13408, 17943, 20693, 21616, 31672,\n", + " 44917, 45838, 46793, 49547, 55105, 56053, 57915, 60713,\n", + " 61631, 64086, 65603, 67372, 71918, 79193, 81014, 82836,\n", + " 84710, 87492, 89375, 90830, 105122, 107828, 108755, 109680,\n", + " 112506, 113420, 117996, 119810, 121610, 134328, 135255, 144872,\n", + " 147479, 152067, 153018, 161781, 164498, 168094, 169043, 169993,\n", + " 186789, 190481],\n", + " dtype='int64'), Int64Index([ 4056, 4976, 10649, 13409, 17944, 20694, 21617, 31673,\n", + " 44918, 45839, 46794, 49548, 55106, 56054, 57916, 60714,\n", + " 61632, 64087, 65604, 67373, 71919, 79194, 81015, 82837,\n", + " 84711, 87493, 89376, 90831, 105123, 107829, 108756, 109681,\n", + " 112507, 113421, 117997, 119811, 121611, 134329, 135256, 144873,\n", + " 147480, 152068, 153019, 161782, 164499, 168095, 169044, 169994,\n", + " 186790, 190482],\n", + " dtype='int64'), Int64Index([ 4057, 4977, 10650, 13410, 17945, 20695, 21618, 31674,\n", + " 44919, 45840, 46795, 49549, 55107, 56055, 57917, 60715,\n", + " 61633, 64088, 65605, 67374, 71920, 79195, 81016, 82838,\n", + " 84712, 87494, 89377, 90832, 105124, 107830, 108757, 109682,\n", + " 112508, 113422, 117998, 119812, 121612, 134330, 135257, 144874,\n", + " 147481, 152069, 153020, 161783, 164500, 168096, 169045, 169995,\n", + " 186791, 190483],\n", + " dtype='int64'), Int64Index([ 4058, 4978, 10651, 13411, 17946, 20696, 21619, 31675,\n", + " 44920, 45841, 46796, 49550, 55108, 56056, 57918, 60716,\n", + " 61634, 64089, 65606, 67375, 71921, 79196, 81017, 82839,\n", + " 84713, 87495, 89378, 90833, 105125, 107831, 108758, 109683,\n", + " 112509, 113423, 117999, 119813, 121613, 134331, 135258, 144875,\n", + " 147482, 152070, 153021, 161784, 164501, 168097, 169046, 169996,\n", + " 186792, 190484],\n", + " dtype='int64'), Int64Index([ 4059, 4979, 10652, 13412, 17947, 20697, 21620, 31676,\n", + " 44921, 45842, 46797, 49551, 55109, 56057, 57919, 60717,\n", + " 61635, 64090, 65607, 67376, 71922, 79197, 81018, 82840,\n", + " 84714, 87496, 89379, 90834, 105126, 107832, 108759, 109684,\n", + " 112510, 113424, 118000, 119814, 121614, 134332, 135259, 144876,\n", + " 147483, 152071, 153022, 161785, 164502, 168098, 169047, 169997,\n", + " 186793, 190485],\n", + " dtype='int64'), Int64Index([ 4060, 4980, 10653, 13413, 17948, 20698, 21621, 31677,\n", + " 44922, 45843, 46798, 49552, 55110, 56058, 57920, 60718,\n", + " 61636, 64091, 65608, 67377, 71923, 79198, 81019, 82841,\n", + " 84715, 87497, 89380, 90835, 105127, 107833, 108760, 109685,\n", + " 112511, 113425, 118001, 119815, 121615, 134333, 135260, 144877,\n", + " 147484, 152072, 153023, 161786, 164503, 168099, 169048, 169998,\n", + " 186794, 190486],\n", + " dtype='int64'), Int64Index([ 4061, 4981, 10654, 13414, 17949, 20699, 21622, 31678,\n", + " 44923, 45844, 46799, 49553, 55111, 56059, 57921, 60719,\n", + " 61637, 64092, 65609, 67378, 71924, 79199, 81020, 82842,\n", + " 84716, 87498, 89381, 90836, 105128, 107834, 108761, 109686,\n", + " 112512, 113426, 118002, 119816, 121616, 134334, 135261, 144878,\n", + " 147485, 152073, 153024, 161787, 164504, 168100, 169049, 169999,\n", + " 186795, 190487],\n", + " dtype='int64'), Int64Index([ 4062, 4982, 10655, 13415, 17950, 20700, 21623, 31679,\n", + " 44924, 45845, 46800, 49554, 55112, 56060, 57922, 60720,\n", + " 61638, 64093, 65610, 67379, 71925, 79200, 81021, 82843,\n", + " 84717, 87499, 89382, 90837, 105129, 107835, 108762, 109687,\n", + " 112513, 113427, 118003, 119817, 121617, 134335, 135262, 144879,\n", + " 147486, 152074, 153025, 161788, 164505, 168101, 169050, 170000,\n", + " 186796, 190488],\n", + " dtype='int64'), Int64Index([ 4063, 4983, 10656, 13416, 17951, 20701, 21624, 31680,\n", + " 44925, 45846, 46801, 49555, 55113, 56061, 57923, 60721,\n", + " 61639, 64094, 65611, 67380, 71926, 79201, 81022, 82844,\n", + " 84718, 87500, 89383, 90838, 105130, 107836, 108763, 109688,\n", + " 112514, 113428, 118004, 119818, 121618, 134336, 135263, 144880,\n", + " 147487, 152075, 153026, 161789, 164506, 168102, 169051, 170001,\n", + " 186797, 190489],\n", + " dtype='int64'), Int64Index([ 4064, 4984, 10657, 13417, 17952, 20702, 21625, 31681,\n", + " 44926, 45847, 46802, 49556, 55114, 56062, 57924, 60722,\n", + " 61640, 64095, 65612, 67381, 71927, 79202, 81023, 82845,\n", + " 84719, 87501, 89384, 90839, 105131, 107837, 108764, 109689,\n", + " 112515, 113429, 118005, 119819, 121619, 134337, 135264, 144881,\n", + " 147488, 152076, 153027, 161790, 164507, 168103, 169052, 170002,\n", + " 186798, 190490],\n", + " dtype='int64'), Int64Index([ 4065, 4985, 10658, 13418, 17953, 20703, 21626, 31682,\n", + " 44927, 45848, 46803, 49557, 55115, 56063, 57925, 60723,\n", + " 61641, 64096, 65613, 67382, 71928, 79203, 81024, 82846,\n", + " 84720, 87502, 89385, 90840, 105132, 107838, 108765, 109690,\n", + " 112516, 113430, 118006, 119820, 121620, 134338, 135265, 144882,\n", + " 147489, 152077, 153028, 161791, 164508, 168104, 169053, 170003,\n", + " 186799, 190491],\n", + " dtype='int64'), Int64Index([ 4066, 4986, 10659, 13419, 17954, 20704, 21627, 31683,\n", + " 44928, 45849, 46804, 49558, 55116, 56064, 57926, 60724,\n", + " 61642, 64097, 65614, 67383, 71929, 79204, 81025, 82847,\n", + " 84721, 87503, 89386, 90841, 105133, 107839, 108766, 109691,\n", + " 112517, 113431, 118007, 119821, 121621, 134339, 135266, 144883,\n", + " 147490, 152078, 153029, 161792, 164509, 168105, 169054, 170004,\n", + " 186800, 190492],\n", + " dtype='int64'), Int64Index([ 4067, 4987, 10660, 13420, 17955, 20705, 21628, 31684,\n", + " 44929, 45850, 46805, 49559, 55117, 56065, 57927, 60725,\n", + " 61643, 64098, 65615, 67384, 71930, 79205, 81026, 82848,\n", + " 84722, 87504, 89387, 90842, 105134, 107840, 108767, 109692,\n", + " 112518, 113432, 118008, 119822, 121622, 134340, 135267, 144884,\n", + " 147491, 152079, 153030, 161793, 164510, 168106, 169055, 170005,\n", + " 186801, 190493],\n", + " dtype='int64'), Int64Index([ 4068, 4988, 10661, 13421, 17956, 20706, 21629, 31685,\n", + " 44930, 45851, 46806, 49560, 55118, 56066, 57928, 60726,\n", + " 61644, 64099, 65616, 67385, 71931, 79206, 81027, 82849,\n", + " 84723, 87505, 89388, 90843, 105135, 107841, 108768, 109693,\n", + " 112519, 113433, 118009, 119823, 121623, 134341, 135268, 144885,\n", + " 147492, 152080, 153031, 161794, 164511, 168107, 169056, 170006,\n", + " 186802, 190494],\n", + " dtype='int64'), Int64Index([ 4069, 4989, 10662, 13422, 17957, 20707, 21630, 31686,\n", + " 44931, 45852, 46807, 49561, 55119, 56067, 57929, 60727,\n", + " 61645, 64100, 65617, 67386, 71932, 79207, 81028, 82850,\n", + " 84724, 87506, 89389, 90844, 105136, 107842, 108769, 109694,\n", + " 112520, 113434, 118010, 119824, 121624, 134342, 135269, 144886,\n", + " 147493, 152081, 153032, 161795, 164512, 168108, 169057, 170007,\n", + " 186803, 190495],\n", + " dtype='int64'), Int64Index([ 4070, 4990, 10663, 13423, 17958, 20708, 21631, 31687,\n", + " 44932, 45853, 46808, 49562, 55120, 56068, 57930, 60728,\n", + " 61646, 64101, 65618, 67387, 71933, 79208, 81029, 82851,\n", + " 84725, 87507, 89390, 90845, 105137, 107843, 108770, 109695,\n", + " 112521, 113435, 118011, 119825, 121625, 134343, 135270, 144887,\n", + " 147494, 152082, 153033, 161796, 164513, 168109, 169058, 170008,\n", + " 186804, 190496],\n", + " dtype='int64'), Int64Index([ 4071, 4991, 10664, 13424, 17959, 20709, 21632, 31688,\n", + " 44933, 45854, 46809, 49563, 55121, 56069, 57931, 60729,\n", + " 61647, 64102, 65619, 67388, 71934, 79209, 81030, 82852,\n", + " 84726, 87508, 89391, 90846, 105138, 107844, 108771, 109696,\n", + " 112522, 113436, 118012, 119826, 121626, 134344, 135271, 144888,\n", + " 147495, 152083, 153034, 161797, 164514, 168110, 169059, 170009,\n", + " 186805, 190497],\n", + " dtype='int64'), Int64Index([ 4072, 4992, 10665, 13425, 17960, 20710, 21633, 31689,\n", + " 44934, 45855, 46810, 49564, 55122, 56070, 57932, 60730,\n", + " 61648, 64103, 65620, 67389, 71935, 79210, 81031, 82853,\n", + " 84727, 87509, 89392, 90847, 105139, 107845, 108772, 109697,\n", + " 112523, 113437, 118013, 119827, 121627, 134345, 135272, 144889,\n", + " 147496, 152084, 153035, 161798, 164515, 168111, 169060, 170010,\n", + " 186806, 190498],\n", + " dtype='int64'), Int64Index([ 4073, 4993, 10666, 13426, 17961, 20711, 21634, 31690,\n", + " 44935, 45856, 46811, 49565, 55123, 56071, 57933, 60731,\n", + " 61649, 64104, 65621, 67390, 71936, 79211, 81032, 82854,\n", + " 84728, 87510, 89393, 90848, 105140, 107846, 108773, 109698,\n", + " 112524, 113438, 118014, 119828, 121628, 134346, 135273, 144890,\n", + " 147497, 152085, 153036, 161799, 164516, 168112, 169061, 170011,\n", + " 186807, 190499],\n", + " dtype='int64'), Int64Index([ 4074, 4994, 10667, 13427, 17962, 20712, 21635, 31691,\n", + " 44936, 45857, 46812, 49566, 55124, 56072, 57934, 60732,\n", + " 61650, 64105, 65622, 67391, 71937, 79212, 81033, 82855,\n", + " 84729, 87511, 89394, 90849, 105141, 107847, 108774, 109699,\n", + " 112525, 113439, 118015, 119829, 121629, 134347, 135274, 144891,\n", + " 147498, 152086, 153037, 161800, 164517, 168113, 169062, 170012,\n", + " 186808, 190500],\n", + " dtype='int64'), Int64Index([ 4075, 4995, 10668, 13428, 17963, 20713, 21636, 31692,\n", + " 44937, 45858, 46813, 49567, 55125, 56073, 57935, 60733,\n", + " 61651, 64106, 65623, 67392, 71938, 79213, 81034, 82856,\n", + " 84730, 87512, 89395, 90850, 105142, 107848, 108775, 109700,\n", + " 112526, 113440, 118016, 119830, 121630, 134348, 135275, 144892,\n", + " 147499, 152087, 153038, 161801, 164518, 168114, 169063, 170013,\n", + " 186809, 190501],\n", + " dtype='int64'), Int64Index([ 4076, 4996, 10669, 13429, 17964, 20714, 21637, 31693,\n", + " 44938, 45859, 46814, 49568, 55126, 56074, 57936, 60734,\n", + " 61652, 64107, 65624, 67393, 71939, 79214, 81035, 82857,\n", + " 84731, 87513, 89396, 90851, 105143, 107849, 108776, 109701,\n", + " 112527, 113441, 118017, 119831, 121631, 134349, 135276, 144893,\n", + " 147500, 152088, 153039, 161802, 164519, 168115, 169064, 170014,\n", + " 186810, 190502],\n", + " dtype='int64'), Int64Index([ 4077, 4997, 10670, 13430, 17965, 20715, 21638, 31694,\n", + " 44939, 45860, 46815, 49569, 55127, 56075, 57937, 60735,\n", + " 61653, 64108, 65625, 67394, 71940, 79215, 81036, 82858,\n", + " 84732, 87514, 89397, 90852, 105144, 107850, 108777, 109702,\n", + " 112528, 113442, 118018, 119832, 121632, 134350, 135277, 144894,\n", + " 147501, 152089, 153040, 161803, 164520, 168116, 169065, 170015,\n", + " 186811, 190503],\n", + " dtype='int64'), Int64Index([ 4078, 4998, 10671, 13431, 17966, 20716, 21639, 31695,\n", + " 44940, 45861, 46816, 49570, 55128, 56076, 57938, 60736,\n", + " 61654, 64109, 65626, 67395, 71941, 79216, 81037, 82859,\n", + " 84733, 87515, 89398, 90853, 105145, 107851, 108778, 109703,\n", + " 112529, 113443, 118019, 119833, 121633, 134351, 135278, 144895,\n", + " 147502, 152090, 153041, 161804, 164521, 168117, 169066, 170016,\n", + " 186812, 190504],\n", + " dtype='int64'), Int64Index([ 4079, 4999, 10672, 13432, 17967, 20717, 21640, 31696,\n", + " 44941, 45862, 46817, 49571, 55129, 56077, 57939, 60737,\n", + " 61655, 64110, 65627, 67396, 71942, 79217, 81038, 82860,\n", + " 84734, 87516, 89399, 90854, 105146, 107852, 108779, 109704,\n", + " 112530, 113444, 118020, 119834, 121634, 134352, 135279, 144896,\n", + " 147503, 152091, 153042, 161805, 164522, 168118, 169067, 170017,\n", + " 186813, 190505],\n", + " dtype='int64'), Int64Index([ 4080, 5000, 10673, 13433, 17968, 20718, 21641, 31697,\n", + " 44942, 45863, 46818, 49572, 55130, 56078, 57940, 60738,\n", + " 61656, 64111, 65628, 67397, 71943, 79218, 81039, 82861,\n", + " 84735, 87517, 89400, 90855, 105147, 107853, 108780, 109705,\n", + " 112531, 113445, 118021, 119835, 121635, 134353, 135280, 144897,\n", + " 147504, 152092, 153043, 161806, 164523, 168119, 169068, 170018,\n", + " 186814, 190506],\n", + " dtype='int64'), Int64Index([ 4081, 5001, 10674, 13434, 17969, 20719, 21642, 31698,\n", + " 44943, 45864, 46819, 49573, 55131, 56079, 57941, 60739,\n", + " 61657, 64112, 65629, 67398, 71944, 79219, 81040, 82862,\n", + " 84736, 87518, 89401, 90856, 105148, 107854, 108781, 109706,\n", + " 112532, 113446, 118022, 119836, 121636, 134354, 135281, 144898,\n", + " 147505, 152093, 153044, 161807, 164524, 168120, 169069, 170019,\n", + " 186815, 190507],\n", + " dtype='int64'), Int64Index([ 4082, 5002, 10675, 13435, 17970, 20720, 21643, 31699,\n", + " 44944, 45865, 46820, 49574, 55132, 56080, 57942, 60740,\n", + " 61658, 64113, 65630, 67399, 71945, 79220, 81041, 82863,\n", + " 84737, 87519, 89402, 90857, 105149, 107855, 108782, 109707,\n", + " 112533, 113447, 118023, 119837, 121637, 134355, 135282, 144899,\n", + " 147506, 152094, 153045, 161808, 164525, 168121, 169070, 170020,\n", + " 186816, 190508],\n", + " dtype='int64'), Int64Index([ 4083, 5003, 10676, 13436, 17971, 20721, 21644, 31700,\n", + " 44945, 45866, 46821, 49575, 55133, 56081, 57943, 60741,\n", + " 61659, 64114, 65631, 67400, 71946, 79221, 81042, 82864,\n", + " 84738, 87520, 89403, 90858, 105150, 107856, 108783, 109708,\n", + " 112534, 113448, 118024, 119838, 121638, 134356, 135283, 144900,\n", + " 147507, 152095, 153046, 161809, 164526, 168122, 169071, 170021,\n", + " 186817, 190509],\n", + " dtype='int64'), Int64Index([ 4084, 5004, 10677, 13437, 17972, 20722, 21645, 31701,\n", + " 44946, 45867, 46822, 49576, 55134, 56082, 57944, 60742,\n", + " 61660, 64115, 65632, 67401, 71947, 79222, 81043, 82865,\n", + " 84739, 87521, 89404, 90859, 105151, 107857, 108784, 109709,\n", + " 112535, 113449, 118025, 119839, 121639, 134357, 135284, 144901,\n", + " 147508, 152096, 153047, 161810, 164527, 168123, 169072, 170022,\n", + " 186818, 190510],\n", + " dtype='int64'), Int64Index([ 4085, 5005, 10678, 13438, 17973, 20723, 21646, 31702,\n", + " 44947, 45868, 46823, 49577, 55135, 56083, 57945, 60743,\n", + " 61661, 64116, 65633, 67402, 71948, 79223, 81044, 82866,\n", + " 84740, 87522, 89405, 90860, 105152, 107858, 108785, 109710,\n", + " 112536, 113450, 118026, 119840, 121640, 134358, 135285, 144902,\n", + " 147509, 152097, 153048, 161811, 164528, 168124, 169073, 170023,\n", + " 186819, 190511],\n", + " dtype='int64'), Int64Index([ 4086, 5006, 10679, 13439, 17974, 20724, 21647, 31703,\n", + " 44948, 45869, 46824, 49578, 55136, 56084, 57946, 60744,\n", + " 61662, 64117, 65634, 67403, 71949, 79224, 81045, 82867,\n", + " 84741, 87523, 89406, 90861, 105153, 107859, 108786, 109711,\n", + " 112537, 113451, 118027, 119841, 121641, 134359, 135286, 144903,\n", + " 147510, 152098, 153049, 161812, 164529, 168125, 169074, 170024,\n", + " 186820, 190512],\n", + " dtype='int64'), Int64Index([ 4087, 5007, 10680, 13440, 17975, 20725, 21648, 31704,\n", + " 44949, 45870, 46825, 49579, 55137, 56085, 57947, 60745,\n", + " 61663, 64118, 65635, 67404, 71950, 79225, 81046, 82868,\n", + " 84742, 87524, 89407, 90862, 105154, 107860, 108787, 109712,\n", + " 112538, 113452, 118028, 119842, 121642, 134360, 135287, 144904,\n", + " 147511, 152099, 153050, 161813, 164530, 168126, 169075, 170025,\n", + " 186821, 190513],\n", + " dtype='int64'), Int64Index([ 4088, 5008, 10681, 13441, 17976, 20726, 21649, 31705,\n", + " 44950, 45871, 46826, 49580, 55138, 56086, 57948, 60746,\n", + " 61664, 64119, 65636, 67405, 71951, 79226, 81047, 82869,\n", + " 84743, 87525, 89408, 90863, 105155, 107861, 108788, 109713,\n", + " 112539, 113453, 118029, 119843, 121643, 134361, 135288, 144905,\n", + " 147512, 152100, 153051, 161814, 164531, 168127, 169076, 170026,\n", + " 186822, 190514],\n", + " dtype='int64'), Int64Index([ 4089, 5009, 10682, 13442, 17977, 20727, 21650, 31706,\n", + " 44951, 45872, 46827, 49581, 55139, 56087, 57949, 60747,\n", + " 61665, 64120, 65637, 67406, 71952, 79227, 81048, 82870,\n", + " 84744, 87526, 89409, 90864, 105156, 107862, 108789, 109714,\n", + " 112540, 113454, 118030, 119844, 121644, 134362, 135289, 144906,\n", + " 147513, 152101, 153052, 161815, 164532, 168128, 169077, 170027,\n", + " 186823, 190515],\n", + " dtype='int64'), Int64Index([ 4090, 5010, 10683, 13443, 17978, 20728, 21651, 31707,\n", + " 44952, 45873, 46828, 49582, 55140, 56088, 57950, 60748,\n", + " 61666, 64121, 65638, 67407, 71953, 79228, 81049, 82871,\n", + " 84745, 87527, 89410, 90865, 105157, 107863, 108790, 109715,\n", + " 112541, 113455, 118031, 119845, 121645, 134363, 135290, 144907,\n", + " 147514, 152102, 153053, 161816, 164533, 168129, 169078, 170028,\n", + " 186824, 190516],\n", + " dtype='int64'), Int64Index([ 4091, 5011, 10684, 13444, 17979, 20729, 21652, 31708,\n", + " 44953, 45874, 46829, 49583, 55141, 56089, 57951, 60749,\n", + " 61667, 64122, 65639, 67408, 71954, 79229, 81050, 82872,\n", + " 84746, 87528, 89411, 90866, 105158, 107864, 108791, 109716,\n", + " 112542, 113456, 118032, 119846, 121646, 134364, 135291, 144908,\n", + " 147515, 152103, 153054, 161817, 164534, 168130, 169079, 170029,\n", + " 186825, 190517],\n", + " dtype='int64'), Int64Index([ 4092, 5012, 10685, 13445, 17980, 20730, 21653, 31709,\n", + " 44954, 45875, 46830, 49584, 55142, 56090, 57952, 60750,\n", + " 61668, 64123, 65640, 67409, 71955, 79230, 81051, 82873,\n", + " 84747, 87529, 89412, 90867, 105159, 107865, 108792, 109717,\n", + " 112543, 113457, 118033, 119847, 121647, 134365, 135292, 144909,\n", + " 147516, 152104, 153055, 161818, 164535, 168131, 169080, 170030,\n", + " 186826, 190518],\n", + " dtype='int64'), Int64Index([ 4093, 5013, 10686, 13446, 17981, 20731, 21654, 31710,\n", + " 44955, 45876, 46831, 49585, 55143, 56091, 57953, 60751,\n", + " 61669, 64124, 65641, 67410, 71956, 79231, 81052, 82874,\n", + " 84748, 87530, 89413, 90868, 105160, 107866, 108793, 109718,\n", + " 112544, 113458, 118034, 119848, 121648, 134366, 135293, 144910,\n", + " 147517, 152105, 153056, 161819, 164536, 168132, 169081, 170031,\n", + " 186827, 190519],\n", + " dtype='int64'), Int64Index([ 4094, 5014, 10687, 13447, 17982, 20732, 21655, 31711,\n", + " 44956, 45877, 46832, 49586, 55144, 56092, 57954, 60752,\n", + " 61670, 64125, 65642, 67411, 71957, 79232, 81053, 82875,\n", + " 84749, 87531, 89414, 90869, 105161, 107867, 108794, 109719,\n", + " 112545, 113459, 118035, 119849, 121649, 134367, 135294, 144911,\n", + " 147518, 152106, 153057, 161820, 164537, 168133, 169082, 170032,\n", + " 186828, 190520],\n", + " dtype='int64'), Int64Index([ 4095, 5015, 10688, 13448, 17983, 20733, 21656, 31712,\n", + " 44957, 45878, 46833, 49587, 55145, 56093, 57955, 60753,\n", + " 61671, 64126, 65643, 67412, 71958, 79233, 81054, 82876,\n", + " 84750, 87532, 89415, 90870, 105162, 107868, 108795, 109720,\n", + " 112546, 113460, 118036, 119850, 121650, 134368, 135295, 144912,\n", + " 147519, 152107, 153058, 161821, 164538, 168134, 169083, 170033,\n", + " 186829, 190521],\n", + " dtype='int64'), Int64Index([ 4096, 5016, 10689, 13449, 17984, 20734, 21657, 31713,\n", + " 44958, 45879, 46834, 49588, 55146, 56094, 57956, 60754,\n", + " 61672, 64127, 65644, 67413, 71959, 79234, 81055, 82877,\n", + " 84751, 87533, 89416, 90871, 105163, 107869, 108796, 109721,\n", + " 112547, 113461, 118037, 119851, 121651, 134369, 135296, 144913,\n", + " 147520, 152108, 153059, 161822, 164539, 168135, 169084, 170034,\n", + " 186830, 190522],\n", + " dtype='int64'), Int64Index([ 4097, 5017, 10690, 13450, 17985, 20735, 21658, 31714,\n", + " 44959, 45880, 46835, 49589, 55147, 56095, 57957, 60755,\n", + " 61673, 64128, 65645, 67414, 71960, 79235, 81056, 82878,\n", + " 84752, 87534, 89417, 90872, 105164, 107870, 108797, 109722,\n", + " 112548, 113462, 118038, 119852, 121652, 134370, 135297, 144914,\n", + " 147521, 152109, 153060, 161823, 164540, 168136, 169085, 170035,\n", + " 186831, 190523],\n", + " dtype='int64'), Int64Index([ 4098, 5018, 10691, 13451, 17986, 20736, 21659, 31715,\n", + " 44960, 45881, 46836, 49590, 55148, 56096, 57958, 60756,\n", + " 61674, 64129, 65646, 67415, 71961, 79236, 81057, 82879,\n", + " 84753, 87535, 89418, 90873, 105165, 107871, 108798, 109723,\n", + " 112549, 113463, 118039, 119853, 121653, 134371, 135298, 144915,\n", + " 147522, 152110, 153061, 161824, 164541, 168137, 169086, 170036,\n", + " 186832, 190524],\n", + " dtype='int64'), Int64Index([ 4099, 5019, 10692, 13452, 17987, 20737, 21660, 31716,\n", + " 44961, 45882, 46837, 49591, 55149, 56097, 57959, 60757,\n", + " 61675, 64130, 65647, 67416, 71962, 79237, 81058, 82880,\n", + " 84754, 87536, 89419, 90874, 105166, 107872, 108799, 109724,\n", + " 112550, 113464, 118040, 119854, 121654, 134372, 135299, 144916,\n", + " 147523, 152111, 153062, 161825, 164542, 168138, 169087, 170037,\n", + " 186833, 190525],\n", + " dtype='int64'), Int64Index([ 4100, 5020, 10693, 13453, 17988, 20738, 21661, 31717,\n", + " 44962, 45883, 46838, 49592, 55150, 56098, 57960, 60758,\n", + " 61676, 64131, 65648, 67417, 71963, 79238, 81059, 82881,\n", + " 84755, 87537, 89420, 90875, 105167, 107873, 108800, 109725,\n", + " 112551, 113465, 118041, 119855, 121655, 134373, 135300, 144917,\n", + " 147524, 152112, 153063, 161826, 164543, 168139, 169088, 170038,\n", + " 186834, 190526],\n", + " dtype='int64'), Int64Index([ 4101, 5021, 10694, 13454, 17989, 20739, 21662, 31718,\n", + " 44963, 45884, 46839, 49593, 55151, 56099, 57961, 60759,\n", + " 61677, 64132, 65649, 67418, 71964, 79239, 81060, 82882,\n", + " 84756, 87538, 89421, 90876, 105168, 107874, 108801, 109726,\n", + " 112552, 113466, 118042, 119856, 121656, 134374, 135301, 144918,\n", + " 147525, 152113, 153064, 161827, 164544, 168140, 169089, 170039,\n", + " 186835, 190527],\n", + " dtype='int64'), Int64Index([ 4102, 5022, 10695, 13455, 17990, 20740, 21663, 31719,\n", + " 44964, 45885, 46840, 49594, 55152, 56100, 57962, 60760,\n", + " 61678, 64133, 65650, 67419, 71965, 79240, 81061, 82883,\n", + " 84757, 87539, 89422, 90877, 105169, 107875, 108802, 109727,\n", + " 112553, 113467, 118043, 119857, 121657, 134375, 135302, 144919,\n", + " 147526, 152114, 153065, 161828, 164545, 168141, 169090, 170040,\n", + " 186836, 190528],\n", + " dtype='int64'), Int64Index([ 4103, 5023, 10696, 13456, 17991, 20741, 21664, 31720,\n", + " 44965, 45886, 46841, 49595, 55153, 56101, 57963, 60761,\n", + " 61679, 64134, 65651, 67420, 71966, 79241, 81062, 82884,\n", + " 84758, 87540, 89423, 90878, 105170, 107876, 108803, 109728,\n", + " 112554, 113468, 118044, 119858, 121658, 134376, 135303, 144920,\n", + " 147527, 152115, 153066, 161829, 164546, 168142, 169091, 170041,\n", + " 186837, 190529],\n", + " dtype='int64'), Int64Index([ 4104, 5024, 10697, 13457, 17992, 20742, 21665, 31721,\n", + " 44966, 45887, 46842, 49596, 55154, 56102, 57964, 60762,\n", + " 61680, 64135, 65652, 67421, 71967, 79242, 81063, 82885,\n", + " 84759, 87541, 89424, 90879, 105171, 107877, 108804, 109729,\n", + " 112555, 113469, 118045, 119859, 121659, 134377, 135304, 144921,\n", + " 147528, 152116, 153067, 161830, 164547, 168143, 169092, 170042,\n", + " 186838, 190530],\n", + " dtype='int64'), Int64Index([ 4105, 5025, 10698, 13458, 17993, 20743, 21666, 31722,\n", + " 44967, 45888, 46843, 49597, 55155, 56103, 57965, 60763,\n", + " 61681, 64136, 65653, 67422, 71968, 79243, 81064, 82886,\n", + " 84760, 87542, 89425, 90880, 105172, 107878, 108805, 109730,\n", + " 112556, 113470, 118046, 119860, 121660, 134378, 135305, 144922,\n", + " 147529, 152117, 153068, 161831, 164548, 168144, 169093, 170043,\n", + " 186839, 190531],\n", + " dtype='int64'), Int64Index([ 4106, 5026, 10699, 13459, 17994, 20744, 21667, 31723,\n", + " 44968, 45889, 46844, 49598, 55156, 56104, 57966, 60764,\n", + " 61682, 64137, 65654, 67423, 71969, 79244, 81065, 82887,\n", + " 84761, 87543, 89426, 90881, 105173, 107879, 108806, 109731,\n", + " 112557, 113471, 118047, 119861, 121661, 134379, 135306, 144923,\n", + " 147530, 152118, 153069, 161832, 164549, 168145, 169094, 170044,\n", + " 186840, 190532],\n", + " dtype='int64'), Int64Index([ 4107, 5027, 10700, 13460, 17995, 20745, 21668, 31724,\n", + " 44969, 45890, 46845, 49599, 55157, 56105, 57967, 60765,\n", + " 61683, 64138, 65655, 67424, 71970, 79245, 81066, 82888,\n", + " 84762, 87544, 89427, 90882, 105174, 107880, 108807, 109732,\n", + " 112558, 113472, 118048, 119862, 121662, 134380, 135307, 144924,\n", + " 147531, 152119, 153070, 161833, 164550, 168146, 169095, 170045,\n", + " 186841, 190533],\n", + " dtype='int64'), Int64Index([ 4108, 5028, 10701, 13461, 17996, 20746, 21669, 31725,\n", + " 44970, 45891, 46846, 49600, 55158, 56106, 57968, 60766,\n", + " 61684, 64139, 65656, 67425, 71971, 79246, 81067, 82889,\n", + " 84763, 87545, 89428, 90883, 105175, 107881, 108808, 109733,\n", + " 112559, 113473, 118049, 119863, 121663, 134381, 135308, 144925,\n", + " 147532, 152120, 153071, 161834, 164551, 168147, 169096, 170046,\n", + " 186842, 190534],\n", + " dtype='int64'), Int64Index([ 4109, 5029, 10702, 13462, 17997, 20747, 21670, 31726,\n", + " 44971, 45892, 46847, 49601, 55159, 56107, 57969, 60767,\n", + " 61685, 64140, 65657, 67426, 71972, 79247, 81068, 82890,\n", + " 84764, 87546, 89429, 90884, 105176, 107882, 108809, 109734,\n", + " 112560, 113474, 118050, 119864, 121664, 134382, 135309, 144926,\n", + " 147533, 152121, 153072, 161835, 164552, 168148, 169097, 170047,\n", + " 186843, 190535],\n", + " dtype='int64'), Int64Index([ 4110, 5030, 10703, 13463, 17998, 20748, 21671, 31727,\n", + " 44972, 45893, 46848, 49602, 55160, 56108, 57970, 60768,\n", + " 61686, 64141, 65658, 67427, 71973, 79248, 81069, 82891,\n", + " 84765, 87547, 89430, 90885, 105177, 107883, 108810, 109735,\n", + " 112561, 113475, 118051, 119865, 121665, 134383, 135310, 144927,\n", + " 147534, 152122, 153073, 161836, 164553, 168149, 169098, 170048,\n", + " 186844, 190536],\n", + " dtype='int64'), Int64Index([ 4111, 5031, 10704, 13464, 17999, 20749, 21672, 31728,\n", + " 44973, 45894, 46849, 49603, 55161, 56109, 57971, 60769,\n", + " 61687, 64142, 65659, 67428, 71974, 79249, 81070, 82892,\n", + " 84766, 87548, 89431, 90886, 105178, 107884, 108811, 109736,\n", + " 112562, 113476, 118052, 119866, 121666, 134384, 135311, 144928,\n", + " 147535, 152123, 153074, 161837, 164554, 168150, 169099, 170049,\n", + " 186845, 190537],\n", + " dtype='int64'), Int64Index([ 4112, 5032, 10705, 13465, 18000, 20750, 21673, 31729,\n", + " 44974, 45895, 46850, 49604, 55162, 56110, 57972, 60770,\n", + " 61688, 64143, 65660, 67429, 71975, 79250, 81071, 82893,\n", + " 84767, 87549, 89432, 90887, 105179, 107885, 108812, 109737,\n", + " 112563, 113477, 118053, 119867, 121667, 134385, 135312, 144929,\n", + " 147536, 152124, 153075, 161838, 164555, 168151, 169100, 170050,\n", + " 186846, 190538],\n", + " dtype='int64'), Int64Index([ 4113, 5033, 10706, 13466, 18001, 20751, 21674, 31730,\n", + " 44975, 45896, 46851, 49605, 55163, 56111, 57973, 60771,\n", + " 61689, 64144, 65661, 67430, 71976, 79251, 81072, 82894,\n", + " 84768, 87550, 89433, 90888, 105180, 107886, 108813, 109738,\n", + " 112564, 113478, 118054, 119868, 121668, 134386, 135313, 144930,\n", + " 147537, 152125, 153076, 161839, 164556, 168152, 169101, 170051,\n", + " 186847, 190539],\n", + " dtype='int64'), Int64Index([ 4114, 5034, 10707, 13467, 18002, 20752, 21675, 31731,\n", + " 44976, 45897, 46852, 49606, 55164, 56112, 57974, 60772,\n", + " 61690, 64145, 65662, 67431, 71977, 79252, 81073, 82895,\n", + " 84769, 87551, 89434, 90889, 105181, 107887, 108814, 109739,\n", + " 112565, 113479, 118055, 119869, 121669, 134387, 135314, 144931,\n", + " 147538, 152126, 153077, 161840, 164557, 168153, 169102, 170052,\n", + " 186848, 190540],\n", + " dtype='int64'), Int64Index([ 4115, 5035, 10708, 13468, 18003, 20753, 21676, 31732,\n", + " 44977, 45898, 46853, 49607, 55165, 56113, 57975, 60773,\n", + " 61691, 64146, 65663, 67432, 71978, 79253, 81074, 82896,\n", + " 84770, 87552, 89435, 90890, 105182, 107888, 108815, 109740,\n", + " 112566, 113480, 118056, 119870, 121670, 134388, 135315, 144932,\n", + " 147539, 152127, 153078, 161841, 164558, 168154, 169103, 170053,\n", + " 186849, 190541],\n", + " dtype='int64'), Int64Index([ 4116, 5036, 10709, 13469, 18004, 20754, 21677, 31733,\n", + " 44978, 45899, 46854, 49608, 55166, 56114, 57976, 60774,\n", + " 61692, 64147, 65664, 67433, 71979, 79254, 81075, 82897,\n", + " 84771, 87553, 89436, 90891, 105183, 107889, 108816, 109741,\n", + " 112567, 113481, 118057, 119871, 121671, 134389, 135316, 144933,\n", + " 147540, 152128, 153079, 161842, 164559, 168155, 169104, 170054,\n", + " 186850, 190542],\n", + " dtype='int64'), Int64Index([ 4117, 5037, 10710, 13470, 18005, 20755, 21678, 31734,\n", + " 44979, 45900, 46855, 49609, 55167, 56115, 57977, 60775,\n", + " 61693, 64148, 65665, 67434, 71980, 79255, 81076, 82898,\n", + " 84772, 87554, 89437, 90892, 105184, 107890, 108817, 109742,\n", + " 112568, 113482, 118058, 119872, 121672, 134390, 135317, 144934,\n", + " 147541, 152129, 153080, 161843, 164560, 168156, 169105, 170055,\n", + " 186851, 190543],\n", + " dtype='int64'), Int64Index([ 4118, 5038, 10711, 13471, 18006, 20756, 21679, 31735,\n", + " 44980, 45901, 46856, 49610, 55168, 56116, 57978, 60776,\n", + " 61694, 64149, 65666, 67435, 71981, 79256, 81077, 82899,\n", + " 84773, 87555, 89438, 90893, 105185, 107891, 108818, 109743,\n", + " 112569, 113483, 118059, 119873, 121673, 134391, 135318, 144935,\n", + " 147542, 152130, 153081, 161844, 164561, 168157, 169106, 170056,\n", + " 186852, 190544],\n", + " dtype='int64'), Int64Index([ 4119, 5039, 10712, 13472, 18007, 20757, 21680, 31736,\n", + " 44981, 45902, 46857, 49611, 55169, 56117, 57979, 60777,\n", + " 61695, 64150, 65667, 67436, 71982, 79257, 81078, 82900,\n", + " 84774, 87556, 89439, 90894, 105186, 107892, 108819, 109744,\n", + " 112570, 113484, 118060, 119874, 121674, 134392, 135319, 144936,\n", + " 147543, 152131, 153082, 161845, 164562, 168158, 169107, 170057,\n", + " 186853, 190545],\n", + " dtype='int64'), Int64Index([ 4120, 5040, 10713, 13473, 18008, 20758, 21681, 31737,\n", + " 44982, 45903, 46858, 49612, 55170, 56118, 57980, 60778,\n", + " 61696, 64151, 65668, 67437, 71983, 79258, 81079, 82901,\n", + " 84775, 87557, 89440, 90895, 105187, 107893, 108820, 109745,\n", + " 112571, 113485, 118061, 119875, 121675, 134393, 135320, 144937,\n", + " 147544, 152132, 153083, 161846, 164563, 168159, 169108, 170058,\n", + " 186854, 190546],\n", + " dtype='int64'), Int64Index([ 4121, 5041, 10714, 13474, 18009, 20759, 21682, 31738,\n", + " 44983, 45904, 46859, 49613, 55171, 56119, 57981, 60779,\n", + " 61697, 64152, 65669, 67438, 71984, 79259, 81080, 82902,\n", + " 84776, 87558, 89441, 90896, 105188, 107894, 108821, 109746,\n", + " 112572, 113486, 118062, 119876, 121676, 134394, 135321, 144938,\n", + " 147545, 152133, 153084, 161847, 164564, 168160, 169109, 170059,\n", + " 186855, 190547],\n", + " dtype='int64'), Int64Index([ 4122, 5042, 10715, 13475, 18010, 20760, 21683, 31739,\n", + " 44984, 45905, 46860, 49614, 55172, 56120, 57982, 60780,\n", + " 61698, 64153, 65670, 67439, 71985, 79260, 81081, 82903,\n", + " 84777, 87559, 89442, 90897, 105189, 107895, 108822, 109747,\n", + " 112573, 113487, 118063, 119877, 121677, 134395, 135322, 144939,\n", + " 147546, 152134, 153085, 161848, 164565, 168161, 169110, 170060,\n", + " 186856, 190548],\n", + " dtype='int64'), Int64Index([ 4123, 5043, 10716, 13476, 18011, 20761, 21684, 31740,\n", + " 44985, 45906, 46861, 49615, 55173, 56121, 57983, 60781,\n", + " 61699, 64154, 65671, 67440, 71986, 79261, 81082, 82904,\n", + " 84778, 87560, 89443, 90898, 105190, 107896, 108823, 109748,\n", + " 112574, 113488, 118064, 119878, 121678, 134396, 135323, 144940,\n", + " 147547, 152135, 153086, 161849, 164566, 168162, 169111, 170061,\n", + " 186857, 190549],\n", + " dtype='int64'), Int64Index([ 4124, 5044, 10717, 13477, 18012, 20762, 21685, 31741,\n", + " 44986, 45907, 46862, 49616, 55174, 56122, 57984, 60782,\n", + " 61700, 64155, 65672, 67441, 71987, 79262, 81083, 82905,\n", + " 84779, 87561, 89444, 90899, 105191, 107897, 108824, 109749,\n", + " 112575, 113489, 118065, 119879, 121679, 134397, 135324, 144941,\n", + " 147548, 152136, 153087, 161850, 164567, 168163, 169112, 170062,\n", + " 186858, 190550],\n", + " dtype='int64'), Int64Index([ 4125, 5045, 10718, 13478, 18013, 20763, 21686, 31742,\n", + " 44987, 45908, 46863, 49617, 55175, 56123, 57985, 60783,\n", + " 61701, 64156, 65673, 67442, 71988, 79263, 81084, 82906,\n", + " 84780, 87562, 89445, 90900, 105192, 107898, 108825, 109750,\n", + " 112576, 113490, 118066, 119880, 121680, 134398, 135325, 144942,\n", + " 147549, 152137, 153088, 161851, 164568, 168164, 169113, 170063,\n", + " 186859, 190551],\n", + " dtype='int64'), Int64Index([ 4126, 5046, 10719, 13479, 18014, 20764, 21687, 31743,\n", + " 44988, 45909, 46864, 49618, 55176, 56124, 57986, 60784,\n", + " 61702, 64157, 65674, 67443, 71989, 79264, 81085, 82907,\n", + " 84781, 87563, 89446, 90901, 105193, 107899, 108826, 109751,\n", + " 112577, 113491, 118067, 119881, 121681, 134399, 135326, 144943,\n", + " 147550, 152138, 153089, 161852, 164569, 168165, 169114, 170064,\n", + " 186860, 190552],\n", + " dtype='int64'), Int64Index([ 4127, 5047, 10720, 13480, 18015, 20765, 21688, 31744,\n", + " 44989, 45910, 46865, 49619, 55177, 56125, 57987, 60785,\n", + " 61703, 64158, 65675, 67444, 71990, 79265, 81086, 82908,\n", + " 84782, 87564, 89447, 90902, 105194, 107900, 108827, 109752,\n", + " 112578, 113492, 118068, 119882, 121682, 134400, 135327, 144944,\n", + " 147551, 152139, 153090, 161853, 164570, 168166, 169115, 170065,\n", + " 186861, 190553],\n", + " dtype='int64'), Int64Index([ 4128, 5048, 10721, 13481, 18016, 20766, 21689, 31745,\n", + " 44990, 45911, 46866, 49620, 55178, 56126, 57988, 60786,\n", + " 61704, 64159, 65676, 67445, 71991, 79266, 81087, 82909,\n", + " 84783, 87565, 89448, 90903, 105195, 107901, 108828, 109753,\n", + " 112579, 113493, 118069, 119883, 121683, 134401, 135328, 144945,\n", + " 147552, 152140, 153091, 161854, 164571, 168167, 169116, 170066,\n", + " 186862, 190554],\n", + " dtype='int64'), Int64Index([ 4129, 5049, 10722, 13482, 18017, 20767, 21690, 31746,\n", + " 44991, 45912, 46867, 49621, 55179, 56127, 57989, 60787,\n", + " 61705, 64160, 65677, 67446, 71992, 79267, 81088, 82910,\n", + " 84784, 87566, 89449, 90904, 105196, 107902, 108829, 109754,\n", + " 112580, 113494, 118070, 119884, 121684, 134402, 135329, 144946,\n", + " 147553, 152141, 153092, 161855, 164572, 168168, 169117, 170067,\n", + " 186863, 190555],\n", + " dtype='int64'), Int64Index([ 4130, 5050, 10723, 13483, 18018, 20768, 21691, 31747,\n", + " 44992, 45913, 46868, 49622, 55180, 56128, 57990, 60788,\n", + " 61706, 64161, 65678, 67447, 71993, 79268, 81089, 82911,\n", + " 84785, 87567, 89450, 90905, 105197, 107903, 108830, 109755,\n", + " 112581, 113495, 118071, 119885, 121685, 134403, 135330, 144947,\n", + " 147554, 152142, 153093, 161856, 164573, 168169, 169118, 170068,\n", + " 186864, 190556],\n", + " dtype='int64'), Int64Index([ 4131, 5051, 10724, 13484, 18019, 20769, 21692, 31748,\n", + " 44993, 45914, 46869, 49623, 55181, 56129, 57991, 60789,\n", + " 61707, 64162, 65679, 67448, 71994, 79269, 81090, 82912,\n", + " 84786, 87568, 89451, 90906, 105198, 107904, 108831, 109756,\n", + " 112582, 113496, 118072, 119886, 121686, 134404, 135331, 144948,\n", + " 147555, 152143, 153094, 161857, 164574, 168170, 169119, 170069,\n", + " 186865, 190557],\n", + " dtype='int64'), Int64Index([ 4132, 5052, 10725, 13485, 18020, 20770, 21693, 31749,\n", + " 44994, 45915, 46870, 49624, 55182, 56130, 57992, 60790,\n", + " 61708, 64163, 65680, 67449, 71995, 79270, 81091, 82913,\n", + " 84787, 87569, 89452, 90907, 105199, 107905, 108832, 109757,\n", + " 112583, 113497, 118073, 119887, 121687, 134405, 135332, 144949,\n", + " 147556, 152144, 153095, 161858, 164575, 168171, 169120, 170070,\n", + " 186866, 190558],\n", + " dtype='int64'), Int64Index([ 4133, 5053, 10726, 13486, 18021, 20771, 21694, 31750,\n", + " 44995, 45916, 46871, 49625, 55183, 56131, 57993, 60791,\n", + " 61709, 64164, 65681, 67450, 71996, 79271, 81092, 82914,\n", + " 84788, 87570, 89453, 90908, 105200, 107906, 108833, 109758,\n", + " 112584, 113498, 118074, 119888, 121688, 134406, 135333, 144950,\n", + " 147557, 152145, 153096, 161859, 164576, 168172, 169121, 170071,\n", + " 186867, 190559],\n", + " dtype='int64'), Int64Index([ 4134, 5054, 10727, 13487, 18022, 20772, 21695, 31751,\n", + " 44996, 45917, 46872, 49626, 55184, 56132, 57994, 60792,\n", + " 61710, 64165, 65682, 67451, 71997, 79272, 81093, 82915,\n", + " 84789, 87571, 89454, 90909, 105201, 107907, 108834, 109759,\n", + " 112585, 113499, 118075, 119889, 121689, 134407, 135334, 144951,\n", + " 147558, 152146, 153097, 161860, 164577, 168173, 169122, 170072,\n", + " 186868, 190560],\n", + " dtype='int64'), Int64Index([ 4135, 5055, 10728, 13488, 18023, 20773, 21696, 31752,\n", + " 44997, 45918, 46873, 49627, 55185, 56133, 57995, 60793,\n", + " 61711, 64166, 65683, 67452, 71998, 79273, 81094, 82916,\n", + " 84790, 87572, 89455, 90910, 105202, 107908, 108835, 109760,\n", + " 112586, 113500, 118076, 119890, 121690, 134408, 135335, 144952,\n", + " 147559, 152147, 153098, 161861, 164578, 168174, 169123, 170073,\n", + " 186869, 190561],\n", + " dtype='int64'), Int64Index([ 4136, 5056, 10729, 13489, 18024, 20774, 21697, 31753,\n", + " 44998, 45919, 46874, 49628, 55186, 56134, 57996, 60794,\n", + " 61712, 64167, 65684, 67453, 71999, 79274, 81095, 82917,\n", + " 84791, 87573, 89456, 90911, 105203, 107909, 108836, 109761,\n", + " 112587, 113501, 118077, 119891, 121691, 134409, 135336, 144953,\n", + " 147560, 152148, 153099, 161862, 164579, 168175, 169124, 170074,\n", + " 186870, 190562],\n", + " dtype='int64'), Int64Index([ 4137, 5057, 10730, 13490, 18025, 20775, 21698, 31754,\n", + " 44999, 45920, 46875, 49629, 55187, 56135, 57997, 60795,\n", + " 61713, 64168, 65685, 67454, 72000, 79275, 81096, 82918,\n", + " 84792, 87574, 89457, 90912, 105204, 107910, 108837, 109762,\n", + " 112588, 113502, 118078, 119892, 121692, 134410, 135337, 144954,\n", + " 147561, 152149, 153100, 161863, 164580, 168176, 169125, 170075,\n", + " 186871, 190563],\n", + " dtype='int64'), Int64Index([ 4138, 5058, 10731, 13491, 18026, 20776, 21699, 31755,\n", + " 45000, 45921, 46876, 49630, 55188, 56136, 57998, 60796,\n", + " 61714, 64169, 65686, 67455, 72001, 79276, 81097, 82919,\n", + " 84793, 87575, 89458, 90913, 105205, 107911, 108838, 109763,\n", + " 112589, 113503, 118079, 119893, 121693, 134411, 135338, 144955,\n", + " 147562, 152150, 153101, 161864, 164581, 168177, 169126, 170076,\n", + " 186872, 190564],\n", + " dtype='int64'), Int64Index([ 4139, 5059, 10732, 13492, 18027, 20777, 21700, 31756,\n", + " 45001, 45922, 46877, 49631, 55189, 56137, 57999, 60797,\n", + " 61715, 64170, 65687, 67456, 72002, 79277, 81098, 82920,\n", + " 84794, 87576, 89459, 90914, 105206, 107912, 108839, 109764,\n", + " 112590, 113504, 118080, 119894, 121694, 134412, 135339, 144956,\n", + " 147563, 152151, 153102, 161865, 164582, 168178, 169127, 170077,\n", + " 186873, 190565],\n", + " dtype='int64'), Int64Index([ 4140, 5060, 10733, 13493, 18028, 20778, 21701, 31757,\n", + " 45002, 45923, 46878, 49632, 55190, 56138, 58000, 60798,\n", + " 61716, 64171, 65688, 67457, 72003, 79278, 81099, 82921,\n", + " 84795, 87577, 89460, 90915, 105207, 107913, 108840, 109765,\n", + " 112591, 113505, 118081, 119895, 121695, 134413, 135340, 144957,\n", + " 147564, 152152, 153103, 161866, 164583, 168179, 169128, 170078,\n", + " 186874, 190566],\n", + " dtype='int64'), Int64Index([ 4141, 5061, 10734, 13494, 18029, 20779, 21702, 31758,\n", + " 45003, 45924, 46879, 49633, 55191, 56139, 58001, 60799,\n", + " 61717, 64172, 65689, 67458, 72004, 79279, 81100, 82922,\n", + " 84796, 87578, 89461, 90916, 105208, 107914, 108841, 109766,\n", + " 112592, 113506, 118082, 119896, 121696, 134414, 135341, 144958,\n", + " 147565, 152153, 153104, 161867, 164584, 168180, 169129, 170079,\n", + " 186875, 190567],\n", + " dtype='int64'), Int64Index([ 4142, 5062, 10735, 13495, 18030, 20780, 21703, 31759,\n", + " 45004, 45925, 46880, 49634, 55192, 56140, 58002, 60800,\n", + " 61718, 64173, 65690, 67459, 72005, 79280, 81101, 82923,\n", + " 84797, 87579, 89462, 90917, 105209, 107915, 108842, 109767,\n", + " 112593, 113507, 118083, 119897, 121697, 134415, 135342, 144959,\n", + " 147566, 152154, 153105, 161868, 164585, 168181, 169130, 170080,\n", + " 186876, 190568],\n", + " dtype='int64'), Int64Index([ 4143, 5063, 10736, 13496, 18031, 20781, 21704, 31760,\n", + " 45005, 45926, 46881, 49635, 55193, 56141, 58003, 60801,\n", + " 61719, 64174, 65691, 67460, 72006, 79281, 81102, 82924,\n", + " 84798, 87580, 89463, 90918, 105210, 107916, 108843, 109768,\n", + " 112594, 113508, 118084, 119898, 121698, 134416, 135343, 144960,\n", + " 147567, 152155, 153106, 161869, 164586, 168182, 169131, 170081,\n", + " 186877, 190569],\n", + " dtype='int64'), Int64Index([ 4144, 5064, 10737, 13497, 18032, 20782, 21705, 31761,\n", + " 45006, 45927, 46882, 49636, 55194, 56142, 58004, 60802,\n", + " 61720, 64175, 65692, 67461, 72007, 79282, 81103, 82925,\n", + " 84799, 87581, 89464, 90919, 105211, 107917, 108844, 109769,\n", + " 112595, 113509, 118085, 119899, 121699, 134417, 135344, 144961,\n", + " 147568, 152156, 153107, 161870, 164587, 168183, 169132, 170082,\n", + " 186878, 190570],\n", + " dtype='int64'), Int64Index([ 4145, 5065, 10738, 13498, 18033, 20783, 21706, 31762,\n", + " 45007, 45928, 46883, 49637, 55195, 56143, 58005, 60803,\n", + " 61721, 64176, 65693, 67462, 72008, 79283, 81104, 82926,\n", + " 84800, 87582, 89465, 90920, 105212, 107918, 108845, 109770,\n", + " 112596, 113510, 118086, 119900, 121700, 134418, 135345, 144962,\n", + " 147569, 152157, 153108, 161871, 164588, 168184, 169133, 170083,\n", + " 186879, 190571],\n", + " dtype='int64'), Int64Index([ 4146, 5066, 10739, 13499, 18034, 20784, 21707, 31763,\n", + " 45008, 45929, 46884, 49638, 55196, 56144, 58006, 60804,\n", + " 61722, 64177, 65694, 67463, 72009, 79284, 81105, 82927,\n", + " 84801, 87583, 89466, 90921, 105213, 107919, 108846, 109771,\n", + " 112597, 113511, 118087, 119901, 121701, 134419, 135346, 144963,\n", + " 147570, 152158, 153109, 161872, 164589, 168185, 169134, 170084,\n", + " 186880, 190572],\n", + " dtype='int64'), Int64Index([ 4147, 5067, 10740, 13500, 18035, 20785, 21708, 31764,\n", + " 45009, 45930, 46885, 49639, 55197, 56145, 58007, 60805,\n", + " 61723, 64178, 65695, 67464, 72010, 79285, 81106, 82928,\n", + " 84802, 87584, 89467, 90922, 105214, 107920, 108847, 109772,\n", + " 112598, 113512, 118088, 119902, 121702, 134420, 135347, 144964,\n", + " 147571, 152159, 153110, 161873, 164590, 168186, 169135, 170085,\n", + " 186881, 190573],\n", + " dtype='int64'), Int64Index([ 4148, 5068, 10741, 13501, 18036, 20786, 21709, 31765,\n", + " 45010, 45931, 46886, 49640, 55198, 56146, 58008, 60806,\n", + " 61724, 64179, 65696, 67465, 72011, 79286, 81107, 82929,\n", + " 84803, 87585, 89468, 90923, 105215, 107921, 108848, 109773,\n", + " 112599, 113513, 118089, 119903, 121703, 134421, 135348, 144965,\n", + " 147572, 152160, 153111, 161874, 164591, 168187, 169136, 170086,\n", + " 186882, 190574],\n", + " dtype='int64'), Int64Index([ 4149, 5069, 10742, 13502, 18037, 20787, 21710, 31766,\n", + " 45011, 45932, 46887, 49641, 55199, 56147, 58009, 60807,\n", + " 61725, 64180, 65697, 67466, 72012, 79287, 81108, 82930,\n", + " 84804, 87586, 89469, 90924, 105216, 107922, 108849, 109774,\n", + " 112600, 113514, 118090, 119904, 121704, 134422, 135349, 144966,\n", + " 147573, 152161, 153112, 161875, 164592, 168188, 169137, 170087,\n", + " 186883, 190575],\n", + " dtype='int64'), Int64Index([ 4150, 5070, 10743, 13503, 18038, 20788, 21711, 31767,\n", + " 45012, 45933, 46888, 49642, 55200, 56148, 58010, 60808,\n", + " 61726, 64181, 65698, 67467, 72013, 79288, 81109, 82931,\n", + " 84805, 87587, 89470, 90925, 105217, 107923, 108850, 109775,\n", + " 112601, 113515, 118091, 119905, 121705, 134423, 135350, 144967,\n", + " 147574, 152162, 153113, 161876, 164593, 168189, 169138, 170088,\n", + " 186884, 190576],\n", + " dtype='int64'), Int64Index([ 4151, 5071, 10744, 13504, 18039, 20789, 21712, 31768,\n", + " 45013, 45934, 46889, 49643, 55201, 56149, 58011, 60809,\n", + " 61727, 64182, 65699, 67468, 72014, 79289, 81110, 82932,\n", + " 84806, 87588, 89471, 90926, 105218, 107924, 108851, 109776,\n", + " 112602, 113516, 118092, 119906, 121706, 134424, 135351, 144968,\n", + " 147575, 152163, 153114, 161877, 164594, 168190, 169139, 170089,\n", + " 186885, 190577],\n", + " dtype='int64'), Int64Index([ 4152, 5072, 10745, 13505, 18040, 20790, 21713, 31769,\n", + " 45014, 45935, 46890, 49644, 55202, 56150, 58012, 60810,\n", + " 61728, 64183, 65700, 67469, 72015, 79290, 81111, 82933,\n", + " 84807, 87589, 89472, 90927, 105219, 107925, 108852, 109777,\n", + " 112603, 113517, 118093, 119907, 121707, 134425, 135352, 144969,\n", + " 147576, 152164, 153115, 161878, 164595, 168191, 169140, 170090,\n", + " 186886, 190578],\n", + " dtype='int64'), Int64Index([ 4153, 5073, 10746, 13506, 18041, 20791, 21714, 31770,\n", + " 45015, 45936, 46891, 49645, 55203, 56151, 58013, 60811,\n", + " 61729, 64184, 65701, 67470, 72016, 79291, 81112, 82934,\n", + " 84808, 87590, 89473, 90928, 105220, 107926, 108853, 109778,\n", + " 112604, 113518, 118094, 119908, 121708, 134426, 135353, 144970,\n", + " 147577, 152165, 153116, 161879, 164596, 168192, 169141, 170091,\n", + " 186887, 190579],\n", + " dtype='int64'), Int64Index([ 4154, 5074, 10747, 13507, 18042, 20792, 21715, 31771,\n", + " 45016, 45937, 46892, 49646, 55204, 56152, 58014, 60812,\n", + " 61730, 64185, 65702, 67471, 72017, 79292, 81113, 82935,\n", + " 84809, 87591, 89474, 90929, 105221, 107927, 108854, 109779,\n", + " 112605, 113519, 118095, 119909, 121709, 134427, 135354, 144971,\n", + " 147578, 152166, 153117, 161880, 164597, 168193, 169142, 170092,\n", + " 186888, 190580],\n", + " dtype='int64'), Int64Index([ 4155, 5075, 10748, 13508, 18043, 20793, 21716, 31772,\n", + " 45017, 45938, 46893, 49647, 55205, 56153, 58015, 60813,\n", + " 61731, 64186, 65703, 67472, 72018, 79293, 81114, 82936,\n", + " 84810, 87592, 89475, 90930, 105222, 107928, 108855, 109780,\n", + " 112606, 113520, 118096, 119910, 121710, 134428, 135355, 144972,\n", + " 147579, 152167, 153118, 161881, 164598, 168194, 169143, 170093,\n", + " 186889, 190581],\n", + " dtype='int64'), Int64Index([ 4156, 5076, 10749, 13509, 18044, 20794, 21717, 31773,\n", + " 45018, 45939, 46894, 49648, 55206, 56154, 58016, 60814,\n", + " 61732, 64187, 65704, 67473, 72019, 79294, 81115, 82937,\n", + " 84811, 87593, 89476, 90931, 105223, 107929, 108856, 109781,\n", + " 112607, 113521, 118097, 119911, 121711, 134429, 135356, 144973,\n", + " 147580, 152168, 153119, 161882, 164599, 168195, 169144, 170094,\n", + " 186890, 190582],\n", + " dtype='int64'), Int64Index([ 4157, 5077, 10750, 13510, 18045, 20795, 21718, 31774,\n", + " 45019, 45940, 46895, 49649, 55207, 56155, 58017, 60815,\n", + " 61733, 64188, 65705, 67474, 72020, 79295, 81116, 82938,\n", + " 84812, 87594, 89477, 90932, 105224, 107930, 108857, 109782,\n", + " 112608, 113522, 118098, 119912, 121712, 134430, 135357, 144974,\n", + " 147581, 152169, 153120, 161883, 164600, 168196, 169145, 170095,\n", + " 186891, 190583],\n", + " dtype='int64'), Int64Index([ 4158, 5078, 10751, 13511, 18046, 20796, 21719, 31775,\n", + " 45020, 45941, 46896, 49650, 55208, 56156, 58018, 60816,\n", + " 61734, 64189, 65706, 67475, 72021, 79296, 81117, 82939,\n", + " 84813, 87595, 89478, 90933, 105225, 107931, 108858, 109783,\n", + " 112609, 113523, 118099, 119913, 121713, 134431, 135358, 144975,\n", + " 147582, 152170, 153121, 161884, 164601, 168197, 169146, 170096,\n", + " 186892, 190584],\n", + " dtype='int64'), Int64Index([ 4159, 5079, 10752, 13512, 18047, 20797, 21720, 31776,\n", + " 45021, 45942, 46897, 49651, 55209, 56157, 58019, 60817,\n", + " 61735, 64190, 65707, 67476, 72022, 79297, 81118, 82940,\n", + " 84814, 87596, 89479, 90934, 105226, 107932, 108859, 109784,\n", + " 112610, 113524, 118100, 119914, 121714, 134432, 135359, 144976,\n", + " 147583, 152171, 153122, 161885, 164602, 168198, 169147, 170097,\n", + " 186893, 190585],\n", + " dtype='int64'), Int64Index([ 4160, 5080, 10753, 13513, 18048, 20798, 21721, 31777,\n", + " 45022, 45943, 46898, 49652, 55210, 56158, 58020, 60818,\n", + " 61736, 64191, 65708, 67477, 72023, 79298, 81119, 82941,\n", + " 84815, 87597, 89480, 90935, 105227, 107933, 108860, 109785,\n", + " 112611, 113525, 118101, 119915, 121715, 134433, 135360, 144977,\n", + " 147584, 152172, 153123, 161886, 164603, 168199, 169148, 170098,\n", + " 186894, 190586],\n", + " dtype='int64'), Int64Index([ 4161, 5081, 10754, 13514, 18049, 20799, 21722, 31778,\n", + " 45023, 45944, 46899, 49653, 55211, 56159, 58021, 60819,\n", + " 61737, 64192, 65709, 67478, 72024, 79299, 81120, 82942,\n", + " 84816, 87598, 89481, 90936, 105228, 107934, 108861, 109786,\n", + " 112612, 113526, 118102, 119916, 121716, 134434, 135361, 144978,\n", + " 147585, 152173, 153124, 161887, 164604, 168200, 169149, 170099,\n", + " 186895, 190587],\n", + " dtype='int64'), Int64Index([ 4162, 5082, 10755, 13515, 18050, 20800, 21723, 31779,\n", + " 45024, 45945, 46900, 49654, 55212, 56160, 58022, 60820,\n", + " 61738, 64193, 65710, 67479, 72025, 79300, 81121, 82943,\n", + " 84817, 87599, 89482, 90937, 105229, 107935, 108862, 109787,\n", + " 112613, 113527, 118103, 119917, 121717, 134435, 135362, 144979,\n", + " 147586, 152174, 153125, 161888, 164605, 168201, 169150, 170100,\n", + " 186896, 190588],\n", + " dtype='int64'), Int64Index([ 4163, 5083, 10756, 13516, 18051, 20801, 21724, 31780,\n", + " 45025, 45946, 46901, 49655, 55213, 56161, 58023, 60821,\n", + " 61739, 64194, 65711, 67480, 72026, 79301, 81122, 82944,\n", + " 84818, 87600, 89483, 90938, 105230, 107936, 108863, 109788,\n", + " 112614, 113528, 118104, 119918, 121718, 134436, 135363, 144980,\n", + " 147587, 152175, 153126, 161889, 164606, 168202, 169151, 170101,\n", + " 186897, 190589],\n", + " dtype='int64'), Int64Index([ 4164, 5084, 10757, 13517, 18052, 20802, 21725, 31781,\n", + " 45026, 45947, 46902, 49656, 55214, 56162, 58024, 60822,\n", + " 61740, 64195, 65712, 67481, 72027, 79302, 81123, 82945,\n", + " 84819, 87601, 89484, 90939, 105231, 107937, 108864, 109789,\n", + " 112615, 113529, 118105, 119919, 121719, 134437, 135364, 144981,\n", + " 147588, 152176, 153127, 161890, 164607, 168203, 169152, 170102,\n", + " 186898, 190590],\n", + " dtype='int64'), Int64Index([ 4165, 5085, 10758, 13518, 18053, 20803, 21726, 31782,\n", + " 45027, 45948, 46903, 49657, 55215, 56163, 58025, 60823,\n", + " 61741, 64196, 65713, 67482, 72028, 79303, 81124, 82946,\n", + " 84820, 87602, 89485, 90940, 105232, 107938, 108865, 109790,\n", + " 112616, 113530, 118106, 119920, 121720, 134438, 135365, 144982,\n", + " 147589, 152177, 153128, 161891, 164608, 168204, 169153, 170103,\n", + " 186899, 190591],\n", + " dtype='int64'), Int64Index([ 4166, 5086, 10759, 13519, 18054, 20804, 21727, 31783,\n", + " 45028, 45949, 46904, 49658, 55216, 56164, 58026, 60824,\n", + " 61742, 64197, 65714, 67483, 72029, 79304, 81125, 82947,\n", + " 84821, 87603, 89486, 90941, 105233, 107939, 108866, 109791,\n", + " 112617, 113531, 118107, 119921, 121721, 134439, 135366, 144983,\n", + " 147590, 152178, 153129, 161892, 164609, 168205, 169154, 170104,\n", + " 186900, 190592],\n", + " dtype='int64'), Int64Index([ 4167, 5087, 10760, 13520, 18055, 20805, 21728, 31784,\n", + " 45029, 45950, 46905, 49659, 55217, 56165, 58027, 60825,\n", + " 61743, 64198, 65715, 67484, 72030, 79305, 81126, 82948,\n", + " 84822, 87604, 89487, 90942, 105234, 107940, 108867, 109792,\n", + " 112618, 113532, 118108, 119922, 121722, 134440, 135367, 144984,\n", + " 147591, 152179, 153130, 161893, 164610, 168206, 169155, 170105,\n", + " 186901, 190593],\n", + " dtype='int64'), Int64Index([ 4168, 5088, 10761, 13521, 18056, 20806, 21729, 31785,\n", + " 45030, 45951, 46906, 49660, 55218, 56166, 58028, 60826,\n", + " 61744, 64199, 65716, 67485, 72031, 79306, 81127, 82949,\n", + " 84823, 87605, 89488, 90943, 105235, 107941, 108868, 109793,\n", + " 112619, 113533, 118109, 119923, 121723, 134441, 135368, 144985,\n", + " 147592, 152180, 153131, 161894, 164611, 168207, 169156, 170106,\n", + " 186902, 190594],\n", + " dtype='int64'), Int64Index([ 4169, 5089, 10762, 13522, 18057, 20807, 21730, 31786,\n", + " 45031, 45952, 46907, 49661, 55219, 56167, 58029, 60827,\n", + " 61745, 64200, 65717, 67486, 72032, 79307, 81128, 82950,\n", + " 84824, 87606, 89489, 90944, 105236, 107942, 108869, 109794,\n", + " 112620, 113534, 118110, 119924, 121724, 134442, 135369, 144986,\n", + " 147593, 152181, 153132, 161895, 164612, 168208, 169157, 170107,\n", + " 186903, 190595],\n", + " dtype='int64'), Int64Index([ 4170, 5090, 10763, 13523, 18058, 20808, 21731, 31787,\n", + " 45032, 45953, 46908, 49662, 55220, 56168, 58030, 60828,\n", + " 61746, 64201, 65718, 67487, 72033, 79308, 81129, 82951,\n", + " 84825, 87607, 89490, 90945, 105237, 107943, 108870, 109795,\n", + " 112621, 113535, 118111, 119925, 121725, 134443, 135370, 144987,\n", + " 147594, 152182, 153133, 161896, 164613, 168209, 169158, 170108,\n", + " 186904, 190596],\n", + " dtype='int64'), Int64Index([ 4171, 5091, 10764, 13524, 18059, 20809, 21732, 31788,\n", + " 45033, 45954, 46909, 49663, 55221, 56169, 58031, 60829,\n", + " 61747, 64202, 65719, 67488, 72034, 79309, 81130, 82952,\n", + " 84826, 87608, 89491, 90946, 105238, 107944, 108871, 109796,\n", + " 112622, 113536, 118112, 119926, 121726, 134444, 135371, 144988,\n", + " 147595, 152183, 153134, 161897, 164614, 168210, 169159, 170109,\n", + " 186905, 190597],\n", + " dtype='int64'), Int64Index([ 4172, 5092, 10765, 13525, 18060, 20810, 21733, 31789,\n", + " 45034, 45955, 46910, 49664, 55222, 56170, 58032, 60830,\n", + " 61748, 64203, 65720, 67489, 72035, 79310, 81131, 82953,\n", + " 84827, 87609, 89492, 90947, 105239, 107945, 108872, 109797,\n", + " 112623, 113537, 118113, 119927, 121727, 134445, 135372, 144989,\n", + " 147596, 152184, 153135, 161898, 164615, 168211, 169160, 170110,\n", + " 186906, 190598],\n", + " dtype='int64'), Int64Index([ 4173, 5093, 10766, 13526, 18061, 20811, 21734, 31790,\n", + " 45035, 45956, 46911, 49665, 55223, 56171, 58033, 60831,\n", + " 61749, 64204, 65721, 67490, 72036, 79311, 81132, 82954,\n", + " 84828, 87610, 89493, 90948, 105240, 107946, 108873, 109798,\n", + " 112624, 113538, 118114, 119928, 121728, 134446, 135373, 144990,\n", + " 147597, 152185, 153136, 161899, 164616, 168212, 169161, 170111,\n", + " 186907, 190599],\n", + " dtype='int64'), Int64Index([ 4174, 5094, 10767, 13527, 18062, 20812, 21735, 31791,\n", + " 45036, 45957, 46912, 49666, 55224, 56172, 58034, 60832,\n", + " 61750, 64205, 65722, 67491, 72037, 79312, 81133, 82955,\n", + " 84829, 87611, 89494, 90949, 105241, 107947, 108874, 109799,\n", + " 112625, 113539, 118115, 119929, 121729, 134447, 135374, 144991,\n", + " 147598, 152186, 153137, 161900, 164617, 168213, 169162, 170112,\n", + " 186908, 190600],\n", + " dtype='int64'), Int64Index([ 4175, 5095, 10768, 13528, 18063, 20813, 21736, 31792,\n", + " 45037, 45958, 46913, 49667, 55225, 56173, 58035, 60833,\n", + " 61751, 64206, 65723, 67492, 72038, 79313, 81134, 82956,\n", + " 84830, 87612, 89495, 90950, 105242, 107948, 108875, 109800,\n", + " 112626, 113540, 118116, 119930, 121730, 134448, 135375, 144992,\n", + " 147599, 152187, 153138, 161901, 164618, 168214, 169163, 170113,\n", + " 186909, 190601],\n", + " dtype='int64'), Int64Index([ 4176, 5096, 10769, 13529, 18064, 20814, 21737, 31793,\n", + " 45038, 45959, 46914, 49668, 55226, 56174, 58036, 60834,\n", + " 61752, 64207, 65724, 67493, 72039, 79314, 81135, 82957,\n", + " 84831, 87613, 89496, 90951, 105243, 107949, 108876, 109801,\n", + " 112627, 113541, 118117, 119931, 121731, 134449, 135376, 144993,\n", + " 147600, 152188, 153139, 161902, 164619, 168215, 169164, 170114,\n", + " 186910, 190602],\n", + " dtype='int64'), Int64Index([ 4177, 5097, 10770, 13530, 18065, 20815, 21738, 31794,\n", + " 45039, 45960, 46915, 49669, 55227, 56175, 58037, 60835,\n", + " 61753, 64208, 65725, 67494, 72040, 79315, 81136, 82958,\n", + " 84832, 87614, 89497, 90952, 105244, 107950, 108877, 109802,\n", + " 112628, 113542, 118118, 119932, 121732, 134450, 135377, 144994,\n", + " 147601, 152189, 153140, 161903, 164620, 168216, 169165, 170115,\n", + " 186911, 190603],\n", + " dtype='int64'), Int64Index([ 4178, 5098, 10771, 13531, 18066, 20816, 21739, 31795,\n", + " 45040, 45961, 46916, 49670, 55228, 56176, 58038, 60836,\n", + " 61754, 64209, 65726, 67495, 72041, 79316, 81137, 82959,\n", + " 84833, 87615, 89498, 90953, 105245, 107951, 108878, 109803,\n", + " 112629, 113543, 118119, 119933, 121733, 134451, 135378, 144995,\n", + " 147602, 152190, 153141, 161904, 164621, 168217, 169166, 170116,\n", + " 186912, 190604],\n", + " dtype='int64'), Int64Index([ 4179, 5099, 10772, 13532, 18067, 20817, 21740, 31796,\n", + " 45041, 45962, 46917, 49671, 55229, 56177, 58039, 60837,\n", + " 61755, 64210, 65727, 67496, 72042, 79317, 81138, 82960,\n", + " 84834, 87616, 89499, 90954, 105246, 107952, 108879, 109804,\n", + " 112630, 113544, 118120, 119934, 121734, 134452, 135379, 144996,\n", + " 147603, 152191, 153142, 161905, 164622, 168218, 169167, 170117,\n", + " 186913, 190605],\n", + " dtype='int64'), Int64Index([ 4180, 5100, 10773, 13533, 18068, 20818, 21741, 31797,\n", + " 45042, 45963, 46918, 49672, 55230, 56178, 58040, 60838,\n", + " 61756, 64211, 65728, 67497, 72043, 79318, 81139, 82961,\n", + " 84835, 87617, 89500, 90955, 105247, 107953, 108880, 109805,\n", + " 112631, 113545, 118121, 119935, 121735, 134453, 135380, 144997,\n", + " 147604, 152192, 153143, 161906, 164623, 168219, 169168, 170118,\n", + " 186914, 190606],\n", + " dtype='int64'), Int64Index([ 4181, 5101, 10774, 13534, 18069, 20819, 21742, 31798,\n", + " 45043, 45964, 46919, 49673, 55231, 56179, 58041, 60839,\n", + " 61757, 64212, 65729, 67498, 72044, 79319, 81140, 82962,\n", + " 84836, 87618, 89501, 90956, 105248, 107954, 108881, 109806,\n", + " 112632, 113546, 118122, 119936, 121736, 134454, 135381, 144998,\n", + " 147605, 152193, 153144, 161907, 164624, 168220, 169169, 170119,\n", + " 186915, 190607],\n", + " dtype='int64'), Int64Index([ 4182, 5102, 10775, 13535, 18070, 20820, 21743, 31799,\n", + " 45044, 45965, 46920, 49674, 55232, 56180, 58042, 60840,\n", + " 61758, 64213, 65730, 67499, 72045, 79320, 81141, 82963,\n", + " 84837, 87619, 89502, 90957, 105249, 107955, 108882, 109807,\n", + " 112633, 113547, 118123, 119937, 121737, 134455, 135382, 144999,\n", + " 147606, 152194, 153145, 161908, 164625, 168221, 169170, 170120,\n", + " 186916, 190608],\n", + " dtype='int64'), Int64Index([ 4183, 5103, 10776, 13536, 18071, 20821, 21744, 31800,\n", + " 45045, 45966, 46921, 49675, 55233, 56181, 58043, 60841,\n", + " 61759, 64214, 65731, 67500, 72046, 79321, 81142, 82964,\n", + " 84838, 87620, 89503, 90958, 105250, 107956, 108883, 109808,\n", + " 112634, 113548, 118124, 119938, 121738, 134456, 135383, 145000,\n", + " 147607, 152195, 153146, 161909, 164626, 168222, 169171, 170121,\n", + " 186917, 190609],\n", + " dtype='int64'), Int64Index([ 4184, 5104, 10777, 13537, 18072, 20822, 21745, 31801,\n", + " 45046, 45967, 46922, 49676, 55234, 56182, 58044, 60842,\n", + " 61760, 64215, 65732, 67501, 72047, 79322, 81143, 82965,\n", + " 84839, 87621, 89504, 90959, 105251, 107957, 108884, 109809,\n", + " 112635, 113549, 118125, 119939, 121739, 134457, 135384, 145001,\n", + " 147608, 152196, 153147, 161910, 164627, 168223, 169172, 170122,\n", + " 186918, 190610],\n", + " dtype='int64'), Int64Index([ 4185, 5105, 10778, 13538, 18073, 20823, 21746, 31802,\n", + " 45047, 45968, 46923, 49677, 55235, 56183, 58045, 60843,\n", + " 61761, 64216, 65733, 67502, 72048, 79323, 81144, 82966,\n", + " 84840, 87622, 89505, 90960, 105252, 107958, 108885, 109810,\n", + " 112636, 113550, 118126, 119940, 121740, 134458, 135385, 145002,\n", + " 147609, 152197, 153148, 161911, 164628, 168224, 169173, 170123,\n", + " 186919, 190611],\n", + " dtype='int64'), Int64Index([ 4186, 5106, 10779, 13539, 18074, 20824, 21747, 31803,\n", + " 45048, 45969, 46924, 49678, 55236, 56184, 58046, 60844,\n", + " 61762, 64217, 65734, 67503, 72049, 79324, 81145, 82967,\n", + " 84841, 87623, 89506, 90961, 105253, 107959, 108886, 109811,\n", + " 112637, 113551, 118127, 119941, 121741, 134459, 135386, 145003,\n", + " 147610, 152198, 153149, 161912, 164629, 168225, 169174, 170124,\n", + " 186920, 190612],\n", + " dtype='int64'), Int64Index([ 4187, 5107, 10780, 13540, 18075, 20825, 21748, 31804,\n", + " 45049, 45970, 46925, 49679, 55237, 56185, 58047, 60845,\n", + " 61763, 64218, 65735, 67504, 72050, 79325, 81146, 82968,\n", + " 84842, 87624, 89507, 90962, 105254, 107960, 108887, 109812,\n", + " 112638, 113552, 118128, 119942, 121742, 134460, 135387, 145004,\n", + " 147611, 152199, 153150, 161913, 164630, 168226, 169175, 170125,\n", + " 186921, 190613],\n", + " dtype='int64'), Int64Index([ 4188, 5108, 10781, 13541, 18076, 20826, 21749, 31805,\n", + " 45050, 45971, 46926, 49680, 55238, 56186, 58048, 60846,\n", + " 61764, 64219, 65736, 67505, 72051, 79326, 81147, 82969,\n", + " 84843, 87625, 89508, 90963, 105255, 107961, 108888, 109813,\n", + " 112639, 113553, 118129, 119943, 121743, 134461, 135388, 145005,\n", + " 147612, 152200, 153151, 161914, 164631, 168227, 169176, 170126,\n", + " 186922, 190614],\n", + " dtype='int64'), Int64Index([ 4189, 5109, 10782, 13542, 18077, 20827, 21750, 31806,\n", + " 45051, 45972, 46927, 49681, 55239, 56187, 58049, 60847,\n", + " 61765, 64220, 65737, 67506, 72052, 79327, 81148, 82970,\n", + " 84844, 87626, 89509, 90964, 105256, 107962, 108889, 109814,\n", + " 112640, 113554, 118130, 119944, 121744, 134462, 135389, 145006,\n", + " 147613, 152201, 153152, 161915, 164632, 168228, 169177, 170127,\n", + " 186923, 190615],\n", + " dtype='int64'), Int64Index([ 4190, 5110, 10783, 13543, 18078, 20828, 21751, 31807,\n", + " 45052, 45973, 46928, 49682, 55240, 56188, 58050, 60848,\n", + " 61766, 64221, 65738, 67507, 72053, 79328, 81149, 82971,\n", + " 84845, 87627, 89510, 90965, 105257, 107963, 108890, 109815,\n", + " 112641, 113555, 118131, 119945, 121745, 134463, 135390, 145007,\n", + " 147614, 152202, 153153, 161916, 164633, 168229, 169178, 170128,\n", + " 186924, 190616],\n", + " dtype='int64'), Int64Index([ 4191, 5111, 10784, 13544, 18079, 20829, 21752, 31808,\n", + " 45053, 45974, 46929, 49683, 55241, 56189, 58051, 60849,\n", + " 61767, 64222, 65739, 67508, 72054, 79329, 81150, 82972,\n", + " 84846, 87628, 89511, 90966, 105258, 107964, 108891, 109816,\n", + " 112642, 113556, 118132, 119946, 121746, 134464, 135391, 145008,\n", + " 147615, 152203, 153154, 161917, 164634, 168230, 169179, 170129,\n", + " 186925, 190617],\n", + " dtype='int64'), Int64Index([ 4192, 5112, 10785, 13545, 18080, 20830, 21753, 31809,\n", + " 45054, 45975, 46930, 49684, 55242, 56190, 58052, 60850,\n", + " 61768, 64223, 65740, 67509, 72055, 79330, 81151, 82973,\n", + " 84847, 87629, 89512, 90967, 105259, 107965, 108892, 109817,\n", + " 112643, 113557, 118133, 119947, 121747, 134465, 135392, 145009,\n", + " 147616, 152204, 153155, 161918, 164635, 168231, 169180, 170130,\n", + " 186926, 190618],\n", + " dtype='int64'), Int64Index([ 4193, 5113, 10786, 13546, 18081, 20831, 21754, 31810,\n", + " 45055, 45976, 46931, 49685, 55243, 56191, 58053, 60851,\n", + " 61769, 64224, 65741, 67510, 72056, 79331, 81152, 82974,\n", + " 84848, 87630, 89513, 90968, 105260, 107966, 108893, 109818,\n", + " 112644, 113558, 118134, 119948, 121748, 134466, 135393, 145010,\n", + " 147617, 152205, 153156, 161919, 164636, 168232, 169181, 170131,\n", + " 186927, 190619],\n", + " dtype='int64'), Int64Index([ 4194, 5114, 10787, 13547, 18082, 20832, 21755, 31811,\n", + " 45056, 45977, 46932, 49686, 55244, 56192, 58054, 60852,\n", + " 61770, 64225, 65742, 67511, 72057, 79332, 81153, 82975,\n", + " 84849, 87631, 89514, 90969, 105261, 107967, 108894, 109819,\n", + " 112645, 113559, 118135, 119949, 121749, 134467, 135394, 145011,\n", + " 147618, 152206, 153157, 161920, 164637, 168233, 169182, 170132,\n", + " 186928, 190620],\n", + " dtype='int64'), Int64Index([ 4195, 5115, 10788, 13548, 18083, 20833, 21756, 31812,\n", + " 45057, 45978, 46933, 49687, 55245, 56193, 58055, 60853,\n", + " 61771, 64226, 65743, 67512, 72058, 79333, 81154, 82976,\n", + " 84850, 87632, 89515, 90970, 105262, 107968, 108895, 109820,\n", + " 112646, 113560, 118136, 119950, 121750, 134468, 135395, 145012,\n", + " 147619, 152207, 153158, 161921, 164638, 168234, 169183, 170133,\n", + " 186929, 190621],\n", + " dtype='int64'), Int64Index([ 4196, 5116, 10789, 13549, 18084, 20834, 21757, 31813,\n", + " 45058, 45979, 46934, 49688, 55246, 56194, 58056, 60854,\n", + " 61772, 64227, 65744, 67513, 72059, 79334, 81155, 82977,\n", + " 84851, 87633, 89516, 90971, 105263, 107969, 108896, 109821,\n", + " 112647, 113561, 118137, 119951, 121751, 134469, 135396, 145013,\n", + " 147620, 152208, 153159, 161922, 164639, 168235, 169184, 170134,\n", + " 186930, 190622],\n", + " dtype='int64'), Int64Index([ 4197, 5117, 10790, 13550, 18085, 20835, 21758, 31814,\n", + " 45059, 45980, 46935, 49689, 55247, 56195, 58057, 60855,\n", + " 61773, 64228, 65745, 67514, 72060, 79335, 81156, 82978,\n", + " 84852, 87634, 89517, 90972, 105264, 107970, 108897, 109822,\n", + " 112648, 113562, 118138, 119952, 121752, 134470, 135397, 145014,\n", + " 147621, 152209, 153160, 161923, 164640, 168236, 169185, 170135,\n", + " 186931, 190623],\n", + " dtype='int64'), Int64Index([ 4198, 5118, 10791, 13551, 18086, 20836, 21759, 31815,\n", + " 45060, 45981, 46936, 49690, 55248, 56196, 58058, 60856,\n", + " 61774, 64229, 65746, 67515, 72061, 79336, 81157, 82979,\n", + " 84853, 87635, 89518, 90973, 105265, 107971, 108898, 109823,\n", + " 112649, 113563, 118139, 119953, 121753, 134471, 135398, 145015,\n", + " 147622, 152210, 153161, 161924, 164641, 168237, 169186, 170136,\n", + " 186932, 190624],\n", + " dtype='int64'), Int64Index([ 4199, 5119, 10792, 13552, 18087, 20837, 21760, 31816,\n", + " 45061, 45982, 46937, 49691, 55249, 56197, 58059, 60857,\n", + " 61775, 64230, 65747, 67516, 72062, 79337, 81158, 82980,\n", + " 84854, 87636, 89519, 90974, 105266, 107972, 108899, 109824,\n", + " 112650, 113564, 118140, 119954, 121754, 134472, 135399, 145016,\n", + " 147623, 152211, 153162, 161925, 164642, 168238, 169187, 170137,\n", + " 186933, 190625],\n", + " dtype='int64'), Int64Index([ 4200, 5120, 10793, 13553, 18088, 20838, 21761, 31817,\n", + " 45062, 45983, 46938, 49692, 55250, 56198, 58060, 60858,\n", + " 61776, 64231, 65748, 67517, 72063, 79338, 81159, 82981,\n", + " 84855, 87637, 89520, 90975, 105267, 107973, 108900, 109825,\n", + " 112651, 113565, 118141, 119955, 121755, 134473, 135400, 145017,\n", + " 147624, 152212, 153163, 161926, 164643, 168239, 169188, 170138,\n", + " 186934, 190626],\n", + " dtype='int64'), Int64Index([ 4201, 5121, 10794, 13554, 18089, 20839, 21762, 31818,\n", + " 45063, 45984, 46939, 49693, 55251, 56199, 58061, 60859,\n", + " 61777, 64232, 65749, 67518, 72064, 79339, 81160, 82982,\n", + " 84856, 87638, 89521, 90976, 105268, 107974, 108901, 109826,\n", + " 112652, 113566, 118142, 119956, 121756, 134474, 135401, 145018,\n", + " 147625, 152213, 153164, 161927, 164644, 168240, 169189, 170139,\n", + " 186935, 190627],\n", + " dtype='int64'), Int64Index([ 4202, 5122, 10795, 13555, 18090, 20840, 21763, 31819,\n", + " 45064, 45985, 46940, 49694, 55252, 56200, 58062, 60860,\n", + " 61778, 64233, 65750, 67519, 72065, 79340, 81161, 82983,\n", + " 84857, 87639, 89522, 90977, 105269, 107975, 108902, 109827,\n", + " 112653, 113567, 118143, 119957, 121757, 134475, 135402, 145019,\n", + " 147626, 152214, 153165, 161928, 164645, 168241, 169190, 170140,\n", + " 186936, 190628],\n", + " dtype='int64'), Int64Index([ 4203, 5123, 10796, 13556, 18091, 20841, 21764, 31820,\n", + " 45065, 45986, 46941, 49695, 55253, 56201, 58063, 60861,\n", + " 61779, 64234, 65751, 67520, 72066, 79341, 81162, 82984,\n", + " 84858, 87640, 89523, 90978, 105270, 107976, 108903, 109828,\n", + " 112654, 113568, 118144, 119958, 121758, 134476, 135403, 145020,\n", + " 147627, 152215, 153166, 161929, 164646, 168242, 169191, 170141,\n", + " 186937, 190629],\n", + " dtype='int64'), Int64Index([ 4204, 5124, 10797, 13557, 18092, 20842, 21765, 31821,\n", + " 45066, 45987, 46942, 49696, 55254, 56202, 58064, 60862,\n", + " 61780, 64235, 65752, 67521, 72067, 79342, 81163, 82985,\n", + " 84859, 87641, 89524, 90979, 105271, 107977, 108904, 109829,\n", + " 112655, 113569, 118145, 119959, 121759, 134477, 135404, 145021,\n", + " 147628, 152216, 153167, 161930, 164647, 168243, 169192, 170142,\n", + " 186938, 190630],\n", + " dtype='int64'), Int64Index([ 4205, 5125, 10798, 13558, 18093, 20843, 21766, 31822,\n", + " 45067, 45988, 46943, 49697, 55255, 56203, 58065, 60863,\n", + " 61781, 64236, 65753, 67522, 72068, 79343, 81164, 82986,\n", + " 84860, 87642, 89525, 90980, 105272, 107978, 108905, 109830,\n", + " 112656, 113570, 118146, 119960, 121760, 134478, 135405, 145022,\n", + " 147629, 152217, 153168, 161931, 164648, 168244, 169193, 170143,\n", + " 186939, 190631],\n", + " dtype='int64'), Int64Index([ 4206, 5126, 10799, 13559, 18094, 20844, 21767, 31823,\n", + " 45068, 45989, 46944, 49698, 55256, 56204, 58066, 60864,\n", + " 61782, 64237, 65754, 67523, 72069, 79344, 81165, 82987,\n", + " 84861, 87643, 89526, 90981, 105273, 107979, 108906, 109831,\n", + " 112657, 113571, 118147, 119961, 121761, 134479, 135406, 145023,\n", + " 147630, 152218, 153169, 161932, 164649, 168245, 169194, 170144,\n", + " 186940, 190632],\n", + " dtype='int64'), Int64Index([ 4207, 5127, 10800, 13560, 18095, 20845, 21768, 31824,\n", + " 45069, 45990, 46945, 49699, 55257, 56205, 58067, 60865,\n", + " 61783, 64238, 65755, 67524, 72070, 79345, 81166, 82988,\n", + " 84862, 87644, 89527, 90982, 105274, 107980, 108907, 109832,\n", + " 112658, 113572, 118148, 119962, 121762, 134480, 135407, 145024,\n", + " 147631, 152219, 153170, 161933, 164650, 168246, 169195, 170145,\n", + " 186941, 190633],\n", + " dtype='int64'), Int64Index([ 4208, 5128, 10801, 13561, 18096, 20846, 21769, 31825,\n", + " 45070, 45991, 46946, 49700, 55258, 56206, 58068, 60866,\n", + " 61784, 64239, 65756, 67525, 72071, 79346, 81167, 82989,\n", + " 84863, 87645, 89528, 90983, 105275, 107981, 108908, 109833,\n", + " 112659, 113573, 118149, 119963, 121763, 134481, 135408, 145025,\n", + " 147632, 152220, 153171, 161934, 164651, 168247, 169196, 170146,\n", + " 186942, 190634],\n", + " dtype='int64'), Int64Index([ 4209, 5129, 10802, 13562, 18097, 20847, 21770, 31826,\n", + " 45071, 45992, 46947, 49701, 55259, 56207, 58069, 60867,\n", + " 61785, 64240, 65757, 67526, 72072, 79347, 81168, 82990,\n", + " 84864, 87646, 89529, 90984, 105276, 107982, 108909, 109834,\n", + " 112660, 113574, 118150, 119964, 121764, 134482, 135409, 145026,\n", + " 147633, 152221, 153172, 161935, 164652, 168248, 169197, 170147,\n", + " 186943, 190635],\n", + " dtype='int64'), Int64Index([ 4210, 5130, 10803, 13563, 18098, 20848, 21771, 31827,\n", + " 45072, 45993, 46948, 49702, 55260, 56208, 58070, 60868,\n", + " 61786, 64241, 65758, 67527, 72073, 79348, 81169, 82991,\n", + " 84865, 87647, 89530, 90985, 105277, 107983, 108910, 109835,\n", + " 112661, 113575, 118151, 119965, 121765, 134483, 135410, 145027,\n", + " 147634, 152222, 153173, 161936, 164653, 168249, 169198, 170148,\n", + " 186944, 190636],\n", + " dtype='int64'), Int64Index([ 4211, 5131, 10804, 13564, 18099, 20849, 21772, 31828,\n", + " 45073, 45994, 46949, 49703, 55261, 56209, 58071, 60869,\n", + " 61787, 64242, 65759, 67528, 72074, 79349, 81170, 82992,\n", + " 84866, 87648, 89531, 90986, 105278, 107984, 108911, 109836,\n", + " 112662, 113576, 118152, 119966, 121766, 134484, 135411, 145028,\n", + " 147635, 152223, 153174, 161937, 164654, 168250, 169199, 170149,\n", + " 186945, 190637],\n", + " dtype='int64'), Int64Index([ 4212, 5132, 10805, 13565, 18100, 20850, 21773, 31829,\n", + " 45074, 45995, 46950, 49704, 55262, 56210, 58072, 60870,\n", + " 61788, 64243, 65760, 67529, 72075, 79350, 81171, 82993,\n", + " 84867, 87649, 89532, 90987, 105279, 107985, 108912, 109837,\n", + " 112663, 113577, 118153, 119967, 121767, 134485, 135412, 145029,\n", + " 147636, 152224, 153175, 161938, 164655, 168251, 169200, 170150,\n", + " 186946, 190638],\n", + " dtype='int64'), Int64Index([ 4213, 5133, 10806, 13566, 18101, 20851, 21774, 31830,\n", + " 45075, 45996, 46951, 49705, 55263, 56211, 58073, 60871,\n", + " 61789, 64244, 65761, 67530, 72076, 79351, 81172, 82994,\n", + " 84868, 87650, 89533, 90988, 105280, 107986, 108913, 109838,\n", + " 112664, 113578, 118154, 119968, 121768, 134486, 135413, 145030,\n", + " 147637, 152225, 153176, 161939, 164656, 168252, 169201, 170151,\n", + " 186947, 190639],\n", + " dtype='int64'), Int64Index([ 4214, 5134, 10807, 13567, 18102, 20852, 21775, 31831,\n", + " 45076, 45997, 46952, 49706, 55264, 56212, 58074, 60872,\n", + " 61790, 64245, 65762, 67531, 72077, 79352, 81173, 82995,\n", + " 84869, 87651, 89534, 90989, 105281, 107987, 108914, 109839,\n", + " 112665, 113579, 118155, 119969, 121769, 134487, 135414, 145031,\n", + " 147638, 152226, 153177, 161940, 164657, 168253, 169202, 170152,\n", + " 186948, 190640],\n", + " dtype='int64'), Int64Index([ 4215, 5135, 10808, 13568, 18103, 20853, 21776, 31832,\n", + " 45077, 45998, 46953, 49707, 55265, 56213, 58075, 60873,\n", + " 61791, 64246, 65763, 67532, 72078, 79353, 81174, 82996,\n", + " 84870, 87652, 89535, 90990, 105282, 107988, 108915, 109840,\n", + " 112666, 113580, 118156, 119970, 121770, 134488, 135415, 145032,\n", + " 147639, 152227, 153178, 161941, 164658, 168254, 169203, 170153,\n", + " 186949, 190641],\n", + " dtype='int64'), Int64Index([ 4216, 5136, 10809, 13569, 18104, 20854, 21777, 31833,\n", + " 45078, 45999, 46954, 49708, 55266, 56214, 58076, 60874,\n", + " 61792, 64247, 65764, 67533, 72079, 79354, 81175, 82997,\n", + " 84871, 87653, 89536, 90991, 105283, 107989, 108916, 109841,\n", + " 112667, 113581, 118157, 119971, 121771, 134489, 135416, 145033,\n", + " 147640, 152228, 153179, 161942, 164659, 168255, 169204, 170154,\n", + " 186950, 190642],\n", + " dtype='int64'), Int64Index([ 4217, 5137, 10810, 13570, 18105, 20855, 21778, 31834,\n", + " 45079, 46000, 46955, 49709, 55267, 56215, 58077, 60875,\n", + " 61793, 64248, 65765, 67534, 72080, 79355, 81176, 82998,\n", + " 84872, 87654, 89537, 90992, 105284, 107990, 108917, 109842,\n", + " 112668, 113582, 118158, 119972, 121772, 134490, 135417, 145034,\n", + " 147641, 152229, 153180, 161943, 164660, 168256, 169205, 170155,\n", + " 186951, 190643],\n", + " dtype='int64'), Int64Index([ 4218, 5138, 10811, 13571, 18106, 20856, 21779, 31835,\n", + " 45080, 46001, 46956, 49710, 55268, 56216, 58078, 60876,\n", + " 61794, 64249, 65766, 67535, 72081, 79356, 81177, 82999,\n", + " 84873, 87655, 89538, 90993, 105285, 107991, 108918, 109843,\n", + " 112669, 113583, 118159, 119973, 121773, 134491, 135418, 145035,\n", + " 147642, 152230, 153181, 161944, 164661, 168257, 169206, 170156,\n", + " 186952, 190644],\n", + " dtype='int64'), Int64Index([ 4219, 5139, 10812, 13572, 18107, 20857, 21780, 31836,\n", + " 45081, 46002, 46957, 49711, 55269, 56217, 58079, 60877,\n", + " 61795, 64250, 65767, 67536, 72082, 79357, 81178, 83000,\n", + " 84874, 87656, 89539, 90994, 105286, 107992, 108919, 109844,\n", + " 112670, 113584, 118160, 119974, 121774, 134492, 135419, 145036,\n", + " 147643, 152231, 153182, 161945, 164662, 168258, 169207, 170157,\n", + " 186953, 190645],\n", + " dtype='int64'), Int64Index([ 4220, 5140, 10813, 13573, 18108, 20858, 21781, 31837,\n", + " 45082, 46003, 46958, 49712, 55270, 56218, 58080, 60878,\n", + " 61796, 64251, 65768, 67537, 72083, 79358, 81179, 83001,\n", + " 84875, 87657, 89540, 90995, 105287, 107993, 108920, 109845,\n", + " 112671, 113585, 118161, 119975, 121775, 134493, 135420, 145037,\n", + " 147644, 152232, 153183, 161946, 164663, 168259, 169208, 170158,\n", + " 186954, 190646],\n", + " dtype='int64'), Int64Index([ 4221, 5141, 10814, 13574, 18109, 20859, 21782, 31838,\n", + " 45083, 46004, 46959, 49713, 55271, 56219, 58081, 60879,\n", + " 61797, 64252, 65769, 67538, 72084, 79359, 81180, 83002,\n", + " 84876, 87658, 89541, 90996, 105288, 107994, 108921, 109846,\n", + " 112672, 113586, 118162, 119976, 121776, 134494, 135421, 145038,\n", + " 147645, 152233, 153184, 161947, 164664, 168260, 169209, 170159,\n", + " 186955, 190647],\n", + " dtype='int64'), Int64Index([ 4222, 5142, 10815, 13575, 18110, 20860, 21783, 31839,\n", + " 45084, 46005, 46960, 49714, 55272, 56220, 58082, 60880,\n", + " 61798, 64253, 65770, 67539, 72085, 79360, 81181, 83003,\n", + " 84877, 87659, 89542, 90997, 105289, 107995, 108922, 109847,\n", + " 112673, 113587, 118163, 119977, 121777, 134495, 135422, 145039,\n", + " 147646, 152234, 153185, 161948, 164665, 168261, 169210, 170160,\n", + " 186956, 190648],\n", + " dtype='int64'), Int64Index([ 4223, 5143, 10816, 13576, 18111, 20861, 21784, 31840,\n", + " 45085, 46006, 46961, 49715, 55273, 56221, 58083, 60881,\n", + " 61799, 64254, 65771, 67540, 72086, 79361, 81182, 83004,\n", + " 84878, 87660, 89543, 90998, 105290, 107996, 108923, 109848,\n", + " 112674, 113588, 118164, 119978, 121778, 134496, 135423, 145040,\n", + " 147647, 152235, 153186, 161949, 164666, 168262, 169211, 170161,\n", + " 186957, 190649],\n", + " dtype='int64'), Int64Index([ 4224, 5144, 10817, 13577, 18112, 20862, 21785, 31841,\n", + " 45086, 46007, 46962, 49716, 55274, 56222, 58084, 60882,\n", + " 61800, 64255, 65772, 67541, 72087, 79362, 81183, 83005,\n", + " 84879, 87661, 89544, 90999, 105291, 107997, 108924, 109849,\n", + " 112675, 113589, 118165, 119979, 121779, 134497, 135424, 145041,\n", + " 147648, 152236, 153187, 161950, 164667, 168263, 169212, 170162,\n", + " 186958, 190650],\n", + " dtype='int64'), Int64Index([ 4225, 5145, 10818, 13578, 18113, 20863, 21786, 31842,\n", + " 45087, 46008, 46963, 49717, 55275, 56223, 58085, 60883,\n", + " 61801, 64256, 65773, 67542, 72088, 79363, 81184, 83006,\n", + " 84880, 87662, 89545, 91000, 105292, 107998, 108925, 109850,\n", + " 112676, 113590, 118166, 119980, 121780, 134498, 135425, 145042,\n", + " 147649, 152237, 153188, 161951, 164668, 168264, 169213, 170163,\n", + " 186959, 190651],\n", + " dtype='int64'), Int64Index([ 4226, 5146, 10819, 13579, 18114, 20864, 21787, 31843,\n", + " 45088, 46009, 46964, 49718, 55276, 56224, 58086, 60884,\n", + " 61802, 64257, 65774, 67543, 72089, 79364, 81185, 83007,\n", + " 84881, 87663, 89546, 91001, 105293, 107999, 108926, 109851,\n", + " 112677, 113591, 118167, 119981, 121781, 134499, 135426, 145043,\n", + " 147650, 152238, 153189, 161952, 164669, 168265, 169214, 170164,\n", + " 186960, 190652],\n", + " dtype='int64'), Int64Index([ 4227, 5147, 10820, 13580, 18115, 20865, 21788, 31844,\n", + " 45089, 46010, 46965, 49719, 55277, 56225, 58087, 60885,\n", + " 61803, 64258, 65775, 67544, 72090, 79365, 81186, 83008,\n", + " 84882, 87664, 89547, 91002, 105294, 108000, 108927, 109852,\n", + " 112678, 113592, 118168, 119982, 121782, 134500, 135427, 145044,\n", + " 147651, 152239, 153190, 161953, 164670, 168266, 169215, 170165,\n", + " 186961, 190653],\n", + " dtype='int64'), Int64Index([ 4228, 5148, 10821, 13581, 18116, 20866, 21789, 31845,\n", + " 45090, 46011, 46966, 49720, 55278, 56226, 58088, 60886,\n", + " 61804, 64259, 65776, 67545, 72091, 79366, 81187, 83009,\n", + " 84883, 87665, 89548, 91003, 105295, 108001, 108928, 109853,\n", + " 112679, 113593, 118169, 119983, 121783, 134501, 135428, 145045,\n", + " 147652, 152240, 153191, 161954, 164671, 168267, 169216, 170166,\n", + " 186962, 190654],\n", + " dtype='int64'), Int64Index([ 4229, 5149, 10822, 13582, 18117, 20867, 21790, 31846,\n", + " 45091, 46012, 46967, 49721, 55279, 56227, 58089, 60887,\n", + " 61805, 64260, 65777, 67546, 72092, 79367, 81188, 83010,\n", + " 84884, 87666, 89549, 91004, 105296, 108002, 108929, 109854,\n", + " 112680, 113594, 118170, 119984, 121784, 134502, 135429, 145046,\n", + " 147653, 152241, 153192, 161955, 164672, 168268, 169217, 170167,\n", + " 186963, 190655],\n", + " dtype='int64'), Int64Index([ 4230, 5150, 10823, 13583, 18118, 20868, 21791, 31847,\n", + " 45092, 46013, 46968, 49722, 55280, 56228, 58090, 60888,\n", + " 61806, 64261, 65778, 67547, 72093, 79368, 81189, 83011,\n", + " 84885, 87667, 89550, 91005, 105297, 108003, 108930, 109855,\n", + " 112681, 113595, 118171, 119985, 121785, 134503, 135430, 145047,\n", + " 147654, 152242, 153193, 161956, 164673, 168269, 169218, 170168,\n", + " 186964, 190656],\n", + " dtype='int64'), Int64Index([ 4231, 5151, 10824, 13584, 18119, 20869, 21792, 31848,\n", + " 45093, 46014, 46969, 49723, 55281, 56229, 58091, 60889,\n", + " 61807, 64262, 65779, 67548, 72094, 79369, 81190, 83012,\n", + " 84886, 87668, 89551, 91006, 105298, 108004, 108931, 109856,\n", + " 112682, 113596, 118172, 119986, 121786, 134504, 135431, 145048,\n", + " 147655, 152243, 153194, 161957, 164674, 168270, 169219, 170169,\n", + " 186965, 190657],\n", + " dtype='int64'), Int64Index([ 4232, 5152, 10825, 13585, 18120, 20870, 21793, 31849,\n", + " 45094, 46015, 46970, 49724, 55282, 56230, 58092, 60890,\n", + " 61808, 64263, 65780, 67549, 72095, 79370, 81191, 83013,\n", + " 84887, 87669, 89552, 91007, 105299, 108005, 108932, 109857,\n", + " 112683, 113597, 118173, 119987, 121787, 134505, 135432, 145049,\n", + " 147656, 152244, 153195, 161958, 164675, 168271, 169220, 170170,\n", + " 186966, 190658],\n", + " dtype='int64'), Int64Index([ 4233, 5153, 10826, 13586, 18121, 20871, 21794, 31850,\n", + " 45095, 46016, 46971, 49725, 55283, 56231, 58093, 60891,\n", + " 61809, 64264, 65781, 67550, 72096, 79371, 81192, 83014,\n", + " 84888, 87670, 89553, 91008, 105300, 108006, 108933, 109858,\n", + " 112684, 113598, 118174, 119988, 121788, 134506, 135433, 145050,\n", + " 147657, 152245, 153196, 161959, 164676, 168272, 169221, 170171,\n", + " 186967, 190659],\n", + " dtype='int64'), Int64Index([ 4234, 5154, 10827, 13587, 18122, 20872, 21795, 31851,\n", + " 45096, 46017, 46972, 49726, 55284, 56232, 58094, 60892,\n", + " 61810, 64265, 65782, 67551, 72097, 79372, 81193, 83015,\n", + " 84889, 87671, 89554, 91009, 105301, 108007, 108934, 109859,\n", + " 112685, 113599, 118175, 119989, 121789, 134507, 135434, 145051,\n", + " 147658, 152246, 153197, 161960, 164677, 168273, 169222, 170172,\n", + " 186968, 190660],\n", + " dtype='int64'), Int64Index([ 4235, 5155, 10828, 13588, 18123, 20873, 21796, 31852,\n", + " 45097, 46018, 46973, 49727, 55285, 56233, 58095, 60893,\n", + " 61811, 64266, 65783, 67552, 72098, 79373, 81194, 83016,\n", + " 84890, 87672, 89555, 91010, 105302, 108008, 108935, 109860,\n", + " 112686, 113600, 118176, 119990, 121790, 134508, 135435, 145052,\n", + " 147659, 152247, 153198, 161961, 164678, 168274, 169223, 170173,\n", + " 186969, 190661],\n", + " dtype='int64'), Int64Index([ 4236, 5156, 10829, 13589, 18124, 20874, 21797, 31853,\n", + " 45098, 46019, 46974, 49728, 55286, 56234, 58096, 60894,\n", + " 61812, 64267, 65784, 67553, 72099, 79374, 81195, 83017,\n", + " 84891, 87673, 89556, 91011, 105303, 108009, 108936, 109861,\n", + " 112687, 113601, 118177, 119991, 121791, 134509, 135436, 145053,\n", + " 147660, 152248, 153199, 161962, 164679, 168275, 169224, 170174,\n", + " 186970, 190662],\n", + " dtype='int64'), Int64Index([ 4237, 5157, 10830, 13590, 18125, 20875, 21798, 31854,\n", + " 45099, 46020, 46975, 49729, 55287, 56235, 58097, 60895,\n", + " 61813, 64268, 65785, 67554, 72100, 79375, 81196, 83018,\n", + " 84892, 87674, 89557, 91012, 105304, 108010, 108937, 109862,\n", + " 112688, 113602, 118178, 119992, 121792, 134510, 135437, 145054,\n", + " 147661, 152249, 153200, 161963, 164680, 168276, 169225, 170175,\n", + " 186971, 190663],\n", + " dtype='int64'), Int64Index([ 4238, 5158, 10831, 13591, 18126, 20876, 21799, 31855,\n", + " 45100, 46021, 46976, 49730, 55288, 56236, 58098, 60896,\n", + " 61814, 64269, 65786, 67555, 72101, 79376, 81197, 83019,\n", + " 84893, 87675, 89558, 91013, 105305, 108011, 108938, 109863,\n", + " 112689, 113603, 118179, 119993, 121793, 134511, 135438, 145055,\n", + " 147662, 152250, 153201, 161964, 164681, 168277, 169226, 170176,\n", + " 186972, 190664],\n", + " dtype='int64'), Int64Index([ 4239, 5159, 10832, 13592, 18127, 20877, 21800, 31856,\n", + " 45101, 46022, 46977, 49731, 55289, 56237, 58099, 60897,\n", + " 61815, 64270, 65787, 67556, 72102, 79377, 81198, 83020,\n", + " 84894, 87676, 89559, 91014, 105306, 108012, 108939, 109864,\n", + " 112690, 113604, 118180, 119994, 121794, 134512, 135439, 145056,\n", + " 147663, 152251, 153202, 161965, 164682, 168278, 169227, 170177,\n", + " 186973, 190665],\n", + " dtype='int64'), Int64Index([ 4240, 5160, 10833, 13593, 18128, 20878, 21801, 31857,\n", + " 45102, 46023, 46978, 49732, 55290, 56238, 58100, 60898,\n", + " 61816, 64271, 65788, 67557, 72103, 79378, 81199, 83021,\n", + " 84895, 87677, 89560, 91015, 105307, 108013, 108940, 109865,\n", + " 112691, 113605, 118181, 119995, 121795, 134513, 135440, 145057,\n", + " 147664, 152252, 153203, 161966, 164683, 168279, 169228, 170178,\n", + " 186974, 190666],\n", + " dtype='int64'), Int64Index([ 4241, 5161, 10834, 13594, 18129, 20879, 21802, 31858,\n", + " 45103, 46024, 46979, 49733, 55291, 56239, 58101, 60899,\n", + " 61817, 64272, 65789, 67558, 72104, 79379, 81200, 83022,\n", + " 84896, 87678, 89561, 91016, 105308, 108014, 108941, 109866,\n", + " 112692, 113606, 118182, 119996, 121796, 134514, 135441, 145058,\n", + " 147665, 152253, 153204, 161967, 164684, 168280, 169229, 170179,\n", + " 186975, 190667],\n", + " dtype='int64'), Int64Index([ 4242, 5162, 10835, 13595, 18130, 20880, 21803, 31859,\n", + " 45104, 46025, 46980, 49734, 55292, 56240, 58102, 60900,\n", + " 61818, 64273, 65790, 67559, 72105, 79380, 81201, 83023,\n", + " 84897, 87679, 89562, 91017, 105309, 108015, 108942, 109867,\n", + " 112693, 113607, 118183, 119997, 121797, 134515, 135442, 145059,\n", + " 147666, 152254, 153205, 161968, 164685, 168281, 169230, 170180,\n", + " 186976, 190668],\n", + " dtype='int64'), Int64Index([ 4243, 5163, 10836, 13596, 18131, 20881, 21804, 31860,\n", + " 45105, 46026, 46981, 49735, 55293, 56241, 58103, 60901,\n", + " 61819, 64274, 65791, 67560, 72106, 79381, 81202, 83024,\n", + " 84898, 87680, 89563, 91018, 105310, 108016, 108943, 109868,\n", + " 112694, 113608, 118184, 119998, 121798, 134516, 135443, 145060,\n", + " 147667, 152255, 153206, 161969, 164686, 168282, 169231, 170181,\n", + " 186977, 190669],\n", + " dtype='int64'), Int64Index([ 4244, 5164, 10837, 13597, 18132, 20882, 21805, 31861,\n", + " 45106, 46027, 46982, 49736, 55294, 56242, 58104, 60902,\n", + " 61820, 64275, 65792, 67561, 72107, 79382, 81203, 83025,\n", + " 84899, 87681, 89564, 91019, 105311, 108017, 108944, 109869,\n", + " 112695, 113609, 118185, 119999, 121799, 134517, 135444, 145061,\n", + " 147668, 152256, 153207, 161970, 164687, 168283, 169232, 170182,\n", + " 186978, 190670],\n", + " dtype='int64'), Int64Index([ 4245, 5165, 10838, 13598, 18133, 20883, 21806, 31862,\n", + " 45107, 46028, 46983, 49737, 55295, 56243, 58105, 60903,\n", + " 61821, 64276, 65793, 67562, 72108, 79383, 81204, 83026,\n", + " 84900, 87682, 89565, 91020, 105312, 108018, 108945, 109870,\n", + " 112696, 113610, 118186, 120000, 121800, 134518, 135445, 145062,\n", + " 147669, 152257, 153208, 161971, 164688, 168284, 169233, 170183,\n", + " 186979, 190671],\n", + " dtype='int64'), Int64Index([ 4246, 5166, 10839, 13599, 18134, 20884, 21807, 31863,\n", + " 45108, 46029, 46984, 49738, 55296, 56244, 58106, 60904,\n", + " 61822, 64277, 65794, 67563, 72109, 79384, 81205, 83027,\n", + " 84901, 87683, 89566, 91021, 105313, 108019, 108946, 109871,\n", + " 112697, 113611, 118187, 120001, 121801, 134519, 135446, 145063,\n", + " 147670, 152258, 153209, 161972, 164689, 168285, 169234, 170184,\n", + " 186980, 190672],\n", + " dtype='int64'), Int64Index([ 4247, 5167, 10840, 13600, 18135, 20885, 21808, 31864,\n", + " 45109, 46030, 46985, 49739, 55297, 56245, 58107, 60905,\n", + " 61823, 64278, 65795, 67564, 72110, 79385, 81206, 83028,\n", + " 84902, 87684, 89567, 91022, 105314, 108020, 108947, 109872,\n", + " 112698, 113612, 118188, 120002, 121802, 134520, 135447, 145064,\n", + " 147671, 152259, 153210, 161973, 164690, 168286, 169235, 170185,\n", + " 186981, 190673],\n", + " dtype='int64'), Int64Index([ 4248, 5168, 10841, 13601, 18136, 20886, 21809, 31865,\n", + " 45110, 46031, 46986, 49740, 55298, 56246, 58108, 60906,\n", + " 61824, 64279, 65796, 67565, 72111, 79386, 81207, 83029,\n", + " 84903, 87685, 89568, 91023, 105315, 108021, 108948, 109873,\n", + " 112699, 113613, 118189, 120003, 121803, 134521, 135448, 145065,\n", + " 147672, 152260, 153211, 161974, 164691, 168287, 169236, 170186,\n", + " 186982, 190674],\n", + " dtype='int64'), Int64Index([ 4249, 5169, 10842, 13602, 18137, 20887, 21810, 31866,\n", + " 45111, 46032, 46987, 49741, 55299, 56247, 58109, 60907,\n", + " 61825, 64280, 65797, 67566, 72112, 79387, 81208, 83030,\n", + " 84904, 87686, 89569, 91024, 105316, 108022, 108949, 109874,\n", + " 112700, 113614, 118190, 120004, 121804, 134522, 135449, 145066,\n", + " 147673, 152261, 153212, 161975, 164692, 168288, 169237, 170187,\n", + " 186983, 190675],\n", + " dtype='int64'), Int64Index([ 4250, 5170, 10843, 13603, 18138, 20888, 21811, 31867,\n", + " 45112, 46033, 46988, 49742, 55300, 56248, 58110, 60908,\n", + " 61826, 64281, 65798, 67567, 72113, 79388, 81209, 83031,\n", + " 84905, 87687, 89570, 91025, 105317, 108023, 108950, 109875,\n", + " 112701, 113615, 118191, 120005, 121805, 134523, 135450, 145067,\n", + " 147674, 152262, 153213, 161976, 164693, 168289, 169238, 170188,\n", + " 186984, 190676],\n", + " dtype='int64'), Int64Index([ 4251, 5171, 10844, 13604, 18139, 20889, 21812, 31868,\n", + " 45113, 46034, 46989, 49743, 55301, 56249, 58111, 60909,\n", + " 61827, 64282, 65799, 67568, 72114, 79389, 81210, 83032,\n", + " 84906, 87688, 89571, 91026, 105318, 108024, 108951, 109876,\n", + " 112702, 113616, 118192, 120006, 121806, 134524, 135451, 145068,\n", + " 147675, 152263, 153214, 161977, 164694, 168290, 169239, 170189,\n", + " 186985, 190677],\n", + " dtype='int64'), Int64Index([ 4252, 5172, 10845, 13605, 18140, 20890, 21813, 31869,\n", + " 45114, 46035, 46990, 49744, 55302, 56250, 58112, 60910,\n", + " 61828, 64283, 65800, 67569, 72115, 79390, 81211, 83033,\n", + " 84907, 87689, 89572, 91027, 105319, 108025, 108952, 109877,\n", + " 112703, 113617, 118193, 120007, 121807, 134525, 135452, 145069,\n", + " 147676, 152264, 153215, 161978, 164695, 168291, 169240, 170190,\n", + " 186986, 190678],\n", + " dtype='int64'), Int64Index([ 4253, 5173, 10846, 13606, 18141, 20891, 21814, 31870,\n", + " 45115, 46036, 46991, 49745, 55303, 56251, 58113, 60911,\n", + " 61829, 64284, 65801, 67570, 72116, 79391, 81212, 83034,\n", + " 84908, 87690, 89573, 91028, 105320, 108026, 108953, 109878,\n", + " 112704, 113618, 118194, 120008, 121808, 134526, 135453, 145070,\n", + " 147677, 152265, 153216, 161979, 164696, 168292, 169241, 170191,\n", + " 186987, 190679],\n", + " dtype='int64'), Int64Index([ 4254, 5174, 10847, 13607, 18142, 20892, 21815, 31871,\n", + " 45116, 46037, 46992, 49746, 55304, 56252, 58114, 60912,\n", + " 61830, 64285, 65802, 67571, 72117, 79392, 81213, 83035,\n", + " 84909, 87691, 89574, 91029, 105321, 108027, 108954, 109879,\n", + " 112705, 113619, 118195, 120009, 121809, 134527, 135454, 145071,\n", + " 147678, 152266, 153217, 161980, 164697, 168293, 169242, 170192,\n", + " 186988, 190680],\n", + " dtype='int64'), Int64Index([ 4255, 5175, 10848, 13608, 18143, 20893, 21816, 31872,\n", + " 45117, 46038, 46993, 49747, 55305, 56253, 58115, 60913,\n", + " 61831, 64286, 65803, 67572, 72118, 79393, 81214, 83036,\n", + " 84910, 87692, 89575, 91030, 105322, 108028, 108955, 109880,\n", + " 112706, 113620, 118196, 120010, 121810, 134528, 135455, 145072,\n", + " 147679, 152267, 153218, 161981, 164698, 168294, 169243, 170193,\n", + " 186989, 190681],\n", + " dtype='int64'), Int64Index([ 4256, 5176, 10849, 13609, 18144, 20894, 21817, 31873,\n", + " 45118, 46039, 46994, 49748, 55306, 56254, 58116, 60914,\n", + " 61832, 64287, 65804, 67573, 72119, 79394, 81215, 83037,\n", + " 84911, 87693, 89576, 91031, 105323, 108029, 108956, 109881,\n", + " 112707, 113621, 118197, 120011, 121811, 134529, 135456, 145073,\n", + " 147680, 152268, 153219, 161982, 164699, 168295, 169244, 170194,\n", + " 186990, 190682],\n", + " dtype='int64'), Int64Index([ 4257, 5177, 10850, 13610, 18145, 20895, 21818, 31874,\n", + " 45119, 46040, 46995, 49749, 55307, 56255, 58117, 60915,\n", + " 61833, 64288, 65805, 67574, 72120, 79395, 81216, 83038,\n", + " 84912, 87694, 89577, 91032, 105324, 108030, 108957, 109882,\n", + " 112708, 113622, 118198, 120012, 121812, 134530, 135457, 145074,\n", + " 147681, 152269, 153220, 161983, 164700, 168296, 169245, 170195,\n", + " 186991, 190683],\n", + " dtype='int64'), Int64Index([ 4258, 5178, 10851, 13611, 18146, 20896, 21819, 31875,\n", + " 45120, 46041, 46996, 49750, 55308, 56256, 58118, 60916,\n", + " 61834, 64289, 65806, 67575, 72121, 79396, 81217, 83039,\n", + " 84913, 87695, 89578, 91033, 105325, 108031, 108958, 109883,\n", + " 112709, 113623, 118199, 120013, 121813, 134531, 135458, 145075,\n", + " 147682, 152270, 153221, 161984, 164701, 168297, 169246, 170196,\n", + " 186992, 190684],\n", + " dtype='int64'), Int64Index([ 4259, 5179, 10852, 13612, 18147, 20897, 21820, 31876,\n", + " 45121, 46042, 46997, 49751, 55309, 56257, 58119, 60917,\n", + " 61835, 64290, 65807, 67576, 72122, 79397, 81218, 83040,\n", + " 84914, 87696, 89579, 91034, 105326, 108032, 108959, 109884,\n", + " 112710, 113624, 118200, 120014, 121814, 134532, 135459, 145076,\n", + " 147683, 152271, 153222, 161985, 164702, 168298, 169247, 170197,\n", + " 186993, 190685],\n", + " dtype='int64'), Int64Index([ 4260, 5180, 10853, 13613, 18148, 20898, 21821, 31877,\n", + " 45122, 46043, 46998, 49752, 55310, 56258, 58120, 60918,\n", + " 61836, 64291, 65808, 67577, 72123, 79398, 81219, 83041,\n", + " 84915, 87697, 89580, 91035, 105327, 108033, 108960, 109885,\n", + " 112711, 113625, 118201, 120015, 121815, 134533, 135460, 145077,\n", + " 147684, 152272, 153223, 161986, 164703, 168299, 169248, 170198,\n", + " 186994, 190686],\n", + " dtype='int64'), Int64Index([ 4261, 5181, 10854, 13614, 18149, 20899, 21822, 31878,\n", + " 45123, 46044, 46999, 49753, 55311, 56259, 58121, 60919,\n", + " 61837, 64292, 65809, 67578, 72124, 79399, 81220, 83042,\n", + " 84916, 87698, 89581, 91036, 105328, 108034, 108961, 109886,\n", + " 112712, 113626, 118202, 120016, 121816, 134534, 135461, 145078,\n", + " 147685, 152273, 153224, 161987, 164704, 168300, 169249, 170199,\n", + " 186995, 190687],\n", + " dtype='int64'), Int64Index([ 4262, 5182, 10855, 13615, 18150, 20900, 21823, 31879,\n", + " 45124, 46045, 47000, 49754, 55312, 56260, 58122, 60920,\n", + " 61838, 64293, 65810, 67579, 72125, 79400, 81221, 83043,\n", + " 84917, 87699, 89582, 91037, 105329, 108035, 108962, 109887,\n", + " 112713, 113627, 118203, 120017, 121817, 134535, 135462, 145079,\n", + " 147686, 152274, 153225, 161988, 164705, 168301, 169250, 170200,\n", + " 186996, 190688],\n", + " dtype='int64'), Int64Index([ 4263, 5183, 10856, 13616, 18151, 20901, 21824, 31880,\n", + " 45125, 46046, 47001, 49755, 55313, 56261, 58123, 60921,\n", + " 61839, 64294, 65811, 67580, 72126, 79401, 81222, 83044,\n", + " 84918, 87700, 89583, 91038, 105330, 108036, 108963, 109888,\n", + " 112714, 113628, 118204, 120018, 121818, 134536, 135463, 145080,\n", + " 147687, 152275, 153226, 161989, 164706, 168302, 169251, 170201,\n", + " 186997, 190689],\n", + " dtype='int64'), Int64Index([ 4264, 5184, 10857, 13617, 18152, 20902, 21825, 31881,\n", + " 45126, 46047, 47002, 49756, 55314, 56262, 58124, 60922,\n", + " 61840, 64295, 65812, 67581, 72127, 79402, 81223, 83045,\n", + " 84919, 87701, 89584, 91039, 105331, 108037, 108964, 109889,\n", + " 112715, 113629, 118205, 120019, 121819, 134537, 135464, 145081,\n", + " 147688, 152276, 153227, 161990, 164707, 168303, 169252, 170202,\n", + " 186998, 190690],\n", + " dtype='int64'), Int64Index([ 4265, 5185, 10858, 13618, 18153, 20903, 21826, 31882,\n", + " 45127, 46048, 47003, 49757, 55315, 56263, 58125, 60923,\n", + " 61841, 64296, 65813, 67582, 72128, 79403, 81224, 83046,\n", + " 84920, 87702, 89585, 91040, 105332, 108038, 108965, 109890,\n", + " 112716, 113630, 118206, 120020, 121820, 134538, 135465, 145082,\n", + " 147689, 152277, 153228, 161991, 164708, 168304, 169253, 170203,\n", + " 186999, 190691],\n", + " dtype='int64'), Int64Index([ 4266, 5186, 10859, 13619, 18154, 20904, 21827, 31883,\n", + " 45128, 46049, 47004, 49758, 55316, 56264, 58126, 60924,\n", + " 61842, 64297, 65814, 67583, 72129, 79404, 81225, 83047,\n", + " 84921, 87703, 89586, 91041, 105333, 108039, 108966, 109891,\n", + " 112717, 113631, 118207, 120021, 121821, 134539, 135466, 145083,\n", + " 147690, 152278, 153229, 161992, 164709, 168305, 169254, 170204,\n", + " 187000, 190692],\n", + " dtype='int64'), Int64Index([ 4267, 5187, 10860, 13620, 18155, 20905, 21828, 31884,\n", + " 45129, 46050, 47005, 49759, 55317, 56265, 58127, 60925,\n", + " 61843, 64298, 65815, 67584, 72130, 79405, 81226, 83048,\n", + " 84922, 87704, 89587, 91042, 105334, 108040, 108967, 109892,\n", + " 112718, 113632, 118208, 120022, 121822, 134540, 135467, 145084,\n", + " 147691, 152279, 153230, 161993, 164710, 168306, 169255, 170205,\n", + " 187001, 190693],\n", + " dtype='int64'), Int64Index([ 4268, 5188, 10861, 13621, 18156, 20906, 21829, 31885,\n", + " 45130, 46051, 47006, 49760, 55318, 56266, 58128, 60926,\n", + " 61844, 64299, 65816, 67585, 72131, 79406, 81227, 83049,\n", + " 84923, 87705, 89588, 91043, 105335, 108041, 108968, 109893,\n", + " 112719, 113633, 118209, 120023, 121823, 134541, 135468, 145085,\n", + " 147692, 152280, 153231, 161994, 164711, 168307, 169256, 170206,\n", + " 187002, 190694],\n", + " dtype='int64'), Int64Index([ 4269, 5189, 10862, 13622, 18157, 20907, 21830, 31886,\n", + " 45131, 46052, 47007, 49761, 55319, 56267, 58129, 60927,\n", + " 61845, 64300, 65817, 67586, 72132, 79407, 81228, 83050,\n", + " 84924, 87706, 89589, 91044, 105336, 108042, 108969, 109894,\n", + " 112720, 113634, 118210, 120024, 121824, 134542, 135469, 145086,\n", + " 147693, 152281, 153232, 161995, 164712, 168308, 169257, 170207,\n", + " 187003, 190695],\n", + " dtype='int64'), Int64Index([ 4270, 5190, 10863, 13623, 18158, 20908, 21831, 31887,\n", + " 45132, 46053, 47008, 49762, 55320, 56268, 58130, 60928,\n", + " 61846, 64301, 65818, 67587, 72133, 79408, 81229, 83051,\n", + " 84925, 87707, 89590, 91045, 105337, 108043, 108970, 109895,\n", + " 112721, 113635, 118211, 120025, 121825, 134543, 135470, 145087,\n", + " 147694, 152282, 153233, 161996, 164713, 168309, 169258, 170208,\n", + " 187004, 190696],\n", + " dtype='int64'), Int64Index([ 4271, 5191, 10864, 13624, 18159, 20909, 21832, 31888,\n", + " 45133, 46054, 47009, 49763, 55321, 56269, 58131, 60929,\n", + " 61847, 64302, 65819, 67588, 72134, 79409, 81230, 83052,\n", + " 84926, 87708, 89591, 91046, 105338, 108044, 108971, 109896,\n", + " 112722, 113636, 118212, 120026, 121826, 134544, 135471, 145088,\n", + " 147695, 152283, 153234, 161997, 164714, 168310, 169259, 170209,\n", + " 187005, 190697],\n", + " dtype='int64'), Int64Index([ 4272, 5192, 10865, 13625, 18160, 20910, 21833, 31889,\n", + " 45134, 46055, 47010, 49764, 55322, 56270, 58132, 60930,\n", + " 61848, 64303, 65820, 67589, 72135, 79410, 81231, 83053,\n", + " 84927, 87709, 89592, 91047, 105339, 108045, 108972, 109897,\n", + " 112723, 113637, 118213, 120027, 121827, 134545, 135472, 145089,\n", + " 147696, 152284, 153235, 161998, 164715, 168311, 169260, 170210,\n", + " 187006, 190698],\n", + " dtype='int64'), Int64Index([ 4273, 5193, 10866, 13626, 18161, 20911, 21834, 31890,\n", + " 45135, 46056, 47011, 49765, 55323, 56271, 58133, 60931,\n", + " 61849, 64304, 65821, 67590, 72136, 79411, 81232, 83054,\n", + " 84928, 87710, 89593, 91048, 105340, 108046, 108973, 109898,\n", + " 112724, 113638, 118214, 120028, 121828, 134546, 135473, 145090,\n", + " 147697, 152285, 153236, 161999, 164716, 168312, 169261, 170211,\n", + " 187007, 190699],\n", + " dtype='int64'), Int64Index([ 4274, 5194, 10867, 13627, 18162, 20912, 21835, 31891,\n", + " 45136, 46057, 47012, 49766, 55324, 56272, 58134, 60932,\n", + " 61850, 64305, 65822, 67591, 72137, 79412, 81233, 83055,\n", + " 84929, 87711, 89594, 91049, 105341, 108047, 108974, 109899,\n", + " 112725, 113639, 118215, 120029, 121829, 134547, 135474, 145091,\n", + " 147698, 152286, 153237, 162000, 164717, 168313, 169262, 170212,\n", + " 187008, 190700],\n", + " dtype='int64'), Int64Index([ 4275, 5195, 10868, 13628, 18163, 20913, 21836, 31892,\n", + " 45137, 46058, 47013, 49767, 55325, 56273, 58135, 60933,\n", + " 61851, 64306, 65823, 67592, 72138, 79413, 81234, 83056,\n", + " 84930, 87712, 89595, 91050, 105342, 108048, 108975, 109900,\n", + " 112726, 113640, 118216, 120030, 121830, 134548, 135475, 145092,\n", + " 147699, 152287, 153238, 162001, 164718, 168314, 169263, 170213,\n", + " 187009, 190701],\n", + " dtype='int64'), Int64Index([ 4276, 5196, 10869, 13629, 18164, 20914, 21837, 31893,\n", + " 45138, 46059, 47014, 49768, 55326, 56274, 58136, 60934,\n", + " 61852, 64307, 65824, 67593, 72139, 79414, 81235, 83057,\n", + " 84931, 87713, 89596, 91051, 105343, 108049, 108976, 109901,\n", + " 112727, 113641, 118217, 120031, 121831, 134549, 135476, 145093,\n", + " 147700, 152288, 153239, 162002, 164719, 168315, 169264, 170214,\n", + " 187010, 190702],\n", + " dtype='int64'), Int64Index([ 4277, 5197, 10870, 13630, 18165, 20915, 21838, 31894,\n", + " 45139, 46060, 47015, 49769, 55327, 56275, 58137, 60935,\n", + " 61853, 64308, 65825, 67594, 72140, 79415, 81236, 83058,\n", + " 84932, 87714, 89597, 91052, 105344, 108050, 108977, 109902,\n", + " 112728, 113642, 118218, 120032, 121832, 134550, 135477, 145094,\n", + " 147701, 152289, 153240, 162003, 164720, 168316, 169265, 170215,\n", + " 187011, 190703],\n", + " dtype='int64'), Int64Index([ 4278, 5198, 10871, 13631, 18166, 20916, 21839, 31895,\n", + " 45140, 46061, 47016, 49770, 55328, 56276, 58138, 60936,\n", + " 61854, 64309, 65826, 67595, 72141, 79416, 81237, 83059,\n", + " 84933, 87715, 89598, 91053, 105345, 108051, 108978, 109903,\n", + " 112729, 113643, 118219, 120033, 121833, 134551, 135478, 145095,\n", + " 147702, 152290, 153241, 162004, 164721, 168317, 169266, 170216,\n", + " 187012, 190704],\n", + " dtype='int64'), Int64Index([ 4279, 5199, 10872, 13632, 18167, 20917, 21840, 31896,\n", + " 45141, 46062, 47017, 49771, 55329, 56277, 58139, 60937,\n", + " 61855, 64310, 65827, 67596, 72142, 79417, 81238, 83060,\n", + " 84934, 87716, 89599, 91054, 105346, 108052, 108979, 109904,\n", + " 112730, 113644, 118220, 120034, 121834, 134552, 135479, 145096,\n", + " 147703, 152291, 153242, 162005, 164722, 168318, 169267, 170217,\n", + " 187013, 190705],\n", + " dtype='int64'), Int64Index([ 4280, 5200, 10873, 13633, 18168, 20918, 21841, 31897,\n", + " 45142, 46063, 47018, 49772, 55330, 56278, 58140, 60938,\n", + " 61856, 64311, 65828, 67597, 72143, 79418, 81239, 83061,\n", + " 84935, 87717, 89600, 91055, 105347, 108053, 108980, 109905,\n", + " 112731, 113645, 118221, 120035, 121835, 134553, 135480, 145097,\n", + " 147704, 152292, 153243, 162006, 164723, 168319, 169268, 170218,\n", + " 187014, 190706],\n", + " dtype='int64'), Int64Index([ 4281, 5201, 10874, 13634, 18169, 20919, 21842, 31898,\n", + " 45143, 46064, 47019, 49773, 55331, 56279, 58141, 60939,\n", + " 61857, 64312, 65829, 67598, 72144, 79419, 81240, 83062,\n", + " 84936, 87718, 89601, 91056, 105348, 108054, 108981, 109906,\n", + " 112732, 113646, 118222, 120036, 121836, 134554, 135481, 145098,\n", + " 147705, 152293, 153244, 162007, 164724, 168320, 169269, 170219,\n", + " 187015, 190707],\n", + " dtype='int64'), Int64Index([ 4282, 5202, 10875, 13635, 18170, 20920, 21843, 31899,\n", + " 45144, 46065, 47020, 49774, 55332, 56280, 58142, 60940,\n", + " 61858, 64313, 65830, 67599, 72145, 79420, 81241, 83063,\n", + " 84937, 87719, 89602, 91057, 105349, 108055, 108982, 109907,\n", + " 112733, 113647, 118223, 120037, 121837, 134555, 135482, 145099,\n", + " 147706, 152294, 153245, 162008, 164725, 168321, 169270, 170220,\n", + " 187016, 190708],\n", + " dtype='int64'), Int64Index([ 4283, 5203, 10876, 13636, 18171, 20921, 21844, 31900,\n", + " 45145, 46066, 47021, 49775, 55333, 56281, 58143, 60941,\n", + " 61859, 64314, 65831, 67600, 72146, 79421, 81242, 83064,\n", + " 84938, 87720, 89603, 91058, 105350, 108056, 108983, 109908,\n", + " 112734, 113648, 118224, 120038, 121838, 134556, 135483, 145100,\n", + " 147707, 152295, 153246, 162009, 164726, 168322, 169271, 170221,\n", + " 187017, 190709],\n", + " dtype='int64'), Int64Index([ 4284, 5204, 10877, 13637, 18172, 20922, 21845, 31901,\n", + " 45146, 46067, 47022, 49776, 55334, 56282, 58144, 60942,\n", + " 61860, 64315, 65832, 67601, 72147, 79422, 81243, 83065,\n", + " 84939, 87721, 89604, 91059, 105351, 108057, 108984, 109909,\n", + " 112735, 113649, 118225, 120039, 121839, 134557, 135484, 145101,\n", + " 147708, 152296, 153247, 162010, 164727, 168323, 169272, 170222,\n", + " 187018, 190710],\n", + " dtype='int64'), Int64Index([ 4285, 5205, 10878, 13638, 18173, 20923, 21846, 31902,\n", + " 45147, 46068, 47023, 49777, 55335, 56283, 58145, 60943,\n", + " 61861, 64316, 65833, 67602, 72148, 79423, 81244, 83066,\n", + " 84940, 87722, 89605, 91060, 105352, 108058, 108985, 109910,\n", + " 112736, 113650, 118226, 120040, 121840, 134558, 135485, 145102,\n", + " 147709, 152297, 153248, 162011, 164728, 168324, 169273, 170223,\n", + " 187019, 190711],\n", + " dtype='int64'), Int64Index([ 4286, 5206, 10879, 13639, 18174, 20924, 21847, 31903,\n", + " 45148, 46069, 47024, 49778, 55336, 56284, 58146, 60944,\n", + " 61862, 64317, 65834, 67603, 72149, 79424, 81245, 83067,\n", + " 84941, 87723, 89606, 91061, 105353, 108059, 108986, 109911,\n", + " 112737, 113651, 118227, 120041, 121841, 134559, 135486, 145103,\n", + " 147710, 152298, 153249, 162012, 164729, 168325, 169274, 170224,\n", + " 187020, 190712],\n", + " dtype='int64'), Int64Index([ 4287, 5207, 10880, 13640, 18175, 20925, 21848, 31904,\n", + " 45149, 46070, 47025, 49779, 55337, 56285, 58147, 60945,\n", + " 61863, 64318, 65835, 67604, 72150, 79425, 81246, 83068,\n", + " 84942, 87724, 89607, 91062, 105354, 108060, 108987, 109912,\n", + " 112738, 113652, 118228, 120042, 121842, 134560, 135487, 145104,\n", + " 147711, 152299, 153250, 162013, 164730, 168326, 169275, 170225,\n", + " 187021, 190713],\n", + " dtype='int64'), Int64Index([ 4288, 5208, 10881, 13641, 18176, 20926, 21849, 31905,\n", + " 45150, 46071, 47026, 49780, 55338, 56286, 58148, 60946,\n", + " 61864, 64319, 65836, 67605, 72151, 79426, 81247, 83069,\n", + " 84943, 87725, 89608, 91063, 105355, 108061, 108988, 109913,\n", + " 112739, 113653, 118229, 120043, 121843, 134561, 135488, 145105,\n", + " 147712, 152300, 153251, 162014, 164731, 168327, 169276, 170226,\n", + " 187022, 190714],\n", + " dtype='int64'), Int64Index([ 4289, 5209, 10882, 13642, 18177, 20927, 21850, 31906,\n", + " 45151, 46072, 47027, 49781, 55339, 56287, 58149, 60947,\n", + " 61865, 64320, 65837, 67606, 72152, 79427, 81248, 83070,\n", + " 84944, 87726, 89609, 91064, 105356, 108062, 108989, 109914,\n", + " 112740, 113654, 118230, 120044, 121844, 134562, 135489, 145106,\n", + " 147713, 152301, 153252, 162015, 164732, 168328, 169277, 170227,\n", + " 187023, 190715],\n", + " dtype='int64'), Int64Index([ 4290, 5210, 10883, 13643, 18178, 20928, 21851, 31907,\n", + " 45152, 46073, 47028, 49782, 55340, 56288, 58150, 60948,\n", + " 61866, 64321, 65838, 67607, 72153, 79428, 81249, 83071,\n", + " 84945, 87727, 89610, 91065, 105357, 108063, 108990, 109915,\n", + " 112741, 113655, 118231, 120045, 121845, 134563, 135490, 145107,\n", + " 147714, 152302, 153253, 162016, 164733, 168329, 169278, 170228,\n", + " 187024, 190716],\n", + " dtype='int64'), Int64Index([ 4291, 5211, 10884, 13644, 18179, 20929, 21852, 31908,\n", + " 45153, 46074, 47029, 49783, 55341, 56289, 58151, 60949,\n", + " 61867, 64322, 65839, 67608, 72154, 79429, 81250, 83072,\n", + " 84946, 87728, 89611, 91066, 105358, 108064, 108991, 109916,\n", + " 112742, 113656, 118232, 120046, 121846, 134564, 135491, 145108,\n", + " 147715, 152303, 153254, 162017, 164734, 168330, 169279, 170229,\n", + " 187025, 190717],\n", + " dtype='int64'), Int64Index([ 4292, 5212, 10885, 13645, 18180, 20930, 21853, 31909,\n", + " 45154, 46075, 47030, 49784, 55342, 56290, 58152, 60950,\n", + " 61868, 64323, 65840, 67609, 72155, 79430, 81251, 83073,\n", + " 84947, 87729, 89612, 91067, 105359, 108065, 108992, 109917,\n", + " 112743, 113657, 118233, 120047, 121847, 134565, 135492, 145109,\n", + " 147716, 152304, 153255, 162018, 164735, 168331, 169280, 170230,\n", + " 187026, 190718],\n", + " dtype='int64'), Int64Index([ 4293, 5213, 10886, 13646, 18181, 20931, 21854, 31910,\n", + " 45155, 46076, 47031, 49785, 55343, 56291, 58153, 60951,\n", + " 61869, 64324, 65841, 67610, 72156, 79431, 81252, 83074,\n", + " 84948, 87730, 89613, 91068, 105360, 108066, 108993, 109918,\n", + " 112744, 113658, 118234, 120048, 121848, 134566, 135493, 145110,\n", + " 147717, 152305, 153256, 162019, 164736, 168332, 169281, 170231,\n", + " 187027, 190719],\n", + " dtype='int64'), Int64Index([ 4294, 5214, 10887, 13647, 18182, 20932, 21855, 31911,\n", + " 45156, 46077, 47032, 49786, 55344, 56292, 58154, 60952,\n", + " 61870, 64325, 65842, 67611, 72157, 79432, 81253, 83075,\n", + " 84949, 87731, 89614, 91069, 105361, 108067, 108994, 109919,\n", + " 112745, 113659, 118235, 120049, 121849, 134567, 135494, 145111,\n", + " 147718, 152306, 153257, 162020, 164737, 168333, 169282, 170232,\n", + " 187028, 190720],\n", + " dtype='int64'), Int64Index([ 4295, 5215, 10888, 13648, 18183, 20933, 21856, 31912,\n", + " 45157, 46078, 47033, 49787, 55345, 56293, 58155, 60953,\n", + " 61871, 64326, 65843, 67612, 72158, 79433, 81254, 83076,\n", + " 84950, 87732, 89615, 91070, 105362, 108068, 108995, 109920,\n", + " 112746, 113660, 118236, 120050, 121850, 134568, 135495, 145112,\n", + " 147719, 152307, 153258, 162021, 164738, 168334, 169283, 170233,\n", + " 187029, 190721],\n", + " dtype='int64'), Int64Index([ 4296, 5216, 10889, 13649, 18184, 20934, 21857, 31913,\n", + " 45158, 46079, 47034, 49788, 55346, 56294, 58156, 60954,\n", + " 61872, 64327, 65844, 67613, 72159, 79434, 81255, 83077,\n", + " 84951, 87733, 89616, 91071, 105363, 108069, 108996, 109921,\n", + " 112747, 113661, 118237, 120051, 121851, 134569, 135496, 145113,\n", + " 147720, 152308, 153259, 162022, 164739, 168335, 169284, 170234,\n", + " 187030, 190722],\n", + " dtype='int64'), Int64Index([ 4297, 5217, 10890, 13650, 18185, 20935, 21858, 31914,\n", + " 45159, 46080, 47035, 49789, 55347, 56295, 58157, 60955,\n", + " 61873, 64328, 65845, 67614, 72160, 79435, 81256, 83078,\n", + " 84952, 87734, 89617, 91072, 105364, 108070, 108997, 109922,\n", + " 112748, 113662, 118238, 120052, 121852, 134570, 135497, 145114,\n", + " 147721, 152309, 153260, 162023, 164740, 168336, 169285, 170235,\n", + " 187031, 190723],\n", + " dtype='int64'), Int64Index([ 4298, 5218, 10891, 13651, 18186, 20936, 21859, 31915,\n", + " 45160, 46081, 47036, 49790, 55348, 56296, 58158, 60956,\n", + " 61874, 64329, 65846, 67615, 72161, 79436, 81257, 83079,\n", + " 84953, 87735, 89618, 91073, 105365, 108071, 108998, 109923,\n", + " 112749, 113663, 118239, 120053, 121853, 134571, 135498, 145115,\n", + " 147722, 152310, 153261, 162024, 164741, 168337, 169286, 170236,\n", + " 187032, 190724],\n", + " dtype='int64'), Int64Index([ 4299, 5219, 10892, 13652, 18187, 20937, 21860, 31916,\n", + " 45161, 46082, 47037, 49791, 55349, 56297, 58159, 60957,\n", + " 61875, 64330, 65847, 67616, 72162, 79437, 81258, 83080,\n", + " 84954, 87736, 89619, 91074, 105366, 108072, 108999, 109924,\n", + " 112750, 113664, 118240, 120054, 121854, 134572, 135499, 145116,\n", + " 147723, 152311, 153262, 162025, 164742, 168338, 169287, 170237,\n", + " 187033, 190725],\n", + " dtype='int64'), Int64Index([ 4300, 5220, 10893, 13653, 18188, 20938, 21861, 31917,\n", + " 45162, 46083, 47038, 49792, 55350, 56298, 58160, 60958,\n", + " 61876, 64331, 65848, 67617, 72163, 79438, 81259, 83081,\n", + " 84955, 87737, 89620, 91075, 105367, 108073, 109000, 109925,\n", + " 112751, 113665, 118241, 120055, 121855, 134573, 135500, 145117,\n", + " 147724, 152312, 153263, 162026, 164743, 168339, 169288, 170238,\n", + " 187034, 190726],\n", + " dtype='int64'), Int64Index([ 4301, 5221, 10894, 13654, 18189, 20939, 21862, 31918,\n", + " 45163, 46084, 47039, 49793, 55351, 56299, 58161, 60959,\n", + " 61877, 64332, 65849, 67618, 72164, 79439, 81260, 83082,\n", + " 84956, 87738, 89621, 91076, 105368, 108074, 109001, 109926,\n", + " 112752, 113666, 118242, 120056, 121856, 134574, 135501, 145118,\n", + " 147725, 152313, 153264, 162027, 164744, 168340, 169289, 170239,\n", + " 187035, 190727],\n", + " dtype='int64'), Int64Index([ 4302, 5222, 10895, 13655, 18190, 20940, 21863, 31919,\n", + " 45164, 46085, 47040, 49794, 55352, 56300, 58162, 60960,\n", + " 61878, 64333, 65850, 67619, 72165, 79440, 81261, 83083,\n", + " 84957, 87739, 89622, 91077, 105369, 108075, 109002, 109927,\n", + " 112753, 113667, 118243, 120057, 121857, 134575, 135502, 145119,\n", + " 147726, 152314, 153265, 162028, 164745, 168341, 169290, 170240,\n", + " 187036, 190728],\n", + " dtype='int64'), Int64Index([ 4303, 5223, 10896, 13656, 18191, 20941, 21864, 31920,\n", + " 45165, 46086, 47041, 49795, 55353, 56301, 58163, 60961,\n", + " 61879, 64334, 65851, 67620, 72166, 79441, 81262, 83084,\n", + " 84958, 87740, 89623, 91078, 105370, 108076, 109003, 109928,\n", + " 112754, 113668, 118244, 120058, 121858, 134576, 135503, 145120,\n", + " 147727, 152315, 153266, 162029, 164746, 168342, 169291, 170241,\n", + " 187037, 190729],\n", + " dtype='int64'), Int64Index([ 4304, 5224, 10897, 13657, 18192, 20942, 21865, 31921,\n", + " 45166, 46087, 47042, 49796, 55354, 56302, 58164, 60962,\n", + " 61880, 64335, 65852, 67621, 72167, 79442, 81263, 83085,\n", + " 84959, 87741, 89624, 91079, 105371, 108077, 109004, 109929,\n", + " 112755, 113669, 118245, 120059, 121859, 134577, 135504, 145121,\n", + " 147728, 152316, 153267, 162030, 164747, 168343, 169292, 170242,\n", + " 187038, 190730],\n", + " dtype='int64'), Int64Index([ 4305, 5225, 10898, 13658, 18193, 20943, 21866, 31922,\n", + " 45167, 46088, 47043, 49797, 55355, 56303, 58165, 60963,\n", + " 61881, 64336, 65853, 67622, 72168, 79443, 81264, 83086,\n", + " 84960, 87742, 89625, 91080, 105372, 108078, 109005, 109930,\n", + " 112756, 113670, 118246, 120060, 121860, 134578, 135505, 145122,\n", + " 147729, 152317, 153268, 162031, 164748, 168344, 169293, 170243,\n", + " 187039, 190731],\n", + " dtype='int64'), Int64Index([ 4306, 5226, 10899, 13659, 18194, 20944, 21867, 31923,\n", + " 45168, 46089, 47044, 49798, 55356, 56304, 58166, 60964,\n", + " 61882, 64337, 65854, 67623, 72169, 79444, 81265, 83087,\n", + " 84961, 87743, 89626, 91081, 105373, 108079, 109006, 109931,\n", + " 112757, 113671, 118247, 120061, 121861, 134579, 135506, 145123,\n", + " 147730, 152318, 153269, 162032, 164749, 168345, 169294, 170244,\n", + " 187040, 190732],\n", + " dtype='int64'), Int64Index([ 4307, 5227, 10900, 13660, 18195, 20945, 21868, 31924,\n", + " 45169, 46090, 47045, 49799, 55357, 56305, 58167, 60965,\n", + " 61883, 64338, 65855, 67624, 72170, 79445, 81266, 83088,\n", + " 84962, 87744, 89627, 91082, 105374, 108080, 109007, 109932,\n", + " 112758, 113672, 118248, 120062, 121862, 134580, 135507, 145124,\n", + " 147731, 152319, 153270, 162033, 164750, 168346, 169295, 170245,\n", + " 187041, 190733],\n", + " dtype='int64'), Int64Index([ 4308, 5228, 10901, 13661, 18196, 20946, 21869, 31925,\n", + " 45170, 46091, 47046, 49800, 55358, 56306, 58168, 60966,\n", + " 61884, 64339, 65856, 67625, 72171, 79446, 81267, 83089,\n", + " 84963, 87745, 89628, 91083, 105375, 108081, 109008, 109933,\n", + " 112759, 113673, 118249, 120063, 121863, 134581, 135508, 145125,\n", + " 147732, 152320, 153271, 162034, 164751, 168347, 169296, 170246,\n", + " 187042, 190734],\n", + " dtype='int64'), Int64Index([ 4309, 5229, 10902, 13662, 18197, 20947, 21870, 31926,\n", + " 45171, 46092, 47047, 49801, 55359, 56307, 58169, 60967,\n", + " 61885, 64340, 65857, 67626, 72172, 79447, 81268, 83090,\n", + " 84964, 87746, 89629, 91084, 105376, 108082, 109009, 109934,\n", + " 112760, 113674, 118250, 120064, 121864, 134582, 135509, 145126,\n", + " 147733, 152321, 153272, 162035, 164752, 168348, 169297, 170247,\n", + " 187043, 190735],\n", + " dtype='int64'), Int64Index([ 4310, 5230, 10903, 13663, 18198, 20948, 21871, 31927,\n", + " 45172, 46093, 47048, 49802, 55360, 56308, 58170, 60968,\n", + " 61886, 64341, 65858, 67627, 72173, 79448, 81269, 83091,\n", + " 84965, 87747, 89630, 91085, 105377, 108083, 109010, 109935,\n", + " 112761, 113675, 118251, 120065, 121865, 134583, 135510, 145127,\n", + " 147734, 152322, 153273, 162036, 164753, 168349, 169298, 170248,\n", + " 187044, 190736],\n", + " dtype='int64'), Int64Index([ 4311, 5231, 10904, 13664, 18199, 20949, 21872, 31928,\n", + " 45173, 46094, 47049, 49803, 55361, 56309, 58171, 60969,\n", + " 61887, 64342, 65859, 67628, 72174, 79449, 81270, 83092,\n", + " 84966, 87748, 89631, 91086, 105378, 108084, 109011, 109936,\n", + " 112762, 113676, 118252, 120066, 121866, 134584, 135511, 145128,\n", + " 147735, 152323, 153274, 162037, 164754, 168350, 169299, 170249,\n", + " 187045, 190737],\n", + " dtype='int64'), Int64Index([ 4312, 5232, 10905, 13665, 18200, 20950, 21873, 31929,\n", + " 45174, 46095, 47050, 49804, 55362, 56310, 58172, 60970,\n", + " 61888, 64343, 65860, 67629, 72175, 79450, 81271, 83093,\n", + " 84967, 87749, 89632, 91087, 105379, 108085, 109012, 109937,\n", + " 112763, 113677, 118253, 120067, 121867, 134585, 135512, 145129,\n", + " 147736, 152324, 153275, 162038, 164755, 168351, 169300, 170250,\n", + " 187046, 190738],\n", + " dtype='int64'), Int64Index([ 4313, 5233, 10906, 13666, 18201, 20951, 21874, 31930,\n", + " 45175, 46096, 47051, 49805, 55363, 56311, 58173, 60971,\n", + " 61889, 64344, 65861, 67630, 72176, 79451, 81272, 83094,\n", + " 84968, 87750, 89633, 91088, 105380, 108086, 109013, 109938,\n", + " 112764, 113678, 118254, 120068, 121868, 134586, 135513, 145130,\n", + " 147737, 152325, 153276, 162039, 164756, 168352, 169301, 170251,\n", + " 187047, 190739],\n", + " dtype='int64'), Int64Index([ 4314, 5234, 10907, 13667, 18202, 20952, 21875, 31931,\n", + " 45176, 46097, 47052, 49806, 55364, 56312, 58174, 60972,\n", + " 61890, 64345, 65862, 67631, 72177, 79452, 81273, 83095,\n", + " 84969, 87751, 89634, 91089, 105381, 108087, 109014, 109939,\n", + " 112765, 113679, 118255, 120069, 121869, 134587, 135514, 145131,\n", + " 147738, 152326, 153277, 162040, 164757, 168353, 169302, 170252,\n", + " 187048, 190740],\n", + " dtype='int64'), Int64Index([ 4315, 5235, 10908, 13668, 18203, 20953, 21876, 31932,\n", + " 45177, 46098, 47053, 49807, 55365, 56313, 58175, 60973,\n", + " 61891, 64346, 65863, 67632, 72178, 79453, 81274, 83096,\n", + " 84970, 87752, 89635, 91090, 105382, 108088, 109015, 109940,\n", + " 112766, 113680, 118256, 120070, 121870, 134588, 135515, 145132,\n", + " 147739, 152327, 153278, 162041, 164758, 168354, 169303, 170253,\n", + " 187049, 190741],\n", + " dtype='int64'), Int64Index([ 4316, 5236, 10909, 13669, 18204, 20954, 21877, 31933,\n", + " 45178, 46099, 47054, 49808, 55366, 56314, 58176, 60974,\n", + " 61892, 64347, 65864, 67633, 72179, 79454, 81275, 83097,\n", + " 84971, 87753, 89636, 91091, 105383, 108089, 109016, 109941,\n", + " 112767, 113681, 118257, 120071, 121871, 134589, 135516, 145133,\n", + " 147740, 152328, 153279, 162042, 164759, 168355, 169304, 170254,\n", + " 187050, 190742],\n", + " dtype='int64'), Int64Index([ 4317, 5237, 10910, 13670, 18205, 20955, 21878, 31934,\n", + " 45179, 46100, 47055, 49809, 55367, 56315, 58177, 60975,\n", + " 61893, 64348, 65865, 67634, 72180, 79455, 81276, 83098,\n", + " 84972, 87754, 89637, 91092, 105384, 108090, 109017, 109942,\n", + " 112768, 113682, 118258, 120072, 121872, 134590, 135517, 145134,\n", + " 147741, 152329, 153280, 162043, 164760, 168356, 169305, 170255,\n", + " 187051, 190743],\n", + " dtype='int64'), Int64Index([ 4318, 5238, 10911, 13671, 18206, 20956, 21879, 31935,\n", + " 45180, 46101, 47056, 49810, 55368, 56316, 58178, 60976,\n", + " 61894, 64349, 65866, 67635, 72181, 79456, 81277, 83099,\n", + " 84973, 87755, 89638, 91093, 105385, 108091, 109018, 109943,\n", + " 112769, 113683, 118259, 120073, 121873, 134591, 135518, 145135,\n", + " 147742, 152330, 153281, 162044, 164761, 168357, 169306, 170256,\n", + " 187052, 190744],\n", + " dtype='int64'), Int64Index([ 4319, 5239, 10912, 13672, 18207, 20957, 21880, 31936,\n", + " 45181, 46102, 47057, 49811, 55369, 56317, 58179, 60977,\n", + " 61895, 64350, 65867, 67636, 72182, 79457, 81278, 83100,\n", + " 84974, 87756, 89639, 91094, 105386, 108092, 109019, 109944,\n", + " 112770, 113684, 118260, 120074, 121874, 134592, 135519, 145136,\n", + " 147743, 152331, 153282, 162045, 164762, 168358, 169307, 170257,\n", + " 187053, 190745],\n", + " dtype='int64'), Int64Index([ 4320, 5240, 10913, 13673, 18208, 20958, 21881, 31937,\n", + " 45182, 46103, 47058, 49812, 55370, 56318, 58180, 60978,\n", + " 61896, 64351, 65868, 67637, 72183, 79458, 81279, 83101,\n", + " 84975, 87757, 89640, 91095, 105387, 108093, 109020, 109945,\n", + " 112771, 113685, 118261, 120075, 121875, 134593, 135520, 145137,\n", + " 147744, 152332, 153283, 162046, 164763, 168359, 169308, 170258,\n", + " 187054, 190746],\n", + " dtype='int64'), Int64Index([ 4321, 5241, 10914, 13674, 18209, 20959, 21882, 31938,\n", + " 45183, 46104, 47059, 49813, 55371, 56319, 58181, 60979,\n", + " 61897, 64352, 65869, 67638, 72184, 79459, 81280, 83102,\n", + " 84976, 87758, 89641, 91096, 105388, 108094, 109021, 109946,\n", + " 112772, 113686, 118262, 120076, 121876, 134594, 135521, 145138,\n", + " 147745, 152333, 153284, 162047, 164764, 168360, 169309, 170259,\n", + " 187055, 190747],\n", + " dtype='int64'), Int64Index([ 4322, 5242, 10915, 13675, 18210, 20960, 21883, 31939,\n", + " 45184, 46105, 47060, 49814, 55372, 56320, 58182, 60980,\n", + " 61898, 64353, 65870, 67639, 72185, 79460, 81281, 83103,\n", + " 84977, 87759, 89642, 91097, 105389, 108095, 109022, 109947,\n", + " 112773, 113687, 118263, 120077, 121877, 134595, 135522, 145139,\n", + " 147746, 152334, 153285, 162048, 164765, 168361, 169310, 170260,\n", + " 187056, 190748],\n", + " dtype='int64'), Int64Index([ 4323, 5243, 10916, 13676, 18211, 20961, 21884, 31940,\n", + " 45185, 46106, 47061, 49815, 55373, 56321, 58183, 60981,\n", + " 61899, 64354, 65871, 67640, 72186, 79461, 81282, 83104,\n", + " 84978, 87760, 89643, 91098, 105390, 108096, 109023, 109948,\n", + " 112774, 113688, 118264, 120078, 121878, 134596, 135523, 145140,\n", + " 147747, 152335, 153286, 162049, 164766, 168362, 169311, 170261,\n", + " 187057, 190749],\n", + " dtype='int64'), Int64Index([ 4324, 5244, 10917, 13677, 18212, 20962, 21885, 31941,\n", + " 45186, 46107, 47062, 49816, 55374, 56322, 58184, 60982,\n", + " 61900, 64355, 65872, 67641, 72187, 79462, 81283, 83105,\n", + " 84979, 87761, 89644, 91099, 105391, 108097, 109024, 109949,\n", + " 112775, 113689, 118265, 120079, 121879, 134597, 135524, 145141,\n", + " 147748, 152336, 153287, 162050, 164767, 168363, 169312, 170262,\n", + " 187058, 190750],\n", + " dtype='int64'), Int64Index([ 4325, 5245, 10918, 13678, 18213, 20963, 21886, 31942,\n", + " 45187, 46108, 47063, 49817, 55375, 56323, 58185, 60983,\n", + " 61901, 64356, 65873, 67642, 72188, 79463, 81284, 83106,\n", + " 84980, 87762, 89645, 91100, 105392, 108098, 109025, 109950,\n", + " 112776, 113690, 118266, 120080, 121880, 134598, 135525, 145142,\n", + " 147749, 152337, 153288, 162051, 164768, 168364, 169313, 170263,\n", + " 187059, 190751],\n", + " dtype='int64'), Int64Index([ 4326, 5246, 10919, 13679, 18214, 20964, 21887, 31943,\n", + " 45188, 46109, 47064, 49818, 55376, 56324, 58186, 60984,\n", + " 61902, 64357, 65874, 67643, 72189, 79464, 81285, 83107,\n", + " 84981, 87763, 89646, 91101, 105393, 108099, 109026, 109951,\n", + " 112777, 113691, 118267, 120081, 121881, 134599, 135526, 145143,\n", + " 147750, 152338, 153289, 162052, 164769, 168365, 169314, 170264,\n", + " 187060, 190752],\n", + " dtype='int64'), Int64Index([ 4327, 5247, 10920, 13680, 18215, 20965, 21888, 31944,\n", + " 45189, 46110, 47065, 49819, 55377, 56325, 58187, 60985,\n", + " 61903, 64358, 65875, 67644, 72190, 79465, 81286, 83108,\n", + " 84982, 87764, 89647, 91102, 105394, 108100, 109027, 109952,\n", + " 112778, 113692, 118268, 120082, 121882, 134600, 135527, 145144,\n", + " 147751, 152339, 153290, 162053, 164770, 168366, 169315, 170265,\n", + " 187061, 190753],\n", + " dtype='int64'), Int64Index([ 4328, 5248, 10921, 13681, 18216, 20966, 21889, 31945,\n", + " 45190, 46111, 47066, 49820, 55378, 56326, 58188, 60986,\n", + " 61904, 64359, 65876, 67645, 72191, 79466, 81287, 83109,\n", + " 84983, 87765, 89648, 91103, 105395, 108101, 109028, 109953,\n", + " 112779, 113693, 118269, 120083, 121883, 134601, 135528, 145145,\n", + " 147752, 152340, 153291, 162054, 164771, 168367, 169316, 170266,\n", + " 187062, 190754],\n", + " dtype='int64'), Int64Index([ 4329, 5249, 10922, 13682, 18217, 20967, 21890, 31946,\n", + " 45191, 46112, 47067, 49821, 55379, 56327, 58189, 60987,\n", + " 61905, 64360, 65877, 67646, 72192, 79467, 81288, 83110,\n", + " 84984, 87766, 89649, 91104, 105396, 108102, 109029, 109954,\n", + " 112780, 113694, 118270, 120084, 121884, 134602, 135529, 145146,\n", + " 147753, 152341, 153292, 162055, 164772, 168368, 169317, 170267,\n", + " 187063, 190755],\n", + " dtype='int64'), Int64Index([ 4330, 5250, 10923, 13683, 18218, 20968, 21891, 31947,\n", + " 45192, 46113, 47068, 49822, 55380, 56328, 58190, 60988,\n", + " 61906, 64361, 65878, 67647, 72193, 79468, 81289, 83111,\n", + " 84985, 87767, 89650, 91105, 105397, 108103, 109030, 109955,\n", + " 112781, 113695, 118271, 120085, 121885, 134603, 135530, 145147,\n", + " 147754, 152342, 153293, 162056, 164773, 168369, 169318, 170268,\n", + " 187064, 190756],\n", + " dtype='int64'), Int64Index([ 4331, 5251, 10924, 13684, 18219, 20969, 21892, 31948,\n", + " 45193, 46114, 47069, 49823, 55381, 56329, 58191, 60989,\n", + " 61907, 64362, 65879, 67648, 72194, 79469, 81290, 83112,\n", + " 84986, 87768, 89651, 91106, 105398, 108104, 109031, 109956,\n", + " 112782, 113696, 118272, 120086, 121886, 134604, 135531, 145148,\n", + " 147755, 152343, 153294, 162057, 164774, 168370, 169319, 170269,\n", + " 187065, 190757],\n", + " dtype='int64'), Int64Index([ 4332, 5252, 10925, 13685, 18220, 20970, 21893, 31949,\n", + " 45194, 46115, 47070, 49824, 55382, 56330, 58192, 60990,\n", + " 61908, 64363, 65880, 67649, 72195, 79470, 81291, 83113,\n", + " 84987, 87769, 89652, 91107, 105399, 108105, 109032, 109957,\n", + " 112783, 113697, 118273, 120087, 121887, 134605, 135532, 145149,\n", + " 147756, 152344, 153295, 162058, 164775, 168371, 169320, 170270,\n", + " 187066, 190758],\n", + " dtype='int64'), Int64Index([ 4333, 5253, 10926, 13686, 18221, 20971, 21894, 31950,\n", + " 45195, 46116, 47071, 49825, 55383, 56331, 58193, 60991,\n", + " 61909, 64364, 65881, 67650, 72196, 79471, 81292, 83114,\n", + " 84988, 87770, 89653, 91108, 105400, 108106, 109033, 109958,\n", + " 112784, 113698, 118274, 120088, 121888, 134606, 135533, 145150,\n", + " 147757, 152345, 153296, 162059, 164776, 168372, 169321, 170271,\n", + " 187067, 190759],\n", + " dtype='int64'), Int64Index([ 4334, 5254, 10927, 13687, 18222, 20972, 21895, 31951,\n", + " 45196, 46117, 47072, 49826, 55384, 56332, 58194, 60992,\n", + " 61910, 64365, 65882, 67651, 72197, 79472, 81293, 83115,\n", + " 84989, 87771, 89654, 91109, 105401, 108107, 109034, 109959,\n", + " 112785, 113699, 118275, 120089, 121889, 134607, 135534, 145151,\n", + " 147758, 152346, 153297, 162060, 164777, 168373, 169322, 170272,\n", + " 187068, 190760],\n", + " dtype='int64'), Int64Index([ 4335, 5255, 10928, 13688, 18223, 20973, 21896, 31952,\n", + " 45197, 46118, 47073, 49827, 55385, 56333, 58195, 60993,\n", + " 61911, 64366, 65883, 67652, 72198, 79473, 81294, 83116,\n", + " 84990, 87772, 89655, 91110, 105402, 108108, 109035, 109960,\n", + " 112786, 113700, 118276, 120090, 121890, 134608, 135535, 145152,\n", + " 147759, 152347, 153298, 162061, 164778, 168374, 169323, 170273,\n", + " 187069, 190761],\n", + " dtype='int64'), Int64Index([ 4336, 5256, 10929, 13689, 18224, 20974, 21897, 31953,\n", + " 45198, 46119, 47074, 49828, 55386, 56334, 58196, 60994,\n", + " 61912, 64367, 65884, 67653, 72199, 79474, 81295, 83117,\n", + " 84991, 87773, 89656, 91111, 105403, 108109, 109036, 109961,\n", + " 112787, 113701, 118277, 120091, 121891, 134609, 135536, 145153,\n", + " 147760, 152348, 153299, 162062, 164779, 168375, 169324, 170274,\n", + " 187070, 190762],\n", + " dtype='int64'), Int64Index([ 4337, 5257, 10930, 13690, 18225, 20975, 21898, 31954,\n", + " 45199, 46120, 47075, 49829, 55387, 56335, 58197, 60995,\n", + " 61913, 64368, 65885, 67654, 72200, 79475, 81296, 83118,\n", + " 84992, 87774, 89657, 91112, 105404, 108110, 109037, 109962,\n", + " 112788, 113702, 118278, 120092, 121892, 134610, 135537, 145154,\n", + " 147761, 152349, 153300, 162063, 164780, 168376, 169325, 170275,\n", + " 187071, 190763],\n", + " dtype='int64'), Int64Index([ 4338, 5258, 10931, 13691, 18226, 20976, 21899, 31955,\n", + " 45200, 46121, 47076, 49830, 55388, 56336, 58198, 60996,\n", + " 61914, 64369, 65886, 67655, 72201, 79476, 81297, 83119,\n", + " 84993, 87775, 89658, 91113, 105405, 108111, 109038, 109963,\n", + " 112789, 113703, 118279, 120093, 121893, 134611, 135538, 145155,\n", + " 147762, 152350, 153301, 162064, 164781, 168377, 169326, 170276,\n", + " 187072, 190764],\n", + " dtype='int64'), Int64Index([ 4339, 5259, 10932, 13692, 18227, 20977, 21900, 31956,\n", + " 45201, 46122, 47077, 49831, 55389, 56337, 58199, 60997,\n", + " 61915, 64370, 65887, 67656, 72202, 79477, 81298, 83120,\n", + " 84994, 87776, 89659, 91114, 105406, 108112, 109039, 109964,\n", + " 112790, 113704, 118280, 120094, 121894, 134612, 135539, 145156,\n", + " 147763, 152351, 153302, 162065, 164782, 168378, 169327, 170277,\n", + " 187073, 190765],\n", + " dtype='int64'), Int64Index([ 4340, 5260, 10933, 13693, 18228, 20978, 21901, 31957,\n", + " 45202, 46123, 47078, 49832, 55390, 56338, 58200, 60998,\n", + " 61916, 64371, 65888, 67657, 72203, 79478, 81299, 83121,\n", + " 84995, 87777, 89660, 91115, 105407, 108113, 109040, 109965,\n", + " 112791, 113705, 118281, 120095, 121895, 134613, 135540, 145157,\n", + " 147764, 152352, 153303, 162066, 164783, 168379, 169328, 170278,\n", + " 187074, 190766],\n", + " dtype='int64'), Int64Index([ 4341, 5261, 10934, 13694, 18229, 20979, 21902, 31958,\n", + " 45203, 46124, 47079, 49833, 55391, 56339, 58201, 60999,\n", + " 61917, 64372, 65889, 67658, 72204, 79479, 81300, 83122,\n", + " 84996, 87778, 89661, 91116, 105408, 108114, 109041, 109966,\n", + " 112792, 113706, 118282, 120096, 121896, 134614, 135541, 145158,\n", + " 147765, 152353, 153304, 162067, 164784, 168380, 169329, 170279,\n", + " 187075, 190767],\n", + " dtype='int64'), Int64Index([ 4342, 5262, 10935, 13695, 18230, 20980, 21903, 31959,\n", + " 45204, 46125, 47080, 49834, 55392, 56340, 58202, 61000,\n", + " 61918, 64373, 65890, 67659, 72205, 79480, 81301, 83123,\n", + " 84997, 87779, 89662, 91117, 105409, 108115, 109042, 109967,\n", + " 112793, 113707, 118283, 120097, 121897, 134615, 135542, 145159,\n", + " 147766, 152354, 153305, 162068, 164785, 168381, 169330, 170280,\n", + " 187076, 190768],\n", + " dtype='int64'), Int64Index([ 4343, 5263, 10936, 13696, 18231, 20981, 21904, 31960,\n", + " 45205, 46126, 47081, 49835, 55393, 56341, 58203, 61001,\n", + " 61919, 64374, 65891, 67660, 72206, 79481, 81302, 83124,\n", + " 84998, 87780, 89663, 91118, 105410, 108116, 109043, 109968,\n", + " 112794, 113708, 118284, 120098, 121898, 134616, 135543, 145160,\n", + " 147767, 152355, 153306, 162069, 164786, 168382, 169331, 170281,\n", + " 187077, 190769],\n", + " dtype='int64'), Int64Index([ 4344, 5264, 10937, 13697, 18232, 20982, 21905, 31961,\n", + " 45206, 46127, 47082, 49836, 55394, 56342, 58204, 61002,\n", + " 61920, 64375, 65892, 67661, 72207, 79482, 81303, 83125,\n", + " 84999, 87781, 89664, 91119, 105411, 108117, 109044, 109969,\n", + " 112795, 113709, 118285, 120099, 121899, 134617, 135544, 145161,\n", + " 147768, 152356, 153307, 162070, 164787, 168383, 169332, 170282,\n", + " 187078, 190770],\n", + " dtype='int64'), Int64Index([ 4345, 5265, 10938, 13698, 18233, 20983, 21906, 31962,\n", + " 45207, 46128, 47083, 49837, 55395, 56343, 58205, 61003,\n", + " 61921, 64376, 65893, 67662, 72208, 79483, 81304, 83126,\n", + " 85000, 87782, 89665, 91120, 105412, 108118, 109045, 109970,\n", + " 112796, 113710, 118286, 120100, 121900, 134618, 135545, 145162,\n", + " 147769, 152357, 153308, 162071, 164788, 168384, 169333, 170283,\n", + " 187079, 190771],\n", + " dtype='int64'), Int64Index([ 4346, 5266, 10939, 13699, 18234, 20984, 21907, 31963,\n", + " 45208, 46129, 47084, 49838, 55396, 56344, 58206, 61004,\n", + " 61922, 64377, 65894, 67663, 72209, 79484, 81305, 83127,\n", + " 85001, 87783, 89666, 91121, 105413, 108119, 109046, 109971,\n", + " 112797, 113711, 118287, 120101, 121901, 134619, 135546, 145163,\n", + " 147770, 152358, 153309, 162072, 164789, 168385, 169334, 170284,\n", + " 187080, 190772],\n", + " dtype='int64'), Int64Index([ 4347, 5267, 10940, 13700, 18235, 20985, 21908, 31964,\n", + " 45209, 46130, 47085, 49839, 55397, 56345, 58207, 61005,\n", + " 61923, 64378, 65895, 67664, 72210, 79485, 81306, 83128,\n", + " 85002, 87784, 89667, 91122, 105414, 108120, 109047, 109972,\n", + " 112798, 113712, 118288, 120102, 121902, 134620, 135547, 145164,\n", + " 147771, 152359, 153310, 162073, 164790, 168386, 169335, 170285,\n", + " 187081, 190773],\n", + " dtype='int64'), Int64Index([ 4348, 5268, 10941, 13701, 18236, 20986, 21909, 31965,\n", + " 45210, 46131, 47086, 49840, 55398, 56346, 58208, 61006,\n", + " 61924, 64379, 65896, 67665, 72211, 79486, 81307, 83129,\n", + " 85003, 87785, 89668, 91123, 105415, 108121, 109048, 109973,\n", + " 112799, 113713, 118289, 120103, 121903, 134621, 135548, 145165,\n", + " 147772, 152360, 153311, 162074, 164791, 168387, 169336, 170286,\n", + " 187082, 190774],\n", + " dtype='int64'), Int64Index([ 4349, 5269, 10942, 13702, 18237, 20987, 21910, 31966,\n", + " 45211, 46132, 47087, 49841, 55399, 56347, 58209, 61007,\n", + " 61925, 64380, 65897, 67666, 72212, 79487, 81308, 83130,\n", + " 85004, 87786, 89669, 91124, 105416, 108122, 109049, 109974,\n", + " 112800, 113714, 118290, 120104, 121904, 134622, 135549, 145166,\n", + " 147773, 152361, 153312, 162075, 164792, 168388, 169337, 170287,\n", + " 187083, 190775],\n", + " dtype='int64'), Int64Index([ 4350, 5270, 10943, 13703, 18238, 20988, 21911, 31967,\n", + " 45212, 46133, 47088, 49842, 55400, 56348, 58210, 61008,\n", + " 61926, 64381, 65898, 67667, 72213, 79488, 81309, 83131,\n", + " 85005, 87787, 89670, 91125, 105417, 108123, 109050, 109975,\n", + " 112801, 113715, 118291, 120105, 121905, 134623, 135550, 145167,\n", + " 147774, 152362, 153313, 162076, 164793, 168389, 169338, 170288,\n", + " 187084, 190776],\n", + " dtype='int64'), Int64Index([ 4351, 5271, 10944, 13704, 18239, 20989, 21912, 31968,\n", + " 45213, 46134, 47089, 49843, 55401, 56349, 58211, 61009,\n", + " 61927, 64382, 65899, 67668, 72214, 79489, 81310, 83132,\n", + " 85006, 87788, 89671, 91126, 105418, 108124, 109051, 109976,\n", + " 112802, 113716, 118292, 120106, 121906, 134624, 135551, 145168,\n", + " 147775, 152363, 153314, 162077, 164794, 168390, 169339, 170289,\n", + " 187085, 190777],\n", + " dtype='int64'), Int64Index([ 4352, 5272, 10945, 13705, 18240, 20990, 21913, 31969,\n", + " 45214, 46135, 47090, 49844, 55402, 56350, 58212, 61010,\n", + " 61928, 64383, 65900, 67669, 72215, 79490, 81311, 83133,\n", + " 85007, 87789, 89672, 91127, 105419, 108125, 109052, 109977,\n", + " 112803, 113717, 118293, 120107, 121907, 134625, 135552, 145169,\n", + " 147776, 152364, 153315, 162078, 164795, 168391, 169340, 170290,\n", + " 187086, 190778],\n", + " dtype='int64'), Int64Index([ 4353, 5273, 10946, 13706, 18241, 20991, 21914, 31970,\n", + " 45215, 46136, 47091, 49845, 55403, 56351, 58213, 61011,\n", + " 61929, 64384, 65901, 67670, 72216, 79491, 81312, 83134,\n", + " 85008, 87790, 89673, 91128, 105420, 108126, 109053, 109978,\n", + " 112804, 113718, 118294, 120108, 121908, 134626, 135553, 145170,\n", + " 147777, 152365, 153316, 162079, 164796, 168392, 169341, 170291,\n", + " 187087, 190779],\n", + " dtype='int64'), Int64Index([ 4354, 5274, 10947, 13707, 18242, 20992, 21915, 31971,\n", + " 45216, 46137, 47092, 49846, 55404, 56352, 58214, 61012,\n", + " 61930, 64385, 65902, 67671, 72217, 79492, 81313, 83135,\n", + " 85009, 87791, 89674, 91129, 105421, 108127, 109054, 109979,\n", + " 112805, 113719, 118295, 120109, 121909, 134627, 135554, 145171,\n", + " 147778, 152366, 153317, 162080, 164797, 168393, 169342, 170292,\n", + " 187088, 190780],\n", + " dtype='int64'), Int64Index([ 4355, 5275, 10948, 13708, 18243, 20993, 21916, 31972,\n", + " 45217, 46138, 47093, 49847, 55405, 56353, 58215, 61013,\n", + " 61931, 64386, 65903, 67672, 72218, 79493, 81314, 83136,\n", + " 85010, 87792, 89675, 91130, 105422, 108128, 109055, 109980,\n", + " 112806, 113720, 118296, 120110, 121910, 134628, 135555, 145172,\n", + " 147779, 152367, 153318, 162081, 164798, 168394, 169343, 170293,\n", + " 187089, 190781],\n", + " dtype='int64'), Int64Index([ 4356, 5276, 10949, 13709, 18244, 20994, 21917, 31973,\n", + " 45218, 46139, 47094, 49848, 55406, 56354, 58216, 61014,\n", + " 61932, 64387, 65904, 67673, 72219, 79494, 81315, 83137,\n", + " 85011, 87793, 89676, 91131, 105423, 108129, 109056, 109981,\n", + " 112807, 113721, 118297, 120111, 121911, 134629, 135556, 145173,\n", + " 147780, 152368, 153319, 162082, 164799, 168395, 169344, 170294,\n", + " 187090, 190782],\n", + " dtype='int64'), Int64Index([ 4357, 5277, 10950, 13710, 18245, 20995, 21918, 31974,\n", + " 45219, 46140, 47095, 49849, 55407, 56355, 58217, 61015,\n", + " 61933, 64388, 65905, 67674, 72220, 79495, 81316, 83138,\n", + " 85012, 87794, 89677, 91132, 105424, 108130, 109057, 109982,\n", + " 112808, 113722, 118298, 120112, 121912, 134630, 135557, 145174,\n", + " 147781, 152369, 153320, 162083, 164800, 168396, 169345, 170295,\n", + " 187091, 190783],\n", + " dtype='int64'), Int64Index([ 4358, 5278, 10951, 13711, 18246, 20996, 21919, 31975,\n", + " 45220, 46141, 47096, 49850, 55408, 56356, 58218, 61016,\n", + " 61934, 64389, 65906, 67675, 72221, 79496, 81317, 83139,\n", + " 85013, 87795, 89678, 91133, 105425, 108131, 109058, 109983,\n", + " 112809, 113723, 118299, 120113, 121913, 134631, 135558, 145175,\n", + " 147782, 152370, 153321, 162084, 164801, 168397, 169346, 170296,\n", + " 187092, 190784],\n", + " dtype='int64'), Int64Index([ 4359, 5279, 10952, 13712, 18247, 20997, 21920, 31976,\n", + " 45221, 46142, 47097, 49851, 55409, 56357, 58219, 61017,\n", + " 61935, 64390, 65907, 67676, 72222, 79497, 81318, 83140,\n", + " 85014, 87796, 89679, 91134, 105426, 108132, 109059, 109984,\n", + " 112810, 113724, 118300, 120114, 121914, 134632, 135559, 145176,\n", + " 147783, 152371, 153322, 162085, 164802, 168398, 169347, 170297,\n", + " 187093, 190785],\n", + " dtype='int64'), Int64Index([ 4360, 5280, 10953, 13713, 18248, 20998, 21921, 31977,\n", + " 45222, 46143, 47098, 49852, 55410, 56358, 58220, 61018,\n", + " 61936, 64391, 65908, 67677, 72223, 79498, 81319, 83141,\n", + " 85015, 87797, 89680, 91135, 105427, 108133, 109060, 109985,\n", + " 112811, 113725, 118301, 120115, 121915, 134633, 135560, 145177,\n", + " 147784, 152372, 153323, 162086, 164803, 168399, 169348, 170298,\n", + " 187094, 190786],\n", + " dtype='int64'), Int64Index([ 4361, 5281, 10954, 13714, 18249, 20999, 21922, 31978,\n", + " 45223, 46144, 47099, 49853, 55411, 56359, 58221, 61019,\n", + " 61937, 64392, 65909, 67678, 72224, 79499, 81320, 83142,\n", + " 85016, 87798, 89681, 91136, 105428, 108134, 109061, 109986,\n", + " 112812, 113726, 118302, 120116, 121916, 134634, 135561, 145178,\n", + " 147785, 152373, 153324, 162087, 164804, 168400, 169349, 170299,\n", + " 187095, 190787],\n", + " dtype='int64'), Int64Index([ 4362, 5282, 10955, 13715, 18250, 21000, 21923, 31979,\n", + " 45224, 46145, 47100, 49854, 55412, 56360, 58222, 61020,\n", + " 61938, 64393, 65910, 67679, 72225, 79500, 81321, 83143,\n", + " 85017, 87799, 89682, 91137, 105429, 108135, 109062, 109987,\n", + " 112813, 113727, 118303, 120117, 121917, 134635, 135562, 145179,\n", + " 147786, 152374, 153325, 162088, 164805, 168401, 169350, 170300,\n", + " 187096, 190788],\n", + " dtype='int64'), Int64Index([ 4363, 5283, 10956, 13716, 18251, 21001, 21924, 31980,\n", + " 45225, 46146, 47101, 49855, 55413, 56361, 58223, 61021,\n", + " 61939, 64394, 65911, 67680, 72226, 79501, 81322, 83144,\n", + " 85018, 87800, 89683, 91138, 105430, 108136, 109063, 109988,\n", + " 112814, 113728, 118304, 120118, 121918, 134636, 135563, 145180,\n", + " 147787, 152375, 153326, 162089, 164806, 168402, 169351, 170301,\n", + " 187097, 190789],\n", + " dtype='int64'), Int64Index([ 4364, 5284, 10957, 13717, 18252, 21002, 21925, 31981,\n", + " 45226, 46147, 47102, 49856, 55414, 56362, 58224, 61022,\n", + " 61940, 64395, 65912, 67681, 72227, 79502, 81323, 83145,\n", + " 85019, 87801, 89684, 91139, 105431, 108137, 109064, 109989,\n", + " 112815, 113729, 118305, 120119, 121919, 134637, 135564, 145181,\n", + " 147788, 152376, 153327, 162090, 164807, 168403, 169352, 170302,\n", + " 187098, 190790],\n", + " dtype='int64'), Int64Index([ 4365, 5285, 10958, 13718, 18253, 21003, 21926, 31982,\n", + " 45227, 46148, 47103, 49857, 55415, 56363, 58225, 61023,\n", + " 61941, 64396, 65913, 67682, 72228, 79503, 81324, 83146,\n", + " 85020, 87802, 89685, 91140, 105432, 108138, 109065, 109990,\n", + " 112816, 113730, 118306, 120120, 121920, 134638, 135565, 145182,\n", + " 147789, 152377, 153328, 162091, 164808, 168404, 169353, 170303,\n", + " 187099, 190791],\n", + " dtype='int64'), Int64Index([ 4366, 5286, 10959, 13719, 18254, 21004, 21927, 31983,\n", + " 45228, 46149, 47104, 49858, 55416, 56364, 58226, 61024,\n", + " 61942, 64397, 65914, 67683, 72229, 79504, 81325, 83147,\n", + " 85021, 87803, 89686, 91141, 105433, 108139, 109066, 109991,\n", + " 112817, 113731, 118307, 120121, 121921, 134639, 135566, 145183,\n", + " 147790, 152378, 153329, 162092, 164809, 168405, 169354, 170304,\n", + " 187100, 190792],\n", + " dtype='int64'), Int64Index([ 4367, 5287, 10960, 13720, 18255, 21005, 21928, 31984,\n", + " 45229, 46150, 47105, 49859, 55417, 56365, 58227, 61025,\n", + " 61943, 64398, 65915, 67684, 72230, 79505, 81326, 83148,\n", + " 85022, 87804, 89687, 91142, 105434, 108140, 109067, 109992,\n", + " 112818, 113732, 118308, 120122, 121922, 134640, 135567, 145184,\n", + " 147791, 152379, 153330, 162093, 164810, 168406, 169355, 170305,\n", + " 187101, 190793],\n", + " dtype='int64'), Int64Index([ 4368, 5288, 10961, 13721, 18256, 21006, 21929, 31985,\n", + " 45230, 46151, 47106, 49860, 55418, 56366, 58228, 61026,\n", + " 61944, 64399, 65916, 67685, 72231, 79506, 81327, 83149,\n", + " 85023, 87805, 89688, 91143, 105435, 108141, 109068, 109993,\n", + " 112819, 113733, 118309, 120123, 121923, 134641, 135568, 145185,\n", + " 147792, 152380, 153331, 162094, 164811, 168407, 169356, 170306,\n", + " 187102, 190794],\n", + " dtype='int64'), Int64Index([ 4369, 5289, 10962, 13722, 18257, 21007, 21930, 31986,\n", + " 45231, 46152, 47107, 49861, 55419, 56367, 58229, 61027,\n", + " 61945, 64400, 65917, 67686, 72232, 79507, 81328, 83150,\n", + " 85024, 87806, 89689, 91144, 105436, 108142, 109069, 109994,\n", + " 112820, 113734, 118310, 120124, 121924, 134642, 135569, 145186,\n", + " 147793, 152381, 153332, 162095, 164812, 168408, 169357, 170307,\n", + " 187103, 190795],\n", + " dtype='int64'), Int64Index([ 4370, 5290, 10963, 13723, 18258, 21008, 21931, 31987,\n", + " 45232, 46153, 47108, 49862, 55420, 56368, 58230, 61028,\n", + " 61946, 64401, 65918, 67687, 72233, 79508, 81329, 83151,\n", + " 85025, 87807, 89690, 91145, 105437, 108143, 109070, 109995,\n", + " 112821, 113735, 118311, 120125, 121925, 134643, 135570, 145187,\n", + " 147794, 152382, 153333, 162096, 164813, 168409, 169358, 170308,\n", + " 187104, 190796],\n", + " dtype='int64'), Int64Index([ 4371, 5291, 10964, 13724, 18259, 21009, 21932, 31988,\n", + " 45233, 46154, 47109, 49863, 55421, 56369, 58231, 61029,\n", + " 61947, 64402, 65919, 67688, 72234, 79509, 81330, 83152,\n", + " 85026, 87808, 89691, 91146, 105438, 108144, 109071, 109996,\n", + " 112822, 113736, 118312, 120126, 121926, 134644, 135571, 145188,\n", + " 147795, 152383, 153334, 162097, 164814, 168410, 169359, 170309,\n", + " 187105, 190797],\n", + " dtype='int64'), Int64Index([ 4372, 5292, 10965, 13725, 18260, 21010, 21933, 31989,\n", + " 45234, 46155, 47110, 49864, 55422, 56370, 58232, 61030,\n", + " 61948, 64403, 65920, 67689, 72235, 79510, 81331, 83153,\n", + " 85027, 87809, 89692, 91147, 105439, 108145, 109072, 109997,\n", + " 112823, 113737, 118313, 120127, 121927, 134645, 135572, 145189,\n", + " 147796, 152384, 153335, 162098, 164815, 168411, 169360, 170310,\n", + " 187106, 190798],\n", + " dtype='int64'), Int64Index([ 4373, 5293, 10966, 13726, 18261, 21011, 21934, 31990,\n", + " 45235, 46156, 47111, 49865, 55423, 56371, 58233, 61031,\n", + " 61949, 64404, 65921, 67690, 72236, 79511, 81332, 83154,\n", + " 85028, 87810, 89693, 91148, 105440, 108146, 109073, 109998,\n", + " 112824, 113738, 118314, 120128, 121928, 134646, 135573, 145190,\n", + " 147797, 152385, 153336, 162099, 164816, 168412, 169361, 170311,\n", + " 187107, 190799],\n", + " dtype='int64'), Int64Index([ 4374, 5294, 10967, 13727, 18262, 21012, 21935, 31991,\n", + " 45236, 46157, 47112, 49866, 55424, 56372, 58234, 61032,\n", + " 61950, 64405, 65922, 67691, 72237, 79512, 81333, 83155,\n", + " 85029, 87811, 89694, 91149, 105441, 108147, 109074, 109999,\n", + " 112825, 113739, 118315, 120129, 121929, 134647, 135574, 145191,\n", + " 147798, 152386, 153337, 162100, 164817, 168413, 169362, 170312,\n", + " 187108, 190800],\n", + " dtype='int64'), Int64Index([ 4375, 5295, 10968, 13728, 18263, 21013, 21936, 31992,\n", + " 45237, 46158, 47113, 49867, 55425, 56373, 58235, 61033,\n", + " 61951, 64406, 65923, 67692, 72238, 79513, 81334, 83156,\n", + " 85030, 87812, 89695, 91150, 105442, 108148, 109075, 110000,\n", + " 112826, 113740, 118316, 120130, 121930, 134648, 135575, 145192,\n", + " 147799, 152387, 153338, 162101, 164818, 168414, 169363, 170313,\n", + " 187109, 190801],\n", + " dtype='int64'), Int64Index([ 4376, 5296, 10969, 13729, 18264, 21014, 21937, 31993,\n", + " 45238, 46159, 47114, 49868, 55426, 56374, 58236, 61034,\n", + " 61952, 64407, 65924, 67693, 72239, 79514, 81335, 83157,\n", + " 85031, 87813, 89696, 91151, 105443, 108149, 109076, 110001,\n", + " 112827, 113741, 118317, 120131, 121931, 134649, 135576, 145193,\n", + " 147800, 152388, 153339, 162102, 164819, 168415, 169364, 170314,\n", + " 187110, 190802],\n", + " dtype='int64'), Int64Index([ 4377, 5297, 10970, 13730, 18265, 21015, 21938, 31994,\n", + " 45239, 46160, 47115, 49869, 55427, 56375, 58237, 61035,\n", + " 61953, 64408, 65925, 67694, 72240, 79515, 81336, 83158,\n", + " 85032, 87814, 89697, 91152, 105444, 108150, 109077, 110002,\n", + " 112828, 113742, 118318, 120132, 121932, 134650, 135577, 145194,\n", + " 147801, 152389, 153340, 162103, 164820, 168416, 169365, 170315,\n", + " 187111, 190803],\n", + " dtype='int64'), Int64Index([ 4378, 5298, 10971, 13731, 18266, 21016, 21939, 31995,\n", + " 45240, 46161, 47116, 49870, 55428, 56376, 58238, 61036,\n", + " 61954, 64409, 65926, 67695, 72241, 79516, 81337, 83159,\n", + " 85033, 87815, 89698, 91153, 105445, 108151, 109078, 110003,\n", + " 112829, 113743, 118319, 120133, 121933, 134651, 135578, 145195,\n", + " 147802, 152390, 153341, 162104, 164821, 168417, 169366, 170316,\n", + " 187112, 190804],\n", + " dtype='int64'), Int64Index([ 4379, 5299, 10972, 13732, 18267, 21017, 21940, 31996,\n", + " 45241, 46162, 47117, 49871, 55429, 56377, 58239, 61037,\n", + " 61955, 64410, 65927, 67696, 72242, 79517, 81338, 83160,\n", + " 85034, 87816, 89699, 91154, 105446, 108152, 109079, 110004,\n", + " 112830, 113744, 118320, 120134, 121934, 134652, 135579, 145196,\n", + " 147803, 152391, 153342, 162105, 164822, 168418, 169367, 170317,\n", + " 187113, 190805],\n", + " dtype='int64'), Int64Index([ 4380, 5300, 10973, 13733, 18268, 21018, 21941, 31997,\n", + " 45242, 46163, 47118, 49872, 55430, 56378, 58240, 61038,\n", + " 61956, 64411, 65928, 67697, 72243, 79518, 81339, 83161,\n", + " 85035, 87817, 89700, 91155, 105447, 108153, 109080, 110005,\n", + " 112831, 113745, 118321, 120135, 121935, 134653, 135580, 145197,\n", + " 147804, 152392, 153343, 162106, 164823, 168419, 169368, 170318,\n", + " 187114, 190806],\n", + " dtype='int64'), Int64Index([ 4381, 5301, 10974, 13734, 18269, 21019, 21942, 31998,\n", + " 45243, 46164, 47119, 49873, 55431, 56379, 58241, 61039,\n", + " 61957, 64412, 65929, 67698, 72244, 79519, 81340, 83162,\n", + " 85036, 87818, 89701, 91156, 105448, 108154, 109081, 110006,\n", + " 112832, 113746, 118322, 120136, 121936, 134654, 135581, 145198,\n", + " 147805, 152393, 153344, 162107, 164824, 168420, 169369, 170319,\n", + " 187115, 190807],\n", + " dtype='int64'), Int64Index([ 4382, 5302, 10975, 13735, 18270, 21020, 21943, 31999,\n", + " 45244, 46165, 47120, 49874, 55432, 56380, 58242, 61040,\n", + " 61958, 64413, 65930, 67699, 72245, 79520, 81341, 83163,\n", + " 85037, 87819, 89702, 91157, 105449, 108155, 109082, 110007,\n", + " 112833, 113747, 118323, 120137, 121937, 134655, 135582, 145199,\n", + " 147806, 152394, 153345, 162108, 164825, 168421, 169370, 170320,\n", + " 187116, 190808],\n", + " dtype='int64'), Int64Index([ 4383, 5303, 10976, 13736, 18271, 21021, 21944, 32000,\n", + " 45245, 46166, 47121, 49875, 55433, 56381, 58243, 61041,\n", + " 61959, 64414, 65931, 67700, 72246, 79521, 81342, 83164,\n", + " 85038, 87820, 89703, 91158, 105450, 108156, 109083, 110008,\n", + " 112834, 113748, 118324, 120138, 121938, 134656, 135583, 145200,\n", + " 147807, 152395, 153346, 162109, 164826, 168422, 169371, 170321,\n", + " 187117, 190809],\n", + " dtype='int64'), Int64Index([ 4384, 5304, 10977, 13737, 18272, 21022, 21945, 32001,\n", + " 45246, 46167, 47122, 49876, 55434, 56382, 58244, 61042,\n", + " 61960, 64415, 65932, 67701, 72247, 79522, 81343, 83165,\n", + " 85039, 87821, 89704, 91159, 105451, 108157, 109084, 110009,\n", + " 112835, 113749, 118325, 120139, 121939, 134657, 135584, 145201,\n", + " 147808, 152396, 153347, 162110, 164827, 168423, 169372, 170322,\n", + " 187118, 190810],\n", + " dtype='int64'), Int64Index([ 4385, 5305, 10978, 13738, 18273, 21023, 21946, 32002,\n", + " 45247, 46168, 47123, 49877, 55435, 56383, 58245, 61043,\n", + " 61961, 64416, 65933, 67702, 72248, 79523, 81344, 83166,\n", + " 85040, 87822, 89705, 91160, 105452, 108158, 109085, 110010,\n", + " 112836, 113750, 118326, 120140, 121940, 134658, 135585, 145202,\n", + " 147809, 152397, 153348, 162111, 164828, 168424, 169373, 170323,\n", + " 187119, 190811],\n", + " dtype='int64'), Int64Index([ 4386, 5306, 10979, 13739, 18274, 21024, 21947, 32003,\n", + " 45248, 46169, 47124, 49878, 55436, 56384, 58246, 61044,\n", + " 61962, 64417, 65934, 67703, 72249, 79524, 81345, 83167,\n", + " 85041, 87823, 89706, 91161, 105453, 108159, 109086, 110011,\n", + " 112837, 113751, 118327, 120141, 121941, 134659, 135586, 145203,\n", + " 147810, 152398, 153349, 162112, 164829, 168425, 169374, 170324,\n", + " 187120, 190812],\n", + " dtype='int64'), Int64Index([ 4387, 5307, 10980, 13740, 18275, 21025, 21948, 32004,\n", + " 45249, 46170, 47125, 49879, 55437, 56385, 58247, 61045,\n", + " 61963, 64418, 65935, 67704, 72250, 79525, 81346, 83168,\n", + " 85042, 87824, 89707, 91162, 105454, 108160, 109087, 110012,\n", + " 112838, 113752, 118328, 120142, 121942, 134660, 135587, 145204,\n", + " 147811, 152399, 153350, 162113, 164830, 168426, 169375, 170325,\n", + " 187121, 190813],\n", + " dtype='int64'), Int64Index([ 4388, 5308, 10981, 13741, 18276, 21026, 21949, 32005,\n", + " 45250, 46171, 47126, 49880, 55438, 56386, 58248, 61046,\n", + " 61964, 64419, 65936, 67705, 72251, 79526, 81347, 83169,\n", + " 85043, 87825, 89708, 91163, 105455, 108161, 109088, 110013,\n", + " 112839, 113753, 118329, 120143, 121943, 134661, 135588, 145205,\n", + " 147812, 152400, 153351, 162114, 164831, 168427, 169376, 170326,\n", + " 187122, 190814],\n", + " dtype='int64'), Int64Index([ 4389, 5309, 10982, 13742, 18277, 21027, 21950, 32006,\n", + " 45251, 46172, 47127, 49881, 55439, 56387, 58249, 61047,\n", + " 61965, 64420, 65937, 67706, 72252, 79527, 81348, 83170,\n", + " 85044, 87826, 89709, 91164, 105456, 108162, 109089, 110014,\n", + " 112840, 113754, 118330, 120144, 121944, 134662, 135589, 145206,\n", + " 147813, 152401, 153352, 162115, 164832, 168428, 169377, 170327,\n", + " 187123, 190815],\n", + " dtype='int64'), Int64Index([ 4390, 5310, 10983, 13743, 18278, 21028, 21951, 32007,\n", + " 45252, 46173, 47128, 49882, 55440, 56388, 58250, 61048,\n", + " 61966, 64421, 65938, 67707, 72253, 79528, 81349, 83171,\n", + " 85045, 87827, 89710, 91165, 105457, 108163, 109090, 110015,\n", + " 112841, 113755, 118331, 120145, 121945, 134663, 135590, 145207,\n", + " 147814, 152402, 153353, 162116, 164833, 168429, 169378, 170328,\n", + " 187124, 190816],\n", + " dtype='int64'), Int64Index([ 4391, 5311, 10984, 13744, 18279, 21029, 21952, 32008,\n", + " 45253, 46174, 47129, 49883, 55441, 56389, 58251, 61049,\n", + " 61967, 64422, 65939, 67708, 72254, 79529, 81350, 83172,\n", + " 85046, 87828, 89711, 91166, 105458, 108164, 109091, 110016,\n", + " 112842, 113756, 118332, 120146, 121946, 134664, 135591, 145208,\n", + " 147815, 152403, 153354, 162117, 164834, 168430, 169379, 170329,\n", + " 187125, 190817],\n", + " dtype='int64'), Int64Index([ 4392, 5312, 10985, 13745, 18280, 21030, 21953, 32009,\n", + " 45254, 46175, 47130, 49884, 55442, 56390, 58252, 61050,\n", + " 61968, 64423, 65940, 67709, 72255, 79530, 81351, 83173,\n", + " 85047, 87829, 89712, 91167, 105459, 108165, 109092, 110017,\n", + " 112843, 113757, 118333, 120147, 121947, 134665, 135592, 145209,\n", + " 147816, 152404, 153355, 162118, 164835, 168431, 169380, 170330,\n", + " 187126, 190818],\n", + " dtype='int64'), Int64Index([ 4393, 5313, 10986, 13746, 18281, 21031, 21954, 32010,\n", + " 45255, 46176, 47131, 49885, 55443, 56391, 58253, 61051,\n", + " 61969, 64424, 65941, 67710, 72256, 79531, 81352, 83174,\n", + " 85048, 87830, 89713, 91168, 105460, 108166, 109093, 110018,\n", + " 112844, 113758, 118334, 120148, 121948, 134666, 135593, 145210,\n", + " 147817, 152405, 153356, 162119, 164836, 168432, 169381, 170331,\n", + " 187127, 190819],\n", + " dtype='int64'), Int64Index([ 4394, 5314, 10987, 13747, 18282, 21032, 21955, 32011,\n", + " 45256, 46177, 47132, 49886, 55444, 56392, 58254, 61052,\n", + " 61970, 64425, 65942, 67711, 72257, 79532, 81353, 83175,\n", + " 85049, 87831, 89714, 91169, 105461, 108167, 109094, 110019,\n", + " 112845, 113759, 118335, 120149, 121949, 134667, 135594, 145211,\n", + " 147818, 152406, 153357, 162120, 164837, 168433, 169382, 170332,\n", + " 187128, 190820],\n", + " dtype='int64'), Int64Index([ 4395, 5315, 10988, 13748, 18283, 21033, 21956, 32012,\n", + " 45257, 46178, 47133, 49887, 55445, 56393, 58255, 61053,\n", + " 61971, 64426, 65943, 67712, 72258, 79533, 81354, 83176,\n", + " 85050, 87832, 89715, 91170, 105462, 108168, 109095, 110020,\n", + " 112846, 113760, 118336, 120150, 121950, 134668, 135595, 145212,\n", + " 147819, 152407, 153358, 162121, 164838, 168434, 169383, 170333,\n", + " 187129, 190821],\n", + " dtype='int64'), Int64Index([ 4396, 5316, 10989, 13749, 18284, 21034, 21957, 32013,\n", + " 45258, 46179, 47134, 49888, 55446, 56394, 58256, 61054,\n", + " 61972, 64427, 65944, 67713, 72259, 79534, 81355, 83177,\n", + " 85051, 87833, 89716, 91171, 105463, 108169, 109096, 110021,\n", + " 112847, 113761, 118337, 120151, 121951, 134669, 135596, 145213,\n", + " 147820, 152408, 153359, 162122, 164839, 168435, 169384, 170334,\n", + " 187130, 190822],\n", + " dtype='int64'), Int64Index([ 4397, 5317, 10990, 13750, 18285, 21035, 21958, 32014,\n", + " 45259, 46180, 47135, 49889, 55447, 56395, 58257, 61055,\n", + " 61973, 64428, 65945, 67714, 72260, 79535, 81356, 83178,\n", + " 85052, 87834, 89717, 91172, 105464, 108170, 109097, 110022,\n", + " 112848, 113762, 118338, 120152, 121952, 134670, 135597, 145214,\n", + " 147821, 152409, 153360, 162123, 164840, 168436, 169385, 170335,\n", + " 187131, 190823],\n", + " dtype='int64'), Int64Index([ 4398, 5318, 10991, 13751, 18286, 21036, 21959, 32015,\n", + " 45260, 46181, 47136, 49890, 55448, 56396, 58258, 61056,\n", + " 61974, 64429, 65946, 67715, 72261, 79536, 81357, 83179,\n", + " 85053, 87835, 89718, 91173, 105465, 108171, 109098, 110023,\n", + " 112849, 113763, 118339, 120153, 121953, 134671, 135598, 145215,\n", + " 147822, 152410, 153361, 162124, 164841, 168437, 169386, 170336,\n", + " 187132, 190824],\n", + " dtype='int64'), Int64Index([ 4399, 5319, 10992, 13752, 18287, 21037, 21960, 32016,\n", + " 45261, 46182, 47137, 49891, 55449, 56397, 58259, 61057,\n", + " 61975, 64430, 65947, 67716, 72262, 79537, 81358, 83180,\n", + " 85054, 87836, 89719, 91174, 105466, 108172, 109099, 110024,\n", + " 112850, 113764, 118340, 120154, 121954, 134672, 135599, 145216,\n", + " 147823, 152411, 153362, 162125, 164842, 168438, 169387, 170337,\n", + " 187133, 190825],\n", + " dtype='int64'), Int64Index([ 4400, 5320, 10993, 13753, 18288, 21038, 21961, 32017,\n", + " 45262, 46183, 47138, 49892, 55450, 56398, 58260, 61058,\n", + " 61976, 64431, 65948, 67717, 72263, 79538, 81359, 83181,\n", + " 85055, 87837, 89720, 91175, 105467, 108173, 109100, 110025,\n", + " 112851, 113765, 118341, 120155, 121955, 134673, 135600, 145217,\n", + " 147824, 152412, 153363, 162126, 164843, 168439, 169388, 170338,\n", + " 187134, 190826],\n", + " dtype='int64'), Int64Index([ 4401, 5321, 10994, 13754, 18289, 21039, 21962, 32018,\n", + " 45263, 46184, 47139, 49893, 55451, 56399, 58261, 61059,\n", + " 61977, 64432, 65949, 67718, 72264, 79539, 81360, 83182,\n", + " 85056, 87838, 89721, 91176, 105468, 108174, 109101, 110026,\n", + " 112852, 113766, 118342, 120156, 121956, 134674, 135601, 145218,\n", + " 147825, 152413, 153364, 162127, 164844, 168440, 169389, 170339,\n", + " 187135, 190827],\n", + " dtype='int64'), Int64Index([ 4402, 5322, 10995, 13755, 18290, 21040, 21963, 32019,\n", + " 45264, 46185, 47140, 49894, 55452, 56400, 58262, 61060,\n", + " 61978, 64433, 65950, 67719, 72265, 79540, 81361, 83183,\n", + " 85057, 87839, 89722, 91177, 105469, 108175, 109102, 110027,\n", + " 112853, 113767, 118343, 120157, 121957, 134675, 135602, 145219,\n", + " 147826, 152414, 153365, 162128, 164845, 168441, 169390, 170340,\n", + " 187136, 190828],\n", + " dtype='int64'), Int64Index([ 4403, 5323, 10996, 13756, 18291, 21041, 21964, 32020,\n", + " 45265, 46186, 47141, 49895, 55453, 56401, 58263, 61061,\n", + " 61979, 64434, 65951, 67720, 72266, 79541, 81362, 83184,\n", + " 85058, 87840, 89723, 91178, 105470, 108176, 109103, 110028,\n", + " 112854, 113768, 118344, 120158, 121958, 134676, 135603, 145220,\n", + " 147827, 152415, 153366, 162129, 164846, 168442, 169391, 170341,\n", + " 187137, 190829],\n", + " dtype='int64'), Int64Index([ 4404, 5324, 10997, 13757, 18292, 21042, 21965, 32021,\n", + " 45266, 46187, 47142, 49896, 55454, 56402, 58264, 61062,\n", + " 61980, 64435, 65952, 67721, 72267, 79542, 81363, 83185,\n", + " 85059, 87841, 89724, 91179, 105471, 108177, 109104, 110029,\n", + " 112855, 113769, 118345, 120159, 121959, 134677, 135604, 145221,\n", + " 147828, 152416, 153367, 162130, 164847, 168443, 169392, 170342,\n", + " 187138, 190830],\n", + " dtype='int64'), Int64Index([ 4405, 5325, 10998, 13758, 18293, 21043, 21966, 32022,\n", + " 45267, 46188, 47143, 49897, 55455, 56403, 58265, 61063,\n", + " 61981, 64436, 65953, 67722, 72268, 79543, 81364, 83186,\n", + " 85060, 87842, 89725, 91180, 105472, 108178, 109105, 110030,\n", + " 112856, 113770, 118346, 120160, 121960, 134678, 135605, 145222,\n", + " 147829, 152417, 153368, 162131, 164848, 168444, 169393, 170343,\n", + " 187139, 190831],\n", + " dtype='int64'), Int64Index([ 4406, 5326, 10999, 13759, 18294, 21044, 21967, 32023,\n", + " 45268, 46189, 47144, 49898, 55456, 56404, 58266, 61064,\n", + " 61982, 64437, 65954, 67723, 72269, 79544, 81365, 83187,\n", + " 85061, 87843, 89726, 91181, 105473, 108179, 109106, 110031,\n", + " 112857, 113771, 118347, 120161, 121961, 134679, 135606, 145223,\n", + " 147830, 152418, 153369, 162132, 164849, 168445, 169394, 170344,\n", + " 187140, 190832],\n", + " dtype='int64'), Int64Index([ 4407, 5327, 11000, 13760, 18295, 21045, 21968, 32024,\n", + " 45269, 46190, 47145, 49899, 55457, 56405, 58267, 61065,\n", + " 61983, 64438, 65955, 67724, 72270, 79545, 81366, 83188,\n", + " 85062, 87844, 89727, 91182, 105474, 108180, 109107, 110032,\n", + " 112858, 113772, 118348, 120162, 121962, 134680, 135607, 145224,\n", + " 147831, 152419, 153370, 162133, 164850, 168446, 169395, 170345,\n", + " 187141, 190833],\n", + " dtype='int64'), Int64Index([ 4408, 5328, 11001, 13761, 18296, 21046, 21969, 32025,\n", + " 45270, 46191, 47146, 49900, 55458, 56406, 58268, 61066,\n", + " 61984, 64439, 65956, 67725, 72271, 79546, 81367, 83189,\n", + " 85063, 87845, 89728, 91183, 105475, 108181, 109108, 110033,\n", + " 112859, 113773, 118349, 120163, 121963, 134681, 135608, 145225,\n", + " 147832, 152420, 153371, 162134, 164851, 168447, 169396, 170346,\n", + " 187142, 190834],\n", + " dtype='int64'), Int64Index([ 4409, 5329, 11002, 13762, 18297, 21047, 21970, 32026,\n", + " 45271, 46192, 47147, 49901, 55459, 56407, 58269, 61067,\n", + " 61985, 64440, 65957, 67726, 72272, 79547, 81368, 83190,\n", + " 85064, 87846, 89729, 91184, 105476, 108182, 109109, 110034,\n", + " 112860, 113774, 118350, 120164, 121964, 134682, 135609, 145226,\n", + " 147833, 152421, 153372, 162135, 164852, 168448, 169397, 170347,\n", + " 187143, 190835],\n", + " dtype='int64'), Int64Index([ 4410, 5330, 11003, 13763, 18298, 21048, 21971, 32027,\n", + " 45272, 46193, 47148, 49902, 55460, 56408, 58270, 61068,\n", + " 61986, 64441, 65958, 67727, 72273, 79548, 81369, 83191,\n", + " 85065, 87847, 89730, 91185, 105477, 108183, 109110, 110035,\n", + " 112861, 113775, 118351, 120165, 121965, 134683, 135610, 145227,\n", + " 147834, 152422, 153373, 162136, 164853, 168449, 169398, 170348,\n", + " 187144, 190836],\n", + " dtype='int64'), Int64Index([ 4411, 5331, 11004, 13764, 18299, 21049, 21972, 32028,\n", + " 45273, 46194, 47149, 49903, 55461, 56409, 58271, 61069,\n", + " 61987, 64442, 65959, 67728, 72274, 79549, 81370, 83192,\n", + " 85066, 87848, 89731, 91186, 105478, 108184, 109111, 110036,\n", + " 112862, 113776, 118352, 120166, 121966, 134684, 135611, 145228,\n", + " 147835, 152423, 153374, 162137, 164854, 168450, 169399, 170349,\n", + " 187145, 190837],\n", + " dtype='int64'), Int64Index([ 4412, 5332, 11005, 13765, 18300, 21050, 21973, 32029,\n", + " 45274, 46195, 47150, 49904, 55462, 56410, 58272, 61070,\n", + " 61988, 64443, 65960, 67729, 72275, 79550, 81371, 83193,\n", + " 85067, 87849, 89732, 91187, 105479, 108185, 109112, 110037,\n", + " 112863, 113777, 118353, 120167, 121967, 134685, 135612, 145229,\n", + " 147836, 152424, 153375, 162138, 164855, 168451, 169400, 170350,\n", + " 187146, 190838],\n", + " dtype='int64'), Int64Index([ 4413, 5333, 11006, 13766, 18301, 21051, 21974, 32030,\n", + " 45275, 46196, 47151, 49905, 55463, 56411, 58273, 61071,\n", + " 61989, 64444, 65961, 67730, 72276, 79551, 81372, 83194,\n", + " 85068, 87850, 89733, 91188, 105480, 108186, 109113, 110038,\n", + " 112864, 113778, 118354, 120168, 121968, 134686, 135613, 145230,\n", + " 147837, 152425, 153376, 162139, 164856, 168452, 169401, 170351,\n", + " 187147, 190839],\n", + " dtype='int64'), Int64Index([ 4414, 5334, 11007, 13767, 18302, 21052, 21975, 32031,\n", + " 45276, 46197, 47152, 49906, 55464, 56412, 58274, 61072,\n", + " 61990, 64445, 65962, 67731, 72277, 79552, 81373, 83195,\n", + " 85069, 87851, 89734, 91189, 105481, 108187, 109114, 110039,\n", + " 112865, 113779, 118355, 120169, 121969, 134687, 135614, 145231,\n", + " 147838, 152426, 153377, 162140, 164857, 168453, 169402, 170352,\n", + " 187148, 190840],\n", + " dtype='int64'), Int64Index([ 4415, 5335, 11008, 13768, 18303, 21053, 21976, 32032,\n", + " 45277, 46198, 47153, 49907, 55465, 56413, 58275, 61073,\n", + " 61991, 64446, 65963, 67732, 72278, 79553, 81374, 83196,\n", + " 85070, 87852, 89735, 91190, 105482, 108188, 109115, 110040,\n", + " 112866, 113780, 118356, 120170, 121970, 134688, 135615, 145232,\n", + " 147839, 152427, 153378, 162141, 164858, 168454, 169403, 170353,\n", + " 187149, 190841],\n", + " dtype='int64'), Int64Index([ 4416, 5336, 11009, 13769, 18304, 21054, 21977, 32033,\n", + " 45278, 46199, 47154, 49908, 55466, 56414, 58276, 61074,\n", + " 61992, 64447, 65964, 67733, 72279, 79554, 81375, 83197,\n", + " 85071, 87853, 89736, 91191, 105483, 108189, 109116, 110041,\n", + " 112867, 113781, 118357, 120171, 121971, 134689, 135616, 145233,\n", + " 147840, 152428, 153379, 162142, 164859, 168455, 169404, 170354,\n", + " 187150, 190842],\n", + " dtype='int64'), Int64Index([ 4417, 5337, 11010, 13770, 18305, 21055, 21978, 32034,\n", + " 45279, 46200, 47155, 49909, 55467, 56415, 58277, 61075,\n", + " 61993, 64448, 65965, 67734, 72280, 79555, 81376, 83198,\n", + " 85072, 87854, 89737, 91192, 105484, 108190, 109117, 110042,\n", + " 112868, 113782, 118358, 120172, 121972, 134690, 135617, 145234,\n", + " 147841, 152429, 153380, 162143, 164860, 168456, 169405, 170355,\n", + " 187151, 190843],\n", + " dtype='int64'), Int64Index([ 4418, 5338, 11011, 13771, 18306, 21056, 21979, 32035,\n", + " 45280, 46201, 47156, 49910, 55468, 56416, 58278, 61076,\n", + " 61994, 64449, 65966, 67735, 72281, 79556, 81377, 83199,\n", + " 85073, 87855, 89738, 91193, 105485, 108191, 109118, 110043,\n", + " 112869, 113783, 118359, 120173, 121973, 134691, 135618, 145235,\n", + " 147842, 152430, 153381, 162144, 164861, 168457, 169406, 170356,\n", + " 187152, 190844],\n", + " dtype='int64'), Int64Index([ 4419, 5339, 11012, 13772, 18307, 21057, 21980, 32036,\n", + " 45281, 46202, 47157, 49911, 55469, 56417, 58279, 61077,\n", + " 61995, 64450, 65967, 67736, 72282, 79557, 81378, 83200,\n", + " 85074, 87856, 89739, 91194, 105486, 108192, 109119, 110044,\n", + " 112870, 113784, 118360, 120174, 121974, 134692, 135619, 145236,\n", + " 147843, 152431, 153382, 162145, 164862, 168458, 169407, 170357,\n", + " 187153, 190845],\n", + " dtype='int64'), Int64Index([ 4420, 5340, 11013, 13773, 18308, 21058, 21981, 32037,\n", + " 45282, 46203, 47158, 49912, 55470, 56418, 58280, 61078,\n", + " 61996, 64451, 65968, 67737, 72283, 79558, 81379, 83201,\n", + " 85075, 87857, 89740, 91195, 105487, 108193, 109120, 110045,\n", + " 112871, 113785, 118361, 120175, 121975, 134693, 135620, 145237,\n", + " 147844, 152432, 153383, 162146, 164863, 168459, 169408, 170358,\n", + " 187154, 190846],\n", + " dtype='int64'), Int64Index([ 4421, 5341, 11014, 13774, 18309, 21059, 21982, 32038,\n", + " 45283, 46204, 47159, 49913, 55471, 56419, 58281, 61079,\n", + " 61997, 64452, 65969, 67738, 72284, 79559, 81380, 83202,\n", + " 85076, 87858, 89741, 91196, 105488, 108194, 109121, 110046,\n", + " 112872, 113786, 118362, 120176, 121976, 134694, 135621, 145238,\n", + " 147845, 152433, 153384, 162147, 164864, 168460, 169409, 170359,\n", + " 187155, 190847],\n", + " dtype='int64'), Int64Index([ 4422, 5342, 11015, 13775, 18310, 21060, 21983, 32039,\n", + " 45284, 46205, 47160, 49914, 55472, 56420, 58282, 61080,\n", + " 61998, 64453, 65970, 67739, 72285, 79560, 81381, 83203,\n", + " 85077, 87859, 89742, 91197, 105489, 108195, 109122, 110047,\n", + " 112873, 113787, 118363, 120177, 121977, 134695, 135622, 145239,\n", + " 147846, 152434, 153385, 162148, 164865, 168461, 169410, 170360,\n", + " 187156, 190848],\n", + " dtype='int64'), Int64Index([ 4423, 5343, 11016, 13776, 18311, 21061, 21984, 32040,\n", + " 45285, 46206, 47161, 49915, 55473, 56421, 58283, 61081,\n", + " 61999, 64454, 65971, 67740, 72286, 79561, 81382, 83204,\n", + " 85078, 87860, 89743, 91198, 105490, 108196, 109123, 110048,\n", + " 112874, 113788, 118364, 120178, 121978, 134696, 135623, 145240,\n", + " 147847, 152435, 153386, 162149, 164866, 168462, 169411, 170361,\n", + " 187157, 190849],\n", + " dtype='int64'), Int64Index([ 4424, 5344, 11017, 13777, 18312, 21062, 21985, 32041,\n", + " 45286, 46207, 47162, 49916, 55474, 56422, 58284, 61082,\n", + " 62000, 64455, 65972, 67741, 72287, 79562, 81383, 83205,\n", + " 85079, 87861, 89744, 91199, 105491, 108197, 109124, 110049,\n", + " 112875, 113789, 118365, 120179, 121979, 134697, 135624, 145241,\n", + " 147848, 152436, 153387, 162150, 164867, 168463, 169412, 170362,\n", + " 187158, 190850],\n", + " dtype='int64'), Int64Index([ 4425, 5345, 11018, 13778, 18313, 21063, 21986, 32042,\n", + " 45287, 46208, 47163, 49917, 55475, 56423, 58285, 61083,\n", + " 62001, 64456, 65973, 67742, 72288, 79563, 81384, 83206,\n", + " 85080, 87862, 89745, 91200, 105492, 108198, 109125, 110050,\n", + " 112876, 113790, 118366, 120180, 121980, 134698, 135625, 145242,\n", + " 147849, 152437, 153388, 162151, 164868, 168464, 169413, 170363,\n", + " 187159, 190851],\n", + " dtype='int64'), Int64Index([ 4426, 5346, 11019, 13779, 18314, 21064, 21987, 32043,\n", + " 45288, 46209, 47164, 49918, 55476, 56424, 58286, 61084,\n", + " 62002, 64457, 65974, 67743, 72289, 79564, 81385, 83207,\n", + " 85081, 87863, 89746, 91201, 105493, 108199, 109126, 110051,\n", + " 112877, 113791, 118367, 120181, 121981, 134699, 135626, 145243,\n", + " 147850, 152438, 153389, 162152, 164869, 168465, 169414, 170364,\n", + " 187160, 190852],\n", + " dtype='int64'), Int64Index([ 4427, 5347, 11020, 13780, 18315, 21065, 21988, 32044,\n", + " 45289, 46210, 47165, 49919, 55477, 56425, 58287, 61085,\n", + " 62003, 64458, 65975, 67744, 72290, 79565, 81386, 83208,\n", + " 85082, 87864, 89747, 91202, 105494, 108200, 109127, 110052,\n", + " 112878, 113792, 118368, 120182, 121982, 134700, 135627, 145244,\n", + " 147851, 152439, 153390, 162153, 164870, 168466, 169415, 170365,\n", + " 187161, 190853],\n", + " dtype='int64'), Int64Index([ 4428, 5348, 11021, 13781, 18316, 21066, 21989, 32045,\n", + " 45290, 46211, 47166, 49920, 55478, 56426, 58288, 61086,\n", + " 62004, 64459, 65976, 67745, 72291, 79566, 81387, 83209,\n", + " 85083, 87865, 89748, 91203, 105495, 108201, 109128, 110053,\n", + " 112879, 113793, 118369, 120183, 121983, 134701, 135628, 145245,\n", + " 147852, 152440, 153391, 162154, 164871, 168467, 169416, 170366,\n", + " 187162, 190854],\n", + " dtype='int64'), Int64Index([ 4429, 5349, 11022, 13782, 18317, 21067, 21990, 32046,\n", + " 45291, 46212, 47167, 49921, 55479, 56427, 58289, 61087,\n", + " 62005, 64460, 65977, 67746, 72292, 79567, 81388, 83210,\n", + " 85084, 87866, 89749, 91204, 105496, 108202, 109129, 110054,\n", + " 112880, 113794, 118370, 120184, 121984, 134702, 135629, 145246,\n", + " 147853, 152441, 153392, 162155, 164872, 168468, 169417, 170367,\n", + " 187163, 190855],\n", + " dtype='int64'), Int64Index([ 4430, 5350, 11023, 13783, 18318, 21068, 21991, 32047,\n", + " 45292, 46213, 47168, 49922, 55480, 56428, 58290, 61088,\n", + " 62006, 64461, 65978, 67747, 72293, 79568, 81389, 83211,\n", + " 85085, 87867, 89750, 91205, 105497, 108203, 109130, 110055,\n", + " 112881, 113795, 118371, 120185, 121985, 134703, 135630, 145247,\n", + " 147854, 152442, 153393, 162156, 164873, 168469, 169418, 170368,\n", + " 187164, 190856],\n", + " dtype='int64'), Int64Index([ 4431, 5351, 11024, 13784, 18319, 21069, 21992, 32048,\n", + " 45293, 46214, 47169, 49923, 55481, 56429, 58291, 61089,\n", + " 62007, 64462, 65979, 67748, 72294, 79569, 81390, 83212,\n", + " 85086, 87868, 89751, 91206, 105498, 108204, 109131, 110056,\n", + " 112882, 113796, 118372, 120186, 121986, 134704, 135631, 145248,\n", + " 147855, 152443, 153394, 162157, 164874, 168470, 169419, 170369,\n", + " 187165, 190857],\n", + " dtype='int64'), Int64Index([ 4432, 5352, 11025, 13785, 18320, 21070, 21993, 32049,\n", + " 45294, 46215, 47170, 49924, 55482, 56430, 58292, 61090,\n", + " 62008, 64463, 65980, 67749, 72295, 79570, 81391, 83213,\n", + " 85087, 87869, 89752, 91207, 105499, 108205, 109132, 110057,\n", + " 112883, 113797, 118373, 120187, 121987, 134705, 135632, 145249,\n", + " 147856, 152444, 153395, 162158, 164875, 168471, 169420, 170370,\n", + " 187166, 190858],\n", + " dtype='int64'), Int64Index([ 4433, 5353, 11026, 13786, 18321, 21071, 21994, 32050,\n", + " 45295, 46216, 47171, 49925, 55483, 56431, 58293, 61091,\n", + " 62009, 64464, 65981, 67750, 72296, 79571, 81392, 83214,\n", + " 85088, 87870, 89753, 91208, 105500, 108206, 109133, 110058,\n", + " 112884, 113798, 118374, 120188, 121988, 134706, 135633, 145250,\n", + " 147857, 152445, 153396, 162159, 164876, 168472, 169421, 170371,\n", + " 187167, 190859],\n", + " dtype='int64'), Int64Index([ 4434, 5354, 11027, 13787, 18322, 21072, 21995, 32051,\n", + " 45296, 46217, 47172, 49926, 55484, 56432, 58294, 61092,\n", + " 62010, 64465, 65982, 67751, 72297, 79572, 81393, 83215,\n", + " 85089, 87871, 89754, 91209, 105501, 108207, 109134, 110059,\n", + " 112885, 113799, 118375, 120189, 121989, 134707, 135634, 145251,\n", + " 147858, 152446, 153397, 162160, 164877, 168473, 169422, 170372,\n", + " 187168, 190860],\n", + " dtype='int64'), Int64Index([ 4435, 5355, 11028, 13788, 18323, 21073, 21996, 32052,\n", + " 45297, 46218, 47173, 49927, 55485, 56433, 58295, 61093,\n", + " 62011, 64466, 65983, 67752, 72298, 79573, 81394, 83216,\n", + " 85090, 87872, 89755, 91210, 105502, 108208, 109135, 110060,\n", + " 112886, 113800, 118376, 120190, 121990, 134708, 135635, 145252,\n", + " 147859, 152447, 153398, 162161, 164878, 168474, 169423, 170373,\n", + " 187169, 190861],\n", + " dtype='int64'), Int64Index([ 4436, 5356, 11029, 13789, 18324, 21074, 21997, 32053,\n", + " 45298, 46219, 47174, 49928, 55486, 56434, 58296, 61094,\n", + " 62012, 64467, 65984, 67753, 72299, 79574, 81395, 83217,\n", + " 85091, 87873, 89756, 91211, 105503, 108209, 109136, 110061,\n", + " 112887, 113801, 118377, 120191, 121991, 134709, 135636, 145253,\n", + " 147860, 152448, 153399, 162162, 164879, 168475, 169424, 170374,\n", + " 187170, 190862],\n", + " dtype='int64'), Int64Index([ 4437, 5357, 11030, 13790, 18325, 21075, 21998, 32054,\n", + " 45299, 46220, 47175, 49929, 55487, 56435, 58297, 61095,\n", + " 62013, 64468, 65985, 67754, 72300, 79575, 81396, 83218,\n", + " 85092, 87874, 89757, 91212, 105504, 108210, 109137, 110062,\n", + " 112888, 113802, 118378, 120192, 121992, 134710, 135637, 145254,\n", + " 147861, 152449, 153400, 162163, 164880, 168476, 169425, 170375,\n", + " 187171, 190863],\n", + " dtype='int64'), Int64Index([ 4438, 5358, 11031, 13791, 18326, 21076, 21999, 32055,\n", + " 45300, 46221, 47176, 49930, 55488, 56436, 58298, 61096,\n", + " 62014, 64469, 65986, 67755, 72301, 79576, 81397, 83219,\n", + " 85093, 87875, 89758, 91213, 105505, 108211, 109138, 110063,\n", + " 112889, 113803, 118379, 120193, 121993, 134711, 135638, 145255,\n", + " 147862, 152450, 153401, 162164, 164881, 168477, 169426, 170376,\n", + " 187172, 190864],\n", + " dtype='int64'), Int64Index([ 4439, 5359, 11032, 13792, 18327, 21077, 22000, 32056,\n", + " 45301, 46222, 47177, 49931, 55489, 56437, 58299, 61097,\n", + " 62015, 64470, 65987, 67756, 72302, 79577, 81398, 83220,\n", + " 85094, 87876, 89759, 91214, 105506, 108212, 109139, 110064,\n", + " 112890, 113804, 118380, 120194, 121994, 134712, 135639, 145256,\n", + " 147863, 152451, 153402, 162165, 164882, 168478, 169427, 170377,\n", + " 187173, 190865],\n", + " dtype='int64'), Int64Index([ 4440, 5360, 11033, 13793, 18328, 21078, 22001, 32057,\n", + " 45302, 46223, 47178, 49932, 55490, 56438, 58300, 61098,\n", + " 62016, 64471, 65988, 67757, 72303, 79578, 81399, 83221,\n", + " 85095, 87877, 89760, 91215, 105507, 108213, 109140, 110065,\n", + " 112891, 113805, 118381, 120195, 121995, 134713, 135640, 145257,\n", + " 147864, 152452, 153403, 162166, 164883, 168479, 169428, 170378,\n", + " 187174, 190866],\n", + " dtype='int64'), Int64Index([ 4441, 5361, 11034, 13794, 18329, 21079, 22002, 32058,\n", + " 45303, 46224, 47179, 49933, 55491, 56439, 58301, 61099,\n", + " 62017, 64472, 65989, 67758, 72304, 79579, 81400, 83222,\n", + " 85096, 87878, 89761, 91216, 105508, 108214, 109141, 110066,\n", + " 112892, 113806, 118382, 120196, 121996, 134714, 135641, 145258,\n", + " 147865, 152453, 153404, 162167, 164884, 168480, 169429, 170379,\n", + " 187175, 190867],\n", + " dtype='int64'), Int64Index([ 4442, 5362, 11035, 13795, 18330, 21080, 22003, 32059,\n", + " 45304, 46225, 47180, 49934, 55492, 56440, 58302, 61100,\n", + " 62018, 64473, 65990, 67759, 72305, 79580, 81401, 83223,\n", + " 85097, 87879, 89762, 91217, 105509, 108215, 109142, 110067,\n", + " 112893, 113807, 118383, 120197, 121997, 134715, 135642, 145259,\n", + " 147866, 152454, 153405, 162168, 164885, 168481, 169430, 170380,\n", + " 187176, 190868],\n", + " dtype='int64'), Int64Index([ 4443, 5363, 11036, 13796, 18331, 21081, 22004, 32060,\n", + " 45305, 46226, 47181, 49935, 55493, 56441, 58303, 61101,\n", + " 62019, 64474, 65991, 67760, 72306, 79581, 81402, 83224,\n", + " 85098, 87880, 89763, 91218, 105510, 108216, 109143, 110068,\n", + " 112894, 113808, 118384, 120198, 121998, 134716, 135643, 145260,\n", + " 147867, 152455, 153406, 162169, 164886, 168482, 169431, 170381,\n", + " 187177, 190869],\n", + " dtype='int64'), Int64Index([ 4444, 5364, 11037, 13797, 18332, 21082, 22005, 32061,\n", + " 45306, 46227, 47182, 49936, 55494, 56442, 58304, 61102,\n", + " 62020, 64475, 65992, 67761, 72307, 79582, 81403, 83225,\n", + " 85099, 87881, 89764, 91219, 105511, 108217, 109144, 110069,\n", + " 112895, 113809, 118385, 120199, 121999, 134717, 135644, 145261,\n", + " 147868, 152456, 153407, 162170, 164887, 168483, 169432, 170382,\n", + " 187178, 190870],\n", + " dtype='int64'), Int64Index([ 4445, 5365, 11038, 13798, 18333, 21083, 22006, 32062,\n", + " 45307, 46228, 47183, 49937, 55495, 56443, 58305, 61103,\n", + " 62021, 64476, 65993, 67762, 72308, 79583, 81404, 83226,\n", + " 85100, 87882, 89765, 91220, 105512, 108218, 109145, 110070,\n", + " 112896, 113810, 118386, 120200, 122000, 134718, 135645, 145262,\n", + " 147869, 152457, 153408, 162171, 164888, 168484, 169433, 170383,\n", + " 187179, 190871],\n", + " dtype='int64'), Int64Index([ 4446, 5366, 11039, 13799, 18334, 21084, 22007, 32063,\n", + " 45308, 46229, 47184, 49938, 55496, 56444, 58306, 61104,\n", + " 62022, 64477, 65994, 67763, 72309, 79584, 81405, 83227,\n", + " 85101, 87883, 89766, 91221, 105513, 108219, 109146, 110071,\n", + " 112897, 113811, 118387, 120201, 122001, 134719, 135646, 145263,\n", + " 147870, 152458, 153409, 162172, 164889, 168485, 169434, 170384,\n", + " 187180, 190872],\n", + " dtype='int64'), Int64Index([ 4447, 5367, 11040, 13800, 18335, 21085, 22008, 32064,\n", + " 45309, 46230, 47185, 49939, 55497, 56445, 58307, 61105,\n", + " 62023, 64478, 65995, 67764, 72310, 79585, 81406, 83228,\n", + " 85102, 87884, 89767, 91222, 105514, 108220, 109147, 110072,\n", + " 112898, 113812, 118388, 120202, 122002, 134720, 135647, 145264,\n", + " 147871, 152459, 153410, 162173, 164890, 168486, 169435, 170385,\n", + " 187181, 190873],\n", + " dtype='int64'), Int64Index([ 4448, 5368, 11041, 13801, 18336, 21086, 22009, 32065,\n", + " 45310, 46231, 47186, 49940, 55498, 56446, 58308, 61106,\n", + " 62024, 64479, 65996, 67765, 72311, 79586, 81407, 83229,\n", + " 85103, 87885, 89768, 91223, 105515, 108221, 109148, 110073,\n", + " 112899, 113813, 118389, 120203, 122003, 134721, 135648, 145265,\n", + " 147872, 152460, 153411, 162174, 164891, 168487, 169436, 170386,\n", + " 187182, 190874],\n", + " dtype='int64'), Int64Index([ 4449, 5369, 11042, 13802, 18337, 21087, 22010, 32066,\n", + " 45311, 46232, 47187, 49941, 55499, 56447, 58309, 61107,\n", + " 62025, 64480, 65997, 67766, 72312, 79587, 81408, 83230,\n", + " 85104, 87886, 89769, 91224, 105516, 108222, 109149, 110074,\n", + " 112900, 113814, 118390, 120204, 122004, 134722, 135649, 145266,\n", + " 147873, 152461, 153412, 162175, 164892, 168488, 169437, 170387,\n", + " 187183, 190875],\n", + " dtype='int64'), Int64Index([ 4450, 5370, 11043, 13803, 18338, 21088, 22011, 32067,\n", + " 45312, 46233, 47188, 49942, 55500, 56448, 58310, 61108,\n", + " 62026, 64481, 65998, 67767, 72313, 79588, 81409, 83231,\n", + " 85105, 87887, 89770, 91225, 105517, 108223, 109150, 110075,\n", + " 112901, 113815, 118391, 120205, 122005, 134723, 135650, 145267,\n", + " 147874, 152462, 153413, 162176, 164893, 168489, 169438, 170388,\n", + " 187184, 190876],\n", + " dtype='int64'), Int64Index([ 4451, 5371, 11044, 13804, 18339, 21089, 22012, 32068,\n", + " 45313, 46234, 47189, 49943, 55501, 56449, 58311, 61109,\n", + " 62027, 64482, 65999, 67768, 72314, 79589, 81410, 83232,\n", + " 85106, 87888, 89771, 91226, 105518, 108224, 109151, 110076,\n", + " 112902, 113816, 118392, 120206, 122006, 134724, 135651, 145268,\n", + " 147875, 152463, 153414, 162177, 164894, 168490, 169439, 170389,\n", + " 187185, 190877],\n", + " dtype='int64'), Int64Index([ 4452, 5372, 11045, 13805, 18340, 21090, 22013, 32069,\n", + " 45314, 46235, 47190, 49944, 55502, 56450, 58312, 61110,\n", + " 62028, 64483, 66000, 67769, 72315, 79590, 81411, 83233,\n", + " 85107, 87889, 89772, 91227, 105519, 108225, 109152, 110077,\n", + " 112903, 113817, 118393, 120207, 122007, 134725, 135652, 145269,\n", + " 147876, 152464, 153415, 162178, 164895, 168491, 169440, 170390,\n", + " 187186, 190878],\n", + " dtype='int64'), Int64Index([ 4453, 5373, 11046, 13806, 18341, 21091, 22014, 32070,\n", + " 45315, 46236, 47191, 49945, 55503, 56451, 58313, 61111,\n", + " 62029, 64484, 66001, 67770, 72316, 79591, 81412, 83234,\n", + " 85108, 87890, 89773, 91228, 105520, 108226, 109153, 110078,\n", + " 112904, 113818, 118394, 120208, 122008, 134726, 135653, 145270,\n", + " 147877, 152465, 153416, 162179, 164896, 168492, 169441, 170391,\n", + " 187187, 190879],\n", + " dtype='int64'), Int64Index([ 4454, 5374, 11047, 13807, 18342, 21092, 22015, 32071,\n", + " 45316, 46237, 47192, 49946, 55504, 56452, 58314, 61112,\n", + " 62030, 64485, 66002, 67771, 72317, 79592, 81413, 83235,\n", + " 85109, 87891, 89774, 91229, 105521, 108227, 109154, 110079,\n", + " 112905, 113819, 118395, 120209, 122009, 134727, 135654, 145271,\n", + " 147878, 152466, 153417, 162180, 164897, 168493, 169442, 170392,\n", + " 187188, 190880],\n", + " dtype='int64'), Int64Index([ 4455, 5375, 11048, 13808, 18343, 21093, 22016, 32072,\n", + " 45317, 46238, 47193, 49947, 55505, 56453, 58315, 61113,\n", + " 62031, 64486, 66003, 67772, 72318, 79593, 81414, 83236,\n", + " 85110, 87892, 89775, 91230, 105522, 108228, 109155, 110080,\n", + " 112906, 113820, 118396, 120210, 122010, 134728, 135655, 145272,\n", + " 147879, 152467, 153418, 162181, 164898, 168494, 169443, 170393,\n", + " 187189, 190881],\n", + " dtype='int64'), Int64Index([ 4456, 5376, 11049, 13809, 18344, 21094, 22017, 32073,\n", + " 45318, 46239, 47194, 49948, 55506, 56454, 58316, 61114,\n", + " 62032, 64487, 66004, 67773, 72319, 79594, 81415, 83237,\n", + " 85111, 87893, 89776, 91231, 105523, 108229, 109156, 110081,\n", + " 112907, 113821, 118397, 120211, 122011, 134729, 135656, 145273,\n", + " 147880, 152468, 153419, 162182, 164899, 168495, 169444, 170394,\n", + " 187190, 190882],\n", + " dtype='int64'), Int64Index([ 4457, 5377, 11050, 13810, 18345, 21095, 22018, 32074,\n", + " 45319, 46240, 47195, 49949, 55507, 56455, 58317, 61115,\n", + " 62033, 64488, 66005, 67774, 72320, 79595, 81416, 83238,\n", + " 85112, 87894, 89777, 91232, 105524, 108230, 109157, 110082,\n", + " 112908, 113822, 118398, 120212, 122012, 134730, 135657, 145274,\n", + " 147881, 152469, 153420, 162183, 164900, 168496, 169445, 170395,\n", + " 187191, 190883],\n", + " dtype='int64'), Int64Index([ 4458, 5378, 11051, 13811, 18346, 21096, 22019, 32075,\n", + " 45320, 46241, 47196, 49950, 55508, 56456, 58318, 61116,\n", + " 62034, 64489, 66006, 67775, 72321, 79596, 81417, 83239,\n", + " 85113, 87895, 89778, 91233, 105525, 108231, 109158, 110083,\n", + " 112909, 113823, 118399, 120213, 122013, 134731, 135658, 145275,\n", + " 147882, 152470, 153421, 162184, 164901, 168497, 169446, 170396,\n", + " 187192, 190884],\n", + " dtype='int64'), Int64Index([ 4459, 5379, 11052, 13812, 18347, 21097, 22020, 32076,\n", + " 45321, 46242, 47197, 49951, 55509, 56457, 58319, 61117,\n", + " 62035, 64490, 66007, 67776, 72322, 79597, 81418, 83240,\n", + " 85114, 87896, 89779, 91234, 105526, 108232, 109159, 110084,\n", + " 112910, 113824, 118400, 120214, 122014, 134732, 135659, 145276,\n", + " 147883, 152471, 153422, 162185, 164902, 168498, 169447, 170397,\n", + " 187193, 190885],\n", + " dtype='int64'), Int64Index([ 4460, 5380, 11053, 13813, 18348, 21098, 22021, 32077,\n", + " 45322, 46243, 47198, 49952, 55510, 56458, 58320, 61118,\n", + " 62036, 64491, 66008, 67777, 72323, 79598, 81419, 83241,\n", + " 85115, 87897, 89780, 91235, 105527, 108233, 109160, 110085,\n", + " 112911, 113825, 118401, 120215, 122015, 134733, 135660, 145277,\n", + " 147884, 152472, 153423, 162186, 164903, 168499, 169448, 170398,\n", + " 187194, 190886],\n", + " dtype='int64'), Int64Index([ 4461, 5381, 11054, 13814, 18349, 21099, 22022, 32078,\n", + " 45323, 46244, 47199, 49953, 55511, 56459, 58321, 61119,\n", + " 62037, 64492, 66009, 67778, 72324, 79599, 81420, 83242,\n", + " 85116, 87898, 89781, 91236, 105528, 108234, 109161, 110086,\n", + " 112912, 113826, 118402, 120216, 122016, 134734, 135661, 145278,\n", + " 147885, 152473, 153424, 162187, 164904, 168500, 169449, 170399,\n", + " 187195, 190887],\n", + " dtype='int64'), Int64Index([ 4462, 5382, 11055, 13815, 18350, 21100, 22023, 32079,\n", + " 45324, 46245, 47200, 49954, 55512, 56460, 58322, 61120,\n", + " 62038, 64493, 66010, 67779, 72325, 79600, 81421, 83243,\n", + " 85117, 87899, 89782, 91237, 105529, 108235, 109162, 110087,\n", + " 112913, 113827, 118403, 120217, 122017, 134735, 135662, 145279,\n", + " 147886, 152474, 153425, 162188, 164905, 168501, 169450, 170400,\n", + " 187196, 190888],\n", + " dtype='int64'), Int64Index([ 4463, 5383, 11056, 13816, 18351, 21101, 22024, 32080,\n", + " 45325, 46246, 47201, 49955, 55513, 56461, 58323, 61121,\n", + " 62039, 64494, 66011, 67780, 72326, 79601, 81422, 83244,\n", + " 85118, 87900, 89783, 91238, 105530, 108236, 109163, 110088,\n", + " 112914, 113828, 118404, 120218, 122018, 134736, 135663, 145280,\n", + " 147887, 152475, 153426, 162189, 164906, 168502, 169451, 170401,\n", + " 187197, 190889],\n", + " dtype='int64'), Int64Index([ 4464, 5384, 11057, 13817, 18352, 21102, 22025, 32081,\n", + " 45326, 46247, 47202, 49956, 55514, 56462, 58324, 61122,\n", + " 62040, 64495, 66012, 67781, 72327, 79602, 81423, 83245,\n", + " 85119, 87901, 89784, 91239, 105531, 108237, 109164, 110089,\n", + " 112915, 113829, 118405, 120219, 122019, 134737, 135664, 145281,\n", + " 147888, 152476, 153427, 162190, 164907, 168503, 169452, 170402,\n", + " 187198, 190890],\n", + " dtype='int64'), Int64Index([ 4465, 5385, 11058, 13818, 18353, 21103, 22026, 32082,\n", + " 45327, 46248, 47203, 49957, 55515, 56463, 58325, 61123,\n", + " 62041, 64496, 66013, 67782, 72328, 79603, 81424, 83246,\n", + " 85120, 87902, 89785, 91240, 105532, 108238, 109165, 110090,\n", + " 112916, 113830, 118406, 120220, 122020, 134738, 135665, 145282,\n", + " 147889, 152477, 153428, 162191, 164908, 168504, 169453, 170403,\n", + " 187199, 190891],\n", + " dtype='int64'), Int64Index([ 4466, 5386, 11059, 13819, 18354, 21104, 22027, 32083,\n", + " 45328, 46249, 47204, 49958, 55516, 56464, 58326, 61124,\n", + " 62042, 64497, 66014, 67783, 72329, 79604, 81425, 83247,\n", + " 85121, 87903, 89786, 91241, 105533, 108239, 109166, 110091,\n", + " 112917, 113831, 118407, 120221, 122021, 134739, 135666, 145283,\n", + " 147890, 152478, 153429, 162192, 164909, 168505, 169454, 170404,\n", + " 187200, 190892],\n", + " dtype='int64'), Int64Index([ 4467, 5387, 11060, 13820, 18355, 21105, 22028, 32084,\n", + " 45329, 46250, 47205, 49959, 55517, 56465, 58327, 61125,\n", + " 62043, 64498, 66015, 67784, 72330, 79605, 81426, 83248,\n", + " 85122, 87904, 89787, 91242, 105534, 108240, 109167, 110092,\n", + " 112918, 113832, 118408, 120222, 122022, 134740, 135667, 145284,\n", + " 147891, 152479, 153430, 162193, 164910, 168506, 169455, 170405,\n", + " 187201, 190893],\n", + " dtype='int64'), Int64Index([ 4468, 5388, 11061, 13821, 18356, 21106, 22029, 32085,\n", + " 45330, 46251, 47206, 49960, 55518, 56466, 58328, 61126,\n", + " 62044, 64499, 66016, 67785, 72331, 79606, 81427, 83249,\n", + " 85123, 87905, 89788, 91243, 105535, 108241, 109168, 110093,\n", + " 112919, 113833, 118409, 120223, 122023, 134741, 135668, 145285,\n", + " 147892, 152480, 153431, 162194, 164911, 168507, 169456, 170406,\n", + " 187202, 190894],\n", + " dtype='int64'), Int64Index([ 4469, 5389, 11062, 13822, 18357, 21107, 22030, 32086,\n", + " 45331, 46252, 47207, 49961, 55519, 56467, 58329, 61127,\n", + " 62045, 64500, 66017, 67786, 72332, 79607, 81428, 83250,\n", + " 85124, 87906, 89789, 91244, 105536, 108242, 109169, 110094,\n", + " 112920, 113834, 118410, 120224, 122024, 134742, 135669, 145286,\n", + " 147893, 152481, 153432, 162195, 164912, 168508, 169457, 170407,\n", + " 187203, 190895],\n", + " dtype='int64'), Int64Index([ 4470, 5390, 11063, 13823, 18358, 21108, 22031, 32087,\n", + " 45332, 46253, 47208, 49962, 55520, 56468, 58330, 61128,\n", + " 62046, 64501, 66018, 67787, 72333, 79608, 81429, 83251,\n", + " 85125, 87907, 89790, 91245, 105537, 108243, 109170, 110095,\n", + " 112921, 113835, 118411, 120225, 122025, 134743, 135670, 145287,\n", + " 147894, 152482, 153433, 162196, 164913, 168509, 169458, 170408,\n", + " 187204, 190896],\n", + " dtype='int64'), Int64Index([ 4471, 5391, 11064, 13824, 18359, 21109, 22032, 32088,\n", + " 45333, 46254, 47209, 49963, 55521, 56469, 58331, 61129,\n", + " 62047, 64502, 66019, 67788, 72334, 79609, 81430, 83252,\n", + " 85126, 87908, 89791, 91246, 105538, 108244, 109171, 110096,\n", + " 112922, 113836, 118412, 120226, 122026, 134744, 135671, 145288,\n", + " 147895, 152483, 153434, 162197, 164914, 168510, 169459, 170409,\n", + " 187205, 190897],\n", + " dtype='int64'), Int64Index([ 4472, 5392, 11065, 13825, 18360, 21110, 22033, 32089,\n", + " 45334, 46255, 47210, 49964, 55522, 56470, 58332, 61130,\n", + " 62048, 64503, 66020, 67789, 72335, 79610, 81431, 83253,\n", + " 85127, 87909, 89792, 91247, 105539, 108245, 109172, 110097,\n", + " 112923, 113837, 118413, 120227, 122027, 134745, 135672, 145289,\n", + " 147896, 152484, 153435, 162198, 164915, 168511, 169460, 170410,\n", + " 187206, 190898],\n", + " dtype='int64'), Int64Index([ 4473, 5393, 11066, 13826, 18361, 21111, 22034, 32090,\n", + " 45335, 46256, 47211, 49965, 55523, 56471, 58333, 61131,\n", + " 62049, 64504, 66021, 67790, 72336, 79611, 81432, 83254,\n", + " 85128, 87910, 89793, 91248, 105540, 108246, 109173, 110098,\n", + " 112924, 113838, 118414, 120228, 122028, 134746, 135673, 145290,\n", + " 147897, 152485, 153436, 162199, 164916, 168512, 169461, 170411,\n", + " 187207, 190899],\n", + " dtype='int64'), Int64Index([ 4474, 5394, 11067, 13827, 18362, 21112, 22035, 32091,\n", + " 45336, 46257, 47212, 49966, 55524, 56472, 58334, 61132,\n", + " 62050, 64505, 66022, 67791, 72337, 79612, 81433, 83255,\n", + " 85129, 87911, 89794, 91249, 105541, 108247, 109174, 110099,\n", + " 112925, 113839, 118415, 120229, 122029, 134747, 135674, 145291,\n", + " 147898, 152486, 153437, 162200, 164917, 168513, 169462, 170412,\n", + " 187208, 190900],\n", + " dtype='int64'), Int64Index([ 4475, 5395, 11068, 13828, 18363, 21113, 22036, 32092,\n", + " 45337, 46258, 47213, 49967, 55525, 56473, 58335, 61133,\n", + " 62051, 64506, 66023, 67792, 72338, 79613, 81434, 83256,\n", + " 85130, 87912, 89795, 91250, 105542, 108248, 109175, 110100,\n", + " 112926, 113840, 118416, 120230, 122030, 134748, 135675, 145292,\n", + " 147899, 152487, 153438, 162201, 164918, 168514, 169463, 170413,\n", + " 187209, 190901],\n", + " dtype='int64'), Int64Index([ 4476, 5396, 11069, 13829, 18364, 21114, 22037, 32093,\n", + " 45338, 46259, 47214, 49968, 55526, 56474, 58336, 61134,\n", + " 62052, 64507, 66024, 67793, 72339, 79614, 81435, 83257,\n", + " 85131, 87913, 89796, 91251, 105543, 108249, 109176, 110101,\n", + " 112927, 113841, 118417, 120231, 122031, 134749, 135676, 145293,\n", + " 147900, 152488, 153439, 162202, 164919, 168515, 169464, 170414,\n", + " 187210, 190902],\n", + " dtype='int64'), Int64Index([ 4477, 5397, 11070, 13830, 18365, 21115, 22038, 32094,\n", + " 45339, 46260, 47215, 49969, 55527, 56475, 58337, 61135,\n", + " 62053, 64508, 66025, 67794, 72340, 79615, 81436, 83258,\n", + " 85132, 87914, 89797, 91252, 105544, 108250, 109177, 110102,\n", + " 112928, 113842, 118418, 120232, 122032, 134750, 135677, 145294,\n", + " 147901, 152489, 153440, 162203, 164920, 168516, 169465, 170415,\n", + " 187211, 190903],\n", + " dtype='int64'), Int64Index([ 4478, 5398, 11071, 13831, 18366, 21116, 22039, 32095,\n", + " 45340, 46261, 47216, 49970, 55528, 56476, 58338, 61136,\n", + " 62054, 64509, 66026, 67795, 72341, 79616, 81437, 83259,\n", + " 85133, 87915, 89798, 91253, 105545, 108251, 109178, 110103,\n", + " 112929, 113843, 118419, 120233, 122033, 134751, 135678, 145295,\n", + " 147902, 152490, 153441, 162204, 164921, 168517, 169466, 170416,\n", + " 187212, 190904],\n", + " dtype='int64'), Int64Index([ 4479, 5399, 11072, 13832, 18367, 21117, 22040, 32096,\n", + " 45341, 46262, 47217, 49971, 55529, 56477, 58339, 61137,\n", + " 62055, 64510, 66027, 67796, 72342, 79617, 81438, 83260,\n", + " 85134, 87916, 89799, 91254, 105546, 108252, 109179, 110104,\n", + " 112930, 113844, 118420, 120234, 122034, 134752, 135679, 145296,\n", + " 147903, 152491, 153442, 162205, 164922, 168518, 169467, 170417,\n", + " 187213, 190905],\n", + " dtype='int64'), Int64Index([ 4480, 5400, 11073, 13833, 18368, 21118, 22041, 32097,\n", + " 45342, 46263, 47218, 49972, 55530, 56478, 58340, 61138,\n", + " 62056, 64511, 66028, 67797, 72343, 79618, 81439, 83261,\n", + " 85135, 87917, 89800, 91255, 105547, 108253, 109180, 110105,\n", + " 112931, 113845, 118421, 120235, 122035, 134753, 135680, 145297,\n", + " 147904, 152492, 153443, 162206, 164923, 168519, 169468, 170418,\n", + " 187214, 190906],\n", + " dtype='int64'), Int64Index([ 4481, 5401, 11074, 13834, 18369, 21119, 22042, 32098,\n", + " 45343, 46264, 47219, 49973, 55531, 56479, 58341, 61139,\n", + " 62057, 64512, 66029, 67798, 72344, 79619, 81440, 83262,\n", + " 85136, 87918, 89801, 91256, 105548, 108254, 109181, 110106,\n", + " 112932, 113846, 118422, 120236, 122036, 134754, 135681, 145298,\n", + " 147905, 152493, 153444, 162207, 164924, 168520, 169469, 170419,\n", + " 187215, 190907],\n", + " dtype='int64'), Int64Index([ 4482, 5402, 11075, 13835, 18370, 21120, 22043, 32099,\n", + " 45344, 46265, 47220, 49974, 55532, 56480, 58342, 61140,\n", + " 62058, 64513, 66030, 67799, 72345, 79620, 81441, 83263,\n", + " 85137, 87919, 89802, 91257, 105549, 108255, 109182, 110107,\n", + " 112933, 113847, 118423, 120237, 122037, 134755, 135682, 145299,\n", + " 147906, 152494, 153445, 162208, 164925, 168521, 169470, 170420,\n", + " 187216, 190908],\n", + " dtype='int64'), Int64Index([ 4483, 5403, 11076, 13836, 18371, 21121, 22044, 32100,\n", + " 45345, 46266, 47221, 49975, 55533, 56481, 58343, 61141,\n", + " 62059, 64514, 66031, 67800, 72346, 79621, 81442, 83264,\n", + " 85138, 87920, 89803, 91258, 105550, 108256, 109183, 110108,\n", + " 112934, 113848, 118424, 120238, 122038, 134756, 135683, 145300,\n", + " 147907, 152495, 153446, 162209, 164926, 168522, 169471, 170421,\n", + " 187217, 190909],\n", + " dtype='int64'), Int64Index([ 4484, 5404, 11077, 13837, 18372, 21122, 22045, 32101,\n", + " 45346, 46267, 47222, 49976, 55534, 56482, 58344, 61142,\n", + " 62060, 64515, 66032, 67801, 72347, 79622, 81443, 83265,\n", + " 85139, 87921, 89804, 91259, 105551, 108257, 109184, 110109,\n", + " 112935, 113849, 118425, 120239, 122039, 134757, 135684, 145301,\n", + " 147908, 152496, 153447, 162210, 164927, 168523, 169472, 170422,\n", + " 187218, 190910],\n", + " dtype='int64'), Int64Index([ 4485, 5405, 11078, 13838, 18373, 21123, 22046, 32102,\n", + " 45347, 46268, 47223, 49977, 55535, 56483, 58345, 61143,\n", + " 62061, 64516, 66033, 67802, 72348, 79623, 81444, 83266,\n", + " 85140, 87922, 89805, 91260, 105552, 108258, 109185, 110110,\n", + " 112936, 113850, 118426, 120240, 122040, 134758, 135685, 145302,\n", + " 147909, 152497, 153448, 162211, 164928, 168524, 169473, 170423,\n", + " 187219, 190911],\n", + " dtype='int64'), Int64Index([ 4486, 5406, 11079, 13839, 18374, 21124, 22047, 32103,\n", + " 45348, 46269, 47224, 49978, 55536, 56484, 58346, 61144,\n", + " 62062, 64517, 66034, 67803, 72349, 79624, 81445, 83267,\n", + " 85141, 87923, 89806, 91261, 105553, 108259, 109186, 110111,\n", + " 112937, 113851, 118427, 120241, 122041, 134759, 135686, 145303,\n", + " 147910, 152498, 153449, 162212, 164929, 168525, 169474, 170424,\n", + " 187220, 190912],\n", + " dtype='int64'), Int64Index([ 4487, 5407, 11080, 13840, 18375, 21125, 22048, 32104,\n", + " 45349, 46270, 47225, 49979, 55537, 56485, 58347, 61145,\n", + " 62063, 64518, 66035, 67804, 72350, 79625, 81446, 83268,\n", + " 85142, 87924, 89807, 91262, 105554, 108260, 109187, 110112,\n", + " 112938, 113852, 118428, 120242, 122042, 134760, 135687, 145304,\n", + " 147911, 152499, 153450, 162213, 164930, 168526, 169475, 170425,\n", + " 187221, 190913],\n", + " dtype='int64'), Int64Index([ 4488, 5408, 11081, 13841, 18376, 21126, 22049, 32105,\n", + " 45350, 46271, 47226, 49980, 55538, 56486, 58348, 61146,\n", + " 62064, 64519, 66036, 67805, 72351, 79626, 81447, 83269,\n", + " 85143, 87925, 89808, 91263, 105555, 108261, 109188, 110113,\n", + " 112939, 113853, 118429, 120243, 122043, 134761, 135688, 145305,\n", + " 147912, 152500, 153451, 162214, 164931, 168527, 169476, 170426,\n", + " 187222, 190914],\n", + " dtype='int64'), Int64Index([ 4489, 5409, 11082, 13842, 18377, 21127, 22050, 32106,\n", + " 45351, 46272, 47227, 49981, 55539, 56487, 58349, 61147,\n", + " 62065, 64520, 66037, 67806, 72352, 79627, 81448, 83270,\n", + " 85144, 87926, 89809, 91264, 105556, 108262, 109189, 110114,\n", + " 112940, 113854, 118430, 120244, 122044, 134762, 135689, 145306,\n", + " 147913, 152501, 153452, 162215, 164932, 168528, 169477, 170427,\n", + " 187223, 190915],\n", + " dtype='int64'), Int64Index([ 4490, 5410, 11083, 13843, 18378, 21128, 22051, 32107,\n", + " 45352, 46273, 47228, 49982, 55540, 56488, 58350, 61148,\n", + " 62066, 64521, 66038, 67807, 72353, 79628, 81449, 83271,\n", + " 85145, 87927, 89810, 91265, 105557, 108263, 109190, 110115,\n", + " 112941, 113855, 118431, 120245, 122045, 134763, 135690, 145307,\n", + " 147914, 152502, 153453, 162216, 164933, 168529, 169478, 170428,\n", + " 187224, 190916],\n", + " dtype='int64'), Int64Index([ 4491, 5411, 11084, 13844, 18379, 21129, 22052, 32108,\n", + " 45353, 46274, 47229, 49983, 55541, 56489, 58351, 61149,\n", + " 62067, 64522, 66039, 67808, 72354, 79629, 81450, 83272,\n", + " 85146, 87928, 89811, 91266, 105558, 108264, 109191, 110116,\n", + " 112942, 113856, 118432, 120246, 122046, 134764, 135691, 145308,\n", + " 147915, 152503, 153454, 162217, 164934, 168530, 169479, 170429,\n", + " 187225, 190917],\n", + " dtype='int64'), Int64Index([ 4492, 5412, 11085, 13845, 18380, 21130, 22053, 32109,\n", + " 45354, 46275, 47230, 49984, 55542, 56490, 58352, 61150,\n", + " 62068, 64523, 66040, 67809, 72355, 79630, 81451, 83273,\n", + " 85147, 87929, 89812, 91267, 105559, 108265, 109192, 110117,\n", + " 112943, 113857, 118433, 120247, 122047, 134765, 135692, 145309,\n", + " 147916, 152504, 153455, 162218, 164935, 168531, 169480, 170430,\n", + " 187226, 190918],\n", + " dtype='int64'), Int64Index([ 4493, 5413, 11086, 13846, 18381, 21131, 22054, 32110,\n", + " 45355, 46276, 47231, 49985, 55543, 56491, 58353, 61151,\n", + " 62069, 64524, 66041, 67810, 72356, 79631, 81452, 83274,\n", + " 85148, 87930, 89813, 91268, 105560, 108266, 109193, 110118,\n", + " 112944, 113858, 118434, 120248, 122048, 134766, 135693, 145310,\n", + " 147917, 152505, 153456, 162219, 164936, 168532, 169481, 170431,\n", + " 187227, 190919],\n", + " dtype='int64'), Int64Index([ 4494, 5414, 11087, 13847, 18382, 21132, 22055, 32111,\n", + " 45356, 46277, 47232, 49986, 55544, 56492, 58354, 61152,\n", + " 62070, 64525, 66042, 67811, 72357, 79632, 81453, 83275,\n", + " 85149, 87931, 89814, 91269, 105561, 108267, 109194, 110119,\n", + " 112945, 113859, 118435, 120249, 122049, 134767, 135694, 145311,\n", + " 147918, 152506, 153457, 162220, 164937, 168533, 169482, 170432,\n", + " 187228, 190920],\n", + " dtype='int64'), Int64Index([ 4495, 5415, 11088, 13848, 18383, 21133, 22056, 32112,\n", + " 45357, 46278, 47233, 49987, 55545, 56493, 58355, 61153,\n", + " 62071, 64526, 66043, 67812, 72358, 79633, 81454, 83276,\n", + " 85150, 87932, 89815, 91270, 105562, 108268, 109195, 110120,\n", + " 112946, 113860, 118436, 120250, 122050, 134768, 135695, 145312,\n", + " 147919, 152507, 153458, 162221, 164938, 168534, 169483, 170433,\n", + " 187229, 190921],\n", + " dtype='int64'), Int64Index([ 4496, 5416, 11089, 13849, 18384, 21134, 22057, 32113,\n", + " 45358, 46279, 47234, 49988, 55546, 56494, 58356, 61154,\n", + " 62072, 64527, 66044, 67813, 72359, 79634, 81455, 83277,\n", + " 85151, 87933, 89816, 91271, 105563, 108269, 109196, 110121,\n", + " 112947, 113861, 118437, 120251, 122051, 134769, 135696, 145313,\n", + " 147920, 152508, 153459, 162222, 164939, 168535, 169484, 170434,\n", + " 187230, 190922],\n", + " dtype='int64'), Int64Index([ 4497, 5417, 11090, 13850, 18385, 21135, 22058, 32114,\n", + " 45359, 46280, 47235, 49989, 55547, 56495, 58357, 61155,\n", + " 62073, 64528, 66045, 67814, 72360, 79635, 81456, 83278,\n", + " 85152, 87934, 89817, 91272, 105564, 108270, 109197, 110122,\n", + " 112948, 113862, 118438, 120252, 122052, 134770, 135697, 145314,\n", + " 147921, 152509, 153460, 162223, 164940, 168536, 169485, 170435,\n", + " 187231, 190923],\n", + " dtype='int64'), Int64Index([ 4498, 5418, 11091, 13851, 18386, 21136, 22059, 32115,\n", + " 45360, 46281, 47236, 49990, 55548, 56496, 58358, 61156,\n", + " 62074, 64529, 66046, 67815, 72361, 79636, 81457, 83279,\n", + " 85153, 87935, 89818, 91273, 105565, 108271, 109198, 110123,\n", + " 112949, 113863, 118439, 120253, 122053, 134771, 135698, 145315,\n", + " 147922, 152510, 153461, 162224, 164941, 168537, 169486, 170436,\n", + " 187232, 190924],\n", + " dtype='int64'), Int64Index([ 4499, 5419, 11092, 13852, 18387, 21137, 22060, 32116,\n", + " 45361, 46282, 47237, 49991, 55549, 56497, 58359, 61157,\n", + " 62075, 64530, 66047, 67816, 72362, 79637, 81458, 83280,\n", + " 85154, 87936, 89819, 91274, 105566, 108272, 109199, 110124,\n", + " 112950, 113864, 118440, 120254, 122054, 134772, 135699, 145316,\n", + " 147923, 152511, 153462, 162225, 164942, 168538, 169487, 170437,\n", + " 187233, 190925],\n", + " dtype='int64'), Int64Index([ 4500, 5420, 11093, 13853, 18388, 21138, 22061, 32117,\n", + " 45362, 46283, 47238, 49992, 55550, 56498, 58360, 61158,\n", + " 62076, 64531, 66048, 67817, 72363, 79638, 81459, 83281,\n", + " 85155, 87937, 89820, 91275, 105567, 108273, 109200, 110125,\n", + " 112951, 113865, 118441, 120255, 122055, 134773, 135700, 145317,\n", + " 147924, 152512, 153463, 162226, 164943, 168539, 169488, 170438,\n", + " 187234, 190926],\n", + " dtype='int64'), Int64Index([ 4501, 5421, 11094, 13854, 18389, 21139, 22062, 32118,\n", + " 45363, 46284, 47239, 49993, 55551, 56499, 58361, 61159,\n", + " 62077, 64532, 67818, 72364, 79639, 81460, 83282, 85156,\n", + " 87938, 89821, 91276, 105568, 108274, 109201, 110126, 112952,\n", + " 113866, 118442, 120256, 122056, 134774, 135701, 145318, 147925,\n", + " 152513, 153464, 162227, 164944, 168540, 169489, 170439, 187235,\n", + " 190927],\n", + " dtype='int64'), Int64Index([ 4502, 5422, 11095, 13855, 18390, 21140, 22063, 32119,\n", + " 45364, 46285, 47240, 49994, 55552, 56500, 58362, 61160,\n", + " 62078, 64533, 67819, 72365, 79640, 81461, 83283, 85157,\n", + " 87939, 89822, 91277, 105569, 108275, 109202, 110127, 112953,\n", + " 113867, 118443, 120257, 122057, 134775, 135702, 145319, 147926,\n", + " 152514, 153465, 162228, 164945, 168541, 169490, 170440, 187236,\n", + " 190928],\n", + " dtype='int64'), Int64Index([ 4503, 5423, 11096, 13856, 18391, 21141, 22064, 32120,\n", + " 45365, 46286, 47241, 49995, 55553, 56501, 58363, 61161,\n", + " 62079, 64534, 67820, 72366, 79641, 81462, 83284, 85158,\n", + " 87940, 89823, 91278, 105570, 108276, 109203, 110128, 112954,\n", + " 113868, 118444, 120258, 122058, 134776, 135703, 145320, 147927,\n", + " 152515, 153466, 162229, 164946, 168542, 169491, 170441, 187237,\n", + " 190929],\n", + " dtype='int64'), Int64Index([ 4504, 5424, 11097, 13857, 18392, 21142, 22065, 32121,\n", + " 45366, 46287, 47242, 49996, 55554, 56502, 58364, 61162,\n", + " 62080, 64535, 67821, 72367, 79642, 81463, 83285, 85159,\n", + " 87941, 89824, 91279, 105571, 108277, 109204, 110129, 112955,\n", + " 113869, 118445, 120259, 122059, 134777, 135704, 145321, 147928,\n", + " 152516, 153467, 162230, 164947, 168543, 169492, 170442, 187238,\n", + " 190930],\n", + " dtype='int64'), Int64Index([ 4505, 5425, 11098, 13858, 18393, 21143, 22066, 32122,\n", + " 45367, 46288, 47243, 49997, 55555, 56503, 58365, 61163,\n", + " 62081, 64536, 67822, 72368, 79643, 81464, 83286, 85160,\n", + " 87942, 89825, 91280, 105572, 108278, 109205, 110130, 112956,\n", + " 113870, 118446, 120260, 122060, 134778, 135705, 145322, 147929,\n", + " 152517, 153468, 162231, 164948, 168544, 169493, 170443, 187239,\n", + " 190931],\n", + " dtype='int64'), Int64Index([ 4506, 5426, 11099, 13859, 18394, 21144, 22067, 32123,\n", + " 45368, 46289, 47244, 49998, 55556, 56504, 58366, 61164,\n", + " 62082, 64537, 67823, 72369, 79644, 81465, 83287, 85161,\n", + " 87943, 89826, 91281, 105573, 108279, 109206, 110131, 112957,\n", + " 113871, 118447, 120261, 122061, 134779, 135706, 145323, 147930,\n", + " 152518, 153469, 162232, 164949, 168545, 169494, 170444, 187240,\n", + " 190932],\n", + " dtype='int64'), Int64Index([ 4507, 5427, 11100, 13860, 18395, 21145, 22068, 32124,\n", + " 45369, 46290, 47245, 49999, 55557, 56505, 58367, 61165,\n", + " 62083, 64538, 67824, 72370, 79645, 81466, 83288, 85162,\n", + " 87944, 89827, 91282, 105574, 108280, 109207, 110132, 112958,\n", + " 113872, 118448, 120262, 122062, 134780, 135707, 145324, 147931,\n", + " 152519, 153470, 162233, 164950, 168546, 169495, 170445, 187241,\n", + " 190933],\n", + " dtype='int64'), Int64Index([ 4508, 5428, 11101, 13861, 18396, 21146, 22069, 32125,\n", + " 45370, 46291, 47246, 50000, 55558, 56506, 58368, 61166,\n", + " 62084, 64539, 67825, 72371, 79646, 81467, 83289, 85163,\n", + " 87945, 89828, 91283, 105575, 108281, 109208, 110133, 112959,\n", + " 113873, 118449, 120263, 122063, 134781, 135708, 145325, 147932,\n", + " 152520, 153471, 162234, 164951, 168547, 169496, 170446, 187242,\n", + " 190934],\n", + " dtype='int64'), Int64Index([ 4509, 5429, 11102, 13862, 18397, 21147, 22070, 32126,\n", + " 45371, 46292, 47247, 50001, 55559, 56507, 58369, 61167,\n", + " 62085, 64540, 67826, 72372, 79647, 81468, 83290, 85164,\n", + " 87946, 89829, 91284, 105576, 108282, 109209, 110134, 112960,\n", + " 113874, 118450, 120264, 122064, 134782, 135709, 145326, 147933,\n", + " 152521, 153472, 162235, 164952, 168548, 169497, 170447, 187243,\n", + " 190935],\n", + " dtype='int64'), Int64Index([ 4510, 5430, 11103, 13863, 18398, 21148, 22071, 32127,\n", + " 45372, 46293, 47248, 50002, 55560, 56508, 58370, 61168,\n", + " 62086, 64541, 67827, 72373, 79648, 81469, 83291, 85165,\n", + " 87947, 89830, 91285, 105577, 108283, 109210, 110135, 112961,\n", + " 113875, 118451, 120265, 122065, 134783, 135710, 145327, 147934,\n", + " 152522, 153473, 162236, 164953, 168549, 169498, 170448, 187244,\n", + " 190936],\n", + " dtype='int64'), Int64Index([ 4511, 5431, 11104, 13864, 18399, 21149, 22072, 32128,\n", + " 45373, 46294, 47249, 50003, 55561, 56509, 58371, 61169,\n", + " 62087, 64542, 67828, 72374, 79649, 81470, 83292, 85166,\n", + " 87948, 89831, 91286, 105578, 108284, 109211, 110136, 112962,\n", + " 113876, 118452, 120266, 122066, 134784, 135711, 145328, 147935,\n", + " 152523, 153474, 162237, 164954, 168550, 169499, 170449, 187245,\n", + " 190937],\n", + " dtype='int64'), Int64Index([ 4512, 5432, 11105, 13865, 18400, 21150, 22073, 32129,\n", + " 45374, 46295, 47250, 50004, 55562, 56510, 58372, 61170,\n", + " 62088, 64543, 67829, 72375, 79650, 81471, 83293, 85167,\n", + " 87949, 89832, 91287, 105579, 108285, 109212, 110137, 112963,\n", + " 113877, 118453, 120267, 122067, 134785, 135712, 145329, 147936,\n", + " 152524, 153475, 162238, 164955, 168551, 169500, 170450, 187246,\n", + " 190938],\n", + " dtype='int64'), Int64Index([ 4513, 5433, 11106, 13866, 18401, 21151, 22074, 32130,\n", + " 45375, 46296, 47251, 50005, 55563, 56511, 58373, 61171,\n", + " 62089, 64544, 67830, 72376, 79651, 81472, 83294, 85168,\n", + " 87950, 89833, 91288, 105580, 108286, 109213, 110138, 112964,\n", + " 113878, 118454, 120268, 122068, 134786, 135713, 145330, 147937,\n", + " 152525, 153476, 162239, 164956, 168552, 169501, 170451, 187247,\n", + " 190939],\n", + " dtype='int64'), Int64Index([ 4514, 5434, 11107, 13867, 18402, 21152, 22075, 32131,\n", + " 45376, 46297, 47252, 50006, 55564, 56512, 58374, 61172,\n", + " 62090, 64545, 67831, 72377, 79652, 81473, 83295, 85169,\n", + " 87951, 89834, 91289, 105581, 108287, 109214, 110139, 112965,\n", + " 113879, 118455, 120269, 122069, 134787, 135714, 145331, 147938,\n", + " 152526, 153477, 162240, 164957, 168553, 169502, 170452, 187248,\n", + " 190940],\n", + " dtype='int64'), Int64Index([ 4515, 5435, 11108, 13868, 18403, 21153, 22076, 32132,\n", + " 45377, 46298, 47253, 50007, 55565, 56513, 58375, 61173,\n", + " 62091, 64546, 67832, 72378, 79653, 81474, 83296, 85170,\n", + " 87952, 89835, 91290, 105582, 108288, 109215, 110140, 112966,\n", + " 113880, 118456, 120270, 122070, 134788, 135715, 145332, 147939,\n", + " 152527, 153478, 162241, 164958, 168554, 169503, 170453, 187249,\n", + " 190941],\n", + " dtype='int64'), Int64Index([ 4516, 5436, 11109, 13869, 18404, 21154, 22077, 32133,\n", + " 45378, 46299, 47254, 50008, 55566, 56514, 58376, 61174,\n", + " 62092, 64547, 67833, 72379, 79654, 81475, 83297, 85171,\n", + " 87953, 89836, 91291, 105583, 108289, 109216, 110141, 112967,\n", + " 113881, 118457, 120271, 122071, 134789, 135716, 145333, 147940,\n", + " 152528, 153479, 162242, 164959, 168555, 169504, 170454, 187250,\n", + " 190942],\n", + " dtype='int64'), Int64Index([ 4517, 5437, 11110, 13870, 18405, 21155, 22078, 32134,\n", + " 45379, 46300, 47255, 50009, 55567, 56515, 58377, 61175,\n", + " 62093, 64548, 67834, 72380, 79655, 81476, 83298, 85172,\n", + " 87954, 89837, 91292, 105584, 108290, 109217, 110142, 112968,\n", + " 113882, 118458, 120272, 122072, 134790, 135717, 145334, 147941,\n", + " 152529, 153480, 162243, 164960, 168556, 169505, 170455, 187251,\n", + " 190943],\n", + " dtype='int64'), Int64Index([ 4518, 5438, 11111, 13871, 18406, 21156, 22079, 32135,\n", + " 45380, 46301, 47256, 50010, 55568, 56516, 58378, 61176,\n", + " 62094, 64549, 67835, 72381, 79656, 81477, 83299, 85173,\n", + " 87955, 89838, 91293, 105585, 108291, 109218, 110143, 112969,\n", + " 113883, 118459, 120273, 122073, 134791, 135718, 145335, 147942,\n", + " 152530, 153481, 162244, 164961, 168557, 169506, 170456, 187252,\n", + " 190944],\n", + " dtype='int64'), Int64Index([ 4519, 5439, 11112, 13872, 18407, 21157, 22080, 32136,\n", + " 45381, 46302, 47257, 50011, 55569, 56517, 58379, 61177,\n", + " 62095, 64550, 67836, 72382, 79657, 81478, 83300, 85174,\n", + " 87956, 89839, 91294, 105586, 108292, 109219, 110144, 112970,\n", + " 113884, 118460, 120274, 122074, 134792, 135719, 145336, 147943,\n", + " 152531, 153482, 162245, 164962, 168558, 169507, 170457, 187253,\n", + " 190945],\n", + " dtype='int64'), Int64Index([ 4520, 5440, 11113, 13873, 18408, 21158, 22081, 32137,\n", + " 45382, 46303, 47258, 50012, 55570, 56518, 58380, 61178,\n", + " 62096, 64551, 67837, 72383, 79658, 81479, 83301, 85175,\n", + " 87957, 89840, 91295, 105587, 108293, 109220, 110145, 112971,\n", + " 113885, 118461, 120275, 122075, 134793, 135720, 145337, 147944,\n", + " 152532, 153483, 162246, 164963, 168559, 169508, 170458, 187254,\n", + " 190946],\n", + " dtype='int64'), Int64Index([ 4521, 5441, 11114, 13874, 18409, 21159, 22082, 32138,\n", + " 45383, 46304, 47259, 50013, 55571, 56519, 58381, 61179,\n", + " 62097, 64552, 67838, 72384, 79659, 81480, 83302, 85176,\n", + " 87958, 89841, 91296, 105588, 108294, 109221, 110146, 112972,\n", + " 113886, 118462, 120276, 122076, 134794, 135721, 145338, 147945,\n", + " 152533, 153484, 162247, 164964, 168560, 169509, 170459, 187255,\n", + " 190947],\n", + " dtype='int64'), Int64Index([ 4522, 5442, 11115, 13875, 18410, 21160, 22083, 32139,\n", + " 45384, 46305, 47260, 50014, 55572, 56520, 58382, 61180,\n", + " 62098, 64553, 67839, 72385, 79660, 81481, 83303, 85177,\n", + " 87959, 89842, 91297, 105589, 108295, 109222, 110147, 112973,\n", + " 113887, 118463, 120277, 122077, 134795, 135722, 145339, 147946,\n", + " 152534, 153485, 162248, 164965, 168561, 169510, 170460, 187256,\n", + " 190948],\n", + " dtype='int64'), Int64Index([ 4523, 5443, 11116, 13876, 18411, 21161, 22084, 32140,\n", + " 45385, 46306, 47261, 50015, 55573, 56521, 58383, 61181,\n", + " 62099, 64554, 67840, 72386, 79661, 81482, 83304, 85178,\n", + " 87960, 89843, 91298, 105590, 108296, 109223, 110148, 112974,\n", + " 113888, 118464, 120278, 122078, 134796, 135723, 145340, 147947,\n", + " 152535, 153486, 162249, 164966, 168562, 169511, 170461, 187257,\n", + " 190949],\n", + " dtype='int64'), Int64Index([ 4524, 5444, 11117, 13877, 18412, 21162, 22085, 32141,\n", + " 45386, 46307, 47262, 50016, 55574, 56522, 58384, 61182,\n", + " 62100, 64555, 67841, 72387, 79662, 81483, 83305, 85179,\n", + " 87961, 89844, 91299, 105591, 108297, 109224, 110149, 112975,\n", + " 113889, 118465, 120279, 122079, 134797, 135724, 145341, 147948,\n", + " 152536, 153487, 162250, 164967, 168563, 169512, 170462, 187258,\n", + " 190950],\n", + " dtype='int64'), Int64Index([ 4525, 5445, 11118, 13878, 18413, 21163, 22086, 32142,\n", + " 45387, 46308, 47263, 50017, 55575, 56523, 58385, 61183,\n", + " 62101, 64556, 67842, 72388, 79663, 81484, 83306, 85180,\n", + " 87962, 89845, 91300, 105592, 108298, 109225, 110150, 112976,\n", + " 113890, 118466, 120280, 122080, 134798, 135725, 145342, 147949,\n", + " 152537, 153488, 162251, 164968, 168564, 169513, 170463, 187259,\n", + " 190951],\n", + " dtype='int64'), Int64Index([ 4526, 5446, 11119, 13879, 18414, 21164, 22087, 32143,\n", + " 45388, 46309, 47264, 50018, 55576, 56524, 58386, 61184,\n", + " 62102, 64557, 67843, 72389, 79664, 81485, 83307, 85181,\n", + " 87963, 89846, 91301, 105593, 108299, 109226, 110151, 112977,\n", + " 113891, 118467, 120281, 122081, 134799, 135726, 145343, 147950,\n", + " 152538, 153489, 162252, 164969, 168565, 169514, 170464, 187260,\n", + " 190952],\n", + " dtype='int64'), Int64Index([ 4527, 5447, 11120, 13880, 18415, 21165, 22088, 32144,\n", + " 45389, 46310, 47265, 50019, 55577, 56525, 58387, 61185,\n", + " 62103, 64558, 67844, 72390, 79665, 81486, 83308, 85182,\n", + " 87964, 89847, 91302, 105594, 108300, 109227, 110152, 112978,\n", + " 113892, 118468, 120282, 122082, 134800, 135727, 145344, 147951,\n", + " 152539, 153490, 162253, 164970, 168566, 169515, 170465, 187261,\n", + " 190953],\n", + " dtype='int64'), Int64Index([ 4528, 5448, 11121, 13881, 18416, 21166, 22089, 32145,\n", + " 45390, 46311, 47266, 50020, 55578, 56526, 58388, 61186,\n", + " 62104, 64559, 67845, 72391, 79666, 81487, 83309, 85183,\n", + " 87965, 89848, 91303, 105595, 108301, 109228, 110153, 112979,\n", + " 113893, 118469, 120283, 122083, 134801, 135728, 145345, 147952,\n", + " 152540, 153491, 162254, 164971, 168567, 169516, 170466, 187262,\n", + " 190954],\n", + " dtype='int64'), Int64Index([ 4529, 5449, 11122, 13882, 18417, 21167, 22090, 32146,\n", + " 45391, 46312, 47267, 50021, 55579, 56527, 58389, 61187,\n", + " 62105, 64560, 67846, 72392, 79667, 81488, 83310, 85184,\n", + " 87966, 89849, 91304, 105596, 108302, 109229, 110154, 112980,\n", + " 113894, 118470, 120284, 122084, 134802, 135729, 145346, 147953,\n", + " 152541, 153492, 162255, 164972, 168568, 169517, 170467, 187263,\n", + " 190955],\n", + " dtype='int64'), Int64Index([ 4530, 5450, 11123, 13883, 18418, 21168, 22091, 32147,\n", + " 45392, 46313, 47268, 50022, 55580, 56528, 58390, 61188,\n", + " 62106, 64561, 67847, 72393, 79668, 81489, 83311, 85185,\n", + " 87967, 89850, 91305, 105597, 108303, 109230, 110155, 112981,\n", + " 113895, 118471, 120285, 122085, 134803, 135730, 145347, 147954,\n", + " 152542, 153493, 162256, 164973, 168569, 169518, 170468, 187264,\n", + " 190956],\n", + " dtype='int64'), Int64Index([ 4531, 5451, 11124, 13884, 18419, 21169, 22092, 32148,\n", + " 45393, 46314, 47269, 50023, 55581, 56529, 58391, 61189,\n", + " 62107, 64562, 67848, 72394, 79669, 81490, 83312, 85186,\n", + " 87968, 89851, 91306, 105598, 108304, 109231, 110156, 112982,\n", + " 113896, 118472, 120286, 122086, 134804, 135731, 145348, 147955,\n", + " 152543, 153494, 162257, 164974, 168570, 169519, 170469, 187265,\n", + " 190957],\n", + " dtype='int64'), Int64Index([ 4532, 5452, 11125, 13885, 18420, 21170, 22093, 32149,\n", + " 45394, 46315, 47270, 50024, 55582, 56530, 58392, 61190,\n", + " 62108, 64563, 67849, 72395, 79670, 81491, 83313, 85187,\n", + " 87969, 89852, 91307, 105599, 108305, 109232, 110157, 112983,\n", + " 113897, 118473, 120287, 122087, 134805, 135732, 145349, 147956,\n", + " 152544, 153495, 162258, 164975, 168571, 169520, 170470, 187266,\n", + " 190958],\n", + " dtype='int64'), Int64Index([ 4533, 5453, 11126, 13886, 18421, 21171, 22094, 32150,\n", + " 45395, 46316, 47271, 50025, 55583, 56531, 58393, 61191,\n", + " 62109, 64564, 67850, 72396, 79671, 81492, 83314, 85188,\n", + " 87970, 89853, 91308, 105600, 108306, 109233, 110158, 112984,\n", + " 113898, 118474, 120288, 122088, 134806, 135733, 145350, 147957,\n", + " 152545, 153496, 162259, 164976, 168572, 169521, 170471, 187267,\n", + " 190959],\n", + " dtype='int64'), Int64Index([ 4534, 5454, 11127, 13887, 18422, 21172, 22095, 32151,\n", + " 45396, 46317, 47272, 50026, 55584, 56532, 58394, 61192,\n", + " 62110, 64565, 67851, 72397, 79672, 81493, 83315, 85189,\n", + " 87971, 89854, 91309, 105601, 108307, 109234, 110159, 112985,\n", + " 113899, 118475, 120289, 122089, 134807, 135734, 145351, 147958,\n", + " 152546, 153497, 162260, 164977, 168573, 169522, 170472, 187268,\n", + " 190960],\n", + " dtype='int64'), Int64Index([ 4535, 5455, 11128, 13888, 18423, 21173, 22096, 32152,\n", + " 45397, 46318, 47273, 50027, 55585, 56533, 58395, 61193,\n", + " 62111, 64566, 67852, 72398, 79673, 81494, 83316, 85190,\n", + " 87972, 89855, 91310, 105602, 108308, 109235, 110160, 112986,\n", + " 113900, 118476, 120290, 122090, 134808, 135735, 145352, 147959,\n", + " 152547, 153498, 162261, 164978, 168574, 169523, 170473, 187269,\n", + " 190961],\n", + " dtype='int64'), Int64Index([ 4536, 5456, 11129, 13889, 18424, 21174, 22097, 32153,\n", + " 45398, 46319, 47274, 50028, 55586, 56534, 58396, 61194,\n", + " 62112, 64567, 67853, 72399, 79674, 81495, 83317, 85191,\n", + " 87973, 89856, 91311, 105603, 108309, 109236, 110161, 112987,\n", + " 113901, 118477, 120291, 122091, 134809, 135736, 145353, 147960,\n", + " 152548, 153499, 162262, 164979, 168575, 169524, 170474, 187270,\n", + " 190962],\n", + " dtype='int64'), Int64Index([ 4537, 5457, 11130, 13890, 18425, 21175, 22098, 32154,\n", + " 45399, 46320, 47275, 50029, 55587, 56535, 58397, 61195,\n", + " 62113, 64568, 67854, 72400, 79675, 81496, 83318, 85192,\n", + " 87974, 89857, 91312, 105604, 108310, 109237, 110162, 112988,\n", + " 113902, 118478, 120292, 122092, 134810, 135737, 145354, 147961,\n", + " 152549, 153500, 162263, 164980, 168576, 169525, 170475, 187271,\n", + " 190963],\n", + " dtype='int64'), Int64Index([ 4538, 5458, 11131, 13891, 18426, 21176, 22099, 32155,\n", + " 45400, 46321, 47276, 50030, 55588, 56536, 58398, 61196,\n", + " 62114, 64569, 67855, 72401, 79676, 81497, 83319, 85193,\n", + " 87975, 89858, 91313, 105605, 108311, 109238, 110163, 112989,\n", + " 113903, 118479, 120293, 122093, 134811, 135738, 145355, 147962,\n", + " 152550, 153501, 162264, 164981, 168577, 169526, 170476, 187272,\n", + " 190964],\n", + " dtype='int64'), Int64Index([ 4539, 5459, 11132, 13892, 18427, 21177, 22100, 32156,\n", + " 45401, 46322, 47277, 50031, 55589, 56537, 58399, 61197,\n", + " 62115, 64570, 67856, 72402, 79677, 81498, 83320, 85194,\n", + " 87976, 89859, 91314, 105606, 108312, 109239, 110164, 112990,\n", + " 113904, 118480, 120294, 122094, 134812, 135739, 145356, 147963,\n", + " 152551, 153502, 162265, 164982, 168578, 169527, 170477, 187273,\n", + " 190965],\n", + " dtype='int64'), Int64Index([ 4540, 5460, 11133, 13893, 18428, 21178, 22101, 32157,\n", + " 45402, 46323, 47278, 50032, 55590, 56538, 58400, 61198,\n", + " 62116, 64571, 67857, 72403, 79678, 81499, 83321, 85195,\n", + " 87977, 89860, 91315, 105607, 108313, 109240, 110165, 112991,\n", + " 113905, 118481, 120295, 122095, 134813, 135740, 145357, 147964,\n", + " 152552, 153503, 162266, 164983, 168579, 169528, 170478, 187274,\n", + " 190966],\n", + " dtype='int64'), Int64Index([ 4541, 5461, 11134, 13894, 18429, 21179, 22102, 32158,\n", + " 45403, 46324, 47279, 50033, 55591, 56539, 58401, 61199,\n", + " 62117, 64572, 67858, 72404, 79679, 81500, 83322, 85196,\n", + " 87978, 89861, 91316, 105608, 108314, 109241, 110166, 112992,\n", + " 113906, 118482, 120296, 122096, 134814, 135741, 145358, 147965,\n", + " 152553, 153504, 162267, 164984, 168580, 169529, 170479, 187275,\n", + " 190967],\n", + " dtype='int64'), Int64Index([ 4542, 5462, 11135, 13895, 18430, 21180, 22103, 32159,\n", + " 45404, 46325, 47280, 50034, 55592, 56540, 58402, 61200,\n", + " 62118, 64573, 67859, 72405, 79680, 81501, 83323, 85197,\n", + " 87979, 89862, 91317, 105609, 108315, 109242, 110167, 112993,\n", + " 113907, 118483, 120297, 122097, 134815, 135742, 145359, 147966,\n", + " 152554, 153505, 162268, 164985, 168581, 169530, 170480, 187276,\n", + " 190968],\n", + " dtype='int64'), Int64Index([ 4543, 5463, 11136, 13896, 18431, 21181, 22104, 32160,\n", + " 45405, 46326, 47281, 50035, 55593, 56541, 58403, 61201,\n", + " 62119, 64574, 67860, 72406, 79681, 81502, 83324, 85198,\n", + " 87980, 89863, 91318, 105610, 108316, 109243, 110168, 112994,\n", + " 113908, 118484, 120298, 122098, 134816, 135743, 145360, 147967,\n", + " 152555, 153506, 162269, 164986, 168582, 169531, 170481, 187277,\n", + " 190969],\n", + " dtype='int64'), Int64Index([ 4544, 5464, 11137, 13897, 18432, 21182, 22105, 32161,\n", + " 45406, 46327, 47282, 50036, 55594, 56542, 58404, 61202,\n", + " 62120, 64575, 67861, 72407, 79682, 81503, 83325, 85199,\n", + " 87981, 89864, 91319, 105611, 108317, 109244, 110169, 112995,\n", + " 113909, 118485, 120299, 122099, 134817, 135744, 145361, 147968,\n", + " 152556, 153507, 162270, 164987, 168583, 169532, 170482, 187278,\n", + " 190970],\n", + " dtype='int64'), Int64Index([ 4545, 5465, 11138, 13898, 18433, 21183, 22106, 32162,\n", + " 45407, 46328, 47283, 50037, 55595, 56543, 58405, 61203,\n", + " 62121, 64576, 67862, 72408, 79683, 81504, 83326, 85200,\n", + " 87982, 89865, 91320, 105612, 108318, 109245, 110170, 112996,\n", + " 113910, 118486, 120300, 122100, 134818, 135745, 145362, 147969,\n", + " 152557, 153508, 162271, 164988, 168584, 169533, 170483, 187279,\n", + " 190971],\n", + " dtype='int64'), Int64Index([ 4546, 5466, 11139, 13899, 18434, 21184, 22107, 32163,\n", + " 45408, 46329, 47284, 50038, 55596, 56544, 58406, 61204,\n", + " 62122, 64577, 67863, 72409, 79684, 81505, 83327, 85201,\n", + " 87983, 89866, 91321, 105613, 108319, 109246, 110171, 112997,\n", + " 113911, 118487, 120301, 122101, 134819, 135746, 145363, 147970,\n", + " 152558, 153509, 162272, 164989, 168585, 169534, 170484, 187280,\n", + " 190972],\n", + " dtype='int64'), Int64Index([ 4547, 5467, 11140, 13900, 18435, 21185, 22108, 32164,\n", + " 45409, 46330, 47285, 50039, 55597, 56545, 58407, 61205,\n", + " 62123, 64578, 67864, 72410, 79685, 81506, 83328, 85202,\n", + " 87984, 89867, 91322, 105614, 108320, 109247, 110172, 112998,\n", + " 113912, 118488, 120302, 122102, 134820, 135747, 145364, 147971,\n", + " 152559, 153510, 162273, 164990, 168586, 169535, 170485, 187281,\n", + " 190973],\n", + " dtype='int64'), Int64Index([ 4548, 5468, 11141, 13901, 18436, 21186, 22109, 32165,\n", + " 45410, 46331, 47286, 50040, 55598, 56546, 58408, 61206,\n", + " 62124, 64579, 67865, 72411, 79686, 81507, 83329, 85203,\n", + " 87985, 89868, 105615, 108321, 109248, 110173, 112999, 113913,\n", + " 118489, 120303, 122103, 134821, 135748, 145365, 147972, 152560,\n", + " 153511, 162274, 164991, 168587, 169536, 170486, 187282, 190974],\n", + " dtype='int64'), Int64Index([ 4549, 5469, 11142, 13902, 18437, 21187, 22110, 32166,\n", + " 45411, 46332, 47287, 50041, 55599, 56547, 58409, 61207,\n", + " 62125, 64580, 67866, 72412, 79687, 81508, 83330, 85204,\n", + " 87986, 89869, 105616, 108322, 109249, 110174, 113000, 113914,\n", + " 118490, 120304, 122104, 134822, 135749, 145366, 147973, 152561,\n", + " 153512, 162275, 164992, 168588, 169537, 170487, 187283, 190975],\n", + " dtype='int64'), Int64Index([ 4550, 5470, 11143, 13903, 18438, 21188, 22111, 32167,\n", + " 45412, 46333, 47288, 50042, 55600, 56548, 58410, 61208,\n", + " 62126, 64581, 67867, 72413, 79688, 81509, 83331, 85205,\n", + " 87987, 89870, 105617, 108323, 109250, 110175, 113001, 113915,\n", + " 118491, 120305, 122105, 134823, 135750, 145367, 147974, 152562,\n", + " 153513, 162276, 164993, 168589, 169538, 170488, 187284, 190976],\n", + " dtype='int64'), Int64Index([ 4551, 5471, 11144, 13904, 18439, 21189, 22112, 32168,\n", + " 45413, 46334, 47289, 50043, 55601, 56549, 58411, 61209,\n", + " 62127, 64582, 67868, 72414, 79689, 81510, 83332, 85206,\n", + " 87988, 89871, 105618, 108324, 109251, 110176, 113002, 113916,\n", + " 118492, 120306, 122106, 134824, 135751, 145368, 147975, 152563,\n", + " 153514, 162277, 164994, 168590, 169539, 170489, 187285, 190977],\n", + " dtype='int64'), Int64Index([ 4552, 5472, 11145, 13905, 18440, 21190, 22113, 32169,\n", + " 45414, 46335, 47290, 50044, 55602, 56550, 58412, 61210,\n", + " 62128, 64583, 67869, 72415, 79690, 81511, 83333, 85207,\n", + " 87989, 89872, 105619, 108325, 109252, 110177, 113003, 113917,\n", + " 118493, 120307, 122107, 134825, 135752, 145369, 147976, 152564,\n", + " 153515, 162278, 164995, 168591, 169540, 170490, 187286, 190978],\n", + " dtype='int64'), Int64Index([ 4553, 5473, 11146, 13906, 18441, 21191, 22114, 32170,\n", + " 45415, 46336, 47291, 50045, 55603, 56551, 58413, 61211,\n", + " 62129, 64584, 67870, 72416, 79691, 81512, 83334, 85208,\n", + " 87990, 89873, 105620, 108326, 109253, 110178, 113004, 113918,\n", + " 118494, 120308, 122108, 134826, 135753, 145370, 147977, 152565,\n", + " 153516, 162279, 164996, 168592, 169541, 170491, 187287, 190979],\n", + " dtype='int64'), Int64Index([ 4554, 5474, 11147, 13907, 18442, 21192, 22115, 32171,\n", + " 45416, 46337, 47292, 50046, 55604, 56552, 58414, 61212,\n", + " 62130, 64585, 67871, 72417, 79692, 81513, 83335, 85209,\n", + " 87991, 89874, 105621, 108327, 109254, 110179, 113005, 113919,\n", + " 118495, 120309, 122109, 134827, 135754, 145371, 147978, 152566,\n", + " 153517, 162280, 164997, 168593, 169542, 170492, 187288, 190980],\n", + " dtype='int64'), Int64Index([ 4555, 5475, 11148, 13908, 18443, 21193, 22116, 32172,\n", + " 45417, 46338, 47293, 50047, 55605, 56553, 58415, 61213,\n", + " 62131, 64586, 67872, 72418, 79693, 81514, 83336, 85210,\n", + " 87992, 89875, 105622, 108328, 109255, 110180, 113006, 113920,\n", + " 118496, 120310, 122110, 134828, 135755, 145372, 147979, 152567,\n", + " 153518, 162281, 164998, 168594, 169543, 170493, 187289, 190981],\n", + " dtype='int64'), Int64Index([ 4556, 5476, 11149, 13909, 18444, 21194, 22117, 32173,\n", + " 45418, 46339, 47294, 50048, 55606, 56554, 58416, 61214,\n", + " 62132, 64587, 67873, 72419, 79694, 81515, 83337, 85211,\n", + " 87993, 89876, 105623, 108329, 109256, 110181, 113007, 113921,\n", + " 118497, 120311, 122111, 134829, 135756, 145373, 147980, 152568,\n", + " 153519, 162282, 164999, 168595, 169544, 170494, 187290, 190982],\n", + " dtype='int64'), Int64Index([ 4557, 5477, 11150, 13910, 18445, 21195, 22118, 32174,\n", + " 45419, 46340, 47295, 50049, 55607, 56555, 58417, 61215,\n", + " 62133, 64588, 67874, 72420, 79695, 81516, 83338, 85212,\n", + " 87994, 89877, 105624, 108330, 109257, 110182, 113008, 113922,\n", + " 118498, 120312, 122112, 134830, 135757, 145374, 147981, 152569,\n", + " 153520, 162283, 165000, 168596, 169545, 170495, 187291, 190983],\n", + " dtype='int64'), Int64Index([11151, 47296, 50050, 58418, 89878, 134831, 153521, 165001], dtype='int64'), Int64Index([115739], dtype='int64'), Int64Index([115740], dtype='int64'), Int64Index([115741], dtype='int64'), Int64Index([115742], dtype='int64'), Int64Index([115743], dtype='int64'), Int64Index([115744], dtype='int64'), Int64Index([115745], dtype='int64'), Int64Index([115746], dtype='int64'), Int64Index([115747], dtype='int64'), Int64Index([115748], dtype='int64'), Int64Index([115749], dtype='int64'), Int64Index([115750], dtype='int64'), Int64Index([115751], dtype='int64'), Int64Index([115752], dtype='int64'), Int64Index([115753], dtype='int64'), Int64Index([115754], dtype='int64'), Int64Index([115755], dtype='int64'), Int64Index([115756], dtype='int64'), Int64Index([115757], dtype='int64'), Int64Index([115758], dtype='int64'), Int64Index([115759], dtype='int64'), Int64Index([115760, 188201], dtype='int64'), Int64Index([30290, 115761, 188202], dtype='int64'), Int64Index([30291, 115762, 188203], dtype='int64'), Int64Index([30292, 115763, 188204], dtype='int64'), Int64Index([30293, 115764, 188205], dtype='int64'), Int64Index([30294, 115765, 188206], dtype='int64'), Int64Index([30295, 115766, 188207], dtype='int64'), Int64Index([30296, 115767, 188208], dtype='int64'), Int64Index([30297, 115768, 188209], dtype='int64'), Int64Index([30298, 115769, 188210], dtype='int64'), Int64Index([30299, 115770, 188211], dtype='int64'), Int64Index([30300, 115771, 188212], dtype='int64'), Int64Index([30301, 115772, 188213], dtype='int64'), Int64Index([30302, 115773, 188214], dtype='int64'), Int64Index([30303, 115774, 188215], dtype='int64'), Int64Index([30304, 115775, 188216], dtype='int64'), Int64Index([30305, 115776, 188217], dtype='int64'), Int64Index([30306, 115777, 188218], dtype='int64'), Int64Index([30307, 115778, 188219], dtype='int64'), Int64Index([30308, 115779, 188220], dtype='int64'), Int64Index([30309, 115780, 188221], dtype='int64'), Int64Index([30310, 115781, 188222], dtype='int64'), Int64Index([30311, 115782, 188223], dtype='int64'), Int64Index([30312, 115783, 188224], dtype='int64'), Int64Index([30313, 115784, 188225], dtype='int64'), Int64Index([30314, 115785, 188226], dtype='int64'), Int64Index([30315, 74227, 115786, 188227], dtype='int64'), Int64Index([30316, 74228, 115787, 188228], dtype='int64'), Int64Index([30317, 74229, 115788, 188229], dtype='int64'), Int64Index([30318, 74230, 115789, 188230], dtype='int64'), Int64Index([30319, 74231, 115790, 188231], dtype='int64'), Int64Index([30320, 74232, 115791, 188232], dtype='int64'), Int64Index([30321, 74233, 115792, 188233], dtype='int64'), Int64Index([30322, 74234, 115793, 188234], dtype='int64'), Int64Index([30323, 74235, 115794, 188235], dtype='int64'), Int64Index([30324, 74236, 115795, 188236], dtype='int64'), Int64Index([30325, 74237, 115796, 188237], dtype='int64'), Int64Index([30326, 74238, 115797, 188238], dtype='int64'), Int64Index([30327, 74239, 115798, 188239], dtype='int64'), Int64Index([30328, 50051, 74240, 115799, 145375, 188240], dtype='int64'), Int64Index([30329, 50052, 74241, 115800, 145376, 188241], dtype='int64'), Int64Index([30330, 50053, 74242, 115801, 145377, 188242], dtype='int64'), Int64Index([30331, 50054, 74243, 115802, 145378, 188243], dtype='int64'), Int64Index([30332, 50055, 74244, 115803, 145379, 188244], dtype='int64'), Int64Index([30333, 40863, 50056, 74245, 115804, 145380, 188245], dtype='int64'), Int64Index([30334, 40864, 50057, 74246, 115805, 145381, 188246], dtype='int64'), Int64Index([30335, 40865, 50058, 74247, 115806, 145382, 188247], dtype='int64'), Int64Index([30336, 40866, 50059, 74248, 115807, 139951, 145383, 188248], dtype='int64'), Int64Index([30337, 40867, 50060, 74249, 115808, 139952, 145384, 188249], dtype='int64'), Int64Index([30338, 40868, 50061, 74250, 77859, 89879, 115809, 139953, 145385,\n", + " 188250],\n", + " dtype='int64'), Int64Index([30339, 40869, 41779, 50062, 74251, 77860, 89880, 115810, 139954,\n", + " 145386, 188251],\n", + " dtype='int64'), Int64Index([ 0, 8360, 30340, 40870, 41780, 43597, 50063, 74252,\n", + " 77861, 89881, 115811, 139955, 145387, 188252],\n", + " dtype='int64'), Int64Index([ 1, 8361, 30341, 40871, 41781, 42689, 43598, 50064,\n", + " 74253, 77862, 89882, 103799, 115812, 139956, 145388, 180412,\n", + " 188253, 190984],\n", + " dtype='int64'), Int64Index([ 2, 8362, 30342, 40872, 41782, 42690, 43599, 50065,\n", + " 74254, 77863, 89883, 103800, 115813, 139957, 145389, 180413,\n", + " 188254, 190985],\n", + " dtype='int64'), Int64Index([ 3, 8363, 19373, 30343, 40873, 41783, 42691, 43600,\n", + " 50066, 73321, 74255, 77864, 89884, 103801, 115814, 139958,\n", + " 145390, 180414, 188255, 190986],\n", + " dtype='int64'), Int64Index([ 4, 8364, 19374, 25757, 30344, 40874, 41784, 42692,\n", + " 43601, 50067, 73322, 74256, 77865, 89885, 103802, 115815,\n", + " 139959, 145391, 180415, 188256, 190987],\n", + " dtype='int64'), Int64Index([ 5, 8365, 19375, 25758, 30345, 40875, 41785, 42693,\n", + " 43602, 50068, 73323, 74257, 77866, 89886, 103803, 115816,\n", + " 125443, 139960, 145392, 180416, 188257, 190988],\n", + " dtype='int64'), Int64Index([ 6, 8366, 19376, 23018, 25759, 30346, 40876, 41786,\n", + " 42694, 43603, 50069, 73324, 74258, 77867, 89887, 103804,\n", + " 115817, 125444, 132738, 139961, 145393, 160459, 180417, 188258,\n", + " 190989],\n", + " dtype='int64'), Int64Index([ 7, 8367, 19377, 23019, 25760, 30347, 40877, 41787,\n", + " 42695, 43604, 50070, 73325, 74259, 77868, 79696, 89888,\n", + " 103805, 115818, 125445, 132739, 139962, 145394, 160460, 180418,\n", + " 188259, 190990],\n", + " dtype='int64'), Int64Index([ 8, 8368, 19378, 23020, 25761, 30348, 40878, 41788,\n", + " 42696, 43605, 50071, 73326, 74260, 77869, 79697, 89889,\n", + " 103806, 115819, 125446, 132740, 139963, 145395, 160461, 180419,\n", + " 188260, 190991],\n", + " dtype='int64'), Int64Index([ 9, 8369, 19379, 23021, 25762, 30349, 40879, 41789,\n", + " 42697, 43606, 48201, 50072, 72421, 73327, 74261, 77870,\n", + " 79698, 89890, 103807, 115820, 125447, 132741, 139964, 145396,\n", + " 160462, 180420, 188261, 190992],\n", + " dtype='int64'), Int64Index([ 10, 8370, 19380, 22119, 23022, 25763, 30350, 40880,\n", + " 41790, 42698, 43607, 48202, 50073, 72422, 73328, 74262,\n", + " 77871, 79699, 89891, 103808, 115821, 125448, 132742, 139965,\n", + " 145397, 160463, 180421, 188262, 190993],\n", + " dtype='int64'), Int64Index([ 11, 8371, 19381, 22120, 23023, 25764, 30351, 40881,\n", + " 41791, 42699, 43608, 48203, 50074, 72423, 73329, 74263,\n", + " 77872, 79700, 89892, 103809, 115822, 125449, 132743, 139966,\n", + " 145398, 160464, 180422, 188263, 190994, 193694],\n", + " dtype='int64'), Int64Index([ 12, 8372, 19382, 22121, 23024, 25765, 30352, 40882,\n", + " 41792, 42700, 43609, 48204, 50075, 72424, 73330, 74264,\n", + " 77873, 79701, 89893, 97364, 103810, 115823, 125450, 132744,\n", + " 139967, 145399, 160465, 180423, 188264, 190995, 193695],\n", + " dtype='int64'), Int64Index([ 13, 8373, 19383, 22122, 23025, 25766, 30353, 40883,\n", + " 41793, 42701, 43610, 48205, 50076, 72425, 73331, 74265,\n", + " 77874, 79702, 89894, 97365, 103811, 115824, 125451, 132745,\n", + " 139968, 145400, 160466, 180424, 188265, 190996, 193696],\n", + " dtype='int64'), Int64Index([ 14, 8374, 19384, 22123, 23026, 25767, 30354, 40884,\n", + " 41794, 42702, 43611, 48206, 50077, 72426, 73332, 74266,\n", + " 77875, 79703, 89895, 97366, 103812, 115825, 125452, 132746,\n", + " 139969, 145401, 160467, 180425, 188266, 190997, 193697],\n", + " dtype='int64'), Int64Index([ 15, 2738, 8375, 19385, 22124, 23027, 25768, 30355,\n", + " 40885, 41795, 42703, 43612, 48207, 50078, 72427, 73333,\n", + " 74267, 77876, 79704, 89896, 97367, 103813, 115826, 125453,\n", + " 132747, 139970, 145402, 160468, 173667, 180426, 188267, 190998,\n", + " 192800, 193698],\n", + " dtype='int64'), Int64Index([ 16, 2739, 8376, 19386, 22125, 23028, 25769, 30356,\n", + " 40886, 41796, 42704, 43613, 48208, 50079, 72428, 73334,\n", + " 74268, 77877, 79705, 89897, 97368, 103814, 115827, 125454,\n", + " 132748, 139971, 145403, 160469, 173668, 180427, 188268, 190999,\n", + " 192801, 193699],\n", + " dtype='int64'), Int64Index([ 17, 2740, 8377, 19387, 22126, 23029, 25770, 30357,\n", + " 40887, 41797, 42705, 43614, 48209, 50080, 72429, 73335,\n", + " 74269, 77878, 79706, 89898, 97369, 103815, 115828, 125455,\n", + " 132749, 139972, 145404, 160470, 173669, 180428, 188269, 191000,\n", + " 192802, 193700],\n", + " dtype='int64'), Int64Index([ 18, 2741, 8378, 19388, 22127, 23030, 25771, 30358,\n", + " 40888, 41798, 42706, 43615, 48210, 50081, 72430, 73336,\n", + " 74270, 77879, 79707, 89899, 97370, 103816, 115829, 125456,\n", + " 132750, 139973, 145405, 160471, 173670, 180429, 188270, 191001,\n", + " 192803, 193701],\n", + " dtype='int64'), Int64Index([ 19, 2742, 8379, 19389, 22128, 23031, 25772, 30359,\n", + " 40889, 41799, 42707, 43616, 48211, 50082, 72431, 73337,\n", + " 74271, 77880, 79708, 89900, 97371, 103817, 115830, 125457,\n", + " 132751, 139974, 145406, 160472, 173671, 180430, 188271, 191002,\n", + " 192804, 193702],\n", + " dtype='int64'), Int64Index([ 20, 2743, 8380, 14817, 19390, 22129, 23032, 25773,\n", + " 30360, 40890, 41800, 42708, 43617, 48212, 50083, 72432,\n", + " 73338, 74272, 77881, 79709, 89901, 97372, 103818, 115831,\n", + " 125458, 132752, 139975, 145407, 160473, 173672, 180431, 188272,\n", + " 191003, 192805, 193703],\n", + " dtype='int64'), Int64Index([ 21, 2744, 8381, 14818, 19391, 22130, 23033, 25774,\n", + " 30361, 40891, 41801, 42709, 43618, 48213, 50084, 72433,\n", + " 73339, 74273, 77882, 79710, 89902, 97373, 103819, 115832,\n", + " 125459, 132753, 139976, 145408, 160474, 173673, 180432, 188273,\n", + " 191004, 192806, 193704],\n", + " dtype='int64'), Int64Index([ 22, 2745, 8382, 14819, 19392, 22131, 23034, 25775,\n", + " 30362, 40892, 41802, 42710, 43619, 48214, 50085, 72434,\n", + " 73340, 74274, 77883, 79711, 89903, 97374, 103820, 115833,\n", + " 125460, 132754, 139977, 145409, 160475, 173674, 180433, 188274,\n", + " 191005, 192807, 193705],\n", + " dtype='int64'), Int64Index([ 23, 2746, 8383, 14820, 19393, 22132, 23035, 25776,\n", + " 30363, 40893, 41803, 42711, 43620, 48215, 50086, 72435,\n", + " 73341, 74275, 77884, 79712, 89904, 97375, 103821, 115834,\n", + " 125461, 132755, 139978, 145410, 160476, 163190, 173675, 180434,\n", + " 188275, 191006, 192808, 193706],\n", + " dtype='int64'), Int64Index([ 24, 2747, 8384, 14821, 19394, 22133, 23036, 25777,\n", + " 30364, 40894, 41804, 42712, 43621, 48216, 50087, 72436,\n", + " 73342, 74276, 77885, 79713, 89905, 97376, 103822, 115835,\n", + " 125462, 132756, 139979, 145411, 160477, 163191, 173676, 180435,\n", + " 188276, 191007, 192809, 193707],\n", + " dtype='int64'), Int64Index([ 25, 2748, 8385, 14822, 19395, 22134, 23037, 25778,\n", + " 30365, 40895, 41805, 42713, 43622, 48217, 50088, 72437,\n", + " 73343, 74277, 77886, 79714, 89906, 97377, 103823, 115836,\n", + " 125463, 132757, 139980, 145412, 160478, 163192, 173677, 180436,\n", + " 188277, 191008, 192810, 193708],\n", + " dtype='int64'), Int64Index([ 26, 2749, 8386, 14823, 19396, 22135, 23038, 25779,\n", + " 30366, 40896, 41806, 42714, 43623, 48218, 50089, 72438,\n", + " 73344, 74278, 77887, 79715, 89907, 97378, 103824, 115837,\n", + " 125464, 132758, 139981, 145413, 160479, 163193, 173678, 180437,\n", + " 188278, 191009, 192811, 193709],\n", + " dtype='int64'), Int64Index([ 27, 2750, 8387, 14824, 19397, 22136, 23039, 25780,\n", + " 30367, 40897, 41807, 42715, 43624, 48219, 50090, 72439,\n", + " 73345, 74279, 77888, 79716, 89908, 97379, 103825, 115838,\n", + " 125465, 132759, 139982, 145414, 160480, 163194, 173679, 180438,\n", + " 188279, 191010, 192812, 193710],\n", + " dtype='int64'), Int64Index([ 28, 2751, 8388, 14825, 19398, 22137, 23040, 25781,\n", + " 30368, 40898, 41808, 42716, 43625, 48220, 50091, 72440,\n", + " 73346, 74280, 77889, 79717, 89909, 97380, 103826, 115839,\n", + " 125466, 132760, 139983, 145415, 160481, 163195, 173680, 180439,\n", + " 188280, 191011, 192813, 193711],\n", + " dtype='int64'), Int64Index([ 29, 2752, 8389, 14826, 19399, 22138, 23041, 25782,\n", + " 30369, 40899, 41809, 42717, 43626, 48221, 50092, 72441,\n", + " 73347, 74281, 77890, 79718, 89910, 97381, 103827, 115840,\n", + " 125467, 132761, 139984, 145416, 160482, 163196, 173681, 180440,\n", + " 188281, 191012, 192814, 193712],\n", + " dtype='int64'), Int64Index([ 30, 2753, 8390, 14827, 19400, 22139, 23042, 25783,\n", + " 30370, 40900, 41810, 42718, 43627, 48222, 50093, 72442,\n", + " 73348, 74282, 77891, 79719, 89911, 97382, 103828, 115841,\n", + " 125468, 132762, 139985, 145417, 160483, 163197, 173682, 180441,\n", + " 188282, 191013, 192815, 193713],\n", + " dtype='int64'), Int64Index([ 31, 2754, 8391, 14828, 19401, 22140, 23043, 25784,\n", + " 30371, 40901, 41811, 42719, 43628, 48223, 50094, 72443,\n", + " 73349, 74283, 77892, 79720, 89912, 97383, 103829, 115842,\n", + " 125469, 132763, 139986, 145418, 160484, 163198, 173683, 180442,\n", + " 188283, 191014, 192816, 193714],\n", + " dtype='int64'), Int64Index([ 32, 2755, 8392, 14829, 19402, 22141, 23044, 25785,\n", + " 30372, 40902, 41812, 42720, 43629, 48224, 50095, 72444,\n", + " 73350, 74284, 77893, 79721, 89913, 97384, 103830, 115843,\n", + " 125470, 132764, 139987, 145419, 160485, 163199, 173684, 180443,\n", + " 188284, 191015, 192817, 193715],\n", + " dtype='int64'), Int64Index([ 33, 2756, 8393, 14830, 19403, 22142, 23045, 25786,\n", + " 30373, 40903, 41813, 42721, 43630, 48225, 50096, 72445,\n", + " 73351, 74285, 77894, 79722, 89914, 97385, 103831, 115844,\n", + " 125471, 132765, 139988, 145420, 160486, 163200, 173685, 180444,\n", + " 188285, 191016, 192818, 193716],\n", + " dtype='int64'), Int64Index([ 34, 2757, 8394, 14831, 19404, 22143, 23046, 25787,\n", + " 30374, 40904, 41814, 42722, 43631, 48226, 50097, 72446,\n", + " 73352, 74286, 77895, 79723, 89915, 97386, 103832, 115845,\n", + " 125472, 132766, 139989, 145421, 160487, 163201, 173686, 180445,\n", + " 188286, 191017, 192819, 193717],\n", + " dtype='int64'), Int64Index([ 35, 2758, 8395, 14832, 19405, 22144, 23047, 25788,\n", + " 30375, 40905, 41815, 42723, 43632, 48227, 50098, 72447,\n", + " 73353, 74287, 77896, 79724, 89916, 97387, 103833, 115846,\n", + " 125473, 132767, 139990, 145422, 160488, 163202, 173687, 180446,\n", + " 188287, 191018, 192820, 193718],\n", + " dtype='int64'), Int64Index([ 36, 2759, 8396, 14833, 19406, 22145, 23048, 25789,\n", + " 30376, 40906, 41816, 42724, 43633, 48228, 50099, 72448,\n", + " 73354, 74288, 77897, 79725, 89917, 97388, 103834, 115847,\n", + " 125474, 132768, 139991, 145423, 160489, 163203, 173688, 180447,\n", + " 188288, 191019, 192821, 193719],\n", + " dtype='int64'), Int64Index([ 37, 2760, 8397, 14834, 19407, 22146, 23049, 25790,\n", + " 30377, 40907, 41817, 42725, 43634, 48229, 50100, 72449,\n", + " 73355, 74289, 77898, 79726, 89918, 97389, 103835, 115848,\n", + " 125475, 132769, 139992, 145424, 160490, 163204, 173689, 180448,\n", + " 188289, 191020, 192822, 193720],\n", + " dtype='int64'), Int64Index([ 38, 2761, 8398, 14835, 19408, 22147, 23050, 25791,\n", + " 30378, 40908, 41818, 42726, 43635, 48230, 50101, 72450,\n", + " 73356, 74290, 77899, 79727, 89919, 97390, 103836, 115849,\n", + " 125476, 132770, 139993, 145425, 160491, 163205, 173690, 180449,\n", + " 188290, 191021, 192823, 193721],\n", + " dtype='int64'), Int64Index([ 39, 2762, 8399, 14836, 19409, 22148, 23051, 25792,\n", + " 30379, 40909, 41819, 42727, 43636, 48231, 50102, 72451,\n", + " 73357, 74291, 77900, 79728, 89920, 97391, 103837, 115850,\n", + " 125477, 132771, 139994, 145426, 160492, 163206, 173691, 180450,\n", + " 188291, 191022, 192824, 193722],\n", + " dtype='int64'), Int64Index([ 40, 2763, 8400, 14837, 19410, 22149, 23052, 25793,\n", + " 30380, 40910, 41820, 42728, 43637, 48232, 50103, 72452,\n", + " 73358, 74292, 77901, 79729, 89921, 97392, 103838, 115851,\n", + " 125478, 132772, 139995, 145427, 160493, 163207, 173692, 180451,\n", + " 188292, 191023, 192825, 193723],\n", + " dtype='int64'), Int64Index([ 41, 2764, 8401, 14838, 19411, 22150, 23053, 25794,\n", + " 30381, 40911, 41821, 42729, 43638, 48233, 50104, 72453,\n", + " 73359, 74293, 77902, 79730, 89922, 97393, 103839, 115852,\n", + " 125479, 132773, 139996, 145428, 160494, 163208, 173693, 180452,\n", + " 188293, 191024, 192826, 193724],\n", + " dtype='int64'), Int64Index([ 42, 2765, 8402, 14839, 19412, 22151, 23054, 25795,\n", + " 30382, 40912, 41822, 42730, 43639, 48234, 50105, 72454,\n", + " 73360, 74294, 77903, 79731, 89923, 97394, 103840, 115853,\n", + " 125480, 132774, 139997, 145429, 160495, 163209, 173694, 180453,\n", + " 188294, 191025, 192827, 193725],\n", + " dtype='int64'), Int64Index([ 43, 2766, 8403, 14840, 19413, 22152, 23055, 25796,\n", + " 30383, 40913, 41823, 42731, 43640, 48235, 50106, 72455,\n", + " 73361, 74295, 77904, 79732, 89924, 97395, 103841, 115854,\n", + " 125481, 132775, 139998, 145430, 160496, 163210, 173695, 180454,\n", + " 188295, 191026, 192828, 193726],\n", + " dtype='int64'), Int64Index([ 44, 2767, 8404, 14841, 19414, 22153, 23056, 25797,\n", + " 30384, 40914, 41824, 42732, 43641, 48236, 50107, 72456,\n", + " 73362, 74296, 77905, 79733, 89925, 97396, 103842, 115855,\n", + " 125482, 132776, 139999, 145431, 160497, 163211, 173696, 180455,\n", + " 188296, 191027, 192829, 193727],\n", + " dtype='int64'), Int64Index([ 45, 2768, 8405, 14842, 19415, 22154, 23057, 25798,\n", + " 30385, 40915, 41825, 42733, 43642, 48237, 50108, 72457,\n", + " 73363, 74297, 77906, 79734, 89926, 97397, 103843, 115856,\n", + " 125483, 132777, 140000, 145432, 160498, 163212, 173697, 180456,\n", + " 188297, 191028, 192830, 193728],\n", + " dtype='int64'), Int64Index([ 46, 2769, 8406, 14843, 19416, 22155, 23058, 25799,\n", + " 30386, 40916, 41826, 42734, 43643, 48238, 50109, 72458,\n", + " 73364, 74298, 77907, 79735, 89927, 97398, 103844, 115857,\n", + " 125484, 132778, 140001, 145433, 160499, 163213, 173698, 180457,\n", + " 188298, 191029, 192831, 193729],\n", + " dtype='int64'), Int64Index([ 47, 2770, 8407, 14844, 19417, 22156, 23059, 25800,\n", + " 30387, 40917, 41827, 42735, 43644, 48239, 50110, 72459,\n", + " 73365, 74299, 77908, 79736, 89928, 97399, 103845, 115858,\n", + " 125485, 132779, 140002, 145434, 160500, 163214, 173699, 180458,\n", + " 188299, 191030, 192832, 193730],\n", + " dtype='int64'), Int64Index([ 48, 2771, 8408, 14845, 19418, 22157, 23060, 25801,\n", + " 30388, 40918, 41828, 42736, 43645, 48240, 50111, 72460,\n", + " 73366, 74300, 77909, 79737, 89929, 97400, 103846, 115859,\n", + " 125486, 132780, 140003, 145435, 160501, 163215, 173700, 180459,\n", + " 188300, 191031, 192833, 193731],\n", + " dtype='int64'), Int64Index([ 49, 2772, 8409, 14846, 19419, 22158, 23061, 25802,\n", + " 30389, 40919, 41829, 42737, 43646, 48241, 50112, 72461,\n", + " 73367, 74301, 77910, 79738, 89930, 97401, 103847, 115860,\n", + " 125487, 132781, 140004, 145436, 160502, 163216, 173701, 180460,\n", + " 188301, 191032, 192834, 193732],\n", + " dtype='int64'), Int64Index([ 50, 2773, 8410, 14847, 19420, 22159, 23062, 25803,\n", + " 30390, 40920, 41830, 42738, 43647, 48242, 50113, 72462,\n", + " 73368, 74302, 77911, 79739, 89931, 97402, 103848, 115861,\n", + " 125488, 132782, 140005, 145437, 160503, 163217, 173702, 180461,\n", + " 188302, 191033, 192835, 193733],\n", + " dtype='int64'), Int64Index([ 51, 2774, 8411, 14848, 19421, 22160, 23063, 25804,\n", + " 30391, 40921, 41831, 42739, 43648, 48243, 50114, 72463,\n", + " 73369, 74303, 77912, 79740, 89932, 97403, 103849, 115862,\n", + " 125489, 132783, 140006, 145438, 160504, 163218, 173703, 180462,\n", + " 188303, 191034, 192836, 193734],\n", + " dtype='int64'), Int64Index([ 52, 2775, 8412, 14849, 19422, 22161, 23064, 25805,\n", + " 30392, 40922, 41832, 42740, 43649, 48244, 50115, 72464,\n", + " 73370, 74304, 77913, 79741, 89933, 97404, 103850, 115863,\n", + " 125490, 132784, 140007, 145439, 160505, 163219, 173704, 180463,\n", + " 188304, 191035, 192837, 193735],\n", + " dtype='int64'), Int64Index([ 53, 2776, 8413, 14850, 19423, 22162, 23065, 25806,\n", + " 30393, 40923, 41833, 42741, 43650, 48245, 50116, 72465,\n", + " 73371, 74305, 77914, 79742, 89934, 97405, 103851, 115864,\n", + " 125491, 132785, 140008, 145440, 160506, 163220, 173705, 180464,\n", + " 188305, 191036, 192838, 193736],\n", + " dtype='int64'), Int64Index([ 54, 2777, 8414, 14851, 19424, 22163, 23066, 25807,\n", + " 30394, 40924, 41834, 42742, 43651, 48246, 50117, 72466,\n", + " 73372, 74306, 77915, 79743, 89935, 97406, 103852, 115865,\n", + " 125492, 132786, 140009, 145441, 160507, 163221, 173706, 180465,\n", + " 188306, 191037, 192839, 193737],\n", + " dtype='int64'), Int64Index([ 55, 2778, 8415, 14852, 19425, 22164, 23067, 25808,\n", + " 30395, 40925, 41835, 42743, 43652, 48247, 50118, 72467,\n", + " 73373, 74307, 77916, 79744, 89936, 97407, 103853, 115866,\n", + " 125493, 132787, 140010, 145442, 160508, 163222, 173707, 180466,\n", + " 188307, 191038, 192840, 193738],\n", + " dtype='int64'), Int64Index([ 56, 2779, 8416, 14853, 19426, 22165, 23068, 25809,\n", + " 30396, 40926, 41836, 42744, 43653, 48248, 50119, 72468,\n", + " 73374, 74308, 77917, 79745, 89937, 97408, 103854, 115867,\n", + " 125494, 132788, 140011, 145443, 160509, 163223, 173708, 180467,\n", + " 188308, 191039, 192841, 193739],\n", + " dtype='int64'), Int64Index([ 57, 2780, 8417, 14854, 19427, 22166, 23069, 25810,\n", + " 30397, 40927, 41837, 42745, 43654, 48249, 50120, 72469,\n", + " 73375, 74309, 77918, 79746, 89938, 97409, 103855, 115868,\n", + " 125495, 132789, 140012, 145444, 160510, 163224, 173709, 180468,\n", + " 188309, 191040, 192842, 193740],\n", + " dtype='int64'), Int64Index([ 58, 2781, 8418, 14855, 19428, 22167, 23070, 25811,\n", + " 30398, 40928, 41838, 42746, 43655, 48250, 50121, 72470,\n", + " 73376, 74310, 77919, 79747, 89939, 97410, 103856, 115869,\n", + " 125496, 132790, 140013, 145445, 160511, 163225, 173710, 180469,\n", + " 188310, 191041, 192843, 193741],\n", + " dtype='int64'), Int64Index([ 59, 2782, 8419, 14856, 19429, 22168, 23071, 25812,\n", + " 30399, 40929, 41839, 42747, 43656, 48251, 50122, 72471,\n", + " 73377, 74311, 77920, 79748, 89940, 97411, 103857, 115870,\n", + " 125497, 132791, 140014, 145446, 160512, 163226, 173711, 180470,\n", + " 188311, 191042, 192844, 193742],\n", + " dtype='int64'), Int64Index([ 60, 2783, 8420, 14857, 19430, 22169, 23072, 25813,\n", + " 30400, 40930, 41840, 42748, 43657, 48252, 50123, 72472,\n", + " 73378, 74312, 77921, 79749, 89941, 97412, 103858, 115871,\n", + " 125498, 132792, 140015, 145447, 160513, 163227, 173712, 180471,\n", + " 188312, 191043, 192845, 193743],\n", + " dtype='int64'), Int64Index([ 61, 2784, 8421, 14858, 19431, 22170, 23073, 25814,\n", + " 30401, 40931, 41841, 42749, 43658, 48253, 50124, 72473,\n", + " 73379, 74313, 77922, 79750, 89942, 97413, 103859, 115872,\n", + " 125499, 132793, 140016, 145448, 160514, 163228, 173713, 180472,\n", + " 188313, 191044, 192846, 193744],\n", + " dtype='int64'), Int64Index([ 62, 2785, 8422, 14859, 19432, 22171, 23074, 25815,\n", + " 30402, 40932, 41842, 42750, 43659, 48254, 50125, 72474,\n", + " 73380, 74314, 77923, 79751, 89943, 97414, 103860, 115873,\n", + " 125500, 132794, 140017, 145449, 160515, 163229, 173714, 180473,\n", + " 188314, 191045, 192847, 193745],\n", + " dtype='int64'), Int64Index([ 63, 2786, 8423, 14860, 19433, 22172, 23075, 25816,\n", + " 30403, 40933, 41843, 42751, 43660, 48255, 50126, 72475,\n", + " 73381, 74315, 77924, 79752, 89944, 97415, 103861, 115874,\n", + " 125501, 132795, 140018, 145450, 160516, 163230, 173715, 180474,\n", + " 188315, 191046, 192848, 193746],\n", + " dtype='int64'), Int64Index([ 64, 2787, 8424, 14861, 19434, 22173, 23076, 25817,\n", + " 30404, 40934, 41844, 42752, 43661, 48256, 50127, 72476,\n", + " 73382, 74316, 77925, 79753, 89945, 97416, 103862, 115875,\n", + " 125502, 132796, 140019, 145451, 160517, 163231, 173716, 180475,\n", + " 188316, 191047, 192849, 193747],\n", + " dtype='int64'), Int64Index([ 65, 2788, 8425, 14862, 19435, 22174, 23077, 25818,\n", + " 30405, 40935, 41845, 42753, 43662, 48257, 50128, 72477,\n", + " 73383, 74317, 77926, 79754, 89946, 97417, 103863, 115876,\n", + " 125503, 132797, 140020, 145452, 160518, 163232, 173717, 180476,\n", + " 188317, 191048, 192850, 193748],\n", + " dtype='int64'), Int64Index([ 66, 2789, 8426, 14863, 19436, 22175, 23078, 25819,\n", + " 30406, 40936, 41846, 42754, 43663, 48258, 50129, 72478,\n", + " 73384, 74318, 77927, 79755, 89947, 97418, 103864, 115877,\n", + " 125504, 132798, 140021, 145453, 160519, 163233, 173718, 180477,\n", + " 188318, 191049, 192851, 193749],\n", + " dtype='int64'), Int64Index([ 67, 2790, 8427, 14864, 19437, 22176, 23079, 25820,\n", + " 30407, 40937, 41847, 42755, 43664, 48259, 50130, 72479,\n", + " 73385, 74319, 77928, 79756, 89948, 97419, 103865, 115878,\n", + " 125505, 132799, 140022, 145454, 160520, 163234, 173719, 180478,\n", + " 188319, 191050, 192852, 193750],\n", + " dtype='int64'), Int64Index([ 68, 2791, 8428, 14865, 19438, 22177, 23080, 25821,\n", + " 30408, 40938, 41848, 42756, 43665, 48260, 50131, 72480,\n", + " 73386, 74320, 77929, 79757, 89949, 97420, 103866, 115879,\n", + " 125506, 132800, 140023, 145455, 160521, 163235, 173720, 180479,\n", + " 188320, 191051, 192853, 193751],\n", + " dtype='int64'), Int64Index([ 69, 2792, 8429, 14866, 19439, 22178, 23081, 25822,\n", + " 30409, 40939, 41849, 42757, 43666, 48261, 50132, 72481,\n", + " 73387, 74321, 77930, 79758, 89950, 97421, 103867, 115880,\n", + " 125507, 132801, 140024, 145456, 160522, 163236, 173721, 180480,\n", + " 188321, 191052, 192854, 193752],\n", + " dtype='int64'), Int64Index([ 70, 2793, 8430, 14867, 19440, 22179, 23082, 25823,\n", + " 30410, 40940, 41850, 42758, 43667, 48262, 50133, 72482,\n", + " 73388, 74322, 77931, 79759, 89951, 97422, 103868, 115881,\n", + " 125508, 132802, 140025, 145457, 160523, 163237, 173722, 180481,\n", + " 188322, 191053, 192855, 193753],\n", + " dtype='int64'), Int64Index([ 71, 2794, 8431, 14868, 19441, 22180, 23083, 25824,\n", + " 30411, 40941, 41851, 42759, 43668, 48263, 50134, 72483,\n", + " 73389, 74323, 77932, 79760, 89952, 97423, 103869, 115882,\n", + " 125509, 132803, 140026, 145458, 160524, 163238, 173723, 180482,\n", + " 188323, 191054, 192856, 193754],\n", + " dtype='int64'), Int64Index([ 72, 2795, 8432, 14869, 19442, 22181, 23084, 25825,\n", + " 30412, 40942, 41852, 42760, 43669, 48264, 50135, 72484,\n", + " 73390, 74324, 77933, 79761, 89953, 97424, 103870, 115883,\n", + " 125510, 132804, 140027, 145459, 160525, 163239, 173724, 180483,\n", + " 188324, 191055, 192857, 193755],\n", + " dtype='int64'), Int64Index([ 73, 2796, 8433, 14870, 19443, 22182, 23085, 25826,\n", + " 30413, 40943, 41853, 42761, 43670, 48265, 50136, 72485,\n", + " 73391, 74325, 77934, 79762, 89954, 97425, 103871, 115884,\n", + " 125511, 132805, 140028, 145460, 160526, 163240, 173725, 180484,\n", + " 188325, 191056, 192858, 193756],\n", + " dtype='int64'), Int64Index([ 74, 2797, 8434, 14871, 19444, 22183, 23086, 25827,\n", + " 30414, 40944, 41854, 42762, 43671, 48266, 50137, 72486,\n", + " 73392, 74326, 77935, 79763, 89955, 97426, 103872, 115885,\n", + " 125512, 132806, 140029, 145461, 160527, 163241, 173726, 180485,\n", + " 188326, 191057, 192859, 193757],\n", + " dtype='int64'), Int64Index([ 75, 2798, 8435, 14872, 19445, 22184, 23087, 25828,\n", + " 30415, 40945, 41855, 42763, 43672, 48267, 50138, 72487,\n", + " 73393, 74327, 77936, 79764, 89956, 97427, 103873, 115886,\n", + " 125513, 132807, 140030, 145462, 160528, 163242, 173727, 180486,\n", + " 188327, 191058, 192860, 193758],\n", + " dtype='int64'), Int64Index([ 76, 2799, 8436, 14873, 19446, 22185, 23088, 25829,\n", + " 30416, 40946, 41856, 42764, 43673, 48268, 50139, 72488,\n", + " 73394, 74328, 77937, 79765, 89957, 97428, 103874, 115887,\n", + " 125514, 132808, 140031, 145463, 160529, 163243, 173728, 180487,\n", + " 188328, 191059, 192861, 193759],\n", + " dtype='int64'), Int64Index([ 77, 2800, 8437, 14874, 19447, 22186, 23089, 25830,\n", + " 30417, 40947, 41857, 42765, 43674, 48269, 50140, 72489,\n", + " 73395, 74329, 77938, 79766, 89958, 97429, 103875, 115888,\n", + " 125515, 132809, 140032, 145464, 160530, 163244, 173729, 180488,\n", + " 188329, 191060, 192862, 193760],\n", + " dtype='int64'), Int64Index([ 78, 2801, 8438, 14875, 19448, 22187, 23090, 25831,\n", + " 30418, 40948, 41858, 42766, 43675, 48270, 50141, 72490,\n", + " 73396, 74330, 77939, 79767, 89959, 97430, 103876, 115889,\n", + " 125516, 132810, 140033, 145465, 160531, 163245, 173730, 180489,\n", + " 188330, 191061, 192863, 193761],\n", + " dtype='int64'), Int64Index([ 79, 2802, 8439, 14876, 19449, 22188, 23091, 25832,\n", + " 30419, 40949, 41859, 42767, 43676, 48271, 50142, 72491,\n", + " 73397, 74331, 77940, 79768, 89960, 97431, 103877, 115890,\n", + " 125517, 132811, 140034, 145466, 160532, 163246, 173731, 180490,\n", + " 188331, 191062, 192864, 193762],\n", + " dtype='int64'), Int64Index([ 80, 2803, 8440, 14877, 19450, 22189, 23092, 25833,\n", + " 30420, 40950, 41860, 42768, 43677, 48272, 50143, 72492,\n", + " 73398, 74332, 77941, 79769, 89961, 97432, 103878, 115891,\n", + " 125518, 132812, 140035, 145467, 160533, 163247, 173732, 180491,\n", + " 188332, 191063, 192865, 193763],\n", + " dtype='int64'), Int64Index([ 81, 2804, 8441, 14878, 19451, 22190, 23093, 25834,\n", + " 30421, 40951, 41861, 42769, 43678, 48273, 50144, 72493,\n", + " 73399, 74333, 77942, 79770, 89962, 97433, 103879, 115892,\n", + " 125519, 132813, 140036, 145468, 160534, 163248, 173733, 180492,\n", + " 188333, 191064, 192866, 193764],\n", + " dtype='int64'), Int64Index([ 82, 2805, 8442, 14879, 19452, 22191, 23094, 25835,\n", + " 30422, 40952, 41862, 42770, 43679, 48274, 50145, 72494,\n", + " 73400, 74334, 77943, 79771, 89963, 97434, 103880, 115893,\n", + " 125520, 132814, 140037, 145469, 160535, 163249, 173734, 180493,\n", + " 188334, 191065, 192867, 193765],\n", + " dtype='int64'), Int64Index([ 83, 2806, 8443, 14880, 19453, 22192, 23095, 25836,\n", + " 30423, 40953, 41863, 42771, 43680, 48275, 50146, 72495,\n", + " 73401, 74335, 77944, 79772, 89964, 97435, 103881, 115894,\n", + " 125521, 132815, 140038, 145470, 160536, 163250, 173735, 180494,\n", + " 188335, 191066, 192868, 193766],\n", + " dtype='int64'), Int64Index([ 84, 2807, 8444, 14881, 19454, 22193, 23096, 25837,\n", + " 30424, 40954, 41864, 42772, 43681, 48276, 50147, 72496,\n", + " 73402, 74336, 77945, 79773, 89965, 97436, 103882, 115895,\n", + " 125522, 132816, 140039, 145471, 160537, 163251, 173736, 180495,\n", + " 188336, 191067, 192869, 193767],\n", + " dtype='int64'), Int64Index([ 85, 2808, 8445, 14882, 19455, 22194, 23097, 25838,\n", + " 30425, 40955, 41865, 42773, 43682, 48277, 50148, 72497,\n", + " 73403, 74337, 77946, 79774, 89966, 97437, 103883, 115896,\n", + " 125523, 132817, 140040, 145472, 160538, 163252, 173737, 180496,\n", + " 188337, 191068, 192870, 193768],\n", + " dtype='int64'), Int64Index([ 86, 2809, 8446, 14883, 19456, 22195, 23098, 25839,\n", + " 30426, 40956, 41866, 42774, 43683, 48278, 50149, 72498,\n", + " 73404, 74338, 77947, 79775, 89967, 97438, 103884, 115897,\n", + " 125524, 132818, 140041, 145473, 160539, 163253, 173738, 180497,\n", + " 188338, 191069, 192871, 193769],\n", + " dtype='int64'), Int64Index([ 87, 2810, 8447, 14884, 19457, 22196, 23099, 25840,\n", + " 30427, 40957, 41867, 42775, 43684, 48279, 50150, 72499,\n", + " 73405, 74339, 77948, 79776, 89968, 97439, 103885, 115898,\n", + " 125525, 132819, 140042, 145474, 160540, 163254, 173739, 180498,\n", + " 188339, 191070, 192872, 193770],\n", + " dtype='int64'), Int64Index([ 88, 2811, 8448, 14885, 19458, 22197, 23100, 25841,\n", + " 30428, 40958, 41868, 42776, 43685, 48280, 50151, 72500,\n", + " 73406, 74340, 77949, 79777, 89969, 97440, 103886, 115899,\n", + " 125526, 132820, 140043, 145475, 160541, 163255, 173740, 180499,\n", + " 188340, 191071, 192873, 193771],\n", + " dtype='int64'), Int64Index([ 89, 2812, 8449, 14886, 19459, 22198, 23101, 25842,\n", + " 30429, 40959, 41869, 42777, 43686, 48281, 50152, 72501,\n", + " 73407, 74341, 77950, 79778, 89970, 97441, 103887, 115900,\n", + " 125527, 132821, 140044, 145476, 160542, 163256, 173741, 180500,\n", + " 188341, 191072, 192874, 193772],\n", + " dtype='int64'), Int64Index([ 90, 2813, 8450, 14887, 19460, 22199, 23102, 25843,\n", + " 30430, 40960, 41870, 42778, 43687, 48282, 50153, 72502,\n", + " 73408, 74342, 77951, 79779, 89971, 97442, 103888, 115901,\n", + " 125528, 132822, 140045, 145477, 160543, 163257, 173742, 180501,\n", + " 188342, 191073, 192875, 193773],\n", + " dtype='int64'), Int64Index([ 91, 2814, 8451, 14888, 19461, 22200, 23103, 25844,\n", + " 30431, 40961, 41871, 42779, 43688, 48283, 50154, 72503,\n", + " 73409, 74343, 77952, 79780, 89972, 97443, 103889, 115902,\n", + " 125529, 132823, 140046, 145478, 160544, 163258, 173743, 180502,\n", + " 188343, 191074, 192876, 193774],\n", + " dtype='int64'), Int64Index([ 92, 2815, 8452, 14889, 19462, 22201, 23104, 25845,\n", + " 30432, 40962, 41872, 42780, 43689, 48284, 50155, 72504,\n", + " 73410, 74344, 77953, 79781, 89973, 97444, 103890, 115903,\n", + " 125530, 132824, 140047, 145479, 160545, 163259, 173744, 180503,\n", + " 188344, 191075, 192877, 193775],\n", + " dtype='int64'), Int64Index([ 93, 2816, 8453, 14890, 19463, 22202, 23105, 25846,\n", + " 30433, 40963, 41873, 42781, 43690, 48285, 50156, 72505,\n", + " 73411, 74345, 77954, 79782, 89974, 97445, 103891, 115904,\n", + " 125531, 132825, 140048, 145480, 160546, 163260, 173745, 180504,\n", + " 188345, 191076, 192878, 193776],\n", + " dtype='int64'), Int64Index([ 94, 2817, 8454, 14891, 19464, 22203, 23106, 25847,\n", + " 30434, 40964, 41874, 42782, 43691, 48286, 50157, 72506,\n", + " 73412, 74346, 77955, 79783, 89975, 97446, 103892, 115905,\n", + " 125532, 132826, 140049, 145481, 160547, 163261, 173746, 180505,\n", + " 188346, 191077, 192879, 193777],\n", + " dtype='int64'), Int64Index([ 95, 2818, 8455, 14892, 19465, 22204, 23107, 25848,\n", + " 30435, 40965, 41875, 42783, 43692, 48287, 50158, 72507,\n", + " 73413, 74347, 77956, 79784, 89976, 97447, 103893, 115906,\n", + " 125533, 132827, 140050, 145482, 160548, 163262, 173747, 180506,\n", + " 188347, 191078, 192880, 193778],\n", + " dtype='int64'), Int64Index([ 96, 2819, 8456, 14893, 19466, 22205, 23108, 25849,\n", + " 30436, 40966, 41876, 42784, 43693, 48288, 50159, 72508,\n", + " 73414, 74348, 77957, 79785, 89977, 97448, 103894, 115907,\n", + " 125534, 132828, 140051, 145483, 160549, 163263, 173748, 180507,\n", + " 188348, 191079, 192881, 193779],\n", + " dtype='int64'), Int64Index([ 97, 2820, 8457, 14894, 19467, 22206, 23109, 25850,\n", + " 30437, 40967, 41877, 42785, 43694, 48289, 50160, 72509,\n", + " 73415, 74349, 77958, 79786, 89978, 97449, 103895, 115908,\n", + " 125535, 132829, 140052, 145484, 160550, 163264, 173749, 180508,\n", + " 188349, 191080, 192882, 193780],\n", + " dtype='int64'), Int64Index([ 98, 2821, 8458, 14895, 19468, 22207, 23110, 25851,\n", + " 30438, 40968, 41878, 42786, 43695, 48290, 50161, 72510,\n", + " 73416, 74350, 77959, 79787, 89979, 97450, 103896, 115909,\n", + " 125536, 132830, 140053, 145485, 160551, 163265, 173750, 180509,\n", + " 188350, 191081, 192883, 193781],\n", + " dtype='int64'), Int64Index([ 99, 2822, 8459, 14896, 19469, 22208, 23111, 25852,\n", + " 30439, 40969, 41879, 42787, 43696, 48291, 50162, 72511,\n", + " 73417, 74351, 77960, 79788, 89980, 97451, 103897, 115910,\n", + " 125537, 132831, 140054, 145486, 160552, 163266, 173751, 180510,\n", + " 188351, 191082, 192884, 193782],\n", + " dtype='int64'), Int64Index([ 100, 2823, 8460, 14897, 19470, 22209, 23112, 25853,\n", + " 30440, 40970, 41880, 42788, 43697, 48292, 50163, 72512,\n", + " 73418, 74352, 77961, 79789, 89981, 97452, 103898, 115911,\n", + " 125538, 132832, 140055, 145487, 160553, 163267, 173752, 180511,\n", + " 188352, 191083, 192885, 193783],\n", + " dtype='int64'), Int64Index([ 101, 2824, 8461, 14898, 19471, 22210, 23113, 25854,\n", + " 30441, 40971, 41881, 42789, 43698, 48293, 50164, 72513,\n", + " 73419, 74353, 77962, 79790, 89982, 97453, 103899, 115912,\n", + " 125539, 132833, 140056, 145488, 160554, 163268, 173753, 180512,\n", + " 188353, 191084, 192886, 193784],\n", + " dtype='int64'), Int64Index([ 102, 2825, 8462, 14899, 19472, 22211, 23114, 25855,\n", + " 30442, 40972, 41882, 42790, 43699, 48294, 50165, 72514,\n", + " 73420, 74354, 77963, 79791, 89983, 97454, 103900, 115913,\n", + " 125540, 132834, 140057, 145489, 160555, 163269, 173754, 180513,\n", + " 188354, 191085, 192887, 193785],\n", + " dtype='int64'), Int64Index([ 103, 2826, 8463, 14900, 19473, 22212, 23115, 25856,\n", + " 30443, 40973, 41883, 42791, 43700, 48295, 50166, 72515,\n", + " 73421, 74355, 77964, 79792, 89984, 97455, 103901, 115914,\n", + " 125541, 132835, 140058, 145490, 160556, 163270, 173755, 180514,\n", + " 188355, 191086, 192888, 193786],\n", + " dtype='int64'), Int64Index([ 104, 2827, 8464, 14901, 19474, 22213, 23116, 25857,\n", + " 30444, 40974, 41884, 42792, 43701, 48296, 50167, 72516,\n", + " 73422, 74356, 77965, 79793, 89985, 97456, 103902, 115915,\n", + " 125542, 132836, 140059, 145491, 160557, 163271, 173756, 180515,\n", + " 188356, 191087, 192889, 193787],\n", + " dtype='int64'), Int64Index([ 105, 2828, 8465, 14902, 19475, 22214, 23117, 25858,\n", + " 30445, 40975, 41885, 42793, 43702, 48297, 50168, 72517,\n", + " 73423, 74357, 77966, 79794, 89986, 97457, 103903, 115916,\n", + " 125543, 132837, 140060, 145492, 160558, 163272, 173757, 180516,\n", + " 188357, 191088, 192890, 193788],\n", + " dtype='int64'), Int64Index([ 106, 2829, 8466, 14903, 19476, 22215, 23118, 25859,\n", + " 30446, 40976, 41886, 42794, 43703, 48298, 50169, 72518,\n", + " 73424, 74358, 77967, 79795, 89987, 97458, 103904, 115917,\n", + " 125544, 132838, 140061, 145493, 160559, 163273, 173758, 180517,\n", + " 188358, 191089, 192891, 193789],\n", + " dtype='int64'), Int64Index([ 107, 2830, 8467, 14904, 19477, 22216, 23119, 25860,\n", + " 30447, 40977, 41887, 42795, 43704, 48299, 50170, 72519,\n", + " 73425, 74359, 77968, 79796, 89988, 97459, 103905, 115918,\n", + " 125545, 132839, 140062, 145494, 160560, 163274, 173759, 180518,\n", + " 188359, 191090, 192892, 193790],\n", + " dtype='int64'), Int64Index([ 108, 2831, 8468, 14905, 19478, 22217, 23120, 25861,\n", + " 30448, 40978, 41888, 42796, 43705, 48300, 50171, 72520,\n", + " 73426, 74360, 77969, 79797, 89989, 97460, 103906, 115919,\n", + " 125546, 132840, 140063, 145495, 160561, 163275, 173760, 180519,\n", + " 188360, 191091, 192893, 193791],\n", + " dtype='int64'), Int64Index([ 109, 2832, 8469, 14906, 19479, 22218, 23121, 25862,\n", + " 30449, 40979, 41889, 42797, 43706, 48301, 50172, 72521,\n", + " 73427, 74361, 77970, 79798, 89990, 97461, 103907, 115920,\n", + " 125547, 132841, 140064, 145496, 160562, 163276, 173761, 180520,\n", + " 188361, 191092, 192894, 193792],\n", + " dtype='int64'), Int64Index([ 110, 2833, 8470, 14907, 19480, 22219, 23122, 25863,\n", + " 30450, 40980, 41890, 42798, 43707, 48302, 50173, 72522,\n", + " 73428, 74362, 77971, 79799, 89991, 97462, 103908, 115921,\n", + " 125548, 132842, 140065, 145497, 160563, 163277, 173762, 180521,\n", + " 188362, 191093, 192895, 193793],\n", + " dtype='int64'), Int64Index([ 111, 2834, 8471, 14908, 19481, 22220, 23123, 25864,\n", + " 30451, 40981, 41891, 42799, 43708, 48303, 50174, 72523,\n", + " 73429, 74363, 77972, 79800, 89992, 97463, 103909, 115922,\n", + " 125549, 132843, 140066, 145498, 160564, 163278, 173763, 180522,\n", + " 188363, 191094, 192896, 193794],\n", + " dtype='int64'), Int64Index([ 112, 2835, 8472, 14909, 19482, 22221, 23124, 25865,\n", + " 30452, 40982, 41892, 42800, 43709, 48304, 50175, 72524,\n", + " 73430, 74364, 77973, 79801, 89993, 97464, 103910, 115923,\n", + " 125550, 132844, 140067, 145499, 160565, 163279, 173764, 180523,\n", + " 188364, 191095, 192897, 193795],\n", + " dtype='int64'), Int64Index([ 113, 2836, 8473, 14910, 19483, 22222, 23125, 25866,\n", + " 30453, 40983, 41893, 42801, 43710, 48305, 50176, 72525,\n", + " 73431, 74365, 77974, 79802, 89994, 97465, 103911, 115924,\n", + " 125551, 132845, 140068, 145500, 160566, 163280, 173765, 180524,\n", + " 188365, 191096, 192898, 193796],\n", + " dtype='int64'), Int64Index([ 114, 2837, 8474, 14911, 19484, 22223, 23126, 25867,\n", + " 30454, 40984, 41894, 42802, 43711, 48306, 50177, 72526,\n", + " 73432, 74366, 77975, 79803, 89995, 97466, 103912, 115925,\n", + " 125552, 132846, 140069, 145501, 160567, 163281, 173766, 180525,\n", + " 188366, 191097, 192899, 193797],\n", + " dtype='int64'), Int64Index([ 115, 2838, 8475, 14912, 19485, 22224, 23127, 25868,\n", + " 30455, 40985, 41895, 42803, 43712, 48307, 50178, 72527,\n", + " 73433, 74367, 77976, 79804, 89996, 97467, 103913, 115926,\n", + " 125553, 132847, 140070, 145502, 160568, 163282, 173767, 180526,\n", + " 188367, 191098, 192900, 193798],\n", + " dtype='int64'), Int64Index([ 116, 2839, 8476, 14913, 19486, 22225, 23128, 25869,\n", + " 30456, 40986, 41896, 42804, 43713, 48308, 50179, 72528,\n", + " 73434, 74368, 77977, 79805, 89997, 97468, 103914, 115927,\n", + " 125554, 132848, 140071, 145503, 160569, 163283, 173768, 180527,\n", + " 188368, 191099, 192901, 193799],\n", + " dtype='int64'), Int64Index([ 117, 2840, 8477, 14914, 19487, 22226, 23129, 25870,\n", + " 30457, 40987, 41897, 42805, 43714, 48309, 50180, 72529,\n", + " 73435, 74369, 77978, 79806, 89998, 97469, 103915, 115928,\n", + " 125555, 132849, 140072, 145504, 160570, 163284, 173769, 180528,\n", + " 188369, 191100, 192902, 193800],\n", + " dtype='int64'), Int64Index([ 118, 2841, 8478, 14915, 19488, 22227, 23130, 25871,\n", + " 30458, 40988, 41898, 42806, 43715, 48310, 50181, 72530,\n", + " 73436, 74370, 77979, 79807, 89999, 97470, 103916, 115929,\n", + " 125556, 132850, 140073, 145505, 160571, 163285, 173770, 180529,\n", + " 188370, 191101, 192903, 193801],\n", + " dtype='int64'), Int64Index([ 119, 2842, 8479, 14916, 19489, 22228, 23131, 25872,\n", + " 30459, 40989, 41899, 42807, 43716, 48311, 50182, 72531,\n", + " 73437, 74371, 77980, 79808, 90000, 97471, 103917, 115930,\n", + " 125557, 132851, 140074, 145506, 160572, 163286, 173771, 180530,\n", + " 188371, 191102, 192904, 193802],\n", + " dtype='int64'), Int64Index([ 120, 2843, 8480, 14917, 19490, 22229, 23132, 25873,\n", + " 30460, 40990, 41900, 42808, 43717, 48312, 50183, 72532,\n", + " 73438, 74372, 77981, 79809, 90001, 97472, 103918, 115931,\n", + " 125558, 132852, 140075, 145507, 160573, 163287, 173772, 180531,\n", + " 188372, 191103, 192905, 193803],\n", + " dtype='int64'), Int64Index([ 121, 2844, 8481, 14918, 19491, 22230, 23133, 25874,\n", + " 30461, 40991, 41901, 42809, 43718, 48313, 50184, 72533,\n", + " 73439, 74373, 77982, 79810, 90002, 97473, 103919, 115932,\n", + " 125559, 132853, 140076, 145508, 160574, 163288, 173773, 180532,\n", + " 188373, 191104, 192906, 193804],\n", + " dtype='int64'), Int64Index([ 122, 2845, 8482, 14919, 19492, 22231, 23134, 25875,\n", + " 30462, 40992, 41902, 42810, 43719, 48314, 50185, 72534,\n", + " 73440, 74374, 77983, 79811, 90003, 97474, 103920, 115933,\n", + " 125560, 132854, 140077, 145509, 160575, 163289, 173774, 180533,\n", + " 188374, 191105, 192907, 193805],\n", + " dtype='int64'), Int64Index([ 123, 2846, 8483, 14920, 19493, 22232, 23135, 25876,\n", + " 30463, 40993, 41903, 42811, 43720, 48315, 50186, 72535,\n", + " 73441, 74375, 77984, 79812, 90004, 97475, 103921, 115934,\n", + " 125561, 132855, 140078, 145510, 160576, 163290, 173775, 180534,\n", + " 188375, 191106, 192908, 193806],\n", + " dtype='int64'), Int64Index([ 124, 2847, 8484, 14921, 19494, 22233, 23136, 25877,\n", + " 30464, 40994, 41904, 42812, 43721, 48316, 50187, 72536,\n", + " 73442, 74376, 77985, 79813, 90005, 97476, 103922, 115935,\n", + " 125562, 132856, 140079, 145511, 160577, 163291, 173776, 180535,\n", + " 188376, 191107, 192909, 193807],\n", + " dtype='int64'), Int64Index([ 125, 2848, 8485, 14922, 19495, 22234, 23137, 25878,\n", + " 30465, 40995, 41905, 42813, 43722, 48317, 50188, 72537,\n", + " 73443, 74377, 77986, 79814, 90006, 97477, 103923, 115936,\n", + " 125563, 132857, 140080, 145512, 160578, 163292, 173777, 180536,\n", + " 188377, 191108, 192910, 193808],\n", + " dtype='int64'), Int64Index([ 126, 2849, 8486, 14923, 19496, 22235, 23138, 25879,\n", + " 30466, 40996, 41906, 42814, 43723, 48318, 50189, 72538,\n", + " 73444, 74378, 77987, 79815, 90007, 97478, 103924, 115937,\n", + " 125564, 132858, 140081, 145513, 160579, 163293, 173778, 180537,\n", + " 188378, 191109, 192911, 193809],\n", + " dtype='int64'), Int64Index([ 127, 2850, 8487, 14924, 19497, 22236, 23139, 25880,\n", + " 30467, 40997, 41907, 42815, 43724, 48319, 50190, 72539,\n", + " 73445, 74379, 77988, 79816, 90008, 97479, 103925, 115938,\n", + " 125565, 132859, 140082, 145514, 160580, 163294, 173779, 180538,\n", + " 188379, 191110, 192912, 193810],\n", + " dtype='int64'), Int64Index([ 128, 2851, 8488, 14925, 19498, 22237, 23140, 25881,\n", + " 30468, 40998, 41908, 42816, 43725, 48320, 50191, 72540,\n", + " 73446, 74380, 77989, 79817, 90009, 97480, 103926, 115939,\n", + " 125566, 132860, 140083, 145515, 160581, 163295, 173780, 180539,\n", + " 188380, 191111, 192913, 193811],\n", + " dtype='int64'), Int64Index([ 129, 2852, 8489, 14926, 19499, 22238, 23141, 25882,\n", + " 30469, 40999, 41909, 42817, 43726, 48321, 50192, 72541,\n", + " 73447, 74381, 77990, 79818, 90010, 97481, 103927, 115940,\n", + " 125567, 132861, 140084, 145516, 160582, 163296, 173781, 180540,\n", + " 188381, 191112, 192914, 193812],\n", + " dtype='int64'), Int64Index([ 130, 2853, 8490, 14927, 19500, 22239, 23142, 25883,\n", + " 30470, 41000, 41910, 42818, 43727, 48322, 50193, 72542,\n", + " 73448, 74382, 77991, 79819, 90011, 97482, 103928, 115941,\n", + " 125568, 132862, 140085, 145517, 160583, 163297, 173782, 180541,\n", + " 188382, 191113, 192915, 193813],\n", + " dtype='int64'), Int64Index([ 131, 2854, 8491, 14928, 19501, 22240, 23143, 25884,\n", + " 30471, 41001, 41911, 42819, 43728, 48323, 50194, 72543,\n", + " 73449, 74383, 77992, 79820, 90012, 97483, 103929, 115942,\n", + " 125569, 132863, 140086, 145518, 160584, 163298, 173783, 180542,\n", + " 188383, 191114, 192916, 193814],\n", + " dtype='int64'), Int64Index([ 132, 2855, 8492, 14929, 19502, 22241, 23144, 25885,\n", + " 30472, 41002, 41912, 42820, 43729, 48324, 50195, 72544,\n", + " 73450, 74384, 77993, 79821, 90013, 97484, 103930, 115943,\n", + " 125570, 132864, 140087, 145519, 160585, 163299, 173784, 180543,\n", + " 188384, 191115, 192917, 193815],\n", + " dtype='int64'), Int64Index([ 133, 2856, 8493, 14930, 19503, 22242, 23145, 25886,\n", + " 30473, 41003, 41913, 42821, 43730, 48325, 50196, 72545,\n", + " 73451, 74385, 77994, 79822, 90014, 97485, 103931, 115944,\n", + " 125571, 132865, 140088, 145520, 160586, 163300, 173785, 180544,\n", + " 188385, 191116, 192918, 193816],\n", + " dtype='int64'), Int64Index([ 134, 2857, 8494, 14931, 19504, 22243, 23146, 25887,\n", + " 30474, 41004, 41914, 42822, 43731, 48326, 50197, 72546,\n", + " 73452, 74386, 77995, 79823, 90015, 97486, 103932, 115945,\n", + " 125572, 132866, 140089, 145521, 160587, 163301, 173786, 180545,\n", + " 188386, 191117, 192919, 193817],\n", + " dtype='int64'), Int64Index([ 135, 2858, 8495, 14932, 19505, 22244, 23147, 25888,\n", + " 30475, 41005, 41915, 42823, 43732, 48327, 50198, 72547,\n", + " 73453, 74387, 77996, 79824, 90016, 97487, 103933, 115946,\n", + " 125573, 132867, 140090, 145522, 160588, 163302, 173787, 180546,\n", + " 188387, 191118, 192920, 193818],\n", + " dtype='int64'), Int64Index([ 136, 2859, 8496, 14933, 19506, 22245, 23148, 25889,\n", + " 30476, 41006, 41916, 42824, 43733, 48328, 50199, 72548,\n", + " 73454, 74388, 77997, 79825, 90017, 97488, 103934, 115947,\n", + " 125574, 132868, 140091, 145523, 160589, 163303, 173788, 180547,\n", + " 188388, 191119, 192921, 193819],\n", + " dtype='int64'), Int64Index([ 137, 2860, 8497, 14934, 19507, 22246, 23149, 25890,\n", + " 30477, 41007, 41917, 42825, 43734, 48329, 50200, 72549,\n", + " 73455, 74389, 77998, 79826, 90018, 97489, 103935, 115948,\n", + " 125575, 132869, 140092, 145524, 160590, 163304, 173789, 180548,\n", + " 188389, 191120, 192922, 193820],\n", + " dtype='int64'), Int64Index([ 138, 2861, 8498, 14935, 19508, 22247, 23150, 25891,\n", + " 30478, 41008, 41918, 42826, 43735, 48330, 50201, 72550,\n", + " 73456, 74390, 77999, 79827, 90019, 97490, 103936, 115949,\n", + " 125576, 132870, 140093, 145525, 160591, 163305, 173790, 180549,\n", + " 188390, 191121, 192923, 193821],\n", + " dtype='int64'), Int64Index([ 139, 2862, 8499, 14936, 19509, 22248, 23151, 25892,\n", + " 30479, 41009, 41919, 42827, 43736, 48331, 50202, 72551,\n", + " 73457, 74391, 78000, 79828, 90020, 97491, 103937, 115950,\n", + " 125577, 132871, 140094, 145526, 160592, 163306, 173791, 180550,\n", + " 188391, 191122, 192924, 193822],\n", + " dtype='int64'), Int64Index([ 140, 2863, 8500, 14937, 19510, 22249, 23152, 25893,\n", + " 30480, 41010, 41920, 42828, 43737, 48332, 50203, 72552,\n", + " 73458, 74392, 78001, 79829, 90021, 97492, 103938, 115951,\n", + " 125578, 132872, 140095, 145527, 160593, 163307, 173792, 180551,\n", + " 188392, 191123, 192925, 193823],\n", + " dtype='int64'), Int64Index([ 141, 2864, 8501, 14938, 19511, 22250, 23153, 25894,\n", + " 30481, 41011, 41921, 42829, 43738, 48333, 50204, 72553,\n", + " 73459, 74393, 78002, 79830, 90022, 97493, 103939, 115952,\n", + " 125579, 132873, 140096, 145528, 160594, 163308, 173793, 180552,\n", + " 188393, 191124, 192926, 193824],\n", + " dtype='int64'), Int64Index([ 142, 2865, 8502, 14939, 19512, 22251, 23154, 25895,\n", + " 30482, 41012, 41922, 42830, 43739, 48334, 50205, 72554,\n", + " 73460, 74394, 78003, 79831, 90023, 97494, 103940, 115953,\n", + " 125580, 132874, 140097, 145529, 160595, 163309, 173794, 180553,\n", + " 188394, 191125, 192927, 193825],\n", + " dtype='int64'), Int64Index([ 143, 2866, 8503, 14940, 19513, 22252, 23155, 25896,\n", + " 30483, 41013, 41923, 42831, 43740, 48335, 50206, 72555,\n", + " 73461, 74395, 78004, 79832, 90024, 97495, 103941, 115954,\n", + " 125581, 132875, 140098, 145530, 160596, 163310, 173795, 180554,\n", + " 188395, 191126, 192928, 193826],\n", + " dtype='int64'), Int64Index([ 144, 2867, 8504, 14941, 19514, 22253, 23156, 25897,\n", + " 30484, 41014, 41924, 42832, 43741, 48336, 50207, 72556,\n", + " 73462, 74396, 78005, 79833, 90025, 97496, 103942, 115955,\n", + " 125582, 132876, 140099, 145531, 160597, 163311, 173796, 180555,\n", + " 188396, 191127, 192929, 193827],\n", + " dtype='int64'), Int64Index([ 145, 2868, 8505, 14942, 19515, 22254, 23157, 25898,\n", + " 30485, 41015, 41925, 42833, 43742, 48337, 50208, 72557,\n", + " 73463, 74397, 78006, 79834, 90026, 97497, 103943, 115956,\n", + " 125583, 132877, 140100, 145532, 160598, 163312, 173797, 180556,\n", + " 188397, 191128, 192930, 193828],\n", + " dtype='int64'), Int64Index([ 146, 2869, 8506, 14943, 19516, 22255, 23158, 25899,\n", + " 30486, 41016, 41926, 42834, 43743, 48338, 50209, 72558,\n", + " 73464, 74398, 78007, 79835, 90027, 97498, 103944, 115957,\n", + " 125584, 132878, 140101, 145533, 160599, 163313, 173798, 180557,\n", + " 188398, 191129, 192931, 193829],\n", + " dtype='int64'), Int64Index([ 147, 2870, 8507, 14944, 19517, 22256, 23159, 25900,\n", + " 30487, 41017, 41927, 42835, 43744, 48339, 50210, 72559,\n", + " 73465, 74399, 78008, 79836, 90028, 97499, 103945, 115958,\n", + " 125585, 132879, 140102, 145534, 160600, 163314, 173799, 180558,\n", + " 188399, 191130, 192932, 193830],\n", + " dtype='int64'), Int64Index([ 148, 2871, 8508, 14945, 19518, 22257, 23160, 25901,\n", + " 30488, 41018, 41928, 42836, 43745, 48340, 50211, 72560,\n", + " 73466, 74400, 78009, 79837, 90029, 97500, 103946, 115959,\n", + " 125586, 132880, 140103, 145535, 160601, 163315, 173800, 180559,\n", + " 188400, 191131, 192933, 193831],\n", + " dtype='int64'), Int64Index([ 149, 2872, 8509, 14946, 19519, 22258, 23161, 25902,\n", + " 30489, 41019, 41929, 42837, 43746, 48341, 50212, 72561,\n", + " 73467, 74401, 78010, 79838, 90030, 97501, 103947, 115960,\n", + " 125587, 132881, 140104, 145536, 160602, 163316, 173801, 180560,\n", + " 188401, 191132, 192934, 193832],\n", + " dtype='int64'), Int64Index([ 150, 2873, 8510, 14947, 19520, 22259, 23162, 25903,\n", + " 30490, 41020, 41930, 42838, 43747, 48342, 50213, 72562,\n", + " 73468, 74402, 78011, 79839, 90031, 97502, 103948, 115961,\n", + " 125588, 132882, 140105, 145537, 160603, 163317, 173802, 180561,\n", + " 188402, 191133, 192935, 193833],\n", + " dtype='int64'), Int64Index([ 151, 2874, 8511, 14948, 19521, 22260, 23163, 25904,\n", + " 30491, 41021, 41931, 42839, 43748, 48343, 50214, 72563,\n", + " 73469, 74403, 78012, 79840, 90032, 97503, 103949, 115962,\n", + " 125589, 132883, 140106, 145538, 160604, 163318, 173803, 180562,\n", + " 188403, 191134, 192936, 193834],\n", + " dtype='int64'), Int64Index([ 152, 2875, 8512, 14949, 19522, 22261, 23164, 25905,\n", + " 30492, 41022, 41932, 42840, 43749, 48344, 50215, 72564,\n", + " 73470, 74404, 78013, 79841, 90033, 97504, 103950, 115963,\n", + " 125590, 132884, 140107, 145539, 160605, 163319, 173804, 180563,\n", + " 188404, 191135, 192937, 193835],\n", + " dtype='int64'), Int64Index([ 153, 2876, 8513, 14950, 19523, 22262, 23165, 25906,\n", + " 30493, 41023, 41933, 42841, 43750, 48345, 50216, 72565,\n", + " 73471, 74405, 78014, 79842, 90034, 97505, 103951, 115964,\n", + " 125591, 132885, 140108, 145540, 160606, 163320, 173805, 180564,\n", + " 188405, 191136, 192938, 193836],\n", + " dtype='int64'), Int64Index([ 154, 2877, 8514, 14951, 19524, 22263, 23166, 25907,\n", + " 30494, 41024, 41934, 42842, 43751, 48346, 50217, 72566,\n", + " 73472, 74406, 78015, 79843, 90035, 97506, 103952, 115965,\n", + " 125592, 132886, 140109, 145541, 160607, 163321, 173806, 180565,\n", + " 188406, 191137, 192939, 193837],\n", + " dtype='int64'), Int64Index([ 155, 2878, 8515, 14952, 19525, 22264, 23167, 25908,\n", + " 30495, 41025, 41935, 42843, 43752, 48347, 50218, 72567,\n", + " 73473, 74407, 78016, 79844, 90036, 97507, 103953, 115966,\n", + " 125593, 132887, 140110, 145542, 160608, 163322, 173807, 180566,\n", + " 188407, 191138, 192940, 193838],\n", + " dtype='int64'), Int64Index([ 156, 2879, 8516, 14953, 19526, 22265, 23168, 25909,\n", + " 30496, 41026, 41936, 42844, 43753, 48348, 50219, 72568,\n", + " 73474, 74408, 78017, 79845, 90037, 97508, 103954, 115967,\n", + " 125594, 132888, 140111, 145543, 160609, 163323, 173808, 180567,\n", + " 188408, 191139, 192941, 193839],\n", + " dtype='int64'), Int64Index([ 157, 2880, 8517, 14954, 19527, 22266, 23169, 25910,\n", + " 30497, 41027, 41937, 42845, 43754, 48349, 50220, 72569,\n", + " 73475, 74409, 78018, 79846, 90038, 97509, 103955, 115968,\n", + " 125595, 132889, 140112, 145544, 160610, 163324, 173809, 180568,\n", + " 188409, 191140, 192942, 193840],\n", + " dtype='int64'), Int64Index([ 158, 2881, 8518, 14955, 19528, 22267, 23170, 25911,\n", + " 30498, 41028, 41938, 42846, 43755, 48350, 50221, 72570,\n", + " 73476, 74410, 78019, 79847, 90039, 97510, 103956, 115969,\n", + " 125596, 132890, 140113, 145545, 160611, 163325, 173810, 180569,\n", + " 188410, 191141, 192943, 193841],\n", + " dtype='int64'), Int64Index([ 159, 2882, 8519, 14956, 19529, 22268, 23171, 25912,\n", + " 30499, 41029, 41939, 42847, 43756, 48351, 50222, 72571,\n", + " 73477, 74411, 78020, 79848, 90040, 97511, 103957, 115970,\n", + " 125597, 132891, 140114, 145546, 160612, 163326, 173811, 180570,\n", + " 188411, 191142, 192944, 193842],\n", + " dtype='int64'), Int64Index([ 160, 2883, 8520, 14957, 19530, 22269, 23172, 25913,\n", + " 30500, 41030, 41940, 42848, 43757, 48352, 50223, 72572,\n", + " 73478, 74412, 78021, 79849, 90041, 97512, 103958, 115971,\n", + " 125598, 132892, 140115, 145547, 160613, 163327, 173812, 180571,\n", + " 188412, 191143, 192945, 193843],\n", + " dtype='int64'), Int64Index([ 161, 2884, 8521, 14958, 19531, 22270, 23173, 25914,\n", + " 30501, 41031, 41941, 42849, 43758, 48353, 50224, 72573,\n", + " 73479, 74413, 78022, 79850, 90042, 97513, 103959, 115972,\n", + " 125599, 132893, 140116, 145548, 160614, 163328, 173813, 180572,\n", + " 188413, 191144, 192946, 193844],\n", + " dtype='int64'), Int64Index([ 162, 2885, 8522, 14959, 19532, 22271, 23174, 25915,\n", + " 30502, 41032, 41942, 42850, 43759, 48354, 50225, 72574,\n", + " 73480, 74414, 78023, 79851, 90043, 97514, 103960, 115973,\n", + " 125600, 132894, 140117, 145549, 160615, 163329, 173814, 180573,\n", + " 188414, 191145, 192947, 193845],\n", + " dtype='int64'), Int64Index([ 163, 2886, 8523, 14960, 19533, 22272, 23175, 25916,\n", + " 30503, 41033, 41943, 42851, 43760, 48355, 50226, 72575,\n", + " 73481, 74415, 78024, 79852, 90044, 97515, 103961, 115974,\n", + " 125601, 132895, 140118, 145550, 160616, 163330, 173815, 180574,\n", + " 188415, 191146, 192948, 193846],\n", + " dtype='int64'), Int64Index([ 164, 2887, 8524, 14961, 19534, 22273, 23176, 25917,\n", + " 30504, 41034, 41944, 42852, 43761, 48356, 50227, 72576,\n", + " 73482, 74416, 78025, 79853, 90045, 97516, 103962, 115975,\n", + " 125602, 132896, 140119, 145551, 160617, 163331, 173816, 180575,\n", + " 188416, 191147, 192949, 193847],\n", + " dtype='int64'), Int64Index([ 165, 2888, 8525, 14962, 19535, 22274, 23177, 25918,\n", + " 30505, 41035, 41945, 42853, 43762, 48357, 50228, 72577,\n", + " 73483, 74417, 78026, 79854, 90046, 97517, 103963, 115976,\n", + " 125603, 132897, 140120, 145552, 160618, 163332, 173817, 180576,\n", + " 188417, 191148, 192950, 193848],\n", + " dtype='int64'), Int64Index([ 166, 2889, 8526, 14963, 19536, 22275, 23178, 25919,\n", + " 30506, 41036, 41946, 42854, 43763, 48358, 50229, 72578,\n", + " 73484, 74418, 78027, 79855, 90047, 97518, 103964, 115977,\n", + " 125604, 132898, 140121, 145553, 160619, 163333, 173818, 180577,\n", + " 188418, 191149, 192951, 193849],\n", + " dtype='int64'), Int64Index([ 167, 2890, 8527, 14964, 19537, 22276, 23179, 25920,\n", + " 30507, 41037, 41947, 42855, 43764, 48359, 50230, 72579,\n", + " 73485, 74419, 78028, 79856, 90048, 97519, 103965, 115978,\n", + " 125605, 132899, 140122, 145554, 160620, 163334, 173819, 180578,\n", + " 188419, 191150, 192952, 193850],\n", + " dtype='int64'), Int64Index([ 168, 2891, 8528, 14965, 19538, 22277, 23180, 25921,\n", + " 30508, 41038, 41948, 42856, 43765, 48360, 50231, 72580,\n", + " 73486, 74420, 78029, 79857, 90049, 97520, 103966, 115979,\n", + " 125606, 132900, 140123, 145555, 160621, 163335, 173820, 180579,\n", + " 188420, 191151, 192953, 193851],\n", + " dtype='int64'), Int64Index([ 169, 2892, 8529, 14966, 19539, 22278, 23181, 25922,\n", + " 30509, 41039, 41949, 42857, 43766, 48361, 50232, 72581,\n", + " 73487, 74421, 78030, 79858, 90050, 97521, 103967, 115980,\n", + " 125607, 132901, 140124, 145556, 160622, 163336, 173821, 180580,\n", + " 188421, 191152, 192954, 193852],\n", + " dtype='int64'), Int64Index([ 170, 2893, 8530, 14967, 19540, 22279, 23182, 25923,\n", + " 30510, 41040, 41950, 42858, 43767, 48362, 50233, 72582,\n", + " 73488, 74422, 78031, 79859, 90051, 97522, 103968, 115981,\n", + " 125608, 132902, 140125, 145557, 160623, 163337, 173822, 180581,\n", + " 188422, 191153, 192955, 193853],\n", + " dtype='int64'), Int64Index([ 171, 2894, 8531, 14968, 19541, 22280, 23183, 25924,\n", + " 30511, 41041, 41951, 42859, 43768, 48363, 50234, 72583,\n", + " 73489, 74423, 78032, 79860, 90052, 97523, 103969, 115982,\n", + " 125609, 132903, 140126, 145558, 160624, 163338, 173823, 180582,\n", + " 188423, 191154, 192956, 193854],\n", + " dtype='int64'), Int64Index([ 172, 2895, 8532, 14969, 19542, 22281, 23184, 25925,\n", + " 30512, 41042, 41952, 42860, 43769, 48364, 50235, 72584,\n", + " 73490, 74424, 78033, 79861, 90053, 97524, 103970, 115983,\n", + " 125610, 132904, 140127, 145559, 160625, 163339, 173824, 180583,\n", + " 188424, 191155, 192957, 193855],\n", + " dtype='int64'), Int64Index([ 173, 2896, 8533, 14970, 19543, 22282, 23185, 25926,\n", + " 30513, 41043, 41953, 42861, 43770, 48365, 50236, 72585,\n", + " 73491, 74425, 78034, 79862, 90054, 97525, 103971, 115984,\n", + " 125611, 132905, 140128, 145560, 160626, 163340, 173825, 180584,\n", + " 188425, 191156, 192958, 193856],\n", + " dtype='int64'), Int64Index([ 174, 2897, 8534, 14971, 19544, 22283, 23186, 25927,\n", + " 30514, 41044, 41954, 42862, 43771, 48366, 50237, 72586,\n", + " 73492, 74426, 78035, 79863, 90055, 97526, 103972, 115985,\n", + " 125612, 132906, 140129, 145561, 160627, 163341, 173826, 180585,\n", + " 188426, 191157, 192959, 193857],\n", + " dtype='int64'), Int64Index([ 175, 2898, 8535, 14972, 19545, 22284, 23187, 25928,\n", + " 30515, 41045, 41955, 42863, 43772, 48367, 50238, 72587,\n", + " 73493, 74427, 78036, 79864, 90056, 97527, 103973, 115986,\n", + " 125613, 132907, 140130, 145562, 160628, 163342, 173827, 180586,\n", + " 188427, 191158, 192960, 193858],\n", + " dtype='int64'), Int64Index([ 176, 2899, 8536, 14973, 19546, 22285, 23188, 25929,\n", + " 30516, 41046, 41956, 42864, 43773, 48368, 50239, 72588,\n", + " 73494, 74428, 78037, 79865, 90057, 97528, 103974, 115987,\n", + " 125614, 132908, 140131, 145563, 160629, 163343, 173828, 180587,\n", + " 188428, 191159, 192961, 193859],\n", + " dtype='int64'), Int64Index([ 177, 2900, 8537, 14974, 19547, 22286, 23189, 25930,\n", + " 30517, 41047, 41957, 42865, 43774, 48369, 50240, 72589,\n", + " 73495, 74429, 78038, 79866, 90058, 97529, 103975, 115988,\n", + " 125615, 132909, 140132, 145564, 160630, 163344, 173829, 180588,\n", + " 188429, 191160, 192962, 193860],\n", + " dtype='int64'), Int64Index([ 178, 2901, 8538, 14975, 19548, 22287, 23190, 25931,\n", + " 30518, 41048, 41958, 42866, 43775, 48370, 50241, 72590,\n", + " 73496, 74430, 78039, 79867, 90059, 97530, 103976, 115989,\n", + " 125616, 132910, 140133, 145565, 160631, 163345, 173830, 180589,\n", + " 188430, 191161, 192963, 193861],\n", + " dtype='int64'), Int64Index([ 179, 2902, 8539, 14976, 19549, 22288, 23191, 25932,\n", + " 30519, 41049, 41959, 42867, 43776, 48371, 50242, 72591,\n", + " 73497, 74431, 78040, 79868, 90060, 97531, 103977, 115990,\n", + " 125617, 132911, 140134, 145566, 160632, 163346, 173831, 180590,\n", + " 188431, 191162, 192964, 193862],\n", + " dtype='int64'), Int64Index([ 180, 2903, 8540, 14977, 19550, 22289, 23192, 25933,\n", + " 30520, 41050, 41960, 42868, 43777, 48372, 50243, 72592,\n", + " 73498, 74432, 78041, 79869, 90061, 97532, 103978, 115991,\n", + " 125618, 132912, 140135, 145567, 160633, 163347, 173832, 180591,\n", + " 188432, 191163, 192965, 193863],\n", + " dtype='int64'), Int64Index([ 181, 2904, 8541, 14978, 19551, 22290, 23193, 25934,\n", + " 30521, 41051, 41961, 42869, 43778, 48373, 50244, 72593,\n", + " 73499, 74433, 78042, 79870, 90062, 97533, 103979, 115992,\n", + " 125619, 132913, 140136, 145568, 160634, 163348, 173833, 180592,\n", + " 188433, 191164, 192966, 193864],\n", + " dtype='int64'), Int64Index([ 182, 2905, 8542, 14979, 19552, 22291, 23194, 25935,\n", + " 30522, 41052, 41962, 42870, 43779, 48374, 50245, 72594,\n", + " 73500, 74434, 78043, 79871, 90063, 97534, 103980, 115993,\n", + " 125620, 132914, 140137, 145569, 160635, 163349, 173834, 180593,\n", + " 188434, 191165, 192967, 193865],\n", + " dtype='int64'), Int64Index([ 183, 2906, 8543, 14980, 19553, 22292, 23195, 25936,\n", + " 30523, 41053, 41963, 42871, 43780, 48375, 50246, 72595,\n", + " 73501, 74435, 78044, 79872, 90064, 97535, 103981, 115994,\n", + " 125621, 132915, 140138, 145570, 160636, 163350, 173835, 180594,\n", + " 188435, 191166, 192968, 193866],\n", + " dtype='int64'), Int64Index([ 184, 2907, 8544, 14981, 19554, 22293, 23196, 25937,\n", + " 30524, 41054, 41964, 42872, 43781, 48376, 50247, 72596,\n", + " 73502, 74436, 78045, 79873, 90065, 97536, 103982, 115995,\n", + " 125622, 132916, 140139, 145571, 160637, 163351, 173836, 180595,\n", + " 188436, 191167, 192969, 193867],\n", + " dtype='int64'), Int64Index([ 185, 2908, 8545, 14982, 19555, 22294, 23197, 25938,\n", + " 30525, 41055, 41965, 42873, 43782, 48377, 50248, 72597,\n", + " 73503, 74437, 78046, 79874, 90066, 97537, 103983, 115996,\n", + " 125623, 132917, 140140, 145572, 160638, 163352, 173837, 180596,\n", + " 188437, 191168, 192970, 193868],\n", + " dtype='int64'), Int64Index([ 186, 2909, 8546, 14983, 19556, 22295, 23198, 25939,\n", + " 30526, 41056, 41966, 42874, 43783, 48378, 50249, 72598,\n", + " 73504, 74438, 78047, 79875, 90067, 97538, 103984, 115997,\n", + " 125624, 132918, 140141, 145573, 160639, 163353, 173838, 180597,\n", + " 188438, 191169, 192971, 193869],\n", + " dtype='int64'), Int64Index([ 187, 2910, 8547, 14984, 19557, 22296, 23199, 25940,\n", + " 30527, 41057, 41967, 42875, 43784, 48379, 50250, 72599,\n", + " 73505, 74439, 78048, 79876, 90068, 97539, 103985, 115998,\n", + " 125625, 132919, 140142, 145574, 160640, 163354, 173839, 180598,\n", + " 188439, 191170, 192972, 193870],\n", + " dtype='int64'), Int64Index([ 188, 2911, 8548, 14985, 19558, 22297, 23200, 25941,\n", + " 30528, 41058, 41968, 42876, 43785, 48380, 50251, 72600,\n", + " 73506, 74440, 78049, 79877, 90069, 97540, 103986, 115999,\n", + " 125626, 132920, 140143, 145575, 160641, 163355, 173840, 180599,\n", + " 188440, 191171, 192973, 193871],\n", + " dtype='int64'), Int64Index([ 189, 2912, 8549, 14986, 19559, 22298, 23201, 25942,\n", + " 30529, 41059, 41969, 42877, 43786, 48381, 50252, 72601,\n", + " 73507, 74441, 78050, 79878, 90070, 97541, 103987, 116000,\n", + " 125627, 132921, 140144, 145576, 160642, 163356, 173841, 180600,\n", + " 188441, 191172, 192974, 193872],\n", + " dtype='int64'), Int64Index([ 190, 2913, 8550, 14987, 19560, 22299, 23202, 25943,\n", + " 30530, 41060, 41970, 42878, 43787, 48382, 50253, 72602,\n", + " 73508, 74442, 78051, 79879, 90071, 97542, 103988, 116001,\n", + " 125628, 132922, 140145, 145577, 160643, 163357, 173842, 180601,\n", + " 188442, 191173, 192975, 193873],\n", + " dtype='int64'), Int64Index([ 191, 2914, 8551, 14988, 19561, 22300, 23203, 25944,\n", + " 30531, 41061, 41971, 42879, 43788, 48383, 50254, 72603,\n", + " 73509, 74443, 78052, 79880, 90072, 97543, 103989, 116002,\n", + " 125629, 132923, 140146, 145578, 160644, 163358, 173843, 180602,\n", + " 188443, 191174, 192976, 193874],\n", + " dtype='int64'), Int64Index([ 192, 2915, 8552, 14989, 19562, 22301, 23204, 25945,\n", + " 30532, 41062, 41972, 42880, 43789, 48384, 50255, 72604,\n", + " 73510, 74444, 78053, 79881, 90073, 97544, 103990, 116003,\n", + " 125630, 132924, 140147, 145579, 160645, 163359, 173844, 180603,\n", + " 188444, 191175, 192977, 193875],\n", + " dtype='int64'), Int64Index([ 193, 2916, 8553, 14990, 19563, 22302, 23205, 25946,\n", + " 30533, 41063, 41973, 42881, 43790, 48385, 50256, 72605,\n", + " 73511, 74445, 78054, 79882, 90074, 97545, 103991, 116004,\n", + " 125631, 132925, 140148, 145580, 160646, 163360, 173845, 180604,\n", + " 188445, 191176, 192978, 193876],\n", + " dtype='int64'), Int64Index([ 194, 2917, 8554, 14991, 19564, 22303, 23206, 25947,\n", + " 30534, 41064, 41974, 42882, 43791, 48386, 50257, 72606,\n", + " 73512, 74446, 78055, 79883, 90075, 97546, 103992, 116005,\n", + " 125632, 132926, 140149, 145581, 160647, 163361, 173846, 180605,\n", + " 188446, 191177, 192979, 193877],\n", + " dtype='int64'), Int64Index([ 195, 2918, 8555, 14992, 19565, 22304, 23207, 25948,\n", + " 30535, 41065, 41975, 42883, 43792, 48387, 50258, 72607,\n", + " 73513, 74447, 78056, 79884, 90076, 97547, 103993, 116006,\n", + " 125633, 132927, 140150, 145582, 160648, 163362, 173847, 180606,\n", + " 188447, 191178, 192980, 193878],\n", + " dtype='int64'), Int64Index([ 196, 2919, 8556, 14993, 19566, 22305, 23208, 25949,\n", + " 30536, 41066, 41976, 42884, 43793, 48388, 50259, 72608,\n", + " 73514, 74448, 78057, 79885, 90077, 97548, 103994, 116007,\n", + " 125634, 132928, 140151, 145583, 160649, 163363, 173848, 180607,\n", + " 188448, 191179, 192981, 193879],\n", + " dtype='int64'), Int64Index([ 197, 2920, 8557, 14994, 19567, 22306, 23209, 25950,\n", + " 30537, 41067, 41977, 42885, 43794, 48389, 50260, 72609,\n", + " 73515, 74449, 78058, 79886, 90078, 97549, 103995, 116008,\n", + " 125635, 132929, 140152, 145584, 160650, 163364, 173849, 180608,\n", + " 188449, 191180, 192982, 193880],\n", + " dtype='int64'), Int64Index([ 198, 2921, 8558, 14995, 19568, 22307, 23210, 25951,\n", + " 30538, 41068, 41978, 42886, 43795, 48390, 50261, 72610,\n", + " 73516, 74450, 78059, 79887, 90079, 97550, 103996, 116009,\n", + " 125636, 132930, 140153, 145585, 160651, 163365, 173850, 180609,\n", + " 188450, 191181, 192983, 193881],\n", + " dtype='int64'), Int64Index([ 199, 2922, 8559, 14996, 19569, 22308, 23211, 25952,\n", + " 30539, 41069, 41979, 42887, 43796, 48391, 50262, 72611,\n", + " 73517, 74451, 78060, 79888, 90080, 97551, 103997, 116010,\n", + " 125637, 132931, 140154, 145586, 160652, 163366, 173851, 180610,\n", + " 188451, 191182, 192984, 193882],\n", + " dtype='int64'), Int64Index([ 200, 2923, 8560, 14997, 19570, 22309, 23212, 25953,\n", + " 30540, 41070, 41980, 42888, 43797, 48392, 50263, 72612,\n", + " 73518, 74452, 78061, 79889, 90081, 97552, 103998, 116011,\n", + " 125638, 132932, 140155, 145587, 160653, 163367, 173852, 180611,\n", + " 188452, 191183, 192985, 193883],\n", + " dtype='int64'), Int64Index([ 201, 2924, 8561, 14998, 19571, 22310, 23213, 25954,\n", + " 30541, 41071, 41981, 42889, 43798, 48393, 50264, 72613,\n", + " 73519, 74453, 78062, 79890, 90082, 97553, 103999, 116012,\n", + " 125639, 132933, 140156, 145588, 160654, 163368, 173853, 180612,\n", + " 188453, 191184, 192986, 193884],\n", + " dtype='int64'), Int64Index([ 202, 2925, 8562, 14999, 19572, 22311, 23214, 25955,\n", + " 30542, 41072, 41982, 42890, 43799, 48394, 50265, 72614,\n", + " 73520, 74454, 78063, 79891, 90083, 97554, 104000, 116013,\n", + " 125640, 132934, 140157, 145589, 160655, 163369, 173854, 180613,\n", + " 188454, 191185, 192987, 193885],\n", + " dtype='int64'), Int64Index([ 203, 2926, 8563, 15000, 19573, 22312, 23215, 25956,\n", + " 30543, 41073, 41983, 42891, 43800, 48395, 50266, 72615,\n", + " 73521, 74455, 78064, 79892, 90084, 97555, 104001, 116014,\n", + " 125641, 132935, 140158, 145590, 160656, 163370, 173855, 180614,\n", + " 188455, 191186, 192988, 193886],\n", + " dtype='int64'), Int64Index([ 204, 2927, 8564, 15001, 19574, 22313, 23216, 25957,\n", + " 30544, 41074, 41984, 42892, 43801, 48396, 50267, 72616,\n", + " 73522, 74456, 78065, 79893, 90085, 97556, 104002, 116015,\n", + " 125642, 132936, 140159, 145591, 160657, 163371, 173856, 180615,\n", + " 188456, 191187, 192989, 193887],\n", + " dtype='int64'), Int64Index([ 205, 2928, 8565, 15002, 19575, 22314, 23217, 25958,\n", + " 30545, 41075, 41985, 42893, 43802, 48397, 50268, 72617,\n", + " 73523, 74457, 78066, 79894, 90086, 97557, 104003, 116016,\n", + " 125643, 132937, 140160, 145592, 160658, 163372, 173857, 180616,\n", + " 188457, 191188, 192990, 193888],\n", + " dtype='int64'), Int64Index([ 206, 2929, 8566, 15003, 19576, 22315, 23218, 25959,\n", + " 30546, 41076, 41986, 42894, 43803, 48398, 50269, 72618,\n", + " 73524, 74458, 78067, 79895, 90087, 97558, 104004, 116017,\n", + " 125644, 132938, 140161, 145593, 160659, 163373, 173858, 180617,\n", + " 188458, 191189, 192991, 193889],\n", + " dtype='int64'), Int64Index([ 207, 2930, 8567, 15004, 19577, 22316, 23219, 25960,\n", + " 30547, 41077, 41987, 42895, 43804, 48399, 50270, 72619,\n", + " 73525, 74459, 78068, 79896, 90088, 97559, 104005, 116018,\n", + " 125645, 132939, 140162, 145594, 160660, 163374, 173859, 180618,\n", + " 188459, 191190, 192992, 193890],\n", + " dtype='int64'), Int64Index([ 208, 2931, 8568, 15005, 19578, 22317, 23220, 25961,\n", + " 30548, 41078, 41988, 42896, 43805, 48400, 50271, 72620,\n", + " 73526, 74460, 78069, 79897, 90089, 97560, 104006, 116019,\n", + " 125646, 132940, 140163, 145595, 160661, 163375, 173860, 180619,\n", + " 188460, 191191, 192993, 193891],\n", + " dtype='int64'), Int64Index([ 209, 2932, 8569, 15006, 19579, 22318, 23221, 25962,\n", + " 30549, 41079, 41989, 42897, 43806, 48401, 50272, 72621,\n", + " 73527, 74461, 78070, 79898, 90090, 97561, 104007, 116020,\n", + " 125647, 132941, 140164, 145596, 160662, 163376, 173861, 180620,\n", + " 188461, 191192, 192994, 193892],\n", + " dtype='int64'), Int64Index([ 210, 2933, 8570, 15007, 19580, 22319, 23222, 25963,\n", + " 30550, 41080, 41990, 42898, 43807, 48402, 50273, 72622,\n", + " 73528, 74462, 78071, 79899, 90091, 97562, 104008, 116021,\n", + " 125648, 132942, 140165, 145597, 160663, 163377, 173862, 180621,\n", + " 188462, 191193, 192995, 193893],\n", + " dtype='int64'), Int64Index([ 211, 2934, 8571, 15008, 19581, 22320, 23223, 25964,\n", + " 30551, 41081, 41991, 42899, 43808, 48403, 50274, 72623,\n", + " 73529, 74463, 78072, 79900, 90092, 97563, 104009, 116022,\n", + " 125649, 132943, 140166, 145598, 160664, 163378, 173863, 180622,\n", + " 188463, 191194, 192996, 193894],\n", + " dtype='int64'), Int64Index([ 212, 2935, 8572, 15009, 19582, 22321, 23224, 25965,\n", + " 30552, 41082, 41992, 42900, 43809, 48404, 50275, 72624,\n", + " 73530, 74464, 78073, 79901, 90093, 97564, 104010, 116023,\n", + " 125650, 132944, 140167, 145599, 160665, 163379, 173864, 180623,\n", + " 188464, 191195, 192997, 193895],\n", + " dtype='int64'), Int64Index([ 213, 2936, 8573, 15010, 19583, 22322, 23225, 25966,\n", + " 30553, 41083, 41993, 42901, 43810, 48405, 50276, 72625,\n", + " 73531, 74465, 78074, 79902, 90094, 97565, 104011, 116024,\n", + " 125651, 132945, 140168, 145600, 160666, 163380, 173865, 180624,\n", + " 188465, 191196, 192998, 193896],\n", + " dtype='int64'), Int64Index([ 214, 2937, 8574, 15011, 19584, 22323, 23226, 25967,\n", + " 30554, 41084, 41994, 42902, 43811, 48406, 50277, 72626,\n", + " 73532, 74466, 78075, 79903, 90095, 97566, 104012, 116025,\n", + " 125652, 132946, 140169, 145601, 160667, 163381, 173866, 180625,\n", + " 188466, 191197, 192999, 193897],\n", + " dtype='int64'), Int64Index([ 215, 2938, 8575, 15012, 19585, 22324, 23227, 25968,\n", + " 30555, 41085, 41995, 42903, 43812, 48407, 50278, 72627,\n", + " 73533, 74467, 78076, 79904, 90096, 97567, 104013, 116026,\n", + " 125653, 132947, 140170, 145602, 160668, 163382, 173867, 180626,\n", + " 188467, 191198, 193000, 193898],\n", + " dtype='int64'), Int64Index([ 216, 2939, 8576, 15013, 19586, 22325, 23228, 25969,\n", + " 30556, 41086, 41996, 42904, 43813, 48408, 50279, 72628,\n", + " 73534, 74468, 78077, 79905, 90097, 97568, 104014, 116027,\n", + " 125654, 132948, 140171, 145603, 160669, 163383, 173868, 180627,\n", + " 188468, 191199, 193001, 193899],\n", + " dtype='int64'), Int64Index([ 217, 2940, 8577, 15014, 19587, 22326, 23229, 25970,\n", + " 30557, 41087, 41997, 42905, 43814, 48409, 50280, 72629,\n", + " 73535, 74469, 78078, 79906, 90098, 97569, 104015, 116028,\n", + " 125655, 132949, 140172, 145604, 160670, 163384, 173869, 180628,\n", + " 188469, 191200, 193002, 193900],\n", + " dtype='int64'), Int64Index([ 218, 2941, 8578, 15015, 19588, 22327, 23230, 25971,\n", + " 30558, 41088, 41998, 42906, 43815, 48410, 50281, 72630,\n", + " 73536, 74470, 78079, 79907, 90099, 97570, 104016, 116029,\n", + " 125656, 132950, 140173, 145605, 160671, 163385, 173870, 180629,\n", + " 188470, 191201, 193003, 193901],\n", + " dtype='int64'), Int64Index([ 219, 2942, 8579, 15016, 19589, 22328, 23231, 25972,\n", + " 30559, 41089, 41999, 42907, 43816, 48411, 50282, 72631,\n", + " 73537, 74471, 78080, 79908, 90100, 97571, 104017, 116030,\n", + " 125657, 132951, 140174, 145606, 160672, 163386, 173871, 180630,\n", + " 188471, 191202, 193004, 193902],\n", + " dtype='int64'), Int64Index([ 220, 2943, 8580, 15017, 19590, 22329, 23232, 25973,\n", + " 30560, 41090, 42000, 42908, 43817, 48412, 50283, 72632,\n", + " 73538, 74472, 78081, 79909, 90101, 97572, 104018, 116031,\n", + " 125658, 132952, 140175, 145607, 160673, 163387, 173872, 180631,\n", + " 188472, 191203, 193005, 193903],\n", + " dtype='int64'), Int64Index([ 221, 2944, 8581, 15018, 19591, 22330, 23233, 25974,\n", + " 30561, 41091, 42001, 42909, 43818, 48413, 50284, 72633,\n", + " 73539, 74473, 78082, 79910, 90102, 97573, 104019, 116032,\n", + " 125659, 132953, 140176, 145608, 160674, 163388, 173873, 180632,\n", + " 188473, 191204, 193006, 193904],\n", + " dtype='int64'), Int64Index([ 222, 2945, 8582, 15019, 19592, 22331, 23234, 25975,\n", + " 30562, 41092, 42002, 42910, 43819, 48414, 50285, 72634,\n", + " 73540, 74474, 78083, 79911, 90103, 97574, 104020, 116033,\n", + " 125660, 132954, 140177, 145609, 160675, 163389, 173874, 180633,\n", + " 188474, 191205, 193007, 193905],\n", + " dtype='int64'), Int64Index([ 223, 2946, 8583, 15020, 19593, 22332, 23235, 25976,\n", + " 30563, 41093, 42003, 42911, 43820, 48415, 50286, 72635,\n", + " 73541, 74475, 78084, 79912, 90104, 97575, 104021, 116034,\n", + " 125661, 132955, 140178, 145610, 160676, 163390, 173875, 180634,\n", + " 188475, 191206, 193008, 193906],\n", + " dtype='int64'), Int64Index([ 224, 2947, 8584, 15021, 19594, 22333, 23236, 25977,\n", + " 30564, 41094, 42004, 42912, 43821, 48416, 50287, 72636,\n", + " 73542, 74476, 78085, 79913, 90105, 97576, 104022, 116035,\n", + " 125662, 132956, 140179, 145611, 160677, 163391, 173876, 180635,\n", + " 188476, 191207, 193009, 193907],\n", + " dtype='int64'), Int64Index([ 225, 2948, 8585, 15022, 19595, 22334, 23237, 25978,\n", + " 30565, 41095, 42005, 42913, 43822, 48417, 50288, 72637,\n", + " 73543, 74477, 78086, 79914, 90106, 97577, 104023, 116036,\n", + " 125663, 132957, 140180, 145612, 160678, 163392, 173877, 180636,\n", + " 188477, 191208, 193010, 193908],\n", + " dtype='int64'), Int64Index([ 226, 2949, 8586, 15023, 19596, 22335, 23238, 25979,\n", + " 30566, 41096, 42006, 42914, 43823, 48418, 50289, 72638,\n", + " 73544, 74478, 78087, 79915, 90107, 97578, 104024, 116037,\n", + " 125664, 132958, 140181, 145613, 160679, 163393, 173878, 180637,\n", + " 188478, 191209, 193011, 193909],\n", + " dtype='int64'), Int64Index([ 227, 2950, 8587, 15024, 19597, 22336, 23239, 25980,\n", + " 30567, 41097, 42007, 42915, 43824, 48419, 50290, 72639,\n", + " 73545, 74479, 78088, 79916, 90108, 97579, 104025, 116038,\n", + " 125665, 132959, 140182, 145614, 160680, 163394, 173879, 180638,\n", + " 188479, 191210, 193012, 193910],\n", + " dtype='int64'), Int64Index([ 228, 2951, 8588, 15025, 19598, 22337, 23240, 25981,\n", + " 30568, 41098, 42008, 42916, 43825, 48420, 50291, 72640,\n", + " 73546, 74480, 78089, 79917, 90109, 97580, 104026, 116039,\n", + " 125666, 132960, 140183, 145615, 160681, 163395, 173880, 180639,\n", + " 188480, 191211, 193013, 193911],\n", + " dtype='int64'), Int64Index([ 229, 2952, 8589, 15026, 19599, 22338, 23241, 25982,\n", + " 30569, 41099, 42009, 42917, 43826, 48421, 50292, 72641,\n", + " 73547, 74481, 78090, 79918, 90110, 97581, 104027, 116040,\n", + " 125667, 132961, 140184, 145616, 160682, 163396, 173881, 180640,\n", + " 188481, 191212, 193014, 193912],\n", + " dtype='int64'), Int64Index([ 230, 2953, 8590, 15027, 19600, 22339, 23242, 25983,\n", + " 30570, 41100, 42010, 42918, 43827, 48422, 50293, 72642,\n", + " 73548, 74482, 78091, 79919, 90111, 97582, 104028, 116041,\n", + " 125668, 132962, 140185, 145617, 160683, 163397, 173882, 180641,\n", + " 188482, 191213, 193015, 193913],\n", + " dtype='int64'), Int64Index([ 231, 2954, 8591, 15028, 19601, 22340, 23243, 25984,\n", + " 30571, 41101, 42011, 42919, 43828, 48423, 50294, 72643,\n", + " 73549, 74483, 78092, 79920, 90112, 97583, 104029, 116042,\n", + " 125669, 132963, 140186, 145618, 160684, 163398, 173883, 180642,\n", + " 188483, 191214, 193016, 193914],\n", + " dtype='int64'), Int64Index([ 232, 2955, 8592, 15029, 19602, 22341, 23244, 25985,\n", + " 30572, 41102, 42012, 42920, 43829, 48424, 50295, 72644,\n", + " 73550, 74484, 78093, 79921, 90113, 97584, 104030, 116043,\n", + " 125670, 132964, 140187, 145619, 160685, 163399, 173884, 180643,\n", + " 188484, 191215, 193017, 193915],\n", + " dtype='int64'), Int64Index([ 233, 2956, 8593, 15030, 19603, 22342, 23245, 25986,\n", + " 30573, 41103, 42013, 42921, 43830, 48425, 50296, 72645,\n", + " 73551, 74485, 78094, 79922, 90114, 97585, 104031, 116044,\n", + " 125671, 132965, 140188, 145620, 160686, 163400, 173885, 180644,\n", + " 188485, 191216, 193018, 193916],\n", + " dtype='int64'), Int64Index([ 234, 2957, 8594, 15031, 19604, 22343, 23246, 25987,\n", + " 30574, 41104, 42014, 42922, 43831, 48426, 50297, 72646,\n", + " 73552, 74486, 78095, 79923, 90115, 97586, 104032, 116045,\n", + " 125672, 132966, 140189, 145621, 160687, 163401, 173886, 180645,\n", + " 188486, 191217, 193019, 193917],\n", + " dtype='int64'), Int64Index([ 235, 2958, 8595, 15032, 19605, 22344, 23247, 25988,\n", + " 30575, 41105, 42015, 42923, 43832, 48427, 50298, 72647,\n", + " 73553, 74487, 78096, 79924, 90116, 97587, 104033, 116046,\n", + " 125673, 132967, 140190, 145622, 160688, 163402, 173887, 180646,\n", + " 188487, 191218, 193020, 193918],\n", + " dtype='int64'), Int64Index([ 236, 2959, 8596, 15033, 19606, 22345, 23248, 25989,\n", + " 30576, 41106, 42016, 42924, 43833, 48428, 50299, 72648,\n", + " 73554, 74488, 78097, 79925, 90117, 97588, 104034, 116047,\n", + " 125674, 132968, 140191, 145623, 160689, 163403, 173888, 180647,\n", + " 188488, 191219, 193021, 193919],\n", + " dtype='int64'), Int64Index([ 237, 2960, 8597, 15034, 19607, 22346, 23249, 25990,\n", + " 30577, 41107, 42017, 42925, 43834, 48429, 50300, 72649,\n", + " 73555, 74489, 78098, 79926, 90118, 97589, 104035, 116048,\n", + " 125675, 132969, 140192, 145624, 160690, 163404, 173889, 180648,\n", + " 188489, 191220, 193022, 193920],\n", + " dtype='int64'), Int64Index([ 238, 2961, 8598, 15035, 19608, 22347, 23250, 25991,\n", + " 30578, 41108, 42018, 42926, 43835, 48430, 50301, 72650,\n", + " 73556, 74490, 78099, 79927, 90119, 97590, 104036, 116049,\n", + " 125676, 132970, 140193, 145625, 160691, 163405, 173890, 180649,\n", + " 188490, 191221, 193023, 193921],\n", + " dtype='int64'), Int64Index([ 239, 2962, 8599, 15036, 19609, 22348, 23251, 25992,\n", + " 30579, 41109, 42019, 42927, 43836, 48431, 50302, 72651,\n", + " 73557, 74491, 78100, 79928, 90120, 97591, 104037, 116050,\n", + " 125677, 132971, 140194, 145626, 160692, 163406, 173891, 180650,\n", + " 188491, 191222, 193024, 193922],\n", + " dtype='int64'), Int64Index([ 240, 2963, 8600, 15037, 19610, 22349, 23252, 25993,\n", + " 30580, 41110, 42020, 42928, 43837, 48432, 50303, 72652,\n", + " 73558, 74492, 78101, 79929, 90121, 97592, 104038, 116051,\n", + " 125678, 132972, 140195, 145627, 160693, 163407, 173892, 180651,\n", + " 188492, 191223, 193025, 193923],\n", + " dtype='int64'), Int64Index([ 241, 2964, 8601, 15038, 19611, 22350, 23253, 25994,\n", + " 30581, 41111, 42021, 42929, 43838, 48433, 50304, 72653,\n", + " 73559, 74493, 78102, 79930, 90122, 97593, 104039, 116052,\n", + " 125679, 132973, 140196, 145628, 160694, 163408, 173893, 180652,\n", + " 188493, 191224, 193026, 193924],\n", + " dtype='int64'), Int64Index([ 242, 2965, 8602, 15039, 19612, 22351, 23254, 25995,\n", + " 30582, 41112, 42022, 42930, 43839, 48434, 50305, 72654,\n", + " 73560, 74494, 78103, 79931, 90123, 97594, 104040, 116053,\n", + " 125680, 132974, 140197, 145629, 160695, 163409, 173894, 180653,\n", + " 188494, 191225, 193027, 193925],\n", + " dtype='int64'), Int64Index([ 243, 2966, 8603, 15040, 19613, 22352, 23255, 25996,\n", + " 30583, 41113, 42023, 42931, 43840, 48435, 50306, 72655,\n", + " 73561, 74495, 78104, 79932, 90124, 97595, 104041, 116054,\n", + " 125681, 132975, 140198, 145630, 160696, 163410, 173895, 180654,\n", + " 188495, 191226, 193028, 193926],\n", + " dtype='int64'), Int64Index([ 244, 2967, 8604, 15041, 19614, 22353, 23256, 25997,\n", + " 30584, 41114, 42024, 42932, 43841, 48436, 50307, 72656,\n", + " 73562, 74496, 78105, 79933, 90125, 97596, 104042, 116055,\n", + " 125682, 132976, 140199, 145631, 160697, 163411, 173896, 180655,\n", + " 188496, 191227, 193029, 193927],\n", + " dtype='int64'), Int64Index([ 245, 2968, 8605, 15042, 19615, 22354, 23257, 25998,\n", + " 30585, 41115, 42025, 42933, 43842, 48437, 50308, 72657,\n", + " 73563, 74497, 78106, 79934, 90126, 97597, 104043, 116056,\n", + " 125683, 132977, 140200, 145632, 160698, 163412, 173897, 180656,\n", + " 188497, 191228, 193030, 193928],\n", + " dtype='int64'), Int64Index([ 246, 2969, 8606, 15043, 19616, 22355, 23258, 25999,\n", + " 30586, 41116, 42026, 42934, 43843, 48438, 50309, 72658,\n", + " 73564, 74498, 78107, 79935, 90127, 97598, 104044, 116057,\n", + " 125684, 132978, 140201, 145633, 160699, 163413, 173898, 180657,\n", + " 188498, 191229, 193031, 193929],\n", + " dtype='int64'), Int64Index([ 247, 2970, 8607, 15044, 19617, 22356, 23259, 26000,\n", + " 30587, 41117, 42027, 42935, 43844, 48439, 50310, 72659,\n", + " 73565, 74499, 78108, 79936, 90128, 97599, 104045, 116058,\n", + " 125685, 132979, 140202, 145634, 160700, 163414, 173899, 180658,\n", + " 188499, 191230, 193032, 193930],\n", + " dtype='int64'), Int64Index([ 248, 2971, 8608, 15045, 19618, 22357, 23260, 26001,\n", + " 30588, 41118, 42028, 42936, 43845, 48440, 50311, 72660,\n", + " 73566, 74500, 78109, 79937, 90129, 97600, 104046, 116059,\n", + " 125686, 132980, 140203, 145635, 160701, 163415, 173900, 180659,\n", + " 188500, 191231, 193033, 193931],\n", + " dtype='int64'), Int64Index([ 249, 2972, 8609, 15046, 19619, 22358, 23261, 26002,\n", + " 30589, 41119, 42029, 42937, 43846, 48441, 50312, 72661,\n", + " 73567, 74501, 78110, 79938, 90130, 97601, 104047, 116060,\n", + " 125687, 132981, 140204, 145636, 160702, 163416, 173901, 180660,\n", + " 188501, 191232, 193034, 193932],\n", + " dtype='int64'), Int64Index([ 250, 2973, 8610, 15047, 19620, 22359, 23262, 26003,\n", + " 30590, 41120, 42030, 42938, 43847, 48442, 50313, 72662,\n", + " 73568, 74502, 78111, 79939, 90131, 97602, 104048, 116061,\n", + " 125688, 132982, 140205, 145637, 160703, 163417, 173902, 180661,\n", + " 188502, 191233, 193035, 193933],\n", + " dtype='int64'), Int64Index([ 251, 2974, 8611, 15048, 19621, 22360, 23263, 26004,\n", + " 30591, 41121, 42031, 42939, 43848, 48443, 50314, 72663,\n", + " 73569, 74503, 78112, 79940, 90132, 97603, 104049, 116062,\n", + " 125689, 132983, 140206, 145638, 160704, 163418, 173903, 180662,\n", + " 188503, 191234, 193036, 193934],\n", + " dtype='int64'), Int64Index([ 252, 2975, 8612, 15049, 19622, 22361, 23264, 26005,\n", + " 30592, 41122, 42032, 42940, 43849, 48444, 50315, 72664,\n", + " 73570, 74504, 78113, 79941, 90133, 97604, 104050, 116063,\n", + " 125690, 132984, 140207, 145639, 160705, 163419, 173904, 180663,\n", + " 188504, 191235, 193037, 193935],\n", + " dtype='int64'), Int64Index([ 253, 2976, 8613, 15050, 19623, 22362, 23265, 26006,\n", + " 30593, 41123, 42033, 42941, 43850, 48445, 50316, 72665,\n", + " 73571, 74505, 78114, 79942, 90134, 97605, 104051, 116064,\n", + " 125691, 132985, 140208, 145640, 160706, 163420, 173905, 180664,\n", + " 188505, 191236, 193038, 193936],\n", + " dtype='int64'), Int64Index([ 254, 2977, 8614, 15051, 19624, 22363, 23266, 26007,\n", + " 30594, 41124, 42034, 42942, 43851, 48446, 50317, 72666,\n", + " 73572, 74506, 78115, 79943, 90135, 97606, 104052, 116065,\n", + " 125692, 132986, 140209, 145641, 160707, 163421, 173906, 180665,\n", + " 188506, 191237, 193039, 193937],\n", + " dtype='int64'), Int64Index([ 255, 2978, 8615, 15052, 19625, 22364, 23267, 26008,\n", + " 30595, 41125, 42035, 42943, 43852, 48447, 50318, 72667,\n", + " 73573, 74507, 78116, 79944, 90136, 97607, 104053, 116066,\n", + " 125693, 132987, 140210, 145642, 160708, 163422, 173907, 180666,\n", + " 188507, 191238, 193040, 193938],\n", + " dtype='int64'), Int64Index([ 256, 2979, 8616, 15053, 19626, 22365, 23268, 26009,\n", + " 30596, 41126, 42036, 42944, 43853, 48448, 50319, 72668,\n", + " 73574, 74508, 78117, 79945, 90137, 97608, 104054, 116067,\n", + " 125694, 132988, 140211, 145643, 160709, 163423, 173908, 180667,\n", + " 188508, 191239, 193041, 193939],\n", + " dtype='int64'), Int64Index([ 257, 2980, 8617, 15054, 19627, 22366, 23269, 26010,\n", + " 30597, 41127, 42037, 42945, 43854, 48449, 50320, 72669,\n", + " 73575, 74509, 78118, 79946, 90138, 97609, 104055, 116068,\n", + " 125695, 132989, 140212, 145644, 160710, 163424, 173909, 180668,\n", + " 188509, 191240, 193042, 193940],\n", + " dtype='int64'), Int64Index([ 258, 2981, 8618, 15055, 19628, 22367, 23270, 26011,\n", + " 30598, 41128, 42038, 42946, 43855, 48450, 50321, 72670,\n", + " 73576, 74510, 78119, 79947, 90139, 97610, 104056, 116069,\n", + " 125696, 132990, 140213, 145645, 160711, 163425, 173910, 180669,\n", + " 188510, 191241, 193043, 193941],\n", + " dtype='int64'), Int64Index([ 259, 2982, 8619, 15056, 19629, 22368, 23271, 26012,\n", + " 30599, 41129, 42039, 42947, 43856, 48451, 50322, 72671,\n", + " 73577, 74511, 78120, 79948, 90140, 97611, 104057, 116070,\n", + " 125697, 132991, 140214, 145646, 160712, 163426, 173911, 180670,\n", + " 188511, 191242, 193044, 193942],\n", + " dtype='int64'), Int64Index([ 260, 2983, 8620, 15057, 19630, 22369, 23272, 26013,\n", + " 30600, 41130, 42040, 42948, 43857, 48452, 50323, 72672,\n", + " 73578, 74512, 78121, 79949, 90141, 97612, 104058, 116071,\n", + " 125698, 132992, 140215, 145647, 160713, 163427, 173912, 180671,\n", + " 188512, 191243, 193045, 193943],\n", + " dtype='int64'), Int64Index([ 261, 2984, 8621, 15058, 19631, 22370, 23273, 26014,\n", + " 30601, 41131, 42041, 42949, 43858, 48453, 50324, 72673,\n", + " 73579, 74513, 78122, 79950, 90142, 97613, 104059, 116072,\n", + " 125699, 132993, 140216, 145648, 160714, 163428, 173913, 180672,\n", + " 188513, 191244, 193046, 193944],\n", + " dtype='int64'), Int64Index([ 262, 2985, 8622, 15059, 19632, 22371, 23274, 26015,\n", + " 30602, 41132, 42042, 42950, 43859, 48454, 50325, 72674,\n", + " 73580, 74514, 78123, 79951, 90143, 97614, 104060, 116073,\n", + " 125700, 132994, 140217, 145649, 160715, 163429, 173914, 180673,\n", + " 188514, 191245, 193047, 193945],\n", + " dtype='int64'), Int64Index([ 263, 2986, 8623, 15060, 19633, 22372, 23275, 26016,\n", + " 30603, 41133, 42043, 42951, 43860, 48455, 50326, 72675,\n", + " 73581, 74515, 78124, 79952, 90144, 97615, 104061, 116074,\n", + " 125701, 132995, 140218, 145650, 160716, 163430, 173915, 180674,\n", + " 188515, 191246, 193048, 193946],\n", + " dtype='int64'), Int64Index([ 264, 2987, 8624, 15061, 19634, 22373, 23276, 26017,\n", + " 30604, 41134, 42044, 42952, 43861, 48456, 50327, 72676,\n", + " 73582, 74516, 78125, 79953, 90145, 97616, 104062, 116075,\n", + " 125702, 132996, 140219, 145651, 160717, 163431, 173916, 180675,\n", + " 188516, 191247, 193049, 193947],\n", + " dtype='int64'), Int64Index([ 265, 2988, 8625, 15062, 19635, 22374, 23277, 26018,\n", + " 30605, 41135, 42045, 42953, 43862, 48457, 50328, 72677,\n", + " 73583, 74517, 78126, 79954, 90146, 97617, 104063, 116076,\n", + " 125703, 132997, 140220, 145652, 160718, 163432, 173917, 180676,\n", + " 188517, 191248, 193050, 193948],\n", + " dtype='int64'), Int64Index([ 266, 2989, 8626, 15063, 19636, 22375, 23278, 26019,\n", + " 30606, 41136, 42046, 42954, 43863, 48458, 50329, 72678,\n", + " 73584, 74518, 78127, 79955, 90147, 97618, 104064, 116077,\n", + " 125704, 132998, 140221, 145653, 160719, 163433, 173918, 180677,\n", + " 188518, 191249, 193051, 193949],\n", + " dtype='int64'), Int64Index([ 267, 2990, 8627, 15064, 19637, 22376, 23279, 26020,\n", + " 30607, 41137, 42047, 42955, 43864, 48459, 50330, 72679,\n", + " 73585, 74519, 78128, 79956, 90148, 97619, 104065, 116078,\n", + " 125705, 132999, 140222, 145654, 160720, 163434, 173919, 180678,\n", + " 188519, 191250, 193052, 193950],\n", + " dtype='int64'), Int64Index([ 268, 2991, 8628, 15065, 19638, 22377, 23280, 26021,\n", + " 30608, 41138, 42048, 42956, 43865, 48460, 50331, 72680,\n", + " 73586, 74520, 78129, 79957, 90149, 97620, 104066, 116079,\n", + " 125706, 133000, 140223, 145655, 160721, 163435, 173920, 180679,\n", + " 188520, 191251, 193053, 193951],\n", + " dtype='int64'), Int64Index([ 269, 2992, 8629, 15066, 19639, 22378, 23281, 26022,\n", + " 30609, 41139, 42049, 42957, 43866, 48461, 50332, 72681,\n", + " 73587, 74521, 78130, 79958, 90150, 97621, 104067, 116080,\n", + " 125707, 133001, 140224, 145656, 160722, 163436, 173921, 180680,\n", + " 188521, 191252, 193054, 193952],\n", + " dtype='int64'), Int64Index([ 270, 2993, 8630, 15067, 19640, 22379, 23282, 26023,\n", + " 30610, 41140, 42050, 42958, 43867, 48462, 50333, 72682,\n", + " 73588, 74522, 78131, 79959, 90151, 97622, 104068, 116081,\n", + " 125708, 133002, 140225, 145657, 160723, 163437, 173922, 180681,\n", + " 188522, 191253, 193055, 193953],\n", + " dtype='int64'), Int64Index([ 271, 2994, 8631, 15068, 19641, 22380, 23283, 26024,\n", + " 30611, 41141, 42051, 42959, 43868, 48463, 50334, 72683,\n", + " 73589, 74523, 78132, 79960, 90152, 97623, 104069, 116082,\n", + " 125709, 133003, 140226, 145658, 160724, 163438, 173923, 180682,\n", + " 188523, 191254, 193056, 193954],\n", + " dtype='int64'), Int64Index([ 272, 2995, 8632, 15069, 19642, 22381, 23284, 26025,\n", + " 30612, 41142, 42052, 42960, 43869, 48464, 50335, 72684,\n", + " 73590, 74524, 78133, 79961, 90153, 97624, 104070, 116083,\n", + " 125710, 133004, 140227, 145659, 160725, 163439, 173924, 180683,\n", + " 188524, 191255, 193057, 193955],\n", + " dtype='int64'), Int64Index([ 273, 2996, 8633, 15070, 19643, 22382, 23285, 26026,\n", + " 30613, 41143, 42053, 42961, 43870, 48465, 50336, 72685,\n", + " 73591, 74525, 78134, 79962, 90154, 97625, 104071, 116084,\n", + " 125711, 133005, 140228, 145660, 160726, 163440, 173925, 180684,\n", + " 188525, 191256, 193058, 193956],\n", + " dtype='int64'), Int64Index([ 274, 2997, 8634, 15071, 19644, 22383, 23286, 26027,\n", + " 30614, 41144, 42054, 42962, 43871, 48466, 50337, 72686,\n", + " 73592, 74526, 78135, 79963, 90155, 97626, 104072, 116085,\n", + " 125712, 133006, 140229, 145661, 160727, 163441, 173926, 180685,\n", + " 188526, 191257, 193059, 193957],\n", + " dtype='int64'), Int64Index([ 275, 2998, 8635, 15072, 19645, 22384, 23287, 26028,\n", + " 30615, 41145, 42055, 42963, 43872, 48467, 50338, 72687,\n", + " 73593, 74527, 78136, 79964, 90156, 97627, 104073, 116086,\n", + " 125713, 133007, 140230, 145662, 160728, 163442, 173927, 180686,\n", + " 188527, 191258, 193060, 193958],\n", + " dtype='int64'), Int64Index([ 276, 2999, 8636, 15073, 19646, 22385, 23288, 26029,\n", + " 30616, 41146, 42056, 42964, 43873, 48468, 50339, 72688,\n", + " 73594, 74528, 78137, 79965, 90157, 97628, 104074, 116087,\n", + " 125714, 133008, 140231, 145663, 160729, 163443, 173928, 180687,\n", + " 188528, 191259, 193061, 193959],\n", + " dtype='int64'), Int64Index([ 277, 3000, 8637, 15074, 19647, 22386, 23289, 26030,\n", + " 30617, 41147, 42057, 42965, 43874, 48469, 50340, 72689,\n", + " 73595, 74529, 78138, 79966, 90158, 97629, 104075, 116088,\n", + " 125715, 133009, 140232, 145664, 160730, 163444, 173929, 180688,\n", + " 188529, 191260, 193062, 193960],\n", + " dtype='int64'), Int64Index([ 278, 3001, 8638, 15075, 19648, 22387, 23290, 26031,\n", + " 30618, 41148, 42058, 42966, 43875, 48470, 50341, 72690,\n", + " 73596, 74530, 78139, 79967, 90159, 97630, 104076, 116089,\n", + " 125716, 133010, 140233, 145665, 160731, 163445, 173930, 180689,\n", + " 188530, 191261, 193063, 193961],\n", + " dtype='int64'), Int64Index([ 279, 3002, 8639, 15076, 19649, 22388, 23291, 26032,\n", + " 30619, 41149, 42059, 42967, 43876, 48471, 50342, 72691,\n", + " 73597, 74531, 78140, 79968, 90160, 97631, 104077, 116090,\n", + " 125717, 133011, 140234, 145666, 160732, 163446, 173931, 180690,\n", + " 188531, 191262, 193064, 193962],\n", + " dtype='int64'), Int64Index([ 280, 3003, 8640, 15077, 19650, 22389, 23292, 26033,\n", + " 30620, 41150, 42060, 42968, 43877, 48472, 50343, 72692,\n", + " 73598, 74532, 78141, 79969, 90161, 97632, 104078, 116091,\n", + " 125718, 133012, 140235, 145667, 160733, 163447, 173932, 180691,\n", + " 188532, 191263, 193065, 193963],\n", + " dtype='int64'), Int64Index([ 281, 3004, 8641, 15078, 19651, 22390, 23293, 26034,\n", + " 30621, 41151, 42061, 42969, 43878, 48473, 50344, 72693,\n", + " 73599, 74533, 78142, 79970, 90162, 97633, 104079, 116092,\n", + " 125719, 133013, 140236, 145668, 160734, 163448, 173933, 180692,\n", + " 188533, 191264, 193066, 193964],\n", + " dtype='int64'), Int64Index([ 282, 3005, 8642, 15079, 19652, 22391, 23294, 26035,\n", + " 30622, 41152, 42062, 42970, 43879, 48474, 50345, 72694,\n", + " 73600, 74534, 78143, 79971, 90163, 97634, 104080, 116093,\n", + " 125720, 133014, 140237, 145669, 160735, 163449, 173934, 180693,\n", + " 188534, 191265, 193067, 193965],\n", + " dtype='int64'), Int64Index([ 283, 3006, 8643, 15080, 19653, 22392, 23295, 26036,\n", + " 30623, 41153, 42063, 42971, 43880, 48475, 50346, 72695,\n", + " 73601, 74535, 78144, 79972, 90164, 97635, 104081, 116094,\n", + " 125721, 133015, 140238, 145670, 160736, 163450, 173935, 180694,\n", + " 188535, 191266, 193068, 193966],\n", + " dtype='int64'), Int64Index([ 284, 3007, 8644, 15081, 19654, 22393, 23296, 26037,\n", + " 30624, 41154, 42064, 42972, 43881, 48476, 50347, 72696,\n", + " 73602, 74536, 78145, 79973, 90165, 97636, 104082, 116095,\n", + " 125722, 133016, 140239, 145671, 160737, 163451, 173936, 180695,\n", + " 188536, 191267, 193069, 193967],\n", + " dtype='int64'), Int64Index([ 285, 3008, 8645, 15082, 19655, 22394, 23297, 26038,\n", + " 30625, 41155, 42065, 42973, 43882, 48477, 50348, 72697,\n", + " 73603, 74537, 78146, 79974, 90166, 97637, 104083, 116096,\n", + " 125723, 133017, 140240, 145672, 160738, 163452, 173937, 180696,\n", + " 188537, 191268, 193070, 193968],\n", + " dtype='int64'), Int64Index([ 286, 3009, 8646, 15083, 19656, 22395, 23298, 26039,\n", + " 30626, 41156, 42066, 42974, 43883, 48478, 50349, 72698,\n", + " 73604, 74538, 78147, 79975, 90167, 97638, 104084, 116097,\n", + " 125724, 133018, 140241, 145673, 160739, 163453, 173938, 180697,\n", + " 188538, 191269, 193071, 193969],\n", + " dtype='int64'), Int64Index([ 287, 3010, 8647, 15084, 19657, 22396, 23299, 26040,\n", + " 30627, 41157, 42067, 42975, 43884, 48479, 50350, 72699,\n", + " 73605, 74539, 78148, 79976, 90168, 97639, 104085, 116098,\n", + " 125725, 133019, 140242, 145674, 160740, 163454, 173939, 180698,\n", + " 188539, 191270, 193072, 193970],\n", + " dtype='int64'), Int64Index([ 288, 3011, 8648, 15085, 19658, 22397, 23300, 26041,\n", + " 30628, 41158, 42068, 42976, 43885, 48480, 50351, 72700,\n", + " 73606, 74540, 78149, 79977, 90169, 97640, 104086, 116099,\n", + " 125726, 133020, 140243, 145675, 160741, 163455, 173940, 180699,\n", + " 188540, 191271, 193073, 193971],\n", + " dtype='int64'), Int64Index([ 289, 3012, 8649, 15086, 19659, 22398, 23301, 26042,\n", + " 30629, 41159, 42069, 42977, 43886, 48481, 50352, 72701,\n", + " 73607, 74541, 78150, 79978, 90170, 97641, 104087, 116100,\n", + " 125727, 133021, 140244, 145676, 160742, 163456, 173941, 180700,\n", + " 188541, 191272, 193074, 193972],\n", + " dtype='int64'), Int64Index([ 290, 3013, 8650, 15087, 19660, 22399, 23302, 26043,\n", + " 30630, 41160, 42070, 42978, 43887, 48482, 50353, 72702,\n", + " 73608, 74542, 78151, 79979, 90171, 97642, 104088, 116101,\n", + " 125728, 133022, 140245, 145677, 160743, 163457, 173942, 180701,\n", + " 188542, 191273, 193075, 193973],\n", + " dtype='int64'), Int64Index([ 291, 3014, 8651, 15088, 19661, 22400, 23303, 26044,\n", + " 30631, 41161, 42071, 42979, 43888, 48483, 50354, 72703,\n", + " 73609, 74543, 78152, 79980, 90172, 97643, 104089, 116102,\n", + " 125729, 133023, 140246, 145678, 160744, 163458, 173943, 180702,\n", + " 188543, 191274, 193076, 193974],\n", + " dtype='int64'), Int64Index([ 292, 3015, 8652, 15089, 19662, 22401, 23304, 26045,\n", + " 30632, 41162, 42072, 42980, 43889, 48484, 50355, 72704,\n", + " 73610, 74544, 78153, 79981, 90173, 97644, 104090, 116103,\n", + " 125730, 133024, 140247, 145679, 160745, 163459, 173944, 180703,\n", + " 188544, 191275, 193077, 193975],\n", + " dtype='int64'), Int64Index([ 293, 3016, 8653, 15090, 19663, 22402, 23305, 26046,\n", + " 30633, 41163, 42073, 42981, 43890, 48485, 50356, 72705,\n", + " 73611, 74545, 78154, 79982, 90174, 97645, 104091, 116104,\n", + " 125731, 133025, 140248, 145680, 160746, 163460, 173945, 180704,\n", + " 188545, 191276, 193078, 193976],\n", + " dtype='int64'), Int64Index([ 294, 3017, 8654, 15091, 19664, 22403, 23306, 26047,\n", + " 30634, 41164, 42074, 42982, 43891, 48486, 50357, 72706,\n", + " 73612, 74546, 78155, 79983, 90175, 97646, 104092, 116105,\n", + " 125732, 133026, 140249, 145681, 160747, 163461, 173946, 180705,\n", + " 188546, 191277, 193079, 193977],\n", + " dtype='int64'), Int64Index([ 295, 3018, 8655, 15092, 19665, 22404, 23307, 26048,\n", + " 30635, 41165, 42075, 42983, 43892, 48487, 50358, 72707,\n", + " 73613, 74547, 78156, 79984, 90176, 97647, 104093, 116106,\n", + " 125733, 133027, 140250, 145682, 160748, 163462, 173947, 180706,\n", + " 188547, 191278, 193080, 193978],\n", + " dtype='int64'), Int64Index([ 296, 3019, 8656, 15093, 19666, 22405, 23308, 26049,\n", + " 30636, 41166, 42076, 42984, 43893, 48488, 50359, 72708,\n", + " 73614, 74548, 78157, 79985, 90177, 97648, 104094, 116107,\n", + " 125734, 133028, 140251, 145683, 160749, 163463, 173948, 180707,\n", + " 188548, 191279, 193081, 193979],\n", + " dtype='int64'), Int64Index([ 297, 3020, 8657, 15094, 19667, 22406, 23309, 26050,\n", + " 30637, 41167, 42077, 42985, 43894, 48489, 50360, 72709,\n", + " 73615, 74549, 78158, 79986, 90178, 97649, 104095, 116108,\n", + " 125735, 133029, 140252, 145684, 160750, 163464, 173949, 180708,\n", + " 188549, 191280, 193082, 193980],\n", + " dtype='int64'), Int64Index([ 298, 3021, 8658, 15095, 19668, 22407, 23310, 26051,\n", + " 30638, 41168, 42078, 42986, 43895, 48490, 50361, 72710,\n", + " 73616, 74550, 78159, 79987, 90179, 97650, 104096, 116109,\n", + " 125736, 133030, 140253, 145685, 160751, 163465, 173950, 180709,\n", + " 188550, 191281, 193083, 193981],\n", + " dtype='int64'), Int64Index([ 299, 3022, 8659, 15096, 19669, 22408, 23311, 26052,\n", + " 30639, 41169, 42079, 42987, 43896, 48491, 50362, 72711,\n", + " 73617, 74551, 78160, 79988, 90180, 97651, 104097, 116110,\n", + " 125737, 133031, 140254, 145686, 160752, 163466, 173951, 180710,\n", + " 188551, 191282, 193084, 193982],\n", + " dtype='int64'), Int64Index([ 300, 3023, 8660, 15097, 19670, 22409, 23312, 26053,\n", + " 30640, 41170, 42080, 42988, 43897, 48492, 50363, 72712,\n", + " 73618, 74552, 78161, 79989, 90181, 97652, 104098, 116111,\n", + " 125738, 133032, 140255, 145687, 160753, 163467, 173952, 180711,\n", + " 188552, 191283, 193085, 193983],\n", + " dtype='int64'), Int64Index([ 301, 3024, 8661, 15098, 19671, 22410, 23313, 26054,\n", + " 30641, 41171, 42081, 42989, 43898, 48493, 50364, 72713,\n", + " 73619, 74553, 78162, 79990, 90182, 97653, 104099, 116112,\n", + " 125739, 133033, 140256, 145688, 160754, 163468, 173953, 180712,\n", + " 188553, 191284, 193086, 193984],\n", + " dtype='int64'), Int64Index([ 302, 3025, 8662, 15099, 19672, 22411, 23314, 26055,\n", + " 30642, 41172, 42082, 42990, 43899, 48494, 50365, 72714,\n", + " 73620, 74554, 78163, 79991, 90183, 97654, 104100, 116113,\n", + " 125740, 133034, 140257, 145689, 160755, 163469, 173954, 180713,\n", + " 188554, 191285, 193087, 193985],\n", + " dtype='int64'), Int64Index([ 303, 3026, 8663, 15100, 19673, 22412, 23315, 26056,\n", + " 30643, 41173, 42083, 42991, 43900, 48495, 50366, 72715,\n", + " 73621, 74555, 78164, 79992, 90184, 97655, 104101, 116114,\n", + " 125741, 133035, 140258, 145690, 160756, 163470, 173955, 180714,\n", + " 188555, 191286, 193088, 193986],\n", + " dtype='int64'), Int64Index([ 304, 3027, 8664, 15101, 19674, 22413, 23316, 26057,\n", + " 30644, 41174, 42084, 42992, 43901, 48496, 50367, 72716,\n", + " 73622, 74556, 78165, 79993, 90185, 97656, 104102, 116115,\n", + " 125742, 133036, 140259, 145691, 160757, 163471, 173956, 180715,\n", + " 188556, 191287, 193089, 193987],\n", + " dtype='int64'), Int64Index([ 305, 3028, 8665, 15102, 19675, 22414, 23317, 26058,\n", + " 30645, 41175, 42085, 42993, 43902, 48497, 50368, 72717,\n", + " 73623, 74557, 78166, 79994, 90186, 97657, 104103, 116116,\n", + " 125743, 133037, 140260, 145692, 160758, 163472, 173957, 180716,\n", + " 188557, 191288, 193090, 193988],\n", + " dtype='int64'), Int64Index([ 306, 3029, 8666, 15103, 19676, 22415, 23318, 26059,\n", + " 30646, 41176, 42086, 42994, 43903, 48498, 50369, 72718,\n", + " 73624, 74558, 78167, 79995, 90187, 97658, 104104, 116117,\n", + " 125744, 133038, 140261, 145693, 160759, 163473, 173958, 180717,\n", + " 188558, 191289, 193091, 193989],\n", + " dtype='int64'), Int64Index([ 307, 3030, 8667, 15104, 19677, 22416, 23319, 26060,\n", + " 30647, 41177, 42087, 42995, 43904, 48499, 50370, 72719,\n", + " 73625, 74559, 78168, 79996, 90188, 97659, 104105, 116118,\n", + " 125745, 133039, 140262, 145694, 160760, 163474, 173959, 180718,\n", + " 188559, 191290, 193092, 193990],\n", + " dtype='int64'), Int64Index([ 308, 3031, 8668, 15105, 19678, 22417, 23320, 26061,\n", + " 30648, 41178, 42088, 42996, 43905, 48500, 50371, 72720,\n", + " 73626, 74560, 78169, 79997, 90189, 97660, 104106, 116119,\n", + " 125746, 133040, 140263, 145695, 160761, 163475, 173960, 180719,\n", + " 188560, 191291, 193093, 193991],\n", + " dtype='int64'), Int64Index([ 309, 3032, 8669, 15106, 19679, 22418, 23321, 26062,\n", + " 30649, 41179, 42089, 42997, 43906, 48501, 50372, 72721,\n", + " 73627, 74561, 78170, 79998, 90190, 97661, 104107, 116120,\n", + " 125747, 133041, 140264, 145696, 160762, 163476, 173961, 180720,\n", + " 188561, 191292, 193094, 193992],\n", + " dtype='int64'), Int64Index([ 310, 3033, 8670, 15107, 19680, 22419, 23322, 26063,\n", + " 30650, 41180, 42090, 42998, 43907, 48502, 50373, 72722,\n", + " 73628, 74562, 78171, 79999, 90191, 97662, 104108, 116121,\n", + " 125748, 133042, 140265, 145697, 160763, 163477, 173962, 180721,\n", + " 188562, 191293, 193095, 193993],\n", + " dtype='int64'), Int64Index([ 311, 3034, 8671, 15108, 19681, 22420, 23323, 26064,\n", + " 30651, 41181, 42091, 42999, 43908, 48503, 50374, 72723,\n", + " 73629, 74563, 78172, 80000, 90192, 97663, 104109, 116122,\n", + " 125749, 133043, 140266, 145698, 160764, 163478, 173963, 180722,\n", + " 188563, 191294, 193096, 193994],\n", + " dtype='int64'), Int64Index([ 312, 3035, 8672, 15109, 19682, 22421, 23324, 26065,\n", + " 30652, 41182, 42092, 43000, 43909, 48504, 50375, 72724,\n", + " 73630, 74564, 78173, 80001, 90193, 97664, 104110, 116123,\n", + " 125750, 133044, 140267, 145699, 160765, 163479, 173964, 180723,\n", + " 188564, 191295, 193097, 193995],\n", + " dtype='int64'), Int64Index([ 313, 3036, 8673, 15110, 19683, 22422, 23325, 26066,\n", + " 30653, 41183, 42093, 43001, 43910, 48505, 50376, 72725,\n", + " 73631, 74565, 78174, 80002, 90194, 97665, 104111, 116124,\n", + " 125751, 133045, 140268, 145700, 160766, 163480, 173965, 180724,\n", + " 188565, 191296, 193098, 193996],\n", + " dtype='int64'), Int64Index([ 314, 3037, 8674, 15111, 19684, 22423, 23326, 26067,\n", + " 30654, 41184, 42094, 43002, 43911, 48506, 50377, 72726,\n", + " 73632, 74566, 78175, 80003, 90195, 97666, 104112, 116125,\n", + " 125752, 133046, 140269, 145701, 160767, 163481, 173966, 180725,\n", + " 188566, 191297, 193099, 193997],\n", + " dtype='int64'), Int64Index([ 315, 3038, 8675, 15112, 19685, 22424, 23327, 26068,\n", + " 30655, 41185, 42095, 43003, 43912, 48507, 50378, 72727,\n", + " 73633, 74567, 78176, 80004, 90196, 97667, 104113, 116126,\n", + " 125753, 133047, 140270, 145702, 160768, 163482, 173967, 180726,\n", + " 188567, 191298, 193100, 193998],\n", + " dtype='int64'), Int64Index([ 316, 3039, 8676, 15113, 19686, 22425, 23328, 26069,\n", + " 30656, 41186, 42096, 43004, 43913, 48508, 50379, 72728,\n", + " 73634, 74568, 78177, 80005, 90197, 97668, 104114, 116127,\n", + " 125754, 133048, 140271, 145703, 160769, 163483, 173968, 180727,\n", + " 188568, 191299, 193101, 193999],\n", + " dtype='int64'), Int64Index([ 317, 3040, 8677, 15114, 19687, 22426, 23329, 26070,\n", + " 30657, 41187, 42097, 43005, 43914, 48509, 50380, 72729,\n", + " 73635, 74569, 78178, 80006, 90198, 97669, 104115, 116128,\n", + " 125755, 133049, 140272, 145704, 160770, 163484, 173969, 180728,\n", + " 188569, 191300, 193102, 194000],\n", + " dtype='int64'), Int64Index([ 318, 3041, 8678, 15115, 19688, 22427, 23330, 26071,\n", + " 30658, 41188, 42098, 43006, 43915, 48510, 50381, 72730,\n", + " 73636, 74570, 78179, 80007, 90199, 97670, 104116, 116129,\n", + " 125756, 133050, 140273, 145705, 160771, 163485, 173970, 180729,\n", + " 188570, 191301, 193103, 194001],\n", + " dtype='int64'), Int64Index([ 319, 3042, 8679, 15116, 19689, 22428, 23331, 26072,\n", + " 30659, 41189, 42099, 43007, 43916, 48511, 50382, 72731,\n", + " 73637, 74571, 78180, 80008, 90200, 97671, 104117, 116130,\n", + " 125757, 133051, 140274, 145706, 160772, 163486, 173971, 180730,\n", + " 188571, 191302, 193104, 194002],\n", + " dtype='int64'), Int64Index([ 320, 3043, 8680, 15117, 19690, 22429, 23332, 26073,\n", + " 30660, 41190, 42100, 43008, 43917, 48512, 50383, 72732,\n", + " 73638, 74572, 78181, 80009, 90201, 97672, 104118, 116131,\n", + " 125758, 133052, 140275, 145707, 160773, 163487, 173972, 180731,\n", + " 188572, 191303, 193105, 194003],\n", + " dtype='int64'), Int64Index([ 321, 3044, 8681, 15118, 19691, 22430, 23333, 26074,\n", + " 30661, 41191, 42101, 43009, 43918, 48513, 50384, 72733,\n", + " 73639, 74573, 78182, 80010, 90202, 97673, 104119, 116132,\n", + " 125759, 133053, 140276, 145708, 160774, 163488, 173973, 180732,\n", + " 188573, 191304, 193106, 194004],\n", + " dtype='int64'), Int64Index([ 322, 3045, 8682, 15119, 19692, 22431, 23334, 26075,\n", + " 30662, 41192, 42102, 43010, 43919, 48514, 50385, 72734,\n", + " 73640, 74574, 78183, 80011, 90203, 97674, 104120, 116133,\n", + " 125760, 133054, 140277, 145709, 160775, 163489, 173974, 180733,\n", + " 188574, 191305, 193107, 194005],\n", + " dtype='int64'), Int64Index([ 323, 3046, 8683, 15120, 19693, 22432, 23335, 26076,\n", + " 30663, 41193, 42103, 43011, 43920, 48515, 50386, 72735,\n", + " 73641, 74575, 78184, 80012, 90204, 97675, 104121, 116134,\n", + " 125761, 133055, 140278, 145710, 160776, 163490, 173975, 180734,\n", + " 188575, 191306, 193108, 194006],\n", + " dtype='int64'), Int64Index([ 324, 3047, 8684, 15121, 19694, 22433, 23336, 26077,\n", + " 30664, 41194, 42104, 43012, 43921, 48516, 50387, 72736,\n", + " 73642, 74576, 78185, 80013, 90205, 97676, 104122, 116135,\n", + " 125762, 133056, 140279, 145711, 160777, 163491, 173976, 180735,\n", + " 188576, 191307, 193109, 194007],\n", + " dtype='int64'), Int64Index([ 325, 3048, 8685, 15122, 19695, 22434, 23337, 26078,\n", + " 30665, 41195, 42105, 43013, 43922, 48517, 50388, 72737,\n", + " 73643, 74577, 78186, 80014, 90206, 97677, 104123, 116136,\n", + " 125763, 133057, 140280, 145712, 160778, 163492, 173977, 180736,\n", + " 188577, 191308, 193110, 194008],\n", + " dtype='int64'), Int64Index([ 326, 3049, 8686, 15123, 19696, 22435, 23338, 26079,\n", + " 30666, 41196, 42106, 43014, 43923, 48518, 50389, 72738,\n", + " 73644, 74578, 78187, 80015, 90207, 97678, 104124, 116137,\n", + " 125764, 133058, 140281, 145713, 160779, 163493, 173978, 180737,\n", + " 188578, 191309, 193111, 194009],\n", + " dtype='int64'), Int64Index([ 327, 3050, 8687, 15124, 19697, 22436, 23339, 26080,\n", + " 30667, 41197, 42107, 43015, 43924, 48519, 50390, 72739,\n", + " 73645, 74579, 78188, 80016, 90208, 97679, 104125, 116138,\n", + " 125765, 133059, 140282, 145714, 160780, 163494, 173979, 180738,\n", + " 188579, 191310, 193112, 194010],\n", + " dtype='int64'), Int64Index([ 328, 3051, 8688, 15125, 19698, 22437, 23340, 26081,\n", + " 30668, 41198, 42108, 43016, 43925, 48520, 50391, 72740,\n", + " 73646, 74580, 78189, 80017, 90209, 97680, 104126, 116139,\n", + " 125766, 133060, 140283, 145715, 160781, 163495, 173980, 180739,\n", + " 188580, 191311, 193113, 194011],\n", + " dtype='int64'), Int64Index([ 329, 3052, 8689, 15126, 19699, 22438, 23341, 26082,\n", + " 30669, 41199, 42109, 43017, 43926, 48521, 50392, 72741,\n", + " 73647, 74581, 78190, 80018, 90210, 97681, 104127, 116140,\n", + " 125767, 133061, 140284, 145716, 160782, 163496, 173981, 180740,\n", + " 188581, 191312, 193114, 194012],\n", + " dtype='int64'), Int64Index([ 330, 3053, 8690, 15127, 19700, 22439, 23342, 26083,\n", + " 30670, 41200, 42110, 43018, 43927, 48522, 50393, 72742,\n", + " 73648, 74582, 78191, 80019, 90211, 97682, 104128, 116141,\n", + " 125768, 133062, 140285, 145717, 160783, 163497, 173982, 180741,\n", + " 188582, 191313, 193115, 194013],\n", + " dtype='int64'), Int64Index([ 331, 3054, 8691, 15128, 19701, 22440, 23343, 26084,\n", + " 30671, 41201, 42111, 43019, 43928, 48523, 50394, 72743,\n", + " 73649, 74583, 78192, 80020, 90212, 97683, 104129, 116142,\n", + " 125769, 133063, 140286, 145718, 160784, 163498, 173983, 180742,\n", + " 188583, 191314, 193116, 194014],\n", + " dtype='int64'), Int64Index([ 332, 3055, 8692, 15129, 19702, 22441, 23344, 26085,\n", + " 30672, 41202, 42112, 43020, 43929, 48524, 50395, 72744,\n", + " 73650, 74584, 78193, 80021, 90213, 97684, 104130, 116143,\n", + " 125770, 133064, 140287, 145719, 160785, 163499, 173984, 180743,\n", + " 188584, 191315, 193117, 194015],\n", + " dtype='int64'), Int64Index([ 333, 3056, 8693, 15130, 19703, 22442, 23345, 26086,\n", + " 30673, 41203, 42113, 43021, 43930, 48525, 50396, 72745,\n", + " 73651, 74585, 78194, 80022, 90214, 97685, 104131, 116144,\n", + " 125771, 133065, 140288, 145720, 160786, 163500, 173985, 180744,\n", + " 188585, 191316, 193118, 194016],\n", + " dtype='int64'), Int64Index([ 334, 3057, 8694, 15131, 19704, 22443, 23346, 26087,\n", + " 30674, 41204, 42114, 43022, 43931, 48526, 50397, 72746,\n", + " 73652, 74586, 78195, 80023, 90215, 97686, 104132, 116145,\n", + " 125772, 133066, 140289, 145721, 160787, 163501, 173986, 180745,\n", + " 188586, 191317, 193119, 194017],\n", + " dtype='int64'), Int64Index([ 335, 3058, 8695, 15132, 19705, 22444, 23347, 26088,\n", + " 30675, 41205, 42115, 43023, 43932, 48527, 50398, 72747,\n", + " 73653, 74587, 78196, 80024, 90216, 97687, 104133, 116146,\n", + " 125773, 133067, 140290, 145722, 160788, 163502, 173987, 180746,\n", + " 188587, 191318, 193120, 194018],\n", + " dtype='int64'), Int64Index([ 336, 3059, 8696, 15133, 19706, 22445, 23348, 26089,\n", + " 30676, 41206, 42116, 43024, 43933, 48528, 50399, 72748,\n", + " 73654, 74588, 78197, 80025, 90217, 97688, 104134, 116147,\n", + " 125774, 133068, 140291, 145723, 160789, 163503, 173988, 180747,\n", + " 188588, 191319, 193121, 194019],\n", + " dtype='int64'), Int64Index([ 337, 3060, 8697, 15134, 19707, 22446, 23349, 26090,\n", + " 30677, 41207, 42117, 43025, 43934, 48529, 50400, 72749,\n", + " 73655, 74589, 78198, 80026, 90218, 97689, 104135, 116148,\n", + " 125775, 133069, 140292, 145724, 160790, 163504, 173989, 180748,\n", + " 188589, 191320, 193122, 194020],\n", + " dtype='int64'), Int64Index([ 338, 3061, 8698, 15135, 19708, 22447, 23350, 26091,\n", + " 30678, 41208, 42118, 43026, 43935, 48530, 50401, 72750,\n", + " 73656, 74590, 78199, 80027, 90219, 97690, 104136, 116149,\n", + " 125776, 133070, 140293, 145725, 160791, 163505, 173990, 180749,\n", + " 188590, 191321, 193123, 194021],\n", + " dtype='int64'), Int64Index([ 339, 3062, 8699, 15136, 19709, 22448, 23351, 26092,\n", + " 30679, 41209, 42119, 43027, 43936, 48531, 50402, 72751,\n", + " 73657, 74591, 78200, 80028, 90220, 97691, 104137, 116150,\n", + " 125777, 133071, 140294, 145726, 160792, 163506, 173991, 180750,\n", + " 188591, 191322, 193124, 194022],\n", + " dtype='int64'), Int64Index([ 340, 3063, 8700, 15137, 19710, 22449, 23352, 26093,\n", + " 30680, 41210, 42120, 43028, 43937, 48532, 50403, 72752,\n", + " 73658, 74592, 78201, 80029, 90221, 97692, 104138, 116151,\n", + " 125778, 133072, 140295, 145727, 160793, 163507, 173992, 180751,\n", + " 188592, 191323, 193125, 194023],\n", + " dtype='int64'), Int64Index([ 341, 3064, 8701, 15138, 19711, 22450, 23353, 26094,\n", + " 30681, 41211, 42121, 43029, 43938, 48533, 50404, 72753,\n", + " 73659, 74593, 78202, 80030, 90222, 97693, 104139, 116152,\n", + " 125779, 133073, 140296, 145728, 160794, 163508, 173993, 180752,\n", + " 188593, 191324, 193126, 194024],\n", + " dtype='int64'), Int64Index([ 342, 3065, 8702, 15139, 19712, 22451, 23354, 26095,\n", + " 30682, 41212, 42122, 43030, 43939, 48534, 50405, 72754,\n", + " 73660, 74594, 78203, 80031, 90223, 97694, 104140, 116153,\n", + " 125780, 133074, 140297, 145729, 160795, 163509, 173994, 180753,\n", + " 188594, 191325, 193127, 194025],\n", + " dtype='int64'), Int64Index([ 343, 3066, 8703, 15140, 19713, 22452, 23355, 26096,\n", + " 30683, 41213, 42123, 43031, 43940, 48535, 50406, 72755,\n", + " 73661, 74595, 78204, 80032, 90224, 97695, 104141, 116154,\n", + " 125781, 133075, 140298, 145730, 160796, 163510, 173995, 180754,\n", + " 188595, 191326, 193128, 194026],\n", + " dtype='int64'), Int64Index([ 344, 3067, 8704, 15141, 19714, 22453, 23356, 26097,\n", + " 30684, 41214, 42124, 43032, 43941, 48536, 50407, 72756,\n", + " 73662, 74596, 78205, 80033, 90225, 97696, 104142, 116155,\n", + " 125782, 133076, 140299, 145731, 160797, 163511, 173996, 180755,\n", + " 188596, 191327, 193129, 194027],\n", + " dtype='int64'), Int64Index([ 345, 3068, 8705, 15142, 19715, 22454, 23357, 26098,\n", + " 30685, 41215, 42125, 43033, 43942, 48537, 50408, 72757,\n", + " 73663, 74597, 78206, 80034, 90226, 97697, 104143, 116156,\n", + " 125783, 133077, 140300, 145732, 160798, 163512, 173997, 180756,\n", + " 188597, 191328, 193130, 194028],\n", + " dtype='int64'), Int64Index([ 346, 3069, 8706, 15143, 19716, 22455, 23358, 26099,\n", + " 30686, 41216, 42126, 43034, 43943, 48538, 50409, 72758,\n", + " 73664, 74598, 78207, 80035, 90227, 97698, 104144, 116157,\n", + " 125784, 133078, 140301, 145733, 160799, 163513, 173998, 180757,\n", + " 188598, 191329, 193131, 194029],\n", + " dtype='int64'), Int64Index([ 347, 3070, 8707, 15144, 19717, 22456, 23359, 26100,\n", + " 30687, 41217, 42127, 43035, 43944, 48539, 50410, 72759,\n", + " 73665, 74599, 78208, 80036, 90228, 97699, 104145, 116158,\n", + " 125785, 133079, 140302, 145734, 160800, 163514, 173999, 180758,\n", + " 188599, 191330, 193132, 194030],\n", + " dtype='int64'), Int64Index([ 348, 3071, 8708, 15145, 19718, 22457, 23360, 26101,\n", + " 30688, 41218, 42128, 43036, 43945, 48540, 50411, 72760,\n", + " 73666, 74600, 78209, 80037, 90229, 97700, 104146, 116159,\n", + " 125786, 133080, 140303, 145735, 160801, 163515, 174000, 180759,\n", + " 188600, 191331, 193133, 194031],\n", + " dtype='int64'), Int64Index([ 349, 3072, 8709, 15146, 19719, 22458, 23361, 26102,\n", + " 30689, 41219, 42129, 43037, 43946, 48541, 50412, 72761,\n", + " 73667, 74601, 78210, 80038, 90230, 97701, 104147, 116160,\n", + " 125787, 133081, 140304, 145736, 160802, 163516, 174001, 180760,\n", + " 188601, 191332, 193134, 194032],\n", + " dtype='int64'), Int64Index([ 350, 3073, 8710, 15147, 19720, 22459, 23362, 26103,\n", + " 30690, 41220, 42130, 43038, 43947, 48542, 50413, 72762,\n", + " 73668, 74602, 78211, 80039, 90231, 97702, 104148, 116161,\n", + " 125788, 133082, 140305, 145737, 160803, 163517, 174002, 180761,\n", + " 188602, 191333, 193135, 194033],\n", + " dtype='int64'), Int64Index([ 351, 3074, 8711, 15148, 19721, 22460, 23363, 26104,\n", + " 30691, 41221, 42131, 43039, 43948, 48543, 50414, 72763,\n", + " 73669, 74603, 78212, 80040, 90232, 97703, 104149, 116162,\n", + " 125789, 133083, 140306, 145738, 160804, 163518, 174003, 180762,\n", + " 188603, 191334, 193136, 194034],\n", + " dtype='int64'), Int64Index([ 352, 3075, 8712, 15149, 19722, 22461, 23364, 26105,\n", + " 30692, 41222, 42132, 43040, 43949, 48544, 50415, 72764,\n", + " 73670, 74604, 78213, 80041, 90233, 97704, 104150, 116163,\n", + " 125790, 133084, 140307, 145739, 160805, 163519, 174004, 180763,\n", + " 188604, 191335, 193137, 194035],\n", + " dtype='int64'), Int64Index([ 353, 3076, 8713, 15150, 19723, 22462, 23365, 26106,\n", + " 30693, 41223, 42133, 43041, 43950, 48545, 50416, 72765,\n", + " 73671, 74605, 78214, 80042, 90234, 97705, 104151, 116164,\n", + " 125791, 133085, 140308, 145740, 160806, 163520, 174005, 180764,\n", + " 188605, 191336, 193138, 194036],\n", + " dtype='int64'), Int64Index([ 354, 3077, 8714, 15151, 19724, 22463, 23366, 26107,\n", + " 30694, 41224, 42134, 43042, 43951, 48546, 50417, 72766,\n", + " 73672, 74606, 78215, 80043, 90235, 97706, 104152, 116165,\n", + " 125792, 133086, 140309, 145741, 160807, 163521, 174006, 180765,\n", + " 188606, 191337, 193139, 194037],\n", + " dtype='int64'), Int64Index([ 355, 3078, 8715, 15152, 19725, 22464, 23367, 26108,\n", + " 30695, 41225, 42135, 43043, 43952, 48547, 50418, 72767,\n", + " 73673, 74607, 78216, 80044, 90236, 97707, 104153, 116166,\n", + " 125793, 133087, 140310, 145742, 160808, 163522, 174007, 180766,\n", + " 188607, 191338, 193140, 194038],\n", + " dtype='int64'), Int64Index([ 356, 3079, 8716, 15153, 19726, 22465, 23368, 26109,\n", + " 30696, 41226, 42136, 43044, 43953, 48548, 50419, 72768,\n", + " 73674, 74608, 78217, 80045, 90237, 97708, 104154, 116167,\n", + " 125794, 133088, 140311, 145743, 160809, 163523, 174008, 180767,\n", + " 188608, 191339, 193141, 194039],\n", + " dtype='int64'), Int64Index([ 357, 3080, 8717, 15154, 19727, 22466, 23369, 26110,\n", + " 30697, 41227, 42137, 43045, 43954, 48549, 50420, 72769,\n", + " 73675, 74609, 78218, 80046, 90238, 97709, 104155, 116168,\n", + " 125795, 133089, 140312, 145744, 160810, 163524, 174009, 180768,\n", + " 188609, 191340, 193142, 194040],\n", + " dtype='int64'), Int64Index([ 358, 3081, 8718, 15155, 19728, 22467, 23370, 26111,\n", + " 30698, 41228, 42138, 43046, 43955, 48550, 50421, 72770,\n", + " 73676, 74610, 78219, 80047, 90239, 97710, 104156, 116169,\n", + " 125796, 133090, 140313, 145745, 160811, 163525, 174010, 180769,\n", + " 188610, 191341, 193143, 194041],\n", + " dtype='int64'), Int64Index([ 359, 3082, 8719, 15156, 19729, 22468, 23371, 26112,\n", + " 30699, 41229, 42139, 43047, 43956, 48551, 50422, 72771,\n", + " 73677, 74611, 78220, 80048, 90240, 97711, 104157, 116170,\n", + " 125797, 133091, 140314, 145746, 160812, 163526, 174011, 180770,\n", + " 188611, 191342, 193144, 194042],\n", + " dtype='int64'), Int64Index([ 360, 3083, 8720, 15157, 19730, 22469, 23372, 26113,\n", + " 30700, 41230, 42140, 43048, 43957, 48552, 50423, 72772,\n", + " 73678, 74612, 78221, 80049, 90241, 97712, 104158, 116171,\n", + " 125798, 133092, 140315, 145747, 160813, 163527, 174012, 180771,\n", + " 188612, 191343, 193145, 194043],\n", + " dtype='int64'), Int64Index([ 361, 3084, 8721, 15158, 19731, 22470, 23373, 26114,\n", + " 30701, 41231, 42141, 43049, 43958, 48553, 50424, 72773,\n", + " 73679, 74613, 78222, 80050, 90242, 97713, 104159, 116172,\n", + " 125799, 133093, 140316, 145748, 160814, 163528, 174013, 180772,\n", + " 188613, 191344, 193146, 194044],\n", + " dtype='int64'), Int64Index([ 362, 3085, 8722, 15159, 19732, 22471, 23374, 26115,\n", + " 30702, 41232, 42142, 43050, 43959, 48554, 50425, 72774,\n", + " 73680, 74614, 78223, 80051, 90243, 97714, 104160, 116173,\n", + " 125800, 133094, 140317, 145749, 160815, 163529, 174014, 180773,\n", + " 188614, 191345, 193147, 194045],\n", + " dtype='int64'), Int64Index([ 363, 3086, 8723, 15160, 19733, 22472, 23375, 26116,\n", + " 30703, 41233, 42143, 43051, 43960, 48555, 50426, 72775,\n", + " 73681, 74615, 78224, 80052, 90244, 97715, 104161, 116174,\n", + " 125801, 133095, 140318, 145750, 160816, 163530, 174015, 180774,\n", + " 188615, 191346, 193148, 194046],\n", + " dtype='int64'), Int64Index([ 364, 3087, 8724, 15161, 19734, 22473, 23376, 26117,\n", + " 30704, 41234, 42144, 43052, 43961, 48556, 50427, 72776,\n", + " 73682, 74616, 78225, 80053, 90245, 97716, 104162, 116175,\n", + " 125802, 133096, 140319, 145751, 160817, 163531, 174016, 180775,\n", + " 188616, 191347, 193149, 194047],\n", + " dtype='int64'), Int64Index([ 365, 3088, 8725, 15162, 19735, 22474, 23377, 26118,\n", + " 30705, 41235, 42145, 43053, 43962, 48557, 50428, 72777,\n", + " 73683, 74617, 78226, 80054, 90246, 97717, 104163, 116176,\n", + " 125803, 133097, 140320, 145752, 160818, 163532, 174017, 180776,\n", + " 188617, 191348, 193150, 194048],\n", + " dtype='int64'), Int64Index([ 366, 3089, 8726, 15163, 19736, 22475, 23378, 26119,\n", + " 30706, 41236, 42146, 43054, 43963, 48558, 50429, 72778,\n", + " 73684, 74618, 78227, 80055, 90247, 97718, 104164, 116177,\n", + " 125804, 133098, 140321, 145753, 160819, 163533, 174018, 180777,\n", + " 188618, 191349, 193151, 194049],\n", + " dtype='int64'), Int64Index([ 367, 3090, 8727, 15164, 19737, 22476, 23379, 26120,\n", + " 30707, 41237, 42147, 43055, 43964, 48559, 50430, 72779,\n", + " 73685, 74619, 78228, 80056, 90248, 97719, 104165, 116178,\n", + " 125805, 133099, 140322, 145754, 160820, 163534, 174019, 180778,\n", + " 188619, 191350, 193152, 194050],\n", + " dtype='int64'), Int64Index([ 368, 3091, 8728, 15165, 19738, 22477, 23380, 26121,\n", + " 30708, 41238, 42148, 43056, 43965, 48560, 50431, 72780,\n", + " 73686, 74620, 78229, 80057, 90249, 97720, 104166, 116179,\n", + " 125806, 133100, 140323, 145755, 160821, 163535, 174020, 180779,\n", + " 188620, 191351, 193153, 194051],\n", + " dtype='int64'), Int64Index([ 369, 3092, 8729, 15166, 19739, 22478, 23381, 26122,\n", + " 30709, 41239, 42149, 43057, 43966, 48561, 50432, 72781,\n", + " 73687, 74621, 78230, 80058, 90250, 97721, 104167, 116180,\n", + " 125807, 133101, 140324, 145756, 160822, 163536, 174021, 180780,\n", + " 188621, 191352, 193154, 194052],\n", + " dtype='int64'), Int64Index([ 370, 3093, 8730, 15167, 19740, 22479, 23382, 26123,\n", + " 30710, 41240, 42150, 43058, 43967, 48562, 50433, 72782,\n", + " 73688, 74622, 78231, 80059, 90251, 97722, 104168, 116181,\n", + " 125808, 133102, 140325, 145757, 160823, 163537, 174022, 180781,\n", + " 188622, 191353, 193155, 194053],\n", + " dtype='int64'), Int64Index([ 371, 3094, 8731, 15168, 19741, 22480, 23383, 26124,\n", + " 30711, 41241, 42151, 43059, 43968, 48563, 50434, 72783,\n", + " 73689, 74623, 78232, 80060, 90252, 97723, 104169, 116182,\n", + " 125809, 133103, 140326, 145758, 160824, 163538, 174023, 180782,\n", + " 188623, 191354, 193156, 194054],\n", + " dtype='int64'), Int64Index([ 372, 3095, 8732, 15169, 19742, 22481, 23384, 26125,\n", + " 30712, 41242, 42152, 43060, 43969, 48564, 50435, 72784,\n", + " 73690, 74624, 78233, 80061, 90253, 97724, 104170, 116183,\n", + " 125810, 133104, 140327, 145759, 160825, 163539, 174024, 180783,\n", + " 188624, 191355, 193157, 194055],\n", + " dtype='int64'), Int64Index([ 373, 3096, 8733, 15170, 19743, 22482, 23385, 26126,\n", + " 30713, 41243, 42153, 43061, 43970, 48565, 50436, 72785,\n", + " 73691, 74625, 78234, 80062, 90254, 97725, 104171, 116184,\n", + " 125811, 133105, 140328, 145760, 160826, 163540, 174025, 180784,\n", + " 188625, 191356, 193158, 194056],\n", + " dtype='int64'), Int64Index([ 374, 3097, 8734, 15171, 19744, 22483, 23386, 26127,\n", + " 30714, 41244, 42154, 43062, 43971, 48566, 50437, 72786,\n", + " 73692, 74626, 78235, 80063, 90255, 97726, 104172, 116185,\n", + " 125812, 133106, 140329, 145761, 160827, 163541, 174026, 180785,\n", + " 188626, 191357, 193159, 194057],\n", + " dtype='int64'), Int64Index([ 375, 3098, 8735, 15172, 19745, 22484, 23387, 26128,\n", + " 30715, 41245, 42155, 43063, 43972, 48567, 50438, 72787,\n", + " 73693, 74627, 78236, 80064, 90256, 97727, 104173, 116186,\n", + " 125813, 133107, 140330, 145762, 160828, 163542, 174027, 180786,\n", + " 188627, 191358, 193160, 194058],\n", + " dtype='int64'), Int64Index([ 376, 3099, 8736, 15173, 19746, 22485, 23388, 26129,\n", + " 30716, 41246, 42156, 43064, 43973, 48568, 50439, 72788,\n", + " 73694, 74628, 78237, 80065, 90257, 97728, 104174, 116187,\n", + " 125814, 133108, 140331, 145763, 160829, 163543, 174028, 180787,\n", + " 188628, 191359, 193161, 194059],\n", + " dtype='int64'), Int64Index([ 377, 3100, 8737, 15174, 19747, 22486, 23389, 26130,\n", + " 30717, 41247, 42157, 43065, 43974, 48569, 50440, 72789,\n", + " 73695, 74629, 78238, 80066, 90258, 97729, 104175, 116188,\n", + " 125815, 133109, 140332, 145764, 160830, 163544, 174029, 180788,\n", + " 188629, 191360, 193162, 194060],\n", + " dtype='int64'), Int64Index([ 378, 3101, 8738, 15175, 19748, 22487, 23390, 26131,\n", + " 30718, 41248, 42158, 43066, 43975, 48570, 50441, 72790,\n", + " 73696, 74630, 78239, 80067, 90259, 97730, 104176, 116189,\n", + " 125816, 133110, 140333, 145765, 160831, 163545, 174030, 180789,\n", + " 188630, 191361, 193163, 194061],\n", + " dtype='int64'), Int64Index([ 379, 3102, 8739, 15176, 19749, 22488, 23391, 26132,\n", + " 30719, 41249, 42159, 43067, 43976, 48571, 50442, 72791,\n", + " 73697, 74631, 78240, 80068, 90260, 97731, 104177, 116190,\n", + " 125817, 133111, 140334, 145766, 160832, 163546, 174031, 180790,\n", + " 188631, 191362, 193164, 194062],\n", + " dtype='int64'), Int64Index([ 380, 3103, 8740, 15177, 19750, 22489, 23392, 26133,\n", + " 30720, 41250, 42160, 43068, 43977, 48572, 50443, 72792,\n", + " 73698, 74632, 78241, 80069, 90261, 97732, 104178, 116191,\n", + " 125818, 133112, 140335, 145767, 160833, 163547, 174032, 180791,\n", + " 188632, 191363, 193165, 194063],\n", + " dtype='int64'), Int64Index([ 381, 3104, 8741, 15178, 19751, 22490, 23393, 26134,\n", + " 30721, 41251, 42161, 43069, 43978, 48573, 50444, 72793,\n", + " 73699, 74633, 78242, 80070, 90262, 97733, 104179, 116192,\n", + " 125819, 133113, 140336, 145768, 160834, 163548, 174033, 180792,\n", + " 188633, 191364, 193166, 194064],\n", + " dtype='int64'), Int64Index([ 382, 3105, 8742, 15179, 19752, 22491, 23394, 26135,\n", + " 30722, 41252, 42162, 43070, 43979, 48574, 50445, 72794,\n", + " 73700, 74634, 78243, 80071, 90263, 97734, 104180, 116193,\n", + " 125820, 133114, 140337, 145769, 160835, 163549, 174034, 180793,\n", + " 188634, 191365, 193167, 194065],\n", + " dtype='int64'), Int64Index([ 383, 3106, 8743, 15180, 19753, 22492, 23395, 26136,\n", + " 30723, 41253, 42163, 43071, 43980, 48575, 50446, 72795,\n", + " 73701, 74635, 78244, 80072, 90264, 97735, 104181, 116194,\n", + " 125821, 133115, 140338, 145770, 160836, 163550, 174035, 180794,\n", + " 188635, 191366, 193168, 194066],\n", + " dtype='int64'), Int64Index([ 384, 3107, 8744, 15181, 19754, 22493, 23396, 26137,\n", + " 30724, 41254, 42164, 43072, 43981, 48576, 50447, 72796,\n", + " 73702, 74636, 78245, 80073, 90265, 97736, 104182, 116195,\n", + " 125822, 133116, 140339, 145771, 160837, 163551, 174036, 180795,\n", + " 188636, 191367, 193169, 194067],\n", + " dtype='int64'), Int64Index([ 385, 3108, 8745, 15182, 19755, 22494, 23397, 26138,\n", + " 30725, 41255, 42165, 43073, 43982, 48577, 50448, 72797,\n", + " 73703, 74637, 78246, 80074, 90266, 97737, 104183, 116196,\n", + " 125823, 133117, 140340, 145772, 160838, 163552, 174037, 180796,\n", + " 188637, 191368, 193170, 194068],\n", + " dtype='int64'), Int64Index([ 386, 3109, 8746, 15183, 19756, 22495, 23398, 26139,\n", + " 30726, 41256, 42166, 43074, 43983, 48578, 50449, 72798,\n", + " 73704, 74638, 78247, 80075, 90267, 97738, 104184, 116197,\n", + " 125824, 133118, 140341, 145773, 160839, 163553, 174038, 180797,\n", + " 188638, 191369, 193171, 194069],\n", + " dtype='int64'), Int64Index([ 387, 3110, 8747, 15184, 19757, 22496, 23399, 26140,\n", + " 30727, 41257, 42167, 43075, 43984, 48579, 50450, 72799,\n", + " 73705, 74639, 78248, 80076, 90268, 97739, 104185, 116198,\n", + " 125825, 133119, 140342, 145774, 160840, 163554, 174039, 180798,\n", + " 188639, 191370, 193172, 194070],\n", + " dtype='int64'), Int64Index([ 388, 3111, 8748, 15185, 19758, 22497, 23400, 26141,\n", + " 30728, 41258, 42168, 43076, 43985, 48580, 50451, 72800,\n", + " 73706, 74640, 78249, 80077, 90269, 97740, 104186, 116199,\n", + " 125826, 133120, 140343, 145775, 160841, 163555, 174040, 180799,\n", + " 188640, 191371, 193173, 194071],\n", + " dtype='int64'), Int64Index([ 389, 3112, 8749, 15186, 19759, 22498, 23401, 26142,\n", + " 30729, 41259, 42169, 43077, 43986, 48581, 50452, 72801,\n", + " 73707, 74641, 78250, 80078, 90270, 97741, 104187, 116200,\n", + " 125827, 133121, 140344, 145776, 160842, 163556, 174041, 180800,\n", + " 188641, 191372, 193174, 194072],\n", + " dtype='int64'), Int64Index([ 390, 3113, 8750, 15187, 19760, 22499, 23402, 26143,\n", + " 30730, 41260, 42170, 43078, 43987, 48582, 50453, 72802,\n", + " 73708, 74642, 78251, 80079, 90271, 97742, 104188, 116201,\n", + " 125828, 133122, 140345, 145777, 160843, 163557, 174042, 180801,\n", + " 188642, 191373, 193175, 194073],\n", + " dtype='int64'), Int64Index([ 391, 3114, 8751, 15188, 19761, 22500, 23403, 26144,\n", + " 30731, 41261, 42171, 43079, 43988, 48583, 50454, 72803,\n", + " 73709, 74643, 78252, 80080, 90272, 97743, 104189, 116202,\n", + " 125829, 133123, 140346, 145778, 160844, 163558, 174043, 180802,\n", + " 188643, 191374, 193176, 194074],\n", + " dtype='int64'), Int64Index([ 392, 3115, 8752, 15189, 19762, 22501, 23404, 26145,\n", + " 30732, 41262, 42172, 43080, 43989, 48584, 50455, 72804,\n", + " 73710, 74644, 78253, 80081, 90273, 97744, 104190, 116203,\n", + " 125830, 133124, 140347, 145779, 160845, 163559, 174044, 180803,\n", + " 188644, 191375, 193177, 194075],\n", + " dtype='int64'), Int64Index([ 393, 3116, 8753, 15190, 19763, 22502, 23405, 26146,\n", + " 30733, 41263, 42173, 43081, 43990, 48585, 50456, 72805,\n", + " 73711, 74645, 78254, 80082, 90274, 97745, 104191, 116204,\n", + " 125831, 133125, 140348, 145780, 160846, 163560, 174045, 180804,\n", + " 188645, 191376, 193178, 194076],\n", + " dtype='int64'), Int64Index([ 394, 3117, 8754, 15191, 19764, 22503, 23406, 26147,\n", + " 30734, 41264, 42174, 43082, 43991, 48586, 50457, 72806,\n", + " 73712, 74646, 78255, 80083, 90275, 97746, 104192, 116205,\n", + " 125832, 133126, 140349, 145781, 160847, 163561, 174046, 180805,\n", + " 188646, 191377, 193179, 194077],\n", + " dtype='int64'), Int64Index([ 395, 3118, 8755, 15192, 19765, 22504, 23407, 26148,\n", + " 30735, 41265, 42175, 43083, 43992, 48587, 50458, 72807,\n", + " 73713, 74647, 78256, 80084, 90276, 97747, 104193, 116206,\n", + " 125833, 133127, 140350, 145782, 160848, 163562, 174047, 180806,\n", + " 188647, 191378, 193180, 194078],\n", + " dtype='int64'), Int64Index([ 396, 3119, 8756, 15193, 19766, 22505, 23408, 26149,\n", + " 30736, 41266, 42176, 43084, 43993, 48588, 50459, 72808,\n", + " 73714, 74648, 78257, 80085, 90277, 97748, 104194, 116207,\n", + " 125834, 133128, 140351, 145783, 160849, 163563, 174048, 180807,\n", + " 188648, 191379, 193181, 194079],\n", + " dtype='int64'), Int64Index([ 397, 3120, 8757, 15194, 19767, 22506, 23409, 26150,\n", + " 30737, 41267, 42177, 43085, 43994, 48589, 50460, 72809,\n", + " 73715, 74649, 78258, 80086, 90278, 97749, 104195, 116208,\n", + " 125835, 133129, 140352, 145784, 160850, 163564, 174049, 180808,\n", + " 188649, 191380, 193182, 194080],\n", + " dtype='int64'), Int64Index([ 398, 3121, 8758, 15195, 19768, 22507, 23410, 26151,\n", + " 30738, 41268, 42178, 43086, 43995, 48590, 50461, 72810,\n", + " 73716, 74650, 78259, 80087, 90279, 97750, 104196, 116209,\n", + " 125836, 133130, 140353, 145785, 160851, 163565, 174050, 180809,\n", + " 188650, 191381, 193183, 194081],\n", + " dtype='int64'), Int64Index([ 399, 3122, 8759, 15196, 19769, 22508, 23411, 26152,\n", + " 30739, 41269, 42179, 43087, 43996, 48591, 50462, 72811,\n", + " 73717, 74651, 78260, 80088, 90280, 97751, 104197, 116210,\n", + " 125837, 133131, 140354, 145786, 160852, 163566, 174051, 180810,\n", + " 188651, 191382, 193184, 194082],\n", + " dtype='int64'), Int64Index([ 400, 3123, 8760, 15197, 19770, 22509, 23412, 26153,\n", + " 30740, 41270, 42180, 43088, 43997, 48592, 50463, 72812,\n", + " 73718, 74652, 78261, 80089, 90281, 97752, 104198, 116211,\n", + " 125838, 133132, 140355, 145787, 160853, 163567, 174052, 180811,\n", + " 188652, 191383, 193185, 194083],\n", + " dtype='int64'), Int64Index([ 401, 3124, 8761, 15198, 19771, 22510, 23413, 26154,\n", + " 30741, 41271, 42181, 43089, 43998, 48593, 50464, 72813,\n", + " 73719, 74653, 78262, 80090, 90282, 97753, 104199, 116212,\n", + " 125839, 133133, 140356, 145788, 160854, 163568, 174053, 180812,\n", + " 188653, 191384, 193186, 194084],\n", + " dtype='int64'), Int64Index([ 402, 3125, 8762, 15199, 19772, 22511, 23414, 26155,\n", + " 30742, 41272, 42182, 43090, 43999, 48594, 50465, 72814,\n", + " 73720, 74654, 78263, 80091, 90283, 97754, 104200, 116213,\n", + " 125840, 133134, 140357, 145789, 160855, 163569, 174054, 180813,\n", + " 188654, 191385, 193187, 194085],\n", + " dtype='int64'), Int64Index([ 403, 3126, 8763, 15200, 19773, 22512, 23415, 26156,\n", + " 30743, 41273, 42183, 43091, 44000, 48595, 50466, 72815,\n", + " 73721, 74655, 78264, 80092, 90284, 97755, 104201, 116214,\n", + " 125841, 133135, 140358, 145790, 160856, 163570, 174055, 180814,\n", + " 188655, 191386, 193188, 194086],\n", + " dtype='int64'), Int64Index([ 404, 3127, 8764, 15201, 19774, 22513, 23416, 26157,\n", + " 30744, 41274, 42184, 43092, 44001, 48596, 50467, 72816,\n", + " 73722, 74656, 78265, 80093, 90285, 97756, 104202, 116215,\n", + " 125842, 133136, 140359, 145791, 160857, 163571, 174056, 180815,\n", + " 188656, 191387, 193189, 194087],\n", + " dtype='int64'), Int64Index([ 405, 3128, 8765, 15202, 19775, 22514, 23417, 26158,\n", + " 30745, 41275, 42185, 43093, 44002, 48597, 50468, 72817,\n", + " 73723, 74657, 78266, 80094, 90286, 97757, 104203, 116216,\n", + " 125843, 133137, 140360, 145792, 160858, 163572, 174057, 180816,\n", + " 188657, 191388, 193190, 194088],\n", + " dtype='int64'), Int64Index([ 406, 3129, 8766, 15203, 19776, 22515, 23418, 26159,\n", + " 30746, 41276, 42186, 43094, 44003, 48598, 50469, 72818,\n", + " 73724, 74658, 78267, 80095, 90287, 97758, 104204, 116217,\n", + " 125844, 133138, 140361, 145793, 160859, 163573, 174058, 180817,\n", + " 188658, 191389, 193191, 194089],\n", + " dtype='int64'), Int64Index([ 407, 3130, 8767, 15204, 19777, 22516, 23419, 26160,\n", + " 30747, 41277, 42187, 43095, 44004, 48599, 50470, 72819,\n", + " 73725, 74659, 78268, 80096, 90288, 97759, 104205, 116218,\n", + " 125845, 133139, 140362, 145794, 160860, 163574, 174059, 180818,\n", + " 188659, 191390, 193192, 194090],\n", + " dtype='int64'), Int64Index([ 408, 3131, 8768, 15205, 19778, 22517, 23420, 26161,\n", + " 30748, 41278, 42188, 43096, 44005, 48600, 50471, 72820,\n", + " 73726, 74660, 78269, 80097, 90289, 97760, 104206, 116219,\n", + " 125846, 133140, 140363, 145795, 160861, 163575, 174060, 180819,\n", + " 188660, 191391, 193193, 194091],\n", + " dtype='int64'), Int64Index([ 409, 3132, 8769, 15206, 19779, 22518, 23421, 26162,\n", + " 30749, 41279, 42189, 43097, 44006, 48601, 50472, 72821,\n", + " 73727, 74661, 78270, 80098, 90290, 97761, 104207, 116220,\n", + " 125847, 133141, 140364, 145796, 160862, 163576, 174061, 180820,\n", + " 188661, 191392, 193194, 194092],\n", + " dtype='int64'), Int64Index([ 410, 3133, 8770, 15207, 19780, 22519, 23422, 26163,\n", + " 30750, 41280, 42190, 43098, 44007, 48602, 50473, 72822,\n", + " 73728, 74662, 78271, 80099, 90291, 97762, 104208, 116221,\n", + " 125848, 133142, 140365, 145797, 160863, 163577, 174062, 180821,\n", + " 188662, 191393, 193195, 194093],\n", + " dtype='int64'), Int64Index([ 411, 3134, 8771, 15208, 19781, 22520, 23423, 26164,\n", + " 30751, 41281, 42191, 43099, 44008, 48603, 50474, 72823,\n", + " 73729, 74663, 78272, 80100, 90292, 97763, 104209, 116222,\n", + " 125849, 133143, 140366, 145798, 160864, 163578, 174063, 180822,\n", + " 188663, 191394, 193196, 194094],\n", + " dtype='int64'), Int64Index([ 412, 3135, 8772, 15209, 19782, 22521, 23424, 26165,\n", + " 30752, 41282, 42192, 43100, 44009, 48604, 50475, 72824,\n", + " 73730, 74664, 78273, 80101, 90293, 97764, 104210, 116223,\n", + " 125850, 133144, 140367, 145799, 160865, 163579, 174064, 180823,\n", + " 188664, 191395, 193197, 194095],\n", + " dtype='int64'), Int64Index([ 413, 3136, 8773, 15210, 19783, 22522, 23425, 26166,\n", + " 30753, 41283, 42193, 43101, 44010, 48605, 50476, 72825,\n", + " 73731, 74665, 78274, 80102, 90294, 97765, 104211, 116224,\n", + " 125851, 133145, 140368, 145800, 160866, 163580, 174065, 180824,\n", + " 188665, 191396, 193198, 194096],\n", + " dtype='int64'), Int64Index([ 414, 3137, 8774, 15211, 19784, 22523, 23426, 26167,\n", + " 30754, 41284, 42194, 43102, 44011, 48606, 50477, 72826,\n", + " 73732, 74666, 78275, 80103, 90295, 97766, 104212, 116225,\n", + " 125852, 133146, 140369, 145801, 160867, 163581, 174066, 180825,\n", + " 188666, 191397, 193199, 194097],\n", + " dtype='int64'), Int64Index([ 415, 3138, 8775, 15212, 19785, 22524, 23427, 26168,\n", + " 30755, 41285, 42195, 43103, 44012, 48607, 50478, 72827,\n", + " 73733, 74667, 78276, 80104, 90296, 97767, 104213, 116226,\n", + " 125853, 133147, 140370, 145802, 160868, 163582, 174067, 180826,\n", + " 188667, 191398, 193200, 194098],\n", + " dtype='int64'), Int64Index([ 416, 3139, 8776, 15213, 19786, 22525, 23428, 26169,\n", + " 30756, 41286, 42196, 43104, 44013, 48608, 50479, 72828,\n", + " 73734, 74668, 78277, 80105, 90297, 97768, 104214, 116227,\n", + " 125854, 133148, 140371, 145803, 160869, 163583, 174068, 180827,\n", + " 188668, 191399, 193201, 194099],\n", + " dtype='int64'), Int64Index([ 417, 3140, 8777, 15214, 19787, 22526, 23429, 26170,\n", + " 30757, 41287, 42197, 43105, 44014, 48609, 50480, 72829,\n", + " 73735, 74669, 78278, 80106, 90298, 97769, 104215, 116228,\n", + " 125855, 133149, 140372, 145804, 160870, 163584, 174069, 180828,\n", + " 188669, 191400, 193202, 194100],\n", + " dtype='int64'), Int64Index([ 418, 3141, 8778, 15215, 19788, 22527, 23430, 26171,\n", + " 30758, 41288, 42198, 43106, 44015, 48610, 50481, 72830,\n", + " 73736, 74670, 78279, 80107, 90299, 97770, 104216, 116229,\n", + " 125856, 133150, 140373, 145805, 160871, 163585, 174070, 180829,\n", + " 188670, 191401, 193203, 194101],\n", + " dtype='int64'), Int64Index([ 419, 3142, 8779, 15216, 19789, 22528, 23431, 26172,\n", + " 30759, 41289, 42199, 43107, 44016, 48611, 50482, 72831,\n", + " 73737, 74671, 78280, 80108, 90300, 97771, 104217, 116230,\n", + " 125857, 133151, 140374, 145806, 160872, 163586, 174071, 180830,\n", + " 188671, 191402, 193204, 194102],\n", + " dtype='int64'), Int64Index([ 420, 3143, 8780, 15217, 19790, 22529, 23432, 26173,\n", + " 30760, 41290, 42200, 43108, 44017, 48612, 50483, 72832,\n", + " 73738, 74672, 78281, 80109, 90301, 97772, 104218, 116231,\n", + " 125858, 133152, 140375, 145807, 160873, 163587, 171404, 174072,\n", + " 180831, 188672, 191403, 193205, 194103],\n", + " dtype='int64'), Int64Index([ 421, 3144, 8781, 15218, 19791, 22530, 23433, 26174,\n", + " 30761, 41291, 42201, 43109, 44018, 48613, 50484, 72833,\n", + " 73739, 74673, 78282, 80110, 90302, 97773, 104219, 116232,\n", + " 125859, 133153, 140376, 145808, 160874, 163588, 171405, 174073,\n", + " 180832, 188673, 191404, 193206, 194104],\n", + " dtype='int64'), Int64Index([ 422, 3145, 8782, 15219, 19792, 22531, 23434, 26175,\n", + " 30762, 41292, 42202, 43110, 44019, 48614, 50485, 72834,\n", + " 73740, 74674, 78283, 80111, 90303, 97774, 104220, 116233,\n", + " 125860, 133154, 140377, 145809, 160875, 163589, 171406, 174074,\n", + " 180833, 188674, 191405, 193207, 194105],\n", + " dtype='int64'), Int64Index([ 423, 3146, 8783, 15220, 19793, 22532, 23435, 26176,\n", + " 30763, 41293, 42203, 43111, 44020, 48615, 50486, 72835,\n", + " 73741, 74675, 78284, 80112, 90304, 97775, 104221, 116234,\n", + " 125861, 133155, 140378, 145810, 160876, 163590, 171407, 174075,\n", + " 180834, 188675, 191406, 193208, 194106],\n", + " dtype='int64'), Int64Index([ 424, 3147, 8784, 15221, 19794, 22533, 23436, 26177,\n", + " 30764, 41294, 42204, 43112, 44021, 48616, 50487, 72836,\n", + " 73742, 74676, 78285, 80113, 90305, 97776, 104222, 116235,\n", + " 125862, 133156, 140379, 145811, 160877, 163591, 171408, 174076,\n", + " 180835, 188676, 191407, 193209, 194107],\n", + " dtype='int64'), Int64Index([ 425, 3148, 8785, 15222, 19795, 22534, 23437, 26178,\n", + " 30765, 41295, 42205, 43113, 44022, 48617, 50488, 72837,\n", + " 73743, 74677, 78286, 80114, 90306, 97777, 104223, 116236,\n", + " 125863, 133157, 140380, 145812, 160878, 163592, 171409, 174077,\n", + " 180836, 188677, 191408, 193210, 194108],\n", + " dtype='int64'), Int64Index([ 426, 3149, 8786, 15223, 19796, 22535, 23438, 26179,\n", + " 30766, 41296, 42206, 43114, 44023, 48618, 50489, 72838,\n", + " 73744, 74678, 78287, 80115, 90307, 97778, 104224, 116237,\n", + " 125864, 133158, 140381, 145813, 160879, 163593, 171410, 174078,\n", + " 180837, 188678, 191409, 193211, 194109],\n", + " dtype='int64'), Int64Index([ 427, 3150, 8787, 15224, 19797, 22536, 23439, 26180,\n", + " 30767, 41297, 42207, 43115, 44024, 48619, 50490, 72839,\n", + " 73745, 74679, 78288, 80116, 90308, 97779, 104225, 116238,\n", + " 125865, 133159, 140382, 145814, 160880, 163594, 171411, 174079,\n", + " 180838, 188679, 191410, 193212, 194110],\n", + " dtype='int64'), Int64Index([ 428, 3151, 8788, 15225, 19798, 22537, 23440, 26181,\n", + " 30768, 41298, 42208, 43116, 44025, 48620, 50491, 72840,\n", + " 73746, 74680, 78289, 80117, 90309, 97780, 104226, 116239,\n", + " 125866, 133160, 140383, 145815, 160881, 163595, 171412, 174080,\n", + " 180839, 188680, 191411, 193213, 194111],\n", + " dtype='int64'), Int64Index([ 429, 3152, 8789, 15226, 19799, 22538, 23441, 26182,\n", + " 30769, 41299, 42209, 43117, 44026, 48621, 50492, 72841,\n", + " 73747, 74681, 78290, 80118, 90310, 97781, 104227, 116240,\n", + " 125867, 133161, 140384, 145816, 160882, 163596, 171413, 174081,\n", + " 180840, 188681, 191412, 193214, 194112],\n", + " dtype='int64'), Int64Index([ 430, 3153, 8790, 15227, 19800, 22539, 23442, 26183,\n", + " 30770, 41300, 42210, 43118, 44027, 48622, 50493, 72842,\n", + " 73748, 74682, 78291, 80119, 90311, 97782, 104228, 116241,\n", + " 125868, 133162, 140385, 145817, 160883, 163597, 171414, 174082,\n", + " 180841, 188682, 191413, 193215, 194113],\n", + " dtype='int64'), Int64Index([ 431, 3154, 8791, 15228, 19801, 22540, 23443, 26184,\n", + " 30771, 41301, 42211, 43119, 44028, 48623, 50494, 72843,\n", + " 73749, 74683, 78292, 80120, 90312, 97783, 104229, 116242,\n", + " 125869, 133163, 140386, 145818, 160884, 163598, 171415, 174083,\n", + " 180842, 188683, 191414, 193216, 194114],\n", + " dtype='int64'), Int64Index([ 432, 3155, 8792, 15229, 19802, 22541, 23444, 26185,\n", + " 30772, 41302, 42212, 43120, 44029, 48624, 50495, 72844,\n", + " 73750, 74684, 78293, 80121, 90313, 97784, 104230, 116243,\n", + " 125870, 133164, 140387, 145819, 160885, 163599, 171416, 174084,\n", + " 180843, 188684, 191415, 193217, 194115],\n", + " dtype='int64'), Int64Index([ 433, 3156, 8793, 15230, 19803, 22542, 23445, 26186,\n", + " 30773, 41303, 42213, 43121, 44030, 48625, 50496, 72845,\n", + " 73751, 74685, 78294, 80122, 90314, 97785, 104231, 116244,\n", + " 125871, 133165, 140388, 145820, 160886, 163600, 171417, 174085,\n", + " 180844, 188685, 191416, 193218, 194116],\n", + " dtype='int64'), Int64Index([ 434, 3157, 8794, 15231, 19804, 22543, 23446, 26187,\n", + " 30774, 41304, 42214, 43122, 44031, 48626, 50497, 72846,\n", + " 73752, 74686, 78295, 80123, 90315, 97786, 104232, 116245,\n", + " 125872, 133166, 140389, 145821, 160887, 163601, 171418, 174086,\n", + " 180845, 188686, 191417, 193219, 194117],\n", + " dtype='int64'), Int64Index([ 435, 3158, 8795, 15232, 19805, 22544, 23447, 26188,\n", + " 30775, 41305, 42215, 43123, 44032, 48627, 50498, 72847,\n", + " 73753, 74687, 78296, 80124, 90316, 97787, 104233, 116246,\n", + " 125873, 133167, 140390, 145822, 160888, 163602, 171419, 174087,\n", + " 180846, 188687, 191418, 193220, 194118],\n", + " dtype='int64'), Int64Index([ 436, 3159, 8796, 15233, 19806, 22545, 23448, 26189,\n", + " 30776, 41306, 42216, 43124, 44033, 48628, 50499, 72848,\n", + " 73754, 74688, 78297, 80125, 90317, 97788, 104234, 116247,\n", + " 125874, 133168, 140391, 145823, 160889, 163603, 171420, 174088,\n", + " 180847, 188688, 191419, 193221, 194119],\n", + " dtype='int64'), Int64Index([ 437, 3160, 8797, 15234, 19807, 22546, 23449, 26190,\n", + " 30777, 41307, 42217, 43125, 44034, 48629, 50500, 72849,\n", + " 73755, 74689, 78298, 80126, 90318, 97789, 104235, 116248,\n", + " 125875, 133169, 140392, 145824, 160890, 163604, 171421, 174089,\n", + " 180848, 188689, 191420, 193222, 194120],\n", + " dtype='int64'), Int64Index([ 438, 3161, 8798, 15235, 19808, 22547, 23450, 26191,\n", + " 30778, 41308, 42218, 43126, 44035, 48630, 50501, 72850,\n", + " 73756, 74690, 78299, 80127, 90319, 97790, 104236, 116249,\n", + " 125876, 133170, 140393, 145825, 160891, 163605, 171422, 174090,\n", + " 180849, 188690, 191421, 193223, 194121],\n", + " dtype='int64'), Int64Index([ 439, 3162, 8799, 15236, 19809, 22548, 23451, 26192,\n", + " 30779, 41309, 42219, 43127, 44036, 48631, 50502, 72851,\n", + " 73757, 74691, 78300, 80128, 90320, 97791, 104237, 116250,\n", + " 125877, 133171, 140394, 145826, 160892, 163606, 171423, 174091,\n", + " 180850, 188691, 191422, 193224, 194122],\n", + " dtype='int64'), Int64Index([ 440, 3163, 8800, 15237, 19810, 22549, 23452, 26193,\n", + " 30780, 41310, 42220, 43128, 44037, 48632, 50503, 72852,\n", + " 73758, 74692, 78301, 80129, 90321, 97792, 104238, 116251,\n", + " 125878, 133172, 140395, 145827, 160893, 163607, 171424, 174092,\n", + " 180851, 188692, 191423, 193225, 194123],\n", + " dtype='int64'), Int64Index([ 441, 3164, 8801, 15238, 19811, 22550, 23453, 26194,\n", + " 30781, 41311, 42221, 43129, 44038, 48633, 50504, 72853,\n", + " 73759, 74693, 78302, 80130, 90322, 97793, 104239, 116252,\n", + " 125879, 133173, 140396, 145828, 160894, 163608, 171425, 174093,\n", + " 180852, 188693, 191424, 193226, 194124],\n", + " dtype='int64'), Int64Index([ 442, 3165, 8802, 15239, 19812, 22551, 23454, 26195,\n", + " 30782, 41312, 42222, 43130, 44039, 48634, 50505, 72854,\n", + " 73760, 74694, 78303, 80131, 90323, 97794, 104240, 116253,\n", + " 125880, 133174, 140397, 145829, 160895, 163609, 171426, 174094,\n", + " 180853, 188694, 191425, 193227, 194125],\n", + " dtype='int64'), Int64Index([ 443, 3166, 8803, 15240, 19813, 22552, 23455, 26196,\n", + " 30783, 41313, 42223, 43131, 44040, 48635, 50506, 72855,\n", + " 73761, 74695, 78304, 80132, 90324, 97795, 104241, 116254,\n", + " 125881, 133175, 140398, 145830, 160896, 163610, 171427, 174095,\n", + " 180854, 188695, 191426, 193228, 194126],\n", + " dtype='int64'), Int64Index([ 444, 3167, 8804, 15241, 19814, 22553, 23456, 26197,\n", + " 30784, 41314, 42224, 43132, 44041, 48636, 50507, 72856,\n", + " 73762, 74696, 78305, 80133, 90325, 97796, 104242, 116255,\n", + " 125882, 133176, 140399, 145831, 160897, 163611, 171428, 174096,\n", + " 180855, 188696, 191427, 193229, 194127],\n", + " dtype='int64'), Int64Index([ 445, 3168, 8805, 15242, 19815, 22554, 23457, 26198,\n", + " 30785, 41315, 42225, 43133, 44042, 48637, 50508, 72857,\n", + " 73763, 74697, 78306, 80134, 90326, 97797, 104243, 116256,\n", + " 125883, 133177, 140400, 145832, 160898, 163612, 171429, 174097,\n", + " 180856, 188697, 191428, 193230, 194128],\n", + " dtype='int64'), Int64Index([ 446, 3169, 8806, 15243, 19816, 22555, 23458, 26199,\n", + " 30786, 41316, 42226, 43134, 44043, 48638, 50509, 72858,\n", + " 73764, 74698, 78307, 80135, 90327, 97798, 104244, 116257,\n", + " 125884, 133178, 140401, 145833, 160899, 163613, 171430, 174098,\n", + " 180857, 188698, 191429, 193231, 194129],\n", + " dtype='int64'), Int64Index([ 447, 3170, 8807, 15244, 19817, 22556, 23459, 26200,\n", + " 30787, 41317, 42227, 43135, 44044, 48639, 50510, 72859,\n", + " 73765, 74699, 78308, 80136, 90328, 97799, 104245, 116258,\n", + " 125885, 133179, 140402, 145834, 160900, 163614, 171431, 174099,\n", + " 180858, 188699, 191430, 193232, 194130],\n", + " dtype='int64'), Int64Index([ 448, 3171, 8808, 15245, 19818, 22557, 23460, 26201,\n", + " 30788, 41318, 42228, 43136, 44045, 48640, 50511, 72860,\n", + " 73766, 74700, 78309, 80137, 90329, 97800, 104246, 116259,\n", + " 125886, 133180, 140403, 145835, 160901, 163615, 171432, 174100,\n", + " 180859, 188700, 191431, 193233, 194131],\n", + " dtype='int64'), Int64Index([ 449, 3172, 8809, 15246, 19819, 22558, 23461, 26202,\n", + " 30789, 41319, 42229, 43137, 44046, 48641, 50512, 72861,\n", + " 73767, 74701, 78310, 80138, 90330, 97801, 104247, 116260,\n", + " 125887, 133181, 140404, 145836, 160902, 163616, 171433, 174101,\n", + " 180860, 188701, 191432, 193234, 194132],\n", + " dtype='int64'), Int64Index([ 450, 3173, 8810, 15247, 19820, 22559, 23462, 26203,\n", + " 30790, 41320, 42230, 43138, 44047, 48642, 50513, 72862,\n", + " 73768, 74702, 78311, 80139, 90331, 97802, 104248, 116261,\n", + " 125888, 133182, 140405, 145837, 160903, 163617, 171434, 174102,\n", + " 180861, 188702, 191433, 193235, 194133],\n", + " dtype='int64'), Int64Index([ 451, 3174, 8811, 15248, 19821, 22560, 23463, 26204,\n", + " 30791, 41321, 42231, 43139, 44048, 48643, 50514, 72863,\n", + " 73769, 74703, 78312, 80140, 90332, 97803, 104249, 116262,\n", + " 125889, 133183, 140406, 145838, 160904, 163618, 171435, 174103,\n", + " 180862, 188703, 191434, 193236, 194134],\n", + " dtype='int64'), Int64Index([ 452, 3175, 8812, 15249, 19822, 22561, 23464, 26205,\n", + " 30792, 41322, 42232, 43140, 44049, 48644, 50515, 72864,\n", + " 73770, 74704, 78313, 80141, 90333, 97804, 104250, 116263,\n", + " 125890, 133184, 140407, 145839, 160905, 163619, 171436, 174104,\n", + " 180863, 188704, 191435, 193237, 194135],\n", + " dtype='int64'), Int64Index([ 453, 3176, 8813, 15250, 19823, 22562, 23465, 26206,\n", + " 30793, 41323, 42233, 43141, 44050, 48645, 50516, 72865,\n", + " 73771, 74705, 78314, 80142, 90334, 97805, 104251, 116264,\n", + " 125891, 133185, 140408, 145840, 160906, 163620, 171437, 174105,\n", + " 180864, 188705, 191436, 193238, 194136],\n", + " dtype='int64'), Int64Index([ 454, 3177, 8814, 15251, 19824, 22563, 23466, 26207,\n", + " 30794, 41324, 42234, 43142, 44051, 48646, 50517, 72866,\n", + " 73772, 74706, 78315, 80143, 90335, 97806, 104252, 116265,\n", + " 125892, 133186, 140409, 145841, 160907, 163621, 171438, 174106,\n", + " 180865, 188706, 191437, 193239, 194137],\n", + " dtype='int64'), Int64Index([ 455, 3178, 8815, 15252, 19825, 22564, 23467, 26208,\n", + " 30795, 41325, 42235, 43143, 44052, 48647, 50518, 72867,\n", + " 73773, 74707, 78316, 80144, 90336, 97807, 104253, 116266,\n", + " 125893, 133187, 140410, 145842, 160908, 163622, 171439, 174107,\n", + " 180866, 188707, 191438, 193240, 194138],\n", + " dtype='int64'), Int64Index([ 456, 3179, 8816, 15253, 19826, 22565, 23468, 26209,\n", + " 30796, 41326, 42236, 43144, 44053, 48648, 50519, 72868,\n", + " 73774, 74708, 78317, 80145, 90337, 97808, 104254, 116267,\n", + " 125894, 133188, 140411, 145843, 160909, 163623, 171440, 174108,\n", + " 180867, 188708, 191439, 193241, 194139],\n", + " dtype='int64'), Int64Index([ 457, 3180, 8817, 15254, 19827, 22566, 23469, 26210,\n", + " 30797, 41327, 42237, 43145, 44054, 48649, 50520, 72869,\n", + " 73775, 74709, 78318, 80146, 90338, 97809, 104255, 116268,\n", + " 125895, 133189, 140412, 145844, 160910, 163624, 171441, 174109,\n", + " 180868, 188709, 191440, 193242, 194140],\n", + " dtype='int64'), Int64Index([ 458, 3181, 8818, 15255, 19828, 22567, 23470, 26211,\n", + " 30798, 41328, 42238, 43146, 44055, 48650, 50521, 72870,\n", + " 73776, 74710, 78319, 80147, 90339, 97810, 104256, 116269,\n", + " 125896, 133190, 140413, 145845, 160911, 163625, 171442, 174110,\n", + " 180869, 188710, 191441, 193243, 194141],\n", + " dtype='int64'), Int64Index([ 459, 3182, 8819, 15256, 19829, 22568, 23471, 26212,\n", + " 30799, 41329, 42239, 43147, 44056, 48651, 50522, 72871,\n", + " 73777, 74711, 78320, 80148, 90340, 97811, 104257, 116270,\n", + " 125897, 133191, 140414, 145846, 160912, 163626, 171443, 174111,\n", + " 180870, 188711, 191442, 193244, 194142],\n", + " dtype='int64'), Int64Index([ 460, 3183, 8820, 15257, 19830, 22569, 23472, 26213,\n", + " 30800, 41330, 42240, 43148, 44057, 48652, 50523, 72872,\n", + " 73778, 74712, 78321, 80149, 90341, 97812, 104258, 116271,\n", + " 125898, 133192, 140415, 145847, 160913, 163627, 171444, 174112,\n", + " 180871, 188712, 191443, 193245, 194143],\n", + " dtype='int64'), Int64Index([ 461, 3184, 8821, 15258, 19831, 22570, 23473, 26214,\n", + " 30801, 41331, 42241, 43149, 44058, 48653, 50524, 72873,\n", + " 73779, 74713, 78322, 80150, 90342, 97813, 104259, 116272,\n", + " 125899, 133193, 140416, 145848, 160914, 163628, 171445, 174113,\n", + " 180872, 188713, 191444, 193246, 194144],\n", + " dtype='int64'), Int64Index([ 462, 3185, 8822, 15259, 19832, 22571, 23474, 26215,\n", + " 30802, 41332, 42242, 43150, 44059, 48654, 50525, 72874,\n", + " 73780, 74714, 78323, 80151, 90343, 97814, 104260, 116273,\n", + " 125900, 133194, 140417, 145849, 160915, 163629, 171446, 174114,\n", + " 180873, 188714, 191445, 193247, 194145],\n", + " dtype='int64'), Int64Index([ 463, 3186, 8823, 15260, 19833, 22572, 23475, 26216,\n", + " 30803, 41333, 42243, 43151, 44060, 48655, 50526, 72875,\n", + " 73781, 74715, 78324, 80152, 90344, 97815, 104261, 116274,\n", + " 125901, 133195, 140418, 145850, 160916, 163630, 171447, 174115,\n", + " 180874, 188715, 191446, 193248, 194146],\n", + " dtype='int64'), Int64Index([ 464, 3187, 8824, 15261, 19834, 22573, 23476, 26217,\n", + " 30804, 41334, 42244, 43152, 44061, 48656, 50527, 72876,\n", + " 73782, 74716, 78325, 80153, 90345, 97816, 104262, 116275,\n", + " 125902, 133196, 140419, 145851, 160917, 163631, 171448, 174116,\n", + " 180875, 188716, 191447, 193249, 194147],\n", + " dtype='int64'), Int64Index([ 465, 3188, 8825, 15262, 19835, 22574, 23477, 26218,\n", + " 30805, 41335, 42245, 43153, 44062, 48657, 50528, 72877,\n", + " 73783, 74717, 78326, 80154, 90346, 97817, 104263, 116276,\n", + " 125903, 133197, 140420, 145852, 160918, 163632, 171449, 174117,\n", + " 180876, 188717, 191448, 193250, 194148],\n", + " dtype='int64'), Int64Index([ 466, 3189, 8826, 15263, 19836, 22575, 23478, 26219,\n", + " 30806, 41336, 42246, 43154, 44063, 48658, 50529, 72878,\n", + " 73784, 74718, 78327, 80155, 90347, 97818, 104264, 116277,\n", + " 125904, 133198, 140421, 145853, 160919, 163633, 171450, 174118,\n", + " 180877, 188718, 191449, 193251, 194149],\n", + " dtype='int64'), Int64Index([ 467, 3190, 8827, 15264, 19837, 22576, 23479, 26220,\n", + " 30807, 41337, 42247, 43155, 44064, 48659, 50530, 72879,\n", + " 73785, 74719, 78328, 80156, 90348, 97819, 104265, 116278,\n", + " 125905, 133199, 140422, 145854, 160920, 163634, 171451, 174119,\n", + " 180878, 188719, 191450, 193252, 194150],\n", + " dtype='int64'), Int64Index([ 468, 3191, 8828, 15265, 19838, 22577, 23480, 26221,\n", + " 30808, 41338, 42248, 43156, 44065, 48660, 50531, 72880,\n", + " 73786, 74720, 78329, 80157, 90349, 97820, 104266, 116279,\n", + " 125906, 133200, 140423, 145855, 160921, 163635, 171452, 174120,\n", + " 180879, 188720, 191451, 193253, 194151],\n", + " dtype='int64'), Int64Index([ 469, 3192, 8829, 15266, 19839, 22578, 23481, 26222,\n", + " 30809, 41339, 42249, 43157, 44066, 48661, 50532, 72881,\n", + " 73787, 74721, 78330, 80158, 90350, 97821, 104267, 116280,\n", + " 125907, 133201, 140424, 145856, 160922, 163636, 171453, 174121,\n", + " 180880, 188721, 191452, 193254, 194152],\n", + " dtype='int64'), Int64Index([ 470, 3193, 8830, 15267, 19840, 22579, 23482, 26223,\n", + " 30810, 41340, 42250, 43158, 44067, 48662, 50533, 72882,\n", + " 73788, 74722, 78331, 80159, 90351, 97822, 104268, 116281,\n", + " 125908, 133202, 140425, 145857, 160923, 163637, 171454, 174122,\n", + " 180881, 188722, 191453, 193255, 194153],\n", + " dtype='int64'), Int64Index([ 471, 3194, 8831, 15268, 19841, 22580, 23483, 26224,\n", + " 30811, 41341, 42251, 43159, 44068, 48663, 50534, 72883,\n", + " 73789, 74723, 78332, 80160, 90352, 97823, 104269, 116282,\n", + " 125909, 133203, 140426, 145858, 160924, 163638, 171455, 174123,\n", + " 180882, 188723, 191454, 193256, 194154],\n", + " dtype='int64'), Int64Index([ 472, 3195, 8832, 15269, 19842, 22581, 23484, 26225,\n", + " 30812, 41342, 42252, 43160, 44069, 48664, 50535, 72884,\n", + " 73790, 74724, 78333, 80161, 90353, 97824, 104270, 116283,\n", + " 125910, 133204, 140427, 145859, 160925, 163639, 171456, 174124,\n", + " 180883, 188724, 191455, 193257, 194155],\n", + " dtype='int64'), Int64Index([ 473, 3196, 8833, 15270, 19843, 22582, 23485, 26226,\n", + " 30813, 41343, 42253, 43161, 44070, 48665, 50536, 72885,\n", + " 73791, 74725, 78334, 80162, 90354, 97825, 104271, 116284,\n", + " 125911, 133205, 140428, 145860, 160926, 163640, 171457, 174125,\n", + " 180884, 188725, 191456, 193258, 194156],\n", + " dtype='int64'), Int64Index([ 474, 3197, 8834, 15271, 19844, 22583, 23486, 26227,\n", + " 30814, 41344, 42254, 43162, 44071, 48666, 50537, 72886,\n", + " 73792, 74726, 78335, 80163, 90355, 97826, 104272, 116285,\n", + " 125912, 133206, 140429, 145861, 160927, 163641, 171458, 174126,\n", + " 180885, 188726, 191457, 193259, 194157],\n", + " dtype='int64'), Int64Index([ 475, 3198, 8835, 15272, 19845, 22584, 23487, 26228,\n", + " 30815, 41345, 42255, 43163, 44072, 48667, 50538, 72887,\n", + " 73793, 74727, 78336, 80164, 90356, 97827, 104273, 116286,\n", + " 125913, 133207, 140430, 145862, 160928, 163642, 171459, 174127,\n", + " 180886, 188727, 191458, 193260, 194158],\n", + " dtype='int64'), Int64Index([ 476, 3199, 8836, 15273, 19846, 22585, 23488, 26229,\n", + " 30816, 41346, 42256, 43164, 44073, 48668, 50539, 72888,\n", + " 73794, 74728, 78337, 80165, 90357, 97828, 104274, 116287,\n", + " 125914, 133208, 140431, 145863, 160929, 163643, 171460, 174128,\n", + " 180887, 188728, 191459, 193261, 194159],\n", + " dtype='int64'), Int64Index([ 477, 3200, 8837, 15274, 19847, 22586, 23489, 26230,\n", + " 30817, 41347, 42257, 43165, 44074, 48669, 50540, 72889,\n", + " 73795, 74729, 78338, 80166, 90358, 97829, 104275, 116288,\n", + " 125915, 133209, 140432, 145864, 160930, 163644, 171461, 174129,\n", + " 180888, 188729, 191460, 193262, 194160],\n", + " dtype='int64'), Int64Index([ 478, 3201, 8838, 15275, 19848, 22587, 23490, 26231,\n", + " 30818, 41348, 42258, 43166, 44075, 48670, 50541, 72890,\n", + " 73796, 74730, 78339, 80167, 90359, 97830, 104276, 116289,\n", + " 125916, 133210, 140433, 145865, 160931, 163645, 171462, 174130,\n", + " 180889, 188730, 191461, 193263, 194161],\n", + " dtype='int64'), Int64Index([ 479, 3202, 8839, 15276, 19849, 22588, 23491, 26232,\n", + " 30819, 41349, 42259, 43167, 44076, 48671, 50542, 72891,\n", + " 73797, 74731, 78340, 80168, 90360, 97831, 104277, 116290,\n", + " 125917, 133211, 140434, 145866, 160932, 163646, 171463, 174131,\n", + " 180890, 188731, 191462, 193264, 194162],\n", + " dtype='int64'), Int64Index([ 480, 3203, 8840, 15277, 19850, 22589, 23492, 26233,\n", + " 30820, 41350, 42260, 43168, 44077, 48672, 50543, 72892,\n", + " 73798, 74732, 78341, 80169, 90361, 97832, 104278, 116291,\n", + " 125918, 133212, 140435, 145867, 160933, 163647, 171464, 174132,\n", + " 180891, 188732, 191463, 193265, 194163],\n", + " dtype='int64'), Int64Index([ 481, 3204, 8841, 15278, 19851, 22590, 23493, 26234,\n", + " 30821, 41351, 42261, 43169, 44078, 48673, 50544, 72893,\n", + " 73799, 74733, 78342, 80170, 90362, 97833, 104279, 116292,\n", + " 125919, 133213, 140436, 145868, 160934, 163648, 171465, 174133,\n", + " 180892, 188733, 191464, 193266, 194164],\n", + " dtype='int64'), Int64Index([ 482, 3205, 8842, 15279, 19852, 22591, 23494, 26235,\n", + " 30822, 41352, 42262, 43170, 44079, 48674, 50545, 72894,\n", + " 73800, 74734, 78343, 80171, 90363, 97834, 104280, 116293,\n", + " 125920, 133214, 140437, 145869, 160935, 163649, 171466, 174134,\n", + " 180893, 188734, 191465, 193267, 194165],\n", + " dtype='int64'), Int64Index([ 483, 3206, 8843, 15280, 19853, 22592, 23495, 26236,\n", + " 30823, 41353, 42263, 43171, 44080, 48675, 50546, 72895,\n", + " 73801, 74735, 78344, 80172, 90364, 97835, 104281, 116294,\n", + " 125921, 133215, 140438, 145870, 160936, 163650, 171467, 174135,\n", + " 180894, 188735, 191466, 193268, 194166],\n", + " dtype='int64'), Int64Index([ 484, 3207, 8844, 15281, 19854, 22593, 23496, 26237,\n", + " 30824, 41354, 42264, 43172, 44081, 48676, 50547, 72896,\n", + " 73802, 74736, 78345, 80173, 90365, 97836, 104282, 116295,\n", + " 125922, 133216, 140439, 145871, 160937, 163651, 171468, 174136,\n", + " 180895, 188736, 191467, 193269, 194167],\n", + " dtype='int64'), Int64Index([ 485, 3208, 8845, 15282, 19855, 22594, 23497, 26238,\n", + " 30825, 41355, 42265, 43173, 44082, 48677, 50548, 72897,\n", + " 73803, 74737, 78346, 80174, 90366, 97837, 104283, 116296,\n", + " 125923, 133217, 140440, 145872, 160938, 163652, 171469, 174137,\n", + " 180896, 188737, 191468, 193270, 194168],\n", + " dtype='int64'), Int64Index([ 486, 3209, 8846, 15283, 19856, 22595, 23498, 26239,\n", + " 30826, 41356, 42266, 43174, 44083, 48678, 50549, 72898,\n", + " 73804, 74738, 78347, 80175, 90367, 97838, 104284, 116297,\n", + " 125924, 133218, 140441, 145873, 160939, 163653, 171470, 174138,\n", + " 180897, 188738, 191469, 193271, 194169],\n", + " dtype='int64'), Int64Index([ 487, 3210, 8847, 15284, 19857, 22596, 23499, 26240,\n", + " 30827, 41357, 42267, 43175, 44084, 48679, 50550, 72899,\n", + " 73805, 74739, 78348, 80176, 90368, 97839, 104285, 116298,\n", + " 125925, 133219, 140442, 145874, 160940, 163654, 171471, 174139,\n", + " 180898, 188739, 191470, 193272, 194170],\n", + " dtype='int64'), Int64Index([ 488, 3211, 8848, 15285, 19858, 22597, 23500, 26241,\n", + " 30828, 41358, 42268, 43176, 44085, 48680, 50551, 72900,\n", + " 73806, 74740, 78349, 80177, 90369, 97840, 104286, 116299,\n", + " 125926, 133220, 140443, 145875, 160941, 163655, 171472, 174140,\n", + " 180899, 188740, 191471, 193273, 194171],\n", + " dtype='int64'), Int64Index([ 489, 3212, 8849, 15286, 19859, 22598, 23501, 26242,\n", + " 30829, 41359, 42269, 43177, 44086, 48681, 50552, 72901,\n", + " 73807, 74741, 78350, 80178, 90370, 97841, 104287, 116300,\n", + " 125927, 133221, 140444, 145876, 160942, 163656, 171473, 174141,\n", + " 180900, 188741, 191472, 193274, 194172],\n", + " dtype='int64'), Int64Index([ 490, 3213, 8850, 15287, 19860, 22599, 23502, 26243,\n", + " 30830, 41360, 42270, 43178, 44087, 48682, 50553, 72902,\n", + " 73808, 74742, 78351, 80179, 90371, 97842, 104288, 116301,\n", + " 125928, 133222, 140445, 145877, 160943, 163657, 171474, 174142,\n", + " 180901, 188742, 191473, 193275, 194173],\n", + " dtype='int64'), Int64Index([ 491, 3214, 8851, 15288, 19861, 22600, 23503, 26244,\n", + " 30831, 41361, 42271, 43179, 44088, 48683, 50554, 72903,\n", + " 73809, 74743, 78352, 80180, 90372, 97843, 104289, 116302,\n", + " 125929, 133223, 140446, 145878, 160944, 163658, 171475, 174143,\n", + " 180902, 188743, 191474, 193276, 194174],\n", + " dtype='int64'), Int64Index([ 492, 3215, 8852, 15289, 19862, 22601, 23504, 26245,\n", + " 30832, 41362, 42272, 43180, 44089, 48684, 50555, 72904,\n", + " 73810, 74744, 78353, 80181, 90373, 97844, 104290, 116303,\n", + " 125930, 133224, 140447, 145879, 160945, 163659, 171476, 174144,\n", + " 180903, 188744, 191475, 193277, 194175],\n", + " dtype='int64'), Int64Index([ 493, 3216, 8853, 15290, 19863, 22602, 23505, 26246,\n", + " 30833, 41363, 42273, 43181, 44090, 48685, 50556, 72905,\n", + " 73811, 74745, 78354, 80182, 90374, 97845, 104291, 116304,\n", + " 125931, 133225, 140448, 145880, 160946, 163660, 171477, 174145,\n", + " 180904, 188745, 191476, 193278, 194176],\n", + " dtype='int64'), Int64Index([ 494, 3217, 8854, 15291, 19864, 22603, 23506, 26247,\n", + " 30834, 41364, 42274, 43182, 44091, 48686, 50557, 72906,\n", + " 73812, 74746, 78355, 80183, 90375, 97846, 104292, 116305,\n", + " 125932, 133226, 140449, 145881, 160947, 163661, 171478, 174146,\n", + " 180905, 188746, 191477, 193279, 194177],\n", + " dtype='int64'), Int64Index([ 495, 3218, 8855, 15292, 19865, 22604, 23507, 26248,\n", + " 30835, 41365, 42275, 43183, 44092, 48687, 50558, 72907,\n", + " 73813, 74747, 78356, 80184, 90376, 97847, 104293, 116306,\n", + " 125933, 133227, 140450, 145882, 160948, 163662, 171479, 174147,\n", + " 180906, 188747, 191478, 193280, 194178],\n", + " dtype='int64'), Int64Index([ 496, 3219, 8856, 15293, 19866, 22605, 23508, 26249,\n", + " 30836, 41366, 42276, 43184, 44093, 48688, 50559, 72908,\n", + " 73814, 74748, 78357, 80185, 90377, 97848, 104294, 116307,\n", + " 125934, 133228, 140451, 145883, 160949, 163663, 171480, 174148,\n", + " 180907, 188748, 191479, 193281, 194179],\n", + " dtype='int64'), Int64Index([ 497, 3220, 8857, 15294, 19867, 22606, 23509, 26250,\n", + " 30837, 41367, 42277, 43185, 44094, 48689, 50560, 72909,\n", + " 73815, 74749, 78358, 80186, 90378, 97849, 104295, 116308,\n", + " 125935, 133229, 140452, 145884, 160950, 163664, 171481, 174149,\n", + " 180908, 188749, 191480, 193282, 194180],\n", + " dtype='int64'), Int64Index([ 498, 3221, 8858, 15295, 19868, 22607, 23510, 26251,\n", + " 30838, 41368, 42278, 43186, 44095, 48690, 50561, 72910,\n", + " 73816, 74750, 78359, 80187, 90379, 97850, 104296, 116309,\n", + " 125936, 133230, 140453, 145885, 160951, 163665, 171482, 174150,\n", + " 180909, 188750, 191481, 193283, 194181],\n", + " dtype='int64'), Int64Index([ 499, 3222, 8859, 15296, 19869, 22608, 23511, 26252,\n", + " 30839, 41369, 42279, 43187, 44096, 48691, 50562, 72911,\n", + " 73817, 74751, 78360, 80188, 90380, 97851, 104297, 116310,\n", + " 125937, 133231, 140454, 145886, 160952, 163666, 171483, 174151,\n", + " 180910, 188751, 191482, 193284, 194182],\n", + " dtype='int64'), Int64Index([ 500, 3223, 8860, 15297, 19870, 22609, 23512, 26253,\n", + " 30840, 41370, 42280, 43188, 44097, 48692, 50563, 72912,\n", + " 73818, 74752, 78361, 80189, 90381, 97852, 104298, 116311,\n", + " 125938, 133232, 140455, 145887, 160953, 163667, 171484, 174152,\n", + " 180911, 188752, 191483, 193285, 194183],\n", + " dtype='int64'), Int64Index([ 501, 3224, 8861, 15298, 19871, 22610, 23513, 26254,\n", + " 30841, 41371, 42281, 43189, 44098, 48693, 50564, 72913,\n", + " 73819, 74753, 78362, 80190, 90382, 97853, 104299, 116312,\n", + " 125939, 133233, 140456, 145888, 160954, 163668, 171485, 174153,\n", + " 180912, 188753, 191484, 193286, 194184],\n", + " dtype='int64'), Int64Index([ 502, 3225, 8862, 15299, 19872, 22611, 23514, 26255,\n", + " 30842, 41372, 42282, 43190, 44099, 48694, 50565, 72914,\n", + " 73820, 74754, 78363, 80191, 90383, 97854, 104300, 116313,\n", + " 125940, 133234, 140457, 145889, 160955, 163669, 171486, 174154,\n", + " 180913, 188754, 191485, 193287, 194185],\n", + " dtype='int64'), Int64Index([ 503, 3226, 8863, 15300, 19873, 22612, 23515, 26256,\n", + " 30843, 41373, 42283, 43191, 44100, 48695, 50566, 72915,\n", + " 73821, 74755, 78364, 80192, 90384, 97855, 104301, 116314,\n", + " 125941, 133235, 140458, 145890, 160956, 163670, 171487, 174155,\n", + " 180914, 188755, 191486, 193288, 194186],\n", + " dtype='int64'), Int64Index([ 504, 3227, 8864, 15301, 19874, 22613, 23516, 26257,\n", + " 30844, 41374, 42284, 43192, 44101, 48696, 50567, 72916,\n", + " 73822, 74756, 78365, 80193, 90385, 97856, 104302, 116315,\n", + " 125942, 133236, 140459, 145891, 160957, 163671, 171488, 174156,\n", + " 180915, 188756, 191487, 193289, 194187],\n", + " dtype='int64'), Int64Index([ 505, 3228, 8865, 15302, 19875, 22614, 23517, 26258,\n", + " 30845, 41375, 42285, 43193, 44102, 48697, 50568, 72917,\n", + " 73823, 74757, 78366, 80194, 90386, 97857, 104303, 116316,\n", + " 125943, 133237, 140460, 145892, 160958, 163672, 171489, 174157,\n", + " 180916, 188757, 191488, 193290, 194188],\n", + " dtype='int64'), Int64Index([ 506, 3229, 8866, 15303, 19876, 22615, 23518, 26259,\n", + " 30846, 41376, 42286, 43194, 44103, 48698, 50569, 72918,\n", + " 73824, 74758, 78367, 80195, 90387, 97858, 104304, 116317,\n", + " 125944, 133238, 140461, 145893, 160959, 163673, 171490, 174158,\n", + " 180917, 188758, 191489, 193291, 194189],\n", + " dtype='int64'), Int64Index([ 507, 3230, 8867, 15304, 19877, 22616, 23519, 26260,\n", + " 30847, 41377, 42287, 43195, 44104, 48699, 50570, 72919,\n", + " 73825, 74759, 78368, 80196, 90388, 97859, 104305, 116318,\n", + " 125945, 133239, 140462, 145894, 160960, 163674, 171491, 174159,\n", + " 180918, 188759, 191490, 193292, 194190],\n", + " dtype='int64'), Int64Index([ 508, 3231, 8868, 15305, 19878, 22617, 23520, 26261,\n", + " 30848, 41378, 42288, 43196, 44105, 48700, 50571, 72920,\n", + " 73826, 74760, 78369, 80197, 90389, 97860, 104306, 116319,\n", + " 125946, 133240, 140463, 145895, 160961, 163675, 171492, 174160,\n", + " 180919, 188760, 191491, 193293, 194191],\n", + " dtype='int64'), Int64Index([ 509, 3232, 8869, 15306, 19879, 22618, 23521, 26262,\n", + " 30849, 41379, 42289, 43197, 44106, 48701, 50572, 72921,\n", + " 73827, 74761, 78370, 80198, 90390, 97861, 104307, 116320,\n", + " 125947, 133241, 140464, 145896, 160962, 163676, 171493, 174161,\n", + " 180920, 188761, 191492, 193294, 194192],\n", + " dtype='int64'), Int64Index([ 510, 3233, 8870, 15307, 19880, 22619, 23522, 26263,\n", + " 30850, 41380, 42290, 43198, 44107, 48702, 50573, 72922,\n", + " 73828, 74762, 78371, 80199, 90391, 97862, 104308, 116321,\n", + " 125948, 133242, 140465, 145897, 160963, 163677, 171494, 174162,\n", + " 180921, 188762, 191493, 193295, 194193],\n", + " dtype='int64'), Int64Index([ 511, 3234, 8871, 15308, 19881, 22620, 23523, 26264,\n", + " 30851, 41381, 42291, 43199, 44108, 48703, 50574, 72923,\n", + " 73829, 74763, 78372, 80200, 90392, 97863, 104309, 116322,\n", + " 125949, 133243, 140466, 145898, 160964, 163678, 171495, 174163,\n", + " 180922, 188763, 191494, 193296, 194194],\n", + " dtype='int64'), Int64Index([ 512, 3235, 8872, 15309, 19882, 22621, 23524, 26265,\n", + " 30852, 41382, 42292, 43200, 44109, 48704, 50575, 72924,\n", + " 73830, 74764, 78373, 80201, 90393, 97864, 104310, 116323,\n", + " 125950, 133244, 140467, 145899, 160965, 163679, 171496, 174164,\n", + " 180923, 188764, 191495, 193297, 194195],\n", + " dtype='int64'), Int64Index([ 513, 3236, 8873, 15310, 19883, 22622, 23525, 26266,\n", + " 30853, 41383, 42293, 43201, 44110, 48705, 50576, 72925,\n", + " 73831, 74765, 78374, 80202, 90394, 97865, 104311, 116324,\n", + " 125951, 133245, 140468, 145900, 160966, 163680, 171497, 174165,\n", + " 180924, 188765, 191496, 193298, 194196],\n", + " dtype='int64'), Int64Index([ 514, 3237, 8874, 15311, 19884, 22623, 23526, 26267,\n", + " 30854, 41384, 42294, 43202, 44111, 48706, 50577, 72926,\n", + " 73832, 74766, 78375, 80203, 90395, 97866, 104312, 116325,\n", + " 125952, 133246, 140469, 145901, 160967, 163681, 171498, 174166,\n", + " 180925, 188766, 191497, 193299, 194197],\n", + " dtype='int64'), Int64Index([ 515, 3238, 8875, 15312, 19885, 22624, 23527, 26268,\n", + " 30855, 41385, 42295, 43203, 44112, 48707, 50578, 72927,\n", + " 73833, 74767, 78376, 80204, 90396, 97867, 104313, 116326,\n", + " 125953, 133247, 140470, 145902, 160968, 163682, 171499, 174167,\n", + " 180926, 188767, 191498, 193300, 194198],\n", + " dtype='int64'), Int64Index([ 516, 3239, 8876, 15313, 19886, 22625, 23528, 26269,\n", + " 30856, 41386, 42296, 43204, 44113, 48708, 50579, 72928,\n", + " 73834, 74768, 78377, 80205, 90397, 97868, 104314, 116327,\n", + " 125954, 133248, 140471, 145903, 160969, 163683, 171500, 174168,\n", + " 180927, 188768, 191499, 193301, 194199],\n", + " dtype='int64'), Int64Index([ 517, 3240, 8877, 15314, 19887, 22626, 23529, 26270,\n", + " 30857, 41387, 42297, 43205, 44114, 48709, 50580, 72929,\n", + " 73835, 74769, 78378, 80206, 90398, 97869, 104315, 116328,\n", + " 125955, 133249, 140472, 145904, 160970, 163684, 171501, 174169,\n", + " 180928, 188769, 191500, 193302, 194200],\n", + " dtype='int64'), Int64Index([ 518, 3241, 8878, 15315, 19888, 22627, 23530, 26271,\n", + " 30858, 41388, 42298, 43206, 44115, 48710, 50581, 72930,\n", + " 73836, 74770, 78379, 80207, 90399, 97870, 104316, 116329,\n", + " 125956, 133250, 140473, 145905, 160971, 163685, 171502, 174170,\n", + " 180929, 188770, 191501, 193303, 194201],\n", + " dtype='int64'), Int64Index([ 519, 3242, 8879, 15316, 19889, 22628, 23531, 26272,\n", + " 30859, 41389, 42299, 43207, 44116, 48711, 50582, 72931,\n", + " 73837, 74771, 78380, 80208, 90400, 97871, 104317, 116330,\n", + " 125957, 133251, 140474, 145906, 160972, 163686, 171503, 174171,\n", + " 180930, 188771, 191502, 193304, 194202],\n", + " dtype='int64'), Int64Index([ 520, 3243, 8880, 15317, 19890, 22629, 23532, 26273,\n", + " 30860, 41390, 42300, 43208, 44117, 48712, 50583, 72932,\n", + " 73838, 74772, 78381, 80209, 90401, 97872, 104318, 116331,\n", + " 125958, 133252, 140475, 145907, 160973, 163687, 171504, 174172,\n", + " 180931, 188772, 191503, 193305, 194203],\n", + " dtype='int64'), Int64Index([ 521, 3244, 8881, 15318, 19891, 22630, 23533, 26274,\n", + " 30861, 41391, 42301, 43209, 44118, 48713, 50584, 72933,\n", + " 73839, 74773, 78382, 80210, 90402, 97873, 104319, 116332,\n", + " 125959, 133253, 140476, 145908, 160974, 163688, 171505, 174173,\n", + " 180932, 188773, 191504, 193306, 194204],\n", + " dtype='int64'), Int64Index([ 522, 3245, 8882, 15319, 19892, 22631, 23534, 26275,\n", + " 30862, 41392, 42302, 43210, 44119, 48714, 50585, 72934,\n", + " 73840, 74774, 78383, 80211, 90403, 97874, 104320, 116333,\n", + " 125960, 133254, 140477, 145909, 160975, 163689, 171506, 174174,\n", + " 180933, 188774, 191505, 193307, 194205],\n", + " dtype='int64'), Int64Index([ 523, 3246, 8883, 15320, 19893, 22632, 23535, 26276,\n", + " 30863, 41393, 42303, 43211, 44120, 48715, 50586, 72935,\n", + " 73841, 74775, 78384, 80212, 90404, 97875, 104321, 116334,\n", + " 125961, 133255, 140478, 145910, 160976, 163690, 171507, 174175,\n", + " 180934, 188775, 191506, 193308, 194206],\n", + " dtype='int64'), Int64Index([ 524, 3247, 8884, 15321, 19894, 22633, 23536, 26277,\n", + " 30864, 41394, 42304, 43212, 44121, 48716, 50587, 72936,\n", + " 73842, 74776, 78385, 80213, 90405, 97876, 104322, 116335,\n", + " 125962, 133256, 140479, 145911, 160977, 163691, 171508, 174176,\n", + " 180935, 188776, 191507, 193309, 194207],\n", + " dtype='int64'), Int64Index([ 525, 3248, 8885, 15322, 19895, 22634, 23537, 26278,\n", + " 30865, 41395, 42305, 43213, 44122, 48717, 50588, 72937,\n", + " 73843, 74777, 78386, 80214, 90406, 97877, 104323, 116336,\n", + " 125963, 133257, 140480, 145912, 160978, 163692, 171509, 174177,\n", + " 180936, 188777, 191508, 193310, 194208],\n", + " dtype='int64'), Int64Index([ 526, 3249, 8886, 15323, 19896, 22635, 23538, 26279,\n", + " 30866, 41396, 42306, 43214, 44123, 48718, 50589, 72938,\n", + " 73844, 74778, 78387, 80215, 90407, 97878, 104324, 116337,\n", + " 125964, 133258, 140481, 145913, 160979, 163693, 171510, 174178,\n", + " 180937, 188778, 191509, 193311, 194209],\n", + " dtype='int64'), Int64Index([ 527, 3250, 8887, 15324, 19897, 22636, 23539, 26280,\n", + " 30867, 41397, 42307, 43215, 44124, 48719, 50590, 72939,\n", + " 73845, 74779, 78388, 80216, 90408, 97879, 104325, 116338,\n", + " 125965, 133259, 140482, 145914, 160980, 163694, 171511, 174179,\n", + " 180938, 188779, 191510, 193312, 194210],\n", + " dtype='int64'), Int64Index([ 528, 3251, 8888, 15325, 19898, 22637, 23540, 26281,\n", + " 30868, 41398, 42308, 43216, 44125, 48720, 50591, 72940,\n", + " 73846, 74780, 78389, 80217, 90409, 97880, 104326, 116339,\n", + " 125966, 133260, 140483, 145915, 160981, 163695, 171512, 174180,\n", + " 180939, 188780, 191511, 193313, 194211],\n", + " dtype='int64'), Int64Index([ 529, 3252, 8889, 15326, 19899, 22638, 23541, 26282,\n", + " 30869, 41399, 42309, 43217, 44126, 48721, 50592, 72941,\n", + " 73847, 74781, 78390, 80218, 90410, 97881, 104327, 116340,\n", + " 125967, 133261, 140484, 145916, 160982, 163696, 171513, 174181,\n", + " 180940, 188781, 191512, 193314, 194212],\n", + " dtype='int64'), Int64Index([ 530, 3253, 8890, 15327, 19900, 22639, 23542, 26283,\n", + " 30870, 41400, 42310, 43218, 44127, 48722, 50593, 72942,\n", + " 73848, 74782, 78391, 80219, 90411, 97882, 104328, 116341,\n", + " 125968, 133262, 140485, 145917, 160983, 163697, 171514, 174182,\n", + " 180941, 188782, 191513, 193315, 194213],\n", + " dtype='int64'), Int64Index([ 531, 3254, 8891, 15328, 19901, 22640, 23543, 26284,\n", + " 30871, 41401, 42311, 43219, 44128, 48723, 50594, 72943,\n", + " 73849, 74783, 78392, 80220, 90412, 97883, 104329, 116342,\n", + " 125969, 133263, 140486, 145918, 160984, 163698, 171515, 174183,\n", + " 180942, 188783, 191514, 193316, 194214],\n", + " dtype='int64'), Int64Index([ 532, 3255, 8892, 15329, 19902, 22641, 23544, 26285,\n", + " 30872, 41402, 42312, 43220, 44129, 48724, 50595, 72944,\n", + " 73850, 74784, 78393, 80221, 90413, 97884, 104330, 116343,\n", + " 125970, 133264, 140487, 145919, 160985, 163699, 171516, 174184,\n", + " 180943, 188784, 191515, 193317, 194215],\n", + " dtype='int64'), Int64Index([ 533, 3256, 8893, 15330, 19903, 22642, 23545, 26286,\n", + " 30873, 41403, 42313, 43221, 44130, 48725, 50596, 72945,\n", + " 73851, 74785, 78394, 80222, 90414, 97885, 104331, 116344,\n", + " 125971, 133265, 140488, 145920, 160986, 163700, 171517, 174185,\n", + " 180944, 188785, 191516, 193318, 194216],\n", + " dtype='int64'), Int64Index([ 534, 3257, 8894, 15331, 19904, 22643, 23546, 26287,\n", + " 30874, 41404, 42314, 43222, 44131, 48726, 50597, 72946,\n", + " 73852, 74786, 78395, 80223, 90415, 97886, 104332, 116345,\n", + " 125972, 133266, 140489, 145921, 160987, 163701, 171518, 174186,\n", + " 180945, 188786, 191517, 193319, 194217],\n", + " dtype='int64'), Int64Index([ 535, 3258, 8895, 15332, 19905, 22644, 23547, 26288,\n", + " 30875, 41405, 42315, 43223, 44132, 48727, 50598, 72947,\n", + " 73853, 74787, 78396, 80224, 90416, 97887, 104333, 116346,\n", + " 125973, 133267, 140490, 145922, 160988, 163702, 171519, 174187,\n", + " 180946, 188787, 191518, 193320, 194218],\n", + " dtype='int64'), Int64Index([ 536, 3259, 8896, 15333, 19906, 22645, 23548, 26289,\n", + " 30876, 41406, 42316, 43224, 44133, 48728, 50599, 72948,\n", + " 73854, 74788, 78397, 80225, 90417, 97888, 104334, 116347,\n", + " 125974, 133268, 140491, 145923, 160989, 163703, 171520, 174188,\n", + " 180947, 188788, 191519, 193321, 194219],\n", + " dtype='int64'), Int64Index([ 537, 3260, 8897, 15334, 19907, 22646, 23549, 26290,\n", + " 30877, 41407, 42317, 43225, 44134, 48729, 50600, 72949,\n", + " 73855, 74789, 78398, 80226, 90418, 97889, 104335, 116348,\n", + " 125975, 133269, 140492, 145924, 160990, 163704, 171521, 174189,\n", + " 180948, 188789, 191520, 193322, 194220],\n", + " dtype='int64'), Int64Index([ 538, 3261, 8898, 15335, 19908, 22647, 23550, 26291,\n", + " 30878, 41408, 42318, 43226, 44135, 48730, 50601, 72950,\n", + " 73856, 74790, 78399, 80227, 90419, 97890, 104336, 116349,\n", + " 125976, 133270, 140493, 145925, 160991, 163705, 171522, 174190,\n", + " 180949, 188790, 191521, 193323, 194221],\n", + " dtype='int64'), Int64Index([ 539, 3262, 8899, 15336, 19909, 22648, 23551, 26292,\n", + " 30879, 41409, 42319, 43227, 44136, 48731, 50602, 72951,\n", + " 73857, 74791, 78400, 80228, 90420, 97891, 104337, 116350,\n", + " 125977, 133271, 140494, 145926, 160992, 163706, 171523, 174191,\n", + " 180950, 188791, 191522, 193324, 194222],\n", + " dtype='int64'), Int64Index([ 540, 3263, 8900, 15337, 19910, 22649, 23552, 26293,\n", + " 30880, 41410, 42320, 43228, 44137, 48732, 50603, 72952,\n", + " 73858, 74792, 78401, 80229, 90421, 97892, 104338, 116351,\n", + " 125978, 133272, 140495, 145927, 160993, 163707, 171524, 174192,\n", + " 180951, 188792, 191523, 193325, 194223],\n", + " dtype='int64'), Int64Index([ 541, 3264, 8901, 15338, 19911, 22650, 23553, 26294,\n", + " 30881, 41411, 42321, 43229, 44138, 48733, 50604, 72953,\n", + " 73859, 74793, 78402, 80230, 90422, 97893, 104339, 116352,\n", + " 125979, 133273, 140496, 145928, 160994, 163708, 171525, 174193,\n", + " 180952, 188793, 191524, 193326, 194224],\n", + " dtype='int64'), Int64Index([ 542, 3265, 8902, 15339, 19912, 22651, 23554, 26295,\n", + " 30882, 41412, 42322, 43230, 44139, 48734, 50605, 72954,\n", + " 73860, 74794, 78403, 80231, 90423, 97894, 104340, 116353,\n", + " 125980, 133274, 140497, 145929, 160995, 163709, 171526, 174194,\n", + " 180953, 188794, 191525, 193327, 194225],\n", + " dtype='int64'), Int64Index([ 543, 3266, 8903, 15340, 19913, 22652, 23555, 26296,\n", + " 30883, 41413, 42323, 43231, 44140, 48735, 50606, 72955,\n", + " 73861, 74795, 78404, 80232, 90424, 97895, 104341, 116354,\n", + " 125981, 133275, 140498, 145930, 160996, 163710, 171527, 174195,\n", + " 180954, 188795, 191526, 193328, 194226],\n", + " dtype='int64'), Int64Index([ 544, 3267, 8904, 15341, 19914, 22653, 23556, 26297,\n", + " 30884, 41414, 42324, 43232, 44141, 48736, 50607, 72956,\n", + " 73862, 74796, 78405, 80233, 90425, 97896, 104342, 116355,\n", + " 125982, 133276, 140499, 145931, 160997, 163711, 171528, 174196,\n", + " 180955, 188796, 191527, 193329, 194227],\n", + " dtype='int64'), Int64Index([ 545, 3268, 8905, 15342, 19915, 22654, 23557, 26298,\n", + " 30885, 41415, 42325, 43233, 44142, 48737, 50608, 72957,\n", + " 73863, 74797, 78406, 80234, 90426, 97897, 104343, 116356,\n", + " 125983, 133277, 140500, 145932, 160998, 163712, 171529, 174197,\n", + " 180956, 188797, 191528, 193330, 194228],\n", + " dtype='int64'), Int64Index([ 546, 3269, 8906, 15343, 19916, 22655, 23558, 26299,\n", + " 30886, 41416, 42326, 43234, 44143, 48738, 50609, 72958,\n", + " 73864, 74798, 78407, 80235, 90427, 97898, 104344, 116357,\n", + " 125984, 133278, 140501, 145933, 160999, 163713, 171530, 174198,\n", + " 180957, 188798, 191529, 193331, 194229],\n", + " dtype='int64'), Int64Index([ 547, 3270, 8907, 15344, 19917, 22656, 23559, 26300,\n", + " 30887, 41417, 42327, 43235, 44144, 48739, 50610, 72959,\n", + " 73865, 74799, 78408, 80236, 90428, 97899, 104345, 116358,\n", + " 125985, 133279, 140502, 145934, 161000, 163714, 171531, 174199,\n", + " 180958, 188799, 191530, 193332, 194230],\n", + " dtype='int64'), Int64Index([ 548, 3271, 8908, 15345, 19918, 22657, 23560, 26301,\n", + " 30888, 41418, 42328, 43236, 44145, 48740, 50611, 72960,\n", + " 73866, 74800, 78409, 80237, 90429, 97900, 104346, 116359,\n", + " 125986, 133280, 140503, 145935, 161001, 163715, 171532, 174200,\n", + " 180959, 188800, 191531, 193333, 194231],\n", + " dtype='int64'), Int64Index([ 549, 3272, 8909, 15346, 19919, 22658, 23561, 26302,\n", + " 30889, 41419, 42329, 43237, 44146, 48741, 50612, 72961,\n", + " 73867, 74801, 78410, 80238, 90430, 97901, 104347, 116360,\n", + " 125987, 133281, 140504, 145936, 161002, 163716, 171533, 174201,\n", + " 180960, 188801, 191532, 193334, 194232],\n", + " dtype='int64'), Int64Index([ 550, 3273, 8910, 15347, 19920, 22659, 23562, 26303,\n", + " 30890, 41420, 42330, 43238, 44147, 48742, 50613, 72962,\n", + " 73868, 74802, 78411, 80239, 90431, 97902, 104348, 116361,\n", + " 125988, 133282, 140505, 145937, 161003, 163717, 171534, 174202,\n", + " 180961, 188802, 191533, 193335, 194233],\n", + " dtype='int64'), Int64Index([ 551, 3274, 8911, 15348, 19921, 22660, 23563, 26304,\n", + " 30891, 41421, 42331, 43239, 44148, 48743, 50614, 72963,\n", + " 73869, 74803, 78412, 80240, 90432, 97903, 104349, 116362,\n", + " 125989, 133283, 140506, 145938, 161004, 163718, 171535, 174203,\n", + " 180962, 188803, 191534, 193336, 194234],\n", + " dtype='int64'), Int64Index([ 552, 3275, 8912, 15349, 19922, 22661, 23564, 26305,\n", + " 30892, 41422, 42332, 43240, 44149, 48744, 50615, 72964,\n", + " 73870, 74804, 78413, 80241, 90433, 97904, 104350, 116363,\n", + " 125990, 133284, 140507, 145939, 161005, 163719, 171536, 174204,\n", + " 180963, 188804, 191535, 193337, 194235],\n", + " dtype='int64'), Int64Index([ 553, 3276, 8913, 15350, 19923, 22662, 23565, 26306,\n", + " 30893, 41423, 42333, 43241, 44150, 48745, 50616, 72965,\n", + " 73871, 74805, 78414, 80242, 90434, 97905, 104351, 116364,\n", + " 125991, 133285, 140508, 145940, 161006, 163720, 171537, 174205,\n", + " 180964, 188805, 191536, 193338, 194236],\n", + " dtype='int64'), Int64Index([ 554, 3277, 8914, 15351, 19924, 22663, 23566, 26307,\n", + " 30894, 41424, 42334, 43242, 44151, 48746, 50617, 72966,\n", + " 73872, 74806, 78415, 80243, 90435, 97906, 104352, 116365,\n", + " 125992, 133286, 140509, 145941, 161007, 163721, 171538, 174206,\n", + " 180965, 188806, 191537, 193339, 194237],\n", + " dtype='int64'), Int64Index([ 555, 3278, 8915, 15352, 19925, 22664, 23567, 26308,\n", + " 30895, 41425, 42335, 43243, 44152, 48747, 50618, 72967,\n", + " 73873, 74807, 78416, 80244, 90436, 97907, 104353, 116366,\n", + " 125993, 133287, 140510, 145942, 161008, 163722, 171539, 174207,\n", + " 180966, 188807, 191538, 193340, 194238],\n", + " dtype='int64'), Int64Index([ 556, 3279, 8916, 15353, 19926, 22665, 23568, 26309,\n", + " 30896, 41426, 42336, 43244, 44153, 48748, 50619, 72968,\n", + " 73874, 74808, 78417, 80245, 90437, 97908, 104354, 116367,\n", + " 125994, 133288, 140511, 145943, 161009, 163723, 171540, 174208,\n", + " 180967, 188808, 191539, 193341, 194239],\n", + " dtype='int64'), Int64Index([ 557, 3280, 8917, 15354, 19927, 22666, 23569, 26310,\n", + " 30897, 41427, 42337, 43245, 44154, 48749, 50620, 72969,\n", + " 73875, 74809, 78418, 80246, 90438, 97909, 104355, 116368,\n", + " 125995, 133289, 140512, 145944, 161010, 163724, 171541, 174209,\n", + " 180968, 188809, 191540, 193342, 194240],\n", + " dtype='int64'), Int64Index([ 558, 3281, 8918, 15355, 19928, 22667, 23570, 26311,\n", + " 30898, 41428, 42338, 43246, 44155, 48750, 50621, 72970,\n", + " 73876, 74810, 78419, 80247, 90439, 97910, 104356, 116369,\n", + " 125996, 133290, 140513, 145945, 161011, 163725, 171542, 174210,\n", + " 180969, 188810, 191541, 193343, 194241],\n", + " dtype='int64'), Int64Index([ 559, 3282, 8919, 15356, 19929, 22668, 23571, 26312,\n", + " 30899, 41429, 42339, 43247, 44156, 48751, 50622, 72971,\n", + " 73877, 74811, 78420, 80248, 90440, 97911, 104357, 116370,\n", + " 125997, 133291, 140514, 145946, 161012, 163726, 171543, 174211,\n", + " 180970, 188811, 191542, 193344, 194242],\n", + " dtype='int64'), Int64Index([ 560, 3283, 8920, 15357, 19930, 22669, 23572, 26313,\n", + " 30900, 41430, 42340, 43248, 44157, 48752, 50623, 72972,\n", + " 73878, 74812, 78421, 80249, 90441, 97912, 104358, 116371,\n", + " 125998, 133292, 140515, 145947, 161013, 163727, 171544, 174212,\n", + " 180971, 188812, 191543, 193345, 194243],\n", + " dtype='int64'), Int64Index([ 561, 3284, 8921, 15358, 19931, 22670, 23573, 26314,\n", + " 30901, 41431, 42341, 43249, 44158, 48753, 50624, 72973,\n", + " 73879, 74813, 78422, 80250, 90442, 97913, 104359, 116372,\n", + " 125999, 133293, 140516, 145948, 161014, 163728, 171545, 174213,\n", + " 180972, 188813, 191544, 193346, 194244],\n", + " dtype='int64'), Int64Index([ 562, 3285, 8922, 15359, 19932, 22671, 23574, 26315,\n", + " 30902, 41432, 42342, 43250, 44159, 48754, 50625, 72974,\n", + " 73880, 74814, 78423, 80251, 90443, 97914, 104360, 116373,\n", + " 126000, 133294, 140517, 145949, 161015, 163729, 171546, 174214,\n", + " 180973, 188814, 191545, 193347, 194245],\n", + " dtype='int64'), Int64Index([ 563, 3286, 8923, 15360, 19933, 22672, 23575, 26316,\n", + " 30903, 41433, 42343, 43251, 44160, 48755, 50626, 72975,\n", + " 73881, 74815, 78424, 80252, 90444, 97915, 104361, 116374,\n", + " 126001, 133295, 140518, 145950, 161016, 163730, 171547, 174215,\n", + " 180974, 188815, 191546, 193348, 194246],\n", + " dtype='int64'), Int64Index([ 564, 3287, 8924, 15361, 19934, 22673, 23576, 26317,\n", + " 30904, 41434, 42344, 43252, 44161, 48756, 50627, 72976,\n", + " 73882, 74816, 78425, 80253, 90445, 97916, 104362, 116375,\n", + " 126002, 133296, 140519, 145951, 161017, 163731, 171548, 174216,\n", + " 180975, 188816, 191547, 193349, 194247],\n", + " dtype='int64'), Int64Index([ 565, 3288, 8925, 15362, 19935, 22674, 23577, 26318,\n", + " 30905, 41435, 42345, 43253, 44162, 48757, 50628, 72977,\n", + " 73883, 74817, 78426, 80254, 90446, 97917, 104363, 116376,\n", + " 126003, 133297, 140520, 145952, 161018, 163732, 171549, 174217,\n", + " 180976, 188817, 191548, 193350, 194248],\n", + " dtype='int64'), Int64Index([ 566, 3289, 8926, 15363, 19936, 22675, 23578, 26319,\n", + " 30906, 41436, 42346, 43254, 44163, 48758, 50629, 72978,\n", + " 73884, 74818, 78427, 80255, 90447, 97918, 104364, 116377,\n", + " 126004, 133298, 140521, 145953, 161019, 163733, 171550, 174218,\n", + " 180977, 188818, 191549, 193351, 194249],\n", + " dtype='int64'), Int64Index([ 567, 3290, 8927, 15364, 19937, 22676, 23579, 26320,\n", + " 30907, 41437, 42347, 43255, 44164, 48759, 50630, 72979,\n", + " 73885, 74819, 78428, 80256, 90448, 97919, 104365, 116378,\n", + " 126005, 133299, 140522, 145954, 161020, 163734, 171551, 174219,\n", + " 180978, 188819, 191550, 193352, 194250],\n", + " dtype='int64'), Int64Index([ 568, 3291, 8928, 15365, 19938, 22677, 23580, 26321,\n", + " 30908, 41438, 42348, 43256, 44165, 48760, 50631, 72980,\n", + " 73886, 74820, 78429, 80257, 90449, 97920, 104366, 116379,\n", + " 126006, 133300, 140523, 145955, 161021, 163735, 171552, 174220,\n", + " 180979, 188820, 191551, 193353, 194251],\n", + " dtype='int64'), Int64Index([ 569, 3292, 8929, 15366, 19939, 22678, 23581, 26322,\n", + " 30909, 41439, 42349, 43257, 44166, 48761, 50632, 72981,\n", + " 73887, 74821, 78430, 80258, 90450, 97921, 104367, 116380,\n", + " 126007, 133301, 140524, 145956, 161022, 163736, 171553, 174221,\n", + " 180980, 188821, 191552, 193354, 194252],\n", + " dtype='int64'), Int64Index([ 570, 3293, 8930, 15367, 19940, 22679, 23582, 26323,\n", + " 30910, 41440, 42350, 43258, 44167, 48762, 50633, 72982,\n", + " 73888, 74822, 78431, 80259, 90451, 97922, 104368, 116381,\n", + " 126008, 133302, 140525, 145957, 161023, 163737, 171554, 174222,\n", + " 180981, 188822, 191553, 193355, 194253],\n", + " dtype='int64'), Int64Index([ 571, 3294, 8931, 15368, 19941, 22680, 23583, 26324,\n", + " 30911, 41441, 42351, 43259, 44168, 48763, 50634, 72983,\n", + " 73889, 74823, 78432, 80260, 90452, 97923, 104369, 116382,\n", + " 126009, 133303, 140526, 145958, 161024, 163738, 171555, 174223,\n", + " 180982, 188823, 191554, 193356, 194254],\n", + " dtype='int64'), Int64Index([ 572, 3295, 8932, 15369, 19942, 22681, 23584, 26325,\n", + " 30912, 41442, 42352, 43260, 44169, 48764, 50635, 72984,\n", + " 73890, 74824, 78433, 80261, 90453, 97924, 104370, 116383,\n", + " 126010, 133304, 140527, 145959, 161025, 163739, 171556, 174224,\n", + " 180983, 188824, 191555, 193357, 194255],\n", + " dtype='int64'), Int64Index([ 573, 3296, 8933, 15370, 19943, 22682, 23585, 26326,\n", + " 30913, 41443, 42353, 43261, 44170, 48765, 50636, 72985,\n", + " 73891, 74825, 78434, 80262, 90454, 97925, 104371, 116384,\n", + " 126011, 133305, 140528, 145960, 161026, 163740, 171557, 174225,\n", + " 180984, 188825, 191556, 193358, 194256],\n", + " dtype='int64'), Int64Index([ 574, 3297, 8934, 15371, 19944, 22683, 23586, 26327,\n", + " 30914, 41444, 42354, 43262, 44171, 48766, 50637, 72986,\n", + " 73892, 74826, 78435, 80263, 90455, 97926, 104372, 116385,\n", + " 126012, 133306, 140529, 145961, 161027, 163741, 171558, 174226,\n", + " 180985, 188826, 191557, 193359, 194257],\n", + " dtype='int64'), Int64Index([ 575, 3298, 8935, 15372, 19945, 22684, 23587, 26328,\n", + " 30915, 41445, 42355, 43263, 44172, 48767, 50638, 72987,\n", + " 73893, 74827, 78436, 80264, 90456, 97927, 104373, 116386,\n", + " 126013, 133307, 140530, 145962, 161028, 163742, 171559, 174227,\n", + " 180986, 188827, 191558, 193360, 194258],\n", + " dtype='int64'), Int64Index([ 576, 3299, 8936, 15373, 19946, 22685, 23588, 26329,\n", + " 30916, 41446, 42356, 43264, 44173, 48768, 50639, 72988,\n", + " 73894, 74828, 78437, 80265, 90457, 97928, 104374, 116387,\n", + " 126014, 133308, 140531, 145963, 161029, 163743, 171560, 174228,\n", + " 180987, 188828, 191559, 193361, 194259],\n", + " dtype='int64'), Int64Index([ 577, 3300, 8937, 15374, 19947, 22686, 23589, 26330,\n", + " 30917, 41447, 42357, 43265, 44174, 48769, 50640, 72989,\n", + " 73895, 74829, 78438, 80266, 90458, 97929, 104375, 116388,\n", + " 126015, 133309, 140532, 145964, 161030, 163744, 171561, 174229,\n", + " 180988, 188829, 191560, 193362, 194260],\n", + " dtype='int64'), Int64Index([ 578, 3301, 8938, 15375, 19948, 22687, 23590, 26331,\n", + " 30918, 41448, 42358, 43266, 44175, 48770, 50641, 72990,\n", + " 73896, 74830, 78439, 80267, 90459, 97930, 104376, 116389,\n", + " 126016, 133310, 140533, 145965, 161031, 163745, 171562, 174230,\n", + " 180989, 188830, 191561, 193363, 194261],\n", + " dtype='int64'), Int64Index([ 579, 3302, 8939, 15376, 19949, 22688, 23591, 26332,\n", + " 30919, 41449, 42359, 43267, 44176, 48771, 50642, 72991,\n", + " 73897, 74831, 78440, 80268, 90460, 97931, 104377, 116390,\n", + " 126017, 133311, 140534, 145966, 161032, 163746, 171563, 174231,\n", + " 180990, 188831, 191562, 193364, 194262],\n", + " dtype='int64'), Int64Index([ 580, 3303, 8940, 15377, 19950, 22689, 23592, 26333,\n", + " 30920, 41450, 42360, 43268, 44177, 48772, 50643, 72992,\n", + " 73898, 74832, 78441, 80269, 90461, 97932, 104378, 116391,\n", + " 126018, 133312, 140535, 145967, 161033, 163747, 171564, 174232,\n", + " 180991, 188832, 191563, 193365, 194263],\n", + " dtype='int64'), Int64Index([ 581, 3304, 8941, 15378, 19951, 22690, 23593, 26334,\n", + " 30921, 41451, 42361, 43269, 44178, 48773, 50644, 72993,\n", + " 73899, 74833, 78442, 80270, 90462, 97933, 104379, 116392,\n", + " 126019, 133313, 140536, 145968, 161034, 163748, 171565, 174233,\n", + " 180992, 188833, 191564, 193366, 194264],\n", + " dtype='int64'), Int64Index([ 582, 3305, 8942, 15379, 19952, 22691, 23594, 26335,\n", + " 30922, 41452, 42362, 43270, 44179, 48774, 50645, 72994,\n", + " 73900, 74834, 78443, 80271, 90463, 97934, 104380, 116393,\n", + " 126020, 133314, 140537, 145969, 161035, 163749, 171566, 174234,\n", + " 180993, 188834, 191565, 193367, 194265],\n", + " dtype='int64'), Int64Index([ 583, 3306, 8943, 15380, 19953, 22692, 23595, 26336,\n", + " 30923, 41453, 42363, 43271, 44180, 48775, 50646, 72995,\n", + " 73901, 74835, 78444, 80272, 90464, 97935, 104381, 116394,\n", + " 126021, 133315, 140538, 145970, 161036, 163750, 171567, 174235,\n", + " 180994, 188835, 191566, 193368, 194266],\n", + " dtype='int64'), Int64Index([ 584, 3307, 8944, 15381, 19954, 22693, 23596, 26337,\n", + " 30924, 41454, 42364, 43272, 44181, 48776, 50647, 72996,\n", + " 73902, 74836, 78445, 80273, 90465, 97936, 104382, 116395,\n", + " 126022, 133316, 140539, 145971, 161037, 163751, 171568, 174236,\n", + " 180995, 188836, 191567, 193369, 194267],\n", + " dtype='int64'), Int64Index([ 585, 3308, 8945, 15382, 19955, 22694, 23597, 26338,\n", + " 30925, 41455, 42365, 43273, 44182, 48777, 50648, 72997,\n", + " 73903, 74837, 78446, 80274, 90466, 97937, 104383, 116396,\n", + " 126023, 133317, 140540, 145972, 161038, 163752, 171569, 174237,\n", + " 180996, 188837, 191568, 193370, 194268],\n", + " dtype='int64'), Int64Index([ 586, 3309, 8946, 15383, 19956, 22695, 23598, 26339,\n", + " 30926, 41456, 42366, 43274, 44183, 48778, 50649, 72998,\n", + " 73904, 74838, 78447, 80275, 90467, 97938, 104384, 116397,\n", + " 126024, 133318, 140541, 145973, 161039, 163753, 171570, 174238,\n", + " 180997, 188838, 191569, 193371, 194269],\n", + " dtype='int64'), Int64Index([ 587, 3310, 8947, 15384, 19957, 22696, 23599, 26340,\n", + " 30927, 41457, 42367, 43275, 44184, 48779, 50650, 72999,\n", + " 73905, 74839, 78448, 80276, 90468, 97939, 104385, 116398,\n", + " 126025, 133319, 140542, 145974, 161040, 163754, 171571, 174239,\n", + " 180998, 188839, 191570, 193372, 194270],\n", + " dtype='int64'), Int64Index([ 588, 3311, 8948, 15385, 19958, 22697, 23600, 26341,\n", + " 30928, 41458, 42368, 43276, 44185, 48780, 50651, 73000,\n", + " 73906, 74840, 78449, 80277, 90469, 97940, 104386, 116399,\n", + " 126026, 133320, 140543, 145975, 161041, 163755, 171572, 174240,\n", + " 180999, 188840, 191571, 193373, 194271],\n", + " dtype='int64'), Int64Index([ 589, 3312, 8949, 15386, 19959, 22698, 23601, 26342,\n", + " 30929, 41459, 42369, 43277, 44186, 48781, 50652, 73001,\n", + " 73907, 74841, 78450, 80278, 90470, 97941, 104387, 116400,\n", + " 126027, 133321, 140544, 145976, 161042, 163756, 171573, 174241,\n", + " 181000, 188841, 191572, 193374, 194272],\n", + " dtype='int64'), Int64Index([ 590, 3313, 8950, 15387, 19960, 22699, 23602, 26343,\n", + " 30930, 41460, 42370, 43278, 44187, 48782, 50653, 73002,\n", + " 73908, 74842, 78451, 80279, 90471, 97942, 104388, 116401,\n", + " 126028, 133322, 140545, 145977, 161043, 163757, 171574, 174242,\n", + " 181001, 188842, 191573, 193375, 194273],\n", + " dtype='int64'), Int64Index([ 591, 3314, 8951, 15388, 19961, 22700, 23603, 26344,\n", + " 30931, 41461, 42371, 43279, 44188, 48783, 50654, 73003,\n", + " 73909, 74843, 78452, 80280, 90472, 97943, 104389, 116402,\n", + " 126029, 133323, 140546, 145978, 161044, 163758, 171575, 174243,\n", + " 181002, 188843, 191574, 193376, 194274],\n", + " dtype='int64'), Int64Index([ 592, 3315, 8952, 15389, 19962, 22701, 23604, 26345,\n", + " 30932, 41462, 42372, 43280, 44189, 48784, 50655, 73004,\n", + " 73910, 74844, 78453, 80281, 90473, 97944, 104390, 116403,\n", + " 126030, 133324, 140547, 145979, 161045, 163759, 171576, 174244,\n", + " 181003, 188844, 191575, 193377, 194275],\n", + " dtype='int64'), Int64Index([ 593, 3316, 8953, 15390, 19963, 22702, 23605, 26346,\n", + " 30933, 41463, 42373, 43281, 44190, 48785, 50656, 73005,\n", + " 73911, 74845, 78454, 80282, 90474, 97945, 104391, 116404,\n", + " 126031, 133325, 140548, 145980, 161046, 163760, 171577, 174245,\n", + " 181004, 188845, 191576, 193378, 194276],\n", + " dtype='int64'), Int64Index([ 594, 3317, 8954, 15391, 19964, 22703, 23606, 26347,\n", + " 30934, 41464, 42374, 43282, 44191, 48786, 50657, 73006,\n", + " 73912, 74846, 78455, 80283, 90475, 97946, 104392, 116405,\n", + " 126032, 133326, 140549, 145981, 161047, 163761, 171578, 174246,\n", + " 181005, 188846, 191577, 193379, 194277],\n", + " dtype='int64'), Int64Index([ 595, 3318, 8955, 15392, 19965, 22704, 23607, 26348,\n", + " 30935, 41465, 42375, 43283, 44192, 48787, 50658, 73007,\n", + " 73913, 74847, 78456, 80284, 90476, 97947, 104393, 116406,\n", + " 126033, 133327, 140550, 145982, 161048, 163762, 171579, 174247,\n", + " 181006, 188847, 191578, 193380, 194278],\n", + " dtype='int64'), Int64Index([ 596, 3319, 8956, 15393, 19966, 22705, 23608, 26349,\n", + " 30936, 41466, 42376, 43284, 44193, 48788, 50659, 73008,\n", + " 73914, 74848, 78457, 80285, 90477, 97948, 104394, 116407,\n", + " 126034, 133328, 140551, 145983, 161049, 163763, 171580, 174248,\n", + " 181007, 188848, 191579, 193381, 194279],\n", + " dtype='int64'), Int64Index([ 597, 3320, 8957, 15394, 19967, 22706, 23609, 26350,\n", + " 30937, 41467, 42377, 43285, 44194, 48789, 50660, 73009,\n", + " 73915, 74849, 78458, 80286, 90478, 97949, 104395, 116408,\n", + " 126035, 133329, 140552, 145984, 161050, 163764, 171581, 174249,\n", + " 181008, 188849, 191580, 193382, 194280],\n", + " dtype='int64'), Int64Index([ 598, 3321, 8958, 15395, 19968, 22707, 23610, 26351,\n", + " 30938, 41468, 42378, 43286, 44195, 48790, 50661, 73010,\n", + " 73916, 74850, 78459, 80287, 90479, 97950, 104396, 116409,\n", + " 126036, 133330, 140553, 145985, 161051, 163765, 171582, 174250,\n", + " 181009, 188850, 191581, 193383, 194281],\n", + " dtype='int64'), Int64Index([ 599, 3322, 8959, 15396, 19969, 22708, 23611, 26352,\n", + " 30939, 41469, 42379, 43287, 44196, 48791, 50662, 73011,\n", + " 73917, 74851, 78460, 80288, 90480, 97951, 104397, 116410,\n", + " 126037, 133331, 140554, 145986, 161052, 163766, 171583, 174251,\n", + " 181010, 188851, 191582, 193384, 194282],\n", + " dtype='int64'), Int64Index([ 600, 3323, 8960, 15397, 19970, 22709, 23612, 26353,\n", + " 30940, 41470, 42380, 43288, 44197, 48792, 50663, 73012,\n", + " 73918, 74852, 78461, 80289, 90481, 97952, 104398, 116411,\n", + " 126038, 133332, 140555, 145987, 161053, 163767, 171584, 174252,\n", + " 181011, 188852, 191583, 193385, 194283],\n", + " dtype='int64'), Int64Index([ 601, 3324, 8961, 15398, 19971, 22710, 23613, 26354,\n", + " 30941, 41471, 42381, 43289, 44198, 48793, 50664, 73013,\n", + " 73919, 74853, 78462, 80290, 90482, 97953, 104399, 116412,\n", + " 126039, 133333, 140556, 145988, 161054, 163768, 171585, 174253,\n", + " 181012, 188853, 191584, 193386, 194284],\n", + " dtype='int64'), Int64Index([ 602, 3325, 8962, 15399, 19972, 22711, 23614, 26355,\n", + " 30942, 41472, 42382, 43290, 44199, 48794, 50665, 73014,\n", + " 73920, 74854, 78463, 80291, 90483, 97954, 104400, 116413,\n", + " 126040, 133334, 140557, 145989, 161055, 163769, 171586, 174254,\n", + " 181013, 188854, 191585, 193387, 194285],\n", + " dtype='int64'), Int64Index([ 603, 3326, 8963, 15400, 19973, 22712, 23615, 26356,\n", + " 30943, 41473, 42383, 43291, 44200, 48795, 50666, 73015,\n", + " 73921, 74855, 78464, 80292, 90484, 97955, 104401, 116414,\n", + " 126041, 133335, 140558, 145990, 161056, 163770, 171587, 174255,\n", + " 181014, 188855, 191586, 193388, 194286],\n", + " dtype='int64'), Int64Index([ 604, 3327, 8964, 15401, 19974, 22713, 23616, 26357,\n", + " 30944, 41474, 42384, 43292, 44201, 48796, 50667, 73016,\n", + " 73922, 74856, 78465, 80293, 90485, 97956, 104402, 116415,\n", + " 126042, 133336, 140559, 145991, 161057, 163771, 171588, 174256,\n", + " 181015, 188856, 191587, 193389, 194287],\n", + " dtype='int64'), Int64Index([ 605, 3328, 8965, 15402, 19975, 22714, 23617, 26358,\n", + " 30945, 41475, 42385, 43293, 44202, 48797, 50668, 73017,\n", + " 73923, 74857, 78466, 80294, 90486, 97957, 104403, 116416,\n", + " 126043, 133337, 140560, 145992, 161058, 163772, 171589, 174257,\n", + " 181016, 188857, 191588, 193390, 194288],\n", + " dtype='int64'), Int64Index([ 606, 3329, 8966, 15403, 19976, 22715, 23618, 26359,\n", + " 30946, 41476, 42386, 43294, 44203, 48798, 50669, 73018,\n", + " 73924, 74858, 78467, 80295, 90487, 97958, 104404, 116417,\n", + " 126044, 133338, 140561, 145993, 161059, 163773, 171590, 174258,\n", + " 181017, 188858, 191589, 193391, 194289],\n", + " dtype='int64'), Int64Index([ 607, 3330, 8967, 15404, 19977, 22716, 23619, 26360,\n", + " 30947, 41477, 42387, 43295, 44204, 48799, 50670, 73019,\n", + " 73925, 74859, 78468, 80296, 90488, 97959, 104405, 116418,\n", + " 126045, 133339, 140562, 145994, 161060, 163774, 171591, 174259,\n", + " 181018, 188859, 191590, 193392, 194290],\n", + " dtype='int64'), Int64Index([ 608, 3331, 8968, 15405, 19978, 22717, 23620, 26361,\n", + " 30948, 41478, 42388, 43296, 44205, 48800, 50671, 73020,\n", + " 73926, 74860, 78469, 80297, 90489, 97960, 104406, 116419,\n", + " 126046, 133340, 140563, 145995, 161061, 163775, 171592, 174260,\n", + " 181019, 188860, 191591, 193393, 194291],\n", + " dtype='int64'), Int64Index([ 609, 3332, 8969, 15406, 19979, 22718, 23621, 26362,\n", + " 30949, 41479, 42389, 43297, 44206, 48801, 50672, 73021,\n", + " 73927, 74861, 78470, 80298, 90490, 97961, 104407, 116420,\n", + " 126047, 133341, 140564, 145996, 161062, 163776, 171593, 174261,\n", + " 181020, 188861, 191592, 193394, 194292],\n", + " dtype='int64'), Int64Index([ 610, 3333, 8970, 15407, 19980, 22719, 23622, 26363,\n", + " 30950, 41480, 42390, 43298, 44207, 48802, 50673, 73022,\n", + " 73928, 74862, 78471, 80299, 90491, 97962, 104408, 116421,\n", + " 126048, 133342, 140565, 145997, 161063, 163777, 171594, 174262,\n", + " 181021, 188862, 191593, 193395, 194293],\n", + " dtype='int64'), Int64Index([ 611, 3334, 8971, 15408, 19981, 22720, 23623, 26364,\n", + " 30951, 41481, 42391, 43299, 44208, 48803, 50674, 73023,\n", + " 73929, 74863, 78472, 80300, 90492, 97963, 104409, 116422,\n", + " 126049, 133343, 140566, 145998, 161064, 163778, 171595, 174263,\n", + " 181022, 188863, 191594, 193396, 194294],\n", + " dtype='int64'), Int64Index([ 612, 3335, 8972, 15409, 19982, 22721, 23624, 26365,\n", + " 30952, 41482, 42392, 43300, 44209, 48804, 50675, 73024,\n", + " 73930, 74864, 78473, 80301, 90493, 97964, 104410, 116423,\n", + " 126050, 133344, 140567, 145999, 161065, 163779, 171596, 174264,\n", + " 181023, 188864, 191595, 193397, 194295],\n", + " dtype='int64'), Int64Index([ 613, 3336, 8973, 15410, 19983, 22722, 23625, 26366,\n", + " 30953, 41483, 42393, 43301, 44210, 48805, 50676, 73025,\n", + " 73931, 74865, 78474, 80302, 90494, 97965, 104411, 116424,\n", + " 126051, 133345, 140568, 146000, 161066, 163780, 171597, 174265,\n", + " 181024, 188865, 191596, 193398, 194296],\n", + " dtype='int64'), Int64Index([ 614, 3337, 8974, 15411, 19984, 22723, 23626, 26367,\n", + " 30954, 41484, 42394, 43302, 44211, 48806, 50677, 73026,\n", + " 73932, 74866, 78475, 80303, 90495, 97966, 104412, 116425,\n", + " 126052, 133346, 140569, 146001, 161067, 163781, 171598, 174266,\n", + " 181025, 188866, 191597, 193399, 194297],\n", + " dtype='int64'), Int64Index([ 615, 3338, 8975, 15412, 19985, 22724, 23627, 26368,\n", + " 30955, 41485, 42395, 43303, 44212, 48807, 50678, 73027,\n", + " 73933, 74867, 78476, 80304, 90496, 97967, 104413, 116426,\n", + " 126053, 133347, 140570, 146002, 161068, 163782, 171599, 174267,\n", + " 181026, 188867, 191598, 193400, 194298],\n", + " dtype='int64'), Int64Index([ 616, 3339, 8976, 15413, 19986, 22725, 23628, 26369,\n", + " 30956, 41486, 42396, 43304, 44213, 48808, 50679, 73028,\n", + " 73934, 74868, 78477, 80305, 90497, 97968, 104414, 116427,\n", + " 126054, 133348, 140571, 146003, 161069, 163783, 171600, 174268,\n", + " 181027, 188868, 191599, 193401, 194299],\n", + " dtype='int64'), Int64Index([ 617, 3340, 8977, 15414, 19987, 22726, 23629, 26370,\n", + " 30957, 41487, 42397, 43305, 44214, 48809, 50680, 73029,\n", + " 73935, 74869, 78478, 80306, 90498, 97969, 104415, 116428,\n", + " 126055, 133349, 140572, 146004, 161070, 163784, 171601, 174269,\n", + " 181028, 188869, 191600, 193402, 194300],\n", + " dtype='int64'), Int64Index([ 618, 3341, 8978, 15415, 19988, 22727, 23630, 26371,\n", + " 30958, 41488, 42398, 43306, 44215, 48810, 50681, 73030,\n", + " 73936, 74870, 78479, 80307, 90499, 97970, 104416, 116429,\n", + " 126056, 133350, 140573, 146005, 161071, 163785, 171602, 174270,\n", + " 181029, 188870, 191601, 193403, 194301],\n", + " dtype='int64'), Int64Index([ 619, 3342, 8979, 15416, 19989, 22728, 23631, 26372,\n", + " 30959, 41489, 42399, 43307, 44216, 48811, 50682, 73031,\n", + " 73937, 74871, 78480, 80308, 90500, 97971, 104417, 116430,\n", + " 126057, 133351, 140574, 146006, 161072, 163786, 171603, 174271,\n", + " 181030, 188871, 191602, 193404, 194302],\n", + " dtype='int64'), Int64Index([ 620, 3343, 8980, 15417, 19990, 22729, 23632, 26373,\n", + " 30960, 41490, 42400, 43308, 44217, 48812, 50683, 73032,\n", + " 73938, 74872, 78481, 80309, 90501, 97972, 104418, 116431,\n", + " 126058, 133352, 140575, 146007, 161073, 163787, 171604, 174272,\n", + " 181031, 188872, 191603, 193405, 194303],\n", + " dtype='int64'), Int64Index([ 621, 3344, 8981, 15418, 19991, 22730, 23633, 26374,\n", + " 30961, 41491, 42401, 43309, 44218, 48813, 50684, 73033,\n", + " 73939, 74873, 78482, 80310, 90502, 97973, 104419, 116432,\n", + " 126059, 133353, 140576, 146008, 161074, 163788, 171605, 174273,\n", + " 181032, 188873, 191604, 193406, 194304],\n", + " dtype='int64'), Int64Index([ 622, 3345, 8982, 15419, 19992, 22731, 23634, 26375,\n", + " 30962, 41492, 42402, 43310, 44219, 48814, 50685, 73034,\n", + " 73940, 74874, 78483, 80311, 90503, 97974, 104420, 116433,\n", + " 126060, 133354, 140577, 146009, 161075, 163789, 171606, 174274,\n", + " 181033, 188874, 191605, 193407, 194305],\n", + " dtype='int64'), Int64Index([ 623, 3346, 8983, 15420, 19993, 22732, 23635, 26376,\n", + " 30963, 41493, 42403, 43311, 44220, 48815, 50686, 73035,\n", + " 73941, 74875, 78484, 80312, 90504, 97975, 104421, 116434,\n", + " 126061, 133355, 140578, 146010, 161076, 163790, 171607, 174275,\n", + " 181034, 188875, 191606, 193408, 194306],\n", + " dtype='int64'), Int64Index([ 624, 3347, 8984, 15421, 19994, 22733, 23636, 26377,\n", + " 30964, 41494, 42404, 43312, 44221, 48816, 50687, 73036,\n", + " 73942, 74876, 78485, 80313, 90505, 97976, 104422, 116435,\n", + " 126062, 133356, 140579, 146011, 161077, 163791, 171608, 174276,\n", + " 181035, 188876, 191607, 193409, 194307],\n", + " dtype='int64'), Int64Index([ 625, 3348, 8985, 15422, 19995, 22734, 23637, 26378,\n", + " 30965, 41495, 42405, 43313, 44222, 48817, 50688, 73037,\n", + " 73943, 74877, 78486, 80314, 90506, 97977, 104423, 116436,\n", + " 126063, 133357, 140580, 146012, 161078, 163792, 171609, 174277,\n", + " 181036, 188877, 191608, 193410, 194308],\n", + " dtype='int64'), Int64Index([ 626, 3349, 8986, 15423, 19996, 22735, 23638, 26379,\n", + " 30966, 41496, 42406, 43314, 44223, 48818, 50689, 73038,\n", + " 73944, 74878, 78487, 80315, 90507, 97978, 104424, 116437,\n", + " 126064, 133358, 140581, 146013, 161079, 163793, 171610, 174278,\n", + " 181037, 188878, 191609, 193411, 194309],\n", + " dtype='int64'), Int64Index([ 627, 3350, 8987, 15424, 19997, 22736, 23639, 26380,\n", + " 30967, 41497, 42407, 43315, 44224, 48819, 50690, 73039,\n", + " 73945, 74879, 78488, 80316, 90508, 97979, 104425, 116438,\n", + " 126065, 133359, 140582, 146014, 161080, 163794, 171611, 174279,\n", + " 181038, 188879, 191610, 193412, 194310],\n", + " dtype='int64'), Int64Index([ 628, 3351, 8988, 15425, 19998, 22737, 23640, 26381,\n", + " 30968, 41498, 42408, 43316, 44225, 48820, 50691, 73040,\n", + " 73946, 74880, 78489, 80317, 90509, 97980, 104426, 116439,\n", + " 126066, 133360, 140583, 146015, 161081, 163795, 171612, 174280,\n", + " 181039, 188880, 191611, 193413, 194311],\n", + " dtype='int64'), Int64Index([ 629, 3352, 8989, 15426, 19999, 22738, 23641, 26382,\n", + " 30969, 41499, 42409, 43317, 44226, 48821, 50692, 73041,\n", + " 73947, 74881, 78490, 80318, 90510, 97981, 104427, 116440,\n", + " 126067, 133361, 140584, 146016, 161082, 163796, 171613, 174281,\n", + " 181040, 188881, 191612, 193414, 194312],\n", + " dtype='int64'), Int64Index([ 630, 3353, 8990, 15427, 20000, 22739, 23642, 26383,\n", + " 30970, 41500, 42410, 43318, 44227, 48822, 50693, 73042,\n", + " 73948, 74882, 78491, 80319, 90511, 97982, 104428, 116441,\n", + " 126068, 133362, 140585, 146017, 161083, 163797, 171614, 174282,\n", + " 181041, 188882, 191613, 193415, 194313],\n", + " dtype='int64'), Int64Index([ 631, 3354, 8991, 15428, 20001, 22740, 23643, 26384,\n", + " 30971, 41501, 42411, 43319, 44228, 48823, 50694, 73043,\n", + " 73949, 74883, 78492, 80320, 90512, 97983, 104429, 116442,\n", + " 126069, 133363, 140586, 146018, 161084, 163798, 171615, 174283,\n", + " 181042, 188883, 191614, 193416, 194314],\n", + " dtype='int64'), Int64Index([ 632, 3355, 8992, 15429, 20002, 22741, 23644, 26385,\n", + " 30972, 41502, 42412, 43320, 44229, 48824, 50695, 73044,\n", + " 73950, 74884, 78493, 80321, 90513, 97984, 104430, 116443,\n", + " 126070, 133364, 140587, 146019, 161085, 163799, 171616, 174284,\n", + " 181043, 188884, 191615, 193417, 194315],\n", + " dtype='int64'), Int64Index([ 633, 3356, 8993, 15430, 20003, 22742, 23645, 26386,\n", + " 30973, 41503, 42413, 43321, 44230, 48825, 50696, 73045,\n", + " 73951, 74885, 78494, 80322, 90514, 97985, 104431, 116444,\n", + " 126071, 133365, 140588, 146020, 161086, 163800, 171617, 174285,\n", + " 181044, 188885, 191616, 193418, 194316],\n", + " dtype='int64'), Int64Index([ 634, 3357, 8994, 15431, 20004, 22743, 23646, 26387,\n", + " 30974, 41504, 42414, 43322, 44231, 48826, 50697, 73046,\n", + " 73952, 74886, 78495, 80323, 90515, 97986, 104432, 116445,\n", + " 126072, 133366, 140589, 146021, 161087, 163801, 171618, 174286,\n", + " 181045, 188886, 191617, 193419, 194317],\n", + " dtype='int64'), Int64Index([ 635, 3358, 8995, 15432, 20005, 22744, 23647, 26388,\n", + " 30975, 41505, 42415, 43323, 44232, 48827, 50698, 73047,\n", + " 73953, 74887, 78496, 80324, 90516, 97987, 104433, 116446,\n", + " 126073, 133367, 140590, 146022, 161088, 163802, 171619, 174287,\n", + " 181046, 188887, 191618, 193420, 194318],\n", + " dtype='int64'), Int64Index([ 636, 3359, 8996, 15433, 20006, 22745, 23648, 26389,\n", + " 30976, 41506, 42416, 43324, 44233, 48828, 50699, 73048,\n", + " 73954, 74888, 78497, 80325, 90517, 97988, 104434, 116447,\n", + " 126074, 133368, 140591, 146023, 161089, 163803, 171620, 174288,\n", + " 181047, 188888, 191619, 193421, 194319],\n", + " dtype='int64'), Int64Index([ 637, 3360, 8997, 15434, 20007, 22746, 23649, 26390,\n", + " 30977, 41507, 42417, 43325, 44234, 48829, 50700, 73049,\n", + " 73955, 74889, 78498, 80326, 90518, 97989, 104435, 116448,\n", + " 126075, 133369, 140592, 146024, 161090, 163804, 171621, 174289,\n", + " 181048, 188889, 191620, 193422, 194320],\n", + " dtype='int64'), Int64Index([ 638, 3361, 8998, 15435, 20008, 22747, 23650, 26391,\n", + " 30978, 41508, 42418, 43326, 44235, 48830, 50701, 73050,\n", + " 73956, 74890, 78499, 80327, 90519, 97990, 104436, 116449,\n", + " 126076, 133370, 140593, 146025, 161091, 163805, 171622, 174290,\n", + " 181049, 188890, 191621, 193423, 194321],\n", + " dtype='int64'), Int64Index([ 639, 3362, 8999, 15436, 20009, 22748, 23651, 26392,\n", + " 30979, 41509, 42419, 43327, 44236, 48831, 50702, 73051,\n", + " 73957, 74891, 78500, 80328, 90520, 97991, 104437, 116450,\n", + " 126077, 133371, 140594, 146026, 161092, 163806, 171623, 174291,\n", + " 181050, 188891, 191622, 193424, 194322],\n", + " dtype='int64'), Int64Index([ 640, 3363, 9000, 15437, 20010, 22749, 23652, 26393,\n", + " 30980, 41510, 42420, 43328, 44237, 48832, 50703, 73052,\n", + " 73958, 74892, 78501, 80329, 90521, 97992, 104438, 116451,\n", + " 126078, 133372, 140595, 146027, 161093, 163807, 171624, 174292,\n", + " 181051, 188892, 191623, 193425, 194323],\n", + " dtype='int64'), Int64Index([ 641, 3364, 9001, 15438, 20011, 22750, 23653, 26394,\n", + " 30981, 41511, 42421, 43329, 44238, 48833, 50704, 73053,\n", + " 73959, 74893, 78502, 80330, 90522, 97993, 104439, 116452,\n", + " 126079, 133373, 140596, 146028, 161094, 163808, 171625, 174293,\n", + " 181052, 188893, 191624, 193426, 194324],\n", + " dtype='int64'), Int64Index([ 642, 3365, 9002, 15439, 20012, 22751, 23654, 26395,\n", + " 30982, 41512, 42422, 43330, 44239, 48834, 50705, 73054,\n", + " 73960, 74894, 78503, 80331, 90523, 97994, 104440, 116453,\n", + " 126080, 133374, 140597, 146029, 161095, 163809, 171626, 174294,\n", + " 181053, 188894, 191625, 193427, 194325],\n", + " dtype='int64'), Int64Index([ 643, 3366, 9003, 15440, 20013, 22752, 23655, 26396,\n", + " 30983, 41513, 42423, 43331, 44240, 48835, 50706, 73055,\n", + " 73961, 74895, 78504, 80332, 90524, 97995, 104441, 116454,\n", + " 126081, 133375, 140598, 146030, 161096, 163810, 171627, 174295,\n", + " 181054, 188895, 191626, 193428, 194326],\n", + " dtype='int64'), Int64Index([ 644, 3367, 9004, 15441, 20014, 22753, 23656, 26397,\n", + " 30984, 41514, 42424, 43332, 44241, 48836, 50707, 73056,\n", + " 73962, 74896, 78505, 80333, 90525, 97996, 104442, 116455,\n", + " 126082, 133376, 140599, 146031, 161097, 163811, 171628, 174296,\n", + " 181055, 188896, 191627, 193429, 194327],\n", + " dtype='int64'), Int64Index([ 645, 3368, 9005, 15442, 20015, 22754, 23657, 26398,\n", + " 30985, 41515, 42425, 43333, 44242, 48837, 50708, 73057,\n", + " 73963, 74897, 78506, 80334, 90526, 97997, 104443, 116456,\n", + " 126083, 133377, 140600, 146032, 161098, 163812, 171629, 174297,\n", + " 181056, 188897, 191628, 193430, 194328],\n", + " dtype='int64'), Int64Index([ 646, 3369, 9006, 15443, 20016, 22755, 23658, 26399,\n", + " 30986, 41516, 42426, 43334, 44243, 48838, 50709, 73058,\n", + " 73964, 74898, 78507, 80335, 90527, 97998, 104444, 116457,\n", + " 126084, 133378, 140601, 146033, 161099, 163813, 171630, 174298,\n", + " 181057, 188898, 191629, 193431, 194329],\n", + " dtype='int64'), Int64Index([ 647, 3370, 9007, 15444, 20017, 22756, 23659, 26400,\n", + " 30987, 41517, 42427, 43335, 44244, 48839, 50710, 73059,\n", + " 73965, 74899, 78508, 80336, 90528, 97999, 104445, 116458,\n", + " 126085, 133379, 140602, 146034, 161100, 163814, 171631, 174299,\n", + " 181058, 188899, 191630, 193432, 194330],\n", + " dtype='int64'), Int64Index([ 648, 3371, 9008, 15445, 20018, 22757, 23660, 26401,\n", + " 30988, 41518, 42428, 43336, 44245, 48840, 50711, 73060,\n", + " 73966, 74900, 78509, 80337, 90529, 98000, 104446, 116459,\n", + " 126086, 133380, 140603, 146035, 161101, 163815, 171632, 174300,\n", + " 181059, 188900, 191631, 193433, 194331],\n", + " dtype='int64'), Int64Index([ 649, 3372, 9009, 15446, 20019, 22758, 23661, 26402,\n", + " 30989, 41519, 42429, 43337, 44246, 48841, 50712, 73061,\n", + " 73967, 74901, 78510, 80338, 90530, 98001, 104447, 116460,\n", + " 126087, 133381, 140604, 146036, 161102, 163816, 171633, 174301,\n", + " 181060, 188901, 191632, 193434, 194332],\n", + " dtype='int64'), Int64Index([ 650, 3373, 9010, 15447, 20020, 22759, 23662, 26403,\n", + " 30990, 41520, 42430, 43338, 44247, 48842, 50713, 73062,\n", + " 73968, 74902, 78511, 80339, 90531, 98002, 104448, 116461,\n", + " 126088, 133382, 140605, 146037, 161103, 163817, 171634, 174302,\n", + " 181061, 188902, 191633, 193435, 194333],\n", + " dtype='int64'), Int64Index([ 651, 3374, 9011, 15448, 20021, 22760, 23663, 26404,\n", + " 30991, 41521, 42431, 43339, 44248, 48843, 50714, 73063,\n", + " 73969, 74903, 78512, 80340, 90532, 98003, 104449, 116462,\n", + " 126089, 133383, 140606, 146038, 161104, 163818, 171635, 174303,\n", + " 181062, 188903, 191634, 193436, 194334],\n", + " dtype='int64'), Int64Index([ 652, 3375, 9012, 15449, 20022, 22761, 23664, 26405,\n", + " 30992, 41522, 42432, 43340, 44249, 48844, 50715, 73064,\n", + " 73970, 74904, 78513, 80341, 90533, 98004, 104450, 116463,\n", + " 126090, 133384, 140607, 146039, 161105, 163819, 171636, 174304,\n", + " 181063, 188904, 191635, 193437, 194335],\n", + " dtype='int64'), Int64Index([ 653, 3376, 9013, 15450, 20023, 22762, 23665, 26406,\n", + " 30993, 41523, 42433, 43341, 44250, 48845, 50716, 73065,\n", + " 73971, 74905, 78514, 80342, 90534, 98005, 104451, 116464,\n", + " 126091, 133385, 140608, 146040, 161106, 163820, 171637, 174305,\n", + " 181064, 188905, 191636, 193438, 194336],\n", + " dtype='int64'), Int64Index([ 654, 3377, 9014, 15451, 20024, 22763, 23666, 26407,\n", + " 30994, 41524, 42434, 43342, 44251, 48846, 50717, 73066,\n", + " 73972, 74906, 78515, 80343, 90535, 98006, 104452, 116465,\n", + " 126092, 133386, 140609, 146041, 161107, 163821, 171638, 174306,\n", + " 181065, 188906, 191637, 193439, 194337],\n", + " dtype='int64'), Int64Index([ 655, 3378, 9015, 15452, 20025, 22764, 23667, 26408,\n", + " 30995, 41525, 42435, 43343, 44252, 48847, 50718, 73067,\n", + " 73973, 74907, 78516, 80344, 90536, 98007, 104453, 116466,\n", + " 126093, 133387, 140610, 146042, 161108, 163822, 171639, 174307,\n", + " 181066, 188907, 191638, 193440, 194338],\n", + " dtype='int64'), Int64Index([ 656, 3379, 9016, 15453, 20026, 22765, 23668, 26409,\n", + " 30996, 41526, 42436, 43344, 44253, 48848, 50719, 73068,\n", + " 73974, 74908, 78517, 80345, 90537, 98008, 104454, 116467,\n", + " 126094, 133388, 140611, 146043, 161109, 163823, 171640, 174308,\n", + " 181067, 188908, 191639, 193441, 194339],\n", + " dtype='int64'), Int64Index([ 657, 3380, 9017, 15454, 20027, 22766, 23669, 26410,\n", + " 30997, 41527, 42437, 43345, 44254, 48849, 50720, 73069,\n", + " 73975, 74909, 78518, 80346, 90538, 98009, 104455, 116468,\n", + " 126095, 133389, 140612, 146044, 161110, 163824, 171641, 174309,\n", + " 181068, 188909, 191640, 193442, 194340],\n", + " dtype='int64'), Int64Index([ 658, 3381, 9018, 15455, 20028, 22767, 23670, 26411,\n", + " 30998, 41528, 42438, 43346, 44255, 48850, 50721, 73070,\n", + " 73976, 74910, 78519, 80347, 90539, 98010, 104456, 116469,\n", + " 126096, 133390, 140613, 146045, 161111, 163825, 171642, 174310,\n", + " 181069, 188910, 191641, 193443, 194341],\n", + " dtype='int64'), Int64Index([ 659, 3382, 9019, 15456, 20029, 22768, 23671, 26412,\n", + " 30999, 41529, 42439, 43347, 44256, 48851, 50722, 73071,\n", + " 73977, 74911, 78520, 80348, 90540, 98011, 104457, 116470,\n", + " 126097, 133391, 140614, 146046, 161112, 163826, 171643, 174311,\n", + " 181070, 188911, 191642, 193444, 194342],\n", + " dtype='int64'), Int64Index([ 660, 3383, 9020, 15457, 20030, 22769, 23672, 26413,\n", + " 31000, 41530, 42440, 43348, 44257, 48852, 50723, 73072,\n", + " 73978, 74912, 78521, 80349, 90541, 98012, 104458, 116471,\n", + " 126098, 133392, 140615, 146047, 161113, 163827, 171644, 174312,\n", + " 181071, 188912, 191643, 193445, 194343],\n", + " dtype='int64'), Int64Index([ 661, 3384, 9021, 15458, 20031, 22770, 23673, 26414,\n", + " 31001, 41531, 42441, 43349, 44258, 48853, 50724, 73073,\n", + " 73979, 74913, 78522, 80350, 90542, 98013, 104459, 116472,\n", + " 126099, 133393, 140616, 146048, 161114, 163828, 171645, 174313,\n", + " 181072, 188913, 191644, 193446, 194344],\n", + " dtype='int64'), Int64Index([ 662, 3385, 9022, 15459, 20032, 22771, 23674, 26415,\n", + " 31002, 41532, 42442, 43350, 44259, 48854, 50725, 73074,\n", + " 73980, 74914, 78523, 80351, 90543, 98014, 104460, 116473,\n", + " 126100, 133394, 140617, 146049, 161115, 163829, 171646, 174314,\n", + " 181073, 188914, 191645, 193447, 194345],\n", + " dtype='int64'), Int64Index([ 663, 3386, 9023, 15460, 20033, 22772, 23675, 26416,\n", + " 31003, 41533, 42443, 43351, 44260, 48855, 50726, 73075,\n", + " 73981, 74915, 78524, 80352, 90544, 98015, 104461, 116474,\n", + " 126101, 133395, 140618, 146050, 161116, 163830, 171647, 174315,\n", + " 181074, 188915, 191646, 193448, 194346],\n", + " dtype='int64'), Int64Index([ 664, 3387, 9024, 15461, 20034, 22773, 23676, 26417,\n", + " 31004, 41534, 42444, 43352, 44261, 48856, 50727, 73076,\n", + " 73982, 74916, 78525, 80353, 90545, 98016, 104462, 116475,\n", + " 126102, 133396, 140619, 146051, 161117, 163831, 171648, 174316,\n", + " 181075, 188916, 191647, 193449, 194347],\n", + " dtype='int64'), Int64Index([ 665, 3388, 9025, 15462, 20035, 22774, 23677, 26418,\n", + " 31005, 41535, 42445, 43353, 44262, 48857, 50728, 73077,\n", + " 73983, 74917, 78526, 80354, 90546, 98017, 104463, 116476,\n", + " 126103, 133397, 140620, 146052, 161118, 163832, 171649, 174317,\n", + " 181076, 188917, 191648, 193450, 194348],\n", + " dtype='int64'), Int64Index([ 666, 3389, 9026, 15463, 20036, 22775, 23678, 26419,\n", + " 31006, 41536, 42446, 43354, 44263, 48858, 50729, 73078,\n", + " 73984, 74918, 78527, 80355, 90547, 98018, 104464, 116477,\n", + " 126104, 133398, 140621, 146053, 161119, 163833, 171650, 174318,\n", + " 181077, 188918, 191649, 193451, 194349],\n", + " dtype='int64'), Int64Index([ 667, 3390, 9027, 15464, 20037, 22776, 23679, 26420,\n", + " 31007, 41537, 42447, 43355, 44264, 48859, 50730, 73079,\n", + " 73985, 74919, 78528, 80356, 90548, 98019, 104465, 116478,\n", + " 126105, 133399, 140622, 146054, 161120, 163834, 171651, 174319,\n", + " 181078, 188919, 191650, 193452, 194350],\n", + " dtype='int64'), Int64Index([ 668, 3391, 9028, 15465, 20038, 22777, 23680, 26421,\n", + " 31008, 41538, 42448, 43356, 44265, 48860, 50731, 73080,\n", + " 73986, 74920, 78529, 80357, 90549, 98020, 104466, 116479,\n", + " 126106, 133400, 140623, 146055, 161121, 163835, 171652, 174320,\n", + " 181079, 188920, 191651, 193453, 194351],\n", + " dtype='int64'), Int64Index([ 669, 3392, 9029, 15466, 20039, 22778, 23681, 26422,\n", + " 31009, 41539, 42449, 43357, 44266, 48861, 50732, 73081,\n", + " 73987, 74921, 78530, 80358, 90550, 98021, 104467, 116480,\n", + " 126107, 133401, 140624, 146056, 161122, 163836, 171653, 174321,\n", + " 181080, 188921, 191652, 193454, 194352],\n", + " dtype='int64'), Int64Index([ 670, 3393, 9030, 15467, 20040, 22779, 23682, 26423,\n", + " 31010, 41540, 42450, 43358, 44267, 48862, 50733, 73082,\n", + " 73988, 74922, 78531, 80359, 90551, 98022, 104468, 116481,\n", + " 126108, 133402, 140625, 146057, 161123, 163837, 171654, 174322,\n", + " 181081, 188922, 191653, 193455, 194353],\n", + " dtype='int64'), Int64Index([ 671, 3394, 9031, 15468, 20041, 22780, 23683, 26424,\n", + " 31011, 41541, 42451, 43359, 44268, 48863, 50734, 73083,\n", + " 73989, 74923, 78532, 80360, 90552, 98023, 104469, 116482,\n", + " 126109, 133403, 140626, 146058, 161124, 163838, 171655, 174323,\n", + " 181082, 188923, 191654, 193456, 194354],\n", + " dtype='int64'), Int64Index([ 672, 3395, 9032, 15469, 20042, 22781, 23684, 26425,\n", + " 31012, 41542, 42452, 43360, 44269, 48864, 50735, 73084,\n", + " 73990, 74924, 78533, 80361, 90553, 98024, 104470, 116483,\n", + " 126110, 133404, 140627, 146059, 161125, 163839, 171656, 174324,\n", + " 181083, 188924, 191655, 193457, 194355],\n", + " dtype='int64'), Int64Index([ 673, 3396, 9033, 15470, 20043, 22782, 23685, 26426,\n", + " 31013, 41543, 42453, 43361, 44270, 48865, 50736, 73085,\n", + " 73991, 74925, 78534, 80362, 90554, 98025, 104471, 116484,\n", + " 126111, 133405, 140628, 146060, 161126, 163840, 171657, 174325,\n", + " 181084, 188925, 191656, 193458, 194356],\n", + " dtype='int64'), Int64Index([ 674, 3397, 9034, 15471, 20044, 22783, 23686, 26427,\n", + " 31014, 41544, 42454, 43362, 44271, 48866, 50737, 73086,\n", + " 73992, 74926, 78535, 80363, 90555, 98026, 104472, 116485,\n", + " 126112, 133406, 140629, 146061, 161127, 163841, 171658, 174326,\n", + " 181085, 188926, 191657, 193459, 194357],\n", + " dtype='int64'), Int64Index([ 675, 3398, 9035, 15472, 20045, 22784, 23687, 26428,\n", + " 31015, 41545, 42455, 43363, 44272, 48867, 50738, 73087,\n", + " 73993, 74927, 78536, 80364, 90556, 98027, 104473, 116486,\n", + " 126113, 133407, 140630, 146062, 161128, 163842, 171659, 174327,\n", + " 181086, 188927, 191658, 193460, 194358],\n", + " dtype='int64'), Int64Index([ 676, 3399, 9036, 15473, 20046, 22785, 23688, 26429,\n", + " 31016, 41546, 42456, 43364, 44273, 48868, 50739, 73088,\n", + " 73994, 74928, 78537, 80365, 90557, 98028, 104474, 116487,\n", + " 126114, 133408, 140631, 146063, 161129, 163843, 171660, 174328,\n", + " 181087, 188928, 191659, 193461, 194359],\n", + " dtype='int64'), Int64Index([ 677, 3400, 9037, 15474, 20047, 22786, 23689, 26430,\n", + " 31017, 41547, 42457, 43365, 44274, 48869, 50740, 73089,\n", + " 73995, 74929, 78538, 80366, 90558, 98029, 104475, 116488,\n", + " 126115, 133409, 140632, 146064, 161130, 163844, 171661, 174329,\n", + " 181088, 188929, 191660, 193462, 194360],\n", + " dtype='int64'), Int64Index([ 678, 3401, 9038, 15475, 20048, 22787, 23690, 26431,\n", + " 31018, 41548, 42458, 43366, 44275, 48870, 50741, 73090,\n", + " 73996, 74930, 78539, 80367, 90559, 98030, 104476, 116489,\n", + " 126116, 133410, 140633, 146065, 161131, 163845, 171662, 174330,\n", + " 181089, 188930, 191661, 193463, 194361],\n", + " dtype='int64'), Int64Index([ 679, 3402, 9039, 15476, 20049, 22788, 23691, 26432,\n", + " 31019, 41549, 42459, 43367, 44276, 48871, 50742, 73091,\n", + " 73997, 74931, 78540, 80368, 90560, 98031, 104477, 116490,\n", + " 126117, 133411, 140634, 146066, 161132, 163846, 171663, 174331,\n", + " 181090, 188931, 191662, 193464, 194362],\n", + " dtype='int64'), Int64Index([ 680, 3403, 9040, 15477, 20050, 22789, 23692, 26433,\n", + " 31020, 41550, 42460, 43368, 44277, 48872, 50743, 73092,\n", + " 73998, 74932, 78541, 80369, 90561, 98032, 104478, 116491,\n", + " 126118, 133412, 140635, 146067, 161133, 163847, 171664, 174332,\n", + " 181091, 188932, 191663, 193465, 194363],\n", + " dtype='int64'), Int64Index([ 681, 3404, 9041, 15478, 20051, 22790, 23693, 26434,\n", + " 31021, 41551, 42461, 43369, 44278, 48873, 50744, 73093,\n", + " 73999, 74933, 78542, 80370, 90562, 98033, 104479, 116492,\n", + " 126119, 133413, 140636, 146068, 161134, 163848, 171665, 174333,\n", + " 181092, 188933, 191664, 193466, 194364],\n", + " dtype='int64'), Int64Index([ 682, 3405, 9042, 15479, 20052, 22791, 23694, 26435,\n", + " 31022, 41552, 42462, 43370, 44279, 48874, 50745, 73094,\n", + " 74000, 74934, 78543, 80371, 90563, 98034, 104480, 116493,\n", + " 126120, 133414, 140637, 146069, 161135, 163849, 171666, 174334,\n", + " 181093, 188934, 191665, 193467, 194365],\n", + " dtype='int64'), Int64Index([ 683, 3406, 9043, 15480, 20053, 22792, 23695, 26436,\n", + " 31023, 41553, 42463, 43371, 44280, 48875, 50746, 73095,\n", + " 74001, 74935, 78544, 80372, 90564, 98035, 104481, 116494,\n", + " 126121, 133415, 140638, 146070, 161136, 163850, 171667, 174335,\n", + " 181094, 188935, 191666, 193468, 194366],\n", + " dtype='int64'), Int64Index([ 684, 3407, 9044, 15481, 20054, 22793, 23696, 26437,\n", + " 31024, 41554, 42464, 43372, 44281, 48876, 50747, 73096,\n", + " 74002, 74936, 78545, 80373, 90565, 98036, 104482, 116495,\n", + " 126122, 133416, 140639, 146071, 161137, 163851, 171668, 174336,\n", + " 181095, 188936, 191667, 193469, 194367],\n", + " dtype='int64'), Int64Index([ 685, 3408, 9045, 15482, 20055, 22794, 23697, 26438,\n", + " 31025, 41555, 42465, 43373, 44282, 48877, 50748, 73097,\n", + " 74003, 74937, 78546, 80374, 90566, 98037, 104483, 116496,\n", + " 126123, 133417, 140640, 146072, 161138, 163852, 171669, 174337,\n", + " 181096, 188937, 191668, 193470, 194368],\n", + " dtype='int64'), Int64Index([ 686, 3409, 9046, 15483, 20056, 22795, 23698, 26439,\n", + " 31026, 41556, 42466, 43374, 44283, 48878, 50749, 73098,\n", + " 74004, 74938, 78547, 80375, 90567, 98038, 104484, 116497,\n", + " 126124, 133418, 140641, 146073, 161139, 163853, 171670, 174338,\n", + " 181097, 188938, 191669, 193471, 194369],\n", + " dtype='int64'), Int64Index([ 687, 3410, 9047, 15484, 20057, 22796, 23699, 26440,\n", + " 31027, 41557, 42467, 43375, 44284, 48879, 50750, 73099,\n", + " 74005, 74939, 78548, 80376, 90568, 98039, 104485, 116498,\n", + " 126125, 133419, 140642, 146074, 161140, 163854, 171671, 174339,\n", + " 181098, 188939, 191670, 193472, 194370],\n", + " dtype='int64'), Int64Index([ 688, 3411, 9048, 15485, 20058, 22797, 23700, 26441,\n", + " 31028, 41558, 42468, 43376, 44285, 48880, 50751, 73100,\n", + " 74006, 74940, 78549, 80377, 90569, 98040, 104486, 116499,\n", + " 126126, 133420, 140643, 146075, 161141, 163855, 171672, 174340,\n", + " 181099, 188940, 191671, 193473, 194371],\n", + " dtype='int64'), Int64Index([ 689, 3412, 9049, 15486, 20059, 22798, 23701, 26442,\n", + " 31029, 41559, 42469, 43377, 44286, 48881, 50752, 73101,\n", + " 74007, 74941, 78550, 80378, 90570, 98041, 104487, 116500,\n", + " 126127, 133421, 140644, 146076, 161142, 163856, 171673, 174341,\n", + " 181100, 188941, 191672, 193474, 194372],\n", + " dtype='int64'), Int64Index([ 690, 3413, 9050, 15487, 20060, 22799, 23702, 26443,\n", + " 31030, 41560, 42470, 43378, 44287, 48882, 50753, 73102,\n", + " 74008, 74942, 78551, 80379, 90571, 98042, 104488, 116501,\n", + " 126128, 133422, 140645, 146077, 161143, 163857, 171674, 174342,\n", + " 181101, 188942, 191673, 193475, 194373],\n", + " dtype='int64'), Int64Index([ 691, 3414, 9051, 15488, 20061, 22800, 23703, 26444,\n", + " 31031, 41561, 42471, 43379, 44288, 48883, 50754, 73103,\n", + " 74009, 74943, 78552, 80380, 90572, 98043, 104489, 116502,\n", + " 126129, 133423, 140646, 146078, 161144, 163858, 171675, 174343,\n", + " 181102, 188943, 191674, 193476, 194374],\n", + " dtype='int64'), Int64Index([ 692, 3415, 9052, 15489, 20062, 22801, 23704, 26445,\n", + " 31032, 41562, 42472, 43380, 44289, 48884, 50755, 73104,\n", + " 74010, 74944, 78553, 80381, 90573, 98044, 104490, 116503,\n", + " 126130, 133424, 140647, 146079, 161145, 163859, 171676, 174344,\n", + " 181103, 188944, 191675, 193477, 194375],\n", + " dtype='int64'), Int64Index([ 693, 3416, 9053, 15490, 20063, 22802, 23705, 26446,\n", + " 31033, 41563, 42473, 43381, 44290, 48885, 50756, 73105,\n", + " 74011, 74945, 78554, 80382, 90574, 98045, 104491, 116504,\n", + " 126131, 133425, 140648, 146080, 161146, 163860, 171677, 174345,\n", + " 181104, 188945, 191676, 193478, 194376],\n", + " dtype='int64'), Int64Index([ 694, 3417, 9054, 15491, 20064, 22803, 23706, 26447,\n", + " 31034, 41564, 42474, 43382, 44291, 48886, 50757, 73106,\n", + " 74012, 74946, 78555, 80383, 90575, 98046, 104492, 116505,\n", + " 126132, 133426, 140649, 146081, 161147, 163861, 171678, 174346,\n", + " 181105, 188946, 191677, 193479, 194377],\n", + " dtype='int64'), Int64Index([ 695, 3418, 9055, 15492, 20065, 22804, 23707, 26448,\n", + " 31035, 41565, 42475, 43383, 44292, 48887, 50758, 73107,\n", + " 74013, 74947, 78556, 80384, 90576, 98047, 104493, 116506,\n", + " 126133, 133427, 140650, 146082, 161148, 163862, 171679, 174347,\n", + " 181106, 188947, 191678, 193480, 194378],\n", + " dtype='int64'), Int64Index([ 696, 3419, 9056, 15493, 20066, 22805, 23708, 26449,\n", + " 31036, 41566, 42476, 43384, 44293, 48888, 50759, 73108,\n", + " 74014, 74948, 78557, 80385, 90577, 98048, 104494, 116507,\n", + " 126134, 133428, 140651, 146083, 161149, 163863, 171680, 174348,\n", + " 181107, 188948, 191679, 193481, 194379],\n", + " dtype='int64'), Int64Index([ 697, 3420, 9057, 15494, 20067, 22806, 23709, 26450,\n", + " 31037, 41567, 42477, 43385, 44294, 48889, 50760, 73109,\n", + " 74015, 74949, 78558, 80386, 90578, 98049, 104495, 116508,\n", + " 126135, 133429, 140652, 146084, 161150, 163864, 171681, 174349,\n", + " 181108, 188949, 191680, 193482, 194380],\n", + " dtype='int64'), Int64Index([ 698, 3421, 9058, 15495, 20068, 22807, 23710, 26451,\n", + " 31038, 41568, 42478, 43386, 44295, 48890, 50761, 73110,\n", + " 74016, 74950, 78559, 80387, 90579, 98050, 104496, 116509,\n", + " 126136, 133430, 140653, 146085, 161151, 163865, 171682, 174350,\n", + " 181109, 188950, 191681, 193483, 194381],\n", + " dtype='int64'), Int64Index([ 699, 3422, 9059, 15496, 20069, 22808, 23711, 26452,\n", + " 31039, 41569, 42479, 43387, 44296, 48891, 50762, 73111,\n", + " 74017, 74951, 78560, 80388, 90580, 98051, 104497, 116510,\n", + " 126137, 133431, 140654, 146086, 161152, 163866, 171683, 174351,\n", + " 181110, 188951, 191682, 193484, 194382],\n", + " dtype='int64'), Int64Index([ 700, 3423, 9060, 15497, 20070, 22809, 23712, 26453,\n", + " 31040, 41570, 42480, 43388, 44297, 48892, 50763, 73112,\n", + " 74018, 74952, 78561, 80389, 90581, 98052, 104498, 116511,\n", + " 126138, 133432, 140655, 146087, 161153, 163867, 171684, 174352,\n", + " 181111, 188952, 191683, 193485, 194383],\n", + " dtype='int64'), Int64Index([ 701, 3424, 9061, 15498, 20071, 22810, 23713, 26454,\n", + " 31041, 41571, 42481, 43389, 44298, 48893, 50764, 73113,\n", + " 74019, 74953, 78562, 80390, 90582, 98053, 104499, 116512,\n", + " 126139, 133433, 140656, 146088, 161154, 163868, 171685, 174353,\n", + " 181112, 188953, 191684, 193486, 194384],\n", + " dtype='int64'), Int64Index([ 702, 3425, 9062, 15499, 20072, 22811, 23714, 26455,\n", + " 31042, 41572, 42482, 43390, 44299, 48894, 50765, 73114,\n", + " 74020, 74954, 78563, 80391, 90583, 98054, 104500, 116513,\n", + " 126140, 133434, 140657, 146089, 161155, 163869, 171686, 174354,\n", + " 181113, 188954, 191685, 193487, 194385],\n", + " dtype='int64'), Int64Index([ 703, 3426, 9063, 15500, 20073, 22812, 23715, 26456,\n", + " 31043, 41573, 42483, 43391, 44300, 48895, 50766, 73115,\n", + " 74021, 74955, 78564, 80392, 90584, 98055, 104501, 116514,\n", + " 126141, 133435, 140658, 146090, 161156, 163870, 171687, 174355,\n", + " 181114, 188955, 191686, 193488, 194386],\n", + " dtype='int64'), Int64Index([ 704, 3427, 9064, 15501, 20074, 22813, 23716, 26457,\n", + " 31044, 41574, 42484, 43392, 44301, 48896, 50767, 73116,\n", + " 74022, 74956, 78565, 80393, 90585, 98056, 104502, 116515,\n", + " 126142, 133436, 140659, 146091, 161157, 163871, 171688, 174356,\n", + " 181115, 188956, 191687, 193489, 194387],\n", + " dtype='int64'), Int64Index([ 705, 3428, 9065, 15502, 20075, 22814, 23717, 26458,\n", + " 31045, 41575, 42485, 43393, 44302, 48897, 50768, 73117,\n", + " 74023, 74957, 78566, 80394, 90586, 98057, 104503, 116516,\n", + " 126143, 133437, 140660, 146092, 161158, 163872, 171689, 174357,\n", + " 181116, 188957, 191688, 193490, 194388],\n", + " dtype='int64'), Int64Index([ 706, 3429, 9066, 15503, 20076, 22815, 23718, 26459,\n", + " 31046, 41576, 42486, 43394, 44303, 48898, 50769, 73118,\n", + " 74024, 74958, 78567, 80395, 90587, 98058, 104504, 116517,\n", + " 126144, 133438, 140661, 146093, 161159, 163873, 171690, 174358,\n", + " 181117, 188958, 191689, 193491, 194389],\n", + " dtype='int64'), Int64Index([ 707, 3430, 9067, 15504, 20077, 22816, 23719, 26460,\n", + " 31047, 41577, 42487, 43395, 44304, 48899, 50770, 73119,\n", + " 74025, 74959, 78568, 80396, 90588, 98059, 104505, 116518,\n", + " 126145, 133439, 140662, 146094, 161160, 163874, 171691, 174359,\n", + " 181118, 188959, 191690, 193492, 194390],\n", + " dtype='int64'), Int64Index([ 708, 3431, 9068, 15505, 20078, 22817, 23720, 26461,\n", + " 31048, 41578, 42488, 43396, 44305, 48900, 50771, 73120,\n", + " 74026, 74960, 78569, 80397, 90589, 98060, 104506, 116519,\n", + " 126146, 133440, 140663, 146095, 161161, 163875, 171692, 174360,\n", + " 181119, 188960, 191691, 193493, 194391],\n", + " dtype='int64'), Int64Index([ 709, 3432, 9069, 15506, 20079, 22818, 23721, 26462,\n", + " 31049, 41579, 42489, 43397, 44306, 48901, 50772, 73121,\n", + " 74027, 74961, 78570, 80398, 90590, 98061, 104507, 116520,\n", + " 126147, 133441, 140664, 146096, 161162, 163876, 171693, 174361,\n", + " 181120, 188961, 191692, 193494, 194392],\n", + " dtype='int64'), Int64Index([ 710, 3433, 9070, 15507, 20080, 22819, 23722, 26463,\n", + " 31050, 41580, 42490, 43398, 44307, 48902, 50773, 73122,\n", + " 74028, 74962, 78571, 80399, 90591, 98062, 104508, 116521,\n", + " 126148, 133442, 140665, 146097, 161163, 163877, 171694, 174362,\n", + " 181121, 188962, 191693, 193495, 194393],\n", + " dtype='int64'), Int64Index([ 711, 3434, 9071, 15508, 20081, 22820, 23723, 26464,\n", + " 31051, 41581, 42491, 43399, 44308, 48903, 50774, 73123,\n", + " 74029, 74963, 78572, 80400, 90592, 98063, 104509, 116522,\n", + " 126149, 133443, 140666, 146098, 161164, 163878, 171695, 174363,\n", + " 181122, 188963, 191694, 193496, 194394],\n", + " dtype='int64'), Int64Index([ 712, 3435, 9072, 15509, 20082, 22821, 23724, 26465,\n", + " 31052, 41582, 42492, 43400, 44309, 48904, 50775, 73124,\n", + " 74030, 74964, 78573, 80401, 90593, 98064, 104510, 116523,\n", + " 126150, 133444, 140667, 146099, 161165, 163879, 171696, 174364,\n", + " 181123, 188964, 191695, 193497, 194395],\n", + " dtype='int64'), Int64Index([ 713, 3436, 9073, 15510, 20083, 22822, 23725, 26466,\n", + " 31053, 41583, 42493, 43401, 44310, 48905, 50776, 73125,\n", + " 74031, 74965, 78574, 80402, 90594, 98065, 104511, 116524,\n", + " 126151, 133445, 140668, 146100, 161166, 163880, 171697, 174365,\n", + " 181124, 188965, 191696, 193498, 194396],\n", + " dtype='int64'), Int64Index([ 714, 3437, 9074, 15511, 20084, 22823, 23726, 26467,\n", + " 31054, 41584, 42494, 43402, 44311, 48906, 50777, 73126,\n", + " 74032, 74966, 78575, 80403, 90595, 98066, 104512, 116525,\n", + " 126152, 133446, 140669, 146101, 161167, 163881, 171698, 174366,\n", + " 181125, 188966, 191697, 193499, 194397],\n", + " dtype='int64'), Int64Index([ 715, 3438, 9075, 15512, 20085, 22824, 23727, 26468,\n", + " 31055, 41585, 42495, 43403, 44312, 48907, 50778, 73127,\n", + " 74033, 74967, 78576, 80404, 90596, 98067, 104513, 116526,\n", + " 126153, 133447, 140670, 146102, 161168, 163882, 171699, 174367,\n", + " 181126, 188967, 191698, 193500, 194398],\n", + " dtype='int64'), Int64Index([ 716, 3439, 9076, 15513, 20086, 22825, 23728, 26469,\n", + " 31056, 41586, 42496, 43404, 44313, 48908, 50779, 73128,\n", + " 74034, 74968, 78577, 80405, 90597, 98068, 104514, 116527,\n", + " 126154, 133448, 140671, 146103, 161169, 163883, 171700, 174368,\n", + " 181127, 188968, 191699, 193501, 194399],\n", + " dtype='int64'), Int64Index([ 717, 3440, 9077, 15514, 20087, 22826, 23729, 26470,\n", + " 31057, 41587, 42497, 43405, 44314, 48909, 50780, 73129,\n", + " 74035, 74969, 78578, 80406, 90598, 98069, 104515, 116528,\n", + " 126155, 133449, 140672, 146104, 161170, 163884, 171701, 174369,\n", + " 181128, 188969, 191700, 193502, 194400],\n", + " dtype='int64'), Int64Index([ 718, 3441, 9078, 15515, 20088, 22827, 23730, 26471,\n", + " 31058, 41588, 42498, 43406, 44315, 48910, 50781, 73130,\n", + " 74036, 74970, 78579, 80407, 90599, 98070, 104516, 116529,\n", + " 126156, 133450, 140673, 146105, 161171, 163885, 171702, 174370,\n", + " 181129, 188970, 191701, 193503, 194401],\n", + " dtype='int64'), Int64Index([ 719, 3442, 9079, 15516, 20089, 22828, 23731, 26472,\n", + " 31059, 41589, 42499, 43407, 44316, 48911, 50782, 73131,\n", + " 74037, 74971, 78580, 80408, 90600, 98071, 104517, 116530,\n", + " 126157, 133451, 140674, 146106, 161172, 163886, 171703, 174371,\n", + " 181130, 188971, 191702, 193504, 194402],\n", + " dtype='int64'), Int64Index([ 720, 3443, 9080, 15517, 20090, 22829, 23732, 26473,\n", + " 31060, 41590, 42500, 43408, 44317, 48912, 50783, 73132,\n", + " 74038, 74972, 78581, 80409, 90601, 98072, 104518, 116531,\n", + " 126158, 133452, 140675, 146107, 161173, 163887, 171704, 174372,\n", + " 181131, 188972, 191703, 193505, 194403],\n", + " dtype='int64'), Int64Index([ 721, 3444, 9081, 15518, 20091, 22830, 23733, 26474,\n", + " 31061, 41591, 42501, 43409, 44318, 48913, 50784, 73133,\n", + " 74039, 74973, 78582, 80410, 90602, 98073, 104519, 116532,\n", + " 126159, 133453, 140676, 146108, 161174, 163888, 171705, 174373,\n", + " 181132, 188973, 191704, 193506, 194404],\n", + " dtype='int64'), Int64Index([ 722, 3445, 9082, 15519, 20092, 22831, 23734, 26475,\n", + " 31062, 41592, 42502, 43410, 44319, 48914, 50785, 73134,\n", + " 74040, 74974, 78583, 80411, 90603, 98074, 104520, 116533,\n", + " 126160, 133454, 140677, 146109, 161175, 163889, 171706, 174374,\n", + " 181133, 188974, 191705, 193507, 194405],\n", + " dtype='int64'), Int64Index([ 723, 3446, 9083, 15520, 20093, 22832, 23735, 26476,\n", + " 31063, 41593, 42503, 43411, 44320, 48915, 50786, 73135,\n", + " 74041, 74975, 78584, 80412, 90604, 98075, 104521, 116534,\n", + " 126161, 133455, 140678, 146110, 161176, 163890, 171707, 174375,\n", + " 181134, 188975, 191706, 193508, 194406],\n", + " dtype='int64'), Int64Index([ 724, 3447, 9084, 15521, 20094, 22833, 23736, 26477,\n", + " 31064, 41594, 42504, 43412, 44321, 48916, 50787, 73136,\n", + " 74042, 74976, 78585, 80413, 90605, 98076, 104522, 116535,\n", + " 126162, 133456, 140679, 146111, 161177, 163891, 171708, 174376,\n", + " 181135, 188976, 191707, 193509, 194407],\n", + " dtype='int64'), Int64Index([ 725, 3448, 9085, 15522, 20095, 22834, 23737, 26478,\n", + " 31065, 41595, 42505, 43413, 44322, 48917, 50788, 73137,\n", + " 74043, 74977, 78586, 80414, 90606, 98077, 104523, 116536,\n", + " 126163, 133457, 140680, 146112, 161178, 163892, 171709, 174377,\n", + " 181136, 188977, 191708, 193510, 194408],\n", + " dtype='int64'), Int64Index([ 726, 3449, 9086, 15523, 20096, 22835, 23738, 26479,\n", + " 31066, 41596, 42506, 43414, 44323, 48918, 50789, 73138,\n", + " 74044, 74978, 78587, 80415, 90607, 98078, 104524, 116537,\n", + " 126164, 133458, 140681, 146113, 161179, 163893, 171710, 174378,\n", + " 181137, 188978, 191709, 193511, 194409],\n", + " dtype='int64'), Int64Index([ 727, 3450, 9087, 15524, 20097, 22836, 23739, 26480,\n", + " 31067, 41597, 42507, 43415, 44324, 48919, 50790, 73139,\n", + " 74045, 74979, 78588, 80416, 90608, 98079, 104525, 116538,\n", + " 126165, 133459, 140682, 146114, 161180, 163894, 171711, 174379,\n", + " 181138, 188979, 191710, 193512, 194410],\n", + " dtype='int64'), Int64Index([ 728, 3451, 9088, 15525, 20098, 22837, 23740, 26481,\n", + " 31068, 41598, 42508, 43416, 44325, 48920, 50791, 73140,\n", + " 74046, 74980, 78589, 80417, 90609, 98080, 104526, 116539,\n", + " 126166, 133460, 140683, 146115, 161181, 163895, 171712, 174380,\n", + " 181139, 188980, 191711, 193513, 194411],\n", + " dtype='int64'), Int64Index([ 729, 3452, 9089, 15526, 20099, 22838, 23741, 26482,\n", + " 31069, 41599, 42509, 43417, 44326, 48921, 50792, 73141,\n", + " 74047, 74981, 78590, 80418, 90610, 98081, 104527, 116540,\n", + " 126167, 133461, 140684, 146116, 161182, 163896, 171713, 174381,\n", + " 181140, 188981, 191712, 193514, 194412],\n", + " dtype='int64'), Int64Index([ 730, 3453, 9090, 15527, 20100, 22839, 23742, 26483,\n", + " 31070, 41600, 42510, 43418, 44327, 48922, 50793, 73142,\n", + " 74048, 74982, 78591, 80419, 90611, 98082, 104528, 116541,\n", + " 126168, 133462, 140685, 146117, 161183, 163897, 171714, 174382,\n", + " 181141, 188982, 191713, 193515, 194413],\n", + " dtype='int64'), Int64Index([ 731, 3454, 9091, 15528, 20101, 22840, 23743, 26484,\n", + " 31071, 41601, 42511, 43419, 44328, 48923, 50794, 73143,\n", + " 74049, 74983, 78592, 80420, 90612, 98083, 104529, 116542,\n", + " 126169, 133463, 140686, 146118, 161184, 163898, 171715, 174383,\n", + " 181142, 188983, 191714, 193516, 194414],\n", + " dtype='int64'), Int64Index([ 732, 3455, 9092, 15529, 20102, 22841, 23744, 26485,\n", + " 31072, 41602, 42512, 43420, 44329, 48924, 50795, 73144,\n", + " 74050, 74984, 78593, 80421, 90613, 98084, 104530, 116543,\n", + " 126170, 133464, 140687, 146119, 161185, 163899, 171716, 174384,\n", + " 181143, 188984, 191715, 193517, 194415],\n", + " dtype='int64'), Int64Index([ 733, 3456, 9093, 15530, 20103, 22842, 23745, 26486,\n", + " 31073, 41603, 42513, 43421, 44330, 48925, 50796, 73145,\n", + " 74051, 74985, 78594, 80422, 90614, 98085, 104531, 116544,\n", + " 126171, 133465, 140688, 146120, 161186, 163900, 171717, 174385,\n", + " 181144, 188985, 191716, 193518, 194416],\n", + " dtype='int64'), Int64Index([ 734, 3457, 9094, 15531, 20104, 22843, 23746, 26487,\n", + " 31074, 41604, 42514, 43422, 44331, 48926, 50797, 73146,\n", + " 74052, 74986, 78595, 80423, 90615, 98086, 104532, 116545,\n", + " 126172, 133466, 140689, 146121, 161187, 163901, 171718, 174386,\n", + " 181145, 188986, 191717, 193519, 194417],\n", + " dtype='int64'), Int64Index([ 735, 3458, 9095, 15532, 20105, 22844, 23747, 26488,\n", + " 31075, 41605, 42515, 43423, 44332, 48927, 50798, 73147,\n", + " 74053, 74987, 78596, 80424, 90616, 98087, 104533, 116546,\n", + " 126173, 133467, 140690, 146122, 161188, 163902, 171719, 174387,\n", + " 181146, 188987, 191718, 193520, 194418],\n", + " dtype='int64'), Int64Index([ 736, 3459, 9096, 15533, 20106, 22845, 23748, 26489,\n", + " 31076, 41606, 42516, 43424, 44333, 48928, 50799, 73148,\n", + " 74054, 74988, 78597, 80425, 90617, 98088, 104534, 116547,\n", + " 126174, 133468, 140691, 146123, 161189, 163903, 171720, 174388,\n", + " 181147, 188988, 191719, 193521, 194419],\n", + " dtype='int64'), Int64Index([ 737, 3460, 9097, 15534, 20107, 22846, 23749, 26490,\n", + " 31077, 41607, 42517, 43425, 44334, 48929, 50800, 73149,\n", + " 74055, 74989, 78598, 80426, 90618, 98089, 104535, 116548,\n", + " 126175, 133469, 140692, 146124, 161190, 163904, 171721, 174389,\n", + " 181148, 188989, 191720, 193522, 194420],\n", + " dtype='int64'), Int64Index([ 738, 3461, 9098, 15535, 20108, 22847, 23750, 26491,\n", + " 31078, 41608, 42518, 43426, 44335, 48930, 50801, 73150,\n", + " 74056, 74990, 78599, 80427, 90619, 98090, 104536, 116549,\n", + " 126176, 133470, 140693, 146125, 161191, 163905, 171722, 174390,\n", + " 181149, 188990, 191721, 193523, 194421],\n", + " dtype='int64'), Int64Index([ 739, 3462, 9099, 15536, 20109, 22848, 23751, 26492,\n", + " 31079, 41609, 42519, 43427, 44336, 48931, 50802, 73151,\n", + " 74057, 74991, 78600, 80428, 90620, 98091, 104537, 116550,\n", + " 126177, 133471, 140694, 146126, 161192, 163906, 171723, 174391,\n", + " 181150, 188991, 191722, 193524, 194422],\n", + " dtype='int64'), Int64Index([ 740, 3463, 9100, 15537, 20110, 22849, 23752, 26493,\n", + " 31080, 41610, 42520, 43428, 44337, 48932, 50803, 73152,\n", + " 74058, 74992, 78601, 80429, 90621, 98092, 104538, 116551,\n", + " 126178, 133472, 140695, 146127, 161193, 163907, 171724, 174392,\n", + " 181151, 188992, 191723, 193525, 194423],\n", + " dtype='int64'), Int64Index([ 741, 3464, 9101, 15538, 20111, 22850, 23753, 26494,\n", + " 31081, 41611, 42521, 43429, 44338, 48933, 50804, 73153,\n", + " 74059, 74993, 78602, 80430, 90622, 98093, 104539, 116552,\n", + " 126179, 133473, 140696, 146128, 161194, 163908, 171725, 174393,\n", + " 181152, 188993, 191724, 193526, 194424],\n", + " dtype='int64'), Int64Index([ 742, 3465, 9102, 15539, 20112, 22851, 23754, 26495,\n", + " 31082, 41612, 42522, 43430, 44339, 48934, 50805, 73154,\n", + " 74060, 74994, 78603, 80431, 90623, 98094, 104540, 116553,\n", + " 126180, 133474, 140697, 146129, 161195, 163909, 171726, 174394,\n", + " 181153, 188994, 191725, 193527, 194425],\n", + " dtype='int64'), Int64Index([ 743, 3466, 9103, 15540, 20113, 22852, 23755, 26496,\n", + " 31083, 41613, 42523, 43431, 44340, 48935, 50806, 73155,\n", + " 74061, 74995, 78604, 80432, 90624, 98095, 104541, 116554,\n", + " 126181, 133475, 140698, 146130, 161196, 163910, 171727, 174395,\n", + " 181154, 188995, 191726, 193528, 194426],\n", + " dtype='int64'), Int64Index([ 744, 3467, 9104, 15541, 20114, 22853, 23756, 26497,\n", + " 31084, 41614, 42524, 43432, 44341, 48936, 50807, 73156,\n", + " 74062, 74996, 78605, 80433, 90625, 98096, 104542, 116555,\n", + " 126182, 133476, 140699, 146131, 161197, 163911, 171728, 174396,\n", + " 181155, 188996, 191727, 193529, 194427],\n", + " dtype='int64'), Int64Index([ 745, 3468, 9105, 15542, 20115, 22854, 23757, 26498,\n", + " 31085, 41615, 42525, 43433, 44342, 48937, 50808, 73157,\n", + " 74063, 74997, 78606, 80434, 90626, 98097, 104543, 116556,\n", + " 126183, 133477, 140700, 146132, 161198, 163912, 171729, 174397,\n", + " 181156, 188997, 191728, 193530, 194428],\n", + " dtype='int64'), Int64Index([ 746, 3469, 9106, 15543, 20116, 22855, 23758, 26499,\n", + " 31086, 41616, 42526, 43434, 44343, 48938, 50809, 73158,\n", + " 74064, 74998, 78607, 80435, 90627, 98098, 104544, 116557,\n", + " 126184, 133478, 140701, 146133, 161199, 163913, 171730, 174398,\n", + " 181157, 188998, 191729, 193531, 194429],\n", + " dtype='int64'), Int64Index([ 747, 3470, 9107, 15544, 20117, 22856, 23759, 26500,\n", + " 31087, 41617, 42527, 43435, 44344, 48939, 50810, 73159,\n", + " 74065, 74999, 78608, 80436, 90628, 98099, 104545, 116558,\n", + " 126185, 133479, 140702, 146134, 161200, 163914, 171731, 174399,\n", + " 181158, 188999, 191730, 193532, 194430],\n", + " dtype='int64'), Int64Index([ 748, 3471, 9108, 15545, 20118, 22857, 23760, 26501,\n", + " 31088, 41618, 42528, 43436, 44345, 48940, 50811, 73160,\n", + " 74066, 75000, 78609, 80437, 90629, 98100, 104546, 116559,\n", + " 126186, 133480, 140703, 146135, 161201, 163915, 171732, 174400,\n", + " 181159, 189000, 191731, 193533, 194431],\n", + " dtype='int64'), Int64Index([ 749, 3472, 9109, 15546, 20119, 22858, 23761, 26502,\n", + " 31089, 41619, 42529, 43437, 44346, 48941, 50812, 73161,\n", + " 74067, 75001, 78610, 80438, 90630, 98101, 104547, 116560,\n", + " 126187, 133481, 140704, 146136, 161202, 163916, 171733, 174401,\n", + " 181160, 189001, 191732, 193534, 194432],\n", + " dtype='int64'), Int64Index([ 750, 3473, 9110, 15547, 20120, 22859, 23762, 26503,\n", + " 31090, 41620, 42530, 43438, 44347, 48942, 50813, 73162,\n", + " 74068, 75002, 78611, 80439, 90631, 98102, 104548, 116561,\n", + " 126188, 133482, 140705, 146137, 161203, 163917, 171734, 174402,\n", + " 181161, 189002, 191733, 193535, 194433],\n", + " dtype='int64'), Int64Index([ 751, 3474, 9111, 15548, 20121, 22860, 23763, 26504,\n", + " 31091, 41621, 42531, 43439, 44348, 48943, 50814, 73163,\n", + " 74069, 75003, 78612, 80440, 90632, 98103, 104549, 116562,\n", + " 126189, 133483, 140706, 146138, 161204, 163918, 171735, 174403,\n", + " 181162, 189003, 191734, 193536, 194434],\n", + " dtype='int64'), Int64Index([ 752, 3475, 9112, 15549, 20122, 22861, 23764, 26505,\n", + " 31092, 41622, 42532, 43440, 44349, 48944, 50815, 73164,\n", + " 74070, 75004, 78613, 80441, 90633, 98104, 104550, 116563,\n", + " 126190, 133484, 140707, 146139, 161205, 163919, 171736, 174404,\n", + " 181163, 189004, 191735, 193537, 194435],\n", + " dtype='int64'), Int64Index([ 753, 3476, 9113, 15550, 20123, 22862, 23765, 26506,\n", + " 31093, 41623, 42533, 43441, 44350, 48945, 50816, 73165,\n", + " 74071, 75005, 78614, 80442, 90634, 98105, 104551, 116564,\n", + " 126191, 133485, 140708, 146140, 161206, 163920, 171737, 174405,\n", + " 181164, 189005, 191736, 193538, 194436],\n", + " dtype='int64'), Int64Index([ 754, 3477, 9114, 15551, 20124, 22863, 23766, 26507,\n", + " 31094, 41624, 42534, 43442, 44351, 48946, 50817, 73166,\n", + " 74072, 75006, 78615, 80443, 90635, 98106, 104552, 116565,\n", + " 126192, 133486, 140709, 146141, 161207, 163921, 171738, 174406,\n", + " 181165, 189006, 191737, 193539, 194437],\n", + " dtype='int64'), Int64Index([ 755, 3478, 9115, 15552, 20125, 22864, 23767, 26508,\n", + " 31095, 41625, 42535, 43443, 44352, 48947, 50818, 73167,\n", + " 74073, 75007, 78616, 80444, 90636, 98107, 104553, 116566,\n", + " 126193, 133487, 140710, 146142, 161208, 163922, 171739, 174407,\n", + " 181166, 189007, 191738, 193540, 194438],\n", + " dtype='int64'), Int64Index([ 756, 3479, 9116, 15553, 20126, 22865, 23768, 26509,\n", + " 31096, 41626, 42536, 43444, 44353, 48948, 50819, 73168,\n", + " 74074, 75008, 78617, 80445, 90637, 98108, 104554, 116567,\n", + " 126194, 133488, 140711, 146143, 161209, 163923, 171740, 174408,\n", + " 181167, 189008, 191739, 193541, 194439],\n", + " dtype='int64'), Int64Index([ 757, 3480, 9117, 15554, 20127, 22866, 23769, 26510,\n", + " 31097, 41627, 42537, 43445, 44354, 48949, 50820, 73169,\n", + " 74075, 75009, 78618, 80446, 90638, 98109, 104555, 116568,\n", + " 126195, 133489, 140712, 146144, 161210, 163924, 171741, 174409,\n", + " 181168, 189009, 191740, 193542, 194440],\n", + " dtype='int64'), Int64Index([ 758, 3481, 9118, 15555, 20128, 22867, 23770, 26511,\n", + " 31098, 41628, 42538, 43446, 44355, 48950, 50821, 73170,\n", + " 74076, 75010, 78619, 80447, 90639, 98110, 104556, 116569,\n", + " 126196, 133490, 140713, 146145, 161211, 163925, 171742, 174410,\n", + " 181169, 189010, 191741, 193543, 194441],\n", + " dtype='int64'), Int64Index([ 759, 3482, 9119, 15556, 20129, 22868, 23771, 26512,\n", + " 31099, 41629, 42539, 43447, 44356, 48951, 50822, 73171,\n", + " 74077, 75011, 78620, 80448, 90640, 98111, 104557, 116570,\n", + " 126197, 133491, 140714, 146146, 161212, 163926, 171743, 174411,\n", + " 181170, 189011, 191742, 193544, 194442],\n", + " dtype='int64'), Int64Index([ 760, 3483, 9120, 15557, 20130, 22869, 23772, 26513,\n", + " 31100, 41630, 42540, 43448, 44357, 48952, 50823, 73172,\n", + " 74078, 75012, 78621, 80449, 90641, 98112, 104558, 116571,\n", + " 126198, 133492, 140715, 146147, 161213, 163927, 171744, 174412,\n", + " 181171, 189012, 191743, 193545, 194443],\n", + " dtype='int64'), Int64Index([ 761, 3484, 9121, 15558, 20131, 22870, 23773, 26514,\n", + " 31101, 41631, 42541, 43449, 44358, 48953, 50824, 73173,\n", + " 74079, 75013, 78622, 80450, 90642, 98113, 104559, 116572,\n", + " 126199, 133493, 140716, 146148, 161214, 163928, 171745, 174413,\n", + " 181172, 189013, 191744, 193546, 194444],\n", + " dtype='int64'), Int64Index([ 762, 3485, 9122, 15559, 20132, 22871, 23774, 26515,\n", + " 31102, 41632, 42542, 43450, 44359, 48954, 50825, 73174,\n", + " 74080, 75014, 78623, 80451, 90643, 98114, 104560, 116573,\n", + " 126200, 133494, 140717, 146149, 161215, 163929, 171746, 174414,\n", + " 181173, 189014, 191745, 193547, 194445],\n", + " dtype='int64'), Int64Index([ 763, 3486, 9123, 15560, 20133, 22872, 23775, 26516,\n", + " 31103, 41633, 42543, 43451, 44360, 48955, 50826, 73175,\n", + " 74081, 75015, 78624, 80452, 90644, 98115, 104561, 116574,\n", + " 126201, 133495, 140718, 146150, 161216, 163930, 171747, 174415,\n", + " 181174, 189015, 191746, 193548, 194446],\n", + " dtype='int64'), Int64Index([ 764, 3487, 9124, 15561, 20134, 22873, 23776, 26517,\n", + " 31104, 41634, 42544, 43452, 44361, 48956, 50827, 73176,\n", + " 74082, 75016, 78625, 80453, 90645, 98116, 104562, 116575,\n", + " 126202, 133496, 140719, 146151, 161217, 163931, 171748, 174416,\n", + " 181175, 189016, 191747, 193549, 194447],\n", + " dtype='int64'), Int64Index([ 765, 3488, 9125, 15562, 20135, 22874, 23777, 26518,\n", + " 31105, 41635, 42545, 43453, 44362, 48957, 50828, 73177,\n", + " 74083, 75017, 78626, 80454, 90646, 98117, 104563, 116576,\n", + " 126203, 133497, 140720, 146152, 161218, 163932, 171749, 174417,\n", + " 181176, 189017, 191748, 193550, 194448],\n", + " dtype='int64'), Int64Index([ 766, 3489, 9126, 15563, 20136, 22875, 23778, 26519,\n", + " 31106, 41636, 42546, 43454, 44363, 48958, 50829, 73178,\n", + " 74084, 75018, 78627, 80455, 90647, 98118, 104564, 116577,\n", + " 126204, 133498, 140721, 146153, 161219, 163933, 171750, 174418,\n", + " 181177, 189018, 191749, 193551, 194449],\n", + " dtype='int64'), Int64Index([ 767, 3490, 9127, 15564, 20137, 22876, 23779, 26520,\n", + " 31107, 41637, 42547, 43455, 44364, 48959, 50830, 73179,\n", + " 74085, 75019, 78628, 80456, 90648, 98119, 104565, 116578,\n", + " 126205, 133499, 140722, 146154, 161220, 163934, 171751, 174419,\n", + " 181178, 189019, 191750, 193552, 194450],\n", + " dtype='int64'), Int64Index([ 768, 3491, 9128, 15565, 20138, 22877, 23780, 26521,\n", + " 31108, 41638, 42548, 43456, 44365, 48960, 50831, 73180,\n", + " 74086, 75020, 78629, 80457, 90649, 98120, 104566, 116579,\n", + " 126206, 133500, 140723, 146155, 161221, 163935, 171752, 174420,\n", + " 181179, 189020, 191751, 193553, 194451],\n", + " dtype='int64'), Int64Index([ 769, 3492, 9129, 15566, 20139, 22878, 23781, 26522,\n", + " 31109, 41639, 42549, 43457, 44366, 48961, 50832, 73181,\n", + " 74087, 75021, 78630, 80458, 90650, 98121, 104567, 116580,\n", + " 126207, 133501, 140724, 146156, 161222, 163936, 171753, 174421,\n", + " 181180, 189021, 191752, 193554, 194452],\n", + " dtype='int64'), Int64Index([ 770, 3493, 9130, 15567, 20140, 22879, 23782, 26523,\n", + " 31110, 41640, 42550, 43458, 44367, 48962, 50833, 73182,\n", + " 74088, 75022, 78631, 80459, 90651, 98122, 104568, 116581,\n", + " 126208, 133502, 140725, 146157, 161223, 163937, 171754, 174422,\n", + " 181181, 189022, 191753, 193555, 194453],\n", + " dtype='int64'), Int64Index([ 771, 3494, 9131, 15568, 20141, 22880, 23783, 26524,\n", + " 31111, 41641, 42551, 43459, 44368, 48963, 50834, 73183,\n", + " 74089, 75023, 78632, 80460, 90652, 98123, 104569, 116582,\n", + " 126209, 133503, 140726, 146158, 161224, 163938, 171755, 174423,\n", + " 181182, 189023, 191754, 193556, 194454],\n", + " dtype='int64'), Int64Index([ 772, 3495, 9132, 15569, 20142, 22881, 23784, 26525,\n", + " 31112, 41642, 42552, 43460, 44369, 48964, 50835, 73184,\n", + " 74090, 75024, 78633, 80461, 90653, 98124, 104570, 116583,\n", + " 126210, 133504, 140727, 146159, 161225, 163939, 171756, 174424,\n", + " 181183, 189024, 191755, 193557, 194455],\n", + " dtype='int64'), Int64Index([ 773, 3496, 9133, 15570, 20143, 22882, 23785, 26526,\n", + " 31113, 41643, 42553, 43461, 44370, 48965, 50836, 73185,\n", + " 74091, 75025, 78634, 80462, 90654, 98125, 104571, 116584,\n", + " 126211, 133505, 140728, 146160, 161226, 163940, 171757, 174425,\n", + " 181184, 189025, 191756, 193558, 194456],\n", + " dtype='int64'), Int64Index([ 774, 3497, 9134, 15571, 20144, 22883, 23786, 26527,\n", + " 31114, 41644, 42554, 43462, 44371, 48966, 50837, 73186,\n", + " 74092, 75026, 78635, 80463, 90655, 98126, 104572, 116585,\n", + " 126212, 133506, 140729, 146161, 161227, 163941, 171758, 174426,\n", + " 181185, 189026, 191757, 193559, 194457],\n", + " dtype='int64'), Int64Index([ 775, 3498, 9135, 15572, 20145, 22884, 23787, 26528,\n", + " 31115, 41645, 42555, 43463, 44372, 48967, 50838, 73187,\n", + " 74093, 75027, 78636, 80464, 90656, 98127, 104573, 116586,\n", + " 126213, 133507, 140730, 146162, 161228, 163942, 171759, 174427,\n", + " 181186, 189027, 191758, 193560, 194458],\n", + " dtype='int64'), Int64Index([ 776, 3499, 9136, 15573, 20146, 22885, 23788, 26529,\n", + " 31116, 41646, 42556, 43464, 44373, 48968, 50839, 73188,\n", + " 74094, 75028, 78637, 80465, 90657, 98128, 104574, 116587,\n", + " 126214, 133508, 140731, 146163, 161229, 163943, 171760, 174428,\n", + " 181187, 189028, 191759, 193561, 194459],\n", + " dtype='int64'), Int64Index([ 777, 3500, 9137, 15574, 20147, 22886, 23789, 26530,\n", + " 31117, 41647, 42557, 43465, 44374, 48969, 50840, 73189,\n", + " 74095, 75029, 78638, 80466, 90658, 98129, 104575, 116588,\n", + " 126215, 133509, 140732, 146164, 161230, 163944, 171761, 174429,\n", + " 181188, 189029, 191760, 193562, 194460],\n", + " dtype='int64'), Int64Index([ 778, 3501, 9138, 15575, 20148, 22887, 23790, 26531,\n", + " 31118, 41648, 42558, 43466, 44375, 48970, 50841, 73190,\n", + " 74096, 75030, 78639, 80467, 90659, 98130, 104576, 116589,\n", + " 126216, 133510, 140733, 146165, 161231, 163945, 171762, 174430,\n", + " 181189, 189030, 191761, 193563, 194461],\n", + " dtype='int64'), Int64Index([ 779, 3502, 9139, 15576, 20149, 22888, 23791, 26532,\n", + " 31119, 41649, 42559, 43467, 44376, 48971, 50842, 73191,\n", + " 74097, 75031, 78640, 80468, 90660, 98131, 104577, 116590,\n", + " 126217, 133511, 140734, 146166, 161232, 163946, 171763, 174431,\n", + " 181190, 189031, 191762, 193564, 194462],\n", + " dtype='int64'), Int64Index([ 780, 3503, 9140, 15577, 20150, 22889, 23792, 26533,\n", + " 31120, 41650, 42560, 43468, 44377, 48972, 50843, 73192,\n", + " 74098, 75032, 78641, 80469, 90661, 98132, 104578, 116591,\n", + " 126218, 133512, 140735, 146167, 161233, 163947, 171764, 174432,\n", + " 181191, 189032, 191763, 193565, 194463],\n", + " dtype='int64'), Int64Index([ 781, 3504, 9141, 15578, 20151, 22890, 23793, 26534,\n", + " 31121, 41651, 42561, 43469, 44378, 48973, 50844, 73193,\n", + " 74099, 75033, 78642, 80470, 90662, 98133, 104579, 116592,\n", + " 126219, 133513, 140736, 146168, 161234, 163948, 171765, 174433,\n", + " 181192, 189033, 191764, 193566, 194464],\n", + " dtype='int64'), Int64Index([ 782, 3505, 9142, 15579, 20152, 22891, 23794, 26535,\n", + " 31122, 41652, 42562, 43470, 44379, 48974, 50845, 73194,\n", + " 74100, 75034, 78643, 80471, 90663, 98134, 104580, 116593,\n", + " 126220, 133514, 140737, 146169, 161235, 163949, 171766, 174434,\n", + " 181193, 189034, 191765, 193567, 194465],\n", + " dtype='int64'), Int64Index([ 783, 3506, 9143, 15580, 20153, 22892, 23795, 26536,\n", + " 31123, 41653, 42563, 43471, 44380, 48975, 50846, 73195,\n", + " 74101, 75035, 78644, 80472, 90664, 98135, 104581, 116594,\n", + " 126221, 133515, 140738, 146170, 161236, 163950, 171767, 174435,\n", + " 181194, 189035, 191766, 193568, 194466],\n", + " dtype='int64'), Int64Index([ 784, 3507, 9144, 15581, 20154, 22893, 23796, 26537,\n", + " 31124, 41654, 42564, 43472, 44381, 48976, 50847, 73196,\n", + " 74102, 75036, 78645, 80473, 90665, 98136, 104582, 116595,\n", + " 126222, 133516, 140739, 146171, 161237, 163951, 171768, 174436,\n", + " 181195, 189036, 191767, 193569, 194467],\n", + " dtype='int64'), Int64Index([ 785, 3508, 9145, 15582, 20155, 22894, 23797, 26538,\n", + " 31125, 41655, 42565, 43473, 44382, 48977, 50848, 73197,\n", + " 74103, 75037, 78646, 80474, 90666, 98137, 104583, 116596,\n", + " 126223, 133517, 140740, 146172, 161238, 163952, 171769, 174437,\n", + " 181196, 189037, 191768, 193570, 194468],\n", + " dtype='int64'), Int64Index([ 786, 3509, 9146, 15583, 20156, 22895, 23798, 26539,\n", + " 31126, 41656, 42566, 43474, 44383, 48978, 50849, 73198,\n", + " 74104, 75038, 78647, 80475, 90667, 98138, 104584, 116597,\n", + " 126224, 133518, 140741, 146173, 161239, 163953, 171770, 174438,\n", + " 181197, 189038, 191769, 193571, 194469],\n", + " dtype='int64'), Int64Index([ 787, 3510, 9147, 15584, 20157, 22896, 23799, 26540,\n", + " 31127, 41657, 42567, 43475, 44384, 48979, 50850, 73199,\n", + " 74105, 75039, 78648, 80476, 90668, 98139, 104585, 116598,\n", + " 126225, 133519, 140742, 146174, 161240, 163954, 171771, 174439,\n", + " 181198, 189039, 191770, 193572, 194470],\n", + " dtype='int64'), Int64Index([ 788, 3511, 9148, 15585, 20158, 22897, 23800, 26541,\n", + " 31128, 41658, 42568, 43476, 44385, 48980, 50851, 73200,\n", + " 74106, 75040, 78649, 80477, 90669, 98140, 104586, 116599,\n", + " 126226, 133520, 140743, 146175, 161241, 163955, 171772, 174440,\n", + " 181199, 189040, 191771, 193573, 194471],\n", + " dtype='int64'), Int64Index([ 789, 3512, 9149, 15586, 20159, 22898, 23801, 26542,\n", + " 31129, 41659, 42569, 43477, 44386, 48981, 50852, 73201,\n", + " 74107, 75041, 78650, 80478, 90670, 98141, 104587, 116600,\n", + " 126227, 133521, 140744, 146176, 161242, 163956, 171773, 174441,\n", + " 181200, 189041, 191772, 193574, 194472],\n", + " dtype='int64'), Int64Index([ 790, 3513, 9150, 15587, 20160, 22899, 23802, 26543,\n", + " 31130, 41660, 42570, 43478, 44387, 48982, 50853, 73202,\n", + " 74108, 75042, 78651, 80479, 90671, 98142, 104588, 116601,\n", + " 126228, 133522, 140745, 146177, 161243, 163957, 171774, 174442,\n", + " 181201, 189042, 191773, 193575, 194473],\n", + " dtype='int64'), Int64Index([ 791, 3514, 9151, 15588, 20161, 22900, 23803, 26544,\n", + " 31131, 41661, 42571, 43479, 44388, 48983, 50854, 73203,\n", + " 74109, 75043, 78652, 80480, 90672, 98143, 104589, 116602,\n", + " 126229, 133523, 140746, 146178, 161244, 163958, 171775, 174443,\n", + " 181202, 189043, 191774, 193576, 194474],\n", + " dtype='int64'), Int64Index([ 792, 3515, 9152, 15589, 20162, 22901, 23804, 26545,\n", + " 31132, 41662, 42572, 43480, 44389, 48984, 50855, 73204,\n", + " 74110, 75044, 78653, 80481, 90673, 98144, 104590, 116603,\n", + " 126230, 133524, 140747, 146179, 161245, 163959, 171776, 174444,\n", + " 181203, 189044, 191775, 193577, 194475],\n", + " dtype='int64'), Int64Index([ 793, 3516, 9153, 15590, 20163, 22902, 23805, 26546,\n", + " 31133, 41663, 42573, 43481, 44390, 48985, 50856, 73205,\n", + " 74111, 75045, 78654, 80482, 90674, 98145, 104591, 116604,\n", + " 126231, 133525, 140748, 146180, 161246, 163960, 171777, 174445,\n", + " 181204, 189045, 191776, 193578, 194476],\n", + " dtype='int64'), Int64Index([ 794, 3517, 9154, 15591, 20164, 22903, 23806, 26547,\n", + " 31134, 41664, 42574, 43482, 44391, 48986, 50857, 73206,\n", + " 74112, 75046, 78655, 80483, 90675, 98146, 104592, 116605,\n", + " 126232, 133526, 140749, 146181, 161247, 163961, 171778, 174446,\n", + " 181205, 189046, 191777, 193579, 194477],\n", + " dtype='int64'), Int64Index([ 795, 3518, 9155, 15592, 20165, 22904, 23807, 26548,\n", + " 31135, 41665, 42575, 43483, 44392, 48987, 50858, 73207,\n", + " 74113, 75047, 78656, 80484, 90676, 98147, 104593, 116606,\n", + " 126233, 133527, 140750, 146182, 161248, 163962, 171779, 174447,\n", + " 181206, 189047, 191778, 193580, 194478],\n", + " dtype='int64'), Int64Index([ 796, 3519, 9156, 15593, 20166, 22905, 23808, 26549,\n", + " 31136, 41666, 42576, 43484, 44393, 48988, 50859, 73208,\n", + " 74114, 75048, 78657, 80485, 90677, 98148, 104594, 116607,\n", + " 126234, 133528, 140751, 146183, 161249, 163963, 171780, 174448,\n", + " 181207, 189048, 191779, 193581, 194479],\n", + " dtype='int64'), Int64Index([ 797, 3520, 9157, 15594, 20167, 22906, 23809, 26550,\n", + " 31137, 41667, 42577, 43485, 44394, 48989, 50860, 73209,\n", + " 74115, 75049, 78658, 80486, 90678, 98149, 104595, 116608,\n", + " 126235, 133529, 140752, 146184, 161250, 163964, 171781, 174449,\n", + " 181208, 189049, 191780, 193582, 194480],\n", + " dtype='int64'), Int64Index([ 798, 3521, 9158, 15595, 20168, 22907, 23810, 26551,\n", + " 31138, 41668, 42578, 43486, 44395, 48990, 50861, 73210,\n", + " 74116, 75050, 78659, 80487, 90679, 98150, 104596, 116609,\n", + " 126236, 133530, 140753, 146185, 161251, 163965, 171782, 174450,\n", + " 181209, 189050, 191781, 193583, 194481],\n", + " dtype='int64'), Int64Index([ 799, 3522, 9159, 15596, 20169, 22908, 23811, 26552,\n", + " 31139, 41669, 42579, 43487, 44396, 48991, 50862, 73211,\n", + " 74117, 75051, 78660, 80488, 90680, 98151, 104597, 116610,\n", + " 126237, 133531, 140754, 146186, 161252, 163966, 171783, 174451,\n", + " 181210, 189051, 191782, 193584, 194482],\n", + " dtype='int64'), Int64Index([ 800, 3523, 9160, 15597, 20170, 22909, 23812, 26553,\n", + " 31140, 41670, 42580, 43488, 44397, 48992, 50863, 73212,\n", + " 74118, 75052, 78661, 80489, 90681, 98152, 104598, 116611,\n", + " 126238, 133532, 140755, 146187, 161253, 163967, 171784, 174452,\n", + " 181211, 189052, 191783, 193585, 194483],\n", + " dtype='int64'), Int64Index([ 801, 3524, 9161, 15598, 20171, 22910, 23813, 26554,\n", + " 31141, 41671, 42581, 43489, 44398, 48993, 50864, 73213,\n", + " 74119, 75053, 78662, 80490, 90682, 98153, 104599, 116612,\n", + " 126239, 133533, 140756, 146188, 161254, 163968, 171785, 174453,\n", + " 181212, 189053, 191784, 193586, 194484],\n", + " dtype='int64'), Int64Index([ 802, 3525, 9162, 15599, 20172, 22911, 23814, 26555,\n", + " 31142, 41672, 42582, 43490, 44399, 48994, 50865, 73214,\n", + " 74120, 75054, 78663, 80491, 90683, 98154, 104600, 116613,\n", + " 126240, 133534, 140757, 146189, 161255, 163969, 171786, 174454,\n", + " 181213, 189054, 191785, 193587, 194485],\n", + " dtype='int64'), Int64Index([ 803, 3526, 9163, 15600, 20173, 22912, 23815, 26556,\n", + " 31143, 41673, 42583, 43491, 44400, 48995, 50866, 73215,\n", + " 74121, 75055, 78664, 80492, 90684, 98155, 104601, 116614,\n", + " 126241, 133535, 140758, 146190, 161256, 163970, 171787, 174455,\n", + " 181214, 189055, 191786, 193588, 194486],\n", + " dtype='int64'), Int64Index([ 804, 3527, 9164, 15601, 20174, 22913, 23816, 26557,\n", + " 31144, 41674, 42584, 43492, 44401, 48996, 50867, 73216,\n", + " 74122, 75056, 78665, 80493, 90685, 98156, 104602, 116615,\n", + " 126242, 133536, 140759, 146191, 161257, 163971, 171788, 174456,\n", + " 181215, 189056, 191787, 193589, 194487],\n", + " dtype='int64'), Int64Index([ 805, 3528, 9165, 15602, 20175, 22914, 23817, 26558,\n", + " 31145, 41675, 42585, 43493, 44402, 48997, 50868, 73217,\n", + " 74123, 75057, 78666, 80494, 90686, 98157, 104603, 116616,\n", + " 126243, 133537, 140760, 146192, 161258, 163972, 171789, 174457,\n", + " 181216, 189057, 191788, 193590, 194488],\n", + " dtype='int64'), Int64Index([ 806, 3529, 9166, 15603, 20176, 22915, 23818, 26559,\n", + " 31146, 41676, 42586, 43494, 44403, 48998, 50869, 73218,\n", + " 74124, 75058, 78667, 80495, 90687, 98158, 104604, 116617,\n", + " 126244, 133538, 140761, 146193, 161259, 163973, 171790, 174458,\n", + " 181217, 189058, 191789, 193591, 194489],\n", + " dtype='int64'), Int64Index([ 807, 3530, 9167, 15604, 20177, 22916, 23819, 26560,\n", + " 31147, 41677, 42587, 43495, 44404, 48999, 50870, 73219,\n", + " 74125, 75059, 78668, 80496, 90688, 98159, 104605, 116618,\n", + " 126245, 133539, 140762, 146194, 161260, 163974, 171791, 174459,\n", + " 181218, 189059, 191790, 193592, 194490],\n", + " dtype='int64'), Int64Index([ 808, 3531, 9168, 15605, 20178, 22917, 23820, 26561,\n", + " 31148, 41678, 42588, 43496, 44405, 49000, 50871, 73220,\n", + " 74126, 75060, 78669, 80497, 90689, 98160, 104606, 116619,\n", + " 126246, 133540, 140763, 146195, 161261, 163975, 171792, 174460,\n", + " 181219, 189060, 191791, 193593, 194491],\n", + " dtype='int64'), Int64Index([ 809, 3532, 9169, 15606, 20179, 22918, 23821, 26562,\n", + " 31149, 41679, 42589, 43497, 44406, 49001, 50872, 73221,\n", + " 74127, 75061, 78670, 80498, 90690, 98161, 104607, 116620,\n", + " 126247, 133541, 140764, 146196, 161262, 163976, 171793, 174461,\n", + " 181220, 189061, 191792, 193594, 194492],\n", + " dtype='int64'), Int64Index([ 810, 3533, 9170, 15607, 20180, 22919, 23822, 26563,\n", + " 31150, 41680, 42590, 43498, 44407, 49002, 50873, 73222,\n", + " 74128, 75062, 78671, 80499, 90691, 98162, 104608, 116621,\n", + " 126248, 133542, 140765, 146197, 161263, 163977, 171794, 174462,\n", + " 181221, 189062, 191793, 193595, 194493],\n", + " dtype='int64'), Int64Index([ 811, 3534, 9171, 15608, 20181, 22920, 23823, 26564,\n", + " 31151, 41681, 42591, 43499, 44408, 49003, 50874, 73223,\n", + " 74129, 75063, 78672, 80500, 90692, 98163, 104609, 116622,\n", + " 126249, 133543, 140766, 146198, 161264, 163978, 171795, 174463,\n", + " 181222, 189063, 191794, 193596, 194494],\n", + " dtype='int64'), Int64Index([ 812, 3535, 9172, 15609, 20182, 22921, 23824, 26565,\n", + " 31152, 41682, 42592, 43500, 44409, 49004, 50875, 73224,\n", + " 74130, 75064, 78673, 80501, 90693, 98164, 104610, 116623,\n", + " 126250, 133544, 140767, 146199, 161265, 163979, 171796, 174464,\n", + " 181223, 189064, 191795, 193597, 194495],\n", + " dtype='int64'), Int64Index([ 813, 3536, 9173, 15610, 20183, 22922, 23825, 26566,\n", + " 31153, 41683, 42593, 43501, 44410, 49005, 50876, 73225,\n", + " 74131, 75065, 78674, 80502, 90694, 98165, 104611, 116624,\n", + " 126251, 133545, 140768, 146200, 161266, 163980, 171797, 174465,\n", + " 181224, 189065, 191796, 193598, 194496],\n", + " dtype='int64'), Int64Index([ 814, 3537, 9174, 15611, 20184, 22923, 23826, 26567,\n", + " 31154, 41684, 42594, 43502, 44411, 49006, 50877, 73226,\n", + " 74132, 75066, 78675, 80503, 90695, 98166, 104612, 116625,\n", + " 126252, 133546, 140769, 146201, 161267, 163981, 171798, 174466,\n", + " 181225, 189066, 191797, 193599, 194497],\n", + " dtype='int64'), Int64Index([ 815, 3538, 9175, 15612, 20185, 22924, 23827, 26568,\n", + " 31155, 41685, 42595, 43503, 44412, 49007, 50878, 73227,\n", + " 74133, 75067, 78676, 80504, 90696, 98167, 104613, 116626,\n", + " 126253, 133547, 140770, 146202, 161268, 163982, 171799, 174467,\n", + " 181226, 189067, 191798, 193600, 194498],\n", + " dtype='int64'), Int64Index([ 816, 3539, 9176, 15613, 20186, 22925, 23828, 26569,\n", + " 31156, 41686, 42596, 43504, 44413, 49008, 50879, 73228,\n", + " 74134, 75068, 78677, 80505, 90697, 98168, 104614, 116627,\n", + " 126254, 133548, 140771, 146203, 161269, 163983, 171800, 174468,\n", + " 181227, 189068, 191799, 193601, 194499],\n", + " dtype='int64'), Int64Index([ 817, 3540, 9177, 15614, 20187, 22926, 23829, 26570,\n", + " 31157, 41687, 42597, 43505, 44414, 49009, 50880, 73229,\n", + " 74135, 75069, 78678, 80506, 90698, 98169, 104615, 116628,\n", + " 126255, 133549, 140772, 146204, 161270, 163984, 171801, 174469,\n", + " 181228, 189069, 191800, 193602, 194500],\n", + " dtype='int64'), Int64Index([ 818, 3541, 9178, 15615, 20188, 22927, 23830, 26571,\n", + " 31158, 41688, 42598, 43506, 44415, 49010, 50881, 73230,\n", + " 74136, 75070, 78679, 80507, 90699, 98170, 104616, 116629,\n", + " 126256, 133550, 140773, 146205, 161271, 163985, 171802, 174470,\n", + " 181229, 189070, 191801, 193603, 194501],\n", + " dtype='int64'), Int64Index([ 819, 3542, 9179, 15616, 20189, 22928, 23831, 26572,\n", + " 31159, 41689, 42599, 43507, 44416, 49011, 50882, 73231,\n", + " 74137, 75071, 78680, 80508, 90700, 98171, 104617, 116630,\n", + " 126257, 133551, 140774, 146206, 161272, 163986, 171803, 174471,\n", + " 181230, 189071, 191802, 193604, 194502],\n", + " dtype='int64'), Int64Index([ 820, 3543, 9180, 15617, 20190, 22929, 23832, 26573,\n", + " 31160, 41690, 42600, 43508, 44417, 49012, 50883, 73232,\n", + " 74138, 75072, 78681, 80509, 90701, 98172, 104618, 116631,\n", + " 126258, 133552, 140775, 146207, 161273, 163987, 171804, 174472,\n", + " 181231, 189072, 191803, 193605, 194503],\n", + " dtype='int64'), Int64Index([ 821, 3544, 9181, 15618, 20191, 22930, 23833, 26574,\n", + " 31161, 41691, 42601, 43509, 44418, 49013, 50884, 73233,\n", + " 74139, 75073, 78682, 80510, 90702, 98173, 104619, 116632,\n", + " 126259, 133553, 140776, 146208, 161274, 163988, 171805, 174473,\n", + " 181232, 189073, 191804, 193606, 194504],\n", + " dtype='int64'), Int64Index([ 822, 3545, 9182, 15619, 20192, 22931, 23834, 26575,\n", + " 31162, 41692, 42602, 43510, 44419, 49014, 50885, 73234,\n", + " 74140, 75074, 78683, 80511, 90703, 98174, 104620, 116633,\n", + " 126260, 133554, 140777, 146209, 161275, 163989, 171806, 174474,\n", + " 181233, 189074, 191805, 193607, 194505],\n", + " dtype='int64'), Int64Index([ 823, 3546, 9183, 15620, 20193, 22932, 23835, 26576,\n", + " 31163, 41693, 42603, 43511, 44420, 49015, 50886, 73235,\n", + " 74141, 75075, 78684, 80512, 90704, 98175, 104621, 116634,\n", + " 126261, 133555, 140778, 146210, 161276, 163990, 171807, 174475,\n", + " 181234, 189075, 191806, 193608, 194506],\n", + " dtype='int64'), Int64Index([ 824, 3547, 9184, 15621, 20194, 22933, 23836, 26577,\n", + " 31164, 41694, 42604, 43512, 44421, 49016, 50887, 73236,\n", + " 74142, 75076, 78685, 80513, 90705, 98176, 104622, 116635,\n", + " 126262, 133556, 140779, 146211, 161277, 163991, 171808, 174476,\n", + " 181235, 189076, 191807, 193609, 194507],\n", + " dtype='int64'), Int64Index([ 825, 3548, 9185, 15622, 20195, 22934, 23837, 26578,\n", + " 31165, 41695, 42605, 43513, 44422, 49017, 50888, 73237,\n", + " 74143, 75077, 78686, 80514, 90706, 98177, 104623, 116636,\n", + " 126263, 133557, 140780, 146212, 161278, 163992, 171809, 174477,\n", + " 181236, 189077, 191808, 193610, 194508],\n", + " dtype='int64'), Int64Index([ 826, 3549, 9186, 15623, 20196, 22935, 23838, 26579,\n", + " 31166, 41696, 42606, 43514, 44423, 49018, 50889, 73238,\n", + " 74144, 75078, 78687, 80515, 90707, 98178, 104624, 116637,\n", + " 126264, 133558, 140781, 146213, 161279, 163993, 171810, 174478,\n", + " 181237, 189078, 191809, 193611, 194509],\n", + " dtype='int64'), Int64Index([ 827, 3550, 9187, 15624, 20197, 22936, 23839, 26580,\n", + " 31167, 41697, 42607, 43515, 44424, 49019, 50890, 73239,\n", + " 74145, 75079, 78688, 80516, 90708, 98179, 104625, 116638,\n", + " 126265, 133559, 140782, 146214, 161280, 163994, 171811, 174479,\n", + " 181238, 189079, 191810, 193612, 194510],\n", + " dtype='int64'), Int64Index([ 828, 3551, 9188, 15625, 20198, 22937, 23840, 26581,\n", + " 31168, 41698, 42608, 43516, 44425, 49020, 50891, 73240,\n", + " 74146, 75080, 78689, 80517, 90709, 98180, 104626, 116639,\n", + " 126266, 133560, 140783, 161281, 163995, 171812, 174480, 181239,\n", + " 189080, 191811, 193613, 194511],\n", + " dtype='int64'), Int64Index([ 829, 3552, 9189, 15626, 20199, 22938, 23841, 26582,\n", + " 31169, 41699, 42609, 43517, 44426, 49021, 50892, 73241,\n", + " 74147, 75081, 78690, 80518, 90710, 98181, 104627, 116640,\n", + " 126267, 133561, 140784, 161282, 163996, 171813, 174481, 181240,\n", + " 189081, 191812, 193614],\n", + " dtype='int64'), Int64Index([ 830, 3553, 9190, 15627, 20200, 22939, 23842, 26583,\n", + " 31170, 41700, 42610, 43518, 44427, 49022, 50893, 73242,\n", + " 74148, 75082, 78691, 80519, 90711, 98182, 104628, 116641,\n", + " 126268, 133562, 140785, 161283, 163997, 171814, 174482, 181241,\n", + " 189082, 191813, 193615],\n", + " dtype='int64'), Int64Index([ 831, 3554, 9191, 15628, 20201, 22940, 23843, 26584,\n", + " 31171, 41701, 42611, 43519, 44428, 49023, 50894, 73243,\n", + " 74149, 75083, 78692, 80520, 90712, 98183, 104629, 116642,\n", + " 126269, 133563, 140786, 161284, 163998, 171815, 174483, 181242,\n", + " 189083, 191814, 193616],\n", + " dtype='int64'), Int64Index([ 832, 3555, 9192, 15629, 20202, 22941, 23844, 26585,\n", + " 31172, 41702, 42612, 43520, 44429, 49024, 50895, 73244,\n", + " 74150, 75084, 78693, 80521, 90713, 98184, 104630, 116643,\n", + " 126270, 133564, 140787, 161285, 163999, 171816, 174484, 181243,\n", + " 189084, 191815, 193617],\n", + " dtype='int64'), Int64Index([ 833, 3556, 9193, 15630, 20203, 22942, 23845, 26586,\n", + " 31173, 41703, 42613, 43521, 44430, 49025, 50896, 73245,\n", + " 74151, 75085, 78694, 80522, 90714, 98185, 104631, 116644,\n", + " 126271, 133565, 140788, 161286, 164000, 171817, 174485, 181244,\n", + " 189085, 191816, 193618],\n", + " dtype='int64'), Int64Index([ 834, 3557, 9194, 15631, 20204, 22943, 23846, 26587,\n", + " 31174, 41704, 42614, 43522, 44431, 49026, 50897, 73246,\n", + " 74152, 75086, 78695, 80523, 90715, 98186, 104632, 116645,\n", + " 126272, 133566, 140789, 161287, 164001, 171818, 174486, 181245,\n", + " 189086, 191817, 193619],\n", + " dtype='int64'), Int64Index([ 835, 3558, 9195, 15632, 20205, 22944, 23847, 26588,\n", + " 31175, 41705, 42615, 43523, 44432, 49027, 50898, 73247,\n", + " 74153, 75087, 78696, 80524, 90716, 98187, 104633, 116646,\n", + " 126273, 133567, 140790, 161288, 164002, 171819, 174487, 181246,\n", + " 189087, 191818, 193620],\n", + " dtype='int64'), Int64Index([ 836, 3559, 9196, 15633, 20206, 22945, 23848, 26589,\n", + " 31176, 41706, 42616, 43524, 44433, 49028, 50899, 73248,\n", + " 74154, 75088, 78697, 80525, 90717, 98188, 104634, 116647,\n", + " 126274, 133568, 140791, 161289, 164003, 171820, 174488, 181247,\n", + " 189088, 191819, 193621],\n", + " dtype='int64'), Int64Index([ 837, 3560, 9197, 15634, 20207, 22946, 23849, 26590,\n", + " 31177, 41707, 42617, 43525, 44434, 49029, 50900, 73249,\n", + " 74155, 75089, 78698, 80526, 90718, 98189, 104635, 116648,\n", + " 126275, 133569, 140792, 161290, 164004, 171821, 174489, 181248,\n", + " 189089, 191820, 193622],\n", + " dtype='int64'), Int64Index([ 838, 3561, 9198, 15635, 20208, 22947, 23850, 26591,\n", + " 31178, 41708, 42618, 43526, 44435, 49030, 50901, 73250,\n", + " 74156, 75090, 78699, 80527, 90719, 98190, 104636, 116649,\n", + " 126276, 133570, 140793, 161291, 164005, 171822, 174490, 181249,\n", + " 189090, 191821, 193623],\n", + " dtype='int64'), Int64Index([ 839, 3562, 9199, 15636, 20209, 22948, 23851, 26592,\n", + " 31179, 41709, 42619, 43527, 44436, 49031, 50902, 73251,\n", + " 74157, 75091, 78700, 80528, 90720, 98191, 104637, 116650,\n", + " 126277, 133571, 140794, 161292, 164006, 171823, 174491, 181250,\n", + " 189091, 191822, 193624],\n", + " dtype='int64'), Int64Index([ 840, 3563, 9200, 15637, 20210, 22949, 23852, 26593,\n", + " 31180, 41710, 42620, 43528, 44437, 49032, 50903, 73252,\n", + " 74158, 75092, 78701, 80529, 90721, 98192, 104638, 116651,\n", + " 126278, 133572, 140795, 161293, 164007, 171824, 174492, 181251,\n", + " 189092, 191823, 193625],\n", + " dtype='int64'), Int64Index([ 841, 3564, 9201, 15638, 20211, 22950, 23853, 26594,\n", + " 31181, 41711, 42621, 43529, 44438, 49033, 50904, 73253,\n", + " 74159, 75093, 78702, 80530, 90722, 98193, 104639, 116652,\n", + " 126279, 133573, 140796, 161294, 164008, 171825, 174493, 181252,\n", + " 189093, 191824, 193626],\n", + " dtype='int64'), Int64Index([ 842, 3565, 9202, 15639, 20212, 22951, 23854, 26595,\n", + " 31182, 41712, 42622, 43530, 44439, 49034, 50905, 73254,\n", + " 74160, 75094, 78703, 80531, 90723, 98194, 104640, 116653,\n", + " 126280, 133574, 140797, 161295, 164009, 171826, 174494, 181253,\n", + " 189094, 191825, 193627],\n", + " dtype='int64'), Int64Index([ 843, 3566, 9203, 15640, 20213, 22952, 23855, 26596,\n", + " 31183, 41713, 42623, 43531, 44440, 49035, 50906, 73255,\n", + " 74161, 75095, 78704, 80532, 90724, 98195, 104641, 116654,\n", + " 126281, 133575, 140798, 161296, 164010, 171827, 174495, 181254,\n", + " 189095, 191826, 193628],\n", + " dtype='int64'), Int64Index([ 844, 3567, 9204, 15641, 20214, 22953, 23856, 26597,\n", + " 31184, 41714, 42624, 43532, 44441, 49036, 50907, 73256,\n", + " 74162, 75096, 78705, 80533, 90725, 98196, 104642, 116655,\n", + " 126282, 133576, 140799, 161297, 164011, 171828, 174496, 181255,\n", + " 189096, 191827, 193629],\n", + " dtype='int64'), Int64Index([ 845, 3568, 9205, 15642, 20215, 22954, 23857, 26598,\n", + " 31185, 41715, 42625, 43533, 44442, 49037, 50908, 73257,\n", + " 74163, 75097, 78706, 80534, 90726, 98197, 104643, 116656,\n", + " 126283, 133577, 140800, 161298, 164012, 171829, 174497, 181256,\n", + " 189097, 191828, 193630],\n", + " dtype='int64'), Int64Index([ 846, 3569, 9206, 15643, 20216, 22955, 23858, 26599,\n", + " 31186, 41716, 42626, 43534, 44443, 49038, 50909, 73258,\n", + " 74164, 75098, 78707, 80535, 90727, 98198, 104644, 116657,\n", + " 126284, 133578, 140801, 161299, 164013, 171830, 174498, 181257,\n", + " 189098, 191829, 193631],\n", + " dtype='int64'), Int64Index([ 847, 3570, 9207, 15644, 20217, 22956, 23859, 26600,\n", + " 31187, 41717, 42627, 43535, 44444, 49039, 50910, 73259,\n", + " 74165, 75099, 78708, 80536, 90728, 98199, 104645, 116658,\n", + " 126285, 133579, 140802, 161300, 164014, 171831, 174499, 181258,\n", + " 189099, 191830, 193632],\n", + " dtype='int64'), Int64Index([ 848, 3571, 9208, 15645, 20218, 22957, 23860, 26601,\n", + " 31188, 41718, 42628, 43536, 44445, 49040, 50911, 73260,\n", + " 74166, 75100, 78709, 80537, 90729, 98200, 104646, 116659,\n", + " 126286, 133580, 140803, 161301, 164015, 171832, 174500, 181259,\n", + " 189100, 191831, 193633],\n", + " dtype='int64'), Int64Index([ 849, 3572, 9209, 15646, 20219, 22958, 23861, 26602,\n", + " 31189, 41719, 42629, 43537, 44446, 49041, 50912, 73261,\n", + " 74167, 75101, 78710, 80538, 90730, 98201, 104647, 116660,\n", + " 126287, 133581, 140804, 161302, 164016, 171833, 174501, 181260,\n", + " 189101, 191832, 193634],\n", + " dtype='int64'), Int64Index([ 850, 3573, 9210, 15647, 20220, 22959, 23862, 26603,\n", + " 31190, 41720, 42630, 43538, 44447, 49042, 50913, 73262,\n", + " 74168, 75102, 78711, 80539, 90731, 98202, 104648, 116661,\n", + " 126288, 133582, 140805, 161303, 164017, 171834, 174502, 181261,\n", + " 189102, 191833, 193635],\n", + " dtype='int64'), Int64Index([ 851, 3574, 9211, 15648, 20221, 22960, 23863, 26604,\n", + " 31191, 41721, 42631, 43539, 44448, 49043, 50914, 73263,\n", + " 74169, 75103, 78712, 80540, 90732, 98203, 104649, 116662,\n", + " 126289, 133583, 140806, 161304, 164018, 171835, 174503, 181262,\n", + " 189103, 191834, 193636],\n", + " dtype='int64'), Int64Index([ 852, 3575, 9212, 15649, 20222, 22961, 23864, 26605,\n", + " 31192, 41722, 42632, 43540, 44449, 49044, 50915, 73264,\n", + " 74170, 75104, 78713, 80541, 90733, 98204, 104650, 116663,\n", + " 126290, 133584, 140807, 161305, 164019, 171836, 174504, 181263,\n", + " 189104, 191835, 193637],\n", + " dtype='int64'), Int64Index([ 853, 3576, 9213, 15650, 20223, 22962, 23865, 26606,\n", + " 31193, 41723, 42633, 43541, 44450, 49045, 50916, 73265,\n", + " 74171, 75105, 78714, 80542, 90734, 98205, 104651, 116664,\n", + " 126291, 133585, 140808, 161306, 164020, 171837, 174505, 181264,\n", + " 189105, 191836, 193638],\n", + " dtype='int64'), Int64Index([ 854, 3577, 9214, 15651, 20224, 22963, 23866, 26607,\n", + " 31194, 41724, 42634, 43542, 44451, 49046, 50917, 73266,\n", + " 74172, 75106, 78715, 80543, 90735, 98206, 104652, 116665,\n", + " 126292, 133586, 140809, 161307, 164021, 171838, 174506, 181265,\n", + " 189106, 191837, 193639],\n", + " dtype='int64'), Int64Index([ 855, 3578, 9215, 15652, 20225, 22964, 23867, 26608,\n", + " 31195, 41725, 42635, 43543, 44452, 49047, 50918, 73267,\n", + " 74173, 75107, 78716, 80544, 90736, 98207, 104653, 116666,\n", + " 126293, 133587, 140810, 161308, 164022, 171839, 174507, 181266,\n", + " 189107, 191838, 193640],\n", + " dtype='int64'), Int64Index([ 856, 3579, 9216, 15653, 20226, 22965, 23868, 26609,\n", + " 31196, 41726, 42636, 43544, 44453, 49048, 50919, 73268,\n", + " 74174, 75108, 78717, 80545, 90737, 98208, 104654, 116667,\n", + " 126294, 133588, 140811, 161309, 164023, 171840, 174508, 181267,\n", + " 189108, 191839, 193641],\n", + " dtype='int64'), Int64Index([ 857, 3580, 9217, 15654, 20227, 22966, 23869, 26610,\n", + " 31197, 41727, 42637, 43545, 44454, 49049, 50920, 73269,\n", + " 74175, 75109, 78718, 80546, 90738, 98209, 104655, 116668,\n", + " 126295, 133589, 140812, 161310, 164024, 171841, 174509, 181268,\n", + " 189109, 191840, 193642],\n", + " dtype='int64'), Int64Index([ 858, 3581, 9218, 15655, 20228, 22967, 23870, 26611,\n", + " 31198, 41728, 42638, 43546, 44455, 49050, 50921, 73270,\n", + " 74176, 75110, 78719, 80547, 90739, 98210, 104656, 116669,\n", + " 126296, 133590, 140813, 161311, 164025, 171842, 174510, 181269,\n", + " 189110, 191841, 193643],\n", + " dtype='int64'), Int64Index([ 859, 3582, 9219, 15656, 20229, 22968, 23871, 26612,\n", + " 31199, 41729, 42639, 43547, 44456, 49051, 50922, 73271,\n", + " 74177, 75111, 78720, 80548, 90740, 98211, 104657, 116670,\n", + " 126297, 133591, 140814, 161312, 164026, 171843, 174511, 181270,\n", + " 189111, 191842, 193644],\n", + " dtype='int64'), Int64Index([ 860, 3583, 9220, 15657, 20230, 22969, 23872, 26613,\n", + " 31200, 41730, 42640, 43548, 44457, 49052, 50923, 73272,\n", + " 74178, 75112, 78721, 80549, 90741, 98212, 104658, 116671,\n", + " 126298, 133592, 140815, 161313, 164027, 171844, 174512, 181271,\n", + " 189112, 191843, 193645],\n", + " dtype='int64'), Int64Index([ 861, 3584, 9221, 15658, 20231, 22970, 23873, 26614,\n", + " 31201, 41731, 42641, 43549, 44458, 49053, 50924, 73273,\n", + " 74179, 75113, 78722, 80550, 90742, 98213, 104659, 116672,\n", + " 126299, 133593, 140816, 161314, 164028, 171845, 174513, 181272,\n", + " 189113, 191844, 193646],\n", + " dtype='int64'), Int64Index([ 862, 3585, 9222, 15659, 20232, 22971, 23874, 26615,\n", + " 31202, 41732, 42642, 43550, 44459, 49054, 50925, 73274,\n", + " 74180, 75114, 78723, 80551, 90743, 98214, 104660, 116673,\n", + " 126300, 133594, 140817, 161315, 164029, 171846, 174514, 181273,\n", + " 189114, 191845, 193647],\n", + " dtype='int64'), Int64Index([ 863, 3586, 9223, 15660, 20233, 22972, 23875, 26616,\n", + " 31203, 41733, 42643, 43551, 44460, 49055, 50926, 73275,\n", + " 74181, 75115, 78724, 80552, 90744, 98215, 104661, 116674,\n", + " 126301, 133595, 140818, 161316, 164030, 171847, 174515, 181274,\n", + " 189115, 191846, 193648],\n", + " dtype='int64'), Int64Index([ 864, 3587, 9224, 15661, 20234, 22973, 23876, 26617,\n", + " 31204, 41734, 42644, 43552, 44461, 49056, 50927, 73276,\n", + " 74182, 75116, 78725, 80553, 90745, 98216, 104662, 116675,\n", + " 126302, 133596, 140819, 161317, 164031, 171848, 174516, 181275,\n", + " 189116, 191847, 193649],\n", + " dtype='int64'), Int64Index([ 865, 3588, 9225, 15662, 20235, 22974, 23877, 26618,\n", + " 31205, 41735, 42645, 43553, 44462, 49057, 50928, 73277,\n", + " 74183, 75117, 78726, 80554, 90746, 98217, 104663, 116676,\n", + " 126303, 133597, 140820, 161318, 164032, 171849, 174517, 181276,\n", + " 189117, 191848, 193650],\n", + " dtype='int64'), Int64Index([ 866, 3589, 9226, 15663, 20236, 22975, 23878, 26619,\n", + " 31206, 41736, 42646, 43554, 44463, 49058, 50929, 73278,\n", + " 74184, 75118, 78727, 80555, 90747, 98218, 104664, 116677,\n", + " 126304, 133598, 140821, 161319, 164033, 171850, 174518, 181277,\n", + " 189118, 191849, 193651],\n", + " dtype='int64'), Int64Index([ 867, 3590, 9227, 15664, 20237, 22976, 23879, 26620,\n", + " 31207, 41737, 42647, 43555, 44464, 49059, 50930, 73279,\n", + " 74185, 75119, 78728, 80556, 90748, 98219, 104665, 116678,\n", + " 126305, 133599, 140822, 161320, 164034, 171851, 174519, 181278,\n", + " 189119, 191850, 193652],\n", + " dtype='int64'), Int64Index([ 868, 3591, 9228, 15665, 20238, 22977, 23880, 26621,\n", + " 31208, 41738, 42648, 43556, 44465, 49060, 50931, 73280,\n", + " 74186, 75120, 78729, 80557, 90749, 98220, 104666, 116679,\n", + " 126306, 133600, 140823, 161321, 164035, 171852, 174520, 181279,\n", + " 189120, 191851, 193653],\n", + " dtype='int64'), Int64Index([ 869, 3592, 9229, 15666, 20239, 22978, 23881, 26622,\n", + " 31209, 41739, 42649, 43557, 44466, 49061, 50932, 73281,\n", + " 74187, 75121, 78730, 80558, 90750, 98221, 104667, 116680,\n", + " 126307, 133601, 140824, 161322, 164036, 171853, 174521, 181280,\n", + " 189121, 191852, 193654],\n", + " dtype='int64'), Int64Index([ 870, 3593, 9230, 15667, 20240, 22979, 23882, 26623,\n", + " 31210, 41740, 42650, 43558, 44467, 49062, 50933, 73282,\n", + " 74188, 75122, 78731, 80559, 90751, 98222, 104668, 116681,\n", + " 126308, 133602, 140825, 161323, 164037, 171854, 174522, 181281,\n", + " 189122, 191853, 193655],\n", + " dtype='int64'), Int64Index([ 871, 3594, 9231, 15668, 20241, 22980, 23883, 26624,\n", + " 31211, 41741, 42651, 43559, 44468, 49063, 50934, 73283,\n", + " 74189, 75123, 78732, 80560, 90752, 98223, 104669, 116682,\n", + " 126309, 133603, 140826, 161324, 164038, 171855, 174523, 181282,\n", + " 189123, 191854, 193656],\n", + " dtype='int64'), Int64Index([ 872, 3595, 9232, 15669, 20242, 22981, 23884, 26625,\n", + " 31212, 41742, 42652, 43560, 44469, 49064, 50935, 73284,\n", + " 74190, 75124, 78733, 80561, 90753, 98224, 104670, 116683,\n", + " 126310, 133604, 140827, 161325, 164039, 171856, 174524, 181283,\n", + " 189124, 191855, 193657],\n", + " dtype='int64'), Int64Index([ 873, 3596, 9233, 15670, 20243, 22982, 23885, 26626,\n", + " 31213, 41743, 42653, 43561, 44470, 49065, 50936, 73285,\n", + " 74191, 75125, 78734, 80562, 90754, 98225, 104671, 116684,\n", + " 126311, 133605, 140828, 161326, 164040, 171857, 174525, 181284,\n", + " 189125, 191856, 193658],\n", + " dtype='int64'), Int64Index([ 874, 3597, 9234, 15671, 20244, 22983, 23886, 26627,\n", + " 31214, 41744, 42654, 43562, 44471, 49066, 50937, 73286,\n", + " 74192, 75126, 78735, 80563, 90755, 98226, 104672, 116685,\n", + " 126312, 133606, 140829, 161327, 164041, 171858, 174526, 181285,\n", + " 189126, 191857, 193659],\n", + " dtype='int64'), Int64Index([ 875, 3598, 9235, 15672, 20245, 22984, 23887, 26628,\n", + " 31215, 41745, 42655, 43563, 44472, 49067, 50938, 73287,\n", + " 74193, 75127, 78736, 80564, 90756, 98227, 104673, 116686,\n", + " 126313, 133607, 140830, 161328, 164042, 171859, 174527, 181286,\n", + " 189127, 191858, 193660],\n", + " dtype='int64'), Int64Index([ 876, 3599, 9236, 15673, 20246, 22985, 23888, 26629,\n", + " 31216, 41746, 42656, 43564, 44473, 49068, 50939, 73288,\n", + " 74194, 75128, 78737, 80565, 90757, 98228, 104674, 116687,\n", + " 126314, 133608, 140831, 161329, 164043, 174528, 181287, 189128,\n", + " 191859, 193661],\n", + " dtype='int64'), Int64Index([ 877, 3600, 9237, 15674, 20247, 22986, 23889, 26630,\n", + " 31217, 41747, 42657, 43565, 44474, 49069, 50940, 73289,\n", + " 74195, 75129, 78738, 80566, 90758, 98229, 104675, 116688,\n", + " 126315, 133609, 140832, 161330, 164044, 174529, 181288, 189129,\n", + " 191860, 193662],\n", + " dtype='int64'), Int64Index([ 878, 3601, 9238, 15675, 20248, 22987, 23890, 26631,\n", + " 31218, 41748, 42658, 43566, 44475, 49070, 50941, 73290,\n", + " 74196, 75130, 78739, 80567, 90759, 98230, 104676, 116689,\n", + " 126316, 133610, 140833, 161331, 164045, 174530, 181289, 189130,\n", + " 191861, 193663],\n", + " dtype='int64'), Int64Index([ 879, 3602, 9239, 15676, 20249, 22988, 23891, 26632,\n", + " 31219, 41749, 42659, 43567, 44476, 49071, 50942, 73291,\n", + " 74197, 75131, 78740, 80568, 90760, 98231, 104677, 116690,\n", + " 126317, 133611, 140834, 161332, 164046, 174531, 181290, 189131,\n", + " 191862, 193664],\n", + " dtype='int64'), Int64Index([ 880, 3603, 9240, 15677, 20250, 22989, 23892, 26633,\n", + " 31220, 41750, 42660, 43568, 44477, 49072, 50943, 73292,\n", + " 74198, 75132, 78741, 80569, 90761, 98232, 104678, 116691,\n", + " 126318, 133612, 140835, 161333, 164047, 174532, 181291, 189132,\n", + " 191863, 193665],\n", + " dtype='int64'), Int64Index([ 881, 3604, 9241, 15678, 20251, 22990, 23893, 26634,\n", + " 31221, 41751, 42661, 43569, 44478, 49073, 50944, 73293,\n", + " 74199, 75133, 78742, 80570, 90762, 98233, 104679, 116692,\n", + " 126319, 133613, 140836, 161334, 164048, 174533, 181292, 189133,\n", + " 191864, 193666],\n", + " dtype='int64'), Int64Index([ 882, 3605, 9242, 15679, 20252, 22991, 23894, 26635,\n", + " 31222, 41752, 42662, 43570, 44479, 49074, 50945, 73294,\n", + " 74200, 75134, 78743, 80571, 90763, 98234, 104680, 116693,\n", + " 126320, 133614, 140837, 161335, 164049, 174534, 181293, 189134,\n", + " 191865, 193667],\n", + " dtype='int64'), Int64Index([ 883, 3606, 9243, 15680, 20253, 22992, 23895, 26636,\n", + " 31223, 41753, 42663, 43571, 44480, 49075, 50946, 73295,\n", + " 74201, 75135, 78744, 80572, 90764, 98235, 104681, 116694,\n", + " 126321, 133615, 140838, 161336, 164050, 174535, 181294, 189135,\n", + " 191866, 193668],\n", + " dtype='int64'), Int64Index([ 884, 3607, 9244, 15681, 20254, 22993, 23896, 26637,\n", + " 31224, 41754, 42664, 43572, 44481, 49076, 50947, 73296,\n", + " 74202, 75136, 78745, 80573, 90765, 98236, 104682, 116695,\n", + " 126322, 133616, 140839, 161337, 164051, 174536, 181295, 189136,\n", + " 191867, 193669],\n", + " dtype='int64'), Int64Index([ 885, 3608, 9245, 15682, 20255, 22994, 23897, 26638,\n", + " 31225, 41755, 42665, 43573, 44482, 49077, 50948, 73297,\n", + " 74203, 75137, 78746, 80574, 90766, 98237, 104683, 116696,\n", + " 126323, 133617, 140840, 161338, 164052, 174537, 181296, 189137,\n", + " 191868, 193670],\n", + " dtype='int64'), Int64Index([ 886, 3609, 9246, 15683, 20256, 22995, 23898, 26639,\n", + " 31226, 41756, 42666, 43574, 44483, 49078, 50949, 73298,\n", + " 74204, 75138, 78747, 80575, 90767, 98238, 104684, 116697,\n", + " 126324, 133618, 140841, 161339, 164053, 174538, 181297, 189138,\n", + " 191869, 193671],\n", + " dtype='int64'), Int64Index([ 887, 3610, 9247, 15684, 20257, 22996, 23899, 26640,\n", + " 31227, 41757, 42667, 43575, 44484, 49079, 50950, 73299,\n", + " 74205, 75139, 78748, 80576, 90768, 98239, 104685, 116698,\n", + " 126325, 133619, 140842, 161340, 164054, 174539, 181298, 189139,\n", + " 191870, 193672],\n", + " dtype='int64'), Int64Index([ 888, 3611, 9248, 15685, 20258, 22997, 23900, 26641,\n", + " 31228, 41758, 42668, 43576, 44485, 49080, 50951, 73300,\n", + " 74206, 75140, 78749, 80577, 90769, 98240, 104686, 116699,\n", + " 126326, 133620, 140843, 161341, 164055, 174540, 181299, 189140,\n", + " 191871, 193673],\n", + " dtype='int64'), Int64Index([ 889, 3612, 9249, 15686, 20259, 22998, 23901, 26642,\n", + " 31229, 41759, 42669, 43577, 44486, 49081, 50952, 73301,\n", + " 74207, 75141, 78750, 80578, 90770, 98241, 104687, 116700,\n", + " 126327, 133621, 140844, 161342, 164056, 174541, 181300, 189141,\n", + " 191872, 193674],\n", + " dtype='int64'), Int64Index([ 890, 3613, 9250, 15687, 20260, 22999, 23902, 26643,\n", + " 31230, 41760, 42670, 43578, 44487, 49082, 50953, 73302,\n", + " 74208, 75142, 78751, 80579, 90771, 98242, 104688, 116701,\n", + " 126328, 133622, 140845, 161343, 164057, 174542, 181301, 189142,\n", + " 191873, 193675],\n", + " dtype='int64'), Int64Index([ 891, 3614, 9251, 15688, 20261, 23000, 23903, 26644,\n", + " 31231, 41761, 42671, 43579, 44488, 49083, 50954, 73303,\n", + " 74209, 75143, 78752, 80580, 90772, 98243, 104689, 116702,\n", + " 126329, 133623, 140846, 161344, 164058, 174543, 181302, 189143,\n", + " 191874, 193676],\n", + " dtype='int64'), Int64Index([ 892, 3615, 9252, 15689, 20262, 23001, 23904, 26645,\n", + " 31232, 41762, 42672, 43580, 44489, 49084, 50955, 73304,\n", + " 74210, 75144, 78753, 80581, 90773, 98244, 104690, 116703,\n", + " 126330, 133624, 140847, 161345, 164059, 174544, 181303, 189144,\n", + " 191875, 193677],\n", + " dtype='int64'), Int64Index([ 893, 3616, 9253, 15690, 20263, 23002, 23905, 26646,\n", + " 31233, 41763, 42673, 43581, 44490, 49085, 50956, 73305,\n", + " 74211, 75145, 78754, 80582, 90774, 98245, 104691, 116704,\n", + " 126331, 133625, 140848, 161346, 164060, 174545, 181304, 189145,\n", + " 191876, 193678],\n", + " dtype='int64'), Int64Index([ 894, 3617, 9254, 15691, 20264, 23003, 23906, 26647,\n", + " 31234, 41764, 42674, 43582, 44491, 49086, 50957, 73306,\n", + " 74212, 75146, 78755, 80583, 90775, 98246, 104692, 116705,\n", + " 126332, 133626, 140849, 161347, 164061, 174546, 181305, 189146,\n", + " 191877, 193679],\n", + " dtype='int64'), Int64Index([ 895, 3618, 9255, 15692, 20265, 23004, 23907, 26648,\n", + " 31235, 41765, 42675, 43583, 44492, 49087, 50958, 73307,\n", + " 74213, 75147, 78756, 80584, 90776, 98247, 104693, 116706,\n", + " 126333, 133627, 140850, 161348, 164062, 174547, 181306, 189147,\n", + " 191878, 193680],\n", + " dtype='int64'), Int64Index([ 896, 3619, 9256, 15693, 20266, 23005, 23908, 26649,\n", + " 31236, 41766, 42676, 43584, 44493, 49088, 50959, 73308,\n", + " 74214, 75148, 78757, 80585, 90777, 98248, 104694, 116707,\n", + " 126334, 133628, 140851, 161349, 164063, 174548, 181307, 189148,\n", + " 191879, 193681],\n", + " dtype='int64'), Int64Index([ 897, 3620, 9257, 15694, 20267, 23006, 23909, 26650,\n", + " 31237, 41767, 42677, 43585, 44494, 49089, 50960, 73309,\n", + " 74215, 75149, 78758, 80586, 90778, 98249, 104695, 116708,\n", + " 126335, 133629, 140852, 161350, 164064, 174549, 181308, 189149,\n", + " 191880, 193682],\n", + " dtype='int64'), Int64Index([ 898, 3621, 9258, 15695, 20268, 23007, 23910, 26651,\n", + " 31238, 41768, 42678, 43586, 44495, 49090, 50961, 73310,\n", + " 74216, 75150, 78759, 80587, 90779, 98250, 104696, 116709,\n", + " 126336, 133630, 140853, 161351, 164065, 174550, 181309, 189150,\n", + " 191881, 193683],\n", + " dtype='int64'), Int64Index([ 899, 3622, 9259, 15696, 20269, 23008, 23911, 26652,\n", + " 31239, 41769, 42679, 43587, 44496, 49091, 50962, 73311,\n", + " 74217, 75151, 78760, 80588, 90780, 98251, 104697, 116710,\n", + " 126337, 133631, 140854, 161352, 164066, 174551, 181310, 189151,\n", + " 191882, 193684],\n", + " dtype='int64'), Int64Index([ 900, 3623, 9260, 15697, 20270, 23009, 23912, 26653,\n", + " 31240, 41770, 42680, 43588, 44497, 49092, 50963, 73312,\n", + " 74218, 75152, 78761, 80589, 90781, 98252, 104698, 116711,\n", + " 126338, 133632, 140855, 161353, 164067, 174552, 181311, 189152,\n", + " 191883, 193685],\n", + " dtype='int64'), Int64Index([ 901, 3624, 9261, 15698, 20271, 23010, 23913, 26654,\n", + " 31241, 41771, 42681, 43589, 44498, 49093, 50964, 73313,\n", + " 74219, 75153, 78762, 80590, 90782, 98253, 104699, 116712,\n", + " 126339, 133633, 140856, 161354, 164068, 174553, 181312, 189153,\n", + " 191884, 193686],\n", + " dtype='int64'), Int64Index([ 902, 3625, 9262, 15699, 20272, 23011, 23914, 26655,\n", + " 31242, 41772, 42682, 43590, 44499, 49094, 50965, 73314,\n", + " 74220, 75154, 78763, 80591, 90783, 98254, 104700, 116713,\n", + " 126340, 133634, 140857, 161355, 164069, 174554, 181313, 189154,\n", + " 191885, 193687],\n", + " dtype='int64'), Int64Index([ 903, 3626, 9263, 15700, 20273, 23012, 23915, 26656,\n", + " 31243, 41773, 42683, 43591, 44500, 49095, 50966, 73315,\n", + " 74221, 75155, 78764, 80592, 90784, 98255, 104701, 116714,\n", + " 126341, 133635, 140858, 161356, 164070, 174555, 181314, 189155,\n", + " 191886, 193688],\n", + " dtype='int64'), Int64Index([ 904, 3627, 9264, 15701, 20274, 23013, 23916, 26657,\n", + " 31244, 41774, 42684, 43592, 44501, 49096, 50967, 73316,\n", + " 74222, 75156, 78765, 80593, 90785, 98256, 104702, 116715,\n", + " 126342, 133636, 140859, 161357, 164071, 174556, 181315, 189156,\n", + " 191887, 193689],\n", + " dtype='int64'), Int64Index([ 905, 3628, 9265, 15702, 20275, 23014, 23917, 26658,\n", + " 31245, 41775, 42685, 43593, 44502, 49097, 50968, 73317,\n", + " 74223, 75157, 78766, 80594, 90786, 98257, 104703, 116716,\n", + " 126343, 133637, 140860, 161358, 164072, 174557, 181316, 189157,\n", + " 191888, 193690],\n", + " dtype='int64'), Int64Index([ 906, 3629, 9266, 15703, 20276, 23015, 23918, 26659,\n", + " 31246, 41776, 42686, 43594, 44503, 49098, 50969, 73318,\n", + " 74224, 75158, 78767, 80595, 90787, 98258, 104704, 116717,\n", + " 126344, 133638, 140861, 161359, 164073, 174558, 181317, 189158,\n", + " 191889, 193691],\n", + " dtype='int64'), Int64Index([ 907, 3630, 9267, 15704, 20277, 23016, 23919, 26660,\n", + " 31247, 41777, 42687, 43595, 44504, 49099, 50970, 73319,\n", + " 74225, 75159, 78768, 80596, 90788, 98259, 104705, 116718,\n", + " 126345, 133639, 140862, 161360, 164074, 174559, 181318, 189159,\n", + " 191890, 193692],\n", + " dtype='int64'), Int64Index([ 908, 3631, 9268, 15705, 20278, 23017, 23920, 26661,\n", + " 31248, 41778, 42688, 43596, 44505, 49100, 50971, 73320,\n", + " 74226, 75160, 78769, 80597, 90789, 98260, 104706, 116719,\n", + " 126346, 133640, 140863, 161361, 164075, 174560, 181319, 189160,\n", + " 191891, 193693],\n", + " dtype='int64'), Int64Index([9269], dtype='int64'), Int64Index([9270], dtype='int64'), Int64Index([9271], dtype='int64'), Int64Index([9272], dtype='int64'), Int64Index([9273, 58419], dtype='int64'), Int64Index([9274, 58420], dtype='int64'), Int64Index([9275, 58421], dtype='int64'), Int64Index([9276, 58422], dtype='int64'), Int64Index([9277, 58423], dtype='int64'), Int64Index([9278, 58424], dtype='int64'), Int64Index([9279, 58425], dtype='int64'), Int64Index([9280, 58426], dtype='int64'), Int64Index([9281, 58427], dtype='int64'), Int64Index([9282, 58428], dtype='int64'), Int64Index([9283, 58429], dtype='int64'), Int64Index([9284, 58430], dtype='int64'), Int64Index([9285, 58431], dtype='int64'), Int64Index([9286, 58432], dtype='int64'), Int64Index([9287, 58433], dtype='int64'), Int64Index([9288, 58434], dtype='int64'), Int64Index([9289, 58435], dtype='int64'), Int64Index([9290, 58436], dtype='int64'), Int64Index([9291, 58437], dtype='int64'), Int64Index([9292, 58438], dtype='int64'), Int64Index([9293, 58439], dtype='int64'), Int64Index([9294, 58440], dtype='int64'), Int64Index([9295, 58441], dtype='int64'), Int64Index([9296, 58442], dtype='int64'), Int64Index([9297, 58443], dtype='int64'), Int64Index([9298, 58444], dtype='int64'), Int64Index([9299, 58445], dtype='int64'), Int64Index([9300, 58446], dtype='int64'), Int64Index([9301, 58447], dtype='int64'), Int64Index([9302, 58448, 137175], dtype='int64'), Int64Index([9303, 58449, 137176], dtype='int64'), Int64Index([9304, 58450, 137177], dtype='int64'), Int64Index([9305, 58451, 137178], dtype='int64'), Int64Index([9306, 58452, 137179], dtype='int64'), Int64Index([9307, 58453, 137180], dtype='int64'), Int64Index([9308, 58454, 137181], dtype='int64'), Int64Index([9309, 58455, 137182], dtype='int64'), Int64Index([9310, 58456, 137183], dtype='int64'), Int64Index([9311, 58457, 137184], dtype='int64'), Int64Index([9312, 58458, 137185], dtype='int64'), Int64Index([9313, 58459, 137186], dtype='int64'), Int64Index([9314, 58460, 137187], dtype='int64'), Int64Index([9315, 58461, 75161, 137188], dtype='int64'), Int64Index([9316, 58462, 75162, 137189, 149814], dtype='int64'), Int64Index([9317, 58463, 75163, 137190, 149815], dtype='int64'), Int64Index([9318, 58464, 75164, 137191, 149816], dtype='int64'), Int64Index([9319, 58465, 75165, 137192, 149817], dtype='int64'), Int64Index([9320, 58466, 75166, 137193, 149818], dtype='int64'), Int64Index([9321, 58467, 75167, 137194, 149819], dtype='int64'), Int64Index([9322, 58468, 75168, 130010, 137195, 149820], dtype='int64'), Int64Index([9323, 58469, 75169, 130011, 137196, 143555, 149821], dtype='int64'), Int64Index([9324, 58470, 75170, 130012, 137197, 143556, 149822], dtype='int64'), Int64Index([9325, 58471, 75171, 130013, 137198, 143557, 149823], dtype='int64'), Int64Index([9326, 58472, 75172, 130014, 137199, 143558, 149824], dtype='int64'), Int64Index([9327, 58473, 75173, 130015, 137200, 143559, 149825], dtype='int64'), Int64Index([9328, 58474, 75174, 130016, 137201, 143560, 149826], dtype='int64'), Int64Index([9329, 58475, 75175, 130017, 137202, 143561, 149827], dtype='int64'), Int64Index([9330, 58476, 75176, 130018, 137203, 143562, 149828], dtype='int64'), Int64Index([9331, 58477, 75177, 130019, 137204, 143563, 149829], dtype='int64'), Int64Index([9332, 58478, 75178, 130020, 137205, 143564, 149830], dtype='int64'), Int64Index([9333, 58479, 75179, 130021, 137206, 143565, 149831], dtype='int64'), Int64Index([9334, 58480, 75180, 130022, 137207, 143566, 149832], dtype='int64'), Int64Index([9335, 58481, 75181, 130023, 137208, 143567, 149833], dtype='int64'), Int64Index([9336, 58482, 75182, 130024, 137209, 143568, 149834], dtype='int64'), Int64Index([9337, 58483, 75183, 130025, 137210, 143569, 149835], dtype='int64'), Int64Index([9338, 58484, 75184, 123025, 130026, 137211, 143570, 149836], dtype='int64'), Int64Index([9339, 58485, 75185, 123026, 130027, 137212, 143571, 149837], dtype='int64'), Int64Index([9340, 58486, 75186, 123027, 130028, 137213, 143572, 149838], dtype='int64'), Int64Index([9341, 58487, 75187, 123028, 130029, 137214, 143573, 149839], dtype='int64'), Int64Index([9342, 58488, 75188, 123029, 130030, 137215, 143574, 149840], dtype='int64'), Int64Index([9343, 58489, 75189, 123030, 130031, 137216, 143575, 149841], dtype='int64'), Int64Index([9344, 58490, 75190, 123031, 130032, 137217, 143576, 149842], dtype='int64'), Int64Index([9345, 58491, 75191, 123032, 130033, 137218, 143577, 149843], dtype='int64'), Int64Index([9346, 58492, 75192, 123033, 130034, 137219, 143578, 149844], dtype='int64'), Int64Index([9347, 58493, 75193, 123034, 130035, 137220, 143579, 149845], dtype='int64'), Int64Index([9348, 58494, 75194, 123035, 130036, 137221, 143580, 149846], dtype='int64'), Int64Index([9349, 58495, 75195, 123036, 130037, 137222, 143581, 149847], dtype='int64'), Int64Index([9350, 58496, 75196, 123037, 130038, 137223, 143582, 149848], dtype='int64'), Int64Index([9351, 58497, 75197, 123038, 130039, 137224, 143583, 149849], dtype='int64'), Int64Index([9352, 58498, 75198, 123039, 130040, 137225, 143584, 149850], dtype='int64'), Int64Index([9353, 58499, 75199, 123040, 130041, 137226, 143585, 149851], dtype='int64'), Int64Index([9354, 58500, 75200, 123041, 130042, 137227, 143586, 149852], dtype='int64'), Int64Index([9355, 58501, 75201, 123042, 130043, 137228, 143587, 149853], dtype='int64'), Int64Index([9356, 58502, 75202, 123043, 130044, 137229, 143588, 149854], dtype='int64'), Int64Index([9357, 58503, 75203, 123044, 130045, 137230, 143589, 149855], dtype='int64'), Int64Index([9358, 58504, 75204, 123045, 130046, 137231, 143590, 149856], dtype='int64'), Int64Index([9359, 58505, 75205, 123046, 130047, 137232, 143591, 149857], dtype='int64'), Int64Index([9360, 58506, 75206, 123047, 130048, 137233, 143592, 149858], dtype='int64'), Int64Index([9361, 58507, 75207, 123048, 130049, 137234, 143593, 149859], dtype='int64'), Int64Index([9362, 58508, 75208, 123049, 130050, 137235, 143594, 149860], dtype='int64'), Int64Index([9363, 58509, 75209, 123050, 130051, 137236, 143595, 149861], dtype='int64'), Int64Index([9364, 58510, 75210, 123051, 130052, 137237, 143596, 149862], dtype='int64'), Int64Index([9365, 58511, 75211, 123052, 130053, 137238, 143597, 149863], dtype='int64'), Int64Index([9366, 58512, 75212, 123053, 130054, 137239, 143598, 149864], dtype='int64'), Int64Index([9367, 58513, 75213, 123054, 130055, 137240, 143599, 149865], dtype='int64'), Int64Index([9368, 58514, 75214, 123055, 130056, 137241, 143600, 149866], dtype='int64'), Int64Index([9369, 58515, 75215, 123056, 130057, 137242, 143601, 149867], dtype='int64'), Int64Index([9370, 58516, 75216, 123057, 130058, 137243, 143602, 149868], dtype='int64'), Int64Index([9371, 58517, 75217, 116720, 123058, 130059, 137244, 143603,\n", + " 149869],\n", + " dtype='int64'), Int64Index([9372, 58518, 75218, 116721, 123059, 130060, 137245, 143604,\n", + " 149870],\n", + " dtype='int64'), Int64Index([9373, 58519, 75219, 116722, 123060, 130061, 137246, 143605,\n", + " 149871],\n", + " dtype='int64'), Int64Index([9374, 58520, 75220, 116723, 123061, 130062, 137247, 143606,\n", + " 149872],\n", + " dtype='int64'), Int64Index([9375, 58521, 75221, 116724, 123062, 130063, 137248, 143607,\n", + " 149873],\n", + " dtype='int64'), Int64Index([9376, 58522, 75222, 116725, 123063, 130064, 137249, 143608,\n", + " 149874],\n", + " dtype='int64'), Int64Index([9377, 58523, 75223, 116726, 123064, 130065, 137250, 143609,\n", + " 149875],\n", + " dtype='int64'), Int64Index([9378, 58524, 75224, 116727, 123065, 130066, 137251, 143610,\n", + " 149876],\n", + " dtype='int64'), Int64Index([9379, 58525, 75225, 116728, 123066, 130067, 137252, 143611,\n", + " 149877],\n", + " dtype='int64'), Int64Index([9380, 58526, 75226, 116729, 123067, 130068, 137253, 143612,\n", + " 149878],\n", + " dtype='int64'), Int64Index([9381, 58527, 75227, 116730, 123068, 130069, 137254, 143613,\n", + " 149879],\n", + " dtype='int64'), Int64Index([9382, 58528, 75228, 116731, 123069, 130070, 137255, 143614,\n", + " 149880],\n", + " dtype='int64'), Int64Index([9383, 58529, 75229, 116732, 123070, 130071, 137256, 143615,\n", + " 149881],\n", + " dtype='int64'), Int64Index([9384, 58530, 75230, 116733, 123071, 130072, 137257, 143616,\n", + " 149882],\n", + " dtype='int64'), Int64Index([9385, 58531, 75231, 116734, 123072, 130073, 137258, 143617,\n", + " 149883],\n", + " dtype='int64'), Int64Index([9386, 58532, 75232, 116735, 123073, 130074, 137259, 143618,\n", + " 149884],\n", + " dtype='int64'), Int64Index([9387, 58533, 75233, 116736, 123074, 130075, 137260, 143619,\n", + " 149885],\n", + " dtype='int64'), Int64Index([9388, 58534, 75234, 116737, 123075, 130076, 137261, 143620,\n", + " 149886],\n", + " dtype='int64'), Int64Index([9389, 58535, 75235, 116738, 123076, 130077, 137262, 143621,\n", + " 149887],\n", + " dtype='int64'), Int64Index([9390, 58536, 75236, 116739, 123077, 130078, 137263, 143622,\n", + " 149888],\n", + " dtype='int64'), Int64Index([9391, 58537, 75237, 116740, 123078, 130079, 137264, 143623,\n", + " 149889],\n", + " dtype='int64'), Int64Index([9392, 58538, 75238, 116741, 123079, 130080, 137265, 143624,\n", + " 149890],\n", + " dtype='int64'), Int64Index([9393, 58539, 75239, 116742, 123080, 130081, 137266, 143625,\n", + " 149891],\n", + " dtype='int64'), Int64Index([9394, 58540, 75240, 116743, 123081, 130082, 137267, 143626,\n", + " 149892],\n", + " dtype='int64'), Int64Index([9395, 58541, 75241, 116744, 123082, 130083, 137268, 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Int64Index([ 9544, 58690, 75390, 116893, 123231, 130232, 137417, 143776,\n", + " 150042, 158887, 196146],\n", + " dtype='int64'), Int64Index([ 9545, 58691, 75391, 116894, 123232, 130233, 137418, 143777,\n", + " 150043, 158888, 196147],\n", + " dtype='int64'), Int64Index([ 9546, 58692, 75392, 116895, 123233, 130234, 137419, 143778,\n", + " 150044, 158889, 196148],\n", + " dtype='int64'), Int64Index([ 9547, 58693, 75393, 116896, 123234, 130235, 137420, 143779,\n", + " 150045, 158890, 196149],\n", + " dtype='int64'), Int64Index([ 9548, 58694, 75394, 116897, 123235, 130236, 137421, 143780,\n", + " 150046, 158891, 196150],\n", + " dtype='int64'), Int64Index([ 9549, 58695, 75395, 116898, 123236, 130237, 137422, 143781,\n", + " 150047, 158892, 196151],\n", + " dtype='int64'), Int64Index([ 9550, 58696, 75396, 116899, 123237, 130238, 137423, 143782,\n", + " 150048, 158893, 196152],\n", + " dtype='int64'), Int64Index([ 9551, 58697, 75397, 116900, 123238, 130239, 137424, 143783,\n", + " 150049, 158894, 196153],\n", + " dtype='int64'), Int64Index([ 9552, 58698, 75398, 116901, 123239, 130240, 137425, 143784,\n", + " 150050, 158895, 196154],\n", + " dtype='int64'), Int64Index([ 9553, 58699, 75399, 116902, 123240, 130241, 137426, 143785,\n", + " 150051, 158896, 196155],\n", + " dtype='int64'), Int64Index([ 9554, 58700, 75400, 116903, 123241, 130242, 137427, 143786,\n", + " 150052, 158897, 196156],\n", + " dtype='int64'), Int64Index([ 9555, 58701, 75401, 116904, 123242, 130243, 137428, 143787,\n", + " 150053, 158898, 196157],\n", + " dtype='int64'), Int64Index([ 9556, 58702, 75402, 116905, 123243, 130244, 137429, 143788,\n", + " 150054, 158899, 196158],\n", + " dtype='int64'), Int64Index([ 9557, 58703, 75403, 116906, 123244, 130245, 137430, 143789,\n", + " 150055, 158900, 196159],\n", + " dtype='int64'), Int64Index([ 9558, 58704, 75404, 116907, 123245, 130246, 137431, 143790,\n", + " 150056, 158901, 195471, 196160],\n", + " dtype='int64'), Int64Index([ 9559, 58705, 75405, 116908, 123246, 130247, 137432, 143791,\n", + " 150057, 158902, 195472, 196161],\n", + " dtype='int64'), Int64Index([ 9560, 58706, 75406, 116909, 123247, 130248, 137433, 143792,\n", + " 150058, 158903, 195473, 196162],\n", + " dtype='int64'), Int64Index([ 9561, 58707, 75407, 116910, 123248, 130249, 137434, 143793,\n", + " 150059, 158904, 195474, 196163],\n", + " dtype='int64'), Int64Index([ 9562, 58708, 75408, 116911, 123249, 130250, 137435, 143794,\n", + " 150060, 158905, 195475, 196164],\n", + " dtype='int64'), Int64Index([ 9563, 58709, 75409, 116912, 123250, 130251, 137436, 143795,\n", + " 150061, 158906, 195476, 196165],\n", + " dtype='int64'), Int64Index([ 9564, 58710, 75410, 116913, 123251, 130252, 137437, 143796,\n", + " 150062, 158907, 195477, 196166],\n", + " dtype='int64'), Int64Index([ 9565, 58711, 75411, 116914, 123252, 130253, 137438, 143797,\n", + " 150063, 158908, 195478, 196167],\n", + " dtype='int64'), Int64Index([ 9566, 58712, 75412, 116915, 123253, 130254, 137439, 143798,\n", + " 150064, 158909, 195479, 196168, 196827],\n", + " dtype='int64'), Int64Index([ 9567, 58713, 75413, 116916, 123254, 130255, 137440, 143799,\n", + " 150065, 158910, 195480, 196169, 196828],\n", + " dtype='int64'), Int64Index([ 9568, 58714, 75414, 116917, 123255, 130256, 137441, 143800,\n", + " 150066, 158911, 195481, 196170, 196829],\n", + " dtype='int64'), Int64Index([ 9569, 58715, 75415, 116918, 123256, 130257, 137442, 143801,\n", + " 150067, 158912, 195482, 196171, 196830],\n", + " dtype='int64'), Int64Index([ 9570, 58716, 75416, 116919, 123257, 130258, 137443, 143802,\n", + " 150068, 158913, 195483, 196172, 196831],\n", + " dtype='int64'), Int64Index([ 9571, 58717, 75417, 116920, 123258, 130259, 137444, 143803,\n", + " 150069, 158914, 195484, 196173, 196832],\n", + " dtype='int64'), Int64Index([ 9572, 58718, 75418, 116921, 123259, 130260, 137445, 143804,\n", + " 150070, 158915, 195485, 196174, 196833],\n", + " dtype='int64'), Int64Index([ 9573, 58719, 75419, 116922, 123260, 130261, 137446, 143805,\n", + " 150071, 158916, 195486, 196175, 196834],\n", + " dtype='int64'), Int64Index([ 9574, 58720, 75420, 116923, 123261, 130262, 137447, 143806,\n", + " 150072, 158917, 195487, 196176, 196835],\n", + " dtype='int64'), Int64Index([ 9575, 58721, 75421, 116924, 123262, 130263, 137448, 143807,\n", + " 150073, 158918, 195488, 196177, 196836],\n", + " dtype='int64'), Int64Index([ 9576, 58722, 75422, 116925, 123263, 130264, 137449, 143808,\n", + " 150074, 158919, 195489, 196178, 196837],\n", + " dtype='int64'), Int64Index([ 9577, 58723, 75423, 116926, 123264, 130265, 137450, 143809,\n", + " 150075, 158920, 195490, 196179, 196838],\n", + " dtype='int64'), Int64Index([ 9578, 58724, 75424, 116927, 123265, 130266, 137451, 143810,\n", + " 150076, 158921, 195491, 196180, 196839],\n", + " dtype='int64'), Int64Index([ 9579, 58725, 75425, 116928, 123266, 130267, 137452, 143811,\n", + " 150077, 158922, 195492, 196181, 196840],\n", + " dtype='int64'), Int64Index([ 9580, 58726, 75426, 116929, 123267, 130268, 137453, 143812,\n", + " 150078, 158923, 195493, 196182, 196841],\n", + " dtype='int64'), Int64Index([ 9581, 58727, 75427, 116930, 123268, 130269, 137454, 143813,\n", + " 150079, 158924, 195494, 196183, 196842],\n", + " dtype='int64'), Int64Index([ 9582, 58728, 75428, 116931, 123269, 130270, 137455, 143814,\n", + " 150080, 158925, 195495, 196184, 196843],\n", + " dtype='int64'), Int64Index([ 9583, 58729, 75429, 116932, 123270, 130271, 137456, 143815,\n", + " 150081, 158926, 195496, 196185, 196844],\n", + " dtype='int64'), Int64Index([ 9584, 58730, 75430, 116933, 123271, 130272, 137457, 143816,\n", + " 150082, 158927, 195497, 196186, 196845],\n", + " dtype='int64'), Int64Index([ 9585, 58731, 75431, 116934, 123272, 130273, 137458, 143817,\n", + " 150083, 158928, 195498, 196187, 196846],\n", + " dtype='int64'), Int64Index([ 9586, 58732, 75432, 116935, 123273, 130274, 137459, 143818,\n", + " 150084, 158929, 195499, 196188, 196847],\n", + " dtype='int64'), Int64Index([ 9587, 58733, 75433, 116936, 123274, 130275, 137460, 143819,\n", + " 150085, 158930, 195500, 196189, 196848],\n", + " dtype='int64'), Int64Index([ 9588, 58734, 75434, 116937, 123275, 130276, 137461, 143820,\n", + " 150086, 158931, 195501, 196190, 196849],\n", + " dtype='int64'), Int64Index([ 9589, 58735, 75435, 116938, 123276, 130277, 137462, 143821,\n", + " 150087, 158932, 195502, 196191, 196850],\n", + " dtype='int64'), Int64Index([ 9590, 58736, 75436, 116939, 123277, 130278, 137463, 143822,\n", + " 150088, 158933, 195503, 196192, 196851],\n", + " dtype='int64'), Int64Index([ 9591, 58737, 75437, 116940, 123278, 130279, 137464, 143823,\n", + " 150089, 158934, 195504, 196193, 196852],\n", + " dtype='int64'), Int64Index([ 9592, 58738, 75438, 116941, 123279, 130280, 137465, 143824,\n", + " 150090, 158935, 195505, 196194, 196853],\n", + " dtype='int64'), Int64Index([ 9593, 58739, 75439, 116942, 123280, 130281, 137466, 143825,\n", + " 150091, 158936, 195506, 196195, 196854],\n", + " dtype='int64'), Int64Index([ 9594, 58740, 75440, 116943, 123281, 130282, 137467, 143826,\n", + " 150092, 158937, 195507, 196196, 196855],\n", + " dtype='int64'), Int64Index([ 9595, 58741, 75441, 116944, 123282, 130283, 137468, 143827,\n", + " 150093, 158938, 195508, 196197, 196856],\n", + " dtype='int64'), Int64Index([ 9596, 58742, 75442, 116945, 123283, 130284, 137469, 143828,\n", + " 150094, 158939, 195509, 196198, 196857],\n", + " dtype='int64'), Int64Index([ 9597, 58743, 75443, 116946, 123284, 130285, 137470, 143829,\n", + " 150095, 158940, 195510, 196199, 196858],\n", + " dtype='int64'), Int64Index([ 9598, 58744, 75444, 116947, 123285, 130286, 137471, 143830,\n", + " 150096, 158941, 195511, 196200, 196859],\n", + " dtype='int64'), Int64Index([ 9599, 58745, 75445, 116948, 123286, 130287, 137472, 143831,\n", + " 150097, 158942, 195512, 196201, 196860],\n", + " dtype='int64'), Int64Index([ 9600, 58746, 75446, 116949, 123287, 130288, 137473, 143832,\n", + " 150098, 158943, 195513, 196202, 196861],\n", + " dtype='int64'), Int64Index([ 9601, 58747, 75447, 116950, 123288, 130289, 137474, 143833,\n", + " 150099, 158944, 195514, 196203, 196862],\n", + " dtype='int64'), Int64Index([ 9602, 58748, 75448, 116951, 123289, 130290, 137475, 143834,\n", + " 150100, 158945, 195515, 196204, 196863],\n", + " dtype='int64'), Int64Index([ 9603, 58749, 75449, 116952, 123290, 130291, 137476, 143835,\n", + " 150101, 158946, 195516, 196205, 196864],\n", + " dtype='int64'), Int64Index([ 9604, 58750, 75450, 116953, 123291, 130292, 137477, 143836,\n", + " 150102, 158947, 195517, 196206, 196865],\n", + " dtype='int64'), Int64Index([ 9605, 58751, 75451, 116954, 123292, 130293, 137478, 143837,\n", + " 150103, 158948, 195518, 196207, 196866],\n", + " dtype='int64'), Int64Index([ 9606, 58752, 75452, 116955, 123293, 130294, 137479, 143838,\n", + " 150104, 158949, 195519, 196208, 196867],\n", + " dtype='int64'), Int64Index([ 9607, 58753, 75453, 116956, 123294, 130295, 137480, 143839,\n", + " 150105, 158950, 195520, 196209, 196868],\n", + " dtype='int64'), Int64Index([ 9608, 58754, 75454, 116957, 123295, 130296, 137481, 143840,\n", + " 150106, 158951, 195521, 196210, 196869],\n", + " dtype='int64'), Int64Index([ 9609, 58755, 75455, 116958, 123296, 130297, 137482, 143841,\n", + " 150107, 158952, 195522, 196211, 196870],\n", + " dtype='int64'), Int64Index([ 9610, 58756, 75456, 116959, 123297, 130298, 137483, 143842,\n", + " 150108, 158953, 195523, 196212, 196871],\n", + " dtype='int64'), Int64Index([ 9611, 58757, 75457, 116960, 123298, 130299, 137484, 143843,\n", + " 150109, 158954, 195524, 196213, 196872],\n", + " dtype='int64'), Int64Index([ 9612, 58758, 75458, 116961, 123299, 130300, 137485, 143844,\n", + " 150110, 158955, 195525, 196214, 196873],\n", + " dtype='int64'), Int64Index([ 9613, 58759, 75459, 116962, 123300, 130301, 137486, 143845,\n", + " 150111, 158956, 195526, 196215, 196874],\n", + " dtype='int64'), Int64Index([ 9614, 58760, 75460, 116963, 123301, 130302, 137487, 143846,\n", + " 150112, 158957, 195527, 196216, 196875],\n", + " dtype='int64'), Int64Index([ 9615, 58761, 75461, 116964, 123302, 130303, 137488, 143847,\n", + " 150113, 158958, 195528, 196217, 196876],\n", + " dtype='int64'), Int64Index([ 9616, 58762, 75462, 116965, 123303, 130304, 137489, 143848,\n", + " 150114, 158959, 195529, 196218, 196877],\n", + " dtype='int64'), Int64Index([ 9617, 58763, 75463, 116966, 123304, 130305, 137490, 143849,\n", + " 150115, 158960, 195530, 196219, 196878],\n", + " dtype='int64'), Int64Index([ 9618, 58764, 75464, 116967, 123305, 130306, 137491, 143850,\n", + " 150116, 158961, 195531, 196220, 196879],\n", + " dtype='int64'), Int64Index([ 9619, 58765, 75465, 116968, 123306, 130307, 137492, 143851,\n", + " 150117, 158962, 195532, 196221, 196880],\n", + " dtype='int64'), Int64Index([ 9620, 58766, 75466, 116969, 123307, 130308, 137493, 143852,\n", + " 150118, 158963, 195533, 196222, 196881],\n", + " dtype='int64'), Int64Index([ 9621, 58767, 75467, 116970, 123308, 130309, 137494, 143853,\n", + " 150119, 158964, 195534, 196223, 196882],\n", + " dtype='int64'), Int64Index([ 9622, 58768, 75468, 116971, 123309, 130310, 137495, 143854,\n", + " 150120, 158965, 195535, 196224, 196883],\n", + " dtype='int64'), Int64Index([ 9623, 58769, 75469, 116972, 123310, 130311, 137496, 143855,\n", + " 150121, 158966, 195536, 196225, 196884],\n", + " dtype='int64'), Int64Index([ 9624, 58770, 75470, 116973, 123311, 130312, 137497, 143856,\n", + " 150122, 158967, 195537, 196226, 196885],\n", + " dtype='int64'), Int64Index([ 9625, 58771, 75471, 116974, 123312, 130313, 137498, 143857,\n", + " 150123, 158968, 195538, 196227, 196886],\n", + " dtype='int64'), Int64Index([ 9626, 58772, 75472, 116975, 123313, 130314, 137499, 143858,\n", + " 150124, 158969, 195539, 196228, 196887],\n", + " dtype='int64'), Int64Index([ 9627, 58773, 75473, 116976, 123314, 130315, 137500, 143859,\n", + " 150125, 158970, 195540, 196229, 196888],\n", + " dtype='int64'), Int64Index([ 9628, 58774, 75474, 116977, 123315, 130316, 137501, 143860,\n", + " 150126, 158971, 195541, 196230, 196889],\n", + " dtype='int64'), Int64Index([ 9629, 58775, 75475, 116978, 123316, 130317, 137502, 143861,\n", + " 150127, 158972, 195542, 196231, 196890],\n", + " dtype='int64'), Int64Index([ 9630, 58776, 62134, 75476, 116979, 123317, 130318, 137503,\n", + " 143862, 150128, 158973, 195543, 196232, 196891],\n", + " dtype='int64'), Int64Index([ 9631, 58777, 62135, 75477, 116980, 123318, 130319, 137504,\n", + " 143863, 150129, 158974, 195544, 196233, 196892],\n", + " dtype='int64'), Int64Index([ 9632, 58778, 62136, 75478, 116981, 123319, 130320, 137505,\n", + " 143864, 150130, 158975, 195545, 196234, 196893],\n", + " dtype='int64'), Int64Index([ 9633, 58779, 62137, 75479, 116982, 123320, 130321, 137506,\n", + " 143865, 150131, 158976, 195546, 196235, 196894],\n", + " dtype='int64'), Int64Index([ 9634, 58780, 62138, 75480, 116983, 123321, 130322, 137507,\n", + " 143866, 150132, 158977, 195547, 196236, 196895],\n", + " dtype='int64'), Int64Index([ 9635, 58781, 62139, 75481, 116984, 123322, 130323, 137508,\n", + " 143867, 150133, 158978, 195548, 196237, 196896],\n", + " dtype='int64'), Int64Index([ 9636, 58782, 62140, 75482, 116985, 123323, 130324, 137509,\n", + " 143868, 150134, 158979, 195549, 196238, 196897],\n", + " dtype='int64'), Int64Index([ 9637, 58783, 62141, 75483, 116986, 123324, 130325, 137510,\n", + " 143869, 150135, 158980, 195550, 196239, 196898],\n", + " dtype='int64'), Int64Index([ 9638, 58784, 62142, 75484, 116987, 123325, 130326, 137511,\n", + " 143870, 150136, 158981, 195551, 196240, 196899],\n", + " dtype='int64'), Int64Index([ 9639, 58785, 62143, 75485, 116988, 123326, 130327, 137512,\n", + " 143871, 150137, 158982, 195552, 196241, 196900],\n", + " dtype='int64'), Int64Index([ 9640, 58786, 62144, 75486, 116989, 123327, 130328, 137513,\n", + " 143872, 150138, 158983, 195553, 196242, 196901],\n", + " dtype='int64'), Int64Index([ 9641, 58787, 62145, 75487, 116990, 123328, 130329, 137514,\n", + " 143873, 150139, 158984, 195554, 196243, 196902],\n", + " dtype='int64'), Int64Index([ 9642, 58788, 62146, 75488, 116991, 123329, 130330, 137515,\n", + " 143874, 150140, 158985, 195555, 196244, 196903],\n", + " dtype='int64'), Int64Index([ 9643, 58789, 62147, 75489, 116992, 123330, 130331, 137516,\n", + " 143875, 150141, 158986, 195556, 196245, 196904],\n", + " dtype='int64'), Int64Index([ 9644, 58790, 62148, 75490, 116993, 123331, 130332, 137517,\n", + " 143876, 150142, 158987, 195557, 196246, 196905],\n", + " dtype='int64'), Int64Index([ 9645, 58791, 62149, 75491, 116994, 123332, 130333, 137518,\n", + " 143877, 150143, 158988, 195558, 196247, 196906],\n", + " dtype='int64'), Int64Index([ 9646, 58792, 62150, 75492, 116995, 123333, 130334, 137519,\n", + " 143878, 150144, 158989, 195559, 196248, 196907],\n", + " dtype='int64'), Int64Index([ 9647, 58793, 62151, 75493, 116996, 123334, 130335, 137520,\n", + " 143879, 150145, 158990, 195560, 196249, 196908],\n", + " dtype='int64'), Int64Index([ 9648, 58794, 62152, 75494, 116997, 123335, 130336, 137521,\n", + " 143880, 150146, 158991, 195561, 196250, 196909],\n", + " dtype='int64'), Int64Index([ 9649, 58795, 62153, 75495, 116998, 123336, 130337, 137522,\n", + " 143881, 150147, 158992, 195562, 196251, 196910],\n", + " dtype='int64'), Int64Index([ 9650, 58796, 62154, 75496, 116999, 123337, 130338, 137523,\n", + " 143882, 150148, 158993, 195563, 196252, 196911],\n", + " dtype='int64'), Int64Index([ 9651, 58797, 62155, 75497, 117000, 123338, 130339, 137524,\n", + " 143883, 150149, 158994, 195564, 196253, 196912],\n", + " dtype='int64'), Int64Index([ 9652, 58798, 62156, 75498, 117001, 123339, 130340, 137525,\n", + " 143884, 150150, 158995, 195565, 196254, 196913],\n", + " dtype='int64'), Int64Index([ 9653, 58799, 62157, 75499, 117002, 123340, 130341, 137526,\n", + " 143885, 150151, 158996, 195566, 196255, 196914],\n", + " dtype='int64'), Int64Index([ 9654, 58800, 62158, 75500, 117003, 123341, 130342, 137527,\n", + " 143886, 150152, 158997, 195567, 196256, 196915],\n", + " dtype='int64'), Int64Index([ 9655, 58801, 62159, 75501, 117004, 123342, 130343, 137528,\n", + " 143887, 150153, 158998, 195568, 196257, 196916],\n", + " dtype='int64'), Int64Index([ 9656, 58802, 62160, 75502, 117005, 123343, 130344, 137529,\n", + " 143888, 150154, 158999, 195569, 196258, 196917],\n", + " dtype='int64'), Int64Index([ 9657, 58803, 62161, 75503, 117006, 123344, 130345, 137530,\n", + " 143889, 150155, 159000, 195570, 196259, 196918],\n", + " dtype='int64'), Int64Index([ 9658, 58804, 62162, 75504, 117007, 123345, 130346, 137531,\n", + " 143890, 150156, 159001, 195571, 196260, 196919],\n", + " dtype='int64'), Int64Index([ 9659, 58805, 62163, 75505, 117008, 123346, 130347, 137532,\n", + " 143891, 150157, 159002, 195572, 196261, 196920],\n", + " dtype='int64'), Int64Index([ 9660, 58806, 62164, 75506, 117009, 123347, 130348, 137533,\n", + " 143892, 150158, 159003, 195573, 196262, 196921],\n", + " dtype='int64'), Int64Index([ 9661, 58807, 62165, 75507, 117010, 123348, 130349, 137534,\n", + " 143893, 150159, 159004, 195574, 196263, 196922],\n", + " dtype='int64'), Int64Index([ 9662, 58808, 62166, 75508, 117011, 123349, 130350, 137535,\n", + " 143894, 150160, 159005, 195575, 196264, 196923],\n", + " dtype='int64'), Int64Index([ 9663, 58809, 62167, 75509, 117012, 123350, 130351, 137536,\n", + " 143895, 150161, 159006, 195576, 196265, 196924],\n", + " dtype='int64'), Int64Index([ 9664, 58810, 62168, 75510, 117013, 123351, 130352, 137537,\n", + " 143896, 150162, 159007, 195577, 196266, 196925],\n", + " dtype='int64'), Int64Index([ 9665, 58811, 62169, 75511, 117014, 123352, 130353, 137538,\n", + " 143897, 150163, 159008, 195578, 196267, 196926],\n", + " dtype='int64'), Int64Index([ 9666, 58812, 62170, 75512, 117015, 123353, 130354, 137539,\n", + " 143898, 150164, 159009, 195579, 196268, 196927],\n", + " dtype='int64'), Int64Index([ 9667, 58813, 62171, 75513, 117016, 123354, 130355, 137540,\n", + " 143899, 150165, 159010, 195580, 196269, 196928],\n", + " dtype='int64'), Int64Index([ 9668, 58814, 62172, 75514, 117017, 123355, 130356, 137541,\n", + " 143900, 150166, 159011, 195581, 196270, 196929],\n", + " dtype='int64'), Int64Index([ 9669, 58815, 62173, 75515, 117018, 123356, 130357, 137542,\n", + " 143901, 150167, 159012, 195582, 196271, 196930],\n", + " dtype='int64'), Int64Index([ 9670, 58816, 62174, 75516, 117019, 123357, 130358, 137543,\n", + " 143902, 150168, 159013, 195583, 196272, 196931],\n", + " dtype='int64'), Int64Index([ 9671, 58817, 62175, 75517, 117020, 123358, 130359, 137544,\n", + " 143903, 150169, 159014, 195584, 196273, 196932],\n", + " dtype='int64'), Int64Index([ 9672, 58818, 62176, 75518, 117021, 123359, 130360, 137545,\n", + " 143904, 150170, 159015, 195585, 196274, 196933],\n", + " dtype='int64'), Int64Index([ 9673, 58819, 62177, 75519, 117022, 123360, 130361, 137546,\n", + " 143905, 150171, 159016, 195586, 196275, 196934],\n", + " dtype='int64'), Int64Index([ 9674, 58820, 62178, 75520, 117023, 123361, 130362, 137547,\n", + " 143906, 150172, 159017, 195587, 196276, 196935],\n", + " dtype='int64'), Int64Index([ 9675, 58821, 62179, 75521, 117024, 123362, 130363, 137548,\n", + " 143907, 150173, 159018, 195588, 196277, 196936],\n", + " dtype='int64'), Int64Index([ 9676, 58822, 62180, 75522, 117025, 123363, 130364, 137549,\n", + " 143908, 150174, 159019, 195589, 196278, 196937],\n", + " dtype='int64'), Int64Index([ 9677, 58823, 62181, 75523, 117026, 123364, 130365, 137550,\n", + " 143909, 150175, 159020, 195590, 196279, 196938],\n", + " dtype='int64'), Int64Index([ 9678, 58824, 62182, 75524, 117027, 123365, 130366, 137551,\n", + " 143910, 150176, 159021, 195591, 196280, 196939],\n", + " dtype='int64'), Int64Index([ 9679, 58825, 62183, 75525, 117028, 123366, 130367, 137552,\n", + " 143911, 150177, 159022, 195592, 196281, 196940],\n", + " dtype='int64'), Int64Index([ 9680, 58826, 62184, 75526, 117029, 123367, 130368, 137553,\n", + " 143912, 150178, 159023, 195593, 196282, 196941],\n", + " dtype='int64'), Int64Index([ 9681, 58827, 62185, 75527, 117030, 123368, 130369, 137554,\n", + " 143913, 150179, 159024, 195594, 196283, 196942],\n", + " dtype='int64'), Int64Index([ 9682, 58828, 62186, 75528, 117031, 123369, 130370, 137555,\n", + " 143914, 150180, 159025, 195595, 196284, 196943],\n", + " dtype='int64'), Int64Index([ 9683, 58829, 62187, 75529, 117032, 123370, 130371, 137556,\n", + " 143915, 150181, 159026, 195596, 196285, 196944],\n", + " dtype='int64'), Int64Index([ 9684, 58830, 62188, 75530, 117033, 123371, 130372, 137557,\n", + " 143916, 150182, 159027, 195597, 196286, 196945],\n", + " dtype='int64'), Int64Index([ 9685, 58831, 62189, 75531, 117034, 123372, 130373, 137558,\n", + " 143917, 150183, 159028, 195598, 196287, 196946],\n", + " dtype='int64'), Int64Index([ 9686, 58832, 62190, 75532, 117035, 123373, 130374, 137559,\n", + " 143918, 150184, 159029, 195599, 196288, 196947],\n", + " dtype='int64'), Int64Index([ 9687, 58833, 62191, 75533, 117036, 123374, 130375, 137560,\n", + " 143919, 150185, 159030, 195600, 196289, 196948],\n", + " dtype='int64'), Int64Index([ 9688, 58834, 62192, 75534, 117037, 123375, 130376, 137561,\n", + " 143920, 150186, 159031, 195601, 196290, 196949],\n", + " dtype='int64'), Int64Index([ 9689, 58835, 62193, 75535, 117038, 123376, 130377, 137562,\n", + " 143921, 150187, 159032, 195602, 196291, 196950],\n", + " dtype='int64'), Int64Index([ 9690, 58836, 62194, 75536, 117039, 123377, 130378, 137563,\n", + " 143922, 150188, 159033, 195603, 196292, 196951],\n", + " dtype='int64'), Int64Index([ 9691, 58837, 62195, 75537, 117040, 123378, 130379, 137564,\n", + " 143923, 150189, 159034, 195604, 196293, 196952],\n", + " dtype='int64'), Int64Index([ 9692, 58838, 62196, 75538, 117041, 123379, 130380, 137565,\n", + " 143924, 150190, 159035, 195605, 196294, 196953],\n", + " dtype='int64'), Int64Index([ 9693, 58839, 62197, 75539, 117042, 123380, 130381, 137566,\n", + " 143925, 150191, 159036, 195606, 196295, 196954],\n", + " dtype='int64'), Int64Index([ 9694, 58840, 62198, 75540, 117043, 123381, 130382, 137567,\n", + " 143926, 150192, 159037, 195607, 196296, 196955],\n", + " dtype='int64'), Int64Index([ 9695, 58841, 62199, 75541, 117044, 123382, 130383, 137568,\n", + " 143927, 150193, 159038, 195608, 196297, 196956],\n", + " dtype='int64'), Int64Index([ 9696, 58842, 62200, 75542, 117045, 123383, 130384, 137569,\n", + " 143928, 150194, 159039, 195609, 196298, 196957],\n", + " dtype='int64'), Int64Index([ 9697, 58843, 62201, 75543, 117046, 123384, 130385, 137570,\n", + " 143929, 150195, 159040, 195610, 196299, 196958],\n", + " dtype='int64'), Int64Index([ 9698, 58844, 62202, 75544, 117047, 123385, 130386, 137571,\n", + " 143930, 150196, 159041, 195611, 196300, 196959],\n", + " dtype='int64'), Int64Index([ 9699, 58845, 62203, 75545, 117048, 123386, 130387, 137572,\n", + " 143931, 150197, 159042, 195612, 196301, 196960],\n", + " dtype='int64'), Int64Index([ 9700, 58846, 62204, 75546, 117049, 123387, 130388, 137573,\n", + " 143932, 150198, 159043, 195613, 196302, 196961],\n", + " dtype='int64'), Int64Index([ 9701, 58847, 62205, 75547, 117050, 123388, 130389, 137574,\n", + " 143933, 150199, 159044, 195614, 196303, 196962],\n", + " dtype='int64'), Int64Index([ 9702, 58848, 62206, 75548, 117051, 123389, 130390, 137575,\n", + " 143934, 150200, 159045, 195615, 196304, 196963],\n", + " dtype='int64'), Int64Index([ 9703, 58849, 62207, 75549, 117052, 123390, 130391, 137576,\n", + " 143935, 150201, 159046, 195616, 196305, 196964],\n", + " dtype='int64'), Int64Index([ 9704, 58850, 62208, 75550, 117053, 123391, 130392, 137577,\n", + " 143936, 150202, 159047, 195617, 196306, 196965],\n", + " dtype='int64'), Int64Index([ 9705, 58851, 62209, 75551, 117054, 123392, 130393, 137578,\n", + " 143937, 150203, 159048, 195618, 196307, 196966],\n", + " dtype='int64'), Int64Index([ 9706, 58852, 62210, 75552, 117055, 123393, 130394, 137579,\n", + " 143938, 150204, 159049, 195619, 196308, 196967],\n", + " dtype='int64'), Int64Index([ 9707, 58853, 62211, 75553, 117056, 123394, 130395, 136715,\n", + " 137580, 143939, 150205, 159050, 195620, 196309, 196968],\n", + " dtype='int64'), Int64Index([ 9708, 58854, 62212, 75554, 117057, 123395, 130396, 136716,\n", + " 137581, 143940, 150206, 159051, 195621, 196310, 196969],\n", + " dtype='int64'), Int64Index([ 9709, 58855, 62213, 75555, 117058, 123396, 130397, 136717,\n", + " 137582, 143941, 150207, 159052, 195622, 196311, 196970],\n", + " dtype='int64'), Int64Index([ 9710, 58856, 62214, 75556, 117059, 123397, 130398, 136718,\n", + " 137583, 143942, 150208, 159053, 195623, 196312, 196971],\n", + " dtype='int64'), Int64Index([ 9711, 58857, 62215, 75557, 117060, 123398, 130399, 136719,\n", + " 137584, 143943, 150209, 159054, 195624, 196313, 196972],\n", + " dtype='int64'), Int64Index([ 9712, 58858, 62216, 75558, 117061, 123399, 130400, 136720,\n", + " 137585, 143944, 150210, 159055, 195625, 196314, 196973],\n", + " dtype='int64'), Int64Index([ 9713, 58859, 62217, 75559, 117062, 123400, 130401, 136721,\n", + " 137586, 143945, 150211, 159056, 195626, 196315, 196974],\n", + " dtype='int64'), Int64Index([ 9714, 58860, 62218, 75560, 117063, 123401, 130402, 136722,\n", + " 137587, 143946, 150212, 159057, 195627, 196316, 196975],\n", + " dtype='int64'), Int64Index([ 9715, 58861, 62219, 75561, 117064, 123402, 130403, 136723,\n", + " 137588, 143947, 150213, 159058, 195628, 196317, 196976],\n", + " dtype='int64'), Int64Index([ 9716, 58862, 62220, 75562, 117065, 123403, 130404, 136724,\n", + " 137589, 143948, 150214, 159059, 195629, 196318, 196977],\n", + " dtype='int64'), Int64Index([ 9717, 58863, 62221, 75563, 117066, 123404, 130405, 136725,\n", + " 137590, 143949, 150215, 159060, 195630, 196319, 196978],\n", + " dtype='int64'), Int64Index([ 9718, 58864, 62222, 75564, 117067, 123405, 130406, 136726,\n", + " 137591, 143950, 150216, 159061, 195631, 196320, 196979],\n", + " dtype='int64'), Int64Index([ 9719, 58865, 62223, 75565, 117068, 123406, 130407, 136727,\n", + " 137592, 143951, 150217, 159062, 179906, 195632, 196321, 196980],\n", + " dtype='int64'), Int64Index([ 9720, 58866, 62224, 75566, 117069, 123407, 130408, 136728,\n", + " 137593, 143952, 150218, 159063, 179907, 195633, 196322, 196981],\n", + " dtype='int64'), Int64Index([ 9721, 58867, 62225, 75567, 117070, 123408, 130409, 136729,\n", + " 137594, 143953, 150219, 159064, 179908, 195634, 196323, 196982],\n", + " dtype='int64'), Int64Index([ 9722, 58868, 62226, 75568, 117071, 123409, 130410, 136730,\n", + " 137595, 143954, 150220, 159065, 179909, 195635, 196324, 196983],\n", + " dtype='int64'), Int64Index([ 9723, 58869, 62227, 75569, 117072, 123410, 130411, 136731,\n", + " 137596, 143955, 150221, 159066, 179910, 195636, 196325, 196984],\n", + " dtype='int64'), Int64Index([ 9724, 58870, 62228, 75570, 117073, 123411, 130412, 136732,\n", + " 137597, 143956, 150222, 159067, 179911, 195637, 196326, 196985],\n", + " dtype='int64'), Int64Index([ 9725, 58871, 62229, 75571, 117074, 123412, 130413, 136733,\n", + " 137598, 143957, 150223, 159068, 179912, 195638, 196327, 196986],\n", + " dtype='int64'), Int64Index([ 9726, 58872, 62230, 75572, 117075, 123413, 130414, 136734,\n", + " 137599, 143958, 150224, 159069, 179913, 183149, 195639, 196328,\n", + " 196987],\n", + " dtype='int64'), Int64Index([ 9727, 58873, 62231, 75573, 117076, 123414, 130415, 136735,\n", + " 137600, 143959, 150225, 159070, 179914, 183150, 195640, 196329,\n", + " 196988],\n", + " dtype='int64'), Int64Index([ 9728, 58874, 62232, 75574, 117077, 123415, 130416, 136736,\n", + " 137601, 143960, 150226, 159071, 179915, 183151, 195641, 196330,\n", + " 196989],\n", + " dtype='int64'), Int64Index([ 9729, 58875, 62233, 75575, 117078, 123416, 130417, 136737,\n", + " 137602, 143961, 150227, 159072, 179916, 183152, 195642, 196331,\n", + " 196990],\n", + " dtype='int64'), Int64Index([ 9730, 58876, 62234, 75576, 117079, 123417, 130418, 136738,\n", + " 137603, 143962, 150228, 159073, 179917, 183153, 195643, 196332,\n", + " 196991],\n", + " dtype='int64'), Int64Index([ 9731, 58877, 62235, 75577, 117080, 123418, 130419, 136739,\n", + " 137604, 143963, 150229, 159074, 179918, 183154, 195644, 196333,\n", + " 196992],\n", + " dtype='int64'), Int64Index([ 9732, 58878, 62236, 75578, 117081, 123419, 130420, 136740,\n", + " 137605, 143964, 150230, 159075, 179919, 183155, 195645, 196334,\n", + " 196993],\n", + " dtype='int64'), Int64Index([ 9733, 58879, 62237, 75579, 117082, 123420, 130421, 136741,\n", + " 137606, 143965, 150231, 159076, 179920, 183156, 195646, 196335,\n", + " 196994],\n", + " dtype='int64'), Int64Index([ 9734, 58880, 62238, 75580, 117083, 123421, 130422, 136742,\n", + " 137607, 143966, 150232, 159077, 179921, 183157, 195647, 196336,\n", + " 196995],\n", + " dtype='int64'), Int64Index([ 9735, 58881, 62239, 75581, 117084, 123422, 130423, 136743,\n", + " 137608, 143967, 150233, 159078, 179922, 183158, 195648, 196337,\n", + " 196996],\n", + " dtype='int64'), Int64Index([ 9736, 58882, 62240, 75582, 117085, 123423, 130424, 136744,\n", + " 137609, 143968, 150234, 159079, 179923, 183159, 195649, 196338,\n", + " 196997],\n", + " dtype='int64'), Int64Index([ 9737, 58883, 62241, 75583, 117086, 123424, 130425, 136745,\n", + " 137610, 143969, 150235, 159080, 179924, 183160, 195650, 196339,\n", + " 196998],\n", + " dtype='int64'), Int64Index([ 9738, 58884, 62242, 75584, 117087, 123425, 130426, 136746,\n", + " 137611, 143970, 150236, 159081, 179925, 183161, 195651, 196340,\n", + " 196999],\n", + " dtype='int64'), Int64Index([ 9739, 58885, 62243, 75585, 117088, 123426, 130427, 136747,\n", + " 137612, 143971, 150237, 159082, 179926, 183162, 195652, 196341,\n", + " 197000],\n", + " dtype='int64'), Int64Index([ 9740, 58886, 62244, 75586, 117089, 123427, 130428, 136748,\n", + " 137613, 143972, 150238, 159083, 179927, 183163, 195653, 196342,\n", + " 197001],\n", + " dtype='int64'), Int64Index([ 9741, 58887, 62245, 75587, 117090, 123428, 130429, 136749,\n", + " 137614, 143973, 150239, 159084, 179928, 183164, 195654, 196343,\n", + " 197002],\n", + " dtype='int64'), Int64Index([ 9742, 58888, 62246, 75588, 117091, 123429, 130430, 136750,\n", + " 137615, 143974, 150240, 159085, 179929, 183165, 195655, 196344,\n", + " 197003],\n", + " dtype='int64'), Int64Index([ 9743, 58889, 62247, 75589, 117092, 123430, 130431, 136751,\n", + " 137616, 143975, 150241, 159086, 179930, 183166, 195656, 196345,\n", + " 197004],\n", + " dtype='int64'), Int64Index([ 9744, 58890, 62248, 75590, 117093, 123431, 130432, 136752,\n", + " 137617, 143976, 150242, 159087, 179931, 183167, 195657, 196346,\n", + " 197005],\n", + " dtype='int64'), Int64Index([ 9745, 58891, 62249, 75591, 117094, 123432, 130433, 136753,\n", + " 137618, 143977, 150243, 159088, 179932, 183168, 195658, 196347,\n", + " 197006],\n", + " dtype='int64'), Int64Index([ 9746, 58892, 62250, 75592, 117095, 123433, 130434, 136754,\n", + " 137619, 143978, 150244, 159089, 179933, 183169, 195659, 196348,\n", + " 197007],\n", + " dtype='int64'), Int64Index([ 9747, 58893, 62251, 75593, 96886, 117096, 123434, 130435,\n", + " 136755, 137620, 143979, 150245, 159090, 179934, 183170, 195660,\n", + " 196349, 197008],\n", + " dtype='int64'), Int64Index([ 9748, 58894, 62252, 75594, 96887, 117097, 123435, 130436,\n", + " 136756, 137621, 143980, 150246, 159091, 179935, 183171, 195661,\n", + " 196350, 197009],\n", + " dtype='int64'), Int64Index([ 9749, 58895, 62253, 75595, 96888, 117098, 123436, 130437,\n", + " 136757, 137622, 143981, 150247, 159092, 179936, 183172, 195662,\n", + " 196351, 197010],\n", + " dtype='int64'), Int64Index([ 9750, 37709, 58896, 62254, 75596, 96889, 117099, 123437,\n", + " 130438, 136758, 137623, 143982, 150248, 159093, 179937, 183173,\n", + " 195663, 196352, 197011],\n", + " dtype='int64'), Int64Index([ 9751, 37710, 58897, 62255, 75597, 96890, 117100, 123438,\n", + " 130439, 136759, 137624, 143983, 150249, 159094, 179938, 183174,\n", + " 195664, 196353, 197012],\n", + " dtype='int64'), Int64Index([ 9752, 37711, 58898, 62256, 75598, 96891, 117101, 123439,\n", + " 130440, 136760, 137625, 143984, 150250, 159095, 179939, 183175,\n", + " 195665, 196354, 197013],\n", + " dtype='int64'), Int64Index([ 9753, 37712, 58899, 62257, 75599, 96892, 117102, 123440,\n", + " 130441, 136761, 137626, 143985, 150251, 159096, 179940, 183176,\n", + " 195666, 196355, 197014],\n", + " dtype='int64'), Int64Index([ 9754, 37713, 58900, 62258, 75600, 96893, 117103, 123441,\n", + " 130442, 136762, 137627, 143986, 150252, 159097, 179941, 183177,\n", + " 195667, 196356, 197015],\n", + " dtype='int64'), Int64Index([ 9755, 37714, 58901, 62259, 75601, 96894, 117104, 123442,\n", + " 130443, 136763, 137628, 143987, 150253, 159098, 179942, 183178,\n", + " 195668, 196357, 197016],\n", + " dtype='int64'), Int64Index([ 9756, 37715, 58902, 62260, 75602, 96895, 117105, 123443,\n", + " 130444, 136764, 137629, 143988, 150254, 159099, 179943, 183179,\n", + " 195669, 196358, 197017],\n", + " dtype='int64'), Int64Index([ 9757, 37716, 58903, 62261, 75603, 96896, 117106, 123444,\n", + " 130445, 136765, 137630, 143989, 150255, 159100, 179944, 183180,\n", + " 195670, 196359, 197018],\n", + " dtype='int64'), Int64Index([ 9758, 37717, 58904, 62262, 75604, 96897, 117107, 123445,\n", + " 130446, 136766, 137631, 143990, 150256, 159101, 179945, 183181,\n", + " 195671, 196360, 197019],\n", + " dtype='int64'), Int64Index([ 9759, 37718, 58905, 62263, 75605, 96898, 117108, 123446,\n", + " 130447, 136767, 137632, 143991, 150257, 159102, 179946, 183182,\n", + " 195672, 196361, 197020],\n", + " dtype='int64'), Int64Index([ 9760, 37719, 58906, 62264, 75606, 96899, 117109, 123447,\n", + " 130448, 136768, 137633, 143992, 150258, 159103, 179947, 183183,\n", + " 195673, 196362, 197021],\n", + " dtype='int64'), Int64Index([ 9761, 37720, 58907, 62265, 75607, 96900, 117110, 123448,\n", + " 130449, 136769, 137634, 143993, 150259, 159104, 179948, 183184,\n", + " 195674, 196363, 197022],\n", + " dtype='int64'), Int64Index([ 9762, 37721, 58908, 62266, 75608, 96901, 117111, 123449,\n", + " 130450, 136770, 137635, 143994, 150260, 159105, 179949, 183185,\n", + " 195675, 196364, 197023],\n", + " dtype='int64'), Int64Index([ 9763, 37722, 58909, 62267, 75609, 96902, 117112, 123450,\n", + " 130451, 136771, 137636, 143995, 150261, 159106, 179950, 183186,\n", + " 195676, 196365, 197024],\n", + " dtype='int64'), Int64Index([ 9764, 37723, 58910, 62268, 75610, 96903, 117113, 123451,\n", + " 130452, 136772, 137637, 143996, 150262, 159107, 179951, 183187,\n", + " 195677, 196366, 197025],\n", + " dtype='int64'), Int64Index([ 9765, 37724, 58911, 62269, 75611, 96904, 117114, 123452,\n", + " 130453, 136773, 137638, 143997, 150263, 159108, 179952, 183188,\n", + " 195678, 196367, 197026],\n", + " dtype='int64'), Int64Index([ 9766, 37725, 58912, 62270, 75612, 96905, 117115, 123453,\n", + " 130454, 136774, 137639, 143998, 150264, 159109, 179953, 183189,\n", + " 195679, 196368, 197027],\n", + " dtype='int64'), Int64Index([ 9767, 37726, 58913, 62271, 75613, 96906, 117116, 123454,\n", + " 130455, 136775, 137640, 143999, 150265, 159110, 179954, 183190,\n", + " 195680, 196369, 197028],\n", + " dtype='int64'), Int64Index([ 9768, 37727, 58914, 62272, 75614, 96907, 117117, 123455,\n", + " 130456, 136776, 137641, 144000, 150266, 159111, 179955, 183191,\n", + " 195681, 196370, 197029],\n", + " dtype='int64'), Int64Index([ 9769, 37728, 58915, 62273, 75615, 96908, 117118, 123456,\n", + " 130457, 136777, 137642, 144001, 150267, 159112, 179956, 183192,\n", + " 195682, 196371, 197030],\n", + " dtype='int64'), Int64Index([ 9770, 37729, 58916, 62274, 75616, 96909, 117119, 123457,\n", + " 130458, 136778, 137643, 144002, 150268, 159113, 179957, 183193,\n", + " 195683, 196372, 197031],\n", + " dtype='int64'), Int64Index([ 9771, 37730, 58917, 62275, 75617, 96910, 117120, 123458,\n", + " 130459, 136779, 137644, 144003, 150269, 159114, 179958, 183194,\n", + " 195684, 196373, 197032],\n", + " dtype='int64'), Int64Index([ 9772, 37731, 58918, 62276, 75618, 96911, 117121, 123459,\n", + " 130460, 136780, 137645, 144004, 150270, 159115, 179959, 183195,\n", + " 195685, 196374, 197033],\n", + " dtype='int64'), Int64Index([ 9773, 37732, 58919, 62277, 75619, 96912, 117122, 123460,\n", + " 130461, 136781, 137646, 144005, 150271, 159116, 179960, 183196,\n", + " 195686, 196375, 197034],\n", + " dtype='int64'), Int64Index([ 9774, 37733, 58920, 62278, 75620, 96913, 117123, 123461,\n", + " 130462, 136782, 137647, 144006, 150272, 159117, 179961, 183197,\n", + " 195687, 196376, 197035],\n", + " dtype='int64'), Int64Index([ 9775, 37734, 58921, 62279, 75621, 96914, 117124, 123462,\n", + " 130463, 136783, 137648, 140864, 144007, 150273, 159118, 179962,\n", + " 183198, 195688, 196377, 197036],\n", + " dtype='int64'), Int64Index([ 9776, 37735, 58922, 62280, 75622, 96915, 117125, 123463,\n", + " 130464, 136784, 137649, 140865, 144008, 150274, 159119, 179963,\n", + " 183199, 195689, 196378, 197037],\n", + " dtype='int64'), Int64Index([ 9777, 37736, 58923, 62281, 75623, 96916, 117126, 123464,\n", + " 130465, 136785, 137650, 140866, 144009, 150275, 159120, 179964,\n", + " 183200, 195690, 196379, 197038],\n", + " dtype='int64'), Int64Index([ 9778, 37737, 58924, 62282, 75624, 96917, 117127, 123465,\n", + " 130466, 136786, 137651, 140867, 144010, 150276, 159121, 179965,\n", + " 183201, 195691, 196380, 197039],\n", + " dtype='int64'), Int64Index([ 9779, 37738, 58925, 62283, 75625, 96918, 117128, 123466,\n", + " 130467, 136787, 137652, 140868, 144011, 150277, 159122, 179966,\n", + " 183202, 195692, 196381, 197040],\n", + " dtype='int64'), Int64Index([ 9780, 37739, 58926, 62284, 75626, 96919, 117129, 123467,\n", + " 130468, 136788, 137653, 140869, 144012, 150278, 159123, 179967,\n", + " 183203, 195693, 196382, 197041],\n", + " dtype='int64'), Int64Index([ 9781, 37740, 58927, 62285, 75627, 96920, 117130, 123468,\n", + " 130469, 133641, 136789, 137654, 140870, 144013, 150279, 159124,\n", + " 178220, 179968, 183204, 195694, 196383, 197042],\n", + " dtype='int64'), Int64Index([ 9782, 37741, 58928, 62286, 75628, 96921, 117131, 123469,\n", + " 130470, 133642, 136790, 137655, 140871, 144014, 150280, 159125,\n", + " 178221, 179969, 183205, 195695, 196384, 197043],\n", + " dtype='int64'), Int64Index([ 9783, 37742, 58929, 62287, 75629, 96922, 117132, 123470,\n", + " 130471, 133643, 136791, 137656, 140872, 144015, 150281, 159126,\n", + " 178222, 179970, 183206, 195696, 196385, 197044],\n", + " dtype='int64'), Int64Index([ 9784, 37743, 58930, 62288, 75630, 96923, 117133, 123471,\n", + " 130472, 133644, 136792, 137657, 140873, 144016, 150282, 159127,\n", + " 178223, 179971, 183207, 195697, 196386, 197045],\n", + " dtype='int64'), Int64Index([ 9785, 37744, 58931, 62289, 75631, 96924, 117134, 123472,\n", + " 130473, 133645, 136793, 137658, 140874, 144017, 150283, 159128,\n", + " 178224, 179972, 183208, 195698, 196387, 197046],\n", + " dtype='int64'), Int64Index([ 9786, 37745, 58932, 62290, 75632, 96925, 117135, 123473,\n", + " 130474, 133646, 136794, 137659, 140875, 144018, 150284, 159129,\n", + " 178225, 179973, 183209, 195699, 196388, 197047],\n", + " dtype='int64'), Int64Index([ 9787, 37746, 58933, 62291, 75633, 96926, 117136, 123474,\n", + " 130475, 133647, 136795, 137660, 140876, 144019, 150285, 159130,\n", + " 178226, 179974, 183210, 195700, 196389, 197048],\n", + " dtype='int64'), Int64Index([ 9788, 37747, 58934, 62292, 75634, 96927, 117137, 123475,\n", + " 130476, 133648, 136796, 137661, 140877, 144020, 150286, 159131,\n", + " 178227, 179975, 183211, 195701, 196390, 197049],\n", + " dtype='int64'), Int64Index([ 9789, 37748, 58935, 62293, 75635, 96928, 117138, 123476,\n", + " 130477, 133649, 136797, 137662, 140878, 144021, 150287, 159132,\n", + " 178228, 179976, 183212, 195702, 196391, 197050],\n", + " dtype='int64'), Int64Index([ 9790, 37749, 58936, 62294, 75636, 96929, 117139, 123477,\n", + " 130478, 133650, 136798, 137663, 140879, 144022, 150288, 159133,\n", + " 178229, 179977, 183213, 195703, 196392, 197051],\n", + " dtype='int64'), Int64Index([ 9791, 37750, 58937, 62295, 75637, 96930, 117140, 123478,\n", + " 130479, 133651, 136799, 137664, 140880, 144023, 150289, 159134,\n", + " 178230, 179978, 183214, 195704, 196393, 197052],\n", + " dtype='int64'), Int64Index([ 9792, 37751, 58938, 62296, 75638, 96931, 117141, 123479,\n", + " 130480, 133652, 136800, 137665, 140881, 144024, 150290, 159135,\n", + " 178231, 179979, 183215, 195705, 196394, 197053],\n", + " dtype='int64'), Int64Index([ 9793, 37752, 58939, 62297, 75639, 96932, 117142, 123480,\n", + " 130481, 133653, 136801, 137666, 140882, 144025, 150291, 159136,\n", + " 178232, 179980, 183216, 195706, 196395, 197054],\n", + " dtype='int64'), Int64Index([ 9794, 37753, 58940, 62298, 75640, 96933, 117143, 123481,\n", + " 130482, 133654, 136802, 137667, 140883, 144026, 150292, 159137,\n", + " 178233, 179981, 183217, 195707, 196396, 197055],\n", + " dtype='int64'), Int64Index([ 9795, 37754, 58941, 62299, 75641, 96934, 117144, 123482,\n", + " 130483, 133655, 136803, 137668, 140884, 144027, 150293, 159138,\n", + " 178234, 179982, 183218, 195708, 196397, 197056],\n", + " dtype='int64'), Int64Index([ 9796, 37755, 58942, 62300, 75642, 96935, 117145, 123483,\n", + " 130484, 133656, 136804, 137669, 140885, 144028, 150294, 159139,\n", + " 178235, 179983, 183219, 195709, 196398, 197057],\n", + " dtype='int64'), Int64Index([ 9797, 37756, 58943, 62301, 75643, 96936, 117146, 123484,\n", + " 130485, 133657, 136805, 137670, 140886, 144029, 150295, 159140,\n", + " 178236, 179984, 183220, 195710, 196399, 197058],\n", + " dtype='int64'), Int64Index([ 9798, 37757, 58944, 62302, 75644, 96937, 117147, 123485,\n", + " 130486, 133658, 136806, 137671, 140887, 144030, 150296, 159141,\n", + " 178237, 179985, 183221, 195711, 196400, 197059],\n", + " dtype='int64'), Int64Index([ 9799, 37758, 58945, 62303, 75645, 96938, 117148, 123486,\n", + " 130487, 133659, 136807, 137672, 140888, 144031, 150297, 159142,\n", + " 178238, 179986, 183222, 195712, 196401, 197060],\n", + " dtype='int64'), Int64Index([ 9800, 37759, 58946, 62304, 75646, 96939, 117149, 123487,\n", + " 130488, 133660, 136808, 137673, 140889, 144032, 150298, 159143,\n", + " 178239, 179987, 183223, 195713, 196402, 197061],\n", + " dtype='int64'), Int64Index([ 9801, 37760, 58947, 62305, 75647, 96940, 117150, 123488,\n", + " 130489, 133661, 136809, 137674, 140890, 144033, 150299, 159144,\n", + " 178240, 179988, 183224, 195714, 196403, 197062],\n", + " dtype='int64'), Int64Index([ 9802, 37761, 58948, 62306, 75648, 96941, 117151, 123489,\n", + " 130490, 133662, 136810, 137675, 140891, 144034, 150300, 159145,\n", + " 178241, 179989, 183225, 195715, 196404, 197063],\n", + " dtype='int64'), Int64Index([ 9803, 37762, 58949, 62307, 75649, 96942, 117152, 123490,\n", + " 130491, 133663, 136811, 137676, 140892, 144035, 150301, 159146,\n", + " 178242, 179990, 183226, 195716, 196405, 197064],\n", + " dtype='int64'), Int64Index([ 9804, 37763, 58950, 62308, 75650, 96943, 117153, 123491,\n", + " 130492, 133664, 136812, 137677, 140893, 144036, 150302, 159147,\n", + " 178243, 179991, 183227, 195717, 196406, 197065],\n", + " dtype='int64'), Int64Index([ 9805, 37764, 58951, 62309, 75651, 96944, 117154, 123492,\n", + " 130493, 133665, 136813, 137678, 140894, 144037, 150303, 159148,\n", + " 178244, 179992, 183228, 195718, 196407, 197066],\n", + " dtype='int64'), Int64Index([ 9806, 37765, 58952, 62310, 75652, 96945, 117155, 123493,\n", + " 130494, 133666, 136814, 137679, 140895, 144038, 150304, 159149,\n", + " 178245, 179993, 183229, 195719, 196408, 197067],\n", + " dtype='int64'), Int64Index([ 9807, 37766, 58953, 62311, 75653, 96946, 117156, 123494,\n", + " 130495, 133667, 136815, 137680, 140896, 144039, 150305, 159150,\n", + " 178246, 179994, 183230, 195720, 196409, 197068],\n", + " dtype='int64'), Int64Index([ 9808, 37767, 58954, 62312, 75654, 96947, 117157, 123495,\n", + " 130496, 133668, 136816, 137681, 140897, 144040, 150306, 159151,\n", + " 178247, 179995, 183231, 195721, 196410, 197069],\n", + " dtype='int64'), Int64Index([ 9809, 37768, 58955, 62313, 75655, 96948, 117158, 123496,\n", + " 130497, 133669, 136817, 137682, 140898, 144041, 150307, 159152,\n", + " 178248, 179996, 183232, 195722, 196411, 197070],\n", + " dtype='int64'), Int64Index([ 9810, 37769, 58956, 62314, 75656, 96949, 117159, 123497,\n", + " 130498, 133670, 136818, 137683, 140899, 144042, 150308, 159153,\n", + " 178249, 179997, 183233, 195723, 196412, 197071],\n", + " dtype='int64'), Int64Index([ 9811, 37770, 58957, 62315, 75657, 96950, 117160, 123498,\n", + " 130499, 133671, 136819, 137684, 140900, 144043, 150309, 159154,\n", + " 178250, 179998, 183234, 195724, 196413, 197072],\n", + " dtype='int64'), Int64Index([ 9812, 37771, 58958, 62316, 75658, 96951, 117161, 123499,\n", + " 130500, 133672, 136820, 137685, 140901, 144044, 150310, 159155,\n", + " 178251, 179999, 183235, 195725, 196414, 197073],\n", + " dtype='int64'), Int64Index([ 9813, 37772, 58959, 62317, 75659, 96952, 117162, 123500,\n", + " 130501, 133673, 136821, 137686, 140902, 144045, 150311, 159156,\n", + " 178252, 180000, 183236, 195726, 196415, 197074],\n", + " dtype='int64'), Int64Index([ 9814, 37773, 58960, 62318, 75660, 96953, 117163, 123501,\n", + " 130502, 133674, 136822, 137687, 140903, 144046, 150312, 159157,\n", + " 178253, 180001, 183237, 195727, 196416, 197075],\n", + " dtype='int64'), Int64Index([ 9815, 37774, 58961, 62319, 75661, 96954, 117164, 123502,\n", + " 130503, 133675, 136823, 137688, 140904, 144047, 150313, 159158,\n", + " 178254, 180002, 183238, 195728, 196417, 197076],\n", + " dtype='int64'), Int64Index([ 9816, 37775, 58962, 62320, 75662, 96955, 117165, 123503,\n", + " 130504, 133676, 136824, 137689, 140905, 144048, 150314, 159159,\n", + " 178255, 180003, 183239, 195729, 196418, 197077],\n", + " dtype='int64'), Int64Index([ 9817, 37776, 58963, 62321, 75663, 96956, 117166, 123504,\n", + " 130505, 133677, 136825, 137690, 140906, 144049, 150315, 159160,\n", + " 178256, 180004, 183240, 195730, 196419, 197078],\n", + " dtype='int64'), Int64Index([ 9818, 37777, 58964, 62322, 75664, 96957, 117167, 123505,\n", + " 130506, 133678, 136826, 137691, 140907, 144050, 150316, 159161,\n", + " 178257, 180005, 183241, 195731, 196420, 197079],\n", + " dtype='int64'), Int64Index([ 9819, 37778, 58965, 62323, 75665, 96958, 117168, 123506,\n", + " 130507, 133679, 136827, 137692, 140908, 144051, 150317, 159162,\n", + " 178258, 180006, 183242, 195732, 196421, 197080],\n", + " dtype='int64'), Int64Index([ 9820, 37779, 58966, 62324, 75666, 96959, 117169, 123507,\n", + " 130508, 133680, 136828, 137693, 140909, 144052, 150318, 159163,\n", + " 178259, 180007, 183243, 195733, 196422, 197081],\n", + " dtype='int64'), Int64Index([ 9821, 37780, 58967, 62325, 75667, 96960, 117170, 123508,\n", + " 130509, 133681, 136829, 137694, 140910, 144053, 150319, 159164,\n", + " 178260, 180008, 183244, 195734, 196423, 197082],\n", + " dtype='int64'), Int64Index([ 9822, 37781, 58968, 62326, 75668, 96961, 117171, 123509,\n", + " 130510, 133682, 136830, 137695, 140911, 144054, 150320, 159165,\n", + " 178261, 180009, 183245, 195735, 196424, 197083],\n", + " dtype='int64'), Int64Index([ 9823, 37782, 58969, 62327, 75669, 96962, 117172, 123510,\n", + " 130511, 133683, 136831, 137696, 140912, 144055, 150321, 159166,\n", + " 178262, 180010, 183246, 195736, 196425, 197084],\n", + " dtype='int64'), Int64Index([ 9824, 37783, 58970, 62328, 75670, 96963, 117173, 123511,\n", + " 130512, 133684, 136832, 137697, 140913, 144056, 150322, 159167,\n", + " 178263, 180011, 183247, 195737, 196426, 197085],\n", + " dtype='int64'), Int64Index([ 9825, 37784, 58971, 62329, 75671, 96964, 117174, 123512,\n", + " 130513, 133685, 136833, 137698, 140914, 144057, 150323, 159168,\n", + " 178264, 180012, 183248, 195738, 196427, 197086],\n", + " dtype='int64'), Int64Index([ 9826, 37785, 58972, 62330, 75672, 96965, 117175, 123513,\n", + " 130514, 133686, 136834, 137699, 140915, 144058, 150324, 159169,\n", + " 178265, 180013, 183249, 195739, 196428, 197087],\n", + " dtype='int64'), Int64Index([ 9827, 37786, 58973, 62331, 75673, 96966, 117176, 123514,\n", + " 130515, 133687, 136835, 137700, 140916, 144059, 150325, 159170,\n", + " 178266, 180014, 183250, 195740, 196429, 197088],\n", + " dtype='int64'), Int64Index([ 9828, 37787, 58974, 62332, 75674, 96967, 117177, 123515,\n", + " 130516, 133688, 136836, 137701, 140917, 144060, 150326, 159171,\n", + " 178267, 180015, 183251, 195741, 196430, 197089],\n", + " dtype='int64'), Int64Index([ 9829, 37788, 58975, 62333, 75675, 96968, 117178, 123516,\n", + " 130517, 133689, 136837, 137702, 140918, 144061, 150327, 159172,\n", + " 178268, 180016, 183252, 195742, 196431, 197090],\n", + " dtype='int64'), Int64Index([ 9830, 37789, 58976, 62334, 75676, 96969, 117179, 123517,\n", + " 130518, 133690, 136838, 137703, 140919, 144062, 150328, 159173,\n", + " 178269, 180017, 183253, 195743, 196432, 197091],\n", + " dtype='int64'), Int64Index([ 9831, 37790, 58977, 62335, 75677, 96970, 117180, 123518,\n", + " 130519, 133691, 136839, 137704, 140920, 144063, 150329, 159174,\n", + " 178270, 180018, 183254, 195744, 196433, 197092],\n", + " dtype='int64'), Int64Index([ 9832, 37791, 58978, 62336, 75678, 96971, 117181, 123519,\n", + " 130520, 133692, 136840, 137705, 140921, 144064, 150330, 159175,\n", + " 178271, 180019, 183255, 195745, 196434, 197093],\n", + " dtype='int64'), Int64Index([ 9833, 37792, 58979, 62337, 75679, 96972, 117182, 123520,\n", + " 130521, 133693, 136841, 137706, 140922, 144065, 150331, 159176,\n", + " 178272, 180020, 183256, 195746, 196435, 197094],\n", + " dtype='int64'), Int64Index([ 9834, 37793, 58980, 62338, 75680, 96973, 117183, 123521,\n", + " 130522, 133694, 136842, 137707, 140923, 144066, 150332, 159177,\n", + " 178273, 180021, 183257, 195747, 196436, 197095],\n", + " dtype='int64'), Int64Index([ 9835, 37794, 58981, 62339, 75681, 96974, 117184, 123522,\n", + " 130523, 133695, 136843, 137708, 140924, 144067, 150333, 159178,\n", + " 178274, 180022, 183258, 195748, 196437, 197096],\n", + " dtype='int64'), Int64Index([ 9836, 37795, 58982, 62340, 75682, 96975, 117185, 123523,\n", + " 130524, 133696, 136844, 137709, 140925, 144068, 150334, 159179,\n", + " 178275, 180023, 183259, 195749, 196438, 197097],\n", + " dtype='int64'), Int64Index([ 9837, 37796, 58983, 62341, 75683, 96976, 117186, 123524,\n", + " 130525, 133697, 136845, 137710, 140926, 144069, 150335, 159180,\n", + " 178276, 180024, 183260, 195750, 196439, 197098],\n", + " dtype='int64'), Int64Index([ 9838, 37797, 58984, 62342, 75684, 96977, 117187, 123525,\n", + " 130526, 133698, 136846, 137711, 140927, 144070, 150336, 159181,\n", + " 178277, 180025, 183261, 195751, 196440, 197099],\n", + " dtype='int64'), Int64Index([ 9839, 37798, 58985, 62343, 75685, 96978, 117188, 123526,\n", + " 130527, 133699, 136847, 137712, 140928, 144071, 150337, 159182,\n", + " 178278, 180026, 183262, 195752, 196441, 197100],\n", + " dtype='int64'), Int64Index([ 9840, 37799, 58986, 62344, 75686, 96979, 117189, 123527,\n", + " 130528, 133700, 136848, 137713, 140929, 144072, 150338, 159183,\n", + " 178279, 180027, 183263, 195753, 196442, 197101],\n", + " dtype='int64'), Int64Index([ 9841, 37800, 58987, 62345, 75687, 96980, 117190, 123528,\n", + " 130529, 133701, 136849, 137714, 140930, 144073, 150339, 159184,\n", + " 178280, 180028, 183264, 195754, 196443, 197102],\n", + " dtype='int64'), Int64Index([ 9842, 37801, 58988, 62346, 75688, 96981, 117191, 123529,\n", + " 130530, 133702, 136850, 137715, 140931, 144074, 150340, 159185,\n", + " 178281, 180029, 183265, 195755, 196444, 197103],\n", + " dtype='int64'), Int64Index([ 9843, 37802, 58989, 62347, 75689, 96982, 117192, 123530,\n", + " 130531, 133703, 136851, 137716, 140932, 143173, 144075, 150341,\n", + " 159186, 178282, 180030, 183266, 195756, 196445, 197104],\n", + " dtype='int64'), Int64Index([ 9844, 37803, 58990, 62348, 75690, 96983, 117193, 123531,\n", + " 130532, 133704, 136852, 137717, 140933, 143174, 144076, 150342,\n", + " 159187, 178283, 180031, 183267, 195757, 196446, 197105],\n", + " dtype='int64'), Int64Index([ 9845, 37804, 58991, 62349, 75691, 96984, 117194, 123532,\n", + " 130533, 133705, 136853, 137718, 140934, 143175, 144077, 150343,\n", + " 159188, 178284, 180032, 183268, 195758, 196447, 197106],\n", + " dtype='int64'), Int64Index([ 9846, 37805, 58992, 62350, 75692, 96985, 117195, 123533,\n", + " 130534, 133706, 136854, 137719, 140935, 143176, 144078, 150344,\n", + " 159189, 178285, 180033, 183269, 195759, 196448, 197107],\n", + " dtype='int64'), Int64Index([ 9847, 37806, 58993, 62351, 75693, 96986, 117196, 123534,\n", + " 130535, 133707, 136855, 137720, 140936, 143177, 144079, 150345,\n", + " 159190, 178286, 180034, 183270, 195760, 196449, 197108],\n", + " dtype='int64'), Int64Index([ 9848, 37807, 58994, 62352, 75694, 96987, 117197, 123535,\n", + " 130536, 133708, 136856, 137721, 140937, 143178, 144080, 150346,\n", + " 159191, 178287, 180035, 183271, 195761, 196450, 197109],\n", + " dtype='int64'), Int64Index([ 9849, 37808, 58995, 62353, 75695, 96988, 117198, 123536,\n", + " 130537, 133709, 136857, 137722, 140938, 143179, 144081, 150347,\n", + " 159192, 178288, 180036, 183272, 195762, 196451, 197110],\n", + " dtype='int64'), Int64Index([ 9850, 37809, 58996, 62354, 75696, 96989, 117199, 123537,\n", + " 130538, 133710, 136858, 137723, 140939, 143180, 144082, 150348,\n", + " 159193, 178289, 180037, 183273, 195763, 196452, 197111],\n", + " dtype='int64'), Int64Index([ 9851, 37810, 58997, 62355, 75697, 96990, 117200, 123538,\n", + " 130539, 133711, 136859, 137724, 140940, 143181, 144083, 150349,\n", + " 159194, 178290, 180038, 183274, 195764, 196453, 197112],\n", + " dtype='int64'), Int64Index([ 9852, 37811, 58998, 62356, 75698, 96991, 117201, 123539,\n", + " 130540, 133712, 136860, 137725, 140941, 143182, 144084, 150350,\n", + " 159195, 178291, 180039, 183275, 195765, 196454, 197113],\n", + " dtype='int64'), Int64Index([ 9853, 37812, 58999, 62357, 75699, 96992, 117202, 123540,\n", + " 130541, 133713, 136861, 137726, 140942, 143183, 144085, 150351,\n", + " 159196, 178292, 180040, 183276, 195766, 196455, 197114],\n", + " dtype='int64'), Int64Index([ 9854, 37813, 59000, 62358, 75700, 96993, 117203, 123541,\n", + " 130542, 133714, 136862, 137727, 140943, 143184, 144086, 150352,\n", + " 159197, 178293, 180041, 183277, 195767, 196456, 197115],\n", + " dtype='int64'), Int64Index([ 9855, 37814, 59001, 62359, 75701, 96994, 117204, 123542,\n", + " 130543, 133715, 136863, 137728, 140944, 143185, 144087, 150353,\n", + " 159198, 178294, 180042, 183278, 195768, 196457, 197116],\n", + " dtype='int64'), Int64Index([ 9856, 37815, 59002, 62360, 75702, 96995, 117205, 123543,\n", + " 130544, 133716, 136864, 137729, 140945, 143186, 144088, 150354,\n", + " 159199, 178295, 180043, 183279, 195769, 196458, 197117],\n", + " dtype='int64'), Int64Index([ 9857, 37816, 59003, 62361, 75703, 96996, 117206, 123544,\n", + " 130545, 133717, 136865, 137730, 140946, 143187, 144089, 150355,\n", + " 159200, 178296, 180044, 183280, 195770, 196459, 197118],\n", + " dtype='int64'), Int64Index([ 9858, 37817, 59004, 62362, 75704, 96997, 117207, 123545,\n", + " 130546, 133718, 136866, 137731, 140947, 143188, 144090, 150356,\n", + " 159201, 178297, 180045, 183281, 195771, 196460, 197119],\n", + " dtype='int64'), Int64Index([ 9859, 37818, 59005, 62363, 75705, 96998, 117208, 123546,\n", + " 130547, 133719, 136867, 137732, 140948, 143189, 144091, 150357,\n", + " 159202, 178298, 180046, 183282, 195772, 196461, 197120],\n", + " dtype='int64'), Int64Index([ 9860, 37819, 59006, 62364, 75706, 96999, 117209, 123547,\n", + " 130548, 133720, 136868, 137733, 140949, 143190, 144092, 150358,\n", + " 159203, 178299, 180047, 183283, 195773, 196462, 197121],\n", + " dtype='int64'), Int64Index([ 9861, 37820, 59007, 62365, 75707, 97000, 117210, 123548,\n", + " 130549, 133721, 136869, 137734, 140950, 143191, 144093, 150359,\n", + " 159204, 178300, 180048, 183284, 195774, 196463, 197122],\n", + " dtype='int64'), Int64Index([ 9862, 37821, 59008, 62366, 75708, 97001, 117211, 123549,\n", + " 130550, 133722, 136870, 137735, 140951, 143192, 144094, 150360,\n", + " 159205, 178301, 180049, 183285, 195775, 196464, 197123],\n", + " dtype='int64'), Int64Index([ 9863, 37822, 59009, 62367, 75709, 97002, 117212, 123550,\n", + " 130551, 133723, 136871, 137736, 140952, 143193, 144095, 150361,\n", + " 159206, 178302, 180050, 183286, 195776, 196465, 197124],\n", + " dtype='int64'), Int64Index([ 9864, 37823, 59010, 62368, 75710, 97003, 117213, 123551,\n", + " 130552, 133724, 136872, 137737, 140953, 143194, 144096, 150362,\n", + " 159207, 178303, 180051, 183287, 195777, 196466, 197125],\n", + " dtype='int64'), Int64Index([ 9865, 37824, 59011, 62369, 75711, 97004, 117214, 123552,\n", + " 130553, 133725, 136873, 137738, 140954, 143195, 144097, 150363,\n", + " 159208, 178304, 180052, 183288, 195778, 196467, 197126],\n", + " dtype='int64'), Int64Index([ 9866, 37825, 59012, 62370, 75712, 97005, 117215, 123553,\n", + " 130554, 133726, 136874, 137739, 140955, 143196, 144098, 150364,\n", + " 159209, 178305, 180053, 183289, 195779, 196468, 197127],\n", + " dtype='int64'), Int64Index([ 9867, 37826, 59013, 62371, 75713, 97006, 117216, 123554,\n", + " 130555, 133727, 136875, 137740, 140956, 143197, 144099, 150365,\n", + " 159210, 178306, 180054, 183290, 195780, 196469, 197128],\n", + " dtype='int64'), Int64Index([ 9868, 37827, 59014, 62372, 75714, 97007, 117217, 123555,\n", + " 130556, 133728, 136876, 137741, 140957, 143198, 144100, 150366,\n", + " 159211, 178307, 180055, 183291, 195781, 196470, 197129],\n", + " dtype='int64'), Int64Index([ 9869, 37828, 59015, 62373, 75715, 97008, 117218, 123556,\n", + " 130557, 133729, 136877, 137742, 140958, 143199, 144101, 150367,\n", + " 159212, 178308, 180056, 183292, 195782, 196471, 197130],\n", + " dtype='int64'), Int64Index([ 9870, 37829, 59016, 62374, 75716, 97009, 117219, 123557,\n", + " 130558, 133730, 136878, 137743, 140959, 143200, 144102, 150368,\n", + " 159213, 178309, 180057, 183293, 195783, 196472, 197131],\n", + " dtype='int64'), Int64Index([ 9871, 37830, 59017, 62375, 75717, 97010, 117220, 123558,\n", + " 130559, 133731, 136879, 137744, 140960, 143201, 144103, 150369,\n", + " 159214, 178310, 180058, 183294, 195784, 196473, 197132],\n", + " dtype='int64'), Int64Index([ 9872, 37831, 59018, 62376, 75718, 97011, 117221, 123559,\n", + " 130560, 133732, 136880, 137745, 140961, 143202, 144104, 150370,\n", + " 159215, 178311, 180059, 183295, 195785, 196474, 197133],\n", + " dtype='int64'), Int64Index([ 9873, 37832, 59019, 62377, 75719, 97012, 117222, 123560,\n", + " 130561, 133733, 136881, 137746, 140962, 143203, 144105, 150371,\n", + " 159216, 178312, 180060, 183296, 195786, 196475, 197134],\n", + " dtype='int64'), Int64Index([ 9874, 37833, 59020, 62378, 75720, 97013, 117223, 123561,\n", + " 130562, 133734, 136882, 137747, 140963, 143204, 144106, 150372,\n", + " 159217, 178313, 180061, 183297, 195787, 196476, 197135],\n", + " dtype='int64'), Int64Index([ 9875, 37834, 59021, 62379, 75721, 97014, 117224, 123562,\n", + " 130563, 133735, 136883, 137748, 140964, 143205, 144107, 150373,\n", + " 159218, 178314, 180062, 183298, 195788, 196477, 197136],\n", + " dtype='int64'), Int64Index([ 9876, 37835, 59022, 62380, 75722, 97015, 117225, 123563,\n", + " 130564, 133736, 136884, 137749, 140965, 143206, 144108, 150374,\n", + " 159219, 178315, 180063, 183299, 195789, 196478, 197137],\n", + " dtype='int64'), Int64Index([ 9877, 37836, 59023, 62381, 75723, 97016, 117226, 123564,\n", + " 130565, 133737, 136885, 137750, 140966, 143207, 144109, 150375,\n", + " 159220, 178316, 180064, 183300, 195790, 196479, 197138],\n", + " dtype='int64'), Int64Index([ 9878, 37837, 59024, 62382, 75724, 97017, 117227, 123565,\n", + " 130566, 133738, 136886, 137751, 140967, 143208, 144110, 150376,\n", + " 159221, 178317, 180065, 183301, 195791, 196480, 197139],\n", + " dtype='int64'), Int64Index([ 9879, 37838, 59025, 62383, 75725, 97018, 117228, 123566,\n", + " 130567, 133739, 136887, 137752, 140968, 143209, 144111, 150377,\n", + " 159222, 178318, 180066, 183302, 195792, 196481, 197140],\n", + " dtype='int64'), Int64Index([ 9880, 37839, 59026, 62384, 75726, 97019, 117229, 123567,\n", + " 130568, 133740, 136888, 137753, 140969, 143210, 144112, 150378,\n", + " 159223, 178319, 180067, 183303, 195793, 196482, 197141],\n", + " dtype='int64'), Int64Index([ 9881, 37840, 59027, 62385, 75727, 97020, 117230, 123568,\n", + " 130569, 133741, 136889, 137754, 140970, 143211, 144113, 150379,\n", + " 159224, 178320, 180068, 183304, 195794, 196483, 197142],\n", + " dtype='int64'), Int64Index([ 9882, 37841, 59028, 62386, 75728, 97021, 117231, 123569,\n", + " 130570, 133742, 136890, 137755, 140971, 143212, 144114, 150380,\n", + " 159225, 178321, 180069, 183305, 195795, 196484, 197143],\n", + " dtype='int64'), Int64Index([ 9883, 37842, 59029, 62387, 75729, 97022, 117232, 123570,\n", + " 130571, 133743, 136891, 137756, 140972, 143213, 144115, 150381,\n", + " 159226, 178322, 180070, 183306, 195796, 196485, 197144],\n", + " dtype='int64'), Int64Index([ 9884, 37843, 59030, 62388, 75730, 97023, 117233, 123571,\n", + " 130572, 133744, 136892, 137757, 140973, 143214, 144116, 150382,\n", + " 159227, 178323, 180071, 183307, 195797, 196486, 197145],\n", + " dtype='int64'), Int64Index([ 9885, 37844, 59031, 62389, 75731, 97024, 117234, 123572,\n", + " 130573, 133745, 136893, 137758, 140974, 143215, 144117, 150383,\n", + " 159228, 178324, 180072, 183308, 195798, 196487, 197146],\n", + " dtype='int64'), Int64Index([ 9886, 37845, 59032, 62390, 75732, 97025, 117235, 123573,\n", + " 130574, 133746, 136894, 137759, 140975, 143216, 144118, 150384,\n", + " 159229, 178325, 180073, 183309, 195799, 196488, 197147],\n", + " dtype='int64'), Int64Index([ 9887, 37846, 59033, 62391, 75733, 97026, 117236, 123574,\n", + " 130575, 133747, 136895, 137760, 140976, 143217, 144119, 150385,\n", + " 159230, 178326, 180074, 183310, 195800, 196489, 197148],\n", + " dtype='int64'), Int64Index([ 9888, 37847, 59034, 62392, 75734, 97027, 117237, 123575,\n", + " 130576, 133748, 136896, 137761, 140977, 143218, 144120, 150386,\n", + " 159231, 178327, 180075, 183311, 195801, 196490, 197149],\n", + " dtype='int64'), Int64Index([ 9889, 37848, 59035, 62393, 75735, 97028, 117238, 123576,\n", + " 130577, 133749, 136897, 137762, 140978, 143219, 144121, 150387,\n", + " 159232, 178328, 180076, 183312, 195802, 196491, 197150],\n", + " dtype='int64'), Int64Index([ 9890, 37849, 59036, 62394, 75736, 97029, 117239, 123577,\n", + " 130578, 133750, 136898, 137763, 140979, 143220, 144122, 150388,\n", + " 159233, 178329, 180077, 183313, 195803, 196492, 197151],\n", + " dtype='int64'), Int64Index([ 9891, 37850, 59037, 62395, 75737, 97030, 117240, 123578,\n", + " 130579, 133751, 136899, 137764, 140980, 143221, 144123, 150389,\n", + " 159234, 178330, 180078, 183314, 195804, 196493, 197152],\n", + " dtype='int64'), Int64Index([ 9892, 37851, 59038, 62396, 75738, 97031, 117241, 123579,\n", + " 130580, 133752, 136900, 137765, 140981, 143222, 144124, 150390,\n", + " 159235, 178331, 180079, 183315, 195805, 196494, 197153],\n", + " dtype='int64'), Int64Index([ 9893, 37852, 59039, 62397, 75739, 97032, 117242, 123580,\n", + " 130581, 133753, 136901, 137766, 140982, 143223, 144125, 150391,\n", + " 159236, 178332, 180080, 183316, 195806, 196495, 197154],\n", + " dtype='int64'), Int64Index([ 9894, 37853, 59040, 62398, 75740, 97033, 117243, 123581,\n", + " 130582, 133754, 136902, 137767, 140983, 143224, 144126, 150392,\n", + " 159237, 178333, 180081, 183317, 195807, 196496, 197155],\n", + " dtype='int64'), Int64Index([ 9895, 37854, 59041, 62399, 75741, 97034, 117244, 123582,\n", + " 130583, 133755, 136903, 137768, 140984, 143225, 144127, 150393,\n", + " 159238, 178334, 180082, 183318, 195808, 196497, 197156],\n", + " dtype='int64'), Int64Index([ 9896, 37855, 59042, 62400, 75742, 97035, 117245, 123583,\n", + " 130584, 133756, 136904, 137769, 140985, 143226, 144128, 150394,\n", + " 159239, 178335, 180083, 183319, 195809, 196498, 197157],\n", + " dtype='int64'), Int64Index([ 9897, 37856, 59043, 62401, 75743, 97036, 117246, 123584,\n", + " 130585, 133757, 136905, 137770, 140986, 143227, 144129, 150395,\n", + " 159240, 178336, 180084, 183320, 195810, 196499, 197158],\n", + " dtype='int64'), Int64Index([ 9898, 37857, 59044, 62402, 75744, 97037, 117247, 123585,\n", + " 130586, 133758, 136906, 137771, 140987, 143228, 144130, 150396,\n", + " 159241, 178337, 180085, 183321, 195811, 196500, 197159],\n", + " dtype='int64'), Int64Index([ 9899, 37858, 59045, 62403, 75745, 97038, 117248, 123586,\n", + " 130587, 133759, 136907, 137772, 140988, 143229, 144131, 150397,\n", + " 159242, 178338, 180086, 183322, 195812, 196501, 197160],\n", + " dtype='int64'), Int64Index([ 9900, 37859, 59046, 62404, 75746, 97039, 117249, 123587,\n", + " 130588, 133760, 136908, 137773, 140989, 143230, 144132, 150398,\n", + " 159243, 178339, 180087, 183323, 195813, 196502, 197161],\n", + " dtype='int64'), Int64Index([ 9901, 37860, 59047, 62405, 75747, 97040, 117250, 123588,\n", + " 130589, 133761, 136909, 137774, 140990, 143231, 144133, 150399,\n", + " 159244, 178340, 180088, 183324, 195814, 196503, 197162],\n", + " dtype='int64'), Int64Index([ 9902, 37861, 59048, 62406, 75748, 97041, 117251, 123589,\n", + " 130590, 133762, 136910, 137775, 140991, 143232, 144134, 150400,\n", + " 159245, 178341, 180089, 183325, 195815, 196504, 197163],\n", + " dtype='int64'), Int64Index([ 9903, 37862, 59049, 62407, 75749, 97042, 117252, 123590,\n", + " 130591, 133763, 136911, 137776, 140992, 143233, 144135, 150401,\n", + " 159246, 178342, 180090, 183326, 195816, 196505, 197164],\n", + " dtype='int64'), Int64Index([ 9904, 37863, 59050, 62408, 75750, 97043, 117253, 123591,\n", + " 130592, 133764, 136912, 137777, 140993, 143234, 144136, 150402,\n", + " 159247, 178343, 180091, 183327, 195817, 196506, 197165],\n", + " dtype='int64'), Int64Index([ 9905, 37864, 59051, 62409, 75751, 97044, 117254, 123592,\n", + " 130593, 133765, 136913, 137778, 140994, 143235, 144137, 150403,\n", + " 159248, 178344, 180092, 183328, 195818, 196507, 197166],\n", + " dtype='int64'), Int64Index([ 9906, 37865, 59052, 62410, 75752, 97045, 117255, 123593,\n", + " 130594, 133766, 136914, 137779, 140995, 143236, 144138, 150404,\n", + " 159249, 178345, 180093, 183329, 195819, 196508, 197167],\n", + " dtype='int64'), Int64Index([ 9907, 37866, 59053, 62411, 75753, 97046, 117256, 123594,\n", + " 130595, 133767, 136915, 137780, 140996, 143237, 144139, 150405,\n", + " 159250, 178346, 180094, 183330, 195820, 196509, 197168],\n", + " dtype='int64'), Int64Index([ 9908, 37867, 59054, 62412, 75754, 97047, 117257, 123595,\n", + " 130596, 133768, 136916, 137781, 140997, 143238, 144140, 150406,\n", + " 159251, 178347, 180095, 183331, 195821, 196510, 197169],\n", + " dtype='int64'), Int64Index([ 9909, 37868, 59055, 62413, 75755, 97048, 117258, 123596,\n", + " 130597, 133769, 136917, 137782, 140998, 143239, 144141, 150407,\n", + " 159252, 178348, 180096, 183332, 195822, 196511, 197170],\n", + " dtype='int64'), Int64Index([ 9910, 37869, 59056, 62414, 75756, 97049, 117259, 123597,\n", + " 130598, 133770, 136918, 137783, 140999, 143240, 144142, 150408,\n", + " 159253, 178349, 180097, 183333, 195823, 196512, 197171],\n", + " dtype='int64'), Int64Index([ 9911, 37870, 59057, 62415, 75757, 97050, 117260, 123598,\n", + " 130599, 133771, 136919, 137784, 141000, 143241, 144143, 150409,\n", + " 159254, 178350, 180098, 183334, 195824, 196513, 197172],\n", + " dtype='int64'), Int64Index([ 9912, 37871, 59058, 62416, 75758, 97051, 117261, 123599,\n", + " 130600, 133772, 136920, 137785, 141001, 143242, 144144, 150410,\n", + " 159255, 178351, 180099, 183335, 195825, 196514, 197173],\n", + " dtype='int64'), Int64Index([ 9913, 37872, 59059, 62417, 75759, 97052, 117262, 123600,\n", + " 130601, 133773, 136921, 137786, 141002, 143243, 144145, 150411,\n", + " 159256, 178352, 180100, 183336, 195826, 196515, 197174],\n", + " dtype='int64'), Int64Index([ 9914, 37873, 59060, 62418, 75760, 97053, 117263, 123601,\n", + " 130602, 133774, 136922, 137787, 141003, 143244, 144146, 150412,\n", + " 159257, 178353, 180101, 183337, 195827, 196516, 197175],\n", + " dtype='int64'), Int64Index([ 9915, 37874, 59061, 62419, 75761, 97054, 117264, 123602,\n", + " 130603, 133775, 136923, 137788, 141004, 143245, 144147, 150413,\n", + " 159258, 178354, 180102, 183338, 195828, 196517, 197176],\n", + " dtype='int64'), Int64Index([ 9916, 37875, 59062, 62420, 75762, 97055, 117265, 123603,\n", + " 130604, 133776, 136924, 137789, 141005, 143246, 144148, 150414,\n", + " 159259, 178355, 180103, 183339, 195829, 196518, 197177],\n", + " dtype='int64'), Int64Index([ 9917, 37876, 59063, 62421, 75763, 97056, 117266, 123604,\n", + " 130605, 133777, 136925, 137790, 141006, 143247, 144149, 150415,\n", + " 159260, 178356, 180104, 183340, 195830, 196519, 197178],\n", + " dtype='int64'), Int64Index([ 9918, 37877, 59064, 62422, 75764, 97057, 117267, 123605,\n", + " 130606, 133778, 136926, 137791, 141007, 143248, 144150, 150416,\n", + " 159261, 178357, 180105, 183341, 195831, 196520, 197179],\n", + " dtype='int64'), Int64Index([ 9919, 37878, 59065, 62423, 75765, 97058, 117268, 123606,\n", + " 130607, 133779, 136927, 137792, 141008, 143249, 144151, 150417,\n", + " 159262, 178358, 180106, 183342, 195832, 196521, 197180],\n", + " dtype='int64'), Int64Index([ 9920, 37879, 59066, 62424, 75766, 97059, 117269, 123607,\n", + " 130608, 133780, 136928, 137793, 141009, 143250, 144152, 150418,\n", + " 159263, 178359, 180107, 183343, 195833, 196522, 197181],\n", + " dtype='int64'), Int64Index([ 9921, 37880, 59067, 62425, 75767, 97060, 117270, 123608,\n", + " 130609, 133781, 136929, 137794, 141010, 143251, 144153, 150419,\n", + " 159264, 178360, 180108, 183344, 195834, 196523, 197182],\n", + " dtype='int64'), Int64Index([ 9922, 37881, 59068, 62426, 75768, 97061, 117271, 123609,\n", + " 130610, 133782, 136930, 137795, 141011, 143252, 144154, 150420,\n", + " 159265, 178361, 180109, 183345, 195835, 196524, 197183],\n", + " dtype='int64'), Int64Index([ 9923, 37882, 59069, 62427, 75769, 97062, 117272, 123610,\n", + " 130611, 133783, 136931, 137796, 141012, 143253, 144155, 150421,\n", + " 159266, 178362, 180110, 183346, 195836, 196525, 197184],\n", + " dtype='int64'), Int64Index([ 9924, 37883, 59070, 62428, 75770, 97063, 117273, 123611,\n", + " 130612, 133784, 136932, 137797, 141013, 143254, 144156, 150422,\n", + " 159267, 178363, 180111, 183347, 195837, 196526, 197185],\n", + " dtype='int64'), Int64Index([ 9925, 37884, 59071, 62429, 75771, 97064, 117274, 123612,\n", + " 130613, 133785, 136933, 137798, 141014, 143255, 144157, 150423,\n", + " 159268, 178364, 180112, 183348, 195838, 196527, 197186],\n", + " dtype='int64'), Int64Index([ 9926, 37885, 59072, 62430, 75772, 97065, 117275, 123613,\n", + " 130614, 133786, 136934, 137799, 141015, 143256, 144158, 150424,\n", + " 159269, 178365, 180113, 183349, 195839, 196528, 197187],\n", + " dtype='int64'), Int64Index([ 9927, 37886, 59073, 62431, 75773, 97066, 117276, 123614,\n", + " 130615, 133787, 136935, 137800, 141016, 143257, 144159, 150425,\n", + " 159270, 178366, 180114, 183350, 195840, 196529, 197188],\n", + " dtype='int64'), Int64Index([ 9928, 37887, 59074, 62432, 75774, 97067, 117277, 123615,\n", + " 130616, 133788, 136936, 137801, 141017, 143258, 144160, 150426,\n", + " 159271, 178367, 180115, 183351, 195841, 196530, 197189],\n", + " dtype='int64'), Int64Index([ 9929, 37888, 59075, 62433, 75775, 97068, 117278, 123616,\n", + " 130617, 133789, 136937, 137802, 141018, 143259, 144161, 150427,\n", + " 159272, 178368, 180116, 183352, 195842, 196531, 197190],\n", + " dtype='int64'), Int64Index([ 9930, 37889, 59076, 62434, 75776, 97069, 117279, 123617,\n", + " 130618, 133790, 136938, 137803, 141019, 143260, 144162, 150428,\n", + " 159273, 178369, 180117, 183353, 195843, 196532, 197191],\n", + " dtype='int64'), Int64Index([ 9931, 37890, 59077, 62435, 75777, 97070, 117280, 123618,\n", + " 130619, 133791, 136939, 137804, 141020, 143261, 144163, 150429,\n", + " 159274, 178370, 180118, 183354, 195844, 196533, 197192],\n", + " dtype='int64'), Int64Index([ 9932, 37891, 59078, 62436, 75778, 97071, 117281, 123619,\n", + " 130620, 133792, 136940, 137805, 141021, 143262, 144164, 150430,\n", + " 159275, 178371, 180119, 183355, 195845, 196534, 197193],\n", + " dtype='int64'), Int64Index([ 9933, 37892, 59079, 62437, 75779, 97072, 117282, 123620,\n", + " 130621, 133793, 136941, 137806, 141022, 143263, 144165, 150431,\n", + " 159276, 178372, 180120, 183356, 195846, 196535, 197194],\n", + " dtype='int64'), Int64Index([ 9934, 37893, 59080, 62438, 75780, 97073, 117283, 123621,\n", + " 130622, 133794, 136942, 137807, 141023, 143264, 144166, 150432,\n", + " 159277, 178373, 180121, 183357, 195847, 196536, 197195],\n", + " dtype='int64'), Int64Index([ 9935, 37894, 59081, 62439, 75781, 97074, 117284, 123622,\n", + " 130623, 133795, 136943, 137808, 141024, 143265, 144167, 150433,\n", + " 159278, 178374, 180122, 183358, 195848, 196537, 197196],\n", + " dtype='int64'), Int64Index([ 9936, 37895, 59082, 62440, 75782, 97075, 117285, 123623,\n", + " 130624, 133796, 136944, 137809, 141025, 143266, 144168, 150434,\n", + " 159279, 178375, 180123, 183359, 195849, 196538, 197197],\n", + " dtype='int64'), Int64Index([ 9937, 37896, 59083, 62441, 75783, 97076, 117286, 123624,\n", + " 130625, 133797, 136945, 137810, 141026, 143267, 144169, 150435,\n", + " 159280, 178376, 180124, 183360, 195850, 196539, 197198],\n", + " dtype='int64'), Int64Index([ 9938, 37897, 59084, 62442, 75784, 97077, 117287, 123625,\n", + " 130626, 133798, 136946, 137811, 141027, 143268, 144170, 150436,\n", + " 159281, 178377, 180125, 183361, 195851, 196540, 197199],\n", + " dtype='int64'), Int64Index([ 9939, 37898, 59085, 62443, 75785, 97078, 117288, 123626,\n", + " 130627, 133799, 136947, 137812, 141028, 143269, 144171, 150437,\n", + " 159282, 178378, 180126, 183362, 195852, 196541, 197200],\n", + " dtype='int64'), Int64Index([ 9940, 37899, 59086, 62444, 75786, 97079, 117289, 123627,\n", + " 130628, 133800, 136948, 137813, 141029, 143270, 144172, 150438,\n", + " 159283, 178379, 180127, 183363, 195853, 196542, 197201],\n", + " dtype='int64'), Int64Index([ 9941, 37900, 59087, 62445, 75787, 97080, 117290, 123628,\n", + " 130629, 133801, 136949, 137814, 141030, 143271, 144173, 150439,\n", + " 159284, 178380, 180128, 183364, 195854, 196543, 197202],\n", + " dtype='int64'), Int64Index([ 9942, 37901, 59088, 62446, 75788, 97081, 117291, 123629,\n", + " 130630, 133802, 136950, 137815, 141031, 143272, 144174, 150440,\n", + " 159285, 178381, 180129, 183365, 195855, 196544, 197203],\n", + " dtype='int64'), Int64Index([ 9943, 37902, 59089, 62447, 75789, 97082, 117292, 123630,\n", + " 130631, 133803, 136951, 137816, 141032, 143273, 144175, 150441,\n", + " 159286, 178382, 180130, 183366, 195856, 196545, 197204],\n", + " dtype='int64'), Int64Index([ 9944, 37903, 59090, 62448, 75790, 97083, 117293, 123631,\n", + " 130632, 133804, 136952, 137817, 141033, 143274, 144176, 150442,\n", + " 159287, 178383, 180131, 183367, 195857, 196546, 197205],\n", + " dtype='int64'), Int64Index([ 9945, 37904, 59091, 62449, 75791, 97084, 117294, 123632,\n", + " 130633, 133805, 136953, 137818, 141034, 143275, 144177, 150443,\n", + " 159288, 178384, 180132, 183368, 195858, 196547, 197206],\n", + " dtype='int64'), Int64Index([ 9946, 37905, 59092, 62450, 75792, 97085, 117295, 123633,\n", + " 130634, 133806, 136954, 137819, 141035, 143276, 144178, 150444,\n", + " 159289, 178385, 180133, 183369, 195859, 196548, 197207],\n", + " dtype='int64'), Int64Index([ 9947, 37906, 59093, 62451, 75793, 97086, 117296, 123634,\n", + " 130635, 133807, 136955, 137820, 141036, 143277, 144179, 150445,\n", + " 159290, 178386, 180134, 183370, 195860, 196549, 197208],\n", + " dtype='int64'), Int64Index([ 9948, 37907, 59094, 62452, 75794, 97087, 117297, 130636,\n", + " 133808, 136956, 137821, 141037, 143278, 144180, 150446, 159291,\n", + " 178387, 180135, 183371, 195861, 196550, 197209],\n", + " dtype='int64'), Int64Index([ 9949, 37908, 59095, 62453, 75795, 97088, 117298, 130637,\n", + " 133809, 136957, 137822, 141038, 143279, 144181, 150447, 159292,\n", + " 178388, 180136, 183372, 195862, 196551, 197210],\n", + " dtype='int64'), Int64Index([ 9950, 37909, 59096, 62454, 75796, 97089, 117299, 130638,\n", + " 133810, 136958, 137823, 141039, 143280, 144182, 150448, 159293,\n", + " 178389, 180137, 183373, 195863, 196552, 197211],\n", + " dtype='int64'), Int64Index([ 9951, 37910, 59097, 62455, 75797, 97090, 117300, 130639,\n", + " 133811, 136959, 137824, 141040, 143281, 144183, 150449, 159294,\n", + " 178390, 180138, 183374, 195864, 196553, 197212],\n", + " dtype='int64'), Int64Index([ 9952, 37911, 59098, 62456, 75798, 97091, 117301, 130640,\n", + " 133812, 136960, 137825, 141041, 143282, 144184, 150450, 159295,\n", + " 178391, 180139, 183375, 195865, 196554, 197213],\n", + " dtype='int64'), Int64Index([ 9953, 37912, 59099, 62457, 75799, 97092, 117302, 130641,\n", + " 133813, 136961, 137826, 141042, 143283, 144185, 150451, 159296,\n", + " 178392, 180140, 183376, 195866, 196555, 197214],\n", + " dtype='int64'), Int64Index([ 9954, 37913, 59100, 62458, 75800, 97093, 117303, 130642,\n", + " 133814, 136962, 137827, 141043, 143284, 144186, 150452, 159297,\n", + " 178393, 180141, 183377, 195867, 196556, 197215],\n", + " dtype='int64'), Int64Index([ 9955, 37914, 59101, 62459, 75801, 97094, 117304, 130643,\n", + " 133815, 136963, 137828, 141044, 143285, 144187, 150453, 159298,\n", + " 178394, 180142, 183378, 195868, 196557, 197216],\n", + " dtype='int64'), Int64Index([ 9956, 37915, 59102, 62460, 75802, 97095, 117305, 130644,\n", + " 133816, 136964, 137829, 141045, 143286, 144188, 150454, 159299,\n", + " 178395, 180143, 183379, 195869, 196558, 197217],\n", + " dtype='int64'), Int64Index([ 9957, 37916, 59103, 62461, 75803, 97096, 117306, 130645,\n", + " 133817, 136965, 137830, 141046, 143287, 144189, 150455, 159300,\n", + " 178396, 180144, 183380, 195870, 196559, 197218],\n", + " dtype='int64'), Int64Index([ 9958, 37917, 59104, 62462, 75804, 97097, 117307, 130646,\n", + " 133818, 136966, 137831, 141047, 143288, 144190, 150456, 159301,\n", + " 178397, 180145, 183381, 195871, 196560, 197219],\n", + " dtype='int64'), Int64Index([ 9959, 37918, 59105, 62463, 75805, 97098, 117308, 130647,\n", + " 133819, 136967, 137832, 141048, 143289, 144191, 150457, 159302,\n", + " 178398, 180146, 183382, 195872, 196561, 197220],\n", + " dtype='int64'), Int64Index([ 9960, 37919, 59106, 62464, 75806, 97099, 117309, 130648,\n", + " 133820, 136968, 137833, 141049, 143290, 144192, 150458, 159303,\n", + " 178399, 180147, 183383, 195873, 196562, 197221],\n", + " dtype='int64'), Int64Index([ 9961, 37920, 59107, 62465, 75807, 97100, 117310, 130649,\n", + " 133821, 136969, 137834, 141050, 143291, 144193, 150459, 159304,\n", + " 178400, 180148, 183384, 195874, 196563, 197222],\n", + " dtype='int64'), Int64Index([ 9962, 37921, 59108, 62466, 75808, 97101, 117311, 130650,\n", + " 133822, 136970, 137835, 141051, 143292, 144194, 150460, 159305,\n", + " 178401, 180149, 183385, 195875, 196564, 197223],\n", + " dtype='int64'), Int64Index([ 9963, 37922, 59109, 62467, 75809, 97102, 117312, 130651,\n", + " 133823, 136971, 137836, 141052, 143293, 144195, 150461, 159306,\n", + " 178402, 180150, 183386, 195876, 196565, 197224],\n", + " dtype='int64'), Int64Index([ 9964, 37923, 59110, 62468, 75810, 97103, 117313, 130652,\n", + " 133824, 136972, 137837, 141053, 143294, 144196, 150462, 159307,\n", + " 178403, 180151, 183387, 195877, 196566, 197225],\n", + " dtype='int64'), Int64Index([ 9965, 37924, 59111, 62469, 75811, 97104, 117314, 130653,\n", + " 133825, 136973, 137838, 141054, 143295, 144197, 150463, 159308,\n", + " 178404, 180152, 183388, 195878, 196567, 197226],\n", + " dtype='int64'), Int64Index([ 9966, 37925, 59112, 62470, 75812, 97105, 117315, 130654,\n", + " 133826, 136974, 137839, 141055, 143296, 144198, 150464, 159309,\n", + " 178405, 180153, 183389, 195879, 196568, 197227],\n", + " dtype='int64'), Int64Index([ 9967, 37926, 59113, 62471, 75813, 97106, 117316, 130655,\n", + " 133827, 136975, 137840, 141056, 143297, 144199, 150465, 159310,\n", + " 178406, 180154, 183390, 195880, 196569, 197228],\n", + " dtype='int64'), Int64Index([ 9968, 37927, 59114, 62472, 75814, 97107, 117317, 130656,\n", + " 133828, 136976, 137841, 141057, 143298, 144200, 150466, 159311,\n", + " 178407, 180155, 183391, 195881, 196570, 197229],\n", + " dtype='int64'), Int64Index([ 9969, 37928, 59115, 62473, 75815, 97108, 117318, 130657,\n", + " 133829, 136977, 137842, 141058, 143299, 144201, 150467, 159312,\n", + " 178408, 180156, 183392, 195882, 196571, 197230],\n", + " dtype='int64'), Int64Index([ 9970, 37929, 59116, 62474, 75816, 97109, 117319, 130658,\n", + " 133830, 136978, 137843, 141059, 143300, 144202, 150468, 159313,\n", + " 178409, 180157, 183393, 195883, 196572, 197231],\n", + " dtype='int64'), Int64Index([ 9971, 37930, 59117, 62475, 75817, 97110, 117320, 130659,\n", + " 133831, 136979, 137844, 141060, 143301, 144203, 150469, 159314,\n", + " 178410, 180158, 183394, 195884, 196573, 197232],\n", + " dtype='int64'), Int64Index([ 9972, 37931, 59118, 62476, 75818, 97111, 117321, 130660,\n", + " 133832, 136980, 137845, 141061, 143302, 144204, 150470, 159315,\n", + " 178411, 180159, 183395, 195885, 196574, 197233],\n", + " dtype='int64'), Int64Index([ 9973, 37932, 59119, 62477, 75819, 97112, 117322, 130661,\n", + " 133833, 136981, 137846, 141062, 143303, 144205, 150471, 159316,\n", + " 178412, 180160, 183396, 195886, 196575, 197234],\n", + " dtype='int64'), Int64Index([ 9974, 37933, 59120, 62478, 75820, 97113, 117323, 130662,\n", + " 133834, 136982, 137847, 141063, 143304, 144206, 150472, 159317,\n", + " 178413, 180161, 183397, 195887, 196576, 197235],\n", + " dtype='int64'), Int64Index([ 9975, 37934, 59121, 62479, 75821, 97114, 117324, 130663,\n", + " 133835, 136983, 137848, 141064, 143305, 144207, 150473, 159318,\n", + " 178414, 180162, 183398, 195888, 196577, 197236],\n", + " dtype='int64'), Int64Index([ 9976, 37935, 59122, 62480, 75822, 97115, 117325, 130664,\n", + " 133836, 136984, 137849, 141065, 143306, 144208, 150474, 159319,\n", + " 178415, 180163, 183399, 195889, 196578, 197237],\n", + " dtype='int64'), Int64Index([ 9977, 37936, 59123, 62481, 75823, 97116, 117326, 130665,\n", + " 133837, 136985, 137850, 141066, 143307, 144209, 150475, 159320,\n", + " 178416, 180164, 183400, 195890, 196579, 197238],\n", + " dtype='int64'), Int64Index([ 9978, 37937, 59124, 62482, 75824, 97117, 117327, 130666,\n", + " 133838, 136986, 137851, 141067, 143308, 144210, 150476, 159321,\n", + " 178417, 180165, 183401, 195891, 196580, 197239],\n", + " dtype='int64'), Int64Index([ 9979, 37938, 59125, 62483, 75825, 97118, 117328, 130667,\n", + " 133839, 136987, 137852, 141068, 143309, 144211, 150477, 159322,\n", + " 178418, 180166, 183402, 195892, 196581, 197240],\n", + " dtype='int64'), Int64Index([ 9980, 37939, 59126, 62484, 75826, 97119, 117329, 130668,\n", + " 133840, 136988, 137853, 141069, 143310, 144212, 150478, 159323,\n", + " 178419, 180167, 183403, 195893, 196582, 197241],\n", + " dtype='int64'), Int64Index([ 9981, 37940, 59127, 62485, 75827, 97120, 117330, 130669,\n", + " 133841, 136989, 137854, 141070, 143311, 144213, 150479, 159324,\n", + " 178420, 180168, 183404, 195894, 196583, 197242],\n", + " dtype='int64'), Int64Index([ 9982, 37941, 59128, 62486, 75828, 97121, 117331, 130670,\n", + " 133842, 136990, 137855, 141071, 143312, 144214, 150480, 159325,\n", + " 178421, 180169, 183405, 195895, 196584, 197243],\n", + " dtype='int64'), Int64Index([ 9983, 37942, 59129, 62487, 75829, 97122, 117332, 130671,\n", + " 133843, 136991, 137856, 141072, 143313, 144215, 150481, 159326,\n", + " 178422, 180170, 183406, 195896, 196585, 197244],\n", + " dtype='int64'), Int64Index([ 9984, 37943, 59130, 62488, 75830, 97123, 117333, 130672,\n", + " 133844, 136992, 137857, 141073, 143314, 144216, 150482, 159327,\n", + " 178423, 180171, 183407, 195897, 196586, 197245],\n", + " dtype='int64'), Int64Index([ 9985, 37944, 59131, 62489, 75831, 97124, 117334, 130673,\n", + " 133845, 136993, 137858, 141074, 143315, 144217, 150483, 159328,\n", + " 178424, 180172, 183408, 195898, 196587, 197246],\n", + " dtype='int64'), Int64Index([ 9986, 37945, 59132, 62490, 75832, 97125, 117335, 130674,\n", + " 133846, 136994, 137859, 141075, 143316, 144218, 150484, 159329,\n", + " 178425, 180173, 183409, 195899, 196588, 197247],\n", + " dtype='int64'), Int64Index([ 9987, 37946, 59133, 62491, 75833, 97126, 117336, 130675,\n", + " 133847, 136995, 137860, 141076, 143317, 144219, 150485, 159330,\n", + " 178426, 180174, 183410, 195900, 196589, 197248],\n", + " dtype='int64'), Int64Index([ 9988, 37947, 59134, 62492, 75834, 97127, 117337, 130676,\n", + " 133848, 136996, 137861, 141077, 143318, 144220, 150486, 159331,\n", + " 178427, 180175, 183411, 195901, 196590, 197249],\n", + " dtype='int64'), Int64Index([ 9989, 37948, 59135, 62493, 75835, 97128, 117338, 130677,\n", + " 133849, 136997, 137862, 141078, 143319, 144221, 150487, 159332,\n", + " 178428, 180176, 183412, 195902, 196591, 197250],\n", + " dtype='int64'), Int64Index([ 9990, 37949, 59136, 62494, 75836, 97129, 117339, 130678,\n", + " 133850, 136998, 137863, 141079, 143320, 144222, 150488, 159333,\n", + " 178429, 180177, 183413, 195903, 196592, 197251],\n", + " dtype='int64'), Int64Index([ 9991, 37950, 59137, 62495, 75837, 97130, 117340, 130679,\n", + " 133851, 136999, 137864, 141080, 143321, 144223, 150489, 159334,\n", + " 178430, 180178, 183414, 195904, 196593, 197252],\n", + " dtype='int64'), Int64Index([ 9992, 37951, 59138, 62496, 75838, 97131, 117341, 130680,\n", + " 133852, 137000, 137865, 141081, 143322, 144224, 150490, 159335,\n", + " 178431, 180179, 183415, 195905, 196594, 197253],\n", + " dtype='int64'), Int64Index([ 9993, 37952, 59139, 62497, 75839, 97132, 117342, 130681,\n", + " 133853, 137001, 137866, 141082, 143323, 144225, 150491, 159336,\n", + " 178432, 180180, 183416, 195906, 196595, 197254],\n", + " dtype='int64'), Int64Index([ 9994, 37953, 59140, 62498, 75840, 97133, 117343, 130682,\n", + " 133854, 137002, 137867, 141083, 143324, 144226, 150492, 159337,\n", + " 178433, 180181, 183417, 195907, 196596, 197255],\n", + " dtype='int64'), Int64Index([ 9995, 37954, 59141, 62499, 75841, 97134, 117344, 130683,\n", + " 133855, 137003, 137868, 141084, 143325, 144227, 150493, 159338,\n", + " 178434, 180182, 183418, 195908, 196597, 197256],\n", + " dtype='int64'), Int64Index([ 9996, 37955, 59142, 62500, 75842, 97135, 117345, 130684,\n", + " 133856, 137004, 137869, 141085, 143326, 144228, 150494, 159339,\n", + " 178435, 180183, 183419, 195909, 196598, 197257],\n", + " dtype='int64'), Int64Index([ 9997, 37956, 59143, 62501, 75843, 97136, 117346, 130685,\n", + " 133857, 137005, 137870, 141086, 143327, 144229, 150495, 159340,\n", + " 178436, 180184, 183420, 195910, 196599, 197258],\n", + " dtype='int64'), Int64Index([ 9998, 37957, 59144, 62502, 75844, 97137, 117347, 130686,\n", + " 133858, 137006, 137871, 141087, 143328, 144230, 150496, 159341,\n", + " 178437, 180185, 183421, 195911, 196600, 197259],\n", + " dtype='int64'), Int64Index([ 9999, 37958, 59145, 62503, 75845, 97138, 117348, 130687,\n", + " 133859, 137007, 137872, 141088, 143329, 144231, 150497, 159342,\n", + " 178438, 180186, 183422, 195912, 196601, 197260],\n", + " dtype='int64'), Int64Index([ 10000, 37959, 59146, 62504, 75846, 97139, 117349, 130688,\n", + " 133860, 137008, 137873, 141089, 143330, 144232, 150498, 159343,\n", + " 178439, 180187, 183423, 195913, 196602, 197261],\n", + " dtype='int64'), Int64Index([ 10001, 37960, 59147, 62505, 75847, 97140, 117350, 130689,\n", + " 133861, 137009, 137874, 141090, 143331, 144233, 150499, 159344,\n", + " 178440, 180188, 183424, 195914, 196603, 197262],\n", + " dtype='int64'), Int64Index([ 10002, 37961, 59148, 62506, 75848, 97141, 117351, 130690,\n", + " 133862, 137010, 137875, 141091, 143332, 144234, 150500, 159345,\n", + " 178441, 180189, 183425, 195915, 196604, 197263],\n", + " dtype='int64'), Int64Index([ 10003, 37962, 59149, 62507, 75849, 97142, 117352, 130691,\n", + " 133863, 137011, 137876, 141092, 143333, 144235, 150501, 159346,\n", + " 178442, 180190, 183426, 195916, 196605, 197264],\n", + " dtype='int64'), Int64Index([ 10004, 37963, 59150, 62508, 75850, 97143, 117353, 130692,\n", + " 133864, 137012, 137877, 141093, 143334, 144236, 150502, 159347,\n", + " 178443, 180191, 183427, 195917, 196606, 197265],\n", + " dtype='int64'), Int64Index([ 10005, 37964, 59151, 62509, 75851, 97144, 117354, 130693,\n", + " 133865, 137013, 137878, 141094, 143335, 144237, 150503, 159348,\n", + " 178444, 180192, 183428, 195918, 196607, 197266],\n", + " dtype='int64'), Int64Index([ 10006, 37965, 59152, 62510, 75852, 97145, 117355, 130694,\n", + " 133866, 137014, 137879, 141095, 143336, 144238, 150504, 159349,\n", + " 178445, 180193, 183429, 195919, 196608, 197267],\n", + " dtype='int64'), Int64Index([ 10007, 37966, 59153, 62511, 75853, 97146, 117356, 130695,\n", + " 133867, 137015, 137880, 141096, 143337, 144239, 150505, 159350,\n", + " 178446, 180194, 183430, 195920, 196609, 197268],\n", + " dtype='int64'), Int64Index([ 10008, 37967, 59154, 62512, 75854, 97147, 117357, 130696,\n", + " 133868, 137016, 137881, 141097, 143338, 144240, 150506, 159351,\n", + " 178447, 180195, 183431, 195921, 196610, 197269],\n", + " dtype='int64'), Int64Index([ 10009, 37968, 59155, 62513, 75855, 97148, 117358, 130697,\n", + " 133869, 137017, 137882, 141098, 143339, 144241, 150507, 159352,\n", + " 178448, 180196, 183432, 195922, 196611, 197270],\n", + " dtype='int64'), Int64Index([ 10010, 37969, 59156, 62514, 75856, 97149, 117359, 130698,\n", + " 133870, 137018, 137883, 141099, 143340, 144242, 150508, 159353,\n", + " 178449, 180197, 183433, 195923, 196612, 197271],\n", + " dtype='int64'), Int64Index([ 10011, 37970, 59157, 62515, 75857, 97150, 117360, 130699,\n", + " 133871, 137019, 137884, 141100, 143341, 144243, 150509, 159354,\n", + " 178450, 180198, 183434, 195924, 196613, 197272],\n", + " dtype='int64'), Int64Index([ 10012, 37971, 59158, 62516, 75858, 97151, 117361, 130700,\n", + " 133872, 137020, 137885, 141101, 143342, 144244, 150510, 159355,\n", + " 178451, 180199, 183435, 195925, 196614, 197273],\n", + " dtype='int64'), Int64Index([ 10013, 37972, 59159, 62517, 75859, 97152, 117362, 130701,\n", + " 133873, 137021, 137886, 141102, 143343, 144245, 150511, 159356,\n", + " 178452, 180200, 183436, 195926, 196615, 197274],\n", + " dtype='int64'), Int64Index([ 10014, 37973, 59160, 62518, 75860, 97153, 117363, 130702,\n", + " 133874, 137022, 137887, 141103, 143344, 144246, 150512, 159357,\n", + " 178453, 180201, 183437, 195927, 196616, 197275],\n", + " dtype='int64'), Int64Index([ 10015, 37974, 59161, 62519, 75861, 97154, 117364, 130703,\n", + " 133875, 137023, 137888, 141104, 143345, 144247, 150513, 159358,\n", + " 178454, 180202, 183438, 195928, 196617, 197276],\n", + " dtype='int64'), Int64Index([ 10016, 37975, 59162, 62520, 75862, 97155, 117365, 130704,\n", + " 133876, 137024, 137889, 141105, 143346, 144248, 150514, 159359,\n", + " 178455, 180203, 183439, 195929, 196618, 197277],\n", + " dtype='int64'), Int64Index([ 10017, 37976, 59163, 62521, 75863, 97156, 117366, 130705,\n", + " 133877, 137025, 137890, 141106, 143347, 144249, 150515, 159360,\n", + " 178456, 180204, 183440, 195930, 196619, 197278],\n", + " dtype='int64'), Int64Index([ 10018, 37977, 59164, 62522, 75864, 97157, 117367, 130706,\n", + " 133878, 137026, 137891, 141107, 143348, 144250, 150516, 159361,\n", + " 178457, 180205, 183441, 195931, 196620, 197279],\n", + " dtype='int64'), Int64Index([ 10019, 37978, 59165, 62523, 75865, 97158, 117368, 130707,\n", + " 133879, 137027, 137892, 141108, 143349, 144251, 150517, 159362,\n", + " 178458, 180206, 183442, 195932, 196621, 197280],\n", + " dtype='int64'), Int64Index([ 10020, 37979, 59166, 62524, 75866, 97159, 117369, 130708,\n", + " 133880, 137028, 137893, 141109, 143350, 144252, 150518, 159363,\n", + " 178459, 180207, 183443, 195933, 196622, 197281],\n", + " dtype='int64'), Int64Index([ 10021, 37980, 59167, 62525, 75867, 97160, 117370, 130709,\n", + " 133881, 137029, 137894, 141110, 143351, 144253, 150519, 159364,\n", + " 178460, 180208, 183444, 195934, 196623, 197282],\n", + " dtype='int64'), Int64Index([ 10022, 37981, 59168, 62526, 75868, 97161, 117371, 130710,\n", + " 133882, 137030, 137895, 141111, 143352, 144254, 150520, 159365,\n", + " 178461, 180209, 183445, 195935, 196624, 197283],\n", + " dtype='int64'), Int64Index([ 10023, 37982, 59169, 62527, 75869, 97162, 117372, 130711,\n", + " 133883, 137031, 137896, 141112, 143353, 144255, 150521, 159366,\n", + " 178462, 180210, 183446, 195936, 196625, 197284],\n", + " dtype='int64'), Int64Index([ 10024, 37983, 59170, 62528, 75870, 97163, 117373, 130712,\n", + " 133884, 137032, 137897, 141113, 143354, 144256, 150522, 159367,\n", + " 178463, 180211, 183447, 195937, 196626, 197285],\n", + " dtype='int64'), Int64Index([ 10025, 37984, 59171, 62529, 75871, 97164, 117374, 130713,\n", + " 133885, 137033, 137898, 141114, 143355, 144257, 150523, 159368,\n", + " 178464, 180212, 183448, 195938, 196627, 197286],\n", + " dtype='int64'), Int64Index([ 10026, 37985, 59172, 62530, 75872, 97165, 117375, 130714,\n", + " 133886, 137034, 137899, 141115, 143356, 144258, 150524, 159369,\n", + " 178465, 180213, 183449, 195939, 196628, 197287],\n", + " dtype='int64'), Int64Index([ 10027, 37986, 59173, 62531, 75873, 97166, 117376, 130715,\n", + " 133887, 137035, 137900, 141116, 143357, 144259, 150525, 159370,\n", + " 178466, 180214, 183450, 195940, 196629, 197288],\n", + " dtype='int64'), Int64Index([ 10028, 37987, 59174, 62532, 75874, 97167, 117377, 130716,\n", + " 133888, 137036, 137901, 141117, 143358, 144260, 150526, 159371,\n", + " 178467, 180215, 183451, 195941, 196630, 197289],\n", + " dtype='int64'), Int64Index([ 10029, 37988, 59175, 62533, 75875, 97168, 117378, 130717,\n", + " 133889, 137037, 137902, 141118, 143359, 144261, 150527, 159372,\n", + " 178468, 180216, 183452, 195942, 196631, 197290],\n", + " dtype='int64'), Int64Index([ 10030, 37989, 59176, 62534, 75876, 97169, 117379, 130718,\n", + " 133890, 137038, 137903, 141119, 143360, 144262, 150528, 159373,\n", + " 178469, 180217, 183453, 195943, 196632, 197291],\n", + " dtype='int64'), Int64Index([ 10031, 37990, 59177, 62535, 75877, 97170, 117380, 130719,\n", + " 133891, 137039, 137904, 141120, 143361, 144263, 150529, 159374,\n", + " 178470, 180218, 183454, 195944, 196633, 197292],\n", + " dtype='int64'), Int64Index([ 10032, 37991, 59178, 62536, 75878, 97171, 117381, 130720,\n", + " 133892, 137040, 137905, 141121, 143362, 144264, 150530, 159375,\n", + " 178471, 180219, 183455, 195945, 196634, 197293],\n", + " dtype='int64'), Int64Index([ 10033, 37992, 59179, 62537, 75879, 97172, 117382, 130721,\n", + " 133893, 137041, 137906, 141122, 143363, 144265, 150531, 159376,\n", + " 178472, 180220, 183456, 195946, 196635, 197294],\n", + " dtype='int64'), Int64Index([ 10034, 37993, 59180, 62538, 75880, 97173, 117383, 130722,\n", + " 133894, 137042, 137907, 141123, 143364, 144266, 150532, 159377,\n", + " 178473, 180221, 183457, 195947, 196636, 197295],\n", + " dtype='int64'), Int64Index([ 10035, 37994, 59181, 62539, 75881, 97174, 117384, 130723,\n", + " 133895, 137043, 137908, 141124, 143365, 144267, 150533, 159378,\n", + " 178474, 180222, 183458, 195948, 196637, 197296],\n", + " dtype='int64'), Int64Index([ 10036, 37995, 59182, 62540, 75882, 97175, 117385, 130724,\n", + " 133896, 137044, 137909, 141125, 143366, 144268, 150534, 159379,\n", + " 178475, 180223, 183459, 195949, 196638, 197297],\n", + " dtype='int64'), Int64Index([ 10037, 37996, 59183, 62541, 75883, 97176, 117386, 130725,\n", + " 133897, 137045, 137910, 141126, 143367, 144269, 150535, 159380,\n", + " 178476, 180224, 183460, 195950, 196639, 197298],\n", + " dtype='int64'), Int64Index([ 10038, 37997, 59184, 62542, 75884, 97177, 117387, 130726,\n", + " 133898, 137046, 137911, 141127, 143368, 144270, 150536, 159381,\n", + " 178477, 180225, 183461, 195951, 196640, 197299],\n", + " dtype='int64'), Int64Index([ 10039, 37998, 59185, 62543, 75885, 97178, 117388, 130727,\n", + " 133899, 137047, 137912, 141128, 143369, 144271, 150537, 159382,\n", + " 178478, 180226, 183462, 195952, 196641, 197300],\n", + " dtype='int64'), Int64Index([ 10040, 37999, 59186, 62544, 75886, 97179, 117389, 130728,\n", + " 133900, 137048, 137913, 141129, 143370, 144272, 150538, 159383,\n", + " 178479, 180227, 183463, 195953, 196642, 197301],\n", + " dtype='int64'), Int64Index([ 10041, 38000, 59187, 62545, 75887, 97180, 117390, 130729,\n", + " 133901, 137049, 137914, 141130, 143371, 144273, 150539, 159384,\n", + " 178480, 180228, 183464, 195954, 196643, 197302],\n", + " dtype='int64'), Int64Index([ 10042, 38001, 59188, 62546, 75888, 97181, 117391, 130730,\n", + " 133902, 137050, 137915, 141131, 143372, 144274, 150540, 159385,\n", + " 178481, 180229, 183465, 195955, 196644, 197303],\n", + " dtype='int64'), Int64Index([ 10043, 38002, 59189, 62547, 75889, 97182, 117392, 130731,\n", + " 133903, 137051, 137916, 141132, 143373, 144275, 150541, 159386,\n", + " 178482, 180230, 183466, 195956, 196645, 197304],\n", + " dtype='int64'), Int64Index([ 10044, 38003, 59190, 62548, 75890, 97183, 117393, 130732,\n", + " 133904, 137052, 137917, 141133, 143374, 144276, 150542, 159387,\n", + " 178483, 180231, 183467, 195957, 196646, 197305],\n", + " dtype='int64'), Int64Index([ 10045, 38004, 59191, 62549, 75891, 97184, 117394, 130733,\n", + " 133905, 137053, 137918, 141134, 143375, 144277, 150543, 159388,\n", + " 178484, 180232, 183468, 195958, 196647, 197306],\n", + " dtype='int64'), Int64Index([ 10046, 38005, 59192, 62550, 75892, 97185, 117395, 130734,\n", + " 133906, 137054, 137919, 141135, 143376, 144278, 150544, 159389,\n", + " 178485, 180233, 183469, 195959, 196648, 197307],\n", + " dtype='int64'), Int64Index([ 10047, 38006, 59193, 62551, 75893, 97186, 117396, 130735,\n", + " 137055, 137920, 141136, 143377, 144279, 150545, 159390, 178486,\n", + " 180234, 183470, 195960, 196649, 197308],\n", + " dtype='int64'), Int64Index([ 10048, 38007, 59194, 62552, 75894, 97187, 117397, 130736,\n", + " 137056, 137921, 141137, 143378, 144280, 150546, 159391, 178487,\n", + " 180235, 183471, 195961, 196650, 197309],\n", + " dtype='int64'), Int64Index([ 10049, 38008, 59195, 62553, 75895, 97188, 117398, 130737,\n", + " 137057, 137922, 141138, 143379, 144281, 150547, 159392, 178488,\n", + " 180236, 183472, 195962, 196651, 197310],\n", + " dtype='int64'), Int64Index([ 10050, 38009, 59196, 62554, 75896, 97189, 117399, 130738,\n", + " 137058, 137923, 141139, 143380, 144282, 150548, 159393, 178489,\n", + " 180237, 183473, 195963, 196652, 197311],\n", + " dtype='int64'), Int64Index([ 10051, 38010, 59197, 62555, 75897, 97190, 117400, 130739,\n", + " 137059, 137924, 141140, 143381, 144283, 150549, 159394, 178490,\n", + " 180238, 183474, 195964, 196653, 197312],\n", + " dtype='int64'), Int64Index([ 10052, 38011, 59198, 62556, 75898, 97191, 117401, 130740,\n", + " 137060, 137925, 141141, 143382, 144284, 150550, 159395, 178491,\n", + " 180239, 183475, 195965, 196654, 197313],\n", + " dtype='int64'), Int64Index([ 10053, 38012, 59199, 62557, 75899, 97192, 117402, 130741,\n", + " 137061, 137926, 141142, 143383, 144285, 150551, 159396, 178492,\n", + " 180240, 183476, 195966, 196655, 197314],\n", + " dtype='int64'), Int64Index([ 10054, 38013, 59200, 62558, 75900, 97193, 117403, 130742,\n", + " 137062, 137927, 141143, 143384, 144286, 150552, 159397, 178493,\n", + " 180241, 183477, 195967, 196656, 197315],\n", + " dtype='int64'), Int64Index([ 10055, 38014, 59201, 62559, 75901, 97194, 117404, 130743,\n", + " 137063, 137928, 141144, 143385, 144287, 150553, 159398, 178494,\n", + " 180242, 183478, 195968, 196657, 197316],\n", + " dtype='int64'), Int64Index([ 10056, 38015, 59202, 62560, 75902, 97195, 117405, 130744,\n", + " 137064, 137929, 141145, 143386, 144288, 150554, 159399, 178495,\n", + " 180243, 183479, 195969, 196658, 197317],\n", + " dtype='int64'), Int64Index([ 10057, 38016, 59203, 62561, 75903, 97196, 117406, 130745,\n", + " 137065, 137930, 141146, 143387, 144289, 150555, 159400, 178496,\n", + " 180244, 183480, 195970, 196659, 197318],\n", + " dtype='int64'), Int64Index([ 10058, 38017, 59204, 62562, 75904, 97197, 117407, 130746,\n", + " 137066, 137931, 141147, 143388, 144290, 150556, 159401, 178497,\n", + " 180245, 183481, 195971, 196660, 197319],\n", + " dtype='int64'), Int64Index([ 10059, 38018, 59205, 62563, 75905, 97198, 117408, 130747,\n", + " 137067, 137932, 141148, 143389, 144291, 150557, 159402, 178498,\n", + " 180246, 183482, 195972, 196661, 197320],\n", + " dtype='int64'), Int64Index([ 10060, 38019, 59206, 62564, 75906, 97199, 117409, 130748,\n", + " 137068, 137933, 141149, 143390, 144292, 150558, 159403, 178499,\n", + " 180247, 183483, 195973, 196662, 197321],\n", + " dtype='int64'), Int64Index([ 10061, 38020, 59207, 62565, 75907, 97200, 117410, 130749,\n", + " 137069, 137934, 141150, 143391, 144293, 150559, 159404, 178500,\n", + " 180248, 183484, 195974, 196663, 197322],\n", + " dtype='int64'), Int64Index([ 10062, 38021, 59208, 62566, 75908, 97201, 117411, 130750,\n", + " 137070, 137935, 141151, 143392, 144294, 150560, 159405, 178501,\n", + " 180249, 183485, 195975, 196664, 197323],\n", + " dtype='int64'), Int64Index([ 10063, 38022, 59209, 62567, 75909, 97202, 117412, 130751,\n", + " 137071, 137936, 141152, 143393, 144295, 150561, 159406, 178502,\n", + " 180250, 183486, 195976, 196665, 197324],\n", + " dtype='int64'), Int64Index([ 10064, 38023, 59210, 62568, 75910, 97203, 117413, 130752,\n", + " 137072, 137937, 141153, 143394, 144296, 150562, 159407, 178503,\n", + " 180251, 183487, 195977, 196666, 197325],\n", + " dtype='int64'), Int64Index([ 10065, 38024, 59211, 62569, 75911, 97204, 117414, 130753,\n", + " 137073, 137938, 141154, 143395, 144297, 150563, 159408, 178504,\n", + " 180252, 183488, 195978, 196667, 197326],\n", + " dtype='int64'), Int64Index([ 10066, 38025, 59212, 62570, 75912, 97205, 117415, 130754,\n", + " 137074, 137939, 141155, 143396, 144298, 150564, 159409, 178505,\n", + " 180253, 183489, 195979, 196668, 197327],\n", + " dtype='int64'), Int64Index([ 10067, 38026, 59213, 62571, 75913, 97206, 117416, 130755,\n", + " 137075, 137940, 141156, 143397, 144299, 150565, 159410, 178506,\n", + " 180254, 183490, 195980, 196669, 197328],\n", + " dtype='int64'), Int64Index([ 10068, 38027, 59214, 62572, 75914, 97207, 117417, 130756,\n", + " 137076, 137941, 141157, 143398, 144300, 150566, 159411, 178507,\n", + " 180255, 183491, 195981, 196670, 197329],\n", + " dtype='int64'), Int64Index([ 10069, 38028, 59215, 62573, 75915, 97208, 117418, 130757,\n", + " 137077, 137942, 141158, 143399, 144301, 150567, 159412, 178508,\n", + " 180256, 183492, 195982, 196671, 197330],\n", + " dtype='int64'), Int64Index([ 10070, 38029, 59216, 62574, 75916, 97209, 117419, 130758,\n", + " 137078, 137943, 141159, 143400, 144302, 150568, 159413, 178509,\n", + " 180257, 183493, 195983, 196672, 197331],\n", + " dtype='int64'), Int64Index([ 10071, 38030, 59217, 62575, 75917, 97210, 117420, 130759,\n", + " 137079, 137944, 141160, 143401, 144303, 150569, 159414, 178510,\n", + " 180258, 183494, 195984, 196673, 197332],\n", + " dtype='int64'), Int64Index([ 10072, 38031, 59218, 62576, 75918, 97211, 117421, 130760,\n", + " 137080, 137945, 141161, 143402, 144304, 150570, 159415, 178511,\n", + " 180259, 183495, 195985, 196674, 197333],\n", + " dtype='int64'), Int64Index([ 10073, 38032, 59219, 62577, 75919, 97212, 117422, 130761,\n", + " 137081, 137946, 141162, 143403, 144305, 150571, 159416, 178512,\n", + " 180260, 183496, 195986, 196675, 197334],\n", + " dtype='int64'), Int64Index([ 10074, 38033, 59220, 62578, 75920, 97213, 117423, 130762,\n", + " 137082, 137947, 141163, 143404, 144306, 150572, 159417, 178513,\n", + " 180261, 183497, 195987, 196676, 197335],\n", + " dtype='int64'), Int64Index([ 10075, 38034, 59221, 62579, 75921, 97214, 117424, 130763,\n", + " 137083, 137948, 141164, 143405, 144307, 150573, 159418, 178514,\n", + " 180262, 183498, 195988, 196677, 197336],\n", + " dtype='int64'), Int64Index([ 10076, 38035, 59222, 62580, 75922, 97215, 117425, 130764,\n", + " 137084, 137949, 141165, 143406, 144308, 150574, 159419, 178515,\n", + " 180263, 183499, 195989, 196678, 197337],\n", + " dtype='int64'), Int64Index([ 10077, 38036, 59223, 62581, 75923, 97216, 117426, 130765,\n", + " 137085, 137950, 141166, 143407, 144309, 150575, 159420, 178516,\n", + " 180264, 183500, 195990, 196679, 197338],\n", + " dtype='int64'), Int64Index([ 10078, 38037, 59224, 62582, 75924, 97217, 117427, 130766,\n", + " 137086, 137951, 141167, 143408, 144310, 150576, 159421, 178517,\n", + " 180265, 183501, 195991, 196680, 197339],\n", + " dtype='int64'), Int64Index([ 10079, 38038, 59225, 62583, 75925, 97218, 117428, 130767,\n", + " 137087, 137952, 141168, 143409, 144311, 150577, 159422, 178518,\n", + " 180266, 183502, 195992, 196681, 197340],\n", + " dtype='int64'), Int64Index([ 10080, 38039, 59226, 62584, 75926, 97219, 117429, 130768,\n", + " 137088, 137953, 141169, 143410, 144312, 150578, 159423, 178519,\n", + " 180267, 183503, 195993, 196682, 197341],\n", + " dtype='int64'), Int64Index([ 10081, 38040, 59227, 62585, 75927, 97220, 117430, 130769,\n", + " 137089, 137954, 141170, 143411, 144313, 150579, 159424, 178520,\n", + " 180268, 183504, 195994, 196683, 197342],\n", + " dtype='int64'), Int64Index([ 10082, 38041, 59228, 62586, 75928, 97221, 117431, 130770,\n", + " 137090, 137955, 141171, 143412, 144314, 150580, 159425, 178521,\n", + " 180269, 183505, 195995, 196684, 197343],\n", + " dtype='int64'), Int64Index([ 10083, 38042, 59229, 62587, 75929, 97222, 117432, 130771,\n", + " 137091, 137956, 141172, 143413, 144315, 150581, 159426, 178522,\n", + " 180270, 183506, 195996, 196685, 197344],\n", + " dtype='int64'), Int64Index([ 10084, 38043, 59230, 62588, 75930, 97223, 117433, 130772,\n", + " 137092, 137957, 141173, 143414, 144316, 150582, 159427, 178523,\n", + " 180271, 183507, 195997, 196686, 197345],\n", + " dtype='int64'), Int64Index([ 10085, 38044, 59231, 62589, 75931, 97224, 117434, 130773,\n", + " 137093, 137958, 141174, 143415, 144317, 150583, 159428, 178524,\n", + " 180272, 183508, 195998, 196687, 197346],\n", + " dtype='int64'), Int64Index([ 10086, 38045, 59232, 62590, 75932, 97225, 117435, 130774,\n", + " 137094, 137959, 141175, 143416, 144318, 150584, 159429, 178525,\n", + " 180273, 183509, 195999, 196688, 197347],\n", + " dtype='int64'), Int64Index([ 10087, 38046, 59233, 62591, 75933, 97226, 117436, 130775,\n", + " 137095, 137960, 141176, 143417, 144319, 150585, 159430, 178526,\n", + " 180274, 183510, 196000, 196689, 197348],\n", + " dtype='int64'), Int64Index([ 10088, 38047, 59234, 62592, 75934, 97227, 117437, 130776,\n", + " 137096, 137961, 141177, 143418, 144320, 150586, 159431, 178527,\n", + " 180275, 183511, 196001, 196690, 197349],\n", + " dtype='int64'), Int64Index([ 10089, 38048, 59235, 62593, 75935, 97228, 117438, 130777,\n", + " 137097, 137962, 141178, 143419, 144321, 150587, 159432, 178528,\n", + " 180276, 183512, 196002, 196691, 197350],\n", + " dtype='int64'), Int64Index([ 10090, 38049, 59236, 62594, 75936, 97229, 117439, 130778,\n", + " 137098, 137963, 141179, 143420, 144322, 150588, 159433, 178529,\n", + " 180277, 183513, 196003, 196692, 197351],\n", + " dtype='int64'), Int64Index([ 10091, 38050, 59237, 62595, 75937, 97230, 117440, 130779,\n", + " 137099, 137964, 141180, 143421, 144323, 150589, 159434, 178530,\n", + " 180278, 183514, 196004, 196693, 197352],\n", + " dtype='int64'), Int64Index([ 10092, 38051, 59238, 62596, 75938, 97231, 117441, 130780,\n", + " 137100, 137965, 141181, 143422, 144324, 150590, 159435, 178531,\n", + " 180279, 183515, 196005, 196694, 197353],\n", + " dtype='int64'), Int64Index([ 10093, 38052, 59239, 62597, 75939, 97232, 117442, 130781,\n", + " 137101, 137966, 141182, 143423, 144325, 150591, 159436, 178532,\n", + " 180280, 183516, 196006, 196695, 197354],\n", + " dtype='int64'), Int64Index([ 10094, 38053, 59240, 62598, 75940, 97233, 117443, 130782,\n", + " 137102, 137967, 141183, 143424, 144326, 150592, 159437, 178533,\n", + " 180281, 183517, 196007, 196696, 197355],\n", + " dtype='int64'), Int64Index([ 10095, 38054, 59241, 62599, 75941, 97234, 117444, 130783,\n", + " 137103, 137968, 141184, 143425, 144327, 150593, 159438, 178534,\n", + " 180282, 183518, 196008, 196697, 197356],\n", + " dtype='int64'), Int64Index([ 10096, 38055, 59242, 62600, 75942, 97235, 117445, 130784,\n", + " 137104, 137969, 141185, 143426, 144328, 150594, 159439, 178535,\n", + " 180283, 183519, 196009, 196698, 197357],\n", + " dtype='int64'), Int64Index([ 10097, 38056, 59243, 62601, 75943, 97236, 117446, 130785,\n", + " 137105, 137970, 141186, 143427, 144329, 150595, 159440, 178536,\n", + " 180284, 183520, 196010, 196699, 197358],\n", + " dtype='int64'), Int64Index([ 10098, 38057, 59244, 62602, 75944, 97237, 117447, 130786,\n", + " 137106, 137971, 141187, 143428, 144330, 150596, 159441, 178537,\n", + " 180285, 183521, 196011, 196700, 197359],\n", + " dtype='int64'), Int64Index([ 10099, 38058, 59245, 62603, 75945, 97238, 117448, 130787,\n", + " 137107, 137972, 141188, 143429, 144331, 150597, 159442, 178538,\n", + " 180286, 183522, 196012, 196701, 197360],\n", + " dtype='int64'), Int64Index([ 10100, 38059, 59246, 62604, 75946, 97239, 117449, 130788,\n", + " 137108, 137973, 141189, 143430, 144332, 150598, 159443, 178539,\n", + " 180287, 183523, 196013, 196702, 197361],\n", + " dtype='int64'), Int64Index([ 10101, 38060, 59247, 62605, 75947, 97240, 117450, 130789,\n", + " 137109, 137974, 141190, 143431, 144333, 150599, 159444, 178540,\n", + " 180288, 183524, 196014, 196703, 197362],\n", + " dtype='int64'), Int64Index([ 10102, 38061, 59248, 62606, 75948, 97241, 117451, 130790,\n", + " 137110, 137975, 141191, 143432, 144334, 150600, 159445, 178541,\n", + " 180289, 183525, 196015, 196704, 197363],\n", + " dtype='int64'), Int64Index([ 10103, 38062, 59249, 62607, 75949, 97242, 117452, 130791,\n", + " 137111, 137976, 141192, 143433, 144335, 150601, 159446, 178542,\n", + " 180290, 183526, 196016, 196705, 197364],\n", + " dtype='int64'), Int64Index([ 10104, 38063, 59250, 62608, 75950, 97243, 117453, 130792,\n", + " 137112, 137977, 141193, 143434, 144336, 150602, 159447, 178543,\n", + " 180291, 183527, 196017, 196706, 197365],\n", + " dtype='int64'), Int64Index([ 10105, 38064, 59251, 62609, 75951, 97244, 117454, 130793,\n", + " 137113, 137978, 141194, 143435, 144337, 150603, 159448, 178544,\n", + " 180292, 183528, 196018, 196707, 197366],\n", + " dtype='int64'), Int64Index([ 10106, 38065, 59252, 62610, 75952, 97245, 117455, 130794,\n", + " 137114, 137979, 141195, 143436, 144338, 150604, 159449, 178545,\n", + " 180293, 183529, 196019, 196708, 197367],\n", + " dtype='int64'), Int64Index([ 10107, 38066, 59253, 62611, 75953, 97246, 117456, 130795,\n", + " 137115, 137980, 141196, 143437, 144339, 150605, 159450, 178546,\n", + " 180294, 183530, 196020, 196709, 197368],\n", + " dtype='int64'), Int64Index([ 10108, 38067, 59254, 62612, 75954, 97247, 117457, 130796,\n", + " 137116, 137981, 141197, 143438, 144340, 150606, 159451, 178547,\n", + " 180295, 183531, 196021, 196710, 197369],\n", + " dtype='int64'), Int64Index([ 10109, 38068, 59255, 62613, 75955, 97248, 117458, 130797,\n", + " 137117, 137982, 141198, 143439, 144341, 150607, 159452, 178548,\n", + " 180296, 183532, 196022, 196711, 197370],\n", + " dtype='int64'), Int64Index([ 10110, 38069, 59256, 62614, 75956, 97249, 117459, 130798,\n", + " 137118, 137983, 141199, 143440, 144342, 150608, 159453, 178549,\n", + " 180297, 183533, 196023, 196712, 197371],\n", + " dtype='int64'), Int64Index([ 10111, 38070, 59257, 62615, 75957, 97250, 117460, 130799,\n", + " 137119, 137984, 141200, 143441, 144343, 150609, 159454, 178550,\n", + " 180298, 183534, 196024, 196713, 197372],\n", + " dtype='int64'), Int64Index([ 10112, 38071, 59258, 62616, 75958, 97251, 117461, 130800,\n", + " 137120, 137985, 141201, 143442, 144344, 150610, 159455, 178551,\n", + " 180299, 183535, 196025, 196714, 197373],\n", + " dtype='int64'), Int64Index([ 10113, 38072, 59259, 62617, 75959, 97252, 117462, 130801,\n", + " 137121, 137986, 141202, 143443, 144345, 150611, 159456, 178552,\n", + " 180300, 183536, 196026, 196715, 197374],\n", + " dtype='int64'), Int64Index([ 10114, 38073, 59260, 62618, 75960, 97253, 117463, 130802,\n", + " 137122, 137987, 141203, 143444, 144346, 150612, 159457, 178553,\n", + " 180301, 183537, 196027, 196716, 197375],\n", + " dtype='int64'), Int64Index([ 10115, 38074, 59261, 62619, 75961, 97254, 117464, 130803,\n", + " 137123, 137988, 141204, 143445, 144347, 150613, 159458, 178554,\n", + " 180302, 183538, 196028, 196717, 197376],\n", + " dtype='int64'), Int64Index([ 10116, 38075, 59262, 62620, 75962, 97255, 117465, 130804,\n", + " 137124, 137989, 141205, 143446, 144348, 150614, 159459, 178555,\n", + " 180303, 183539, 196029, 196718, 197377],\n", + " dtype='int64'), Int64Index([ 10117, 38076, 59263, 62621, 75963, 97256, 117466, 130805,\n", + " 137125, 137990, 141206, 143447, 144349, 150615, 159460, 178556,\n", + " 180304, 183540, 196030, 196719, 197378],\n", + " dtype='int64'), Int64Index([ 10118, 38077, 59264, 62622, 75964, 97257, 117467, 130806,\n", + " 137126, 137991, 141207, 143448, 144350, 150616, 159461, 178557,\n", + " 180305, 183541, 196031, 196720, 197379],\n", + " dtype='int64'), Int64Index([ 10119, 38078, 59265, 62623, 75965, 97258, 117468, 130807,\n", + " 137127, 137992, 141208, 143449, 144351, 150617, 159462, 178558,\n", + " 180306, 183542, 196032, 196721, 197380],\n", + " dtype='int64'), Int64Index([ 10120, 38079, 59266, 62624, 75966, 97259, 117469, 130808,\n", + " 137128, 137993, 141209, 143450, 144352, 150618, 159463, 178559,\n", + " 180307, 183543, 196033, 196722, 197381],\n", + " dtype='int64'), Int64Index([ 10121, 38080, 59267, 62625, 75967, 97260, 117470, 130809,\n", + " 137129, 137994, 141210, 143451, 144353, 150619, 159464, 178560,\n", + " 180308, 183544, 196034, 196723, 197382],\n", + " dtype='int64'), Int64Index([ 10122, 38081, 59268, 62626, 75968, 97261, 117471, 130810,\n", + " 137130, 137995, 141211, 143452, 144354, 150620, 159465, 178561,\n", + " 180309, 183545, 196035, 196724, 197383],\n", + " dtype='int64'), Int64Index([ 10123, 38082, 59269, 62627, 75969, 97262, 117472, 130811,\n", + " 137131, 137996, 141212, 143453, 144355, 150621, 159466, 178562,\n", + " 180310, 183546, 196036, 196725, 197384],\n", + " dtype='int64'), Int64Index([ 10124, 38083, 59270, 62628, 75970, 97263, 117473, 130812,\n", + " 137132, 137997, 141213, 143454, 144356, 150622, 159467, 178563,\n", + " 180311, 183547, 196037, 196726, 197385],\n", + " dtype='int64'), Int64Index([ 10125, 38084, 59271, 62629, 75971, 97264, 117474, 130813,\n", + " 137133, 137998, 141214, 143455, 144357, 150623, 159468, 178564,\n", + " 180312, 183548, 196038, 196727, 197386],\n", + " dtype='int64'), Int64Index([ 10126, 38085, 59272, 62630, 75972, 97265, 117475, 130814,\n", + " 137134, 137999, 141215, 143456, 144358, 150624, 159469, 178565,\n", + " 180313, 183549, 196039, 196728, 197387],\n", + " dtype='int64'), Int64Index([ 10127, 38086, 59273, 62631, 75973, 97266, 117476, 130815,\n", + " 137135, 138000, 141216, 143457, 144359, 150625, 159470, 178566,\n", + " 180314, 183550, 196040, 196729, 197388],\n", + " dtype='int64'), Int64Index([ 10128, 38087, 59274, 62632, 75974, 97267, 117477, 130816,\n", + " 137136, 138001, 141217, 143458, 144360, 150626, 159471, 178567,\n", + " 180315, 183551, 196041, 196730, 197389],\n", + " dtype='int64'), Int64Index([ 10129, 38088, 59275, 62633, 75975, 97268, 117478, 130817,\n", + " 137137, 138002, 141218, 143459, 144361, 150627, 159472, 178568,\n", + " 180316, 183552, 196042, 196731, 197390],\n", + " dtype='int64'), Int64Index([ 10130, 38089, 59276, 62634, 75976, 97269, 117479, 130818,\n", + " 137138, 138003, 141219, 143460, 144362, 150628, 159473, 178569,\n", + " 180317, 183553, 196043, 196732, 197391],\n", + " dtype='int64'), Int64Index([ 10131, 38090, 59277, 62635, 75977, 97270, 117480, 130819,\n", + " 137139, 138004, 141220, 143461, 144363, 150629, 159474, 178570,\n", + " 180318, 183554, 196044, 196733, 197392],\n", + " dtype='int64'), Int64Index([ 10132, 38091, 59278, 62636, 75978, 97271, 117481, 130820,\n", + " 137140, 138005, 141221, 143462, 144364, 150630, 159475, 178571,\n", + " 180319, 183555, 196045, 196734, 197393],\n", + " dtype='int64'), Int64Index([ 10133, 38092, 59279, 62637, 75979, 97272, 117482, 130821,\n", + " 137141, 138006, 141222, 143463, 144365, 150631, 159476, 178572,\n", + " 180320, 183556, 196046, 196735, 197394],\n", + " dtype='int64'), Int64Index([ 10134, 38093, 59280, 62638, 75980, 97273, 117483, 130822,\n", + " 137142, 138007, 141223, 143464, 144366, 150632, 159477, 178573,\n", + " 180321, 183557, 196047, 196736, 197395],\n", + " dtype='int64'), Int64Index([ 10135, 38094, 59281, 62639, 75981, 97274, 117484, 130823,\n", + " 137143, 138008, 141224, 143465, 144367, 150633, 159478, 178574,\n", + " 180322, 183558, 196048, 196737, 197396],\n", + " dtype='int64'), Int64Index([ 10136, 38095, 59282, 62640, 75982, 97275, 117485, 130824,\n", + " 137144, 138009, 141225, 143466, 144368, 150634, 159479, 178575,\n", + " 180323, 183559, 196049, 196738, 197397],\n", + " dtype='int64'), Int64Index([ 10137, 38096, 59283, 62641, 75983, 97276, 117486, 130825,\n", + " 137145, 138010, 141226, 143467, 144369, 150635, 159480, 178576,\n", + " 180324, 183560, 196050, 196739, 197398],\n", + " dtype='int64'), Int64Index([ 10138, 38097, 59284, 62642, 75984, 97277, 117487, 130826,\n", + " 137146, 138011, 141227, 143468, 144370, 150636, 159481, 178577,\n", + " 180325, 183561, 196051, 196740, 197399],\n", + " dtype='int64'), Int64Index([ 10139, 38098, 59285, 62643, 75985, 97278, 117488, 130827,\n", + " 137147, 138012, 141228, 143469, 144371, 150637, 159482, 178578,\n", + " 180326, 183562, 196052, 196741, 197400],\n", + " dtype='int64'), Int64Index([ 10140, 38099, 59286, 62644, 75986, 97279, 117489, 130828,\n", + " 137148, 138013, 141229, 143470, 144372, 150638, 159483, 178579,\n", + " 180327, 183563, 196053, 196742, 197401],\n", + " dtype='int64'), Int64Index([ 10141, 38100, 59287, 62645, 75987, 97280, 117490, 130829,\n", + " 137149, 138014, 141230, 143471, 144373, 150639, 159484, 178580,\n", + " 180328, 183564, 196054, 196743, 197402],\n", + " dtype='int64'), Int64Index([ 10142, 38101, 59288, 62646, 75988, 97281, 117491, 130830,\n", + " 137150, 138015, 141231, 143472, 144374, 150640, 159485, 178581,\n", + " 180329, 183565, 196055, 196744, 197403],\n", + " dtype='int64'), Int64Index([ 10143, 38102, 59289, 62647, 75989, 97282, 117492, 130831,\n", + " 137151, 138016, 141232, 143473, 144375, 150641, 159486, 178582,\n", + " 180330, 183566, 196056, 196745, 197404],\n", + " dtype='int64'), Int64Index([ 10144, 38103, 59290, 62648, 97283, 117493, 130832, 137152,\n", + " 138017, 141233, 143474, 144376, 150642, 159487, 178583, 180331,\n", + " 183567, 196057, 196746, 197405],\n", + " dtype='int64'), Int64Index([ 10145, 38104, 59291, 62649, 97284, 117494, 130833, 137153,\n", + " 138018, 141234, 143475, 144377, 150643, 159488, 178584, 180332,\n", + " 183568, 196058, 196747, 197406],\n", + " dtype='int64'), Int64Index([ 10146, 38105, 59292, 62650, 97285, 117495, 130834, 137154,\n", + " 138019, 141235, 143476, 144378, 150644, 159489, 178585, 180333,\n", + " 183569, 196059, 196748, 197407],\n", + " dtype='int64'), Int64Index([ 10147, 38106, 59293, 62651, 97286, 117496, 130835, 137155,\n", + " 138020, 141236, 143477, 144379, 150645, 159490, 178586, 180334,\n", + " 183570, 196060, 196749, 197408],\n", + " dtype='int64'), Int64Index([ 10148, 38107, 59294, 62652, 97287, 117497, 130836, 137156,\n", + " 138021, 141237, 143478, 144380, 150646, 159491, 178587, 180335,\n", + " 183571, 196061, 196750, 197409],\n", + " dtype='int64'), Int64Index([ 10149, 38108, 59295, 62653, 97288, 117498, 130837, 137157,\n", + " 138022, 141238, 143479, 144381, 150647, 159492, 178588, 180336,\n", + " 183572, 196062, 196751, 197410],\n", + " dtype='int64'), Int64Index([ 10150, 38109, 59296, 62654, 97289, 117499, 130838, 137158,\n", + " 138023, 141239, 143480, 144382, 150648, 159493, 178589, 180337,\n", + " 183573, 196063, 196752, 197411],\n", + " dtype='int64'), Int64Index([ 10151, 38110, 59297, 62655, 97290, 117500, 130839, 137159,\n", + " 138024, 141240, 143481, 144383, 150649, 159494, 178590, 180338,\n", + " 183574, 196064, 196753, 197412],\n", + " dtype='int64'), Int64Index([ 10152, 38111, 59298, 62656, 97291, 117501, 130840, 137160,\n", + " 138025, 141241, 143482, 144384, 150650, 159495, 178591, 180339,\n", + " 183575, 196065, 196754, 197413],\n", + " dtype='int64'), Int64Index([ 10153, 38112, 59299, 62657, 97292, 117502, 130841, 137161,\n", + " 138026, 141242, 143483, 144385, 150651, 159496, 178592, 180340,\n", + " 183576, 196066, 196755, 197414],\n", + " dtype='int64'), Int64Index([ 10154, 38113, 59300, 62658, 97293, 117503, 130842, 137162,\n", + " 138027, 141243, 143484, 144386, 150652, 159497, 178593, 180341,\n", + " 183577, 196067, 196756, 197415],\n", + " dtype='int64'), Int64Index([ 10155, 38114, 59301, 62659, 97294, 117504, 130843, 137163,\n", + " 138028, 141244, 143485, 144387, 150653, 159498, 178594, 180342,\n", + " 183578, 196068, 196757, 197416],\n", + " dtype='int64'), Int64Index([ 10156, 38115, 59302, 62660, 97295, 117505, 130844, 137164,\n", + " 138029, 141245, 143486, 144388, 150654, 159499, 178595, 180343,\n", + " 183579, 196069, 196758, 197417],\n", + " dtype='int64'), Int64Index([ 10157, 38116, 59303, 62661, 97296, 117506, 130845, 137165,\n", + " 138030, 141246, 143487, 144389, 150655, 159500, 178596, 180344,\n", + " 183580, 196070, 196759, 197418],\n", + " dtype='int64'), Int64Index([ 10158, 38117, 59304, 62662, 97297, 117507, 130846, 137166,\n", + " 138031, 141247, 143488, 144390, 150656, 159501, 178597, 180345,\n", + " 183581, 196071, 196760, 197419],\n", + " dtype='int64'), Int64Index([ 10159, 38118, 59305, 62663, 97298, 117508, 130847, 137167,\n", + " 138032, 141248, 143489, 144391, 150657, 159502, 178598, 180346,\n", + " 183582, 196072, 196761, 197420],\n", + " dtype='int64'), Int64Index([ 10160, 38119, 59306, 62664, 97299, 117509, 130848, 137168,\n", + " 138033, 141249, 143490, 144392, 150658, 159503, 178599, 180347,\n", + " 183583, 196073, 196762, 197421],\n", + " dtype='int64'), Int64Index([ 10161, 38120, 59307, 62665, 97300, 117510, 130849, 137169,\n", + " 138034, 141250, 143491, 144393, 150659, 159504, 178600, 180348,\n", + " 183584, 196074, 196763, 197422],\n", + " dtype='int64'), Int64Index([ 10162, 38121, 59308, 62666, 97301, 117511, 130850, 137170,\n", + " 138035, 141251, 143492, 144394, 150660, 159505, 178601, 180349,\n", + " 183585, 196075, 196764, 197423],\n", + " dtype='int64'), Int64Index([ 10163, 38122, 59309, 62667, 97302, 117512, 130851, 137171,\n", + " 138036, 141252, 143493, 144395, 150661, 159506, 178602, 180350,\n", + " 183586, 196076, 196765, 197424],\n", + " dtype='int64'), Int64Index([ 10164, 38123, 59310, 62668, 97303, 117513, 130852, 137172,\n", + " 138037, 141253, 143494, 144396, 150662, 159507, 178603, 180351,\n", + " 183587, 196077, 196766, 197425],\n", + " dtype='int64'), Int64Index([ 10165, 38124, 59311, 62669, 97304, 117514, 130853, 137173,\n", + " 138038, 141254, 143495, 144397, 150663, 159508, 178604, 180352,\n", + " 183588, 196078, 196767, 197426],\n", + " dtype='int64'), Int64Index([ 10166, 38125, 59312, 62670, 97305, 117515, 130854, 137174,\n", + " 138039, 141255, 143496, 144398, 150664, 159509, 178605, 180353,\n", + " 183589, 196079, 196768, 197427],\n", + " dtype='int64'), Int64Index([ 10167, 38126, 59313, 62671, 97306, 117516, 130855, 138040,\n", + " 141256, 143497, 144399, 150665, 159510, 178606, 180354, 183590,\n", + " 196080, 196769, 197428],\n", + " dtype='int64'), Int64Index([ 10168, 38127, 59314, 62672, 97307, 117517, 130856, 138041,\n", + " 141257, 143498, 144400, 150666, 159511, 178607, 180355, 183591,\n", + " 196081, 196770, 197429],\n", + " dtype='int64'), Int64Index([ 10169, 38128, 59315, 62673, 97308, 117518, 130857, 138042,\n", + " 141258, 143499, 144401, 150667, 159512, 178608, 180356, 183592,\n", + " 196082, 196771, 197430],\n", + " dtype='int64'), Int64Index([ 10170, 38129, 59316, 62674, 97309, 117519, 130858, 138043,\n", + " 141259, 143500, 144402, 150668, 159513, 178609, 180357, 183593,\n", + " 196083, 196772, 197431],\n", + " dtype='int64'), Int64Index([ 10171, 38130, 59317, 62675, 97310, 117520, 130859, 138044,\n", + " 141260, 143501, 144403, 150669, 159514, 178610, 180358, 183594,\n", + " 196084, 196773, 197432],\n", + " dtype='int64'), Int64Index([ 10172, 38131, 59318, 62676, 97311, 117521, 130860, 138045,\n", + " 141261, 143502, 144404, 150670, 159515, 178611, 180359, 183595,\n", + " 196085, 196774, 197433],\n", + " dtype='int64'), Int64Index([ 10173, 38132, 59319, 62677, 97312, 117522, 130861, 138046,\n", + " 141262, 143503, 144405, 150671, 159516, 178612, 180360, 183596,\n", + " 196086, 196775, 197434],\n", + " dtype='int64'), Int64Index([ 10174, 38133, 59320, 62678, 97313, 117523, 130862, 138047,\n", + " 141263, 143504, 144406, 150672, 159517, 178613, 180361, 183597,\n", + " 196087, 196776, 197435],\n", + " dtype='int64'), Int64Index([ 10175, 38134, 59321, 62679, 97314, 117524, 130863, 138048,\n", + " 141264, 143505, 144407, 150673, 159518, 178614, 180362, 183598,\n", + " 196088, 196777, 197436],\n", + " dtype='int64'), Int64Index([ 10176, 38135, 59322, 62680, 97315, 117525, 130864, 138049,\n", + " 141265, 143506, 144408, 150674, 159519, 178615, 180363, 183599,\n", + " 196089, 196778, 197437],\n", + " dtype='int64'), Int64Index([ 10177, 38136, 59323, 62681, 97316, 117526, 130865, 138050,\n", + " 141266, 143507, 144409, 150675, 159520, 178616, 180364, 196090,\n", + " 196779, 197438],\n", + " dtype='int64'), Int64Index([ 10178, 38137, 59324, 62682, 97317, 117527, 130866, 138051,\n", + " 141267, 143508, 144410, 150676, 159521, 178617, 180365, 196091,\n", + " 196780, 197439],\n", + " dtype='int64'), Int64Index([ 10179, 38138, 59325, 62683, 97318, 117528, 130867, 138052,\n", + " 141268, 143509, 144411, 150677, 159522, 178618, 180366, 196092,\n", + " 196781, 197440],\n", + " dtype='int64'), Int64Index([ 10180, 38139, 59326, 62684, 97319, 117529, 130868, 138053,\n", + " 141269, 143510, 144412, 150678, 159523, 178619, 180367, 196093,\n", + " 196782, 197441],\n", + " dtype='int64'), Int64Index([ 10181, 38140, 59327, 62685, 97320, 117530, 130869, 138054,\n", + " 141270, 143511, 144413, 150679, 159524, 178620, 180368, 196094,\n", + " 196783, 197442],\n", + " dtype='int64'), Int64Index([ 10182, 38141, 59328, 62686, 97321, 117531, 130870, 138055,\n", + " 141271, 143512, 144414, 150680, 159525, 178621, 180369, 196095,\n", + " 196784, 197443],\n", + " dtype='int64'), Int64Index([ 10183, 38142, 59329, 62687, 97322, 117532, 130871, 138056,\n", + " 141272, 143513, 144415, 150681, 159526, 178622, 180370, 196096,\n", + " 196785, 197444],\n", + " dtype='int64'), Int64Index([ 10184, 38143, 59330, 62688, 97323, 117533, 130872, 138057,\n", + " 141273, 143514, 144416, 150682, 159527, 178623, 180371, 196097,\n", + " 196786, 197445],\n", + " dtype='int64'), Int64Index([ 10185, 38144, 59331, 62689, 97324, 117534, 130873, 138058,\n", + " 141274, 143515, 144417, 150683, 159528, 178624, 180372, 196098,\n", + " 196787, 197446],\n", + " dtype='int64'), Int64Index([ 10186, 38145, 59332, 62690, 97325, 117535, 130874, 138059,\n", + " 141275, 143516, 144418, 150684, 159529, 178625, 180373, 196099,\n", + " 196788, 197447],\n", + " dtype='int64'), Int64Index([ 10187, 38146, 59333, 62691, 97326, 117536, 130875, 138060,\n", + " 141276, 143517, 144419, 150685, 159530, 178626, 180374, 196100,\n", + " 196789, 197448],\n", + " dtype='int64'), Int64Index([ 10188, 38147, 59334, 62692, 97327, 117537, 130876, 138061,\n", + " 141277, 143518, 144420, 150686, 159531, 178627, 180375, 196101,\n", + " 196790, 197449],\n", + " dtype='int64'), Int64Index([ 10189, 38148, 59335, 62693, 97328, 117538, 130877, 138062,\n", + " 141278, 143519, 144421, 150687, 159532, 178628, 180376, 196102,\n", + " 196791, 197450],\n", + " dtype='int64'), Int64Index([ 10190, 38149, 59336, 62694, 97329, 117539, 130878, 138063,\n", + " 141279, 143520, 144422, 150688, 159533, 178629, 180377, 196103,\n", + " 196792, 197451],\n", + " dtype='int64'), Int64Index([ 10191, 38150, 59337, 62695, 97330, 117540, 130879, 138064,\n", + " 141280, 143521, 144423, 150689, 159534, 178630, 180378, 196104,\n", + " 196793, 197452],\n", + " dtype='int64'), Int64Index([ 10192, 38151, 59338, 62696, 97331, 117541, 130880, 138065,\n", + " 141281, 143522, 144424, 150690, 159535, 178631, 180379, 196105,\n", + " 196794, 197453],\n", + " dtype='int64'), Int64Index([ 10193, 38152, 59339, 62697, 97332, 117542, 130881, 138066,\n", + " 141282, 143523, 144425, 150691, 159536, 178632, 180380, 196106,\n", + " 196795, 197454],\n", + " dtype='int64'), Int64Index([ 10194, 38153, 59340, 62698, 97333, 117543, 130882, 138067,\n", + " 141283, 143524, 144426, 150692, 159537, 178633, 180381, 196107,\n", + " 196796, 197455],\n", + " dtype='int64'), Int64Index([ 10195, 38154, 59341, 62699, 97334, 117544, 130883, 138068,\n", + " 141284, 143525, 144427, 150693, 159538, 180382, 196108, 196797,\n", + " 197456],\n", + " dtype='int64'), Int64Index([ 10196, 38155, 59342, 62700, 97335, 117545, 130884, 138069,\n", + " 141285, 143526, 144428, 150694, 159539, 180383, 196109, 196798,\n", + " 197457],\n", + " dtype='int64'), Int64Index([ 10197, 38156, 59343, 62701, 97336, 117546, 130885, 138070,\n", + " 141286, 143527, 144429, 150695, 159540, 180384, 196110, 196799,\n", + " 197458],\n", + " dtype='int64'), Int64Index([ 10198, 38157, 59344, 62702, 97337, 117547, 130886, 138071,\n", + " 141287, 143528, 144430, 150696, 159541, 180385, 196111, 196800,\n", + " 197459],\n", + " dtype='int64'), Int64Index([ 10199, 38158, 59345, 62703, 97338, 117548, 130887, 138072,\n", + " 141288, 143529, 144431, 150697, 159542, 180386, 196112, 196801,\n", + " 197460],\n", + " dtype='int64'), Int64Index([ 10200, 38159, 59346, 62704, 97339, 117549, 130888, 138073,\n", + " 141289, 143530, 144432, 150698, 159543, 180387, 196113, 196802,\n", + " 197461],\n", + " dtype='int64'), Int64Index([ 10201, 38160, 59347, 62705, 97340, 117550, 130889, 138074,\n", + " 141290, 143531, 144433, 150699, 159544, 180388, 196114, 196803,\n", + " 197462],\n", + " dtype='int64'), Int64Index([ 10202, 38161, 59348, 62706, 97341, 117551, 130890, 138075,\n", + " 141291, 143532, 144434, 150700, 159545, 180389, 196115, 196804,\n", + " 197463],\n", + " dtype='int64'), Int64Index([ 10203, 38162, 59349, 62707, 97342, 117552, 130891, 138076,\n", + " 141292, 143533, 144435, 150701, 159546, 180390, 196116, 196805,\n", + " 197464],\n", + " dtype='int64'), Int64Index([ 10204, 38163, 59350, 62708, 97343, 117553, 130892, 138077,\n", + " 141293, 143534, 144436, 150702, 159547, 180391, 196117, 196806,\n", + " 197465],\n", + " dtype='int64'), Int64Index([ 10205, 38164, 59351, 62709, 97344, 117554, 130893, 138078,\n", + " 141294, 143535, 144437, 150703, 159548, 180392, 196118, 196807,\n", + " 197466],\n", + " dtype='int64'), Int64Index([ 10206, 38165, 59352, 62710, 97345, 117555, 130894, 138079,\n", + " 141295, 143536, 144438, 150704, 159549, 180393, 196119, 196808,\n", + " 197467],\n", + " dtype='int64'), Int64Index([ 10207, 38166, 59353, 62711, 97346, 117556, 130895, 138080,\n", + " 141296, 143537, 144439, 150705, 159550, 180394, 196120, 196809,\n", + " 197468],\n", + " dtype='int64'), Int64Index([ 10208, 38167, 59354, 62712, 97347, 117557, 130896, 138081,\n", + " 141297, 143538, 144440, 150706, 159551, 180395, 196121, 196810,\n", + " 197469],\n", + " dtype='int64'), Int64Index([ 10209, 38168, 59355, 62713, 97348, 117558, 130897, 138082,\n", + " 141298, 143539, 144441, 150707, 159552, 180396, 196122, 196811,\n", + " 197470],\n", + " dtype='int64'), Int64Index([ 10210, 38169, 59356, 62714, 97349, 117559, 130898, 138083,\n", + " 141299, 143540, 144442, 150708, 159553, 180397, 196123, 196812,\n", + " 197471],\n", + " dtype='int64'), Int64Index([ 10211, 38170, 59357, 62715, 97350, 117560, 130899, 138084,\n", + " 141300, 143541, 144443, 150709, 159554, 180398, 196124, 196813,\n", + " 197472],\n", + " dtype='int64'), Int64Index([ 10212, 38171, 59358, 62716, 97351, 117561, 130900, 138085,\n", + " 141301, 143542, 144444, 150710, 159555, 180399, 196125, 196814,\n", + " 197473],\n", + " dtype='int64'), Int64Index([ 10213, 38172, 59359, 62717, 97352, 117562, 130901, 138086,\n", + " 141302, 143543, 144445, 150711, 159556, 180400, 196126, 196815,\n", + " 197474],\n", + " dtype='int64'), Int64Index([ 10214, 38173, 59360, 62718, 97353, 117563, 130902, 138087,\n", + " 141303, 143544, 144446, 150712, 159557, 180401, 196127, 196816,\n", + " 197475],\n", + " dtype='int64'), Int64Index([ 10215, 38174, 59361, 62719, 97354, 117564, 130903, 138088,\n", + " 141304, 143545, 144447, 150713, 159558, 180402, 196128, 196817,\n", + " 197476],\n", + " dtype='int64'), Int64Index([ 10216, 38175, 59362, 62720, 97355, 117565, 130904, 138089,\n", + " 143546, 144448, 150714, 159559, 180403, 196129, 196818, 197477],\n", + " dtype='int64'), Int64Index([ 10217, 38176, 59363, 62721, 97356, 117566, 130905, 138090,\n", + " 143547, 144449, 150715, 159560, 180404, 196130, 196819, 197478],\n", + " dtype='int64'), Int64Index([ 10218, 38177, 59364, 62722, 97357, 117567, 130906, 138091,\n", + " 143548, 144450, 150716, 159561, 180405, 196131, 196820, 197479],\n", + " dtype='int64'), Int64Index([ 10219, 38178, 59365, 62723, 97358, 117568, 130907, 138092,\n", + " 143549, 144451, 150717, 159562, 180406, 196132, 196821, 197480],\n", + " dtype='int64'), Int64Index([ 10220, 38179, 59366, 62724, 97359, 117569, 130908, 138093,\n", + " 143550, 144452, 150718, 159563, 180407, 196133, 196822, 197481],\n", + " dtype='int64'), Int64Index([ 10221, 38180, 59367, 62725, 97360, 117570, 130909, 138094,\n", + " 143551, 144453, 150719, 159564, 180408, 196134, 196823, 197482],\n", + " dtype='int64'), Int64Index([ 10222, 38181, 59368, 62726, 97361, 117571, 130910, 138095,\n", + " 143552, 144454, 150720, 159565, 180409, 196135, 196824, 197483],\n", + " dtype='int64'), Int64Index([ 10223, 38182, 59369, 62727, 97362, 117572, 130911, 138096,\n", + " 143553, 144455, 150721, 159566, 180410, 196136, 196825, 197484],\n", + " dtype='int64'), Int64Index([ 10224, 38183, 59370, 62728, 97363, 117573, 130912, 138097,\n", + " 143554, 144456, 150722, 159567, 180411, 196137, 196826, 197485],\n", + " dtype='int64'), Int64Index([6431], dtype='int64'), Int64Index([6432], dtype='int64'), Int64Index([6433], dtype='int64'), Int64Index([6434], dtype='int64'), Int64Index([6435], dtype='int64'), Int64Index([6436], dtype='int64'), Int64Index([6437], dtype='int64'), Int64Index([6438], dtype='int64'), Int64Index([6439], dtype='int64'), Int64Index([6440], dtype='int64'), Int64Index([6441], dtype='int64'), Int64Index([6442], dtype='int64'), Int64Index([6443], dtype='int64'), Int64Index([6444], dtype='int64'), Int64Index([6445], dtype='int64'), Int64Index([6446], dtype='int64'), Int64Index([6447], dtype='int64'), Int64Index([6448], dtype='int64'), Int64Index([6449], dtype='int64'), Int64Index([6450], dtype='int64'), Int64Index([6451], dtype='int64'), Int64Index([6452], dtype='int64'), Int64Index([6453], dtype='int64'), Int64Index([6454], dtype='int64'), Int64Index([6455], dtype='int64'), Int64Index([6456], dtype='int64'), Int64Index([6457], dtype='int64'), Int64Index([6458], dtype='int64'), Int64Index([6459], dtype='int64'), Int64Index([6460], dtype='int64'), Int64Index([6461], dtype='int64'), Int64Index([6462], dtype='int64'), Int64Index([6463], dtype='int64'), Int64Index([6464], dtype='int64'), Int64Index([6465], dtype='int64'), Int64Index([6466], dtype='int64'), Int64Index([6467], dtype='int64'), Int64Index([6468], dtype='int64'), Int64Index([6469], dtype='int64'), Int64Index([6470], dtype='int64'), Int64Index([6471], dtype='int64'), Int64Index([6472], dtype='int64'), Int64Index([6473], dtype='int64'), Int64Index([6474], dtype='int64'), Int64Index([6475], dtype='int64'), Int64Index([6476], dtype='int64'), Int64Index([6477], dtype='int64'), Int64Index([6478], dtype='int64'), Int64Index([6479], dtype='int64'), Int64Index([6480], dtype='int64'), Int64Index([6481], dtype='int64'), Int64Index([6482], dtype='int64'), Int64Index([6483], dtype='int64'), Int64Index([6484, 32175], dtype='int64'), Int64Index([6485, 32176], dtype='int64'), Int64Index([6486, 32177], dtype='int64'), Int64Index([6487, 24832, 32178], dtype='int64'), Int64Index([6488, 24833, 32179], dtype='int64'), Int64Index([6489, 24834, 32180], dtype='int64'), Int64Index([6490, 24835, 32181], dtype='int64'), Int64Index([6491, 24836, 32182, 51898], dtype='int64'), Int64Index([6492, 24837, 32183, 51899], dtype='int64'), Int64Index([6493, 24838, 32184, 51900], dtype='int64'), Int64Index([6494, 24839, 32185, 51901], dtype='int64'), Int64Index([6495, 24840, 32186, 51902], dtype='int64'), Int64Index([6496, 24841, 32187, 38184, 51903, 141305], dtype='int64'), Int64Index([6497, 24842, 32188, 38185, 51904, 141306, 147982], dtype='int64'), Int64Index([6498, 24843, 32189, 38186, 51905, 141307, 147983], dtype='int64'), Int64Index([6499, 24844, 32190, 38187, 51906, 141308, 147984], dtype='int64'), Int64Index([6500, 24845, 32191, 38188, 51907, 141309, 147985], dtype='int64'), Int64Index([6501, 23921, 24846, 32192, 38189, 51908, 141310, 147986], dtype='int64'), Int64Index([6502, 23922, 24847, 32193, 38190, 51909, 75990, 141311, 147987], dtype='int64'), Int64Index([6503, 23923, 24848, 32194, 38191, 51910, 75991, 141312, 147988,\n", + " 187292],\n", + " dtype='int64'), Int64Index([ 6504, 23924, 24849, 32195, 38192, 51911, 75992, 141313,\n", + " 147989, 166773, 187293, 191892],\n", + " dtype='int64'), Int64Index([ 6505, 23925, 24850, 32196, 38193, 51912, 75993, 141314,\n", + " 147990, 166774, 187294, 191893],\n", + " dtype='int64'), Int64Index([ 6506, 23926, 24851, 32197, 38194, 51913, 75994, 141315,\n", + " 147991, 166775, 187295, 191894],\n", + " dtype='int64'), Int64Index([ 6507, 23927, 24852, 32198, 38195, 51914, 75995, 141316,\n", + " 147992, 166776, 187296, 191895],\n", + " dtype='int64'), Int64Index([ 6508, 23928, 24853, 32199, 38196, 51915, 75996, 141317,\n", + " 147993, 166777, 187297, 191896],\n", + " dtype='int64'), Int64Index([ 6509, 23929, 24854, 32200, 38197, 51916, 75997, 141318,\n", + " 147994, 166778, 187298, 191897],\n", + " dtype='int64'), Int64Index([ 6510, 23930, 24855, 32201, 38198, 51917, 75998, 141319,\n", + " 147995, 166779, 187299, 191898],\n", + " dtype='int64'), Int64Index([ 6511, 23931, 24856, 32202, 38199, 51918, 75999, 141320,\n", + " 147996, 166780, 187300, 191899],\n", + " dtype='int64'), Int64Index([ 6512, 23932, 24857, 32203, 38200, 51919, 76000, 141321,\n", + " 147997, 166781, 187301, 191900],\n", + " dtype='int64'), Int64Index([ 6513, 23933, 24858, 32204, 38201, 51920, 76001, 141322,\n", + " 147998, 166782, 187302, 191901],\n", + " dtype='int64'), Int64Index([ 6514, 23934, 24859, 32205, 38202, 51921, 76002, 141323,\n", + " 147999, 166783, 187303, 191902],\n", + " dtype='int64'), Int64Index([ 6515, 23935, 24860, 32206, 38203, 51922, 76003, 141324,\n", + " 148000, 166784, 187304, 191903],\n", + " dtype='int64'), Int64Index([ 6516, 23936, 24861, 32207, 38204, 51923, 76004, 141325,\n", + " 148001, 166785, 187305, 191904],\n", + " dtype='int64'), Int64Index([ 6517, 23937, 24862, 32208, 38205, 51924, 76005, 141326,\n", + " 148002, 166786, 187306, 191905],\n", + " dtype='int64'), Int64Index([ 6518, 23938, 24863, 32209, 38206, 51925, 76006, 141327,\n", + " 148003, 166787, 187307, 191906],\n", + " dtype='int64'), Int64Index([ 6519, 23939, 24864, 32210, 38207, 51926, 76007, 141328,\n", + " 148004, 166788, 187308, 191907],\n", + " dtype='int64'), Int64Index([ 6520, 23940, 24865, 32211, 38208, 51927, 76008, 141329,\n", + " 148005, 166789, 187309, 191908],\n", + " dtype='int64'), Int64Index([ 6521, 23941, 24866, 32212, 38209, 51928, 76009, 141330,\n", + " 148006, 166790, 187310, 191909],\n", + " dtype='int64'), Int64Index([ 6522, 23942, 24867, 32213, 38210, 51929, 76010, 141331,\n", + " 148007, 166791, 187311, 191910],\n", + " dtype='int64'), Int64Index([ 6523, 23943, 24868, 32214, 38211, 51930, 76011, 141332,\n", + " 148008, 166792, 187312, 191911],\n", + " dtype='int64'), Int64Index([ 6524, 23944, 24869, 32215, 38212, 51931, 76012, 141333,\n", + " 148009, 166793, 187313, 191912],\n", + " dtype='int64'), Int64Index([ 6525, 23945, 24870, 32216, 38213, 51932, 59371, 76013,\n", + " 141334, 148010, 166794, 187314, 191913],\n", + " dtype='int64'), Int64Index([ 6526, 23946, 24871, 32217, 38214, 51933, 59372, 76014,\n", + " 141335, 148011, 166795, 187315, 191914],\n", + " dtype='int64'), Int64Index([ 6527, 23947, 24872, 32218, 38215, 51934, 59373, 76015,\n", + " 141336, 148012, 166796, 187316, 191915],\n", + " dtype='int64'), Int64Index([ 6528, 23948, 24873, 32219, 38216, 51935, 59374, 76016,\n", + " 141337, 148013, 166797, 187317, 191916],\n", + " dtype='int64'), Int64Index([ 6529, 23949, 24874, 32220, 38217, 51936, 59375, 76017,\n", + " 141338, 148014, 166798, 187318, 191917],\n", + " dtype='int64'), Int64Index([ 6530, 23950, 24875, 32221, 38218, 51937, 59376, 76018,\n", + " 141339, 148015, 166799, 187319, 191918],\n", + " dtype='int64'), Int64Index([ 6531, 23951, 24876, 32222, 38219, 51938, 59377, 76019,\n", + " 141340, 148016, 166800, 187320, 191919],\n", + " dtype='int64'), Int64Index([ 6532, 23952, 24877, 32223, 38220, 51939, 59378, 76020,\n", + " 141341, 148017, 166801, 187321, 191920],\n", + " dtype='int64'), Int64Index([ 6533, 23953, 24878, 32224, 38221, 51940, 59379, 76021,\n", + " 141342, 148018, 166802, 187322, 191921],\n", + " dtype='int64'), Int64Index([ 6534, 23954, 24879, 32225, 38222, 51941, 59380, 76022,\n", + " 141343, 148019, 166803, 187323, 191922],\n", + " dtype='int64'), Int64Index([ 6535, 23955, 24880, 32226, 38223, 51942, 59381, 76023,\n", + " 141344, 148020, 166804, 187324, 191923],\n", + " dtype='int64'), Int64Index([ 6536, 23956, 24881, 32227, 38224, 51943, 59382, 76024,\n", + " 141345, 148021, 166805, 187325, 191924],\n", + " dtype='int64'), Int64Index([ 6537, 23957, 24882, 32228, 38225, 51944, 59383, 76025,\n", + " 141346, 148022, 166806, 187326, 191925],\n", + " dtype='int64'), Int64Index([ 6538, 23958, 24883, 32229, 38226, 51945, 59384, 76026,\n", + " 141347, 148023, 166807, 187327, 191926],\n", + " dtype='int64'), Int64Index([ 6539, 23959, 24884, 32230, 38227, 51946, 59385, 76027,\n", + " 141348, 148024, 166808, 187328, 191927],\n", + " dtype='int64'), Int64Index([ 6540, 23960, 24885, 32231, 38228, 51947, 59386, 76028,\n", + " 141349, 148025, 166809, 187329, 191928],\n", + " dtype='int64'), Int64Index([ 6541, 23961, 24886, 32232, 38229, 51948, 59387, 76029,\n", + " 141350, 148026, 166810, 187330, 191929],\n", + " dtype='int64'), Int64Index([ 6542, 23962, 24887, 32233, 38230, 51949, 59388, 76030,\n", + " 141351, 148027, 166811, 187331, 191930],\n", + " dtype='int64'), Int64Index([ 6543, 23963, 24888, 32234, 38231, 51950, 59389, 76031,\n", + " 141352, 148028, 166812, 187332, 191931],\n", + " dtype='int64'), Int64Index([ 6544, 23964, 24889, 32235, 38232, 51951, 59390, 76032,\n", + " 141353, 148029, 166813, 187333, 191932],\n", 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dtype='int64'), Int64Index([ 6552, 23972, 24897, 32243, 38240, 51959, 59398, 76040,\n", + " 141361, 148037, 166821, 187341, 191940],\n", + " dtype='int64'), Int64Index([ 6553, 23973, 24898, 32244, 38241, 51960, 59399, 76041,\n", + " 141362, 148038, 166822, 187342, 191941],\n", + " dtype='int64'), Int64Index([ 6554, 23974, 24899, 32245, 38242, 51961, 59400, 76042,\n", + " 141363, 148039, 166823, 187343, 191942],\n", + " dtype='int64'), Int64Index([ 6555, 23975, 24900, 32246, 38243, 51962, 59401, 76043,\n", + " 141364, 148040, 166824, 187344, 191943],\n", + " dtype='int64'), Int64Index([ 6556, 23976, 24901, 32247, 38244, 51963, 59402, 76044,\n", + " 141365, 148041, 166825, 187345, 191944],\n", + " dtype='int64'), Int64Index([ 6557, 23977, 24902, 32248, 38245, 51964, 59403, 76045,\n", + " 141366, 148042, 166826, 187346, 191945],\n", + " dtype='int64'), Int64Index([ 6558, 23978, 24903, 32249, 38246, 51965, 59404, 76046,\n", + " 141367, 148043, 166827, 187347, 191946],\n", + " dtype='int64'), Int64Index([ 6559, 23979, 24904, 32250, 38247, 51966, 59405, 76047,\n", + " 141368, 148044, 166828, 187348, 191947],\n", + " dtype='int64'), Int64Index([ 6560, 23980, 24905, 32251, 38248, 51967, 59406, 76048,\n", + " 141369, 148045, 166829, 187349, 191948],\n", + " dtype='int64'), Int64Index([ 6561, 23981, 24906, 32252, 38249, 51968, 59407, 76049,\n", + " 141370, 148046, 166830, 187350, 191949],\n", + " dtype='int64'), Int64Index([ 6562, 23982, 24907, 32253, 38250, 51969, 59408, 76050,\n", + " 141371, 148047, 166831, 187351, 191950],\n", + " dtype='int64'), Int64Index([ 6563, 23983, 24908, 32254, 38251, 51970, 59409, 76051,\n", + " 141372, 148048, 166832, 187352, 191951],\n", + " dtype='int64'), Int64Index([ 6564, 23984, 24909, 32255, 38252, 51971, 59410, 76052,\n", + " 141373, 148049, 166833, 187353, 191952],\n", + " dtype='int64'), Int64Index([ 6565, 23985, 24910, 32256, 38253, 51972, 59411, 76053,\n", + " 141374, 148050, 166834, 187354, 191953],\n", + " dtype='int64'), Int64Index([ 6566, 23986, 24911, 32257, 38254, 51973, 59412, 76054,\n", + " 141375, 148051, 166835, 187355, 191954],\n", + " dtype='int64'), Int64Index([ 6567, 23987, 24912, 32258, 38255, 51974, 59413, 76055,\n", + " 141376, 148052, 166836, 187356, 191955],\n", + " dtype='int64'), Int64Index([ 6568, 23988, 24913, 32259, 38256, 51975, 59414, 76056,\n", + " 141377, 148053, 166837, 187357, 191956],\n", + " dtype='int64'), Int64Index([ 6569, 23989, 24914, 32260, 38257, 51976, 59415, 76057,\n", + " 141378, 148054, 166838, 187358, 191957],\n", + " dtype='int64'), Int64Index([ 6570, 23990, 24915, 32261, 38258, 51977, 59416, 76058,\n", + " 141379, 148055, 166839, 187359, 191958],\n", + " dtype='int64'), Int64Index([ 6571, 23991, 24916, 32262, 38259, 51978, 59417, 76059,\n", + " 141380, 148056, 166840, 187360, 191959],\n", + " dtype='int64'), Int64Index([ 6572, 23992, 24917, 32263, 38260, 51979, 59418, 76060,\n", + " 141381, 148057, 166841, 187361, 191960],\n", + " dtype='int64'), Int64Index([ 6573, 23993, 24918, 32264, 38261, 51980, 59419, 76061,\n", + " 141382, 148058, 166842, 187362, 191961],\n", + " dtype='int64'), Int64Index([ 6574, 23994, 24919, 32265, 38262, 51981, 59420, 76062,\n", + " 141383, 148059, 166843, 187363, 191962],\n", + " dtype='int64'), Int64Index([ 6575, 23995, 24920, 32266, 38263, 51982, 59421, 76063,\n", + " 141384, 148060, 166844, 187364, 191963],\n", + " dtype='int64'), Int64Index([ 6576, 23996, 24921, 32267, 38264, 51983, 59422, 76064,\n", + " 141385, 148061, 166845, 187365, 191964],\n", + " dtype='int64'), Int64Index([ 6577, 23997, 24922, 32268, 38265, 51984, 59423, 76065,\n", + " 141386, 148062, 166846, 187366, 191965],\n", + " dtype='int64'), Int64Index([ 6578, 23998, 24923, 32269, 38266, 51985, 59424, 76066,\n", + " 141387, 148063, 166847, 187367, 191966],\n", + " dtype='int64'), Int64Index([ 6579, 23999, 24924, 32270, 38267, 51986, 59425, 76067,\n", + " 141388, 148064, 166848, 187368, 191967],\n", + " dtype='int64'), Int64Index([ 6580, 24000, 24925, 32271, 38268, 51987, 59426, 76068,\n", + " 141389, 148065, 166849, 187369, 191968],\n", + " dtype='int64'), Int64Index([ 6581, 24001, 24926, 32272, 38269, 51988, 59427, 76069,\n", + " 141390, 148066, 166850, 187370, 191969],\n", + " dtype='int64'), Int64Index([ 6582, 24002, 24927, 32273, 38270, 51989, 59428, 76070,\n", + " 141391, 148067, 166851, 187371, 191970],\n", + " dtype='int64'), Int64Index([ 6583, 24003, 24928, 32274, 38271, 51990, 59429, 76071,\n", + " 141392, 148068, 166852, 187372, 191971],\n", + " dtype='int64'), Int64Index([ 6584, 24004, 24929, 32275, 38272, 51991, 59430, 76072,\n", + " 141393, 148069, 166853, 187373, 191972],\n", + " dtype='int64'), Int64Index([ 6585, 24005, 24930, 32276, 38273, 51992, 59431, 76073,\n", + " 141394, 148070, 166854, 187374, 191973],\n", + " dtype='int64'), Int64Index([ 6586, 24006, 24931, 32277, 38274, 51993, 59432, 76074,\n", + " 141395, 148071, 166855, 187375, 191974],\n", + " dtype='int64'), Int64Index([ 6587, 24007, 24932, 32278, 38275, 51994, 59433, 76075,\n", + " 141396, 148072, 166856, 187376, 191975],\n", + " dtype='int64'), Int64Index([ 6588, 24008, 24933, 32279, 38276, 51995, 59434, 76076,\n", + " 141397, 148073, 166857, 187377, 191976],\n", + " dtype='int64'), Int64Index([ 6589, 24009, 24934, 32280, 38277, 51996, 59435, 76077,\n", + " 141398, 148074, 166858, 187378, 191977],\n", + " dtype='int64'), Int64Index([ 6590, 24010, 24935, 32281, 38278, 51997, 59436, 76078,\n", + " 141399, 148075, 166859, 187379, 191978],\n", + " dtype='int64'), Int64Index([ 6591, 24011, 24936, 32282, 38279, 51998, 59437, 76079,\n", + " 141400, 148076, 166860, 187380, 191979],\n", + " dtype='int64'), Int64Index([ 6592, 24012, 24937, 32283, 38280, 51999, 59438, 76080,\n", + " 141401, 148077, 166861, 187381, 191980],\n", + " dtype='int64'), Int64Index([ 6593, 24013, 24938, 32284, 38281, 52000, 59439, 76081,\n", + " 141402, 148078, 166862, 187382, 191981],\n", + " dtype='int64'), Int64Index([ 6594, 24014, 24939, 32285, 38282, 52001, 59440, 76082,\n", + " 141403, 148079, 166863, 187383, 191982],\n", + " dtype='int64'), Int64Index([ 6595, 24015, 24940, 32286, 38283, 52002, 59441, 76083,\n", + " 141404, 148080, 166864, 187384, 191983],\n", + " dtype='int64'), Int64Index([ 6596, 24016, 24941, 32287, 38284, 52003, 59442, 76084,\n", + " 141405, 148081, 166865, 187385, 191984],\n", + " dtype='int64'), Int64Index([ 6597, 24017, 24942, 32288, 38285, 52004, 59443, 76085,\n", + " 141406, 148082, 166866, 187386, 191985],\n", + " dtype='int64'), Int64Index([ 6598, 24018, 24943, 32289, 38286, 52005, 59444, 76086,\n", + " 141407, 148083, 166867, 187387, 191986],\n", + " dtype='int64'), Int64Index([ 6599, 24019, 24944, 32290, 38287, 52006, 59445, 76087,\n", + " 141408, 148084, 166868, 187388, 191987],\n", + " dtype='int64'), Int64Index([ 6600, 24020, 24945, 32291, 38288, 52007, 59446, 76088,\n", + " 141409, 148085, 166869, 187389, 191988],\n", + " dtype='int64'), Int64Index([ 6601, 24021, 24946, 32292, 38289, 52008, 59447, 76089,\n", + " 141410, 148086, 166870, 187390, 191989],\n", + " dtype='int64'), Int64Index([ 6602, 24022, 24947, 32293, 38290, 52009, 59448, 76090,\n", + " 141411, 148087, 166871, 187391, 191990],\n", + " dtype='int64'), Int64Index([ 6603, 24023, 24948, 32294, 38291, 52010, 59449, 76091,\n", + " 141412, 148088, 166872, 187392, 191991],\n", + " dtype='int64'), Int64Index([ 6604, 24024, 24949, 32295, 38292, 52011, 59450, 76092,\n", + " 141413, 148089, 166873, 187393, 191992],\n", + " dtype='int64'), Int64Index([ 6605, 24025, 24950, 32296, 38293, 52012, 59451, 76093,\n", + " 141414, 148090, 166874, 187394, 191993],\n", + " dtype='int64'), Int64Index([ 6606, 24026, 24951, 32297, 38294, 52013, 59452, 76094,\n", + " 141415, 148091, 166875, 187395, 191994],\n", + " dtype='int64'), Int64Index([ 6607, 24027, 24952, 32298, 38295, 52014, 59453, 76095,\n", + " 141416, 148092, 166876, 187396, 191995],\n", + " dtype='int64'), Int64Index([ 6608, 24028, 24953, 32299, 38296, 52015, 59454, 76096,\n", + " 141417, 148093, 166877, 187397, 191996],\n", + " dtype='int64'), Int64Index([ 6609, 24029, 24954, 32300, 38297, 52016, 59455, 76097,\n", + " 141418, 148094, 166878, 187398, 191997],\n", + " dtype='int64'), Int64Index([ 6610, 24030, 24955, 32301, 38298, 52017, 59456, 76098,\n", + " 141419, 148095, 166879, 187399, 191998],\n", + " dtype='int64'), Int64Index([ 6611, 24031, 24956, 32302, 38299, 52018, 59457, 76099,\n", + " 141420, 148096, 166880, 187400, 191999],\n", + " dtype='int64'), Int64Index([ 6612, 24032, 24957, 32303, 38300, 52019, 59458, 76100,\n", + " 141421, 148097, 166881, 187401, 192000],\n", + " dtype='int64'), Int64Index([ 6613, 24033, 24958, 32304, 38301, 52020, 59459, 76101,\n", + " 141422, 148098, 166882, 187402, 192001],\n", + " dtype='int64'), Int64Index([ 6614, 24034, 24959, 32305, 38302, 52021, 59460, 76102,\n", + " 141423, 148099, 166883, 187403, 192002],\n", + " dtype='int64'), Int64Index([ 6615, 24035, 24960, 32306, 38303, 52022, 59461, 76103,\n", + " 141424, 148100, 166884, 187404, 192003],\n", + " dtype='int64'), Int64Index([ 6616, 24036, 24961, 32307, 38304, 52023, 59462, 76104,\n", + " 141425, 148101, 166885, 187405, 192004],\n", + " dtype='int64'), Int64Index([ 6617, 24037, 24962, 32308, 38305, 52024, 59463, 76105,\n", + " 141426, 148102, 166886, 187406, 192005],\n", + " dtype='int64'), Int64Index([ 6618, 24038, 24963, 32309, 38306, 52025, 59464, 76106,\n", + " 141427, 148103, 166887, 187407, 192006],\n", + " dtype='int64'), Int64Index([ 6619, 24039, 24964, 32310, 38307, 52026, 59465, 76107,\n", + " 141428, 148104, 166888, 187408, 192007],\n", + " dtype='int64'), Int64Index([ 6620, 24040, 24965, 32311, 38308, 52027, 59466, 76108,\n", + " 141429, 148105, 166889, 187409, 192008],\n", + " dtype='int64'), Int64Index([ 6621, 24041, 24966, 32312, 38309, 52028, 59467, 76109,\n", + " 141430, 148106, 166890, 187410, 192009],\n", + " dtype='int64'), Int64Index([ 6622, 24042, 24967, 32313, 38310, 52029, 59468, 76110,\n", + " 141431, 148107, 166891, 187411, 192010],\n", + " dtype='int64'), Int64Index([ 6623, 24043, 24968, 32314, 38311, 52030, 59469, 76111,\n", + " 141432, 148108, 166892, 187412, 192011],\n", + " dtype='int64'), Int64Index([ 6624, 24044, 24969, 32315, 38312, 52031, 59470, 76112,\n", + " 141433, 148109, 166893, 187413, 192012],\n", + " dtype='int64'), Int64Index([ 6625, 24045, 24970, 32316, 38313, 52032, 59471, 76113,\n", + " 141434, 148110, 166894, 187414, 192013],\n", + " dtype='int64'), Int64Index([ 6626, 24046, 24971, 32317, 38314, 52033, 59472, 76114,\n", + " 141435, 148111, 166895, 187415, 192014],\n", + " dtype='int64'), Int64Index([ 6627, 24047, 24972, 32318, 38315, 52034, 59473, 76115,\n", + " 141436, 148112, 166896, 187416, 192015],\n", + " dtype='int64'), Int64Index([ 6628, 24048, 24973, 32319, 38316, 52035, 59474, 76116,\n", + " 141437, 148113, 166897, 187417, 192016],\n", + " dtype='int64'), Int64Index([ 6629, 24049, 24974, 32320, 38317, 52036, 59475, 76117,\n", + " 141438, 148114, 166898, 187418, 192017],\n", + " dtype='int64'), Int64Index([ 6630, 24050, 24975, 32321, 38318, 52037, 59476, 76118,\n", + " 141439, 148115, 166899, 187419, 192018],\n", + " dtype='int64'), Int64Index([ 6631, 24051, 24976, 32322, 38319, 52038, 59477, 76119,\n", + " 141440, 148116, 166900, 187420, 192019],\n", + " dtype='int64'), Int64Index([ 6632, 24052, 24977, 32323, 38320, 52039, 59478, 76120,\n", + " 141441, 148117, 166901, 187421, 192020],\n", + " dtype='int64'), Int64Index([ 6633, 24053, 24978, 32324, 38321, 52040, 59479, 76121,\n", + " 141442, 148118, 166902, 187422, 192021],\n", + " dtype='int64'), Int64Index([ 6634, 24054, 24979, 32325, 38322, 52041, 59480, 76122,\n", + " 141443, 148119, 166903, 187423, 192022],\n", + " dtype='int64'), Int64Index([ 6635, 24055, 24980, 32326, 38323, 52042, 59481, 76123,\n", + " 141444, 148120, 166904, 187424, 192023],\n", + " dtype='int64'), Int64Index([ 6636, 24056, 24981, 32327, 38324, 52043, 59482, 76124,\n", + " 141445, 148121, 166905, 187425, 192024],\n", + " dtype='int64'), Int64Index([ 6637, 24057, 24982, 32328, 38325, 52044, 59483, 76125,\n", + " 141446, 148122, 166906, 187426, 192025],\n", + " dtype='int64'), Int64Index([ 6638, 24058, 24983, 32329, 38326, 52045, 59484, 76126,\n", + " 141447, 148123, 166907, 187427, 192026],\n", + " dtype='int64'), Int64Index([ 6639, 24059, 24984, 32330, 38327, 52046, 59485, 76127,\n", + " 141448, 148124, 166908, 187428, 192027],\n", + " dtype='int64'), Int64Index([ 6640, 24060, 24985, 32331, 38328, 52047, 59486, 76128,\n", + " 141449, 148125, 166909, 187429, 192028],\n", + " dtype='int64'), Int64Index([ 6641, 24061, 24986, 32332, 38329, 52048, 59487, 76129,\n", + " 141450, 148126, 166910, 187430, 192029],\n", + " dtype='int64'), Int64Index([ 6642, 24062, 24987, 32333, 38330, 52049, 59488, 76130,\n", + " 141451, 148127, 166911, 187431, 192030],\n", + " dtype='int64'), Int64Index([ 6643, 24063, 24988, 32334, 38331, 52050, 59489, 76131,\n", + " 141452, 148128, 166912, 187432, 192031],\n", + " dtype='int64'), Int64Index([ 6644, 24064, 24989, 32335, 38332, 52051, 59490, 76132,\n", + " 141453, 148129, 166913, 187433, 192032],\n", + " dtype='int64'), Int64Index([ 6645, 24065, 24990, 32336, 38333, 52052, 59491, 76133,\n", + " 141454, 148130, 166914, 187434, 192033],\n", + " dtype='int64'), Int64Index([ 6646, 24066, 24991, 32337, 38334, 52053, 59492, 76134,\n", + " 141455, 148131, 166915, 187435, 192034],\n", + " dtype='int64'), Int64Index([ 6647, 24067, 24992, 32338, 38335, 52054, 59493, 76135,\n", + " 141456, 148132, 166916, 187436, 192035],\n", + " dtype='int64'), Int64Index([ 6648, 24068, 24993, 32339, 38336, 52055, 59494, 76136,\n", + " 141457, 148133, 166917, 187437, 192036],\n", + " dtype='int64'), Int64Index([ 6649, 24069, 24994, 32340, 38337, 52056, 59495, 76137,\n", + " 141458, 148134, 166918, 187438, 192037],\n", + " dtype='int64'), Int64Index([ 6650, 24070, 24995, 32341, 38338, 52057, 59496, 76138,\n", + " 141459, 148135, 166919, 187439, 192038],\n", + " dtype='int64'), Int64Index([ 6651, 24071, 24996, 32342, 38339, 52058, 59497, 76139,\n", + " 141460, 148136, 166920, 187440, 192039],\n", + " dtype='int64'), Int64Index([ 6652, 24072, 24997, 32343, 38340, 52059, 59498, 76140,\n", + " 141461, 148137, 166921, 187441, 192040],\n", + " dtype='int64'), Int64Index([ 6653, 24073, 24998, 32344, 38341, 52060, 59499, 76141,\n", + " 141462, 148138, 166922, 187442, 192041],\n", + " dtype='int64'), Int64Index([ 6654, 24074, 24999, 32345, 38342, 52061, 59500, 76142,\n", + " 141463, 148139, 166923, 187443, 192042],\n", + " dtype='int64'), Int64Index([ 6655, 24075, 25000, 32346, 38343, 52062, 59501, 76143,\n", + " 141464, 148140, 166924, 187444, 192043],\n", + " dtype='int64'), Int64Index([ 6656, 24076, 25001, 32347, 38344, 52063, 59502, 76144,\n", + " 141465, 148141, 166925, 187445, 192044],\n", + " dtype='int64'), Int64Index([ 6657, 24077, 25002, 32348, 38345, 52064, 59503, 76145,\n", + " 141466, 148142, 166926, 187446, 192045],\n", + " dtype='int64'), Int64Index([ 6658, 24078, 25003, 32349, 38346, 52065, 59504, 76146,\n", + " 141467, 148143, 166927, 187447, 192046],\n", + " dtype='int64'), Int64Index([ 6659, 24079, 25004, 32350, 38347, 52066, 59505, 76147,\n", + " 141468, 148144, 166928, 187448, 192047],\n", + " dtype='int64'), Int64Index([ 6660, 24080, 25005, 32351, 38348, 52067, 59506, 76148,\n", + " 141469, 148145, 166929, 187449, 192048],\n", + " dtype='int64'), Int64Index([ 6661, 24081, 25006, 32352, 38349, 52068, 59507, 76149,\n", + " 141470, 148146, 166930, 187450, 192049],\n", + " dtype='int64'), Int64Index([ 6662, 24082, 25007, 32353, 38350, 52069, 59508, 76150,\n", + " 141471, 148147, 166931, 187451, 192050],\n", + " dtype='int64'), Int64Index([ 6663, 24083, 25008, 32354, 38351, 52070, 59509, 76151,\n", + " 141472, 148148, 166932, 187452, 192051],\n", + " dtype='int64'), Int64Index([ 6664, 24084, 25009, 32355, 38352, 52071, 59510, 76152,\n", + " 141473, 148149, 166933, 187453, 192052],\n", + " dtype='int64'), Int64Index([ 6665, 24085, 25010, 32356, 38353, 52072, 59511, 76153,\n", + " 141474, 148150, 166934, 187454, 192053],\n", + " dtype='int64'), Int64Index([ 6666, 24086, 25011, 32357, 38354, 52073, 59512, 76154,\n", + " 141475, 148151, 166935, 187455, 192054],\n", + " dtype='int64'), Int64Index([ 6667, 24087, 25012, 32358, 38355, 52074, 59513, 76155,\n", + " 141476, 148152, 166936, 187456, 192055],\n", + " dtype='int64'), Int64Index([ 6668, 24088, 25013, 32359, 38356, 52075, 59514, 76156,\n", + " 141477, 148153, 166937, 187457, 192056],\n", + " dtype='int64'), Int64Index([ 6669, 24089, 25014, 32360, 38357, 52076, 59515, 76157,\n", + " 141478, 148154, 166938, 187458, 192057],\n", + " dtype='int64'), Int64Index([ 6670, 24090, 25015, 32361, 38358, 52077, 59516, 76158,\n", + " 141479, 148155, 166939, 187459, 192058],\n", + " dtype='int64'), Int64Index([ 6671, 24091, 25016, 32362, 38359, 52078, 59517, 76159,\n", + " 141480, 148156, 166940, 187460, 192059],\n", + " dtype='int64'), Int64Index([ 6672, 24092, 25017, 32363, 38360, 52079, 59518, 76160,\n", + " 141481, 148157, 166941, 187461, 192060],\n", + " dtype='int64'), Int64Index([ 6673, 24093, 25018, 32364, 38361, 52080, 59519, 76161,\n", + " 141482, 148158, 166942, 187462, 192061],\n", + " dtype='int64'), Int64Index([ 6674, 24094, 25019, 32365, 38362, 52081, 59520, 76162,\n", + " 141483, 148159, 166943, 187463, 192062],\n", + " dtype='int64'), Int64Index([ 6675, 24095, 25020, 32366, 38363, 52082, 59521, 76163,\n", + " 141484, 148160, 166944, 187464, 192063],\n", + " dtype='int64'), Int64Index([ 6676, 24096, 25021, 32367, 38364, 52083, 59522, 76164,\n", + " 141485, 148161, 166945, 187465, 192064],\n", + " dtype='int64'), Int64Index([ 6677, 24097, 25022, 32368, 38365, 52084, 59523, 76165,\n", + " 141486, 148162, 166946, 187466, 192065],\n", + " dtype='int64'), Int64Index([ 6678, 24098, 25023, 32369, 38366, 52085, 59524, 76166,\n", + " 141487, 148163, 166947, 187467, 192066],\n", + " dtype='int64'), Int64Index([ 6679, 24099, 25024, 32370, 38367, 52086, 59525, 76167,\n", + " 141488, 148164, 166948, 187468, 192067],\n", + " dtype='int64'), Int64Index([ 6680, 24100, 25025, 32371, 38368, 52087, 59526, 76168,\n", + " 141489, 148165, 166949, 187469, 192068],\n", + " dtype='int64'), Int64Index([ 6681, 24101, 25026, 32372, 38369, 52088, 59527, 76169,\n", + " 141490, 148166, 166950, 187470, 192069],\n", + " dtype='int64'), Int64Index([ 6682, 24102, 25027, 32373, 38370, 52089, 59528, 76170,\n", + " 141491, 148167, 166951, 187471, 192070],\n", + " dtype='int64'), Int64Index([ 6683, 24103, 25028, 32374, 38371, 52090, 59529, 76171,\n", + " 141492, 148168, 166952, 187472, 192071],\n", + " dtype='int64'), Int64Index([ 6684, 24104, 25029, 32375, 38372, 52091, 59530, 76172,\n", + " 141493, 148169, 166953, 187473, 192072],\n", + " dtype='int64'), Int64Index([ 6685, 24105, 25030, 32376, 38373, 52092, 59531, 76173,\n", + " 141494, 148170, 166954, 187474, 192073],\n", + " dtype='int64'), Int64Index([ 6686, 24106, 25031, 32377, 38374, 52093, 59532, 76174,\n", + " 141495, 148171, 166955, 187475, 192074],\n", + " dtype='int64'), Int64Index([ 6687, 24107, 25032, 32378, 38375, 52094, 59533, 76175,\n", + " 141496, 148172, 166956, 187476, 192075],\n", + " dtype='int64'), Int64Index([ 6688, 24108, 25033, 32379, 38376, 52095, 59534, 76176,\n", + " 141497, 148173, 166957, 187477, 192076],\n", + " dtype='int64'), Int64Index([ 6689, 24109, 25034, 32380, 38377, 52096, 59535, 76177,\n", + " 141498, 148174, 166958, 187478, 192077],\n", + " dtype='int64'), Int64Index([ 6690, 24110, 25035, 32381, 38378, 52097, 59536, 76178,\n", + " 141499, 148175, 166959, 187479, 192078],\n", + " dtype='int64'), Int64Index([ 6691, 24111, 25036, 32382, 38379, 52098, 59537, 76179,\n", + " 141500, 148176, 166960, 187480, 192079],\n", + " dtype='int64'), Int64Index([ 6692, 24112, 25037, 32383, 38380, 52099, 59538, 76180,\n", + " 141501, 148177, 166961, 187481, 192080],\n", + " dtype='int64'), Int64Index([ 6693, 24113, 25038, 32384, 38381, 52100, 59539, 76181,\n", + " 141502, 148178, 166962, 187482, 192081],\n", + " dtype='int64'), Int64Index([ 6694, 24114, 25039, 32385, 38382, 52101, 59540, 76182,\n", + " 141503, 148179, 166963, 187483, 192082],\n", + " dtype='int64'), Int64Index([ 6695, 24115, 25040, 32386, 38383, 52102, 59541, 76183,\n", + " 141504, 148180, 166964, 187484, 192083],\n", + " dtype='int64'), Int64Index([ 6696, 24116, 25041, 32387, 38384, 52103, 59542, 76184,\n", + " 141505, 148181, 166965, 187485, 192084],\n", + " dtype='int64'), Int64Index([ 6697, 24117, 25042, 32388, 38385, 52104, 59543, 76185,\n", + " 141506, 148182, 166966, 187486, 192085],\n", + " dtype='int64'), Int64Index([ 6698, 24118, 25043, 32389, 38386, 52105, 59544, 76186,\n", + " 141507, 148183, 166967, 187487, 192086],\n", + " dtype='int64'), Int64Index([ 6699, 24119, 25044, 32390, 38387, 52106, 59545, 76187,\n", + " 141508, 148184, 166968, 187488, 192087],\n", + " dtype='int64'), Int64Index([ 6700, 24120, 25045, 32391, 38388, 52107, 59546, 76188,\n", + " 141509, 148185, 166969, 187489, 192088],\n", + " dtype='int64'), Int64Index([ 6701, 24121, 25046, 32392, 38389, 52108, 59547, 76189,\n", + " 141510, 148186, 166970, 187490, 192089],\n", + " dtype='int64'), Int64Index([ 6702, 24122, 25047, 32393, 38390, 52109, 59548, 76190,\n", + " 141511, 148187, 166971, 187491, 192090],\n", + " dtype='int64'), Int64Index([ 6703, 24123, 25048, 32394, 38391, 52110, 59549, 76191,\n", + " 141512, 148188, 166972, 187492, 192091],\n", + " dtype='int64'), Int64Index([ 6704, 24124, 25049, 32395, 38392, 52111, 59550, 76192,\n", + " 141513, 148189, 166973, 187493, 192092],\n", + " dtype='int64'), Int64Index([ 6705, 24125, 25050, 32396, 38393, 52112, 59551, 76193,\n", + " 141514, 148190, 166974, 187494, 192093],\n", + " dtype='int64'), Int64Index([ 6706, 24126, 25051, 32397, 38394, 52113, 59552, 76194,\n", + " 141515, 148191, 166975, 187495, 192094],\n", + " dtype='int64'), Int64Index([ 6707, 24127, 25052, 32398, 38395, 52114, 59553, 76195,\n", + " 141516, 148192, 166976, 187496, 192095],\n", + " dtype='int64'), Int64Index([ 6708, 24128, 25053, 32399, 38396, 52115, 59554, 76196,\n", + " 141517, 148193, 166977, 187497, 192096],\n", + " dtype='int64'), Int64Index([ 6709, 24129, 25054, 32400, 38397, 52116, 59555, 76197,\n", + " 141518, 148194, 166978, 187498, 192097],\n", + " dtype='int64'), Int64Index([ 6710, 24130, 25055, 32401, 38398, 52117, 59556, 76198,\n", + " 141519, 148195, 166979, 187499, 192098],\n", + " dtype='int64'), Int64Index([ 6711, 24131, 25056, 32402, 38399, 52118, 59557, 76199,\n", + " 141520, 148196, 166980, 187500, 192099],\n", + " dtype='int64'), Int64Index([ 6712, 24132, 25057, 32403, 38400, 52119, 59558, 76200,\n", + " 141521, 148197, 166981, 187501, 192100],\n", + " dtype='int64'), Int64Index([ 6713, 24133, 25058, 32404, 38401, 52120, 59559, 76201,\n", + " 141522, 148198, 166982, 187502, 192101],\n", + " dtype='int64'), Int64Index([ 6714, 24134, 25059, 32405, 38402, 52121, 59560, 76202,\n", + " 141523, 148199, 166983, 187503, 192102],\n", + " dtype='int64'), Int64Index([ 6715, 24135, 25060, 32406, 38403, 52122, 59561, 76203,\n", + " 141524, 148200, 166984, 187504, 192103],\n", + " dtype='int64'), Int64Index([ 6716, 24136, 25061, 32407, 38404, 52123, 59562, 76204,\n", + " 141525, 148201, 166985, 187505, 192104],\n", + " dtype='int64'), Int64Index([ 6717, 24137, 25062, 32408, 38405, 52124, 59563, 76205,\n", + " 141526, 148202, 166986, 187506, 192105],\n", + " dtype='int64'), Int64Index([ 6718, 24138, 25063, 32409, 38406, 52125, 59564, 76206,\n", + " 141527, 148203, 166987, 187507, 192106],\n", + " dtype='int64'), Int64Index([ 6719, 24139, 25064, 32410, 38407, 52126, 59565, 76207,\n", + " 141528, 148204, 166988, 187508, 192107],\n", + " dtype='int64'), Int64Index([ 6720, 24140, 25065, 32411, 38408, 52127, 59566, 76208,\n", + " 141529, 148205, 166989, 187509, 192108],\n", + " dtype='int64'), Int64Index([ 6721, 24141, 25066, 32412, 38409, 52128, 59567, 76209,\n", + " 141530, 148206, 166990, 187510, 192109],\n", + " dtype='int64'), Int64Index([ 6722, 24142, 25067, 32413, 38410, 52129, 59568, 76210,\n", + " 141531, 148207, 166991, 187511, 192110],\n", + " dtype='int64'), Int64Index([ 6723, 24143, 25068, 32414, 38411, 52130, 59569, 76211,\n", + " 141532, 148208, 166992, 187512, 192111],\n", + " dtype='int64'), Int64Index([ 6724, 24144, 25069, 32415, 38412, 52131, 59570, 76212,\n", + " 141533, 148209, 166993, 187513, 192112],\n", + " dtype='int64'), Int64Index([ 6725, 24145, 25070, 32416, 38413, 52132, 59571, 76213,\n", + " 141534, 148210, 166994, 187514, 192113],\n", + " dtype='int64'), Int64Index([ 6726, 24146, 25071, 32417, 38414, 52133, 59572, 76214,\n", + " 141535, 148211, 166995, 187515, 192114],\n", + " dtype='int64'), Int64Index([ 6727, 24147, 25072, 32418, 38415, 52134, 59573, 76215,\n", + " 141536, 148212, 166996, 187516, 192115],\n", + " dtype='int64'), Int64Index([ 6728, 24148, 25073, 32419, 38416, 52135, 59574, 76216,\n", + " 141537, 148213, 166997, 187517, 192116],\n", + " dtype='int64'), Int64Index([ 6729, 24149, 25074, 32420, 38417, 52136, 59575, 76217,\n", + " 141538, 148214, 166998, 187518, 192117],\n", + " dtype='int64'), Int64Index([ 6730, 24150, 25075, 32421, 38418, 52137, 59576, 76218,\n", + " 141539, 148215, 166999, 187519, 192118],\n", + " dtype='int64'), Int64Index([ 6731, 24151, 25076, 32422, 38419, 52138, 59577, 76219,\n", + " 141540, 148216, 167000, 187520, 192119],\n", + " dtype='int64'), Int64Index([ 6732, 24152, 25077, 32423, 38420, 52139, 59578, 76220,\n", + " 141541, 148217, 167001, 187521, 192120],\n", + " dtype='int64'), Int64Index([ 6733, 24153, 25078, 32424, 38421, 52140, 59579, 76221,\n", + " 141542, 148218, 167002, 187522, 192121],\n", + " dtype='int64'), Int64Index([ 6734, 24154, 25079, 32425, 38422, 52141, 59580, 76222,\n", + " 141543, 148219, 167003, 187523, 192122],\n", + " dtype='int64'), Int64Index([ 6735, 24155, 25080, 32426, 38423, 52142, 59581, 76223,\n", + " 141544, 148220, 167004, 187524, 192123],\n", + " dtype='int64'), Int64Index([ 6736, 24156, 25081, 32427, 38424, 52143, 59582, 76224,\n", + " 141545, 148221, 167005, 187525, 192124],\n", + " dtype='int64'), Int64Index([ 6737, 24157, 25082, 32428, 38425, 52144, 59583, 76225,\n", + " 141546, 148222, 167006, 187526, 192125],\n", + " dtype='int64'), Int64Index([ 6738, 24158, 25083, 32429, 38426, 52145, 59584, 76226,\n", + " 141547, 148223, 167007, 187527, 192126],\n", + " dtype='int64'), Int64Index([ 6739, 24159, 25084, 32430, 38427, 52146, 59585, 76227,\n", + " 141548, 148224, 167008, 187528, 192127],\n", + " dtype='int64'), Int64Index([ 6740, 24160, 25085, 32431, 38428, 52147, 59586, 76228,\n", + " 141549, 148225, 167009, 187529, 192128],\n", + " dtype='int64'), Int64Index([ 6741, 24161, 25086, 32432, 38429, 52148, 59587, 76229,\n", + " 141550, 148226, 167010, 187530, 192129],\n", + " dtype='int64'), Int64Index([ 6742, 24162, 25087, 32433, 38430, 52149, 59588, 76230,\n", + " 141551, 148227, 167011, 187531, 192130],\n", + " dtype='int64'), Int64Index([ 6743, 24163, 25088, 32434, 38431, 52150, 59589, 76231,\n", + " 141552, 148228, 167012, 187532, 192131],\n", + " dtype='int64'), Int64Index([ 6744, 24164, 25089, 32435, 38432, 52151, 59590, 76232,\n", + " 141553, 148229, 167013, 187533, 192132],\n", + " dtype='int64'), Int64Index([ 6745, 24165, 25090, 32436, 38433, 52152, 59591, 76233,\n", + " 141554, 148230, 167014, 187534, 192133],\n", + " dtype='int64'), Int64Index([ 6746, 24166, 25091, 32437, 38434, 52153, 59592, 76234,\n", + " 141555, 148231, 167015, 187535, 192134],\n", + " dtype='int64'), Int64Index([ 6747, 24167, 25092, 32438, 38435, 52154, 59593, 76235,\n", + " 141556, 148232, 167016, 187536, 192135],\n", + " dtype='int64'), Int64Index([ 6748, 24168, 25093, 32439, 38436, 52155, 59594, 76236,\n", + " 141557, 148233, 167017, 187537, 192136],\n", + " dtype='int64'), Int64Index([ 6749, 24169, 25094, 32440, 38437, 52156, 59595, 76237,\n", + " 141558, 148234, 167018, 187538, 192137],\n", + " dtype='int64'), Int64Index([ 6750, 24170, 25095, 32441, 38438, 52157, 59596, 76238,\n", + " 141559, 148235, 167019, 187539, 192138],\n", + " dtype='int64'), Int64Index([ 6751, 24171, 25096, 32442, 38439, 52158, 59597, 76239,\n", + " 141560, 148236, 167020, 187540, 192139],\n", + " dtype='int64'), Int64Index([ 6752, 24172, 25097, 32443, 38440, 52159, 59598, 76240,\n", + " 141561, 148237, 167021, 187541, 192140],\n", + " dtype='int64'), Int64Index([ 6753, 24173, 25098, 32444, 38441, 52160, 59599, 76241,\n", + " 141562, 148238, 167022, 187542, 192141],\n", + " dtype='int64'), Int64Index([ 6754, 24174, 25099, 32445, 38442, 52161, 59600, 76242,\n", + " 141563, 148239, 167023, 187543, 192142],\n", + " dtype='int64'), Int64Index([ 6755, 24175, 25100, 32446, 38443, 52162, 59601, 76243,\n", + " 141564, 148240, 167024, 187544, 192143],\n", + " dtype='int64'), Int64Index([ 6756, 24176, 25101, 32447, 38444, 52163, 59602, 76244,\n", + " 141565, 148241, 167025, 187545, 192144],\n", + " dtype='int64'), Int64Index([ 6757, 24177, 25102, 32448, 38445, 52164, 59603, 76245,\n", + " 141566, 148242, 167026, 187546, 192145],\n", + " dtype='int64'), Int64Index([ 6758, 24178, 25103, 32449, 38446, 52165, 59604, 76246,\n", + " 141567, 148243, 167027, 187547, 192146],\n", + " dtype='int64'), Int64Index([ 6759, 24179, 25104, 32450, 38447, 52166, 59605, 76247,\n", + " 141568, 148244, 167028, 187548, 192147],\n", + " dtype='int64'), Int64Index([ 6760, 24180, 25105, 32451, 38448, 52167, 59606, 76248,\n", + " 141569, 148245, 167029, 187549, 192148],\n", + " dtype='int64'), Int64Index([ 6761, 24181, 25106, 32452, 38449, 52168, 59607, 76249,\n", + " 141570, 148246, 167030, 187550, 192149],\n", + " dtype='int64'), Int64Index([ 6762, 24182, 25107, 32453, 38450, 52169, 59608, 76250,\n", + " 141571, 148247, 167031, 187551, 192150],\n", + " dtype='int64'), Int64Index([ 6763, 24183, 25108, 32454, 38451, 52170, 59609, 76251,\n", + " 141572, 148248, 167032, 187552, 192151],\n", + " dtype='int64'), Int64Index([ 6764, 24184, 25109, 32455, 38452, 52171, 59610, 76252,\n", + " 141573, 148249, 167033, 187553, 192152],\n", + " dtype='int64'), Int64Index([ 6765, 24185, 25110, 32456, 38453, 52172, 59611, 76253,\n", + " 141574, 148250, 167034, 187554, 192153],\n", + " dtype='int64'), Int64Index([ 6766, 24186, 25111, 32457, 38454, 52173, 59612, 76254,\n", + " 141575, 148251, 167035, 187555, 192154],\n", + " dtype='int64'), Int64Index([ 6767, 24187, 25112, 32458, 38455, 52174, 59613, 76255,\n", + " 141576, 148252, 167036, 187556, 192155],\n", + " dtype='int64'), Int64Index([ 6768, 24188, 25113, 32459, 38456, 52175, 59614, 76256,\n", + " 141577, 148253, 167037, 187557, 192156],\n", + " dtype='int64'), Int64Index([ 6769, 24189, 25114, 32460, 38457, 52176, 59615, 76257,\n", + " 141578, 148254, 167038, 187558, 192157],\n", + " dtype='int64'), Int64Index([ 6770, 24190, 25115, 32461, 38458, 52177, 59616, 76258,\n", + " 141579, 148255, 167039, 187559, 192158],\n", + " dtype='int64'), Int64Index([ 6771, 24191, 25116, 32462, 38459, 52178, 59617, 76259,\n", + " 141580, 148256, 167040, 187560, 192159],\n", + " dtype='int64'), Int64Index([ 6772, 24192, 25117, 32463, 38460, 52179, 59618, 76260,\n", + " 141581, 148257, 167041, 187561, 192160],\n", + " dtype='int64'), Int64Index([ 6773, 24193, 25118, 32464, 38461, 52180, 59619, 76261,\n", + " 141582, 148258, 167042, 187562, 192161],\n", + " dtype='int64'), Int64Index([ 6774, 24194, 25119, 32465, 38462, 52181, 59620, 76262,\n", + " 141583, 148259, 167043, 187563, 192162],\n", + " dtype='int64'), Int64Index([ 6775, 24195, 25120, 32466, 38463, 52182, 59621, 76263,\n", + " 141584, 148260, 167044, 187564, 192163],\n", + " dtype='int64'), Int64Index([ 6776, 24196, 25121, 32467, 38464, 52183, 59622, 76264,\n", + " 141585, 148261, 167045, 187565, 192164],\n", + " dtype='int64'), Int64Index([ 6777, 24197, 25122, 32468, 38465, 52184, 59623, 76265,\n", + " 141586, 148262, 167046, 187566, 192165],\n", + " dtype='int64'), Int64Index([ 6778, 24198, 25123, 32469, 38466, 52185, 59624, 76266,\n", + " 141587, 148263, 167047, 187567, 192166],\n", + " dtype='int64'), Int64Index([ 6779, 24199, 25124, 32470, 38467, 52186, 59625, 76267,\n", + " 141588, 148264, 167048, 187568, 192167],\n", + " dtype='int64'), Int64Index([ 6780, 24200, 25125, 32471, 38468, 52187, 59626, 76268,\n", + " 141589, 148265, 167049, 187569, 192168],\n", + " dtype='int64'), Int64Index([ 6781, 24201, 25126, 32472, 38469, 52188, 59627, 76269,\n", + " 141590, 148266, 167050, 187570, 192169],\n", + " dtype='int64'), Int64Index([ 6782, 24202, 25127, 32473, 38470, 52189, 59628, 76270,\n", + " 141591, 148267, 167051, 187571, 192170],\n", + " dtype='int64'), Int64Index([ 6783, 24203, 25128, 32474, 38471, 52190, 59629, 76271,\n", + " 141592, 148268, 167052, 187572, 192171],\n", + " dtype='int64'), Int64Index([ 6784, 24204, 25129, 32475, 38472, 52191, 59630, 76272,\n", + " 141593, 148269, 167053, 187573, 192172],\n", + " dtype='int64'), Int64Index([ 6785, 24205, 25130, 32476, 38473, 52192, 59631, 76273,\n", + " 141594, 148270, 167054, 187574, 192173],\n", + " dtype='int64'), Int64Index([ 6786, 24206, 25131, 32477, 38474, 52193, 59632, 76274,\n", + " 141595, 148271, 167055, 187575, 192174],\n", + " dtype='int64'), Int64Index([ 6787, 24207, 25132, 32478, 38475, 52194, 59633, 76275,\n", + " 141596, 148272, 167056, 187576, 192175],\n", + " dtype='int64'), Int64Index([ 6788, 24208, 25133, 32479, 38476, 52195, 59634, 76276,\n", + " 141597, 148273, 167057, 187577, 192176],\n", + " dtype='int64'), Int64Index([ 6789, 24209, 25134, 32480, 38477, 52196, 59635, 76277,\n", + " 141598, 148274, 167058, 187578, 192177],\n", + " dtype='int64'), Int64Index([ 6790, 24210, 25135, 32481, 38478, 52197, 59636, 76278,\n", + " 141599, 148275, 167059, 187579, 192178],\n", + " dtype='int64'), Int64Index([ 6791, 24211, 25136, 32482, 38479, 52198, 59637, 76279,\n", + " 141600, 148276, 167060, 187580, 192179],\n", + " dtype='int64'), Int64Index([ 6792, 24212, 25137, 32483, 38480, 52199, 59638, 76280,\n", + " 141601, 148277, 167061, 187581, 192180],\n", + " dtype='int64'), Int64Index([ 6793, 24213, 25138, 32484, 38481, 52200, 59639, 76281,\n", + " 141602, 148278, 167062, 187582, 192181],\n", + " dtype='int64'), Int64Index([ 6794, 24214, 25139, 32485, 38482, 52201, 59640, 76282,\n", + " 141603, 148279, 167063, 187583, 192182],\n", + " dtype='int64'), Int64Index([ 6795, 24215, 25140, 32486, 38483, 52202, 59641, 76283,\n", + " 141604, 148280, 167064, 187584, 192183],\n", + " dtype='int64'), Int64Index([ 6796, 24216, 25141, 32487, 38484, 52203, 59642, 76284,\n", + " 141605, 148281, 167065, 187585, 192184],\n", + " dtype='int64'), Int64Index([ 6797, 24217, 25142, 32488, 38485, 52204, 59643, 76285,\n", + " 141606, 148282, 167066, 187586, 192185],\n", + " dtype='int64'), Int64Index([ 6798, 24218, 25143, 32489, 38486, 52205, 59644, 76286,\n", + " 141607, 148283, 167067, 187587, 192186],\n", + " dtype='int64'), Int64Index([ 6799, 24219, 25144, 32490, 38487, 52206, 59645, 76287,\n", + " 141608, 148284, 167068, 187588, 192187],\n", + " dtype='int64'), Int64Index([ 6800, 24220, 25145, 32491, 38488, 52207, 59646, 76288,\n", + " 141609, 148285, 167069, 187589, 192188],\n", + " dtype='int64'), Int64Index([ 6801, 24221, 25146, 32492, 38489, 52208, 59647, 76289,\n", + " 141610, 148286, 167070, 187590, 192189],\n", + " dtype='int64'), Int64Index([ 6802, 24222, 25147, 32493, 38490, 52209, 59648, 76290,\n", + " 141611, 148287, 167071, 187591, 192190],\n", + " dtype='int64'), Int64Index([ 6803, 24223, 25148, 32494, 38491, 52210, 59649, 76291,\n", + " 141612, 148288, 167072, 187592, 192191],\n", + " dtype='int64'), Int64Index([ 6804, 24224, 25149, 32495, 38492, 52211, 59650, 76292,\n", + " 141613, 148289, 167073, 187593, 192192],\n", + " dtype='int64'), Int64Index([ 6805, 24225, 25150, 32496, 38493, 52212, 59651, 76293,\n", + " 141614, 148290, 167074, 187594, 192193],\n", + " dtype='int64'), Int64Index([ 6806, 24226, 25151, 32497, 38494, 52213, 59652, 76294,\n", + " 141615, 148291, 167075, 187595, 192194],\n", + " dtype='int64'), Int64Index([ 6807, 24227, 25152, 32498, 38495, 52214, 59653, 76295,\n", + " 141616, 148292, 167076, 187596, 192195],\n", + " dtype='int64'), Int64Index([ 6808, 24228, 25153, 32499, 38496, 52215, 59654, 76296,\n", + " 141617, 148293, 167077, 187597, 192196],\n", + " dtype='int64'), Int64Index([ 6809, 24229, 25154, 32500, 38497, 52216, 59655, 76297,\n", + " 141618, 148294, 167078, 187598, 192197],\n", + " dtype='int64'), Int64Index([ 6810, 24230, 25155, 32501, 38498, 52217, 59656, 76298,\n", + " 141619, 148295, 167079, 187599, 192198],\n", + " dtype='int64'), Int64Index([ 6811, 24231, 25156, 32502, 38499, 52218, 59657, 76299,\n", + " 141620, 148296, 167080, 187600, 192199],\n", + " dtype='int64'), Int64Index([ 6812, 24232, 25157, 32503, 38500, 52219, 59658, 76300,\n", + " 141621, 148297, 167081, 187601, 192200],\n", + " dtype='int64'), Int64Index([ 6813, 24233, 25158, 32504, 38501, 52220, 59659, 76301,\n", + " 141622, 148298, 167082, 187602, 192201],\n", + " dtype='int64'), Int64Index([ 6814, 24234, 25159, 32505, 38502, 52221, 59660, 76302,\n", + " 141623, 148299, 167083, 187603, 192202],\n", + " dtype='int64'), Int64Index([ 6815, 24235, 25160, 32506, 38503, 52222, 59661, 76303,\n", + " 141624, 148300, 167084, 187604, 192203],\n", + " dtype='int64'), Int64Index([ 6816, 24236, 25161, 32507, 38504, 52223, 59662, 76304,\n", + " 141625, 148301, 167085, 187605, 192204],\n", + " dtype='int64'), Int64Index([ 6817, 24237, 25162, 32508, 38505, 52224, 59663, 76305,\n", + " 141626, 148302, 167086, 187606, 192205],\n", + " dtype='int64'), Int64Index([ 6818, 24238, 25163, 32509, 38506, 52225, 59664, 76306,\n", + " 141627, 148303, 167087, 187607, 192206],\n", + " dtype='int64'), Int64Index([ 6819, 24239, 25164, 32510, 38507, 52226, 59665, 76307,\n", + " 141628, 148304, 167088, 187608, 192207],\n", + " dtype='int64'), Int64Index([ 6820, 24240, 25165, 32511, 38508, 52227, 59666, 76308,\n", + " 141629, 148305, 167089, 187609, 192208],\n", + " dtype='int64'), Int64Index([ 6821, 24241, 25166, 32512, 38509, 52228, 59667, 76309,\n", + " 141630, 148306, 167090, 187610, 192209],\n", + " dtype='int64'), Int64Index([ 6822, 24242, 25167, 32513, 38510, 52229, 59668, 76310,\n", + " 141631, 148307, 167091, 187611, 192210],\n", + " dtype='int64'), Int64Index([ 6823, 24243, 25168, 32514, 38511, 52230, 59669, 76311,\n", + " 141632, 148308, 167092, 187612, 192211],\n", + " dtype='int64'), Int64Index([ 6824, 24244, 25169, 32515, 38512, 52231, 59670, 76312,\n", + " 141633, 148309, 167093, 187613, 192212],\n", + " dtype='int64'), Int64Index([ 6825, 24245, 25170, 32516, 38513, 52232, 59671, 76313,\n", + " 141634, 148310, 167094, 187614, 192213],\n", + " dtype='int64'), Int64Index([ 6826, 24246, 25171, 32517, 38514, 52233, 59672, 76314,\n", + " 141635, 148311, 167095, 187615, 192214],\n", + " dtype='int64'), Int64Index([ 6827, 24247, 25172, 32518, 38515, 52234, 59673, 76315,\n", + " 141636, 148312, 167096, 187616, 192215],\n", + " dtype='int64'), Int64Index([ 6828, 24248, 25173, 32519, 38516, 52235, 59674, 76316,\n", + " 141637, 148313, 167097, 187617, 192216],\n", + " dtype='int64'), Int64Index([ 6829, 24249, 25174, 32520, 38517, 52236, 59675, 76317,\n", + " 141638, 148314, 167098, 187618, 192217],\n", + " dtype='int64'), Int64Index([ 6830, 24250, 25175, 32521, 38518, 52237, 59676, 76318,\n", + " 141639, 148315, 167099, 187619, 192218],\n", + " dtype='int64'), Int64Index([ 6831, 24251, 25176, 32522, 38519, 52238, 59677, 76319,\n", + " 141640, 148316, 167100, 187620, 192219],\n", + " dtype='int64'), Int64Index([ 6832, 24252, 25177, 32523, 38520, 52239, 59678, 76320,\n", + " 141641, 148317, 167101, 187621, 192220],\n", + " dtype='int64'), Int64Index([ 6833, 24253, 25178, 32524, 38521, 52240, 59679, 76321,\n", + " 141642, 148318, 167102, 187622, 192221],\n", + " dtype='int64'), Int64Index([ 6834, 24254, 25179, 32525, 38522, 52241, 59680, 76322,\n", + " 141643, 148319, 167103, 187623, 192222],\n", + " dtype='int64'), Int64Index([ 6835, 24255, 25180, 32526, 38523, 52242, 59681, 76323,\n", + " 141644, 148320, 167104, 187624, 192223],\n", + " dtype='int64'), Int64Index([ 6836, 24256, 25181, 32527, 38524, 52243, 59682, 76324,\n", + " 141645, 148321, 167105, 187625, 192224],\n", + " dtype='int64'), Int64Index([ 6837, 24257, 25182, 32528, 38525, 52244, 59683, 76325,\n", + " 141646, 148322, 167106, 187626, 192225],\n", + " dtype='int64'), Int64Index([ 6838, 24258, 25183, 32529, 38526, 52245, 59684, 76326,\n", + " 141647, 148323, 167107, 187627, 192226],\n", + " dtype='int64'), Int64Index([ 6839, 24259, 25184, 32530, 38527, 52246, 59685, 76327,\n", + " 141648, 148324, 167108, 187628, 192227],\n", + " dtype='int64'), Int64Index([ 6840, 24260, 25185, 32531, 38528, 52247, 59686, 76328,\n", + " 141649, 148325, 167109, 187629, 192228],\n", + " dtype='int64'), Int64Index([ 6841, 24261, 25186, 32532, 38529, 52248, 59687, 76329,\n", + " 141650, 148326, 167110, 187630, 192229],\n", + " dtype='int64'), Int64Index([ 6842, 24262, 25187, 32533, 38530, 52249, 59688, 76330,\n", + " 141651, 148327, 167111, 187631, 192230],\n", + " dtype='int64'), Int64Index([ 6843, 24263, 25188, 32534, 38531, 52250, 59689, 76331,\n", + " 141652, 148328, 167112, 187632, 192231],\n", + " dtype='int64'), Int64Index([ 6844, 24264, 25189, 32535, 38532, 52251, 59690, 76332,\n", + " 141653, 148329, 167113, 187633, 192232],\n", + " dtype='int64'), Int64Index([ 6845, 24265, 25190, 32536, 38533, 52252, 59691, 76333,\n", + " 141654, 148330, 167114, 187634, 192233],\n", + " dtype='int64'), Int64Index([ 6846, 24266, 25191, 32537, 38534, 52253, 59692, 76334,\n", + " 141655, 148331, 167115, 187635, 192234],\n", + " dtype='int64'), Int64Index([ 6847, 24267, 25192, 32538, 38535, 52254, 59693, 76335,\n", + " 141656, 148332, 167116, 187636, 192235],\n", + " dtype='int64'), Int64Index([ 6848, 24268, 25193, 32539, 38536, 52255, 59694, 76336,\n", + " 141657, 148333, 167117, 187637, 192236],\n", + " dtype='int64'), Int64Index([ 6849, 24269, 25194, 32540, 38537, 52256, 59695, 76337,\n", + " 141658, 148334, 167118, 187638, 192237],\n", + " dtype='int64'), Int64Index([ 6850, 24270, 25195, 32541, 38538, 52257, 59696, 76338,\n", + " 141659, 148335, 167119, 187639, 192238],\n", + " dtype='int64'), Int64Index([ 6851, 24271, 25196, 32542, 38539, 52258, 59697, 76339,\n", + " 141660, 148336, 167120, 187640, 192239],\n", + " dtype='int64'), Int64Index([ 6852, 24272, 25197, 32543, 38540, 52259, 59698, 76340,\n", + " 141661, 148337, 167121, 187641, 192240],\n", + " dtype='int64'), Int64Index([ 6853, 24273, 25198, 32544, 38541, 52260, 59699, 76341,\n", + " 141662, 148338, 167122, 187642, 192241],\n", + " dtype='int64'), Int64Index([ 6854, 24274, 25199, 32545, 38542, 52261, 59700, 76342,\n", + " 141663, 148339, 167123, 187643, 192242],\n", + " dtype='int64'), Int64Index([ 6855, 24275, 25200, 32546, 38543, 52262, 59701, 76343,\n", + " 141664, 148340, 167124, 187644, 192243],\n", + " dtype='int64'), Int64Index([ 6856, 24276, 25201, 32547, 38544, 52263, 59702, 76344,\n", + " 141665, 148341, 167125, 187645, 192244],\n", + " dtype='int64'), Int64Index([ 6857, 24277, 25202, 32548, 38545, 52264, 59703, 76345,\n", + " 141666, 148342, 167126, 187646, 192245],\n", + " dtype='int64'), Int64Index([ 6858, 24278, 25203, 32549, 38546, 52265, 59704, 76346,\n", + " 141667, 148343, 167127, 187647, 192246],\n", + " dtype='int64'), Int64Index([ 6859, 24279, 25204, 32550, 38547, 52266, 59705, 76347,\n", + " 141668, 148344, 167128, 187648, 192247],\n", + " dtype='int64'), Int64Index([ 6860, 24280, 25205, 32551, 38548, 52267, 59706, 76348,\n", + " 141669, 148345, 167129, 187649, 192248],\n", + " dtype='int64'), Int64Index([ 6861, 24281, 25206, 32552, 38549, 52268, 59707, 76349,\n", + " 141670, 148346, 167130, 187650, 192249],\n", + " dtype='int64'), Int64Index([ 6862, 24282, 25207, 32553, 38550, 52269, 59708, 76350,\n", + " 141671, 148347, 167131, 187651, 192250],\n", + " dtype='int64'), Int64Index([ 6863, 24283, 25208, 32554, 38551, 52270, 59709, 76351,\n", + " 141672, 148348, 167132, 187652, 192251],\n", + " dtype='int64'), Int64Index([ 6864, 24284, 25209, 32555, 38552, 52271, 59710, 76352,\n", + " 141673, 148349, 167133, 187653, 192252],\n", + " dtype='int64'), Int64Index([ 6865, 24285, 25210, 32556, 38553, 52272, 59711, 76353,\n", + " 141674, 148350, 167134, 187654, 192253],\n", + " dtype='int64'), Int64Index([ 6866, 24286, 25211, 32557, 38554, 52273, 59712, 76354,\n", + " 141675, 148351, 167135, 187655, 192254],\n", + " dtype='int64'), Int64Index([ 6867, 24287, 25212, 32558, 38555, 52274, 59713, 76355,\n", + " 141676, 148352, 167136, 187656, 192255],\n", + " dtype='int64'), Int64Index([ 6868, 24288, 25213, 32559, 38556, 52275, 59714, 76356,\n", + " 141677, 148353, 167137, 187657, 192256],\n", + " dtype='int64'), Int64Index([ 6869, 24289, 25214, 32560, 38557, 52276, 59715, 76357,\n", + " 141678, 148354, 167138, 187658, 192257],\n", + " dtype='int64'), Int64Index([ 6870, 24290, 25215, 32561, 38558, 52277, 59716, 76358,\n", + " 141679, 148355, 167139, 187659, 192258],\n", + " dtype='int64'), Int64Index([ 6871, 24291, 25216, 32562, 38559, 52278, 59717, 76359,\n", + " 141680, 148356, 167140, 187660, 192259],\n", + " dtype='int64'), Int64Index([ 6872, 24292, 25217, 32563, 38560, 52279, 59718, 76360,\n", + " 141681, 148357, 167141, 187661, 192260],\n", + " dtype='int64'), Int64Index([ 6873, 24293, 25218, 32564, 38561, 52280, 59719, 76361,\n", + " 141682, 148358, 167142, 187662, 192261],\n", + " dtype='int64'), Int64Index([ 6874, 24294, 25219, 32565, 38562, 52281, 59720, 76362,\n", + " 141683, 148359, 167143, 187663, 192262],\n", + " dtype='int64'), Int64Index([ 6875, 24295, 25220, 32566, 38563, 52282, 59721, 76363,\n", + " 141684, 148360, 167144, 187664, 192263],\n", + " dtype='int64'), Int64Index([ 6876, 24296, 25221, 32567, 38564, 52283, 59722, 76364,\n", + " 141685, 148361, 167145, 187665, 192264],\n", + " dtype='int64'), Int64Index([ 6877, 24297, 25222, 32568, 38565, 52284, 59723, 76365,\n", + " 141686, 148362, 167146, 187666, 192265],\n", + " dtype='int64'), Int64Index([ 6878, 24298, 25223, 32569, 38566, 52285, 59724, 76366,\n", + " 141687, 148363, 167147, 187667, 192266],\n", + " dtype='int64'), Int64Index([ 6879, 24299, 25224, 32570, 38567, 52286, 59725, 76367,\n", + " 141688, 148364, 167148, 187668, 192267],\n", + " dtype='int64'), Int64Index([ 6880, 24300, 25225, 32571, 38568, 52287, 59726, 76368,\n", + " 141689, 148365, 167149, 187669, 192268],\n", + " dtype='int64'), Int64Index([ 6881, 24301, 25226, 32572, 38569, 52288, 59727, 76369,\n", + " 141690, 148366, 167150, 187670, 192269],\n", + " dtype='int64'), Int64Index([ 6882, 24302, 25227, 32573, 38570, 52289, 59728, 76370,\n", + " 141691, 148367, 167151, 187671, 192270],\n", + " dtype='int64'), Int64Index([ 6883, 24303, 25228, 32574, 38571, 52290, 59729, 76371,\n", + " 141692, 148368, 167152, 187672, 192271],\n", + " dtype='int64'), Int64Index([ 6884, 24304, 25229, 32575, 38572, 52291, 59730, 76372,\n", + " 141693, 148369, 167153, 187673, 192272],\n", + " dtype='int64'), Int64Index([ 6885, 24305, 25230, 32576, 38573, 52292, 59731, 76373,\n", + " 141694, 148370, 167154, 187674, 192273],\n", + " dtype='int64'), Int64Index([ 6886, 24306, 25231, 32577, 38574, 52293, 59732, 76374,\n", + " 141695, 148371, 167155, 187675, 192274],\n", + " dtype='int64'), Int64Index([ 6887, 24307, 25232, 32578, 38575, 52294, 59733, 76375,\n", + " 141696, 148372, 167156, 187676, 192275],\n", + " dtype='int64'), Int64Index([ 6888, 24308, 25233, 32579, 38576, 52295, 59734, 76376,\n", + " 141697, 148373, 167157, 187677, 192276],\n", + " dtype='int64'), Int64Index([ 6889, 24309, 25234, 32580, 38577, 52296, 59735, 76377,\n", + " 141698, 148374, 167158, 187678, 192277],\n", + " dtype='int64'), Int64Index([ 6890, 24310, 25235, 32581, 38578, 52297, 59736, 76378,\n", + " 141699, 148375, 167159, 187679, 192278],\n", + " dtype='int64'), Int64Index([ 6891, 24311, 25236, 32582, 38579, 52298, 59737, 76379,\n", + " 141700, 148376, 167160, 187680, 192279],\n", + " dtype='int64'), Int64Index([ 6892, 24312, 25237, 32583, 38580, 52299, 59738, 76380,\n", + " 141701, 148377, 167161, 187681, 192280],\n", + " dtype='int64'), Int64Index([ 6893, 24313, 25238, 32584, 38581, 52300, 59739, 76381,\n", + " 141702, 148378, 167162, 187682, 192281],\n", + " dtype='int64'), Int64Index([ 6894, 24314, 25239, 32585, 38582, 52301, 59740, 76382,\n", + " 141703, 148379, 167163, 187683, 192282],\n", + " dtype='int64'), Int64Index([ 6895, 24315, 25240, 32586, 38583, 52302, 59741, 76383,\n", + " 141704, 148380, 167164, 187684, 192283],\n", + " dtype='int64'), Int64Index([ 6896, 24316, 25241, 32587, 38584, 52303, 59742, 76384,\n", + " 141705, 148381, 167165, 187685, 192284],\n", + " dtype='int64'), Int64Index([ 6897, 24317, 25242, 32588, 38585, 52304, 59743, 76385,\n", + " 141706, 148382, 167166, 187686, 192285],\n", + " dtype='int64'), Int64Index([ 6898, 24318, 25243, 32589, 38586, 52305, 59744, 76386,\n", + " 141707, 148383, 167167, 187687, 192286],\n", + " dtype='int64'), Int64Index([ 6899, 24319, 25244, 32590, 38587, 52306, 59745, 76387,\n", + " 141708, 148384, 167168, 187688, 192287],\n", + " dtype='int64'), Int64Index([ 6900, 24320, 25245, 32591, 38588, 52307, 59746, 76388,\n", + " 141709, 148385, 167169, 187689, 192288],\n", + " dtype='int64'), Int64Index([ 6901, 24321, 25246, 32592, 38589, 52308, 59747, 76389,\n", + " 141710, 148386, 167170, 187690, 192289],\n", + " dtype='int64'), Int64Index([ 6902, 24322, 25247, 32593, 38590, 52309, 59748, 76390,\n", + " 141711, 148387, 167171, 187691, 192290],\n", + " dtype='int64'), Int64Index([ 6903, 24323, 25248, 32594, 38591, 52310, 59749, 76391,\n", + " 141712, 148388, 167172, 187692, 192291],\n", + " dtype='int64'), Int64Index([ 6904, 24324, 25249, 32595, 38592, 52311, 59750, 76392,\n", + " 141713, 148389, 167173, 187693, 192292],\n", + " dtype='int64'), Int64Index([ 6905, 24325, 25250, 32596, 38593, 52312, 59751, 76393,\n", + " 141714, 148390, 167174, 187694, 192293],\n", + " dtype='int64'), Int64Index([ 6906, 24326, 25251, 32597, 38594, 52313, 59752, 76394,\n", + " 141715, 148391, 167175, 187695, 192294],\n", + " dtype='int64'), Int64Index([ 6907, 24327, 25252, 32598, 38595, 52314, 59753, 76395,\n", + " 141716, 148392, 167176, 187696, 192295],\n", + " dtype='int64'), Int64Index([ 6908, 24328, 25253, 32599, 38596, 52315, 59754, 76396,\n", + " 141717, 148393, 167177, 187697, 192296],\n", + " dtype='int64'), Int64Index([ 6909, 24329, 25254, 32600, 38597, 52316, 59755, 76397,\n", + " 141718, 148394, 167178, 187698, 192297],\n", + " dtype='int64'), Int64Index([ 6910, 24330, 25255, 32601, 38598, 52317, 59756, 76398,\n", + " 141719, 148395, 167179, 187699, 192298],\n", + " dtype='int64'), Int64Index([ 6911, 24331, 25256, 32602, 38599, 52318, 59757, 76399,\n", + " 141720, 148396, 167180, 187700, 192299],\n", + " dtype='int64'), Int64Index([ 6912, 24332, 25257, 32603, 38600, 52319, 59758, 76400,\n", + " 141721, 148397, 167181, 187701, 192300],\n", + " dtype='int64'), Int64Index([ 6913, 24333, 25258, 32604, 38601, 52320, 59759, 76401,\n", + " 141722, 148398, 167182, 187702, 192301],\n", + " dtype='int64'), Int64Index([ 6914, 24334, 25259, 32605, 38602, 52321, 59760, 76402,\n", + " 141723, 148399, 167183, 187703, 192302],\n", + " dtype='int64'), Int64Index([ 6915, 24335, 25260, 32606, 38603, 52322, 59761, 76403,\n", + " 141724, 148400, 167184, 187704, 192303],\n", + " dtype='int64'), Int64Index([ 6916, 24336, 25261, 32607, 38604, 52323, 59762, 76404,\n", + " 141725, 148401, 167185, 187705, 192304],\n", + " dtype='int64'), Int64Index([ 6917, 24337, 25262, 32608, 38605, 52324, 59763, 76405,\n", + " 141726, 148402, 167186, 187706, 192305],\n", + " dtype='int64'), Int64Index([ 6918, 24338, 25263, 32609, 38606, 52325, 59764, 76406,\n", + " 141727, 148403, 167187, 187707, 192306],\n", + " dtype='int64'), Int64Index([ 6919, 24339, 25264, 32610, 38607, 52326, 59765, 76407,\n", + " 141728, 148404, 167188, 187708, 192307],\n", + " dtype='int64'), Int64Index([ 6920, 24340, 25265, 32611, 38608, 52327, 59766, 76408,\n", + " 141729, 148405, 167189, 187709, 192308],\n", + " dtype='int64'), Int64Index([ 6921, 24341, 25266, 32612, 38609, 52328, 59767, 76409,\n", + " 141730, 148406, 167190, 187710, 192309],\n", + " dtype='int64'), Int64Index([ 6922, 24342, 25267, 32613, 38610, 52329, 59768, 76410,\n", + " 141731, 148407, 167191, 187711, 192310],\n", + " dtype='int64'), Int64Index([ 6923, 24343, 25268, 32614, 38611, 52330, 59769, 76411,\n", + " 141732, 148408, 167192, 187712, 192311],\n", + " dtype='int64'), Int64Index([ 6924, 24344, 25269, 32615, 38612, 52331, 59770, 76412,\n", + " 141733, 148409, 167193, 187713, 192312],\n", + " dtype='int64'), Int64Index([ 6925, 24345, 25270, 32616, 38613, 52332, 59771, 76413,\n", + " 141734, 148410, 167194, 187714, 192313],\n", + " dtype='int64'), Int64Index([ 6926, 24346, 25271, 32617, 38614, 52333, 59772, 76414,\n", + " 141735, 148411, 167195, 187715, 192314],\n", + " dtype='int64'), Int64Index([ 6927, 24347, 25272, 32618, 38615, 52334, 59773, 76415,\n", + " 141736, 148412, 167196, 187716, 192315],\n", + " dtype='int64'), Int64Index([ 6928, 24348, 25273, 32619, 38616, 52335, 59774, 76416,\n", + " 141737, 148413, 167197, 187717, 192316],\n", + " dtype='int64'), Int64Index([ 6929, 24349, 25274, 32620, 38617, 52336, 59775, 76417,\n", + " 141738, 148414, 167198, 187718, 192317],\n", + " dtype='int64'), Int64Index([ 6930, 24350, 25275, 32621, 38618, 52337, 59776, 76418,\n", + " 141739, 148415, 167199, 187719, 192318],\n", + " dtype='int64'), Int64Index([ 6931, 24351, 25276, 32622, 38619, 52338, 59777, 76419,\n", + " 141740, 148416, 167200, 187720, 192319],\n", + " dtype='int64'), Int64Index([ 6932, 24352, 25277, 32623, 38620, 52339, 59778, 76420,\n", + " 141741, 148417, 167201, 187721, 192320],\n", + " dtype='int64'), Int64Index([ 6933, 24353, 25278, 32624, 38621, 52340, 59779, 76421,\n", + " 141742, 148418, 167202, 187722, 192321],\n", + " dtype='int64'), Int64Index([ 6934, 24354, 25279, 32625, 38622, 52341, 59780, 76422,\n", + " 141743, 148419, 167203, 187723, 192322],\n", + " dtype='int64'), Int64Index([ 6935, 24355, 25280, 32626, 38623, 52342, 59781, 76423,\n", + " 141744, 148420, 167204, 187724, 192323],\n", + " dtype='int64'), Int64Index([ 6936, 24356, 25281, 32627, 38624, 52343, 59782, 76424,\n", + " 141745, 148421, 167205, 187725, 192324],\n", + " dtype='int64'), Int64Index([ 6937, 24357, 25282, 32628, 38625, 52344, 59783, 76425,\n", + " 141746, 148422, 167206, 187726, 192325],\n", + " dtype='int64'), Int64Index([ 6938, 24358, 25283, 32629, 38626, 52345, 59784, 76426,\n", + " 141747, 148423, 167207, 187727, 192326],\n", + " dtype='int64'), Int64Index([ 6939, 24359, 25284, 32630, 38627, 52346, 59785, 76427,\n", + " 141748, 148424, 167208, 187728, 192327],\n", + " dtype='int64'), Int64Index([ 6940, 24360, 25285, 32631, 38628, 52347, 59786, 76428,\n", + " 141749, 148425, 167209, 187729, 192328],\n", + " dtype='int64'), Int64Index([ 6941, 24361, 25286, 32632, 38629, 52348, 59787, 76429,\n", + " 141750, 148426, 167210, 187730, 192329],\n", + " dtype='int64'), Int64Index([ 6942, 24362, 25287, 32633, 38630, 52349, 59788, 76430,\n", + " 141751, 148427, 167211, 187731, 192330],\n", + " dtype='int64'), Int64Index([ 6943, 24363, 25288, 32634, 38631, 52350, 59789, 76431,\n", + " 141752, 148428, 167212, 187732, 192331],\n", + " dtype='int64'), Int64Index([ 6944, 24364, 25289, 32635, 38632, 52351, 59790, 76432,\n", + " 141753, 148429, 167213, 187733, 192332],\n", + " dtype='int64'), Int64Index([ 6945, 24365, 25290, 32636, 38633, 52352, 59791, 76433,\n", + " 141754, 148430, 167214, 187734, 192333],\n", + " dtype='int64'), Int64Index([ 6946, 24366, 25291, 32637, 38634, 52353, 59792, 76434,\n", + " 141755, 148431, 167215, 187735, 192334],\n", + " dtype='int64'), Int64Index([ 6947, 24367, 25292, 32638, 38635, 52354, 59793, 76435,\n", + " 141756, 148432, 167216, 187736, 192335],\n", + " dtype='int64'), Int64Index([ 6948, 24368, 25293, 32639, 38636, 52355, 59794, 76436,\n", + " 141757, 148433, 167217, 187737, 192336],\n", + " dtype='int64'), Int64Index([ 6949, 24369, 25294, 32640, 38637, 52356, 59795, 76437,\n", + " 141758, 148434, 167218, 187738, 192337],\n", + " dtype='int64'), Int64Index([ 6950, 24370, 25295, 32641, 38638, 52357, 59796, 76438,\n", + " 141759, 148435, 167219, 187739, 192338],\n", + " dtype='int64'), Int64Index([ 6951, 24371, 25296, 32642, 38639, 52358, 59797, 76439,\n", + " 141760, 148436, 167220, 187740, 192339],\n", + " dtype='int64'), Int64Index([ 6952, 24372, 25297, 32643, 38640, 52359, 59798, 76440,\n", + " 141761, 148437, 167221, 187741, 192340],\n", + " dtype='int64'), Int64Index([ 6953, 24373, 25298, 32644, 38641, 52360, 59799, 76441,\n", + " 141762, 148438, 167222, 187742, 192341],\n", + " dtype='int64'), Int64Index([ 6954, 24374, 25299, 32645, 38642, 52361, 59800, 76442,\n", + " 141763, 148439, 167223, 187743, 192342],\n", + " dtype='int64'), Int64Index([ 6955, 24375, 25300, 32646, 38643, 52362, 59801, 76443,\n", + " 141764, 148440, 167224, 187744, 192343],\n", + " dtype='int64'), Int64Index([ 6956, 24376, 25301, 32647, 38644, 52363, 59802, 76444,\n", + " 141765, 148441, 167225, 187745, 192344],\n", + " dtype='int64'), Int64Index([ 6957, 24377, 25302, 32648, 38645, 52364, 59803, 76445,\n", + " 141766, 148442, 167226, 187746, 192345],\n", + " dtype='int64'), Int64Index([ 6958, 24378, 25303, 32649, 38646, 52365, 59804, 76446,\n", + " 141767, 148443, 167227, 187747, 192346],\n", + " dtype='int64'), Int64Index([ 6959, 24379, 25304, 32650, 38647, 52366, 59805, 76447,\n", + " 141768, 148444, 167228, 187748, 192347],\n", + " dtype='int64'), Int64Index([ 6960, 24380, 25305, 32651, 38648, 52367, 59806, 76448,\n", + " 141769, 148445, 167229, 187749, 192348],\n", + " dtype='int64'), Int64Index([ 6961, 24381, 25306, 32652, 38649, 52368, 59807, 76449,\n", + " 141770, 148446, 167230, 187750, 192349],\n", + " dtype='int64'), Int64Index([ 6962, 24382, 25307, 32653, 38650, 52369, 59808, 76450,\n", + " 141771, 148447, 167231, 187751, 192350],\n", + " dtype='int64'), Int64Index([ 6963, 24383, 25308, 32654, 38651, 52370, 59809, 76451,\n", + " 141772, 148448, 167232, 187752, 192351],\n", + " dtype='int64'), Int64Index([ 6964, 24384, 25309, 32655, 38652, 52371, 59810, 76452,\n", + " 141773, 148449, 167233, 187753, 192352],\n", + " dtype='int64'), Int64Index([ 6965, 24385, 25310, 32656, 38653, 52372, 59811, 76453,\n", + " 141774, 148450, 167234, 187754, 192353],\n", + " dtype='int64'), Int64Index([ 6966, 24386, 25311, 32657, 38654, 52373, 59812, 76454,\n", + " 141775, 148451, 167235, 187755, 192354],\n", + " dtype='int64'), Int64Index([ 6967, 24387, 25312, 32658, 38655, 52374, 59813, 76455,\n", + " 141776, 148452, 167236, 187756, 192355],\n", + " dtype='int64'), Int64Index([ 6968, 24388, 25313, 32659, 38656, 52375, 59814, 76456,\n", + " 141777, 148453, 167237, 187757, 192356],\n", + " dtype='int64'), Int64Index([ 6969, 24389, 25314, 32660, 38657, 52376, 59815, 76457,\n", + " 141778, 148454, 167238, 187758, 192357],\n", + " dtype='int64'), Int64Index([ 6970, 24390, 25315, 32661, 38658, 52377, 59816, 76458,\n", + " 141779, 148455, 167239, 187759, 192358],\n", + " dtype='int64'), Int64Index([ 6971, 24391, 25316, 32662, 38659, 52378, 59817, 76459,\n", + " 141780, 148456, 167240, 187760, 192359],\n", + " dtype='int64'), Int64Index([ 6972, 24392, 25317, 32663, 38660, 52379, 59818, 76460,\n", + " 141781, 148457, 167241, 187761, 192360],\n", + " dtype='int64'), Int64Index([ 6973, 24393, 25318, 32664, 38661, 52380, 59819, 76461,\n", + " 141782, 148458, 167242, 187762, 192361],\n", + " dtype='int64'), Int64Index([ 6974, 24394, 25319, 32665, 38662, 52381, 59820, 76462,\n", + " 141783, 148459, 167243, 187763, 192362],\n", + " dtype='int64'), Int64Index([ 6975, 24395, 25320, 32666, 38663, 52382, 59821, 76463,\n", + " 141784, 148460, 167244, 187764, 192363],\n", + " dtype='int64'), Int64Index([ 6976, 24396, 25321, 32667, 38664, 52383, 59822, 76464,\n", + " 141785, 148461, 167245, 187765, 192364],\n", + " dtype='int64'), Int64Index([ 6977, 24397, 25322, 32668, 38665, 52384, 59823, 76465,\n", + " 141786, 148462, 167246, 187766, 192365],\n", + " dtype='int64'), Int64Index([ 6978, 24398, 25323, 32669, 38666, 52385, 59824, 76466,\n", + " 141787, 148463, 167247, 187767, 192366],\n", + " 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dtype='int64'), Int64Index([ 6986, 24406, 25331, 32677, 38674, 52393, 59832, 76474,\n", + " 141795, 148471, 167255, 187775, 192374],\n", + " dtype='int64'), Int64Index([ 6987, 24407, 25332, 32678, 38675, 52394, 59833, 76475,\n", + " 141796, 148472, 167256, 187776, 192375],\n", + " dtype='int64'), Int64Index([ 6988, 24408, 25333, 32679, 38676, 52395, 59834, 76476,\n", + " 141797, 148473, 167257, 187777, 192376],\n", + " dtype='int64'), Int64Index([ 6989, 24409, 25334, 32680, 38677, 52396, 59835, 76477,\n", + " 141798, 148474, 167258, 187778, 192377],\n", + " dtype='int64'), Int64Index([ 6990, 24410, 25335, 32681, 38678, 52397, 59836, 76478,\n", + " 141799, 148475, 167259, 187779, 192378],\n", + " dtype='int64'), Int64Index([ 6991, 24411, 25336, 32682, 38679, 52398, 59837, 76479,\n", + " 141800, 148476, 167260, 187780, 192379],\n", + " dtype='int64'), Int64Index([ 6992, 24412, 25337, 32683, 38680, 52399, 59838, 76480,\n", + " 141801, 148477, 167261, 187781, 192380],\n", + " 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dtype='int64'), Int64Index([ 7000, 24420, 25345, 32691, 38688, 52407, 59846, 76488,\n", + " 141809, 148485, 167269, 187789, 192388],\n", + " dtype='int64'), Int64Index([ 7001, 24421, 25346, 32692, 38689, 52408, 59847, 76489,\n", + " 141810, 148486, 167270, 187790, 192389],\n", + " dtype='int64'), Int64Index([ 7002, 24422, 25347, 32693, 38690, 52409, 59848, 76490,\n", + " 141811, 148487, 167271, 187791, 192390],\n", + " dtype='int64'), Int64Index([ 7003, 24423, 25348, 32694, 38691, 52410, 59849, 76491,\n", + " 141812, 148488, 167272, 187792, 192391],\n", + " dtype='int64'), Int64Index([ 7004, 24424, 25349, 32695, 38692, 52411, 59850, 76492,\n", + " 141813, 148489, 167273, 187793, 192392],\n", + " dtype='int64'), Int64Index([ 7005, 24425, 25350, 32696, 38693, 52412, 59851, 76493,\n", + " 141814, 148490, 167274, 187794, 192393],\n", + " dtype='int64'), Int64Index([ 7006, 24426, 25351, 32697, 38694, 52413, 59852, 76494,\n", + " 141815, 148491, 167275, 187795, 192394],\n", + " dtype='int64'), Int64Index([ 7007, 24427, 25352, 32698, 38695, 52414, 59853, 76495,\n", + " 141816, 148492, 167276, 187796, 192395],\n", + " dtype='int64'), Int64Index([ 7008, 24428, 25353, 32699, 38696, 52415, 59854, 76496,\n", + " 141817, 148493, 167277, 187797, 192396],\n", + " dtype='int64'), Int64Index([ 7009, 24429, 25354, 32700, 38697, 52416, 59855, 76497,\n", + " 141818, 148494, 167278, 187798, 192397],\n", + " dtype='int64'), Int64Index([ 7010, 24430, 25355, 32701, 38698, 52417, 59856, 76498,\n", + " 141819, 148495, 167279, 187799, 192398],\n", + " dtype='int64'), Int64Index([ 7011, 24431, 25356, 32702, 38699, 52418, 59857, 76499,\n", + " 141820, 148496, 167280, 187800, 192399],\n", + " dtype='int64'), Int64Index([ 7012, 24432, 25357, 32703, 38700, 52419, 59858, 76500,\n", + " 141821, 148497, 167281, 187801, 192400],\n", + " dtype='int64'), Int64Index([ 7013, 24433, 25358, 32704, 38701, 52420, 59859, 76501,\n", + " 141822, 148498, 167282, 187802, 192401],\n", + " dtype='int64'), Int64Index([ 7014, 24434, 25359, 32705, 38702, 52421, 59860, 76502,\n", + " 141823, 148499, 167283, 187803, 192402],\n", + " dtype='int64'), Int64Index([ 7015, 24435, 25360, 32706, 38703, 52422, 59861, 76503,\n", + " 141824, 148500, 167284, 187804, 192403],\n", + " dtype='int64'), Int64Index([ 7016, 24436, 25361, 32707, 38704, 52423, 59862, 76504,\n", + " 141825, 148501, 167285, 187805, 192404],\n", + " dtype='int64'), Int64Index([ 7017, 24437, 25362, 32708, 38705, 52424, 59863, 76505,\n", + " 141826, 148502, 167286, 187806, 192405],\n", + " dtype='int64'), Int64Index([ 7018, 24438, 25363, 32709, 38706, 52425, 59864, 76506,\n", + " 141827, 148503, 167287, 187807, 192406],\n", + " dtype='int64'), Int64Index([ 7019, 24439, 25364, 32710, 38707, 52426, 59865, 76507,\n", + " 141828, 148504, 167288, 187808, 192407],\n", + " dtype='int64'), Int64Index([ 7020, 24440, 25365, 32711, 38708, 52427, 59866, 76508,\n", + " 141829, 148505, 167289, 187809, 192408],\n", + " dtype='int64'), Int64Index([ 7021, 24441, 25366, 32712, 38709, 52428, 59867, 76509,\n", + " 141830, 148506, 167290, 187810, 192409],\n", + " dtype='int64'), Int64Index([ 7022, 24442, 25367, 32713, 38710, 52429, 59868, 76510,\n", + " 141831, 148507, 167291, 187811, 192410],\n", + " dtype='int64'), Int64Index([ 7023, 24443, 25368, 32714, 38711, 52430, 59869, 76511,\n", + " 141832, 148508, 167292, 187812, 192411],\n", + " dtype='int64'), Int64Index([ 7024, 24444, 25369, 32715, 38712, 52431, 59870, 76512,\n", + " 141833, 148509, 167293, 187813, 192412],\n", + " dtype='int64'), Int64Index([ 7025, 24445, 25370, 32716, 38713, 52432, 59871, 76513,\n", + " 141834, 148510, 167294, 187814, 192413],\n", + " dtype='int64'), Int64Index([ 7026, 24446, 25371, 32717, 38714, 52433, 59872, 76514,\n", + " 141835, 148511, 167295, 187815, 192414],\n", + " dtype='int64'), Int64Index([ 7027, 24447, 25372, 32718, 38715, 52434, 59873, 76515,\n", + " 141836, 148512, 167296, 187816, 192415],\n", + " dtype='int64'), Int64Index([ 7028, 24448, 25373, 32719, 38716, 52435, 59874, 76516,\n", + " 141837, 148513, 167297, 187817, 192416],\n", + " dtype='int64'), Int64Index([ 7029, 24449, 25374, 32720, 38717, 52436, 59875, 76517,\n", + " 141838, 148514, 167298, 187818, 192417],\n", + " dtype='int64'), Int64Index([ 7030, 24450, 25375, 32721, 38718, 52437, 59876, 76518,\n", + " 141839, 148515, 167299, 187819, 192418],\n", + " dtype='int64'), Int64Index([ 7031, 24451, 25376, 32722, 38719, 52438, 59877, 76519,\n", + " 141840, 148516, 167300, 187820, 192419],\n", + " dtype='int64'), Int64Index([ 7032, 24452, 25377, 32723, 38720, 52439, 59878, 76520,\n", + " 141841, 148517, 167301, 187821, 192420],\n", + " dtype='int64'), Int64Index([ 7033, 24453, 25378, 32724, 38721, 52440, 59879, 76521,\n", + " 141842, 148518, 167302, 187822, 192421],\n", + " dtype='int64'), Int64Index([ 7034, 24454, 25379, 32725, 38722, 52441, 59880, 76522,\n", + " 141843, 148519, 167303, 187823, 192422],\n", + " dtype='int64'), Int64Index([ 7035, 24455, 25380, 32726, 38723, 52442, 59881, 76523,\n", + " 141844, 148520, 167304, 187824, 192423],\n", + " dtype='int64'), Int64Index([ 7036, 24456, 25381, 32727, 38724, 52443, 59882, 76524,\n", + " 141845, 148521, 167305, 187825, 192424],\n", + " dtype='int64'), Int64Index([ 7037, 24457, 25382, 32728, 38725, 52444, 59883, 76525,\n", + " 141846, 148522, 167306, 187826, 192425],\n", + " dtype='int64'), Int64Index([ 7038, 24458, 25383, 32729, 38726, 52445, 59884, 76526,\n", + " 141847, 148523, 167307, 187827, 192426],\n", + " dtype='int64'), Int64Index([ 7039, 24459, 25384, 32730, 38727, 52446, 59885, 76527,\n", + " 141848, 148524, 167308, 187828, 192427],\n", + " dtype='int64'), Int64Index([ 7040, 24460, 25385, 32731, 38728, 52447, 59886, 76528,\n", + " 141849, 148525, 167309, 187829, 192428],\n", + " dtype='int64'), Int64Index([ 7041, 24461, 25386, 32732, 38729, 52448, 59887, 76529,\n", + " 141850, 148526, 167310, 187830, 192429],\n", + " dtype='int64'), Int64Index([ 7042, 24462, 25387, 32733, 38730, 52449, 59888, 76530,\n", + " 141851, 148527, 167311, 187831, 192430],\n", + " dtype='int64'), Int64Index([ 7043, 24463, 25388, 32734, 38731, 52450, 59889, 76531,\n", + " 141852, 148528, 167312, 187832, 192431],\n", + " dtype='int64'), Int64Index([ 7044, 24464, 25389, 32735, 38732, 52451, 59890, 76532,\n", + " 141853, 148529, 167313, 187833, 192432],\n", + " dtype='int64'), Int64Index([ 7045, 24465, 25390, 32736, 38733, 52452, 59891, 76533,\n", + " 141854, 148530, 167314, 187834, 192433],\n", + " dtype='int64'), Int64Index([ 7046, 24466, 25391, 32737, 38734, 52453, 59892, 76534,\n", + " 141855, 148531, 167315, 187835, 192434],\n", + " dtype='int64'), Int64Index([ 7047, 24467, 25392, 32738, 38735, 52454, 59893, 76535,\n", + " 141856, 148532, 167316, 187836, 192435],\n", + " dtype='int64'), Int64Index([ 7048, 24468, 25393, 32739, 38736, 52455, 59894, 76536,\n", + " 141857, 148533, 167317, 187837, 192436],\n", + " dtype='int64'), Int64Index([ 7049, 24469, 25394, 32740, 38737, 52456, 59895, 76537,\n", + " 141858, 148534, 167318, 187838, 192437],\n", + " dtype='int64'), Int64Index([ 7050, 24470, 25395, 32741, 38738, 52457, 59896, 76538,\n", + " 141859, 148535, 167319, 187839, 192438],\n", + " dtype='int64'), Int64Index([ 7051, 24471, 25396, 32742, 38739, 52458, 59897, 76539,\n", + " 141860, 148536, 167320, 187840, 192439],\n", + " dtype='int64'), Int64Index([ 7052, 24472, 25397, 32743, 38740, 52459, 59898, 76540,\n", + " 141861, 148537, 167321, 187841, 192440],\n", + " dtype='int64'), Int64Index([ 7053, 24473, 25398, 32744, 38741, 52460, 59899, 76541,\n", + " 141862, 148538, 167322, 187842, 192441],\n", + " dtype='int64'), Int64Index([ 7054, 24474, 25399, 32745, 38742, 52461, 59900, 76542,\n", + " 141863, 148539, 167323, 187843, 192442],\n", + " dtype='int64'), Int64Index([ 7055, 24475, 25400, 32746, 38743, 52462, 59901, 76543,\n", + " 141864, 148540, 167324, 187844, 192443],\n", + " 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dtype='int64'), Int64Index([ 7063, 24483, 25408, 32754, 38751, 52470, 59909, 76551,\n", + " 141872, 148548, 167332, 187852, 192451],\n", + " dtype='int64'), Int64Index([ 7064, 24484, 25409, 32755, 38752, 52471, 59910, 76552,\n", + " 141873, 148549, 167333, 187853, 192452],\n", + " dtype='int64'), Int64Index([ 7065, 24485, 25410, 32756, 38753, 52472, 59911, 76553,\n", + " 141874, 148550, 167334, 187854, 192453],\n", + " dtype='int64'), Int64Index([ 7066, 24486, 25411, 32757, 38754, 52473, 59912, 76554,\n", + " 141875, 148551, 167335, 187855, 192454],\n", + " dtype='int64'), Int64Index([ 7067, 24487, 25412, 32758, 38755, 52474, 59913, 76555,\n", + " 141876, 148552, 167336, 187856, 192455],\n", + " dtype='int64'), Int64Index([ 7068, 24488, 25413, 32759, 38756, 52475, 59914, 76556,\n", + " 141877, 148553, 167337, 187857, 192456],\n", + " dtype='int64'), Int64Index([ 7069, 24489, 25414, 32760, 38757, 52476, 59915, 76557,\n", + " 141878, 148554, 167338, 187858, 192457],\n", + " dtype='int64'), Int64Index([ 7070, 24490, 25415, 32761, 38758, 52477, 59916, 76558,\n", + " 141879, 148555, 167339, 187859, 192458],\n", + " dtype='int64'), Int64Index([ 7071, 24491, 25416, 32762, 38759, 52478, 59917, 76559,\n", + " 141880, 148556, 167340, 187860, 192459],\n", + " dtype='int64'), Int64Index([ 7072, 24492, 25417, 32763, 38760, 52479, 59918, 76560,\n", + " 141881, 148557, 167341, 187861, 192460],\n", + " dtype='int64'), Int64Index([ 7073, 24493, 25418, 32764, 38761, 52480, 59919, 76561,\n", + " 141882, 148558, 167342, 187862, 192461],\n", + " dtype='int64'), Int64Index([ 7074, 24494, 25419, 32765, 38762, 52481, 59920, 76562,\n", + " 141883, 148559, 167343, 187863, 192462],\n", + " dtype='int64'), Int64Index([ 7075, 24495, 25420, 32766, 38763, 52482, 59921, 76563,\n", + " 141884, 148560, 167344, 187864, 192463],\n", + " dtype='int64'), Int64Index([ 7076, 24496, 25421, 32767, 38764, 52483, 59922, 76564,\n", + " 141885, 148561, 167345, 187865, 192464],\n", + " dtype='int64'), Int64Index([ 7077, 24497, 25422, 32768, 38765, 52484, 59923, 76565,\n", + " 141886, 148562, 167346, 187866, 192465],\n", + " dtype='int64'), Int64Index([ 7078, 24498, 25423, 32769, 38766, 52485, 59924, 76566,\n", + " 141887, 148563, 167347, 187867, 192466],\n", + " dtype='int64'), Int64Index([ 7079, 24499, 25424, 32770, 38767, 52486, 59925, 76567,\n", + " 141888, 148564, 167348, 187868, 192467],\n", + " dtype='int64'), Int64Index([ 7080, 24500, 25425, 32771, 38768, 52487, 59926, 76568,\n", + " 141889, 148565, 167349, 187869, 192468],\n", + " dtype='int64'), Int64Index([ 7081, 24501, 25426, 32772, 38769, 52488, 59927, 76569,\n", + " 141890, 148566, 167350, 187870, 192469],\n", + " dtype='int64'), Int64Index([ 7082, 24502, 25427, 32773, 38770, 52489, 59928, 76570,\n", + " 141891, 148567, 167351, 187871, 192470],\n", + " dtype='int64'), Int64Index([ 7083, 24503, 25428, 32774, 38771, 52490, 59929, 76571,\n", + " 141892, 148568, 167352, 187872, 192471],\n", + " dtype='int64'), Int64Index([ 7084, 24504, 25429, 32775, 38772, 52491, 59930, 76572,\n", + " 141893, 148569, 167353, 187873, 192472],\n", + " dtype='int64'), Int64Index([ 7085, 24505, 25430, 32776, 38773, 52492, 59931, 76573,\n", + " 141894, 148570, 167354, 187874, 192473],\n", + " dtype='int64'), Int64Index([ 7086, 24506, 25431, 32777, 38774, 52493, 59932, 76574,\n", + " 141895, 148571, 167355, 187875, 192474],\n", + " dtype='int64'), Int64Index([ 7087, 24507, 25432, 32778, 38775, 52494, 59933, 76575,\n", + " 141896, 148572, 167356, 187876, 192475],\n", + " dtype='int64'), Int64Index([ 7088, 24508, 25433, 32779, 38776, 52495, 59934, 76576,\n", + " 141897, 148573, 167357, 187877, 192476],\n", + " dtype='int64'), Int64Index([ 7089, 24509, 25434, 32780, 38777, 52496, 59935, 76577,\n", + " 141898, 148574, 167358, 187878, 192477],\n", + " dtype='int64'), Int64Index([ 7090, 24510, 25435, 32781, 38778, 52497, 59936, 76578,\n", + " 141899, 148575, 167359, 187879, 192478],\n", + " dtype='int64'), Int64Index([ 7091, 24511, 25436, 32782, 38779, 52498, 59937, 76579,\n", + " 141900, 148576, 167360, 187880, 192479],\n", + " dtype='int64'), Int64Index([ 7092, 24512, 25437, 32783, 38780, 52499, 59938, 76580,\n", + " 141901, 148577, 167361, 187881, 192480],\n", + " dtype='int64'), Int64Index([ 7093, 24513, 25438, 32784, 38781, 52500, 59939, 76581,\n", + " 141902, 148578, 167362, 187882, 192481],\n", + " dtype='int64'), Int64Index([ 7094, 24514, 25439, 32785, 38782, 52501, 59940, 76582,\n", + " 141903, 148579, 167363, 187883, 192482],\n", + " dtype='int64'), Int64Index([ 7095, 24515, 25440, 32786, 38783, 52502, 59941, 76583,\n", + " 141904, 148580, 167364, 187884, 192483],\n", + " dtype='int64'), Int64Index([ 7096, 24516, 25441, 32787, 38784, 52503, 59942, 76584,\n", + " 141905, 148581, 167365, 187885, 192484],\n", + " dtype='int64'), Int64Index([ 7097, 24517, 25442, 32788, 38785, 52504, 59943, 76585,\n", + " 141906, 148582, 167366, 187886, 192485],\n", + " dtype='int64'), Int64Index([ 7098, 24518, 25443, 32789, 38786, 52505, 59944, 76586,\n", + " 141907, 148583, 167367, 187887, 192486],\n", + " dtype='int64'), Int64Index([ 7099, 24519, 25444, 32790, 38787, 52506, 59945, 76587,\n", + " 141908, 148584, 167368, 187888, 192487],\n", + " dtype='int64'), Int64Index([ 7100, 24520, 25445, 32791, 38788, 52507, 59946, 76588,\n", + " 141909, 148585, 167369, 187889, 192488],\n", + " dtype='int64'), Int64Index([ 7101, 24521, 25446, 32792, 38789, 52508, 59947, 76589,\n", + " 141910, 148586, 167370, 187890, 192489],\n", + " dtype='int64'), Int64Index([ 7102, 24522, 25447, 32793, 38790, 52509, 59948, 76590,\n", + " 141911, 148587, 167371, 187891, 192490],\n", + " dtype='int64'), Int64Index([ 7103, 24523, 25448, 32794, 38791, 52510, 59949, 76591,\n", + " 141912, 148588, 167372, 187892, 192491],\n", + " dtype='int64'), Int64Index([ 7104, 24524, 25449, 32795, 38792, 52511, 59950, 76592,\n", + " 141913, 148589, 167373, 187893, 192492],\n", + " dtype='int64'), Int64Index([ 7105, 24525, 25450, 32796, 38793, 52512, 59951, 76593,\n", + " 141914, 148590, 167374, 187894, 192493],\n", + " dtype='int64'), Int64Index([ 7106, 24526, 25451, 32797, 38794, 52513, 59952, 76594,\n", + " 141915, 148591, 167375, 187895, 192494],\n", + " dtype='int64'), Int64Index([ 7107, 24527, 25452, 32798, 38795, 52514, 59953, 76595,\n", + " 141916, 148592, 167376, 187896, 192495],\n", + " dtype='int64'), Int64Index([ 7108, 24528, 25453, 32799, 38796, 52515, 59954, 76596,\n", + " 141917, 148593, 167377, 187897, 192496],\n", + " dtype='int64'), Int64Index([ 7109, 24529, 25454, 32800, 38797, 52516, 59955, 76597,\n", + " 141918, 148594, 167378, 187898, 192497],\n", + " dtype='int64'), Int64Index([ 7110, 24530, 25455, 32801, 38798, 52517, 59956, 76598,\n", + " 141919, 148595, 167379, 187899, 192498],\n", + " dtype='int64'), Int64Index([ 7111, 24531, 25456, 32802, 38799, 52518, 59957, 76599,\n", + " 141920, 148596, 167380, 187900, 192499],\n", + " dtype='int64'), Int64Index([ 7112, 24532, 25457, 32803, 38800, 52519, 59958, 76600,\n", + " 141921, 148597, 167381, 187901, 192500],\n", + " dtype='int64'), Int64Index([ 7113, 24533, 25458, 32804, 38801, 52520, 59959, 76601,\n", + " 141922, 148598, 167382, 187902, 192501],\n", + " dtype='int64'), Int64Index([ 7114, 24534, 25459, 32805, 38802, 52521, 59960, 76602,\n", + " 141923, 148599, 167383, 187903, 192502],\n", + " dtype='int64'), Int64Index([ 7115, 24535, 25460, 32806, 38803, 52522, 59961, 76603,\n", + " 141924, 148600, 167384, 187904, 192503],\n", + " dtype='int64'), Int64Index([ 7116, 24536, 25461, 32807, 38804, 52523, 59962, 76604,\n", + " 141925, 148601, 167385, 187905, 192504],\n", + " dtype='int64'), Int64Index([ 7117, 24537, 25462, 32808, 38805, 52524, 59963, 76605,\n", + " 141926, 148602, 167386, 187906, 192505],\n", + " dtype='int64'), Int64Index([ 7118, 24538, 25463, 32809, 38806, 52525, 59964, 76606,\n", + " 141927, 148603, 167387, 187907, 192506],\n", + " dtype='int64'), Int64Index([ 7119, 24539, 25464, 32810, 38807, 52526, 59965, 76607,\n", + " 141928, 148604, 167388, 187908, 192507],\n", + " dtype='int64'), Int64Index([ 7120, 24540, 25465, 32811, 38808, 52527, 59966, 76608,\n", + " 141929, 148605, 167389, 187909, 192508],\n", + " dtype='int64'), Int64Index([ 7121, 24541, 25466, 32812, 38809, 52528, 59967, 76609,\n", + " 141930, 148606, 167390, 187910, 192509],\n", + " dtype='int64'), Int64Index([ 7122, 24542, 25467, 32813, 38810, 52529, 59968, 76610,\n", + " 141931, 148607, 167391, 187911, 192510],\n", + " dtype='int64'), Int64Index([ 7123, 24543, 25468, 32814, 38811, 52530, 59969, 76611,\n", + " 141932, 148608, 167392, 187912, 192511],\n", + " dtype='int64'), Int64Index([ 7124, 24544, 25469, 32815, 38812, 52531, 59970, 76612,\n", + " 141933, 148609, 167393, 187913, 192512],\n", + " dtype='int64'), Int64Index([ 7125, 24545, 25470, 32816, 38813, 52532, 59971, 76613,\n", + " 141934, 148610, 167394, 187914, 192513],\n", + " dtype='int64'), Int64Index([ 7126, 24546, 25471, 32817, 38814, 52533, 59972, 76614,\n", + " 141935, 148611, 167395, 187915, 192514],\n", + " dtype='int64'), Int64Index([ 7127, 24547, 25472, 32818, 38815, 52534, 59973, 76615,\n", + " 141936, 148612, 167396, 187916, 192515],\n", + " dtype='int64'), Int64Index([ 7128, 24548, 25473, 32819, 38816, 52535, 59974, 76616,\n", + " 141937, 148613, 167397, 187917, 192516],\n", + " dtype='int64'), Int64Index([ 7129, 24549, 25474, 32820, 38817, 52536, 59975, 76617,\n", + " 141938, 148614, 167398, 187918, 192517],\n", + " dtype='int64'), Int64Index([ 7130, 24550, 25475, 32821, 38818, 52537, 59976, 76618,\n", + " 141939, 148615, 167399, 187919, 192518],\n", + " dtype='int64'), Int64Index([ 7131, 24551, 25476, 32822, 38819, 52538, 59977, 76619,\n", + " 141940, 148616, 167400, 187920, 192519],\n", + " dtype='int64'), Int64Index([ 7132, 24552, 25477, 32823, 38820, 52539, 59978, 76620,\n", + " 141941, 148617, 167401, 187921, 192520],\n", + " dtype='int64'), Int64Index([ 7133, 24553, 25478, 32824, 38821, 52540, 59979, 76621,\n", + " 141942, 148618, 167402, 187922, 192521],\n", + " dtype='int64'), Int64Index([ 7134, 24554, 25479, 32825, 38822, 52541, 59980, 76622,\n", + " 141943, 148619, 167403, 187923, 192522],\n", + " dtype='int64'), Int64Index([ 7135, 24555, 25480, 32826, 38823, 52542, 59981, 76623,\n", + " 141944, 148620, 167404, 187924, 192523],\n", + " dtype='int64'), Int64Index([ 7136, 24556, 25481, 32827, 38824, 52543, 59982, 76624,\n", + " 141945, 148621, 167405, 187925, 192524],\n", + " dtype='int64'), Int64Index([ 7137, 24557, 25482, 32828, 38825, 52544, 59983, 76625,\n", + " 141946, 148622, 167406, 187926, 192525],\n", + " dtype='int64'), Int64Index([ 7138, 24558, 25483, 32829, 38826, 52545, 59984, 76626,\n", + " 141947, 148623, 167407, 187927, 192526],\n", + " dtype='int64'), Int64Index([ 7139, 24559, 25484, 32830, 38827, 52546, 59985, 76627,\n", + " 141948, 148624, 167408, 187928, 192527],\n", + " dtype='int64'), Int64Index([ 7140, 24560, 25485, 32831, 38828, 52547, 59986, 76628,\n", + " 141949, 148625, 167409, 187929, 192528],\n", + " dtype='int64'), Int64Index([ 7141, 24561, 25486, 32832, 38829, 52548, 59987, 76629,\n", + " 141950, 148626, 167410, 187930, 192529],\n", + " dtype='int64'), Int64Index([ 7142, 24562, 25487, 32833, 38830, 52549, 59988, 76630,\n", + " 141951, 148627, 167411, 187931, 192530],\n", + " dtype='int64'), Int64Index([ 7143, 24563, 25488, 32834, 38831, 52550, 59989, 76631,\n", + " 141952, 148628, 167412, 187932, 192531],\n", + " dtype='int64'), Int64Index([ 7144, 24564, 25489, 32835, 38832, 52551, 59990, 76632,\n", + " 141953, 148629, 167413, 187933, 192532],\n", + " dtype='int64'), Int64Index([ 7145, 24565, 25490, 32836, 38833, 52552, 59991, 76633,\n", + " 141954, 148630, 167414, 187934, 192533],\n", + " dtype='int64'), Int64Index([ 7146, 24566, 25491, 32837, 38834, 52553, 59992, 76634,\n", + " 141955, 148631, 167415, 187935, 192534],\n", + " dtype='int64'), Int64Index([ 7147, 24567, 25492, 32838, 38835, 52554, 59993, 76635,\n", + " 141956, 148632, 167416, 187936, 192535],\n", + " dtype='int64'), Int64Index([ 7148, 24568, 25493, 32839, 38836, 52555, 59994, 76636,\n", + " 141957, 148633, 167417, 187937, 192536],\n", + " dtype='int64'), Int64Index([ 7149, 24569, 25494, 32840, 38837, 52556, 59995, 76637,\n", + " 141958, 148634, 167418, 187938, 192537],\n", + " dtype='int64'), Int64Index([ 7150, 24570, 25495, 32841, 38838, 52557, 59996, 76638,\n", + " 141959, 148635, 167419, 187939, 192538],\n", + " dtype='int64'), Int64Index([ 7151, 24571, 25496, 32842, 38839, 52558, 59997, 76639,\n", + " 141960, 148636, 167420, 187940, 192539],\n", + " dtype='int64'), Int64Index([ 7152, 24572, 25497, 32843, 38840, 52559, 59998, 76640,\n", + " 141961, 148637, 167421, 187941, 192540],\n", + " dtype='int64'), Int64Index([ 7153, 24573, 25498, 32844, 38841, 52560, 59999, 76641,\n", + " 141962, 148638, 167422, 187942, 192541],\n", + " dtype='int64'), Int64Index([ 7154, 24574, 25499, 32845, 38842, 52561, 60000, 76642,\n", + " 141963, 148639, 167423, 187943, 192542],\n", + " dtype='int64'), Int64Index([ 7155, 24575, 25500, 32846, 38843, 52562, 60001, 76643,\n", + " 141964, 148640, 167424, 187944, 192543],\n", + " dtype='int64'), Int64Index([ 7156, 24576, 25501, 32847, 38844, 52563, 60002, 76644,\n", + " 141965, 148641, 167425, 187945, 192544],\n", + " dtype='int64'), Int64Index([ 7157, 24577, 25502, 32848, 38845, 52564, 60003, 76645,\n", + " 141966, 148642, 167426, 187946, 192545],\n", + " dtype='int64'), Int64Index([ 7158, 24578, 25503, 32849, 38846, 52565, 60004, 76646,\n", + " 141967, 148643, 167427, 187947, 192546],\n", + " dtype='int64'), Int64Index([ 7159, 24579, 25504, 32850, 38847, 52566, 60005, 76647,\n", + " 141968, 148644, 167428, 187948, 192547],\n", + " dtype='int64'), Int64Index([ 7160, 24580, 25505, 32851, 38848, 52567, 60006, 76648,\n", + " 141969, 148645, 167429, 187949, 192548],\n", + " dtype='int64'), Int64Index([ 7161, 24581, 25506, 32852, 38849, 52568, 60007, 76649,\n", + " 141970, 148646, 167430, 187950, 192549],\n", + " dtype='int64'), Int64Index([ 7162, 24582, 25507, 32853, 38850, 52569, 60008, 76650,\n", + " 141971, 148647, 167431, 187951, 192550],\n", + " dtype='int64'), Int64Index([ 7163, 24583, 25508, 32854, 38851, 52570, 60009, 76651,\n", + " 141972, 148648, 167432, 187952, 192551],\n", + " dtype='int64'), Int64Index([ 7164, 24584, 25509, 32855, 38852, 52571, 60010, 76652,\n", + " 141973, 148649, 167433, 187953, 192552],\n", + " dtype='int64'), Int64Index([ 7165, 24585, 25510, 32856, 38853, 52572, 60011, 76653,\n", + " 141974, 148650, 167434, 187954, 192553],\n", + " dtype='int64'), Int64Index([ 7166, 24586, 25511, 32857, 38854, 52573, 60012, 76654,\n", + " 141975, 148651, 167435, 187955, 192554],\n", + " dtype='int64'), Int64Index([ 7167, 24587, 25512, 32858, 38855, 52574, 60013, 76655,\n", + " 141976, 148652, 167436, 187956, 192555],\n", + " dtype='int64'), Int64Index([ 7168, 24588, 25513, 32859, 38856, 52575, 60014, 76656,\n", + " 141977, 148653, 167437, 187957, 192556],\n", + " dtype='int64'), Int64Index([ 7169, 24589, 25514, 32860, 38857, 52576, 60015, 76657,\n", + " 141978, 148654, 167438, 187958, 192557],\n", + " dtype='int64'), Int64Index([ 7170, 24590, 25515, 32861, 38858, 52577, 60016, 76658,\n", + " 141979, 148655, 167439, 187959, 192558],\n", + " dtype='int64'), Int64Index([ 7171, 24591, 25516, 32862, 38859, 52578, 60017, 76659,\n", + " 141980, 148656, 167440, 187960, 192559],\n", + " dtype='int64'), Int64Index([ 7172, 24592, 25517, 32863, 38860, 52579, 60018, 76660,\n", + " 141981, 148657, 167441, 187961, 192560],\n", + " dtype='int64'), Int64Index([ 7173, 24593, 25518, 32864, 38861, 52580, 60019, 76661,\n", + " 141982, 148658, 167442, 187962, 192561],\n", + " dtype='int64'), Int64Index([ 7174, 24594, 25519, 32865, 38862, 52581, 60020, 76662,\n", + " 141983, 148659, 167443, 187963, 192562],\n", + " dtype='int64'), Int64Index([ 7175, 24595, 25520, 32866, 38863, 52582, 60021, 76663,\n", + " 141984, 148660, 167444, 187964, 192563],\n", + " dtype='int64'), Int64Index([ 7176, 24596, 25521, 32867, 38864, 52583, 60022, 76664,\n", + " 141985, 148661, 167445, 187965, 192564],\n", + " dtype='int64'), Int64Index([ 7177, 24597, 25522, 32868, 38865, 52584, 60023, 76665,\n", + " 141986, 148662, 167446, 187966, 192565],\n", + " dtype='int64'), Int64Index([ 7178, 24598, 25523, 32869, 38866, 52585, 60024, 76666,\n", + " 141987, 148663, 167447, 187967, 192566],\n", + " dtype='int64'), Int64Index([ 7179, 24599, 25524, 32870, 38867, 52586, 60025, 76667,\n", + " 141988, 148664, 167448, 187968, 192567],\n", + " dtype='int64'), Int64Index([ 7180, 24600, 25525, 32871, 38868, 52587, 60026, 76668,\n", + " 141989, 148665, 167449, 187969, 192568],\n", + " dtype='int64'), Int64Index([ 7181, 24601, 25526, 32872, 38869, 52588, 60027, 76669,\n", + " 141990, 148666, 167450, 187970, 192569],\n", + " dtype='int64'), Int64Index([ 7182, 24602, 25527, 32873, 38870, 52589, 60028, 76670,\n", + " 141991, 148667, 167451, 187971, 192570],\n", + " dtype='int64'), Int64Index([ 7183, 24603, 25528, 32874, 38871, 52590, 60029, 76671,\n", + " 141992, 148668, 167452, 187972, 192571],\n", + " dtype='int64'), Int64Index([ 7184, 24604, 25529, 32875, 38872, 52591, 60030, 76672,\n", + " 141993, 148669, 167453, 187973, 192572],\n", + " dtype='int64'), Int64Index([ 7185, 24605, 25530, 32876, 38873, 52592, 60031, 76673,\n", + " 141994, 148670, 167454, 187974, 192573],\n", + " dtype='int64'), Int64Index([ 7186, 24606, 25531, 32877, 38874, 52593, 60032, 76674,\n", + " 141995, 148671, 167455, 187975, 192574],\n", + " dtype='int64'), Int64Index([ 7187, 24607, 25532, 32878, 38875, 52594, 60033, 76675,\n", + " 141996, 148672, 167456, 187976, 192575],\n", + " dtype='int64'), Int64Index([ 7188, 24608, 25533, 32879, 38876, 52595, 60034, 76676,\n", + " 141997, 148673, 167457, 187977, 192576],\n", + " dtype='int64'), Int64Index([ 7189, 24609, 25534, 32880, 38877, 52596, 60035, 76677,\n", + " 141998, 148674, 167458, 187978, 192577],\n", + " dtype='int64'), Int64Index([ 7190, 24610, 25535, 32881, 38878, 52597, 60036, 76678,\n", + " 141999, 148675, 167459, 187979, 192578],\n", + " dtype='int64'), Int64Index([ 7191, 24611, 25536, 32882, 38879, 52598, 60037, 76679,\n", + " 142000, 148676, 167460, 187980, 192579],\n", + " dtype='int64'), Int64Index([ 7192, 24612, 25537, 32883, 38880, 52599, 60038, 76680,\n", + " 142001, 148677, 167461, 187981, 192580],\n", + " dtype='int64'), Int64Index([ 7193, 24613, 25538, 32884, 38881, 52600, 60039, 76681,\n", + " 142002, 148678, 167462, 187982, 192581],\n", + " dtype='int64'), Int64Index([ 7194, 24614, 25539, 32885, 38882, 52601, 60040, 76682,\n", + " 142003, 148679, 167463, 187983, 192582],\n", + " dtype='int64'), Int64Index([ 7195, 24615, 25540, 32886, 38883, 52602, 60041, 76683,\n", + " 142004, 148680, 167464, 187984, 192583],\n", + " dtype='int64'), Int64Index([ 7196, 24616, 25541, 32887, 38884, 52603, 60042, 76684,\n", + " 142005, 148681, 167465, 187985, 192584],\n", + " dtype='int64'), Int64Index([ 7197, 24617, 25542, 32888, 38885, 52604, 60043, 76685,\n", + " 142006, 148682, 167466, 187986, 192585],\n", + " dtype='int64'), Int64Index([ 7198, 24618, 25543, 32889, 38886, 52605, 60044, 76686,\n", + " 142007, 148683, 167467, 187987, 192586],\n", + " dtype='int64'), Int64Index([ 7199, 24619, 25544, 32890, 38887, 52606, 60045, 76687,\n", + " 142008, 148684, 167468, 187988, 192587],\n", + " dtype='int64'), Int64Index([ 7200, 24620, 25545, 32891, 38888, 52607, 60046, 76688,\n", + " 142009, 148685, 167469, 187989, 192588],\n", + " dtype='int64'), Int64Index([ 7201, 24621, 25546, 32892, 38889, 52608, 60047, 76689,\n", + " 142010, 148686, 167470, 187990, 192589],\n", + " dtype='int64'), Int64Index([ 7202, 24622, 25547, 32893, 38890, 52609, 60048, 76690,\n", + " 142011, 148687, 167471, 187991, 192590],\n", + " dtype='int64'), Int64Index([ 7203, 24623, 25548, 32894, 38891, 52610, 60049, 76691,\n", + " 142012, 148688, 167472, 187992, 192591],\n", + " dtype='int64'), Int64Index([ 7204, 24624, 25549, 32895, 38892, 52611, 60050, 76692,\n", + " 142013, 148689, 167473, 187993, 192592],\n", + " dtype='int64'), Int64Index([ 7205, 24625, 25550, 32896, 38893, 52612, 60051, 76693,\n", + " 142014, 148690, 167474, 187994, 192593],\n", + " dtype='int64'), Int64Index([ 7206, 24626, 25551, 32897, 38894, 52613, 60052, 76694,\n", + " 142015, 148691, 167475, 187995, 192594],\n", + " dtype='int64'), Int64Index([ 7207, 24627, 25552, 32898, 38895, 52614, 60053, 76695,\n", + " 142016, 148692, 167476, 187996, 192595],\n", + " dtype='int64'), Int64Index([ 7208, 24628, 25553, 32899, 38896, 52615, 60054, 76696,\n", + " 142017, 148693, 167477, 187997, 192596],\n", + " dtype='int64'), Int64Index([ 7209, 24629, 25554, 32900, 38897, 52616, 60055, 76697,\n", + " 142018, 148694, 167478, 187998, 192597],\n", + " dtype='int64'), Int64Index([ 7210, 24630, 25555, 32901, 38898, 52617, 60056, 76698,\n", + " 142019, 148695, 167479, 187999, 192598],\n", + " dtype='int64'), Int64Index([ 7211, 24631, 25556, 32902, 38899, 52618, 60057, 76699,\n", + " 142020, 148696, 167480, 188000, 192599],\n", + " dtype='int64'), Int64Index([ 7212, 24632, 25557, 32903, 38900, 52619, 60058, 76700,\n", + " 142021, 148697, 167481, 188001, 192600],\n", + " dtype='int64'), Int64Index([ 7213, 24633, 25558, 32904, 38901, 52620, 60059, 76701,\n", + " 142022, 148698, 167482, 188002, 192601],\n", + " dtype='int64'), Int64Index([ 7214, 24634, 25559, 32905, 38902, 52621, 60060, 76702,\n", + " 142023, 148699, 167483, 188003, 192602],\n", + " dtype='int64'), Int64Index([ 7215, 24635, 25560, 32906, 38903, 52622, 60061, 76703,\n", + " 142024, 148700, 167484, 188004, 192603],\n", + " dtype='int64'), Int64Index([ 7216, 24636, 25561, 32907, 38904, 52623, 60062, 76704,\n", + " 142025, 148701, 167485, 188005, 192604],\n", + " dtype='int64'), Int64Index([ 7217, 24637, 25562, 32908, 38905, 52624, 60063, 76705,\n", + " 142026, 148702, 167486, 188006, 192605],\n", + " dtype='int64'), Int64Index([ 7218, 24638, 25563, 32909, 38906, 52625, 60064, 76706,\n", + " 142027, 148703, 167487, 188007, 192606],\n", + " dtype='int64'), Int64Index([ 7219, 24639, 25564, 32910, 38907, 52626, 60065, 76707,\n", + " 142028, 148704, 167488, 188008, 192607],\n", + " dtype='int64'), Int64Index([ 7220, 24640, 25565, 32911, 38908, 52627, 60066, 76708,\n", + " 142029, 148705, 167489, 188009, 192608],\n", + " dtype='int64'), Int64Index([ 7221, 24641, 25566, 32912, 38909, 52628, 60067, 76709,\n", + " 142030, 148706, 167490, 188010, 192609],\n", + " dtype='int64'), Int64Index([ 7222, 24642, 25567, 32913, 38910, 52629, 60068, 76710,\n", + " 142031, 148707, 167491, 188011, 192610],\n", + " dtype='int64'), Int64Index([ 7223, 24643, 25568, 32914, 38911, 52630, 60069, 76711,\n", + " 142032, 148708, 167492, 188012, 192611],\n", + " dtype='int64'), Int64Index([ 7224, 24644, 25569, 32915, 38912, 52631, 60070, 76712,\n", + " 142033, 148709, 167493, 188013, 192612],\n", + " dtype='int64'), Int64Index([ 7225, 24645, 25570, 32916, 38913, 52632, 60071, 76713,\n", + " 142034, 148710, 167494, 188014, 192613],\n", + " dtype='int64'), Int64Index([ 7226, 24646, 25571, 32917, 38914, 52633, 60072, 76714,\n", + " 142035, 148711, 167495, 188015, 192614],\n", + " dtype='int64'), Int64Index([ 7227, 24647, 25572, 32918, 38915, 52634, 60073, 76715,\n", + " 142036, 148712, 167496, 188016, 192615],\n", + " dtype='int64'), Int64Index([ 7228, 24648, 25573, 32919, 38916, 52635, 60074, 76716,\n", + " 142037, 148713, 167497, 188017, 192616],\n", + " dtype='int64'), Int64Index([ 7229, 24649, 25574, 32920, 38917, 52636, 60075, 76717,\n", + " 142038, 148714, 167498, 188018, 192617],\n", + " dtype='int64'), Int64Index([ 7230, 24650, 25575, 32921, 38918, 52637, 60076, 76718,\n", + " 142039, 148715, 167499, 188019, 192618],\n", + " dtype='int64'), Int64Index([ 7231, 24651, 25576, 32922, 38919, 52638, 60077, 76719,\n", + " 142040, 148716, 167500, 188020, 192619],\n", + " dtype='int64'), Int64Index([ 7232, 24652, 25577, 32923, 38920, 52639, 60078, 76720,\n", + " 142041, 148717, 167501, 188021, 192620],\n", + " dtype='int64'), Int64Index([ 7233, 24653, 25578, 32924, 38921, 52640, 60079, 76721,\n", + " 142042, 148718, 167502, 188022, 192621],\n", + " dtype='int64'), Int64Index([ 7234, 24654, 25579, 32925, 38922, 52641, 60080, 76722,\n", + " 142043, 148719, 167503, 188023, 192622],\n", + " dtype='int64'), Int64Index([ 7235, 24655, 25580, 32926, 38923, 52642, 60081, 76723,\n", + " 142044, 148720, 167504, 188024, 192623],\n", + " dtype='int64'), Int64Index([ 7236, 24656, 25581, 32927, 38924, 52643, 60082, 76724,\n", + " 142045, 148721, 167505, 188025, 192624],\n", + " dtype='int64'), Int64Index([ 7237, 24657, 25582, 32928, 38925, 52644, 60083, 76725,\n", + " 142046, 148722, 167506, 188026, 192625],\n", + " dtype='int64'), Int64Index([ 7238, 24658, 25583, 32929, 38926, 52645, 60084, 76726,\n", + " 142047, 148723, 167507, 188027, 192626],\n", + " dtype='int64'), Int64Index([ 7239, 24659, 25584, 32930, 38927, 52646, 60085, 76727,\n", + " 142048, 148724, 167508, 188028, 192627],\n", + " dtype='int64'), Int64Index([ 7240, 24660, 25585, 32931, 38928, 52647, 60086, 76728,\n", + " 142049, 148725, 167509, 188029, 192628],\n", + " dtype='int64'), Int64Index([ 7241, 24661, 25586, 32932, 38929, 52648, 60087, 76729,\n", + " 142050, 148726, 167510, 188030, 192629],\n", + " dtype='int64'), Int64Index([ 7242, 24662, 25587, 32933, 38930, 52649, 60088, 76730,\n", + " 142051, 148727, 167511, 188031, 192630],\n", + " dtype='int64'), Int64Index([ 7243, 24663, 25588, 32934, 38931, 52650, 60089, 76731,\n", + " 142052, 148728, 167512, 188032, 192631],\n", + " dtype='int64'), Int64Index([ 7244, 24664, 25589, 32935, 38932, 52651, 60090, 76732,\n", + " 142053, 148729, 167513, 188033, 192632],\n", + " dtype='int64'), Int64Index([ 7245, 24665, 25590, 32936, 38933, 52652, 60091, 76733,\n", + " 142054, 148730, 167514, 188034, 192633],\n", + " dtype='int64'), Int64Index([ 7246, 24666, 25591, 32937, 38934, 52653, 60092, 76734,\n", + " 142055, 148731, 167515, 188035, 192634],\n", + " dtype='int64'), Int64Index([ 7247, 24667, 25592, 32938, 38935, 52654, 60093, 76735,\n", + " 142056, 148732, 167516, 188036, 192635],\n", + " dtype='int64'), Int64Index([ 7248, 24668, 25593, 32939, 38936, 52655, 60094, 76736,\n", + " 142057, 148733, 167517, 188037, 192636],\n", + " dtype='int64'), Int64Index([ 7249, 24669, 25594, 32940, 38937, 52656, 60095, 76737,\n", + " 142058, 148734, 167518, 188038, 192637],\n", + " dtype='int64'), Int64Index([ 7250, 24670, 25595, 32941, 38938, 52657, 60096, 76738,\n", + " 142059, 148735, 167519, 188039, 192638],\n", + " dtype='int64'), Int64Index([ 7251, 24671, 25596, 32942, 38939, 52658, 60097, 76739,\n", + " 142060, 148736, 167520, 188040, 192639],\n", + " dtype='int64'), Int64Index([ 7252, 24672, 25597, 32943, 38940, 52659, 60098, 76740,\n", + " 142061, 148737, 167521, 188041, 192640],\n", + " dtype='int64'), Int64Index([ 7253, 24673, 25598, 32944, 38941, 52660, 60099, 76741,\n", + " 142062, 148738, 167522, 188042, 192641],\n", + " dtype='int64'), Int64Index([ 7254, 24674, 25599, 32945, 38942, 52661, 60100, 76742,\n", + " 142063, 148739, 167523, 188043, 192642],\n", + " dtype='int64'), Int64Index([ 7255, 24675, 25600, 32946, 38943, 52662, 60101, 76743,\n", + " 142064, 148740, 167524, 188044, 192643],\n", + " dtype='int64'), Int64Index([ 7256, 24676, 25601, 32947, 38944, 52663, 60102, 76744,\n", + " 142065, 148741, 167525, 188045, 192644],\n", + " dtype='int64'), Int64Index([ 7257, 24677, 25602, 32948, 38945, 52664, 60103, 76745,\n", + " 142066, 148742, 167526, 188046, 192645],\n", + " dtype='int64'), Int64Index([ 7258, 24678, 25603, 32949, 38946, 52665, 60104, 76746,\n", + " 142067, 148743, 167527, 188047, 192646],\n", + " dtype='int64'), Int64Index([ 7259, 24679, 25604, 32950, 38947, 52666, 60105, 76747,\n", + " 142068, 148744, 167528, 188048, 192647],\n", + " dtype='int64'), Int64Index([ 7260, 24680, 25605, 32951, 38948, 52667, 60106, 76748,\n", + " 142069, 148745, 167529, 188049, 192648],\n", + " dtype='int64'), Int64Index([ 7261, 24681, 25606, 32952, 38949, 52668, 60107, 76749,\n", + " 142070, 148746, 167530, 188050, 192649],\n", + " dtype='int64'), Int64Index([ 7262, 24682, 25607, 32953, 38950, 52669, 60108, 76750,\n", + " 142071, 148747, 167531, 188051, 192650],\n", + " dtype='int64'), Int64Index([ 7263, 24683, 25608, 32954, 38951, 52670, 60109, 76751,\n", + " 142072, 148748, 167532, 188052, 192651],\n", + " dtype='int64'), Int64Index([ 7264, 24684, 25609, 32955, 38952, 52671, 60110, 76752,\n", + " 142073, 148749, 167533, 188053, 192652],\n", + " dtype='int64'), Int64Index([ 7265, 24685, 25610, 32956, 38953, 52672, 60111, 76753,\n", + " 142074, 148750, 167534, 188054, 192653],\n", + " dtype='int64'), Int64Index([ 7266, 24686, 25611, 32957, 38954, 52673, 60112, 76754,\n", + " 142075, 148751, 167535, 188055, 192654],\n", + " dtype='int64'), Int64Index([ 7267, 24687, 25612, 32958, 38955, 52674, 60113, 76755,\n", + " 142076, 148752, 167536, 188056, 192655],\n", + " dtype='int64'), Int64Index([ 7268, 24688, 25613, 32959, 38956, 52675, 60114, 76756,\n", + " 142077, 148753, 167537, 188057, 192656],\n", + " dtype='int64'), Int64Index([ 7269, 24689, 25614, 32960, 38957, 52676, 60115, 76757,\n", + " 142078, 148754, 167538, 188058, 192657],\n", + " dtype='int64'), Int64Index([ 7270, 24690, 25615, 32961, 38958, 52677, 60116, 76758,\n", + " 142079, 148755, 167539, 188059, 192658],\n", + " dtype='int64'), Int64Index([ 7271, 24691, 25616, 32962, 38959, 52678, 60117, 76759,\n", + " 142080, 148756, 167540, 188060, 192659],\n", + " dtype='int64'), Int64Index([ 7272, 24692, 25617, 32963, 38960, 52679, 60118, 76760,\n", + " 142081, 148757, 167541, 188061, 192660],\n", + " dtype='int64'), Int64Index([ 7273, 24693, 25618, 32964, 38961, 52680, 60119, 76761,\n", + " 142082, 148758, 167542, 188062, 192661],\n", + " dtype='int64'), Int64Index([ 7274, 24694, 25619, 32965, 38962, 52681, 60120, 76762,\n", + " 142083, 148759, 167543, 188063, 192662],\n", + " dtype='int64'), Int64Index([ 7275, 24695, 25620, 32966, 38963, 52682, 60121, 76763,\n", + " 142084, 148760, 167544, 188064, 192663],\n", + " dtype='int64'), Int64Index([ 7276, 24696, 25621, 32967, 38964, 52683, 60122, 76764,\n", + " 142085, 148761, 167545, 188065, 192664],\n", + " dtype='int64'), Int64Index([ 7277, 24697, 25622, 32968, 38965, 52684, 60123, 76765,\n", + " 142086, 148762, 167546, 188066, 192665],\n", + " dtype='int64'), Int64Index([ 7278, 24698, 25623, 32969, 38966, 52685, 60124, 76766,\n", + " 142087, 148763, 167547, 188067, 192666],\n", + " dtype='int64'), Int64Index([ 7279, 24699, 25624, 32970, 38967, 52686, 60125, 76767,\n", + " 142088, 148764, 167548, 188068, 192667],\n", + " dtype='int64'), Int64Index([ 7280, 24700, 25625, 32971, 38968, 52687, 60126, 76768,\n", + " 142089, 148765, 167549, 188069, 192668],\n", + " dtype='int64'), Int64Index([ 7281, 24701, 25626, 32972, 38969, 52688, 60127, 76769,\n", + " 142090, 148766, 167550, 188070, 192669],\n", + " dtype='int64'), Int64Index([ 7282, 24702, 25627, 32973, 38970, 52689, 60128, 76770,\n", + " 142091, 148767, 167551, 188071, 192670],\n", + " dtype='int64'), Int64Index([ 7283, 24703, 25628, 32974, 38971, 52690, 60129, 76771,\n", + " 142092, 148768, 167552, 188072, 192671],\n", + " dtype='int64'), Int64Index([ 7284, 24704, 25629, 32975, 38972, 52691, 60130, 76772,\n", + " 142093, 148769, 167553, 188073, 192672],\n", + " dtype='int64'), Int64Index([ 7285, 24705, 25630, 32976, 38973, 52692, 60131, 76773,\n", + " 142094, 148770, 167554, 188074, 192673],\n", + " dtype='int64'), Int64Index([ 7286, 24706, 25631, 32977, 38974, 52693, 60132, 76774,\n", + " 142095, 148771, 167555, 188075, 192674],\n", + " dtype='int64'), Int64Index([ 7287, 24707, 25632, 32978, 38975, 52694, 60133, 76775,\n", + " 142096, 148772, 167556, 188076, 192675],\n", + " dtype='int64'), Int64Index([ 7288, 24708, 25633, 32979, 38976, 52695, 60134, 76776,\n", + " 142097, 148773, 167557, 188077, 192676],\n", + " dtype='int64'), Int64Index([ 7289, 24709, 25634, 32980, 38977, 52696, 60135, 76777,\n", + " 142098, 148774, 167558, 188078, 192677],\n", + " dtype='int64'), Int64Index([ 7290, 24710, 25635, 32981, 38978, 52697, 60136, 76778,\n", + " 142099, 148775, 167559, 188079, 192678],\n", + " dtype='int64'), Int64Index([ 7291, 24711, 25636, 32982, 38979, 52698, 60137, 76779,\n", + " 142100, 148776, 167560, 188080, 192679],\n", + " dtype='int64'), Int64Index([ 7292, 24712, 25637, 32983, 38980, 52699, 60138, 76780,\n", + " 142101, 148777, 167561, 188081, 192680],\n", + " dtype='int64'), Int64Index([ 7293, 24713, 25638, 32984, 38981, 52700, 60139, 76781,\n", + " 142102, 148778, 167562, 188082, 192681],\n", + " dtype='int64'), Int64Index([ 7294, 24714, 25639, 32985, 38982, 52701, 60140, 76782,\n", + " 142103, 148779, 167563, 188083, 192682],\n", + " dtype='int64'), Int64Index([ 7295, 24715, 25640, 32986, 38983, 52702, 60141, 76783,\n", + " 142104, 148780, 167564, 188084, 192683],\n", + " dtype='int64'), Int64Index([ 7296, 24716, 25641, 32987, 38984, 52703, 60142, 76784,\n", + " 142105, 148781, 167565, 188085, 192684],\n", + " dtype='int64'), Int64Index([ 7297, 24717, 25642, 32988, 38985, 52704, 60143, 76785,\n", + " 142106, 148782, 167566, 188086, 192685],\n", + " dtype='int64'), Int64Index([ 7298, 24718, 25643, 32989, 38986, 52705, 60144, 76786,\n", + " 142107, 148783, 167567, 188087, 192686],\n", + " dtype='int64'), Int64Index([ 7299, 24719, 25644, 32990, 38987, 52706, 60145, 76787,\n", + " 142108, 148784, 167568, 188088, 192687],\n", + " dtype='int64'), Int64Index([ 7300, 24720, 25645, 32991, 38988, 52707, 60146, 76788,\n", + " 142109, 148785, 167569, 188089, 192688],\n", + " dtype='int64'), Int64Index([ 7301, 24721, 25646, 32992, 38989, 52708, 60147, 76789,\n", + " 142110, 148786, 167570, 188090, 192689],\n", + " dtype='int64'), Int64Index([ 7302, 24722, 25647, 32993, 38990, 52709, 60148, 76790,\n", + " 142111, 148787, 167571, 188091, 192690],\n", + " dtype='int64'), Int64Index([ 7303, 24723, 25648, 32994, 38991, 52710, 60149, 76791,\n", + " 142112, 148788, 167572, 188092, 192691],\n", + " dtype='int64'), Int64Index([ 7304, 24724, 25649, 32995, 38992, 52711, 60150, 76792,\n", + " 142113, 148789, 167573, 188093, 192692],\n", + " dtype='int64'), Int64Index([ 7305, 24725, 25650, 32996, 38993, 52712, 60151, 76793,\n", + " 142114, 148790, 167574, 188094, 192693],\n", + " dtype='int64'), Int64Index([ 7306, 24726, 25651, 32997, 38994, 52713, 60152, 76794,\n", + " 142115, 148791, 167575, 188095, 192694],\n", + " dtype='int64'), Int64Index([ 7307, 24727, 25652, 32998, 38995, 52714, 60153, 76795,\n", + " 142116, 148792, 167576, 188096, 192695],\n", + " dtype='int64'), Int64Index([ 7308, 24728, 25653, 32999, 38996, 52715, 60154, 76796,\n", + " 142117, 148793, 167577, 188097, 192696],\n", + " dtype='int64'), Int64Index([ 7309, 24729, 25654, 33000, 38997, 52716, 60155, 76797,\n", + " 142118, 148794, 167578, 188098, 192697],\n", + " dtype='int64'), Int64Index([ 7310, 24730, 25655, 33001, 38998, 52717, 60156, 76798,\n", + " 142119, 148795, 167579, 188099, 192698],\n", + " dtype='int64'), Int64Index([ 7311, 24731, 25656, 33002, 38999, 52718, 60157, 76799,\n", + " 142120, 148796, 167580, 188100, 192699],\n", + " dtype='int64'), Int64Index([ 7312, 24732, 25657, 33003, 39000, 52719, 60158, 76800,\n", + " 142121, 148797, 167581, 188101, 192700],\n", + " dtype='int64'), Int64Index([ 7313, 24733, 25658, 33004, 39001, 52720, 60159, 76801,\n", + " 142122, 148798, 167582, 188102, 192701],\n", + " dtype='int64'), Int64Index([ 7314, 24734, 25659, 33005, 39002, 52721, 60160, 76802,\n", + " 142123, 148799, 167583, 188103, 192702],\n", + " 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dtype='int64'), Int64Index([ 7322, 24742, 25667, 33013, 39010, 52729, 60168, 76810,\n", + " 142131, 148807, 167591, 188111, 192710],\n", + " dtype='int64'), Int64Index([ 7323, 24743, 25668, 33014, 39011, 52730, 60169, 76811,\n", + " 142132, 148808, 167592, 188112, 192711],\n", + " dtype='int64'), Int64Index([ 7324, 24744, 25669, 33015, 39012, 52731, 60170, 76812,\n", + " 142133, 148809, 167593, 188113, 192712],\n", + " dtype='int64'), Int64Index([ 7325, 24745, 25670, 33016, 39013, 52732, 60171, 76813,\n", + " 142134, 148810, 167594, 188114, 192713],\n", + " dtype='int64'), Int64Index([ 7326, 24746, 25671, 33017, 39014, 52733, 60172, 76814,\n", + " 142135, 148811, 167595, 188115, 192714],\n", + " dtype='int64'), Int64Index([ 7327, 24747, 25672, 33018, 39015, 52734, 60173, 76815,\n", + " 142136, 148812, 167596, 188116, 192715],\n", + " dtype='int64'), Int64Index([ 7328, 24748, 25673, 33019, 39016, 52735, 60174, 76816,\n", + " 142137, 148813, 167597, 188117, 192716],\n", + " 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dtype='int64'), Int64Index([ 7350, 24770, 25695, 33041, 39038, 52757, 60196, 76838,\n", + " 142159, 148835, 167619, 188139, 192738],\n", + " dtype='int64'), Int64Index([ 7351, 24771, 25696, 33042, 39039, 52758, 60197, 76839,\n", + " 142160, 148836, 167620, 188140, 192739],\n", + " dtype='int64'), Int64Index([ 7352, 24772, 25697, 33043, 39040, 52759, 60198, 76840,\n", + " 142161, 148837, 167621, 188141, 192740],\n", + " dtype='int64'), Int64Index([ 7353, 24773, 25698, 33044, 39041, 52760, 60199, 76841,\n", + " 142162, 148838, 167622, 188142, 192741],\n", + " dtype='int64'), Int64Index([ 7354, 24774, 25699, 33045, 39042, 52761, 60200, 76842,\n", + " 142163, 148839, 167623, 188143, 192742],\n", + " dtype='int64'), Int64Index([ 7355, 24775, 25700, 33046, 39043, 52762, 60201, 76843,\n", + " 142164, 148840, 167624, 188144, 192743],\n", + " dtype='int64'), Int64Index([ 7356, 24776, 25701, 33047, 39044, 52763, 60202, 76844,\n", + " 142165, 148841, 167625, 188145, 192744],\n", + " 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dtype='int64'), Int64Index([ 7364, 24784, 25709, 33055, 39052, 52771, 60210, 76852,\n", + " 142173, 148849, 167633, 188153, 192752],\n", + " dtype='int64'), Int64Index([ 7365, 24785, 25710, 33056, 39053, 52772, 60211, 76853,\n", + " 142174, 148850, 167634, 188154, 192753],\n", + " dtype='int64'), Int64Index([ 7366, 24786, 25711, 33057, 39054, 52773, 60212, 76854,\n", + " 142175, 148851, 167635, 188155, 192754],\n", + " dtype='int64'), Int64Index([ 7367, 24787, 25712, 33058, 39055, 52774, 60213, 76855,\n", + " 142176, 148852, 167636, 188156, 192755],\n", + " dtype='int64'), Int64Index([ 7368, 24788, 25713, 33059, 39056, 52775, 60214, 76856,\n", + " 142177, 148853, 167637, 188157, 192756],\n", + " dtype='int64'), Int64Index([ 7369, 24789, 25714, 33060, 39057, 52776, 60215, 76857,\n", + " 142178, 148854, 167638, 188158, 192757],\n", + " dtype='int64'), Int64Index([ 7370, 24790, 25715, 33061, 39058, 52777, 60216, 76858,\n", + " 142179, 148855, 167639, 188159, 192758],\n", + " dtype='int64'), Int64Index([ 7371, 24791, 25716, 33062, 39059, 52778, 60217, 76859,\n", + " 142180, 148856, 167640, 188160, 192759],\n", + " dtype='int64'), Int64Index([ 7372, 24792, 25717, 33063, 39060, 52779, 60218, 76860,\n", + " 142181, 148857, 167641, 188161, 192760],\n", + " dtype='int64'), Int64Index([ 7373, 24793, 25718, 33064, 39061, 52780, 60219, 76861,\n", + " 142182, 148858, 167642, 188162, 192761],\n", + " dtype='int64'), Int64Index([ 7374, 24794, 25719, 33065, 39062, 52781, 60220, 76862,\n", + " 142183, 148859, 167643, 188163, 192762],\n", + " dtype='int64'), Int64Index([ 7375, 24795, 25720, 33066, 39063, 52782, 60221, 76863,\n", + " 142184, 148860, 167644, 188164, 192763],\n", + " dtype='int64'), Int64Index([ 7376, 24796, 25721, 33067, 39064, 52783, 60222, 76864,\n", + " 142185, 148861, 167645, 188165, 192764],\n", + " dtype='int64'), Int64Index([ 7377, 24797, 25722, 33068, 39065, 52784, 60223, 76865,\n", + " 142186, 148862, 167646, 188166, 192765],\n", + " dtype='int64'), Int64Index([ 7378, 24798, 25723, 33069, 39066, 52785, 60224, 76866,\n", + " 142187, 148863, 167647, 188167, 192766],\n", + " dtype='int64'), Int64Index([ 7379, 24799, 25724, 33070, 39067, 52786, 60225, 76867,\n", + " 142188, 148864, 167648, 188168, 192767],\n", + " dtype='int64'), Int64Index([ 7380, 24800, 25725, 33071, 39068, 52787, 60226, 76868,\n", + " 142189, 148865, 167649, 188169, 192768],\n", + " dtype='int64'), Int64Index([ 7381, 24801, 25726, 33072, 39069, 52788, 60227, 76869,\n", + " 142190, 148866, 167650, 188170, 192769],\n", + " dtype='int64'), Int64Index([ 7382, 24802, 25727, 33073, 39070, 52789, 60228, 76870,\n", + " 142191, 148867, 167651, 188171, 192770],\n", + " dtype='int64'), Int64Index([ 7383, 24803, 25728, 33074, 39071, 52790, 60229, 76871,\n", + " 142192, 148868, 167652, 188172, 192771],\n", + " dtype='int64'), Int64Index([ 7384, 24804, 25729, 33075, 39072, 52791, 60230, 76872,\n", + " 142193, 148869, 167653, 188173, 192772],\n", + " 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dtype='int64'), Int64Index([ 7392, 24812, 25737, 33083, 39080, 52799, 60238, 76880,\n", + " 142201, 148877, 167661, 188181, 192780],\n", + " dtype='int64'), Int64Index([ 7393, 24813, 25738, 33084, 39081, 52800, 60239, 76881,\n", + " 142202, 148878, 167662, 188182, 192781],\n", + " dtype='int64'), Int64Index([ 7394, 24814, 25739, 33085, 39082, 52801, 60240, 76882,\n", + " 142203, 148879, 167663, 188183, 192782],\n", + " dtype='int64'), Int64Index([ 7395, 24815, 25740, 33086, 39083, 52802, 60241, 76883,\n", + " 142204, 148880, 167664, 188184, 192783],\n", + " dtype='int64'), Int64Index([ 7396, 24816, 25741, 33087, 39084, 52803, 60242, 76884,\n", + " 142205, 148881, 167665, 188185, 192784],\n", + " dtype='int64'), Int64Index([ 7397, 24817, 25742, 33088, 39085, 52804, 60243, 76885,\n", + " 142206, 148882, 167666, 188186, 192785],\n", + " dtype='int64'), Int64Index([ 7398, 24818, 25743, 33089, 39086, 52805, 60244, 76886,\n", + " 142207, 148883, 167667, 188187, 192786],\n", + " dtype='int64'), Int64Index([ 7399, 24819, 25744, 33090, 39087, 52806, 60245, 76887,\n", + " 142208, 148884, 167668, 188188, 192787],\n", + " dtype='int64'), Int64Index([ 7400, 24820, 25745, 33091, 39088, 52807, 60246, 76888,\n", + " 142209, 148885, 167669, 188189, 192788],\n", + " dtype='int64'), Int64Index([ 7401, 24821, 25746, 33092, 39089, 52808, 60247, 76889,\n", + " 142210, 148886, 167670, 188190, 192789],\n", + " dtype='int64'), Int64Index([ 7402, 24822, 25747, 33093, 39090, 52809, 60248, 76890,\n", + " 142211, 148887, 167671, 188191, 192790],\n", + " dtype='int64'), Int64Index([ 7403, 24823, 25748, 33094, 39091, 52810, 60249, 76891,\n", + " 142212, 148888, 167672, 188192, 192791],\n", + " dtype='int64'), Int64Index([ 7404, 24824, 25749, 33095, 39092, 52811, 60250, 76892,\n", + " 142213, 148889, 167673, 188193, 192792],\n", + " dtype='int64'), Int64Index([ 7405, 24825, 25750, 33096, 39093, 52812, 60251, 76893,\n", + " 142214, 148890, 167674, 188194, 192793],\n", + " dtype='int64'), Int64Index([ 7406, 24826, 25751, 33097, 39094, 52813, 60252, 76894,\n", + " 142215, 148891, 167675, 188195, 192794],\n", + " dtype='int64'), Int64Index([ 7407, 24827, 25752, 33098, 39095, 52814, 60253, 76895,\n", + " 142216, 148892, 167676, 188196, 192795],\n", + " dtype='int64'), Int64Index([ 7408, 24828, 25753, 33099, 39096, 52815, 60254, 76896,\n", + " 142217, 148893, 167677, 188197, 192796],\n", + " dtype='int64'), Int64Index([ 7409, 24829, 25754, 33100, 39097, 52816, 60255, 76897,\n", + " 142218, 148894, 167678, 188198, 192797],\n", + " dtype='int64'), Int64Index([ 7410, 24830, 25755, 33101, 39098, 52817, 60256, 76898,\n", + " 142219, 148895, 167679, 188199, 192798],\n", + " dtype='int64'), Int64Index([ 7411, 24831, 25756, 33102, 39099, 52818, 60257, 76899,\n", + " 142220, 148896, 167680, 188200, 192799],\n", + " dtype='int64'), Int64Index([33103], dtype='int64')])" + ] + }, + "execution_count": 153, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "g.groups.values()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### Group By + Apply" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665956377370 + }, + "jupyter": { + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Martin Hronec\\AppData\\Local\\Temp\\ipykernel_27088\\2047160843.py:4: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " some_countries['deaths_per_case'] = some_countries.new_deaths/some_countries.new_cases\n" + ] + }, + { + "data": { + "text/html": [ + "
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iso_codedatenew_casesnew_deathsicu_patientshosp_patientscontinentlocationpopulationdeaths_per_case
10225AUT2020-02-252.0NaNNaNNaNEuropeAustria8922082.0NaN
10226AUT2020-02-26NaNNaNNaNNaNEuropeAustria8922082.0NaN
10227AUT2020-02-270.0NaNNaNNaNEuropeAustria8922082.0NaN
10228AUT2020-02-280.0NaNNaNNaNEuropeAustria8922082.0NaN
10229AUT2020-02-292.0NaNNaNNaNEuropeAustria8922082.0NaN
.................................
170491SWE2022-09-03NaNNaNNaNNaNEuropeSweden10467097.0NaN
170492SWE2022-09-04NaNNaNNaNNaNEuropeSweden10467097.0NaN
170493SWE2022-09-05NaNNaNNaNNaNEuropeSweden10467097.0NaN
170494SWE2022-09-06NaNNaNNaNNaNEuropeSweden10467097.0NaN
170495SWE2022-09-07NaNNaNNaNNaNEuropeSweden10467097.0NaN
\n", + "

8415 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " iso_code date new_cases new_deaths icu_patients \\\n", + "10225 AUT 2020-02-25 2.0 NaN NaN \n", + "10226 AUT 2020-02-26 NaN NaN NaN \n", + "10227 AUT 2020-02-27 0.0 NaN NaN \n", + "10228 AUT 2020-02-28 0.0 NaN NaN \n", + "10229 AUT 2020-02-29 2.0 NaN NaN \n", + "... ... ... ... ... ... \n", + "170491 SWE 2022-09-03 NaN NaN NaN \n", + "170492 SWE 2022-09-04 NaN NaN NaN \n", + "170493 SWE 2022-09-05 NaN NaN NaN \n", + "170494 SWE 2022-09-06 NaN NaN NaN \n", + "170495 SWE 2022-09-07 NaN NaN NaN \n", + "\n", + " hosp_patients continent location population deaths_per_case \n", + "10225 NaN Europe Austria 8922082.0 NaN \n", + "10226 NaN Europe Austria 8922082.0 NaN \n", + "10227 NaN Europe Austria 8922082.0 NaN \n", + "10228 NaN Europe Austria 8922082.0 NaN \n", + "10229 NaN Europe Austria 8922082.0 NaN \n", + "... ... ... ... ... ... \n", + "170491 NaN Europe Sweden 10467097.0 NaN \n", + "170492 NaN Europe Sweden 10467097.0 NaN \n", + "170493 NaN Europe Sweden 10467097.0 NaN \n", + "170494 NaN Europe Sweden 10467097.0 NaN \n", + "170495 NaN Europe Sweden 10467097.0 NaN \n", + "\n", + "[8415 rows x 10 columns]" + ] + }, + "execution_count": 156, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "interesting_countries = ['Austria', 'Poland', 'Germany', 'Czechia', 'Slovakia', 'Hungary', 'France', 'Denmark', 'Sweden']\n", + "\n", + "some_countries = covid[covid.location.isin(interesting_countries)]\n", + "some_countries['deaths_per_case'] = some_countries.new_deaths/some_countries.new_cases\n", + "some_countries" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "## Merging and joing datasets\n", + "\n", + "https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html\n", + "\n", + "* `pd.concat` - alignment (along index or columns)\n", + "* `pd.merge` - combining data (along columns, by values)\n", + " * `df.join` - merge on index\n", + "\n", + "\n", + "### Concatenate\n", + "![concatenate](./img/concatenate.png)\n", + "\n", + "### Merge\n", + "![merge](./img/merge.png)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* good to know when working with TS: `merge_as_of`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "## Rolling object" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665957850171 + }, + "jupyter": { + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = czech_cases.plot(label='original',**plotconfig,legend=True)\n", + "czech_cases.rolling(3).mean().plot(label='3 days rolling',ax=ax,legend=True)\n", + "czech_cases.rolling(5).mean().plot(label='5 days rolling',ax=ax,legend=True)\n", + "czech_cases.rolling(10).mean().plot(label='10 days rolling',ax=ax,legend=True)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.7" + }, + "microsoft": { + "host": { + "AzureML": { + "notebookHasBeenCompleted": true + } + } + }, + "nteract": { + "version": "nteract-front-end@1.0.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/05_AdvancedPandas/data/covid.zip b/05_AdvancedPandas/data/covid.zip new file mode 100644 index 0000000..f2c6452 Binary files /dev/null and b/05_AdvancedPandas/data/covid.zip differ diff --git a/05_AdvancedPandas/img/concatenate.png b/05_AdvancedPandas/img/concatenate.png new file mode 100644 index 0000000..647bc24 Binary files /dev/null and b/05_AdvancedPandas/img/concatenate.png differ diff --git a/05_AdvancedPandas/img/merge.png b/05_AdvancedPandas/img/merge.png new file mode 100644 index 0000000..969e705 Binary files /dev/null and b/05_AdvancedPandas/img/merge.png differ diff --git a/06_HTML_XML_JSON.ipynb b/06_HTML_XML_JSON.ipynb new file mode 100644 index 0000000..5e737cb --- /dev/null +++ b/06_HTML_XML_JSON.ipynb @@ -0,0 +1,1358 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import requests\n", + "import json\n", + "\n", + "import os\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "from bs4 import BeautifulSoup\n", + "\n", + "import boto3\n", + "\n", + "from IPython.core.display import HTML, display" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#quick warmup\n", + "\n", + "# imagine a key-value data container\n", + "inventory = {\n", + " \"001\": {\"name\": \"Milk\", \"quantity\": 34, \"price\": 1.99},\n", + " \"002\": {\"name\": \"Bread\", \"quantity\": 20, \"price\": 2.49},\n", + " \"003\": {\"name\": \"Nutella\", \"quantity\": 5, \"price\": 2.49},\n", + " }\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#How would a store manager find out how much is milk\n", + "\n", + "#How do we add new item? What do we need to do?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "name_to_item = {}\n", + "for item_id, details in inventory.items():\n", + " # Here we are assuming product names are unique\n", + " name_to_item[details[\"name\"]] = {\"id\": item_id, \"quantity\": details[\"quantity\"], \"price\": details[\"price\"]}\n", + "\n", + "print(name_to_item)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lecture 05 - JSON, XML, HTML, Requests and APIs\n", + "by Jan Šíla, Vítek Macháček
\n", + "November 7, 2023\n", + "\n", + "### Contents\n", + "\n", + "* Standardized data representation\n", + "* JSON\n", + "* XML\n", + "* Introduction to BeautifulSoup\n", + "* Basics of HTML (+ Element Inspection)\n", + "* Introduction to Requests (GET vs. POST) and APIs\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Goals:\n", + " \n", + "* work with data online/real-time data\n", + "* acquisition, processing - > results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Date exchange formats - JSON, XML\n", + "\n", + "`Language of the internet`\n", + "\n", + "* You can send/receive a message with (almost) any service\n", + "\n", + "* send .docx -> what if I do not have MS Word?\n", + "* we need a simple data format which would work on any machine (system agnostic), is general (can write anything) and is ediatable in basic editors\n", + "\n", + "\n", + "\n", + "* More complex than simple tables\n", + "* Highly structured - if you dont follow the rules, you are out\n", + "* Both sides need to understand the structure\n", + "* only data. It does not do anything - no code to be run (security measure)\n", + "* programming language/machine agnostic\n", + "* distributed as text/string (to be precise as `bytes` literals) \n", + "* parsed to objects - easy to work with straight away\n", + "* Can be persisted as special files, or some data streams from APIs. \n", + "* Human readable\n", + "* Hierarchical\n", + "* Can be fetched using standard web APIs\n", + "\n", + "### Purpose\n", + "\n", + "1. Communication \n", + " * All imaginable communication channels\n", + " * Applications within single server/machine\n", + " * Only transferring of data\n", + " * Both sides need to understand the structure\n", + "\n", + "2. Storing\n", + " * self-descriptive\n", + " * human readable\n", + " * also in DBs - SQL, MongoDB etc.\n", + "\n", + "3. Standardization\n", + " * predictability\n", + " * cooperation\n", + " * spillovers from standardization\n", + "\n", + "\n", + "### Dimensionality problem\n", + "\n", + "* rich information comes at costs of data complexity \n", + "* to interrelate information, you need to high dimensionality (or A LOT of columns) or declaratory formats such as protobuf\n", + "* Strongly object-oriented\n", + "\n", + "\n", + "### 1D:\n", + "* logs\n", + "\n", + "### 2D: CSVs\n", + "* tabular data (like pandas DFs)\n", + "\n", + "### 3+D:\n", + "#### XML\n", + "* eXtensible Markup Language is a software- and hardware-independent tool for storing and transporting data.\n", + "* Officialy defined at 1998, but its roots are even older.\n", + "* XML was designed to carry data - with focus on what data is\n", + "* HTML was designed to display data - with focus on what data should look like displayed \n", + "* XML tags are not predefined like HTML tags are\n", + "* more verbose than JSON\n", + "* can have comments !actually a really cool in useful feature!\n", + "* used historically as a transaction format in many areas: \n", + " * Scientific measurements\n", + " * News information\n", + " * Wheather measurements\n", + " * Financial transactions\n", + "* Necessary to use XML parser to use in Python or in JavaScript\n", + "\n", + "\n", + "### JSON\n", + "* JavaScript Object Notation\n", + "* often *.json* files\n", + "* but also used in the web etc.\n", + "* supports standard datatypes - strings, integers, floats, lists\n", + "* No comments\n", + "* More compact, less verbose\n", + "* No closing tags\n", + "* Used EVERYWHERE, BUT [NOT LICENSED FOR EVIL](https://www.json.org/license.html). If you want to do evil stuff, use XML instead.\n", + "* Native in JavaScript and close to native in Python (dictionary)\n", + "* Jupyter Notebooks\n", + "\n", + "\n", + "* common pitfals: properly formatted JSON is different to python dict. -> check: https://jsonlint.com/\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# JSON" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# general representation of a dictionary\n", + "# emphasis on accessibility -> key-value ( hash table )\n", + "# contains records, lists, or other dictionaries\n", + "\n", + "teachers = [\n", + " {'name':'Jozef Baruník','titles':['doc.','PhDr.','Ph.D.','Bc.','Mgr.'],'ID':1234,'courses':['JEM005','JEM116','JEM059','JEM061']},\n", + " {'name':'Martin Hronec','titles':['Bc.','Mgr.'],'ID':3421,'courses':['JEM005','JEM207']},\n", + "]\n", + "\n", + "courses = {\n", + " \"JEM005\":{'name':'Advanced Econometrics','ECTS':6,'teachers':[3421,1234]},\n", + " 'JEM207':{'name':'Data Processing in Python','ECTS':5,'teachers':[3421]},\n", + " 'JEM116':{'name':'Applied Econometrics','ECTS':6,'teachers':[1234]},\n", + " 'JEM059':{'name':'Quantitative Finance I.','ECTS':6,'teachers':[1234,5678]},\n", + " 'JEM061':{'name':'Quantitative Finance II.','ECTS':6,'teachers':[1234,5678]}\n", + "}\n", + "jsondata = {'teachers':teachers,'courses':courses}\n", + "jsondata" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "is this a valid JSON?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "https://jsonformatter.curiousconcept.com/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![python and JSON](./img/python_json.png)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "jsondata" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "js = json.dumps(\n", + " jsondata\n", + ") #json formatted string!\n", + "\n", + "isinstance(js,str)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "jsondata" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "jsondata['courses']['JEM005']['test']='test'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pd.DataFrame(jsondata['courses']).transpose()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dfc = pd.read_json(json.dumps(jsondata['courses']),orient='index')\n", + "dfc" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# lets come back to this a little later" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# eXtensible Markup Language (XML)\n", + "\n", + "* elements\n", + "* attributes\n", + "* tags\n", + "\n", + "### Tag\n", + "> <>\n", + "\n", + "### Element" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Convert to python data-types" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#either\n", + "'''content'''\n", + "\n", + "#or self-closing (no content)\n", + "'''''';\n", + "#
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Attributes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "'''''';" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![XML tree structure](./img/xml_tree_structure.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```xml\n", + "\n", + " \n", + " Everyday Italian\n", + " AAaAA\n", + " Giada De Laurentis\n", + " 2005\n", + " 30.00\n", + " \n", + "\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```json\n", + "{\n", + " \"bookstore\":[\n", + " {\n", + " \"title\":\"Everyday Italian\",\n", + " \"lang\":\"ENG\",\n", + " \"author\":\"Giada de Laurentis\",\n", + " \"year\":2005,\n", + " \"price\":30\n", + " }\n", + " ]\n", + "}\n", + "```\n", + "\n", + "\n", + "Takeaway: JSON and XML are not equivalents and cannot be freely mirrored. Unfortunately.\n", + "\n", + "JSON cannot have multiple tags with different properties ->title_en, title_cze perhaps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Navigation\n", + "* Xpath\n", + "* CSS selectors \n", + "* **BeautifulSoup**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BeatifulSoup in detail\n", + "each BS object represents\n", + "* an element\n", + "* the position in tree" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "'''string on more \n", + "nes '''" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "xml = '''\n", + "\n", + "\n", + " \n", + " \n", + " 3421\n", + " 1234\n", + " \n", + " \n", + " 3421\n", + " \n", + " \n", + " 1234\n", + " \n", + " \n", + " 1234\n", + " 5678\n", + " \n", + " \n", + " 1234\n", + " 5678\n", + " \n", + " \n", + " \n", + " \n", + " Martin Hronec\n", + " \n", + " \n", + " Jozef Baruník\n", + " \n", + " \n", + " Lukáš Vácha\n", + " \n", + " \n", + "\n", + "'''\n", + "\n", + "#unlike HTML, those tag names are defined by Vitek - no one else 'can' understand them -> flexibility is limited. But same issue with JSON to be fair\n", + "\n", + "soup = BeautifulSoup(xml)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dir(soup)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```find()``` will find a **first** element given the input\n", + "\n", + "```find_all()``` or ```findAll()``` finds a **all** elements given the input" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "soup.find_all('course')[0].find('teacher-id')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "jem059 = soup.find('course',{'id':'JEM059'}) #looking for a tag with attrbitues (optional)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "jem059" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "soup.findAll('teacher-id')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`soup['attr']` will return the value of attribute" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(jem059['ects'])\n", + "print(jem059['name'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "soup.findAll('teacher-id')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "jem059" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "you can also navigate horizontally" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "jem059.findNext('course').findNext('course')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "jem059.findPrevious('course').findPrevious('course')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "and even upstream!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "jem059.parent.parent" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#get all teacher ids\n", + "teacher_ids = [int(t.text) for t in soup.findAll('teacher-id')]\n", + "print(teacher_ids)\n", + "#get unique\n", + "set(teacher_ids)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "course = soup.find('course')\n", + "d = {\n", + " 'id':course['id'],\n", + " 'name':course['name'],\n", + " 'ects':course['ects'],\n", + " 'teachers':[int(t.text) for t in course.findAll('teacher-id')]\n", + "}\n", + "d" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Can convert to JSON-like" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "l = []\n", + "for course in soup.findAll('course'):\n", + " d = {'id':course['id'],\n", + " 'name':course['name'],\n", + " 'ects':course['ects'],\n", + " 'teachers':[int(t.text) for t in course.findAll('teacher-id')]}\n", + " l.append(d)\n", + "l" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Or in list-comprehension syntax" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "l = [{\n", + " 'id':course['id'],\n", + " 'name':course['name'],\n", + " 'ects':course['ects'],\n", + " 'teachers':[int(t.text) for t in course.findAll('teacher-id')]\n", + "} for course in soup.findAll('course')]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "l" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pd.DataFrame(l)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# HTML\n", + "standard web-page consists of:\n", + "\n", + "* Browser-executed code (`front-end`)\n", + " * HTML \"DOM\" structure - the website content\n", + " * List of elements that are on website\n", + " * Links to CSS classes, ids and\n", + " * CSS stylesheets - website graphics\n", + " * JavaScripts - website interactivity \n", + "\n", + "* Server-executed (`back-end`)\n", + " * Server, database, app logic etc.\n", + " * Not available for scraping!\n", + " * May be available as API\n", + "\n", + "\n", + "## Web-scraping\n", + "* client side only\n", + "* Navigating HTML DOM by taking advantage of CSS structure\n", + "\n", + "## DOM (Document Object Module):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "html = '''\n", + "\n", + " \n", + " Sample page\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " My page header\n", + "
\n", + "
\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
namenumber
B2
C3
\n", + "
\n", + "
\n", + " \n", + " \n", + "\n", + "'''\n", + "display(HTML(html))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "soup = BeautifulSoup(html,'html')\n", + "soup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rows = soup.findAll('tr',{'class','normalRow'})\n", + "rows" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "d = {}\n", + "\n", + "for row in rows:\n", + " key = row.findAll('td')[0].text\n", + " val = int(row.findAll('td')[1].text)\n", + " d[key] = val\n", + "pd.Series(d)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "d" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pd.Series({\n", + " row.findAll('td')[0].text:int(row.findAll('td')[1].text) \n", + " for row in BeautifulSoup(html).findAll('tr',{'class':'normalRow'})})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "soup = BeautifulSoup(html)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "row = soup.findAll('tr',{'class':'normalRow'})[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "row" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "row.findAll('td')[0].text" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "int(row.findAll('td')[1].text)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "{row.findAll('td')[0].text:int(row.findAll('td')[1].text) for row in soup.findAll('tr',{'class':'normalRow'})}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## HTML Inspection\n", + "http://ies.fsv.cuni.cz/cs/node/51" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import requests" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# requests and internet communication\n", + "\n", + "* `Client` asks/requests questions (your Jupyter client)\n", + "* `Server` replies/serve answers (your Jupyter server)\n", + "\n", + "\n", + "API = *Application Programming Interface*\n", + "\n", + "very general term! Not only used in web communication\n", + "\n", + "## HTTP requests\n", + "\n", + "A most standard webserver communication channel around\n", + "\n", + "A standard HTTP request contains:\n", + "\n", + "* URL \n", + "\n", + " * domain\n", + " * route\n", + " * parameters\n", + "\n", + "* Request Type - GET, POST, PUT, DELETE (see below)\n", + "\n", + "* Content specification - \n", + " * Application/JSON\n", + " * Application/XML\n", + " * text/html\n", + " * text/css\n", + "\n", + "* Content\n", + "\n", + "* Outcoming data (will see below)\n", + "\n", + "* Cookies \n", + "\n", + "* Status Code:\n", + "\n", + " * 200 - success\n", + " * 404 - resource does not exist\n", + " * 500 - the server failed during processing your request\n", + "\n", + "\n", + "1) REST API - use HTTP request and returns JSON\n", + "\n", + "2) SOAP API - use HTTP request and returns XML\n", + "\n", + "3) Website - use HTTP request and returns set of HTML, JavaScript, CSS and other files\n", + "\n", + "### When to use?\n", + "* whenever more applications need to communicate\n", + "* user-friendly interface for complicated tasks - DEEP AI, Google Maps\n", + "* Data - Golemio, OpenStreetMaps\n", + "\n", + "### GET request\n", + "* fast\n", + "* public\n", + "* data flow only one direction\n", + "* parameters via request adress\n", + "\n", + "> https://www.google.com/search?q=how+to+understand+url+parameters&rlz=1C1GCEU_csCZ860CZ860&oq=how+to+understand+url+parameters&aqs=chrome..69i57j33i22i29i30l7.5237j0j4&sourceid=chrome&ie=UTF-8\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "r = requests.get('https://cs.wikipedia.org/wiki/Institut_ekonomick%C3%BDch_studi%C3%AD_Fakulty_soci%C3%A1ln%C3%ADch_v%C4%9Bd_Univerzity_Karlovy')\n", + "#plain request - like browser\n", + "r.text" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "soup = BeautifulSoup(r.text,'html')\n", + "tags=soup.findAll('span', {'class':\"wd\"})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tags" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### POST request\n", + "* slow\n", + "* private\n", + "* both sides can send data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Static pages x Dynamic pages x JavaScript-rendered pages\n", + "\n", + "### Static\n", + "\n", + "* pages that do not get updated instantly\n", + "* all information necessary for rendering a website is available after entering the URL\n", + "* It may ask the database, but the output is stable.\n", + "* all parameters within the adress!\n", + "* Typical example:\n", + " \n", + "### JavaScript rendered: \n", + "* Defacto static, but you cannot take advantage of HTML/CSS structure\n", + "\n", + "### Dynamic content\n", + "* webpage instantly communicates with the webserver and the database\n", + "* \n", + "* solution -> Selenium!\n", + "\n", + "### Is this website static or dynamic?\n", + "\n", + "1. Facebook\n", + "2. Sreality.cz\n", + "3. IES website\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How to chose data source for project\n", + "\n", + "You need to know in advance what data you will download:\n", + "\n", + "1. full or satisfactory access to API\n", + "2. the web-page is parsable (prefer not too much javascript)\n", + "3. plan to generate all requests" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# APIs Example\n", + "### Get wiki data using GET" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#if time, return to geodata" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lets start with a basic request" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "api_url = 'https://krcgc3uqga.execute-api.eu-central-1.amazonaws.com'\n", + "#this api implements three routers\n", + "# GET /time\n", + "# GET /stocks\n", + "# POST /hashme" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "route = 'time'\n", + "# route /ruːt/\n", + "response = requests.get(f'{api_url}/{route}')\n", + "response.json()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# route = stocks\n", + "\n", + "route = 'stocks'\n", + "# route /ruːt/\n", + "response = requests.get(f'{api_url}/{route}')\n", + "print(response.json())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "route = \"hashme\"\n", + "url = f\"https://krcgc3uqga.execute-api.eu-central-1.amazonaws.com/{route}\"\n", + "\n", + "payload = json.dumps({\n", + " \"name\": \"Jan Sila\"\n", + "})\n", + "headers = {\n", + " 'Content-Type': 'application/json'\n", + "}\n", + "\n", + "response = requests.post(url, headers=headers, data=payload)\n", + "\n", + "print(response.json())\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "response = requests.get('https://en.wikipedia.org/wiki/Charles_University')\n", + "soup = BeautifulSoup(response.text)\n", + "div = soup.find('div',{'id':'mw-content-text'}) # #mw-content-text > div > p:nth-child(10)texts)\n", + "article = ' '.join([p.text for p in div.find_all('p')])\n", + "print(article)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bonus example:\n", + "\n", + "## GeoJSON\n", + "\n", + "* One standardized data format for transferring geodata\n", + "* Plenty of geodata out there\n", + "* see for example http://opendata.iprpraha.cz/CUR/OVZ/OVZ_Klima_ZnecOvzdusi_p/WGS_84/OVZ_Klima_ZnecOvzdusi_p.json\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "verbose_request = requests.get('http://opendata.iprpraha.cz/CUR/OVZ/OVZ_Klima_ZnecOvzdusi_p/WGS_84/OVZ_Klima_ZnecOvzdusi_p.json')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "print(verbose_request.status_code)\n", + "dir(verbose_request)\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "verbose_request.json()\n", + "\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "#get already json\n", + "d = requests.get('http://opendata.iprpraha.cz/CUR/OVZ/OVZ_Klima_ZnecOvzdusi_p/WGS_84/OVZ_Klima_ZnecOvzdusi_p.json').json()\n", + "\n", + "d['features'][0]['properties']" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "import branca\n", + "import folium\n", + "\n", + "colorscale = branca.colormap.linear.YlOrRd_09.scale(0, 5)\n", + "\n", + "def style_function(feature):\n", + " gridvalue = feature['properties']['GRIDVALUE']\n", + " return {\n", + " 'fillOpacity': 0.5,\n", + " 'weight': 0,\n", + " 'fillColor': colorscale(gridvalue)\n", + " }\n", + "\n", + "m = folium.Map(location=[50.085,14.45],zoom_start=11)\n", + "folium.GeoJson('http://opendata.iprpraha.cz/CUR/OVZ/OVZ_Klima_ZnecOvzdusi_p/WGS_84/OVZ_Klima_ZnecOvzdusi_p.json',style_function=style_function).add_to(m)\n", + "m" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/07_Algorithmic problem solving.ipynb b/07_Algorithmic problem solving.ipynb new file mode 100644 index 0000000..b9f7994 --- /dev/null +++ b/07_Algorithmic problem solving.ipynb @@ -0,0 +1,375 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "05d4d58b-d56b-44f3-8d52-fc798723172b", + "metadata": {}, + "source": [ + "## Lecture 7 - Algorithmic problem solving" + ] + }, + { + "cell_type": "markdown", + "id": "c599074a-8b96-41ac-9404-2981b9a2f691", + "metadata": {}, + "source": [ + "# Goals:\n", + "- Algorithms are designed to solve computational problems.\n", + "- Communicate the way of solving, searching for correct (prove) and efficient solutions.\n", + "\n", + "# Problem:\n", + "- Set of inputs -> space of outputs (m -> n) mapping.\n", + "- Binary relation between inputs and outputs, not necessarily strictly bijection (one-to-one).\n", + "- Specify a predicate: Are there two people with the same birthday in the class today?\n", + "- Mathematic problems - Scientific computing:\n", + " - What is the derivative of x^2 at x=1?\n", + "- The algorithm should be general enough to apply to, say, C2 functions.\n", + " - Apply to arbitrarily large inputs.\n", + "\n", + "# Algorithm:\n", + "- Procedure (function) generating outputs, ideally correct outputs.\n", + "- m -> 1\n", + "\n", + "# Question:\n", + "- Devise an algorithm that finds out if two people have the same birthday.\n", + "\n", + "\n", + "# Efficiency, complexity:\n", + "- Abstract sense, not seconds or hours\n", + "- how many fundamental operations can algorithm do over real time\n", + "- dont measure time, count ops (operations) -> asymptotic analysis\n", + "- does not even need to be connected to the implementation - certain tasks has certain efficiency\n", + "- exact performance depends on size of the input space (birthday problem for 10 or 1000 people in class)\n", + "- most efficient O(1) access of item in dictionary\n", + "- least efficient O(n!)\n", + " - Traveling Salesman Problem (**TSP**): The TSP involves finding the shortest possible route that visits a set of cities and returns to the origin city. A brute force approach, which tries every possible permutation to find the shortest tour, has a time complexity of O(n!).\n", + "\n", + "# For Us:\n", + "- Breaking down complex problems into smaller, manageable parts.\n", + "- Pieces should be clearly defined and well-tested (later in your coding life).\n", + "- Efficient use of data structures.\n", + "- We use only built-in libraries today.\n" + ] + }, + { + "cell_type": "markdown", + "id": "a20dde5e-0202-40de-8ecf-3a14b98748aa", + "metadata": {}, + "source": [ + "### warm up task\n", + "\n", + "- find a maximum value in a list\n", + "- what steps would you take?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "91aa260f-55b9-4d8e-ae7b-1d2af7a6bb7e", + "metadata": {}, + "outputs": [], + "source": [ + "def find_maximum(arr):\n", + " pass\n", + "\n", + "# Example usage\n", + "arr = [3, 6, 2, 8, 4]\n", + "print(f\"Maximum number in the array is: {find_maximum(arr)}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "99926678-b66d-497c-aca8-4d9993246af2", + "metadata": {}, + "outputs": [], + "source": [ + "# give an array of arbitrary length, find on what position lies a value\n", + "# assume array is of integers\n", + "# if value is not found, return None" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "533961bd-a01e-40a1-af09-af713065c766", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Element found at index: 1\n" + ] + } + ], + "source": [ + "def linear_search(arr, x):\n", + " pass\n", + "# Example usage\n", + "arr = [3, 4, 1, 7, 9]\n", + "x = 4\n", + "print(f\"Element found at index: {linear_search(arr, x)}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "e0d11799-fc5b-495e-8130-ee1c4c7bb387", + "metadata": {}, + "source": [ + "## Linear Search\n", + "\n", + "### Time Complexity\n", + "- **O(n)**, where `n` is the number of elements in the list.\n", + "\n", + "### Performance Characteristics\n", + "- **Worst-Case Scenario**: The element is at the end or not present, requiring `n` comparisons.\n", + "- **Average-Case Scenario**: On average, `n/2` comparisons are made.\n", + "\n", + "### Efficiency\n", + "- Linear search is less efficient for larger datasets, with performance degrading linearly with the size of the data.\n", + "\n", + "## Question:\n", + "- What is the complexity of searching in `m x n` matrix?\n", + "\n", + "## Can we devise a better algorithm?" + ] + }, + { + "cell_type": "markdown", + "id": "edebf315-f531-4776-b9db-d124526dd8a9", + "metadata": {}, + "source": [ + "## Binary Search" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "7746271a-cab9-4199-98d3-9db30d254c1c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Element found at index: 0\n" + ] + } + ], + "source": [ + "def binary_search(arr, x):\n", + " pass\n", + "# Example usage\n", + "arr = [1, 3, 5, 7, 9]\n", + "x = 1\n", + "print(f\"Element found at index: {binary_search(arr, x)}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "08a296b4-c0d4-469b-af51-808c3f013314", + "metadata": {}, + "source": [ + "\n", + "\n", + "### Time Complexity\n", + "- **O(log n)**, assuming the data is sorted.\n", + " - How would we achieve this in real life?\n", + "\n", + "### Performance Characteristics\n", + "- **Worst-Case and Average-Case Scenario**: The search space is halved with each step, significantly reducing the number of comparisons.\n", + "\n", + "### Efficiency\n", + "- Significantly more efficient than linear search for large, sorted datasets. Efficiency increases as the dataset size grows.\n", + "\n", + "## Comparison\n", + "\n", + "- **Dataset Size Dependency**: Linear search's performance is proportional to the dataset size, while binary search's performance is logarithmically proportional.\n", + "- **Precondition**: Binary search requires sorted data, unlike linear search.\n", + "- **Practical Implications**: For small datasets, the speed difference might be negligible. However, for large datasets, especially if sorted, binary search is exponentially faster than linear search." + ] + }, + { + "cell_type": "markdown", + "id": "f79e199b-2f0d-43f7-bab4-65c26eb3f031", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "e1f78c59-be1a-4559-8a3d-4e4b09460583", + "metadata": {}, + "source": [ + "### Find sum of all subarrays of size k in an array" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "20389694-c8ba-4c11-a691-66bd2675baca", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[13, 20, 24, 28]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def double_iteration(arr:list[int], k:int)->list[int]:\n", + " pass\n", + "arr = [1,5,7,8,9,11]\n", + "double_iteration(arr, 3) #O(n*k)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "e6b6e17f-e862-48a9-9df5-4bedc2ca54cb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.2 ms ± 588 µs per loop (mean ± std. dev. of 10 runs, 10 loops each)\n" + ] + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "71036d24-e149-4b27-becd-4f28a529664e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[13, 20, 24, 28]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def fixed_sliding_window(arr:list[int], k:int)->list[int]:\n", + " pass\n", + "\n", + "arr = [1,5,7,8,9,11] \n", + "fixed_sliding_window(arr, 3)#O(n)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "2d0ffb86-4760-4464-a88c-41599bfad16e", + "metadata": {}, + "outputs": [], + "source": [ + "#lets compare the algorithms\n", + "import random\n", + "\n", + "arr = [random.randint(0, 10000) for _ in range(1000)]\n", + "k = 120" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "273e4662-8129-46fa-9e8c-7d03c0c656e5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "21.4 ms ± 2.77 ms per loop (mean ± std. dev. of 10 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%timeit -r 10 -n 10 double_iteration(arr, k)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "fd06c4e2-52fc-4877-a9d0-54723b6887d5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "608 µs ± 60.7 µs per loop (mean ± std. dev. of 10 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%timeit -r 10 -n 10 fixed_sliding_window(arr, k)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "92e15508-e51d-4dc0-8801-a823910c9bf8", + "metadata": {}, + "outputs": [], + "source": [ + "## imagine sliding windows with more complicated functions (mean, meadian) \n", + "# create a structure which adds -> removes elements from the end and start of the window\n", + "# idea is to always keep one pass through the data " + ] + }, + { + "cell_type": "markdown", + "id": "42a6c6ea-e586-4002-b817-3866ee8c0836", + "metadata": {}, + "source": [ + "# lets get back to data processing level a bit\n", + "## Lets try to make a small project of putting together a dataset of Tarantino movies from wikipedia" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "id": "1abb1df0-1e3a-4b54-a944-3bcac8164888", + "metadata": {}, + "outputs": [], + "source": [ + "#lets prepare step by step what we will do" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/07_DataScience_intro/07_ds_intro.ipynb b/07_DataScience_intro/07_ds_intro.ipynb new file mode 100644 index 0000000..1ecb9b2 --- /dev/null +++ b/07_DataScience_intro/07_ds_intro.ipynb @@ -0,0 +1,2138 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## L7 [Data Science Introduction](#ds)\n", + "\n", + "Martin Hronec" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Python as a \"go-to\" language for data science (DS) / machine learning because of its (open-source) libraries\n", + "* we've seen pandas for powerful data manipulation and numpy for numerical computations, but this is far from what Python has to offer for DS\n", + "* we need tools for typical DS tasks such as **Regression**, **Classification**, **Clustering**\n", + "\n", + "* to name a few data-science libraries (in no particular order)\n", + " * [scikit-learn]()\n", + " * supervised vs. unsupervised learning\n", + " * validation \n", + " * evaluation\n", + " * CPU optimized\n", + "\n", + " * [scipy]()\n", + " * optimization algorithms\n", + " * statistics\n", + " * fourier transforms\n", + "\n", + " * [torch]() / [tensorflow]() (+ [keras]())\n", + " * neural networks\n", + " * GPU optimized \n", + " * state-of-the-art for NLP/vision/...\n", + " * [lightgbm]() / [xgboost]() / [catboost]()\n", + " * tree methods\n", + " * state-of-the-art for tabular datasets\n", + "\n", + "* we will look at scikit-learn and lightgbm using some [toy datasets](https://scikit-learn.org/stable/datasets/toy_dataset.html)\n", + "\n", + "* we will not focus on the algorithmic/estimation part of the models but on the programming side\n", + "\n", + "* [mlflow](https://mlflow.org/) is a great platform which can help you in different parts of your ML/DS lifecycle, e.g.\n", + " * tracking experiments\n", + " * package DS projects\n", + " * register and deploy trained models\n", + "\n", + "* [kaggle](https://www.kaggle.com/) great resource to see the state-of-the-art approaches and methods to different problems\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import sklearn \n", + "import sklearn.datasets\n", + "from typing import List, Dict, Union\n", + "\n", + "import lightgbm\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Scikit-learn \n", + "\n", + "* contains implementations of a number of ML algorithms - called **estimators**\n", + "* estimators typically have a standardized method names / attributes - e.g. `.fit()` and `.predict()` methods\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Regression (California housing)\n", + "\n", + "* was going to use [boston housing prices dataset](), which is a standard but apparently there are some [ethical concerns]()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\Martin Hronec\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\n", + "\n", + " The Boston housing prices dataset has an ethical problem. You can refer to\n", + " the documentation of this function for further details.\n", + "\n", + " The scikit-learn maintainers therefore strongly discourage the use of this\n", + " dataset unless the purpose of the code is to study and educate about\n", + " ethical issues in data science and machine learning.\n", + "\n", + " In this special case, you can fetch the dataset from the original\n", + " source::\n", + "\n", + " import pandas as pd\n", + " import numpy as np\n", + "\n", + " data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n", + " raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n", + " data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n", + " target = raw_df.values[1::2, 2]\n", + "\n", + " Alternative datasets include the California housing dataset (i.e.\n", + " :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n", + " dataset. You can load the datasets as follows::\n", + "\n", + " from sklearn.datasets import fetch_california_housing\n", + " housing = fetch_california_housing()\n", + "\n", + " for the California housing dataset and::\n", + "\n", + " from sklearn.datasets import fetch_openml\n", + " housing = fetch_openml(name=\"house_prices\", as_frame=True)\n", + "\n", + " for the Ames housing dataset.\n", + " warnings.warn(msg, category=FutureWarning)\n" + ] + } + ], + "source": [ + "sklearn.datasets.load_boston()['DESCR'];" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* we will use California house prices dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['data', 'target', 'frame', 'target_names', 'feature_names', 'DESCR'])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sklearn.datasets.fetch_california_housing().keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data_raw = sklearn.datasets.fetch_california_housing()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".. _california_housing_dataset:\n", + "\n", + "California Housing dataset\n", + "--------------------------\n", + "\n", + "**Data Set Characteristics:**\n", + "\n", + " :Number of Instances: 20640\n", + "\n", + " :Number of Attributes: 8 numeric, predictive attributes and the target\n", + "\n", + " :Attribute Information:\n", + " - MedInc median income in block group\n", + " - HouseAge median house age in block group\n", + " - AveRooms average number of rooms per household\n", + " - AveBedrms average number of bedrooms per household\n", + " - Population block group population\n", + " - AveOccup average number of household members\n", + " - Latitude block group latitude\n", + " - Longitude block group longitude\n", + "\n", + " :Missing Attribute Values: None\n", + "\n", + "This dataset was obtained from the StatLib repository.\n", + "https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html\n", + "\n", + "The target variable is the median house value for California districts,\n", + "expressed in hundreds of thousands of dollars ($100,000).\n", + "\n", + "This dataset was derived from the 1990 U.S. census, using one row per census\n", + "block group. A block group is the smallest geographical unit for which the U.S.\n", + "Census Bureau publishes sample data (a block group typically has a population\n", + "of 600 to 3,000 people).\n", + "\n", + "An household is a group of people residing within a home. Since the average\n", + "number of rooms and bedrooms in this dataset are provided per household, these\n", + "columns may take surpinsingly large values for block groups with few households\n", + "and many empty houses, such as vacation resorts.\n", + "\n", + "It can be downloaded/loaded using the\n", + ":func:`sklearn.datasets.fetch_california_housing` function.\n", + "\n", + ".. topic:: References\n", + "\n", + " - Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,\n", + " Statistics and Probability Letters, 33 (1997) 291-297\n", + "\n" + ] + } + ], + "source": [ + "print(data_raw['DESCR'])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "label = pd.Series(data_raw['target'], name = data_raw['target_names'][0])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(data_raw['data'], columns = data_raw['feature_names'])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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MedIncHouseAgeAveRoomsAveBedrmsPopulationAveOccupLatitudeLongitude
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" + ], + "text/plain": [ + " MedInc HouseAge AveRooms AveBedrms Population AveOccup Latitude \\\n", + "0 8.3252 41.0 6.984127 1.023810 322.0 2.555556 37.88 \n", + "1 8.3014 21.0 6.238137 0.971880 2401.0 2.109842 37.86 \n", + "2 7.2574 52.0 8.288136 1.073446 496.0 2.802260 37.85 \n", + "3 5.6431 52.0 5.817352 1.073059 558.0 2.547945 37.85 \n", + "4 3.8462 52.0 6.281853 1.081081 565.0 2.181467 37.85 \n", + "\n", + " Longitude \n", + "0 -122.23 \n", + "1 -122.22 \n", + "2 -122.24 \n", + "3 -122.25 \n", + "4 -122.25 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "MedInc 0.0\n", + "HouseAge 0.0\n", + "AveRooms 0.0\n", + "AveBedrms 0.0\n", + "Population 0.0\n", + "AveOccup 0.0\n", + "Latitude 0.0\n", + "Longitude 0.0\n", + "dtype: float64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isnull().mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " MedInc HouseAge AveRooms AveBedrms Population \\\n", + "count 20640.000000 20640.000000 20640.000000 20640.000000 20640.000000 \n", + "mean 3.870671 28.639486 5.429000 1.096675 1425.476744 \n", + "std 1.899822 12.585558 2.474173 0.473911 1132.462122 \n", + "min 0.499900 1.000000 0.846154 0.333333 3.000000 \n", + "25% 2.563400 18.000000 4.440716 1.006079 787.000000 \n", + "50% 3.534800 29.000000 5.229129 1.048780 1166.000000 \n", + "75% 4.743250 37.000000 6.052381 1.099526 1725.000000 \n", + "max 15.000100 52.000000 141.909091 34.066667 35682.000000 \n", + "\n", + " AveOccup Latitude Longitude \n", + "count 20640.000000 20640.000000 20640.000000 \n", + "mean 3.070655 35.631861 -119.569704 \n", + "std 10.386050 2.135952 2.003532 \n", + "min 0.692308 32.540000 -124.350000 \n", + "25% 2.429741 33.930000 -121.800000 \n", + "50% 2.818116 34.260000 -118.490000 \n", + "75% 3.282261 37.710000 -118.010000 \n", + "max 1243.333333 41.950000 -114.310000 " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* we want to be able to predict the prices of unseen houses\n", + " * \"explainning\"/overfitting the already seen data is very easy with high capacity models (e.g. trees, neural nets, etc.)\n", + "* to achieve this, we usually split the available data according to some validation scheme\n", + "* validation scheme needs to respect the nature of a forecasting problem \n", + " * cross-validation \n", + " * temporal validation\n", + "\n", + "* you can use sklearn validation utils for standard tasks or write your own for more exotic\n", + "* don't forget to set random_state so that your results can be reproduced" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# simple random split using sklearn\n", + "X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(df, label, test_size = 0.3, random_state = 42)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "df.shape, X_train.shape, X_test.shape;" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* machine learning models have number of hyperparameters that need to be tweaked\n", + " * searching for optimal using only in-sample (seen) data not good\n", + " * using test-set from above leads to \"overfitting\" on the test set\n", + "* typical solution is to introduce so called validation set used only for evaluating hyperparameters\n", + " * splitting dataset into 3 parts however makes our dataset we can learn from drastically smaller \n", + "* cross-validation (or temporal validation)\n", + " * cross validation iterators are handy and return the indices of the original dataframes \n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "kf5 = sklearn.model_selection.KFold(n_splits = 5)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.concat([df, label], axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "split no. 0\n", + "[ 4128 4129 4130 ... 20637 20638 20639] [ 0 1 2 ... 4125 4126 4127]\n", + "split no. 0\n", + "[ 0 1 2 ... 20637 20638 20639] [4128 4129 4130 ... 8253 8254 8255]\n", + "split no. 0\n", + "[ 0 1 2 ... 20637 20638 20639] [ 8256 8257 8258 ... 12381 12382 12383]\n", + "split no. 0\n", + "[ 0 1 2 ... 20637 20638 20639] [12384 12385 12386 ... 16509 16510 16511]\n", + "split no. 0\n", + "[ 0 1 2 ... 16509 16510 16511] [16512 16513 16514 ... 20637 20638 20639]\n" + ] + } + ], + "source": [ + "i = 0\n", + "for train_idx, test_idx in kf5.split(data):\n", + "\n", + " print(f'split no. {i}')\n", + " print(train_idx, test_idx)\n", + "\n", + " train_data = data.loc[train_idx,:]\n", + " test_data = data.loc[test_idx,:]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* typical situation arises when you need to respect certain classes/groups in the data during splitting process\n", + " * use `GroupKFold` data for this\n", + "* to respect time-series nature of the problem, you can use `TimeSeriesSplit`\n", + "\n", + "\n", + "* we want to fit the model using the train data in each fold and predict the test data in each fold\n", + " * we can start with the simplest \"model\" -> sample mean\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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MedIncHouseAgeAveRoomsAveBedrmsPopulationAveOccupLatitudeLongitudeMedHouseVal
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18.301421.06.2381370.9718802401.02.10984237.86-122.223.585
27.257452.08.2881361.073446496.02.80226037.85-122.243.521
35.643152.05.8173521.073059558.02.54794537.85-122.253.413
43.846252.06.2818531.081081565.02.18146737.85-122.253.422
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" + ], + "text/plain": [ + " MedInc HouseAge AveRooms AveBedrms Population AveOccup Latitude \\\n", + "0 8.3252 41.0 6.984127 1.023810 322.0 2.555556 37.88 \n", + "1 8.3014 21.0 6.238137 0.971880 2401.0 2.109842 37.86 \n", + "2 7.2574 52.0 8.288136 1.073446 496.0 2.802260 37.85 \n", + "3 5.6431 52.0 5.817352 1.073059 558.0 2.547945 37.85 \n", + "4 3.8462 52.0 6.281853 1.081081 565.0 2.181467 37.85 \n", + "... ... ... ... ... ... ... ... \n", + "20635 1.5603 25.0 5.045455 1.133333 845.0 2.560606 39.48 \n", + "20636 2.5568 18.0 6.114035 1.315789 356.0 3.122807 39.49 \n", + "20637 1.7000 17.0 5.205543 1.120092 1007.0 2.325635 39.43 \n", + "20638 1.8672 18.0 5.329513 1.171920 741.0 2.123209 39.43 \n", + "20639 2.3886 16.0 5.254717 1.162264 1387.0 2.616981 39.37 \n", + "\n", + " Longitude MedHouseVal \n", + "0 -122.23 4.526 \n", + "1 -122.22 3.585 \n", + "2 -122.24 3.521 \n", + "3 -122.25 3.413 \n", + "4 -122.25 3.422 \n", + "... ... ... \n", + "20635 -121.09 0.781 \n", + "20636 -121.21 0.771 \n", + "20637 -121.22 0.923 \n", + "20638 -121.32 0.847 \n", + "20639 -121.24 0.894 \n", + "\n", + "[20640 rows x 9 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "split no. 0\n", + "split no. 1\n", + "split no. 2\n", + "split no. 3\n", + "split no. 4\n" + ] + } + ], + "source": [ + "label = 'MedHouseVal'\n", + "kf_predictions = pd.DataFrame()\n", + "\n", + "i = 0\n", + "for train_idx, test_idx in kf5.split(data):\n", + " print(f'split no. {i}')\n", + " train_data = data.loc[train_idx,:]\n", + " test_data = data.loc[test_idx,:]\n", + "\n", + " # \"fitting\" part\n", + " sample_mean = train_data[label].mean()\n", + " # prediction part \n", + " test_data['prediction'] = sample_mean\n", + "\n", + " # save predictions\n", + " test_data['split'] = i\n", + " kf_predictions = pd.concat([kf_predictions, test_data], axis = 0)\n", + " i = i + 1 " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* we have our first predictions, even though simple ones\n", + "* NOTE: when you have large data that don't fit into RAM, you can do the forecasting/predicting \"lazily\"\n", + " * save individual split results or save only split-metrics \n", + "\n", + "### Evaluation\n", + "\n", + "* regression task:\n", + " * typically MSE, MAE, MAPE, wMAPE or r2\n", + "* evaluation strategy should take into account what stakeholder wants, the kitchen-sink approach not recommended\n", + "* we are interested in out-of-sample!\n", + " * sometimes it is interesting to look at in-sample and compare errors in-sample vs out-of-sample\n", + "* scikit-learn offers number of most commonly used metrics, see [sklearn.metrics](https://scikit-learn.org/stable/modules/model_evaluation.html)\n", + "\n", + "* `kf_predicitions` contains out-of-sample predictions from individual splits (even though for now these are simply sample means)\n", + " * we can look at the overall score as well as specific splits (shows stability)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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....................................
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" + ], + "text/plain": [ + " MedInc HouseAge AveRooms AveBedrms Population AveOccup Latitude \\\n", + "0 8.3252 41.0 6.984127 1.023810 322.0 2.555556 37.88 \n", + "1 8.3014 21.0 6.238137 0.971880 2401.0 2.109842 37.86 \n", + "2 7.2574 52.0 8.288136 1.073446 496.0 2.802260 37.85 \n", + "3 5.6431 52.0 5.817352 1.073059 558.0 2.547945 37.85 \n", + "4 3.8462 52.0 6.281853 1.081081 565.0 2.181467 37.85 \n", + "... ... ... ... ... ... ... ... \n", + "20635 1.5603 25.0 5.045455 1.133333 845.0 2.560606 39.48 \n", + "20636 2.5568 18.0 6.114035 1.315789 356.0 3.122807 39.49 \n", + "20637 1.7000 17.0 5.205543 1.120092 1007.0 2.325635 39.43 \n", + "20638 1.8672 18.0 5.329513 1.171920 741.0 2.123209 39.43 \n", + "20639 2.3886 16.0 5.254717 1.162264 1387.0 2.616981 39.37 \n", + "\n", + " Longitude MedHouseVal prediction split \n", + "0 -122.23 4.526 2.16493 0 \n", + "1 -122.22 3.585 2.16493 0 \n", + "2 -122.24 3.521 2.16493 0 \n", + "3 -122.25 3.413 2.16493 0 \n", + "4 -122.25 3.422 2.16493 0 \n", + "... ... ... ... ... \n", + "20635 -121.09 0.781 2.02067 4 \n", + "20636 -121.21 0.771 2.02067 4 \n", + "20637 -121.22 0.923 2.02067 4 \n", + "20638 -121.32 0.847 2.02067 4 \n", + "20639 -121.24 0.894 2.02067 4 \n", + "\n", + "[20640 rows x 11 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kf_predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "split_results = pd.concat([\n", + " kf_predictions.groupby(['split']).apply(lambda x: sklearn.metrics.mean_squared_error(x[label], x['prediction'])),\n", + " kf_predictions.groupby(['split']).apply(lambda x: sklearn.metrics.mean_absolute_error(x[label], x['prediction'])),\n", + " kf_predictions.groupby(['split']).apply(lambda x: sklearn.metrics.r2_score(x[label], x['prediction'])),\n", + " ], axis = 1)\n", + "split_results.columns = ['MSE','MAE', 'r2']" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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MSEMAEr2
split
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" + ], + "text/plain": [ + " MSE MAE r2\n", + "split \n", + "0 1.306462 0.971558 -0.216137\n", + "1 1.198934 0.794484 -0.024237\n", + "2 1.572735 0.944996 -0.093293\n", + "3 1.258655 0.939208 -0.072854\n", + "4 1.514490 0.974571 -0.039344" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "split_results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* now we have a basic modelling workflow implemented and we can fit an actual model\n", + "* models/estimators in scikit-learn have standardized methods which provides great modularity\n", + " * we will see in a couple of minutes, that we can simply switch one model from another and reuse the whole pipeline!\n", + "\n", + "* `.fit(X,y)`\n", + " * `X` - sample matrix (n_samples, n_features)\n", + " * `y` - the target values y which (e.g. real numbers for regression, or ints for classification)\n", + " * `y` not specified for the regression tasks \n", + "\n", + "* `.predict(X)`\n", + " * \n", + "\n", + "* lets replace the sample mean with some econometrics model" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup',\n", + " 'Latitude', 'Longitude'],\n", + " dtype='object')" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'MedHouseVal'" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "label" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "split no. 0\n", + "split no. 1\n", + "split no. 2\n", + "split no. 3\n", + "split no. 4\n" + ] + } + ], + "source": [ + "import sklearn.linear_model\n", + "\n", + "features = ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude']\n", + "labels = ['MedHouseVal']\n", + "\n", + "\n", + "kf_predictions = pd.DataFrame()\n", + "i = 0\n", + "for train_idx, test_idx in kf5.split(data):\n", + " print(f'split no. {i}')\n", + " train_data = data.loc[train_idx,:]\n", + " test_data = data.loc[test_idx,:]\n", + "\n", + " # \"fitting\" part\n", + " model = sklearn.linear_model.LinearRegression()\n", + " model.fit(train_data[features], train_data[label])\n", + "\n", + " # prediction part \n", + " test_data['prediction'] = model.predict(test_data[features])\n", + "\n", + " # save predictions\n", + " test_data['split'] = i\n", + " kf_predictions = pd.concat([kf_predictions, test_data], axis = 0)\n", + " i = i + 1 \n", + "\n", + "split_results = pd.concat([\n", + " kf_predictions.groupby(['split']).apply(lambda x: sklearn.metrics.mean_squared_error(x[label], x['prediction'])),\n", + " kf_predictions.groupby(['split']).apply(lambda x: sklearn.metrics.mean_absolute_error(x[label], x['prediction'])),\n", + " kf_predictions.groupby(['split']).apply(lambda x: sklearn.metrics.r2_score(x[label], x['prediction'])),\n", + " ], axis = 1)\n", + "split_results.columns = ['MSE','MAE', 'r2']" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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MSEMAEr2
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" + ], + "text/plain": [ + " MSE MAE r2\n", + "split \n", + "0 0.484859 0.545994 0.548663\n", + "1 0.622497 0.566178 0.468207\n", + "2 0.646210 0.576550 0.550784\n", + "3 0.543200 0.531906 0.536987\n", + "4 0.494685 0.516853 0.660514" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "split_results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* using OLS instead of just looking at the mean helps a lot (of course)\n", + "* before trying another model, let's put above code into a function so it is reusable" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "def train_predict(data: pd.DataFrame, n_splits: int, features: List[str], label: str, model, model_args: Union[None,Dict]):\n", + " \"\"\"\n", + "\n", + " Args: \n", + "\n", + " Returns:\n", + " \n", + " \"\"\"\n", + "\n", + " kfold_predictions = pd.DataFrame()\n", + "\n", + " i = 0\n", + " kfold = sklearn.model_selection.KFold(n_splits=n_splits)\n", + "\n", + " for train_idx, test_idx in kfold.split(data):\n", + "\n", + " train_data = data.loc[train_idx,:]\n", + " test_data = data.loc[test_idx,:]\n", + "\n", + " # model initialization\n", + " if model_args is not None:\n", + " split_model = model(**model_args)\n", + " else:\n", + " split_model = model()\n", + "\n", + " # fit/estimate the model \n", + " split_model.fit(X = train_data[features], y = train_data[label])\n", + "\n", + " # prediction on unseen data using fit model\n", + " test_data['prediction'] = split_model.predict(test_data[features])\n", + "\n", + " # save split name and predictions\n", + " test_data['split'] = i\n", + " kfold_predictions = pd.concat([kfold_predictions, test_data], axis = 0)\n", + " i = i + 1 \n", + "\n", + " return kfold_predictions\n", + "\n", + "def eval_predicted(split_predictions: pd.DataFrame,label: str,eval_metrics = [sklearn.metrics.mean_squared_error, sklearn.metrics.r2_score]):\n", + "\n", + " split_results = pd.concat([\n", + " split_predictions.groupby(['split']).apply(lambda x: eval_metric(x[label], x['prediction'])) for eval_metric in eval_metrics], axis = 1)\n", + " split_results.columns = [m.__name__ for m in eval_metrics]\n", + "\n", + " return split_results " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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mean_squared_errormean_absolute_percentage_error
split
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" + ], + "text/plain": [ + " mean_squared_error mean_absolute_percentage_error\n", + "split \n", + "0 0.484859 0.472863\n", + "1 0.622497 0.268254\n", + "2 0.646210 0.277037\n", + "3 0.543200 0.331847\n", + "4 0.494685 0.292861" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# we could pack 2 functions below into separate function\n", + "model_args = {'fit_intercept': True}\n", + "predictions = train_predict(data = data, n_splits = 5, model = sklearn.linear_model.LinearRegression, features = features, label = label, model_args= model_args)\n", + "eval_predicted(split_predictions = predictions, label = 'MedHouseVal', eval_metrics= [sklearn.metrics.mean_squared_error,sklearn.metrics.mean_absolute_percentage_error])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* now let's switch sklearn model for LGBM!\n", + "* our pipeline should still work (LGBM models also have `.fit()` and `.predict()`)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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mean_squared_errormean_absolute_percentage_error
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" + ], + "text/plain": [ + " mean_squared_error mean_absolute_percentage_error\n", + "split \n", + "0 0.370611 0.355926\n", + "1 0.348273 0.198146\n", + "2 0.361325 0.227280\n", + "3 0.362462 0.258604\n", + "4 0.453471 0.221525" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lgb_args = {\n", + " 'boosting_type': 'gbdt',\n", + " 'learning_rate': 0.1\n", + "}\n", + "predictions = train_predict(data = data, n_splits = 5, model = lightgbm.LGBMRegressor, features = features, label = label, model_args= lgb_args)\n", + "eval_predicted(split_predictions = predictions, label = 'MedHouseVal', eval_metrics= [sklearn.metrics.mean_squared_error,sklearn.metrics.mean_absolute_percentage_error])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* 2-times better with default hyperparamters!\n", + " * NOTE: they really should teach more trees at IES!\n", + "\n", + "* let's look at another example, classification this time" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Classifying recognizing hand-written digits\n", + "\n", + "* [example from scikit website](https://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html#sphx-glr-auto-examples-classification-plot-digits-classification-py)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# Import datasets, classifiers and performance metrics\n", + "from sklearn import datasets, svm, metrics\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['data', 'target', 'frame', 'feature_names', 'target_names', 'images', 'DESCR'])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "digits = datasets.load_digits()\n", + "digits.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0., 0., 5., 13., 9., 1., 0., 0.],\n", + " [ 0., 0., 13., 15., 10., 15., 5., 0.],\n", + " [ 0., 3., 15., 2., 0., 11., 8., 0.],\n", + " [ 0., 4., 12., 0., 0., 8., 8., 0.],\n", + " [ 0., 5., 8., 0., 0., 9., 8., 0.],\n", + " [ 0., 4., 11., 0., 1., 12., 7., 0.],\n", + " [ 0., 2., 14., 5., 10., 12., 0., 0.],\n", + " [ 0., 0., 6., 13., 10., 0., 0., 0.]])" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "digits.images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_, axes = plt.subplots(nrows=1, ncols=4, figsize=(10, 3))\n", + "for ax, image, label in zip(axes, digits.images, digits.target):\n", + " ax.set_axis_off()\n", + " ax.imshow(image, cmap=plt.cm.gray_r, interpolation=\"nearest\")\n", + " ax.set_title(\"Training: %i\" % label)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "n_samples = len(digits.images)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "# flatten the images\n", + "data = digits.images.reshape((n_samples, -1))" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1797, 64)" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "digits.images.reshape((n_samples,-1)).shape" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1797, 8, 8)" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "digits.images.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a classifier: a support vector classifier\n", + "clf = svm.SVC(gamma=0.001)\n", + "clf_alt = lightgbm.LGBMClassifier()\n", + "\n", + "# Split data into 50% train and 50% test subsets\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " data, digits.target, test_size=0.5, shuffle=False\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "# Learn the digits on the train subset\n", + "clf.fit(X_train, y_train)\n", + "clf_alt.fit(X_train, y_train)\n", + "\n", + "# Predict the value of the digit on the test subset\n", + "predicted = clf.predict(X_test)\n", + "predicted_alt = clf_alt.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_, axes = plt.subplots(nrows=1, ncols=4, figsize=(10, 3))\n", + "\n", + "for ax, image, prediction in zip(axes, X_test, predicted):\n", + " ax.set_axis_off()\n", + " image = image.reshape(8, 8)\n", + " ax.imshow(image, cmap=plt.cm.gray_r, interpolation=\"nearest\")\n", + " ax.set_title(f\"Prediction: {prediction}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_, axes = plt.subplots(nrows=1, ncols=4, figsize=(10, 3))\n", + "\n", + "for ax, image, prediction in zip(axes, X_test, predicted_alt):\n", + " ax.set_axis_off()\n", + " image = image.reshape(8, 8)\n", + " ax.imshow(image, cmap=plt.cm.gray_r, interpolation=\"nearest\")\n", + " ax.set_title(f\"Prediction: {prediction}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 0.99 0.99 88\n", + " 1 0.99 0.97 0.98 91\n", + " 2 0.99 0.99 0.99 86\n", + " 3 0.98 0.87 0.92 91\n", + " 4 0.99 0.96 0.97 92\n", + " 5 0.95 0.97 0.96 91\n", + " 6 0.99 0.99 0.99 91\n", + " 7 0.96 0.99 0.97 89\n", + " 8 0.94 1.00 0.97 88\n", + " 9 0.93 0.98 0.95 92\n", + "\n", + " accuracy 0.97 899\n", + " macro avg 0.97 0.97 0.97 899\n", + "weighted avg 0.97 0.97 0.97 899\n", + "\n" + ] + } + ], + "source": [ + "# classification report -> good resource -> https://en.wikipedia.org/wiki/Evaluation_of_binary_classifiers?oldformat=true\n", + "print(metrics.classification_report(y_test, predicted))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.92 0.99 0.95 88\n", + " 1 0.86 0.84 0.85 91\n", + " 2 0.99 0.90 0.94 86\n", + " 3 0.92 0.84 0.87 91\n", + " 4 0.99 0.90 0.94 92\n", + " 5 0.89 0.92 0.91 91\n", + " 6 0.96 0.98 0.97 91\n", + " 7 0.92 0.96 0.94 89\n", + " 8 0.88 0.86 0.87 88\n", + " 9 0.80 0.92 0.86 92\n", + "\n", + " accuracy 0.91 899\n", + " macro avg 0.91 0.91 0.91 899\n", + "weighted avg 0.91 0.91 0.91 899\n", + "\n" + ] + } + ], + "source": [ + "print(metrics.classification_report(y_test, predicted_alt))" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion matrix:\n", + "[[87 0 0 0 1 0 0 0 0 0]\n", + " [ 0 88 1 0 0 0 0 0 1 1]\n", + " [ 0 0 85 1 0 0 0 0 0 0]\n", + " [ 0 0 0 79 0 3 0 4 5 0]\n", + " [ 0 0 0 0 88 0 0 0 0 4]\n", + " [ 0 0 0 0 0 88 1 0 0 2]\n", + " [ 0 1 0 0 0 0 90 0 0 0]\n", + " [ 0 0 0 0 0 1 0 88 0 0]\n", + " [ 0 0 0 0 0 0 0 0 88 0]\n", + " [ 0 0 0 1 0 1 0 0 0 90]]\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "disp = metrics.ConfusionMatrixDisplay.from_predictions(y_test, predicted)\n", + "disp.figure_.suptitle(\"Confusion Matrix\")\n", + "print(f\"Confusion matrix:\\n{disp.confusion_matrix}\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernel_info": { + "name": "python3" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.7" + }, + "microsoft": { + "ms_spell_check": { + "ms_spell_check_language": "en" + } + }, + "nteract": { + "version": "nteract-front-end@1.0.0" + }, + "vscode": { + "interpreter": { + "hash": "1f0e6d99f3103fd78365fe1cf7b2d51239fa0878786db9cbdfe89bc88a3151d3" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/08_intro_to_databases/08_intro_to_sql.ipynb b/08_intro_to_databases/08_intro_to_sql.ipynb new file mode 100644 index 0000000..1587e44 --- /dev/null +++ b/08_intro_to_databases/08_intro_to_sql.ipynb @@ -0,0 +1,677 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Warmup\n", + "\n", + "# Why is this useful?\n", + "![alt text](bstree.png \"Our DB\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lecture 8 - Intro to databases\n", + "\n", + "### Contents:\n", + "\n", + "* Databases - dockerized versions(quickly as it is way above the course)\n", + "* DataTypes\n", + "* Tables\n", + "* Joins\n", + "* Python - SQLAlchemy\n", + "* SQL + Pandas implementation!\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Why do I need it?\n", + "\n", + "* Peristence of data\n", + "* Csvs might not be suitable anymore:\n", + " * No data sanitation\n", + " * Cannot share between clients (download continually data from multiple sources and create a single file)\n", + " * Permissions handling\n", + " * Files can get corrupted, inconsistent, no security, easily deleted etc...\n", + " * What if something happens during a write? Your computer crashes? File will have issues\n", + " * Parallel writing\n", + " * Speed of writing/reading\n", + "\n", + "* Lookup in the dataset! Always need to load the whole thing\n", + " * DB finds only the required data and returns them\n", + " \n", + "## Cons?\n", + "\n", + "* Large overhead (DB server vs file)\n", + "* Bandwidth limits (bottlenecks in connection)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Relational databases\n", + "\n", + "* optimize storage -> use normalized data - discover relations using joins\n", + "* normalization of data - each table contains its specific data and relates to others through keys\n", + "* designed on ACID principle - Atomicity, Consistency, Isolation, Durability\n", + "- **Atomicity**: Guarantees that each transaction is treated as a single unit, which either completes entirely or not at all.\n", + "- **Consistency**: Ensures that a transaction only brings the database from one valid state to another, maintaining data integrity.\n", + "- **Isolation**: Keeps transactions separate from each other until they're completed, preventing concurrent transactions from affecting each other.\n", + "- **Durability**: Assures that once a transaction is committed, it remains so even in the case of a system failure, ensuring data permanence.\n", + "\n", + "* store huge data amount of data -> gigabytes\n", + "* read it very fast - depending on the design\n", + "* Many different applications!\n", + " * Business\n", + " * Web-servers\n", + " * Big data\n", + " \n", + "* Protected access with username / password, vpns\n", + "* Users have specific permissions! Read/write/delete\n", + "\n", + "## SQL\n", + "*Structured Query Language*\n", + "* Human (easily) readable\n", + "* Different implementations\n", + " * engines: SQLite, MySQL, Oracle, PostgreSQL\n", + "* SQL is only a language\n", + "* Data are stored in *Tables* \n", + "* Connected via *Relations*\n", + "* NoSQL - MongoDB, CouchDB, DynamoDB - they optimize access speed, instead of storage\n", + " * (now storage is cheap), async, scalable and latency optimal\n", + "* Distributed databases such as Apache Hive - big data databases (map-reduce)\n", + "* How does Google get this so quickly? \n", + "![image.png](google.png)\n", + "\n", + "\n", + "\n", + "### Database Layers\n", + "![alt text](sql_struktura.png \"sql structures\")\n", + "\n", + "\n", + "we can have more schemas within a single DB server instance -> saves money on hardware, but still limited resources\n", + "### Tables\n", + "Outline of today's problems\n", + "![alt text](stock-db.png \"Our DB\")\n", + "\n", + "\n", + "### Data Types\n", + "depends on specific application\n", + "* numeric\n", + " * INT, INTEGER, REAL, FLOAT, DOUBLE etc.\n", + "* strings\n", + " * STRING, TEXT, VARCHAR\n", + "* more specialized\n", + " * DATE, TIME etc.\n", + "\n", + "\n", + "## How to use it? \n", + "* Command-line\n", + "* Python drivers\n", + "* Programming interface\n", + "* GUI Interface - [DBeaver](https://dbeaver.io/)\n", + "* Integration with existing software - MS Office, etc\n", + "\n", + "\n", + "We always connect to the server, to establish a connection. Then use a cursor (client) to send commands and retreive results the DB prepares for us." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## My problem - I want to keep data about stocks for analysis\n", + "\n", + "* Would I always need to download data which does not chage?\n", + "* Run different queries - analysis\n", + "* More stocks can be added any day\n", + "* Keep format" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import yfinance as yf\n", + "\n", + "msft = yf.Ticker(\"MSFT\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#data like this so what do I want to keep? and how?\n", + "msft.info\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lets create a database for this data\n", + "\n", + "* Where to store it?\n", + " * memory: fast, will be lost once exited\n", + " * personal computer - why not, but can be lost, ?performance?\n", + " * cloud server - SaaS - https://aws.amazon.com/rds/postgresql/ - if you want to learn this, drop as an email, we might add it to the course\n", + "\n", + "\n", + "* Demo - postgresql server instance running in Docker on your computer - quick to start using, no installation etc.\n", + " * ! docker will create a container where the data will be stored - if you lose the image, you lose the data!\n", + " * It is possible to create have the data in a specific directory, thus persistent - if you really really need persistence of the data, get the cloud server, or read the manual https://hub.docker.com/_/postgres\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### which docker image? \n", + "https://hub.docker.com/_/postgres\n", + " \n", + "docker allows me to easily specify versions, size of image and other things!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### if you have docker running on your machine, you can easily start the database from terminal / command line\n", + "\n", + "* I am running latest postgres 12 based on alpine linux system (dont really care when in docker, but it is slim)\n", + "* Specifying the image name `--name your name` - I can stop it `docker stop name` and start it again `docker start name`\n", + " * if not supplied, it will be created by docker with some funny adjective of a scientist like 'crazy einstein' etc.\n", + "* specify env variables which will customize the DB (password and postgres user)\n", + "* specify on which port I can access the db -p 5423:5432 `-p 54322:5432` - in the docker it runs on default postgres 5432, I want to get there through my own 54322 port, since nothing is running there. \n", + "\n", + "* recommending add `-d` so it runs in backgroung\n", + "\n", + "* access logs `docker logs stock-db`\n", + "\n", + "\n", + "`docker run -d --name stock-db -e POSTGRES_PASSWORD=iesFTW -e POSTGRES_USER=honza -p 54322:5432 postgres:12-alpine`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### how to connect?\n", + "\n", + "* Now we have a server - we need a client (like with requests - browser)\n", + " * Not a bad idea to get familiar with command line tools `psql` client - on MacOS `brew install libpq`\n", + " * GUI clients - multiplatform https://dbeaver.io/ and others - on macOS `brew cask install dbeaver-community` \n", + " \n", + "* terminal connect:\n", + " * `psql -h localhost -U honza postgres` and put in password `iesFTW`\n", + " * `\\dt+` command to show all tables\n", + " * default database name is `postgres`, thats the last parameter. You can customize it with docker\n", + " * by default `psql` would connect you to database with name same as the user (jansila) in my case, so do not get confused here\n", + " \n", + "* DBeaver as shown in video" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Crucial commands\n", + "\n", + "* CREATE TABLE\n", + "* INSERT INTO ... VALUES ....\n", + "* SELECT * FROM ...\n", + "\n", + "## Data types\n", + "\n", + "like in python - int, string (varchar, text), float, boolean, even json, arrays, coordinates etc..\n", + "https://www.postgresql.org/docs/10/datatype.html\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#save some data - design a database\n", + "\n", + "# determine appropriate structure!\n", + "\n", + "# tables - company, financials, prices\n", + "# each has own purpose\n", + "\n", + "sql_create_company = \"\"\" CREATE TABLE IF NOT EXISTS company (\n", + " ticker VARCHAR(5) PRIMARY KEY, --max length of a ticker is 5\n", + " name TEXT NOT NULL, --cannot be empty\n", + " sector TEXT,\n", + " state TEXT,\n", + " summary TEXT)\n", + "\"\"\"\n", + "\n", + "\n", + "sql_create_financials = \"\"\"CREATE TABLE financials (\n", + " ticker VARCHAR(5) PRIMARY KEY, -- in more advanced designs, we would create this as foreign key! only one observation per ticker\n", + " shares BIGINT,\n", + " div_yield REAL,\n", + " beta REAL\n", + ")\"\"\"\n", + "\n", + "sql_create_prices = \"\"\"CREATE TABLE IF NOT EXISTS prices (\n", + " ticker VARCHAR(5),\n", + " ts DATE NOT NULL,\n", + " price REAL,\n", + " volume BIGINT --in milions\n", + " )\n", + " \"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# lets connect\n", + "import psycopg2 #only for PostgreSQL\n", + "\n", + "connection = psycopg2.connect(\"dbname='postgres' user='honza' host='db' password='iesFTW'\") \n", + "connection.autocommit = True #bit advanced\n", + "# in order to work with the DB, we need a cursor \n", + "\n", + "cursor = connection.cursor() #this object interacts with the DB with a proper protocol - like browser\n", + "\n", + "for sql_statement in [sql_create_company, sql_create_financials, sql_create_prices]:\n", + " print(sql_statement)\n", + " cursor.execute(sql_statement)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def write_company_data(cursor, ticker, td):\n", + " cursor.execute(\"INSERT INTO company (ticker, name, sector, state, summary) VALUES (%s, %s, %s, %s, %s)\", \n", + " (ticker, td.get('shortName'), td['sector'], td['state'], td['longBusinessSummary'])\n", + " )\n", + "def write_financial_data(cursor, ticker, td):\n", + " cursor.execute(\"INSERT INTO financials (ticker, shares, div_yield, beta) VALUES (%s, %s, %s, %s)\", \n", + " (ticker, td['floatShares'], td['dividendYield'],td['beta'])\n", + " )\n", + "\n", + "def write_prices(cursor, ticker, data):\n", + " for row in data.iterrows():\n", + " ts = row[0]\n", + " close = row[1]['Close']\n", + " vol = row[1]['Volume']\n", + " cursor.execute(\"INSERT INTO prices (ticker, ts, price, volume) VALUES (%s, %s, %s, %s)\", \n", + " (ticker, ts, close,vol)\n", + " )\n", + " \n", + "## add some data in the db\n", + "tickers = ['MSFT', 'FB','GOOG','GS','INTC', 'AAL', 'AAPL']\n", + "\n", + "#yf api https://aroussi.com/post/python-yahoo-finance\n", + "\n", + "for ticker in tickers: \n", + " td = yf.Ticker(ticker)\n", + " print(f'processing {ticker}')\n", + " #write some company info and then check it\n", + " write_company_data(cursor, ticker, td.info)\n", + " write_financial_data(cursor, ticker, td.info)\n", + " write_prices(cursor, ticker, td.history('ytd'))\n", + " \n", + "print('we are done')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cursor.execute(\"SELECT * FROM company;\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cursor.fetchall()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#check all companies we downloaded\n", + "\n", + "cursor.execute(\"SELECT ticker, name, sector FROM company;\")\n", + "\n", + "for row in cursor.fetchall(): #cursor.fetchone(), \n", + " print(f'downloaded {row[1]} that operates in {row[2]} and has ticker: {row[0]}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cursor.fetchall()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cursor.execute(\"SELECT ticker, name, sector from company;\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cursor.fetchone()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#or iterate \n", + "cursor.execute(\"SELECT ticker, name, sector from company;\")\n", + "\n", + "for row in cursor:\n", + " print(row)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#check all technology companies we downloaded\n", + "\n", + "#df[df.sector=='Tech'] #where clause\n", + " \n", + "#case 1\n", + "cursor.execute(\"SELECT ticker, name FROM company WHERE sector = 'Technology';\")\n", + "for row in cursor.fetchall():\n", + " print(f'downloaded {row[1]} and has ticker: {row[0]}')\n", + "\n", + "# print('-----')\n", + "#check all technology companies we downloaded\n", + "#case 2\n", + "# cursor.execute(\"SELECT ticker, name from company where sector = %s;\", ('Technology', )) #input needs to be a tuple!\n", + "# for row in cursor.fetchall():\n", + "# print(f'downloaded a tech company {row[1]} and has ticker: {row[0]}')\n", + " \n", + " \n", + " \n", + "# print('-----')\n", + "\n", + "#check all Tech and Industrial companies we downloaded\n", + "#case 3\n", + "# for industry in [('Technology',), ('Industrials',)]:\n", + "\n", + "# cursor.execute(\"SELECT ticker, name from company where sector = %s;\", industry) #input needs to be a tuple!\n", + "# for row in cursor.fetchall():\n", + "# print(f'downloaded a {industry[0]} company {row[1]} and has ticker: {row[0]}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# joins\n", + "# just like in pandas\n", + "\n", + "# I have information in company table as well as financials which I wan to use at the same time!\n", + "\n", + "cursor.execute(\"\"\"SELECT comp.ticker, comp.sector, fin.shares \n", + " \n", + " from company as comp \n", + " \n", + " join financials as fin \n", + " \n", + " on fin.ticker=comp.ticker\n", + " ;\"\"\")\n", + "for row in cursor.fetchall():\n", + " print(f'ticker {row[0]} in sector {row[1]} with {row[2]} shares outstanding')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### JOINS \n", + "\n", + "* connecting tables - relations!\n", + "\n", + "\n", + "\n", + "### Inner\n", + "* most common - give me the match!\n", + "* when you see match, keep it, otherwise drop it.\n", + "\n", + "### Left \n", + "* INNER + rows from LEFT with no match in the RIGHT" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cursor.execute(\"\"\"SELECT comp.ticker, comp.sector, fin.shares \n", + " from company as comp \n", + " left join financials as fin \n", + " on fin.ticker=comp.ticker;\"\"\")\n", + "for row in cursor.fetchall():\n", + " print(f'ticker {row[0]} in sector {row[1]} with {row[2]} shares outstanding')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install sqlalchemy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sqlalchemy import *\n", + "import pandas as pd\n", + "\n", + "# connect as driver://username:password@host:port/database\n", + "engine = create_engine('postgresql://honza:iesFTW@db:5432/postgres') #postgresql.connection - similar object" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#pandas + psycopg2\n", + "\n", + "pd.read_sql_query(\"\"\"SELECT comp.ticker, comp.sector, fin.shares \n", + " from company as comp \n", + " left join financials as fin \n", + " on fin.ticker=comp.ticker;\"\"\", connection, index_col='ticker')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#pandas + sqlalchemy\n", + "pd.read_sql_query(\n", + "\"\"\"SELECT comp.ticker, comp.sector, fin.shares \n", + " from company as comp \n", + " left join financials as fin \n", + " on fin.ticker=comp.ticker;\"\"\"\n", + " ,con=engine,index_col='ticker')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#multiple joins with WHERE clause!\n", + "\n", + "pd.read_sql_query(\"\"\"SELECT comp.ticker, fin.shares,fin.div_yield, px.price as lprice\n", + " \n", + " from company as comp \n", + " \n", + " join financials as fin\n", + " on fin.ticker=comp.ticker\n", + " \n", + " join prices as px\n", + " on px.ticker=comp.ticker\n", + " \n", + " WHERE px.ts='2022-03-18'\n", + " \"\"\",connection, index_col='ticker')\n", + "\n", + "#SQL has order of business, it selects on WHERE, then joins" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#algebra within a query\n", + "\n", + "pd.read_sql_query(\"\"\"SELECT comp.ticker, fin.shares, px.price as lprice, fin.shares*px.price/1e9 as mktcap_in_billions\n", + " from company as comp \n", + " join financials as fin\n", + " on fin.ticker=comp.ticker\n", + " join prices as px\n", + " on px.ticker=comp.ticker\n", + " where px.ts='2020-01-02'\n", + " \"\"\",connection, index_col='ticker')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#all prices and calculated market caps\n", + "\n", + "pd.read_sql_query(\"\"\"SELECT comp.ticker, px.price as lprice, px.ts, fin.shares*px.price/1e9 as mktcap_in_billions\n", + " from company as comp \n", + " join financials as fin\n", + " on fin.ticker=comp.ticker\n", + " join prices as px\n", + " on px.ticker=comp.ticker;\n", + " \"\"\",connection, index_col='ticker')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#create turnover variable\n", + "\n", + "cursor.execute(\"\"\"\n", + " ALTER TABLE prices \n", + " ADD COLUMN IF NOT EXISTS turnover REAL;\n", + " \"\"\")\n", + "\n", + "cursor.execute(\"UPDATE prices SET turnover = volume*price\") #df['turnover']=df.volume*df.price\n", + "\n", + "pd.read_sql_query(\"\"\"SELECT * from prices WHERE ticker='AAPL';\n", + " \"\"\",connection, index_col='ticker')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SANITIZE YOUR INPUTS\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.1" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/08_intro_to_databases/bstree.png b/08_intro_to_databases/bstree.png new file mode 100644 index 0000000..785f5c7 Binary files /dev/null and b/08_intro_to_databases/bstree.png differ diff --git a/08_intro_to_databases/companies-bcp.txt b/08_intro_to_databases/companies-bcp.txt new file mode 100644 index 0000000..bca43d2 --- /dev/null +++ b/08_intro_to_databases/companies-bcp.txt @@ -0,0 +1,35 @@ +[('FB', + 'Meta Platforms, Inc.', + 'Communication Services', + 'CA', + "Meta Platforms, Inc. develops products that enable people to connect and share with friends and family through mobile devices, personal computers, virtual reality headsets, wearables, and in-home devices worldwide. It operates in two segments, Family of Apps and Reality Labs. The Family of Apps segment's products include Facebook, which enables people to share, discover, and connect with interests; Instagram, a community for sharing photos, videos, and private messages, as well as feed, stories, reels, video, live, and shops; Messenger, a messaging application for people to connect with friends, family, groups, and businesses across platforms and devices through chat, audio and video calls, and rooms; and WhatsApp, a messaging application that is used by people and businesses to communicate and transact privately. The Reality Labs segment provides augmented and virtual reality related products comprising virtual reality hardware, software, and content that help people feel connected, anytime, and anywhere. The company was formerly known as Facebook, Inc. and changed its name to Meta Platforms, Inc. in October 2021. Meta Platforms, Inc. was incorporated in 2004 and is headquartered in Menlo Park, California."), + ('GOOG', + 'Alphabet Inc.', + 'Communication Services', + 'CA', + 'Alphabet Inc. provides various products and platforms in the United States, Europe, the Middle East, Africa, the Asia-Pacific, Canada, and Latin America. It operates through Google Services, Google Cloud, and Other Bets segments. The Google Services segment offers products and services, including ads, Android, Chrome, hardware, Gmail, Google Drive, Google Maps, Google Photos, Google Play, Search, and YouTube. It is also involved in the sale of apps and in-app purchases and digital content in the Google Play store; and Fitbit wearable devices, Google Nest home products, Pixel phones, and other devices, as well as in the provision of YouTube non-advertising services. The Google Cloud segment offers infrastructure, platform, and other services; Google Workspace that include cloud-based collaboration tools for enterprises, such as Gmail, Docs, Drive, Calendar, and Meet; and other services for enterprise customers. The Other Bets segment sells health technology and internet services. The company was founded in 1998 and is headquartered in Mountain View, California.'), + ('GS', + 'Goldman Sachs Group, Inc. (The)', + 'Financial Services', + 'NY', + "The Goldman Sachs Group, Inc., a financial institution, provides a range of financial services for corporations, financial institutions, governments, and individuals worldwide. It operates through four segments: Investment Banking, Global Markets, Asset Management, and Consumer & Wealth Management. The company's Investment Banking segment provides financial advisory services, including strategic advisory assignments related to mergers and acquisitions, divestitures, corporate defense activities, restructurings, and spin-offs; and middle-market lending, relationship lending, and acquisition financing, as well as transaction banking services. This segment also offers underwriting services, such as equity underwriting for common and preferred stock and convertible and exchangeable securities; and debt underwriting for various types of debt instruments, including investment-grade and high-yield debt, bank and bridge loans, and emerging-and growth-market debt, as well as originates structured securities. Its Global Markets segment is involved in client execution activities for cash and derivative instruments; credit and interest rate products; and provision of equity intermediation and equity financing, clearing, settlement, and custody services, as well as mortgages, currencies, commodities, and equities related products. The company's Asset Management segment manages assets across various classes, including equity, fixed income, hedge funds, credit funds, private equity, real estate, currencies, and commodities; and provides customized investment advisory solutions, as well as invests in corporate, real estate, and infrastructure entities. Its Consumer & Wealth Management segment offers wealth advisory and banking services, including financial planning, investment management, deposit taking, and lending; private banking; and unsecured loans, as well as accepts saving and time deposits. The company was founded in 1869 and is headquartered in New York, New York."), + ('INTC', + 'Intel Corporation', + 'Technology', + 'CA', + 'Intel Corporation engages in the design, manufacture, and sale of computer products and technologies worldwide. The company operates through CCG, DCG, IOTG, Mobileye, NSG, PSG, and All Other segments. It offers platform products, such as central processing units and chipsets, and system-on-chip and multichip packages; and non-platform or adjacent products, including accelerators, boards and systems, connectivity products, graphics, and memory and storage products. The company also provides high-performance compute solutions for targeted verticals and embedded applications for retail, industrial, and healthcare markets; and solutions for assisted and autonomous driving comprising compute platforms, computer vision and machine learning-based sensing, mapping and localization, driving policy, and active sensors. In addition, it offers workload-optimized platforms and related products for cloud service providers, enterprise and government, and communications service providers. The company serves original equipment manufacturers, original design manufacturers, and cloud service providers. Intel Corporation has a strategic partnership with MILA to develop and apply advances in artificial intelligence methods for enhancing the search in the space of drugs. The company was incorporated in 1968 and is headquartered in Santa Clara, California.'), + ('AAL', + 'American Airlines Group, Inc.', + 'Industrials', + 'TX', + 'American Airlines Group Inc., through its subsidiaries, operates as a network air carrier. The company provides scheduled air transportation services for passengers and cargo through its hubs in Charlotte, Chicago, Dallas/Fort Worth, Los Angeles, Miami, New York, Philadelphia, Phoenix, and Washington, D.C., as well as through partner gateways in London, Madrid, Seattle/Tacoma, Sydney, and Tokyo. As of December 31, 2021, it operated a mainline fleet of 865 aircraft. The company was formerly known as AMR Corporation and changed its name to American Airlines Group Inc. in December 2013. American Airlines Group Inc. was founded in 1930 and is headquartered in Fort Worth, Texas.'), + ('AAPL', + 'Apple Inc.', + 'Technology', + 'CA', + 'Apple Inc. designs, manufactures, and markets smartphones, personal computers, tablets, wearables, and accessories worldwide. It also sells various related services. In addition, the company offers iPhone, a line of smartphones; Mac, a line of personal computers; iPad, a line of multi-purpose tablets; AirPods Max, an over-ear wireless headphone; and wearables, home, and accessories comprising AirPods, Apple TV, Apple Watch, Beats products, HomePod, and iPod touch. Further, it provides AppleCare support services; cloud services store services; and operates various platforms, including the App Store that allow customers to discover and download applications and digital content, such as books, music, video, games, and podcasts. Additionally, the company offers various services, such as Apple Arcade, a game subscription service; Apple Music, which offers users a curated listening experience with on-demand radio stations; Apple News+, a subscription news and magazine service; Apple TV+, which offers exclusive original content; Apple Card, a co-branded credit card; and Apple Pay, a cashless payment service, as well as licenses its intellectual property. The company serves consumers, and small and mid-sized businesses; and the education, enterprise, and government markets. It distributes third-party applications for its products through the App Store. The company also sells its products through its retail and online stores, and direct sales force; and third-party cellular network carriers, wholesalers, retailers, and resellers. Apple Inc. was incorporated in 1977 and is headquartered in Cupertino, California.'), + ('MSFT', + 'Microsoft Corporation no bugs please', + 'Technology', + 'WA', + 'Microsoft Corporation develops, licenses, and supports software, services, devices, and solutions worldwide. Its Productivity and Business Processes segment offers Office, Exchange, SharePoint, Microsoft Teams, Office 365 Security and Compliance, and Skype for Business, as well as related Client Access Licenses (CAL); Skype, Outlook.com, OneDrive, and LinkedIn; and Dynamics 365, a set of cloud-based and on-premises business solutions for organizations and enterprise divisions. Its Intelligent Cloud segment licenses SQL, Windows Servers, Visual Studio, System Center, and related CALs; GitHub that provides a collaboration platform and code hosting service for developers; and Azure, a cloud platform. It also offers support services and Microsoft consulting services to assist customers in developing, deploying, and managing Microsoft server and desktop solutions; and training and certification on Microsoft products. Its More Personal Computing segment provides Windows original equipment manufacturer (OEM) licensing and other non-volume licensing of the Windows operating system; Windows Commercial, such as volume licensing of the Windows operating system, Windows cloud services, and other Windows commercial offerings; patent licensing; Windows Internet of Things; and MSN advertising. It also offers Surface, PC accessories, PCs, tablets, gaming and entertainment consoles, and other devices; Gaming, including Xbox hardware, and Xbox content and services; video games and third-party video game royalties; and Search, including Bing and Microsoft advertising. It sells its products through OEMs, distributors, and resellers; and directly through digital marketplaces, online stores, and retail stores. It has collaborations with Dynatrace, Inc., Morgan Stanley, Micro Focus, WPP plc, ACI Worldwide, Inc., and iCIMS, Inc., as well as strategic relationships with Avaya Holdings Corp. and wejo Limited. Microsoft Corporation was founded in 1975 and is based in Redmond, Washington.')] \ No newline at end of file diff --git a/08_intro_to_databases/google.png b/08_intro_to_databases/google.png new file mode 100644 index 0000000..e3f11e6 Binary files /dev/null and b/08_intro_to_databases/google.png differ diff --git a/08_intro_to_databases/sql_order.png b/08_intro_to_databases/sql_order.png new file mode 100644 index 0000000..370ac90 Binary files /dev/null and b/08_intro_to_databases/sql_order.png differ diff --git a/08_intro_to_databases/sql_struktura.png b/08_intro_to_databases/sql_struktura.png new file mode 100644 index 0000000..4dfc29d Binary files /dev/null and b/08_intro_to_databases/sql_struktura.png differ diff --git a/08_intro_to_databases/stock-db.png b/08_intro_to_databases/stock-db.png new file mode 100644 index 0000000..7ca3768 Binary files /dev/null and b/08_intro_to_databases/stock-db.png differ diff --git a/08_packages_docs_tests/08a_pkg_doc.ipynb b/08_packages_docs_tests/08a_pkg_doc.ipynb new file mode 100644 index 0000000..9580d98 --- /dev/null +++ b/08_packages_docs_tests/08a_pkg_doc.ipynb @@ -0,0 +1,657 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lecture 8\n", + "\n", + "by **Martin Hronec** \n", + "\n", + "Contents:\n", + "1. [How to structure your projects](#Repository-structure)\n", + "2. [Python packaging](#Packaging)\n", + "3. [Documentation](#Documentation)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Repository structure\n", + "\n", + "* “structure” means making clean code whose logic and dependencies are clear as well as how the files and folders are organized in the filesystem\n", + "\n", + "* a repository template:\n", + "\n", + " ```\n", + " README.md\n", + " LICENSE\n", + " setup.py\n", + " requirements.txt\n", + " app/__init__.py\n", + " app/main.py\n", + " app/helpers.py\n", + " docs/conf.py\n", + " docs/index.rst\n", + " tests/test_basic.py\n", + " tests/test_advanced.py\n", + " data/\n", + " .gitignore ```\n", + " \n", + "* `./app/`\n", + " * module package (if module consists of only a single file, it can be placed in the root of your repository\n", + " ( `./sample.py`)\n", + "* `./LICENSE`\n", + " * the full license text and copyright claims\n", + " * you are also free to publish code without a license, but this would prevent many people from potentially using or contributing to your code\n", + " * more on licenses [here](https://choosealicense.com/licenses/)\n", + "* `./setup.py`\n", + " * package and distribution management\n", + " * more in [the next section](#Packaging)\n", + "* `./requirements.txt`\n", + " * a pip requirements file\n", + " * should be placed at the root of the repository\n", + " * should specify the dependencies required to contribute to the project (testing, building, and generating documentation)\n", + "* `./docs/`\n", + " * package reference documentation\n", + " * more in [the documentation section](#Documentation)\n", + "* `./tests/`\n", + "\n", + " * more in [the testing section](#Tests)\n", + "* `./Makefile`\n", + " * for generic management tasks\n", + " * other generic management scrips (e.g. `manage.py`) belong at the root of the repository as well" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Packaging \n", + "\n", + "* why packaging?\n", + " * because we want modular programming\n", + " \n", + "* why modularing (modules)?\n", + " * simplicity\n", + " * maintainability\n", + " * reusability\n", + " * scoping - separate namespace\n", + "\n", + "* functions, modules and packages already offer modularization\n", + "\n", + "* Python is a general-purpose programming language => can be used in many ways\n", + " * scientific computing\n", + " * websites\n", + " * scraping, etc.\n", + " \n", + "* this flexibility is the reason you need to think about:\n", + " * the project's customers/users\n", + " * the environment where the project will run\n", + "\n", + "* not necessary bad idea to think about packaging before starting to code\n", + "* what is a package? ... a collection of:\n", + " * modules \n", + " * documentation\n", + " * tests\n", + " * tools to build and install it, etc. \n", + "\n", + "### Deployment \n", + "* projects (packages) exist to be deployed (installed)\n", + "* before you package anything, ask questions like:\n", + "\n", + " * who are your users? (software (python) developers, business people)\n", + " * where will your software run? (servers, desktops, mobiles)\n", + " * how is your software deployed? (part of the large software stack, individually, etc.)\n", + "* packaging libraries and tools (technical audience) vs. packaging applications (non-technical audience)\n", + "\n", + "### Packaging libraries and tools\n", + "\n", + "* you've probably heard about PyPI, `setup.py` and [wheels](https://pythonwheels.com/) \n", + "\n", + "* **modules**\n", + " * simply a python file - can be distributed \n", + " * care about the right version of Python (and only relies on the standard library)\n", + " * great for sharing simple scripts and snippets (email, StackOverflow, [GitHub gists](https://gist.github.com/)\n", + " * ! this does not scale for projects with multiple files, need additional libraries or specific Python versions\n", + "\n", + "* let's look at what's going on with modules\n", + " * look at the objects defined in example_module.py (below)\n", + " * text (string)\n", + " * f (function)\n", + " * AClass (class)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# %load example_module.py\n", + "text = \"modularity is the key\"\n", + "\n", + "def f(arg):\n", + " print(f'This function takes as an argument: {arg}')\n", + "\n", + "class AClass:\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* (if example_module.py is in appropriate location) these objects can be imported using `import` call in python\n", + " * (delete them before trying with import)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "del AClass, f, text" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'f' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn [3], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mf\u001b[49m\n", + "\u001b[1;31mNameError\u001b[0m: name 'f' is not defined" + ] + } + ], + "source": [ + "f" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import example_module" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['AClass',\n", + " '__builtins__',\n", + " '__cached__',\n", + " '__doc__',\n", + " '__file__',\n", + " '__loader__',\n", + " '__name__',\n", + " '__package__',\n", + " '__spec__',\n", + " 'f',\n", + " 'text']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dir(example_module)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'modularity is the key'" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "example_module.text" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* what happens when the interpreter executes the above `import` statement? \n", + "* interpreter searches for *example_module.py* in **the module search path** (list of directories ):\n", + " * the current working directory\n", + " * the list of directories contained in the PYTHONPATH environment variable\n", + " * an installation-dependent list of directories configured at the time Python is installed\n", + "* the resulting search path is accessible in the Python variable `sys.path`" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['c:\\\\Users\\\\Martin Hronec\\\\Projects\\\\phd\\\\teaching\\\\PythonDataIES\\\\08_packages_docs_tests',\n", + " 'c:\\\\Users\\\\Martin Hronec\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310\\\\python310.zip',\n", + " 'c:\\\\Users\\\\Martin Hronec\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310\\\\DLLs',\n", + " 'c:\\\\Users\\\\Martin Hronec\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310\\\\lib',\n", + " 'c:\\\\Users\\\\Martin Hronec\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310',\n", + " '',\n", + " 'C:\\\\Users\\\\Martin Hronec\\\\AppData\\\\Roaming\\\\Python\\\\Python310\\\\site-packages',\n", + " 'C:\\\\Users\\\\Martin Hronec\\\\AppData\\\\Roaming\\\\Python\\\\Python310\\\\site-packages\\\\win32',\n", + " 'C:\\\\Users\\\\Martin Hronec\\\\AppData\\\\Roaming\\\\Python\\\\Python310\\\\site-packages\\\\win32\\\\lib',\n", + " 'C:\\\\Users\\\\Martin Hronec\\\\AppData\\\\Roaming\\\\Python\\\\Python310\\\\site-packages\\\\Pythonwin',\n", + " 'c:\\\\Users\\\\Martin Hronec\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310\\\\lib\\\\site-packages',\n", + " 'c:\\\\users\\\\martin hronec\\\\projects\\\\phd\\\\teaching\\\\dd',\n", + " 'c:\\\\Users\\\\Martin Hronec\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310\\\\lib\\\\site-packages\\\\redata-0.1-py3.10.egg']" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sys\n", + "sys.path" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* to ensure that your module is found, you need to do one of the following:\n", + " * put example_module.py in the directory where the input script is located or the current working directory\n", + " * add directory where `example_module.py` is located to PYTHONPATH environment variable \n", + " * put example_module.py anywhere you like and modify `sys.path` at runtime so that it contains that directory (see below)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['c:\\\\Users\\\\Martin Hronec\\\\Projects\\\\phd\\\\teaching\\\\PythonDataIES\\\\08_packages_docs_tests',\n", + " 'c:\\\\Users\\\\Martin Hronec\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310\\\\python310.zip',\n", + " 'c:\\\\Users\\\\Martin Hronec\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310\\\\DLLs',\n", + " 'c:\\\\Users\\\\Martin Hronec\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310\\\\lib',\n", + " 'c:\\\\Users\\\\Martin Hronec\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310',\n", + " '',\n", + " 'C:\\\\Users\\\\Martin Hronec\\\\AppData\\\\Roaming\\\\Python\\\\Python310\\\\site-packages',\n", + " 'C:\\\\Users\\\\Martin Hronec\\\\AppData\\\\Roaming\\\\Python\\\\Python310\\\\site-packages\\\\win32',\n", + " 'C:\\\\Users\\\\Martin Hronec\\\\AppData\\\\Roaming\\\\Python\\\\Python310\\\\site-packages\\\\win32\\\\lib',\n", + " 'C:\\\\Users\\\\Martin Hronec\\\\AppData\\\\Roaming\\\\Python\\\\Python310\\\\site-packages\\\\Pythonwin',\n", + " 'c:\\\\Users\\\\Martin Hronec\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310\\\\lib\\\\site-packages',\n", + " 'c:\\\\users\\\\martin hronec\\\\projects\\\\phd\\\\teaching\\\\dd',\n", + " 'c:\\\\Users\\\\Martin Hronec\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310\\\\lib\\\\site-packages\\\\redata-0.1-py3.10.egg',\n", + " 'C:\\\\Users\\\\Martin Hronec\\\\Projects']" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sys.path.append(r'C:\\Users\\Martin Hronec\\Projects')\n", + "sys.path" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* once a module has been imported, you can determine the location where it was found with the module's `__file__` attribute" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'c:\\\\Users\\\\Martin Hronec\\\\Projects\\\\phd\\\\teaching\\\\PythonDataIES\\\\08_packages_docs_tests\\\\example_module.py'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import example_module\n", + "example_module.__file__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* possible to do `from import *`\n", + " * this is not recommended (especially in production code)\n", + "* also possible to use aliases\n", + " * `import pandas as pd` - `pd` is alias\n", + "* ! modules are loaded only once per session\n", + " * if you make a change to a module and need to reload it, you need to either restart the interpreter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Continuing with the distribution options you have ...\n", + "\n", + "* **PACKAGES**\n", + "\n", + " * a \"package\" is essentially a module with other modules (potentially in it)\n", + " * ↑ number of modules => ↑ mess\n", + " * packages allow hierarchical structuring of the module namespace\n", + "\n", + "* package = a directory with an `__init__.py` and any number of other python files or other package directories\n", + " ```\n", + " a_package\n", + " __init__.py\n", + " module_a.py\n", + " a_sub_package\n", + " __init__.py\n", + " module_b.py\n", + " ```\n", + "\n", + "* `__init__.py` can be empty or not (it will be run when the package is imported)\n", + "* example project from the Python Packaging Authority (real thing) [here](https://github.com/pypa/sampleproject)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### setuptools\n", + "\n", + "* `setup.py` tells setuptools how to package, build and install the package\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# %load setup.py\n", + "from setuptools import setup\n", + "\n", + "setup(\n", + " name='PackageName',\n", + " version='0.1',\n", + " author='YoursTruly',\n", + " author_email='yourstruly@fsv.cuni.cz',\n", + " #packages=['package_name','package_name.test'],\n", + " url='',\n", + " license='LICENSE.txt',\n", + " description='Exemplatory package.',\n", + " #long_description=open('README.md').read(),\n", + " install_requires=[\n", + " \"Django >= 1.1.1\",\n", + " \"pytest\",\n", + " ],)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* with a `setup.py` script, setuptools can:\n", + " * build a source distribution `python setup.py sdist`\n", + " * build wheels `./setup.py bdist_wheel` (the wheel package needed)\n", + " * build from source `python setup.py build`\n", + " * install `python setup.py install`\n", + " \n", + "* we can also install in develop/editable mode: `python setup.py develop` or `pip install -e ./`\n", + " * your package is installed, but any changes will immediately take effect\n", + " * no `sys.path` manipulation!\n", + "\n", + "* you can also upload your package to [PyPI](https://pypi.org/)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* **Quick exercise**: Create a new package\n", + " 1. create the basic package structure\n", + " 2. write a setup.py\n", + " 3. install the package with a `setup.py`\n", + " 4. import it from somewhere else" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* **Notes**:\n", + " * for larger projects, it is good idea tu use templates, e.g. from [Cookie Cutter](https://cookiecutter.readthedocs.io/en/latest/)\n", + " * quality packaging materials:\n", + " * from the Python Packaging authority [here](https://packaging.python.org/)\n", + " * [practical tutorial](https://python-packaging-tutorial.readthedocs.io/en/latest/setup_py.html)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* **Discussion**: Is data science different?\n", + " * https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/overview" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Documentation\n", + "\n", + "* why documentation?\n", + " * let's ask [write-the-docs community](https://www.writethedocs.org/guide/writing/beginners-guide-to-docs/)\n", + "\n", + "* write docstrings at minimum:\n", + "\n", + "* example from [sphinx](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html) below\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "def function_with_types_in_docstring(param1, param2):\n", + " \"\"\"Example function with types documented in the docstring.\n", + "\n", + " `PEP 484`_ type annotations are supported. If attribute, parameter, and\n", + " return types are annotated according to `PEP 484`_, they do not need to be\n", + " included in the docstring:\n", + "\n", + " Args:\n", + " param1 (int): The first parameter.\n", + " param2 (str): The second parameter.\n", + "\n", + " Returns:\n", + " bool: The return value. True for success, False otherwise.\n", + "\n", + " .. _PEP 484:\n", + " https://www.python.org/dev/peps/pep-0484/\n", + "\n", + " \"\"\"\n", + "\n", + "\n", + "def function_with_pep484_type_annotations(param1: int, param2: str) -> bool:\n", + " \"\"\"Example function with PEP 484 type annotations.\n", + "\n", + " Args:\n", + " param1: The first parameter.\n", + " param2: The second parameter.\n", + "\n", + " Returns:\n", + " The return value. True for success, False otherwise.\n", + "\n", + " \"\"\"\n", + "\n", + "\n", + "def module_level_function(param1, param2=None, *args, **kwargs):\n", + " \"\"\"This is an example of a module level function.\n", + "\n", + " Function parameters should be documented in the ``Args`` section. The name\n", + " of each parameter is required. The type and description of each parameter\n", + " is optional, but should be included if not obvious.\n", + "\n", + " If \\*args or \\*\\*kwargs are accepted,\n", + " they should be listed as ``*args`` and ``**kwargs``.\n", + "\n", + " The format for a parameter is::\n", + "\n", + " name (type): description\n", + " The description may span multiple lines. Following\n", + " lines should be indented. The \"(type)\" is optional.\n", + "\n", + " Multiple paragraphs are supported in parameter\n", + " descriptions.\n", + "\n", + " Args:\n", + " param1 (int): The first parameter.\n", + " param2 (:obj:`str`, optional): The second parameter. Defaults to None.\n", + " Second line of description should be indented.\n", + " *args: Variable length argument list.\n", + " **kwargs: Arbitrary keyword arguments.\n", + "\n", + " Returns:\n", + " bool: True if successful, False otherwise.\n", + "\n", + " The return type is optional and may be specified at the beginning of\n", + " the ``Returns`` section followed by a colon.\n", + "\n", + " The ``Returns`` section may span multiple lines and paragraphs.\n", + " Following lines should be indented to match the first line.\n", + "\n", + " The ``Returns`` section supports any reStructuredText formatting,\n", + " including literal blocks::\n", + "\n", + " {\n", + " 'param1': param1,\n", + " 'param2': param2\n", + " }\n", + "\n", + " Raises:\n", + " AttributeError: The ``Raises`` section is a list of all exceptions\n", + " that are relevant to the interface.\n", + " ValueError: If `param2` is equal to `param1`.\n", + "\n", + " \"\"\"\n", + " if param1 == param2:\n", + " raise ValueError('param1 may not be equal to param2')\n", + " return True" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### mkdocs\n", + "* nothing wrong with sphinx, however mkdocs more user-friendly -> we will look at the example\n", + "* if you want to use markdown, look at [mkdocs](https://www.mkdocs.org/)\n", + "* example config\n", + "\n", + "```\n", + "site_name: example\n", + "nav:\n", + " - \"Home\" : index.md\n", + " - \"About\" : about.md\n", + " - \"Pipeline\" : pipeline.md\n", + "\n", + "docs_dir: docs\n", + "plugins:\n", + " - search\n", + " - mkdocstrings:\n", + " default_handler : python\n", + " handlers:\n", + " python:\n", + " setup_commands:\n", + " - import sys\n", + " - sys.path.append(\"app/\")\n", + " rendering:\n", + " show_source: true\n", + " show_root_heading: true\n", + "extra_css:\n", + " - stylesheets/extra.css\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* **Ex**: build basic docs structure for yourself\n", + " * (later) host it on GitHub pages - https://pages.github.com/\n", + " " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.7 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.7" + }, + "vscode": { + "interpreter": { + "hash": "1f0e6d99f3103fd78365fe1cf7b2d51239fa0878786db9cbdfe89bc88a3151d3" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/08_packages_docs_tests/08b_testing.ipynb b/08_packages_docs_tests/08b_testing.ipynb new file mode 100644 index 0000000..f3deb00 --- /dev/null +++ b/08_packages_docs_tests/08b_testing.ipynb @@ -0,0 +1,1133 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lecture 8b\n", + "by Martin Hronec\n", + "\n", + "[Testing](#Testing)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing\n", + "\n", + "* many ways to test code\n", + "* you've all done an exploratory/manual testing\n", + "* to cover the whole codebase with manual tests, it is necessary:\n", + " * list all the code/projects features\n", + " * collect all (different) types of inputs it \n", + " * collect the corresponding expected results\n", + "* !problem: change in code => change the above\n", + " * not fun => **automated testing**\n", + " * running test from script instead of manually\n", + " \n", + "* 2 main test categories:\n", + " * integration tests - testing multiple if multiple components work together\n", + " * unit tests - testing a single component\n", + "\n", + "* (most) functional tests consist of:\n", + " 1. **Arrange** - conditions in/for which we test\n", + " 2. **Act** - running the behaviour we want to test\n", + " 3. **Assert** - check if behaviour produced expected result\n", + " 4. **Cleanup** - don't influence other tests\n", + "\n", + "* the most basic test can be done using `assert` method\n", + " * e.g. lets check/test if `len` method is the same as `__len__`" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "a_list = [1,2,3,5] \n", + "assert len(a_list) == a_list.__len__(), \"Function len returned differnt result than method __len__\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* we could try different data-structure" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "a_tuple = (1,2,3,5)\n", + "assert len(a_tuple) == a_tuple.__len__(), \"Function len returned differnt result than method __len__\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "a_tuple = (1,2,3,5)\n", + "assert len(a_tuple) == a_tuple.__len__(), \"Function len returned differnt result than method __len__\"" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "ename": "AssertionError", + "evalue": "2. Your result is off.", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAssertionError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn [4], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28msum\u001b[39m([\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m1\u001b[39m]) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m3\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2. Your result is off.\u001b[39m\u001b[38;5;124m'\u001b[39m\n", + "\u001b[1;31mAssertionError\u001b[0m: 2. Your result is off." + ] + } + ], + "source": [ + "assert sum([1,1]) == 3, '2. Your result is off.'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* instead of testing on the REPL, we can put our tests into a test script and run it " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All tests passed.\n" + ] + } + ], + "source": [ + "# %load test_1.py\n", + "def test_sum():\n", + " assert sum([1,1]) == 2, \"Should be 2\"\n", + " \n", + "def test_len_vs__len__():\n", + " a_tuple = (1,2,3,5)\n", + " assert len(a_tuple) == a_tuple.__len__(), \"Function len returned differnt result than method __len__\"\n", + " \n", + "if __name__ == \"__main__\":\n", + " test_sum()\n", + " test_len_vs__len__()\n", + " print('All tests passed.')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All tests passed.\n" + ] + } + ], + "source": [ + "%run test_1.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* OK for simple check, cumbersome for more tests\n", + " * => **test runners**\n", + "* test runner = application designed for running tests\n", + " * check the output\n", + " * offer tools for diagnosing\n", + " \n", + "* many test runners available for Python\n", + " * *unittest* (built into the Python standard library)\n", + " * nose/nose2\n", + " * doctest\n", + " * robot\n", + " * **pytest**, ...\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## pytest" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* a framework for building simple and scalable tests\n", + "* one of the most popular Python testing frameworks\n", + " * feature-rich\n", + " * a lot of available [plugins](https://docs.pytest.org/en/latest/reference/plugin_list.html)\n", + " \n", + "* pytest works with the simple assert statements\n", + " * not necessarily the case with other test runners\n", + "\n", + "* how does pytest know which tests to run?\n", + " * by default it runs all files of the form `test_*.py` or `*_test.py` in the current directory and subdirectories\n", + " * however check [conventions for test discovery rules](https://docs.pytest.org/en/6.2.x/goodpractices.html#test-discovery)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "============================= test session starts =============================\n", + "platform win32 -- Python 3.10.7, pytest-7.1.3, pluggy-1.0.0\n", + "rootdir: c:\\Users\\Martin Hronec\\Projects\\phd\\teaching\\PythonDataIES\\06_packages_docs_tests, configfile: pytest.ini\n", + "plugins: anyio-3.6.1\n", + "collected 17 items\n", + "\n", + "test_1.py .. [ 11%]\n", + "test_2.py F. [ 23%]\n", + "test_naive.py .. [ 35%]\n", + "tests\\test_3.py F. [ 47%]\n", + "tests\\test_fixture_smtp.py . [ 52%]\n", + "tests\\test_fixtures_data.py E [ 58%]\n", + "tests\\test_mark_example.py .. [ 70%]\n", + "tests\\test_parametrize_example.py ..F.. [100%]\n", + "\n", + "=================================== ERRORS ====================================\n", + "______________________ ERROR at setup of test_addressing ______________________\n", + "\n", + " @pytest.fixture\n", + " def data_names():\n", + " import pandas as pd\n", + "> df = pd.read_csv('tests/data/test_data_names.csv')\n", + "\n", + "tests\\test_fixtures_data.py:6: \n", + "_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _\n", + "..\\..\\..\\..\\..\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\util\\_decorators.py:311: in wrapper\n", + " return func(*args, **kwargs)\n", + "..\\..\\..\\..\\..\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:678: in read_csv\n", + " return _read(filepath_or_buffer, kwds)\n", + "..\\..\\..\\..\\..\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:575: in _read\n", + " parser = TextFileReader(filepath_or_buffer, **kwds)\n", + "..\\..\\..\\..\\..\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:932: in __init__\n", + " self._engine = self._make_engine(f, self.engine)\n", + "..\\..\\..\\..\\..\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:1216: in _make_engine\n", + " self.handles = get_handle( # type: ignore[call-overload]\n", + "_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _\n", + "\n", + "path_or_buf = 'tests/data/test_data_names.csv', mode = 'r'\n", + "\n", + " @doc(compression_options=_shared_docs[\"compression_options\"] % \"path_or_buf\")\n", + " def get_handle(\n", + " path_or_buf: FilePath | BaseBuffer,\n", + " mode: str,\n", + " *,\n", + " encoding: str | None = None,\n", + " compression: CompressionOptions = None,\n", + " memory_map: bool = False,\n", + " is_text: bool = True,\n", + " errors: str | None = None,\n", + " storage_options: StorageOptions = None,\n", + " ) -> IOHandles[str] | IOHandles[bytes]:\n", + " \"\"\"\n", + " Get file handle for given path/buffer and mode.\n", + " \n", + " Parameters\n", + " ----------\n", + " path_or_buf : str or file handle\n", + " File path or object.\n", + " mode : str\n", + " Mode to open path_or_buf with.\n", + " encoding : str or None\n", + " Encoding to use.\n", + " {compression_options}\n", + " \n", + " .. versionchanged:: 1.0.0\n", + " May now be a dict with key 'method' as compression mode\n", + " and other keys as compression options if compression\n", + " mode is 'zip'.\n", + " \n", + " .. versionchanged:: 1.1.0\n", + " Passing compression options as keys in dict is now\n", + " supported for compression modes 'gzip', 'bz2', 'zstd' and 'zip'.\n", + " \n", + " .. versionchanged:: 1.4.0 Zstandard support.\n", + " \n", + " memory_map : bool, default False\n", + " See parsers._parser_params for more information.\n", + " is_text : bool, default True\n", + " Whether the type of the content passed to the file/buffer is string or\n", + " bytes. This is not the same as `\"b\" not in mode`. If a string content is\n", + " passed to a binary file/buffer, a wrapper is inserted.\n", + " errors : str, default 'strict'\n", + " Specifies how encoding and decoding errors are to be handled.\n", + " See the errors argument for :func:`open` for a full list\n", + " of options.\n", + " storage_options: StorageOptions = None\n", + " Passed to _get_filepath_or_buffer\n", + " \n", + " .. versionchanged:: 1.2.0\n", + " \n", + " Returns the dataclass IOHandles\n", + " \"\"\"\n", + " # Windows does not default to utf-8. Set to utf-8 for a consistent behavior\n", + " encoding = encoding or \"utf-8\"\n", + " \n", + " # read_csv does not know whether the buffer is opened in binary/text mode\n", + " if _is_binary_mode(path_or_buf, mode) and \"b\" not in mode:\n", + " mode += \"b\"\n", + " \n", + " # validate encoding and errors\n", + " codecs.lookup(encoding)\n", + " if isinstance(errors, str):\n", + " codecs.lookup_error(errors)\n", + " \n", + " # open URLs\n", + " ioargs = _get_filepath_or_buffer(\n", + " path_or_buf,\n", + " encoding=encoding,\n", + " compression=compression,\n", + " mode=mode,\n", + " storage_options=storage_options,\n", + " )\n", + " \n", + " handle = ioargs.filepath_or_buffer\n", + " handles: list[BaseBuffer]\n", + " \n", + " # memory mapping needs to be the first step\n", + " handle, memory_map, handles = _maybe_memory_map(\n", + " handle,\n", + " memory_map,\n", + " ioargs.encoding,\n", + " ioargs.mode,\n", + " errors,\n", + " ioargs.compression[\"method\"] not in _compression_to_extension,\n", + " )\n", + " \n", + " is_path = isinstance(handle, str)\n", + " compression_args = dict(ioargs.compression)\n", + " compression = compression_args.pop(\"method\")\n", + " \n", + " # Only for write methods\n", + " if \"r\" not in mode and is_path:\n", + " check_parent_directory(str(handle))\n", + " \n", + " if compression:\n", + " if compression != \"zstd\":\n", + " # compression libraries do not like an explicit text-mode\n", + " ioargs.mode = ioargs.mode.replace(\"t\", \"\")\n", + " elif compression == \"zstd\" and \"b\" not in ioargs.mode:\n", + " # python-zstandard defaults to text mode, but we always expect\n", + " # compression libraries to use binary mode.\n", + " ioargs.mode += \"b\"\n", + " \n", + " # GZ Compression\n", + " if compression == \"gzip\":\n", + " if is_path:\n", + " assert isinstance(handle, str)\n", + " # error: Incompatible types in assignment (expression has type\n", + " # \"GzipFile\", variable has type \"Union[str, BaseBuffer]\")\n", + " handle = gzip.GzipFile( # type: ignore[assignment]\n", + " filename=handle,\n", + " mode=ioargs.mode,\n", + " **compression_args,\n", + " )\n", + " else:\n", + " handle = gzip.GzipFile(\n", + " # No overload variant of \"GzipFile\" matches argument types\n", + " # \"Union[str, BaseBuffer]\", \"str\", \"Dict[str, Any]\"\n", + " fileobj=handle, # type: ignore[call-overload]\n", + " mode=ioargs.mode,\n", + " **compression_args,\n", + " )\n", + " \n", + " # BZ Compression\n", + " elif compression == \"bz2\":\n", + " # No overload variant of \"BZ2File\" matches argument types\n", + " # \"Union[str, BaseBuffer]\", \"str\", \"Dict[str, Any]\"\n", + " handle = bz2.BZ2File( # type: ignore[call-overload]\n", + " handle,\n", + " mode=ioargs.mode,\n", + " **compression_args,\n", + " )\n", + " \n", + " # ZIP Compression\n", + " elif compression == \"zip\":\n", + " # error: Argument 1 to \"_BytesZipFile\" has incompatible type \"Union[str,\n", + " # BaseBuffer]\"; expected \"Union[Union[str, PathLike[str]],\n", + " # ReadBuffer[bytes], WriteBuffer[bytes]]\"\n", + " handle = _BytesZipFile(\n", + " handle, ioargs.mode, **compression_args # type: ignore[arg-type]\n", + " )\n", + " if handle.mode == \"r\":\n", + " handles.append(handle)\n", + " zip_names = handle.namelist()\n", + " if len(zip_names) == 1:\n", + " handle = handle.open(zip_names.pop())\n", + " elif len(zip_names) == 0:\n", + " raise ValueError(f\"Zero files found in ZIP file {path_or_buf}\")\n", + " else:\n", + " raise ValueError(\n", + " \"Multiple files found in ZIP file. \"\n", + " f\"Only one file per ZIP: {zip_names}\"\n", + " )\n", + " \n", + " # XZ Compression\n", + " elif compression == \"xz\":\n", + " handle = get_lzma_file()(handle, ioargs.mode)\n", + " \n", + " # Zstd Compression\n", + " elif compression == \"zstd\":\n", + " zstd = import_optional_dependency(\"zstandard\")\n", + " if \"r\" in ioargs.mode:\n", + " open_args = {\"dctx\": zstd.ZstdDecompressor(**compression_args)}\n", + " else:\n", + " open_args = {\"cctx\": zstd.ZstdCompressor(**compression_args)}\n", + " handle = zstd.open(\n", + " handle,\n", + " mode=ioargs.mode,\n", + " **open_args,\n", + " )\n", + " \n", + " # Unrecognized Compression\n", + " else:\n", + " msg = f\"Unrecognized compression type: {compression}\"\n", + " raise ValueError(msg)\n", + " \n", + " assert not isinstance(handle, str)\n", + " handles.append(handle)\n", + " \n", + " elif isinstance(handle, str):\n", + " # Check whether the filename is to be opened in binary mode.\n", + " # Binary mode does not support 'encoding' and 'newline'.\n", + " if ioargs.encoding and \"b\" not in ioargs.mode:\n", + " # Encoding\n", + "> handle = open(\n", + " handle,\n", + " ioargs.mode,\n", + " encoding=ioargs.encoding,\n", + " errors=errors,\n", + " newline=\"\",\n", + " )\n", + "E FileNotFoundError: [Errno 2] No such file or directory: 'tests/data/test_data_names.csv'\n", + "\n", + "..\\..\\..\\..\\..\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\io\\common.py:786: FileNotFoundError\n", + "================================== FAILURES ===================================\n", + "__________________________________ test_sum ___________________________________\n", + "\n", + " def test_sum():\n", + "> assert sum([1,1]) == 3, \"Should be 2\"\n", + "E AssertionError: Should be 2\n", + "E assert 2 == 3\n", + "E + where 2 = sum([1, 1])\n", + "\n", + "test_2.py:3: AssertionError\n", + "__________________________________ test_sum ___________________________________\n", + "\n", + " def test_sum():\n", + "> assert sum([1,1]) == 3, \"Should be 2\"\n", + "E AssertionError: Should be 2\n", + "E assert 2 == 3\n", + "E + where 2 = sum([1, 1])\n", + "\n", + "tests\\test_3.py:3: AssertionError\n", + "______________________________ test_eval[6*9-42] ______________________________\n", + "\n", + "test_input = '6*9', expected = 42\n", + "\n", + " @pytest.mark.parametrize(\"test_input,expected\", [(\"3+5\", 8), (\"2+4\", 6), (\"6*9\", 42)])\n", + " def test_eval(test_input, expected):\n", + "> assert eval(test_input) == expected\n", + "E AssertionError: assert 54 == 42\n", + "E + where 54 = eval('6*9')\n", + "\n", + "tests\\test_parametrize_example.py:32: AssertionError\n", + "=========================== short test summary info ===========================\n", + "FAILED test_2.py::test_sum - AssertionError: Should be 2\n", + "FAILED tests/test_3.py::test_sum - AssertionError: Should be 2\n", + "FAILED tests/test_parametrize_example.py::test_eval[6*9-42] - AssertionError:...\n", + "ERROR tests/test_fixtures_data.py::test_addressing - FileNotFoundError: [Errn...\n", + "==================== 3 failed, 13 passed, 1 error in 3.98s ====================\n" + ] + } + ], + "source": [ + "!pytest" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* what does it tell us:\n", + " * the system tests are run on (Python, pytest version, and any pluggins\n", + " * *rootdir* : where are we running things from\n", + " * [XX%] next to each test script shows success rate of all tests\n", + " * it will show you a failure report with detailed explanation (not here)\n", + " * lets fail" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "#%%writefile test_2.py\n", + "#%%read test_2.py\n", + "\n", + "def test_sum():\n", + " assert sum([1,1]) == 3, \"Should be 2\"\n", + "\n", + "def test_len_vs__len__():\n", + " a_tuple = (1,2,3,5)\n", + " assert len(a_tuple) == a_tuple.__len__(), \"Function len returned differnt result than method __len__\"" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "============================= test session starts =============================\n", + "platform win32 -- Python 3.7.3, pytest-6.0.1, py-1.9.0, pluggy-0.13.1\n", + "rootdir: C:\\Users\\Martin Hronec\\Projects\\phd\\teaching\\DPP_IES\\10_testing, configfile: pytest.ini\n", + "collected 15 items\n", + "\n", + "test_2.py F. [ 13%]\n", + "test_naive.py .. [ 26%]\n", + "tests\\test_3.py F. [ 40%]\n", + "tests\\test_fixture_smtp.py . [ 46%]\n", + "tests\\test_fixtures_data.py F [ 53%]\n", + "tests\\test_mark_example.py .. [ 66%]\n", + "tests\\test_parametrize_example.py ..F.. [100%]\n", + "\n", + "================================== FAILURES ===================================\n", + "__________________________________ test_sum ___________________________________\n", + "\n", + " def test_sum():\n", + "> assert sum([1,1]) == 3, \"Should be 2\"\n", + "E AssertionError: Should be 2\n", + "E assert 2 == 3\n", + "E + where 2 = sum([1, 1])\n", + "\n", + "test_2.py:3: AssertionError\n", + "__________________________________ test_sum ___________________________________\n", + "\n", + " def test_sum():\n", + "> assert sum([1,1]) == 3, \"Should be 2\"\n", + "E AssertionError: Should be 2\n", + "E assert 2 == 3\n", + "E + where 2 = sum([1, 1])\n", + "\n", + "tests\\test_3.py:3: AssertionError\n", + "_______________________________ test_addressing _______________________________\n", + "\n", + "data_names = Title Surname Addressing\n", + "0 Mgr. Kalerab Mgr. Kalerab\n", + "1 Ing. Mrkvicka Ing. Mrkvicka\n", + "2 NaN Slanina Slanina\n", + "\n", + " def test_addressing(data_names):\n", + " df = data_names\n", + " titles = df['Title']\n", + " surnames = df['Surname']\n", + " expected = df['Addressing']\n", + "> assert (titles + ' ' + expected == surnames).all()\n", + "E assert False\n", + "E + where False = ()\n", + "E + where = 0 Mgr. Mg...\\ndtype: object == 0 Kalerab... dtype: object\n", + "E Use -v to get the full diff.all\n", + "\n", + "tests\\test_fixtures_data.py:14: AssertionError\n", + "______________________________ test_eval[6*9-42] ______________________________\n", + "\n", + "test_input = '6*9', expected = 42\n", + "\n", + " @pytest.mark.parametrize(\"test_input,expected\", [(\"3+5\", 8), (\"2+4\", 6), (\"6*9\", 42)])\n", + " def test_eval(test_input, expected):\n", + "> assert eval(test_input) == expected\n", + "E AssertionError: assert 54 == 42\n", + "E + where 54 = eval('6*9')\n", + "\n", + "tests\\test_parametrize_example.py:32: AssertionError\n", + "=========================== short test summary info ===========================\n", + "FAILED test_2.py::test_sum - AssertionError: Should be 2\n", + "FAILED tests/test_3.py::test_sum - AssertionError: Should be 2\n", + "FAILED tests/test_fixtures_data.py::test_addressing - assert False\n", + "FAILED tests/test_parametrize_example.py::test_eval[6*9-42] - AssertionError:...\n", + "======================== 4 failed, 11 passed in 0.84s =========================\n" + ] + } + ], + "source": [ + "!pytest" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* output next to the script indecates the status of each test:\n", + " * \".\" - test passed\n", + " * \"F\" - test failed\n", + " * \"E\" - test raised an unexcpected exception\n", + "\n", + "* it does not only show you the AssertionError though\n", + " * what does it show us (compared to the simple assert statement)?\n", + "\n", + "* if we want to run only some tests, we can specify which to ignore\n", + " * `--ignore`\n", + " * `--ignore-glob` - using glob (wildcard like patterns)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting tests/test_3.py\n" + ] + } + ], + "source": [ + "%%writefile tests/test_3.py\n", + "\n", + "def test_sum():\n", + " assert sum([1,1]) == 3, \"Should be 2\"\n", + "\n", + "def test_len_vs__len__():\n", + " a_tuple = (1,2,3,5)\n", + " assert len(a_tuple) == a_tuple.__len__(), \"Function len returned differnt result than method __len__\"" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C:\\Users\\pcz02m4h\\Projects\\DPP_IES\\10_testing\n" + ] + } + ], + "source": [ + "# checking where we are\n", + "!cd" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "============================= test session starts =============================\n", + "platform win32 -- Python 3.9.4, pytest-6.2.3, py-1.10.0, pluggy-0.13.1\n", + "rootdir: C:\\Users\\pcz02m4h\\Projects\\DPP_IES\\10_testing\n", + "plugins: anyio-2.2.0\n", + "collected 4 items\n", + "\n", + "test_1.py .. [ 50%]\n", + "test_2.py F. [100%]\n", + "\n", + "================================== FAILURES ===================================\n", + "__________________________________ test_sum ___________________________________\n", + "\n", + " def test_sum():\n", + "> assert sum([1,1]) == 3, \"Should be 2\"\n", + "E AssertionError: Should be 2\n", + "E assert 2 == 3\n", + "E + where 2 = sum([1, 1])\n", + "\n", + "test_2.py:3: AssertionError\n", + "=========================== short test summary info ===========================\n", + "FAILED test_2.py::test_sum - AssertionError: Should be 2\n", + "========================= 1 failed, 3 passed in 0.11s =========================\n" + ] + } + ], + "source": [ + "!pytest --ignore=tests/" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "============================= test session starts =============================\n", + "platform win32 -- Python 3.9.4, pytest-6.2.3, py-1.10.0, pluggy-0.13.1\n", + "rootdir: C:\\Users\\pcz02m4h\\Projects\\DPP_IES\\10_testing\n", + "plugins: anyio-2.2.0\n", + "collected 5 items\n", + "\n", + "test_1.py .. [ 40%]\n", + "test_2.py F. [ 80%]\n", + "tests\\test_not_to_run.py . [100%]\n", + "\n", + "================================== FAILURES ===================================\n", + "__________________________________ test_sum ___________________________________\n", + "\n", + " def test_sum():\n", + "> assert sum([1,1]) == 3, \"Should be 2\"\n", + "E AssertionError: Should be 2\n", + "E assert 2 == 3\n", + "E + where 2 = sum([1, 1])\n", + "\n", + "test_2.py:3: AssertionError\n", + "=========================== short test summary info ===========================\n", + "FAILED test_2.py::test_sum - AssertionError: Should be 2\n", + "========================= 1 failed, 4 passed in 0.11s =========================\n" + ] + } + ], + "source": [ + "# when not ignoring\n", + "!pytest --ignore-glob=*_3.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* in most modern code editors, managing a set of tests is more user friendly than from command line\n", + "\n", + "\n", + "* tests often depend on:\n", + " * data\n", + " * test doubles\n", + "* we don't want to mess with the originals => pytest **fixtures**\n", + "\n", + "### Fixtures\n", + "* \"arranging\" part of the test\n", + "\n", + "* a method for providing:\n", + " * data\n", + " * test doubles\n", + " * state setup \n", + "\n", + "* more tests using the same underlying dataset -> use fixture\n", + " * (repeating) data provided by a single function [decorated](#Decorators) with `@pytest.fixture`\n", + " \n", + "* test depending on a fixture needs to have a fixture as an argument\n", + "\n", + "* let's look at the test double first" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C:\\Users\\pcz02m4h\\Projects\\DPP_IES\\10_testing\n" + ] + } + ], + "source": [ + "!cd" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "# %load test_fixture_smtp.py\n", + "import pytest\n", + "\n", + "@pytest.fixture\n", + "def smtp():\n", + " \"\"\"Initialize and return SMTP client session object\"\"\"\n", + " import smtplib\n", + " return smtplib.SMTP(\"smtp.gmail.com\")\n", + "\n", + "def test_ehlo(smtp):\n", + " \"\"\"Test response from sending Extended Helo (EHLO) is 250.\"\"\"\n", + " response, msg = smtp.ehlo()\n", + " assert response == 250\n", + " # assert 0 " + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "============================= test session starts =============================\n", + "platform win32 -- Python 3.9.4, pytest-6.2.3, py-1.10.0, pluggy-0.13.1\n", + "rootdir: C:\\Users\\pcz02m4h\\Projects\\DPP_IES\\10_testing\n", + "plugins: anyio-2.2.0\n", + "collected 1 item\n", + "\n", + "test_fixture_smtp.py . [100%]\n", + "\n", + "============================== 1 passed in 0.16s ==============================\n" + ] + } + ], + "source": [ + "!pytest test_fixture_smtp.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* now fixture for providing data\n", + " * note: when providing path, think about the sourcedirectory! " + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [], + "source": [ + "# %load tests/test_fixtures_data.py\n", + "import pytest \n", + "\n", + "@pytest.fixture\n", + "def data_names():\n", + " import pandas as pd\n", + " df = pd.read_csv('data/test_data_names.csv')\n", + " return df\n", + "\n", + "def test_addressing(data_names):\n", + " df = data_names\n", + " titles = df['Title']\n", + " surnames = df['Surname']\n", + " expected = df[['Addressing']]\n", + " assert (titles + ' ' + expected == surnames).all()" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "============================= test session starts =============================\n", + "platform win32 -- Python 3.9.4, pytest-6.2.3, py-1.10.0, pluggy-0.13.1\n", + "rootdir: C:\\Users\\pcz02m4h\\Projects\\DPP_IES\\10_testing\n", + "plugins: anyio-2.2.0\n", + "collected 1 item\n", + "\n", + "tests\\test_fixtures_data.py F [100%]\n", + "\n", + "================================== FAILURES ===================================\n", + "_______________________________ test_addressing _______________________________\n", + "\n", + "data_names = Title Surname Addressing\n", + "0 Mgr. Kalerab Mgr. Kalerab\n", + "1 Ing. Mrkvicka Ing. Mrkvicka\n", + "2 NaN Slanina Slanina\n", + "\n", + " def test_addressing(data_names):\n", + " df = data_names\n", + " titles = df['Title']\n", + " surnames = df['Surname']\n", + " expected = df['Addressing']\n", + "> assert (titles + ' ' + expected == surnames).all()\n", + "E assert False\n", + "E + where False = .all of 0 False\\n1 False\\n2 False\\ndtype: bool>()\n", + "E + where .all of 0 False\\n1 False\\n2 False\\ndtype: bool> = 0 Mgr. Mg...\\ndtype: object == 0 Kalerab... dtype: object\n", + "E Use -v to get the full diff.all\n", + "\n", + "tests\\test_fixtures_data.py:14: AssertionError\n", + "=========================== short test summary info ===========================\n", + "FAILED tests/test_fixtures_data.py::test_addressing - assert False\n", + "============================== 1 failed in 0.53s ==============================\n" + ] + } + ], + "source": [ + "!pytest tests/test_fixtures_data.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* when to avoid fixtures:\n", + " * using fixtures fixtures is as bas as using tests redundantly\n", + " * => **marks**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Marks - test filtering\n", + "\n", + "* you might want to only run couple of your tests\n", + " * full suite of tests only sometimes\n", + " \n", + "* to filter which tests to run:\n", + " * name-based filtering\n", + " * directory scoping \n", + " * **test categorization** (`-m` parameter)\n", + " \n", + "* create **marks** (custom labels) to label any test you like (can have multiple labels)\n", + " * e.g. you can categorize your tests by dependencies (e.g. access to database - could be `@pytest.mark.database_access`\n", + "* to run only tests in specific category (mark) `pytest -m `\n", + "* to *not* run tests with specific mark `pytest -m \"not \"`\n", + "\n", + "* you should also [register the custom markers](https://stackoverflow.com/questions/60806473/pytestunknownmarkwarning-unknown-pytest-mark-xxx-is-this-a-typo) in *pytest.ini* file" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [], + "source": [ + "# %load tests/test_mark_example.py\n", + "import pytest \n", + "\n", + "@pytest.mark.database\n", + "def test_pg_read():\n", + " pass\n", + "\n", + "@pytest.mark.database\n", + "def test_pg_write():\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "!pytest -m database" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* there are few marks out of the box:\n", + " * **skip** skips a test unconditionally\n", + " * **skipif** skips a test if the expression passed to it evaluates to True\n", + " * **parametrize** creates multiple variants of a test with different values as arguments\n", + " \n", + "* you can see a list of all the marks pytest knows about by running `pytest --markers`" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "!pytest --markers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Test parametrization\n", + "\n", + "* using only slightly different input and output would lead to repeating test definitions\n", + " * DRY!\n", + "* fixtures not very good with only slightly different inputs and expected outputs\n", + " * **parametrize** a single test definition a get variants of the test for you with the parameters you specify\n", + " * mind the syntax\n" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [], + "source": [ + "# %load tests/test_parametrize_example.py\n", + "import pytest\n", + "import unicodedata\n", + "\n", + "#######\n", + "# Function we would like to test should be defined in package code, not here.\n", + "########\n", + "def drop_diacritics(text: str) -> str:\n", + " \"\"\"\n", + " Strip accents from input String.\n", + " \n", + " :param text: The input string.\n", + " :returns: The processed string.\n", + " \"\"\"\n", + " if not isinstance(text, str):\n", + " raise TypeError(f'Input text should be a string, not %s', type(text))\n", + " \n", + " # Return the normal form for the Unicode string\n", + " # 'NFKD' stands for the normal form KD \n", + " text = unicodedata.normalize('NFKD',text)\n", + " output = ''\n", + " \n", + " for char in text:\n", + " if not unicodedata.combining(char):\n", + " output += char\n", + " \n", + " return output\n", + "#### \n", + "\n", + "\n", + "@pytest.mark.parametrize(\"test_input,expected\", [(\"3+5\", 8), (\"2+4\", 6), (\"6*9\", 42)])\n", + "def test_eval(test_input, expected):\n", + " assert eval(test_input) == expected\n", + " \n", + "@pytest.mark.parametrize(\n", + " 'original,output',\n", + " [\n", + " ('řeřicha', 'rericha'),\n", + " ('čeština', 'cestina')\n", + " ]\n", + ") \n", + "def test_drop_diacritics(original:str, output:str) -> None:\n", + " assert drop_diacritics(original) == output\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "============================= test session starts =============================\n", + "platform win32 -- Python 3.9.4, pytest-6.2.3, py-1.10.0, pluggy-0.13.1\n", + "rootdir: C:\\Users\\pcz02m4h\\Projects\\DPP_IES\\10_testing, configfile: pytest.ini\n", + "plugins: anyio-2.2.0\n", + "collected 5 items\n", + "\n", + "tests\\test_parametrize_example.py ..F.. [100%]\n", + "\n", + "================================== FAILURES ===================================\n", + "______________________________ test_eval[6*9-42] ______________________________\n", + "\n", + "test_input = '6*9', expected = 42\n", + "\n", + " @pytest.mark.parametrize(\"test_input,expected\", [(\"3+5\", 8), (\"2+4\", 6), (\"6*9\", 42)])\n", + " def test_eval(test_input, expected):\n", + "> assert eval(test_input) == expected\n", + "E AssertionError: assert 54 == 42\n", + "E + where 54 = eval('6*9')\n", + "\n", + "tests\\test_parametrize_example.py:32: AssertionError\n", + "=========================== short test summary info ===========================\n", + "FAILED tests/test_parametrize_example.py::test_eval[6*9-42] - AssertionError:...\n", + "========================= 1 failed, 4 passed in 0.10s =========================\n" + ] + } + ], + "source": [ + "!pytest tests/test_parametrize_example.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Testing features to explore\n", + "\n", + "* [plugins](https://docs.pytest.org/en/latest/reference/plugin_list.html)\n", + " * requests-mock\n", + " * database-mock\n", + "\n", + "* [CI/CD](https://docs.github.com/en/actions/guides/about-continuous-integration)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.7 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.7" + }, + "vscode": { + "interpreter": { + "hash": "1f0e6d99f3103fd78365fe1cf7b2d51239fa0878786db9cbdfe89bc88a3151d3" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/08_packages_docs_tests/pytest.ini b/08_packages_docs_tests/pytest.ini new file mode 100644 index 0000000..de66922 --- /dev/null +++ b/08_packages_docs_tests/pytest.ini @@ -0,0 +1,3 @@ +[pytest] +markers = + database: mark a test needing access to database \ No newline at end of file diff --git a/08_packages_docs_tests/setup.py b/08_packages_docs_tests/setup.py new file mode 100644 index 0000000..c50707d --- /dev/null +++ b/08_packages_docs_tests/setup.py @@ -0,0 +1,13 @@ +from setuptools import setup, find_packages + +setup( + name='PackageName', + version='0.1', + author='YoursTruly', + author_email='yourstruly@fsv.cuni.cz', + packages= ["src"], #find_packages(), + description='Exemplatory package.', + #long_description=open('README.md').read(), + install_requires=[ + "pytest", + ],) \ No newline at end of file diff --git a/08_packages_docs_tests/src/__init_.py b/08_packages_docs_tests/src/__init_.py new file mode 100644 index 0000000..e69de29 diff --git a/08_packages_docs_tests/src/example_module.py b/08_packages_docs_tests/src/example_module.py new file mode 100644 index 0000000..2696bd7 --- /dev/null +++ b/08_packages_docs_tests/src/example_module.py @@ -0,0 +1,7 @@ +text = "modularity is the key" + +def f(arg): + print(f'This function takes as an argument: {arg}') + +class AClass: + pass \ No newline at end of file diff --git a/08_packages_docs_tests/test_1.py b/08_packages_docs_tests/test_1.py new file mode 100644 index 0000000..cf948b7 --- /dev/null +++ b/08_packages_docs_tests/test_1.py @@ -0,0 +1,11 @@ +def test_sum(): + assert sum([1,1]) == 2, "Should be 2" + +def test_len_vs__len__(): + a_tuple = (1,2,3,5) + assert len(a_tuple) == a_tuple.__len__(), "Function len returned differnt result than method __len__" + +if __name__ == "__main__": + test_sum() + test_len_vs__len__() + print('All tests passed.') diff --git a/08_packages_docs_tests/test_2.py b/08_packages_docs_tests/test_2.py new file mode 100644 index 0000000..f228521 --- /dev/null +++ b/08_packages_docs_tests/test_2.py @@ -0,0 +1,7 @@ + +def test_sum(): + assert sum([1,1]) == 3, "Should be 2" + +def test_len_vs__len__(): + a_tuple = (1,2,3,5) + assert len(a_tuple) == a_tuple.__len__(), "Function len returned differnt result than method __len__" diff --git a/08_packages_docs_tests/test_naive.py b/08_packages_docs_tests/test_naive.py new file mode 100644 index 0000000..300fc2a --- /dev/null +++ b/08_packages_docs_tests/test_naive.py @@ -0,0 +1,11 @@ +def test_sum(): + assert sum([1,1]) == 2, "Should be 2" + +def test_len_vs__len__(): + a_tuple = (1,2,3,5) + assert len(a_tuple) == a_tuple.__len__(), "Function len returned differnt result than method __len__" + +if __name__ == "__main__": + test_sum() + test_len_vs__len__() + print('All tests passed.') \ No newline at end of file diff --git a/08_packages_docs_tests/tests/test_3.py b/08_packages_docs_tests/tests/test_3.py new file mode 100644 index 0000000..8718d6f --- /dev/null +++ b/08_packages_docs_tests/tests/test_3.py @@ -0,0 +1,7 @@ + +def test_sum(): + assert sum([1,1]) == 3, "Should be 2" + +def test_len_vs__len__(): + a_tuple = (1,2,3,5) + assert len(a_tuple) == a_tuple.__len__(), "Function len returned differnt result than method __len__" \ No newline at end of file diff --git a/08_packages_docs_tests/tests/test_fixture_smtp.py b/08_packages_docs_tests/tests/test_fixture_smtp.py new file mode 100644 index 0000000..240572d --- /dev/null +++ b/08_packages_docs_tests/tests/test_fixture_smtp.py @@ -0,0 +1,13 @@ +import pytest + +@pytest.fixture +def smtp(): + """Initialize and return SMTP client session object""" + import smtplib + return smtplib.SMTP("smtp.gmail.com") + +def test_ehlo(smtp): + """Test response from sending Extended Helo (EHLO) is 250.""" + response, msg = smtp.ehlo() + assert response == 250 + # assert 0 \ No newline at end of file diff --git a/08_packages_docs_tests/tests/test_fixtures_data.py b/08_packages_docs_tests/tests/test_fixtures_data.py new file mode 100644 index 0000000..14cf1b5 --- /dev/null +++ b/08_packages_docs_tests/tests/test_fixtures_data.py @@ -0,0 +1,14 @@ +import pytest + +@pytest.fixture +def data_names(): + import pandas as pd + df = pd.read_csv('tests/data/test_data_names.csv') + return df + +def test_addressing(data_names): + df = data_names + titles = df['Title'] + surnames = df['Surname'] + expected = df['Addressing'] + assert (titles + ' ' + expected == surnames).all() \ No newline at end of file diff --git a/08_packages_docs_tests/tests/test_mark_example.py b/08_packages_docs_tests/tests/test_mark_example.py new file mode 100644 index 0000000..7c2eae7 --- /dev/null +++ b/08_packages_docs_tests/tests/test_mark_example.py @@ -0,0 +1,9 @@ +import pytest + +@pytest.mark.database +def test_pg_read(): + pass + +@pytest.mark.database +def test_pg_write(): + pass \ No newline at end of file diff --git a/08_packages_docs_tests/tests/test_parametrize_example.py b/08_packages_docs_tests/tests/test_parametrize_example.py new file mode 100644 index 0000000..d166eaa --- /dev/null +++ b/08_packages_docs_tests/tests/test_parametrize_example.py @@ -0,0 +1,43 @@ +import pytest +import unicodedata + +####### +# Function we would like to test should be defined in package code, not here. +######## +def drop_diacritics(text: str) -> str: + """ + Strip accents from input String. + + :param text: The input string. + :returns: The processed string. + """ + if not isinstance(text, str): + raise TypeError(f'Input text should be a string, not %s', type(text)) + + # Return the normal form for the Unicode string + # 'NFKD' stands for the normal form KD + text = unicodedata.normalize('NFKD',text) + output = '' + + for char in text: + if not unicodedata.combining(char): + output += char + + return output +#### + + +@pytest.mark.parametrize("test_input,expected", [("3+5", 8), ("2+4", 6), ("6*9", 42)]) +def test_eval(test_input, expected): + assert eval(test_input) == expected + +@pytest.mark.parametrize( + 'original,output', + [ + ('řeřicha', 'rericha'), + ('čeština', 'cestina') + ] +) +def test_drop_diacritics(original:str, output:str) -> None: + assert drop_diacritics(original) == output + \ No newline at end of file diff --git a/Lecture2.ipynb b/Lecture2.ipynb new file mode 100644 index 0000000..1b4a1fd --- /dev/null +++ b/Lecture2.ipynb @@ -0,0 +1,1742 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "63662a76", + "metadata": {}, + "source": [ + "# Lecture II - basic python II" + ] + }, + { + "attachments": { + "23511f5c-cc46-4228-a97d-7bae6fc066c1.png": { + "image/png": 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IdoKsp59xupwigoDAHUcIfYeeAZbwE7bdy6+8LDrDQMZZIejD3AkIw184dD5w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Ni+B6lSBqJcuTLioRsWFoUjR48hW/YcKPRLHml+s3XbIfHVoIdGzpwZ8fTxOyELSfbVrlNTvnfl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It5feo+TEFgWUHtd9nqKoeBbHoyWiqrUVNYKk5/YstmcCYliJh60M1SvICVugD0xXk9piS4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mo5ObG70eU/cdrVg6+rkAOFXabs6xU4xazWOZPELuO7alS2r7yOuT/Yo6Liam1HFJmzz1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UuXIbe4WBwu2rVqiqF9e4gw5O2MVKz7ZTM2bt7Okw1enur8LCkpkmsVrQGp/Dtkn9O1TFQVWj/b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5cnQtticxhFe3C6VRA1Ms9Ig2IDxOGSZz0hNh4Br+ocOcIBxCCI2ojF0IaZGNi2M0b0HYRc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6o/zC7/vvPWvHP28/x2Xp6Sq8yaXSzTlryXEVYV5/3CrkXPcIPjAAP3CASa216zCJEY06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\n39fm21v83NeG6LQ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xuDQnAgEUFOTb68IR1NXVu59ZlYyCVjw4Z7hX8/nz89i+4VlUvFcS\nAHUVfD1rp8FizAazP7Xk3lXgHauC6ySvX0Eaq5zyfc9QAt62qA3OPeMsTBp3ppKPIHsMEcCqbevw\nypzZWLduPSafcy7OGDMOWaEQyvfvwuadWzH3zYVYtXaN1gJ+Xg6t5wJpaHLMH7KquCZQU4M/l11p\nRhAt9Y2INDajvoa6F9VoqKlDmKKbTS2W8Kfq6xxuh/9HkZ17bSrgonl56GcYHzX5Uy/CdLiv4s8O\n/brP0Eud0BUFlrLz81DQoQ1K2rdF2/btkiAtNRL4ORQ75PzgGFC7WCQiUJxATG1tjfZRc01J0zyQ\nEGlaml7DPVRigmGzTfUsF44dJvF8nvxstsrU7N+f/Jm/Ls5lAv48PMtGoLJrueN3cHxQYFW2puyr\nb2wSA6B+d5X5cweAouIS+b4TADhm2NFfqAGwaeMmzHhkBp54/Amp/8u6sF073HfffbLP0z12+xv7\n/fnZnjXk11XfAskVjnEJQap8BNApvxBdO7TBUUd0xtFdu6JnSTG6FhWiTU4OAi0Rsz3m+kVwjsKU\noUw0JtLQEI3hQEsYzdEWVFXvQU1FGYb36oOOJSVIhILY0RLGb+fOx3MrPpVSf5QRYDyG/MJ8LF2y\nRP32Pq5IHW98xvfc+0v87Gc/1xiT40Y8jnYJ4JyuvXDpKWMx9MheKCjIBfJysKv+AB6ZvQAPL5wj\nrQLGW6YaQeVuq3h2zctGkXRewqisbdD5KIwkKB40nYdBQwbjt7/+DcaeOja5D/yj8fxFP/cJuA+Y\nU19/uKT6wQcfxA9/+EPFSdynmWDeeuutGrOHO1LFCDmeV6xYoeD+jTfeUHsAxQP9mPAgHsdr7969\nceaZZ8oLnH271BagkwDp7DwvigjOmjVLiUFqgvtF1/v/h997ttiha1/qtXGNJuuGPdD/7JG6hn7B\n8nvQR9984/dx94/vRrtOHRBn6xOLj2GzwdZ64oX2Igmgvgk7Vq7G7BdnYsHrC7Fm22ZUhmvEdOGc\nKMkvMn2JW25BqKgAicZmOQfMmTMbN91yC0pLd5p1utNm+bxr5B7KYkteXj4efOABXHLZpfZysZMi\n+HT1aumU0EHjqx5JggWZlTHuza1s1LffeRcPPvRXzJk3XyCeDvVap6u/H5lFCPUegiEnjUX7XgPQ\npmsvRGhvS3cSgSnJmi8CXsjbA+ApD8qDxenMwZqaZevXUF+Lur3lyIweQDOdRXauQ+WWNWjcWw5E\nGY8QZGR3RTrCYduUhw4ahksvvwyTp0xGL/bB/w8cTOYp0Ld7927FvGwNYmsYizMc94e6fnB8sz2I\n7+PvfIzv26r9e5i7kTlEMJfPmevLwEEDMWBAf2zbug1XXnkVqqqqXQsxMH36n3D1NZcrz16zfjX+\n6+f34o2Fb+CEE4bj5ptvwNixJxtgoiTfHo4AEYklprIJPQjgBtw/0gBIJs8ZGdhfs19VUlETDtSj\nICdPtP2rL78SnfLN5o+bIx/T/PcW4+HHH8On69eqCs9NIqkWz4qqQ/nNt5G2S7TFiqg3lFWZbh07\n4vxJ5+G0cePVV8bN1l0SdlRWYtbrC2QXuGPnDmTl5qIlGnY9O44JQFuQDPPK5uaUKvjHgJUbAPuJ\nFWzH46qsMbGgsBp/xgTFW2ep8u2Sj+TmI3s26+3nouargqk/S7Ut9K0UfD+rYKrMCIiwiqFHybzI\nEimc/JklYybwp4BE4EamKmgMjnh4USifpGshdr2jYg84Kr+n/KqirXYjL0ZotHZfGfRq3nwenLRM\ncJjQGV2b1WtL9Lhx8nM6tG2H9FgCQ48aiCunTkPvDt0QSERlLVcbbsSLs1/Fq/PnYve+KuQVFiCQ\nHkAkTobGgaTGgFV+HBOClVynfEqElD26tNJSRZaq4U7Z1aryhgpy8DGAJQVMyazsVyD7PR4SlXIT\nlYCWAlv1wdr18fCuDR4k8AGAWkkca0W2fmEKYIUUPPNa+vXoiWumXJQEAF5e8Bpmvj4Pe2trBUK0\n3mvzu+W91qLptCl8Yu+vn//+IgCA1X9V29Wak5FsG7ANxPzH+3XvhRuvvAb9uvbW7ElDEFury/Dw\n04/j47WrEA3EdZ+8jaZEJAX4mD2YHBMcs8YHZEaxN491JqK+JYKfwbHKnyVF/GTbafaTXI8MHDBx\nOgIYchRwQqPWV27/5v1nwspFk5/JecnAzotqMsH2Aow8Fy7CastxiuY8F4JbnH+28FJPoXWuqTeZ\nSaATJ5MThOuRN/s4a5/hn9bzf7ANqh8XvmLPc+C1eCaPZxDYPbMxbSwIm+f+SGUIeB0Bq7ybdafO\nwTk82Lphz5tjgywcL8gpIJFVx6QmCNlJtCI1MIfnz2oX10ICBbxfTU3NAgy5OTFg9q0wNr+th87G\nvXddiZhtYXq6o4RHpKQuoUaJFxrbieucr1YY0GptOFwXBZy4Co23IUwnsBiOom/33rjy4mk4fsDR\niLGfOpCGRR+8hxdem4nyXbvEBut+RDdMGH0KBvbvj85dO2tD+2jtcrUBrV67BvXScTFNAAZ5EoV1\nDgEZbnzV19ajfl8NmmrqsG93FSK1Da0JvxVfHLD2+VHFYbroDnrD4YLgg5L2fxIAcNv0YU/qoOTP\nu7IE05BVUoQ27duhXccOyMrLEU2brDkm2wQBUg//zPg7jgUC69KooLBiU7OcBTgGxVLiGuHWXh/4\nMPnXfJC6vTFOkq937Ax+n8YEAXtXCRFoJFDJWDN2HgFzp3Hjp6WuHvXOBYAVJh5MgsdPmKgKEPs+\nzYuk9TiUAUDq/6/uvVfJGdcZBl6cP6zq3nHHHQfZALLyTwq57+325+0/nWsVI1+b2+YXzv8LAPTO\nzUWftm0wsEc3DOrVC0d17Ya2dNQhq4rjknuxE61SFwmDfa632SGEW5oQitJJJw3p2VnYG0jDr15+\nDU8uX4EqAQCWHARD6Vi4YCFOPumkpE1y6rrCoVVeuQuTz5uC5R997JiCQE4MGBTMwumDBuOycWNx\nRNsSBAvz0BAHVm7ciT8+9xwW7NqGJjKAKDjI73RqaIwpg9T7SXa/sCAARNx61qlzZ7VjfP/73zd9\nha/h8Iyn4uJirTufl1CT3cGqP9c59uDz7wQBvJDqoadzKIjg3UvKy8sFBMycOVNsU1buvP6VH9vc\nU6j6T0VwMgNefPFFPPPMM8k9m44E11xzzUGsq6/hdvxf/RE+j6AAGrUVKIZ2EDvqS17dP+gQOOy7\nuf7bep7AsUcfi+uuvVa2cEXt2iaxV6/fpQ8g1b0lisjufaipqMTe3XtQtnc3lq78BHtr9yOnIA8j\nR43C+NMmmu4Vo6poDLV1dbjuhu/hpVdmIjPd2hIJMHwRSEEAgPsT16obrr8BZEZwjeYb169fh2u+\n8x1ceeWVGktf58G9lIJxHy3/GNMfmI6XXpyJmtoDzoGAgXQ2EA0A+W1R2Hcoug08Dv+Hve8As7K8\ntl6nz5k+MIXepEmvItJ7tSCI2BVr1LQbjck17aaZRE3UaMSugA0FEVHEAhZQqQrS+wwwDNN7OfV/\n1t7ve+Ywooiacu/zf8+DOMMpX3nL3muvvVan/kOR3roDIr5EBLgvsFVITbW+sPLGn6upwcZEzxnX\n1VdUIlpViUh1JcK1Fag6nodjez9D8aGdiBTmAdE6IELhbf0k2xFHIekJ4yfi0ssul4SXVX/BVOMc\nN7kkm86/7/KWSbWfoDDjTq4dZBtRA+DEvVcLRy+99JK4y+zbty8W49v1hmsHk30yCGg/yvWD/0/n\nEYqQxouOHjx0AEPPHhYDABzOCObNe1AAgGhEbapLiqvw4N/n4d6//k1ynauvvgwXXHAuBvTrp8x3\nccoj4KMURrar2HVUHdJ4kGHRhAHQ77xpUesTLy9hL4qh2PPB0FLsogtm4PwJk9EsyXZ6q/ftilXv\n4omF88U6LImVOLEOc8Lt8wg1k1VQu9mz4sjFwQbvVJSXntRIWMTkxg8fhYvOPR/9unYXJIODl0lX\nIAps3PIZXn71FWzaugUVtdXSA8neSEm6qKJs9ADsBm4r+rKBG9qlTWyYPIqqsglWRJXfjDx77ZYd\nwCRQe+wbfbAZNNvkX4Sx4np04zdnpY+r/ZQGSBS8M6Japi9YKP2ml9YK0fGzmUzxPdLbJ5VG1T7g\n9/Ha2OPJQEySaSYMRhiJibtNBnjdFAFs2udMdoC1prICb5aazt/bXmu5ly6llFoHBfoW9+rcDVdc\nNAd9OnZHEvvIqZZdX4V3P/4Qb6x6Bzv37UU9qy7NM6U/p6q+EjV1NfIZtkXDBlxEZj0JCdJ3S/s/\nH9sPxAZD/a7BdhRWI0TMigreSvsVMIACbdGIqGvbSScJiAlq7ASTMWKqlEwAhXnBhN6IxDEQtSru\n0tNuKttMkkWbwJegzghO4IzW7RQA6DNQOseWvrkMLy5bimJW00xSK6JyHFMU6qLwGMeOAQGscrpF\nSC0QoJRuAgYMmPUcBdChABmFsnjlAkTpRLYtJwySO7dph7mXXC5tCW6ohUhtJIgnFr+AJSteQyCq\nThm2v0xaWSQ4D8v8ob6Hx6eCb/yZCTj/3+oAkELNxJ4Hr43jlZW9mNiLafURSj490zmGhEXCRJGq\n7KTcakKgOgfxLiM6zm3PugA9bmfMhpPfSRq/OgMYUb+4HnwKkXGNURcB7UGnUBsr9pxvMQaSaXex\nz5yfa/vhSRuX5MRYYzIBUiqzAnE8rCaKFSHls5C10qwbosZv7P6UYhyJMQVUK0FFQ8kUsO0PTJh5\n7vyZTB0+f957BrasBgjYEQyhtKRE5j8BWX4P6dYi+CeWnwRvnKI/Yde6xnNXlo/PmxDTFrDWdlaD\nguJ4XD/I8pAk2lD6LWCQblpnOD8lWTSuBwKqNflZrGOdqpLNQ5gSFCiVdiqnVN3JVOndtQe+d/V1\n6NWhq2h87D58EE8+u0CqIKQxhwy1MCM9A0MGD8I5w4agdeuWsmYdyj2Ed1avwicb1qOytkZcQCiM\nJ4wggpURBxqqa1GYX4CCI0dRWVwK1JHiaPa/+Mzc2IaeMnr7iuhOK0onxh9fqNhLWBrHAGiqBRD3\n9pPSX+N/yWTanI/9HnVpYjO3C8nNm6FD5zPQvEW2tAzUNjSIbaCyTbSdxTLMhB3mdqOqukrmt45D\ntnqoCGwMODe9/ZxrklAnJcl4ISuKLhXch3T8qK2jMGrYe29YApaFZ1sC+FrLXpG9yeOTeUqQu6Kw\nCKW5R+VviH6IagCMHDVKqnFfBwB45+23wSqxWuSFUV3F9cwp7/35z3+O4cOHx5JMriu0jmPvJoED\nJisnANSqeSeggzPCvnzCAHyaKmpFU950rxtZKSno2ro1erZpg4HtO6JrZhZaJiSitqQIiQluuGh7\nxTFA0cloGAnJyagrKZfA15XoQZXPhwUfrsMjK9/GPu59fLh8rmHgkUcflv5zrqOi2h034FiVI6jw\njwcfwi9u+5lU9Tge3OEImJqflZ6KW8aPx5i+/ZCUkQFnQiIa6sJ45s038cCqt7G7rqrRspIS3aIt\n5QTBOiYt4qhB60BqEXk8yM7OxjXXXI1bb/0+WuTkxNxIvk0ETjo+hf2OHTsmDA0CNfY4GRBgNQA4\njtnaQVCHf6TV7iQi0PGfZYEqWyjhmsv1lO0BVPl++eWXDYipoJY9+NkdO3aUsUEGgGWH0kmAIFKL\nFi10P/iK7/829+g/6b02jj4ZYyP+PO+77z4RUuM+G38vv9NrsYugxEgKADDYoG7V0LPPxpVXXoVJ\nUyajdYd2QkXnZGaaJMmsBWYJkFdWoba0AmEr2pzkR2pOpuYZXi0gEBh79LHH8F8/vU3dxgwUqUpL\nX31YAICv6tevn9gidunaFSUlxSIMyDF099134yc/+cm3vj3xY/Dw0cN44pkn8cz8+Ti0+4BZVAyF\nnnT/5ObwdeiFdn2GoPugochsdwaqCfqyoGra7Zxen9ps2/tmSQOxfSlugzJ6PJw6jJnrystljXTX\nlKMibwe2fPA6CretAwLVSmFn9kDaEZXeuZ6mZGDyxCmYNm0qxo4djbbtWqs0ToSfZ5xndBn9px1M\n6tkCYI+77rpL1pf4yj/HA3UB+G8UurQHCyxU+u/Tp4/og1irUSb98eBi43xQltuePXsxYsQoFBYe\nV2FcAwBcd+2ViIYJkEcRZoUfLmzesh33/PUBvLR4GdJTUjBn5gW47JKLMXjwAPgSCQRQgl1VLeTJ\nUIPBiMTqAt8EAOg5eXxUPN2FMmKEoIx3L7296cc8cdRosL5K4IxHWXUNXl6+DC8tW4ry2iq4SRFu\n0F58CXhp2ycJOqtE7BkL0Rg2puTNwLe2oR5+Vis8bqFl0j++d5fuQisfPWIkEqmMaCiRgVAEh44c\nxmsrV2DpiuWS4LCvTajUFL4jzT/RL3RZPhzp5TeCQyJQZmz9GJyLWiwFD4Pqs86fSV/mtVvFRgmW\njDpnA6uakpi4lAJslMFZ0efv1U1Ae+htYssgh8G6pb3zdSL4R3ssCiDVsBee7gU+sEefh7WsYYBl\nGQMUOJSgiwEWkyd6YZvqC6v3RgNLNmKLsmoyaSqsBEBsP46p1EhV2mxuViSNFE9eD5MPqy7OcxKR\nM5dL6S/hMNpktcDs6edjyrBxUF17rR6888l7ojCfV5Avat8c1QwimFiHocrdPGwSGpsAPCepKFF0\nKyLUq6QEvwSk1BvwJ6n2AK/PUnyt4BufKe9xckpKrCrM77D0dBWzUocHMlksA6NRTdkmZvqc7Hub\nBoAK9pBp4UDHlq2lBWBorwFy7a+uXIb3P1qLcmpOMJhjNda0FwiDgIEw7VCMDzmvgUmS9ZO37Sj8\nblXGVnaGTSjVVowiKmoVKaGooV9LlTkYxpwLLsSMSdOQ5EhQcRCXB2s3f4JXV7+tVR32ehrRKEnM\nGRxS4M+0TvB6xW+ejAuXS6r6NlDiusDAXBYSngeBA6MiznGk9pWa/CrNWyvXqpGhbAqKzvBoVPE3\nfuSmSmgTT1aZVcyTCu1kHmgCzvdxXqrXuLIGROgnXnnfKPZLm1CY/uasfKorRHwLQAwYJM2MmhhG\nPV2Tc/VB53y2QAXvldoRstoclGRJFfPJU6IlnLULZPBJjYCgtOyI3z1Bq7h+ablH5vPjGUtcIwXA\nM4wEYQaYliTef95XJkicg/wM3guep+0hk/WtTpXr+Rr643I9YkWLcyQ9vZmsH3yupLtyk6KqONer\n8vIyuUe8b9KuZzQO+D7LhklOZNJQH2M/ca0g68L6cDM45ufw4Hfz4M/UPuCjT01LE3ouBfcYhLVo\nloXrr7wGA3r2hsfhxSe7PsUrry9H/rF8IyapjIWMZs1A14jWrVugf7++GNCnn5zryrffwvtr16Cw\nrASJyVT4D6CmsloAhuqyCpQdL0bRkXygnn2NTllXLM3fSRVi0+r0tSOIU0R3cTpOJ3xk49sMWGTj\npHjhv5OcRHy+L5/xhV80vikW/xKAkhOJwpngQ6v2bZHVogW8iQkIcNKQJSYMD6esiRzraampMoY4\nTjgGBQAwNMdGgCdRwDhh7RmFdevCIwwgw8Th53CesAphgQK7noqehwEP+PwIPHCd4zPmeBbXEI6p\nmhocy81D5ZFjqCsuE+oq50lqehrOOWcYfvHLX2BQfwKvJx5NGQAbN2yQqu5bb72FgwcPyecyUfzl\nL3+Ja6+9VmIEGyRLVZARmNMhgdzajz6SZHTrlq1yfgHDvJP1SwJ+N0JUDqd1ViQo1scSL1MwEUAz\nAN0ys9C3XXuc3e1MdG6Rg9aZKfDSGjcYFpE+AfO9PtRVVmurpjuKgN+PdUcLcf/S1/BhQQFEN9o8\n94tmzZT2BSaZdl2wd0BouXCId/fF51+I/Xv3SZuXV9S2w2jrAmZ07Y7rp5+Htjkt4EtMkuB+085d\neGjlW3hn1w4cjwQQ4PZs5wr1Y4wGDtd6cVnyJ2DE8BG44IILMGPGBWjVspUAh5IEfu2JdPIXsnpG\n0a21a9fivPPOw29/+1t06dIlpidhwXL7blbj2PPPscdKGtsB+Od0DpvExn82gQBWrckwIK2XMZra\nMqtrjnXJ4li3z4FrHxMBMgRkp/0/CwB8cREsKytDfv4xdOzYQTSdYmPSCAITzKES+j/3UGarxrwh\n+D1++JzUHKtFECwoeURAdM4lczB71mzktGwhtpjcM8Q9RhivDfC6PAiXV8NhNDw4LwMuwJ2YgNpA\ng4BLR/MOi+3c5i1b1LLZsDeFEXiKi5QWAFP0455OoGnylMnCXHl43jwZYwQhL7300u/kdtHmedmr\nS3H/3x+QVmoEInCwEMNqvysFcCbA1aY9eg4djbb9RiClzRmIJCQixCKAAbVdIJhbD4+PGh+c5wY4\nMUuT7SyPLQAGUHGTxy/MTyd80QiSQgEU7tmC0n0bsW7ly6jK20UpQhWplUo192kHhp4zEuefOwOT\nJ05Gjx7dRJPw33E89vjj+N73btLim9OJRx55JMbMsIw3aokQFNixY0fsFHv06CEaMxQTJcW/aUtS\n/NpgtR5UfDmMvfv2YfSosSgqKpb1NsYAmHu5KHlJXM98lJmAwy9F8edffAX3/+0B7NzymQDMo0eP\nxKyLZmDMuFFo0TJbhANd3KeitHyMixmaVCwcfSaNi0qV24hbifWC24su7TvguquuwVn9+8kHMORl\nPay4sgovLV2Cxa+9KtV4X2KiVKCskrqlKXNz4mLJarX0AZv+ewafQoM1TANuvkxwSA+lInOnNu0w\nbvQYTJsyFW1yWogadDgUFe/Iitp6vPrmG3jyhWeRX1UGX1IC/A4VEHKy99O0GfBytTCi4oI2SWm6\nSCuarrTh+ARaepaJt0gyp7RbDngRSoIqIEuSZHpfLeXe0hulkiwUWK2CCRvA1Uj3loTX9IwzIRUP\naQMoaJ+zMhSURaB9tPJ602NigQbqH2hFWzUbbIWdQR7PjwGdvSf0j6fYIV/DxYgBuvhPC2XEJFYi\nnqIVcvk+sS/RnnK2TMycdi4uHDcJKS4fDYQQQBhbcvdi/ksvYMOmTWgINiCjWYYI5jHZYQVctA1Y\ngTYBIz+XtEweTFpsRcPvU2BG6Mdi+aZ2URQ5Y0IVq+ibnnKt9qoavSC0BtmiMvAJNGefVyryfDbs\nYSfVOaZwH2pM+jhWrXiisDVMa4UIVAXVaaJNVg5unH0ZxgwaJsDG4SN5MctEyxyQpI0JsNGrIBhm\n+/etaJxVu+cz5xBlcmZ95DlmGSTapFr0LMx4kfEmlnJBUdFOTUrGwH794XdSJ0CBHVKqDx87iojb\nKdW6iFBN3TFwQdojbI82E3q2RzCBcGsSQbBCxrFx7qCYp/SpkwVBNFd0MyKIsHdfLKG031oq/MYe\nk0E1zyWm1m5+ZoKgdCRTbTJQriQSpM0yufdpck0VduY0nG8qjEiF96g6gtDdwFTDKB5J0EoYGOIo\noP6ilglC5J5glD1UMNNYJwr1Xu+brSBpW4beY94ramQweWK1ubSCia7ak0nNgeJp7JEzugSKUlvW\nkXEFERqeZXSEhOfGzYHjiaAGgS724JOyndMiR3rb+XuCDRynBDZ5f9u0ai2J2NGj+crQ8ni05cqh\nLS3axuOSBIf/rhoCEST4EozNnYo+sq2CY5tzROaMQ50XyFThuOP3k3VRWV0l4y47o5kkP0zi+Iwt\nhZzPTAKvaBRcW8hQsHZlVMTlmkTAk+CCAF6G3UL70PS0dKSJzoYXlXW1ovPBCyFbQNw0ggEB/w4c\nOoDC4kK5pznZOQLmHCs4hu07dmLHnt3iUFBbXokjuXk4evgoassrgHoaEsc1058qOvuWUUbTJKip\nTee3/PjY2myBCzt2T6iqyZTS+cjBSW/3pIx0YQNktWsNNs2VU98EEbmvPIL1DXI/rYMMf6ftLQpq\nW6V/9pHK+kvhUGFeaYuWuAoY5pndL7hucM2RtiHD9uP5WtcJ2+bC71FhUZeAVDx4CVXFJcIAqCkp\nlYoT38v9ZNr0abjjjjvQvUv3OABAH6wCAGrFSU0d2nI9/cwzeH35cgHVuZ+QyULK+k033SR6ALHD\nxAehhiDKKyvw4dq1eO315VjxxpsoLGA1huAz18KIiKpqYZ4rLWssCn7LPsk/XKu41hgwIDMxBV1a\ntcCADm1xducz0CszG+lRIFniHRJTKMrLERwWNmV+KIqH31iJFz7fhiLTXkkBWjoA3H33X0TgTK43\nXkhYWll4p4AYFoMcAAAgAElEQVT77vsbfn7HHcrWMpRdOgIMSWuOi4cOx/TBA5GZ1RzwuVBVV49l\nq9fiyTdXYmNDpQAOZIIRnKmvrZP134oZ9+rdCzMvvBAzZ84USqtlMH0X45qfwbHz61//GqwYc5xQ\n8JFAA+2+2DMbz3ggKEMrx3vuuUf2BFbW/vSnP8m9iX/dtzk3ukJQQJJV2TVr1mhLmrGZtWumpbnz\n7yuuuEIsARl3fJNzONXy9G0Blm9zL/S98Yup6mzxOj/++GOZZ/fcfa/sOfae8B07d+4U/QS6Lvxz\nDweYcGpbqwvpiWlIdiSgsroMVVCAjfquvIdDBg0SJg3BGuqlEJ3meiYAOzWpWIU27UnCzOQ89Xok\nD+BryLKh6CMp/zK/TJ/1qZ6frA9m/+f/81zHjR2L1q1bY8HChfJvzTMzRUCOLKWmR9PPl7zDCBuK\n+DKLS5KsqjDxqvffw5///Cd8+P57uha42GpgROFcfiC5NTIGjUCfoaOR3bErkJiMsNsrIoBsxw2b\n4iClQXVbaawYW/aZPUdqr0Uc1GuidaALjqhTLEl9bLd1OVBffAw1h/aiKm878rZ9gEPbN8DNWIVF\nJaeyxAcNGYBzzzsXY8eMQ+/efZBsRMrtnvBtx//XeT7x9/z+B+4XTREFp3148vEnQAtZ21bMcc+9\nhGChrsdRWbPIdrHssq8/5nVuUfdo7NjxKCg4Lrmo2+3A/X+/FzdcdxUc0XphnCm/hZuMsj4dcKHg\neDFWvf0+Fi58HitWvkuMAGee2RmjRo2UtoN+/fsip2U2klOTkOClA53uWPZamHc4eowbJZ+bwA2Z\n/q7JKejfszduuPIanNGG/ZcmmabIW00tnnp2Ad5avQqFpcXSb0iKoVU2tj2xQns3FkiyYTLhN1R4\ntSRSX3tJZE1FlA+awWE4EJCgu1/vvqIUPWzAIOpSysH5yYBx/bbP8NLK17Fu0wb4jChZNVE/UgoT\n6DvtFlaApXoz2LD0e160UPxlUWflXpEu6ZFNSY4pw8v5i/2VVmdlQJpEQhgBhvpvvafFD9nr+YIi\nOVXqmdhw82DSxsBdKiYmURB6XbzPt0kebU+0ta2RhcT0x52oKK1AhIAFhmZsByZBOXdcO4FQjQ2Y\nwNdYqzQL3pAOzapLcXGxJFBpKalCraVewzlDzsYlM2aipS8VKksHHKsqxeMvP4/3PvkIpWVlSEqi\ndzsBhoDQm6W6aVomeD+tyr5N5mVDZVJB+q4wJkgPV1aGVkkjmggZ5Xg+CyYnai2nrRhW6d9+Ju+R\nJD8m0WWQalkPYtUY0YnN65Mkj4uoqfJaMbd4eptVnScQkZ2aLgDA2MHDEURQ6IMUW0tw+GJ8CCZL\nKg2jia4GivxJf+8lTV8CSP43Ag9IsWJaaXp45JWmaiikHyKlSjSjtj9fq0sBP0s9yOjryt4fpsYU\nzJQ2CalWhSRwJVjD9/Nz+b38nYaz/Cx+rxZJVXuD39LYI60sD/6Lfob65vJ3fKcNiHWZbUztTWAu\nr+InMkjV67Pfqt+kyDJhNZnf0bCIzej5hhAx58/3y8Jl7qR+G3FkoptiVCfX3hiq6D2032evle/i\nvdTz0XurT4H3RO+zfU5u+EzPr71vBEB5RiFzDXoXvPCZ+8M7xGegbQP2eXpE5k6bevn5YqsZu3aS\n4HT9rKqpQWJKEjzCrdHnxD88V34WITf9DL2nPBf9HtKRtT7Ja+Jr7f3id/N8+O+SKJkxROq9TWj4\nLr0mvX6ycPgttuapZ05apX6GPXitDaJZ7kAwquAAv4dHkAwtri9OF2rq1DmA678AXpEISstKY64G\nHLcptOk0Ghq0pSTYxLWrpq4OVdWV8tqy0jJJFgk4lZaUSrWz+HgR8vYdEIG/YLWClqpYFgcAxM74\nn/M/TYP+ExLz7+ArrSsPmRvcC8rLy08Aq+WZ6KKgIIBhvTCh8yUlolWn9shu2xokBtZHQ6rN4nBI\notdQXy8AL/caauOIa4bHg4z0dFkfKyuqTLCgoCn3C4LL4l9t9iwV8lNGG9drJgMqSKkglMxUCgIL\ne07FCHkkJLAFwSNCfUKpJvuoshrFB/NQcbxIhIJ5Yc2zmmH4iOG446d3YMiQs+MoERYAUIqoBQCW\nLF6MRx59FJs3bVZdErINa2px2WWXSb9mhw4dYtofZLbs2rkTDz74EN5ZtQpH8slCCSMq7h46Q5TA\npOuWU9wn9P85Q9jVK+sePaqFAUFCohHXItDtdMEfCaNn81QMad8Jw7p3R6eMZmiflQWPxwUEGuBu\nqJfAuxIuvLblc9z33vvYLqKjnI+0MvbgvPPOFcV9JsTx44vPPhDWCnVJcYlYrm1cvyE26vxRoJ3D\ng0ndeuCWiZPRtXULRJLccKal4dCePDy+/HU8tWUDjolVmMYDNjaiHR7p+FTe7tmzp85vozfyHQzr\nEz6CFHz2QDNx5HcMGTJEXBsmTZokr7PVM44dJmHsoeb5sKeWVoi2Av9dnhc1AljpY2JGIMAKOMsS\nYzSG+Ddpvqze9u7d+xsxAL5qqYrfT7/La/v6n3Wys3MIuEsg5oG//x0P/+NhodrHHxyrpLNbx5mv\n/32n90rueZylEqvDjWR3IrpltkdxUSEOh/M1npFiCgtJUXgdDhnPc6+9FsOHjxAASePSsMSYLEgx\nlrZtbATAqT326quv4qG/P4j9B0mjP/0jvgBp323nMcf71KlThXlyAjhpXvhl46MpC76woBD33H0P\nnlowH8UlRaIdJlV/ZyIQdAD+FPg6dceZI6egbZ8hSMppi7qIQxzb4pPkeIBR1m5Rkz/5NdPi2+nh\nmheGM+pFkicBHoKHwXqUHDmI4gM7ETi2D0d3bED+nvVAQ7WJqZw4s2cfXDX3akw/dxo6dGinLdIm\nJ7Lf9l2M/9MNBR544H7ROOGRkdEMry59BcOHDZf1hu0/v/jFL7Bw4cLYXKdgIOcCbQpP/9CzYwsA\nAQAWd5hiEgD469/+gpu/d+0JAIDuRLoFah2NxTK/uAFu2LARr7/xhrCXtm3bJU7G6ek+tOvYDplZ\nmdJWyv2Qey4PFpMqqyrh6DZuZJToJTfyZH8Szhl8Fi6eMRPdO3SQh0VgjJW4ovIKLH9zhVT+i8pK\nRO2fgRrD7vjEXzYK09cuXtumAt7YG6yjieGzWA6ZyhOvjEiW9IuS4ur2oE12C5w/aYrQ29gSwBnN\nMcLw8oNNG/GPJx7VaqcjigYGmARISLnm3WFlL6w2IZocq1q83D+TyPNv0uw58KwomFbrNZDVvmwN\nLnhYUSMr6sVnEA8AiL5AWB0KLCJ6KgBAbcO0wsLvlWooJ6/pobR9//x+20fC18pDpC0H1f8NamZ7\nd201k8glA2sm/lLNCFHwkMJ2blF2lsoq+5wttdqKfLHiQURRVIWjovh/zaWXo2t2B1AShQlCTage\ni5YtxeK3Xhd7EGVQsKeuXCjlpHKL/7uco/WR18q27a22/ejS02+qT1qBtaCRSZ2tz3ecO4GwVgzq\nyXshdHMD1IitldDP1Xtdet0ZpFFo0dg48j0MdmVSfUlfLn8vdHTz7AkA0AZw0tljJNHad/gAqgO1\nUonlQEny+pCVnCYbSHFNJfYdzkWVcSqIr9zZ79NqNceWtm3Yqj9BCo5HTlZek4xbqSQrRb9tTisM\n6HomEp1euKMOhB0OVEcC+PjTTThWWiRVM6K1DWSGkPXF6r55BvF/c35YhFXFKFXsxetyI6d5FnqL\nO4cL73/0AY4cLxAqaCRI+pYTETJTokymNTCU+UKxQEOrtTRLjgW5x6bSTm9x7Z3Xzbtrh47o062n\n3M/NWz7Dgfw8hJ3UBdBnRvq7VB9lroWkQs1qeJdOZ8gnFJaXYN2mjUKDJ/hHBoOsjqTU2+faxG/X\nzn+OO2GaiM2ogkkKzLHS7UOaPxndWrWXcygoK8HuQ/tQE2Bbi4qZqVOBXo+0cMS114jOCNc0VlmJ\n2hpGBVtcWrVoCTJemqWmoV1OK/g9BIGiOHgkF5/v3Y2ahvpYEkWUnHRDqtnzugcNHiSJ8Wfbt0py\nTfBSxjedRJISY21Idv2hjSCFJDlu7HXatgsGOB3btxN19SQvTc/YClGHjz5aiwN5uUJ/TExNgdvr\nxrFjBcIQYFXfipTybwvk8tq5DlEZXh0kdG23bCVhgFnQ18w5rnO81yKu6vWI7SrXQ7IPCArIewlY\n0rKSzi8NATijDlSWlOHYocM4mncE4YaA7oqq09ZImT9d6P/0d+8T9hL79u8aALBgJBNr+sIzGWOv\n+ueffy5AioDT4vPX5NqFzeOUfTo1qznadu4ET7IfYQqzilaP7pFCTzUWnHYflz1c9jxtvxKwzrA9\nFPjWtiiu2RYMkDlq1lbuXRaQtq8V1p8BDjgf5D5R9NXuyVGgprQcpblHTgAAEhITMGz4MPzyF7/A\nqBGjDVwZC4UU64kDAFavWo0331yBDz78ENu3bZe+XoInFNgii4BUep47nQueeOJJzJv3iNxPqfSb\nw8t+e2kVsWsIgQBSYBUAsPAmX06/atrZahte4zor95Diq4xBJEwDOqQmoG+bdjirc1f0ad0GHVJS\nkCngnhNRVwI+Ky3F7955A2/sPyhUXIUaolJ5Z2sCe87jDw1jdAXnmsTXMHitqVTBS7bXZjiA3mnN\n8eNxUzCyd2/4MlPga9YM9RV1WPTBh/jFy88hP9LojmQrX0yyb7zxRtUcMrR3G4N8w6nypW/jekF9\nhoceeigmxErAhtV9SdKMKCXXCSpu8w/XHirwU++BQEV8Bfrbnl/8Z1ENnDR/+ntv2bIlphEg9tb1\n9bFnQ9tAu7eezvf/7wMAnMg/dgQ33XgTli9/HQMGDJTnwbHJOODAgQPinLBx40aN/5rYn53OvTnV\nay0AwDiCkaIfbgxvN0DAw92FB1EUKkOQa1JUoToWGkTfwudD3379MGHiRFlbWrVtg9T0dLWGFrE+\niLDbhg0bRAF+3cefSE7yTQ67ftt9wcaBNlbludBeklXlpu0udpVrupVpEYYt2WEBD99+ayXu+/M9\neHfVKrMnUbWfVfko4M4Csjui3fCJ6D54OJq3aYOQLwG1VP53U0yblfvGKzsdAIBvdLGYS//6sAs+\nMqBqKlGetw/lebsRrSpA3q71OLz1ExX740IdiaJDh4646567MePCGfI8pKWGxVhhlpHzrce/AwC4\n//77hAHA50bdDwLKffv0FeD9D3/4Ax5//HH5fx5cdx588EEBIr/J3Leb9v79+zBu3ATk5ubJek/C\n5F/+8kf8+Ec3y32zDIDYrmeC9miUuarPtO5TO8CFqspKbP18K1asWIE1az5C3uF8lJSWorLCxEkk\ngwg7H1K8dAw4f2qUAS7F16ZMmIDZF8xETkYGwgF9KOzF27JrF15ctAibPvsUDdKP4JQgldV/oQua\nCjcXRFbbbdCv1nd1shBIldcIrJF+zgCZwaRVkmUgwICPiakABwywG4JonpyKS2fNxsxzz0fzlCTt\nuXNCak8bt32Oxxc8JfaD7NfxJvkl4SIllgBCSlKSiMvZyWstzERpXzziw/An0urLJckFkwirAs+b\nzfMXkT0zKLk4SI+5qcTH/MINjd4GM9ZTWYTECICYNgSbeDSl9MdX+bkI8P2SvIs1F6nujS0ATRch\nj8+tln62rSDOFYC/43VKRZ2VcPbS19fHkCCKOPE+W4Ex9qiLNZSxG2SVqEWz5rjxqrkY2YfVlxAS\n4EE9Avhky0YR79p1aD/cCT6h9ZO+UlJaIp8vfvMej+hCiHga/dgpjkffc2MPJa0AVJ8PBaXHn/RR\nnivfw5/5fopAMSng2GDgaEXbhAngJHuA3ZfaKiECVEZbQenNECYCgzPLsuD9kx518SpXYUJL/bdA\njE3m+Px4/8UZIRxC26wcXHXeTEwbMUEew+vvrcC6zz+TXltS49tlt8Dk4aORmZ6JTQd2YNFrS7Hn\n4H4V2vMwmaeIo7aExJY4OW8V3RK7PZMoiTK+zydjkmOTizQTQVLHZk45F+MGDIFPFMCAgCOK9zet\nx6MLn0Z1SG1vmBixDygUDggAYN0f+HpVolebNxWtUzCO8y4ajMDndGPIgIGYe+VVSE1MwV/uuxdr\nNq6D1++XnUfGkkMBKAJrNvina4IN/G0yLD8bChyfDZk5ykRiR61T9AuumHOJ9JXPe/xRvL/xY1Q1\n0MkhJL2FHJ8EDXiutVXVaJPTEpPGjsOF089Dsj8FKz58Fw899oiKAbpdQtcXCy4DAMje1qRR21Ly\nmAgzqeU8l/tofHi1FzqKDi1aY+6Fc9C6RUt8vm83nluyCIUVxcJNYKVPKtWk9VKgz2mZJ5qFKgBA\nIUcCAGKWKwBn7x490b1zV+QfPiJWRWPOGYGxo0bL+w/m5+EfTzyG/YcOyhrGtix+TqLPL9XRMSNH\nSSXzcP5ReV1FTaUkzY0qr3pfLYAmLCdSo6XvWxX67VwhWNi+TVtcecllGDbkHJTXVCA5KQnJ8GH/\noX0iovb6W2+iqLoCnkSdI5wLaWlpUi3mnwRaRBolcLYB8LB6JVxb+R4rPmfXLd5jAp0xUNWMQZlz\nIlzaoJVIviYK+Jz6N1ldBUfzcexwPvIP5iFaF5CxR0CKGiMSYBGttlPr/wgAYO+btdKjyBCDbT6H\ng4cOYeXKlaKfEAMBmmwQFLFiUJfeKkdAAG9KolSuCQywFaWitFSYaVwH1d0jIi0pHCv8d7a48Jlx\nvIh2QFqaAMpcQ1m953PjM6cWC4EwMrYUaNJ2EXGWCQZRVloqP1PpnQ+pqqpa1j37vUzU8w/movpY\nIWpKyoSHQgXr7JwsjB4zBrfffhv69aYNoH2wJ2cAkKL5ypIlErjT05nzjudCAICK/3379sX+/fuF\nDUDBJ56bYidM7gE3gcpIRBJ2Jhhs/mPzE+MN9cAmSNr43a1bt5H7wZhGnoNJ2vXvCFyUZjH+xu4Q\nkBYFWnnd6NOyFS4bNQpn52Qjg2W0sAu5DQ14aOOHeH7DOhRHgHpyeQwYQws60uRZrJFAUJJiDZEb\ngvXweRJwvPi4WK99+P6HOgqiQJILaOP0YM6ZA3DF1Ono2KMLQi4nPt2+A08sexWv7tqGIgPCcN7x\nGbMCz177oUOHxijfNiluynhpGo98058pxkhFf7IAuDawYkU9gOuvv173FNMKyWSJFTeOSQbo999/\nvzAfvsvDJmv2WnlPCBJxTLH6Z+m/PKeWLVsKOMB7JVtNE5GtU53X/zYAgPeG4/zm792M5ezxjzok\nEbrqqqvQpk0bcUkgA8AmvjbRPdV9+Gb/rkw/8t/IfaOa1oweE3FGq3bYfXQ/th7YiaKGKtQLx40W\n4i7UR+qkqEHmHHf9ZhkZaNehAzp26iTxJ+Og48cLBADg+iHXYebSN7mWeADAjivuowI0RKPSQkIx\nOQJd8ZaV8fcjXveEr2FRM0QmXUUZ7vnbvXj6qadQdKTAvIWrmIdlZMCfhhZDJ6L72eOR3X0gIr4k\nuLxOKZbCy3U5BNp6f1MAQFhRFGVmr3ltAMGyUpTu34lI+TEES3JxZN+nyP18LXd1BfJZVU/PEGvF\nH972YyQnpcje5DIFPgts2Gv/twEAP/6xFGB79eotDICOHToKE4jrE9nRPMj8eeyxx2SttLEux45t\nkfx641l3ntzcQ8IAOHBgv8Znriju+tPv8NPbfigigF8AADTMi9EBdFwZPQwpYJkdLQoUl1SgqLBE\nQACumfE6VynJyXCMveziaLP0DEyZMBFTxk9EWlKSCNrwcfHP5u3bMO/xx7Bj104JrpnIsR+WlSkG\n3JJAMLiNswDi+TE5loQ7gd7MiupYcSEmcqRwC8VefKJZMdc+FlaO5YbqLieUVIIT/Xv1xiUXzUbv\nM3vCYzZiTuBPtmzGg4/Nw+6D+yVQ5eBmD6kI8QktlfZxAZnYYgtm7LL4HRKYs4+FVTaXVqt4faK8\nbpI00mkZqPCw4mlMTvl+JkJ8vfSuSx+/DnImtlKtDaq3slSkTbLJ4IgJrbxOxAJdMfEkPiAm39Yq\nTJIf2r8Zb3BbseS9FtE10T2g2B5BFooMUr1cxQwFmDFggnVj4AbPc+Ozkfe4CcyoFaKt4lqBG15P\nii8RF06ZhkvOn4kkeM1CC2zL3YWFixdh255dqKQrg6EOioCFUZi3TgsWeGACrfZujYKAIihlevht\nbyF/ZwVTKOjG4IqVJklQ45J7FaDSxEBbNdS9Qp+rJhi8j2JDFkdvFFaHof0zGWcSaNE7y0SwY1E0\nLLwesX9hoEsA4OrzZ2HK8HGygSxc8gKWrXobAWa04TAG9uiNm6+4GpkZmdi8bwcWvPwCduzdIz1m\n/A4LNMgi7vFIgstzZBuIBnRqp0fARBgALrf0RPMC+Iyym2diytjxuHDSNLROSDetAk5sP7oHDzz9\nGDbu+BwpzdIFrCB4FqH4lJf3XJcjPhteP79b2S8KTtlKucyVugAcoQhGnX0Orr36GjRLycAjC57A\na+++Lb3EwuQhY8boXzDAtoJ7UhUkw8RoAVh2jIJoHgH3OPY4f5hgZ6dn4rLzZmHamMloiDbg6ecW\n4N11a1AbImHZOIZEomLjyfOl0FuL9GaYOe08TB83UZKUVRs/wrynH5eEguckYmQOsnWMOA/XENP3\nTgo6wSb+zc9j8m81OPi3rVboWHSgR8cuuP2GW5CTloXdBYfwx7/+BcXVZaiPMDlXXQtNqJUBIesW\nQW6hQ6v4CtcPVlmrKyvRvnVbXHTBTHTteAY+WfsRtm3dipycFhg5eiSS0lKx/+BBrHhzhfwdiIQE\nAJA1Jwqc2eEMXDDtXAzqPwjvr/sQDz/9BIrLy2Sei4gfk68qdRFg4sZEjddTV0OWAEUDPY0tTaGw\nJPBDBg7CTVddI1WsR555UtaNQX364dyxk+GIRvDKsqV4dvkSFFaUyvdw8+Y6UVTMLmWIxoiIk4oF\nIAOaiIxpKxTH77XCsEItNloofC3vD++bPC+jGszzJYBsASVed7i+AfWVNSgpKMThA4dQW1TRSEk0\n0fNJc/7YBvn1tuJ/1avsvnIypsCJ//bVSAZFh6iOz6D78NEjeOH55+X5Sypr2D6c/7GDTBy/D117\n90TLDm2FCUAh3rqa6pj+CEHSplZ9FOmzArdct7h/xAIewwAQzRvDAtJ1WPdBHqqFo3OZz1XXCurM\nNOqksEJPsPnYwVyUHz2GYGm5qBUTAGjdprVU6W6/7Tb07zvgpACAVgAppFslCtvs32aSJtZb4YiM\nUyb8BAEWLVokdFv6NstBgpLq1sIdgYgdU8yvpcePZslpKK6qxOFQLUqoWWMeCYsiLLDRqYhrE6ug\n9ItmGxwt5bZv325AOCqPm0DKw0DbKRRZggtpAK4ePQo3nnUWWvFFISeq3C6sPnoAD7/1JtYUlaCO\nwKlh/tE3mokVK808NFnQVqZ4rOupp54UMSqJO0Iq3JUNYGR2e3xvzqXo3bc33v74A7zw7kp8dPAg\nqjQ81+JMVOc0BRMprEfQx9Kh7f7xXc8Tm/Rw3fr9738v/f2WOk5BLaqjDxo0KAbwk4JLdgLXWDIA\nCAD8M1oATnadPEcCFHQi4DjiGkqrO4IltCT8JiyE/20AANeXsrJSAWYWL35FbhPXdgJTXBvImPjX\nHYYLLVhbBMlw4KI+k3HeiEmorqrA4cJj2H00F5/t24m8ugLUSwMdW+KjotnRQOCLRCjZvGWExzxb\nrBW5vZZviifHg1eyHpqWZa6H1LpgVZnMLnucDECKlxkUNxEA6zasx9333IPFi16Sa5BWhyAB8GSA\ngtCduqHL+KnoNmQ0kjLbIMqeeynAUyeq0ZnmlPZ+sXujZ6gxvrZPM/bzOFxwNwQRKipF8b5d8NYW\no744F/t2fIwjBz9HuK4ULhcLIbpW3nrrLfjJ7bchOztHPq/pfY4fO/8OAIB6K+znZ1BCAclXlrwi\neRqZVQQBOMfJxuMaRPHSpsfpAaSaqB85chgTJ07Gzp0UFVQA4Ne/vhM/+9mP4XGyPTOe2vhlI1LH\nxYnjlCxA8s9sLKFjPL5H0nHFj34QveySSzHirCFCVZNBajaFlatW4flXXsbuA/skiGaywEqd9Jaz\nb9pjkmZjzSZJRTAYCwCIVLXMbiGVJakuGG90voaq39ykxOfbWLoxsaiqUR9w0hk4yITKXFsHV9SB\nc84agpnnXyCBawKVv6UmHcX6LZtw/7x/YMf+vfBRdM7rVpXhSFR62IVebyi+Fgho7ENn9ZV0VVX3\nFkEzi+Ka1Tkm2kfRPFOdl0TTJL5K01e2AJOfmJ2by9ihGUEjBlgiGBZX0ZfAyAgwxlMobdXUUmk5\nCG2llt8ronUM4AL1cr+sbzjvsU1krDCNCoKxCu+WDZ5JGL20iQDxmkkb4WdL/6bTieKSYmFsjB46\nHN+77Cq0T80xGGsEucX5ePrF57D6ow/h9vsUmTcBHp8Xlbv5XFlZIZuAiYd4o1sqPpM0w5gQMUVS\nJNkyYHr+LUOB18PAk5aCfL/YNRprRI5RFUvktSrtnPeO7AAeIjrJHlJSXYyfOs9Fqo/GM12U3QMq\nAsh7wp9ra+tMguyOAQMWreX5N0tKxvWzLsHUEeMEP164+EU8//qrqKYwTCiM/mf2xM9u+SFaZ7ZE\nblk+Frz0Ij7evNEodGuFmQko761VoCfNk+dmj3iAigkSX2/P8ez+A3HtnMvRIb0FEtSpGRWRWjy3\n9GUsfpPuGCFhwWgfK6vtIQmNGUiL37yAK6rsbO8pRW3kOYjQoxdOrjWBMPhd1111NXKSs/Hk4vlY\nsWY16hFBHedtkCJ0po2EjhqkcLPn2OeTaqKKi+kzY/VZ1hS2+oi7CJk3/H4XWmRk4ZJzCahMRG24\nBk8ufAYffrpOxCU5hxhgcU2QviVaXtUHRYfh4nMvwLljJ8LlcGH11k8w76knhDYu64bMMQJy+hkp\nSckyp+MZEErX1UWR6wT/nwKZ/JuVbM53UtkH9uyDH8+9SRKBfccP49d3/R4FFUVwet0gNVmV+xXY\n4ecrg/clsYIAACAASURBVEPbCThWbTtEgtcnTKSUBD9uuPpajBwwHNV1FWrx5/GgoroKH21Yj/Wb\nNghdn+BqcWW5ABqck0neBEwdPR4XTjtPNCQWvvoCnlu6GPCq5aLOFRW5VBtJBWYFNDF6GFqJaLSX\n5Bxla8+lM2bK+vPAY/NQWFyEVE8CfnLLDzCgWy+hTj78/NN49c3XTUWHtqUKdnA9IrBIEUN+thXW\ntLor1AIh0FLD+xmOyDpg57AVKWXfJYXOuB5aRxIyt6R9h5T/+gAqi8tw5MAhlFDZP0C6ewzg1tYl\n/onb0mIB9b+YCfCFSOBLfsF7xYSXh+2dt4yNxrfEhz5fTBHs6zkHmzVvjvETJoiAFCnt6z76yHxM\n/F0xv6IKf3YmuvTqgeTm6SivodCkVwAkzjVJmI34kezlMbYOBIi1NoJcd+OZcBaQ5nvs6zgfuG7Z\nxJ+/51jl9/A7rAYAxw+fPdeMYFUNjh/IRRVtAM2amJGZIQkgA7CJEyeKeoYeGupYvQsGyQSwWP0n\nNZlMgIJj+RLSd+7cWSrjnEus4FK9WQAYU8n3OwFfBGibkoDeLVujX9sO6JLZAlnpGaiPRPDh9i1Y\nueMz7C6vAbvz6a4SjRBoaaTpX3TRbPzut7+Vc2Lv+IsvLsLOXTtiDhmiss8mG7auUVkfwPTevfC7\naVPRjgW5iBthtwe5DVWY/+H7mL95M44yWCeQTYuspCSh5JMWz0tXVqHqfsQHfaVlJbjmmrlyDqri\nD2Q43Wjv8mNw9x5o2aoF3tvwMTYWF0r8RNHugMFr+PxmzJghiTiZJl8cl43tk193vJ/qdfFJM60Y\nKejH5yP7eUKCsDZ+85vfCKjJtZrgBJkQXDOotk1QhO4B/6qD30sgYOvWrTLe2JJDEM7uJ6eXAHy1\nXMl3kQB9u/vyxbXHgn8/+vGP8NBD/xDA+2Rg5rf73q/7btsMrTXyxCgwqs0g3DDjCmT702QfL62r\nwoHjR7Bp7zas3/4piiNl0i7TgDB8Rsia+2JIio5a7WfMIQU9M7O+DZbcFADgmKZ7BUErrmkE9k7F\nGhE3KRYsnE4BbclE4VqzY+cu006pa4sk/ikt0HLAUHQaMQ45PfohmtIcURcZoQ2qCyCxt26OltkQ\nnx5+MYU8MasUdydTQPLyO6vrUXn4MEr370ZiQxXcNcexbeNqHM3dhmi4IvY9yUmpovPxXz/5ITp0\n7GSKTqaS+2V7ZZPU9euOivjXfRXAdrLPu/ev9+KnP7lN/oltIi+/9BLee+89cR6x1X+6yXDdsaLr\n8Z9zevNfz47V+amTp2P9hnUmRovgv++8Db/65R3wullI+jIAQAGEkx/6b7a4Z62r7WvlPBkTrtu2\nLdqj+5lCYTZubaiorcOqNR/i6ecWYm/eQaSkp8niyw9jssSDASQTAekJND7copAvVFOHCFqMPPsc\njBw4BJ3bd0RmZvNYdYEq1xRQI9WwtLICxWWlKCouluS/uq5WAncOekmyoxDqL1WLa6tr0KJ5plBh\np06aTOtjmaK1iGLTjq148NF52HVgnwgdSQJKmz9W0EknFWVv9uqFhWJKcEBQ8ojSFSV4kYBUdne5\nNv5RinTjLdYqn1H4Nr+WKqrpcWZATxorD36PzEtJFDQp0cqj9kUzaGd7Aqvktm1AklSjQ8D38N/4\nmpiSaJx/fXVtDaSVx/iz235bsgiUbaG2QwyobfLMxUZtzpRazftAMSgefLbiNV5eIQvTTVfNxZhu\ng+CMaj82K7NL3l2BJ55bAH53SlqqDCJrB6WsDnU/sAwAAUUMNVsBIO2JlteFKAwWkoRYGSTqnx5f\nUQ5K8qBuBzx4f/haTj7p2zbCgfx3+lhbRoGAIA4INZUJH73rmfwSDCJQoM9fk2HLIOD/M/Gz7QRM\nZthXzcfPZ5eekIgbLrpUAAAyABYsfgGLVixHdaAergjQsU1b3DL3OvTv2ge1kTq8tvINvLxiOcpr\nq9Wz3WgVSL8TwQ32ShOkSvDLM+QzsqrYZHdw7DKhZFJNitpVF1+CEWcOgN+EweQ2vL3+Q3FhKK+u\nQsRJQEzvMQ9hFdiqlRnjKtClDAPeC86zMMURDZJPzQeKPA7o1Rffm3sdMn1pWLDkWbz24SrUhAkU\nOKT1hyBOgk9bFggo2YCNlTz1q3aqCBddBmKAWtQwgxQMyM7IwpxpCgDURWrw1IJnsHrdWkk81UaT\ngp0uARw5DigS2jw5BXPOuxAzxk+G2+HG6s/XSTWciQUTZrJ/RMHX7VJ2iAElVTNCGQLWJpMJOgFN\nvob0P14DK9acN3wmZ3bsglsuvwYd27THln07cd+8B1EdqKPgrTAzmMhy7DI4J0jBNZJ/2DLE4Jyf\n43Co5CJtUi2z4vqr56J5Sjr8SDCyfVHsOLAbS5Yvw/rPNsOXnCjMGjKteB50n5g97XxMPmc8Agjg\nbw8/gFXrPoI/LUVUqjmOCLCkJJNSZwCJSFjOLSOdTICAvE6r9Ebl3emUVoTLZ14swQ5bCvg5Wcnp\nuO3WH+KMFu0QQAgrPlmNJ5+bLwJxlpnDcxJwp75OxAs5Py0AyeC4cX5qNdqCnFZETDQEDEU8UB9Q\nbRc+G3rK+/yoqaxCSXExSo8X4/C+XEQbQrQ6UKl1a6NndnYbztiNnn/HKJP/gSCA7YvlPaM9HdkX\nTCTi/YQN0dzsLl8ewrC1h+sH5xeT4+//4Af4za//R/pvRd+D+xwVgriBGX0UJqKO5ES06NAOGTmZ\n8KckCUOptqYmptdgmW7C1GALC9dM4wzAMW8p/vo6l6ytXB9tmxWvkfNNWswM24p7Eg/u+9wTkhLp\nAuGROSRMIe6f9Q0oOpiH8oJCRGjNSuaQz40BAwdKUjp+zPgvMABkbplr4/ilfgXbIvhn165doiVC\ncK9bt+6gOwVpvRZcd1G3AkAbDzC8aw8MbtcR3ZtnoVVKMpII9jMgcjlRHY1g/b79WPzJx1hVWER9\ncThJoQ2rtCn3Ot6L7996K/5y959FlOl4QQHWrF2D555/Vlg90qYXJH2XwIoYOmFAZhZ+d8FUDM3O\nQXLQjYa6BjiSE/DO3j24b9V72FRWhCquzKblkCAPe04bBaestGdjfMLPfevdtzB9+rkI1gcl/vA7\n3fCFwkhwelAVUTYju8eIp7l9btRzfgHCLmAvPtkMykgzrTVxEebpBbhfEps2+XWMdRgKCVODCb61\n4WOVnz9TVZ69t/x33gOuIWyH4///qxgA8adtnTAE6DWigF9G4f6qu/C/jQFg2UVse7hm7lzU1SoA\nbMGify0YoC0AVsiYoFqHhFa4edZcnN2hN3whB6JUow/Wo6y6DAfyD2H9ts3YnbcXxwNFIhxsVMzI\nM0ZSs3Q0IIT80iJEY7aYX28Mf9mrOId4b2zRj8w8Omqwz5zg0alYI1qx108vOHZMqv7zn5mPspIS\n1Y+SNlA/4E0F2nZGjzGT0HngUGS27yR9/iGHBwE6KIVsXuE1DIAvAwBOvBILEtjfMqbjIfph5VWo\n3HMQNUdy4Q9XIlh+BPs+W4PCw7vgitYibLQXcrJa4aKLLsZ//deP0PGMtpq02pyWRIIvuXnfBQB2\nugDAY48/hptuuEHOqGevXnjiscfFpYT7CQ8y76gJwlYyrgE2l7OXcHrrIx2oyI4LYMaMmXjzTX6H\nnvHNt1yHv9571ykAABsiNAUBTCukiesVBNAkVpgbca5YjrpwNOph4sAH4gQKiopF6f+TTRtRVFEK\nZtkUnbLBH1F0XiSDflKX2f9KGj83a6n8OZzo1bMnrrziCgzs0w+JpIjEOQnEn6rFL5gum68/Ac3m\n0ObvmXBXVlSgrKQURbRKiAJnnzUEfp8L4Sg3MlUlfnPNajz0+KOopoiWl7YWWiGQSeZySkDMBYqJ\nKp0GWAFv3ryZ9AGxQpGanGwU+o0aLkERQ3nmzbMK+naxi/fljX/wJyS/VKunHYnRQ+DnMVHlYfvY\n7Xut1ZP2oCsI4U9Mkkrbrj17cODQIe2lT6DXegAUbvP6fSKgZSvVkrQaX1YBBIzqve3vYwAm9oAO\nh6il1lRXwiMggQqtVVdWISszE5decikmDBqFNPZLhRqkp2j99s/wyHPPYN/hQxLs8V7yWq2tnBVA\nJEXS9vkq6GKs54xQlKWGq5q/R+4rEyexNktIiLWK8GehoNJT3VDL+V38Xn4u/00t9YzVohH6Y4DK\nZy4sEmNBaKn/1gHAgiWshNtz4O9sUGtpiFqRV8E3Jp/XzbwYU0cwEFUA4IU3XkOYcyoclfaZyy6e\ng2nDJ7NLHJ98th7/WPAk8osLVUVb2hU0qBKEmeqp0oeuAI0E1oaSz9dysSUDJhwMYdKEiZg9/Ty0\n9mYgGm0Qu789hw/iHwufwpad28Uvm9daUVkhglUy6MiAMMKUlhXB6iOffawfyJ8AX4Jf5goTOr6O\nNqAD+/TFrTfchCxXKha+8ixefe8dlNVXS1Ih1XIjgBkKBmIWjbaKy3tqq4KWDs7xLuwg8VXmfXAg\nIykds6fOwNSxkxEMBzD/+YV4Y9VbkmRzjCb4leXBXrWAEULMSEzGnPNnYOaEqaKY//ZWtgAYBgAZ\nDhTzirPd5NohYpNGuIzgiFD0Q3R/sECc0mmt5oa0k9MCLzUDN192Nbqe0RmHi4/joccfkTXRneAV\ndXqCBxw7Kcmp2hpVVyeJtlDjKehJ7YggEyOP9Pun+PwYOnCwaJq0yszBkSNHQBvD1q1ay6a4ecdW\n/O7eP6MuHERCcpKAbJzPOenNMXvqebh0+myU1pXibw/9HZv37IDLT32KgLAWpGfbVJY5jsnw8Xk8\nsr5xXLE3m9fEcWWvu0+PXrj0govkmd/3j78L/f6KWXMwatAwqbOyAvLcysWifWDBRLHFZPsRW4cM\noGlblhTcVCFTjmkCK5Z1wPHO+2XbsDguZO4GgvA63cLU4gpUW1GF40ePIe/QIVQfL5UAR9wv6Loo\n/RWmXGn2NOtYIUlNkz//aj2A0wkVJWh2anWaVU/eV/Zysvf0iwCA/WT2szd+C/c0sivcZJ40BHDh\nRbPw/Vt/IL3TtDKTpN+26sjNYTLJzT4ET7N0tO3cESnZzcTSU1p3DBPN7vXaCke6pztmfct1SkTh\nDKAm89pU662eht3PGrVwtGWNQILsv26Kg2m7FpN30fgIBFFVUorK/OOoKCwSAEBEQP0JkpiSljl0\nyNATbAC1MkdRWW3l4vH551tx//0PYNGiF2U+EgBV0F7vG9tx2K4VjYSRAmBY+9aY0KcvBrXthFY+\nv1j1Oepr4aXYMFsdGTtQg8PlxeFAAxauW4tln23BEZEWcaGOQqhO/e6s5pl4/vnnMGbsGP0yB3D0\n8BE89tijePqZp3DsWL6As0JOc7jRPtGP2T0647Kzh6JbYjO46wNw+jzIDTTggdXv4eUtm1BIpX9h\n7kTQrl173PmLO3HdtdfGCnVsAYyv2lmGxFXXXIX5T8+PMT5pz0VmkQ22pdJp8DSn24nx4ybIuOnX\nr19M9M9qHcWP69MLcE9nRuhr9+zZIwry9JDnd3GtpqgcK24cd+z/p+0e12r231M48NsDAF9G8G5S\nG7UWaXFuUPYcT1XF/bI78WXfHEsoTv8WfsfvODkLgHaJpEC//vqK2N76HX/xKT9OxrCh7XNccxYm\nwoPrJl2OS4dNg7taY1N6odcF60QYryZQg6KKYhwrLcDx0iIcLzyOkNuBPqPOwbDJ4/H3Jx/Fotde\nEZZAjJ8e95C+rN7a9GTtWwhqS6HK6GyMHTsOf7rrLgw+a7C8pXEd1YIkNVdMC7f8O2N8Lvo7t+/A\nr375Kyx9dZkmcKTucCtMaA6kt0RW1z7oN2YSmp3ZC87UDNQH1C/I5fKo0GrUFDqpNxJzN1HR5y+O\nNTKLLEBgnZv0Zx9bZkNBlBYUoDw3F/7SCqQ7gjietx3bNr+PqoI9LBGJL5NcH4Dr5t6EX/36N2jb\nlrR/6ZEU20FjI9WEtn7infy69/ubzq+m71u8ZLHsx9yvunfrLhasP/zBD0U3husP9RqsTeA3Y0jF\nr8B8/irgPPviOXj55cVyb9hNN+fiWXjm6UfhdAZO0QLADe4kVy8P1vxD7CF/8YWOYFQxO57W5q3b\nMf/F50RUL8heD9KkWSU324bQWUjjNSr3NmmkuBXFJPhnQJ9++N71N6JLp05CcyO2zLph7qFcqa7Z\nHk9WkVjlTEzU6qel8sUT/PjwbYXHokFM+N2kA4dJt28kBKpYTxTL31kpbQsldUxWXPC5vCJWxJ5C\nqh7yYC91/9590aFtO7TMyUHzjAzpXRWVa4faf9kJYM9HLc8aOyi+SNLUm2vtuOz/2+1ZH4e+xtqS\nWUEja/zG16qYtVqVyUQ193D7kTxs3PIpjh4vMJRypXe4qLbv9UpPDYM49oFq/z/p46SA6yYqQbnp\n02TVhUDA8YIjKC48jCS/Fx4q6jcEBQmaOnESZs+YhQS44YeLRmXYX3AEjz83Hx99ulGqq0keihOG\nUNtQK5VXBvb8YytJVsWeQaq0jZjKIxdkbUnQypIN3KRSJD+7jO+7Lli8NlvVlETOWB4ygbOibVY/\ngNdoK4v8mwkKFZqtZZUFVeJF6TiGrRuAFeCzlWKyCfgZrEZQcZX959fPmoNpAgBE8Mzi5/HK6rdR\nE6hXMZVIFBPGT8Do4SOQ6k/C5s824+XXl6G4skzuPcd8XV2tVoadDhXMdLnlmTFJ43Njgi4aEaYa\nzWseNuRsnDdhMnq37wo3Rwjp8YF6zF/0PFauWY26iIpFCnMhQFaB0dMQcOXELqt4rQereWHHC38m\nCEAa9sABA/C9a29Aa08mnlk8H0veXoHaSFDaa0SXIkIXq3p5tjxnSXhFl0F9auM3N9uSIs/b6dT2\nnGgUGUlpuPS8mbhw4gWoDtfgaVqMvvcuagMUj3PLuOJ9IpAjtP5AEGkJiZhzwYW4aDLp8D4s37QK\njz7zlFSHuJmmpGoVnEwk27pD+qilyUvLEXUAQmFRmufrmKwK+EEBTKnmu6UdpE1mNn71g5+gU4sO\n2Jm/H/c+9ACOlxcLyCAtKJx/RkOCm6yMOdPOpJUSJ+qDYbmfCIYxsFcfXH3p5eic0xGbt24Uehlb\nVK64/HJ07dAZFQ3V+Ou8h/D2+6uRmZMt46C8rBzpiUm4dMYs3DB7LkrqSnDP/X/Dpp3bEGEFmDaK\nHupGaEKldjqq1G5FMZX1QzcUpzBfCNzy+gf37Y/LZ8yWPr41H3+Egf0HYFjfIbLysNdw077P8dDC\nx7HzwF65Nj5nfhbvoRXzYysAx4CdewSICQ7zd2QuJdHujayocEgADc5ngnwW2OL102Y0GgiiuqQc\nh3btwfG8o4jWMHjj2sWF09qxCRqsu53d206y950qqD5lZHnCC75IIyC2RtA0ECTwfXqf9gXGHtkn\niUk4Z+hQTJk6BYmJydi0aZPQ2AvyJc2UexYMGJtDo+Uh+4tQwOMPB37/hz9i/LjxkkSt+eA9sYIi\nzV8BQKMJwKCSLTrpqejY+0xxB+CcoEAfnUOEmSStWQpucY4QhI5ndsjaWs95HJEigMwvgjxxTBH9\nvQpIWsDVMm3IeiDDgIwrEf4NhXD0wEGU5xcgUFIuGy3XijZt28l9IRW8T8/eJ1SL7JPhedmDLQBM\nCj/55BO1AWaRzIiOqlkYY4qwVP4HZWTjmtEjMLh9O6QTKK2pAT1fvI4o3CJYGkFEgFRq7XjAZqZq\nN7B86ybcv/p9FLmAMqlk8Us0UJ41cyYWLFgAOqbYaigdB27/6e2iTSA1e4IMLi9coQAGN0/Hj849\nDxNatkNyXQM8Pi8Kgg1YlZeHR995B5+Wl6HKoVZmaWmpGDVmNO787//GWYM1gThhMsR+dmD3nt1i\neVZ4vFAtCg0D0Y4Xywzjfjxz1iz89PY7pKr1TRPZ05wFX/lyijPedtttOHbsmMSaXL/Zc0uWCzUC\nCAAwXqAt4iOPPPItAYD/7Br8d3lfv/lnxa8yNqJ1YMWKN6TdxPb9W5D9m3/P6b1TAQA9OMUJBlNt\nbEqvUfjRBVcjLaItodwjvWlJsg4m05mgtk6cAugq0xCoQ35tGc6eMw1JZ3bA1POm4p2172vBMz4Z\nMUXV+Lj/q8jXdCTj4qymvmrb2659R6kms2WFMV95RTnSMtPRslUO3IyZiM+KtRsQbAjD7XU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Arz6BDyDufjo48+xrxHHsL+AwdQXlWt4Hs9Xb+i6N6tI156cQF69eps7utJaBs8wW8LAPzhmaei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UJfgZpoAg108xMV1A5POVwppYiGpuP5EISUyQXWhta4sjBAjT5F5ANJaM92gMGZ502ELAFx9+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\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B9IlT0dLdhjXrPsKp6ipRdKdLQm11DaJMFhEJHDVyJDIyMmVNkE0+GBSQa/+Bw9i5dx86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6bprstnl9Lco+zkJ+fUh4qHyPYq1IakcwiKT4BPTv2Ru9evSUcfHFgf04cOSwjO2IOYjWVi+c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R5zQziQdG4mLkwEuSExyeFmxc9gonXezLn44o23i6L+oy89jRffWI1h/7Ak+SsWXyytAQkW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\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1/Dkr7+wp5dgEgwGpzHKseH5KU0AlzqzWch6RNs7nBdsFVLuBelDpihPViLUGAV/vY2uK\nIQQ32kYiIJVNfl82KCaf3IzFiYHsDatoO4hllUVZHYrnrtmMgoJ8zJ07D/X1p3Dg4KFRjQqCBVTF\nzsjIlPve3dklDB5hmLDbbmTEYG8QlFAVdl6rVNnF4pLMEbsE5UxctVIsAYjPtBtZFHDFe+D3B+R9\neb1SUTXmAO8fwS8CA27a7HkDMIUjIlJYPmG8VAmrT56UZJnJBMEDEUZkK5Ak4Kpqz7HmuXKseBDo\nIQDB+y0UeQPoEQqlAfip6+C64PpS7hL8yutl5V+DFgQUeN7CfBhWAB3/rj2a2ULFajO1Tmjh5Bv2\nijp0kOuOAEEkJJX6FE+StFgQROAezHMmy4PaHfxMPowImAjQGWNbpX9UU6K/r08ACiqW83pE9yQa\nFmFKAkLUjxBdDrtDQEzeBwJBHBu+hlEHFWP1HtLT2oHK3Xsx2DysNkg1bEiryEP5pHLZb3ubu1G1\n/zAQUBReOXQrwCgI8I8DAEQh2djf5dT4vDbs6Rgalidk4aJFZ2JR8SQR72IPpsXtQua0cUgozYMp\nJxVIdSs4/q/JHGs+ugnYt2cv1q5dixb6fRtgYdgAZ/4+4ck/+btyEBxmTJw8Eb1d3WhvaVdzxA7k\nlZcguzCPZGcRsOWzwzfkReOxWgx09TLKEhVKrtczzjgTt69fj0ULFsFld51uATDaODQDYDgYwIt/\nfh0/+N7jqKmsFFFh0nAtNjsqJk3BeeeeL217Bfl5AmC5PfECrLc0NQvgOX58xega5lZKNwAyr/hs\n5Jri81dsmYwol/HQT3/yLH787I+FAemIxjAx1Y1z5s1HgsmBvQcOoKqjFX0RYIjIwLipAAAgAElE\nQVRsAO7N1NtxxuPMFctx2RcvxZmrVyM9k6T3/xsH47jb77hjNJi2UVsoEhVq8oKcFNy2ciVmZGUj\nkdoMAQr+GbXDz1f+JUnl4uaezIlgRchsQ1cwiHePVOLRdzehzQBfuHST7XZkJqXAZbLBFASSnEko\nSsuFwx/GYG8fBgM+AWZz45OQEqfYgGS+eeI9CqQwQXr993XVia3eMAKIM7mQ6UrClNxSTM0pRprD\ng5GBQQwOD6K1p1MJ8haPR0lREeq7W9Heo+JnsmYLsnPlmTLgHcHJnnZsOr4fR4ebpVWBibwHZswv\nnIpp2WVIgAPmAJ9lTDhVuxqZNUycGfNpW2G2E7B/XLSrEEYfgtjVXos9LccwDL+w6Mig8fmGkeyJ\nx+2334Y7779H2QEKq41thao9Qw5hzhh7t2ykBhBAts7xOrz6uxdQdeAgplWUY9a4MgEAnvrTa9h+\npArnLpyCJ575DlInpCFmC55+T2Pt8PP47DXRdleUzvn8ZSukDWZbnCpzEvrxhaQo2N3cCv8Qdb7i\nsGN3Jb77o1+hZ3AEDhNw/boz8NADFB80A0GOYAjRWABmpyFsNwYoCgTCcDo8iIbZJpiII0fa8LU7\nH8WOvU2SdKfEm/DEYw9g3eVrFCWFq9ZmQpQOUkZx1GR3IEZGE9vuDOctjhP/rYZNS+mPkcUf80wc\nHAzhvm/9ED9//j0Rj+Xp2aPAjNQirMiZiLLELHEECwfDMI26i0QRNsfgNYfxyYnD2NtzEuXZ5Vha\nNg0JURsi/hAcBDu0WwI1oig4Z4qgP+pHbV8LdlYfQDf8cCCGGRYLrl22BJcvWgAn7YUpZvc5FxNe\nixTFrIzjzNh45Ci+v/l9HPV7EWA3CuN+RzyuuPIqfPuhB5GZlQHLGBvAfwUAgPvA7t27ccEFF4gb\nDNcGCyZr1qxBaWkZBgaG0DfYhxN1x3DyZDUCAwFJ/jPigQfWn41rzp8Ea6gXPZ0D+HRvJfZV1kju\nceaiMixcsVChk9E4AYOe/MWfsb8uIvT/xTOKcM4ZCzB7ZjkS423oamnC8MggsktyxTnERIENthL3\nD1OHE3VN3airqUa8xY81axYDHqOfig/bUAz+/kHVep3okcJvoKUDfV3dMM2+6rwYK5NiCyV9u/ZR\nOniCOwHrb/0PlJWU4aOPdojK7bx58xAf78YLL7yAqZMm4upLLxeU/e0t7+H9bVulYkjXAH/fIN54\n4w1s370Lgz4v8ooKcNUXr8D5i8+SRfLie6/ixZf/hOGhwdGKKGmvVL+86NzzhQHgsXuwr7YSDz78\nbfFMv+0rt2LBtNk4cuwonnzup6hraxr1QWWQrJIPVrzMEvBSeGzW9BmYOX3GqL+8VHZJVY1SYM2n\nklb+jtmMmrpa7Nm/T1wNSLWVBG1YAQAcGyYqHCdFt43D9VdejQsXnSXVh57gkCQ88XAiiACO15zE\nW2+9JfTrb957H4oSc9Dv68c7m9/DkNFn3d3fh+r6eowMDuGOm29FSW6hJJQhxPDBvo+w6cOt6B8Y\nEPAlOSkZkyZNwuRJk0SUqKe7GzWn6sTjva27QwW2TCADQWEk5Gbm4AvnXYBFM2aju7cHP/7t86Io\nLpVYM5Cakiy0kL7+Aamoi42RIHgq0ee/mTyKRZ9RseZYMZnnz5lESv8LxR4jUVXlNyz3dN+8vMZi\nkrHiPWGLCA8RMDPsaqSiGonK/WKiqqvhfJ0k8SLwGBq19BPFf75OqPo+YT8wMBN7vmBQzoO/c1r5\n3C8AgL5/PDeqo+ueacV2UCKYTI7k/ccwEKSf32KVxFdrFvC6iMqylYBCVaS+S7XZbBbWA5M/xUBQ\nDzcm7NI2IAi3SiIJACinCjIq2PuuEmsGHVqwkNc4MDQon69AANJOlduFuHYwaTZaBzRIwDHR7RDK\n/lCxerTIIiu9ZObwfAhgjKsYhwnjJ6DuVB0OHjyEYDT8F5ZW4gxBdw/eK4PmLkk6gTe2xUSUN6+D\nlXYGH1wjYsmpKvlMyjVjgQ8UPcb8fSawnF+qFUeJK6q+XQKVFGSkrSPFKvvkmj0upUWixeuEgUCQ\niZRczmObRbl+GKyDOIrvGLRTXZUnCMExIQDA+yMquEbLhohBarYHRVj4vl4lXsnz1qreWjuC98/t\noQaAWeYVmRhihWpoPeh2EraYMNAaGhhU7iV2xVQSxoiF6rtkoVhl/RIp5jXxffhA57zkGIr4kQF+\nsdUkMSFR1gZjNK1donUxdJuKyI3xoSVtK2pvZJwiIofCXlFq6zpR5tjqlgi2Eg11daP5VCNaa5oh\nCkCS2QJZpXlC4SY7ob62HjUEAZjhUN1Yt+X+A1rcP88I0Net0ytNRHTBhApPLi6csgBnTp2LeIrL\nURw1Nx1Fs6fAPL5QUf5ZnjEYYKPohi5kiPtGWAAcBqzPPf881t9+u2qTGYuF/N/I7T7D7hGwhW1q\nCQ6UlpdhoK8Pbc2tqtqb4MKchfPgcMejd3gAzniX7DnDvQNoPnFKtACEZeQPIDE5GZdeciluveUW\nTJ04RRAcYQAoLvmon3soGsCIL4CX39yAX/78ORz+9FMEyRSzmBAW73Irzl69BvffdQ/KSovRN9iL\nwqJ8xLuc0j5UU30SSxYvRlZ2lsGWMYn1KdsDMjIy5A4SUKupqUFhYaE4YlDvZP3628QDmmsmPgKs\nmz0VVy0/AyVJ6WjpaMeGfbux5fBB1AwHVTXaTBVorlelnzN91ixceNFFWLVqlbgcaQaYnjJaWFeP\n53/HePlnnGZSozdaL5n8k0a7efNmxTSNRBBnMSE+EsPqoixcu+IMzMzNRxxbRcA2J2nuPJ2M6qR0\n9EKN0o3FjGg4il6zDe/VNuDHb7yFEz4fGG0wJaOAYJLTieGefnm3qTnjMT9/MpJs8eIgc+DUQdhg\nx4KJszA+LReRQBBVDbWicVNRUIyyklLp+z/cUov3Tu5Da7AfVliQEZeAXFsCKlJzMbWgXApOtS2N\nUijLy8xEfkoG8t3JaOtsx7GuJtjiXchKSpV2T9/ICHqHh+A1R1HV04rNdYfRgWFGC2xawQRnDhZW\nTENOfAqcEYuAsvxjNZJLsc418nMZJQPotNMGls5OVhMaRnrxcXs1jvc1ittBiicZye5kNLQ1IIwQ\nzjp7JX70zFPIy8+Viq4kubqEy6+MX/hVqmRk/vll3lK5rHHXPjz9xFP4w4bNSPTEYfXSpRjx+/Dm\n1g/lvB5/6FZcde2FMGU4EDWrGETdvs8xwz6jO6C0ZFSDj1JAF7CDTdDUiPDF0Fvfiz++sgmP/PQ3\n6BgIYlJhPH73qx9gyvRUIDpgzBelbh8jYEG9JgEANDrNQSMdnzpP7GWPx8OP/hJPPPOOfAz1XS+9\ncAXuvftGjBuXLSJ5cv1MjnUSP1rzH1vhVg87zXRUFzsWADBgSsYlJif+8OdNuOOuR9BBZUo+YmJm\nZMCJL0xahMlpBTD7w3LNo2KhYjUbQdhiwqHmGrzfdBAeRxKWjZuGoqQMmIJROEw2REOMZZQIZpQW\n32TgmiLoDg1id+0RVPY2CGU9BcBlkyfi1lVnIt/jRsyIufRp6yUWk4IhdZEcqO3pww83voeN9XXo\nEMlFsR9DXn4hHn/8+zhnzTlwsr3ZYB1aqan0v5wBwGfYq6++issuu0yeZ1dddRWuueYazJo1C4lJ\nyVRgE7ekqpOVuPvOO7DpzU2IB1CaCfzokXVYMi1JQK6+Jj9+98JraGjtxdSpeVi+ZBwKi7Mw3D2E\nvh5W5x0ImGxo6BuCMz4eFQWZSE6Mk2IE115dZRUaGhswa+F8JORmiX2nf2QYdSeq4Y73IKewUPQ9\nBod6kVWcD3R3o6ejA7A5kJqdDdA1TiwAzcDgMHobWzHU2wdT+XlLYnzI8WHEB5ymk0rV0+HEzTfc\nhOVLlsPrG1E9ys5knGyqxiMPP4KcrCxcaQzMtk8+EsswJqekofe0dYjtH9OdYCyKwpJinHP2alFC\nJZKycdsW7Ni1U4TKGMQLvSYaQ2pCEi4+93ysXblatoBPjuzHvfffj9nTpuPB+7+JRKcHz/3uV3jj\n/Y3wkf5OcTCD+s/z0xVEVnt5sPdQU77Fhz3MSrchAmZsRgwSmQxIBZxcNGMTZAVXK7nriiqTGxmH\npGRcdM55uHDFWag9WYP3dmzDxEkTcfbiFeIr3jbQLowI2rA9/NC3kRufjv2V+/Du+5uQlpmBzMws\n7D10ALsOHUBWeiYevfsbKEzLEQBgIOrFxk+2Y8vO7aiqqpJ9pLysHLNnzRIwg1Tod95+G/VNDYja\nTDjV1ChtARxH0tYGevtQVliKm6+7HlMKy7Hr4Kf4yQu/Rr9vWBT0hf5vLExFNVKiIzo55d81AKDQ\nWZUck17PeUHghP8mQpxAizkmrsMjsvB1NVUSDYMXrBwEFCrKsdP0fG3NJ4mQUbWWvi2NoOrkxPhd\nJeSnrPqkYkvtAvZFGxV7occbzhOyN8meqzZm3QJw+tqULoD4EFvMcm2a5aAUzRUD4TTbQDkFMJnX\nIoCSTMSICFrltZLMGRW/00yFiCTJPLRFpWIl+KR/WwV8Rp+XoePA10oSS3FGI5HUDAglUKio6Kol\nYEzPvDEm6hoVdcxHL2yxx1TOC6MtGgQr3G7kZGcjKzsbnZ2dUkUXmzljHHmfmZQzWRUV+UBQ7pMW\n/GTSKa0HQu83QAlhcag+Ra4jgTsM5JzXw58JWMB+f0PBmO+pk38dIDBZ1SCNvkZB2fn7YtOpet6o\nQ8DfoTaDCFiKtadq6eB9IripexuZePM9CUbwnlMDQM9tvger4FpjgqdNzQQGM5zrwpqgS0pSkoBR\n4nYiTACLVAf5lWChiFKSjWEk6losk/ee5zxEmikFNQXIsgngE5ZKAm3JXBJ8EsQhACBaFdzTvL5R\nZgbvGe89X0P1f60BwOtke5B8xtCQnB+ZJwSNuMfyvBI8iXLvCFpopowwTQxB0giBHoeyAGUwR0tR\nt9WBwLAXxw8fR1ddi+Izc3m4zEjNzUTB+FIB4Prbu1F/vAb9rT2n3QP+CQAAoYAzwHLYZWyZ+NsQ\nQ0VKIdbOW4EziqbCHbHA7LQhtTQPKVNKYRtfAKTGIWYi7EI1erXO1Uai/+gqDwNkE7zDI7jr7rvx\ni1/+Utbt/0UAQO+zugWK/07ITkFeUT66OzrRWd/GOAfpBbkoqSgXYSPRwyETJRREX0c3Btt64O0b\nkn2G85G6EosWLsS999yD5UuXw25xnAYADAaAdMjGKAIawYEjJ/DsM8/izT+/DO8IxcgomGsW9fA7\nv34XvnHP3di8aTO2bd+Cm26+EaVlJaK3oS1wDx8+LM+ViooK2R/HJi5c99wn2Q7An7355hu46647\ncaq2DtYYkG8F7j1vDVaNG48UE51ErOgI+lDV0Y739u/Hx9XVaApBetK5NPRTmHszbaBWrlyJFStW\nYNGiRfL+PA+9l4/mvP9L/jKW+MuxPXz4EG75yi3YvWuX0dMsUwHJAFbkZ+GWs1ZiamYmXOxvJXhs\nHpOwCc32c0CASreAWAQhFnPsThzq6cNDf34Nu7t6pDefW5XDbkOy2wNHBOgf6EEiPJhZMAmz8sbD\nZbKipqUOx+tPwONwY97UGchPSJG2rqMNtWhoa8Sk3DLkFeSjKzSCA8012NV+HIRjE80u5MUno8KT\niZKULKS6EuQ50dzVIaB8Xlo6JhWVIdnqFNedyrZTcKcmIS8zW2Jcts+e6mhDzUgvjva24HiwEyOI\nwgYrMi0ezM4qweTsIngsLlijKhnW7ZB6H5L00yjQ622JuxLZZMOxEKp6mvFhSxV6okPSH1yaVYCU\n+GTU1NehPdInFdtHHn4Al3/xUmWrqMeYib8oykdFsFqNc1RcGGRNUlivfwjbtm7Hw089jYPHa+Q5\nz0iGc3rhrBI8/fj9GD93PGALIjZ6L5VW12c6oz7n4KDbGUanOYsL0mdlh79tCP/5i5fwk1+8iFMD\nMXgjwGWrJ+Lnzz6EhPQI4O8FTAZNzRIWfSyJQ2VjMubTqD4N41NywDz4+XNv4I67fitjSQo3f2HS\nhAQ8+MAdOOuc5UCMOcaY9oUxNHmV4P4XDLe/AgAwx4maHKit78P1/3YHdu6uMYhlZiRGrFhZOFWq\n+k5/DOYgC0innyRyPQ4rjnc2YeOJ/QgggmUV01CRWQhHmEwahVcI3EEXAgGJzPAjjKGYH0faarCz\n8QjY4MzsaGl2Br51wXmYmpNNWqMAcoKVjIbNvO+8dio6ODDgD+GtQ4fwq21bcTAagZci3FEzrA4H\nLr30Ejz++A+QlpY6Oh5SpBodGz1D/5dsYMZpcg8m9f/BBx+Uqv/Xv/710eeCHipfmDFtDCerj+LR\nBx7E6394DSWZwM9/uB5TS+LgG+zHBx9UYtOmDzFtchGuuOwsZGZbERrqweYNW9HX3YulS+Yhf1wp\nKcQqcfHSCScEE7UmGIOO+BGNmeBKSIRJioVKl8Pf3ydjLD3+Hrea54zpevrQ29iMpNQMmD0eII6C\nmmx/icjP+ru6QRt7U/Hq+TH2siZT2KmvX+j9TIQVxTSGubPmYsniJRLkaSG9j3d+LH2ORL3LSook\n2G1tb5dgmdVd2p+QWsqqHmlOrMZR2I6VAFbuWAXrH+iHL+CHL+iXpIMBbJzThWWLluCyCy5EUXIO\nBv2DeHvrZrz2+uu46sJLcOk5F6G6pRYPf/8xHK+rgSvRA6tDVRilXz2qqnRM8Pmwlj75zynOMzhj\nMK6TCgYaksBR7Cus2hG0JWAgSBtB4jmqR1ESG/ZdOxwS9E8sq0BRRq5QdbtHBkXFf8WsBfD5RrB5\n+za88e7boiNw7513Iy89E+9ueBcbtn6A5Mx0SS4outbd04uJ5RW46ytfxbjsEgE9+qMjeGf7B3h/\nxza0trZJAE/3hfHjximvd4cD1cdOoLO7Ew4qLJtj6OzukvMixZhiigvnzBfNhGRzHH7/6ot44a3X\nYImzIzMzU5SNBwcH5Hrj4txCq2eCo90cVBKuNh7ue0wmlaWf6jvhOChmAJskDAFGAzTQvfjSY00E\nkmJ57OMzevsFbDEqxNr+T6qxojeglO21U4MIihl975Lwieq+oitpAUHeY1aQuVB1ZZkJNt+PB/u8\ntS6E9PiZTDJHeR2qAq9cCLQIoIyD9kof06rAhF23mEh7Q4TCiYpKz0otx5Djp/UJ9Dwk2KL7+ZnQ\nSd+PkSiyEk02DSuxHF+eG4NLff5cT5wnvF5WypUopUp0eY4cH85DViEFzCBNnq4VItgYFZDBZjvt\n0TqaXI9hAOh2Ao6hCBHSfcCo3kkSzpYSsiT4cDCSewFGKP5o9AkqtXNWjilXqu6N6lsj+0i6y1TF\nxwBnJKg1BCC1O4C6/+oe6jYBnj+/7zDELXkuAjwJO8Qm99BP3QBW3yj6KcmyV9lK2pXIICn0BLYI\namghy9MMCdpCKvBJRDUNETutSyFeykYrAwFK3RbDv3OeikdvWCnw8npkj+C6INPDAIJ09V3NRQVc\n6flKfFwDiqwyc52R+scHseydBHfYzsQ/pEsSZDFEMwkCCCjJ8MjQnuB94Hsw+edYklUj7ZqGZgNB\nUdXuQiBGtZpIyxTPN6QEX+XfbP8JB2Wvtkcg9oCDfX3obGrFqcPViFCPkvGQzYK4NDcmT58irSI9\nXT2or6lDW3Mb4FUsDokM/yoQ8PdJkcdWmCTW03ZVEsLQBsmB2UUTsHTSTMwsrEB8kFolHiQWZiFz\nahks4/Mk+WfvejAWkiCK7gw2bRswWsHSAaFiCFDA9Utfuh7btm8fxQn+AfjHP0V0pde61WVH0cRS\nxCW40dzQgN72TlgT4lBWUQFPSpIAX6KxYdBmfYPD6G5okxaAMPv/CbyYTZg1cybuvuturD5rNeIN\nRo9sLobvFPsvlWq1BSfqm/DN+76J1156yej+jsJic+LMVatx1513Y8r4iTh4YD8GRvpx3gXnSoLI\nPYO0bB7bt28XN50lS5YIaMr9vLe3V+If2WtZnTY0XR7+zrfx3e8+iqA/KBT2aakJuGfNaiwuLISV\n+zn1ZuLjpe+2bXgYO45WYcOBQ6jq7kWrUUFTT6nTB90F+NkLFy7E0qVLBRhgTKIB4H+KG/w/nMTn\n06GdO3di/fqv4eCBA7LXsC2L6zIRMawtyMH1K89AeXo6Eqmjw4ojnxkGHVmYSSrIUJ/6+QqyBRgI\nhVEdjOJHb76Nt2vq0W9sT3xuOB1WREf8SIg5UZSUi5LkPCS5EmQPHO7vh8tiQqLThYzkFImd2GLH\neEW810MhpCcko8c/jO31VagZ6EAfhuCADblxSShyp2Fh6WRkepKlBaShrh4usw2ZqanSrua0WFGe\nVyjPsI7+bgwGvMrO1WwWO9i+aBBvVe/D3q5T0l7Avn0nbJiQlIfFBROQ704Vizedu37eZ42g5Gi7\n+eg9icEXC6ErMIzdDcewa6hWCb/ZPZhcUIaUuEScam3Bge5aDCOEG668BN977BGkZGSqvVI0GRTw\nxP/MdlbgKfonPXMqH2W1OBJDpK8fG955B8//7iV8eLRaXpJgBm699TKsv+9m2DNdQISFojFtt5+5\nf8b+PLpjGsUo/W/a/wZ9cNrcCHdFsGXTLjz48FM4VteNEQr8x4AbvjgNz/zwfjidAUgGHKQbRAwR\nC6+affF6nzYYCMaAqWcDE9t4/OKXr+GOu/4gjynCHUanPyrKEvHsUw9g3uKZigGggRCTVvjn+Ku2\niNHEeeza+DwAEDNL3DgwFML+g/V4+LEfY/vHR0HbSp4WW4imuQuwavJcYZZYA7TnMxnvHZOk3mS3\noiUwgA9qDuNUXzPm5U7EnLJJcMOOmDekbN94iyyG+gnHyRyD3xpF3UAb3qv8GN3wiTRiRbwD9y5f\nirUzpquiFlmsekLJAPHaeM/ZpxAPWJw43lSPX2/dij811KNBimSMF6LIzs3Gj370NM4//zyJpxlD\ncHtWLpOayTOW/fG/YSdT58iYk8Avte40IKuf7VqrwcY4MhzEH3//e9x261cwNBLAZatnY3xRChoa\n6rDpwxqMeIH16/Jxz1evhgU+HN27Bzu3f4riknwsXzkXtngrEO8BvEF0tXYhHA4gPScR1jgbQGty\ni016yLyDw9LGaXO7xcoPdBTz+qUwFIpF4MxMU3YqA/2AJU5sG4XF6B1R9zIWhS/ghaugAKZJF6+M\nSdXbCO6ZsPOhzInKh11yYrIkJQwMWf0aGBhAXV2dJPYMtClwpW22tLiW2x0vD0t66w70DUhwHedx\nw5OQINR1VumZ7EvQbjOLFRiTqYK8fHz1K7dgeslkEWfasHUjNnz4AYqLi3H9F6+UYP+lV/6MzVu3\nYNjvFfo5LfdENZ0We4YQHs+LPc88/8SkROn9Jo1XJ4W8Dp7/4MCgbPIM/CWwJrLH3vJweExCRgCA\nAFlIkjBR9DZE8ZiMu6wOSfxSszKl1yvBbBcEmQ4FrZ0il4GF8+fD44rDvv370dTZhvjEBEWPtpjh\n9wWQnpSMRTPmYOK4Chk3+oN/cmAfahpPCaWdf9JSUrFw/gIcr6pCVeVR8U3mfSNlm4gv2xb4cGXQ\nvnDOPFx83lpMLp2I1o4mPP+bX+PTo4fECz4rK1Mq+Gwr4H1hgsklOlb5noAI76Uo30vSGScbBF9D\nBsXnQRYmNBwTJmhit0h/WqMirKvD2jVAV3V5r8TOz+ip1DZLYxkAsrlqb1OLWRI4qfobLAyeB+ld\nqnrDJE9VllVyTvs1KrQrOzqeG89f6wCMWggaSRmDOt5HDTSoWMMki4qHrlBroTXNUhC/asPCTyWA\nqp+OgIAodjL5ZD+WiLAZoIrR669AEpWgj/bxGf3o0mYgQjnqd5S1oKK6awBGt1zwvmtle6VrQDtC\nn8zD5JRk2ZA5HroHXhT4pWdf/Z4aJwJnNqk0c8Pj3CRgwc+QincgIEEwk0yZZ8KmCQlAppNfof6H\nlQUoq+0E/vh+fMDp8dRjoETplGin0ESleq+q7Fr/QIEeCsVnCwbXquxT0otNGi3R/phQ5nnuIn7E\n6r7BECATQCwVCWiZlagjz1sxRZROAX9fi3pyjDiHCOjw4J7BtcfKOseU48BkgXNFtUspRwfuPRwv\noflTJ4KtKcbn6ioiq/I8uN4ITPA1vL44A4xiMkRnDgIATEZ4DbwPbGuwOx2y1/HekzGg2TkinDji\nGxWPJLDE8SIbQuYMtReoMWCcr245ITiimToCHvG62c9H5oLFLOrUlAUi5dQSohAXwR8qnJuEot1Q\nXY/BvmHV0BwHTJgzFXlFhQKsDgwMCtW7taEZw2QDqNxstEii2dtjKxt/y1BgLADA95UHtmhBx1Ca\nlIMVU2ZhemE58hJS4YxZkJSQgsKKcniKsmEuygRSHAixsdTC3nGScY2uB6pdS58V3/R0FSvGvc5h\nx4Z338GXrr8eHZ3daq7/V7jH3/Ji/+HvZazrMQgP1zLnkAiJZqVh8txp8Ab9qK2pEYYhrfBy8vME\nWOHepkFZYen5w2ivbURfe7eIABJ95l5w+eWX499vuVWsSV12pcUyFgBg6h9DRMjT727bgYce/DY+\n/XCrvEycaCxWlI4bj7lz5mHpwkVYtmQxCorzYbGZUFtXh7raGnE5mjhh4mjVXbf7cd02NjZK8Efb\nKz2/SPO+4Ybr8d7Gd2ELR6Vd+IszpuArSxaiNM4JK0VbxQHEBpPZKpXRoNOJ2sEhvH/oMD6pqUVl\nS5t40/MJQzqt7IMwyb8JUhcUFGD+wvlSgJkxY4Y4uCQnp8j+8dePz0NOf+eAe+zHjfkolTpKWoRP\nP90t7RtsL5P9JEaP8xgSWIEsKMBXz1iMmTlZsMEMO0mgYQWJRKXialIOAJI/GBVESeiM1cVCWTSK\nLqsNT254D3/aewi9KmWR/SzBFQcnW7OGB5ECDxZXzEFRQhYigTCqm0+ht7cLFRm5mFBUIir9nIsH\nDx8SzZrCggLRmOHe2TzchzeP7MKpcAecsCPZ7MLUnCLMKByHXE8qUtyJaEMyLs0AACAASURBVGpt\nQV11LXJT0lGUlyfv1dLchJlTpommCxmmPUP92HPsiLCRpk+cjPbQCH67bysOdjeIoCDvao45GbOL\nKjAtvQCJJodkudoAkTHFKGAmm9tnAQDFhInCb4miYbgHW4/tQ22MIxLDtOQ8jM8qgMcWh/ahAWw/\ndQQDGMGU8aX44RPfE30NPi/VERObaXUHIwh7vTh+4BAaampRMb4CxYWFo4zdSH8fXnr1TTzw7C/R\nOjiEZeNy8eh378HMs2cighFYaBsoAMDnGBxGxqzYQkxy1c95/sISYHygd9KwAwe2VeFb938fu/bX\nyWMnalGOoiuXZOLnz34HeXl89iqxQcaEEbOSYGW8Iv8Z7y8JLhN4+akN4aADjz76Szz59FZZd5ks\nYtrt6CNLjnN0STkeeuguTJlGNgNPlXoQhhCgjPfpkrk0Lii6wZjr5TAy6eIvW3Gk8hj+9PLb+GDb\nXhypasAINwbDwYynVGJOxJLSqZiZVQIX3RYVnqHuCWNdCzBkjeKjxuM40HQUk1NLsXDyDKSa4xEe\n9gtgIOMo10iCDMcihpDdhMbhTuxrPYHq7ib4zTEkRyP4yrRJuPnss5DosMu6HGUBGJ8pD3ly3M0u\nsDmd7habjlTiJ7t24VDAhxFqYJkYw5pw2aWX4IknnhQdM9nfTWxZ0Xf+77wX/Z2ehWNbsPRHjAVj\nGSs5jKIMf97a1IzvPfYYfvGzn8pcdJPlRs29EJCbAVxxbhFWLZiC4FAv6o5XIcHlxPwFM5FbmAq4\n6JIRxciQD2+8+S4SkzyYN38KEpLdsKdmADYXMOhDU2M77HGJSMvIkSK602aRz+ioq0FDQyNmL5wD\nczIL+LTnpCuWDQgHUFdbK7FhVl6+FJWsmZkwzV13UYyJgRY7YyKnxd54U6VaLj67yuKKDx+pBksy\nRoRKqbtzUHTAzj46BtHUDGAizoCTQnJ8DYNUvj+rilKZpbjKYL8kmPPmzME1V1yF/Ow81NSexMuv\nvoLjNdW44ILzsWLpMrz15pvY9P5mqWwyGBY1RlbmJBmwSkCu1ch5M2hn5va4R3uyRdDCSCR1JU6q\nluKXTdE0ZQmohezIJOBzRxIc43cZeOgJIBVQwwqM58CJEPKqPnL2PrtosWf0TRO8YA+vRWzgSHlW\n7AS+loAI6alpaWnyWUw6hv2qv1164w39AU0ZZsLD62WyxwSHDyy+N0VgXDY7vnjxJTh3kdJa+MWf\nfo0N728SRNvlVjTkIG1bpLpvVAM1bV0s+CjoZ1des6Q987oc9lE6tYAgVovMC1VxHVZ+tOImwAqk\nIahntFrYrSpZ4pjpSrhSorfJv5lAMenixGRVkvNlLFuA56mrvqRoKxuzEaFxK+BKWT/ysyU5NWiT\nOulW4nOKJq8ZDFoEkPNX9far6rWozVOkSUARCPXa5w/IPZC+cQNo4ut0kksfdgaJOnEVxotQxKkB\nEJV7wmCSfxel9UhEgC+OCc9Vaynw/TQLguNAoTfOEbahsBLP9cO5wSq99Ju63bIG/cYYcj7we3wP\nzSZQFWGlq8B7LQr7hgCeUrRVFXX+XFp+4lxyLfp1BH54jppdw/FjYMrfERq90Tc/ChqL6KNik/Ac\nxVaT6vV+v6LLM8k0BPUIyEjCb+yq+r34u/w+D7vNIWPHipxolNi4j8TLvsN7w+sjAEkAg+8nLCJa\n4VlUgq0ZPZyr0vrg88q+oUQBY9KawfPlPeP942eL7gQVbkfBBlYpY+p7FH8U4E6BRbwfRGJ5rQRw\nNOg1lkGixIGMPnzD3pLziOuIIA3vM+fx6DhHojInNQDA9+I58rrFocK4v1rUksAp1xCBGAWMKdFN\nce3wE1j1j+4zPH+h/hstExxjETEMhcB5zPXFz+McsxIk4PwaGRGbSZ4TLaccJit6u3rRcKpePisj\nKwPxCR4BSQToocVWJCLzqLO1A12NbRjq6IHILKutVrkSSMsXAb6/baY8FgCQHJPnHAOyLG58YcXZ\nWDF1NuLMCuxNycpA0aTxiM9MhSk3EyDSbjWqO3pmjrWa04nIZxIf9jKH8Y0HH8D3vv89KaDpEFCF\nbf+qhwpy1f+f7oUV0qqVbbYm5FeUIq+8SBwu2tvaBEAUQNJuGwU8FRNFubyERgICMA109iJIqyKD\n8UIBpptuuBHLly2TPmsZVxYoyEri+iXggCiGfX786fV38NNnf4Yjn36KSIhOHGQ9MgGJoHTcFDzx\n2PexbNkSEQGMxMI4fvwYTpw4jnFl5Zg+fbp6b7bEBAJS9WGvv4DbMe4XI7KvEAigM8BVV16BI5VH\niYGhGMCNy5fjmrnTkWoiEKzYYRaVNYgVYZgApc2K4WgUzT3d2Ftdi4+qanC0sxMthmAd2wPEGYas\nHEPhnjoGEyomYOrkqZg7d66wAcvGlSM1LQ2eRI9iJMhcY9LEu6Errpz9KvBmLMhqlcqf1Z4myZGR\nsKhqstqnxuYvn5m9Yyfz56jboxVjxlBy3iZ8/MknuP+++7Bj2w5Yed8iYTiFdB3DWTl5+PL552By\nRjKckcBpiruxIcheYoh1KiqIkUQyA5KAz8IiNNpNDjy/bRt+9tFH6JSghtdkRrzVJj3+bjjgtjhh\nj1pQlJ6NCTlFItRHq2bKe2Sxjz8jEyOhgOjM9NPOur8fKYnJKCwqFJG2k50t2N10Au3eXrhgQ35i\nGlZUTEN5dgFGvD5htAYGRwRopJgkqbUslPH5kpGWBnecS/bhQDSMpt5OmJx2ON1xONRaj3dP7kdr\ndMTAUs2YlVSEheVTkGKlH3wUTrNdaapIF4Ruq1R3hWNEwEj2OaMXgEyx3ogPlb0t+KjhMAbghwdO\nzM8rR3FyBhwWO7p8wzjU3YRjPTWIt1lx952346s3fxmOuHhE6abFIoMwB3kXQ+hrbMBPfvgU/vTS\nHzBl8kRcd806rFy6XGU2dhteff0N3PbgIxj2BnH9hSvw4APr4SmmZR/tgfyA6N2EJc6QmWokh6p4\no9qr5I++z5yjkZAIAJqsiWg41IAfPvUS/vCH92Q1TSqvwEgsiiM1J5GSBDz6yFdx+VUrgUifMLeY\n7NCC20x9L85pY3x0Ii2DR7G6mANNtQO4447v4f0Pm8SC8osTpqIkNwevfPIRqkaGBZhbs2YhnvnB\n/cgoTkc0MoKoha4jCliIRSk0yoQ7Ir31sUAEVoKUtC8UlXizCH+0t/Xj0/3H8LuXXsEH2z5GH7UK\n9aGxkRgZajbMTC/DmWUzkRy1CyPjNLtAOTUEXRYcG+jAjqN7kGhzY9H02SiMz0B0hK4n6hjV9GFr\nrymGoDkKry2Kg+01+Lj2EAYQhgMRnJGYiLsuuwRzSwpg8o8o/R9xL9Qgt7pfUbJeyXaMWVDT2o7f\nffQRXqurQTtM6DE+NTs9G489+hiuXrdOMRCY/AoA83/roNbJ9V+6Hnt375L5WpQCnLksA6tXLUR/\nfwCb3tmAjGQ7zl42G+MKk5Cfl84KnzxfTCzCOG3Yc+QA3Am0oMyXImF8cgYQnwJELPC29KG+O4AN\n7+/A7l1VuOSCBbhs7RK01Vah+vhJLFi6BPbSHGEAHNq8A6UFBbAnO9HZ3430jEw4XAkI93jhH/TB\nNO3y82Ojnu+k+7qUC8DpQFFVf6WiqhXRtRI4+4sNFXc+OLQ/tqomMohWaukaJWOgqQNaXYXj5s7A\nlP2qWZlZKM4vkCSFvfPVtSelCjdz5gxJjnft2iWiegQPRBwuFJYFwUOLoKlkkVRy1cvMYFvFbaen\nIateQjeX3m/SYk0YFOVuRRVmgsuEQiWGTFxMkhwJfd3nH61a6yogq4Ja6EZXqZkgCCXdxl5zVgnp\n867U8nU7AR/G4kVuKEbLwmUwQ6s12reRqm21CHOC58tWA6U+7lB0aqOKKtR9Vo7Z51VQiIvOuwBz\nJk7H4eOVeOLnz6J7oE8syriymVByvLXzgkoAw6NK7HwvXZXnZ4ttGAMpo1qrk1ZN/2YwJyJ6UvlW\nyTiTTh4CEJn4nFAVC80U4dwQujOTS763QfuXBN5oL5CEnkrl2m/W2EOUfoNZUaPFBSAgCZ2qjEdG\nLfwU3dkkyvJaeI7nxXPiuMq8EKr16eo0k2I9V/m5in6t3AJGVdODKjnl/NX0bgqxjdUmUNeu5g6/\naoE/riFxpzBU7vk6SSpJTxeWgsFuMO4xp6w/yN4fBbJxTtNJQFfOBQDwUtBQBW68p1xz/AxhJVhY\n+Vfnq4UXNUuB31P0e9LF1VzX900LxDGh1p8ljAmjD15raPA9OJaMPWQtCHuITAufXLsCilwIh1Xi\nyUMLjfLvrIpoZoRuRdDODvq1vA6uV84L9T0FNgoAQXDKmH/S72+AlOq81DwWrQFep7Ge9LkrCr+y\nZlSVf5UAc60yeRX3hoQE+f5o0k2RTwpamk3S5sS1qhX3mUhL+4cBMPJzhV1BJgB7XZMpuwP09bEi\nQ1BW6QQI2CjzUOkWqHUSGZ2H0k4xxh2C+wevXe0ZKtH3ev0Gw4EOB1YBmvT4sQWG9007RCjg06Bd\nG2tKJwLCLDEiCAKiMn9pmajBNc6rGIQqy5YBznuLhR316iCLQTIMri3+CUUQ6B8RfYDelg50t3Yi\n5GNbz5gS+d84Q/4LAIDnCyvGezLx1au+hIWTp8s1OJI8yCwrBKjy73YAcU6J1WKMnKSSoynHxsX9\nV18iEWm/uvzqq7B1m6L/89BPm7/x5f0PJ/P/8senq1x8wlp01Vdh4kgpykR+eTFcSW60dnbIeiK9\nXYvIck0RVBJgVfbBEHrauzHc3gvfAEEnAlvcu2KYNGUq7rv3Xpy7eg0SDXCWybXs1Vw3ZiZCUXj9\nQWzethMv/PYF0QAwc6+Pqn0nKS0L9917P2667nocq6rEyVPVWLZ8KVJTU2Tv45zmeqXdMPdRWv7x\nua70PSjgGpaqLq1Qx48fj48//hjXXXctWlpaQSf1BYkpuHnlGTi7rAAufmbMcK4RmrFkUoo4YqwN\nVsiGoya0eSM4WN+ET2trcKihAXUjw6IEL1VOAxRQUrKATaxLVXtmfkEhJk2eiHHjK1BSUixirunp\naXI9ZENJ4cBMi9nToBvHQdl9qcqoPuS5PTZMNwrsfxG5j1YFx86z0zNct1fzO9s/2o5vffNbkvyP\nAnGIgWbBZxWVCFgyPScTDjARV/vZXx48K70ODWSNCh7M/E1mDNvj8Kude/DsBxvRxEKBhUVaM1xm\nO5y0zYtEkWPNwNSyiXAwhh0eEWYp/56ckCR2fwkWhxJUbqPNXAjji0vgsFhlPx3y+9DY24mGvg6E\n4xQ7DsN+zCobj4Xlk2EORVHT0oS62jrkpWSIK1bvUL/ME+o65WRmSkGkp6dbBLzIgi2qKEN/yIej\nrQ3YdHg3qoO9GBL2ilns31aWTUdFSi6cJqsw2knp/qv0cqnymhAxABsyJfgfKcC0itt26ihODDaB\nXK6yhFzMzC5Bmj1ePmcwEkDNYBcqm09iEMM4e8VSPHTnPZg0Zw5A3Stis1YzLPzggA/h4SG8/+67\neOUPf8Se3YeRl+nBdVdehQsuuhg+mx13PfwIfvPOZrlTc8tysXzpHMxdNh1zFs1AUmo8bA4W6agn\nwHXAZwSTY6NCLfoG6hq5Dvk6ISKwGBO0INoTw5M/+BWe/fkr4HYwJz0TV15yKY43NuP377wujI9F\nS0vxzFP3IH9cGqKBHoQifpjNbBF0CCRG9It7gfJrtgLBsNCpwwErnn3mj3j6qbfQ6wUmxyVh/Yoz\nMXvCeLy1fw9+vWUTTihXYTz6jVtwy/obxNIwDOUexIJGjIKCkisTAIjBQmETevvRySAEtDQ045VX\n38VLL2/AifoO9I1EACvtsc1AnAsIeYGQXwAPCqDHw4zxCblYWTwTBa5U6c03VAxkjBi/hh0WnPL2\nY9fRg+iLDGBBxWxMzSwFfKFR9122rellzHVJFoDXEsHxnkbsbzqJem8vzAiATcf/vuYcfGH2NCSa\nVRGFLAn+vrowi4gJxqwxaRG0xezwjfix42glXvh4J7b4vOgyoGCO9blnn4unf/Qj5JQUwmRRoM7Y\nveb/5RPqH/VZfG79+Mc/xve+8y1EBkZw7SXT8O83LkOCx4UXX/sUT/xwK1Yvi8et11+ISUVxMNso\nEtiNTZu3IjE1GfOWLkBycboBUlnRcqIR7Z1+pKbmo7trEFt27MHGHSdxopbtmcD9d83HNTeeC3Q3\nYaBrAIlZOUCCG9G+ARzfvgupKW5kTMiBKcUNuFOAU61ob+xG0BuCqfTc5TEKw2nqNKs/XIhxFK2y\nWZXqOCuJTIqZiBiVUJkenJAU9jL8Q6V9gBZoIZVUMGl20FbQoNQzSdJVbw6SUl1nZZWoN6uZPkGu\nPEKTtWLYS1mUqAjXKZV23ygKKsnZqKCfUb037OIYXGsqNxMiRZlVPcE8N11p5feE7ms2obevVwJr\n0m1FeZzXIb7bXOinAQDSqrmRKKq6SrqY/I/S2Uk3NfrLlWid6mvXFV+hpBriZ1otk/9m4qDoUEYA\naYjh8TukRSmFfpX88xq0MBkV9qWqarGKwBgdABbNnYecjCxUVh3Fh3t2weWJlwoi7yuRaKV6rpIH\nfa+YWKiEUQEEvJeSuJpNUhHkwWvV1VUmW+q6lDOAooRHDNs/Rcfm2KmeaPWAZ5LLZEwr/cvvs4Vk\nTJU5Lk4l7rqCznnBcxOGiV8lwzqZVXPIB6dLCapp5gY/S/f90+aQ39fzVosNivUcAywKItIeT+te\nkPo9MqySblLlSRuXREjb8ynGAMdJKuc25VSggRHec7637ssmQ0NZISrAgAd1NnhwvIWiaqjBc2x4\naCaDvF7aJVVPnq6I8TUC3JBuL32OqirPP7ynvCc8IhFWsj4LADCx04k8317ZPqqEWjMbeN0M2MkM\n4rmLQKJBSddCj0qMUP2eUDV1e4Kx/jUbRAsW6nmt+ufVdeo5xLHWgA6/f5qC75XKlVTlDYDn8xoL\ncj+FpaJU9emawfGTuSrWi2PmruFgQTCB160tTwmq8JwIQnIMWb3h+XPN8HOlVSIcVnPdALfIQuJr\ntD2k6DSIEwbvvzGHjZYIzk1WP/lw5XtznTCxkP2VVoC8BkP1XwEoXtnvNFDEMdd7llqDap0KM8Gi\nbCt5CDPKYGBo4InnzPcTQUkROVVAmWZZ2CwK3OI5sRWA810sNcliCQWFwWIns4nrm/uCSe0bKmBj\nRcLQR2EAapT35Su1Q2CC02JD1B9CNBhB2BdETeUJNJyoF54uvbW5Pv+Wx+cBAAYzbphRYknGnTd8\nGReec56wxmwJ8bBkJAPyULQDdlpHauorMEa36n88vX179+CiSy9BE1Xu9f5tfP3XBQB4gbolggCA\nOqT26zJj8ryZ0gJAjR+qkvMQ5g3bCw3mE/8t4JMB8vV29GCgtRv+gWEE/EFpA4jzeLB4yRLcfeed\nWDBvgey3khAZwqNMqFUfZgjBSAytnf347iOP4T9//jPaeiMUJThmw9lrzsMjDz+KCWXj8PHHH+FE\nzXHRAMjNzZa1xvMgSL9x40ZkZ2dLD75iuHlF1Z1tg1zDav8O4cUXX8TNN/+bMBUoZLe2tAxfPvMM\nzEpPhCUwIlpBisrMbOazAMCogrvJghiTdJMFI8EQjjU04nB9I/bX1qG+px9NsaD0s7OkwUQvwH1S\nihmnK8E8R23ZmpKcguKiYqRnZqCgoFB0OXJz86U1KTU1VfY3l8sJp8MOV5xTFSgMRpigAmNKdWPX\n0VjK618sBqONSqkBEY+x4o23Xscjjz4qtphUN+dYcH5QHmx1Xq6o/U/LzUeClTHCCExKKvavHJwb\nemYpYE5cSsx2BKxxeP94LR545VU0REPwGryDtAQ3bKQ7+sIwRcPIRhrmjp+BBGc8enq70dzVLvtY\nQVI6JhaUIN5iQ119PU50N4oA3+LJM5CRmor+oA+HT1Vjf3s14uJ57xMw0t2L0uRMzCqfiPz0DIm3\n6OLEmLA4N1/mSFtvF7o6OlGeWyA0fz7jjh6vQlNHKxKSEoW9QUHB1/d9hA+bj6ALQRGqTIAN07JK\nMSu3DJmOBEn+ybYizUH22r+yr5ABIAmu/I9zIwZ/NIjq3ja8V3cAvfBKb/iMwgpUpOaIEKIwZywm\ntAeGcLDhBE752pDmduPb/7Ee1950vRJKIX2emjO8t6EYwoPDsEbCaKqpwbFDB1G5fz/y0jNQMnkq\ntp2sx9MvvIh2r1fuYhwcwhoozM/A1MnFWHPWQpy9ahEyclKAeMYzflHoV4tYVcdHvVlNMYS58TKu\nJ3DWH8WrL32A7z78LOq7g5jgjMMd556DpdNn4mhdM37x9pvY0NUi53zDujNx++3rkFFAgTTFLDVb\nCAcR8uAAEnimwqTDcKux4b2NH+PB7/wMp2qCslesnTARt608BxkOF/rCIfzmg0341ZEDIJxekp2I\nx77/AFZesBhwRxEODKrnhTzlTLAQRJMHH1uO4tDV0o2PP96Pl1/fiLc27CBzGzGTC3AmARn5SJ84\nFQkJbjQc2o1w4wnA3wc7cwOyUkzxOKt4DiZlFIkgpm6DEa0lxtN2CzrCPhw8dQInuk+hIrMMS8un\nwxliQUEDZZ9dTjzXoCWKzvAQDrTUYE/rSXErSwdw6ZQp+OqqM1CQ6EEkSIEf1aqjZpXy4xAuPxlN\nRCYjQHNbB17ZtxfPnahCtQFY8vXpyWl44FsP4MYv3ywtdLzPusj2Pz5I/wVeoHM4usX8+01XY7j5\nEG5ZtxrnnTkRLR09uP3hF7H7wDBWzXTi3644C4tnZMEa86O2th3/+cImlFQk45IrL0JyeaZy4egO\n4OVXt2PLriYMBOJQVd2Ixi6lxZwUDyyeBtx1+xcxc3E5EB1RvQYRG2JeoPZYLYbbOjB+aimcUzOB\neDtwqgfDXYOwpafDkZgE0/iLzopJlUwowqryzweMpluz55QPPNKipcIYDErQKkJqFEkTAT7lqy6C\ne+whZXAaiaqqueGfrdSwVbCp6c4q8DWp/t5IGMND9JuOKdVxBxM6VsJUv41SYLciOSlJKlxMBBjM\n8neZjDBYJa2W762TAVYY6R/OgJlJMh+UkkTygU9letJ5TapfUXzbKVZnMQuNWCmWG5U5Y2xEyV6C\nbuUJrgAMh1AEebBySBEZ2gYJ5X3EK9U9Jhc8T11JlARRaMcK1BDLOiaCBsVcVySTkpMksWTSwEN6\nsA0auFakZ3Kmk1oidARQMlLTRMHbFwhgwD8inrGjPux2JjSqojq2Gspr0WJDHFse0ostAZayV+Qx\nFtxQDAG1ajWowL/rPn++PwEiTUlXtHnFJuEfKldyHJQInk/pSjBRNnqihEFizJexvf+836pab1iW\nMb4yqUrp2Aopr5nfF9AkHDYq41T2V/7S4gBg9OnzPbXlnFTqKahHUEDum65GqR5ubsSk67AyrWjx\nLqneDw0Py1phcsdx4vzmPNJAlWapcJ7wM/iZaakpkkSzasBrFSo16abDCoSIcys6OJM7jrFqybGK\npSI/n2uF90kDQhxzvkbfYwZQugVA9+dLECugjAKnCMLp8eVn63YOaU8wxJhEnZc2REaVWjFQ+E6K\nvsv5wbnOrzpx5TnwvmpxP91aohNlEcUb4/Agn2e1GsKbkHYQiuzxvvAzuOZE5M9qA3VGeD1M3rku\nCYSpaouqsPPnPI+hIXVPuH8xGeY4qnmhgDp9yINV2CdKyE+DYXym8725l4lw1NCQ/K6eBwIEsZ/K\nsAzk+ymmk0q4uTx4fmPBPQEa7QSEzLI/8felXcMYW92iovv0tf6Efh33PQHchCFEi8rTAA3nGqt/\nmm3CJFtU6bk2RBxSa2Yo7RPNcFL3k3s4W3nMpzVB2P5DRW3u37QLNLRi1DpXNkVCsvyMuLNwd1UF\ngm0nFitGBkcQHPbDY3WhuaYB7fUtGBkk0PG3fep/HgDgvE2BDUVw47Zrb8Ill10KV4IHUZcVAZsJ\nzswkmJOUmE40SuEodWgK5f/I4bda8Z+/+AVuue2r8PkNkSzDkkvG6G97ef9872bs/xwvSdEsQEFF\nEYqnVIjombSvuVTcoEBKH1JSUmVP4PxlEiW2mNxDAxHUHTmB/s5exAJMEoDUzExcccUVuO7aazFl\n8hQJ5xX91rDYosgl2UGmCCIxE7bs2I3vPvJd7NyyBbFoADZ6WEejyC8qwcozV+FLV6/DnNkzYXMp\ntldrWwtO1dVJz3dBfoEI/rFFSz8L2MbY3t4uoADbAYStZrHgvvvuw/ce+648P5KiwDWTJuKmFSsw\nzuOERYJoTXPRPeunGQCGTpeyNBRbQ2UxFiXDC2a0U4W+uR0nu7txsLkRTf396BjoxwCFXY0ZwN1b\nlg6v3xh7KZyOsk9U3VBau1wueSbxK/fFxIQEpKYREPBIPEUhWmolcY/UjCn9lfuuvuaxa0udhmKm\n6YOf9+nu3Xjuueek4h0JxySf5C5L6GRxXjbuufAiFDvj4CSwK/an7DH+rzYBAgDsTFa1TL4/BcfC\nLg/2tnTjqdfexJbOFlEjlz0bgId6KREr0qwJSLUlIMnuRmZiGuJtDgQZp1o55qy0WuC22MWSj93i\nHUMUrvYj15Ms7Xb1Pe04WH8SI3YWcx3wdvUiKy4RiyqmIC3eg/qGeomNPAlKY0pYpEODIhaXEBeP\nFEc84tj6yLjQN4IgY1lWRG0WHGiswabaw2iMDGIQQcTBjnxrApZMmIFCTzrcJnp3Mwk2iOb/xSYi\n42IUk1TfewwDwRHsbazGR93VkpAXulIws6gC2fGJMHFNcc+2mtHtG8bJ7mbs7jkho3vV8lV44P67\nkDupBIgFhEbPfuQ9O3bjxNETKC8twqSyMrilgh5AddVR/ODnz+GN46fE3jJKXfm4BEDyR37yEGwI\nIjvJivNWLcYX1p6FufMmw50ZR7U7wGyIxLKlQ54fym5PduBgDNaoE3s3fIKHvvNTHDrcKonqDavX\n4IpZ05EQiSFscuKNPXvxk4+2oMpPAB+4+IuzceP/R957gNdZn2fjyIp+2QAAIABJREFU99lD0tHe\nsiTLsuS9jbHBGDxYttkECBAgAQKUEEiTtE3T9EvStAlN06ZJ0xJIQ9KkGUBZYWMDBjwx3rYs25Jl\na+919vpf9/O8P+nYgaTN13++9Pve6/JlWT7jHb/xjHvccy1mzqwGqadEyGlVmnOR185NyonIWAKb\nNm3Ht7/9Q+zePwpG7zP8Hty74SqsndqIrJQNoUQC20+14F/e3Yx3u3tkXi1ZPAMP/+OXMHNeDWyu\nKJLpGEjysacdcLDolGZBz4s9e4/gse//BM/8ajO6BpOAl5RSH+1PUL1oBaYvPQ951XUYHuhF94Gd\nOLTpWaD9MLw2xopErAHrKpdiac1MuVUuS2nOaek2pd1OjKTjaOpqw862gyjLKcEF9QtQ4s2VAo/s\n5RZsRHQIrdmTcKQQtCfQNNiBN4/vw3BiRObmqrJSfPrCVVg1awaS0ZDo/cj6air5YnVnU+2pSBxO\njw+psSA2HTmMf9z5LnaNhZV+Q/qhzYHly1cICmD+gnnauJL4/P8NIoDoiFmU47deewavPPV9TCl0\noLLIi+bWTnz7RzswHgLOnZGFmzeeh/PnlaKswIvevgG89OqrmDF3DlZcuBJxG5unTnR3jOPLf/UI\n3tlHRQ1FhnEs5gWAO29dhmvWzcWM+jyk3SE4bQk4U3Ykx2w4enQQzzzxLKryfbjmpsuQPaeQVgLo\nPdQOjzsXuUuXydphW3jTFWmjXs4AlMJfPBgo8zDJNBdYbtTGLkdUwGmpZKmBazeQkFCFaBvEwIRS\nvAUj5WcaCKp0vsXLVLv5XJyMFzzXPAmIqa5o8XHVumtS5E2FvRRuLuOPSaAF6c7clEThXzi7qqxt\nKtrceEkxkOCYndOMbjSLD9qhUK48EzJ1NdCKHw8mygwW+P88RDjRgnjz31L0sKvyNwN1hTRqQiBd\nUBsTMMLeLUiv1V3M/DfnIBMfw9MW3n7cUsKne4FVHOGzIGeXZ8bCA58X0Re+QJYUNiQhSCSkEyA2\nX5Y6Ojd/Axs2kGlDCRGeIBNEIiKYbGUo4fM6mFgQgq9caPeEtzg7nTz4bAjR5vcaATe5fib9vCYR\ny9NATPUbFDYp7yWKg933uFrPGSSA6crwNUpj0G4odRNM8YefzeKPChayuKKJjxSNnOyYqpggg1Hq\nNogAlfDtyTu1LORo6xdPSHfJBFKGdsHvFkSGtagxkOWhCZvlcpAgrYKFNdXMMF73huKh8yCp1At2\n103RxaKkSIJHaoaPNllKr+D3G4s+PkPRNWAxKqn310D4zT1UhIpqb/DzhEdviQyKrR8T1TgLfnru\nfL+x6uM4F3cMFh8oTme5XzCI5PUIRYjcJL+6FPAZGTqDdvXUAUIRAAnr+an4nklCzbyhhgXvmylA\n8VoUjaDdbrXcM/adKl5oCljc6AUxYnUZDQ3F3C9ZL6wsk99rij/UEMikCPD/OG75elMI4L3gzTHK\n+zqGiIpgsYj2eUSQqNgkx5jQHZLUJ1CCH21UmZzzPbTJ5D2l4woLFVKYEI0Pn479ZGJCTFCRJ065\np2IXaRU1eZ1G0Z/XoWuzijLyEFqBZV3Ja6MGgPD73XpuLPoYIU8mAry3XNdMIUsLvLpW8NmIFoYg\nobQY6iRqiLoj4YhVVLAjKztHzpFzl+uIcAiNJaSTVAClNL2/bScGD3egtLoc5y46B/09/di1fRdi\nodikRfPESv67//BBBQBKIi5xV+LOG2/BhhuvQ159rXD9keVR+D+Vrp2TRRrZHzKS+N+UxYfGxnD7\n7bfjieeeU0SDtRYY2O7/9QWAzEdlB0prCP2vhSPXj7AUcFnUtRA/iaSsuRx7fE6iizI6JuOfFLXQ\n0DhaDx3DWP+IFrdYOPf7RRX/7rvvxprVa0QEMFNc23StyX0OhiP42ZPP4tHv/wAnDh1CTJAvXEd1\nfixcvAxf/cv/hfWXr5UgiknTkaZD2L5tG+bNnYdl5yzTfTyVEv0iFgMKCgok8TfNAxZ+uR7edddd\neP21V6VoVgXgs2tW4fpFi1BqT8Mu+58pAEwWyMQO9YxCGaHPTmusKS0nSTQE93i7C3G7Hf1jozjV\n24PTvX0Ce27p70cn9TVYXCVaLcNog9dEX2r9m2G8pb5p2Bra1LMOy0Tegu8aRCPnsMzfDHFcM6fM\nvnIGUtFOircWkLkvjI+xo6+VL0ZFjCbLAFzWWI+7LrscddkBeJnliHq65Vv2YXAboeFYFArLTSrt\n8eDgwKDY/W3p7JB8U6MqncaOpAP5tmycWzkH9aU1CIbGcOzYUcQTUSxunItphaXi6DE0PipIIMZM\nQqkUtxu3dL0HRobQ2t+NoC2BqCONtrZWVOcUYdWCpajOLcRAby/2Nh2SmGd63TSJY46fViTAOfMX\nYNqUGsQjUTQ3N0tcUllVBaIzgum4JP+bD7+Pw7FejFtFjVy4sXzKDMyrqEOOwwsXYfGE/7PYxdz4\nAxcRS3mD8Sl1lxJxhNIJ9ERH8PbRfTga65Hu9zml9ZhZQrSFB3aJsXnX04ikk+gOj2Bn+1F0xfpQ\nkZWLzz5wH26/5Xo42KFPRtHX1Y3HHv0xnnzqeZTXlOPqjRuwbtkKeJxOvPbmJvzkuV9hR3s/hmky\nmFWGxes2IunKRsvxwxjtPgKMdgPBUXmCdSU5+Og167Fx3QrMnl0NT5FL0Qai9UBYvARQUgS0pTwY\nPNKB//XFh/GLVw4KbeT2pUvwiUsvQzGTlnAUsXAMQyngxcMH8csdW7E7NC5jYeGiYqxasQgXLFmE\n6VOnoLAoX6j40VAQI6PjON7SiVc2b8NrW3aj/VRI7BHLAdy98kJsmLcQxUmbJNwJCpS6nXit+TB+\nvv0dbB0els+/6/bL8cUvPYj8imxxHaBwqd3pA0IJHG9uwxPPv4qnn38d7x/oEGQP3HlAXgWKF5+H\n2kUrUDRtJrKKSkWfoKOlGRjqwuFNz2Jo6wtwJseQSKTFom9BVg1WzVyMEnc27LGEOGdQ2FJ0FDxu\nxGxAy2A33jiyS7SSVs9ciik5xbDFGF+lLAvAtBayJZdngSgFu9+FltFebG7eg5YxKo9A1q9PrViG\nm1etFBtE0iVszE34eJLMUwjn0+anFOLYUEik0D42ike3bcOTh47iFK3ABb2q1NOvfPWr+MxnPqMa\napZr0u++q//PfOdrLz2He+66AWNDFHum1h0QSVhDHcDKBXk4f1Y5Vi6ehiWLp2JsfBA5+cU42TWG\n/3hhF071RDAWdeCFTUek48/nIf24FHD1Jfn4yhfvRsWUbAS7WzHc14Msjxd5BcVoPzWGv/3u83h7\nyyiuXA188p4rUTarGB1tLYj0hlFd0QiHv4RmA1oA4CLFjZkbbkFhgQW3Z9KSksoxD/G4jsWkE8ag\nlj+zokpIHoW4uCkY7qnhl0swzIDZ8v2eSCbYFbL4tCpiphZwCj2m9ZhFYBEeuHbGBEkgCQu79NpR\n58/qF66FAwpqmddMBMRWgqnaAFrQMKJsRAwYSy5TAKD4l+gUMKl10d+dib0GEMK5z1iMmfxLoG0l\nbyKUxaA6qPdKYcUBSSRN59CgBfTcmeS4JyDfcn/ZdRd4tJWQEhpsfQ8DD7lWgUSrloDcDxYwCM0V\nATq13SMcjPaKRD5w/jKBYiLGhN8khLzXIoyXIfon981K/oxIm0ka+V2idWBRLCRpEWV9VX1XKLci\nHoRrL6JqitJgYiA2c5aWgBE7lO6pS7nL4uMuvGItKPBZEZZpOtiGd89rEZQBURzSXVU9AJ4DP98U\nmFQjgZ7xWiSSIpNA703BRjUAMjnvZowY3YtINCYFECJiDHLBwPzl/IIhq5vPZ52DRELHAyF0VHP3\ne3xyDbw2hcU7JhAOYuUnTwqSCHLci4+9lUDx+kiD4f0RGLxDNQCMEwPvh+geWBaC7ETwPnBcsxCm\nnWGtsEtRxOpW832qtJ8Wi0ktFHhB+oWhgJj5wzkpiJ6z+PQmmRaZIGoqWFBU/m3oCTxv3ltuAnyu\nEqS5SQOxqEIU77MKfWJDY2kRGBSSzCeXumrwMzkmjNgd5wLnjs45xwRvn+gdnoPoECQTKgpoUVR4\nDgJ7JfrDQgGYucDz5D3gn9FRCiGyS+8WcU1xA6CIp9s9keRz3POcmcyIACFpApadoNw7S1xQkFUi\nCqnPdWIuCcIoBZcIbFpaAMaRwe1R4UqKHQa1i2rex88ySAB+P9dCed4W0kXXKV0TtTDC4ooWSvk7\nSe4Z+Lp03RF0jkVHMffJfL4pnBllYNncrbGkSYDCt813cf8w8GwWDmQNYnKQBk4ea0F/Zw+mFFei\nOK8Qnac6ceTgEcRDVG7+70uTzy4A8NzYUTnXW407b74NV935Mbhm1GkBwEO9AuMzpoiGjPRoMur4\nDad39NBhXL7+cpzs6FDpNSvB+n+qAGCRTQNlAVQ3TIW/IICEE3B5PUhE4xMFKq594tRhrVEUGeWz\n170D6G3vxtDpPsTGw0oBEKFXF+bOnYvPffZz2LBhA7IpPsv7bCWz/EsEOMkptjmxc89B/ODRf8VT\nP/0pxsZUY9/hcKNmaj0e/PRDuHLDRuzevQvHW5qx8coNqK6pkvWZKC6Obwr/cS/iwTWGaD4jAsj4\nhjo8LAzccccd2Lt3r4g8Nfic+OKll+DyGY3IJmfXKugLKoZJv2WDKh+aWQCwOmySn0+MPc3WCfnn\neznH+XPK4UTfeAjtoyPoGBnBse5OtA3249RAP0aiUQyPhTBO9LRVEOBqa1ACGrbrnwxDNu2oW6M8\nc4hbDB75H4MoMHWDzNfJIyCM3xLzZKwmhVc2eC3bzQoAl8yoxydWX4RZhYXw0rtNoGOGCz4hcz45\n38xPpgBAUTUWW50uNA8O4R+ffRbPtLQKPYKJI4v4pJWnmDzCjmpPGVbVL0a+y4/h0UEcaTmEHFc2\nFjXOxpTsPDiSaUGn0KK2vbsTfq8fU4pLpWDr8LpwtO0Ejnaegs3nwuDoMLKdHqyZvxRzaqbBzXHa\n04PO/l7k5AZQWlSM/sEBnOrpFLTT8rkLxFJwLBTCsRPHZd0nFYONl4jHjl/t34E3j+3DsUg/CJr3\nw4H67DKsqJqJuvxyuNJ2RYVY9m9SADj7zshDyCgASKPMhpFUBPu6W/Fuy170IYxiVx7OKZ+G+vxS\nQTukoqrnwpg3bktj1J7A+92tONR3TD7t4lXn4UsPfgp15cVwphKCIt264z288OrreOe9naKB0Di1\nHhWVlRiNRdDc2YNtTa0IO3LhqF+M6+5+EDnlU3D8RBNON+1Az7H9GG89AQz1w56IIhsxLKmvxJXr\nL8ClV6xE7bRyuHOoLk9OPosAVCt3ID4Ux3cefgSPfe85SSovnT4Vn11/BaYHcpEKReT5ccFI2B0Y\nstnwRnMzfrL9HezrZ1FFl/Xach+m1VUjNz8HLjqNhUMYGBhCe0c/2tqjUjxjBNTgCeDimXPwkcVL\nMC07GzZaqlmo44jTgZ54FK82H8EPd25DUziELB/wZ39+Hz7x8WvhIaTam4WxvhG88NzLeOKpF/Dq\nloMY5+Tz5AJJDzyzl2L+RZehcuEypHMLEXZ4kBBqnB3J4CgSQz04+savcPrFf9OCiS0tdoD13hKc\nP30+6vLK4CEigm6zNkvniHEfbOiOjGBz826MBoO4cOZS1OaVw5O0SRGLgASF8ltUbTvFAFNweJ1S\n+NnW3oTdXU0ghoEZ3vXVFbh/4wZMKyqSAmYyqbpBNoPQkDiSGgoM5hQhErHZ8dLBJvzone3YFgyi\nT9Y5bcSeu3yFIIHq66dJk9I0e359kv/f+5sXX3wOV197JWIR5hw6xHNcwKoV0zA81IXukyH4EsCN\nV83EHbddjkCuFy2nuvHLZ9/Cj39xHCMJLfDyUfpswLKldZg7uwq2RDcuWVmPVSvnAM44RjraERoM\nIpBViKy8CjzxH5vx/R/vwrRpOXjwvo2YMa8ccIQRHRqALemEO68W+3e04vHHn1QNgMzA1HTnDGyV\nnSPD2TNQVxYBxHqMYnIeqkVr8maSHGMryEdrhOxUiV25rJoIuFXhe1wFtxgEMDA1AmYSyKYZILNC\nyyBSu4xGNEw6dVbHmME3A26T2BvEQKYIYOb3moRwoiNPfQMbRckUwimWYtb5JhMxGfe8Zu2uU6Ve\n4eYmMTffx8SKFWUmaaJS76AtHUUVra6aBWeXpCBBwTrVQDCTgx1IBuY8TAGAC7vymZ2S1PBZTHDp\nWQBIaafEKVKkTERYkXSKCrM4GlCNXZL8SaSELA0ZwS67GPxMHoYfbp47P0NEyiwrMQOllw646Bao\nnZv6oVq2W0ZsTwTQ0tLFkfNnJ9lSVDeBuhEBNMuA4YerpaR6rTMxmSg+ZGgCqNAbVf7JS1arP1Po\n4fPiwxRPdIGuK71AIekq7Mdxy/Om1oMRaDQigEzOJLERzYJJpwtBBUiFlPxvn3ZkTRHG7bHs/0hZ\nUR/1WDAkYmhGv0Gh6EQK2BBNxMVvnZBQUwDI1Dlg4jc8OqTXJ84JTovCkpTgUPibZ0Hz2b3mODId\nYuHCMVG0CiNaFNEOvdoDMnnUEENdL1QNWsX9tIAyQalhtztOpX2fVUAgFYKuBJPaHmeLALI4xEOu\ny0IR8DkYHQh2YbQYMen2wHHF7+VYJISXrzXnJYJclrOGcSXQdcBCEQkSTR0h+H4WRWQtETrRpCAN\nv9MIGwqNwOEUu0teu3F1YPGAqCDqChj6BMcV1xKTmJNXa2wniTqZ0BuxhCo55qVwYDkPKP1hki4l\n6yapE5Y2iLlOnrOhCfFecO3k3FMXAi1giXNFQtcDQYaIcKWusXyvWLRayBbOJ9GcsNYBQzOghoRQ\nm4hoEsSSopMEEWGNa0VqqWo/C15EAnDNkUKtgTBTBJRUHhZrQiGMj4wqbQV25OYXIK+oCIGcPMlK\nmvYdQsuBI6qULBmRSU/+94OBswsAVMfOhR3n59Thzltux0W3XIuseY2QSN7YOZ2laP5fOYuXf/Ur\n3HTzzWJLy3ls6StOFIr/+0ob/5Wz+v2/NqskC9NmNcBDOoXbLuKlnD+cGxMIFSuxNmgXzgmhDZJi\nEo1huHdINADCIyEpuBFu6s7yYePGjfj85z4vKv0UwstMiM4uAHT2DQvX/yePPSYIgJSkwSksW34h\nPvnJe3H5pZeK7V/LyeO4aPUqFJcUwGVXBM7gwCCeffZZzJkzB0uWLJE9gx1/zhUiAYwlFPmdV155\nJZqamqRjtqAgG3+1cT3Or6yEm3ueCJbqWYoDmDW/NLXPJNqTjqMpuI3cCZbXLBuvSb97QvntIiVA\nazwmbvw7SspkJIz+0WEMjo2ha2AAA+NBdAwMYSQaw1AwiFEWBkIRRNMKHWWnnN/Gn/lHIzH9nRLC\n1AedZ59JJ8gw3jvj3vM9Mf6nSwt/TIB5Oeb17PxfUV+NO9auQUNxCXzJFNz8Nqt4qJPkwwsAaSbB\nLjpFOZCgBWk8ie+8/jp+vGs3wrAhyDvjsCHH5UOeMxs5di8KfLnw292oyC+CjeOH5xNPIT8nAJ/b\nI7D8VDQOO11oEnF0DPZJ4l9TVIYsrw9RWwLtw/043N6KkdC4vH5GZQ3mldWiIq8AyRRF2Yh8pSVf\nWiD9w2MjUmil7WBxdq5cVjQdx3goBJfTI7aqdB0YtCfwSst+7Og4js4Epf+SKIUfS6fMwJKK6Sh0\n+kVpXceAjhQLIH/mhDYFABaGuF+zieFyojM0iC0nD+JQP63ykpgaqMCSsjqUOrPgE1HBlDTsSIlh\n4jfmSOJ0YhTvNu1Bd2oE1QWF+KMbbsA5jbQ3DKC4IB+eIo77BHbu2YvHf/ELPLt9p4yXguIiDEcS\n6B0LAqUNWHDD3ahfeTES/oBaUo8NYryjFb0Hd+P03p0InWyBbawfWekg8vzs1DfimvUX4bIVC1Ba\nma+ZEd0CYk78x0+ewV89/M8Y6I5jeZ4Hn7n+JpxTW4/U0KhQI4SIRn0EIifTSYymU2jq7cbLe97H\n9raTOBYal7FucDhGYYKRNcc2C8LUo1hcVY8NC5diUUU1qr1ZsEliS5KNzl+K39mz/Dg6OoSf7nsP\nT723E91poLI6B//wzb/E6kvXoO1UO37wo3/HDx//GXoHOR/cgCOgXf8l52P2qsuQV9uAlM+PccYt\nNjsi0QgCfi/8RG6MDOP426/i2FOPAIMdsDHuTURRRPRGzSycUz0buWkP7PGkUPCYE3HvJTpkOB3D\nlrYDONF1CgurZmJOVT2y0k4RE2T5neOQP/OIO0gLoMViGuO2OJpHe7Dp0C70IgQaMS8E8Llr1mPd\nnAVw8fnFgorA5qZGrRJLq0W5SjEgEUYsBfRHbPjXTW/j0aZDoAKOxO/Mp2DHl770JTz46Qe0SWLt\nsZn78+9/l/r/4xvNDq/rOuNrxqjM1e67/x48/sMfw+EFkmEdd1dfmI8vfu52ON1xbN2+F88/txfp\nlA/nrliAUDSE/Yea0dTSh5auiRq3JP/L57nxF396OxYsJLqoF14nGzdqm83NIRFKIxLLwv7mMXzz\n736EooAdn/njuzBz7Vxg6DT6jx9DfiAAR20tkMjB9/7+aXz979+BrXLt8jQfUKb9H4NH8s2ZwBIK\nx43QIAGMujYTEiYMtECKRaISdDNR4MWbRJ1JI/l/DHCl+8gAlkJw1oYotmtiHaYJtWxGFjedP0sX\nTmCuKhJmOLLq6a7QWBYbJLhnoGFZ62U+ZrWL0+TWKPCbIgPPVbqoGRoAfB2DfKm8i4o763UK+TZB\nt+nuaafSNmEbxw4Br50iZjwvwhiNCJsEQ5kwcxY7pCussQKTOSPKN0EBYIIqQnX6PRIsZYi4CQ2D\nHD0mdzH1apfENDtLsCKEVicsOC8hy+z0M4gxEF/D9R4Pjst9EK48kQmWxZ5wWehPbiUGxl+cwZnC\nnjkucsD3i52fpdAvHGu7TcTDiBLRe68CgDw/dlYMEoB7nYGnZ+o3MAnjtbLIwu8x8GWjpyBdXgu5\nwufFe85zMr7S5DYycSL0WpAOFuJEFd4JJ3dJwsdEndej8GxVgtbusUUfIJdfkA+TKvQ6TrW4wiCW\n41mcHkTnwoW8QB7Gh4bRevQY2o6eEGsdHnx+JWWlKCwoRGlZqVgBpdx2JB028fPks56gRAhtQYMj\nJlrCTU+npNAhhQDRUWABzgOvxyfnwyq3dvNVN4C/Y4Ku6AHVmNDkW+HmqtLPRJgBeFSs9ThfPVTP\ntYQZ+XsVAdTXsFhkbOZ0LKrgJ9cMU9gS7QdLN0IXfrqLaCKuVBxN4nmuek1qiWi0Abj28Nmx28b5\ny7WJr2Gnm4gijiWq6nM+jQwNyeeJbaKFcjDFFqOpwe81KArlxiesOa7nbw7OI56fWIJaf4ylH++V\ncefgNWXqWUwKabrUjtSyvTTCjEQpkFol95hJSSol18TX8Xd8lsZlg98j1nxi2ahWn7ruqNWgsTs1\nooNcPMxabJAvptjJsckCo7EvM+dsKCc8D7UcdOh4suxFaZ/K8S7z1HJ4IP+Q1x8aG1cLVxYqKQAY\nTyI4EkRvR5fY/rFAEI/GkAonNQIT3BrgLspDYXGx+Fr3tneJOq4kcyx+WEmI4fpab5lIJExHUpI9\n67WZnUzz/DRgNtZ0/K1uzgVw4oLANNx1+yew7CMbkLN4JuBi8kX0lPKvP/zI5ALwVRkJXBr47Gc+\ng+/+03eREN6lldtIQqef+IdRAPgw/mXG2Z2dVZ91Q37tv80vbECgKBtTZ01HTnEBkhyuTvJF1RnG\nQELN3qnFKXWAkXXdmn9c6shfbD96UjQAIuPsxKUFfXXdddfh/j/6IyxcsEiKth90NRxF46EIXtn8\nDr71zW/hwHu7EA8HxSYvzHXSn41Fixbjro9/Atddd6V0ZHiwe3vw4AFZkxumN4jKP/e92tpaWZ9O\nnjyJ1tZWLF26VOYs11Q6Baxfvx6dHR3CoV1WnItvXHMlFhfkw8n1IxMKQgRiBirkjABYbNBURFUm\nSZqJtMYZBPDrDxTSUiE9XqM6bWqBUw6HTdw3olyvSZ/muhmJYHBsHGMsqoTCGI/GEIyEEaQmUDCM\nMLVMIvx9FON0WMlwTRHKluXoM4nyUZFXzTuZdNKNyo4IqZWBbHQND2NkeGTCcYARW6ENWDuzAQ+s\nvRD1OdlwsVbI4rFbrXsVYGyu0yoATqAj1BZO9EUYJ7m96ApF8ZM338Y/bdshnHOw6SROECnkwov6\n/Co0VNSiPL8IPZ3dONbSLOiMxTUzUFVQKp9HRGNHVwe8Ljeqi0rhcXsRTOoe6kqo6GrUnkR3cET4\n1YwVppeUY9HUBrHQ62g5iY6eTtTW1IjoH3V4WjtOiUh1ZVkZ8rJzcLrlJIaHh+DN9qG8shK+rBy0\ndXWitacT7YkgtnQdx5HRblDemqWv+flVWF43G5W+PHiTRFMo99r4MrDh+psoAEyCiWSgZt6xwU68\n0bofpyLdKEAOFk5txMy8cvhigJ9U1ITaEgsFACmMOpM4PNyJg92taAv3yf2qzyvAnMpqTMnJRX52\nNsprq7Bw2Tlw+vx4/Mkn8LPXXkYvta1sbiQ4+j25yD33Iqy58yGMZxUg6vGLwr6T+kS0ABzqxGhr\nE9r37UbP0f0InW4GosOSQNbkOnHLpStx3ca1qJs1FYH8fOzedQhf+eo3sedAt/D+/2T9OqxpnIk8\nh0d59lyviQBIqzYVXYzcPi9CqST6Egns7urG9uPHcOBUC/pGRxBkk8hahzniitxuFHv9OKdhJi5Z\nci4acgvhi8SBIKmsfGHUwszr6OS8C/rc2DM+jB+88Rpeb2uTruzGjcswe+4s7NxzAJvffQ/D9O8k\nUsVTAGf9fNQvW4WGZavgLK5CMO1AiGhHUpY9LtGaoP6FnehXAINH9qDpqe9jbN82gd6no2MgbW1W\noAyrG5ehzJ0HJ3XLLDoqCwHUhxhDDLu6j+NQ23FU5pRgxdwlyEk74ZIaWxo2NvZkLBFYoXaAzGcS\nbhv6kiGhirw/3CJICN7rm+ZMx/2XXI5iIroTEdgsNzSuQWZe4fTPAAAgAElEQVQ8yvqVigNpFf1O\n27x4o/kk/vL5V9Ccism4ConTiAPz5s/HD//1B1iwYL6cjyKOJzVDftOu+z/j/87GTelZk3b2yiuv\n4M67P4Ge/j5xkfCngKUNfjz08fOxfu1cmYHdAyn8/T9vwb8/dUiMI8JxRQpEhf4D5AeA/Cwn8n0J\n3PnRc3DzLWsBbwSIj2KkuxO5ObmAOxtIeDA+nMazL+/Goz/fjmMtwF3XlOIv/+Ie2EqcCPWcRntz\nC6oqp8IZKMbJngT+7C//FW9u64dt6oYL0wwC5cQtrj0DREnmQSitCvyZAoBAhONqI0folfB1E8r9\nN/B18ZEWUSz1QGfX1cCPhVcvqtMJSVjJmzSIAQbmGoBzw9H3SxBl6Qto1075/AxO2YlkAmNU2Vkw\nEN5glnrU0wKOQat4f/t8ck1MEIWbTz0BNwsIavMlCtpuDd4ZEDOpZQJLhIOxjpuA9Vo+49Jtt9mF\nI8hjaGhIklnClIVXJt3EcQl4jNquJPOiXaB2gzwXg15g0sOD382DSQNfw43IqHpzAonHuVAh7FL5\nlY60IOssH/Z4TDrXKnKoMF+TTOt90X+fAXEOhyee8QQ/XwSPVFnUwHx53srtVogxP8Pcf5PUyzXb\n7aCIoT577TKbTrJRcjdJqUFimIIMr938zs0xYFUPTXVtYnEQeKVqShhnBdPp5r8lIUpr8cG8VwpC\nGZ1uFqAM/UAh9Rp4EfbNxFdtk2gXx24ru990fVZIezQSRiBbNRT4uRz3VJ4tzMlD94nT2Ln5HSSG\nQtJeEW9sZi9qC42S8nIUV5agrK4SWUUBRMjjZ6Kc7Vc43/CIQPAK8/MFRSH2jfEofFl05+C4Ccs5\nuN3UONBnoMk4K5AqcqhUDKWJGBqFKUSZMSB0C8L8KZhp0VaMAJQK4tHxQD252UHmnBP6j6VnQSFH\noSZYkHuuFRQyFFVtsZhU3iGLR0agkXQigwDiYimFBasDLsKAFtddOvayKytigeNDqAAU//Rrt57C\noSoixnUgLSgTHvw+widZFOLvDIye/2doRopo0sIb5xm7+uIY4SZFQq+TRT1+H4tMXOtYUBKhUBEf\ndQi1h+sY1yqukVzDVMyQ/GUtShlUitxDa1zyZyOwZSD3hkIjNBhLV4PnawqcBhlhXDX4fyx8mIKc\nriOTLhX8f6PYzzlr0B8TIqhE2Eyo+eu4F6VhS91fQiCeJy0gI1qkYOeLBT65Rtgx1j+MjhPtaD96\nSpS3wcIA9RhMlsYdTZIVFXXjX3SDYu1XxMHsNsRTaVDmNG0jPSYFZzKNquwcuJMpRMNBBDxOVBUW\no72nDydZcIYDUVtSLdWSaTjp/c3z5lpPFwk7BWRZuLXBZncgL+XEJRVzcMett2PBtZfAPXca4EnT\nmZqazWdm6WegAcwGn1Hl1wxM/rS3ncR1116LXbvfl3v3QUWJ/9OBjCmH/FohwoKI6rM5q8ghPtCT\n0FFeg9pC6WFs6ZjDeQp9qJ/ZIIr/kUQU0URMEiIWYoQax73O0kbhGspilFlDhQZGupQ4XngRD8XQ\n29KJ/o5eS5lfi06zZ83CA5/6FK6//iOiaH8mLJoGXNqxGxwewy+ffgFPPvEE9m7firGRQeGnR+MJ\n+LMDuOnGj0oBoCA/D/EUodlFSCXjePmll8XzfeX5Kye6/Ga/GBgYkPlbWVk5sedt3rxZEABcxwif\nPX9KOf766iswy++Dk4hBobSoQJt6gf+GQ4af5RFoTZozCxwWkkA+4uynqA9J0mdRhFdrOKMOn/m3\nvNsG0UkQm2ci1EjL47ptrfOmS2d0VLh9svjNNzosKoMUuXxehN0O7OvqxOYTLXi/5SQiVtDK7Y2d\nrqsaa3HbmjWYWUTYf2JCVPMsiSbrkpQOIL7wwgm3w0FEhMOFWCqOUZ8P/7L5LTy6+W0RG+P8TyEJ\nKh654UAWXJhTMBVzaqfD7/LgZFsbjne3CSLgotmLUJydJ0WL9s4OHDt5HFVFFZhaMUUck0QElUhG\nOuEgiZFYCAdamnFipBPVBWU4v3o6Ftc1Sve86fARjI6NiEVkQ309BgcHcORoE4oKCtBY34Asnw8n\nWlpwipZ/Pi+qamvgCeTg1EAfDrSfxL7e09jW24puSZEcKHXm4IqZyzA1q0AU/yXBs7r7Zu010/SD\nRhDnNvcviguOpyPYfmI/3u1qwghCmJtTg4VTGlDhYWGB81n54JxrdjqeeJ1oCw6KXeCJIAsSWghn\nJkDXlCKXH8WBPBE2zqGFrc+NYx2nELWnMUg0HxXtXXnwLLoISzZcj8KZcxD1ZSNKqkHaAWfSDnsq\nDkc6CJ89jmBPO07t34Xj2zcj2XEC6GiFPR1FuRtYNmc6bth4CaZNq8NjP38Kz7/4Lmia+9GVy3HX\n2nXIpXjheAROJ3cMvUlc9xWhqHeGz1e1MxwYiYQxMDKMnsEBjEvTQPXM+EzKi0tQGMhFUSAXzkRa\nEmsmyVzftMSlHHpBYZh57HZjwGnDW+2t+O4bL2P/0Ig4+aWcNgyOMLEm0T0LKKlB+QUXo3bJeSir\nn4OIzYMkxzDP1UJdilkjY2drLlOjyTHYgSNPfR+tLz8JBIcFJeOhg4UzG2umLUVDUbXoNxjsE3MO\nOx2enGkcG+3G7iMH4bI7cc6cRagJlAC8X3QlYAGAaGXRFtF7xLUy5bJL4Wtvx3FsPr0XYUTlua8q\nzsdfbNiABeWlQDIm2SgLs1qss5q0dIeYIBOpEOvpYAxfe+4VvHDyBFIuHwbjUcQtu9MHH3gAX/va\n1yRW+kNM/n9bgf7DSueTa/HkK9iAe+qpp/DkU0/gxIlmHDxwFNTzZIFl3aJCfOVPPoZpNUl4HGNw\n2e3oGbDjL/72BTzxUqfQt5izsfHDDIMmAHfduhqz6wrhxAimN5aguDQLNgcb7iFEx0Pwu3PgyC4G\nIn68t6sdf/2dn+PNQ1ECsvDVBxbi7tsvwXDvcdicaeRW1GJ0KI1fPrkdz72yB1sPhIUuY5t308Y0\nH46BvZpE33BDmVwwqZbOWYKcWk2AePDfIgJImzML9m06pdJdFoE6FTMzyalRnmY3i51NBs4FBXky\nKYaGhieCVNPtVuiwWqtpIpbRsWOhQDhoajfHg0kPXycJA0UzLB64bICWVoBUo6QDoR72/Fn+TRg5\nkwZLyEntxdwTfu4ibmgl1sb6TxMjy79bOstKhzABhLG9MwJ3pgtuzsckAfwck6QJp85CHTB5E494\nS8jN8OBl0aPYh9UlNtfN35sOoOm6857x/XyN+rITOq9Bn/CxLQSEBPiWMrrYfhmNAkvF3CR9xqpP\nx4TqCJh7z+4ygzaF5WtiKjoL0vE1lAEVazTCgCbxN0l2ZrHAWDEqd1ufFzvVslmJ8ruiA0xCy6Rd\n3RUUaq3JrLpECKTZTsi5JkksIlFFn8kck1VJgC0qiRasVM/A3BNTvGGiJWPIQnCwOy52RdxIqNDf\nO4TmHfvRc/gU3Han2Npw8U6wgyO2gtYYdgGBsgKU1VagumEasgtyZTOPJSn+FlKoF+31BPatFjDx\nJAs/XHQ06E2n+Ht9tpwjXIRMQWRSFE7HpymumWKUIlK0aKBoEH0+piuu40dF+HitvDZ2wXhvDf3C\n41PUD+8Nk10R/bKqvMaFQceeWnWaZzDpGKKCgrzOTEQK30ulfQbw6gZAvQyPPAtDH+D98VIUzCpq\nGSpQZpdNGgbSPVd6Db/XUAjUcSBH5rtxoeB3cK4yMVE0hdqYmnMUwUiLSy9+ucKnt+gzlmCleRZE\nU/C1LCLw/UZDwMxl3kfzOz4fXifvv/DzJdGeFMUUOgwRHA6n5WSgyCbjLmJEEFnUlLnocKh1H1Vp\nLYcLQ2/g5wg6wO0Rm1dDtzHUFBbwOIaIiBGaBEU2Q2GB0LLzz2cjoqhpG44fbEbHkRY6L4kHNwOM\nbAamYIJteMg2xOxEidiRiKVE+4nCTnWF+agitNrhxMGefpwaGpD3VOXmoCGvGIUOJxqnVKBhSiWK\ncwLYtf8gnntvD1qj5BuqJZqbux2LXEm1oqsuKMbieQtQXVkl9I1tu3eh++BxbKxZiJs+ciPmfeRS\nOGbWgNjgBC2KLK6t2dNMEsR/a6BzVogg4q0K23rtlZdx4403YnB4VIoaiT/ACsCv4yGsK/2tBQAt\nDAgClCGgkUrgfCLF1e+BLy8LZbVVKCwvEcgzlae1CM9Cke4JfL/ZT8VSk0417B4buonxeYcNQz0D\nGDjVK2KAQtPjnheJijr/5z/3OXzs1o9JUc0kkboKUqiVlKUk7E4v9h48im9+85t4/omfIUoHHOu4\ncM0l+PKXv4rzly9Fc/MJbNr8OhYvWYS5c2ZPoG84x44dO4auri4p6peVlUlRz+y5REOyIPD222/j\ngQceQCoagTdNjnIdvnzFekxz2uHi2i57skJntQDw4WGk4XJPnOh/6YfJzvlve9sZyIMPeHHmuJex\nb41xUngEJcNrYGGWIqB+H95ubsJP33gDm1vbhYvvInVS1PWBi6dXiyPCrKIiSSwcFj1w8nOtE9DK\nhUUDkCxVutlOJ2NHD+B0YyiVxLN79uAbL76I41GuKQSAO+G2pVCSnYvKrBIUuLKFS8+EjpoiRJDl\nFOQh2+VBuTsHqXBUqHa0o+S4y/L6EfBliUUpEz82fuKERiciYhN4oOUIwohhdkUdLpu9CGW+HHmc\n7PizmMwxkZsXQDQclvWTWj+MnRi/aJFWKZGE/dOeMJ2Xg11tx/DS3p04FhtGVJQKgHkldbhs+iLk\nUmjNSgzNWDHNoN/0XAXibXMh6bahZbgTbzftxrFYDzxw4dyqWZhdWoushANucROgsCndnFKwe5wI\nOVI4PtKDt9uP4nSYmAo1tON8csOGfEeW6BswLuoZGZBzjrNLKcr3diBQiYJFqzD3khuQ3zAXCb9P\nHCriQg2zw5EiTJ21nLjYArqQgC8RQl/TXpzevRXd+3Yhdvo4XJFRuBNhzMjPRn1dDbbuO4RUAtgw\nvQafXL8R84rLYRsdhy2u8aNUSPhHHIeYrGtzyhxONt7YRLSQc0y8ScEwSDaJQ3leTJDlJy0AC53N\nQm5Jgi4MMf2baJOIx42+LA++++5m/PvWLehPqj2nlLHtfnhmL8aSy69B4bwlSOTkI+3OQjBKhO9k\nA+PsOSYfTTRdcAhdbz2DvU//G9B5Aq50FI5kEqV2P1ZVz8ec8jq40zal+VqrCdfjuB3ojI5gx8G9\nGI9HsHjWfDQUVMFlhZd8Dce3XC+Tf3EMSQk6mKihk2O9ePnYLnTF9fnP9Djx0IrluGbJIni5r1Ks\n086dXBuTk4feb46WZDyCZFYALxxqwTefehrHkADBEGm7QxpajY2NogVw7rnn/kHqAHzADj9xmdbo\n+JApqPeAzjM8trz5Bv7k85/Hvv17kWJ3Iyk1MxBc4koDf3zrbDx03/VIOweRRaBI0oY33j6Cb/3o\nXWx+LyhjifOFJS4WUO+7dQbuveNy5PqokTcAu9sOl9ct40W6icEo4mEgPG5HVx/w4yd34GcvHkR/\nQpEDj3ztUqxZWoXmg+9h5uzZsM9ZjHDLOO5/6F/wq82dgmJxuGywzf/oFWlufBSu40GROB6mAECV\nbw5ict85gJkAmIBflMe52VvdRoUIKx9YYbDqO69VVishZLA2EdgygHYgP5+QXxsGB4csjrt60vMw\n3eJMfr9AmsXTm2rUZPtoosc/TFIZ0Ep32qUd/pT4odO3XYNmCrrxYMdWAmRy96kWzoDDUtjX70tK\nAYOPmIkzv9dAzMW6z7LjMiKABmXAf7PTweSe94vJEjvKhivNz+HBcxVYrgh+MfGi7Zq6KPDg75js\nsOhikhzDQ1R+PBc3IgrOLHzo+5T5JPxvK4HneZhE2jxj0VmwRMZM4UCTDeoxaCeT/zYoCL4vswDA\n4E40BixkAL+bMHMeWqxJS0dIu6fjcq2G822+z6AlVJWegk6aoEjBh91ES2cgM/ni57NrJF1uk5Bl\nWLtlFkEMbYLvUVqDWtrxcxncmKTPJHl8nSzWdGgQVMWkkrWhwEhnlI4S9Fcl/cHhRLY/WyqxLQeP\n4sCbO5AciEl3khzFaVU1Ehyc7OsWYSGxhyQ8lBuP34GKqVWinu3K9sCT5YE/2y+LdIgQWSbHLkL/\nOeZJv1BxRX4nrcekIsxigQXZEgtACxXBucvXGmFGdrB57kzuONZoMcnXsyhirOnMPJbrppeuhdiR\nbr5VmDJdcyrNcx7y30TecFM2CBbSMugLnUkB4r2WJJxd5DjFP9VCz8wJY0XKa+P1cG4yCOF9YqJt\nCgCyPtlsyMnKkQ3coGJIGZF1g+gDUjTE79ptFS20iGCKN8ZCjwgHXrMKIfql68H1jsUkcvxZKOK8\n5/1i8MdzMS4aRAgR8cN7yHvLeSLaAXSasAqNvP7MwodBNHGt4bzh95p7xHM3SCq9bstWUQoxlouI\npV3B9cRoVghP37JgNc+cwYsUxiz0jXT6xOpQC1u85wxi5Zla7he8p+IiYhXPxEElFkckHEWWj2G2\nHZFgWISGTp9ow/F9R4ARzhHAlwRqvF40lhTDl4giFAmjbWwM7XFIksDVgKs6u6Zryktx2ZKlaKyt\nxmAsjp++sx2vHTogm+D0inJMcfixYtp0XHn+CuQwuUBaoMZbW1vxxI5teLuvB0GrScPkg9danFeA\nG6+9HlddcQXq6+tFQPOeT92HQ9u347bpq3Hbx27HjI9cQklqpO1cZ6yCpOFdn6WN8kEFAC4LNqcb\n8WgYX/vaX+Nv/uZvJorPlluT3M8/lGMyZDzzjDR8UU2MiTxs4iUGFTGZuJp7QTsoFv2KyopRWFYM\nV7YXdo8LLp9bdBA0VlSaEOeLatlYNpSW4wrHMRsOLOBx7phAfLBnACMdAxjpHRT7z2QsJiK6l15y\nCT79wKdxztJzLD0TPVHpbDHMtAoALrcXB5pacc8n78HOtzfDxaIpg9RkGvmFJbjwwtX47B//MWqq\np6Dp6BGUFBeKSBV1MEyRksk/kXwUbaMAYGbizDWAexgRAPfcc49oXXC1uWL2DHzpsotR57TDae1X\n0v3nzTi7AHCGHZZyRpT68rsc//kCQOanZ17TGQUvVfCaQNzxvNiRZzeUKWTS7sCYz4fXDx/GDzdt\nxu7evgkXAs5rdm1vmDMDt61Zi9rcAFzxKJQNPJmgnVGIkC6rjhcRgrNT0ygJJ2317C4MpOx4/vBR\nfP/V17BveAwhAm/SLilGeZHGFE8hFlU3YvqUWrSS+33iuCB6FjTMQUP1VNEAiI6Oo7e7RwQpA3m5\nEsfxnttTLFKqc5LD7cRYLIz+8SG09XSKKn1deQXm1EyFJxxH3EKZMennXsVYjvsD99DK0jJZ91tO\nnZQYuryoWNaeUDyKQ8ebNUkszsWbJw7j9eY9GESC5QuUuXLE9m9GoAxececzWkoqovafOSQ5ZXc5\ny42t7Yfx1oldCCOB2kAFllfPQpk7Bx4m4pIEauIs5Ra3A0PpCA70tGFb13EMpYLIhQcVeYXSWaRj\nAl/pzvIilIxgNBpByu5CKJVCgoi8/FJMW3cdGs6/DN7y6Uj5A4g6bRiPR7VgmKaSvv7NIpgU350O\nBLwuIDgkwnddTftx6K2XETm8SzjKfhuF7vT8FlUW4k+v3IALaurgGI5IYYe0BV4vO99MzFkA0Eae\nYfj/utWcWkMbqone0YnxLv0vS7TSQsFJwmfmp4Wo5RkRWZBwudDndeORHe/gF9u34nQiiiTTNUce\nAstWYdqqdahZtAxJdwDhNEU8VftDdCwsodyznykfnzivpaOINO/F1p8/huCet+CMB6UpVwwPlpU2\nYknNDGTb3cLnJxLHONQk7MBIOoqdTQfQNtKDGdXTsaSqEb6kItRETNJCwcpUI0qCdBGrOz+EKN5o\n3YP93RynSdFFuLNBBTuriwqQotAxm4+mUGLtGeY6LPwt4MtGZzCBbz/9HH5y7AhoxMxpzf2ee8C9\n992Hr3/962c0j/8z4/v38ZrfvQCgrQFe4zPPPIc/uvtODA30Sz7AhH/pdD8Wzq0XjZae9lP4xHWX\nobqqGDsP7sXcWbOwqH4GvvfIz/Dos0cxHBMXYtSUASV+YFq5G5+4eR0aGwrhEw3+OGLxNFx2L2wu\nvyAtY8EY+nuDOHFyCG/sbMWTb57AqUFJWXDJRYX4+p9fgcr8OFKRKNxZhUBuJU4cHsRtD34P+09w\n7AJf/uqXYavbeFHa8L85+JVHPNmNoygJr5RJgApEaeJpYK20eZNOATn4lt2XerSrd7xqR6kwgoG/\nms3WLHpOtk8slXbzWqMcyYRQEzTtWpvfC1+LC1OKyY5ahLFzZrp7ykG31LSThEHTSk8LAEIlYKAb\nVmqCFA9IVSDMmsJjlgI9BzjvB8+T0ENjl2eScoV+a3LPgwmPBD0ZXGRuOPwOQqwZZKuImoriSUfW\nUk/PXJwyEQCZyAG+x3QP+XpJiNlzs9T3+X9ql6edctI3zLnymZikiYkN7xeTGEIB1eecUOKQQP5M\nQsbFidBz0UwQtXxNhI0Kv1jmGai95X+u9AsVcGNXlWODGhM8zPer7oKODwnkKG5iCeuZ5y88faEw\n6CIpopMWGsJ4DzOwJIzR5fbos0+oSwQPQzfIhF9zw2ZhxtAXjG0ZA1Edqyq2xkO657w2oiNY6HI6\npXAksPCg5T1PcRMHJIlmXyI/kI8sjw9Hdu3F/te3SVeU1fTzlpyLz957P0pKy/Dy1rewacub2Pv+\nHkmUubHRm1iq2l4gr7II02ZMw/SZDSLUMxKOStVWBe/Izec1slCkxQmPW+3djGihFGysboRRgzed\ndeOCMDF2iCiIKwSS99fw3A2iQ5EApG4o9SZTq2GC+hFXC0Z2oo1AnkEViHaACNcoKsXYA0pSH2Kg\noRZ1TLIFlmoJhIprBC3CrKIk55B0DymEZ80ZGfuc1xQ5FKFI2kjGJ6gERrBUOhvCOZ5EOxirU943\nESm1LPUkoLAoIGacZyrq876JNoglUCk8ZiniWHOQcEIXaRI63nn+RkBTkShq/6fFI7WjNPdK3Soc\nct3822gw8LW8ryxYCcXEQukYHQXOMRYRjLaKCApa/GtBWRBZNIGQUQQQr1O1EHj/3dLVV/FGRZ0I\nEsqyzeT9E6QUtQCcbowNjSHb40NoZAw7tmxFpHtM2vzshDbk+nDVoiVYUFGOPCdEnfho/wBe3nsA\n+4aDahNEznRhAPetWYe1c+agIC8XXaOj+Mazz+OpXbtRmO1DkduHZeW1uO68lZhZXAgni8hE8Pg8\n6LKl8f3XX8OPdu3CEOMc8RN3YsGcufjYrbfi8vXrUVlfLxDIkZ5uXH3dNWjauRMfm74an/jEnZh+\n06VAVb54ODscFNEkRWUyAfutCACro9PT04M77rgdL73ymnT/efyhFgDM1X0Q3PHszrDp7egVZfZA\nCGNVBXenz4nc/DzhOfvzAsgvKYTb5wGt+EgBYEIlvtEW/UjcIexK0TD0GEE3CUqFHfO08LFJAeg7\n2YXuU51IRnUdp33ax265Fbfeeitmz5ot89zE6OqIJCsobHBgPBzFpi3b8NWvfBV7t78Lhy0FLzuT\n4QhSiTiWLV8lYoIrzluO7Gw/nBThikbQ2dkpRe3Kikr09fWhsKhI9jme7+DQoPxuypQpsk/ymt54\n4w1ce+21CI+PC1xz46xGfOHiNZiZ5YWdbkOiIq5BuKAAzhD+y0z2f/8FAMJMTReVY93A/eX3TFQy\nEJZSKOfeyN/bnAi6XHj2/ffx0zffwv4xtV2jkCDbGeQQr2uowT0XrsP88nJRCk/GolK0ziSx/1oB\nQCJwyWQVAUC6oc+LoN2ObSfb8aWfPolj0ThGLfsrF9zwskMNH6YVVWJqYSlK8gpxorUFw4NDKM0r\nRG1ZJaoKipEMRzEwMCic/ILCQgRyc3U9pKgxdWNIO4nFJCHqHh3A6e4OKa7XlJVjxcw5KPJloefU\nKQz298kaWFVVJZpHXd1d6O7uRnXVFJSXlkq819nTjd6+XkypqJC5QY2Ftr4ejKWT6EpF8fLRvWgN\nDYKylKQuLM6bhgtmLUKezS0JrhBBjCDUby0IZRRU0jaMOZN48ehO7O9vhhdOLK2eg6WVjcL9tyUt\n9A4UsUjNqKTHge74GHafPo5d/Sdk7tRmFaC+tALJSAyhaBgD0XH0R0bRlxiX++P1ZmOEoq2BAiy+\n7lZMu/AqxAPlGI05YPNlgx7zpGsooygJJ7vnXFpFRZ46GETkQpAbeT4X0uFR9B7Zi6bXnkHvtk3A\nWA+c6Rg8SOHacxbi85etxazsHEQ6h+CCU2DrPFwJfq4WACQDk6rAZKpoBEfN6pWKq5CxrM8WSlhQ\nuxPUHBYBJt+fOT7Vq8KOsM2GEZsdrzYdxo92bcWhUAiD1OrIr0Txoguw7MqPwFNTh3GbC3FeL2ME\nEK2ZkD0zc0/JtClW+Zk0sh1pOAe7cfDlp9Dy5GOwR0cFiZELFxbl1mLF9HkocPmE268FAC2ZktNP\nJ4n9bcewv7sVJfnFWDdjCXxxxheMPJnxK+JBdXYU+UzhZT6McUcCe3pPYEfrQQylxuFMpXCRz4f7\nL7scKxvr4WDO4mIJ74MP5keCtqAYtt2FTUeP4ytPP4MjcXVZYGmGhTfGvY8++ijWrVsnH2Tib/P3\nh3z87+XX/zsFAN7T9w4cwGWXXorBzk7ZT6hLkucDHv+7+3DphYsRTsZx4MAhDHYNYvvOfdiy6yCm\n1tbhxvUb8chj/4Z3jwxKYebuWxbjthvWojg7jTxWBGODsKVDcGXZkEzbEYm5kRUoQ2g4goN7DsJn\nt6GwaAreb+rFNx55AXu6VOR1UT3w11+8GasWF8IWH4A9i1jLbLQc6cPffOdJ/HLzKMbSwKfu/yS+\n8fDDsDVcvTbNCWIU7VnhZpJNJAAnCTm2wuvNzpbE0lAFRJxNNADUDlCh4C5J9rhxqHVXWLq/AqPP\ncACQAJicELfy7yd975Unwv9nUmW67grxZ+eKivLKzQdO9SsAACAASURBVFVfcDv8WfQlJx9aFRFF\n1ErU6TWYZyeB1yeCeRa/1fC8TZLIAgJnCYWwCBs1XTVZW6yiuHDuLZgyR6YiDVQdn/fCKP2r8ji7\njhTSUhtE7dBq4US8xZmgug2kXbv84iAgnVGFs5sklp+f+V0K89bChXTKyU1mx1f+KJTb+C4TKs9n\nadAUmrgoP5yHWAYKzF+REsZijPdOxcbUY57Xxs9UGgYLCepGwGBMkRhaOGHCx/eYpIbPmOfIhFWK\nBVZhiJuuuT7eE5NYKm9bAySeOz8ri37rFnyd1y22ghMJlrotuOm37ldbSL7GOBYwaSdU+ezvUoE/\nWhfquXPT4LUxKBDlcuGBq+Ub4fqqGaHFD0FdRNW2kvoLDHalU5tIIuDPEZ/qkwebsXfTu0iPpOC3\nO3DxytX4/j98F8WNDUAygj0H9+O5p5/Bli1vY9+hgxgNjctzkJXaDfiL/KisrUJxVQVySkrh8vsk\nOaNitN9PqL4KjfB7Wbgxivey6VkOFZMWiaroz+fIApVJOo2GgLG+43sNksVA3/lZHI8GwssxIV1T\ndgKkAp+SbqCIeVq6CLynvO8mmWV3nfNN6BqW+4Wx0GMRQwqDlk+4sdFjoUXg6uNjMhf5HFiU43Nh\n54XnwN9xXhGazufHscPEl916fibXKyI0xsZGJ7roZsxLMYWqvGP03VUVfSJ1DGpAEEYulwSHLBjx\nPrDzz+tlAm+0INip59onc9HqsmfqWPA6+Zm871zrOO5Gx8ZUlJG6ApzLImRJBIVLilgGCcR1goUK\nbtgqWqr0EiNUaDZP/s6sobwmvs44AZDeIoXSyOQ5GhtQs4bwNTwPg7zQua8IHkFXCSSXSBeF7ycj\nCfjsHnS1nsL+LbuU058EprrtuHjWTGyYMweLpk5Bfo4X4VgMze29+Nk72/CLpmPoT6TEc/mmZUtw\n+9qL0FBSLGP+VGcXHn75Vby4l3ZdQHVWLq6YtRBXLT8PBW4HUlG12rTl5uBoJISHn/wlXjilMLZc\ntweXrl6Lj3/sNqy95GLYCnLp2yrtjrHeXtz40Zuw86038eA5V+OT99yLoqsvAnKJAtI5wQ7ghyv1\nKUrojINFg1QKW7a8hauuukosI3Xe/c8pAGij1ySi+rcU56xOmF6vzk1yhoWuRVQZKUy0epNCgJXX\nem3ILykWwbOc/IDQnNx+DyJJcsxpdck1yydjimsr55zRCOG3UDNDqFOEjIbjop1CXYlYiHugDYX5\nBVi4cCEeeughXLDyApm35n5rTiggdSTSCQyNjOPF197E4z98HG+98hJsadIDUnC43Kirm47Pf/5P\nsWzZMpxsbUEwNI5LLl4rRd2XX34Jvb192LB+A440HZG9YPnycyUvPXz4EE6dOoX58+ejoqJC5vCr\nr76Km266CcGRES0AzJ6BP1u3erIAwPFnJdQTmvjmfp+NAPityd6Zw+9MGPF/HQFwdsHnzE/X4Nxo\nhAgaj6KxXj+6k2n8x7bt+OXO7Tg6HpJgU9DTafUR39A4DR9fdzFq/X5kE1VCDQ5RqJ+sf/zadyvl\n2sIo0waODQ4bwlk+7OrtwDd+/iS29oxiVEkocNucKPUGUJFVgBJvLvKyckRkixzuIm8WCtx+TC2r\nFHj/+OiYFExJw5JDYjmz39BCma4qcREAHA6O4mh7q7g3lQdyMbumDvNrpsGVTCEYHkNvb4/s/xUV\n5bImCwKAlsyME5xOlBQXS3I9ODIsVIL+4SG5L76ifLSGhrHp8F6809GMUen+OzHFU4TV0xdgekGF\nqLsT0WCOzO7/mbgQwxqfTFlkHrqcONrfjucOb8NwOojpWeVYUFWP6XmV0glWvRiL/y8w8DRCzhRO\njg9g58mjOBbuhgduLC6uRXVuEdxserjsOD7cg+MDneiMDMHhdYOJdDLthnvFxdhwz2fhLK9HX5hJ\npVttKu0sehFRKZ4VAh9n99nGzqXLC9jdsh9oHJ6C12lDDhIYbjqEfS89hd53fgV7aABZ6QSWVZbj\nvjXn4fzqKQhEAUcijZhN9yG6OogIIAsArLjK+FJbQBm7mVaultHLhLPG2YPdWuc+DIFD/Zi0zY0h\nuxMvNh/Fz3duw86RdvSxU1NQg8YrbsKUJRcgb+p0RO1OERyk8ozoWFiH8PbF1txCnYpmFQtQmv8k\nk6RHpOBPRNGx403s/el3kTh9ROw1nYkoFmXXYMmUBtQVlMEeScCdgSgStIXLgabuU3i79SAC2Tm4\ncOpcFLtp9a1WvywA8NAyKW9TWtZzaeIKDaQTO9oO48R4j6TsUwHcdcGFuHHJEhQFsoAIvTY+uASg\nCBTNndJ0oUgk8U8vbcIzB/ajCxAqAPOPRCqN+++/Hw8//LDlxjXpSPbb1qMPfGT/jb/8XQsAjNNa\nT7Xh+ptvwh5qAEXVUtzjcyIZiuK5x+/GxRep+OF7e4/j4W/9AvsOd6G7X4dtYb4XfUMR6T34HMCf\n3j0fn7v/OsQSPUjHgrBFk0hHYvDm+JGK2rH/xCjae8Po7R3Bgfd3Y+7UMlxy+ZX41daj+PI/voCu\nOFAUAL50/2Lcf+taYKwfsdA4PLlFgKcMf/edZ/G3//yeCKg2zJiBl157CcVEuM287lKhAEg1Pm64\n4qqOL/7W3AQIB8pQoOf9FwiwBROX9dXi+WqSpJ1u2txpN1mTenaXmVBwIqvNHIXVjAHN5FPV5FKT\nLePXzYFkoOUmQaaAoNdH5frYhPOAgRjLoE8kJcDWLqZyvU2yKcUCS2Ge50W4KI+cQGCi+8jFncUL\nTmeTyBs4vgbdyvnntfB6WRzhYQoA2gVXT3axObSswTSAIXVC70umZoBBDJjigtEX0PsZFes5vkeE\nzCzxJjOh+bmiaSCdSEU68PsnCyyaxE5yMlUoTpJ8UfV3yT2RgoN4K6uvp/lc84wNV1x/rxB5HhwP\nfA/fb5JIbj6GXmIKLpkwevWE1040EzMDD1crO7tAx/gdRtzO3DvTURKYIhd6ixhqtAaMUrIWbtR/\nOlOZ3qjWG/FE7YZQu8DirjLxSiSkIGSKFJL0G24ZFdDJPxOFe69UZaPhqBQAxroH8N6rWxDpCYkP\na31RJb73jb/D6o1XAAGfbFrBoQFJ/je99Rae/9Vz2L9/v/D7RXeGm5oXyC0vRMP8+cgvLoLNaZPK\nvMvt1KQ7rbx1k3ByjvEeMojhPOE5Ge93QUrESBtwi/MBX2eoMnxtJlqCY4Vzjq8jLSASJY9cE3x+\nHscHxxQ7+ExO8/JzJSGgQj8h7UYDwCBDwiEV7TPfYQp1SjtRJACvg+c2IeSXUQBjEm+KMvx+IpEm\nKsgZ6xPfy/Ph5xmaiSKagjK2TcFNBCytYEETcbd4jnO88Zz4HeLwYCEBODaNJaisKZZ7gVIWKEZE\nm0z1Vzbrk+HjcwM2c9UUSYTeY6GhzLnwmkzBRAqXFkXKiJmy6MDzEOROhgCpoQ+oBoZC+c06w/MT\nrQQ6tFiWjwa9YK5Nkj5r2TVFUd5vs7ZxQ3My4SM1Ksm9IAm/w4fYWATH9h5G+6EWKQAUO51YXl6K\ni6ZNxerGesyrrwbysiQI3LPnCH66ZTueaDmJUDyFpYEAPrlhPVbOm4WCwnygrweHT7bhb1/fhDdP\nnEaJ14GZBSW447wLsbJxJuzJOGLjY7JRhrL8+Pn7u/DIptdwhDpH5NFOrcefPPRZXHP9dXCWFUry\nz/vqdLgx3NmFm2+5GXvfegcPrb4R99x7H7IvPgfI0a4kCxzCVf+wFoeSQ88IN5LsbNls+Na3voUv\nfOELqqViHRpg/TdGJ/8NH5WZ5vNnAaZTn04BsBMNNO3T6a3gyOb/CuxbeuwMIG0S5I1TVVvlTgQ8\nIZwNO8Suj9ZgBaVFyC8tRChFSTUW4BXlI/QkC3nCtUA0Wugo4mAhjLo0KfR39IgNYGh4TBIOIlOY\noM2ePVtEAC+7/HJBCugekNn4ox5EGom0Dc2t7fjzL/w5nvvFv8v18Tw5vy5ctQZf+MIXhYu6Y+d2\ndHW144YbPiIUrbaTbejq7sHSJUul2899vHZq7QTNkEVAUgK08ZAQCsAtt9yCsZER4WtePrMBf37J\n2jMLAJkUgExI9/+JAkBmgvlrKnxnDjJJ/i3h3aTNgYjDjc5oDD975x08u+s9nGKsxLfwmaeAElIg\n5s7EbStXoSE3D15CspNx6wEROWA9gzOuO/M7OZBSiIbC8OTkIuLyYMvxZnz31Rfxbs8oonYHwtJF\nTsIDG6ZmlWFh3SyUBwoRHB3D/qYjkhTNn1KHKflFCLi8CI2Po62jHb6sLJSUliiFwSoAyL4khSyH\nJE+hcBAn2lqElje1qhKNZVUigpcDB/ykcdmZ+KoYLmMJFoKLiopkLAwODCDMsZGfL0VecuvbB/sw\nODoqqLlEthdbO4/jxb070RZnV5ef68LCsgasqJ6JQlGSswDWlv2uSYjOKM+J6P3ZBYA0ElQMd6ax\n5cgebOtpkuLCotI6LK5qFOs4V9Iq1vFxyWekpZM+akvgYH87trUcxCAiKHLnYmHhFFRk58HrdmMo\nHsK+3jacGOnBGNn/LABQmjxQhoaPP4TGNVch5srBcJDoS5IxrC9gy18c6nmlKbHDdDCmsjnF84BW\njhpJMAlNwZNOwkeXqO427H7mcXRteQWO2AgKEcaqkiLcuW4tzq2sQQB2Qaqy4E9ROykAWBROXZ9/\nPfmXNeI/WwAwJPmz1tuUw40gPNjcdBSPbd+C3eEB9JCMllOMsnXXYf7l18BVXoNQ2oFEjOgKKh1Y\nSCmrqCoFAI4fxhQpbfSZPECahT4PPNRQSybQu2crDv3sEYwfeQ+wReBIRDHTU4olVdPRWFQlBR0V\n9bMcMvgdTjtODHbh7bbDkohfWDcXVdmFQvgX5JUR+Z1AmExeJK0Bx+wxQQBs6zoKlvVkPaupw2dX\nr8HcijKkk8yfPljcRl08iRBPIe1II+L24d0Tp/GPzzyHt0PjSn9xuRCJx1FXN1VQABddtFpHgCUi\n/j+tAGCElHkNd9/zSTz2+A/EzpgCldOmTkU8FcVw70l87Op5mF6Tj6HhYew7dAqvbBkSdX+GlZwD\nIuNFVCUU9v/FP1qCW29YDU8WXShSGO0JYce776O2aioGx4EfPf0O3j/cizBNAILA2mUBrDjvAjzy\nxOvYdozeC8Dq87Lxna98BNOr/WjfexjjozHMWHIB+oN+PPCF7+PFt07JM/nMn30GX/jSZ5Dt8cI2\n98b1aQauhkPLTpjpcsaiMfjFAkwh0gox10RWbLwsNUsmbppcKcRvklJA2Dd911MT/ugmgdNgmh3p\nSXV401nl9xixOqlCC7dbBcm0G8uCBRNH8v/IpdYut3THEiwysDPPChg1AbSoYaD/DK61e6m6ARKg\nM4CmByj50lbCJ4mUCcwtTQEm45IoWj7eZvCaDmDmoDZ86cw1xfy/4SGbe2WSBL7HaB1kfpZ29fTe\nmsKBdAIslXNev+H6Kwxek2kmDGciLDTBZmeSh3RT7DZBPogjg8czAXNnsUG7mkZvQQX/eDA5lApi\nRJEfqiugHGWjYM77rXRCpYbo2FFrOwP5F3j0BK2E3WaFcLPjacQowxYMnN/Be2MKODwPeX4UAWSH\n2zhR+GntF1dRunhcklgWPUwn1YjRMfgUODWDnZSlEWHZovGz+T205qMQnaIwqKSvXuqmmCAihtzE\nyI2WsUlApA32aAqH392FjqY2OKKAP23DzauvwE3XXIdzV54H99QpgM8uKrLBcBA7d+zEs0/9B958\nYzOajzUjRnsVWTyBQHkxispLUVJRirTLBqfPjdyiAgnAqP/ACrLRwOA1Ceebyv6SLBtov3PCi1sS\nWoeOA14775MRVWTRgAmkFhYoPJgBX80IIifGpmgkqN4AD85RFt3ERspyI2CAzwTazDsjJsjnrkUL\n8pvUkZpBFN8rFpDk9wcCUshg15zdeCN6x9eoTkFKRABF7JOK8RQTlLVBi5A8B71GRdToOZImxAJI\nXAqCfn+2rH1EPhGlQE4yuzu8JywikfrDg2gEMy55Hix40AWCSTLHJT/XFPzMPeY84X3kWmKoFrRD\n5HMhcoGFAnaUhItvOZHwuRk9BL2PmnAaCoOhoRiqjIiqWigBXhevm59BZBDX47FR9s90rvO+8Nr4\nWaJhwoDFmiu65iqaRKgpCWq5OIVHzTU1xk5w2gG/04e+tm4c3X0Io6cHhNNf6/bioilVWDu7EStm\n16NqaiWQ7QGiMbz61g787I2teKmtEy6bA7fMmovbL74Y1dXl8OXlAO3t2H34MP7urbfwfmePdPxX\nTKvHPWsvRn1eIQc54uMh8RY+Fg7hH97ZgqePHJTuQl5OLv74trvw8Y/fgaI5jVYy6lCLAZsdzfsO\n4Jqrr0Z/20n8zc2fwR2ffhCYVaVKZdKJSQik8r9SAODnkid+55134pVXXj0jZPxDLACoApTeGv4R\nlz7LjIQ4D94K+gzTz57BSJbLIzZpHHMU1mNHlrOH3Uv6bfcnYgimVIBKKgkMxiWiA+weO0qnlGPq\nzAa4cnyI26j3E50oMBtdD84JQTQRGeT1q+0ZUYPD4xhu78No3xAinH9pG2jnetWVV+Lee+/FnDlz\nZe2azCW1f8OQmAUAph09Q2P49AMP4vknfoF0Mo7sQDaGR0ZRWVGNdRdfgnvvuRfz5s0R+zB2K4cG\nB2Vdz8kOiHgc9ySuoRR36+zqwMyZMyZoM729vbI27dixA3fddZesG7kMvGqn4MsbL8esbD/sUZ63\nqmMzFbLZSIfIcAL4fRcAMoTD5DlJAeA3VKkcTqEB8PmHnG6ciKXx482b8dzu99DFt1EbTMMAVJPz\nP3sWbr7gAkwpyEc6HIKXsQi/wrpmddH5DTKI3H+pSxJPIuT2oCkYxNd/+u94u2dAED6MVGT9ggN5\nyEZFoBgNVVNREihALBjGoQMHUFlShjl105Ht9kjXuaOjA8daT6CsrBz1tdMk+SV/WhIWW1og0HGk\nRbjy2MkTGOjvRVVRES5Yugy5DjfCg0MIDgzC53HBm+0V5x2u1RSAZDxcXFwstLXBgUEkI0qPDFL/\nwuWAhy4+FGcNBLCv+xR+eXAbtnWeEKpENjyoyyrG8vq5qAuUwhFJSgFyIum3xkZm8i9aRSLiNlkA\nMHEVk/+O6Cg27d+Blkg3KvxFWDllFhoLqmAPq8Apvd/lsTNntqWRdDnQFw9iZ8dx7Os7Lhzm6UXV\naMwpQaHHL6jGk0M92N1zEp3xIdhdNiRcbqRDaeRccDnOvesh2MqmIRxl44q0MNPN1cQ3Ld+nor/i\nS0EUDnn7abpruUU0jagckXNMJ+BJp5DvSGO8tRmvP/49xPe9g+xEP7KRwpqKMvzJ+o2oz8qBw+L+\nu8QW3PCtkireJ2LYvz6m5T4aLYIzVmr9x+RMsO44ofUZBbJxmxt7B4L40ZY38XL3AfTa/Ij5yzB9\nw0cwY/01CAcKECXl0umGLUGnnDgcpM9ZmhaKW7EoN7wrjDmJgLAagYwX1EXJLsr/sbZjOPTzx9C5\nYxMQ7oczFUcV7RzLpmF+xVTkOX2CdjEFAF4DhXVPjw5ga+dRcT5YXjMTM8tqYI9TIJJuGYqK4BgT\nRI51nwyFIO5IoXm4Ay8f243+BLv9CUx3uPAXF67B1YsXwm5nujpZAMikk8mIlNhP1+G43YmhpA0/\neHUzfrBvD3rgQIziuJaNOylz3/72d2TuGMrpBzyW3+uvflcEAIvAV119tcRKHIPl5eXIy83F0eaj\nonniUV1PCUW4RaoegkxkawlOoywPWLWgGCVZKSxbMA8l+T6UFroxvbEBx0714V//7WkMjtkwGHL8\nf9S9B5ydZZ02fJ3ez5w501umJJNMeg8hkEJCL6HDIkUERcQVdF1dXVfdXd1VdFXAsryuugoqIiKQ\nUIQEkpBCSCOVTMokmZLp9fR+3t/1v5/7zBBh8Xu/7/3efc/vN4SZOfOc57mf+7nvf7kK3jo0iFGa\n6tAe0AMsmV6K4cFBHD6jjjtzmgm3XL8U99y+DI7cKPrPdCMcNiFuqcW6Te/ikZ9vk317+ZoL8YW/\nfwCz5pEGOTouAqjF7bRfvCR3tM+RrpNKvLk5MgBlQJ8wbL8ogsXuGjdRduo4ybVInxKfoyJ7XCYh\ng1X+PY/Dz5MOr51dRUOUL6K68OwWaG91hU5QHB4G+ez2BwIBCawZRDDA0MWBiQrm/BvhC0qHWBUn\nxFfdSBylk5gzeK8MugnZp8J8XHmhM9BQfunK0pAJKb/XHF7CGrUV2dDwkJwb38Nkk+8RSLzTaRQy\nFL9Zc9hJGWAwrvnookkgyZp6n1Zf1wmc2IQZtn28Zp2I8zwZyOikWj85+kHXnKeJLgVMmjVPnomM\nFDSkUEf3gnFFVZXAqftXEKuTSTze7Rf3BKM4wmRCeMUG1Fw2RUPVnImW5klzTgmVwEAKMAGRAoKR\nBGmuOL/n+AnkW5wWDBsSQyuC1yooFWNzVBZ2hqCfIbrG6+EYsRPLOah91Dk/mZCJ4rqVInfj+gic\n69KFFecCZSGpOv9Kh0Bv1gI3E/tFVUhgV1d43DY7LDkTzh47hf1bdyM/TF4bUGP144rlF+GKSy/D\n/KWLUb14NuBRvTaGrmPDI9i54y08//zzeHXjBnT0nFWbt4gRArYiLyrqqhGsLEPlpBqB2Saz5Nua\nBRnCAo4USWSDzIu4GxM3Bp8MbBn8kG/L+cPx4MbDcSCyhVBJnocSsWSRRRXNpFtviD5qoT3eL6Gr\n2Ng9twocUrlFOIT+IygEqvbnckKN0cE9j1UYS0NPQOa3IXLHe6ELV2qjNMQrBUGnaDK6aMd/xT5M\n1hpSOVSBQYk4WuRcOMelYGM1SzLMzxe6iFiSqYRaUxO4LOgOvQRbRpGP18yCJ18ataIoNjaZTxwL\n6jLoOSHIAUPLQmhHRkCnETj8O84jopJ0oKEKMUQhGG4IVosUHXgt1Jrg2qM1AFiE4LhP7CJo3QTt\nVCExj4GoEQqOAbPW2hD8vQQeE9wxiBLQLgy8Jj4n8nyRmpFXFATmEXaLA/lkDh3H26W4FTo7LK6W\n0z1+XF5fj5XTJ2PpghkI1pVLohAKRfDqll343RvbsKl/WJKlv1u+GlcumI/y+ho4CTHs7MSb+w/g\nkW3bcHxoBCU2C65atBAfvXAFKuwuIE6KWRppuw2vt7XhX1/fgIPRMVEDn97UjJ/887/igisuB4l3\nRMWw4CQy0mYT3tn+FtasWgN7JoNH//rruPW++4Ep5crrSinXFISx3jfq0IrThYRJhT8bN2zE7Xfc\ngf6hYVXJN16yFhlezYW12Ag1/58BA/6CdxeiV3UC/K8KOI0u/3hzvpD4c7UphQl+hxPFPj/8DhvK\n/Q747Vb4nR5J/qnvYLfYZA5Eo3GxYkuy6BEJoW14EG3DQ+iJxzDEgvQEBHehFmA3YfKMaSiT++tG\nMptEJB6RZ02QUkYgynkrcUWM9BdV3M3G0+h4t01EABPhiMSdZRUVuPWWW/CJ++7DjOkzDIrTe0Zc\nKADk3IajcTz1hxfw6A8exbEDe+XZIVqKqI1ELIXq6lpBbdz9sbvhcVvx6msbsW/vblx55VWYMWOm\nrI+iN5TNoLOzE52dHZgzZw5KSoKy/nDN4Vq0c+dO0SQY7O+XjtkFVZX45vVXYabXBRuRc1w/DDs+\nusOIQ8z/MgXgvUBwHcNMnHU6NfzzOaxmxXtmqGTjE6u5EwjUIlxmQor7ndeDI/2DeHzDJmw82ooe\nJhGslRPxa8D+r5s7D59cuQK1ToeCfkvBX+2RUoAtfLShuC4fO8FykolgNo0Ek1xPEVrHQnj0pfXY\neKJdbEEZDfJP/LChzl+B2pJq5BIZxMYiggagTZ0bVtRX1aCsuATRcFiGmetb/+AAqiqr4LY5JRFm\nAYBWeIwfHR4XxtIJdA/14WR7G9xWKxY2TcX85mmwsWOdzSI8PCQJrKfICyuRAAb9jLEri8REwokj\nSjKDZCqFnsFexFNJQcHY/T5EzMCO9uN4cvdm9OYSolJRZvbi/NppAtH3muzIRInoVAVseYYnFIcK\nidaEAoAxeqqxgixitjx297Zhy7Fd8vczK5uwpLIZRVk7HGYHLCbFRecNI0I/ZwbSVhPORIaxrf0o\numLqGufVtqDBXQyv1YFEPo3jA2exf7AdIXb/LXlkHB7AXY4Zt96LpstvxIjJjWzaBC+5/xn6A6iX\nSuwVb1YSXUGPZQRhR/FGp91t6KRw5SX1J4tMMo4gY+lsDp273sQ7z/wUyZP7YUoPYyrRJY1N+Miy\nCzGnth7psbBCEhJdo50Acir+fb+XsvJ7/+KTnPOfLbNKs4MQ/pTFjNPRBJ7Y+Q6ebd2LHuQQcXhQ\nvuwaLLnxLmRq6hC1O5AXaAVgyVphyiu9IxGmMcY9neSepJyRCpRTQ6DZRnFi6jlRr8xignN0AEee\n+RVOv7EeCJ+FLZtGESyYXzoZi6ono9JVZAg6jj/XPGZffAxv9RxHR/9ZzKuagvlN02HP0CtDoXvl\neaTw3znwNBZsTDaz6EG80roHraEu0Z0qQw6fnDId9196CUqK+PwYuA0JRdXdFn3HcxByLMCaHB7s\nONOJf35+HfaEiC8xI2NlgyYjriq/e/oPWLbsAmneapv39715/z/98MN22veuvio2JAqMCLBnnnnG\nEBXPIVhcLHO+r7ffECYfF6CkeOikuhqcd95iHD54AHt27ZI19FMfXY0Hb78ASI5iy9bDeO3VTagM\nurBw8RK0tg9g4/Z3cboLSHFNY+GV8WgWWLlsKqzI4s3tbZhUBXz09mswr6UILZO9KA1k4HCZgVge\nIyEbvvHYevzyj52i13LB6gvw5a9+CXPmTUIoehTDo+0wzbzlyjwXNw3RLfCbRfRPfRHmqL2ntYiU\nEh7LSgCseaksEjBY1UIcTCrZQSYPl+8vcOONLrwkUJoHTAEFdu4NBXwGyZJoFITv0oZCvoLaSmeM\n1SWBTavbqAWyhKOcUTBnwgt5zjx/Ld4m/GGDxMWdfQAAIABJREFU/81OniSJRgGASAD1sCpout4r\ndeVMLzYiQia0ByUmqNW6WZjge0SzQDocyp9c4IMCFVb0CrEHJFxf7NUs0o3T1nharV/OURSU+TcK\nrq87+qqTrX6ukhtVtS94i2eNhEg0ClSBhV1Ovojy4Dmw88/P5/ULFYDwvmhEuuY8LkdVLByppZDL\nFcTamGyLWJlViYrxWie6I0hybKiNi9aDTVW5Nfxb6COG0JlOtgvjS1johKSLnyFB2YQETxIidoxi\ncZWYTDjeRJSEtv8TagKpFgbiQLzehW/N8af4HwUj7arTKfSHrIwRX1I4SKXlOjl/Of5MxPj/TNR4\njHBIcdWJFpAEOpFCPBRFz6lOtO48CIwlJVGqLyrD6vlLsPaKq7D60ovhqqsG3DYJumShMZvEq3jT\ntq148vdPYdu2bYgPh8ZL1VbAUxVEfXMTguVlcAd9MHntiKXjokFApwCn3Srq2TmOV5oKoC6ViGdy\nGBkYEttDn98n2gWkFChhGMEHqkCVIQH1dbjYyxyjUwcpJzkpMuj5zO85z1ViaZax024TvDcCdVdh\nTQEtwrnAMeL84TMjgpged0GZXwqI+Zx0olnUY2GC64AuLoiPeCQiY604/jalvC/XbpduCo8tyBoK\n11FwyWk3zlHNPxY32CFnEVOjWBjMafFOFq340m4CnMdUwOc5CJ+elnjU9TBbUOT3KaeTRFzpMbhI\nzzHL80RNB/KdWYXndWtLQKElJRPSaWHBUNYOjiXhgSJwpsZL0wj4O73W8Lw0NYefr19aU0O7a/Be\nSIFL1mdleanQEdT1UBBEKaCwbmUjLcgKUyaLZDxhuLrYZC5yvCVBo7Ao1xqTBfGxGNqOtqHndC9i\nvSNCcZnl8+O6Kc04r7EOi5fORml1mUyg0bEoXtq0Ay9s34Vtvf0ot9nxxVUrcfnC+fBXlEoAkOru\nwZZDR/C9zVvQHYuh0mLCbStX4foFSxBg8MBiksOBs+k0frFpE359aD96ZU7ZsGb5Snzz7/4O8y44\nX3nu2I2yuwj7ZbB902ZcdvGVqPGW4nuf+wdcfffHgGo/pCKn20PnBBp6HVIz14A4TnxP3ozvf+dh\nfP4rX5Gx4t44sStybtyiFe51oKH/HQ8sjICq8IdGT2JikmZA3nXgNf5WJe1sM1MZncg0OqSr+gZX\nfP5bzKTfYUFVoBhlLjfqgkEE3W6UBQIo8rgQcDvgdToEWq8cZUnf496ZQZLCq9TZyOYQiidxdjSE\nk4NDONh7FkdGB9CXTgkqgA1hLfAp7iM+J0prq1FWVwGrmyrr47QdXeDmc8Jngc+I0o6xITYSRs+J\nDkSGx5AzknGeF3n7RACsWb2m4FKkkiD22BmUqgJAJJbA4z9/Ar/8xa/QemAfkFfuLFzf3G4f7rzz\no/jkffehqrpKqEHhyBg62tuxYsUKWU943cdaj8m+PG/eHBlmapgcOnQQFRUVIgLHZ3Pfvn249tpr\n0dvVBS+suKCyEl+7ehUWlRfDnEhJVywn3vIKAWD+rwoAH+oC8Gch6Hum2Lmz571ZjWT0xvu1j6OC\n5At/nN3YVB5WFtpY6MtnkbVbEHc5sK+nG0++/gZebD0tiTjvMWcmH51JFuCWmbNw98UXo5RIQ+F1\n69MyOtYSk6m1TJGEJz4lgpWRI2YpImuzoz1vxo9eehnPHXwXI8bDJ8KjMKPOX4UZ1VNQEShDZ3c3\njncclQB4XnUL5je0wMPCpAk4deqUrPXVlZWC6mPsyg4s1dApSmdhYcNsQtKcQ1dkGAdbjyCVSWBJ\ny2zMqqyFPZVGJhFDWTCAYMAn1xSh4NvIsKzBLKSzIUYUCOMdXheRI5w7sURE4iuP14fhXBr7R3ux\n8dhh7BvsQgQ5OGFGs78Ka6bMRR1VuWlrR+tSrr2FcTp39Rj/Xvmxq8SaWkBWuxl9mQiePbQNp8e6\nMMlWgvMmz8SM0knIhYg3oIMOmxnqzjFPpWp81JzD3q6T2NJzVKDIdY4g5tQ1ocJJ81ZgJJPArpNH\ncCYzKKgFjmGaZuYtC7Hy3s+ieOYSDCX5vCvnJ6KodKNJ9iSiDIQWqqmbqvvM+yDUYNIqJXbOIUkk\nKvd70v9MVjiTYZzZuQF71z0FHN+H4nwMtchi7fS5uHf1alRabEoIj1OZFDzpaqiYQeywz3nxvptI\nlC9MTWP1NSywxwsAuijFhpcVKYcLraPD+MOu3djUdRZH43GMutyoOm85Zl17B5wN0xB3UPhOiWRb\nZZ6phE+aFSI2yLmmy7Hjzg4aSS1jQkcqsSlOgxqHpcjg9OvrseepnwODp4F0WAQvp3mrsbphJuq9\nJbDklLq/frasJjPCSGFfXxsOnmlFjacMS2bOQ7krgBw96Izu/7ncNFkDJJHPI+EwY+vpI9jVeRhj\niMmesdoTxKevvByLptbAnmNMrKgcBY0KxS0YH1tBqJAC7MCwyYJfbN2Cx9/cAspjJ6n1RkSx2YTb\n7/qYoAC8frdaAv4yw4sPfjD+D/zmnXfewZVXXilCoOe+KBb78Y9/AgsXL8aePXsk7qK2zMzpM1EU\n8GLrpjdww3VXIhxO4pt/fyMeuvMCHG99Fz/+9VY8v/4YnGagvr4IoxETegdCiCRZVFNiq1x7r7qw\nDrfdeiN++pvnsXXnGXz+vgvxt5+5FdZcO0zpflhyCVitTqCsCcOdGdz6mR9g096orN9f/eqX8IUv\n/i18XgdGw51IJsMwNV69Ks/AUSdyulOtuolOCX4ZYE4UqVMdUassptzsCrZ7ohOgFLe5mSr+rLrD\nOiHQ0HhaenGxTqSUpziDUIpi8MUuMRNPLjAi3JfNiZCQ/I4BtAFhl+TB6ZBgWcTAcuQuG8G8ofpP\npVcVx7EooDzrtR0Wf66/52ovHUoRv6LvuSpGsHvN85EkO5NWybPR7Wb30+lmJ1XBcHkOwj03OL4i\nosbE3JglXBR5nTyW4s4zMVBuBtoiUHMl+R52RihEwkIACxW8bt0tL3Ro88p2UdEIxi1RVIdabfpa\ng4HnwuR+Ip9+YmGjAA8yoEJC8xA1Y/ptqwRZ318NTdY6AxR95OZEkTHhd+Zz0o1mspVOJSVpK9BH\nDKV+LRYniZlRwBCbHt4DI5EjLFuOYYgjijihYaMo94mFCNrDGdztiYJsTHgIv+a5an43jzPRkk26\nOvm8JGP8OZ0ReA9Z5efmJygTwsXdKlHltfF8dce0INImSBV1ntyLRIsiZ8X+N3ehY/+7ItHJ4LzJ\nW4zLL1iFG65ai/POPx/OsiBMPg9Q5FYEIW4IVhNaT53EH599Fut//wcc3L8fKT5LDqV0D7cdLr8P\n1ZMnobShCu4inqtCkFhY1UVOvpcKryEcFxsOobujE9lUBi3TW1BSXopQIoq0OSebEJWAuU0nE3xu\nOTeVSr0YbWVS6v4JJWbcspLPN++3OErElFAbn1ctRqfnAcdC4OxMloxKtCrgaZtFpfehikEKts/P\nIe+PCbLWB+BzRQg71xAWIij+KN8bBQCFGKGmQELuHxXAub7I/RJKkILpi8aIob+hAioVpGhND83z\nFwQIu6H8e6MoJe4hxnPOz+A857wRZIXYk9rkeeFaxHXDJqKbaq3j3OI1kmZkppuGodjPf5lki1hq\nRqGQeP9YLOE91eKMGinEtUBz0YTrzzXY6Nrr51KPJ4siej3WCBntMCBBgFAmzBKM5knfoEaLUChM\noAMME0IHkQtcPxNJZBNZdLR1oqerD8OdvcIDnu4vwhW1k7CwrhrLVy5GBcvSMGF0aBQvb96Bddvf\nxra+AZRbrfjCRStxxcL58EoBwIZ4dw82HzyE721+E72xOCZZzbjr4ktw5awF8DNBScQQtVmwe2gI\n33v+OWwfG5OEJOgJ4r6P3YuHHrgPpU0N4m1MOX7VgZQVBBvWr8f1a29BQ0k1HvnyN3DxLbcC5R6h\n1iiC6J/X//+8ADCxWwqkwhHcc++9+N1zf5QiycQCwPvFIroAoIGS6j3jxkq6fDD+KRNKBefkfiJI\na8Sq4vFsBAXEqHCX88MKr8WCEqcLFeQqux2YVFaECp8btcWlKPf6UOxwgbucTQr8Jnk+hFKTyiCe\nohZOHIl0RuxF4+k0EqRsWamN4UTO6kA4m0dnNIz9/d04MtCLrngUo3Tj0Q01Q0jA7HWirLZSigD+\nYJF0BhkERuNxw2FF7YW6MMx5nwjFMNrZj2yUDhXKfpfF1JkzZ+HBzzyIm266qSAAe24BQDRRUlmc\n7R/Bw99+GL/+2U+Rp3NEnjRDK+bOmY9/+IevY8WK5XjyyScQCo3ioYc+I37ukm+KuGsae/fslcRp\nyZJF8nOuJ1ve3CLJ/+zZs+VnpADccMMN6KV7ACy4oLwSX796JRaUBWBnYpfLI8fu5/9nBYBzk2c1\ni87JuY3u+sT5qsojes4V/hWotpGg5zh7+OxYkKBAnNuJ7adO4hevvYbtXf2gqhEpICwo8asUwE1L\nF+He85ehmmKvNq4Vfz7z1XOkkAAFETahRhgFLmnNmhBKpzBst+Ph517AC0dOYJA7js0MU1rc5tHg\nqUBNcRVKPAG4bE50nz2LeDiMEq8XU2vrMbmiVt6bSCbQ2tqKQFEAdTW1YGIk65dhh8bkn8gMFoqG\nExGcHDyL/uF+lPv8WD53AWqcXkT6+pFJJ2C3mdHUMEkaJ3QR6O7ukTiiqbFJikfcQ06fOi3PDWM0\nCl173C54fB6EYgkcHx3An868iy2njqGHCSrMqHEUYVbFJCypaUaRySHNM7FeJU/bQABMXHv0iOoC\niureqv2SHJ6cNY9jI2fx3KFtiOTjWFrCAuwMBC1uQUnQuUHF3JyFapUhbWcon8Rbp97FnpEzcrw5\nRXVoKa+F3+YQgcDO6Aj2dxxHTy4ksGW5W64gXMuvxkUffQDmYA3CGXVfeXyh8U5A6IrQYqGZp6iX\nvM6JyB8drzLGZ4OFxXuHzQUX05SxHpzasQFH1j8NtL8LLyKYbnLglnnzcP2i81BhdsJrYRyUhNXl\nkCIfEV+ahjhxJnKNlAKALj7phLWQuKoxVagUfrHY4cYAzHh63048uXsrTgMYswbhmTYHi2/8CIJz\nz0fM5UOKGjJCgzZWeOMhKKAHjTXFZtX7s9KmYQFJ8hhDXJtITBEFtAABZDC4fye2PPE40LoHlmxc\nYrl6ZzFW101Hc6AKThOLvUpZU9QAqE3hMOHocBd2tx6Ex+HG/JbZaCiqgJmkc9lu1JpQaGYaz6vK\ndTKw+N04NtKLrUf34nSqW/aHGTDj9qXn4yMrzkOxwEeMFefch91YbsRakAX7VB4mnx97e7rxrad+\ni7fCIcSNfZJnU1PfhEce+xGuWXuFmlzvs3a83z763+lnjzzyCL74xS8WkKk8N+Yt559/vojVXn31\n1ao5bIyVaBKZrXKtFI5dtGAmTp8+i7tvW4LPfexy/Ozn/4nfvdaJvmEg4OactSCesiAp9Iosijw+\nOJ12+J0pfPnTN2HqtGY89NVHceDdXiydBnzijuVYe8Vs2E0RREfGAJMXMQTxxp4ufOm7z+HsCODz\nOvHDH/5IkAuRSBguNsYsJpgarlqZ17Z07LJKZcro/sjiJGrAqovF33HCaJs4fh8OjQkHQmz4jMRs\n3HOcHee8JOn8DAbW7LBpgTouIBKgGxxgBuga5isBcyQqXSkel7B/Bqqjo2MG31up1IuwCTuRspCy\ny6uuQTpdBr+T8Fr+TC884ygHxZXlgs7Ov3T5XKrLq4XyxJJMkkZlN8gNhv8yKFcicUrM7IMKAOwO\n6sSH3The88QCAO2IVFeRgZHqCvKzWFzgODookGQ4KAjkmKrvBodIwZJVRVX/TIsAjnOhlbo7x1MX\nL8Z92mlrR8hvunCOvN6CSBy7khRBFNcGJcamkQg6wdA2acohQCnkqyTcLOgQzhVunoI0oNK+UQCR\nIpKZkHlaoamkTAvaKcEUlcSq9UtBwiVJy4x73PNYop5vFHiU+KHq7uqXVlDn91pgUBdKJqJeNOVD\niaeparJwpYXeMg79l2VQizAaavjCpzaug9aLct8cDvidXvSd7sKeLTsQ7x8TGCL9cat8QVw8fyk+\ncu1NWHbe+bAVB5SEp8sqPMIUsjDbbYI0OLBrN1555WU8t+4FnDjVNt7gcZhh9rnROG0KahtqhevK\nRlMiFRdUjbIxtEh33Mx9tW8YXafbcbajUxLW2vpJmLFwNhCwIW9X1jLk6mXTWan0an0MEerkGJtN\nqqtO1ISBbtHFM93949hIh93QBGFQrwTZyGsf56rzfcoGz670J4wEWxWqlBUj57wgXHg/iOYxikZa\nKFDmGEyysIpFIO2jjOIhz7E4GJTiXSIWFUcRKQ66PQUkiF4ntOilLuqIm0AuKwU3JncCubfapcio\nYMuE5BPKmJOOj9BIiHCh6jNhzExkEkr8lJ0hzh0RLTWbVYGEDgZEsJhNhkCp6rLwGCxQCtze0Cfh\nesC1QNA9hpAon+OJlomCfjGsODUdQCNepNiYoFaC0ung2LEIwSKSFKgcDoSj9BxmAYQ2hFak6RvL\nMSV/LZVBIhJHLpVBmpSHRBLxUBzRUAyR0SgGe/rhyAONTjdWllViTnkpLl91PppaJktHPjwyglc2\nvokXdrwtFIBSuxVfWrkKVyxcIAUAClpGO7uw6eAhfH/LVvRGE2hyWfHxK67CmuaZ8Al5LomhfA5P\nvXsYj23ciA6hiFlx4byl+NrffwVLVyyFxeNEjmgVw5JWHt50FE/+7Od44IHPYXrtZPzoG/+GJVdf\nBfhtigT/FxQA1CJgpOhG3tJxsg0XrVmNjr5uCas/rACgQ3AJxTRyU5KRcWDPh8EQ1VrGEl1eePr8\nYjeA/P06WrBZHagtLUF1cTHK/V6UFflRXV6GYMCLoiKXFG/oB27Pm6WAQ62SsVgUoVQKvWMhjCbi\nGI1EEWMhnfOV1LdUWv7lF9dGh80pXy6HWzpfI5kUuuIRvDvch+7IGGIs2nPZpKaiMVY2t12KACWV\n5fD4veIQoH2oKZjFJ5ZdZz5L7KJlI0mMdPQhOjiG8NgokMrBXxzAtWvX4jMPPoj58+a/twNlJDYM\nhHmceDKDto5ufPMb/4LnnvotrITiyhZiwpw5C/DZhz6H5cuXY+vWNxEKj+Kjd90pLkIC9+/owuTJ\nU1BeXib3dGR0RNaNxsb6Am9WNzG2bt2Ku+66C31d3ULyXFAUxNeuWo0L6ypgT2fEz9wIuWEy0znl\nv6AA/EUIgP+VAsDErvu54bNOwtkBsSNDepbbhUET8KeTp/DzV1/FgcFRgY1mpTiYQxBK7f/aufNw\n++qLUOdxwUQkk51IwQ+awaoAIWcvHUMmEhpLwEK3FadTafx2xw78ascudPJXhK1ToBkmVLqCaKmc\nLEnc2eE+sQIttXtQV1SKScEqoaskKRKaTMiaNTo4JFSAgN+vtMkoQscGijxreUlwI7kkjnWeQl9o\nCFVlQZw/YwYml5bDHs9gtK9fCrm0+gsGA8LNjoYihYYT5wb3fu2gwvkh+wHRgXQbsDswmIjjyEgf\nXjp1EAf6uqRYaYcFC8sm4/zmmah0+GGhm4qxL1qkjPdhL6OYZIxmypzDUCaMHScOYOfgMXhNLlw6\neR6mBWtgy1tgzokOv5FfjXfGkxbgVGgAO44fRntuGG64sLRmKmr9JbIkJk05HOlrR+tQF0boDM8i\nFtdgRwkmf+xvMe3S65Fz+kgMEG65xGaCtBin6H5YAYD7uG6QEaGXypBmSBSTU5Ak1nQErtgwTm5+\nBQde+C3QfxJluThazFZcP2ch/mrJCvhSWbgcJln3he4lwr5/rgPw4QUADqiBGkMGabMdMXsA6/bv\nx3+89QZa0zHQ5yNbORlzbr4b9cvWIOMJIElkbp4oWBb4STNWMaqODVXRRq2DbCJIEcxQ3lexkqI+\nKjqlcqQAXREsOZj6O7HtNz/D8KYXYUqMyppfAjuW10zDnMoG+K1uWNmxMVo2LABk7CacTY5i17FD\nGE3EMGtyC2aX1cOZUfRn/TJAP+rcjB8yzrJ5XBjNJfFm6z68PXgUSWRQAuDSykr8/dprMLW0xLDL\nPUcUV0GJZCOjWCZpTxZ6GlgcCJks+I/X/oRH396BUS4zZlnORdPjnns/ie98+1soKSXW4P++F5Po\np556qoByufjii/HAAw/goosuKiCHpQErIuFM/g0nEsanyRRmzZyGM+1n0FDlxaIZzdi+4x2cCZNu\nahJLUC6DNlI/Fi7CTX91Gy6++CJ4HRZYEcWhvRuwYcPreOq5rRgOZ1DjAL72hdW4/eaVCA93ITw0\nhv1Hu7DhnW68vusMTnYBJZUl+JvPfxY33XSjov86nCgrrUYiEYOp5cbL8loMjIGl7nSSKxUaC0vF\nSsFIFYxdJ9G8bQIljUYMiDeFAVUyJrwmM7uCTPzUJGc3i8dQIn66+g+xCuLzojtaTEZVwmCXAJud\nMSYGmvc+0deesNt4IiYFBg2fZaCuIfQM+MlHZgdPQ3NFuM04dyaVurssvFyLCs51AUPg5AYPWJ9z\ngXNLWCzPK5t+XwqAVCQNn13+yyBHc70mUgDYhdGFC54Xr11XChnojoXCBW40kxNJhgzLQ2Wfprjo\nTFoZ8LMoIcm1aCDwe1IkcjI+DPq1AJncJ4OuoQTVlDgcedy8R7oAIt1KI3mRcROBOcWb5+KmiwK0\n5uNx5P4a9ATZDAxOHpM6rbzPcef3E+cTkQb8mSRJxqaqxdE4D3juFITiBjI6MiJjxjnF5I88diae\nmi6gKtPKAYLnUBB7NDqlYl+ZoVCiEpbU+hXczPUY8zp4LqVBLoWKA064Kn+vUQ86IdQWdIqzTUFM\n5YjA7ivFa/pOd+L0oWMYOs3+BmDPAxUWFy5efAHuvvE2XLh8OcxBP+BxAF6ngjNbzchQpAwQteEd\nb+/EupfWY/3LL6Ovu0dhMmk563ehvLIctQ11KK0shZUeu2ZBBhvdFzOSkTh62jpw4shRpMJxZRjq\noE2RF+XNCqpbUlpibFa0p7HLPShUqUVsUSF75H7TZUCKHlaxQePPef9lHmfSBVFBJsD8ncB+jURV\nF4c07UXrbnDO0h5M20jyueXfFAWKhGPJTr8u4vFZ5P1hcYMQe45/MpdFKBJW4261IeDzK0qQxYzQ\n2BjiyaQEaUyqVceTiB5aIypvbybuSmdDoZCIaBC6jlWplHMOcr4SQSPPjYgVhsQJgsrJ7Pqxk8Gg\nMxmLSbGBtlHJTAadZ7sMpArh9qoQQiVqXdRSKBmFIuDaw3PiS7udaGi/Fi/VAYZQlKSLxIBCwfw5\nhzUKQJ5DQrgpzmWsyTLXY3FBVEkBgFaIgvSxiiAb9SvS0QRC/cMIDY1gbGhExGbiobASW0ur66S/\ncWQkIvlVtcmExcWlmBUM4rKli3HewtmwlAeQCI3h5T9twPM73sbGgRCCDhv+btVFuGLBAgSIALCa\nEenoxKZDh/C9TW+iL5HCFLcNn1p7HZbXN8OXySKXjKMjk8J3t27Bb44clQAi4PPgobvvx/2fvB+B\n2jJY3E6AHSwjWWVXKB0awfe/811881//DXMbp+Pfv/cYZl+8RqndCdPlwxEAOoorNC3zwMvr1uGu\nuz+KUdKp/iIEgAq2JiJRRVvCqAeIB46R3+kOja4TaOE+Ld7HxL8EJpS6nKgs9qPC6cTM8go0BkvR\nUFGBUr8PJQEqLRjEV/5hLoNoPIHwWBTRaBJj7LJHYugfG8NQnH7fKQzHYwgn4wIJ5nNE0SZ+cYmh\ndZcAyUjNypNXqqCudp8XMasJA/kkhtNxjJHSwxghbThoMNdjvGgHiivKUFZZDn8wAC9dQ1gE5/NK\nsUnqm1Dgl+ie4RCi3cMY7u5DfEw56lRWVeGaq68piAAWqC/GTVGa2BRiTgsC4NdP/xHf/tbD6Dp5\nQtToq6orReGfz8rsWQukc8NAyOm0SPFxeHhYnrO+vj5MnToNJSXFSMRTUhRo72jH3LlzEAgUyV4x\nODgozxjf++lPf1q0ALhaNJqs+OrVl+CqWS1wEy1ZoA5yrinr1A/UAPjfWgAohP5/HmUbIsJMv/Ie\nF4ZyWby4dx/+/dWNaM+oQJ2JGROQYpgwNxjE1fPm4rZly+BNp4AMHV/MoqT/wQUsXQAgn0yJtSnR\nME5+M8YcDvzH5jfx8ze2oYsURYPOwXS3wV2G6bVNqHKXyT062HFUlOVnlU7CnPqpqPKVC/3r8Olj\n0v2fXDsJZYFiSba5L6jEh+u/SrBZpKBXfVt/F06d7YDH48SKxYuweHIjLBRkHQ0jHopIjEHHhzOd\n7ejt6YHP5RZKAYX/2K0+ceKEwP/JZ6b9H3VSmNz1Dg5jIB5FZzKGtzpPYmvPKYxIZ5rPrAcXTZuL\nWdWNsGdNMBMhwqaGjK/Safmgl+pP6x6+SjSzDjNahzux4/h+nIx3ozlQhyumLkSZyY10ii5bhrYQ\nOfkGNJ7rT9wK7O48jt1nuY7mUGELYmn9NLFQZBwet2Sx60wrTkcHEDVnkGN8mbWI5d15n/0XFM85\nHzkiQcmDzivKKbvR2kFmolYXYzE2FFQndBwBoDvkosvFPdDO5zCFXNosbg5MdHymFHJ9nTj4p2fR\ntv4J2JMjKKYlnt2De85bhYumTkeJ2y40LzZMRGnNaExMHEeuWdIt/0AEAG0qFdU1b84iarXj7e5h\n/H7PHqzvOIwRmx9pfyUWXP1XqFl5FXKl1UgR9i+FTKU9ICKIhquQNKoM9C/Pg9QjpRulrI4ZjyhX\nMurr2OCw25AQN6WMuDO4bUBRNoF9zz+F08/+Ehjtkj6/F8CysmbMr50i9pe2rELVcObwi7oNI6YE\n9p85LvoNLZOmYEnNVHiySgTw3NfEn/CcuBZTHPJw3xm80rYbEcTgRA7THTZ85ZLLcUlLi4gBKxiA\nRhUY/p5SACDiSX2SOW+ByepEGhbs7OrEPzz7GxwKJ1jfEJHAaC6PmvoGfPffvovrr79Oocj/L3rx\nHjPR37Jli5z1vffei8cee8yIr97LZ2BLWBn0AAAgAElEQVRzls10VepXvyOi7JJL1mDHWztFAszn\n8ApaZ4B6aWx65oErL71M9pdVq1fDSkR0nrlZDsjG8S9f/Vs89tjPEKLmKO1XV1bgkW/9DcqrXIh0\ndyA8msSr2w7haz/ejLMhdce++MUv4OGH/xWR2Ji43FRV1giir69vUBUAdHLPwFqSRCOwoQsAlfQ1\nlIEBMzvUMmmokh6LSZc3UByQ5EjxoiCBvPZUt/EBNRJu8ZM2lK3VD8mfJ6xdCVvxxWSVwbBO4giz\nVXBwZSHIpEJzjVksYJVFw+5lodecFzk8H35lU8dFSie6TBrZDWcyzXNmIMwkgp1xwnrI9WKCS74x\nA3o+rIR5aegsj0P1bgoMseAwFqJkDWRs2HVkIsgOIhcIn4ePL7mEFPWKSEWaaAYRKsukEQqNScGE\nQRDPXRcYeHw+UJFoTJJxJi9MDLRvOI8pQb2JYtukByjNBO0ioAoBysPZ7VI6BBparAXAxDGB9AsD\nLcEii+7oi9o9rZv44Go6B8VMDM92PdZaWEQpluckQRJ7SBFQjMvkLQhHSmGBtlBETdgNaxTFcebf\nCDzfgDLrNWEcRpOV5IYbh/ytVHzHq74a9SHQZaMarBXn5e8MFwUeV81x9XkancHP4Wfzd9ryh/96\njcSWxxL+GsfKSEAFAWKcO+8L760SujKLECTvDTtMDphx5vBxHNt7CPHhGExJVR2mwMvc+qm4/457\nsGzBItTU1cJMH/MiUgLYJSbHVaoJ8gf9AwN44tdP4ne//z32H9gvHVr9cgQ8mNRUj8nTp4hNl8Vp\nE+92xlv9Xb04uu8QkoOjav0ueC8r7HBJQwkaDV0BOgywq0L4/Gg4rKxr7Hbh6rPzweee80XRKJS+\ng5o3Cn2hBTcFiWHQBXjPtcODRgOIq0IuK8dSzz1FQrk+5CUh1bZ4snEaFqWCRCFvjtB5cSAxCbpB\nEjGq1VMoMJeHz+1GPplBwOeThL63r1cKKpMaGsRZgAJRZ3u65XOpa+B2KqFTFo1Ix2GRQMP5uQaS\nBqVpIRPnEdExVIETQcFUGh67E5OqakQmu7Q4iKnTpqF3eBCvvfG6zFUWrPgaCRGRkIPb65H5xnPg\nF2+11hrQtCa+Xzuz8PmVcRclYRVQaEcDjabRyJwCkoBewMZLNBdYvKCeA22VOLvE3ioDr8cnHd5c\nIou2Iydw7MARJEJR5Og7Y+z5KlJS+xnjaskv00CZyYSpbh+avT6snj0LV61ZAX+5H8lEBFu2bMWL\nu/dhfXuvvP8TCxZJB7GiqlwVe3p78Mqu3fjhjp0YSCTR6LQLAuCiKdPhTPIcM9jZ04Uvvrge74Tj\nkkjPapmKb33561ixchVsFcXIWtlhIeJLrfcMDMNDffjy57+Ap558GisWL8d3v/NvmLJ4IWCnDaOG\nHho+0oURGu/k6B/JfkI/awbViSS+/rWv4Tvf+64SLpvwYiBWEFUVDZisqP8qr3o1Xmz0NNbVYNa0\n6agMluHo8ePYffQQEqmUjKtmjNKLO53LCYefSiRl5F07nWgqKUFNURGa6mpRXlmBipJiVBT54SNy\nhvZ4SgGNUQaSsShGYwn0jkTQPRrC2ZERDCeT6BoewVg8Kec2lkggws45XTG4F9AClHNZySPKbVd2\ngLTxMgJ2BWwXxAU7hBlzHknkkOCcovSC7MFiwFfoQtIdwOPzobi8BG6/T7zLSYEhYiNNiprTLnN5\nbGBYdCVCA8PIslAvriweUf9nQHTtddcWbABFzX28vywBOVXq1r/6Or7z8Hexc9Mm6VNSwI9WTHab\nSzr8pBF8/vN/A6/XgRMnTmLzlk247rrrUVZGa7cM9uzeI3eVugOMTRi/6D1Lio50HnE6pZDw4x/9\nWNZnWuF9fOE83LNqBepZLI1RlyQDi80mgmLipvJf2OB9kA+5ml46gJQd9z3KZR9KAVAL6wScrSpI\nKO2uLHI2C8xeL9pTCfxs8yb8futO9CpkPpIM5JFFAMCKykm4dclirJk9He5sAhYKD4vuLSvQE4ob\nE56HAleYs9rE7qCyeyYoI22yIOlw4uUDB/HIqxtwOBSTgoPFaoclY4Lf5MSU8irUFVcgHyfFKoMz\nPWfgNJmwqGEamqvqYbc4EUsm0dbTgVgihqbqOpT4i2TdN0DdcjacV0RbcU8cjoWw+/A+2T+mTarH\n4unTUOdzwZXPo6+7W5KpSdW1oBB2e2enBMtFXi8aJtUbCNYMTp8+g5GRYZSXl6O6qgrlJcVyHofb\nTqM3k8K+oR5s6ziBd0f7Qek/D6yYVzoFy5pmoNTuFmE+2fzFgpXFgHEr1vcsKBOWKEEaaVFFswkR\nSxo7zhzCrtMHZEyXNc/DorImuNOkCIgnsuj5MB6TLzYd3E70JsN4/cQBHB05I5aB04sbMLeqAUV2\nF6LZJPqSISkQdMaHkHfZkOKx0lagZQlWPfjP8DXPQ4xaVkTtiDo8YDNZkU6qphr3TC3mK3HiBK46\n11Etwqy6o2o9yVuV24g5Z5WiMpMmL5PhbALRU0ew83ePY+ytDbDno5iEHM5zlOC+NZdhQVUNvGzx\nOzinTciw+E2tqGSy0I2nwwvHd4Ia5XuHmMUBE/VurMg7LGgNj+EHb7yOl9pbMQYPokUVaFpzHRav\nvRUoqcJoDiCKIiMTTCFbzKI3wy6/QkiKeLRR5FLFSqJWFYJUu1EVToL7CjcFwuetZlhMOXjNWZzZ\nvgH7f/lDoOOwrHA2pDHbU4slDdNR7y2DO69oALoAkDXnEDGncbDzJN7pOomqkgqsaJwNH50XDHrG\nRAyR/nyNXFAFEDO6U2G82Po22uN9yJvSKM/n8cC8BfjE6jUodtiAVHy8ci2FFSPhZQGABT5dV2d5\n2+JAfzaLn2x+Db/buUdQADm7AyFaUiOP+x64H9/45jdREigu5Jfnzv//jt9z7i5cuBAHDhyQ+/zc\nc8/huuuuKyCYx89Zr9U6TlBBBx8puhY98cQv1TrM8N5qxerL1+DSSy/BqmUrMGNai+SEpO+q1TuP\nfDqKX//qV/jqV/4R/f1Dgva99so5ePDui7B0Qb3oRYR6BpHO+fHy1qP4zDf/iLGUavL++snf4oYb\nrhHNG85DNnpisTT6WQCYvHa1FACYELH7ryHcWuSM4g+0kmJyqRN7wvg135cdYzoBEFpKv3d2uDQk\nXWBOtHkxbCCYROluLKviTKBIAXBShIgbJzmJDDiNxUQ8yVOKk6z5q/wZA1jpcDOQoKWLKFdT+CqN\nYHHQCIqpHq1UwZnQM6EYHR01hM0UpYGJhurqsTNok+RaqpKkPExUfWf3QwRVDNiT8KFp76PEuiZC\n4DhOokBueOWQB69FCbVQH8dZFOyZ8KcUXJjXIXAqw/eeY8DrVN0DpcbPcdAJq4QDXECkWEOeNqtN\nKiFTegdq3LiZ8x5xQyYig4kCk3QN1+e4cbLJ9cfi8h52LwtQ67Tif/PvtD6Brvjyswl/0xQGnpu2\n7+NxlSe7QlmoZEWN97hNJBd/A5FhBEgC+SYMlclOVkGXWfTh37FbybnFhIibOBMiBq76eEIpMawJ\nNSVFJ0QcSy1WowX+OGd5/3TSxWMyydVUBIFEGwKLLOzIPcnR1WK82CKFsHhcQaoNSza+j57ptMTJ\nW3JS2c6E4gj1DOPo7kOIDEbEDJRFPR51clEVrll1CS5duRrz5syBr7oCqCrh7qr4foZQpgizpdPY\nvWcPnn76abz44ktoP30GGd1VcZoRrCxFoKwY5TVVKC4pkSD88DuHcPbgcVlw7CYLyktKpYslI89d\nwAJ4S91onDoZvtIAzA4bSivLZXM22a2IGxQdzmnebz577L7IYmKMC/m9im7B+aUgTxqpMnEh1wUa\nziXpWJMKwGdElP2VNRjngHYLITWH94UFOq0BoAuBRAelYkkJLJiM8Jis+lsyecyeMhXLl12A/pEh\nvPHmJoxGwmhumQa3zyfdnS4GfHQbYeJmskjwoQpwCSXKKd7lSqWcaxLnmBZA1UU6sQy1W2R9RDqD\nqpIyLJg5B06LFZPrG1FXPwlH2k7g+ZfWC6KAx6QAICGuhl6y6GTIumB0BSaOm2hbGFaNam1QtCet\nAaC1L2QNMpwTChaOQuVSKBQplFLIM5sp6DPw/0VgkEKvTPHMNoGHHzvYihOHjiE9GhOhqoI5vAh5\nGTBHC9GuHDMrEqGkJKmT7E40FxVjXk0VLlu2BDOnN8JpN+PtnW/hhZ178MzxDsSzwC3Nzbjz4osx\nY3KjdECyg/14fvt2PLbtLYxkMmhyOXDf1dcIBcBBfnBoEK8eb8UXNr6JPgP6fs0lq/EvX/0nNM2c\nAfjcyBhClEr1mWtiHu0nj+KBe+/D9q1v4+bLb8A3H/4WKmZQV3pi0v+XFQCMUA+h/n7cftedeG3j\nBkU3MfZ2rYxsmHfCKqKjav32elyora7G4vnzsWj+AjTW1yMWjuL1ja9jy/Zt6BzoR9K4/4T184vj\n6TZbUOnzYnJpEC1VFZhaXo7J5WWoCRTDTx4pnztNeeDkicUFsXe2rw9n+/sxyA5/KIrugTEMx5MY\nTDDJymEkEZOEnS8+/+/VJlBPqkYcqJCFKC5KrikUG5Nqhv5M2NTKrQoF/Cocz2SRsaHEKAciTyix\noTDPpN/mdMAfKBJ4P23T+BzZHFZERkIY7R6UAgA1AGQfS2ewYP5CPPTQg7j++htk79KRJkEAqkDM\n5ykrooVnzvbjke8/il/99KdIJogiUJBbagDc87GP46LVF6G0tET4j2OhERw8eBBLly5FZUWlqJav\nX/+SPBfXXneNurZMFm2n2qQrzC9BBZpM+NnPfobPPvRZZQ2KPC6tKsdnr74K84oCcBFRJ17zZgms\nuX9LJ+h9iwDj3d33D3j/3xUAVAdYUbzkJhBhyOIUnW9sNpwMhfCHnTvxxK69GDYKVYks+f4m+JDH\nRdWNuHXZMqxsboI9GYXdymSGcQWTfxY2KHb456/xAoASGsuzoyycYyDn8WPriTY8/vIr2NQ3KJ1/\ngYHDjApHALWBUuGkx8eiSMfSCHj88DjsqAwEMSlQBreV9s4ZWp0jnKSuSg4B8mRZpBO7UuOMuH3a\nbEjmMhiKh0Txf2h4ELOmNGOSrxj2RAJlHhtqK0rF6g+kHhr2rixQlZSWCsUsPBaSIinjSIW2VAkd\n56jFlIXN5cZgOoeDg314pe0IdnWfwUA2KSrs9ZZiLJ82D1ODVbDRL5xFIeGc/wUFgAJNSBUAMmYT\n0uY8hvMxvHJwG86O9qDGG8QFM+ah0VWKzGjMsMkcLwBw7SdNw+yw43R4EOuOvI3BTBg+2DCnYgpa\nSmvgttkxlonj1Ggf9vedxkA6AjgtSHOAnUEEV67F0jsfRMpfoWz8eAUmdsCzkohqZCv3IO32pTRp\nFH2YcZysK+Iqpe4N38fpmMoRuUnnJNLdWDTNw+O0wWXKwxEdwvCB7Xj72V8hfmIvvOkQZsGGK5tn\n4Ya5S9DERpqd8TonLB2H8oC48ygUrNJJmYAAOHeaEr2QMcPk9mAIOaw7vB8/3P4GWkl9svoRmHsB\nVnz887DXTZGCbyyXQcZOHAqRD4r2xyKZLgDoWHOcDqAQhLx+riOaiqebJHzylURaXlAVLH743HYM\nHNyJw0/+ELEDO4E8i+BZTLGXicbDtOIaePPU5BF5S5Uc0hLaksGJ/rPY3nZEmo7LJ89BlbtYhIA/\n6CU9fYOiwELeCFLY1nkU+3qOIWpOwJvL4OrySvz15VdgXnWl2BKChTwREGQhUX2+rL+s7LEoK40Z\nXpQNKYcDu3u78b3fP4M3R8aQpSOK3Y5oOoWqxnr86Cc/xtWXXq7mQ8E9aeJ694Gn/n/sF7y3hPzT\nBnD69OmCAtM5j85T1clNXBUNSohoP+Tw+P94HF/60t8ZSFkTrr7mavz7//gxqsorBBEk+w4dviyq\neZPPp/GfP3sc3/32d9BxuhvpfA5lQeD7D38BN18xD4nhMzh95AC6uoZw9FQEz73xDt4+mRLhwNtv\nuwP/+PV/QkVFWcG+nTeQRTc+b6amay7KMxjkxkoBPQoEaLssqpyzAxYaC0kiWhIMysLHAoCC4hvd\nXgr2SLfRWBgpoGfwdzkQXCj1Q6Fhr7wwaVhk2BFmoKs6gYoPrJAI5K+qQoNSu6ddFqkJEwsAihM7\nXgDQBQY5/sQqqoEMUJ13bRnGoodHkkBW7pxiUUgBpIQE1Lw+JsPUIdC8eXbvmRjye3YIWBhglYUv\nJrwSpBt2XyIYFlW+27wGBuSFBMhAW4hqqvDa1Xu4yXAMeHx2SVl0YXGAP1PBxzhMmd/z73X3j8HI\nezroRtmPvA8pMBiJvOYFSSU2qxwgtIUiNwx+r7vtmout+Y/8G20XyM/VGgra/533Uh+X914Sf+P+\nT7z3PL5WYtcaAOIYQJ92w4pMF4wmCggqdIFa1AQibYjOaJuViXoCnNNSRBJ7Q8XF4ryUzUfb64hX\nvFJG1xSV/6oAoMUINaf63AKASg7ZDaQ+RQ6JTEw4ij6XD7asFYNn+nFs/1GMdfeD+FeXKO7n4Le4\nsGzmPFy7aAWWLVqMqecvBiZVAl6rqrrqBUXxZdDT04vNm7eIUOCGjRtFMI/JsgAG/FYESoKoa5gE\nb5EfJ44eQ9/Js4J+Xjx7Hu6566PY+uZW/P65P4rAE7dqi9UEF5VZbWaYHRZMaqzH5JlT4Sj1IZJS\nwnrs6OskmLoOupCkFilFEZpY6ebcmljwIcKIzy/h95wLgi4wlO95DHEWyWUNnpJCyKQMXRI1R6l0\nTisnhSJhOZWKuLy/aVCwz4JcIoXmmnrcd9udmFzThCM9J7Bx2xac6eoU2D0hzqPhEGJJFh6doAZz\nJskEXFXwHRRapIOGUcTjM8m5wjlyrtcwA9uUYXtlzmThd7gwZ+p0BFxeLJw9R8Z+8663sH3PLqGu\n8HoSPHcnrYwUikp3MfW6JfojpKdYlQ4Dn1uuIbxu7aLCYoIgMYw1RNv26fVTFRQNepPhaqFEXGlf\nqOg9SmjRIsUKUzqPk0eOo/WddxEbCMOUsyKfzst4pDOqIMsN3UREUDYFl9eJ4hIfvG4X+rr6kAvF\npVM42R9Ahc2CFXNm4JqV56Ouvgadx4/iue1v45d7W9ETjePCQAB3X3opLlqwAG6KOIVG8fLOt5QG\nQCyOBocV9199LS5pmQ17NoPuUB9+u/MtPLznsCQKFR4XPvPJT+KT998PX0U5QGSWCEuqAoVAwi0m\nHDywC/fecRdOHT6Jez9yL/7pO9+Cq6xEukXvUU87R4VY71M6wlDOGCpzOXrgAK67+SYcP3NGtmnF\n31RrqFhtCRfWJPZj01qmobGuDgtmzcCCmbPQVDsJ8WgUO1gQefFFbN65A2FSPmg7RbtSI/GfbHOg\nhgJnFVWYPbUJdXVlqKuthN/rE6cGAQyzuCzZVBbJaAxnh0bQ1teLjpERnB4eQU8ojIFwFKFoHBnp\nuKTlGde0AykwwAI/i6kOJ3xuJ4oDRfC4nfD7PEbRVhXB2bFkEV2t6dQISUvHk4WESCqJoZFRhBNJ\nDIYjgjDopeWeYeFG/jOLAlntP11IgvPigsEOucPthNvnQWlFmdD9aAE42DeASCwi2hPck6+84kpJ\ntimyNFGFWhcAZO80AyORiNgA/vuPH8eRPezkp2VPZUja3NyCv/38F0S9f/36dRJsX37FpaipqZY1\na3h4VOD/kYha60pLg0r52mqWYimL9ixE62bJunXrcM899wg9i4SdyQC+cNXluKZxCopYkCXig9NB\n3ACIAPkgFMD/3gIA1zxZY4xOKAszJrcTCasZh4eH8ZMNm/Gno60gdpOoDl6fI0/eMbBmagtuW7wY\nC2uqIXjEbBJmBwsqrAsaHd6CP/17Y3K5TwanRQrh5jyypPlZPdjb1YMfvPQytrR3YsgoQpFaUu72\nY2ZpDcq9RRgOjaGzq0s+p7miEdMrG1HmDYiQ5NDQIBLJGMpKS+F1e9U+ILbOqmEi1rxGgsku2lgy\ngqNdp3C2uwtzJ0/F/IYp8EZTiPT1weu1oXHyJNTU1yI6NorOE8eRjEZQVVOBmto65HMWdHX1oL+/\nX+Lj8ooyaTJxDT1xvBXZdAyBqkpEnB682X4KTx/ag9booIy6D3YsLp2CVdMXwMtOc4o0SIOSIMhp\nVZj5IBC0YimpKJbPUIpuPB4rDnSdwBtH3kYyH8OKyXOxsKkFrrQZidEo7BaOpCoAiC0fEbBOO+L5\nDPZ2nsTGrsMIIYGpjkosbJiGCgfdDkwYziSwv/MEjod6ECec25JHmivGpFloWXs76pdeAvjLkcoq\nfQGYiFpiXKVoq3xpmi73Gl0AUHx30l9VQVrHboLMINKE8b/VDmuWgtlZWOzUhEoI5qjEaYE9MoxT\nW1/G/md+ClPfCZQhjyaTAzfNXoYr5s5D0JqGx5qHgwUA6aYrqBWLANIM+K8KAPydw4Oo2YZt7R14\n7KXnsSczhH7O9YrpWH3P51C+7CoMkdcOJSiqCnoW5USgjDRk/dCxicprjIRP4kyektKv0q/xPYbx\nsXo+hS5BrR63A9mekzjy2x+hZ8vLQDomFIcqeHBew0wsqJoCX84mOgDUTZGiLOeFOYez4SFsP3lY\nKBVLmmZiSkUdGJec6wAw8TxkjvG5NFuQsAJtoX5sPLgDXRiCE3ksdntw23nn4ZbFi+AnkT8Tl5g7\nB8a3hn2lURhW1DO2Q7mmWZC12BDO5PHkpi348ds70UPAqd2FcCou1nZ33PMxfP/h76KMjSrJHY2k\nUPlivHdB+W/yHZP83/zmN3j88cfxjW98AytXrlRr+4e8NB4ga1DBqQHAfJH77aTaSRNQWnkkWGhl\nQzxvQnh0DI9+/wd45NHvIxyOC+yfn0Zg+f33rUXzpApkI0PIR0YwOBzHj58kkhKwFjlwzfU34Wtf\n+UdUV9VJk523K5VUZ2Kj0Cqn8pTr1uQlCWRATcRPJi0bHYPdJP2hmXDa7NJpFpVzQ7CN3U6qpJKP\nzQ2TXVUG5QxMJTEmn8fnFZEPdm6ZuBUVBSShl2RMkg5WGTNSKedLEANWmwo8qCEQUYgCnaCy0yoD\nI+4DLCykVdeP1VTy0WjxwnMW/rtNumsUXRDov0CMlRcnFyGeo3STDfV3HpubPI9BYSweU66JyuHJ\npHTGNY+ef8fv2bFkwmonFNiA+XMsCJemEjpfFElkcsxrUkJ5Zrnx/BxCgu3ssLJrbajgK3i+RYof\nIhgoBRK7JAwadaGDID78FFBkssAOKY8xMjwinTkmGUocTSVhvG5eD+8DaQ+859zQhI9saAhw3Pj5\n7DCKOiiTCIoACn7VSPAMzo5OoFXxgUgMxRlXyUpO7hM/j3xo8rm11aRGDEgxggkIhdOI6iAHm3Bq\no1PJjgnPn4JVvF+iRWF0m4nk4LVzjLXirO7uy1wwRFe0OI1GGRA+zs9Uc1DNB14vv9fQdo7lRAoA\nC0CKHkCUhbKcVImREk7Uooi87+Jrb1AYBKVAr95kRDiKbpcXtrwVuVgW4YExnDrWhv4TZ4i1FFgk\nr9eTM2GaJYirLrwIl15+GeavuADOlnrAqy3OBPahdh3BDpnR3nYK6196Eb97+mnseGsHzDYzsmnF\nAXYGXLC7nAJPT5CvnQHWnLcUj33/BygOFuNfH/4OXnx1I0bCYYxGRhQ7gKsLRaHdNlQ11qK6uQ7e\nkiKZ57zPotHBYh0hb3blDMFgiHNHhCiZoDOxNZvgsJNOoYo1hU3PgK2zYCFz3eC/C//JapX5wHnM\nhFmgi0YSrPnwnAPiOkAxOwpusXtvd4jAGLs/JS4frllzKa5dfrl0Kk6He/DMi8/jeNtJeZbEezyd\nQpjqww4HXPQ5jyek0MD7RwEoXpPWCKANmSRAxrzkPGVRjq+xSAhZs5oDRHN4bQ4pAJR4/Fi2cInA\n1V/dsQVv7dutOmVGwVM0FogwMgQCOU6KM5lT40uqDZN4AyWlBVj1HiPPLLsKxpcuLPIZ5DzWooxa\nA0DpLmgtDNJTuB5bxAse8bTA/Q/t3g9E6BXFxjLFDPPgZgWzDXB7YfcFYXdRyJXWqhZ4PRZ4nVbE\nR0YxcKYdpngSVVYbKmw2TCsN4vrlS3HpBUuQTkbwwpvb8fOte3FiaBR1JgvuvuJK3Lx8GfwshMQj\n2HRgPx7d/CZO9A+iwgx8au01uHL6bOFSt4304acbN+LJNgpTAaumz8DXvvwlrLjsYphKgvIMSBdb\nKMaqCMS5eWD/Lnz63k/i2L5DePTbj+Ijn/mUAf8/J7D4kAIA7wcDVH7IOzt34rY7bkdHZ6eMPT+X\niXVZMIgFC+Zj3vz5ovtAtfiZM2aghOstCy7hiAjGEbHz7Lp1ePfkMUToSEMaRRZKYM3pQLXbjfkN\nDThvxgxMralF0O+BKeBSPAuuS7SYgxmjQyMYGRhEx9kedAyMoK2nRziEg7EY+uJxqfyzWKIFA7kT\nBb1eBP1eNFRWoipYjFKPD1XBIKoCAZBy4LBzzrFAyiGl6KtSdxcHDAOdRkEwfhENkOTvJHkzIZpI\nY5BrSCqFtv5+nBocwLGuDvSGIhiIJKQgwDPnS3PLkwZuTvZ+M1AU9MPv8yMeTWB0ZEzEAvkKFgVw\n2WWX4a8//WksWrRYwc4lylZCipoGw/PtHRrE65t3iAvAkX17YZbgWFG9+Lrl5tvxz9/4Z2zY8BqG\nhvrxiU98HFVVlTh27Bi2b9+BuXPmYtHiBfLe9jOd2Lx5Ey64cBmmTJki65eIiWazggTYtWsXbr75\nFtEKcOdzoPLCHbNn4qHlq1HJu2Q1IUuOMlt8gpIf50GPx4rv3+0a7x29XxD83s6SvKPwIyPMFD9H\ng5vLOyTJCgsR9OS2I+lyYl93h3TgN3b2ImyxIZGVdE/mDOfjZc0tuHHpEqwkUocaO/EYXG46Qajj\nmtkdVkhoRQop2PwZt0eSA34wkXB58QKPW2w4PRbHo88+h9fazoAu9KoECpQ5/GgsrcDU0ipYsjkM\njI7IPHDAihl1zWguqYXH5kLP0Jv6LZYAACAASURBVADau9oFhzKjpQUOk0269IwjhRZmPM8sAHD9\nzVhNaOtqR2vHSQTcbqyYMQfz6xpgC0WRCo3C4bKhtLwE3iIvouExREeGxQqwKOCDy0VHKafYS/Z0\ndwsN1OejsKZf1ui+vl7EkzGYA34cGhnGy0cO4s3eDgzl0qJFMMldgtVN8zCtrAapaBxuh6Jijt8y\ndpI/ONHhmOrf81qYoI3Zs9j67l68030ERVYPrpm3DHXeEinismDGZrji3KtijjhrOazoiY1h07v7\nsC/SKQWC+cEGLGqcLkJxqXwO/bk4dhzbj870gAhDChXX7IJ7xbVYsPY2OCrqkXP6AavLED/luqDW\nXYnHZQ1mIUbZiCqqKDuNKqbXtGLtGKZjNN4z2cezFO5Wn6vssgGXhcJ4JuR6TuLES7/BoT89A3t8\nEEHkMdtcjBsXLsHqpnpMKS1Gju5ITK64qPKzyb9nflCgwOhxHgfDsyOdsrlxKprAf27bhudO7EO/\n2YyIy4cpl92MxdfdiVF/LSLUx3ERXRoVmpG4NxgpGw8vmmFyHeP0PI6H1hZivMS9+Nz3MN5NJpXj\nDy12ZUxsFrhiAzj0u5+g7aWngfiY6G34YMX5dbOwrG46inJETCjkoiB8DO5Yb3QU73SfQvdAH2ZO\nahYNDRZ6zVy3J1r2GQtQYS7ykeYzZDFhIB7C9uP7cTBCud0UKpHDVS0t+JvLLkMdtXZybDpy3Sc8\nXRXTWQ4wGUUAQfsYlBXk2eQBDg+O4OvPr8NbQ/0ydixkRXNZ1NY34JHvPYK1a9dK7K9IY7K4/7cu\nAAh9WAQcDTt6g3asm6Tj6/v4/00kb/F2sYtPpxK1fstDVHgzLUXdTg+GRwbxyXs/gZfWvSBLaWN9\nMc5fMA1FXguOH39X4pLRkTBmTp2GW6+7Br/89bN45rUjSFqsuO+v78eX/v7LKA2WyiJNFB/r0tRT\nYWPb7WbsbYJp2s2X5wWGGktI0smOLhW0R0MhZW1lVsr0TAJHR0fkYfV7vcKNFl47fblzWRR5fUqd\nOptW0EeHU6BmsWwSVNanurSIZNE3WIIfi/CM0+QgpnSXiUgej1IBT6QkAPYV+SUYJ72ASZjDo/zY\nOakTkgSkFX+eCTaPZ3S8xEbL6ZKFl4sOFznhaxtWKoQl8vwkKUce8XBUiYYZyTMrfcKJZGJqFAOE\np8jkOZcVDQAtUMbAgAmKg+clAUdaYNmK1qB4eAIvj1IzQcGjuSjIpsUOPiHHhngXu7jshBR7/fIz\nuQ/JhHTfVAHDKmI0HHsWENihYDEjzeqhjZVUah+lZSHkNWjxKem4s3pJz/Gssi4T6DWvnkmZWDZa\nRfWerglMJOT4DqIuMgLJZ3GFmyCvKxKOSGJPkTXCOTmhhY8cV51Vi4sVLCCfSCk+pyGEUkCCGIr+\nGm7NDYO+sOKakCf0XxUeUgKbs8t1awFCFl9k0zC42hxPrW8gHQwHgbQKTcKXFq7UXWrOK9UxUMkb\nX+KUQJ96zr98XjQtOI+L/EUFEUQK0Wm6CIsknFdEkOiEVDjsUtiwquIQ1eHNVJK3iSYAbYm8Lo9U\nQUeHR9F64AjGuvqRHCMlIA9bGvDnrSLmc8nCZVh7zVqsWHs57DMahUcpwaRBLVEBnuowsYCzb99e\n/OqJJ/DMH36P/v4BuSaLTW1MIoRESkI6h5aqWnzrm/+Cq+68A7lwGNu37cDJU21S1dyx523QtIhl\nRkL/8xTXcZjgK/KhvLoSHr8PRSUB6Wzzi0lsNEHaCKkeqmPNzYjuBYLqYRIrEGK6h6QEpWClfoNs\nIIo4IxB10ZhQWhtSAGAH3ijacQ7yvYLsyGbVnKT4qFhNKogufyZQw1gcMxom46+uuxFVpeV493gr\n3jq0TxTb2fVnIWT5giXSod389g50dp1FRaAEwZISdAz0YCSkCktMlgWFRD2PSEQ2I66DfHaIiOJz\nyGsht9TESirfzzXC4UJL42TUlJRjyex5Unz5w6sv4a139iiKiNMhEEehNfDkjcBJdweYlIvThxRB\niJXNybpZTHErpwNDQ0OytvC5leKdQYORYp3hqKEdAARtEE+gyK+CVVI26GkeKC6W5D8RTcJjsuPI\n9n04svsAkMxK8MEAR9GMrIDLh0BDC0obp6OosgFOX0DQX7HQIPKxYaTHeuHOxxDq6UBf2yl4M3k0\n+YpRabFgbk0ZbrxkJebPmoKDR1vxq41b8do7R0S2aNnU6bj/4pWYN6MZSMbxzpkzePS117Dj2GkR\nPLp9+Xm4+7yl0qHeduIUHnvxRWyNJ5GxW/Dpv7oDD376r1HRPJneNtLR50xStFoW/yzIpRPYs+tt\n/M2nPoOxM334xX/+JxavvVwJRhkFqcJue44I0Z8jAKiErZKo2FgIj3z/B4K6Ydd+ytRmVNTVyL+r\nVq3CnLmzZZ0WcSpObqKB4nF0HT2Gp379NP64bj3aes4K3JMvXmuz1YKWYDEWNDVien0dpjbVozRY\nLLBUENHD86UDTTqLkWgcJ7p70NrRhWNnz+JU/yBGMllEKFhKJWwKJIodIFAZKEJVkR+NZWWoCBSj\nqqYKwWCxdBy4LjlYxGPAbDgSFPjlknjrjFLae+PCDxKbScanYJ+G0JqI/Umn3yxf4XhcOrhEIHR0\n9eF0Ty+O9/bh5OAQzqZT0vUlOD9rsLWFWmaguhgIWmwOGpnKWkqUCIX4aK906SWXForAKkxUXxTz\nY6Gae9mx4+346le+gnV//J3w3NmloiZBY2MzbrjxJtxw/Q1oamqA2ZKXZI70xcHBIXS0d6CisgLV\nVdXSRBwYGMKRI4eF78n38Vz4/LE5wjVqZGQEd951F1566RXYuecgh/OLi3HvvCW4uHkaiqy8DiZX\nKZgdVikMq/jnHPfJcxNA7XzznrzwfUH2hRFQeohGiJmh3yxb9Mb+IMUPiraZYXb7EIYNOzra8aMN\nr2B7b78Ui1LS0jfBlc2BvahrZyzA7RevRlOxH6ZEBGYWBwSxR8E/gwZkXAgLoIwfqDyvu9X62WJi\nIDZtdLZxOnFoaBiPv7EFL+47IogDhZUzocwcEGh/mT8gyMexkRF4bA7UFpejoaQSxQ4PLBlIzDkw\nMoQI92q7VTrxTouhwm6k1RQW1ft+3m5Bx3A/TnW1S2wxraoGc8sqUevzormhGn6XA/FQCCODQxLL\nMPatqasRBAELbENDIzBZrPD4/BI0e30e9A/2SfzFollJWRlyNidOjA1j3dH9eLX1IE5nhfiCSgSw\ncNI0zK9sRLHVqcSkjRhvPC1QzYoPesl4ZlX3nHFi2m3BmdQwXtm5GYOpIcyd1IJVjfPhTpul0SX0\nOSK6jAIApwCbFnmvA/sHOrD56D70IwIfXLiwZhqmlzcIoT1uBo6EuvH26UOIIgqznTGRBXGTF9U3\nfQrTL7kOOZcbFrcXeYsdKSMGkSKhOIapAhvjIOn4G05NjNcYCwmK1Cj8i9bOhDVY6bYoxwY+Y7KG\nk9ZK0WiYxbnEk44ieXo/3ln/FHp2bIA3F0URMlhRXo8Hl65Bk8UBez6DQFUZxGpJOEwMYrhoR8Tl\nwmIhXc+JNOl8QstNI8KECE68dORd/GLXdhxJhpGADc7J87D6E5+Dr2Uuhkl/sjOeVBS6gmMOY0g6\nl4hzT6YgcqwRDuqeKutkaRYJzdpI2KXRrZBBGqFjE8FsE+wOF5zRYZx4+bc4+MKTwEA7TJkE3CYH\nWoI1WFnVgqaiCkEMSPoo80M1bcaySRwa6EBr+2mUF5VgQcssBC0u2P4neW8dZWd5ro1f23Vmj7tm\nJjpxDyQhhgYJUNylUAqltKWHthRoD22pAKVI0mKBkiBB2iAREojLxGWSjGZmMppx3S6/dd3P+06G\n2vnW769vnW+zWITMni2vPM99X/clwjL95zVEVgyNpaPYDMqLYHdNGba0nEI/fHAgjLkpqfjR/PlY\nUFykpEyCtuvTehUpKve6rA7fBFp4f3fEDHht+26s2rNLYj7pduj1hxCJGXHHXffgmd/9FgkphHX+\n7wcA/u3N+n/wg+EgwLmjJAdyaMvlMVDXRhQvvPhH/OSnP0UkEJK0pduun4FHH74GeZkJ+HrjLnz5\n5R60dYVRWFQie+ZHf1+HMx0B5I0ai1ffehvnzZih+jrW3+zNtWE41zL6nHAQZBh59RKysxALRYRu\nwIm4JzEBQcarBOnlaBA9LlF/boAs1D1x8Uj0eDDQ1y+FuuS9GziVG5RsThbkhnAUHV0dYB5wYnIS\nBnr7ZNImmnguCBK7aYTBYREAgKik7uLOhjkwqGLxHPFu5QI+MKBuILOaUltiBimOSfORyDgrNflq\nOqAvSPzinMyJM7uY5alJrzQMGvtAImSoUWdkBxc2usvHuYVWyiaDN72YANJYLBaT6TlvaIIihQWF\nggSdPHVSPgMbCdEpU4ekueqz6GIhT52w3+uTm5wyAgIENK/hwi5NJSnR4YhqnswWJMd55H37vQOS\nncxmgseDjAwyI/haBD5ooMT3IwDA5j3RFacah2gE3YypsSmWA4EX/k4kEITDalNsDQIVPGBkF2jx\nbXwNnh8uyAJS8OJhcUZ0l8fG6ZLmmBsmjwGRVwIZFqddUYk4DTabxd1ZNoggc5HPRdFwsXTYaF7I\n4+vT9GQqGz0UCGDSmDFITUnC8ZPH0drRAbPVDle8R4Adng81GVUbj2jONNmJfA05hqqZlAZYM//T\n4yl1XwROkflcaTJpOKflUYuub5hmSs/o1ZMCBOSRzYNGdUpnJsY3URrcKFdcshJ4zXBCLdIayYZX\nk1mdJib+ClEgOOgXb4Cu5jZUnahApJPUN5XpnQI7Fkybiwd/8D1Mpx6V/H3hip07ljoAwO9KJ1kC\nUTt27sDy5cuxZcsWeb8wj7+Rw0+T0MFYbjB+8JUVy2HhJJuNQDSC/bt24cc//QkOHj8itDwuS6JV\n002b7WqNMrvMSExNEUCAGl7qefU0Bomti3EWr2mMQ0EpsPUYQf6cry07liaHIcNDPBisynyUjSqv\nVWHQSE55QO5nO8+Z5vHA7yVMAPHtYBmpovTsBhNuXLoMF85ayFAfPPfSH3Gitgq2OOVtkuZJwgO3\n3ImReUVYv/NrHDl2HFMnTcZgwI8dB/fibE8HgkHFUCJoqYqZKHwDPuRnZmN00UjYzFa5l1mokr5M\ne9um1ib09PZKE87neexOTB83Uda9jXt2CANAmEJ2mwKXvFRjRyXiUwBDGitxWsE4QGZrE+SjgSZB\nRItV1kxhCVF7Kt4nKo5R7gPNA4XXvE4v1aekvNf4+zwd3r4BiXAjqMHiwT/gR+WRE6g5eFJYAGQ6\nkB5MjScd9eOyc5E5ajzyp86DI70AjqQsRQll4T7QA19nE7oaKjHYWoNwbxs6z9Sir/ok0q1u5Dud\nSEEUk/Izcd91VyIxMR5//2o73tvwFU56/RjhTsTd82bhuoXz4IhzormnGys+X4+1u/bLJT27MA9P\nXHU5Up1ubDx+Cs989hkq2dwmJ+K3P/05rr76WpiSEmBw8xrR909tSyWAEwO+Wr8RTzz6GOINNrz2\nxuvIZeSlTcuoG14L/Q8AgKrjuDmI8hOBgUE01p+Rz5memQF7PEEINj/QYghDypWa61VPL+oqyvHh\n6g/w8cdr0dFNlaWi4rNRHxkXj5k5mTh/zChMKi5CUnoq4LTJ78t6R+ZO3yBaW9vR2NqG8rp61LS0\nosPnQ0cgIE00p/AsTzPi45GTlIjR2VnIS01GUVY60txupJErqINNwrTTGvzhWdjDj4E2eT+nV9cO\nlv4cnX2kHxdV5w49hteZ6v4BvJEYGnv7UdbYhENnGnCsvhF1nZ1opfu11giyaeKxsVpNCgCIsZEj\nhT8mucdkANxzz71DUkEdABgOKPcO+lFaeggvPPcsvt70uezNQlM1mLFo0YWYNm0GZsyYjmXLlgqu\nceJkudD7Z0yfrmoKsxmNjU2yf+Tk5MiEhK9PcFilaKhmTW9UfvLYT/Hss8/L/mZGGIUmOy7NKsC8\n3AKUpKcj0WmFK4ERcWxSZVSoAbjaAdMAlKGDpzXiMlHXJvi678a55+ithQ5/cPo+DACQpnvYv3wa\no/6sNnRFjdh87CRe27ABx0N+MeZS0V0GmGJRpAO4qWQK7r7wIhS4nTAGueYEZMigry36cdeRDJpA\n8mYQMzuNrj70HCZJRAKIuV2oHPDi1S834b1DJ9DGPU7AbEZZ2jEmtRD5CalinHiythp96EMq4jBn\n9BQUpWaJiS4n8NyzPQkJUmcYTZSE0oz2nBGuSA7F9E5Fm3UM9OJQxUkMDPRhZGY2JubmIT0KxBuB\nnLw0pCYnor+rR5r9hvozSElNRSGbHJMR3R1d6OrsRnt7h9TDI4pHICEpAWcaz+Ds2RaZf6Zm58Do\nScaWylP4qKwUB7qahe1C7X+JKx+zi0qQ7YiHi1FxAk7+47T/PwMAMv0PMy1GNnD0GYMoba7AnvID\ncBjNWDjtPIx2ZcFMo0SyEyWOWxlqincUqb5mMzqiPmxrOIU9DWWS7pFjScacrGIUJmQgGjWjGyHs\n76zDseZKBA0+Wc+C3AzsGRh938+QN2cxomQScOLJZClheqsGWJo+bc0YzgSQ+0TraxQzUsmDZUAi\n9ZAClXhEzskxNXBJp9BHSCQ3I9FqgmOgDad3bsTeD96AsbMBjtggxtlcuCV3EuZnFGBEokciT2PG\nIGJmbS3mcCcWFl8HNgBcV2Dm8KofVqcNMU8CDrZ14TcfvI/tA83oN3oAVzqmXn0LChYtRTAxVTLs\nOVzUa0pdBqiAdqMwogi88Dvy+8mwQ7smlSmyZsSogRs6ACIsU0bwSS0YhYFMaHqNma1IMobRtGs9\ndq5aATSegiHshdVgQVF8GhZnl6DYkyG3OFnWjOjkvchDNmiMoLq3DQcrTsBud2LWxClIN7lh+3cA\nAPEDad5j0idJHYkY6r3d+Lz6MBp9bNcDmOB04btTpuL6WbPgFuml7iQ9TGPNAZVcDeoaP8dMiiFo\ntWNfQyue++gj7B3oQ1CVnMIOKCweg+de/BOWXHqhmEsKe+X/YgbAuZ3u/9+f9K3yfxI47Cndg9vu\nuA2na2tkk3cagPkzPHjml3di0vQSNJW14It1e1HZ6MWx8jMoPVQrtQBXw8ce/xmefPrXggNTctvb\nrZJuOLxNoME9B0B+v0QRGkZfvihG0yKnzSETOzadg37v0ESdTTEXXxbc+ZqLNqebpL4JY8BsRXZa\nOubMmIXMrCwcOXkcZ5oapRkliNDZ34MgdfUBv5o8+wPISs+Q6XzvYD8a21tlcm+hRl6TILDZpOaR\nqL43GhJKvoOxe2aLRAfx5uFEzmG1CipMcIBmgWJ2ZzGLvpdTARbJbHb5b5zTLY1yR2+3FPd8LU4I\nMlJTpWns6uxSGmqyAOw2MS6SZlDy6E2K/sxM7qCiLnNxk0YupMw9GPfFRofTOdkQNT0oC3ApDjmp\nINWY9GiTSZoBanfYBJPdQL+FnKxsOQbNTU3SEMgknWZkmnEMCxo2//w+bGZoTFddV4M+7wDiEjzq\nc2nGeV6/XyazbFApxZBGKhCUaRIZHCxGgtEw/LGIgBE890JhMhiQlpwsyHpbewcCpP4TDCBjw2aD\nxxWHwf5+uYgKCvJRWcUYnoAwNQiq8NHV3a3pSHmnx6SpEc1URC0PSZ5EOVechnb3daOjrxshps3E\ne/Dot7+DnIxMbNz6FT7buB4DQb80nPxsbKr5EI8Cs0lzn1eZ8bpvATeY4XRx3exQ6FgaK4OyDmlk\nAgFp0mU6Szo7qedkkGivx7/3ehkHyKmvWab9fD5fn6+rAwv8HYIEfG/+nA/1HjHR/8v1arHIpqzH\nNPL37RYbHBY7vD0DqKusQUP5aXibe+VasYlbcAR3XncrHvreQxg3dTIFVGraOTT7oQxAm2pKPUgA\nJIjKykr89a9/xarVq9HY1CwZ87y/+D34mJhfhOd+9QwWXHmlaoqs6jVKd+7EmtXvYfe2HcLkMJiN\naOvtREtXt3Iyt5ISrHUwVsBit4qUIN7jEdCMrvbuxHjY3U51zRqjCEaDKvLOzHuIxnrnzDd53hT4\nxamFQY6dbrCnyzt4LxMA5LUu50NzwidTSNcXigSBbCGrA7ddfT1mTZqG3t4evLfmA6F/0oGckU0T\nRo7BwimzEO9wo2OwF83tbejzDsq1Vlp2CA6PGy4XI/4YIUmWTQgOixtuqxPzZszGnKkzkJWSpnwD\n2LBbTejx9WDn/j3YumcnOrq6JEaK06sFs87D6HFjsXnPDuwo3aPOGacB0bBcZyxmh5gqBDJFH2eT\nySz/TLCU6w5lVmSikPXC3xdvlHBIAAjegzSvEhNEzStBB5lUHBEnj1SQODFn4jSUjBqLQDCMTV9u\nwtqP/o7Gk+UyOiAjhWbGAX+YuhGkjipB4XnzkTF+KmwpeQianIgaydxQRY+B2cthP/rbWzBw9gy8\nZxvQXV+OlsN7gPY6pNqt8ESNyLSYcfm0ibjpikvR19uNd79Yj48OlUm++KVFebj/2qswdfxY0cZ/\nvmMv3lz7BWoH/ciw2/GLSy7BlKIi7GlqwpPvvy8xYczF/e/HH8e0884DPKSj0ohMAwD0HZXshUgU\n7638K178/XNYdN58/Pdvfg1TZorSY9OzYXjT+n8CAGj3mzAjdL0f9waZzCkDI14TUtUI4BJCpLMT\nG9evx6p3V2Hnrt3wDgSFXk06foHJgmnpGZgzeiQmji5GbkYaLG76yHADDMl9eqa7C/Udnahr7sTp\n5rOoazuLtu5uDMhsHGCCcp7Hg4LUFBTn5aKoaITo+FNTEsXHghR0mprBy+JXawi1Tl2aZvne5yb9\nw3Wrw0ubf9JistL8xjRpWPf/jQZneKnDxAALBoNh9AZCaDzbhvKaWuwuL8f+1jacidIwEAiyzxF/\nDEXhlbQdtxuTJk+WFIAbr7t+aEakF5n6aSergkDJe+9+iHfefgtlxw8IYYpTJgIALrcHOdm5uOuu\nu3D33XciKdmNLzdukli3yy69VMB8vu1rr74hk+Drr79e1pSu7i5hApSUlEgUHMEADjoIfr//7vt4\n6DsPYTDAAt2MxJgRc5KzMDenCEmBKDIcVpQUZyM5wQWbVRn9CjtEtLLnpl1ilKg1TEpgr8kFZDT6\nD5M7ralSzRWZRzojQ7+otaxuGgaTO0UQ3OVGF8xYs3M3Vu/ZjfJBznkhMW/RqAG2aBjFBjMuHjsO\ndy9egpEJHpiDfkRZD1mNQ/u6fl1ohG/5X8UwVLOtITcD7SMzASFkMqIhFMY7+w7hjc1bxWiQTTKv\n4QQ4kedOQ1FKNhKsTqltTjc3yP2ZnZCCCUWjkGBzyXXMtZXrcnpqmjQ+jKQUXI63nXYRqEZG069b\njThxuhK1DWeQ7vFg7tixmFpQAA914UHu+2RSWZCkZZETAJDhTFy8GmyYjcIMaa1vFDA6JTsDdllr\ngwIA+H1eOFNSUO8LYOPJY9hUewLNEfr+GxEPBy4onITp+aNhC8XUUPqfpv/qCv5PDACVY6+kfsx6\nr+tvw6YTpWj1tmFcaj7mjp+OhLAd5hCHYCoBh8MM3eSElQIHRHV9HVhffRin+s7ACjPGpxRieko+\nki1uxAwWNAcHsK25HNV9zYgYQ5JcIQ6LaaMx/Ts/Q+rk2QjzMwijlFJbGuCp+D/Jt9dIDLoJoAIg\n1DUt+n/+ngZQD/eK4u8Lu0prnP/RY0ddbwbEW0xwBgYwWH0Cxz77AE2HdyDW34ycaBSzTWm4cfJs\nzMrIhjMahC2OTGDWE2rACKaj0AOKbCzedg6XRP9RNjXoicPm1jr8ZdMGNMai8Jnikb3wKkxZej0M\n2YXwWu0wWJnkwZdQLIZv6vs5FFMSZN0YW4/H5ieXP0fDQ+CPgB+aFEAMvaUmUKA6N2oCidzzk80x\n+KqP4suVf4L/6HYYIj6h++fYE7EkbyJGJmTAHDPBzr2G5tYS4wsEzQY0DHZhf3kZ/JGIGBIXuVNh\nIyHt3zAAojL9jUl9Lh8DMQzYDfiy6ggONp9CFCFJObk5bwQeWno5cjzxMFCSpun91W8NpzXpQLx2\nGbIPDEZA9fobmzfhxQOlYubr1MrWsMGM2++9B7/5w29FIq6GR/+bIYDhu+s//5m9B++Xh7//MF5+\n6WUxx2U0pi0awM3XXID/+uF1SM9IwskD1Vj1/kZsKa3Eoap28QuPS4jHQw8/gu8+9DDc8fFK/gOg\ns0MxguMTVIIfexUeYxqiGn7xlxdjeXn5ghSy2ed0n0V3Zf1p7Dt0EA1nGuC028VRe8niJUhMT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sot9nVxWQAlcKZ/3T8t6l9UeUjDPqcxXLZgj8GJrka19wKBVFfd9vAiHDGpjhYIB65lDzMfx3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IbtCUSRREaoFsI86301LJ9PvekUYFWi0A29nfjkyA7YYhEsnTQbk3LHwWf3IOxP2KxJnMbY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\nNUVl8GSsSESi0maZ8aCbmmpW/5l6qQdhYpOuN2T+KdZhxm5Bb3Qcm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ElypQJRJd3S/iiq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\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OB7x+v4wrXQRwsNhCQN1ITKUFY2GRdAkYGR3BmTNnZA0tKS5DqGSKAApNJw+j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QNMBDnpOqkqg2IvF3ZzuA8fgqoFmxSaSXlq+jFSn7WwOjwgA4/PofMXpsv/Sac5zI+po5cyZ+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44fWtec8BROx6wF58Pkzke/zYlobgYcebkwcawz/wgHlKOVgXG72m/lfDQ9AP2zfrQwMaj3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\nat5IAwuc3gqcMOtqeMvHI2i1I+H1wVIyAimnXVo7hcZPEWltD1Tg+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5aDEVx73W+xbGUY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umwe26oygKic63Z7zk/OMz59ttFQyqUwZibAR8BCTvgi9J06zBjhX9DaDurGhAmT4DGh2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\nVc7HadOq5P2am1tEPldUVCgtD/k7rBb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FixeLNwzL6GPHT+Hee5cKm2rzpq0iZdu9ezc6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\nZx+McGKAdwBmZ2XinqFZiDWaUFNSgT5LrzBJI2PixWmwiZGQLS1yjnj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dP0GYDPsPH8TO\nwwdQUl0JlUGLXr4WE9jpdEumBU1yeAP2OTAsI1Nc/vsFR2Hdzs34cv1atPR0ijGPyu5Av4hovP7S\n75EanYjD547jr8s/RpuVUhEuzHRNV8wc6bTN5lg+K3FhqwVpCYl4+fGnER8bh4rWBny8/DNcuHBB\nzgMXAzaWXkYvBPn7SzGv4zRdo0Tf0ShFZ9Sjz2FHfkEBahsa5HfYKMydNhMPzrsbYd4h2H3hMN5d\n9jHqW1uxYNadePmBx2BW68XjYdPObTh45rh8HhVzWAl8kU3Qa0dWajqeeeQx9AuORkVDFd7++ye4\nXJCPgOBAjB6ag4fn3oUw3xB8vnk11v28GRYCV9x3CNDQzTY8CtGh4W7ncO7Nv0zxWfxS8lJcWS66\ne55jMi2eevhR+Gq9sGn7Zqze8CNE+qZTPA5sPRYkxsTjt488jtjQKDEjyy27ir1HDuHqjXzF+yAw\nGBNGjcHwodkoLC7Cug3rkV9cKM+/aO4CWWSOnDqOr79bLZPUyaPH4Zl77pNG/WR+Lj5Y8Sna6Tjs\n7S3NP8EjXkOeiCdPrKTiPK2X+4mFoMT7mUxyf/J9cNLviYEicsrnMJuprWJecZ/c4zSZ4r+T9dHe\n2g6TmpPCJtSVlsPV3gstgX53FBy0XlCHJyFt4jwk3n4PXAHhDESDw+UQqr+kFbBAc9IlnQ0o0xfo\nWaBQ/T0GjLfWlgpIoYAMNyOO/gmG+1Ut6ok/chupKvFwytRb67LBV+NEw5XTOPLFu0BVEeAgQ0Ax\nNYQ2CIEpoxEaNxB2uxoZmYNlsygtKURRQR4YcDhsUBbamutw/OgudJblS5OmU7lgc7FZc9PA1V4w\neAUiKDQBg4ZOQmRMP7jULly9cQWllWUCQA7IHISEhH4oyC/E4UNHEBcbD5qt9TmdOHPuPGrrGxAU\nFo6UjAxpAhy2XtTV1qKzuxN+wSHQeJuhDwxGSEoaelRq9FLWAgfUBhUC9Wp05Odi+7J3gRvnoEE7\nfH2cmD9vAubcORHjbxsKkxePPedqjHe0wNnbDTVZUWRMiF5cuQ+Uec4vbZ+nQRB2jBBA7AKiKKJA\nRlPq0NbpwMWr5di3/yK2bj6JklIrbA4ttOYI+EUkweAbjtrGToFNFi19CGNGj8axg3uxeeNPsHS2\nIWfceEyePkP8WDb/+CPqa2vh6++P+H79UVlVI0Y+ks5is6Fb3JEtCAxlDroeZi8Tmppa4BcYrFQn\nLppROcQw1+zjCx1BWZUKVeXl4nFCXxgCgPR9oBdAckoSYnleSkuRf60AJq0BY4dlw+Sy4+iBbbh4\n+QCsnTXQap0IDfZDfS1p+op1liEgGokpgxEZ2x96kxe6ehjvqoVOZ4TZxySmTQRIWUj4B4QISNnZ\n0SpGvAIim3Rob6zCuRP74bQ2Kj4Tov//PwAA/3SvKCCcWqYyPsHRSM2ehIjYZNRW1cDW04KrF3fD\n1l4rky4Cf1xTFi5cKNF1mQNpDfXLQ64M3rsyBiZgCJw9fQkfffQJdu/egfY2mu+SBeSEb0Ag3njj\nT8gYkCERSldyLyEgKBQ/b/tZmAHPPvccqspLRPdNvf+wYUOlAPvqyxVCbZ44aaLQOrlvUut5/OhR\nPP7kU8ImoEHgK6+8KkUf/QW4/544eQJ5ebn4zW+W4tFHl+Lrb7/HIw89CP/AIKzfsF4MDdd89x0e\nf+ghhEZGyc+GBvrg7fc+kuLs7bffxkvPP4Xd+w7jvvvuk2nN22+/iWtX8/Hoo4/Jvrhi+QoMGzoU\n63/6Cd+uXo25c+bIdGjZp5/hh3XrJIZqxoyp+P77n8QXgJP4d995V8z7Xnn1VZEWPPzQg9LEf/DB\nh8i/ni8ABp39q6qrsWLFCowZOxZTp96O1rZ2of5zij927FiZJHNvp4yEwAXZC1wrWa+RQcm1no7/\nfBBI4YM/x/uXBTPBaYmrdccQs27z0Hd5P3Gf8AAFrGluNWjmvSfNvHhAKECuR6bF12GC1dlz58To\n8NCRwzJpI7gtAxQaKfO6orksJ/XuZl5Mr28ZOIlLCUEMtyRPGK/uZiPQ1x+TJ07CosWLMH3eHLfx\n5r+6S5QI7X//+OV7n326QrwMZC8lyNDXiwitDlP6JWBySiomZWQiyC8A9opqFORehdalQ/KAdCXy\npKcbBecuwdJtQXBYKKIS+4H5q5aaGpnCsqshoKn3MaOrtVVqPafNBoNKAyeNnbVaWDVaFFt78MPV\ny9hUlId2owntPDpB8cicdhdGTZmHSyeP49Sqj4H2Qlm3Z00biNf+8AgGZyYKsMJz0NHZia9WbcDf\nP92KEp52/zhoU7MxdPY9iBqQBS1rsY4OdJdVwNBSjUOrP0JX1TWJe+UNbIIe8V7ByPQJQ0ZoLOKC\no9HS0YHCphp0wYaAkCCpd/Kry9Da2IzM0FikhsVKioKOAPA/LVX0m5H/BCRUpGfucCU5m0x76VPZ\n0ad3IL+5HEcq70RrfwAAIABJREFUr6DS0aIYwsmJ08LkF4PMIdMQlJQFW1iERN8Wn9mP0rMHgLpC\n6FQWzJ4Yjf984zEMHJ0GaGzCauJr0dNMAG6yHpp78fobK7ByzWlYnd7wGzgRE5Y8i9aGWhz+8i/Q\nt1bAWwXkhITj3n4DkG4yo194CPy9TOhob0VXeztcdhe8zT7wDggQSTEbTkpcacJIDxtKWbx8fNDV\n2Izq2lrR15v9/RAeHSX1VGlxmTAnTAYToqPpb6VCZXMzcmurcaGhAWdYYxkNmBQTjjEJsfCy9kFl\nscrn4D4VFhIsoCPr58aGBrS20AYTiEuIh3d4KFoqysW1ntgKQSG/sCB535VV1Yo8Q6WFzhyMkkYr\nOvvUIilykImsUqPa2o5j5Xko7iiRo5cdEIjXZ87AYDrx23uV/V7Sd/73AIBAMBod2rqsWHXgED7O\ny0X1LbB2QEAQfvf734vHjF5qe4h8tqCQcXzxAqYxJYdMiw0bNkg9xpQlz2Pduh8kzu+hhx5CZHio\neHT8/PN25F25IrIpAs6sG5kUc/r0GaxY8TkCAwIlwYtsKbLWcnNzb/bIlCZTYtDe3oaHH34E40cO\nxZUb5Zg7d74kMbzz7ttYtGg+8vNL8PTTz4hxP/1qGNNKgJlr/Mu/exYdHb2YOWOGrMVffPklfP18\nJNGvva1DAAoycQkkq9Ln3MGQDph0emmSiTJ66Q24b8lvMOG2CZIAQFf0QyePo7SyXLTXMmVVkmkU\nF0e3Vp8HhYuAJ9dcCmZBUJWFjoABv+9Z9pS4LDaTii6YbrKc8LgrP/ldT447Fyk2RCxUrBbGWOjl\n+zRO45+K673ppgTAYwRIdDk9LU0mnp2tragoKpGJy/O/fQ6h/r64kH8D9U2NiE2Iw6kzp/HT+g3y\nnFwtxowahQGp6bJZVdfW4Mjxo3IM6B7r4+eH2dOnY9JIxTxxy/afcfTkCXGVzRowAEvnLMTIIcNQ\n1tyAVWu+xcmzZ9Bl7RHnemrIpZGmUSHj69yf02OkSHSJx5CflceLdGkeK3Hh5+ZHbatEL1Izbhd0\nnp9TzAvdLAj+jFCsGQvoplyzUfYwBHiM2TDywZ/ptVoQFxaJh+9egmkTJkMPDc5euYh3P/oANc2N\n8A0JgsHHC3VNTEVwT3BFmutEX7cFaf0S8YcXXkZmXDL2nDiET9Z8hbq2ZsXh1uFCekIifv/cC0gI\njcGJy2fw1+WfoKmrHTq1TuQZEsFooz+BViYa1ELy83Z0tiMrJQ2vPPEs0hNSUd7TgI+Wf4aTp04p\n8Xh6rUTt3DV/AeKjY+CtN4k8hbpxHhuNmli5DlVdtfhxw3rs2r9f5ANsXKfeNhEPzL0b8eGxOHX9\nIpat/AK5BdcxfPAwvProMxgQmQAu5QR11u/ehgOnjqGlixpNg0wRxuWMxJJ5CxEbTIqpCwdOHMba\nnzeitrlR9FqDU9Nx3+z5SIiMx4pNq/Hjti2KWQ8n26L+1uCZBx/BxDHjYFAbZKOiA6oOzKy1QwMd\netCLbXt3YceBvWhpa8OgARl4+pHHEG4IxPHzJ7Dj0D5UNtXL+xKwx+ZAWnx/PLH0QaTFJqPD0YXz\nebnYuneXxBcSib173gKMGTYcJQWF+OHHdSgoLoLOyyhmV5S9EH3ctP1n7NqzWxrniSNH4/n7HoHZ\n4I3jNy7hwxWfoaG5SWFVcPIi5r6Km7LQ97VKFrrEWYoRj5J8wX/jNciHxwiUUyqyAuR7bvBDfpf6\nMZn20iRSgx5br7BNuMIScKouLEZzSRXYQ7LPEia9OP56w5w2Eqkz7kdc9nj0atSwudNDuA7Z7HR2\ndimu4RJLyAJSyXeXZt9d8HkWeIkYdacZEAjwrGX/rqjjz3seSpqwm30gEXUOmE1a6LrqcWrtClSe\nPABVRzO0dvdnox+AXxISB49HXP8s9PQqTr0ZA9IQEuKDksIbqKqogtPRC5NJhZ6WGpSfP8J5nuKF\n4Ox1N2xaqHSUYZhh9o1FaHi8AFIEH7v6LNAY6EPhhNVqh685AMHBoXJNFxYVoa6hAVlDhiJr6DC0\ndXZh544dwq6i1m3W7Nmy+Z45f04icHzCI5E9dSYC4uLRYbOii5GmXlpJGgjUalFx9jiOrvwYIFCh\nYrPfLZvpqOHxGJjZD0MHJyE+JhAR4TTVYbwowRgCNUpPz+tALXFSvz7aIp2SOFi76Fu7OrrQ3tyO\n7m4bTpzOw43iWpy+VIWqGveFofaFyuiLyIQ0BEQlo6mjF2ERcVBpmGjiDR9vb6E99vXSR8UiTXlH\ntxIhFREagfDQUClq8nJzUVFahsnTZ2HQ0GxpZDatX4f2mhpMnjsfaenpuHolFwd27caAIcMko724\n6Dq2b/sZ/v6BmL9gIWLj43Hk8CHsW7cO8YOyMHvOneIIfOjAATGEpT6dkbwHDh3BwX374KXWYOGs\naWhvKMfxQz+jpvKK6GXJrCJ41t7WBYeN5JEkjL5tGrx9Q3D+wmVxbZ4wYTKSU7JQVVkjBo+15aUY\nNWEipkyZgo7OLhw+dAgXTh1HdEK80N3pXP3T6uUovnLGPf23/T8HAHgmPU2RS+uF8ORhSB2YI0rt\nxtpy3Lh6GLYOAgBOAQG4Xi25dwlefvllKaa4N976kKJeqn7Ja0BDfRs+/OAj/GPl52hrbxP/Ci8f\nX9H+sojixJ6mSTJBDw7GA/c/IFKMdT/+KBNtHtPnn39OrvcVy5eLjp7X2rhx4/D5F5/LAIPUe2ox\ns3NyhLpPenxZWRm2bdsmFFBO69977x3x3/jbe+/JPs0cbPrhMLqNE3QCoPT6YcoQwaGU1BRxzS64\nUSBaf05x5s+bL5GsO3ZsR1paCtLT0lFdVSVFHesAZj7TsJXv8fLlS5gyeQpmzZqBispqHDx0SIyt\nkpKTUVFejn+sXCkN+G+WLBFW5sYNG+TvpP8TZOVey9xwshPoKs3mN/dyrqzpLBjpSUGKKVkNERGR\nCA8PEwYCQcGtW7cgLS1NjhGP1ZYtW2QQM23aNDlVBA744HSKxs0E7/k1m3qyG5jCwNf1GMSScksD\nPYmjbmuTmsUDHlA2wJ/j7/L7ZLdxcEDmgewzwmpU0gTYnFASwfdDyi4ZN5zkiWrAfRFJT+Fe3z3+\nL7dwVm7CjwQK/M1+SE1Jxb2LF4H54VGxMYBB80+spV+vVQqj7X8CAZSfr6yolikkQQteL/w9k8OG\nRG8vZEdEYnbWYAyLioWmtROqTovE4VJeq/ExigsBGUZcOCmN5YLJPZhFPsEOHgu/oEAxNCX4w9rK\nz+yDSJq0dXaK3KuqvR1VTjsO1dVg8/XLaDYa0U7+bXA/ZN8xD0mpWVj/9Rfou3EasDUhI9kXyz5+\nAyOHJ0GrUhJ7tMI0AGw9Wixbtgnvf/YTakgRixqAiPGzMHbuPdD7+qKV6TNV1VDVleLY98tgaShh\nloIcB1+YkOQbjpFRiYg2+kPT60Ivh4FmA/rUThi9jCitq8a1qnJpSEfEJiMxOFJqJj2PNQEhxR9e\njjsjCD3HX5FgqKRWUaBl7pmATeWEw+BAWXc9jlbkobCnRiIdxWmeDatXKNIyJiI6YwR6gmiaace1\nA+tRz1QeRyf8TTa89uJUPPnEPJjCfJUptXCXyT5w1wPU+tl0WL1qO/7wxteobqDZQRJue+QPMBl1\n2P35n+CqL4FZpcb4mAQ8kDYIGSYfeLN+clHeqRKTUabK2O10nydbToHHFT8vsuJsUtey7mcdTUDA\npWPahVPWLQ48+ebYT3DJtFis6OroRp9GgxaXE+dr63Cxg34OLiTpncgMDUKgWovY0FAZVJKF4EUP\nN5sSK83jyIQ3vrbeqIdOT2ZQOzra2gQAkLSAiFABAOrqG9BrtUGrM8DkF4aiBivKGzvgVLPWZY2m\nQYvTgtNV+bjayCSAXqQaTHhj5nSM7RcvgxKyDvjeeN4UDwBRcfzq8d88AAQA0KL9JgCQJwCAwjlU\nIStrEN77299k7aKW3tfXTzT7fG6b3YGzZ8+JufbMmTPEpJy/x5/jGpyVmSUDK9YBjGZkjCkp+9xf\nOro6ZS8heM1erKioEDt27sLHH3180zMmOjJE2G/3LrlP5FOpKSmSEJOSEo+Ll67hww8/lHM6d958\nAQ249vESHz58OPbt24/Nm7ZIok94eAQOH9qNzi6bAMeHDx+W+FUCEGfPnsFbb7+FnOGZWPbpSvz9\n78tlvfzDH17B7NkzFQkAG28asNGcyNLZJa7uc2bPFnMEavNO5uXii6+/wqUrudK4SnScTmlYJNeb\n6Kp7cs0PSx02bzbJY3fTe5WpILWw1CRrBY1iw8KNjAZcYgTY1SVa8F9v8JzMKTFQbBTEOd/LSzYj\n/g51LuKwbDTKJsCNXRIG2ChDhZjwSCxZtAjZQ4ahIP86Th49gsT4fuKsrNaqcDYvD1fzryEmIgoR\nYWEy7ePvF5eWiHMypQmTxo9GjxM4e/ECvl//A8qqKkXHzxMcFhyCro5O1NTVyeehWdawzCwx04uP\nicHl0gK8/9kyeT7KJ+jVKk0gJ9OMbLMqenKJruNUiYudGxBhs+FpKDwbG39WQcYV13WyFxQqneKC\nz4enSOK5UaacnA4r+fOKtpwxgYpBHo8dGR2kjseGReLBRUtw+/gJ4jpbWVeFVau/xfm8S4BBy9gB\nNHe0yflm80RTK95/BEAC/QPw3GNPYubIiThz+Rze/GIZ6jtaFE17dw+GDsjEqy/9Tujwl29cwVvL\nP0FlfR38fP3keWhMQ6nAyOzhCAkMEuSaCwk1yWOHj8Dto8fD38sfZ0rysPK7b5F7NU/AKF5jNCCi\nbp3ngkioxHWRpWW3wWAyCGOhqqYaO/fswZX8fLmGyR6ZPGY8Hpy9EMkx/XGpNB/Lv12F3OJC+WwP\nzL8H98++R5BIOxy4XHoDn/5jOQpKSuRY+hhNWDxvIRZOmQWH0yYRf2s2/oT9Z05IpCEBiLT4fnho\n4SLERcXh0x++wrZ9u91Z4Yo7Pt/n8KwhGJWTo3gCaJh84SPXLotRoo4NLc04n3sJBWUl4pYdFx2D\nJfPvwrh0xbU7t6QANypKcfTMSYl1E9KaVi/MhJCAQMmC5fXKyBt+btJT58ycjajgUGzdsAmXLlwU\nLWdCchJS01Lh7+MrCOj+I4ckfpHJHHOmzcQD8+6GQWPAxcoCfLB8maDLPIYSByjUfrVMQnlNKtcy\nwSYl7o9Nv4BaDoUeprCGjArw1Eu6mHJ9sujh/SxsELcfAg02+fxsXoWp0m0B+uzoaWlDbXEF2qsY\nLaeg+/Kg07g5DKGjZiLz9vkw90uHRcMNUjpwiebj12Ra8PlYfJPe+f8KAJD70/1mPOuWAvgoOj6d\nTo0gnRP1l44ib89m+HW3ofDcCcBJDrsG8InCoDEzEBSehivXy2Q6Oeeu+RgxfIhQ8I8fOS6N6sQJ\nYxHmr8P+Td+gpjhPiWqjIyDnFu5CR0wGnDSG80ZMbDKyBucgKS0DDU2tOHH6FKpqG5E6cBhGjBwr\nwMjOXTtR39SAO+fOxaDsbJQUlWDn5q3icp89agzGTZgoBcauPTtxef9eIDwcMx97EoH9EtHF3GUq\nLPVa9Nqs8PbSQ2frQVXeORSdPormC6eA2jLA1gX0KSaxfNDaJShYg+Bgf4SGBcOfNDwV9wRFEiWq\np1tSHDznjCAtYysJTnS2d6G9pRvd3XbUN4mRt/LQ8UQHQmMKQUT/AYhNykBq5hBU/H+0fQd4XOW1\n7ZpeNKPee3W3ZWFjWe7dBoONC8bGGAwJEAKBUAK5EEoC4YZAbi41hAA2zfQSG3DB3cZN7rItN3Wr\n19FIM6Op71v7zLEFgST3vvcOH5+l0cyZU/7z/3uvvfZajW0YOnI0OtvbsPGLD6WKnpSWgyVLb4DN\nZsfe3buw/au10FnMWLBkGUYUjkT5iVPYuH4DzEYjpk6bIqI7HV0ONLfTBzmAIQML5Ct7XW5UVJzH\n8CGDkZgQh4qaahw4UCpJ1IJrFkigfuz4URw7WYbsvBwMHjgQnR1dOHnqjAT0RYWFEuidP1OOproq\n2HQB9HZewNmTe9DdWgOdjnO8HwFRv+OgNgGGGIycPAtzFy5FS4cTmzdtgcvRhQkl45GdnY+2tk7s\n23cAjY1NGDd+PLKzMtHa1iz9gg31jcjJzsKQYQMR8HZh6/oP0UjAJtQnQZey/b9jAKj3nfcxpDFC\nE5mErPzhGDSwUForyo5sRZ+DdGavRGlkEJGm/9hjj2HatOkKa7DfJsBimAHA4Hb3zv147PHfYtfO\nbeLMIpZnJrO0hyhWbXr85Nbb8fO77sSnn3yC1/76V5m3mJCyYs7qCxX4GbBR2f4/Hn5Y4o0XX3oR\nTU2NkuCzAnb33XdLZZtBGsWhGOgRADh+vEzmko8++hAFBTmYO3cetm7ZjHGTJmD7tk0yT11zzWIJ\n0qjmf9ONy9HW2ompU6fibPkpvL1mjST+b7zxhvTyU7GZGgIEAR544AFRMafjAAM8JvHvvbdGKKm3\n3nqrzBVs3SF4umjxYqn6HD50GNU1NSJMR9E3R1eXJO2jRo1CfFwU3n7nPYm7brv9dgHnOjocwkYg\nO2TkyBEytqkBkJmdJUKCfE9bR6ckEVaqSEMRE1PjL8YanO84t6sxBxl+3PpT/fvfQ/5dbQeQmIYF\npXAir/6rvkfto+//+e9X2fvHSvRg625rEwDgYGmpgBEVlecFWCVIxmRbtV8WtqKI8SoaJYSqU+OT\nkZCQhIGDB2H8pIkYdfloaREyR9kVlJKGM98Zkd/95d8FAPipzZu3iid52QmOIS20AS+iDDrkWKyY\nkZmPCRk5GJmYglR6ulMJvrYOXQ4nLHYbsgZkQ0Nl8i4HzpWfQcAbFFeC1LRUwGKSnn9Xn1viW7Zs\nMEYQDS4T+6O7UNPehmqPB8d7e7H+ZBmaNCF06SzQpQ7AzKsWwtfbiy3v/g0INsOi8+EPj9+EG5bO\nQYRNA03QB6PWoLTc0AZVY4KzR4///ssn+O1/fYigOUVaAYoXXo8BlxejpbERwdYWtJWV4sja1dA4\nm8PtYEAcbBifPwKjU/Kg6XKhs7FV4jxrQixCBg36vF4psFS3NUEf0mJEajbSo+NFI8hEp6zwWJOp\nkaVNrqlhuEcdO+r6TGcSf4jM5gBCpiCafV040HAax9sq4SSfTQqXrBTYkZM/FoPGTIU+MwcerxMH\n1r0Nx+kD0IV6MSTPildeugsTpo+QVgvRVQkDAJKfSv5CFoAOpbtO4YGH/oydBzsAYzJy51yH7OxM\n7Hz/JfibqxABLWbmDcQtgy/DUHME/F0d0IUCwnyIzcpEyOEUJiQruWRn0hkgjpR/Fkk6u9DW2Czx\nObVn4mkRadSjo75eaP86nQEp6RlITM+Qtrmqiko01TVKb742MgqHautwqKkVvqAPmSYgO9KOeHOE\nMBAo9hlps8JC1y1a+lFjwmZHZk4+tEYT6mur0dVFEVAL4mNjxFWN/e3UCmCMGBefAJPZil6XBy3d\nXpxrdqHNFYQrAPiEkqtHrz6EYy3ncbjhJHrQC5bVHpk+HfMuK4SW7YLCqFPyw2C4nTNcW7740P0Y\nANDd24c3t2zD82UKAKBEShoRRP3b669jQMEAmRso+NsfANiyZasUBeZePVe0zAhGUTz29OnTwoSa\nNmWKFJ137twlczbb/5YsuQ5Wsx4tHQ5R+yfoRrB40ODBigV5hA0HSkslFqdFIwVgGfdSm2XOFdN4\na/DRRx/jF7/4BfLzC2SdGD9+FNranfjlPffjxIkTovr/4osvorWlQwAH6takpibhmWeew9NPPonh\nI0di584daGyqxwsvvARHd6+4zQwfMVg0APbtOwi9zgDNuOsWhqhkHx8bJwskkzCupwMLCmTBJeJT\nVnEezz7/Z5ytqpCgn5VjCrYxcHe53JKM97cu42uCQhkMghwzMZVKNBP9cL+7CNTpwsm/Viv7YOLF\n4J8TuJoIcx+qoJ3SG6+Hjf13Wo0g2dz4IKgLCD9PNJsgBc8jOTZehPyuuuJKEX7p5cJkNEny1dbV\nhS+3bJJqMhXwb15xIwYV5MnEX93QhI8+/AiJcXGySHPs1XV04PmXXxQqNSnXZLSognP8N8Jiht/t\nwdSS8bh12QrplVm/ZwdeXfUG2jraFQV9g0EmNFJoxEeSlH1/QNgLnJB5jrx+4ttut4eTJCUQ46Ir\nwnG9VO5XPqOhzZUKuIRZEAxouF8+DKSIEOEmtYygC++dqsDOhJLHwQSoz+VBQnQMhuQVID0lFUMH\nDhYqINH4tV9/iYMnjsHpcYuOgfR8C9uDMjsaxbItGJTE9N7lP0W7ox2PvfAcjp0/LRVc0u8G5uRh\n+bXXobhwNMqrzuKZV55HZV2dJHqM7/0eL7LS0nHPnXehcMAwdDk6BM0lsphgiYVeEvEgPt60Dp9/\nvQ6NrS2IjY8XYEjAJb8fFpNZ8aMNV3GZbFLJmxVkRbXeB4PJLA4SnQ4HJo4eg5VXLULR0OGobmrA\n2598iP3lZWhmT09aBlYuXY6SkaOklWXrnl1Yu/FrtHd1SLsGWwAKBw/FvBmzUThsBGqa6gUA2Lp/\nj4AyFB6aNXEKVl57HeKj4vHcmy9j+97dYXRaNHwka/W72QZik3Ng4sxKEa8rk3DeV86NbL0hqCBo\nr9GI4mEjcd2MK5GTliW0xTpHE15bvQpnKs/JZEuEnwwdRfHfL8AB7WL4N7biMIjUBkJwtnVgWMEg\nYbqkZ2UiLjFB7Cxff/MNNDQ3yxiLtEQI42DK5eNl0ly/bzvefO9tdLt6pdJAkEUYQeGxK891mAXA\n59nbp4w7uc9MuMN2gAobxS/icRzzKuWTr6nzhCT8pCd7+yTI4dzR6+wRjQj6EDfX1KP61HnAxT7y\nsBosA1CNBZqMIRgxczEGTFsAb0QsPBSvAVuKFNcIIv8MxjmpK1oDil3h9yv8/9MWAGW9V0JCRehJ\nAXsU2zulMmOBD3ZfJw5++TGSPA7UHd2P6vKDSqBijUVibhGyCkrQ7dbBEwQKiy5DZnoKSvfvRX1d\nrSzGqSkJiLHpUX/2ME4e3Qt3d7uSWDPQkMRHFUEKAXqzCDPFJWYgM2sAzBY7tHojetmQZ4qC2xcS\ncVJbVJRUWHxkDnk9AgSTgs6FQmeyICk1TdhPDHmdPQ74yUSKioM+PgG21BSYY2PhJiXfoIPb74HZ\nqEWELgRHXTV6aivReuYUWk6fQHdDLdDZDvT1Ajr2NPqlp1L4uKrj3w9F1eprnKD5PvEUDP8c4A96\nGKLjJTjQGE0wmOwwWuIRHZ+J9Lwh8Id0Anby3IsnTIW7x4GNX6wRACAmKQvzF14n6v1lR47g1JGD\nyMzMQGxyCrp73fB7AxhQUCCMg+aGOmz95mvYo2KwdOXt0BlM2LdrGw7t34fZV1+DAQX5NCrEZ59/\nBnOEHQUDB8o62OdyoezQEWknmDB9Gmou1KF07x4kJyVj8rSZ6O1149tdO9HZ1oLs1CSMv3w4ak4f\nxJefvY3eztqw/Bgrf0YBkP19QERcHnIGjoQxKhbxqRmif0BBtFibHV3tndLb7vZ4UThilNgJsfJ8\n5uxptLY24rKikcjKyIXL1YNduzbB09uM1rqT8PewPKUkN5eQtR/Kcv4vgQGqhmtNMMeno6R4mug6\nHNq3GW62AFBqLVwLYO87Kxtz5879h4Pgs0XPbgnnNDps2rgdz/7xORw+XCq2cKyAkhnEv9GBgQNm\n/qIlorb8xedfYNWbb8LX14fikhK8/PLLovZPBkBbcxPSMjMlqOOc8OtfP4gjh+m4oMHkKVOkUsOY\ng4k3rQLJmmKfJwW5Vq16U/o94+JiUVFRIQkd1aXvueduqWSLOv/Zs8jOypJ7T+bb7m+/lTiHYoRM\n+l977a9iF3jNNQvw/prV2P3tATz22KOyxj333HMSpL7++io8/funMW/e1fjTn/4ktpEP/upXUsl/\n/LdPYNLk8Vi79msBDChUd8MN1+PA/lI53msWLMCIYYOw7qv14iZABfvxJcVwOnvx0ksvSRvezbfc\njLi4GElMOeZY5ecaFRVpk4CSegnx8bFyHtxIKWVMQxFAzqdkRfAaUcuAG4UDCUJwHxyHLNbwb+Ji\n5PFIhYvAhrAbuW55PBetBJX59Ls2rd8fDGqCJ+ASx4XPLxRb2QIh+Ht7UVNTJc8EWxmqqirR1Nwk\nLk5dXZ1y/QkwkXnG4k5aShpKLh+HgQMHIzktFQnZmcp8w6RfjuXSHP9jGMC/AwAIQzzMnnv2uWdF\n1FHYoLTNNmgRr9NhuD0WI6LicNWwIoyh4F/Qj7aaOrQ1tcFqsyG1IBN6uw2etg5Una8U83laAidn\npDNQk/FMtgSBHbJG/J4+0cDQ0IrXbocrFEJVTw++bWzEhtOnUOX1oMsQgei8kbh6wbXY+tVnqD++\nG3qTC8sWlOD3j9yO+Bg9DCal6q4NaKGlG5DLIX32MNpQfqYFP7vnj9h56AIQNxADr16KMXPnic2y\nwdGBE5u/ROXWz6D1OqQtj3dqgD0dxdlDkRsRD4s3BJ2folAatDg70eNxS3zk1wTR4/OKLlAkDEi0\nx0DLarTBKHGg6AcpfHFoqDfyIwCAOKyEtAIAaIxBtPscKGuvxqGG02iFGz7uQ5AEM1IyRuKySXOg\nTUlHl6MFe9a9jUDzWUTpfVixbDwef2oF4lPMgI5CrGRhkKWrsA8U2TpW33VormrDk79/HS+/eQDQ\n2BA5cjzy8nJxateX6Gu+ACuAWXmDcdPAQhRoDdD0OEkyQVRsDOKTkxDq86G9rR2O7h6JK+gUwzmF\ncxTF/Xod3fI6gYHY1GSphLU0NwnQy8OgA0xyRoYUIauqatDV1gmdyQyfyYTanl4cbmyETwvEmzTI\nToxDlMECb28vzCY9MlOTYfB74etxQse2HL0JKakZMMYloOn0KbS3twjtPzY5EcE+D6qqqqSgSzF2\nWhDroqLhcThRUd+Gs80utPYGRQAwqDcJEOM2AKV15TjScBK9cCMBPjw4bhyWjh8LXcgPnRQCVPZG\nmMOjUnnsocM7AAAgAElEQVTCD+APAgBaA7pdnn8AABjJsPXxjp/fiQfuv19YFv1bAChwSAFdPufN\nra2i/J+Xm4/YuDiZu8SxRKORfIDzAZlNZPrRrSbSZsLxU2fw2GOPC4uJdrGzZs+W1upTp8qlfYgA\n3dyrrsbKm1YiLy8T73/wCf766qvScsm1bmThSGEo7tixXY6D24YN36CpsQnTZ0zDQw/dI9PR3/72\nPl544QXJ42gXS7HW9evXy9w5fnwJHnv8cRzcsxe/efIp/OY3v0R3txcrVtyEjRs2QfOXTz4MzZo5\nS/zBiedyiSQxXO38a+rsxOtvrcb6rZslASNiyJtK0TYuEox1WZmXfuvwBK1QeoOS1PPqqbR0VRSM\nEy03eoATOCBY4OqlZ69BknsRmHMTRFCSXC4QTOqZzPCCcJJmoqCI39llUub7WQ3iMTH5Z6BNkUIm\n++xXu2xkEfLzcqEncsQqv8eDU2fOYO/hUlygbZPZihXXL8eYUaOlKkhaNAM4q8ksi3tOfi5OnTmN\nL9d/habWZplg2APNY5OqOgGMQAjRZgvmzZyDlcuWS6K/+tMPxGOdAjsUqxNLNKtVJnyn6BQYLlLf\nuB+VjsxrpVCUFUG1/oufkrDQZUER2OC++DleF6FPB8giUF7jteDGJEq12ZHqrJ90ITIRFAFCakCQ\nxsVJlPeWAnz33n03Ykx2tDha8eLfXhU7PjYvUP1dEdQJyrG7qTKu1SIvNQOP33UfBmUX4MUPV+GL\nb74WhX9SHYNeHwpycjFt4mTpx9+8h+0UtWLhxJ403pcYWyRmT5uBvKxspCYlIzoyUhgV0ZYoePxu\noR2///knOFdbBa3BIBQnLg58wGQgihq0ok3BNgJJvsD7QwERndKz7vVJCwB7bcYWXoZb5l+LsUWj\nJMn+autmfLB+HerbWpRAx2ZHdmq6VK1JsacQIIEEVkzkOvkDSElIxMD8AiSnJGP/kUM4U1Mp+4+x\n2XH7yltw5dhpaOhsENp8+bmzslgSuNIa9EK5Z5BCMUVuinimTq4nx5UiKKQsgGQxSMsDRaRy8nHH\ndStQkJoLF3zYV3YIq957R/rkObYI6pCVooI0qhaCOCUYjVIpItiXn56Jn994C8YUjaLLMHrgwX//\n7RXsPrBP7pE+pMGkUcW44drrkJmUjoaOZry0+m8oLTsmrRcMCqlczEqsSstUVUxZxSFYwTWUTAWF\n/q8IH4q7h08Z6wS1PG66Mij2lKJgy+eJtnLkd2lCIq5E0EnmEbYMkPbr84sVTt3ZSvTWUTSM+kIS\nmcHPRZsCQgWXYdT8lUgpLIGLQKKG7BiyXoLo8zPh14iX8iXBwX8MMH+o4vRjwZ76ev82ACLWKh2R\nvc9asmYCfYgy+NB4dB8u7NiENJ0PB3Z8DZ+rU/qNtfFZiE0qxIjRMxCflo/zlbWoPH8GERY9Rgwf\nAItFh7LjR9FUX4Pk+GhkpCTA63bg8P5dcDVWQ6MLIBToVXSqL1LkeG30gN6GyKhE5OUMQUpaDmob\n23HiTCUSswtwDcGqpCRs3PAVDu3ZIcI2S2+4CUkpKThUuh+ffPqxzP/zFixA0ZhilJ+txKZvtqM3\npEHh9KmIIiXWakWI83OIfe9sZwkq1n/suA/60VJ1Hk3nz6C98ixcrU3wuxzwuboUK6merkuUczLB\nSFkMt4z8wzXnc2E0KUiazsAbC+iNgCkCWpNVNL8nTpuDnEFFCFBISqMXan7dmVMCXi9bfpPMvUeO\n7BWnBoPBItUJamZkZmRKYsb7SFu4zu5uDL9slAi69XlcOHnkIPZs+QbxCUmYetUi6S8/caQUu3fu\nQMkEWsQNQ1drIz784EMkp2dj5pzZ8Pp6sOGrdehsbMLQEZdh+pXzsHfffpRu2YjErCzMnTcfrt4e\n7Nj2DRxtzchJS4DN4MPJw9vR21EnYn9Kex2vBAPMgIBFcxfcjvxBRThRflIYInqrFRMnTMZlRaNR\nfqpcrB3ttihcNXc+8nILUFlRIe0AHZ2tuPKKOSgcPhIXaquxfv2ncDsb0dtegZDXEbZcUqw2f5zC\nLF34/+px+PG/y/PK+2dFcvZg2CNsqDpbBn8flcSpjcHYQoNrl1wn9OjCwsJ/HAaS2ymglFaj2AC+\n8PyLeP+DNahh/7MqJmi0YPToMRg8eCh2794jlFT6OV899ypZP7766iukJKfI+sGWsuEjhuHAgQOo\nrKwQQJ7q0FTT51q5d+/ei7FHUmKi0MqZNJJWyRjkP379H9izbw8GDxqE//zDHzB92ni8/8Gn0svP\nHk0m5DmZyXj0t0/j9088juS0DLHjmzp1Mnbv2iP9+qy2MynnfE8AgfMrVafJDunqpLWjK2w7qTge\nydxpYaXOpMy1Btq/cp33SwWKWklkyvH8aqprJJaKio6SmIHJL9dhtjrGxcSIH/bxsuOSjPM5YMzC\n9iC2GUycOFnYEkz2N23aKF7Sy5dfL+slGRCHDh3Cb37zG/luugCQ3s9glvTY0tJS+Q6yD6hUz4Rf\ndQhgVYpUWrIu+H38HBkWBBO4b36ecRCBFZ4fA3ApgJCJaKRAYYv8TjcoAY7ZKupyI8pOSrbIl4cR\nYi4UenHl8PQ44fEo3t6MP3kdg2RfUntHb0BicgrscQnSQy8ABJejcD9SGOL9YQeT74xShYr+z7Yw\nTixvaWxqwoO/elDaFlh5pO83beASzGaMiEvC+Pg0jMvMRpLJiGSKIFPbJhCCy0dldh8izWwx0cFs\nZSIKuJ29cHZ3S7wURcFGK236dAg6e9Ha3IRep0NAgei4BFQ4uvFNTRXeP7gf5SE3OnRWZAyZgIzs\nXOzdsQ6h3joUj0jBs7+/D2OK2I4TdmLhpWWfHWNOA+OVkIAOAY0dr7y6Dvc88jeEzMmIK56JkiU3\nQKM3wNTdjtKvPkbNzi+BgEviBFNIgzEpQzEyOU9E/SI1RkSxrdfvl/iLiT/jG+YhpOhzCaB1oiGk\ngVFDm2SyKpXVVmUAcM0TGUD1vhEYCrOMOWsEtXopCGn1Abi1fajqacKeiuM4H2gHFa4UfR4jbLZM\njJ48G9G5BaitO4/Dmz6C1t2E3Djg179ahhV3XAmjXWnslPk5xJIVmQAskXH980j12t3uwp+fX4P/\n/K+N6AnaAGsMCoYMxoUzx+Bpb0GU3ohJGbm4PmeIaAHEmg3w0AowFJIxSQtyjketlU4LWkWPTXU5\nEs9vJZ7nNaGDEgmAeotJHAOC/iD8PX1o73IgaLfAQPtZP3ChpQ31rh7Uez043tKMDq8LPn8vzHot\nrDojjBoNYsgySUhARlSktAVkRcciSmeEu8Mpltd9bpe4ydDWNj4pQe6V6C54/Ohs75B7riMwpDfA\n6dfjbIsLzU6fXP/eYAiOYADVjlZpwzjbVoU+9CEJXtxROAI/mTFVQBC2UF5kNxLUUcG4H324iNBp\npdjj9gfw7q5v8edDh1Cj1gzYhRETLyDrG2++JksZLyXZcnv378P4CePE+tTt9QmDiCr/N664ERPH\nl8iqt2fffikSc6556qknpX3A5erDX/7yquRkbOWjCwfnLwqzUoi1uqpa4gsCnGJb39srzx//53e8\n8sor8vr7a95HSclo+PwhFI8ZK/oJ9993H264YQku1DfgvXc/kGJFe3uHsNY473LOJXg7e9Y4HC+r\nws033yxgwF133YmyE8eE4WAxk2Gsg9Gkk7xI86s/PB3ipCzejQaDLDJcTJgksDeoorJK/O3bnQ4Y\nIyyCNspEyUTK64PRZJYTVMVcOBgVOq9GqCj8nxM0kxv+TWj+4q1NsTuD4r8t1nZK/7Dah6tScgUN\nZq87vcvDk72a5HFClmpIKCSgARM1VrilTz5MI+PrTCoILLAazsmEwAD3xT4NB/12vV5pgUhLSZUF\nVBKQUAgdnR0CBjBRoxo7ewo7ujqkoiy6A+HvpqI8k2ES4gvzB+DGJUsxclghzlVV4JV33sTB40cV\nun6YHqdS4ZhMcuZTe9/4vWQ+ELnhxlYK0tFYtSWwwd/5dx4L+zhIYSOSx75CsimImPN8OYDoq05U\nkMGL2mLBiUFRsVcSNvaEURFVLOSCjLX1sm8GHWQD3Hjd9ZgxZqJQ4Fd99C4+Wfu5aBiwQsjWBS44\nvK9Ko7YG0eYISUyvmDobX+/bhjfWvA2HyykJnMftErvAzJQ0GM0mtDkdYisp9pEEIMKCKQQf0pJS\nhIrO3lwivqSod3Z24OCRwzhfUy3q80xyef0IYqjCibLguShqo5P7yM3T55HeIRF1kSqvoknBYx+Y\nlYMb5y3GzOLJAhQcPXsS76z9HHuOHBSAR8a126Mk++wlFysSZTFRkD8XDKS808+bzBNWuzUhxR6k\n6HIsXbwYMYZIrN2yDh9+8SkcPU5ZG0UJP4xckrHCMcaN40jU8OnQQHs9BnB8fszKufDnjNQ0XL/o\nWkwtKhFcu6K+Blt37sCmrZtFoZgindLWQd9VXluxFaRAJz21tXJO1NDgM5RCJ4AbbsbYkaPR1NYs\ntn9r/v4pXNInr8WAzGzcvHgZxgy7DD74sXH7Frz18ftw0ALQqBczNy6mAgiGQSomVeo5MLGWZ1wq\nJazqKCChWBqy3176/g0SpKpJM/1mhfHD/m5xcFASdIImPCcKevJ+s2rm7OhC24VGtFQ3AB0KG4is\nb5/kLCbAnoTMiVdjyPR5sA8YBoeIibKySUCNc43y3PJYlPmGrJyLMsJK0Pd9C8F/I935LgAgTcqK\nTgI0cLt9MBm1sEdooGmvx6E1q5EY6EXtiQNoa65BSB8CzDFIzytBUfEVsEZnoPxsNcqOHREAYMKE\nInbi4NjRw+hxdgvQdVnhcESY9Ni3aysqy48CXifg6wpXHb6XoOksgFgTGWCOiEV2/kDYYpMRlZCG\nuJR0OHvcIu7T3twg7hnF4yfBYrOi7NhhlJUdk/l/wpQpyB4wEO0d3dj37X44vUGkDB2KtKHDoImO\nFhCA45v3W+naU6yIuFl0Gug8bvE3dre3oLPpAjqa6+Hu6YDP7YTP5QQvAQN2MhAYnLNCK/eWiLtY\nDtLyiC1grCDrYLBYxZbJGGGDKSISOr1Rkt5Iewys1ih0O3phtdhQefoMfI4uWPU6JLCf0uvBmAnj\nBQhgUrRp40Zpd5g2Yyby8weg29GDv3/xOUxmC4aOLJTx3tHehpSEBNiNRgGvTlZUyxo4ZDCp/B2o\noSJzIIDLRg6XRCqk1SMyOhKBgAfuXidampqRkpwKvdmGpqYWRFpNyM3OxLlzp+Hq7UJSnF0S4JNH\nDqCFrR0Bh1TjKVDk9TLSNkrVnIMgPmswJk9dBGgs6OzqQHNrkzwz7OlOTEiUHk8GJKRxU+mbNoCd\nHZ1SwdUbtNDT7rGlGbHRNkTaOH42oOYM+//DLRpS4vznyYtyd/+3IIBGAsMQIzSNWQFwfHyOyTAT\nVQ/ExERj8bVL8OCDDyI7K/ui1an6GPKbGR8w2qTey+nyc3jkkUfx5VfrZI4gWE1hSYoB/uSnt+Hq\nq+bhz39+Hju2bUdCSgreffsdCZQpAlVTVSFg0qO/oU3gHXjllZfx5BNPSLh45dVXY82aNYiy6XHn\nPQ/hlZdeEjD3znvuEbs+6jgw+CovPyOV+OTkRHEvYGsAezs3fbMRjz36KDKzMqVtgD2d7PV/881V\nksyy8jNv3hy8/8FnuHnlSlxx5ZX4+OMPUHrgEK5beh0WL1osjAAKdP32id/ixZdewiMPP4y77/mF\n9KOuWLFCgIF33n4bYy4vwoZNW/DX117DyMJCofeXnzktmgZsQ6HIK1kAb739Fu6442eYMmUKamtr\n8MLzLyAnOxt33XkXqqqrhKlAoaqZM6ejr88nKti8B/n5OcJ+b25pRm1tNYqKRkrMQds+JuIMevk7\n51QWZlSLY94znivnd8acLEiotn79EzS+7/tFDynukD1JUegw4/OfTcWi5SJrrQIKKwpi39vUuV01\n/eZ7/XTfEeGZi/O/qjjOFy4pWP2jQOkPH88/BwDkEL73+JCFcscdP8fOXduFOUQQiwlgvEaH0TFJ\nGJuSgZFJSSjKzkEMnX46u8RJoM/tRXxsLFKyFG0C9HmEJef1eEX4NTElWWL2zvZ2cf2ysYAS8Aqj\n1GyOgCsiAtsb6vHuoX0o7W1Hu86CxOzLZH67cHoffTrwyH0L8dC9K2E2e4lVC+Ve3Ff4uLL/Xtqu\naLHHiMqM8vJ2LFv5Hyg774IhdzTGLrsFMUnJsHS3Yuenb6PxyC5pNWIsE60x4/Kkgbg8cwiiQyYY\nvAFhOnBNZu4h7bMieBuQgo3kEezRpwMR41eSfRiL8IJKwq+DhqB3PwCA90hiFhbKOHdQCDFEAC0A\nr9aHBncbDtSW47irXuwAFbNHI6yWZBSVTIUlORXnK8pRVboJppALkwutePiRn6DkisthiCDYQFoa\n11d+L2MkhdamhUvAbXdbJ959bxOe+M9P0dAKaCIikZ6Ria62Jjjb2hBjNKMkORM3DijEtJw8JMRG\nAt0OdLS2osfRLWzZ2IR4RCQmSNGRgn7dDlouAHYm52kp0vPuaW5FTVWVXKuMnCxEpaYAvgAazlSh\npr4BhtR45BQOl+r71tJS/H3XdlQ7HWiBDz0IiKCvMt4pL6uQ8+wGPeIMJiQaTBgzYDDGFgyG0dGL\nYLcT8VGRiI9mAtwDl6cXlkibFGsJAPT0uIQ6T0UoDpOAKRKVnT409njR4/Ohrc+Nys521DnbUNvb\nDA/6BOJJgR8rB+ThZ3NmwW42QCuWwTI7XFLxCGPIP/rs/QsAIDenAE8+9RRmz54d1jQwwR5pQVt7\nl7RQSfzh8wu4SNCTICpbzTk/kUVEgJjz76JFi6UCTweBTz79VGL5McXFGD36MsEfN2/ZgT8++yza\nWtqwfPkNuO++u+T1J596Fu9/8IGArdOmTcPChQuFos+5l+vS8BHDUXb8BEJBrfx808rlOHBgH25c\ncYsAADweihJSM4aA6YYNG5QCpcmEdevWibvLlq1bMGxwBp58+s/4/VPPiJvMN5s3wB6ph2by0sUh\nUTGOox+00ovLRJAWAu1tbTBRRZf2dAGmACFE0PaPD58vIIkKkwwCB9wUwblLdmCkhUtlL/x3Tsac\ngJRkmGJ2OsUnO6weK9TccMVbTYqZrPC9DPpVmq6KeLMiqgjhhVW6tfxuUrg4+V8S9+IEorgKKMJi\nXEBU33Umd9yHUCoDAfS6Ffq90MnZiqAKeYVtXSzseQ4DDO4+j3iKMmnmFmuLxC3XLsWC6bPh9Pfh\n3Q/fx6dfr0MnheMoVGOzCWJH9IeJpCR8YbcEfp7nrCSpqm3Zpco/k1YVBOHxsV+cASwnLwbqfE3p\nqWY1SwEAeN04GLgprgtGWXx5v8WZwWoVAEAs2jiB8njCIo0UVBkxYBAWzZkrFL/t+7/Fug3r4XRT\nHCsQbkNQPOCVe6lH0N2HuZOmYdmSpejo7ca3hw5g8+7tYiHIe8wk3xQGeUgz6vV4ZKwxqeM19bLi\nR9q/ULkUATheW44TMijIAuF30dlBBZek0kzqWbidgqwAjgteB1JKmWAzCeZ15djj2FCAkCBsRjMm\njyrGHSt/ghhLJJxeFzbt2YV3P/0ILQ72OVoUYIcaC2F7E7lPBn6/IkLGv4maccAnY4jPz5SSCbjn\npp8iUmPF1/s348O1n4l4IpXz6RvMhIP9+MIEEOV7GhJB7hXPg4AOJxOeMy+CnF8oJBWNvMwsXH3F\nXCQnJAqlklUW7pef7XHx3gSF7aE3GuQ6MbZQx4UIw1DEJdz3SHvMWVOni1bAgb37cL6yQoAZxvwJ\nMXG4bv4CXDFhCiIMVlQ3X8Crq9/AsTOnRFiGzA7OC/QR5rGStcBnl2CVfIffL4AVnyOltYVMDLa7\nsMKiHAc3Vv/5u+qbrDADwurJHPMB3yXdBKojh4M2zil9vS6EPH2oOXUOXVXNogTIPnvR0mDvn8YM\nc/ZQ5IybidRJV8CYmgk/xXjCvX1UCWH15JIGwP9rAEBRHCYSrtAAFUFgJl8aXR9i4Ma5r/+OliP7\nkGE1iP2a19cNRERhyGUzEJM0EE0dPiSn5Aj639rSAJ+3C96+XkTZo5AQH4e2lhb0dDuE0ZEQFwuD\nLgBHRxPOnTsGT1ut0pMpi7kgEEo1jD+Qqg8dbEnpSEnLFIsnj9uHXpcfUbHJGFp4OaKi43Du7GkZ\np/kDCkT0ioh13YULqKu/gJi4eGTk5kNviURlUzv6TFakFRZCHxsjFXkKDZL1IDGuYIS0YwpBR/aS\niLzRBjCAbke79IDyGpGayoSwz62wwNyuHni9tA5kwV8Brwh2kk1CG1lxSTGaYbTaYTQp4G9UhAUR\nBi1qThzFye1bxa3DGJmI0SWTYbNHI+ByYuPH78DV7cDP7/016ptb0dbajM6OdmGriCBpMIi0tHTE\nRMcKQ+lCba1QnjmGR40Zi1HFJTJOv/j0IwkSrpq3QH5f//WXaKmpxa333Ce2Qjt2bEY5NWYysjB5\n8hS4+/pkAad4amZamlQo3T1dqD5/HG5nMzTBHrQ0VaGruY59URL8c6xbzTa4PH74fSFEpuZg3NS5\nyMwejONHTqCs7AwGDy/CjFmz0Nvbjf17d6DsyF6h/F+zaAW0egtKDxzA0WNHxHlgyZIlYgn3zab1\n2P3NlygqLsLkCcX44O3X0FR1TKRIhbknJZH/fwCAPOdhMa4QBTw5UNiGo0BGikyTRoNJkyfjqaee\nQklJiZLQ9du+DwC89dZ7eOLx36KurlbRBhDQj9UiLbKyqMqcI60WdCfivmjn2+1w4HhZGYYPGyZB\nHUFt+i4fO3ZM1g4KPZExxgCM6+r69V/L2sAeTs5drIifOnVKKKC8XlS/ZxtBe3sb3n77bWkroCDT\nL395j8RDrPKcPn1GWgKoHl12vExYAazUJyUno6RkHMZcPlrmcgIDb739Np588ne45+470ef2C4BA\nsTgm7zffvBKNTY14/PHHcerkKfz6oYfEYaCppQkff/KJVIBYgadf/Jr316CocKSoQ589dw5btmzG\nlCmTRYCPyTtbAbieESAgkM5kXa2mky1ht0fi0MFDUukaNnyQ3AWHo1uuEYNWnjfF+1iI4HrEQoaq\n9cJWiJycHHm2GDAz4O2vCfBDSX1/Sj+/6/ssSHUY9Kf9CvgcBhbU17lu0tHjIhrZP+PurzAqipLh\nvhNZIwS5vii2JgnRdxRJ/x3w638OAHC4rv96I267/ae40HABRiPjXyN0Hh/iNXoUJaRgYkoGZg4d\njtyYGPi7u9He0iLrPNsiCRrwWzlvubqcAhLTB54CgdVVVeKSoBS8ksWjnRaS3S4v3DYbTgV8+PDY\nIeztaITDZEXBkHEiVNlZdxJpyVp8sPpJTJgwRDRr/FRmpygEr5s/JMxRjfTABxHUUqeENod23PXL\n/8Rr7+wAEgYjb/YiDCksgrmrCevffU0cALR+Si1DRP9GRGZhbPZwxOkiJCZ1ebrhdDthZ5+62QBX\n0A93wAePXymWRUbY5X1Grqt+ssxC0g6g9N4T5GfroSI8rI4fNY/gDMNVh/MEhfZC+iA6g04cbjiH\nb1vPwSG0fc6CjE9iMLRoLIyxCThx4hB66ssQZQjipnnpeOJ3dyMyj6ALS4BM9sNsuxDjPOoP8bVe\nYTX5u7rw0Udb8eiTn6ClU4eCQYPFmaq9sws+lweRehNKkjMEAJiek4ekCAvcXe1o62iT+IrxKNek\nmORkYUE2t7bIs8uNiSnbjnl+ZNO0iVCzDjFxsYiMiQEbdhtr6tHlcqHbpEFEbhbKuzrx4bYtONRY\nxSMkTCGwB8e+XtD7IFVm5Eoo9pcKxyHDYEFRajpm5OVjYEwshmTlSKzubm5GXW21tArqWYTUGxGX\nmCQgfUCjRXNnF9r7gjhR70B5Sxsu9Hai2tGOWmcneuGHGy7omYOEfEgLAYuT4vDLBfORYLdK3HDR\ni4PtFSoD4EeXKIUBQNjA7Qvi3V278V8HDwoDQAQgEcKI4SPx/PMvSmsS57/s7GwsXHSNhEkdXd14\n8803waIU52mjXocLDY1YvfotYSJdc818pKemyPsYhx/YfwCDBg/FgAEDYTKbRGeEcx1zEhbYOZ9T\nsJj6awnxCTJv3vfA/cIMp1Us510KAG76Zqe0lrGgTebbz+64HR3tXXj44YdR31AvjnmMKzs6OrF8\n+XKJK+jsw+1nP/uZVPpffvkljJ8wHs/+8VkZC2znJ6OI7U9kfHEtI+tAM3HZtSEGeKy0EWXl5M1A\niwsVLTT4MFHhkA+cl3ZtJt5+wdYkcGZATn9NJqhqJY2VSqn06RTvUSaMvbT5YjJD2ubFmZuFHoXi\nzmo6afmcm9UAj1UeLkREkkhll15z6fFh5VAvCSsvHhN3oT2ZzfI30pJVkIDJFScKUY0OsJKi2Ary\nGLkP0t6ZmNMSRnr0jQp1jg+axCNsy42wiuWdj8FpIHBRtZ779Pi88pAR+GBytuSqeWKjeOjoEXz+\n5Vqcq65U/GH1Okl01fYGAhpsX+DnmfRIgEvhM6NR/B15XRUau+Zi/z7PRaHcK7ZNZjoyGHTyd25q\ncqeCKAQ+RKk9LKyjtAUooIII7fBahRkDYqdNdoROI8ENe7+DHi8GZmRj8sSJcPa5UXr0MI6dOCHH\nK1oOTP7Yy007HaKOPb3SBjB9ylSUTJyATqcDn3z5BQ4eOyL3wEiRFp+CLHLK14vyP/3h9fI/GSDc\nRN+BOgg6Ah3sG/fLOXMcSAXb4w67Q4RgFnE4iuaxJ51FJKNMRKrdZKTdJsmn2MAFaTmpk2STY4uI\naozFhqtnzcHSBYthtdrQ5XPh5ddfExo8xwkTLw4EVpM4rvjQEKBhf75YL2qoSeeTxZUsAR4z7QVX\nzl8i/YZ/+utLaOxsk2eH94PAhhLeXrKd4/jhpgI8EmjIvkgjk+xR6U9nBVWsxqxS2aIQGvv9WYnk\nZMKxwUCVHs8RdrtMPOz/J5DAWFjE+fR6Sax4Tdk7xr5OZxfp4wfkOWdln+Mp0mrDA3f/EpOGFMuk\nvKWj218AACAASURBVGn7Nnzw6cdodXQIC4T9eHz+hG4mTh4BAVdUcE/mCFKWxTEi3GNPGhvvefgZ\n5iRG4EgcPcLCUfwbj5PzDME6nqOIh1J41GKVZIvAm6hpM/nTaKQNoPnsBbjbncJMYu8ksWsZauZo\nWHKHI2P6QqRfPgHWhHhZ9P38Y1ADk8Ek11YU+3+gx1RpRVCSjn/HBUACjUuKhApjjWOafYb8WyAE\ng0kPX9CNGH0ArlNHcfSrzzEsIRKnD+9FTcNZSVhzR0+DTxONxo4Axk+chVGFo1C6fw+OHd+PHmcn\nLh81CmPHjMGpsuM4cfy4WM+VFBdj8MAB6O5uQ+mhPThfdlDolYKMiDoy2RoeoWcK949lPEVlT56b\nALUAQnpY7PGwRafAEhEJT69T6LHpOTkifkWQib2jRKdjExKRWTAEOms0jp+pRZ/JhryScYjPzxPh\nKaL9pEDKAFYsYySgJlhB9WBhgbDCEwrAZGb7UhB+L0E1skgoiEqxUZmFLy4ZqqsJ52gKxHG8URWZ\nbR9CCWXFwqgFvL1oOHkUbccOwtPRCT/MGF08EZk5A1B9/iz2frNWAIAZs+fjRPkZ6PRaTJk6CVpN\nEF9/9RVqKioxamwJJk+eiraWVhw7dBBVlVVi5TdkxEhEJ6XIOP12+zY5NVtUNBzCVnBJAD5hwhRZ\n93bt2gaPqxeZGVkoHjsWve5e7N65HRlJ8UhJiEdTQz1K9+6AzRyA392B5rozYiMlSkDSQkr6shZ6\now1+LxCTnovBI4sxpHAsdHorjh48jJMnz2B40WjRioi0W3D44F7s3rYBcbHxmHXFYuiNVpw+c0rm\nI7bdUWwoIsKOg6X7UFlRjmGDc9DdXocDOzcA/h6p0yjCemHk5kcpzP9OAnRpuf/+T2rcdrEAGmYc\nKEGesm/+R5r4M398RoKY/nZv6uf6MwB279qLV155VXycm5sahO5MBocoYLOfWKPDvGsW4qe33YbN\n32zGiy+8gJDfi5IJk/D+mjXYuHEjbv/Z7ZyMhWn27rvvYdrUKWLRxgo6gbO4xGS8++670qv/9NNP\ni2gg19JHf/OoiD0y6Fu58kbp86erg6O9FZOmTcOWzevR1NyBlTfdhC2bvsEtt92KJ3/3OxEWfOih\nBwVMmr9gCT75dI3gFs8//5Ls68EHf4Uvv/wK27ZtRUJcvAR9LNjQnnjrtq3iOkBaPS2qPv/0M5w9\new43rFguVoaklLMflGsMgR8mc/SLpsUVx2NVZYUEuaMvv1yC0IrzFdi9ezdSkpNx9dVXY+fOnfjs\n888xccIELLluMcrLz6G8/LQAJTNmTEdubraALWQTzJg+A5MmTZS1ma0zXIvIgOD8/qc/PSeWgtwP\n1+aGxgYBQUpKxgobgGsOWy6ysrKEQaAq+dMiKzU19aILAH9nME0wgRtBcK496u+MYfm76hLA97CQ\nxc9wvmHBius0k1bGWWqxhHMira01hjAQJchteIQKCBCex2WM9hvJ8pz8GAPm36D/f++jLMZIgUmv\nxedffI6f33kbmpqpMm4FvAGYQkBsUIORMYkoyc7FwNg4xOn1yEtNQ7TVirbWFjjaKRyn6NtQVFV8\n2o0U5nOivrZGnHTY+sb4UqMNweX1osnhRItGi2qdDmvLT2BfZyPcVhsysweJTV1fWzWmjcvHqjd/\nh8QUWzj54pzLdVRpCVYumfLsBrUBBEIUurbh9TfX4ue//At82hhEjJ2NCVNnwNTZiLVvvgR42qBj\ngS4E5NuSMSFtGAbHZMDsVaBBZ6gX2gijaMs4vW40drWLDhXjGsaOFB+0m6yIibAhxhSBaILBXNZI\nQmCSGGKBghX5715oAQ7pXBS+lZJc6kPo0/pwvLUKm+qOoQ1uiQs4Ees0dgwcWgRLTAwOH9qLoKsJ\nqTbgoTvG4Bf3rEAgNgFBkxF6Ldd5rnVc94wIwCy/a9GjCKv29uCtVWvxxO/XwRyRJK4daz78GPXN\nTjkSelhNyszFspwhGJuQhEhaGLt7YLCYEB0TA5ezV1pGxeKbGkpaKOAWiykut/zPvIuUf1ukTYpN\nXZ0OtLa2IRTSIDEpFW6jDrsqyrHt3CkcbG1CPfyS+DOLEJvWi/OwAoxIGycvQ3haNhHMD1GwERgd\nFYvZI4swMjcfieYIaFwuBF1ueKUtWAMDNScyM+HVaNDlcqO8uhoHz1TgZEMHqru70eTvRgf64Ide\nEnLaS4ouGQH4EHBFlA0PXbsYadE2GNUYQgJiAgDhA/m+92P4nkrELACAFm6f0gJwCQBQ2txzsvPx\nu989iVmzZgkAy9yBVXWdnhbkXmE9saA2c8YMyXcYW3/zzTdy/adOmQKb1SwaQR999BH+/sXfsXjx\nEixdulQ6EveXHhXVfVbmf3H3LzB71lR4PCE884dnxKaV38nW4ZJx4yQ/ptgr28+czh5hG5BZRRcX\narxw3iMIXnG+CnfeeRfuuONm3Hffw5LcX3XVVbIeyb0LhcS1gOvS9OnjcPZsLaZPn4mWllYBoJ9+\n+nGsWvWuAAtOArzF1y4IMSliUsfJU2g14aSRwT0nSQICTCa4yPFiMMBlQs7J00Ufx0BAEUQI0+5F\nmIcJAZPYiAipOPJhVWn+HJTq72riyn8Z3PX/nYkKE25O3pxYuHCo1DKpHtDWi8l/2D5MsU9hQEjv\nZYVCzaSByZtKgWbyx4D+oho+k1KxfPBLIsfjlcCUVnFMzOlvbjFJ8K9Qm1WxQsNFyrbqg85qzvAh\nQyUpramtRWVVpQxmJuqqrZ8kTAa9UMmYyCj93QZJpBTqswJMqIkUPZ7VhF1NlJVFLij9d7TeoD8k\n36P2kMt+KAIYrh4ziGTCy9e5Dy6O/DvBE8VeUVFPZYWc94bXgJUA6TfyB1GQnw+zxYraC7XCDOHn\neI2UMaEm1mR3eIWORc0F0l8IDuwvLcW5ygo5Z54X74WcI5O+MDWc53IpoL8kWCgOEWwl4X7Z08hK\nMoXzwvecgQW/k4kzN45T3jOeGz/H82BFjxvfo1adVYCE99Lf50FcdAzycnLFVslstWDtV1/hxKmT\nsh/pyXcr+6AaNDcmoRJg0NKRPVgkejGBCtPHr5x9Ba6ZexXWrV2LLzesv9TDT+q/z6eMc44L9utr\nNBIocVNbP9jawcSY14zXgOfAscwFm6i+UFvDKvZqtVxVPpbWl/AzLIExnR7CXskqs4R/V9sYCCoR\n3Op1OuX55fPE68rrRz2MohGFEmgeP3lCKr9y7U0GeWZEXDCc7KrXVn2GuQ95jWBJT89FQIOvKaKR\nBOXomhGptCwQTPN4lHmkX8LN+yuOHiIYqpXv53v5s7S+aDVwtnWhs6ZJmACBXqV3mImg9PgxAY1M\nQXLJFcieMBOxg0cgaI2UxZD0QY2qGhzOdeTaSMKurCJ8bvsfD1/7vlCg6r6h6hv0T3SEpSSAKMEn\nhSmjAH5B2PVBGLqacHDtR8gIueFqqMCh0l1yzWJyhsKekA+fLgHDRoxFUmwizp89g+6eNhiNGuRk\npiHKakFdZYXiABJmqPR0dsFqMSMiKgJOdw/aO1pRffYk0N0Gu90MvZWBYBf8tDqVK6RUWtUgVqPl\nXED0xnKpb1ZOOnxW4qUdgtEWBYstBqbIROgi4mGIToOJIoOjxyChoAA9mgA0ZrZTEQa4RNxTr416\nfZVqLzfl34stFxcj7h8OrgVkUco8ii3QpV9BB2eCHSb643W0oqe+DkGHE85WCiUq2ij2mCgBAlvr\n60WFl6rJY4ovh6fXgcYL9ejtcSE+PkEWfbIO+LvFbEF8QqK0xp05fxaWCDtGjBgNrdaAE0ePoKmp\nHjOvmI6kxHjs2LJZxHqycgtQXDwGTQ01qDh3UhSzk+JiYDXo0dnahIrTR9DSWKPoIEBhOojwdLjQ\nrTPa4evjRGlCUsYgzFv8E7n2x8uPorbuAgbkD4fdHgNvwCMLv8moF+CeDJEIq10s4SjkmpKaJFZy\nnEtOl5dftDFiNaGp7jS++uwv6OuqU8ZD/4BKLv+PlVgu3ZuL7xB1/35PQD9bzP7PxcVx8A/pk5L+\nXxwTGg2uWbgQzzzzB/E77g8A8Dj5br+wWTilGtDZ6cR/Pv0HrHn/PTTWX1DAI/axmixiAUiNB2uE\nDXn5BWhubhKLPNoAZmVnC6WelfFVb76hiI35fRg/frwEhF9//aWsfVmZWeK7TVVnAqis+hMwZ6sC\nKzscv+xvHzt2jBwTg0qCmNQjWHHjCgnuTp44KXO99Ia2t0uMlZySErZA1mDAwAES2LGKwyT8ueee\nxf3334dVb7wOsyVCqP3Lr1+Cbdt2Y+asmcjKzsInn3yK0UVD8asHHxUggw4CDBwJ1pGNQFBq9apV\nqKupFQYBfamvv36ZMBfYWsDk/K4770Rd7QWsfustcTq45ZabZU5mcMnEmcJVtCo8cfKkBKhkSfA6\nkWl38NAhZGVmIj8/T+wzaSHIShPbCxin79mzFympqchMz4BeT6pskzgtcB9KMuOTY1GsCan/rWxs\nKeAapToKUDeAMQyvn+ocxffxPRdZgGF2GV9X177+LQYXmQEej4wnrt2KmJVSeOB8yqS5/2ck/gvR\nPem7LhT9565/HN//ij3zQ0/Ed1979913BJCS+JU6TyyS9HklMcuLjMOwpGSMGzAY04pGIz8uAa6G\nJpw7ckxE2uJjohCfHIducQJqkIpxcpRdWiw9jh4Z+y6vE9EpSUgaOAhlza3YVH4WG86exjGPA30s\nGOiAAWkxGJFsxszJI7Bs5XwYEmzhNglVWyF8zOqzzpiQ/7FQZ7Fix84yXHnNw3D1maAbNhHTZl0B\nfWcD1n+wGvB0yRpsgBFDE3IxM/cyxPoNMHoDMNotaEIPmrzdqGppQF17C5w+N9xejwj6snE0AB+M\n0MOmtSI9LhF5CalIsUYhMqiDMaCFhuCptAmwHSB8tyhXILdGozAX+J/MW9Qa6kONtwOfV+xDPRzQ\niVAgBZWjkZqeDYvdispzJwCPAwPSgEcfmI+FS2YDCWmiO6DVc21nrzoncCrgUIOLLEO2APiELv/O\nW1/iqafXIidnEFbetAxP/uFZnK7pUXIN6DA2KQOLMgowJT0LJrcT8LqRkp4GW2IiQj09Ml+xNZXx\nXWRcDJIowqnVor32ApobmiVGSqRVJwUgqZ9R34ALlXXQmC2Iy8nGSUcrVn27BVsbq8HmASnRsN2U\nsZ/ZhNy8fGFD8XlmfEz7zAMHSyWRVQTaFVifMVY0gGS9BYOSU1GUnYvC1EzkxScgitZ7zm50BL3o\nRlAE1E/V1uLkhTpUdXSgU1J9HVzwCDzCHIjfz1zJ5XJK/BkfCmEctHhk6bUYkhoPKykJUgsIAwD8\nUUs7xEsFgv5sIKXwQPavBj19Prz37R78d+khVIdjPbaKmoxWXHvtEmFl0cHA6w0IG4vCoPPnz0d6\neqqAsbu/3S0immR7ZaQmi4bU1m3bZF5nrz+vFd1WIqw2AVvIJGhobMTnn30m8x5jgLFjS4Q9TFCA\n3zdt+nQ8/sQTSEmOxpFjpzF58mQBXletXi0Wg4wTyQRg0ZprDIHSzMxsXDX3atGv4TrCY922bZsI\nzvJ4r5x7Jb7+6mthjbFdi/Mk1y3GoIxlx48rET2CDes3iPaNpuS6xSF+kSQypGf280QV2nnYjkWl\nWfe3aOFAI4WWk6IkD2F/eFYEuRBKoE7hMlLExRbMJ4OWExmrh2ri1j/AVkECeS1cMeJg4P44oRB4\nYMLISZrHxqSQdFDul4sVA20OIqn0U826j7Z3SuVRXRAUGzwCAYo/OSvKakLFpJBBnyThWqXvWrXc\nk8SZffrhSrqInDEACWsU8JhZ5Vct0Rhs8XzsUoVWdAr4ryrMx0mdAiQCOrCH332pz5n74Pnw+Em9\n42IniVOYxcB7xfYJPhGkXRPw4Pt5HhTd40aaPFsN1NYCnqdir2aQKiy/n/dYfWh47lxIJdHyKdeT\nQa/VbJXz5v1V2heU/m1eF0nO2JJAhfaeHrk2TOqYLBMoUHvR1WVNhCCZPHo8Ao7wZxXU4XulOi/e\ntUovvNDs2fvHXtGwHgSPV01SBUjie8LAjmI3qdgcMoEmSspAjp9hwqiKS/LvDEQ4tljh5OdY3ae/\nsVTRxWmBXqeKywQ/LwyLcPKtjHc+VAqrhMdGQIDnxSCGDx+pn6y4SOVdx3Gq9JxzC8giqYxpJonc\neG3V50/QPPn+gFTYec3l72HVej5bfE0qA6IX4ZJjFJFJs1mOg2jlRZu+sKAkxyDHi9DkeW3CTBh+\nhuOYIJuIIFHXgG0mBHsExDIr4zOsH0FRQgb6RC4JwMk5EkgMH5dawaEwnwq+8Vqr7TqifcBWHJ1i\nfSnK/yqQFwYLuUaL0GgYeOP7eRzcDz/D4xVXDR5ITx+qTp5Gw/kqhLxcUi7VZQJaC/SZgzFi9iJE\nDiuGMSlLKONc2C9WIcOFHbXFQF5nVaNfwq+KB6ljWbknCvtIuX9Ey78b9ImaM4X/+MyE2QS8B9ys\nuhCi4MaxjV/AVH8eUT4n9n27WbQljNEpGHzZFMSmD4feGIsjBw4L2DFpUgliY2w4VLoHZ06VoSA7\nG/PnzZMAf9Omb7B9yxa5RtNnzsTo4jE4V1UlivW1R/dJO8CUWTOE/HbySCnami4gyKoEV9WLFS6V\nBRtWT1akrvuBBOy7N0BrMAH6CGip1hyXgewRxYjJHYionHxYU1PQTQklzsF+lbb3w8Hu98F71Tnh\nX4XGKlCg5qec47+/6UNB2LQaxNITu7kRWz7/DDUHDyKZbTQLFiEmKgpv//WvkqyZ7FacKD+BoN+L\nEcOGIS05A/X1jdj17S5JGEcWjpJ5mFtDQz0OHymFPTIaxeOmwmy24kjpPlyoq8Loy4uQnBiLvbt3\nSEtTWkYexo0bi7OnD+PA3u3obm+WFgW2OjTXVQFBpTIk4rQSmKq+1WxoNcJsT0RKWgFM5mhk5w5D\n0eiJcPX5sHnHJlnYx0+YhtzcPFRVVWDH9m0yJw8oGIgpk6cJ9fXzzz7H+VOnMKxoBBYtWgCHo1Oh\nm1eex/Qr56KkeCyOH9yOtR/8F+DrvPRAXCrC//NbEX7fDwEAUrD5X2hohEM8+d7o2BgsXLQIvxIN\ngKyL7YThUF6GLVmE3HRaI/bvO4hf/8cj+PbbXTIfyloSDCI2LhF3330vJk2ajGeeeRYbN2wUAPaX\n99wt+kCk6rOqzTVuzpzZuOvOn2PL1q149o9/RMDnhi0qBn/5y1+wcOE1+OMf/yRK/ARN0zIyxCaP\na9711y8XgUBHF8PbEKbPmC7JNOe6xYsXYveuXWL1+Iu778Yzf/gd6uqaMWL4CFk313+9HrNnT0J1\nTbOIN3E+5VrFueWxx36jAMWs4NY3YNfOXcIsYNLNvl/OJwSHuU5S44Frb8EAxZaS1XmnsxtZWZlI\niI+XijlBB1JemWgTACLVn2sZe4k5lrj2EDTiOkhmAedfMuyYFHOeMRADBNDV5cSZ06cxdNgQhQ2n\n06G2liASxEGDt577YCsYW06UtVyP7q5uOT9LWKhOHWDqWFGSDIUJ9s+2/pRuldl4cV7ox+bqH7PK\ntN7PYpC/q20l6v76Ww5+N6H4104E/2re+t/8nU4U999/v9wn1WaRx6UjG5VWhRoTsqNjMXbAYIzJ\nK0BkIIRIrQ4JUXZER1pQXVUpcxnZvAWZmUiOjUNPpxONDRfEhSUmLRkJeQU47+jBtnPn8eGePdjf\n1Yg+nQYRpiB+cdM1+PmCyXA56pA2MBOm1AShuyuJ2A+wHMI99oxvdBYLjh6vwRXz7kZTixbmYRMw\nacoMuOrOYvcXH0gCx2pvBKwoSh2I8RmDEaMxSmthq8eBo82VON1ah7a+XrhCfnhlvVIB5RAMUIpp\nBAQsMCAaJgxITMfwtBxE6yyIoJUhjWmYtIbb8bhSKBpMGsW1UOlPU9KNoB/1fgfW1pTiXF+TiA2y\nfddijoXZYkNI40d7cw2itSFMHZuGO2+ZjElzSoDETEWIVmrpF3kFCIUUEW4N/ZpCfvS1deC9d9bh\nz//9NSZNnIkbli/Aw088hT1lDfCHjLBCh9HxqbgyPgPTsnJh9bmgD3gFeIuJj4enp0e0ZtgayfFK\n5nZ0TLSMYbbj9PayhY2FQdpfG0V02BcIweF0IWCz4kR7Mz7YswO7HA2ShAepbSWWsiHR96GgJ2nk\nuXl5oqNFIV/Gx7Se++DDD6Q3naykgJ+ihIRgyJzxgk2sORFxGJqUImMx0WKF1+9FdVcr6hwdqGlp\nQUfQB8LcPujgN5jgYcE2XHzg/DFh/ASJnbk+MRdIJEUfwK/mz8e4/AzYVPefiwAAQTu1gBF+sr6j\n9UEAgDlefwDg8HcAgLTUDNx77/2499574PUq7HIykThPTpo0CVnZaWBa++6ad1F24gR+8pOfYvDA\nfAEA6EyzZcsWYYL995/+KHPiuXPVIgLI/cycNQuXjx6N6Jgo7Ny5G6tXrcbwESNwx8/uEBYV59K/\nr/27WLSyqEgRVYq8Xr98OYqLi+U6E0BlTL1hw3qMGXMZPvl4rbDSyKAqHjMGc66YI2Ar17k777xT\nAGna2+4JC9WS4UvANz09GUuWXC/gwE0rbxLNA645mlEL54XUqp1MhmExLAn8mTzSsi5cheXEqSKj\nTDCUqip7fJlQ+iTp4ASlWn6pQl6seqlVMwbB/YNimZDDSRGTgYuggFr1IyExDCwolHAKpZFOrSCM\nAhQIlVRR4+fCa7WyN1Tp6VcE1pSEsf95qACA9Ef7vLLY8H+pVobF0xRRtrCFYTjpFP0DUoeETq4k\nkEyUVPSZyZOaDHHhZTWXiSVfF80EJr1hJVLZf79qrUqh5qLPRVj62ClCaI+Ue8EkjwujKkJBwQ0G\nPwQY1Eoy963S3xTAhT2kCqNARcNVlwZJrCRA+m4Lh1pVFu0cKFR0Jn48VqlKh8+XgILFapbrLgll\nWFtBBPIEjFBU3oWpEU561EWd/fycdCWBCyfQqoaEBG5h1gj3RdSM111NztR+caEIs6YTTqBVerCa\ncKmAC7+T14XnyUBHJk4K+xFjCgsK0bNereQSbePvfR7aCJqkB4j3hmrPHEeqlgLHMfuQSZcViimT\nun7KxJw4ldesAjKpAQY/z+uuXgO2yXBTACyOX0Uws8/jluoJFyCVIs9zkXHsI8slJGON91fYMuHz\nUqvsrNDL9Q7b76lBEp9XbrzGvL9SMTcoyT6TeFY61EDqEvDBVg1lDMt56C4p63Jf6v1VwZqLbgDQ\nyELC+8zrwWeZ155UK2mHCTM0OGZV20uKWRFYIRjF1hK+T23/4WtqFZ3PKgEOq8EEi0aPrsYWnD56\nHD3tXQgS2RcRKA1CeiMCWiOiCoqQNeEaJAwZA0tSMoImM4I8X+lFVnJcyWc4VvrVPfvPT+o4UgM5\n3sf/CQCgJiX8vEnjR7QhgMo9W+E9dRgZFg3279yE5sZ6wBCBkZOuQMGIKejoDmDb1p1Iik/A0oXz\nEQp6sXnrBlRVnsPAvDxcOWcOIm2R2LFzJw4fPSKipRSLZCWxo9uByqpzqDxfLmr8uVmZsBo1cHXV\n4+ypI+jsoOUbAQCFoq9h2wndAS1R0OhM8HBcU3VYy6We8xKdHCywR8chOikdhphkBO1xiMzMRUxO\nHiwpqYDNij6ND37p2WOz2I9Xwv5/AgCM8aT6Q9CypxtVxw6jo+IcNL3d0Hnd4tjiaO3CsKGDoTX4\ncercKXj8QQwbUYTs9FwR0KmsrRbaf1JSqnj2chxmZWfCQB0HDXDm9BlRU4+Ni4azqwMtF2phNRtQ\nMCBPbJmaG5sRZbcgFHTh3OljqKs6w+bccJDIiEYRMZUlUGtCKKCBxsaATo9AH/v981E8fjpy8oZL\nEnbs8G4R3Ro4eAxskdFoba/D+crziLTHwWwkI0a8+xBhU1oqSNJhu4vdFiE+zWTHMfGiAnBUZAQ0\nAQ9aG86idOuHQMjTr9gfpiD8kEpZ/ywmfGtJCeWPfJYknlaDbTES+DGK9I+nQ+qIISPr8uJiodfP\nmDZNWqcubQpbpz8A8OknX+Dhh38j1eWw+1dYndyCm25ciTlzrsR7772PdWvXiR3jjStukPn9rdVv\nSTuGLTJKaOzXLLgGmzdvxpfr1kmgRpo9hQKHDRuOV1/9q9gDEpzNLxiARx55WKz3XnjxBWG/sQ93\n587tUrV56KGHYLPb8NZbq5VA+uRJAYep8E/QoOL8OQFd+RlurKwz0aMYFMcXaaGcM9gfetvtP0F1\nVZ2IRbEaN236NHy59jPp31658ibxg2Yb0LKly0QVmvZRLEIMGz5M9n/yxAmpsit6RBECAowZMya8\nviqaNFdfPQ8TJ0yU6hADXI45JgTjxo3BwUPH8P4H78uxTJ40Dhfqm/HiSy9IAMzjNeg0eGPVajQ2\nNom+Aa/rqlWrpcWB5xsXE4Xdu/diz55vcduttyE6JlLOlT2r1CqgJSDPldeAATHFqtTCDQNbri3U\nK2CMSR9u/s7rzYSdzAAeMytvogMVDEq/K99LvQGZ3kMhtLe3S3zEGEpaAsPaUv1/loTte0CuOuZU\n8OB/k8j/33yGgl6PPvqo6FKom7Thhh3nuaITLknWGlCYnYcBickYlp6OFLMJNoSQmZgk7YL1La0C\nzIS8fbBbzYiPjxaGgN5qh0Onx/HOTry3awe+baoTSnhyJPDEvddixaxiNF84g5isZNhzMgGzWfRs\ntHp+6/fmdyngMEYOQW+NxOmzrZg7/y5U1rgQUzgREyZOQePJQzi4dRM0uiD0gSDiNJEYmzcChcnZ\nIhZd19uG0rMncL67Xoj4ARYKAmROhun8bE8Ot5CpnrB69vqHfLDDiNyoFAxKzUaaLQH0N7P4AXNA\nI60BEvtIfbFfRTk8WXD6bEUvNjccx6G2syIEyPjMHpEAny8Il8eJkLcbmTbg1mWTsXB2HgqK8oGU\nHMASiZAI2LHnn60A/BIWHiXQFAEgb2sbVr/xCV5+5RssX34jFi2ahft+/Rus310tNHi2NBIAcfib\nYAAAIABJREFUmGVPxtzBw5AeE4FAjxO+sFMWxx+teqMT4kTEte1CPVzOHmio2E/XLGGtGdFygfaW\nTrEANicmwGnSYVflaXxyaA9O9XSIw4GTtXeuPQgJGPjLe+/F0uuuQwrX8HB8yPiLG1v1GIeVlZXh\nxZdexHvvvScgACvsWrZ6sKUXAREMZEOCMdxj76fWF9uLw7J9HFMGg1mAbtE7CwWFYbVg4QJcf/31\nqKutEyu7uroaARWGArhzylTMu2w4oi7Kv6igE7//XwMA5Cs4PX1Ys2cv/nzguwyAAQWD8LfX3xDG\nE9uWmKDPmDFZ7AAJtlKZn2BqSmqyCI47up2S7zCnYz7G+ZTx9JSpUyR2ZlX+L6++KnnbiOHDJUEf\nPCgfO3bswdJly1BQMEDsZysqKpGQGI8pU6dJbnj9sutx6223Sg7wwAMPSBsUhWIzqLvk7JY5lvPh\nxx9/ipdffgVnT53E/EULhdXF5+zFF17B+XOV2L59u7RUvvLK87LGr137JW64YQWmTi3BK6+8iSee\neEJYbWQbUOhVM2TOTFlRRVxNr1QweUBMnKWyG6bIq0memkgroIGinK309Sqol1otVyvqTATUAEAE\nmwyGi4kYv4/f0Z/6rwIAyr4Uexup6FLNV5DhwEWNAH6WVWYuANyUXnxW4rySQPCzdAVgBVIVx1Mr\nsSoNmcmEVF7oI2+1yiBXj4HJrvQzUSVUr1D0eQ5qwsk2AVZ0VdqykqSw717RI2BS1tPjlASdg4NJ\nD5MJEXfjgxK2A+R+eV68Nqwmi2aB0ISV5Fft7WC1lptUdaVy6kcgoGoQKJOw0out9P4zqZM+IbFg\nU4AJ3l9+juegggL8nQG+QsVTGBNMnPiaVEEpshJmUCifCch+RYlbrySCDDyZ0ArDIMwAYXIk2g60\nm5AKtE8AAT70TAgJGEiwGKat91dgV8EQJo+8pgRSFNs4pd+J14fvIZtCbRPg9VcSRFailXtuDQs0\nqmAA77eAXHRIkAQ9JBUT9Zx4XckOUJkEVKkXpgjBpHCrC79fBcjYz00ggPeX15EJLnuTyXKRhD4Y\nRHRUlIwFXleV7cB9CBjxf5j7Dug4y2vbPVWj3nuXZbn33hsYN5pNCYQApoUWUwK5kNBNCJ0AoZga\nMDU0Q2zcKS7g3i3Lkmz1Llm9zGjKW/t8/yeNjQ1J7r3vvVnLy9Joyl++cs4+++xNBoaBWvqrx/d+\njhqbGrwhGKPHuj4nbrZaDFFAI6OVg/eT94BAAD9bu0mQkqpbIoi0c8xo9wQyWDh3tM0SwQvqHWjX\nDgWStRvtI0rXgt/Pz5TWoKBAWUd4TfiZZKPw3uuKvzAOhGHDtiOq9il2j2Yd6FYAniPnkGgm9AQU\nynaSY43nLgwHriEmE8ICAmFyeVBytBBF+QVwd7Dnl1VBVgF90o7iDYhC+ICp6DdlHiJzBsEXHgkL\nxVnOAABIIGi0j/BncfuQTcugqxvA2c+3AJD5pLyJtcZFTwDn64bN24H6Q3vgyz+IZJsHe7auR3Fh\nnmysmaOmIiFjJOKSc9Da1oWGmjrEhAbDZvbBYleURbbqsEeddRCCs2Ex0TJWCtinW1GBgGAHJk6e\niIzMdNTX1GDTxm9QX3kcXmctXJ0N6GhVKvO9fAkrbI4wpKb3Q3R8iljqkRbn6uiCq4vskVCER8XC\nFhSMgIhYeEOjaFIMU2QUwlJS4GTV32aRHlDeXxtFZM8AAGjQxT8w/rcZAKpFU+bWTx8MEi3wOF0w\nuZwIMXlhaWnEvu824tj3GzmJxRaH3YdWa5f0gMampMHjNSEkgE4sJrE0Yk9kXS0TDOUr3zcnGwEO\namW048C+PUL/TkhOQIDVjJK8PISHBCM2IUb6qyuKS+B2sZJLNlorutuVYjMDWY4HD0VwzYrBBVMw\nQuKSRafAHhSBqroWBIRGY9LUsxDoiEBL8wmsXPGeQHa/vvxGpKVn4Icd32L1VysQk5iJSy76lex3\n5eVFWPHVP2R9PP/8izBi+GgcP1aI5cvfkUDu17++DNnZWdi4fiX27fwODnMHaooPwELhLiIRWv1f\nLmlve8hpLnBPX6iOzaR/lHNFt9D8NwAAfUdHjh6Fp55+GtOnTVeaKD0PRd/tBQDYAtCCJ598Gu++\n+45Yyal9Nhid7Z1SDYqIiEZYeIQom9PmsqK8HLm5h2UfufnmmxEeHiHq/FS351rKqgoBAfZ9UvyP\nQC9p/ey7nDXrLKnMrFixAm6XCwmJSeIEMHfebHz66ed48onHJemme8WvfnUJli5dip07d2DJkiXi\nFDBt6jRs3LhG9tfLL/8NPvnHx8LYo90kdQK4/h3JOyLBNkX0aAPFnlGKQXFvYeB82+23S7sAmQZF\nBfnS603RQr3mSGxBFIjpDvcOQ2BYMe1UISU0JBRRFAoLC0dWZh+pPk2eMlmqSdu2bcdtS5Zg/ITR\nKCoqwwcffohZs2Zh9JhRwv4kNZbjfwQVxQEUFRWLNRXVr7ku0qKK13HkyFGwW82orq7DscJCodJy\nqPAYqNfAe8Fkng8yMZi88zj0g+dPwIQVQq6/ZBZw32IgrtmS3H91KwD3HO65qrWwl0nA91CgkNe2\np1DV3S3PRUZG9qztvG78RzCen+XPEpB94QwAwWnnyP/QkxQaY28vNR3IUOQxCehmiOMGmCFq+Fxx\n48x2oWL3jY5CZnQ0po4aKQJkVSeaVczs6kJkWAgsJp8UNlxuH2gcnNfShO/zclHi7JSZlpUE/Onm\nC3H+hEGorypCaEo8osguCVLaWla70qk66WFYgnMpsQaG4+jRKsw/70YcK3MjPLs/pk6bheN79+Dw\nnp0CYLChMNEciUmDRiE1Og71TSewvegQipurwQZL7s8en1vo7HHx8Rg0YCCyUtMEs+Y83XdgP2rr\napXCrlDTKXfnRYIlEjnx6egbk4x4ewjCaHbc7VOW1z2MP76DonNqBaNAb5O5C5urc7GlYj/aeBXs\ndkSGx6Or04X2zhY40ImcKOC+Wy/GpGHhiM+OAxLTgMgY+Sxl+qMKQnI32KhvVNkbCo7j3bc+xfL3\ntuHuP9yJKVOG4rY/3IcvviuX9Ymh4KiEVJwVlohF1HXJSoG7uQn1lVVwd7kktouKiUJUfKwAm7XF\npehi6zZZmyEhiEhMgNURKEyc5nYnWj1Arbsb3xw9gO/Kc1FD5o64JbH2bpJWiLTMDHEyIbDnoDYP\nY1PJqXyip6RjHoLSBFsoRLds2Wv4+zvLUVZaYYQOymaTIKC0yxijopecr+SGzIxUzFZ0uz2iOXXJ\npRfj4osvwpSpU6V1MTc3D1dc8Wvs338AIV43CN1dP24Crpo8HpHSnqYAZmFtSMXiFADgpHnJ5EJZ\nMZ4JAJg8aaokyXQ0eW3Za5g4cSIuuvgCuXPbftwlvf0U01u06Hy43F5s37ETzzzzLMaPH4ebbr5Z\n1hGCrR988KFcp8WLr0F6eoasOWyd5frHdY2vy809IleFsTU1XBjrrPjyS2lzmjx5ChITE6SNmUAz\n12eCIs8+8xhKSmpw1dVXCjA6Yfwk/P6uuwREffXVV6WSHxsbh6N5BQiwO4Q1kJyShLfeehGVVSdw\n0403y97Bnn+une+88w527dqNuvo6AShMQxfM8TGx4mIiVVcjkVcbJ72Rleq7Fhpj4sCFUfX8q0RL\nU+o1msq/q6CelX+z0g0wqpOa7q8ABToIqKBaEkuDCSAIruEIID1aRsVSklNWoQ3ROC5QTKK42alK\npV1R6bs6jH53Lv6qZUCqm8IgUFRslXS7heHAJEknWQyieS34eiYrTHC1SBnfw9fxNfIwtA74o646\nMwkSkTWPorBxPErPv6GPwNdqgIFJj1DpjaRCtS8EyoLO+yBonyNQfubGpf9OVJ7HJNoCHiZCylqR\n1WdtHSfLGjcI9rWRbm8yCeqtk0YNpPTQrgVMUH3yvKf8LibCrBALy4HghM0ugQcpfZy8PE6iTPxf\nBA35t/Z2ESVhoi62hOynY1sEbfsMer4AQYbgoTgpsFXBoNjzuBU4w++3iggfr5f09RvtKT3VabH0\nJSKsqtm8XhSZ5HhV14sJtwJLaIdIEESPJX4PQQWOGaH1G+CU3GNSy6kyboBHGtjQtEyOGwEeLEr9\nXlo/QoKlaiGgRFCgAoA6OmWMiGCi0cLA79XzQ4Acu1XmGI9VByqKycJ9xyb9ldICY1T7NVjGZJ4P\n0vIlIKGdpMEU4JyQsW64ZMhcNIADza6Q1h5eczoaGN+n2l7UPOT3cG7qdhE9R+V3o59I2j/Y2kMR\nSGOO+rcqCKjINoIugnRsb6Dgn2qXkOtMkUVHQC9gYARsGmySthMXNTEUoKaDOs2+EWCLCDbnP68P\nz9HpRnF+IcryC0XVhkCMzazcEFweC0zhqUgbMQWpE85GdL8hcJHB4QgQIEa3O8n19OtH9wdmTgVr\n9Fg8U4ynwTf+XdkgKncS0QNg75q7A56qUrgP70WsuxXbv12JspJ8WROiMwbAbYvH+Rddjb59B2Ll\nl//EoZ07ZZM9Z95sZPXJQllFBdavXScCpaNGj8bEGdOkheDb9RtxYN9eOIIdmHnWLAwaNBjNjY3Y\nuHo1KkuOorujHO2t1YYbgAcmUb9W9ogBwXEYM2k2MvsOgwt0GHGgq71NMZLIopGW9ABYwiMRnJCO\nwPhEWCLD0UZ7JrJEzFzPDbqsz2Rs1qe/Qv9qwq/ffer1lrXzDBdf30PFMjDBRkC5qwOd1ZUo2bcb\nTSXFqM3PB05UA+5GINiK6IREsTzzdHUjyBGIyJgIsRtsamyVCpDaF+ly0SUBjUdcbixwk57Kdb+1\nDYEc42aIs4BXV/vN5Jp6VbFKonY2+lM4lOweG6zhScjKHiDey6T+p/UbjsiEVKmcVFfVor2pVVge\nXN8psBREGz++1mZDWXkFQoJDkZKUIkAa16GCgqOyLw4cOBgx0bEy/3bt3CmA8cCB/REZGYb2lhrs\n37EORXm74GpvkOpZz7XUEfIZr6666D0tNH73QPZsr0+Ot6Gt5ZSq/ZlmyumfJ9OIQnJPPPkkMlLT\nTvNZPr/9lPepBc8+85z0rZeWHJN0IDg0TNhcVluAtGsQK4qKjhG1Zlbgj+XnIzwqCksfWSrzkxZ7\nhUePwOZw4A933y3JMIUh3//gfVmDmHxRMI9VFIIFrLqwP3NA/wESRLN/dvfunfjss8+wafMmWaPm\nzpuLpUsfRnFxsYgGFhQUiOATAziuhQzSKHa3adP32LBuDQYNHorrb7hBvo/shD27d8Bspa6KXUTA\nCCoIK45MSLcLFrbkSGzh6hU8E8tapTsiILERHHM+SAsmQW1jTVKFGzraWITxR9Vr9p/ymHisPO5J\nkycjJTVZNHE++/xzsQykVzaH4Yqvvpb9ev7c2XIce/cfQHV1Dc466ywR8m1r7ZDPoS0hO71oDcYA\nmWCaFidmXMPjZKKv9xs5Jz/bP90eICy6U+wA9Xv82Wt8v/9n+a8jOon3f71Odvi/jmv9P/d0ib9m\nBejPEYFpo8Ci9lujQGW0uP17M+DkVzN54Nh+5dVlOHzooJE0KjFVu5lFMrdUX5lYM5Fji0CwzYqo\noCBEhYUjKjzWsKVVCjmtLc2yTtCdpM3jQWFzo6SurF9z1+yXAfz57sUYnxmDxroyBMbHIH7IECA4\nDM6OTmFvMmvVAIk+Wt0+aQmPwtHdebjttkfR2GFFTNZAOIJjsGPTdpSXl0myHgAT+oQkYfTQ4Wjs\nbMPeIwdQ01UPH51pfCz4OTBoyGCcdfZZmDVzFrKz+iA5PkHAb1p1Hzx0EMvffw/rNmxATU0VzD3W\nez6EwoHUsBgMSkhDRmgsoswBcHgoMmdYGCvFHwEA1FrmhTMQyG0rx3cFe1HqbhUAwGENlqJot9cF\ns8eJ2f2C8dCSX6FvkhmhUVaY2BaRnA7YIlViau5Q672JtrsqEUVXN45vP4SXn38LuUeq8TjXtD6x\nuOtPS/HOigJhAFAMd2BsIsaHxuGCYSMwLCEa3Q11cLZ3ioMWeXi0/+ODbWPMf+3MEaxmtJLxa7Gg\nyeVETWcryltbcKCsAntKi1HkbaUMofT78x/jei7xg4YNxaOPPYa5c+f2tMaI9KoUARWJSs0F5aJm\ntxlxp6tLaO1vv/0uvl69Bi2N9TpqkvNn4cVsIXubxT62s3JtUrEP9ypqqDDxP+ec2cjISJfP5jpZ\nW1+PG264AV9+8YWM3XgAlwwagiWzpiOR+ZKJPf9qlxJxxVOrCKcCALxeLMa5fXh382Y8s2M3qAyj\nXQ2GDxuJv/71BXGY4frDeJ6WwDznnJwcNDbSqlzlsEzOCbbu3LVL2EoLFsyVvST3SB7uvvtulJWV\n4+mnnsbcObNEgPrQoVwBiclAIsAyZcoUsQy/evG1KDp+XM5z8eLLZIddtuzvwiqbNn0Gxo4bKwyA\nXbt2ITYmTtbNb7/dKGc8efI0PPTQ3ejoIDtsOdav34ivPv8Cz77wHG747TVobm7Df/3hv8TiOi0t\nXTQM3njjdTkPMggoUsvj4Hr+/PMvqBYAbgyqv1xVurloSa8+q5qGsJ6uXOrgXXqExXJPBfOKCaCo\nV3yNVKeNCrAkuobyviRlVpvqpzZ653UfugYA/H9XNmuqx1ZtUsqqSzY0UvD9khwet+r9V73ZchwC\nDqiqvVYf57EI+mv0/eiE9yR7sq4uqfpyEGuRQdWbrr5XJZc2mYSqHUHZy2mfanVuqvLH5JCJEiv/\n0sPPpNToG2Z1lsmhHAsF7khr50SXvnoFsKgNiECIqv6y+s1NXQJALlhORR3n/RC02vBYl43V6NHX\nGxoTQ34WA0L90OAEx4B2UhAgyOeTyaqPS9oVDPV/8aoXxJX0dyXexoful1fgiVUSfU0pF0aH9HSr\nnno5NrIUxJpRecHrFgleA1GQN8aLThQ0MMIARoEDSpdAgB3Sxd3dPX3ZHGfCQDGofpqpQiCJ40FU\ngekbLwAAFWtV5V+upbZaFGq/YjhoCj3PUVUPAGe3apXRCbh8v8GeIUjABJ3jWgE2ihWiv4/nT5q0\n1gDQQIh/AswEQ88r/1BAMwG0MBFppPyZoIV/S4Vus9AsCW72uo2D11YSfaNKwvNmJYTXhgAO77G0\n69CGLEgp8HNMab0Ff6BAjxsFoKjX8Dm5P2ShGI4UegxLy4LBZpGxr9cMD5FhQ9egvUM2ZV573cYh\nIJIxDrUgkwYYKPRDl4TGmjoU5eWjpaJe0HebidUuSjUKKRwBCf0wYOYihGUPRVBqOtUXYQ4MEBTd\nQ6qebgUwnEBOG7AZmY8/OHC61+ljlUBW25UamxQV7s1mD6wnauE+tA8R7fXYs2kl8nN3S/AQkdoX\nSdljMW3WBYiNScKPm39EbUUFHHYrMrPSRVymsrZW7GYYFBB4ioiOVq1PThfsNgta21ukV5kBBNfS\nPunp8Lmb8ePmL1FUsN8QfPPCLOdN+bwAhMdlYsY5FyM4MgU1jbxPVpioVRIaIlRMr8MOc1gIzGHh\nCIyOhyU0HE4r0M1WH5NP4h2pODIOOl2PqN+F+t8FABTFkPOXYpCkpVso9Epws6MdXXXVqDqai/Kj\nB9HZWIGu1gZ4XJ3orq3hQkZUF7CpCqLXwzVfBbpUvu7uplYGnQwJ0KkAiRUZBpasRCn1aNWSFiD2\nql50uxitBainzYGA2QFTQDAGDxuDidPnSK/myi+/QGdXN0aMn4oxE6dIC8eqlatQW1SKoSPGYMKk\nGVK1W7fqE5SXl2LStJkYMXKUrGNUiSc1nC1jkyZNBtlLP/64TdTW++XkYOjQwbBazNi3fw8qKssw\n/+xJyNu7DlvWfdrrtNBTyv+JOt/PTYMemEDaiSR5UG0j7V6Xn4fDv5/6xMTFYvY55+B3t94qNEi9\nVupPIg1atwXw5+83b8Xtt9+J/Px8tfYa1m4hoZGYO28e0tOz8O1332P3jp2ITUzCRQsXSr/n0aP5\nskZTuI5rEQNTrndM7rl/V1VWYOGihZg7Zw6+Xr0aX3zxuew7rEBTpZ+JLns42efJHvuMjDTRBBg6\ndIjYR7Jaw5YzsgGpKE3wgCrvy155Wfp0SQGdv2A+XnzxBfywZbP07xIg4HrNwJJCgeIHTyYkk3wj\nJlI8bDL1lO1WQkIcomNieoo0aalpmDhRCRmSEcR/bGPgeK2prkFTczO++eYbFBUVoaG+wRDYtWLo\n0GHSvrBw4YX44osV4gRw1913qWNyu3HfffdJQLl48ZVS3KSVFucae2npjLNpy1YZe6xmsfJ/8PBR\noaveeOMN6NMnC6UlZVi27BVcddVV6NcvR/bWt99+G5MnT5Zj1YxBUt+HDBkiz/G+UFeHexTZARoA\noAUhq/f8xwfZA3wwiNbJORNn/6r/qUCi3tslsTBYaTrZ/yXa/6kAw6mAgB6r/xOsARVr+kQAlJoU\nH7z/PiorylV7ltHCK8LMPWZuXMbYWqt2P6Nzv4fXo7VytBysgipU4xZNqof1s+Hh31+FEUmh6Gqp\nhSUiDImDBwNhUSISS7ox90x9DfQ5SmzOfc5qR8HeXGzbsh9WRwyctlAcLa7H+x+sRllVvSS0jGqH\nJeRIm82h0kKU1JfDbPaBeGtWVjYuu/wKnEVNm9GjBQATXU+eiOFuyxM/UX8CX/1zJV57/Q3s2LlL\ntQUz/haRQBOSzOHoF5+C7OhkxAWGItBkkVaDbrEMN8FsAAA2mwVtJifyWyuwvSQX+S118FqtsJuU\n1bTT0ynU9Kunp+HOq+YhPqQbQZFWeCId8ASHwJ7Uj358QHcTYKclonKRkoNtdmLb1zvw0nNvwGwL\nwbN/fQZBYSbc/9jzeOW9Q+jyUZDbjMzoGPSxBmFEQiL6RoYg1OdDenwictIyEWSxo6qsXNYYETkP\nCUM8mXdmE45VVOBgQT5yS4txrKEatc4uVMIFSkyz6cwtbRQKxA6y2yXZvOue/8LMGTOUK5Xh4iQj\ngMxUA0TSc0XlPkZLgI8FJLPYF37x+Qp8//33AsQwWe1oaxH3Fe6HJuYb4sbqEVcaOrrMnDFT1j/O\n6+CQIEGRaUXJRJkxEsVYn3ziCRmrsQAWZGXj3tmzkRFC8JYONaplkZbPJ7X8q8nrt8kwrjSBFrPt\nLg/e3LgRfztwWAAA4VJTaDA2AZde+iuxyyOQX15eKe1abFsiIywjIxklJZXCvOFaf+uS3yE5OQEH\nDh4WII6xwYULFwqDiayLvCN5aGtvR0hwiMRjbBHgdaWODYFN6jSw957zgwr/8+fOQFuHF8+/8Dwe\neeQREfL782OPITMzCQvmL8L6r1fhmt/ehMcf/4t8/rJlr4vI7Lx584SF9sbrb+HxRx/Eol/9Ctdd\nd40Izz/66GOoqqzFzTffgscfX4qtW3+Uvam0rEzW6rfferunrcw07Nx5MjzZo8wHEzYt/iWVPUPN\nW1fJ/Xvp1Was/Om1MrsgyAyktdWe0NqVQrquXvoDACIiZlO+4NpPnMeh1PJtYjGikzIlpsZEXiXF\nPHCd9EoCIUr3pH1Tl8AqqI3Lqaqh/J0bBhMU/z5ujiAtQsbEXKqnBiVdJV70vG83hNAU/ZwbP4+B\nrydFXQnyKUo6E3cmYXww4aeIhviympSdn4APfgAA9QcEpTcU7vkaqVQb94GexboCL9dVBBCJivJn\nt+z9oktg0JJPolsbII5uPSAToEeN36iYK9CnV8SM5yvJsVi4eeCwE/Hrpf3zmvD6saKuWAJK1VwS\netFDIGjk6VF0Z/+UgCGGkJ6/hgPvH62W+OBnaEE1TSGX5NRsEVqSP4CkLfIESDBKVlrXQEQKDcre\nqei7iPB5lZOFVMhlk+q9fnrz4v2QJZvMC0EmVQVFsRIIeimHCwGcHKxEuAU9ZFWer5XefioTBwdL\ni4gGrAh+8LUaxBIRTV4DhwJPFJCm9Cr4YAWcyQaBDo5LPnjcnCei8G8wT/h6UiL5nBZx1Mm7sHC4\n8BMdNtpn/AE1UXTX32e0mMjn2pTDga7s02GCjAYt1KiBLh6Xbj/Q1Rm+n8cogRTvEZMBg72h2QME\nR3gNeU0ZsPD1MgcMdgA/g/PEawBDCmxUm5diKehQhQCOWzYwJrjBdgfcHV2oPF6C8mPFcDZ1KARe\nKNZki3iAoFgEJQxCvwlzENF/JEzRcbBHh8McEohukcAHzESruXGd4vqkDqCXMK/tGeX50zxkDBqJ\nv1Yw13OV522xmRDc0QrvoX0IbChHxZHt2LF1PVzuNgTGpmDwuLnI6DMCjbUtsMCK/gP6y5qye8d2\nUTFPSc/A6PHjlePG9h0oPHpU7tn4SRMxavRIFBYexYavv0bp4VwkxcVh1uyZ8HrasG3rShw/fsgA\nAn0GAECEPQgxSf0wdvoF6DaHoKKuiY2ewhSJiotDeGICzBFhMEVHwB1ghz0kFC6fF91cg0j/E2qh\ncZF4qYzq+5muz/8aAKDtuUTIUYUyQhdndc5nAq2MrNzfXO3wtDbC1N6M3O1bUHV0P5pz9wM2rgFM\n6ru5ycBqC1I9jyIURYs+N9DdAbPVBC/nLQEuhtjGesIk2AM3ODIl8mZA5LNKRcgcFo+MrCGwOyLg\n6jYhOzsHsTFRMh+OFB6X/2Nj6LseShMW1De0oLqiDhlpfZCRloWuznYcyduPxuYGZOfkSOBEEOLw\n4UPSf00AYPKkKcJcID1x3779SEqg5dQCOGnnSvu/omMY0i8Zxw9uQHXZEfi4Lgnd04+0+Qvs/5Pu\nqeH0GGyyIZgVKZ8XzV1tsr7500DPNA7O9DydZqhQf89/3SNU/F8CAFatXoe77/6DJIGcZ3Rv4Jy3\n2gIxceJk9MnOwa7de3Fw3z4MHDpMEnoKtW7d+oOIO3JfS05OEuo/q/Ksykj5yuTDHXfeicWLr8Zf\n/vIYPvroYxG/iomPw0MPP4h+Of2kTYAK/GUlRaLWT3V99sKT4n7NtdeisrxUTvOGG38rvexkCFA0\nkMdJX2mugU3NTehme6UhzivWarRKtiodHGGsedzSv0+l6GHDhhmAshmJiYlCYSWdn+tbvjt+AAAg\nAElEQVQs13Uy9eLj46V1gnsCdVPYkqZjFcYbpJYyiV616mt8+MFHaG1rxujR44W2T4YCk420jHTp\nj+VnEqjmfsOAVpiBdsUiYyxAGi9BFFWh9yIkJFTWZ+4lB/bvR1p6KtLTUiVGoCji8OHDRPiK6yTp\nsgykdZsfrxWfYyLP7+KDFThWr6gZoLWQeJ+Y3JOtcLqKO2MP9vLyOvA1qkXQJWAZ4wV+vo5fCBTo\n+6GTWmGrSiGqpwn55KHvp6/kn+Tze6U48D/YLuAPRnCdILjEMbdxwzfYtWOnFCTobiM0aa5FIrSm\nqr5S0hU72JMfcnzSBkehPMOuVfZwYOSwYDx2z/UYFGlFZ1MV3AFWZAwYAFNMHEDWibQfKB0picm1\nwxBde5hUtregprgM6KJLVQwKqpuwetMhvPjWajS0qD58rsf9k7Mkzj1SfgweswfBQVbMnzcfl112\nNaZNn4mwcLZkMfZWGmJmC+N5twjRscjAOIzFyB+3b8ffXnoZX678JzpbmmA226XvX7capIckID06\nHvEh4Qi2B0osxuq/xasYAB5vN1o8HahyN6GgoQIlTfXoZgEJNnUdfS7EOYA/XTYBvz57FEKsXbAF\nAr4wG5w2E2zBobClpqqNj2AFC2Nmr9jSdlW3YvvaXLzxynsYPmo4fv+H2+FGO556aTn+8vI2dHjJ\n6HUizBaAgG4XguBDTEgg4kNCMDwtG0PSs0XM0NXeiZa2VrSRteFzw223oralGWV1tShtqEej1yUY\nMyNGIeabrLJHk6rDPna2q112yaW49ZZbRfGfDkJ8CPPVEBAXZwSxRlZMUX+uF+cIGb/CHGd1XazY\n3ZLEFxWVIDfviKwnhfkF4gqWEBsnLieDBw3C4IGD0K9/f1gdNrVXGi3RVMxPSk6WdtwnnngC991/\nP2xuD0J8XkyOisOfF5yLAXGkzTsFAOA/vaufMhn9fu0FAFo6nXh51Wq8cawIFcZ8MFktYtd7ySWX\nCnOLawvFFtnG1N7eJroIXDO43nEfYPvJeeefj/j4aBw4cBjXX38DSstKxZ7v2muuEh2f1157U1q2\nuP8QVFh00QXiunzX3X/ERx9+hEmTpsjr+/ZNxe49ufjwo48EaOb3l5aWCFubGizMddmvv3PXbrFv\npVbNJ598im3bdsjrY2NjBIzle+iOw3Xhww8/EnY2hVs5pykaS8CF8SAZNCwUPf30M5gxY4bYf7PV\nSloAxHuUVXpDpVprAWh6OsVcWIkmSED6u6aCK8s6m0ExU0loT2XesCnT9Hat7q8r37oFwJ8aryvr\nPXaBFK8z6N3+N1knMJJYMqEWBXKzoTivME3SzjjCeG588Nx6Bjf70V2qnYDJhFCZxYdeCdmp19Ob\nmjQ7RXPXtGNS8jkpNJjBHmlJDkVVX1G3pY3AZBKkmq/VoAnPi4nVmVoAJLg0AjCdhIoQn7GJ6D5w\ntVkpezgilqpq7pVKqyCvRt+OWvNVmwXfK8mWUb1WAolMClWrgoiBuJXyP6+TEjI0o7OjS1HFDfCF\nn6P746Rf3q4cIHT7CM9PUeAUCMTvlnTAQNX1GJHn5FhpP6K8b/k7e2H5vwgYGkGMpqlrBob0tht9\n8Qo4UvoCurot7AHD9pDXngGFtk4kgKGvrctp2AAZbSiikG9QzXVfvL42/JvSolA2kkyGBYgQIUdF\nxdTnqc9VNAloVWdYPBI0YiuNvl4ECmwcYwZy/5MEWOyJlCMCz43XVANevE98sKLORVgn0fxfBx5y\nLwjOiVuBOnYGabrNh8cpGgt00DBaCTQYIAGdAf5xIeEGz3WAD93CwPPg+/Vc0G0FymZSjSXOQraA\nMICSlhDDkaGnhciwbeT8YwsLP4NBKh+i52AyG/oPikrJtYfXnGNdtZSo6oM1wC7tH146W7i9sknW\nlVehNL8I3rYulbHLvGEoHADYY5E4cCL6TlwAS0I6ApKZ2Iai26Yq9eIjLPSxXiVzTY/2z2l/CQDQ\nLgc8Hw20aRFRmetmH8I93XAdPgBz5XHY2yqxYdXHaGmtgSUkAoPHz0dEdBa2bd2DyROn4pw5s0UF\n+MPly+X/wUOH4Zz5cyUBp0Lv0dwjIh4zavQoaREgJTJv/z501tQiLNAGe7AZxcVHUF9diNbWOqNS\nTQYAKyGs+TgQkzUcA8fPgS0iCY0dLlgDguTahkRFwR4eDmtkOLzhwei2KhqhgEE9Ip0i6tEbMJyJ\nn28s6KdW4k6JTX/y66mvP7PAnPpi9XfV76g1a8SOlHoupMv63Ai1WUQo8ERJIapz96KtthxhDhtK\nC46iJu+wqC/nZPdHSGg49h/YLy0AjiA7WprqUVdZDi/5eOQYmKzi7sGfbSa7YpMEcF91AUHBCImM\ng8kSIv8mTDoL2TlD0NbRjb27d6Mo/zAyMjMxeeZs2AJs2L1tM/bt3YO0jCxMm3YWgoOjUHS8BDu3\nbRd65eSpkxAeGSZiSfv275XAadiwocLYUi0ABTLPCS5wnrOC3N7WIuKFEREhcDrbsXf7RlTnb4XJ\nq1rIVDnF74b9KwCAXzZBhkW4LUg8xls72sTdmfCfYkP8hw8TMHzkSLz6yisYN2bsadsJhCkmGIsN\nZWWVePGFv+GTT/4hQZi0WZkt6BINGTtsdrbUeeVa01ng8OFcvP7a63B2diI9IxMjR46Qnkz2RbPX\nPzMzQ4AHCjHt2bMbISFBMgYGDhyAGTOmo7KyHLt27xIRwJiYaFx6yaWorqrCp599avS+jxAG184d\nO6UgoNr0VDFAs5703qFbKNnSxAcFEAkMECxlG8LUKVOlmMAHbZ9o5RcZGSVrINd2Bo/8/J5qrNHL\nKzGI0aJ16l3w37eYSNNjesOGb8QdIDIqSvr5CYbQuYBAAf2rCUyxPYD76j1/vFeC5CuvvAJxcTF4\nf/kH2LFzB25bchuyszOxadMP+Oqrf+KGG64X3QmOzU8//VSAhbTUZJmVXLeY+OsqP8UXucfMnz9f\nDpdaBGSMUbyKD4r9cf9Qewj3ByWAq5lj/ueoE2ZdRNIWyNwbeQ/4j9dYryunCvHqwo0uIp16/XS8\n69+OoD/LX3fqPxz9P3mbP7ugF1jwoaqyCuvWrhO7xaLiYuzdvw8N9fUCXolKvlI8/ulhSLFD7Drk\nb4b3i+pUMgEjhgTg5SfuRp9gN+rLCuH0+tBnQH844uOFOSdZnHDFCS74zXNmQ2xPLS9BfUUZ0lPT\n4PNZUVh+Ap+t3YMnX9+EJm7LzJNtDsSERIiOULOrHVnZGbjqN5fjkosuRp+sftKKw4fPQx0LteB0\nOJWuFDWMZGX3Y20cyS/AG2+9Kf3OJ2obBH8V9qmCgBEEK8JNQSIeLDE1AQBpVWOy7EOH14kOK4GA\nLtGzUVa26kHSbXoY8MRv52LW8HSEBlrQ7e2C0+RGcESw/Gxy2BEQGQVTciIQyBYJH9DZhvbSJqz+\ndCdWfbkBiy49FwsuPQ8ebzte+vsKPPD0N2jrtsPMHIqMVj8YneUhznr9P8t1PCK16yhCGVcM/i9q\nH2YTXGTTirmBFnOFWMcNHz0Si69ZjAXz5iM0hKKFGq6nnarnJJtVWUfUlTdALMV+08VezUbhtef8\nILtABNEBNLe0CKMoNDBIAOGeNmqnE2HREXLgkn8YDFMlhq5ANoqZXnnllehsakIgfBgeEIxnFy7E\niNREwNMFn1/fv8wB/+3lFBcAMgC8ZitOtLXj+a/+iXfLKlEpoAjrGjbcfsfv8evLr0B5RQW2b9+O\n22+/HRERocJi2rl7vwCdTJYJQDIu3Llzp4wZAgUEDMgeY2+9EnE1CVtry5atskZdedWViImOkUSc\nAoNr1qwRjZUHH3wQQwZnY9eeXBGb5Tq6ZetWRISHIDf3KFavWSOU/XFjxyElJRXPP/881q1fJ4n9\n0089KwXa66+/Dtu2bsFV116Dt197AcXljZgxY6bEzJ9++gmSkhJEH+bvb76JIcOHY8PGjQgLt+Pq\nq26W9Ze2t3f9/vcKANCDWwSA/GxSBO01LPd00qQTOr5O92ZLOwCpaUYiLJVgo/+fgb1OILRAmBbg\n81cMF0q5kSxzEAljgL7gVF1nv5rBBGAirynMwkowEgAej/Tcc/AzuZdKNhMeQ9DNoLcwgdIJO4+X\nVVux9qMVGRMioyItWgfdpOdzcBsVYUMjQVneKHszPmQCGEk6J4cgkkaLBDdfblB89AjYyfF5ZbDT\nApCTgJQT/s4byN/F317OleeghNq4OfFzBbAwUGyOfg1s6EBIb0o8Fx6jAlvMcm5in8ZkVvrCVW+6\nCNoZff5aDE8WO4OVwM9jEiuJtyTWSiNAXkPEkCCKKM0rGw1tCym0WAP91q8XSziDbs/7yP53no9m\nZTBA0Buofp7fo6nqBAz09wiF3+hDZ3DFMapF/9SGr86Bx6Msldg2oVpBdBVeW1VJT7YhSCnnQAp7\noENAFu1uod/L49HnyOPldVH0nma5d1pIj2NI3y+lQaGut3YcIGrNqpvTr+dfAyz8Dr5XCyPqNgKe\nJ+eHbqGRFhfRqWFwS82Abgk0+XeCVRSt8mcWaKaMsDsMAcUekTqjLYLzimOE7A3uH5ptoMYV23kM\nsR329FutAh7oAEiNVXV9dVVAu0botaUn0CW4YQiQ6nuu20CkQsXrxXFlMBP03FXtAAQK2nvYHiLo\ny141ChNZLLCZrehqbsWR3QfQVl0vO6QWtPEILcwOa1gKBo29AHEDxsGXnAJbcjwQFiA0dlIC7RY7\nuru0mI9aJck0kLzISHxIQ/OvqJza86/nEf8/VQPAZPbB092FEJ8Httpa1B3YjXBnPX5c95kos1tD\nw5Ez4iykZI1Aa7sXYaGRojDPKnZLU6swTJhe8f7IdWTLDQElr0ecEGqra8AK6oCcbKTExqCu+jg2\nb/4K5aV56GxhbxuJoirZUGwHBnFBcGQMwbjzLkdYWn90wo6A4AhYmEDYbLDQvcECdFNPTAIMpfGv\nwVuFs6hK+L+S93Et0PsO77f+uSfiOoOAoP77zwEIcp96ClonUwMlaOS6RmaSUaemt7apoxWhtAz1\nAXnbf0Bt7kE4qPbeJ1uSIXpQJ6YkwRpoR0nRMdSVlqOprk4Ar8CQIISEh8q84lrQ1e0WwcSQ8HBk\n9++L6Og4bP5+G6qr6zF2wgQMHjxUqgYb160XVV9WNaeffY4AXDu3/4gftm6VRPWcc+YiIDBEqH9b\nNn0nbIy5c+dLpTr3yEGsXbtGgPj58xYgLSUdFZXl+Pzzf8g6cN55CzFgwECpFG7csA7wOjHnnJlw\nBPrw8fJlaK3IhYnq/zIIeMt0/4txhX+pfO9/WRlESxJhRgSCEBYehgZXC5roZf0fPpjA09v4yaee\nQlZmVo9gqv44fiMZXTxystUqy+vE5u8f//gYNbWVcAQGCegqvZwmO4YOGiF2T26PCwvOPRflldVY\nseIrWZdZSaFYH0UBSWkl7X/ajOlYsuR3KC4+jldffRkF1IzweXDu+edKEPbNNxvE09nV1Y6gkDCp\n2nCtf/bZZ9HYUCcOGj3HalT19fopLD6y5ywWoZcykGSrxqSJk5CekY7QkBDExccJuMN2HwJ7+tEj\nrHya6rJ0oRgiyLImys8qptI2soxZ/JN//blvv/2O0F3LKyolMWYF/6abbsKddy5BSUk1li1bJsdK\nemxQkB2vv/m2WJERJKGI1eFDufjhhx+wcOEiJCXF42j+Maz44gsJdPv3U3PowQfux7nnnovZs8+W\ntf2Jxx+XHtzZs2fLek7LMRZPKBSowWbub9xr+XfNciNQw72cwTef5x7Pqn46fdENZxred15X3Qao\n9yD/ar4GTH5uiPbsT34toDoB1387XaX/1PXp1Nf4Awenahr8J1OG+2Z9Q720/TDpOJp3RAQnq2pq\nBSCiPpZYaLtZKFCxNI8pPiFeLEE72tpE2JLjhiN38phQvPPygwjqrEJD2XGY3VbRzwiPi4I1JQkg\n25WFCiZe1JNgHC1MKDPQ0oq6w4fh7e5EXFYSfFYHysoa8c/1+3HfcxvQRoxACFKqndPpdmLgoEG4\n8847MH/+HMTT977HyuNkELEHjJf9t1eJRBcHTzQ3Yv26dXj0kT9LOxBjY9mSpFBD4UHtfyR1bmO7\nMkBj+MAdVX4juK2Zh0YSPmN4LB64YhYGpYUjNC4S3a4uFBUWo29WOkxWJ+prSmELCUJ4TiYQEyqO\nCWjqRNH2Anz51R4UFlXgpiW/Qf9hfeELNGHlt3txy3+9jUoa8jBkJiuCbbrcj/idTIw9qhrP5F+f\nu16aGaEwTnJyrhvMDa4BuhWY/7MSzEryxZdeIvNDABHRhfo5lFe1Zvo/dB6o5pHKSQi8MWbk31Tu\nZQijk0lt5FVkyLKIwCJyWHg4ujo70draJgk2RezolCK5k8ctFnmXX3YZ8o4eUfaCAB6bfRYWjBwG\nuGmnaABZPZa5Z5op3NlN8FgsqGpuxpOfforP6ltAaViCsNQ8uePOu2W9YwsY2Vhcc8hI4lpDd5Li\nkhLMnzdP2JRkNzzyyFJxByJDYca0Ceh0KjtAurUMGzpctGr69EkVAT6ukVTaJ5j64MMPyTpEy76N\nGzbI2kogdduOHZIfP3D/AyDLlvsOdQQaTpzAhvUbMGLEYHzwwSd45dWXJcG/+aZbBXR47bVloqFB\nfZyEhEQBSWlHW11di0WLFmHa9GnYvmM7Nm3aJPdo6aOPyv3mXsVc5fjxIgF0e1oAJLil0IMhAihU\ncSbPLlVZVdVepfAulT0jyfevwKieefZiG5RyCrH53RudmOp+dz1QtBaAslULlAPlzwQAqCKu1PiV\nGrquIMv3i1hgL6Wbi7pQ/8WhwFCuZ7AvNpe94IZOMvT3cILrga6cBNTAp6AKKfasuPRcH9rqGVVX\nrY8gFUij/1+1B2hRQtUeoBOoXiq5QV/WlkmGt7v/hsLJrqnDmoYtlH+j9085NSi9gJ6goEeoQ+kb\nyCZo0Mq1iJ+IG/pVYXnsepNlEMT7zPstNCu/yr+cv19VncfKZJ6oIa8Hj0cHGZqJwPumKtJGkmG0\nCfhvmP7XThwYDAaGfo3Q9g1BHh4fE3qlgGuSBUW3H/D4ZEwYOgD6HuqNXiuwa+VjsaY0ro/YS2rt\nCw2OGICA6mlXffP8fqXmT4u6UNUT5nT29OhzQ1XuDOr+asCEz0sfsbav9CrwRQAzeGRe8eEf7Ojf\n+R7d/85zUkCC0R4jqLjfWDLGYS8oRgq+oszrZFxfF21L6f+7LOpGiwnnoYxbAfeV5gWr9wrUUKwf\n0RDo7FJ+sNpVwuOVe8JrSj9abgqaAioMBkMMUd9rVrnUWHIpkIdMAD8bRGFPG4AXj0ODGXpz4zHr\nNUH/r6s1Xmc32mobcTz3KNrrTwhsTmSXU8MjCW8wQqMHYuCk+QgfMAKW5GR4okJgCg2Gj20WLg+s\n7HnTi5gBMpwaipyWUqnFcwy9ArV+qDWoR4zLROaQU3rSA1vbUXdoN4KbK7F/8yqUF+6CKTAIg0bP\nRr+hk5DWZygOHszD5o0bEBMZifnzL0BCfDxyD+/HDz9skc+dMGkSho0cLl7g32/4BscLCkVEZuqU\niUiKj8Thgz9i7er30N5czcUUtE3SAIDce7ZIWMIQ2Hc4xl90FZKHjENjpxdtTi8Cg0OkEkOZOIr8\neQ1TZTH5Y87ol4j4Bwln2pr18xLUGPuFnvu97/GnHp7+k34OAJDWpV5G608+gIUZ/mPVR4QLCfzB\nh0CiO20d6KqqQkBbM+zOLuzY+gNaGk8gNSUJY8aNRUNzI7Zs2gSr24vJ4yegua0VBccK4TX5kJGZ\ngeSUFHQ42f9uQ21dHZJT4hEcHIIT9a2yH3W7u8TmU4SR2JJgU9Ruri8E3pi0MoFuMUS6GByRdsn9\nl9V8WWMlolUMGFa/hY1H3Z5uFxob62W8xcUlICIiUtZpJrFtTQ0YMrgPXJ21+Oqzd+FpqYbJ5wdy\nCUNGVlNVEfslC7/e2NtgfVBz2YwER5TQFAtrStHerSq0/8mD/eznnn8ebr/tdgwY0P8nLQAq+FdB\nLBOPdWu+w5133IXjRYXS08mqolxwkxn9cwbh9t/dhT2792H5e38XsCwuPgnRMfEYNWoMiouKsGPn\nNgm02ScfFRWBsrJSEYbi9czKykBYaDCOHStEY2MDoqIj0dbaIsFidEy07F3sq+faKu47UpxQLWA8\nBmG4cd7ZA6TazTYAsjZ4nfg7LexSklOk4q9YXIrpd9ICZKwrer873TUlFZo3zz/JpS6S0iNRmkqa\npXfy+2nBdwy/+90SfL95k4DHFy26GEtuu00sMOmQMGXqZLE7//GHvSLANnfeLBmHa9d/J0DweefN\nEVCrpLhSgl8m9na7GY2NTQKmhwQFCqOL16upsVGxyrgfGnEbYzwKKvLBAJzXgaJVjDmZ0JKWq5kA\nFMkijZX0WT7o+kDmC3UE+OBcYkDN1xM00GsU2wH4OWyj0NeR7Ad+LwNz/V4mJ3yO15ExHY+Bn+N/\n7f3H9f8PAID//eR+ymugLRK5d5IJyXvPn/m/ahMJkvMmk4NU5z//+c+qpQPAzAlheP/1RxFiaUZV\nQR5MbT64O7vhMbmRkJmOsNQUIDwMsFvhpfUzrfg4/pxudFRW4/i+fYiMCUfyqAGiB1CeX4O13x3G\n3U+uQYs4dDMOUvEFK55LbluCWTNniEVa73X++STV/5z1fOl2KwFhqsnTipLOCWT0+BeXTrcm+T/H\nJFHmkrFnszofbAJ+M38QbrtwIjISg2CNj5RWiNzvtiE2OAixSWHoqC2Fi+KFEcEISI2FPTwaqHfh\n+w17sOa7QwiPi8M1N16MuMxYYQ78eKAUV934LEpL1XclJiRJe09+/lEUlxT35C76PDnOdBGw191K\ntU9yL+VzfFATg4k/2TYE19gupHOw3vXll1bl3j2Y7+U80DabOl4VtyYBAEyS0PN5Jrv6wTHI53k8\nWrdJXDxoiy5Fu2BZG+k2xTWE4Dm1Q9at/hp2nxep8OFPk8bj8knjYKYbhKynbFtRFvA/caHo+WaT\nuO14rBYUNdQLAPBFfSvqlXSa7LEXXXwpfv2b32DokKESf1JjhAAm5wNtVqOjoiUmJZDY1eXE99+r\nhJoAptY9oxbAqpUrBUB+9NE/y97PcXjH7+/E6q+/FsHDt95+C+GhdhQVV+G6664TgI7MKrIESM3/\n+zt/FwCC94lWgQUFhTJep0+fjnFjx4obDFvKKHJLcb9/fPIx4uMi8OfHnhLAgO/j3KXuyhW/uVJi\n9eeeew7nn38e7n/gAWEfUA+HbQNffvkRDhwoEEDotAwAnTxKf7+B7OiB479QCy2clX/2MhvBraa7\nS2JJdXeqkxs+7NIDbyRDmu5MKrVWG9eJgEZCuRESnyNqxE1L9w0zqeVDknWjIszftfK7Ut83mAAe\nnwIuDPtBoi26ssxjlEFoiLjxM3SiRFqrqk4pmrEsAvJcrxicXqA4iJl0+W8AOpgVUMJQYJdKnRYv\nNGjbFNRhFVgPJg1eiCK7cQ68hvp3dZ69QIBuoeipFBtuC/p1TPyZcCjtAPU+LabIpNaf9SFCbX5C\nhny9pv7rz2NgwwSQD2kjYJKoVc21loBBYVQOD4rCzvPm+fjbD/E5Urp1PzkTR319ef10Qq0rxny/\nvq4arNBgFD+HD22xSHSN95bVIb5HJeBqkdLVAIUIU/BPjWG5N7p1xTg3ATEM20MZc+IkoRwNOMm5\n+PFn/tNUQvYP8TpzY9WUQq1+r9sU+Fms/JusppMAAD7vT8OXa2RYQ3Ic8Prp4+Hxi02k3Au3zC2y\nFvS4YZJAEE/sxsSxQSnqa/BOJ9O6LUffI5kTBuuFQTS3JenvN4AcPRYZrLFf1WEPUGwFggQGm4aB\nnVSfBLkmyKTaQrQOgqqGk72i+hVlPMnxWns1F0jtNWhpugdTWkMMgM1fj4THwnvHBElaXaRvz4Rg\nexAqi0txZPc+gNV8tnT3oOQUZwtFTNpg9J8wH2HZg9EUEgx7QhwCIkKV4J1oAvam+HKNjCLp6bZO\nDajotUHmzRk0APS8EKXmrk7UHtoNx4kSFO3bhCPb14oI3YAxszBq4mxExWUhL+84Cg4fQWhQKMaP\nmyLVwSN5B3HwkPKGpvr4kIGDUFVdjdz8PHR1dCI8KAQOK9DVQfutvSgq2CU0Ot3TSBEn1ahP8T47\nrNGpiBkwGgPnXoSEgSPR3OFBe2c3HEHBsAcGwsV5afbBZxGtYLFg/P8ZAOC9O1ObgAIAfAIAuPkL\nfLBzfrjcQJcTgT4gkJaYFZWoyi9EfWkZMhPjpV2irq5GFLRDA4PRv38/dHd7kJd/FE0tzeiTnS3B\nVlBICOpONGLFis/gcbVh8JChGDNhBgIDg3H44G5sWLsGdqsdF1ywCFl9+2HP7t1Yu+ormaOTpk3H\nnHnzcPjIYby/fDm6GpswbfYcnHXOAlRWVeCzT95HQ101srP64pyz58BktogIYG7uPgwfNhxjx0yC\nIyAIB/bvQe6RXMQkJmDq1KlwdrSjqvQo9u74GhWFewGPyEP1aKmcCmaducVCjX7//F/WIwKBDJq5\n5vPak9Xh1yL1S+HmqX8n6DFm7Bg8snQpJk6YeIoNoKqW+QMA237Yg9/feTcOHTogQmgMKuFzw0R/\n9KEjsej8X2Hbj9uwdv1qWdcio+MxctRYqa4cPHgAH7y/XOKKyy6/HGNGj8b777+HHdu3is/2fffd\ni4sWXoinnnoC7y3/u9Fk7UVIWKgkmaSE7t65nbuElPKo2k/BPgINo0eNxpSpU8QpgL36TPgTkxJ7\n9n0tJmo1+szVPtfr3iLzU+IQw573Zy4k11xJXoz9rjcu6W1V02JejIR7kx4VTL/x5ltCH+1sb8Mt\nt94uIleff75CWgLuv/9PcHZ148W/vSSVKOot2O0OvPrqa8g/ehQPP3K/rEuvLXtTgtxrr12MESOG\nYf2GtdiwYS2uXbwYY0aNQFtHhwhfsd2LrAnua7TcYlWKdodc2zdu3ChUWyYxfDX1J28AACAASURB\nVDBJ515NzQvuB0z++WBfP68Xz5cJLhkKfPB3Jbhr2E8bhQTR6zFsAPVl5GcxuWfcoN/L/VGzNrm/\n87M1QKDjXQ2y6HVf/69bBnVcp7/nVJBAM0H1d/qDNv/uXPF/vf/namDc/xhOB1YwVqGaOcUFFTXe\nhwsmR+KNlx9AZLQPtSXH0FXWjM66FjTU1SIgKBAhMVGIz0xDRHoSEBJIZBOe+ka0lFejuqhU2tQy\nBvZF0phBgMWOurwqfLu1ADc/+iUaJXQkm5UMkqniQ88KbDQp4sZDjc2fAwBOvkq9gFlvYsh7xwop\n7TopeEmrNIIiPwWc1WdpNqpqM1HWasKQ9XoQZQfuvGoGLpnaDwlxAbBnp1A4DXVb96KppBR9UqLh\nc3cKU6yTFt0hgQiNScaJRjNWrtuG3blFmDV/NhYsmo6ASBsQGYKduwpxxbV/wbEiFliApMQUqYAz\nzlz59Srp81YtrD9t4fAHAvgzdTGoacE5w6SQYnuaEaPzKh3ncYyyhfjnHz9NsHviRD8XDvVZhm4U\ncw4/vQz/uaDfy7ml41xq62jQgnEn48/7H7gfzz/7LAJMJsR53bilf3/cOHc2HDaftO5RB/CXAQCh\nKMNjteJgRQWe+PRTrG51ooXdMF4IvX7BeRdI4YSJNpm7BCcPHjokcSzXaOa31HbZsX0H5s2fL4Kq\npPVzHHGe9MnMEos9tld8s/EbaXdi7J+cmiIgAXPPDz74QD4zu2820tPS5N5wTFF4lesb6firVq2S\nMfriiy9i2KBstHb48KtfXSaMJmFKedyyNpYUl0mu8cjSRwR4pRYNwYEhg4fg6sWLUVpSij8/9hf5\n/KWPLpXnCARQzJZtbQQ977//PonD+V2mgXPO7oHmdUVe6OXdLlBZnN6xRGj40DRmvXioZI3oaK9A\nmbbYk35go7de+qQ9hniHHyWux+rPsGcTqjarIoZ4ChN+6X82fMwZ4OvkU1epeYO4mGtEUyd7kiwK\n/YR92sqdgINMhN/87Fh0BVdb5eg+fiYrknh5WAHuPT+dTOlNUych0oNOeruRzHNDl+TUbOrZRKRX\njQmnn70aAQCiWEzctKq6StBUv7duBdAMDL6Gx8i/izMARfh0C4NBxdeCeDxGncATEeL9FNo3NQIM\nxwEGeHqT0roI3Iz5+aqlQ1UxdAsAP1P300nPjlHx1kmOWkANgECQSKXsL8HiKXRFAQRoK2d8h16I\npIplsA3820T4et530vy1taMcp2H/x/drFwBdfdcACf8mCachxCcbrogUKXsXTV3ihs/vIbtC6SFw\nHCjhI+3QwO/UCaw/04DXlq/RYns8Rv5dAwa8L/q4hHbPhMNhh8uYP4qOp6rceiPSTBDeG75foa1K\nUJOJOJMFvcHr68vjVUCSAo70fdS9W5wzam7QqkVVmHrGM20Bu12CxJKSJAuywVzQwBmvi4g6CvWM\nooS9aYMGV3iNWEHiP/pwS5huaBloEIzHSRcMXYnQSDKDdgIbbP1hEKJbALQAo4BmnDOGXoOuavDz\nGMCRPqqBl5ioGHS1deD44TzUlJbD1+GEycVKr7LC6WaSYg5HSs5EDJw0F87IRJhi42CNDYM9MhTd\nrJKzncFoNZNjNyrHMqbUyO7ZQ0/X868ZIT/VAKDdDhBEyrmzEyeOHoC19jhq83dh3zefCfti6JRz\nEBmfhdoTLqSl9UVmUhps5gDkHi6Q5CY8KgQZWWny/SXHi1BVUiaA4uDRIxAXFwtXUzMO7P4R+Ud3\noqamEL6uBljEvcQu65Xq0FaIBgEARCYhc/xM5Jx9IcIz+6O10wOT1QGni5VLm6JE8lqQASA2PD8F\nANRc17ZKvxBeCMjaa5N1cpDz32cAaPDmtImsgERKB8bDVheKxZGW7XQhyGoXJXsGtFaee5cTQQx4\nOjuwceVK+FxOjBk5UgRnWTUQ6nFGhggZ0d6MYnxc0zP6ZCL38AF0trdIwJGYkinren1tFQry8uCw\nOTBs+AhEx8ZLxed4fp5csMw+2UhISkZ1bRVKiovR0tSMfv0HIjE5TYTi6mrK0XSiAQmxiUhJThWm\nWnV1BUpLi5CcnIKsjAEiRpufdxiFxwoRFBEh1ViHzYrSwn3YtO49tNUXGd2jSvBSrrZB49d3TfsZ\nnOku6jskrR8wwWGhWKei21JLvIszjBoxBgj2C9Hmaf88asxooVZOmzrtF1sAamua8eory/DGG6+p\nih85GLYAcW2ICI9BVHg82tu6EOCwC8DS6fSIKwDZfqwIxcXFS9W3qekEKirK0NLajJiYSHR1teOi\ni9hOkSM2fsePFWLQoIEoryhXdmZW7mPdImpF0ILrPCuqgwYPFHCAVbj+OTmKWm2sl1pjSSf96hr6\naY4Ia9EQ4jJzv/+lYP3ky8d9hCKQ/AwmBW3trdi6ZaucHzUFGMieWgXlvM0vKMQFF1yII7mHkdN/\nEObOnSf2UdQ2It2Uazq1jyjExmvGlgqzWYnjxifEyHXo6CAzzo6oyAhERIbJ9aNWxehRI5EQHyt7\nDL2sWQm84FxVwa+srkbTiUax2OJaT8FDHh/nFll/aVT1N+KP4uIicSFQcalLRMdoJ6YT9oaGBqG6\n+ov3Mf7yFxjUez73kZ7x7lGWw/o5fX303qoTGPb48rP9GQH+/fnSHy2uR4q9qh//2wCAf+J/akvB\n6VoM+Hq9HzMpvvHGG/HFF1+omMrnw5Xz0/Hsk3cgOC0I7oYaNBwqRXtFAyqOl6KpsUnE3BIzUxGb\nnozYxDhh2FUXl6ClvAZN9Q3wBdgxYNxIRA7OFtvTxrxKbNlehGsf/BQNwgAwYcTI0WLByYorHwqw\nUXGNevzrAIAqYmmvg58uJwStCCQxmeL5kr3j/+B3ct4S6GHMe+zYcbz0t5dQV18DlpiGJlrwh+sW\nYcrgWMQlBcOclaRmbU0zDqz9Bg4vkJmcJO11LW3NsFAM0xqG/PIOfL76W3T6PLji2ssxevJAeALc\nsKWn4PDe4/jtzU9h6zZa6RH3twt9/Mrf/AbVNdXYvWc3OJ4JUrE6zvEpbdQej8Q7PF5W3Pk/Pedp\nX9e3b18V6xuJuP/Y1EUwFjh/uU/v5D2Y38lj0ewZHi+PR4Nq4vxkCH7z+nL868IY51t1VTXCI8Ll\n2FTxrFuAUx4Tz0HrWT399NN4kIkqfIj2eHBlajpuv/A8hAdSsLEbFo2FnKQ3ZIyXU5wAnDY7tpeU\n4MnPPsePTg+aed5eYNz4CXj+pZcxcPBgsdTj9SW4SHX9+NgoGXUHDh6RBLqtrVUS8nPPPU/MgT77\n/Cu8+867IhD/0ksvoV+/LOzdewh33H6n2ASy6v/JJ58gMioU69d/h6sXXy20fn7GX597Tu7zvffe\nK4KBXIsXXrgQR/OPivAqBROZqz3zzDMi4EvmFB0COCY6O5144/U3JLlna9ijjy7F5MlT8Mc//RFf\nf/kV4pKSccNvb0BmVpa4qrDNgvv/zbfcIs+zNY3nSvb0LbfeohgAqlKnKsTK390rVTbpR3ar6qlO\nRvk8F3YmEVr9XtuIiRq3USXUnt8ML7l4c2Jx8dXJiiSxhvo/n2MSJiJ5UuFUYIFYzlmUZ7YophuV\neL5GBrH0vJzao6ICSv4TajoTIKP6qRXs1abh7PFW1337/N9/4WdQIOJOYrdnVEGN89e/q6RSCbEx\n+dS/q6qy0ihgQusv0sbXyAZByqnR068EzSgYSPRMVem1O4E+Xw20aJvAHiaEwTDQ1176kC2Klq9b\nOvw3Hv9N/3RIMM9NLxKy/BoVfM0O0NoPskkHOuQc/e+fZpCItoD0+9NVQJ0T7wvHEDdnTaHn8aiN\nuvc1/F5t7SfVbSOAlN53WkwaFXu14PSKMmqRP92uoO0reT76XHWSIb/THs4Aq7SDgd7kJRmQ5Fex\nRPhZOjmX5NlsEtVoBRYpBgLvmW5Z0boSfB3pN7oi05MAB7DvTVlWysZnjHUtXMnP4Wv9e6QFnGCf\nu3FvxbrFYMPwGHhtFKhFz2jaEDrFtokPcZ/o0Y5QSLcS52Nbg6JiSwIr1pkKHJBWE4IFRksJr4Ny\nR1DjmoJfqupPkULON75eVV1Ua4/SsdABMq+Doraqfxyr7E3ksejNQ7ErlKWnje0xMhcUe0YBKhR5\nVGPpTBUTqQiRPRHggMnrQ2dzG2rLKlCZVwh0UrEdCDBbReGXRkGwRiJr0BQMHLMA3SGR6AixIDA5\nDsGJCWh1u8QikIm1JJIcr2ytkNXBT/Dup/HGT55RTCG1pokOhc8Mq8eDMJ8H9Xn7gapCuKoLsOWr\n94DuTgyZejYCQmKx73AJpk+fg0ljxsPZ7sTXX28QlHfg0H6YOWu6rI8b165DYW4eoqKjMXHWdGRl\npqIs9yC2b1mPI3nb4e5kgMGmSzOCHKHoNMTrjM5vwAAAUsdMx4A5ixCW2R/NXR5YHcGKNixsJYvy\n4SWK7seNODXd9wcATq0q64viH96d6dL9u8zxk9c2Xl/1yb090+p3mXO8j8a885AFQMEs3hc+x0SY\nl8pnhpniRmzFIRugrAylhw7D1ulE33QKW3UjPz9P7sWE8eOEurlv3z6sWb1aEpjzzp2Hzi4n4hJS\nUF1dhR83fyPzLTW9D0aPGQu3sxu5Bw+itrpKqH9DR46Wcc1Kct6RIxg0ZAgGDBosfSvV1ZXYsW2L\ngEbzFlyIyPBoGdtr16xEW/sJjB4zCulpWWhr7RQhIrYHDerfDxGRUWhq75AWAIvPhTC7C1vWfyBW\nVR6fAdIKa1zV0+X8BRryKnuoXxjXIiIuA4LymnYkhMciPTYJxWUlqHLWwyl+Xf+yLMRJ38b9hV7K\nDHRIy9dgsn6RyHoRQDJ5YTXbUFFRj4cfWoovvvgMDQ01cASEITAwAK1tJ1TrG4/QEoRBg4djyZI7\nUFpRheeeewaNTTWIjIzGrJkLxGt805aN+PCDtxGbEIfn//osPD43lr36Mnbt2gF3t1Oo66TsU5G+\nubEJtoAAsQJkvyirPNRzIAOMYn3ilW48/MFSSWuEgaL+qB1ttPit0s3hmsf1TrkFyT3S1Ffjfawk\nsf+Te37fnL5il8h1kr30S5culWRn/PixUl2l4B6V9a+44goJCFn18n/ws6uqa3DNtddh7dq10rKQ\nlJKOF1/8m8whWkjFxyfiphtvQmpqOl56eZkEvE88/hhy+mXh6adexK5de/DQww9g+NBMfPLZGqxZ\n87W4IQwdnI3NW7fjq6++xPwF8zBjymS4vT5s2bJFqrP33nMP4uNi8Y9/fCLj96mnnpI9YPPmLRIo\nP/nkEwgPD0Nubp44MTz00MPo0ycT9fUnsOzVV4XS2jeHOgONeOedt6UXVhwyzBQ+LZbEj5VmZYVs\n6hHxIpCg4z4m9qT7UldAP8eki5+htQd4vfwBAH4WwQXuUZpBwNdozaaesWrsedLmY7BU9Z7G1+i4\n4F/YRv5HX6LjH4JFpCfv379frk+gz4t7rhmL25ZchNB+sYC7C60HClFfUIGK/FI0VNfLXhAaHoqo\n2Cg4Au0IsFnQ1doCJ0H4bhfsUeEYMGEMgjJTxGa28Wg5tu8px3UPfYzKDiVSfcEFC/HCiy8gkVoO\npzVa+NcBgJ9emJ8HkbWYsn6f1naSeekF/v735VLhZQuQw+vG2FQbHrzt1xjSNxxRKeFAarxyGPJa\nsOPzddjyz60YP4zrcDS6XZ1oam5Di9OEfQU12J93FDkDUnDtjVcgLM4BS1gALGlJqK/uwO23P4MV\nXx2EPTAYrW0uXHXllXji8SfE0cP/waSa96uXCWs1Yj3FetY27qeL6/3BLDXuZNTJx+u8jD8rdpAC\nsPizrtbrsXrquOaxEADgeufPXiUAywq3nhNMRgnAsPVJ9mSPErcWAN7tQVCww2hZBd5//0PcctNN\nApyHen04LzwKdy1aiKy4MNi9LhF67tV+ODnCYLyozswkukhtZgtWHTyMV9atx36PD22MYTxeZGVm\n46XXX8PkqVPxwQcfSu6anp4hjD6yl3bt2inAaU5OP/nbiRONWLt2nZzj7279neRuZAxs3LBRgCSK\nBRIQTU5KwabNm6SFiY4HtH6l1Tm1DRjbUuWfYAfX1w8/+lC+75mnnxEw+fU33hQXAgr+LV68WNZ6\nrlsEmcl0io2Jx9YffsT6dWuRlpaKRx5+RPYY6hhQE4XtAQRcsnP6CqOquLhEGCRso6Po7YovVuDR\nhx+W63PT726Fqf/sWT4ubnpj1ZVhvsBGz2KpSNJKT2kAaKE53Xuu+317PLkl6VPJEhdap6H0qAJ6\nvl/dHOkxN/y9leCe6kkSsEGSdgUCWNnrTTsN9nQZyuv8WWwCaUfX2aHsAK1KGE0n0nyNXuh1HzfB\nDSYfTGj4Ot3XrdBalXCzuqyTLCIvVisF6jTlXSWi+nM5IdTnqHPVE0knekzc+Rp/CzddnZWk1+ib\nZjCqVd55XXld9Pt0Hz4/m8mmvg9MIDgReczcqJgQ8jN4rlpsjs9pSzq90XPAaus/isRpwUF1v+09\n1Qld4dagje7/lntigAsCZPjZD/K66fGgqPRq6VIUICZrFORTrQ2nUtR0Mse/+S9U8pmG6KBmW/hf\nZya1cg6GToVOCDXIImPbQEG5QfuPCxljbFMxRPQISEmPvzhGqIVVjQuVfPo//BdYLV7GAFMDXwJq\nGTR3aQPp6Qs1Em0uegREKFPih9LKfTA0L3QfqdY+4Gcq2r4ak/yf6tWyaNOSxWBa6Io67xOrM9rB\ngsem0Fm1yzLAVOehWBpikWiMZ547z1hsMa1se1BghJ438hmcN7QWlGtMxJ4bhvpf2nYsZtgNpV2t\nXaAZECJGadjOaOoYrzOrproNh7sBrf3EkM3Y9DQLQaPgEqgYFRwCCTw/bkQCbDidImLK/uoAqw3t\nJ5pRmpePhuPlsFIgEqQns2WRvfAWBASmYMjI+YjNGgJnWDCsiXGwJcaJO0CH1y1VYgvHL4N1KhIb\n/vJn7kE7acioTc+vAij9j2RpeL0IhQ+txflA5XEEtNXgn28+L9TlIZOmIToxEy2dZkSExyE9MRlW\nkxVVlayKtSMiOhQhIYHyue2trdJ/LaANRdGsPtQXH8HhfVtRWZ0PH3u9aZ0UEKTsKjtae1R+ZTwQ\nAAiNR+rYGRh27qWI6jcMVS2d8NnoCEIsXgvFaW2z3jlxJgDgTMm/vjK/FNr9dwCAXvV/9W1qrdIB\nj7IRYpor663CApWYohYX8pLdoBxWqB5tY2DR1o4AVzccLjdqiopQXVqC9NRkBAU4pKdfBFbdHgFX\nnJ0dCAm2S9Wmb3/2GHZh9/bvpG88Ka0PzjprjlT216xaifrqSowbPx5zz70QHq8Pq1d+KSKAY8aO\nw5Tp0yUR37dvDwqOHJDK/rwFF8HnNcPV2YHvv9+IuvpK8ZfPzqa9TzXWrVuDphMnxON5yOChqKyu\nw7o1qxAbaYOvqw77f/gaNqsL3WS3ieilAj1k/MCMQO7V8KHF1fGLKv5cQtj7S1qmA3YM6zsYNo8J\n+ccLUIcm0Y3Qnewnr6I/nR+nPhMeGSHV6D/84W6paugAU7+ObT7SpmviDDZjy9bduP7636KwkOOd\noqAUuzKj28N7EYZB/Yehvd2Fo0cL0advDqz2QJSVl2HAwGzxWfZ57BgxYiRsdhOO5h9CZ2cLZsyc\nhozMdLzy8t9QU0kXaaP8JJUkHzKy+uC+++7DooWLEBEeKm1DAooIJnLmfUOfg1Hn7wEACKSoGIDr\nM8TbmesnxaQcjiARASWLRwas2STJ8f333y/034ULF2LJkiVS5Wf/+6233ipe1GPGjMKry16VIPHe\ne+6VdX3Zq8sEFDj5oQCGe/94nwgv0ks9MjpanBWGDR+Ot958C+XlFbjxxpsRn5CA95Z/iPyCfNxy\n603SgvTCC68I8HDHHbchNTUF7y5fLgDEb397AyZNGI6P/vEFlr32KubNm4PbliyBzWrB2rXr8e67\n74r/N90Vtv2wDes3rBenAapZN55oFqcFAhvR0ZFyuKTCMjjXEki85jo24XNMNsju0/EE7wPXS/8E\nXSdSOnaVNcLYZ/RzOunx/1//rGMA/7/JEunneMT7SIYaH9qlQPrrDbu10wEAug301PaBX54t//4r\nemMAE1auWolrFl8jjAsed1SAFw/dMgvXXj0Xjkwqt7uA2ibUFpTjyLbDaKpuFICfSvwhQQ7YbdQy\n4SykgHcHukxuZI4chLQhA4GwYCAgGM25x/HjnlJc+8DHqOpQLKFZM8/CG2+8IePl/zYAcLor1ns/\ngQceeEREKrkXB3i6MSPTjsfvvQGJiSZEpkUBcTHwkglrDcDxHw7hj9c9h9hgC4YMH4TQsDBU19Sj\nvLoedS2diE2MxJy5YzDt7HGwhwcAUaFAZDi6uyx44rF38NzzX6IbFE13Y8GCc/H8X/+KzExK4PU+\n/MeWHmtnKuL17LFn2EQ1wHjy5xBQ7VYizoa2DP9Opg7XF52b+I99f3aBf/ytAS3/eL7nmLxAXV29\nxCoR4coGlPOWrXQsLHEs3nD9dairrQHVIEaZ7bjnkosxJiMBDrrqMI6Vdd8PMTJ03nrXXLWWNVus\n+Hz/Qby4fiOOASDnI8QehAEDB2Hxb2/A4GFDMW78WDk0O8kcDU148smnsGnT99Knf/mvL5NWVopJ\nXn31NaKF8vlnnyEyzAGXFzj33Auwbt16qeg/8vB9smbn5h6X9ZIgPPf17zdvFCmau+76I156SQnx\nPfnUkyLke+RILs4++2zRAHj5FYr7lYuL0+uvvYH4hAi0tbkxd8452PPjNkQnpeLOO++UtgDmhmQT\nfPzxx5g7dy6WL38bLpdXWg+OFxeJ4N+kyZORl5cndoPffPutxMq0kib7kxotpuyZ03xcjHRypZJK\nJfrH6hmTVFYwxBaPybnVaiR0KnCSpJ5VNENsjhdRtwYwutQ6AP4Dg6/RgnH8WSdr8rNBkVcgAAcD\nB6RSFdcV2h6xQS0OaNCY+H5Nc2ICoEAERftXg1l5h8rrxBZN0bP8N+mTPNLlr6rUp/vQ9SahK5ja\nvUDOiUwAVj4pAGSIW/E5Xa3ma5hQ6wqqTOaeJFkJzelkTSdparNQ1HD13crdgKgZg03Vk09GhrJx\nFF92qZIaGgLsqyfV3kjue1onjHunk0x13yiGpyjdGiTRKLgGZLQInFZkZx+72OHx+8SBQQFAivqm\nKrb6OzQFnmNLV85VYqkYJ6qq3uubq1FJ/wWGzymU02wwO5RHr1b415un/iz/zdkfZJAxoJVhmWga\n9n5aU0CYKAbzQY8RrYlAAEWs9TxeqQQpfQXFFNDXXUT+DG9c7SKhWQD+AA9tbVhZ1vdWxpERHPD+\navs+3ZKhhR9VJbzXekfmkAFc9IIoHCMEXjToonpIe0XXCGwosIPzToIfv3kh4oFSIVXgFucdK809\n45Hz3tktVH2OSRU8qfso48AAALQIoO6t05aRwvLxA+400qzHgGhWGCJ6GjTicXDeshqt+8b0GBWN\nEa+3BwDgWKDll1hhWiwIMFvQWncCJXkFaK6sgbdbkW7ZCmAzWeD0WWELSELOsKmI7zscnvAYODJS\nYY2LhifQBpPDBsq6ybWXNmdJH6Ve+q8+/AEuJljsj7abTAjm5zXUwHk8D2HOFnzyyrOSaIycPB1Z\nA4YjPiUHu3ftF4p4TEQkpk2dKchyUVEB9u7dLXN25OjRyOqbjdaWFuzdvhMVx4+gu7UM1eV5cHs0\n1ZGiO7S0cUmQxodO0il8iOB4JA+biGELLkXckDGocnrgNNEiVek3aCaOWo5UxiyAyE/aexQk8P8a\nAGCOpNdqHrGyeeW6RNCK95+gLnvgycqwSJVKkdl7BQRZB+c+IJrCLjeigoNh9/hQfDAX9QVFmD52\nvATBO7dvR2lRCfpkZGD40KGAz4Xl772B+poq3HDDLRg7bizefuc1HDy4H4MHDcfMGbNQX1uPTd9/\nL2s50XuqwHMNLzx2DPRGZqWAQnhMIlj94dzjPEpMSBS1elZo+XNYWISIALJSwD2PAnYEzutPnEC3\n0wOHNQhxkcEoyv8Bufs2w9XegG5XqyTO/KcBABssop2RGBuP9q4O1LSeOK31nh7vmi0g6xYsCLU6\nEBYYiubWZnTBiS6/d/8S2HO6OcR9i9TgP/3pPqHSa4lC/VoeK6vIwgAwWVBwrBx/uPsefPPNevFy\nZjVIrDpNFiQmJOOyS69EaUkZPl/xCdw+tkkFIaffACxadKlQ0jdt/lbigeHDRiAtPQW7dv2A2rpK\naQXramuF1U4LyU5jzfaJeCAr0XPmzBH6s1onVUyhQQD1rPojbUj9f5fXC3NMsQel11hiF8Vu4pr4\nf5h7C/CorvVrfM1kJsnEDRKCBQIE9yJFixYv1CgUq1AvpYUqLXUD6nLr3t4a1OW2QIHi7h4SNO4+\n9j3r3WfPnJkEer/f9/2/5z/Pcy9NMnPmnH322ft917vetRbeu1BiiocXPYzysnLpFWWlikEk/Z1p\nE8WgkAkmBfAeeeQRafcgK4DAxPr1f2PEyBFipche1fvvux/LVyzHm2+8iUmTJgUMu3LNsOCtt9/B\nnXfOE5eClq1a46knnxZ6MRNrrgFkuZSWlqF1erq0A2zYuBElJcUCPHRoT8vMaOlPvfKqqcLgyMvL\nxZ49u6Ri37NXd4SF2bFz5y7k5+Zh4MABwjxlDEnrutQmqZIMnMw+LckohatYXS4rq8Cff67EkCGD\nEU9hS1EMPyhVxqa0XAOk+s/jJKc09hUatFOPbhnLzc2V4glbDfS+wr2EtpH8nQaZCNxpQTMdV7K9\njM8hwQd+lmwBHt98LH6O90bvY3wPXxRBU/u8ip1lBW0AIPp/DwBYUV1bJWJijzy82Nc2kRzlxIsP\nTcWUiX1hTaKfPVEWD2pySrD3733YvmY7IuxRYqcXbg+BIywEEY5QVFeVobq2EvFNE9FpSC/ENktW\nbgFcFw5nYsP2LMx58AvkypZkEfV0VivZc23StDbNy//JymFeoc6/CzU0QGle7wAAIABJREFU/vp3\nZJaQJfTvf3/BVQYxAEa3CcPjC2cjuWko4tqm0KsYCKetcBhcp0vx+bLP8N2XW4EoO7yh0ah1eVBU\nVIImjR24dHRfTJrSDylp8UBMBBAfJ5+tqbTgg7d+xLPPf4zS6hCUlrnRuUtnvPH6Gz5BS301weBT\nQ2tmQ/OqofepLdu3+/vAcV35V8eRZnuZsyyOMXbUbTT8HQFFsmX088H2AMbiBNr0HOdzxRefGdFj\ntTD5LxSaO9tMKbQnbn5eCNDHvY697I8++gjY6kNJUPp6zB95Kab06gyHi+xtLX4tVET1qgcAqOer\n1BaGN1evxcc7diJbrBKtiLI7BADo0L0L4pISxf6Pa8K6deskXlTtQyxkkyWrNL9UcTZE4gTmwnTb\noN5I1oksKcLS0ezMmbMYOvQSsYIlYMC8Jr8gH1Muv1wsW8lm+ubbb2VPIqhN21m2cr791tvSo8+2\nhOYtmqOgoFCAW7Y3MRfJPHFcnOISExrJGsvqPyv/u3bsFCYBBaEpnMh4mqBvfmEBnnvueXEJIMD9\n+Rdf4M0XX0Bq63QBV7WTi6Xd8Eu8TPqEZm5Y6HEsuREJhcKn0KwoIdp+TAffpEXp3nVdKeScYnJV\nSzVwQxCCN0J68EVIz99XrSk4KjkgxdopiYbQhim4ZlV+93xJ24GREIhVGen4YbQgZK++oj6bK+ai\nC+CqUx6OLpept9mPDCtBPCPxNBZlTfNmosEHwNyTLZ7zbI9gPyhttwwbPZ14a2qXTsa5EfKcGeDp\nBEcE6Uh9pjWdKXlSm4OqlAsLgRRqQ9yG4yP98ka1WyvXC63eYFVoMTceR4v+aQ2AqiraDLJnLVJZ\nARq96jw3cwWV4y1UcmM8dXLtT8pUYCLXxASO80NTGI0kQPelaycBLdinqeycZ9rmTbkkqN5b/eK4\nyjWQ6u/xGH3vHkHw+WKVWANIivGgAADdXqBBAjpIiFChoYqqKe/iXiABlzGfJdH2t59oirlug+Hn\nBNgxjqUYM7r33WsCbdRAcH5ocEx+Zr+/tHsoRojuPZQxNXrsZX6Tci9Ve78GgDgHGDY0TKi1IwSZ\nHfw2zlEBbAx7RvYFcfxYgVeCUXbpKdMsE3OyJsm/4Xqh2RFa4ZXzmy8u0JzbCsTxyOLvU57l+Sse\nq2+x54KlxlWxdtT8VawGvQbw/gsV1mib0S4JZOdo+r9vzhBYNGjaOnnWrTUaFDNvbnL+FB3zOXv4\nxbBYpSUIkHfqLA7s2A13ZQ0gIAAQEWpHldh6hsMe0QKde49AYqvOqImOgzMqClFNG8PROB4ugnQE\nAIwqsdtIGM3ncKEqp+8ahD3jEQqlnTohVIKvLEdd1jHEuaqx4sN34KmrQ48BA9Gha2/YHLE4uP8Q\njh8+gMbxCeh7UV80TmqEgwf3Y/funQLc9ezdC0nJjVBdWY5j+/bi6D6CANvhqiP1X52VBaECWlXV\n0G6T/Z/qTwYPBHAkIbnjReg05gokdemNUpsD1UwKjTVbzUXVNy//XgAAUN934dc/VYRVMHaBAC7o\n8OaATgBEA4ZQz6xVtC30HkQbRoIsLledUKvttjCVjfEz9A82BAKFCG8IJYZSUNPphM1rQbTFBntF\nHVBRjTMnTiCPPed1dYiLjELTJilwu2qwb892OGur0apFK3EHOHriMGiHW1NZg7ycPEme+vXrj8jo\naGzcsAGHDxyQZ+7S8ePEGmnn9h0iTkdWwYABA9GocbI831mZx7D+73UIC7Xh0kvHolvXXtKv+s03\nXyEs3IYZM6ZLsvfjr79i2+Zt6JTeAZPGDMWPK97E9nU/qcRfPKeU2JdueyAAEBEaDg/tRFFDecDz\nAgD8DBNwtuER7gkNsSM5PglVJeWodXHW2FFmggD+R2G81SK93s8884yIWpldbyTm82qbUlb6Q1GQ\nX4Znn30e//73Z8jLpUicGGNxpUSTxs1wcb9BIr60e+8OxMbFITwiGlVVdejatSfOnTuL3NzTsicn\nJSbD6arFyVNH4QVZHaodUMaNtp1hdkyfPl2S8hbNWygtlgZmqpLKZFZtAGLGPNJzmmwPVmL27d0n\n/0Y4HGiVloa0tBbCeKDl1NixY2XtZe9n1olsSZa4Tw8YOAB3L7hHqqfz5t3lE8tjH/fll0+R5Hjp\nsmUSII659FLpI+V6T9sqVpv+9a+30Levqnypl2LfcYx/+PEnCVoZW7Ru3RrRMbGyBvTt21d6jN9/\n/31pJWjZsoXoJhw4cFC0V1ihT09vjf0HDqG6qgZPP7MECxbcis8+/w4PLXoA06ZNxeNPLIbdAjz9\n3PP45uuvseT5JbjkkqES+E+9eqowWW6//Q7s379P7LZumjsX48ePFQDgpZde8imbh4ba8O23K4Qi\nO2PGTDRp0hjrN2zCTz/+iJtvuVkqykxQ2EtLNkG7tm3lKukCwIom2Q9aJJk/kzFBAEUL/fG4TOYp\n6KlBc1L/eZ10EeBaziSGzDMG4/qlAQANLpiTfTNQqtcqndTJHWjA1vF8K6j5c/+wzJ7nz+qJ5P/n\n5OVK4rD86+XyXna9tkqx4fUnrsPISzoRJ+MjJBVNizsEx7cfw+tL30HhuRo0S00T5ktcdCScVaWo\nKMtH45RoXDplNFJ6tmWvHRDmoE8tKjOzsW5LJubc/xlyFC6CVmmt8dPPP4vLx/kv/3+yevw3EHTg\n0OgxZYyzb99+XH/DTdi5YxtCLR6kx9tw1UVpmDt9FGKbhyMyrTEQGq7+xwGrBgp2n8a/XvkMP605\njhqP0h0OswFD+qZj1syxaN8tCeEJdljjYmGJihABQVjj8MW7P+LpZ95FblkI8gtqpY2PgN0VV1wR\ncIIqEVU24Oai5P/0/jOGYsWfc5hxMnMlVSxVYDITeVa8+eLc5zNKKjpfjO3YLkLwi0kp5y4V7Rnb\nEbRW+6xVgD6+evTo7gMAcnPyxUWELTpkJ2tHNSbTUrhxu/D4Y4/ixx9WwOEB+I039u2HWy4ZhAgK\nsxuC6gq0N67eANMCYgCCgvZQPPzlN/jj5GnQaVHiGYRg/ISJuPG2W9CufYbktUuWLsWK5ctlvyGj\nisl59slTuOP224XOT2HUu+++Bw5HKF5//S3Zk5j4c/0dMmSgCDiSPXbk0GGMnzgR3y7/QqbFG299\nJG0kjGkfWrQIN980F19//a0I852mzkxZqbQHVJWW4p2PPsLsGVdi1ZotGDt2HNw1tWjZpg2ee/45\njB83UmLpJUtewROPLkZaerq4D9Auk6K1f/z2G+ISEnDxgAEC+K5Y8Z0IBDKPGj9xguSEvI7rr79e\n7ivXcEun0aO8WqiEyThvmLYdkyTDoO5ziJmYMPHRE08PNDdMLY7HzUOSVMPT0UHveUNRXejqYWEB\nc1VX9flLoTXTQcSw8eN5cDKZEyYtlqfF3jRlX1cZ1cOh/KRJR3cQnTPpBDA508rzfuV+fxgajAgS\nVKoznY9ugeD56veqCpNVztUn8CM9/irBNAe5Wg2diZz2nVfXHioPofKjVwkwkzKq+aqqlfK0l5qV\n/pmJL4UTq5TGAr8rNDxMjqNF3oTKbbIU5HEC+hA9Xh8djQsAv1torNqtQJc1jLvG89Nigfxv3g/p\nkzcSLs0+kPNn4m3YEQlyZqr8q+vnd7FPXPvKm4Vf1F2TZNEQkRK6vkldlO+gZZao4rsIHFGvIVAo\niRVqjpuu8OskVKPwuo9Hg1s8b1WpVyr1ehNX1HV/hVuhgeqZUHR85Zjh29SN8eL7hAFiAFga1OAz\npy01NXVeU/95//jSwIxZE0AHwAo480rAKFZTevx5fwxRQToAMKkhg4f3SCiFHn9Liog90jbG8G6V\nZ5/CfNXq+xUdX7Ed+JJNx7gXWmdAs2v4d2F7UFjO0A/gv9IyQJBCGBFqTHXbilpTlKo/gQWCH3y/\nk97YpPbTEtSmkjbOAy62/Lye72xR4rMg65K4Ljjk2Saiqt0qtEuDsAloa0lr0+oanMzKxrlDR4EK\nkflX1gCCXDLKsSOucRt07nUJ4pp0RilCUWJ3oXH7dDhSkuENC2chRPWXGyI0vsKewZrQzJ6GNmZJ\nLDVabXx1mBeIoCDiqUwkwoOfP/9Cno1+w4YjKbUpzpzNhbO2BpEUEPV4UJpfJPkI1fmj42LETeL0\n6Wy46mqQGOtATek5bN+8CqX5mYCF3rnqTDRXwVd4kv5t1f9NyzpvSCTi2nVH2rAJaD9sLIq8Iaiz\nqvWd//MHqOqA8ruGLtK3Jyt/YF2F1wyCC3zkv/6TgT2Z9n/1vOq1WfQxZP0wbGmF2ea3eeUzoYNz\ndV7KLlPvh7L+ipOQtrVV4nZi2UcQABZE28NRW1yKM4cOw1tRibbNUlFWkIdtWzYhLjoGQ/oPQl1l\ntfQTlpeXolvv7sjIaIudW7dj+9Zt4oE8efIU2MLCsGXrFmzftk0S6qumTRUf+J1bt2DH1q1ih3v1\nVVejrLxckkIGT2xV4DMdE00P+cYCKB8/fgQ1tVXo2bM7HJER4gaRn5OLaFsoWjaJwca1X+N05i7A\n4lLAj/E/YT0Q1HBEwlVN0I2whwe1hg5AQzdFVf+N+UMAwB4m/3NX1SA1LE7ojQdys5FTWSQAgWqz\n+CfIJ/CbCJBShO6+++5F9+7dpcdS5pKhSaJFHlW5woI1azbhgfsfwq5d2+UehYeGC8OnkgK8XFPt\nEahy1qBJo2aYMXM2oqLj8Nbbb+HM2ZMyl9NbZ4go4969u3Do8E5J+gcM7IdWrVtJL3tBfq7QgSdN\nvkwElVqnKYquL/nXYoe6mOCtgc1iF6qzzExdAhOHgiq8994H+OTjT3H06BHUOWsR4QhDSpMUTJk8\nGffcswBlpWW48oqrpDrERJgB8s+//CJr3gMP3I9bb78NH3z4AR588EHcfNPN2Lt3r1Ta2ON/Lucc\nPvrwI6xdu04Agblzb8Lff68TWzSyKhY/slgKAv6XirUYM5CGOmvWbKxe/Zd6vsTGkKC3VtSn7o5q\nP+M6rNd9r1cxzpqkpmLU6LGYNnUmomPicDzzKLKyT6BN29a4qHcviWt27dop7Jcpl0+RJJvimb/9\n+pv02rOfn2wAJtPsg2aMwKqVuEbZlBgu5z7jOVbgmbRzH+a6rxNwsgycLjf27d2LTp07qcq2SYCR\ne5deK/T+1dBapuMA+Zf2sazgs2AhIK7f5cWsqE5qPPdhFeeQyaEYnvz6kmLFEJD12HBr0La4WoSY\nfwsWLeTvtKtBAKvJaG3U/d/mJyiY9RgY3+r10ovdB/biqquuxpH9h+WZ5orXo30E/rXkdvTo1gwI\ns8BdyzjTA1tELGrySvH0k2/ggy9OISwE8Fj5bLFPFejWzoKbb52MwVNGAI3jADf3H/EjhjP7NP5a\ndxBzHvwcZwrVmZLBQZsyqqPr9bjhTeB/b+34Z/j5PN9irFFfff0N7rzrbuSfOwMHvBjRJRW3jOmB\nbp0aI7FzCkKbJMJrCYPFzlZpTgQ+J6HY/sdmvPvud9iyNQes5bRMjcCVEwfhsilDENvUDm+UBYiK\ngIVi1uKkE4NVP2zBoodfw6EzNSgu8yA0JFRYNwsW3uOLQ4Nzk4bAIjO7JJhpYmYf6itXsadqJ9aF\nWSU86pUqdEJCvK/9k59hfKbFmvmzXi/M81Evc8K8Er0n9W262MB/zUyPclog5+cjrWWa7/d0Bnjg\ngfuwZOkScIVqSkvKpql49KopSLZRVJZtxR7YZBduOAKR2WKz4nBFJRb8+0tsKK4AI1syAKzWUEye\ncjmeFV2RWIlPmZBTdI/XN3LkCNEqIOPpk08+FvHUy6+4AtOmKUDmu+9/xcpVqyTu7tW7t/TsszXq\nlZdfkT2uXUaGgI5ct3759RcBuCh2SIDzrrvmCYvrxZdewtp164TOT5HYv9evFzbEoMGDsWPHTnEV\n4LPBseG53HTTzZKbEADm5xmfUFS1Z6+eAg5zzab1Nl1z5s6dixUrlosQIdeML7/6N3r37oVPP/0c\nTzzxpKyZBHIsHYcP82oKLVFQqbyziuZmn7zqN9bq1VoMTCz8mCQZgawWzpBWAMNjXpkUc8NXQZh5\nsppvGKsckgRIn7ii5QcjplLJkX44P9VWkgxW4myqYqr77nWCIVVgETULnBw6gRGqnVDuFSXU/0AE\nLTIef8KsEy2zZZ8k/UZgzP+WXmddITaqTuYjin+zUOZ5buzbVwmTVr6XSW1UwxUqZg5ASTFXrQ1q\n81BCYrIpGO4Dcg+MseR7NJ1NLx6KhaEq5iKuaAi+6Y1QJ6iqbcBuVL78C6WmivOzqmVBqcFrurud\nXtS0EpSEmEJtihKu/6et/jinpF/czeTVqE4aVUVzEi3ik8b1yPkaYIGckQZ4mcQY36GAKI9iD5BJ\nQpcIg3GhGSLcZDmGRPKZQErbg8FqILOA3y+VdwMc0eNNAMAnzCfJPhdPJZangQXfeKuIQhA7H6Bj\nWPVx3kmPlcHe4DkqJFa1p5iTGPUMqfHhmPJchKFgiA1KImkISkpN1gAtKH7C55I9VTJmGtgzWXZK\nVZTHNMADNQ95Hn5Ahp7ivmMaWgYaxOL48KVbHNT4KJYDj8ln3x6mHBQ0pZX/6vVCiYYqSj2PqfUh\nhAUkjAwFHOgNhNcuSZ2MA9sRFL1fnj8m3nK//MKJHE+xMdTigQZ4wXvENePEwSMoOHYKnlqXLzNm\n1VJdfyRSm3dC+07DENkoDWVhNrhio+Fo1hSh9IZ2OMC7pd0B9BOi2ybMAID5+ddrmbrPqs+X7QR2\nLxBWVwNvzmnEeTxY/f3PMsH7DhuO0IhIZGUeh8NmQ4f0VqgsLMKa/6xEcVExLurXHxkdOqC0vAR7\n9+5ETUURoh1eZB3dgZysfYCVWx6rlkalX/e56+fHqHZzTfaCtogORLfuhLThk9Bp5AQUwQZnCK0k\n1VwJXMcVAHChl36u5LppLRUA4F3wo//VH3XfvhmMlWeGz6f4i6vzC2ZJ6X2CSub6moLtGmXOyjWr\nU1HXQFtOxahhVR4uLyIo+FpegZDaGsTYQnDmxDEcPbAfqQmN0L9rH5QXleLgof2yXkVGO6RaWFRQ\nKEwCWmjyuJVV1aDgXUx8nCR7FOArLy1Bk6RE2EOskviQ5keK4aFDB0XZfvJlkxEREYXVq1dL33VS\nUgImTBiH2rpqSVbPnDuD3v36YkDfi7Bn8ybs2Pgnzh3fBFhJ/VfzwaqL2uLmoFQtGFLFIBL28FDk\n1hSLnkRDL9n7DPaAaptgmm9FGELQo2lbYeftOn0ERXXlhn6EyZLRzDy9wJ1ulNxYaIw333ILWrdS\nGgB+AEC1KKmprFLw335ZhQcfXIQDB/bJ+qoUDagB4EII2D4RJslYuCMKM2bMkorJe++/i7y8HDbk\nIK1lO4wcMUqqX+s3rEFZeT669+iGzp074vfff0NhUb7Qzd97712MHD7cHzqo7N5gj7iVMJhYWlUh\nxBaKvIJSlJVWICUpEVFiU+cRb+h77rlHrqdzp07C2Pjrr9WyXt5000144P4HUVfjxLXXzsDhQ4fE\nZaKosFD2N1bJ582/Cx06tpfjEAD415v/ksBxy5atGDFCKarTB3379h3SEsCqD+2meHwGkX36mKv/\nahy1nRf/pQgVxbh4XWwHpUhl5vHjPhcgL+0VLVxvdVuNEvtlNZDe0zfffBO++OJ7LFv2Am6Yez1u\nuWUmSktrcNf8eQIOEdSJo34JgI8//UyUyy/u20eS6vff/0AszNq0aS1K4a+99ppUiMePHSO55C+/\n/ipznlU57hEUu1q3bi1GjhyFZs1SUVNTJ8wAzhcyEngNxcVFMjZdunaRZ4kvBtKk1pJNwWeRt5HU\nXQbIvXv39s1M9tES/GiX0U6Q36wTJ0RfiTRixhBM1hlPULOA51deUYHSklJp4dHiwcr9KUTaFMj8\nIUtOAwBmpqcA6U6nHFMDBfpEWIHl/h8gVhekw2R+nAIEj+sxDDhfuQBYsPzHHzB71mxUlJaJPk6o\nF5h6WWc8uOBqpKcnAF6ntIyJxk5SMuC24D8/rMETT36K7NNAlROyj6VEArfcNArTZo1CTOtG7PU1\nLEHZs+2F50wO1qw7iNn3f4KT+epM2bpCEUhasZn1GP6rDeD/ozdx/3j1tdew8L4HgNpqRHi9mD26\nG+6Y3BexsXVI6p0OJMbAU0ehOQMA4DPPIlGVF5vXH8BX//4JRYVVGDroYkweOwhRcRbYE2zwRoVI\n8u+lO5qoD0Vg78ZM3Hb7E9hxrAbVThu8Ti/umX83lix73tcywjkR7CrBdhS2lkiRzBBF1oUXn7uX\nYdGnhyoYSJD18wLMEx7X7IyhVf91mws/T3E/xpktW7QUwCsvt0C0S2h7GhunrDVLS8pF6Z5gH8E5\nanswKeZxSKeniwH3sIqKKgEknn76Kbz15muw1NSB/JqLk+KxeNIEdEiMg4sAAONNC52izgcAWOhL\ni9WZmXjqz5XYVFwBcmsZt7Vr1wlzb74F10y9Ruxzf/jhB4wYORI3Xj9D1qOVf67Dhx99KOsEgVC6\nv5zIOinrGvf+vn36Yt5dt6OosFwq6rQKvPLKq4Qh0Kt7O+w9lC3APkHYiRMnCjBA0J7iqWRjE9Sm\nDezLr7wi/fsU6GML1R133Ik/fvweIRExePvtt3HVVZdh2bJX8ejDjyA0wiFaTHQSePnl57B16x5c\nc81UFBYWiOsANV0Yk3Pd3rNnNy677DIBMrjWt++QgXFjx+HXX38TIViui7l5ebCk9+vtJTWBk0Jb\nZ6lgh5U/Bth23Vrh01tQQZ1KQpmkMlHUtG6dlOqkiig3fyeVeSpJGx7xejIKo8CgIfN3OrHhgq3b\nDNQxSU+jaJuq2sjnrErlWVnVKRoyB0Crr6uKaiB1yB7C/mFV0RQKtql9V2IiSWrU//TPrIryZ03N\nDgYAtKI3z5Eie2LPRmFAinEFeXfqcdNghkblNAWdMJkZAFB95UYQywTRqyqxMlZMcA2rHMV8sCml\nY+2QYPIB1si3CDrSYpBCfgIAKOBFj4dWVxehuTC7THZz36XYdEgPm/Jc104EKgFnAKj0Bniv/QIz\nhs6DMWcIMfM4ur/IX4VT99EMyHAcOe78PY8pyZ4R9fG5p22k9HgbtojSvkCVeia6TO6N89Q9TBxb\nVpH43QS8dBuAVr+X1hcPaf2GT32Iqu5rJX3plzeU+amPrQEAJulafM+/Hqk+Yt4X/axosIffK8KF\nhl4CkyNpHRBgymyjqOYpX9Jjb7hQcPGTlhmjPUYDYqK6b7BLeD8q2AcrrTwqgTMv9j4rQwOg0wmO\nP2lyiQUnx0xvPOb2BN0KoDdt3/XL+HB0PGIdJwKSBC54rXYm2C7DLUEJGfL+iSsH2ySYTBlaBJwH\n1ADRbhYaXFN2gIrhY7Op1hgZf7aDEJgy1jN1rsp2U4MUaizJJnCIM8CpIydQWVImfeCi9UHbP2o3\nCEocjVYte6Jl24sQndoOZbYwuOJjEJrSGOGNk2CJCIPL61K2eMaCZmbXyDoqA67+qL2+BTiSCpJR\nWeMzzwqqsxaevLOIcXuwfc3f4uPeb8RwuX852SdRW1aOeAe9lp3IPXtWnl3a8YSGhaG6qhZuVyUs\nnlLs37seR/ZvAlzlEripLzcAAON0fEJ3Bu1X5peY7oQivHkGWg+fiG5jJ6PEFuEDAIIrCEIB//8B\nAGCuKutz1KAPf9bJv1lBXrEDDFcHg72l9yQzEMDE0r9mK7BXLDnZjsO9sdaNUJtdUcOp1O6shbuy\nEu7yCkTDhtyDmcg/k4N2nTKQ0b4d1q/9Cyezs9Gxc0fxmS/Ky8e6v9aIGGB6u3boP2QgzuXmYu3q\nv5CdeQxTJk5A504dkJ2VLQkPeyxFWTkqRmwFOd/4eyY7DKD69u0t57JmzWoUFBVIj3jP7p2xf+tG\nrPzla3gqTwOWSlWxohCm6ugw6udsEGECb0WrxGaodTtxtOQ06i4AAEjl3ZjkSq7Ti6YxyXBYQlFW\nWYZidyVqvE6pdOl9SS1Cxmj/Q1EvJi4Ww4YNl2SxX5++6jimio8JupeDHj2cjWVLX8C3y79BWWmJ\nAMwEASywISoqRvozqT9DQaSEpARERDrE4rJjh25y7tu2r0dUVAQc4bHCJLLZ2KpXIWtYDYXNqssx\n5YrLRWU+KiLCaBnR16P6YYpyz2LzmjWIi45Cn/4XIaewEIufeQG79x/E/HnzcBX7Mk+exq233oSd\nuzdjxIhLMH7cBMTFJWD+/LtFBfrWW2/D/fc9IADA9OnXYuPGDVLxrqiqwjVXXy2tBxRzYmsK7Z5o\nA8WKUWZmlthWtWjZHFGRUaiorJR+WibTrKwz4T18+LBQUqmSf77An/vn1199jVmzZwuW4fYqfRtd\nZZYWMy/3SsWcoQgkVbBZTSNQsWTpMiQ1SsSji5/Gx598ikWPPIQbbpiO0tJqvP/eOzhwkO0DLTFn\nziyp3lOwilTWObNniWsUabVxcfGYM2sGikrKsHTpEnTp0hVXTJkiSfRXX36DPXv34N777kNkVKQI\nir333nsYMOBiDB08SCbXl19/K+c7efIkmZm0GSQFlhWzZk2bgfaLO3btRPbJkxg7Zowwe/hisk8W\nAdsd9L7Ln1lxY0LPx6G4qFCqgy3S2J0M1FTXyV6bEK9aAZQDjmLdcYlkRTPEFpioqLxdzWBzUnbh\nKrhepYxl3QC39TEulMgFf4889SJE68LTzy+ROWVhEc7tRXIk8NDCqZgxczgiksOAumpAXLEM1Dsk\nFJWFVfjjl/X49OMVOHKsDi2ah2H61FEYPro/Grdh338IvE6usyEKHSBTobgM69bsx5wF7yEzl7EN\nletD8emnn9ajuwde6f+bnxgz6Dl+z4KFUqWlA0yyFZg7pR9mjemGsMgqpAzoAsQ44K2lM06oamzn\nsyAFmRDAZUdJfimKSyrEuSLKYYMt3AprJLUBQmBhUUrkgzywWsKRn10uAMAff59EWVWIiJdec9U1\neOGlZUhOUWCVuaKvR0MXhPTPet9jrGQWkeS+QaBFi3rr97NFhvv2aIAqAAAgAElEQVSGfhF0Isik\nQQXGdKT+81/2+qviiwfZ2dny/OrzysnJkWdGW3OeOXMOhw8dlsq4tsokQHb06FF5DzU9qPHBBJnM\nLjLcmIsROOA18NxXr16J2265GeUFedIG0MURikWjRmJou3Q27gnAa/OeHwCgfbM13IEVO3bgyVV/\n4pjbi3LWwuyhGDzoEtxzz0KMHDkcn3zyqawdZB3dOe8WuYW//74KDz+8SFox+FwMHtQH+w8exy23\n3iq0eib17771mrR4PPPMc/jmm28EIL388itE9I/9+2wpYPWegON9994nMS7BgmMH92PClCvwyaef\nYP3Gjdi8ebO0U/G6qZXCtay0pAyXTZ6Mxx57DO+/9x7eefddYQeQlUE3nIULF8ixn3rqCezdtw8X\nX3yxtFIVFhaJcGHmkcPSRsi2npdfeRl//ParsEUZ2yx+5FH06XORCANa4pulenWPsi8ZFds1LkxM\n2ulvq6jNqrpmqCYbyVydS4nUMTBSVTu/5Y2AAiZ0khuFquD6F0IFILCPP1AsT/MTGeSqhFJRrJng\n6JdKopX1BV9C+5dKIqnNqjrNz5mDDSZQUkU3qMMMAHzHM4J0qSYYAZJBKA1ISgMeNgM9M1dtdSWV\nLAhJbExrl6766V+ZEys+nGw34LnrTYRjyzHiS2zpqLpuqmr5/luqo25JLLS4IcEHLc6oHQd0PzmP\npyrGBFXU8fk5vk8ndtoD3lzmoLWcTsgV4MIFzCQ6WOeSh1dXghkgSMJs/Ktav/3zwtwywMRQ9577\nrJF0VVoU+5UDQcDLYJrI9UgvPCndRqJraneQDZbINsfTsJxUFECjF9uoWhPsUSwHAgCK2UAhEJ4b\n5yGvXZJtw+WAtE3+Xi+MrHrLHin/r96nWQscW44ZE1feB/432QlaJFFVtgJFEPV94b8aINAMAOUs\noEQelYWg+i5zQi52lEblUlPwNcDA+8xz0S0xWrfCLKpHkTHf/fQQJKyGIyJC7pOwFjzuAA0JtQEZ\ngmpWC2rqakU538y8YSDJ4/B9HFt5XuR3qvVA/ewH/YRJ4iJooFoFZHwNgUW9Bih2gKHn4UP1FJCj\nWyt4D30ijaS1wwpPjRtFBUVwupVmgrw0KEgVeMShWbOuyOg6FPaEpqgIDYclIQHhTRohMjkRHrsF\nLqsURAQIkHsolHp1qGBwWrsASGLKtgSpmirbNbvbCXdBDiJcLhzcvgsxUbHo2revUGprC4uxf9sO\nHNq1G40T4jFsyBAkJsQL1fn40WOIjY5Fx/ZpKC0+jl9//gKVZecAJ32WjMsyEj2VCllhN6hztGgj\n1Zsv1iIIRYQ1aYP04RPQbezlKHPEos5GFwC/2I6v2v5fAAC66q8AQhU8/N98sYe6oRxSA5Ja/0OD\nyD6w2NDbIIuE90QDaGrd9TNguLzoti8FVKsWKHmGeCl1am2zOQhEKVV+C58pVidqXDi+bTdKcvLR\npUsntGrRAmtW/oncc2eR3jYd3bt1Q3FeHvbt3ovykhI0b9kSGZ07SpJ/+OBBFOXno2/vHkhv1Qqn\nTp2U5I37GwXnoqNjsG3rNqGOt2mdjj59+6K4uBAbNvwtvet9+vRGdHQk9h7Yh6MHdyMxMgQnDm5H\nTQkFmaplgnKOMi7XCTznYGJkHOIjYuCqqEF+dSHK4RIV/wu9OH9tFivCQ0Ll85xb5aVlqEK1WACK\n2KWhE6Caof57AIAPR5eu3bBkyfNSoVE8A+PZMjr8/T9ZkHXinGgALP/2a6n40tlARD5tYUhITMLs\nWXNkDN9+512UVLAJ2YOMjM64+64HZE16+tlFyM1lH6tDxOt69eqK7374Viq+bK0goDb/7rvxwrLn\n/V9rFP7Vqu/GS88/i6/efw+jBg/GzOnTsGnXbsx/8lmUO934/LPPcNn48Vi1cg1unDsHSY0iMWbs\nKAwcNBgRjihcc810nMzOxa233oiHFy0W8OL662/AT7/8LDAGZ/vUqVOl2hMVy35dt7QAPPTQQ1i3\ndh2SkhoJqyAz87io9FMg8tSp0xg+fDgmT54sOjp0DWDv7YwZM3z7tI5fzEkGK+ELF94rfb3s6SVD\ngarkTMy5plKQ75effsHIkaPhCI9ASkoTqXIxoG+S2hTbd+yEx2PB2HHjUFZRhuXLv4EtxIJHHlkk\nbIrPPv8Mix56UFgrx45l4tdffpGeemqUNE1tKokPtQratGmL8ePGyXjTcou05H59L4LLzXYdCw4f\nPS6WjFpImO8LD1fJvFmeVUoZbPFh3GIkVDIVDRsy/rcwM3WbgMEi0uwyHWtVlVciIlq1TpC9qIsD\nqs2QblUKzKatmVD/bWxtU8r/BAEUaU8BAg0yl0xtMrrYI3uJMGHVWhUcb+q/NwQA6M/pIpn/WVao\ncGl5Ge64az4+/eAj2QLDvUDXpiF4YvFtGDqmG0IT7ICzlgEivLSrZqtliB2whqK2oAIFuaUoKXMi\nLNyOhEQrEtgbbwNqGbc6IW2F0mLHwltlNdav3oc5d7+D42cVYMZUg9VOJkcNgR/6mvW//zf3j/Md\ni3He1VOnCjMhzOtF63Bg9oTemHRJBuKSbUju1xmICIPX6YaFYyGuSJpRwQqXnZZBaqFjS6UsttRC\nsMMr2k8h8LA9VnxoHagtduKRxa/j/c/Wo7wyVFgv7dpk4F9vvYlLhg31Jf9moEj/t3nMztcCoK37\n9NzRn+Uzo2N1PRZmUEGPuWYGnW8e6s8yP1Mtf4YLigfS6sQX9al0aHb2bC7ipW1HxX+6jkB3j0Sy\npKIipNVt8IAByD19Cla3G62twINDh2By7x6wWFwigBviYUGwYQaAmy5ZIXa88euveHvfARwz4nJb\nWDgmjJuEDh06CsuGGgZcowhekgmQm5OL2LhY9OvbTwT5SP8/dPiQAB5sbSIAz5iB+zF1d4YMGSpC\npVyrFixYKDH4lVddiWeffRxV1W588sknePPNN2UMrptzvaxvZO2NGj0a3Xv0wJmzZzDvznk4dewo\nHnv2Odxx5x3CEqKrAOchizyXTZqEYcP7Y/fu49LeRcA/rWVLeS8ZTjr51/d10qSJ0jZAC0CKnm7b\nvhWb164TQIRgG10Fnl+yFJZQK8k+/pdW2JaqBz2QOZGhKM5cLHRV11cVCaqwBz9QUmkyfYNTCBhK\nZVkmk/GBBvs4LP7qiz6u2v79N1xoh4avtuaEa2qyfMYQ3fFVP40vlMqVT5DHb90ieb/hv6tWeCNg\nNR2HN99cNT8f6qqWg38Ini5AvVHz2hh308A2PN3VG3S1yzdepgTZfG90NdJXiAkAavQ7VXIcWK3R\nvHv1nuDrM98fHZqbQ3RWzfQiEnA+xvfbxINU3WINHskG5nuzkVor82mFvJro1OrYRhJu0FOVCrQf\nFFAfM5eg/Ef3DbkPlbf6GBDmOcrqNmvc+ijS42q+IF8vsuaSqLESWE3YMDyueumH1iikBRwnuGrv\nB0YUeGFOcOm4wYTfB7h4lKq0eX7qYEYo0uZ5bpxLcAVbWjmMTaIhO0TzJQf+tx4Z/2z1VcBNVTyd\nwJ/vGdJq8wF/NxJtIf6a8kl5UoJ6jElHC7wtgWtHRGikPDPFlUwGDEU7/quXGZcS6WnZsitaZ/RB\ndEpblLqtcEeGIaZpCsIbJ8Aa40CdFXByjTDAOdGX00+v6YE1tzLx7Xwf/8w6pc3rhqckD7baWtSU\nVCAuNh7R8Ynw1LlRdOoMsg4cwqnDR5AUE4thQ4cgzG7Dpo3rcCo7E6mNkxATZcWuHSuRn3MUcDP5\nNwaHQQblOMjkcAMZjVuhTXJz1LrqsOXYXpQ4K+V6ldakHeEprZHSeyguvmomquKSUeHVDBsjYDUm\nqmYAKOaIAvq0UKv+WYvInn+e/M//otaHwOfOHFDrthozgyZwnhlGgEZgzTPRAqYCCgiAqO6i7xmV\nwF25tTD4JyVP32f1pGt7QS9sBCeqauEprYSlrEoCwfKSYoTaldJ+VUU5nFVVaNG8mQAKVEuuLFVu\nDR07dZLvzj17SuwEa+tqERsTK73WIpwUGSHCcfwMA5P+F/dHZUU5vv9+udgMTp48Ee3apuPLr/+N\n44f3IDKkEmdPHACqOc+VsKZ+cRRJfk6IikNiTAIqK6qQV5ZP6Ttf8n6hu0SdDqvLi+aNUuGwhSH3\nXI5UaKLs0aiy1qG0tsInJKh2s/8eAGBbBOmSrMbQVik4TgjeXXftOoz77r0fa9f9JZoZ8dEJAoxW\nV1fKPezatRtqapw4cOAYUpo0FauuouIS9OrZGwkJCfj9919UK2JouCSRGe3TsG8fFe9LUF5RCq/H\nhceeeBSLFj3oA0/15chz7KzBwttvwa61f2HSJcPRPDEZv69dj4/WrkHfkSPxxquvokO7DKxZtRY3\n33wDbGFOjLp0BC6+eCCSk5vgmqnTkZdXgNmz5+ChBxchIT4R1193A1atWingN9tDKGbHKv+QS4YK\n+Pzee+8IA4A9rB3ad8Lbb/8L333/ndhFdu/RE/l5+VKJo0UgrbxY9eOLgaVW7zYnBuY1NDv7pLyX\nABoTfAViq0LOC8tewosvvow5s6/DCREn/AiDh1yKp55+RlgGt952Oy6/4kq8+ebr2Lhps7JybNUC\nr7zykjAlOab5+XnYsnkzevXqLYklBfwIWsyZM1u0DEiJZRD+wAML5XmYPm22AM6vvvoqIiLt2LZ9\nNx5Z/AhmzpyJK6+8HHV1bmELVFVW4L777xddGF4vaa883rBhqjWCrRDnzp2TYFrp+7ixetUqESRj\n5Z8CgwzC2UM7cMAAdO7SST6Xey5XgLex48b6dCj27t0vx2jbto0kOCezz8hcS22aKhaUbAc4cviI\nBOQEBXhc0p4J4LMqSrcKsiC0gDSZiZyDWkmdRST+jq0VOonnz9rulufF82eyEGwdqNc+VnW1vlfw\nfkjHketvnIu1q1ch1G5FmNODyRc3x30L5qDjkAwghMC8EfdJXKOfYgZpWjHAplhQdoKnLtBRRtiP\n4pbjgcfKVjc3qxjYv+MUrrnxRRw4USPFrDqXG4sXL8ajjz4qp6bbFszW2ow9gunvvvXLWL+lfdYo\nBuq/NRRXqNhHrRzCAm4gBmfVm4AZnynqH/ZtbMXMiRejV5ckpHVMRXSHVvCEsvpM9yub0gJiMEJm\nn7SHGSxj6i7xb4wLjCHU4r8s9ljt4ap64LLh5Ze/wJIXv0NBkRW1dW559gnujR8/zkfx1y3AvNe6\nUKOvlcAxCyTabtI8PrJGGfaU5n/1e3T7t9ZZ4nv4O7NtJkEEfq8WCuR7aDnLedy4cSM51KlTZ4Rx\nxCo/K/8EufbvPygtNXzGQ0NDUFxUJgwsJs3t27dRz1VOvhSA5PwjI6RFhpogY0ePxoGD+wQ/agxg\nbtt03DpuDKLCLCK4G9wCwP1R4muyr0PsOFdTi8c+/RQ/l5Sh2GYVV4bomHhcftnlAlKePHUKw0eM\nwJzZ10j8s2TJi/j22+Xo1q2rVPBjYx34+af/yDqR1KgRXnr5ZRHYY/X9mmumYcOGjWKXevXVVyM3\nNw933TVf7GRJv7/t9tsETCA7YNWqVYZg4Hvo1KkVXnvtfQEF2OJGAT+u7VnZWeKE0T6jvYAmd82f\nL9/DNYttcGPGDEN2dp5Q/Ldt3SrMjUUPPYSuXbvKs8O5yt8RDH3kkYeFkUGhwkGDBkqaxHYL6mmF\nh4ULaLlu7VpY2sY29e2hPmEdo7dXEnWLQFR+GqRB5dOAgIRfQbuwObgIFpyTgM2wu/ElQMaDaD5O\n8EMp5+b1V/s5afxscVUB1WJTgRmrP2E0pyS+hDCgXOfv91eVPBVgSphoLHy6ks1qI5Mx/3caj5KJ\nDszfnE/0SFeKgx9Sc1CmojRfyGScjXn5NkMhRv4i46Te4wtazyO8pALcQDjBBwz4RJa8QjNUi4Zx\nXNFz0QCOMb7G/Qnu8TWDMToFN3+jHNc3dPrcVXIvug56DI0/SXuEUV4VgCVg7tUHjCQBNg2qIJq+\nITQUmv23LnBwpdIsS6fv9+zb0wk2/+ifJ+otwqYxqgrGyKg/sEfdBGT45p8JCAi498YPAc+PL/FV\nf1Qq5aSTGy0C7OU33qPFJJ21foaAbjvxiwYqEIALuOg2iOMGrbHUS7Z6oyWF1yVWhIYjgZ5bfpBG\nUUF1+4pcu2m++I5pBqRIIQ1aO3S7hDoBAhj+A6n7bTyTxnE0+ivjaVQT/DeRDhNqI663nsi8sMBm\nDZVqDEXCcosLUVxXDg9p82rZE1FAiycM8IajZVpXpLXtg8jE5qgLCYfTEY7IpskIaxwPb1Q4XKEh\n8NitAkppAMDcHhA8uYQBKI+3BUSrQ1iLLyuCp7pKEqlQVt4tdlSXVaIirwC1RSVIZfXN7cHpE1kI\nsXjQuFEcvO4qlBScwrZNK5F77jDgqTDmnIyYupFuIDosCj3adEbnFu3QPKYxMk+dwPdb/kSRq0Lc\n71hNZ6kmNKkFUnoNxoCrZqMuuSVK3Vx3OSD+J5XnrNYPQ0jLAEr186+owVwn/nuLxHoP3wV+IQCD\nrF0NAwDme24GawMBIgUA+J4lk+WkrpqptV+diAI6DJADbAtisMFxCYSzZVXhdPRSGCsU9jo3yk/l\nIC8rGwlRUUhJSkJFcTF2bdkCh92GPr16gX73WzZtRv6Zc5LgDB12CWJiorFp/TpkZWVK4DV65CgJ\n8EmtpEAa6cgUjKNLzunTJ+VZiYmOlKootTvKy0qksmJ1l+NM5g4c37sJcNbAKiC8cU2G4Fe4xSbe\nyDYLmTh14kpQ5apDYXWxtPJc6EUAINRjRZP4RqgurkStpxLtkzPQuFkKth7Zg7zyQp+OgO9IekkN\nzuCDvqhxSrIEP6R6i/J6AHRRL/TA5s178PTTVLn/G6XFxYgMjxKthoqaUuPIKmEJgQPXTJ0hFnek\n0OcXqKQ4JbkJbrjhBqkMvf/+2ygoOodwB22topCbc1YSvBdfegG33XaLJML6egQMI7RSVYanHn4A\n+zb/jYnDRqAwKxdf/vALdpUW4+a778YjixYhMT4ep7NOYcbM6di5ZwtGXUoRqEtFDZ1CT6zYU8mf\nQEZEeJSI+7HaRJbHkSOHRTGalHX6SLdIS8P3368QhX9W1NtndMS+fXuE9hkWGo5x48ZJuxPHjur9\nMtdNLX++eW0CwcxrZXBFlpaLtLVjL+nLL70ilqJcKHnOpWVlYit47cyZKC4qwaZNm5HWqpWI+LES\nxUpVcUkh1q75CzfdPBcD+vVC5olTQr299NIxeOzRR+SZKSgoxpNPPSkJAtkOrD5S4IrA15lTp4VG\nzH5x3pPikmL8/MvPkrR37txBwJ3ffvtVAAb21rKqxz2NVFrSXi+7bLI8z9u2bRPa7aRJlyE+Plbq\nPAygK8rLcdmkCWLlyM/t2r0L6a3T0TiZbuRsB6+TucHjGgRUUMSM1X9WUtn6ptY9C+y0OjBeZAU4\nIpQANpMmagXoFwN1LWqo749O9HkveL+YHJkTu2CKt76P52MAnO/vfD/v03XX34iDB/bDZvEgwQbM\nHdcN99w1A3GdkwEb91C2rBnXI/GXoZ2lAg6paiunFGPdl95sHUB54dYAgMuFk0dLcfV1S7BtX5G4\nIDndHtxxxx1iZ6mvl+cbKNDacJ+6+XqDwX+5DxcqsJnaJ/S90BV0tlVxXtJJgx3so9LCcO2EfkhL\nj0Dn/h1ha5pM8QLpsKMOhhIRlrOWxMTtVS5TbK+W2I/7p0kQz2NVxSAv91Y7WY0h+PLfqzB/Pu3g\nLKhxsnXRjg8/+EDcRvjSOhHaip3Vc/0s89o5J/geJu26ck9QgO8j8KfHMzMzU8AkzmE9L9j2wt8R\nFOSLz5y2+NMgCcEqAiN8DvXr0KEjcnw6l/B5ILhFUJqWoWSgsfWFNH8ye+hqIiy5Wo/oA7BFqWnT\nFPncdyt+kOP26NlTMBQygqjJ8dzTz+D9994FmeYp8GJCUiLuu+pytEiIRm1pKcJs4caeYNpIeEC2\n7drt2JCVhRe+/wF/V9ehnG5xLjeSGzdBcqMUpDRpgn+987bch+++WyGAB8ENthoRYCe1niLbbPXh\nNXAuEQDt17+/rAG//vabCP9FRkXJ85vRLkNYBalNm2L1qtUCUhL44Dp03XXX4cyZM/jxx5/FVYV5\nJNepw0eOCFjw8cfvyXW/++6neOXVV3H82DEMGXaJAL0EIT/77HOJC8hYJLhy78J75f48/thjvliA\nDBrGB/zeVq3SRGfljz/+FJbgxQP644nHH0dVZTUeeuAh7Nq+TcAqy6w+4+ptwZqmqoMb8+IhQZ8h\nmKYqHoEf14Gv73EIfgBZjWXVK6gy7euJNx1OJfjsJffNt4AHWj3walHSk9QsFKjbCOTvQeehFwZ5\nDH0S3oHfo7ABI201Pu8DAAyP+OAFpiHxr4YYKjrR1t/Y0OKl/qbG2H+lpnP0/6d6J8fWCFQbSniC\n3q7GTcSf6o+vCtxV8s+XTtR1Euu/f/6/y/tEBd2/8QUv0JLQ66xd7R3mtMJ3IubPyX0wqffq+ch/\n64mAGUKCci6ssJpFHgx2gO93BvjjB4MCwRANVvk3PhkRwz7NT8czj6uMhpGE62HVgIA+JzMwUw8A\nUaPd4DiYr1vNDOV/rcEkZZlZK5sCF2VRa3V7pIWEbQ9c1InKcmNS81h99p8AAEFTqVfABcM4M3NF\n1HxeSvDQvyqY2Q36efXNdSaR9e6PeT2hiqy6Uv3yTR0TsKOSNK2TEHgPldWb//P1GA6CX1qFrped\nn4O/9+9AibMcbosS4AxDOOrYNoRQeCzhaNq8E1ql90JMQjpqrQ64YxywJsYivEkCbAkxcIUzGDDs\nETk/gwBB81yRVEQo7AQAlKFauLcOruoqUQGuq3KiqqQS7upauKtrEObxIi2pMSpyC7Bx1Sq4aqow\nbPhAJCWG4a8/v8LWv38VjVs1HqxMMGjgz0BqTCK6t+2MbumdEWOLktaHY2ey8PO2lSh0l8NrJZtE\nAQDW2BQkdx+AwdNugDs1HSUe5aziX0IVGKtHWq3nDQMBDa05/6e/0+BCQwCAeS761gxTwhMMAIiF\nfNDaolkz8l6DJePbL0T520TD5VAYk9KPVSnFe+4d0oPOwxAYJLhWXYsoul2UlKCmpATemhqEsq2g\nplY0HaKoT+LxIDE+TtwumMjk5eeiprpahIf4HbReysvNRfuMDPTq3QunTmXjzz9+h9Xilcp/k5Rk\nLP/2Wxw8sA/9+vZEXBTwx48foTwvC3DVEc7XxSi1b3q8iI+MgbOShH0n0hqlITGxEQ5nHUNOTRFc\n/wAA6PsZDTuiEILWcc3QvVMXHD2Vhd3njqHQWS5BGqFRvT765sA/AACscFOxnuJLtG37JwDgl1//\nwjtvv4vt27fg7ClWr6m/z9YWLyId0bJOkF4bEhKKa6ZNR2rTZnjrrbeQV5AjfeyxsQkYP2484hPi\n8NFH76G0rBBR0ZFCSc05lwWL1S603Lk3zJFL8LU06NaEuio8vvh+bN/8F6ZMnIjtG/fi069+QEhE\nFB579lnccvstsie5nbVSnXl+6VKhujOhZQX5ww8/EFuo66+/DvPuvAuhdgdmzZwlCfBLL70sye/S\nJUukN5WV7t59+uDQ4QNCXZ1741yh53PO/PTTT5I0Dh48RLQidOVQi4Xx3M0VVg146b1e3x+zuBjn\n/arVf+HHH38SYcGsrJNITk7Fnj37pS/1ngULkJefj9dff130DOh3zeeGKtdsd/ns87dRWlqLYcOH\n4tprp2Hh/Duwe99BodjGxMTiot4XoU3rNGzbvlNoqq3T03HjjTegqKgYH330IVJTm2LmjGsF1Prx\nx18FVBg9ciQaNUqSwJdjFO5woHnzpmK/xW5RTV1m9YtBPZNoHX/YQyxwuqnxwLaDSIWTuij8yQoi\nmW/+vn32+PPF3mW+qEVFxfCM9hmS6PCVcy5PqP60H9S4Z+aJLAHykpLoZq6Wk9LSMsTFx4hAL/UM\nzMr+uq1SCgtG8q/jbVWEUXEH/2e+fxdKdAPikyDEnZ/7+KNPMP+uBSgpoV2sB00dwOJZw3H9TVcA\nzSKUUIiX+7qvN065iMi6oII4j1HIscpix7XP1Khj8cLDQgVBAyaWZ524csaz2LQzR4rfxExYQf34\n44+lgm9O/M1AyD8l+P/0d703XGisNADAc5l3550oKS0F0+RruiZi2oQ+iGzkRM8RfYBkChyGCrNO\nKXVrpqxiTJPeL+dOhjJFokO4Zwi9WAJTN+NkCqyTEcv2aa8XGzcew4xpT+DcOQ9qDIyWQN4dt98h\nt5BAEee5rtLrudIQG0CPRb38RFyYFJsiYE8zxKX1XDFT/vWYSEEuiFF6vr2crS8UrmU1n0UhxiJl\nZcqdKTEhVqZNeVm1AAp8PkSryuuRViNqr7DXPTUlBSv/8wdmzZyJquoqNIIHF1kteHDqlbioVXN4\nKspht2pXOSPOY9uF7NteVNrt+GDdWny4YSsOMTIKsSPCHi6WvK3btBHw9KXXlsk6cd11N2Ldur9x\n9z13447bbiRRBVdccRUOHz6Crt26SotKdEw4rr/xVnz3/fcSXxMgeuGF5/DBB5/j5uuvx/BLx4jY\nH4EMJt4UZ927Zy/Gjx+P559/BqdOncWEiZNw/MgR9O7XT1xemKiv+O47ofQnxMeLng+dfg4dPowZ\nM2fghReex1tvvSffHxERKYAD7XBfevElZGdlYfq06cjKOoGWLdNkzeQ9uvLKK5BzMhuXjB4lYMaO\nHduRk3MOHTt2EHbfubPnBPyhHa7l2k7DA7ZgThxORp0U+JMV1dshPds+ACAwOJfNQ7NojZlhzruN\nfEsFHw0k5DqA8ofwDDpt0oqgX7ofXv/M4+geaP076Q/2HSQwQfV1Z/sqhoHVDfPxJZkOKn7oRVg5\nINSPXswBjkxJAgUXiHiDEyB5q+ncVf7tM+oO/LO5JheQEP2Tn6wSVJTKlXFy+lq0ir0+ZZVc6itQ\nzgn+BL+BFg0DAAi+v8GJl69yrKv5xliqTc0/A6iUzVdDAK/W0nMAACAASURBVI4aX3V29f5uqMKr\nXdffAhCwGerr4gZ1HpZE8P0X6r/hSkHNCbldQZPcfL91xVp9r1IQF4V8Q2RQC475bntQhBzAADDO\n0X+u7CFTisF86V536kiILV5khCz2op1hOBBEREbK+WuV/mDwS11LIAAhOh02Jd5XT4MhYHYrJXHz\nJQgCXi8J9s+/YLZKvXljAAD+8woE5FSLjn9+qiEytz0E3iNW7cwv+snSLtFiC0VJXQ22Hz+Io2dO\noIY0aYNREBESBqfHBZeXqVM4WjbrijbpfRGX3BolHgsq7TY4miQhoVUzhMRFw03nARYCDF2AwPmg\n54tRQTdYIRSsIQAQ4qmDRbQ7nKgsqUBFYRlYnbV7LQh1eeEsLoertAwFJ08izOpC09RYlJVkY8vG\nn1BVlgOLl7oC1JKgwjT1Jqpgt1iQ5IhFy0ZNER8eB3eNm05mKK4uR1bZWZS4yuGxuBWbhuttZCIa\nd70YQ6ffCG+LDihyW0SJ3ndveD8FADDYXPJ9SuhVrDoNLQsdoFxg+Tvvn1TbTmC7l6Li6wWZa7y6\nl2bAUT3u/hHXASR/rwMY/5dqLZLANhldPZHrFaA3cA4xiCHQybVQa5b4wRDzOSkAQEAxFiUYQNU4\nITULusUQFKipQcHZcygrKEZ8dAxap6XBXVuHY4cOoyA/D2mtW4lrQFVZmfxcV0OrONpjKjvDyAjq\nxlQjJ+eM2NO1aN5Uzon9k1UVpWjRvBGyj+3E3o0/A+4KZjimvmgmx0B8VAys1W6JxJsmpUolKK+w\nAFkFp0Dd5PO5AJhvHnUlHLChW3IbpKc0l+DiZNlZ5KBCYmTOSZ+WgPkR/AcAgN/Rrn17oQhfccXl\nqqIW9DIfYsXy3/Diiy9h954d0mJBDQAmIzZbmCSlgwcNRX5BAdb+vU7okg5HJLKys9Grd09ER0Vj\n48YtMn6OiHCEO+yIinIgn9Z/Fq8k5h53HV59/VXcfutNylnAEOTVLBCvsxrPPv0oVv/1K0aOGIl1\na7bgjz/+Rpeu3fHUM89i1NhRxnplxY7tW3Hb7XdKBTY1tYn0tkdEhKNz505yrWPGjJeRe/ftt6Wq\ndt9998lavmrlSpnLgwYPElqq1uQhO02SD49LqllUt6c9pN7TNd3fPHzaFUgH9vVjGvUccg84d47a\nBLcKCMBKUnqbDJw8dQbNmrXAvHnzcePcWXj99Xdx7913o23HTtiwYYMkyRRwzMhoJ2J8ZWUlQuk9\neTJLkvFDBw9i6dKlAjAvW7pMaL9kK5Bqy2sjdf+dd94WhhqPQTZIr149sHjxY/huxQpMmTJZaLCs\n1NIJgeOybNlSYQOsX78Rn3/+uYgT3n77rVL9W7lylVT/KeJFXY78/EKxoCMwPmHCeAHzKHBG1wUG\n8mwZ4WvLls3YuXMnZsyYieho7qte/PTzTxg4YCCSkuLlPceOnZAxJ9WfgCmnBgUd4+LjER+nqq06\n6WevNBkTtNqNcIQbf3PJOGv7X3F0MmwZ9VrKa1DC0HQUMLSv+DsBBOqzrfR9bUhBXsdNjz72OJ59\n6lk4ndWyNjRzAM/dPhFXTb8UaHo+AIAMAD8AILGYEcBLBEHkWwc1utdNWgBqkZfrxuSpj2P73iLU\nGg8vkxr223OumkWuGb/oPvHzFckkHjNcybRjmZ7jwfGEBlvM1H9zsqzXfj5ry5YulfnEO3fzgKa4\nemxveKOr0GNkf6BZE8BJFxgDAKB1gorCVEJv/CS7JIdCbhWBFON9BAW451NnikBBeCiOHi7EFVMe\nwpGjlag13InvvutuPPbY49LKwrnF6yHzRb9YiWaVX9hRpjiU6wXXN2UDTuvkamlZYquJjv/IYKOr\nDJk5WluMInK8ZoJq+neHDh2W47RurSxPy8rKRZOG2hxcc5jsU8iOgCNFVlnlr6qsERCSLLVLhg0R\nMI1im5mZJ9CvX384HGEoMlps2rZri+HDL0FtnVOq3TW1tRgxYgRaNGuGPbt2YeiQoaitqUaUx4n2\nABaMH4PRHTIQQZqOkutR7SfyryqXklVSZLPjmR9/woojmSC/y2sNhcMejutmX4fb77wTRWWl2LFn\nt7CIxEKdTIbWrYWZwN9xDyV7iVV86s8QnPh2+be4c948GbOHH16MCeNHYf+B48ImYtU/88QJGVPq\n9Dz99OPYuHGr3D8ek2w96s/Q5puA44IFBACaY9GiR/HqsmUSawwbMw5v/utNfPzJJ6IpwHvFdYci\nf2+88aYAJXNmz5HWhejIKMy98UYBxhcuXCjvJfOCLKbNWzYhPj4B/3rzTbFAfeKJx4UZwD6H+Xff\nI0wtglyWHrZk3/5pfsDMk0knvZJoG37u/k1ERfe+irCRq8rnZQ1QAbpOdBvqMTbnEIF/N0TdTAmJ\nphj7H/CAfMV3WvqifAm4LwkxhwtG4hcQkPhZDfUqlgIIBCICkoyZPl8fXfT3r/vBFFOAGkRp1Itm\ncFzkS6rMf9DUfNMABtew/fdJr8bqX9+5GJfjXwT9m4gGc8wMAQbdASNYL3EOqsAGAD1KYV8PmN4w\n/MdTlTP9d/l+gzGirkP5iporuhoA0H83/9vQWARvIuq760ehar76f68TR51s1GdwGEJq2nLO8BuX\nZIHAmZFAKYApMEFR65Z/wxQNeu2SoFEOvc5poUPpVyYYw2qEAgBIGQtuGaDAm8/H3KWE+/T3mcdH\nCSL60hgf40PPR98wBdxvf2uM73nU2X7Q4PufC3Xtvp+DEBaz6JHaPA1RPR8wEbjeNCxx4f/y8wE7\nvvnEoEkERr3w2qwod9UiK+eMJD48U/7ZzzLhGDEBCUOz5Dbo0Lk/HEltUWuNQG0IEJoQh+jUVNjJ\nBAi1wGnxIDSczAF1DFVtJojA79R1WLnBSpSNa6XLKdUrAhNM9twsBdS64S2vQ2VeCYqyziLU40FG\nq+aIi/Bgx8afsGvrn3DW0FS5xuT0oOYGh03UJtz+qq+SP1RAW510a7Nn0wAAOA9CoxHT4SIMuvp6\nRHXshzL6mAvoRcEjVlONA8vDGsggClxv/vmn4BYBzmlx8RA3Fw/C7A4lxikOKBRA5b6iQFXFTgrm\noAV+p56u5mpH8Jp4vmqJ7zKD6NF8v9hX0gI0mOFmWkp8LVMC8qjxJ8+D1nkslIUQaGfbKAHBmjrU\nllUi2haGyuISlObRA70E6e3boGXzZijPL8DalatELLBfnz4SgJDat33bNsTHx2DAxX0QHmbD5o3r\nkXPuHBonJWPUiCEozMvEB++8gKqCTAUAiO2tvn+sT7lp+IeWESno2bE7nB43NuzYgmKUo1pmxz/o\n2JDW6rUg3GtHLCLQPa0DLG4vDpw6iDJUSkJaK0KCnv8RAEAGQN/+/STJY5VcC5g2OLO8Fvz993Y8\n9eRTWLtutbRBKNFAq/QZJ8Q2woxrr5fk+MuvP0NlNfUWXIiKTMbMGTPRMq0VPvn4E+w7sFeuOi2t\nBUaPHoFNmzdg965dKsj0uvHckudw74L59XYN2ZncbixZ8izeeONVEYs6ffqcKMTPue4GLLx3IZoI\n9dtwMvACK777Hl/8+wsf3XboJUMxeNAgtGjZQhJBztucczlyuQwgza967Ye+xVpPwvNHAwrENu28\nxjzWsZ5vfTSqzrVOp1jzzZt3F1x13MNCcM21M3HZZZdj587dYolH9f7yinLExsbJWsdgn9X1559/\nXiyuHnzofmS0a4NPP31PgJZ58+YJuLZy5UrExkZh754DePaZZzDtmmmYMGEMysqqpFeYVFbSdWl5\nxuoVA14CB6qq75DgmL2y1BJgr/vYsWPRqFE8KitqZGzJDHjggfvFNefAgYP4/vvvJblnBY+0ffbo\n7tq1CzfdNBet01oiN69AvjcuNlYCfwpJE6RhVZKJKv9lIkXHpbjYKBw9dkLW9fT0lqirI7jvEQ0E\nPqMUxqOu1tEjWZIstWyZCqerThIkVhfJ/mjRormsDcdPZEl8xESL+1ZtTQ2OHT2GtLSWoiTPF9sN\nKisqEJ+QIFZq1CHh2FPwkhoNZBTwttKCkO4IBAl4Phw7ghxmgV+2vVXX1Uq/8Advv4swgrgeD7q1\nTMQjN03EmMsHA43t8KJG9bkH7f3muaj2BLWXGTiAMYV0HsC/8fmpw9kzVZg19wWs2pAj+xMZAOxz\n/vLLL+WZMb909dlckebfzWJ3an9QrEZqNwScV1B8qo9jjtODW2JIdWfPN+cJHVFi4cFdl7bGmMEd\nxMav0+hBQJxDbFxDrKFGBUpoyL59XRcgffGJbJWm2FXvG2KF7YTFYUdRgQdjx9yNLdtLfRpEAy4e\nhLffegcZ7doZGjtsx6ahAgXOGVf6hXU1U4TXw6Q1KipaaUJ5vCgsKpJCURNjDeHXE4yirSjZRJw3\nfBGYIjAQHk57WnXKBMn47Og5SJs+Pm+cl/oySNkvKy1Djx7dlR6iG/jjj5UiHNqrZ085NmcAwVbO\n0whHqAzXrt0H5BjdunaU9/znjzXS7rRgwXyZR6dPnha71ewTmYiBBwnw4PpevXHToAGIoeMZxXgl\nhiQDwwunh+2bIQhxOLD51Dk88t3P2FFWLqo3NfAiLiYRH3z0CS4dOwY7d+/BjFkzxd1g2bJl6NGj\nE/bvPypAIkGVp555GgMH9MN//rNK+vUZw2e0b4/BQ4bIc7R161YcO3pcnj/qhNAqddPmTfj551+k\nfemdd97B5s1bsGTJEuzevVdcf6h/0r59e8yffw9WLF+B3hf1Ft2RcePHi5UirfmmTLkc73/wAdas\nWSM2hHQdoCbQCy+8IEUHqvzv27ULoY4ILFu6RPQLFt77MN585VWkpafjqy+/wr59e0VEkO0GTPbZ\n3sRxpQjspaNHi+4B57flopDGvl3AHCybAxu9KfBf3feo7V8arGD7nkBa8+lbr+OOwE2pQUDA9ATr\nBzX4e9TvNUW4fjigE2YzG0AlVvpzBsXd+KjvewLq2/VFxbQooJyPmb5ubJSyOOmKkTHtzSFqcEIS\nXL3SV6IXEB+QoYdNi4YZC5um6OuKfoOBUcCKqIJ23/cE5L6Bomo6YPUDAGox1+esFvoAREKPprpy\no7rpr9pp4MH0/aZz848FK4oqsfCBEL7x9QtRmi8rIMFsIO45XwvIP41XYHzvH7t6QIIxDlxkeS6q\nfYYvzVBRPdQqnzdECk3/bbxV3RrTva7XJG+qcOqquO7LF1TblBHzu+pcyvaRL6qbBwNx+t7q8TO3\nTKgkK2iEAkUXZLH3zW/mmUG2l8GAmBYelODVCKHV3ugHpWRDlsfUeMB8mJFfI0KNv+/pOD+Doz62\nY1qdCCCr79I2V7CHoLymGjUWD8q8NaD8kRyCXQJkQThJOaHieTiSGrVCq4wBSErNgBM2VPJQMTHw\nRDoQ26IJHAmxsFIXQJgABk2SAA4DFgHS1OCqW069C6XDzw2N4lHhtMbxAGW5RSg9lY+Kc0WoyitF\npM2K9FYpqCrOwsaV/0buqd3qOBaXYTlFN5YweKnIK8eUWSjfYwcDOmXRqEQprQh1hKGqjkJtBAFI\nZ3TAkd4NfSfPQPOBY1FmDYfb5ZTqlJUBhdwanfz/HwAAhoaK+Yb4lxNDf8are+w5LjodVZs91z7z\nLKj/LKtzDK7wNPTMXxgE8FNv1TgbP2vq/z8IrZ4XWGfAVedERGiYMDw8VbWoK6uEt7oObmkJqEN8\nUpzYyZWcycHpY8fhra1DcmKCCAeeyDyOw4cOIjYmCsOHD4bdZsHvv/6CnLNnpZ98+JCLsX/3Ovyw\n/EPUFZEOr1w21LQjRcWKSFgRTtu/uBZIiktESXkZsgpPog4eVKFWIIB/ErIlmJRgi0H75m3gLq9F\nEcWGUC1JijPMg8xz2ZL8BxrK/tPKq/5OG0AG4xSDY6Xogi+vBceOnsajjz6G//znFxQV5sJu59xV\nTg8R4fHo3bO/RLV/b1iFcEcYoqPiUVlZjS6du0i1asvWLfIV7IXNyjqOuPhoOF01UplmO5HXU4eH\nHn4Yjz22WPX4NqBxwUom7ZfY88kEc+DAQUL3pCezDrTVukt0ziLBNJNZBpUUItS9uaz0SoOSITQp\nVqUm8FSv/wEViP9uWBtkL9b/KDVUFMuhsKgYzz77HF5YukxYQr379MOHH36CffsOiOvCwUOHUVtV\nhbm33oqXXnoaP/y4ClOvugqt27bFli0bhfZ++ZTJ0jRBC8WWLVtIpV3EDW02cSSgsBXF8lidJ2Dw\n2muvyRh++ulniIhQ685zz72A7du248knn0C7jNYoyC/C0qXLMGLEcIweOUy2S4IurHy1y8jAnFnT\nJcEMMfYxUv65R9OLmxU/utDw+8+ePSs/c+w1Y4Ljzz5bVtJUZdoCZ50Hb739Nvr3749evbrJ9/3+\n+5/CwhgzdozElwQIaAPWt28fqXSy5WDD+k2SQHXt2lHew8IAxSdF8Izq6IaLHL8z1NiveT/YrsJE\nX79YSTWz2HQHnWYW6PeJdrO0oxv2m0H0bY4BQZ2T587gvnvvxU/LvxNom+nz4C6tsOjWSRg4uieQ\nQMcEZbUcEPMYDDD1fWoVVrGoKaI1BxAy/ox7nMjJqcXsuUvx59qz4NQm3bpPnz7CxKD9XPB6zZ85\nvuKi4GPcqHYNHdv8N9Nej0Xwe4PXftLSKZhJK1YymxLhxvwJbXDpwA4IT3KgzeDeQDytpBlPMQFV\nz7H6nxJ31sUtPwAQFEj5AABmy7RKsKG6KgwTJ96LlX+dVYIiALp26Y43XnsTFw/o5zttLgs5uQYo\nmNI4oGWCzwvXLo6VbqVgAk92EwEozgnOFV5jo0aJkjvzd1Tf5+e0TgWBJlrPdu7S0feeUyfPSO97\nuwwl3MfzINOAYFhsHJUSgNoat6xlvI/SMmrslQQkCAZQfI5MBjqLUNwzIS4SxaWV2LRpi9K0SUnB\nsWPH0LhxsrDhjhw6iHl33I51f/+FWLIfvB6MT0vHI+PHoTEZT1JQc6uigMUrWgFhUVGos9rw5YbN\neH71emTRcltcCGLQtn0HXD39WnTp1g179u7F2XPnhFHRs0cvceTiWkxmD8VWp02fJnT6jz/+BK+8\n8qr87Ya5N0piTX2Pt956X0BMVtVbt2uHvXt3IjQE+OKbH/DkE0/IOsB9+OFHFguIywR8yOBBIvS3\nZMlSrFixXCbK4MGD8eOP3+L48dO44sqrcPTIEcBZh2lzrsOiRQ+J28pTTz8l1f9mzZpi4KCBwoT6\n5edfhB12UZ8+wnI6sG+/3I8ZvL6uXbBlyxZhQPG8yQaZP3++PEdsFeAYy/Mwq3V/X4hsrmqaEwku\nWMHgwIUra/5HjA+J+RXALNDUbVOQHqwNUJ9ybD6aIdB0HqBbJVtG/7whjqeTDvGpNxYsvan6ci9z\n0hAU3AVX8c8XgMoiLZWeQNG5ehXsAAaAX4Xcn9oY12skRDpl8J2HkQTJz/Lx8wyGCh2Mlanh98iE\nMAmz8V3BjAvd++sDYIwNoEGAJgggka2iXkIWeC56tAIAKINhopM+oQf77os6YH3mRQPhjMkdoOEN\nQ5+L/5w0Oqze/98BAKolxVjBjXPnBqrHzPfdUhk2M0rqtyLUE2kM8v1V1270X4sooZkGSIsiZ4Ct\nZDADgJVd8/jVAzaCNBqC/24W6eNx/glo0Z+XPmrfXKhvhyS5v8EWMa81Gn8IBAAaAOqMQQ4+3+D7\nro9N2q8o2NusUq2sdNXhVGkBDhSeQRkDb1Hs42bNf0KM5JrWYmlom9EbKU06wGOPQpnbizJWIhIT\nEdfUAAEcYbA7QuXYJFRr20CFPLAmzCRaqceHhljhcSkaf3iIHd4aJ3JOnERlbiFQXotoazisrlpU\nVJzF/p2rUZZzAJ66Ilis1HKg8FQIqiorEQI7Eh2xaB3XBJHWUFHVlfYTQ7uE85rXeaYkH2dL8lDu\nrAQdWqR1wRqGkNS26Db6cnQcNxXltki4RC9Cr0WkL6rngetFAxIqDT9ewb815pYCg9QP4pIg4oiq\nQqmBEmWXKIugL9jUYnvnx3jO/7w2dIL/ExDgQhfqc71ooNKqP8fkmgwezoKwELsAAV6XG67aOrjr\n6iRxcVVVoSwvH8mR0WgaG49TR47i5IlMSRTatmktiWn2iWOoralEclKiJKUULqoqL0BO9l5kHdwM\nOIuMQEl/sxV2bwhSEYn2rdoiv6IMmfknBRJKjU1B00bJOHEmG6eq8wQMaOilV8lwhKF1SpqICJ44\neVxYBZ3i0tG2Yzusz9yBIzlZ0kbg75f3dxNfGMBRAAArGHfceSd6GpWk846514Kvv/oRr736Oo4c\n3S90c1KoqSMUFZGA+HgqVVulH7LOVY2Bgwahfbsu+OuvNTieeUQCeiYUl44ei759L8Y333yN3Xs2\ngU4irOJSUNTlopfzHLz8ykuINKjbwedDT2xSWdevXy/tFAzwRo0cJQGwf90ydnKxAyWlmzbKyhqW\nL+3SIgweA0xmciiMqCBW3f8OAKAB2vrHafCJ8Nk+5xcW4bnnnhfVf4/Li569++Cu+ffg448+lYp5\nVHQ0du3eLT2pV0+9Gus3bMC3y5ejQ/sOaNW6FQoL89E0NRWVlWVYvXoVBg0ciHsW3C1fumjRImkv\nYzVy9qzZohnw1ddfS8WLgmZzb7oJEydNQmFRId58/Q385/ffRShr6JAB2Lf3AK67bg5Gjx6Nhx58\nEFGR4Vi7Zj3+XLkSJcXFmD1nDnr37Iot23Zg7dq1YlE4YthQ6f0nRZm9tyNHjkS3Lp1QWFwqVTee\nC0EIAgPr1q0TCu60adNVwuQF1v+9ES1atkSLFqmyLZRUVKOyohJNUpLk73xmyQ5X+6pIwqgnSDSJ\nVM5CN2MC8rzvNaJJwTaVEBERpDYBQQtZB41Kre6jrrd/0ddc1xr4FQbL3Pw782fMSfD/Yu0twKO6\nuu/hNZ6JG8RDcHcoVqB43UvdW6rUXd4KpfLWqQv1UqWlRgWKU1yDBhKCBkJcx+d71j73ztyZBOjv\n/37Tpw/JZObec4/uvfbaa/vIVNhdhJtuvhnLFy4RaJhJJOec0g+PTb0A3Yd2AuIJCv8PAIDR2JHD\nM4B9pbW4ZsorWLbqCExWEzzeoJSX3Lx5swaGqLWh78dGDYTQvmlYK//msGkNhI3e7/W+YST2ggsu\nwPq166VYbqbVj/vO7YVJw7sgvk0csvt3A9rEy1n5PwMAcqT5ATvTyZJw+RVP4dvvC9U5DKB/3wH4\n9JPP0K17txB4yOetq1M583RejXsGK1vQkdZBLNLzyYDJzMiUChR8UXGfonb9+vUJOferVq0R+nh6\neqo49vw7xQKHDxseKl3J9V1TXSP7GZl4bANTdKhaT6CAL7J4qKLPvXXE8OGIibFhx45dwvJh5LxL\n1y7i/FMnJCs7C1Nvvx1NzS588tHnwpa84cZrBKCbPXuOrKlhJw1WqVK33gSTzyvpGIPiEjDttDPR\nv6CdKk9p8sFvUTC1lbaZxYbDDU14/895+HjXblSIZWXGM889h8HDhuODjz7G0uXL0bFTZ/zxx88S\n0535wSdCyZ988WRMnz4dqWkpeOPNt1Bauhd795biggsvRPcePYSWT3CF/V5cskcEVgluch+55NJL\nhN2wc+cOKbVaVUXBxHb44otPwEqYTz31PN59+wPxDfr16y1pXvm5+fj0089kTydwYrbaZBxYuo/5\n+6zewvSCL7+aJSkcHdoXSCnC/Lw82auWLl4s/X7W2Wcjo21bLFu6DIcOHhQWGUsMMt2Jn8vKysKb\nb7wh6UhkOCya9yfLQsH037FXhgGAYxgqxuicvkiiHeHWF6FCkCOo1FGUSXEYDBZcy1zuyLymCAq+\nJiJ3vA0gWm3fHpVDGI5Oaxu2vlFLLrshu1jPNzV6sCp0GXH7CKBEM2TpZEUe2grFbdX4kcuFOyTS\nuNXc+wjAxNA/oWyEE4AAhhZHO+5EVY11NfU8M+NDhkW/dJX2YzvgLRgPLRGACEZC9FhGp1xEUoZ1\nGuOxXYDo6x2fsaJDt/oBpEXqQ20OAwQtHGUtNUTyuUgN1srLEOk3/h7toCuFbdV+45qKBjRkDWqG\noQ6K6XoV3JD4kvy2qPkodY+10jgs5xdm1GjUem0WCjU9CiBpAUIZ2qj3a4RKv85KOQa1NGQRRUSx\nIvO8mfMnUVutqxVlXr3CFQfC430i4OdEAABpXewzEWszm+EJ+FVVHjNQ7mrA8uJt2F62DzXe5rAb\npLFT2EiLJRYJCZkoKOiPrLxuiE3NRL3fhEqXF6a4eNiSEhCXkoyEtCRYY+0wx9rgMwfhM9EdMsFM\nAEDEAlTqkZ0ghD8Ad2MTmusbUVtehfrySjj9ZiTZYpBgtsMSbMKWrUuwY9U8IFgjRH4aVsScGDW2\nUePc7MSATr0wrtdQ+a5x3rHPaHD6bWZsP1iCv9csQ1kzo7ZMBWDfWoC0XHQ55Qz0Pe9qNDqTEeAc\n4oEhVWHIZlBjoHKs/40J1vIzOsGDa1oHEuhsBamuT8fN75MSlEEe6hRcslhDKQhkNsj+eRyRxUg6\nzbFBImPL/v8EAYR9FrXfRc9XmQXamiXww/NJbEItZ4R6EGaGyFgiyedHusWOfVu3Y/WypUhPScbZ\nZ58pTumPP36P+toaTBw3Bp07dMCq1aux5p8FaKgshqfuIBBgDXs6w/rTmuCABSM79oPFB+zYuweN\nIiBpQlZKBjLT07Bzz26U+aqPCwDYzFZYAxYk2OJh8pJtEkC37I5IsDlR0VCNbZV70Gj2yLoK4Uea\nYj5bommJt5gcus3BFID+AwdICgDp1ydKAfjgvc/x3vsfYO/e3aiurhCPyAQr0tOykZLcFs1NHhws\nOyBRzXHjJqJXzwH4e/4CbNteKBFaRqgK2hWgU8eu2LhpA9yeetgdZilX19TUIMb6hEkT8NFHHyA3\nO/uYE19X0WZ0l8rSdPRa24uM80F3zvVyZupvWp61OiFauV8Y5Po3q5AisVImS4vOHf874dTNol1F\nuP2OOzGP+fKJaTh55Gg4YmLx0+wfMXbCJNx08y3iFg6XagAAIABJREFUdFD86u133kZObg6efOop\nKUl1/wP3SQSSSttNDQ0YfNIgSW+hYdqtW1eh165btxbjxo4VI5Z5uDSomQvcu3cv/PnXXyKKlZ2V\nhYsnT5Ym/7P8H0mBodM8Yfx4yeGl0fzznDnivNNJobHOdJirrr5Komr898YbbxRFbjrZ8/5eiHfe\neRfjx43DlJumSFSeIow//jgHjz76CEaPHIEFi5aqto0fj+ysbNlHmfPPcoNr162T6NoZZ54he2/x\n7lJs3bIFI0acjIw2yahvcEvOMJ+hX/9ewsJasmiF6B0MGz5YHDKPV+VP02nr3rWLCJKRnUBngH3W\nJj1VnpcRWoJTubnhOUdHjjnFTJGgwy/lbKvrkZScDM0kQHWVKjnI73L+8Rlp0+nz7s8Ff0uVidLd\nJYil3RLw4/KzhuHRuycjr3cWYPciEHS1ZAC0SP06BgMgNME08NYC7NhyCFfd+DI2bauDR8MWSZkm\nA4CRSt1eoS3AiDNTF3Qnl2uE/7MSBftMwIGAEpZmSeToFADmpfMzurgg6f0i+CgsDF07KWw/0ZYi\n0HTF5VdIe5gul2v348Hz+2PS0E5IzkhASrd2QGYSwHQ04U38vzIAtHLDtB2ltGwSbr71JXz08Uph\nDfLVuVMXzPryKwwaPACsJMEXaeB6udroFADZJTTwnGOtdEEUMKTHhvgzxSp1QUuxraKAJH3YvF5l\nF3FP0sWkeX1GkrkOSkv3S38Kc8ZmkbQd0s0J6FB0juviwIHDIm5H55+pTdzFvvrqa7G7rrj8YrnV\n3wtWyB57wUXnSDt37CjGz3N+kuj/4bJDGDpkEBqrqxATDKIzzLh76AhceNJgWFlnMuCF38qdkpo2\nVpjiErCwcDNem/MT1geDOAQgPTFZAIDc/PZYvHQpDpaVSeoDS+qxn3779VepuEDggukAmW2TccU1\nNwpNnuJ7s76ahdGjhmHt+kJJHdq/txSXX3U1PvjgXbDw0xPTXsC7774nz8J5ybJ8rOwy4/U3REiS\nAr08k0tLCcZk4OSRw3HbrbciJSkV9913Pz6dOVPyTac98yweePAWVFR68Mwz0/HO228DnmacceFk\nSdVJTk4SBsIP33+P2Ph43HfvvXI9AjEErhj9J7Pqww8+EM0ClrLk+PHv1A5yOGIkBY5znHuD6b3T\nprQ4VXQnnzkmxuiiTBSthF9rxnhrh0k0AyDawDJSvPXJe7xDqYXBb4jit/q9KFHCkGqc9mGjYawi\n4FEiUlG+tNGBkkvo5ciic0H1eDGRSoMD2YI5YaRsy+f0PPlj9YIWsW1RRUFzWtVyb/FlFTHXHiZC\nsyH8vsIzIh+4tQg0ry5OE++i0750FXbZabTbG+qc6w2KdoCPN9ZqPhyvBFU4cnyi60T/vQUwYSh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Rq2HFEFQD9Mwg/WqqbAie2s8H7cQsArcvTV8xkv2AKyCTnBqn9U1EackNZqpkY0uKXzb7wV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\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OHugJmqfHSwCiPFMkIoA7jHtoTCBOyysCnEP0Q4Psl8q2Zmd1X0ZxQbuA8nDerBW50NXd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WzZogXffmtJd9u0bq199t1XWe3bq6y42NQ6hBakpqVp/4mT1KtXb/v+nK++snKDqNbZq0go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ccXXhXd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\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i5dJtCAZjqKgpwvGnTkZ+FUOmMcCpcqc51pe9W48//O5PKC8rxcVXfA1Fozl5KDSm0TVD\nMZRhe3mJpXksEsU3KlCpxFV2GSUQ+gXkU+ju6MHzz/0RUyYfhjlHzVBJsMaxb08XLrv0GtTvbsKN\nN16Py6/ZHwDgx3Zs3ylRpLKyMtncleJoul8HYrixydeXLl0mQAwrGOTn5Qs6y1SA8opS+ZoSobSj\nrzeK3/3u96iv34W7775TBAm/+GNoBoAqf0Tgk5JizPszf27/ELoisqs0CUURVyNjADDYP76t39fw\nAsd+HAlJd2EKlE1qM9IpVkiu+rzhrKRJdQa+dv4leO/dD5BIsqyYSs9hJQXWdD/5y6fh1m/fKobL\n6Wecij/9+Vd4/vnXceUVN4ko5IJ/vIFly/6FSy6+EZGIqpVsp1Ia5xpzjOtqcOzcY/DGm/Owt2GP\nCJZVVJTiZ/fdi7PPPgmLF6/AjTeS8rdGHFCn1Y+5x3wJN9x4PU46aQaWLtuAE088HoFwDy6/7FLc\nccdtKC8vw6JF/8Jpp56Bs886Cw8/8iB8GXYZPwfiT/xPuSbmTdq8piaSCWzbuQ3vvvcO5r3+Nyxf\nsgShvog4+067TF9kpR0oyc5BbU0l5hwxEzMOm4jK8mL4M31q7loc2LmbRvQK/OPDj7Bq3Sa0dAcl\nsiw9a1HlY/1ZWcj152D8yPGoLa9DbdlIZDiykestlvUhZbUjmlTRatLik/EoekN9Ugvel5uBEdVF\nqB5djtxSHywECe2sLpQyFgc1LuWfsdb1gwDUzTGiSYwWMvJCai6jMjr6c8IJJ4gBTwNTxuR+AICZ\nAWDF4sVLcfdd9+DDDxciyfJzZDc5XMivHYsTv3ER5l5wAbrIdLDYke/PQnkWx3QKjV19CMViKCnO\nRY7PhlAE6KbRngbyc3PgcwHBIATYpbFDVXuXyy4ARyyeFME9Ou+ZHp9E+1mFlc4/Y0iKSKrmpRnY\n06CavCZMBtIsWdrXcP4TQHMI6E6kEbMqYID6BAzciMhiPKbEUUkF4BwkC9Fi5PmLuLMqHqyiPVY4\n7Crqr3M6uWfxdXbX+iX/xCN334725YuQ73Jj1shJqM4vhcfuRlNnB5bu3IBgNIzRpWUCAOTY3LCm\n0ggl41ixdSNW7NqKlmAvoumE2F0a9GF5xW9ddJFE7qidMDRAORxI+cWvwAe7wmBbhamLXNtIOTZH\nzLX9QUeHNGNGQMWYTaVkLNN5oO2inTi+zqghnTutkG+2o5gPvm7dOnF6GERh0IMOOPOMtdq6Xje0\n88JKOlQf53f1dcwAAHUAOLcIBPDQzo0+D+/puOOOk2vyXthnfA46ZvybziXp1gQANF1fPzc/Q3YB\nUxT4HtkRvBedl03bjc45HT06dnRuddtSH4nMHzpcdGJ1dJr3x8g5n4t0ct67bl9SzPk5zZpR9YS4\n5iRxw4034MnHf9XftVw7mAuvD/O9H6z/B79vBlx4/xR7I6Vep4AQAGc76Gdj4IvjheNm8FhiaijB\nCTrCfBaufQRYaD9rBgDBA4oGSt1242A/koVBluzgw7yX0DF94403JCpPJ5djgQwARtY5lg4cnR44\ns0rfTYjtxefVz6r7XgNgBG8IPnG9Nu9j5nvk+CGYy/ukXgCj0gQ09FjUn6WDzkg8WRs8yPqkZoT5\nIFhFwEQ7hByDZIYwRYCOPe+P8+XHP/6xjB/zXGC7k4ExGACQMZVOyzOQdcG+0QwHgh8EqAcDABTR\nJEhNAIAHgWq2A0Evc3rPJ8faFw8A8Jqk1xN0uemmG7Fu7br+29B7cqbNhYrsAkysGYmynALYqedt\ntSKaTmJj/S5s2bNTbAmmpdPYc7icMj5ZAtDF9Bxx7GkHWgRwdlJ0mJsNS9/KfkRRPyUES1BANNyY\nfiBpnEwNUEEtfl+0A9K0Cw370zBf9VhVa52RTkAAIJ5OppW2oN5cVaaCggIY701LlJvvK8HziJiz\nartVCsrcJPk5foaboeQoCKqhnH6Jx1GMTkKTabl5JVGmPB5SIZh5J4sR6/iK28jrWMWw1tsbYy12\ngRd4b4xLSOak/MVKBAQIYqkEXFZ+S2UE0vgaiITqyCBLPanBoySs+B8dVi2zlZLyRol0HCmrBW29\n7djbsBf+zGxUlVbBaePZlZhCe2cbnnn2D2IcjxxpEqo7RABAjya98HDz4ILDzU5T2vR7GsE91EV3\nwKlX1G5uHkSFdX1YbSAqhXDDtdERItJV2Ik8ZOBaRXTllutvwFfOOA0ur4ryKEfJoK2IKAVZAoAt\nbRUwh5DLmp5V2BLcgKQ7hVB3HBVZVZiUOwFFVkYvLXDYOLiVaZNKAAsWLERjSwtOOflkFOXnHjSB\nQpdO6+0J4/77f4Ff/epxZGT6cPjhMzFt2nSh6AWDfbI5c6FRhxrB/WODPGw6UprJpJJLlYoXxQLD\nSknfS4SLF2SoiHRdPm3Cht3bmtHdGUBlVTlyit3KWrelQYqqzBSjzqdEsOTyxsgjQ4BGqL4RneZp\nElfv6wshHIkgPz9X7oltZLerrXvTuu246sobUVFRi+uuvwaHzx6jImRGZI9q4HffdTf21tcLCp6X\nm6fqkxq1zA82ltrb28SRpuI/D8EvBlWTbG1twxvz3kMkEsW5550pKRjMQZI62eKcflHH0ACAXhTT\n1gQC8U6kGOo1HH2WVlE8gP1dV80aGFDfVwuldNUglWLz03CtoVALHUG3IxN2SV/hGqVmRz8LyJhK\n1E845pgzsWTJCjjtLoQTAVn76mrrBKA77dRTMW7CeMyafTjOOfc8/OG5x3Dttd/F00+9gGuvvx4P\nPvQ9PPeHt3DDtd9DcXEJTjhxNrw+J8LhCGw2O3Jz8tDYuA9PP/UUiorycMHXz8Ls2TNxwglfQjgU\nwvnnf00YHcWFZZg29XCsXbsVba3tuPSyb+GWW29EX6ALX/nKKahv2IHi4iLcdtu3cdWV12DVyo04\n6cTTcM45Z+PhRx6A10ch0f2hlcG9/FkAAA23fBaXRhtBRLmZp/fuO2+L479h/RokoykRiPOxyEgS\ncFiAsbXlOHzKOMw8bBLGjxsvTjxr17Pfe3t6sKehCQuXrMJHy1ZKnn9HRKXVKF0NINvrQYG3GJVF\nNRg9ajwqK+qQl18CpzsTTneGMNucabtUoqEeQILUPbsFQarcW+Ow+6wori1G9dhKlFRlGmuGcvxF\nDE7mjy7jaIAAAgBwz1StzXFM1V8+M0WfaKgzsqOFrRilYWTwiiuuEGqo2XBVDID9AYA9e+pxxx13\n4Y15b0oJrnQyAXemH87sQowYPwlX3X03KieMFMy+N5QWxX6Ww/MaoKLV40TaYkEkEEKoLygli4oK\nMuBmCdJACvs6OqR0UX5GBvKy7AingLauANq7Q/DYHKjJ8aPUa0WRAyL4N7j69AC3R7UDrQ+1lit7\nhUA0JRwbSO2PJNFLXIHUXhvXTMDrUKyv3nBKBP68Hrcwr9ieXL8oeutwsjazsDsRSabEcXPbHPBn\n2WX96wumEBFxTEZprMhw2LBxycd44PbvoHvlUpRlZeCo0VNQnl0Ml82LfV2dWLJjA0LREEaPKMOR\n4yfDb3HCklDnWbl1E1bVb0dLuA8RAwAQeSHDljp6zhy89PJLKCku+T+XAjB47aRzR2dexq4RiaJT\nyugyD4550ruZo07Ant9n1J758IxIapuGzt/PfvYzoRBrp0PTyXV0lnOAziCdJM4LRrl5fTqA+vqa\nFs55QceH+ct6zedrOt1An5PXJJVaf2awE0gwgk4Tv0eqOB1GGvuaBk2HSwMAPAcdHE3z1hFgAvVk\n8DDvn1Ftfqa6ulrSQJmXTuCD0WF9MOJKp5ROF6PHmsmp702DD2wHRt75fd4TgSw7562xnumZ9Ogj\nj+AWPiOdkjQEtCDAoPvSHIji74fqCOv71WxXfo/gDPO/NZuBEWlGiXW7MOWVFRh03+tz8F7IxGD7\n0rGkbcvoOPPTuRby+XhutglTN5hSoMcbafNkW5A1oZ1THTTj+fU+QsYt8/Jpj5Muz3viGKEjr0EW\n8/cOZOUw6k9wg+UP9TgfyrZgOxPE0Pc6+Px8PqaT8H1G9ekncLwMtt+5/xEQIgDE59Hn1eOefgDH\nIkEhfZBJQ6ed7aLvje1McWjuH5qiz3vi5whu0Vk3MwDMgUT2B+c6z8F5RrBOAzFkYLBP+H2mABDs\nYl/zoAYA+4f20IHb94sHANjOJMnfcsvNeOzRx4bs4iyHGzW5RZhcMxolOfkSWub46wwFsHjzOqzZ\nvVl2KbuF5WjpDyifU2tzqdCT2t9Z3o++Gn1nfokBJgIAwgaw24RFx++5mLJCQMBiVWKExqECSkrI\nksx7pin0Awc2JQiv5zFZBJZYOpmmUrgCABStQJ1A6RZH0kkpO8C/aJTYrGnEUzGpravMIDrTKuIu\nVMkYa4LTytrfk9G+JNEO3m/SKI9Ag1yJERimMtFvohQWqibS4ac4kkAkqh6vbBxEOehgSDBaHNRE\nKi6idbGUEkNwWGxwOVhdwKEcUm2NGiFX5T7RhCBDgc9iMjmFz6zKRDEup4CFGOwsiwaeT90zB0cg\nGBCq2aRJU6VuY/9BpaJDYADoz/f1BWRjIMVk7956eZkGve6Pg0X9hxqZalEwK3wPfIoDpd/BP8DK\nZfc4kIjFlaVvsWFEcQku+uaFuPbaa1BaWtLPsaaAkdik6YQKnNHiQhqMlW3u2oQNPWvRG+tDqb8S\nk4umIB958CPbqKCgnbIk9uzei/ff/we+fPLJKBtRctAq5oJocaLI8wCrV6/FO++8jY7OdhlnDQ2N\nYkB0dnbIRvDoo4+YIkl6UBBKixrejFMNUKtFNB8ITqgNUuWB8tkS8QhE24rMBYdXqX1RuUq63+DC\nW+xIilCXU8pLOZxG2J7jShpKAV6M9Eh76T5IstwLYLE75bliiSTsDn42JZNdcArJyVdq0dS4WLly\nLYqKSlFVXaxyuQUAUB5Cd3ePRPCPmnMUjpozR9opHlcgBBcGUe8f9qAaKXUhBuaHFirT32O+fyDA\nUp1OZGa5pQ+Maaqe47N4cge4o4O9pdBPGvlxqbiwatsiJG3Me+I8UIusRJYGOffa2Vf0K+2GKjXW\n/j+HuLiVSEvKBpctC2PqJqOyeAzJz8ZqqKBSzSbgeai9ccLx5+Gfi5bjB3f8ABlZbqzbsFpEf2bP\nmo3sbBs2rN+Hw6bNxHnnn4s77vw+Tjn1ZGEYMepz2LQKvPD8+/j+bT/C6aefip/edxsyMlUJc4LG\nfMDnn38Tl15yBa64/Ft49OF7YWjx4MUXX8YFF3wDVosDjzz8OL565nl4+KFf4bHHHhdw6eGHH0Td\nyGqc+dXTUb93JyyWNCZOHIc77rgb2f5CnHP2+eJE/uy+HyOvQMVldUsN1y+aMaHeP/Bg2B+SMSLe\n5ot84us6fkWjLSHGzsIPP8Qb8+bhH/MXYNeOHRLxpRI7a1daEkBhtg/jRtZhyvhxmDJxLCaMrUam\nj9omOYjE4pKXvnLVGnz08WIsWrIcjS19oGuiRwVXdb/Hh6LCAoytGofx5ZNRUzoamRk58Pj84vxT\nBDAsFL007CKewb1M7SW9oR74C7NQVlOCoop8jBiVpdJ9TM8WJ8JH1hwpgf2tppz+AYaK0eIG0s9I\nzxtvzMMvfnE/NmxYL+rCdjsVzBNiBNMZIfVXeuEADIDm5hY89OAj+POfX5XUlHCIESQrnIVlqJs2\nE9fceTcmHzlWcNGW7hQaO5qRlZWJigKCX0B7FOjq7kGmyy3CSFwPBHB02ORnlOsZyxbZ7HC7rIgl\nCHawhGgU7lQaY/N9GOFQtH+ucU6jxK0eBuY6GGr0qZ7hb7QWwrBIzn9DNIEe5vun7bA7LDI32H7J\neFoYB2mrDTYKEhrldcUGERX+pDK+eD4utjYVuWUkxud1SMoVWQDhWEw0csgWzHQ7sXHpYtx3283o\nW78KI3NzMXfsVJT5CxGPplHf3opFm9cgEA5gxpjxOHzMRAEAWG4wnEpg9Y7NWF2/A23hAMIEAAgK\nG+OB9lRpaTFuvOkmXHnFFf3l4lRQQFdZOdiq+J97f7DdMnXqVMlr104vHVbSfxkZpOPGsUmWGdMe\n6QDwb5Z2o+OlHUe+zvxoUu7NUfyhHFEdfdWpAXSkZZeWMrdWSaukwc1ScaTs03HS7zECSQeFzjYB\nCn6eDg2BdH2YnWK+xvJmdJj4OiPbdBYJeNFIFyG0669TAICRY69F7yTCZ7OJo0ZniwAAFcdJweZB\n0IIMAEb7GaVlSoTZmCdYQqFkgn1sHzrHBP8IDvB3nSsu82i/NKC03Bsj/3QWKChG9unVV18l/cEd\njCkHBD40U+JQnd6hRt1gh5ugBDVK9LPQ+WUb6D5iQIxBGwIXg/PU2WYUUCT4QWeXTilztHnwPdLg\nCXbQ8WbEn+3P6zAlg7oAXA/pqLGdCA4xAm+ucsDnJ/uBTqseE9Q9YJ9+mrbgd3kOOu8cb7wHjjn2\nDfvZXIWA6QVMbRjq4Hl4Pxx/nAvsZ44PDWiZ74msDw0A8FwEVahtoccrnW0CJ/ycbldG9fl85vQG\nPVa4f2jmA6/Ne2C7MsI/OAVA3zsp/I8+9qgE4XjwegxM8Dju+OPxzNNPC3jBFASei32twbVbjCIA\nACAASURBVC6KQGpmx4FWry+yDKC+LtM/rr72Grz//vtIGfT8fneSgQCnB3V5xZhcOwZFObmww4oM\nnw/NvV1YsOZf2NxYD3Lec/KKUFhWikA4jL5AH8KhsKoCwLWAGyUBUf6UwCI3aodhGCg/zgiRGgxV\n5YUqT1S7Yeo32vUstUtgntFFzmuyfqUUoMES0HawJZlOSWxeHftvr7JYGO8N2CfmCoX7d41ElIe0\n+Ic2/DTqYTztficbiEsMZzSaTc+BYaApvaqDVAFf+V15pzqR0DBcdQxYWtu4/oDxr76hhPD0Parz\nKuxGRw3Vu+r75gjiwHOZWs9YfM2UHQovEXW9/4EHJFeVlGpF56BaMQVO9i/zd6AJ8YW9x7R0m11E\nKCj6RIeFFLeyEeUysMybysA9EF6JoSfdjU1tmwT9qi2qg0+SSrxGaogT4kQZbUzafzAQRElpqTie\nB/MfkwmVh6ydzng8jUBfn4BHXGw3rN+AJUuXCIJ81FFzcN5555jcFhMAIKF/o26amcLNcKEc2rPm\n5GQYXH+WlRZUjXQVTUuooZCmVW8dPtd+wHcxzm92MXgCo857/6hUKmBqTBvD7UDel9FwNHwI3Hk9\nHqXq3Z9vYIzOA3roKgquwBwFvClgkNRMJUj1nzZCBzcB25+co0Q6hvq2zXh35cvoijTKc4vyuLCN\nlBPEcavzrfSzKJaSahszYKBeGBBpUTNeNbIl4YLLkosjpx6HMeVTJA1AL80DmgPkUqtSZaeefCEW\nfrgE8978G447fhzaO3vgy/DB61aOx/JluzD3mC/jiCNn48hZM/GTn96LSy79Jn732/vk1l556R/4\n/m0/xAXf+Bp+/JOr+pcucYJSBAD+jksvuhLfve07uO9nA4ra3/nO96RSSU52rqQR1NSMwO3f+zl+\n9vP74bK7xYCaMXMGTjv1ZHR2t4nWCx2io48+Cmd99Wzcfc8Pcc7Z5+HBh34uoMNBJ6duNNP6OdyX\nzM6//l1aN6FUdmX6sQviKrdONksqdMdi2LlrJxZ8+A+pGf3B/AXo6eiUuu5WRriEjwHkZ/sxecI4\nzDp8OiaNH40RRQXwZ/gEUSfI1rivBUtWrMZ7H36M1Zu2oLmzW5JHKD0qTr/VjqyMLNRUjkRd9VjU\nVU9AblYJcjJLZa4z6i1l+aQkkF1Fo1MsK9eHeDIGh8+B7MJsjKgtQdWYEXAXArEwRIhODSLtxg6/\ngu+/Fhq7DoHXtCpjRZHYX//6N5g//33s29cs4BtBODo2pGsyYrS/8f9JBsCSJcvw43t/goULP0Ii\nwXJGQVrTcJZU4IxvXYavXnIZ4M2QZBeCgDmZdsTiaUQSZDdY0cdSutEIiv2Z8LitCMaA9q6glPTL\n8rpRnkcaO9AWYvm/LjgpMpyViXyXBdkWoMSpNFIN7VODWzaoTQa2434mIUcJ0xCp+N8YTaG+OwA4\nPbClLcjOsovj3tcXR4RCAyAo5ITPaxFBwmAkCvIgvR4HvC6lZdLdF5O1wuN28PFBzDQcYdldm5QZ\nJLFJbDWpCACsXrIYD911O9oXf4TRRUU4dtxUVBeWCqtg+769WLhxlbC4Zo4ahyPGTka2hTRPBzrj\nISzdtA6rd25FdzwMuOzoZq4ES6gyEEO7MJHE5MmThBrPaKjobhgA5sHEXA62fw4/2j6fd+h0EfDh\n+kqnngAi/9aRdZa/o3PPaDcj59oRJCBAUTw6G3Tq6DTrfHc64aQzk+6uD7PQHaO1SpzS0S/kR2dL\ns1+0A8vz0bGnA8jcfjrcOlpOVgLZAGQ98h6ZTsB7JpjGe2Hu8lCOMHOnSRfmeX5wxx0CAuhc+Vhc\nKaGLBoABAPD+tQPG+yODgM4ao/gU6SObh9elU0aGDwEGshm0Mv5QvcQxyrahk8ln4z8yB/iTdHqO\nIe006u9rBXLar2yLK668At1d3fI2HVI6pkPbeP/eOCFYQideO6Z0MumE6oNOPNMiBgs86jaj087x\nQceY7A462TwX25xRZPYXAROm02q2Ascf+49OPz9H9gBBAVLpCT5xnaTzyr/Zzoy4a3udNi9BCoID\nOrXjYC1ABioF8HS0nffO1AS+RrCFdHf2MfufdjXBLfbVUOwKPisBMZ6D98qxNpSoJccrI/pat4Fa\nFdwHeE5e6y9/+YuMa84V/s3xQNbNUMr7/A5TUagNQDuan2dKAducaQh04AezQ7Qw4y233oJ7f/xj\nuJ0uXHHVlf3pHby3F55/AVWVlZJywzZlgIPXIojFviM4cOAUAN3ywxnCn3310wyMYCCABx58EA89\n8rA47PFYTJzrFEFtm0NAbkssIbn/U0aPQ1F2Lvy+DPjcHrT09eDNZYuweV8jIrDjlHO/hiNOPAFp\nlwsdnZ3o7upBS0uriObyfD6vF6FQH1qamhAM9MHpcgkozXUqFApIWV6X3QZLKoVIKIhwb5+keEpK\njqH2j3DI0MUxNn+VJ6BNDGVDSYDBMDvS+0G0n/BKDja2/+PvD44amW9Iu3cDrw316cGvmQcTnfzB\nZxmgPJnFxvob9CAqvXoR1Yjizp27ZTN77g/PIRanAInKoREkRxgAQ5dL+Z9ueFLGqfrP++7p7hO7\ngzSep37/FMrLK+R2htoQaZxS4K8j3SUUmCxx/JnmoZ7RnibHY4AmrlUwheApqscHjnKQxUBDVEWm\nVfRZR6EpYKcP5gOzDBrBi/3jljy/ih8pQi1/pxOoPA5LPwDQz8k00CQDvhIBSxMeyDCjWPNE7w4A\nAOzXgWbQSQMOqsSkgSYYkS4DGOifpmYkQMcKlaOpnTM9zvqXykF09gPT98yu2ODF9EDv/c+Nzk/O\naFKs1L/d7evxxvJn0JNsEnaOjCUyKOzU+1DUKoIbUu3AqHggTCRTONYMaAr8Yq6dSTDBYoc16YYb\nBZg6djZGlUwUAID4rHLpONLZdgN8+VNO/Bbmz1+Et9+Zh6Pnju5X4NdkjGVLd+LoOcdj7rHHIyPL\nh1dffRl3//B23Hnn9dK3r7y8AN/99p245FvfwI/uvdoANYUMLl3/0ivv4Btfvxy33Hwjfvbft8rY\n4bi/7rob8cQTT6K4sBTLlq3EiLIcXHv1D/Dkr38Lr9uHJ5/8tWy+jMjR+Rs3bjSWLluMjq425OXm\nor2zA5dfehUefPAXhwgADLXemseRGVY28y6U062dQBlNnMukvtBwtqQRjYSxdMliLHjvfXz4wQdY\ns36NEnBKAD6nFfZkGj6nAyOKinDkzBmYOf0wjB5Zi9LiQjhsVA2nqFsQuxr2YdHiZfjwo39i+epN\nEtmmMgoBHLK9smwulBUUoqaqEmNGjsHI6rHI8hYiy1eEVNKFeJIAsVXYZ2TdEOQkOBaNxRBPROH2\nOVBcXiT/KkYVKESBAp5GdTyCVUOD5ocyh1QUmCU6KXpLAODJXz8p1Feu0VwX+frs2XPESKbhfzAA\nYNnS5bjnh/di8eIl6Av0AMmYRMGzxk7ERTfdimO+8lU0dHYjmbagtLgEFdk2dEeB5s5exFIpMWLo\n1HtsViSlwoEVgUgEEcTgczlQmsWyomQKpIRtwfKmJRkeVGcqACCH0ilG/Ry9AqqWMBa1/bdnpZQM\nq/RZSyKBvZEYAhYHQkJaswrzz+exCaVfVx/gvCcrhMSQSCwpUU8L1wPm+RvLfDSm1lOv2yqsnXiS\nLI60UWrJIWOIS6lUL7JYsW7lCjxy911oXvwRKr1eHDNuMsZV1YjBuLOpAQs3rEI4GsFhVaNwxLjJ\nyLV5ZX9vTyoAYM3OLeiJhZG0WdAXicpy4fZ6EQ8rRXau44zg0RlhtFc71Z/O/jmUMfX5f4bOG50l\nOnp0aLWTr6O0dIZIM6fjQxuCdGECAsxR5t98T6uHM5L961//WhwoHmwX5g6LrlFzs9CJSW+maByv\nS+OZzjwdRa5tPC+j6DzouJARwHWDwASjjnQI+T1+ljnldK7IHOSc4sHP817o6A91DAUAuKgiyWzC\nwQCAEfcwpwHwd7YDo96MivKeePB1MhF4fuZM03nUQIMuV6gjwEPdl3YmeR4yG5hHrqneMru0k2Cx\nSN47r61LiQ7WAPg8R8hgAIAMWB3F53UoCEjQROfNa8ffnI9OUITPQzo8HVM+C+cIndzBAMBgRoqZ\nOs/r0aZlugkp6UwvYbSfgItOK+D9EYDimDlUHQCmbrA92W+8f4I7BCsI8HDNZmoqzyeOnMUi/c/P\nDwW4fB4AAJ+TIALBDV2OkSwMggQEHsyHbh+m0nD/IBDAviCYwPnJtid7RqfSaMdZ2c4WCeTd8u1b\nJV3g+uuuE6YPDwJdzz//AqoNEUH2MZ1+HgRfCPyw/Q8NAPg8R+QAO4hnfenFF4V9RQ0KMnm43uvw\nXLbbJ864LZFCZX4xpowZj4KsbORm+QUYaOxox7ylC7GxdR+82aW49Nbv4JSLLoQrz49QJIFAMIRg\nMIRIKCwBDTepmukU9jU0gMBDRoZPGMJkmVPgOcFUU5YJtdnQ3dmBhvp6STEmUOD0uhGNxdHU3CTn\nS8cTUoY3FAygt6dLgVVMyydgwH05kRBmsmV/AODzbcj/ibN9ug1wuE8PhyD1gyfGoxgRP8MJ+CwA\ngN60uBBwgf3ud7+HF194UQkLpRLIzyuUiU9kjgbkf5oBYE4VoOhXtj9bNsi+PmosAzNnzsDTTz8j\nSqR60u/X74bdFgaVllNwMUMmZekvh6EFMPR3aDzbbJJgb0SfD05zlFRWw3DbW9+INWvXIhQMSv3r\nUaPrVIm6foxBjwFzn/N3KcA0AABoKX0jjULfXz+WZjiJWu9dPTzPQReCAAAn86EAAEOBbrrmV3+r\nmAAArQCogQJT2ons5Oah+slzD7cBfnKumh18ebj+j6iNydwin4Ta/ifmvn7cgZ5U/dgPALStxxvL\nnkXE2qlSMKR8isqPUhubBnqMPKq0ytkf4LVb+sVU9PMoUc+Bvxw2L5B0IT+rAtPHz0aBtww2OI3s\n/08CAMwAOfXkS/D++4vwzjtv4djjatHVncT/Y+88wOwqq/X/njrnTC9JJpNM+qR3CIEkoKEooiBF\nkeYVRClSRdGLVOkIqIhiwwaCXi5FBCnSU4D0SkhIQnrvmczMmdP/z2/t882cHNOAKPf+r9tnHHJm\n732+/e2vrPWud72LFKBoBGp5UNOmrdLYsUfqs589UZ/+zKd1xZWX6Yunn6yHH37AqMx/fvRVXfXN\n/9Ql3/i6br4VBoDXA47P9PgTb+jM07+qiy++QL/4xbVt9eW//e2rdd9P7lNleZ1e/vsE1dZ21X/8\nx1c08c2JGn3oKD344K+tb3AYYSFdd901Vrnitttv1uq1K4yVcsHXvqEf/ehelZbl563s7W3va3V2\nY2rvIICNKhOq91Bsyt+tW7da8+bO0QsvvqDJEydq/do1SlLXHQJORiryS+VFAaP5f3LsOI057Aj1\n6NFNZZWlBigGsllt37ZTCxcv1ZszZ+n5CW9o1aZNatxFfWiu9ymQCaiyuEzdaus1YtAI9WsYqNqO\nXRQpKlc0UqlsBnCP/GC/UkTfTRDWp6wvo+ZYo7K+pMqrilVdW6FeA3uqY5dqU7JLJbIKlniOo6XN\nEeTNMVI+3HzxAAD2CZhZf3n6L5anOG36NK9EaK6M15DBQyzCgsI5Ds6+UgCMjXb/T/X7Pzyk9evW\nSpmEVBRRpHdfjT/5VJ15wYUqqelg6WAwHSAUxVoSSqViKin28hfBaJqa4sbsqoyEbZq1hlKW7+5P\n++RH/wTLJplWUSar7pVRdQ17RVPAR+hNeDzW/raRneuhPPCTPme1bZVPGxCBbWrV+taEssEilVMP\n2XL4E959SGM0MACavxSDtZCIKxQMKhIOKuwj11MGHKCqHAnlqJU+qgKkFcxk7TyYBI3NrQYXAxqE\nEBb0+zV/5kz95Ps3at1bE1UXDGpc/yEa1qevrTnL9wIAwEjaksoBAMvf0854TBU1ldq6c4eaEgkr\nZZxNZ63MLgfOJxFlnFgX/f5g9s+HG2Uf5SoXOcUJKowu8m+cTRx0VMOJ1Lpyb9B/cX6JAuIM4Zww\nbl2FFJwDDpw+hNVwTIh2OgfEObz5Dgnnk2ePE0eUF4YgUV5U6RH2c0rzXEsk2Qnf4SzhBHMvIunQ\n4V3JvsK++aAAQElJqY1BosRub4a27iLNOP0usAIIgbAbB9R5AkcAH04EFGfJRcrzgzH5wADPBngE\nwEGfk+Jge0ceAACwABsCMIXPoW7ni+h9lPFQeG0hAEBqAzoAzvmFEQD7wEWuuZ7INz+UFOSdsaaR\nqw8ARJ/sCwBw9ykMfNBf/ODkwjgg2g0zANYNP4xLxikAHH2Tr2Owr/7ge2CSwHJxfcwYIfLP2Cai\nzphEFJNzaQOOOdUROAqDagcLAGAd4blwDHHeAZScuGD+8+TPJ8AU5jHPjhAlY5Q5nM8AoL2ACG1V\nG/w+A1V+8ctf6p677zYAgH4AAHj00T8ZAIBzDagCQ4iD82FLwND4OA439phLF15wgR56+GEvTzfj\nMdz5O3n/VaXl2tW40/YG2F6HDByi2qoalRVFjSWwYcdWvTp3hmavXq7Sjt10+fdv1vizzlC6NKRY\nEhsgK3/QE+4zJioZZ0jnm3aelEikTBzawBW/THCWtLFMMqV4a6v5OIlU0kQFw5GoEsmkiT7yOaBx\nKBBUC0yBlpjKS8sMEIBBic/FPQAK/o8BALbUFUSA9z3EdneYDg4AwDfy0pkMl19+heVp4viOH3+0\nTjv1NFuYJ0ycYChmcXHENqr88g3/yklB5NDljDMIMao61XZQSXGJli9faU2hFisLGvl9/3Bg7KJM\nGSSm5ile2tHGZPfeR/sGRMqDRy9n8JO7sj+usYv4r1mzTj+894f6qamkZozuxmbK5AL1P+bYo3Pi\nd/lSUruPCZd60h7Vdw6fZ3la89tYISwIDiTKwfm+HGPjgBkAeU56QXSr3ROlvS7CvwcAYE/4lTmF\n/PhNUZiFAaOn8Ng7A8C73jPgQgJYmTf/HR16yCGq6VAjvx+02uUTOyfugwsCfdSxvPtsdgAAS2tK\nKzct0svTHlc61OQ5Jul0G1fDIvn2HnOpGqQz5FKF3NN4tEifaZLkhqxHy3ONzgZUUlKpitKO6lhZ\nr/pOfVQTrbVKFjkoIY8BkGNmZKWTPvd1vfDiqzr5pJM1aHBf7dy51XQ/MCxOPe0TmjF9pY4YM1an\nnnKabr71ZnPkZ8yaZtoW48YO1p8efVmXX/otXfXNS/T9W76RAwC8sZlMSk8/PUFnfOnL+vKX/0O/\n/e0dCueU1H7ykwf0vWtuVGtrVueccYGKi8v04O9/rrLiYl111RW67vortGLFZh111CeVSfv029/9\nWsceN0633naTfvjDey0n+usGAPzwIAEArlfbR8EepwCo+KpVevqpJ/XsM09r4bvvaPPmnaZQHwHX\nIesmI3WsjmjkkMEaM2qUDhs5Qn169lRZSblR8+PphDZs2qR5c+frtdcnaPK0GVq/o9Xy+3HqWNdK\nQmF1rqpV/579NKBnXw1oGKC62npFo+VSpkiJOHm8tDUoPykGRGtju8zx9xgAGVV1qlDXHp3VrWdn\nFVcVyUeqhFvv0PRIJgyMclVk9iuisM8J4qXiWNQ0ndKkiZP04G8e1ORJk8Va6A5opSg5Dx06dN8M\nAPltnt92+x166qm/WImwjNUa9Umd6zV43FG69NrrNHBwgzV7Z6u0dasnAlhdGVVp1Eu329mc1rYd\nzSoKhtWhJKKSUlk/b9yxXa2xtCnjlxcVqTIUUllW6lwsqx6UK35gzXYJUTmYLgcC5e8dzHC/dgI2\n+GV5/+taYtrOXpr1q0NpiUX9aWezARRZRYuKVE5pxawsNaEp1mqssPKSIkUDXjWCHTE0kPyqKuMd\ncU7GjK2SUFAdysLm+G/dEVNrJquS0mIjpfgz0qyp0/TjG663FIDOlKbrO0j967sbO2BZLgWgNR7X\n6H6DdcSAYaoORC0laWsypmkL52v28vfUGG/R0CFDtHr9Wq3YvNnbq3yeU+eig1DScQ5cRYf/6QCA\no+3juKMk7qL/vEkcEBxrIqlEQomIsldx8Bn5zUSpccgAAxjnlGHj+bkPjjuRRTR+sB+ckJxNtz1U\nNXLODAAK8wGxMT7D0cdBc9dBhyYPnSgwB9R7WABUI6ANiABCU2dPKXTQHADAddffcIMBNvtiAFz1\nzassPx9QgmfHIeO+9AulOMn75zt4PhwkBBJdn+J40UcIBSLQxvXkyudHtXk+fpx95SLotI88eMAT\nM2lyqan8Jj/8rLPOsog11yK4CBvC9c9H3bPzry8EAIja84yuvQjVkW+eH/kHKMKuA4hxonowOADF\naOe+UgDygQ7mEgAQ0Xf6jd+MU36wZQEWcN5JnwJcAgAgOs84xZZgvO3vANiBas9zuWg2z8TYZ4yv\nW7fOWAtoFLj+pQQiY5q2FUbADxYA4ErauXnCOAVoKRzPbWBxxrOnyYPngCXoGACMQze3AASwY6i8\n4FgbnTrXWhrAKy+/rMcf+2+7fvSYI/ToI4+ooXcfE+qkjxwAAPiCPwFr5199MD6cmCilCFkXAJbc\nYbLy/qA6V3dQabTYKi2Fsj716VyvUYOGqlunOou+I8C3K9GqF6e/pXnrVqmsrocu/f7N+tyFZ2lD\nq0wgMBwtUjTqaYy1xpNq2dWiUCCsqoqo7T07dyTVGo8pQtnZCEL8gBDI0zldIYTqM8ZgYyzBUGuN\ntdr491iF2CteGjntoe3sR5EQadf6vwoA2HL3IcfV3mjQ+7rd7tFRR8km34VNBQSwsrLaFhUmDaVH\nLrrwIlXXVOu8c8+10l+o2lPblIFpLzqdtpdciG5/mIcqdAALN4rS0mIzWIkwtcRarC68TeDRo2yB\npL48iwaIvQMB3IaF0I1X8aE9Tt7Wg23dsj9A5sCiyzASnnryKT308ENasXyF9V+vnr3U1NxspTvG\nj/+kbr/9NnXvgWJq/ncGrHya6Qj40TLwct5RL/Byvp3D7XJp3LVO4c4RgtpZqvQPqQl7cq7zGeRt\n7ytLyayE5s1dqEQircMOG6GK6vZIMxt+4y7q4Pq0Ytka9R8wwBStUQVlwcB5mTrVy1E8/PDhXjQU\nqzQXrcaY4d0w5sh9c0dhesA/jh9vjLGArFq5Xld908snf+jhP6i0rCjXVwf2fj7M2DyQa5wiiXeu\nm9dehZHW1E6t2bxYO5q2mNFkBptz9Hd7Ee3ABYIpbRoAHui7m0Db7m3yqbi4XMWRMpUUVSqQDas0\nVJGj/3sj3dUbaItlpqUvn3OZnnj8GVVXdVB9fTd16dpZvfvU62tfP1cDB3bXrJlLdMzRnzFA8M+P\n/UZ/f/l1XXDB1412d8/dP9ATTzyj22/BeL5cl15+tnzGPPHSlXisXz/4F33nO9fpa+efq7vu/E8T\nAQQk27p1h66/7nZNnjBT69d5IjQdOxfrrLNO1cWXXKhevWu04J2VOuXk01RUVKxf/ernGveJoVq4\ncLGtRdOnz9YVl39Ld9xxuyJRX5vuxt7f0/7mttOVcGOdeG9epZZEXDOnTdfzf3tWr7z0dy15b5Fa\nWpKm5G59m5FKI1J9XWcdMnyYDj/0UA3q108dq6tUWVEufyCgrdu3a/HS5Zo8eapmzHtHsxcuVGMr\npWQ9WQHyu4tDxepS00UN3ftpaP+Rqq/rqfquPRUg18+MY79ScdJHggr6iwywbWlpVDqbVDASUKDI\npw51VerRr7uq66oVLPbLTyI7jqyNnzwlnTYhvxzEWLilHMigz184zOX28tqJgkJPfvKpJ7Vxw2b7\njDWI6AoGHw5PvkH5D1UA5NekSW/qyiuv0rzZsy2ykE61KEOYv1NXHf2FM/TVb31XZZ07KkYHZqR0\nrMVW96KSiCLFASUTUqI1YROHCATfH0LsL5NWK+V1QxHFm2IqDfhVV1KkLkGpkrKMuWh/e82DdkUi\n20Hc1LY8L4SHJThoazPSqh1xZSNBZRFLDUpbdiYUT2ZVEi1SBxz+lNQU98QIcey5l4kTksMf8NLG\nnDgx850DA4zHJm2Ac8n7dxVdEwnYAh67KxiWwn5p7sx39dNbvq/lL/xNtZEijR88Qn1q6yxlZfGq\n5aYBgNDakYNH6vAcAED/bG1t0tRF8wwA2BVv0egRhygYDuv1KW8ZZy4EfRxxwnTaHBQODHGc4BEj\nR3pRQ/QncotWHjzZBnZ+oCF1kE/GniBSS86vM6zdvoiTRRSUtZl69Ygpu2fEhiC6D9ABAEB+MtcR\nHcV54u/Qw3HGud45sOxX+RoD7nHyHV8ilEQYifYSqaRt2F0cjlrOZ7SNfofijFiaix7THhxn2ACF\nDhp0bhxyDgCAG268wZT2OWjbxRdfZHMUXYcOnTrqycefMJCD5yK1wX0HAQvOR4DM2VP0ASweJxrn\nqhTQr/wAUOBw4qySBsFvHMzFixdbCgOfca3TRhgyZIitGeyL7uA7yR8nCs9vDpxr8tT3REn/qMMF\nB94xQ2gbNHAL2gDW+3zmTOIcO0eV/qbveQc4Z66uPM486RoARQ4AwOk+5phjjMHgAAzXXvocEId+\nBmBj3PG7pqbG7sNvDgAAWAj8zVH36Q9Hnac9jm1ie1LOLqf9gDmAOIg68h7dGOA7oblzHeOPUoeM\n/6qqKntn3Jvy36RqFPY56zhVCbgWFgTCefkigG5u8e7oV5xrDmw/gA1n8wGcMd5srfP7rS8d08L1\nkbuXx/jd1ZYCwPk46aQxEK135fsQJQRMI9UHgACWilOoGzp8uK2/SxYvMbtx2PDh+u/HHlPfPg0W\n4ARw4t3RPq7lvcLA2HuA6qOOvH1fz1rEM1I+lCMYCiuFqLek8lCxBjf0t33v3cULlU7HNbBzLx02\neJjqO3ZWqhX2m9QYj+nVudP1zvo1KqnroUu+f5PGfemLao0WGTsgVByxamXs4NDxW5piJjQfCXtK\n/5k0/p68crk+Kd7KZgQAQNqzX+FwwIDt5paWnDZbyKvqkfU+M5UqhOuDYQtikY5GVEmwMQAAIABJ\nREFUCgPBW9JgYZb9H2QAHMyBs38Dtz2S7H0vL4fNBFSVidO1S71F6qDgMNhB4ljEL730Uh19zNGm\n7HzFFZcbCnWwnP78HtgbAMDi5ammSmVlperRs4dteCxUzsH+/OdP1soVK/X662+0qZJC1WMh8Ghn\nXpTYqPCuLp2z9XNaa/uuJE5L9+9gYqDNmDnDFrHt27YbonrsceRVJbRgwbs2iWtrO+kzx39a0eKi\n3QAAWAZ+HyCAT2GTis4JOxUa5jlmvxf+zYUcbfXEYQxYmUD0N3z+tCqqPTMWsIQI0279XVAVD40H\nn0L6za8e189+9qA6dqzRnXfdrNFH9GtbPF9+5TWVlkYtZ/na792kyy69TMd/5lijSNG/L780WXfc\nfq9isRY98PMfa9iIARb1Jf8cRXoUvTE8oDUyvj7IYZEncpzT0u9+94gZKrfddosuufSCA3AAP8g3\nfZhz91YGxpuXGStOiqfisSdc0cp2lod3ljfKcgyAXDO8CiEeTcvzCbz/T7bVifSu8pIK0LHwfihT\n6or/eVfli6Z6CQQTJszQli071aGmVsFgxN55z15V9s5ggm3d2qKHfv9nyxG+6JJzhQ/w0MNPCMHL\nEz93knbu3KXF7y3RiOGD1K171W4AAE/z7sLVmjJlhgYPGqBxY7zUHHe8+84qLZj/vqZNnW/j5fAx\nQ/Tp48erUyfPY922vUUP/vpBdejQSSed9Fl16FhmpRyfe+55TZ0yXUcdeZyOPXac6RbArGkvU7en\n97e39dF5c21cizyWT1a7tm/X5EmT9Jcnn9Rbkydp9fLV1m/RsM9U4a3Ym9+nhj49deTYw83xH9Cv\nv8qLS3OAo7R56xbNmD1bE956WzNnz9fKVZuMLk49CPqoJBJWWUmZakprNKz/MA1qGKxe9Q3qVFMv\nXwbHP6R4MqlQJGSVSpKtWaWSufJ8fpyMrIrLi9SlZxdVd65UTV2Fx2F3WqGe3ETO/mkX121zZN3K\n9pHKZOTGeYZ2pU286/6f3K9p06d7IzYjY5HhaBF1wnnIP/YEACxctFg333yrremtLU2Kx3YqBYrR\ntV7nXvVdnXr+hdoSS2pnU4vqu3QSmn7oMe5oyaqxudn6n1KKFVGvvOnmnTHFWuMKh0OqriyxcoCo\nK5aBKUSkDj4JKbf2SL/XQtdjXmKLNz85PKUWT6dha1Zasj2uHcm0QsVhlZogodQcTyuWziocDIpM\nFRx0riPf3+/zyhlDhzRtyYgUi6PsnzBNkPJS9gIE/zI2vonQmK4MpWDjHruLyDyRf8CB1mRKpSVB\nrX5/jX79gzs1/Y+/V00opKOHjFDfuq4KZGR1oAEAEGMd1XfQbgDArlRc0xbO08wV76mptUVjhh8i\nHLPXp76tmUsWWfnYcJS13yuTh7MP8H7hRRdZFZHq6hqnWpNTG2nfb/a/c36YNfeDX0N+L9FCF6F3\nzpyjPNtYzTlOOFT84CCx1+C0OAVyziM6jOOPk0b6AGXgXHmy/Kg/9ovL589nUDgHGzuFoAvOJbYY\nTg3tKxSbs1U+V+rOOWM4VlDBYdRZxau8A+CAqDb3u/a66/YLAEx4/Q0R8UUbgfaQFsr35OsCuKis\nE3Kz+ZHLF8+335zT7NrszuM3kVsCTtzfATEAHFDPnbPrgj/0J463K8sGGOAi6wfbIeP7cXRpOxF2\nWCKkYzjQg+/OBwBwCmFLAAAgWosTz/OQHoPQI4ACzwGoAZiwJwAAu4lgG840UXbPVm0PqDmnnj4m\ntYDxx3fQPpxm1lFXUaAQAHBDwbEuSB+5/vrr29T/85kW7lz61CqjFBUZEMF3YasBdOTPFc7nc1cl\ngfQNUkAKAQC+g0obDgDgHuTUU27RjX9AYRx4W1P8fmOD8KxuDOSPIf6b74H27xT/mdOAkFRSADzj\nOv4OQ4J39MILL+jkU04xpzOnuuxR6IMwfLMaOmyYsQEAQ1inmMe8V/qNNBcAp3yRzw++6nzwK9yz\nA1zccMP1mj59hrW1uLhElNOmKk82k9TA7g06dMhwY20uAACIxzWwvpcOGzJMtZU18qWonBfQ9liz\nXp49Ve+sW6NI5646/5prNOrkk1QEgE4uaJDKYlkT+jO7wOSNfFZ1jeApOjMlJSGF/D5TtWpuxnrB\n/vGbHkE47PPYbcmURfeNPQQoQJnbpmYrO4wOTrS42FiOsZgHdHFvNLE8XYD8N/7B++xjv+LjpcB9\ncAAANJbFB2oXeWwXXHChLrzwItV17qyFixbZgnzYqMMM3du6bau+9KXTNXHiBFt8mBCguG6Tyt/w\nPuyL2BMA4BRkm5pQ009bNJva7p/69Kcsrx6HGhCDdgJe/OEPD5myLfdi0SFfrG/fvjmDLWGCWoF0\nLrJqyk6e2+UR1F10bG9PsG8zxokAvvbqGzrtC6fZTf7y1F909DGfbBMGtEWuLVU+N9NyolKU6sLR\nao15tBkCwIH8PT0jrXtvu5Yt3aqyshINH1PnBXMJKwHzBVAEld5f3Ki/PfuSGvr21Gc/N0qBXNS1\n0L4vZADgoK5ft1M3XvOA5s97T+eef5rOPudUVdZQXUBau2aLRTzO/9q5hvb+5sGHdO+9P9Qxx3xC\n0RK/NqzbriuvvFp+X7ES8bhOOe14nfPlL8gfSCmdSSrgj2rnzkZddNHFmj5tmp548kmNHDk8r7ML\nnbE9vwfWq82bm22jmDL1Lf31mcfVv3/DHoUfP+xY/GDXeXNv7zPQ+4vjnniR+N2dCc+B350N1F4R\nxfvcu6ot/JgHBzjn36sIUvg/B1y1AwDcA2iA0mcIUhJN9p7Y1CcQJ6TEWCKjUNivluasGf0daz0O\nOYs6pScNTyOqjAh+bmp48VBvJtm8gpacc6oQMLPygLnrnKRFawyHJq6KyiIP9MrpZAD0ADBUVeXq\n0udmMfMk3po21Xwr321+RsbAgfxj99G0p7ezp89AA9Nau2a10QSh+k95803t2uHlPldGAVYsl0iV\npSXq16ePjjvmaI0dM1p1XTuqtDiqZCyp1pa41m3YpDnzF2ji21M0Y958Ld+03ZrKhhzPplWisCrK\nK9SnVx/16z1QA/sMU33nHiorr1KA6H4iq2w6IF/WE4lMphKKxWOWX+cP+S3a37GuRr0auqumrlL+\nGp+J+nkwfh5e2eaH5aUb5dKeXIlONx68/vswVABvTCWsLGnQ6MDQZV997VXFWhCp8tK3MKhZQxBs\nyj/2BABARXzwN7/Vfff9RKtWrVQ25UWd/d176bSvXaCzL7lczYyRpmbVd+moDkUSZ2zZlbbPMCw6\nVJeoPOjl06/evM3ojcXREnUsLVVtxKP7g5GW5rouwDgq0Pv3JAw8WUuP4+BvA3B2StqUzmp7IqlU\nNmwlF3GkAaOKgoj7eTMilcxamN+jPXplXCGw8TuE8w8GEIB+6eX/B4N+IVnAWofdyj4L9TIEMIcQ\nYNJzvIqj1Fh25QDjKi0p0o7NO/Tg3Xfp9Z/fr3KfT+MHD9eQHr1s3AEATFow2+iaI3r2bQcA/EE1\npeLGAAAAaG5t1hGDh+uTnxyvhStX6KkXn1MLFE+YAKFQe3Tc57PSbohTXXzJN1RaVuaVSi7IGf6f\nAAAQgSZKSETNqKlU7rAFZPcj3wHnWXGuUWYn+uoowrAleW6AAQAtzoMqzb5IJBVnDJYL4ABOHg4V\nzhR7Fk65O2DBEREldRFngyiti4rnt8oFQWx25lIK+O0inbATChkALgWA50FA7nvXXWdluDgAky++\nCAbAH5ROJtWxtpMAANBPYqwRqYapx3c4YT/6iu/AQcQpIp0n31l14IDrW2+db9/TiCa7sm4IvcFO\ncECBo5oXAgCwTRGcpCoD98dedaUPDzYAgKNKOUeenwg4zhf/dgfgTD4A0KNHD1FfHvYGNjTUcVIV\nuBZn3ukW7A0AoC8BRHGusVULKe+ub12/ExUnBYB/49ji4OLouvfj+sM5/PmpCqQVOP0KPue7Hfji\n2Bvu+9zfHbsDO5sxC4iVzwKA8QLwwXmMc9g1+wMAuDfsEdrtxgcl/1xpR/5O1J1+dWPBjXl+M4bo\nAwACy1dPp/WHP/zBovYwKQCMaCOgAk48oDOMgQsuvECPP/FEG1hl1PVczuyQoUNtPA/sP8D+zrNy\nPwAq3jmAYX45wn9YMP4JHxBgWbFyuYGMpBe59dQDefxKJ1Km7vSJw8ZoSN8Bmjdvjt5ZslCpHAAw\nesgwVZeUW4oAcx6a/0uzpmrumpUq6tRFF11/vcaedoqCHTxNIMoFt7JHwZILekA1plpLq1dKPFIU\nVMjv7Y2EsmIx6hIxjijp5wk+oxPg2TeU/oPF7O1xza0JpYj6ZzIqikQtXZL9EOZRa0uLAr6AVQb7\n/wIA2NdY+DBm1QcbWx8MgmDBYvKQ33LbbbeboE9pSanlk3ol7aDWekJRVAY497yv2ObEpBg9+nAr\nZzJ//jzTBICKuu9jd+dmT+cWIoxu82DyeYtj1koZBQNe2REiD0fmDMmKikqjo1PLFHQZRJ4DpJFF\nqkt9nVGxcXsCmVxCpnkmULQdALA/03ffZoyrNb906TLbtN56a7LuufteowaCrnlK7149+gwicH5H\nzfeM51Q8rXQ8oCUL12jn9hbV1FRqwJBaEyk0vYKYdOfNv9ULz72tI444THfee5ECpCa5kBXGf1aa\nP7VR37joKo0aPVR33PVNFVfmIoAFAzCXmpNzuPiCpDZu2KVbbvitOnao0yVXnKmOtQiMQfGXJk2Y\nY2IplIH7y1NPaeXKdUYBq62Nmpc3dcpc/fY3D2vI4FHGxujbv17nf/1shcMe0JFK+8w5eO65F3Th\nBReaAWGLdDEUfvplLwBA7mNzPJ0DmZZeemmSvv7183X1d76lK674RoG4Yv4I+2fPvHYAYH/zFSec\nsWD0/9xzeRv3gZjIzljdfS4VqkjYhplHVXbVK7yr3BrRfg/3X7QL4z0EkGRujt9K+QFsIbqJcJlr\nAe+CKWl6NFSiZIzmpbMYG8HowB5GxeK/Wx31vPrPOLaIzTDfcRJts/V8bC8nnnlrcyaVM3KDpgGA\nk+Ts97wS7W1Pue8R0N4P3uxDVT2pWTNn6aW//13P/vUZLXznHVOPLw7Dq8iqyB9QOOBXt65d1NCr\np8YfdZRGjRyprnV1noObSWrr9m2aP2+BJkx8U9NmzdG6jZu1vSluDiP8Fe6EY9i5ppOG9B2svr36\nakD/wepYXafiUIU5/Ey2xl0tCoejymb8ovgHDKbmeKNaUzFVVpPbX6eGwX0ULvV7teqsqmRWiQzR\nY0TmrGChZ7DlhAtzFKi2Rc6c/xwQYAZGG1Tz0eYLuilEaTCcXnjxRW3f5lE/GQ/sMThSTv3cvSMr\nL5snIElrVq9Zp3vu+aEe/fOftRP6aLLVRACLevRSz+Ejddl1N2jA8P6Kp72CDICfjFWeCUGjGNH4\nUED+NMZGSqEiLw2A7gjEk+oS9KsuElBNzthJi+iFB4V4opyeQZQPELeL/UkkQq2KpbSuuUlx+VRT\nWWHr5OZdSTU27VI0GFKnDmUWoSdNAaEjP8aRP2BUSpgrHJTjxPAEjGPsoxHqBnE8nrZ3mfPbjAEC\n1TJcFPDAA+6RgbaZ8iL0YWn75kY9cNutmvTrB1ScThsAMKJ3X/vexSuXWxUAAICRvfrp8AFDVR2M\nKOQPakcrGgDzNHPle2qOt2jckJE6bvzRas1k9Nizf9X81cuV9gcMiAdc4VkQfjKgpVNH3XjTTWYb\nlCG0kLfStK1H+1sc/8l/x6HF5gFsdM4W4wE2IQ4bzoL7HOcIpqOjqtM0HCgcUGjUUKWZWzj3UI0Z\n1/kOeH4E3D0WkUgcE/LjHb2fSDtic0S4AcXc57SDQAu/nf3D92F/IQLHD44qbSaquyc23YknnaTn\ncloHt91+u67+ztU5HSPmy+4pADUdOmjihAnGAKDt0PQRF7TSvegm5RZavh8AAIYPYArPhGYCfwfc\nIwq7p1Jw+Y4j9iKpIwj8uXWBfxMlz08B4BrAGhgARNN5V1RhQPCNfjnYAMDcuXONMg7YjRPP9ziB\nRfoAmy4fAEC8kHeHngnvFDCIfrGxnwd88EykAJCSUZgCQB8CaJC2mj9+vL0waf1Kn/M3GAboRfA3\nUkdgfmBD4aji4PPDmKB/eW84vk4fgAg9VHucSdc2lN0Zv5xL4MlR8qHBu/KV9DP+AeAIgFe+jY4G\nAGJ9fAeMB6j+e3r3jgHAPTnXMQocQIF9zmdujOH4s3eQ3uAO965xzmFckOvPQd8gBojzD1DhUgBg\nVCDC6FgxXPfJ8eNNnwbn3xdA/BXnVho8dIgJew4aMLBtTDH3aS/Xw+jIH7//5GXKC7atW6tvf/tb\nBkyYnUP1lVzpZ+w5bKGOkQodM/Yo1dd01MJF72re0oVKJuIaBANg8DBVRIpVighgIKjNTY2WAjBl\n+Xsqre2u7959j0afcrJaQgHTRHPeG/soew1VoC3lLIWGgM+L5lNuEFq/pDhpMVnKV/tFNVsYaVHT\nE8r5Kth8qbSyfsrbpiyHDRAA+5J7REM+xeIZCxR6KXoEOv6XMwD+2QPjwO6/N0e73aijm6EPsZhy\nMFluuP4GK63nDiZjGupHKGgLClGYNye/qeEjRui8885Vjx49DSH+yX33ae68OZbbUVJa2lbL01so\nvGigI6B6YhDt7YOiyeLYEot51Kfcl7PYsQDzGRswv8mhP+XkU2zBYlKy4LKAsAA6mpYL3LAIgCJC\nB+IcFr577rlbQbOWPCe0rbZXXkx1NxX9PXb2gQEAjY1Nljbx6KOPWCoAFB426n3KPRANaklrx4a4\nXv7LLE2ZNEv9BvTSxVecJF85XE+pdZV04/d+o78984ZO+9LJuu3e0z2parqZV0fyalqa+vIOfeNr\nV+rYT43WPfdfan+j1JdjHlDNEYeJiJP5BmacmymqluaMbrnxlyovr9AV3zpHpWVeLnQ2E9Dzz72t\nxx57UkcfO0YzZky3KOxtt96q2jrUvKUFC5br7rt/pM2bdmrb1m0686wv6JtXfTVXHzshv0lrSS3N\nrXrsvx/T3579m846+yx7v8ccc7RqaqrbDUcvvdZs8GXvb9Y9d9+j5qZm9e7TXRdc+BV1ra/Txg1b\ndNxxx6uhT389/sSfLEcJr7ot3YMotAmT7U6NPLB59EHP2j/Ate877s/p2v/93Qza853yx+7+77Un\nKGz/K8vuT1h4/r6fcC/gT9st//FuB/IU+S0q/H6jvPmoDb9FE19/Xc8//VdNfestrV67zkrTRHBy\nMimVBAOKBHzqVd9Fow4ZodGHj9ahh4xUh+oqj/lAbu7KtXpr2hxNmDJV0+bO0YqN2zx19ty0DPl8\nqiwpV+eOdRo8cKj69h6oAX0GqzhSoWi0zPLVLU1HQRPQYbNPJynLFzBBnqTiKqoOqFtDF/Xp10cl\ncNYZ1o5egfNckOKz5/G2v17b3zjc9yh2OawYLj+5/yemeeKMfFdbHECUyEbhaDEQwDvZ4u1Tpk7X\n1Vd/V1MmTzarxJ8FlvIp0ruPOg8cpEu/d72GHzHCANItO9PaQfWKcEjdOhSr2C+tS2W1ZedOq8xQ\nV1WtumJqYiAYmFQomVLv4qhqMHasG10RP4/6aNQVh9bmNZR3Sr7/VoQEk9L65pga4foHgqpC1Y/1\nLZ42tkY4GFA0ErZnApjIJFq90rq+sAGARYGsios8kKypJaUYUf5QWBUlHjLQFMua4RcJBRQtAkTz\nRAQZE9FowABPADpugEYA6zug2fJlq/SrH9yl2Y8+pHBrqz4xcIgO6dtfgXRWy9au0eRF800DYHS/\nQTpi0FBVWF5BVs2ptKYufEezjAHQosMHDdNnPjFe1eVleu3tt/X8rOna0LzLdkxKLKJrgbPkIr5Q\n5XE0iTyz7+Jk8tsVt/1oI+uDrsft59M+xiXOu6OPO+cDxwM9EyKq7NEuqsg1MCBJh3SK/r179zbH\nBHArX6QMdgDOEBRxp2uT78wRlICViM1FbjzgAge2DNRn8sxddNm1C0cKarMrP+aiurQLeww6N84x\n3wOogCPGMXX6NL300suaN2+uXn/tdW3dssUWoT4NDTps1CgdcsihOvFzn1Pffv0MyHj44YcsvaS6\npkavvfqqKfLzHbAlaBd90J6C6amOw8rE8WU+k+JI1Jv+BUghZYRUCSLnsBqw41wKAc+Gs4rdCGDh\nNBawFWk/dlrh4VIAYHnSB4ABTvPgowIAzt3gPqS68k5ph2O14tTCVAA0Iq8fajiRaXegHs97xXnH\naWZsOJG+/OfAxkVMG4YHYwRBNydKx/0BAAhoFT6PA6roP/qO9gGeuvWUdAXGEIwTGCk4/ABDAAEc\nTo+Cc1hz77zzDjU27rK/IeZ26WWXGhDDXgeNm9xv8rYZV4xLVw3A09eauFupRu5BfwB+0W6+A60A\nUjncczhGAe+boJx7ZuYR9jl57TwDwAH/ZmzYupFO2ziELcNaQv/BvIFhQ7tw5i046fdb5QjAOMYo\nbBgYwfyN7+N9MEbdnAHAIf3A1itU7PF2JfXr309P/+VpY/q47+EcWCfoUjBPCCQWMjQ+/IrkXelA\nBccczhGBtW7dRt155+164IGf5RI7vfOxCRD8TacBqv0qD0R14tGf0oBuPTR12hTNWbbIUimH9uyj\ncSNHWRlAwG/K7W1tadIrs6dr2rLFinTqqstvuUVHnHaafDUl2tmasPTg0pDnwEPXJ+UQ+6iiuMjM\nDD5rBQjMFVyy9K9AwHSFSHGj7eVUq0FcOJNRHC0QSykIGBvOq+AGoJC2VDQ+S7Kf8y4yWWPG/RsA\n+Kgj6gCuZxFlYEPBIYceFVPoYUxct2m5hdENUCbU5s1bzCkMh0JmeGCszp0313LFcNKvu/Y6m0gs\nUqDZXrQubHmYqE2b8++iUbl2UuJo7NhxWrVypVasXmWfOmSXTdUJ8/D9NdU1GjJ4sI4cd6TV89y+\nbZuV7Eil06qqrDRdAJ+xFTwDl4UZJxxmAtffeust5oyDdOZc3oLechHBfXXigURppfffX66vnnee\nJk2eZAsk1RUsYrIvA93Z5U3S737wmh757VMaMLC3PnfqZxTzN1tkdOvqJk1/6x0tWbJaQ0cM0mdO\nHKOlK+Zq7YblOu2sz+oTp4wwDuzEZ9fpym9crZNOHq9b7r5QKpfWrt9u4nksNrF4s0Wg+g/orU6d\nKm3dSSY9mm6sWbrphvvNib7x5ktVXuHUZUN66skJ+uUvf6PRhw/T1m2btW1Lo0497RQdetgQ9elT\nr3fmL9U9d/9Ya9ds1LZt23XiSZ/Rf17zTUWKyZPGyG4X+KGXN27YZFEJxh4CRZEoNev9tqiwWCxf\nvlZvTp6q3zz4O6sFPurQQ3XCCcfqvPPPVK9e3dXSnNCR48aboNzUqW9r85b1qqgoV1lZueVK8d53\n7NhuKSP22v+/Pg7e+P3f0k17cmUL0yjaw925p2ITzWUKrFi5Qq+//pqeeeZpzZg2VY2bNhs1LYxT\nGA4pGgyqprxcDd266YjDDtURh41U34beqqqusnSMzVu2aPGSJZozd47efHu6Zs9bou2xOGnlBjbG\nUeD1BdS5pqN6dK7XsH6DNKBhoLrX91JZSbUCPiL8MGy8XG+LTuQ2RUSKHC2+srJKPRrq1alXjcLl\nAQ/0yzn+WR+bsqf+QOQ6X3jtX/0e3X6BMUz+LAYY1Fi3JmPIjR8/3gxH8mF3P7zxa1UucgAAGgD3\n3Xe/nn3uOW3dvFlZcgbZfGo76/Pnf01nf+NShSur1BxPqKg4oiRip9m0iq0qgrQrlVA8nVIynlRV\nJKKSVFyVRSHTbCj1BVRfFGqTSnASmZZMkyuramPHCAMeCwBHnXe7OSut2RVTUyCoFPWYMXqg5Sfi\nyqZTihZHVVpCDXny95O2dgMElEUok5gV+kk4XaVFAQUxjIioJDLmeAC4ownAdpmzTzGMrBwggK3h\nPLnUgZQv52CTTgA6YOllfm3ctEm///GPNPFXv1BxPAcANPRTJp4wAGDSwvkWuR/df4jGDRmmimBA\nvkxWTamMpr27oA0AGDdspD4LABCJaMX69Xrlnfl6Y84MZaHOk7YQColqArxfDDqe6cijjrKAwuc+\n91mrd0tkx2XpfBwAgBuTOJLYKo4iTF/hrMMUxNlwhwUicjYEzhTlxpy6u1N2h70C5X7Lli1taQQ4\nIDgzOIo4wajp43zgFCHyhzPFgY3l7o8zCI0bgIDoJXaUcwxwOHB2ACYc/di1DQeJKCjODAcBEHKx\nX371VV1/w/Xm0Fk01VESLU/Le0L2RKLOMO+IIOPIcy5RXoANV52Hz2AWOIV4B0Dwm3lMJJz+AUDB\nAW1zZqghHomYY0ZUFkaBcwoBFXDiqGTgaOtch1YBfcR5hQfiiwA3K1eubBN4RpQNpsHBOIj645CT\nuoEjaXnJBWkhvAPeD1F39AsAYOhH1jLeH7R3wCP+hs1Jaq2LlGPPOgAAZx8GAO13AtqMLwcAFD6P\nY5E4hxo73ZUWdG10jJF99QXvAUCGtnprhCxlFgZv3779LEhicziXb8p7gm3gUml5BvoIoCz/cAAA\nn8FWwTFn7LuxwOdUF8MnwNdw495VLIDxAqNh9OjRxp6ACeAYD4AXfC9tZ2zSJsAzHHQHONIvAEKu\nnB/zGLYBfwcUywdr+Iwovyub6J6D/mPMM/5oO0ATFTccw4DzaD9pCgcbAHBtYH/A9ubYvn2X/vM/\nv6vf/e635kM53Rk2ItZRxIARbM20JlUWLNJZnz9VA7p211tT3tKkedNtPQYAGDN8pDpVVCtB2dZM\nVo3JuAEAs9euUHGnrvrGjTfp8NNOU6ayXK3ZpIrCQVVC24fKb9lqMHZTKi0Km9XOZ7sQ1A34bY8x\nsV3SSDgPyyMrVcEkoBJOKmUAgIH2nGti1z6FAx63jozleCJpqQRFRWFjlhIr/jcAcDBWtP3cA6QT\nNJBFiAHPBshm5VT93QbEv5moe0JZoaiwpzBAWESYGGeecaYtdEy6K668wuhB+I63AAAgAElEQVTu\np5x6ispKywzxZZHJWOmzPAZAMKhzv/IVbdi4US+88LyVkHAHlDwQO1Bw/nvXrkY1Ne6yzbCurrMt\nxkwGjvcWLfTEV/r0yuUpZczxP/74z1hlAGgq5JhAMQNR9ICOAgW8fWRwt3fpgQEAU6ZMy5URWqtH\nHnlUZ535JcuPwen2DozcAvfFRZxapF/d9aIe/tWTKi4qVllluTZs32jGYIeyzhbKR4CjuAQHN6ON\nm1bJ52/RqWd/St/8/vkqrpb+/tg83XzDbfqPc7+or3/jS3pl0tt69vnnNG0atWq3qbW1WZ1qq/W9\na6/WF0//fC5y5CF+zz3zmv7r0Zc1cOAAXfbNs1RZFTLlfZ8vqJdfmqb77vupjj52nJqbdmnZslU6\n86wzNHzEAHXq1EEbN2zTc397SRvWb9ai9xZpxYqlOuPM03TJZeeb+RzyewwAd0ydOs2cBKInw4YP\nM2opNUtZRJLJjNas3qBnnnlOf3rkz7Yw3333nbr6Oxd7PchCsytt75PyYBgKEyZM0osvvKqJE6aq\ncWeLbQSVVaUaM/YQ3X7HTerWveP+cjz+BTPwn/UV//cAgPye9GbT7nUYvL97c7YtJSKd1azZM/XK\nKy/r+ef/ptmzZiqRiBtyXUyaQzqr0iK/6ms76dBhwzX6kFE6ZMSh6tmrh0JFXo77mjVr9c6ChZo8\ndbrenjFTC5atM4q/+zac/vLiclVXdFCP+p4aNniY+vbqpy41XVVVVmNzOZWA4knE1mfsGujv2WxS\n8VRMiUxc2QBl/KrUrVe9ejT0UIgkdcLXeV5UJuCtIw70YPv+OAEA9z4wXHH8TQPg1VeNFeYZmAFz\nXDAuiXjtfrSPX49yD3Cc1uNPPKU7f/ADLV60SJk47rdPqumoMy6/Uid+9TylwhETBuxaV2lifs3Q\nJ7c2K5HMqCIaUW2Vl8LU0tiiYDqh+qoK1fh9Qs0Cw8ZIFLml2PP781ImEOMiNcRPclRQKMg0SloZ\nk1bv3KFgcbGKS8KWNkBK5I7mVkuDqSqLqjiC0B8CSEl7tyVRAAAP2AQsALgOhdFF8PZS0gQAnjha\nErDvUqYVACCAbw87AFCgOBJWBNG/eNL0BsJWTcRTbkaAyUqMZjJ65Gf36/Fbb1ZJGwOgn0WDlqxe\npYnvzlUsHtdhfQdq3LARqoa6iVJ0IrUbAHDk8JE6ftxR6lRappbWuBatW6/n35yod9evUsZyfjzh\nKEtRIxoEE0LSEWPHmIM5+vDDTRCKfHMvIeVff7iIPoAUUXbAKXfwb6LyOPaO/lzYQpx07Bd3wGyB\nmkwJQCji+QJ47r+xm9w9ico6x8c5EJyHg4/TTh45jj4Oi3OycaC5NxHxfGeKNjjHjyg40WCcUSLv\npF/c95P79P6SpUZvxm7Ld2T5N8xHhNAYI9htL7744m4AAA4sDpfNgmzW5jDP78TWnFPrctpx7mkj\neh/ub4WOqcsn555WQzzXLvqHSDV2HKwMouOFGgZcgxPqyri5SDh0eqLHH5UBwPeT6026A4djX/Df\nLi/eRa35LL/2PM8J0wH7BfsUkIB+wkmnzfmpHA4AOOGEE8yexRF34xIglHWSvtzX8/A+nOp+4Rh1\n7S68nmcAVCSCjWMMwMNBOt+137vWwKJwiFXQA71cqiBjFoYGz8Zz4Iznl1903097GMPOIUdvCwfb\nvSf6CBue+eNYIIXjEsYFoAmMXsYaQIxLeWBsu4PP3PM50UOALEAsIv8AN4BvDgCAuUO/5vcJ6Q+A\nDdzXgWn0HXsRbeRvgGoER53uAu8JlgvAy8E+8lnWBACg/TM+nrXx6LETvT3JsdH88sNooJxeOq2O\nkTJdeM5X1L26k16f8Lpem/m2+WRDezZYCkBdZbVSgFWs7amE3pg/WzOXL1Np13p98/Y7NPJzn1NL\nOKR0CNG+oIqRRktmLD2M74+GvbQQbBQcffoSoDfLfoj+Ags/gF8ooJKAZ3kkSAcAEIc9HvaZTQ8w\nzzNEgkHbq9CCsrQTKocFg5bWC17wbwDgYI+wgvsx6HGUyFcDtUVIg0nMJHAAAOeARJMDAzKWj1i7\nybR8+UqbpPX1XdpK8bGocCx+b6kN4u49uuvGG27Un//rz7ZRsfGQc5MPANR2rtV1111vzjkb2Oq1\nXt1ddzARWWCgw4FqM2DISYGNQES9piMOHUqVcYt6NLc0qVfvXgoFEQdLGhWOzZoFAweccoFselVV\nNTmaeD4IcPAcqDfemKhTT/2Cdu5o1AsvvKjjP3O0Ys1pRZlhbQfGe0H2NhMqHdATv52sX/z4IbW2\nJNS9R0/V96pX92595E9GNfG1N7V46Xu2ODT07a0ePbuoqkORjvrUSI05vr8CYWnN+4166HePmFp/\nPBXX1sZtGveJIw0cSSbJEWtRSWlEI0cOUW3nanMieLdQsC675LvauDapc845W6ee/klV1oTNcGBz\nmD9vmR56+BENGz5IGzas147tjbrmmv9UeXnQo6JmpbfenK0VK1Zr2tSpmjDxdR3/mWN06+03KhTy\nK+gLWpQIVJBF+Prrb9CAAf1t02CTNKosm1HGZ0rY5D8jULdt63YDAKBdd+laJVIYAKuffvJVnXHG\nl3TOl0/Xzx74qXY1tmjtms2aOGGaXn9tkm3MAAC9+3TRF08/Sd16dPonz7CP8/YHb/x+nE/xwb+7\nHUjbuwyjtzlNev0NvfbyK3r++ee0ZMl7am6JGwxYGvGbEwc8N7Cht8aOGqlPjDlCQwcMVE11R2UV\n1PbGRq1ct1IzZs3UtGnTNWPWHG3Z2eIh41mpKAB1MaMOFTWq71SvgX0GaiA5/n0GqLzcK8sYSIct\nT51ICxQ7nFy0DEzZP5NQRjFlAyl16tpR3ft0U7d+XT2n35GWvAT1tsMThXSCkp6o48cJADijCoOR\niArsJwwyM2FywmVEwYioYGztbrC2j18v4u7T8hWr9LOf/0J/ePiP2kl0lDwJog11XTXi2E/plK99\nTR179lJpWYXqaiIWzd+ZTGvNthZ7Z2WhkGqrgqahkG6JKaKsupcXC9kUujWX6S+rUsrak8N3nUAn\nTngGY4eyRwoJwb/taWj/KW1pblakOKriYg8AwFZlTQLcIcc0AOUpV7rTlfGDXRII+BS2sqkefX9X\nc8orIRkNKkpmGp+1Uis9o5IitAJ83hjjvGxKZcURRRBRypUOREeArwJYsIw7jLBUWr/70Q/19G23\nKGIMgME6pKG/SiNRLV61wgxBAIXhPRt05LARqiEKA7gBALBwgWYt91IAjhgyTMcdPkbdajooHotr\nVyKpaQvf0fNTJmtLPOZpI5CLbRVrfFbWCeCffqMs4Le/9S2dcMJnVVNV1aZ78sHn98G5gkAHYn2U\n0XMOC1FN7APGYX60NX9cQukGsHLjF6cC5wIgARo+jji2FDZUPnvAol65Mc8T8DeixDi+2DUACARe\nOOfUU081J5RzAA+glD///PMWRXfggXOcHIgApRyaMwEdDrR4Xnzp7/r7Cy/ulgoE49FYRQyY3EEQ\nBUCEOZrPAECUj0i3A0NgWJImCujhHHvaQQk67EhSPsj/51moHOCO/Ofe09tzIofQxnHgoH8XVjBw\n1+GQYR+4yDz9R9tdmslHGR2wCngXjuGRv04VPgvOOxFrxgNtwZYCPMA+JfrtgBocScAcBzTRF9g2\nPAegBTY3DqXrIwSqGVMwSvYHAMDGYCzm60u48bG3fqCdBPweeugPFkEHbKysKjetguOOPa6ttLLb\nWNxz0KYrr7yyDZRCpBDnPP8A/KEKhjsYB7TRPRv7gANN3H3zf3MdewFgA34GaQawdJ1IZj6IYutz\nLkDI/fFNAFJggnAe7wT9BvYbbEveK0FOnt/Nb66HxcBcyx+jCF8SHGTeIVAI2JHfr1QY+KAVqz7I\nuISVO2nyZH37O1drplXNQS8saAxAAwDYQGC+hSPq06dBG1etUtOOrepZ0UGXnPc1VRWVmNbO5Pkz\n7DUO6d5Hh/QfpC7VNQrBuw2HtSPeqhdnTtFbSxaotLabrvvRjzX6lFOEOk86tx9RFSdNWdkM1cik\n8iJEbdOKp7LmA9i+xf4C4J3IWFpANOKl1QEykPMPTY09k83VsiNhF+f0C4y5xjkmchtUEIHcXClB\nS9v9twbABxk2+z+XCZgfxQfZI68GpA5hC5BKFlQQMCj30F6ofwlSCP2ehYpNAMeQScLmQ377V8/7\nqnr26qkf/vAeawQGDFGxisoKq+u4avUqU/Dk2vO/er4mTppghgEAAP+DFsgEGzhgoLWhoW+Dqcnf\n9YO72jXOs1mbvLQVZLANCc/Z++vWrDXHnk2UIxAOauNGT7jDU/338r+vvNITKoFqglN5y6236Lpr\nr/VoKbvJ4B88B4pI++lfJMVirX56/wP6ylfOM7Vmsy/zcu/b+DBWUJMwDqBAWDMnLdKf/vi4RXJO\nOvUkDR45SJ1rInr3raR+cMt9mj5rukYcNkhfv+QsDRzWS9GygCq70KcYoOTT+DTlzXd0/09+aTPz\nu9deqcHD+nlK04XEhzxF9qVLlunCC76pdLzCqGpfPPMTilC72mPlasOGRs2YMUuVVWVav36dduxo\n1HnnnWNCVBa1T0g///mvzThgwaD84dlnn6G6zhVKpDKmDOrynJ588i+2EF955RUmokOky0ToLFME\ntJFz270dU4/ICZTgRM2evUinnPxF05l44qlHdcSYQ6ydpmmAinzu0rbnzUX3vFnz8USj9j9jP8oZ\nB2/8fpRW/CuvbRsnZhxQzSCnN+IGrALatm2jZkyfoReee94AgOVLlxqIGKG0aCyuSNinjjXVaujd\nW2MPP1yjDx2pQX37qKqi3AQA165br/eWrtTb02fp7RnTtWjpEu1siVt+v0u/rywuU01VR3Xp2kMN\nPftpWP+h6lJbr8ryGqWzfqVNtVfKxNhAg0qjpp5NKa20UbHZVVNKqGuvGvXs202du3f2dlrn8JtY\nX3vugmM7eBnz7ufjBwDcuydnGgcF497lkTrKLwYbhiU0y90jfv8IAEydPkPf+e41envqNC+5PRFX\nqLRMgbou6nXIKJ1xwYXq1m+gAtGoKiqj8qUzSlsefcTWoxgR8YBPxdmUaqNFqg96+f67SSfkCWZ6\nNP+sktm00pYn6jHFyPnfooxW7EyqJYNCsk8lpWGLdBDhbyKSWVaqDuUBhP61oyWjplir0Sqry8MK\n50QAtzS1Wi5keVFIZVG/iRdaib9MWkWUUsrlVxoQisBfyKPrUvHPxJPCniAvARVAI2MIgBnn1kbb\nB/3Sps1N+sWdd2jCz36iaDKpY4cN16gBA40B8N6K5XrrvQVqao1pZJ9+Gjt0uKowxnx+NcaTmvZu\nuwbAmKHD9emxR6pTSZkycQArckmb9fq8GXpt5nQrf5iGBUDE2U9WKpoFOcG4TFa1dZ0txxgHZMSw\n/Gov/5pVIj96jpNOpBmnAOo6jgCOPE62cyz2RPElYIDNhKK7s0eI3nPgaLDXcU+i5a7OuXMknUNH\nOxBQI0KJLYP+hRv7fCfRfuwc5gg58ziQ5J3v6XDPRPoB7cd5xdaiPBxiZvfce69mzpipbdu3GSBj\n9lkurSEUDJl2A44Sjhf0dxxS7oGDi53EvVxEmev4G++PVAb+DXjA8xMdxmak73C6sOMAWRzAUuiY\n5jtcRMuhz5M+wG/n3O2p/+l3bBGqI3APGES0GdbDRz3obxw++mLZsmV7BHD4DkBL1jKeDceS90xb\ncMaJ+OcfpHMANDFuGGecB8uD/uFZAUwACWAfYB8DgvAeoejvCwDgO8hHB1SFTs97wLZ3DAsnzsi/\nzbkKBi2lAvsYZ5l8ctIvGnfttDRaxmxhygXC1LA9eXewQXDw8Qm4F30E5T//wDHm+R1Lgn/jwLv3\nyH1Y6wEJnJCkS/Fl3FAlAwCNvcCNa5x/2EMwxxzd380nfnM9VRfuvvtu05jIP0hZAaRgj0FwkTFd\neAByASjB1uDAV6CNsBBoE++Y+QcYgX+B38O7A7D+qCkA+WCee16AC9p09Xe+o3cXvmttMkZaFhA8\nI380qkwybo71MaedqX69++j5x/9La5YuUo+KaksB6NetQU8/+4zeeneWGb8j+/TXqAFDVBEpsULQ\npdFibY/H9LepkzVtxWKV1tbruz+4R0d+8XQ1UUIZxx9wBcFJv0zvKESZJ9hoSU98kACe+S9oBFi6\noodLIFjLpYlkyjsv7FWUYv9tJbWhyLsPjAGrDpVMKxIIKhryyuci1OtLspeF/g0AfNQFrfD6QtSN\ngUbdUBYeFh0WM6Lz5JDdcMMNNsFZ6DhA7kDzoPSDYP7grh8oFC6yPBqcesrcsaGaqvvzz9l5oMff\nufo7Ruviu8mbAUlsat7lRQYsp6VdFvBzJ5xgf+/evYcJySAIN2PmzLbHYMIhtAIowcbY1BgzqmNV\nVbn5cLNmzjWkFbCiuKzIoiCIgASCQdvYGbRz575jGy51MgEEunbtYosEAMjeDNC9v4d9pwA45HzR\noqX6/k23atrUGerff5DOOvMcEyusKPdry5YmU9kkmlNSFlGEWk+EqAAsWpPyB6LasblRK1ausWcc\nOHSgiqD/tkrvvr1D37/uLi1+f7E+/4Xjdd2tF6kILisaTlBVM+QSeWkG06e8o7vu/JG+dMbpOuPs\nE3JieE6OaQ8ogOjPObrphru0dZN08imf1yWXn6GyinbWAhN9+w70A8I2hmwR7+Xl7WGY0v9z5szX\nrFkzDdGn36uqSttAHVelbcniZbr8isuNmjZu7FjVdu5oBq1FUfKU5PMddfJyTUKBettLVurSSy7X\n5k3bdfc9P9CnPj3Wy5fKMRl8pm6Y7+QXOscHlspxsOfjP/d+/9cAgHbH1+tXjz/i3vsaIp1vvG4/\nGBXr16y1mrh+DyUz2lpFaYmGDx5sZfxGDh+u/n37GiWttTVmuf0z58zRjFlztWjpMi1+f5kS6Gfg\nh+NEBkKqKq9WdVmlBvbpr369+6tXz/7qWF2rDuU1Xvm2ZNaocF5xnIB8SZSHUdBtVTIbV9qfUiga\nVPee0Py7qaq+1Iv258Q5PdnfPJp/AXqXz31wpR8/bnDL5W5isFNh5qmnnmqjXLq0LlJ+EILa/dgd\nACBZbNXqtbr/Zw/o8Sef1PrVawwA8BVFFKqv16fPOFv/ccllCpZVaMO2bQYAl0SLVBaNqhxhvRSU\n/IR86YRqi4LqXRqxkn9F+ZlXDiTM7UmuBUQ4cJ0oKcjKTIx1YyKp5dtjSvmC6lAaVU2xz9INdu5K\nqiUes/KP1aUhxVPSzljGIuzRoqCKi4Lm2EP7RyyQNa44RCUAn1ri6OR4FEjWttaWVtsn0Z+AIYDj\nH2vlM1LYwhaB4T4tyZQx9qKhgAEHpEDw3mFXwYzbvqNRD95zt17/6X2KJhI6Zugwjeo/UOl4QvPe\nW6S3F79rQMeo/oM0dugwVQVDxgDYZSkAAACL1NwaUz4AgP4CLn46GNCyrRv17MQ3NHfVSiX8PmX8\nAUsJMK2CHJ3YGbs8L1RpHNpxY8eporz8IxvSB7qO5hvcXIPDTlk5nBH2f3KL9+d0cS6aSex32CA4\n8cZWyysjSGAFJxgHC0eZfGXnFOH4k+MOxd3RvJ0wmaMZ8xvHEMcOh8QDxPcuXOvsOlhxOGg4mIMG\nD1YoHDb2BW3NTz0wXY1s1mp6E73H6cRG4yAKjtODQ42juCcnB8eTqDu2FvnuPIur4uH6mL4FLCB4\nxP2INvPjhP4AT3A4hw0bZs4uNif9ua/D2VPoNnBvzudaF/Q50HGwr/NoP8AFtiz9j/AfbeW7rV8H\nDbL3x29YsQCbOKa8H4AU/uYO917oU4TteA84z4wBHEjWv3wAB+E+HHnWQjcW9tVW+pJ78/35qRXO\n6XfOtQMGGKeALYzF999fqqVLl9g7GTxksI495picHlb+N3osFuYEgBlrNyUq8QVgDeeX5eMqwA6A\nDpxp3i1OtxPBdHdlXAAmQPF31HvaxBhkDjLeaaebh/QD/UYJRkA1xhLPzfkIYsL8gC3B3C08AJvp\nH/wB+jy/ooRno3rPxlxFp4w1FFCG95zPdAYMYm7xjmEFAALsLUXog45B9kc3txlv+Fs//8XP1RJr\n8bZuKtQYI426tCGbzyoK6YRzvqIzzjxPC+fM04M/vkuNa1epa1mZzv3iGerdpbeeeuavmjh/qgG5\no/oOMgHXymipgeJUAdjSskuvzZ+tGcuWqrhzna685TYdfebZamVzCnmxtkQ8ZftVlP8GTM/6LXpP\nShGxFUsHyMDQ9ZuODkuUlbNNZy2lLcgGZVkBaY/pq6BdmxIVbqgWwz7jFxq3AAnbk2kvFS7jU0nk\n3wDABx1LB3S+Q38d0gyazSQgB4xNwqHgLGSgbixKvGgmO4s1zjJIJ0DBrFmzdeGFF9nkAekDwcXx\nh4qzYf0GYxKAiLMY/Ndj/2VgwIaNG2xAeHm4noHA+IYFQOUBWAbBIqixaT34mwctFYAFwG0sTExQ\n2i9+4UuaN+ddvf7aBFOn7dOnm3bujBmVCWodKG1tXYV27Ijp5VdescWWZwIF/+ZVV+nhP/5BgUBW\n6XTSgA6oXLvX9jw4DpQFHyXt2B6zHPmrv32NmZfXXnOdKisqNGnyRDXt2qk+DT00/uhxquvSyVPj\nt3SANg6q58/k/HUTyJC0aO5mPf3UC4rFW3Tsp47UuPFDlPV7qREgbDgvhsbFs1q4cJHee2+BTjzx\nBBWXRDwWkX3R3g82+pdenKi/Pz/d+u/hR3+syupw3nWeyIc7cOhd3XgmNGIe0IJQwKbfI5GoRfIp\nI2JHllqhGYv6s6gzhhgrxcVFtpDklyTbrZU49ma4e5za9RvW6b1Fi9W370B16945JyjkSiru6Rn/\nDQB4/fm/C/hwvto/8jXynf/2/07GY5o7Z47eeutNc/yJhCECaelDDJ2EDHmu69RBh40cqeM+8Qkd\ncdgodaisUijg1/p167Vo8RLNXPCu5i5arFkLFmjDlu3mBMazshy5kD+g6ooade/S06L9/XoNUJ+e\nfVVRUqVIsFiBbFCZVEaJ1rg3N3I50CZsiZJuolX+sFRcGVHvAT3UMLCX/Oy4XiqmsinyMdPys5lm\nvNJ4rjyeJ9HTfnjSf/nHx8tscVF+DD0or4gz4RA4NWoMP6KHUEXJ29z9+EcAoKklpv967HHd+6Mf\n6f3FSwwAoHZeuL6bTjjnP/TlK65QoLxCm7ZuM0G64pKoaYhEsjI6faIloTJ/Vt1KitSJ6opgK7lB\nlQuyWBNcKoCDkHD+yT7dioPPTyptAnmtWb85u4axEu1AdR+xKhPsowQjACbGkYztxJoICERqCOeU\nwN3PANRmjC7Jmgc9M4IBlqUSQNzE/nDqQ0GfqKxr5f3IAiFf0nIqvRJMGFjFobDtky3xVovcBVhr\nA0FjJDzy05/qmbvuUHGiVeMHDdHIvv2UbGnVO0sWa+qyRTY2R/UbaABAZTCEqZYHAFAGsFVjBg/z\nGABQnCmdh/BfuEgtmYyenzxJz82cqtaATyn6JbfAe4UTvHFotkeu1FanzrU668yzdPGFF2rAwIFt\npbgOyIj5kCflR6H35ug7w74QLCj8ynw7ishgYV1wxr4LKDD+nTPGZy4dwPXJbv2TExwsdLwPtD2c\nZ/nVudKyfAfgjicJmr9WeAY8DI19gQt7em7XXp7bOZe7rUN5JV3dMxL9Jj0hHwAAfHDBofz3cSDv\nKb9/C5mtH3J47KaRAGuJ94bz7xxq55jSvnyNLL4vn45e+CyF6Rr5zqNzQgvHwoE+gxt7+ffZ17tw\nf2tny2VzwSCHMntneO/Asw/cvfku/AHAof2NGXcN78ZpALh+oj8dMOz6ws0V91731C98P0wXxhBM\nZfaPfel1tK05OcZLoaZEfkCUc/N1zvLnt82hNNWj2ktN7q2/D/S95b9vwBVYEWh/AHDYWHJ0QvYo\ns/dzpflKSjVg9CG66prrNWDgoXryT3/S7350hxKb16u+olwXf+V8da3poqeeeUavz3zLSqQf1neQ\nRg8erg4l5coiROwPaGPjDk16b77eXrxQxbV1uvr2u3TkF76kJlvUSV/z9i3zz5IeAzcY9BK2+aGM\nM4WhDagiRQEtAJiLKQwbAGyfkGRrTVMtICVfJqOqYJEFPBqzSVP8LwpQ/s+zYtjHmkC4wR/Y04MG\nIOcVz9xrz+ZD+PknfbzGzwcZCB/HuaDSTmSDl0hOCygl0XV35NO0nEopKB3UHpzy3//+D5o9e47R\nyC648AJb4P/48B8NxeFaUGEYAiBo1193nZavWGER6WaUKLMYQTiQXi5aXadaPfLoIzrm2OPcCmRi\ngDfddKN+/eCDFvUwWgqiQkccoZtuvEVrVm3Qo4+g1jnUcvfYhGfOnKFXXnnVUL9PHj3ewIC//vUZ\nQ/DGjBlroMSUKVP1xz/+Xjsa11utedRCyWvzUgV2Wz5zw31Pb+jAnCcGPM3GBtq5I6ZLLrlCr706\nUf369jNEcpHRfDL67InH61vfutKU/u2w3dqVsGtfDdro7DnHe9cuD3WzUsu+jAJBr5RGwOdFDHDA\nQee2b282zYFw2G9/w6lAyG9fx6ZNG7V9a7N+/sCjhvbfesd31LG2zHKLvQ2Eauh7jzFapIEFBDo2\nOdEpQImi3PjwUENK9z3++JO20ZIeEA6FFYR3lMuX/cf2ufmOUeP1UUbUxqYeqUeJzYAu7iNi0k6T\n5voDe48fxxw9sO/c2/rnrt7X+vi/Z40sfIrdW54bZ6DMwaDWrVqp+fPm6ZVXX9HUaVO1YMF8xWMx\nTyuClJKMRIW2hl49NXLoUB0zfrxGDB2mTjU1ijU3a9n772vBO+9q+owZmrvgXS1eucHoaTiBzMQw\nSrihsDpUVquhZ4MaejSoX++B6tqxXhWlNYqES5SOZ+VL+02cLZ1Keqh5yKuTToQ4kUooFA2ZqF+v\nfr1U3alMaGJm/RmyfnLof3uKjDfYvfVgzwDA/8xxzBaO0UhEjfxYovMM+dAAACAASURBVBy2vEEV\nz2RMeBbaaKGidP4cZV7DAFj6/nJdc931lt+YaImZ8BEgSrBzFw066pM647LL1X3AQGMklVeEDKjZ\nsa1VqaaYulVXqbZUlu9PFVUKJ5i0YM7zJ+LBkmp1i3NTB8ef6D8/vP81sYw2NDUb66OotFSRYs8g\nao7J9rQif1YV5SUW0dgRz2jz9mb5AgGVRkMqK/HqJTc2Jiwnn/KEFVGPHkmaAJ+xZ7LPOXyU/Q7B\nWgvY5tZxcCDah6CgCwzBIDPA1aq3UHawNRftgrrppWL9969+rYeuvUYl8ZiOHDhAh/Ttp3RLXEtX\nr9KM5UvUmkxoRJ9+OmLIUFUCOPj82hVPanqbBkAeAwAAwAS5sgqGwiZ28LdJE/XM9Le0C+Pekj5z\nE81PeS6ey2dVEfIPWBKHjz7cmF+fPv54VVYTAT6Ia1LB5lRoThaCAPnO/94AgnzDP9+RK4wI5jsX\nzo5yjqD77YTQ8oMbrn/yHcoDiQbn9yvn8+iMJXdYQMBSWHIzKwcQuPzxQsBhT2kQFvEjsgdFJafj\nsb/n3tcelu+8uj5w2gnu/oXP5drJ9+YDKQe2V+77rEKnrtAZ3dO798oKezoXnoPo9bnpLDB/91Bu\nyPUt9y90pA8UgOI73Ln5YIj7fH990w4AAD6mrGx3bkXJdZInGullz3kl9vYHQrn28BtnGnt8f8BE\noaOd/4ZcmoATYHT7xoE+Y5sFVABI7enzwvlaOFIcoGZrct68+ijjDhCEIBvlwR944Odtt2LP8ASM\nSQUmBQD6v1e3+1Onn66zLr5APfsPUjyW1bP//Zge/+V92rbiffWsqtIVX79InSo66i9/fUavzXjT\nHPgRvfrpsAFD1b1TZ+Pih4NBbW7epVfnz9SERXNU3rG7vn//zzTuCydpO4J8sMmKg6YtQ1oaAtx+\nn1+lRaQm8nf2q5Sy/oCBAsi9MU5iVBsxINzTB0CstjWVNmcfJkJN2KtutDOdVsqqBgRN6JYZQ+aj\nmxdtgZ79AwD7itL+zzSIPsqA+ajX5i/WROnZeN1CixMMHYj8/r3hLiwC0MIQ4YNG29rqbejQbzp3\nrrUos6dW6S0WHTrU6NRTT9PkyZO0cOFCVVRU2sIIgodCsZUbyT3Up4/7lLEQqP0egMYYTxgTYOGC\nd03cZubMmR6VJDcBx449Sg0Ng7Vk8ftWZgU62NhxY427QvkWDLL+AwYqncpo165mLVu2QmWl5Ro5\n8hATkps3b6bmv/u25MO880RzyNXyFm9PBdXbbD48wGTimdbBdldzPJYuXa758xdp4YKFSqbiKisr\n0c6dO9TQt49OOulEVddUWmk+ckP3euSahEhVflTUG/GYvaBznngeTjpAi80Uo2RiaLqK5F4Zvj09\nIfdKJxNWunHx0tWm+jxsWIO3IOUqFjgXHKqnbR7W5FzE3YyvgvnZhud5DSeaFQqFFWtNKxymPmju\nidvB5330v3dvVE4R2LI8apwBf1hBkMmUBQf3YU/m99xHnVkf1/X7W//21a6DaGgfpMffH5SRY8R5\njlrb8PLq3UOZW/7+Uk2eMFGvUfd67lyt2bBeyTQRCI8FUxqSaspDGtq3r8YePlqHjjpEQ4cMVWm0\nRLt2NWnR4qWaPmu2Jk+ZqgXvLdG6LbtsNnHwnWFfWBVllaquqjYV/0H9hqi+rrt6duul4qISc/gz\nKPmbkjtq/t6cS2WTSmQSak40G8W/oqZCPRq6q3ufrioqDsiXi/a3rRU+z4DfXZPEdfKHX4/2/Jr2\nBeEdnBeLIQd9FJE0xMGcQ0QEB9VrUgBc7m/7N7aPbQcAUGnhom9coumzZikSDCvT1KJAOKJMebl6\njz5cJ55/kUaMGasutV6t4iZJmzbsUjSVVq+aStVFZWr//I2ohaVJ5BVgyS3T3tsO+I3yj+NPzv/m\ntLShOaXtsZiJ3BWXRhUmW4u/t0iJWEIUdaF8k68oYMyoeKvH4gqFfJa7z5GKE4HxK4RjbwrIXnQk\nHAoqHPIrnsxYaglrdgl5lj6phSgKUa9wSEW58lDxZNZYXqg1OwMM48uYU6bs7Y1ZNAESMenRnz6g\nR2+9VdFYk8YM6K2RDX3kj2e0fO0avbVogZKZtEb06quxI0aqPBz0RACb45q9eJHmrHxfTbFmUQXg\n2CPGqGNJidKo56NDEI2qJZXWsxPe0Evz5minz6+uDQPVpb6HVr6/TBvXrFYoQIWLZisThRgv0jYc\nRTDEsjLg7ZhPHaezzzlH4446SmWkBRSmt+zFiD84I/Tjucv+AImPp1Uf7VsPZmT0o7XkX3H13vdf\nz3EuYGgVxDP3l25yMPpy/71wsPeTdmr9/r/74z1jf/17IPbIgT5BPtUfPwzWND7Yk089aQB5G+5p\nPkNAoaKoyeNT/hcOT7i6s0aMO0qXXXONGkYMV2MMADyrZ//8iB758d1qXLlCA7t201e/dJZqK2r0\n58ce05SFc2yvGdXgMQA6llYo3tRsaVfbEzG9NHuaAQAdujbomnvu1REnn6zWsCc9xlCNhr1gSUvc\nAz2Ki7y/AQo4P4w9jz0wYSmOXnqA2fGWNuCBBwEfooEBs8et4k0mY/5NNOAzXQBfKmN7GxCUAQkJ\nwIX9MgD2Zfy61/JvECB/gLoBjzAe4inkcXHg6ELxZyA653d/Azuftsa5+Si329iIvrs8H6LdrgwJ\n5zMgAAFSKY8iRFkNxGUMMbV6F0SOGSgBPfLHP+rqb39bm7dstmYxR0qjpRowcKiKIsWaPm26Eqm4\nOlR3VDRanFP5zGjLlu0qCkdUVlahcChiyv/lZZUaNnykmpq26rUJzyrW6gm4oGpKf6DgyrG/xWF/\n/WP32A0AAE31RFXcYdF8gny5Z23PO8Jx38c35Gz2/JoBObKW6WWDHHpkYKiYXr1Qk+wwAADkPJUT\n3/NCjfsEAEJhK9uBWjXnebEdVog8hSqXb21tdtGffQEAdI7P0jz8/pCV/KBb/MFcS9y97X55OQYF\nXcLY2rh9k+L+uKo71qjKX20tAQCwpN19AgAH8gb/J5/z/9/6lz8OC8ekW8mhzlvFsdyrWb18hWbN\nm6PJb07WpIkT9d6CBUrF4uYcgaExrsjvr+tUo2ED+uqYI8dq7KhD1K2+q6IlJVq3YZMWLlysufPf\n1aQp0/Xe+8u0YvMOj2YdouRRWpFwsSpLq9Sjay/16dlHfXv3V22HOnWr62nMExgxaJF4VS28qAnz\nnKhqLEl+f1L+sE/1vbqqe+9uqutWQ/VOhbyl5h/Eptp2r30uAv+Tx2b7c/FfRIPIqyUFgFxOIn+s\n7YC2lPRCZZmc2t0N4n8EADZu2qIf3nefHvnTn7R9yzZlmmPyBcOq6tlHnzj1izrt4stUVlupZDxh\nICU50L5kQh2DIXUs8qtjyMusyCUqyUcGotMSKehOl+tPvv+m1qy2xhJqTKQVjEYUjiBvR05+ylhN\nRYGwiqnFh9CgleNLyRcIqjSCw+4ZTI27Wo22T/SlOEQJNsr7ZRVraVEgm1VZSYkBBbF42ij9pC5Q\nhpIDxgGGYJEf1hTVJf4fe+8BZldZbo+v0/uc6b1nkumT3ihKUxDxoqCC5Sro1Ysi4oWrf0GIDWxY\nUPSC96qAoF7wUkSalFQgvUxJZpJJMumTyfSZ0+v/We+395mTSUhHyo+dJ08mZ87Ze5+9v/1977ve\n9a6VRCQagdVmF5omxxvBeGq+sM2K7QJ8TkKadW9g1I8//uJXePqun8EeDeI9LdMwp7YWToMF23ft\nwdL2DaJRMLumFvOaW+CxmoUZ4Q9GsKGrCxt3bRcAgBaBkwEAqwAAMfx92TK81NGOkaQBl179r7jq\n09dgc/tmPHT/H9C7swuI034qouBhTRE65bagaSuUl5Xhg5d9CFd94pOYMWsmPC5nah0+XvXxrf80\nvHuG76wrcPLr7zsR8Hln3dPDv83xyivHK6FMZhRx3WNL4mOPP47nnn0Obe1thx9QghwjSAWkD5Gw\n/eIReMur8NHPfAbnXHwxqqjt4XYgFE2AnLInH3oAD/74Bxjt2YnqvCJc87GrUJiZg8eeeBxrt7ZL\nC9j82iYsaJoBKyuG0RgyM7wYi0fw3LpX8VpXJ7wllbh+0SKcdcXliDkdysmFwn/sUKNIv4hBqqCL\nwLYUHqncz5a3uGphI9jFdYfRuugFMK6hAKUBcIheADAaiiFpoOuNST7LmIlsXbLNKASYbTdLoYZt\nbwKMH5sBcPIP4Dt5sJ7od+OgZMWcffMU2tMTd/bSpPthnuj+0tsE9J+Z6BNU4IBPp9boYio6/VOs\nAGm1VVuHP/7xQcyZN0/np6koXyv5+cfHBSC466d3IRFXNldMZV2uDEn4qV2g95ZJz6SV2s60IWJf\npfK79XqzpHc9Eo6juakFDqcFK1cvxtj4oFwDshhotcP2gsmUqhO9FpPfdzgAoL4SEwqeG3tz+KxH\no7TYMKhBr9HrJO5nKeeoVCMNntOiqPRESV0u3QiMuGFCAkV+b7tZlRmZvBtJ3+fJCkJ99GlMLVYT\nXrDpWEYKAJhcRE/pFkhacwS4kL4AylyXzruVt2u9Erraf3p57oibwKpvBD2Du7F8+wpkZmfi7GkL\nUWwvfBcASF2rtx8Aqg+p1wMA+NVGRkfRvXUbVr/yKl599RWsWrsGff19Mta5YIkoH4B8rwsVRYWY\n3dIsFf+mumkoKy4UEKx/cBBburfjldXrsHZDB7p27MaQPyBUc26k+Ge7M+BxeFBeWoWKsmpMrapD\nQW4J8nMLidEjEdNWQj5TmlsFs8BINIRAJIQw4nBkuFA5pUL+evNsym9Ob7cUIoz6pkcDHI9XITrV\neemf+TlWPgg4s8WKIoCkPOpUP64TrPzTepZMgMPpsEcCAP0DQ/jjn/6M3/3hD9jRrTQAkvE4shvm\n4NM3/Aeu+LerMRgEegeGkOGwIt9lQ47ZiGKHCcRauCqIMj2imv4MPRg0d5E0sIWVf2EP8G8wjrEA\nq9bsbTfA4bYqQT6K+AWCMCQTyHY74LIblUCgL4ZAKCKtbpkeq4wLP0UAQ2FpS7LZzBL8UMeF/fBJ\nLQCyWiwSWJEBwLY0Mgl0LQHO4xwLpFFySuQayN5tm43rLKmXmo+y0SiCgWwrCEWTCCai0hLjHxvH\ng3f/Ei/8+KewxyI4r6Uec+vr4DLZsXVnD5a0rZe2gbnTGjC3qQku6hVQmyBpxPotnVjXsw3jfh/O\nbtEZAA6NAQDoAMBTy5bh7+0bEDHZ8bHPX4cbb7kdEVjwxGN/xcP33Y3hXdtIf5CnS9Y+UBAzqRwY\nzMr+UvEyjCgtK8eHP/JhNDfX45JLLhZdGL2X+HjU5n/m2H73WP8vX4GTzz/eBQDeXuPldAEA/dtS\n8JDt1dQne/KJJzBM0UiTGVG2jVAjxkzGWELaqUiRT1CGPymd8Sia3oJrr/8Szv/AJbB63AjSLpjs\nMKdL3Iue/OMf8eCP7sTY3t2oySvGp6/4KIqzc/H0s89g5eZNUoWcUTkNs2sbke10w2YwS8s2AYCn\nVi7Fqh1dcOeX4YvfvBUXfvoTiDsdiLPlg4sUC2jxJGySrwB+lv9NSnybGjf8ORIlK5vtHhZp82La\nQKFAMrtihiTsZLbFqLeURBRKn4zaNEQK2BJmtZhEE4ztfMZEDHkuJ3LY4sZ18F0A4Mw+MPoE9Kc/\n/UlsLsbYQK5R63TK+7EYAOlVfv3MjtYuwGq/LhCji32wb4zvJSuAFSHanrAnjcHM5z73Ofzkrrvg\n8bgPYwDIMTQgYM+u3bju3/8dz73wDyXSZORDQzq73puUno0SseLxFBWXnSt2m2IBsNeJv7NYjejr\n34t4fKIv8Y477sDXv/71lGjJ6QbgKQBAu1jjPj+6urrw+GOPCbpHdwIqjDMNoF4CfWVbWpq1SthR\nEvMUhUyVQVU9n9MEWylUiq7Uv1WYOxAalMqR0+xCtj1Lkg2peOkndnTx/8MGHa9qIqF6OCeSMo1h\nIEbZkzCEFI9Wd8+e+B6Tx4q0JpACJOpZE7oHKVBicgvBYWemklsfIli8czFWblyF2sI6XDL/fcg1\neWHmJKp5b5/Zp+itsreTD0DeKmd+7PNQKfFkbImf2d7djbVr1mDVypVYvXIVdm7fiUDAL0KebAOJ\nsr/NBOTlZGJaZSUWzJiBhbNmibI/bfwioRAOHupD25YOrFy3Dhs7tqBj606MkdImhBF2hhuRnZWF\nsvwi1JRUoLSgBDVTalFcVAGz0Q67xQ2DgS1KMcTJZ+PGUqYpIWr+Y/5hWOxm5BXlobJ2CgpLi2DL\nUt5sgnEx43md0sE7NUCkyBGtk2gVRpXydKCYAACFX4/08T4SAKAg46JvfxfPvfCCiIrGAkzTTTCV\nVOO8D38UV3z+i8gsKUHSmESmzYhyJySYIO3fqfc+S/KvuFOssihmlJL943TGznbudRzAtiE/RiPs\n47XA7rSLUBGrHdRgYIAkisigmFFCMdpgQjSmGqPsNgNi4bjMv3xFNEnY32+k8GkSsWgEbpsJGU6z\nJMEEFIIRxVgRxyVNQJVVfR6DrVIyXWudVtLzT7sm9v1rrBiprkvbiXqdo5NgRSgUwZ/u+TX++u3v\nwRUJ473N9Zg1bRpsMKN7924s79iEUCSMefVNmFPfINRMAgAwWrBuyxas2d6Fcb8fZzVNx4ULFiDf\no7cATAAAf1u+DM+1bUDQ4sRHrv0CvvT1W5BXWYD+gQCef/wRPPWXh7BnawciI8PKM0pby4SnRvCb\nGgcGC+JJUrd4RQg+JzFz5gxxJ7rwwgtkjWTbyLsgwNtjJn9nn+XJr7/v1Pn9nXqfTxcAoAMZRdPp\ngPPcc8+Jk4KEC6L1wvUjBouRgtdJmG02YZQJdcvqgMHtQv28Ofj8V76M+eeeg6TZhEAkDDOp8hR7\njSfgG/fh6T/9CX/5+V3w7d2DqYVl+NRHrkRpTi6ef/EfeGnNqwIYz6quw5z6JhEBdJitIg4+FPLj\npbY1eLVrMzKKKnDDd76LCz/1CYSsPE4MVodZkngex21VQn3jYa57qjBLly7dQ1wI24YkwlEKw5I5\nYIGBWjBcJ7n2UdsvDjiocUSGQBSIhqLIsFrgsimbw1AwBGMyjmKXS8B61eL5es3ochlP/gF8pw7U\nE/1eepWJSv/sP1EiH0ot/ZiX+nUOMPlzR2sD0AVESG+nKAiTf4ICIoBiMqGosEj676+48soJs/YJ\ndOGwI1NB+qs33CBe0qR3Mmg51qYSeEWFZ5DHIEoFe1JiRzTO5Dueuga0ZqGty2TrkhO9vsd635o1\nG/DgAw+ga+tWLF78gpxTcVEhcnJzQVuboqJCcVagJ6kSPVHdx0qHgE8cbe1U7zwRQKJvqpYVxwBG\nMB71C9Mh2+iFFy6MxIbR2b8VfQOHMKt6JvIc+bDFbaJarrfpq/NNChJHS8eJTV0fFYzxmMr/Wl1P\nBsCKxSC2HvqH1EtHaOop4UYFIKTemlD+0JyIRBWalFDphdCagHhObA2xmDVxHZNMmFQ2IDVIFK6N\ndDiIC3PiQPIAXu1YhbVdHZjTPBeX1l0AR5IBMwW1jkfUOhN3983Yx9tn/jtaMq9fsckiUgkq3hvU\nveYWDAWxYd16vPTii1j12mvY3r0Ng4f6EQiGJcHRZSPcRqCmrBgN06Zg5vRmzGppxtTKKfC6vSJg\nuXPXLrRt2YLX1q5Fa1cn9g8MYDQQg4HVx3hCWEMFOUWoKp2CqpJqlBWUobZiCjKcGXC7KR3HsW6R\nnjai9ETu41y0TMB4aAyBeAAGlxGlU0pROa0CeUW5sDg1FhO/iDYMdXJL2ktvxuD5px6TDC2Ky/72\nt78VP2WuBZz/qSZ97bXX4pprrhFLtMOFyBSDKBaPSnJoNFvQuXUbvnT9DVi5erWaAzivOBxIOt2o\nnDkXF37yGsw7973I9piRbwNqTIArbYxwTDGZNh/WVsT51STMDx9dSWhHBGAoCvSPx+GPxmFzWGF3\nKqXicX9YBPt4rhkeu/RJcmoe94UQThhgt9ngYquBlYk+EAiGhMzlcNjF3SUUZqAUg9VkQKZTiQCK\nUwtF/SgO6DTJmGKPf5wtY+yppPMD50xWUuIJOB1mib0idArwh2RdcDttEkiFwzERWCLgIEACv5Pf\nj/t/9nM8/uOfwB4K4L1NKtF3WWzoZJDaukHEKRc2tWAWRRQJTrMyZbVjVVsb1nRvFYbAgsYmAQAK\nPEoEME5FaasdEZMRjy1+Gc+3b0TI6sSnv3oTrr3xazDR/pdONJEodm3tQseaVVj81BNoXbEEiPEL\nT4gWiCBt6uHQuWxKq4aijhTora2dJiA5wSJqEZFReDSQfvJrZ6Kd75/6wLx7sLfBFXj7rL9vg4v5\nlj1FBRUnZa3RtYeOdrJ6HLNnzx5xPmPyz7WOdp4645lxPfMfSZLNVoQj1IOxSvVfYGM2yRsNKGhp\nxsc/91nMPGchqmunSRzLjUABBQLjRoNyFQqH8fJjj+H+O7+P8T27UVdSicsvuhi5LjdWvLYCK9rW\nSwIuLgD1Lcj1eMX5xe1yYYQaAJtWCQCQWzEVX//Rj3Duxy/HCIshmrYYhfyklYwaAGajtATwNIOs\n3kfjsMIEr02F7aMhIMQWL665JhMcJpOA6QKax5Iwc43SnEzHw3FY4hBmXqYmXhuO0EmADgCqTU+6\nd98FAM78c8E+f/q20p9Wp2KeqaMcjSEwkcRSIImJrVIIlQfGYMAVH7kCv73vPngzvTDRNyJ9mySa\nws/8z3//N75/xx2gQj37fI+1qdxvAgRgz+eEvwYfawoATvSYZ2dnS6A6f/58AQUm24ac7HVSQjBK\nC+Duu38tla5wJIT6uloR/Zs1a6YENgQ2dH9keqzKWdMhIUm7Mtp/KKCD0kzqbBmwJhBBCN2jO7B9\nYDcGfcNi11FbUoOZRQ1ynDU7N2DUP4bZNTNQ7i2DG27FDhAQIQlqcXKfikHASU494VGpkhFgYFis\nABQej+/lFpE/SszJbXDDqeQ7ZBLjvnhNFcmTVSh1j8xaqpaiPKdpFdAXlF1DNplSjIiIUzrPg//y\n2LS+MsEGm7yP+7TLNMGz4hGjOIRhPLLyGYwMj+Ez512NKmeZTJvc3ztze3sEIJNbVI51L8QtwmiC\nP+DH3v17Begjer586TIc2LcPQX9A/NITtMbTnuSSvGyh+c9sbMDc6c0CABQXFUgC5PMFsaNnNza0\ntmHdxk1o3dKFA4NDGEm57JiRmZmF/Nw8lJeUY2p5DSqKqlBdUgOPzQOnxQFDgtVJZbdGkUmCb0xI\nWcGNRYKIxsOwe2worCpCVUM1HFkO2DKPPuYmroX6abJp3ztznELYXk8++STuv/9+sZbV5zfqrbAV\njS0As2bNmvT1FQBAeiHzRNrpDY2M4jf/dR9+/8ADYtFopX6I0QB7fh7ee/kVuOILX4E3Jwf2WADV\n2R5U20xC+09vjZIWqDQuE/9Hh+IQkvDBgN5ADH2hIEJJE6x2p+DFMoNp/7LtSPr+6QRhVTR9irZS\nX4aMAFqYMnBidZ5JPOd+3SOZQEEgGEXCQKDADLuZrPiownbZXmAjM00J44U0SyTS/KXPn4EZVZU4\nl9pMAgAEowlpIaMuALUFeC4MzMT2zWiA1WiCgS1mkTAeve9e/P6O78PqG8M5jc2Y19gMp9mKru3d\nKQBgQWMzZk6rhdPEYM8Eg9l2GAAwv6EBFy1ciEKPB4lQSHzkKVYVs5jx5LKleHL9akQcblx78/8n\nAEDCZUeEz0vcIPoF0bFxrF+xFC//7TGsf3UFRnbvUyBASixNtajJWqKx1PTgWdYUWi5azKIXwfFC\nT26C9nT80X3s9UH0rmbAO3U2eat8r7fH+vtWuVpvx/Ngwq3kqDgrTYAA/C7SJsxeeTITt2/HsmXL\n8Morr2Dr1q0St7CdevI2MZdBWGV0jjAYuMaYAasdGQX5mHH2Alz52U9h1rkLEaW7i8mEaDAqVXWr\nzYRgKIEIe+6TSbisZvzjf/+C+759G4Z39WBafimuvOSDAgAsXb5UWgCYY82tacDchmZk2JwwJw1w\nOhw45B/F8s5NeGVLB7yllbjpjjtx3tUfQ8hmEPG+cJhONSYBrZnwM+Yx6G5eBjr4GGClZZ+sf6p1\nLWFkTpeA22QU6r846LAdIKZyPVL9qc+TjCeRZbWhzG6EV+uIJPmBwDdZXwq3frcF4A15Zqg+SQSd\nQdkbAQDowd2xTl5vM3A7nbjnV/fgms9/TvPKmxQ4T3aBpJ3d4CDuvvuX+PGPf4wwPY+OsU0UfzXp\nOqkwKLqnOHJLJXsCROAD+uCDD4pA4uTK5KndDBXEbtrUgeu++CVs3LABZ529EHf/8meoq6+FzWaX\n43Ay4YRBX1xdEFAEyORPBD0jOzHkV97WhVmFyLcUSOp7EAfx6taV6A30Ce01MOJHsbsACytnI8eV\ngz19SgU9Py8LhZ48xBJRGKImOI1MbtwYhw+BeFCCLZ6D0+aEXTPIGsUIgnEqN9PKBYiEIiK8aIcd\nYxjF7oH9GAyOoaKwHFMsxZK8M4gmK4C8hEgiBKso8qsaLb+JqsCpPzooMIoxDERGEA1FkG3KQLYr\nCwHpnmYwqK5BIBqA0+JM7csX98FjciEf2TArgoH0La0aa8Pjf/8bLjvrUsytmgknSa5J5bv8ztve\n+gFIevKvEt7Dt3SrKY5Btg3t2r0Lixe/jGefexZr1q3FyMiI9LoRgY9FKHxDNX6gND8H06dU4Ow5\ns9DS2IiK0jLk5eYISn6wvw+dO3diw5YtWNvWgc7uHTg44BNFd45Gh9WNTG82SovKUFZUhurSKkwp\nm4LywgrYLQ7YzA4BGSgkycorqXoyHs1GYR0FwgFR9s8pykR+cS6qplbBnctUMyUxr0hH8oLulXG0\nK/DOG5WTvxHnM/ZAPvzww/jLX/4ivZA6UExGWHNzM26//XZcfvnlkz46Mb4JvJClMe4P4r9/93v8\n5r770Lt3PwwR1fqUUVeH62+9FR+6+ioRHXJGwihz2oT2z9lIqcVojwAAIABJREFUzcI6BUO7D1qr\nEiFO8sBI+x+IAbuGxjFABxq7Hdle8gcAPyv5oYjYl3pcDjhJJOOcmCDFPwZTwqCEjGxmkEg1FgbG\nAxT9M0tST6VkOkOwEsJxQRqltEHK8hOHMZmUhJtUeHEGYP8kGVJioaecAMSYQBgBBgQZmIpLSww2\nrdLPACoYCkkvJkWWZIWLJKWanwkDHv/D7/DzRbcgPjyEBY2NWNA8Aw6DCV0MXts2CgNgbl0jZtfV\nwc2+TbNFAtN0BsDCpmYFAGSQARBGIhqHie1+GgPgyQ1rEfN4ce1N38Bnb7gBUbsFUWl7oFOBUYLF\nZCiGgQN7senV1/D0Q3/B1k2tCI4MKr2bJCv+vCMiIaW19ykwXFp8otFUNU0fLGVlZQIGkEHCvy0t\nLaisrBRAgAWHd6v/7/w55s35hm/99ffNuS7vnKPqAID+jRgLMGkfGh4WdzLqD23t2ioMXlqdk+k2\n2T6TILfP55PX2f7MGIextoqGWPG3wF5UjAsv+xAuvOwyVDfUIrc4D0arWWKOUIQMOAIGNlkfhCkW\np9WesjJ+9i+P4r9uuwUDPTtRl1+Kqz50OfJcbrz48ktYsZkMAGBOFTVfWpDjzhDrPeobsQVgccc6\nvEoAoKQSN915J877xMfhp2MShf4o8qelSuymDccVK5csY651wizj+8iGTCYkJvPQYlyTLiNwLuK1\nBEr4ZoMBUe40GkeexYZcC0DTV93xmNeYwLzkP2S8HZ8BoIKro5uY6cHWmQj8J4ex+nA4E/s+kYfl\nzB6fFe5/+Zd/OWXa/9HOOHUl2I+v19wpJGEySgBNmncirSFepeEGzJ0zR1oRGpsbpWKRqgToX1lK\n1WmZgyaKt3f3HtEMoHfm0a6OOh8Ouol7pbAEHWDQVbhUf6WeIPIc//266wSUYKXh9DZOFuxNtuB/\n//x/+OSnPi2n86eHH8ZVn/yYBkBQ0Zle1uzxVOwH3ZeXpzqcHMa+4X3o2NEOWJIwWUxw2V0oySpB\nbnYu9sT24cV1L8MPH7KysmENm2EeN2LBlLmoLqyCPxSGP+7D/vHdCEcCcMSsYIdNYWY5crMLsTu6\nBz29uySQ5MWyO1woLixGrjEbnb1bsH9oL3JysmCJWeD3+WG0m1FWUSq6BW3dHdgz3ovqsko0eCvh\niNsxHojCk+FFLBZC/1AfHB4bsnKzZd9DAyNIsoeI99DC2QVwezzo7TuEsTEfbAYbSj0FyC/Mw47R\nXdg/dBAmgw3Z3hyMDg3JxGmymqUiNzgyiCxzBs6vPQflrhJhLoQRRT9G8Zd//B/yMwpw0YILkW/I\ngoXtC5MAgH/Wk3t64ye17BxnN8eaH968b5pO+9cwmkkAQFL6y/jsUQ22Y/Nm/P3pp7B4yWJsat0E\nn2+cLjj6MinPuddqQE1FJebNmoXZTQ2YPrUCVSXFQrtm9XXf/l6sb92E1Rs2oLVrK3b3HcJokKrq\nysvdY8tAQUEJigtKUF02BZWllagqrUJBVj68Ti9ioYRYZ5I3Qqo1V17aTMYSMcQNcbHxixvjyC3I\nRVl1CYoqC+HIsMKod89o5hhk10zoa74eAPDm3ZszMy6Pvxc9+SKI8+c//1lcADZu3Cjznd4aRjba\nN77xDSxcuPCIFgDlVa3aQSg2tGvPPtx62+146pln5N7EAwHAZIexqBCf/MIXcO2/fR7lhTkoMKrA\nwiqAo845Utoo+nqitFMgyT/7/Wn1t388iv5ACEGY4PA4JcDiXQqEgdGxkIg0OW1GqdxT/TicSCAQ\nCEnQ43E44LQbhcU5HlGtAmSQuZ1myWsD/qCsKR6PRSr1gQikNcVhs8k+xfU1qTEEaBNoMYkPM/UC\nOLapMeDU9CNGQlFEYwQIuH+6ULAVICrezE6nAzZOeey7DIeQ57Aj3wI88vuH8N1v/icCA4cwnwDA\ndAKkJmzp7say1vXy2dnT6jC3oUEAAAaJBABWtrZiTfc2aQFY2NSEixaehUKPW+inNpMFCaMJvkQM\nj770Ip5v34RYRhY+c+NN+OR118GWl40IlayTpLySFirxnwSv4eEA+rq2Y8+2brS3teHvTz6B4T27\nFAjA1jN6R1McSwP5WUGSXlmTWXQY9GpaeqWf42XKlCki5EtggJoBbOerr68H2X3UD3h3e/cKnLkr\n8M/IP87c2b67pxO/AnraQTB279692LZtq9ict7e3Ydeu3di7Z7cA20EKwWot1PwM53iV4KvchXMY\n21vDZHSRH8+NXHerDVPq62F0unHu+y/GZR/9KAorKpA0A1GptFNsj5V2zplJRGJcayIiek5nGepl\nW43A84/8r2IA9OxAQ3GVaACQAfD8C89jZftGqbzPmlInNoBsAZAEHQaETEk8+doSrNy6BXkVNbj5\nhz/EWR/7CIbISjDSklYB0azsm6x0EyMonRQ2G5N9stbGghGZhx1mI1ys+vP7c46ng0wkjrBWNHGa\njZL/jfpYuLOg2umQyj/XaPb96/kgW9i4JZRG4PFaAI4XIJ+JAOvNfsDP7PGJoDPYuvvuu88oAKCn\n1SkyPamHfChIG5GbrNr7VC2GQnSK5vH5z16Lu37+U7iz2WPLQaYZ27HnM5VB6PdRBQC0BeS2q2cX\nFt2+SCw1WPlIBQIakiRFEx1J0lrL5Wykf579vAapHvM4HJw8v1gyjhmzZ+HXv/oVFs5fIA/hqW/8\ntrSyMOCB3z2ML37xOricbry05GXMnjdDQRSTGQ5pBwsZwjiEAbT2tMJjcKGsuBgGqwFrN6/BgcH9\naJnbgrgZWLVpFfwJH8rzy5GbyEOxrRgVWeVSSY8hgr2JvVi6cykGB/sxxVqOhuIm5HiL4DdFsGLP\nKxiKDKE4twgBfxj9g6MozS/DrLJmtG3bgG2920SUsNRdjN6DB7GldxvKK8tQkVOEzp1b0DnajeKS\nQuQnMuCOuWCNO5GTnYsw/NjVtxOBxBicmW4h8I/3B5BtyBWBtb7IQQwFh+F0e+E/6EdzbhMqMsvh\nsbkRtAaxdmANtuzZimTQilkNc5BpdaCrcwsOjvajsLIYA74BDOztx6VN78f7p10orQFkRPgRwEsb\nl6B/ZByXnX8ZCuCFJW5CQsAlnXXwdiJeH+/5P9boPJ2xe+qjXsa19rzpRh58jUmMhVxmPpfsg6M6\n+egwtnRswUsvLsbiJUvwyquvIhRnqq7WSlNc9YMV53kxs6kJMxoa0FxXi2lV1SgsyIXVbhb1/63b\nt2NT+2asb23H5m3d2HdwTPQB9EU8w5GF0pJK1FROQ2lRBUqLy1FRUoksb56AR3wOad9HrQsi2rGE\nQdTlY9GAtMGMh8ZhdphQWFGIovJCFFcUwZapaWmkA5T6ZUtd+uPdvzfvHp3eHT65T9OhhS0A9957\nr9Ak9SoJqyMUgP3KV74iidvRbQDVukCBx9379uO6L38Fr772mrJbpMsLRV2zcjBz3jx8+9ZbcO6c\nhlTfvy72p4VjEvjIXM/7azJJ1X8EwO5QQiz+RCCVCaYJiGlK+rQTdJipwK+aFcNhiLuMqPJTLZ9T\ni4jNKvcVAkek4xM/JqWRsSCDJQJJHA0uF7VLuA8q+cckYDRbqFUCWY+ibBswGiRG5PuIm5NlwM1t\nN0tlRhSWhYJKLZQJu1ld85TAmSkUQhnFKG1muR5//dMj+MZNN2Hk0EEsaG7CgpbpcBpM6OjqxNK2\nDYhEo1hQz9dbBABgxT5htIgN4PL2NviCAZzVTAYAAQC2AIRFKNBotWAoHMITSxbj5c4OhJ0efPbG\nm/GZr34VpiyPtGiQkcDvy+vEogDX8Xg0Ai/bKEwG9PaN4eUXXsa6Fa9i64b12N+6EYgFYbIYENeE\npRgMi/BvjDO9ihMIDqWvoxN4v4amACgoyBdGQEFBAaqqqgQMaGpqEv2J/Px80RGgaPGxmAI6K09n\nBSpWxuHP7uut5a/HPtNZkJMFDdP383Znrr0eNH3ENHly08kJv/t0j39in399AP7EPn/CX+f/mTfq\nz4b+TKZrw6Q/p/xZeuppj0pBcQ1YTm9FZn+9TtUX/3k6bunPruaowsp2JBJFwO+X6v6h/n7s7NmJ\nrVu7cWD/fhzo7cXmzR0YHBxSgKQUMxOwmG1SFZd4JZX468kGxWPV/C+FRzNpYyr3gDcLl1x5Ba66\n5jPIKS6Ew+OByWaFkYwlzU2I8xxZWFYzdXAAvz8soC9tz61WJePlHw3g6T8/hId+8kP49+1Fc2k1\nPvXhK5HrVgDAa23rRWdrVk09ZtU2wmO1w2pkq5gBfsTw9OrlWN3dheyyKnzr7l9gzhWXYc/oGBx2\nF5w2E0Jx5VKmRGjNEr/xXPiXLQGi/m8ywMUWNK0dgucfSAD+cBwRAraiHZCA1ZhEMhRAkcsNeiiR\nnadJCmiCfxPDW67BiQMAb9RzcazgLX0Ke6OCuDfm+GeddRZWrlx52gCAPERUeSQ9RbscqoMvVX/X\nusJV3Z3fhsEAuZOkMvIz/3bN5/C9O76PnJJi2UM0wYeL+nYGCRZSV1kSB+5XdQjqV7y1tV2E8xhc\nKsxI9eSo+j77d7QeHh1ZSi9LJgGn0QITJw0JKVTHe9Jkxp0/+iFu+s+bT2tgqXCQJB7g3nt+i699\n9SY47S4sXroYs+fO1HqLXn+JCBpC6Ip3o6unE9MKalDmKUEUYWzs24Dlrcswfd4MFGeWon1bG8YC\no5hTNxfN9ha44BGaPq+UDyPY7u/GM93/kJ7lC8vPQX1BI2v92Dbcg3/sfBlhSwhZtgyY4maEowaU\nF1SgpbAW7dtasWt4D947/1wUoxBbejejo68L1VOqMd1Th3X716J9fAtKKorgjTjhCbtRn9cEj8GD\nPfGdaN+9CUPhQQTiIQQCUdhjTswrWwB6Pbf1bcKOQz2CbOZb8nFZ3aUoMbGSb0E/DuGl3ucEAMi1\nluF9My9GDtxY3b0K2/f34KzzzsFAuB9rlq3G/JI5eF/jRfAiQ3QB2E7w8pql4qV96bmXIpeaB5zk\nRUeBI1MJJ4o8oPh/v5X1Ad6Y5/+0BvUJfjh9VKfPjsyxSSujem3rxo149umnsfLV19C6qQ2jgTFY\nRPchKZXPLI8VNSUlaKqtxfSmZjQ11KOsqBgu+rCbzdizfx9aO7eImv+6jRvRs6cXvjBELYM0cFKj\ncz35KC+qQElRBaZUTkV1eY1Y+LkdHqU4LqJqum2msrRhghWJxhAM+xBPBuFwW5BfnI/yKeUoKi+A\nkUYBGttfn4x0ctORw+l491DNae/0rb+/X6r/bK+iC4oexJEiTxeAb33rW3jPe95zFABAYFzRXuAs\nPTw6hh/f9VP85ZFHMTgwiFg4AndmDgwZ2bj0ssvwn1++Dg1TimFPaS+qkaiMY3W+oHrmOU4Osrrj\nC2P3mA9RGFGUnSX9+6xgBKO0+fNLxcNLEUCrWVwCxn0x0L6WQZTH4xChPZ4lgyJfICI0dY/TCaeD\nNrTs5Y8I2GV3KO2VYCAmQSSp+mQHcPOxvSAaFYVmO8s7rPxHqTNByjuTfHXO8n8TE2EyBJSNLBWV\n45GoChZ5LnzGkkCGASgyJ+Fly0EsgYce+CNuveVWjA4cwoImlei7jCa0d3Zices66dM8u6kFCzUA\ngMdMGExYt2UCAJhgACgAgOB5OgCwuLMDIbsbn/nqTfjU9dfDXpAta76NfayRmAi/mqxsLVBADMUN\nmcT7/BGYDRZER/3Y29mFV599Bv/42+PoHzgAhPwTrgHCJFXJvdxZYYho8QaLA2nPkl5sUECBCvqZ\nJBAIIBOAiUNOTg7Ky8tFUJBMAbIEMjIyBBjQWwgIEBCo0i2M05Py9J+PZx2cLrisP+96YqMn/a+n\nW3AsIOBYgMHRQIl/JqjwZsOfp3v8N+7zKhZ5q8z8R8Ow/xlr0omAZpOfiRPR9tCBOv17kYYugtEG\ng9iFB4JBYR0OHBpAd3e39PCzd5/26IcOHRKwlsk+45RU33464KdZZPMamalbxuJhNAqDWYn4JQkS\npIpOLGezWd6GksZmLDjvfDTOmo2WuXOQX86inlFpn0qBlKAvmcMEqROi6SW986TdSxUdiMRUOxwB\n6Xg4jOf+98944Md3YnRPDxryK/DRD1wmAMCSZYuxumPTBABQ14hMuxMG2vrZbBgM+fDculexZtc2\n5JVW4/Zf/RLzPnIZ+qMxsbwl25igLa83vwrnVruVdq3Ub03AzDXImIDdCOn5Z63UF0tiPBxFlDQ4\nsxG8NKEwEA374LYYke+wIYfCgdIpOfEMSLE3LTd7FwCQp+94wSPfc3IBJHtRGhsbQaXK0930RJs0\nDqdItBkQ0hJeveKSPrEIhURL07U1HB++9DJc89lr0DijBUWlJfKNGRRayD/RqzUaGqRwNLXEp1+Z\ntes34Hvf+S6ee/pp+UoiPifogarqp66SVGm0caax0K1JyGDMgUWqyEmPAzvG+/HFG27A7Xd+H25P\nxikH6KxTjCZ84jf+ix/+Ar/91W/htNixdMkSzJ47S+t2ODYAsDPZgzVda4WGn5OXjWgsjB37u9G1\nvxO1DbUoy6rADlqhjflxbsu5qDXXwwY7jElWzWJIGMLYPLIZi3etkN6lCyvPRm1OLejs2T68Dcv2\nr4LJbUCRLRMZZiZFNuR585FnycXq9tXoCwwIAFCIbGzauxE7BnpQM7UGze5GrDqwCisPrUZeWR4y\nwk4UJAtxXsn75PqvG3sNHQfaMR4PIRgOIuwLoMCaj4tqL0JpVglWD63B9kPb5T5mmbNxYc2FyEW+\nQDcjGMTiPc9i576dKHVOw0UzLoYbDixrW46dB3bhve97L8LxADo2dKAhvwGzq2fCBTtCiKDbtxcr\nVr2GWdUzMLt6BpywiFjhUMyHiDEuqtq8VpkWN7KcXtiNhALeKsvw5CfyzD//p/vMn8zn9Wd/QmIT\nGB0PYPGSpXjqyb/hleXL0btnL8LRgFThCdF4jAZUleShaWoFaqvK0VRXi6rKSuQXFsHicGLY50P3\nzh60dnRgU8cWbO7ehf0HD8EXpIykAhq5SBUWFqKmfCpqyxpRVlCJkuJy5OcWIR5JwOXIQJyJfpzK\ntiahE1OkjElmMByGP+iXyq0z04ayaQUoLs9HTlGummp1+RBt2lXzjDZ+0rXMUhfqePfw5Obvk7n+\nb6X3DgwM4IEHHhAQIB0AYHBBDYBFixbhox/96KRTnhhBOgCw/8BB/OSnP8OfH3kEYwODai2IJ1Ha\nPBNfu/lmfOojlyPbZYGVqLTimqXmf/6gW+OR9s/K/4EQ0Dvugz8eRdJoQk4G53tSHxNS3RCXAXEf\nkUYxYbFwHrWaFBWS7V1SUeK6wo6ReFLGkQDdVHpmgh5m64AFbhf3RvArJuJ5dpsVdgfZJwzqYgiF\nw+KSQ9E/xovRSBxmgwl2iwK0yAbwh1UySxCNMRbXU4IMVlZakFQKyokkss0mZJqVAwKxqlgkggf/\n8AAW3f5tjAz04aymZsxvboLLaFYAwKZ1ct5nNzZhYct0uMgAMBqlBeBkAYCww4PPfu1mfOaGr8Kc\n4wX7R23s34/ExcHFxCqXySBzMZNmBrtk9fG70m+abQJkd+3bvRsb21vRu3cPOlavQXdrKxLjI9IH\nS0+phDgAaQCuFpzL05ReOJCOD9VXOjlR1quEEh8Im8Ms15/JflZWlujx8GcCAgQK+DOBA/5MsIDv\n4f8JKujv5Xu4D4IFbOnT2/kmVzD1ga4DYaealE9OoE51P2/UXHG6CfTpntfpHv/N/vzpfv+34+dP\nBrTSn2s+ZwT0mNzzeWRr1dDwkCTxfYcO4UDvAaHx8/2jI6MgID06OorhwSHs338AAX8APj/5YBN5\nF1X6xYmK8yDZXakWZgUmktYvrbvUSuEcy4mYUxPnN85NrPhbXZg2ay6mNTWiqLIKc85aiIbpM+Bw\nmaUVKkr1f5nLjTIfkhnFv5LHGIyIETSNxaX9y+FUxyEDjS40FAE0JxN4/tFHcP8PvofBnu1oKqrC\nxz5wGXJcBACWCAOAxRJhANQ1IsvholuxzFG948N4qX0tVu3oQm5JFRb9/Be44OOXYzAJHIrGkGTF\nP8WQVhV/k9kAYzQBF4ygwRFb0gi2Sz3EAAzFYhgOhZE0WWGxWwQUGAtEYYhHkOOwo9CsKv/MGQl9\n8/vo1rapsSqWgifVAvBGDfPjBW+p1PINOoEzf3w6AJBqycGfvgCeyhdgld0NE0ozslGXXYjizCwE\n/D4EwkGMB/0IhMIYGx+Xm8wKLK0lxhPsr4xLPy63bIsHjY1NqJ/eglkL5sKZ6YU3NxuFpSXIzstF\nZnaWosRo7ydqZ7PaZNCwUkhKDNGx9WvX4q4f/QRPPvmE9ggzGtf4CKxSqJZzCcAYFLkMFriNJpRn\nZqPYmYEpWYVwZ2Vh9f4dWLK9DZ+76Wu45fvfhdPJnsFTSxApebdzZC+Gx8bw8zt+hr/+8RE4THYs\nffllzJ2nKV6nRyuTbkLYEEFv8iA6e7vgj40jEg8pSztrEntG9qCotAjFGcXYvWM34uNxzKyehWkZ\ntbAnHdJ2QKs+UvF7hnehJ7QbkUgYDd4aAQ0MsGJvpA/r+9oRM0VRaMuENWZCOBBHSUEp8p352Njd\nigH/MGbPmIFceNF9sAsHRntRVVmNclsZOvo6sPLAatgz7cgxZqLUVo75hecimAhizb7l2OfbKxZd\nDrsDyWAU+bYcTC+cDpfJhU1DG3Eo0C9K2uHRKErtZXAyzXe4Yc4woGtgA/b27kWpuwYt02bDCgs2\ntG3E7r79mDN/Fqw2A4b6hlCeXYl8dz4SyShChgieXbtYbLA+fNYHkWP0Cv9q2DeCjTvaEUiE4QsE\nBBypKalGQ3UdnEaOpVO7v6fyzJzcZ878839yxz/9d5O6zMCayPry5Svw92eexaZNraCGB2XNmVoR\n6vNaXZhSUYLa6lLMbalF89RyVJQVwel0SxWyb3AYmzZ3on1rN9o6t2Hrjp0YGgshygVKKG1GZGZk\norS4FFUVlSguKhZBv6rCqXCaPXC7MkSAjYi21WITxXYu7LSYC0cjMi5C0TBMVhMysjNQOaUcZVOK\n4SxW6H4qLpigOqkWv3cBgGMOElbDef8ZnD311FO45557DmsB0J1P/uM//kMYAIdvEwAAE2+6AHRt\n68Z1X74eK19bqfEQGXRZkTVlGr5/5w/w2Y99UIIWViaU66imSaMtILT4Y6//QAIYCCfhSxoQZBDn\nMKqALJpEOByCyZSEy26D00JhPiAQZZU+Lq1mrNB7HGaYTUaEwgw6YzAkTPI6fY7JchkNkbWQgNuh\nWgFCoYQEi6LUz6RcM7vxh3j+SbG0lc42AxCOxIXez7neSuVnk1q/xHaZTnnatBCLJYWpQIoo22Ss\nxDxCAeTabci3qkCLY5T9mrwPD97/AG6/bRFG+vtwDgGApmY4TSa0dXZiiQYALKirx4JmrQXAaILR\nYsO6zgkGwPyGRly0YAGKvF5hABDSMFqtGAoH8cSSJVjc2Y6oKxNf+MYtuPbGGxF1WmWddliMiIYV\nKGMw0XWBPa2cG+gKRJqoQjikMY8MBjK2Yqr3NRwI4GDPTrz87NNY8eIL6Nu3W9w3wr5xVV4Sm1nJ\n4lOyP/qyKgzANO0h9TYVwOu6O7w2J7MxwXA6ScO1Kv2SaBSZmZkoLS0VIICJPwEAjnuCBfwdGQX8\nl8dk9Y3v4+fJMtCBB37ucBtMyH7SN342fdN1g/TXJgMAPAbfw2Nw0//9ZwEFp5tAn8x9Odp7T/f4\nb/bnT/f7v9U/T12n9I3PB58nvs41g4m3VOwDAfmXrzHZ5//9fr/kMfyZADNzm97eXhHT5s98bWBo\nUMCAsbExzW0kbS3XfjSZrDLXMtlnMs/qv8wTnDstZqnscxywoJd6zuiQwvYj6tOQ+sWNRUtRdU3C\nkZeLc867AKVVdTj3ovejur4Only3MLtkmg6rFkeuLazqs+DgD6vjch6jHhrXF06LxBUIknKelMo/\ntcI0ZzGCvi/89RH8/s7v4dC2Tswsr8WnP3wlcrQWgGUbVgkDYHZNPWbXNiLb6YHLahcG26HgGJZ2\nbcKrnZuRUVSC237yU7z/kx/FqAHoo/AxAFuSa6lB1hm6ApDm5rVakEPafwJwUcpAE2UmaD2UiGOM\nDm9GstiMsmax3ZMtZWSiEV7n0qfnYrLCpz9kWj0kVSg+tg3gGz283+wA/Mwff926dWJxp1NkTscF\ngDe+2OrAe6Y04JKaFtRlFcAeiyMaDsJgNcMfCgqtMBCNIGG3YDQcxMD4KHzREEYCPlFVHh32Y9Qf\nxFg0iABpj+xxdNuQXVaMosoytLRMxwcu/QCqp0wRSg176Lml9/roo2Dt2g246uqrsGf3bpkk+FCL\nd2cyBo/BDFcSoBzdFFc2anMKMC2vCCXebOQ4PTDGDOhHFH/uXI0Xdm/BF75xM277wR2wmrjgnlqC\nGEECPb4DQlW+92f34onfPwy7IwMvPPMsFi6cLwwAemsqwRC1pQcAMUNChO2GE8MYj4wgHA3CabHC\nardhU28nLG4r8jyZGDx4CPa4E/Ul9fAkvLAm1ITGqDGSiCCY9CNkDMgE57VkwWaww0I/c8RwMDEA\nfzggysymBC2W7MjJyJUWgvHIOIYDo8jJzIYLVoxFRkSNP9OVKb8fjY6iZ6wHRqsRGUY3nAYPip0V\nIMRzKLgH46ER2GwuOCxOuMxOWBMWuExuROIRjCfGEI6HJBgaGRxFJBCH3eyA15slwXgoPozhkSG4\nrFnIysiHMWnCocFBjIV8yM3PhpvK0+EwnBaP2LL5Yj5079uBtp2dmD19NlpyG+GABeYkKUth+OJ+\nBBNhUXCn76rLniEWbzaDqsq9Nbcz//yf6e85WfFW3z8Xbi66FPdasXwZXn75JaxbuxajvoBM/lI9\nTSRRnOFEfVU15s6YgeaGOpSV5KGoKBturxmhWBTb9xzCitfWYWNrB3b07MOhgVGM+YIi3MZEym1z\noaS4FMUFpSgvqcCU8hoU5BaiIKcQVpMdTpsH0XBcqrOTycxRAAAgAElEQVTs7bZYbAiHIwiGwvLc\nRUlXi4cQM0VQWJaHytpy5BbnwpltVyuVhkanxshkws5xB8/x7uE7mwGgrzMMyB599FE88sgjWLFi\nhQwTjhEmRVdddRVuvPFGoWEfvh0OALACsfdALxZ9+7v429NPS89mPBSB2elBZctMfPpfP4vPfOxK\nFGaaJYkkvdyi0zbZc8/eRAOwNwTs8oUxEo4iw+WAy0NfZQoaheEPMaGPimUdf5flZlOK6mkcGo2I\ntarDbkF2hkOqG7RLGhkJS3WErSkep2IChCjwFwrB6bLDYYO0pvh8fqnkeN12OMwUAAT80SBPFFaL\nBVarSbWkaP3+NlbhacEUZNCreucdNqU5QLAiEAiLDoLb4YDdmIQ9FkOBzSKBloctp2msSlanHn7o\nT6KZM7h/P85uUC0AdpMRmzZ3YHn7Rgk8FzY0Y25dA5wmivYxiLNgdQd/3y7smLn1dbj47LNR6MkQ\nFwAzQQKrFYPhEB5fvBhLujoQc2fhK7d/W1wAgmYDuA6y6sSWBYIyBEhoY0W2hJWBtNxmjWMhzxfL\n9hOtWYohEYF/fBS9+/YizCLDyAjaVq/Cay/8Azu3bBGRBYIJsXA41dalxp7iC+rzFBNiJhg6AKDi\nbcNR2QHpY/H1qMrH69fXwQYm3vyZbAJ+hufBvwQT+Dv9L19L3yYDAAQOhCGpgQX8PZMibnxd74Nm\nHKEfgz9zndXBAP5MpgILKbQDk2dRm8f4eT0e4c/6+ertCwQ0eP2YjDFJIwNCByX4Gjcd3GC8EY6o\nlhWOJYqhTU74Jtc/eGx+nvtMj4vS4yN+X35Xvo8/63ZrOuiin4eV+5CEbGKbDKDEojE4NJCF++H+\n+L35l+ctVPEoVdhVH/kRLBLpQjmcYZJ+PqzmjoyOwuVyyv3n74ZHRuD3qWqzy+mS4zNZJQ2dtpbv\nf//75bqeiguV/hmeMyntBw4cSLlM6efOcUA1eqs1/RqrhW2y7TXHGc+Zn9XHWrqNN++DLmKdLuzK\n4zMxJ1DG1/Xxw9f068yfGSNwTDBJ5798jRuZysPDw/IaP8vf8y/Pg6/xLz9LEED//1Fjm2Ouz5oo\nbNoHFVio6tIWI7VayAJTILLymyFYa0NU2EfcuQWw2CXhn3f2QkxraoDBasHUxnrMnTcfOVk5wjqL\nEMDVXYGMWkah6cZIjKG1NnPqIyDA8SZIL+dNLgIseEYTUvm32iwCGHBo013m+Ucew72LbsXw9h2o\nKypXDACnCy8vfRmvbd4ksdbcqQ2YV9+CDIsdbqtdxvLe4X683LkBq7Z3Ire0CjfdcQfO/9TVGDcb\nMRZLCFBLhjS/vJ/WsuEwvGYLsvjXCHjJktTaLWVN4jophVnlqMO2Nj5/LgIGZjOYuSlbbn5//bkU\nf4AUa0t02KQ9j83Tx7UBPNPh7OT9HS94k+XjlBPE45/9mT/+X//6V3z84x8/7eo/z53xcY7Jipa8\nEizIK0e1LQMNBUWYkl+ITKdTKC1BQxJjiSiMmR44srwisiePGAH/JP2BjRgd86Fn/1709O7Dms1t\n2DPUhwPhIZA7wMesccYszJw9C/PPWoiptXWYv3ChcgzQL48WKyxesgzXXnMNDh3oRSQWFmQqAwZ4\nYEG+KxP1peWoyc7F1Ox8FNncSIyMwcBBGgxjZGgUB4wxPLq/A5siI/jeT+/CdTd+VQVBp5gikgGw\nbXg3+gYH8dtf3ItH/+sP8GYX4OV/vIDZs1pS7MX0BTd9gaGAYTSp1MfVFJEQmcQo4mg/tB0GhxGZ\nHifGBoaR48xDgTMf1iRt71hHmWBHMtE2GGOwGKwgUZTfR/fBjoF/GHyxDs56LANHS2rS42/pKj3x\n4PIB5pRoQhxR+ORRT8IKVtK5bxV2JhCUR5iv6fszsC0haVDCjAZRW5DjUn2BE4VBRESYkPPG0lBQ\nplvZpxxPqLVKYUJ0wePcBxdqEw6MH8CmrW3IzMvB1IqpyAaBEIvQWNUmR5E/qlPULN/ijXx6j/98\nH+8dZ/75P94RT+X3qX47jbrGhXn9+vV4+umn8fyzz2DPrl0IhhXSr1O/KkqzUV8zBefMnIFplVVo\nrGuAx+2RFpfBsX5s2rwOa1pbsa69G7v39WNgRAmocfEwwYosby4K8gpQUVaGqVOmYtqUOmR5cgRE\nspqogm5XYyZG2ppZ3EWInrMqy6AuEAyItR9FxvIKs1HTUInS2gKlOMghJ7C87kh++Axw3Jz/sIt4\nvHv41h6BpzIeJn+G44P0y/vvvx8PPfSQ2CXpG4Pic889V3RcaE17eAVUVW8lNGXFFkYJgH55z6/x\nX/fdh4GBIQFwHBlZyK2swRUf+zg+/4mrUVHoUkrHCSaY6m4xqBgDMBgDDkWAg6EYfOEI8jKcsFm1\noCUUkDYAzklUt6cWAJcZfzAChoEWq0m1dQrtPibiSLo1E3WdWKnh4ThqzCZWcAgEqHHED7GKY7cb\nkdDKGwyauBoaydvXWhmirKpLwmZVIoIMqkJxUYDm61RYZkBHkUrVeJqAIRqBwwDkO+wosJKVp4aw\nmvbU8TnrPfTHh3Dbt27D0P4DeE9jM86aMRM2owGbtnSIDSDvxfy6Bsypa4DLbBRNAeoirG5XAAAZ\nMme3NONCMgDcGYgHQ0LbJwAwHI3giaVLsXhzGyJuL7586+245savIsTKP9X8pZVC3Quu/bSD4nTB\nq80k0EDEJlVhY6I10bohtohGRcFlMCwAYiyB2OgIutetwfaONoyNjIog4VD/ADo7NuNgby+GBgeF\n1ivEgLQWAH2MMblhonK0IoieCL1egWRyBT0dRDhaj7K+Hz1BT39G9DaA1wMZ1PDXrl1SawEhk0TX\nhUgDAJjA6olYOjtAFy3UP6Pvj4CUvslMlXad+B4m2Xryxt/x/0y4mMTx2jGRZJLHjckbj8//64k2\nAXfRXtDON65XS7WDTp79+N50ZsXRGA76eqNrMuigAj/H76czOuSz+pjTYji23aRvqrdasSN0MIH3\nSMAZrT2M+5M+aApQatVhuSdM7Eh31q6Zfv/047NSy/dzvbFarBKzUmSOVnE+v1/iFwsnDu3YjAN5\n3PPPPx8/+MEPxNryZDcpfBmN2L9/P774xS9iyZIl6plLE5vmNeZ7+NdMmrpGnknRZzT6uX5sXWRP\nF8rkMfQKPV/j7wUkiccFmCG4xGtIIEY/bvo10q08ddp++u/SASj9+Pqzlf4MTL4ur8tmPgkA4Ijn\nVi08EnFw/JKVhWQEsDmlum1xe9A8ax4uvuxf0DS9BTkFeSgoLYHJbhS2M0FaJTCu5zuT0kX1wGns\nZmX/ysOJOw1bA0QInfOmmgsZ/3KBkfmQxfhoHBkuE556+H9x36LbENq3H7WFZbj0vecj02rDsldX\nYP32LRKPz6qqxaxpDajKL4IxTstZM3r6e7FsWxte3b4Z3vwS3Pj97+L8T38CIYcd4TifWcUs8wej\niETDcJrNKLTb4TUofRllsZuSXtMSd+Wq44snRPvGajFKO5mXa2rqpinnNQUDEABQWhi8HHyVAAJN\nwJk/Gd5lAEwuOR0x9I+ZoOqf1p+Dn//857j55puP8NI92YlGvZ+hkhFMK91IyIBoMmXhw3POxkVT\nmgTlfLZjHZbubIMrvxB5xUUozy9EttuD4uxcZGdkIjsnF/lZOciwOpAMR3Bw714MDw1g7fr1eKl1\nPTbFhrEPDMCA2uppqG1uxgWXvB8f+NBlKC4qgpmBkAF45sUX8e1vL0Lr2jWwJBPwwoxSONHozUFj\naTmKC4qQm5ktD8346JjQ8v1jo0iEfIj4fDAQiHBa8Lf+bei3GnHf//wBH//MJydkxE/hAjGxPhAa\nQN9AP+75yT146Nf/A09GNh78/f249IOXiEWg3aHUh7lx8jsMAEjGJVFWaxcfFKbD9BWI46B/BAab\nCTYz+5388Dq8sCSp3KySd7YAKEYk/xBNVDZ/MLCixR0SVOADRjhSpfcqVVEjRV+YtfA7JVcjiD15\nPdynmefCx5SfIiTJfXH//D/pW5wwlQWKvGbgMRT0kDBwUY3DmFACVyq+MYggm0gxGrUqjXS2MpWj\nwisLQ/QSjSIRi0gPlM1ik1l2JDQsVH+P1wub0Q6XQer/ilulKUZzz2IDJpuyJXlXBPAUBvakj0h/\nXCIBWr0tW7YMzz77LNasWSMVCFYzrWbVy1aUbUd5US7qa6owZ0YzairKkePNhNebLT38O/ceQOvm\nTmzp3oq2za3Yd3Bc7Ps42qRNwJ2B/OwClBWVY1pNnSj5FxWUwePywu30KBAryRlJAVQc51GuwjCI\nx3mQgWs4LBY7ngwPcouzUVZbiMx8D5zZWmCowdHxGGngBA4myGr6Uj3x9U9ExuldAIBjg0Hv888/\nD64/HBvyBFJkLpHAjBkz8N3vfhe0Azy8AnUkADAyOo47fvBDPPbkE9jXs0uVk41m5NY14pu3fAuf\nuPxDKHRzDExshJ4OhIGekQBG2DfpdsJiJfjM3nglAhlPsnc0CafDImrHnI7IyAwmaOcXkDkqJ9Mu\n/Y58fXg8LIKRLpsNHo9JplAWrwJBJvAmuEj9t1AUkAlZHA6LDU67CgYD4SQCkbAcJ4NUACYAVPWP\nxiQ4Y1JisKhAj/MtkyYuEUxAeBxxP0gm4bCahN5vCsel57/AArCeyz3q319sNtmqEI3gd7/7Pe74\n/h0Y6j2A8xun45yZs2R23dS5WWwABYxpmYU59fVi6UTwWwcAVpABEAljYVMDLpg/H/lON+L+oACs\nJpsdo/EYnly2DC92bETYkYHrbvkW/vX66wG3Q4JhgVX0ihcFAJUW8ES/PqtjGlVf1i6tJK3a/5SY\nr8i3UvGbQIrRhEy7VSpRhKIJ53Kmj4XC2L2zBwcPHBCLruUrlqGtrQ0HDx6URIVzFJOX9MTkaDOg\nXrk/VlJ+rN9NTriZ7PD9TEr1+VIXDZxM+08/n3QAYvLxJLE20RVhggGgJ8B8b3p1lvs8WvX6sGMd\nw5HoRK4Rj60XM1IJmR54TnJM0PenMwCOBqjowEP6sdMTPf26pbPQDmN2iNCkJvykF3GO8h3TE0s9\nBtP3yWeHz1Bqm5RQknUiYm1M2I62b/3zGhCREpTTmSeaDa7+Wf070aabYCkBl5Nt1+C+Vq9ejauv\nvlq0vvSxrH+HiXNVUZ7avxb9pSYO9UX19q10AOtoDhj6a3rFXv9s+njQxwf3ReCA/+psHB2Q4PXX\nGRSTE//JY1gixknsnSPG6UkAAOnjQEA5IxX5LcjML4DVYYczw4Papka0zJqJnPw8UbgvrqxE04zp\ncLvtoJO2zx9Q7VxkRrDoJELpEh2rJFcbSoLfyhc67B+ZuFMMgMM+Q3caCvOxRYyuMMpanfR8ugD8\nz3cWIbh/P2ZV1+KD510Ar4UAwHKs3NIqk+zMqlrMntqAqSXlcFls8I2OYSQWwpKuTVjStgG2nFxc\n961v4eJ/uwYxt0sAADJrCWLHQiG4DElkms3INVkEYGZjkqxc+vkngahRtdcFWWpTcghwWlTiL6At\nKPyubHlVkZDAPK+TykjU/9l6kEBIynVJChbycqSmC7WwSZCmv3bYf44yT+kHU2mJvjP9hhxtYpt4\nTaVPh01AqQOnp9aHjzL1m8mJu55YTU7Jj3UGxwse5RF43R2oI00kdBzUP7jzh7h90aLU+U3ujzv2\n9Zj0W5k4tGYUqOAjDwY0OfJw7dwLZLCt3NWFh1Y+iwOIISQDhwPCAIfovRuQ58lBRVExaorKUODK\nQKk3C16rHb6hEXQd2I+NI4ewrmcHBhKjYltBionB4sT0hfNw8SUfQGleIbZ2bcWTzzyFnm3bYEmE\nMdWZh9q8YkwvqUB1ZpYMwMGRURwaHMa+gwelCk2quTkRh9sQEwElKoJvC47i/wa3Ann5ePTPj+Lc\n89474VNxUhdGvZkTQF9wCAf7+vD7e/+ARx9+BIZYEvPmzMH555+H0dERXHbZB4Vmx2Dl3Pe8R5Sh\n08efwcDgVCXSDCz5ZLGWHWN/ECwIy1Xlg2UStFAme6HUKJVl/kcVoPSUXvkxKL8DbnqCw5Zsvl8p\nS/Oj0ofKqI37MGqsAt3QXYZ4FHEDjdoZcEuNZ+LU9b5MLRRNSoVnwopPJeNaQs5HXMpAEx7dxAFj\niSgMRqtAFSbNGJQTJ6l5BA9o08bZkhZaMUNUuAaheFiSQLIgGEgLOJGyfWHCQcsYFXSfFrpzCuPh\n1D5yrDlgcv3k1I5wvBkofTzqP+v6xQywWNFdsmQx/vH881i1aiUOHTyoBbkQQa+qskJMm1KGKZUl\naKqrQUVZMdwOm/iM+/1B7Nl3EKvWt2NDayc6u3uk4snRwmoqY6pMbwYK80pQW1OPuikNqK6oQWFe\nAQxJM0xGB4wG/mvSeKyk804kFiPjY5L865VWV4Yb+YX5KKsoQ2aBFQavGgVE3MUvntVKWt3EwjJ+\nFACge5hMXg+OpBAeeS3f3Pt35kfEye1Rr24yMCQA8Mtf/lJaAPQqmcvtxkc+/GHc+LWvoaW5WSpg\nh483NUuRIUQdgNb2zfjW7YuwbMUKxAJBxYM0mlHcMgs//dnP8cHz5sNGan4oDJvdJveWQcmeELBz\ncBwj8RiyC7LgsbEvn+9jEMrKlQFOu/I+puEMqf0EpWgVSEcIk8kIp90kvsvMt8YD7As1wmZl779q\nP+eUFwyreIQifZrjpQRqtNTjxmIOk/1QPCbjzEVV5SiZXkrtmf/n+A3Hk5oooEXAB1bP9eU2GIjD\nEI/DbbPAlowhx2ZBjrb+iq9y2hXkHB43mjDgD+LX9/wav/nZz+Af7MN7a5tw7syZMCUTigHQ3ipr\nBF0ACAC4ad3E5MZswar2DhAAYDX33BnTccG8ech1uBDz+cWth+Kc4/EE/rZ8GZ5v34CQ1YV//+at\n+PT118OY4UJC1iqh/KkkiRVIE9cnzT6RM7EEvpplsFYGk2uaJvfL55Pna4hE4bXZkWk1pgAPPsX8\n3owtNAxPlptQKIxgKIj+/kNCGaal15JlS9G5rRuHBgawY+cuSTjM0m5hkqIF7QnlJr9O0srLqyeu\nBJD1OOvwSFS9SvBHbRPWY6wsc02inpEeJ3Ku4euTN5UYqmdAD4UnwAu2OJDZpI7Bz7Oyx2eN85nY\nLUryyjXQKP/X9yPMmtdJ+GV9FUZKQpIZDkgdwDj8/FLZ/UScrIM4co+V3WPqOGLhrK+9+lVJe9q1\nUF+fMyQhk/AyPQXQVZwVoCFru17l1/N0ybS0imp6JC6nq83HKqyRZ072o7lLCGAgYYq6VtJWQlYF\nYyHazBHIUbdTjWO+jwBACmhIR9+081YUFPVF9dupP8wp+Qpdl0IVatwuF775zW/K36ONiyMGyqQX\nXnrpJQEA2HolY4MtDQRctbEiIBR1sVL3Q4FsExmdNlYkEZ64HvKhNDBJT8Dlk9p3FIaBdl/0eV4V\nedQ9EZtUK7U/OPfSYi4l35G6TIeNDt5fLZ5U90Cn6aduxJGX47C06Cg5kjZGNCXu1LhQmiRxZHgy\nkJNfiPnnnocPXnElzC47THY7MnNzkZGVLX34ZodFrGIDZDokkrBpDBQGLyLmJ8U6xRwR5qKWKgmz\nX9c1Ew0jjSXA65AGAPB6ik6RsGYMMFtMlBwQ/QA+31w/krE4Xvq/v+J/vrcI4zt7UF9aIS0A2S62\nACzGirZ1ct/n1qgWgHy3VxywqFswFg9jcedGLO/YJAXaG77zHWEARFxO+GNxqfqbknFkO2zINVrg\nRFwKq8IjTkxU7fX2IQK9o7GwrF0WkxUOs0r+tdlLSn/8q+AmlYEQLCCzT4p/qiQpAABnRmEAhJOK\nNM7gnpUbh9Ei/X0Tea/+nwnEXJtvtQeOUIRRDjQWCsFiZ/eyRb6EQfp6GDgemUfrpkETU496k15B\n5OnSn9bEZIKwjNx0VXAUL2DNeIifYALHr25hBTQelb453kl9MZyYGfQBnTZRaUP7mEDWYcNfXV5W\nSTkXW82KeKEWVwNu+eZt+MlP7pKeu6RUhY+9cQBKP4oGEmr/qEtMOorwW9Q+dAJ4LoALsmpw2eyz\nUFmQjzU7u/CH9SuwMzKGiDwQ6nnWQRgyB5j2OmjDZM1CgSsTpd4cZDk8sJtsMJqt2NXXi+0DvfC7\nbehHBAejPmTm5aM0t0T4MHa7GdU5Oai2OFFsscMSDon68vbeg9je14u9o4PoCwxJ3/tUZynOqm5A\nlTMDBckEwqOjiJstWN7bgz8Ob8Ws912E3937O1RUVxw2Dx3vWk3+PcfBId8w9h04gN/++l6sfm0V\nGmvrhbZM5WsGHvPmzpM+vLbWNlxw4QXwZnrlmvLasvLDQEldYJ0SqY3MlDCiHn5MKCXqD9zhJ6+N\nX1nY0id6LYl83QGmvzftDakIa2LkTgAMJ3aV1NmkP3j6k6YfR62MKgBMqwil7X4CzuN6rwlMpa6T\ntrAfI4g7Fnh2Yt/i+O9KC13kzUdUcY67i8l7OPwDeqBy3N2c4Bv0qpQg/npwq+zPhXmiRosBoyNj\n2LhxE1546QUsXboU27Z1YXhgWBBjYkakKteUF2Be4zRMrSpB1dQKlJQVweXxIBSNYWhkHF3btmPN\nuo3o6NiK/v5RhAJJ6YHmN3abXMjPycG0qRWoqa5BU/1s5GWXIMOdK4k/MwaK4giuTOBLrq0Cvag0\nzuvsDwWQsCTgyHAgOy8b2XlZKKsogolIJadF7bFiS4rMZ+oOaVdKe02eF31MHu1enMjM/Hr38EQ+\ne4I37i3+tr6+PqloUQOAtksUb+Jmdzpw8cUX4+v/+XXMnTdXVb81EDN9YRYAIBrHprZ23LboO1i8\nbJnkZ/QlNtjsKG+Yji99+Su48gMfQE6GWZL2kNGIwYQRPvbk046PiTXbqjhVkkYficBBez4CiFrC\nzcoU59xAiMJ+SXhsZARAqti+ANetmAS39EgWv+MoqZ4JcZ5ghZ9DSTybgzGpjrssRnkmqH8XDNPi\n1qDYAVquF4ywJSUkyYXLaYNNez0cTUrCzSSKwaYkzFzPCSCEtfaGRASFLjMyYQT5L3ZtOqUrgsGo\n2l2MJgt4pTf3+fHrX92DR3/zKxhGD+GC5uk4Z0YLTPEY1rW1YXlHB8xGC85qbMTsulq4CGAwgTNZ\n8FpbK17p6JAYYn59Hd5/1tkocHtUCwDbEmwOjEVi+NvSpXhu8wZEHBm45sab8K/XfxnWvCxxRBCg\ngNecqp2iAaLBFJIUktVFUcCoHIOBNN/L9h3+jq07vK5mowHGWFQsp3Ks5hQFNf0JnQyJHr6qqMRh\nPBzCgeFhdO7Yhdb2LeJ+YHPYMDY6hH09PRgd6EdwbEyYBuNjY+je1i2ixpK0EzWSBEQBGjLzSCyn\nr9OqKk92n1w/LaFUcVJSvMKlAiv/j0tsw2CcAA9dJ+xWmyR8TOqpccHkgcdinKmSNmYPypM7HGGn\nLAFwswIwSZ83qh5+qovL86PbJnIu15I5fY6TvJnnkVAilBECH5p7hQib6TGfRhnXpxjuRwX9jP+0\n+yj/5zkwYWfcoo4nOaEgPcRS2cqXDk9NWsvSQA59rZT7l5omyaAwy7Vjksb4SDACaQdVwC+rlpIl\ncROXF0W9TzCLoMV0KvbRRoYe3hx1imZSrhoFCZiwJkmGpSSxGohDkEQKEeqLwmC1SoyvF14IKkVC\nis14BAODAnLS6sKEWYERkuvSsi6ZQHlFOR5/4jFMnz49xQKYzAaYfNq8VJxbf/Ob34i1avom7JNE\nQsYWn1uZH3TDbDJstOoq8wa24MixUhVXVaKV54vXOgV7MROaXCalbZy6Zoxj5RbS2k47Wepe8fnX\nVe7puMKWKQp+6sUng9mGJEE49kFx0wE5fVxrz5365aTYlBeRrS0EvESsb6KdyGa1w53hkfFOa0Ae\nM8PtRmlJIS5534WYOb0FTtp+Olzw5uQgp6gIUWpXUKSPgAVTWA5n3nIOMbYmaYefvJpLiUtL+vXW\nsdQzpP0gIKfsS3u2NeCEY4UtY2yL1b8jj8c1UI1x1b5qMQBLnnwS9912K/q2d6KhoAJXXnopslxO\nvLR0MdZ0cl43YWF9C6ZX1yLb7oKBujFOJ3rHhvFixzqs3r4FldMa8aVbvoX3fPJqjJqAoUhAhHRz\nzEYUmZwi3ke2GM3FU7odaeLseuTko4AuNUpMzNhTU0gqpuI6LjmzNPnGpdKvJBZVETFGrQlaNUrr\nQQyGjti2pMPkgAceWGGFCw6Y0xg5aoZTENJheYl2N2LRkNjOjEbC2PT/s/cd4HFV19ZrelfvXbZk\n9ereOx1C7yb0BAhgQ4BgCA6QUA2EBAi9hG5TYoxx7yq2LFvNkovcZat3aTR95v/2PvdKY9kYeLz8\nSd6XSfTJ2NLMveeesvfaa6+1txbNPV0YlZqKtKgkrkD70+X8A44h12DxcMR/U42TNlgxv7g0Kecw\n0ilEdEIBaAxVi1wMACigZXq29OKypviz/2edvCUO/depjqHDEyba/SgolljP8hORxoE3Yy9wzTXz\nsGTpUu65+34AwH8q+7jPj7J2Lx1qHDur+KERxd0fi5OXIunmR0GJaRHp+OXMs9gP8ovdO/BVVTH6\n9SokZ2UgP28MXDYHmhuOsbhPf08PrF296O3p4klGffsB0CAcRsQFRsEUEIDI5CScfdXlSByTi2aN\nG20D/Qg1hyBArUUM2TfZnTi+bjPKNm5AVe1OVNTvwUFXLzrh5SoQDTlVSNKCYjE6diSSlXqkKXXQ\neTywalT4vLIU63ztuOKuO/DC04tZJEXNxsrf91TO/Pc0Nm09XWg4cRzPPf0MtpWU4v75C3D+ueey\nKnBvbx+6u8iMSsHeo3S4kXp5UnIy99/wIxzsEfopl3G6Y0HaRs6YEA+/H/l0PNN9Dg+5/mdj9X2/\n9X1Vih/7KT+VPvdj3/fH/tzwEfQ/Kgfpjz/2zU7zc/+s+xvs65fiNzrHCVQ8euQIq/l/t2Il9xdS\nn6287mnbIyAvPESDsQW5mD1tIsZkpyE6MpSkZA1l6QQAACAASURBVNHY1sIV/p01ddhbfxRHj7fg\nRGM329royCpMqUGIKQRBpmAUZhcgPzcXCfFRTO8PMEdApTDATpxsHx1F4nShA4W0RriiRcmdxwWX\n2wm9QQ9ToBnBEcGISYyBOYqyOCnxFwtLUI45Cv7+Bf7PGt+f8cj/436V1jD1pH788cf44osvQEK0\n/PikoDh/dCEWL16MmTNm8llIoc0QGMNZCj8jStIpiX78ySfxwYcfobu7iyvtPo0OwUmpmHPO+fjV\nL29E5qgYbhk7arWhrqkL0BoRGxYkqitaoKPXCZvTxVWoEIuWVfapqtLT2zeoF0GVKb1ajWCDDnot\niSAJ6j61kBj0Whipl5/o/OQOYLVCp1XDYtLx9dAZTO0BBIQZSdiPAAgftTURg8vHqv2yeQTb/RH1\nX0F9/+L8lrdoTmgYaBf9pNS+plOSVZ5QZw5Q+RBOzAVuv6P2F2lqSNOZgqg+AM0uYE+bFe+/+Ta+\n+8tiKLqaMTsn1w8AqBEAgEKLSdmZGJ0+Cia2OFTAq1ajpKoaWyUAYGJWBs6eNAkRJgEAqBRKaHUG\n9Dnd+MemTfiudhccOgtumr8A1991J3QRoXApREKgocSY7asoNhfBD0cQZKdFCZZU5ZOTA0pWaJ1y\nnzolr1LyH0RjLe01Z2Jy+u+9or5EMQttAyr0wIduaglyeqHSaKHRqTj26O/sgIUovCQU2t0jHAia\nmhgwsg4MoLOzA90dnbAYTejq6EJtbS0aW0k+WMXChn39Vp671KJG72cfsMFoMnI7EdlHUvWSeqM5\nWddqeTxcDgdI8JEEDClIY60ESja81Nog2HeEmzDcQMms18UxGN0JRZa0rcmVbNHMRwG0mAxifMQe\nJ7R+xLobshoT1WkuVNHao0oxARDEMx6ciPwBQ5NLSug5cVUTlZtABEJuPcIHnVaxZInGeyv3r1Ac\ndbq9dmjvZbyBknZJl4BBFI1WsAgoSyJggRJsnheCwSAY1nIrluAUiiK1sGljJiT97mArpRyxngEA\nIGFg0n+RLNDousS4cimcq7r0Re0BLCEsWcTRdWp0Gn6eKq0GHpdLJJrEDZeuh+9DspCjYVFrDTwn\nBIjg5XlA+1JvXzcnyQsXPoyFjyzkuXK6VpGT57iIF3fv3o0777wDJcUlg20gciwpl9jpvUXtTrbM\nllsl6FLpPqX0jakRciO71MxOGxIzWyR6qQRyiYqjhjY/HgePJODHhUFpr6d2LKpks+mvi3SpaG0T\ncORjZhTZg1JSzuVEmjMSgMCTjxJLvYEV7LlSQEwCkwUREZEMtpHoY1BICCfdeqMRloAA6AwGBtFo\nDyFwmUQso6KiERgcxG8ZEBSEQLMZibFRSE2K4/iF1gxdAT01goxoD+2hHMJDelxCI0blkWjrBACc\n8UQ+c/JA7VzMmFAJhheBnZwO8/ORLQHl05CcUQQAINhTCha5Xb1kCd5a9Ci6j9YjLTIBl513PoLM\nBgYAttVUsZjhhPRc5I9MQ7glkAVZDVodWqw92FBXgaI9lUgZlYMFT/4RBRdeiB419fDbYNIAUSol\nwqGDmRN/cTbLAZ983PhnGy6XUzAeGOA5+d7luSr2LOEy4CRdH3ktkyYct2j3ofF4E5Z/sxyKR+r/\n5Is0RyAtOBVpuhSEIwxGn1S+8YcH6bSVEE8BN8phKYFHQjiNLMT2NB1FY3sb0lPTkTcyk8UJBOla\ntoLxxy2G4EG6YFosjGvQ3HcAxw7SMaJA4qgAUVE65SVAALeX/NjpYWmgdADtLU6otEBwmFbaoaXF\n5F+ZlTbtkxEG/w+Qpp2slksVUK8SAz1AbU0bOttsyM5NQGyyWEsCjfUJAGDJ55IwzskKqYNP9qSA\n2AuDWoAuD8+/H/GB4aiuqMLRlkZsKS9Bl6tPHDbyvKD17AaCoUQUNLg8bRzmFo6DT63E39csx7qW\nw8ibOwM3334r+vp6UbK1CD0dndTUwr35tXV7GHV3eJ2DjALqN7FAj7Ej8nHrtTdh8k3zgDgD3Cpu\nQ6dZBJzohatyN9asWIGVW9dh7aEd6IWPBSlEb4kY6mhzCAINJnhcbkS4lfhFUjriAkNwwjWAT8s2\nYY/CjsUfvIsb5t0gfvH7AeszLnt55tAh3tLWgkWPLcJHf/8Q06dMRXpaGgcGpBLd2NLCT50Ebdrb\n2mA0GLFw4UKcc/ZcESANo+n9eCxiOEAkPd3/aACAQ4SflAD9qxO4/zQAwL+fkgM5Asf6rajbXYdN\nmzZj3fp1KN2+DVaHjYM1eiK09dEyiY2wIDdzJKaML8DYgmyMSE7ggPBwQwPKKyqxq7oONXUH0dDU\niX4btZdo4aFEjBJ/SwgSYxKROSID+Zl5iIuIRXx8IqP01ODicZG4E8UKojJBByaxPtzUhkLHicoH\nj9IDc5ARliAzQiNDER4eBhWpo9FLLhNK4CxtUgzGDPb4n35a/avnz0+a7P+GPywDSURLXrlyJd56\n6y2sWLFi8Ep1Bj2uuOIK/Pa3DzAr6lQAQKYQU9CjgN3pxpNPPYV3338f7a2t8Dm4KQxB6YW49e4F\nuPSqK6GxaNDuGIDV60FbjxVGnQnxIaSqTVUcUqCn3n+RhBkMQ9oP5A7ByZdP9KZSJUivpYoj4HBR\noEYihC6u0HJPOgW1Um83BZdUWaNqslatgkUnaPukYUFVfprFep0KGinOtnlEcEeUfzqXpMIreqnt\nAJQsk0AXKfGLHY/ex2l3szq/RaHgykyQSgj++fvUiGocJSlAJ4AWnw/tbgUOd9jw6VtvY9PLzwMd\njTgrOxfTCvK4pWxH9e7vBQA8ajVKq2u4BYCud0pOFuZOnIhISyC8NjtXdwkA6Hd5GABYsXsn7Foz\nAwDzfnMXtOEh8JL6Pyv7C5qkDABQoO5UCK4khdXUJsGiWRQEy+0AVMxjBpIXevgQqqEikDjL6WeF\n6OL3v8RpIWintFtZ4WPBwm6qsPuoVUPHQTcFG1Qw1MILs0oFs0IFncfLWgjyqUPfWYpUCoQJQOyx\n9qHTOoABnwLNPX1o6u7ieUD6CL0dHWhrbmGQhNh+/X39bCk2MGDlxJ+CZHK6aW9pYS0ft82OPVVV\n6O/thFmtQ1igBQEGPbv+NHR2oa27m0GSELOZbRhJdZISTLfPi/buLnT32xBuMsFkNLJeA00qvm+q\nJnpJQdzN+6mdbNbkjjwo2A2AXvQzVIGjRIqALk605dEdjENE9XEwmZdjcLm5mX9O0huSz2qpgCRE\nfIalS1QNlDnE/Kj8z3dJQFOUpIeqbQLFEO8lLxxmRghWxWBlbfDzJfq51I4hQiCJxTDYXkA/Qyk+\nVXhpTnqZtSLLElOcKTgAnJ4xIETjQ3sE7QGDyRpX1qkqLJgrSh3NWvGzakriFQo4mUVCEu466E0m\n0V7B9h/0mT7oNRouiHk9bsyePQOLX1jMe+OPAQDo1tauX4fbbrsdRw8fHvwdfy2JQTcDotMPlqal\n8aXHQ7pKrNTpkSrw0lhzz4CGrUF57RM1apChKudf4hkrCcjyeKDz+mBUa6BRkM2qBw6fm0FWYqqQ\nQ5Neo+W38Lid8KlVTD3vpNYugwkRSUlQm02cIJKwZBgl79HRQlSS26JUiI6KQXh4OIMxFENHREbw\nEyJxSnawoL3DDwAIsJhhMZtZ4JWLpT5AL+2jzI2VdKFoGGQAlfYMq48stcVzpUKryiu44MPju1N3\nop8GANDnUuV/KMmWmSND4BbNNQYKyBJQocS3H32Et//wezhajiMzJgmXnn8egkxGbgEoqtx1EgAQ\nGxrOEt/EAOmwW7Gusgxb6yqQlJKJex7/A/IvvggDBFp7HAjQqhGuUCAQBDoroJKAQtoSZBht+N3x\n6cPL9tT79gcAaM9yeRU40dKD9vZu7K3bjf179+DEsSM4sH8fjhw6iPa2Viiu23e7z+g2IFEZizGR\nhRgbPBoBMEkSTzI2x/Iw0kYvb/lioVLNnv5EC8ChcKPTbUPd0cPYUV2Di84+D6NMoRCdgjIdXu7B\nlqUbJbYXtycL0TSlF+g/Aaz6Zicam5tx423nIyBGVo8WWILbTjmtB4ZgqgAQAEB0DTWsLcDbb3yJ\n8GgLrrruLKiI/05vyIm8xC3h/ek02LY8psOhF/nvvUDphgaUbt2D8u11GDsxF3c/MAtqPb29qHhd\nffV1WLp0iVR9GY5dDUdohcgfMSXOnTwDn771PnpaOxESGIS2rg688s7rePXjtzHAxPqhFx/OIBBA\nhVRYcOm4KZiWkYMjrU14ffNa+KJDMP/3v0VITCSee+FF7NyxEwE6A+KiYxAUEIjW9jZ09vfwEFBP\njtrlhabHCWXHAGLUFtx2512Y+9tfAxEmFoEqWboMh7/bghC1DpsryrB5707sRS9DOvJQhQQEIy8r\nG1qFEocPH8GRpqNI1gbhV4UzkBgeifUHd+OLumIY4xLwyYpvkJeb978CALhJ3V6hwEsvvYSFv3uY\nRYzErBWUNdlbVK3Vi6BUqcKjjz6KJx5fxKqfw9fRjwcA/jcSmh/e3iT6zT8t9fgvA+DMQ/u/naDK\nfZX0qbQBk3Xf6pWrsGHdRtTvPwC3l0y9xG5LdGWLVonUpARkZ6SgIDcT40bnITw0CEqlF3X79mN7\nZTW276zC/vpDaGxyDpKmXFAgQBuA6PBwZKSmISstG9npOYiPimN7RqoR2AbsUKi1osrkUcJuc3Il\njWjYHh+9l/ifJdiMkMgQmEPMMAcbYQ4zDto/cf/kKViY377HDBv/VXXyCvvfHt9/2kL5N31jeT6R\nhROJQ77xxhtYv379YLWXKjGXX3457rrrLmRn5zB1+GQGgGyxJVxjqnfX4ok//gmr16yFvY90/UkD\nQAtLSjb+8NJfMH7uJJzotqHD2o2gsBCmdhNtnsRjrP0uWF0OhISaYdGTrzGJ+dlFxUinFTR+LRew\n4LDZhe0VnfV9VhatsljUgyJ+vdZ+ZmxZ9HroNKIi0291wOWmdgAVQkwCWGDmgFNQW/UaBWti2B0+\n9BGVEz5Y9Bq2cqJLZKaAiwoGXqa7y7Z53DJHBVCHGwafFyEUmGmJ9i/EmCjgYlqy1FcpV60aXS50\n+BQYUKnR1OXGR6++iuJXFgPtjTibAID8PE5edtQQA6CW6c2TJQaAUWIAMABQU4Ot1QIAmJqbwwAA\n2QASA8DnckNrMMHq9uLrjZuwfPcOODRm3HSvYABoI0IAHekYEFgyBABQgEgxEVFrKWEmZoOBn73w\nm6YxIBCERG8VHheMaiVCSEhRCrO4MCmlp4PF6dOsAZm5SVsAjUsHfGi32+AkSj19uXxCfJdIjgov\ndOQ0QF7XSjWCNIJtwdxlyQ6UxkBUTwUARBRWN5ToIsDF60EfK6FrofZQ4YR6bUViyur3FLjT5JJ6\nzn1eNwasfQgwmaDyeFFWXIxXnnseVWWlSAgLw+TCfGQkxjPddMPOchTv3MXzcc7kCZiWmwely8ns\nAvr3kvJyHDhwEDOnTENwUCBsZMXMIl4kOkjJvwsOUmpXKFCxuxZ7Dh2GR6FEWloGxowdy3acvf39\n/DMGk4kBACchrgrRAkqgjNyzzdR/ac8kVgSNBzlGEAhG9HMqbISGhcLhduJYY5NoYSF1frd7UK9A\npOCUqAkHATnjIavAAUoA6d+MBgbb6P1Im0F2HqDqvBCdo8sTAJqa6OxkOabTISoyAkHBgYLhRa2m\nSiVXFanAwgKQtFYUIpkLDAnlSjMJvhHTlZ5b/Z49OHb0IBegAo0mRAaFcHxK19/Q1ow+h433g9Dg\nYAQZjAIAkFgBTWxV7IRbpUVqZhaLVivVarZgpnul6n5bWxvPB9r7SFyORoIIn3Qfm9auQVNDA7xO\nJ1wOO8LCQvDcc8/ipptuOu0OPzxCo2f07Ypv8avbb+fPoRdbDRoN/HwoWY6MjOTvYZGRnOAL/IXe\nSbg10Jlrc3nRZR1Am9XKc0aAKz5mvhj1Bi4KEFDkoxZhunsCRElclyxBG09gZ1ERuo4fR3ZiMlLj\nE0jMDfWHDqL++BEucpEYeDy5hZksDH7ZnXbeJyoPHMChtnakjB6DW+fPx4jcbKgNBhbPpLkguynI\nFX0C7eT9T8aPuHPBDxj0P/4puyMBPWbbuF1sgxpqNsIgeWDJQKETCgz4gB6vB3YCciWqOs0Zjt99\nJGoqc+DPFJWfOWJnsg939QjQk9g3ostH9FuIAqC80wmWOwFMlFvSmiRg5R/vvYd3Hn8M6GxDbnIq\nLjv/PARKAMCWnTugVqgxMVO0AAQbTNDTnuAlBpQNm3dXoGRPFeKSU3HLQw9i0rVXwqlTQ0vnjE7L\nhVwDMYO8pJMmMW3YvWaoNO1/hzIocrq75mv3ebFm3Vqs27ABJ5paUVVdj472bvR0tsPttPMc8nmc\n3BLCLl9X19/i07u0iFVGIt08CqMjCpGoSeR+VAoSyRRGNvQSzoF0acJijD6QpAvES1iqeRVKdHrs\nWFa8BWEhobggexwfotyvIomQ+dPorc2A1QoEx7LdI99162EPDuy0Yt3K7TjR1Ihb77ocY2eZBqH4\n2tIWHDvQDqfLiouuHoc+JzEFlAg0W1C/E3j0d08gpzAZD/1+HjR0ikttLi67lz3QRZOJNLmIWUVz\nQSvlo/LIytmtDE+rge4mN9Z+VwGDJgKtjT3YvW8XfrtoHqISiNZFgY0dkydNRXn5TkmBeTgD4FQA\ngCYfUfI/ffENzJg4BRuLtuKvr7zCG2NaZjqKKspQcWg3twTQJpgQFo2EuATU1NbA5hqACQqkaINx\n89S5SI9PRn1rK1aXlyBj1ljkz56KNz79BKs3bEJafCLuuOEmZMcmY3vZdnyxYSXaBnoQn5SAjIQR\nGJ+QDk9DJ6pWb4XGp8I9v38YKRNH44vVy/D3t95GksKEjIwMLCndgOPODhRmFEBvNqJy3x509nZj\n0pTJGDN+LLZuL0VFRQV6rb04J6UQ9069EP19vXh/5wasPFyFsy+5BG+8/z4CCT2kqg9xOv+HWbf/\n5vzee+/int/czegvocF6jQ6pqaP4EKJDlFkBdjtbxtx55524/obrJHhxCBH3B8oHj8yfVNH/qVnC\nvx4A+KlX/O/288NHUKSbQ14EFPzK/bAyS0eumsrihSeh9xI9UgZG6N9I6IdopUQTJNVgepHgFSn4\nBpIrgm6InuSf4MtjJR8+snUPBQ5FRUWcsJWUlODIkWNM8yS0nI57glTDAo0ozBmFiYVZKMhJQ+rI\nRP7s9s4u1Oyrx7bKWuyoqsOBIyfQ2+cc7O03qXTQKDWIjY5Demoaxo0Zi+SEZMTHJsBgMIGqsASG\n0hjRSFFfKjO4XOT/S1RpwBJghiVAD71Jj9DwcBgDzdAGEFoo9lIZGKD3IKHMIWq0/9M4BRXwO2j/\n3WbRf/71kFDksmXL8Pbbb7MGwKBVl1bLvf+/+c3dDAScCgCIkEwU+BQoK9+JRx9bhKLiErg9Lk7a\n3D4FjImZOHfeLzHm/LMRHB3FpqShwSYoNcIogIgCA1YbJzPR4SYYieZoBVq6url6RDosZqOG6f6U\nbDvsTl5P9LtdXT3s1W2xkD4A0Gv1iAorVaQDTDy/HE7q6/ZAp1ZAq6Z+bZqzVDNUM4WfmNHc6yl0\nS4V5AXeXCZCXaJ3cQuPHOKP9nhgPZGlIlbQA8mVXKhCsU8CoGHKs5KK0FJQRtNUOL5pdTvQSYwJa\nuJQKtHd7seTtN7Hqz09D0dKAuVnZmFFQyHoZxADYXLcbGmgwIz8XeSkjYSRKrVbNLQBFFRVYX7mL\nwfgJpAEwYSISw8Lh7LMyAKDRGzHgAZZt3YoVVeUY0Bpw073z8ct774EyJID7pXmPoxhL1nuVKrCk\nx0ADoCHhViXgojVOP6tWQkHj6XHBrFQgUEORHCV7QuyP6K/D+2q/b5XQmFALYLfbgy6fFzYJ9KNi\njk6j4r2F4kayjDSSEKNCiQAlBNPAr/DM7+/nPkwhGEVQbfCg1euBTSkUrVkLSqpIcz+/y8X3TUky\n99sT3Zdmj8vDz5u2LaqIVpWW4dnHHsOuTRsQbTbinEkTkJMYj4DAQHy1eTM2bC/nMbr5mqsxMzcH\nGrcLAUEBMAUGYu2WIuyqqMK5c89CVnoa+vu6OM7QagzCzcHt4Kq1Uq/HslVrsWLjZhAL5byLfoEH\nH/4dIqOiYKOkiMae/O4l5gAn+sR04b57al9w8jygM4USe9IhoMknW+pS0YL0BzRGPfrdThxt68CA\nZE1I5wsBuIPCcBoNFJSAEthElGECYryUoLkkoTcqQns4Lurr64fNJjziaQ2zJSYLN6rhsztg8PkQ\nqFUjNiwYYUEWBAWbJctYFVx2JzMuaU0Tw4Facimho4quU0ntrEr4OPkHFL1WfLv0c7zzt1fg6OlG\nenQc5k6eitS4JLR0tmPlti3YtXc34mLjMHXseCSGhMHndEKp1+BQUyO2VlTgRHcvVIFhuPLGG3HL\nb+6AnltAyFqQBAU9bJdIY0ltaqzt4SYBY6pEq/D4wkfwwdtvMbXbZRMipwsWLOAWqR9iANDYuN0u\n7v9/4IEH4aE2DB8QmxCP66+/HolJSYiLi0VBQQHCQsM4mfav5MpMWXm9dHjIOaUbLsqyJQDApDfw\nn6mqzm09SmI+ueH0OuFzO/kZ7NlRjqd/txD1ZTtw0dTpmD1xMoN/67dswoayIs7axmbkYHRaJpIj\noxjstHvdONLegs011ag+fhyZ06bid08/jZG5OXCz7Z1w0eAlKLlfiLaMU8Nzf3CQhUdprUkaUexE\n4nIzc9hETB+NCgR2MtBIIA4nW2qu/vf6gG5iy6jI1s/HJldD2gfCok9ByOHpAnM/UEv+oz9QOVjk\nlzpk5PyT75A7gIZYALK+Ba1DwiKp5ZFBA2KYuL1Y9flneG3hQ3B1NCE/IRVXXHghzAYtVq9byy0A\nBAAUJI9C3ohRiAuNhE6t5nixx+XAxpqdKKuvRXh0Am6+fwEuuutXrOYfqFYhkEDZQfV+aeOTvvmX\njoenR8OzSPn+6d76Bvox74Zf4ptlX0ui3xKdgAqlJDpLtjxUAmDmhhKKO2t/6zMq9EgwxSIleASS\nLSNgQRA6fN2MDJrVRoRqghHEeLgPPejGAGxwQqiChyIYRpb9E5kybVUDUOHb2u2MYl0+fjos/url\nQmOF90GCjMs32tFw7ATOvngkjMECANi47Ai+eK8U+2ub4HBbccnV03DT3dMRFANY24CP3lmLvTtP\nICg4APc8cinUZgEe6M3A5i+68eKzr2DMxEw8+Mil0IWIoICYqPvr2hAYrEdkHPXXAfX72tDR3I+o\nmHAkppuhktSGqdxeX9WD9tYemAKMGJESBnMwcLTehk3rd2H65Mno7gRWrP4Gl86bjvS8QA5Curs7\nkF9QiKNHjp3BBvDkx0foZYjGiDUfLIEeKlxz601o6m3CxOR83DBvHgKiwrHwT3/AvhOHGZRJCI/F\nnXfcgcbmJrzx9ptweF0wwYtJwYn4xcTpSA6Pwe76vShv2IvAUYmoaj6BXfv2IiUlBXff+itMTM1G\ncXER/vLxO6g/0QCtToXp+WPx0JU3Y5QpHJ/99R3U1dZi7PTpCE9NxPsrvkRbUxMuKZwKncWElzd/\nBadKhVsvvxZTJkzEic42bCotQpe1D712K6rramF1WBGjsuC+627BL3In4du1a/DS+i9xyN2D5175\nC1ej+EXovdSL/3NDcOoVfO2VV7GrfCcSYuOQlZnBLgYkSknIcnx8PFN/evt6kTxiBIykKCVDmn4f\nPnyv+edWKP8LAPzc5346AICRW7nPkUVRpH5YqmIMir4Mte0MP/gpgJA9l9euXYvVq1ezloTZYsHI\nkSM5MCLfdQLE7r77bkbP5YPT/7uYXqLCSu9Hif+WLVvw7bfforS0FE2NjVKgTdVIFQJMeoQFW5CZ\nOgJj87P4a0RCJJQKD44cOYKK6t0oKduF2gPH0NBqRR8xhCXlFNJx0Wv1SE5IwNgxozE6Px/JyUmI\ni4nlxF9DVQe7Q/R7SqDWIJeLrdCEAA5RAUNCAhEcagE3b9M+TV8SiOpVUCgvKJyCaSOEp2TByJMO\nKxlcP4ki8D9E+37uRPk/+vsy4NTe3o4lS5bg9ddfR01NzeB8pIBq8pSpeOLJJzBx4kTR63qSBoA0\nCyQAoLm1Hc8+/zw++3wJt5ApFG7YiKIYPQKjpkzDhIvOx7gpkxETaIDDBti91EdM1UnpnOV+VMmf\nman9ROkn4UGaY27oSJ2aqPwul0hy1Ao4yRKA+4JlzTRhrUdxMQV1TnKtoGRVo4ZRJ3rx7Q43V/Sp\nf5uE1Gm5O+xUSSW1dkp2pECPOticXk50qOJt0hE9c4iBS/2wSpcHFq8HwSoVwvVqFuiTtYtkTSFi\n4MmU1U6vG50+N+wUyLpIpE8Jqx349G/vYukzj0HZegJzM3MwvbCQg8iyqmoGAAgsm5mfi/yUkVyN\np4SFaLmU1KyTAIAp2dmYPXYsEkLDmObupRYegwn9bh/+sWULvqveKQEAC3DjPXdDFRbEjjWyyBmN\nvSg4ihYIr7zfUTsRJcdqUvkW96/xuhGg9HAl3sjR3FDYKdb0mV8yxEf15E4P0OVywUo2ipRAMJhJ\nFXISZiZQwcviU+R+YJE8rmnX9BdGlwtxcqIkj3er14luSnwpgKVkkp47C+IJlgPdJ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4gQGA3v5eTt6JMRQebMCoEYls3zd+dC5SR8TBoFPB5XCiZvcelO4gG7+92FPfiN4BQaWWCgAw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f+5RUVYWLQOrbp0wjNvvY6sbp1h9Lhgs9jFgcZOPUlNlJaOANKWoBUvOK7r6ARBwFMTaGQL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\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hcTihpoTSZ1WoDQQ39PTVtiGt/WxxQuUF0+sGG+glKrUCe7fuwufDh2HXulWI0uvQs21r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Qv4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jp6B8wABodouwEbvbO7B142Y01tRKZVy4AyLQlRFM1GCjvoKsVU0CcbvJhuKsUjisTqHm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Cq9VMYpkM5uksOG3u8TjxsKOxYe1a/OPDd9GTCcNc5IC7vw/6dAbVjkIcuf9sHDxhmrRJ\nNbW1oaG9GasbNqM90I+akSNx46034+yzzgYsWpF4//idD3DtVddIPJhfXAwjrYLj7KUNiUiYyaBH\niHasUbZkqTB11Bg89qd7MeOA/aRCrFQs1aC4M8XSEEkgGgqJ+JXb6RItD7IsqADe3NWJ1u5u9Lrd\n6A/4pYLqj8cQztB1iXWvlFxzjldoUHP9Zp8357Qadn0BrEa7aAXoVDoY1SboNUwGFQasSv4yt0Yo\n65WRegMqtdD0JYFVsQ2MhQ2WMxLY0d6AUMYnbQDcZ+kQRNYF9WBqa2qkMFNWWo5zzj4H++03Q9aB\neCIhAT/BQIPRiCXLl+GpJ59Cd2cHtMk05s7YDxPHjcPmxu1YvHwZauvqRczvwBn7Y/mK5XjhzVcR\nD4Rx2ann4LwTTkVfZyfe/OBdLG/ehrg6gyPnzMUhB8/BkpXL8fpbb6BQb8O999yD444/XgCAex97\nCFG1GQeedQnOuvY2uDJ62NQZtC/6CMs+fAub1ywXm+dCkwm1hXYcOnE0jjt4JupLi7BhZzPuefU9\nrO3ux/jhlbjjztslQX/wnnsR6g3gvOMOw7EHTINZA/S4nCJCybjT46ZOkBZFeXmoKnbAYTbI37k8\nHnT1u8UGsbKqFHl2VkzTSAT8iDjdwtqwFRQoxa5swUtBX3K2TUwNUxLjMhFS9oRcqy2LmgqbU4p9\nZHAQGBc8X7ESlH/yZQQCEin0+qNY1dCF9xYtxY87diEgYCptBNkOUAANKQ/CASTbhhpBZFD54A/5\nREiQYHPU74M5ncFvJuyHZ555EqXTxiKpIfChhbu9E48/8hiefP5ZoZgfc/RxOOnEk4QRUl5WhtLy\nMtgddkVMlDonyCAYCGLJ0qV4+pln8fVXXyGViguRnUn64489igsvvVBhUSlu39nrZR+0abeLA8JJ\nrPloIa6+7gZs9tO3g2MvD/mFhSJf7e7rhiYRQ7HGiIkVtagqoI1fUqjn1E9TmU1YtHoNvMwNakai\niBoxkSD8YR86elqlPdYgrX4TRNOitasF7oQbxcMKoTImZF72tXvhanDi4Mlj8MZLL0Ft1OH3t9yO\nTz//HvFkGrZ8i7SMpCkZkQCO2G8SnnvyKdSNGwOPz49X3nwLd/7xbqYnOPH4aXj44VtRW1soDGWl\njUyLFLUazAaJ95JZME+kYxjiZRm8dDtTSTsQ/1MLlTB9uB/SBY/rKwEFCuKr4GzvwMcfz8dzz76C\nLdtocK7cZ5sjK7HAbV2R4hCjhVjWOINLN0FGik3m2fNlDRg+YjTsjnwk4nEkUjE0tzdh6fIflbhV\nq1g+8t6xgMg13+/1CUBotdsUJkE6nczkNu/BYTRPPhyJw2TVC1LCHi6pvyVTWLNyFS694ioRJuOg\nqQJw3ujxOH3GdIwuKQR1rgkASPNfRoeEzowdvhAefOcdLOjvRFilhS6jwszK8TiibjJMkYxU/3ke\n9Num8mWXs1dUCiuLy1CQX4hoMo1urxPuRAhedRRt0T6h6LPKXmWrQJEpD7a0AYVqMxLROLrDXtiK\n8mA0aBBIhbDL143mQJdQdlNZyX5+02U0yNNYUJ9XDrveIj6nvlgYgUwEVACIcnpzElBkkACAzop7\n7/ojzr34Qujy8ohM4Kt338fLzzyPMePH4cJrLkfd1ImAxSgq9xIWxVJY8co/8fcH/4qKojJcfu31\nqDroYPiCbrzy4et4/IWnBY2O09O0uB5jDjwc9bOPgLF8GFJGM1J6KmamZYClBWFUknFlO+EipAAA\nss2olAVL9kAmE0zcxQOW1ZAUzAZ6p9IuRel/p1UJlWhpGZFib5PZJn2B7KsSCokg/6zxUDc3u/hx\nOc2uCmx/VM4oA20yjJ7t69C0ZjG6Fi8APK00C0WeBTjiyOk475yTMXP6WLz00lt46K8fIJFkoGoU\ncRv2yPOcitgWUluHW34zFxPKy+H1R/H2shV48qeVaOAsZeIBLYZVjRC7mkQqqExLUWrOVlqHRLfc\nIMXXVxQxtburGbJB50Tchnwf/Bbs23e73dK/z4NCWux/5nvyvg0+fpawZCvDg+m7OUXgoZRe/pxL\nbvYm4pa7Dr6OInl8DQNNInpMWNn/yyBSWgS0RJkVyy8BhbPigDxnfr5C1/25hzoDvn0dBB4GHzwn\n9hzznBQv5T2r9EMTqn+lJ3twBZvXdcMNN0gLAO/7v9tzvs+LkTZSRTCP36m2zx5L0k8l0NVqJQiW\ne5VtoxFLPiZCcj9zyffAuMs909w42FvlffD1KcNuoGK/5/hRIU7AlX3IalYHdLBaLXsKLcq43zv4\nxXce2uP+awDQ0OfD3CB3H5icMMnkc2YC7Pf7shoSSt/+4OOXRAh/DSDI/T53nkNfn/t/jkOpSFKo\nbdAhooFZEGyApqewGHgM1agYOj6Gjle+XoS/UkoiwIorA21pdZK1USO0Os433hsG5wL6ZNJKny/R\n9H1QPMTnOXdkAVZ+JjUOSPnjfM4JYnLN4f/lkn9qk8h4ZLmAbVQ52n8kIus6EzeCAewL/t1ll+GC\nCy781fmzauUaPPf837Fw4UL09fbJWKNImkZtFLcWCvqxcpRnL4LNmq/0T2cBAAWEiCMa88Mf6Jf2\ngNLiCkkcQ5GAqGAXFJXLbuFnNdfdB2RIU4/J2i8BMNRSQaKlXK7+atGbxR41Ew2gDg5MzKvB6LJq\nrG/biYXhBtCsl6vSaUcch3NPOhXtXZ34y3NPoMXVr7TnZ4Cjp+2P22+4EcPHjMKqdavx5ntvw261\nYu6sA3H4QXNgoSCeTgtVvh1q2gXqNUiQAp/KIBWJw6jWS9/wzff+Ee+89x7GDavHzTf8HscdcwyM\ndPjhEY9LksZ1imOC6zAraCra6O3swrvvvIe//vN1bOptUZTCoUaZoxi1pRVgYsmx1evsR5/XLcUH\nq9GK6qIyscHrdHbBFXDJx1QWVqAsv1RA/Eg0LgAAqflmilTpVej3OdHu7FBEBKXmQ/0GlVSrOD75\nnAxGhVLKMSqWwLTA01PwTqeMcyaVWXFX/h33Pj4TrsdKm0kaZaZ83HHB1Th29mHw9Dvxxifv4bvW\nTfAjLlRrh96I4aUVmDpiDOIun4hUbm7bhSZ3F5JaLcZOm4Tf33ozjj7maNH6YEDduqsV5559Dtav\nXSejobiqEiqzAT5SvzO0n8tDPBRCf3sH0qEwWD+96PQzcPetN6O4IE+hhLPoxkJIhoUOoyLmyC+p\niKSASBRhtwderw+dPb3o6O5BU3MLGltasaOzHV0eN/qCfqlSMrYQOYhskZZe9zKdpSikA5WjTAbO\nM6P09Vq1Vhg0RsX+Wl6XA3iz0Rop0lq2PzIxoMskwScqLnEOK64/vd4edPpzbgBK1zD/Zty48bDa\nrKAuTFlpGS695BIBy/hMKPhH/RCzzQpHfh4WfvUV7rv3XnhcfRK3zx42DvfcdSdMBQ7c//hj+Hzx\nt5IAnHHyqThs3jx89PFH+Gr+5yjR23DhKWfgrONOREavwduLF+LjbxaA7i91w+pRU1mFtl0tWL/6\nJ4waORJHHXc0drQ04x8fvYcEDDj4wqtx0m+vhV9thBVJmFs2IbB9A7asXoXlSxbDHfDS1BpFGuCy\n00/E4dOnYVtTCz5dtxVfrFzJsBlz5hyEqvJKfPreB7CrNTjqoNmoKy3Azsbt2N7cKnT9QqsFXo8P\n4XgahQ4H9hs7AlPHjoTFaMC27Y3o8wYxdepkjB9ZCaiTSASDcPX1wevyCmhrsjvkffII+lFnRqOT\n5F1pCZWAWQTpJAaWAilbAbKC5gqyL0lBWq1ka2rxgWPpNgv4yHNXKsOxuAodnhQWrW3Ac599ia2u\nfmEMJVU65BdUQaezZCvGSjzBURCO+BEM+wWEIIgbDwVgSKUx3JCHvz7yFxx10ZkCFgnzI6PG2sXL\ncNVVV2Pjju3SLn3FFVfgpptugr2wAEm2EPB1KojrAzWWXn/jDfy4ZAk8flpkkrrPqDot+cqrzz2P\nU88/W/S1uI/tAQAI5V1xM0q7fXjjib/jjw/dh24kwK714vIRsj6Egi7E/V4YMwmcPfcYHDVxOgL9\nXJd6sHztaskfSofVos3nwdodDUirjRg/aTIKCgvQ0d2OxqbtwgbQ6LWoqKxASUkRgqkAVMVa1E0Z\nBlOhRsZ7f6sHjSu2oDCpx59vvwtjJozD3X+6D5/OXyJV++r6KgS8Xvh6gjCngUfuuhWXXnQRGpvb\n8c2PP+KjLz7DklXrUVoEXHfdubjyqjNhMXPt4ISnW4Re1o5QMokuZz86u3vg7O+H3+WSXJhsEpvF\ngrLyUowYMVzyAhszedHmygBGAoYsHpmgphg+x5O0k6SxddU6PPLIE3B7A6iqrcGkqZNhs1tFgy4R\nC8v47uhxoa/fA7fLg+bmdnR29CMYIBDAPMYIo8WBquphKCkrlfaRHU3bsW37lt0xB/dqOkeJDWoy\nCY/HIwKYBHi1jKEyuVLVbpSSDKq0CD9lODCIHqfTMGgzsFLgKpPGw/ffh7v/fK/cGF7SJIsdl8+Y\njKPGjEaByazYNHACMSlNqRE327CgYQcefv89bEyzD4aUezOOGjsLk/OqEO33wRsKwZGfD4vFJhuR\nx+eGz+2FWWsQoROdyYxAOo6m/g40uNvRFKFNnxfFKgcm1o9FgTkfnvY+mOIqmA0mRFQphCkYkgzD\nXpoHd8KPTW0NCNCZUqrXGREzpA7u+LIRqLGXoqe9G66IT+gRMVUCnf5eAQBytGsGGcUGG27+/Y34\n3VVXQl9YIBO+b1sDHrvnPny38BtMmjoFV1x/DaYffihQ5ADSCQEJ+ldtxLvPvYwl33yPY044EUee\ndjpSdiOe/+BV/O2V5xGIKXQQCk7A5EDhmAmonToLFeNnQl9cLUBAQmNAgum2qC3nqscDfWvy1Lk5\nC3GNGz8DI9Lrs5U8KsZGg4j5nAh6ehEJBJEKK6rTGqMKGa0OprwK6C0FMNDXVatDGCnE0mwPUdAp\nCqCQDcDJQVBBl07AnI6it3E9Gld+i7a1i4GeZiAdpjIaqsqAq688D+eeewqqyguAVBQb1+/AOefd\njq2NQRTkWRCK0t4mLvRqBzKYW1mNW448FPtV1yAUTeGLDZvw1KrlWOVxSQ8VB/1/XXApzjjjDEAd\nQzozAAAMTXCY8PKLAQ2/GBQzecgFPEMTyqEJLN+PwQ/76F577TVJfq666ioccMABIr5Eb+E9gbMh\nquhE9oWVMcDaEC/ubEV+bxXOX6JwKwJEcbH5ev/990UR/sUXXxQRIFqELFmyRKiOolCsY0+uWgIH\n0jh5Xbxu/r1SGSRNSfku+9QvUMZzuckvJfIUR7zzzjvlZY8++qiIow0+9tBk2MvnDE3AJNHKJuW8\nDwxuqPY+bdq0vQIWQ//+8JUGQwAAIABJREFUP/k5l+CR2rp58xZs3rRZQBUm/0rFK5dUqqR6L4rb\nuyvfe9LvhwIA3KR4DKV0D4gvZjf9IUKA2egBXT09Qv3s6mjHcSeciNNOO23Qe0mPjwh15o6hCX/u\n83fnmP+mSwP3Kia37DNjRYkKsnfdeZeI97FHXBLcXxg/uWv+pfE89L4M/nnw3w5+pnSJ4Di/7LLL\nUFs7LKuBMZBhsw+UB58dVa4pRMQxzmSHifXe20gGPmFvTITBAEDjjh2SHHPO0W/bYrGKpSop2KyK\n8pDWgCwwM5ThwXVHwFuFurUbWMqBtgTteL5MInnOb7zxhsz3iooKXHvttRg1apT8P0GBdevX48OP\nPpaqMecHk8AVK1aIPZjY/4hVoxoTJozHE3/7G2bPnDVILWXvM2XHjl0CgNEGsIsiQymqaxNIt0ny\n6PH2SbnCYS9AQX6xMLEUACC3xqUQCLrF/s/usIhImqgWJzMoLCoRVetgKACPx4l0MiJVT40qLe1+\nFDRl72IOVrNo9JgweqysYazWdvZ2iCsHY4f6/DK0e7rQjYhUveryy3DdpZfj9GOOR3NHK+7474fw\n9U+rJKB1aNW46qzzcc7Jp6G7twdrNq3HkrUrsWnrZlQUFOGCM87ECYcfCYcjD1qHHdp8O2A17rb3\npBUYq4YLF32DK276PZz9blx3yaW45YbfS5+0tMrR95sXQ6YQ7XpF00UDGHWK2XkohdWfLcDTr72K\nr1YtlTYAjhYrDKgpLkexNU8E7yjO1edxo8vrRiyRQL7NjoI8irpF0NnTiVAmCovOhrrKYbBZHIhG\n4uICQOqw2WZmwQpdrm50ODuFPSj641qTAEocpzarTeaB0WgQ2zfqFXEscj3LsbpoJSb7hEYtex9b\noxg0ck4pIpUKJbjOUYY/nH4pTjr4CCkQbOttwwtLP8f85d9i9IgRyHfYkYhGkYzE0NvRKe4G4UwS\nB86Zg3POPw8HHzpHgnsrtReyxzN/exa33XKb0LjpdFBUU4WEVoW4JgNLng0lxYXoaW9Dx7ZGIBxD\nmcGC+++6E+eed4ZU2wdsB1mSUPZcgkrKPp9V5WSSR3AjGkMyEoXf40PIE4TX7UVbZzcaW5r/F3Xv\nHR9Vlf//P6fXTHrvARJ67yC9CSIq2LDv2l372svaFXWta1+7shZAkaaAoNJ7hwCBEEJ6bzOZ/nu8\nz00wsOzqfna/f/zm8cjiwmTunXvPPee8X+9XYffhQyrqURgCnrDsJf34hBl5kvrdLnsUWYBZmTk6\nLU6cBicOkwOTQTb8BgLKHPHXZ02eVbnW4ikgc4Hss4PSPVRM0zA6k4GAzseRUkmBqCMQ9iMg4Yjh\nI9T5SxKRAGo5OZ24bPZl9OndRwEz2v3SoiArq6pYv3EDPy1fSaTEHtY00C+nEzdffz09hw9h9fo1\nPPfm31i9eT0mm5UZ50xn0ohRbFqzlu8WLiI1LoE/X3eTWmvqAh4++3Y+C75fTMHRI5hDBsaNHKWk\np5u3bMYVG01McgLLVv2INxhi0i0PMmn2H3EbbESEfMSXH6Jh7xYcQR/NjfX8tGUrBwsKOF5eRqrR\nxIWjx9CvT2/cdgufL13Mj9t3Y7PoiLA4CDY2k5uQSEZSIpUN1Rw+Xkqz1FRAug1VxFT7Q7h9YXom\nRDBr6tlE2R2sWbOeerePC6afzbj+nQk01VNYVMqBwiIKa2oorqpWhf7IAf04Z1h/LBYTQb2ZFmm1\nosMtwJkugNVmVoBLO9CrmmtKwqFyr1UNIcaNKspPyf4kNURkqSrfV1sLfAIg2dBb4jje4OPj5at5\nZ/4CKpScE+zORByOmLYkhzYzTdlz+1qUBECee6PZSFBM4QI+ojBww5XX8PicJyW3UIFjym2i0cvD\nDzzEq39/j9awOBDpSE9PJ6dTJ2JjYhVAJM/CgfwDSjte11CvfEMUR0V8XNoYuOLV8cXcz5k4/Wy1\nr1ayv5NMUjk/LepYUghqC4/zwN338fX33+E2mFWjMj42XUluvC31mEOtpJgc3DjzIvomZ1JUeJSN\nh/ayr7CAlPQ0Rk8YT0Bn4M2PP6akuYnIhAT69+2nOvaHjhyitKJYzV9Wp47E5HhsCXasmU46DexM\nRIpd7T3CDUEKthykam8pM8ZNY9iwIbzzzgcsX7ENnUVP11551FZUUn2khp4ZKbz54hxSkpP5x8JF\nfPj5ZypC0xGhZ/KUocy+bCpDh3VV0bQmg51gqwA6evIPFvLTph38vH6TSk/wedyYwgLQ2hT4p5Iw\nDAblI9C/V0+GDOxLl04ZOBIiCLtQEix92EEoqEdvamuUhPQ0HjnC8eKj2JwuEtJyiIhOAKNNMxqS\nGjUQpLG+kXq5XzUVKj1u82YxBs7n8OFSquuCaGItEzGJCQrQEuZVZXWlBloZDLhsdmIio9RaJnOE\nzOHCPBLgUfagbQDAqRSl+jqJ8HETE5dASVUzRUXFRDvNDOjTicMHDnLdtdewYdMG9RBIKvX0nK78\ncUB3+iTEYjGJXkyKVE2zLv/RZLXx93XreWPVSiracP50ojh3wFgSQmaMvjBho0ElAIiBhSw6DouF\n+JhYlSdaW1uPV0CIqAgCZhQAsK54L7X+epKIJDcpC4NPT7jZT3pMktJUtIR9nKgqo7i5WDkQN+u8\nHK4pEgtABQDIBTaEdGpDMSCzJ7HGCIqOHMOnCxOXloQ73Er+icMqNqa9xJPHJd7q4u7b7uC2P98N\n4hosE3yrjyPrt/DkQ4+ydtcaMuNzGHv2JMZOmcCI8WMkOBdKK/ll3kK+/PgzahsaSOmeR8hlYfX2\nNdS2NhAdF6UoKWUVrQRkrRJU0hINaT3JGzyGnF79iEzJpNVgxW8wExIqmrrObRwdtSpLha4hdGLe\nKI4BlnBAMetaG2upLyli9y8/4ZOYoOrjUF+rHCvVii6zqs0BtniyevQnt89ArAnJEJukYgGFtiOd\nfpn+TKJDC4YxhwIc27+bAxtXU7vtJ6gvAl+Dmv9cUTBhQh9u+dOVjBjeT5OQ6CTzNETQb+LBe1/m\njbeWYrCoZRt3qx9jSDZEYUYmpXLfxLEMy8pS3ZOf8w/xt/VrWX2iiGahNFqdPPbIU9z34O3aRNyu\nsW5bZ89EJT+92PhXlOPTt8XtxWhFRYWK5ZL4sQEDBqjF+0yvdrBIFUVtHT3ZWLUXhqd3hDsWhqfT\nfk///HaGgyC4paWlCv2XQqTjdzlZOLX9crsZUPvv/lYH/vcU0B3PWVzKr732WrKzs3n77bf/KZbt\n93zev7rmHRkdv3Vt/i/Haf8d2dS2a1zdbplSRXWjUflFDiMAjvx7m++j6oQqt+uTpcqvaQpSvMnm\nv/0atReF7f9fPrdj8oJqVrdvEFV4RjucpD3PUhB8/PEniPv93X++m+7dtExoNb46ytLOcAHOVESf\nCXBq/9V/7rq3y4o0xsk3C75Rhens2bOV3lI2r1Ik/DsJwG89Z6czJc405jv+nQARMubGjBmjTMlO\nba9rQJZQJn2+gNpIaMll2vOnOp+/wXA5/XxPYWvo9SqW75tvv6Vr1zymTDlbddHaz0HumGIHiH9H\nmxTrFHZGuxSn4xfqgB6qeeK0OWzdunWKjp+ZmcmV4tYcKy4p2ks2dfv27SfCFamcv9es+YV3332P\n/PwDqigUJ2FZ6AcOGshbb75J7z59TjEtPdMzc+zYcebP/4a5c+dyYH++uo4x0Yk47KI59VNTW4nf\n71b0fzEnslsjT2EAyKZMulc6cTG3SERdIw31jdgdLqw2Oy2tLQoAkAQXMVWymg34Wt306dWTUSNH\nsm7NLxQcOqQojSOHDWf6pLMpLinhq8ULOVp2Qp2yJHBEYMQbls23AavBzNQRY7j8glmcNXS40oq/\nO/9z3vnkA3WNenbpwgO33MaYYSMor6igXhJlgq34w0GqSsuw6QzkJKYoCYErNhZrbCShCKkwhJIu\n65aBypJS7n/oIb5YshSX1cobf32J86ZNV/ph8W0Rt26/V8tcl5fy3DEb8YUCqoMvZoKHf97A11/O\n49sffiC/qYxAm+Vyoj2GBGc0idHxuJwuvMEgpbU1VNRU42lpISbShcmsp1k0td5WWgN+MlOzSYhL\npKXJowr0dgAgqA9xoqpEAQC+cEDFFwqrUtyfNfaIdJ81cFjmNo19pAFRMvalwKytq1XAmrhLy16s\nurpGyX3k3srnyTwhfMDOsSlcNeocpg0drajpAgA8N+8Dlm5YrZlV2a3UNTYQExer9lBJyclMn3Ge\niqob0LcfNoktdLcq4MFhs+B2exV1ecXKFeoaSmyaKymBgFmPKcJGSkaq8s0tOnSIsv0HwROkf2Yn\nXpnzHINHDNRYqm19kbDsgdpkcwrgUHHJOmXgdlL2ICCASCBaveD2E3D7VKhQq9+vfJoqG+opq60j\nv6iQbXt3qU63mFjKeiGGeqLLbXB7RISoCv4oZwxOvYMIswuLQdhqWhRax6AhAQDkJYwBMbETYEV8\nOhQAoIjnYiQdpra5nOKqYwTxM2HieAYNGsyunbs4cEAM0Lz06z+Aq6+6mi6dO+H2iNcDiuK9Z+8e\n5i9YwLZtWwm5vQzr0YeLJ51N9+Q0EqKiiJaY4QgHmwvyeeyl51m29idsZjMXTJzC+NFjWLR0GTu3\nbmNk3wFqn9unfz9OVJfz1ucf8/pbbyrN+PRxU5g6cTKlJSW0+DzUtTTx/tzP8BstnPOn+xh9/mwC\nZifOoJeDCz9n75J5DMvN4uorZtMS1rFl914++8eX7D+UTyerixlTz2bIqCHsLy7k9c/mUlhRowjx\nyWa7igEUA7/1B/bRpKK+Ic1lo3+GpIg42VNRRf6xYpKB2dMmkZORxVcLFnKwooKp48dyzvDeNFSW\ns33PIbYeOMTRhhZqQ1q876yRg/nD1PHoQwFONDRzvLJSNVKaGmqItpvJTk+h34C+xMfFakWv8v0R\nYEn70WRmPvTynCmfFJGbtInS2/cGktQR0KO3x0kAKD/uzufxt95hb3Wdtpc1RhAZlaB8I0RWp9zf\nxQjQ78bjkYz5FpWSFfC1qv28HHlU7/787eWX6DZ8CAG9BjAZ9Vb2rdvEVddex97D+ejNEj+oucar\n4r4tTed0xqqaTyWyLyRnpycxMorPPv2YIaOGKXNT0borqnu71408L6q4M1C4aw+XXn01+44dpVlA\nEIuTCEc87oZmCDZjwUemNYoRPXrRLT6FA3v3cry+iqjEeIafNZJzzz8fs8XOnx99lG83/0RYb2FA\nv4HK36K0rJSdu7fjCbqVvN4eARFp0WQOzSOpRzr2RCcWmwV70ErDsSoOrN2BI2giL7sTm9ZvZc/O\n4zjjI8jsnEFp4XFaTjQxc+I4Hr3vz+zYtZM5r/+N/GOlKu5vytm9uP+BG+nWIx2d3qcZbActlJfU\n8s28xfzww0r25VcrXb7DKbp8KylJSURHxhHGQumJcmrKK3BaTAzt14cxIwaSkRqLPdqEPdWBLSYC\nuyOKgDL/DlJbWUVDWZ1KrUtJSyQ2JQ1sMYIUSHGjzUkKQFIabi1lRjQMwjArq2L79v1s2LCHnTsP\nc/hICdV1jdS73QqsCgV1NDY3q7VH5vA4hwDI0SouU+Yu2SfI3qSitJQ6SdrTGAC/AgCyJsiiLwCA\n0RLB8p83s3vXXsaNHMS4Ub156fm/8uTjj6pJS1QRXbAye8ggLu6TS4KktNkF8hB+lDYBy0A8UF/P\nM0uXsvhYITJVWTExMDKXs7r0JSKow6XoUOKeGaaopoK6+jqSXNHqYfYFgioHvcnnIzUjnahoF4X1\nZSzJX09laz12DERjJx4XOfEZJEbF0+Rxc6CkEB8BYqIi8JqC7KkspB6ZrKWk13ZdUmdbRELgiCcr\nMgmrTlRKYeoDHoqbK6hsqW17r2ZxJwCAU6JsJp/DIw8+RGrvnmqylgffU1jCB++8x3Nv/lXU90To\n7CRERzNt0iQunjWLzH79obqGN557nm+WLeJYay0+uc96OGtsPy7/w6WKLrhu/Sa++mYph465NZf4\nsAN0Nszp2WT3Hkhy937E5/aCyAR8JjstgRB6s0UzlmvLpBcGhnT9xSAnzmmnrjCf9d9+RtmuzVBf\nD74m8NViNAVxGHVEOu1Ki1ZR10jQJ8I3G6SmkjV0PF3HXYwrozsho0S86dC3NuGpPEHN4d3s/GkF\njfl7obESArVqQkxPhFFn5THrkqkMP2swsfGxatMpSQta7o3MJhZ+mL+c2257nKJKsDittHh8ilEi\nhNhBMfHcP3EsY3Jz1eR0oKKa11evZt7BvQgJ328wceMfb+aVl1/BKPvvDh3W3yo4zrTp/W/+7lQF\n9qmf9FsF2n9yXClEZOPVbozXXsi3AxTyWR3/+18Vev+L69ORNi+mJV9++aXqMF1yySUkJiaeNDv8\nX4AN/8k1+r++V7rmWoGvuboq7XY7g6+d2d6hUJMavS1lTB2yTR3wuw9/krHf5i3U8RelmyiUP3kp\nWYzBgFeMg/wBVbzK/ROMT2v6i1nZ6SXw7z6Nf/lGOYbGHNDMQlXxrIemphaFIkdHRatrJOcnL6G+\nC8vk9Ei///5MTv2E9vsk5yNItoApqssWCLalYggzo0O77Vfu4skPOlOB3/6PZwJMtOv9q7xBum+1\ndXU47HatY6yivWSjpMk4TiHS/I7jn+kadQRFBGQRM77o6GhV/Kviq92UsS0pQuZ6YTivXr1Kuf2v\nW7deaba1MR1SJoB333UXM2fO+s1bsm3bTj744EOWLF5CcXEJNqsTpyMKuy1GbYJr6ypoaqpX40Hi\nd13OGCxmh/ICkA2HjIFQyIfeGMTnbaGhoU4bH2aTkkuJ2Zrs6MSFPlJixxrr8LmbGDVyBPfedaei\nPm5av06ZGg4ZNIjs9EyWLl/OB199waGio2pNlHGgYdY6XHoLYwYPY1iPvirXuWuvHiRmpLLr8H4e\neexRNm7dxM1X/YE7/nQr0Qnx4NNYPdKZ1zts+Jqbmff5F9QUnWDmOTNIkVhHm5lQhFX9KfOBdP8P\n7tnHnGfnsHz1KpVG9OGHHzJo1CjRSWosnHbfD+WDII5k0o2V5U4cyrVEoIbDRfzw5UKWrVzJin1b\nlfmvABjS+de3BkmKTiAnI0cZ/ImJmNCuq6sqaRJzr6BHnbcwkyRG1up00aVLV0ISOVVZrdZmcYBu\n8jZzvKKYutYGvEE/DnsEnTt3UdRUnfIx6QAwqrQZieDVnh8pfkRGJqwrYdII203Gu/x/YWFIMRET\nHaMkBwIGRRntjO89lLNHj1fr/OIff2DxupU0+uVcw7T6fegsRmyREfQbNpS/PP4YvXv0UiaAMU4X\nMa4I9RzLY2KzWfjm2++44oorFHNHbrDZ5cIWG43ObiatcxY2h00Z5hYXFFC+J18ZNJ49eDh/f/MN\nElPjCQRaNQBAnlfp1J7G0tJqsnb9tjZ/KoctRd0OE/aJYZ+wXq1ajKPQeCWox++jWIwDi4qorK1W\n11AMtSR1Yu+RQr5d+j2VDY3YLS5cRhexzjjsJicGyXoPahKCk3NMGzgs40rouTJWlJmxFJEqKlYi\ns8N4go0cPr6fAF4GDR2kpH5Hjhyl8GihYuSOHDmKCy+8kPSMNMVKqKyqpra2hl07d7JkyVIOHsxX\n+9XM2ERefOQxpg4ahremjsrScmJTkrCmJrJmz3YeePYJ9hzcr+7BReeeR0ZaBj8sXUZjbR2333wL\nV11ymTrH9+d+ypzXXqGypYG05DTuvP5mzj17KhEuF59+OZf7n/oLHgxM/9O9TLroSkyOGKqLCvnk\nyQcp3rqKiwYP4OmH7yM1L4+WlhY2bNnO8y+9wpaDB+mWmMI1F51Hn4H9mfP3j/lhzTrVdusUncj4\nXt2or69mlTjYhyHNZmV49+4Mzs2ltrmZZXt2s7/wOANiozl/0mRSUlP5aN58fjx2jCiHjT6dMhWY\nuP3QURqEeS3rqsQnApOHDGd4XhdVbG4rLKTgxHHCIjMx6ciLctA/L4dJk8eRmZWh5LeSuCHmj6E2\nEEDFBfrdmnzD6yPc4sbT0IC7uVlJryJio0HmETUApLCzU1DZyAsfz2XBtj1ISJ3IJuzOGBwOWUva\n5hsBjQjQ2iqsFOnmCgtRrIhFguYjymRVgObd99+jmQG2tqgEilCjm5fnvMxzL75Isz5Eq09M208F\n6E+Nrdb8wURCJtCTvLNbSgZffjmXrv16KTNCg1GPTlgryjwtrLrZYgYrEafzPv6UGx+9nwYxKZQm\nicmG1RKJ3+1V1P8omwl/SyMxNhuJdiex9ghS4xPp3rUbA4cOpnuv3rg9XuYu/IZ3vv4HhbXlJCak\n0bN3X7ytAQqOHqa4tIAAQYw2HdaESDqN7EdsbibWhGgcrijinfH46xs4tnsLBdt3omsMUFdeS01Z\nM9EpUSRnJFNypJBQVSv33nILo0YM5qPPPuGrxT+q7nmfLgbuu/d6zp81SfnFSf1rNLk4dKCUDz/8\nig8/XowEDozoZ2T8qMGKjVpR08zmPcUcO1FHZWUjddWNSjGSGB1B725dGNyvJz27ZWOzhXHFmEnJ\nTsSV6FKeHofydytD3PjYTCIiE3FEyPzmUjWRyMJUQ1cBPTIvyU8bonnSa0p1h/E1+jh6+AQ7tuxl\n9aqf2bZjC8VlddRKaE/bZGOzGUiIiFNGgZJC5Q8GSIiPZ/CAgYRa/axcvvzMDABZH5ubveKdwrLV\nG9mzZz9XzJxORnIM11x1KUuWfacGSyQwJSGdiwcNYGRGIi59GJ1FTCZkV2pUAIBfD9urq3hwwQK2\n19cjGLkNA1PShtMnqROxYuih01FbXUdrwKco52arFbtR04SJo6JQ4aTbbbYJPSJMYX0py49uo8Jd\nQ5o1lgRTJJ2iU4m1ujDpzeQfLaDcV4/N7qBrbmfKmqrZdHwf5X7JApbBru3uhbYmsYFOzHSJzyQt\nPlmhJ4WVJRR7a2gMa8iq5garAQCCQGbHpjFp9FhGjx1DnxGDSU1PV+Y1Bzds5M4H72PXwd3YVVBY\nCKfeTK/uPZgydaq6+CuWLmXp6mVUq9wF6JHr5K4/38w5l0xTB/C1+sg/XMSPa3ew9Pu17Nx2gOra\nNp25MQISsojq0pusfkNJ6tKDqJQMQmYHIYMVb0A0mn6lITHpQkSZ9RzZuoGfv/4Eju4AdzWGUCu9\nu2YwcnhPenRNJzHOSZzksDa3cvhIET9t3MaKtTupE18+ZzSpE65gzHlXorc5KCos4MjerZTkb4eC\n7eCphoAYBcHAPumMGNqD6dNG0bNnNlHxDrx+NyY1HozohZuoNDUO9TuNx8v5020P8vmSAlGZEBKd\njF+TlAyIjefOs0YwpUd3bAYzx+oa+GDdBj7bvlExSAJmM+dPm8mnn3yCySG5A9qm5n9R3J6+Q+7Y\nOW/3GWiXDchCrDbZ/2Jb/b8AADp2INuP397NVOfW1m2SU2gvDk5nGXQ8vf/2GrWfT/ufshmXjaP8\nKSCAbBrb/+3/DwDA6UBJ+/WRSVoWXXm1uD04nTa1V1TFt6S7hEQb/X+rvv8dACDmaKLlUoW1AD4d\nik8BQ1Vmd9tL0iEELOjoVP2bFd7veEPH8SPnqrFXtF+UIkCyfc/0nPy3Y+tMp/bvmCsdnw05TwXM\ndNTb/44C/F/d/391LootYjAoYEZJcrx+rEJhbgv1+CeFRfvC3faBv3WN5PPbPUI6Ps8dv6saG21G\ngOq/wwLa+9m8eZNy+1+8eImijIoEQM6tW7du3H7bbVx88SW/KaPZtHELL7/8qvoMj7sFuz2GCFXk\nR2K12pQ5VV1DNR5Ps+oqCQDgtEdhMlk1eqgCiySaq5Xm5joaGmpVp186GuLMLd/DHhGLKzIGi9FA\nS61QmqsY2ncAjz38gKJPNtXXKtNLMQCWLuvi5T/w7KsvU1BcpMy1ZIcjI1DA4gtHn60MyoSh4oyK\nYsioEWR1y6WyqoJnnnqSHes3cdcdtzH9ghnoxW9Buf7LpKlp2vYfOMAdt9xKrM3BK8++QGJsvLTL\nwGnR6PsikVI9Eh3FhwvYn5+PRJ+OGHUWUQkJWoemAwDQ7j8hYLyKdzMaVJSWWuRqmtjw9VKWLFnG\nyn3bOVZfhU5ovDq9aj7YDTZS41NJTUglKjJabcyFHVFcVkz+8cM0+puIjYhTp+MLhcnK6oTNaFVa\nURmARquBupZ6iqtKaPQ3qyIqMjKGzp26KACpfdycHNttAKTMMfJc79u3TzEA2g00lfFZmz+KAABC\nMRUAoL6hgZbmFgXOJ0bEkBwbr4q6w0cO00wLaSnpZGdls/fAfirkXuph6uWX8tdXXiE5KpamhgZ0\n/iBRDqfa49lsZopLSrnuhuv5YdkyDdE0mXFERxK2mIhLTyajc7Yyhg60eig8kE/twWNqzzWqZx/+\n/rc36NIzF5XxFRCfoHYAoF0meRp6q23pTlKoVONEa85pml8p/sUyICR7NFnjpaGiyTvEvCzs82sN\nOoOBI6WlPPTUU8xbulRFY7os0cRHJuKyRCkzbSVDOA0AUIeX3xcJntWsCniJNFTzayCsHMv9uCks\nOURLqEFpePO65qlrrnlM2Jl89lRmzJhBTGw0wk786eef2bd3D1UVlRzYt19F/InxlyEYpF/nXK6b\nMZOZ4ydzaPc+vl26hPieuQyZNI6DRUd4/9OP2LZzuwKZ8zp1oaayWoEwg3r15fF7HqBfn77MW7SQ\np156niPVZbQSZvqoCTz16GP06j+A+XM/55b776Kixc24q25kxuV/JD45ky0//cS8118kWHSIc3vn\ncvd119C5R3f1TMl5vvHhR8xdsgy/z8sV06Zw3Q038Oo/vuS9z77AT4CR3fowqFMG+fv2sL3wGBFm\nI0M7d2Zot244DSZ2HMhn84li6hoamNSvH1NGj1Hxcx/Mn8+S/fuUNMip1+EOiapeDRf1kilEbAFT\nImKIMZuorq2lNCztQm1I9EmJ59IRQxjWNYeMtDhc4gki8goF5EngmNaLF9aG3t+CLuCFllZaG5qp\nLaukuaERpzOClOzN9aMAAAAgAElEQVR0iDRr3VwRvRvs+HROlmzayevfLGFjcSmt4mJmicBhj8Zi\nsWMymrV455CfYKBVUb/FS0UREOSo8swG/EwbPFLNb3mjhyjQQswC5Qvu3bCNO+/6M7/s3KbMGzuy\nFNWX77hAtUdVqm+jNUQH5HZjwfyvSMpOV58rDABJPdKLsYascTIf+sFz6DjPvvAif134OeJioTOa\nsVgjsNqiaKlvwuh30zevM7XlJXgbG8hNy6BTSjqjBw5l9MiRCkyra2zkREkZtuhIjlaW8cXihZQ3\nNpOT102ZApZXlHPg4A5avI3KU04f5SBnxABiu2RjTYjHFhFDQmwqhoCXhrJ88jdtpHDrAdyVWgxm\nXEKskkUVHSwiJzKOB+/6MzV15bzx9lucqPOqgIBzJmfy4gsPkpwWjc4qfiFWQn4Lb7zxCS+8+D7i\nNTpkQCxXXzieTmnxeD0SnbmWr77Lp6FVu2YOix6HzSqG++hDMkf51RLSvVs6Q4f3o/+QHnTplYHJ\nKhK6UqJiYolO6gK2OAgKyGhRXm2K0S0TUVi4YX5tTmhLX9DUS/I/4uci4KasZSFCjV6qKqrYsmUz\nS35Yper14nLhlmgBDonRsZjMNhWzKuzE5IQkJo4ZS6fkdA7s3ntmBoCMkcaGZtURPlbVoHJqp44a\nzo5Na7ls9oXU1FRi0xtJCcNVvfsws39/siRGR7k7tmlkhKyi09OiD7Msfz8PLVpMmWgzhUaPjZk9\nJtLZlUKUzUZrSzMVVVVqsZECQkxMqhvqOX6iWGl1OmXm4LK7aHA3c6SqhEJ3JdtrCmgINNInKpte\nyZ2hwYenvkVpsRyRLmpbm6ior0Zn0hO06SluqaSoqRw3beZ2CkfR0SUyGafOQoTJQVjo8MEwTYFW\nKmhSDANZANoLzPaHxIaJlGhx441VFKzRQ4czos8AtZj97e/vMH/Zd/jazAP9IR8Wk4lom4u46BhO\nlBynJeDFboEBAzsz5ewhTJ52FglZ8QR0AaXdDutNeENGSisa2bIpnxUrNrBi5VqOlwi1Rp42B8Qm\nY01IIafPQDJ7DiChSz/lwoo4M4dDWHVBDm34kc0LPiR0ZDeGcCuDeqTRr3s8M2acxfBR/bDYguiN\nfoyya/YZCHtDlJRV8NWClXz4j9XsP+4m5EgnsUsvGptb8FSXQ3MVhJvB4CPOBVMn9+bsycPp0TOX\nzp0yNf+HcEgZPsmmoj2yTDNX0TplelnhPH7ef+1jbnvkS9xB2XPpCPq12KY+UTHcMmQQ5/Xrp9gh\nle5W5m3fzburVnBYJBxGE8MHjlAAQHrn9P/nAIBs2qULsnnzZmUGmJeXpzbV7fuI/5cAgDZnnxrr\ndkrR0h7J1gaA/Bal+nfUf7/5lo4gQDsr4fRzbD9v+fPfFz3tV69j5fabp/A/e8PpBWBlRRWVlVVk\nZGQqCmz7v0vU1oED+STGJ5KckqLy1YVqbpW40//w9e8AAPmokpIyFY8px4+OiVXAihxfNnr19XWK\nzpuanHQSzNeu3L8ahf/ZyUkBKjp5+d7SWReAQYEA/+wXSX1do6IVi1ayI/vkPzvi73v36fdJ/r+s\nFxKFJl0KdQVOs9/o4F508iD/jgHwr8aqdCnlePJd2+sG6XAKA0KOKZsNGQvC8DvF8O8MAIQW7vlv\nXh2e9fbvLD4aSlpy2jzQDkDKJwpLZNWq1bz55pvK5Em67tr7oUePHrzy6iu/ywOguLiUd995j3nz\n51FwuBCb1UVsTDIGvTABRKDlpaq6VHXl5PMdNhcx0QkaC0DlZcs+WXLVm9T7PJ5GtXlV1ypswmaP\nIzIqTtE3g/5W6uWzGsvIS8vi4QfuYdaMaQS8LcpbR7wnJFP7sy+/5Jm/vkBlXb00apUMTcQf4zr3\n59KpM4hLTmLpzo14DGHFchg8dDAFhUd4+dnn8JbVcMftt9Jz5CDhuirfHyWvCwSUM/yChd8ob4tz\nppzNvbffidHfZl5sMYIAXbIpVOi/kbDEf8kmTQq2NlNVVRicwXxWM4DUjESVlEB+gkYOfbmUHxZ/\nz+ajB9ldcpTjzVWSWEekMxrJm3cY7HROzSE7KZNIu0P5IzR7m9hz5AC7D+9XzuXikyRgeVxMPNFR\nsbjdHiWp8IV81DbXUVZbrrwCZOcSH5eo9OJC/ZT7paLn2lkAbQwA6fDW1FSzc+dONW5sIgUUiYky\nqJP5W6/uvfh/iARIzPNE1qHMvlqa1UY0iBcHVsL6MDarhWH9B6rj/7xpI01+D/3Hj+Xqa6/lvKnT\niImIpKKsnJaGRjqlZ2G3mfnb229z34P3425pxmCTmD0TBhVraCAzN4fEtBS8vlZCrV4O7NhJy/FK\n8WgjOz6R+267nSljzyI9LVnbkSvzRdkky43755QV1Z9S02UbRK+YiUJp0lgAbk8r+QUF7Nq/j+U/\n/cj+PXsZ0asf99/9ZzLzcjX6lTxYZhM+r5sP5s7l3sefoNkXJNoaT6Q9llhXAuGAyA5OfeJFPqa9\ntCQso7XNNFeeDxkzch4So2j0U1JdRFVzqTKjk86j/I7H7SErO4eZsy5k2LBhRERGKIbQF19+waaN\nG6koKcXn8Sg/DaddouYiqD5+nP6p2Tx6213kJKfx7qef8O3WNYyYNJ4b/3At1bU1vPDGq6zduIHW\nQBCX0aIsKpNcMVx3yeWcc/ZU9hcW8NJbb7D/aAG13iYctgguPO98brvqWsXieOLVF/jmp59JHzCc\n6+64j6ycriz49FMO/7yCuJZ6LhnSn8umTlEJE8Iiq6quZG9RIUu2bWHdti2M6NuXW+++m7cXzOPv\nn3+h7szIXv1IcNg4sGMHHq+HXhlZDO/bXYEaJ4rLaQ6EKXa3UFZRwZDuXRncv5/qRH/z4yrWHimg\niTBCEI0ym0iKTzjZ7RbgvLiqEi9aspnIATyYlfGm/HdOZASXjR7OtGH9SY024xI5kIwnuzSvxKtB\nT1CxvQKEfI3oAz5CzT689W7qymuoKhVzUwM5XXOISImQ+u4k8xVjFJUePa/NW8L73y+nWjwadBbs\nDvE/i1S+F5qEUIq/ALXVZUoOIENZcERhAIgUN9Pq4sbrr+O2B+/GGB2pmQ+KyVyLnxeff5GX33yD\nqqZ6AqIlV0BA2+skA6ht/MscbhHlv/Tl3Fx14cW89trLmFzi9i8GxMJS0jzENGa3GSrrObR+O29+\n+Hfe/WWpYnXrJGnMGoErKomG2loC7nr6ZmWRKpz5lhaSomMZN2o0Z0+eorxllv7wvUohiImJ48ab\nb1LpI8/97TUWbl1DbFwWfXoOUuPqyLF9nCgrVI3bkENP2oAexHXJwRafgCUylsjYVFxOOwZfPfvX\nr2XX8jX4a+uxmnWkpSQrGWDJ0VrG9O/NzBkzWLJ8EWs37FRrdbQTbvzjeB588CYMNpkDRDpoY+HC\nlTz55It4PGHGjelOl07phP1+KsoqqCyppb7Wg8MaT2pSBllpGcTHxmKxmRWQJXtH8cUSts6Jare6\nNlnZJi6ZNZnxEwcTFWcmKSMVfUwK6Oyau6iMQknBaUuVEORQpNLaHKWZT7bv72SNVVGTIamlghjE\nU0A5sluor21m7dqNfPTRXL7/fhNC0JT52mJzar534TAWvYFenfIY3rOvYr+3SQDaJ8O2QRKAlqZW\nqusbxbuG5JQEIoxw9x2389pbr2HX6bGFw/SLjOa6fv2YkJdLhFnMxjTam9otCiVQb6DeqOPDNT/z\nwroNNIk7axi6WBM5p9toOkenEPR4aPW4cUukixgfmcwqfqdetIJutzL0Uy7ABrMq3Y83VbG36hg7\nG0UfFaRnZAY5rkQigxYFAMTGJxAZF0uT101BcSE17ga85jCVgUbKvNUqXkfFe0g3P6xjQHIu6ZEJ\nVJXXqM213WAlJjWBIk81B6qOquWjHQJQ7DIFHOiJtjoJtnqxm8zE2J10y8ohNjJKOVfu3L+biqYa\nRTERRr2sFzLNR1v0pGcm47Ab6ZabxZixAxk2shtRKS4wi/O+FBUSP2FQRok6vR2jKYr6Oi8b1m9n\nxYq1LF+xjkOHaxXlU9HcZMpKyMTZqRfZvQbRqWdfEhOTKS8sYOG7L0PBFhIjgvTqmsbFMycwdUp/\nEpOsYPKBKaDmjKBP8nRFj2BWHQ2vx8j8hZt44vmPOHhcvoW2SFntenI7xZKR6qJvnxzOOqs3Q4fl\n4YqxKqqj3NtgUIfVJN68UsqLIaHQ6n7N0w2HgprTalDHjl92cc2fnmPXwWbVkJHNo1zbnpFRXNev\nDzMHDSLWbqfRF2DZ3gP8bekSdgZbadHp6de9Ly/99SVGTxytzFh+q7P2+8qMM79LqM+iw33//Q/Y\nv38/Y8aM5rHHHqN3n16/+bH/bVkrnWYBVeQcpDsjnT7ZDDgj7LS2+tXiL10ycS2VYi0lJRGrbCrO\nQC//zZP9V2/owBrdunWP0pDm5uapDrimsYaGBo+iyImOzWazYrG20aH/7QX4VUChcWza66d2s6bT\nTqgDpeJ/BRv8Wlhqx7/33vvYsGEjTzz+JGPHjtIK3zDMm/ctc+Y8z4033sSVV16haggt5uU/v6od\nQfjTvBdZt3Y9zz03R+ncBw4axCOPPkbPnj2Zv2AeP//8k/ICKCsrY9asC/nTLbeQlpbSduX+NwBA\nO71cNmkbNmxgy5ZtqrsqQN4111xNTk4WixYtUc+DAATdunVVcVJ9TnkWTj+X3/8UnA5giQv+smXL\nFB16ypQp5OTktCU21ChzxKKiIrp17cb06dOVE+8/exyeei6/BQC0Mw7UXQ3DuvXr1XiQjYRkJ587\n41wcdhtNLS0sX76CVT+uIjMri9mXXkpaanKbkrfjmPj1+Nr68e+vRfu/yveSgkxMBiXqSwq49peY\nqW3atEkBNZ26dCErK1t1Kb/55lslATh69KjSbjc2NahnNS83lxdefJExo8f85mAtLSln7j/+wbx5\n89m5Yw9mUwSJCRkYdJJzbsZk0VFXX0FjU43a8Bj1FmUGaDSKoZzmnSHt1MamaprddXh9TWptCIUM\nuCITiYpKx2CwSigXgYCH2spimlsqiY9wcetN13LXrTcS9guPUajTOqoaW3jxtdf5+8cfaby9sBT/\nRqbk9OO8EePITE6lLuTlu0Nb2VtWxAUXnM+555zDzm3b+PjNt+mXmskf/nA1Kf270yqdukAIq92p\ndN/i5vzks89QXHycRx54iH59+kGTSBRUZUzIoFN6aenQyh5LuSarB1+6NB0uZQcAQJn/KQlA28Qo\nCJFy6g6obPrqNTtZ9s0SthccZM2+HRS0SEdViW0wqUaEk5SoRDpLbFZcsnI4lzW0tL6c9Ts2U1Rb\nQWRkNNG2SJU/73C62rr0QZo8TVQ2VlJeV6nEjjKnCgCQnZ1DhFMaA9o62f6MKSmETphMJgoLCzl8\n+BARZivJCYnqu4rUpaq+TktnSpJ1xap+V8w/pRstkc0en0ft2YZ17UlNeTnHykvUPq1Heg5njTyL\nVWvXkl9chDE2krFTJvOHq6/h7AkTFOPz8P6D9O3Ri+rKSm689SZ+XLVS3eColGSckU4qqiuVr0JK\nlxzSOmVpudvBMDs3bqK1og6rDrpmZBP2tNKjUyduufEGho8aKYJolX+tAQAd15hTzVjlqqskJNlg\nS4czrKOxpo633n2PN959h1qvR425zqlpXDPrYq6efbliQbSDPmIwqjfq2LZ3D9fffQ/7jxzDaYvF\nqLOSFJOK1WhDF1QWbScHy0kAQDbkinBgVmwiqUj8Xr9K8pB/MVigou4ERVWHlSSgPWFHGGKDhwzh\n4osvJSMrE6vdhrvVzcLvFrLwm28oKijAEAozdsRITHq9ygaX/fra75aSYHXyyJ/vwxTh4LpH7qOs\noYY7/3QbF150EfsKDjLnhRcoPHSEEUOGoguE2LNtB7mZOZx/wQUcKz3BkaJjJKWmsX7rJrYd2qPm\nssvOmcVFsy4k/1gBi376GX1CGml5PfH6QuzftBHvkYMMTI7n5vOm0yUliS3btyuj0tycLIaMGU2x\n381Lb79Na7ObC6+8gi9XLGP1xi0IBNUlKRWdtxVvXT0ZsQlMGn0WGSlx7Nyxg3UbtpKYnoUrOYkt\nO7YroCwlJUnthY6WVlAaFKmvnj7du3HZRbNIi0/A7/bg83hVM2vzrt0UFB1VAH7nLnk0uQNs3rqd\nyspydKFW0ow6BuZlMbBHNt0yU8lMSiAuJhqzzMPS5FLDJYSvtUEZbetaAwSbvFQXV3Jk/xHqq5vI\nyE6n68BczFI8qSrCQBAbOmcCy7fn8/g7H7GnoRE3FoxmpwIApIEpUh2dknz6aayvVMkrigUgvmVS\np4b8Kv2id+euPP3sU4ycOB6E3WQ0K0+vA9u2c9edd/PT5rWK9SCR5CdfbfHEakiGwW53qrhyMUN3\nWiw8/vBD/PGm6xXo5Pd6FBghDCGJdJdAVn9JDQc27SDc2Mr3a39izvwPqVPziJQjEcQl51BfW4e/\nqZoIfIzJ6UZmTKyKHbz66qsYMHAQTe5mFaO6dfs24mLjlZQlIS6Ol999i1e++JyAxUan7G6kJKdS\nXlnCwcN7cbc2ErBAfNdM4jtnYY+PwxQVg84eTWJysvI5K83PZ8eyldQVFZGeFE1spIuS4nLqKloY\nP2IwaSkpLPl+MV5vgKAP4qLhr3Nu4rwLxhMK+tHbYlj/8wFuu/1BSsvdXH/t+eRmx7J183r27jmI\nw+KiR5de9OvRn0G9BxLnisFmNCmvBFnzBFwVeZY0RkpLK9i0YzuLflzJobJGEiLgyWf+xMRzRmBL\nkjlEaqq2lAEVoSu89PY6qe1P1TRVBXUH5oa2CVZguzDuDQI0y17briILZW0qPV7Gs8++wgcfLMYX\nRHkDGG02JePyNrcQZbbRJyuPjMQUYXcE1XrQHnOl1thmP54mPw0NjRgtJuITY9m3dxeXX3k5+w4d\nxBwOkECYqZ1yuXZAf3rGx6n3KYhKxRzIORqUi/xRv4/nvv6Sb48VKzREHoWBiT2YmDeIOOlYS7SQ\np1XTtzmceFtbVfSRTLLJSclYjCaKj5/AIwYG8TFYohz8vG8rmyoL1OTZOSoJe0uIXonZJDqjlYFL\nYfFxApJ1H2HDFGUjv+woR5srqfY30SweAG2tIvHP7BmRRaf4dAIevzLTkevtM4UpcldR0lKlYllO\nMXZr28QZxeRHWA5C8xM3fMLI1J0ZHUOn7DS8QTcHCo5Q7w3TGoAEJ1x4wRTOnjKKyEgLCUkxRMc5\nMEUKBcSvQdIn6aKah4L8hMNCcRf+j/xpYv/+Y6zfsIuVK9azadtBTpS7CUvBrR5sA9acLiqupfTo\nMRoO7iLJFmDc0E5c8YfJnDW2Lw6XdDTEOER+tCGnCvKTP9LCsdBU18qrb33FX577Xu13nBYj18ye\nzg3XTCQjw0FEjAXsMji9GkilBQG2/cg40GYdhR6eLOza0HhhioR01Ja1cvf9b/OPr9bgEyalomaZ\nyTIYmd0jjyvHjiI9PkEVvVuLjvPigvn81NRAvU5PVnIWzz77LBdfcdH/XgTdcW8HLFiwiIcfeoT8\n/EOq++qKjOCVV15i2tSJJzuCv7mz/j+8od10btPm7Xz22WesX7eeiAgXt91+O+edN40F3yzh7bfe\n4tjREyTEpSuzmj/88RLOO2+KZlQrz/X/oUA9/VR9Hli1cgNrftnIli1blA9Bz149mDbtbMaM68Pe\n3UXMnfuVKhhkcu3VqwfXXncFPXtnnJy//vnrt/dStZ20lq+qxCLaWJYNtFq92rhpinKp/QTaQBGh\nNMmk9vvLy399E5qbPaxcsYLLLr9cddsnjJ/Ea6++Sm5eNit/XM1tt97OwYOHmTx5Cn/5y18YNKiv\nNr7/y+sr84eKs6kS/e1R3n33Xb744mv15Eg37q67HlBpFzffch3bt22mT7++CgBo9XhZv3493brl\nniIB+G+BsPZO/pIlS7jjjjspKDisnl7peD76yKPKSE4i5USbrP5ep2PmBTP54MMPiIjQMob/mY3Q\nAbn5jeegvTiRPwVse/zxx/n+++9V4saoUaP44osvlJ75kUceYc6cOaoLKYZm48dP4Oabb2b69HNO\nHuFMrJR/d/iO76+pqeGjjz5m8aKlqgCS4kjA2ccff4xLZ89m/vz5PPXUk6p4zMjIYPjwEcw471xG\njBihohqFFdDeoe94zN8aq3IOcu1vuOEG6urq1XcdMWIkLzz/PBmZ6SxfvlJFYgoQJC8pBh56+CH1\nHkkqEQaAgEfyEkBQ5CSSIy7XRsaRdNVPPZ9Tz2jz5q3KSFBAl4ryGiIjEomOSiIckiQFGyaTnvqG\nShrE90Ucms12bLYI7Dan6lQIeO/3e3F7Gmhx1xMIShJDgEhXEi5XEg5nkmpWBcI+wiEPjbVl1DWU\nYCbEVbMv5PGH78NlN6vsZXm4tu0/yKPPPMPajRvVabswMTqjDzeNO4/c+FSafR4KWqr4av86fjm8\ni5FjRnHBjHPJ37qDVQu+5Y8zZnDBBefiyk4jJMBoSHyJrNDiUUyJex66n4S4eN5/7Q2SsnIINzae\nBJNl46yXOF+1HGvO3yc7ugq5O/XaqfGjpjUNQDhp0GEQNqQs70b8+4pY/PV3/Lh+Exv37+Kw+4Sa\n5jRTMT0WzEoOkBAZQ6eUbDLis0iMi8cX8rDnyD42HNiJPxwiLS4Vk86I3SZeFJHKk6miroLyxnKa\n/S1Kfy9re1ZmjmIsiX+IMiANSZxlu0lpGKPJgNvjZs++PbjrG0i2ORnSp5+SjhSeKCG/qBh3IEBc\nYrwyB5S1WF5CDZbxIKaOCc4Inv/THSo7/u8L51Pc2EBKRBRXzr6CstIKvlm2hMqAh7TczoyeNIlr\nrrqGzskZ1FdUY7PY+PCjD3jhlecJCIJktpDZKRtnlJ2jx47gqawmOiuTtLwclW/vqW/g0K49+Gub\niLHb+MOFM2msqmHhomVkpmXywtPPctaYEQTMfkIGidw71ay3fYvVDjd7fT4sMh4C4Klr4KO5c3n0\niafUPZEi+ro/XkvfXr1ISUhUHfr2l7rNcr+Dfo6Vl/HgnDl8uWQxYSQO0EFKTApR9miBdTSAoR3g\nVvOCXxVxsswJ7VvMAsWDQYCmdh8NeW4bPDXsKdmqqLxRUdFKeiQO5pMmTWbCxElERkVhMJto9Xv5\nfvn3zPv6S/J375YSk8umnk9qUiL17iZiXC4Mbi8/L1tOelwil119FTsKD/P2px8Sthi55U+3cPEF\ns/hx2Q9s/mktF5wzA3+rl3fee48j5SX0HzRIOccnJyZx9RVXsf/gQd799CPyiwqUx0SP7t0ZPmAg\nGZ26kNKjH2t37OSDjz+m8cRxhiYlcsOs8xnQI5fvlixiwcoVqrF3wxVXcN1NN4HNypyXX2HeosVk\n5eWxfvd2qurqVOe+R0wcuamp6AMBlQYyYdxYsrOyKCgo4MCBg1RUVtHU4mbz7p0crS4lItJFXmo2\nBmk8mU3kdM1j4jlTGDCwv5JDhL1+WhqaVFdbjC3LysuIiHIRGxdHQ2ML+YcK2LRtK6tWr6K+rhqB\nf+Q+j85L4ZKRgxjaPZeY2CgMVhNhmVslri3gUyaCQkPX+UN4Sms4sC2f4/klCnTr2rszienxGCMt\nqpErNVHA5KCswcfT733O13sL8BocBDFhc0RitTmVDEDWD/G0aGqoUl4AyvFeGAvCPFDRfCLxtXD1\nzIt49OGHcXYWIMyj0di9fj5+7wOefu5ZCqvFbFQISCZFAVcvNXVpAJkyN/SJASCMG34Wb7/9Bild\nOktXUFvH2+U0sg+raWLuK+/iqahn0ujxrNm9hQfff5HjbuFaSNkQRUJ6N9VQrC89jDFQz2X9R5EW\nFc36TRu57c47mDBhIsdLSvj0i7lKejFwwECmTZum6qiPvv4Hny9fwtHyMlxR0WRndsZmi+TEiRMU\nFO4nqA9iibeSO6AHEUkxeE1GDJGxRMTEK4a4p6qWAz+vp2jnNtJTY0iIiubI/uO0tvjp1acrrcIW\nOV6CQRemqTnMzPN68+wzN5CaHk3IG+bYsQYee/xDlv6wBbMdhg/tQ6RFj0UXpluXPAb3HUD37M5K\nnh72BjAaTDRW1Sp2mzfgVUaKwvaWWECTTq8iWo+eKGfBsu/5btVPTJg+loeeuIuorHit/pI7oxpM\nsjhI41eTmJy8ScpF/FQG0683sK3MVsuS1G9ayona/+kMHJeEhgee5KuvNqt5xuZ0qNpBHwwQbPES\naXaRk54jc1hbz0QztFSxcNWl5TRU16uDR0VH4Ii08/LfXuXpl+YQ1BlwhIPk6YxcNngwF3TPI1XM\nWRQAoC10ckQ59xajkXWVVTz5j8/Z4ZbUSojCyti8IQxJy8UmtaOa+DT9kwx65bDql429WUUIiU6/\nvrFBURjsURGY7GY2HtrDz0X71UPYPzMXc6Mfa0uQBLtLZftWVFdT3dJMbGoikfGRHG+sYF3hXioD\nzTSHJCNZr3I7xdAvx5igNP0uZ6TK3K6orqC0pZoyGvAQUADAqUFvGqVZ6ZvVKhJGJ1rdti5/jFnH\n+VMnKBfI3fv2sHXPQfRGPSOG9uL6ay+l7xDpGns0hFoMBOWinymc6aSZlRT2bawKgU/EddlvRNh5\n+fmlrPxxGz+s+IVtu3ZQ3+Rv+yiDyn00BX0My47goQeuYeTZ3TGLP6PBrI23NqsInRo42uZGvWQ4\nSJa7Tsfu7ce4+obn2bO3Xrll9uySyHVXTuT8c0cSnSzmhB6UkEbcQsUszKAZWcjnaaoi6WtocYTa\niUker7axozWI12Pj8Wfm8sbbX+NuFcqLEUNYRxJhJqUk8ceJ4xnQqZNaeA+UlfLqd4v4rrwM8YiN\ni4pXG99b7/iThsD9P3rJmd9336O8+MJLKt/z6muuVnmfl1wyi9hYl9Zk+H/0kge3+Hg5L/71Zd55\n5138vlbs9kieeeZZrrz6GtUpfn7OHGKiErj80j+SmZHG1Gln0bVb2skR9V8VqG213I/LN/Lg/U9T\nV9tMSmqyKmjrvhkAACAASURBVAolA/6uu2/nlltu5InHH1cgiRQC7mahyYaYMHEEr7z2HNHxWv7s\nP7+0D5fJU8UXtUHSAqqpZIq6FqqOlRFlisAbCNBsCJDULVsjo7TVMFoQ0393A0T3KHnfb731Nk8+\n+TTnnXeBAgC++PJLLpw1i9mzL+Le++6lqdHN/fc/oIr03bt38tDDDzBx4tj/8s5r16CpqZmdu3bx\nyccS+fYBN95wE2PHTuCdd95HrzerMXfZZbNwRNh59ZWXMZnNVFZWc/VVVxET4/qfAgDyhaTYvuOO\nO/jo44/VdxYmgjAApKt80003KoT/6WefVYkYixZ+pxzAH3/iccVGODMAcHLX8buulyxJ4itx+eWX\nI7F/Mt+K7Eb+7p133lHpA5JxLMdPSkpi27ZtSqIjAIEYUsrftXfy/1MfinZtvXSpZsw4nwH9B3PW\nWaNUx33RokXcdfed3HzzTcyaOUt1Tl988QV+XPWjAiruvPNOnnrqiZNmjf8pACEXR9gWEydOZOvW\nrbz++uuUlVXwxhtvcNuttzFx0kQefuhhtm7byvXXXU/R8eMsXPQdw4cP5+mnn6K2to6PPvqQRYsW\nK5BOmDjBUIDevXpx4003cdlll/3T9T/9+Vm9+md1vBUrVtLS7MVmjSYyIh6D5Ho7XW1mkDU0NVej\nN4iLvx2b1YHRKCZZGvgfDAfwelvwtDbQ6m1EKLdRkSm4XMnYbPFaJyzsg7CPmqpi6htKJXyLaePG\n8tLzT5MSH6Oi9IRW+sXCJTz23HOcKC1R7LA4TFw/bCaz+o5SnYxSdz1bKgr4bOcqNhXnk945m8H9\n+1Nz9DiekhL+cstNjB83BlNOhtYZDgm7sYVtW7apeMVvlq9gwtBBfPj6W0RGx0JQbIDbZEtirmhs\nYwK0eXEoAEB1XP553lH3Ww11LY5XdQplGZR9kWy2BVEoruOX71awaOkKdh8+yImWSjy6gMqtl822\ndNb9Ia/0BImzxRNjS6Rn156kpyVQXlfO6u3rqayrwWGNINoVjcNqawMAWqmsr+BE/QkVfawcp9Er\n/X9SUooGBkkR1MGvQCincpoSvSgAl94fxBWGob37kJ6UjNvnZ+Pe/RRXV+EQwz4BFcJhReEeNnSo\nmg9279uNzECXnDWKfj16kl92gm++/54yj5sZYyczasRZLFi8mFU7NxGy2kjrmsu1f7iWiyZPJzkm\ngV/WrOHue+7m4JH96CSdwGKjW+8eWJ1mjh49TM3xEzgTEsjokYfZYqapqobCAwcJNbiJcVi578Zr\nmTx2DO+9/wlfzP+O3JRMnn3uaYaPHYJB9iZKPN2elPTrUtR+97RelQmaWln5w3Juf+B+qurr1fN2\n2cWXkp2arrm/+3wdElq0x0gFDYZCNDQ38d5XX/LYCy/iDcqOx0ycM5bkmGT0IQEAtLg1eclzGQz6\nCAQ1WZHZ5FDJCtJlFWBGQF+hW8u81eyr41DJbrzhFqwWm2o+SNynAAB5eV2x2KzKF8sb9LFm3RoW\nzPuaHRs3YQj4ueWiK5g0Zhx78vfx6Scfc86ESYw/axRzP/pEMQmnXziL4ppK3vz0fRKSk7j/zrsZ\nkNeTvWs3Y8aggMWFy5fx+aJvMTlsyr/KJi7/k6fSs0dPFQn4j28XsHzjWtx+D72zu3DTzbcycvJ0\nvvj2W1569WVy4mO466ILmTJsMLt3buOOB+6lvA3qevW5OcyU+UhnYOWy75WEQpgwhSVixBckU2fm\nnptuYviQQURHRaokC4N0udu0aK0NDcr88vixIo6WFFMZ9CjpcEZ0Ai6zDacrgsxOOTgT4sBqEWqq\nSvQQsz78fgUwChhmcjrUnlekqfL0SgzkL+vW89O6tazZtIGqlkbFRjinSxLXnT+VrlmpOF12dOL7\nIhIQqV0EAAi6MQiy6Q5SdaiY0sPlBFuDREc7ccU4iE4RszcTIYuJkMkmw40PFq/ixe/XUouZoN6K\nxe7C4XBhEMNtaaB2AADaI/lUfacawkFM4TADs3N5/OFHmDDrfHU+AW+rSlOrO17C83Oe5++ffUqd\nt0Vjm8gY1OsUm0mlDYmMTqdTjJHsuERefv4Fpl56EfhbNWNBh0MBckhcYJOHH778lk+ee51Zk6Yz\nfvR4th7awwPvPsuu0iIl7dYJWyyzlwJJPdXH8dedYExGHiN69ebokSOkpqXhionm4JEC0rIzmTzl\nbLp27aZkLT/88L2qtw5Xl/LO5x8T1OtISskgPSWX5sYWCo8dpsFdQ8gWIrtnDild0gjZLbSEjTij\nE4iJSyTc6uPQxi0U7tpBWpyDpMg4Dmw/QnOzj+T0KCWTcpjseJrqsZkDPPYX6f4PwhFpo77Kx/vv\nLeTlVxZQ3wROM3TKjGL6hPGMGT5c+biJLMbb3ExtRRV1NTVUV9ZQXlGlmFKNzZJ+FlReABLbmp6U\nSFxsgvoOHozMXfoDm/Zs59qbrmD6xdPAKZKjFtD52xrnZo3q3+Y3o5kZyV2T2vh0bWNbqaZY4lrc\nafvr13XJQFFhCff++VnmL1ivHhtnRCQWaQAoqRPERMb+CgAE3NBU0UJFYRnuunpCrQ1YrUYiYiMp\nr6/mseeeZPWmX5RXZAwhpsZncvmQIQzLTsEu85tk3irGgmgTQoT8AeqNJj7fuZPXlv+o9P/yVbJN\nCUwbMJrO9hjMPqE9dKBHteVZywAyGk0E/QEtSk3oEfK5Ju1n27F8VhzeoaaSCb2H4CmroaWqnoyY\nBHrn5KlcVEE43QEvGRkpBBwG1hQf4HB9Ca34MeuNKsouwxpP15gMTH49Iv2TB8PtdVPhqeOov0rJ\nBbSj/zONVOXmqozMNgOZdnmA0NKyYpk0sj9du2Sit5rRm8Kkp8XQp08etjgH6INtJkQagNBm6X3q\n5qydvqbcadrdrWTjJLR6EyarS/XLQ34HRUXl7Nixi2XLVrNrx1H27srHEA6R4jJw+5WTuP6G8zCm\nW8AkEYhiNCGTgcR7CFAjOpDTBL5qA6PH3WTknvvf4ePPflEsBpESCZXl/GkjmH3RaPr1z8IaobQU\nBM1GDGJ0FBZZgoGQHEd5HLfrV9rauTLAG1upL23kl/X5vPbhD6zbWqA2hX6fjC49QnocYLdz67nT\nGZOXh81goLiujneWfc+n+flUyQKpM6tN7XMvPIvZKu6pGrXxv+2Anr5DFo+nJ554hmeenqPGdXp6\nBj16dOOyyy/hwgsvwGI+gzj6d5U5//5NSq8ZhFdffZPXXntDy/YWGUl0Io/95QkunX0Z993/gMoJ\nj4yI4qbrb2P8uNEMHJSLM0ocucNKi/tfvcLQ3BDi9lvv5ct/LKRnzz4MHTqIfft3Ex0TyXXX/pGY\n2BhuvfU2mhpbeOwvT7JnzwHe/+A9TOYAc7/4kKEjev9LhoY8VxJPYtLL1ArhVhG+tOVml9Sx7bsf\n0Ve4FWUxY+IgXH0yNBaAPkRIF1AU1JM6ztO+6O+VCEjTSRDm8RPGc6SgkBdfeF11ZO655066d8/j\nootn8sijjzJuzAQ+++xzNm3azOVXzOaBB+7lvvvuOWnY93+7zhoLQhZz6WhI0f3jj6tV0T10yAg+\n+uhTMjM7M3bcWC6dfREtzbXcePPN3HDD9fTt3UPrtSt8sAPN/AyFyX96bkI9lyI0MzOLzVu2KJBL\n5D2ffvoZ11x1JY4IF8888wyDBw0i0uUiKzMLm5iaqNeZGAD/GQAgRbh0+iX2TszLHnjgAQYNGqT0\n1BIDeM899yg3dAEDpGCW7rbEYsp7vv322/8JACAd9pkzLyQvtxvdu/dkxYoV7N37/9F2HuBRVdvb\n/03PpPdCgCSQBBJ67yAdBFQQEVRUbKhgRUARQcWGiooVG9gLAhYsFCmCSK8CIQlpkN7bZGYy7XvW\nngmi1+vfe73fPA9iQsqZc/bZZ613veVXZbI3ZMhgNbVo3749H370Ae+887b6/JJHHuG+++/9P432\n/up6iM/DlClTGDJkiDrHp09lcNVV00hMSmTOnDksmL+A1A6prF27VgEf4ydcqhoyAbBSU1PU9H7D\nhvUKSJdnlLAjuvfozpNPPkm/vv18sY2/HcEfAYDMzCzWrHmPb775htzcc3hcBoICI9BrzURExuBy\nOmi01GK11WI06TDqhTwqSREGJQFQni+Sj+2yKe1qvaVKpQqFhMQpAMBkCvex2iSeykFtTSm11cU4\nXNV0SU7mpRVP07dnd7QSW+Rwsnzla7z8xpsKnA9EQz9zIvOnzqJrRBsFDOfaa9iSdZjPjuwgq74E\nj9btNbxraKJ720RefngRnbukQ0wElfW1HDhylG07drL/4CEyTp9WFM5F8x7gzptuabmZvCfH1zQ6\n9d6CWRo35Tvha0BanjNeb5vfXheeQT4AQJkWipO29KCi9yyu58S2X/jpxz2cKymhoL6c4toKHBq3\nyoCvqa9VgKA06mZ9EFq3mcjQCJLaxBMZHUZOYS4nzpzC7vEQHRVDkDmQsJAwtQaKK4s4X1uIze2l\n/4scKzWlA7HRsb9jfqhIP7fUXm6crmaKS4o4l5+PSYwtHU7C9HpG9uxH+6T2ZBYW8cOeHQSagxk2\nYrjSWx88dJABgwbitDdTkp9PXWU5IXoDt1x7HT07d+KXAwd5/YtPCAmP5b777+P02SxefW+1SljS\nhIVw5eSrWDD7bhJbtWHhgw/y2RefYW2uVzVHWKs4kjum4NK4OHeugMpzhegC/GnXtZMCPquKSijL\nK4CmZgUAPHDbTO6/8zZKS0t4c9Vq3vvwK+Iio3hu+TOMGD0CjBJN5XtGt9TULUM3tV0pp2lKTmWy\n4MFFbNn/C/cuXMDcO+bgtjvx1xnQC+AjQxGZnvmGVfKtqvQWcMftYvPPe5g7fwF5JWXKDDDQEEh8\nVGvMhkC07hZGpHdwJPu9w2lT9YRBL3GBXhmAGEiKrEpAAAFrrK4GsotPYHdb1McStStGnhKjKF9v\nMpu9zDm9Vhkwbvz6K7Z9/z3N9Q3MmnI10y67nNioKB57dClnMjJY9uhjKs5t+TPLVZ1+3awblLb/\ns/VfEBEVxaMPPEjP+GQ+fGcNQXFRtE5px96jRzh84rjKbbdZbWg9GpWWMX7IcE5mZbL41RXszTii\nqrwrLr2c/oNGsnnrj0qONfv6a+kaH4fB1sTeHdu5Z+E8qoS11KsPTz/1BKk9eypJzvnsHK65/XYy\n8/OpbaonXGPkxhHjeXj+AwSlyDPfF7sjN5G4q0lCTbMwiNx4nOKRYcWukymnv/ImE9M60eoLQ1hd\nYgET1b6kUakfwrRQUZgCKKhhl3LcVc2X2+FRA4CSwmL2HNzP599/yS8/7yXZBHOnXsalvTsRGR4E\ngf5eAEBABYnHdDahczm8P6e2icaKRpqqGxTg0Gy3EhDuT1hslIiy1bTX6tCwYd9Rln65iTwhxOKH\nzhhAaHiUmiyrRAiHMAAqFQNAZFQtUzovCCAx5i5CjX7KkPyxR5bSPq0DylxM3k+zQ5m8Pbb0Mbbs\n+FF5kkmT7hvDKVmp1P8yg4wLj+TB++dx6+23Q5AZp82Cxd6kZCcaSRdoaOL7737gqYVLaGcI5+E7\n7yNZ7Q25PPnBSrYc/kUlGrh1AcS164pZzFstFZSdPUNSQACjuveiU6sEftqxg8KaCoaNGcmDDz5I\nYFiYYpZIvPDBvfu4fOoULHoPDz35GPlV1fgHBpCYkEZwYCjFJefJPZet9vfQxHBSe3RQwE5JRSPm\n0HDiktoQEuRPydmzZB8+TBBuYgLCOXM4R8lCQiOM+AeZlVeN+GRcNiad5U8/QFRrE1a7ky1bT/L4\n0tfIzmwi0l/L7GumMLxvTxLjYgkLDqG6po7TmdkcP5lBYUm58ko7X1JKVYNFmeUrM0CdVxYqsvyQ\nYAPhYWEkJiSQmJxKVZOTL77+kvDoYJY9s5SRl40Co1x4Cx63ROK2gJU+ZuyF58qfMQBaKq1/BQCc\nDofXeFZN/kycOnKWe+5+mD17M7zG/B4NZj8zAf4BStKnaXa7PVq3Bku1lYLMc1SeqyJAZ1AmuAL+\nmEIC+HrTRpateJr6pga14cQAc9P7MqlTOimtwjHKKhLKmw8AkKmeHEi5TsfzW7ex4fhJ7wIBuock\nM67HICJdBgI0BhyCvvkoaS2xV/KD5A2o2A2NBqPJpOhhQvfzCw/keFEOP5w4oBrZLhFtCJcM1oBg\n4sKicFvEEMdFlbURjVGPyU9PdsV5TllKKbLXYnU3q0UfgI7urVLpFptM+bky8ioLMej9iWwVRa3b\nytGSbGpcEjrXUtReVDQpBoC0tgIAKKtX1URLQyOPXqHTtAqEnl0T6D+4H2np7UlNbk1Suzi0AXKe\nHDg83lgGf9mA/lW4+pt+TQCAFiqOeEO6POiFsqYkAeIg64dBG6icJJ31btZ+tpWVK14jOyeHfulx\nvPLsnST3agP+LjxaFy7JN1cAgOMCAPB7m1pvM6EKGW0EL72wgcef/pgaXyCCaI/8JKqvWyh33DGV\nieMHoAmQEyqmSV7/Ai8AIMfXAgDI5uVQb8Ne0cSZEzls/HIbGzbuIatMQFOvMlYVU26BNVx0Mhq5\nc8J4JnTuRJjRSKXFypqt21h1+BAlko+Oh+lXTeftd99WZmm/xbj9w6b3T6rz777fweLFS1Uutuix\nZH326duLTz/9kPbt2vyn/dVffv3FOk2hpc1/4GGVQyoUwLy8ArXBLHviKTWdnD9/ATt3/kSXTt0Y\n2H8ICW3i6d2nIz17pSoz43/88kBVuZO75yxg4zebsTlsREWEkpDYmvvuu4erpo1gx/ZfmTv3LjVF\nX7dug5IBPPrYEjUh/PyL9xkxesBfSjRahAA0e/DUNFJ/rowApw69fxi5P/zE8a37SevelY63TQNd\nE/WOOoITY8HsjUvxjth+78bf0oL+XeJ5bU2DAlPeefs94mLaqozm6poKbrr5ekUfXrToIXLOFnDp\nhImKfuhvNvH0M0/QpWv6PzzF3iOVhtdqtXP33Xfz3nsf0C6pvSreq6vrueHGm5ky5Uq+2fgVr7/+\nKo0NNbRLTmbunDuZOfM6wsND/6cAgKw/0ZELBV0ovqNGj2bZE0/QtWsXPvzwY6Xjk0Wo1RtVFN60\nq65SUhxxB/cCTv8cAJBjmDx5sqKh33HHHTzxxBPcfPPNtGnTho4dO3Lrrbdy2223cdNNN5GZmcmu\nXbtU0y+ghRTHLa//BhBs+R5pqBYtWszuXXuoqakj+2y2MrB66qknufTS8bz62mvs+uknFZd26tRJ\n1ZzPf+ABYmKj/9GakN8vGv7S0lLOnMlUgNP27dsV7V8m+ELxl/crAMGZM2f4+NNPGT9+PAsXLlST\nvZUrVypgwOn0AgBS6Pbo0V2BCQP69f+dnM0Ly/x+v8zLy+f99z9QQMqpkxloxJjOLGvMRFRkrNI5\nSgJAs7MRP5mw4sHaJDFmehVFJZFD3hQLeb7ZlFzAZncoGYFIAMTTRqRsXpzKibWpVgEAjZYSWkWE\nsuzRRUwTx36PTKYruGv+g/ywbat6krTCnxu7jWLmoHGEOHV4/Awcrj7HNyf38vWJPZS7G1XjKD9a\npmMTBw7mpcWLaZOUAOHBHDl+jHkPLODQiVOKFhtiNnH9tKu5f85ctX682v7fwDSlitNLyJfXcPLi\nvVklsbSYY10MAAj7USuFmZcBIEN/Yaipz8kH1XayftzL8Z+Pcb60lJNl+Rw9e4qaplq1lgKCA9Eb\njDgl3ldrwt7kUf4Xcq46J6cRHBbImdxsSmoqMZr8iAyJJDw0XDm4l9WWca76HMK1FABAGokOqR2J\niY65MP1XccFuaTK9g4Umq4U8MXarqiI8KJDIgEAqSktJCgpn2tSrsWlg9UcfYwwwM3vOnRw+eoQN\n332NWWeic2oHxg8axoa1n1NUX8782XOYPHgIWWdzWPDSSkob6rnh+usorarg8+++wSaNmZ+JpHYp\n3D/7LuJj4rjr3rspKj2nmjNJROjSoxuRcdHUNtZRVVFJQUaWAgba9+iq6O5leeepKypFNIOhwUYW\nzruVeffOVhrws5l5fLN+K2ve/JB2sW15/vnn6dBNopp9fgAXLXUvC9JX6zTZ2PXDNj794gv6jB7B\ndTffhEnYILX13kQAaTyFQl1X9ztwT75fwFGRnRZWVDB33gNs33dA8hgwaf2IDIoiJizutwQkX10r\nAIBIABT7TSvTf70CzlpYAHLvyvpyYCW7+FeanF7DxU6dOqt9pkePHorO3WS1qrUpjVJtfS1bt2xm\n3ccfU1lcTPeUjvTomM78uXeRcfIUb7zxhsoBnyheKTExfP3FeqXLvmrG1Rw8fpR1339N99ROzJ95\nGw219WzYvpn4lHakpKXx/ZbN1NTXKV+InOwcOiS055bp1xEcFsaHm7/lh10/UldfjcloJimpgwKd\nhBk4ZPAAodvgqCynrqiYPT/vodreTOdePenetwcGsx/4BVJ6JovJN97A8TMZqlGN1/mz5LpbuXn2\nbdA+TtWOHpdvuKPmST5WrGpyvPpooceLF5RifMl9rKJRHHicLjRiwOn0Mnm18v9y0Vpc9YRi3TJI\ncHlwW0Vr7/WvctmtvPbRapYufRQZt906ZgjXD+1Jm8ggNGGhCjxweDQKANK6rOhUI+cDE2wuXBV1\n2GstWOrrcXiaCQkPw1+aagm19+jZfjqHuz/8glMWWWN+qoaPiIpTAwiJdnU57D4JgMUHAFx4snlV\nRnKUHhehfgHcNOM67rnnbiJbx+HRaRRY7rbaOfzzPj758CO19xWVlVJuqVWDWJFayrNh0ID+XD7p\nMq6cPJngiAg15RPAU1gl0sjLffbFZ2t5dMljVBSdZ8EVtzJr8tXoPDpqmxt4ee07fPbjt0hgul1j\nIjGtN3o/Mw5rHRXncjBaLbT1D2J0p55UFBYpP7bnV75I69bx1Dc08vm6dXz+6ecM7NOXG2++CYdR\nx/zHHmH74YMqXSA6ohXt23VQUZynzpxUiQCGMBMJaQm4DToqqiwYA4NI6NSeuPgYrLU1/LpvP66K\nGsIMgRRklmDWaYiKMtMmsRXniorRYeXl5+Yzblx/NCY3mVmFLH7kLTZ/d5oIf1g6fwGXjbwEV2MD\nuWfOkJ+Xp/whTpzOoLLOgd4PNTDW+flh8DcrL55gsz9+PumGmBdam23KBF2vF1kcVNdZKampVyPl\n62ZMYPGjC4hJiQePFUt9BVpPM2aRUKpnp/RBLbKhP5/+e1eCd1B7cYEttaQCFqV5Vz/Dny1fb+P+\n+Y9zJqdM+TgYTH74+fupaN4LEgC7xUV9dT3lhVW47C4c1iZiYqKwNDfw2NOP8sU3X6gN06zRkO4X\nyJwe/RmX1pFAf8mSFBBVr6gMkt8r+bPycabFwqIvv2J3kThuSuNoYEy7vgxs3xl/m1BYxE3z9zFq\nXkWC9yR4H7JeOYGYoTVr3DhMHnZnHOVwSR5Go4mu4W1IjWpNkNFMTXUNeSWFahNIjk8gMjqS7OI8\n9uadoBgrNdhVvIjICoIx0Tk2iW5R7WiqrKe0qkpNsR0mDdWuJs7WnseijvpfGQAXTr6PUiPFukT2\nXMDXxPRXzEKFTW+AqAgDw4f24pZZ0+jfJw2N3oEE1jrsFqWp9MaE/J5l4NUPttjVKmTF+0c0RE5v\ntqzQLl0OHXp9MDRqObT7OK+/+Qk7d/xCfVMz4wansvz5W4lPi/yd3kQ2StGgeB+ALfTrFuM1LzVI\nFTu6SFa/sZlFS9+mzAYmD7Q3+BNs1JJnaSQpHu656QomXzUaU3wwiJOmFIWSU638ALwZ4gp/dzTR\nVG5hx9aTrHrzC3buK0aFVej91can1bpwi9jc4yHQ4yRVZ+SmkZdwdf/ehGo1WB0evtizjxd++okC\nj4tmjZZ+gwax5r01yhjs/xcDQM5QdbWN9eu+4ufdezh2/BgnT56gbUJrvvzyC3p0/6dN4O/7BTnv\ncn1lDVdX1/Hww8s4ePAoEydOVLRcaQgWLnyIO26/k6efXq58CUaNHEur6ARyc84SERnAZZePoVV8\nOOZAHyvnv21J3JB1uoqF85eyZfM2IiMjSGrfWkV7DRs2lKunTyfnbD7Ln3lG1UiSP/z11xt55ZWX\naN0mildee4H0Lol/CQCoQ/PJj6i1YT+UQea+Y7RrnYi13k5mRi7tRFuYnEBh9XlCu7YlsHt7r/9E\nSzGglqyXpyPynovvpL8LAmz8eifXz7yJ2gaZUbgJDghh2/Yt9OqdzsqVr/LAAw9eIOrcfdccnlvx\njNeUp2W69F+dY7nPvBu81Wrjyy+/Vtc3KiqalS+9QlV1DZ07d1dafNF4v/vuO6z94jPOnctTU4f1\nX67nissnXWjh/hfsF5kcy8RdaOAizbDa7KSkpPLqq68oavuGDV+quLCffvpJmQTJ/vHO2+9w8y03\nqiZE3PD/1QPA97D6G3INuY5yDCkpKcrgUmj3QnuVibs0+/fffz/PPfecmr6LOaKwJqRZlyapV69e\nREVF/e5K/KfnpEURJ/rwp596huDgUOW6Lrna+w/sZ8Kll/Lsc88S36oV27ZvV+cqKyuTl1e+zNy7\n5vw+hvC/WhOotJHbb79daflDQ8MZO3Ys8x+Yr6LAPvv0MxY/spiyslL102NbxfPII4vV10h034oV\nz1NaWoZe7917ZZqYkpLM4kceYerUqf+nZOaATG9ff10xAOpqG9Rk388UhFbjr8z+xKSsoaEGvVHW\nbjNuVzP19Q0Y9f4EBoR6p5l6HW6PA4fDSlVNmQLkzf6hhIW1wt9f9I8iFZN7ViaqTYoFUF2dT6BJ\nx8Txo7hv7p10TE0lK/sst911NydPZyjVT4/gtsy/5CoGxnfA7NFR0WzhQH0h647s5JdzGZQ569RU\nW6ZbQumdOmY0ryxfTkyb1irSr/h8IfPvfYBvN29WhdiQnj157KFF9O/bRw0wpPFqid1U60ZqmhZW\no9CxfMD4b8CS1x1bmXZdMP37PYAgKURSUKt9yCmVtZayn49y8Ic9nMnNZVPmAU6WZBEWHKRMxWT6\nbzT5PhgmAQAAIABJREFUYzQFYDYFofMYMeqMKqvcoNeraCu7y05xRYkCAIL9QwkOClW/QBIA8qsK\nFACgHq06A+kd0omKjPJNhLy+EHK/yDURSmyjpYGszDPY7DbCtHomD7qE2rJyjmT9ysRJVxAVG8cb\n776NQ6shJa2j8gEQxpTQxMcNvoQ7pl3LurVr+eLnTcy5fhbTh49i34EDPP7eexRZ6xjSpTsmfzOb\n9+9VaQfi7STJHb169FHHdPDwAV855cY/Moxho4cqOWt5VSU1lTUUnz2HvcFCm+6dCQgKoCg7j4aS\ncq+ZYzCMv3Y4t98zi+R2SQTpArCVWNmydjMfv/4hg/oO5LFlj6OVRAcZUMne5GselXGwmtxowe7k\nxN4DKuat6yVDVZLVIQHetmzlfFaOKu5lPxowcACpKSm/AYxSkamUFLfSoj/x7HO8snoNzdLooyc8\nIJI2UQn46QNEWeJ17/axFAUAEI8QebPCKhHjSAEBWtI75Je4tHYKKrOoaCzCZPBn5MiRzL79dlXv\n1NbUqMemw+VS+7J/gJndu37izddeJT8rS7l9i7f7o/PmM+6SEZw5ncHLq16nrKGWB+bNw9jsYsMn\nnxNoDmDo8Es4cPIYO7ZtZ2i33sy+8052nTrGe2s/ZcqVV3rZXx9/pKIdDTo9RefleEwEh4YSER9H\nSXkJp06doMHaiA4/Jlw6gaVLH6ZDj65QUwPNdhyVVVRXVOEfGq4YfcYQM1q5HsYAqvIKGDn5Cs7k\n5aravH1wFEtm3srU62dCm0gllVXTfh8410Jy89avPlRH1bTeal19RlHcfe77LY8f+VsnBn6+r/VZ\nX6l/1ujQyhDP6vACdTaHYicuf/tVnl6xQgEA1/Ttyi0jepMSH4E+PAyXGHbrxRhT2AM2tO5mtB4v\na1jV8zIatjTjdgjjo9nLHtHoMRhMGAz+/JJXzIPrv2N3YYUy4BZWb1BwuAIAhf5pa2rAbqunublJ\nvXevfMQbPa3eotupsC2Xx01SaCRz77iTuffejT7AX8kSxPNAfAl+/Xkvm778hn17flE+GhKpGRYX\nQ1qPrnTu04M27RPRm0zKq0Wurzo9IiewNPHBxx/x4ooXlddNp+hElt+ziC5tkrFbmxFd8Kdbv+LF\nj96mnGZsmAiLSyQiJk7t7bWVJVQWFRCGh6EJHUmObUV2XhbzFj6ghp8/7d7NocNHiI6M4fZbbqN9\ncjLny4p5YdVr/LD/Z2xyHxkDaN22HaHBYZRVlFFYeh6H00JE+yi0ZgMNVjseg4EOPTqT0jGFZpuN\n7OMnyfzlEJ4GiS8Efx0M6tUBp72ejLMlXH/jaO6bdz3RsWFYG2y8/PL7PPXURpUq8shdMxg3aiT7\nDhxj165fyMk6S2V5PXYPRARBYrs2dEhNoWPHFELDQpXJqniriBGqXDfxShKPFrvbqYbbwpyuqq7n\n5137VW1cbrGR3CaCJUvmMfnGa5Sc1W2tpqoqm9CwADRGP/QGccAQY1C1gP+iivB5xf2hrhKAV14K\nyJIhe4OLhx56mlffWOc1KdaY0JiEQWQSDxPvXaXWlAcaaq3UVNZRVV6nGtO8wrM8uOgBsvMy1UM4\nGg3D4xO5f/Bw0sNC0JkkhsmniRfnXJcLhyCcOi17CgtZuOFrzlgdShMejJnL0wfTLS5RbUDiNCo6\nv4s1DBcDAGqz9N304hXg1ENhQzlbD/9CLrVE+kcxrH0XWvmFUF9ercxFZPIvWqA24dHKyOZUfhYn\nSs9yjkasBg1NjiaFfkkOaJIxhpSgWBKiW+MWeml1OVkl52jQ2KlxW6iTuDsfBHDhHF94vsvJ16vY\nvcTO3YmKiaOusZH8wvM019ZBQ6PXwMEhOJYdo85F146R3HnbdCZNHEFknAQZ2XDaa9Bq3F5ksgUE\naGn+1c7UssnJxZSm2hsfI8WCTiPUaRM0m9i6bgePL3megnNStKEWbNe0cN5+90ESuseDNNcSZK6k\nGi0Ly2sc6H2TLTQ5rYq80MjmpovknRVf8chjq6n3aDE6PUxN7UHPDqn8eHQvxwoL6NAKbrptGhOv\nHoNftBlCJH6nBVjQqYgif6M/lYWlfL3+R95861t+zbYpvRdiGpLeR2nbirJ/paYgG9wO5TGRZDAx\nuWc3bhk+kLZBQTTbPfxw7ARLhZqKR31/YkqKmlZePPX7L2vuf/ttsvxWvLCKQwePMmLEKOWAvGDB\n/bRpE8/u3TtISIj9X/9K1QDJZn/y5BmmX32jctQV3fHmzZvZunUzkydP4cUXX8LaZGP//sOsW7uB\n40dPKjr0ZZPGcdNNM2nXPp42CbFKvvVnEW5/66Dd8MuubG6eNVf9rjdWvUa3buksfmSROpa0jp1I\nTU1j06YfaJsQz/btn7N27Q4efvghBg7uw0svP0tElFDe/s1vk5MrNqXyMKlsgIpGHL/mU5tfoph0\nsW0SIakDlFZQeOgoDp2TpEsHQFq8BPx6WQDSgIuHgM+MQbS2f2QA/BUnRB7eZaXVzL3zQTZ+s4m5\nc++kqamRN995g5tmzeTe++Ywb9795OWd57Zbb1fF8759v3D19Klce920v3Ua//0X/QYAiATg1KnT\nylBPJqhLlyzj8WVPKF+Fl1a+TN8+/dS6qKmpUs124fmz3DdvHs8/99z/FAAQ9FiabImPGT/+Ul57\n4w3WfvYZ8+bP5+677yErK0sVIDJlXvb44zTbHbz91lvMvP4a39v8ZwwA2fPlHktOTkaM+EQHP2bM\nGDXt37Nnj2qMxYBPGn7xBRDN/5IlSxRbQHwBFi9e/LvT/Z8CAC3fLPKD6qoa1q//UrHIPv9sPTfe\neCOjR4/k3dXvEhEeofbZe+69lzdXvcktt96iTCP/GSCEcmOfPn260lffcsstRIRH8v0PP9C3T1+S\n2rXjvTVrOJ1xmokTJqqmcPuOHfQfMIDFix9W8ggxC/355z3U19f7mj07vfv05vbZtzPt6mn4mYS/\n9e9f27ZtZ8WKFQrgsTXZVOawuPYbdIFEhIsTvJma2mp0OidanQtrUx02m12BBF4AwOSbZIoZoJWa\nukpFqfUzBxEcHE1gYAxanb9PCezG7bZjaaykrCQHvcZObFQIl08Yz6RLJ1BaVs68RQ+p6WYgekbE\nd+WJy28mQReCw9ZMob2eH0sy1PT/eJlUBE2K1mgXd3Zg+pVX8NbrryvHaHWxLBY+f/8T7plzl2Iy\niqv73bfNpk3b1kqfq3yMJF5H9iUB2b3uyN7IP18d4gUufotyk821BQT4F0MYFcHlazikxhI3Qaee\nuqNZ/LrpAD/8uJmNZ3bjCtEyaEAfoqIi2bf/EKczc3G4JFXJTExoFLERMSr273xRIbXWBsxBZmos\n1QrkiAyOJiYqTh1jSVUp52vEVFB8hf4cAGjxAJB/F6C5vKKU8uJiPDY74VoD14+ZSGq7dqz5ci2m\nsGBMQf4cO/krFqtMtFwqCUAosaKBbR0WxUN33kNGRgYvfvoOl48cxdXDx7Bn734+2LpZRRbOvGIy\nFTWVvP3tV2oyqXFJmlAgWrOZuiaLkpS4HJJIpCelSxqdJa3BZaOqto7qsmoKTuUqdllEYrxK2yjL\nK6TifCHOpiY8/hA/MJwxV46kY/tUkmMT0VTZ0deC7Vwjx/YcVfK0GGGAiE+RrAvRg8t18bkBKgaM\nw4VNsssNRmptNl567TXWfPgB9Q0SDgej+g9Q7KLBgwerPaflpSozBXhq1Hpc/81G7luyhBqLRRnV\nBugDSYpNJsgvVFkfiVu3DIu8L3EN9+aGtkSJqvQI30sxNfTNZBefpLqpHL1Wr+7f2bfNVp4PDY0N\nqmETrwdhyIYEB3Fg/z6eeXIZJUWFaoIsmvTpw8dx7ZSpqlk5k3OWD9Z9TlOThRmXTSEqKJQj+/Yr\n8+1O3buqPfTMqdOMmzSRsVdN5u0PP1BZ7TLp/3HnDgYPHETfrj05fuwY323ZQp29kXZt2tOrezf8\nDFpy83I5ePSEumfumHMrS5YsRieTSDH0lPcq4mpxP5epmHRlMu3UGSnLzWf0FZPJzM/FgI4OUa1Y\nesNsLrvuGogJUQCAN1bTW3y3+G8qE8YLkrffAAD1NS1MngvzRC8K5/W69t7X8rfSwqt2S4vWrcXV\naMdpseNotPFr1hkefWUFe/fvI14L0wb14oZh3UmMDUUbGoLH5IfGZPaBDUL/t4Nb/ojBtg98EPmH\nNHIiFXB5cNnEf8CNUWfiREUd93y0jl0FFbg1BiUBCAmLVIM9t9OhdPiWhiolp5Jph+xtJpNZAe3C\nAPG4nBgNWjUdl1XYMaEdixY+yJQrp6h4SbejGa3kptY2krH/MD98vVExuMZeNpGuQwcqVhSy70lb\nKJIDmwCgwkjWUFlcxMpXXlagkUOiIdFy86Tp3DTpKoI0fkoKIgaS3+3fxpOrVlLobsKGDnNQFBHR\ncQQGB2O3WqgqL8RWXUYbrT8TBw/FY7VwviCPsNhIwiMj0etMyrS8f/8BysNm7769FFVVcCj7NJlF\n52lCS3RMW9q2TlK9z5ms09Q0VhLZNozQuBDqHRZcOi1t2yWT1D5FnbszR09wavcBXLU21dO2bx1O\nemIU53IziYkL5Klnl9CzXyclC9mxdT+PPLScU6csXDaiPw/MmcWWTT/w5ntf0eSAEH/olJ5Gr57d\n6No1nbZtWhEeHkJoSKDyrxPAWyN9mM/DRWP298W+GkAk0XozHquTvLP5PPTgw2zavp2wACM3zZzM\nbXfcTGx6B9A347YVKdasWQwEjX7C3/KyXkS68m9ffw4AyJcLK0Wuo9rf3HoyThZw/fVzOXq6WOXY\nybNOI0MCZQL4h5GZrcHOnt1HFQVvz8FdvLt6Fc22RsKAeHRM79qPWwcMIMTjQBMgERFSh/vycCVS\nHjfNHhcbDh9hyfY9yKxCj5EEcwyXdx9EYmAEWpvd2ycqrfhvJfqfAQAKhBezQD2U2GrYdfoIp+uL\nCTWH0b91KkaLk+ZGK5FhEYSGh1FRVUlFXY2i3kWEhVLpamR/RS65ljLVPJvRE40/XVslE+cXikFj\nUJEvNVYL9fYmKu31VDrqqEPMM7zxDN6d5w/DLa0Zv/Y9GDxpGmFt29GsMVBf10RDTT3VxSVYy4up\nyv6V5qJM8NSLvRZRwTBh/ECmTJ3I0KE9CPa3o9GLBb4Y5ImI0hcfpH6hbFRCVpRdy+t+5haMVLkK\nu70TBYuGtR99yzPPrKa01E6YToPJz0y+pUn1R2++NItJUy9RE1OnqwmNybvxqT1QLQ9xnryoRZLr\nKBorQQTrtDz/5Fpef/0rGl0QSwDzho5lSHo6JQ1VfLB5Azsry0hu78/Cu65n1OXDIEKaMgV3+9za\n/CjMr+StNz7lw09/JL9SPAMMEJ5IuwEj6Dl8PFqng0MbPyd3706w1mHCQbRGy8A2ccyfMIZerVvj\ntHvYn3+eh9ev55ilUdGD/MNCWP3uaiZfMfmiB+f/VgJQVW3lkmGjlAnciy+uVO7bS5c+Qp++Pdi8\n+VtCQ/+6oP7LavtP/vG3h5yGzz/fwOKHn8RudylNXXb2GTV9F/nBBx+8R0pKojDsuPzyqezc+aOC\nhu67514WLFigHGglzlmVGBIX8t9ow91w/HAJM66+UU0DBQBISGjFPffeQ3ZWDmlpnVRm9P79++jW\nPY0ffviIAwfO8tCiBRQUnOXDT9YwZFivfwMAeJkmouVsKiih9GgG9uwi/EoacFZbcGi1pA8ZBsMH\nwy8nyfh8I0ajjtBu7dAkRaJJjEDfOpyAeImCMarNzDsB+O1Py/T/r1aEoPIbN25mypTrCA6IZO3n\n68WakOkzJitBzzPLn1T066FDh/PJx5+qqfSVU0WjPYB33n3jP728f/j63yYYhw4dVFKKO++Yw8hR\no3l55SuseOFFJl12OXPm3MXbb62mY8d0xo0bzZ1zZ3PsyH4eW/aoMoVT7/O/ub7/5uilAZSi/uHF\ni5UeXCQAs2bdpJpxcZoX87TGxgbFVhg6eKiaGKelpSARVV7TyT+wmS5snn/v3hQa24gRIxTVvVu3\nbvTu3Vs1tvIe5XcJ6Ccmfenp6WryLfRWod0tW7ZMTeQvfv2352Xu3LkqZnDRQ4tpFR/P+nXr+fXk\nr7zy8kraJ7fn448/VkDIqjdWUVpWyrLHl/HQogf/MQNAEg+E7SMNh+j2DymzutV06dqF1NRU3nnn\nHVJTUln+7HJOnPiVa669Rrm8f/DBB6rhl2sjTBwBA+U6BAcH0bVbVxY//LBXOyx51n/xysnJ47nn\nnlXNgDAJRIan0RgxGUKIihR6qpH6hhp0OhdOt5UmS4267oGB4QSYQxRtXQojKSrtzVYaGmuxWCzo\nDRJZF0FwcCuMxiCf67HQ5Z3YrDWUFGXjcFQq3liInx+Xjh2HR6tl3bff4HY41ATu8vaDeGTSjUQ4\njYqZkllTyuen9rA95zjnHVUq+Ue8SaqqK3C63YwfP5r3319DeFS0YszIKL4sM48brrueA0cPctvM\nWdx/+x1Ei2wjxKxYAvIs9DSK5taDzt/f27z81cuX1uN9bv9mDHyRq+6F2GEpmsUjx55dwpnNh/j6\nmw3sKtxPVEo0HTq0Iyw8gtNZeWza9jMldTb0+BEdEEb72La0iYihsqqaY3mZ1Loly8immAEhhjBa\nRccrwListpzC2iJcPgaAUIk7deykGADKm0GafqUR9f6/zdZEUXEhTVXVdGmdRIfoVoRo9YwcMZwz\nhbm89sEa6jRORowahaWihiN795PUpo1aR6ezM9m3fz9jR45R8c/fbv6WAb16MKBzb85m57Jt7x7G\njBrNFcNH8f2PW3hz+3eKkTlmwDBOZWVTq/dQXFNFZWGxGnbJZHjQJYOJiA/B7rZT19hE3pk88k+c\nVQ8xv6hQWsW3wtVgo7TgnIqz8pghfngoqb1SCDdHEqwz42mwMKHvCKb2m0Tx6fOEBEcQEB2rjM0U\nACEAgJLk+9iVMqxt9mrucbjJOHWGW2ffzsm8PBLi47h79u1MvWKKN4bTYFBmgC0vYXaIRES5Qdkc\nZGRnc+M9d3M0M0PVtEb8iA9rS1RwrKL6y/xFq/XdfyrOTeZxypFNrTcxZBOpiByLaPzdeocyAaxs\nFF8BjYpLE0AjOCSYyqoqlbghJnRCzY+MiODkr8d5dvnTZJw+pVgwEUYzC2+4hVZhEVQ21BEmaV06\nnfIPqW9sYMbV02kdHcvBPXtxW2wqXnj/iaOcyT2rAMPeAwfw3hef88XGbyitraZnl27cfMXV2Bot\nfPrNBk5nZSkT0EljxjJ90gTFJnl37eds2rudsNAgVr78EpOmiqmcmO/ZvQZ80nwIoGYSho3KjqMk\nr4Bxl11BZl6uepZ1bt2OxTffzuUzpkOYUON9Jn0q6aqllmkBA7zPFG8J661rZQSgbrUWfoAievgG\nAy0sAfWxFwiS79EJOKcm/06aqr0Gg5t27uSJ11+mtq6KgZGhzBo3hGEdY4kJ9wdF5RfprQAZ0vtI\ngy8yV2/Cg3ysGMyy3yrXPV/UpM2Js7ZBAXqZ9Vbu/fhLduaUKW81aRYDgkJVEyuLxWZtxG4V8bT8\nLJ0CX9slJSuWkLA+oqMiyc3OpLzS29fIyhrZZyBPPP4YPUV+IZPoqhpMvsjwxpJSyqsrceo0tOuS\nhjbAhNbnGeCxyT5vwF5eq+7rlW+/yeaftmFziTsB9GmVzCN33EenhGQcDU1q79H6G9l2bA+PrHyW\nc64mvDCEnrCIOKKiW6m0teqqIhqrK9C5bAxL7cwVfQawc8smRlw2lnETJqJzG9i5YxdlNdXk5ucR\nYPan/5BBZBWf4+1PPqa4sRFDQAiJbVIJD4skLz+HwrICQuMCSOrSFl24hsZmG376MFrFJmEyBpF5\n/DSnfzmIq6FBAcF9uyVgMtiVtGnhAzOYecMMMPlRXlbP/Aee5vuvDtM6RM+Kp55Rcb+PPf6omoeO\nGzuSAX160zk9jbj4VspzQ+7TC/Iuj5ifG5Vxqc5P/CRkrapINO9A1SMDcR1adT3dfPDe+zy+7HEa\nqkoZ3D2Za2deyRU3XI02PABcDTQ11dDYUE5wSAB+gRL7K+DR7yn+v38U/XsAoAUE0IheQWQBdi3P\nPbuKJ598iyanNOwmxQbQNFkcHrNy8RPgSoy1hJru5sSxbE6cPM0b77zGwf0/i9qcQI+HAcZQbhsx\nirGpKapRc+ucirKl04hphUyOdbh0OurdTt7+cRsvHjmpjD/MmOgTn87I5G6EuPSYDXplIiOOlxcq\ndx/FrqVj+E0C4L2bbYixYDO7Tx3hUGUWadGpdA6OI8CpVchJcGAwTY2NanJUWVtDQGgQoZFh1Gqa\n2ZF3nILGCpw4CcJE18gExvQajKPGwuFDRylyVxMbkUBUfAx55YWcLM3C6ssAUBN3RRdrqW19J14X\nTNL4mfQYdyXFNgd+oZEEB4bhaXZjcHtoKivCcj6b8rMnyM88gr0kC1y1aDw2EtsEM7BvGtfPGMfg\nAd3w9zd6zSCUjtan+1cGgEIpks3K6DVP8mjQyYKSBWbX8P5bn/L6C6spKobWfn70SknhfFkZh8ol\nCxiuu6wTTzx+LyFJkeBuwGVw4PbpJEVzL1Q1hRKp/dT3tzykNHoKTpeycP4qtu88rW6kXgFxLL1s\nKt1btaaxuYmfc07zzt5tZNZUM6B7HI8svZv2fduBTjRNXhOKY5kFrPnwKz78cBc1KvBYi6lzP9KG\nXkrH/sMxxcRDUyMZ36/jwNr3obZMaXTEa6BHeAgLxo9mhKDuTU4yKqp4atMPbC8qQtwZtP5+LH/6\nOe6+e673IfD/wQSwttZGly49KCw8z4jhYzl+/BgNDfUsf/Zp7rrrFrX3/y9fMoGVdS9O5/PmPciq\nVe9h0PkxavRIsrLOkJObRUxsJKvefIOJEy6lotzKlMlTOHDgZ5LbJ/L6a6vo13cAW7fswORnot+A\nLoRFBHkfRv9pk+iBkkIrjy55mk2bNisKqsGoJTo6SjWlolM9k5HFxo3fUFRcwCWXDKO8vIxjx4+S\nnp7Kex+8Q2pHcVH+szPkAwAEoa6qx3muDGdhJfa8MuqLK7DK1NA/CHNgGJXnSvGrtyuzR098KKGd\nEgno0BpnuBl3iBFDsNAJhUnkNcv8zxgAcDY7n1k33sHevYcYMmi4YkPt2P0Do0dewjPLn1KT78OH\nj3LrrbOVm60AHm+sepXJky8X1ug/erVM5GR626VLVzUt7dG9J7m5ebSKb82qN9+kdeu2jB0zntOn\nM0hP70hJaRF9+nbjiWWPqxidFrnUPzqQi75ZmmvRm+fnF+AfEKAmVRJ9KECIRA9Kcy7AidDtxVDq\nxhuu9363z5zoXwGAvyvE+O0eloZWmnBJJJB7Qu5tAQN27NihPAFeeOGFC7IfKVzFCPCGG24gLk40\no7+9/uM17/tWAfomTZxEfkEBOuGMabWMGjmSzz77VMmAJM7IKTRL3PTu3VdJJnr36vUPTSG99P9L\nL72UmpoalXIgTACZkEgqRVhoKE8+9ZSKRZT/l4mwMKxGjRrJSytXKu+AhQsWcvrUr5j8/FU/KvrE\nXr16Kg+A/n37/Sk0c/H5qqysZs177/HJxx9z8tQp77l3a9HrAmkV10bd8/ZmGwaDh6amOhoaqlXD\nEhocib85VAEAWl+usd1uxdJUT6OlXhXgIgMQAMBkCkWrE6ccN1q9JAbUUl6ah7WpEj+NVzwmU2a3\nRqMM8qRCiMHItLTh3D/2WgKaNZQ0VLIz+ygbzuzncG0eMvMPDAxRkcVFJYXUNNXTrXNHvvt+I/Ft\nEhRdVqbPTWU1vPrSy7z2xutcMWkSD89boLT3JXWV1DTWYRVTp6YmYmJj6ZyeToAYhfn2FZ8y0Zs9\nIg1kyzPTN/3xSvZ8rL2W+KYWx+2WzHmtAUdBFdmbDvLLz9vJbs7iaN4Rzp+ro2//dFq1bc/OX45x\nPFMm+ULjNqp44/T49ooNklNayMn8TGo9deq5HaQPUT4AElFZb2uguLZUrP3UJZVpszi2S9a2ypr3\nPSMFABCmZl1dLWdzslTO+owRlzJ7xnUc2LVLNXJx7RNY/cUn7M/PZdbMawn06Fj30cd0T03j5ptu\nIrfoHC++/poyxwsLCaautITktm3p3qUnBbnnqCqv4PabbyGhVWueffkFtueeomt6OrNn3sTqTz+h\noNmCISyIY3sPgUlLZFwcw8cMR2t2Ywow0Wixc2D3AQpPitxJhykiRA0+gs0BlJ8v9DaTJghMg6Ao\nf7ql9GD00OF0S+9AWmwirRANvxuXza3A65LyUuLaxhMeHwsmvQIDZP2pyZ3NpiagQtW2NzhU3Xv2\nfB5dunZV8XPid6JM5y7EJ3rvGGkyhUkiAxcp1iWOcOnyp3nrww9pEqtmrYlAXQhtYhIwmwLQuuW8\nC8nda8QsDVLLo9H7s4XerVMMGmnqtUYXOcWnKak5rz437JJhSg4mhqAVFeXYmptxKLNfPbEx0WRm\nZrBm9Tts2bJZLcUe7VNZvexZdA4Xj69YjinAzKTxl6rmcZ0YhTqdTJw4gaKcAjL2HiQ9tSNpvbop\nzX9dbS3LX3iBzPPnuO722xD3r7CAEAYld6Z7x45ExEVzKiOD8+eKcTc76NohmREjh1PvbObTr9ez\n9ZefGDioH/fdcz/Dhw9H6YRtdm9DI02S1LnC3jObqczLVwCAAApyPrskpbDo9jlcOe0qCDB6Q6rE\nWFwxJn4/zFCO+BfPr1pCzS58zgsUCLjyuxK3hQ3gGz8q2r806TaXcpOvqqrhl8OHePiZpzhfmMeQ\nVjHcOmE4g5IjiAjSg6QHBAYqM01h38i1FG8HrdYb0SfpKyoiXE1Z5QjEIU5AJnBW1asYubwmF3PX\nfMGOnFJEkCvx2waDWTFRhBkjDAA8IkP2snlk2NKlSzdCQkKVH05sdDQH9uxh955dWFxe1lOQR8tN\n113HvPkPEN8hVbSFIPHm0mf5GE3qWoj02KDD42xWx9/c2MSp4yf57P1PWbfhS4obqhWMKJdK5MwV\nd38KAAAgAElEQVS3j7yCO6++Hp2AWEIY8Ggwhvjz4/E9LH7pGUpE/qA30OR0Y9QHEhAkUgY9Lo/X\nyJBmG5HomDHoEiJMJoaMHsaQMWPBZeSNF1ayZcc2Ujp0YNLECXTs2oU9J47w+EsrVMKAgEZt41Jo\n0yaByqpycgsyCYo00nVwGrFdoimvq6WhwkN4YGtwmcg5mcnZo0fR2S0E+eno0D6OqspCLhmWzmNL\n7yU2UXy7/Nj8/c/cfucS6ss93Hv9ZKZdfjlPPPs8pzJOcu30KUyfNoWYiHD0AgRLfysGnTLt1xuV\nZEIjWYHigi6sFsWY1iuwS6mM1A0u7FQ/L7PbT8u509kqKWj7tu9ICjIzduxgpt14FT3GDlXAkfRN\nTQ3lWCw1+JmNBIVFeZ3zvYjln0ygvf3Uv6XYipG4rEO3Br0xgF8PZnLbrQ9x9EQuDq1RMcg09XV2\nj5+fEZGdtDwgBJisrbZw8vRpFi5awP69P+OngRiPi6kJqdw2ahRJQYF4mhoxBoohXcuE3GsGaNVo\nKLQ1s3zdetYXFlOPhkgCGdKhB/1ikwlR2ahenXmLq+7FIMBvb8ir0/LSGKBZryGnoZTNR3ZTSSMj\nOwxmYFwyRoeHMmsDWXk5ClRoHR1HfHi08gw4VV3IkaJsCptrqMGCXnER3AyJ70Kfdp0Usp2bm091\ns43Q2Gh0QWayS/I531iqggClQfYCAKLf8dUCWmnWdfin9qbnlDsIbN8Fi06M6cQ4UVJY5Xrr8ddp\n0Tc3ga2BysI88k8e4tzxPVBwCpw1qoBIiDMxedIobrj+Srp1a69YAh5Ns9KGNDtsGI1e8yHRCMmx\nOJqbMeoCcDdoeH/NepYtfxNLNaRrYeaYEXRL7sCR7Bye27wFGbYLw2fJ4lnMumEiBsn4McmCaMKt\nc3vNALU6HHYnRh9SqSJrPCYVZ7J61Rc8/vw6LI2QhJEbh43i6p59CWr2FhgWg5bvTh3lna0bqQau\nunYUd98+lZCoUOrLLWz55QhvfrOJ7ftzvPppjwn/7sPoNm4KiQOGUq81ovULxN/dTOXhXWxb/SoU\nnMHktoHLRrK/H7N692Tm4KGEujSU25t5eO3nbCkuROZbdo2We+95gBcknlIuj1YeoC07/9+bNv6d\npunjj9YqZ/aiomL18JwzZ66KIAwL/Qt6+9/5wf/ma+RBJ1PQV16WBIA36ZTeRbmmigv5SytfVA+W\n+QvmM/Wq6Xzz1SbefvsdgoP8uOqqK7l62lUcO3qaO++4RxnZrXprBePGj/D2Z0Jn/Q9Pi9SxmRn5\n/PjjTvbt269ugBkzrmb8pd4IvNOnCqirq1dTxw3r16tIlKunTeWuu+eQ2qF1i53Hn79TXxSNV+Yk\nCDxQb4XKOgUKlJw6y6kd+5RbdHLPrl4RVkwwhAdCSICviPA+VxVNV72/3wAA30/9twqEloOS93j8\n+GmuuupqZAIqTV23Lp157fVXGTCgL7t27WHRooc5duy4evg+8/RTzLhmOkbj780H/8ElV/R+mSY/\n+dTTKqO4Y4c0Ne3tP6C3Wtvvvfcezz+/QtE3hw4dzIKF8+ncKd1bwP6f7/DvH5nsNQI+yaR96aOP\nqkJDmusZ02coivLPe/YwZfJkqqqq1ZqUibvk3v/+9UcGwN9fdLL2pdmWif5LL72kGleh+yckJKiP\nL7vsMmWQJ1T/devWKR8AYbxMmjTJ+zzx6SP//jv+86+U6/Hdxm/5aPUHHDt6TOVPCxOi/7BBZB4/\nxbRp0yguK+GeefdxzczraNcu6X8CQMrvFYaD/BFQSDT38+c/oGQZ8tq7dy+rVr3J7t271AN84KCB\nygehW9duijGxcOECfj15Un2tNA1CFU1PS+fRxx7liismX9C4/7vzk5WdwwsrXuDHH7eSk5Ojph1y\nfxiMgYSFRvhqBLdyMRc2ksctciU9wUERBAWGq7mnTiM0Ui8JzO5oorq2HHtzA2ZzMGFhifj7R6PX\n+6tG1eGspampisa6CqyN1fjpITjAjEGjo66+gTpXk/R5pGkimN57DNf2EsaYi9MNuaw78iPfnz1C\nuUdmTwG0b5uK0d9ARtZJFTMYERnCD5u+pnP3LiqJQGi30oCc+GU/y59/lsCAICaNn0Rtbb2ajNXV\n16shhtDck1OSGT50CDHhIV6qskzNFWX5Qg6XAsZk8OERloRQu+VEmXSKvttCM/but14ATMUHSkVd\nbSf3m32cOPwzlYGF5FRmUVdrQZjwaWnd2XfoFFv2/ooDvdoSzRoTSRFxpLRtj9nkz6mzZ8isOKuS\nA6S2DDGHKoNKi81CtbXO+7tVV6RRCR1yD/v7B/gmzr9deQG1c85mKbnDiI7duObyyYQGBfLTrp1U\n1FZT1dTI0ZxMAoKD6ZbSkbL8c3gsVm67aZYyK3xjzWoO5ucRbjQwrENnYkPDySkporGugb6pnZl2\n1dUU1Fby4KOLlSzhzlvvoEPHNBY9/SQNQWZ6Dx/Kgf0HyM44Q0KXdvQb0g9zsEkB1yWFpezdsY/K\n7DICZbASF4ul2aZo/A2VFV45Y7OTiFgDY0dfwh23zlaATVBIoMq5NjSKntVJQWYe2zZJ9ryFYaOH\n031Ifwjww62T2lOKymaaa6qorqsiLCIagz5UNWJaSfcRVplE0ooereVZ9S87nW8SrYYnWjas/5K7\n58+n1GnFJdfarSc2LJbY0AhMGjN6jzSNepwq111V3BdJSuSe8coAJL3D6KfjfOlZimoKFMEkKTlJ\nyb7ED6aqqlJJpZTGzxcvaDTqWb9+Le+88xbNzQ5SomNZeutcOiQksXH7Vr7bukkxZKdOmUK31DR+\n2r2Lzbu2E2T2p3N8EiF6P9VU6o1GjmT8SseuXenZpx9Llj/DzowjGDHSNjCM7u2SmXnlFNonJnK+\npIy31rzL0cxj9O3Vl/vm3IOEXby4ZhXf/7SDoT37sPLllbTt3kmtM6zSQUoDKp5hXo+NamHVXT2d\nA8ePqI5z/NgxLLpvHj27dwe5DtKQuLwAwO+HPF7WxJ8zzrzn1vvyUv5/97pgJODzCvBNbMWMWL5c\nfCrKKitYtvxplVIRqzUwY/gApvVOIU7Kj3iJF5T3YVL9gWIYeJzKF0iaFeXlJJGOBo3Pc1CuqxcA\nEGsxV42V/AY3i9dt4qsjYn4odYtBRaqqOEjxiPA4cTt9AIDGQHhEJD179aJr124qoUeuW1FeHtt3\nbGfrnu3e9+j2EBMQxOyZ13Pv3LsIS07ynj85LLUefcNFYfm6nOTn5XL81xPsPXiATVu3kl9wHmuz\nQx2mYDTydtqHRbNk5h0M69obh93hTX5xujGHB/PpT9/y0vtvEBodRkFZGdU2iU7Xo9Ea8ZPGWRKb\nHDZcVgnDc9M9LJbLhg1n4ujRdOrTH0+9lYULFmG32rhh1o107t6NrOICVm9cz1c7tlBYXYFkQ4UF\nRZOe1gmN3kNO7mnqm0oZMK4vCf0SqbHJnmDA5AnH1eih4HQmBRknsNfVEmjSEx1mIiZcw5Il9zB0\nZD+MwYHYah0sf/ZNlj23gYRwf15+5CGKcrJ4cdWHjB7bm9tmzSQtJQWd0Rs9KYbmTo0Mtw0Y/YPQ\nBgSBOQhkmq7WjtzHf2BievUmXn29SJDsNj58/wMW3HsvRo+DEQN6M2xwV6bfcBX+HSXtwusj57TV\nUJyfoaLa2yS2V9HvAj643RoMLVp9n4/FBRPsvyx45Djk2vvx2JIVPP/CezTKBRb5gt3mEQ8FdfBS\nLEgB5TVygk2bdnLb7FsoOpePH27a4uHOnn2Z1qcv0f7+aGRxXtCU/4Z8N+r07C8r5+kv1nOgyerV\ncRDAZb2H0TEolgCXr6lVyFyL7qkFUf/DfdqC0Hm02PRwsiKfbaf2CCmGEWkD6RbeGpMLyhrryMjJ\nVoVj147ptA6NwuJsZkvGIU42nFMGgBLrJ+8sGD0DYzrSIbqNQrJkCCmIbU5pEcV1lVQ662nAjl0j\nfgZiKyRX1mus4N1TjGCOou+0W4kfdAW24CiaDVpcOq/rr6DrklxikE3GaUfndmMWkxd7Iw3ns/l1\n92ZKj/wM5QUgzb6zmU6pEcy8ZhKzZl1JdEwgDrcVj5BqdG6l+ZGCSW8KBKsdmg289cpHrHh2NWUN\n0pzD9K4duXLgQOLCIsitrufVrdvZlptNjcg24gNYsuBWJl9+CbpQuastODV2dEYtGqMJh8WKQVBu\nmbW4JU3AxY/f/sRzy9/k2FkbkvR3RUIHbhw5mpTgEMwSsSLyba2GvIY6Pti+hfXnzhIRZmLZwjvU\njbPh+x18/P2PHC6ppVnQJX0wwWl96DlhOq169KcpIBCLTIp0BgI8DpznzrD38zVUbd+I1mVR2tDW\nOh1XpnXg3kmX0VprpMZuY/m337LuzGkqFFQCU6dcywcffIifWYqrizf9v990/OW94/vH3bv3UFBQ\nQFRkNP369SckRDgK/0Bf/3/8UmkE6uoaOH0qi8jIaNolJanGTLK/5cEiU0+ZeBUXV1BdVU1UZBgx\nMTGIse7zz7/FoocfZeyYkXz8yZuEibXpP3xJcSpO9bK2AwL9FWAo2l+D6NV8ktgzGTmKipqcnKjY\nUH/r5TNl8np3C7osCLoWGpuhuJrGY5kExsRAYhwESyyJjzqoaMZu7+ba4pnR4gNw0aP/78+eITe3\nQDkVy0Rl0MCBtGnT+gJgUlvbqFzXJY+4nS/5QQHq/wMGSEtRI/f58WPHqa6ppVVcKzp3TrvQ20tB\nl5+fd6EZjhDH3v8Pr5ZjkeLyyJEjaqIuhnvykimjfF4M4qqrq5kxY4by7/hfv1pAADkW8RoQ2UWX\nLl2U+ZW6wz0e1ZzK9ZDPiRSg5fP/7cT/4vcgP1+OQZo7R5OdmspqQkNDMPr5KH4S556Tp1y123VI\n9ppZ+V7/i98vP0rOswAfMsGXa93C4JHjEn2/OP7Ls1pYGEJPlmMW9oDIIYQhINdHvkeaWTl3AhII\neCI/769ee/fu5/nnnlceAALmKUaN24PBGEBYqCQ96BQDSuj9jmbRWLqVa3VwUDhBgWEqAk1Febq9\nYKMkAdQ3VtFkrVasMgEAJA5QZACSh253VmOxVNIocVfWWuXMHx4chElnpMFioaJOpWSTRgQ3D57M\n5WmX0GhvZGvxfj45+D3H6koV0y3K1JqENu2xua3kns/G6qjDoPXw6efvM+mKy/CIVl85Lulw1Tbw\n/eZN5OcV4G8MVPrqwKAgxaqQc2k2+xMkjvjhYZiNWqVlbbBasLucqjGuqK2hpLxMUXElxsrW0EhS\nXDyXX3E5ka2ilRGh7Eni6+MWWrDa27xAnQKo6p2UbjrCLzt/ICgNQtoG0qtnf3bu2Mu585Vs2b6X\nn/afUlMaqyQqeZyEagOUV1Fy63bUW+o5mnuCMnutkvH5mwIwGYxqPdaLYZj3yaT+K88EAQGCgrws\nsIvX6fnCc5w9m4XG4STBGER0YBApqclExkTx2dfr6ZiWRkh0FD/+tIswox9dOnTk3NmzDOnfn2tn\nzGDNxx/y9a6d9OjYkTnjrsRWU8en339NkDmAuTNupG1iEptOHOTx159nREo35aXy/b7dPPveO9gD\n/Rg+aQLtktuxbdc2DMFGkju2JygkSDUdGcczFADQXOskrlVbotq1w+50UFtRRtn5AlUz6V1u+ndJ\n5/HFixgybCA6gw6Nyaio/DLAkESZ7zd8w0/bdhATHcVlV02h54hBEOgDAOQ6WRrJzzhFVV053Xr2\nQ2+KVAAABh+42wIASE78vzAMLwI6VR+qJS8jm3vmz+f7Ywdxq+hIjfJqCvcPJja0FYF6yd42KomK\nV3/uY5ZetH+0GL6J90hZ5XnOVeTixEZYeJiSJ1w6YQJ2q1XJfMQcWwoff38/AoMC+OGH73jj9VcV\neBZhMNE9LoEhffozfOwoDh49wkdrPyE4MIinHllKtET/LX+SE0ePMXH4KGICQqgrraBTp06ExEUp\nU76kpBROFeTz+OpX0KNjeIcehHm09O/QgVnXX4cxLo4P1rzL62veVvGTc2+aw02zb2Pb/p+Zffcc\nxHv/uutmMvveObRJTESjQLiLTa81nDh2lKuuu4bz5aXKj+CJZctU3KxKCRDpgNNratkij2z5W87d\nH/v63+9tFwEAf5x8tDRScv4VWOYz2ZZmQOpShQ018+PWLcpk9XxJIWlhIUzr3ZFLurYjOTEGk9Qj\nZpMa9HrlBh4VX6ruPQVuyM9yKGBAo4okL0UelwFqnRTVu3n48+9Zd/C4os9LcpbJ4KeAOodICQT+\nE0BcGlC3qA5CSE3tQPfu3emQ2oHI0HDC/QM5V1jAF99/xa5fdqv9WOvykBQRxahLhjN+0gTiWsUR\nHhGh9nFhlgl4L8/U/8fef0BJWZ/9//hretnZMtsrZWEXlt6LdEWUYsEeEWtMNCqWRBOjyaNRkxh7\nR7FgAY3YYkGUXhSQ3payLGVZtpfZMr39zvW5Z5aFYOIT/Z7n+f/PM+dwaDsz9/25P+W63tf7er93\n7dqprFD3HthPXUtLTPL8xAgKtCS50mVnTeWOi67CabDh9nmxmq1KBFTYGQ/Me5LvDmzjjw/8nhdf\nf50Vm3coLQC96o3XY7WIQHpQAXe6SFjlEsN79mH6+EmU9OilbKO/WfMNF547nekzprO38ghv/eMD\nPl6zFFfIR4Cwmi4Om5Pu3XuQkZlKbX0lDU0VFA8rpHhiL0ImMwF3EtZIKkFXiL1btrFv2wa8za3Y\nTJCeqOfWX17CbbddgzlRq+Dv23OU2269lw0bqrn6svO49OxJPPLAfeitBn79mzuYcMYYTGJFLzbp\nRgsG0c4xWdDZHIo+jwIGRDNGEr7OFJQOTo+2tuNyMQpNgfpjx7lzzhy+XPI5/fMLmDy8hHNmTGD4\nz2bEnK2ke8RDyNNA6a7tKhYcPnI0Dme6mgMqNu5gmnX0vvybECw2vyNmli1Zx42/+DUVNdKoL5os\nwWhUglipMvn9PmUNIABAwA9333MPL859AYJ+bNEwgxMSuWfCJCYXFWORixCxC6GSqE3shIVOq17P\n+3v28PySrykTHTz09LRmcd6ICeQbk7BKy3tH9T82YN/XNhrv64nq8Rthr6uSr7evwYadYb0HkhYx\nYg1CiiNRqTH6wkElBuj3+nFmpnO0qZZdNYfYH6jHpUKFEEWWLKYVDqV7ag7t4QBHaqpUcikggtsY\noaKlluZIm5p8Gvlfjbz2kus0JmMrGsmUq2+D/F747El4oyElrCDvkINWWZdGwhh0YZU0iS2DRa/D\nFPAScjVQs3cXO1cuxle6HtpFBTSoBCcuu2gy995zM127pqJPkBJKq6I/6cRcUGcn7Arw/Atv8Jen\n3kYc6YTMcnn/gVw6bDBdk5OUC0PUnsTaA0d4Y/lyVrfW4QaKu6Rw/TUzOW/6KPJ7ZIBTgrSAhsYq\ntUkdwbYg1RUuRBH9ldcXUXnMr6j4U/K6cuXQMxjapSs2k9ybAACagI3b52P1gf08t2YZFeEIl0yc\ngDM7h9e++IwjbW7cYjsoLgVFwzn3mptJLe6H22AiZDLiV17EeuwGHYmBdvYu/YTtbz4Hrmpl/SIy\nieOyMrjz/PMZk9+Fdq+ftzduZO7K5YiUhTBLxow5iwULFirVeTWnOmLxnwYAUMC/xHOxzw0GIxgN\nmq/z/6tXZx0A+Y5AIKz630/3kqcQvxRZs1IpePihp1j/7WZ+f9/vGDuuP4GgiHD9BL6A8aa6GGiu\ntgDp+1YJuAY0q3GKreV4heOHJEXxfbJjWP2irKvTBALlsJb+XKW3pPUBdopkT2yInQYo/hP/nVkg\n16/21thFnHQ/gk/KJUW0lr+OdqD/zhec5gEK0yPuMd55nNRm32mOxZPizh8hgYLm9/rTvTSapQy5\nXrFQ5NrkFW9N6ZxAnHpNP0ULTpxm26F0HGeKxZWPT/P3uAOIStp/op4cTYA21iusFXVOLDR55hLn\n/bRD30nkSqpJ2jlyujE93VyQZyUMAGFJSJuE9N1L8i9ijmIbdvvttysnhX+3FkXb4+FHHlbuC0pH\nQAWfAgDYFQAgVe+WlmZCAv7HvKlFF+BUAEBscWUjCEeDeHwttLbV4wsESUrMIS0tH4s1mWDQTyDo\nwutpor21kUCghSSxtnMm47Am4PX5qG6swxgMUIKTX597LWfk9KOyuZaFh1fwwY7lCHnWgImu6SVk\npufS4m/m6PFy2gMNaht64I/3cN/9v1diSgGPH3NCshIedTc10dLoUtV1SWwE3JJfMm7SdtHc3ERt\nfT0VVRUcPHKIfQf2U9NQj6u9lZqGBjx+H1arRf2sTIMeubnce8/dzL5mtkZtlgUsbIGI2JMJKVh0\ne3ToxaPZDe2r9rL4wwWk9bfQe1ghSUlOdpUeYtueo7zz988pr6ghJSWLNp+PdqECizOJIZGiLj1J\nSU7iSPVhjtRWKNtho9GMSWi8Yg0XDsSSEW1jcjgSFUgmyaMEtJ3Xb0VlBfv2l2KMwMSefTEEguyu\nOEBRYQ9aPe3U1TfQo3shXfML2F+6TxWIHMmJeNvauOeWOWzbvpV5//iYq6ZO49fn/Yya/Yf4cvVy\n+pb0ZfrYM4maTTz78ULeX/wBf7rhLvoPH8w9857m0y2bVExSNKCQS2dfii/qp6qpDluCHZvVjtfl\nYcuajezZsgeCRnK6dCerZw88Xg9NR49Rd6wCXTSElSjTxo/hr4/8icLePQjrIxhEiVsoYS4/O9du\n5PVXX6emqoopkycz4+ILyCjqgk5YqwqPEaV2D7WHyzHZjKQWdIeQ+MLHhYxj61wYALE9UK96vuJN\nZqfuu3JehXj2pZf4wxOP0i60cGHQyD6KmczkbNITc7EbHRqhQEJm1TfV+TjT1rzsYwIANLfUcaSq\nDB/tqso88+JLueaaa7GYzarVVYBhOQcciQ5S05ysXbuKZ55+ioamJrplZJJrTyLZZOG6WbNV68CK\ntav5YsliBg8axFmTJtFUU8uSxYvVBaQlJmON6Bk7chS9+5bQ7vPR7PayevtWXvroPRxmG3POv4ws\njCTrdUwcPw5HZjq79u9Viu4Hjx7BE41y/kUXKWbWkpXLeOXdt6lqb+bn11/HbbffTo5UNMUXTbt5\n2uqbmDf/Df7r8UeVePiYQcN4/tlnKRkxVBtnv5+o36dYEvH98MRo/TsAIB6wx/bxzo9LHa6xQCbe\n2xNnIcZYddFAkNrKY7z7/t957tVXaGttZVCajcsnjmTasBJyc1IIizWWVajuktob0KsKYOx7RVbe\nJHNF/N61++0AAFojonnMgx98xYJ1G3HLhFT6KUYl1BeWZgjJr2L6Pqq9QDEErGSkZdCjsCd9i3sz\ntN8AzBYTm3ZtZcnyryg/Uq7mhEwplcA7EhSoKTa9YpftanYpZpkUmETUMxyVMmPs1ek8k0u3RaGn\nI4OHb/8tQ7r0ROcLYbSKQJ1OMQBKj5Tx6IK5FPQr5Pm5z/Lr++/jpYWLCGDC6khS+jHK7yUiYpXa\nPBXFN/mVIoByggNXcxPd0/O49qLLMJjNvP7pIjZX7FcMBFVyjbk6GnVmsrPzlVNAKOKnrv4YKQUO\n+pzZm8SsdMy6DCyk0FThYus3G9j13Tp8rW6lX3DhtBE88uDtFBblqLYh8cj+7ONl3HjtAyQZ4eXn\nnmbzdxuY+9J7XH75OG679VYys/LVyRI1mFSPv8kq4n5WsMQU+uUZq18x4Cg+hh2I1Kl7RIyFYjDx\n0fw3ueXmm8m0W5g+YgD5BU6mzJ5Oz0kj1DhJ+4/68Iif7Rs3cOjgfkYMG0x+cS+UEJ7B3MFtOZnv\n+j1xoMxnFcOYqCiv5pZbfsfXK3arRrF/YgDIYW+xmKmvd3He+efz3XdrsQnCGI1wbl4Bt40ew7Cc\nXOUhLxQVo1JVlYsV5UsdkUCA45EIc7/7jgWbtiGpnIikjMzozcSSIWQY7ZiCmuxfB331dMm/mvmx\nhSRjF2MA7HEdY/n2teQkZDO4uC80u0k2WMhJy8BqtuBqa6WxxaWqF5l5uRgTrWw/fpCllVsVA0Bo\nKEOdRZxXPIKcBCfVbc2s3fwdDdE2Crr2wmPTsf3wXqr99THF/Rj1v+N6zGBJZ/AVc+g+egq+5FQC\nFht+AQBUz0tUiTFJhVypc8rYCIigNyq6kiEcJtFoxEaY5vK9HFj9BeUbV0JzDfiaMEb8TBpdzB23\nXMf48UNwOOX7zODVEWj28/zTr/LSSwuo8YgjA8zs1pPLR46kT7oTq8pYdAR1Rrx6K2sPHebtLRv5\ntrpCQR8CWI4bW8TMS86hpH8RztQkkh2JSlDpaHUNWzbvYv3qraxatpMWj6B/cEZaOpePHsf4bsUK\nDBB2pyxNcUdWCyAUZX91NXPXrmJvUwvO9FwONTdT6m/GLRYpIpQorR+X3kj+0DF4jGbcQo0yGpXN\nmNvjwWY0kKiP0rBjAxtefwpf2Q4Ie0kiQi+bmesnTWT2yNGEvAG+PnCAxz/6kMNAM3p6FPXl9dfn\nc8aYISfmi7Ztfs9q+HH/LFXauG2Ptjn/uM879d2COkuVTjvwYofUKei3Vg3UEwwHMRm03nehAwvl\nVwpOtXUu/L4Q3bqlK4qoTvxxhZ2i+gX/+xd8AnHXKE7xJK1zVbIz9VoTg4tT8/7993Ve/pLIxPtV\n1akq/6k+Sw2Cpt7badDiW8Spj/uHAgBCrZKqprxOXHenL+j8QafuU//+1n7Q5OicaHd+g8Y61Sjx\nnZO+U5/HD/qS/+CH4t8Z/12CBvEXjgclasxilH3t8fyHYpOdhztWfZd77pwAy7qQBC3+3Z2/93QJ\n8X9wu6d/S+dzXLG7YnM7BobF589Pce8/9Jo7f1fnPwszQ5wQysrK1B4i7UrCIhAbxQcffFC5KcTH\n8Pu+q7mphT//5c+89957qtVCLKZEjdhkEQDAqdgHPp+cJkIr1apdnQEAo96uPK41AECw5SDBkIc2\ndyOt7a2YzclkZ3XFaktRBYdQyK0q/572RvyBVtWel5mSqvR8/MEAtU31GIN+epHCfef9inTMzpYA\nACAASURBVIFphXxXvot5+5awsaEMTzSM1ZhI98y+JDpSaA+3UV6xD0+wSbVlTjl3Mu8seBtHolP5\ngqvqrt5Ie00dFYeO0NbYSsDrV0KOVcePq/abI0ePKIaFUICP19XSHhY2nk714Bb37sWwkSPo2r2b\nGoclSxazaf16FWyfPWECr7z8Etn5+apCrcbHKKK7Su1AaRoIK8Dg1RPeeIT33pjHgfZSBo8foJK3\n47XNLPpsJWu/3YbNmEh+fldcbW00uJpoD7uVUnmBs4D87BxCYT/7yvfjCmtWnKIgL8ywoCpBnDj/\npIVE2hnS09LVfOi8dhUAsLdUaSxMLBnIkN4l7NqxXRWARDhy2YoVVDTUcvVlV5DtzGDBewvxmKC6\noZGrZpxHe2srH69Zze0XXcptUy6mqvQgAX2EbgVdcSY6aW1r5ZE3XsIXDHD3z39FY8DNDU89wLaG\nBhU65PfJ5eJZF6K3GxXQIerXhoiRqvJKvl26hpryGgwmC5n5XUjKzVGslpaqagItTRh1OszRCBef\nOYE/P/wA+b1Fe0imo0a7j1S6+OyDT5j/5ny6F3Zn9lWzGHzGSMJ2A4YEmQOxFSCVL0HOpRhikJGQ\nlEEq9+GYcJxWfZWtXpToDUJd1w6jf6aeK8qvngN793HjnXPYtGsn3hiyrceARZ9AenKO1rJhtKk1\nItTiOAggsbc8R1nTAtaYLAZa2hoUoBXUefBGfEwYfyZzbr9DsVUaGxrw+XzKwtFmt6oWrW1bN/PG\n/NcpK9tPt+xcLp0ylaqDh6g7XsXokSO57IrLFaD1+LNPKdBo1vkXk2i2qPbCPTt2YorolDaG6IYU\ndO3Ksapa/vTM03y8dR1WjNw17VKmDx1FbmoytfXVfL58KYmpKUwcP5GQiHZ+vUT5pYtw4oTJZ/Hp\niqU88dJzNEb83HT9DTz04MPKP12Nv8HI0Z2l3DznNpZu1+wgzxs9gfOnTVMK8wVdCxg2dAgpqalK\nYC8ed5zYu/49ABAXA9RaBU5CWk48xo71olHoOwIMSZC9Pg4eKmfhxx/yystzCYWjTO6ZzS3njmdo\n7wKMyUaMNonBxEXAAD6taCHWff6wlwSnDb20CkjfvQIY9BAUFlCEZp+JRz5eypur1iJSfzpxR4lI\nfCW1/6BqO06yJihRVY9XCWipnEviPdGXyErPpKRHEWlpqbj9bsVS2H9wPx6/V/XfS9uT3LEIQqoZ\nKyyW2OBp5TvZCvXKqUQJUsaAbnk0dr2RpIiB26+4losnnYM9pMOiN2JLdCidovVrv+HLtctJ6deN\n6+74Jd37FjFr9mz+vmQZEb2N9Mwcgj4/gfY2ELtXdTcRBUgLIBby+TVmBBGcOjtdU9IV46ss0BhL\n/PUak0pdt3bRKYkZyhI6OUVEMGswJEUpmVhEXs8uJNqzMUUdHN5zjM1r17N783pC7X4yEuDRh+5m\n1pXTMElB1aTD6w7y5FOv8cTD73LZ2UO4Yfa1PPL433C1ufjdb3/N+IlnYkvKICwtymaL0jZQVX/Z\nH+RcUxX/GL0/VjztmJPfBwCotiwBi6zUHSznzttuZ/nSxYzt3oX+JQV075vLpTf+DJuwSw3CeRP4\nQfIrPwd3bWfjulX06duHweMmElWuA/9iHzr1cFcAgHy/Nj8fefgZ/vr4O4r5rgsFNNUM1ecelUqj\n9sFLvlzBVbOuosXViCkaIA+4Zshwfj56FFn62KYlNDdRdFcKu0FlPSDjsaetjQeWLGFVVZ3yek/A\nzNSikYwq7Is5EMEQiimeiuLuv0rUOvl6ymYpLQDb6g6yfM9auljyOGf0RJL8AurolcqpBAxmEVER\nywx5aBYjFa0NbKksY7enUlX0Ra5lZHYJYwtKcERN+MJhjlZX4fJ5sWSkcMjdwO6qg7ilBQB/zHIv\n3sYnwhlOEnuNYPzVv8bcpRd+i0WteaGnhaIRLEJbkkmrUGOxLwkp1E6mjPS5yGKUAoH0n1mjEcxe\nL+Wb17F71T8IHNgCovgaDNMlzc4tN1/Dr267CluSjbYj9cx75W2eeO4dBaCK2/WorG5cM3Y0AzLT\nSTTo0EdCRFSPotCPzLiNZpaV7eWTzd+xWtR+Y+ejIwUKu3YhLTUFh0N6B31UVNVxrKKGFhekKNEl\nGJXfhSvGjqdvVg6JgoSpik5A67WXUrPXR8QXpD2iY1tNPRuPV/NN5XH2tbqoCLTi01kx9h7FyAtm\n0WXEeHz2RMExCInFTax8ZjboCXg9ShTS3FTF1nee5+jSf4D0jBIk2wAz+pbw26kzSLXY2FHXwP3z\n5rFdmB7iOutI5dnnnufqay77fwQAfB86FU94floqgJbcxb+z83efch1xvcYOcZBOh9s/Zcin7gj/\nvb9rtledr+vEYXoCADl1HDpZZf2LNf59o6tw85D0mwlTJUa/lOAodiXxA6xDdP6UZPyHAgBaD+aJ\nq4j3YP73Ruj/fvr/X0fg+1e/dsc/EQb0o4dPQKRNmzYpS0Sh78dZJfLv48aN4w9/+AMTJ048aa6r\n6z8FwRQRwOeff573//4+hw5LNUkCNfnBWACrnW5aS5yq4mlOMkL/Fx0AkzFBtQDEAQD52WDYqxgA\nHq+bQDhCdlYXUp05qlIVDHqU6JHP20I44CYa9uJ0JClHH6lo17U2onN76I2T+y66lUJnLu+v/IL5\nh1ZQpZoLjeSmdSEvrYcKwuvaazhWdxhfSLMxzu+SzWeffkr/Pv1iwkwS5OtYtWIlL704l/1lB/F4\nvSqRErBEWghVoqHoxaLgbCQnP48hw4Zy7rnnMmbcOHLz80iwGZWDqVCur551pUqGiwu68sHCBfQd\nPEgDLYM+NU4RxVoyKgaAHP6GoBl21fL+6/N5/ev38JgDSkeqqq6eyjrNerh3Vg+6FXSjrrGJgxWH\naRDxYCDdnEqPgm44LGYOVRzheHs9PtUQGe8dN5LgcGhMjTaXEgLMyc5ROgACAAgAFAdrDx4s4/Dh\ncmV1l2qxU5LXhbHFfRkzYBC6UJh9FYd4fcknKq56+v6H2LNvL39b8Lp0IJKXkYXVYKTy2HFmjhzL\nH2f9El9NIw5nMmnpGYplUXawjPe/+pxRE8fTp09fXnrvHeat+oxG0crSwxnnjuasqRPwhvxKKD4a\nNlB1tJrt67dytLSMsAiy6czYk1IwJyUp1oXdYqa5uhIRTLBEotx4/lT+/NB/kZiboeIvo6he+3XU\n7j7Ei8+8wN6yfVx82SXMuGAGCekpmqhcXFNLBb7S+6qptivKVTQmSi1WgZ0SwzjQ1rFeOpDn2A4R\nTzAD0qsLL8ybx2tvzqeq+jgtfnk+BkTe0mJKIC+zAKc1BUPUTDisU5egWQLGdXp0is1qlPaToIdj\n1YdxeRsIRgMMGjyMm2/+FYMGDOD48UoFyMl75dLT0pw0NTfwwgvPs3nzd2p8/njz7WSnpvHXpx7D\n6/Uw/6WXGTNuLDfdfTsfLV7CWX0H8KurriUv0cmnH3xAs8vFpLPPZNK5Z2NISKRx/1Eee3kub61f\npopcVxQN5+5rb6Bbr0JWLPuSl9+YR3pmBr/77e8pGDCQbZu2sGDRIiqPVzNj+nlMnzqdr1av5M4n\nH1BjcOvNt/LLm24iNSMNXYKDJQv/zi9uvYUG/KoiPbFkkBLh23G4VFHShw8YyJNPPsGAgf1P2wKg\nMojOe5gMRMxOWxVGJN4UZpher1r7ZJ1UHjvG9m3bOHz4CLnZ2YpS36NX7w6tgZPiL3m8wTAH9uxX\nGjBfrlupmKm3Th7JeWOHkJ+XooAadRL4JXx3U3n4KIFgO7YkMxkFadidCZiTEzWhOEkk24JEmgI0\n+Iw8vngN81euoUklyEZMmFTiKwW23LQMenUtxGFPUBbjFVVVNLe1aDofqqxqwGwwKUaqgHZy6yLg\nqTEITjF3i9EJpbgrQKGaaxJPSXFB2pxlDoXCWPQGTKGoAtemjpzEQ3fegzUQxRiMYDGYFBi5Zes2\nNm/ejLNrHlf9fg55IwdwfO8urrruer7ZugdbgpP8vK4KRPW0i/tZRIluiv6ExWxVwIDP7SHg8xFQ\nzgmh2F1HlI7aCWlMQd40QEZcEIQBkZffVVkES0za6Kmlx4gCeg3qRWpqFi2NPg7sOMymtRs5smuX\nKlD27WLntblPMHJ0CZiEymvk2NFa7v3D43z9yUae+t2tygL1lQXvMHrieG6ecwvdZC44hHIvYxp7\ntrG2EK3qHytGqWD0FGDJbCbk9WCUvFjRMExqr4qIk4XsAPL3UIQ1n3/JXb+6mZr6aqaO6EtJbiJj\nJ49ixOyLIdGqWBlSaFTZeDjMikWL2LJlI5fMvpSCHt0xJqRp3x3vv/1XEYQU5sNi22tWLXD/WPgZ\nt875E82tUXSRUFTa406KZmR+PPLIozz79NMEvG70/jaGmxO5dtwYLhjYh4SQ3JBR0ep0UqmRzTMk\n1LOweoBrjh3nj0u+Yqvbpx1aukQuGTSBYmcuhnAUg7DjYrSbHwIAyMUJA8BrjLKqfBtbKnbS1ZTD\nuaMmkK63EfEFOFpfTUtbKwXZuTisNow6A+0hP98dLKW09RiHaSZIiAzsjO3SnyFZPbCG9KrHP2o0\n0B4MKLBgZ0MF5W1VBAjhVVtQbDtQcJkZEnIZcumNZI6cQjQtF78SD9X6kGUs1JyI0Zs6BOlkcSl9\nRD0GEYNQugJaBT3FlkikpQHXoe2Ub1jGsXVfQ+1RdNEgKRYd11w3kymTxvHN0jUseONjvGEtQT+n\nsD/n9O3LyIJsksQSUCnaimdxrNcQA0GdHrfZxO76etYdOsTa0t0cqK9VCvoysWQbiP8e71BKR0dx\nRhbjiooYW1xMSXoGCWLVGNIQ8VA0oMALQQ7xeAl7QwT0Zhoier7ef4A3t25mp7uVFhmS7n0YPvMG\nuo2YRDgplSZ/AL3NSkQdtHGPSi2QlLFJCbk5vuITvnn9WWipxRTxIvJi4/MzeeSiS+ienMphVxt/\nXbiQZY3S0iF9Uxbuve9+HnjwXrXgpNr904Xmp0H5/2mhnbIB/MhQ/p8BgHglvWMmnvQNJ0SmfmI+\ncqdvUfNZaWB0GltBE09iQPw4AKDzXcbvVAAAk1Au5UA/ReDvxwEApwdWtCDvXwE6/1vSvR85yf7v\n7T94BP5/BQCQGxLrRrFw/OSTT1QyK4wNp9Op3BNEKHHy5Mn/JJJ4KgCwZ89eXp33Kl8s/oLqqirc\nnrYYANApa1Kstrgwmqox4bALmOzEbHIoEUAtdNGpfT2ghACr8frdClBPTkknzZmj7K58fjd+Xzte\nTzN+TyuRkIckq5301HRVMaqUSo8/QF8yuWvmL0hPSGbuJ+/wZfsO2qV9TJdAUUFvHKYUFXQdqj1I\njes4gaiPqC6E2WrkxRee55orZyuqvGp5i8Lqr1Zw9933sHn3Ni3Z15uUzZbY5EmCXNClgKKiIsaP\nn8DYseMo6duH1JSTxS6l1Xznzp2cc/ZkXA2NJNvtLP7wI0aOHaMpaIV8REUyW6zGxNtANmtpHwsZ\noayFL9/7kIdffZbS+nLxU4pFG1KDNtM3rye9CoupbWhgf0U5te46db7ZsNIlKw+nPZFGVyMVTbV4\n8Mfq/jqMOqPqo5fER/rA5WU2m5SOh+h1KPaaxCnRKLW11RwoLVWtilIHEpvn8weM4spp59FYXcPB\nY0c47m1h87atTBg2isnTpvLku2/w7S5NZDLLYadHeg5drEn8+aa7iLZ5FSPUmZFBqK2dnbt30+hu\npXjIILYcKuPR+S+zu71Bs/DNsXPZ1ZfTu19P2t3ttLp87Niym/27D1BzuFKpsYsYXUREtxyJmBIS\nVCVYOnyO7NsL7e0I+/ru2ZfzwH2/w5CRrJ0TolXT0M6G5d/ywXsfkF2Qw7W/uIGCXoWaaJvELTJa\nsZZVJd4YCqvilbb3a/FTPH2KcVU1Rl6MlaSI++rgja+DOMVcrxiRGM3s2raDefNeofLwIVURr2z3\nKFqzSW8nPSWDHHHD0NuJRITyLQCAqIdru40UVyRRkNxD6M5VtRUKAPCFfeTmdeHmm29m0oQJigEg\nzBVhsAnjLjk5ifr6WubOfZGt27aIbxSjRYzxggs5WlnBqlUrGTZoEDMvmsnS1StY+M47pBnN/PqG\nX3L+mEk01NZS53ZR1Lc3qVmZ+JvbaKyoY9/xSp5f8jHrt29irLMrc666mjPOGMbxmgq+XP4VVpuN\nqWdPJTO3AH8Y9h85yudLl9LS2k7vnr0YOmoUq3ds4qm5L+APB7nsksu47Ve3UDhgAFtXrebyq6+i\nwiNqVTB10Gj6F/WirPIwW3Zuo97dytSzzmLevJdx5GQTbWuPjVMcnollEB2ificAAHk6gYi035oV\ne2PXrl18/unnfPXVV4olJcU/eYp9evbknrvv4YILz49pCsRK4drD0Bj8rT7WrvuWJ158kS2b1zE2\nO5XZUycxsndX0hOsigTtbQ+wb9dhjldU0KdPV7r3zCVkCmJySN4gfeNSRRY2b4RQvY/qtiiPf7mK\nhd9upLkTAJBosZGXmU5xt+6UdOuhdD4CkYiyu6uqr6OmsYHq+joaXC4FzslcFUaQxjOKFXNPG6eK\nmKsww8yKZi4aFKIbIuw6qSoK9CW7djIGzh4+jmsuvoLBRb1V8h/2+pXLzJoN39Lq9zLojFGcfdH5\n2Iu7Kl2mRQvmc8ttd+AOGcjNL8RudVBfX0+zq1GttahBj9OZSqozjUggrEAAOZ88ApIKy1Gp50dU\nPhaIhPCFRaw8qJg5ajWqRNuA2WhVQqLS0tDibyKtZyL9R/QlwZFAe4ufsl3H2LhmI41Hj2KMwoQh\n+Tz31J8oGVok5W6VAK9cvoE5v3kEfdDIn+bczsfvvkdzexu33HUnk6ZPw5TiBEsSUen/V4wQrZdf\nK+pK5e3k1pGOqFB+TBiRwpKUllV/gKpjIqroJznDqQAQlQdarIRqGvnzgw8oN6kch4mLRvYnJyeJ\nsVfMoNdZZxCxGNQztxq0loOGvXt5de5zFPbK45zpU0jOzQOTXUviOuhMpzz0+IXpdAR8HvSRCEab\ngz3rdzNr9m8oP9KiMQBkjneOe13NbfzsZ7PZtnkLHlczSWE3F+X34tIRQxjaNRMLYv0n1H+DdqAI\njSrsV+qzHpOBd7/bxGNr1nNYIXE6ipLzuGzgRLINDoVmyIQWiry20f6LoDrGAOgAAExRPtuxlgON\nh+iXUsSY/kOJtniQBSOVDuFja8rHIYL+AN5IkOPtzWyvLmd/uBYhouTrkpnYazC9EnMw+CNU1FZj\ntFvVAVPR1si6o6VU06LYEv6wABgaDVsdCAY7tn4TOPvaOYRzuuO3J+ELhZQCuRyqcUFD6dmX+SoH\nitB3ZKIrjU+jUfm5qo1eicBIMUKPRaoCAS/hxuNUblnDrmUfw8GtGI0BQv4oORlW2lt92PyQC5xb\nNIhLx4ymODUFS9AnKA56q0W1H6hxlUmotE10hAwmwkaTot3vPXaMA3V17DhUxtHaSpr9GhIoejcZ\njiSyE1MZUtSbHjk5FBfk4TAZsIharVjghDR/WqGmiNWJ5GVRr4+QJ0zAaOVAazsLN2zgo0OlHDPZ\nCWblMOiSqykcO51oUhaeQFD1/OutWhBlkgM1GlX/JpuRjElSyEPowDaWvPKkYkMYQm5VnRjotPPQ\n9BmM6dmLOreflxd/yZt7dtGEHh96LrzgEubPf5XERPtPqAHwQ5J/bQ7/lHXAzgI3Jz731PQ4vtAF\n9ImJU8r23dkPJ35pPzjV+f4fFGaQBgB06tRXAIDQtGNfdFrWgbb+f8jrn94u8zcYjNEupSdT1FRP\nQ//vDEp0+qLvh4F+6HM93VWf7l5+2P39kDH4v5/5vxHoPAI/fPVIETOoqjJ/+ctfVIArCYq0AQgT\nIC8vTwEAV1xxxT+1AJwKAIgl7mOPP8biLxarnmvlaa3OYVE7js11BbJq+46iMUdF7MlBYkIqFrMD\nk9CbhTEgAmh6A6GQTzEA/CFhAPhVsi0MAGdKOsFggGDQqwCANlcD4aAbm9GsGAB6s5Gq1gYibV76\nkc31M36m6K1vLV1EOU20EqJLchHZzlzM0tynD7P/+F6a/c0xXSZpy4tyyUUzmfvMC6Q505Syu9Bs\nWxtalIvF2+8twJmWpsQ9BSiRRFmE80r6ligAIDUtV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uh1hMPCkIiQQBS7z8fBVYvZ/t4L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\nWrj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+XLtKOdTLw2gypiojQINB1Mby/GkTLIAEGKsi+bTKZC0cCirTV5/fqZgA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P0BdTNPzjVUfmoX/IPh0s7fDT7t2Ys2geA3hcnJFvi74ezpw9g6UrlmnTGrR+YSMltyM6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I7zILzPfEczJDqIN1DDL5fshY0A8GFPp11fhXmk+2h/53fkco11Ei9nZ5bh68hl4tgoGuDnix\ngbmocQ4QYC8QlHeoSuIoeZG4/Wttry/2YjYqMgMlMeJ96dpOnufo9VTFBbYnv68DlKSE85zMTOt0\ncC6qbA++C7bF0Wuo9663Iz/nM7B9+KzsT7r/Cb8j70VkDiq7weCf/y5UdbMySeWYYRCqZBrsG2bN\nG0WxqfQ21YMpMh9EDmC2CO2frBse/Fx/lxxfutGqSAzE3EgZs/IeuHgqzxWVRdBL5ZAlIVULLEoK\nINVXqLPTnkUMSTVplt62fDe8N2ZIZExrfi48j4wtmbvNUpJMQEyLWdqJ19THH30LlNmp6qPSFhYL\naKRHs02CFxxP/HfODQSM2rxt8jn7JfsQAzmCAhyHBD34b7rciOOS/UmX4vAeCThIpkWy1MzUcuya\nYXU4JfP/1gcLsGbteqlYQD8Lemj07z9AnMOnTr3kP0oACDws+PRTzP/XvwT4OAoAdGcA6ML5Y/M1\n3bP/akB1MwEWrxhVmpAjii12gicFs8+figtPniCbcq6NNGxLSkmCw+OS38eCQcUKEKWhQZXvcjkE\nKF269gf87YN3sbu+Dg4yws49B3+6/iZkZ+YgynfDd8sLUtalU/27mxLrmsyu0XHs/8QMJuyurMKM\nW27CwXAH0i1ODEgrgK/Zi6Ahgd4jhsBdnI1PvlyGlg4/bM4kpKVkwel0HwU+RL7FvUAQ/mCH1O5u\n72xXc5MjCdmZhXCxIo22ge5SVOoaBQEAKDsMo72jVarYpKQmiVkfvUSYZaLbPs+hlThQD/E7DIBj\nAQDtebsAAJbt5ZwURKcGAIT87bL5ZsUlmlo5EglkmCwY268fTh8+CicPGgmj044Pf/wGT817G50w\nCAV+8LDhePnVVzGgXz8BACwsKdmVa1TlJ3kv78/7ALdeNwvmaAAzzrwQD947F6nZGZKNJyVf5oEu\n8MaAaKdfxrYjMx2dPj8+XfgFnn3xJVS1NSEFJtxw6TTMvGIaMnLSEQ74YXUmccFDNBwQFkObtwOP\nvfISXlnwCTpDERS5U3HeKRNx/fQrMGTIQIBGaVE/HZskU8dornuZ6+49RAAAwSisiHfG0OoP4U/P\nPo2PV3wuossIYjj3nPMwbMRwPPfss+IXdPrpZ+DJp58SMFEYKMcBAGwXtQZG8cwzz+CBBx6QuW38\n+PFYvny5zNv/v47y8gpMPP10sDKBhaW3hwzHtdfNRF1TC+JmMyoPHsQP33yD5voahAIscBgVKUwR\n2V/Z+Rg7biwMdis+WLgAlUdqMGXSJFx21tl45uln8NPuHcJQbKprlLF55Znn4d47ZyOMCN54/z28\n+v77Ygx59023YMqkS+BMS8eq1Wvw7Juv4euf1yLNZsets67HjVddgfS0NBzcV4FnXnoZby5bKkHd\nVZMn486bbkZxQT5ifj9+3rEbf3/+BXy3ebMwFji0bQkjyjy5OOOU07DvYDm+3/GT9EDWXbrgggvw\n5DNPo6RHiTjZs40J9jJxsHjxInz77bdSRYT7t0jMgJS0PGTQk8ydiZiBcJO2/yVtvpsPmvQZcpkS\nISSiAXQ01aK6Yh9CIRo5u5BR0A9p2SWwOjyIRBNCL+e83j0QloSi5i1AKZCJ1QIifgQ6GtDaVInW\nlhpE6VtBACBOaaniDHMvyKw/AQCHw6Wy7xanMFr8ASa52qQiD5N8LmeaAJVKrd49GBBXby38VRKF\ncIRVS3zCiIzTsJwMGyP1/lYxHCTwyGsy0KeBs0hpNVN1shNEPqdVvzJZFBtAJQAVy1o/ZFU5Ji5R\nrAq1lDDRTFDBIKVJW9tqEAl3wGSMoyBfsRrr6xtRV1ffBQAoMYdab9JT3SJfDYYiaGxpQ2cwqMAR\nutRYbCKfkIpF4U60tzbD19omMm3Gau7kJJT27IGRo0ajubEJK5YtR9jPKm0xCaIvPe1CPPe3R5BU\nVApQCujzwcDnJluUjAunTWRNXMToxi+xj92G7du2YOHyL/DZii+xecc2WSc43wwaOBCXTZ0qBp1M\nANptti6JpMw3oSiq9u6X/vvGx+8jEA0h2ZWEa6++BiOGjZA1n2P3w48/QmX1IQGXZ0yfIezcrVu3\nYPOmjTh0qBLBUPDXAICYnwgSY8DSbdvx2Pc/YGdnJ2xwYFh+XwEA8uxJ4uBMt8xE9FgJwH9iAOga\nQdn8mYD2RBhbjxzA+vr9qAk1SzZ7aEEfjCnshzSjAzHWbuzOANB7CzuCOYavKrdiQ90BtFOlQeg4\nYYO5bAgGXDwThSeeijADDC4+mk5fD+7V5lAFrapcVqLL3E9l+FXGWpUHPLpB16nssnnUsv6/CwCw\nhBUdMcXxni6WURhYVg4JuC1WmJurUbl8HtZ99hHgb4XNYIEpEUN2WjoC1P0E/QhEpDKkhhKrAFI/\n2H2TTQ70zC1CssmO1tpGWIxW5GXnIZdIrLcJa/ZtgjfBYiiKqUEAQM5x3JjX92s8u9WejWFjLkFO\nj+HooM4nLx+OXmUIu+yIkt6mMwB4vv8AAEhw0c0DQK9NKu3HoFWqNqgnOr6OKAduktGEzn2bseWD\n51G7/hsgEYYLMZSagGtPHY8rhw5DNoM00tFjwKoDlXhx+XJsDAVgdFmRk+zGbTdeg0suORMOu0mC\nA/YnamR1NgJ1uwz2eJ/cODPryo08s70MOBh0kyXAzC//zsmGG3edPcAMJNFOIozJGelSe5NsAGai\n2X+YjWVGnW1BfSszkwwSSUsme4CUohZSxDVHejEBTE0RxI80c+r82W+ZBdAz78wKsB4197c8R0py\ncpc5DrO6DLaY3WRFAQao3DSRns7AgffCjCiDNOrEySzgAsjz89l0nTwpznpgQto1gxkGfby2t80r\nTqjym8xMmBx2RHx0q24Q6QTZNsxOu52krUfkc7IO+M5J7aYTOzd2LFtH1JNtSbM3ZpDZR5gdIgWe\n74jX4P2aLGa0t7WhinIAzY+AmVf2G4IVegk6Ahakurs8bkUTrK6W0oFkAZCqb3fY5LlY055/ktkh\nn0uptg7JdBPIIGsiOytb2oksCnm21lZ5B2wPSjfYfaml5HNQg897ZXDH9mRg197ulWCc7crMJp+T\n52DfYMenbpZZbbYLgzlmgvlbXpP3xfJvrFjAczGAZP/VDRk57/BzKWtnYdWKZJELEEjiNfgfD94T\n24Tfp8ZcqlxYrJJ5J/uEcg1S+BubGuUe2Ra6Zl/MG5uape9Kxly8FWj2R5O+GnlX7K+ZGRkqA97W\nJpl6HnzHXFQZfLGfMbtMYIP9goaLZHe0i4yjXvoV75ESAgbSfJ8NDY1i1kmmC++JbcLv0aiO/87+\nWFJcJH8S8OH7Yf9mwMy+pPshSH/1euWe+N7cHreMD37eyLJ8ZF/kFyArO0vujywEPp/bpdgg6dk5\niFNWcFAZKKpKCb2EMcF3WE/2QGWljJsyVlxISVHvua1VrsH3yaw/x50OrrHSBO+J5+EY1eUANArj\nb2n0mEkDRTKI6uvw888/yz2PGjlKqLk8Dh88JLKClpY25BUWod0fwUcLluKrb9bA621XKQx6AEya\njAcffFDM2I7XnOp/5xrHtuUGmBUDVqxYLoDi0fzk8XlKPUt0NBz5FQCgzeld20ndmZ6hahxirHdG\njxMwZ8Z1GFjWS/YTHR3tsNJ7xOWQDGRCA866NolaZonmiI1eL15csgD/XKjKrxUnpeCBa2/GBadM\ngNVCwJ/McU0CwCCyWxUftas8/pm0z7T1iJv8Jm8nbrrnbnxzYKcwL1Nghd1sgys1FaV9eqLi8EFU\nVFXB5kqFJyldaXAtVkV3Bct8BhCNBBEIdsizBcNBoROzXFd2dgGS3Jmi2WXbqd90P1Tmm3M/M4qB\noE+yePz/YIj0XD88NHPKzoXFSIaTVtJQnu23TQD/GwCgtbUBoUC7ZOKlZHE8gUKnB5PGjcfZQ4eh\nKC1DmA71kQCeXPwRFv28XrTZ9EGYeuk0PProYzKfU3ZCPx+d/KsIx8oouaqyGrOuuQ6rf/gaPZMz\n8ehdc3HBmafDkKQF7bqEoxsFMuBth4OSG4sNvroWvPr223juPYIPCRQ4kzBt0iRcf/mlUpKT2X9W\ngHJ5dADVji3bd+CBl57H6k2bqEhCmsODy88+GzdeOQ1lJYWAkdl/v2IECADwO0eCIDaNwiwIeyk7\ncGD9vr247d652FNXAbAuu8Mm6wSzbn37noDHn3gcZ559VpcPlYJYj27HdE8bXvGOO+4APQB4zJw5\nE6+//vrv3cn/6nNKic495xxhjfEoyC9EemYWKg4dEjNhPrg5qqp3DOxRhEG9S9G3sACBllYMOWEg\nxp46EV9t3oS/PvEEqutbcOWUKZgzYwbeeuMNfPDlUvQ6YQB5qCIrYgrq5mkzcN/tdyAWC+OxV57D\n/IWfCoNg7py7MPmiSbCnZ2DLL+vwzEuvYOX3a8R88t7bb8KVl0xGUmoa9mzbjoeefxZfrPlJwtbp\nU6birpnXoCAnB/5QAstW/4BHXnsN28sPqMwrEkiFA5NPPR8epwMrvluFg/4GdCKCsoISPP/Sizhl\n4gTs2rMHn376qUiruKZxPacEzkawlSxXgw1FJf2Rll6AuMGOuMGiAQBKm350K83EaQKBQDsCIS8S\nkSBiviDaGusR6GwGAcWkrJ5Izy6FzZkswINEWkzgSDUHdSYlR6bhuBEmgwGRQDvaKedtqIavrQkG\nUwI2eqZEA4gG2jQAQPUiAaUSBODtcDqT4HQqeSwlRAQfKYFVhqPJSElmxR1q6n+bCcV70f2MaAjo\n87ULiBkJkw3ACgGU0Fol6835zGp1ihM9AWkxOKcRelSxqwlwcM9LUJ3zowLllU9W19z+mwCAAtd1\nAICsnNbWerR5a4BEUJgk2VmZcn7uUwIByhV0ewQG2wQZNNIXgdRYt9L0BrOAtmPHTcQJ/YcI8F6+\nfzv27tqG2kOUEkRhdVjRu19fDB85SoyRv1jyGfbt2oOslHSJEetaGpDlSsFT9/wNl199nej8o03N\niAZDIgHm6zDQe0bipDgSFjNqGuuweNnn+HjBJ1i74ReReTDxm5mdhcumXoarr5qOYUOHyHNzXyj+\nSPSt4xrGRSgQxrw338LDTzyGvdT4m4wY1LcfZl07U/ZK3LuSYfveB+9jf8UBAWuuufoanDxufBer\ndffOPdi2dWt3AEA5souvn7AADFi0cTMe+fY7lEfD8CAJg3N74sTsMuQ7U1QoSVRf6gEf7UDHU1lk\nUevWv7oHeQQAWhJBcfHfWH8ATVEvMuDE8OK+OLGwHzxxC2KUGBwHAHDTyQxOfdyPFQe3YmtzJWjD\nFmPJFqMblv4nYthlNyF70Ej4ibiQwk+TEmaJrNau7J2eme5afFnyT6txzcZn5q27zp+Lp2R7SWWR\n78WEyvJbEgCF4hEh1mrcG7k4U1FmgD0egysaRNuuX7Bp/kto3L1RMhV2WFGcmSOI36GGwwgJVqmQ\nczUINAMjLbPFCS4JVmQ5UxDzs/5BFL0yeiEnPRsd7e1SG7IOnWhHEBFlj3hUBvA7AACvY7VnoccJ\nE9F76AS0mx2wFhTB07cXIh4nAomIQrEEG9M0UCxbomXFmUFV5nfMFJK2oyY03WCPAXT3v3Pzzjbm\n4BOTRC3jKveRSCCJjq+7N2L924+jefuPMvF4EEcvM3DdhFNx2dBhyODgoGY/DqwqP4gXv/wSm0IB\nFqBFhtuB2bfNwqSLToPbxcy/W96/t00ZlPHgxlyM37TSXXpddAYQDDo4ABnkq/5ilGCbZfw4GTW3\ntkowzNqgAgykpgjTg0ih1AcPBJCZRS04la9AZ5tXggtuSnLy85DMoDDK7zfiSPVhCbyovSfqyHYk\nzZMadbYZafEMPvQyb9T58H54n/ycbc6goo1ghd8v1G5WJmBAyN8wEBaqvsejHEANBnFf57PxcwIJ\nrOcufc1oENdzvaa9/n2+Xz3QJtuAwSUDKga/POh8Tyq9BPSZmV3no6M4g3oCIgw8Kalg4EWZAp+D\n2V22K6/DSgO+jk4NsIiIrIFBIxcS3g9p37wHBpfUU/MgGMJrMDPNZyCd3u50iNmjTvtnMJ2ekS7X\n4XPzczJC+DnPw8BT+SPwPGFpPxrxsc/wunxv/B3Pr7TaNHxMyDMQ8GFf4XvWaegsF0ZggJ9TlsHz\ncd7gvZNmyDYijZ7Px4mewTODdl5bp9MrFoRRwBNq+cnAYDDMdy4+Fa3U9jH8URUcdFM5yld4bX6H\nbcH+IeUpm5vl+lyIGWzrpqaUFLCvUvpFkxtelzMOwRyCYgxuaVhF91gevCbBB/YH0vt5bT5bc0uL\nBOecFzmueF0evKZUEzCZpR8TAOD7b2/zChDD77OyBgMG9k+eo66Wfg0t8l36O1D2EQkEpAwlz8f+\n27t3T9jtyoSP8hipYEPvDA2sUFKEGgEg2N7U0/P67CcEJPh8PA/vn9RhAgik9/Nd8POyklJk5OUi\n3OkTYID+COwnDKb5G26CCJLs27tXxh8Der5PzmOcg3mvnE/ycnNlLLJ9mYX5+edf4G1rk2oFvXv3\nlowxq4ls3bZN2rR///7ILiqW7HWn1ysAADWOlBSwogSDY8oW6Fvg8wdgtTtx8HA9/rVkOdb8tB5+\nX0DtgowmnHfe+Xj44YcxYEC/XzEA9IQ42433ydJg9933Z/z0049qfdckAFL2lRVnhJ/NWZhmaUJr\nk0BBsjlaqHSspvZo5Mbvs3Q674vkeBtLf5qsuGHyFFxz/mR4jBatypIY5gjAaLASBlZZUQK0ZAOI\nJNBE08QYNh06iP95+SV8d3C37LGGZ5bi73fOwch+fYEoTRpNYt7E80nALzRX7Z70nWF3NFz/N60M\nHEGAj5d+gfueexItYuDHfZhF6rtnZmei5kg1/MEQ0tLyhIZPB25ugMVxJxGFr6NFC/69CIS4cear\ntkjWPjMjD2azS8pq6UyZX/n9SBlilYwIRwJoo3FgZxuiLLVnMIh8IC09EyYDS5vSxFIzhvyvAQCN\n4m/kpl1JAAgABAkAkJJKs794AieW9MadV16FUqdH6NkhsxGrtm/CI/PfxT4vc/+Qyja3/WE27r57\nrpQqI/DaHQCQNtAptqE4Hn7oETz00N9hjodw2diJuPuWm3HCwD5IGOgZoX1Te2UspUtZCE31aDQG\nkw37d+7GIy+/hM/XfCfvJy8pBVdPugQzp18lYG0w4JNnctodMHpSEe/0Y+W6X/Dwiy9i3YHdMMOM\nAflFuPycczD9wguQQQaCmTXDtb7SLULv2jNLWD0AACAASURBVMtKDoX7MRPi4QQSIRNMVhc6onG8\n+v57eHfRv7D/yEGEEYPb6cTpZ5yBa6+5VoJ/YUdpZsj6flkfJVynKbXjGkDTP5b9o1TqiSeeEP3/\n/6bsn7Z9lD/0fsb/5xiffOGFsn6R/q4fuktIpseBFJsdJTlZOOOUkzDlwnPhb25CW30jstNz4M7M\nwWPvvovXPl6IGLXWxcW48eIpAto98soryM7Nx6XnXiSA/cKvlsocPf2s8/Cnu+bA5Lbi6RdfwEef\nLBTj1Nm334qrLp8Gu9Uhcr9Hn38Bny5dgqLMVMy57RZcdt6Fslbtq67GQ08/g8WrfxAp0dyZMzBz\nxgwkZebD6wviw2XL8PAzz6KutZGFsGW/PaJ4IIb3GiBr1vJ136MOrcKdnXj26SgsLcHmLVskM8r5\nrytB2a09jI509Ow1HA5XBgxmh+SFEzp1ncGtsJu4N4wgFPKitbVOlagL+GGKUiYSRSTig8XmhiMl\nHxm5ZXC40gQAINOMaxTHrWKnMbFokHFjYT34SAiNtYfQUF+JYKeqNpWckYW0zEwJxA9X7FSSFS0R\nJ6ahklzjuHPD4+HegnEMmZ7tyrBXAAIPPO4UMR89WgZVRRndJV+cq5Run+xSSpiUf0ksSgafSk2a\nzAoAYIUAh8MDs8XWxbRk2yjDaGUEzvHCtZElYiUWOK6M5TEsABlsjDN4EIxmDBdEc0sN/J11gCGG\npCSXJBrEf6ulTcVcJH1J2fOjfgNmM1nZNOe0oaCoWAB/eqcUFpXhpDHjReKxd+8efLtqGfbs2ILW\n5kYku90o69MDI0aPRGnPntixfQfmf/AhDNE4rrjkUikR/dATj6Pd24rzRpyKF558Gvm9eiLMfS4r\ngFmskhiD7KVoct2J1b+sxXuffITFy79AMELPkRhKy3ri9DPPwBVXXIExJ50EC9c+bTDqxuPCOtTk\nYZW792L2H/6Az75bKe1PAOHqaVfg9AmniXm47M2am/D2u+9gX+V+YYZcftnl4gHE2Ie+Sm1t7ags\nr4AhMfceVcldD9S5ALBMViSBRRs24ukfVuMQ0QlTOkaW9MfwjCKkmdQUIciOGFD8/nE8etqdARCx\nAIcDbdhQvRc7W6rQEetEviUFw0v6YkhOT3hgQyQYlk2IKj2nuiY3kVFDAodDXnyxfz32dtZK94ga\nbEBSHpJHnYZRV8yCMasAYc0Jk5tC3fVbZwGoQJQdVHUUpU03HuMkzc+lzqZmYMGOTESbvxVac4Qu\n10S1zFq2WAVr/I9BITP+LOMVMxIdVO6cyYkITLV7sHXZR9i/Yr7Qe7iPssKKAcW9JYCqbq5VtH2j\nZpyhVSTgJMHOre26xPSIIACBk94FvRAzmVBZXQ1fvFPYIlGrEW1hn1DSyCVQy2kCcb0x9RektS0d\nPqMxJ4p7jMWwky9Ah9UFZOYIAID0FHTSXI8go9BamaFRHgd8Xp16y7ZSVGFlmihUaI06LJtHjcor\nQb+UnNTdrI9doAgAcHpq37Uea994GB37NsrWMwlxDHU7MGviRJzbtw9SNbdrbxz4qrwSLyxbhi3h\nIBJOK1Kddvzp7j9g8qTTYTZFZVAycCVdifID7vdEpiGbLKUH0qlJarFUzASdlkVXctL89fKWBEN0\n6YP0FXo9EN3Uav9S5y6IrshKjtYD1lk2QoGn87nmfi5VKaysoasZhXDaiylTKv7J4EMfe/pmRHqD\nhqTqbc/v8NripE6vC6NBsvL8U9z2NYkC+zsz1rq7OQ3YeOiO/kojpe5P1aG3yr/x+fhMqvbq0WoK\nDI6VbwBNZAICulEioAM7vAepDkDwxQChsOqeAbxXaVfNj0P6z3EMHIJyvB7fYdc4096dLBNxJQdQ\nbvBsS0VZ/61Sk/J7QmwmNebZn9kPdBd6vkf6g/AZSGGjCZ/uXdHpY51cTc5BUyttfOqgklpKj1Yc\n0WdInpuoO3/L98N71Td1fCZu/EQOY1TmmcpQVKHgXEhlzhLzIPWZ0OOl2oBiMlEWIBIUyo26SQb0\nzZ8y4VRVS4QmrwUYSuKhvDnYvjqFnqCFDs6F5fuKLs/suMyBlGZoVR94bRoEdR3C4leSDgIK3aVT\nQnmmAFF13q5KF1aLovjrlRXYRhyEuuSCz81/F9CQYwsJuW+ZV6Kq/fgs7NO6F4suO+I8rksp2AZk\nZgS1Ch76mNfnKVWHW1V04ZqjS0cIHEitZ43qKFVCwDHll/epAx5632T7sA+pd2YWoJHgjpIJ0MFY\nlajluFCO/1qJWaNR2paacCldGuXmMiT9jCAV24GURqkEYDShqaUV33y/FvP+tQTrN28X3wSy8whQ\njBw5Cvff/xcpB8ixqB968M/n43viBp1eC++8+w7+Nf9f4oshAH4sDjNLzbqSkWJ1CuDQEPIJCGCn\ntClBKZJFyXQ0u1leg1p8tivdt5PspPQn4A8FBfyU/XMc8AAYnFuEe666FqcMGAy3w4XO2npUHTmC\n1Ix0ZGRmSBZVIQOaIpPSKBpLBcPwh+NYsvYH3Pv2K2iJcx6KYfLwUzB3+jUYUFyMeIySNZZ/MsHk\ndAD0qBF5SEISA0IT+JUKW7WQEChYdSQaw6crVuL5d97BrhbuNcyICZ2TWmM1Bq1WNxx2+scQ/LGB\nY8Xv74DPR9qtD6EwK28oeq7V6kBqCtlcmUCCfMDfquWuyyyOlsxlHe/6xmq0d7TIBtzpSkNWZgFc\nTo+qRc7gobvGX/byx+7OjmcA6Ls3BfAwAUIsnX4DNWhra1Tl3hIEXhKq9O6IMfjDZVfCLiVubWiO\nxvD8/A+wZMvP6GCCKgHkFxTi8WefxfkXXACnADjHJon0/qfjR6tWfYPrb5yFw4cqUGhx4sE5d+Kq\nqRfDTBCDv5fn0BJO+ovh/Mbkk80h/Wjjpq34x9NP4cutm6Q1+2bm4rpLL8f0iyfDTfdtGc9mljVh\nhgP+QAhvz5+PZ955G3WdLbAZTRg3eCguHT8Rl5x1NuwpbiTCfjHuUu52Bnnn1PVybhYQSgznmG40\nAyGaetmJfmBrZTnmPvYgVv74rfSttNQUvPDCC7hkypQuaZQOVB6/c5a1LRoVc9spU6aI0RfnFBoB\n0kvg/+fxBT0GrrtWGEQRNjXiKHI50SMrDRNOHAm31Sol9fJyskRCMXjQQNisVrjdyXAkp+ObH37G\nQ/98Axv2HwAV/x6rBSN69cXEU07FohXLpMTqnKtmYtoVl+OrDWvw6JOPwecN4sqLzsGf59wtScan\nX3sTH326ADa7GXfddhtmTr0UFqcDe3ZtE4fzj5Z+jR6F6Xjq/v/BmOGjZH36Yf0v+NsTj2Pn/kpM\nPe0k/HnuPcjMKwSsDrQEI5i/aDFefeNN1NQ1IhiNIcuUirP6jcXYoaPRGQ5gxS/f4rvKXxAwxhGJ\nh2X+7JpbZMybVYwhNHYj7O4sFJYMgd1J1qIVRq0yEdc9m5Vl9MIIB9sRCbWjreUwfO0NCAe9Ylan\nOo+yRzTYPEjNLEJGdjHMdg/CEZUc5DOJvEwCde5fzDBbY4iHO9FUfRAtDUcQpN7daEJGXgny8svg\ndKego70F5Xs2IehvAhLKFNBIQEpjLIsEze4Wt3fOU4xVmBwiRZxZeI+HBsJkCDCRoXqWqlqi5HcS\nF4oHBoMvmgVwD+hDNB6Cr9OLYJCsOh3K4++4R0pWFQBkT69kCZxbRNbGPaxeIU281Myy/+HzS8JQ\n2z9330CIFFv2D9wL0Zib2f86JGIdcpsuDw2Q7QJsRCgXjyjfG7X/osGqA8lJKSgsLIYnKQUlPXtj\nzLiTBTQN01smogyaydT75eefsOHnn9DS3ACHxYoT+p+AcRPGo7isTKpdLV60GL+s+QF2iw2vPPUs\nzjv7XMyeMweLPlsIF2y47657cPsttwprQ2ZdFVwgEQhgb8U+fLJkMeZ9+jEqGqslXiUzeOKEibhh\n1kyMPvFEOJwqCSk/1Z6h+76e3mXsI6++/DLuvede+Fk+1mhCYWERbrnpJpSVliIcDElCjSbB/3zz\nDVRWKXNwlhI99ZRT4LA5Zd4K0RuF8mMFAGhzmdx1XHLFHRHgk59/wfNr1+AI4si35mB0aX8MySiC\nJ65RN7QMwb8DAI6fsLoDACELcNDfgo2H92F3cxUCcT+KrGkY3aM/eqcVwhWnBlNlkdmDBQjgRspq\nQcSQwH5vPZYd2IhD4Wbib4gaHEBmDxSdeQkGTp6GdqMqlaQHb/J03XTqejfRAYCujZG2M+LnOkKr\nvwh9I61v8IRQr2lS5cVpv5XNJzPg1GlT22ygxCAGm8WO1JgfdT8uweqPX0OierfWWc1IsniQZLDL\nJpBZe0XbZy6B5Q2Zi1CLjwAA7Fukf8OKgVk9UJSWLZPR7oOVqGlvQLozA5mFOSinzKDpMOIsZUYq\nn2bNKIuZfnT7XxphxBNu5BQMw/Bx5yPsyUQsPROevr1hykyD3xBFmLpyQfbi4qoqUhDN4E+VlDMK\nsqkCXzWBiP5a0zoJM4Au1bG4bAYFxdLlFBJ0qx5FAMATj6Fh8xr89OYjiFTRTIUMgATGpiThhtMm\nYGJZDyTLoDGiHUasKq/A80uXYrMOALgc+Mu9f8SV0y6A39cqTvCCjrrc0jd4L/yMG2xOwswY6AEp\ns4zUF1OzzE246KYjrLHeIllRHnS2Zyk7HsxCS7bUZJYsMTOZPIjI8VycmDPS05HMzGEsJtQ70vsZ\nZJDKzu9zkiT9jIE4A0FmExl4sq2YrfSLxtsgAVhSisquEsHn+blgMevKDC4DhM6OTtTVNch1GWjo\nNGqdTi6l4JxOlYFmNlZQVGZK43IvzOzq9Hr+G/ulTmenKQnHRGNDQ5cBIduOddylmkEgIMZwlE8w\nCGfGWjcVJGOCGVYuCqSwciLkwc/4G56XDAFmWMmmkBJ47e1SCo3nETmF2SwTmLApolEJrCjBoI8B\n3yUndAZKDpdDqN38d36fnwurweFAFoMLyiPCYcmW6+wI3qf4U2g0cS70DCiJNJNVwOwTWQJkA9Cw\nj1l8OnNT1877oWs9g3bSsUjv5wLG96NT48ke4L+x/UkT53l4sM8wQOXmg/fEzxmIcnF0u1zCIODB\nZ2CfJahCujlZIwyGKQXgYsajO/uCrAy2H89Ddgmvw40m+wEZEgzeyKbgRpPPTCM7ZqEYLJCezrYi\nOMH3w8w8D74bMimEEdLmlecj6KP6WY6MAT6v0EuNBmk7qbDhckobcZHiM5JhwffGAJiUZm58eQ+U\nIoi5odHQla3n95ktyshIlz8JMDU1Nsh5+H71sUuAkcwBvm/2E2kLl6sbi8Mn15OqCyynGY3KmCZ7\ngQATn1nKcxmMqKupkb6hu/FzXLCfMDgmy4IAQG5unjwDxyVZM/w3vv/iomLF1IhGhM1QVa3YA8z6\ns4/xIAuBkgYeZA/kl5ZKsFJfWycmfNysDRs2HNk0bjQYUV1RId/nM7JaRVnPHlKpg1U6Pv9yFd7+\ncKF4AfD69EEO+oPo0aMn7rzzTsyYMUPGb9c6p3nh6Wsa24umjZ988gnef/99uY4OaJH2nurwwBJO\nIBAKScUdrknMVJFlT78P6bMhH+z/j7XvAJOqyrZelWNX5xygyTmDiAoighEVBDFnMCsqgmmcGUdn\n1HHUMQfUEUfFiKAgNsGsCEpsMg1N03TOoXL4v7XPPU3jvPf+mfe9ms8Bqqtv3Xvuuefsvfbaa9lc\nklgTXBEtFaoxe32y3ja3tRo+8QpYtcRNcCOO0wcOx6Jr52J034HYt3krVq4pERVwUpRzs7MESJeA\niuJKgSD8dY2i7Wfx+PDF1l/xyMdvY0d9BWwifxvD5ROm4przZqB/YRGcNiviBOBpN8h9T4AEUeVV\nzID/BgDguFAN2+LxorapDfc8+ig+2fgt+LSefPIp8Ac7sWXLZmWjFaNllBMeT5K4ctB+tL29VRJ/\n6V1lNY9aRLAgyZsqAIDDYfhq6/LdsR25qweX8YUGnyggWFd/RDy6CbqnpmUhMyNfVL+7AsfuCf+/\nDQCo2EoDAMFQB+rrjooOgNlE+y0WYRIC1lw8fiJuv+xK2MIRERj9fucuPPraSyiPdor4H0f25BNP\nwguLF6P/wAHsCPgfAYBYNIHGpmbccOstWPnpMtgjIZwx9gT87cEHUdyr57Gqj9y37lRQUjTICIvA\n4XQjHIjghTffwMOvPI+WaFx61YcV9caC66/H9ClTYBZrxxgSLNaQreb2ouLAAbz07jt465MP0RTx\nI9XuxFmjxmHGaVMxeeJEYWJGIiE4PQ7Y3E4BACxOFcvxRVBEXBqiBGyp3WGB2ZOET9etwd1/ehCH\nao/CypjHbMKCBQvw0EMPyTqlnzcd53S/7cqyNiqJ/+zZs2WNJvuppKRExEz/r16cLx8sXYq5114r\n7ZCsajsRxUUTJuDK6WfBlQgjGgogpzAfvU8Yh/CRCgEt9+49gL4DBqNg9Al4b8k7ePil13CUFsEJ\n1apKFs+sCy8UluhHH32A4pRMvL74VQwcPQjvvP8O/v7MU6ir6cDsqadiwV0LYfem4KkXnsdrS99F\nfroPi268CVfMngWrzYJD5Qfx5EsvCfPp1iuvw5ihihbdGfRjw46t2Fa6FWOHDsWQwYPgS89EwuZA\nwuZEeyCM9z78GH96/Ak0+gNIMSdjgKcQ5516FkaMGIHPv/sSS75+X8SyWZGmiw/XXq7VXNvJGNuz\nZ68AnWwbSs7shYKikbA5UiVh5L5rYbWeYKIUuYLobKtHe2stOlprEQmSkUdOMmNbAlh2wOaGw5WC\nzOwieJMzqQKHcFRZZTMR1gAAe/g9HlpHt6Ku6hDqKg4iFumE1eFGakYOcgt6C5OAjcQEZVrqy1F1\nZC9ikVaYzCzwcS0ic1i1JnCfZBLOwhefcIKE3O94DW43RZ6p33TMrjTCfMtEUFfrErA4rNwBSKcX\nIAAxBEN+dHS0gSKBCujgi8AJNYmcEhPz7y6HR2xNhcTPgl+MoS//NDR4RB/AKgUWxqD8ne4VZcac\noufgoEp/GPV1R9DRWW/4TqilW8RTw8q+2w7asKYJ65Z746ABAwUoLyrqCbfXJ4VLAjhs7Whr70Ag\nFMTefXvw66aN2L1rJ2qqKuG02aS1kGydPgMHCBC9c9cuvLH4DdRVV6NXQQ98/Pa7GDF2DL5aXYLL\nL7scrf5WjB8xDm+9slgAULZN0dWILkIla9dg2efLsWVfKdiM7bS7Me2cs3HpZZfi3LPPkXYOvbbp\nol5XeibzjMwuit+YREx47ry5+OTTT9UaxFaYS67AqaeeKvmCLn4xV3jxpZew/9B+YWbMuOACTJ9+\nLswJE6JhVTCSomRi4UKDAWDcQ1McUZMFLaE43vn+B7z0y0+oQwI9PQWY0Gc4BiXnwCWFIwOR/U07\n+X+yQAWtCexvr8OWo/uxv6UKoXgAfTzZmNBvOApd6XByYRVk2hD1VSun2AcScN1WfQgl5VtRh4Bg\nzDGrBygYguEzr0LByacj7FLCQt2rf90tAKVaKwJOrLKqXn+ZxoYIoBay4Ht6sSaNX/ucq8oxvT+V\nrzcrQ6yQ6gqbJLqsZtIXnMqUkRC8dgsCh3dh17LXUfndKiDSITcu2eZDiiMJPqtL+kHK2+rRGfOz\n1oBUbzI6Q0GDMmIkyCYTPHEzJmQOwIjiAWhqa8HB8sMIhWPo32cQ0ryZ6EAI3x7ehP2N5YjRLoQU\nSgMA6NIB+E17hUq9vUhO7Y/RE8+DLbMHQikZSOrfF/bcLATMCgCICJKnxJx4/v8uAKDGUn1eFiTZ\n13/T+WgEMS5qJcRCKP9uNX5+43Gg+QgQj4Bp77ScTFx/2mSMzy+QzZ5rbZvJIgAAGQBsASADIMPn\nxQOL5uOyS6fD39EslG8CAKQyK9AhoTzuI2FZSJhQadq/+JgHg5IAKe91l/RLk9rPBIkUcC4uXGQZ\n5DMJYq85H0Im1kRgOd+Elm9QiiloowEAJgV8UDl3mThzA+IcUjZvKikkXVn3b/N8iOx1BwDEGqyp\nSSXIkQhShXadIgkAA/r6eibIpEr5pKed18bP8nw4vwkiiKe8hT70fhkLHp+JGgNZvpgsEnjgvGcA\nQ0CCx+Gc53eQPq4o3F5JIvkzHruluVmU2nkurG4qgbNYl/Agb7s3ySd96PwdJp5MDrV4H/uv2fsk\n49fcIsJqvA6xLXQ4hClDajw3bSaeTPT0veMY8lqoJs72AYfHg0AHARGVYP67AAATST7fpLHyPAkw\n8DyY/DHBEeBB+lxV3z/HlUEc1xD2jHnc3uMAAJ1syz1NJNDW3n4cAEC1aiahnI8Ek3RFWwMATLg5\nBwg2MZnjPWJCz2MRMNDUeEm2jfcJDPDzXIcIFvD3eO/ks90q+hxDJuEMuHjPed+Su7XH8HtFXC4e\nl/vMc1Jocki0BYKBEJKSfMo+0mqVdgOCDwyAtYsGE29+L90EeG3JSbTXPAYA8L4R6OF9FH0HcWVR\ndH3FJnEonQUmWeGQWH1pAICaCTL3BNVvkPsgrhXZWSL6SS0Ftn1wnvO+MdHnePO5YZJPUEKDJAQT\nlEtDNaqrquXz0kqRkiLHUdoIDSL4RsBDLEKBLgCAz5A4XxiWiEysqVDM8+lBe62MDHnO6Caxd99e\nCQAV7b9QwEHqCrC/n5X80aPHyLFIiScwwGvgHCYQVtSTThY21NY3Yv23P+Ef7y3D5m27ZMwEYI3E\nMGTwYNx99wIRFepOISZWLXRRg47MNY1aBo8//rg4AWiwh9clwZzVAUtI9f6ne9JhsVtR09aIUCwE\nl9kmnsikZwtrxJRAMwFUVtiQgJOVb6cNLe1tEpzaDd0dJlMEl6gHMOe0M3HLzDkiNvfKe2/jq5++\nwVWzL8Wsc85DFv2P2bBNkcZQFC1Ha9HGykpbEz79ZQO+2LkVRztbldWX7GDApP7DcPMFczBuwCCx\ngIKdSsxEDXREoWKJLqeCbvu8jmPIXrD6fNh/pBpzF9yDXysOwp2Rjg8++hC5eTm44oorsPHnTcdV\n+lSHu6Fgb+jtcH2lKw+D5KzMfFHgJhtQCUj+dyUUcvXINlGsJNp41TceBftxSRnOzs5HanIW4nHF\n4PyXhPJ/CQDQL7y+/iiC/jbYLZBEQ6j9AC6dOAXzLr4Uh/ftlyv8YVcpPlizGnW0KjSuZMb0C/Ds\nCy8gvzBPEXz+m6BQk41Z9fzbs3/H3595Gk1VVbK///WehZIImsn+kB4TXaU6Hq+hna7N6oDJ6caf\nn/4b/rL4ZfjjgJPVQZgxccgILLz5VowbO1bYHwKoUjSY8yEexw8//YS/v/4aVm/6AaFEAgXeJJwy\nagzGDR4Gf4vSbxkxYhjGjBwOdwohECqvxxC1KIcAm8WDSGcMJqsbTZ0hlHz7Pf6x9F38uHWj0P95\n3ykI3L9/f7zyyiui4i8FKUME97f3jOAu9yYq/t9www2yDp9xxhnyby2s+p/E2P/dZzlfXn3lFdx6\n003GR8wguf2eWRdiwdxrsG3LRlTWVGLWVZdTQQ3h/WU4vO8A1nyxBiNGjMGJE0/DByVr8YfFr6Mx\nEIKNRAiCxNEg+vXugxHDhmNNSQkaO9sxY+rpePSh+5Cfm4XPVq7ACy+8iD37jmLmmafjzw/8Hq2d\n7XjsleexdPkq5Oem4LpLL8c1589CSlIqyvfsFbvk3kU9BCyWUjZZfTYzOkMBqQxT58PsIEBjgYn2\nznYnyg5X4I57FuHrDb/AknDAl3CjX4++og1zsOoQSqt3UqEDo8eOxa233oYxY8fIODC++/bbb/H6\n66+jdHupuJ/lFw9FRuZAAXko2kc6uYkASZQAfQc62hvR1HgUgdY6BViRNSOpGRNftgulIjktEx5v\nGhzOZFgdXoS5lDGxM5iUpHEzjhbZiUQITfWHUV15AJHOZvl8bl4fZGQVSBLrp414LAarOQFLPIjy\nslI01R2EiSAnW3GZI8WjwnxiQV9aDMCWAkqG0yGFQrkOiaEcTpso+9OJwWSxoTMQEe0KggDCuhOx\nXJUXyV5hxPpiaRoJiy4JgQDVykBGNdd7M6wOm4DjbqdHwHZpTRLBMK5lZHkyb6JY4rEVgkAI46Ou\nl7AWWDgku5i98J1obKAAYgfM1qhiABnOCxTRzkzPwMiBQzB+7FgU9ugh+2VqeoaMFVs2GKORgdDR\n4QdJZK3t7di/f7+0vW3ftlWKAHarFYUF+Zhw4niMPWEcktPTxNJyzdq1WPbRxwj7O3Dq+In49MOP\nkZybjmBDG2ZeOBNrfliH/OQcvPHcizht4qnY+OsmfL/hJ6xY9Rm27SxFJ0Lim1FY2BO33XEH5lxy\nCbJzaSmsxlW1PxNcVFffHQCgppguCn2ybBluvOEG2Uf5UTpczL1+rhQWCbCI8DpbnFtb8OZbb2Er\nnYQSMZx00smYM/si+DwqHtWMbVNi4QKVwhtKlvFEDHGrHY2hOP7x9dd4bcvPoHNgv+SeOKnfCPR2\npcLBnqduVIX/hAHQfUEiALCntQZbqw6grLkKEQTRz5uLiYNGIdvigy1sWOcZLQB6EoZNCYRsJvxc\nvgfrqrahXVQLTIjbfUCfcTj5shvhGzgCMY9Sy9UK10TbuIgqWrpKYNWE48RVavCqH4f/KcSeQSr/\nrRZtoxvFQK+MXanrkrpAEYMyzgda6K1h1bvoNSfgiXVge8kn2PnRa0BrtSS0FP/zWT2wRU3olVMo\n6v0/HypFEGFlNeFIQmuoUxBW8ail/6TJjKSEFef0n4A0mwe/7tyO5kQrBmUPRr9eAxAOxrGzfC92\ntZShMdEmyBMrMYrwyKrMbwWHjIkn3+GC29NTAABv/gAEk9KlBcCZn4O2aBBRK8X/LIJcxkhZjasJ\nRRSPC4HQkbtaALQmgFJKV/ReUp1oD2iFxWaTh1wJhaiSlN4U3YghOerH7jXLsPnNJ4FAg1RvGCCc\nX5iL6ydPxojsbCQJZUkBAOtoGbNyJTaHVAsAAYD7F96BmWwBMMckMVP32aKq/gZDRNPcNdgjFGzC\nJWIpo+4//1PK6Ooc1TOg5oQouVvMw/PMMwAAIABJREFU6roM9XoRQjFYJKJILZ/TxiKGuYlxiO6B\ngF5o+Xk+rPJ93dWruwWqumeqO1NFU7J5D9hrpQEpBXJYlFo5Kcz8X4IAiKIU8z0GPKKxEaftZbBL\nJFMxO9Q1M/mIslWB1pqGpoN+NtleIPffpujOavNQDgTcXPl9fF+xLKj9oMJDAmf6eplwcz5Q/VSS\nYcM9oMs5wGCZMJgTbQnaJtmUq4GwUrq1AHDxZvVet6foPj/lOqFpr6oFQAXp1uNsQruvVwQSCRSK\nEj3pvHbVTiLWLobyfZfFqQyIUtDv/lI0P1XVlys3WlD4Vx5L33vddqKBKRkbo3WG5ynJXbe5xHHi\n2qZtOAlE8RgcVw1ocu4R/OEc5nuscpNyT6V5XelVbgps2VCUfbbJcP3kBkQQhL/LhJvXK3Oda6XB\n3lGU++N3A2kBYLAibggqINZjIOCGkQjyd/W80XOJ6wvtOGXt7WazqsdT0ri4eqb13OHY6nYXjp3W\nHOHzJ0wk7UZgUfOTDCTF4lJ7Gmn5yiWDXk7qajhvOa+0y4Bew4TWb1E0d754XWJxJD2fam5qxwi+\nz3EjwMJnkN+t10iCRjwGQRv+jN/DYxPYI+Ci7D5V65TQOsmwoU1pU6PSW6HbhtWO7bv3iw3g2q++\nR1tzi9rTEwmcefY5ePjhP2Hs6JES+OhXdwCA50rmyt69e3HzzTdj48aN6mPGPsj1lXuYDRa4YcfA\nnn1FB2Xb/t1oD3Ygw5uM7IxM2ZdEByRM5pQbLrsT7Z0daAo0iwI4VzMPg0+bTcBSAgCcU6yLMsG8\n6ZzZuHTGTGzfW4rnnn9OBFV/d9c9mDxiDOzScmpGzB9A49EabD2wD299VYJvq/ZC1YSOlQr4N6Zr\nQ7yZuPiMc3Hy6LHISk9DTi7FRDlfGHRFRFhUV3TlcexWQRfNAurbeNx469MVWPj4X9CEBCacPBHf\nfPeN2BKy8lJWdgh2G/VGmALzJMXDRf133OPAnv0sZGcVwuVKFu/w7vvBcQuFnAzHhm1CbK8Iobm5\nAQ2NVYjF2GrjRm4uj0MtGvUl/38A4Dd2Y4bYszQryVwnxZSCqk3CAAiHOuCymREJBYR1yFT8hOK+\nGNCjJ7Zv2YKOUBAhmwVV7a3wmwFiQ7TuvfmmW/HHhx+GLz3lOACADIPjX0ZxJwGs//Y7zL9zPloa\n69FYcQQnFvfCXx58EONOOEH9igYAjHHR4sOMP1g1NVnt+MOTT+CZd/4pQIR2mkiFGdMnno6Ft9yG\n4l69pAhjJhDE/YbaIQ3N+LRkNV54bwk27d0jv5ficsEVtyAc8sNqsmJw/76Ydc65OPesqUjLSBaR\nwLCVzEwLHGYvTFYvqhtasGL1Gixe8jZ2HdyL1OQUjD1xHL7b8CMaW6jVY8K9996LBx98UJ59EUA2\nqNrdwRuuBVw7qNnx7LPPyqXffvvt4gjwf/nidz/91FNYdM89XfwXPn93zjgHs6ZOQVVVBdKyMjB6\n1gw07tiOVR99gh7ZuSjKLURhQTGa2wN4e+UqPLN8OYIwo2dqFkYOH4F9VRX4cesv4oPeEehEW4g/\nBR68dR4W3HaLzLENP23A4sVvoPSXLbhxziW4ft5cHPG34pFnn8bqb76C1+HGLXOuxA0XXwE7S0yh\nsOqjFpZBAmYHAQCL5JMWp13+pC2fxemSuJQM5kgigceffhZPv/Aa2kNhuKAq0bwP7TFW6GPo3asY\nd9x5By697DKkpyo2ZSgSw9KlS0UItfzgYVgcydL/7/YUIBg2weWmaB7ZEW0IBqjxcxRtrXWIiCWd\n3v/IvnLCYvPAnZQGX2oGPEnJcDi9iETNMFsdUv2PSEufEtoj0E2dDEsiiqb6SjTVlCPQ1qi0RrLy\nkZ3dG05XMjoDfoRoW24yweW0wW6Owd9ej507NiARC8DK99lWSOYO259iYbEx1ELijEYY19rFxd4C\nu0t9t83ulD2E4YqFYC3t+yR/0QxnFu3YZqfcsbhH2eyMt+hO0iIuAdEY2wH59KnWB5OVDAC6RDkF\npGMlmoKn1F8g60CYAIYrmC7SSg+/8eK6LGuaWcq7si6xiMfjUyCSuR2ZCqTAE8g55aSTUJSdB4fE\nZwlEEnGE2L5Kq/d4TPZRas9wLQlGIlJ8eX/pUvzyyy/K/jqREIvVsWPGYPSYUbJekCW2Z+8eMPHe\nvW2baEpcPutivP7aYphFywN46L5FePyZJ0Wt4IZLr0a/Pn3w4aefYMuO7ehIhOQ7eaypU6di5oWz\nMP7EE+H0uI7tDVHq5KlWW2lT6vbSOnVkY1ZVVuKaa6/F2rVr4LA7JE6bOeNCTDxloowTi5sE3xnr\ncC9fseIzfPPD9+jobEev4j64eM4c9OnVW2IuAg7SUppYeJeh/GfgsezrjcZQF4zj5dVfYOmBnaCm\nc39PT5zSdwT6+NJhjxBhUn1dupX8f7M4BawJbK8/jB3VB1HRUSfGKcPSewgDIM+VhkQHFSSPjRP3\nAFKLEi47akPt+HbvNmxtq0AbH3kGX7ZkeMafg0mX3wRzdiFC0mur/Cb5YoCmg3VB3qSP85gGgNiw\nsfeSCamBqHOi6x5svWAf6zlV9l4Mvpl8MAlhdUniJsOOisl/OBCAz+OGJ9KBRPkufPH6M+jcsxHm\nWCfccCDF40M4GJV2gR6p2Ygkoihrr0ZnLAg3bHBY7GiLsSVAqWnyTsUTUWTZU9DLmwV71IKjbTXI\n9+ZjYHFfoaAeajyCDvjhsjjhSPXgaHsTmkPtQvOTxI+uBCo+PPbq+rsDTk8hRkw4G9l9x6HF5oGz\ndzEcuVmIuazSfkHHBQE8YqoPUr/0hNW9iMLAYBLNVgEjCWYgzBeTEyaS0mqhk+puya4nEUFKqBVb\nPn8Xpe+9AIQY1EIAgCv6FWPelNPRNzkZTmm3MBktAAfw7MpV2MzeXpcNWSk+3HHzdbjgvNOQ5LWL\nKj3veWcwKH1DDK4oAsekl0E1q8q8p0wCkrw+eVh0P7v0uptNEqiLHVeMPVFhoV5zLHR/L5kBvEYm\nw/yTlVJWXnm9FPpi/zhfpNGzAq778tmfxRer+KxwctxYheOcYgLEqqu2i+K5kl7P3iQ6ElCEj2PM\nc5TEgNS15BRhD+i+bh6L1VVWDoWCLMFeu9gTMTljYsEqqu6z17RuXldSklcQVAI8PE+eE+8tK7g8\nLz47Womf1X9WypN8ZE045HnSbRA8R2kHSCLN3opWQyiR183v0eAAkwMyCzi3WMnmtfC543doQTey\nFHgsHpNMBF0x58JImjjHmwmdjAfdNyhkJ/fOKRX2tlZSaRlM22VMeBwm7KT3Kzq7R4TpOBc43myP\nEP0Km03aLJjUilCj0VbAa9JMERmn5hapinNB5njzZzLH/KrCLtfscsv3cAHne5wb/IwwC1LZ422T\n9zkveS28Nn6e58w5x/YPLuY8R1az9bmyRYCf55hx7AQciERQXVUlFEDOSVauuQ6S5cJqvTyT2v1C\nko6InKu4XxhtCJzHPA43G95TzkfODSamvJ88d943aTtJShLwhfOPx2GFnXOG94AtLDwmnxEyaggG\nqGtOk5YR3a7B5NDhdErFXNtEUqWZ9H9hA6SnS+8cx4j0e34vx4hij7qfX7dl8P5KO4BXtQMw2Oa1\ncdljBV/188Nwb2gTwINK4mSvqOtqkgRexB5T05CckirPE1koZC7we9neQfYA5xFbIHTrD0UmyR7g\nWFdVVUkVn4yL4p7FMvd4/hTp5HFENDKX1kaZ8lwerqgQpgCvjSwBsgH4qjx6VIKTSCyOnLx8HKlu\nwPufrsZnq0rQ2a7Enjh/zj7rbHEBGDp0qNLBM9Z5cVczFm4tLMrvf+qpp4QBUFNXa+ydSkHfKQR7\nM7xmN9JSUkXvoaGpUfZHMpv4TLS0NKO8rkIa2IpSC9C/Z28cPHwQZU0V0tJGWS4m4KQMc+3ii/3X\ndgutG4ModCRhwdwbMWnYCHy1pgRvffguzj/rLFw3fTYyyQKQCk47tu4qxYvvLsGm5qOy7rfTIlXo\n8gQvlbaFLUFKKL3IgV7p+Zg0eizG9R+EvoVF6NmzUMQj2cJgZnKh2wK6B18cK6sJLdEw5v/pEXzy\n7bc0h8M99z+APz/6MJa88w5uvOEmhMNRFBUWS9WMLApS/9n/z4ocK3FKTIBuLeki/Ge3eWGCw6De\nKjciDdLy64+BvUqDhjExHQTq6qvQKV7mJrhd7GstBjV7aBcrvy/AXrcA8l8YALrm3m3L71ZxYmxA\n4TH2FtfUViAQbIPTSitbZanJsUw2U6ODHtImONwuxGwW1Le1izh1NAGkJvnw0O9+j9vn36nqSmyv\n7nJ/MGLGbmMseismM37dtgO333GHtAwe3r0bwZo6XDX9fCy46RbkZmaBltHEOWjbqQAbNYkFlI/G\nhbX0l78/g+feXwpGFwR/GPt0xILIsiXh3mtuxJUXXQSrx46IKQZ7qk89CBYbGqtr8MGK5Xj65RdR\n2aFsl3l0Ro0uAk6JOCYPHooH7roLE8aPQZgq6E4TzPRatyejvKIG//zgEyxe8k9UNdVJj/Dca67C\nnXfPx/1//D3efvcdOU+uAa+++qpU9HWxSIPa+lpk/25sxNy5c/Hll19K69ITTzyB6667TsVQvxFN\n6zaU//ZftYbKvfcuwjPP/F1YnNkZKciwW3DO6GHI9bhQXNgDU0+bApvZjM9XrMCP33+HC2fMxLCh\nI8RvfsuWHfh6eymeWfU5AgkTxvcehCsuuQzbDu7F4rffQmvYj4yUVPQf1B9bNm9CflYG5s+biyvO\nnykaOI0VFVi/ajX2bPoV06ZNxZjJE1HWUItHXnwOK9f+iJ6ZKXjozoU4f+IUZQdriiFO5g6fVeOZ\npRCqarEnEmAVq0ozQQDGyXY7Plu5Gnfc/SAO033I5DA83hNI9vowdtRIzJx5PmZdMgdpmQpg5auz\nI4DHHntM3BooQO72FaC41zBYbakiIhdPBBENd0iff3NTtTwjiLMXW0q5MuHNDrLvMpCckgWvL00S\na1b4STuX6rehCyC6ZlJ7JUvYIs4lLY01qCzfj2hnq7Q6F/bsjfTMXFjMbvgDYZiklYh5Cp/GONjV\nFAm3Yt/uX9DeUgs76fJWM5K9Lkw/Ywpyktz45pv1KKuuRn0rV2UlJO+AVdZjm9khILgA8wSeybgW\nO2qlmcb2Aa4xVibwJosAA6qSr9izpOUz6afAKRl5nf52ScxpFagQdBYKbXDYXSIUSACA5y6sHToD\nMOYSwJwJadQoth6z446yuEhrUtogdraKK4lJIJ447FY7Bg4agkmnTsbwUSORwljU6UZrI+O0GMIs\nViGGQDwqLIBksmA9SQI+ByJh7CgtlX76o5WVcDhc8qwNHDQYJ554Ivr26yPxMFs0ab+5+svVFNVC\nssWOe+9egEV/+L2sb8G2VixYuACvvvWG5GdFmTnKAaqVUDHvrUksJ+ded70I/DEW08969z+PS6K6\nP8lGckVRwZdfeQX33XcvOmhxazZj+LDhuHjOJeIwJJpFzDmpqUCQq6MDX339NVZ+sRotbc3S7nHl\n5Vfg5AkTxNqYzxRzBFNi4Z2qzhEzbqrFBH/chMqOIF5evRoflu9RAICtABN6D8XAjFzYuNILQE0x\nlC4ZmX97AdIfJACwufYQdtaUo7KzTm7sqKzeGN9nCDIsSUBA4VWaDsENIMRqW5IL5e0NWLfzF+wP\n16NT+lMsSCTlIG3iLEy8dB5C7mRExMuaFSFN9VdVFC7GivqvnlkmhHzpqj+REf1StBYtAmhYdhhV\nAlnEiVL9BtmWipBRPSL1xpKIw8V+/vojOPDFUpQufxemeCcc8QiyfOmyUbU3t8FrcyIrJRWN7U0o\nD9ZIzd5LpM5kQ1uCgaoZEURgFbvABFLNHqTGHUgyuzFg4GD4PMmor6zG7qpdiJB2aWHi5kZLqANH\nAk0ICgeAcoBM/pVG5nGYfNc/bLA4sjB4zFTkDzgZHa5UuPv0gqdHPsxJTnRGwghRcIuJhJuJMAU+\nIkb1VVXEuqpi3LiNyqae8DrAYeCiNBnMMmn5EkqQMem9iTB8HbX4+eN/YP9nbwHhdtmVMwFcPagP\n5k6ZgiK3Fw7ePwEATFhbVqYYAOGwAAAZyUm47cZrMP3siXA7LbIIEPghAshgnt/LBIYPDoN8Ut15\nPQycWYGmrgKDef5MC5IxIWCQznnExExXT/meCOQRaIpGVB81K1uGzSAXO6UroHqmiIwSseMM0jR7\nvs+Fh8kIH2iCC/w8v4vvMUkiMKCF9ET0zuGQ3nEmY5KwGVV4lVC7VeUzosAqSfSN8+ft5nH4HQRh\nqHXApI0vATCCqjIsyaXLJQkOFxomZ/wZx87lcMrPparLKj/H3aCKicALxZOiavz4XxdQ4nZJgtYZ\nCMq4auE/VRm1yPGpos4qHQEAJksuJm5+tjXUy3jxs+JOYDYJIKEAi0iXHgATR84n2tsxIeY9Zt+6\nbhMQirNU8m2qXUMYHKqVg+PE97lg831el6ZEq2RYaUiwDYDAAzcvEU40xkmqyf4AOjv8XQJv2imB\nmwqr7rx3BFx0X6jMv0BA7i+Pxe/QSTVF5vhz/ltUtaVKHBP2DP9k4C/3wWBMcHz4eX6O72tXDmFv\nGNRDdd8Um0ADEjxvNYfpchKXZ0SAT4vZuA6lN6DvZyighOyYVPP3GIhznLjO8t9MkjVwIy4AVjpx\neAQQ4fzn7zMR5/EkcU9nom+Tfwv4FQjI3CbIxHHieRIA4p+8P106DlGCEh0CSvBz+vPqmaaFkWpd\noA4GN3reZ7Fp7GiXz2sHDM5FBuBiuWi1IidbaSZwT+B943fwHjPJJ6DDvYGABKm7fM7Yl08AjyAY\nAQO2RnBMBQCgI4LJhMojR7psA+kmkJyeITaDBw8dFBCA40bKcHpGpjCIWGmmwwGPw/YBghhiKSqW\nmBUiWuX2JmHH7gOiAfD1dxuURkmUbCtSmEeIsj8TDwJy/xUAwOvjeNNVgv3/7733Hnbv3g1p8I8D\nXpsbPbIL4LU6RVegpr5e9uWc9EwJvCjuR8AjxHU1wr53E5IpCOVwoLWjDXWhJpEHtAsnFYiYlAAs\nsfmCbNpCposfeSjYjt7edDxx+10YP2wYXv/nm2L/Nf2U03DKqHFI8/rQ0NKMDaVbsfrnH9BsNaPV\nbsF3u0rRTmBZRPAIKKtjE8ykUxG9dJh2D0zNx4nDR2BQ/37Iy0xH76JCFOblwO5yiXp59yo6442E\n3Yz1v27C3AfuR30oAos7CWvWr8fo0aNwzTVX459L3oHV7kJxjz6wk35M/ZsY5yLdLSqRoD0xxSot\ntPxMRX5eD1itFGKyIRpRjCLZF1n9M2KSY4EUGQAqHW1vb0ZjEx1hWmG22JCSnIOszDyjne5fSfaa\nTXe8COD/HwAgrbi1pQG1tZUSdPPfrCqyldFNwD4UQk5mFtIz06WSvufgATS3dUpOwPGmQ8Xzz72A\nM8486zcAgCGmeRwlIiHCZBRG/H7jRtx22x0C3NZUVKCsdKfY+y689gacM2UqMvNzJfGzUVWbInzi\n7qicKWC2orOmHr9//DG8uHK5xDVZFhey0rNQXlctGkrDM4rx14cfxpgxI6CyJIv0cJutTkqGY8/O\nXfjLs8/gox++UvVLq03WTp6fJRpDoS8Nd910E66+/FLYHVYkHCYEIlHs3HMQSz/8FO8vW4HGtlbZ\no2+6cR5umTcXPfr1wYoVy3DZlVdIoYBrDsW4mNDTjUS/ujNHua6RhcOEn7Z01EJh68CFF17YxRj4\njwPt3/wC10Gui9ddey3eW7pUiOpFeRnISXJgfN8ipJhMOGHoGEybdhbKd5Sivq5W6N5cl1NTM9DY\n2IKGxlbsbWjEk8uXIWy2osiVikV33AWHz4M771+EhlAHsnJz8NorL2DDjz/gT4/9DX1zMzD/6utw\nyXnnweXzAs3N2PHDT6iuPCr91Fl9emN7xUE8t3gxtm/bgbtuuR2zpp0FC8M7ikmQ+UcNEwMEkDhR\nAyJkJ3I2sD3AAAC2bC3FjbcswLZd+yQeZiHQ6/PhwhkzMPv8GRg//gSkZace51K2d2+ZJFnLP/1I\nOEnp2X1RVDSA6SaCQbZI1qG9rQ4dLfVIxMmEYw5BEItgkh02Vwp8yVlITs6Bw5kk7CwJT0XoloK4\nx1hyfGrFpYTMYJcdHW2NqKk+hNaaKthcScjJLUAak38bLapVriLOQ5KAE7QkAy2GaKQNtdX7UXv0\nIBALwG2xoigvG3fOuxazp0zCquXLsGz1arhT09AeDGHPvjLZQxX8auYKZejlM7+Q0UPMAIotBn2f\nQACTdlH4F8iIDEouXCpBV7E940nGHUHFHIwQihPfFAUEWOwCHvF5ZwuCAhjUfiT6HF36bGT8KdFs\nCq1GoiEE/C3wd7BdllT6BOxgAjwCp54+Tej+1IshGzXF6UFnW7uI4zEhr21tEgCA883Dfd7hgtfp\nQou/Q8RuS75YLd/N/YdaTuNPnCCsI8bBXHs3/bwBJSVfouzAPmmHoAPe00/+DbOuvFzYDxTnJJBW\nWVslKxtXa7KglAsBNUdtAmwuuOtuoenrhKtLjFUzzn7D8D2WgApNC1s3b5bq/47t2+VHjAFmzpyJ\nUSNHi8gj40MnY0/anNNOt6O9CwBobm2WPOai2bMxedKpwlQQjhpbARIL5x8PAJjM8CfMONDUilfW\nlGBZ5QGwLjnIXigMgN6pWbAZk1iwLFIDJZOWKfAva9NxFpO/+WnABmyuLceuusOo6WyAJRbDqPze\nGNtzILwRG6xRRVnrqlKw95zCLE4rdtQexlf7NuMoO90FjDLDnN0H/WfegEFTZ8JvdSIkg8uHxKDu\nGyrCOtnkA8mh0NR+nehT/Vm/RJlcq8B3AwN0Mmu1KgRLxI6oAWBYBEplWxTYbfCYTXAG2nF041fY\n8/kSNO3eDFMiCqfJgiSXB+64BZkWD/oU9UQgHsbOw3tRHayH1W5DXko2Av6QKNXKIiYnxvOOIgc+\n9LPlYFCPfsjv3Uv6PShgMqh4EArzixBKhLCneg82HNyMFkQRYuU+QWSOR1EMgGOUUINGKAsAt4Qk\n9BxyCvqOOhNBehUXFSC1Tw9E7GbRACByzluuFNP/C2DEiDJlbKnga9D/BXwxqhQcWwF4DIFHubJu\nLQDeRAjOhkP4+p0XUfPNciBMpVMg3wRcP3wQrjv1VGQ7XLALVzPxLwBA3GVDdmoybr3xakw/Z5IA\nABSrYqJBtFhRy1WyxUCRqKEoiwvtVbXFUPWfgY1yLIgJKtudsscT0naHes4IS4NBym9Nt7vNf02n\nJwiinxstjqitP5RzgJqLug/rmH6C0lLQNH6Or6C2Bqqqj8/qlD5fXqcGZoRyLe2v6lz1Swm0EIll\nqwCrcur31X+qUiWUbBFvsSASUirw/G6dOEpVxlC0J11VfZ59Z4YquKFaL+NktCTwuPwufd78UyhR\n8sypIFnTuQk08DuZgDMR0tfEAIvAi7goGK4UqofM0nVfNdVbq8xLCzC/S/uLGwNxvG6IuYtFpCts\nTPD0+Ks+Xu7HXCuOtYsI4ygSlcCe48Pv1kr+2glDt+JIIt6NjqPnTtc8MdYXYV5JAKysYmRcWV3o\n5tQg7RwUKrJaZY5zTvPFf+s5wPdJbefxOPeZZPOlwS4JDVhxsilxHr6kdYpaJ8ZGrd0uuIP/dvxk\nPLRDRreqlcwhmavc/FlHVACRHN+w8NNtNuJqIAmSou7zJY4WGizkvBDXDi0odGzd1okVk3U9H7Vb\ngm53Ue0JytZVzzHd/sJz5Hy0G60v6hlMHDfvZLxI5aTXeLc2EKVqrMZeV2bJ2JD2M94noxWD5yVu\nCoZDB4+hxRq18j+fX92Woh009Bzi/Cfrgwh3Y3MrVpasx+v//EhEAJVAeUKYABS4oiXbJZdcLCDf\n/wQAlJWVCf141apVcv7SXWVY/ean5iDbmybCvAerjyKMEArd2SjKz8eRmmpUt9fKDpXjyRaaP3Vp\nGuJ0oCFxNypV44Q5LkEdbyGvy+tJwrRpZ6IgvwgrV63Cvr17YEcE5w8ZjUfvWgB7JIav16xBU30j\nThkzDn2y8iXWbgx2oLy5ATXhEFZu2oDlv/yEdrECJrvbCmsiIno4TA4lNuDzDQLxJmR6fBjUqxcG\n9+iJPI8bQ/v2xYgRw7tEW7vmlwloi/jx0NNPYvGar0T48Jzp5+HzFcuxr6wMU047DZUVlVLlKyzo\nJcEtRe3Ump1AR2crWtvrpG9Vu5t4vMnwuJPhdpFpZRfdDm0tzLmqQcJje4lJBAWbmuqEAUDLL5vD\ng5zMYni9qcaa8++3APw2Ruvec6pSqBiaG2vR0MDvisDGFgha1qZnIh6OCIOO/dJJKT4cOnIY3278\nES3+kEQMfE2cNAmvLX4DPYuLVUgotuSqYi/r1W8AALYztgda8fKrr+NPDz+CzNR0hPydqK+vQe+s\nbAxMysDgHsU457zpGDF6FCwEsCgsbGO7lkVF3M0dqNhzAH996UW8+fPXsq9lWz3S893cEcT+moOI\nIYzLz56F+266CQU9C2UcZb2xORDrDGHX9l149vXXsPSn9QiaTdJrTfCXwF48FAXTiYvPm4V7Fy1C\n3wF9se9wGVavKcEHH36CzVu2IxSLISU5DfPn345rr74KhT3zJVZpaW7ETTffjKXvfyDjQyYTxQDn\nzZvX1dOv1wi9Pq1cuRK33nqr6NXw2X3rrbekKqkZqN3CiP/4r3qvJPhJMOLrr7+WtoeeuWnI9Nkx\nflgfpJktGJzVCxdceDFCfMaqq6R3+NdNv0gi6vYlo9/gofhw3Vd45O23kXC44QzG8NDdizBq6DDc\n/eB9+PnIPhHdfO+NVzFs0CDcfPtdWPvt9yhMScbN11yFeZfMEgFW1LegrqJSQLqM7ByY3C5U1teh\npqkROXl5AggxzqDgJOhgQoq0VPwN7Q7uTUb4wmqryWqo95uAI9X1uOeBR/DJypXKshq03c3CnfPv\nxKyZFyInNxcer/O4ItiXJWu8M7YhAAAgAElEQVSxcOFClJaWwmb3oUeP4UjLyBOWZFNzDdrbqhDs\nbAJi5AKxjdIi1oCcL053CrwpWbDYvZL8E3ZU5iWq5VjiA7ljxwA77tMCsiXCqKosQ21tuSTLOfl9\nRTDQZqOocUziGgLmCRFgNzQ/xLWDldxOhPx1KNu7DeZICPG4HxkeL04Y0AfnjR2FSHOTCN7lFuTD\n4fVid9l+1DY3IRJJwN8alHvazhajUBCNQT+aAn40BjokS6DAJpN5vphEUnRTDfixpJ/7KWM7tsMx\n1hFgl/R7tnwa2mjKZoDjoT6r2lO5GrMlQLkvSZ5l2IaLAw8Fz2mfF+pEe2u9XCf3EK41wwcNw8TJ\nk9F3yBBhe0TiUbgdTmQmpYrQHQGA5vY2VNRWgXbznrQUOK025Li9cDscWPHlKixe/BqCnR3IyVQA\ncK/efXH61Gko7t0XwVAY7R1t4gpQQo2Tmmopug7NK8Z7b7+NASeORcnaL3Hv/Yuwq3SnPENsVwnK\nGat2No4S+eBFhT1w/wP3Y86ci+Egy1CAE2PeGm3Pv3Vr6XqwDRHARQsXShsQszPuE1NPnyq2fj5v\nkjBqWODQ7ZscR4L5X339DdZ+tV5aCfk679zpOOfMsyQHolOBuCYkFt3FMrbKCRPKQzJgtqG0pgmL\n163FZ9VlCNIn1dsTpw4cjRybBzajIi4AHCn6xyX//woCdF+luluXBW1AaeNR7Kk/grrOJljjcQzP\n6YkRhX1h88eFpiJ9KMZLWt1sVrQgjA0V+7Dh6G7UogMxUZm0w1Y0BGOuuhPpwyYgYncLFUzlmgpJ\n57UxiJK+YSOYlUnbzWZLenyNHm7+DmmwPAcdNEqyb/Rlc2FiD6rQ3EXYTtnoKGoMhSDjsJltSIpH\nED+yH7vWfoIDX74HW6QTlhg9lW1w0iYjHMHYvP4YOmAQDtQdwY87f0F9ohluuxsFKTlSRazyNyJu\nVOWIaHthw5DUXji5aDji7WHsOXII9ZEW5Hlz0adXX7jsLrR0tmJn1R7sbN2PFoQE8VNsBeVb2gUA\nyPPcHQDgRdiRUzwOIydehIA7E/GcbCT36gEkuRDifWcSDRNCoYDYZSmdhGPq/7oFgMmaBPricXqs\nf14H1OrWHutN1AAAz9KXCMBWtRslbzyD5l/WsxlXPl1sBeYNH4prJ01CKpFJiTISaEuQAaBdAEKI\nuWzIy0zDHbdchwumnwafl5YkipbDxFMSLmF/qASXCaTq/1VJJROAKINII3niHJA2FPZ9G20ipJHy\nd/j9DJhZadZ2X6oHXlWCdYWV1XfOQb7E7aGtTfr0pX3ArZIVVh+F9m82iy+6SlbVgkrqsyzGBuWb\nCwcr+GLtx15aEemjurRqB6Dgif483+fxdfVW2SFahILM9+lIwIWD58wqLf3X+T5bDagTwOMx6BKB\nOR5LlPub5Xf4fayuK0Vb1U7Ba5MEi1aDbo9qR7CYhT6uqsRsOyC9P1nmB4UPdZWd1+dLSlJWbYGA\nVHF5TFaatQUdn9WW1pYuVgOPxe/id4rLAOebCVKtFdp/OCznpKrHdrkOh10lavxebl5MqrqPH4+j\n7eNYCVZU+qg4NXC+MAHjOTGZEaq+wSDh8Xk+LpkDFFIMyPVxnNgbLXMGFDRX7/P6ddWf73exUUhj\nJhvF6ZJ1iYrNur9cqb7aBADgNbM6zZf47ErVX7WvUAxPA108X95faUWg4jSvwUUniDT5XTITyPDQ\nOgV2p0pcCdaRHaNUeclGoeK5W5JMzlXNauH56HlGFgdbGhjYMvGUFhabzbj/HV2tC7wPHC/OYX4H\nv4vHl3MVfRYlWCgAnAh4KkV5Wt5wkxbGjrQ6qD5O/pttKvw92t5x3mlnD95nXp/Q+A23DF4PWyn0\n/Ob38juIqgt7gLZ7dAhJTpaWBhlTowVCC4qyhYDnzfOU1girRe5/ZlaWAC2cR3xfPdPpwoDgPWMg\nzqo7n0OqfWs2CBOPw4crlJtAj55q7KxWER5kSwDvW05Orggl2p1OHDxUgXc++ARLln6KyppGVVWh\n7VIogkGDh2DRokWiOkxATSUBXHOPpYMEd/iM79ixQxTLtQaAStrMksKT+t8zK1+shOpbWtDY2oii\n1BzkZeeiqq4Wjc1NQpdmhTjJ5hRxr/LWWiXMx1YerwvN9LBPxEUdnn2MDARnzJyFkaPGomTNWqwu\n+RLmaAg+xHD9tAtw08WXwudwonTvbkT9ARSlZMLn8Yg928G6Wvy8dy+WfL4CpW3VkG5L9p26XbDG\nY3Kfg10Aqwq+CVAwWOPTN7p3f2SxXzYYxCUXzMS006YcA3xMZkQsJqz87ivM/+PvUcfxtNvw1pJ3\nMGfObAFUnnryrzBb2J6SjeysAqH0s69fPL1ZvYoGYbZE0dbeiNq6KsWAEq0YG5KSkuHxEAxgW5DS\nHtJ6EcfiJaVDxMpjfcNRsf+Lx4JwuZNRkNcPdptHVaiN4Fn1lf+GDfAvlaXu8ZnBXSSL0fAyZ1Wz\nqaEGjY01kphQPd9DQc70TGEBnHjCiejXv5+smb/s2IIvv/8KHWRGiFyECZdedgWefe55+EQwz2D0\ndaPsawBAAcmy82L9N19h/l33oHTzNrXXwgyH24Z5F1+G4WnZWL/8c6SkpeGq667B8NEjQeo32Qfs\nMQ43NGHf9p3yu++t+gxrjxyQOCcTLgztNQSpqTn4dcd2HA1XoyA1HQ/Mm4fLZ81EwqJAS6vNiVC7\nH2tWr8Gr7/wT6w/ulKA9Iz1T2D919Q1obGiQ8Rk+eDguvvgSuLwufPPDN/j2h+9QV8t2pwQGDRqK\nm266ETMvOB9pacnSWkJ9CbaWlKwpwe23z8feffvk+saNG4eXXnoJI0eO7AIzuZfpden5558XrQCu\nPSeddBJee+01YQTxpeOqY3PkP/ubBvD37NmDK6+4Ept+2STPQ2FKEtKcZlw5+ywk07UFHvTIyUda\nbqa0i1UfqcShAwcxZtwJsDncCCdM+Nvrb2DJ+q9gcnthjSQwf+5NmDr+JCx5/z28ueZzWL1O/OWB\nRbjq8suxavVaPPTHh7GrrAw+MzDv8jm49aqrlLhneycSIQWoi8uH24l4OACz2wE/W+6cTtjo3EDb\nTkmcmOBJT68CA4w5rgAnBaRbPG60N7fj2ZffwiOPPSEgVCgShMebhPl33omLZl2Eoh5FSCYTodsj\n8/dnXxD9CrbvWR0pGDz4ZDhdPhw5ehBVVfuBcINBoqcApBuu5Gy4vGnwpWSJOr/JTClFq1gcSh6a\n4MpJ+zuVJzCJPhaDk/0K2C0JtNYfxdEjZcLwScstRG7hQFhsZJgphjKr7PLkJpS9niGKAYdo63Qi\nEW1FedlOtDZWwilFEz96eLw4c9gQXHr2WRg2YCAOHtiPfQcOICkjDT0H9ReBvNJfdmD71lKEkUC/\nYcPhTE/Fhm1b8d2mTahuqEcoHEFTawuCZMsY7TC6PYZ/ajiA+yBzKVrrKYFBWg8qoT8CnGRFKQBE\nCffxXpNNEI9Te4rsApvEUNy4ZB6wkGUxIRKPiNBgKNQuTGoWPgsz8jD93PMwbNQoOFOTEeSzEwpI\nLF+QmSNtqWyLKzt0SIQiHV63tI+4yRj1JOFIeTmeeOZJHDl8GL2Lewn7MxgOiz3ghJNOgcvjRSAY\nQjDgx6H9e7CmZDV2790DcySKKcPH4e1//AO2dB9mXToHP3z/rQjO9nSmoEd2Do7UVKEz1IECR7qs\nWXvrj6Iq2oG+AwfjsaefwkmTJhvWrKJeaNjQ/g/PsNmEz5evwA033ih7PzWtxo0ei3PPPVfYjC6n\nsjvuXrQTBgABgG++xtr161XrJSI4ffI0nD/9PCSRvWo2S/xkSiy6V6nhdSk9AiGrExsOVuHl1V/i\nm7YqWRBPyBiE8b2GINfmgYVeiyalOCgAgDR78SL05tJtk+lGh5f5SxVJbgyki1iBsrY6bD20F+3R\nENJ8yeiZlIEBOYVwx0wwhRnwdReFoKOGDdWhdqw/VIodTYfRQnk7swOwJsMz+AScct3dsBX0RYhC\nGyJGxy9VapNSiTPEV1RiQkVKfkbh111+2kblVUS/uln8yRV2r+omqK7BgJiLkFNUJ6PRgIhh0GfT\nZXXCEgasTdVo/mUNvv/4DcSr9wqyngQb3CD6aBUPyVMGjxQ7wcPNNTjSVodqPy2prEgxuxCMBMA6\nSsRC4EL132UgCSPzB2Js0VBUH6zC7toyoeYMHzYMHYF2HKw6jKbOVlgdTtSHmtGENgQok2RUJ/gg\n6vvQde/EFUEJ3RGZS0rvhbGnXQZTek9EMrLh7dkD5nQfonYin6yncOooiqJGlnVPm64aMzgRb3JD\nJI1BsugEyCJwDEvsAob4ngEopCCA2N6fsfq1p9G5ayMsZC/EgP5OC+6bPAnnDRkGNxcSmX8EAMz4\nYs9+vFSyBlvCfgMASMXCu27GjPOnwGFNGEJ0JDAouxLdH6+rdaQH85x5/nyP1T3ecyYuqlqu6OAa\nAGC/MpM8nbTx2rhgM+Eg/ZVXKFoAouSpLE/4Mx6TCYdWQuV7ujrLxIM/Y7LMxI+BIV9MjpigiVqq\nTXnVSg9VNCrnxP+YuBBh5Ut6tQ0AQAv/sdKrP8vvZrWcSZKmXaskRVX/uTDqZF5TyvndbJlg8E5k\nmAmstlvj2GmbMX5eWhFIw2V/m/jdq0qr+MHGol3+8BxDLdzHY3FBY6LFhJsvni+DZxHEMxJAjhW/\nmwkmf87fJ11Sjysp0Oy37p4AcjyU+4EC+VRCT+GbiIy3JPSuY8k5v6+1rU2OKfR+A7xRvfodci94\nj5lgqiRZVXBJIyfIQxCF84YJ47HzV2q0/H7xrRdBRVXd5ndrtgQDQgID/FML8HEu8DgKYVdRC48j\nz1+cST0hT5OsG9w8FTPHLOfDdUMzIyQwN8QTuTlz89VzUlej1POg2ks0g4rJv070tRsEj6/nK39H\nA1M8HkEPJsQ8UwF0ODetNpnHvDbF4iCdWQFQPDbBJo4Xr4ugEdV9KQSo5x9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2V0AceDK8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Zd8C9mTTsEQETQxWTQmDMPMTxVwI7cT6xm9CdURgMPFg96wD7smG462htutbOhl76sunEiP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\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VuO/Bx7F8xRr0SWzmAHfXxMlzMOVX18GeScuXCMLcOKwmmPjHbPReR9ThrBfO/0fbe4BX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\nEBquw8WoJ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XBl9rtgKvKJDABmFhJpSctQn4diQMk3U6Ogw8O/+hUeeuxxBBNZzCgqx8WL\nj0OlyYmRzi4BXw896gjUHDATL6x4Dz9/4nHMaZiOO370I8ybPQsGiwk9Pd1o2bsXtUVlKCsuUa80\nf3lZIxo5/WcvId8miSP8cuWnGBkYwMJFh8JVXSFG0CPd/bjuRz/Gy1+ulnSMeqMVZzUeiDMmzkSV\nkQxzHYJ2E/667Ws8+/lytJBhygFSkgCYFTU1dTj22OPQ0NAgEjOy5Zga5SVI6HCiqalJmv/169cL\nmKoNGTg4oyyfSR+1tbUyaOdnLuBAKiX1BH+GNTv3wmQyi/6+ANrautHR0YVkKoaBwS7s3rsB0TBN\n/D046MCDUF1Ri7raegUAEF0oLSuTxUXKP01g+ERTJk8WZIJLcl879db9QsXjTcOoMH4AgaFBaeB5\nEJHCoB3wnIbwSzukiOxxShKLUUeqctMZtUPjLSJ72RQ1sllBo7Zub8KNP7wXa77ZIweOGGWYHJg0\nZwG8lROxad16JJt3w2YK4tILl+CO2y+G25uCmbp/oezqocsqLm/q/+PsPcDsLMv08fv0Xqacc6Zn\nkkmfJKSQRgk1IF0QEQVRFKSIYFd2xdW1IBZ0wS4W7K4IIqBICL0ESELqJNMyvZfTe/tf9/N+38xJ\nZPnt/g9XrgmTmVO+8r7Pcz93IdRDbZORmznlCh6g5MLzT7+OWz727zg6HIWxfgUuu+NLctCf/OHX\nkRvuBIzk+ZXgrXLglhs/iI/dcB38DpY4nI7mVCAYG3WtEZUGu8SGrCCNkt6QK+MedQfri7dMD7XK\npJLaz+/rAEDl9yuLp4q1YK6o0p9bb/L1n1cLEgsA3ZSrPKfFVM+jIZw0chNqvhYJqGkNVYY7keYy\nfvO7h/GNb30fkQzTDAjdcQNzwNO8VFa9xFAH6h0Z3PnRd+LDH7oSjmC1OAZDo9jPvW9Nu6UOiAHF\nTA4P/u5vuOPOHyNR5O5iU3u7HMMSXJYC7vrc7bjphmtgNimZBFkddBqW/0g5E+rkvOmi0iUr12dV\nCKlCXr82dSmEHBv5iGxgFEigswN4PLiyskCU4yhaXzY9BBlI7zdjaHAMzz3zqji1d3Z1CwhAIxsW\nAzrnQS9NxZjPYoTTZoHf60a13yeafBrduB0OkS7U+P3iNeG02+TfuPkaGZWiXGfU7IPRWKTEGtlY\nc5JOzZlJJvn8nh7Tps2clGrXxGZETcNJA1MTbFXMqymhuh71SC5OCpS5nzLT4RTcJKCQEalcASNT\nYew+0okX9x5BX7iAupZ2tLa2w2x1I1soIZ3LIJWLidP0zNSoul9yCujjJVHklEBOclkkGELVN6mG\nn0aAzA33uBWl31MTkom/SmxgYoyNzDbt2lVFuK7blgWdawop+EJ/IHChuhwdhFPO9KoplaaVTIi5\ne1PJBDg1JTtCrhetnFH36/xZ1d3JzbyXeW2VCtIkk4lCOQo/azw6hVRyCulUWOj5KaLuAnYogE3R\ngN2w2dySmW610QjQhanpEUxM9Ah7hkomT7UfGZqQBgI4fetpOP+c89DQ0ICDRzrx2FPPwOutwuZ1\n60Wb57BZMDIyhL8+/iiO9Q1j/drTUaAJkMWEppZ6rFjchLpAtZgtHTvWL00OJ1WMjTy4fx9K2TQG\nezvxwo6/o9ZrwwXnnYHTTtmIhoY6tDQ3wud3w8Wmm7IHmSxqTZk2+lBTXCUpkcmveEXo5VZZzKQS\nsSx+8qPf4b7/ekA0mUGTH+3ekDAAFnlqUWVT8ZfiAVABANBfgdp/HQDIpxir5IDDYz+OAbB3eBBf\nJwNgqh8rWhfgyndegM3rVqCxJYiqpgCKFjPMDq/EDhHU4H0kcijSzysYAFLcEdDQpFuK4TDPZpJr\nqqz0/nI+Jd3GIvsAj4OAlVp0qNKZKoNRgoviOF0ka4TGoYzrK0jTrqIzLXP6f/4MG3PS0nkNEmCn\n/p+bPpkLgyMTyNDBP5eXJIdHHn8Kzzz3ohiJ8hzU19fj/e+/Dtdddx2WLVs25zGjr8cnAgA7nt6B\nB372AF548UWlF2Y6Sgk4acFCUHaxcfUabD/rTMyGJ7HzxedxuL9f1gQCuTOROEYis0iWc6BqXrw1\njmvY53cv5UBiFA0s9wFOftQ+pky5dADAALKX8rLnqEP/Fs20VMhqryIA4PMGRQKgewAcv2cquZbO\nAmDtQVO8oeFuAenqGAmcz2B2dkzWfjIanHY3fL4qkXhJ9JtsL2oyp64H7Q/p8RLpSGqySiyIJyel\n8dZWmcq3otYBmWby20YxMOXaR2BZRb6y5VVeK/rj+HqBcaYEoBQIVTkg0KWIBOxoUkjb3EiUzIRZ\nWau457H5D5mAjS3NWFlTA4fZiu6paXQc64OdAMDqVW8JABgNVjSFFuGTt9+JM049B8e6+4W6veX0\ndfA0KLzifwsAqHxnI3a/3IkjB3owNjyBbDKDdWtW4/DR/bjv5/diJj6hDF+lJSDgy715XoNuKFqx\n9aRz8K6LrkWtpx4uhxc1wRrMxGYwMjmCNScvw5nnLJHJu/IwUuuTumJU0/y/feg1BAvwu75wF77z\nnXvlWHKSbjK4cMbGC3HdVR9C24KFiIfjcnq5J7ExdlVZsOm0lbBXUUbHQYb+upXT37d/J5Xrj/6T\nrKsVQ3SeZcMPNz4+hc9+5tP44x/+KHs+NdesTVmri8RPG26w/VxWvQg+dw3G4gnEMxmJsGXCTiof\nRyKt/EcIF0hSlJbcYXcFUFu7HFXVTdKo89qnAaTK7lDHVeqvkgFmo1MShGBII5UOIxmfxezMKNKJ\nGRjKytST+4ny95CKS86T0uhbQLP/okTGaef9fwUA8PVEvASbzY9avs+yHeWyReM0liX5gPRph8sF\ni81awQJQV8f/DACoqf1bnQ/FBlYmgVy3yEjOJmYxPtqFSHQAMOQVu9HkwMKlG2ChL5KB3kyshY6H\nOP9fAIBaOSh1LGN68hhG+t8ADJQaavIs2BDw18hhY2KFz+1BldsHi8A9BvjcbjmfiXRSGH21Hr+A\nz5l8ToBnp9mKaDgMGkDyUzG62VdVJWvy0a6jYi5YtJlwdHwEmVIWHhjx7nMuxJc/+xmEFjQD9EMh\nizZXQnJgGCO9fQi0NKBqdRtQ40a5mNMkuuoekOafgItEbVkwMDyCL9/zLfzy4UfFyuPCFZvwrs3b\nUFsoY7SnFwWzAZdcdw2KoRp87htfw7OvvoRbb7kJn7jpI5JWx6jq2GwEXrPm9aDVe7J2kx1s4+Rf\n6J4KAGAa3Ng4+rq64aupQmhhIyxkOiZz+Nb37sPXf86apYggTLhs+Um4Yc1mLLJ7ZO2dtRjw4L5X\n8Yvdz2OIYherWaJJ2SuddcY7cOmll0l05uTUhDB729oWwe3xoe9YH3bs2IGDBw/KXs96g8Ae71Oa\n/dHjY9GiRbIfC1NOY4TzCiXL0Olyyf3MU55KZpGI5zAwMI6u7h7YHUYMDB5FV89eSaOgh80Zp5+J\nxrpmhAIhGPa99GSZU2Gd1slJPulE/EATE+OYmpyA1WJBQ0O9FFts+vsH+gSNo76QGde8cEh9nJ2Z\nwexsWDYDahqcTpUkwA8l+ccOmxQuOrWaRk+swUnXsDAirVBAMp3Go0/swJ13fQsz0aLEOjGegijd\naWedi/FYHntffBlIRbB5dSPu++7HsGnrIsCQEL1+KZuZAwBY4JVI6+SGbyJ90wqjhRpID3a/sB+f\n+tzX8cKbA4CvAWddfZPE3xzb+ReYDem5ou6cczbh3z77cWxdtxqFdAxWE7WOaZkekd5J0wW9EGCB\nyEKOm6/QgOece1VzLU0l0VdOFkycJCin40pPAJ78Si8F/ryg/Nr3T1xwTvQA0A3/dLq/fl4ri4M5\nCrwUp4xXMkphyuxlaf40AGBOCmCyY+czu3DPt76PNzt6pcGVpsdgAexeiZGzpiaxosGCL95+OS68\neBsD1wHKLujdwJt5rneaL6C52pEC9MeHnsTHPnM/wuLxw3kqKRYiYoLbbsSdn/4orn//lXC7lKZT\nTP2s2rFL52HmcdaYFFxAeQ74IOVUHF4phRDzNmXkpdP7eSxFzysmZWqKKZMUxgDqAIDo+NkoqGab\nRSB1m30DI/j5z3+LP//5cUxPx2Vx5Du2kipstorBF4t1FlIetws1VV74XNTqu8WrIFDth4v0ZaHv\nK68AydwWAJ5RgPxMBmSLRN+pVS9qmn5Gj2Xh0CY/+mBbnwJpPPi53HueQ+UrQa0q379KrZB9mfTn\nfF6KSDa9+gSJx4fXCCeaEr/FLUaOoQGRWBoT0TgODY7gjaP9mEiYYXTUwmr3SzxkIplGIpVAoZxB\nLp+E2UqpDJssynwKMlliASqgBSUMpPyVjDCZGQPoEoqtFMFmB/kMgNWuzGGkCVNminpMzxxzgZ+H\nH4yfyajoxwLa08SvRMLa/L3I86o3bGx+la+rikLTGTFc71jCELTQZQTigcDipsjkDxY0fAEeI44U\nlPQkS4Q8yelaHvFYBOHwBIqZsND+dNaMwWgTU0KukYwEq6lplLhDm50GgGQplSSupadvH8pF5Rkg\nD4cDzQvbsOnkjdiyeauY/tEL42jvIMwWGxrq6rCwuVmYIZHILA53HsLf/74DdYFF2LT1DARaGqUo\n9nlsMBmYVzyF/r4+1AVr0b5yhdCJX37pZfEP2Pf6qxjsOgSrNHEKrODhqamyobmlAc3NjViydLFE\nGjY1NSBUFxQzOJ5TmvzxGPHcWCkFUL+ueUhQVsLrt4wHHvgjvnnP/UhnSwoA8NVjuS+EVncNajgF\nLZWRLRYEAKAHwDTj1mIRVNts2LZ+A1qqqpGNx+F0O+D0OQQAUAyAEvaPjeLuRx/Gm+O9OHPdWnzo\nuquwdu0S2H1W2AM+pDm5t3qRzSt9uQAWWmyrXAsaI0DAIYl0ZHynWtul4NKYWASxBahIppQkwkVz\nNzpLF0Xbx7WXx43yOqbq8Ge5F3JP5N9JeQ0GgnLs+X1qevlv3I9bFy5EdS39lYHBvn709ffJPRAK\nhrBkyRIY6EWSyeKNvfswPjktk+SZcBw7X9iFV3a9oWKD8gWhBV566aUSW0bDLd2Mr7LNYOHAQbQu\nAfjBD34AZodPTlKeYoDHYseWpUvR4HSD44BrrroSq1YtxcjUBP7xzHOYno2iMdiEaCKFnbtewWR0\nFjPxiPSt1JXKZFAr6dVdox6KAKwZ5GlMLdYV8ovCxinLmqdfQSeWxnOfQe+jpHC0ora6Hi5XjRgW\nVk7FFeinJAn8w/2B6y5ZSJQAxGLT8Hpr5Rgx9pMArN9dhbxEVZE1oJz7FXtIGefqfiAiN5RYQ94s\nTImgB5IBkegYYvEZqY8UE6ACMNATA6Te5frPpzbLJIf+ABaLA2Yjy15t2qkNFoTsptUOArKZ51kN\nc94zAqgwhjaLdDYhpoaTkyNKP10uylTWUS5idY0fa+pCaOReZXfgyNQ0DpMBYHdgU3s76m1mxAp5\nPEEGwEwEhC9NRjvqa1vwsZs/iXPPvBDDgyMI1FZj0ylLYaTPsnbf6EyHeRZGxd6vi6zLwOxwHIcP\nDCCTLIrmm0Bq+4o2PPaPv+Anv/4BCoas6PeVQ7ti8ilmoXrw/bQ1rsLn7vgPVDnqMDk2i7ali2Fz\nOTA2NYnqkAsbtixGVUBdWjQE1c/D2wMAbw0M6HJMZnrfestHMROmvpubkwMtgaW4/r03Yfu28zA9\nNo1kLCmMWRojTswOoW1FHVqXNsDq5cT5+HSi/w0QcWL9x//XGwauO2TVynpdAo4dG8BNN38EO3c+\npa3BgNflQ12oXtaaHD2SuJaVgUW+hVi2eBX2He1CMpuV+O9COYfp2CiSOXpzFWCympEj2EADTJMd\ngcAC1AaXiXcNG8JKrx1dNEgQrZAvoVSwSC0BAwdoGTHMTcVnkYpNoEQ5QT4nkWhkqxjN/EwFFCgv\n4p5LxJ/SHfrNyPBAue9LwswclKPurXmgTbvWpA6gCThTjwLwuIJAmUgQQQCD4AMc4nm8Pri8Hlmr\n1PWlCU0qLtnjr4ZKAGBuAZLjrAMA6nwSAABM3D8yEQwM7kcyOa2STMoleENLUd+0RI4hh5S6hICf\nW72NeQ8C+f8TJE0qSppPlcf4cBcmR/cr9rJIazQPKeEXqDWEP+syuWA3WeC2O4U5wBQ2i82CYHUt\nHAaLSNMCoaDIa+PTEdhMFvGhYfww62km1HBg0Nvbjfb2JTB5nfjTs09hKheTuzNoceH+u76Ed158\nEcBIUPYTBESPdmPw0FFh4NWtXwb4HSib2Z9p4K2WSDYXiU3A1WzFkzufxaf/8z/R1X0MC101uPac\nC3H5hi0oRmPoGTiGVadswbKzTsOfn/w7vv2D+9BQH8JX77wTK2WftCEfT8JcYJpVAWabRRIf5B7h\nII0DNg75OMglCCB7vAHpqWmEY2H4QtWydxvMdvzm57/Cp+65R1KFnCjjokUrccfJZ2Cpwyv9w4Sx\nhAfeeBG/2/8KRgGkJTPdiLpQMy668BIsWtgm+wCHb8FQLRx2mxh+v/jiS3j66aelbuZDzDiNRrS3\nt4vmf/HixZo3DhPHMlIf0ICXPZ4wBSjltdnFKJxM92LRJKztzq5OZLKzmJjqw/BghwzCmhpbRUa6\nfu0GFHIFGJITveW+vj6h/rOAoaFIaMECuqJh3949kgJAc4HVq1ehobkRscgMOjoOI5lKyCJB6QA3\nSjaPTAFgIaP8A1jY2oTqyxhANht+v18YBbxR6R/AwokHu6aqSvSzbP46unpwz70/wO8eek4Ngi28\nWXO4+t1n4Myzz8L3fvLfOLK7AzU+M7742Q/jY7deDIMhMq81l4pTQ1SlIND+TjCAf5jbanCjc+8Q\nPvnpr+Hp1/tQsLqwfO1WiVtIzwyJcR23zwVNtfj8527F5ZddAKtkjuak8CdyJAeeJmuai6iuHxU9\ntZgDKXMoUoB4gth86RpHIjYsPggAUPesu/cTXeVJZVPG78nmJlRidfPqmn49LUD/N76m/vP6c+g/\nz9/RGQcsUvWfU/IDItFquqCnAMj3tMkGzxkXR76VsZFZfP8Hv8Bv/vhXpAslKXy4WIINDXKothTx\ngUvX4KNXbUJzox85iw/WpiWA0y/yjXkAYH7jliixXArPvbgPV73/y6BxdbPfKZO5wXBS8GCHzYyP\n3/Zh3Prh98LvtcNkMavpWlkZ2smUSVs1edzVdFdNyBXooXKl9Wgp1ejPJy0oAsBbb/RsDBQzQE2G\nWVCbSkbRk/3hz0/g37/4NQzNxGVp9TudEq1X5XSjrqYadTW1aKhjHJVXNmU3c1YNRViMZVlvrFaT\nWnwlKjMjOcVsJu1SCBaQJ8WazvTUcokmVkXNKfd+FTzPc6WocaqJUdMJRRlVU3EFPEnmNcseTqd5\nr4r2nZssi1qlC9SBI/X79I8pSBNG4xIlMSAl3oJspoRj41N4tbsbLx44gtFZTvtsKPK+op7V4pwj\nrZHNY7QrcIbgAf+ozHgT7DanaF8tFjuMTE8g+s1WnbF/emwWYxMpU9GMNLlhk82gPxT9jp9ZgRpy\nX9JATPZmpbMwF01S7OrXtF5kySRNmAIl2RBoGiiZ9VSlc2MQOYXwSOR7RgOPPQGAjPgsFItZMfJL\nJCIyVaPEgSkV/MMiu5xL84CJlIV+FSaTC15PAG5nNaqqghLVQvCNrySDZn4OoTzn0HPsAIbHOlDM\nx2Azqcg5uuPWt7Rg6YqVaGhmlrAXDY1NqPbVYmxiCuPTU/IcThrDNdUjX87gr3/9G8JTCbzzsndj\n3cYNmAyHMRWOwcFoMX72fFZAXCL6ZqtNorye/MfjeOmZHUjPTKDW64LNSAA/JRR2SldObGEcDqN4\nxdAktqmhXjbf1gUt8nd+9TidcLm4YSm2F43xaKj205/9Gt/77k+QzgABYxVOqmnCMl8IzXYfau1u\ncCrFLPmI+ADkMJNQAIDPzniy1VgSqkc2HoXNZUN1qApmtxFmuwU0lTs0OYG7H/0L9oz04PST1uAT\nt9+IZcubULYU4aurQZqGtUbS9khDV+dc1+LyEtBlXbJGi0O9Yh6JV4rW9HMNUSwcpkHQO8QgNH76\nBPDe4UZNNhd/hwC5Lr2iFjccichzkvbO64ANJ/1x2ORzTySq39KyAF6vR153cmJSCnau2ZTm0SvA\n6nIjEYni8NEuhKNxuXZ7+gYlBnD33v3SlEnBZzRi48aNuOGGG3DllVfKHnzigwU9m1U+XnjhJfzo\nRz/Gk0/+Q/S3fFQ7nDh3/QY0MXpwYgJXXnoRTjt9s7CKGE/K+9pSNsl7jCQSWNC2CEd6OjExNSWR\npUNjo+jq78dMLA7O+uLpNImwmiSL9owGpMtskAna2pAnm4ZrmckggJZ2m89tISeu1vM1uhF2mxt+\nf63kXYtRH+8tNs4mBQbTb4AgAP1tuJbQsdtkKmF8oh/TM+MCGvj9NagL0UPAgCIbd9LoJetb9xlR\n70Bn3FUCE2oJ0Wn7BLwo66PXSVRiNHkPKCDgeOryXM2i1S6SKCGyAL9iBOgxsrqkRpNt6OdS9jqu\ngZr8kEMGrvUEB2LJWYTj48imVEoDp4VcRVuddmysq0Orx4NqpwM5oxFdsRj2dByRyd8pq1ej0W5H\nXAMADtLZXl7QjKC/Cbfd/Amcu+0CTE1MyTHcsLEddUuq2O1oEYeEjrWHHDI96UFjBZWBQhTYv7sL\n46OzyKQpncjAYjOgd+Ag/vTQr9A5cFCZqrJWKANup0vtxcW8SPEkYadshtcTxLVX3YBNK09HLlmG\n2+tFy8KFIv9M5+OoqbNj2coQ7B71fvh8anV/u+n/W0/mdRZAd1c3brnlVjzz7HPKC8JghcvixeUX\nvAeXbX8vqlwB5LMZGXCJUfL4AKrrXVi/dRU8DaTkc9/SAaH/fywA2ccq/KZUEpEafD/xxD/xic9+\nCt0dh4WIaTXSKLle8uCnaaCcVdG2lPm1BBfDULSgmOPn4NpbQK6UQywZBswlWJ2U5maQodlr0Qin\npxn1DUtgd1ZJrK5abColquq4StIQm1iw4aZkwyTmtrHZSeQzKRjLlHDKL2vrFc8t6ehJSeCanBrU\npAJyx6kfk8afQLoLNjNNU/kZUiK5k1pGMwlUDBvVXLIGNZvd8LoCsNv8MBnpNcDahACgCWabXTxV\nbASwJRVKeSAJq6GCI/IvE39NHjC3pmp0djVA0j4Xe2D5k0cyNoG+Y4eQzc1qn8uK6rpWBOqXASbG\nldN3qYJ5eCIAcIKmiUeW/bXRmEP3kT1IzPaSszv/drS/6VW3flZ0ZgFHHTazVRh9dosVpkJZWALV\nfr+6FgoG1AfrRdoYC0cQqK6RIU5PZxfc9CdathiJQho7D7yB7qkRRFAgxxofOO18fPMr/wlHaz2Q\npZy6CDD5bXxSHO2rmkMwB/xyYDi0mZtYaesjBMT9AAAgAElEQVSfYlwapP+jmexdX70bP33gl9ID\nbVm6Cjecfwk2ta+C0+9BcOlC6m8wMTWB3z/4W7z24st43zvfhUsvuHD+zMk1pp8TtWOw7jPYzOIF\nZLSq69MgjG5OEQuIxSPCAnOIca4Jj/73Q/jUV+/GRCQsPKTzWpbiY2tOwUn+gAy3h4tZ3PfyTvy1\ncy8mFQwDu82LbaefjZNP3iQDRA7bWC/R8Z/11ksvvYx9+/Zr0nij9MXclygVOOOMM7Bk6VLZh1hL\n8Lpkn80agzWFMHnINI0nxSiQkdelMkGFOHa99ibC4UnMhgcwMNSBdHpG6v21qzZi26lnobG+Ufkh\nzA52lGn6R+OBmtparFi+XKgfLFQoBZicGIPH7RYGAHNyw5FZpEhHNDJr3Ckohm7uQ9diNphq8yFa\nzoLJiGyGcVhsPmzStMkCSiSWcHmJTR4npTYpZP6+83l883s/woHOsbmLuK7Bjh9+7/NYuLAVH7jx\nLhzYN4Szty7CPV/9JE5eXwcYFXKi7coVf+cZ1RdWlTOZZXav2Y+uN0dw223/gef2jqFktoCO95ys\nWtiIZTLwOSx431WX4dOfvAGhoE8KOt2AT6cDS5MkNCd9ekwKqUJ1dYq/6sO0246NJA+6TJvouM+N\nkpTH+Ug5fdKvT+z155PLV0N7K6nsciFrdGX9Z+ZfuxJxV7+vo9fiPcD4Mhb12gai63j1woZIJos8\naikLOSOeeeYV3P3N+3HoaK8AB6IPp3OsCVhQBXzqQ1tx+bYlSIQnYfY0ILhsPVC/EGWTTa3D0khW\nAgDkJRnw3I5duOLqL8NcBq6+YJs4W/7thX2IFsoyHf/YrdfjthuvRrDWI80kAQC+ZzHrE9fkCpkD\nad8apV0/NrIRVSyceoHPnyOqrGI0VJGvgBGN3ilRVpya0+yGSK4BDhNj70q4/0e/wd3fuh8lGlkx\nGk94C2WJT6K+utrrFeSQjrosugPBarjdjP0i5dskTREj+Zx0Fmfra1Vxfpz3cFKeKeQk+1NF9im2\noJBmRcevlJBCnaU3BpvICud1OX/aZ1a05nkwaZ7hoMzmZKqt0cXEe0KYAOp4yjZr1lIPpEw3o5gr\n42B3L148cgSvHe3BeIxyGIs4y9J0h3Q7FtEWk00aOE4qaRzIqD9u3Izu4/XEaROjsKw2RrCZ5Gcs\nBBtIy6YswWgWt342ppyO6ZM7hf4rzbKSdaiYRUHsNesPpUAmUcQAC5s8UvXkXLL4V9c9jwt/qkBJ\njxwv1SAIFsTjjoJYC0r8m0gBKO0pIJOOopBPI52KSWoJ9VypWERlFsmrkqFigplSDqcTfqY2uKvE\nidhi8cBkdEkxooAp0sB1cIWgFcGMArq630T/4F4gF5bPLzINsxULlyxF+0knwWx3wOP3Y+PJm7Cg\nrgm9ff14+uWXpEmneU1zQwgWaxnHjvXi0P6jaGtbivZVa9B1bABT0TSWti1Ge2sTfG4nYlznB4cw\nNc3YqgS6Og9gx+OPoMpiwrmnbEF7WxtyqbRk1zMjemBoSDwtOPWKJpISJcQZBAtqSlMIErAcdNiB\nQG0NgrW1khXf2FwHf5UyiuWyv3PnC3jisWeAkgW1Jh9WVzViiSeAJrsPIacXLotNrriJeAypYg6z\npFPHwnDbLNi4ahVW1DeikErC6rSguq4KZpdRGACkwx+aGNcAgF6cvnYVbr7x/Vi2ohl2twXuGi+y\nIiF1wmCmj8Q8pbxSRqSvseq6IRCnvA1It5aIKjFLLciURJfLiAkpmS6y7/E6U/uC7u0iKQA0bNSa\nNZ5X3o9kFshaxCaVzDCyfWg0qqcPWCwK7DMrcJh7M9+HgLcmK7y+KkSiMbz6+h4xAXzu+ZeRiMfl\nPTidbpx33nn40Ic+JFTCExkAcwx2xhhFonh659O49zv3itEg3xenduZ8HsuDQTR7vbjs3HNw0faz\n0XOsAwaaaNWGsHD1WsT7+vG7X/9Gaocrr3kf4HVjpKsbr7/2OnLFktAox2ZmcGxoGGMzsxgkQ6FU\nRiSeQDSZQJHmoOUSyJnQW2PF4plnDGgl3BwQoHUY2r7PLwQWCbZ6xeCS3UKppGilKrqRALui1Mv3\nqMUVllgRk1NDmJji3MaIKl8AdcFmRfEtESxVe4M0BlrDWNkIVEZHVh5Prkmk4DucnMrkEInNShoI\nQf/5KfTbNX7ch8yiIeWEh8whvn+lk9WjBRXIcWJjIr4cvCENJUTjM0hlZxUYKSu0yrLe0NSAVdU1\ncHOCZDaiYLejIxrFvs4ueMwWidtstNnmGAAHw4oBwHW+2lOHmz98G8476yJMjk2iUEhj89Y1WLSm\nSRpGRWVWTDO9B9ONmeaBamDgQASH93Wiv39Y4mSrqmuRL6Xw4B9+gL0HXkZBe0UBdcuAi8dBmFsl\nuT+ElccVx2DFgoaluODUy3DeWRdLv8F0jKraIFK5OGZiw9h8yhq0LHJJE6mkf4p/8vaP48+Pfpx5\nD3NS+sADv8CXvvyfiCcSAmSXinmsWrgBH3j3bThj63ZYjEYMDQxI4e9wW5EqxLBszWI0L62R7VIw\n4ONazP/X+zn+XIscXjP/k1hb8XHi/K6AH/30x/j8Xf+GbDqh1V6Ah3IzmxtOu0sislORqLov7B7k\nsyW0BBpkbyLgOhOdlXz7oiGPkjGPDOMvYIDb14xQcBlc7iDycn8q8OnEOov/rySYeugt108jCpk0\nYjOTAkCLm6YO+HO/FtA1DbuzhGR6GmPjvcxomm/epH7kPexCKNACk8Em8rp0NgyzRdUylKUxjk95\nCMxrvrnv2qxkXlbDaa+Cycw4UzUY5ADCTMZPdbWAZiLREs+I46+QfwEA/ofTNSdLVG0sDGQ58kgV\nM5ieHMTg4EGqxOU9Ojw1qK1bAYcnKHWQaheo7dcAqkoWwnEAgDq2BN9KhTi6j+xGLj4qAICCEFTN\nWNGCaFilNhhVplCKU6PVj/xZB8N56bFULMJj98Fld0lcKE1gW5uahRGQisWxuHUBGmqq0D/Uh3Ah\ng77ZCXQM98nrLbd48asf/QhrzzsTSHIoIho5ThpRSidhdFppXCCu10XWPfIZVcKHPoTSDy1rnwMH\nO/HZL3wRL7+xGx6zDZdsOg1XbD8fZ5+5DZbGIOCzC6A12NmD7gMd8Fmd2HLypjk/ouObQvV/BADK\nwgAww8ipnNT5oq+VMpPgLwFoI1neZjN2/PUx3H7XlzAwNSk1ztZAEz698UxsrW+R1LGOyDTufelp\n7BjuRJTPb7Fiy5YzcfLJW6TeZeoGqfyU8NEY+JmdO/HSyy/JgJz7MusJGvxRkr969WqR5XMYwLWG\nezH7TfYUfC4y5gkKkN1LnyX6fVhtjOA04c19Hejs7IXdYcLg0AH0HSMrhMxVEy658AqsXb1Brkky\nEA1Hdz9Xrq6pkSKGxcX4+Lg4EHJKvXL5cmUCkstidGwEk5MTkpPd1NSIYIh8qjKmJiYQDs/KhIPu\nxjStIirB56CmMRBQVAdea0TqGC3C3yN4wPgyi8GAVCIl08OO7j7cfe/38ZcnX1VniItjAXjPVRtx\n/7c/j+hsHFdfeycOHBjDzR88B1//6ifgdhMpVNuSPI7P0KlgVfGkGpEzcHLjxSvPHsInP3U39nXF\nhBBmcSgnZ2qs+Tht0xp88vabcM5ZG8W9uzJOr7LB50nRKaMnuv2LyYro6BXtX4+L4sSeTSwfovOm\nTIGUa+219ck+GzQVQaWeQ9HX5wNT9ca/0lNAZwnoP6/rV+Wzac/Di4bPI5u0FgMnBozUwAjTQOmf\nhcokO4wRTrsPQ8NT+PZ3f4Rf/+4hZRajLcVr2iy49sJluPrCDairtmOouxcGQxVqFq2CY+U6FE1a\n7oeM1zRbI/n9ohQLj/zlH3jfB3+AVYv9+MonPoC9e/fh2796HlGtNr/lI+/H7Te9D00N1XMAAJsn\nbnYswHn8RaMmBbVydhaGAot0bTpXWYz/bwAAxRIoC3jFr3w+i8kMp9Uh04pvfu9n+MWv/iQazSp/\ntTTv1Epx/SBqyHsmT1CAUTWMDrFQr2lCda0fLk4t/V54nS40B+tEh0Xtt0T72W0w8zxrsgAbEXMx\nysxrNGoVMUeNup61rFNapbAS9Fo1K7ruXbTIwiYh60SjMWvMD4nZEoYBs2uLco3yuPE5uOjT1Z6n\nKidabyCfyqOnfwgdg0M4OjyG4ZkkphIZROlHQCMS0stsHiRTWY1ez6KViR9WhWKS+iasC0YnKWd5\nblZWB3PPGWNSBYfLK02NyczNUDFNSL3lpJHTMFXYa4WPUPVU86+m+pXzOMUEYbUnn1u0xnrxLIuF\nuJmLtzQbA6kVWFDTS6QAI+mIpQJy6aRm4pcQAICuqsxv5XRH8cl4rTDDlqCGB1VVtRK9R/o1vU/y\neV6fhBPMIKguxYVQ/AxCFTYbyOQwwERPhnwKPd170T+0G2VOCTT5jMHmwJJly9G++iTUBkOoriF1\nvx1um1OSJXYdOojRyQksWtCCxQta0NoYxPDQIP6x42kkklls3nQquLyYbW7ZxJc0heDzezGdymLP\ngcPYs3u/6DIZIfTGCzvQ7Pfhgq2n4NyTN8KpsX1oCkdwhQ7C0+EIYskkRqcnxS14ZnYa07MziCZj\niGfzak3VlmRefbp9W8sCmqkFkIylMDE8iVymgGqDC6uqG9HmqkWzw4eQwwun1SbXxXQ8jnS5iEgy\nKlNUh8mA9cuXo72pBYZ8VgEAZAC4jLA6rWJId3B8DN949GHsHe7GqetX4uaPvB/LlrVIAW73OpBn\nA251w2C2IUcQtmIKqGIvNZNDUp80oEkHefnvXD+op+U96SKLRUsS4bouGj6DUTZumvvwEdZSAHhP\ncYPnH661sVhcnIA5ySRLqHVBK4wuJ0rJlBj90YWfG3/rwlY0tbVJwx8ZG0NnZ6fsF5xYLVq8DO6a\nAKIzM5IC8Ns/PYJnnn1RyVzKEK+IG2/8iMQANlMicsIEiU0DG0VigJOTk3jttdfwi1/+QnKIx8cn\nVP58NofF1TVodLtwxfnn48xTN2FiYkjoug67C/XBOqSicRzt6JA64BSaDXpcONRxGG+++SZWLF+B\njZu2SOTYgcMdSNFE1enA0MioRHxR9scJTEdPJ7oH+mGwWZAp5hHJ60pgdSHNqbdP7Jl1B23e30ZS\n6NV6oyJGtWafTDqjWZhH/MpJqaxxFuqmDYjGpjA6Nix7ns9Tg1CwSWn+BVxXgKgCANTjfwYA5qt1\nYSYJKKkm/pQ30BeAJnwKBDjRz0B78rkv2hRMo70bKQ+gOap8PloyEtSY9wggWKUPY/j+OA2NpyIi\nAUA5IzCdhdcggPV19VhGc0mXE+V0SvaWksOBQ7Oz2N99DD6LBaeftFZSAGL5Ap7o7sLBSBRl7g9l\nM+wmN659z/XYfuaFMiksl3LYvHUtWtvr1I0vcYHzMnfVjsjKr3n8ADPDaXQfGkEsnBEGKmvMZSuX\nYnj8GB548HsYnWEUsAYAaYxKa9kgRTCvj2hCDX5Ua0X+mBsbl56Gb999P2LxDCanw2hsWYBcMYPx\n6X5s3LIKbcvq4KQNlFjEFDUp14nHvfL//wcWgMiEjNi7503ceutteG3367Bwb2ITDjsu3f4+fPj9\nN6O5rglHDh+RfWXR4oUYnRqCu9qBlScvR02IOvCK8fJxb6PieL3F25u7/mSgpMUc5svIZBhly+SR\nKL7y1a/gJz/7iZjtztU8cjwNcNm8QLaEVlcdHDYnOmcH4XVVo61xkQDTA1MjCCdmRR5DCV9aEi1I\nw69BY9NyeLyNMBjtAtpVNsn6+iI1sjAxlWSPwzfpAY0GpGkGPjMJE4E11mfHcXtY4xZhsecxGx5C\nNDamNPP6tSM1rBUOWz0CNQqki8fDSGVmxDBUZENllXohIAD3WZ3kIesDfTY88LoDcDpqUCq7RLoo\n0J7JIFJj+gGowRBlrv86RHu7q0VVFdp6pQ27FABAQ0Req0WYDAX09x3B1CSt4uLclFETWo66hsUy\nHBDWoUFLHzr+5Y9bv1m7s/ywmMtIRcdx7OhelHMzoG9K5Zit8v1qMI2KVq/4Kf0Q8SsHP9wVFTtG\neRnw77RydNCrgHWLyYzWpiZJEuH5W7pkCYamJvDcvjckWYc8s9uvuwFf+NIXZOhXjidVc+12Kv8O\nSaEqqul/ZaCLxkDVVlmRhVDiWC6Z8PCjf8e/f/VrGBybxKpgMy7ddjY+cOllWNC+HGipockBCtEE\n0tE4jLkiXA4XikmyTLRLQE3F5g6HGLlTeqsnQ8mkTTFZBAQQ9Lmo/m614cUnd+C2O7+AzpERuW2X\nWty4feM2XLBiDTxWO3YN9eGel57Cq5ExYbq5q6px5buuQUN9izD8WBNQ089h8isvv4ydz+yU5p+1\nBOt57lk0+9u69RRhCSRTKRkWU6qqZIhqiE5jUfZrTMkQ6aHTI6wcgqD9A2M4dLgTM+GoMGi6ut/A\nNP2kjAYEaoO4+t3XiP4/nSIzqQqG3//03vIFF1wAf309RYB4+fnncejQIRAUuOLyy2GyWUVXf/Dg\nfvT0dMPhsGHFimUCAFBTMTI8hKmpSaH9s8BgccNmkzFE1EHS7Ix0Bza5RD1YDHHxIr0xUF0FA6ed\n6TzGp2P46S9/hx//8veI5RQwyHEoDSN/ct+duPyi0/Hm6/tw110/RVdXP26/7Vrc9rFrWV7JB50/\nq5V3jDJyU9c5KcLUAtlRLDrxh988ia987cfoHaO2ySYFOR3l+XDZLPjANVfgjttuQF3Ii1KRjYuK\n1dObfB0E4Pf5eXUDPT2PVTX08+YsKsdXxUjxYpAMdhOjuBQ4wufW5QMsDnW2Ab9fSe/XX5fPo/Lo\n1YWjGnqFWlb+vJi2aM8hdHBNZqAXMby9SR9V4IK6ExUjQDl7ZrJpoWV63VUolSz42S9+j298+z7M\nRuhUKV6vOG9LEHfecAq2bmwS0KYwHUNiqghbdQscazeizGOu67eLKlZF3ZREA0r47a/+iI/e8Ru8\n55LV+PxH3o2HHv4bvvrL3YL9s5G46fr34uO3XouFLSFZ8JQplHrPZI6w8RdZAPPJjcyX59RYxbzp\nfgf6xqTHTM1//reWAAgTQEwZ6d6tZBQWoxlOix2TU1H81/cfxI9/8mvZANcvXydN/OjQsNCrWcz3\n9vUiS827xyNNQTrP/FVtfdFlRwYjAlXV8rs0jvO6XQhW++H3eFBbWy3/X1Ptg10zBqQ0gPpt3hzc\nONUEXBk3irEfwRwa7WnIu+RqG5VhIR88PnqsCM+1MvlSIBSLGTbo/Mx6HjdfKpdWWetFjSmQT9Pc\nDAinMhiPJtHRP44X9u7H4PSsREQuXrQSLS1tmJyOiFs2i9bGhia5ztmEz4ZnROecSMZFH8w4lGiM\nFGbG/tlEr+euqhVzFMoBTLxvbHaZ6FEbS8mAAgNUTKUw7YQDp5lAngAACI1XmzLoxYiuzWMByOkf\nsR51WKnVp9cCrxs2/3lMjgzKBCWRiIuBaSlLAFOdSbPNIdngNTUhmdAT1HC5fQJkuL1+KdaLuQLS\nGQKIxNFMIu0wWIwwWxVdksY8c4VBuYhEZAbHeg9gdPQQcvmYgCS8d3iP1obqsO2ss3Hyps0yKWA+\neY2vCnaHC4NT0zjYcUQ2itO2bhJkvrPrCJ579RX0Dw5j29YzsW7tRokbZAPEa8vj88DitGNkbBwH\nD3RgZPAY+nsO4PDul7EkFMQFm7fgtOUrJfeWBYOQITWQRf7foEoNugOzcUqkUpgkEBCeFRfpcCSK\n6ekwZsIR0YRnWZhpC7XdakUpz8QWE/wGB1b46rHIVYMFripU0wzSSCZKGSnuD6UioumYUKgtpeIc\nAEDyuNVuRnXID4vHBIvDgmQ6qwCAv/4Fe0Z7ccq6dtxx+0ewfFkrDGZOonktG2C2u2Gy0V/ieMBI\nZ2dJVGVWeUDomn+CR7I+0uuFgColMjbbnCxFogsTvD6o+3cJ84fXHjd5XvMEBigTII2f63AymRKt\nPgtuskUkBtDpEtfhY729GBsbB82XGhobRfbBQmR6dEyYebzfyaBpblkId7AesalJPPK3v+O/H35C\nPAAIvpPaz/SASy6+BB/84AdF3se94sSHTsoi5Z8uxH/80x/x9I6n0T8wIJ/fZ7HijFWrEaIWMpXA\nyiWLcNHF54rXxLGePoyPjGHDmrXYesopMlXc8+ZeTIZnxMOguakJja2tyEaiGOgfxOjYqFy/7qVL\nkRkYwDM7n0UgWIeaYABP/PNJjEyMYfHK5QjHY3j+lV3IlSyIJFKYjoQRSSWgw4rzUhTdNVsvGfXS\nV037CRqScaQkG0pfr6JblWketa9ut0PiSAliZbJJeN21CIUalJeQpL+ofVVnVr3VFHBu4qdP0rQ0\nGTb93EtFembiGsyEj4SwKPMFHQQ4oco/7gTpn4fsKYswCTg2MxmtmleAQ30e3YxYYxgSAEpnUkhl\nOItik8XJfwmEpFYEg9hQ14CQww5DkVMusp2AnNWCA9PTONDTh2q7HdvWrkOQ7uL5HJ7o6sbhcBi8\nuk0mB7yOKgEAOG13OzzIZlJY0d6GlhXVcwBAZRMyd5Y0mhbN3F96dg8S4RIsRof4StH1v3VxIx5/\n8iH87k8/Q7qQkGZDmhGTRRr/gNsn+3ssncLY5Lg0boqkzYcd6xduxb9/9itwOKswORVGNT02bCZk\ni3EYrXls2LQSdY1OTQf8/w8A0Nlx3EcSsRTuuedbYgao0kFYQ5qxuGkNrrnqepy+aRssBoukfQSC\ntbDYzegd6sHK9Suw8qQ61uzKl0AtrdpDZrba3/9nGYL8wAkAACf/vGb7+/rx8Y/fgR1P/1MGLUrA\nouoPnheu3xZYsK6uHQ6rE/0z4zBYLLCb7VJLjUTGYbLw3imiWM6L47vZ7IG/uhU1NQthtfmQp8Ed\nG6iKJllnkfKt8fV4n1UCAOytEgREZyZhZd1b4GBDUe754LrqclmQyU1jcrqfSuoKvwei/ATlLAhU\nL4XF5EY2yyqRPhcRpLIxWIxKAqFHX5bYG1SyTstkFrrgclCOHITZFJDEAvFZoukzqfDC3CQIwBQk\nNquV7cXb3av6GiTV5ZwMU9pnyg3KbKKVXj+XTeLYsYNIJ5TEweppxvLl61CmCbYWa6ye5fjXqwRw\nZdjButRcQmSyHwNH9wJIMOBY/ZbGoNK+zJ1//oOywdQzRtQARa46kV5RhlqCgYbKEjOo0W+ECcmr\nW4E7FODyOHusTtQFgpicmcZUJiF7PJms61rb8MPv3os17e0op9LifG+lFJYgAC0cyK7Shjf6ET4e\noCboyuuDUiIL4qkc/uMb38IvH/ydXPeb2lbhPaefhSve9U5UrV/OyDB1E+XySsufzSMXTbDFUMIC\nASS1k6noEShQp29RiVeqjtTtmjQ2AM8/rx+bHa88+zzuuPOL2D/QLxKqIIBrl6zHe7ZsQ8jtw8s9\nnfj680/iQC4i91iwvglXXP5e1NaGpLejLJ6fr6uzCzufeQa9vT1K/qtJkKn1J+2fIAFBvFSag3F6\nNNgFBOAjTVBAahDKz61SV1ut9IpxIBJJYf/BI+js7tUSmhI4cnQXskyUMpmwpn0Nzt9+ARxWh4B+\nfgIAf3nwB2VqBP1VVdKojAwPS2SZ2+WS/GGvxy3TTNkcMynYbMw8t4nmgY1DOpkUJ2UuyiyweSOR\noiB0WzaV8uH0BY0GehlpPJxOO6zMuM8wTsSKf+7chS98+RvoH4soWggvIidw6w3n4AufuQFVTgN6\nO3rxywcew9HOY/jUZ2/G1rM2AqV4BUJ4IgNA24JkQqAmcAanH1ODEXzzmz/Hg7/ZiWiGLEoHSoai\nMBcIGG7esAYfvfk6bD/nFDhsNAtSxnz6NF6n51fS9E/U5bPZUvmlahHUkw/06eq8gZma/svNODeV\nVVNc/Xs6HVUWhEqGg75dHOdMrTU8mn8A32Oln4BMSTUpgMgLhFqq8pJ1HwBZO3RX+JLaUBx2J+wO\nH3Y+twtf/PI3sPvNo7KYEexf02DER69chmuu3AxTnQNIZDDVFYXFVQffipNgqGuSBk0lytHUxAiT\nylGULO+H//wwvvfdX+GW69+Fi087Gffe/3P87PEeRMtG0eteedl5+Mzt12Pt6qXiTqofB6Fxa4VW\n5QLCm4MPHvMTjxcBAL0IzjLvnhQfFod6fGBFDKJQ80WeocksGONltiEey+F73/8VfvjDX0rxfvKq\nDXAaLTh2tAfL2hajsb4Bw2NjMqGoqQtiemYaYfpgmAyY5PSY+nCZj5fgkIa2JJR/p5nH2SqMGZ/X\nA7+P02SPYgY4XWipq4PXYUfA75cGiQwaTnfIOOB9RmaAgZRmLlpawUptO7Fc5fyr/Cm4uEgCgFDi\n+W8aMCVZwSpOKJlMCphBAz1Ba3lPcgqWV6wGRvBF0kW8fqQPT738GjoGB5GGARtXn4rly9cgGk8j\nm81jdGQUmVQaHo9HdM0ECfk8bIhYaJC2PjY+hmgshmQmreISUymlFhERv1qwyQYQY0UHjdacsjB6\nvH7Y7R4pDlkkCtChNahqI5EzqGHaynuAoIH4Echkl7+lsRKKOWQzCeSzNG9LIhKdQTIRE70imxoN\nuhF2ArW59HbweaoRCjWJNEKZENLdneuJovgTseWaSHM1aUL4PstFON1O2J2MJi0hHYsgnYggPDMi\nX0mPTMZmYDHl4PU64fH5MDkzi97+AXhranHeRRfhzO3bRWt94OAhuO0OMXQh66L32AC6e3oQDNSi\noT6AbCGL6XgUhw4dRltTK6649F3IpPLo7h9FrGhGXagW65c1oJCOYnxiEgf378fzT+/AgTd2YePy\nNjHQ27ioDTYWmWx+NS0K1zV+TlImZR/V/ESUTl4xh6LxKKZnZjE6Nobp2VlMTs0gEo9hinTNYg7J\nYk6kHgQRa41eLPOEsNQfQsjskhhAZs/T0I8U00y5gGhKAQB8xTVtbThp4SJYyyU4XXZ4a9xwVNlg\ntJmQyxdxmB4AjzyE1wY7sailBZde8ouuBO4AACAASURBVA4sXdwqPjH0sq4JBbBg0RLUBGtRNtAR\nXa0JukGtvtYTLNO4V0Lh5aSe95C+fggAp3lv6OuPgGgiMWHDp6RdOkAprv8ELOmtMudlYRBQTl9z\nKwFaYe4wQ5v3LP0kNINCfuU0gcV+iZF0MAqI9spru/H4k8+KBwABAPoJEIRat249brzxRlx88cUS\n11v54D2q9ie+jwJ2Pr0TP/zhD/Hcc8/Jvcl7yG+1Y8PCRag2GRFw2LFp3Wpc/q6LMDjYj4cfegTT\nE1PYtvVUXHLRRZiZmcWTT/1TJrTNLS047ZRTYLPbcaTrKAYGB1BdVYX6UAjVXp/IXyanpxQzj1GC\neRb/9D9oke+PjE/B669DR2cP3jywH54qP8LxOI72dGNselJkA9PxGOJk7GiwHK/TlMjwdDKgCJK0\npof7LMtZMuDYKBjkvHId4B7PyTzplC4nXf+5tii2wLw+VTUoAl3rjb62d4tMSWR9ld4AWgmvAbVk\nWiiJHz0i0ojHI1pCgA6LaVXp3AmqbATVN3WPF71S5TqjDwu4f+tpN0qsQEaWSSbg9NSgcVV7bR3a\n6+qwwOWGKaeiFXlrk4GUNZv/BQCos1oQzmTx964edETCUHa3Zrht1bjqimtw2YVXiQ/J9OQkfH4H\nVq1biMBC+v4oX3e9gVHNp9rz+RjpjuHwvm6EpzNgz2x3WODx23Coczfu/+E3MTjRpdZ9mKVZtck+\nWZZYMrJHZ6MRTM1MIy0xi2rEZypbsaB6Ga67+iacvPF05AtGWWMCoQD8tS4MjfbA5TNj09Y1cLjJ\nJpM76rj74V//560bcFWEKBLji8/vwifu+AQOHN7H21Wl1Rhd2LLhdHzw6htw8kmbMDo4hvBsBLXB\nAMwOM4rGPFasXYy6RVZRj8kQX+Yv87Wydsa1Tq6iC62sA7VBiFyTxbJEDXN9YqTYLTffjP7+Y+JJ\nkysX4bUY4Xc4xcgvkmd9WoYdNngcPknNoFzxWH8vMmUyvQrikM9rgw7xJqMNXn89amra4HTXSaMq\nUj0a5uirZIX0Va7Vt2AAkCodnZ1GYnZasuBVDLGSPPHatVipyc9hfIpNDD0zONzT7gvx1jDD6aiF\n38spZhaJxCycTsoG2YfkYDKQ+WORmjpXoiyDYH5anoIyS9G1g/GZHrhdQbhdTUJ6F5Cda0ipJCAt\n11cxVDSqqOz/20NPFpr3I2PNy3XExH0tl4PL6cDs7Aj6BvahkIrAYHEgFGqGxxOAx8NIRS1/SBrz\nSlbR8dcBz3UuE8P0eDfGhzpUzCAHFLwf+KdkFHkmX1c1+Oo9qaQG3V5Uk09WXupzp1VJFHUlxdzX\nE65KAWS1t8afUWJQ4N9uuAmfvu1jAi7l00kBXMUEy8Y/ZpQKTAGYP7qVAIC8SwGXVOwj0+DeOHAY\nn/78Xdi9/yD8RjvOXr4GH77uOpxx2fkwBv0AYy71Gpg6oHRRksYU00SBF3O3GAEoiwkGym/FSJyS\nOsWm0TS36rl4zDxe7HnuRdz6yc/hSH8/6FhDr4PtNc246cJ3YnGgDg89+wx+fWQ/OshGQRkb1m/B\nRZdeKdHE3Hc52O3p6cGe3Xuwe8+eORNumuXTNH/btm1YtWqVDBgY8cfEJOlNJLVLRcanUjR4pMEy\n6xXCLGXxVEonyTY34Y09+6TestotmJgcwJGOV4R/yaSpC99xIdauXis1vdftFYaiYaz7zfIszaGm\npuRJqbMPBALS7HIaMD42Kvr/lStXwCUpAAVhAoyNj4qBVFODMjTgDczJBCcgPr9PqIdsIOj0z+fg\nplddUyW/ww/IyZ9MrItlDI/O4O5v/xiPP/XKPCnOAGzZ3IDvfOMOnLJ5KZBLIDk+jX27u3C4owvv\nfM9lCNJggguEHgkj6+cJCJ0+/RdneQsMZjf2vHoIn/23e/HanjGZMBZhQa6YldzLQJUH1193NW6+\n4RoEatzI5VKiW9Kp9AQv9KZTdHZa065eWjXuupZcpsZmmkIVpQhk888LXAdLJEebLuJsU7TGSxpv\nTQ6gT+yVO7iaaMsipzne66+tT/j52jq9n+9HZxXw7zqrQKiMGpuBXyVmRXu+ShmCvhUJPVSMsMge\ncKBvaArf/M738de/PYVivgwnjGhxlXDNdi9uufE8+JcGZSVN9caQSBjhXbQS9sYFgIfaZ5rLcBE3\nycJkKBlkMvTU43/Ds/98Ah++5j1oqPLi/gf+Gw/u6EUfhxcATt+yFp//5A04+4wtyGrSCRXtpopr\nnd6vLx55raDmDaSfF5ngaGaB+rREFXOKFkQARP99BZCoJZIbFI+dHONcXhgAY6Oz+O59v8Rvfvtn\nkUe0hJpR6/BiYahJzLCOdBwRM5kFbW0YGh+VY7egsVEiRw4P9sr0wmFW3gN00qa5Dk0wORFKlzMy\nX1ap8IDDRp8DiF8AG//62lr43S4E/FUIBqoRCNVKCgNNBi0mat6FIC+SBJo9SUGqFQn8DEQTdZaI\naP7NSiqhI/BcA/hzBACYBqJcuNntKaYIF2Kz2S4GOgQAntt7EDtf3Y2jwyOy9axZvBHr1m5GJJZC\nNpNXk8zRMbnm+YdrSSgUkvWFawGBATYa3HCJvDNvllRypomQQZRMx4U1xNxabsQCxpTZsJHZYJVY\nQLvTI5p7u9MhfyQWhSCjy4NCkfcoC36af5qFjs9zRnqw0VhCJkdzuSlEZiaQioeRSce1mCw2uBYp\nNp0uN9zuKtgYS+iuhsXiFIdu+i+4nVXSNNCchU7G4o2hey6YlZ+COHVLHJFIyeDxOiTPeGJ8UCYh\nI0M9yGTCyKXjgq1zk3LazQgEaiQbfmJ6Bj39fZKIwOb/3AsvFACgs6tLcnvfsf1CBKvrEYkm8PKu\nXThy5AgWNDdg7bqTUF0fwEsvvoTuI0fxketvEGPK/Yd7MBovijxr66oW5JKzGBubEADgmX/+E137\n38TWVctx5vqT0N7QKE23bI7CadbiIUmpJoDLSBr5jBqX1WBCgtO5iXFp4mgMS+kADdnY0CdzGcRz\naUQ40U9nkEhnEDD6sNQTxBICABYXvBYCTwaJ3MyXy8gy2YCRVMk4HCYjVixowUkL20CRgMfrhLfa\nDWe1TSIz6ZH25tAQvvaXP+H18T7OShVozQhOp01MAx0eN+obm6QxMFnKsi811NejurpamGwEAujc\nz3NPIE4ZumosGiZ1aJ+dawNBIu5xBFl5v/D+4jpCPxxq9blWkxWn33fRaFTALzZtXo8XNbUB2TPi\n8YSk7nByy/VpYetCodGzGKHvQjgclrU/GAxKQoC4FhPsOHgEg4MjUtSHo0m88Ooe/O2xf2BmalaA\nQXoAXHbZZZICwALjrRgAevlFM6Fnn31WPACef+F5kTawUbYUiljg9qDF58f7r3gnTtu8Hm6v0t52\ndXaj2leFxmAdJsbGlL+B2YxUXumz49EY4ok4gnVBtK9ul2O3+/XX0N/Ti5XLV2DrqafgcEcHXtv1\nOs4640wsWrIE3R0d4jK9evVauJsWYuBQBw4d6cCqNWskhmr/oUOwuxzIlUt4s+MQ9h/pgNVhR6ZY\nRM/goLiYFwyMvta8UDQiq5T+Rq5dBhRI65fmT0WVcY/nAIAPUux5z/Kr7h3A889zRjacvlcog8Hj\nDUYVMKAq2nlzWsXUqnwIAFvKI5niOhfTagZlWDv/0Irf+R5L+6fjm9JKk+DK11DwpzL88xptaPR4\n0B4MoMnlgp/yKlJPDWQnvD0AMJvOCABwJBrRAAAyl7w467Tz8Z53XYdFLUsxM0ln8zQ2n7YazUtr\n1RBHzPvU5xHzQd7VJSA6UUDH/h6MDU1jsH9c6KitrU0oGdJ49B+/x1+f/KPMFnMoMsBNfpOO5crM\nkRp31o1FyaJPFdNKaiYAgAkN3kW4cPuVuOKK98PjrcXY2CRcHieWty/E6OQoBse6ce75p8JXTYaI\nrpR+u/bu7QEA9huRmTh+/IMf45577kayGBYpQL5sRpUniGuuuB4Xb38nvA4/BvuHZN+pb2rATHQG\n/joX1m1dAtdxvpw855UGkbqh5HyXVDnU0D0AlA8E16O0yE4ffPBX+PKXvySJVUyooUiizuPB8pYm\nYdfsH1T7tdSGoBeRX/a0TIFGfym5Lqw2i+iq2Rz7vCH4qhgD2wIYXRSTKB8Fk2amWFEH60fzrQAA\niYyOzCITC0ssGQ0e5d6QQUUBFmsJqcwswpERlEqpitqejRsbIhdqqxthNrsQng2jWEzD4WDEYRJZ\nsmphh99bI0yBWHwWhXIU5bIauKgLUUFRwn+weFHtb4HNVkXrX2RpYEOWsIlDEYsacjqdSmLxf3qo\nHkSlAai0ACkzxbBaGRnzXBmNefT3H8DsaI80aZTn1VQ3oql5CXIFTrPNKJt0gYQGRVdQ2PmsEiGb\njWFs6DCmx/k8NCnm2SnLqdEwKhho4KyZq6rBiJria25X8wyAEy93XX4sC1qFmqICAJArVjtEMmUX\nTywFAKwKNeDXP/0pFm/YgNLMtPKKIEpGd2EBtwtvAwCQLaxd2Hy3BhOSuSL+/Ojj+MJXvo5IOIoW\nux/vvvhSXPXuK9C+4SRGHKhrko07kbgCXYzzKGSyMshhXcH3p2NKJZtZmA4y4NA1WzTilsGGGEmp\na8bmwIFdr+O2T3we++iuD4JnwAqzE9dtvwCnrFiNR595Fr/atwtTin+Cc89+B84+/0KYLDapc0dG\nRrHjqadwtPMoopGoDI2y+TQWLViMc7dvx5LFi2X/1PchsgIlqUpY3cr3THkFKONMxSq3CABQzJsx\nORnBG3veBAebNFI9eOgNzEx2yVocrG3GZRdfhsWLFqGYL8BhtcPn9cGQDw+VDxw4gN5jx8Tsb8uW\nLfA3NEgKwPNPPy0pAK2tC3Da6afC5XaK23Vn11EMDQ1KOkBLcxOCQVXEdHd3C4uAaAd9Aojms3Af\nHxubAwCUs3FRXA+ZXTwxE8Ef/vsx/PzBRxBPsUGmHrYAvxf4989/BB+9+Z0wG2JAMox0eBaJaAyJ\nVAahBQvhDAVVTFalTuc4AEBhPuoPMx4t4Jpy73d/jm/+118RzQNOuxPZfBk50SLnce5ZW3HHbTfi\nnG3ULEble8qJXSHtevNUCQLokXv8ntI506Gdmm+lRWQhxO9TM8qimcUgFwE2CqSA8GoUsIBZ1Jpp\nlE5f52vqAIA8B2nu/0cAQDV6ivKoAwBqRdLyNrVM9H9hF3AD1yyY2cQWSwbkSlbRvn/vvp8hHkuJ\nBGCpD3j3GW589JaLULW0Qfav3GAM02MJWP0NqF24FKgLiSt6ibpMYlBlE4zFMnIz03jl6X9gZvAo\nzjptkyB+v3n0RTzwRDcODmVkkVq2uAn/8blbcPml588BAPp2qL9nXRrBj1XJAJB1q4KWqZsw6uv5\niTMWnY4jBm0a/Udi4cikyOVho86mfwzfve8X+NNDj0mzXu8KoLkqiJOWtCMTS0lR7PB54a2qwsDo\niGjpV7S2SQHfNzkGh9OBZc0L5S3sPXwQDg83NT/Gpsexb/AwrCaboHOJeAw5LSPXJlpPZaLIY0TP\nAKfDLveYz+NGQ6gWNVU+NNUH4bJb4KEGmQ0sp5CajlXJQdTkX88bJ5uH16AOHvFaIWBFlNoqzAAl\nMeAmKEx6ouuwIJ0tYzqRxqsdHXjq5V04NjEJIxxY374F60/ajHzegFCITqOQDHPKfyYmxzE5OSb6\n+Wg0LMYpyonbjIaGRmmGbE63mP81NDQJVTqXyUqSCLPtR0eHxLV4emYCkcgMwpyelxWNjPR7Nk6k\n2Ht8Xrm2yRCwOb0wmuwytSdzQQcwJHMXWQyPHkFkdgS5qBblJOQ4E2wONxxuD3w0BaJxodEKj7dG\nEiBIIaRpKPV6nLzzWiGoKTGTnCSIdly5CJfFbNOIIpM9cilwASqX0piaGMDQYDfi0SnZ/I0WI0qU\nb5howcPpd0rYA1wr6IHAtdLEaK5TTsW577gA6VwOCdLNi0WcvH4zArUNGJ2YwQuvvY6+nl6sWbIE\n55x5BqoCPjz2xGN49oUXcN45Z+O8M08TYCaeNcDtrxbw6NChgxgZGhVq5qvP78Bw52GcvX4tztyw\nFotCtaIbVgwA0j3VuiHTkmIRM+HZOUPAZJZmfQnMxuISv0XjwEQiIceHMY/MeufnkLxnofZxolpC\nrcGLpb4gFhMAsLols5fVpUyFS0C2ggFAj4zFDQ1Y1bwATpMBNTX+fwEA9vT34xuP/AV7J4ZBm0JZ\nEzQuiLymdvOzDiCzjlnwZMDRm4D7Ext5glSMSAwFKMlxiSSO1yPBcTaDBJlIHU/EI0gkFFLJhp5A\nFh/U0xMAIKBG0x96BZDxQm+cRJxgVhFer08AHl7/BAZIj/8XAMBixtDgoAD0/P36uno0NDbATEZP\nKoOOQ0cxNDwmRUI4nsILL+/G3/+xA1OTU3LfcpJ94w034gMf+ICYC73dIxKOiRb74Ucexj//+U8c\nPdqpJFUlYGVdPVY0NOCK87dj8YIGvPDi08JUOu3U07Fo1RpMdXbj0Uf+ipNOWoONp58ujtHjExM4\ncqhDagCmRKxa3S5+AwMDfcIGIJhBliH3RfohLG5bIgAM85DJCuJUhEUKPwNBQT7IDuI6uqC1FXan\nHX2D/RgdH4fd5cHQ6Dj2dxyFy+cXL4iunl4MDo8iUyhKpCQ9SsJ5TjcVa4JflXmp7o+iwE7FkOL6\nzytFTV9UvJ7ai8kKo0aYTYuZMbek91YAyJUmVropoD6ZnwMHxAi5hHyBbKNpqQ/0xJr5c/RWAMC/\nNqQKAFBmlZUPxf0C6uwutHqqsajKj3q3FU7ieGSOEMzi0MtUflsGQCUAkJSagEfFhfal63D9NTfj\n5LVbEJmNIpEMY82GxVixpgUGJ+eQ5LhpDaIEkZkwMRhB16F+TI1GkY7nMT01I1rWBQub8eaBXfjV\n776PWGZaJmh0EpHm1OjAwvpGeH1+HO7vEWrsyrYlUj8d6etEnvM4Lk8lIOBoxlmnXYxLL3kfWluX\niQyJRzFYH0AsOYNYehpnbd8EL5tu4//dBPC4A6xhPfk48NTfd+BLX7oLbx59Te4ZybQoWbCseR3e\nc+m1OPv0c2E3OzE5MSPHr7a+FoncLFpW1GLRsgYyjCtWJUXS12boc23aXM1SUeOSoq3YpKrF5FQ8\nm8nhM5/9HH77+9/C7rIiE49JPvuyUD0W1voRzyaxe3gIYe5JErUlFajUyOJZJM2Weg/5fBpmkxuB\nYBtqAgtgNLtRZIRemW71vJBUCs/x1G0lq1SUa16F8x4AmVQK6UQU+WQMJjZ/JSWzoqs/B3kWW1qo\n/+k092Nq+PWBHt+kDQ5bCMFAk0h1ZmamxfOCe1g6ExMAwGJ0oro6JB8qHJ1G2ZiRVAHKeCWOV4/m\nFu2XCS5PDTzuEKzWahSLrG1YF/C2JxDLf+d+T1aBYom+FQP3rddUXQJQWWmSmFkW/w5Gi9oJsGRi\n6O7ch3RsWJpWh6MKLQuWwe4MoEywUksrUs+ihl7y0I4LB4zZdBhDA/sQZ4JZOQO3yYQ6hxNVThfi\niQxmUglE1V2iLT/zoIQGX6pn14fjlSQDSXDQOB7yfe18HE9E0D6kMvOT817BAvjKpz6N2z76UcZo\nqetakCH1GUqUArwFA0BuLRp6y2WovMh4vRlsNkyFo7j7O/+Fn/z81/JUqxYsxnvPOh/XXv1u1Cxr\nAey8LgsKAOAL8DmyORTTOek5JGZQx2nFiJCggTb5Z9evAQDKD6AMA//dYsHAkW7cctun8OK+vchq\n9ork021ta8PmJe3Yf7QLO/o7keTYwWTGRRdfim1nbhcDbPbFr7/xOnbt2oWRkREBRi1Gq+yBW7du\nxebNTNUxCWuPX/WeT6IvxXBYsbv0pr9YKGkSRQO8nmpk00a8umsPenqPSQJICVkcPPwaUrERAVra\nFrbjsksuR1trC7LptEg7bAS6e/a/XOaboxkRC+hQMAi3xyMX2tjoKJKJuEz+60IhcYS3263IZBnh\nl4TDyQs5JxsikQhuzNzI2JSwkSLar+QN8y4MLOr43PJhcwX8+g9/wY8f+C2GhuPiEinRifkC3nXR\nOnz33jvRvMgLJMclmi81NozYxKhQgata2gC3V05MpUZHLha5QTR8iyKrMpsnK1By4KWdr+Nzd34D\nh7rjKIgbs0202rwerFYDPvTBd+Pjt92IBQ0Bof/mSzlktAaJGy2bdC4ClaZ6ynBIuT3LRExLAuD3\nOSHQTep0nwD5/FyIyAoiuqNt3KLjlrzzeQmAuE9r31Mf61/vvH+huc+5uqt8YD5k6l9QdH6Z0mh6\nf25GvLj4ULFwFRpRAxuPnNx8FrlBLAIAPPLoU7jrP76B8GxcULD2oBFXn1WFG298Bzy8AYk29o5h\ncmAGJrMftU1tsDY1AR6PZnvLD046YAnTRzvQvf81eM1ptC0ISe79U68dw7d+ux/7jiUEfW5pCuKr\nX7gdV1z2jrnpqk7d0zPreQxFq0+gRtPqnvg9iYHSpjz6sZTjIx4M8xOauWNMJgCzQ+W8qLgdM8wY\n6B8XAOCPDz0mjZCfzuUuPwJuP6wwy0QsR5p7IolsOq/id7x+meJNRWbh9/mxuLlVmgRqW1e2r5RI\nzd6hPuztPiw5qYGaWgwwOmt6Gk6XQ0ADNlvRLAuaMuwWOzJ5ItuqwHKYrXA6aDRpQOD/Y+09wOQ8\ny3Phe3rvMzvbd7XaVZdlSZYsWbIty5Y7xjghAWNKCD8n/PDnQAhwDpArEMIfCJg4IYBJCIEQbAjN\ncQPcrWLL6r1t77PTdnov57qfdz5pbROSc50zvnRJWo+mfOV9n+d+7hLwoberC+GAFz1tfgQ8brkn\nSRmiVIDHR6jLjbrIcKhT1SL1Lk/8W1GAjG3jhcrrmJq/TLGKZLqAqakFjM7O4+zMBIZnZ5GrsNgz\nYdOqbdi57SbJeicA0N83ALvdgampScxHZhGNzWNycgyJeEw+CxtjsoYYYyIpAHoD/IEQwu2dYlJC\n8MTnccPn94ibtt1uFhDh3PnTSKQSiCaTmFuYl8aCgIDIOoRy29qTjBYxe1MmnARW3CJn4fuREp7N\nLkCvq6BWYmGSF1yclP0Kp/oEkpkyIS7HSuNvtjhgMFmFEUCAgfnBZNFY6U5Nx3QadTHHFQ2Ju6Hu\nneermM9hMbEgVP9cJop8Lo5msxWGpmugu6cLmzdfg7ZQJ1479BpOnTiqgEE2IDRvZCGl02PP7Xfi\nxt27hUpAx2J6rbBxY9FDVkaxWkOpUEKjWERPJymbXhw/eRxHT52E1+PGA793H5b19kvhQzbO+PQM\nXt63HzNT0+J5cPbYq4hPDOP2rVsEAOhrD8maTKMc6g1zpZJIW6bn5uS9owsRLERjWExnpOkiCJBr\nUKlNHaBZUhYU5VAdR+UsogpvovGcQQR1LgwRAPC1I2x2wGkwK6O9ckXOAY36UgVO2rPwOGxY1taG\nNT29cBmNaG8LwOtzwu6zS051PLGIo9OT+OpT/45z2RiaBh4nH1ApI5tOSewQJRg6ox65Qh6lSl0c\nw7WhAD8XyxSaG7HBd9pMkthBNJ7NP/X49GUge4K/2tsDAsRpEbcExQnaaICvhe7fLVYAad90LidQ\npHThBIEbKh+7VdxJ9GqL7k9GGg8+J+pa5jz3AwKcElFV437DNY8FcBkHDx3DY08+i5de3o9sOqcS\nRAwG3HXX3fjwhz8s4D7Bjf/oQQYATfueevppPP/cc+IFxKxgm06PHWvXYXkwiH6/FwPd7WLUReDQ\n5XJjOfONCyXEolF0dXYiEArh6IkTcDhc6O3qho0Rk+c5NJhEIOTHrt03CmPn1VdeEcnKdTt2YHDd\nOuTiCRWFStCrVhUwIp1KYc+ePdBbrTi0b78AIdRJEgzhmkL2Rs/q1TIdOrbvFUQX07h2+w4B31/c\nuw8z8QR8oZCsMeeGh3Fq+JIUwmRMMNIsnk6j0IprLFdVljlNrFTYKifYylhUqpgl6SqseZQBH4s9\nlXqkgQSS5iDSMTW91dYiRQJQLu3q/1FKU0Q6kxA/CCW1XTr9XWpgpp213wYAtBhtraeyxnaZzNjQ\nO4CVLh/CZiPMjSKatbLyZ+EclPsBDUgNOpRMJpyil8jIKAIW5QEQtliQEAbAMC6kGQOorAs5aV05\nsB733X0/rttyI0rFGkrlHFau7cVVmwdgciuRm8p04HsZpZh/4ZdHcO7kMALuNtRKLNABl8eJxXwC\nP/zRd7Hv8K+k9Zc1V8iqOtiMVgx09Qjocm5sRKJEyYDj1h1ZXECqkJJRJhsPS9OFFX1X475734Md\n192MQKANxXIRw2MjsNj1WLaiHZu29oLKDgUAvIleseT2+C3Tf60XagDRmUWcO3UeL774Ah76+lfE\nMK/KL9swQt+04oaNN+P+33k3Nl61BUZYcf78RfhDAbgDdmTrUay+agA9/f6WIJvHS3O40DqlKxNo\nTXpC1grr7Ste72y4DShkSzh75jw+/omP48y50+AlWkwnEYAJm/uXoc1qQK5SxNlUEsPJTMtYc+mK\nQHCTsWcQnwp+KK+nA8HwIKz2oHhoUV5N5i4bY83A7TcBAFzqVbyq5gDRlDW4lMugQWatnvHYRSWX\n0tnEzb5SjyGWmBRDPwUAaDcfX8eKkH8IBr0VxVIWlWpJ9nGzxYBiISlMLAv1/U63DAKYWiPrnY5r\nVQbVSr5lEqhN5rkXmeDxhuG0d8BgcEKns8u/begqLU8AG7w+DgFUQsvlYVLrkP3GPrj1/yQKUApN\nBeQJCCjDNvZFKkaWDIhYZByz06dQKzO5xQK/vwud3athMLtQqSt6/hWZhUYwUq9Nhla1ksbYpddQ\nykQkMt1nNGJduB2re3vF1+zM2AiG0wlkuHfIa6kGWGH5S77B0gn/ZW+cKxaNqs9aAkr+po1EjKjV\nZcHoQ/5Oud4Pv/0dLL/6KiC3KPIS1kciEOI62YrZlk/W+jPfRXzCyMpQaEAr3lmHht6A8yPj+Ngn\nP42XDx+T1ILdQ1fhgw+8C3vucAgOCwAAIABJREFU3ANT2MP8Z5G+CDIoviM0tKqiVihJOhRvTzGO\ntnECcIXdKNeqlCWt763FBNtsmB+bxEc+8qd4/tBBifhjncD+h1KAbrdX6qnJbA4ZVOFx+nDzLbfi\n+ut3C+Bz5OgRvHLgFWFEsndkj8jEHDb/W7ZcI5IyesNprFieF5oASvTwZXa4uh3Eq63J2HCaEFOi\nD4yMzOP8+RFk8zlJFonFpzDKuMkSjaSBVUNX464770Zne5vyONTpYWHC1l995mPN++67T4wHSPt9\n9dVXJRKQFP5du26UCCcaix07fhRzc7OwWE3YuHGD0P75OHr4sEwvuPH39PZIk1GplgU8yGbTokX1\n+b1CfaJOkaZQpNQSzT1y4iw++dm/xLHTo8pXq3VtDg048bnPfBD3v/suNGsx6EjhyWVRXlhAanhY\nMimdbZ0wsakMBVumaIxra7mpyOuoxZNFsd7MU2TF3EQSn//8N/Hoj/ehwY3P7JBiirQnLqj9/Z34\n7Gc+hnvu2A0z0cJmVUzeaASiNe+cosrmzmW6BTbIQiw0QlIElf5I0exVI6I1/FrEE58vN28L6eGf\nJRngsjGgraVNbKomkmZUep0URlqeND83/RQEFWJ+Z6to1EAYviaBEv7iosWfi/EgjY9oGtgye+PV\noWnDlR7cIgUoPz+LVToJ87WZ/WkwWpBIl/HP3/83fOXBb6BcYAAccN2QA390Tx/ecs9mWJb3AUyO\nmI1i+tw4miUzvMEeeHuWwRDqAGzUVSmzDSSTGDt2FMXkHMJ+HazmGmxOK46NZvBn33gVzxyPSxHr\nclrwp3/8B/jIH70fBm58ZUVRI2DFTYjU9KUSCwW8UBPXOg8stqmVNKnJjpaVzD9f1um2kg/UpaMW\nRUXZEi6nTGtkEt40IJsp4++/8X188+F/RkFSDfTiAuq12uE229DT3imO102J1ekRuUM0FlOa2mZD\nbnKrw4FkMolSroChgeWy2CUyKSxWC1i36Wro6k2Mj45Ko06UsFgpY2xiXAAQl9cjxjsTU9QiVWDW\nmSXPNEsNu8xN6nJ4PWYHXE4rwm0BhDjB9HlENhD0eRBye2ARY0MIg6DWqIj5HV3v6UNA0KFUKUsG\na6laR7ZQxvRCAqeHJzA6MYd4ZFEAtHg1h1SpIE0UaXS94UFcu+V6cT5lgee0u4T+zug4zQyL1xfX\nGpqsxRMxYQNRCkOmAItwGtyRhs0FkB4hpFAzS9ntodFoN0xmIyYnJ9DX1w+r1SG+AYwrijP3NL2I\nyZlp0SLztQri1K8BJQrkYcNFd37eGyzWNT0VJ7ui3WcTzfuRv5fL0mzw89G9ldeaIhYrkYbOpJzE\ng4F2tAU6EQ50w+VgpFIetWYVhQonGlGhd2fSSVTpfiVbR0OMVHt6etHb24Nt27ZiaMWgsDOeeupJ\n/NtPHkUhW0B3ICwmaaeHh0VHd8stt+H2O+9CjeuByyl66GPHT2D92vW4ZtMWDPQuQ6FQwhO/fFpS\nW6695hqZok5OzWBkZBS9XT3YePVmBALt0jwyO5eAzHxkBvHIDI4f3I/szBTuvX4H7rxhB8IhHyq1\nMhL5HMbm5nBpahLDk5OYj0aRS+XgZIVZqSujvCbvYasYH/LvBKVsnKaSwVCvo0BAhMCIjvi0MqYq\n1ypwwowBawArQl3ocnjhaEUQFYtkPDWlISvWmFldhtWoR28ohJVdXXAZDOhtD8NuMcNiZWKEHnPR\nOI7OTuA7B1/E+XIEYXc7Nm3YgHafB8VsGjpDHQ63A7zwUzkCRkymyUmSQjZXRIJARpkGajWJC+Sj\nNbeQe0o2YCNN/uwClPNe5l7YQX8Ol1NYcZQTkBlH4Crg8YgnB699gnj8dx6PS6ZLNN2LLMSEstvb\n2w0vGW0s/qtlzExNivTFTNlMVzdcPu63OlkvLl0almKBwMDyFSvgD7fJzx97/GkxaX3ttaMoF9We\nxrV29+7dYgJ42223CVCx9KHVqLzWCY4+88yz4gGwfx+TBKqSsmJpAqu7e9DpsKPNbMA9e3bjlt27\nMDo8jIOHDglQw0n+XffeK1OeJ598CkePHcfu3bfg+j23SgF37uV9OH7sCKxOK9atX4OgP4DR8TGM\njI/juuuvlz2GTBROwdavWQO71YLTp07LYGLrtddKoX/q1ElhA2zevh3ZRALnzp9DuKsd/avXkBqA\nE0ePomk0ymehV/XI5BRCAwPo7u1Dei6KEydPomylWaQNzWod07OzGJmbEelAsVzF5MyMyG2YbkE/\nkhylZCYLCtWK7PkEopa2i2pi/3pqP0EBmvIpY15lhy8sJ2Eg8WcEe1RRKgZr9YI0zjkaHLb28ytF\n9hI545I/vn7icaU5eGM9rqj/Rly/ai1WOT1wE+gVPTTN6lQEM0Nb+WAOeqrRxLnFFC6Mj6HL4cLm\nlSvQ6fJgKpnA4+fOYbpeFiCSalp6y7gsPuzafjvuvv130ds7JNP+pr6M/uVtGFzTDrOtKbWgjsOX\nqgnR2QKOvHoKs5PzUn84bU647R7MRKbwy31P4JUjLyCdjcharqfTu8kqEzvxzeC6USqLFID3G4Hz\nLI1kBWzk/iyOaMIs7A4tx/2/8//glt13I9jWKQ3dmQunYLDUsPW6NegfDCoAQCR+/xkA8Ju6nNbP\nWlPE1HwOJw+fFs+bHz76r/jxLx5FBWQvkktmhk3vxO6de/D+d/83BH0dqJabwgTo6etCVVeAyd7A\n6nXL4O4i86kuwAQbxWqNYCCZJlfkiUs/japSKC2lUz9rFSOKqQp++m8/w///119APBERXxtO2jug\nx7YVKxGkNMOow6lEAidmF8St/ApsRBaqFTarTe5psrV8jjBcnnbYvJ0wUe5GLTYZIMIQUAC5xKe9\nYTCl/q4MOBVxsYZyIYdUPCZO+HrWVKxU6iWVsGQko47DhSnkCzGl/Re3+NYEmKw8cwB+b7/Qnfn5\neF3QWJNU+lxuAYVCCoaW6XGBbDsYEfANIRAIIrHImoCGewRNlB5cvTjZPXa4XW1wOpls5hSwmvIc\nXldsVQm0c63nQ0DBN3hu/UcgwGUA4E2XUOvd2YPQ46hZxPjwMSwuTImOw2L3YXBoM8xWNpUUb2j3\nuGJbLGU8cz2pVzMYOf8qyrkFOW5M+Vjh8WDn2lXoD7gxPD2J/ZPTOLeYQ0Mm3EboKUvW+Dkt530m\nSLUycFoQlGqMZelpAZNkAnJwdNmWQNsUte+oSQZaYALZrMZaDR9/4P343J9/FnBQ2ldkoLT6FyK7\naE3fl7AQSNAgkGBYQmpiY85exWK1if/Ez594Gp/9y79GMpGGy2jGzVu34SP3P4BN120FbE2AAzLq\n5CmX4zVYq8vAqUEAnQeVLBWrSumSYYQgAq3Vl7rbFqtLotpNJiwuxPCZ//Fn+Mmvfs3sBpUmJDGK\nmpcC7RcN8s0Glw/inrfeh8HB1Th3/iIO7D+AS5cuolAoCqOcbMNNmzYKAEDWIHs/Xlf03qMHBfsZ\njTlO4NPpdAhrkKaZXFMlWQ8G2C00Ga/glYOnMDsXlf68VEpjfu4CxifOyWfjpP/GG27Ctm3bxXtC\nY7HJMPijH7i/+aEPfQgrVq6SFyf1jzQFbqK/c999CIQCqJWL2Ld/HxYW5uHzubF27RqZfrAwPnPq\nlOgZ+Pzevh7RznAyQOOzbCaFjo6QFDwsRDLZPHK5EowmK2KxFP7+4e/ie488KToXRkQ0aozgAf7g\ngTvwyY+9Gx3dTtSrizCwBCMasrCAwugImqUyjG4f7J1dzDmShlN12pqTiobc0mjCAdRMyCdr+O4/\nP4YvfeV7SGYgRmKcLhXKRRiZCWxs4M47duN/fuq/Y/VgLwx1Ng41lKjbbznsaxF70tC3pswsDEj1\nZDMp1P6WVpTP1UwAlaO8ogJrzbgmIdBymdUJr0ghpOn4Ob0h3Y3TH06glefA0qk+C4iWJIAmdi2f\ngCumgkqnS7T2CgXxzSwCTfMuhYDxSkyaROs1akKz4nSDNOjJmQT+5u++je//4BcCqnGedOs1IXzm\nfZuwaUsPGh1h6Gk0FYtj5swICtEKHLYADE4f2vvJ2nCpc1XOI5eIITI2AlOzgICf918BDpcVCwUP\nPvXQfvz02RFJAuBF/L533YE///QnEPR4UK+X0dSTzsXFUBVZXGyXygFUtKL6mXasiZwRlOH34c94\nfjTpgEbv0pgA8nP+R22aTHEVc8Vud6NRM+LRHz0uZoiRxYywFDhtcZrNAgRYaaoFHfrC3Vjduwr5\nVB7jlMx4POgKhWWqPzEzLduP02hFV7hDCsm5hYi4qrv9XqF308hroL8fXd3diC0mEYvHBTXkhsRG\nd3R0VAokmpllqiXMRhfkNS02m0S1sbkpgg2nCFlEk0X/gPZQEF1tIbhsdoR8XnR3dshma7eY4HM5\n4PM4ZSHhVCoepx4vI6Z++XINaTIAUlkk42lkSyWMRGeQLKoYTpbHZrjQFu5GMBDG8mVDCPra4PME\nhEHA71SmIz4XcdLgajQDtQuiSXYR1w1OMsUIMJ8TYJFeAOJTQN+QLDOL9Qi3twnThkYmq1etFc8R\nunhbbGbYXbS60iEaSyIaj2MuOi+ygWRyHrOz47JBKgKwAgj5iSVn26bypZWpFu9Bs9zLZC+IQRNT\nQuo15HMF5HIFaRAZS9fkpKBZFVdup8OLjrZ+dHf2IBj0IRaP4Mz5s7IW1pssSBTzx+Nxo7unEzt3\n7MS2rdsQDASEEsamsFgu4PHHH8OjP3pEJvkbBlbC6XLjyLmzKNcaeMtb34abb70NmWIRmWJe9Jw8\nVpQ0XLtpKwZ7BuTb7T38GsYmJrBhzWpsWHcVJian8dhjT6HW0GPPnjvFOZaSFnqeGI0NTM+MYu+L\nz+D0kUMwlou4a8e1uGn7VtRqZVwcHcGZkWFcmpiQ67DEGDeDSQAmv84qubxuuxMOiw1Os1U8DMh6\nIdOE62C6kEOikEW+UUOuWUNJV5d1lYZLBHgcMKLD7EKvK4BeXwg2vVGBMNTtcfWvK5C2Vi3DatCj\nt60NK7q64DQa4KZ5n9Eka7gYEJrNuJhN4usvPY7hfAxGnRnXbt6Mvs4wmrUSzGblxk4QgOeov69D\nZG2RaAxNvRH5chWZfBGL6SxyxSIWEkkkUykx1hFPilIRlZrEGUthxr5DahiZxiiAgBt4ONwmrLmA\n2w2n3SYgFq/bgYF+BIJ+KfyUdMwgUyrK6BjLaHfaUC7mEY1GxCuA+0E7IyX9QWFWpWNJjI+NC3jD\na7ajp0uMxQgIPP30s3jk3x7H3r2vSEY5izauFffeey8eeOABSQHg/fZGAIAFnkoIqWDfvv148MEH\nZZopDaLBJAyADrcbAwE/7t6+FffuuRmhgF+M7OhDcf7iBZisFrztvvukLnzt8GE5XqtWrEJ3sB3z\n07Mo5XKSbDI+NYbh4YtoCwSwYdMm2H1elOtVvPTCCxi+eBFrVtLdWy+v393ZJdKA4eERAZGWDSwX\niQD3PN6XNOmqNqqYmpgA4+G4TpqddlDSOHlxBP3Ll2MbQYkmcPaFvQIAXH/7Lehdvx4olTB54SIW\n0iksW7ECmXwBx06eEkYMZTvpXB7TkQgWFtOIJhaRE/ZGRVgDlBNIg9byn1e2gOrRUurKXqFWGe79\nNFk0wcAccou9BQSYVNKUrip05myeFOYWI+iyS1XrhV/X/P+WhvQN/4vFPM/2hnA3dvb1w1ykgRRN\nKlmOGYRdpZfoXBphWRCr1HBiYQETkVksc/lwzaqVCNmdmEwm8cT5M5hhHG2r6aOPAqenq/o24L3v\n+iCu33mr+KucOnMCOn0Z12xbhWu3r5M9upCu4+D+0xgbnpO9kzcOp7Y+rxuT4+P496d+hv0nX0CF\nexUj35jOAgvsJhtsXC/dbtEvUz4TdHrQ19uLibkZzKcWJBKLQLWKNeMxb6A3uBwf+oOPY8O6bWjq\nzeIL0zTUkS3GsGxFCIOruiUZwEh53H/9cL75mS0AoJyq4+DeIyjkygJa/9P3/gHPHfglagICEAKw\nos3bgXe/4w9x7aadWNazEvPTUQGUPUE3dKYazPY6Vl01AG+n/fJU/XLb94aGU2tdebVJygRZnA0D\n6lU9ZkYjePhb38KjP/kXibasV/Iypew1W7Cpvx8hi1HA4xPxOI7PRi4nLWnNMD1DKCEgQMRI34Cn\nCw53GGZ3G8CBGSeqcmWr+D6umTLV/o0AgGIOctpYymexmIgCtYo0/5dzZRrVyx5blWoOi+lplKtc\nu7QIP05vWftS8hiA2ehFhfI1B31VbGg0+FnzSKWmUSwzhtkg5mmlKplWZOZ1wOsNoVLJIrk4Ky75\neoE9tPBX1b6ROUAAwOnsQLNpUce0qTxodEajAABur0dF+EqvoS4HVSf+5sfrAQCtJ1n6XMqAmrCY\ndShk53H+7DE0mNphtKOndw2Cbb2oN82oX+bIKw+kpQAAB6v1ShYj5w+gkqeckPtpA70mM9Z3BLH7\nqtXS6L86PoV950exIKQbo5gH05yVnirCgFZqDkkEUUCH6DpkQi9/v+xNokCIyyaob/ry2gqounkm\nNpmgw1Vt3fj6176C7bffCNQLqJdyMrhV3kitCfwSCYL04iIluCJX4EdiDWZkmhr9hMpV/N23vouv\nPPR1lJtV9LhCeO+eu/COt9yNgVXLUKuVhGhsop6cQAB9AQTva8q/l+/VYkJrSQGXfY44SxYTS/oU\nNKA3G1HOFPC3X/4aHv7u9zFLdhPp+WwiLz/4fWvw+UK4+667cc2Wa5HLlvHkk7/E6TNnpE/kkMnj\n9WLt2rXYuPFq8R3ius/+inub1NHsu1oxo1IztgzKq7x3yFYWlqBiVTmtPszOJXHoyFnEEotiplos\nJDA2chSLyYisiatXrJHPQykiQX3KF2XQy/rj33/4nSaNubihKm2wKgToTMgmql6lG7VT9dYiGW8K\nTY8TQpoksZgnvY4TO14kpOWYzUb5O5tOmuswjkY0DzScM1oQi2Xw/e//GN/57g+xkCzBwLgJ6uz1\nwE27hvCFz30UWzYOoFFJodkoCTUIjPtIRFGeGEYuGkWdC3tbOyzdvTAHAoLkyIck/4HmGYKyk2Zv\nQrNgxC9+fgCf/NRfYyZRh8fpgt1pwVwkLhsaT2FPRxAf+cgH8IfvfQeMugp0rYKT4x420VrMnjYt\n10AA/lz72WV2QEtrr9E2lyL2Sk+tptM8kRpyuhQg0OQFQhWS+CDVuGpGEDwvpCSxUeHFwckNTyzf\nj1IMLa5KSQ+I1qmLiGiyavbVBcY/X56Wt1gOWtOsyQTkkDIORSL1DLg0OouvfO2bePpXe4VGY24C\nb9/Vi89/aCd6+qwA46p8fiCTQ35sHpNnZmAzOIUBwvPPhZS6c17M1VIOxUwcDnsTDk9dpgdcC0rG\nXnz+4aP41qOHUW656t5200Z87UtfQF9Xu5i3Nejo3eBnUkpHfj9+Zu17aU2/tkhrQICmo+Fzea1r\nD3U+FUCjbj4NAFATAqElEp0122E02nHmzCi+8rVv4BdP/1qm3ywmqGcmHMEtkr+8dIq3e6BvGMQJ\nf3l3Hzq9QSQjUczNzqIjGMZQzzJptCcjs8LM4MLMXOxkPoNUPgOPww1PwIdIMi600XVDq+T8zsQW\nYDWZsTzYKVFkl2anxGW9t71LNu+ZhXlZ+M1mA2KLcczluDnwUqiLrwCveTqtkxIf8Plk6u9101zQ\nI8U36c9GSnt4DdabMOkMMBJ8InZTrUgk12KxiJGZeYxOzyKeSiErBSJLAyLJVgH6fJ42dITpE9KO\nznA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3Bp6qiVeu+QJsvQGoUHJJGWcin82gVMhLTDGnsHR219pHE+M49fRhSSGZmkGpnFxi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OPyxE0M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\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fzCyXG8PmgN5oxdF3LuPzv/+XGByaF0qOvlzCB556DH/yh7+Hrp42zM7NCJrPoo/Z8yzI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\ngLsOSTq6nCU4wHOsPTHt3eyQUnq34UHJcfMz80oe0s4lfxr5y5pYme9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+ } + }, + "cell_type": "markdown", + "id": "488bbb9b-0923-496f-83aa-daba0502abd9", + "metadata": {}, + "source": [ + "![python-comic.png](attachment:23511f5c-cc46-4228-a97d-7bae6fc066c1.png)\n", + "\n", + "generated with OpenAI DALL-E3" + ] + }, + { + "cell_type": "markdown", + "id": "d2509b00-dd89-4166-bc92-fad73a9f623a", + "metadata": {}, + "source": [ + "#### issues with installations, homework understanding?" + ] + }, + { + "cell_type": "markdown", + "id": "691ba451-05f4-4cb9-a54a-bc186791c631", + "metadata": {}, + "source": [ + "## Recap\n", + "\n", + "what data types do you remember from last lecture?" + ] + }, + { + "cell_type": "markdown", + "id": "96447e1b-814c-4fc9-a65e-3789aff50a59", + "metadata": {}, + "source": [ + "shout any buzz word you remember" + ] + }, + { + "cell_type": "markdown", + "id": "7914b0c4-32e8-4baf-ba5c-ec1874bee647", + "metadata": {}, + "source": [ + "your new homepage\n", + "\n", + "https://stackoverflow.com/" + ] + }, + { + "cell_type": "markdown", + "id": "ac36bc30-bbbe-4862-a8af-74788f38da1c", + "metadata": {}, + "source": [ + "## Docs\n", + "https://docs.python.org/3/tutorial/index.html\n", + "https://docs.python.org/3/tutorial/datastructures.html" + ] + }, + { + "cell_type": "markdown", + "id": "a1c6f332", + "metadata": {}, + "source": [ + "## Lists" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "id": "38df9067", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "my_list = [0, 1, 2, 3, 4, 5] #collettion of items\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "id": "5f2390ac-555b-4f26-977c-246a299238a5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 'a', (x)>]" + ] + }, + "execution_count": 176, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[0,'a',lambda x: x+1] #very versatile" + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "id": "a5f08f76-e6b7-4263-9a41-12c9f8fafe6d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 2, 3]" + ] + }, + "execution_count": 177, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subset = my_list[1:4] #how to look into object\n", + "#general concept of iterables \n", + "subset\n" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "id": "450f5a99-751e-4d05-b891-954dfa97f5b2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + "2\n", + "3\n", + "4\n", + "5\n" + ] + } + ], + "source": [ + "for item in my_list:\n", + " print(item)" + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "id": "dd85bfd3-5397-45d5-b43f-01dab4c50f1b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 0\n", + "1 1\n", + "2 2\n", + "3 3\n", + "4 4\n", + "5 5\n" + ] + } + ], + "source": [ + "for idx, item in enumerate(my_list):\n", + " print(idx, item)" + ] + }, + { + "cell_type": "code", + "execution_count": 180, + "id": "c9552e98-85f8-4018-8448-9fc7ebd4c575", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['h', 'e', 'l', 'l', 'o']" + ] + }, + "execution_count": 180, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list('hello')" + ] + }, + { + "cell_type": "code", + "execution_count": 181, + "id": "c8d97b85", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 2, 4]" + ] + }, + "execution_count": 181, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subset = my_list[::2]\n", + "subset\n" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "id": "f9c6fcbe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[5, 4, 3, 2, 1, 0]" + ] + }, + "execution_count": 182, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "reversed_list = my_list[::-1]\n", + "reversed_list\n" + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "id": "b8f16358-611b-4708-9570-1b3f67c293f4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 1, 4, 9, 16]" + ] + }, + "execution_count": 183, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Creating a list of squared numbers\n", + "squares = [x**2 for x in range(5)]\n", + "squares" + ] + }, + { + "cell_type": "code", + "execution_count": 184, + "id": "36d47197-ef07-4aac-bf80-e3e847b1abb4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1, 2, 3], [4, 5, 6], [7, 8, 9]]\n", + "[1, 2, 3, 4, 5, 6, 7, 8, 9]\n" + ] + } + ], + "source": [ + "# Creating a flat list from a matrix\n", + "matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]\n", + "print(matrix)\n", + "flat_list = [element for row in matrix for element in row]\n", + "print(flat_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 185, + "id": "f17c936b-553f-483f-bcfd-196359d850b1", + "metadata": {}, + "outputs": [], + "source": [ + "# Creating a list of even numbers\n", + "evens = [x for x in range(10) if x % 2 == 0]\n", + "\n", + "#python built-in filter\n", + "# DYI! Try to look up how to filter values in python array (list)! Lets filter even values" + ] + }, + { + "cell_type": "code", + "execution_count": 186, + "id": "49b2f2b6-47cc-49bf-92a1-a23ef3f32d59", + "metadata": {}, + "outputs": [], + "source": [ + "# Using append and extend\n", + "my_list = [1, 2, 3]\n", + "my_list.append(4) # [1, 2, 3, 4]\n", + "my_list.extend([5, 6]) # [1, 2, 3, 4, 5, 6]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 187, + "id": "a70b811c-4f70-4fa0-9d3f-9cb7095b1e73", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "21" + ] + }, + "execution_count": 187, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Using sum, min, max, and len - basic operations on arrays\n", + "sum(my_list) # 21\n", + "# min(my_list) # 1\n", + "# max(my_list) # 6\n", + "# len(my_list) # 6\n" + ] + }, + { + "cell_type": "code", + "execution_count": 188, + "id": "628e51b4-9e71-40a5-9613-5698371dc4c2", + "metadata": {}, + "outputs": [], + "source": [ + "#merge lists\n", + "\n", + "list_a = [1, 2, 3]\n", + "list_b = [4, 5, 6]\n", + "# [*list_a, *list_b]\n", + "# list_a+list_b\n", + "\n", + "# how does this differ?\n", + "list_a.extend(list_b)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 189, + "id": "4781af74-6416-4443-81d5-f02dc2a90c85", + "metadata": {}, + "outputs": [], + "source": [ + "# Creating a list of all coordinates within a 3x3 grid - list comprehension\n", + "coordinates = [(x, y) for x in range(3) for y in range(3)]\n", + "# coordinates" + ] + }, + { + "cell_type": "code", + "execution_count": 190, + "id": "c78c6fe4-f6d8-48e2-8cbd-672201df7921", + "metadata": {}, + "outputs": [], + "source": [ + "# Transposing a matrix using nested list comprehension - two for loops in one comprehension\n", + "matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]\n", + "transposed = [[row[i] for row in matrix] for i in range(3)]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 191, + "id": "08c588b7-5ea9-437c-ba9a-f3123852d920", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "12" + ] + }, + "execution_count": 191, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Repeating lists\n", + "repeated = my_list * 2 # [1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6]\n", + "len(repeated)" + ] + }, + { + "cell_type": "markdown", + "id": "b087e69c", + "metadata": {}, + "source": [ + "## Iterating Backward" + ] + }, + { + "cell_type": "code", + "execution_count": 192, + "id": "59a4a1e1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[6, 5, 4, 3, 2, 1]" + ] + }, + "execution_count": 192, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# Iterating backward through a list\n", + "[item for item in reversed(my_list)]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "id": "b3093d79", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['c', 'i', 'n', 'o', 'h', 't', 'y', 'P']" + ] + }, + "execution_count": 193, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_str = \"Pythonic\"\n", + "\n", + "# Iterating backward through a string\n", + "[char for char in reversed(my_str)]\n" + ] + }, + { + "cell_type": "markdown", + "id": "2c57a27b", + "metadata": {}, + "source": [ + "## Using Negative Indices" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "id": "29393fce", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6" + ] + }, + "execution_count": 194, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "last_element = my_list[-1]\n", + "last_element\n" + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "id": "8f6da6df", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[4, 5]" + ] + }, + "execution_count": 195, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "subset = my_list[-3:-1]\n", + "subset\n" + ] + }, + { + "cell_type": "markdown", + "id": "efc592b7", + "metadata": {}, + "source": [ + "## Strings and Slicing" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "id": "8a71aab0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'yth'" + ] + }, + "execution_count": 196, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "my_str = \"Pythonic\"\n", + "substring = my_str[1:4]\n", + "substring\n" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "id": "8e8d3bcc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Ptoi'" + ] + }, + "execution_count": 197, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "skipped_string = my_str[::2]\n", + "skipped_string\n" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "id": "d8942737", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'cinohtyP'" + ] + }, + "execution_count": 198, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "reversed_string = my_str[::-1]\n", + "reversed_string\n" + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "id": "3b90ba63-83e6-4a80-a6a1-091a74278f35", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'my string is Pythonic'" + ] + }, + "execution_count": 199, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#f-strings\n", + "\n", + "f'my string is {my_str}'" + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "id": "aa16a3bc-3887-40ab-bdb9-93f9d69a143c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'my string is Pythonic, no kidding!'" + ] + }, + "execution_count": 200, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f'my string is {my_str+ \", no kidding!\"}' #notice the \"\" in f string" + ] + }, + { + "cell_type": "code", + "execution_count": 201, + "id": "b5c4ab02-7e0c-4de8-8fa1-df302ca814f0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'PYTHON IS fantastic!'" + ] + }, + "execution_count": 201, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Example: Converting, replacing, and formatting strings\n", + "original = \"Python is fun\"\n", + "formatted = original.upper().replace(\"FUN\", \"fantastic\")\n", + "final = f\"{formatted}!\"\n", + "final" + ] + }, + { + "cell_type": "code", + "execution_count": 202, + "id": "2076e192-1526-45c2-9825-eb2f649e8b82", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Pi is 3.14'" + ] + }, + "execution_count": 202, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f\"Pi is {22/7:.2f}\" #rounding floats in strings" + ] + }, + { + "cell_type": "code", + "execution_count": 203, + "id": "a76c0ca6-c41b-4710-ae0d-00fd842a7c99", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6" + ] + }, + "execution_count": 203, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(\"Python\") #number of characters\n" + ] + }, + { + "cell_type": "code", + "execution_count": 204, + "id": "b1fe3ab4-9644-4750-ad3b-296038d99bb7", + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "'str' object does not support item assignment", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[204], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmy_str\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mZ\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;66;03m#immutable \u001b[39;00m\n", + "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment" + ] + } + ], + "source": [ + "my_str[2]='Z' #immutable " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ef5e858-2927-4f95-8cdb-166107290964", + "metadata": {}, + "outputs": [], + "source": [ + "escaped = \"This is a \\\"quote\\\".\"\n", + "newline = \"This is a line.\\\\nAnd this is another.\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "047b4d09-8bf5-4d97-98f5-2a776e7881fe", + "metadata": {}, + "outputs": [], + "source": [ + "\"This is a snowman: \\u2603\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b7d12168-3723-41dc-9e46-6f1b6d345951", + "metadata": {}, + "outputs": [], + "source": [ + "multiline = \"\"\"This is a\n", + "multiline string.\"\"\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9c6ec83-5336-4424-8347-c1b3db2e4b65", + "metadata": {}, + "outputs": [], + "source": [ + "#create simple csv\n", + "to_csv = ('list_me_to_csv') \n", + "','.join(to_csv)+\"\\n\"#add line break - bonus\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57b265bf-c8ad-4466-9d48-f263d7acc72d", + "metadata": {}, + "outputs": [], + "source": [ + "#create manual csv file with integers\n", + "','.join(list(range(3)))" + ] + }, + { + "cell_type": "markdown", + "id": "39effd52", + "metadata": {}, + "source": [ + "## Dictionaries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9a1150b3-0aa7-4383-9c97-81d25cc03a16", + "metadata": {}, + "outputs": [], + "source": [ + "#very useful containers -> quick O(1) access to their values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bc8f46d0", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "my_dict = {'a': 1, 'b': 2, 'c': 3}\n", + "for key, value in my_dict.items():\n", + " print(key, value)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "56993a3c-e425-485d-821b-9d6be333e71e", + "metadata": {}, + "outputs": [], + "source": [ + "# Creating and accessing elements\n", + "print(my_dict['a']) # Output: 1\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3db58c5d", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "squares = {x: x**2 for x in range(5)}\n", + "squares\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d3da64a9", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "even_squares = {x: x**2 for x in range(5) if x % 2 == 0}\n", + "even_squares\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d56ccd9", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "dict1 = {'a': 1, 'b': 2}\n", + "dict2 = {'c': 3, 'd': 4}\n", + "merged = {**dict1, **dict2}\n", + "merged\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cc514d6c-13a6-4cb7-a048-6260e9f60e66", + "metadata": {}, + "outputs": [], + "source": [ + "# Using dictionary comprehension - notice similar behavior like array\n", + "squared_dict = {k: v**2 for k, v in my_dict.items()}\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5ab1085a-e299-42ef-8665-47679629c8b9", + "metadata": {}, + "outputs": [], + "source": [ + "# Iterating through keys and values\n", + "for key, value in my_dict.items():\n", + " print(f\"{key}: {value}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54fd8766-1c62-45df-82cd-5f6be0ae25d6", + "metadata": {}, + "outputs": [], + "source": [ + "# Accessing\n", + "my_dict['a']\n", + "# my_dict['c']\n", + "# my_dict.get('c')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26e8a32a-6caf-41e6-a5c8-12389ea0097b", + "metadata": {}, + "outputs": [], + "source": [ + "# Iterating through keys, values, and items individually\n", + "my_dict = {'a': 1, 'b': 2, 'c': 3}\n", + "for key in my_dict.keys():\n", + " print(key)\n", + "for value in my_dict.values():\n", + " print(value)\n", + "for key, value in my_dict.items():\n", + " print(key, value)\n", + "\n", + "\n", + "#best is last one, always can ommit value" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5c87c27-af87-43d4-9589-6fe183da8d6a", + "metadata": {}, + "outputs": [], + "source": [ + "# Using get, pop, and update\n", + "value = my_dict.get('a', 0) # Get value with a default\n", + "popped = my_dict.pop('b', 0) # Remove item, default if not found\n", + "my_dict.update({'d': 4, 'e': 5}) # Add multiple key-value pairs\n" + ] + }, + { + "cell_type": "markdown", + "id": "ddc19af0", + "metadata": {}, + "source": [ + "## Sets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62b5c2aa", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "my_set = {1, 2, 3}\n", + "my_set.add(4)\n", + "my_set\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9f97205d", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "squared_set = {x**2 for x in my_set}\n", + "squared_set\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2c1a412a", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "another_set = {3, 4, 5}\n", + "union_set = my_set.union(another_set)\n", + "intersection_set = my_set.intersection(another_set)\n", + "difference_set = my_set.difference(another_set)\n", + "union_set, intersection_set, difference_set\n", + "\n", + "#useful with strings, Getting subsets of some collections" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2d7bf9c0-0624-484b-8b1e-0b8a2bdf50f0", + "metadata": {}, + "outputs": [], + "source": [ + "# Using set comprehension\n", + "squared_set = {x**2 for x in my_set}\n", + "squared_set" + ] + }, + { + "cell_type": "markdown", + "id": "75f0a72c", + "metadata": {}, + "source": [ + "## Tuples" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fe9e2277", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "my_tuple = (1, \"apple\", 3.14)\n", + "element = my_tuple[1]\n", + "element\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e2d8d434", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "x, y, z = my_tuple #get individual values easily (same in .items() in dictionaries\n", + "x, y, z\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32ca76fb", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "nested_data = {\n", + " 'fruit': {'apple': 3, 'banana': 5},\n", + " 'vegetable': {'carrot': 4, 'pepper': 7}\n", + "}\n", + "for key, value in nested_data.items():\n", + " print(f\"{key}:\")\n", + " for inner_key, inner_value in value.items():\n", + " print(f\" {inner_key}: {inner_value}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "08d061a2", + "metadata": {}, + "source": [ + "## Defaultdict" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3914d594", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "from collections import defaultdict #import library, top of the script!!\n", + "\n", + "fruits = ['apple', 'banana', 'cherry', 'apple', 'cherry'] #could how many of each we have\n", + "fruit_counts = defaultdict(int) \n", + "for fruit in fruits:\n", + " fruit_counts[fruit] += 1\n", + "fruit_counts\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "554f55c3-d5ba-4bde-9686-773f6152d0bc", + "metadata": {}, + "outputs": [], + "source": [ + "# Using list as a default factory - too sophisticated, but why not\n", + "words = [\"apple\", \"banana\", \"cherry\", \"apple\", \"banana\"]\n", + "word_count = defaultdict(list)\n", + "\n", + "for word in words:\n", + " word_count[word].append(1)\n", + "\n", + "# Summing counts\n", + "word_sum = {word: sum(counts) for word, counts in word_count.items()}\n", + "word_sum" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1bc4889b-b766-42ab-86bb-ac1bb04965bf", + "metadata": {}, + "outputs": [], + "source": [ + "#useful when we do not know how many items we will get (keys in dict)\n", + "\n", + "# Using int as a default factory\n", + "word_count = defaultdict(int)\n", + "\n", + "for word in words:\n", + " word_count[word] += 1\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "212b3768-0348-4c46-92ac-b304e31ee580", + "metadata": {}, + "outputs": [], + "source": [ + "# Sample data: (category, item)\n", + "data = [\n", + " (\"fruit\", \"apple\"),\n", + " (\"fruit\", \"banana\"),\n", + " (\"fruit\", \"apple\"),\n", + " (\"veggie\", \"carrot\"),\n", + " (\"veggie\", \"potato\"),\n", + " (\"veggie\", \"carrot\"),\n", + " (\"veggie\", \"potato\"),\n", + " (\"veggie\", \"potato\"),\n", + " (\"fruit\", \"banana\"),\n", + "]\n", + "\n", + "# A nested defaultdict to store counts\n", + "counter = defaultdict(lambda: defaultdict(int))\n", + "\n", + "# Counting items in categories\n", + "for category, item in data:\n", + " counter[category][item] += 1\n", + "\n", + "# Display counts\n", + "for category, items in counter.items():\n", + " print(f\"{category.capitalize()}s:\")\n", + " for item, count in items.items():\n", + " print(f\" {item}: {count}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "91ac1579-5f44-47a2-bb28-04accf5f82f0", + "metadata": {}, + "source": [ + "## Functions in Python\n", + "\n", + "Functions are foundational in Python for creating reusable, organized, and modular code. \n", + "Modular code can be easily maintained" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2bf16110-d958-42ff-9608-df190e02139e", + "metadata": {}, + "outputs": [], + "source": [ + "# 1. Defining and Calling Functions\n", + "# Definition: Using the def keyword.\n", + "# Calling: Using the function name followed by parentheses.\n", + "\n", + "def greet():\n", + " print(\"Hello, World!\")\n", + "\n", + "greet()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c6f42dea-6b88-43c7-b922-4f6d395cce54", + "metadata": {}, + "outputs": [], + "source": [ + "# 2. Parameters and Arguments\n", + "# Parameters: Variables listed inside the parentheses in the function definition.\n", + "# Arguments: Values sent to the function when it is called.\n", + "\n", + "def greet(name):\n", + " print(f\"Hello, {name}!\")\n", + "\n", + "greet(\"Python\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10e1bba1-0024-46a3-a9e5-5393d73e2f08", + "metadata": {}, + "outputs": [], + "source": [ + "# 3. Default Parameter Values\n", + "# Specify a default value for a parameter that will be used if the function is called without an argument for that parameter.\n", + "\n", + "def greet(name=\"World\"):\n", + " print(f\"Hello, {name}!\")\n", + "\n", + "greet()\n", + "greet(\"Python\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9613fc54-7ed1-4d14-b171-6df0e83e6997", + "metadata": {}, + "outputs": [], + "source": [ + "# 4. Return Values\n", + "# Using the return statement to let a function return a value.\n", + "\n", + "def add(a, b):\n", + " return a + b\n", + "\n", + "result = add(3, 4)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d0c7ef3e-c58e-4661-95b4-d7b32bf26d37", + "metadata": {}, + "outputs": [], + "source": [ + "# 5. Variable Scope\n", + "# Understanding of local and global variable scope\n", + "\n", + "def my_function():\n", + " local_var = 10 # Local variable\n", + " print(local_var)\n", + "\n", + "global_var = 5 # Global variable - not a good practice, but nevertheless possible. Drawback of notebook structure in general.\n", + "my_function()\n", + "print(global_var)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c314e2e0-43d1-41b3-9b97-b07f253fc394", + "metadata": {}, + "outputs": [], + "source": [ + "# 6. Positional vs. Keyword Arguments\n", + "# Positional arguments: Must be passed in the correct positional order.\n", + "# Keyword arguments: Passed by keyword and can be in any order.\n", + "\n", + "def example_function(arg1, arg2):\n", + " print(arg1, arg2)\n", + "\n", + "example_function(1, 2) # Positional\n", + "example_function(arg2=2, arg1=1) # Keyword\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "73717cab-41b8-4f22-a433-3d087cff9d01", + "metadata": {}, + "outputs": [], + "source": [ + "# 7. Arbitrary Argument Lists\n", + "# *args: For arbitrary number of positional arguments.\n", + "# **kwargs: For arbitrary number of keyword arguments.\n", + "\n", + "def example_function(*args, **kwargs):\n", + " print(args) # Tuple\n", + " print(kwargs) # Dictionary\n", + "\n", + "example_function(1, 2, 3, a=4, b=5)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "356b2937-29b5-4a19-a561-1f839d1ff9fc", + "metadata": {}, + "outputs": [], + "source": [ + "# 8. Lambda Functions\n", + "# Anonymous functions defined with the lambda keyword.\n", + "\n", + "square = lambda x: x ** 2\n", + "print(square(4))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc100338-4b05-4970-9ef8-2bbb2d879510", + "metadata": {}, + "outputs": [], + "source": [ + "# 9. Docstrings\n", + "# Documentation strings providing a description of the function's purpose.\n", + "\n", + "def greet():\n", + " \"\"\"\n", + " This function prints a greeting.\n", + " \"\"\"\n", + " print(\"Hello, World!\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fb2a8722-08e7-4aaa-ab26-6a0467c761ee", + "metadata": {}, + "outputs": [], + "source": [ + "# 10. Recursion\n", + "# Functions calling themselves to solve problems.\n", + "def factorial(n):\n", + " return 1 if n == 0 else n * factorial(n-1)\n", + "\n", + "print(factorial(5))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0ef00fa5-2183-4596-b822-f437e2ee3801", + "metadata": {}, + "outputs": [], + "source": [ + "# 11. First-Class Functions\n", + "# Functions in Python are first-class citizens, meaning they can be passed around and used as arguments.\n", + "\n", + "def greet():\n", + " return \"Hello, World!\"\n", + "\n", + "def shout(func):\n", + " print(func().upper()) #calling on some objects passed as input to function. Like trying 5(), but still possible.\n", + "\n", + "shout(greet)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3c2011ca-4f9c-4833-af4c-f05e8153ddf8", + "metadata": {}, + "outputs": [], + "source": [ + "# 12. Decorators\n", + "# Functions that modify the behavior of other functions.\n", + "\n", + "def my_decorator(func):\n", + " def wrapper():\n", + " print(\"Something is happening before the function is called.\")\n", + " func()\n", + " print(f\"Something is happening after the function '{func.__name__}' is called.\")\n", + " return wrapper\n", + "\n", + "@my_decorator\n", + "def greet():\n", + " print(\"Hello, World!\")\n", + "\n", + "greet()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "30c7a307-759d-426d-a147-2b5152f0b2cf", + "metadata": {}, + "source": [ + "## Input/Output (I/O) Operations in Python" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "053772be-d296-429c-8c16-486cbcd1baa2", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Handling I/O operations effectively is crucial for various applications. \n", + "\n", + "# 1. Basic File I/O\n", + "# Reading and Writing Text Files" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8430fcfc-ddfe-4bd0-851c-ea04fbc0ff8a", + "metadata": {}, + "outputs": [], + "source": [ + "# Writing to a file\n", + "with open('example.txt', 'w') as file:\n", + " file.write(\"Hello, Python!\")\n", + "\n", + "# Reading from a file\n", + "with open('example.txt', 'r') as file:\n", + " content = file.read()\n", + " print(content)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2565c6a6-7a89-4b4b-b414-c372f4e73f0d", + "metadata": {}, + "outputs": [], + "source": [ + "# Reading file line by line\n", + "with open('example.txt', 'r') as file:\n", + " for line in file:\n", + " print(line)\n", + " print(line.strip())\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "327faf3b-46f9-494e-b227-c7613e7de5b1", + "metadata": {}, + "outputs": [], + "source": [ + "import csv\n", + "\n", + "data = [[\"Name\", \"Age\"], [\"Alice\", 30], [\"Bob\", 28]]\n", + "\n", + "# Writing to a CSV file\n", + "with open('people.csv', 'w', newline='') as file:\n", + " writer = csv.writer(file)\n", + " writer.writerows(data)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "42e81473-ea6b-41a7-b29b-0b32dea575ce", + "metadata": {}, + "outputs": [], + "source": [ + "# Reading from a CSV file\n", + "with open('people.csv', 'r') as file:\n", + " reader = csv.reader(file)\n", + " for row in reader:\n", + " print(row)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ee32032a-a276-4fc1-9e77-0ec112b67420", + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " with open('nonexistent.txt', 'r') as file:\n", + " content = file.read()\n", + "except FileNotFoundError:\n", + " print(\"File not found.\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d1282dcb-f59e-4b1f-b3e4-cf99506855ef", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "# List directory content\n", + "print(os.listdir('.'))\n", + "\n", + "# Change directory\n", + "os.chdir('path')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f3087dcd-3231-4ec7-bdf1-b7097e752489", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "# Get current directory\n", + "current_dir = os.getcwd()\n", + "\n", + "# Join paths\n", + "path = os.path.join(current_dir, 'example.txt')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bbad71a5-2693-417f-a90f-9324c8673690", + "metadata": {}, + "outputs": [], + "source": [ + "path" + ] + }, + { + "cell_type": "markdown", + "id": "12ccfbad-646e-4ca4-ad39-5e291aa334ff", + "metadata": {}, + "source": [ + "## Error and Exception Handling in Python\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2296202c-f3d8-4ccf-b01e-7957a5245a5a", + "metadata": {}, + "outputs": [], + "source": [ + "# 1. Basic Try/Except Block\n", + "# Handling exceptions with try and except.\n", + "\n", + "try:\n", + " result = 10 / 0\n", + "except ZeroDivisionError:\n", + " print(\"You can't divide by zero!\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "545622cc-da80-4a8d-ba3b-e010ee6c17de", + "metadata": {}, + "outputs": [], + "source": [ + "# 2. Handling Multiple Exceptions\n", + "# Catching different exception types.\n", + " \n", + "try:\n", + " result = 10 / 0\n", + "except (TypeError, ZeroDivisionError) as e:\n", + " print(f\"An error occurred: {str(e)}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae583796-6d68-43ed-a6c6-a2e4afe38518", + "metadata": {}, + "outputs": [], + "source": [ + "# 3. Else and Finally Clauses\n", + "# else: Runs when no exception is raised in the try block.\n", + "# finally: Always runs, whether an exception is raised or not.\n", + "try:\n", + " result = 10 / 5\n", + "except ZeroDivisionError:\n", + " print(\"You can't divide by zero!\")\n", + "else:\n", + " print(\"Division successful!\")\n", + "finally:\n", + " print(\"This block will run no matter what.\")\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a2e853a-56cb-4faf-86e2-a685c9223a40", + "metadata": {}, + "outputs": [], + "source": [ + "class CustomError(Exception):\n", + " pass\n", + "\n", + "try:\n", + " raise CustomError(\"This is a custom exception\")\n", + "except CustomError as e:\n", + " print(f\"A CustomError occurred: {str(e)}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1f2642bb-b6e1-4736-9ca2-2aff630feb9c", + "metadata": {}, + "outputs": [], + "source": [ + "# 5. Assertion and the Assert Statement\n", + "# Using assert to automatically trigger an exception when a condition is not met.\n", + "\n", + "x = -1\n", + "assert x >= 0, \"Only non-negative numbers are allowed!\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "90dd266e-21f8-4575-b6a4-7f19d8cfd468", + "metadata": {}, + "outputs": [], + "source": [ + "# 7. Handling Exceptions in Functions\n", + "# Creating functions that handle exceptions.\n", + "# python\n", + "def safe_divide(a, b):\n", + " try:\n", + " return a / b\n", + " except ZeroDivisionError:\n", + " print(\"Can't divide by zero!\")\n", + " return None\n", + "\n", + "result = safe_divide(10, 0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9125ce39-864e-4c3f-81df-b34ddc54506b", + "metadata": {}, + "outputs": [], + "source": [ + "# 8. Raising Exceptions\n", + "# Using raise to trigger an exception.\n", + "\n", + "def validate_age(age):\n", + " if age < 0:\n", + " raise ValueError(\"Age cannot be negative\")\n", + "\n", + "try:\n", + " validate_age(-1)\n", + "except ValueError as e:\n", + " print(f\"Validation failed: {str(e)}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57216bd4-a74f-46bd-ac12-e46a6c8deebf", + "metadata": {}, + "outputs": [], + "source": [ + "# 9. Chaining Exceptions\n", + "# Using from with raise to chain exceptions and maintain tracebacks.\n", + "\n", + "def example():\n", + " try:\n", + " int(\"not_a_number\")\n", + " except ValueError as e:\n", + " raise RuntimeError(\"A parsing error occurred\") from e\n", + "\n", + "try:\n", + " example()\n", + "except RuntimeError as e:\n", + " print(f\"An error occurred: {str(e)}\")\n", + " print(f\"Due to: {str(e.__cause__)}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4115fb83-7962-4b51-95ef-65f7f11d13ac", + "metadata": {}, + "outputs": [], + "source": [ + "### simple data pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c31cd5b1-cb82-4a62-a4d0-acbd359452e4", + "metadata": {}, + "outputs": [], + "source": [ + "def read_data(file_path):\n", + " data = []\n", + " with open(file_path, 'r') as file:\n", + " for line in file:\n", + " name, age = line.strip().split(',')\n", + " data.append((name, int(age)))\n", + " return data\n", + "\n", + "def calculate_average_age(data):\n", + " total_age = sum(age for _, age in data)\n", + " average_age = total_age / len(data)\n", + " return average_age\n", + "\n", + "def display_average_age(average_age):\n", + " print(f\"The average age is: {average_age:.2f}\")\n", + "\n", + "def main(file_path):\n", + " data = read_data(file_path)\n", + " average_age = calculate_average_age(data)\n", + " display_average_age(average_age)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21019019-d720-4fee-8bb5-09d6003875c0", + "metadata": {}, + "outputs": [], + "source": [ + "main('sample.txt')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f694dd79-2e62-4517-8d56-4852a698870f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/README.md b/README.md index ac4783f..1362dce 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,18 @@ # Data Processing in Python (JEM207) The course site for the Data Processing in Python from [IES](http://ies.fsv.cuni.cz/). See information on [SIS](https://is.cuni.cz/studium/predmety/index.php?do=predmet&kod=JEM207). The course is taught by [Martin Hronec](mailto:martin.hronec@fsv.cuni.cz), [Jan Šíla](mailto:jan.sila@fsv.cuni.cz) and -[Alena Pavlovova](mailto:alena.pavlovova@fsv.cuni.cz). +[Alena Pavlova](mailto:alena.pavlova@fsv.cuni.cz). ## Communication Please direct all questions at [Alena Pavlova](mailto:alena.pavlova@fsv.cuni.cz) only. +## Final project +* [Submit you proposal here](https://forms.gle/w7Ct7nCr5P5JM3Uz8) +* See full instructions below schedule + + +## Project - paring +* If you are looking for a partner [use this google sheet](https://docs.google.com/spreadsheets/d/1edVcoztzwrkDS2uqdw_t45c4bt5AX_2IthaPTEG-D78/edit#gid=0) with your CUNI account logged in. If you have a partner, delete your info, please, to make it easier for others. + # Schedule @@ -32,11 +45,11 @@ Please direct all questions at [Alena Pavlova](mailto:alena.pavlova@fsv.cuni.cz) | 7 | 14.11. | L | Algorithmic problem solving | Jan | | | 8 | 21.11. | - | MIDTERM | Alena, Jan & Martin | | | 9 | 27.11. | S | MIDTERM solution | Alena | | -| 9 | 28.11. | L | Data science | Martin | Project proposal | -| 10 | 5.12. | L | How to code (avoiding spaghetti code) | Martin | Topic approved | +| 9 | 28.11. | L | Data science | Martin | | +| 10 | 5.12. | L | How to code (avoiding spaghetti code) | Martin | Project proposal | | 11 | 11.12. | S | Seminar 5: Data science case-study | Alena | | -| 11 | 12.12. | L | Databases | Jan | | -| 12 | 19.12. | L | Guest lecture (TBA) + Python Beer | Alena, Jan & Martin | | +| 11 | 12.12. | L | Databases | Jan | Topic approved | +| 12 | 19.12. | L | Python profiling + Beer after lecture @ [Pivo klub](https://pivo-klub.cz/) | [Miloš Kozák](https://www.linkedin.com/in/milo%C5%A1-koz%C3%A1k-1b837927/) | | | 2.1. | - | - | WiP: Project consultations | Alena, Jan & Martin | | | 9.1. | - | - | WiP: Project consultations | Alena, Jan & Martin | | @@ -47,34 +60,36 @@ At least 50% from the homeworks assignments and work-in-progress presentation is ## Final project (60%) * Students in teams by 2 +* [Submit you proposal here](https://forms.gle/w7Ct7nCr5P5JM3Uz8) * Deadline for topic approval: 5th of December 2023 * Deadline: 9th of February 2024 -### Projects' Evaluation critera +### Projects' Evaluation criteria * Use of git by both - 5pts * meaningful commit messages * pythonic code principles - 5 pts + * Provide requirements.txt file of the dependencies with versions (can use pip freeze) so that we can install with `pip install -r requirements.txt` * code is more often read than written, EAFP -* runability - 15 pts - * by far the most important one! Project needs to run from scratch after installing versioned requirements. +* Runnable code - 15 pts + * by far the most important one! The project needs to run from scratch after installing versioned requirements. * provide requirements.txt file with specific versions of packages (use pip freeze to get it), and specify your precise Python version. * code structure - 15 pts * functions (classes), properly named variables * README, documentation - 5 pts * analysis, visualization - 15 pts - * highlight key poins of your projet + * highlight key points of your project, give it some narrative ## Project work - presentation (10%) * Presentation of work-in-progress related to the final project. * Prepare questions, understand the goals of your project ## Midterm exam (25%) -Live coding (80 minutes), "open browser", no collaboration between the students. More details during the lecture week before +Live coding (80 minutes), "open browser", no collaboration between the students. More details during the lecture the week before ## Homework Assignments (5%) * Create leetcode.com account -* You are expected to submit in a specified Google form +* You are expected to submit in a specified Google form: https://forms.gle/jkoRpZ7yZoQYSYjY7 * link to the problem * Print page showing your solution and submission statistics *Like this: [Path Sum III - Submission Detail - LeetCode.pdf](https://github.com/vitekzkytek/PythonDataIES/files/12743340/Path.Sum.III.-.Submission.Detail.-.LeetCode.pdf) diff --git a/Seminar1/data_describtion.txt b/Seminar1/data_describtion.txt new file mode 100644 index 0000000..cba0710 --- /dev/null +++ b/Seminar1/data_describtion.txt @@ -0,0 +1,523 @@ +MSSubClass: Identifies the type of dwelling involved in the sale. + + 20 1-STORY 1946 & NEWER ALL STYLES + 30 1-STORY 1945 & OLDER + 40 1-STORY W/FINISHED ATTIC ALL AGES + 45 1-1/2 STORY - UNFINISHED ALL AGES + 50 1-1/2 STORY FINISHED ALL AGES + 60 2-STORY 1946 & NEWER + 70 2-STORY 1945 & OLDER + 75 2-1/2 STORY ALL AGES + 80 SPLIT OR MULTI-LEVEL + 85 SPLIT FOYER + 90 DUPLEX - ALL STYLES AND AGES + 120 1-STORY PUD (Planned Unit Development) - 1946 & NEWER + 150 1-1/2 STORY PUD - ALL AGES + 160 2-STORY PUD - 1946 & NEWER + 180 PUD - MULTILEVEL - INCL SPLIT LEV/FOYER + 190 2 FAMILY CONVERSION - ALL STYLES AND AGES + +MSZoning: Identifies the general zoning classification of the sale. + + A Agriculture + C Commercial + FV Floating Village Residential + I Industrial + RH Residential High Density + RL Residential Low Density + RP Residential Low Density Park + RM Residential Medium Density + +LotFrontage: Linear feet of street connected to property + +LotArea: Lot size in square feet + +Street: Type of road access to property + + Grvl Gravel + Pave Paved + +Alley: Type of alley access to property + + Grvl Gravel + Pave Paved + NA No alley access + +LotShape: General shape of property + + Reg Regular + IR1 Slightly irregular + IR2 Moderately Irregular + IR3 Irregular + +LandContour: Flatness of the property + + Lvl Near Flat/Level + Bnk Banked - Quick and significant rise from street grade to building + HLS Hillside - Significant slope from side to side + Low Depression + +Utilities: Type of utilities available + + AllPub All public Utilities (E,G,W,& S) + NoSewr Electricity, Gas, and Water (Septic Tank) + NoSeWa Electricity and Gas Only + ELO Electricity only + +LotConfig: Lot configuration + + Inside Inside lot + Corner Corner lot + CulDSac Cul-de-sac + FR2 Frontage on 2 sides of property + FR3 Frontage on 3 sides of property + +LandSlope: Slope of property + + Gtl Gentle slope + Mod Moderate Slope + Sev Severe Slope + +Neighborhood: Physical locations within Ames city limits + + Blmngtn Bloomington Heights + Blueste Bluestem + BrDale Briardale + BrkSide Brookside + ClearCr Clear Creek + CollgCr College Creek + Crawfor Crawford + Edwards Edwards + Gilbert Gilbert + IDOTRR Iowa DOT and Rail Road + MeadowV Meadow Village + Mitchel Mitchell + Names North Ames + NoRidge Northridge + NPkVill Northpark Villa + NridgHt Northridge Heights + NWAmes Northwest Ames + OldTown Old Town + SWISU South & West of Iowa State University + Sawyer Sawyer + SawyerW Sawyer West + Somerst Somerset + StoneBr Stone Brook + Timber Timberland + Veenker Veenker + +Condition1: Proximity to various conditions + + Artery Adjacent to arterial street + Feedr Adjacent to feeder street + Norm Normal + RRNn Within 200' of North-South Railroad + RRAn Adjacent to North-South Railroad + PosN Near positive off-site feature--park, greenbelt, etc. + PosA Adjacent to postive off-site feature + RRNe Within 200' of East-West Railroad + RRAe Adjacent to East-West Railroad + +Condition2: Proximity to various conditions (if more than one is present) + + Artery Adjacent to arterial street + Feedr Adjacent to feeder street + Norm Normal + RRNn Within 200' of North-South Railroad + RRAn Adjacent to North-South Railroad + PosN Near positive off-site feature--park, greenbelt, etc. + PosA Adjacent to postive off-site feature + RRNe Within 200' of East-West Railroad + RRAe Adjacent to East-West Railroad + +BldgType: Type of dwelling + + 1Fam Single-family Detached + 2FmCon Two-family Conversion; originally built as one-family dwelling + Duplx Duplex + TwnhsE Townhouse End Unit + TwnhsI Townhouse Inside Unit + +HouseStyle: Style of dwelling + + 1Story One story + 1.5Fin One and one-half story: 2nd level finished + 1.5Unf One and one-half story: 2nd level unfinished + 2Story Two story + 2.5Fin Two and one-half story: 2nd level finished + 2.5Unf Two and one-half story: 2nd level unfinished + SFoyer Split Foyer + SLvl Split Level + +OverallQual: Rates the overall material and finish of the house + + 10 Very Excellent + 9 Excellent + 8 Very Good + 7 Good + 6 Above Average + 5 Average + 4 Below Average + 3 Fair + 2 Poor + 1 Very Poor + +OverallCond: Rates the overall condition of the house + + 10 Very Excellent + 9 Excellent + 8 Very Good + 7 Good + 6 Above Average + 5 Average + 4 Below Average + 3 Fair + 2 Poor + 1 Very Poor + +YearBuilt: Original construction date + +YearRemodAdd: Remodel date (same as construction date if no remodeling or additions) + +RoofStyle: Type of roof + + Flat Flat + Gable Gable + Gambrel Gabrel (Barn) + Hip Hip + Mansard Mansard + Shed Shed + +RoofMatl: Roof material + + ClyTile Clay or Tile + CompShg Standard (Composite) Shingle + Membran Membrane + Metal Metal + Roll Roll + Tar&Grv Gravel & Tar + WdShake Wood Shakes + WdShngl Wood Shingles + +Exterior1st: Exterior covering on house + + AsbShng Asbestos Shingles + AsphShn Asphalt Shingles + BrkComm Brick Common + BrkFace Brick Face + CBlock Cinder Block + CemntBd Cement Board + HdBoard Hard Board + ImStucc Imitation Stucco + MetalSd Metal Siding + Other Other + Plywood Plywood + PreCast PreCast + Stone Stone + Stucco Stucco + VinylSd Vinyl Siding + Wd Sdng Wood Siding + WdShing Wood Shingles + +Exterior2nd: Exterior covering on house (if more than one material) + + AsbShng Asbestos Shingles + AsphShn Asphalt Shingles + BrkComm Brick Common + BrkFace Brick Face + CBlock Cinder Block + CemntBd Cement Board + HdBoard Hard Board + ImStucc Imitation Stucco + MetalSd Metal Siding + Other Other + Plywood Plywood + PreCast PreCast + Stone Stone + Stucco Stucco + VinylSd Vinyl Siding + Wd Sdng Wood Siding + WdShing Wood Shingles + +MasVnrType: Masonry veneer type + + BrkCmn Brick Common + BrkFace Brick Face + CBlock Cinder Block + None None + Stone Stone + +MasVnrArea: Masonry veneer area in square feet + +ExterQual: Evaluates the quality of the material on the exterior + + Ex Excellent + Gd Good + TA Average/Typical + Fa Fair + Po Poor + +ExterCond: Evaluates the present condition of the material on the exterior + + Ex Excellent + Gd Good + TA Average/Typical + Fa Fair + Po Poor + +Foundation: Type of foundation + + BrkTil Brick & Tile + CBlock Cinder Block + PConc Poured Contrete + Slab Slab + Stone Stone + Wood Wood + +BsmtQual: Evaluates the height of the basement + + Ex Excellent (100+ inches) + Gd Good (90-99 inches) + TA Typical (80-89 inches) + Fa Fair (70-79 inches) + Po Poor (<70 inches + NA No Basement + +BsmtCond: Evaluates the general condition of the basement + + Ex Excellent + Gd Good + TA Typical - slight dampness allowed + Fa Fair - dampness or some cracking or settling + Po Poor - Severe cracking, settling, or wetness + NA No Basement + +BsmtExposure: Refers to walkout or garden level walls + + Gd Good Exposure + Av Average Exposure (split levels or foyers typically score average or above) + Mn Mimimum Exposure + No No Exposure + NA No Basement + +BsmtFinType1: Rating of basement finished area + + GLQ Good Living Quarters + ALQ Average Living Quarters + BLQ Below Average Living Quarters + Rec Average Rec Room + LwQ Low Quality + Unf Unfinshed + NA No Basement + +BsmtFinSF1: Type 1 finished square feet + +BsmtFinType2: Rating of basement finished area (if multiple types) + + GLQ Good Living Quarters + ALQ Average Living Quarters + BLQ Below Average Living Quarters + Rec Average Rec Room + LwQ Low Quality + Unf Unfinshed + NA No Basement + +BsmtFinSF2: Type 2 finished square feet + +BsmtUnfSF: Unfinished square feet of basement area + +TotalBsmtSF: Total square feet of basement area + +Heating: Type of heating + + Floor Floor Furnace + GasA Gas forced warm air furnace + GasW Gas hot water or steam heat + Grav Gravity furnace + OthW Hot water or steam heat other than gas + Wall Wall furnace + +HeatingQC: Heating quality and condition + + Ex Excellent + Gd Good + TA Average/Typical + Fa Fair + Po Poor + +CentralAir: Central air conditioning + + N No + Y Yes + +Electrical: Electrical system + + SBrkr Standard Circuit Breakers & Romex + FuseA Fuse Box over 60 AMP and all Romex wiring (Average) + FuseF 60 AMP Fuse Box and mostly Romex wiring (Fair) + FuseP 60 AMP Fuse Box and mostly knob & tube wiring (poor) + Mix Mixed + +1stFlrSF: First Floor square feet + +2ndFlrSF: Second floor square feet + +LowQualFinSF: Low quality finished square feet (all floors) + +GrLivArea: Above grade (ground) living area square feet + +BsmtFullBath: Basement full bathrooms + +BsmtHalfBath: Basement half bathrooms + +FullBath: Full bathrooms above grade + +HalfBath: Half baths above grade + +Bedroom: Bedrooms above grade (does NOT include basement bedrooms) + +Kitchen: Kitchens above grade + +KitchenQual: Kitchen quality + + Ex Excellent + Gd Good + TA Typical/Average + Fa Fair + Po Poor + +TotRmsAbvGrd: Total rooms above grade (does not include bathrooms) + +Functional: Home functionality (Assume typical unless deductions are warranted) + + Typ Typical Functionality + Min1 Minor Deductions 1 + Min2 Minor Deductions 2 + Mod Moderate Deductions + Maj1 Major Deductions 1 + Maj2 Major Deductions 2 + Sev Severely Damaged + Sal Salvage only + +Fireplaces: Number of fireplaces + +FireplaceQu: Fireplace quality + + Ex Excellent - Exceptional Masonry Fireplace + Gd Good - Masonry Fireplace in main level + TA Average - Prefabricated Fireplace in main living area or Masonry Fireplace in basement + Fa Fair - Prefabricated Fireplace in basement + Po Poor - Ben Franklin Stove + NA No Fireplace + +GarageType: Garage location + + 2Types More than one type of garage + Attchd Attached to home + Basment Basement Garage + BuiltIn Built-In (Garage part of house - typically has room above garage) + CarPort Car Port + Detchd Detached from home + NA No Garage + +GarageYrBlt: Year garage was built + +GarageFinish: Interior finish of the garage + + Fin Finished + RFn Rough Finished + Unf Unfinished + NA No Garage + +GarageCars: Size of garage in car capacity + +GarageArea: Size of garage in square feet + +GarageQual: Garage quality + + Ex Excellent + Gd Good + TA Typical/Average + Fa Fair + Po Poor + NA No Garage + +GarageCond: Garage condition + + Ex Excellent + Gd Good + TA Typical/Average + Fa Fair + Po Poor + NA No Garage + +PavedDrive: Paved driveway + + Y Paved + P Partial Pavement + N Dirt/Gravel + +WoodDeckSF: Wood deck area in square feet + +OpenPorchSF: Open porch area in square feet + +EnclosedPorch: Enclosed porch area in square feet + +3SsnPorch: Three season porch area in square feet + +ScreenPorch: Screen porch area in square feet + +PoolArea: Pool area in square feet + +PoolQC: Pool quality + + Ex Excellent + Gd Good + TA Average/Typical + Fa Fair + NA No Pool + +Fence: Fence quality + + GdPrv Good Privacy + MnPrv Minimum Privacy + GdWo Good Wood + MnWw Minimum Wood/Wire + NA No Fence + +MiscFeature: Miscellaneous feature not covered in other categories + + Elev Elevator + Gar2 2nd Garage (if not described in garage section) + Othr Other + Shed Shed (over 100 SF) + TenC Tennis Court + NA None + +MiscVal: $Value of miscellaneous feature + +MoSold: Month Sold (MM) + +YrSold: Year Sold (YYYY) + +SaleType: Type of sale + + WD Warranty Deed - Conventional + CWD Warranty Deed - Cash + VWD Warranty Deed - VA Loan + New Home just constructed and sold + COD Court Officer Deed/Estate + Con Contract 15% Down payment regular terms + ConLw Contract Low Down payment and low interest + ConLI Contract Low Interest + ConLD Contract Low Down + Oth Other + +SaleCondition: Condition of sale + + Normal Normal Sale + Abnorml Abnormal Sale - trade, foreclosure, short sale + AdjLand Adjoining Land Purchase + Alloca Allocation - two linked properties with separate deeds, typically condo with a garage unit + Family Sale between family members + Partial Home was not completed when last assessed (associated with New Homes) diff --git a/Seminar1/seminar_empty.ipynb b/Seminar1/seminar_empty.ipynb new file mode 100644 index 0000000..ae3ae05 --- /dev/null +++ b/Seminar1/seminar_empty.ipynb @@ -0,0 +1,674 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "# Seminar: Exploratory Data Analysis in Python/Pandas environment\n", + "\n", + "* We will explore and understand the Ames Housing dataset of real estate sales\n", + "* The content is based on the Kaggle Competition House Prices Advanced Regression Techniques. See details [here](https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "## Definitions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "gather": { + "logged": 1665932462185 + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import math\n", + "import matplotlib.pyplot as plt\n", + "import scipy.stats as stats\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "## Task 1: Read data and undestand it's structure\n", + "\n", + "### 1a. Load training dataset (in `./data/train.csv`) and display 5 random rows\n", + "\n", + "Hint: Use `.style` attribute to display all columns\n", + "\n", + "Hint: See `./data/data_description.txt` for documentation of variables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665932463360 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### 1b. What is the distribution `SalePrice` variable?\n", + "\n", + "* plot histogram (`.hist()` on `pd.Series`) with bin width $10,000\n", + "\n", + "Hint: Specify bins using range" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665932220142 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### 1c. Split columns between quantitative and qualitative variables\n", + "Store column names in separate lists of strings `quantitative` and `qualitative`\n", + "\n", + "Hint: `.dtypes` attribute contains a series with strings describing dtype of the data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665932487403 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### 1d. Are `dtype`s correct? \n", + "\n", + "* visually check whether all the columns look correctly parsed" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665932227042 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665932228725 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### 1e. Plot number of missing data for all columns\n", + "* You can drop columns with no missing data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665932234934 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "## Bonus: Distribution of all variables\n", + "\n", + "### Quantitative\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665932268139 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### Quantitative" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665932290215 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "## Task 2: Study relationships between variables\n", + "\n", + "### 2a. see correlation matrix" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665932337283 + }, + "jupyter": { + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### 2b. Boxplots for categorical variables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665932338324 + }, + "jupyter": { + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665932339270 + }, + "jupyter": { + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "## Task 3: ANOVA disparity estimations \n", + "\n", + "### One-way ANOVA \n", + "* Question: Do sale prices differ across categories of certain feature?\n", + "* Test: Do price means across factors with a feature differ in their underlying distribution? \n", + "* Are prices of houses with pool drawn from distribution with different mean than prices of house without a pool? \n", + "\n", + "* We will test this on all features and plot results\n", + "\n", + "### 3a. Function for estimating ANOVA for one feature\n", + "* Your task is to complete a following snippet:\n", + "\n", + "\n", + "```python\n", + "\n", + " def anova_feature(qualitative_series, quantitative_series):\n", + " '''\n", + " Performs One-way ANOVA testing whether all levels of `qualitative` series are drawn from distributions with equal means\n", + "\n", + " Expects:\n", + " - 'qualitative_series': Series with categorical data delienating indivudal groups\n", + " - 'quantitative_series': Series with value data on which the distribution is tested\n", + " \n", + " Uses `scipy.stats.f_oneway` to deliver the test.\n", + "\n", + " Returns pd.Series with `statistic`, `p_value` and `disparity` measure. `statistic` and `p_value` are calculated by `scipy.stats.f_oneway`. Disparity is calculated as 1/log(p_value).\n", + " '''\n", + " samples = {\n", + " factor: quantitative_series.loc[qualitative_series.fillna('MISSING') == factor] for factor in qualitative_series.fillna('MISSING').unique()\n", + " }\n", + "\n", + " anova_result = stats.f_oneway(*samples.values())\n", + " \n", + " pass\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665932355553 + }, + "jupyter": { + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### 3b. Generate dataframe with ANOVA test of all quantitative columns on `SalePrice` in the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665932371237 + }, + "jupyter": { + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### 3c. Plot the disparity measure" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665932381965 + }, + "jupyter": { + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "## Task 4: Encode qualitative variables as quantitative\n", + "### 4a. Feature-level function\n", + "\n", + "* Write a function that an input dataframe with encoded version\n", + "* Complete the following snippet" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665932501723 + }, + "jupyter": { + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "## 4b. Apply on all qualitative features\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665932504214 + }, + "jupyter": { + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665932509900 + }, + "jupyter": { + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernel_info": { + "name": "python38-azureml" + }, + "kernelspec": { + "display_name": "Python 3.8 - AzureML", + "language": "python", + "name": "python38-azureml" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "microsoft": { + "host": { + "AzureML": { + "notebookHasBeenCompleted": true + } + } + }, + "nteract": { + "version": "nteract-front-end@1.0.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Seminar1/seminar_solved.ipynb b/Seminar1/seminar_solved.ipynb new file mode 100644 index 0000000..72f7dc6 --- /dev/null +++ b/Seminar1/seminar_solved.ipynb @@ -0,0 +1,3335 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "# Seminar: Exploratory Data Analysis in Python/Pandas environment\n", + "\n", + "* We will explore and understand the Ames Housing dataset of real estate sales\n", + "* The content is based on the Kaggle Competition House Prices Advanced Regression Techniques. See details [here](https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Keyboard shortcuts" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Shift + Enter: run cell, select below\n", + "\n", + "Alt + Enter: run cell, insert below" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "## Definitions" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "gather": { + "logged": 1665932462185 + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import math\n", + "import matplotlib.pyplot as plt\n", + "import scipy.stats as stats\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "## Task 1: Read data and undestand it's structure\n", + "\n", + "### 1a. Load training dataset (in `./data/train.csv`) and display 5 random rows\n", + "\n", + "Hint: Use `.style` attribute to display all columns\n", + "\n", + "Hint: See `./data/data_description.txt` for documentation of variables" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665932463360 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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 MSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondYearBuiltYearRemodAddRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQCCentralAirElectrical1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFunctionalFireplacesFireplaceQuGarageTypeGarageYrBltGarageFinishGarageCarsGarageAreaGarageQualGarageCondPavedDriveWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaPoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
Id                                                                                
69550RM51.0000006120PavenanRegLvlAllPubCornerGtlBrkSideNormNorm1Fam1.5Fin5619361950GableCompShgWd SdngWd SdngNone0.000000TAFaBrkTilTATANoUnf0Unf0927927GasATAYSBrkr106747201539001131TA5Typ0nanDetchd1995.000000Unf2576TATAY11200000nanMnPrvnan042009WDNormal141500
40750RL51.00000010480PavenanRegLvlAllPubInsideGtlSWISUNormNorm1Fam1.5Fin6519361950GableCompShgMetalSdMetalSdNone0.000000TATABrkTilTATANoUnf0Unf010641064GasAExYFuseA116604731639001031TA6Maj20nanDetchd1936.000000Unf1240TATAY000000nannannan032008WDNormal115000
1359160FVnan2117PavenanRegLvlAllPubInsideGtlSomerstNormNormTwnhs2Story6520002000GableCompShgMetalSdMetalSdBrkFace216.000000GdTAPConcGdTANoGLQ378Unf0378756GasAExYSBrkr76980401573002131Gd5Typ0nanDetchd2000.000000Unf2440TATAY0320000nannannan062010WDNormal177500
13160RL88.00000014200PavenanRegLvlAllPubCornerGtlNAmesNormNorm1Fam2Story7619661966GableCompShgMetalSdMetalSdBrkFace309.000000TATACBlockTATANoRec445Unf0479924GasAExYSBrkr121694102157002141Gd8Typ2GdAttchd1966.000000Fin2487TATAY105660000nanGdPrvnan052006WDNormal226000
1173160FV35.0000004017PavePaveIR1LvlAllPubInsideGtlSomerstNormNormTwnhsE2Story7520062007GableCompShgMetalSdMetalSdNone0.000000GdTAPConcGdTANoUnf0Unf0625625GasAExYSBrkr62562501250002121Gd5Typ0nanDetchd2006.000000Fin2625TATAY0540000nannannan032008WDNormal171900
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MSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilitiesLotConfig...PoolAreaPoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
Id
160RL65.08450PaveNaNRegLvlAllPubInside...0NaNNaNNaN022008WDNormal208500
220RL80.09600PaveNaNRegLvlAllPubFR2...0NaNNaNNaN052007WDNormal181500
360RL68.011250PaveNaNIR1LvlAllPubInside...0NaNNaNNaN092008WDNormal223500
470RL60.09550PaveNaNIR1LvlAllPubCorner...0NaNNaNNaN022006WDAbnorml140000
560RL84.014260PaveNaNIR1LvlAllPubFR2...0NaNNaNNaN0122008WDNormal250000
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5 rows × 80 columns

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" + ], + "text/plain": [ + " MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", + "Id \n", + "1 60 RL 65.0 8450 Pave NaN Reg \n", + "2 20 RL 80.0 9600 Pave NaN Reg \n", + "3 60 RL 68.0 11250 Pave NaN IR1 \n", + "4 70 RL 60.0 9550 Pave NaN IR1 \n", + "5 60 RL 84.0 14260 Pave NaN IR1 \n", + "\n", + " LandContour Utilities LotConfig ... PoolArea PoolQC Fence MiscFeature \\\n", + "Id ... \n", + "1 Lvl AllPub Inside ... 0 NaN NaN NaN \n", + "2 Lvl AllPub FR2 ... 0 NaN NaN NaN \n", + "3 Lvl AllPub Inside ... 0 NaN NaN NaN \n", + "4 Lvl AllPub Corner ... 0 NaN NaN NaN \n", + "5 Lvl AllPub FR2 ... 0 NaN NaN NaN \n", + "\n", + " MiscVal MoSold YrSold SaleType SaleCondition SalePrice \n", + "Id \n", + "1 0 2 2008 WD Normal 208500 \n", + "2 0 5 2007 WD Normal 181500 \n", + "3 0 9 2008 WD Normal 223500 \n", + "4 0 2 2006 WD Abnorml 140000 \n", + "5 0 12 2008 WD Normal 250000 \n", + "\n", + "[5 rows x 80 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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MSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilitiesLotConfig...PoolAreaPoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
Id
145660RL62.07917PaveNaNRegLvlAllPubInside...0NaNNaNNaN082007WDNormal175000
145720RL85.013175PaveNaNRegLvlAllPubInside...0NaNMnPrvNaN022010WDNormal210000
145870RL66.09042PaveNaNRegLvlAllPubInside...0NaNGdPrvShed250052010WDNormal266500
145920RL68.09717PaveNaNRegLvlAllPubInside...0NaNNaNNaN042010WDNormal142125
146020RL75.09937PaveNaNRegLvlAllPubInside...0NaNNaNNaN062008WDNormal147500
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" + ], + "text/plain": [ + " MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", + "Id \n", + "1456 60 RL 62.0 7917 Pave NaN Reg \n", + "1457 20 RL 85.0 13175 Pave NaN Reg \n", + "1458 70 RL 66.0 9042 Pave NaN Reg \n", + "1459 20 RL 68.0 9717 Pave NaN Reg \n", + "1460 20 RL 75.0 9937 Pave NaN Reg \n", + "\n", + " LandContour Utilities LotConfig ... PoolArea PoolQC Fence MiscFeature \\\n", + "Id ... \n", + "1456 Lvl AllPub Inside ... 0 NaN NaN NaN \n", + "1457 Lvl AllPub Inside ... 0 NaN MnPrv NaN \n", + "1458 Lvl AllPub Inside ... 0 NaN GdPrv Shed \n", + "1459 Lvl AllPub Inside ... 0 NaN NaN NaN \n", + "1460 Lvl AllPub Inside ... 0 NaN NaN NaN \n", + "\n", + " MiscVal MoSold YrSold SaleType SaleCondition SalePrice \n", + "Id \n", + "1456 0 8 2007 WD Normal 175000 \n", + "1457 0 2 2010 WD Normal 210000 \n", + "1458 2500 5 2010 WD Normal 266500 \n", + "1459 0 4 2010 WD Normal 142125 \n", + "1460 0 6 2008 WD Normal 147500 \n", + "\n", + "[5 rows x 80 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### 1b. What is the distribution `SalePrice` variable?\n", + "\n", + "* plot histogram (`.hist()` on `pd.Series`) with bin width $10,000\n", + "\n", + "Hint: Specify bins using range" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665932220142 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df.SalePrice.hist(bins=range(0,df.SalePrice.max(),10000))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### 1c. Split columns between quantitative and qualitative variables\n", + "Store column names in separate lists of strings `quantitative` and `qualitative`\n", + "\n", + "Hint: `.dtypes` attribute contains a series with strings describing dtype of the data" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665932487403 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual', 'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature', 'SaleType', 'SaleCondition']\n", + "['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold', 'SalePrice']\n" + ] + } + ], + "source": [ + "qualitative = [column for column in df.columns if df.dtypes[column] == 'object']\n", + "quantitative = [column for column in df.columns if df.dtypes[column] != 'object']\n", + "\n", + "print(qualitative)\n", + "print(quantitative)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### 1d. Are `dtype`s correct? \n", + "\n", + "* visually check whether all the columns look correctly parsed" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665932227042 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 MSZoningStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinType2HeatingHeatingQCCentralAirElectricalKitchenQualFunctionalFireplaceQuGarageTypeGarageFinishGarageQualGarageCondPavedDrivePoolQCFenceMiscFeatureSaleTypeSaleCondition
Id                                           
209RLPavenanIR1LowAllPubInsideModSawyerWNormNorm1Fam2StoryGableCompShgPlywoodPlywoodBrkFaceGdTACBlockGdTAGdGLQUnfGasAExYSBrkrTATypGdAttchdFinTATAYnannannanWDNormal
308RMPaveGrvlIR1LvlAllPubInsideGtlIDOTRRArteryNorm1Fam1.5FinGableCompShgMetalSdMetalSdNoneTAFaCBlockTATANoUnfUnfGasATAYFuseAFaTypnannannannannanNnanMnPrvnanWDNormal
910RLPavenanIR2LvlAllPubInsideGtlGilbertNormNorm1Fam2StoryGableCompShgVinylSdVinylSdNoneGdTAPConcGdTANoUnfUnfGasAExYSBrkrGdTypGdAttchdFinTATAYnannannanWDNormal
194RMPavenanRegLvlAllPubInsideGtlEdwardsNormNormTwnhs2StoryGableCompShgVinylSdVinylSdStoneGdTAPConcGdTANoUnfUnfGasAExYSBrkrGdMaj1nanDetchdUnfTATAYnannannanWDNormal
863RLPavenanRegLvlAllPubCornerGtlSawyerWNormNorm1Fam1StoryHipCompShgHdBoardPlywoodNoneTATAPConcGdTANoGLQUnfGasATAYSBrkrTATypnanAttchdUnfTATAYnanGdPrvnanWDNormal
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 MSSubClassLotFrontageLotAreaOverallQualOverallCondYearBuiltYearRemodAddMasVnrAreaBsmtFinSF1BsmtFinSF2BsmtUnfSFTotalBsmtSF1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrTotRmsAbvGrdFireplacesGarageYrBltGarageCarsGarageAreaWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaMiscValMoSoldYrSoldSalePrice
Id                                     
2548085.00000093506719641991108.00000027058045213021302001302012031701964.0000001309333000000102007158000
5667066.000000685864191519500.0000000080680684180601647101141601920.0000001216066136000052010128000
77520110.000000142268520062006375.00000000193519351973001973002031912006.0000003895315450000072007395000
1435071.000000852054195219520.000000507040391091047501385002041602000.0000002720000000062010166000
2292070.000000852155196719670.00000084207091291200912001031511974.0000001336000000052010125000
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Plot number of missing data for all columns\n", + "* You can drop columns with no missing data" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665932234934 + }, + "jupyter": { + "outputs_hidden": false, + "source_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df.isna().sum().sort_values().plot.bar(figsize=(15, 5))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "col_names_with_miss_val = [col for col in df.columns if df[col].isna().sum()!=0]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df[col_names_with_miss_val].isna().sum().sort_values().plot.bar(figsize=(5, 3))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "## Task 2: Study relationships between variables\n", + "\n", + "### 2a. see correlation matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665932337283 + }, + "jupyter": { + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_236/3149907857.py:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n", + " sns.heatmap(df.corr(),vmin=-1,cmap='coolwarm',annot=True,fmt='0.1f')\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig=plt.subplots(1,1,figsize=(15,12))\n", + "sns.heatmap(df.corr(),vmin=-1,cmap='coolwarm',annot=True,fmt='0.1f')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### 2b. Boxplots for categorical variables" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665932338324 + }, + "jupyter": { + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.boxplot(data=df,x='MSZoning',y='SalePrice')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "## Task 3: ANOVA disparity estimations \n", + "\n", + "### One-way ANOVA \n", + "* Question: Do sale prices differ across categories of certain feature?\n", + "* Test: Do price means across factors with a feature differ in their underlying distribution? \n", + "* Are prices of houses with pool drawn from distribution with different mean than prices of house without a pool? \n", + "\n", + "* We will test this on all features and plot results\n", + "\n", + "### 3a. Function for estimating ANOVA for one feature\n", + "* Your task is to complete a following snippet:\n", + "\n", + "\n", + "```python\n", + "\n", + " def anova_feature(qualitative_series, quantitative_series):\n", + " '''\n", + " Performs One-way ANOVA testing whether all levels of `qualitative` series are drawn from distributions with equal means\n", + "\n", + " Expects:\n", + " - 'qualitative_series': Series with categorical data delienating indivudal groups\n", + " - 'quantitative_series': Series with value data on which the distribution is tested\n", + " \n", + " Uses `scipy.stats.f_oneway` to deliver the test.\n", + "\n", + " Returns pd.Series with `statistic`, `p_value` and `disparity` measure. `statistic` and `p_value` are calculated by `scipy.stats.f_oneway`. Disparity is calculated as 1/log(p_value).\n", + " '''\n", + " samples = {\n", + " factor: quantitative_series.loc[qualitative_series.fillna('MISSING') == factor] for factor in qualitative_series.fillna('MISSING').unique()\n", + " }\n", + "\n", + " anova_result = stats.f_oneway(*samples.values())\n", + " \n", + " pass\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665932355553 + }, + "jupyter": { + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "statistic 8.467220e+01\n", + "p_value 1.054025e-64\n", + "disparity 1.473128e+02\n", + "dtype: float64" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def anova_feature(qualitative_series, quantitative_series):\n", + " '''\n", + " Performs One-way ANOVA testing whether `quantitative_series` across levels of `qualitative_series` are drawn from distributions with equal means\n", + "\n", + " Expects:\n", + " - 'qualitative_series': Series with categorical data delienating indivudal groups\n", + " - 'quantitative_series': Series with value data on which the distribution is tested\n", + " \n", + "\n", + " Returns Series with test statistic, p-value\n", + " '''\n", + " samples = {\n", + " factor: quantitative_series.loc[qualitative_series.fillna('MISSING') == factor]for factor in qualitative_series.fillna('MISSING').unique()\n", + " }\n", + "\n", + " statistic, pvalue = stats.f_oneway(*samples.values())\n", + " \n", + " return pd.Series({\n", + " 'statistic':statistic,\n", + " 'p_value':pvalue,\n", + " 'disparity': math.log(1./pvalue)\n", + " })\n", + "\n", + "anova_feature(df.MasVnrType, df.SalePrice)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### 3b. Generate dataframe with ANOVA test of all quantitative columns on `SalePrice` in the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665932371237 + }, + "jupyter": { + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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statisticp_valuedisparity
MSZoning43.8402828.817634e-3578.413725
Street2.4592901.170486e-012.145166
Alley15.1766142.996380e-0715.020691
LotShape40.1328526.447524e-2555.700931
LandContour12.8501882.742217e-0817.411914
Utilities0.2988045.847168e-010.536628
LotConfig7.8099543.163167e-0612.663937
LandSlope1.9588171.413964e-011.956188
Neighborhood71.7848651.558600e-225517.637858
Condition16.1180178.904549e-0816.234118
Condition22.0738994.342566e-023.136705
BldgType13.0110772.056736e-1022.304730
HouseStyle19.5950013.376777e-2556.347706
RoofStyle17.8054973.653523e-1737.848255
RoofMatl6.7273057.231445e-0816.442242
Exterior1st18.6117432.586089e-4398.061012
Exterior2nd17.5008404.842186e-4397.433793
MasVnrType84.6722011.054025e-64147.312830
ExterQual443.3348311.439551e-204469.363028
ExterCond8.7987145.106681e-0714.487546
Foundation100.2538515.791895e-91207.778784
BsmtQual316.1486358.158548e-196449.207612
BsmtCond19.7081398.195794e-1634.737740
BsmtExposure63.9397617.557758e-50113.106680
BsmtFinType164.6882002.386358e-71162.613773
BsmtFinType27.5653785.225649e-0816.767102
Heating4.2598197.534721e-047.190819
HeatingQC88.3944622.667062e-67153.292224
CentralAir98.3053441.809506e-2250.063818
Electrical18.4601928.226925e-1839.339119
KitchenQual407.8063523.032213e-192440.987045
Functional4.0578754.841697e-047.633075
FireplaceQu121.0751212.971217e-107245.287633
GarageType80.3799926.117026e-87198.513827
GarageFinish213.8670286.228747e-115262.968110
GarageQual25.7760935.388762e-2555.880312
GarageCond25.7501535.711746e-2555.822103
PavedDrive42.0241791.803569e-1840.856764
PoolQC10.5098537.700989e-0714.076747
Fence13.4332769.379977e-1123.089859
MiscFeature2.5936223.500367e-023.352302
SaleType28.8630545.039767e-4295.091214
SaleCondition45.5784287.988268e-4499.235770
\n", + "
" + ], + "text/plain": [ + " statistic p_value disparity\n", + "MSZoning 43.840282 8.817634e-35 78.413725\n", + "Street 2.459290 1.170486e-01 2.145166\n", + "Alley 15.176614 2.996380e-07 15.020691\n", + "LotShape 40.132852 6.447524e-25 55.700931\n", + "LandContour 12.850188 2.742217e-08 17.411914\n", + "Utilities 0.298804 5.847168e-01 0.536628\n", + "LotConfig 7.809954 3.163167e-06 12.663937\n", + "LandSlope 1.958817 1.413964e-01 1.956188\n", + "Neighborhood 71.784865 1.558600e-225 517.637858\n", + "Condition1 6.118017 8.904549e-08 16.234118\n", + "Condition2 2.073899 4.342566e-02 3.136705\n", + "BldgType 13.011077 2.056736e-10 22.304730\n", + "HouseStyle 19.595001 3.376777e-25 56.347706\n", + "RoofStyle 17.805497 3.653523e-17 37.848255\n", + "RoofMatl 6.727305 7.231445e-08 16.442242\n", + "Exterior1st 18.611743 2.586089e-43 98.061012\n", + "Exterior2nd 17.500840 4.842186e-43 97.433793\n", + "MasVnrType 84.672201 1.054025e-64 147.312830\n", + "ExterQual 443.334831 1.439551e-204 469.363028\n", + "ExterCond 8.798714 5.106681e-07 14.487546\n", + "Foundation 100.253851 5.791895e-91 207.778784\n", + "BsmtQual 316.148635 8.158548e-196 449.207612\n", + "BsmtCond 19.708139 8.195794e-16 34.737740\n", + "BsmtExposure 63.939761 7.557758e-50 113.106680\n", + "BsmtFinType1 64.688200 2.386358e-71 162.613773\n", + "BsmtFinType2 7.565378 5.225649e-08 16.767102\n", + "Heating 4.259819 7.534721e-04 7.190819\n", + "HeatingQC 88.394462 2.667062e-67 153.292224\n", + "CentralAir 98.305344 1.809506e-22 50.063818\n", + "Electrical 18.460192 8.226925e-18 39.339119\n", + "KitchenQual 407.806352 3.032213e-192 440.987045\n", + "Functional 4.057875 4.841697e-04 7.633075\n", + "FireplaceQu 121.075121 2.971217e-107 245.287633\n", + "GarageType 80.379992 6.117026e-87 198.513827\n", + "GarageFinish 213.867028 6.228747e-115 262.968110\n", + "GarageQual 25.776093 5.388762e-25 55.880312\n", + "GarageCond 25.750153 5.711746e-25 55.822103\n", + "PavedDrive 42.024179 1.803569e-18 40.856764\n", + "PoolQC 10.509853 7.700989e-07 14.076747\n", + "Fence 13.433276 9.379977e-11 23.089859\n", + "MiscFeature 2.593622 3.500367e-02 3.352302\n", + "SaleType 28.863054 5.039767e-42 95.091214\n", + "SaleCondition 45.578428 7.988268e-44 99.235770" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "anova = pd.DataFrame({\n", + " col: anova_feature(df[col],df.SalePrice) for col in qualitative\n", + "}).T\n", + "anova" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "### 3c. Plot the disparity measure" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665932381965 + }, + "jupyter": { + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "anova.disparity.sort_values(ascending=False).plot.bar(figsize=(12,3))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "## Task 4: Encode qualitative variables as quantitative\n", + "### 4a. Feature-level function\n", + "\n", + "* Write a function that an input dataframe with encoded version\n", + "* Complete the following snippet" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665932504214 + }, + "jupyter": { + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [], + "source": [ + "def encode_qualitative_feature(quantitative_series, qualitative_series, sorting_function=np.mean,suffix='_E'):\n", + " '''\n", + " Ranks `qualitative_series` according to sorting function applied on `quantitative_series` and return its rank as a series. \n", + " \n", + " Series name is extended with `suffix`.\n", + " '''\n", + " encode_df = pd.DataFrame({\n", + " quantitative_series.name:quantitative_series,\n", + " qualitative_series.name:qualitative_series \n", + " })\n", + "\n", + " sorting = encode_df.groupby(qualitative_series.name)[quantitative_series.name].apply(np.mean).rank()\n", + "\n", + " return qualitative_series.map(sorting).rename(f'{qualitative_series.name}{suffix}')" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Id\n", + "1 3.0\n", + "2 2.0\n", + "3 3.0\n", + "4 3.0\n", + "5 3.0\n", + " ... \n", + "1456 2.0\n", + "1457 2.0\n", + "1458 3.0\n", + "1459 3.0\n", + "1460 2.0\n", + "Name: KitchenQual_E, Length: 1460, dtype: float64" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "encode_qualitative_feature(df.SalePrice, df.KitchenQual)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nteract": { + "transient": { + "deleting": false + } + } + }, + "source": [ + "## 4b. Apply on all qualitative features\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false, + "gather": { + "logged": 1665932509900 + }, + "jupyter": { + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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MSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilitiesLotConfig...GarageType_EGarageFinish_EGarageQual_EGarageCond_EPavedDrive_EPoolQC_EFence_EMiscFeature_ESaleType_ESaleCondition_E
Id
160RL65.08450PaveNaNRegLvlAllPubInside...5.02.03.05.03.0NaNNaNNaN5.05.0
220RL80.09600PaveNaNRegLvlAllPubFR2...5.02.03.05.03.0NaNNaNNaN5.05.0
360RL68.011250PaveNaNIR1LvlAllPubInside...5.02.03.05.03.0NaNNaNNaN5.05.0
470RL60.09550PaveNaNIR1LvlAllPubCorner...2.01.03.05.03.0NaNNaNNaN5.02.0
560RL84.014260PaveNaNIR1LvlAllPubFR2...5.02.03.05.03.0NaNNaNNaN5.05.0
..................................................................
145660RL62.07917PaveNaNRegLvlAllPubInside...5.02.03.05.03.0NaNNaNNaN5.05.0
145720RL85.013175PaveNaNRegLvlAllPubInside...5.01.03.05.03.0NaN3.0NaN5.05.0
145870RL66.09042PaveNaNRegLvlAllPubInside...5.02.03.05.03.0NaN4.02.05.05.0
145920RL68.09717PaveNaNRegLvlAllPubInside...5.01.03.05.03.0NaNNaNNaN5.05.0
146020RL75.09937PaveNaNRegLvlAllPubInside...5.03.03.05.03.0NaNNaNNaN5.05.0
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1460 rows × 123 columns

\n", + "
" + ], + "text/plain": [ + " MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", + "Id \n", + "1 60 RL 65.0 8450 Pave NaN Reg \n", + "2 20 RL 80.0 9600 Pave NaN Reg \n", + "3 60 RL 68.0 11250 Pave NaN IR1 \n", + "4 70 RL 60.0 9550 Pave NaN IR1 \n", + "5 60 RL 84.0 14260 Pave NaN IR1 \n", + "... ... ... ... ... ... ... ... \n", + "1456 60 RL 62.0 7917 Pave NaN Reg \n", + "1457 20 RL 85.0 13175 Pave NaN Reg \n", + "1458 70 RL 66.0 9042 Pave NaN Reg \n", + "1459 20 RL 68.0 9717 Pave NaN Reg \n", + "1460 20 RL 75.0 9937 Pave NaN Reg \n", + "\n", + " LandContour Utilities LotConfig ... GarageType_E GarageFinish_E \\\n", + "Id ... \n", + "1 Lvl AllPub Inside ... 5.0 2.0 \n", + "2 Lvl AllPub FR2 ... 5.0 2.0 \n", + "3 Lvl AllPub Inside ... 5.0 2.0 \n", + "4 Lvl AllPub Corner ... 2.0 1.0 \n", + "5 Lvl AllPub FR2 ... 5.0 2.0 \n", + "... ... ... ... ... ... ... \n", + "1456 Lvl AllPub Inside ... 5.0 2.0 \n", + "1457 Lvl AllPub Inside ... 5.0 1.0 \n", + "1458 Lvl AllPub Inside ... 5.0 2.0 \n", + "1459 Lvl AllPub Inside ... 5.0 1.0 \n", + "1460 Lvl AllPub Inside ... 5.0 3.0 \n", + "\n", + " GarageQual_E GarageCond_E PavedDrive_E PoolQC_E Fence_E MiscFeature_E \\\n", + "Id \n", + "1 3.0 5.0 3.0 NaN NaN NaN \n", + "2 3.0 5.0 3.0 NaN NaN NaN \n", + "3 3.0 5.0 3.0 NaN NaN NaN \n", + "4 3.0 5.0 3.0 NaN NaN NaN \n", + "5 3.0 5.0 3.0 NaN NaN NaN \n", + "... ... ... ... ... ... ... \n", + "1456 3.0 5.0 3.0 NaN NaN NaN \n", + "1457 3.0 5.0 3.0 NaN 3.0 NaN \n", + "1458 3.0 5.0 3.0 NaN 4.0 2.0 \n", + "1459 3.0 5.0 3.0 NaN NaN NaN \n", + "1460 3.0 5.0 3.0 NaN NaN NaN \n", + "\n", + " SaleType_E SaleCondition_E \n", + "Id \n", + "1 5.0 5.0 \n", + "2 5.0 5.0 \n", + "3 5.0 5.0 \n", + "4 5.0 2.0 \n", + "5 5.0 5.0 \n", + "... ... ... \n", + "1456 5.0 5.0 \n", + "1457 5.0 5.0 \n", + "1458 5.0 5.0 \n", + "1459 5.0 5.0 \n", + "1460 5.0 5.0 \n", + "\n", + "[1460 rows x 123 columns]" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def encode_columns(df, columns, sorting_function=np.mean,suffix='_E'):\n", + " for col in columns:\n", + " ranked_col = encode_qualitative_feature(df.SalePrice, df[col])\n", + " df[ranked_col.name] = ranked_col\n", + "\n", + " return df\n", + "\n", + "df_encoded =encode_columns(df.copy(), qualitative, sorting_function=np.mean,suffix='_E')\n", + "df_encoded" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernel_info": { + "name": "python38-azureml" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + }, + "microsoft": { + "host": { + "AzureML": { + "notebookHasBeenCompleted": true + } + } + }, + "nteract": { + "version": "nteract-front-end@1.0.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Seminar1/train.csv b/Seminar1/train.csv new file mode 100644 index 0000000..d68e0d7 --- /dev/null +++ b/Seminar1/train.csv @@ -0,0 +1,1461 @@ +Id,MSSubClass,MSZoning,LotFrontage,LotArea,Street,Alley,LotShape,LandContour,Utilities,LotConfig,LandSlope,Neighborhood,Condition1,Condition2,BldgType,HouseStyle,OverallQual,OverallCond,YearBuilt,YearRemodAdd,RoofStyle,RoofMatl,Exterior1st,Exterior2nd,MasVnrType,MasVnrArea,ExterQual,ExterCond,Foundation,BsmtQual,BsmtCond,BsmtExposure,BsmtFinType1,BsmtFinSF1,BsmtFinType2,BsmtFinSF2,BsmtUnfSF,TotalBsmtSF,Heating,HeatingQC,CentralAir,Electrical,1stFlrSF,2ndFlrSF,LowQualFinSF,GrLivArea,BsmtFullBath,BsmtHalfBath,FullBath,HalfBath,BedroomAbvGr,KitchenAbvGr,KitchenQual,TotRmsAbvGrd,Functional,Fireplaces,FireplaceQu,GarageType,GarageYrBlt,GarageFinish,GarageCars,GarageArea,GarageQual,GarageCond,PavedDrive,WoodDeckSF,OpenPorchSF,EnclosedPorch,3SsnPorch,ScreenPorch,PoolArea,PoolQC,Fence,MiscFeature,MiscVal,MoSold,YrSold,SaleType,SaleCondition,SalePrice 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+25,20,RL,NA,8246,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,1Story,5,8,1968,2001,Gable,CompShg,Plywood,Plywood,None,0,TA,Gd,CBlock,TA,TA,Mn,Rec,188,ALQ,668,204,1060,GasA,Ex,Y,SBrkr,1060,0,0,1060,1,0,1,0,3,1,Gd,6,Typ,1,TA,Attchd,1968,Unf,1,270,TA,TA,Y,406,90,0,0,0,0,NA,MnPrv,NA,0,5,2010,WD,Normal,154000 +26,20,RL,110,14230,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NridgHt,Norm,Norm,1Fam,1Story,8,5,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,640,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1566,1566,GasA,Ex,Y,SBrkr,1600,0,0,1600,0,0,2,0,3,1,Gd,7,Typ,1,Gd,Attchd,2007,RFn,3,890,TA,TA,Y,0,56,0,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,256300 +27,20,RL,60,7200,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,7,1951,2000,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,Mn,BLQ,234,Rec,486,180,900,GasA,TA,Y,SBrkr,900,0,0,900,0,1,1,0,3,1,Gd,5,Typ,0,NA,Detchd,2005,Unf,2,576,TA,TA,Y,222,32,0,0,0,0,NA,NA,NA,0,5,2010,WD,Normal,134800 +28,20,RL,98,11478,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,1Story,8,5,2007,2008,Gable,CompShg,VinylSd,VinylSd,Stone,200,Gd,TA,PConc,Ex,TA,No,GLQ,1218,Unf,0,486,1704,GasA,Ex,Y,SBrkr,1704,0,0,1704,1,0,2,0,3,1,Gd,7,Typ,1,Gd,Attchd,2008,RFn,3,772,TA,TA,Y,0,50,0,0,0,0,NA,NA,NA,0,5,2010,WD,Normal,306000 +29,20,RL,47,16321,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,6,1957,1997,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,Gd,BLQ,1277,Unf,0,207,1484,GasA,TA,Y,SBrkr,1600,0,0,1600,1,0,1,0,2,1,TA,6,Typ,2,Gd,Attchd,1957,RFn,1,319,TA,TA,Y,288,258,0,0,0,0,NA,NA,NA,0,12,2006,WD,Normal,207500 +30,30,RM,60,6324,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,BrkSide,Feedr,RRNn,1Fam,1Story,4,6,1927,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,520,520,GasA,Fa,N,SBrkr,520,0,0,520,0,0,1,0,1,1,Fa,4,Typ,0,NA,Detchd,1920,Unf,1,240,Fa,TA,Y,49,0,87,0,0,0,NA,NA,NA,0,5,2008,WD,Normal,68500 +31,70,C (all),50,8500,Pave,Pave,Reg,Lvl,AllPub,Inside,Gtl,IDOTRR,Feedr,Norm,1Fam,2Story,4,4,1920,1950,Gambrel,CompShg,BrkFace,BrkFace,None,0,TA,Fa,BrkTil,TA,TA,No,Unf,0,Unf,0,649,649,GasA,TA,N,SBrkr,649,668,0,1317,0,0,1,0,3,1,TA,6,Typ,0,NA,Detchd,1920,Unf,1,250,TA,Fa,N,0,54,172,0,0,0,NA,MnPrv,NA,0,7,2008,WD,Normal,40000 +32,20,RL,NA,8544,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Sawyer,Norm,Norm,1Fam,1Story,5,6,1966,2006,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,1228,1228,GasA,Gd,Y,SBrkr,1228,0,0,1228,0,0,1,1,3,1,Gd,6,Typ,0,NA,Attchd,1966,Unf,1,271,TA,TA,Y,0,65,0,0,0,0,NA,MnPrv,NA,0,6,2008,WD,Normal,149350 +33,20,RL,85,11049,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,CollgCr,Norm,Norm,1Fam,1Story,8,5,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Ex,TA,Av,Unf,0,Unf,0,1234,1234,GasA,Ex,Y,SBrkr,1234,0,0,1234,0,0,2,0,3,1,Gd,7,Typ,0,NA,Attchd,2007,RFn,2,484,TA,TA,Y,0,30,0,0,0,0,NA,NA,NA,0,1,2008,WD,Normal,179900 +34,20,RL,70,10552,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,5,1959,1959,Hip,CompShg,BrkFace,BrkFace,None,0,TA,TA,CBlock,TA,TA,No,Rec,1018,Unf,0,380,1398,GasA,Gd,Y,SBrkr,1700,0,0,1700,0,1,1,1,4,1,Gd,6,Typ,1,Gd,Attchd,1959,RFn,2,447,TA,TA,Y,0,38,0,0,0,0,NA,NA,NA,0,4,2010,WD,Normal,165500 +35,120,RL,60,7313,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,TwnhsE,1Story,9,5,2005,2005,Hip,CompShg,MetalSd,MetalSd,BrkFace,246,Ex,TA,PConc,Ex,TA,No,GLQ,1153,Unf,0,408,1561,GasA,Ex,Y,SBrkr,1561,0,0,1561,1,0,2,0,2,1,Ex,6,Typ,1,Gd,Attchd,2005,Fin,2,556,TA,TA,Y,203,47,0,0,0,0,NA,NA,NA,0,8,2007,WD,Normal,277500 +36,60,RL,108,13418,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,2Story,8,5,2004,2005,Gable,CompShg,VinylSd,VinylSd,Stone,132,Gd,TA,PConc,Ex,TA,Av,Unf,0,Unf,0,1117,1117,GasA,Ex,Y,SBrkr,1132,1320,0,2452,0,0,3,1,4,1,Gd,9,Typ,1,Gd,BuiltIn,2004,Fin,3,691,TA,TA,Y,113,32,0,0,0,0,NA,NA,NA,0,9,2006,WD,Normal,309000 +37,20,RL,112,10859,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,CollgCr,Norm,Norm,1Fam,1Story,5,5,1994,1995,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1097,1097,GasA,Ex,Y,SBrkr,1097,0,0,1097,0,0,1,1,3,1,TA,6,Typ,0,NA,Attchd,1995,Unf,2,672,TA,TA,Y,392,64,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,145000 +38,20,RL,74,8532,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,6,1954,1990,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,650,TA,TA,CBlock,TA,TA,No,Rec,1213,Unf,0,84,1297,GasA,Gd,Y,SBrkr,1297,0,0,1297,0,1,1,0,3,1,TA,5,Typ,1,TA,Attchd,1954,Fin,2,498,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,10,2009,WD,Normal,153000 +39,20,RL,68,7922,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,7,1953,2007,Gable,CompShg,VinylSd,VinylSd,None,0,TA,Gd,CBlock,TA,TA,No,GLQ,731,Unf,0,326,1057,GasA,TA,Y,SBrkr,1057,0,0,1057,1,0,1,0,3,1,Gd,5,Typ,0,NA,Detchd,1953,Unf,1,246,TA,TA,Y,0,52,0,0,0,0,NA,NA,NA,0,1,2010,WD,Abnorml,109000 +40,90,RL,65,6040,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Duplex,1Story,4,5,1955,1955,Gable,CompShg,AsbShng,Plywood,None,0,TA,TA,PConc,NA,NA,NA,NA,0,NA,0,0,0,GasA,TA,N,FuseP,1152,0,0,1152,0,0,2,0,2,2,Fa,6,Typ,0,NA,NA,NA,NA,0,0,NA,NA,N,0,0,0,0,0,0,NA,NA,NA,0,6,2008,WD,AdjLand,82000 +41,20,RL,84,8658,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,5,1965,1965,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,101,TA,TA,CBlock,TA,TA,No,Rec,643,Unf,0,445,1088,GasA,Ex,Y,SBrkr,1324,0,0,1324,0,0,2,0,3,1,TA,6,Typ,1,TA,Attchd,1965,RFn,2,440,TA,TA,Y,0,138,0,0,0,0,NA,GdWo,NA,0,12,2006,WD,Abnorml,160000 +42,20,RL,115,16905,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Timber,Norm,Norm,1Fam,1Story,5,6,1959,1959,Gable,CompShg,VinylSd,VinylSd,None,0,TA,Gd,CBlock,TA,TA,Gd,BLQ,967,Unf,0,383,1350,GasA,Gd,Y,SBrkr,1328,0,0,1328,0,1,1,1,2,1,TA,5,Typ,2,Gd,Attchd,1959,RFn,1,308,TA,TA,P,0,104,0,0,0,0,NA,NA,NA,0,7,2007,WD,Normal,170000 +43,85,RL,NA,9180,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,SawyerW,Norm,Norm,1Fam,SFoyer,5,7,1983,1983,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,Gd,TA,Av,ALQ,747,LwQ,93,0,840,GasA,Gd,Y,SBrkr,884,0,0,884,1,0,1,0,2,1,Gd,5,Typ,0,NA,Attchd,1983,RFn,2,504,TA,Gd,Y,240,0,0,0,0,0,NA,MnPrv,NA,0,12,2007,WD,Normal,144000 +44,20,RL,NA,9200,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,CollgCr,Norm,Norm,1Fam,1Story,5,6,1975,1980,Hip,CompShg,VinylSd,VinylSd,None,0,TA,TA,CBlock,Gd,TA,Av,LwQ,280,BLQ,491,167,938,GasA,TA,Y,SBrkr,938,0,0,938,1,0,1,0,3,1,TA,5,Typ,0,NA,Detchd,1977,Unf,1,308,TA,TA,Y,145,0,0,0,0,0,NA,MnPrv,NA,0,7,2008,WD,Normal,130250 +45,20,RL,70,7945,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,6,1959,1959,Gable,CompShg,BrkFace,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,ALQ,179,BLQ,506,465,1150,GasA,Ex,Y,FuseA,1150,0,0,1150,1,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1959,RFn,1,300,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,5,2006,WD,Normal,141000 +46,120,RL,61,7658,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,TwnhsE,1Story,9,5,2005,2005,Hip,CompShg,MetalSd,MetalSd,BrkFace,412,Ex,TA,PConc,Ex,TA,No,GLQ,456,Unf,0,1296,1752,GasA,Ex,Y,SBrkr,1752,0,0,1752,1,0,2,0,2,1,Ex,6,Typ,1,Gd,Attchd,2005,RFn,2,576,TA,TA,Y,196,82,0,0,0,0,NA,NA,NA,0,2,2010,WD,Normal,319900 +47,50,RL,48,12822,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Mitchel,Norm,Norm,1Fam,1.5Fin,7,5,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Ex,TA,No,GLQ,1351,Unf,0,83,1434,GasA,Ex,Y,SBrkr,1518,631,0,2149,1,0,1,1,1,1,Gd,6,Typ,1,Ex,Attchd,2003,RFn,2,670,TA,TA,Y,168,43,0,0,198,0,NA,NA,NA,0,8,2009,WD,Abnorml,239686 +48,20,FV,84,11096,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,1Story,8,5,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,Av,GLQ,24,Unf,0,1632,1656,GasA,Ex,Y,SBrkr,1656,0,0,1656,0,0,2,0,3,1,Gd,7,Typ,0,NA,Attchd,2006,RFn,3,826,TA,TA,Y,0,146,0,0,0,0,NA,NA,NA,0,7,2007,WD,Normal,249700 +49,190,RM,33,4456,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,2fmCon,2Story,4,5,1920,2008,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,736,736,GasA,Gd,Y,SBrkr,736,716,0,1452,0,0,2,0,2,3,TA,8,Typ,0,NA,NA,NA,NA,0,0,NA,NA,N,0,0,102,0,0,0,NA,NA,NA,0,6,2009,New,Partial,113000 +50,20,RL,66,7742,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,1Story,5,7,1966,1966,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,TA,TA,No,BLQ,763,Unf,0,192,955,GasA,Ex,Y,SBrkr,955,0,0,955,1,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1966,Unf,1,386,TA,TA,Y,0,0,0,0,0,0,NA,MnPrv,NA,0,1,2007,WD,Normal,127000 +51,60,RL,NA,13869,Pave,NA,IR2,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,1Fam,2Story,6,6,1997,1997,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,Av,GLQ,182,Unf,0,612,794,GasA,Gd,Y,SBrkr,794,676,0,1470,0,1,2,0,3,1,TA,6,Typ,0,NA,Attchd,1997,Fin,2,388,TA,TA,Y,0,75,0,0,0,0,NA,NA,NA,0,7,2007,WD,Normal,177000 +52,50,RM,52,6240,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,1.5Fin,6,6,1934,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,PConc,TA,TA,No,Unf,0,Unf,0,816,816,GasA,TA,Y,SBrkr,816,0,360,1176,0,0,1,0,3,1,TA,6,Typ,1,Gd,Detchd,1985,Unf,2,528,TA,TA,Y,112,0,0,0,0,0,NA,MnPrv,Shed,400,9,2006,WD,Normal,114500 +53,90,RM,110,8472,Grvl,NA,IR2,Bnk,AllPub,Corner,Mod,IDOTRR,RRNn,Norm,Duplex,1Story,5,5,1963,1963,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Fa,TA,CBlock,Gd,TA,Gd,LwQ,104,GLQ,712,0,816,GasA,TA,N,SBrkr,816,0,0,816,1,0,1,0,2,1,TA,5,Typ,0,NA,CarPort,1963,Unf,2,516,TA,TA,Y,106,0,0,0,0,0,NA,NA,NA,0,5,2010,WD,Normal,110000 +54,20,RL,68,50271,Pave,NA,IR1,Low,AllPub,Inside,Gtl,Veenker,Norm,Norm,1Fam,1Story,9,5,1981,1987,Gable,WdShngl,WdShing,Wd Shng,None,0,Gd,TA,CBlock,Ex,TA,Gd,GLQ,1810,Unf,0,32,1842,GasA,Gd,Y,SBrkr,1842,0,0,1842,2,0,0,1,0,1,Gd,5,Typ,1,Gd,Attchd,1981,Fin,3,894,TA,TA,Y,857,72,0,0,0,0,NA,NA,NA,0,11,2006,WD,Normal,385000 +55,80,RL,60,7134,Pave,NA,Reg,Bnk,AllPub,Inside,Mod,NAmes,Norm,Norm,1Fam,SLvl,5,5,1955,1955,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,ALQ,384,Unf,0,0,384,GasA,TA,Y,SBrkr,1360,0,0,1360,0,0,1,0,3,1,TA,6,Min1,1,TA,Detchd,1962,Unf,2,572,TA,TA,Y,0,50,0,0,0,0,NA,MnPrv,NA,0,2,2007,WD,Normal,130000 +56,20,RL,100,10175,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,5,1964,1964,Gable,CompShg,HdBoard,Plywood,BrkFace,272,TA,TA,CBlock,TA,TA,No,BLQ,490,Unf,0,935,1425,GasA,Gd,Y,SBrkr,1425,0,0,1425,0,0,2,0,3,1,TA,7,Typ,1,Gd,Attchd,1964,RFn,2,576,TA,TA,Y,0,0,0,407,0,0,NA,NA,NA,0,7,2008,WD,Normal,180500 +57,160,FV,24,2645,Pave,Pave,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,Twnhs,2Story,8,5,1999,2000,Gable,CompShg,MetalSd,MetalSd,BrkFace,456,Gd,TA,PConc,Gd,TA,No,GLQ,649,Unf,0,321,970,GasA,Ex,Y,SBrkr,983,756,0,1739,1,0,2,1,3,1,Gd,7,Typ,0,NA,Attchd,1999,Fin,2,480,TA,TA,Y,115,0,0,0,0,0,NA,NA,NA,0,8,2009,WD,Abnorml,172500 +58,60,RL,89,11645,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,CollgCr,Norm,Norm,1Fam,2Story,7,5,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,860,860,GasA,Ex,Y,SBrkr,860,860,0,1720,0,0,2,1,3,1,Gd,7,Typ,0,NA,Attchd,2004,RFn,2,565,TA,TA,Y,0,70,0,0,0,0,NA,NA,NA,0,8,2006,WD,Normal,196500 +59,60,RL,66,13682,Pave,NA,IR2,HLS,AllPub,CulDSac,Gtl,StoneBr,Norm,Norm,1Fam,2Story,10,5,2006,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,1031,Ex,TA,PConc,Ex,TA,Gd,Unf,0,Unf,0,1410,1410,GasA,Ex,Y,SBrkr,1426,1519,0,2945,0,0,3,1,3,1,Gd,10,Typ,1,Gd,BuiltIn,2006,Fin,3,641,TA,TA,Y,192,0,37,0,0,0,NA,NA,NA,0,10,2006,New,Partial,438780 +60,20,RL,60,7200,Pave,NA,Reg,Bnk,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,5,7,1972,1972,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,TA,TA,Av,ALQ,632,Unf,0,148,780,GasA,Ex,Y,SBrkr,780,0,0,780,0,0,1,0,2,1,TA,4,Typ,0,NA,Detchd,1973,Unf,1,352,TA,TA,Y,196,0,0,0,0,0,NA,MnPrv,NA,0,1,2008,WD,Normal,124900 +61,20,RL,63,13072,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SawyerW,RRAe,Norm,1Fam,1Story,6,5,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,No,ALQ,941,Unf,0,217,1158,GasA,Ex,Y,SBrkr,1158,0,0,1158,1,0,1,1,3,1,Gd,5,Typ,0,NA,Detchd,2006,Unf,2,576,TA,TA,Y,0,50,0,0,0,0,NA,NA,NA,0,5,2006,New,Partial,158000 +62,75,RM,60,7200,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,IDOTRR,Norm,Norm,1Fam,2.5Unf,5,7,1920,1996,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,BrkTil,TA,Fa,No,Unf,0,Unf,0,530,530,GasA,TA,N,SBrkr,581,530,0,1111,0,0,1,0,3,1,Fa,6,Typ,0,NA,Detchd,1935,Unf,1,288,TA,TA,N,0,0,144,0,0,0,NA,NA,NA,0,3,2007,WD,Normal,101000 +63,120,RL,44,6442,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,TwnhsE,1Story,8,5,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,178,Gd,TA,PConc,Gd,Gd,Mn,GLQ,24,Unf,0,1346,1370,GasA,Ex,Y,SBrkr,1370,0,0,1370,0,0,2,0,2,1,Gd,6,Typ,1,Gd,Attchd,2006,RFn,2,484,TA,TA,Y,120,49,0,0,0,0,NA,NA,NA,0,10,2007,WD,Normal,202500 +64,70,RM,50,10300,Pave,NA,IR1,Bnk,AllPub,Inside,Gtl,OldTown,RRAn,Feedr,1Fam,2Story,7,6,1921,1950,Gable,CompShg,Stucco,Stucco,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,576,576,GasA,Gd,Y,SBrkr,902,808,0,1710,0,0,2,0,3,1,TA,9,Typ,0,NA,Detchd,1990,Unf,2,480,TA,TA,Y,12,11,64,0,0,0,NA,GdPrv,NA,0,4,2010,WD,Normal,140000 +65,60,RL,NA,9375,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,7,5,1997,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,573,TA,TA,PConc,Gd,TA,No,GLQ,739,Unf,0,318,1057,GasA,Ex,Y,SBrkr,1057,977,0,2034,1,0,2,1,3,1,Gd,8,Typ,0,NA,Attchd,1998,RFn,2,645,TA,TA,Y,576,36,0,0,0,0,NA,GdPrv,NA,0,2,2009,WD,Normal,219500 +66,60,RL,76,9591,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,2Story,8,5,2004,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,344,Gd,TA,PConc,Ex,TA,Av,Unf,0,Unf,0,1143,1143,GasA,Ex,Y,SBrkr,1143,1330,0,2473,0,0,2,1,4,1,Gd,9,Typ,1,Gd,BuiltIn,2004,RFn,3,852,TA,TA,Y,192,151,0,0,0,0,NA,NA,NA,0,10,2007,WD,Normal,317000 +67,20,RL,NA,19900,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,PosA,Norm,1Fam,1Story,7,5,1970,1989,Gable,CompShg,Plywood,Plywood,BrkFace,287,TA,TA,CBlock,Gd,TA,Gd,GLQ,912,Unf,0,1035,1947,GasA,TA,Y,SBrkr,2207,0,0,2207,1,0,2,0,3,1,TA,7,Min1,1,Gd,Attchd,1970,RFn,2,576,TA,TA,Y,301,0,0,0,0,0,NA,NA,NA,0,7,2010,WD,Normal,180000 +68,20,RL,72,10665,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,167,Gd,TA,PConc,Gd,TA,Av,GLQ,1013,Unf,0,440,1453,GasA,Ex,Y,SBrkr,1479,0,0,1479,1,0,2,0,3,1,Gd,7,Typ,0,NA,Attchd,2003,RFn,2,558,TA,TA,Y,144,29,0,0,0,0,NA,NA,NA,0,6,2007,WD,Normal,226000 +69,30,RM,47,4608,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,OldTown,Artery,Norm,1Fam,1Story,4,6,1945,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,Gd,CBlock,TA,TA,No,Unf,0,Unf,0,747,747,GasA,TA,Y,SBrkr,747,0,0,747,0,0,1,0,2,1,TA,4,Typ,0,NA,Attchd,1945,Unf,1,220,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,6,2010,WD,Normal,80000 +70,50,RL,81,15593,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,ClearCr,Norm,Norm,1Fam,1.5Fin,7,4,1953,1953,Gable,CompShg,BrkFace,AsbShng,None,0,Gd,TA,CBlock,TA,TA,No,BLQ,603,Unf,0,701,1304,GasW,TA,Y,SBrkr,1304,983,0,2287,0,0,2,0,3,1,TA,7,Typ,1,TA,Attchd,1953,Fin,2,667,TA,TA,Y,0,21,114,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,225000 +71,20,RL,95,13651,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,7,6,1973,1973,Gable,CompShg,Plywood,Plywood,BrkFace,1115,TA,Gd,CBlock,Gd,TA,Gd,ALQ,1880,Unf,0,343,2223,GasA,Ex,Y,SBrkr,2223,0,0,2223,1,0,2,0,3,1,TA,8,Typ,2,Gd,Attchd,1973,Fin,2,516,TA,TA,Y,300,0,0,0,0,0,NA,NA,NA,0,2,2007,WD,Normal,244000 +72,20,RL,69,7599,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,Mitchel,Norm,Norm,1Fam,1Story,4,6,1982,2006,Gable,CompShg,HdBoard,Plywood,None,0,TA,TA,CBlock,TA,TA,No,ALQ,565,Unf,0,280,845,GasA,TA,Y,SBrkr,845,0,0,845,1,0,1,0,2,1,TA,4,Typ,0,NA,Detchd,1987,Unf,2,360,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,6,2007,WD,Normal,129500 +73,60,RL,74,10141,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,1Fam,2Story,7,5,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,40,TA,TA,PConc,Gd,TA,No,Unf,0,Unf,0,832,832,GasA,Gd,Y,SBrkr,885,833,0,1718,0,0,2,1,3,1,TA,7,Typ,1,TA,Attchd,1998,Fin,2,427,TA,TA,Y,0,94,0,0,291,0,NA,NA,NA,0,12,2009,WD,Normal,185000 +74,20,RL,85,10200,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,7,1954,2003,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,104,TA,TA,CBlock,TA,TA,No,ALQ,320,BLQ,362,404,1086,GasA,Gd,Y,SBrkr,1086,0,0,1086,1,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1989,Unf,2,490,TA,TA,Y,0,0,0,0,0,0,NA,GdWo,NA,0,5,2010,WD,Normal,144900 +75,50,RM,60,5790,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,OldTown,Norm,Norm,1Fam,2Story,3,6,1915,1950,Gambrel,CompShg,VinylSd,VinylSd,None,0,Gd,Gd,CBlock,Fa,TA,No,Unf,0,Unf,0,840,840,GasA,Gd,N,SBrkr,840,765,0,1605,0,0,2,0,3,2,TA,8,Typ,0,NA,Detchd,1915,Unf,1,379,TA,TA,Y,0,0,202,0,0,0,NA,NA,NA,0,5,2010,WD,Normal,107400 +76,180,RM,21,1596,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,MeadowV,Norm,Norm,Twnhs,SLvl,4,5,1973,1973,Gable,CompShg,CemntBd,CmentBd,None,0,TA,TA,CBlock,Gd,TA,Gd,GLQ,462,Unf,0,0,462,GasA,TA,Y,SBrkr,526,462,0,988,1,0,1,0,2,1,TA,5,Typ,0,NA,BuiltIn,1973,Unf,1,297,TA,TA,Y,120,101,0,0,0,0,NA,GdWo,NA,0,11,2009,WD,Normal,91000 +77,20,RL,NA,8475,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,4,7,1956,1956,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,CBlock,TA,TA,No,ALQ,228,Unf,0,724,952,GasA,Ex,Y,FuseA,952,0,0,952,0,0,1,0,2,1,TA,4,Typ,0,NA,Detchd,1956,Unf,1,283,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,4,2008,WD,Normal,135750 +78,50,RM,50,8635,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,1.5Fin,5,5,1948,2001,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,BLQ,336,GLQ,41,295,672,GasA,TA,Y,SBrkr,1072,213,0,1285,1,0,1,0,2,1,TA,6,Min1,0,NA,Detchd,1948,Unf,1,240,TA,TA,Y,0,0,0,0,0,0,NA,MnPrv,NA,0,1,2008,WD,Normal,127000 +79,90,RL,72,10778,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,Duplex,1Story,4,5,1968,1968,Hip,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,1768,1768,GasA,TA,N,SBrkr,1768,0,0,1768,0,0,2,0,4,2,TA,8,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,0,0,0,0,0,0,NA,NA,NA,0,4,2010,WD,Normal,136500 +80,50,RM,60,10440,Pave,Grvl,Reg,Lvl,AllPub,Corner,Gtl,OldTown,Norm,Norm,1Fam,2Story,5,6,1910,1981,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,PConc,TA,TA,No,Unf,0,Unf,0,440,440,GasA,Gd,Y,SBrkr,682,548,0,1230,0,0,1,1,2,1,TA,5,Typ,0,NA,Detchd,1966,Unf,2,440,TA,TA,Y,74,0,128,0,0,0,NA,MnPrv,NA,0,5,2009,WD,Normal,110000 +81,60,RL,100,13000,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,1Fam,2Story,6,6,1968,1968,Gable,CompShg,VinylSd,VinylSd,BrkFace,576,TA,Gd,CBlock,Gd,TA,No,Rec,448,Unf,0,448,896,GasA,TA,Y,SBrkr,1182,960,0,2142,0,0,2,1,4,1,Gd,8,Typ,1,Gd,Attchd,1968,Fin,1,509,TA,TA,Y,0,72,0,0,252,0,NA,NA,NA,0,6,2009,WD,Normal,193500 +82,120,RM,32,4500,Pave,NA,Reg,Lvl,AllPub,FR2,Gtl,Mitchel,Norm,Norm,TwnhsE,1Story,6,5,1998,1998,Hip,CompShg,VinylSd,VinylSd,BrkFace,443,TA,Gd,PConc,Ex,Gd,No,GLQ,1201,Unf,0,36,1237,GasA,Ex,Y,SBrkr,1337,0,0,1337,1,0,2,0,2,1,TA,5,Typ,0,NA,Attchd,1998,Fin,2,405,TA,TA,Y,0,199,0,0,0,0,NA,NA,NA,0,3,2006,WD,Normal,153500 +83,20,RL,78,10206,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,1Story,8,5,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,468,TA,TA,PConc,Gd,TA,No,GLQ,33,Unf,0,1530,1563,GasA,Ex,Y,SBrkr,1563,0,0,1563,0,0,2,0,3,1,Gd,6,Typ,1,Gd,Attchd,2007,RFn,3,758,TA,TA,Y,144,99,0,0,0,0,NA,NA,NA,0,10,2008,WD,Normal,245000 +84,20,RL,80,8892,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,5,1960,1960,Gable,CompShg,MetalSd,MetalSd,BrkCmn,66,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,1065,1065,GasA,Gd,Y,SBrkr,1065,0,0,1065,0,0,1,1,3,1,TA,6,Typ,0,NA,Detchd,1974,Unf,2,461,TA,TA,Y,74,0,0,0,0,0,NA,NA,NA,0,7,2007,COD,Normal,126500 +85,80,RL,NA,8530,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,SLvl,7,5,1995,1996,Gable,CompShg,HdBoard,HdBoard,BrkFace,22,TA,TA,PConc,Gd,TA,No,Unf,0,Unf,0,384,384,GasA,Gd,Y,SBrkr,804,670,0,1474,0,0,2,1,3,1,TA,7,Typ,1,TA,BuiltIn,1995,Fin,2,400,TA,TA,Y,120,72,0,0,0,0,NA,NA,Shed,700,5,2009,WD,Normal,168500 +86,60,RL,121,16059,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NoRidge,Norm,Norm,1Fam,2Story,8,5,1991,1992,Hip,CompShg,HdBoard,HdBoard,BrkFace,284,Gd,TA,CBlock,Gd,TA,No,Unf,0,Unf,0,1288,1288,GasA,Ex,Y,SBrkr,1301,1116,0,2417,0,0,2,1,4,1,Gd,9,Typ,1,TA,Attchd,1991,Unf,2,462,TA,TA,Y,127,82,0,0,0,0,NA,NA,NA,0,4,2006,WD,Normal,260000 +87,60,RL,122,11911,Pave,NA,IR2,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,2Story,6,5,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,Av,Unf,0,Unf,0,684,684,GasA,Ex,Y,SBrkr,684,876,0,1560,0,0,2,1,3,1,Gd,6,Typ,1,Gd,BuiltIn,2005,Fin,2,400,TA,TA,Y,100,38,0,0,0,0,NA,NA,NA,0,3,2009,WD,Normal,174000 +88,160,FV,40,3951,Pave,Pave,Reg,Lvl,AllPub,Corner,Gtl,Somerst,Norm,Norm,TwnhsE,2Story,6,5,2009,2009,Gable,CompShg,VinylSd,VinylSd,Stone,76,Gd,TA,PConc,Gd,TA,Av,Unf,0,Unf,0,612,612,GasA,Ex,Y,SBrkr,612,612,0,1224,0,0,2,1,2,1,Gd,4,Typ,0,NA,Detchd,2009,RFn,2,528,TA,TA,Y,0,234,0,0,0,0,NA,NA,NA,0,6,2009,New,Partial,164500 +89,50,C (all),105,8470,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,IDOTRR,Feedr,Feedr,1Fam,1.5Fin,3,2,1915,1982,Hip,CompShg,Plywood,Plywood,None,0,Fa,Fa,CBlock,TA,Fa,No,Unf,0,Unf,0,1013,1013,GasA,TA,N,SBrkr,1013,0,513,1526,0,0,1,0,2,1,Fa,6,Typ,0,NA,NA,NA,NA,0,0,NA,NA,N,0,0,156,0,0,0,NA,MnPrv,NA,0,10,2009,ConLD,Abnorml,85000 +90,20,RL,60,8070,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,4,5,1994,1995,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,No,GLQ,588,Unf,0,402,990,GasA,Ex,Y,SBrkr,990,0,0,990,1,0,1,0,3,1,TA,5,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,0,0,0,0,0,0,NA,NA,NA,0,8,2007,WD,Normal,123600 +91,20,RL,60,7200,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,4,5,1950,1950,Gable,CompShg,BrkFace,Wd Sdng,None,0,TA,TA,Slab,NA,NA,NA,NA,0,NA,0,0,0,GasA,TA,Y,FuseA,1040,0,0,1040,0,0,1,0,2,1,TA,4,Typ,0,NA,Detchd,1950,Unf,2,420,TA,TA,Y,0,29,0,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,109900 +92,20,RL,85,8500,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,3,1961,1961,Hip,CompShg,HdBoard,HdBoard,BrkCmn,203,TA,TA,CBlock,TA,TA,No,Rec,600,Unf,0,635,1235,GasA,TA,Y,SBrkr,1235,0,0,1235,0,0,1,0,2,1,TA,6,Typ,0,NA,Attchd,1961,Unf,2,480,TA,TA,Y,0,0,0,0,0,0,NA,GdWo,NA,0,12,2006,WD,Abnorml,98600 +93,30,RL,80,13360,Pave,Grvl,IR1,HLS,AllPub,Inside,Gtl,Crawfor,Norm,Norm,1Fam,1Story,5,7,1921,2006,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,Gd,BrkTil,Gd,TA,No,ALQ,713,Unf,0,163,876,GasA,Ex,Y,SBrkr,964,0,0,964,1,0,1,0,2,1,TA,5,Typ,0,NA,Detchd,1921,Unf,2,432,TA,TA,Y,0,0,44,0,0,0,NA,NA,NA,0,8,2009,WD,Normal,163500 +94,190,C (all),60,7200,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,OldTown,Norm,Norm,2fmCon,2.5Unf,6,6,1910,1998,Hip,CompShg,MetalSd,MetalSd,None,0,TA,TA,BrkTil,TA,Fa,Mn,Rec,1046,Unf,0,168,1214,GasW,Ex,N,SBrkr,1260,1031,0,2291,0,1,2,0,4,2,TA,9,Typ,1,Gd,Detchd,1900,Unf,2,506,TA,TA,Y,0,0,0,0,99,0,NA,NA,NA,0,11,2007,WD,Normal,133900 +95,60,RL,69,9337,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,6,5,1997,1997,Gable,CompShg,VinylSd,VinylSd,None,0,TA,Gd,PConc,Gd,TA,No,GLQ,648,Unf,0,176,824,GasA,Ex,Y,SBrkr,905,881,0,1786,1,0,2,1,3,1,Gd,7,Typ,0,NA,Attchd,1997,RFn,2,684,TA,TA,Y,0,162,0,0,0,0,NA,NA,NA,0,5,2007,WD,Normal,204750 +96,60,RL,NA,9765,Pave,NA,IR2,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,1Fam,2Story,6,8,1993,1993,Gable,CompShg,VinylSd,VinylSd,BrkFace,68,Ex,Gd,PConc,Gd,Gd,No,ALQ,310,Unf,0,370,680,GasA,Gd,Y,SBrkr,680,790,0,1470,0,0,2,1,3,1,TA,6,Typ,1,TA,BuiltIn,1993,Fin,2,420,TA,TA,Y,232,63,0,0,0,0,NA,NA,Shed,480,4,2009,WD,Normal,185000 +97,20,RL,78,10264,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,1999,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,183,Gd,TA,PConc,Gd,TA,Av,ALQ,1162,Unf,0,426,1588,GasA,Ex,Y,SBrkr,1588,0,0,1588,0,0,2,0,3,1,Gd,6,Typ,0,NA,Attchd,1999,RFn,2,472,TA,TA,Y,158,29,0,0,0,0,NA,NA,NA,0,8,2006,WD,Normal,214000 +98,20,RL,73,10921,Pave,NA,Reg,HLS,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1Story,4,5,1965,1965,Hip,CompShg,HdBoard,HdBoard,BrkFace,48,TA,TA,CBlock,TA,TA,No,Rec,520,Unf,0,440,960,GasA,TA,Y,FuseF,960,0,0,960,1,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1965,Fin,1,432,TA,TA,P,120,0,0,0,0,0,NA,NA,NA,0,5,2007,WD,Normal,94750 +99,30,RL,85,10625,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,1Fam,1Story,5,5,1920,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,TA,TA,No,ALQ,108,Unf,0,350,458,GasA,Fa,N,SBrkr,835,0,0,835,0,0,1,0,2,1,TA,5,Typ,0,NA,Basment,1920,Unf,1,366,Fa,TA,Y,0,0,77,0,0,0,NA,NA,Shed,400,5,2010,COD,Abnorml,83000 +100,20,RL,77,9320,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,4,5,1959,1959,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,CBlock,TA,TA,No,ALQ,569,Unf,0,381,950,GasA,Fa,Y,SBrkr,1225,0,0,1225,1,0,1,1,3,1,TA,6,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,352,0,0,0,0,0,NA,NA,Shed,400,1,2010,WD,Normal,128950 +101,20,RL,NA,10603,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,1Story,6,7,1977,2001,Gable,CompShg,Plywood,Plywood,BrkFace,28,TA,TA,PConc,TA,TA,Mn,ALQ,1200,Unf,0,410,1610,GasA,Gd,Y,SBrkr,1610,0,0,1610,1,0,2,0,3,1,Gd,6,Typ,2,TA,Attchd,1977,RFn,2,480,TA,TA,Y,168,68,0,0,0,0,NA,NA,NA,0,2,2010,WD,Normal,205000 +102,60,RL,77,9206,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SawyerW,Norm,Norm,1Fam,2Story,6,5,1985,1985,Gable,CompShg,HdBoard,HdBoard,BrkFace,336,Gd,TA,CBlock,Gd,TA,No,Unf,0,Unf,0,741,741,GasA,TA,Y,SBrkr,977,755,0,1732,0,0,2,1,3,1,Gd,7,Typ,1,TA,Attchd,1985,Fin,2,476,TA,TA,Y,192,46,0,0,0,0,NA,NA,NA,0,6,2010,WD,Normal,178000 +103,90,RL,64,7018,Pave,NA,Reg,Bnk,AllPub,Inside,Gtl,SawyerW,Norm,Norm,Duplex,1Story,5,5,1979,1979,Gable,CompShg,HdBoard,HdBoard,None,0,TA,Fa,Slab,NA,NA,NA,NA,0,NA,0,0,0,GasA,TA,Y,SBrkr,1535,0,0,1535,0,0,2,0,4,2,TA,8,Typ,0,NA,Attchd,1979,Unf,2,410,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,6,2009,WD,Alloca,118964 +104,20,RL,94,10402,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2009,2009,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1226,1226,GasA,Ex,Y,SBrkr,1226,0,0,1226,0,0,2,0,3,1,Gd,6,Typ,0,NA,Attchd,2009,RFn,3,740,TA,TA,Y,0,36,0,0,0,0,NA,NA,NA,0,5,2010,WD,Normal,198900 +105,50,RM,NA,7758,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,IDOTRR,Norm,Norm,1Fam,1.5Fin,7,4,1931,1950,Gable,CompShg,Stucco,Stucco,BrkFace,600,TA,Fa,PConc,TA,TA,No,LwQ,224,Unf,0,816,1040,GasA,Ex,Y,FuseF,1226,592,0,1818,0,0,1,1,4,1,TA,7,Typ,2,TA,Detchd,1951,Unf,1,240,TA,TA,Y,0,0,0,0,184,0,NA,NA,NA,0,6,2007,WD,Normal,169500 +106,60,FV,75,9375,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,2Story,8,5,2003,2004,Hip,CompShg,VinylSd,VinylSd,BrkFace,768,Gd,TA,PConc,Ex,TA,No,Unf,0,Unf,0,1053,1053,GasA,Ex,Y,SBrkr,1053,939,0,1992,0,0,2,1,3,1,Gd,9,Typ,1,Gd,Attchd,2003,RFn,2,648,TA,TA,Y,140,45,0,0,0,0,NA,NA,NA,0,8,2008,WD,Normal,250000 +107,30,RM,60,10800,Pave,Grvl,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,1Story,4,7,1885,1995,Mansard,CompShg,VinylSd,VinylSd,None,0,TA,TA,BrkTil,Fa,TA,No,Unf,0,Unf,0,641,641,GasA,Gd,Y,SBrkr,1047,0,0,1047,0,0,1,0,2,1,TA,6,Typ,0,NA,Detchd,1954,Unf,1,273,Fa,Fa,N,0,0,0,0,0,0,NA,NA,Shed,450,8,2007,WD,Normal,100000 +108,20,RM,50,6000,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,1Story,5,5,1948,1950,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,CBlock,TA,TA,No,ALQ,104,BLQ,169,516,789,GasA,Ex,Y,SBrkr,789,0,0,789,0,0,1,0,2,1,TA,5,Typ,0,NA,Detchd,1948,Unf,1,250,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,4,2008,WD,Partial,115000 +109,50,RM,85,8500,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,IDOTRR,Artery,Norm,1Fam,1.5Fin,5,7,1919,2005,Gable,CompShg,CemntBd,CmentBd,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,793,793,GasW,TA,N,FuseF,997,520,0,1517,0,0,2,0,3,1,Fa,7,Typ,0,NA,NA,NA,NA,0,0,NA,NA,N,0,0,144,0,0,0,NA,NA,NA,0,8,2007,WD,Normal,115000 +110,20,RL,105,11751,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,1Story,6,6,1977,1977,Hip,CompShg,Plywood,Plywood,BrkFace,480,TA,TA,CBlock,Gd,TA,No,BLQ,705,Unf,0,1139,1844,GasA,Ex,Y,SBrkr,1844,0,0,1844,0,0,2,0,3,1,TA,7,Typ,1,TA,Attchd,1977,RFn,2,546,TA,TA,Y,0,122,0,0,0,0,NA,MnPrv,NA,0,1,2010,COD,Normal,190000 +111,50,RL,75,9525,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1.5Fin,6,4,1954,1972,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,Fa,No,Rec,444,Unf,0,550,994,GasA,Gd,Y,SBrkr,1216,639,0,1855,0,0,2,0,4,1,TA,7,Typ,0,NA,Attchd,1954,Unf,1,325,TA,TA,Y,182,0,0,0,0,0,NA,NA,NA,0,10,2006,WD,Normal,136900 +112,80,RL,NA,7750,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,SLvl,7,5,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,No,GLQ,250,Unf,0,134,384,GasA,Ex,Y,SBrkr,774,656,0,1430,0,0,2,1,3,1,TA,7,Typ,1,TA,BuiltIn,2000,Fin,2,400,TA,TA,Y,180,0,0,0,0,0,NA,NA,NA,0,4,2010,WD,Normal,180000 +113,60,RL,77,9965,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,7,5,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,220,Gd,TA,PConc,Ex,TA,Av,GLQ,984,Unf,0,280,1264,GasA,Ex,Y,SBrkr,1282,1414,0,2696,1,0,2,1,4,1,Ex,10,Typ,1,Gd,BuiltIn,2007,Fin,3,792,TA,TA,Y,120,184,0,0,168,0,NA,NA,NA,0,10,2007,New,Partial,383970 +114,20,RL,NA,21000,Pave,NA,Reg,Bnk,AllPub,Corner,Gtl,Crawfor,Norm,Norm,1Fam,1Story,6,5,1953,1953,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,184,TA,Gd,CBlock,Gd,TA,Mn,ALQ,35,Rec,869,905,1809,GasA,TA,Y,SBrkr,2259,0,0,2259,1,0,2,0,3,1,Gd,7,Typ,2,Gd,Basment,1953,Unf,2,450,TA,TA,Y,166,120,192,0,0,0,NA,MnPrv,NA,0,10,2007,COD,Abnorml,217000 +115,70,RL,61,7259,Pave,NA,IR1,Lvl,AllPub,Inside,Mod,Crawfor,Norm,Norm,1Fam,2Story,6,8,1945,2002,Gambrel,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,ALQ,774,LwQ,150,104,1028,GasA,Ex,Y,SBrkr,1436,884,0,2320,1,0,2,1,3,1,Gd,9,Typ,1,TA,Detchd,1945,Unf,1,180,TA,TA,Y,224,0,0,0,0,0,NA,MnPrv,NA,0,7,2007,WD,Normal,259500 +116,160,FV,34,3230,Pave,Pave,Reg,Lvl,AllPub,Corner,Gtl,Somerst,Norm,Norm,TwnhsE,2Story,6,5,1999,1999,Gable,CompShg,MetalSd,MetalSd,BrkFace,1129,TA,TA,PConc,Gd,TA,No,GLQ,419,Unf,0,310,729,GasA,Gd,Y,SBrkr,729,729,0,1458,0,0,2,1,2,1,TA,5,Typ,1,Fa,Detchd,1999,Unf,2,440,TA,TA,Y,0,32,0,0,0,0,NA,NA,NA,0,6,2007,WD,Normal,176000 +117,20,RL,NA,11616,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,1Story,5,5,1962,1962,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,116,TA,TA,CBlock,TA,TA,No,LwQ,170,BLQ,670,252,1092,GasA,TA,Y,SBrkr,1092,0,0,1092,0,1,1,0,3,1,TA,6,Typ,1,Po,Attchd,1962,Unf,1,288,TA,TA,Y,0,20,144,0,0,0,NA,NA,NA,0,9,2009,WD,Normal,139000 +118,20,RL,74,8536,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,1Fam,1Story,5,5,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1125,1125,GasA,Gd,Y,SBrkr,1125,0,0,1125,0,0,1,1,2,1,TA,5,Typ,0,NA,Attchd,2007,Unf,2,430,TA,TA,Y,80,64,0,0,0,0,NA,NA,NA,0,4,2007,New,Partial,155000 +119,60,RL,90,12376,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,SawyerW,Norm,Norm,1Fam,2Story,7,5,1990,1990,Hip,CompShg,Plywood,Plywood,None,0,TA,TA,PConc,Gd,TA,Mn,GLQ,1470,Unf,0,203,1673,GasA,Gd,Y,SBrkr,1699,1523,0,3222,1,0,3,0,5,1,Gd,11,Typ,2,TA,Attchd,1990,Unf,3,594,TA,TA,Y,367,0,0,0,0,0,NA,NA,NA,0,5,2010,WD,Normal,320000 +120,60,RL,65,8461,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,6,5,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,728,728,GasA,Ex,Y,SBrkr,728,728,0,1456,0,0,2,1,3,1,Gd,8,Typ,1,Gd,Attchd,2005,Fin,2,390,TA,TA,Y,0,24,0,0,0,0,NA,NA,NA,0,7,2006,New,Partial,163990 +121,80,RL,NA,21453,Pave,NA,IR1,Low,AllPub,CulDSac,Sev,ClearCr,Norm,Norm,1Fam,SLvl,6,5,1969,1969,Flat,Metal,Plywood,Plywood,None,0,TA,TA,CBlock,TA,TA,Gd,ALQ,938,Unf,0,0,938,GasA,Ex,Y,SBrkr,988,0,0,988,1,0,1,0,1,1,TA,4,Typ,2,TA,Attchd,1969,Unf,2,540,TA,TA,Y,0,130,0,130,0,0,NA,NA,NA,0,10,2006,WD,Normal,180000 +122,50,RM,50,6060,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,IDOTRR,Norm,Norm,1Fam,1.5Fin,4,5,1939,1950,Gable,CompShg,AsbShng,AsbShng,None,0,TA,TA,PConc,TA,TA,No,Unf,0,Unf,0,732,732,GasA,Gd,Y,SBrkr,772,351,0,1123,0,0,1,0,3,1,TA,4,Typ,0,NA,Detchd,1979,Unf,1,264,TA,TA,P,0,0,140,0,0,0,NA,MnPrv,NA,0,6,2007,WD,Normal,100000 +123,20,RL,75,9464,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,7,1958,1958,Hip,CompShg,MetalSd,MetalSd,BrkFace,135,TA,Gd,CBlock,TA,TA,No,BLQ,570,Unf,0,510,1080,GasA,Gd,Y,SBrkr,1080,0,0,1080,0,0,1,0,3,1,TA,5,Typ,0,NA,Attchd,1958,Unf,1,288,TA,TA,Y,0,0,0,0,130,0,NA,NA,NA,0,6,2008,WD,Normal,136000 +124,120,RL,55,7892,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SawyerW,Norm,Norm,TwnhsE,1Story,6,5,1993,1993,Gable,CompShg,Plywood,Plywood,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,300,Unf,0,899,1199,GasA,Ex,Y,SBrkr,1199,0,0,1199,0,0,2,0,2,1,Gd,5,Typ,0,NA,Attchd,1993,RFn,2,530,TA,TA,Y,0,63,0,0,0,0,NA,NA,NA,0,3,2008,WD,Normal,153900 +125,20,RL,48,17043,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,NWAmes,Norm,Norm,1Fam,1Story,6,5,1979,1998,Gable,CompShg,HdBoard,HdBoard,None,0,TA,Gd,CBlock,Gd,Fa,No,Unf,0,Unf,0,1362,1362,GasA,TA,Y,SBrkr,1586,0,0,1586,0,0,2,0,3,1,TA,7,Typ,1,TA,Attchd,1979,Unf,2,435,TA,TA,Y,192,0,0,0,0,0,NA,NA,NA,0,1,2009,WD,Normal,181000 +126,190,RM,60,6780,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,IDOTRR,Norm,Norm,2fmCon,1.5Fin,6,8,1935,1982,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,Fa,CBlock,TA,TA,Av,GLQ,490,Unf,0,30,520,GasA,Gd,N,SBrkr,520,0,234,754,1,0,1,0,2,1,TA,5,Typ,0,NA,NA,NA,NA,0,0,NA,NA,N,53,0,0,0,0,0,NA,NA,NA,0,6,2006,WD,Normal,84500 +127,120,RL,NA,4928,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NPkVill,Norm,Norm,TwnhsE,1Story,6,5,1976,1976,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,CBlock,Gd,TA,No,ALQ,120,Unf,0,958,1078,GasA,TA,Y,SBrkr,958,0,0,958,0,0,2,0,2,1,TA,5,Typ,1,TA,Attchd,1977,RFn,2,440,TA,TA,Y,0,205,0,0,0,0,NA,NA,NA,0,2,2007,WD,Normal,128000 +128,45,RM,55,4388,Pave,NA,IR1,Bnk,AllPub,Inside,Gtl,OldTown,Feedr,Norm,1Fam,1.5Unf,5,7,1930,1950,Gable,CompShg,WdShing,Wd Sdng,None,0,TA,Gd,BrkTil,TA,TA,No,LwQ,116,Unf,0,556,672,GasA,Ex,Y,SBrkr,840,0,0,840,0,0,1,0,3,1,TA,5,Typ,1,TA,NA,NA,NA,0,0,NA,NA,N,0,0,0,0,0,0,NA,NA,NA,0,6,2007,WD,Normal,87000 +129,60,RL,69,7590,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,PosN,Norm,1Fam,2Story,6,5,1966,1966,Gable,CompShg,VinylSd,VinylSd,BrkFace,266,TA,TA,CBlock,TA,TA,No,BLQ,512,Unf,0,148,660,GasA,TA,Y,SBrkr,660,688,0,1348,0,0,1,1,3,1,TA,6,Typ,1,Fa,Attchd,1966,RFn,2,453,TA,TA,Y,188,108,0,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,155000 +130,20,RL,69,8973,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,7,1958,1991,Gable,CompShg,Plywood,Plywood,BrkFace,85,TA,TA,CBlock,TA,TA,No,Rec,567,BLQ,28,413,1008,GasA,TA,Y,FuseA,1053,0,0,1053,0,1,1,1,3,1,Ex,6,Typ,0,NA,2Types,1998,RFn,2,750,TA,TA,Y,0,80,0,180,0,0,NA,MnWw,NA,0,7,2006,WD,Abnorml,150000 +131,60,RL,88,14200,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,1Fam,2Story,7,6,1966,1966,Gable,CompShg,MetalSd,MetalSd,BrkFace,309,TA,TA,CBlock,TA,TA,No,Rec,445,Unf,0,479,924,GasA,Ex,Y,SBrkr,1216,941,0,2157,0,0,2,1,4,1,Gd,8,Typ,2,Gd,Attchd,1966,Fin,2,487,TA,TA,Y,105,66,0,0,0,0,NA,GdPrv,NA,0,5,2006,WD,Normal,226000 +132,60,RL,NA,12224,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,1Fam,2Story,6,5,2000,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,40,Gd,TA,PConc,Gd,TA,No,GLQ,695,Unf,0,297,992,GasA,Ex,Y,SBrkr,1022,1032,0,2054,1,0,2,1,3,1,Gd,7,Typ,1,TA,BuiltIn,2000,RFn,2,390,TA,TA,Y,24,48,0,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,244000 +133,20,RL,75,7388,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,6,1959,2002,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,Rec,405,Unf,0,658,1063,GasA,Gd,Y,SBrkr,1327,0,0,1327,1,0,1,0,3,1,Gd,7,Typ,0,NA,Detchd,1974,Unf,2,624,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,7,2007,WD,Normal,150750 +134,20,RL,NA,6853,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Timber,Norm,Norm,1Fam,1Story,8,5,2001,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,136,Gd,TA,PConc,Ex,TA,No,GLQ,1005,Unf,0,262,1267,GasA,Ex,Y,SBrkr,1296,0,0,1296,1,0,2,0,2,1,Gd,6,Typ,0,NA,Attchd,2001,Fin,2,471,TA,TA,Y,192,25,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,220000 +135,20,RL,78,10335,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,1Story,5,6,1968,1993,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,CBlock,TA,TA,No,Rec,570,Unf,0,891,1461,GasA,Gd,Y,SBrkr,1721,0,0,1721,0,0,2,1,3,1,TA,7,Min1,1,TA,Attchd,1968,RFn,2,440,TA,TA,Y,0,96,180,0,0,0,NA,MnPrv,NA,0,7,2006,WD,Normal,180000 +136,20,RL,80,10400,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,1Story,7,6,1970,1970,Hip,CompShg,Plywood,Plywood,BrkFace,288,TA,TA,PConc,TA,TA,No,Unf,0,Unf,0,1304,1304,GasA,Gd,Y,SBrkr,1682,0,0,1682,0,0,2,0,3,1,TA,7,Typ,1,Gd,Attchd,1970,Unf,2,530,TA,TA,Y,98,0,0,0,0,0,NA,MnPrv,NA,0,5,2008,WD,Normal,174000 +137,20,RL,NA,10355,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,5,1967,1967,Gable,CompShg,MetalSd,MetalSd,BrkFace,196,TA,TA,CBlock,TA,TA,No,BLQ,695,Unf,0,519,1214,GasA,TA,Y,SBrkr,1214,0,0,1214,0,0,2,0,3,1,TA,5,Typ,1,Fa,Attchd,1967,RFn,1,318,TA,TA,Y,0,111,0,0,0,0,NA,NA,NA,0,7,2007,WD,Normal,143000 +138,90,RL,82,11070,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Mitchel,Norm,Norm,Duplex,1Story,7,5,1988,1989,Gable,CompShg,VinylSd,VinylSd,BrkFace,70,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,1907,1907,GasA,Gd,Y,SBrkr,1959,0,0,1959,0,0,3,0,5,2,TA,9,Typ,0,NA,2Types,1989,Unf,3,766,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,7,2006,WD,Family,171000 +139,60,RL,73,9066,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,8,5,1999,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,320,Gd,TA,PConc,Gd,TA,Mn,GLQ,668,Unf,0,336,1004,GasA,Ex,Y,SBrkr,1004,848,0,1852,0,0,2,1,3,1,Gd,7,Typ,2,TA,Attchd,1999,Fin,3,660,TA,TA,Y,224,106,0,0,0,0,NA,GdPrv,NA,0,12,2008,WD,Normal,230000 +140,60,RL,65,15426,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,6,5,1997,1997,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,No,GLQ,821,Unf,0,107,928,GasA,Ex,Y,SBrkr,928,836,0,1764,1,0,2,1,3,1,Gd,7,Typ,0,NA,Attchd,1997,RFn,2,470,TA,TA,Y,276,99,0,0,0,0,NA,MnPrv,NA,0,8,2009,WD,Normal,231500 +141,20,RL,70,10500,Pave,NA,Reg,Lvl,AllPub,FR2,Gtl,NAmes,Norm,Norm,1Fam,1Story,4,5,1971,1971,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,TA,TA,No,ALQ,432,Unf,0,432,864,GasA,TA,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,TA,5,Typ,1,Po,NA,NA,NA,0,0,NA,NA,Y,0,0,0,0,0,0,NA,NA,NA,0,4,2010,ConLI,Normal,115000 +142,20,RL,78,11645,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,Av,GLQ,1300,Unf,0,434,1734,GasA,Ex,Y,SBrkr,1734,0,0,1734,1,0,2,0,3,1,Gd,7,Typ,0,NA,Attchd,2005,Fin,2,660,TA,TA,Y,160,24,0,0,0,0,NA,NA,NA,0,1,2006,WD,Normal,260000 +143,50,RL,71,8520,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NAmes,Artery,Norm,1Fam,1.5Fin,5,4,1952,1952,Gable,CompShg,BrkFace,Wd Sdng,None,0,TA,Fa,CBlock,TA,TA,No,Rec,507,Unf,0,403,910,GasA,Fa,Y,SBrkr,910,475,0,1385,0,0,2,0,4,1,TA,6,Typ,0,NA,Detchd,2000,Unf,2,720,TA,TA,Y,0,0,0,0,0,0,NA,MnPrv,NA,0,6,2010,WD,Normal,166000 +144,20,RL,78,10335,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,1999,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,183,Gd,TA,PConc,Gd,TA,Gd,GLQ,679,Unf,0,811,1490,GasA,Ex,Y,SBrkr,1501,0,0,1501,1,0,2,0,3,1,Gd,6,Typ,0,NA,Attchd,1999,RFn,2,577,TA,TA,Y,144,29,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,204000 +145,90,RM,70,9100,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Sawyer,RRAe,Norm,Duplex,1Story,5,5,1963,1963,Gable,CompShg,HdBoard,HdBoard,BrkFace,336,TA,TA,CBlock,TA,TA,No,Rec,1332,Unf,0,396,1728,GasA,TA,Y,SBrkr,1728,0,0,1728,1,0,2,0,6,2,TA,10,Typ,0,NA,Detchd,1963,Unf,2,504,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,11,2006,ConLI,Abnorml,125000 +146,160,RM,24,2522,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Twnhs,2Story,6,5,2004,2006,Gable,CompShg,VinylSd,VinylSd,Stone,50,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,970,970,GasA,Ex,Y,SBrkr,970,739,0,1709,0,0,2,0,3,1,Gd,7,Maj1,0,NA,Detchd,2004,Unf,2,380,TA,TA,Y,0,40,0,0,0,0,NA,NA,NA,0,4,2006,WD,Normal,130000 +147,30,RM,51,6120,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,BrkSide,Norm,Norm,1Fam,1Story,5,7,1931,1993,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,TA,TA,No,BLQ,209,Unf,0,506,715,GasA,TA,Y,FuseA,875,0,0,875,1,0,1,0,2,1,TA,5,Typ,0,NA,Detchd,1931,Unf,1,180,Fa,TA,Y,48,0,0,0,0,0,NA,NA,NA,0,11,2009,WD,Normal,105000 +148,60,RL,NA,9505,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Gilbert,Norm,Norm,1Fam,2Story,7,5,2001,2001,Gable,CompShg,VinylSd,VinylSd,BrkFace,180,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,884,884,GasA,Ex,Y,SBrkr,884,1151,0,2035,0,0,2,1,3,1,Gd,8,Typ,1,Gd,BuiltIn,2001,Fin,2,434,TA,TA,Y,144,48,0,0,0,0,NA,NA,NA,0,5,2010,WD,Normal,222500 +149,20,RL,63,7500,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SawyerW,Norm,Norm,1Fam,1Story,7,5,2004,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,120,TA,TA,PConc,Gd,TA,No,GLQ,680,Unf,0,400,1080,GasA,Ex,Y,SBrkr,1080,0,0,1080,1,0,1,0,3,1,Gd,6,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,0,0,0,0,0,0,NA,NA,NA,0,4,2008,WD,Normal,141000 +150,50,RM,NA,6240,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,1.5Fin,5,4,1936,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,BrkTil,Gd,TA,No,Unf,0,Unf,0,896,896,GasA,Gd,Y,FuseA,896,448,0,1344,0,0,1,0,3,1,TA,7,Typ,0,NA,Detchd,1936,Unf,1,240,Fa,TA,Y,200,114,0,0,0,0,NA,NA,NA,0,4,2006,WD,Normal,115000 +151,20,RL,120,10356,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,CollgCr,Norm,Norm,1Fam,1Story,5,6,1975,1975,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,TA,TA,Av,BLQ,716,Unf,0,253,969,GasA,TA,Y,SBrkr,969,0,0,969,0,0,1,1,3,1,TA,5,Typ,0,NA,Attchd,1975,Unf,2,440,TA,TA,Y,0,0,0,0,0,0,NA,MnPrv,NA,0,1,2007,WD,Normal,122000 +152,20,RL,107,13891,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,1Story,8,5,2007,2008,Hip,CompShg,VinylSd,VinylSd,Stone,436,Gd,TA,PConc,Ex,TA,Gd,GLQ,1400,Unf,0,310,1710,GasA,Ex,Y,SBrkr,1710,0,0,1710,1,0,2,0,2,1,Gd,6,Typ,1,Gd,Attchd,2007,RFn,3,866,TA,TA,Y,0,102,0,0,0,0,NA,NA,NA,0,1,2008,New,Partial,372402 +153,60,RL,NA,14803,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,NWAmes,Norm,Norm,1Fam,2Story,6,5,1971,1971,Gable,CompShg,HdBoard,HdBoard,BrkFace,252,TA,TA,CBlock,TA,TA,No,Rec,416,Unf,0,409,825,GasA,Gd,Y,SBrkr,1097,896,0,1993,0,0,2,1,4,1,TA,8,Typ,1,Gd,Attchd,1971,RFn,2,495,TA,TA,Y,0,66,0,0,0,0,NA,GdWo,NA,0,6,2006,WD,Normal,190000 +154,20,RL,NA,13500,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,ClearCr,Norm,Norm,1Fam,1Story,6,7,1960,1975,Flat,CompShg,BrkFace,Plywood,None,0,TA,TA,CBlock,Gd,TA,Gd,BLQ,429,ALQ,1080,93,1602,GasA,Gd,Y,SBrkr,1252,0,0,1252,1,0,1,0,1,1,TA,4,Typ,1,Gd,Attchd,1960,RFn,2,564,TA,TA,Y,409,0,0,0,0,0,NA,NA,NA,0,3,2008,WD,Normal,235000 +155,30,RM,84,11340,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,OldTown,Norm,Norm,1Fam,1Story,6,5,1923,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,1200,1200,GasA,TA,Y,FuseA,1200,0,0,1200,0,0,1,0,4,1,TA,7,Typ,0,NA,Detchd,1923,Unf,1,312,Fa,Fa,Y,0,0,228,0,0,0,NA,NA,NA,0,3,2006,WD,Family,125000 +156,50,RL,60,9600,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,Edwards,Artery,Norm,1Fam,1.5Fin,6,5,1924,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,572,572,Grav,Fa,N,FuseF,572,524,0,1096,0,0,1,0,2,1,TA,5,Typ,0,NA,NA,NA,NA,0,0,NA,NA,N,0,8,128,0,0,0,NA,NA,NA,0,4,2008,WD,Normal,79000 +157,20,RL,60,7200,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,7,1950,1950,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,NA,NA,NA,NA,0,NA,0,0,0,GasA,TA,Y,FuseF,1040,0,0,1040,0,0,1,0,2,1,TA,5,Typ,0,NA,Detchd,1950,Unf,2,625,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,6,2006,WD,Normal,109500 +158,60,RL,92,12003,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,Timber,Norm,Norm,1Fam,2Story,8,5,2009,2010,Gable,CompShg,VinylSd,VinylSd,BrkFace,84,Gd,TA,PConc,Ex,TA,No,Unf,0,Unf,0,774,774,GasA,Ex,Y,SBrkr,774,1194,0,1968,0,0,2,1,4,1,Ex,8,Typ,1,Gd,BuiltIn,2009,Fin,3,680,TA,TA,Y,0,75,0,0,0,0,NA,NA,NA,0,5,2010,New,Partial,269500 +159,60,FV,100,12552,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,Somerst,Norm,Norm,1Fam,2Story,7,5,2004,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,222,Unf,0,769,991,GasA,Ex,Y,SBrkr,991,956,0,1947,0,0,2,1,3,1,Gd,8,Typ,1,Gd,Attchd,2004,RFn,2,678,TA,TA,Y,0,136,0,0,0,0,NA,GdWo,NA,0,5,2010,WD,Normal,254900 +160,60,RL,134,19378,Pave,NA,IR1,HLS,AllPub,Corner,Gtl,Gilbert,Norm,Norm,1Fam,2Story,7,5,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,456,Gd,TA,PConc,Gd,TA,Mn,GLQ,57,Unf,0,1335,1392,GasA,Ex,Y,SBrkr,1392,1070,0,2462,1,0,2,1,4,1,Gd,9,Typ,1,Gd,Attchd,2006,RFn,2,576,TA,TA,Y,239,132,0,168,0,0,NA,NA,NA,0,3,2006,New,Partial,320000 +161,20,RL,NA,11120,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Veenker,Norm,Norm,1Fam,1Story,6,6,1984,1984,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,PConc,Gd,TA,No,BLQ,660,Unf,0,572,1232,GasA,TA,Y,SBrkr,1232,0,0,1232,0,0,2,0,3,1,TA,6,Typ,0,NA,Attchd,1984,Unf,2,516,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,6,2008,WD,Normal,162500 +162,60,RL,110,13688,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,2Story,9,5,2003,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,664,Gd,TA,PConc,Ex,TA,Av,GLQ,1016,Unf,0,556,1572,GasA,Ex,Y,SBrkr,1572,1096,0,2668,1,0,2,1,3,1,Ex,10,Typ,2,Gd,BuiltIn,2003,Fin,3,726,TA,TA,Y,400,0,0,0,0,0,NA,NA,NA,0,3,2008,WD,Normal,412500 +163,20,RL,95,12182,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NridgHt,Norm,Norm,1Fam,1Story,7,5,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,226,Gd,TA,PConc,Gd,TA,Mn,BLQ,1201,Unf,0,340,1541,GasA,Ex,Y,SBrkr,1541,0,0,1541,0,0,2,0,3,1,Gd,7,Typ,1,Gd,Attchd,2005,RFn,2,532,TA,TA,Y,0,70,0,0,0,0,NA,NA,NA,0,5,2010,New,Partial,220000 +164,45,RL,55,5500,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,1.5Unf,4,6,1956,1956,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,882,882,GasA,Ex,Y,SBrkr,882,0,0,882,0,0,1,0,1,1,TA,4,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,0,0,0,0,0,0,NA,MnPrv,NA,0,4,2007,WD,Normal,103200 +165,40,RM,40,5400,Pave,Pave,Reg,Lvl,AllPub,Corner,Gtl,OldTown,Norm,Norm,1Fam,1Story,6,7,1926,2004,Gable,CompShg,MetalSd,MetalSd,None,0,TA,Gd,BrkTil,TA,TA,Mn,LwQ,370,Unf,0,779,1149,GasA,Gd,Y,FuseA,1149,467,0,1616,0,0,2,0,3,1,Gd,5,Typ,0,NA,Detchd,1926,Unf,1,216,TA,TA,Y,0,0,183,0,0,0,NA,NA,NA,0,10,2007,WD,Normal,152000 +166,190,RL,62,10106,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,2fmCon,1.5Fin,5,7,1940,1999,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,Gd,BrkTil,TA,TA,No,ALQ,351,Rec,181,112,644,GasA,Gd,Y,SBrkr,808,547,0,1355,1,0,2,0,4,2,TA,6,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,140,0,0,0,0,0,NA,NA,NA,0,9,2008,WD,Normal,127500 +167,20,RL,NA,10708,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,ClearCr,Norm,Norm,1Fam,1Story,5,5,1955,1993,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Gd,TA,CBlock,TA,TA,No,LwQ,379,BLQ,768,470,1617,GasA,Ex,Y,FuseA,1867,0,0,1867,1,0,1,0,2,1,TA,7,Typ,3,Gd,Attchd,1955,Fin,1,303,TA,TA,Y,476,0,0,0,142,0,NA,GdWo,NA,0,11,2009,COD,Normal,190000 +168,60,RL,86,10562,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,2Story,8,5,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,300,Gd,TA,PConc,Ex,TA,No,GLQ,1288,Unf,0,294,1582,GasA,Ex,Y,SBrkr,1610,551,0,2161,1,0,1,1,3,1,Ex,8,Typ,1,Gd,Attchd,2007,Fin,3,789,TA,TA,Y,178,120,0,0,0,0,NA,NA,NA,0,11,2007,New,Partial,325624 +169,60,RL,62,8244,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,2Story,7,5,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,840,840,GasA,Ex,Y,SBrkr,840,880,0,1720,0,0,2,1,3,1,Gd,7,Typ,1,Gd,Attchd,2004,Fin,2,440,TA,TA,Y,100,48,0,0,0,0,NA,NA,NA,0,5,2007,WD,Normal,183500 +170,20,RL,NA,16669,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Timber,Norm,Norm,1Fam,1Story,8,6,1981,1981,Hip,WdShake,Plywood,Plywood,BrkFace,653,Gd,TA,CBlock,Gd,TA,No,Unf,0,Unf,0,1686,1686,GasA,TA,Y,SBrkr,1707,0,0,1707,0,0,2,1,2,1,TA,6,Typ,1,TA,Attchd,1981,RFn,2,511,TA,TA,Y,574,64,0,0,0,0,NA,NA,NA,0,1,2006,WD,Normal,228000 +171,50,RM,NA,12358,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,OldTown,Feedr,Norm,1Fam,1.5Fin,5,6,1941,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,Rec,360,Unf,0,360,720,GasA,TA,Y,SBrkr,854,0,528,1382,0,0,1,1,2,1,TA,7,Typ,0,NA,Detchd,1991,Unf,2,660,TA,TA,Y,237,0,0,0,0,0,NA,NA,NA,0,5,2007,WD,Normal,128500 +172,20,RL,141,31770,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,5,1960,1960,Hip,CompShg,BrkFace,Plywood,Stone,112,TA,TA,CBlock,TA,Gd,Gd,BLQ,639,Unf,0,441,1080,GasA,Fa,Y,SBrkr,1656,0,0,1656,1,0,1,0,3,1,TA,7,Typ,2,Gd,Attchd,1960,Fin,2,528,TA,TA,P,210,62,0,0,0,0,NA,NA,NA,0,5,2010,WD,Normal,215000 +173,160,RL,44,5306,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,StoneBr,Norm,Norm,TwnhsE,2Story,7,7,1987,1987,Gable,CompShg,HdBoard,HdBoard,None,0,Gd,Gd,PConc,Gd,Gd,No,GLQ,495,Rec,215,354,1064,GasA,Gd,Y,SBrkr,1064,703,0,1767,1,0,2,0,2,1,Gd,5,Typ,1,TA,Attchd,1987,RFn,2,504,Gd,TA,Y,441,35,0,0,0,0,NA,NA,NA,0,6,2006,WD,Normal,239000 +174,20,RL,80,10197,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,5,1961,1961,Gable,CompShg,WdShing,Wd Shng,BrkCmn,491,TA,TA,CBlock,TA,TA,No,ALQ,288,Rec,374,700,1362,GasA,TA,Y,SBrkr,1362,0,0,1362,1,0,1,1,3,1,TA,6,Typ,1,TA,Attchd,1961,Unf,2,504,TA,TA,Y,0,20,0,0,0,0,NA,NA,NA,0,6,2008,COD,Normal,163000 +175,20,RL,47,12416,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Timber,Norm,Norm,1Fam,1Story,6,5,1986,1986,Gable,CompShg,VinylSd,Plywood,Stone,132,TA,TA,CBlock,Gd,Fa,No,ALQ,1398,LwQ,208,0,1606,GasA,TA,Y,SBrkr,1651,0,0,1651,1,0,2,0,3,1,TA,7,Min2,1,TA,Attchd,1986,Fin,2,616,TA,TA,Y,192,0,0,0,0,0,NA,NA,NA,0,11,2008,WD,Normal,184000 +176,20,RL,84,12615,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,1Fam,1Story,6,7,1950,2001,Gable,CompShg,WdShing,Wd Shng,None,0,TA,TA,CBlock,TA,Gd,Av,ALQ,477,Unf,0,725,1202,GasA,TA,Y,SBrkr,2158,0,0,2158,1,0,2,0,4,1,Gd,7,Typ,1,Gd,Attchd,1950,Unf,2,576,TA,TA,Y,0,29,39,0,0,0,NA,MnPrv,NA,0,6,2007,WD,Normal,243000 +177,60,RL,97,10029,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,ClearCr,Norm,Norm,1Fam,2Story,6,5,1988,1989,Gable,CompShg,Plywood,Plywood,BrkFace,268,Gd,TA,PConc,Gd,TA,No,GLQ,831,Unf,0,320,1151,GasA,TA,Y,SBrkr,1164,896,0,2060,0,1,2,1,4,1,TA,8,Typ,1,TA,Attchd,1988,Unf,2,521,TA,TA,Y,0,228,0,0,192,0,NA,NA,NA,0,9,2007,WD,Normal,211000 +178,50,RL,NA,13650,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,1.5Fin,5,5,1958,1958,Gable,CompShg,MetalSd,MetalSd,None,0,Gd,Gd,CBlock,TA,TA,No,ALQ,57,BLQ,441,554,1052,GasA,Ex,Y,SBrkr,1252,668,0,1920,1,0,2,0,4,1,Gd,8,Typ,1,Gd,Attchd,1958,Unf,2,451,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,172500 +179,20,RL,63,17423,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,StoneBr,Norm,Norm,1Fam,1Story,9,5,2008,2009,Hip,CompShg,VinylSd,VinylSd,Stone,748,Ex,TA,PConc,Ex,TA,No,GLQ,1904,Unf,0,312,2216,GasA,Ex,Y,SBrkr,2234,0,0,2234,1,0,2,0,1,1,Ex,9,Typ,1,Gd,Attchd,2009,Fin,3,1166,TA,TA,Y,0,60,0,0,0,0,NA,NA,NA,0,7,2009,New,Partial,501837 +180,30,RM,60,8520,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,1Story,5,6,1923,2006,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Gd,TA,CBlock,TA,TA,No,Unf,0,Unf,0,968,968,GasA,TA,Y,SBrkr,968,0,0,968,0,0,1,0,2,1,TA,5,Typ,0,NA,Detchd,1935,Unf,2,480,Fa,TA,N,0,0,184,0,0,0,NA,NA,NA,0,7,2007,WD,Normal,100000 +181,160,FV,NA,2117,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,Twnhs,2Story,6,5,2000,2000,Gable,CompShg,MetalSd,MetalSd,BrkFace,456,Gd,TA,PConc,Gd,TA,No,GLQ,436,Unf,0,320,756,GasA,Ex,Y,SBrkr,769,756,0,1525,0,0,2,1,3,1,Gd,5,Typ,1,TA,Detchd,2000,Unf,2,440,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,6,2007,WD,Normal,177000 +182,70,RL,54,7588,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Crawfor,Norm,Norm,1Fam,2Story,7,6,1920,1950,Gable,CompShg,Stucco,Stucco,None,0,TA,TA,BrkTil,Fa,TA,No,LwQ,352,Unf,0,441,793,GasA,Gd,Y,SBrkr,901,901,0,1802,0,0,1,1,4,1,TA,9,Typ,1,Gd,Detchd,1920,Unf,1,216,Fa,TA,Y,0,0,40,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,200100 +183,20,RL,60,9060,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Artery,Norm,1Fam,1Story,5,6,1957,2006,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,98,TA,TA,PConc,NA,NA,NA,NA,0,NA,0,0,0,GasA,Ex,Y,SBrkr,1340,0,0,1340,0,0,1,0,3,1,TA,7,Typ,1,Gd,Attchd,1957,RFn,1,252,TA,TA,Y,116,0,0,180,0,0,NA,MnPrv,NA,0,6,2007,WD,Normal,120000 +184,50,RM,63,11426,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,1.5Fin,7,5,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1362,1362,GasA,Ex,Y,SBrkr,1362,720,0,2082,0,0,2,1,3,1,Gd,6,Mod,0,NA,Detchd,2003,Unf,2,484,TA,TA,N,280,238,0,0,0,0,NA,NA,NA,0,6,2008,WD,Normal,200000 +185,50,RL,92,7438,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,BrkSide,RRAn,Feedr,1Fam,1.5Fin,5,8,1908,1991,Gable,CompShg,AsbShng,Plywood,None,0,TA,TA,PConc,Fa,TA,No,Unf,0,Unf,0,504,504,GasA,Gd,Y,SBrkr,936,316,0,1252,0,0,1,0,3,1,TA,5,Typ,0,NA,Attchd,1986,Unf,2,576,TA,TA,Y,104,0,0,0,0,0,NA,MnPrv,NA,0,6,2006,WD,Normal,127000 +186,75,RM,90,22950,Pave,NA,IR2,Lvl,AllPub,Inside,Gtl,OldTown,Artery,Norm,1Fam,2.5Fin,10,9,1892,1993,Gable,WdShngl,Wd Sdng,Wd Sdng,None,0,Gd,Gd,BrkTil,TA,TA,Mn,Unf,0,Unf,0,1107,1107,GasA,Ex,Y,SBrkr,1518,1518,572,3608,0,0,2,1,4,1,Ex,12,Typ,2,TA,Detchd,1993,Unf,3,840,Ex,TA,Y,0,260,0,0,410,0,NA,GdPrv,NA,0,6,2006,WD,Normal,475000 +187,80,RL,NA,9947,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Mitchel,Norm,Norm,1Fam,SLvl,7,5,1990,1991,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,PConc,Gd,TA,Av,GLQ,611,Unf,0,577,1188,GasA,Ex,Y,SBrkr,1217,0,0,1217,1,0,2,0,3,1,Gd,6,Typ,0,NA,Attchd,1990,Unf,2,497,TA,TA,Y,168,27,0,0,0,0,NA,GdPrv,NA,0,6,2009,WD,Normal,173000 +188,50,RL,60,10410,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,1.5Fin,5,7,1916,1987,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,Fa,TA,No,Unf,0,Unf,0,660,660,GasA,Ex,Y,SBrkr,808,704,144,1656,0,0,2,1,3,1,TA,8,Min2,0,NA,Detchd,1916,Unf,1,180,Fa,Fa,N,0,0,0,140,0,0,NA,MnPrv,NA,0,8,2009,WD,Normal,135000 +189,90,RL,64,7018,Pave,NA,Reg,Bnk,AllPub,Inside,Gtl,SawyerW,Feedr,Norm,Duplex,SFoyer,5,5,1979,1979,Gable,CompShg,Plywood,Plywood,Stone,275,TA,TA,CBlock,Gd,TA,Av,GLQ,1086,Unf,0,0,1086,GasA,TA,Y,SBrkr,1224,0,0,1224,2,0,0,2,2,2,TA,6,Typ,2,TA,Detchd,1979,Unf,2,528,TA,TA,Y,120,0,0,0,0,0,NA,NA,NA,0,6,2009,WD,Alloca,153337 +190,120,RL,41,4923,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,StoneBr,Norm,Norm,TwnhsE,1Story,8,5,2001,2002,Gable,CompShg,CemntBd,CmentBd,None,0,Gd,TA,PConc,Ex,TA,Av,GLQ,1153,Unf,0,440,1593,GasA,Ex,Y,SBrkr,1593,0,0,1593,1,0,1,1,0,1,Ex,5,Typ,1,Gd,Attchd,2001,Fin,2,682,TA,TA,Y,0,120,0,0,224,0,NA,NA,NA,0,8,2008,WD,Normal,286000 +191,70,RL,70,10570,Pave,NA,Reg,Bnk,AllPub,Inside,Mod,Crawfor,Norm,Norm,1Fam,2Story,8,8,1932,1994,Hip,CompShg,BrkFace,BrkFace,None,0,Gd,TA,CBlock,Gd,Gd,No,Rec,297,Unf,0,556,853,GasA,TA,Y,SBrkr,1549,1178,0,2727,0,0,2,1,3,1,Gd,10,Maj1,2,TA,Detchd,1932,Unf,2,440,TA,TA,Y,0,74,0,0,0,0,NA,NA,NA,0,12,2007,WD,Normal,315000 +192,60,RL,NA,7472,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,NAmes,Norm,Norm,1Fam,2Story,7,9,1972,2004,Gable,CompShg,HdBoard,HdBoard,BrkFace,138,TA,TA,CBlock,TA,TA,No,ALQ,626,Unf,0,99,725,GasA,Gd,Y,SBrkr,725,754,0,1479,1,0,1,1,4,1,Gd,7,Typ,0,NA,Attchd,1972,Fin,2,484,TA,TA,Y,0,32,0,0,0,0,NA,NA,NA,0,6,2007,WD,Normal,184000 +193,20,RL,68,9017,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,1999,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,Av,GLQ,560,Unf,0,871,1431,GasA,Ex,Y,SBrkr,1431,0,0,1431,1,0,2,0,3,1,Gd,6,Typ,0,NA,Attchd,1999,Fin,2,666,TA,TA,Y,0,35,0,0,0,0,NA,NA,NA,0,9,2009,WD,Normal,192000 +194,160,RM,24,2522,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Twnhs,2Story,7,5,2004,2004,Gable,CompShg,VinylSd,VinylSd,Stone,50,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,970,970,GasA,Ex,Y,SBrkr,970,739,0,1709,0,0,2,0,3,1,Gd,7,Maj1,0,NA,Detchd,2004,Unf,2,380,TA,TA,Y,0,40,0,0,0,0,NA,NA,NA,0,5,2006,WD,Normal,130000 +195,20,RL,60,7180,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,5,7,1972,1972,Hip,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,TA,TA,Av,ALQ,390,Unf,0,474,864,GasA,TA,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,TA,5,Typ,0,NA,Detchd,1989,Unf,1,352,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,5,2008,WD,Normal,127000 +196,160,RL,24,2280,Pave,NA,Reg,Lvl,AllPub,FR2,Gtl,NPkVill,Norm,Norm,Twnhs,2Story,6,6,1976,1976,Gable,CompShg,Plywood,Brk Cmn,None,0,TA,TA,CBlock,Gd,TA,No,ALQ,566,Unf,0,289,855,GasA,TA,Y,SBrkr,855,601,0,1456,0,0,2,1,3,1,TA,7,Typ,1,TA,Attchd,1976,Unf,2,440,TA,TA,Y,87,0,0,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,148500 +197,20,RL,79,9416,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,1Story,7,5,2007,2007,Hip,CompShg,CemntBd,CmentBd,Stone,205,Ex,TA,PConc,Ex,TA,No,GLQ,1126,Unf,0,600,1726,GasA,Ex,Y,SBrkr,1726,0,0,1726,1,0,2,0,3,1,Ex,8,Typ,1,Gd,Attchd,2007,Fin,3,786,TA,TA,Y,171,138,0,0,266,0,NA,NA,NA,0,9,2007,New,Partial,311872 +198,75,RL,174,25419,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NAmes,Artery,Norm,1Fam,2Story,8,4,1918,1990,Gable,CompShg,Stucco,Stucco,None,0,Gd,Gd,PConc,TA,TA,No,GLQ,1036,LwQ,184,140,1360,GasA,Gd,Y,SBrkr,1360,1360,392,3112,1,1,2,0,4,1,Gd,8,Typ,1,Ex,Detchd,1918,Unf,2,795,TA,TA,Y,0,16,552,0,0,512,Ex,GdPrv,NA,0,3,2006,WD,Abnorml,235000 +199,75,RM,92,5520,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,OldTown,Norm,Norm,1Fam,2.5Fin,6,6,1912,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,755,755,GasA,Ex,Y,SBrkr,929,929,371,2229,0,0,1,0,5,1,TA,8,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,0,198,30,0,0,0,NA,MnPrv,NA,0,7,2009,WD,Abnorml,104000 +200,20,RL,76,9591,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,1Story,8,5,2004,2005,Hip,CompShg,VinylSd,VinylSd,BrkFace,262,Gd,TA,PConc,Ex,TA,Av,GLQ,1088,Unf,0,625,1713,GasA,Ex,Y,SBrkr,1713,0,0,1713,1,0,2,0,3,1,Ex,7,Typ,1,Gd,Attchd,2004,Fin,3,856,TA,TA,Y,0,26,0,0,170,0,NA,NA,NA,0,1,2009,WD,Normal,274900 +201,20,RM,80,8546,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,1Fam,1Story,4,5,2003,2004,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1121,1121,GasA,Ex,Y,SBrkr,1121,0,0,1121,0,0,2,0,2,1,TA,5,Typ,0,NA,Attchd,2003,RFn,2,440,TA,TA,Y,132,64,0,0,0,0,NA,NA,NA,0,3,2010,WD,Normal,140000 +202,20,RL,75,10125,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Mitchel,Norm,Norm,1Fam,1Story,6,6,1977,1977,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,CBlock,TA,TA,No,ALQ,641,LwQ,279,276,1196,GasA,TA,Y,SBrkr,1279,0,0,1279,0,1,2,0,3,1,TA,6,Typ,2,Fa,Detchd,1980,Unf,2,473,TA,TA,Y,238,83,0,0,0,0,NA,MnPrv,NA,0,2,2008,WD,Normal,171500 +203,50,RL,50,7000,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,OldTown,Artery,Norm,1Fam,1.5Fin,6,6,1924,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,Gd,BrkTil,Fa,TA,No,LwQ,617,Unf,0,0,617,GasA,Gd,Y,SBrkr,865,445,0,1310,0,0,2,0,2,1,TA,6,Min1,0,NA,Attchd,1924,Unf,1,398,TA,TA,Y,0,0,126,0,0,0,NA,NA,NA,0,5,2006,COD,Normal,112000 +204,120,RM,NA,4438,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,TwnhsE,1Story,6,5,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,205,Gd,TA,PConc,Gd,TA,Av,GLQ,662,Unf,0,186,848,GasA,Ex,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Gd,3,Typ,1,Gd,Attchd,2004,RFn,2,420,TA,TA,Y,149,0,0,0,0,0,NA,NA,NA,0,1,2008,WD,Normal,149000 +205,50,RM,50,3500,Pave,Grvl,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,1.5Fin,5,7,1947,1950,Gable,CompShg,AsbShng,AsbShng,None,0,TA,TA,CBlock,TA,TA,No,LwQ,312,Unf,0,408,720,GasA,TA,Y,SBrkr,720,564,0,1284,0,0,1,1,2,1,TA,5,Typ,0,NA,Detchd,1948,Unf,1,240,TA,TA,Y,0,35,0,0,0,0,NA,MnWw,NA,0,4,2009,WD,Normal,110000 +206,20,RL,99,11851,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,1Fam,1Story,7,5,1990,1990,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1424,1424,GasA,Ex,Y,SBrkr,1442,0,0,1442,0,0,2,0,3,1,TA,5,Typ,0,NA,Attchd,1990,RFn,2,500,TA,TA,Y,0,34,0,508,0,0,NA,NA,NA,0,5,2009,WD,Normal,180500 +207,20,RL,40,13673,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Sawyer,RRAe,Norm,1Fam,1Story,5,5,1962,1962,Gable,CompShg,HdBoard,HdBoard,None,0,TA,Gd,CBlock,TA,TA,No,Unf,0,Unf,0,1140,1140,GasA,TA,Y,SBrkr,1696,0,0,1696,0,0,1,1,3,1,TA,8,Min2,1,TA,Attchd,1962,RFn,1,349,TA,TA,Y,0,30,0,0,0,0,NA,NA,NA,0,3,2007,WD,Normal,143900 +208,20,RL,NA,12493,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,4,5,1960,1960,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,PConc,TA,TA,No,ALQ,419,Rec,306,375,1100,GasA,TA,Y,SBrkr,1100,0,0,1100,1,0,1,0,3,1,TA,6,Typ,1,Po,Attchd,1960,RFn,1,312,TA,TA,Y,355,0,0,0,0,0,NA,GdWo,NA,0,4,2008,WD,Normal,141000 +209,60,RL,NA,14364,Pave,NA,IR1,Low,AllPub,Inside,Mod,SawyerW,Norm,Norm,1Fam,2Story,7,5,1988,1989,Gable,CompShg,Plywood,Plywood,BrkFace,128,Gd,TA,CBlock,Gd,TA,Gd,GLQ,1065,Unf,0,92,1157,GasA,Ex,Y,SBrkr,1180,882,0,2062,1,0,2,1,3,1,TA,7,Typ,1,Gd,Attchd,1988,Fin,2,454,TA,TA,Y,60,55,0,0,154,0,NA,NA,NA,0,4,2007,WD,Normal,277000 +210,20,RL,75,8250,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,7,1964,1964,Hip,CompShg,HdBoard,HdBoard,Stone,260,TA,TA,CBlock,Gd,TA,No,Rec,787,Unf,0,305,1092,GasA,Ex,Y,SBrkr,1092,0,0,1092,1,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1964,RFn,2,504,TA,Gd,Y,0,0,0,0,0,0,NA,MnPrv,NA,0,7,2008,WD,Normal,145000 +211,30,RL,67,5604,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1Story,5,6,1925,1950,Gable,CompShg,Stucco,Stucco,None,0,TA,TA,CBlock,TA,TA,No,Rec,468,Unf,0,396,864,GasA,TA,N,FuseA,864,0,0,864,1,0,1,0,2,1,TA,5,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,0,0,96,0,0,0,NA,NA,NA,0,4,2008,WD,Normal,98000 +212,20,RL,83,10420,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,1Fam,1Story,6,5,2009,2009,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,Mn,GLQ,36,Unf,0,1176,1212,GasA,Ex,Y,SBrkr,1212,0,0,1212,0,0,2,0,3,1,Gd,6,Typ,0,NA,Attchd,2009,RFn,2,460,TA,TA,Y,100,22,0,0,0,0,NA,NA,NA,0,3,2010,WD,Normal,186000 +213,60,FV,72,8640,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,2Story,7,5,2009,2009,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,No,GLQ,822,Unf,0,78,900,GasA,Ex,Y,SBrkr,932,920,0,1852,1,0,2,1,3,1,Gd,7,Typ,1,TA,Attchd,2009,RFn,2,644,TA,TA,Y,168,108,0,0,0,0,NA,NA,NA,0,7,2009,New,Partial,252678 +214,20,RL,43,13568,Pave,NA,IR2,Lvl,AllPub,CulDSac,Gtl,CollgCr,Norm,Norm,1Fam,1Story,5,5,1995,1995,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,No,ALQ,716,Unf,0,274,990,GasA,Ex,Y,SBrkr,990,0,0,990,0,1,1,0,3,1,TA,5,Typ,0,NA,Attchd,1996,Unf,2,576,TA,TA,Y,224,0,0,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,156000 +215,60,RL,NA,10900,Pave,NA,IR1,Lvl,AllPub,FR2,Gtl,CollgCr,Norm,Norm,1Fam,2Story,6,7,1977,1977,Gable,CompShg,HdBoard,HdBoard,BrkFace,153,TA,TA,CBlock,Gd,TA,No,GLQ,378,Unf,0,311,689,GasA,Ex,Y,SBrkr,689,703,0,1392,0,0,1,1,3,1,TA,6,Typ,0,NA,Attchd,1977,Fin,1,299,TA,TA,Y,0,36,0,0,0,0,NA,MnPrv,Shed,450,3,2010,WD,Normal,161750 +216,20,RL,72,10011,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,6,1957,1996,Gable,CompShg,HdBoard,HdBoard,BrkFace,64,TA,TA,CBlock,TA,TA,No,BLQ,360,Unf,0,710,1070,GasA,TA,Y,SBrkr,1236,0,0,1236,0,1,1,0,2,1,Gd,6,Min1,1,Fa,Attchd,1957,Unf,1,447,TA,TA,Y,0,0,0,0,0,0,NA,MnPrv,NA,0,5,2006,WD,Normal,134450 +217,20,RL,65,8450,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,266,Gd,TA,PConc,Gd,TA,Mn,GLQ,946,Unf,0,490,1436,GasA,Ex,Y,SBrkr,1436,0,0,1436,1,0,2,0,3,1,Gd,8,Typ,0,NA,Attchd,2004,Unf,2,484,TA,TA,Y,139,98,0,0,0,0,NA,NA,NA,0,4,2008,WD,Normal,210000 +218,70,RM,57,9906,Pave,Grvl,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,2Story,4,4,1925,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,686,686,GasA,Fa,N,SBrkr,810,518,0,1328,0,0,1,0,3,1,TA,8,Typ,0,NA,Detchd,1940,Unf,1,210,TA,TA,Y,0,172,60,0,0,0,NA,NA,NA,0,9,2006,WD,Family,107000 +219,50,RL,NA,15660,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Crawfor,Norm,Norm,1Fam,1.5Fin,7,9,1939,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,312,Gd,Gd,CBlock,TA,TA,No,BLQ,341,Unf,0,457,798,GasA,Ex,Y,SBrkr,1137,817,0,1954,0,1,1,1,3,1,Gd,8,Typ,2,TA,Attchd,1939,Unf,2,431,TA,TA,Y,0,119,150,0,0,0,NA,NA,NA,0,5,2008,WD,Normal,311500 +220,120,RL,43,3010,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Blmngtn,Norm,Norm,TwnhsE,1Story,7,5,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,16,Gd,TA,PConc,Gd,TA,Av,GLQ,16,Unf,0,1232,1248,GasA,Ex,Y,SBrkr,1248,0,0,1248,0,0,2,0,2,1,Gd,5,Typ,0,NA,Attchd,2005,Fin,2,438,TA,TA,Y,108,0,0,0,0,0,NA,NA,NA,0,3,2006,New,Partial,167240 +221,20,RL,73,8990,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,Mn,Unf,0,Unf,0,1498,1498,GasA,Ex,Y,SBrkr,1498,0,0,1498,0,0,2,0,2,1,Gd,5,Typ,0,NA,Attchd,2006,RFn,2,675,TA,TA,Y,351,33,0,0,0,0,NA,NA,NA,0,4,2006,New,Partial,204900 +222,60,RL,NA,8068,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,2Story,6,5,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1010,1010,GasA,Ex,Y,SBrkr,1010,1257,0,2267,0,0,2,1,4,1,Gd,8,Typ,1,TA,BuiltIn,2002,RFn,2,390,TA,TA,Y,120,46,0,0,0,0,NA,NA,NA,0,12,2009,ConLI,Normal,200000 +223,60,RL,85,11475,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,RRAn,Norm,1Fam,2Story,6,6,1975,1975,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,CBlock,Gd,TA,No,ALQ,550,Unf,0,163,713,GasA,TA,Y,SBrkr,811,741,0,1552,1,0,2,1,3,1,TA,6,Typ,1,TA,Attchd,1975,RFn,2,434,TA,TA,Y,209,208,0,0,0,0,NA,MnPrv,NA,0,2,2006,WD,Normal,179900 +224,20,RL,70,10500,Pave,NA,Reg,Lvl,AllPub,FR2,Gtl,NAmes,Norm,Norm,1Fam,1Story,4,6,1971,1971,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,TA,TA,No,ALQ,524,LwQ,180,160,864,GasA,Gd,Y,SBrkr,864,0,0,864,0,0,1,0,2,1,TA,4,Typ,0,NA,Detchd,1989,Unf,2,576,TA,TA,Y,216,0,0,0,0,0,NA,NA,NA,0,3,2009,WD,Abnorml,97000 +225,20,RL,103,13472,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,1Story,10,5,2003,2003,Hip,CompShg,VinylSd,VinylSd,BrkFace,922,Ex,TA,PConc,Ex,TA,Gd,GLQ,56,Unf,0,2336,2392,GasA,Ex,Y,SBrkr,2392,0,0,2392,0,0,2,0,3,1,Ex,8,Typ,1,Ex,Attchd,2003,Fin,3,968,TA,TA,Y,248,105,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,386250 +226,160,RM,21,1680,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrDale,Norm,Norm,Twnhs,2Story,5,5,1971,1971,Gable,CompShg,HdBoard,HdBoard,BrkFace,142,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,630,630,GasA,TA,Y,SBrkr,630,672,0,1302,0,0,2,1,3,1,TA,6,Typ,0,NA,Detchd,1991,Unf,1,280,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,5,2009,COD,Abnorml,112000 +227,60,RL,82,9950,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NoRidge,Norm,Norm,1Fam,2Story,7,5,1995,1995,Gable,CompShg,VinylSd,VinylSd,BrkFace,290,Gd,TA,PConc,Gd,TA,No,GLQ,565,Unf,0,638,1203,GasA,Ex,Y,SBrkr,1214,1306,0,2520,0,0,2,1,4,1,Gd,9,Typ,1,TA,Attchd,1995,RFn,3,721,TA,TA,Y,224,114,0,0,0,0,NA,NA,NA,0,6,2007,WD,Abnorml,290000 +228,160,RM,21,1869,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrDale,Norm,Norm,Twnhs,2Story,6,6,1970,1970,Gable,CompShg,HdBoard,HdBoard,BrkFace,127,TA,TA,CBlock,TA,TA,No,Rec,321,Unf,0,162,483,GasA,TA,Y,SBrkr,483,504,0,987,0,0,1,1,2,1,TA,5,Typ,0,NA,Detchd,1987,Unf,1,280,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,9,2008,WD,Normal,106000 +229,20,RL,70,8521,Pave,NA,Reg,Lvl,AllPub,FR2,Gtl,Sawyer,Feedr,Norm,1Fam,1Story,5,5,1967,1967,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,TA,TA,No,ALQ,842,Unf,0,70,912,GasA,TA,Y,SBrkr,912,0,0,912,0,0,1,0,3,1,TA,5,Typ,1,Fa,Detchd,1974,Unf,1,336,TA,TA,Y,0,0,0,0,0,0,NA,MnPrv,NA,0,5,2010,WD,Normal,125000 +230,120,RL,43,3182,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Blmngtn,Norm,Norm,TwnhsE,1Story,7,5,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,16,Gd,TA,PConc,Gd,TA,Av,GLQ,16,Unf,0,1357,1373,GasA,Ex,Y,SBrkr,1555,0,0,1555,0,0,2,0,2,1,Gd,7,Typ,1,TA,Attchd,2005,Fin,2,430,TA,TA,Y,143,20,0,0,0,0,NA,NA,NA,0,5,2009,WD,Normal,192500 +231,20,RL,73,8760,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,6,1959,1959,Hip,CompShg,MetalSd,MetalSd,BrkFace,220,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,1194,1194,GasA,TA,Y,SBrkr,1194,0,0,1194,1,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1959,RFn,1,312,TA,TA,Y,0,0,120,0,0,0,NA,NA,NA,0,4,2010,WD,Normal,148000 +232,60,RL,174,15138,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NoRidge,Norm,Norm,1Fam,2Story,8,5,1995,1996,Gable,CompShg,VinylSd,VinylSd,BrkFace,506,Gd,TA,PConc,Gd,TA,No,GLQ,689,Unf,0,773,1462,GasA,Ex,Y,SBrkr,1490,1304,0,2794,1,0,2,1,4,1,Ex,9,Typ,1,TA,Attchd,1995,Fin,3,810,TA,TA,Y,0,146,202,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,403000 +233,160,RM,21,1680,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrDale,Norm,Norm,Twnhs,2Story,6,5,1972,1972,Gable,CompShg,HdBoard,HdBoard,BrkFace,297,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,483,483,GasA,TA,Y,SBrkr,483,504,0,987,0,0,1,1,2,1,TA,5,Typ,1,Po,Attchd,1972,Unf,1,288,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,6,2006,WD,Normal,94500 +234,20,RL,75,10650,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,CollgCr,Norm,Norm,1Fam,1Story,5,6,1976,1976,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,TA,Gd,Av,LwQ,182,ALQ,712,0,894,GasA,TA,Y,SBrkr,894,0,0,894,1,0,1,0,3,1,TA,5,Typ,0,NA,Attchd,1976,Unf,1,308,TA,TA,Y,365,0,0,0,0,0,NA,MnPrv,NA,0,2,2010,WD,Normal,128200 +235,60,RL,NA,7851,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,2Story,6,5,2002,2002,Gable,CompShg,VinylSd,VinylSd,NA,NA,Gd,TA,PConc,Gd,TA,No,GLQ,625,Unf,0,235,860,GasA,Ex,Y,SBrkr,860,1100,0,1960,1,0,2,1,4,1,Gd,8,Typ,2,TA,BuiltIn,2002,Fin,2,440,TA,TA,Y,288,48,0,0,0,0,NA,NA,NA,0,5,2010,WD,Normal,216500 +236,160,RM,21,1680,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrDale,Norm,Norm,TwnhsE,2Story,6,3,1971,1971,Gable,CompShg,HdBoard,HdBoard,BrkFace,604,TA,TA,CBlock,TA,TA,No,ALQ,358,Unf,0,125,483,GasA,TA,Y,SBrkr,483,504,0,987,0,0,1,1,2,1,TA,5,Typ,0,NA,Detchd,1971,Unf,1,264,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,8,2008,WD,Normal,89500 +237,20,RL,65,8773,Pave,NA,Reg,Lvl,AllPub,FR2,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,98,Gd,TA,PConc,Gd,TA,Av,GLQ,24,Unf,0,1390,1414,GasA,Ex,Y,SBrkr,1414,0,0,1414,0,0,2,0,3,1,Gd,6,Typ,0,NA,Attchd,2004,RFn,2,494,TA,TA,Y,132,105,0,0,0,0,NA,NA,NA,0,5,2010,WD,Normal,185500 +238,60,RL,NA,9453,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,SawyerW,RRNe,Norm,1Fam,2Story,7,7,1993,2003,Gable,CompShg,HdBoard,HdBoard,None,0,Gd,TA,PConc,Gd,TA,No,BLQ,402,Unf,0,594,996,GasA,Ex,Y,SBrkr,1014,730,0,1744,0,0,2,1,3,1,Gd,7,Typ,0,NA,Attchd,1993,RFn,2,457,TA,TA,Y,370,70,0,238,0,0,NA,NA,NA,0,2,2010,WD,Normal,194500 +239,20,RL,93,12030,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,1Story,8,5,2007,2007,Hip,CompShg,VinylSd,VinylSd,BrkFace,254,Ex,TA,PConc,Ex,TA,No,Unf,0,Unf,0,1694,1694,GasA,Ex,Y,SBrkr,1694,0,0,1694,0,0,2,0,3,1,Gd,7,Typ,0,NA,Attchd,2007,Fin,3,818,TA,TA,Y,168,228,0,0,0,0,NA,NA,NA,0,12,2007,New,Partial,318000 +240,50,RL,52,8741,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1.5Fin,6,4,1945,1950,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,CBlock,TA,Fa,No,LwQ,94,Unf,0,641,735,GasA,TA,Y,FuseA,798,689,0,1487,0,0,1,1,3,1,TA,7,Typ,1,Gd,Detchd,1949,Unf,1,220,TA,TA,Y,0,140,0,0,0,0,NA,MnPrv,NA,0,4,2010,WD,Normal,113000 +241,20,FV,75,9000,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,1Story,8,5,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,36,Gd,TA,PConc,Gd,TA,Av,GLQ,1078,Unf,0,488,1566,GasA,Ex,Y,SBrkr,1566,0,0,1566,1,0,2,0,3,1,Gd,7,Typ,0,NA,Attchd,2008,RFn,2,750,TA,TA,Y,144,168,0,0,0,0,NA,NA,NA,0,4,2010,WD,Normal,262500 +242,30,RM,40,3880,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,1Story,5,9,1945,1997,Gable,CompShg,VinylSd,VinylSd,None,0,TA,Gd,CBlock,TA,TA,No,ALQ,329,Unf,0,357,686,GasA,Gd,Y,SBrkr,866,0,0,866,0,0,1,0,2,1,Gd,4,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,58,42,0,0,0,0,NA,NA,NA,0,8,2007,WD,Normal,110500 +243,50,RM,63,5000,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,OldTown,Norm,Norm,1Fam,1.5Fin,5,4,1900,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,540,540,GasA,Gd,N,FuseA,889,551,0,1440,0,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1940,Unf,1,352,Fa,TA,Y,0,0,77,0,0,0,NA,NA,NA,0,4,2006,WD,Normal,79000 +244,160,RL,75,10762,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,SawyerW,Norm,Norm,TwnhsE,2Story,6,6,1980,1980,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,CBlock,Gd,TA,No,Unf,0,Unf,0,626,626,GasA,TA,Y,SBrkr,626,591,0,1217,0,0,1,1,3,1,TA,6,Typ,1,TA,Attchd,1980,RFn,1,288,TA,TA,Y,0,28,0,0,0,0,NA,NA,NA,0,4,2009,WD,Normal,120000 +245,60,RL,NA,8880,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,SawyerW,Norm,Norm,1Fam,2Story,7,5,1994,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,695,Unf,0,253,948,GasA,Ex,Y,SBrkr,1222,888,0,2110,1,0,2,1,3,1,Gd,8,Typ,2,Fa,Attchd,1994,RFn,2,463,TA,TA,Y,0,130,0,0,0,0,NA,NA,NA,0,5,2010,WD,Normal,205000 +246,20,RL,80,10400,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,1Story,7,5,1988,1988,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,102,TA,TA,CBlock,Gd,TA,Av,GLQ,929,Unf,0,916,1845,GasA,Gd,Y,SBrkr,1872,0,0,1872,0,1,2,0,3,1,TA,6,Typ,1,TA,Attchd,1988,Fin,2,604,TA,TA,Y,197,39,0,0,0,0,NA,NA,NA,0,6,2006,WD,Normal,241500 +247,190,RM,69,9142,Pave,Grvl,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,2fmCon,2Story,6,8,1910,1950,Gable,CompShg,AsbShng,AsbShng,None,0,TA,Fa,Stone,Fa,TA,No,Unf,0,Unf,0,1020,1020,GasA,Gd,N,FuseP,908,1020,0,1928,0,0,2,0,4,2,Fa,9,Typ,0,NA,Detchd,1910,Unf,1,440,Po,Po,Y,0,60,112,0,0,0,NA,NA,NA,0,4,2006,WD,Normal,137000 +248,20,RL,75,11310,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,5,1954,1954,Hip,CompShg,Wd Sdng,BrkFace,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,1367,1367,GasA,Ex,Y,SBrkr,1375,0,0,1375,0,0,1,0,2,1,TA,5,Typ,1,TA,Attchd,1954,Unf,2,451,TA,TA,Y,0,30,0,0,0,0,NA,NA,NA,0,6,2006,WD,Normal,140000 +249,60,RL,72,11317,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,7,5,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,101,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,840,840,GasA,Ex,Y,SBrkr,840,828,0,1668,0,0,2,1,3,1,Gd,8,Typ,0,NA,Attchd,2003,RFn,2,500,TA,TA,Y,144,68,0,0,0,0,NA,NA,NA,0,9,2007,WD,Normal,180000 +250,50,RL,NA,159000,Pave,NA,IR2,Low,AllPub,CulDSac,Sev,ClearCr,Norm,Norm,1Fam,1.5Fin,6,7,1958,2006,Gable,CompShg,Wd Sdng,HdBoard,BrkCmn,472,Gd,TA,CBlock,Gd,TA,Gd,Rec,697,Unf,0,747,1444,GasA,Gd,Y,SBrkr,1444,700,0,2144,0,1,2,0,4,1,Gd,7,Typ,2,TA,Attchd,1958,Fin,2,389,TA,TA,Y,0,98,0,0,0,0,NA,NA,Shed,500,6,2007,WD,Normal,277000 +251,30,RL,55,5350,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,1Story,3,2,1940,1966,Gable,CompShg,Wd Sdng,Plywood,None,0,TA,Po,CBlock,TA,TA,No,Unf,0,Unf,0,728,728,GasA,Ex,Y,SBrkr,1306,0,0,1306,0,0,1,0,3,1,Fa,6,Mod,0,NA,NA,NA,NA,0,0,NA,NA,Y,263,0,0,0,0,0,NA,GdWo,Shed,450,5,2010,WD,Normal,76500 +252,120,RM,44,4750,Pave,NA,IR1,HLS,AllPub,Inside,Mod,Crawfor,Norm,Norm,TwnhsE,1Story,8,5,2006,2007,Hip,CompShg,VinylSd,VinylSd,Stone,481,Gd,TA,PConc,Gd,TA,Gd,GLQ,1573,Unf,0,0,1573,GasA,Ex,Y,SBrkr,1625,0,0,1625,1,1,2,0,2,1,Gd,5,Typ,1,Gd,Attchd,2006,Fin,2,538,TA,TA,Y,123,0,0,0,153,0,NA,NA,NA,0,12,2007,WD,Family,235000 +253,60,RL,65,8366,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,SawyerW,Norm,Norm,1Fam,2Story,6,5,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,798,798,GasA,Ex,Y,SBrkr,798,842,0,1640,0,0,2,1,3,1,Gd,6,Typ,0,NA,Attchd,2004,RFn,2,520,TA,TA,Y,138,45,0,0,0,0,NA,NA,NA,0,12,2008,WD,Normal,173000 +254,80,RL,85,9350,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,SLvl,6,7,1964,1991,Hip,CompShg,HdBoard,HdBoard,BrkFace,108,TA,TA,CBlock,Gd,TA,Gd,LwQ,270,ALQ,580,452,1302,GasA,Ex,Y,SBrkr,1302,0,0,1302,0,1,2,0,3,1,Gd,7,Min1,0,NA,Attchd,1964,RFn,1,309,TA,TA,Y,333,0,0,0,0,0,NA,MnPrv,NA,0,10,2007,CWD,Normal,158000 +255,20,RL,70,8400,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,6,1957,1957,Gable,CompShg,MetalSd,MetalSd,None,0,TA,Gd,CBlock,TA,TA,No,Rec,922,Unf,0,392,1314,GasA,TA,Y,SBrkr,1314,0,0,1314,1,0,1,0,3,1,TA,5,Typ,0,NA,Attchd,1957,RFn,1,294,TA,TA,Y,250,0,0,0,0,0,NA,NA,NA,0,6,2010,WD,Normal,145000 +256,60,RL,66,8738,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,2Story,7,5,1999,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,302,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,975,975,GasA,Ex,Y,SBrkr,1005,1286,0,2291,0,0,2,1,4,1,Gd,8,Typ,1,TA,BuiltIn,1999,Fin,2,429,TA,TA,Y,192,0,0,0,0,0,NA,NA,NA,0,2,2006,WD,Normal,230000 +257,60,FV,64,8791,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,2Story,6,5,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,Rec,503,Unf,0,361,864,GasA,Ex,Y,SBrkr,864,864,0,1728,0,0,2,1,3,1,Gd,7,Typ,0,NA,Attchd,2003,RFn,2,673,TA,TA,Y,216,56,0,0,0,0,NA,NA,NA,0,5,2008,WD,Normal,207500 +258,20,RL,68,8814,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,180,Gd,TA,PConc,Gd,TA,No,GLQ,1334,Unf,0,270,1604,GasA,Ex,Y,SBrkr,1604,0,0,1604,1,0,2,1,3,1,Gd,8,Typ,1,Gd,Attchd,2006,RFn,2,660,TA,TA,Y,123,110,0,0,0,0,NA,NA,NA,0,3,2009,WD,Abnorml,220000 +259,60,RL,80,12435,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,7,5,2001,2001,Gable,CompShg,VinylSd,VinylSd,BrkFace,172,Gd,TA,PConc,Gd,TA,No,GLQ,361,Unf,0,602,963,GasA,Ex,Y,SBrkr,963,829,0,1792,0,0,2,1,3,1,Gd,7,Typ,1,TA,Attchd,2001,RFn,2,564,TA,TA,Y,0,96,0,245,0,0,NA,NA,NA,0,5,2008,WD,Normal,231500 +260,20,RM,70,12702,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,1Story,5,5,1956,1956,Gable,CompShg,BrkFace,BrkFace,None,0,TA,TA,PConc,NA,NA,NA,NA,0,NA,0,0,0,GasA,Gd,Y,FuseA,882,0,0,882,0,0,1,0,2,1,TA,4,Typ,0,NA,Detchd,1956,Unf,1,308,TA,TA,Y,0,45,0,0,0,0,NA,NA,NA,0,12,2008,WD,Normal,97000 +261,80,RL,120,19296,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NAmes,Artery,Norm,1Fam,SLvl,6,5,1962,1962,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,399,TA,TA,CBlock,TA,TA,Gd,Rec,672,ALQ,690,0,1362,GasA,TA,Y,SBrkr,1382,0,0,1382,1,0,1,0,3,1,TA,6,Typ,1,TA,Attchd,1991,Unf,2,884,TA,TA,Y,0,0,252,0,0,0,NA,GdWo,NA,0,5,2009,WD,Normal,176000 +262,60,RL,69,9588,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,8,5,2007,2007,Gable,CompShg,CemntBd,CmentBd,Stone,270,Gd,TA,PConc,Ex,TA,No,Unf,0,Unf,0,1482,1482,GasA,Ex,Y,SBrkr,1482,1092,0,2574,0,0,2,1,3,1,Ex,10,Typ,1,Gd,BuiltIn,2007,Fin,3,868,TA,TA,Y,0,148,0,0,0,0,NA,NA,NA,0,11,2007,New,Partial,276000 +263,80,RL,88,8471,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,1Fam,SLvl,6,7,1977,1995,Gable,CompShg,HdBoard,Plywood,BrkFace,46,TA,TA,CBlock,Gd,Gd,Av,ALQ,506,Unf,0,0,506,GasA,TA,Y,SBrkr,1212,0,0,1212,1,0,1,0,3,1,TA,6,Typ,1,TA,Attchd,1978,Unf,2,492,TA,TA,Y,292,12,0,0,0,0,NA,GdWo,NA,0,7,2006,WD,Normal,151000 +264,50,RM,50,5500,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,OldTown,Norm,Norm,1Fam,1.5Fin,5,7,1929,2001,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,TA,TA,No,LwQ,234,ALQ,692,0,926,GasA,TA,Y,SBrkr,926,0,390,1316,1,0,1,0,3,1,TA,6,Typ,0,NA,Detchd,1974,Unf,2,484,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,4,2010,WD,Normal,130000 +265,30,RM,30,5232,Pave,Grvl,IR3,Bnk,AllPub,Inside,Gtl,OldTown,Artery,Norm,1Fam,1Story,5,5,1925,2004,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,Fa,TA,No,Unf,0,Unf,0,680,680,GasA,Gd,N,FuseP,764,0,0,764,0,0,1,0,2,1,TA,4,Typ,0,NA,Detchd,1965,Unf,2,504,TA,TA,N,0,0,0,0,0,0,NA,NA,NA,0,6,2008,WD,Normal,73000 +266,20,RL,78,12090,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,1Story,6,6,1981,1981,Gable,CompShg,MetalSd,MetalSd,BrkFace,210,TA,Gd,CBlock,Gd,TA,No,GLQ,588,LwQ,228,606,1422,GasA,TA,Y,SBrkr,1422,0,0,1422,0,0,2,0,3,1,Gd,7,Typ,1,TA,Attchd,1981,Fin,2,576,TA,TA,Y,276,0,0,0,0,0,NA,GdPrv,NA,0,6,2008,WD,Normal,175500 +267,60,RL,70,11207,Pave,NA,IR1,HLS,AllPub,FR2,Gtl,Gilbert,Norm,Norm,1Fam,2Story,6,5,1997,1997,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,Av,GLQ,714,Unf,0,88,802,GasA,Gd,Y,SBrkr,802,709,0,1511,1,0,2,1,3,1,TA,8,Typ,1,TA,Attchd,1997,Fin,2,413,TA,TA,Y,95,75,0,0,0,0,NA,NA,NA,0,6,2006,WD,Normal,185000 +268,75,RL,60,8400,Pave,NA,Reg,Bnk,AllPub,Inside,Mod,SWISU,Norm,Norm,1Fam,2.5Fin,5,8,1939,1997,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,PConc,TA,TA,No,LwQ,378,Unf,0,342,720,GasA,Ex,Y,SBrkr,1052,720,420,2192,0,0,2,1,4,1,Gd,8,Typ,1,Gd,Detchd,1939,Unf,1,240,TA,TA,Y,262,24,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,179500 +269,30,RM,71,6900,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,IDOTRR,Norm,Norm,1Fam,1Story,5,6,1940,1955,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,CBlock,TA,TA,No,ALQ,403,Rec,125,212,740,GasA,Ex,Y,SBrkr,778,0,0,778,0,0,1,0,2,1,TA,4,Typ,1,Gd,Detchd,1966,Fin,1,924,Ex,Ex,Y,0,25,0,0,0,0,NA,NA,NA,0,2,2008,WD,Normal,120500 +270,20,RL,NA,7917,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,1Fam,1Story,6,7,1976,1976,Hip,CompShg,HdBoard,HdBoard,BrkFace,174,TA,Gd,CBlock,TA,Gd,No,BLQ,751,Unf,0,392,1143,GasA,TA,Y,SBrkr,1113,0,0,1113,1,0,1,1,3,1,TA,6,Typ,1,Fa,Attchd,1987,RFn,1,504,TA,Gd,Y,370,30,0,0,0,0,NA,GdPrv,NA,0,5,2007,WD,Normal,148000 +271,60,FV,84,10728,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,2Story,8,5,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,Mn,Unf,0,Unf,0,1095,1095,GasA,Gd,Y,SBrkr,1095,844,0,1939,0,0,2,1,3,1,Gd,8,Typ,1,Gd,Attchd,2006,RFn,3,1053,TA,TA,Y,192,51,0,0,0,0,NA,NA,NA,0,8,2006,New,Partial,266000 +272,20,RL,73,39104,Pave,NA,IR1,Low,AllPub,CulDSac,Sev,ClearCr,Norm,Norm,1Fam,1Story,7,7,1954,2005,Flat,Membran,Plywood,Plywood,None,0,TA,TA,CBlock,Gd,TA,Gd,LwQ,226,GLQ,1063,96,1385,GasA,Ex,Y,SBrkr,1363,0,0,1363,1,0,1,0,2,1,TA,5,Mod,2,TA,Attchd,1954,Unf,2,439,TA,TA,Y,81,0,0,0,0,0,NA,NA,NA,0,4,2008,WD,Normal,241500 +273,60,RL,92,11764,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,NoRidge,Norm,Norm,1Fam,2Story,8,7,1999,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,348,Gd,TA,PConc,Gd,TA,No,GLQ,524,Unf,0,628,1152,GasA,Ex,Y,SBrkr,1164,1106,0,2270,0,0,2,1,4,1,Gd,9,Typ,1,Gd,Attchd,1999,Fin,3,671,TA,TA,Y,132,57,0,0,0,0,NA,NA,NA,0,4,2010,WD,Normal,290000 +274,20,RL,80,9600,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Feedr,Norm,1Fam,1Story,6,6,1958,1988,Hip,CompShg,Wd Sdng,Wd Sdng,BrkCmn,183,TA,TA,CBlock,TA,TA,No,Rec,620,LwQ,620,0,1240,GasA,Gd,Y,SBrkr,1632,0,0,1632,1,0,2,0,3,1,TA,6,Min1,1,Gd,Attchd,1958,RFn,1,338,TA,TA,Y,289,0,0,0,0,0,NA,MnPrv,NA,0,4,2009,WD,Normal,139000 +275,20,RL,76,8314,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,Mitchel,Norm,Norm,1Fam,1Story,5,7,1982,1982,Gable,CompShg,HdBoard,ImStucc,None,0,TA,TA,CBlock,TA,TA,Gd,ALQ,546,Unf,0,270,816,GasA,TA,Y,SBrkr,816,0,0,816,0,0,1,0,2,1,TA,5,Typ,0,NA,Attchd,1982,Unf,1,264,TA,TA,Y,168,0,0,0,0,0,NA,NA,NA,0,6,2007,WD,Normal,124500 +276,50,RL,55,7264,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,1.5Fin,7,7,1925,2007,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Gd,Gd,BrkTil,TA,TA,No,Unf,0,Unf,0,952,952,GasW,Gd,N,SBrkr,952,596,0,1548,0,0,2,1,3,1,Ex,5,Typ,0,NA,Detchd,1978,Unf,2,672,TA,TA,Y,74,0,0,0,144,0,NA,NA,NA,0,10,2009,WD,Normal,205000 +277,20,RL,129,9196,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Mitchel,Norm,Norm,1Fam,1Story,7,5,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Ex,TA,No,Unf,0,Unf,0,1560,1560,GasA,Ex,Y,SBrkr,1560,0,0,1560,0,0,2,0,3,1,Gd,7,Typ,0,NA,Attchd,2003,Fin,2,573,TA,TA,Y,100,150,0,0,0,0,NA,NA,NA,0,4,2010,WD,Normal,201000 +278,20,RL,140,19138,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,1Fam,1Story,4,5,1951,1951,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,CBlock,TA,TA,No,LwQ,120,Unf,0,744,864,GasA,Ex,Y,SBrkr,864,0,0,864,0,0,1,0,2,1,TA,4,Typ,0,NA,Detchd,1951,Unf,2,400,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,6,2010,WD,Normal,141000 +279,20,RL,107,14450,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,1Story,9,5,2006,2007,Gable,CompShg,CemntBd,CmentBd,BrkFace,315,Ex,TA,PConc,Ex,TA,Gd,Unf,0,Unf,0,2121,2121,GasA,Ex,Y,SBrkr,2121,0,0,2121,0,0,2,1,3,1,Ex,8,Typ,1,Ex,Attchd,2007,Fin,3,732,TA,TA,Y,124,98,0,0,142,0,NA,NA,NA,0,5,2007,New,Partial,415298 +280,60,RL,83,10005,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,ClearCr,Norm,Norm,1Fam,2Story,7,5,1977,1977,Hip,CompShg,Plywood,Plywood,BrkFace,299,TA,TA,CBlock,Gd,TA,No,BLQ,392,Unf,0,768,1160,GasA,Ex,Y,SBrkr,1156,866,0,2022,0,0,2,1,4,1,TA,8,Typ,1,TA,Attchd,1977,Fin,2,505,TA,TA,Y,288,117,0,0,0,0,NA,NA,NA,0,3,2008,WD,Normal,192000 +281,60,RL,82,11287,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SawyerW,Norm,Norm,1Fam,2Story,7,6,1989,1989,Gable,CompShg,Plywood,Plywood,BrkFace,340,Gd,TA,CBlock,Gd,TA,Av,GLQ,421,Unf,0,386,807,GasA,Gd,Y,SBrkr,1175,807,0,1982,0,0,2,1,3,1,Gd,7,Typ,1,TA,Attchd,1989,Fin,2,575,TA,TA,Y,0,84,0,196,0,0,NA,NA,NA,0,1,2007,WD,Normal,228500 +282,20,FV,60,7200,Pave,Pave,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,1Story,6,5,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,68,Gd,TA,PConc,Gd,TA,No,GLQ,905,Unf,0,357,1262,GasA,Gd,Y,SBrkr,1262,0,0,1262,0,0,2,0,2,1,Gd,5,Typ,0,NA,Attchd,2006,Fin,2,572,TA,TA,Y,0,120,0,0,0,0,NA,NA,NA,0,5,2006,New,Partial,185000 +283,120,RL,34,5063,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,Twnhs,1Story,7,5,2007,2008,Gable,CompShg,VinylSd,VinylSd,Stone,166,Gd,TA,PConc,Gd,TA,No,GLQ,904,Unf,0,410,1314,GasA,Ex,Y,SBrkr,1314,0,0,1314,1,0,2,0,2,1,Gd,6,Typ,1,Gd,Attchd,2008,RFn,2,626,TA,TA,Y,172,62,0,0,0,0,NA,NA,NA,0,4,2009,ConLw,Normal,207500 +284,20,RL,74,9612,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Feedr,Norm,1Fam,1Story,8,5,2008,2009,Gable,CompShg,VinylSd,VinylSd,Stone,72,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1468,1468,GasA,Ex,Y,SBrkr,1468,0,0,1468,0,0,2,0,3,1,Gd,6,Typ,1,Gd,Attchd,2008,Fin,3,898,TA,TA,Y,210,150,0,0,0,0,NA,NA,NA,0,12,2009,New,Partial,244600 +285,120,RL,50,8012,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SawyerW,Norm,Norm,TwnhsE,1Story,6,5,1992,1992,Gable,CompShg,Plywood,ImStucc,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,430,Unf,0,1145,1575,GasA,Gd,Y,SBrkr,1575,0,0,1575,1,0,2,0,2,1,Gd,5,Typ,0,NA,Attchd,1992,RFn,2,529,TA,TA,Y,0,0,52,0,0,0,NA,NA,NA,0,7,2007,WD,Normal,179200 +286,160,FV,35,4251,Pave,Pave,IR1,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,TwnhsE,2Story,7,5,2006,2007,Gable,CompShg,MetalSd,MetalSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,625,625,GasA,Ex,Y,SBrkr,625,625,0,1250,0,0,2,1,2,1,Gd,5,Typ,0,NA,Detchd,2006,RFn,2,528,TA,TA,Y,0,54,0,0,0,0,NA,NA,NA,0,6,2007,New,Partial,164700 +287,50,RL,77,9786,Pave,NA,IR1,Bnk,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1.5Fin,6,7,1962,1981,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,Rec,600,Unf,0,312,912,GasA,TA,Y,SBrkr,1085,649,0,1734,0,0,1,1,3,1,Gd,7,Typ,1,Gd,Attchd,1962,RFn,2,440,TA,TA,Y,0,0,0,0,128,0,NA,GdPrv,NA,0,6,2006,WD,Normal,159000 +288,20,RL,NA,8125,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,1Fam,1Story,4,4,1971,1971,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,TA,TA,No,BLQ,614,Unf,0,244,858,GasA,TA,Y,SBrkr,858,0,0,858,0,0,1,0,3,1,TA,5,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,0,0,0,0,0,0,NA,NA,NA,0,6,2006,WD,Normal,88000 +289,20,RL,NA,9819,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,1Story,5,5,1967,1967,Gable,CompShg,MetalSd,MetalSd,BrkFace,31,TA,Gd,CBlock,TA,TA,No,BLQ,450,Unf,0,432,882,GasA,TA,Y,SBrkr,900,0,0,900,0,0,1,0,3,1,TA,5,Typ,0,NA,Detchd,1970,Unf,1,280,TA,TA,Y,0,0,0,0,0,0,NA,MnPrv,NA,0,2,2010,WD,Normal,122000 +290,70,RL,60,8730,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrkSide,RRAn,Norm,1Fam,2Story,6,7,1915,2003,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,698,698,GasA,Ex,Y,FuseA,698,698,0,1396,0,0,1,0,3,1,TA,7,Typ,0,NA,Detchd,2003,Unf,1,384,TA,TA,Y,0,0,0,0,259,0,NA,NA,NA,0,7,2007,WD,Normal,153575 +291,60,RL,120,15611,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,8,5,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,Av,Unf,0,Unf,0,1079,1079,GasA,Ex,Y,SBrkr,1079,840,0,1919,0,0,2,1,3,1,Gd,8,Typ,1,Gd,Attchd,2006,RFn,2,685,Gd,TA,Y,0,51,0,0,0,0,NA,NA,NA,0,7,2006,New,Partial,233230 +292,190,RL,55,5687,Pave,Grvl,Reg,Bnk,AllPub,Inside,Gtl,SWISU,Norm,Norm,2fmCon,2Story,5,6,1912,2000,Gable,CompShg,VinylSd,VinylSd,None,0,TA,Fa,PConc,TA,Fa,No,Rec,210,Unf,0,570,780,GasA,Ex,N,SBrkr,936,780,0,1716,1,0,2,0,6,1,Fa,9,Typ,0,NA,NA,NA,NA,0,0,NA,NA,N,0,184,0,0,0,0,NA,NA,NA,0,3,2008,WD,Normal,135900 +293,50,RL,60,11409,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1.5Fin,5,4,1949,2008,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,LwQ,292,Unf,0,476,768,GasA,Gd,Y,SBrkr,1148,568,0,1716,0,0,1,1,3,1,TA,8,Min2,1,Gd,Attchd,1949,Unf,1,281,TA,TA,Y,0,0,0,0,160,0,NA,NA,NA,0,1,2009,WD,Normal,131000 +294,60,RL,NA,16659,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NWAmes,PosA,Norm,1Fam,2Story,7,7,1977,1994,Gable,CompShg,Plywood,Plywood,BrkFace,34,TA,TA,CBlock,TA,TA,No,ALQ,795,Unf,0,0,795,GasA,Fa,Y,SBrkr,1468,795,0,2263,1,0,2,1,3,1,Gd,9,Typ,1,TA,Attchd,1977,Fin,2,539,TA,TA,Y,0,250,0,0,0,0,NA,NA,NA,0,3,2006,WD,Normal,235000 +295,20,RL,80,9600,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,5,1953,1953,Hip,CompShg,HdBoard,HdBoard,Stone,238,TA,TA,CBlock,TA,TA,No,GLQ,1285,Unf,0,131,1416,GasA,TA,Y,SBrkr,1644,0,0,1644,1,0,1,0,3,1,TA,7,Typ,2,Gd,Attchd,1953,Fin,2,418,TA,TA,Y,110,0,0,0,0,0,NA,NA,NA,0,10,2009,WD,Normal,167000 +296,80,RL,37,7937,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Mitchel,Norm,Norm,1Fam,SLvl,6,6,1984,1984,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,TA,TA,Av,GLQ,819,Unf,0,184,1003,GasA,TA,Y,SBrkr,1003,0,0,1003,1,0,1,0,3,1,TA,6,Typ,0,NA,Detchd,1984,Unf,2,588,TA,TA,Y,120,0,0,0,0,0,NA,GdPrv,NA,0,3,2006,WD,Normal,142500 +297,50,RM,75,13710,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,IDOTRR,Norm,Norm,1Fam,1.5Fin,5,5,1950,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,BLQ,420,Unf,0,490,910,GasA,TA,Y,FuseA,910,648,0,1558,0,0,1,1,4,1,TA,6,Typ,0,NA,Attchd,1950,Unf,1,282,TA,TA,Y,289,0,0,0,0,0,NA,MnPrv,NA,0,6,2007,WD,Normal,152000 +298,60,FV,66,7399,Pave,Pave,IR1,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,2Story,7,5,1997,1998,Hip,CompShg,VinylSd,VinylSd,BrkFace,1600,Gd,TA,PConc,Gd,TA,No,BLQ,649,Unf,0,326,975,GasA,Ex,Y,SBrkr,975,975,0,1950,0,0,2,1,3,1,Gd,7,Typ,1,TA,Detchd,1997,RFn,2,576,TA,TA,Y,0,10,0,0,198,0,NA,NA,NA,0,6,2007,WD,Normal,239000 +299,60,RL,90,11700,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,2Story,6,6,1968,1968,Mansard,CompShg,HdBoard,AsphShn,BrkFace,365,Gd,TA,CBlock,TA,TA,No,ALQ,384,Rec,175,143,702,GasA,Gd,Y,SBrkr,1041,702,0,1743,0,1,1,2,3,1,TA,7,Typ,1,Gd,Attchd,1968,Unf,2,539,TA,TA,Y,224,0,0,0,0,0,NA,NA,NA,0,6,2007,WD,Normal,175000 +300,20,RL,80,14000,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Crawfor,Norm,Norm,1Fam,1Story,6,8,1950,2004,Gable,CompShg,HdBoard,HdBoard,None,0,TA,Gd,CBlock,TA,TA,No,Unf,0,Unf,0,1092,1092,GasA,Ex,Y,SBrkr,1152,0,0,1152,0,1,1,0,3,1,Gd,6,Typ,1,Gd,Attchd,1950,Unf,1,300,TA,TA,Y,0,36,0,0,0,0,NA,GdPrv,NA,0,8,2009,WD,Family,158500 +301,190,RL,90,15750,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,Crawfor,Norm,Norm,2fmCon,1Story,5,5,1953,1953,Hip,CompShg,MetalSd,MetalSd,BrkFace,56,TA,TA,CBlock,TA,TA,Mn,BLQ,841,Unf,0,324,1165,GasA,TA,Y,SBrkr,1336,0,0,1336,1,0,1,0,2,1,TA,5,Typ,2,Gd,Attchd,1953,Unf,1,375,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,6,2006,WD,Normal,157000 +302,60,RL,66,16226,Pave,NA,IR3,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,8,5,1998,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,281,Unf,0,747,1028,GasA,Ex,Y,SBrkr,1210,1242,0,2452,0,0,2,1,4,1,Gd,9,Typ,1,TA,BuiltIn,1998,Fin,2,683,TA,TA,Y,208,50,0,0,0,0,NA,NA,NA,0,5,2007,WD,Normal,267000 +303,20,RL,118,13704,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2001,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,150,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1541,1541,GasA,Ex,Y,SBrkr,1541,0,0,1541,0,0,2,0,3,1,Gd,6,Typ,1,TA,Attchd,2001,RFn,3,843,TA,TA,Y,468,81,0,0,0,0,NA,NA,NA,0,1,2006,WD,Normal,205000 +304,20,RL,70,9800,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,CollgCr,Norm,Norm,1Fam,1Story,5,7,1972,1972,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,TA,TA,No,ALQ,894,Unf,0,0,894,GasA,TA,Y,SBrkr,894,0,0,894,1,0,1,0,3,1,TA,5,Typ,0,NA,Attchd,1975,Unf,2,552,TA,TA,Y,256,0,0,0,0,0,NA,GdWo,NA,0,7,2006,WD,Abnorml,149900 +305,75,RM,87,18386,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,2.5Fin,7,9,1880,2002,Gable,CompShg,CemntBd,CmentBd,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,1470,1470,GasA,Ex,Y,SBrkr,1675,1818,0,3493,0,0,3,0,3,1,Gd,10,Typ,1,Ex,Attchd,2003,Unf,3,870,TA,TA,Y,302,0,0,0,0,0,NA,NA,NA,0,5,2008,WD,Normal,295000 +306,20,RL,80,10386,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,8,5,2004,2005,Gable,CompShg,CemntBd,CmentBd,Stone,246,Gd,TA,PConc,Gd,TA,No,GLQ,1464,Unf,0,536,2000,GasA,Ex,Y,SBrkr,2000,0,0,2000,1,0,2,0,3,1,Gd,8,Typ,0,NA,Attchd,2004,Fin,3,888,TA,TA,Y,168,0,0,0,0,0,NA,NA,NA,0,7,2007,WD,Normal,305900 +307,60,RL,116,13474,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SawyerW,Feedr,Norm,1Fam,2Story,7,5,1990,1991,Gable,CompShg,HdBoard,Plywood,BrkFace,246,Gd,TA,CBlock,Gd,TA,No,ALQ,700,Unf,0,0,700,GasA,Gd,Y,SBrkr,1122,1121,0,2243,1,0,2,1,4,1,Gd,8,Typ,1,TA,Attchd,1990,RFn,3,746,TA,TA,Y,127,44,224,0,0,0,NA,NA,NA,0,6,2007,WD,Normal,225000 +308,50,RM,NA,7920,Pave,Grvl,IR1,Lvl,AllPub,Inside,Gtl,IDOTRR,Artery,Norm,1Fam,1.5Fin,6,7,1920,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,Fa,CBlock,TA,TA,No,Unf,0,Unf,0,319,319,GasA,TA,Y,FuseA,1035,371,0,1406,0,0,1,0,3,1,Fa,6,Typ,0,NA,NA,NA,NA,0,0,NA,NA,N,0,144,0,0,0,0,NA,MnPrv,NA,0,3,2008,WD,Normal,89500 +309,30,RL,NA,12342,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1Story,4,5,1940,1950,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,CBlock,TA,TA,No,BLQ,262,Unf,0,599,861,GasA,Ex,Y,SBrkr,861,0,0,861,0,0,1,0,1,1,TA,4,Typ,0,NA,Detchd,1961,Unf,2,539,TA,TA,Y,158,0,0,0,0,0,NA,NA,NA,0,3,2009,WD,Normal,82500 +310,20,RL,90,12378,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,1Story,9,5,2003,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Ex,TA,Gd,GLQ,1274,Unf,0,622,1896,GasA,Ex,Y,SBrkr,1944,0,0,1944,1,0,2,0,3,1,Ex,8,Typ,3,Ex,Attchd,2003,Fin,3,708,TA,TA,Y,208,175,0,0,0,0,NA,NA,NA,0,11,2006,WD,Normal,360000 +311,60,RL,NA,7685,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,2Story,6,5,1993,1994,Gable,CompShg,HdBoard,HdBoard,BrkFace,112,TA,TA,PConc,Gd,TA,No,ALQ,518,Unf,0,179,697,GasA,Gd,Y,SBrkr,697,804,0,1501,0,0,2,1,3,1,Gd,6,Typ,1,TA,Attchd,1993,Fin,2,420,TA,TA,Y,190,63,0,0,0,0,NA,NA,NA,0,5,2006,WD,Normal,165600 +312,20,RL,50,8000,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,6,1948,2002,Gable,CompShg,VinylSd,VinylSd,None,0,TA,Gd,CBlock,TA,TA,No,ALQ,680,Unf,0,292,972,GasA,Ex,Y,SBrkr,972,0,0,972,1,0,1,0,2,1,TA,5,Typ,1,Gd,Detchd,1948,Unf,1,240,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,5,2009,WD,Normal,132000 +313,190,RM,65,7800,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Artery,Norm,2fmCon,1.5Fin,5,7,1939,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,Gd,TA,Mn,Rec,507,Unf,0,286,793,GasA,TA,Y,SBrkr,793,325,0,1118,1,0,1,0,3,1,TA,5,Typ,1,Gd,Detchd,1939,Unf,2,410,TA,TA,Y,0,0,0,0,271,0,NA,MnPrv,NA,0,5,2006,WD,Normal,119900 +314,20,RL,150,215245,Pave,NA,IR3,Low,AllPub,Inside,Sev,Timber,Norm,Norm,1Fam,1Story,7,5,1965,1965,Hip,CompShg,BrkFace,BrkFace,None,0,TA,TA,CBlock,Gd,TA,Gd,ALQ,1236,Rec,820,80,2136,GasW,TA,Y,SBrkr,2036,0,0,2036,2,0,2,0,3,1,TA,8,Typ,2,Gd,Attchd,1965,RFn,2,513,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,375000 +315,70,RM,60,9600,Pave,Grvl,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,2Story,7,7,1925,1990,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,TA,Gd,No,LwQ,16,Unf,0,712,728,GasA,Ex,Y,SBrkr,832,809,0,1641,0,1,1,1,3,1,Ex,6,Typ,1,Gd,Detchd,1925,Unf,2,546,Fa,TA,Y,0,0,234,0,0,0,NA,NA,NA,0,8,2006,WD,Normal,178000 +316,60,RL,71,7795,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,2Story,7,5,2004,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,425,Unf,0,291,716,GasA,Ex,Y,SBrkr,716,716,0,1432,1,0,2,1,3,1,Gd,6,Typ,1,Gd,Attchd,2004,Fin,2,432,TA,TA,Y,100,51,0,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,188500 +317,60,RL,94,13005,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NWAmes,Norm,Norm,1Fam,2Story,7,7,1980,1980,Gable,CompShg,CemntBd,CmentBd,BrkFace,278,Gd,TA,CBlock,Gd,TA,No,GLQ,692,Unf,0,153,845,GasA,TA,Y,SBrkr,1153,1200,0,2353,1,0,2,1,4,1,Ex,10,Typ,1,TA,Attchd,1983,RFn,2,484,TA,TA,Y,288,195,0,0,0,0,NA,GdPrv,NA,0,8,2009,WD,Normal,260000 +318,60,FV,75,9000,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,2Story,8,5,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,Av,Unf,0,Unf,0,1088,1088,GasA,Ex,Y,SBrkr,1088,871,0,1959,0,0,2,1,3,1,Gd,8,Typ,1,Gd,Attchd,2006,RFn,3,1025,TA,TA,Y,208,46,0,0,0,0,NA,NA,NA,0,12,2007,WD,Normal,270000 +319,60,RL,90,9900,Pave,NA,Reg,Low,AllPub,Inside,Mod,NoRidge,Norm,Norm,1Fam,2Story,7,5,1993,1993,Gable,CompShg,HdBoard,HdBoard,BrkFace,256,Gd,TA,PConc,Gd,TA,Gd,GLQ,987,Unf,0,360,1347,GasA,Ex,Y,SBrkr,1372,1274,0,2646,1,0,2,1,4,1,Gd,9,Typ,1,TA,Attchd,1993,RFn,3,656,TA,TA,Y,340,60,144,0,0,0,NA,NA,NA,0,4,2009,WD,Normal,260000 +320,80,RL,NA,14115,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,SLvl,7,5,1980,1980,Gable,CompShg,Plywood,Plywood,BrkFace,225,TA,TA,CBlock,Gd,TA,Av,GLQ,1036,Unf,0,336,1372,GasA,TA,Y,SBrkr,1472,0,0,1472,1,0,2,0,3,1,TA,6,Typ,2,TA,Attchd,1980,Unf,2,588,TA,TA,Y,233,48,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,187500 +321,60,RL,111,16259,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NridgHt,Norm,Norm,1Fam,2Story,9,5,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,370,TA,TA,PConc,Ex,Gd,Av,Unf,0,Unf,0,1249,1249,GasA,Ex,Y,SBrkr,1249,1347,0,2596,0,0,3,1,4,1,Gd,9,Typ,0,NA,Attchd,2006,RFn,3,840,TA,TA,Y,240,154,0,0,0,0,NA,NA,NA,0,9,2006,New,Partial,342643 +322,60,RL,99,12099,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,2Story,8,5,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,388,Gd,TA,PConc,Ex,TA,Av,GLQ,970,Unf,0,166,1136,GasA,Ex,Y,SBrkr,1136,1332,0,2468,1,0,2,1,4,1,Gd,10,Typ,1,Gd,BuiltIn,2004,Fin,3,872,TA,TA,Y,184,154,0,0,0,0,NA,NA,NA,0,6,2007,WD,Normal,354000 +323,60,RL,86,10380,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,SawyerW,Norm,Norm,1Fam,2Story,7,5,1986,1987,Gable,CompShg,Plywood,Plywood,BrkFace,172,Gd,TA,CBlock,TA,TA,Gd,LwQ,28,ALQ,1474,0,1502,GasA,Ex,Y,SBrkr,1553,1177,0,2730,1,0,2,1,4,1,Gd,8,Typ,1,TA,Attchd,1987,Fin,2,576,TA,TA,Y,201,96,0,0,0,0,NA,MnPrv,NA,0,8,2007,WD,Normal,301000 +324,20,RM,49,5820,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,1Story,3,8,1955,2005,Gable,CompShg,VinylSd,VinylSd,None,0,TA,Gd,CBlock,TA,TA,No,ALQ,256,Unf,0,906,1162,GasA,Ex,Y,SBrkr,1163,0,0,1163,1,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1955,Unf,1,220,Fa,TA,Y,142,98,0,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,126175 +325,80,RL,96,11275,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NAmes,PosN,Norm,1Fam,SLvl,7,7,1967,2007,Mansard,WdShake,Wd Sdng,Wd Sdng,BrkFace,300,Gd,Gd,CBlock,Gd,TA,No,Unf,0,Unf,0,710,710,GasA,Ex,Y,SBrkr,1898,1080,0,2978,0,0,2,1,5,1,Gd,11,Typ,1,Gd,BuiltIn,1961,Fin,2,564,TA,TA,Y,240,0,0,0,0,0,NA,NA,NA,0,6,2010,WD,Normal,242000 +326,45,RM,50,5000,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,IDOTRR,RRAe,Norm,1Fam,1.5Unf,5,6,1941,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,Av,BLQ,116,Unf,0,604,720,GasA,Po,N,FuseF,803,0,0,803,0,0,1,0,2,1,TA,5,Typ,0,NA,Detchd,1941,Unf,2,360,TA,TA,Y,0,0,244,0,0,0,NA,NA,NA,0,12,2007,WD,Normal,87000 +327,120,RL,32,10846,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Veenker,Norm,Norm,TwnhsE,1Story,8,5,1993,1993,Gable,CompShg,BrkFace,BrkFace,None,0,Gd,TA,PConc,Gd,TA,Gd,GLQ,1619,Unf,0,100,1719,GasA,Ex,Y,SBrkr,1719,0,0,1719,2,0,1,1,1,1,Gd,6,Typ,2,Gd,Attchd,1993,Fin,2,473,TA,TA,Y,122,30,0,0,0,0,NA,NA,NA,0,5,2008,Con,Normal,324000 +328,20,RL,80,11600,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,5,1960,1960,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,175,TA,TA,CBlock,TA,TA,No,Rec,565,Unf,0,818,1383,GasA,TA,Y,SBrkr,1383,0,0,1383,0,0,1,1,3,1,TA,7,Typ,0,NA,Attchd,1960,RFn,1,292,TA,TA,Y,0,45,0,0,0,0,NA,NA,NA,0,4,2006,WD,Normal,145250 +329,75,RL,NA,11888,Pave,Pave,IR1,Bnk,AllPub,Inside,Gtl,BrkSide,PosN,Norm,1Fam,2.5Unf,6,6,1916,1994,Gable,CompShg,Wd Sdng,Wd Shng,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,844,844,GasA,Gd,N,FuseA,1445,689,0,2134,0,0,2,0,5,1,Gd,10,Typ,0,NA,Detchd,1930,Unf,2,441,TA,TA,Y,0,60,268,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,214500 +330,70,RM,60,6402,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,IDOTRR,Norm,Norm,1Fam,2Story,5,5,1920,1950,Gable,CompShg,Wd Sdng,Wd Shng,None,0,TA,TA,PConc,TA,TA,Mn,Unf,0,Unf,0,596,596,GasA,TA,N,SBrkr,596,596,0,1192,0,0,1,0,3,1,TA,6,Typ,0,NA,Detchd,1920,Unf,1,189,Fa,Fa,N,0,0,137,0,0,0,NA,GdWo,NA,0,7,2009,WD,Normal,78000 +331,90,RL,NA,10624,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,Duplex,1Story,5,4,1964,1964,Gable,CompShg,HdBoard,HdBoard,BrkFace,84,TA,TA,CBlock,TA,TA,No,GLQ,40,Rec,264,1424,1728,GasA,TA,Y,SBrkr,1728,0,0,1728,0,1,2,0,6,2,TA,10,Typ,0,NA,Detchd,2002,Unf,1,352,TA,TA,Y,155,0,0,0,0,0,NA,NA,NA,0,11,2007,WD,Normal,119000 +332,20,RL,70,8176,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,6,1958,1992,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,Rec,846,Unf,0,210,1056,GasA,Fa,Y,SBrkr,1056,0,0,1056,1,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1958,RFn,1,308,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,8,2007,WD,Normal,139000 +333,20,RL,85,10655,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,1Story,8,5,2003,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,296,Gd,TA,PConc,Gd,TA,No,GLQ,1124,NA,479,1603,3206,GasA,Ex,Y,SBrkr,1629,0,0,1629,1,0,2,0,3,1,Gd,7,Typ,1,Gd,Attchd,2003,RFn,3,880,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,10,2009,WD,Normal,284000 +334,120,RM,59,8198,Pave,NA,Reg,Lvl,AllPub,FR3,Gtl,NridgHt,Norm,Norm,TwnhsE,1Story,7,5,2004,2004,Gable,CompShg,VinylSd,VinylSd,Stone,146,Gd,TA,PConc,Gd,TA,Av,GLQ,720,Unf,0,638,1358,GasA,Ex,Y,SBrkr,1358,0,0,1358,1,0,2,0,2,1,Gd,6,Typ,1,Gd,Attchd,2004,RFn,2,484,TA,TA,Y,192,30,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,207000 +335,60,RL,59,9042,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,2Story,6,5,1998,1998,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,Gd,GLQ,828,Unf,0,115,943,GasA,Gd,Y,SBrkr,943,695,0,1638,1,0,2,1,3,1,TA,7,Typ,2,TA,Attchd,1998,Fin,2,472,TA,TA,Y,100,38,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,192000 +336,190,RL,NA,164660,Grvl,NA,IR1,HLS,AllPub,Corner,Sev,Timber,Norm,Norm,2fmCon,1.5Fin,5,6,1965,1965,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,CBlock,TA,TA,Gd,ALQ,1249,BLQ,147,103,1499,GasA,Ex,Y,SBrkr,1619,167,0,1786,2,0,2,0,3,1,TA,7,Typ,2,Gd,Attchd,1965,Fin,2,529,TA,TA,Y,670,0,0,0,0,0,NA,NA,Shed,700,8,2008,WD,Normal,228950 +337,20,RL,86,14157,Pave,NA,IR1,HLS,AllPub,Corner,Gtl,StoneBr,Norm,Norm,1Fam,1Story,9,5,2005,2006,Hip,CompShg,VinylSd,VinylSd,Stone,200,Gd,TA,PConc,Ex,TA,Gd,GLQ,1249,Unf,0,673,1922,GasA,Ex,Y,SBrkr,1922,0,0,1922,1,0,2,0,3,1,Gd,8,Typ,1,Gd,Attchd,2005,Fin,3,676,TA,TA,Y,178,51,0,0,0,0,NA,NA,NA,0,7,2007,WD,Normal,377426 +338,20,RL,70,9135,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2002,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,113,Gd,TA,PConc,Gd,TA,Av,GLQ,810,Unf,0,726,1536,GasA,Ex,Y,SBrkr,1536,0,0,1536,1,0,2,0,3,1,Gd,7,Typ,0,NA,Attchd,2002,RFn,2,532,TA,TA,Y,192,74,0,0,0,0,NA,NA,NA,0,12,2008,WD,Normal,214000 +339,20,RL,91,14145,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NWAmes,Norm,Norm,1Fam,1Story,7,7,1984,1998,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Gd,TA,CBlock,Gd,TA,Mn,ALQ,213,Unf,0,995,1208,GasA,Ex,Y,SBrkr,1621,0,0,1621,1,0,2,0,3,1,Gd,8,Typ,0,NA,Attchd,1984,RFn,2,440,TA,TA,Y,108,45,0,0,0,0,NA,NA,Shed,400,5,2006,WD,Normal,202500 +340,20,RL,66,12400,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Feedr,Norm,1Fam,1Story,6,7,1958,1998,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,176,TA,TA,CBlock,TA,Fa,No,Rec,585,Unf,0,630,1215,GasA,TA,Y,FuseA,1215,0,0,1215,0,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1958,Unf,1,297,TA,TA,Y,0,0,0,0,234,0,NA,NA,NA,0,6,2009,WD,Normal,155000 +341,60,RL,85,14191,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Timber,Norm,Norm,1Fam,2Story,8,5,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,967,967,GasA,Ex,Y,SBrkr,993,915,0,1908,0,0,2,1,4,1,Gd,9,Typ,0,NA,Attchd,2002,Fin,2,431,TA,TA,Y,135,0,0,0,0,0,NA,NA,NA,0,4,2010,WD,Normal,202900 +342,20,RH,60,8400,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SawyerW,Feedr,Norm,1Fam,1Story,4,4,1950,1950,Gable,CompShg,Wd Sdng,AsbShng,None,0,Fa,Fa,CBlock,TA,Fa,No,Unf,0,Unf,0,721,721,GasA,Gd,Y,SBrkr,841,0,0,841,0,0,1,0,2,1,TA,4,Typ,0,NA,CarPort,1950,Unf,1,294,TA,TA,N,250,0,24,0,0,0,NA,NA,NA,0,9,2009,WD,Normal,82000 +343,90,RL,NA,8544,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,Duplex,1Story,3,4,1949,1950,Gable,CompShg,Stucco,Stucco,BrkFace,340,TA,TA,Slab,NA,NA,NA,NA,0,NA,0,0,0,Wall,Fa,N,FuseA,1040,0,0,1040,0,0,2,0,2,2,TA,6,Typ,0,NA,Detchd,1949,Unf,2,400,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,5,2006,WD,Normal,87500 +344,120,RL,63,8849,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,TwnhsE,1Story,9,5,2005,2005,Hip,CompShg,MetalSd,MetalSd,BrkFace,616,Ex,TA,PConc,Ex,TA,No,GLQ,28,Unf,0,1656,1684,GasA,Ex,Y,SBrkr,1684,0,0,1684,0,0,2,0,2,1,Ex,6,Typ,1,Ex,Attchd,2005,RFn,2,564,TA,TA,Y,495,72,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,266000 +345,160,RM,36,2592,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,MeadowV,Norm,Norm,TwnhsE,2Story,5,3,1976,1976,Gable,CompShg,CemntBd,CmentBd,None,0,TA,TA,CBlock,Gd,TA,No,Rec,129,BLQ,232,175,536,GasA,TA,Y,SBrkr,536,576,0,1112,0,0,1,1,3,1,TA,4,Typ,0,NA,Attchd,1976,Unf,1,336,TA,TA,Y,182,0,0,0,0,0,NA,NA,NA,0,4,2010,WD,Normal,85000 +346,50,RL,65,6435,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrkSide,RRAn,Norm,1Fam,1.5Fin,6,5,1939,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,972,972,GasA,Gd,Y,SBrkr,972,605,0,1577,0,0,1,0,3,1,Fa,6,Typ,1,Gd,Detchd,1939,Unf,1,312,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,10,2006,WD,Normal,140200 +347,20,RL,NA,12772,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,8,1960,1998,Hip,CompShg,MetalSd,MetalSd,None,0,TA,Gd,CBlock,TA,TA,Mn,BLQ,498,Unf,0,460,958,GasA,TA,Y,SBrkr,958,0,0,958,0,0,1,0,2,1,TA,5,Typ,0,NA,Attchd,1960,RFn,1,301,TA,TA,Y,0,0,0,0,0,0,NA,NA,Gar2,15500,4,2007,WD,Normal,151500 +348,20,RL,NA,17600,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,5,1960,1960,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,30,TA,TA,CBlock,TA,TA,No,BLQ,1270,Unf,0,208,1478,GasA,Ex,Y,FuseA,1478,0,0,1478,1,0,2,0,3,1,TA,6,Typ,2,Gd,Attchd,1960,Unf,2,498,TA,TA,Y,0,40,0,0,0,0,NA,NA,NA,0,12,2009,WD,Normal,157500 +349,160,RL,36,2448,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,Twnhs,2Story,7,5,2003,2004,Gable,CompShg,VinylSd,Wd Shng,Stone,106,Gd,TA,PConc,Gd,TA,No,GLQ,573,Unf,0,191,764,GasA,Ex,Y,SBrkr,764,862,0,1626,1,0,2,1,2,1,Gd,6,Typ,0,NA,BuiltIn,2003,RFn,2,474,TA,TA,Y,0,27,0,0,0,0,NA,NA,NA,0,10,2008,WD,Normal,154000 +350,60,RL,56,20431,Pave,NA,IR2,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,2Story,9,5,2005,2006,Hip,CompShg,CemntBd,CmentBd,BrkFace,870,Ex,TA,PConc,Ex,TA,No,GLQ,1410,Unf,0,438,1848,GasA,Ex,Y,SBrkr,1848,880,0,2728,1,0,2,1,4,1,Ex,10,Typ,2,Ex,Attchd,2006,Fin,3,706,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,4,2006,New,Partial,437154 +351,120,RL,68,7820,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,TwnhsE,1Story,9,5,2007,2007,Hip,CompShg,MetalSd,MetalSd,BrkFace,362,Ex,TA,PConc,Ex,TA,No,Unf,0,Unf,0,1869,1869,GasA,Ex,Y,SBrkr,1869,0,0,1869,0,0,2,0,2,1,Ex,6,Typ,1,Gd,Attchd,2007,RFn,2,617,TA,TA,Y,210,54,0,0,0,0,NA,NA,NA,0,12,2007,New,Partial,318061 +352,120,RL,NA,5271,Pave,NA,IR1,Low,AllPub,Inside,Mod,ClearCr,Norm,Norm,1Fam,1Story,7,5,1986,1986,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,PConc,Gd,TA,Gd,GLQ,1082,Unf,0,371,1453,GasA,Gd,Y,SBrkr,1453,0,0,1453,1,0,1,1,2,1,Gd,6,Typ,1,TA,Attchd,1986,RFn,2,445,TA,TA,Y,0,80,0,0,184,0,NA,NA,NA,0,12,2006,WD,Abnorml,190000 +353,50,RL,60,9084,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Artery,Norm,1Fam,1.5Fin,5,6,1941,1950,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,CBlock,TA,Fa,Mn,LwQ,236,Rec,380,0,616,GasA,TA,N,SBrkr,616,495,0,1111,0,1,1,0,3,1,TA,5,Typ,0,NA,Detchd,1941,Unf,1,200,TA,Fa,Y,48,0,0,0,0,0,NA,NA,NA,0,3,2008,ConLw,Normal,95000 +354,30,RM,60,8520,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,1Story,6,8,1928,2003,Gable,CompShg,VinylSd,VinylSd,None,0,TA,Gd,BrkTil,TA,TA,No,Unf,0,Unf,0,624,624,GasA,Gd,Y,SBrkr,720,0,0,720,0,0,1,0,2,1,TA,5,Typ,0,NA,Detchd,2005,Unf,2,484,TA,TA,Y,106,0,0,0,0,0,NA,NA,NA,0,5,2010,WD,Normal,105900 +355,50,RL,60,8400,Pave,NA,Reg,Bnk,AllPub,Inside,Gtl,SWISU,Norm,Norm,1Fam,1.5Fin,6,5,1940,2000,Gable,CompShg,Wd Sdng,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,LwQ,388,Unf,0,552,940,GasA,Ex,Y,SBrkr,1192,403,0,1595,0,0,1,0,2,1,TA,6,Typ,2,Gd,Attchd,1940,Unf,1,240,TA,TA,Y,0,0,108,0,0,0,NA,NA,NA,0,6,2006,WD,Normal,140000 +356,20,RL,105,11249,Pave,NA,IR2,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,6,5,1995,1995,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,Gd,PConc,Gd,Gd,No,ALQ,334,BLQ,544,322,1200,GasA,Ex,Y,SBrkr,1200,0,0,1200,1,0,2,0,3,1,Gd,6,Typ,0,NA,Attchd,1995,RFn,2,521,TA,TA,Y,0,26,0,0,0,0,NA,NA,NA,0,8,2007,WD,Normal,177500 +357,20,RL,NA,9248,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,1Story,6,6,1992,1992,Gable,CompShg,HdBoard,HdBoard,BrkFace,106,TA,TA,PConc,Gd,TA,No,GLQ,560,Unf,0,598,1158,GasA,Gd,Y,SBrkr,1167,0,0,1167,1,0,2,0,3,1,Gd,6,Typ,0,NA,Attchd,1992,RFn,2,400,TA,TA,Y,120,26,0,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,173000 +358,120,RM,44,4224,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,MeadowV,Norm,Norm,TwnhsE,1Story,5,5,1976,1976,Gable,CompShg,CemntBd,CmentBd,None,0,TA,TA,PConc,Gd,TA,No,ALQ,874,Unf,0,268,1142,GasA,TA,Y,SBrkr,1142,0,0,1142,1,0,1,1,3,1,TA,6,Typ,1,Po,Attchd,1976,Fin,2,528,TA,TA,Y,536,90,0,0,0,0,NA,MnPrv,NA,0,8,2007,WD,Normal,134000 +359,80,RL,92,6930,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,ClearCr,Norm,Norm,1Fam,SLvl,5,4,1958,1958,Hip,CompShg,Wd Sdng,ImStucc,BrkFace,120,TA,TA,CBlock,TA,TA,Av,BLQ,300,Rec,294,468,1062,GasA,Ex,Y,FuseF,1352,0,0,1352,0,1,1,0,3,1,Gd,6,Min2,0,NA,BuiltIn,1958,Unf,1,288,TA,TA,Y,168,0,294,0,0,0,NA,NA,NA,0,7,2006,WD,Abnorml,130000 +360,60,RL,78,12011,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,NoRidge,Norm,Norm,1Fam,2Story,8,5,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,530,Gd,TA,PConc,Gd,TA,Av,GLQ,956,Unf,0,130,1086,GasA,Ex,Y,SBrkr,1086,838,0,1924,1,0,2,1,3,1,Gd,7,Typ,1,TA,Attchd,1998,RFn,2,592,TA,TA,Y,208,75,0,0,374,0,NA,NA,NA,0,6,2006,WD,Normal,280000 +361,85,RL,NA,7540,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Mitchel,Norm,Norm,1Fam,SFoyer,6,6,1978,1978,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,CBlock,Gd,TA,Av,GLQ,773,Unf,0,115,888,GasA,Ex,Y,SBrkr,912,0,0,912,1,0,1,0,2,1,TA,5,Typ,1,TA,Attchd,1978,RFn,2,470,TA,TA,Y,0,0,0,0,192,0,NA,MnPrv,NA,0,6,2007,WD,Normal,156000 +362,50,RL,NA,9144,Pave,Pave,Reg,Lvl,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,1.5Fin,5,5,1940,1982,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,Rec,399,Unf,0,484,883,GasA,Gd,Y,SBrkr,988,517,0,1505,1,0,1,0,3,1,TA,8,Typ,0,NA,Detchd,1940,Unf,1,240,TA,TA,N,0,0,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,145000 +363,85,RL,64,7301,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,1Fam,SFoyer,7,5,2003,2003,Gable,CompShg,HdBoard,HdBoard,BrkFace,500,Gd,TA,Slab,NA,NA,NA,NA,0,NA,0,0,0,GasA,Ex,Y,SBrkr,495,1427,0,1922,0,0,3,0,4,1,Gd,7,Typ,1,Ex,BuiltIn,2003,RFn,2,672,TA,TA,Y,0,0,177,0,0,0,NA,NA,NA,0,7,2009,ConLD,Normal,198500 +364,160,RM,21,1680,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrDale,Norm,Norm,Twnhs,2Story,6,8,1972,2007,Gable,CompShg,HdBoard,HdBoard,BrkFace,510,TA,TA,CBlock,TA,TA,No,ALQ,162,Unf,0,321,483,GasA,Gd,Y,SBrkr,483,504,0,987,0,0,1,1,2,1,Gd,5,Typ,0,NA,Detchd,1972,Unf,1,264,TA,TA,Y,250,0,0,0,0,0,NA,NA,NA,0,5,2009,WD,Normal,118000 +365,60,RL,NA,18800,Pave,NA,IR1,Lvl,AllPub,FR2,Gtl,NWAmes,Norm,Norm,1Fam,2Story,6,5,1976,1976,Gable,CompShg,HdBoard,HdBoard,BrkFace,120,TA,TA,PConc,Gd,TA,Mn,GLQ,712,Unf,0,84,796,GasA,TA,Y,SBrkr,790,784,0,1574,1,0,2,1,3,1,TA,6,Typ,1,TA,Attchd,1976,Fin,2,566,TA,TA,Y,306,111,0,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,190000 +366,70,RM,59,10690,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,IDOTRR,Norm,Norm,1Fam,2Story,5,7,1920,1997,Hip,CompShg,VinylSd,VinylSd,None,0,TA,Gd,CBlock,TA,Fa,No,Rec,456,Unf,0,216,672,GasA,Gd,Y,FuseA,672,672,0,1344,0,0,1,0,3,1,TA,6,Typ,0,NA,Detchd,1964,Unf,1,468,TA,Fa,Y,0,128,218,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,147000 +367,20,RL,NA,9500,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,5,1963,1963,Gable,CompShg,Plywood,Plywood,BrkFace,247,TA,TA,CBlock,Gd,TA,No,BLQ,609,Unf,0,785,1394,GasA,Gd,Y,SBrkr,1394,0,0,1394,1,0,1,1,3,1,TA,6,Typ,2,Gd,Attchd,1963,RFn,2,514,TA,TA,Y,0,76,0,0,185,0,NA,NA,NA,0,7,2009,WD,Normal,159000 +368,80,RL,101,9150,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,1Fam,SLvl,6,5,1962,1962,Gable,Tar&Grv,Plywood,Plywood,BrkFace,305,TA,TA,CBlock,Gd,TA,Gd,GLQ,371,Unf,0,728,1099,GasA,Gd,Y,SBrkr,1431,0,0,1431,0,1,1,0,3,1,TA,6,Typ,1,Gd,Basment,1962,RFn,1,296,TA,TA,Y,64,110,0,0,0,0,NA,NA,NA,0,12,2008,WD,Normal,165000 +369,20,RL,78,7800,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,6,1954,1954,Gable,CompShg,HdBoard,HdBoard,BrkFace,200,TA,TA,PConc,TA,TA,No,LwQ,540,Unf,0,728,1268,GasA,Gd,Y,SBrkr,1268,0,0,1268,0,0,1,0,2,1,TA,7,Typ,1,Gd,Attchd,1954,Fin,1,244,TA,TA,Y,0,98,0,0,0,0,NA,NA,NA,0,3,2010,WD,Normal,132000 +370,20,RL,NA,9830,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,7,1959,2006,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,Gd,CBlock,TA,TA,No,ALQ,72,Rec,258,733,1063,GasA,Ex,Y,SBrkr,1287,0,0,1287,1,0,1,0,3,1,Gd,7,Typ,1,Gd,Detchd,1997,Fin,2,576,TA,TA,Y,364,17,0,0,182,0,NA,NA,NA,0,3,2010,WD,Normal,162000 +371,60,RL,NA,8121,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,2Story,6,5,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,No,Unf,0,Unf,0,953,953,GasA,Ex,Y,SBrkr,953,711,0,1664,0,0,2,1,3,1,TA,7,Typ,1,TA,Attchd,2000,RFn,2,460,TA,TA,Y,100,40,0,0,0,0,NA,NA,NA,0,1,2006,WD,Normal,172400 +372,50,RL,80,17120,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,ClearCr,Feedr,Norm,1Fam,1.5Fin,4,4,1959,1959,Gable,CompShg,WdShing,Plywood,None,0,TA,TA,CBlock,NA,NA,NA,NA,0,NA,0,0,0,GasA,TA,Y,SBrkr,1120,468,0,1588,0,0,2,0,4,1,TA,7,Min2,1,Gd,Detchd,1991,Fin,2,680,TA,TA,N,0,59,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,134432 +373,120,RL,50,7175,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SawyerW,Norm,Norm,TwnhsE,1Story,6,5,1984,1984,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,CBlock,Gd,TA,No,ALQ,623,LwQ,121,0,744,GasA,TA,Y,SBrkr,752,0,0,752,1,0,1,0,2,1,TA,4,Typ,0,NA,Attchd,1984,Unf,1,264,TA,TA,Y,353,0,0,0,90,0,NA,MnPrv,NA,0,2,2010,WD,Normal,125000 +374,20,RL,79,10634,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,6,1953,1953,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,PConc,TA,TA,No,BLQ,428,LwQ,180,0,608,GasA,TA,Y,SBrkr,1319,0,0,1319,1,0,1,0,3,1,TA,5,Min2,0,NA,Attchd,1953,Unf,1,270,TA,TA,Y,66,0,0,0,0,0,NA,GdWo,NA,0,11,2009,WD,Normal,123000 +375,60,RL,65,8200,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,7,5,2003,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,847,847,GasA,Ex,Y,SBrkr,847,1081,0,1928,0,0,2,1,4,1,Gd,8,Typ,1,Gd,BuiltIn,2003,Fin,2,434,TA,TA,Y,100,48,0,0,0,0,NA,NA,NA,0,7,2007,WD,Normal,219500 +376,30,RL,NA,10020,Pave,NA,IR1,Low,AllPub,Inside,Sev,Edwards,Norm,Norm,1Fam,1Story,1,1,1922,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Fa,Fa,BrkTil,Fa,Po,Gd,BLQ,350,Unf,0,333,683,GasA,Gd,N,FuseA,904,0,0,904,1,0,0,1,1,1,Fa,4,Maj1,0,NA,NA,NA,NA,0,0,NA,NA,Y,0,0,0,0,0,0,NA,NA,NA,0,3,2009,WD,Normal,61000 +377,85,RL,57,8846,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,CollgCr,Norm,Norm,1Fam,SFoyer,5,5,1996,1996,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,Av,GLQ,298,Unf,0,572,870,GasA,Ex,Y,SBrkr,914,0,0,914,0,0,1,0,2,1,TA,5,Typ,0,NA,Detchd,1998,Unf,2,576,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,148000 +378,60,FV,102,11143,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Somerst,Norm,Norm,1Fam,2Story,8,5,2004,2005,Gable,CompShg,CemntBd,CmentBd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1580,1580,GasA,Ex,Y,SBrkr,1580,886,0,2466,0,0,3,0,4,1,Gd,8,Typ,1,Gd,Attchd,2004,RFn,2,610,TA,TA,Y,159,214,0,0,0,0,NA,NA,NA,0,12,2007,WD,Normal,340000 +379,20,RL,88,11394,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,StoneBr,Norm,Norm,1Fam,1Story,9,2,2010,2010,Hip,CompShg,VinylSd,VinylSd,Stone,350,Gd,TA,PConc,Ex,TA,Av,GLQ,1445,Unf,0,411,1856,GasA,Ex,Y,SBrkr,1856,0,0,1856,1,0,1,1,1,1,Ex,8,Typ,1,Ex,Attchd,2010,Fin,3,834,TA,TA,Y,113,0,0,0,0,0,NA,NA,NA,0,6,2010,New,Partial,394432 +380,60,RL,60,8123,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,1Fam,2Story,6,5,2000,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,16,TA,TA,PConc,Gd,TA,No,Unf,0,Unf,0,982,982,GasA,Ex,Y,SBrkr,1007,793,0,1800,0,0,2,1,3,1,TA,7,Typ,1,TA,Attchd,2000,Fin,2,463,TA,TA,Y,100,63,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,179000 +381,50,RL,50,5000,Pave,Pave,Reg,Lvl,AllPub,Inside,Gtl,SWISU,Norm,Norm,1Fam,1.5Fin,5,6,1924,1950,Gable,CompShg,BrkFace,Wd Sdng,None,0,TA,TA,BrkTil,TA,TA,No,LwQ,218,Unf,0,808,1026,GasA,TA,Y,SBrkr,1026,665,0,1691,0,0,2,0,3,1,Gd,6,Typ,1,Gd,Detchd,1924,Unf,1,308,TA,TA,Y,0,0,242,0,0,0,NA,NA,NA,0,5,2010,WD,Normal,127000 +382,20,FV,60,7200,Pave,Pave,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,1Story,7,5,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,Gd,No,Unf,0,Unf,0,1293,1293,GasA,Ex,Y,SBrkr,1301,0,0,1301,1,0,2,0,2,1,Gd,5,Typ,1,Gd,Attchd,2006,RFn,2,572,TA,TA,Y,216,121,0,0,0,0,NA,NA,NA,0,8,2006,New,Partial,187750 +383,60,RL,79,9245,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,7,5,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,Av,Unf,0,Unf,0,939,939,GasA,Ex,Y,SBrkr,939,858,0,1797,0,0,2,1,3,1,Gd,8,Typ,0,NA,Attchd,2006,RFn,2,639,TA,TA,Y,144,53,0,0,0,0,NA,NA,NA,0,4,2007,WD,Normal,213500 +384,45,RH,60,9000,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,SawyerW,Norm,Norm,1Fam,1.5Unf,6,3,1928,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,Fa,Fa,No,Unf,0,Unf,0,784,784,GasA,TA,N,FuseA,784,0,0,784,0,0,1,0,2,1,TA,5,Typ,0,NA,Detchd,1950,Unf,2,360,Fa,Fa,N,0,0,91,0,0,0,NA,NA,NA,0,10,2009,WD,Normal,76000 +385,60,RL,NA,53107,Pave,NA,IR2,Low,AllPub,Corner,Mod,ClearCr,Feedr,Norm,1Fam,2Story,6,5,1992,1992,Gable,CompShg,HdBoard,HdBoard,None,0,Gd,TA,PConc,Gd,TA,Av,GLQ,985,Unf,0,595,1580,GasA,Ex,Y,SBrkr,1079,874,0,1953,1,0,2,1,3,1,Gd,9,Typ,2,Fa,Attchd,1992,Fin,2,501,TA,TA,Y,216,231,0,0,0,0,NA,NA,NA,0,6,2007,WD,Normal,240000 +386,120,RL,43,3182,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Blmngtn,Norm,Norm,TwnhsE,1Story,8,5,2004,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,16,Gd,TA,PConc,Gd,TA,No,GLQ,24,Unf,0,1232,1256,GasA,Ex,Y,SBrkr,1269,0,0,1269,0,0,2,0,2,1,Gd,6,Typ,1,TA,Attchd,2004,Fin,2,430,TA,TA,Y,146,20,0,0,144,0,NA,NA,NA,0,4,2010,WD,Normal,192000 +387,50,RL,58,8410,Pave,NA,Reg,Lvl,AllPub,FR2,Gtl,Edwards,Feedr,Norm,1Fam,1.5Fin,5,3,1910,1996,Gambrel,CompShg,Wd Sdng,VinylSd,None,0,TA,Fa,PConc,TA,TA,No,Unf,0,Unf,0,658,658,GasA,TA,Y,SBrkr,658,526,0,1184,0,0,1,0,5,1,TA,8,Typ,0,NA,NA,NA,NA,0,0,NA,NA,N,0,151,0,0,0,0,NA,NA,NA,0,5,2006,WD,AdjLand,81000 +388,80,RL,72,7200,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,SLvl,6,6,1976,1976,Hip,CompShg,MetalSd,MetalSd,BrkFace,255,TA,TA,CBlock,TA,TA,Av,ALQ,631,Unf,0,410,1041,GasA,Ex,Y,SBrkr,1125,0,0,1125,1,0,1,0,3,1,TA,6,Typ,1,Fa,Detchd,1977,Unf,1,352,TA,TA,Y,296,0,0,0,0,0,NA,GdWo,NA,0,10,2009,WD,Abnorml,125000 +389,20,RL,93,9382,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,1999,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,125,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1468,1468,GasA,Ex,Y,SBrkr,1479,0,0,1479,0,0,2,0,3,1,Gd,6,Typ,0,NA,Attchd,1999,RFn,2,577,TA,TA,Y,120,25,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,191000 +390,60,RL,96,12474,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,2Story,10,5,2007,2008,Gable,CompShg,VinylSd,VinylSd,Stone,272,Ex,TA,PConc,Ex,TA,Av,GLQ,1280,Unf,0,402,1682,GasA,Ex,Y,SBrkr,1742,590,0,2332,1,0,2,1,3,1,Ex,9,Typ,1,Ex,BuiltIn,2008,Fin,3,846,TA,TA,Y,196,134,0,0,0,0,NA,NA,NA,0,8,2008,New,Partial,426000 +391,50,RL,50,8405,Pave,Grvl,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1.5Fin,5,8,1900,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,BrkTil,TA,Gd,No,Rec,241,BLQ,391,229,861,GasA,Ex,Y,SBrkr,961,406,0,1367,1,0,1,0,4,1,TA,7,Typ,0,NA,Detchd,1978,Unf,1,384,TA,TA,Y,0,130,112,0,0,0,NA,MnPrv,NA,0,4,2008,WD,Normal,119000 +392,60,RL,71,12209,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Mitchel,Norm,Norm,1Fam,2Story,6,5,2001,2002,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Ex,TA,No,ALQ,690,Unf,0,114,804,GasA,Ex,Y,SBrkr,804,1157,0,1961,1,0,2,1,3,1,Gd,7,Typ,1,TA,BuiltIn,2001,Fin,2,560,TA,TA,Y,125,192,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,215000 +393,20,RL,NA,8339,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,7,1959,1959,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,Slab,NA,NA,NA,NA,0,NA,0,0,0,GasA,TA,Y,SBrkr,882,0,0,882,0,0,1,0,3,1,TA,5,Typ,0,NA,Attchd,1959,RFn,1,294,TA,TA,Y,0,0,0,0,0,0,NA,MnPrv,Shed,1200,7,2007,WD,Normal,106500 +394,30,RL,NA,7446,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,BrkSide,Feedr,Norm,1Fam,1Story,4,5,1941,1950,Gable,CompShg,WdShing,Wd Shng,None,0,TA,TA,CBlock,TA,TA,No,Rec,266,Unf,0,522,788,GasA,TA,Y,FuseA,788,0,0,788,0,0,1,0,2,1,TA,4,Typ,2,TA,NA,NA,NA,0,0,NA,NA,Y,0,0,0,0,0,0,NA,GdWo,NA,0,4,2006,WD,Abnorml,100000 +395,50,RL,60,10134,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,1.5Fin,5,6,1940,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,735,735,GasA,Gd,Y,FuseA,735,299,0,1034,0,0,1,0,2,1,TA,5,Typ,0,NA,Detchd,1940,Unf,1,240,TA,TA,Y,0,39,0,0,0,0,NA,NA,NA,0,7,2007,WD,Normal,109000 +396,20,RL,68,9571,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1Story,5,6,1956,1956,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,Av,BLQ,739,Unf,0,405,1144,GasA,TA,Y,SBrkr,1144,0,0,1144,1,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1956,Unf,1,596,TA,TA,Y,44,0,0,0,0,0,NA,NA,NA,0,6,2010,WD,Normal,129000 +397,20,RL,60,7200,Pave,NA,Reg,Low,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,5,5,1972,1972,Hip,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,Av,Rec,777,Unf,0,117,894,GasA,TA,Y,SBrkr,894,0,0,894,0,0,1,0,2,1,TA,6,Typ,0,NA,Detchd,1985,RFn,2,600,TA,TA,Y,215,0,0,0,0,0,NA,NA,NA,0,9,2009,WD,Normal,123000 +398,60,RL,69,7590,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,PosN,Norm,1Fam,2Story,5,5,1962,1962,Gable,CompShg,VinylSd,VinylSd,BrkFace,288,TA,TA,CBlock,TA,TA,No,ALQ,540,Unf,0,324,864,GasA,TA,Y,SBrkr,876,936,0,1812,0,0,2,0,4,1,TA,8,Typ,1,TA,Attchd,1962,RFn,1,264,TA,TA,Y,0,168,0,0,0,0,NA,NA,NA,0,7,2007,WD,Normal,169500 +399,30,RM,60,8967,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,IDOTRR,Norm,Norm,1Fam,1Story,5,2,1920,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,Fa,BrkTil,Fa,Po,No,Unf,0,Unf,0,961,961,GasA,Gd,Y,Mix,1077,0,0,1077,0,0,1,0,2,1,TA,6,Maj2,0,NA,Detchd,1920,Unf,1,338,Po,Po,N,0,0,0,0,0,0,NA,NA,NA,0,11,2007,WD,Abnorml,67000 +400,60,FV,65,8125,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,2Story,7,5,2006,2007,Gable,CompShg,CemntBd,CmentBd,Stone,100,Gd,TA,PConc,Gd,TA,No,GLQ,812,Unf,0,280,1092,GasA,Ex,Y,SBrkr,1112,438,0,1550,1,0,2,0,2,1,Gd,7,Typ,0,NA,Attchd,2007,Fin,2,438,TA,TA,Y,0,168,0,0,0,0,NA,NA,NA,0,10,2009,WD,Normal,241000 +401,120,RL,38,14963,Pave,NA,IR2,Lvl,AllPub,Inside,Gtl,Veenker,Norm,Norm,TwnhsE,1Story,8,5,1996,1996,Gable,CompShg,BrkFace,BrkFace,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,786,Unf,0,474,1260,GasA,Ex,Y,SBrkr,1288,0,0,1288,1,0,1,1,1,1,Ex,4,Typ,2,Gd,Attchd,1996,Fin,2,500,TA,TA,Y,120,30,0,0,224,0,NA,NA,NA,0,12,2008,WD,Normal,245500 +402,20,RL,65,8767,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,Av,GLQ,24,Unf,0,1286,1310,GasA,Ex,Y,SBrkr,1310,0,0,1310,0,0,2,0,3,1,Gd,6,Typ,1,Gd,Attchd,2005,Fin,2,400,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,7,2006,New,Partial,164990 +403,30,RL,60,10200,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,1Story,5,8,1940,1997,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,PConc,TA,TA,No,Unf,0,Unf,0,672,672,GasA,Ex,Y,SBrkr,672,0,0,672,0,0,1,0,2,1,TA,4,Typ,0,NA,Detchd,1940,Unf,1,240,TA,TA,N,168,0,0,0,0,0,NA,GdPrv,NA,0,8,2008,WD,Normal,108000 +404,60,RL,93,12090,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NoRidge,Norm,Norm,1Fam,2Story,8,5,1998,1998,Hip,CompShg,VinylSd,VinylSd,BrkFace,650,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1141,1141,GasA,Gd,Y,SBrkr,1165,1098,0,2263,0,0,2,1,4,1,Gd,10,Typ,1,TA,BuiltIn,1998,Fin,2,420,TA,TA,Y,144,123,0,0,0,0,NA,NA,NA,0,7,2006,WD,Abnorml,258000 +405,60,RL,NA,10364,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,2Story,6,5,1995,1996,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,PConc,Gd,TA,No,Unf,0,Unf,0,806,806,GasA,Gd,Y,SBrkr,806,766,0,1572,0,0,2,1,3,1,TA,7,Typ,1,TA,BuiltIn,1995,Fin,2,373,TA,TA,Y,0,40,0,0,0,0,NA,NA,NA,0,5,2007,WD,Normal,168000 +406,20,RL,NA,9991,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Sawyer,Feedr,Norm,1Fam,1Story,4,4,1976,1993,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,CBlock,TA,TA,No,BLQ,1116,Unf,0,165,1281,GasA,Ex,Y,SBrkr,1620,0,0,1620,1,0,2,0,3,1,TA,8,Min1,1,TA,Attchd,1993,Unf,2,490,TA,TA,Y,120,78,0,0,0,0,NA,GdWo,NA,0,6,2009,WD,Normal,150000 +407,50,RL,51,10480,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SWISU,Norm,Norm,1Fam,1.5Fin,6,5,1936,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,1064,1064,GasA,Ex,Y,FuseA,1166,0,473,1639,0,0,1,0,3,1,TA,6,Maj2,0,NA,Detchd,1936,Unf,1,240,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,3,2008,WD,Normal,115000 +408,70,RL,63,15576,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Crawfor,Norm,Norm,1Fam,2Story,6,7,1915,1976,Gable,CompShg,Wd Sdng,Plywood,None,0,TA,TA,BrkTil,Gd,TA,No,Unf,0,Unf,0,840,840,GasA,Ex,Y,SBrkr,840,840,0,1680,0,0,2,0,4,1,TA,8,Typ,0,NA,Attchd,1960,Unf,1,308,TA,TA,Y,0,0,160,0,0,0,NA,NA,NA,0,3,2008,WD,Normal,177000 +409,60,RL,109,14154,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NridgHt,Norm,Norm,1Fam,2Story,7,5,2006,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,350,Gd,TA,PConc,Ex,Gd,No,Unf,0,Unf,0,1063,1063,GasA,Ex,Y,SBrkr,1071,1101,0,2172,0,0,2,1,3,1,Gd,9,Typ,1,Gd,Attchd,2006,RFn,3,947,TA,TA,Y,192,62,0,0,0,0,NA,NA,NA,0,8,2007,New,Partial,280000 +410,60,FV,85,10800,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,2Story,8,5,2007,2008,Gable,CompShg,VinylSd,VinylSd,Stone,100,Gd,TA,PConc,Ex,TA,No,GLQ,789,Unf,0,245,1034,GasA,Ex,Y,SBrkr,1050,1028,0,2078,1,0,2,1,3,1,Ex,8,Typ,1,Gd,Attchd,2008,Fin,3,836,TA,TA,Y,0,102,0,0,0,0,NA,NA,NA,0,4,2008,New,Partial,339750 +411,20,RL,68,9571,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1Story,5,3,1958,1958,Gable,CompShg,BrkComm,Brk Cmn,None,0,TA,Fa,CBlock,TA,Fa,No,Unf,0,Unf,0,1276,1276,GasA,TA,Y,FuseA,1276,0,0,1276,0,0,1,0,3,1,TA,5,Mod,0,NA,Attchd,1958,Unf,1,350,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,6,2009,COD,Abnorml,60000 +412,190,RL,100,34650,Pave,NA,Reg,Bnk,AllPub,Inside,Gtl,Gilbert,Norm,Norm,2fmCon,1Story,5,5,1955,1955,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,Mn,Rec,1056,Unf,0,0,1056,GasA,TA,N,SBrkr,1056,0,0,1056,1,0,1,0,3,1,TA,5,Typ,0,NA,Attchd,1955,Fin,2,572,TA,TA,Y,264,0,0,0,0,0,NA,NA,NA,0,1,2006,WD,Normal,145000 +413,20,FV,NA,4403,Pave,NA,IR2,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,1Story,7,5,2009,2009,Gable,CompShg,MetalSd,MetalSd,Stone,432,Ex,TA,PConc,Ex,TA,Av,GLQ,578,Unf,0,892,1470,GasA,Ex,Y,SBrkr,1478,0,0,1478,1,0,2,1,2,1,Gd,7,Typ,1,Gd,Attchd,2009,Fin,2,484,TA,TA,Y,0,144,0,0,0,0,NA,NA,NA,0,6,2010,New,Partial,222000 +414,30,RM,56,8960,Pave,Grvl,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Artery,Norm,1Fam,1Story,5,6,1927,1950,Gable,CompShg,WdShing,Wd Shng,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,1008,1008,GasA,Gd,Y,FuseA,1028,0,0,1028,0,0,1,0,2,1,TA,5,Typ,1,Gd,Detchd,1927,Unf,2,360,TA,TA,Y,0,0,130,0,0,0,NA,NA,NA,0,3,2010,WD,Normal,115000 +415,60,RL,59,11228,Pave,NA,IR2,Lvl,AllPub,CulDSac,Gtl,SawyerW,Norm,Norm,1Fam,2Story,7,5,1993,1993,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,BLQ,50,GLQ,531,499,1080,GasA,Ex,Y,SBrkr,1080,1017,0,2097,0,1,2,1,3,1,Gd,9,Typ,1,TA,Attchd,1993,Unf,3,678,TA,TA,Y,196,187,0,0,0,0,NA,NA,NA,0,12,2008,WD,Normal,228000 +416,20,RL,73,8899,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,1Story,7,5,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,Av,GLQ,24,Unf,0,1316,1340,GasA,Ex,Y,SBrkr,1340,0,0,1340,0,0,2,0,3,1,Gd,6,Typ,0,NA,Attchd,2007,Fin,2,396,TA,TA,Y,100,30,0,0,0,0,NA,NA,NA,0,8,2007,New,Partial,181134 +417,60,RL,74,7844,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,2Story,6,7,1978,1978,Hip,CompShg,HdBoard,HdBoard,BrkFace,203,TA,TA,CBlock,TA,TA,No,ALQ,209,Unf,0,463,672,GasA,TA,Y,SBrkr,672,728,0,1400,0,0,1,1,3,1,TA,6,Typ,1,TA,Attchd,1978,Fin,2,440,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,3,2006,WD,Normal,149500 +418,70,RL,86,22420,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Crawfor,Feedr,Norm,1Fam,2Story,6,6,1918,1950,Hip,CompShg,Wd Sdng,Stucco,None,0,TA,TA,BrkTil,Gd,TA,No,BLQ,1128,Unf,0,242,1370,GasW,TA,N,FuseA,1370,1254,0,2624,1,0,2,1,4,1,TA,10,Typ,1,Gd,Detchd,1918,Unf,3,864,TA,TA,N,0,0,0,0,0,0,NA,NA,NA,0,11,2007,WD,Normal,239000 +419,50,RL,60,8160,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1.5Fin,5,6,1940,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,BrkTil,TA,TA,No,ALQ,312,Unf,0,444,756,GasA,Fa,N,FuseF,756,378,0,1134,1,0,1,1,3,1,TA,7,Typ,0,NA,Detchd,1940,Unf,1,240,TA,TA,P,0,0,0,0,0,0,NA,NA,NA,0,4,2007,WD,AdjLand,126000 +420,20,RL,65,8450,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,6,1968,1968,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,CBlock,TA,TA,No,BLQ,775,Unf,0,281,1056,GasA,Ex,Y,SBrkr,1056,0,0,1056,1,0,1,0,3,1,TA,6,Typ,1,Fa,Attchd,1968,Unf,1,304,TA,TA,Y,0,85,184,0,0,0,NA,NA,NA,0,7,2010,WD,Normal,142000 +421,90,RM,78,7060,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Mitchel,Norm,Norm,Duplex,SFoyer,7,5,1997,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,200,TA,Gd,PConc,Gd,Gd,Gd,GLQ,1309,Unf,0,35,1344,GasA,Ex,Y,SBrkr,1344,0,0,1344,2,0,2,0,2,2,TA,8,Typ,0,NA,Attchd,1997,Fin,4,784,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,11,2008,WD,Alloca,206300 +422,20,RL,NA,16635,Pave,NA,IR1,Lvl,AllPub,FR2,Gtl,NWAmes,Norm,Norm,1Fam,1Story,6,7,1977,2000,Gable,CompShg,CemntBd,CmentBd,Stone,126,Gd,TA,CBlock,Gd,TA,No,ALQ,1246,Unf,0,356,1602,GasA,Gd,Y,SBrkr,1602,0,0,1602,0,1,2,0,3,1,Gd,8,Typ,1,TA,Attchd,1977,Fin,2,529,TA,TA,Y,240,0,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,215000 +423,20,RL,100,21750,Pave,NA,Reg,HLS,AllPub,Inside,Mod,Mitchel,Artery,Norm,1Fam,1Story,5,5,1954,1954,Hip,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,988,988,GasA,Ex,Y,FuseA,988,0,0,988,0,0,1,0,2,1,TA,4,Typ,0,NA,Attchd,1954,RFn,2,520,TA,TA,N,0,0,0,0,0,0,NA,NA,NA,0,2,2008,WD,Normal,113000 +424,60,RL,80,9200,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NoRidge,Norm,Norm,1Fam,2Story,8,5,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,473,Gd,TA,PConc,Gd,TA,No,GLQ,986,Unf,0,484,1470,GasA,Gd,Y,SBrkr,1470,1160,0,2630,1,0,2,1,4,1,Gd,8,Typ,1,TA,Attchd,1998,Fin,3,696,TA,TA,Y,0,66,0,0,0,0,NA,NA,NA,0,6,2008,WD,Normal,315000 +425,20,RL,72,9000,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,5,1956,1956,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,74,TA,TA,CBlock,Gd,TA,No,LwQ,616,Unf,0,580,1196,GasA,Gd,Y,FuseA,1196,0,0,1196,1,0,1,0,2,1,TA,6,Typ,1,Gd,Attchd,1956,RFn,1,297,TA,TA,Y,0,44,0,0,0,0,NA,NA,NA,0,5,2008,WD,Normal,139000 +426,60,RM,60,3378,Pave,Grvl,Reg,HLS,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,2Story,7,8,1946,1992,Gable,CompShg,HdBoard,HdBoard,None,0,TA,Gd,CBlock,TA,TA,No,Unf,0,Unf,0,651,651,GasA,Gd,Y,SBrkr,707,682,0,1389,0,0,1,1,3,1,TA,6,Typ,2,Gd,Detchd,1947,Unf,1,240,TA,TA,P,0,0,126,0,0,0,NA,NA,NA,0,9,2009,WD,Normal,135000 +427,80,RL,NA,12800,Pave,NA,Reg,Low,AllPub,Inside,Mod,SawyerW,Norm,Norm,1Fam,SLvl,7,5,1989,1989,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,145,Gd,TA,PConc,Gd,TA,Gd,GLQ,1518,Unf,0,0,1518,GasA,Gd,Y,SBrkr,1644,0,0,1644,1,1,2,0,2,1,Gd,5,Typ,1,TA,Attchd,1989,Fin,2,569,TA,TA,Y,80,0,0,0,396,0,NA,NA,NA,0,8,2009,WD,Normal,275000 +428,20,RL,77,8593,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,4,6,1957,1957,Hip,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,Rec,288,Unf,0,619,907,GasA,Ex,Y,SBrkr,907,0,0,907,0,0,1,0,3,1,TA,5,Typ,0,NA,Detchd,1964,Unf,1,352,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,109008 +429,20,RL,64,6762,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2007,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,108,Gd,TA,PConc,Gd,TA,No,GLQ,664,Unf,0,544,1208,GasA,Ex,Y,SBrkr,1208,0,0,1208,1,0,2,0,2,1,Gd,6,Typ,0,NA,Attchd,2007,RFn,2,628,TA,TA,Y,105,54,0,0,0,0,NA,NA,NA,0,9,2007,New,Partial,195400 +430,20,RL,130,11457,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Timber,Norm,Norm,1Fam,1Story,6,5,1988,1988,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,Gd,TA,Mn,GLQ,1005,Unf,0,387,1392,GasA,TA,Y,SBrkr,1412,0,0,1412,1,0,2,0,3,1,Gd,6,Typ,1,TA,Attchd,1988,Unf,2,576,TA,TA,Y,0,0,169,0,0,0,NA,NA,NA,0,3,2009,WD,Normal,175000 +431,160,RM,21,1680,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrDale,Norm,Norm,Twnhs,2Story,6,5,1971,1971,Gable,CompShg,HdBoard,HdBoard,BrkFace,232,TA,TA,CBlock,TA,TA,No,ALQ,387,Unf,0,96,483,GasA,TA,Y,SBrkr,483,504,0,987,0,0,1,1,2,1,TA,4,Typ,0,NA,Detchd,1971,Unf,1,264,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,7,2008,COD,Abnorml,85400 +432,50,RM,60,5586,Pave,NA,IR1,Bnk,AllPub,Inside,Gtl,OldTown,Feedr,Norm,1Fam,1.5Fin,6,7,1920,1998,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,901,901,GasA,Gd,Y,SBrkr,1088,110,0,1198,0,0,1,0,4,1,TA,7,Typ,0,NA,NA,NA,NA,0,0,NA,NA,N,0,98,0,0,0,0,NA,MnPrv,NA,0,9,2008,ConLD,Abnorml,79900 +433,160,RM,24,1920,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrDale,Norm,Norm,TwnhsE,2Story,5,5,1971,1971,Gable,CompShg,HdBoard,HdBoard,BrkFace,376,TA,TA,CBlock,TA,TA,No,ALQ,471,Unf,0,294,765,GasA,Ex,Y,SBrkr,765,600,0,1365,1,0,1,1,2,1,TA,6,Min1,0,NA,Detchd,1971,Unf,2,440,TA,TA,Y,240,36,0,0,0,0,NA,NA,NA,0,8,2007,WD,Normal,122500 +434,60,RL,100,10839,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,1Fam,2Story,6,5,1997,1998,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,No,Unf,0,Unf,0,926,926,GasA,Ex,Y,SBrkr,926,678,0,1604,0,0,2,1,3,1,TA,7,Typ,1,TA,Attchd,1997,Fin,2,470,TA,TA,Y,0,36,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,181000 +435,180,RM,21,1890,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,MeadowV,Norm,Norm,Twnhs,SFoyer,4,7,1972,1972,Gable,CompShg,CemntBd,CmentBd,None,0,TA,Gd,CBlock,Gd,TA,Av,ALQ,495,Unf,0,135,630,GasA,Gd,Y,SBrkr,630,0,0,630,1,0,1,0,1,1,TA,3,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,88,0,0,0,0,0,NA,NA,NA,0,6,2008,WD,Normal,81000 +436,60,RL,43,10667,Pave,NA,IR2,Lvl,AllPub,CulDSac,Gtl,CollgCr,PosN,Norm,1Fam,2Story,7,6,1996,1996,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,Av,GLQ,385,ALQ,344,70,799,GasA,Ex,Y,SBrkr,827,834,0,1661,1,0,2,1,3,1,Gd,6,Typ,1,TA,Attchd,1996,RFn,2,550,TA,TA,Y,158,61,0,0,0,0,NA,NA,NA,0,4,2009,ConLw,Normal,212000 +437,50,RM,40,4400,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,1.5Fin,6,8,1920,1950,Gable,CompShg,Stucco,Stucco,None,0,TA,TA,BrkTil,Fa,TA,No,Unf,0,Unf,0,648,648,GasA,TA,Y,FuseA,734,384,0,1118,0,0,1,0,2,1,TA,6,Typ,0,NA,Detchd,1990,Unf,2,440,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,10,2006,WD,Normal,116000 +438,45,RM,50,6000,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,1.5Unf,6,7,1926,2004,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Gd,TA,PConc,TA,TA,No,Unf,0,Unf,0,884,884,GasA,Gd,Y,SBrkr,904,0,0,904,0,0,1,0,2,1,TA,4,Typ,0,NA,Detchd,1926,Unf,1,180,TA,TA,Y,0,0,105,0,0,0,NA,NA,NA,0,1,2009,WD,Normal,119000 +439,30,RL,40,4280,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Crawfor,Norm,Norm,1Fam,1Story,5,6,1913,2002,Gable,CompShg,WdShing,Stucco,None,0,TA,TA,PConc,TA,TA,No,LwQ,365,Unf,0,75,440,GasA,TA,N,SBrkr,694,0,0,694,0,0,1,0,2,1,Gd,4,Typ,1,Gd,Detchd,1990,Unf,1,352,Gd,TA,P,0,0,34,0,0,0,NA,MnPrv,NA,0,3,2007,WD,Normal,90350 +440,50,RL,67,12354,Pave,Grvl,Reg,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,1Fam,1.5Fin,6,8,1920,2000,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,TA,Fa,Mn,Unf,0,Unf,0,684,684,GasA,Gd,Y,SBrkr,684,512,0,1196,0,0,1,0,3,1,Gd,7,Typ,0,NA,Detchd,2005,Unf,2,528,TA,TA,Y,0,46,0,0,0,0,NA,GdPrv,Shed,800,8,2009,ConLI,Normal,110000 +441,20,RL,105,15431,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,1Story,10,5,2008,2008,Hip,CompShg,VinylSd,VinylSd,Stone,200,Ex,TA,PConc,Ex,TA,Gd,GLQ,1767,ALQ,539,788,3094,GasA,Ex,Y,SBrkr,2402,0,0,2402,1,0,2,0,2,1,Ex,10,Typ,2,Gd,Attchd,2008,Fin,3,672,TA,TA,Y,0,72,0,0,170,0,NA,NA,NA,0,4,2009,WD,Normal,555000 +442,90,RL,92,12108,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Duplex,1Story,4,4,1955,1955,Gable,CompShg,VinylSd,VinylSd,BrkFace,270,TA,TA,CBlock,TA,TA,No,ALQ,133,Unf,0,1307,1440,GasA,TA,N,FuseF,1440,0,0,1440,0,0,2,0,4,2,Fa,8,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,0,0,0,0,0,0,NA,NA,NA,0,9,2008,WD,Normal,118000 +443,50,RM,52,6240,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,1.5Fin,5,7,1930,1992,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,PConc,TA,TA,No,Unf,0,Unf,0,1078,1078,GasA,TA,Y,SBrkr,1128,445,0,1573,0,0,2,0,3,1,TA,8,Typ,1,Gd,Detchd,1930,Unf,2,360,TA,TA,P,0,0,0,0,0,0,NA,NA,NA,0,6,2008,WD,Normal,162900 +444,120,RL,53,3922,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Blmngtn,Norm,Norm,TwnhsE,1Story,7,5,2006,2007,Gable,CompShg,WdShing,Wd Shng,BrkFace,72,Gd,TA,PConc,Ex,TA,Av,Unf,0,Unf,0,1258,1258,GasA,Ex,Y,SBrkr,1258,0,0,1258,0,0,2,0,2,1,Gd,6,Typ,1,Gd,Attchd,2007,Fin,3,648,TA,TA,Y,144,16,0,0,0,0,NA,NA,NA,0,6,2007,New,Partial,172500 +445,60,RL,70,8750,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,7,5,1994,1995,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,Gd,PConc,Gd,TA,No,GLQ,642,Unf,0,273,915,GasA,Ex,Y,SBrkr,933,975,0,1908,1,0,2,1,4,1,Gd,8,Typ,1,TA,Attchd,1994,Unf,2,493,TA,TA,Y,144,133,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,210000 +446,20,RL,73,9855,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,1Fam,1Story,6,5,1956,1956,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,1436,1436,GasA,Fa,Y,SBrkr,1689,0,0,1689,0,0,1,0,3,1,TA,7,Typ,1,Gd,Attchd,1956,Unf,2,480,TA,TA,Y,0,0,0,0,0,0,NA,MnPrv,NA,0,11,2009,COD,Normal,127500 +447,20,RL,137,16492,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NAmes,PosA,Norm,1Fam,1Story,6,6,1966,2002,Gable,CompShg,BrkFace,Plywood,None,0,Gd,TA,CBlock,TA,TA,No,ALQ,247,Rec,713,557,1517,GasA,Ex,Y,SBrkr,1888,0,0,1888,0,0,2,1,2,1,Gd,6,Mod,1,Gd,Attchd,1966,Fin,2,578,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,6,2010,WD,Normal,190000 +448,60,RL,NA,11214,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,1Fam,2Story,7,5,1998,1999,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,930,930,GasA,Gd,Y,SBrkr,956,930,0,1886,0,0,2,1,4,1,Gd,10,Typ,1,TA,Attchd,1998,Fin,2,431,TA,TA,Y,89,0,0,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,199900 +449,50,RM,50,8600,Pave,NA,Reg,Bnk,AllPub,Inside,Gtl,IDOTRR,Norm,Norm,1Fam,1.5Fin,6,6,1937,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,780,780,GasA,TA,Y,SBrkr,780,596,0,1376,0,0,2,0,3,1,TA,7,Typ,1,Gd,Detchd,1937,Unf,1,198,TA,TA,N,0,0,0,0,0,0,NA,NA,NA,0,6,2006,WD,Normal,119500 +450,50,RM,50,6000,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,1.5Fin,3,7,1948,2002,Gable,CompShg,MetalSd,MetalSd,None,0,TA,Gd,CBlock,TA,TA,No,ALQ,331,Unf,0,318,649,GasA,Ex,Y,SBrkr,679,504,0,1183,0,0,1,1,2,1,TA,6,Typ,0,NA,Detchd,1981,Unf,1,308,TA,TA,Y,0,176,0,0,0,0,NA,NA,NA,0,6,2007,WD,Normal,120000 +451,30,RM,70,5684,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,1Story,6,8,1930,2005,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,813,813,GasA,Ex,Y,FuseA,813,0,0,813,0,0,1,0,2,1,Gd,5,Typ,0,NA,Detchd,1932,Unf,1,270,Fa,Fa,N,0,113,0,0,0,0,NA,NA,NA,0,6,2006,WD,Normal,110000 +452,20,RL,62,70761,Pave,NA,IR1,Low,AllPub,Inside,Mod,ClearCr,Norm,Norm,1Fam,1Story,7,5,1975,1975,Gable,WdShngl,Plywood,Plywood,None,0,TA,TA,CBlock,Gd,TA,Gd,ALQ,655,Unf,0,878,1533,GasA,TA,Y,SBrkr,1533,0,0,1533,1,0,2,0,2,1,Gd,5,Typ,2,TA,Attchd,1975,Unf,2,576,TA,TA,Y,200,54,0,0,0,0,NA,NA,NA,0,12,2006,WD,Normal,280000 +453,60,RL,NA,9303,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Timber,Norm,Norm,1Fam,2Story,6,5,1996,1997,Hip,CompShg,VinylSd,VinylSd,BrkFace,42,Gd,TA,PConc,Ex,TA,No,ALQ,742,Unf,0,130,872,GasA,Ex,Y,SBrkr,888,868,0,1756,1,0,2,1,3,1,TA,7,Typ,0,NA,Attchd,1996,Fin,2,422,TA,TA,Y,144,122,0,0,0,0,NA,NA,NA,0,7,2007,WD,Normal,204000 +454,60,FV,75,9000,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,2Story,8,5,2008,2008,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,768,768,GasA,Ex,Y,SBrkr,786,804,0,1590,0,0,2,1,3,1,Gd,6,Typ,0,NA,Attchd,2008,RFn,2,676,TA,TA,Y,0,30,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,210000 +455,90,RL,63,9297,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Mitchel,Norm,Norm,Duplex,1Story,5,5,1976,1976,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,CBlock,TA,TA,No,ALQ,1606,Unf,0,122,1728,GasA,TA,Y,SBrkr,1728,0,0,1728,2,0,2,0,4,2,TA,8,Typ,0,NA,Detchd,1976,Unf,2,560,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,7,2006,WD,Family,188000 +456,20,RL,80,9600,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,1Story,7,6,1973,1973,Hip,CompShg,HdBoard,HdBoard,BrkFace,320,TA,TA,CBlock,TA,TA,No,ALQ,916,Unf,0,326,1242,GasA,Fa,Y,SBrkr,1242,0,0,1242,0,0,1,1,3,1,TA,6,Typ,1,TA,Attchd,1973,Unf,2,528,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,9,2007,WD,Normal,175500 +457,70,RM,34,4571,Pave,Grvl,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,2Story,5,5,1916,1950,Gable,CompShg,AsbShng,AsbShng,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,624,624,GasA,Fa,N,SBrkr,624,720,0,1344,0,0,1,0,4,1,TA,7,Typ,0,NA,Detchd,1916,Unf,3,513,Fa,Fa,Y,0,0,96,0,0,0,NA,NA,NA,0,5,2008,COD,Abnorml,98000 +458,20,RL,NA,53227,Pave,NA,IR1,Low,AllPub,CulDSac,Mod,ClearCr,Norm,Norm,1Fam,1Story,4,6,1954,1994,Flat,Tar&Grv,Plywood,Plywood,None,0,TA,TA,CBlock,Gd,TA,Gd,BLQ,1116,Unf,0,248,1364,GasA,Ex,Y,SBrkr,1663,0,0,1663,1,0,1,0,2,1,Gd,6,Min1,2,Gd,Attchd,1954,Fin,2,529,TA,TA,Y,224,137,0,0,0,0,NA,NA,NA,0,3,2008,WD,Normal,256000 +459,70,RM,NA,5100,Pave,Grvl,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,2Story,8,7,1925,1996,Hip,CompShg,Stucco,Wd Shng,None,0,TA,Gd,PConc,TA,TA,No,Unf,0,Unf,0,588,588,GasA,Fa,Y,SBrkr,833,833,0,1666,0,0,1,0,3,1,Gd,7,Typ,1,Gd,Detchd,1925,Unf,1,228,TA,TA,Y,192,63,0,0,0,0,NA,MnPrv,NA,0,6,2008,WD,Normal,161000 +460,50,RL,NA,7015,Pave,NA,IR1,Bnk,AllPub,Corner,Gtl,BrkSide,Norm,Norm,1Fam,1.5Fin,5,4,1950,1950,Gable,CompShg,MetalSd,MetalSd,BrkCmn,161,TA,TA,CBlock,TA,TA,No,LwQ,185,Unf,0,524,709,GasA,TA,Y,SBrkr,979,224,0,1203,1,0,1,0,3,1,Gd,5,Typ,1,TA,Detchd,1950,Unf,1,352,TA,TA,Y,0,0,248,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,110000 +461,60,FV,75,8004,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Somerst,RRAn,Norm,1Fam,2Story,8,5,2009,2009,Gable,CompShg,VinylSd,VinylSd,Stone,110,Gd,TA,PConc,Gd,TA,No,GLQ,544,Unf,0,288,832,GasA,Ex,Y,SBrkr,832,1103,0,1935,1,0,2,1,3,1,TA,8,Typ,0,NA,BuiltIn,2009,Fin,2,552,TA,TA,Y,0,150,0,0,0,0,NA,NA,NA,0,12,2009,New,Partial,263435 +462,70,RL,60,7200,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SWISU,Feedr,Norm,1Fam,2Story,7,9,1936,2007,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Gd,Gd,PConc,Gd,Gd,No,ALQ,350,BLQ,210,0,560,GasA,Ex,Y,SBrkr,575,560,0,1135,1,0,1,0,3,1,Gd,6,Typ,0,NA,Detchd,1971,RFn,2,576,TA,TA,Y,256,0,0,0,0,0,NA,MnPrv,NA,0,4,2009,WD,Normal,155000 +463,20,RL,60,8281,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,1Story,5,5,1965,1965,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,Rec,553,BLQ,311,0,864,GasA,Gd,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,TA,5,Typ,1,Po,Detchd,1965,Unf,1,360,TA,TA,Y,0,0,236,0,0,0,NA,GdWo,NA,0,12,2009,WD,Normal,62383 +464,70,RL,74,11988,Pave,NA,IR1,HLS,AllPub,Inside,Mod,Crawfor,Norm,Norm,1Fam,2Story,6,7,1934,1995,Hip,CompShg,Stucco,Stucco,None,0,TA,TA,CBlock,TA,TA,No,LwQ,326,Unf,0,389,715,GasA,Fa,Y,FuseA,849,811,0,1660,0,0,1,1,3,1,TA,6,Typ,1,Gd,Detchd,1939,Unf,1,240,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,8,2008,WD,Normal,188700 +465,20,RL,60,8430,Pave,NA,Reg,HLS,AllPub,Inside,Mod,CollgCr,Norm,Norm,1Fam,1Story,5,5,1978,1978,Gable,CompShg,HdBoard,HdBoard,BrkFace,136,TA,TA,CBlock,Gd,TA,No,Rec,616,Unf,0,424,1040,GasA,TA,Y,SBrkr,1040,0,0,1040,0,0,2,0,3,1,TA,5,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,0,0,0,0,0,0,NA,NA,NA,0,8,2009,WD,Normal,124000 +466,120,RM,NA,3072,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Blmngtn,Norm,Norm,TwnhsE,1Story,7,5,2004,2004,Hip,CompShg,VinylSd,VinylSd,BrkFace,18,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1375,1375,GasA,Ex,Y,SBrkr,1414,0,0,1414,0,0,2,0,2,1,Gd,6,Typ,1,TA,Attchd,2004,Fin,2,398,TA,TA,Y,144,20,0,0,0,0,NA,NA,NA,0,5,2006,WD,Normal,178740 +467,20,RL,85,10628,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,7,5,1970,1970,Flat,Tar&Grv,Plywood,Plywood,None,0,TA,Gd,CBlock,TA,Gd,Gd,GLQ,778,Unf,0,499,1277,GasA,TA,Y,SBrkr,1277,0,0,1277,1,0,1,0,2,1,TA,5,Typ,1,Po,Attchd,1970,Unf,2,526,TA,TA,Y,0,0,0,0,176,0,NA,GdWo,NA,0,4,2007,WD,Normal,167000 +468,70,RL,79,9480,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Artery,Norm,1Fam,2Story,5,7,1942,1995,Gable,CompShg,MetalSd,MetalSd,Stone,224,TA,TA,CBlock,TA,TA,No,LwQ,386,Unf,0,342,728,GasA,Ex,Y,SBrkr,888,756,0,1644,0,0,1,1,3,1,Gd,7,Typ,2,Gd,Attchd,1942,Unf,1,312,TA,TA,Y,168,0,0,0,0,0,NA,NA,NA,0,6,2007,WD,Normal,146500 +469,20,RL,98,11428,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,1Story,8,5,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,248,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1626,1626,GasA,Ex,Y,SBrkr,1634,0,0,1634,0,0,2,0,3,1,Gd,7,Typ,1,Gd,Attchd,2006,RFn,3,866,TA,TA,Y,0,44,0,0,0,0,NA,NA,NA,0,5,2007,WD,Normal,250000 +470,60,RL,76,9291,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,SawyerW,RRNe,Norm,1Fam,2Story,6,5,1993,1993,Gable,CompShg,HdBoard,HdBoard,BrkFace,120,Gd,TA,PConc,Gd,TA,No,GLQ,426,Unf,0,406,832,GasA,Ex,Y,SBrkr,832,878,0,1710,0,0,2,1,3,1,Gd,7,Typ,0,NA,Attchd,1993,RFn,2,506,TA,TA,Y,144,70,0,0,0,0,NA,NA,NA,0,6,2008,WD,Normal,187000 +471,120,RL,NA,6820,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,StoneBr,Norm,Norm,TwnhsE,1Story,8,5,1985,1985,Gable,CompShg,HdBoard,HdBoard,None,0,Gd,TA,PConc,Gd,TA,Av,GLQ,368,BLQ,1120,0,1488,GasA,TA,Y,SBrkr,1502,0,0,1502,1,0,1,1,1,1,Gd,4,Typ,0,NA,Attchd,1985,RFn,2,528,TA,TA,Y,0,54,0,0,140,0,NA,NA,NA,0,6,2010,WD,Normal,212000 +472,60,RL,92,11952,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,PosA,Norm,1Fam,2Story,7,6,1977,1977,Mansard,WdShake,WdShing,Plywood,None,0,TA,TA,CBlock,Gd,TA,No,Unf,0,Unf,0,808,808,GasA,TA,Y,SBrkr,1161,808,0,1969,0,0,2,1,3,1,TA,8,Typ,1,Gd,Attchd,1977,RFn,2,534,TA,TA,Y,0,0,0,0,276,0,NA,NA,NA,0,11,2007,WD,Normal,190000 +473,180,RM,35,3675,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,TwnhsE,SLvl,6,5,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,80,TA,TA,PConc,Gd,TA,Gd,GLQ,459,Unf,0,88,547,GasA,Ex,Y,SBrkr,1072,0,0,1072,1,0,1,0,2,1,TA,5,Typ,0,NA,Basment,2005,RFn,2,525,TA,TA,Y,0,28,0,0,0,0,NA,NA,NA,0,6,2008,WD,Normal,148000 +474,20,RL,110,14977,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,1Story,8,5,2006,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,304,Gd,TA,PConc,Ex,TA,Gd,GLQ,1350,Unf,0,626,1976,GasA,Ex,Y,SBrkr,1976,0,0,1976,1,0,2,0,2,1,Gd,7,Typ,1,Ex,Attchd,2006,RFn,3,908,TA,TA,Y,250,63,0,0,0,0,NA,NA,NA,0,7,2007,New,Partial,440000 +475,120,RL,41,5330,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,StoneBr,Norm,Norm,TwnhsE,1Story,8,5,2000,2000,Gable,CompShg,CemntBd,CmentBd,None,0,Gd,TA,PConc,Gd,TA,Av,GLQ,1196,Unf,0,298,1494,GasA,Ex,Y,SBrkr,1652,0,0,1652,1,0,2,0,2,1,Ex,6,Typ,0,NA,Attchd,2000,RFn,2,499,TA,TA,Y,96,48,0,0,0,0,NA,NA,NA,0,8,2007,WD,Normal,251000 +476,20,RL,80,8480,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,1Fam,1Story,5,6,1963,1963,Hip,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,TA,TA,No,GLQ,630,Unf,0,340,970,GasA,TA,Y,SBrkr,970,0,0,970,1,0,1,0,2,1,TA,5,Typ,0,NA,Detchd,1996,Unf,2,624,TA,TA,Y,0,24,0,0,192,0,NA,NA,NA,0,7,2007,WD,Normal,132500 +477,20,RL,75,13125,Pave,NA,Reg,Lvl,AllPub,Inside,Mod,CollgCr,Norm,Norm,1Fam,1Story,6,5,1997,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,215,TA,TA,PConc,Gd,TA,Gd,GLQ,994,Unf,0,484,1478,GasA,Ex,Y,SBrkr,1493,0,0,1493,1,0,2,0,3,1,Gd,7,Typ,1,TA,Attchd,1997,Fin,2,508,TA,TA,Y,140,39,0,0,0,0,NA,NA,NA,0,4,2008,WD,Normal,208900 +478,60,RL,105,13693,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,2Story,9,5,2006,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,772,Ex,TA,PConc,Gd,TA,Av,Unf,0,Unf,0,2153,2153,GasA,Ex,Y,SBrkr,2069,574,0,2643,0,0,2,1,3,1,Ex,9,Typ,1,Gd,BuiltIn,2006,Fin,3,694,TA,TA,Y,414,84,0,0,0,0,NA,NA,NA,0,3,2007,WD,Normal,380000 +479,20,RL,79,10637,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,8,5,2007,2008,Hip,CompShg,VinylSd,VinylSd,Stone,336,Gd,TA,PConc,Ex,TA,Gd,GLQ,1288,Unf,0,417,1705,GasA,Ex,Y,SBrkr,1718,0,0,1718,1,0,2,0,3,1,Gd,7,Typ,1,Gd,Attchd,2007,RFn,3,826,TA,TA,Y,208,44,0,0,0,0,NA,NA,NA,0,9,2009,WD,Normal,297000 +480,30,RM,50,5925,Pave,NA,Reg,Bnk,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,1Story,4,7,1937,2000,Hip,CompShg,Stucco,Stucco,BrkCmn,435,TA,TA,BrkTil,Fa,TA,No,Rec,168,Unf,0,739,907,GasA,TA,Y,SBrkr,1131,0,0,1131,0,0,1,0,2,1,TA,7,Typ,0,NA,Detchd,1995,Unf,2,672,TA,TA,Y,0,72,0,0,0,0,NA,MnPrv,NA,0,3,2007,WD,Alloca,89471 +481,20,RL,98,16033,Pave,NA,IR1,Lvl,AllPub,FR2,Gtl,NridgHt,Norm,Norm,1Fam,1Story,9,5,2004,2005,Hip,CompShg,VinylSd,VinylSd,BrkFace,378,Gd,TA,PConc,Ex,TA,Gd,GLQ,1261,Unf,0,572,1833,GasA,Ex,Y,SBrkr,1850,0,0,1850,1,0,2,0,3,1,Gd,8,Typ,1,Gd,Attchd,2004,Fin,3,772,TA,TA,Y,519,112,0,0,0,0,NA,NA,NA,0,3,2006,WD,Normal,326000 +482,20,RL,72,11846,Pave,NA,IR1,HLS,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,1Story,9,5,2003,2004,Hip,CompShg,VinylSd,VinylSd,BrkFace,562,Gd,TA,PConc,Ex,TA,Gd,GLQ,1567,Unf,0,225,1792,GasA,Ex,Y,SBrkr,1792,0,0,1792,1,0,2,0,2,1,Ex,6,Typ,1,Gd,Attchd,2003,Fin,3,874,TA,TA,Y,206,49,0,0,0,0,NA,NA,NA,0,8,2006,WD,Normal,374000 +483,70,RM,50,2500,Pave,Pave,Reg,Lvl,AllPub,Corner,Gtl,OldTown,Norm,Norm,1Fam,2Story,7,8,1915,2005,Gable,CompShg,Stucco,Stucco,None,0,Gd,TA,PConc,TA,TA,No,ALQ,299,Unf,0,611,910,GasA,Ex,Y,SBrkr,916,910,0,1826,1,0,1,1,4,1,Ex,7,Min2,1,Gd,Attchd,1915,Unf,1,164,Fa,Fa,Y,0,0,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,155000 +484,120,RM,32,4500,Pave,NA,Reg,Lvl,AllPub,FR2,Gtl,Mitchel,Norm,Norm,Twnhs,1Story,6,5,1998,1998,Hip,CompShg,VinylSd,VinylSd,BrkFace,116,TA,TA,PConc,Ex,TA,No,GLQ,897,Unf,0,319,1216,GasA,Ex,Y,SBrkr,1216,0,0,1216,1,0,2,0,2,1,TA,5,Typ,0,NA,Attchd,1998,Unf,2,402,TA,TA,Y,0,125,0,0,0,0,NA,NA,NA,0,5,2006,WD,Normal,164000 +485,20,RL,NA,7758,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,1Fam,1Story,5,7,1962,2001,Gable,CompShg,HdBoard,Plywood,None,0,TA,Gd,CBlock,TA,TA,No,ALQ,588,Unf,0,411,999,GasA,Gd,Y,SBrkr,999,0,0,999,1,0,1,0,3,1,Gd,6,Typ,0,NA,Detchd,1963,Unf,1,264,TA,TA,Y,0,132,0,0,0,0,NA,NA,NA,0,3,2007,WD,Normal,132500 +486,20,RL,80,9600,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,7,1950,2007,Gable,CompShg,MetalSd,MetalSd,None,0,Gd,TA,CBlock,TA,TA,No,ALQ,607,Unf,0,506,1113,GasA,Gd,Y,SBrkr,1113,0,0,1113,0,0,1,0,3,1,Gd,5,Typ,1,Gd,Attchd,1950,Unf,1,264,TA,TA,Y,0,80,120,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,147000 +487,20,RL,79,10289,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,7,1965,1965,Hip,CompShg,MetalSd,MetalSd,BrkFace,168,TA,TA,CBlock,TA,TA,No,ALQ,836,Unf,0,237,1073,GasA,TA,Y,SBrkr,1073,0,0,1073,1,0,1,1,3,1,TA,6,Typ,0,NA,Attchd,1965,RFn,2,515,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,6,2007,WD,Normal,156000 +488,20,RL,70,12243,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,1Story,5,6,1971,1971,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,CBlock,Gd,TA,Av,ALQ,998,Unf,0,486,1484,GasA,Gd,Y,SBrkr,1484,0,0,1484,0,0,2,0,3,1,TA,7,Typ,1,TA,Attchd,1971,Unf,2,487,TA,TA,Y,224,0,0,0,180,0,NA,NA,NA,0,2,2007,WD,Normal,175000 +489,190,RL,60,10800,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,OldTown,Norm,Norm,2fmCon,1.5Fin,5,4,1900,1970,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,Fa,CBlock,TA,Fa,No,BLQ,664,Unf,0,290,954,GasA,TA,N,FuseA,1766,648,0,2414,0,0,2,0,3,2,TA,10,Mod,1,Gd,Attchd,1970,Unf,2,520,TA,Fa,N,142,0,0,0,0,0,NA,NA,NA,0,5,2006,ConLD,Normal,160000 +490,180,RM,21,1526,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,MeadowV,Norm,Norm,Twnhs,SFoyer,4,8,1970,2002,Gable,CompShg,CemntBd,CmentBd,None,0,TA,Gd,CBlock,Gd,TA,Av,GLQ,515,Unf,0,115,630,GasA,TA,Y,SBrkr,630,0,0,630,1,0,1,0,1,1,Gd,3,Typ,0,NA,Attchd,1970,Unf,1,286,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,5,2009,WD,Normal,86000 +491,160,RM,NA,2665,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,MeadowV,Norm,Norm,TwnhsE,2Story,5,6,1976,1976,Gable,CompShg,CemntBd,CmentBd,None,0,TA,TA,PConc,Gd,TA,Mn,Unf,0,Unf,0,264,264,GasA,TA,Y,SBrkr,616,688,0,1304,0,0,1,1,3,1,TA,4,Typ,1,Gd,BuiltIn,1976,Fin,1,336,TA,TA,Y,141,24,0,0,0,0,NA,NA,NA,0,6,2008,WD,Normal,115000 +492,50,RL,79,9490,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Artery,Norm,1Fam,1.5Fin,6,7,1941,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,BLQ,403,Rec,165,238,806,GasA,TA,Y,FuseA,958,620,0,1578,1,0,1,0,3,1,Fa,5,Typ,2,TA,Attchd,1941,Unf,1,240,TA,TA,Y,0,0,32,0,0,0,NA,MnPrv,NA,0,8,2006,WD,Normal,133000 +493,60,RL,105,15578,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,1Fam,2Story,6,5,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,728,728,GasA,Gd,Y,SBrkr,728,728,0,1456,0,0,2,1,3,1,TA,8,Typ,0,NA,Attchd,2006,RFn,2,429,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,5,2006,New,Partial,172785 +494,20,RL,70,7931,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,6,1960,1960,Gable,CompShg,BrkFace,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,BLQ,374,LwQ,532,363,1269,GasA,TA,Y,FuseA,1269,0,0,1269,0,0,1,1,3,1,TA,6,Typ,1,Fa,Detchd,1964,Unf,1,308,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,6,2008,WD,Normal,155000 +495,30,RM,50,5784,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Artery,Norm,1Fam,1Story,5,8,1938,1996,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,BrkTil,Fa,TA,No,Unf,0,Unf,0,190,190,GasA,Gd,Y,FuseA,886,0,0,886,0,0,1,0,2,1,TA,4,Typ,0,NA,Attchd,1938,Unf,1,273,TA,TA,Y,144,20,80,0,0,0,NA,NA,NA,0,12,2009,WD,Normal,91300 +496,30,C (all),60,7879,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,IDOTRR,Norm,Norm,1Fam,1Story,4,5,1920,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,Rec,495,Unf,0,225,720,GasA,TA,N,FuseA,720,0,0,720,0,0,1,0,2,1,TA,4,Typ,0,NA,NA,NA,NA,0,0,NA,NA,N,0,523,115,0,0,0,NA,GdWo,NA,0,11,2009,WD,Abnorml,34900 +497,20,RL,NA,12692,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NoRidge,Norm,Norm,1Fam,1Story,8,5,1992,1993,Hip,CompShg,BrkFace,BrkFace,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,1231,Unf,0,1969,3200,GasA,Ex,Y,SBrkr,3228,0,0,3228,1,0,3,0,4,1,Gd,10,Typ,1,Gd,Attchd,1992,RFn,2,546,TA,TA,Y,264,75,291,0,0,0,NA,NA,NA,0,5,2007,WD,Normal,430000 +498,50,RL,60,9120,Pave,Pave,Reg,Lvl,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,1.5Fin,7,6,1925,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,Gd,PConc,TA,TA,No,Rec,329,Unf,0,697,1026,GasA,Ex,Y,SBrkr,1133,687,0,1820,1,0,2,0,4,1,TA,8,Typ,0,NA,Detchd,1925,Unf,1,240,TA,TA,N,0,100,0,0,0,0,NA,GdPrv,NA,0,6,2008,WD,Normal,184000 +499,20,RL,65,7800,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,1Story,5,7,1967,2004,Hip,CompShg,HdBoard,HdBoard,BrkFace,89,TA,TA,PConc,TA,TA,No,ALQ,450,Unf,0,414,864,GasA,Ex,Y,SBrkr,899,0,0,899,0,0,1,0,3,1,Gd,5,Typ,0,NA,Attchd,1967,Fin,1,288,TA,TA,Y,64,0,0,0,0,0,NA,MnPrv,NA,0,6,2009,WD,Normal,130000 +500,20,RL,70,7535,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,7,1958,1985,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,BLQ,111,LwQ,279,522,912,GasA,Fa,Y,SBrkr,912,0,0,912,0,1,1,0,2,1,TA,5,Typ,0,NA,Attchd,1958,Fin,1,297,TA,TA,Y,12,285,0,0,0,0,NA,MnWw,Shed,480,6,2007,WD,Normal,120000 +501,160,RM,21,1890,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrDale,Norm,Norm,Twnhs,2Story,6,5,1973,1973,Gable,CompShg,HdBoard,HdBoard,BrkFace,285,TA,TA,CBlock,TA,TA,No,BLQ,356,Unf,0,316,672,GasA,TA,Y,SBrkr,672,546,0,1218,0,0,1,1,3,1,TA,7,Typ,0,NA,Detchd,1973,Unf,1,264,TA,TA,Y,144,28,0,0,0,0,NA,NA,NA,0,5,2007,WD,Normal,113000 +502,60,FV,75,9803,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,2Story,7,5,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,400,Unf,0,466,866,GasA,Gd,Y,SBrkr,866,902,0,1768,0,0,2,1,3,1,Gd,7,Typ,0,NA,Attchd,2005,RFn,2,603,TA,TA,Y,0,108,0,0,0,0,NA,NA,NA,0,2,2008,WD,Normal,226700 +503,20,RL,70,9170,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,Edwards,Feedr,Norm,1Fam,1Story,5,7,1965,1965,Hip,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,ALQ,698,GLQ,96,420,1214,GasA,Ex,Y,SBrkr,1214,0,0,1214,1,0,1,0,2,1,TA,6,Typ,0,NA,Detchd,1965,Unf,2,461,Fa,Fa,Y,0,0,184,0,0,0,NA,GdPrv,Shed,400,4,2007,WD,Normal,140000 +504,20,RL,100,15602,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Crawfor,Norm,Norm,1Fam,1Story,7,8,1959,1997,Gable,CompShg,BrkFace,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,ALQ,1247,Unf,0,254,1501,GasA,TA,Y,SBrkr,1801,0,0,1801,1,0,2,0,1,1,TA,6,Typ,2,TA,Attchd,1959,Fin,2,484,TA,TA,Y,0,54,0,0,161,0,NA,GdWo,NA,0,3,2010,WD,Normal,289000 +505,160,RL,24,2308,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NPkVill,Norm,Norm,TwnhsE,2Story,6,5,1974,1974,Gable,CompShg,Plywood,Brk Cmn,None,0,TA,TA,CBlock,TA,TA,No,ALQ,257,Rec,495,103,855,GasA,TA,Y,SBrkr,855,467,0,1322,0,1,2,1,3,1,TA,6,Typ,1,Fa,Attchd,1974,Unf,2,440,TA,TA,Y,260,0,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,147000 +506,90,RM,60,7596,Pave,Grvl,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Artery,Norm,Duplex,2Story,5,5,1952,1952,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,360,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,960,960,GasA,Gd,Y,SBrkr,960,1000,0,1960,0,0,2,0,4,2,TA,10,Typ,0,NA,Detchd,1952,Unf,2,400,TA,TA,N,0,0,0,0,0,0,NA,NA,NA,0,7,2009,COD,Normal,124500 +507,60,RL,80,9554,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,SawyerW,Norm,Norm,1Fam,2Story,8,5,1993,1994,Gable,CompShg,VinylSd,VinylSd,BrkFace,125,Gd,TA,PConc,Gd,TA,No,GLQ,380,Unf,0,397,777,GasA,Ex,Y,SBrkr,1065,846,0,1911,0,0,2,1,3,1,Gd,8,Typ,1,TA,Attchd,1993,RFn,2,471,TA,TA,Y,182,81,0,0,0,0,NA,NA,NA,0,9,2006,WD,Normal,215000 +508,20,FV,75,7862,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,1Story,6,5,2009,2009,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,27,Unf,0,1191,1218,GasA,Ex,Y,SBrkr,1218,0,0,1218,0,0,2,0,2,1,Gd,4,Typ,0,NA,Attchd,2009,Fin,2,676,TA,TA,Y,0,102,0,0,0,0,NA,NA,NA,0,9,2009,New,Partial,208300 +509,70,RM,60,9600,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,2Story,7,9,1928,2005,Gambrel,CompShg,MetalSd,MetalSd,None,0,TA,Ex,BrkTil,TA,TA,No,Rec,141,Unf,0,548,689,GasA,Ex,Y,SBrkr,689,689,0,1378,0,0,2,0,3,1,Gd,7,Typ,1,Gd,Detchd,1928,Unf,2,360,TA,TA,N,0,0,116,0,0,0,NA,NA,NA,0,10,2008,WD,Normal,161000 +510,20,RL,80,9600,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,6,1959,1959,Gable,CompShg,MetalSd,MetalSd,BrkFace,132,TA,TA,CBlock,TA,TA,No,ALQ,991,Unf,0,50,1041,GasA,Ex,Y,SBrkr,1041,0,0,1041,1,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1959,RFn,1,270,TA,TA,Y,224,88,0,0,0,0,NA,MnPrv,NA,0,7,2009,WD,Normal,124500 +511,20,RL,75,14559,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,7,1951,2000,Hip,CompShg,Wd Sdng,Wd Sdng,BrkCmn,70,Gd,TA,CBlock,TA,TA,No,BLQ,650,Rec,180,178,1008,GasA,Ex,Y,SBrkr,1363,0,0,1363,1,0,1,0,2,1,TA,6,Min1,2,TA,CarPort,1951,Unf,1,288,TA,TA,Y,324,42,0,0,168,0,NA,NA,Shed,2000,6,2009,WD,Normal,164900 +512,120,RL,40,6792,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,TwnhsE,1Story,7,5,2005,2006,Gable,CompShg,VinylSd,VinylSd,Stone,94,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1368,1368,GasA,Ex,Y,SBrkr,1368,0,0,1368,0,0,2,0,2,1,Gd,6,Typ,1,Gd,Attchd,2005,RFn,2,474,TA,TA,Y,132,35,0,0,0,0,NA,NA,NA,0,3,2006,New,Partial,202665 +513,20,RL,70,9100,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NAmes,Feedr,Norm,1Fam,1Story,5,5,1958,1958,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,CBlock,TA,TA,No,BLQ,521,LwQ,174,169,864,GasA,TA,Y,SBrkr,864,0,0,864,1,0,1,0,3,1,TA,5,Typ,0,NA,Detchd,1964,Unf,2,624,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,129900 +514,20,RL,71,9187,Pave,NA,Reg,Bnk,AllPub,Corner,Gtl,Mitchel,Norm,Norm,1Fam,1Story,6,5,1983,1983,Gable,CompShg,VinylSd,VinylSd,None,0,TA,Gd,PConc,TA,TA,No,ALQ,336,Unf,0,748,1084,GasA,TA,Y,SBrkr,1080,0,0,1080,0,0,1,1,3,1,TA,5,Typ,0,NA,Attchd,1983,Unf,2,484,TA,TA,Y,120,0,158,0,0,0,NA,NA,NA,0,6,2007,WD,Normal,134000 +515,45,RL,55,10594,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Crawfor,Norm,Norm,1Fam,1.5Unf,5,5,1926,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,768,768,Grav,Fa,N,SBrkr,789,0,0,789,0,0,1,0,2,1,TA,5,Typ,0,NA,Detchd,1926,Unf,1,200,Po,Po,Y,0,0,112,0,0,0,NA,MnPrv,NA,0,6,2007,WD,Normal,96500 +516,20,RL,94,12220,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,1Story,10,5,2009,2009,Hip,CompShg,CemntBd,CmentBd,BrkFace,305,Ex,TA,CBlock,Ex,TA,No,GLQ,1436,Unf,0,570,2006,GasA,Ex,Y,SBrkr,2020,0,0,2020,1,0,2,1,3,1,Ex,9,Typ,1,Gd,Attchd,2009,Fin,3,900,TA,TA,Y,156,54,0,0,0,0,NA,NA,NA,0,9,2009,New,Partial,402861 +517,80,RL,NA,10448,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NWAmes,Norm,Norm,1Fam,SLvl,6,6,1972,1972,Gable,CompShg,HdBoard,HdBoard,BrkFace,333,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,689,689,GasA,TA,Y,SBrkr,1378,741,0,2119,0,0,2,1,3,1,TA,7,Typ,1,TA,Attchd,1972,RFn,2,583,TA,TA,Y,0,104,0,0,0,0,NA,GdPrv,NA,0,8,2009,COD,Abnorml,158000 +518,60,RL,79,10208,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NoRidge,Norm,Norm,1Fam,2Story,7,5,1996,1997,Gable,CompShg,VinylSd,VinylSd,BrkFace,921,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1264,1264,GasA,Ex,Y,SBrkr,1277,1067,0,2344,0,0,2,1,3,1,Gd,7,Typ,1,TA,Attchd,1996,RFn,3,889,TA,TA,Y,220,0,0,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,265000 +519,60,RL,NA,9531,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,CollgCr,Norm,Norm,1Fam,2Story,6,5,1998,1998,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,Mn,GLQ,706,Unf,0,88,794,GasA,Ex,Y,SBrkr,882,914,0,1796,1,0,2,1,3,1,TA,7,Typ,0,NA,Attchd,1998,RFn,2,546,TA,TA,Y,0,36,0,0,0,0,NA,MnPrv,NA,0,5,2007,WD,Normal,211000 +520,70,RL,53,10918,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Crawfor,Norm,Norm,1Fam,2Story,7,9,1926,2004,Gambrel,CompShg,MetalSd,MetalSd,None,0,Gd,TA,BrkTil,Gd,TA,No,Unf,0,Unf,0,1276,1276,GasA,Ex,Y,SBrkr,1276,804,0,2080,0,0,1,1,3,1,Gd,9,Typ,2,Gd,Detchd,1926,Unf,1,282,TA,TA,Y,0,0,0,0,145,0,NA,MnPrv,NA,0,6,2009,WD,Normal,234000 +521,190,RL,60,10800,Pave,Grvl,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,2fmCon,2Story,4,7,1900,2000,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,BrkTil,NA,NA,NA,NA,0,NA,0,0,0,GasA,TA,N,FuseA,694,600,0,1294,0,0,2,0,3,2,TA,7,Typ,0,NA,NA,NA,NA,0,0,NA,NA,N,220,114,210,0,0,0,NA,NA,NA,0,8,2008,WD,Normal,106250 +522,20,RL,90,11988,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NAmes,Feedr,Norm,1Fam,1Story,6,6,1957,1957,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,CBlock,TA,TA,No,Rec,777,Unf,0,467,1244,GasA,Ex,Y,FuseA,1244,0,0,1244,0,0,1,1,3,1,TA,6,Typ,2,Gd,Attchd,1957,Unf,1,336,TA,TA,Y,0,40,0,0,0,0,NA,NA,NA,0,5,2007,WD,Normal,150000 +523,50,RM,50,5000,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,BrkSide,Feedr,Norm,1Fam,1.5Fin,6,7,1947,1950,Gable,CompShg,CemntBd,CmentBd,None,0,TA,Gd,CBlock,TA,TA,No,ALQ,399,Unf,0,605,1004,GasA,Ex,Y,SBrkr,1004,660,0,1664,0,0,2,0,3,1,TA,7,Typ,2,Gd,Detchd,1950,Unf,2,420,TA,TA,Y,0,24,36,0,0,0,NA,NA,NA,0,10,2006,WD,Normal,159000 +524,60,RL,130,40094,Pave,NA,IR1,Bnk,AllPub,Inside,Gtl,Edwards,PosN,PosN,1Fam,2Story,10,5,2007,2008,Hip,CompShg,CemntBd,CmentBd,Stone,762,Ex,TA,PConc,Ex,TA,Gd,GLQ,2260,Unf,0,878,3138,GasA,Ex,Y,SBrkr,3138,1538,0,4676,1,0,3,1,3,1,Ex,11,Typ,1,Gd,BuiltIn,2007,Fin,3,884,TA,TA,Y,208,406,0,0,0,0,NA,NA,NA,0,10,2007,New,Partial,184750 +525,60,RL,95,11787,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NoRidge,Norm,Norm,1Fam,2Story,7,5,1996,1997,Gable,CompShg,VinylSd,VinylSd,BrkFace,594,Gd,TA,PConc,Gd,TA,No,GLQ,719,Unf,0,660,1379,GasA,Ex,Y,SBrkr,1383,1015,0,2398,1,0,2,1,3,1,Gd,8,Typ,1,TA,Attchd,1996,Fin,3,834,TA,TA,Y,239,60,0,0,0,0,NA,NA,NA,0,8,2007,WD,Normal,315750 +526,20,FV,62,7500,Pave,Pave,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,1Story,7,5,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1257,1257,GasA,Ex,Y,SBrkr,1266,0,0,1266,0,0,2,0,3,1,Gd,6,Typ,1,TA,Attchd,2005,Unf,2,453,TA,TA,Y,38,144,0,0,0,0,NA,NA,NA,0,4,2006,WD,Normal,176000 +527,20,RL,70,13300,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,7,1956,2000,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,Gd,TA,No,Rec,377,Unf,0,551,928,GasA,TA,Y,SBrkr,928,0,0,928,0,0,1,0,2,1,TA,4,Typ,0,NA,Attchd,1956,Unf,1,252,TA,TA,Y,261,0,156,0,0,0,NA,NA,NA,0,6,2007,WD,Normal,132000 +528,60,RL,67,14948,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,2Story,9,5,2008,2008,Hip,CompShg,VinylSd,VinylSd,Stone,268,Ex,TA,PConc,Ex,TA,Av,GLQ,1330,Unf,0,122,1452,GasA,Ex,Y,SBrkr,1476,1237,0,2713,1,0,2,1,3,1,Ex,11,Typ,1,Gd,Attchd,2008,Fin,3,858,TA,TA,Y,126,66,0,0,0,0,NA,NA,NA,0,11,2008,New,Partial,446261 +529,30,RL,58,9098,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1Story,4,7,1920,2002,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,TA,TA,Mn,ALQ,348,Unf,0,180,528,GasA,Ex,Y,SBrkr,605,0,0,605,1,0,1,0,2,1,TA,5,Typ,0,NA,NA,NA,NA,0,0,NA,NA,N,0,0,144,0,0,0,NA,NA,NA,0,7,2007,WD,Normal,86000 +530,20,RL,NA,32668,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Crawfor,Norm,Norm,1Fam,1Story,6,3,1957,1975,Hip,CompShg,Wd Sdng,Stone,NA,NA,Gd,TA,PConc,TA,TA,No,Rec,1219,Unf,0,816,2035,GasA,TA,Y,SBrkr,2515,0,0,2515,1,0,3,0,4,2,TA,9,Maj1,2,TA,Attchd,1975,RFn,2,484,TA,TA,Y,0,0,200,0,0,0,NA,NA,NA,0,3,2007,WD,Alloca,200624 +531,80,RL,85,10200,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Timber,Norm,Norm,1Fam,SLvl,6,5,1988,1989,Gable,CompShg,HdBoard,HdBoard,BrkFace,219,Gd,TA,CBlock,Gd,TA,Av,GLQ,783,Unf,0,678,1461,GasA,Ex,Y,SBrkr,1509,0,0,1509,1,0,2,0,3,1,Gd,5,Typ,1,Fa,Attchd,1988,RFn,2,600,TA,TA,Y,224,0,0,0,0,0,NA,NA,NA,0,8,2008,WD,Abnorml,175000 +532,70,RM,60,6155,Pave,NA,IR1,Lvl,AllPub,FR3,Gtl,BrkSide,RRNn,Feedr,1Fam,2Story,6,8,1920,1999,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,Fa,Fa,Mn,Unf,0,Unf,0,611,611,GasA,Ex,Y,SBrkr,751,611,0,1362,0,0,2,0,3,1,TA,6,Typ,0,NA,Detchd,1920,Fin,2,502,TA,Fa,Y,0,0,84,0,0,0,NA,NA,NA,0,6,2008,WD,Normal,128000 +533,20,RL,60,7200,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,7,1955,2007,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,Slab,NA,NA,NA,NA,0,NA,0,0,0,GasA,Ex,Y,SBrkr,827,0,0,827,0,0,1,0,2,1,TA,5,Mod,1,Po,Detchd,1967,Unf,1,392,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,4,2010,WD,Normal,107500 +534,20,RL,50,5000,Pave,NA,Reg,Low,AllPub,Inside,Mod,BrkSide,Norm,Norm,1Fam,1Story,1,3,1946,1950,Gable,CompShg,VinylSd,VinylSd,None,0,Fa,Fa,Slab,NA,NA,NA,NA,0,NA,0,0,0,GasA,Fa,N,FuseF,334,0,0,334,0,0,1,0,1,1,Fa,2,Typ,0,NA,NA,NA,NA,0,0,NA,NA,N,0,0,0,0,0,0,NA,NA,NA,0,1,2007,WD,Normal,39300 +535,60,RL,74,9056,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,2Story,8,5,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Ex,Gd,Av,Unf,0,Unf,0,707,707,GasA,Ex,Y,SBrkr,707,707,0,1414,0,0,2,1,3,1,Gd,6,Typ,1,Gd,Attchd,2004,Fin,2,403,TA,TA,Y,100,35,0,0,0,0,NA,NA,NA,0,10,2006,WD,Normal,178000 +536,190,RL,70,7000,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,2fmCon,2Story,5,7,1910,1991,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,Gd,TA,Gd,GLQ,969,Unf,0,148,1117,GasA,TA,Y,SBrkr,820,527,0,1347,1,0,1,0,3,1,TA,5,Typ,0,NA,NA,NA,NA,0,0,NA,NA,N,85,0,148,0,0,0,NA,NA,NA,0,1,2008,WD,Normal,107500 +537,60,RL,57,8924,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,CollgCr,Norm,Norm,1Fam,2Story,7,5,1998,1999,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,Av,Unf,0,Unf,0,880,880,GasA,Ex,Y,SBrkr,880,844,0,1724,0,0,2,1,3,1,Gd,8,Typ,0,NA,Attchd,1998,Fin,2,527,TA,TA,Y,120,155,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,188000 +538,20,RL,NA,12735,Pave,NA,IR1,Lvl,AllPub,FR2,Gtl,NAmes,Norm,Norm,1Fam,1Story,4,5,1972,1972,Hip,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,BLQ,600,Unf,0,264,864,GasA,TA,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,TA,5,Typ,0,NA,Detchd,1980,Unf,2,576,TA,TA,Y,216,0,0,0,0,0,NA,MnWw,NA,0,4,2008,COD,Normal,111250 +539,20,RL,NA,11553,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,1Story,5,5,1968,1968,Hip,CompShg,Plywood,Plywood,BrkFace,188,TA,TA,CBlock,TA,TA,No,BLQ,673,Unf,0,378,1051,GasA,TA,Y,SBrkr,1159,0,0,1159,0,0,1,1,3,1,TA,7,Typ,1,Fa,Attchd,1968,Unf,1,336,TA,TA,Y,466,0,0,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,158000 +540,20,RL,NA,11423,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,8,5,2001,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,479,Gd,TA,PConc,Gd,TA,Av,GLQ,1358,Unf,0,223,1581,GasA,Ex,Y,SBrkr,1601,0,0,1601,1,0,2,0,3,1,Gd,6,Typ,1,TA,Attchd,2001,RFn,2,670,TA,TA,Y,180,0,0,0,0,0,NA,MnPrv,Shed,2000,5,2010,WD,Normal,272000 +541,20,RL,85,14601,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Timber,Norm,Norm,1Fam,1Story,9,5,2006,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,584,Ex,TA,PConc,Ex,TA,Av,GLQ,1260,Unf,0,578,1838,GasA,Ex,Y,SBrkr,1838,0,0,1838,1,0,2,0,2,1,Ex,8,Typ,1,Gd,Attchd,2006,Fin,3,765,TA,TA,Y,270,68,0,0,0,0,NA,NA,NA,0,3,2009,WD,Normal,315000 +542,60,RL,NA,11000,Pave,NA,Reg,Lvl,AllPub,FR2,Gtl,NoRidge,Norm,Norm,1Fam,2Story,8,5,2000,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,72,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,969,969,GasA,Ex,Y,SBrkr,997,1288,0,2285,0,0,2,1,4,1,Gd,8,Typ,1,TA,BuiltIn,2000,Fin,3,648,TA,TA,Y,0,56,0,0,0,0,NA,NA,NA,0,6,2007,WD,Normal,248000 +543,20,RL,78,10140,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,RRAn,Norm,1Fam,1Story,7,5,1998,1999,Hip,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Ex,TA,No,LwQ,144,GLQ,1127,379,1650,GasA,Ex,Y,SBrkr,1680,0,0,1680,1,0,2,0,3,1,Gd,7,Maj1,1,TA,Attchd,1998,Fin,2,583,TA,TA,Y,78,73,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,213250 +544,120,RH,34,4058,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,TwnhsE,SFoyer,7,5,1998,1998,Gable,CompShg,MetalSd,MetalSd,BrkFace,182,TA,TA,PConc,Gd,TA,Av,GLQ,584,LwQ,139,0,723,GasA,Ex,Y,SBrkr,767,0,0,767,1,0,1,0,1,1,TA,4,Typ,0,NA,Attchd,1998,Fin,1,367,TA,TA,Y,120,40,0,0,0,0,NA,NA,NA,0,6,2007,WD,Normal,133000 +545,60,RL,58,17104,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,2Story,7,5,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,Gd,Av,GLQ,554,Unf,0,100,654,GasA,Ex,Y,SBrkr,664,832,0,1496,1,0,2,1,3,1,Gd,7,Typ,1,Gd,Attchd,2006,RFn,2,426,TA,TA,Y,100,24,0,0,0,0,NA,NA,NA,0,9,2006,New,Partial,179665 +546,50,RL,NA,13837,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NWAmes,Norm,Norm,1Fam,1.5Fin,7,5,1988,1988,Gable,CompShg,HdBoard,HdBoard,BrkFace,178,Gd,Gd,PConc,Gd,Gd,No,GLQ,1002,LwQ,202,0,1204,GasA,Gd,Y,SBrkr,1377,806,0,2183,0,0,2,1,4,1,Gd,9,Typ,0,NA,Attchd,1988,Unf,3,786,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,2,2006,WD,Normal,229000 +547,50,RL,70,8737,Pave,NA,IR1,Bnk,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,1.5Fin,6,7,1923,1950,Gable,CompShg,BrkFace,Wd Sdng,None,0,TA,TA,BrkTil,Gd,TA,No,Rec,300,Unf,0,765,1065,GasA,Ex,Y,FuseA,915,720,0,1635,0,0,1,1,3,1,TA,6,Typ,1,Gd,Detchd,1950,Unf,2,440,TA,TA,Y,0,38,0,144,0,0,NA,NA,NA,0,5,2007,WD,Normal,210000 +548,85,RL,54,7244,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Mitchel,Norm,Norm,1Fam,SFoyer,5,7,1970,1970,Gable,CompShg,VinylSd,VinylSd,None,0,TA,Gd,CBlock,Gd,TA,Av,ALQ,619,Unf,0,149,768,GasA,Ex,Y,SBrkr,768,0,0,768,1,0,1,0,2,1,TA,5,Typ,0,NA,Detchd,1987,Unf,2,624,TA,TA,Y,104,0,0,0,0,0,NA,NA,NA,0,4,2007,WD,Normal,129500 +549,20,RM,49,8235,Pave,NA,IR1,HLS,AllPub,Inside,Gtl,OldTown,Feedr,RRNn,1Fam,1Story,5,7,1955,1995,Gable,CompShg,MetalSd,MetalSd,None,0,TA,Gd,CBlock,TA,TA,No,LwQ,180,Rec,645,0,825,GasA,TA,Y,SBrkr,825,0,0,825,1,0,1,0,2,1,TA,4,Typ,0,NA,Detchd,1963,RFn,2,720,TA,TA,Y,140,50,0,0,0,0,NA,MnPrv,NA,0,6,2008,WD,Normal,125000 +550,60,FV,75,9375,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,2Story,7,5,2003,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,912,912,GasA,Ex,Y,SBrkr,912,1182,0,2094,0,0,2,1,4,1,Gd,8,Typ,1,Gd,BuiltIn,2003,Fin,2,615,TA,TA,Y,182,182,0,0,0,0,NA,NA,NA,0,11,2009,WD,Normal,263000 +551,120,RL,53,4043,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NPkVill,Norm,Norm,TwnhsE,1Story,6,6,1977,1977,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,CBlock,Gd,TA,No,ALQ,559,Unf,0,510,1069,GasA,TA,Y,SBrkr,1069,0,0,1069,0,0,2,0,2,1,TA,4,Typ,0,NA,Attchd,1977,RFn,2,440,TA,TA,Y,0,55,0,0,200,0,NA,NA,NA,0,10,2008,COD,Abnorml,140000 +552,20,RM,50,6000,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,1Story,5,6,1957,1957,Hip,CompShg,BrkFace,BrkFace,None,0,TA,TA,CBlock,TA,TA,No,Rec,308,Unf,0,620,928,GasA,Gd,Y,FuseA,928,0,0,928,0,0,1,0,3,1,TA,5,Typ,0,NA,Attchd,1957,Fin,1,288,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,6,2008,WD,Normal,112500 +553,20,RL,87,11146,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,1Story,8,5,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,250,Gd,TA,PConc,Ex,TA,Av,Unf,0,Unf,0,1709,1709,GasA,Ex,Y,SBrkr,1717,0,0,1717,0,0,2,0,3,1,Gd,7,Typ,1,Gd,Attchd,2006,RFn,3,908,TA,TA,Y,169,39,0,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,255500 +554,20,RL,67,8777,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Feedr,Norm,1Fam,1Story,4,5,1949,2003,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,CBlock,NA,NA,NA,NA,0,NA,0,0,0,GasA,Ex,Y,SBrkr,1126,0,0,1126,0,0,2,0,2,1,Gd,5,Typ,0,NA,Detchd,2002,Fin,2,520,TA,TA,N,0,96,0,0,0,0,NA,MnPrv,NA,0,5,2009,WD,Normal,108000 +555,60,RL,85,10625,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,2Story,7,5,2003,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,292,Gd,TA,PConc,Gd,TA,No,GLQ,866,Unf,0,132,998,GasA,Ex,Y,SBrkr,1006,1040,0,2046,1,0,2,1,3,1,Gd,8,Typ,1,Gd,BuiltIn,2003,RFn,3,871,TA,TA,Y,320,62,0,0,0,0,NA,NA,NA,0,8,2008,WD,Normal,284000 +556,45,RM,58,6380,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,1.5Unf,5,6,1922,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,BrkTil,TA,Fa,No,Unf,0,Unf,0,993,993,GasA,TA,Y,FuseA,1048,0,0,1048,0,0,1,0,2,1,TA,5,Typ,1,Gd,Detchd,1922,Unf,1,280,TA,TA,Y,0,0,116,0,0,0,NA,NA,NA,0,8,2006,WD,Normal,113000 +557,20,RL,69,14850,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,5,1957,1957,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,Rec,895,Unf,0,197,1092,GasA,TA,Y,FuseA,1092,0,0,1092,1,0,1,0,2,1,TA,6,Typ,1,TA,Attchd,1957,Fin,1,299,TA,TA,Y,268,0,0,0,122,0,NA,MnWw,NA,0,5,2006,WD,Normal,141000 +558,50,C (all),60,11040,Pave,NA,Reg,Low,AllPub,Inside,Mod,IDOTRR,Norm,Norm,1Fam,1.5Fin,4,6,1920,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,Rec,637,Unf,0,0,637,GasA,Gd,Y,SBrkr,897,439,0,1336,0,0,1,1,3,1,TA,7,Typ,0,NA,CarPort,1994,Unf,1,570,TA,TA,Y,0,47,120,0,0,0,NA,NA,NA,0,9,2006,COD,Normal,108000 +559,60,RL,57,21872,Pave,NA,IR2,HLS,AllPub,FR2,Gtl,Gilbert,Norm,Norm,1Fam,2Story,7,5,1996,1997,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,PConc,Gd,TA,Gd,GLQ,604,Unf,0,125,729,GasA,Ex,Y,SBrkr,729,717,0,1446,0,1,2,1,3,1,TA,6,Typ,1,TA,Attchd,1996,Unf,2,406,TA,TA,Y,264,22,0,0,0,0,NA,NA,NA,0,8,2008,WD,Normal,175000 +560,120,RL,NA,3196,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Blmngtn,Norm,Norm,TwnhsE,1Story,7,5,2003,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,18,Gd,TA,PConc,Gd,TA,Gd,Unf,0,Unf,0,1374,1374,GasA,Ex,Y,SBrkr,1557,0,0,1557,0,0,2,0,2,1,Gd,7,Typ,1,TA,Attchd,2003,Fin,2,420,TA,TA,Y,143,20,0,0,0,0,NA,NA,NA,0,10,2006,WD,Normal,234000 +561,20,RL,NA,11341,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,1Story,5,6,1957,1996,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,180,TA,TA,CBlock,Gd,TA,No,ALQ,1302,Unf,0,90,1392,GasA,TA,Y,SBrkr,1392,0,0,1392,1,0,1,1,3,1,TA,5,Mod,1,Gd,Detchd,1957,Unf,2,528,TA,TA,Y,0,0,0,0,95,0,NA,NA,NA,0,5,2010,WD,Normal,121500 +562,20,RL,77,10010,Pave,NA,Reg,Lvl,AllPub,Inside,Mod,Mitchel,Norm,Norm,1Fam,1Story,5,5,1974,1975,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,Gd,TA,Av,ALQ,1071,LwQ,123,195,1389,GasA,Gd,Y,SBrkr,1389,0,0,1389,1,0,1,0,2,1,TA,6,Typ,1,TA,Attchd,1975,RFn,2,418,TA,TA,Y,240,38,0,0,0,0,NA,NA,NA,0,4,2006,WD,Normal,170000 +563,30,RL,63,13907,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1Story,5,6,1940,1969,Gable,CompShg,WdShing,Wd Shng,None,0,TA,TA,CBlock,TA,TA,No,BLQ,290,Unf,0,706,996,GasA,Ex,Y,SBrkr,996,0,0,996,1,0,1,0,3,1,TA,6,Typ,1,Gd,NA,NA,NA,0,0,NA,NA,Y,144,0,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,108000 +564,50,RL,66,21780,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1.5Fin,6,7,1918,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,Gd,TA,Mn,Unf,0,Unf,0,1163,1163,GasA,Ex,Y,SBrkr,1163,511,0,1674,0,0,2,0,4,1,TA,8,Typ,1,Gd,Detchd,1955,Fin,2,396,TA,TA,N,72,36,0,0,144,0,NA,NA,NA,0,7,2008,WD,Normal,185000 +565,60,RL,NA,13346,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,NoRidge,Norm,Norm,1Fam,2Story,7,5,1992,2000,Gable,CompShg,HdBoard,HdBoard,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,728,Unf,0,367,1095,GasA,Ex,Y,SBrkr,1166,1129,0,2295,1,0,2,1,4,1,Gd,9,Typ,1,TA,Attchd,1992,RFn,2,590,TA,TA,Y,0,40,0,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,268000 +566,70,RL,66,6858,Pave,NA,Reg,Bnk,AllPub,Corner,Gtl,SWISU,Norm,Norm,1Fam,2Story,6,4,1915,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,PConc,Gd,TA,No,Unf,0,Unf,0,806,806,GasA,TA,N,FuseF,841,806,0,1647,1,0,1,1,4,1,Fa,6,Typ,0,NA,Detchd,1920,Unf,1,216,TA,TA,Y,0,66,136,0,0,0,NA,NA,NA,0,5,2010,WD,Normal,128000 +567,60,RL,77,11198,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,StoneBr,Norm,Norm,1Fam,2Story,9,5,2005,2007,Hip,CompShg,VinylSd,VinylSd,BrkFace,245,Gd,TA,PConc,Gd,Gd,No,Unf,0,Unf,0,1122,1122,GasA,Ex,Y,SBrkr,1134,1370,0,2504,0,0,2,1,4,1,Ex,11,Typ,1,Gd,BuiltIn,2005,Fin,3,656,TA,TA,Y,144,39,0,0,0,0,NA,NA,NA,0,6,2008,WD,Normal,325000 +568,20,RL,70,10171,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,1Story,7,5,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,168,Gd,TA,PConc,Gd,TA,No,GLQ,2,Unf,0,1515,1517,GasA,Ex,Y,SBrkr,1535,0,0,1535,0,0,2,0,3,1,Gd,7,Typ,0,NA,Attchd,2004,RFn,2,532,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,3,2010,WD,Normal,214000 +569,50,RL,79,12327,Pave,NA,IR1,Low,AllPub,Inside,Mod,SawyerW,Norm,Norm,1Fam,1.5Fin,8,8,1983,2009,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Gd,TA,CBlock,Gd,TA,Gd,GLQ,1441,Unf,0,55,1496,GasA,Ex,Y,SBrkr,1496,636,0,2132,1,0,1,1,1,1,Gd,5,Min2,1,Gd,BuiltIn,1983,Fin,2,612,Gd,TA,Y,349,40,0,0,0,0,NA,NA,NA,0,9,2009,WD,Normal,316600 +570,90,RL,NA,7032,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,Duplex,SFoyer,5,5,1979,1979,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,Gd,TA,Gd,GLQ,943,Unf,0,0,943,GasA,TA,Y,SBrkr,943,0,0,943,1,0,1,0,2,1,TA,4,Typ,2,TA,Detchd,1979,Unf,2,600,TA,TA,Y,42,0,0,0,0,0,NA,NA,NA,0,12,2006,WD,Normal,135960 +571,90,RL,74,13101,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,Duplex,1Story,5,5,1965,1965,Gable,CompShg,HdBoard,HdBoard,BrkFace,108,TA,TA,CBlock,TA,TA,No,LwQ,231,Unf,0,1497,1728,GasA,TA,Y,SBrkr,1728,0,0,1728,0,0,2,0,6,2,TA,10,Typ,0,NA,Detchd,1987,Unf,2,576,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,11,2008,WD,Normal,142600 +572,20,RL,60,7332,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,6,1959,1959,Gable,CompShg,WdShing,Wd Shng,BrkFace,207,TA,TA,CBlock,TA,TA,No,BLQ,414,Unf,0,450,864,GasA,Ex,Y,SBrkr,864,0,0,864,1,0,1,0,2,1,Gd,4,Typ,0,NA,Attchd,1959,Unf,1,288,TA,TA,Y,168,0,0,0,0,0,NA,NA,NA,0,10,2006,WD,Abnorml,120000 +573,60,RL,83,13159,Pave,NA,IR1,HLS,AllPub,Corner,Gtl,Timber,Norm,Norm,1Fam,2Story,7,5,2009,2009,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Ex,TA,Av,Unf,0,Unf,0,846,846,GasA,Gd,Y,SBrkr,846,846,0,1692,0,0,2,1,3,1,Gd,6,Typ,0,NA,Attchd,2009,RFn,2,650,TA,TA,Y,208,114,0,0,0,0,NA,NA,NA,0,7,2009,New,Partial,224500 +574,80,RL,76,9967,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,SLvl,7,5,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,No,Unf,0,Unf,0,384,384,GasA,Ex,Y,SBrkr,774,656,0,1430,0,0,2,1,3,1,TA,8,Typ,1,TA,BuiltIn,2000,RFn,2,400,TA,TA,Y,100,0,0,0,0,0,NA,NA,NA,0,12,2007,WD,Normal,170000 +575,80,RL,70,10500,Pave,NA,Reg,Lvl,AllPub,FR2,Gtl,NAmes,Norm,Norm,1Fam,SLvl,5,7,1971,2005,Gambrel,CompShg,MetalSd,AsphShn,BrkFace,82,TA,TA,CBlock,TA,TA,Av,ALQ,349,Unf,0,23,372,GasA,TA,Y,SBrkr,576,533,0,1109,0,1,1,0,3,1,TA,5,Typ,0,NA,BuiltIn,1971,Unf,1,288,TA,TA,Y,35,0,0,0,0,0,NA,GdWo,NA,0,12,2007,WD,Normal,139000 +576,50,RL,80,8480,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1.5Fin,5,5,1947,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,Rec,442,Unf,0,390,832,GasA,TA,Y,SBrkr,832,384,0,1216,0,0,1,0,2,1,TA,6,Typ,0,NA,Detchd,1947,Unf,1,336,TA,TA,Y,158,0,102,0,0,0,NA,NA,NA,0,10,2008,COD,Abnorml,118500 +577,50,RL,52,6292,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SWISU,Norm,Norm,1Fam,1.5Fin,7,7,1928,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,861,861,GasA,Gd,Y,SBrkr,877,600,0,1477,0,1,2,0,3,1,TA,6,Typ,1,Gd,Detchd,1928,Unf,1,216,TA,TA,Y,0,50,0,0,0,0,NA,NA,NA,0,8,2009,WD,Normal,145000 +578,80,RL,96,11777,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,SLvl,5,6,1966,1966,Gable,CompShg,VinylSd,VinylSd,BrkFace,97,TA,TA,CBlock,TA,TA,Av,LwQ,328,ALQ,551,285,1164,GasA,Ex,Y,SBrkr,1320,0,0,1320,1,0,1,0,3,1,TA,6,Typ,2,Fa,Attchd,1966,RFn,2,564,TA,TA,Y,160,68,240,0,0,0,NA,NA,NA,0,5,2006,WD,Abnorml,164500 +579,160,FV,34,3604,Pave,Pave,Reg,Lvl,AllPub,Corner,Gtl,Somerst,Norm,Norm,TwnhsE,2Story,7,5,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,689,689,GasA,Ex,Y,SBrkr,703,689,0,1392,0,0,2,0,2,1,Gd,5,Typ,0,NA,Detchd,2007,Unf,2,540,TA,TA,Y,0,102,0,0,0,0,NA,NA,NA,0,2,2008,WD,Abnorml,146000 +580,50,RM,81,12150,Pave,Grvl,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,1.5Fin,5,5,1954,1954,Gable,CompShg,MetalSd,MetalSd,BrkFace,335,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,1050,1050,GasA,Ex,N,FuseF,1050,745,0,1795,0,0,2,0,4,1,TA,7,Typ,0,NA,Attchd,1954,Unf,1,352,Fa,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,11,2008,WD,Normal,131500 +581,20,RL,NA,14585,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,6,1960,1987,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,85,TA,TA,CBlock,TA,TA,No,BLQ,594,Rec,219,331,1144,GasA,Ex,Y,SBrkr,1429,0,0,1429,0,1,1,0,3,1,Gd,7,Typ,2,Gd,Attchd,1960,Unf,2,572,TA,TA,Y,216,110,0,0,0,0,NA,NA,NA,0,6,2007,WD,Normal,181900 +582,20,RL,98,12704,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,1Story,8,5,2008,2009,Hip,CompShg,VinylSd,VinylSd,BrkFace,306,Ex,TA,PConc,Ex,TA,No,Unf,0,Unf,0,2042,2042,GasA,Ex,Y,SBrkr,2042,0,0,2042,0,0,2,1,3,1,Ex,8,Typ,1,Gd,Attchd,2009,RFn,3,1390,TA,TA,Y,0,90,0,0,0,0,NA,NA,NA,0,8,2009,New,Partial,253293 +583,90,RL,81,11841,Grvl,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Duplex,SFoyer,6,5,1990,1990,Gable,CompShg,HdBoard,HdBoard,BrkFace,104,TA,Gd,CBlock,Gd,TA,Av,GLQ,816,Unf,0,0,816,GasA,TA,Y,SBrkr,816,0,0,816,1,0,1,0,3,1,TA,5,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,0,32,0,0,0,0,NA,NA,NA,0,5,2007,WD,Normal,118500 +584,75,RM,75,13500,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Artery,PosA,1Fam,2.5Unf,10,9,1893,2000,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Ex,Ex,BrkTil,TA,TA,No,Unf,0,Unf,0,1237,1237,GasA,Gd,Y,SBrkr,1521,1254,0,2775,0,0,3,1,3,1,Gd,9,Typ,1,Gd,Detchd,1988,Unf,2,880,Gd,TA,Y,105,502,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,325000 +585,50,RM,51,6120,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,1.5Fin,4,7,1935,1995,Gable,CompShg,AsbShng,AsbShng,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,884,884,GasA,Ex,Y,SBrkr,989,584,0,1573,0,0,1,0,3,1,Gd,6,Typ,0,NA,Detchd,1935,Unf,1,240,TA,TA,Y,0,0,54,0,120,0,NA,NA,NA,0,7,2009,WD,Normal,133000 +586,20,RL,88,11443,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Timber,Norm,Norm,1Fam,1Story,8,5,2005,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,208,Gd,TA,PConc,Ex,TA,Gd,GLQ,1460,Unf,0,408,1868,GasA,Ex,Y,SBrkr,2028,0,0,2028,1,0,2,0,2,1,Gd,7,Typ,2,Gd,Attchd,2005,RFn,3,880,TA,TA,Y,326,66,0,0,0,0,NA,NA,NA,0,3,2006,New,Partial,369900 +587,30,RL,55,10267,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrkSide,RRAn,Norm,1Fam,1Story,6,7,1918,2000,Gable,CompShg,Stucco,Wd Shng,None,0,TA,Gd,BrkTil,TA,Gd,Mn,Rec,210,ALQ,606,0,816,GasA,Ex,Y,SBrkr,838,0,0,838,1,0,1,0,2,1,Fa,5,Typ,0,NA,Detchd,1961,Fin,1,275,TA,TA,N,0,0,112,0,0,0,NA,MnWw,NA,0,5,2008,WD,Normal,130000 +588,85,RL,74,8740,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,SFoyer,5,6,1982,1982,Hip,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,TA,TA,Av,ALQ,672,Unf,0,168,840,GasA,TA,Y,SBrkr,860,0,0,860,1,0,1,0,2,1,TA,4,Typ,0,NA,Detchd,1996,Unf,2,528,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,137000 +589,20,RL,65,25095,Pave,NA,IR1,Low,AllPub,Inside,Sev,ClearCr,Norm,Norm,1Fam,1Story,5,8,1968,2003,Flat,Tar&Grv,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,Gd,GLQ,1324,Unf,0,113,1437,GasA,Ex,Y,SBrkr,1473,0,0,1473,2,0,1,0,1,1,Ex,5,Typ,2,Gd,Attchd,1968,Unf,1,452,TA,TA,Y,0,48,0,0,60,0,NA,NA,NA,0,6,2009,WD,Partial,143000 +590,40,RM,50,9100,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrkSide,RRAn,Feedr,1Fam,1Story,5,6,1930,1960,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,742,742,GasA,TA,Y,FuseA,779,0,156,935,0,0,1,0,2,1,TA,4,Typ,0,NA,Detchd,1988,Unf,1,308,TA,TA,P,0,0,0,0,0,0,NA,NA,Shed,600,8,2008,WD,Normal,79500 +591,60,RL,64,8320,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,7,5,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,490,Unf,0,280,770,GasA,Ex,Y,SBrkr,770,812,0,1582,0,0,2,1,3,1,Gd,6,Typ,0,NA,Attchd,2004,RFn,2,520,TA,TA,Y,0,45,0,0,0,0,NA,NA,NA,0,9,2008,WD,Normal,185900 +592,60,RL,97,13478,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NridgHt,Norm,Norm,1Fam,2Story,10,5,2008,2008,Gable,CompShg,CemntBd,CmentBd,Stone,420,Ex,TA,PConc,Ex,TA,Gd,GLQ,1338,Unf,0,384,1722,GasA,Ex,Y,SBrkr,1728,568,0,2296,1,0,2,1,3,1,Ex,10,Typ,1,Gd,BuiltIn,2008,RFn,3,842,TA,TA,Y,382,274,0,0,0,0,NA,NA,NA,0,6,2009,ConLI,Normal,451950 +593,20,RL,60,6600,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Mitchel,Norm,Norm,1Fam,1Story,5,8,1982,2003,Gable,CompShg,HdBoard,HdBoard,None,0,TA,Gd,PConc,TA,Gd,No,GLQ,816,Unf,0,0,816,GasA,Ex,Y,SBrkr,816,0,0,816,1,0,1,0,2,1,TA,4,Typ,0,NA,Detchd,1985,Fin,2,816,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,6,2008,WD,Normal,138000 +594,120,RM,NA,4435,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,TwnhsE,1Story,6,5,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,170,Gd,TA,PConc,Gd,TA,Av,GLQ,685,Unf,0,163,848,GasA,Ex,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Gd,4,Typ,0,NA,Attchd,2003,Fin,2,420,TA,TA,Y,140,0,0,0,0,0,NA,NA,NA,0,5,2009,WD,Normal,140000 +595,20,RL,88,7990,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,5,6,1975,1975,Hip,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,Gd,TA,No,Unf,0,Unf,0,924,924,GasA,TA,Y,SBrkr,924,0,0,924,0,0,1,0,3,1,TA,5,Typ,0,NA,Detchd,1981,Unf,1,280,TA,TA,Y,0,0,0,0,0,0,NA,MnPrv,NA,0,4,2008,WD,Normal,110000 +596,20,RL,69,11302,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,StoneBr,Norm,Norm,1Fam,1Story,8,5,2005,2006,Gable,CompShg,VinylSd,Other,BrkFace,238,Gd,TA,PConc,Gd,TA,Gd,GLQ,1422,Unf,0,392,1814,GasA,Ex,Y,SBrkr,1826,0,0,1826,1,0,2,0,3,1,Gd,7,Typ,1,TA,Attchd,2005,Fin,3,758,TA,TA,Y,180,75,0,0,120,0,NA,NA,NA,0,8,2006,New,Partial,319000 +597,70,RM,60,3600,Pave,Grvl,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,2Story,6,7,1910,1993,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,684,684,GasA,Ex,N,FuseA,684,684,0,1368,0,0,1,0,3,1,TA,7,Typ,0,NA,Detchd,1930,Unf,1,216,TA,Fa,N,0,158,0,0,0,0,NA,NA,NA,0,10,2006,WD,Normal,114504 +598,120,RL,53,3922,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Blmngtn,Norm,Norm,TwnhsE,1Story,7,5,2006,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,72,Gd,TA,PConc,Ex,TA,Av,Unf,0,Unf,0,1258,1258,GasA,Ex,Y,SBrkr,1402,0,0,1402,0,2,0,2,2,1,Gd,7,Typ,1,Gd,Attchd,2006,Fin,3,648,TA,TA,Y,120,16,0,0,0,0,NA,NA,NA,0,2,2007,New,Partial,194201 +599,20,RL,80,12984,Pave,NA,Reg,Bnk,AllPub,Inside,Gtl,Crawfor,Norm,Norm,1Fam,1Story,5,6,1977,1977,Gable,CompShg,Plywood,Plywood,BrkFace,459,TA,TA,CBlock,Gd,TA,Mn,ALQ,1283,LwQ,147,0,1430,GasA,Ex,Y,SBrkr,1647,0,0,1647,1,0,2,0,3,1,Gd,7,Typ,1,TA,Attchd,1977,Fin,2,621,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,3,2006,WD,Normal,217500 +600,160,RM,24,1950,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Blueste,Norm,Norm,Twnhs,2Story,6,6,1980,1980,Gable,CompShg,MetalSd,MetalSd,None,0,TA,Gd,CBlock,Gd,TA,No,LwQ,81,GLQ,612,23,716,GasA,TA,Y,SBrkr,716,840,0,1556,1,0,2,1,3,1,TA,6,Typ,1,TA,Attchd,1980,Fin,2,452,TA,TA,Y,161,0,0,0,0,0,NA,GdPrv,NA,0,7,2008,COD,Normal,151000 +601,60,RL,74,10927,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,2Story,8,5,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,280,Gd,TA,PConc,Gd,TA,Av,GLQ,546,Unf,0,512,1058,GasA,Ex,Y,SBrkr,1058,846,0,1904,1,0,2,1,3,1,Ex,8,Typ,1,Gd,BuiltIn,2003,Fin,2,736,TA,TA,Y,179,60,0,0,0,0,NA,NA,NA,0,6,2006,WD,Normal,275000 +602,50,RM,50,9000,Pave,NA,Reg,Bnk,AllPub,Inside,Gtl,IDOTRR,Norm,Norm,1Fam,1.5Fin,6,6,1937,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,Gd,PConc,TA,TA,No,Unf,0,Unf,0,780,780,GasA,TA,Y,SBrkr,780,595,0,1375,0,0,1,1,3,1,Gd,6,Typ,1,Gd,Detchd,1979,Unf,1,544,TA,TA,P,0,162,0,0,126,0,NA,NA,NA,0,12,2007,WD,Normal,141000 +603,60,RL,80,10041,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,SawyerW,Norm,Norm,1Fam,2Story,8,5,1992,1992,Gable,CompShg,HdBoard,HdBoard,None,0,Gd,TA,PConc,Gd,TA,Mn,GLQ,789,Unf,0,119,908,GasA,Ex,Y,SBrkr,927,988,0,1915,1,0,2,1,3,1,Gd,8,Typ,1,TA,Attchd,1992,Fin,2,506,TA,TA,Y,120,150,0,0,0,0,NA,NA,NA,0,2,2006,WD,Abnorml,220000 +604,160,FV,30,3182,Pave,Pave,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,TwnhsE,2Story,7,5,2004,2005,Gable,CompShg,MetalSd,MetalSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,600,600,GasA,Ex,Y,SBrkr,600,600,0,1200,0,0,2,1,2,1,Gd,4,Typ,0,NA,Detchd,2004,RFn,2,480,TA,TA,Y,0,172,0,0,0,0,NA,NA,NA,0,6,2010,WD,Normal,151000 +605,20,RL,88,12803,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2002,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,99,Gd,TA,PConc,Gd,TA,Mn,GLQ,922,Unf,0,572,1494,GasA,Ex,Y,SBrkr,1494,0,0,1494,1,0,2,0,3,1,Gd,6,Typ,1,TA,Attchd,2002,RFn,2,530,TA,TA,Y,192,36,0,0,0,0,NA,NA,NA,0,9,2008,WD,Normal,221000 +606,60,RL,85,13600,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,2Story,7,6,1965,1990,Gable,CompShg,HdBoard,HdBoard,BrkFace,176,TA,TA,CBlock,TA,TA,No,BLQ,454,Unf,0,314,768,GasA,TA,Y,SBrkr,1186,800,0,1986,0,0,2,1,3,1,TA,7,Typ,3,Fa,Attchd,1965,Unf,2,486,TA,TA,Y,0,42,0,0,189,0,NA,NA,NA,0,10,2009,WD,Normal,205000 +607,20,RL,82,12464,Pave,NA,IR2,Low,AllPub,Corner,Mod,CollgCr,Norm,Norm,1Fam,1Story,5,5,1996,1996,Gable,CompShg,VinylSd,VinylSd,None,0,TA,Gd,PConc,Gd,TA,No,GLQ,732,Unf,0,308,1040,GasA,Gd,Y,SBrkr,1040,0,0,1040,1,0,1,0,3,1,Gd,6,Typ,0,NA,Detchd,2000,Unf,2,576,TA,TA,Y,168,0,0,0,0,0,NA,GdPrv,NA,0,11,2009,WD,Normal,152000 +608,20,RL,78,7800,Pave,NA,Reg,Bnk,AllPub,Inside,Mod,Edwards,Norm,Norm,1Fam,2Story,5,8,1948,2002,Gable,CompShg,MetalSd,MetalSd,None,0,TA,Gd,CBlock,TA,Gd,No,GLQ,603,Unf,0,293,896,GasA,Ex,Y,SBrkr,1112,896,0,2008,1,0,3,0,3,1,Ex,8,Typ,0,NA,Attchd,1948,Unf,1,230,TA,TA,Y,103,0,0,0,0,0,NA,NA,NA,0,8,2006,WD,Normal,225000 +609,70,RL,78,12168,Pave,NA,Reg,HLS,AllPub,Inside,Mod,Crawfor,Norm,Norm,1Fam,2Story,8,6,1934,1998,Gable,CompShg,BrkFace,Wd Sdng,None,0,TA,TA,PConc,Gd,TA,Mn,BLQ,428,Unf,0,537,965,GasA,TA,Y,SBrkr,1940,1254,0,3194,0,0,2,1,4,1,TA,10,Typ,2,Gd,Basment,1934,Unf,2,380,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,9,2007,WD,Alloca,359100 +610,20,RL,61,7943,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,1Fam,1Story,4,5,1961,1961,Gable,CompShg,VinylSd,VinylSd,BrkCmn,192,TA,Fa,CBlock,TA,TA,Mn,Rec,903,Unf,0,126,1029,GasA,Gd,Y,SBrkr,1029,0,0,1029,1,0,1,0,3,1,TA,5,Typ,0,NA,Attchd,1961,Unf,1,261,TA,TA,Y,64,0,39,0,0,0,NA,NA,NA,0,4,2007,WD,Normal,118500 +611,60,RL,NA,11050,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,PosN,Norm,1Fam,2Story,9,5,2000,2000,Hip,CompShg,VinylSd,VinylSd,BrkFace,204,Gd,TA,PConc,Ex,TA,Mn,GLQ,904,Unf,0,536,1440,GasA,Ex,Y,SBrkr,1476,677,0,2153,1,0,2,1,3,1,Ex,8,Typ,2,Ex,Attchd,2000,Fin,3,736,TA,TA,Y,253,142,0,0,0,0,NA,NA,NA,0,5,2009,WD,Normal,313000 +612,80,RL,NA,10395,Pave,NA,IR1,Lvl,AllPub,FR2,Gtl,NWAmes,Norm,Norm,1Fam,SLvl,6,6,1978,1978,Gable,CompShg,HdBoard,HdBoard,BrkFace,233,TA,TA,CBlock,Gd,TA,Av,ALQ,605,Unf,0,427,1032,GasA,TA,Y,SBrkr,1032,0,0,1032,0,1,2,0,3,1,TA,6,Typ,1,TA,Attchd,1978,Unf,2,564,TA,TA,Y,0,0,0,0,0,0,NA,MnPrv,Shed,500,7,2007,WD,Normal,148000 +613,60,RL,NA,11885,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,8,5,2001,2001,Gable,CompShg,VinylSd,VinylSd,BrkFace,108,Gd,TA,PConc,Gd,TA,Av,GLQ,990,Unf,0,309,1299,GasA,Ex,Y,SBrkr,1299,573,0,1872,1,0,2,1,3,1,Ex,7,Typ,1,TA,BuiltIn,2001,RFn,2,531,TA,TA,Y,160,122,0,0,0,0,NA,NA,NA,0,11,2009,WD,Normal,261500 +614,20,RL,70,8402,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Mitchel,Feedr,Norm,1Fam,1Story,5,5,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,No,ALQ,206,Unf,0,914,1120,GasA,Ex,Y,SBrkr,1120,0,0,1120,0,0,1,0,3,1,TA,6,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,0,30,0,0,0,0,NA,NA,NA,0,12,2007,New,Partial,147000 +615,180,RM,21,1491,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,MeadowV,Norm,Norm,TwnhsE,SFoyer,4,6,1972,1972,Gable,CompShg,CemntBd,CmentBd,None,0,TA,TA,CBlock,Gd,TA,Av,LwQ,150,GLQ,480,0,630,GasA,Ex,Y,SBrkr,630,0,0,630,1,0,1,0,1,1,TA,3,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,96,24,0,0,0,0,NA,NA,NA,0,5,2010,WD,Normal,75500 +616,85,RL,80,8800,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Feedr,Norm,1Fam,SFoyer,6,7,1963,1963,Gable,CompShg,MetalSd,MetalSd,BrkFace,156,TA,Gd,PConc,TA,TA,Gd,GLQ,763,Unf,0,173,936,GasA,Ex,Y,SBrkr,1054,0,0,1054,1,0,1,0,3,1,Gd,6,Typ,0,NA,Attchd,1963,RFn,2,480,TA,TA,Y,120,0,0,0,0,0,NA,MnPrv,NA,0,5,2010,WD,Abnorml,137500 +617,60,RL,NA,7861,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,2Story,6,5,2002,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,457,Unf,0,326,783,GasA,Ex,Y,SBrkr,807,702,0,1509,1,0,2,1,3,1,Gd,7,Typ,1,Gd,Attchd,2002,Fin,2,393,TA,TA,Y,100,75,0,0,0,0,NA,NA,NA,0,6,2006,WD,Normal,183200 +618,45,RL,59,7227,Pave,NA,Reg,HLS,AllPub,Corner,Mod,NAmes,Artery,Norm,1Fam,1.5Unf,6,6,1954,1954,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,832,832,GasA,Gd,Y,SBrkr,832,0,0,832,0,0,1,0,2,1,Gd,4,Typ,0,NA,Detchd,1962,Unf,2,528,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,6,2008,WD,Normal,105500 +619,20,RL,90,11694,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,1Story,9,5,2007,2007,Hip,CompShg,CemntBd,CmentBd,BrkFace,452,Ex,TA,PConc,Ex,TA,Av,GLQ,48,Unf,0,1774,1822,GasA,Ex,Y,SBrkr,1828,0,0,1828,0,0,2,0,3,1,Gd,9,Typ,1,Gd,Attchd,2007,Unf,3,774,TA,TA,Y,0,108,0,0,260,0,NA,NA,NA,0,7,2007,New,Partial,314813 +620,60,RL,85,12244,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Timber,Norm,Norm,1Fam,2Story,8,5,2003,2003,Hip,CompShg,VinylSd,VinylSd,Stone,226,Gd,TA,PConc,Gd,TA,Gd,GLQ,871,Unf,0,611,1482,GasA,Ex,Y,SBrkr,1482,780,0,2262,1,0,2,1,4,1,Gd,10,Typ,2,Gd,Attchd,2003,Fin,3,749,TA,TA,Y,168,0,0,0,0,0,NA,NA,NA,0,8,2008,WD,Normal,305000 +621,30,RL,45,8248,Pave,Grvl,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1Story,3,3,1914,1950,Gable,CompShg,Stucco,Stucco,None,0,TA,TA,BrkTil,TA,TA,No,BLQ,41,Unf,0,823,864,GasA,TA,N,FuseF,864,0,0,864,1,0,1,0,2,1,TA,5,Typ,0,NA,NA,NA,NA,0,0,NA,NA,N,0,0,100,0,0,0,NA,NA,NA,0,9,2008,WD,Normal,67000 +622,60,RL,90,10800,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,2Story,6,7,1974,1997,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,TA,TA,No,ALQ,956,Rec,182,384,1522,GasA,TA,Y,SBrkr,1548,1066,0,2614,0,0,2,1,4,1,TA,9,Typ,1,TA,Attchd,1974,RFn,2,624,TA,TA,Y,38,243,0,0,0,0,NA,NA,NA,0,6,2008,WD,Normal,240000 +623,20,RL,71,7064,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,1Story,5,6,1977,1977,Gable,CompShg,Plywood,Plywood,BrkFace,153,TA,TA,CBlock,TA,TA,No,BLQ,560,Unf,0,420,980,GasA,TA,Y,SBrkr,980,0,0,980,0,0,1,0,3,1,TA,6,Typ,0,NA,Detchd,1986,Unf,2,484,TA,TA,Y,192,0,0,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,135000 +624,160,FV,NA,2117,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,TwnhsE,2Story,6,5,2000,2000,Gable,CompShg,MetalSd,MetalSd,BrkFace,513,Gd,TA,PConc,Gd,TA,No,GLQ,420,Unf,0,336,756,GasA,Ex,Y,SBrkr,756,756,0,1512,0,0,2,1,2,1,Gd,4,Typ,1,TA,Detchd,2000,Unf,2,440,TA,TA,Y,0,32,0,0,0,0,NA,NA,NA,0,6,2007,WD,Normal,168500 +625,60,RL,80,10400,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,2Story,6,5,1972,1972,Gable,CompShg,VinylSd,VinylSd,None,288,TA,TA,CBlock,TA,TA,No,Rec,247,Unf,0,485,732,GasA,Gd,Y,SBrkr,1012,778,0,1790,1,0,1,2,4,1,TA,8,Min2,1,TA,Attchd,1972,RFn,2,484,TA,TA,Y,148,0,0,0,147,0,NA,NA,NA,0,11,2006,WD,Normal,165150 +626,20,RL,87,10000,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,6,1962,1962,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,261,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,1116,1116,GasA,TA,Y,SBrkr,1116,0,0,1116,0,0,1,1,3,1,TA,5,Typ,0,NA,Attchd,1962,Unf,2,440,TA,TA,Y,0,0,0,0,385,0,NA,NA,NA,0,2,2010,WD,Normal,160000 +627,20,RL,NA,12342,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,5,1960,1978,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,978,978,GasA,TA,Y,SBrkr,1422,0,0,1422,0,0,1,0,3,1,TA,6,Min1,1,TA,Attchd,1960,RFn,1,286,TA,TA,Y,0,0,36,0,0,0,NA,GdWo,Shed,600,8,2007,WD,Normal,139900 +628,80,RL,80,9600,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,SLvl,6,6,1955,1972,Gable,CompShg,AsbShng,AsbShng,BrkFace,164,TA,TA,CBlock,TA,TA,Av,BLQ,674,LwQ,132,350,1156,GasA,Ex,Y,SBrkr,1520,0,0,1520,1,0,1,0,3,1,TA,7,Typ,2,Gd,Basment,1955,RFn,1,364,TA,TA,Y,0,0,189,0,0,0,NA,NA,NA,0,3,2010,WD,Normal,153000 +629,60,RL,70,11606,Pave,NA,IR1,HLS,AllPub,Inside,Sev,NAmes,Norm,Norm,1Fam,2Story,5,5,1969,1969,Gable,CompShg,Plywood,Plywood,BrkFace,192,TA,TA,PConc,Gd,TA,Av,Rec,650,Unf,0,390,1040,GasA,TA,Y,SBrkr,1040,1040,0,2080,0,1,1,2,5,1,Fa,9,Typ,2,TA,Attchd,1969,Unf,2,504,TA,TA,Y,335,0,0,0,0,0,NA,NA,NA,0,9,2007,WD,Family,135000 +630,80,RL,82,9020,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NAmes,Feedr,Norm,1Fam,SLvl,6,5,1964,1964,Gable,WdShngl,Plywood,Wd Sdng,BrkFace,259,TA,TA,CBlock,TA,TA,Gd,GLQ,624,Rec,336,288,1248,GasA,TA,Y,SBrkr,1350,0,0,1350,1,0,1,1,3,1,TA,6,Typ,0,NA,Attchd,1964,RFn,2,520,TA,TA,Y,176,0,0,0,0,0,NA,GdPrv,NA,0,6,2008,WD,Normal,168500 +631,70,RM,50,9000,Pave,Grvl,Reg,Lvl,AllPub,Corner,Gtl,OldTown,Artery,Norm,1Fam,2Story,5,6,1880,1991,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,BrkTil,Fa,Fa,No,Unf,0,Unf,0,636,636,GasA,TA,Y,FuseA,1089,661,0,1750,0,0,1,0,3,1,Ex,8,Typ,0,NA,Detchd,1937,Unf,1,240,Fa,Po,N,0,0,293,0,0,0,NA,MnPrv,NA,0,6,2006,WD,Abnorml,124000 +632,120,RL,34,4590,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,Twnhs,1Story,8,5,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,108,Gd,TA,PConc,Gd,Gd,Mn,GLQ,24,Unf,0,1530,1554,GasA,Ex,Y,SBrkr,1554,0,0,1554,0,0,2,0,2,1,Gd,6,Typ,1,Gd,Attchd,2006,RFn,2,627,TA,TA,Y,156,73,0,0,0,0,NA,NA,NA,0,8,2007,WD,Normal,209500 +633,20,RL,85,11900,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,1Story,7,5,1977,1977,Hip,CompShg,Plywood,Plywood,BrkFace,209,TA,Gd,CBlock,TA,TA,No,ALQ,822,Unf,0,564,1386,GasA,TA,Y,SBrkr,1411,0,0,1411,0,0,2,0,3,1,TA,6,Typ,1,TA,Attchd,1977,Fin,2,544,TA,TA,Y,192,0,0,0,0,0,NA,NA,NA,0,4,2009,WD,Family,82500 +634,20,RL,80,9250,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,7,1954,2005,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,BLQ,480,LwQ,468,108,1056,GasA,TA,Y,SBrkr,1056,0,0,1056,0,1,1,0,3,1,TA,6,Typ,0,NA,Attchd,1954,Unf,1,260,TA,TA,Y,390,0,0,0,0,0,NA,NA,NA,0,7,2007,WD,Normal,139400 +635,90,RL,64,6979,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,Duplex,SFoyer,6,5,1980,1980,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,CBlock,TA,TA,No,GLQ,1056,Unf,0,0,1056,GasA,Gd,Y,SBrkr,1056,0,0,1056,2,0,0,0,0,2,TA,4,Typ,0,NA,Detchd,1980,Unf,2,576,TA,TA,Y,264,56,0,0,0,0,NA,GdPrv,Shed,600,6,2010,WD,Normal,144000 +636,190,RH,60,10896,Pave,Pave,Reg,Bnk,AllPub,Inside,Gtl,SWISU,Feedr,Norm,2fmCon,2.5Fin,6,7,1914,1995,Hip,CompShg,VinylSd,VinylSd,None,0,Fa,TA,CBlock,TA,Fa,No,LwQ,256,Unf,0,1184,1440,GasA,Ex,Y,FuseA,1440,1440,515,3395,0,0,2,0,8,2,Fa,14,Typ,0,NA,NA,NA,NA,0,0,NA,NA,N,0,110,0,0,0,0,NA,NA,NA,0,3,2007,WD,Abnorml,200000 +637,30,RM,51,6120,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,1Story,2,3,1936,1950,Gable,CompShg,AsbShng,AsbShng,None,0,Fa,Fa,BrkTil,TA,Fa,No,Unf,0,Unf,0,264,264,Grav,Fa,N,FuseA,800,0,0,800,0,0,1,0,1,1,Fa,4,Maj1,1,Po,NA,NA,NA,0,0,NA,NA,N,0,0,0,0,0,0,NA,NA,NA,0,1,2009,ConLw,Normal,60000 +638,190,RM,50,6000,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,2fmCon,1.5Fin,5,4,1954,1954,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,811,811,GasA,TA,Y,FuseA,811,576,0,1387,0,0,2,0,3,2,Gd,7,Typ,0,NA,BuiltIn,1954,Unf,1,256,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,11,2009,WD,Normal,93000 +639,30,RL,67,8777,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Feedr,Norm,1Fam,1Story,5,7,1910,1950,Gable,CompShg,MetalSd,Wd Sdng,None,0,TA,TA,CBlock,Fa,TA,No,Unf,0,Unf,0,796,796,GasA,Gd,Y,FuseA,796,0,0,796,0,0,1,0,2,1,TA,4,Typ,0,NA,NA,NA,NA,0,0,NA,NA,P,328,0,164,0,0,0,NA,MnPrv,NA,0,5,2008,WD,Normal,85000 +640,120,RL,53,3982,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Blmngtn,Norm,Norm,TwnhsE,1Story,8,5,2006,2006,Hip,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,Gd,Av,GLQ,1154,Unf,0,366,1520,GasA,Ex,Y,SBrkr,1567,0,0,1567,1,0,2,0,1,1,Ex,7,Typ,1,Gd,Attchd,2006,Fin,3,648,TA,TA,Y,312,0,0,0,0,0,NA,NA,NA,0,10,2006,New,Partial,264561 +641,120,RL,62,12677,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,TwnhsE,1Story,8,5,2003,2004,Hip,CompShg,MetalSd,MetalSd,BrkFace,472,Ex,TA,PConc,Ex,TA,Gd,GLQ,1218,Unf,0,300,1518,GasA,Ex,Y,SBrkr,1518,0,0,1518,0,0,1,1,1,1,Ex,6,Typ,1,Gd,Attchd,2003,RFn,2,588,TA,TA,Y,185,140,0,0,0,0,NA,NA,NA,0,4,2008,WD,Normal,274000 +642,60,FV,NA,7050,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,2Story,7,5,2001,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,738,Unf,0,319,1057,GasA,Ex,Y,SBrkr,1057,872,0,1929,1,0,2,1,3,1,Gd,7,Typ,1,TA,Attchd,2001,Fin,2,650,TA,TA,Y,0,235,0,0,0,0,NA,NA,NA,0,5,2007,WD,Normal,226000 +643,80,RL,75,13860,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,SLvl,8,7,1972,1995,Gable,CompShg,Plywood,Wd Sdng,None,0,Gd,TA,CBlock,Gd,TA,Gd,GLQ,1410,Unf,0,542,1952,GasA,Gd,Y,SBrkr,2000,704,0,2704,1,0,2,1,4,1,Ex,9,Typ,3,TA,Attchd,1972,Fin,2,538,TA,TA,Y,269,111,0,0,0,0,NA,MnPrv,NA,0,7,2009,WD,Normal,345000 +644,60,RL,80,10793,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,RRAn,Norm,1Fam,2Story,5,5,1969,1969,Mansard,CompShg,WdShing,HdBoard,BrkFace,263,TA,TA,CBlock,TA,TA,No,Rec,493,BLQ,287,0,780,GasA,Ex,Y,SBrkr,780,840,0,1620,0,0,2,1,4,1,TA,7,Min1,0,NA,Attchd,1969,Fin,2,462,TA,TA,Y,208,0,0,0,0,0,NA,GdWo,NA,0,4,2007,WD,Normal,152000 +645,20,FV,85,9187,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,1Story,9,5,2009,2009,Gable,CompShg,CemntBd,CmentBd,Stone,162,Ex,TA,PConc,Ex,TA,Mn,GLQ,1121,Unf,0,645,1766,GasA,Ex,Y,SBrkr,1766,0,0,1766,1,0,2,1,2,1,Ex,7,Typ,1,Gd,Attchd,2009,Fin,3,478,TA,TA,Y,195,130,0,0,0,0,NA,NA,NA,0,10,2009,New,Partial,370878 +646,20,RL,NA,10530,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,5,1971,1971,Hip,CompShg,Plywood,Plywood,None,0,TA,TA,CBlock,TA,TA,No,ALQ,282,LwQ,35,664,981,GasA,TA,Y,SBrkr,981,0,0,981,1,0,1,1,3,1,TA,5,Typ,0,NA,Detchd,1979,Unf,2,576,TA,TA,Y,0,312,40,0,0,0,NA,NA,NA,0,3,2007,WD,Normal,143250 +647,20,RL,60,7200,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,5,1950,1950,Hip,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,NA,NA,NA,NA,0,NA,0,0,0,GasA,Gd,Y,SBrkr,1048,0,0,1048,0,0,1,0,3,1,TA,7,Min1,0,NA,Detchd,1950,Unf,2,420,TA,TA,Y,0,27,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,98300 +648,20,RL,85,10452,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1Story,6,5,1953,1953,Hip,CompShg,Wd Sdng,Wd Sdng,Stone,216,TA,TA,CBlock,TA,TA,Mn,Rec,500,Unf,0,594,1094,GasA,Ex,Y,SBrkr,1094,0,0,1094,0,0,1,0,3,1,TA,5,Typ,2,Gd,Attchd,1953,RFn,2,495,TA,TA,Y,0,0,0,0,287,0,NA,NA,NA,0,6,2008,WD,Normal,155000 +649,60,RL,70,7700,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,PosN,Norm,1Fam,2Story,6,5,1966,1966,Gable,CompShg,MetalSd,MetalSd,BrkFace,351,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,756,756,GasA,TA,Y,SBrkr,1051,788,0,1839,0,0,1,1,4,1,TA,7,Typ,1,TA,Attchd,1966,Unf,2,442,TA,TA,Y,0,124,216,0,0,0,NA,NA,NA,0,6,2010,WD,Normal,155000 +650,180,RM,21,1936,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,MeadowV,Norm,Norm,Twnhs,SFoyer,4,6,1970,1970,Gable,CompShg,CemntBd,CmentBd,None,0,TA,TA,CBlock,Gd,TA,Av,BLQ,131,GLQ,499,0,630,GasA,Gd,Y,SBrkr,630,0,0,630,1,0,1,0,1,1,TA,3,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,0,0,0,0,0,0,NA,MnPrv,NA,0,12,2007,WD,Normal,84500 +651,60,FV,65,8125,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,2Story,7,6,2007,2007,Gable,CompShg,CemntBd,CmentBd,NA,NA,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,813,813,GasA,Ex,Y,SBrkr,822,843,0,1665,0,0,2,1,3,1,Gd,7,Typ,0,NA,Attchd,2007,RFn,2,562,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,5,2008,WD,Normal,205950 +652,70,RL,60,9084,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Artery,Norm,1Fam,2Story,4,5,1940,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,Mn,Unf,0,Unf,0,755,755,GasA,TA,Y,SBrkr,755,755,0,1510,1,0,1,0,4,1,TA,7,Typ,1,Gd,Detchd,1940,Unf,1,296,Fa,Po,P,120,0,0,0,0,0,NA,MnPrv,NA,0,10,2009,WD,Normal,108000 +653,60,RL,70,8750,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,7,5,1996,1996,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,880,880,GasA,Ex,Y,SBrkr,909,807,0,1716,0,0,2,1,2,1,Gd,7,Typ,1,TA,Attchd,1996,RFn,2,512,TA,TA,Y,0,120,0,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,191000 +654,50,RM,60,10320,Pave,Grvl,Reg,Lvl,AllPub,Inside,Gtl,IDOTRR,Norm,Norm,1Fam,1.5Fin,6,7,1906,1995,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,756,756,GasA,Ex,Y,SBrkr,756,713,0,1469,0,0,1,0,3,1,TA,7,Typ,0,NA,Detchd,1906,Unf,1,216,TA,TA,Y,57,0,239,0,0,0,NA,MnPrv,NA,0,6,2008,WD,Normal,135000 +655,20,RL,91,10437,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NoRidge,Norm,Norm,1Fam,1Story,8,6,1995,1995,Hip,CompShg,MetalSd,MetalSd,BrkFace,660,Gd,Gd,PConc,Gd,TA,Gd,GLQ,1696,Unf,0,413,2109,GasA,Ex,Y,SBrkr,2113,0,0,2113,1,0,2,1,2,1,Gd,7,Typ,1,TA,Attchd,1995,Fin,3,839,TA,TA,Y,236,46,0,0,0,0,NA,NA,NA,0,8,2008,WD,Normal,350000 +656,160,RM,21,1680,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrDale,Norm,Norm,Twnhs,2Story,6,5,1971,1971,Gable,CompShg,HdBoard,ImStucc,BrkFace,381,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,525,525,GasA,TA,Y,SBrkr,525,567,0,1092,0,0,1,1,3,1,TA,6,Typ,0,NA,Detchd,1971,Unf,1,264,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,3,2010,WD,Family,88000 +657,20,RL,72,10007,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,7,1959,2006,Gable,CompShg,HdBoard,HdBoard,BrkFace,54,Gd,TA,CBlock,TA,TA,No,ALQ,806,Unf,0,247,1053,GasA,Ex,Y,SBrkr,1053,0,0,1053,1,0,1,1,3,1,Gd,5,Typ,0,NA,Attchd,1959,RFn,1,312,TA,TA,Y,0,0,0,0,0,0,NA,MnPrv,NA,0,8,2008,WD,Normal,145500 +658,70,RL,60,7200,Pave,NA,Reg,HLS,AllPub,Inside,Mod,Crawfor,Norm,Norm,1Fam,2Story,7,6,1931,2000,Gable,CompShg,Stucco,Wd Shng,None,0,TA,Fa,BrkTil,Gd,TA,No,Unf,0,Unf,0,776,776,GasA,TA,Y,SBrkr,851,651,0,1502,0,0,1,1,3,1,TA,6,Typ,1,Gd,Attchd,1931,RFn,1,270,TA,TA,P,0,0,112,0,0,0,NA,MnPrv,NA,0,2,2008,WD,Normal,149000 +659,50,RL,78,17503,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Artery,Norm,1Fam,1.5Fin,6,5,1948,1950,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,912,912,GasA,TA,Y,SBrkr,912,546,0,1458,0,1,1,0,3,1,TA,6,Typ,1,Gd,Attchd,1948,Unf,1,330,TA,TA,Y,192,0,0,0,0,0,NA,NA,NA,0,1,2010,WD,Abnorml,97500 +660,20,RL,75,9937,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,1Fam,1Story,5,7,1964,1999,Hip,CompShg,MetalSd,MetalSd,None,0,TA,Gd,PConc,TA,TA,No,BLQ,637,Unf,0,849,1486,GasA,Ex,Y,SBrkr,1486,0,0,1486,1,0,1,0,3,1,TA,7,Typ,0,NA,Detchd,1968,Fin,2,480,TA,TA,Y,0,0,0,0,0,0,NA,MnPrv,NA,0,3,2009,WD,Normal,167000 +661,60,RL,NA,12384,Pave,NA,Reg,Lvl,AllPub,CulDSac,Gtl,NWAmes,Norm,Norm,1Fam,2Story,7,7,1976,1976,Gable,CompShg,Plywood,Plywood,BrkFace,233,TA,TA,CBlock,Gd,TA,No,Unf,0,Unf,0,793,793,GasA,TA,Y,SBrkr,1142,793,0,1935,0,0,2,1,3,1,TA,7,Typ,1,TA,Attchd,1976,RFn,2,550,TA,TA,Y,0,113,252,0,0,0,NA,NA,NA,0,11,2007,WD,Normal,197900 +662,60,RL,52,46589,Pave,NA,IR2,Lvl,AllPub,CulDSac,Gtl,NoRidge,Norm,Norm,1Fam,2Story,8,7,1994,2005,Hip,CompShg,VinylSd,VinylSd,BrkFace,528,Gd,TA,PConc,Gd,Gd,No,GLQ,1361,Rec,180,88,1629,GasA,Ex,Y,SBrkr,1686,762,0,2448,1,0,2,1,4,1,Gd,8,Typ,1,TA,Attchd,1994,RFn,3,711,TA,TA,Y,517,76,0,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,402000 +663,20,RL,120,13560,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,3,1968,1968,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,216,TA,TA,CBlock,Fa,Fa,No,Unf,0,Unf,0,1392,1392,GasA,Gd,Y,SBrkr,1392,0,0,1392,1,0,1,0,2,1,TA,5,Maj2,2,TA,Attchd,1968,RFn,2,576,TA,TA,Y,0,0,240,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,110000 +664,85,RL,90,10012,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,SFoyer,4,5,1972,1972,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,CBlock,Gd,TA,Av,BLQ,920,Rec,180,38,1138,GasA,TA,Y,SBrkr,1181,0,0,1181,1,0,2,0,3,1,TA,6,Typ,0,NA,Detchd,1974,RFn,2,588,TA,TA,Y,0,0,180,0,0,0,NA,MnPrv,NA,0,4,2008,WD,Normal,137500 +665,20,RL,49,20896,Pave,NA,IR2,Lvl,AllPub,CulDSac,Gtl,Somerst,RRAn,Norm,1Fam,1Story,8,5,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Ex,TA,Mn,GLQ,1721,Unf,0,356,2077,GasA,Ex,Y,SBrkr,2097,0,0,2097,1,0,1,1,1,1,Ex,8,Typ,1,Ex,Attchd,2005,Fin,3,1134,TA,TA,Y,192,267,0,0,0,0,NA,NA,NA,0,1,2006,New,Partial,423000 +666,60,RL,106,11194,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,1Fam,2Story,8,5,2000,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,40,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1406,1406,GasA,Ex,Y,SBrkr,1454,482,0,1936,0,0,2,1,3,1,Gd,7,Typ,1,TA,Attchd,2000,RFn,2,504,TA,TA,Y,188,124,0,0,0,0,NA,NA,NA,0,11,2006,WD,Normal,230500 +667,60,RL,NA,18450,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,2Story,6,5,1965,1979,Flat,Tar&Grv,Plywood,Plywood,BrkCmn,113,TA,Gd,CBlock,Gd,TA,No,LwQ,187,Rec,723,111,1021,GasA,TA,Y,SBrkr,1465,915,0,2380,0,0,2,1,3,1,TA,7,Sev,1,Po,CarPort,1965,Unf,2,596,TA,TA,Y,0,265,0,0,0,0,NA,NA,NA,0,8,2007,WD,Abnorml,129000 +668,20,RL,65,8125,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SawyerW,Norm,Norm,1Fam,1Story,6,5,1994,1998,Gable,CompShg,HdBoard,HdBoard,BrkFace,258,TA,TA,PConc,Gd,TA,No,GLQ,1138,Unf,0,270,1408,GasA,Ex,Y,SBrkr,1679,0,0,1679,1,0,2,0,3,1,Gd,7,Typ,1,Fa,Attchd,1994,RFn,2,575,TA,TA,Y,224,42,0,0,0,0,NA,NA,NA,0,10,2008,WD,Normal,193500 +669,20,RL,NA,14175,Pave,NA,Reg,Bnk,AllPub,Corner,Mod,Sawyer,Norm,Norm,1Fam,1Story,5,6,1956,1987,Gable,CompShg,CemntBd,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,Rec,988,Unf,0,200,1188,GasA,Gd,Y,SBrkr,1437,0,0,1437,1,0,1,1,3,1,TA,6,Min2,1,TA,Detchd,1999,Unf,2,576,TA,TA,Y,304,0,0,0,0,0,NA,NA,NA,0,11,2006,WD,Normal,168000 +670,30,RL,80,11600,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Crawfor,Norm,Norm,1Fam,1Story,4,5,1922,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,BrkTil,Fa,TA,No,Unf,0,Unf,0,700,700,GasA,Ex,Y,SBrkr,1180,0,0,1180,0,0,1,0,2,1,Fa,5,Typ,1,Gd,Detchd,1922,Unf,1,252,TA,Fa,Y,0,0,67,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,137500 +671,60,RL,64,8633,Pave,NA,Reg,Lvl,AllPub,FR2,Gtl,CollgCr,Norm,Norm,1Fam,2Story,6,5,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,193,Unf,0,545,738,GasA,Ex,Y,SBrkr,738,738,0,1476,1,0,2,1,3,1,Gd,7,Typ,0,NA,Attchd,2005,Fin,2,540,TA,TA,Y,100,35,0,0,0,0,NA,NA,NA,0,2,2009,WD,Normal,173500 +672,70,RH,54,6629,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Artery,Norm,1Fam,2Story,6,6,1925,1950,Gambrel,CompShg,Wd Sdng,Wd Sdng,None,0,TA,Gd,BrkTil,TA,TA,No,BLQ,551,Unf,0,121,672,GasA,TA,N,SBrkr,697,672,0,1369,1,0,2,0,3,1,TA,6,Typ,0,NA,Detchd,1930,Unf,1,300,TA,TA,Y,147,0,0,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,103600 +673,20,RL,NA,11250,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Veenker,Norm,Norm,1Fam,1Story,6,6,1977,1977,Gable,CompShg,Plywood,Plywood,None,0,Gd,TA,CBlock,Gd,TA,No,ALQ,767,Unf,0,441,1208,GasA,TA,Y,SBrkr,1208,0,0,1208,1,0,1,1,3,1,TA,6,Typ,1,TA,Attchd,1977,RFn,2,546,TA,TA,Y,198,42,0,0,0,0,NA,NA,NA,0,6,2006,WD,Normal,165000 +674,20,RL,110,14442,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Crawfor,Norm,Norm,1Fam,1Story,6,7,1957,2004,Hip,CompShg,CemntBd,CmentBd,BrkFace,106,TA,TA,PConc,TA,TA,No,GLQ,1186,Unf,0,291,1477,GasA,Ex,Y,SBrkr,1839,0,0,1839,1,0,2,0,3,1,Gd,7,Typ,2,TA,Attchd,1957,Fin,2,416,TA,TA,Y,0,87,0,0,200,0,NA,NA,NA,0,6,2007,WD,Normal,257500 +675,20,RL,80,9200,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,6,1965,1965,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,TA,TA,No,Rec,892,Unf,0,244,1136,GasA,TA,Y,SBrkr,1136,0,0,1136,1,0,1,0,3,1,TA,5,Typ,1,Gd,Attchd,1965,RFn,1,384,TA,TA,Y,426,0,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,140000 +676,160,RL,24,2289,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NPkVill,Norm,Norm,Twnhs,2Story,6,6,1978,1978,Gable,CompShg,Plywood,Brk Cmn,None,0,TA,TA,CBlock,TA,TA,No,ALQ,311,Unf,0,544,855,GasA,TA,Y,SBrkr,855,586,0,1441,0,0,2,1,3,1,TA,7,Typ,1,TA,Attchd,1978,Unf,2,440,TA,TA,Y,28,0,0,0,0,0,NA,NA,NA,0,4,2009,WD,Normal,148500 +677,70,RM,60,9600,Pave,Grvl,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,2Story,4,2,1900,1950,Gable,CompShg,AsbShng,Stucco,None,0,TA,TA,BrkTil,TA,Fa,No,Unf,0,Unf,0,1095,1095,GasW,Fa,N,SBrkr,1095,679,0,1774,1,0,2,0,4,2,TA,8,Min2,0,NA,2Types,1920,Unf,3,779,Fa,Fa,N,0,0,90,0,0,0,NA,NA,NA,0,5,2006,WD,Normal,87000 +678,30,RL,52,9022,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,1Story,5,8,1924,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,768,768,GasA,Ex,Y,SBrkr,792,0,0,792,0,0,1,0,2,1,Gd,5,Typ,0,NA,Detchd,1924,Unf,1,240,Fa,Fa,N,316,0,120,0,0,0,NA,NA,NA,0,5,2009,WD,Normal,109500 +679,20,RL,80,11844,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,StoneBr,Norm,Norm,1Fam,1Story,8,5,2008,2008,Hip,CompShg,VinylSd,VinylSd,Stone,464,Gd,TA,PConc,Ex,TA,Mn,Unf,0,Unf,0,2046,2046,GasA,Ex,Y,SBrkr,2046,0,0,2046,0,0,2,1,3,1,Gd,7,Typ,1,Gd,Attchd,2008,Fin,3,834,TA,TA,Y,322,82,0,0,0,0,NA,NA,NA,0,7,2009,New,Partial,372500 +680,20,RL,NA,9945,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,1Story,5,5,1961,1961,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,57,TA,TA,CBlock,TA,TA,No,Rec,827,Unf,0,161,988,GasA,TA,Y,SBrkr,988,0,0,988,1,0,1,0,3,1,TA,5,Typ,0,NA,Detchd,1963,Unf,2,572,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,10,2007,WD,Normal,128500 +681,120,RL,50,8012,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SawyerW,Norm,Norm,TwnhsE,1Story,6,5,1980,1980,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,CBlock,Gd,TA,No,BLQ,543,BLQ,119,261,923,GasA,TA,Y,SBrkr,923,0,0,923,0,0,2,0,2,1,TA,5,Typ,1,TA,Attchd,1980,RFn,1,264,TA,TA,Y,80,0,0,0,0,0,NA,NA,NA,0,5,2010,WD,Normal,143000 +682,50,RH,55,4500,Pave,Pave,IR2,Bnk,AllPub,Inside,Gtl,SWISU,Norm,Norm,1Fam,1.5Fin,5,5,1932,2000,Gable,CompShg,VinylSd,Stucco,None,0,TA,TA,BrkTil,TA,TA,No,Rec,182,Unf,0,611,793,GasA,Ex,Y,SBrkr,848,672,0,1520,0,0,1,0,3,1,TA,6,Typ,0,NA,Detchd,1968,Unf,1,281,TA,TA,Y,0,0,56,0,0,0,NA,NA,NA,0,7,2009,WD,Abnorml,159434 +683,120,RL,NA,2887,Pave,NA,Reg,HLS,AllPub,Inside,Gtl,ClearCr,Norm,Norm,1Fam,1Story,6,5,1996,1997,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,PConc,Gd,TA,Mn,GLQ,1003,Unf,0,288,1291,GasA,Ex,Y,SBrkr,1291,0,0,1291,1,0,1,0,2,1,Gd,6,Typ,1,Gd,Attchd,1996,Unf,2,431,TA,TA,Y,307,0,0,0,0,0,NA,NA,NA,0,11,2008,WD,Normal,173000 +684,20,RL,90,11248,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,CollgCr,Norm,Norm,1Fam,1Story,9,5,2002,2002,Hip,CompShg,VinylSd,VinylSd,Stone,215,Gd,TA,PConc,Gd,TA,Av,GLQ,1059,Unf,0,567,1626,GasA,Ex,Y,SBrkr,1668,0,0,1668,1,0,2,0,3,1,Gd,7,Typ,1,TA,Attchd,2002,Fin,3,702,TA,TA,Y,257,45,0,0,0,0,NA,NA,NA,0,7,2007,WD,Normal,285000 +685,60,RL,58,16770,Pave,NA,IR2,Lvl,AllPub,CulDSac,Gtl,NoRidge,Norm,Norm,1Fam,2Story,7,5,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,30,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1195,1195,GasA,Gd,Y,SBrkr,1195,644,0,1839,0,0,2,1,4,1,TA,7,Typ,0,NA,Attchd,1998,Fin,2,486,TA,TA,Y,0,81,0,0,0,0,NA,NA,NA,0,6,2010,WD,Normal,221000 +686,160,RL,NA,5062,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,StoneBr,Norm,Norm,TwnhsE,2Story,7,5,1984,1984,Gable,CompShg,HdBoard,HdBoard,None,0,Gd,TA,CBlock,Gd,TA,Mn,GLQ,828,LwQ,182,180,1190,GasA,Gd,Y,SBrkr,1190,900,0,2090,1,0,2,0,3,1,Gd,6,Min1,1,TA,Attchd,1984,Fin,2,577,TA,TA,Y,219,0,0,0,0,0,NA,NA,NA,0,9,2007,WD,Normal,207500 +687,60,FV,84,10207,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,2Story,7,6,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,874,874,GasA,Ex,Y,SBrkr,874,887,0,1761,0,0,3,0,3,1,Gd,7,Typ,0,NA,Attchd,2007,Fin,2,578,TA,TA,Y,144,105,0,0,0,0,NA,NA,NA,0,8,2007,New,Partial,227875 +688,160,FV,NA,5105,Pave,NA,IR2,Lvl,AllPub,FR2,Gtl,Somerst,Norm,Norm,TwnhsE,2Story,7,5,2004,2004,Gable,CompShg,MetalSd,MetalSd,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,239,Unf,0,312,551,GasA,Ex,Y,SBrkr,551,551,0,1102,0,0,2,1,2,1,Gd,4,Typ,0,NA,Detchd,2004,Unf,2,480,TA,TA,Y,0,60,0,0,0,0,NA,NA,NA,0,3,2007,WD,Normal,148800 +689,20,RL,60,8089,Pave,NA,Reg,HLS,AllPub,Inside,Gtl,StoneBr,Norm,Norm,1Fam,1Story,8,6,2007,2007,Gable,CompShg,MetalSd,MetalSd,BrkFace,0,Gd,TA,PConc,Gd,TA,Av,GLQ,945,Unf,0,474,1419,GasA,Ex,Y,SBrkr,1419,0,0,1419,1,0,2,0,2,1,Gd,7,Typ,1,Gd,Attchd,2007,RFn,2,567,TA,TA,Y,140,0,0,0,0,0,NA,NA,NA,0,10,2007,New,Partial,392000 +690,120,RL,61,7577,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NridgHt,Norm,Norm,TwnhsE,1Story,6,5,2005,2006,Gable,CompShg,VinylSd,VinylSd,Stone,256,Gd,TA,PConc,Gd,TA,Av,ALQ,20,Unf,0,1342,1362,GasA,Ex,Y,SBrkr,1362,0,0,1362,0,0,2,0,2,1,Gd,6,Typ,1,Gd,Attchd,2005,RFn,2,460,TA,TA,Y,192,28,0,0,0,0,NA,NA,NA,0,6,2007,WD,Normal,194700 +691,120,RM,NA,4426,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,TwnhsE,1Story,6,5,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,147,Gd,TA,PConc,Gd,TA,Gd,GLQ,697,Unf,0,151,848,GasA,Ex,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Gd,3,Typ,1,TA,Attchd,2004,RFn,2,420,TA,TA,Y,149,0,0,0,0,0,NA,NA,NA,0,5,2008,WD,Normal,141000 +692,60,RL,104,21535,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NoRidge,Norm,Norm,1Fam,2Story,10,6,1994,1995,Gable,WdShngl,HdBoard,HdBoard,BrkFace,1170,Ex,TA,PConc,Ex,TA,Gd,GLQ,1455,Unf,0,989,2444,GasA,Ex,Y,SBrkr,2444,1872,0,4316,0,1,3,1,4,1,Ex,10,Typ,2,Ex,Attchd,1994,Fin,3,832,TA,TA,Y,382,50,0,0,0,0,NA,NA,NA,0,1,2007,WD,Normal,755000 +693,60,RL,42,26178,Pave,NA,IR1,Lvl,AllPub,Inside,Mod,Timber,Norm,Norm,1Fam,2Story,7,5,1989,1990,Hip,CompShg,MetalSd,MetalSd,BrkFace,293,Gd,TA,PConc,Gd,TA,Gd,GLQ,965,Unf,0,245,1210,GasA,Ex,Y,SBrkr,1238,1281,0,2519,1,0,2,1,4,1,Gd,9,Typ,2,Gd,Attchd,1989,RFn,2,628,TA,TA,Y,320,27,0,0,0,0,NA,NA,NA,0,4,2006,WD,Normal,335000 +694,30,RL,60,5400,Pave,NA,Reg,Lvl,AllPub,Corner,Sev,OldTown,Norm,Norm,1Fam,1Story,5,6,1921,1968,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,1073,1073,GasA,Ex,Y,SBrkr,1073,0,0,1073,0,0,1,0,2,1,TA,4,Typ,0,NA,Detchd,1968,Unf,1,326,TA,TA,Y,0,0,112,0,0,0,NA,NA,NA,0,12,2006,WD,Abnorml,108480 +695,50,RM,51,6120,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,BrkSide,Norm,Norm,1Fam,1.5Fin,5,6,1936,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,Fa,BrkTil,TA,TA,No,Unf,0,Unf,0,927,927,GasA,TA,Y,SBrkr,1067,472,0,1539,0,0,1,1,3,1,TA,5,Typ,0,NA,Detchd,1995,Unf,2,576,TA,TA,Y,112,0,0,0,0,0,NA,MnPrv,NA,0,4,2009,WD,Normal,141500 +696,20,RL,54,13811,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Timber,Norm,Norm,1Fam,1Story,6,6,1987,1987,Gable,CompShg,HdBoard,HdBoard,BrkFace,72,TA,TA,CBlock,Gd,Gd,No,GLQ,980,LwQ,40,92,1112,GasA,Gd,Y,SBrkr,1137,0,0,1137,1,0,2,0,2,1,Gd,5,Typ,1,TA,Attchd,1987,Unf,2,551,TA,TA,Y,125,0,0,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,176000 +697,30,RM,50,6000,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,1Story,5,7,1921,1950,Gable,CompShg,Wd Sdng,Wd Shng,None,0,TA,TA,CBlock,TA,TA,No,LwQ,616,Unf,0,0,616,GasA,Gd,Y,SBrkr,616,0,0,616,0,0,1,0,2,1,TA,4,Typ,0,NA,Detchd,1921,Unf,1,205,TA,TA,Y,0,0,129,0,0,0,NA,NA,NA,0,6,2006,WD,Normal,89000 +698,20,RL,57,6420,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1Story,5,7,1952,1952,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,PConc,Ex,Gd,Mn,LwQ,210,ALQ,551,219,980,GasA,Fa,Y,FuseA,1148,0,0,1148,0,1,1,0,2,1,TA,6,Typ,0,NA,Detchd,1952,Unf,1,308,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,9,2006,WD,Normal,123500 +699,20,RL,65,8450,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Sawyer,RRAe,Norm,1Fam,1Story,5,8,1965,2009,Gable,CompShg,MetalSd,MetalSd,None,0,TA,Gd,CBlock,TA,TA,No,GLQ,553,BLQ,117,224,894,GasA,Ex,Y,SBrkr,894,0,0,894,1,0,1,0,3,1,TA,5,Typ,1,Gd,Detchd,1973,Unf,1,336,TA,TA,Y,416,144,0,0,0,0,NA,MnPrv,NA,0,4,2010,WD,Normal,138500 +700,120,FV,59,4282,Pave,Pave,IR2,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,TwnhsE,1Story,7,5,2004,2004,Gable,CompShg,MetalSd,MetalSd,None,0,Gd,TA,PConc,Gd,TA,Mn,GLQ,16,Unf,0,1375,1391,GasA,Ex,Y,SBrkr,1391,0,0,1391,0,0,2,0,2,1,Gd,5,Typ,0,NA,Attchd,2004,RFn,2,530,TA,TA,Y,156,158,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,196000 +701,20,RL,85,14331,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Timber,Norm,Norm,1Fam,1Story,8,5,2002,2002,Hip,CompShg,VinylSd,VinylSd,BrkFace,630,Gd,TA,PConc,Ex,TA,Gd,GLQ,1274,Unf,0,526,1800,GasA,Ex,Y,SBrkr,1800,0,0,1800,1,0,2,0,3,1,Gd,7,Typ,1,Gd,Attchd,2002,Fin,3,765,TA,TA,Y,270,78,0,0,0,0,NA,NA,NA,0,5,2006,WD,Normal,312500 +702,20,RL,80,9600,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,1Story,7,5,1969,1969,Hip,CompShg,HdBoard,HdBoard,BrkFace,168,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,1164,1164,GasA,TA,Y,SBrkr,1164,0,0,1164,0,0,1,1,3,1,TA,6,Typ,0,NA,Attchd,1969,Unf,2,528,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,7,2006,COD,Normal,140000 +703,60,RL,82,12438,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,StoneBr,Norm,Norm,1Fam,2Story,8,5,2006,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,466,Ex,TA,PConc,Ex,Gd,No,Unf,0,Unf,0,1234,1234,GasA,Ex,Y,SBrkr,1264,1312,0,2576,0,0,2,1,4,1,Ex,10,Typ,1,Gd,BuiltIn,2006,Fin,3,666,TA,TA,Y,324,100,0,0,0,0,NA,NA,NA,0,7,2006,New,Partial,361919 +704,190,RM,76,7630,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Feedr,Norm,2fmCon,2Story,5,9,1900,1996,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,Gd,BrkTil,Gd,TA,No,Unf,0,Unf,0,360,360,GasA,Gd,Y,SBrkr,1032,780,0,1812,0,0,2,0,4,2,Gd,8,Typ,1,Po,Detchd,1999,Unf,2,672,TA,TA,N,344,0,40,0,0,0,NA,MnPrv,NA,0,5,2010,WD,Normal,140000 +705,20,RL,70,8400,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2004,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,109,Gd,TA,PConc,Gd,TA,Av,GLQ,712,Unf,0,761,1473,GasA,Ex,Y,SBrkr,1484,0,0,1484,1,0,2,0,3,1,Gd,7,Typ,0,NA,Attchd,2004,RFn,2,606,TA,TA,Y,0,35,0,144,0,0,NA,NA,NA,0,5,2010,WD,Normal,213000 +706,190,RM,70,5600,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,IDOTRR,Norm,Norm,2fmCon,2Story,4,5,1930,1950,Hip,CompShg,VinylSd,Wd Shng,None,0,Fa,Fa,Slab,NA,NA,NA,NA,0,NA,0,0,0,GasA,Fa,N,SBrkr,372,720,0,1092,0,0,2,0,3,2,Fa,7,Mod,0,NA,NA,NA,NA,0,0,NA,NA,N,0,0,0,0,0,0,NA,NA,Othr,3500,7,2010,WD,Normal,55000 +707,20,RL,NA,115149,Pave,NA,IR2,Low,AllPub,CulDSac,Sev,ClearCr,Norm,Norm,1Fam,1Story,7,5,1971,2002,Gable,CompShg,Plywood,Plywood,Stone,351,TA,TA,CBlock,Gd,TA,Gd,GLQ,1219,Unf,0,424,1643,GasA,TA,Y,SBrkr,1824,0,0,1824,1,0,2,0,2,1,Gd,5,Typ,2,TA,Attchd,1971,Unf,2,739,TA,TA,Y,380,48,0,0,0,0,NA,NA,NA,0,6,2007,WD,Normal,302000 +708,120,RL,48,6240,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,TwnhsE,1Story,8,5,2006,2006,Hip,CompShg,MetalSd,MetalSd,BrkFace,176,Gd,TA,PConc,Gd,TA,No,GLQ,863,Unf,0,461,1324,GasA,Ex,Y,SBrkr,1324,0,0,1324,1,0,2,0,2,1,Gd,6,Typ,1,Gd,Attchd,2006,Fin,2,550,TA,TA,Y,192,38,0,0,0,0,NA,NA,NA,0,12,2009,WD,Normal,254000 +709,60,RL,65,9018,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,2Story,7,5,2007,2007,Hip,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,Av,Unf,0,Unf,0,728,728,GasA,Ex,Y,SBrkr,728,728,0,1456,0,0,2,1,3,1,Gd,8,Typ,1,Gd,Attchd,2007,Fin,2,400,TA,TA,Y,100,24,0,0,0,0,NA,NA,NA,0,7,2007,New,Partial,179540 +710,20,RL,NA,7162,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,1Story,5,7,1966,1966,Gable,CompShg,HdBoard,HdBoard,BrkCmn,41,TA,TA,PConc,TA,TA,No,Unf,0,Unf,0,876,876,GasA,TA,Y,SBrkr,904,0,0,904,0,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1966,Unf,1,408,TA,TA,Y,0,0,0,0,0,0,NA,MnPrv,NA,0,12,2008,WD,Abnorml,109900 +711,30,RL,56,4130,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,1Story,3,6,1935,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,CBlock,TA,TA,No,Unf,0,Unf,0,270,270,GasA,Gd,Y,SBrkr,729,0,0,729,0,0,1,0,2,1,TA,5,Maj2,0,NA,NA,NA,NA,0,0,NA,NA,N,0,0,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,52000 +712,50,C (all),66,8712,Pave,Pave,Reg,HLS,AllPub,Inside,Mod,IDOTRR,Norm,Norm,1Fam,1.5Fin,4,7,1900,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,Stone,TA,TA,Mn,Unf,0,Unf,0,859,859,GasA,Gd,Y,SBrkr,859,319,0,1178,0,0,1,0,2,1,TA,7,Typ,0,NA,Detchd,1964,RFn,1,384,TA,TA,N,68,0,98,0,0,0,NA,NA,NA,0,1,2010,WD,Abnorml,102776 +713,120,RL,40,4671,Pave,NA,IR1,HLS,AllPub,Inside,Gtl,StoneBr,Norm,Norm,TwnhsE,1Story,8,5,1988,1989,Gable,CompShg,HdBoard,HdBoard,None,0,Gd,TA,PConc,Gd,TA,Mn,GLQ,767,Unf,0,461,1228,GasA,Gd,Y,SBrkr,1228,0,0,1228,1,0,2,0,2,1,Gd,5,Typ,1,Gd,Attchd,1988,Fin,2,472,TA,TA,Y,168,120,0,0,0,0,NA,NA,NA,0,10,2008,WD,Normal,189000 +714,190,RL,60,9873,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrkSide,RRAn,Norm,2fmCon,1Story,4,5,1970,1970,Gable,CompShg,HdBoard,HdBoard,BrkFace,160,TA,TA,CBlock,TA,TA,Av,ALQ,789,Unf,0,171,960,GasW,TA,N,SBrkr,960,0,0,960,1,0,1,0,3,1,TA,6,Typ,0,NA,Detchd,1970,Unf,2,576,TA,TA,Y,0,288,0,0,0,0,NA,NA,NA,0,5,2006,WD,Normal,129000 +715,60,RL,NA,13517,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Sawyer,RRAe,Norm,1Fam,2Story,6,8,1976,2005,Gable,CompShg,HdBoard,Plywood,BrkFace,289,Gd,TA,CBlock,TA,TA,No,GLQ,533,Unf,0,192,725,GasA,Ex,Y,SBrkr,725,754,0,1479,0,0,2,1,3,1,Gd,6,Typ,0,NA,Attchd,1976,RFn,2,475,TA,TA,Y,0,44,0,0,0,0,NA,NA,NA,0,3,2010,WD,Normal,130500 +716,20,RL,78,10140,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,1Story,6,5,1974,1974,Hip,CompShg,HdBoard,HdBoard,BrkFace,174,TA,TA,CBlock,Gd,TA,No,Unf,0,Unf,0,1064,1064,GasA,TA,Y,SBrkr,1350,0,0,1350,0,0,2,0,3,1,TA,7,Typ,1,TA,Attchd,1974,RFn,2,478,TA,TA,Y,0,0,0,0,0,0,NA,MnPrv,NA,0,8,2009,WD,Normal,165000 +717,70,RM,60,10800,Pave,Grvl,Reg,Bnk,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,2Story,7,8,1890,1998,Gable,CompShg,Wd Sdng,VinylSd,None,0,TA,Gd,BrkTil,TA,TA,No,Unf,0,Unf,0,718,718,GasA,Ex,Y,SBrkr,1576,978,0,2554,0,0,1,1,3,1,TA,8,Typ,0,NA,Detchd,1996,Unf,2,704,TA,TA,P,0,48,143,0,0,0,NA,NA,NA,0,7,2007,WD,Normal,159500 +718,20,RL,80,10000,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,1Story,5,6,1973,2000,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,Gd,TA,No,BLQ,1084,Unf,0,92,1176,GasA,Gd,Y,SBrkr,1178,0,0,1178,0,1,1,1,3,1,Gd,5,Typ,1,Fa,Attchd,1973,Unf,2,439,TA,TA,Y,224,0,0,0,0,0,NA,MnPrv,NA,0,11,2008,WD,Normal,157000 +719,60,RL,96,10542,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NoRidge,Norm,Norm,1Fam,2Story,7,5,1993,1994,Hip,CompShg,Wd Sdng,ImStucc,BrkFace,651,Gd,TA,PConc,Gd,TA,Gd,GLQ,1173,Unf,0,138,1311,GasA,Ex,Y,SBrkr,1325,1093,0,2418,1,0,2,1,3,1,Gd,9,Typ,1,TA,Attchd,1993,RFn,3,983,TA,TA,Y,250,154,216,0,0,0,NA,NA,NA,0,8,2008,WD,Normal,341000 +720,20,RL,69,9920,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,5,6,1969,1969,Gable,CompShg,HdBoard,Plywood,None,0,TA,TA,CBlock,Gd,TA,Gd,ALQ,523,Unf,0,448,971,GasA,TA,Y,SBrkr,971,0,0,971,0,0,1,1,3,1,TA,5,Typ,1,Po,Attchd,1969,Unf,1,300,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,5,2006,WD,Normal,128500 +721,120,RL,NA,6563,Pave,NA,IR1,Low,AllPub,CulDSac,Mod,StoneBr,Norm,Norm,1Fam,1Story,8,5,1985,1985,Gable,CompShg,HdBoard,HdBoard,None,0,Gd,TA,PConc,Gd,TA,Gd,GLQ,1148,Unf,0,594,1742,GasA,TA,Y,SBrkr,1742,0,0,1742,1,0,2,0,2,1,Gd,5,Typ,1,TA,Attchd,1985,RFn,2,564,TA,TA,Y,114,28,234,0,0,0,NA,NA,NA,0,12,2006,WD,Normal,275000 +722,120,RM,NA,4426,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,TwnhsE,1Story,6,5,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,169,Gd,TA,PConc,Gd,TA,Av,GLQ,662,Unf,0,186,848,GasA,Ex,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Gd,3,Typ,0,NA,Attchd,2004,RFn,2,420,TA,TA,Y,160,0,0,0,0,0,NA,NA,NA,0,5,2010,WD,Normal,143000 +723,20,RL,70,8120,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,4,7,1970,1970,Gable,CompShg,MetalSd,MetalSd,None,0,TA,Gd,CBlock,TA,TA,No,ALQ,191,Unf,0,673,864,GasA,Ex,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,TA,5,Typ,0,NA,Detchd,1994,Unf,2,463,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,124500 +724,50,RL,60,8172,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1.5Fin,4,6,1954,1972,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,PConc,TA,TA,No,Unf,0,Unf,0,941,941,GasA,Ex,Y,SBrkr,997,473,0,1470,0,0,2,0,4,1,TA,7,Typ,0,NA,Detchd,1958,Unf,1,548,TA,TA,Y,0,0,0,0,156,0,NA,NA,NA,0,5,2008,WD,Normal,135000 +725,20,RL,86,13286,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1Story,9,5,2007,2008,Hip,CompShg,CemntBd,CmentBd,Stone,340,Ex,TA,PConc,Ex,TA,No,GLQ,1234,Unf,0,464,1698,GasA,Ex,Y,SBrkr,1698,0,0,1698,1,0,2,0,3,1,Ex,8,Typ,1,Gd,Attchd,2007,Fin,3,768,TA,TA,Y,327,64,0,0,0,0,NA,NA,NA,0,2,2009,WD,Normal,320000 +726,20,RL,60,6960,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,4,6,1970,1970,Gable,CompShg,HdBoard,Plywood,None,0,TA,TA,CBlock,TA,TA,No,ALQ,375,BLQ,239,250,864,GasA,TA,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,Gd,5,Typ,0,NA,Detchd,1989,Unf,2,660,TA,TA,Y,96,0,0,0,0,0,NA,NA,Shed,500,11,2009,WD,Normal,120500 +727,20,RL,NA,21695,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Crawfor,Norm,Norm,1Fam,1Story,6,9,1988,2007,Hip,CompShg,Wd Sdng,Plywood,BrkFace,260,Gd,Gd,CBlock,Gd,TA,No,GLQ,808,Unf,0,72,880,GasA,Ex,Y,SBrkr,1680,0,0,1680,1,0,2,0,3,1,Gd,5,Typ,1,Gd,Attchd,1988,Fin,2,540,TA,TA,Y,292,44,0,182,0,0,NA,NA,NA,0,12,2009,WD,Normal,222000 +728,20,RL,64,7314,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,82,Gd,TA,PConc,Gd,TA,Av,GLQ,724,Unf,0,508,1232,GasA,Ex,Y,SBrkr,1232,0,0,1232,1,0,2,0,2,1,Gd,6,Typ,0,NA,Attchd,2007,RFn,2,632,TA,TA,Y,132,0,0,0,0,0,NA,NA,NA,0,2,2009,WD,Normal,194500 +729,90,RL,85,11475,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,Duplex,1Story,5,5,1958,1958,Gable,CompShg,VinylSd,VinylSd,BrkFace,95,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,1584,1584,GasA,TA,Y,SBrkr,1776,0,0,1776,1,0,2,0,4,2,TA,9,Typ,0,NA,Detchd,1968,Unf,3,888,TA,TA,Y,0,25,0,0,0,0,NA,NA,NA,0,7,2009,COD,Abnorml,110000 +730,30,RM,52,6240,Pave,Grvl,Reg,Lvl,AllPub,Inside,Gtl,IDOTRR,Norm,Norm,1Fam,1.5Fin,4,5,1925,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,BLQ,152,Unf,0,628,780,GasA,TA,Y,FuseA,848,0,360,1208,0,0,1,0,2,1,TA,5,Typ,0,NA,Detchd,1962,Unf,2,539,TA,TA,Y,0,23,112,0,0,0,NA,NA,NA,0,1,2009,WD,Normal,103000 +731,120,RL,39,5389,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,StoneBr,Norm,Norm,TwnhsE,1Story,8,5,1995,1996,Gable,CompShg,CemntBd,CmentBd,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,1180,Unf,0,415,1595,GasA,Ex,Y,SBrkr,1616,0,0,1616,1,0,2,0,2,1,Gd,5,Typ,1,TA,Attchd,1995,RFn,2,608,TA,TA,Y,237,152,0,0,0,0,NA,NA,NA,0,3,2010,WD,Normal,236500 +732,80,RL,73,9590,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Timber,Norm,Norm,1Fam,SLvl,7,5,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,442,Gd,TA,PConc,Ex,TA,Av,GLQ,786,Unf,0,82,868,GasA,Ex,Y,SBrkr,1146,0,0,1146,1,0,2,0,3,1,Gd,6,Typ,1,Gd,Attchd,2003,Fin,2,438,TA,TA,Y,160,22,0,0,0,0,NA,NA,NA,0,5,2007,WD,Normal,187500 +733,60,RL,75,11404,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,7,5,1998,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,202,Gd,TA,PConc,Gd,TA,Av,ALQ,252,Unf,0,901,1153,GasA,Ex,Y,SBrkr,1153,878,0,2031,0,0,2,1,3,1,Gd,8,Typ,1,TA,Attchd,1998,Fin,2,541,TA,TA,Y,192,84,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,222500 +734,20,RL,80,10000,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,Sawyer,Feedr,Norm,1Fam,1Story,5,6,1961,1983,Hip,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,TA,TA,No,BLQ,594,Unf,0,270,864,GasA,Ex,Y,SBrkr,1144,0,0,1144,1,0,1,0,3,1,TA,6,Typ,1,TA,Attchd,1961,RFn,1,264,TA,TA,Y,165,0,0,0,0,0,NA,GdWo,Shed,400,3,2009,WD,Normal,131400 +735,20,RL,NA,8978,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,1Fam,1Story,5,5,1968,1968,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,PConc,TA,TA,No,Unf,0,Unf,0,948,948,GasA,TA,Y,SBrkr,948,0,0,948,0,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1968,Unf,1,300,TA,TA,Y,147,0,0,0,0,0,NA,NA,NA,0,5,2007,WD,Family,108000 +736,75,RM,60,10800,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,2.5Unf,7,7,1914,1970,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,Gd,TA,Mn,Rec,390,Unf,0,490,880,GasW,Fa,N,SBrkr,880,888,0,1768,0,0,1,1,2,1,TA,6,Typ,2,TA,Detchd,1914,Unf,2,320,TA,TA,N,0,341,0,0,0,0,NA,NA,NA,0,10,2006,WD,Normal,163000 +737,90,RL,60,8544,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,Duplex,1Story,3,4,1950,1950,Gable,CompShg,Stucco,Stone,None,0,TA,TA,CBlock,NA,NA,NA,NA,0,NA,0,0,0,GasA,Gd,N,FuseF,1040,0,0,1040,0,0,2,0,2,2,TA,6,Typ,0,NA,Detchd,1949,Unf,2,400,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,93500 +738,60,RL,72,10463,Pave,NA,IR1,HLS,AllPub,CulDSac,Gtl,Gilbert,Norm,Norm,1Fam,2Story,8,5,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,893,893,GasA,Ex,Y,SBrkr,901,900,0,1801,0,0,2,1,3,1,Gd,8,Typ,1,Gd,Attchd,2005,Fin,3,800,TA,TA,Y,0,116,0,0,0,0,NA,NA,NA,0,6,2006,WD,Normal,239900 +739,90,RL,60,10800,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Duplex,1Story,5,5,1987,1988,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,CBlock,Gd,Gd,Gd,GLQ,1200,Unf,0,0,1200,GasA,TA,Y,SBrkr,1200,0,0,1200,3,0,3,0,3,1,TA,5,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,120,0,0,0,0,0,NA,NA,NA,0,3,2009,WD,Alloca,179000 +740,60,RL,65,9313,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,7,5,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,864,864,GasA,Ex,Y,SBrkr,864,864,0,1728,0,0,2,1,3,1,Gd,7,Typ,0,NA,Attchd,2004,RFn,2,572,TA,TA,Y,187,56,0,0,0,0,NA,NA,NA,0,4,2009,WD,Normal,190000 +741,70,RM,60,9600,Pave,Grvl,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,2Story,5,7,1910,2002,Gable,CompShg,Wd Sdng,Wd Shng,None,0,TA,Gd,BrkTil,Fa,Fa,No,Unf,0,Unf,0,264,264,GasA,Ex,Y,SBrkr,768,664,0,1432,0,0,2,0,2,1,TA,7,Typ,0,NA,Detchd,1910,Unf,2,360,TA,Gd,Y,270,0,112,0,0,0,NA,GdPrv,NA,0,5,2007,WD,Abnorml,132000 +742,20,RL,65,6768,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,1Fam,1Story,6,8,1961,1996,Hip,CompShg,HdBoard,HdBoard,None,0,TA,Gd,CBlock,TA,TA,Mn,GLQ,832,Unf,0,80,912,GasA,Gd,Y,SBrkr,912,0,0,912,1,1,1,0,3,1,Gd,5,Typ,0,NA,Detchd,1962,Unf,1,288,TA,TA,Y,168,0,0,0,0,0,NA,GdPrv,NA,0,5,2008,WD,Normal,142000 +743,20,RL,65,8450,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SawyerW,Norm,Norm,1Fam,1Story,7,5,2000,2001,Gable,CompShg,VinylSd,VinylSd,BrkFace,108,TA,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1349,1349,GasA,Ex,Y,SBrkr,1349,0,0,1349,0,0,2,0,3,1,TA,6,Typ,0,NA,Attchd,2000,Unf,2,539,TA,TA,Y,120,55,0,0,0,0,NA,GdPrv,NA,0,12,2007,WD,Normal,179000 +744,80,RL,70,12886,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,SLvl,5,6,1963,1999,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,Gd,TA,Av,ALQ,444,Unf,0,76,520,GasA,Ex,Y,SBrkr,1464,0,0,1464,0,1,2,0,3,1,TA,6,Min2,1,TA,Attchd,1997,RFn,2,480,TA,TA,Y,302,0,0,0,100,0,NA,NA,NA,0,10,2009,WD,Normal,175000 +745,120,RL,41,5395,Pave,NA,IR1,HLS,AllPub,Inside,Gtl,StoneBr,Norm,Norm,TwnhsE,1Story,8,5,1993,1993,Gable,CompShg,HdBoard,HdBoard,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,733,Unf,0,604,1337,GasA,Gd,Y,SBrkr,1337,0,0,1337,1,0,2,0,2,1,Gd,5,Typ,1,TA,Attchd,1993,RFn,2,462,TA,TA,Y,96,0,70,168,0,0,NA,NA,NA,0,10,2008,WD,Normal,180000 +746,60,RL,NA,8963,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,2Story,8,9,1976,1996,Hip,CompShg,VinylSd,VinylSd,BrkFace,289,Ex,Gd,CBlock,TA,Gd,No,GLQ,575,ALQ,80,487,1142,GasA,Ex,Y,SBrkr,1175,1540,0,2715,0,1,3,1,4,1,Gd,11,Typ,2,TA,BuiltIn,1994,Fin,2,831,TA,TA,Y,0,204,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,299800 +747,60,RL,NA,8795,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,2Story,7,5,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,300,Unf,0,652,952,GasA,Ex,Y,SBrkr,980,1276,0,2256,0,0,2,1,4,1,Gd,8,Typ,1,TA,BuiltIn,2000,Fin,2,554,TA,TA,Y,224,54,0,0,0,0,NA,NA,NA,0,4,2009,WD,Normal,236000 +748,70,RM,65,11700,Pave,Pave,IR1,Lvl,AllPub,Corner,Gtl,OldTown,Norm,Norm,1Fam,2Story,7,7,1880,2003,Mansard,CompShg,Stucco,Stucco,None,0,Gd,TA,Stone,TA,Fa,No,Unf,0,Unf,0,1240,1240,GasW,TA,N,SBrkr,1320,1320,0,2640,0,0,1,1,4,1,Gd,8,Typ,1,Gd,Detchd,1950,Unf,4,864,TA,TA,N,181,0,386,0,0,0,NA,NA,NA,0,5,2009,WD,Normal,265979 +749,20,RL,59,10593,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NoRidge,Norm,Norm,1Fam,1Story,7,5,1996,1996,Hip,CompShg,VinylSd,VinylSd,BrkFace,338,Gd,TA,PConc,Gd,TA,No,GLQ,919,Unf,0,801,1720,GasA,Ex,Y,SBrkr,1720,0,0,1720,1,0,2,0,3,1,Gd,7,Typ,1,TA,Attchd,1996,Fin,2,527,TA,TA,Y,240,56,154,0,0,0,NA,NA,NA,0,3,2010,WD,Normal,260400 +750,50,RL,50,8405,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1.5Fin,4,3,1945,1950,Gable,CompShg,WdShing,Wd Shng,None,0,TA,TA,Slab,NA,NA,NA,NA,0,NA,0,0,0,Wall,TA,N,FuseF,1088,441,0,1529,0,0,2,0,4,1,TA,9,Mod,0,NA,Detchd,1945,Unf,1,240,TA,TA,N,92,0,185,0,0,0,NA,NA,NA,0,4,2009,WD,Normal,98000 +751,50,RM,55,8800,Pave,Grvl,Reg,Lvl,AllPub,Corner,Gtl,OldTown,Norm,Norm,1Fam,1.5Fin,4,7,1910,2004,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,TA,Fa,No,Unf,0,Unf,0,576,576,GasA,Gd,Y,SBrkr,792,348,0,1140,0,0,1,0,3,1,TA,7,Min2,0,NA,NA,NA,NA,0,0,NA,NA,N,0,160,0,0,0,0,NA,NA,NA,0,6,2010,WD,Normal,96500 +752,60,RL,NA,7750,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,1Fam,2Story,7,5,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,660,660,GasA,Ex,Y,SBrkr,660,660,0,1320,0,0,2,1,3,1,Gd,6,Typ,0,NA,Attchd,2003,Fin,2,400,TA,TA,Y,0,48,0,0,0,0,NA,NA,NA,0,8,2007,WD,Normal,162000 +753,20,RL,79,9236,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,6,5,1997,1997,Gable,CompShg,VinylSd,VinylSd,None,0,TA,Gd,PConc,Gd,TA,Gd,GLQ,1200,Unf,0,279,1479,GasA,Ex,Y,SBrkr,1494,0,0,1494,1,0,2,0,3,1,Gd,6,Typ,0,NA,Attchd,1997,RFn,2,576,TA,TA,Y,168,27,0,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,217000 +754,60,RL,80,10240,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,2Story,8,5,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,178,Gd,TA,PConc,Gd,TA,Mn,Unf,0,Unf,0,1030,1030,GasA,Gd,Y,SBrkr,1038,1060,0,2098,0,0,2,1,3,1,Ex,8,Typ,1,Gd,Attchd,2005,RFn,3,878,TA,TA,Y,192,52,0,0,0,0,NA,NA,NA,0,3,2006,WD,Normal,275500 +755,20,RL,61,7930,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,8,1969,2005,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,CBlock,TA,TA,No,GLQ,439,LwQ,472,115,1026,GasA,Gd,Y,SBrkr,1026,0,0,1026,1,0,1,0,3,1,Gd,5,Typ,0,NA,Detchd,1969,RFn,2,440,TA,TA,Y,171,48,0,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,156000 +756,160,FV,34,3230,Pave,Pave,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,TwnhsE,2Story,6,5,1999,1999,Gable,CompShg,MetalSd,MetalSd,BrkFace,894,TA,TA,PConc,Gd,TA,No,GLQ,381,Unf,0,348,729,GasA,Gd,Y,SBrkr,742,729,0,1471,0,0,2,1,3,1,TA,6,Typ,0,NA,Detchd,1999,Unf,2,440,TA,TA,Y,0,24,0,0,0,0,NA,NA,NA,0,3,2009,WD,Normal,172500 +757,60,RL,68,10769,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,8,5,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,Av,GLQ,20,Unf,0,846,866,GasA,Ex,Y,SBrkr,866,902,0,1768,0,0,2,1,3,1,Gd,7,Typ,0,NA,Attchd,2007,RFn,2,578,TA,TA,Y,144,105,0,0,0,0,NA,NA,NA,0,4,2009,WD,Normal,212000 +758,60,RL,NA,11616,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Sawyer,Norm,Norm,1Fam,2Story,6,5,1978,1978,Hip,CompShg,HdBoard,HdBoard,BrkCmn,328,TA,TA,CBlock,TA,TA,Mn,Rec,438,Unf,0,234,672,GasA,TA,Y,SBrkr,672,714,0,1386,0,0,2,1,3,1,TA,6,Typ,1,TA,Attchd,1978,Fin,2,440,TA,TA,Y,335,0,0,0,0,0,NA,GdPrv,NA,0,4,2010,WD,Abnorml,158900 +759,160,FV,24,2280,Pave,Pave,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,Twnhs,2Story,7,5,1999,1999,Gable,CompShg,MetalSd,MetalSd,BrkFace,360,TA,TA,PConc,Gd,TA,No,ALQ,549,Unf,0,195,744,GasA,Gd,Y,SBrkr,757,744,0,1501,0,0,2,1,3,1,TA,6,Typ,0,NA,Detchd,1999,Unf,2,440,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,8,2008,WD,Normal,179400 +760,60,RL,65,12257,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NoRidge,Norm,Norm,1Fam,2Story,8,5,1995,1995,Gable,CompShg,VinylSd,VinylSd,BrkFace,513,Gd,TA,PConc,Gd,TA,Av,LwQ,56,ALQ,64,1198,1318,GasA,Ex,Y,SBrkr,1328,1203,0,2531,0,0,2,1,4,1,Gd,9,Typ,1,TA,Attchd,1995,RFn,3,752,TA,TA,Y,222,98,0,0,0,0,NA,NA,NA,0,11,2007,WD,Normal,290000 +761,20,RL,70,9100,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,6,1959,1959,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,Rec,612,Unf,0,252,864,GasA,Ex,Y,SBrkr,864,0,0,864,0,0,1,0,2,1,TA,5,Typ,0,NA,Detchd,2008,Unf,1,300,Ex,Ex,Y,0,0,0,0,0,0,NA,NA,Shed,450,10,2009,WD,Normal,127500 +762,30,RM,60,6911,Pave,NA,Reg,Lvl,AllPub,FR2,Gtl,BrkSide,Feedr,Norm,1Fam,1Story,5,5,1924,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,PConc,TA,TA,Mn,LwQ,405,Unf,0,740,1145,GasA,TA,Y,SBrkr,1301,0,0,1301,0,0,1,0,2,1,Fa,5,Min1,0,NA,Detchd,1965,Unf,2,440,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,10,2009,WD,Normal,100000 +763,60,FV,72,8640,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,2Story,7,5,2009,2009,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,Mn,GLQ,24,Unf,0,732,756,GasA,Ex,Y,SBrkr,764,783,0,1547,0,0,2,1,3,1,Gd,7,Typ,0,NA,Attchd,2009,Unf,2,614,TA,TA,Y,169,45,0,0,0,0,NA,NA,NA,0,6,2010,Con,Normal,215200 +764,60,RL,82,9430,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NoRidge,Norm,Norm,1Fam,2Story,8,5,1999,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,673,Gd,TA,PConc,Gd,TA,Mn,GLQ,1163,Unf,0,89,1252,GasA,Ex,Y,SBrkr,1268,1097,0,2365,1,0,2,1,3,1,Gd,8,Typ,1,Gd,Attchd,1999,RFn,3,856,TA,TA,Y,0,128,0,0,180,0,NA,NA,NA,0,7,2009,WD,Normal,337000 +765,120,RL,30,9549,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Veenker,Norm,Norm,TwnhsE,1Story,8,5,1995,1996,Hip,CompShg,BrkFace,BrkFace,None,0,Gd,Gd,PConc,Gd,Gd,Av,LwQ,437,GLQ,1057,0,1494,GasA,Ex,Y,SBrkr,1494,0,0,1494,1,0,1,1,2,1,Ex,6,Typ,1,Gd,Attchd,1995,Fin,2,481,TA,TA,Y,0,30,0,0,216,0,NA,NA,NA,0,4,2006,WD,Normal,270000 +766,20,RL,75,14587,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,1Story,9,5,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,284,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1498,1498,GasA,Ex,Y,SBrkr,1506,0,0,1506,0,0,2,0,2,1,Ex,6,Typ,1,Gd,Attchd,2008,Fin,2,592,TA,TA,Y,0,174,0,0,0,0,NA,NA,NA,0,8,2008,New,Partial,264132 +767,60,RL,80,10421,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,2Story,7,5,1988,1988,Gable,CompShg,HdBoard,HdBoard,BrkFace,42,TA,TA,CBlock,Gd,TA,No,GLQ,394,Unf,0,586,980,GasA,TA,Y,SBrkr,980,734,0,1714,0,0,2,1,3,1,TA,7,Typ,1,TA,Attchd,1988,Unf,2,496,TA,TA,Y,228,66,156,0,0,0,NA,MnPrv,Shed,500,3,2010,WD,Normal,196500 +768,50,RL,75,12508,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Mitchel,Norm,Norm,1Fam,1.5Fin,6,7,1940,1985,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,CBlock,Gd,TA,Mn,ALQ,660,Unf,0,323,983,GasA,Ex,Y,SBrkr,983,767,0,1750,1,0,2,0,4,1,TA,7,Mod,0,NA,Attchd,1989,Unf,1,423,TA,TA,Y,245,0,156,0,0,0,NA,NA,Shed,1300,7,2008,WD,Normal,160000 +769,20,RL,70,9100,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2004,2005,Hip,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,24,Unf,0,1836,1860,GasA,Ex,Y,SBrkr,1836,0,0,1836,0,0,2,0,3,1,Gd,8,Typ,1,Gd,Attchd,2004,Fin,2,484,TA,TA,Y,120,33,0,0,0,0,NA,NA,NA,0,10,2006,WD,Normal,216837 +770,60,RL,47,53504,Pave,NA,IR2,HLS,AllPub,CulDSac,Mod,StoneBr,Norm,Norm,1Fam,2Story,8,5,2003,2003,Hip,CompShg,CemntBd,Wd Shng,BrkFace,603,Ex,TA,PConc,Gd,TA,Gd,ALQ,1416,Unf,0,234,1650,GasA,Ex,Y,SBrkr,1690,1589,0,3279,1,0,3,1,4,1,Ex,12,Mod,1,Gd,BuiltIn,2003,Fin,3,841,TA,TA,Y,503,36,0,0,210,0,NA,NA,NA,0,6,2010,WD,Normal,538000 +771,85,RL,NA,7252,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Sawyer,Norm,Norm,1Fam,SFoyer,5,5,1982,1982,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,Gd,TA,Av,GLQ,685,Unf,0,173,858,GasA,TA,Y,SBrkr,858,0,0,858,1,0,1,0,2,1,TA,5,Typ,0,NA,Detchd,1983,Unf,2,576,TA,TA,Y,120,0,0,0,0,0,NA,NA,NA,0,4,2009,WD,Normal,134900 +772,20,RL,67,8877,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1Story,4,5,1951,1951,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,Fa,Fa,No,LwQ,836,Unf,0,0,836,GasA,TA,Y,FuseF,1220,0,0,1220,0,0,1,0,2,1,TA,6,Typ,0,NA,Detchd,1951,Unf,2,396,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,4,2006,COD,Normal,102000 +773,80,RL,94,7819,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,SLvl,6,5,1976,1976,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,CBlock,TA,TA,Av,ALQ,422,BLQ,127,480,1029,GasA,TA,Y,SBrkr,1117,0,0,1117,1,0,1,0,3,1,TA,6,Typ,1,TA,Detchd,1976,Unf,2,672,TA,TA,Y,144,0,0,0,0,0,NA,MnPrv,NA,0,3,2010,WD,Abnorml,107000 +774,20,RL,70,10150,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Feedr,Norm,1Fam,1Story,5,5,1958,1958,Gable,CompShg,Wd Sdng,Wd Sdng,None,1,TA,TA,CBlock,TA,TA,No,Rec,456,Unf,0,456,912,GasA,Ex,Y,FuseA,912,0,0,912,0,0,1,0,2,1,TA,5,Typ,0,NA,Attchd,1958,RFn,1,275,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,7,2007,COD,Normal,114500 +775,20,RL,110,14226,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NridgHt,Norm,Norm,1Fam,1Story,8,5,2006,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,375,Gd,TA,PConc,Gd,TA,Av,Unf,0,Unf,0,1935,1935,GasA,Gd,Y,SBrkr,1973,0,0,1973,0,0,2,0,3,1,Gd,9,Typ,1,Gd,Attchd,2006,Fin,3,895,TA,TA,Y,315,45,0,0,0,0,NA,NA,NA,0,7,2007,New,Partial,395000 +776,120,RM,32,4500,Pave,NA,Reg,Lvl,AllPub,FR2,Gtl,Mitchel,Norm,Norm,TwnhsE,1Story,6,5,1998,1998,Hip,CompShg,VinylSd,VinylSd,BrkFace,320,TA,TA,PConc,Ex,TA,No,GLQ,866,Unf,0,338,1204,GasA,Ex,Y,SBrkr,1204,0,0,1204,1,0,2,0,2,1,TA,5,Typ,0,NA,Attchd,1998,Fin,2,412,TA,TA,Y,0,247,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,162000 +777,20,RL,86,11210,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,240,Gd,TA,PConc,Gd,TA,Av,GLQ,20,Unf,0,1594,1614,GasA,Ex,Y,SBrkr,1614,0,0,1614,0,0,2,0,3,1,Gd,7,Typ,0,NA,Attchd,2005,RFn,3,865,TA,TA,Y,144,59,0,0,0,0,NA,NA,NA,0,7,2006,New,Partial,221500 +778,20,RL,100,13350,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,1Story,5,5,1974,1974,Hip,CompShg,HdBoard,Plywood,None,0,TA,TA,CBlock,TA,TA,No,ALQ,762,Unf,0,102,864,GasA,TA,Y,SBrkr,894,0,0,894,1,0,1,0,3,1,TA,5,Typ,1,Fa,Attchd,1974,Unf,2,440,TA,TA,Y,241,0,0,0,0,0,NA,MnPrv,NA,0,6,2006,WD,Normal,142500 +779,90,RH,60,8400,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SawyerW,Feedr,Norm,Duplex,1Story,5,5,1977,1977,Gable,CompShg,Plywood,Plywood,BrkFace,320,TA,TA,Slab,NA,NA,NA,NA,0,NA,0,0,0,GasA,TA,Y,SBrkr,2020,0,0,2020,0,0,2,0,4,2,TA,10,Typ,2,TA,Detchd,1977,Unf,2,630,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,10,2007,WD,Normal,144000 +780,90,RL,78,10530,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Mitchel,Norm,Norm,Duplex,SFoyer,6,5,1977,1977,Gable,CompShg,Plywood,ImStucc,BrkFace,90,TA,TA,CBlock,Gd,TA,Gd,GLQ,975,Unf,0,0,975,GasA,TA,Y,SBrkr,1004,0,0,1004,1,0,1,0,2,1,TA,4,Typ,0,NA,Attchd,1977,Unf,2,504,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,5,2006,WD,Normal,135000 +781,20,RL,63,7875,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,1Story,7,5,1995,1996,Gable,CompShg,HdBoard,HdBoard,BrkFace,38,TA,TA,PConc,Gd,Gd,No,Unf,0,Unf,0,1237,1237,GasA,Gd,Y,SBrkr,1253,0,0,1253,0,0,2,0,3,1,TA,6,Typ,1,TA,Attchd,1995,Fin,2,402,TA,TA,Y,220,21,0,0,0,0,NA,NA,NA,0,6,2007,WD,Normal,176000 +782,60,RL,65,7153,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SawyerW,Norm,Norm,1Fam,2Story,6,5,1992,1992,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,Gd,PConc,Gd,TA,No,ALQ,387,Unf,0,374,761,GasA,Ex,Y,SBrkr,810,793,0,1603,0,0,2,1,3,1,Gd,7,Typ,0,NA,Attchd,1992,RFn,2,484,TA,TA,Y,0,124,0,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,175900 +783,20,RL,67,16285,Pave,NA,IR2,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2001,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1413,1413,GasA,Ex,Y,SBrkr,1430,0,0,1430,0,0,2,0,3,1,Gd,6,Typ,0,NA,Attchd,2001,RFn,2,605,TA,TA,Y,0,33,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,187100 +784,85,RL,NA,9101,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Mitchel,Norm,Norm,1Fam,SFoyer,5,6,1978,1978,Gable,CompShg,Plywood,Plywood,BrkFace,104,TA,Gd,PConc,Gd,TA,Av,GLQ,1097,Unf,0,0,1097,GasA,Ex,Y,SBrkr,1110,0,0,1110,1,0,1,0,1,1,Gd,4,Typ,1,TA,Attchd,1978,Fin,2,602,TA,TA,Y,303,30,0,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,165500 +785,75,RM,35,6300,Pave,Grvl,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,2.5Unf,6,6,1914,2001,Gable,CompShg,Wd Sdng,Wd Shng,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,742,742,GasA,Ex,Y,SBrkr,742,742,0,1484,0,0,2,0,3,1,TA,9,Typ,1,Gd,NA,NA,NA,0,0,NA,NA,Y,0,291,134,0,0,0,NA,NA,NA,0,6,2008,WD,Normal,128000 +786,20,RL,NA,9790,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,Feedr,Norm,1Fam,1Story,6,5,1967,1967,Gable,CompShg,BrkFace,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,Rec,251,LwQ,630,491,1372,GasA,TA,Y,SBrkr,1342,0,0,1342,0,0,2,0,3,1,TA,7,Typ,1,Gd,Attchd,1967,Unf,2,457,TA,TA,Y,0,0,0,0,197,0,NA,NA,NA,0,9,2009,WD,Normal,161500 +787,50,RM,60,10800,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Artery,Norm,1Fam,1.5Fin,5,6,1915,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,Gd,PConc,Fa,TA,No,LwQ,686,Unf,0,0,686,GasA,TA,Y,SBrkr,966,686,0,1652,1,0,2,0,4,1,TA,7,Typ,0,NA,Detchd,1961,Unf,1,416,TA,TA,Y,0,0,196,0,0,0,NA,NA,Shed,1200,6,2010,WD,Normal,139000 +788,60,RL,76,10142,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,SawyerW,Norm,Norm,1Fam,2Story,7,5,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,656,Unf,0,300,956,GasA,Ex,Y,SBrkr,956,1128,0,2084,1,0,2,1,4,1,Gd,8,Typ,0,NA,BuiltIn,2004,RFn,2,618,TA,TA,Y,0,45,0,0,0,0,NA,NA,NA,0,1,2010,WD,Normal,233000 +789,20,RM,50,6000,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,OldTown,Norm,Norm,1Fam,1Story,4,7,1954,2000,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,901,901,GasA,Ex,Y,SBrkr,901,0,0,901,0,0,1,0,2,1,TA,4,Typ,0,NA,Detchd,1954,Unf,1,281,Fa,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,8,2008,WD,Normal,107900 +790,60,RL,NA,12205,Pave,NA,IR1,Low,AllPub,Inside,Gtl,ClearCr,Norm,Norm,1Fam,2Story,6,8,1966,2007,Gable,CompShg,HdBoard,HdBoard,BrkFace,157,TA,TA,CBlock,TA,Fa,Gd,LwQ,568,Unf,0,264,832,GasA,Gd,Y,SBrkr,976,1111,0,2087,0,0,2,1,5,1,Gd,9,Typ,0,NA,Attchd,1966,Fin,2,444,TA,TA,Y,133,168,0,0,0,0,NA,NA,NA,0,7,2007,WD,Normal,187500 +791,120,RL,43,3182,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Blmngtn,Norm,Norm,TwnhsE,1Story,7,5,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,11,Gd,TA,PConc,Gd,TA,No,GLQ,16,Unf,0,1129,1145,GasA,Ex,Y,SBrkr,1145,0,0,1145,0,0,2,0,2,1,Gd,5,Typ,1,Gd,Attchd,2005,Fin,2,397,TA,TA,Y,100,16,0,0,0,0,NA,NA,NA,0,9,2009,WD,Normal,160200 +792,80,RL,NA,11333,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Mitchel,Norm,Norm,1Fam,SLvl,6,5,1976,1976,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,PConc,Gd,TA,Av,ALQ,539,Unf,0,490,1029,GasA,TA,Y,SBrkr,1062,0,0,1062,1,0,1,0,3,1,TA,5,Typ,2,TA,Attchd,1976,RFn,2,539,TA,TA,Y,120,0,0,0,0,0,NA,NA,NA,0,5,2007,WD,Normal,146800 +793,60,RL,92,9920,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,NoRidge,Norm,Norm,1Fam,2Story,7,5,1996,1997,Gable,CompShg,MetalSd,MetalSd,None,0,Gd,TA,PConc,Gd,TA,Av,GLQ,862,Unf,0,255,1117,GasA,Ex,Y,SBrkr,1127,886,0,2013,1,0,2,1,3,1,TA,8,Typ,1,TA,Attchd,1997,Unf,2,455,TA,TA,Y,180,130,0,0,0,0,NA,NA,NA,0,6,2007,WD,Normal,269790 +794,20,RL,76,9158,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,1Story,8,5,2007,2007,Gable,CompShg,CemntBd,CmentBd,Stone,140,Gd,TA,PConc,Gd,TA,Av,Unf,0,Unf,0,1496,1496,GasA,Ex,Y,SBrkr,1496,0,0,1496,0,0,2,0,3,1,Gd,7,Typ,0,NA,Attchd,2007,Fin,2,474,TA,TA,Y,168,130,0,0,0,0,NA,NA,NA,0,6,2007,New,Partial,225000 +795,60,RL,NA,10832,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,1Fam,2Story,7,5,1994,1996,Gable,CompShg,MetalSd,MetalSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,712,712,GasA,Ex,Y,SBrkr,1086,809,0,1895,0,0,2,1,3,1,Gd,7,Typ,1,TA,Attchd,1994,Fin,2,409,TA,TA,Y,143,46,0,0,0,0,NA,NA,Shed,500,10,2008,WD,Normal,194500 +796,60,RL,70,8400,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SawyerW,Norm,Norm,1Fam,2Story,6,6,1980,1981,Gable,CompShg,HdBoard,HdBoard,BrkFace,130,TA,TA,CBlock,Gd,TA,No,Unf,0,Unf,0,650,650,GasA,TA,Y,SBrkr,888,676,0,1564,0,0,2,1,3,1,TA,7,Typ,1,TA,Attchd,1980,Unf,2,476,TA,TA,Y,0,50,0,0,204,0,NA,MnPrv,NA,0,4,2010,WD,Normal,171000 +797,20,RL,71,8197,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,1Story,6,5,1977,1977,Gable,CompShg,Plywood,Plywood,BrkFace,148,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,660,660,GasA,Ex,Y,SBrkr,1285,0,0,1285,0,0,1,1,3,1,TA,7,Typ,1,TA,Attchd,1977,RFn,2,528,TA,TA,Y,138,0,0,0,0,0,NA,MnPrv,NA,0,4,2007,WD,Normal,143500 +798,20,RL,57,7677,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,5,1953,1953,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,CBlock,TA,TA,No,BLQ,570,Unf,0,203,773,GasA,Gd,Y,SBrkr,773,0,0,773,0,0,1,0,2,1,TA,4,Typ,0,NA,Attchd,1953,Unf,1,240,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,4,2008,WD,Abnorml,110000 +799,60,RL,104,13518,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,2Story,9,5,2008,2009,Hip,CompShg,VinylSd,VinylSd,Stone,860,Ex,TA,PConc,Ex,TA,No,Unf,0,Unf,0,1926,1926,GasA,Ex,Y,SBrkr,1966,1174,0,3140,0,0,3,1,4,1,Ex,11,Typ,2,Gd,BuiltIn,2009,Fin,3,820,TA,TA,Y,144,78,0,0,0,0,NA,NA,NA,0,7,2009,New,Partial,485000 +800,50,RL,60,7200,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,SWISU,Feedr,Norm,1Fam,1.5Fin,5,7,1937,1950,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,252,TA,TA,BrkTil,Gd,TA,No,ALQ,569,Unf,0,162,731,GasA,Ex,Y,SBrkr,981,787,0,1768,1,0,1,1,3,1,Gd,7,Typ,2,TA,Detchd,1939,Unf,1,240,TA,TA,Y,0,0,264,0,0,0,NA,MnPrv,NA,0,6,2007,WD,Normal,175000 +801,60,RL,79,12798,Pave,NA,IR1,HLS,AllPub,Inside,Mod,ClearCr,Feedr,Norm,1Fam,2Story,6,5,1997,1997,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,Gd,GLQ,462,Unf,0,154,616,GasA,Gd,Y,SBrkr,616,1072,0,1688,1,0,2,1,4,1,Gd,8,Typ,0,NA,Attchd,1997,RFn,2,603,TA,TA,Y,403,114,185,0,0,0,NA,NA,Shed,400,5,2008,WD,Normal,200000 +802,30,RM,40,4800,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,IDOTRR,Norm,Norm,1Fam,1Story,4,7,1916,1990,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,LwQ,197,Unf,0,999,1196,GasA,Ex,Y,FuseA,1196,0,0,1196,1,0,1,0,2,1,TA,5,Typ,0,NA,Detchd,1957,Unf,2,440,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,7,2007,WD,Normal,109900 +803,60,RL,63,8199,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,7,5,2005,2005,Gable,CompShg,WdShing,Wd Shng,None,0,Gd,TA,PConc,Gd,TA,Av,GLQ,648,Unf,0,80,728,GasA,Ex,Y,SBrkr,728,728,0,1456,1,0,2,1,3,1,Gd,7,Typ,1,Gd,Attchd,2005,Fin,2,410,TA,TA,Y,36,18,0,0,0,0,NA,NA,NA,0,10,2008,WD,Normal,189000 +804,60,RL,107,13891,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,2Story,9,5,2008,2009,Hip,CompShg,VinylSd,VinylSd,Stone,424,Ex,TA,PConc,Ex,TA,Gd,Unf,0,Unf,0,1734,1734,GasA,Ex,Y,SBrkr,1734,1088,0,2822,0,0,3,1,4,1,Ex,12,Typ,1,Gd,BuiltIn,2009,RFn,3,1020,TA,TA,Y,52,170,0,0,192,0,NA,NA,NA,0,1,2009,New,Partial,582933 +805,20,RL,75,9000,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,5,1954,1954,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,LwQ,812,Unf,0,124,936,GasA,TA,Y,SBrkr,1128,0,0,1128,0,0,1,0,2,1,TA,5,Min1,0,NA,Attchd,1954,Unf,1,286,TA,TA,Y,0,0,0,0,0,0,NA,GdWo,NA,0,6,2006,WD,Family,118000 +806,20,RL,91,12274,Pave,NA,IR1,Lvl,AllPub,FR2,Gtl,Somerst,Norm,Norm,1Fam,1Story,7,5,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,256,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1417,1417,GasA,Ex,Y,SBrkr,1428,0,0,1428,0,0,2,0,3,1,Ex,6,Typ,0,NA,Attchd,2008,RFn,2,554,TA,TA,Y,0,60,0,0,0,0,NA,NA,NA,0,7,2008,New,Partial,227680 +807,80,RL,75,9750,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,SLvl,5,5,1967,1967,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,TA,TA,Av,ALQ,400,Rec,480,100,980,GasA,Gd,Y,SBrkr,980,0,0,980,0,0,2,0,3,1,TA,6,Typ,0,NA,Attchd,1967,Fin,1,384,TA,TA,Y,68,0,0,0,0,0,NA,NA,NA,0,10,2006,WD,Normal,135500 +808,70,RL,144,21384,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,2Story,5,6,1923,2004,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,Gd,GLQ,1309,Unf,0,15,1324,GasA,Ex,Y,SBrkr,1072,504,0,1576,2,0,1,1,3,1,Gd,6,Typ,1,TA,Attchd,1923,RFn,2,528,TA,TA,Y,0,312,0,0,0,0,NA,NA,NA,0,5,2009,WD,Normal,223500 +809,80,RL,85,13400,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,SLvl,5,5,1966,1966,Gable,CompShg,VinylSd,VinylSd,BrkFace,1047,TA,TA,CBlock,TA,TA,Av,ALQ,516,BLQ,128,380,1024,GasA,TA,Y,SBrkr,1086,0,0,1086,1,0,1,0,3,1,TA,6,Typ,1,Gd,Attchd,1966,RFn,2,484,TA,TA,Y,0,0,0,0,0,0,NA,GdWo,NA,0,6,2006,WD,Normal,159950 +810,75,RM,90,8100,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,OldTown,Norm,Norm,1Fam,2.5Unf,5,5,1898,1965,Hip,CompShg,AsbShng,AsbShng,None,0,TA,TA,PConc,TA,TA,No,Unf,0,Unf,0,849,849,GasA,TA,N,FuseA,1075,1063,0,2138,0,0,2,0,2,3,TA,11,Typ,0,NA,Detchd,1910,Unf,2,360,Fa,Po,N,40,156,0,0,0,0,NA,MnPrv,NA,0,11,2009,WD,Normal,106000 +811,20,RL,78,10140,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,1Story,6,6,1974,1999,Hip,CompShg,HdBoard,HdBoard,BrkFace,99,TA,TA,CBlock,TA,TA,No,ALQ,663,LwQ,377,0,1040,GasA,Fa,Y,SBrkr,1309,0,0,1309,1,0,1,1,3,1,Gd,5,Typ,1,Fa,Attchd,1974,RFn,2,484,TA,TA,Y,265,0,0,0,0,648,Fa,GdPrv,NA,0,1,2006,WD,Normal,181000 +812,120,RM,NA,4438,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,TwnhsE,1Story,6,5,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,169,Gd,TA,PConc,Gd,TA,Gd,GLQ,662,Unf,0,186,848,GasA,Ex,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Gd,4,Typ,1,Gd,Attchd,2004,Fin,2,420,TA,TA,Y,140,0,0,0,0,0,NA,NA,NA,0,6,2008,ConLD,Normal,144500 +813,20,C (all),66,8712,Grvl,NA,Reg,Bnk,AllPub,Inside,Mod,IDOTRR,Norm,Norm,1Fam,1Story,5,5,1952,1952,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Fa,TA,CBlock,TA,TA,Av,Unf,0,Unf,0,540,540,GasA,TA,N,FuseA,1044,0,0,1044,0,0,1,0,2,1,Fa,4,Typ,0,NA,Basment,1952,Unf,2,504,TA,TA,N,0,0,0,0,0,0,NA,NA,Shed,54,6,2010,WD,Alloca,55993 +814,20,RL,75,9750,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,6,1958,1958,Gable,CompShg,MetalSd,MetalSd,BrkFace,243,TA,TA,CBlock,TA,TA,No,Rec,608,Unf,0,834,1442,GasA,Gd,Y,SBrkr,1442,0,0,1442,0,0,1,1,4,1,TA,7,Typ,0,NA,Attchd,1958,RFn,1,301,TA,TA,Y,0,0,275,0,0,0,NA,NA,Shed,500,4,2007,COD,Normal,157900 +815,50,RL,45,8248,Pave,Grvl,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1.5Fin,5,7,1918,1950,Gable,CompShg,Stucco,Stucco,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,686,686,GasW,Gd,Y,SBrkr,686,564,0,1250,0,1,1,1,3,1,Fa,7,Typ,0,NA,Detchd,1955,Unf,1,280,TA,TA,P,207,0,96,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,116000 +816,20,RL,48,12137,Pave,NA,IR2,Lvl,AllPub,CulDSac,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,442,TA,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1649,1649,GasA,Ex,Y,SBrkr,1661,0,0,1661,0,0,2,0,3,1,Gd,6,Typ,0,NA,Attchd,1998,RFn,2,598,TA,TA,Y,0,34,0,0,0,0,NA,NA,NA,0,5,2010,WD,Normal,224900 +817,20,RL,NA,11425,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,6,1954,1954,Gable,CompShg,BrkFace,BrkFace,None,0,TA,TA,CBlock,TA,TA,No,BLQ,486,Unf,0,522,1008,GasA,Gd,Y,SBrkr,1008,0,0,1008,0,0,1,0,2,1,TA,4,Typ,1,Gd,Attchd,1954,RFn,1,275,TA,TA,Y,0,0,120,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,137000 +818,20,RL,NA,13265,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Mitchel,Norm,Norm,1Fam,1Story,8,5,2002,2002,Hip,CompShg,CemntBd,CmentBd,BrkFace,148,Gd,TA,PConc,Gd,TA,No,GLQ,1218,Unf,0,350,1568,GasA,Ex,Y,SBrkr,1689,0,0,1689,1,0,2,0,3,1,Gd,7,Typ,2,Gd,Attchd,2002,RFn,3,857,TA,TA,Y,150,59,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,271000 +819,80,RL,80,8816,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,ClearCr,Norm,Norm,1Fam,SLvl,6,7,1971,1971,Gable,CompShg,HdBoard,HdBoard,BrkFace,80,TA,TA,CBlock,TA,TA,Av,GLQ,504,Unf,0,506,1010,GasA,Gd,Y,SBrkr,1052,0,0,1052,1,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1971,Unf,2,440,TA,TA,Y,0,0,0,0,0,0,NA,MnPrv,NA,0,6,2010,WD,Normal,155000 +820,120,RL,44,6371,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,TwnhsE,1Story,7,5,2009,2010,Gable,CompShg,VinylSd,VinylSd,Stone,128,Gd,TA,PConc,Gd,TA,Mn,GLQ,733,Unf,0,625,1358,GasA,Ex,Y,SBrkr,1358,0,0,1358,1,0,2,0,2,1,Gd,6,Typ,1,Gd,Attchd,2010,RFn,2,484,TA,TA,Y,192,35,0,0,0,0,NA,NA,NA,0,6,2010,New,Partial,224000 +821,60,RL,72,7226,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,CollgCr,Norm,Norm,1Fam,2Story,7,5,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,798,798,GasA,Ex,Y,SBrkr,798,842,0,1640,0,0,2,1,3,1,Gd,6,Typ,0,NA,Attchd,2003,RFn,2,595,TA,TA,Y,0,45,0,0,0,0,NA,NA,NA,0,6,2008,WD,Normal,183000 +822,20,RM,60,6000,Pave,Pave,Reg,Bnk,AllPub,Inside,Mod,OldTown,Norm,Norm,2fmCon,1Story,4,4,1953,1953,Gable,CompShg,MetalSd,MetalSd,None,0,Fa,TA,CBlock,Fa,TA,No,Unf,0,Unf,0,936,936,GasA,TA,N,SBrkr,936,0,0,936,0,0,1,0,2,1,TA,4,Min2,0,NA,Detchd,1974,Unf,2,576,TA,TA,Y,0,32,112,0,0,0,NA,NA,NA,0,2,2009,WD,Normal,93000 +823,60,RL,NA,12394,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,1Fam,2Story,7,5,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,Gd,Unf,0,Unf,0,847,847,GasA,Ex,Y,SBrkr,847,886,0,1733,0,0,2,1,3,1,Gd,7,Typ,1,Gd,BuiltIn,2003,Fin,2,433,TA,TA,Y,100,48,0,0,0,0,NA,NA,NA,0,10,2007,WD,Family,225000 +824,50,RL,60,9900,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SWISU,Norm,Norm,1Fam,1.5Fin,6,7,1940,1950,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,778,778,GasA,TA,Y,SBrkr,944,545,0,1489,0,0,2,0,3,1,TA,7,Typ,1,Gd,Detchd,1940,Unf,1,240,TA,TA,Y,335,0,0,0,0,0,NA,GdWo,NA,0,7,2009,WD,Normal,139500 +825,20,FV,81,11216,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,1Story,8,5,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,Gd,No,Unf,0,Unf,0,1489,1489,GasA,Ex,Y,SBrkr,1489,0,0,1489,0,0,2,0,3,1,Gd,7,Typ,1,Gd,Attchd,2006,RFn,2,776,TA,TA,Y,0,140,0,0,0,0,NA,NA,NA,0,6,2006,New,Partial,232600 +826,20,RL,114,14803,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,PosN,PosN,1Fam,1Story,10,5,2007,2008,Hip,CompShg,CemntBd,CmentBd,BrkFace,816,Ex,TA,PConc,Ex,TA,Av,GLQ,1636,Unf,0,442,2078,GasA,Ex,Y,SBrkr,2084,0,0,2084,1,0,2,0,2,1,Ex,7,Typ,1,Gd,Attchd,2007,Fin,3,1220,TA,TA,Y,188,45,0,0,0,0,NA,NA,NA,0,6,2008,New,Partial,385000 +827,45,RM,50,6130,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,1.5Unf,5,6,1924,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,BrkTil,TA,TA,No,ALQ,784,Unf,0,0,784,GasA,Gd,Y,SBrkr,784,0,0,784,1,0,1,0,2,1,Gd,5,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,0,0,116,0,0,0,NA,NA,NA,0,5,2008,WD,Normal,109500 +828,20,RL,65,8529,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,SawyerW,Norm,Norm,1Fam,1Story,7,5,2001,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,20,Unf,0,1434,1454,GasA,Ex,Y,SBrkr,1434,0,0,1434,0,0,2,0,3,1,Gd,6,Typ,1,TA,Attchd,2001,RFn,2,527,TA,TA,Y,290,39,0,0,0,0,NA,NA,NA,0,4,2009,WD,Normal,189000 +829,60,RL,NA,28698,Pave,NA,IR2,Low,AllPub,CulDSac,Sev,ClearCr,Norm,Norm,1Fam,2Story,5,5,1967,1967,Flat,Tar&Grv,Plywood,Plywood,None,0,TA,TA,PConc,TA,Gd,Gd,LwQ,249,ALQ,764,0,1013,GasA,TA,Y,SBrkr,1160,966,0,2126,0,1,2,1,3,1,TA,7,Min2,0,NA,Attchd,1967,Fin,2,538,TA,TA,Y,486,0,0,0,225,0,NA,NA,NA,0,6,2009,WD,Abnorml,185000 +830,160,FV,24,2544,Pave,Pave,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,Twnhs,2Story,7,5,2005,2005,Gable,CompShg,MetalSd,MetalSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,600,600,GasA,Ex,Y,SBrkr,520,623,80,1223,0,0,2,1,2,1,Gd,4,Typ,0,NA,Detchd,2005,RFn,2,480,TA,TA,Y,0,166,0,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,147400 +831,20,RL,80,11900,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,5,1957,1957,Gable,CompShg,HdBoard,HdBoard,BrkFace,387,TA,TA,CBlock,TA,TA,No,Rec,1040,Unf,0,352,1392,GasA,TA,Y,FuseA,1392,0,0,1392,1,0,1,1,3,1,TA,6,Typ,2,Gd,Attchd,1957,RFn,2,458,TA,TA,Y,0,0,0,0,192,0,NA,NA,NA,0,6,2008,WD,Normal,166000 +832,160,FV,30,3180,Pave,Pave,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,TwnhsE,2Story,7,5,2005,2005,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,PConc,Gd,TA,No,Unf,0,Unf,0,600,600,GasA,Ex,Y,SBrkr,520,600,80,1200,0,0,2,1,2,1,Gd,4,Typ,0,NA,Detchd,2005,RFn,2,480,TA,TA,Y,0,166,0,0,0,0,NA,NA,NA,0,6,2006,WD,Normal,151000 +833,60,RL,44,9548,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,CollgCr,Norm,Norm,1Fam,2Story,7,6,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,223,Gd,TA,PConc,Gd,TA,No,GLQ,483,Unf,0,458,941,GasA,Ex,Y,SBrkr,941,888,0,1829,1,0,2,1,3,1,Gd,7,Typ,1,TA,Attchd,2003,RFn,2,613,TA,TA,Y,192,39,0,0,0,0,NA,NA,NA,0,1,2010,WD,Normal,237000 +834,20,RL,100,10004,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,6,1964,1964,Gable,CompShg,HdBoard,Plywood,BrkFace,180,TA,TA,CBlock,TA,TA,No,Rec,196,BLQ,345,975,1516,GasA,TA,Y,SBrkr,1516,0,0,1516,0,0,1,1,3,1,TA,6,Typ,0,NA,Attchd,1964,RFn,2,472,TA,TA,Y,0,0,0,0,152,0,NA,NA,NA,0,2,2009,WD,Normal,167000 +835,20,RL,75,7875,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,6,1961,1961,Gable,CompShg,VinylSd,VinylSd,BrkFace,136,TA,TA,CBlock,TA,TA,No,Rec,572,Unf,0,572,1144,GasA,Gd,Y,SBrkr,1144,0,0,1144,1,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1961,Unf,2,456,TA,TA,Y,0,0,0,0,0,0,NA,GdWo,NA,0,9,2008,WD,Normal,139950 +836,20,RL,60,9600,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,1Story,4,7,1950,1995,Gable,CompShg,VinylSd,HdBoard,None,0,TA,TA,CBlock,Gd,TA,No,BLQ,442,Unf,0,625,1067,GasA,TA,Y,SBrkr,1067,0,0,1067,0,0,2,0,2,1,Gd,4,Min2,0,NA,Attchd,1996,Unf,2,436,TA,TA,Y,290,0,0,0,0,0,NA,NA,NA,0,2,2010,WD,Normal,128000 +837,30,RM,90,8100,Pave,Pave,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,1Story,5,6,1948,1973,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,BrkTil,TA,TA,No,Rec,338,Unf,0,1221,1559,GasA,Gd,Y,SBrkr,1559,0,0,1559,1,0,1,0,2,1,TA,5,Min2,0,NA,Detchd,1948,Unf,2,812,TA,TA,Y,0,116,230,0,0,0,NA,GdWo,NA,0,6,2007,COD,Normal,153500 +838,160,RM,21,1680,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrDale,Norm,Norm,Twnhs,2Story,6,5,1973,1973,Gable,CompShg,HdBoard,HdBoard,BrkFace,158,TA,TA,CBlock,TA,TA,No,BLQ,330,Unf,0,153,483,GasA,TA,Y,SBrkr,483,504,0,987,1,0,1,1,2,1,TA,5,Typ,0,NA,Detchd,1973,Unf,1,264,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,11,2008,WD,Normal,100000 +839,20,RL,75,9525,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,5,6,1995,2006,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1099,1099,GasA,Ex,Y,SBrkr,1099,0,0,1099,0,0,1,1,3,1,Gd,6,Typ,0,NA,Attchd,1999,Unf,1,352,TA,TA,Y,278,0,0,0,0,0,NA,NA,NA,0,6,2008,WD,Normal,144000 +840,50,RL,70,11767,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1.5Fin,5,6,1946,1995,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,BLQ,352,Unf,0,416,768,GasA,Ex,Y,SBrkr,768,432,0,1200,0,0,1,0,3,1,TA,6,Typ,0,NA,Detchd,1946,Unf,1,240,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,5,2008,WD,Normal,130500 +841,70,RH,NA,12155,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,SWISU,Norm,Norm,1Fam,2Story,6,8,1925,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,TA,TA,No,BLQ,156,Unf,0,516,672,GasA,TA,N,SBrkr,810,672,0,1482,0,0,2,0,4,1,Fa,7,Typ,0,NA,Detchd,1934,Unf,1,400,TA,TA,P,0,0,254,0,0,0,NA,NA,NA,0,3,2008,WD,Normal,140000 +842,70,RM,60,10440,Pave,Grvl,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,2Story,5,8,1904,2002,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,PConc,TA,TA,No,Unf,0,Unf,0,650,650,GasA,Gd,Y,SBrkr,958,581,0,1539,0,0,2,0,3,1,Gd,8,Typ,1,Po,Detchd,1983,Unf,2,686,Gd,TA,P,70,78,68,0,0,0,NA,NA,NA,0,6,2008,WD,Normal,157500 +843,80,RL,82,9020,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,SLvl,6,7,1966,1966,Gable,CompShg,HdBoard,HdBoard,BrkFace,183,TA,TA,CBlock,TA,TA,Gd,Rec,312,ALQ,539,276,1127,GasA,TA,Y,SBrkr,1165,0,0,1165,1,0,1,1,3,1,TA,6,Typ,0,NA,Attchd,1966,RFn,2,490,Gd,Gd,Y,0,129,0,0,0,0,NA,GdPrv,NA,0,5,2008,WD,Normal,174900 +844,90,RL,80,8000,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NAmes,Artery,Norm,Duplex,1Story,5,4,1961,1961,Gable,CompShg,BrkFace,BrkFace,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,1800,1800,GasA,Ex,N,SBrkr,1800,0,0,1800,0,0,2,0,6,2,TA,10,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,0,0,0,0,0,0,NA,NA,NA,0,7,2007,WD,Normal,141000 +845,50,RM,100,12665,Pave,Grvl,IR1,Lvl,AllPub,Inside,Gtl,OldTown,Artery,Norm,1Fam,1.5Fin,5,8,1915,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,TA,TA,Mn,Unf,0,Unf,0,876,876,GasA,Gd,Y,SBrkr,876,540,0,1416,0,0,1,1,4,1,TA,7,Typ,1,Gd,Detchd,1949,Unf,3,720,TA,TA,Y,418,0,194,0,0,0,NA,NA,NA,0,6,2008,WD,Normal,153900 +846,85,RL,NA,16647,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Sawyer,RRAe,Norm,1Fam,SFoyer,5,5,1975,1981,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,Gd,TA,Gd,ALQ,1390,Unf,0,0,1390,GasA,TA,Y,SBrkr,1701,0,0,1701,1,0,2,0,3,1,TA,6,Min2,2,TA,Basment,1975,Fin,2,611,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,1,2007,WD,Normal,171000 +847,60,RL,75,9317,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SawyerW,Norm,Norm,1Fam,2Story,7,5,1993,1993,Gable,CompShg,HdBoard,HdBoard,BrkFace,137,Gd,TA,PConc,Gd,TA,No,ALQ,513,Unf,0,227,740,GasA,Ex,Y,SBrkr,1006,769,0,1775,1,0,2,1,3,1,Gd,7,Typ,1,TA,Attchd,1993,Unf,2,425,TA,TA,Y,234,72,192,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,213000 +848,20,RL,36,15523,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,CollgCr,Norm,Norm,1Fam,1Story,5,6,1972,1972,Gable,CompShg,HdBoard,Plywood,None,0,TA,TA,CBlock,TA,TA,Av,BLQ,460,Unf,0,404,864,GasA,Ex,Y,SBrkr,864,0,0,864,1,0,1,0,3,1,TA,5,Typ,1,Fa,Attchd,1972,Unf,1,338,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,8,2009,WD,Normal,133500 +849,50,RL,75,45600,Pave,NA,IR2,Bnk,AllPub,Inside,Gtl,ClearCr,Norm,Norm,1Fam,1.5Fin,6,8,1908,1997,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,907,907,GasA,TA,Y,SBrkr,1307,1051,0,2358,0,0,3,0,5,1,TA,10,Typ,1,Gd,Detchd,1908,Unf,2,360,Fa,TA,Y,486,40,0,0,175,0,NA,NA,NA,0,9,2008,WD,Normal,240000 +850,80,RL,80,9600,Pave,NA,Reg,Lvl,AllPub,FR2,Gtl,Veenker,Feedr,Norm,1Fam,SLvl,6,7,1976,1994,Hip,CompShg,Plywood,Plywood,BrkFace,360,Gd,Gd,CBlock,TA,TA,No,Unf,0,Unf,0,528,528,GasA,Ex,Y,SBrkr,1094,761,0,1855,0,0,2,1,3,1,TA,7,Typ,1,TA,Attchd,1976,RFn,2,512,TA,TA,Y,113,100,0,0,0,0,NA,NA,NA,0,8,2007,WD,Normal,187000 +851,120,RM,36,4435,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,TwnhsE,1Story,6,5,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,170,Gd,TA,PConc,Gd,TA,Av,GLQ,659,Unf,0,189,848,GasA,Ex,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Gd,3,Typ,0,NA,Attchd,2003,Fin,2,420,TA,TA,Y,140,0,0,0,0,0,NA,NA,NA,0,11,2007,WD,Normal,131500 +852,120,RL,NA,3196,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Blmngtn,Norm,Norm,TwnhsE,1Story,8,5,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,40,Gd,TA,PConc,Gd,TA,Gd,Unf,0,Unf,0,1273,1273,GasA,Ex,Y,SBrkr,1456,0,0,1456,0,0,2,0,2,1,Gd,7,Typ,1,TA,Attchd,2003,Fin,2,400,TA,TA,Y,143,20,0,0,0,0,NA,NA,NA,0,5,2006,WD,Normal,215000 +853,75,RL,53,7128,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Crawfor,Norm,Norm,1Fam,2.5Unf,7,5,1941,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,Gd,CBlock,TA,TA,No,Rec,364,Unf,0,554,918,GasA,Gd,Y,SBrkr,918,728,0,1646,0,0,2,0,4,1,TA,7,Typ,2,Gd,Detchd,1941,Unf,1,240,TA,TA,Y,0,0,0,0,126,0,NA,MnPrv,NA,0,8,2007,WD,Normal,164000 +854,80,RL,NA,12095,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,1Fam,SLvl,6,6,1964,1964,Gable,CompShg,MetalSd,HdBoard,BrkFace,115,TA,Gd,CBlock,TA,TA,Gd,Rec,564,Unf,0,563,1127,GasA,TA,Y,SBrkr,1445,0,0,1445,0,0,1,1,3,1,TA,7,Typ,1,Fa,Attchd,1964,RFn,2,645,TA,TA,Y,180,0,0,0,0,0,NA,MnPrv,NA,0,8,2009,WD,Normal,158000 +855,20,RL,102,17920,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,1Story,5,4,1955,1974,Hip,CompShg,Wd Sdng,Plywood,None,0,TA,TA,CBlock,TA,TA,Mn,ALQ,306,Rec,1085,372,1763,GasA,TA,Y,SBrkr,1779,0,0,1779,1,0,1,1,3,1,TA,6,Typ,1,Gd,Attchd,1955,Unf,2,454,TA,TA,Y,0,418,0,0,312,0,NA,NA,NA,0,7,2006,WD,Abnorml,170000 +856,20,RL,NA,6897,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,1Fam,1Story,5,8,1962,2010,Gable,CompShg,HdBoard,HdBoard,None,0,TA,Gd,CBlock,TA,TA,No,ALQ,659,Unf,0,381,1040,GasA,Ex,Y,SBrkr,1040,0,0,1040,1,0,1,1,3,1,TA,6,Typ,0,NA,Detchd,1962,Unf,1,260,TA,TA,Y,0,104,0,0,0,0,NA,NA,NA,0,4,2010,WD,Normal,127000 +857,80,RL,NA,10970,Pave,NA,IR1,Low,AllPub,Inside,Mod,CollgCr,Norm,Norm,1Fam,SLvl,6,6,1978,1978,Gable,CompShg,Plywood,HdBoard,None,0,TA,TA,CBlock,Gd,Gd,Gd,GLQ,505,LwQ,435,0,940,GasA,TA,Y,SBrkr,1026,0,0,1026,1,0,1,0,3,1,TA,5,Typ,0,NA,Detchd,1981,Unf,2,576,TA,Fa,Y,0,0,34,0,0,0,NA,MnPrv,NA,0,10,2008,WD,Normal,147000 +858,60,RL,65,8125,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,2Story,6,5,1994,1995,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,PConc,Gd,TA,No,Unf,0,Unf,0,702,702,GasA,Gd,Y,SBrkr,702,779,0,1481,0,0,2,1,3,1,TA,6,Typ,1,TA,Attchd,1994,Fin,2,343,TA,TA,Y,0,36,0,0,0,0,NA,NA,NA,0,3,2009,WD,Normal,174000 +859,20,RL,80,10400,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,1Story,7,5,1976,1976,Gable,CompShg,HdBoard,HdBoard,BrkFace,189,TA,TA,CBlock,Gd,TA,No,Unf,0,Unf,0,1090,1090,GasA,TA,Y,SBrkr,1370,0,0,1370,0,0,2,0,3,1,TA,6,Typ,1,TA,Attchd,1976,RFn,2,479,TA,TA,Y,0,0,0,0,0,0,NA,MnPrv,NA,0,6,2009,WD,Family,152000 +860,60,RL,NA,11029,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NWAmes,PosA,Norm,1Fam,2Story,6,7,1968,1984,Gable,CompShg,HdBoard,HdBoard,BrkFace,220,TA,TA,CBlock,TA,TA,Mn,BLQ,619,Unf,0,435,1054,GasA,TA,Y,SBrkr,1512,1142,0,2654,1,0,2,1,4,1,Gd,9,Typ,1,Gd,Attchd,1968,Unf,2,619,TA,TA,Y,0,65,0,0,222,0,NA,NA,NA,0,8,2006,WD,Normal,250000 +861,50,RL,55,7642,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,Crawfor,Norm,Norm,1Fam,1.5Fin,7,8,1918,1998,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Gd,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,912,912,GasA,Gd,Y,SBrkr,912,514,0,1426,0,0,1,1,3,1,Gd,7,Typ,1,Gd,Detchd,1925,Unf,1,216,TA,TA,Y,0,240,0,0,0,0,NA,GdPrv,NA,0,6,2007,WD,Normal,189950 +862,190,RL,75,11625,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,2fmCon,1Story,5,4,1965,1965,Hip,CompShg,Plywood,HdBoard,None,0,TA,TA,PConc,TA,TA,Mn,BLQ,841,Unf,0,198,1039,GasA,Ex,Y,SBrkr,1039,0,0,1039,1,0,1,1,3,1,TA,6,Typ,0,NA,Attchd,1965,Unf,2,504,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,4,2010,WD,Normal,131500 +863,20,RL,81,9672,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,SawyerW,Norm,Norm,1Fam,1Story,6,5,1984,1985,Hip,CompShg,HdBoard,Plywood,None,0,TA,TA,PConc,Gd,TA,No,GLQ,338,Unf,0,702,1040,GasA,TA,Y,SBrkr,1097,0,0,1097,0,0,2,0,3,1,TA,6,Typ,0,NA,Attchd,1986,Unf,2,480,TA,TA,Y,0,0,0,0,0,0,NA,GdPrv,NA,0,5,2010,WD,Normal,152000 +864,20,RL,70,7931,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,5,1959,1959,Hip,CompShg,BrkFace,Plywood,None,0,TA,TA,CBlock,TA,TA,No,BLQ,1148,Unf,0,0,1148,GasA,TA,Y,SBrkr,1148,0,0,1148,1,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1959,Unf,1,672,TA,TA,Y,0,0,0,0,0,0,NA,GdPrv,NA,0,7,2009,WD,Normal,132500 +865,20,FV,72,8640,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,1Story,7,5,2007,2008,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Ex,TA,No,Unf,0,Unf,0,1372,1372,GasA,Ex,Y,SBrkr,1372,0,0,1372,0,0,2,0,3,1,Gd,6,Typ,0,NA,Attchd,2008,Fin,2,529,TA,TA,Y,0,140,0,0,0,0,NA,NA,NA,0,5,2008,New,Partial,250580 +866,20,RL,NA,8750,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,6,1970,1970,Gable,CompShg,MetalSd,MetalSd,BrkFace,76,TA,TA,CBlock,TA,TA,No,BLQ,828,Unf,0,174,1002,GasA,TA,Y,SBrkr,1002,0,0,1002,1,0,1,0,3,1,TA,5,Typ,0,NA,Detchd,1973,Unf,2,902,TA,TA,Y,0,0,0,0,0,0,NA,MnPrv,NA,0,8,2009,WD,Normal,148500 +867,20,RL,67,10656,Pave,NA,IR1,HLS,AllPub,Inside,Gtl,Timber,Norm,Norm,1Fam,1Story,8,5,2006,2007,Gable,CompShg,VinylSd,VinylSd,Stone,274,Gd,TA,PConc,Gd,TA,Av,Unf,0,Unf,0,1638,1638,GasA,Ex,Y,SBrkr,1646,0,0,1646,0,0,2,0,3,1,Gd,6,Typ,1,Gd,Attchd,2007,RFn,3,870,TA,TA,Y,192,80,0,0,0,0,NA,NA,NA,0,11,2007,New,Partial,248900 +868,20,RL,85,6970,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,Sawyer,Feedr,Norm,1Fam,1Story,4,5,1961,1961,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,CBlock,TA,TA,No,ALQ,932,Unf,0,108,1040,GasA,TA,Y,SBrkr,1120,0,0,1120,1,0,1,1,3,1,Fa,5,Typ,0,NA,Attchd,1961,RFn,2,544,TA,TA,Y,168,0,0,0,0,0,NA,NA,Shed,400,5,2007,WD,Normal,129000 +869,60,RL,NA,14762,Pave,NA,IR2,Lvl,AllPub,Corner,Gtl,Gilbert,Feedr,Norm,1Fam,2Story,5,6,1948,1950,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,Slab,NA,NA,NA,NA,0,NA,0,0,0,GasA,Gd,Y,SBrkr,1547,720,53,2320,0,0,2,0,2,1,TA,7,Typ,1,TA,Attchd,1979,Unf,2,672,TA,TA,P,120,144,0,0,0,0,NA,NA,NA,0,5,2006,WD,Normal,169000 +870,60,RL,80,9938,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SawyerW,Norm,Norm,1Fam,2Story,7,5,1993,1994,Gable,CompShg,MetalSd,MetalSd,BrkFace,246,Gd,TA,PConc,Gd,TA,No,GLQ,750,Unf,0,300,1050,GasA,Ex,Y,SBrkr,1062,887,0,1949,1,0,2,1,3,1,Gd,8,Typ,1,TA,Attchd,1993,Fin,2,574,TA,TA,Y,156,90,0,0,0,0,NA,GdPrv,NA,0,6,2010,WD,Normal,236000 +871,20,RL,60,6600,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,PosN,Norm,1Fam,1Story,5,5,1962,1962,Hip,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,894,894,GasA,Gd,N,SBrkr,894,0,0,894,0,0,1,0,2,1,TA,5,Typ,0,NA,Detchd,1962,Unf,1,308,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,8,2009,WD,Normal,109500 +872,60,RL,70,8750,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,6,5,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,116,TA,TA,PConc,Gd,TA,No,GLQ,505,Unf,0,299,804,GasA,Ex,Y,SBrkr,804,878,0,1682,0,0,2,1,3,1,Gd,7,Typ,0,NA,Attchd,1998,RFn,2,523,TA,TA,Y,0,77,0,0,0,0,NA,NA,NA,0,6,2010,WD,Normal,200500 +873,20,RL,74,8892,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,7,1953,1996,Gable,CompShg,WdShing,Wd Shng,None,0,Gd,TA,Stone,TA,TA,Av,Unf,0,Unf,0,105,105,GasA,Gd,Y,SBrkr,910,0,0,910,0,0,1,0,3,1,Gd,5,Typ,0,NA,Attchd,1953,Unf,2,414,TA,TA,Y,196,0,150,0,0,0,NA,GdWo,NA,0,10,2008,WD,Normal,116000 +874,40,RL,60,12144,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,7,1949,1950,Gable,CompShg,HdBoard,HdBoard,None,0,Gd,TA,CBlock,TA,TA,No,Rec,375,Unf,0,457,832,GasA,Gd,Y,SBrkr,1036,0,232,1268,0,0,1,0,3,1,TA,6,Typ,1,Gd,Attchd,1949,Unf,1,288,TA,TA,Y,0,28,0,0,0,0,NA,NA,Othr,0,9,2009,WD,Normal,133000 +875,50,RM,52,5720,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Artery,Norm,1Fam,1.5Fin,5,6,1941,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,676,676,GasA,Ex,Y,SBrkr,676,455,0,1131,0,0,1,1,3,1,TA,5,Typ,0,NA,Detchd,1941,Unf,1,200,TA,TA,Y,26,0,0,0,0,0,NA,NA,NA,0,8,2009,WD,Abnorml,66500 +876,60,FV,75,9000,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,2Story,8,5,2007,2007,Gable,CompShg,CemntBd,CmentBd,None,0,Gd,TA,PConc,Gd,TA,Av,GLQ,64,Unf,0,1120,1184,GasA,Ex,Y,SBrkr,1184,1426,0,2610,0,0,2,1,4,1,Ex,11,Typ,1,Gd,BuiltIn,2007,Fin,2,550,TA,TA,Y,208,364,0,0,0,0,NA,NA,NA,0,8,2007,New,Partial,303477 +877,20,RL,94,25286,Pave,NA,Reg,HLS,AllPub,Inside,Mod,Mitchel,Norm,Norm,1Fam,1Story,4,5,1963,1963,Gable,CompShg,HdBoard,Plywood,None,0,TA,TA,PConc,TA,TA,Gd,ALQ,633,Unf,0,431,1064,GasA,Gd,Y,SBrkr,1040,0,0,1040,1,0,1,0,3,1,TA,5,Typ,0,NA,Attchd,1963,Unf,2,648,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,1,2007,WD,Normal,132250 +878,60,RL,74,8834,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,2Story,9,5,2004,2005,Hip,CompShg,VinylSd,VinylSd,Stone,216,Gd,TA,PConc,Ex,TA,No,GLQ,1170,Unf,0,292,1462,GasA,Ex,Y,SBrkr,1462,762,0,2224,1,0,2,1,4,1,Ex,10,Typ,1,Gd,Attchd,2004,Fin,3,738,TA,TA,Y,184,0,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,350000 +879,85,RL,88,11782,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,SFoyer,5,7,1961,1995,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,TA,TA,Av,ALQ,899,Unf,0,210,1109,GasA,TA,Y,SBrkr,1155,0,0,1155,1,0,1,0,3,1,Gd,6,Min2,0,NA,Detchd,1987,Unf,2,576,TA,TA,Y,192,0,0,0,0,0,NA,MnPrv,Shed,400,6,2010,WD,Normal,148000 +880,20,RL,NA,7000,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,CollgCr,Norm,Norm,1Fam,1Story,5,8,1978,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,90,Gd,Gd,CBlock,TA,TA,No,ALQ,646,Unf,0,218,864,GasA,Ex,Y,SBrkr,864,0,0,864,1,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1978,Unf,1,336,TA,TA,Y,0,0,0,0,0,0,NA,GdWo,NA,0,7,2009,WD,Normal,136500 +881,20,RL,60,7024,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1Story,5,5,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Ex,Gd,No,ALQ,980,Unf,0,110,1090,GasA,Gd,Y,SBrkr,1090,0,0,1090,1,0,1,1,2,1,TA,5,Typ,0,NA,Attchd,2005,Fin,2,450,TA,TA,Y,0,49,0,0,0,0,NA,NA,NA,0,6,2007,WD,Normal,157000 +882,50,RL,44,13758,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Timber,Norm,Norm,1Fam,1.5Fin,7,5,1990,1991,Gable,CompShg,HdBoard,HdBoard,BrkFace,117,Gd,Gd,CBlock,Gd,TA,Mn,LwQ,902,Unf,0,254,1156,GasA,Ex,Y,SBrkr,1187,530,0,1717,0,0,2,1,3,1,Gd,7,Typ,1,TA,Attchd,1990,RFn,2,400,TA,TA,Y,168,36,0,0,0,0,NA,NA,NA,0,4,2007,WD,Normal,187500 +883,60,RL,NA,9636,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,1Fam,2Story,6,5,1992,1993,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,No,Unf,0,Unf,0,808,808,GasA,Gd,Y,SBrkr,808,785,0,1593,0,0,2,1,3,1,TA,7,Typ,1,TA,BuiltIn,1993,RFn,2,389,TA,TA,Y,342,40,0,0,0,0,NA,MnPrv,NA,0,12,2009,WD,Normal,178000 +884,75,RL,60,6204,Pave,NA,Reg,Bnk,AllPub,Inside,Gtl,SWISU,Norm,Norm,1Fam,2.5Fin,4,5,1912,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Gd,TA,PConc,TA,Fa,No,Unf,0,Unf,0,795,795,GasA,TA,N,SBrkr,954,795,481,2230,1,0,1,0,5,1,TA,10,Typ,0,NA,Detchd,1997,Unf,1,440,TA,Gd,Y,0,188,0,0,0,0,NA,NA,NA,0,3,2006,WD,Normal,118500 +885,20,RL,65,7150,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,5,1967,1967,Gable,CompShg,HdBoard,HdBoard,BrkFace,60,TA,TA,CBlock,TA,TA,No,BLQ,432,Unf,0,460,892,GasA,TA,Y,SBrkr,892,0,0,892,0,0,1,0,3,1,TA,5,Typ,0,NA,Attchd,1967,RFn,1,288,TA,TA,Y,0,0,0,0,0,0,NA,GdWo,NA,0,7,2009,WD,Normal,100000 +886,120,FV,50,5119,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Somerst,Norm,Norm,TwnhsE,1Story,9,5,1999,2000,Gable,CompShg,MetalSd,MetalSd,BrkFace,60,Gd,TA,PConc,Ex,TA,Av,GLQ,1238,Unf,0,460,1698,GasA,Ex,Y,SBrkr,1709,0,0,1709,1,0,2,0,2,1,Gd,5,Typ,1,TA,Attchd,1999,Fin,2,506,TA,TA,Y,97,65,0,0,0,0,NA,NA,NA,0,1,2008,CWD,Abnorml,328900 +887,90,RL,70,8393,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,Duplex,1Story,5,5,1959,2005,Gable,CompShg,MetalSd,MetalSd,BrkFace,122,TA,TA,CBlock,TA,TA,No,LwQ,528,Unf,0,1098,1626,GasA,Ex,Y,SBrkr,1712,0,0,1712,0,0,2,0,4,2,TA,8,Typ,0,NA,Attchd,2005,Fin,2,588,TA,TA,Y,272,54,0,0,0,0,NA,NA,NA,0,6,2006,WD,Family,145000 +888,50,RL,59,16466,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1.5Fin,5,7,1955,1955,Gable,CompShg,MetalSd,MetalSd,None,0,TA,Gd,PConc,TA,TA,No,Unf,0,Unf,0,816,816,GasA,TA,Y,SBrkr,872,521,0,1393,0,0,1,1,3,1,TA,8,Typ,0,NA,Attchd,1955,Unf,1,300,TA,TA,Y,121,0,0,0,265,0,NA,NA,NA,0,4,2008,WD,Normal,135500 +889,20,RL,95,15865,Pave,NA,IR1,Lvl,AllPub,Inside,Mod,NAmes,Norm,Norm,1Fam,1Story,8,6,1970,1970,Flat,Tar&Grv,Wd Sdng,Wd Sdng,None,0,Gd,Gd,PConc,TA,Gd,Gd,ALQ,351,Rec,823,1043,2217,GasA,Ex,Y,SBrkr,2217,0,0,2217,1,0,2,0,4,1,Gd,8,Typ,1,TA,Attchd,1970,Unf,2,621,TA,TA,Y,81,207,0,0,224,0,NA,NA,NA,0,10,2007,WD,Normal,268000 +890,20,RL,128,12160,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Feedr,Norm,1Fam,1Story,6,4,1953,1953,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,90,TA,TA,CBlock,TA,TA,No,BLQ,1024,Unf,0,481,1505,GasA,Ex,Y,SBrkr,1505,0,0,1505,1,0,1,0,2,1,TA,6,Typ,1,TA,Attchd,1953,RFn,2,505,TA,TA,Y,0,0,0,162,0,0,NA,NA,NA,0,2,2009,WD,Normal,149500 +891,50,RL,60,8064,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NAmes,Artery,Norm,1Fam,1.5Fin,5,7,1949,2006,Gable,CompShg,MetalSd,MetalSd,None,0,TA,Gd,CBlock,TA,TA,Mn,Unf,0,Unf,0,672,672,GasA,Ex,Y,SBrkr,672,252,0,924,0,0,1,0,3,1,TA,6,Typ,1,Po,Detchd,2003,Unf,2,576,TA,TA,Y,0,0,0,0,0,0,NA,MnPrv,Shed,2000,7,2007,WD,Normal,122900 +892,60,RL,70,11184,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,2Story,6,5,1978,1978,Hip,CompShg,HdBoard,HdBoard,BrkFace,92,TA,TA,CBlock,TA,TA,No,LwQ,226,Rec,500,192,918,GasA,Gd,Y,SBrkr,918,765,0,1683,0,0,2,1,3,1,TA,7,Typ,1,TA,Attchd,1978,RFn,2,440,TA,TA,Y,243,0,0,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,172500 +893,20,RL,70,8414,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,1Story,6,8,1963,2003,Hip,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,TA,TA,No,GLQ,663,Unf,0,396,1059,GasA,TA,Y,SBrkr,1068,0,0,1068,0,1,1,0,3,1,TA,6,Typ,0,NA,Attchd,1963,RFn,1,264,TA,TA,Y,192,0,0,0,0,0,NA,MnPrv,NA,0,2,2006,WD,Normal,154500 +894,20,RL,NA,13284,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Sawyer,PosN,Norm,1Fam,1Story,5,5,1954,1954,Gable,CompShg,Wd Sdng,Plywood,None,0,TA,TA,PConc,Gd,TA,Mn,BLQ,1064,Unf,0,319,1383,GasA,TA,Y,SBrkr,1383,0,0,1383,1,0,1,0,3,1,TA,6,Typ,1,Gd,Attchd,1954,Unf,1,354,TA,TA,Y,511,116,0,0,0,0,NA,GdPrv,NA,0,6,2008,WD,Normal,165000 +895,90,RL,64,7018,Pave,NA,Reg,Bnk,AllPub,Inside,Gtl,SawyerW,Norm,Norm,Duplex,1Story,5,5,1979,1979,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,Slab,NA,NA,NA,NA,0,NA,0,0,0,GasA,TA,Y,SBrkr,1535,0,0,1535,0,0,2,0,4,2,TA,8,Typ,0,NA,Attchd,1979,Unf,2,400,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,6,2009,WD,Alloca,118858 +896,60,RL,71,7056,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,2Story,6,5,1963,1963,Hip,CompShg,HdBoard,HdBoard,BrkFace,415,TA,TA,CBlock,TA,TA,No,BLQ,400,Unf,0,380,780,GasA,TA,Y,SBrkr,983,813,0,1796,1,0,1,1,4,1,TA,8,Typ,1,TA,Attchd,1963,RFn,2,483,TA,TA,Y,0,50,0,0,0,0,NA,NA,NA,0,10,2008,WD,Normal,140000 +897,30,RM,50,8765,Pave,Grvl,Reg,Lvl,AllPub,Inside,Gtl,IDOTRR,Norm,Norm,1Fam,1Story,4,6,1936,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,TA,TA,No,ALQ,285,Unf,0,666,951,GasA,Ex,N,SBrkr,951,0,0,951,0,0,1,0,2,1,TA,6,Typ,0,NA,Detchd,1936,Unf,1,327,TA,TA,Y,0,28,0,0,0,0,NA,NA,NA,0,4,2006,WD,Abnorml,106500 +898,90,RL,64,7018,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SawyerW,Feedr,Norm,Duplex,2Story,5,5,1979,1979,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,Slab,NA,NA,NA,NA,0,NA,0,0,0,GasA,TA,Y,SBrkr,1120,1120,0,2240,0,0,2,0,6,2,TA,12,Typ,0,NA,Detchd,1979,Unf,2,528,TA,TA,Y,154,0,0,0,0,0,NA,NA,NA,0,6,2009,WD,Alloca,142953 +899,20,RL,100,12919,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,1Story,9,5,2009,2010,Hip,CompShg,VinylSd,VinylSd,Stone,760,Ex,TA,PConc,Ex,TA,Gd,GLQ,2188,Unf,0,142,2330,GasA,Ex,Y,SBrkr,2364,0,0,2364,1,0,2,1,2,1,Ex,11,Typ,2,Gd,Attchd,2009,Fin,3,820,TA,TA,Y,0,67,0,0,0,0,NA,NA,NA,0,3,2010,New,Partial,611657 +900,20,RL,65,6993,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,1Fam,1Story,5,7,1961,1994,Gable,CompShg,HdBoard,Plywood,None,0,TA,TA,CBlock,TA,TA,No,BLQ,465,Unf,0,447,912,GasA,TA,Y,SBrkr,1236,0,0,1236,0,0,1,0,3,1,TA,6,Typ,1,TA,Attchd,1961,Unf,1,288,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,6,2006,WD,Normal,135000 +901,20,RL,NA,7340,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,4,6,1971,1971,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,TA,TA,No,ALQ,322,Unf,0,536,858,GasA,TA,Y,SBrkr,858,0,0,858,0,0,1,0,2,1,TA,4,Typ,0,NA,Detchd,1979,Unf,1,684,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,6,2007,WD,Normal,110000 +902,20,RL,64,8712,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,7,1957,2000,Hip,CompShg,MetalSd,MetalSd,None,0,TA,Gd,CBlock,TA,TA,Mn,BLQ,860,Unf,0,132,992,GasA,TA,Y,SBrkr,1306,0,0,1306,1,0,1,0,2,1,TA,5,Typ,0,NA,Detchd,1968,Unf,1,756,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,5,2009,WD,Normal,153000 +903,60,RL,63,7875,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,2Story,7,5,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,783,783,GasA,Ex,Y,SBrkr,807,702,0,1509,0,0,2,1,3,1,Gd,8,Typ,1,Gd,Attchd,2003,Fin,2,393,TA,TA,Y,0,75,0,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,180000 +904,20,RL,50,14859,Pave,NA,IR1,HLS,AllPub,CulDSac,Gtl,Gilbert,Norm,Norm,1Fam,1Story,7,5,2006,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,27,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1670,1670,GasA,Ex,Y,SBrkr,1670,0,0,1670,0,0,2,0,3,1,Gd,7,Typ,1,Gd,Attchd,2006,RFn,3,690,TA,TA,Y,144,60,0,0,0,0,NA,NA,NA,0,8,2006,New,Partial,240000 +905,20,RL,NA,6173,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,1Story,5,6,1967,1967,Gable,CompShg,HdBoard,Wd Sdng,BrkFace,75,TA,TA,CBlock,TA,TA,No,GLQ,599,Unf,0,277,876,GasA,TA,Y,SBrkr,902,0,0,902,0,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1967,Unf,1,288,TA,TA,Y,0,0,0,0,0,0,NA,MnPrv,NA,0,8,2007,WD,Normal,125500 +906,20,RL,80,9920,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,5,1954,1954,Gable,CompShg,HdBoard,HdBoard,Stone,110,TA,TA,CBlock,TA,TA,No,Rec,354,LwQ,290,412,1056,GasA,TA,Y,SBrkr,1063,0,0,1063,1,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1954,RFn,1,280,TA,TA,Y,0,0,164,0,0,0,NA,MnPrv,NA,0,2,2010,WD,Normal,128000 +907,20,RL,116,13501,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Somerst,Norm,Norm,1Fam,1Story,8,5,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,208,Gd,TA,PConc,Gd,TA,No,GLQ,63,Unf,0,1560,1623,GasA,Ex,Y,SBrkr,1636,0,0,1636,1,0,2,0,3,1,Gd,8,Typ,1,Gd,Attchd,2006,RFn,3,865,TA,TA,Y,0,60,0,0,0,0,NA,NA,NA,0,6,2007,WD,Normal,255000 +908,50,RL,86,11500,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Crawfor,Norm,Norm,1Fam,1.5Fin,7,7,1936,1987,Gable,CompShg,BrkFace,BrkFace,None,0,Gd,TA,CBlock,Gd,TA,No,Rec,223,Unf,0,794,1017,GasA,Gd,Y,SBrkr,1020,1037,0,2057,0,0,1,1,3,1,Gd,6,Typ,1,Gd,Attchd,1936,Fin,1,180,Fa,TA,Y,0,0,0,0,322,0,NA,NA,NA,0,6,2006,WD,Normal,250000 +909,20,RL,NA,8885,Pave,NA,IR1,Low,AllPub,Inside,Mod,Mitchel,Norm,Norm,1Fam,1Story,5,5,1983,1983,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,Gd,TA,Av,BLQ,301,ALQ,324,239,864,GasA,TA,Y,SBrkr,902,0,0,902,1,0,1,0,2,1,TA,5,Typ,0,NA,Attchd,1983,Unf,2,484,TA,TA,Y,164,0,0,0,0,0,NA,MnPrv,NA,0,6,2006,WD,Normal,131000 +910,60,RL,149,12589,Pave,NA,IR2,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,2Story,6,5,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,742,742,GasA,Ex,Y,SBrkr,742,742,0,1484,0,0,2,1,3,1,Gd,8,Typ,1,Gd,Attchd,2005,Fin,2,390,TA,TA,Y,36,24,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,174000 +911,90,RL,80,11600,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NAmes,Feedr,Norm,Duplex,2Story,5,5,1960,1960,Gable,CompShg,MetalSd,MetalSd,BrkFace,361,TA,TA,CBlock,TA,TA,No,Rec,443,Unf,0,662,1105,GasA,TA,Y,FuseA,1105,1169,0,2274,0,0,2,0,5,2,TA,12,Typ,0,NA,Detchd,1960,Unf,2,480,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,1,2010,WD,Normal,154300 +912,20,RL,NA,9286,Pave,NA,IR1,Lvl,AllPub,CulDSac,Mod,CollgCr,Norm,Norm,1Fam,1Story,5,7,1977,1989,Gable,CompShg,HdBoard,Plywood,None,0,TA,TA,CBlock,Gd,Gd,Av,ALQ,196,Unf,0,1072,1268,GasA,TA,Y,SBrkr,1268,0,0,1268,0,0,1,1,3,1,Gd,5,Typ,0,NA,Detchd,1978,Unf,1,252,TA,TA,Y,173,0,0,0,0,0,NA,NA,NA,0,10,2009,WD,Normal,143500 +913,30,RM,51,6120,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,1Story,5,7,1925,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,BrkTil,TA,TA,No,Rec,489,Unf,0,279,768,GasA,TA,N,SBrkr,1015,0,0,1015,0,0,1,0,3,1,TA,6,Min1,0,NA,Detchd,1925,Unf,1,450,TA,TA,Y,0,0,112,0,120,0,NA,MnPrv,Shed,620,7,2006,WD,Abnorml,88000 +914,90,RH,82,6270,Pave,NA,Reg,HLS,AllPub,Inside,Gtl,Crawfor,Norm,Norm,Duplex,2Story,5,6,1949,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,BLQ,284,Unf,0,717,1001,GasA,TA,N,FuseA,1001,1001,0,2002,0,0,2,0,4,2,TA,8,Typ,0,NA,2Types,1949,Unf,3,871,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,8,2007,WD,Normal,145000 +915,160,FV,30,3000,Pave,Pave,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,TwnhsE,2Story,6,5,2009,2009,Gable,CompShg,VinylSd,VinylSd,Stone,76,Gd,TA,PConc,Gd,TA,Av,GLQ,294,Unf,0,318,612,GasA,Ex,Y,SBrkr,612,612,0,1224,0,0,2,1,2,1,Gd,4,Typ,0,NA,Detchd,2009,RFn,2,528,TA,TA,Y,0,234,0,0,0,0,NA,NA,NA,0,6,2009,New,Partial,173733 +916,160,RM,21,2001,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,MeadowV,Norm,Norm,Twnhs,2Story,4,5,1970,1970,Gable,CompShg,CemntBd,CmentBd,BrkFace,80,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,546,546,GasA,Fa,Y,SBrkr,546,546,0,1092,0,0,1,1,3,1,TA,6,Typ,0,NA,Attchd,1970,Unf,1,286,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,1,2007,WD,Normal,75000 +917,20,C (all),50,9000,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,IDOTRR,Norm,Norm,1Fam,1Story,2,3,1949,1950,Gable,CompShg,AsbShng,AsbShng,None,0,TA,TA,CBlock,TA,TA,Av,BLQ,50,Unf,0,430,480,GasA,TA,N,FuseA,480,0,0,480,1,0,0,0,1,1,TA,4,Typ,0,NA,Detchd,1958,Unf,1,308,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,10,2006,WD,Abnorml,35311 +918,20,RL,NA,17140,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1Story,4,6,1956,1956,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,CBlock,TA,TA,No,ALQ,1059,Unf,0,75,1134,GasA,Ex,Y,FuseA,1229,0,0,1229,0,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1956,RFn,1,284,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,4,2009,WD,Normal,135000 +919,60,RL,103,13125,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,SawyerW,Norm,Norm,1Fam,2Story,7,5,1991,1991,Gable,CompShg,HdBoard,HdBoard,None,0,Gd,TA,PConc,Ex,TA,Mn,BLQ,48,GLQ,634,422,1104,GasA,Ex,Y,SBrkr,912,1215,0,2127,1,0,2,1,4,1,Gd,8,Typ,1,TA,Attchd,1991,RFn,3,833,TA,TA,Y,72,192,224,0,0,0,NA,GdPrv,NA,0,11,2007,WD,Normal,238000 +920,20,RL,87,11029,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,8,1958,2002,Hip,CompShg,MetalSd,MetalSd,None,0,Ex,TA,CBlock,Gd,TA,No,ALQ,528,BLQ,411,245,1184,GasA,Ex,Y,SBrkr,1414,0,0,1414,1,0,1,0,3,1,TA,6,Min1,1,TA,Attchd,1990,Unf,2,601,TA,TA,Y,0,51,0,0,190,0,NA,NA,NA,0,5,2008,WD,Normal,176500 +921,60,RL,70,8462,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,SawyerW,Norm,Norm,1Fam,2Story,6,5,1994,1994,Gable,CompShg,HdBoard,HdBoard,BrkFace,105,Gd,Gd,PConc,Gd,Gd,No,GLQ,814,Unf,0,114,928,GasA,Ex,Y,SBrkr,936,785,0,1721,0,1,2,1,3,1,Gd,7,Typ,0,NA,Attchd,1994,RFn,2,471,TA,TA,Y,300,87,0,0,0,0,NA,NA,NA,0,7,2007,WD,Normal,201000 +922,90,RL,67,8777,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Feedr,Norm,Duplex,1.5Fin,5,7,1900,2003,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,ALQ,1084,Unf,0,188,1272,GasA,Gd,Y,SBrkr,1272,928,0,2200,2,0,2,2,4,2,TA,9,Typ,0,NA,NA,NA,NA,0,0,NA,NA,N,0,70,0,0,0,0,NA,GdPrv,NA,0,9,2008,WD,Normal,145900 +923,20,RL,65,10237,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,1Fam,1Story,6,5,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,28,Unf,0,1288,1316,GasA,Ex,Y,SBrkr,1316,0,0,1316,0,0,2,0,3,1,Gd,6,Typ,1,Gd,Attchd,2005,Fin,2,397,TA,TA,Y,100,0,0,23,0,0,NA,NA,NA,0,10,2006,New,Partial,169990 +924,120,RL,50,8012,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SawyerW,Norm,Norm,TwnhsE,1Story,6,5,1993,1994,Gable,CompShg,Plywood,Plywood,None,0,Gd,TA,PConc,Gd,TA,No,LwQ,165,GLQ,841,598,1604,GasA,Ex,Y,SBrkr,1617,0,0,1617,1,0,2,0,2,1,Gd,5,Typ,1,Fa,Attchd,1993,RFn,2,533,TA,TA,Y,0,69,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,193000 +925,20,RL,79,10240,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,1Story,6,6,1980,1980,Gable,CompShg,Plywood,Plywood,BrkFace,157,TA,Gd,CBlock,Gd,TA,No,BLQ,625,LwQ,1061,0,1686,GasA,TA,Y,SBrkr,1686,0,0,1686,1,0,2,0,3,1,TA,7,Typ,1,TA,Attchd,1980,Unf,2,612,TA,TA,Y,384,131,0,0,0,0,NA,NA,NA,0,5,2006,WD,Normal,207500 +926,20,RL,NA,15611,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NWAmes,Norm,Norm,1Fam,1Story,5,6,1977,1977,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,Av,ALQ,767,LwQ,93,266,1126,GasA,TA,Y,SBrkr,1126,0,0,1126,0,1,2,0,3,1,Ex,6,Typ,0,NA,Attchd,1977,RFn,2,540,TA,TA,Y,180,0,0,0,0,0,NA,NA,NA,0,3,2008,WD,Abnorml,175000 +927,60,RL,93,11999,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,2Story,8,5,2003,2004,Hip,CompShg,VinylSd,VinylSd,BrkFace,340,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1181,1181,GasA,Ex,Y,SBrkr,1234,1140,0,2374,0,0,2,1,4,1,Ex,10,Typ,1,Gd,BuiltIn,2003,Fin,3,656,TA,TA,Y,104,100,0,0,0,0,NA,NA,NA,0,5,2007,WD,Normal,285000 +928,60,RL,NA,9900,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,Feedr,Norm,1Fam,2Story,7,5,1968,1968,Gable,CompShg,MetalSd,MetalSd,BrkFace,342,TA,TA,CBlock,TA,TA,No,BLQ,552,Unf,0,280,832,GasA,Gd,Y,SBrkr,1098,880,0,1978,0,0,2,1,4,1,TA,9,Typ,1,Gd,Attchd,1968,RFn,2,486,TA,TA,Y,0,43,0,0,0,0,NA,GdPrv,NA,0,4,2008,WD,Normal,176000 +929,20,RL,NA,11838,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,8,5,2001,2001,Hip,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,Av,Unf,0,Unf,0,1753,1753,GasA,Ex,Y,SBrkr,1788,0,0,1788,0,0,2,0,3,1,Ex,7,Typ,1,TA,Attchd,2001,RFn,2,522,TA,TA,Y,202,151,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,236500 +930,60,RL,NA,13006,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,1Fam,2Story,7,5,1997,1997,Gable,CompShg,HdBoard,HdBoard,BrkFace,285,TA,TA,PConc,Gd,TA,No,Unf,0,Unf,0,964,964,GasA,Gd,Y,SBrkr,993,1243,0,2236,0,0,2,1,4,1,Gd,8,Typ,1,TA,BuiltIn,1997,Fin,2,642,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,11,2006,WD,Normal,222000 +931,20,RL,73,8925,Pave,NA,IR1,HLS,AllPub,Inside,Gtl,Timber,Norm,Norm,1Fam,1Story,8,5,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,Av,GLQ,16,Unf,0,1450,1466,GasA,Ex,Y,SBrkr,1466,0,0,1466,0,0,2,0,3,1,Gd,7,Typ,0,NA,Attchd,2007,Fin,3,610,TA,TA,Y,100,18,0,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,201000 +932,20,RL,70,9100,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,6,1965,1965,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,CBlock,TA,TA,No,BLQ,338,Rec,466,121,925,GasA,Ex,Y,SBrkr,925,0,0,925,0,1,1,0,2,1,TA,5,Typ,0,NA,Detchd,1965,Unf,1,429,TA,TA,Y,0,0,0,0,0,0,NA,GdWo,NA,0,7,2009,WD,Normal,117500 +933,20,RL,84,11670,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Somerst,RRNn,Norm,1Fam,1Story,9,5,2006,2006,Hip,CompShg,VinylSd,ImStucc,Stone,302,Ex,TA,PConc,Ex,Gd,No,Unf,0,Unf,0,1905,1905,GasA,Ex,Y,SBrkr,1905,0,0,1905,0,0,2,0,3,1,Ex,8,Typ,1,Gd,Attchd,2006,Fin,3,788,TA,TA,Y,0,191,0,0,0,0,NA,NA,NA,0,3,2007,WD,Normal,320000 +934,20,RL,63,8487,Pave,NA,Reg,Lvl,AllPub,FR2,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,210,Gd,TA,PConc,Gd,TA,Av,GLQ,20,Unf,0,1480,1500,GasA,Ex,Y,SBrkr,1500,0,0,1500,0,0,2,0,3,1,Gd,6,Typ,0,NA,Attchd,2004,RFn,2,570,TA,TA,Y,192,36,0,0,0,0,NA,NA,NA,0,8,2009,WD,Normal,190000 +935,20,RL,313,27650,Pave,NA,IR2,HLS,AllPub,Inside,Mod,NAmes,PosA,Norm,1Fam,1Story,7,7,1960,2007,Flat,Tar&Grv,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,Gd,TA,Gd,GLQ,425,Unf,0,160,585,GasA,Ex,Y,SBrkr,2069,0,0,2069,1,0,2,0,4,1,Gd,9,Typ,1,Gd,Attchd,1960,RFn,2,505,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,11,2008,WD,Normal,242000 +936,30,RL,52,5825,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,1Story,4,5,1926,1953,Gable,CompShg,MetalSd,MetalSd,BrkFace,108,TA,Gd,PConc,Fa,TA,Mn,Unf,0,Unf,0,600,600,GasA,Gd,Y,SBrkr,747,0,0,747,0,0,1,0,1,1,TA,5,Typ,0,NA,Detchd,1953,Unf,2,528,TA,TA,Y,0,0,32,0,0,0,NA,NA,NA,0,6,2006,WD,Normal,79900 +937,20,RL,67,10083,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SawyerW,Norm,Norm,1Fam,1Story,7,5,2003,2003,Gable,CompShg,VinylSd,VinylSd,NA,NA,Gd,TA,PConc,Gd,TA,No,GLQ,833,Unf,0,343,1176,GasA,Ex,Y,SBrkr,1200,0,0,1200,1,0,2,0,2,1,Gd,5,Typ,0,NA,Attchd,2003,RFn,2,555,TA,TA,Y,0,41,0,0,0,0,NA,NA,NA,0,8,2009,WD,Normal,184900 +938,60,RL,75,9675,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,2Story,7,5,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,Mn,GLQ,341,Unf,0,772,1113,GasA,Ex,Y,SBrkr,1113,858,0,1971,0,0,2,1,3,1,Gd,8,Typ,1,Gd,Attchd,2005,RFn,2,689,TA,TA,Y,0,48,0,0,0,0,NA,NA,NA,0,2,2009,WD,Normal,253000 +939,60,RL,73,8760,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,7,5,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,Gd,Mn,GLQ,464,Unf,0,927,1391,GasA,Ex,Y,SBrkr,1391,571,0,1962,0,0,2,1,3,1,Gd,7,Typ,0,NA,Attchd,2006,RFn,3,868,TA,TA,Y,0,90,0,0,0,0,NA,NA,NA,0,8,2006,New,Partial,239799 +940,70,RL,NA,24090,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,ClearCr,Norm,Norm,1Fam,2Story,7,7,1940,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,Gd,CBlock,TA,TA,Mn,Unf,0,Unf,0,1032,1032,GasA,Ex,Y,SBrkr,1207,1196,0,2403,0,0,2,0,4,1,TA,10,Typ,2,TA,Attchd,1940,Unf,1,349,TA,TA,Y,56,0,318,0,0,0,NA,NA,NA,0,6,2010,COD,Normal,244400 +941,90,RL,55,12640,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Mitchel,Norm,Norm,Duplex,1Story,6,5,1976,1976,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,CBlock,TA,TA,Gd,Rec,936,LwQ,396,396,1728,GasA,TA,Y,SBrkr,1728,0,0,1728,0,0,2,0,4,2,TA,8,Typ,0,NA,Attchd,1976,Unf,2,574,TA,TA,Y,40,0,0,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,150900 +942,60,RL,NA,8755,Pave,NA,IR1,Lvl,AllPub,FR2,Gtl,Gilbert,RRNn,Norm,1Fam,2Story,7,5,1999,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,298,Gd,TA,PConc,Gd,TA,No,ALQ,772,Unf,0,220,992,GasA,Ex,Y,SBrkr,1022,1038,0,2060,1,0,2,1,3,1,Gd,8,Typ,1,TA,BuiltIn,1999,RFn,2,390,TA,TA,Y,0,0,0,168,0,0,NA,GdPrv,NA,0,6,2009,WD,Normal,214000 +943,90,RL,42,7711,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Duplex,1Story,4,3,1977,1977,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,PConc,Gd,TA,Gd,GLQ,1440,Unf,0,0,1440,GasA,TA,Y,SBrkr,1440,0,0,1440,2,0,2,0,4,2,TA,8,Typ,0,NA,NA,NA,NA,0,0,NA,NA,N,321,0,0,0,0,0,NA,NA,NA,0,8,2007,Oth,Abnorml,150000 +944,90,RL,100,25000,Pave,NA,Reg,Low,AllPub,Inside,Gtl,Mitchel,Norm,Norm,Duplex,1Story,5,4,1967,1967,Gable,CompShg,HdBoard,Plywood,None,0,TA,TA,CBlock,TA,TA,Av,Unf,0,Unf,0,1632,1632,GasA,TA,Y,SBrkr,1632,0,0,1632,0,0,2,0,4,2,TA,8,Typ,0,NA,Attchd,1967,Unf,2,576,TA,TA,P,0,0,0,0,0,0,NA,NA,NA,0,6,2007,WD,Normal,143000 +945,20,RL,NA,14375,Pave,NA,IR1,Lvl,NoSeWa,CulDSac,Gtl,Timber,Norm,Norm,1Fam,SLvl,6,6,1958,1958,Gable,CompShg,HdBoard,HdBoard,BrkFace,541,TA,TA,CBlock,TA,TA,No,GLQ,111,Rec,354,354,819,GasA,Gd,Y,FuseA,1344,0,0,1344,0,1,1,0,3,1,Gd,7,Typ,1,Gd,Basment,1958,RFn,2,525,TA,TA,Y,0,118,0,0,233,0,NA,NA,NA,0,1,2009,COD,Abnorml,137500 +946,50,RM,98,8820,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,OldTown,Norm,Norm,1Fam,1.5Fin,5,6,1890,1996,Hip,CompShg,VinylSd,VinylSd,None,0,TA,TA,BrkTil,TA,TA,No,LwQ,1088,Unf,0,0,1088,GasA,TA,Y,SBrkr,1188,561,120,1869,0,0,1,0,2,1,TA,7,Typ,0,NA,Detchd,1963,Unf,2,456,TA,TA,Y,48,0,244,0,0,0,NA,MnWw,NA,0,9,2009,WD,Normal,124900 +947,80,RL,70,8163,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,SLvl,5,6,1959,1959,Gable,CompShg,HdBoard,HdBoard,BrkFace,128,TA,Gd,CBlock,TA,TA,Av,ALQ,748,BLQ,294,102,1144,GasA,TA,Y,SBrkr,1144,0,0,1144,1,0,1,0,3,1,TA,6,Typ,1,TA,Attchd,1959,RFn,1,796,TA,TA,Y,86,0,0,0,0,0,NA,NA,NA,0,3,2006,WD,Normal,143000 +948,20,RL,85,14536,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Timber,Norm,Norm,1Fam,1Story,8,5,2002,2003,Hip,CompShg,VinylSd,VinylSd,BrkFace,236,Gd,TA,PConc,Gd,TA,Av,GLQ,1300,Unf,0,316,1616,GasA,Ex,Y,SBrkr,1629,0,0,1629,1,0,2,0,3,1,Gd,9,Typ,1,Gd,Attchd,2002,Fin,3,808,TA,TA,Y,0,252,0,0,0,0,NA,NA,NA,0,11,2007,WD,Normal,270000 +949,60,RL,65,14006,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,7,5,2002,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,144,Gd,TA,PConc,Gd,TA,NA,Unf,0,Unf,0,936,936,GasA,Ex,Y,SBrkr,936,840,0,1776,0,0,2,1,3,1,Gd,7,Typ,1,TA,Attchd,2002,RFn,2,474,TA,TA,Y,144,96,0,0,0,0,NA,NA,NA,0,2,2006,WD,Normal,192500 +950,20,RL,78,9360,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,1Story,6,7,1972,2006,Hip,CompShg,Plywood,Plywood,None,0,TA,TA,CBlock,TA,TA,No,ALQ,982,Unf,0,179,1161,GasA,TA,Y,SBrkr,1381,0,0,1381,1,0,1,1,3,1,Gd,5,Typ,1,TA,Attchd,1972,RFn,2,676,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,3,2010,WD,Normal,197500 +951,20,RL,60,7200,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,8,1950,2002,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,CBlock,TA,TA,No,ALQ,398,BLQ,149,317,864,GasA,Gd,Y,SBrkr,864,0,0,864,1,0,1,0,3,1,Gd,5,Typ,0,NA,Detchd,1980,RFn,2,720,TA,TA,Y,194,0,0,0,0,0,NA,NA,NA,0,7,2007,WD,Normal,129000 +952,20,RH,60,7800,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,SawyerW,Norm,Norm,1Fam,1Story,5,5,1965,1965,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,CBlock,TA,TA,No,BLQ,641,Unf,0,187,828,GasA,Gd,Y,SBrkr,965,0,0,965,1,0,1,0,3,1,TA,6,Typ,0,NA,Detchd,1979,Unf,1,300,TA,TA,Y,421,0,0,0,0,0,NA,MnPrv,NA,0,7,2006,WD,Abnorml,119900 +953,85,RL,60,7200,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,SFoyer,5,8,1972,2003,Gable,CompShg,WdShing,HdBoard,None,0,TA,Gd,CBlock,Gd,TA,Av,GLQ,660,Unf,0,108,768,GasA,Gd,Y,SBrkr,768,0,0,768,0,1,1,0,2,1,TA,5,Typ,0,NA,Detchd,1974,Fin,1,396,TA,TA,Y,192,0,0,0,0,0,NA,MnPrv,NA,0,4,2009,WD,Normal,133900 +954,60,RL,NA,11075,Pave,NA,IR1,Lvl,AllPub,Inside,Mod,Mitchel,Norm,Norm,1Fam,2Story,5,4,1969,1969,Gable,CompShg,HdBoard,HdBoard,BrkFace,232,TA,TA,CBlock,TA,TA,Av,ALQ,562,LwQ,193,29,784,GasA,Ex,Y,SBrkr,1168,800,0,1968,0,1,2,1,4,1,TA,7,Min2,1,Po,Attchd,1969,RFn,2,530,TA,TA,Y,305,189,0,0,0,0,NA,MnPrv,Shed,400,9,2008,WD,Normal,172000 +955,90,RL,35,9400,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Edwards,Norm,Norm,Duplex,SFoyer,6,5,1975,1975,Flat,Tar&Grv,WdShing,Plywood,BrkFace,250,TA,TA,CBlock,Gd,Gd,Gd,GLQ,945,Unf,0,0,945,GasA,TA,Y,SBrkr,980,0,0,980,0,2,2,0,4,0,TA,4,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,0,0,0,0,0,0,NA,NA,NA,0,10,2006,WD,AdjLand,127500 +956,90,RH,82,7136,Pave,NA,IR1,HLS,AllPub,Inside,Gtl,Crawfor,Norm,Norm,Duplex,2Story,6,6,1946,1950,Gable,CompShg,MetalSd,MetalSd,BrkFace,423,TA,TA,CBlock,Gd,TA,No,Rec,484,Unf,0,495,979,GasA,TA,N,FuseF,979,979,0,1958,0,0,2,0,4,2,TA,8,Typ,0,NA,Attchd,1946,Unf,2,492,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,8,2007,WD,Normal,145000 +957,160,RM,24,1300,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Blueste,Norm,Norm,TwnhsE,2Story,6,6,1980,1980,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,Gd,TA,No,ALQ,285,Unf,0,276,561,GasA,TA,Y,SBrkr,561,668,0,1229,0,0,1,1,2,1,TA,5,Typ,1,TA,Attchd,1980,Fin,2,462,TA,TA,Y,150,0,0,0,0,0,NA,GdPrv,NA,0,5,2009,WD,Normal,124000 +958,20,RL,70,7420,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,1Story,5,5,1962,1962,Hip,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,Rec,417,Unf,0,640,1057,GasA,TA,Y,SBrkr,1057,0,0,1057,0,0,1,0,3,1,TA,6,Typ,0,NA,Detchd,1977,Fin,2,576,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,4,2007,WD,Normal,132000 +959,20,RL,65,8450,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2003,2003,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,Mn,GLQ,699,Unf,0,638,1337,GasA,Ex,Y,SBrkr,1337,0,0,1337,1,0,2,0,3,1,Gd,6,Typ,0,NA,Attchd,2003,RFn,2,531,TA,TA,Y,0,39,0,0,0,0,NA,NA,NA,0,10,2007,WD,Normal,185000 +960,160,FV,24,2572,Pave,NA,Reg,Lvl,AllPub,FR2,Gtl,Somerst,Norm,Norm,Twnhs,2Story,7,5,1999,1999,Hip,CompShg,MetalSd,MetalSd,None,0,Gd,TA,PConc,Gd,TA,No,ALQ,604,Unf,0,92,696,GasA,Ex,Y,SBrkr,696,720,0,1416,1,0,2,1,3,1,Gd,6,Typ,0,NA,Detchd,1999,Unf,2,484,TA,TA,Y,0,44,0,0,0,0,NA,NA,NA,0,5,2010,WD,Normal,155000 +961,20,RL,50,7207,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,1Story,5,7,1958,2008,Gable,CompShg,Wd Sdng,Plywood,None,0,TA,Gd,CBlock,TA,TA,Gd,BLQ,696,Unf,0,162,858,GasA,Gd,Y,SBrkr,858,0,0,858,1,0,1,0,2,1,TA,4,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,117,0,0,0,0,0,NA,NA,NA,0,2,2010,WD,Normal,116500 +962,60,RL,NA,12227,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NWAmes,PosN,Norm,1Fam,2Story,6,7,1977,1995,Gable,CompShg,HdBoard,HdBoard,BrkFace,424,TA,Gd,CBlock,Gd,Gd,No,ALQ,896,Unf,0,434,1330,GasA,TA,Y,SBrkr,1542,1330,0,2872,1,0,2,1,4,1,TA,11,Typ,1,TA,Attchd,1977,Fin,2,619,TA,TA,Y,550,282,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,272000 +963,160,RL,24,2308,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NPkVill,Norm,Norm,TwnhsE,2Story,6,6,1976,1976,Gable,CompShg,Plywood,Brk Cmn,None,0,TA,TA,CBlock,Gd,TA,No,ALQ,556,Unf,0,248,804,GasA,TA,Y,SBrkr,804,744,0,1548,1,0,2,1,3,1,Gd,7,Typ,1,TA,Detchd,1976,Unf,2,440,TA,TA,Y,48,0,0,0,0,0,NA,NA,NA,0,7,2007,WD,Normal,155000 +964,20,RL,122,11923,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,CollgCr,Norm,Norm,1Fam,1Story,9,5,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Ex,TA,No,Unf,0,Unf,0,1800,1800,GasA,Ex,Y,SBrkr,1800,0,0,1800,0,0,2,0,2,1,Ex,7,Typ,0,NA,Attchd,2007,Fin,2,702,TA,TA,Y,288,136,0,0,0,0,NA,NA,NA,0,5,2009,WD,Normal,239000 +965,60,RL,80,11316,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,Timber,Norm,Norm,1Fam,2Story,7,5,2002,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,44,Gd,TA,PConc,Gd,TA,No,GLQ,624,Unf,0,193,817,GasA,Ex,Y,SBrkr,824,1070,0,1894,1,0,2,1,4,1,Gd,8,Typ,1,Gd,BuiltIn,2002,Fin,2,510,TA,TA,Y,0,40,0,0,0,0,NA,NA,NA,0,2,2010,WD,Normal,214900 +966,60,RL,65,10237,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,1Fam,2Story,6,5,2005,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,783,783,GasA,Ex,Y,SBrkr,783,701,0,1484,0,0,2,1,3,1,Gd,8,Typ,1,Gd,Attchd,2005,Fin,2,393,TA,TA,Y,0,72,0,0,0,0,NA,NA,NA,0,7,2007,New,Partial,178900 +967,50,RL,130,9600,Pave,NA,IR1,HLS,AllPub,Inside,Gtl,Crawfor,Norm,Norm,1Fam,1.5Fin,5,7,1940,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Gd,Gd,BrkTil,TA,Fa,No,Rec,428,Unf,0,300,728,GasA,Ex,Y,SBrkr,976,332,0,1308,1,0,1,1,2,1,TA,7,Min2,2,TA,Detchd,1940,Unf,1,256,TA,TA,Y,0,70,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,160000 +968,20,RL,NA,7390,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,7,1955,1955,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,151,TA,TA,CBlock,TA,TA,No,ALQ,902,Unf,0,196,1098,GasA,TA,Y,SBrkr,1098,0,0,1098,1,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1955,Unf,1,260,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,135000 +969,50,RM,50,5925,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,1.5Fin,3,6,1910,1950,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,600,600,Grav,Fa,N,SBrkr,600,368,0,968,0,0,1,0,2,1,TA,6,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,0,0,0,0,0,0,NA,GdWo,NA,0,5,2009,WD,Abnorml,37900 +970,190,RL,75,10382,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,2fmCon,SLvl,6,5,1958,1958,Hip,CompShg,HdBoard,HdBoard,BrkFace,105,TA,Fa,CBlock,TA,TA,Gd,ALQ,513,Unf,0,75,588,GasA,TA,Y,SBrkr,1095,0,0,1095,1,0,1,0,2,1,TA,6,Typ,0,NA,Attchd,1958,RFn,1,264,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,3,2006,ConLD,Normal,140000 +971,50,RL,60,10800,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1.5Fin,4,4,1949,1950,Gable,CompShg,AsbShng,AsbShng,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,720,720,GasA,TA,N,FuseA,720,472,0,1192,0,0,1,1,4,1,TA,6,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,0,0,0,0,0,0,NA,NA,NA,0,12,2006,WD,Abnorml,135000 +972,160,RL,36,2268,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,Twnhs,2Story,7,5,2003,2004,Gable,CompShg,VinylSd,Wd Shng,Stone,106,Gd,TA,PConc,Gd,TA,No,GLQ,567,Unf,0,197,764,GasA,Ex,Y,SBrkr,764,862,0,1626,0,0,2,0,2,1,Gd,6,Typ,0,NA,BuiltIn,2003,RFn,2,474,TA,TA,Y,0,27,0,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,173000 +973,120,RL,55,7892,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SawyerW,Norm,Norm,TwnhsE,1Story,6,5,1979,1979,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,CBlock,Gd,TA,No,Unf,0,Unf,0,918,918,GasA,TA,Y,SBrkr,918,0,0,918,0,0,2,0,2,1,TA,5,Typ,1,TA,Attchd,1979,Unf,1,264,TA,TA,Y,28,0,0,0,0,0,NA,NA,NA,0,4,2010,WD,Normal,99500 +974,20,FV,95,11639,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,Somerst,Norm,Norm,1Fam,1Story,7,5,2007,2008,Gable,CompShg,CemntBd,CmentBd,NA,NA,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1428,1428,GasA,Ex,Y,SBrkr,1428,0,0,1428,0,0,2,0,3,1,Gd,6,Typ,0,NA,Attchd,2007,Fin,2,480,TA,TA,Y,0,120,0,0,0,0,NA,NA,NA,0,12,2008,New,Partial,182000 +975,70,RL,60,11414,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,BrkSide,RRAn,Feedr,1Fam,2Story,7,8,1910,1993,Gable,CompShg,HdBoard,HdBoard,None,0,TA,Gd,BrkTil,Gd,TA,No,Unf,0,Unf,0,728,728,GasA,TA,N,SBrkr,1136,883,0,2019,0,0,1,0,3,1,Gd,8,Typ,0,NA,Detchd,1997,Unf,2,532,TA,TA,Y,509,135,0,0,0,0,NA,GdPrv,NA,0,10,2009,WD,Normal,167500 +976,160,FV,NA,2651,Pave,NA,Reg,Lvl,AllPub,FR2,Gtl,Somerst,Norm,Norm,Twnhs,2Story,7,5,2000,2000,Gable,CompShg,MetalSd,MetalSd,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,641,Unf,0,32,673,GasA,Ex,Y,SBrkr,673,709,0,1382,1,0,2,1,3,1,Gd,6,Typ,0,NA,Detchd,2000,Unf,2,490,TA,TA,Y,153,50,0,0,0,0,NA,NA,NA,0,4,2006,WD,Normal,165000 +977,30,RL,51,5900,Pave,NA,IR1,Bnk,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,1Story,4,7,1923,1958,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,PConc,Gd,TA,No,Unf,0,Unf,0,440,440,GasA,TA,Y,FuseA,869,0,0,869,0,0,1,0,2,1,Fa,4,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,0,0,0,0,0,0,NA,NA,NA,0,8,2006,WD,Normal,85500 +978,120,FV,35,4274,Pave,Pave,IR1,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,TwnhsE,1Story,7,5,2006,2007,Gable,CompShg,VinylSd,VinylSd,NA,NA,Gd,TA,PConc,Gd,TA,No,GLQ,1106,Unf,0,135,1241,GasA,Ex,Y,SBrkr,1241,0,0,1241,1,0,1,1,1,1,Gd,4,Typ,0,NA,Attchd,2007,Fin,2,569,TA,TA,Y,0,116,0,0,0,0,NA,NA,NA,0,11,2007,New,Partial,199900 +979,20,RL,68,9450,Pave,NA,Reg,Bnk,AllPub,Inside,Mod,Edwards,Norm,Norm,1Fam,1Story,4,5,1954,1954,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,LwQ,552,Unf,0,342,894,GasA,Ex,Y,SBrkr,894,0,0,894,0,0,1,0,3,1,TA,5,Typ,0,NA,Detchd,1999,Unf,2,400,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,5,2007,WD,Abnorml,110000 +980,20,RL,80,8816,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,Sawyer,Feedr,Norm,1Fam,1Story,5,6,1963,1963,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,TA,TA,No,Rec,651,Unf,0,470,1121,GasA,TA,Y,SBrkr,1121,0,0,1121,1,0,1,0,3,1,TA,5,Typ,0,NA,Detchd,1963,Unf,2,480,TA,TA,Y,0,80,0,0,0,0,NA,MnPrv,NA,0,6,2009,WD,Normal,139000 +981,85,RL,NA,12122,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,1Fam,SFoyer,7,9,1961,2007,Gable,CompShg,CemntBd,CmentBd,Stone,210,Ex,TA,CBlock,TA,TA,Av,ALQ,867,Unf,0,77,944,GasA,Gd,Y,SBrkr,999,0,0,999,1,0,1,0,3,1,Ex,6,Typ,0,NA,Attchd,1961,RFn,2,588,TA,TA,Y,144,76,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,178400 +982,60,RL,98,12203,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NoRidge,Norm,Norm,1Fam,2Story,8,5,1998,1999,Hip,CompShg,VinylSd,VinylSd,BrkFace,975,Gd,TA,PConc,Gd,TA,No,GLQ,854,Unf,0,371,1225,GasA,Ex,Y,SBrkr,1276,1336,0,2612,1,0,2,1,4,1,Gd,8,Typ,1,TA,Attchd,1998,Fin,3,676,TA,TA,Y,250,0,0,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,336000 +983,20,RL,43,3182,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Blmngtn,Norm,Norm,1Fam,1Story,7,5,2007,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,16,Gd,TA,PConc,Gd,TA,Av,Unf,0,Unf,0,1266,1266,GasA,Ex,Y,SBrkr,1266,0,0,1266,0,0,2,0,2,1,Gd,6,Typ,1,Gd,Attchd,2007,Fin,2,388,TA,TA,Y,100,16,0,0,0,0,NA,NA,NA,0,3,2008,WD,Normal,159895 +984,60,RL,NA,11250,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,CollgCr,Norm,Norm,1Fam,2Story,8,5,2002,2002,Gable,CompShg,CemntBd,CmentBd,None,0,Gd,TA,PConc,Gd,TA,Mn,Unf,0,Unf,0,1128,1128,GasA,Ex,Y,SBrkr,1149,1141,0,2290,0,0,2,1,4,1,Gd,9,Typ,1,Gd,Attchd,2002,Unf,2,779,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,5,2008,WD,Normal,255900 +985,90,RL,75,10125,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Mitchel,Norm,Norm,Duplex,1.5Fin,5,5,1977,1977,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,CBlock,NA,NA,NA,NA,0,NA,0,0,0,GasA,TA,Y,SBrkr,1302,432,0,1734,0,0,2,0,4,2,Gd,8,Typ,0,NA,Attchd,1977,Unf,2,539,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,8,2009,COD,Normal,126000 +986,190,RL,68,10880,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,2fmCon,1Story,5,5,1950,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,ALQ,1040,Unf,0,124,1164,GasW,TA,N,SBrkr,1164,0,0,1164,1,0,1,0,3,1,TA,5,Typ,0,NA,Detchd,1950,Unf,1,240,TA,TA,Y,0,48,0,0,0,0,NA,NA,NA,0,8,2008,ConLD,Normal,125000 +987,50,RM,59,5310,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,OldTown,Feedr,Norm,1Fam,1.5Fin,6,8,1910,2003,Hip,CompShg,VinylSd,VinylSd,None,0,TA,Gd,CBlock,TA,Fa,No,Unf,0,Unf,0,485,485,GasA,Gd,Y,SBrkr,1001,634,0,1635,0,0,1,0,2,1,Gd,5,Typ,0,NA,Attchd,1950,Unf,1,255,Fa,TA,Y,394,0,0,0,0,0,NA,NA,NA,0,6,2006,WD,Normal,117000 +988,20,RL,83,10159,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,1Story,9,5,2009,2010,Hip,CompShg,VinylSd,VinylSd,Stone,450,Ex,TA,PConc,Ex,TA,Av,GLQ,1646,Unf,0,284,1930,GasA,Ex,Y,SBrkr,1940,0,0,1940,1,0,2,1,3,1,Ex,8,Typ,1,Gd,Attchd,2010,Fin,3,606,TA,TA,Y,168,95,0,0,0,0,NA,NA,NA,0,4,2010,New,Partial,395192 +989,60,RL,NA,12046,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,2Story,6,6,1976,1976,Gable,CompShg,Plywood,Plywood,BrkFace,298,TA,TA,CBlock,TA,TA,No,LwQ,156,Unf,0,692,848,GasA,TA,Y,SBrkr,1118,912,0,2030,0,0,2,1,4,1,Gd,8,Typ,1,TA,Attchd,1976,Fin,2,551,TA,TA,Y,0,224,0,0,0,0,NA,NA,NA,0,6,2007,WD,Normal,195000 +990,60,FV,65,8125,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,2Story,7,5,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,Gd,No,Unf,0,Unf,0,770,770,GasA,Ex,Y,SBrkr,778,798,0,1576,0,0,2,1,3,1,Gd,6,Typ,0,NA,Attchd,2006,RFn,2,614,TA,TA,Y,0,50,0,0,0,0,NA,NA,NA,0,8,2006,New,Partial,197000 +991,60,RL,82,9452,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NoRidge,Norm,Norm,1Fam,2Story,8,5,1997,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,423,Gd,TA,PConc,Gd,TA,No,GLQ,1074,Unf,0,322,1396,GasA,Ex,Y,SBrkr,1407,985,0,2392,1,0,2,1,3,1,Gd,7,Typ,1,TA,Attchd,1997,Fin,3,870,TA,TA,Y,0,70,0,0,0,0,NA,NA,NA,0,6,2006,WD,Normal,348000 +992,70,RM,121,17671,Pave,Grvl,Reg,Lvl,AllPub,Corner,Gtl,OldTown,Artery,Norm,1Fam,2Story,8,9,1882,1986,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Gd,Gd,BrkTil,TA,TA,No,BLQ,216,Unf,0,700,916,GasA,Gd,Y,SBrkr,916,826,0,1742,0,0,1,1,4,1,Gd,8,Typ,1,Gd,Attchd,1925,Unf,2,424,TA,TA,P,0,169,0,0,0,0,NA,NA,NA,0,11,2009,WD,Normal,168000 +993,60,RL,80,9760,Pave,NA,Reg,Lvl,AllPub,Inside,Mod,NAmes,Norm,Norm,1Fam,2Story,6,8,1964,1993,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,340,TA,TA,CBlock,TA,TA,Gd,BLQ,536,Rec,117,169,822,GasA,Gd,Y,SBrkr,1020,831,0,1851,0,0,2,1,3,1,Gd,7,Typ,1,Fa,Attchd,1964,RFn,2,440,TA,TA,Y,239,42,0,0,0,0,NA,MnWw,NA,0,7,2007,WD,Normal,187000 +994,60,RL,68,8846,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,6,5,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,No,Unf,0,Unf,0,750,750,GasA,Ex,Y,SBrkr,750,750,0,1500,0,0,2,1,3,1,Gd,6,Typ,0,NA,Attchd,2005,RFn,2,564,TA,TA,Y,0,35,0,0,0,0,NA,NA,NA,0,8,2006,New,Partial,173900 +995,20,RL,96,12456,Pave,NA,Reg,Lvl,AllPub,FR2,Gtl,NridgHt,Norm,Norm,1Fam,1Story,10,5,2006,2007,Hip,CompShg,CemntBd,CmentBd,Stone,230,Ex,TA,PConc,Ex,TA,Gd,GLQ,1172,Unf,0,528,1700,GasA,Ex,Y,SBrkr,1718,0,0,1718,1,0,2,0,3,1,Ex,7,Typ,1,Gd,Attchd,2008,Fin,3,786,TA,TA,Y,216,48,0,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,337500 +996,50,RL,51,4712,Pave,NA,IR1,Lvl,AllPub,Inside,Mod,BrkSide,Feedr,Norm,1Fam,1.5Fin,4,7,1946,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,ALQ,384,Unf,0,363,747,GasA,TA,Y,SBrkr,774,456,0,1230,1,0,1,1,3,1,TA,5,Typ,0,NA,Detchd,1946,Unf,1,305,TA,TA,Y,0,57,0,0,63,0,NA,MnPrv,NA,0,8,2006,WD,Abnorml,121600 +997,20,RL,NA,10659,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,6,1961,1961,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,Rec,915,Unf,0,135,1050,GasA,TA,Y,SBrkr,1050,0,0,1050,1,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1961,Unf,1,368,TA,TA,Y,0,319,0,0,0,0,NA,NA,NA,0,1,2006,COD,Normal,136500 +998,20,RL,NA,11717,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NWAmes,PosA,Norm,1Fam,1Story,6,6,1970,1970,Hip,CompShg,HdBoard,HdBoard,BrkFace,571,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,1442,1442,GasA,TA,Y,SBrkr,1442,0,0,1442,0,0,2,0,2,1,TA,6,Typ,1,TA,Attchd,1970,RFn,2,615,TA,TA,Y,371,0,0,0,0,0,NA,NA,NA,0,2,2009,WD,Normal,185000 +999,30,RM,60,9786,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,IDOTRR,Norm,Norm,1Fam,1Story,3,4,1922,1950,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,TA,Fa,No,Unf,0,Unf,0,1007,1007,GasA,Fa,N,SBrkr,1077,0,0,1077,0,0,1,0,3,1,TA,6,Typ,1,Gd,Detchd,1922,Unf,1,210,TA,Fa,P,0,100,48,0,0,0,NA,NA,NA,0,5,2006,WD,Normal,91000 +1000,20,RL,64,6762,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,24,Gd,TA,PConc,Gd,TA,Av,GLQ,686,Unf,0,501,1187,GasA,Ex,Y,SBrkr,1208,0,0,1208,1,0,2,0,2,1,Gd,6,Typ,0,NA,Attchd,2006,RFn,2,632,TA,TA,Y,105,61,0,0,0,0,NA,NA,NA,0,2,2010,WD,Normal,206000 +1001,20,RL,74,10206,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,1Fam,1Story,3,3,1952,1952,Flat,Tar&Grv,BrkComm,Brk Cmn,None,0,TA,TA,Slab,NA,NA,NA,NA,0,NA,0,0,0,GasW,Fa,N,FuseF,944,0,0,944,0,0,1,0,2,1,Fa,4,Min1,0,NA,Detchd,1956,Unf,2,528,TA,Fa,Y,0,0,0,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,82000 +1002,30,RL,60,5400,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,OldTown,Norm,Norm,1Fam,1Story,5,6,1920,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,Fa,TA,No,Unf,0,Unf,0,691,691,GasA,Ex,Y,FuseA,691,0,0,691,0,0,1,0,2,1,Ex,4,Typ,0,NA,Detchd,1920,Unf,1,216,Fa,TA,N,0,20,94,0,0,0,NA,NA,NA,0,1,2007,WD,Abnorml,86000 +1003,20,RL,75,11957,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Somerst,RRAn,Norm,1Fam,1Story,8,5,2006,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,53,Gd,TA,PConc,Gd,TA,No,GLQ,24,Unf,0,1550,1574,GasA,Ex,Y,SBrkr,1574,0,0,1574,0,0,2,0,3,1,Gd,7,Typ,1,Gd,Attchd,2006,RFn,3,824,TA,TA,Y,144,104,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,232000 +1004,90,RL,NA,11500,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NWAmes,Feedr,RRAn,Duplex,1Story,5,6,1976,1976,Gable,CompShg,VinylSd,VinylSd,BrkFace,164,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,1680,1680,GasA,Fa,Y,SBrkr,1680,0,0,1680,0,0,2,0,4,2,TA,8,Typ,0,NA,Detchd,1976,Unf,2,528,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,6,2007,WD,Normal,136905 +1005,120,RL,43,3182,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Blmngtn,Norm,Norm,TwnhsE,1Story,7,5,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,16,Gd,TA,PConc,Gd,TA,No,GLQ,16,Unf,0,1330,1346,GasA,Ex,Y,SBrkr,1504,0,0,1504,0,0,2,0,1,1,Gd,7,Typ,1,Gd,Attchd,2005,Fin,2,457,TA,TA,Y,156,0,0,0,0,0,NA,NA,NA,0,5,2009,WD,Normal,181000 +1006,80,RL,65,8385,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,SLvl,5,8,1977,1977,Gable,CompShg,HdBoard,HdBoard,BrkFace,220,Gd,TA,CBlock,Gd,Gd,Av,GLQ,595,Unf,0,390,985,GasA,TA,Y,SBrkr,985,0,0,985,0,0,2,0,3,1,TA,6,Typ,0,NA,Attchd,1977,Unf,1,328,TA,TA,Y,210,0,0,0,0,0,NA,NA,NA,0,11,2008,WD,Normal,149900 +1007,20,RL,NA,12155,Pave,NA,IR3,Lvl,AllPub,Inside,Gtl,NAmes,PosN,Norm,1Fam,1Story,6,3,1970,1970,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,CBlock,Gd,TA,No,LwQ,1237,Unf,0,420,1657,GasA,Gd,Y,SBrkr,1657,0,0,1657,0,1,2,0,3,1,TA,7,Typ,1,TA,Attchd,1970,Unf,2,484,TA,TA,Y,0,0,0,0,147,0,NA,NA,NA,0,3,2007,WD,Normal,163500 +1008,160,RM,21,2217,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,MeadowV,Norm,Norm,TwnhsE,2Story,4,4,1970,1970,Gable,CompShg,CemntBd,CmentBd,None,0,TA,TA,CBlock,TA,TA,No,BLQ,273,LwQ,273,0,546,GasA,TA,Y,SBrkr,546,546,0,1092,0,0,1,1,3,1,TA,6,Typ,0,NA,Attchd,1970,RFn,1,286,TA,TA,Y,238,0,0,0,0,0,NA,NA,NA,0,8,2009,WD,Normal,88000 +1009,20,RL,43,12118,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Mitchel,Norm,Norm,1Fam,1Story,7,5,2004,2005,Hip,CompShg,VinylSd,VinylSd,Stone,108,Gd,TA,PConc,Ex,TA,Mn,Unf,0,Unf,0,1710,1710,GasA,Ex,Y,SBrkr,1710,0,0,1710,0,0,2,0,3,1,Gd,7,Typ,1,Gd,Attchd,2004,Fin,2,550,TA,TA,Y,100,48,0,0,180,0,NA,NA,NA,0,4,2009,WD,Normal,240000 +1010,50,RL,60,6000,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SWISU,Norm,Norm,1Fam,1.5Fin,5,5,1926,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,Fa,BrkTil,TA,TA,No,Unf,0,Unf,0,1008,1008,GasA,Ex,Y,SBrkr,1008,0,514,1522,0,0,2,0,4,1,TA,7,Typ,0,NA,NA,NA,NA,0,0,NA,NA,P,0,0,138,0,0,0,NA,NA,NA,0,6,2006,WD,Normal,102000 +1011,50,RL,115,21286,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,1.5Fin,5,5,1948,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,720,720,GasA,TA,Y,SBrkr,720,551,0,1271,0,0,2,0,4,1,TA,7,Typ,1,Gd,Attchd,1948,Unf,1,312,TA,TA,Y,0,0,108,0,0,0,NA,NA,NA,0,8,2008,WD,Normal,135000 +1012,90,RL,75,9825,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Duplex,1Story,5,5,1965,1965,Hip,CompShg,AsphShn,AsphShn,None,0,TA,TA,CBlock,NA,NA,NA,NA,0,NA,0,0,0,GasA,TA,N,SBrkr,1664,0,0,1664,0,0,2,0,4,2,TA,8,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,0,0,0,0,0,0,NA,NA,NA,0,5,2010,WD,Normal,100000 +1013,70,RL,55,10592,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Crawfor,Norm,Norm,1Fam,2Story,6,7,1923,1996,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,TA,Gd,PConc,TA,Fa,No,Unf,0,Unf,0,602,602,GasA,TA,Y,SBrkr,900,602,0,1502,0,0,1,1,3,1,Gd,7,Typ,2,TA,Detchd,1923,Unf,1,180,TA,TA,Y,96,0,112,0,53,0,NA,NA,NA,0,8,2007,WD,Normal,165000 +1014,30,RM,60,7200,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,1Story,5,4,1910,2006,Hip,CompShg,MetalSd,Stucco,None,0,TA,TA,BrkTil,TA,TA,No,ALQ,247,Rec,465,310,1022,GasW,TA,N,SBrkr,1022,0,0,1022,1,0,1,0,2,1,TA,4,Maj2,0,NA,Detchd,1956,Unf,1,280,TA,TA,Y,0,30,226,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,85000 +1015,20,RL,60,11664,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Artery,Norm,1Fam,1Story,6,5,1948,1950,Gable,CompShg,MetalSd,MetalSd,BrkFace,206,TA,TA,CBlock,TA,Fa,No,BLQ,336,Unf,0,746,1082,GasA,TA,Y,SBrkr,1082,0,0,1082,0,0,1,0,2,1,TA,5,Typ,1,Gd,Detchd,1948,Unf,1,240,TA,TA,Y,0,130,0,0,0,0,NA,NA,NA,0,11,2007,WD,Normal,119200 +1016,60,RL,70,8400,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,2Story,8,6,2001,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,643,Unf,0,167,810,GasA,Ex,Y,SBrkr,810,855,0,1665,1,0,2,1,3,1,Gd,6,Typ,0,NA,Attchd,2001,Fin,2,528,TA,TA,Y,0,45,0,0,0,0,NA,NA,NA,0,11,2009,WD,Normal,227000 +1017,20,RL,73,11883,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,1996,1996,Hip,CompShg,VinylSd,VinylSd,BrkFace,196,Gd,TA,PConc,Gd,TA,Gd,GLQ,690,Unf,0,814,1504,GasA,Ex,Y,SBrkr,1504,0,0,1504,1,0,2,0,3,1,Gd,6,Typ,1,TA,Attchd,1996,Fin,2,478,TA,TA,Y,115,66,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,203000 +1018,120,RL,NA,5814,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,StoneBr,Norm,Norm,TwnhsE,1Story,8,5,1984,1984,Gable,CompShg,HdBoard,HdBoard,None,0,Gd,TA,CBlock,Gd,TA,Av,GLQ,1036,Unf,0,184,1220,GasA,Gd,Y,SBrkr,1360,0,0,1360,1,0,1,0,1,1,Gd,4,Typ,1,Ex,Attchd,1984,RFn,2,565,TA,TA,Y,63,0,0,0,0,0,NA,NA,NA,0,8,2009,COD,Abnorml,187500 +1019,80,RL,NA,10784,Pave,NA,IR1,Lvl,AllPub,FR2,Gtl,Gilbert,Norm,Norm,1Fam,SLvl,7,5,1991,1992,Gable,CompShg,HdBoard,HdBoard,BrkFace,76,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,384,384,GasA,Gd,Y,SBrkr,802,670,0,1472,0,0,2,1,3,1,Gd,7,Typ,1,TA,Attchd,1991,RFn,2,402,TA,TA,Y,164,0,0,0,0,0,NA,NA,NA,0,5,2007,WD,Normal,160000 +1020,120,RL,43,3013,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Blmngtn,Norm,Norm,TwnhsE,1Story,7,5,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,145,Gd,TA,PConc,Gd,TA,Gd,GLQ,16,Unf,0,1346,1362,GasA,Ex,Y,SBrkr,1506,0,0,1506,0,0,2,0,2,1,Gd,6,Typ,1,Gd,Attchd,2005,Fin,2,440,TA,TA,Y,142,20,0,0,0,0,NA,NA,NA,0,4,2006,WD,Normal,213490 +1021,20,RL,60,7024,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1Story,4,5,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,No,GLQ,1024,Unf,0,108,1132,GasA,Ex,Y,SBrkr,1132,0,0,1132,1,0,1,1,2,1,Gd,5,Typ,0,NA,Attchd,2005,Fin,2,451,TA,TA,Y,252,64,0,0,0,0,NA,NA,NA,0,6,2008,WD,Normal,176000 +1022,20,RL,64,7406,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,84,Gd,TA,PConc,Gd,TA,Av,GLQ,684,Unf,0,515,1199,GasA,Ex,Y,SBrkr,1220,0,0,1220,1,0,2,0,2,1,Gd,6,Typ,0,NA,Attchd,2006,RFn,2,632,TA,TA,Y,105,54,0,0,0,0,NA,NA,NA,0,7,2006,New,Partial,194000 +1023,50,RM,52,9439,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,1.5Fin,5,5,1930,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,LwQ,324,Unf,0,588,912,GasA,Gd,Y,FuseA,912,336,0,1248,0,0,1,0,2,1,TA,6,Typ,0,NA,Detchd,1957,Unf,1,160,Fa,Fa,Y,0,0,192,0,0,0,NA,NA,NA,0,3,2007,WD,Normal,87000 +1024,120,RL,43,3182,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Blmngtn,Norm,Norm,TwnhsE,1Story,7,5,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,14,Gd,TA,PConc,Gd,Gd,No,GLQ,16,Unf,0,1330,1346,GasA,Ex,Y,SBrkr,1504,0,0,1504,0,0,2,0,2,1,Gd,7,Typ,1,Gd,Attchd,2005,Fin,2,437,TA,TA,Y,156,20,0,0,0,0,NA,NA,NA,0,5,2008,WD,Normal,191000 +1025,20,RL,NA,15498,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Timber,Norm,Norm,1Fam,1Story,8,6,1976,1976,Hip,WdShake,Stone,HdBoard,None,0,Gd,TA,CBlock,Gd,TA,Av,ALQ,1165,LwQ,400,0,1565,GasA,TA,Y,SBrkr,2898,0,0,2898,1,0,2,0,2,1,Gd,10,Typ,1,Gd,Attchd,1976,Fin,2,665,TA,TA,Y,0,72,174,0,0,0,NA,NA,NA,0,5,2008,COD,Abnorml,287000 +1026,20,RL,70,7700,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,CollgCr,Norm,Norm,1Fam,1Story,5,5,1972,1972,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,CBlock,TA,TA,No,LwQ,138,Rec,468,276,882,GasA,TA,Y,SBrkr,882,0,0,882,1,0,1,0,3,1,TA,5,Typ,0,NA,Detchd,1980,Unf,2,461,TA,TA,Y,96,0,0,0,0,0,NA,MnPrv,NA,0,3,2007,WD,Normal,112500 +1027,20,RL,73,9300,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Feedr,Norm,1Fam,1Story,5,5,1960,1960,Gable,CompShg,MetalSd,HdBoard,BrkFace,324,TA,TA,CBlock,TA,TA,No,Rec,697,Unf,0,571,1268,GasA,TA,Y,SBrkr,1264,0,0,1264,1,0,1,0,3,1,TA,6,Typ,2,Gd,Attchd,1960,Unf,2,461,TA,TA,Y,0,0,0,0,143,0,NA,NA,NA,0,4,2010,WD,Normal,167500 +1028,20,RL,71,9520,Pave,NA,IR1,HLS,AllPub,Inside,Gtl,Timber,Norm,Norm,1Fam,1Story,8,5,2007,2008,Gable,CompShg,VinylSd,VinylSd,Stone,338,Gd,TA,PConc,Gd,TA,Gd,GLQ,1513,Unf,0,125,1638,GasA,Ex,Y,SBrkr,1646,0,0,1646,1,0,2,0,3,1,Gd,7,Typ,1,Gd,Attchd,2008,RFn,3,800,TA,TA,Y,192,44,0,0,0,0,NA,NA,NA,0,4,2008,New,Partial,293077 +1029,50,RL,79,9492,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Artery,Norm,1Fam,1.5Fin,5,5,1941,1950,Gable,CompShg,WdShing,Wd Shng,None,0,TA,TA,CBlock,TA,TA,No,Rec,368,BLQ,41,359,768,GasA,TA,Y,SBrkr,968,408,0,1376,1,0,1,0,3,1,TA,6,Typ,1,Gd,Attchd,1941,Unf,1,240,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,4,2007,WD,Normal,105000 +1030,160,RM,21,1680,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrDale,Norm,Norm,Twnhs,2Story,6,7,1972,1972,Gable,CompShg,HdBoard,HdBoard,BrkFace,281,TA,TA,CBlock,TA,TA,No,BLQ,317,Unf,0,355,672,GasA,Gd,Y,SBrkr,672,546,0,1218,0,1,1,1,3,1,TA,7,Typ,0,NA,Detchd,1972,Unf,1,264,TA,TA,Y,0,28,0,0,0,0,NA,NA,NA,0,5,2006,WD,Normal,118000 +1031,190,RH,NA,7082,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SWISU,Norm,Norm,2fmCon,2Story,5,8,1916,1995,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,TA,TA,Mn,Unf,0,Unf,0,686,686,GasA,Gd,Y,SBrkr,948,980,0,1928,0,0,2,0,5,2,TA,10,Typ,0,NA,NA,NA,NA,0,0,NA,NA,N,0,0,228,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,160000 +1032,75,RL,102,15863,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,SWISU,Norm,Norm,1Fam,2.5Fin,7,3,1920,1970,Gable,CompShg,Wd Sdng,Plywood,None,0,TA,TA,BrkTil,TA,TA,No,GLQ,523,Unf,0,301,824,GasA,Ex,Y,SBrkr,1687,998,397,3082,1,0,2,1,5,1,TA,12,Typ,2,TA,Basment,1970,Fin,2,672,TA,TA,Y,136,63,0,0,0,0,NA,NA,NA,0,8,2009,WD,Normal,197000 +1033,60,RL,NA,14541,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NoRidge,Norm,Norm,1Fam,2Story,8,7,1993,1993,Gable,CompShg,MetalSd,MetalSd,None,0,Gd,Gd,PConc,Gd,Gd,No,GLQ,1012,Unf,0,326,1338,GasA,Ex,Y,SBrkr,1352,1168,0,2520,1,0,2,1,5,1,Gd,10,Typ,1,TA,Attchd,1993,RFn,3,796,TA,TA,Y,209,55,0,0,0,0,NA,NA,NA,0,11,2006,WD,Abnorml,310000 +1034,20,RL,NA,8125,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2002,2002,Gable,CompShg,VinylSd,VinylSd,Stone,295,Gd,TA,PConc,Gd,TA,No,GLQ,986,Unf,0,668,1654,GasA,Ex,Y,SBrkr,1654,0,0,1654,1,0,2,0,3,1,Gd,6,Typ,0,NA,Attchd,2002,Unf,3,900,TA,TA,Y,0,136,0,0,0,0,NA,NA,NA,0,2,2006,WD,Normal,230000 +1035,30,RL,50,6305,Pave,NA,Reg,Bnk,AllPub,Inside,Gtl,Crawfor,Norm,Norm,1Fam,1Story,5,7,1938,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,Gd,PConc,Fa,Fa,No,Unf,0,Unf,0,920,920,GasA,Ex,Y,SBrkr,954,0,0,954,0,0,1,0,2,1,Fa,5,Typ,1,Gd,Basment,1938,Unf,1,240,Fa,TA,Y,0,0,0,0,0,0,NA,MnPrv,NA,0,6,2007,WD,Normal,119750 +1036,20,RL,NA,11500,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Edwards,Norm,Norm,1Fam,1Story,4,3,1957,1957,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,Gd,Slab,NA,NA,NA,NA,0,NA,0,0,0,GasA,Ex,N,SBrkr,845,0,0,845,0,0,1,0,3,1,TA,5,Typ,0,NA,Detchd,1957,Unf,1,290,TA,TA,N,186,0,0,0,0,0,NA,NA,NA,0,1,2009,WD,Normal,84000 +1037,20,RL,89,12898,Pave,NA,IR1,HLS,AllPub,Inside,Gtl,Timber,Norm,Norm,1Fam,1Story,9,5,2007,2008,Hip,CompShg,VinylSd,VinylSd,Stone,70,Gd,TA,PConc,Ex,TA,Gd,GLQ,1022,Unf,0,598,1620,GasA,Ex,Y,SBrkr,1620,0,0,1620,1,0,2,0,2,1,Ex,6,Typ,1,Ex,Attchd,2008,Fin,3,912,TA,TA,Y,228,0,0,0,0,0,NA,NA,NA,0,9,2009,WD,Normal,315500 +1038,60,RL,NA,9240,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,8,5,2001,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,396,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1055,1055,GasA,Ex,Y,SBrkr,1055,1208,0,2263,0,0,2,1,3,1,Gd,7,Typ,1,TA,BuiltIn,2001,Fin,2,905,TA,TA,Y,0,45,0,0,189,0,NA,NA,NA,0,9,2008,WD,Normal,287000 +1039,160,RM,21,1533,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,MeadowV,Norm,Norm,Twnhs,2Story,4,6,1970,2008,Gable,CompShg,CemntBd,CmentBd,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,546,546,GasA,TA,Y,SBrkr,798,546,0,1344,0,0,1,1,3,1,TA,6,Typ,1,TA,NA,NA,NA,0,0,NA,NA,Y,0,0,0,0,0,0,NA,NA,NA,0,5,2009,WD,Normal,97000 +1040,180,RM,21,1477,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,MeadowV,Norm,Norm,TwnhsE,SFoyer,4,4,1970,1970,Gable,CompShg,CemntBd,CmentBd,None,0,TA,TA,CBlock,Gd,TA,Av,GLQ,509,Unf,0,121,630,GasA,TA,Y,SBrkr,630,0,0,630,1,0,1,0,1,1,TA,3,Typ,0,NA,Attchd,1970,Unf,1,286,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,4,2009,WD,Normal,80000 +1041,20,RL,88,13125,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,1Fam,1Story,5,4,1957,2000,Gable,CompShg,Wd Sdng,Wd Sdng,BrkCmn,67,TA,TA,CBlock,TA,TA,No,Rec,168,BLQ,682,284,1134,GasA,Ex,Y,SBrkr,1803,0,0,1803,1,0,2,0,3,1,TA,8,Maj1,1,TA,Attchd,1957,RFn,2,484,TA,TA,Y,0,0,0,0,0,0,NA,GdPrv,NA,0,1,2006,WD,Normal,155000 +1042,60,RL,NA,9130,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,Feedr,Norm,1Fam,2Story,6,8,1966,2000,Hip,CompShg,HdBoard,HdBoard,BrkFace,252,TA,TA,CBlock,TA,TA,No,GLQ,400,Rec,64,336,800,GasA,Gd,Y,SBrkr,800,832,0,1632,0,1,1,1,4,1,Gd,7,Typ,0,NA,Attchd,1966,Unf,2,484,TA,TA,Y,0,40,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,173000 +1043,120,RL,34,5381,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,Twnhs,1Story,6,5,2005,2005,Gable,CompShg,VinylSd,VinylSd,Stone,135,Gd,TA,PConc,Gd,TA,Av,ALQ,900,Unf,0,406,1306,GasA,Ex,Y,SBrkr,1306,0,0,1306,1,0,2,0,1,1,Gd,5,Typ,1,Gd,Attchd,2005,RFn,2,624,TA,TA,Y,170,63,0,0,0,0,NA,NA,NA,0,8,2009,WD,Normal,196000 +1044,60,RL,86,11839,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SawyerW,Norm,Norm,1Fam,2Story,7,5,1990,1990,Hip,CompShg,HdBoard,HdBoard,BrkFace,99,TA,TA,PConc,Gd,TA,No,GLQ,1085,Unf,0,390,1475,GasA,Ex,Y,SBrkr,1532,797,0,2329,1,0,2,1,4,1,Gd,10,Typ,1,Ex,Attchd,1990,Unf,2,514,TA,TA,Y,192,121,0,0,0,0,NA,NA,NA,0,5,2008,WD,Normal,262280 +1045,20,RL,80,9600,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,PosN,Norm,1Fam,1Story,8,5,1981,1981,Hip,WdShngl,BrkFace,BrkFace,None,0,Gd,TA,PConc,Gd,TA,No,ALQ,1104,Unf,0,1420,2524,GasA,TA,Y,SBrkr,2524,0,0,2524,1,0,2,1,4,1,Gd,9,Typ,1,Gd,Attchd,1981,Fin,2,542,TA,TA,Y,474,120,0,0,0,0,NA,MnPrv,NA,0,7,2009,WD,Normal,278000 +1046,20,RL,NA,13680,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Edwards,Norm,Norm,1Fam,1Story,3,5,1955,1955,Hip,CompShg,BrkFace,Wd Sdng,None,0,TA,TA,Slab,NA,NA,NA,NA,0,NA,0,0,0,GasA,Ex,Y,FuseA,1733,0,0,1733,0,0,2,0,4,1,TA,8,Min2,1,Gd,Attchd,1955,Unf,2,452,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,139600 +1047,60,RL,85,16056,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,StoneBr,Norm,Norm,1Fam,2Story,9,5,2005,2006,Hip,CompShg,CemntBd,CmentBd,Stone,208,Gd,TA,PConc,Ex,TA,Av,GLQ,240,Unf,0,1752,1992,GasA,Ex,Y,SBrkr,1992,876,0,2868,0,0,3,1,4,1,Ex,11,Typ,1,Gd,BuiltIn,2005,Fin,3,716,TA,TA,Y,214,108,0,0,0,0,NA,NA,NA,0,7,2006,New,Partial,556581 +1048,20,RL,57,9245,Pave,NA,IR2,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,5,5,1994,1995,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,No,GLQ,686,Unf,0,304,990,GasA,Ex,Y,SBrkr,990,0,0,990,0,1,1,0,3,1,TA,5,Typ,0,NA,Detchd,1996,Unf,2,672,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,2,2008,WD,Normal,145000 +1049,20,RL,100,21750,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Mitchel,Norm,Norm,1Fam,1Story,5,4,1960,2006,Hip,CompShg,HdBoard,HdBoard,BrkFace,75,TA,Fa,Slab,NA,NA,NA,NA,0,NA,0,0,0,GasA,TA,Y,SBrkr,1771,0,0,1771,0,0,1,0,3,1,TA,9,Min1,1,TA,Attchd,1960,Unf,2,336,TA,TA,Y,0,0,0,0,0,0,NA,GdPrv,NA,0,11,2009,WD,Normal,115000 +1050,20,RL,60,11100,Pave,NA,Reg,Low,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1Story,4,7,1946,2006,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,NA,NA,NA,NA,0,NA,0,0,0,GasA,Ex,Y,SBrkr,930,0,0,930,0,0,1,0,2,1,Gd,6,Typ,0,NA,Detchd,1946,Unf,1,308,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,4,2010,WD,Abnorml,84900 +1051,20,RL,73,8993,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,1Story,7,5,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,Av,Unf,0,Unf,0,1302,1302,GasA,Ex,Y,SBrkr,1302,0,0,1302,0,0,2,0,3,1,Gd,6,Typ,0,NA,Attchd,2007,Fin,2,436,TA,TA,Y,0,22,0,0,0,0,NA,NA,NA,0,8,2007,New,Partial,176485 +1052,20,RL,103,11175,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2007,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,Av,Unf,0,Unf,0,1316,1316,GasA,Ex,Y,SBrkr,1316,0,0,1316,0,0,2,0,3,1,Gd,6,Typ,1,Gd,Attchd,2007,Fin,2,440,TA,TA,Y,0,20,0,0,0,0,NA,NA,NA,0,10,2007,New,Partial,200141 +1053,60,RL,100,9500,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NAmes,Artery,Norm,1Fam,2Story,6,6,1964,1978,Gable,CompShg,VinylSd,VinylSd,BrkCmn,272,TA,TA,CBlock,TA,TA,No,Rec,442,Unf,0,374,816,GasA,TA,Y,SBrkr,1127,850,0,1977,0,1,1,1,4,1,TA,9,Typ,1,TA,Attchd,1964,RFn,2,540,TA,TA,Y,0,52,0,0,0,0,NA,GdPrv,NA,0,6,2007,WD,Normal,165000 +1054,20,RL,68,8562,Pave,NA,Reg,Lvl,AllPub,Inside,Mod,Edwards,Norm,Norm,1Fam,1Story,5,6,1957,2002,Hip,CompShg,HdBoard,HdBoard,Stone,145,TA,TA,CBlock,TA,TA,Av,Rec,383,Unf,0,833,1216,GasA,Ex,Y,FuseA,1526,0,0,1526,0,0,1,0,4,1,TA,7,Min2,1,Gd,Basment,1957,Unf,1,364,TA,TA,Y,116,78,0,0,0,0,NA,NA,NA,0,5,2010,WD,Normal,144500 +1055,60,RL,90,11367,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,CollgCr,Norm,Norm,1Fam,2Story,8,5,2002,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,210,Gd,TA,PConc,Gd,TA,Mn,GLQ,932,Unf,0,133,1065,GasA,Ex,Y,SBrkr,1091,898,0,1989,1,0,2,1,3,1,Gd,7,Typ,1,Gd,Attchd,2002,Fin,2,586,TA,TA,Y,199,60,0,0,0,0,NA,NA,NA,0,11,2006,WD,Normal,255000 +1056,20,RL,104,11361,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,1Story,6,5,1976,1976,Gable,CompShg,Plywood,Plywood,BrkFace,160,TA,TA,CBlock,Gd,TA,No,ALQ,644,Unf,0,549,1193,GasA,TA,Y,SBrkr,1523,0,0,1523,0,1,2,0,3,1,TA,7,Typ,1,TA,Attchd,1976,Fin,2,478,TA,TA,Y,0,0,0,0,189,0,NA,MnPrv,NA,0,5,2008,COD,Abnorml,180000 +1057,120,RL,43,7052,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,TwnhsE,1Story,7,5,2005,2005,Gable,CompShg,VinylSd,VinylSd,Stone,240,Gd,TA,PConc,Gd,TA,Av,GLQ,659,Unf,0,705,1364,GasA,Ex,Y,SBrkr,1364,0,0,1364,1,0,2,0,2,1,Gd,6,Typ,1,Gd,Attchd,2005,RFn,2,484,TA,TA,Y,192,36,0,0,0,0,NA,NA,NA,0,6,2006,WD,Normal,185850 +1058,60,RL,NA,29959,Pave,NA,IR2,Lvl,AllPub,FR2,Gtl,NoRidge,Norm,Norm,1Fam,2Story,7,6,1994,1994,Gable,CompShg,HdBoard,HdBoard,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,595,Unf,0,378,973,GasA,Ex,Y,SBrkr,979,871,0,1850,0,0,2,1,3,1,Gd,7,Typ,1,Gd,BuiltIn,1994,Fin,2,467,TA,TA,Y,168,98,0,0,0,0,NA,NA,NA,0,1,2009,WD,Normal,248000 +1059,60,RL,96,11308,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,2Story,9,5,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,154,Ex,TA,PConc,Ex,TA,Av,GLQ,936,Unf,0,168,1104,GasA,Ex,Y,SBrkr,1130,1054,0,2184,1,0,2,1,3,1,Ex,10,Typ,1,Gd,Attchd,2008,Fin,3,836,TA,TA,Y,0,102,0,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,335000 +1060,50,RL,NA,11275,Pave,NA,IR1,HLS,AllPub,Corner,Mod,Crawfor,Norm,Norm,1Fam,1.5Fin,6,7,1932,1950,Gable,CompShg,MetalSd,MetalSd,BrkFace,480,TA,TA,CBlock,TA,TA,Mn,Rec,297,LwQ,557,0,854,GasA,TA,Y,SBrkr,1096,895,0,1991,0,0,1,1,3,1,TA,7,Typ,1,Gd,Detchd,1977,Unf,2,432,TA,Fa,Y,0,0,19,0,0,0,NA,NA,NA,0,3,2007,WD,Normal,220000 +1061,120,RL,41,4920,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,StoneBr,Norm,Norm,TwnhsE,1Story,8,5,2001,2001,Gable,CompShg,CemntBd,CmentBd,None,0,Gd,TA,PConc,Gd,TA,Mn,GLQ,616,Unf,0,722,1338,GasA,Ex,Y,SBrkr,1338,0,0,1338,1,0,2,0,2,1,Gd,6,Typ,0,NA,Attchd,2001,Fin,2,582,TA,TA,Y,0,0,170,0,0,0,NA,NA,NA,0,4,2010,WD,Normal,213500 +1062,30,C (all),120,18000,Grvl,NA,Reg,Low,AllPub,Inside,Gtl,IDOTRR,Norm,Norm,1Fam,1Story,3,4,1935,1950,Gable,CompShg,MetalSd,MetalSd,None,0,Fa,TA,CBlock,TA,TA,No,Unf,0,Unf,0,894,894,GasA,TA,Y,SBrkr,894,0,0,894,0,0,1,0,2,1,TA,6,Typ,0,NA,Detchd,1994,RFn,3,1248,TA,TA,Y,0,20,0,0,0,0,NA,NA,Shed,560,8,2008,ConLD,Normal,81000 +1063,190,RM,85,13600,Pave,Grvl,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,2fmCon,2Story,5,5,1900,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,662,662,GasA,TA,N,SBrkr,1422,915,0,2337,0,0,2,0,5,2,TA,10,Min2,0,NA,Detchd,1945,Unf,2,560,TA,TA,Y,0,57,0,0,0,0,NA,NA,NA,0,9,2007,WD,Normal,90000 +1064,30,RM,50,6000,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Artery,Norm,1Fam,1Story,6,6,1925,1980,Gable,CompShg,MetalSd,MetalSd,None,0,TA,Gd,BrkTil,TA,TA,No,BLQ,397,Unf,0,706,1103,GasA,Gd,Y,SBrkr,1103,0,0,1103,0,0,1,0,2,1,Gd,5,Typ,1,Gd,Detchd,1976,Unf,2,440,TA,TA,Y,166,120,0,0,0,0,NA,MnPrv,NA,0,7,2006,WD,Normal,110500 +1065,20,RL,NA,11000,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,6,1966,1966,Gable,CompShg,Plywood,Plywood,BrkFace,200,TA,TA,CBlock,TA,TA,Mn,BLQ,740,Rec,230,184,1154,GasA,Ex,Y,SBrkr,1154,0,0,1154,0,0,1,1,3,1,TA,6,Typ,1,Po,Attchd,1966,RFn,2,480,TA,TA,Y,0,58,0,0,0,0,NA,MnPrv,NA,0,11,2009,WD,Normal,154000 +1066,60,RL,80,14000,Pave,NA,Reg,Lvl,AllPub,Inside,Mod,ClearCr,Norm,Norm,1Fam,2Story,7,5,1996,1997,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Gd,TA,PConc,Ex,TA,Gd,GLQ,1201,Unf,0,105,1306,GasA,Ex,Y,SBrkr,1306,954,0,2260,1,0,2,1,3,1,Gd,7,Typ,0,NA,Attchd,1996,RFn,2,533,TA,TA,Y,296,44,0,0,0,0,NA,NA,NA,0,4,2010,WD,Normal,328000 +1067,60,RL,59,7837,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,2Story,6,7,1993,1994,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,799,799,GasA,Gd,Y,SBrkr,799,772,0,1571,0,0,2,1,3,1,TA,7,Typ,1,TA,Attchd,1993,RFn,2,380,TA,TA,Y,0,40,0,0,0,0,NA,NA,NA,0,5,2009,WD,Normal,178000 +1068,60,RL,80,9760,Pave,NA,Reg,Lvl,AllPub,Inside,Mod,NAmes,Norm,Norm,1Fam,2Story,6,6,1964,1964,Gable,CompShg,HdBoard,HdBoard,BrkFace,360,TA,TA,CBlock,TA,TA,Gd,GLQ,674,LwQ,106,0,780,GasA,TA,Y,SBrkr,798,813,0,1611,1,0,1,1,4,1,TA,7,Typ,0,NA,Attchd,1964,RFn,2,442,TA,TA,Y,328,128,0,0,189,0,NA,NA,NA,0,6,2008,WD,Normal,167900 +1069,160,RM,42,3964,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,MeadowV,Norm,Norm,TwnhsE,2Story,6,4,1973,1973,Gable,CompShg,CemntBd,CmentBd,None,0,TA,TA,CBlock,Gd,TA,No,ALQ,837,Unf,0,105,942,GasA,Gd,Y,SBrkr,1291,1230,0,2521,1,0,2,1,5,1,TA,10,Maj1,1,Gd,Attchd,1973,Fin,2,576,TA,TA,Y,728,20,0,0,0,0,NA,GdPrv,NA,0,6,2006,WD,Normal,151400 +1070,45,RL,60,9600,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1.5Unf,5,7,1949,2003,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,ALQ,220,Unf,0,625,845,GasA,TA,Y,SBrkr,893,0,0,893,0,1,1,0,2,1,Gd,4,Typ,0,NA,Detchd,1985,Unf,2,576,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,5,2007,WD,Normal,135000 +1071,20,RL,72,10152,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,5,1956,1956,Hip,CompShg,MetalSd,MetalSd,BrkFace,120,TA,TA,CBlock,TA,TA,No,BLQ,586,Unf,0,462,1048,GasA,TA,Y,SBrkr,1048,0,0,1048,1,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1956,Unf,1,286,TA,TA,Y,0,20,0,0,192,0,NA,NA,NA,0,6,2007,WD,Normal,135000 +1072,60,RL,78,11700,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,RRAn,Norm,1Fam,2Story,6,6,1968,1968,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,Rec,298,Unf,0,429,727,GasA,Ex,Y,SBrkr,829,727,0,1556,0,0,1,1,4,1,TA,8,Typ,0,NA,Attchd,1968,Unf,2,441,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,5,2009,WD,Normal,154000 +1073,50,RL,50,7585,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Artery,Norm,1Fam,1.5Fin,5,3,1948,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,Fa,Fa,Mn,Unf,0,Unf,0,810,810,GasA,Fa,Y,FuseA,1002,454,0,1456,1,1,1,0,4,1,TA,7,Typ,1,TA,Detchd,1954,Unf,1,280,TA,TA,P,0,0,0,0,0,0,NA,NA,NA,0,8,2006,WD,Normal,91500 +1074,60,RL,75,7950,Pave,NA,IR1,Bnk,AllPub,Corner,Gtl,Edwards,Norm,Norm,1Fam,2Story,6,6,1977,1977,Hip,CompShg,HdBoard,Plywood,BrkFace,140,TA,TA,CBlock,TA,TA,No,BLQ,535,Unf,0,155,690,GasA,TA,Y,SBrkr,698,728,0,1426,0,0,1,1,3,1,TA,6,Typ,0,NA,Attchd,1977,Fin,2,440,TA,TA,Y,252,0,0,0,0,0,NA,MnPrv,NA,0,7,2009,WD,Normal,159500 +1075,20,RL,74,8556,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,Av,Unf,0,Unf,0,1240,1240,GasA,Ex,Y,SBrkr,1240,0,0,1240,0,0,2,0,2,1,Gd,5,Typ,0,NA,Attchd,2006,RFn,3,826,TA,TA,Y,140,93,0,0,0,0,NA,NA,NA,0,5,2007,WD,Normal,194000 +1076,70,RL,75,13125,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Crawfor,Norm,Norm,1Fam,2Story,7,6,1940,1984,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,BLQ,410,Unf,0,390,800,GasA,TA,Y,SBrkr,960,780,0,1740,0,0,1,1,3,1,TA,6,Typ,2,Gd,Attchd,1940,Unf,1,240,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,7,2007,CWD,Normal,219500 +1077,50,RL,60,10800,Pave,Grvl,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,1.5Fin,5,8,1936,1989,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,Fa,TA,No,ALQ,626,Unf,0,170,796,GasA,Gd,Y,SBrkr,1096,370,0,1466,0,1,2,0,3,1,Gd,7,Min1,1,TA,Attchd,1950,Unf,2,566,TA,TA,Y,436,21,0,0,0,0,NA,NA,Shed,500,4,2006,WD,Normal,170000 +1078,20,RL,NA,15870,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,5,1969,1969,Gable,CompShg,VinylSd,Plywood,None,0,TA,TA,CBlock,TA,TA,Mn,BLQ,75,Rec,791,230,1096,GasA,Ex,Y,SBrkr,1096,0,0,1096,1,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1969,Fin,1,299,TA,TA,Y,240,32,0,0,0,0,NA,NA,NA,0,3,2006,WD,Abnorml,138800 +1079,120,RM,37,4435,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,TwnhsE,1Story,6,5,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,169,Gd,TA,PConc,Gd,TA,Mn,GLQ,662,Unf,0,186,848,GasA,Ex,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Gd,3,Typ,1,Gd,Attchd,2004,RFn,2,420,TA,TA,Y,140,0,0,0,0,0,NA,NA,NA,0,5,2006,WD,Normal,155900 +1080,20,RL,65,8775,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,5,5,1994,1994,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,No,GLQ,495,Unf,0,495,990,GasA,Gd,Y,SBrkr,990,0,0,990,0,0,1,0,3,1,TA,5,Typ,0,NA,Attchd,1996,Unf,1,299,TA,TA,Y,0,64,0,0,0,0,NA,NA,NA,0,4,2007,WD,Normal,126000 +1081,20,RL,80,11040,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,1Story,6,7,1971,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,144,Gd,Gd,CBlock,TA,TA,No,ALQ,656,Unf,0,602,1258,GasA,Ex,Y,SBrkr,1258,0,0,1258,0,1,2,0,3,1,Gd,5,Typ,0,NA,Attchd,1971,RFn,2,528,TA,TA,Y,55,0,0,216,0,0,NA,NA,NA,0,10,2008,COD,Abnorml,145000 +1082,20,RL,75,7500,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,Sawyer,Feedr,Norm,1Fam,1Story,5,5,1963,1963,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,TA,TA,No,ALQ,824,Unf,0,216,1040,GasA,Fa,Y,SBrkr,1040,0,0,1040,1,0,1,1,3,1,TA,5,Typ,0,NA,Attchd,1963,Fin,1,308,TA,TA,Y,0,0,220,0,0,0,NA,MnPrv,NA,0,6,2010,WD,Normal,133000 +1083,20,RL,70,8749,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2002,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,100,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1459,1459,GasA,Ex,Y,SBrkr,1459,0,0,1459,0,0,2,0,3,1,Gd,6,Typ,1,Gd,Attchd,2002,RFn,2,527,TA,TA,Y,192,39,0,0,0,0,NA,NA,NA,0,9,2007,WD,Normal,192000 +1084,20,RL,80,8800,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,6,1964,1964,Hip,CompShg,HdBoard,HdBoard,BrkFace,425,TA,TA,CBlock,TA,TA,No,BLQ,553,Unf,0,698,1251,GasA,TA,Y,SBrkr,1251,0,0,1251,1,0,1,0,3,1,TA,6,Typ,2,Gd,Attchd,1964,RFn,1,461,TA,TA,Y,0,116,0,0,0,0,NA,MnPrv,Shed,700,3,2006,WD,Normal,160000 +1085,60,RL,NA,13031,Pave,NA,IR2,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,1Fam,2Story,6,5,1995,1996,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,PConc,Gd,TA,No,ALQ,592,Unf,0,99,691,GasA,Gd,Y,SBrkr,691,807,0,1498,0,0,2,1,3,1,TA,6,Typ,1,TA,Attchd,1995,Fin,2,409,TA,TA,Y,315,44,0,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,187500 +1086,85,RL,73,9069,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SawyerW,Norm,Norm,1Fam,SFoyer,6,6,1992,1992,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,PConc,Gd,TA,Av,GLQ,747,Unf,0,189,936,GasA,Ex,Y,SBrkr,996,0,0,996,1,0,1,0,2,1,Gd,5,Typ,0,NA,Attchd,1992,Unf,2,564,TA,TA,Y,120,0,0,0,0,0,NA,NA,NA,0,4,2010,WD,Normal,147000 +1087,160,RM,NA,1974,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,MeadowV,Norm,Norm,TwnhsE,2Story,4,5,1973,1973,Gable,CompShg,CemntBd,CmentBd,None,0,TA,TA,CBlock,TA,TA,No,BLQ,334,Unf,0,212,546,GasA,TA,Y,SBrkr,546,546,0,1092,0,0,1,1,3,1,TA,6,Typ,0,NA,Attchd,1973,RFn,1,286,TA,TA,Y,120,96,0,0,0,0,NA,NA,NA,0,5,2010,WD,Normal,83500 +1088,60,FV,85,10574,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,2Story,8,5,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,Mn,Unf,0,Unf,0,1082,1082,GasA,Ex,Y,SBrkr,1082,871,0,1953,0,0,2,1,3,1,Gd,9,Typ,1,Gd,Attchd,2005,RFn,3,1043,TA,TA,Y,160,50,0,0,0,0,NA,NA,NA,0,5,2009,WD,Normal,252000 +1089,160,RM,24,2522,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,Twnhs,2Story,7,5,2004,2004,Gable,CompShg,VinylSd,VinylSd,Stone,50,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,970,970,GasA,Ex,Y,SBrkr,970,739,0,1709,0,0,2,0,3,1,Gd,7,Maj1,0,NA,Detchd,2004,Unf,2,380,TA,TA,Y,0,40,0,0,0,0,NA,NA,NA,0,4,2006,WD,Normal,137500 +1090,120,FV,37,3316,Pave,Pave,IR1,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,TwnhsE,1Story,8,5,2005,2005,Gable,CompShg,MetalSd,MetalSd,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,1039,Unf,0,208,1247,GasA,Ex,Y,SBrkr,1247,0,0,1247,1,0,1,1,1,1,Gd,4,Typ,1,Gd,Attchd,2005,Fin,2,550,TA,TA,Y,0,84,0,0,0,0,NA,NA,NA,0,4,2006,WD,Normal,197000 +1091,90,RL,60,8544,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,Duplex,1Story,3,4,1950,1950,Gable,CompShg,BrkFace,BrkFace,None,0,TA,TA,Slab,NA,NA,NA,NA,0,NA,0,0,0,Wall,Fa,N,FuseA,1040,0,0,1040,0,0,2,0,2,2,TA,6,Typ,0,NA,Detchd,1987,Unf,2,400,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,92900 +1092,160,FV,24,2160,Pave,Pave,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,Twnhs,2Story,7,5,1999,2000,Gable,CompShg,MetalSd,MetalSd,BrkFace,212,Gd,TA,PConc,Gd,TA,No,BLQ,510,Unf,0,90,600,GasA,Ex,Y,SBrkr,624,628,0,1252,1,0,2,1,2,1,Gd,4,Typ,0,NA,Detchd,1999,Unf,2,462,TA,TA,Y,0,48,0,0,0,0,NA,NA,NA,0,3,2008,WD,Normal,160000 +1093,50,RL,60,8400,Pave,NA,Reg,Bnk,AllPub,Inside,Gtl,SWISU,Norm,Norm,1Fam,1.5Fin,6,5,1925,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,PConc,TA,TA,No,Rec,423,Unf,0,758,1181,GasA,Fa,Y,SBrkr,1390,304,0,1694,0,0,2,0,4,1,TA,7,Typ,1,Gd,Detchd,1925,Unf,2,576,TA,TA,Y,342,0,128,0,0,0,NA,NA,NA,0,6,2008,WD,Normal,136500 +1094,20,RL,71,9230,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NAmes,Feedr,Norm,1Fam,1Story,5,8,1965,1998,Hip,CompShg,MetalSd,MetalSd,BrkFace,166,TA,TA,CBlock,TA,TA,Mn,GLQ,661,Unf,0,203,864,GasA,Gd,Y,SBrkr,1200,0,0,1200,1,0,1,1,1,1,Gd,6,Typ,0,NA,Detchd,1977,Unf,2,884,TA,TA,Y,0,64,0,0,0,0,NA,MnPrv,NA,0,10,2006,WD,Normal,146000 +1095,20,RL,74,5868,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,7,1956,2000,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,BLQ,248,Rec,240,448,936,GasA,Ex,Y,SBrkr,936,0,0,936,1,0,1,0,2,1,TA,4,Typ,0,NA,Attchd,1956,Fin,1,308,TA,TA,Y,0,0,80,0,160,0,NA,NA,NA,0,5,2010,WD,Normal,129000 +1096,20,RL,78,9317,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,6,5,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,24,Unf,0,1290,1314,GasA,Gd,Y,SBrkr,1314,0,0,1314,0,0,2,0,3,1,Gd,6,Typ,1,Gd,Attchd,2006,RFn,2,440,TA,TA,Y,0,22,0,0,0,0,NA,NA,NA,0,3,2007,WD,Normal,176432 +1097,70,RM,60,6882,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,IDOTRR,Norm,Norm,1Fam,2Story,6,7,1914,2006,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,PConc,TA,TA,No,Unf,0,Unf,0,684,684,GasA,TA,Y,SBrkr,773,582,0,1355,0,0,1,1,3,1,Gd,7,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,136,0,115,0,0,0,NA,NA,NA,0,3,2007,WD,Normal,127000 +1098,120,RL,NA,3696,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,StoneBr,Norm,Norm,TwnhsE,1Story,8,5,1986,1986,Gable,CompShg,HdBoard,HdBoard,None,0,Gd,TA,CBlock,Gd,TA,No,Unf,0,Unf,0,1074,1074,GasA,Ex,Y,SBrkr,1088,0,0,1088,0,0,1,1,2,1,Gd,5,Typ,0,NA,Attchd,1987,RFn,2,461,TA,TA,Y,0,74,137,0,0,0,NA,NA,NA,0,10,2007,WD,Normal,170000 +1099,50,RM,50,6000,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,1.5Fin,4,6,1936,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,BrkTil,TA,TA,No,BLQ,672,Unf,0,0,672,GasA,TA,Y,SBrkr,757,567,0,1324,0,0,1,0,3,1,TA,6,Typ,0,NA,Detchd,1936,Unf,1,240,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,128000 +1100,20,RL,82,11880,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NWAmes,RRAn,Norm,1Fam,1Story,7,5,1978,1978,Gable,CompShg,Plywood,Plywood,BrkFace,206,TA,TA,CBlock,Gd,TA,No,ALQ,704,Unf,0,567,1271,GasA,TA,Y,SBrkr,1601,0,0,1601,0,0,2,0,3,1,TA,7,Typ,1,TA,Attchd,1978,RFn,2,478,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,4,2009,COD,Abnorml,157000 +1101,30,RL,60,8400,Pave,NA,Reg,Bnk,AllPub,Inside,Gtl,SWISU,Norm,Norm,1Fam,1Story,2,5,1920,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,Fa,No,Rec,290,Unf,0,0,290,GasA,TA,N,FuseF,438,0,0,438,0,0,1,0,1,1,Fa,3,Typ,0,NA,Detchd,1930,Unf,1,246,TA,TA,N,0,0,0,0,0,0,NA,NA,NA,0,1,2009,WD,Normal,60000 +1102,20,RL,61,9758,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,5,1971,1971,Gable,CompShg,HdBoard,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,BLQ,412,LwQ,287,251,950,GasA,TA,Y,SBrkr,950,0,0,950,0,0,1,0,3,1,TA,5,Typ,0,NA,Detchd,1981,Unf,1,280,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,7,2007,WD,Normal,119500 +1103,20,RL,70,7000,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,7,1960,2002,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,45,TA,TA,CBlock,TA,TA,No,Rec,588,Unf,0,422,1010,GasA,Ex,Y,SBrkr,1134,0,0,1134,0,0,1,0,2,1,TA,6,Typ,0,NA,Attchd,1960,RFn,1,254,TA,TA,Y,0,16,0,0,0,0,NA,MnWw,NA,0,4,2007,WD,Family,135000 +1104,20,RL,79,8910,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,6,1959,1959,Hip,CompShg,BrkFace,BrkFace,None,0,TA,TA,CBlock,TA,TA,Mn,ALQ,655,Unf,0,0,655,GasA,Ex,Y,SBrkr,1194,0,0,1194,0,1,1,0,3,1,TA,6,Typ,1,Fa,BuiltIn,1954,Fin,2,539,TA,TA,Y,0,0,192,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,159500 +1105,160,RM,24,2016,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrDale,Norm,Norm,TwnhsE,2Story,5,5,1970,1970,Gable,CompShg,HdBoard,HdBoard,BrkFace,304,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,630,630,GasA,TA,Y,SBrkr,630,672,0,1302,0,0,2,1,3,1,TA,6,Typ,0,NA,Detchd,1970,Unf,2,440,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,4,2007,WD,Normal,106000 +1106,60,RL,98,12256,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NoRidge,Norm,Norm,1Fam,2Story,8,5,1994,1995,Gable,CompShg,HdBoard,HdBoard,BrkFace,362,Gd,TA,PConc,Ex,TA,Av,GLQ,1032,Unf,0,431,1463,GasA,Ex,Y,SBrkr,1500,1122,0,2622,1,0,2,1,3,1,Gd,9,Typ,2,TA,Attchd,1994,RFn,2,712,TA,TA,Y,186,32,0,0,0,0,NA,NA,NA,0,4,2010,WD,Normal,325000 +1107,20,RL,114,10357,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,SawyerW,Feedr,Norm,1Fam,1Story,7,5,1990,1991,Hip,CompShg,HdBoard,HdBoard,None,0,Gd,TA,PConc,Gd,TA,Mn,GLQ,738,Unf,0,172,910,GasA,Gd,Y,SBrkr,1442,0,0,1442,1,0,2,0,3,1,Gd,6,Typ,1,TA,Attchd,1990,Fin,2,719,TA,TA,Y,0,244,0,0,0,0,NA,NA,NA,0,5,2007,WD,Normal,179900 +1108,60,RL,168,23257,Pave,NA,IR3,HLS,AllPub,CulDSac,Gtl,Gilbert,Norm,Norm,1Fam,2Story,7,5,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Ex,Gd,No,Unf,0,Unf,0,868,868,GasA,Ex,Y,SBrkr,887,1134,0,2021,0,0,2,1,3,1,Gd,9,Typ,1,Gd,BuiltIn,2006,RFn,2,422,TA,TA,Y,0,100,0,0,0,0,NA,NA,NA,0,9,2006,New,Partial,274725 +1109,60,RL,NA,8063,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,2Story,6,5,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,No,Unf,0,Unf,0,924,924,GasA,Ex,Y,SBrkr,948,742,0,1690,0,0,2,1,3,1,TA,7,Typ,1,TA,Attchd,2000,RFn,2,463,TA,TA,Y,100,48,0,0,0,0,NA,NA,NA,0,11,2007,WD,Abnorml,181000 +1110,20,RL,107,11362,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,1Story,8,5,2004,2005,Gable,CompShg,MetalSd,MetalSd,Stone,42,Gd,TA,PConc,Ex,TA,Mn,GLQ,1039,Unf,0,797,1836,GasA,Ex,Y,SBrkr,1836,0,0,1836,1,0,2,0,3,1,Gd,7,Typ,1,Gd,Attchd,2004,Fin,3,862,TA,TA,Y,125,185,0,0,0,0,NA,NA,NA,0,3,2009,WD,Normal,280000 +1111,60,RL,NA,8000,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,2Story,6,5,1995,1996,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,PConc,Gd,TA,No,GLQ,219,Unf,0,554,773,GasA,Gd,Y,SBrkr,773,885,0,1658,1,0,2,1,3,1,TA,8,Typ,1,TA,Attchd,1995,Fin,2,431,TA,TA,Y,224,84,0,0,0,0,NA,NA,NA,0,6,2008,WD,Normal,188000 +1112,60,RL,80,10480,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,2Story,7,6,1976,1976,Hip,CompShg,Plywood,Plywood,BrkFace,660,TA,TA,CBlock,TA,TA,No,ALQ,403,Unf,0,400,803,GasA,TA,Y,SBrkr,1098,866,0,1964,0,0,2,1,4,1,TA,8,Typ,1,Gd,Attchd,1976,RFn,2,483,TA,TA,Y,0,69,0,0,0,0,NA,NA,NA,0,9,2008,WD,Normal,205000 +1113,20,RL,73,7100,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,7,1957,1957,Gable,CompShg,WdShing,Wd Shng,None,0,TA,TA,CBlock,TA,TA,No,GLQ,708,Unf,0,108,816,GasA,TA,Y,FuseA,816,0,0,816,1,0,1,0,2,1,TA,5,Typ,0,NA,Detchd,1957,Unf,1,308,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,129900 +1114,20,RL,66,8923,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,7,1953,2006,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,BLQ,643,Unf,0,365,1008,GasA,Gd,Y,SBrkr,1008,0,0,1008,1,0,1,0,2,1,Gd,6,Typ,0,NA,Attchd,1953,Unf,1,240,TA,TA,Y,0,18,0,0,0,0,NA,NA,NA,0,5,2007,WD,Normal,134500 +1115,20,RL,90,5400,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,1Story,5,7,1954,2000,Gable,CompShg,MetalSd,MetalSd,None,0,TA,Gd,CBlock,TA,TA,No,Rec,415,Unf,0,418,833,GasA,Ex,Y,SBrkr,833,0,0,833,0,0,1,0,2,1,Gd,4,Typ,0,NA,Detchd,1955,Unf,1,326,TA,TA,Y,0,0,0,0,0,0,NA,MnPrv,NA,0,8,2006,WD,Normal,117000 +1116,20,RL,93,12085,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,1Story,8,5,2007,2007,Hip,CompShg,VinylSd,VinylSd,Stone,328,Gd,TA,PConc,Ex,TA,No,GLQ,1004,Unf,0,730,1734,GasA,Ex,Y,SBrkr,1734,0,0,1734,1,0,2,0,3,1,Ex,7,Typ,1,Gd,Attchd,2007,RFn,3,928,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,11,2007,New,Partial,318000 +1117,80,RL,NA,7750,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,SLvl,8,5,2002,2002,Hip,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,353,Unf,0,55,408,GasA,Ex,Y,SBrkr,779,640,0,1419,1,0,2,1,3,1,Gd,7,Typ,1,TA,BuiltIn,2002,Fin,2,527,TA,TA,Y,120,0,0,0,0,0,NA,NA,NA,0,3,2009,WD,Normal,184100 +1118,20,RL,57,9764,Pave,NA,IR1,Lvl,AllPub,FR2,Gtl,Sawyer,Feedr,Norm,1Fam,1Story,5,7,1967,2003,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,CBlock,TA,TA,No,BLQ,702,Unf,0,192,894,GasA,Ex,Y,SBrkr,894,0,0,894,1,0,1,0,3,1,Gd,5,Typ,0,NA,Attchd,1967,RFn,2,450,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,5,2008,WD,Normal,130000 +1119,80,RL,85,13825,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,SLvl,5,6,1958,1987,Gable,CompShg,MetalSd,MetalSd,None,0,TA,Gd,CBlock,TA,TA,No,Unf,0,Unf,0,533,533,GasA,TA,Y,SBrkr,1021,580,0,1601,0,1,1,0,3,1,TA,6,Min2,0,NA,BuiltIn,1958,RFn,1,300,TA,TA,Y,280,34,0,0,0,0,NA,NA,NA,0,12,2008,WD,Normal,140000 +1120,20,RL,70,7560,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,5,1959,1959,Gable,CompShg,BrkFace,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,LwQ,369,Unf,0,671,1040,GasA,TA,Y,FuseA,1040,0,0,1040,0,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1959,RFn,1,286,TA,TA,Y,140,0,252,0,0,0,NA,GdWo,NA,0,7,2006,WD,Normal,133700 +1121,30,RM,59,8263,Pave,NA,Reg,Bnk,AllPub,Inside,Mod,IDOTRR,Norm,Norm,1Fam,1Story,6,5,1920,1950,Gable,CompShg,BrkFace,BrkFace,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,1012,1012,GasA,TA,Y,FuseA,1012,0,0,1012,0,0,1,0,2,1,TA,6,Typ,1,Gd,Detchd,1920,Unf,1,308,TA,TA,Y,0,22,112,0,0,0,NA,MnPrv,NA,0,5,2007,WD,Normal,118400 +1122,20,RL,84,10084,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,196,Gd,TA,PConc,Gd,TA,Av,GLQ,24,Unf,0,1528,1552,GasA,Ex,Y,SBrkr,1552,0,0,1552,0,0,2,0,3,1,Gd,7,Typ,0,NA,Attchd,2005,RFn,3,782,TA,TA,Y,144,20,0,0,0,0,NA,NA,NA,0,7,2006,New,Partial,212900 +1123,20,RL,NA,8926,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,1Fam,1Story,4,3,1956,1956,Gable,CompShg,AsbShng,AsbShng,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,672,672,GasA,Ex,Y,FuseA,960,0,0,960,0,0,1,0,3,1,TA,5,Typ,0,NA,Basment,1956,Unf,1,288,TA,TA,Y,64,0,0,0,160,0,NA,MnPrv,NA,0,10,2009,COD,Abnorml,112000 +1124,20,RL,50,9405,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1Story,5,9,1947,2008,Hip,CompShg,VinylSd,VinylSd,None,0,TA,Ex,CBlock,TA,TA,No,Unf,0,Unf,0,698,698,GasA,Ex,Y,SBrkr,698,0,0,698,0,1,1,0,2,1,TA,4,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,0,200,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,118000 +1125,80,RL,NA,9125,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,SLvl,7,5,1992,1992,Gable,CompShg,HdBoard,HdBoard,BrkFace,170,TA,TA,PConc,Gd,TA,No,Unf,0,Unf,0,384,384,GasA,Gd,Y,SBrkr,812,670,0,1482,0,0,2,1,3,1,Gd,7,Typ,1,TA,Attchd,1992,Fin,2,392,TA,TA,Y,100,25,0,0,0,0,NA,NA,NA,0,7,2007,WD,Normal,163900 +1126,20,RL,60,10434,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,4,5,1955,1955,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,1005,1005,GasA,TA,Y,SBrkr,1005,0,0,1005,0,0,1,0,2,1,Fa,5,Typ,1,TA,Detchd,1977,Unf,2,672,Fa,Fa,Y,0,0,0,0,0,0,NA,NA,NA,0,11,2009,WD,Normal,115000 +1127,120,RL,53,3684,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Blmngtn,Norm,Norm,TwnhsE,1Story,7,5,2007,2007,Hip,CompShg,VinylSd,VinylSd,BrkFace,130,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1373,1373,GasA,Ex,Y,SBrkr,1555,0,0,1555,0,0,2,0,2,1,Gd,7,Typ,1,TA,Attchd,2007,Fin,3,660,TA,TA,Y,143,20,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,174000 +1128,20,RL,182,14572,Pave,NA,IR3,Lvl,AllPub,Corner,Gtl,Gilbert,Norm,Norm,1Fam,1Story,7,5,2004,2004,Hip,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,Av,GLQ,1300,Unf,0,230,1530,GasA,Ex,Y,SBrkr,1530,0,0,1530,1,0,2,0,3,1,Gd,7,Typ,1,Gd,Attchd,2004,Fin,3,630,TA,TA,Y,144,36,0,0,0,0,NA,NA,NA,0,11,2007,WD,Family,259000 +1129,60,RL,59,11796,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,2Story,7,5,2004,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,Av,Unf,0,Unf,0,847,847,GasA,Ex,Y,SBrkr,847,1112,0,1959,0,0,2,1,4,1,Gd,8,Typ,1,Gd,BuiltIn,2004,Fin,2,434,TA,TA,Y,100,48,0,0,0,0,NA,NA,NA,0,7,2007,WD,Normal,215000 +1130,90,RM,60,7200,Pave,Grvl,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,Duplex,SFoyer,5,5,1980,1980,Gable,CompShg,MetalSd,MetalSd,BrkFace,180,TA,TA,CBlock,Gd,TA,Gd,GLQ,936,Unf,0,0,936,GasA,TA,Y,SBrkr,936,0,0,936,1,0,1,0,2,1,TA,4,Typ,0,NA,Detchd,1980,Unf,2,672,TA,TA,Y,49,0,0,0,0,0,NA,NA,NA,0,8,2007,WD,Normal,140000 +1131,50,RL,65,7804,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SWISU,Norm,Norm,1Fam,1.5Fin,4,3,1928,1950,Gable,CompShg,WdShing,Plywood,None,0,TA,TA,BrkTil,TA,TA,No,BLQ,622,Unf,0,500,1122,GasA,TA,Y,SBrkr,1328,653,0,1981,1,0,2,0,4,1,Gd,7,Min2,2,TA,Detchd,1981,Unf,2,576,TA,TA,Y,431,44,0,0,0,0,NA,MnPrv,NA,0,12,2009,WD,Normal,135000 +1132,20,RL,63,10712,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Mitchel,Norm,Norm,1Fam,1Story,5,5,1991,1992,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,PConc,Gd,TA,Mn,BLQ,212,Unf,0,762,974,GasA,TA,Y,SBrkr,974,0,0,974,0,0,1,0,3,1,TA,5,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,0,28,0,0,0,0,NA,MnPrv,NA,0,9,2007,Oth,Abnorml,93500 +1133,70,RM,90,9900,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,2Story,6,4,1880,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,TA,TA,Mn,Unf,0,Unf,0,1008,1008,GasW,TA,Y,SBrkr,1178,1032,0,2210,0,0,2,0,5,1,Fa,8,Typ,0,NA,Detchd,1930,Unf,1,205,Fa,TA,N,0,48,0,0,0,0,NA,NA,NA,0,5,2007,WD,Normal,117500 +1134,60,RL,80,9828,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,SawyerW,Norm,Norm,1Fam,2Story,8,5,1995,1995,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,584,Unf,0,544,1128,GasA,Ex,Y,SBrkr,1142,878,0,2020,0,0,2,1,3,1,Gd,8,Typ,1,TA,Attchd,1995,RFn,2,466,TA,TA,Y,0,155,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,239500 +1135,60,RL,57,8773,Pave,NA,IR1,HLS,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,2Story,6,5,1997,1997,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,Av,Unf,0,Unf,0,916,916,GasA,Gd,Y,SBrkr,916,684,0,1600,0,0,2,1,3,1,TA,7,Typ,1,TA,Attchd,1997,Fin,2,460,TA,TA,Y,100,38,0,0,0,0,NA,NA,NA,0,8,2007,WD,Normal,169000 +1136,30,RM,60,6180,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,BrkSide,Norm,Norm,1Fam,1Story,6,5,1926,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,960,960,GasA,TA,N,SBrkr,986,0,0,986,0,0,1,0,2,1,TA,5,Typ,1,Gd,Detchd,1926,Unf,1,180,TA,TA,Y,0,128,0,0,0,0,NA,NA,NA,0,5,2007,WD,Normal,102000 +1137,50,RL,80,9600,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1.5Fin,6,5,1950,1950,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,CBlock,TA,TA,No,BLQ,280,Unf,0,752,1032,GasA,TA,Y,FuseA,1032,220,0,1252,0,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1950,Unf,1,288,TA,TA,Y,0,0,96,0,0,0,NA,NA,NA,0,4,2008,WD,Abnorml,119000 +1138,50,RL,54,6342,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,1Fam,1.5Fin,5,8,1875,1996,Gable,CompShg,VinylSd,VinylSd,None,0,TA,Gd,CBlock,TA,TA,No,Unf,0,Unf,0,780,780,GasA,Gd,N,SBrkr,780,240,0,1020,0,0,1,0,2,1,TA,6,Typ,0,NA,NA,NA,NA,0,0,NA,NA,N,0,0,176,0,0,0,NA,NA,NA,0,5,2010,WD,Normal,94000 +1139,20,RL,NA,9819,Pave,NA,IR1,Lvl,AllPub,Inside,Mod,Mitchel,Norm,Norm,1Fam,1Story,6,5,1977,1977,Gable,CompShg,Plywood,ImStucc,None,0,TA,TA,PConc,TA,TA,Gd,ALQ,1567,Unf,0,0,1567,GasA,TA,Y,SBrkr,1567,0,0,1567,1,0,2,0,2,1,Gd,5,Typ,2,TA,Attchd,1977,RFn,2,714,TA,TA,Y,264,32,0,0,0,0,NA,NA,NA,0,5,2009,WD,Normal,196000 +1140,30,RL,98,8731,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,1Story,5,5,1920,1950,Gable,CompShg,Stucco,Stucco,None,0,TA,Fa,BrkTil,TA,TA,No,BLQ,645,Unf,0,270,915,GasA,TA,Y,SBrkr,1167,0,0,1167,0,0,1,0,3,1,TA,6,Maj1,1,Gd,Detchd,1972,Unf,2,495,TA,TA,Y,0,0,216,0,126,0,NA,NA,NA,0,5,2007,WD,Normal,144000 +1141,20,RL,60,7350,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,7,1951,1951,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,TA,TA,Mn,ALQ,852,Unf,0,100,952,GasA,TA,Y,SBrkr,952,0,0,952,1,0,1,0,2,1,TA,4,Typ,0,NA,Detchd,1988,Unf,2,840,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,6,2008,COD,Abnorml,139000 +1142,60,RL,NA,10304,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,NWAmes,PosN,Norm,1Fam,2Story,5,7,1976,1976,Gable,CompShg,Plywood,Plywood,BrkFace,44,TA,Gd,CBlock,TA,TA,No,ALQ,381,Unf,0,399,780,GasA,Ex,Y,SBrkr,1088,780,0,1868,1,0,2,1,4,1,Gd,9,Typ,1,TA,Attchd,1976,Unf,2,484,TA,TA,Y,448,96,0,0,0,0,NA,NA,NA,0,10,2009,WD,Normal,197500 +1143,60,RL,77,9965,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,8,5,2006,2007,Hip,CompShg,VinylSd,VinylSd,Stone,340,Gd,TA,PConc,Ex,TA,Gd,GLQ,1150,Unf,0,316,1466,GasA,Ex,Y,SBrkr,1466,1362,0,2828,1,0,3,0,4,1,Gd,11,Typ,1,TA,BuiltIn,2006,RFn,3,1052,TA,TA,Y,125,144,0,0,0,0,NA,NA,NA,0,4,2007,New,Partial,424870 +1144,20,RL,NA,9000,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,1Story,5,3,1959,1959,Gable,CompShg,Wd Sdng,Plywood,None,0,TA,TA,CBlock,TA,TA,No,GLQ,288,Unf,0,718,1006,GasA,TA,Y,SBrkr,1006,0,0,1006,0,0,1,0,3,1,TA,5,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,0,24,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,80000 +1145,190,RL,60,12180,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,2fmCon,1.5Fin,4,4,1941,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,Fa,BrkTil,Gd,TA,No,BLQ,348,Unf,0,324,672,Grav,Fa,N,FuseA,672,252,0,924,1,0,1,0,2,1,Fa,5,Typ,0,NA,Detchd,1941,Unf,1,280,TA,TA,Y,0,0,0,0,0,0,NA,MnPrv,NA,0,7,2010,WD,Normal,80000 +1146,50,RM,52,6240,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,1.5Fin,5,6,1928,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,1042,1042,GasA,Ex,Y,SBrkr,1042,534,0,1576,0,0,1,0,3,1,TA,8,Typ,1,Gd,Detchd,1928,Unf,1,225,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,8,2006,WD,Family,149000 +1147,20,RL,NA,11200,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SawyerW,Norm,Norm,1Fam,1Story,6,5,1985,1985,Gable,CompShg,Wd Sdng,Wd Shng,BrkFace,85,Gd,TA,CBlock,Gd,TA,No,GLQ,1258,Unf,0,40,1298,GasA,TA,Y,SBrkr,1298,0,0,1298,1,0,2,0,3,1,Gd,5,Typ,1,TA,Attchd,1985,Unf,2,403,TA,TA,Y,165,26,0,0,0,0,NA,NA,NA,0,5,2006,WD,Normal,180000 +1148,70,RL,75,12000,Pave,NA,Reg,Bnk,AllPub,Inside,Gtl,Crawfor,Norm,Norm,1Fam,2Story,7,7,1941,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,Rec,275,Unf,0,429,704,GasA,Ex,Y,SBrkr,860,704,0,1564,0,0,1,1,3,1,Fa,7,Typ,1,Gd,Attchd,1941,Unf,1,234,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,174500 +1149,50,RM,NA,5700,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,1.5Fin,7,7,1926,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,PConc,TA,TA,No,Unf,0,Unf,0,572,572,GasA,TA,Y,SBrkr,572,539,0,1111,0,0,1,0,2,1,TA,5,Typ,1,Gd,Detchd,1982,Unf,1,288,TA,TA,Y,0,0,176,0,0,0,NA,NA,NA,0,8,2008,WD,Normal,116900 +1150,70,RM,50,9000,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Artery,Norm,1Fam,2Story,7,9,1920,1988,Hip,CompShg,VinylSd,VinylSd,None,0,TA,Gd,PConc,TA,TA,No,ALQ,624,Unf,0,26,650,GasA,Ex,Y,SBrkr,832,650,0,1482,0,1,1,0,3,1,TA,7,Typ,0,NA,Detchd,1930,Unf,2,324,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,143000 +1151,20,RL,57,8280,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,5,1950,1950,Gable,CompShg,BrkFace,BrkFace,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,932,932,GasA,Ex,Y,FuseA,932,0,0,932,0,0,1,0,2,1,Gd,4,Typ,1,Gd,Attchd,1950,Unf,1,306,TA,TA,Y,0,0,214,0,0,0,NA,GdPrv,NA,0,11,2007,WD,Normal,124000 +1152,20,RL,134,17755,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1Story,5,4,1959,1959,Gable,CompShg,HdBoard,Plywood,BrkFace,132,TA,TA,CBlock,TA,TA,No,BLQ,176,Unf,0,1290,1466,GasA,TA,Y,SBrkr,1466,0,0,1466,0,0,1,1,3,1,Fa,6,Typ,2,Gd,Attchd,1959,Fin,2,528,TA,TA,Y,0,140,0,0,100,0,NA,NA,NA,0,11,2006,WD,Normal,149900 +1153,20,RL,90,14115,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Crawfor,Norm,Norm,1Fam,1Story,6,7,1956,2004,Gable,CompShg,Stone,Stone,None,0,TA,TA,PConc,TA,TA,No,ALQ,296,GLQ,547,230,1073,GasA,Ex,Y,SBrkr,1811,0,0,1811,0,0,1,0,2,1,Ex,6,Typ,1,Gd,Attchd,1956,Fin,2,470,TA,TA,Y,0,0,280,0,0,0,NA,NA,NA,0,7,2006,WD,Abnorml,230000 +1154,30,RM,NA,5890,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,IDOTRR,Norm,Norm,1Fam,1Story,6,8,1930,2007,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Gd,Gd,BrkTil,TA,TA,Av,ALQ,538,Unf,0,278,816,GasA,Ex,Y,SBrkr,816,0,0,816,0,0,1,0,2,1,TA,5,Typ,0,NA,Detchd,2002,Unf,1,432,TA,TA,Y,0,0,96,0,0,0,NA,NA,NA,0,6,2008,WD,Normal,120500 +1155,60,RL,NA,13700,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,2Story,7,6,1965,1988,Gable,CompShg,VinylSd,VinylSd,Stone,288,TA,TA,CBlock,TA,TA,Gd,ALQ,454,Unf,0,410,864,GasA,TA,Y,SBrkr,902,918,0,1820,0,0,1,2,4,1,Gd,8,Typ,2,Gd,Attchd,1965,Unf,2,492,TA,TA,Y,60,84,0,0,273,0,NA,GdPrv,NA,0,5,2008,WD,Normal,201800 +1156,20,RL,90,10768,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Veenker,Norm,Norm,1Fam,1Story,5,8,1976,2004,Gable,CompShg,Plywood,Plywood,None,0,Gd,Gd,CBlock,Gd,TA,Gd,ALQ,1157,Unf,0,280,1437,GasA,TA,Y,SBrkr,1437,0,0,1437,1,0,2,0,3,1,Gd,6,Typ,1,Fa,Attchd,1976,RFn,2,528,TA,TA,Y,0,21,0,0,180,0,NA,NA,NA,0,7,2007,WD,Normal,218000 +1157,80,RL,85,9350,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,SLvl,5,8,1965,1999,Gable,CompShg,BrkFace,BrkFace,None,0,TA,Gd,PConc,TA,TA,Gd,ALQ,633,Unf,0,586,1219,GasA,Gd,Y,SBrkr,1265,0,0,1265,0,1,2,0,3,1,Gd,6,Typ,1,Gd,Attchd,1965,RFn,2,502,TA,TA,Y,0,92,0,96,0,0,NA,MnPrv,NA,0,10,2008,WD,Normal,179900 +1158,120,RL,34,5001,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,Twnhs,1Story,7,5,2007,2008,Gable,CompShg,VinylSd,VinylSd,Stone,166,Gd,TA,PConc,Gd,TA,No,GLQ,904,Unf,0,410,1314,GasA,Ex,Y,SBrkr,1314,0,0,1314,1,0,2,0,2,1,Gd,6,Typ,1,Gd,Attchd,2008,RFn,2,626,TA,TA,Y,172,62,0,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,230000 +1159,20,RL,92,11932,Pave,NA,Reg,Lvl,AllPub,FR2,Gtl,Somerst,Feedr,Norm,1Fam,1Story,8,5,2007,2008,Gable,CompShg,VinylSd,VinylSd,Stone,186,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1580,1580,GasA,Ex,Y,SBrkr,1580,0,0,1580,0,0,2,0,3,1,Gd,7,Typ,0,NA,Attchd,2008,RFn,3,830,TA,TA,Y,0,24,0,0,0,0,NA,NA,NA,0,6,2008,ConLD,Partial,235128 +1160,60,RL,76,9120,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,2Story,6,6,1974,1974,Hip,CompShg,HdBoard,HdBoard,BrkFace,270,Gd,TA,CBlock,TA,TA,No,ALQ,442,Unf,0,459,901,GasA,TA,Y,SBrkr,943,933,0,1876,0,0,2,1,4,1,Gd,8,Typ,1,TA,Attchd,1974,RFn,2,540,Gd,TA,Y,0,69,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,185000 +1161,160,RL,24,2280,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NPkVill,Norm,Norm,Twnhs,2Story,6,5,1978,1978,Gable,CompShg,Plywood,Brk Cmn,None,0,TA,TA,CBlock,Gd,TA,No,ALQ,311,Unf,0,544,855,GasA,Fa,Y,SBrkr,855,601,0,1456,0,0,2,1,3,1,TA,7,Typ,1,TA,Attchd,1978,Unf,2,440,TA,TA,Y,26,0,0,0,0,0,NA,NA,NA,0,7,2010,WD,Normal,146000 +1162,20,RL,NA,14778,Pave,NA,IR1,Low,AllPub,CulDSac,Gtl,Crawfor,PosN,Norm,1Fam,1Story,6,7,1954,2006,Hip,CompShg,HdBoard,HdBoard,BrkFace,72,Gd,TA,CBlock,TA,TA,No,BLQ,728,Unf,0,568,1296,GasA,Ex,Y,SBrkr,1640,0,0,1640,1,0,1,0,3,1,Gd,7,Typ,1,Gd,Detchd,1993,Unf,2,924,TA,TA,Y,108,0,0,216,0,0,NA,NA,NA,0,11,2008,WD,Normal,224000 +1163,20,RL,109,8724,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,1Story,5,5,1968,1968,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,CBlock,Gd,TA,No,BLQ,492,Unf,0,402,894,GasA,Gd,Y,SBrkr,894,0,0,894,0,0,1,0,3,1,TA,5,Typ,1,Po,Attchd,1968,Fin,2,450,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,5,2007,WD,Normal,129000 +1164,90,RL,60,12900,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,Duplex,SFoyer,4,4,1969,1969,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,CBlock,Gd,TA,Av,GLQ,1198,Unf,0,0,1198,GasA,TA,Y,SBrkr,1258,0,0,1258,2,0,0,2,0,2,TA,6,Typ,0,NA,CarPort,1969,Unf,2,400,Fa,TA,Y,120,0,0,0,0,0,NA,NA,NA,0,1,2008,WD,Alloca,108959 +1165,80,RL,NA,16157,Pave,NA,IR1,Lvl,AllPub,FR2,Gtl,Veenker,Feedr,Norm,1Fam,SLvl,5,7,1978,1978,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,PConc,Gd,TA,Gd,ALQ,680,Rec,391,289,1360,GasA,Ex,Y,SBrkr,1432,0,0,1432,1,0,1,1,2,1,Gd,5,Typ,1,TA,Attchd,1978,Unf,2,588,TA,TA,Y,168,180,0,0,0,0,NA,NA,NA,0,6,2007,WD,Normal,194000 +1166,20,RL,79,9541,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,1Story,7,5,2009,2009,Gable,CompShg,VinylSd,VinylSd,Stone,268,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1502,1502,GasA,Ex,Y,SBrkr,1502,0,0,1502,0,0,2,0,3,1,Gd,7,Typ,0,NA,Attchd,2009,RFn,2,644,TA,TA,Y,0,114,0,0,0,0,NA,NA,NA,0,9,2009,New,Partial,233170 +1167,20,RL,64,10475,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,CollgCr,Norm,Norm,1Fam,1Story,8,5,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,72,Gd,TA,PConc,Gd,TA,Av,Unf,0,Unf,0,1694,1694,GasA,Ex,Y,SBrkr,1694,0,0,1694,0,0,2,0,3,1,Gd,7,Typ,0,NA,Attchd,2008,RFn,3,776,TA,TA,Y,160,33,0,0,0,0,NA,NA,NA,0,2,2010,WD,Normal,245350 +1168,60,RL,58,10852,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,1Fam,2Story,6,5,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,No,GLQ,786,Unf,0,173,959,GasA,Ex,Y,SBrkr,959,712,0,1671,1,0,2,1,3,1,TA,7,Typ,1,TA,Attchd,2000,Fin,2,472,TA,TA,Y,0,38,0,0,0,0,NA,NA,NA,0,2,2006,WD,Normal,173000 +1169,70,RL,120,13728,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,1Fam,2Story,6,7,1935,1986,Hip,CompShg,Stucco,Stucco,None,0,TA,TA,CBlock,TA,TA,No,Rec,626,Unf,0,501,1127,GasA,Ex,Y,SBrkr,1236,872,0,2108,0,0,2,0,4,1,Gd,7,Typ,2,TA,Basment,1935,Unf,2,540,TA,TA,Y,0,0,0,0,90,0,NA,NA,NA,0,7,2008,WD,Normal,235000 +1170,60,RL,118,35760,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,NoRidge,Norm,Norm,1Fam,2Story,10,5,1995,1996,Hip,CompShg,HdBoard,HdBoard,BrkFace,1378,Gd,Gd,PConc,Ex,TA,Gd,GLQ,1387,Unf,0,543,1930,GasA,Ex,Y,SBrkr,1831,1796,0,3627,1,0,3,1,4,1,Gd,10,Typ,1,TA,Attchd,1995,Fin,3,807,TA,TA,Y,361,76,0,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,625000 +1171,80,RL,76,9880,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Mitchel,Norm,Norm,1Fam,SLvl,6,6,1977,1977,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,CBlock,TA,TA,Av,ALQ,522,Unf,0,574,1096,GasA,TA,Y,SBrkr,1118,0,0,1118,1,0,1,0,3,1,TA,6,Typ,1,Po,Attchd,1977,Fin,1,358,TA,TA,Y,203,0,0,0,0,576,Gd,GdPrv,NA,0,7,2008,WD,Normal,171000 +1172,20,RL,76,9120,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,6,1958,1958,Hip,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,ALQ,662,Unf,0,599,1261,GasA,Ex,Y,SBrkr,1261,0,0,1261,1,0,1,0,3,1,TA,6,Typ,1,TA,Attchd,1958,RFn,2,433,TA,TA,Y,0,0,0,0,288,0,NA,NA,Shed,1400,11,2008,WD,Normal,163000 +1173,160,FV,35,4017,Pave,Pave,IR1,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,TwnhsE,2Story,7,5,2006,2007,Gable,CompShg,MetalSd,MetalSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,625,625,GasA,Ex,Y,SBrkr,625,625,0,1250,0,0,2,1,2,1,Gd,5,Typ,0,NA,Detchd,2006,Fin,2,625,TA,TA,Y,0,54,0,0,0,0,NA,NA,NA,0,3,2008,WD,Normal,171900 +1174,50,RL,138,18030,Pave,NA,IR1,Bnk,AllPub,Inside,Gtl,ClearCr,Norm,Norm,1Fam,1.5Fin,5,6,1946,1994,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,Rec,152,BLQ,469,977,1598,GasA,TA,Y,SBrkr,1636,971,479,3086,0,0,3,0,3,1,Ex,12,Maj1,1,Gd,NA,NA,NA,0,0,NA,NA,Y,122,0,0,0,0,0,NA,MnPrv,NA,0,3,2007,WD,Normal,200500 +1175,70,RL,80,16560,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Crawfor,Norm,Norm,1Fam,2Story,6,8,1932,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,Gd,TA,No,Rec,503,Unf,0,449,952,GasA,TA,Y,SBrkr,1170,1175,0,2345,0,0,2,1,4,1,TA,9,Typ,1,Gd,Detchd,1932,Unf,2,360,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,239000 +1176,50,RL,85,10678,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NoRidge,Norm,Norm,1Fam,1.5Fin,8,5,1992,2000,Hip,CompShg,HdBoard,HdBoard,BrkFace,337,Gd,TA,PConc,Gd,TA,No,GLQ,700,Unf,0,983,1683,GasA,Ex,Y,SBrkr,2129,743,0,2872,0,0,2,1,4,1,Gd,9,Typ,1,TA,Attchd,1992,Fin,2,541,TA,TA,Y,0,33,0,0,0,0,NA,NA,NA,0,4,2007,WD,Normal,285000 +1177,20,RL,37,6951,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Mitchel,Norm,Norm,1Fam,1Story,5,5,1984,1985,Gable,CompShg,HdBoard,Plywood,None,0,TA,TA,CBlock,TA,TA,No,ALQ,658,Unf,0,218,876,GasA,TA,Y,SBrkr,923,0,0,923,1,0,1,0,3,1,TA,5,Typ,0,NA,Attchd,1984,Unf,1,264,TA,TA,Y,362,0,0,0,0,0,NA,MnPrv,NA,0,10,2008,WD,Normal,119500 +1178,50,RM,NA,3950,Pave,Grvl,Reg,Bnk,AllPub,Inside,Gtl,OldTown,Artery,Norm,1Fam,1.5Fin,6,8,1926,2004,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,Rec,468,Unf,0,350,818,GasA,TA,Y,SBrkr,818,406,0,1224,0,0,1,0,3,1,TA,5,Typ,0,NA,Detchd,1926,Unf,1,210,TA,TA,N,0,0,116,0,0,0,NA,NA,NA,0,12,2009,WD,Normal,115000 +1179,50,RL,54,7681,Pave,NA,IR1,Lvl,AllPub,FR2,Gtl,Crawfor,Norm,Norm,1Fam,1.5Fin,5,6,1921,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,731,731,GasA,Ex,Y,SBrkr,820,523,0,1343,0,0,1,1,3,1,TA,7,Typ,1,Gd,Detchd,1921,Unf,1,186,Fa,TA,Y,192,0,102,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,154900 +1180,20,RL,77,8335,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,1Fam,1Story,5,5,1954,1954,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,Slab,NA,NA,NA,NA,0,NA,0,0,0,GasA,Gd,Y,SBrkr,1124,0,0,1124,0,0,1,0,3,1,TA,5,Min2,1,Gd,NA,NA,NA,0,0,NA,NA,N,0,36,190,0,0,0,NA,NA,NA,0,4,2006,WD,Normal,93000 +1181,60,RL,NA,11170,Pave,NA,IR2,Lvl,AllPub,Corner,Gtl,Timber,Norm,Norm,1Fam,2Story,7,5,1990,1991,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,Wood,Gd,TA,No,LwQ,1216,Unf,0,0,1216,GasA,Ex,Y,SBrkr,1298,1216,0,2514,0,0,2,1,4,1,TA,8,Typ,0,NA,Attchd,1990,Fin,2,693,TA,TA,Y,0,0,0,0,0,0,NA,GdPrv,NA,0,4,2006,WD,Normal,250000 +1182,120,RM,64,5587,Pave,NA,IR1,HLS,AllPub,Inside,Mod,Crawfor,Norm,Norm,TwnhsE,1Story,8,5,2008,2008,Hip,CompShg,CemntBd,CmentBd,Stone,186,Ex,TA,PConc,Ex,TA,Gd,GLQ,1480,Unf,0,120,1600,GasA,Ex,Y,SBrkr,1652,0,0,1652,1,1,2,0,2,1,Gd,5,Typ,1,Gd,Attchd,2008,Fin,2,482,TA,TA,Y,162,53,0,153,0,0,NA,NA,NA,0,11,2008,New,Partial,392500 +1183,60,RL,160,15623,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NoRidge,Norm,Norm,1Fam,2Story,10,5,1996,1996,Hip,CompShg,Wd Sdng,ImStucc,None,0,Gd,TA,PConc,Ex,TA,Av,GLQ,2096,Unf,0,300,2396,GasA,Ex,Y,SBrkr,2411,2065,0,4476,1,0,3,1,4,1,Ex,10,Typ,2,TA,Attchd,1996,Fin,3,813,TA,TA,Y,171,78,0,0,0,555,Ex,MnPrv,NA,0,7,2007,WD,Abnorml,745000 +1184,30,RL,60,10800,Pave,Grvl,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,1Story,5,6,1920,1950,Hip,CompShg,Stucco,Stucco,None,0,TA,TA,BrkTil,TA,TA,No,Rec,821,Unf,0,299,1120,GasA,Ex,Y,SBrkr,1130,0,0,1130,1,0,1,0,2,1,TA,5,Typ,1,Gd,Detchd,1970,Unf,2,720,TA,TA,Y,229,0,0,0,0,0,NA,NA,NA,0,6,2006,WD,Normal,120000 +1185,20,RL,50,35133,Grvl,NA,Reg,Lvl,AllPub,Inside,Mod,Timber,Norm,Norm,1Fam,1Story,5,4,1963,1963,Hip,CompShg,MetalSd,MetalSd,BrkFace,226,TA,TA,CBlock,TA,TA,Gd,Rec,1159,Unf,0,413,1572,GasA,Gd,Y,SBrkr,1572,0,0,1572,1,0,1,1,3,1,TA,5,Typ,2,TA,2Types,1963,RFn,3,995,TA,TA,Y,0,263,0,0,263,0,NA,NA,NA,0,5,2007,WD,Normal,186700 +1186,50,RL,60,9738,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1.5Fin,5,7,1924,1950,Gable,CompShg,AsbShng,AsbShng,None,0,TA,Gd,BrkTil,TA,TA,No,BLQ,392,Unf,0,392,784,GasA,Gd,Y,SBrkr,949,272,0,1221,1,0,1,0,4,1,TA,7,Typ,0,NA,Attchd,1965,Unf,1,392,TA,TA,Y,0,0,236,0,0,0,NA,NA,NA,0,3,2006,WD,Normal,104900 +1187,190,RL,107,10615,Pave,NA,IR1,Bnk,AllPub,Corner,Mod,OldTown,Artery,Artery,2fmCon,2Story,3,5,1900,1970,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,Fa,TA,Mn,BLQ,440,Unf,0,538,978,GasA,TA,Y,SBrkr,1014,685,0,1699,1,0,2,0,3,2,TA,7,Typ,0,NA,CarPort,1920,Unf,2,420,Fa,Fa,Y,0,74,0,0,0,0,NA,NA,NA,0,8,2009,WD,Abnorml,95000 +1188,20,RL,89,12461,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NoRidge,Norm,Norm,1Fam,1Story,8,5,1994,1995,Gable,CompShg,ImStucc,ImStucc,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,1456,Unf,0,168,1624,GasA,Ex,Y,SBrkr,1624,0,0,1624,1,0,2,0,2,1,Gd,5,Typ,1,Fa,Attchd,1994,RFn,3,757,TA,TA,Y,0,114,192,0,0,0,NA,GdPrv,NA,0,7,2006,WD,Normal,262000 +1189,60,RL,68,8935,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,7,5,2002,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,95,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,831,831,GasA,Ex,Y,SBrkr,831,829,0,1660,0,0,2,1,3,1,Gd,7,Typ,0,NA,Attchd,2002,RFn,2,493,TA,TA,Y,144,68,0,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,195000 +1190,60,RL,60,7500,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,2Story,7,5,1999,1999,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,TA,TA,No,Unf,0,Unf,0,994,994,GasA,Gd,Y,SBrkr,1028,776,0,1804,0,0,2,1,3,1,Gd,7,Typ,1,TA,Attchd,1999,Fin,2,442,TA,TA,Y,140,60,0,0,0,0,NA,NA,NA,0,6,2010,WD,Normal,189000 +1191,190,RL,NA,32463,Pave,NA,Reg,Low,AllPub,Inside,Mod,Mitchel,Norm,Norm,2fmCon,1Story,4,4,1961,1975,Gable,CompShg,MetalSd,MetalSd,Stone,149,TA,Gd,CBlock,TA,TA,Av,BLQ,1159,Unf,0,90,1249,GasA,Ex,Y,SBrkr,1622,0,0,1622,1,0,1,0,3,1,TA,7,Typ,1,TA,2Types,1975,Fin,4,1356,TA,TA,Y,439,0,0,0,0,0,NA,NA,NA,0,3,2007,WD,Normal,168000 +1192,160,FV,24,2645,Pave,Pave,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,Twnhs,2Story,8,5,1999,2000,Gable,CompShg,MetalSd,MetalSd,BrkFace,456,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,776,776,GasA,Ex,Y,SBrkr,764,677,0,1441,0,0,2,1,2,1,Gd,5,Typ,0,NA,Detchd,1999,Unf,2,492,TA,TA,Y,206,0,0,0,0,0,NA,NA,NA,0,11,2007,WD,Normal,174000 +1193,50,RM,60,9600,Pave,Grvl,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,1.5Fin,5,8,1925,1994,Gambrel,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,TA,TA,Mn,Unf,0,Unf,0,702,702,GasA,Gd,Y,SBrkr,842,630,0,1472,0,0,1,0,3,1,Gd,6,Typ,0,NA,Detchd,1925,Unf,1,250,TA,Fa,P,0,0,84,0,0,0,NA,GdWo,NA,0,7,2007,WD,Normal,125000 +1194,120,RM,NA,4500,Pave,NA,Reg,Lvl,AllPub,FR2,Gtl,Mitchel,Norm,Norm,TwnhsE,1Story,6,5,1999,1999,Hip,CompShg,VinylSd,VinylSd,BrkFace,425,TA,TA,PConc,Ex,TA,No,GLQ,883,Unf,0,341,1224,GasA,Ex,Y,SBrkr,1224,0,0,1224,1,0,2,0,2,1,TA,5,Typ,0,NA,Attchd,1999,Fin,2,402,TA,TA,Y,0,304,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,165000 +1195,60,RL,80,9364,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,Sawyer,Norm,Norm,1Fam,2Story,6,7,1969,1969,Gable,CompShg,HdBoard,HdBoard,Stone,143,TA,TA,CBlock,TA,TA,No,ALQ,371,Unf,0,292,663,GasA,TA,Y,SBrkr,663,689,0,1352,0,0,1,1,4,1,TA,7,Typ,0,NA,Attchd,1969,Fin,1,299,TA,TA,Y,379,36,0,0,0,0,NA,MnPrv,NA,0,3,2010,WD,Normal,158000 +1196,60,RL,51,8029,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,2Story,6,5,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,No,Unf,0,Unf,0,728,728,GasA,Ex,Y,SBrkr,728,728,0,1456,0,0,2,1,3,1,Gd,8,Typ,0,NA,Attchd,2005,Fin,2,400,TA,TA,Y,100,24,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,176000 +1197,60,RL,58,14054,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,2Story,7,5,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,Av,Unf,0,Unf,0,879,879,GasA,Ex,Y,SBrkr,879,984,0,1863,0,0,2,1,4,1,Gd,9,Typ,1,Gd,BuiltIn,2006,Fin,3,660,TA,TA,Y,100,17,0,0,0,0,NA,NA,NA,0,11,2006,New,Partial,219210 +1198,75,RM,65,8850,Pave,NA,IR1,Bnk,AllPub,Corner,Gtl,OldTown,Norm,Norm,1Fam,2.5Unf,7,6,1916,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,815,815,GasA,Ex,Y,SBrkr,815,875,0,1690,0,0,1,0,3,1,TA,7,Typ,1,Gd,Detchd,1916,Unf,1,225,TA,TA,Y,0,0,330,0,0,0,NA,NA,NA,0,7,2006,ConLw,Normal,144000 +1199,20,RL,70,9100,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2001,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1212,1212,GasA,Ex,Y,SBrkr,1212,0,0,1212,0,0,2,0,3,1,Gd,6,Typ,0,NA,Attchd,2001,RFn,2,573,TA,TA,Y,356,0,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,178000 +1200,20,RL,75,11235,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,1Story,4,5,1963,1979,Gable,CompShg,HdBoard,HdBoard,BrkFace,51,TA,TA,CBlock,TA,TA,No,Rec,547,Unf,0,504,1051,GasA,Gd,Y,SBrkr,1382,0,0,1382,0,0,1,1,3,1,TA,6,Typ,1,Po,Attchd,1974,Unf,2,459,TA,TA,Y,0,82,0,0,0,0,NA,NA,NA,0,10,2006,WD,Normal,148000 +1201,20,RL,71,9353,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,4,5,1970,1970,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,864,864,GasA,Gd,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,TA,5,Typ,0,NA,Attchd,1972,Unf,1,280,TA,TA,Y,0,0,0,0,0,0,NA,NA,Shed,0,7,2006,Oth,Abnorml,116050 +1202,60,RL,80,10400,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,CollgCr,Norm,Norm,1Fam,2Story,7,5,1998,1998,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,Av,Unf,0,Unf,0,866,866,GasA,Ex,Y,SBrkr,866,913,0,1779,0,0,2,1,3,1,Gd,6,Typ,0,NA,Attchd,1998,RFn,2,546,TA,TA,Y,198,36,0,0,0,0,NA,NA,NA,0,3,2009,WD,Normal,197900 +1203,50,RM,50,6000,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,BrkSide,Norm,Norm,1Fam,1.5Fin,5,8,1925,1997,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,884,884,GasA,Ex,Y,SBrkr,884,464,0,1348,1,0,1,0,3,1,TA,5,Typ,1,Fa,Detchd,1960,Unf,1,216,TA,TA,N,0,0,208,0,0,0,NA,NA,NA,0,5,2009,WD,Normal,117000 +1204,20,RL,75,9750,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2000,2001,Gable,CompShg,VinylSd,VinylSd,BrkFace,171,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1630,1630,GasA,Ex,Y,SBrkr,1630,0,0,1630,0,0,2,0,3,1,Gd,6,Typ,1,TA,Attchd,2000,Unf,2,451,TA,TA,Y,74,234,0,0,0,0,NA,NA,NA,0,10,2009,WD,Normal,213000 +1205,20,RL,78,10140,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,1Story,5,6,1975,1975,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,Gd,TA,No,ALQ,788,Unf,0,268,1056,GasA,Ex,Y,SBrkr,1074,0,0,1074,1,0,1,1,3,1,TA,6,Typ,0,NA,Attchd,1975,RFn,2,495,TA,TA,Y,0,88,0,0,0,0,NA,MnPrv,NA,0,7,2006,WD,Normal,153500 +1206,20,RL,90,14684,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,SawyerW,Norm,Norm,1Fam,1Story,7,7,1990,1991,Hip,CompShg,HdBoard,HdBoard,BrkFace,234,Gd,TA,CBlock,Gd,TA,Mn,ALQ,485,BLQ,177,1496,2158,GasA,Gd,Y,SBrkr,2196,0,0,2196,0,0,2,0,3,1,Gd,7,Typ,1,TA,Attchd,1990,RFn,3,701,TA,TA,Y,84,70,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,271900 +1207,20,RH,NA,8900,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SawyerW,Norm,Norm,1Fam,1Story,4,4,1966,1966,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,TA,TA,No,Rec,1056,Unf,0,0,1056,GasA,TA,Y,SBrkr,1056,0,0,1056,1,0,1,0,2,1,TA,5,Typ,0,NA,Detchd,1966,Unf,1,384,TA,TA,Y,0,42,0,0,0,0,NA,MnPrv,NA,0,11,2006,WD,Normal,107000 +1208,20,RL,70,9135,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,6,5,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,120,Gd,TA,PConc,Gd,TA,Av,GLQ,340,Unf,0,1342,1682,GasA,Ex,Y,SBrkr,1700,0,0,1700,1,0,2,0,3,1,Gd,7,Typ,0,NA,Attchd,2003,RFn,2,544,TA,TA,Y,192,23,0,0,0,0,NA,NA,NA,0,5,2006,WD,Normal,200000 +1209,20,RL,70,7763,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,7,1962,1980,Gable,CompShg,MetalSd,MetalSd,None,0,TA,Gd,CBlock,TA,TA,No,Rec,504,BLQ,108,319,931,GasA,TA,Y,SBrkr,1283,0,0,1283,1,0,1,0,3,1,TA,6,Typ,0,NA,Detchd,1980,Unf,2,506,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,10,2008,WD,Normal,140000 +1210,20,RL,85,10182,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Somerst,RRNn,Norm,1Fam,1Story,8,5,2006,2006,Hip,CompShg,VinylSd,VinylSd,Stone,420,Gd,TA,PConc,Ex,TA,Mn,GLQ,1220,Unf,0,440,1660,GasA,Ex,Y,SBrkr,1660,0,0,1660,1,0,2,0,3,1,Gd,8,Typ,1,Gd,Attchd,2006,RFn,2,500,TA,TA,Y,322,50,0,0,0,0,NA,NA,NA,0,5,2006,New,Partial,290000 +1211,60,RL,70,11218,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SawyerW,Norm,Norm,1Fam,2Story,6,5,1992,1992,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1055,1055,GasA,Ex,Y,SBrkr,1055,790,0,1845,0,0,2,1,3,1,Gd,8,Typ,1,TA,Attchd,1992,RFn,2,462,TA,TA,Y,635,104,0,0,0,0,NA,GdPrv,Shed,400,5,2010,WD,Normal,189000 +1212,50,RL,152,12134,Pave,NA,IR1,Bnk,AllPub,Inside,Mod,Gilbert,Norm,Norm,1Fam,1.5Fin,8,7,1988,2005,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Gd,TA,Wood,Gd,TA,Av,GLQ,427,Unf,0,132,559,GasA,Gd,Y,SBrkr,1080,672,0,1752,0,0,2,0,4,1,TA,8,Typ,0,NA,Basment,1988,RFn,2,492,TA,TA,Y,325,12,0,0,0,0,NA,NA,NA,0,6,2010,WD,Normal,164000 +1213,30,RL,50,9340,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1Story,4,6,1941,1950,Hip,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,Rec,344,Unf,0,328,672,GasA,TA,Y,SBrkr,672,0,0,672,1,0,1,0,2,1,TA,4,Typ,0,NA,Attchd,1941,Unf,1,234,TA,TA,N,0,113,0,0,0,0,NA,NA,NA,0,8,2009,WD,Normal,113000 +1214,80,RL,NA,10246,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Sawyer,Norm,Norm,1Fam,SLvl,4,9,1965,2001,Gable,CompShg,VinylSd,VinylSd,None,0,TA,Gd,CBlock,TA,Gd,Av,GLQ,648,Unf,0,0,648,GasA,Ex,Y,SBrkr,960,0,0,960,1,1,0,0,0,1,TA,3,Typ,0,NA,Attchd,1965,Unf,1,364,TA,TA,Y,88,0,0,0,0,0,NA,NA,NA,0,5,2006,WD,Normal,145000 +1215,85,RL,69,10205,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,SFoyer,5,5,1962,1962,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,Av,BLQ,784,Unf,0,141,925,GasA,TA,Y,SBrkr,999,0,0,999,1,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1962,Unf,1,300,TA,TA,Y,150,72,0,0,0,0,NA,NA,NA,0,5,2006,WD,Normal,134500 +1216,20,RL,99,7094,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,1Story,5,5,1966,1966,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,CBlock,TA,TA,No,Rec,180,LwQ,374,340,894,GasA,TA,Y,SBrkr,894,0,0,894,0,0,1,0,3,1,TA,5,Typ,0,NA,Detchd,1966,RFn,1,384,TA,TA,Y,0,0,0,0,0,0,NA,MnPrv,NA,0,5,2007,WD,Normal,125000 +1217,90,RM,68,8930,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Sawyer,RRAe,Norm,Duplex,1.5Fin,6,5,1978,1978,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,Slab,NA,NA,NA,NA,0,NA,0,0,0,GasA,TA,Y,SBrkr,1318,584,0,1902,0,0,2,0,4,2,TA,8,Typ,0,NA,Attchd,1978,Unf,2,539,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,4,2010,WD,Normal,112000 +1218,20,FV,72,8640,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,1Story,8,5,2009,2009,Gable,CompShg,CemntBd,CmentBd,Stone,72,Gd,TA,PConc,Gd,TA,Mn,GLQ,936,Unf,0,364,1300,GasA,Ex,Y,SBrkr,1314,0,0,1314,1,0,2,0,3,1,Gd,6,Typ,0,NA,Attchd,2009,RFn,2,552,TA,TA,Y,135,112,0,0,0,0,NA,NA,NA,0,9,2009,New,Partial,229456 +1219,50,RM,52,6240,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,1.5Fin,4,5,1947,1950,Gable,CompShg,AsbShng,AsbShng,None,0,TA,TA,Slab,NA,NA,NA,NA,0,NA,0,0,0,GasA,Gd,N,SBrkr,672,240,0,912,0,0,1,0,2,1,TA,3,Typ,0,NA,NA,NA,NA,0,0,NA,NA,N,0,0,0,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,80500 +1220,160,RM,21,1680,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrDale,Norm,Norm,Twnhs,2Story,6,5,1971,1971,Gable,CompShg,CemntBd,CmentBd,BrkFace,236,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,672,672,GasA,TA,Y,SBrkr,672,546,0,1218,0,0,1,1,3,1,TA,7,Typ,0,NA,NA,NA,NA,0,0,NA,NA,N,201,0,0,0,0,0,NA,NA,NA,0,4,2006,WD,Abnorml,91500 +1221,20,RL,66,7800,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,5,1964,1964,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,Rec,312,LwQ,600,0,912,GasA,TA,Y,SBrkr,912,0,0,912,0,0,1,0,2,1,TA,5,Typ,0,NA,Attchd,1964,Unf,1,288,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,11,2006,WD,Abnorml,115000 +1222,20,RL,55,8250,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,Norm,1Fam,1Story,5,5,1968,1968,Hip,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,TA,TA,No,BLQ,250,LwQ,492,210,952,GasA,Ex,Y,SBrkr,1211,0,0,1211,0,0,1,0,3,1,TA,5,Typ,1,TA,Attchd,1968,Unf,1,322,TA,TA,Y,0,63,0,0,0,0,NA,NA,NA,0,8,2008,WD,Normal,134000 +1223,50,RL,78,10496,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Artery,Norm,1Fam,1.5Fin,6,6,1949,1950,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,320,TA,TA,CBlock,TA,TA,Mn,Rec,196,Unf,0,844,1040,GasA,Ex,Y,SBrkr,1168,678,0,1846,0,0,2,0,3,1,TA,7,Typ,1,Gd,Attchd,1949,Unf,1,315,TA,TA,Y,0,0,0,0,0,0,NA,GdWo,NA,0,1,2007,WD,Normal,143000 +1224,20,RL,89,10680,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,3,1951,1951,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,44,TA,TA,CBlock,TA,Fa,No,LwQ,756,Unf,0,1380,2136,GasA,TA,N,FuseA,2136,0,0,2136,0,0,2,0,4,1,TA,7,Mod,0,NA,Detchd,1951,Unf,2,528,TA,TA,Y,0,30,0,0,0,0,NA,MnPrv,NA,0,10,2006,WD,Normal,137900 +1225,60,RL,60,15384,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,1Fam,2Story,7,5,2004,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,Av,GLQ,724,Unf,0,64,788,GasA,Ex,Y,SBrkr,788,702,0,1490,1,0,2,1,3,1,Gd,8,Typ,1,Gd,Attchd,2004,Fin,2,388,TA,TA,Y,100,75,0,0,0,0,NA,NA,NA,0,2,2008,WD,Normal,184000 +1226,80,RL,65,10482,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,SLvl,6,8,1958,1958,Hip,CompShg,VinylSd,VinylSd,BrkFace,63,TA,Gd,CBlock,TA,TA,Av,GLQ,507,Unf,0,81,588,GasA,Ex,Y,SBrkr,1138,0,0,1138,0,1,1,0,3,1,TA,6,Typ,0,NA,Attchd,1958,RFn,1,264,TA,TA,Y,224,0,0,0,0,0,NA,MnWw,NA,0,6,2007,WD,Normal,145000 +1227,60,RL,86,14598,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Somerst,Feedr,Norm,1Fam,2Story,6,5,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,74,Gd,TA,PConc,Gd,TA,Mn,Unf,0,Unf,0,894,894,GasA,Ex,Y,SBrkr,894,1039,0,1933,0,0,2,1,4,1,Gd,9,Typ,1,Gd,BuiltIn,2007,Fin,3,668,TA,TA,Y,100,18,0,0,0,0,NA,NA,NA,0,1,2008,WD,Normal,214000 +1228,20,RL,72,8872,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,8,1965,2008,Gable,CompShg,VinylSd,VinylSd,BrkFace,300,TA,TA,CBlock,TA,TA,No,ALQ,595,Unf,0,317,912,GasA,Ex,Y,SBrkr,912,0,0,912,1,0,1,0,2,1,Gd,5,Typ,0,NA,Detchd,1992,Unf,2,576,TA,TA,Y,0,240,0,0,0,0,NA,NA,NA,0,12,2008,WD,Normal,147000 +1229,120,RL,65,8769,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NridgHt,Norm,Norm,TwnhsE,1Story,9,5,2008,2008,Hip,CompShg,MetalSd,MetalSd,BrkFace,766,Ex,TA,PConc,Ex,TA,No,GLQ,1540,Unf,0,162,1702,GasA,Ex,Y,SBrkr,1702,0,0,1702,1,0,1,1,1,1,Ex,7,Typ,1,Gd,Attchd,2008,Fin,3,1052,TA,TA,Y,0,72,0,0,224,0,NA,NA,NA,0,10,2008,New,Partial,367294 +1230,80,RL,70,7910,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,SLvl,5,5,1960,1960,Hip,CompShg,BrkFace,HdBoard,None,0,TA,TA,CBlock,TA,TA,No,ALQ,666,Unf,0,409,1075,GasA,Gd,Y,SBrkr,1507,0,0,1507,0,0,2,0,4,1,TA,7,Maj1,0,NA,Basment,1960,Unf,1,404,TA,TA,Y,0,0,0,0,0,0,NA,GdWo,NA,0,8,2008,WD,Normal,127000 +1231,90,RL,NA,18890,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Sawyer,Feedr,RRAe,Duplex,1.5Fin,5,5,1977,1977,Shed,CompShg,Plywood,Plywood,None,1,TA,TA,CBlock,Gd,TA,No,GLQ,498,Rec,211,652,1361,GasA,Ex,Y,SBrkr,1361,1259,0,2620,0,0,2,2,4,2,TA,12,Typ,1,TA,BuiltIn,1977,RFn,2,600,TA,TA,N,155,24,145,0,0,0,NA,NA,Gar2,8300,8,2007,WD,Normal,190000 +1232,90,RL,70,7728,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,Duplex,SLvl,5,6,1962,1962,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,120,TA,TA,CBlock,TA,TA,Av,ALQ,803,Unf,0,303,1106,GasA,TA,Y,SBrkr,1190,0,0,1190,1,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1962,Unf,2,540,TA,TA,Y,0,18,0,0,0,0,NA,GdWo,NA,0,5,2006,WD,Normal,132500 +1233,90,RL,70,9842,Pave,NA,Reg,Lvl,AllPub,FR2,Gtl,NAmes,Norm,Norm,Duplex,1Story,4,5,1962,1962,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,Slab,NA,NA,NA,NA,0,NA,0,0,0,GasA,TA,Y,SBrkr,1224,0,0,1224,0,0,2,0,2,2,TA,6,Typ,0,NA,CarPort,1962,Unf,2,462,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,3,2007,WD,Normal,101800 +1234,20,RL,NA,12160,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,5,1959,1959,Hip,CompShg,Plywood,Plywood,BrkFace,180,TA,TA,CBlock,TA,TA,No,Rec,1000,Unf,0,188,1188,GasA,Fa,Y,SBrkr,1188,0,0,1188,1,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1959,RFn,2,531,TA,TA,Y,0,0,0,0,0,0,NA,MnPrv,NA,0,5,2010,COD,Abnorml,142000 +1235,70,RH,55,8525,Pave,NA,Reg,Bnk,AllPub,Inside,Gtl,SWISU,Norm,Norm,1Fam,2Story,5,6,1911,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,PConc,TA,TA,Av,Unf,0,Unf,0,940,940,GasA,TA,N,FuseA,1024,940,0,1964,0,0,1,1,4,1,TA,7,Typ,0,NA,NA,NA,NA,0,0,NA,NA,N,0,192,0,0,0,0,NA,NA,NA,0,11,2008,WD,Abnorml,130000 +1236,70,RL,96,13132,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Crawfor,Norm,Norm,1Fam,2Story,5,5,1914,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,Gd,TA,Mn,Unf,0,Unf,0,747,747,GasA,Gd,Y,FuseF,892,892,0,1784,0,0,1,1,4,1,TA,9,Typ,0,NA,Detchd,1914,Unf,1,180,Fa,Fa,N,203,40,0,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,138887 +1237,160,RL,36,2628,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,Twnhs,2Story,7,5,2003,2003,Gable,CompShg,VinylSd,Wd Shng,Stone,106,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,764,764,GasA,Ex,Y,SBrkr,764,862,0,1626,0,0,2,1,2,1,Gd,6,Typ,0,NA,BuiltIn,2003,RFn,2,474,TA,TA,Y,0,27,0,0,0,0,NA,NA,NA,0,6,2010,WD,Normal,175500 +1238,60,RL,41,12393,Pave,NA,IR2,Lvl,AllPub,FR2,Gtl,CollgCr,Norm,Norm,1Fam,2Story,7,5,2004,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,847,847,GasA,Ex,Y,SBrkr,847,1101,0,1948,0,0,2,1,4,1,Gd,8,Typ,1,Gd,BuiltIn,2004,Fin,2,434,TA,TA,Y,100,48,0,0,0,0,NA,NA,NA,0,9,2006,WD,Normal,195000 +1239,20,RL,63,13072,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SawyerW,RRAe,Norm,1Fam,1Story,6,5,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1141,1141,GasA,Ex,Y,SBrkr,1141,0,0,1141,0,0,1,1,3,1,TA,6,Typ,0,NA,Detchd,2005,Unf,2,484,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,3,2006,WD,Abnorml,142500 +1240,20,RL,64,9037,Pave,NA,IR1,HLS,AllPub,Inside,Gtl,Timber,Norm,Norm,1Fam,1Story,8,5,2006,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,32,Gd,TA,PConc,Gd,TA,Av,GLQ,428,Unf,0,1048,1476,GasA,Ex,Y,SBrkr,1484,0,0,1484,0,0,2,0,2,1,Ex,6,Typ,1,Gd,Attchd,2006,RFn,2,472,TA,TA,Y,120,33,0,0,0,0,NA,NA,NA,0,12,2007,WD,Normal,265900 +1241,60,RL,65,8158,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,7,5,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,252,Gd,TA,PConc,Gd,TA,No,GLQ,550,Unf,0,334,884,GasA,Ex,Y,SBrkr,884,884,0,1768,1,0,2,1,3,1,Gd,8,Typ,0,NA,Attchd,2003,RFn,2,543,TA,TA,Y,0,63,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,224900 +1242,20,RL,83,9849,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,1Story,7,6,2007,2007,Hip,CompShg,VinylSd,VinylSd,Stone,0,Gd,TA,PConc,Gd,TA,Av,Unf,0,Unf,0,1689,1689,GasA,Ex,Y,SBrkr,1689,0,0,1689,0,0,2,0,3,1,Gd,7,Typ,0,NA,Attchd,2007,RFn,3,954,TA,TA,Y,0,56,0,0,0,0,NA,NA,NA,0,6,2007,New,Partial,248328 +1243,85,RL,85,10625,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,SFoyer,7,6,1974,1974,Gable,CompShg,Plywood,Plywood,BrkFace,81,TA,TA,CBlock,Gd,TA,Gd,GLQ,885,LwQ,168,0,1053,GasA,TA,Y,SBrkr,1173,0,0,1173,1,0,2,0,3,1,Gd,6,Typ,2,TA,Attchd,1974,RFn,2,528,TA,TA,Y,0,120,0,0,0,0,NA,MnPrv,NA,0,1,2010,WD,Family,170000 +1244,20,RL,107,13891,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,1Story,10,5,2006,2006,Gable,CompShg,VinylSd,VinylSd,NA,NA,Ex,TA,PConc,Ex,Gd,Gd,GLQ,1386,Unf,0,690,2076,GasA,Ex,Y,SBrkr,2076,0,0,2076,1,0,2,1,2,1,Ex,7,Typ,1,Gd,Attchd,2006,Fin,3,850,TA,TA,Y,216,229,0,0,0,0,NA,NA,NA,0,9,2006,New,Partial,465000 +1245,70,RL,NA,11435,Pave,NA,IR1,HLS,AllPub,Corner,Mod,Crawfor,Norm,Norm,1Fam,2Story,8,7,1929,1950,Gable,CompShg,BrkFace,Stucco,None,0,TA,TA,PConc,Gd,TA,No,Unf,0,Unf,0,792,792,GasA,Fa,Y,SBrkr,792,725,0,1517,0,0,1,0,3,1,Gd,7,Typ,2,Gd,Detchd,1931,Unf,2,400,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,6,2006,WD,Normal,230000 +1246,80,RL,78,12090,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,SLvl,6,7,1984,2003,Hip,CompShg,VinylSd,VinylSd,BrkFace,74,TA,TA,CBlock,Gd,TA,No,Unf,0,Unf,0,585,585,GasA,Ex,Y,SBrkr,1140,728,0,1868,0,0,3,1,3,1,TA,7,Typ,1,TA,BuiltIn,1984,Fin,2,477,TA,TA,Y,268,112,0,0,147,0,NA,NA,NA,0,1,2007,WD,Abnorml,178000 +1247,60,FV,65,8125,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,2Story,7,5,2005,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,756,756,GasA,Ex,Y,SBrkr,756,797,0,1553,0,0,2,1,3,1,Gd,6,Typ,0,NA,Attchd,2005,RFn,2,615,TA,TA,Y,0,45,0,0,0,0,NA,NA,NA,0,3,2006,New,Partial,186500 +1248,80,RL,NA,12328,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Mitchel,Norm,Norm,1Fam,SLvl,6,5,1976,1976,Gable,CompShg,HdBoard,HdBoard,BrkFace,335,TA,TA,CBlock,TA,TA,Av,GLQ,539,Unf,0,473,1012,GasA,TA,Y,SBrkr,1034,0,0,1034,1,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1976,Unf,3,888,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,5,2010,WD,Normal,169900 +1249,75,RM,60,9600,Pave,Grvl,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,2.5Unf,6,5,1917,1950,Gable,CompShg,AsbShng,AsbShng,None,0,TA,TA,BrkTil,Gd,TA,No,Rec,319,Unf,0,416,735,OthW,Fa,N,SBrkr,1134,924,0,2058,0,0,1,1,3,1,TA,8,Typ,1,Gd,Detchd,1950,Unf,2,396,Fa,Fa,P,0,0,259,0,0,0,NA,NA,NA,0,4,2008,WD,Normal,129500 +1250,20,RL,60,7200,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,7,1950,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,BLQ,534,Rec,96,246,876,GasA,TA,Y,SBrkr,988,0,0,988,0,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1950,Unf,1,276,TA,TA,Y,0,80,0,0,0,0,NA,NA,NA,0,5,2007,WD,Normal,119000 +1251,20,RL,93,11160,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,1Fam,1Story,7,5,1968,1968,Hip,CompShg,BrkFace,BrkFace,None,0,Gd,TA,CBlock,TA,TA,No,ALQ,1065,Unf,0,1045,2110,GasA,Ex,Y,SBrkr,2110,0,0,2110,1,0,2,1,3,1,Ex,8,Typ,2,TA,Attchd,1968,Fin,2,522,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,4,2010,WD,Normal,244000 +1252,120,RL,NA,3136,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NridgHt,Norm,Norm,TwnhsE,1Story,7,5,2003,2003,Gable,CompShg,VinylSd,Wd Shng,Stone,163,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1405,1405,GasA,Ex,Y,SBrkr,1405,0,0,1405,0,0,2,0,2,1,Gd,6,Typ,1,Gd,Attchd,2003,RFn,2,478,TA,TA,Y,148,36,0,0,0,0,NA,NA,NA,0,3,2006,WD,Normal,171750 +1253,20,RL,62,9858,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Mitchel,Norm,Norm,1Fam,1Story,5,6,1968,1968,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,TA,TA,No,BLQ,510,Unf,0,354,864,GasA,TA,Y,SBrkr,874,0,0,874,1,0,1,0,3,1,TA,5,Typ,0,NA,Attchd,1968,RFn,1,288,TA,TA,Y,33,0,0,0,0,0,NA,GdWo,Shed,600,11,2009,WD,Normal,130000 +1254,60,RL,NA,17542,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Veenker,Norm,Norm,1Fam,2Story,7,7,1974,2003,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,Gd,TA,CBlock,TA,TA,Gd,LwQ,125,ALQ,1031,36,1192,GasA,TA,Y,SBrkr,1516,651,0,2167,1,0,2,1,3,1,Gd,9,Typ,2,Gd,Attchd,1974,RFn,2,518,TA,TA,Y,220,47,0,0,0,0,NA,MnPrv,NA,0,7,2007,WD,Normal,294000 +1255,60,RL,60,6931,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,2Story,7,5,2003,2004,Gable,CompShg,VinylSd,VinylSd,Stone,92,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,746,746,GasA,Ex,Y,SBrkr,760,896,0,1656,0,0,2,1,3,1,Gd,7,Typ,1,Gd,BuiltIn,2003,Fin,2,397,TA,TA,Y,178,128,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,165400 +1256,50,RM,52,6240,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,1.5Fin,6,6,1931,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,TA,Fa,No,LwQ,425,Unf,0,459,884,GasA,TA,Y,FuseA,959,408,0,1367,0,0,1,0,3,1,TA,6,Typ,1,Gd,Detchd,1978,Unf,1,560,TA,TA,Y,0,0,0,0,120,0,NA,NA,NA,0,11,2007,WD,Normal,127500 +1257,20,RL,91,14303,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NoRidge,Norm,Norm,1Fam,1Story,8,5,1994,1994,Hip,CompShg,HdBoard,HdBoard,BrkFace,554,Gd,TA,PConc,Gd,TA,Gd,GLQ,1314,Unf,0,672,1986,GasA,Ex,Y,SBrkr,1987,0,0,1987,1,0,2,0,2,1,Gd,7,Typ,1,TA,Attchd,1994,Fin,2,691,TA,TA,Y,262,36,0,0,0,0,NA,NA,NA,0,8,2008,WD,Normal,301500 +1258,30,RL,56,4060,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,Edwards,Feedr,Norm,1Fam,1Story,5,8,1922,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,PConc,Fa,TA,No,Unf,0,Unf,0,864,864,GasA,Ex,Y,SBrkr,864,0,0,864,0,0,1,0,2,1,TA,4,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,0,96,0,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,99900 +1259,80,RL,59,9587,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,SLvl,7,5,2005,2005,Gable,CompShg,VinylSd,VinylSd,Stone,182,Gd,TA,PConc,Gd,TA,Gd,GLQ,655,Unf,0,201,856,GasA,Ex,Y,SBrkr,1166,0,0,1166,1,0,2,0,2,1,Gd,5,Typ,0,NA,Attchd,2005,Fin,2,400,TA,TA,Y,212,0,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,190000 +1260,20,RL,65,9750,Pave,NA,Reg,Lvl,AllPub,FR2,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,8,1969,1969,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,Gd,TA,No,ALQ,602,LwQ,438,14,1054,GasA,Gd,Y,SBrkr,1054,0,0,1054,1,0,1,1,3,1,TA,6,Typ,0,NA,Attchd,1969,Unf,2,460,TA,TA,Y,180,0,0,0,80,0,NA,NA,NA,0,7,2008,WD,Normal,151000 +1261,60,RL,NA,24682,Pave,NA,IR3,Lvl,AllPub,CulDSac,Gtl,Gilbert,RRAn,Norm,1Fam,2Story,6,5,1999,1999,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,No,Unf,0,Unf,0,841,841,GasA,Ex,Y,SBrkr,892,783,0,1675,0,0,2,1,3,1,TA,7,Typ,1,TA,BuiltIn,1999,Fin,2,502,TA,TA,Y,0,103,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,181000 +1262,20,RL,80,9600,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,6,1956,1956,Hip,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,Rec,504,Unf,0,546,1050,GasA,Gd,Y,SBrkr,1050,0,0,1050,0,0,1,0,2,1,TA,5,Typ,0,NA,Attchd,1956,Unf,1,338,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,128900 +1263,50,RL,NA,11250,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,ClearCr,Norm,Norm,1Fam,1.5Fin,4,5,1957,1989,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,Av,Unf,0,Unf,0,1104,1104,GasA,Ex,Y,FuseA,1104,684,0,1788,1,0,1,0,5,1,TA,8,Min2,2,TA,Attchd,1957,Unf,1,304,TA,TA,Y,120,0,0,0,0,0,NA,NA,NA,0,11,2009,WD,Normal,161500 +1264,70,RL,60,13515,Pave,Pave,Reg,Lvl,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,2Story,6,6,1919,1950,Gambrel,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,PConc,TA,TA,No,Unf,0,Unf,0,764,764,GasA,Ex,Y,FuseA,1060,764,0,1824,0,0,1,0,3,1,TA,8,Typ,1,Gd,Detchd,1940,Unf,2,520,TA,TA,N,0,0,126,0,0,0,NA,GdPrv,NA,0,7,2007,WD,Normal,180500 +1265,120,RH,34,4060,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,TwnhsE,1Story,6,5,1998,1999,Gable,CompShg,MetalSd,MetalSd,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,266,Unf,0,1139,1405,GasA,Ex,Y,SBrkr,1337,0,0,1337,1,0,2,0,2,1,Gd,5,Typ,0,NA,Attchd,1998,Fin,2,511,TA,TA,Y,144,68,0,0,0,0,NA,NA,NA,0,8,2008,COD,Abnorml,181000 +1266,160,FV,35,3735,Pave,NA,Reg,Lvl,AllPub,FR3,Gtl,Somerst,Norm,Norm,TwnhsE,2Story,7,5,1999,1999,Hip,CompShg,MetalSd,MetalSd,BrkFace,218,Gd,TA,PConc,Gd,TA,No,GLQ,450,Unf,0,241,691,GasA,Ex,Y,SBrkr,713,739,0,1452,1,0,2,1,3,1,Gd,6,Typ,0,NA,Detchd,1999,Unf,2,506,TA,TA,Y,0,34,0,0,0,0,NA,NA,NA,0,3,2006,WD,Normal,183900 +1267,190,RM,60,10120,Pave,NA,IR1,Bnk,AllPub,Inside,Gtl,OldTown,Feedr,Norm,2fmCon,2.5Unf,7,4,1910,1950,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,Fa,TA,CBlock,TA,TA,No,Unf,0,Unf,0,925,925,GasA,TA,N,FuseF,964,925,0,1889,0,0,1,1,4,2,TA,9,Typ,1,Gd,Detchd,1960,Unf,1,308,TA,TA,N,0,0,264,0,0,0,NA,MnPrv,NA,0,1,2007,WD,Normal,122000 +1268,20,RL,89,13214,Pave,NA,IR1,HLS,AllPub,Inside,Gtl,Timber,Norm,Norm,1Fam,1Story,9,5,2008,2009,Hip,CompShg,Stucco,CmentBd,None,0,Ex,TA,PConc,Ex,TA,Gd,Unf,0,Unf,0,2002,2002,GasA,Ex,Y,SBrkr,2018,0,0,2018,0,0,2,0,3,1,Ex,10,Typ,1,Gd,Attchd,2009,Fin,3,746,TA,TA,Y,144,76,0,0,0,0,NA,NA,NA,0,5,2010,WD,Normal,378500 +1269,50,RL,NA,14100,Pave,NA,IR1,Lvl,AllPub,Inside,Mod,Crawfor,Norm,Norm,1Fam,1.5Fin,8,9,1935,1997,Gable,CompShg,Stucco,Stucco,BrkFace,632,TA,Gd,CBlock,TA,TA,Mn,Rec,192,Unf,0,536,728,GasA,Ex,Y,SBrkr,1968,1479,0,3447,0,0,3,1,4,1,Gd,11,Typ,2,Gd,BuiltIn,1982,Unf,3,1014,TA,TA,Y,314,12,0,0,0,0,NA,GdWo,NA,0,5,2008,WD,Normal,381000 +1270,50,RL,78,11344,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Feedr,Norm,1Fam,1.5Fin,5,5,1958,1958,Gable,CompShg,MetalSd,MetalSd,BrkFace,180,TA,TA,CBlock,TA,TA,No,BLQ,460,Unf,0,414,874,GasW,TA,Y,FuseA,874,650,0,1524,0,0,1,1,3,1,TA,7,Typ,0,NA,Attchd,1958,Unf,1,315,TA,TA,Y,0,0,0,0,0,0,NA,GdWo,NA,0,7,2007,WD,Normal,144000 +1271,40,RL,NA,23595,Pave,NA,Reg,Low,AllPub,Inside,Sev,ClearCr,Norm,Norm,1Fam,1Story,7,6,1979,1979,Shed,WdShake,Plywood,Plywood,None,0,Gd,TA,PConc,Gd,TA,Gd,GLQ,1258,Unf,0,74,1332,GasA,TA,Y,SBrkr,1332,192,0,1524,2,0,0,1,0,1,Gd,4,Typ,1,TA,Attchd,1979,Fin,2,586,TA,TA,Y,268,0,0,0,0,0,NA,NA,NA,0,4,2010,WD,Normal,260000 +1272,20,RL,NA,9156,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NWAmes,PosN,Norm,1Fam,1Story,6,7,1968,1968,Hip,CompShg,BrkFace,BrkFace,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,1489,1489,GasA,Gd,Y,SBrkr,1489,0,0,1489,0,0,2,0,3,1,Gd,7,Typ,1,Gd,Attchd,1968,RFn,2,462,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,8,2009,WD,Normal,185750 +1273,20,RL,NA,13526,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Sawyer,Norm,Norm,1Fam,1Story,5,6,1965,1965,Hip,CompShg,HdBoard,Plywood,BrkFace,114,TA,TA,CBlock,TA,TA,No,BLQ,560,LwQ,375,0,935,GasA,TA,Y,SBrkr,935,0,0,935,1,0,1,0,3,1,TA,5,Typ,0,NA,Attchd,1965,Unf,1,288,TA,TA,Y,180,0,0,0,0,0,NA,MnPrv,NA,0,11,2006,WD,Normal,137000 +1274,80,RL,124,11512,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Edwards,Norm,Norm,1Fam,SLvl,6,7,1959,2006,Gable,CompShg,Plywood,Plywood,BrkFace,84,TA,TA,CBlock,TA,TA,Av,ALQ,719,Unf,0,300,1019,GasA,Gd,Y,SBrkr,1357,0,0,1357,1,0,1,0,2,1,Ex,5,Typ,1,Gd,Basment,1959,RFn,1,312,TA,TA,Y,0,0,0,0,163,0,NA,GdPrv,NA,0,5,2008,WD,Normal,177000 +1275,50,RL,53,5362,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,Crawfor,Norm,Norm,1Fam,1.5Fin,5,6,1910,2003,Gable,CompShg,Wd Sdng,Wd Shng,None,0,TA,TA,PConc,TA,TA,No,Unf,0,Unf,0,661,661,GasA,Ex,Y,SBrkr,661,589,0,1250,0,0,2,0,3,1,TA,8,Typ,1,Gd,Detchd,1985,Unf,2,552,TA,TA,Y,242,0,81,0,0,0,NA,NA,NA,0,11,2007,WD,Normal,139000 +1276,90,RL,95,11345,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NAmes,Feedr,Norm,Duplex,2Story,5,5,1948,1950,Gable,Roll,AsbShng,AsbShng,Stone,567,TA,TA,CBlock,TA,TA,No,Rec,220,Unf,0,708,928,GasA,Gd,Y,FuseA,928,992,0,1920,0,0,2,0,4,2,TA,10,Typ,0,NA,Detchd,1948,Unf,2,400,TA,Fa,Y,0,0,0,0,0,0,NA,NA,NA,0,7,2007,WD,Normal,137000 +1277,60,RL,NA,12936,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,NWAmes,Norm,Norm,1Fam,2Story,6,6,1972,1972,Gable,CompShg,HdBoard,Plywood,None,0,TA,TA,CBlock,TA,Gd,No,BLQ,593,Unf,0,130,723,GasA,TA,Y,SBrkr,735,660,0,1395,0,1,1,1,3,1,TA,6,Typ,1,TA,Attchd,1972,Unf,2,497,TA,TA,Y,294,116,0,0,0,0,NA,NA,NA,0,12,2009,WD,Normal,162000 +1278,80,RL,NA,17871,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,NWAmes,Norm,Norm,1Fam,SLvl,6,5,1967,1976,Gable,CompShg,HdBoard,HdBoard,BrkFace,359,TA,TA,CBlock,Gd,TA,Av,ALQ,528,Unf,0,1152,1680,GasA,Fa,Y,SBrkr,1724,0,0,1724,1,0,1,1,3,1,TA,7,Typ,1,Gd,Attchd,1967,RFn,2,480,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,197900 +1279,60,RL,75,9473,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,8,5,2002,2002,Gable,CompShg,VinylSd,VinylSd,NA,NA,Gd,TA,PConc,Gd,TA,No,GLQ,804,Unf,0,324,1128,GasA,Ex,Y,SBrkr,1128,903,0,2031,1,0,2,1,3,1,Gd,7,Typ,1,Gd,Attchd,2002,RFn,2,577,TA,TA,Y,0,211,0,0,0,0,NA,NA,NA,0,3,2008,WD,Normal,237000 +1280,50,C (all),60,7500,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,IDOTRR,Norm,Norm,1Fam,1.5Fin,4,4,1920,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,Gd,CBlock,TA,TA,No,Unf,0,Unf,0,698,698,GasA,TA,Y,FuseA,698,430,0,1128,0,0,1,0,2,1,TA,6,Typ,0,NA,Detchd,1980,RFn,2,528,TA,TA,Y,30,0,164,0,0,0,NA,NA,NA,0,4,2010,COD,Abnorml,68400 +1281,20,RL,67,9808,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2002,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,110,Gd,TA,PConc,Gd,TA,No,GLQ,788,Unf,0,785,1573,GasA,Ex,Y,SBrkr,1573,0,0,1573,1,0,2,0,3,1,Gd,6,Typ,0,NA,Attchd,2002,RFn,2,544,TA,TA,Y,0,72,0,0,0,0,NA,NA,NA,0,3,2009,WD,Normal,227000 +1282,20,RL,50,8049,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Timber,Norm,Norm,1Fam,1Story,7,5,1990,1990,Hip,CompShg,HdBoard,HdBoard,BrkFace,54,TA,TA,CBlock,Gd,TA,No,ALQ,1053,Unf,0,256,1309,GasA,TA,Y,SBrkr,1339,0,0,1339,1,0,2,0,2,1,TA,6,Typ,1,TA,Attchd,1990,Fin,2,484,Gd,Gd,Y,0,58,0,0,90,0,NA,NA,NA,0,7,2006,WD,Normal,180000 +1283,20,RL,61,8800,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,5,7,1977,2008,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,Gd,TA,Mn,LwQ,532,Rec,144,364,1040,GasA,TA,Y,SBrkr,1040,0,0,1040,0,0,2,0,3,1,Gd,5,Typ,0,NA,Detchd,1977,Unf,2,484,TA,TA,Y,0,0,0,0,288,0,NA,NA,NA,0,9,2009,WD,Normal,150500 +1284,90,RL,94,9400,Pave,NA,Reg,Low,AllPub,Corner,Gtl,Mitchel,Norm,Norm,Duplex,2Story,6,5,1971,1971,Mansard,CompShg,MetalSd,Wd Shng,None,0,TA,TA,CBlock,TA,TA,Av,Unf,0,Unf,0,912,912,GasA,TA,Y,SBrkr,912,912,0,1824,0,0,2,2,4,2,TA,8,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,128,0,0,0,0,0,NA,NA,NA,0,4,2010,WD,Normal,139000 +1285,50,RL,50,9638,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SWISU,Feedr,Norm,1Fam,1.5Fin,6,7,1919,1990,Gable,CompShg,Wd Sdng,Wd Shng,None,0,TA,TA,PConc,TA,TA,No,Unf,0,Unf,0,804,804,GasA,Ex,Y,SBrkr,1699,748,0,2447,0,0,2,0,4,1,Gd,10,Min2,1,Gd,Detchd,1969,Unf,1,336,TA,TA,Y,272,0,42,0,116,0,NA,NA,NA,0,3,2010,WD,Normal,169000 +1286,50,RM,50,6000,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,1.5Fin,6,6,1939,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,Fa,CBlock,TA,TA,No,Unf,0,Unf,0,780,780,GasA,Ex,Y,FuseF,825,587,0,1412,0,0,1,0,4,1,TA,6,Typ,1,Gd,Detchd,1939,Unf,1,280,TA,TA,Y,45,0,0,0,0,0,NA,NA,NA,0,5,2009,WD,Normal,132500 +1287,20,RL,NA,9790,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,Feedr,Norm,1Fam,1Story,6,5,1963,1963,Hip,CompShg,HdBoard,HdBoard,BrkFace,451,TA,TA,CBlock,TA,TA,No,ALQ,569,Rec,81,678,1328,GasA,TA,Y,SBrkr,1328,0,0,1328,1,0,1,1,3,1,TA,6,Typ,2,Gd,Attchd,1963,Unf,2,528,TA,TA,Y,0,26,0,0,0,0,NA,NA,NA,0,6,2010,WD,Normal,143000 +1288,20,RL,NA,36500,Pave,NA,IR1,Low,AllPub,Inside,Mod,ClearCr,Norm,Norm,1Fam,1Story,5,5,1964,1964,Gable,CompShg,Wd Sdng,Wd Sdng,BrkCmn,621,TA,Gd,CBlock,TA,TA,Av,Rec,812,Unf,0,812,1624,GasA,Fa,Y,SBrkr,1582,0,0,1582,0,1,2,0,4,1,TA,7,Typ,0,NA,Attchd,1964,Unf,2,390,TA,TA,N,168,198,0,0,0,0,NA,NA,NA,0,6,2006,WD,Normal,190000 +1289,120,RL,40,5664,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,StoneBr,Norm,Norm,TwnhsE,1Story,8,5,2000,2000,Gable,CompShg,CemntBd,CmentBd,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,1158,Unf,0,343,1501,GasA,Ex,Y,SBrkr,1659,0,0,1659,1,0,2,0,2,1,Ex,5,Typ,1,Ex,Attchd,2000,Fin,2,499,TA,TA,Y,212,59,0,0,0,0,NA,NA,NA,0,10,2009,WD,Normal,278000 +1290,60,RL,86,11065,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,1Fam,2Story,8,5,2006,2006,Gable,CompShg,VinylSd,VinylSd,Stone,788,Gd,TA,PConc,Gd,TA,Mn,Unf,0,Unf,0,1085,1085,GasA,Ex,Y,SBrkr,1120,850,0,1970,0,0,2,1,3,1,Ex,8,Typ,1,Gd,BuiltIn,2006,Fin,3,753,TA,TA,Y,177,74,0,0,0,0,NA,NA,NA,0,10,2006,New,Partial,281000 +1291,80,RL,NA,14112,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,1Fam,SLvl,5,7,1964,1964,Hip,CompShg,Wd Sdng,HdBoard,BrkFace,86,TA,TA,PConc,TA,TA,Av,GLQ,1014,Unf,0,138,1152,GasA,TA,Y,SBrkr,1152,0,0,1152,1,0,1,0,3,1,TA,6,Typ,1,Gd,Attchd,1964,RFn,2,484,TA,TA,Y,227,0,0,0,0,0,NA,NA,NA,0,4,2010,WD,Normal,180500 +1292,160,RM,21,1680,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrDale,Norm,Norm,Twnhs,2Story,5,7,1972,1972,Gable,CompShg,CemntBd,CmentBd,BrkFace,268,TA,TA,CBlock,TA,TA,No,ALQ,231,Unf,0,399,630,GasA,TA,Y,SBrkr,630,672,0,1302,0,0,2,1,3,1,TA,6,Typ,0,NA,Detchd,1972,Unf,1,264,TA,TA,Y,185,0,0,0,0,0,NA,NA,NA,0,2,2009,WD,Normal,119500 +1293,70,RM,60,6600,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,OldTown,Norm,Norm,1Fam,2Story,5,4,1892,1965,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,Stone,TA,TA,No,Unf,0,Unf,0,994,994,GasA,TA,N,SBrkr,1378,994,0,2372,0,0,2,0,4,2,TA,11,Min2,0,NA,Attchd,1985,RFn,1,432,TA,TA,Y,0,287,0,0,0,0,NA,NA,NA,0,12,2009,WD,Normal,107500 +1294,60,RL,78,10140,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,2Story,7,5,1976,1976,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,PConc,Gd,TA,No,GLQ,194,Unf,0,638,832,GasA,TA,Y,SBrkr,832,832,0,1664,0,0,2,1,4,1,TA,8,Typ,1,TA,Attchd,1976,RFn,2,528,TA,TA,Y,0,28,0,0,259,0,NA,GdWo,NA,0,3,2006,WD,Normal,162900 +1295,20,RL,60,8172,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1Story,5,7,1955,1990,Hip,CompShg,WdShing,Plywood,None,0,TA,TA,CBlock,TA,TA,No,Rec,167,Unf,0,697,864,GasA,TA,Y,SBrkr,864,0,0,864,1,0,1,0,2,1,TA,5,Typ,0,NA,Detchd,1957,Unf,2,572,TA,TA,N,0,0,0,0,0,0,NA,NA,NA,0,4,2006,WD,Normal,115000 +1296,20,RL,70,8400,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Feedr,Norm,1Fam,1Story,5,5,1968,1968,Hip,CompShg,HdBoard,HdBoard,BrkFace,168,TA,TA,CBlock,TA,TA,Av,BLQ,1016,Unf,0,36,1052,GasA,Gd,Y,SBrkr,1052,0,0,1052,1,0,1,1,3,1,TA,5,Typ,0,NA,Attchd,1968,RFn,1,288,TA,TA,Y,356,0,0,0,0,0,NA,GdWo,NA,0,11,2006,WD,Normal,138500 +1297,20,RL,80,8700,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,6,1963,1963,Hip,CompShg,MetalSd,MetalSd,BrkFace,148,TA,Gd,CBlock,TA,TA,Mn,ALQ,776,Unf,0,344,1120,GasA,Gd,Y,SBrkr,1128,0,0,1128,1,0,2,0,3,1,TA,6,Typ,0,NA,Attchd,1963,RFn,2,525,TA,TA,Y,192,20,123,0,0,0,NA,MnPrv,NA,0,12,2008,WD,Normal,155000 +1298,180,RM,35,3675,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,TwnhsE,SFoyer,6,5,2005,2006,Gable,CompShg,VinylSd,VinylSd,BrkFace,82,TA,TA,PConc,Gd,TA,Gd,GLQ,547,Unf,0,0,547,GasA,Gd,Y,SBrkr,1072,0,0,1072,1,0,2,0,2,1,TA,5,Typ,0,NA,Basment,2005,Fin,2,525,TA,TA,Y,0,44,0,0,0,0,NA,NA,NA,0,6,2006,New,Partial,140000 +1299,60,RL,313,63887,Pave,NA,IR3,Bnk,AllPub,Corner,Gtl,Edwards,Feedr,Norm,1Fam,2Story,10,5,2008,2008,Hip,ClyTile,Stucco,Stucco,Stone,796,Ex,TA,PConc,Ex,TA,Gd,GLQ,5644,Unf,0,466,6110,GasA,Ex,Y,SBrkr,4692,950,0,5642,2,0,2,1,3,1,Ex,12,Typ,3,Gd,Attchd,2008,Fin,2,1418,TA,TA,Y,214,292,0,0,0,480,Gd,NA,NA,0,1,2008,New,Partial,160000 +1300,20,RL,75,7500,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,7,1959,1994,Hip,CompShg,BrkFace,BrkFace,None,0,TA,TA,CBlock,TA,TA,No,LwQ,340,Rec,906,0,1246,GasA,Ex,Y,SBrkr,1246,0,0,1246,1,0,1,1,3,1,Gd,6,Typ,0,NA,Attchd,1959,RFn,1,305,TA,TA,Y,218,0,0,0,0,0,NA,GdPrv,NA,0,5,2010,WD,Normal,154000 +1301,60,RL,NA,10762,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Gilbert,Norm,Norm,1Fam,2Story,7,5,1999,1999,Gable,CompShg,VinylSd,VinylSd,None,344,Gd,TA,PConc,Gd,TA,No,GLQ,694,Unf,0,284,978,GasA,Ex,Y,SBrkr,1005,978,0,1983,0,0,2,1,3,1,Gd,9,Typ,1,TA,Attchd,1999,Fin,2,490,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,5,2009,WD,Normal,225000 +1302,70,RL,NA,7500,Pave,NA,IR1,Bnk,AllPub,Inside,Gtl,Crawfor,Norm,Norm,1Fam,2Story,6,7,1942,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,BLQ,547,Unf,0,224,771,GasA,Fa,Y,SBrkr,753,741,0,1494,0,0,1,0,3,1,Gd,7,Typ,2,Gd,Attchd,1942,Unf,1,213,TA,TA,P,0,0,0,0,224,0,NA,NA,NA,0,11,2009,WD,Normal,177500 +1303,60,RL,92,10120,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NoRidge,Norm,Norm,1Fam,2Story,8,5,1994,1994,Hip,CompShg,VinylSd,VinylSd,BrkFace,391,Gd,TA,PConc,Gd,TA,No,GLQ,740,Unf,0,425,1165,GasA,Ex,Y,SBrkr,1203,1323,0,2526,1,0,2,1,4,1,Gd,8,Typ,1,TA,Attchd,1994,RFn,3,844,TA,TA,Y,309,78,0,0,0,0,NA,NA,NA,0,12,2006,WD,Normal,290000 +1304,20,RL,73,8688,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,1Story,7,5,2005,2005,Gable,CompShg,VinylSd,VinylSd,BrkFace,228,Gd,TA,PConc,Gd,TA,Av,Unf,0,Unf,0,1616,1616,GasA,Ex,Y,SBrkr,1616,0,0,1616,0,0,2,0,3,1,Gd,7,Typ,0,NA,Attchd,2005,RFn,3,834,TA,TA,Y,208,59,0,0,0,0,NA,NA,NA,0,4,2006,WD,Normal,232000 +1305,160,RM,32,3363,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,TwnhsE,2Story,7,5,2004,2004,Gable,CompShg,VinylSd,VinylSd,Stone,117,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,976,976,GasA,Ex,Y,SBrkr,976,732,0,1708,0,0,2,0,3,1,Gd,7,Maj1,0,NA,Detchd,2004,Unf,2,380,TA,TA,Y,0,40,0,0,0,0,NA,NA,NA,0,4,2006,WD,Normal,130000 +1306,20,RL,108,13173,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NridgHt,Norm,Norm,1Fam,1Story,9,5,2006,2007,Hip,CompShg,VinylSd,VinylSd,Stone,300,Gd,TA,PConc,Ex,TA,No,GLQ,1572,Unf,0,80,1652,GasA,Ex,Y,SBrkr,1652,0,0,1652,1,0,2,0,2,1,Ex,6,Typ,2,Ex,Attchd,2006,Fin,2,840,TA,TA,Y,404,102,0,0,0,0,NA,NA,NA,0,11,2009,WD,Normal,325000 +1307,120,RL,48,6955,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NridgHt,Norm,Norm,TwnhsE,1Story,7,5,2005,2006,Gable,CompShg,VinylSd,VinylSd,Stone,94,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1368,1368,GasA,Ex,Y,SBrkr,1368,0,0,1368,0,0,2,0,2,1,Gd,6,Typ,1,Gd,Attchd,2005,RFn,2,474,TA,TA,Y,132,35,0,0,0,0,NA,NA,NA,0,9,2006,New,Partial,202500 +1308,20,RL,60,8072,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,5,5,1994,1995,Gable,CompShg,VinylSd,VinylSd,None,0,TA,Gd,PConc,Gd,Gd,No,ALQ,746,Unf,0,244,990,GasA,Ex,Y,SBrkr,990,0,0,990,1,0,1,0,3,1,TA,5,Typ,0,NA,Detchd,2000,Unf,2,480,TA,TA,Y,0,64,0,0,0,0,NA,NA,NA,0,5,2009,WD,Normal,138000 +1309,20,RM,100,12000,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,1Story,5,7,1948,2005,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,GLQ,144,ALQ,608,172,924,GasA,Ex,Y,SBrkr,1122,0,0,1122,1,0,1,0,2,1,Gd,6,Typ,0,NA,Attchd,1948,Unf,2,528,TA,TA,Y,0,36,0,0,0,0,NA,GdWo,NA,0,5,2008,WD,Normal,147000 +1310,20,RL,NA,7153,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SawyerW,Norm,Norm,1Fam,1Story,6,5,1991,1991,Gable,CompShg,HdBoard,HdBoard,BrkFace,88,TA,TA,CBlock,Gd,TA,No,GLQ,1200,Unf,0,78,1278,GasA,Gd,Y,SBrkr,1294,0,0,1294,1,0,2,0,3,1,Gd,6,Typ,0,NA,Attchd,1991,RFn,2,496,TA,TA,Y,112,51,0,0,0,0,NA,GdWo,NA,0,6,2008,WD,Normal,179200 +1311,20,RL,100,17500,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Crawfor,PosA,Norm,1Fam,1Story,7,8,1959,2002,Gable,CompShg,BrkFace,HdBoard,None,0,Gd,Gd,PConc,Gd,TA,Av,GLQ,1406,Unf,0,496,1902,GasA,TA,Y,SBrkr,1902,0,0,1902,1,0,2,0,3,1,Ex,7,Typ,2,TA,Attchd,1959,Fin,2,567,TA,TA,Y,0,207,162,0,0,0,NA,NA,NA,0,5,2010,WD,Normal,335000 +1312,20,RL,68,8814,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2005,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,80,Gd,TA,PConc,Gd,TA,No,GLQ,925,Unf,0,349,1274,GasA,Ex,Y,SBrkr,1274,0,0,1274,1,0,2,0,3,1,Gd,6,Typ,0,NA,Attchd,2005,RFn,2,508,TA,TA,Y,264,98,0,0,0,0,NA,NA,NA,0,1,2007,New,Partial,203000 +1313,60,RL,NA,9572,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NoRidge,Norm,Norm,1Fam,2Story,8,5,1990,1990,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,336,Gd,TA,PConc,Ex,TA,No,GLQ,482,Unf,0,971,1453,GasA,Ex,Y,SBrkr,1453,1357,0,2810,0,0,2,1,4,1,Gd,9,Typ,1,Ex,Attchd,1990,RFn,2,750,Gd,Gd,Y,500,0,0,0,0,0,NA,NA,NA,0,6,2007,WD,Normal,302000 +1314,60,RL,108,14774,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NoRidge,Norm,Norm,1Fam,2Story,9,5,1999,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,165,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1393,1393,GasA,Ex,Y,SBrkr,1422,1177,0,2599,0,0,2,1,4,1,Gd,10,Typ,1,TA,BuiltIn,1999,Fin,3,779,TA,TA,Y,668,30,0,0,0,0,NA,NA,NA,0,5,2010,WD,Normal,333168 +1315,20,RL,60,8190,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1Story,4,6,1954,1954,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,Rec,732,Unf,0,216,948,GasA,Ex,Y,SBrkr,948,0,0,948,1,0,1,0,3,1,TA,5,Typ,1,TA,Detchd,1956,Unf,1,280,TA,TA,Y,0,36,0,0,0,0,NA,NA,NA,0,10,2007,WD,Normal,119000 +1316,60,RL,85,11075,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,2Story,6,5,1969,1969,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,Fa,TA,Mn,ALQ,500,LwQ,276,176,952,GasA,TA,Y,SBrkr,1092,1020,0,2112,0,0,2,1,4,1,TA,9,Typ,2,Gd,Attchd,1969,Unf,2,576,TA,TA,Y,280,0,0,0,0,0,NA,NA,NA,0,6,2008,WD,Normal,206900 +1317,20,RL,61,10226,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,8,5,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,270,Gd,TA,PConc,Ex,TA,Gd,Unf,0,Unf,0,1622,1622,GasA,Ex,Y,SBrkr,1630,0,0,1630,1,0,2,0,3,1,Ex,8,Typ,1,Gd,Attchd,2008,RFn,3,860,TA,TA,Y,172,42,0,0,0,0,NA,NA,NA,0,1,2009,WD,Normal,295493 +1318,120,FV,47,4230,Pave,Pave,Reg,Lvl,AllPub,Corner,Gtl,Somerst,Norm,Norm,TwnhsE,1Story,7,5,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Ex,Gd,No,Unf,0,Unf,0,1352,1352,GasA,Ex,Y,SBrkr,1352,0,0,1352,0,0,2,0,2,1,Gd,5,Typ,1,Gd,Attchd,2006,RFn,2,466,TA,TA,Y,0,241,0,0,0,0,NA,NA,NA,0,4,2007,New,Partial,208900 +1319,20,RL,NA,14781,Pave,NA,IR2,Lvl,AllPub,CulDSac,Gtl,CollgCr,Norm,Norm,1Fam,1Story,8,5,2001,2002,Hip,CompShg,VinylSd,VinylSd,BrkFace,178,Gd,TA,PConc,Gd,TA,Gd,Unf,0,Unf,0,1753,1753,GasA,Ex,Y,SBrkr,1787,0,0,1787,0,0,2,0,3,1,Gd,7,Typ,1,TA,Attchd,2001,RFn,3,748,TA,TA,Y,198,150,0,0,0,0,NA,NA,NA,0,8,2006,WD,Normal,275000 +1320,20,RL,75,10215,Pave,NA,Reg,Bnk,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1Story,4,5,1954,1954,Hip,CompShg,Wd Sdng,Wd Sdng,BrkFace,132,TA,TA,PConc,TA,TA,No,ALQ,492,Unf,0,372,864,GasA,Ex,Y,SBrkr,948,0,0,948,0,0,1,0,3,1,TA,5,Typ,0,NA,Attchd,1954,Unf,1,248,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,2,2007,WD,Normal,111000 +1321,20,RL,70,8400,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,3,1957,1957,Hip,CompShg,BrkFace,BrkFace,None,0,TA,TA,CBlock,TA,TA,No,ALQ,189,Rec,661,628,1478,GasA,Gd,Y,SBrkr,1478,0,0,1478,1,0,1,1,3,1,TA,6,Typ,2,Gd,Attchd,1957,RFn,2,442,TA,TA,Y,114,0,0,0,216,0,NA,NA,NA,0,6,2009,WD,Normal,156500 +1322,20,RL,NA,6627,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,BrkSide,Feedr,Norm,1Fam,1Story,3,6,1949,1950,Hip,CompShg,VinylSd,VinylSd,None,0,TA,TA,CBlock,NA,NA,NA,NA,0,NA,0,0,0,Floor,TA,N,SBrkr,720,0,0,720,0,0,1,0,2,1,TA,4,Typ,0,NA,Detchd,1955,Unf,1,287,TA,Fa,Y,0,0,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,72500 +1323,60,RL,107,10186,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NoRidge,Norm,Norm,1Fam,2Story,7,5,1992,1992,Gable,CompShg,HdBoard,HdBoard,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,674,Unf,0,76,750,GasA,Ex,Y,SBrkr,1061,862,0,1923,1,0,2,1,3,1,Gd,8,Typ,1,TA,Attchd,1992,RFn,2,564,TA,TA,Y,240,39,0,0,0,0,NA,NA,NA,0,6,2010,WD,Normal,190000 +1324,30,RL,50,5330,Pave,NA,Reg,HLS,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,1Story,4,7,1940,1950,Hip,CompShg,VinylSd,VinylSd,None,0,Fa,TA,CBlock,TA,TA,No,LwQ,280,Unf,0,140,420,GasA,Gd,Y,SBrkr,708,0,0,708,0,0,1,0,2,1,Fa,5,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,164,0,0,0,0,0,NA,NA,NA,0,12,2009,WD,Normal,82500 +1325,20,RL,75,9986,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,1Story,8,5,2006,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,428,Gd,TA,PConc,Ex,TA,Av,Unf,0,Unf,0,1795,1795,GasA,Ex,Y,SBrkr,1795,0,0,1795,0,0,2,0,2,1,Gd,7,Typ,1,Gd,Attchd,2007,RFn,3,895,TA,TA,Y,0,49,0,0,0,0,NA,NA,NA,0,2,2007,New,Partial,147000 +1326,30,RM,40,3636,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,IDOTRR,Norm,Norm,1Fam,1Story,4,4,1922,1950,Gable,CompShg,AsbShng,AsbShng,None,0,TA,TA,BrkTil,TA,Fa,No,Unf,0,Unf,0,796,796,GasA,Fa,N,SBrkr,796,0,0,796,0,0,1,0,2,1,TA,5,Typ,0,NA,NA,NA,NA,0,0,NA,NA,N,0,0,100,0,0,0,NA,MnPrv,NA,0,1,2008,WD,Normal,55000 +1327,30,RH,70,4270,Pave,NA,Reg,Bnk,AllPub,Inside,Mod,Edwards,Norm,Norm,1Fam,1Story,3,6,1931,2006,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,BrkTil,TA,TA,No,Rec,544,Unf,0,0,544,GasA,Ex,Y,SBrkr,774,0,0,774,0,0,1,0,3,1,Gd,6,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,0,0,286,0,0,0,NA,NA,NA,0,5,2007,WD,Normal,79000 +1328,20,RL,60,6600,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Mitchel,Norm,Norm,1Fam,1Story,5,9,1982,2008,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,Gd,CBlock,TA,TA,No,ALQ,641,Unf,0,175,816,GasA,Ex,Y,SBrkr,816,0,0,816,0,1,1,0,3,1,Gd,5,Typ,1,Ex,Attchd,1982,Unf,1,264,TA,TA,Y,0,0,0,0,0,0,NA,MnPrv,NA,0,10,2008,WD,Normal,130500 +1329,50,RM,60,10440,Pave,Grvl,Reg,Lvl,AllPub,Corner,Gtl,OldTown,Norm,Norm,1Fam,1.5Fin,6,7,1920,1950,Gable,CompShg,BrkFace,Wd Sdng,None,0,Gd,Gd,BrkTil,Gd,TA,No,LwQ,493,Unf,0,1017,1510,GasW,Ex,Y,SBrkr,1584,1208,0,2792,0,0,2,0,5,1,TA,8,Mod,2,TA,Detchd,1920,Unf,2,520,Fa,TA,Y,0,547,0,0,480,0,NA,MnPrv,Shed,1150,6,2008,WD,Normal,256000 +1330,60,RL,63,9084,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,2Story,7,5,1998,1998,Hip,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,No,Unf,0,Unf,0,935,935,GasA,Gd,Y,SBrkr,955,677,0,1632,0,0,2,1,3,1,TA,8,Typ,1,TA,Attchd,1998,Fin,2,462,TA,TA,Y,0,28,0,0,0,0,NA,NA,NA,0,6,2006,WD,Normal,176500 +1331,20,RL,85,10000,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,1Story,8,5,2006,2006,Hip,CompShg,VinylSd,VinylSd,Stone,410,Gd,TA,PConc,Gd,Gd,Av,Unf,0,Unf,0,1588,1588,GasA,Ex,Y,SBrkr,1588,0,0,1588,0,0,2,0,3,1,Gd,7,Typ,1,Gd,Attchd,2006,RFn,3,825,TA,TA,Y,144,45,0,0,0,0,NA,NA,NA,0,12,2007,WD,Normal,227000 +1332,80,RL,55,10780,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,SLvl,5,5,1976,1976,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,Av,ALQ,483,Unf,0,428,911,GasA,Gd,Y,SBrkr,954,0,0,954,0,0,1,0,3,1,TA,6,Typ,0,NA,Detchd,1976,Unf,2,576,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,132500 +1333,20,RL,67,8877,Pave,NA,Reg,Lvl,AllPub,Inside,Mod,Edwards,Norm,Norm,1Fam,1Story,4,6,1938,1958,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,Mn,ALQ,690,Unf,0,126,816,GasA,Ex,Y,SBrkr,816,0,0,816,1,0,1,0,2,1,TA,3,Typ,1,Gd,Detchd,1958,Unf,1,288,Fa,Fa,Y,0,0,0,0,0,0,NA,NA,NA,0,5,2009,WD,Normal,100000 +1334,50,RM,60,7200,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,IDOTRR,Norm,Norm,1Fam,1.5Fin,5,6,1938,1995,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,803,803,GasA,Ex,Y,SBrkr,803,557,0,1360,0,0,1,1,2,1,Gd,6,Typ,0,NA,Detchd,1951,Unf,1,297,TA,TA,Y,0,65,190,0,0,0,NA,MnPrv,NA,0,7,2006,WD,Normal,125500 +1335,160,RM,24,2368,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrDale,Norm,Norm,TwnhsE,2Story,5,6,1970,1970,Gable,CompShg,HdBoard,HdBoard,None,312,TA,TA,CBlock,TA,TA,No,LwQ,765,Unf,0,0,765,GasA,TA,Y,SBrkr,765,600,0,1365,0,0,1,1,3,1,TA,7,Min1,0,NA,Attchd,1970,Unf,2,440,TA,TA,Y,0,36,0,0,0,0,NA,NA,NA,0,5,2009,WD,Normal,125000 +1336,20,RL,80,9650,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,1Story,6,5,1977,1977,Gable,CompShg,Plywood,Plywood,BrkFace,360,TA,TA,CBlock,Gd,TA,No,ALQ,686,Unf,0,664,1350,GasA,TA,Y,SBrkr,1334,0,0,1334,0,1,2,0,2,1,TA,6,Typ,1,TA,Attchd,1977,RFn,2,630,TA,TA,Y,0,16,0,0,0,0,NA,NA,NA,0,4,2009,WD,Normal,167900 +1337,90,RL,87,9246,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NWAmes,Feedr,Norm,Duplex,1Story,5,5,1973,1973,Gable,CompShg,Plywood,Plywood,BrkFace,564,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,1656,1656,GasA,TA,Y,SBrkr,1656,0,0,1656,0,0,2,0,4,2,TA,8,Typ,0,NA,Detchd,1973,Unf,2,506,TA,TA,Y,0,211,0,0,0,0,NA,NA,NA,0,11,2008,WD,Normal,135000 +1338,30,RM,153,4118,Pave,Grvl,IR1,Bnk,AllPub,Corner,Mod,OldTown,Feedr,Norm,1Fam,1Story,4,4,1941,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,693,693,Grav,Fa,N,FuseA,693,0,0,693,0,0,1,0,2,1,Fa,4,Typ,0,NA,NA,NA,NA,0,0,NA,NA,N,0,20,0,0,0,0,NA,NA,NA,0,3,2006,WD,Normal,52500 +1339,60,RL,95,13450,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,CollgCr,Norm,Norm,1Fam,2Story,7,5,2002,2002,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,700,Unf,0,216,916,GasA,Ex,Y,SBrkr,920,941,0,1861,1,0,2,1,3,1,Gd,8,Typ,0,NA,BuiltIn,2002,RFn,2,492,TA,TA,Y,146,91,0,0,0,0,NA,NA,NA,0,6,2006,WD,Normal,200000 +1340,20,RL,120,9560,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,CollgCr,Norm,Norm,1Fam,1Story,5,7,1972,1972,Hip,CompShg,MetalSd,MetalSd,None,0,TA,Gd,CBlock,TA,TA,Mn,Rec,360,Unf,0,504,864,GasA,Ex,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,TA,5,Typ,0,NA,Attchd,1972,RFn,1,288,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,6,2006,WD,Normal,128500 +1341,20,RL,70,8294,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,4,5,1971,1971,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,858,858,GasA,TA,Y,SBrkr,872,0,0,872,0,0,1,0,3,1,TA,5,Typ,0,NA,Detchd,1974,Unf,4,480,TA,TA,Y,0,0,0,0,0,0,NA,GdWo,NA,0,6,2007,WD,Normal,123000 +1342,20,RL,66,13695,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SawyerW,RRAe,Norm,1Fam,1Story,6,5,2003,2004,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,No,GLQ,814,Unf,0,300,1114,GasA,Ex,Y,SBrkr,1114,0,0,1114,1,0,1,0,3,1,Gd,6,Typ,0,NA,Detchd,2004,Unf,2,576,TA,TA,Y,0,78,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,155000 +1343,60,RL,NA,9375,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,8,5,2002,2002,Gable,CompShg,VinylSd,VinylSd,BrkFace,149,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1284,1284,GasA,Ex,Y,SBrkr,1284,885,0,2169,0,0,2,1,3,1,Gd,7,Typ,1,Gd,Attchd,2002,RFn,2,647,TA,TA,Y,192,87,0,0,0,0,NA,NA,NA,0,8,2007,WD,Normal,228500 +1344,50,RL,57,7558,Pave,NA,Reg,Bnk,AllPub,Inside,Gtl,Crawfor,Norm,Norm,1Fam,1.5Fin,6,6,1928,1950,Gable,CompShg,BrkFace,Stone,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,896,896,GasA,Gd,Y,SBrkr,1172,741,0,1913,0,0,1,1,3,1,TA,9,Typ,1,TA,Detchd,1929,Unf,2,342,Fa,Fa,Y,0,0,0,0,0,0,NA,NA,NA,0,3,2009,WD,Normal,177000 +1345,60,RL,85,11103,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,CollgCr,Norm,Norm,1Fam,2Story,7,5,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,728,728,GasA,Ex,Y,SBrkr,728,728,0,1456,0,0,2,1,3,1,Gd,8,Typ,1,TA,Attchd,2006,Fin,2,440,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,7,2007,New,Partial,155835 +1346,30,RM,50,6000,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,1Story,4,4,1920,1950,Hip,CompShg,MetalSd,MetalSd,None,0,TA,TA,PConc,TA,TA,No,ALQ,250,Unf,0,710,960,GasA,Gd,Y,FuseA,960,0,0,960,0,0,1,0,2,1,Fa,5,Typ,0,NA,Detchd,1997,Unf,1,308,TA,TA,Y,0,0,168,0,0,0,NA,NA,NA,0,7,2007,WD,Normal,108500 +1347,20,RL,NA,20781,Pave,NA,IR2,Lvl,AllPub,CulDSac,Gtl,NWAmes,PosN,Norm,1Fam,1Story,7,7,1968,2003,Hip,CompShg,BrkFace,HdBoard,None,0,TA,TA,CBlock,TA,TA,No,BLQ,297,Rec,68,1203,1568,GasA,TA,Y,SBrkr,2156,0,0,2156,0,0,2,0,3,1,TA,9,Typ,1,Gd,Attchd,1968,RFn,2,508,Gd,TA,Y,0,80,0,290,0,0,NA,NA,NA,0,6,2006,WD,Normal,262500 +1348,20,RL,93,15306,Pave,NA,IR1,HLS,AllPub,Corner,Gtl,Timber,Norm,Norm,1Fam,1Story,8,5,2006,2007,Gable,CompShg,VinylSd,VinylSd,Stone,100,Gd,TA,PConc,Ex,TA,Gd,GLQ,80,Unf,0,1652,1732,GasA,Ex,Y,SBrkr,1776,0,0,1776,1,0,2,0,3,1,Gd,7,Typ,1,Gd,Attchd,2006,Fin,3,712,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,5,2007,New,Partial,283463 +1349,20,RL,NA,16196,Pave,NA,IR3,Low,AllPub,Inside,Gtl,SawyerW,Norm,Norm,1Fam,1Story,7,5,1998,1998,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,Gd,GLQ,1443,Unf,0,39,1482,GasA,Ex,Y,SBrkr,1494,0,0,1494,1,0,2,0,3,1,Gd,5,Typ,1,Fa,Attchd,1998,RFn,2,514,TA,TA,Y,402,25,0,0,0,0,NA,NA,NA,0,8,2007,WD,Normal,215000 +1350,70,RM,50,5250,Pave,Pave,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,2Story,8,5,1872,1987,Gable,CompShg,MetalSd,MetalSd,None,0,TA,Gd,BrkTil,TA,Fa,No,LwQ,259,Unf,0,425,684,OthW,Fa,N,SBrkr,938,1215,205,2358,0,0,2,0,4,1,TA,8,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,0,54,20,0,0,0,NA,NA,NA,0,12,2008,WD,Normal,122000 +1351,90,RL,91,11643,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Artery,Norm,Duplex,2Story,5,5,1969,1969,Gable,CompShg,MetalSd,MetalSd,BrkFace,368,TA,TA,CBlock,TA,TA,No,LwQ,500,Unf,0,748,1248,GasA,TA,Y,SBrkr,1338,1296,0,2634,1,1,2,2,6,2,TA,12,Typ,0,NA,Detchd,1969,Unf,4,968,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,8,2009,WD,Normal,200000 +1352,60,RL,70,9247,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,2Story,6,6,1962,1962,Gable,CompShg,HdBoard,HdBoard,BrkFace,318,TA,TA,CBlock,TA,TA,No,Rec,319,Unf,0,539,858,GasA,Ex,Y,SBrkr,858,858,0,1716,0,0,1,1,4,1,TA,8,Typ,1,Gd,Attchd,1962,Fin,2,490,TA,TA,Y,0,84,0,0,120,0,NA,NA,NA,0,3,2008,WD,Normal,171000 +1353,50,RM,50,6000,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,1.5Fin,6,9,1937,2000,Gable,CompShg,MetalSd,MetalSd,None,0,Gd,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,698,698,GasA,TA,Y,SBrkr,786,390,0,1176,0,0,1,0,2,1,TA,4,Typ,0,NA,Detchd,1999,Unf,2,624,TA,TA,N,210,0,0,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,134900 +1354,50,RL,56,14720,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,NoRidge,Norm,Norm,1Fam,1.5Fin,8,5,1995,1996,Hip,CompShg,VinylSd,VinylSd,BrkFace,579,Gd,TA,PConc,Gd,TA,Av,GLQ,816,Unf,0,1217,2033,GasA,Ex,Y,SBrkr,2053,1185,0,3238,1,0,2,1,4,1,Gd,9,Typ,1,Ex,Attchd,1996,Fin,3,666,TA,TA,Y,283,86,0,0,0,0,NA,NA,NA,0,3,2010,WD,Normal,410000 +1355,60,RL,NA,10316,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,7,5,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,735,Unf,0,257,992,GasA,Ex,Y,SBrkr,992,873,0,1865,1,0,2,1,3,1,Gd,7,Typ,1,TA,Attchd,2000,RFn,3,839,TA,TA,Y,0,184,0,0,0,0,NA,NA,NA,0,6,2008,WD,Normal,235000 +1356,80,RL,102,10192,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,SLvl,7,6,1968,1992,Gable,CompShg,MetalSd,MetalSd,BrkFace,143,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,570,570,GasA,Gd,Y,SBrkr,1222,698,0,1920,0,0,3,0,4,1,Gd,8,Typ,1,TA,Attchd,1968,RFn,2,487,TA,TA,Y,0,98,0,0,0,0,NA,GdPrv,NA,0,9,2006,WD,Normal,170000 +1357,20,RL,NA,9477,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,5,1966,1966,Gable,CompShg,HdBoard,HdBoard,BrkFace,65,TA,TA,CBlock,TA,TA,No,Rec,340,Unf,0,524,864,GasA,TA,Y,SBrkr,892,0,0,892,0,0,1,0,3,1,TA,5,Typ,0,NA,Attchd,1966,RFn,1,264,TA,TA,Y,0,0,0,0,0,0,NA,GdWo,NA,0,10,2008,WD,Normal,110000 +1358,20,RL,NA,12537,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,6,1971,2008,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,CBlock,TA,TA,No,GLQ,734,Unf,0,344,1078,GasA,Ex,Y,SBrkr,1078,0,0,1078,1,0,1,1,3,1,TA,6,Typ,1,Fa,Attchd,1971,Fin,2,500,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,4,2010,WD,Normal,149900 +1359,160,FV,NA,2117,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,Twnhs,2Story,6,5,2000,2000,Gable,CompShg,MetalSd,MetalSd,BrkFace,216,Gd,TA,PConc,Gd,TA,No,GLQ,378,Unf,0,378,756,GasA,Ex,Y,SBrkr,769,804,0,1573,0,0,2,1,3,1,Gd,5,Typ,0,NA,Detchd,2000,Unf,2,440,TA,TA,Y,0,32,0,0,0,0,NA,NA,NA,0,6,2010,WD,Normal,177500 +1360,20,RL,129,16737,Pave,NA,Reg,Lvl,AllPub,FR3,Gtl,NridgHt,Norm,Norm,1Fam,1Story,9,5,2004,2005,Hip,CompShg,VinylSd,VinylSd,BrkFace,66,Gd,TA,PConc,Ex,TA,Av,GLQ,1447,Unf,0,533,1980,GasA,Ex,Y,SBrkr,1980,0,0,1980,1,0,2,0,3,1,Ex,8,Typ,1,Gd,Attchd,2004,Fin,3,770,TA,TA,Y,194,45,0,0,0,0,NA,NA,NA,0,9,2006,WD,Normal,315000 +1361,70,RL,51,9842,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SWISU,Feedr,Norm,1Fam,2Story,5,6,1921,1998,Gable,CompShg,MetalSd,Wd Sdng,None,0,TA,TA,BrkTil,TA,Fa,No,Unf,0,Unf,0,612,612,GasA,Ex,Y,SBrkr,990,1611,0,2601,0,0,3,1,4,1,TA,8,Typ,0,NA,BuiltIn,1998,RFn,2,621,TA,TA,Y,183,0,301,0,0,0,NA,NA,NA,0,5,2008,WD,Normal,189000 +1362,20,RL,124,16158,Pave,NA,IR1,Low,AllPub,Inside,Mod,StoneBr,Norm,Norm,1Fam,1Story,7,5,2005,2005,Hip,CompShg,VinylSd,VinylSd,Stone,16,Gd,TA,PConc,Ex,TA,Av,ALQ,1274,Unf,0,256,1530,GasA,Ex,Y,SBrkr,1530,0,0,1530,1,0,2,0,3,1,Gd,7,Typ,1,Gd,Attchd,2005,Fin,2,430,TA,TA,Y,168,36,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,260000 +1363,50,RL,NA,12513,Pave,NA,IR1,Lvl,AllPub,FR2,Gtl,NAmes,Feedr,Norm,1Fam,1.5Fin,4,4,1920,2007,Gable,CompShg,VinylSd,VinylSd,None,0,TA,Gd,BrkTil,TA,Fa,No,Unf,0,Unf,0,715,715,GasA,Gd,Y,SBrkr,1281,457,0,1738,0,0,2,0,4,1,TA,7,Typ,1,Gd,Attchd,1920,Unf,1,368,TA,TA,Y,55,0,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,104900 +1364,60,RL,73,8499,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,2Story,6,5,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,No,Unf,0,Unf,0,616,616,GasA,Ex,Y,SBrkr,616,796,0,1412,0,0,2,1,3,1,Gd,6,Typ,1,Gd,BuiltIn,2007,Fin,2,432,TA,TA,Y,0,36,0,0,0,0,NA,NA,NA,0,3,2007,New,Partial,156932 +1365,160,FV,30,3180,Pave,Pave,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,TwnhsE,2Story,7,5,2005,2005,Gable,CompShg,MetalSd,MetalSd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,600,600,GasA,Ex,Y,SBrkr,520,600,80,1200,0,0,2,1,2,1,Gd,4,Typ,0,NA,Detchd,2005,RFn,2,480,TA,TA,Y,0,166,0,0,0,0,NA,NA,NA,0,4,2006,WD,Abnorml,144152 +1366,60,FV,NA,7500,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,2Story,7,5,2000,2000,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,533,Unf,0,281,814,GasA,Ex,Y,SBrkr,814,860,0,1674,1,0,2,1,3,1,Gd,7,Typ,0,NA,Attchd,2000,RFn,2,663,TA,TA,Y,0,96,0,0,0,0,NA,NA,NA,0,1,2010,WD,Normal,216000 +1367,60,RL,68,9179,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,7,5,1999,1999,Gable,CompShg,VinylSd,VinylSd,BrkFace,158,Gd,TA,PConc,Gd,TA,No,GLQ,633,Unf,0,240,873,GasA,Ex,Y,SBrkr,882,908,0,1790,1,0,2,1,3,1,Gd,7,Typ,0,NA,Attchd,1999,RFn,2,588,TA,TA,Y,0,88,0,0,0,0,NA,NA,NA,0,6,2008,WD,Abnorml,193000 +1368,160,RM,41,2665,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,MeadowV,Norm,Norm,TwnhsE,2Story,5,6,1977,1977,Gable,CompShg,CemntBd,CmentBd,None,0,TA,TA,PConc,TA,TA,No,ALQ,548,Rec,173,36,757,GasA,Ex,Y,SBrkr,925,550,0,1475,0,0,2,0,4,1,TA,6,Typ,1,TA,Attchd,1977,RFn,1,336,TA,TA,Y,104,26,0,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,127000 +1369,120,RM,NA,4435,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,TwnhsE,1Story,6,5,2003,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,170,Gd,TA,PConc,Gd,TA,Av,GLQ,685,Unf,0,163,848,GasA,Ex,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Gd,4,Typ,0,NA,Attchd,2003,Fin,2,420,TA,TA,Y,140,0,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,144000 +1370,20,RL,48,10635,Pave,NA,IR2,Lvl,AllPub,FR2,Gtl,CollgCr,Norm,Norm,1Fam,1Story,8,5,2003,2003,Hip,CompShg,VinylSd,VinylSd,BrkFace,171,Gd,TA,PConc,Gd,TA,Av,BLQ,370,GLQ,972,315,1657,GasA,Ex,Y,SBrkr,1668,0,0,1668,1,0,2,0,3,1,Gd,8,Typ,1,TA,Attchd,2003,Fin,2,502,TA,TA,Y,0,262,0,0,0,0,NA,NA,NA,0,5,2010,WD,Normal,232000 +1371,50,RL,90,5400,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,OldTown,Artery,Norm,1Fam,1.5Fin,4,6,1920,1950,Gable,CompShg,CBlock,CBlock,None,0,Fa,TA,PConc,TA,TA,No,ALQ,315,Rec,105,420,840,GasA,Ex,Y,SBrkr,840,534,0,1374,0,0,1,0,2,1,TA,6,Typ,0,NA,Detchd,1967,Fin,1,338,TA,TA,Y,0,0,198,0,0,0,NA,NA,NA,0,10,2009,WD,Normal,105000 +1372,80,RL,80,9600,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,SLvl,6,6,1955,1996,Hip,CompShg,AsbShng,AsbShng,None,0,TA,TA,CBlock,TA,TA,Av,BLQ,831,Unf,0,161,992,GasA,Gd,Y,SBrkr,1661,0,0,1661,1,0,1,0,3,1,Gd,8,Typ,1,TA,BuiltIn,1955,RFn,1,377,TA,TA,Y,0,28,0,0,178,0,NA,MnPrv,NA,0,10,2008,WD,Normal,165500 +1373,60,RL,75,9750,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,CollgCr,Norm,Norm,1Fam,2Story,7,6,1998,1998,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,Av,GLQ,975,Unf,0,133,1108,GasA,Ex,Y,SBrkr,1108,989,0,2097,1,0,2,1,3,1,Gd,8,Typ,1,TA,Detchd,1998,RFn,2,583,TA,TA,Y,253,170,0,0,0,0,NA,NA,NA,0,6,2006,WD,Normal,274300 +1374,20,RL,NA,11400,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NoRidge,Norm,Norm,1Fam,1Story,10,5,2001,2002,Hip,CompShg,VinylSd,VinylSd,BrkFace,705,Ex,TA,PConc,Ex,TA,Gd,GLQ,1282,Unf,0,1351,2633,GasA,Ex,Y,SBrkr,2633,0,0,2633,1,0,2,1,2,1,Ex,8,Typ,2,Gd,Attchd,2001,RFn,3,804,TA,TA,Y,314,140,0,0,0,0,NA,NA,NA,0,3,2007,WD,Normal,466500 +1375,60,FV,85,10625,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,2Story,7,5,2005,2005,Gable,CompShg,CemntBd,CmentBd,None,0,Gd,TA,PConc,Gd,TA,No,Unf,0,Unf,0,1026,1026,GasA,Ex,Y,SBrkr,1026,932,0,1958,0,0,2,1,3,1,Gd,9,Typ,1,Gd,Attchd,2005,Fin,3,936,TA,TA,Y,154,210,0,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,250000 +1376,20,RL,89,10991,Pave,NA,IR1,HLS,AllPub,Inside,Gtl,Timber,Norm,Norm,1Fam,1Story,8,5,2007,2007,Gable,CompShg,VinylSd,VinylSd,BrkFace,80,Gd,TA,PConc,Gd,TA,Gd,Unf,0,Unf,0,1571,1571,GasA,Ex,Y,SBrkr,1571,0,0,1571,0,0,2,0,3,1,Gd,7,Typ,1,Gd,Attchd,2007,Fin,3,722,TA,TA,Y,100,36,0,0,0,0,NA,NA,NA,0,12,2007,New,Partial,239000 +1377,30,RL,52,6292,Pave,NA,Reg,Bnk,AllPub,Inside,Gtl,SWISU,Norm,Norm,1Fam,1Story,6,5,1930,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,Gd,TA,Mn,Rec,384,Unf,0,384,768,GasA,TA,N,SBrkr,790,0,0,790,0,0,1,0,2,1,TA,4,Typ,0,NA,Detchd,1925,Unf,1,160,Fa,TA,Y,0,141,0,0,0,0,NA,NA,NA,0,4,2008,WD,Normal,91000 +1378,50,RL,60,10998,Pave,Grvl,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1.5Fin,5,5,1941,1960,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,LwQ,408,BLQ,420,156,984,GasA,Ex,Y,SBrkr,984,620,0,1604,0,0,2,0,3,1,TA,6,Min2,0,NA,Detchd,1977,Unf,2,660,TA,TA,Y,0,68,0,0,0,0,NA,NA,NA,0,7,2009,WD,Normal,117000 +1379,160,RM,21,1953,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrDale,Norm,Norm,Twnhs,2Story,6,5,1973,1973,Gable,CompShg,HdBoard,HdBoard,BrkFace,408,TA,TA,CBlock,TA,Fa,No,BLQ,309,Unf,0,174,483,GasA,TA,Y,SBrkr,483,504,0,987,0,0,1,1,2,1,TA,5,Typ,0,NA,Detchd,1973,Unf,1,264,TA,TA,Y,72,0,0,0,0,0,NA,NA,NA,0,6,2006,WD,Normal,83000 +1380,80,RL,73,9735,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Timber,Norm,Norm,1Fam,SLvl,5,5,2006,2007,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,Gd,TA,No,Unf,0,Unf,0,384,384,GasA,Gd,Y,NA,754,640,0,1394,0,0,2,1,3,1,Gd,7,Typ,0,NA,BuiltIn,2007,Fin,2,400,TA,TA,Y,100,0,0,0,0,0,NA,NA,NA,0,5,2008,WD,Normal,167500 +1381,30,RL,45,8212,Pave,Grvl,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1Story,3,3,1914,1950,Gable,CompShg,Stucco,Stucco,None,0,TA,Fa,BrkTil,TA,Fa,No,Rec,203,Unf,0,661,864,GasA,TA,N,FuseF,864,0,0,864,1,0,1,0,2,1,TA,5,Typ,0,NA,Detchd,1938,Unf,1,200,TA,Fa,Y,0,0,96,0,0,0,NA,NA,NA,0,6,2010,WD,Normal,58500 +1382,20,RL,NA,12925,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,7,1970,1970,Gable,CompShg,BrkFace,Plywood,None,0,TA,TA,CBlock,TA,TA,Mn,BLQ,865,Unf,0,340,1205,GasA,Ex,Y,SBrkr,2117,0,0,2117,0,0,2,1,4,1,TA,7,Typ,2,Gd,Attchd,1970,Fin,2,550,TA,TA,Y,0,42,0,0,0,0,NA,NA,NA,0,5,2008,WD,Normal,237500 +1383,70,RM,60,7200,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,OldTown,Norm,Norm,1Fam,2Story,7,7,1920,1950,Hip,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,Fa,TA,No,Unf,0,Unf,0,596,596,GasA,Ex,Y,SBrkr,998,764,0,1762,1,0,1,1,4,1,Gd,8,Typ,0,NA,Detchd,1989,Unf,2,576,TA,TA,N,36,0,221,0,0,0,NA,NA,NA,0,10,2006,WD,Normal,157000 +1384,30,RL,NA,25339,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,1Story,5,7,1918,2007,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,Gd,BrkTil,TA,TA,No,Unf,0,Unf,0,816,816,GasA,Ex,Y,SBrkr,1416,0,0,1416,0,0,2,0,3,1,Gd,7,Typ,0,NA,Attchd,2007,Unf,2,576,TA,TA,N,0,0,112,0,0,0,NA,NA,NA,0,8,2007,WD,Normal,112000 +1385,50,RL,60,9060,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1.5Fin,6,5,1939,1950,Gable,CompShg,WdShing,Wd Shng,None,0,TA,TA,BrkTil,TA,TA,Mn,Rec,204,Unf,0,356,560,GasA,TA,Y,SBrkr,698,560,0,1258,0,0,1,0,2,1,TA,6,Typ,0,NA,Detchd,1939,Unf,1,280,TA,TA,P,0,0,0,0,0,0,NA,MnPrv,NA,0,10,2009,WD,Normal,105000 +1386,50,RM,40,5436,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,IDOTRR,Norm,Norm,1Fam,1.5Fin,4,8,1922,2007,Gable,CompShg,VinylSd,VinylSd,None,0,TA,Gd,BrkTil,TA,TA,No,BLQ,735,Unf,0,61,796,GasA,Gd,Y,SBrkr,796,358,0,1154,1,0,1,0,3,1,Gd,7,Typ,0,NA,Detchd,1922,Unf,1,240,TA,TA,N,0,96,0,0,0,0,NA,MnPrv,NA,0,5,2010,WD,Normal,125500 +1387,60,RL,80,16692,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NWAmes,RRAn,Norm,1Fam,2Story,7,5,1978,1978,Gable,CompShg,Plywood,Plywood,BrkFace,184,TA,TA,CBlock,Gd,TA,No,BLQ,790,LwQ,469,133,1392,GasA,TA,Y,SBrkr,1392,1392,0,2784,1,0,3,1,5,1,Gd,12,Typ,2,TA,Attchd,1978,RFn,2,564,TA,TA,Y,0,112,0,0,440,519,Fa,MnPrv,TenC,2000,7,2006,WD,Normal,250000 +1388,50,RM,60,8520,Pave,Grvl,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Artery,Norm,1Fam,1.5Fin,6,7,1916,1950,Gable,CompShg,Stucco,Stucco,None,0,TA,Gd,BrkTil,TA,TA,No,Rec,168,LwQ,546,0,714,GasW,TA,N,SBrkr,1664,862,0,2526,0,0,2,0,5,1,Gd,10,Typ,1,Gd,Detchd,1916,Unf,1,216,TA,TA,Y,88,15,0,0,0,0,NA,GdWo,NA,0,8,2007,CWD,Family,136000 +1389,20,RL,42,14892,Pave,NA,IR1,HLS,AllPub,CulDSac,Gtl,Gilbert,Norm,Norm,1Fam,1Story,9,5,2006,2007,Gable,CompShg,VinylSd,VinylSd,Stone,160,Ex,TA,PConc,Ex,TA,Gd,GLQ,1320,Unf,0,426,1746,GasA,Ex,Y,SBrkr,1746,0,0,1746,1,0,2,0,3,1,Ex,7,Typ,2,Gd,Attchd,2006,Fin,3,758,TA,TA,Y,201,39,0,0,0,0,NA,NA,NA,0,10,2009,WD,Normal,377500 +1390,50,RM,60,6000,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,1.5Fin,6,6,1941,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,Gd,BrkTil,TA,Gd,No,ALQ,375,Unf,0,360,735,GasA,Ex,Y,SBrkr,869,349,0,1218,0,1,1,0,3,1,TA,6,Typ,1,Gd,Detchd,2003,Unf,2,440,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,3,2007,WD,Normal,131000 +1391,20,RL,70,9100,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2000,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,244,Gd,TA,PConc,Gd,TA,Av,GLQ,1400,Unf,0,125,1525,GasA,Ex,Y,SBrkr,1525,0,0,1525,1,0,2,0,3,1,Gd,6,Typ,0,NA,Attchd,2000,RFn,2,541,TA,TA,Y,219,36,0,0,0,0,NA,NA,NA,0,9,2006,WD,Normal,235000 +1392,90,RL,65,8944,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,Duplex,1Story,5,5,1967,1967,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,CBlock,TA,TA,No,Unf,0,Unf,0,1584,1584,GasA,TA,Y,SBrkr,1584,0,0,1584,0,0,2,0,4,2,TA,8,Mod,0,NA,Detchd,1967,Unf,3,792,TA,TA,Y,0,152,0,0,0,0,NA,NA,NA,0,4,2009,WD,Normal,124000 +1393,85,RL,68,7838,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,SFoyer,5,5,1967,1967,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,TA,TA,Av,ALQ,769,Unf,0,95,864,GasA,TA,Y,SBrkr,900,0,0,900,1,0,1,0,3,1,TA,6,Typ,1,Po,Attchd,1967,RFn,1,288,TA,TA,Y,175,144,0,0,0,0,NA,MnWw,NA,0,12,2006,WD,Normal,123000 +1394,190,RM,60,10800,Pave,Pave,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,2fmCon,1.5Fin,6,7,1905,2000,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,Fa,TA,No,Unf,0,Unf,0,482,482,GasA,Ex,N,SBrkr,1221,691,0,1912,0,0,2,0,3,2,TA,7,Typ,1,TA,Detchd,2003,Unf,2,672,Gd,TA,Y,0,25,212,0,0,0,NA,NA,NA,0,4,2008,WD,Normal,163000 +1395,120,RL,53,4045,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Blmngtn,Norm,Norm,TwnhsE,1Story,7,5,2006,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,45,Gd,TA,PConc,Gd,TA,Av,GLQ,1070,Unf,0,286,1356,GasA,Ex,Y,SBrkr,1500,0,0,1500,1,0,2,0,2,1,Gd,6,Typ,1,Gd,Attchd,2006,Fin,3,648,TA,TA,Y,161,20,0,0,0,0,NA,NA,NA,0,10,2006,New,Partial,246578 +1396,60,RL,88,12665,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Timber,Norm,Norm,1Fam,2Story,8,5,2005,2006,Hip,CompShg,VinylSd,VinylSd,BrkFace,245,Gd,TA,PConc,Gd,Gd,Gd,Unf,0,Unf,0,1094,1094,GasA,Ex,Y,SBrkr,1133,1349,0,2482,0,0,2,1,4,1,Gd,9,Typ,1,Gd,BuiltIn,2005,Fin,3,642,TA,TA,Y,144,39,0,0,0,0,NA,NA,NA,0,2,2007,WD,Normal,281213 +1397,20,RL,NA,57200,Pave,NA,IR1,Bnk,AllPub,Inside,Sev,Timber,Norm,Norm,1Fam,1Story,5,5,1948,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,Av,BLQ,353,Rec,334,60,747,GasA,TA,Y,SBrkr,1687,0,0,1687,1,0,1,0,3,1,TA,7,Min1,2,TA,Detchd,1966,Unf,2,572,TA,TA,N,0,0,50,0,0,0,NA,NA,NA,0,6,2010,WD,Normal,160000 +1398,70,RM,51,6120,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,2Story,5,8,1920,2004,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,BrkTil,TA,TA,Mn,Unf,0,Unf,0,939,939,GasA,Ex,Y,SBrkr,939,574,0,1513,0,0,1,1,4,1,TA,8,Typ,0,NA,Detchd,1933,Unf,1,180,Fa,Fa,N,24,0,150,0,0,0,NA,NA,NA,0,5,2007,WD,Normal,137500 +1399,50,RL,60,7200,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1.5Fin,5,4,1950,1982,Gable,CompShg,VinylSd,Wd Sdng,None,0,TA,TA,CBlock,TA,TA,No,Rec,180,BLQ,352,676,1208,GasA,Gd,Y,FuseA,1136,768,0,1904,1,0,1,1,3,1,TA,7,Min1,0,NA,Attchd,1950,Unf,1,240,TA,TA,Y,0,0,168,0,0,0,NA,GdPrv,NA,0,5,2009,WD,Normal,138000 +1400,50,RL,51,6171,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,SWISU,Norm,Norm,1Fam,1.5Fin,6,6,1925,1990,Gable,CompShg,WdShing,Wd Shng,None,0,TA,TA,BrkTil,TA,TA,No,BLQ,264,Unf,0,712,976,GasA,Ex,Y,SBrkr,1160,448,0,1608,0,0,2,1,3,1,Gd,7,Typ,1,Gd,Detchd,1925,Unf,1,216,Fa,TA,Y,147,16,0,0,0,0,NA,MnPrv,NA,0,10,2009,WD,Normal,137450 +1401,50,RM,50,6000,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,BrkSide,Norm,Norm,1Fam,1.5Fin,6,7,1929,1950,Gable,CompShg,WdShing,Wd Shng,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,862,862,GasA,TA,Y,SBrkr,950,208,0,1158,0,0,1,0,3,1,TA,5,Typ,1,Gd,BuiltIn,1929,RFn,1,208,TA,TA,Y,0,0,112,0,0,0,NA,NA,NA,0,7,2008,WD,Normal,120000 +1402,60,RL,62,7415,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,2Story,6,5,2004,2004,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,PConc,TA,TA,No,GLQ,759,Unf,0,80,839,GasA,Ex,Y,SBrkr,864,729,0,1593,1,0,2,1,3,1,TA,8,Typ,1,TA,Attchd,2004,Fin,2,398,TA,TA,Y,100,75,0,0,0,0,NA,NA,NA,0,4,2008,WD,Normal,193000 +1403,20,RL,64,6762,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2006,2006,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,Gd,Av,Unf,0,Unf,0,1286,1286,GasA,Ex,Y,SBrkr,1294,0,0,1294,0,0,2,0,2,1,Gd,6,Typ,1,Gd,Attchd,2006,RFn,2,662,TA,TA,Y,168,55,0,0,0,0,NA,NA,NA,0,7,2006,New,Partial,193879 +1404,20,RL,49,15256,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Somerst,RRAn,Norm,1Fam,1Story,8,5,2007,2007,Gable,CompShg,VinylSd,VinylSd,Stone,84,Gd,TA,PConc,Gd,TA,Gd,GLQ,929,Unf,0,556,1485,GasA,Ex,Y,SBrkr,1464,0,0,1464,1,0,2,0,3,1,Gd,6,Typ,0,NA,Attchd,2007,Unf,3,754,TA,TA,Y,168,160,0,0,0,0,NA,NA,NA,0,8,2007,WD,Normal,282922 +1405,50,RL,60,10410,Pave,Grvl,Reg,Lvl,AllPub,Corner,Gtl,OldTown,Artery,Norm,1Fam,1.5Fin,3,4,1915,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,PConc,TA,TA,No,Unf,0,Unf,0,672,672,GasA,TA,Y,SBrkr,694,520,0,1214,0,0,1,0,3,1,TA,6,Typ,0,NA,Detchd,1998,Unf,3,936,TA,TA,Y,216,0,160,0,0,0,NA,MnPrv,NA,0,1,2006,WD,Family,105000 +1406,120,RM,44,3842,Pave,NA,IR1,HLS,AllPub,Inside,Mod,Crawfor,Norm,Norm,TwnhsE,1Story,8,5,2004,2005,Hip,CompShg,CemntBd,CmentBd,Stone,174,Gd,TA,PConc,Ex,TA,Gd,GLQ,1373,Unf,0,221,1594,GasA,Ex,Y,SBrkr,1646,0,0,1646,1,1,2,0,2,1,Gd,5,Typ,1,Gd,Attchd,2004,Fin,2,482,TA,TA,Y,128,53,0,0,155,0,NA,NA,NA,0,1,2008,WD,Normal,275000 +1407,85,RL,70,8445,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,CollgCr,Norm,Norm,1Fam,SFoyer,5,7,1972,2007,Gable,CompShg,HdBoard,Wd Shng,None,0,TA,TA,CBlock,Gd,TA,Av,GLQ,656,Unf,0,112,768,GasA,TA,Y,SBrkr,768,0,0,768,1,0,1,0,2,1,TA,5,Typ,0,NA,Detchd,1988,Unf,2,396,TA,TA,Y,58,0,0,0,0,0,NA,MnPrv,NA,0,3,2009,WD,Normal,133000 +1408,20,RL,NA,8780,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,Mitchel,Norm,Norm,1Fam,1Story,5,5,1985,1985,Gable,CompShg,HdBoard,Plywood,None,0,TA,TA,CBlock,TA,TA,No,ALQ,625,Unf,0,208,833,GasA,Ex,Y,SBrkr,833,0,0,833,1,0,1,0,3,1,TA,5,Typ,0,NA,NA,NA,NA,0,0,NA,NA,Y,0,0,0,0,0,0,NA,MnPrv,NA,0,3,2009,WD,Normal,112000 +1409,70,RM,60,7740,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,2Story,4,7,1910,1950,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,CBlock,Fa,TA,No,Unf,0,Unf,0,622,622,GasA,Gd,Y,SBrkr,741,622,0,1363,0,0,1,0,3,1,TA,6,Typ,0,NA,Detchd,1966,Unf,2,528,TA,TA,Y,0,0,0,0,168,0,NA,NA,NA,0,6,2010,WD,Normal,125500 +1410,60,RL,46,20544,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,NWAmes,Norm,Norm,1Fam,2Story,7,6,1986,1991,Gable,CompShg,Plywood,Plywood,BrkFace,123,TA,Gd,CBlock,Gd,TA,No,Unf,0,Unf,0,791,791,GasA,Gd,Y,SBrkr,1236,857,0,2093,0,0,2,1,3,1,TA,7,Typ,1,TA,Attchd,1986,Fin,2,542,TA,TA,Y,364,63,0,0,0,0,NA,MnPrv,NA,0,11,2008,WD,Normal,215000 +1411,60,RL,79,12420,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,7,5,2001,2001,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,TA,No,GLQ,666,Unf,0,278,944,GasA,Ex,Y,SBrkr,944,896,0,1840,1,0,2,1,3,1,Gd,6,Typ,0,NA,Attchd,2001,RFn,2,622,TA,TA,Y,0,45,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,230000 +1412,50,RL,80,9600,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1.5Fin,6,8,1950,2005,Gable,CompShg,VinylSd,VinylSd,None,0,TA,Gd,CBlock,TA,TA,No,BLQ,120,Unf,0,736,856,GasA,Ex,Y,SBrkr,1112,556,0,1668,0,0,1,1,3,1,TA,6,Min2,0,NA,Attchd,1950,Unf,1,271,TA,TA,Y,0,0,0,0,0,0,NA,MnPrv,NA,0,9,2009,WD,Normal,140000 +1413,90,RL,60,7200,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,Duplex,1Story,4,5,1949,1950,Gable,CompShg,BrkFace,Stone,None,0,TA,TA,Slab,NA,NA,NA,NA,0,NA,0,0,0,Wall,Fa,N,FuseF,1040,0,0,1040,0,0,2,0,2,2,TA,6,Typ,0,NA,Detchd,1956,Unf,2,420,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,6,2009,WD,Normal,90000 +1414,20,RL,88,10994,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,SawyerW,Norm,Norm,1Fam,1Story,8,5,2005,2006,Gable,CompShg,VinylSd,VinylSd,Stone,366,Gd,TA,PConc,Gd,Gd,No,GLQ,976,Unf,0,868,1844,GasA,Ex,Y,SBrkr,1844,0,0,1844,1,0,2,0,2,1,Gd,7,Typ,1,Gd,Attchd,2005,Fin,2,620,TA,TA,Y,165,44,0,0,0,0,NA,NA,NA,0,9,2009,COD,Abnorml,257000 +1415,50,RL,64,13053,Pave,Pave,Reg,Bnk,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,1.5Fin,6,7,1923,2000,Gambrel,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,833,833,GasA,Gd,Y,SBrkr,1053,795,0,1848,0,0,1,1,4,1,Gd,8,Typ,1,Gd,Detchd,1922,Unf,2,370,TA,TA,N,0,0,0,0,220,0,NA,NA,NA,0,6,2008,WD,Normal,207000 +1416,120,RL,51,3635,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Blmngtn,Norm,Norm,TwnhsE,1Story,7,5,2007,2007,Hip,CompShg,VinylSd,VinylSd,BrkFace,130,Gd,TA,PConc,Gd,TA,No,ALQ,988,Unf,0,398,1386,GasA,Ex,Y,SBrkr,1569,0,0,1569,0,1,2,0,1,1,Gd,7,Typ,1,TA,Attchd,2007,RFn,3,660,TA,TA,Y,143,20,0,0,0,0,NA,NA,NA,0,5,2009,WD,Normal,175900 +1417,190,RM,60,11340,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,2fmCon,2Story,4,6,1885,1950,Gable,CompShg,VinylSd,AsbShng,None,0,TA,TA,PConc,TA,TA,No,Unf,0,Unf,0,777,777,GasA,Gd,Y,SBrkr,1246,1044,0,2290,0,0,2,0,4,2,TA,11,Typ,0,NA,Detchd,1971,Unf,2,560,TA,TA,N,0,0,114,0,0,0,NA,NA,NA,0,4,2010,WD,Normal,122500 +1418,60,RL,NA,16545,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NoRidge,Norm,Norm,1Fam,2Story,8,5,1998,1998,Gable,CompShg,VinylSd,VinylSd,BrkFace,731,Gd,TA,PConc,Gd,TA,Mn,GLQ,781,Unf,0,503,1284,GasA,Ex,Y,SBrkr,1310,1140,0,2450,1,0,2,1,3,1,Gd,7,Typ,1,TA,Attchd,1998,Fin,3,1069,TA,TA,Y,0,126,0,0,0,0,NA,NA,NA,0,5,2009,WD,Normal,340000 +1419,20,RL,71,9204,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,5,1963,1963,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,TA,TA,No,BLQ,25,Rec,872,247,1144,GasA,TA,Y,SBrkr,1144,0,0,1144,1,0,1,1,3,1,TA,6,Typ,0,NA,Detchd,1962,Unf,1,336,TA,TA,Y,0,88,0,0,0,0,NA,NA,NA,0,8,2008,COD,Normal,124000 +1420,20,RL,NA,16381,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Crawfor,Norm,Norm,1Fam,1Story,6,5,1969,1969,Gable,CompShg,Plywood,Plywood,BrkFace,312,Gd,Gd,CBlock,TA,TA,Av,Rec,1110,Unf,0,734,1844,GasA,Gd,Y,SBrkr,1844,0,0,1844,1,0,2,0,3,1,Gd,7,Typ,1,TA,Attchd,1969,RFn,2,540,TA,TA,Y,0,73,216,0,0,0,NA,NA,NA,0,12,2006,WD,Normal,223000 +1421,60,RL,90,11700,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,NWAmes,Norm,Norm,1Fam,2Story,6,6,1968,1968,Gable,CompShg,HdBoard,HdBoard,BrkFace,420,TA,TA,CBlock,TA,TA,No,ALQ,404,Unf,0,304,708,GasA,Gd,Y,SBrkr,708,708,0,1416,0,0,2,1,3,1,TA,7,Typ,1,TA,Attchd,1968,RFn,2,776,TA,TA,Y,0,169,0,0,119,0,NA,NA,NA,0,5,2006,WD,Normal,179900 +1422,120,RL,53,4043,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NPkVill,Norm,Norm,TwnhsE,1Story,6,5,1977,1977,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,CBlock,Gd,TA,No,ALQ,360,Unf,0,709,1069,GasA,TA,Y,SBrkr,1069,0,0,1069,0,0,2,0,2,1,TA,4,Typ,1,Fa,Attchd,1977,RFn,2,440,TA,TA,Y,0,55,0,0,165,0,NA,NA,NA,0,7,2010,WD,Normal,127500 +1423,120,RM,37,4435,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,TwnhsE,1Story,6,5,2003,2003,Gable,CompShg,VinylSd,VinylSd,BrkFace,170,Gd,TA,PConc,Gd,TA,Av,GLQ,686,Unf,0,162,848,GasA,Ex,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Gd,3,Typ,0,NA,Attchd,2003,Fin,2,420,TA,TA,Y,140,0,0,0,0,0,NA,NA,NA,0,3,2008,WD,Normal,136500 +1424,80,RL,NA,19690,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Edwards,Norm,Norm,1Fam,SLvl,6,7,1966,1966,Flat,Tar&Grv,Plywood,Plywood,None,0,Gd,Gd,CBlock,Gd,TA,Av,Unf,0,Unf,0,697,697,GasA,TA,Y,SBrkr,1575,626,0,2201,0,0,2,0,4,1,Gd,8,Typ,1,Gd,Attchd,1966,Unf,2,432,Gd,Gd,Y,586,236,0,0,0,738,Gd,GdPrv,NA,0,8,2006,WD,Alloca,274970 +1425,20,RL,NA,9503,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,5,1958,1983,Hip,CompShg,HdBoard,HdBoard,None,0,TA,TA,CBlock,TA,TA,No,ALQ,457,Rec,374,193,1024,GasA,TA,Y,SBrkr,1344,0,0,1344,1,0,1,0,2,1,TA,6,Min1,1,TA,Detchd,1970,Unf,1,484,TA,TA,Y,316,28,0,0,0,0,NA,GdWo,NA,0,6,2007,WD,Normal,144000 +1426,20,RL,80,10721,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,6,1959,1959,Hip,CompShg,HdBoard,HdBoard,Stone,243,Gd,TA,CBlock,TA,TA,No,Unf,0,Unf,0,1252,1252,GasA,Ex,Y,SBrkr,1252,0,0,1252,0,0,1,0,3,1,Gd,7,Typ,0,NA,Detchd,1960,Unf,2,528,TA,TA,Y,0,39,0,0,0,0,NA,NA,NA,0,10,2008,WD,Normal,142000 +1427,60,RL,81,10944,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NoRidge,Norm,Norm,1Fam,2Story,7,5,1994,1994,Gable,CompShg,VinylSd,VinylSd,BrkFace,448,Gd,TA,PConc,Gd,TA,No,GLQ,1000,Unf,0,223,1223,GasA,Ex,Y,SBrkr,1223,904,0,2127,1,0,2,1,3,1,Gd,5,Typ,2,TA,Attchd,1994,RFn,2,525,TA,TA,Y,171,132,0,0,0,0,NA,NA,NA,0,8,2008,WD,Normal,271000 +1428,50,RL,60,10930,Pave,Grvl,Reg,Bnk,AllPub,Inside,Gtl,NAmes,Artery,Norm,1Fam,1.5Fin,5,6,1945,1950,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,BLQ,580,Unf,0,333,913,GasA,TA,Y,FuseA,1048,510,0,1558,1,0,1,1,3,1,TA,6,Typ,1,TA,Attchd,1962,Unf,1,288,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,4,2008,WD,Normal,140000 +1429,30,RM,60,7200,Pave,NA,Reg,Lvl,AllPub,Corner,Gtl,OldTown,Norm,Norm,1Fam,1Story,5,7,1940,1992,Gable,CompShg,MetalSd,MetalSd,Stone,294,TA,Gd,CBlock,TA,TA,No,BLQ,510,Unf,0,278,788,GasA,TA,Y,SBrkr,804,0,0,804,1,0,1,0,2,1,Gd,4,Typ,2,Gd,Attchd,1940,Unf,1,240,TA,TA,Y,0,0,154,0,0,0,NA,MnPrv,NA,0,2,2010,WD,Abnorml,119000 +1430,20,RL,NA,12546,Pave,NA,IR1,Lvl,AllPub,Corner,Gtl,NWAmes,Norm,Norm,1Fam,1Story,6,7,1981,1981,Gable,CompShg,MetalSd,MetalSd,BrkFace,310,Gd,Gd,CBlock,Gd,TA,No,BLQ,678,Unf,0,762,1440,GasA,Ex,Y,SBrkr,1440,0,0,1440,0,0,2,0,3,1,Gd,7,Typ,1,TA,Attchd,1981,Fin,2,467,TA,TA,Y,0,0,99,0,0,0,NA,NA,NA,0,4,2007,WD,Normal,182900 +1431,60,RL,60,21930,Pave,NA,IR3,Lvl,AllPub,Inside,Gtl,Gilbert,RRAn,Norm,1Fam,2Story,5,5,2005,2005,Gable,CompShg,VinylSd,VinylSd,None,0,Gd,TA,PConc,Gd,Gd,Av,Unf,0,Unf,0,732,732,GasA,Ex,Y,SBrkr,734,1104,0,1838,0,0,2,1,4,1,TA,7,Typ,1,Gd,BuiltIn,2005,Fin,2,372,TA,TA,Y,100,40,0,0,0,0,NA,NA,NA,0,7,2006,WD,Normal,192140 +1432,120,RL,NA,4928,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,NPkVill,Norm,Norm,TwnhsE,1Story,6,6,1976,1976,Gable,CompShg,Plywood,Plywood,None,0,TA,TA,CBlock,Gd,TA,No,LwQ,958,Unf,0,0,958,GasA,TA,Y,SBrkr,958,0,0,958,0,0,2,0,2,1,TA,5,Typ,0,NA,Attchd,1976,RFn,2,440,TA,TA,Y,0,60,0,0,0,0,NA,NA,NA,0,10,2009,WD,Normal,143750 +1433,30,RL,60,10800,Pave,Grvl,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Norm,Norm,1Fam,1Story,4,6,1927,2007,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,656,656,GasA,TA,Y,SBrkr,968,0,0,968,0,0,2,0,4,1,TA,5,Typ,0,NA,Detchd,1928,Unf,1,216,Fa,Fa,Y,0,0,0,0,0,0,NA,NA,NA,0,8,2007,WD,Normal,64500 +1434,60,RL,93,10261,Pave,NA,IR1,Lvl,AllPub,Inside,Gtl,Gilbert,Norm,Norm,1Fam,2Story,6,5,2000,2000,Gable,CompShg,VinylSd,VinylSd,BrkFace,318,TA,TA,PConc,Gd,TA,No,Unf,0,Unf,0,936,936,GasA,Ex,Y,SBrkr,962,830,0,1792,1,0,2,1,3,1,TA,8,Typ,1,TA,Attchd,2000,Fin,2,451,TA,TA,Y,0,0,0,0,0,0,NA,NA,NA,0,5,2008,WD,Normal,186500 +1435,20,RL,80,17400,Pave,NA,Reg,Low,AllPub,Inside,Mod,Mitchel,Norm,Norm,1Fam,1Story,5,5,1977,1977,Gable,CompShg,BrkFace,BrkFace,None,0,TA,TA,CBlock,TA,TA,No,ALQ,936,Unf,0,190,1126,GasA,Fa,Y,SBrkr,1126,0,0,1126,1,0,2,0,3,1,TA,5,Typ,1,Gd,Attchd,1977,RFn,2,484,TA,TA,P,295,41,0,0,0,0,NA,NA,NA,0,5,2006,WD,Normal,160000 +1436,20,RL,80,8400,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,6,9,1962,2005,Gable,CompShg,Wd Sdng,Wd Sdng,BrkFace,237,Gd,Gd,CBlock,TA,TA,No,Unf,0,Unf,0,1319,1319,GasA,TA,Y,SBrkr,1537,0,0,1537,1,0,1,1,3,1,Gd,7,Typ,1,Gd,Attchd,1962,RFn,2,462,TA,TA,Y,0,36,0,0,0,0,NA,GdPrv,NA,0,7,2008,COD,Abnorml,174000 +1437,20,RL,60,9000,Pave,NA,Reg,Lvl,AllPub,FR2,Gtl,NAmes,Norm,Norm,1Fam,1Story,4,6,1971,1971,Gable,CompShg,HdBoard,HdBoard,None,0,TA,TA,PConc,TA,TA,No,ALQ,616,Unf,0,248,864,GasA,TA,Y,SBrkr,864,0,0,864,0,0,1,0,3,1,TA,5,Typ,0,NA,Detchd,1974,Unf,2,528,TA,TA,Y,0,0,0,0,0,0,NA,GdWo,NA,0,5,2007,WD,Normal,120500 +1438,20,RL,96,12444,Pave,NA,Reg,Lvl,AllPub,FR2,Gtl,NridgHt,Norm,Norm,1Fam,1Story,8,5,2008,2008,Hip,CompShg,VinylSd,VinylSd,Stone,426,Ex,TA,PConc,Ex,TA,Av,GLQ,1336,Unf,0,596,1932,GasA,Ex,Y,SBrkr,1932,0,0,1932,1,0,2,0,2,1,Ex,7,Typ,1,Gd,Attchd,2008,Fin,3,774,TA,TA,Y,0,66,0,304,0,0,NA,NA,NA,0,11,2008,New,Partial,394617 +1439,20,RM,90,7407,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,OldTown,Artery,Norm,1Fam,1Story,6,7,1957,1996,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,No,GLQ,600,Unf,0,312,912,GasA,TA,Y,FuseA,1236,0,0,1236,1,0,1,0,2,1,TA,6,Typ,0,NA,Attchd,1957,Unf,2,923,TA,TA,Y,0,158,158,0,0,0,NA,MnPrv,NA,0,4,2010,WD,Normal,149700 +1440,60,RL,80,11584,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NWAmes,Norm,Norm,1Fam,SLvl,7,6,1979,1979,Hip,CompShg,HdBoard,HdBoard,BrkFace,96,TA,TA,CBlock,TA,TA,No,GLQ,315,Rec,110,114,539,GasA,TA,Y,SBrkr,1040,685,0,1725,0,0,2,1,3,1,TA,6,Typ,1,TA,Attchd,1979,RFn,2,550,TA,TA,Y,0,88,216,0,0,0,NA,NA,NA,0,11,2007,WD,Normal,197000 +1441,70,RL,79,11526,Pave,NA,IR1,Bnk,AllPub,Inside,Mod,Crawfor,Norm,Norm,1Fam,2.5Fin,6,7,1922,1994,Gable,CompShg,MetalSd,MetalSd,None,0,TA,TA,BrkTil,Ex,TA,No,Unf,0,Unf,0,588,588,GasA,Fa,Y,SBrkr,1423,748,384,2555,0,0,2,0,3,1,TA,11,Min1,1,Gd,Detchd,1993,Fin,2,672,TA,TA,Y,431,0,0,0,0,0,NA,NA,NA,0,9,2008,WD,Normal,191000 +1442,120,RM,NA,4426,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,TwnhsE,1Story,6,5,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,147,Gd,TA,PConc,Gd,TA,Av,GLQ,697,Unf,0,151,848,GasA,Ex,Y,SBrkr,848,0,0,848,1,0,1,0,1,1,Gd,3,Typ,1,TA,Attchd,2004,RFn,2,420,TA,TA,Y,149,0,0,0,0,0,NA,NA,NA,0,5,2008,WD,Normal,149300 +1443,60,FV,85,11003,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Somerst,Norm,Norm,1Fam,2Story,10,5,2008,2008,Gable,CompShg,VinylSd,VinylSd,Stone,160,Ex,TA,PConc,Ex,TA,Av,GLQ,765,Unf,0,252,1017,GasA,Ex,Y,SBrkr,1026,981,0,2007,1,0,2,1,3,1,Ex,10,Typ,1,Ex,Attchd,2008,Fin,3,812,TA,TA,Y,168,52,0,0,0,0,NA,NA,NA,0,4,2009,WD,Normal,310000 +1444,30,RL,NA,8854,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,BrkSide,Norm,Norm,1Fam,1.5Unf,6,6,1916,1950,Gable,CompShg,Wd Sdng,Wd Sdng,None,0,TA,TA,BrkTil,TA,TA,No,Unf,0,Unf,0,952,952,Grav,Fa,N,FuseF,952,0,0,952,0,0,1,0,2,1,Fa,4,Typ,1,Gd,Detchd,1916,Unf,1,192,Fa,Po,P,0,98,0,0,40,0,NA,NA,NA,0,5,2009,WD,Normal,121000 +1445,20,RL,63,8500,Pave,NA,Reg,Lvl,AllPub,FR2,Gtl,CollgCr,Norm,Norm,1Fam,1Story,7,5,2004,2004,Gable,CompShg,VinylSd,VinylSd,BrkFace,106,Gd,TA,PConc,Gd,TA,Av,Unf,0,Unf,0,1422,1422,GasA,Ex,Y,SBrkr,1422,0,0,1422,0,0,2,0,3,1,Gd,7,Typ,0,NA,Attchd,2004,RFn,2,626,TA,TA,Y,192,60,0,0,0,0,NA,NA,NA,0,11,2007,WD,Normal,179600 +1446,85,RL,70,8400,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Sawyer,Norm,Norm,1Fam,SFoyer,6,5,1966,1966,Gable,CompShg,VinylSd,VinylSd,None,0,TA,TA,CBlock,TA,TA,Gd,LwQ,187,Rec,627,0,814,GasA,Gd,Y,SBrkr,913,0,0,913,1,0,1,0,3,1,TA,6,Typ,0,NA,Detchd,1990,Unf,1,240,TA,TA,Y,0,0,252,0,0,0,NA,NA,NA,0,5,2007,WD,Normal,129000 +1447,20,RL,NA,26142,Pave,NA,IR1,Lvl,AllPub,CulDSac,Gtl,Mitchel,Norm,Norm,1Fam,1Story,5,7,1962,1962,Gable,CompShg,HdBoard,HdBoard,BrkFace,189,TA,TA,CBlock,TA,TA,No,Rec,593,Unf,0,595,1188,GasA,TA,Y,SBrkr,1188,0,0,1188,0,0,1,0,3,1,TA,6,Typ,0,NA,Attchd,1962,Unf,1,312,TA,TA,P,261,39,0,0,0,0,NA,NA,NA,0,4,2010,WD,Normal,157900 +1448,60,RL,80,10000,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,CollgCr,Norm,Norm,1Fam,2Story,8,5,1995,1996,Gable,CompShg,VinylSd,VinylSd,BrkFace,438,Gd,TA,PConc,Gd,TA,No,GLQ,1079,Unf,0,141,1220,GasA,Ex,Y,SBrkr,1220,870,0,2090,1,0,2,1,3,1,Gd,8,Typ,1,TA,Attchd,1995,RFn,2,556,TA,TA,Y,0,65,0,0,0,0,NA,NA,NA,0,12,2007,WD,Normal,240000 +1449,50,RL,70,11767,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,2Story,4,7,1910,2000,Gable,CompShg,MetalSd,HdBoard,None,0,TA,TA,CBlock,Fa,TA,No,Unf,0,Unf,0,560,560,GasA,Gd,N,SBrkr,796,550,0,1346,0,0,1,1,2,1,TA,6,Min2,0,NA,Detchd,1950,Unf,1,384,Fa,TA,Y,168,24,0,0,0,0,NA,GdWo,NA,0,5,2007,WD,Normal,112000 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+1459,20,RL,68,9717,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,NAmes,Norm,Norm,1Fam,1Story,5,6,1950,1996,Hip,CompShg,MetalSd,MetalSd,None,0,TA,TA,CBlock,TA,TA,Mn,GLQ,49,Rec,1029,0,1078,GasA,Gd,Y,FuseA,1078,0,0,1078,1,0,1,0,2,1,Gd,5,Typ,0,NA,Attchd,1950,Unf,1,240,TA,TA,Y,366,0,112,0,0,0,NA,NA,NA,0,4,2010,WD,Normal,142125 +1460,20,RL,75,9937,Pave,NA,Reg,Lvl,AllPub,Inside,Gtl,Edwards,Norm,Norm,1Fam,1Story,5,6,1965,1965,Gable,CompShg,HdBoard,HdBoard,None,0,Gd,TA,CBlock,TA,TA,No,BLQ,830,LwQ,290,136,1256,GasA,Gd,Y,SBrkr,1256,0,0,1256,1,0,1,1,3,1,TA,6,Typ,0,NA,Attchd,1965,Fin,1,276,TA,TA,Y,736,68,0,0,0,0,NA,NA,NA,0,6,2008,WD,Normal,147500 diff --git a/Seminar2/seminar_2_empty.ipynb b/Seminar2/seminar_2_empty.ipynb new file mode 100644 index 0000000..408ba8a --- /dev/null +++ b/Seminar2/seminar_2_empty.ipynb @@ -0,0 +1,263 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "81a927b5", + "metadata": {}, + "source": [ + "# Seminar: Lists, dictionaries, functions, exceptions\n" + ] + }, + { + "cell_type": "markdown", + "id": "7917a993", + "metadata": {}, + "source": [ + "### Task 1. \n", + "\n", + "Write a function which will sum up all values in the list" + ] + }, + { + "cell_type": "markdown", + "id": "1401ca62", + "metadata": {}, + "source": [ + "#### a. For the case when input is list of integers." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8750cd32", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "e06770f1", + "metadata": {}, + "source": [ + "#### b. What will be if the input is list of strings?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c4aa17cc", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "d67d8c92", + "metadata": {}, + "source": [ + "#### c. Rewrite the function in the way that we will get a message 'can not work with not numerical types' if list contains not numerical elements." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d95e80d8", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "3240b759", + "metadata": {}, + "source": [ + "#### d. Why do we need to write the exceptions?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad2390cf", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "278db275", + "metadata": {}, + "source": [ + "### Task 2." + ] + }, + { + "cell_type": "markdown", + "id": "d3d3a943", + "metadata": {}, + "source": [ + "In this task we will work with the dictionary which is the plan for the grocery store:" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "id": "eba4a98b", + "metadata": {}, + "outputs": [], + "source": [ + "dic_food = {'apple':5, 'orange':4, 'onion':9}" + ] + }, + { + "cell_type": "markdown", + "id": "3074601e", + "metadata": {}, + "source": [ + "#### a. Calculate how many products in total do we have. How many fruits?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d13457fd", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "a3ca5ec2", + "metadata": {}, + "source": [ + "#### b. Add one more apple and change the numbers of onions to 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d85c63a1", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "7c03197c", + "metadata": {}, + "source": [ + "#### c. Make the same dictionary from the lists: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "167aca00", + "metadata": {}, + "outputs": [], + "source": [ + "prod_name = ['apple', 'orange', 'onion']\n", + "prod_amount = [1,5,5]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "31f17c02", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "3649c1cb", + "metadata": {}, + "source": [ + "#### d. What will happened if we have dublicates in the list of keys?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c66a0a13", + "metadata": {}, + "outputs": [], + "source": [ + "prod_name = ['apple', 'orange', 'onion', 'apple']\n", + "prod_amount = [1,5,3,5]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c5aec52", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "873a48f6", + "metadata": {}, + "source": [ + "### Task 3 (from HW) leetcode 219. Contains Duplicate II\n", + "https://leetcode.com/problems/contains-duplicate-ii/\n", + "\n", + "Given an integer array nums and an integer k, return true if there are two distinct indices i and j in the array such that \n", + "\n", + "nums[ i ] == nums[ j ] and abs(i - j) <= k." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e168222a", + "metadata": {}, + "outputs": [], + "source": [ + "def containsNearbyDuplicate(self, nums, k):\n", + " \"\"\"\n", + " :type nums: List[int]\n", + " :type k: int\n", + " :rtype: bool\n", + " \"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae231b73", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "689d6ae1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Seminar2/seminar_2_solved.ipynb b/Seminar2/seminar_2_solved.ipynb new file mode 100644 index 0000000..5171618 --- /dev/null +++ b/Seminar2/seminar_2_solved.ipynb @@ -0,0 +1,584 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "fa9e11ab", + "metadata": {}, + "source": [ + "# Seminar: Lists, dictionaries, functions, exceptions\n" + ] + }, + { + "cell_type": "markdown", + "id": "d0dda828", + "metadata": {}, + "source": [ + "### Task 1. \n", + "\n", + "Write a function which will sum up all values in the list" + ] + }, + { + "cell_type": "markdown", + "id": "1401ca62", + "metadata": {}, + "source": [ + "#### a. For the case when input is list of integers." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "8750cd32", + "metadata": {}, + "outputs": [], + "source": [ + "def sum_up(my_list):\n", + " res_of_sum = 0\n", + " for num in my_list:\n", + " res_of_sum += num\n", + " return res_of_sum" + ] + }, + { + "cell_type": "markdown", + "id": "b68bdfb6", + "metadata": {}, + "source": [ + "res_of_sum += num \n", + "\n", + "is equal to:\n", + "\n", + "res_of_sum = res_of_sum + num" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "fefdbef8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum_up([1,2,3])" + ] + }, + { + "cell_type": "markdown", + "id": "e06770f1", + "metadata": {}, + "source": [ + "#### b. What will be if the input is list of strings?" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c4aa17cc", + "metadata": {}, + "outputs": [], + "source": [ + "def sum_up_str(my_list):\n", + " res_of_sum = ''\n", + " for el in my_list:\n", + " res_of_sum += el\n", + " return res_of_sum" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "10fac1f0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'admm'" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum_up_str(['a', 'd', 'mm'])" + ] + }, + { + "cell_type": "markdown", + "id": "d67d8c92", + "metadata": {}, + "source": [ + "#### c. Rewrite the function in the way that we will get a message 'can not work with not numerical types' if list contains not numerical elements." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "d95e80d8", + "metadata": {}, + "outputs": [], + "source": [ + "def sum_up_ex(my_list):\n", + " res_of_sum = 0\n", + " for num in my_list:\n", + " try:\n", + " res_of_sum += num\n", + " except Exception:\n", + " print('can not work with not numerical types')\n", + " return\n", + " return res_of_sum" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "90d87780", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "can not work with not numerical types\n" + ] + } + ], + "source": [ + "sum_up_ex([1, 4, 'd'])" + ] + }, + { + "cell_type": "markdown", + "id": "3240b759", + "metadata": {}, + "source": [ + "#### d. Why do we need to write the exceptions?" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "ad2390cf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1\n", + "can not work with not numerical types\n", + "Step 2: None\n", + "Step 3\n" + ] + } + ], + "source": [ + "print('Step 1')\n", + "print(f'Step 2: {sum_up_ex([1, 4, \"d\"])}')\n", + "print('Step 3')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "ec92a671", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1\n" + ] + }, + { + "ename": "TypeError", + "evalue": "unsupported operand type(s) for +=: 'int' and 'str'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [22]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mStep 1\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mStep 2: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msum_up([\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m4\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124md\u001b[39m\u001b[38;5;124m\"\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mStep 3\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "Input \u001b[0;32mIn [21]\u001b[0m, in \u001b[0;36msum_up\u001b[0;34m(my_list)\u001b[0m\n\u001b[1;32m 2\u001b[0m res_of_sum \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m num \u001b[38;5;129;01min\u001b[39;00m my_list:\n\u001b[0;32m----> 4\u001b[0m res_of_sum \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m num\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res_of_sum\n", + "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +=: 'int' and 'str'" + ] + } + ], + "source": [ + "print('Step 1')\n", + "print(f'Step 2: {sum_up([1, 4, \"d\"])}')\n", + "print('Step 3')" + ] + }, + { + "cell_type": "markdown", + "id": "278db275", + "metadata": {}, + "source": [ + "### Task 2." + ] + }, + { + "cell_type": "markdown", + "id": "d3d3a943", + "metadata": {}, + "source": [ + "In this task we will work with the dictionary which is the plan for the grocery store:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "eba4a98b", + "metadata": {}, + "outputs": [], + "source": [ + "dic_food = {'apple':5, 'orange':4, 'onion':9}" + ] + }, + { + "cell_type": "markdown", + "id": "3074601e", + "metadata": {}, + "source": [ + "#### a. Calculate how many products in total do we have. How many fruits?" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "65fc0173", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "18" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum(dic_food.values())" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "f35e6c38", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "18" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum([v for v in dic_food.values()])" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "c205408f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list_fruits = ['apple', 'orange']\n", + "sum([v for k, v in dic_food.items() if k in list_fruits])" + ] + }, + { + "cell_type": "markdown", + "id": "a3ca5ec2", + "metadata": {}, + "source": [ + "#### b. Add one more apple and change the numbers of onions to 1" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "d85c63a1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'apple': 5, 'orange': 4, 'onion': 9}" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dic_food" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "831fe4ba", + "metadata": {}, + "outputs": [], + "source": [ + "dic_food['apple'] += 1" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "0b9ace80", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'apple': 6, 'orange': 4, 'onion': 9}" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dic_food" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "6d513519", + "metadata": {}, + "outputs": [], + "source": [ + "dic_food['onion'] = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "9e7d3831", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'apple': 6, 'orange': 4, 'onion': 1}" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dic_food" + ] + }, + { + "cell_type": "markdown", + "id": "7c03197c", + "metadata": {}, + "source": [ + "#### c. Make the same dictionary from the lists: " + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "cc3b305e", + "metadata": {}, + "outputs": [], + "source": [ + "prod_name = ['apple', 'orange', 'onion']\n", + "prod_amount = [1,5,5]" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "6672ef13", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'apple': 1, 'orange': 5, 'onion': 5}" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "new_dict = {name:amount for name,amount in zip(prod_name, prod_amount)}\n", + "new_dict" + ] + }, + { + "cell_type": "markdown", + "id": "3649c1cb", + "metadata": {}, + "source": [ + "#### d. What will happened if we have dublicates in the list of keys?" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "c66a0a13", + "metadata": {}, + "outputs": [], + "source": [ + "prod_name = ['apple', 'orange', 'onion', 'apple']\n", + "prod_amount = [1,5,3,2]" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "a1188206", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'apple': 2, 'orange': 5, 'onion': 3}" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "{name:amount for name,amount in zip(prod_name, prod_amount)}" + ] + }, + { + "cell_type": "markdown", + "id": "4b140a01", + "metadata": {}, + "source": [ + "### Task 3 (from HW) leetcode 219. Contains Duplicate II\n", + "https://leetcode.com/problems/contains-duplicate-ii/\n", + "\n", + "Given an integer array nums and an integer k, return true if there are two distinct indices i and j in the array such that \n", + "\n", + "nums[ i ] == nums[ j ] and abs(i - j) <= k." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "93e34d47", + "metadata": {}, + "outputs": [], + "source": [ + "def containsNearbyDuplicate(nums, k):\n", + " \"\"\"\n", + " :type nums: List[int]\n", + " :type k: int\n", + " :rtype: bool\n", + " \"\"\"\n", + " if len(set(nums))==len(nums):\n", + " return False\n", + " for n in set(nums):\n", + " ind = [i for i,j in enumerate(nums) if j==n]\n", + " if len(ind)>1:\n", + " r = min([abs(ind[i]-ind[i+1]) for i in range(len(ind)-1)])\n", + " if r<=k:\n", + " return True\n", + " return False\n" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "3228cbdb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "containsNearbyDuplicate([1,4,5,1], 2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ab34c2a6", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Seminar3/seminar_3_solved.ipynb b/Seminar3/seminar_3_solved.ipynb new file mode 100644 index 0000000..ae7000f --- /dev/null +++ b/Seminar3/seminar_3_solved.ipynb @@ -0,0 +1,3097 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "492eb907-b454-403f-914a-281a61751e07", + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "import pandas as pd\n", + "import time" + ] + }, + { + "cell_type": "markdown", + "id": "9b9babd9-e6ad-49bd-9e5e-c479363d305b", + "metadata": { + "tags": [] + }, + "source": [ + "# Seminar - APIs, DBs and Live coding" + ] + }, + { + "cell_type": "markdown", + "id": "4d7941ab-1155-4c89-8095-94edf11f889d", + "metadata": { + "tags": [] + }, + "source": [ + "## Task 1: Requesting API\n", + "### 1a. Create a function requesting data from sreality\n", + "\n", + "\n", + "```python\n", + "base_url = 'https://www.sreality.cz/api/cs/v2/estates?category_main_cb=1&category_type_cb=1&locality_region_id=10&per_page60&page={}'.format(i)\n", + "\n", + "r = requests.get(base_url)\n", + "d = r.json()\n", + "```\n", + "\n", + "* function should parametrize: \n", + " * `category_main_cb` - `{'flat':1, 'house':2, 'land':3 }`\n", + " * `category_type_cb` - `{'sell':1,'rent':2}`\n", + " * `locality_region_id` - use 10 as default value\n", + " * `page` parameter\n", + "* use string inputs for `category_main_cb` and `category_type_cb`\n", + "* include try/except clause to handle errors\n", + "* function should return JSON data in python types\n", + "* do not forget to sleep each request at least 0.5s" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3bf7c9dc-be77-48ad-b373-c1525983da7a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hi\n", + "CPU times: user 74 µs, sys: 15 µs, total: 89 µs\n", + "Wall time: 99.2 µs\n" + ] + } + ], + "source": [ + "%%time\n", + "print('Hi')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8d075a9e-e094-483d-9a9f-2fd5a7eae194", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1.6 ms, sys: 1.12 ms, total: 2.72 ms\n", + "Wall time: 5.01 s\n" + ] + } + ], + "source": [ + "%%time\n", + "time.sleep(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b8758ddd-8357-4b98-b9ac-e8cf0c3629b4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 2.35 ms, sys: 0 ns, total: 2.35 ms\n", + "Wall time: 1 s\n" + ] + } + ], + "source": [ + "%%time\n", + "time.sleep(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "55cca328-f8c4-4fba-a0b3-6d20c990d712", + "metadata": {}, + "outputs": [], + "source": [ + "def request_sreality(page, category_main_str, category_type_str, locality_region_id=10):\n", + " time.sleep(0.5)\n", + " category_mains = {'flat':1, 'house':2, 'land':3 }\n", + " category_types = {'sell':1,'rent':2}\n", + " template_url = 'https://www.sreality.cz/api/cs/v2/estates?category_main_cb={category_main}&category_type_cb={category_type}&locality_region_id={locality_region_id}&per_page60&page={page}'\n", + " try:\n", + " request_url = template_url.format(\n", + " category_main=category_mains[category_main_str],\n", + " category_type=category_types[category_type_str],\n", + " locality_region_id=locality_region_id,\n", + " page=page\n", + " )\n", + " r = requests.get(request_url)\n", + " return r.json()\n", + " except Exception as e:\n", + " print(e)\n", + "d = request_sreality(0, 'flat', 'sell', 10)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "1a40af97-3f19-4a45-ab62-13ed539b6c86", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['meta_description', 'result_size', '_embedded', 'filterLabels', 'title', 'filter', '_links', 'locality', 'locality_dativ', 'logged_in', 'per_page', 'category_instrumental', 'page', 'filterLabels2'])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "768f1e7e-5330-4f55-b846-7bd02252d45b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'5045 realit v nabídce prodej bytů Praha. Vyberte si novou nemovitost na sreality.cz s hledáním na mapě a velkými náhledy fotografií nabízených bytů.'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d['meta_description']" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "78942b2d-e947-47e0-8dc2-fe333f7c31dc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5045" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d['result_size']" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "32a6da59-18de-4ce0-8127-873f6ec29a0a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['estates', 'is_saved', 'not_precise_location_count'])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d['_embedded'].keys()" + ] + }, + { + "cell_type": "markdown", + "id": "8b718701-e4a4-4fe4-bf34-d03913765b2a", + "metadata": {}, + "source": [ + "### 1b. Create a function converting sreality json data into pandas dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "c1eae9b3-0571-4699-9933-3868f362ef83", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "21" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(d['_embedded']['estates'])" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "1c60d718-0c53-4b00-9de3-234c72e938ca", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "27" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(d['_embedded']['estates'][4].keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "99e70448-9475-465b-a85c-e4bc5cd1778a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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labelsReleasedhas_panoramalabelsis_auctionlabelsAllseoexclusively_at_rkcategoryhas_floor_plan_embedded...hash_idattractive_offerpriceprice_czk_linksrusnameregion_tipgpshas_matterport_url
0[[balcony, parking_lots, garage], []]0[Balkon, Parkování, Garáž]False[[personal, balcony, brick, elevator, parking_...{'category_main_cb': 1, 'category_sub_cb': 8, ...010{'favourite': {'is_favourite': False, '_links'......58234188012862000{'value_raw': 12862000, 'unit': '', 'name': 'C...{'dynamicDown': [{'href': 'https://d18-a.sdn.c...FalseProdej bytu 4+kk 128 m²2693402{'lat': 50.12603618747833, 'lon': 14.561554812...False
1[[not_furnished], [restaurant, drugstore]]0[Nevybavený, Restaurace 1 min. pěšky, Lékárna ...False[[new_building, personal, elevator, not_furnis...{'category_main_cb': 1, 'category_sub_cb': 2, ...011{'favourite': {'is_favourite': False, '_links'......8942906803990000{'value_raw': 3990000, 'unit': '', 'name': 'Ce...{'dynamicDown': [{'href': 'https://d18-a.sdn.c...FalseProdej bytu 1+kk 24 m²0{'lat': 50.09041518747833, 'lon': 14.531943812...False
2[[], []]0[]False[[new_building, personal, brick, cellar, eleva...{'category_main_cb': 1, 'category_sub_cb': 6, ...011{'favourite': {'is_favourite': False, '_links'......567759948021978000{'value_raw': 21978000, 'unit': '', 'name': 'C...{'dynamicDown': [{'href': 'https://d18-a.sdn.c...FalseProdej bytu 3+kk 122 m²0{'lat': 50.06292218747833, 'lon': 14.381577812...False
3[[], []]0[]False[[new_building, personal, brick, cellar, eleva...{'category_main_cb': 1, 'category_sub_cb': 6, ...011{'favourite': {'is_favourite': False, '_links'......618091596018559000{'value_raw': 18559000, 'unit': '', 'name': 'C...{'dynamicDown': [{'href': 'https://d18-a.sdn.c...FalseProdej bytu 3+kk 103 m²0{'lat': 50.06292218747833, 'lon': 14.381577812...False
4[[], [kindergarten, drugstore]]0[Školka 6 min. pěšky, Lékárna 5 min. pěšky]False[[new_building, personal, brick], [candy_shop,...{'category_main_cb': 1, 'category_sub_cb': 8, ...011{'favourite': {'is_favourite': False, '_links'......973042764021876000{'value_raw': 21876000, 'unit': '', 'name': 'C...{'dynamicDown': [{'href': 'https://d18-a.sdn.c...FalseProdej bytu 4+kk 139 m²0{'lat': 50.06782018747833, 'lon': 14.507568812...True
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5 rows × 27 columns

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" + ], + "text/plain": [ + " labelsReleased has_panorama \\\n", + "0 [[balcony, parking_lots, garage], []] 0 \n", + "1 [[not_furnished], [restaurant, drugstore]] 0 \n", + "2 [[], []] 0 \n", + "3 [[], []] 0 \n", + "4 [[], [kindergarten, drugstore]] 0 \n", + "\n", + " labels is_auction \\\n", + "0 [Balkon, Parkování, Garáž] False \n", + "1 [Nevybavený, Restaurace 1 min. pěšky, Lékárna ... False \n", + "2 [] False \n", + "3 [] False \n", + "4 [Školka 6 min. pěšky, Lékárna 5 min. pěšky] False \n", + "\n", + " labelsAll \\\n", + "0 [[personal, balcony, brick, elevator, parking_... \n", + "1 [[new_building, personal, elevator, not_furnis... \n", + "2 [[new_building, personal, brick, cellar, eleva... \n", + "3 [[new_building, personal, brick, cellar, eleva... \n", + "4 [[new_building, personal, brick], [candy_shop,... \n", + "\n", + " seo exclusively_at_rk \\\n", + "0 {'category_main_cb': 1, 'category_sub_cb': 8, ... 0 \n", + "1 {'category_main_cb': 1, 'category_sub_cb': 2, ... 0 \n", + "2 {'category_main_cb': 1, 'category_sub_cb': 6, ... 0 \n", + "3 {'category_main_cb': 1, 'category_sub_cb': 6, ... 0 \n", + "4 {'category_main_cb': 1, 'category_sub_cb': 8, ... 0 \n", + "\n", + " category has_floor_plan \\\n", + "0 1 0 \n", + "1 1 1 \n", + "2 1 1 \n", + "3 1 1 \n", + "4 1 1 \n", + "\n", + " _embedded ... hash_id \\\n", + "0 {'favourite': {'is_favourite': False, '_links'... ... 58234188 \n", + "1 {'favourite': {'is_favourite': False, '_links'... ... 89429068 \n", + "2 {'favourite': {'is_favourite': False, '_links'... ... 567759948 \n", + "3 {'favourite': {'is_favourite': False, '_links'... ... 618091596 \n", + "4 {'favourite': {'is_favourite': False, '_links'... ... 973042764 \n", + "\n", + " attractive_offer price \\\n", + "0 0 12862000 \n", + "1 0 3990000 \n", + "2 0 21978000 \n", + "3 0 18559000 \n", + "4 0 21876000 \n", + "\n", + " price_czk \\\n", + "0 {'value_raw': 12862000, 'unit': '', 'name': 'C... \n", + "1 {'value_raw': 3990000, 'unit': '', 'name': 'Ce... \n", + "2 {'value_raw': 21978000, 'unit': '', 'name': 'C... \n", + "3 {'value_raw': 18559000, 'unit': '', 'name': 'C... \n", + "4 {'value_raw': 21876000, 'unit': '', 'name': 'C... \n", + "\n", + " _links rus \\\n", + "0 {'dynamicDown': [{'href': 'https://d18-a.sdn.c... False \n", + "1 {'dynamicDown': [{'href': 'https://d18-a.sdn.c... False \n", + "2 {'dynamicDown': [{'href': 'https://d18-a.sdn.c... False \n", + "3 {'dynamicDown': [{'href': 'https://d18-a.sdn.c... False \n", + "4 {'dynamicDown': [{'href': 'https://d18-a.sdn.c... False \n", + "\n", + " name region_tip \\\n", + "0 Prodej bytu 4+kk 128 m² 2693402 \n", + "1 Prodej bytu 1+kk 24 m² 0 \n", + "2 Prodej bytu 3+kk 122 m² 0 \n", + "3 Prodej bytu 3+kk 103 m² 0 \n", + "4 Prodej bytu 4+kk 139 m² 0 \n", + "\n", + " gps has_matterport_url \n", + "0 {'lat': 50.12603618747833, 'lon': 14.561554812... False \n", + "1 {'lat': 50.09041518747833, 'lon': 14.531943812... False \n", + "2 {'lat': 50.06292218747833, 'lon': 14.381577812... False \n", + "3 {'lat': 50.06292218747833, 'lon': 14.381577812... False \n", + "4 {'lat': 50.06782018747833, 'lon': 14.507568812... True \n", + "\n", + "[5 rows x 27 columns]" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw.head()" + ] + }, + { + "cell_type": "markdown", + "id": "fc2cde54-c6c3-4baf-9e4c-b740d8eb4dbd", + "metadata": { + "tags": [] + }, + "source": [ + "### 1c. link function `1b` into function `1a`" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "e8da8611-df45-4f30-87d6-8059f61f810d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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labelsReleasedhas_panoramalabelsis_auctionlabelsAllseoexclusively_at_rkcategoryhas_floor_plan_embedded...hash_idattractive_offerpriceprice_czk_linksrusnameregion_tipgpshas_matterport_url
0[[new_building, garage], []]0[Novostavba, Garáž]False[[new_building, personal, terrace, elevator, p...{'category_main_cb': 1, 'category_sub_cb': 6, ...011{'favourite': {'is_favourite': False, '_links'......568091724021760000{'value_raw': 21760000, 'unit': '', 'name': 'C...{'dynamicDown': [{'href': 'https://d18-a.sdn.c...FalseProdej bytu 3+kk 123 m²0{'lat': 50.06301418747833, 'lon': 14.376991812...False
1[[], [post_office, medic]]0[Pošta 6 min. pěšky, Lékař 6 min. pěšky]False[[personal, brick], [candy_shop, small_shop, t...{'category_main_cb': 1, 'category_sub_cb': 4, ...010{'favourite': {'is_favourite': False, '_links'......2946720844024335000{'value_raw': 24335000, 'unit': '', 'name': 'C...{'dynamicDown': [{'href': 'https://d18-a.sdn.c...FalseProdej bytu 2+kk 160 m²0{'lat': 50.07837518747833, 'lon': 14.436064812...False
2[[], [metro, shop]]0[Metro 5 min. pěšky, Obchod 5 min. pěšky]False[[personal, balcony, cellar, elevator, parking...{'category_main_cb': 1, 'category_sub_cb': 6, ...010{'favourite': {'is_favourite': False, '_links'......400340300014034000{'value_raw': 14034000, 'unit': '', 'name': 'C...{'dynamicDown': [{'href': 'https://d18-a.sdn.c...FalseProdej bytu 3+kk 108 m²0{'lat': 50.03316718747833, 'lon': 14.336494812...False
3[[after_reconstruction], [metro, shop]]0[Po rekonstrukci, Metro 2 min. pěšky, Obchod 3...False[[personal, after_reconstruction, brick, parki...{'category_main_cb': 1, 'category_sub_cb': 2, ...011{'favourite': {'is_favourite': False, '_links'......114434158007017000{'value_raw': 7017000, 'unit': '', 'name': 'Ce...{'dynamicDown': [{'href': 'https://d18-a.sdn.c...FalseProdej bytu 1+kk 39 m²0{'lat': 50.05947018747833, 'lon': 14.419744812...False
4[[], [post_office]]0[Pošta 6 min. pěšky]False[[personal, terrace, elevator], [small_shop, t...{'category_main_cb': 1, 'category_sub_cb': 4, ...011{'favourite': {'is_favourite': False, '_links'......162754337208694000{'value_raw': 8694000, 'unit': '', 'name': 'Ce...{'dynamicDown': [{'href': 'https://d18-a.sdn.c...FalseProdej bytu 2+kk 46 m²0{'lat': 50.092200187478326, 'lon': 14.46233681...False
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5 rows × 27 columns

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" + ], + "text/plain": [ + " labelsReleased has_panorama \\\n", + "0 [[new_building, garage], []] 0 \n", + "1 [[], [post_office, medic]] 0 \n", + "2 [[], [metro, shop]] 0 \n", + "3 [[after_reconstruction], [metro, shop]] 0 \n", + "4 [[], [post_office]] 0 \n", + "\n", + " labels is_auction \\\n", + "0 [Novostavba, Garáž] False \n", + "1 [Pošta 6 min. pěšky, Lékař 6 min. pěšky] False \n", + "2 [Metro 5 min. pěšky, Obchod 5 min. pěšky] False \n", + "3 [Po rekonstrukci, Metro 2 min. pěšky, Obchod 3... False \n", + "4 [Pošta 6 min. pěšky] False \n", + "\n", + " labelsAll \\\n", + "0 [[new_building, personal, terrace, elevator, p... \n", + "1 [[personal, brick], [candy_shop, small_shop, t... \n", + "2 [[personal, balcony, cellar, elevator, parking... \n", + "3 [[personal, after_reconstruction, brick, parki... \n", + "4 [[personal, terrace, elevator], [small_shop, t... \n", + "\n", + " seo exclusively_at_rk \\\n", + "0 {'category_main_cb': 1, 'category_sub_cb': 6, ... 0 \n", + "1 {'category_main_cb': 1, 'category_sub_cb': 4, ... 0 \n", + "2 {'category_main_cb': 1, 'category_sub_cb': 6, ... 0 \n", + "3 {'category_main_cb': 1, 'category_sub_cb': 2, ... 0 \n", + "4 {'category_main_cb': 1, 'category_sub_cb': 4, ... 0 \n", + "\n", + " category has_floor_plan \\\n", + "0 1 1 \n", + "1 1 0 \n", + "2 1 0 \n", + "3 1 1 \n", + "4 1 1 \n", + "\n", + " _embedded ... hash_id \\\n", + "0 {'favourite': {'is_favourite': False, '_links'... ... 568091724 \n", + "1 {'favourite': {'is_favourite': False, '_links'... ... 2946720844 \n", + "2 {'favourite': {'is_favourite': False, '_links'... ... 400340300 \n", + "3 {'favourite': {'is_favourite': False, '_links'... ... 1144341580 \n", + "4 {'favourite': {'is_favourite': False, '_links'... ... 1627543372 \n", + "\n", + " attractive_offer price \\\n", + "0 0 21760000 \n", + "1 0 24335000 \n", + "2 0 14034000 \n", + "3 0 7017000 \n", + "4 0 8694000 \n", + "\n", + " price_czk \\\n", + "0 {'value_raw': 21760000, 'unit': '', 'name': 'C... \n", + "1 {'value_raw': 24335000, 'unit': '', 'name': 'C... \n", + "2 {'value_raw': 14034000, 'unit': '', 'name': 'C... \n", + "3 {'value_raw': 7017000, 'unit': '', 'name': 'Ce... \n", + "4 {'value_raw': 8694000, 'unit': '', 'name': 'Ce... \n", + "\n", + " _links rus \\\n", + "0 {'dynamicDown': [{'href': 'https://d18-a.sdn.c... False \n", + "1 {'dynamicDown': [{'href': 'https://d18-a.sdn.c... False \n", + "2 {'dynamicDown': [{'href': 'https://d18-a.sdn.c... False \n", + "3 {'dynamicDown': [{'href': 'https://d18-a.sdn.c... False \n", + "4 {'dynamicDown': [{'href': 'https://d18-a.sdn.c... False \n", + "\n", + " name region_tip \\\n", + "0 Prodej bytu 3+kk 123 m² 0 \n", + "1 Prodej bytu 2+kk 160 m² 0 \n", + "2 Prodej bytu 3+kk 108 m² 0 \n", + "3 Prodej bytu 1+kk 39 m² 0 \n", + "4 Prodej bytu 2+kk 46 m² 0 \n", + "\n", + " gps has_matterport_url \n", + "0 {'lat': 50.06301418747833, 'lon': 14.376991812... False \n", + "1 {'lat': 50.07837518747833, 'lon': 14.436064812... False \n", + "2 {'lat': 50.03316718747833, 'lon': 14.336494812... False \n", + "3 {'lat': 50.05947018747833, 'lon': 14.419744812... False \n", + "4 {'lat': 50.092200187478326, 'lon': 14.46233681... False \n", + "\n", + "[5 rows x 27 columns]" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def request_sreality(page, category_main_str, category_type_str, locality_region_id=10):\n", + " category_mains = {'flat':1, 'house':2, 'land':3 }\n", + " category_types = {'sell':1,'rent':2}\n", + " template_url = 'https://www.sreality.cz/api/cs/v2/estates?category_main_cb={category_main}&category_type_cb={category_type}&locality_region_id={locality_region_id}&per_page60&page={page}'\n", + " \n", + " request_url = template_url.format(\n", + " category_main=category_mains[category_main_str],\n", + " category_type=category_types[category_type_str],\n", + " locality_region_id=locality_region_id,\n", + " page=page\n", + " )\n", + " \n", + " try: \n", + " r = requests.get(request_url)\n", + " return convert_sreality_data_to_df(r.json())\n", + " except Exception as e:\n", + " print(f'error requesting url {request_url}. Reason: {e.message}')\n", + " \n", + "df = request_sreality(0, 'flat', 'sell', 10)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "4ebab853-b6f2-4335-b13a-6c3cbba1951b", + "metadata": {}, + "source": [ + "### 1c. Combining multiple requests into single df\n", + "\n", + "* Function should parametrize:\n", + " * `start_page` and `end_page`\n", + " * request parameters\n", + "* construct a list of individual request dfs\n", + "* then feed it into `pd.concat` function" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "bc61d311-c46a-4aee-a004-8349ec3ce0de", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(21, 27)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "b1d9bef1-7e5c-4648-89a4-6f472968f3c6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "request_sreality" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "284687ef-aba6-4bbf-b7bf-c42dafda4cb4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(103, 27)" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def request_multiply_sreality(start_page, end_page, category_main_str, category_type_str, locality_region_id=10):\n", + " pages = range(start_page, end_page + 1)\n", + " list_of_dfs = [request_sreality(page, category_main_str, category_type_str, locality_region_id) for page in pages]\n", + " return pd.concat(list_of_dfs)\n", + "\n", + "df = request_multiply_sreality(1, 5, 'flat', 'sell',10)\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "cb5b33f7-fce3-4331-9d3e-ecc7b5184253", + "metadata": {}, + "outputs": [], + "source": [ + "df = df.reset_index().drop('index', axis=1)" + ] + }, + { + "cell_type": "markdown", + "id": "bdde40e7-f68e-4859-878e-772c112f7355", + "metadata": {}, + "source": [ + "## Task 2: Cleaning data\n", + "\n", + "### 2a. Filter columns\n", + "* filter only columns: `['locality', 'price', 'name', 'gps','hash_id','exclusively_at_rk']`\n", + "* use `.copy()` to avoid `SettingWithCopyWarning` later\n" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "34d14f44-48f4-4bcd-bac0-ddf282242464", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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localitypricenamegpshash_idexclusively_at_rk
0Praha 9 - Kbely12862000Prodej bytu 4+kk 128 m²{'lat': 50.12603618747833, 'lon': 14.561554812...582341880
1Praha 2 - Vinohrady21566000Prodej bytu 2+kk 126 m²{'lat': 50.06495918747833, 'lon': 14.454340812...41077899000
2Praha 5 - Sobín17382000Prodej bytu 3+kk 97 m²{'lat': 50.052054187478326, 'lon': 14.28598081...19728725241
3Praha 5 - Stodůlky18286000Prodej bytu 4+kk 122 m²{'lat': 50.02775118747833, 'lon': 14.324684812...8663509240
4Praha 5 - Stodůlky14140000Prodej bytu 3+kk 88 m²{'lat': 50.02775118747833, 'lon': 14.324684812...37352548600
.....................
98Praha 8 - Karlín10236000Prodej bytu 1+kk 60 m²{'lat': 50.08081318747833, 'lon': 14.459052812...19180022520
99Praha 4 - Michle29614000Prodej bytu 3+kk 272 m²{'lat': 50.03685218747833, 'lon': 14.467224812...28106192121
100Praha 4 - Modřany14018000Prodej bytu 3+kk 100 m²{'lat': 49.989115187478326, 'lon': 14.41775681...15670208760
101Praha 9 - Kbely11121000Prodej bytu 3+kk 88 m²{'lat': 50.11815518747833, 'lon': 14.550433812...16840428280
102Praha 5 - Stodůlky11421000Prodej bytu 2+kk 74 m²{'lat': 50.03348418747833, 'lon': 14.323431812...3470633721
\n", + "

103 rows × 6 columns

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" + ], + "text/plain": [ + " locality price name \\\n", + "0 Praha 9 - Kbely 12862000 Prodej bytu 4+kk 128 m² \n", + "1 Praha 2 - Vinohrady 21566000 Prodej bytu 2+kk 126 m² \n", + "2 Praha 5 - Sobín 17382000 Prodej bytu 3+kk 97 m² \n", + "3 Praha 5 - Stodůlky 18286000 Prodej bytu 4+kk 122 m² \n", + "4 Praha 5 - Stodůlky 14140000 Prodej bytu 3+kk 88 m² \n", + ".. ... ... ... \n", + "98 Praha 8 - Karlín 10236000 Prodej bytu 1+kk 60 m² \n", + "99 Praha 4 - Michle 29614000 Prodej bytu 3+kk 272 m² \n", + "100 Praha 4 - Modřany 14018000 Prodej bytu 3+kk 100 m² \n", + "101 Praha 9 - Kbely 11121000 Prodej bytu 3+kk 88 m² \n", + "102 Praha 5 - Stodůlky 11421000 Prodej bytu 2+kk 74 m² \n", + "\n", + " gps hash_id \\\n", + "0 {'lat': 50.12603618747833, 'lon': 14.561554812... 58234188 \n", + "1 {'lat': 50.06495918747833, 'lon': 14.454340812... 4107789900 \n", + "2 {'lat': 50.052054187478326, 'lon': 14.28598081... 1972872524 \n", + "3 {'lat': 50.02775118747833, 'lon': 14.324684812... 866350924 \n", + "4 {'lat': 50.02775118747833, 'lon': 14.324684812... 3735254860 \n", + ".. ... ... \n", + "98 {'lat': 50.08081318747833, 'lon': 14.459052812... 1918002252 \n", + "99 {'lat': 50.03685218747833, 'lon': 14.467224812... 2810619212 \n", + "100 {'lat': 49.989115187478326, 'lon': 14.41775681... 1567020876 \n", + "101 {'lat': 50.11815518747833, 'lon': 14.550433812... 1684042828 \n", + "102 {'lat': 50.03348418747833, 'lon': 14.323431812... 347063372 \n", + "\n", + " exclusively_at_rk \n", + "0 0 \n", + "1 0 \n", + "2 1 \n", + "3 0 \n", + "4 0 \n", + ".. ... \n", + "98 0 \n", + "99 1 \n", + "100 0 \n", + "101 0 \n", + "102 1 \n", + "\n", + "[103 rows x 6 columns]" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clean = df[['locality', 'price', 'name', 'gps','hash_id','exclusively_at_rk']].copy()\n", + "clean" + ] + }, + { + "cell_type": "markdown", + "id": "80deec04-4959-4d9a-8a3a-7cf616e8558a", + "metadata": { + "tags": [] + }, + "source": [ + "### 2b: GPS\n", + "* Convert dictionary in `gps` column into two columns - `lat` and `lon`\n", + "* use apply function on gps column\n", + "* Note apply can return multiple columns" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "68f281f1-5169-47f6-a89d-ed1d9f416a48", + "metadata": {}, + "outputs": [], + "source": [ + "clean[['lat', 'lon']] = clean.gps.apply(lambda x: pd.Series({'lat': x['lat'], 'lon': x['lon']}))" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "fbd73a3c-83d5-4b74-8232-58ed33ee1edc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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localitypricenamegpshash_idexclusively_at_rklat1lon1latlon
0Praha 9 - Kbely12862000Prodej bytu 4+kk 128 m²{'lat': 50.12603618747833, 'lon': 14.561554812...58234188050.12603614.56155550.12603614.561555
1Praha 2 - Vinohrady21566000Prodej bytu 2+kk 126 m²{'lat': 50.06495918747833, 'lon': 14.454340812...4107789900050.06495914.45434150.06495914.454341
2Praha 5 - Sobín17382000Prodej bytu 3+kk 97 m²{'lat': 50.052054187478326, 'lon': 14.28598081...1972872524150.05205414.28598150.05205414.285981
3Praha 5 - Stodůlky18286000Prodej bytu 4+kk 122 m²{'lat': 50.02775118747833, 'lon': 14.324684812...866350924050.02775114.32468550.02775114.324685
4Praha 5 - Stodůlky14140000Prodej bytu 3+kk 88 m²{'lat': 50.02775118747833, 'lon': 14.324684812...3735254860050.02775114.32468550.02775114.324685
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98Praha 8 - Karlín10236000Prodej bytu 1+kk 60 m²{'lat': 50.08081318747833, 'lon': 14.459052812...1918002252050.08081314.45905350.08081314.459053
99Praha 4 - Michle29614000Prodej bytu 3+kk 272 m²{'lat': 50.03685218747833, 'lon': 14.467224812...2810619212150.03685214.46722550.03685214.467225
100Praha 4 - Modřany14018000Prodej bytu 3+kk 100 m²{'lat': 49.989115187478326, 'lon': 14.41775681...1567020876049.98911514.41775749.98911514.417757
101Praha 9 - Kbely11121000Prodej bytu 3+kk 88 m²{'lat': 50.11815518747833, 'lon': 14.550433812...1684042828050.11815514.55043450.11815514.550434
102Praha 5 - Stodůlky11421000Prodej bytu 2+kk 74 m²{'lat': 50.03348418747833, 'lon': 14.323431812...347063372150.03348414.32343250.03348414.323432
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103 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " locality price name \\\n", + "0 Praha 9 - Kbely 12862000 Prodej bytu 4+kk 128 m² \n", + "1 Praha 2 - Vinohrady 21566000 Prodej bytu 2+kk 126 m² \n", + "2 Praha 5 - Sobín 17382000 Prodej bytu 3+kk 97 m² \n", + "3 Praha 5 - Stodůlky 18286000 Prodej bytu 4+kk 122 m² \n", + "4 Praha 5 - Stodůlky 14140000 Prodej bytu 3+kk 88 m² \n", + ".. ... ... ... \n", + "98 Praha 8 - Karlín 10236000 Prodej bytu 1+kk 60 m² \n", + "99 Praha 4 - Michle 29614000 Prodej bytu 3+kk 272 m² \n", + "100 Praha 4 - Modřany 14018000 Prodej bytu 3+kk 100 m² \n", + "101 Praha 9 - Kbely 11121000 Prodej bytu 3+kk 88 m² \n", + "102 Praha 5 - Stodůlky 11421000 Prodej bytu 2+kk 74 m² \n", + "\n", + " gps hash_id \\\n", + "0 {'lat': 50.12603618747833, 'lon': 14.561554812... 58234188 \n", + "1 {'lat': 50.06495918747833, 'lon': 14.454340812... 4107789900 \n", + "2 {'lat': 50.052054187478326, 'lon': 14.28598081... 1972872524 \n", + "3 {'lat': 50.02775118747833, 'lon': 14.324684812... 866350924 \n", + "4 {'lat': 50.02775118747833, 'lon': 14.324684812... 3735254860 \n", + ".. ... ... \n", + "98 {'lat': 50.08081318747833, 'lon': 14.459052812... 1918002252 \n", + "99 {'lat': 50.03685218747833, 'lon': 14.467224812... 2810619212 \n", + "100 {'lat': 49.989115187478326, 'lon': 14.41775681... 1567020876 \n", + "101 {'lat': 50.11815518747833, 'lon': 14.550433812... 1684042828 \n", + "102 {'lat': 50.03348418747833, 'lon': 14.323431812... 347063372 \n", + "\n", + " exclusively_at_rk lat1 lon1 lat lon \n", + "0 0 50.126036 14.561555 50.126036 14.561555 \n", + "1 0 50.064959 14.454341 50.064959 14.454341 \n", + "2 1 50.052054 14.285981 50.052054 14.285981 \n", + "3 0 50.027751 14.324685 50.027751 14.324685 \n", + "4 0 50.027751 14.324685 50.027751 14.324685 \n", + ".. ... ... ... ... ... \n", + "98 0 50.080813 14.459053 50.080813 14.459053 \n", + "99 1 50.036852 14.467225 50.036852 14.467225 \n", + "100 0 49.989115 14.417757 49.989115 14.417757 \n", + "101 0 50.118155 14.550434 50.118155 14.550434 \n", + "102 1 50.033484 14.323432 50.033484 14.323432 \n", + "\n", + "[103 rows x 10 columns]" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clean" + ] + }, + { + "cell_type": "markdown", + "id": "36c22408-c327-4c17-b1b4-de54f63f0627", + "metadata": {}, + "source": [ + "### 2b. Get flat type from name\n", + "* Name is always represented by string `Prodej bytu [type of flat] [Area] m^2`\n", + "* try picking third word in string\n", + "* check meaningfulness using `.value_counts()`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "150a551c-f321-408d-b8bc-dee6c2fb2adf", + "metadata": {}, + "outputs": [], + "source": [ + "clean['flat_type'] = clean.name.apply(lambda nm:nm.split()[2])" + ] + }, + { + "cell_type": "markdown", + "id": "5e233b14-db62-41f8-be82-45c861d62e3e", + "metadata": {}, + "source": [ + "### 2c. Get area from name\n", + "* Naive: select the word before last word\n", + "* Then try navigating using the index of `'m²'`\n", + "* if this also fail, then you will need to use regex" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "id": "94a6f0cf-2c35-42fa-a518-d1249487da1e", + "metadata": {}, + "outputs": [], + "source": [ + "clean['area_1'] = clean.name.apply(lambda nm:nm.split()[3])" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "id": "acfe4582-583d-42b9-acf8-72ccf97cfad5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Prodej', 'bytu', '4+kk', '128', 'm²']\n" + ] + }, + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n = 'Prodej bytu 4+kk 128 m²'\n", + "splited = n.split()\n", + "print(splited)\n", + "splited.index('m²')" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "id": "02903e8f-5ef2-4cc6-bdcc-fb68d88ea3ba", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "128" + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "int(splited[3])" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "id": "150ff188-b6d0-4326-95c3-cfc30e6fcb03", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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localitypricenamegpshash_idexclusively_at_rklat1lon1latlonflat_typeareaarea_1area_2
0Praha 9 - Kbely12862000Prodej bytu 4+kk 128 m²{'lat': 50.12603618747833, 'lon': 14.561554812...58234188050.12603614.56155550.12603614.561555[Prodej, bytu, 4+kk, 128, m²]128128128
1Praha 2 - Vinohrady21566000Prodej bytu 2+kk 126 m²{'lat': 50.06495918747833, 'lon': 14.454340812...4107789900050.06495914.45434150.06495914.454341[Prodej, bytu, 2+kk, 126, m²]126126126
2Praha 5 - Sobín17382000Prodej bytu 3+kk 97 m²{'lat': 50.052054187478326, 'lon': 14.28598081...1972872524150.05205414.28598150.05205414.285981[Prodej, bytu, 3+kk, 97, m²]979797
3Praha 5 - Stodůlky18286000Prodej bytu 4+kk 122 m²{'lat': 50.02775118747833, 'lon': 14.324684812...866350924050.02775114.32468550.02775114.324685[Prodej, bytu, 4+kk, 122, m²]122122122
4Praha 5 - Stodůlky14140000Prodej bytu 3+kk 88 m²{'lat': 50.02775118747833, 'lon': 14.324684812...3735254860050.02775114.32468550.02775114.324685[Prodej, bytu, 3+kk, 88, m²]888888
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98Praha 8 - Karlín10236000Prodej bytu 1+kk 60 m²{'lat': 50.08081318747833, 'lon': 14.459052812...1918002252050.08081314.45905350.08081314.459053[Prodej, bytu, 1+kk, 60, m²]606060
99Praha 4 - Michle29614000Prodej bytu 3+kk 272 m²{'lat': 50.03685218747833, 'lon': 14.467224812...2810619212150.03685214.46722550.03685214.467225[Prodej, bytu, 3+kk, 272, m²]272272272
100Praha 4 - Modřany14018000Prodej bytu 3+kk 100 m²{'lat': 49.989115187478326, 'lon': 14.41775681...1567020876049.98911514.41775749.98911514.417757[Prodej, bytu, 3+kk, 100, m²]100100100
101Praha 9 - Kbely11121000Prodej bytu 3+kk 88 m²{'lat': 50.11815518747833, 'lon': 14.550433812...1684042828050.11815514.55043450.11815514.550434[Prodej, bytu, 3+kk, 88, m²]888888
102Praha 5 - Stodůlky11421000Prodej bytu 2+kk 74 m²{'lat': 50.03348418747833, 'lon': 14.323431812...347063372150.03348414.32343250.03348414.323432[Prodej, bytu, 2+kk, 74, m²]747474
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103 rows × 14 columns

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" + ], + "text/plain": [ + " locality price name \\\n", + "0 Praha 9 - Kbely 12862000 Prodej bytu 4+kk 128 m² \n", + "1 Praha 2 - Vinohrady 21566000 Prodej bytu 2+kk 126 m² \n", + "2 Praha 5 - Sobín 17382000 Prodej bytu 3+kk 97 m² \n", + "3 Praha 5 - Stodůlky 18286000 Prodej bytu 4+kk 122 m² \n", + "4 Praha 5 - Stodůlky 14140000 Prodej bytu 3+kk 88 m² \n", + ".. ... ... ... \n", + "98 Praha 8 - Karlín 10236000 Prodej bytu 1+kk 60 m² \n", + "99 Praha 4 - Michle 29614000 Prodej bytu 3+kk 272 m² \n", + "100 Praha 4 - Modřany 14018000 Prodej bytu 3+kk 100 m² \n", + "101 Praha 9 - Kbely 11121000 Prodej bytu 3+kk 88 m² \n", + "102 Praha 5 - Stodůlky 11421000 Prodej bytu 2+kk 74 m² \n", + "\n", + " gps hash_id \\\n", + "0 {'lat': 50.12603618747833, 'lon': 14.561554812... 58234188 \n", + "1 {'lat': 50.06495918747833, 'lon': 14.454340812... 4107789900 \n", + "2 {'lat': 50.052054187478326, 'lon': 14.28598081... 1972872524 \n", + "3 {'lat': 50.02775118747833, 'lon': 14.324684812... 866350924 \n", + "4 {'lat': 50.02775118747833, 'lon': 14.324684812... 3735254860 \n", + ".. ... ... \n", + "98 {'lat': 50.08081318747833, 'lon': 14.459052812... 1918002252 \n", + "99 {'lat': 50.03685218747833, 'lon': 14.467224812... 2810619212 \n", + "100 {'lat': 49.989115187478326, 'lon': 14.41775681... 1567020876 \n", + "101 {'lat': 50.11815518747833, 'lon': 14.550433812... 1684042828 \n", + "102 {'lat': 50.03348418747833, 'lon': 14.323431812... 347063372 \n", + "\n", + " exclusively_at_rk lat1 lon1 lat lon \\\n", + "0 0 50.126036 14.561555 50.126036 14.561555 \n", + "1 0 50.064959 14.454341 50.064959 14.454341 \n", + "2 1 50.052054 14.285981 50.052054 14.285981 \n", + "3 0 50.027751 14.324685 50.027751 14.324685 \n", + "4 0 50.027751 14.324685 50.027751 14.324685 \n", + ".. ... ... ... ... ... \n", + "98 0 50.080813 14.459053 50.080813 14.459053 \n", + "99 1 50.036852 14.467225 50.036852 14.467225 \n", + "100 0 49.989115 14.417757 49.989115 14.417757 \n", + "101 0 50.118155 14.550434 50.118155 14.550434 \n", + "102 1 50.033484 14.323432 50.033484 14.323432 \n", + "\n", + " flat_type area area_1 area_2 \n", + "0 [Prodej, bytu, 4+kk, 128, m²] 128 128 128 \n", + "1 [Prodej, bytu, 2+kk, 126, m²] 126 126 126 \n", + "2 [Prodej, bytu, 3+kk, 97, m²] 97 97 97 \n", + "3 [Prodej, bytu, 4+kk, 122, m²] 122 122 122 \n", + "4 [Prodej, bytu, 3+kk, 88, m²] 88 88 88 \n", + ".. ... ... ... ... \n", + "98 [Prodej, bytu, 1+kk, 60, m²] 60 60 60 \n", + "99 [Prodej, bytu, 3+kk, 272, m²] 272 272 272 \n", + "100 [Prodej, bytu, 3+kk, 100, m²] 100 100 100 \n", + "101 [Prodej, bytu, 3+kk, 88, m²] 88 88 88 \n", + "102 [Prodej, bytu, 2+kk, 74, m²] 74 74 74 \n", + "\n", + "[103 rows x 14 columns]" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def name_to_area(nm):\n", + " splitted= nm.split()\n", + " m2_idx = splitted.index('m²')\n", + " return int(splitted[m2_idx-1])\n", + "\n", + "clean['area_2'] = clean.name.apply(name_to_area)\n", + "clean" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "id": "3bc089d8-eab3-4e85-9f79-ce30291b456c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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localitypricenamegpshash_idexclusively_at_rklat1lon1latlonflat_typeareaarea_1area_2
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" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [locality, price, name, gps, hash_id, exclusively_at_rk, lat1, lon1, lat, lon, flat_type, area, area_1, area_2]\n", + "Index: []" + ] + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clean[clean['area_1']==clean['area_2']]" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "id": "fbc4fd99-9dd9-43d0-9e4b-e49e48556534", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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localitypricenamegpshash_idexclusively_at_rklat1lon1latlonflat_typeareaarea_1area_2
0Praha 9 - Kbely12862000Prodej bytu 4+kk 128 m²{'lat': 50.12603618747833, 'lon': 14.561554812...58234188050.12603614.56155550.12603614.561555[Prodej, bytu, 4+kk, 128, m²]128128128
1Praha 2 - Vinohrady21566000Prodej bytu 2+kk 126 m²{'lat': 50.06495918747833, 'lon': 14.454340812...4107789900050.06495914.45434150.06495914.454341[Prodej, bytu, 2+kk, 126, m²]126126126
2Praha 5 - Sobín17382000Prodej bytu 3+kk 97 m²{'lat': 50.052054187478326, 'lon': 14.28598081...1972872524150.05205414.28598150.05205414.285981[Prodej, bytu, 3+kk, 97, m²]979797
3Praha 5 - Stodůlky18286000Prodej bytu 4+kk 122 m²{'lat': 50.02775118747833, 'lon': 14.324684812...866350924050.02775114.32468550.02775114.324685[Prodej, bytu, 4+kk, 122, m²]122122122
4Praha 5 - Stodůlky14140000Prodej bytu 3+kk 88 m²{'lat': 50.02775118747833, 'lon': 14.324684812...3735254860050.02775114.32468550.02775114.324685[Prodej, bytu, 3+kk, 88, m²]888888
.............................................
98Praha 8 - Karlín10236000Prodej bytu 1+kk 60 m²{'lat': 50.08081318747833, 'lon': 14.459052812...1918002252050.08081314.45905350.08081314.459053[Prodej, bytu, 1+kk, 60, m²]606060
99Praha 4 - Michle29614000Prodej bytu 3+kk 272 m²{'lat': 50.03685218747833, 'lon': 14.467224812...2810619212150.03685214.46722550.03685214.467225[Prodej, bytu, 3+kk, 272, m²]272272272
100Praha 4 - Modřany14018000Prodej bytu 3+kk 100 m²{'lat': 49.989115187478326, 'lon': 14.41775681...1567020876049.98911514.41775749.98911514.417757[Prodej, bytu, 3+kk, 100, m²]100100100
101Praha 9 - Kbely11121000Prodej bytu 3+kk 88 m²{'lat': 50.11815518747833, 'lon': 14.550433812...1684042828050.11815514.55043450.11815514.550434[Prodej, bytu, 3+kk, 88, m²]888888
102Praha 5 - Stodůlky11421000Prodej bytu 2+kk 74 m²{'lat': 50.03348418747833, 'lon': 14.323431812...347063372150.03348414.32343250.03348414.323432[Prodej, bytu, 2+kk, 74, m²]747474
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103 rows × 14 columns

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" + ], + "text/plain": [ + " locality price name \\\n", + "0 Praha 9 - Kbely 12862000 Prodej bytu 4+kk 128 m² \n", + "1 Praha 2 - Vinohrady 21566000 Prodej bytu 2+kk 126 m² \n", + "2 Praha 5 - Sobín 17382000 Prodej bytu 3+kk 97 m² \n", + "3 Praha 5 - Stodůlky 18286000 Prodej bytu 4+kk 122 m² \n", + "4 Praha 5 - Stodůlky 14140000 Prodej bytu 3+kk 88 m² \n", + ".. ... ... ... \n", + "98 Praha 8 - Karlín 10236000 Prodej bytu 1+kk 60 m² \n", + "99 Praha 4 - Michle 29614000 Prodej bytu 3+kk 272 m² \n", + "100 Praha 4 - Modřany 14018000 Prodej bytu 3+kk 100 m² \n", + "101 Praha 9 - Kbely 11121000 Prodej bytu 3+kk 88 m² \n", + "102 Praha 5 - Stodůlky 11421000 Prodej bytu 2+kk 74 m² \n", + "\n", + " gps hash_id \\\n", + "0 {'lat': 50.12603618747833, 'lon': 14.561554812... 58234188 \n", + "1 {'lat': 50.06495918747833, 'lon': 14.454340812... 4107789900 \n", + "2 {'lat': 50.052054187478326, 'lon': 14.28598081... 1972872524 \n", + "3 {'lat': 50.02775118747833, 'lon': 14.324684812... 866350924 \n", + "4 {'lat': 50.02775118747833, 'lon': 14.324684812... 3735254860 \n", + ".. ... ... \n", + "98 {'lat': 50.08081318747833, 'lon': 14.459052812... 1918002252 \n", + "99 {'lat': 50.03685218747833, 'lon': 14.467224812... 2810619212 \n", + "100 {'lat': 49.989115187478326, 'lon': 14.41775681... 1567020876 \n", + "101 {'lat': 50.11815518747833, 'lon': 14.550433812... 1684042828 \n", + "102 {'lat': 50.03348418747833, 'lon': 14.323431812... 347063372 \n", + "\n", + " exclusively_at_rk lat1 lon1 lat lon \\\n", + "0 0 50.126036 14.561555 50.126036 14.561555 \n", + "1 0 50.064959 14.454341 50.064959 14.454341 \n", + "2 1 50.052054 14.285981 50.052054 14.285981 \n", + "3 0 50.027751 14.324685 50.027751 14.324685 \n", + "4 0 50.027751 14.324685 50.027751 14.324685 \n", + ".. ... ... ... ... ... \n", + "98 0 50.080813 14.459053 50.080813 14.459053 \n", + "99 1 50.036852 14.467225 50.036852 14.467225 \n", + "100 0 49.989115 14.417757 49.989115 14.417757 \n", + "101 0 50.118155 14.550434 50.118155 14.550434 \n", + "102 1 50.033484 14.323432 50.033484 14.323432 \n", + "\n", + " flat_type area area_1 area_2 \n", + "0 [Prodej, bytu, 4+kk, 128, m²] 128 128 128 \n", + "1 [Prodej, bytu, 2+kk, 126, m²] 126 126 126 \n", + "2 [Prodej, bytu, 3+kk, 97, m²] 97 97 97 \n", + "3 [Prodej, bytu, 4+kk, 122, m²] 122 122 122 \n", + "4 [Prodej, bytu, 3+kk, 88, m²] 88 88 88 \n", + ".. ... ... ... ... \n", + "98 [Prodej, bytu, 1+kk, 60, m²] 60 60 60 \n", + "99 [Prodej, bytu, 3+kk, 272, m²] 272 272 272 \n", + "100 [Prodej, bytu, 3+kk, 100, m²] 100 100 100 \n", + "101 [Prodej, bytu, 3+kk, 88, m²] 88 88 88 \n", + "102 [Prodej, bytu, 2+kk, 74, m²] 74 74 74 \n", + "\n", + "[103 rows x 14 columns]" + ] + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clean[clean['area_1'].astype(int)==clean['area_2']]" + ] + }, + { + "cell_type": "markdown", + "id": "ce71f809-7a5a-487e-882a-6aa9c7124727", + "metadata": {}, + "source": [ + "## Bonus: Convert `labelsAll` into categorical variables\n", + "\n", + "### Task 4a. Get all possible label names\n", + "* deal with nested-list structure\n", + "* Hint: try to sum the whole column\n", + "* Needed to Iterate through all labels in all rows and " + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "id": "03f3d060-5967-48af-9789-cade7acb715b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['d', 'c']" + ] + }, + "execution_count": 141, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "['d'] + ['c']" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "id": "073e90c7-bec6-4b04-bba3-cf095cdc65f5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['natural_attraction',\n", + " 'kindergarten',\n", + " 'tram',\n", + " 'movies',\n", + " 'cellar',\n", + " 'brick',\n", + " 'candy_shop',\n", + " 'train',\n", + " 'metro',\n", + " 'bus_public_transport',\n", + " 'playground',\n", + " 'personal',\n", + " 'tavern',\n", + " 'loggia',\n", + " 'elevator',\n", + " 'school',\n", + " 'small_shop',\n", + " 'parking_lots',\n", + " 'partly_furnished',\n", + " 'new_building',\n", + " 'vet',\n", + " 'theater',\n", + " 'balcony',\n", + " 'not_furnished',\n", + " 'shop',\n", + " 'medic',\n", + " 'post_office',\n", + " 'sightseeing',\n", + " 'restaurant',\n", + " 'in_construction',\n", + " 'atm',\n", + " 'sports',\n", + " 'garage',\n", + " 'drugstore']" + ] + }, + "execution_count": 152, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "possible_labels = list(set([i for sublist in raw.labelsAll.sum() for i in sublist]))\n", + "possible_labels" + ] + }, + { + "cell_type": "markdown", + "id": "db0b86aa-57b0-439d-a82f-1d8f962be7c2", + "metadata": {}, + "source": [ + "### 4b. Test existence of label `cellar` for offers\n", + "* again deal with nested list of list structure\n", + "* write generic function `test_existence_of_label(offer_labels,label)`" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "id": "a633c468-e096-46bf-a51e-0f30f11cca26", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "1 False\n", + "2 True\n", + "3 True\n", + "4 False\n", + "5 True\n", + "6 True\n", + "7 False\n", + "8 True\n", + "9 True\n", + "10 True\n", + "11 True\n", + "12 True\n", + "13 True\n", + "14 True\n", + "15 True\n", + "16 True\n", + "17 True\n", + "18 True\n", + "19 True\n", + "20 True\n", + "Name: labelsAll, dtype: bool" + ] + }, + "execution_count": 163, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def test_existence_of_label(offer_labels,label):\n", + " return 'cellar' in [item for sublist in offer_labels for item in sublist]\n", + "\n", + "raw.labelsAll.apply(lambda offer_labels: test_existence_of_label(offer_labels, 'cellar'))" + ] + }, + { + "cell_type": "markdown", + "id": "d5e22365-b2d8-4c57-a0cd-7297efb8b948", + "metadata": {}, + "source": [ + "### 4c. Test existence of all possible labels\n", + "* use apply returning series with all labels" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "id": "8165a5a4-a52c-453a-b3e9-39d868fe5501", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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21 rows × 34 columns

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Create a function requesting data from sreality\n", + "\n", + "\n", + "```python\n", + "base_url = 'https://www.sreality.cz/api/cs/v2/estates?category_main_cb=1&category_type_cb=1&locality_region_id=10&per_page60&page={}'.format(i)\n", + "\n", + "r = requests.get(base_url)\n", + "d = r.json()\n", + "```\n", + "\n", + "* function should parametrize: \n", + " * `category_main_cb` - `{'flat':1, 'house':2, 'land':3 }`\n", + " * `category_type_cb` - `{'sell':1,'rent':2}`\n", + " * `locality_region_id` - use 10 as default value\n", + " * `page` parameter\n", + "* use string inputs for `category_main_cb` and `category_type_cb`\n", + "* include try/except clause to handle errors\n", + "* function should return JSON data in python types\n", + "* do not forget to sleep each request at least 0.5s" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "55cca328-f8c4-4fba-a0b3-6d20c990d712", + "metadata": {}, + "outputs": [], + "source": [ + "def request_sreality(page, category_main_str, category_type_str, locality_region_id=10):\n", + " category_mains = {'flat':1, 'house':2, 'land':3 }\n", + " category_types = {'sell':1,'rent':2}\n", + " template_url = 'https://www.sreality.cz/api/cs/v2/estates?category_main_cb={category_main}&category_type_cb={category_type}&locality_region_id={locality_region_id}&per_page60&page={page}'\n", + " request_url = template_url.format(\n", + " category_main=category_mains[category_main_str],\n", + " category_type=category_types[category_type_str],\n", + " locality_region_id=locality_region_id,\n", + " page=page\n", + " )\n", + " r = requests.get(request_url)\n", + " return r.json()\n", + "d = request_sreality(0, 'flat', 'sell', 10)" + ] + }, + { + "cell_type": "markdown", + "id": "8b718701-e4a4-4fe4-bf34-d03913765b2a", + "metadata": {}, + "source": [ + "### 1b. Create a function converting sreality json data into pandas dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "71e52613-8e0e-4b5a-a579-76d803eafa31", + "metadata": {}, + "outputs": [], + "source": [ + "def convert_sreality_data_to_df(sreality_data):\n", + " return\n", + "\n", + "raw = convert_sreality_data_to_df(d)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "219610c5-fea8-487c-8682-2e803d1fc2d1", + "metadata": {}, + "outputs": [], + "source": [ + "raw.head()" + ] + }, + { + "cell_type": "markdown", + "id": "fc2cde54-c6c3-4baf-9e4c-b740d8eb4dbd", + "metadata": { + "tags": [] + }, + "source": [ + "### 1c. link function `1b` into function `1a`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e8da8611-df45-4f30-87d6-8059f61f810d", + "metadata": {}, + "outputs": [], + "source": [ + "df = request_sreality(0, 'flat', 'sell', 10)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "4ebab853-b6f2-4335-b13a-6c3cbba1951b", + "metadata": {}, + "source": [ + "### 1c. Combining multiple requests into single df\n", + "\n", + "* Function should parametrize:\n", + " * `start_page` and `end_page`\n", + " * request parameters\n", + "* construct a list of individual request dfs\n", + "* then feed it into `pd.concat` function" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "bc61d311-c46a-4aee-a004-8349ec3ce0de", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(21, 27)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "b1d9bef1-7e5c-4648-89a4-6f472968f3c6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "request_sreality" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "284687ef-aba6-4bbf-b7bf-c42dafda4cb4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(103, 27)" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def request_multiply_sreality(start_page, end_page, category_main_str, category_type_str, locality_region_id=10):\n", + " \n", + " return pd.concat(list_of_dfs)\n", + "\n", + "df = request_multiply_sreality(1, 5, 'flat', 'sell',10)\n", + "df.shape" + ] + }, + { + "cell_type": "markdown", + "id": "bdde40e7-f68e-4859-878e-772c112f7355", + "metadata": {}, + "source": [ + "## Task 2: Cleaning data\n", + "\n", + "### 2a. Filter columns\n", + "* filter only columns: `['locality', 'price', 'name', 'gps','hash_id','exclusively_at_rk']`\n", + "* use `.copy()` to avoid `SettingWithCopyWarning` later\n" + ] + }, + { + "cell_type": "markdown", + "id": "80deec04-4959-4d9a-8a3a-7cf616e8558a", + "metadata": { + "tags": [] + }, + "source": [ + "### 2b: GPS\n", + "* Convert dictionary in `gps` column into two columns - `lat` and `lon`\n", + "* use apply function on gps column\n", + "* Note apply can return multiple columns" + ] + }, + { + "cell_type": "markdown", + "id": "36c22408-c327-4c17-b1b4-de54f63f0627", + "metadata": {}, + "source": [ + "### 2b. Get flat type from name\n", + "* Name is always represented by string `Prodej bytu [type of flat] [Area] m^2`\n", + "* try picking third word in string\n", + "* check meaningfulness using `.value_counts()`" + ] + }, + { + "cell_type": "markdown", + "id": "5e233b14-db62-41f8-be82-45c861d62e3e", + "metadata": {}, + "source": [ + "### 2c. Get area from name\n", + "* Naive: select the word before last word\n", + "* Then try navigating using the index of `'m²'`\n", + "* if this also fail, then you will need to use regex" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "150ff188-b6d0-4326-95c3-cfc30e6fcb03", + "metadata": {}, + "outputs": [], + "source": [ + "def name_to_area(nm):\n", + "\n", + " return \n", + "\n", + "clean['area_2'] = clean.name.apply(name_to_area)\n", + "clean" + ] + }, + { + "cell_type": "markdown", + "id": "ce71f809-7a5a-487e-882a-6aa9c7124727", + "metadata": {}, + "source": [ + "## Bonus: Convert `labelsAll` into categorical variables\n", + "\n", + "### Task 4a. Get all possible label names\n", + "* deal with nested-list structure\n", + "* Hint: try to sum the whole column\n", + "* Needed to Iterate through all labels in all rows and " + ] + }, + { + "cell_type": "markdown", + "id": "db0b86aa-57b0-439d-a82f-1d8f962be7c2", + "metadata": {}, + "source": [ + "### 4b. Test existence of label `cellar` for offers\n", + "* again deal with nested list of list structure\n", + "* write generic function `test_existence_of_label(offer_labels,label)`" + ] + }, + { + "cell_type": "markdown", + "id": "d5e22365-b2d8-4c57-a0cd-7297efb8b948", + "metadata": {}, + "source": [ + "### 4c. Test existence of all possible labels\n", + "* use apply returning series with all labels" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8eceb6c0-9af6-4fb9-b178-f371dd453d39", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Seminar5/DS_case_study_empty.ipynb b/Seminar5/DS_case_study_empty.ipynb new file mode 100644 index 0000000..ac53f44 --- /dev/null +++ b/Seminar5/DS_case_study_empty.ipynb @@ -0,0 +1,1116 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2d667870", + "metadata": {}, + "source": [ + "# Heart Attack Predicting Case Study" + ] + }, + { + "cell_type": "markdown", + "id": "be742eee", + "metadata": {}, + "source": [ + "## Problem definition: Predict whether a patient will have a heart attack or not" + ] + }, + { + "cell_type": "markdown", + "id": "6c9c4ad8", + "metadata": {}, + "source": [ + "### Features:\n", + "Age : Age of the patient\n", + "\n", + "Sex : Sex of the patient\n", + "\n", + "exang: exercise induced angina (1 = yes; 0 = no)\n", + "\n", + "ca: number of major vessels (0-3)\n", + "\n", + "cp : Chest Pain type chest pain type\n", + "\n", + "Value 1: typical angina\n", + "Value 2: atypical angina\n", + "Value 3: non-anginal pain\n", + "Value 4: asymptomatic\n", + "trtbps : resting blood pressure (in mm Hg)\n", + "\n", + "chol : cholestoral in mg/dl fetched via BMI sensor\n", + "\n", + "fbs : (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)\n", + "\n", + "rest_ecg : resting electrocardiographic results\n", + "\n", + "Value 0: normal\n", + "Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)\n", + "Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria\n", + "thalach : maximum heart rate achieved\n", + "\n", + "target : 0= less chance of heart attack 1= more chance of heart attack" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "25294e0a", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import sklearn\n", + "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.metrics import confusion_matrix\n" + ] + }, + { + "cell_type": "markdown", + "id": "0cd1b4db", + "metadata": {}, + "source": [ + "## Data preparation" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "cad732a7", + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv(\"heart.csv\")\n", + "o2_saturation = pd.read_csv(\"o2Saturation.csv\").head(303)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "190ade33", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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max77.0000001.0000003.000000200.000000564.0000001.0000002.000000202.0000001.0000006.2000002.0000004.0000003.0000001.000000
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" + ], + "text/plain": [ + " age sex cp trtbps chol fbs \\\n", + "count 303.000000 303.000000 303.000000 303.000000 303.000000 303.000000 \n", + "mean 54.366337 0.683168 0.966997 131.623762 246.264026 0.148515 \n", + "std 9.082101 0.466011 1.032052 17.538143 51.830751 0.356198 \n", + "min 29.000000 0.000000 0.000000 94.000000 126.000000 0.000000 \n", + "25% 47.500000 0.000000 0.000000 120.000000 211.000000 0.000000 \n", + "50% 55.000000 1.000000 1.000000 130.000000 240.000000 0.000000 \n", + "75% 61.000000 1.000000 2.000000 140.000000 274.500000 0.000000 \n", + "max 77.000000 1.000000 3.000000 200.000000 564.000000 1.000000 \n", + "\n", + " restecg thalachh exng oldpeak slp caa \\\n", + "count 303.000000 303.000000 303.000000 303.000000 303.000000 303.000000 \n", + "mean 0.528053 149.646865 0.326733 1.039604 1.399340 0.729373 \n", + "std 0.525860 22.905161 0.469794 1.161075 0.616226 1.022606 \n", + "min 0.000000 71.000000 0.000000 0.000000 0.000000 0.000000 \n", + "25% 0.000000 133.500000 0.000000 0.000000 1.000000 0.000000 \n", + "50% 1.000000 153.000000 0.000000 0.800000 1.000000 0.000000 \n", + "75% 1.000000 166.000000 1.000000 1.600000 2.000000 1.000000 \n", + "max 2.000000 202.000000 1.000000 6.200000 2.000000 4.000000 \n", + "\n", + " thall output \n", + "count 303.000000 303.000000 \n", + "mean 2.313531 0.544554 \n", + "std 0.612277 0.498835 \n", + "min 0.000000 0.000000 \n", + "25% 2.000000 0.000000 \n", + "50% 2.000000 1.000000 \n", + "75% 3.000000 1.000000 \n", + "max 3.000000 1.000000 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "17e69c68", + "metadata": {}, + "outputs": [], + "source": [ + "# what is the distribution of age column?\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "caba3e33", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Numbers if man: 0.68\n", + "Numbers if woman: 0.32\n" + ] + } + ], + "source": [ + "# What is the share of wemales in the sample? What is the share of males?\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "f0dd15a8-1502-40c5-8522-2b3737f89bac", + "metadata": {}, + "outputs": [], + "source": [ + "# Add o2_saturation data in the data frame 'data'\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "195ae311", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "corr = data.corr()\n", + "\n", + "plt.figure(figsize=(15, 10))\n", + "sns.heatmap(corr, annot=True)" + ] + }, + { + "cell_type": "markdown", + "id": "076fb645", + "metadata": {}, + "source": [ + "## Random Forest Classifier" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "0fe76502", + "metadata": {}, + "outputs": [], + "source": [ + "# Specify X and y and split the dataset\n", + "X = \n", + "y = \n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state = 14)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "183fd155", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((212, 14), (91, 14), (212,), (91,))" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train.shape, X_test.shape, y_train.shape, y_test.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "077d86fe", + "metadata": {}, + "outputs": [], + "source": [ + "# specify model\n", + "forest = RandomForestClassifier(random_state = 14)\n", + "\n", + "# train model\n", + "forest.fit(X_train, y_train)\n", + "\n", + "# make prediction\n", + "forest_preds = forest.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "40e251ea-7fcd-4aee-b638-f29309b68b89", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1,\n", + " 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0,\n", + " 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n", + " 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0,\n", + " 0, 1, 1])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "forest_preds" + ] + }, + { + "cell_type": "markdown", + "id": "080c566f-6f78-43d2-8782-008a738e0697", + "metadata": {}, + "source": [ + "## Evaluate the result" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "525bd174-6241-4d14-946a-f41fe31e6c7b", + "metadata": {}, + "outputs": [], + "source": [ + "# Manually calculate number of True positive and False negative predictions\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "e5252037-b250-4ae4-99c5-1138bfd537e1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Confusion Matrix')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.heatmap(confusion_matrix(y_test, forest_preds), annot=True)\n", + "plt.xlabel(\"Predicted Labels\")\n", + "plt.ylabel(\"Actual Labels\")\n", + "plt.title(\"Confusion Matrix\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "3d3f1e98-c3cd-428d-aa14-d099db19e3a6", + "metadata": {}, + "outputs": [], + "source": [ + "# Based on the confusion matrics fill:\n", + "true_positive = \n", + "true_negative = \n", + "false_positive = \n", + "false_negative = " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "a5f55d8d-f413-4d0a-9b7b-23efc31c257d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.82\n", + "0.82\n" + ] + } + ], + "source": [ + "# Precision: measure of how many of the positive predictions made are correct (true positives)\n", + "print(round( ,2))\n", + "print(round(sklearn.metrics.precision_score(y_test, forest_preds), 2))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "979fb237-0771-4585-ae70-74a8b4db6077", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.84\n", + "0.84\n" + ] + } + ], + "source": [ + "# Recall: is a measure of how many of the positive cases the classifier correctly predicted\n", + "print(round( ,2))\n", + "print(round(sklearn.metrics.recall_score(y_test, forest_preds), 2))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "008cd394-6520-4888-863f-6463fe3fa9ec", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.83\n", + "0.83\n" + ] + } + ], + "source": [ + "# F1-Score: is a measure combining both precision and recall 2(prec*recall)/(prec+recall)\n", + "print(round( ,2))\n", + "print(round(sklearn.metrics.f1_score(y_test, forest_preds), 2))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "c6fac62f-c5a8-4ba5-b7ee-82ec647b9ef0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.81\n", + "0.81\n" + ] + } + ], + "source": [ + "# Accuracy: describing the number of correct predictions over all predictions\n", + "print(round( ,2))\n", + "print(round(sklearn.metrics.accuracy_score(y_test, forest_preds), 2))" + ] + }, + { + "cell_type": "markdown", + "id": "e43acac7-9bca-461c-9e32-8542a3fd04ef", + "metadata": {}, + "source": [ + "## Save the result" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "922aecda-8067-4b9c-8f2c-ad83e345b42a", + "metadata": {}, + "outputs": [], + "source": [ + "# create empty df with indexes: ['precision','recall', 'f1_score','accuracy']\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "1469766a-232b-4931-a7d6-43ee8b22141c", + "metadata": {}, + "outputs": [], + "source": [ + "# write a function which will return 4 metrics:\n", + "# [round(sklearn.metrics.precision_score(y_test, forest_preds), 2),round(sklearn.metrics.recall_score(y_test, forest_preds), 2),round(sklearn.metrics.f1_score(y_test, forest_preds), 2),round(sklearn.metrics.accuracy_score(y_test, forest_preds), 2)]\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "ce75bce6-17e4-46fc-bd25-c31bdc178d10", + "metadata": {}, + "outputs": [], + "source": [ + "# add the result of model_1 into the df 'result'\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "e6c6db57-954c-4449-bc97-5c3ef07e2020", + "metadata": {}, + "source": [ + "### Change the threshold" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "cabae7b5-dc23-41bc-8cba-f9ba3e1df5ae", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 0, 0, 1])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "forest.predict(X_test)[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "bd4b584e-9b3d-47b2-ae9e-396c6f6af482", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.82, 0.18],\n", + " [0.37, 0.63],\n", + " [0.81, 0.19],\n", + " [0.79, 0.21],\n", + " [0.33, 0.67]])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "forest.predict_proba(X_test)[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0df4b875-4107-4ce8-837a-1c9b5bfcb669", + "metadata": {}, + "outputs": [], + "source": [ + "# get the result for a threshold = 0.7\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "21754b98-1b7b-43f4-b2ad-18507ba9c86d", + "metadata": {}, + "outputs": [], + "source": [ + "# get the result for any threshold from 0.1 till 0.9 and add it into 'result' df\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "3b87475b-d862-44a7-a667-5962099483ee", + "metadata": {}, + "outputs": [], + "source": [ + "# print the result sorted by accuracy\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "45d0fcc7", + "metadata": {}, + "source": [ + "### Using RandomizedSearchCV for hyperparameter turning" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "7c1501e3", + "metadata": {}, + "outputs": [], + "source": [ + "estimator = RandomForestClassifier()\n", + "grid = {\"n_estimators\": [80, 90, 100, 110, 120],\n", + " \"max_depth\": [5, 10, 15],\n", + " \"max_features\" : [\"auto\", \"sqrt\", \"log2\"],\n", + " \"min_samples_split\": [2, 1, 3, 4],\n", + " \"min_samples_leaf\": [1, 2, 3, 4]}\n", + "\n", + "rand_search_model = RandomizedSearchCV(estimator=estimator,\n", + " param_distributions=grid)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "a9188e30", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Retrain the model with tuned parameters and compare with model 1\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "acbb111b-8b61-46b2-92b3-511588758d0f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'n_estimators': 90,\n", + " 'min_samples_split': 3,\n", + " 'min_samples_leaf': 3,\n", + " 'max_features': 'log2',\n", + " 'max_depth': 10}" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rand_search_model.best_params_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10b109c8", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "b084c123", + "metadata": {}, + "source": [ + "## Feature importance" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "44066cb9-2bcc-4450-935b-8c5fb86d48b6", + "metadata": {}, + "outputs": [], + "source": [ + "importances = forest.feature_importances_" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "b86599aa-8ef2-49c8-8aea-1674a1a9bc5a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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PH0VEREiSZs6cqaVLl2r27NkaOnRopv716tVTvXr1JCnL+yUpLi7O6fN33nlHxYsXV2Jiopo1a5bdEgEAAPKkbI3YXb58WYmJiWrVqtXvB3B3V6tWrbR+/focK+rMmTOSpHvuuSfHjgkAAGB32RqxO3HihNLT01WiRAmn9hIlSuiHH37IkYIyMjI0YMAA3X///apWrVqWfS5duqRLly45Pj979myOfG0AAIDczOVWxfbt21c7d+7U/Pnzb9gnNjZW/v7+jluZMmXuYoUAAACuKVvBLiAgQB4eHjp69KhT+9GjR2+4MCI7IiMj9fnnn2vVqlW69957b9hv2LBhOnPmjOOWkpLyp782AABAbpetYOfp6ak6deooPj7e0ZaRkaH4+Hg1atTojoswxigyMlKLFy/WypUrVb58+Zv29/Lykp+fn9MNAAAgr8v2qthBgwape/fuqlu3rurXr6/JkyfrwoULjlWy3bp1U+nSpRUbGyvp6oKL3bt3Oz7+8ccftW3bNhUsWFAVK1aUdPX067x58/Tf//5XhQoVUmpqqiTJ399fPj4+OfJEAQAA7C7bwa5z5846fvy4YmJilJqaqrCwMMXFxTkWVCQnJ8vd/feBwCNHjqhWrVqOzydMmKAJEyaoefPmWr16tSRpxowZkqQWLVo4fa05c+aoR48e2S0RAAAgT8p2sJOuzoWLjIzM8r5rYe2aoKAgGWNuerxb3Q8AAIBbc7lVsQAAALgzBDsAAACbuKNTsUDQ0KU5erykcW1y9HgAAORFjNgBAADYBMEOAADAJgh2AAAANkGwAwAAsAmCHQAAgE0Q7AAAAGyCYAcAAGATBDsAAACbINgBAADYBMEOAADAJgh2AAAANkGwAwAAsAmCHQAAgE0Q7AAAAGyCYAcAAGATBDsAAACbINgBAADYBMEOAADAJgh2AAAANkGwAwAAsAmCHQAAgE0Q7AAAAGyCYAcAAGAT+awuAPirBA1dmqPHSxrXJkePBwBATmPEDgAAwCYIdgAAADZBsAMAALAJgh0AAIBNEOwAAABsgmAHAABgEwQ7AAAAmyDYAQAA2MQdBbvp06crKChI3t7eatCggTZu3HjDvrt27dLjjz+uoKAgubm5afLkyX/6mAAAAMgs28FuwYIFGjRokEaNGqUtW7aoZs2aCg8P17Fjx7Lsn5aWpuDgYI0bN06BgYE5ckwAAABklu1gN2nSJPXp00cREREKDQ3VzJkz5evrq9mzZ2fZv169enr11Vf1xBNPyMvLK0eOCQAAgMyyFewuX76sxMREtWrV6vcDuLurVatWWr9+/R0VcCfHvHTpks6ePet0AwAAyOuyFexOnDih9PR0lShRwqm9RIkSSk1NvaMC7uSYsbGx8vf3d9zKlClzR18bAADATnLlqthhw4bpzJkzjltKSorVJQEAAFguX3Y6BwQEyMPDQ0ePHnVqP3r06A0XRvwVx/Ty8rrhfD0AAIC8Klsjdp6enqpTp47i4+MdbRkZGYqPj1ejRo3uqIC/4pgAAAB5UbZG7CRp0KBB6t69u+rWrav69etr8uTJunDhgiIiIiRJ3bp1U+nSpRUbGyvp6uKI3bt3Oz7+8ccftW3bNhUsWFAVK1a8rWMCAADg1rId7Dp37qzjx48rJiZGqampCgsLU1xcnGPxQ3Jystzdfx8IPHLkiGrVquX4fMKECZowYYKaN2+u1atX39YxAQAAcGvZDnaSFBkZqcjIyCzvuxbWrgkKCpIx5k8dEwAAALeWK1fFAgAAIDOCHQAAgE0Q7AAAAGyCYAcAAGATBDsAAACbINgBAADYBMEOAADAJgh2AAAANnFHGxQD+POChi7N8WMmjWuT48cEAOQejNgBAADYBMEOAADAJgh2AAAANkGwAwAAsAmCHQAAgE0Q7AAAAGyCYAcAAGATBDsAAACbINgBAADYBMEOAADAJgh2AAAANkGwAwAAsIl8VhcAwHUFDV2ao8dLGtcmR48HAHDGiB0AAIBNEOwAAABsgmAHAABgEwQ7AAAAmyDYAQAA2ATBDgAAwCYIdgAAADZBsAMAALAJgh0AAIBNEOwAAABsgmAHAABgEwQ7AAAAmyDYAQAA2MQdBbvp06crKChI3t7eatCggTZu3HjT/h9//LEqV64sb29vVa9eXV988YXT/efPn1dkZKTuvfde+fj4KDQ0VDNnzryT0gAAAPKsbAe7BQsWaNCgQRo1apS2bNmimjVrKjw8XMeOHcuyf0JCgrp06aJevXpp69atateundq1a6edO3c6+gwaNEhxcXF6//339f3332vAgAGKjIzUp59+eufPDAAAII/JdrCbNGmS+vTpo4iICMfImq+vr2bPnp1l/ylTpqh169aKjo5WlSpVNHbsWNWuXVvTpk1z9ElISFD37t3VokULBQUF6ZlnnlHNmjVvORIIAACA32Ur2F2+fFmJiYlq1arV7wdwd1erVq20fv36LB+zfv16p/6SFB4e7tS/cePG+vTTT/Xjjz/KGKNVq1Zp7969euihh7I85qVLl3T27FmnGwAAQF6XrWB34sQJpaenq0SJEk7tJUqUUGpqapaPSU1NvWX/qVOnKjQ0VPfee688PT3VunVrTZ8+Xc2aNcvymLGxsfL393fcypQpk52nAQAAYEsusSp26tSp2rBhgz799FMlJiZq4sSJ6tu3r7766qss+w8bNkxnzpxx3FJSUu5yxQAAAK4nX3Y6BwQEyMPDQ0ePHnVqP3r0qAIDA7N8TGBg4E37X7x4UcOHD9fixYvVpk0bSVKNGjW0bds2TZgwIdNpXEny8vKSl5dXdkoHAACwvWyN2Hl6eqpOnTqKj493tGVkZCg+Pl6NGjXK8jGNGjVy6i9JK1ascPS/cuWKrly5Ind351I8PDyUkZGRnfIAAADytGyN2ElXtybp3r276tatq/r162vy5Mm6cOGCIiIiJEndunVT6dKlFRsbK0nq37+/mjdvrokTJ6pNmzaaP3++Nm/erFmzZkmS/Pz81Lx5c0VHR8vHx0flypXTmjVrNHfuXE2aNCkHnyoAAIC9ZTvYde7cWcePH1dMTIxSU1MVFhamuLg4xwKJ5ORkp9G3xo0ba968eRoxYoSGDx+ukJAQLVmyRNWqVXP0mT9/voYNG6annnpKp06dUrly5fTyyy/r2WefzYGnCAAAkDdkO9hJUmRkpCIjI7O8b/Xq1ZnaOnbsqI4dO97weIGBgZozZ86dlAIgjwsaujRHj5c0rk2OHg8A7iaXWBULAACAP49gBwAAYBMEOwAAAJsg2AEAANgEwQ4AAMAmCHYAAAA2QbADAACwCYIdAACATdzRBsUAgNuT0xsoS2yiDODGGLEDAACwCYIdAACATRDsAAAAbIJgBwAAYBMEOwAAAJsg2AEAANgEwQ4AAMAmCHYAAAA2QbADAACwCYIdAACATRDsAAAAbIJgBwAAYBMEOwAAAJsg2AEAANgEwQ4AAMAmCHYAAAA2QbADAACwCYIdAACATRDsAAAAbIJgBwAAYBMEOwAAAJsg2AEAANgEwQ4AAMAmCHYAAAA2QbADAACwCYIdAACATdxRsJs+fbqCgoLk7e2tBg0aaOPGjTft//HHH6ty5cry9vZW9erV9cUXX2Tq8/3336tt27by9/dXgQIFVK9ePSUnJ99JeQAAAHlStoPdggULNGjQII0aNUpbtmxRzZo1FR4ermPHjmXZPyEhQV26dFGvXr20detWtWvXTu3atdPOnTsdfQ4cOKAmTZqocuXKWr16tb777juNHDlS3t7ed/7MAAAA8phsB7tJkyapT58+ioiIUGhoqGbOnClfX1/Nnj07y/5TpkxR69atFR0drSpVqmjs2LGqXbu2pk2b5ujzwgsv6OGHH9b48eNVq1YtVahQQW3btlXx4sXv/JkBAADkMdkKdpcvX1ZiYqJatWr1+wHc3dWqVSutX78+y8esX7/eqb8khYeHO/pnZGRo6dKluu+++xQeHq7ixYurQYMGWrJkyQ3ruHTpks6ePet0AwAAyOuyFexOnDih9PR0lShRwqm9RIkSSk1NzfIxqampN+1/7NgxnT9/XuPGjVPr1q21fPlytW/fXh06dNCaNWuyPGZsbKz8/f0dtzJlymTnaQAAANiS5atiMzIyJEmPPfaYBg4cqLCwMA0dOlSPPPKIZs6cmeVjhg0bpjNnzjhuKSkpd7NkAAAAl5QvO50DAgLk4eGho0ePOrUfPXpUgYGBWT4mMDDwpv0DAgKUL18+hYaGOvWpUqWK1q1bl+Uxvby85OXllZ3SAQAAbC9bI3aenp6qU6eO4uPjHW0ZGRmKj49Xo0aNsnxMo0aNnPpL0ooVKxz9PT09Va9ePe3Zs8epz969e1WuXLnslAcAAJCnZWvETpIGDRqk7t27q27duqpfv74mT56sCxcuKCIiQpLUrVs3lS5dWrGxsZKk/v37q3nz5po4caLatGmj+fPna/PmzZo1a5bjmNHR0ercubOaNWumBx54QHFxcfrss8+0evXqnHmWAIAbChq6NEePlzSuTY4eD8Dty3aw69y5s44fP66YmBilpqYqLCxMcXFxjgUSycnJcnf/fSCwcePGmjdvnkaMGKHhw4crJCRES5YsUbVq1Rx92rdvr5kzZyo2NlbPP/+8KlWqpIULF6pJkyY58BQBAADyhmwHO0mKjIxUZGRklvdlNcrWsWNHdezY8abH7Nmzp3r27Hkn5QAAAEAusCoWAAAAOYNgBwAAYBMEOwAAAJsg2AEAANgEwQ4AAMAmCHYAAAA2QbADAACwCYIdAACATRDsAAAAbIJgBwAAYBMEOwAAAJsg2AEAANgEwQ4AAMAmCHYAAAA2QbADAACwCYIdAACATRDsAAAAbIJgBwAAYBP5rC4AAIBbCRq6NEePlzSuTY4eD3AVjNgBAADYBMEOAADAJgh2AAAANkGwAwAAsAmCHQAAgE0Q7AAAAGyCYAcAAGATBDsAAACbINgBAADYBMEOAADAJgh2AAAANkGwAwAAsAmCHQAAgE0Q7AAAAGyCYAcAAGATBDsAAACbuKNgN336dAUFBcnb21sNGjTQxo0bb9r/448/VuXKleXt7a3q1avriy++uGHfZ599Vm5ubpo8efKdlAYAAJBnZTvYLViwQIMGDdKoUaO0ZcsW1axZU+Hh4Tp27FiW/RMSEtSlSxf16tVLW7duVbt27dSuXTvt3LkzU9/Fixdrw4YNKlWqVPafCQAAQB6X7WA3adIk9enTRxEREQoNDdXMmTPl6+ur2bNnZ9l/ypQpat26taKjo1WlShWNHTtWtWvX1rRp05z6/fjjj+rXr58++OAD5c+f/86eDQAAQB6WrWB3+fJlJSYmqlWrVr8fwN1drVq10vr167N8zPr16536S1J4eLhT/4yMDHXt2lXR0dGqWrXqLeu4dOmSzp4963QDAADI67IV7E6cOKH09HSVKFHCqb1EiRJKTU3N8jGpqam37P/KK68oX758ev7552+rjtjYWPn7+ztuZcqUyc7TAAAAsCXLV8UmJiZqypQpeuedd+Tm5nZbjxk2bJjOnDnjuKWkpPzFVQIAALi+bAW7gIAAeXh46OjRo07tR48eVWBgYJaPCQwMvGn/r7/+WseOHVPZsmWVL18+5cuXT4cPH1ZUVJSCgoKyPKaXl5f8/PycbgAAAHldtoKdp6en6tSpo/j4eEdbRkaG4uPj1ahRoywf06hRI6f+krRixQpH/65du+q7777Ttm3bHLdSpUopOjpay5Yty+7zAQAAyLPyZfcBgwYNUvfu3VW3bl3Vr19fkydP1oULFxQRESFJ6tatm0qXLq3Y2FhJUv/+/dW8eXNNnDhRbdq00fz587V582bNmjVLklS0aFEVLVrU6Wvkz59fgYGBqlSp0p99fgAAAHlGtoNd586ddfz4ccXExCg1NVVhYWGKi4tzLJBITk6Wu/vvA4GNGzfWvHnzNGLECA0fPlwhISFasmSJqlWrlnPPAgAAANkPdpIUGRmpyMjILO9bvXp1praOHTuqY8eOt338pKSkOykLAAAgT7N8VSwAAAByBsEOAADAJgh2AAAANkGwAwAAsAmCHQAAgE0Q7AAAAGyCYAcAAGATBDsAAACbINgBAADYBMEOAADAJgh2AAAANkGwAwAAsAmCHQAAgE0Q7AAAAGyCYAcAAGATBDsAAACbyGd1AQAA5HZBQ5fm+DGTxrXJ8WPC/hixAwAAsAmCHQAAgE0Q7AAAAGyCYAcAAGATBDsAAACbINgBAADYBMEOAADAJgh2AAAANkGwAwAAsAmCHQAAgE0Q7AAAAGyCYAcAAGATBDsAAACbyGd1AQAA4K8XNHRpjh4vaVybHD0ecgYjdgAAADZBsAMAALAJgh0AAIBNEOwAAABs4o6C3fTp0xUUFCRvb281aNBAGzduvGn/jz/+WJUrV5a3t7eqV6+uL774wnHflStX9K9//UvVq1dXgQIFVKpUKXXr1k1Hjhy5k9IAAADyrGwHuwULFmjQoEEaNWqUtmzZopo1ayo8PFzHjh3Lsn9CQoK6dOmiXr16aevWrWrXrp3atWunnTt3SpLS0tK0ZcsWjRw5Ulu2bNGiRYu0Z88etW3b9s89MwAAgDwm28Fu0qRJ6tOnjyIiIhQaGqqZM2fK19dXs2fPzrL/lClT1Lp1a0VHR6tKlSoaO3asateurWnTpkmS/P39tWLFCnXq1EmVKlVSw4YNNW3aNCUmJio5OfnPPTsAAIA8JFvB7vLly0pMTFSrVq1+P4C7u1q1aqX169dn+Zj169c79Zek8PDwG/aXpDNnzsjNzU2FCxfO8v5Lly7p7NmzTjcAAIC8LlvB7sSJE0pPT1eJEiWc2kuUKKHU1NQsH5Oampqt/r/88ov+9a9/qUuXLvLz88uyT2xsrPz9/R23MmXKZOdpAAAA2JJLrYq9cuWKOnXqJGOMZsyYccN+w4YN05kzZxy3lJSUu1glAACAa8rWJcUCAgLk4eGho0ePOrUfPXpUgYGBWT4mMDDwtvpfC3WHDx/WypUrbzhaJ0leXl7y8vLKTukAAAC2l60RO09PT9WpU0fx8fGOtoyMDMXHx6tRo0ZZPqZRo0ZO/SVpxYoVTv2vhbp9+/bpq6++UtGiRbNTFgAAAJTNETtJGjRokLp37666deuqfv36mjx5si5cuKCIiAhJUrdu3VS6dGnFxsZKkvr376/mzZtr4sSJatOmjebPn6/Nmzdr1qxZkq6Guv/7v//Tli1b9Pnnnys9Pd0x/+6ee+6Rp6dnTj1XAAAAW8t2sOvcubOOHz+umJgYpaamKiwsTHFxcY4FEsnJyXJ3/30gsHHjxpo3b55GjBih4cOHKyQkREuWLFG1atUkST/++KM+/fRTSVJYWJjT11q1apVatGhxh08NAAAgb8l2sJOkyMhIRUZGZnnf6tWrM7V17NhRHTt2zLJ/UFCQjDF3UgYAAACuc0fBDgAAIKcFDV2ao8dLGtcmR4+XG7jUdicAAAC4cwQ7AAAAmyDYAQAA2ATBDgAAwCYIdgAAADZBsAMAALAJgh0AAIBNEOwAAABsgmAHAABgEwQ7AAAAmyDYAQAA2ATBDgAAwCYIdgAAADZBsAMAALAJgh0AAIBNEOwAAABsgmAHAABgEwQ7AAAAmyDYAQAA2ATBDgAAwCYIdgAAADaRz+oCAAAAcoOgoUtz/JhJ49rk6PEYsQMAALAJgh0AAIBNEOwAAABsgmAHAABgEwQ7AAAAmyDYAQAA2ATBDgAAwCYIdgAAADZBsAMAALAJgh0AAIBNEOwAAABsgmAHAABgE3cU7KZPn66goCB5e3urQYMG2rhx4037f/zxx6pcubK8vb1VvXp1ffHFF073G2MUExOjkiVLysfHR61atdK+ffvupDQAAIA8K9vBbsGCBRo0aJBGjRqlLVu2qGbNmgoPD9exY8ey7J+QkKAuXbqoV69e2rp1q9q1a6d27dpp586djj7jx4/X66+/rpkzZ+rbb79VgQIFFB4erl9++eXOnxkAAEAek+1gN2nSJPXp00cREREKDQ3VzJkz5evrq9mzZ2fZf8qUKWrdurWio6NVpUoVjR07VrVr19a0adMkXR2tmzx5skaMGKHHHntMNWrU0Ny5c3XkyBEtWbLkTz05AACAvCRfdjpfvnxZiYmJGjZsmKPN3d1drVq10vr167N8zPr16zVo0CCntvDwcEdoO3TokFJTU9WqVSvH/f7+/mrQoIHWr1+vJ554ItMxL126pEuXLjk+P3PmjCTp7Nmzt/U8Mi6l3Va/23W7X/d25XR9kuvXmNP1Sa5fI+9zznD1Gnmfc4ar18j7nDNcvUar3udrfYwxtz6gyYYff/zRSDIJCQlO7dHR0aZ+/fpZPiZ//vxm3rx5Tm3Tp083xYsXN8YY88033xhJ5siRI059OnbsaDp16pTlMUeNGmUkcePGjRs3bty45ZlbSkrKLbNatkbsXMWwYcOcRgEzMjJ06tQpFS1aVG5ubjnyNc6ePasyZcooJSVFfn5+OXLMnOTq9UnUmFNcvUZXr0+ixpzg6vVJ1JgTXL0+KW/WaIzRuXPnVKpUqVv2zVawCwgIkIeHh44ePerUfvToUQUGBmb5mMDAwJv2v/bv0aNHVbJkSac+YWFhWR7Ty8tLXl5eTm2FCxfOzlO5bX5+fi77jSO5fn0SNeYUV6/R1euTqDEnuHp9EjXmBFevT8p7Nfr7+99Wv2wtnvD09FSdOnUUHx/vaMvIyFB8fLwaNWqU5WMaNWrk1F+SVqxY4ehfvnx5BQYGOvU5e/asvv322xseEwAAAJll+1TsoEGD1L17d9WtW1f169fX5MmTdeHCBUVEREiSunXrptKlSys2NlaS1L9/fzVv3lwTJ05UmzZtNH/+fG3evFmzZs2SJLm5uWnAgAF66aWXFBISovLly2vkyJEqVaqU2rVrl3PPFAAAwOayHew6d+6s48ePKyYmRqmpqQoLC1NcXJxKlCghSUpOTpa7++8DgY0bN9a8efM0YsQIDR8+XCEhIVqyZImqVavm6DNkyBBduHBBzzzzjE6fPq0mTZooLi5O3t7eOfAU74yXl5dGjRqV6ZSvq3D1+iRqzCmuXqOr1ydRY05w9fokaswJrl6fRI234mbM7aydBQAAgKvjWrEAAAA2QbADAACwCYIdAACATRDsAAAAbIJgB+QiFy9etLoEAIALI9gBLub555/Psv3ChQt6+OGH73I1N7d//34tW7bMEThZZG9fxhje3xzG62lfZ8+e1ZIlS/T999/f9a+dK68V+1c5ffq0Nm7cqGPHjikjI8Ppvm7dullUlbP//e9/+vTTT5WcnKzLly873Tdp0iRLavr0009vu2/btm3/wkpu388//6y3337b8Z+uSpUq6tmzp+655x6LK5OWLl2qIkWKaMyYMY62CxcuqHXr1hZW5ezkyZPq3LmzVq5cKTc3N+3bt0/BwcHq1auXihQpookTJ1pa37vvvquAgAC1adNG0tW9MmfNmqXQ0FB9+OGHKleunCV1vf7667fd90YB/257++239dprr2nfvn2SpJCQEA0YMEC9e/e2uLKratWqleU1wt3c3OTt7a2KFSuqR48eeuCBByyoTurRo4emT5+uAgUKOLUnJSWpa9eu+vrrry2p63rp6el65513FB8fn+Xvv5UrV1pUWe7RqVMnNWvWTJGRkbp48aLq1q2rpKQkGWM0f/58Pf7443etFvax+81nn32mp556SufPn5efn5/TDwo3NzedOnXKwuquio+PV9u2bRUcHKwffvhB1apVc3zj1K5d27L/fNdvSH0zbm5uSk9P/4urubW1a9eqbdu28vPzU926dSVJiYmJOn36tD777DM1a9bM0voOHDigpk2basiQIRowYIDOnTun8PBw5cuXT19++WWmXxBW6Natm44dO6a33npLVapU0fbt2xUcHKxly5Zp0KBB2rVrl6X1VapUSTNmzFDLli21fv16tWrVSq+99po+//xz5cuXT4sWLbKkrvLly99WPzc3Nx08ePAvrubWYmJiNGnSJPXr189xicf169dr2rRpGjhwoF588UWLK5SGDRumGTNmqHr16qpfv74kadOmTfruu+/Uo0cP7d69W/Hx8Vq0aJEee+yxu15frVq1dPbsWb3//vuO1/Ddd9/V888/r5YtW2rx4sV3vaY/ioyM1DvvvKM2bdqoZMmSmYLya6+9ZlFlv7vRAML1Af52/3/9FQIDA7Vs2TLVrFlT8+bN06hRo7R9+3a9++67mjVrlrZu3Xr3ijEwxhgTEhJi+vfvby5cuGB1KTdUr149ExMTY4wxpmDBgubAgQPm3Llzpm3btuY///mPxdXlHtWqVTN9+vQxv/76q6Pt119/Nc8884ypVq2ahZX9bvv27eaee+4xU6ZMMQ0bNjTNmzc358+ft7oshxIlSpht27YZY37/XjTGmAMHDpgCBQpYWZoxxhgfHx9z+PBhY4wxQ4YMMV27djXGGLNz504TEBBgZWm5SkBAgJk3b16m9nnz5pmiRYtaUFFmvXv3Ni+++GKm9rFjx5revXsbY4yJiYkxderUudulGWOMuXz5shk8eLDx9PQ0w4YNMx07djQFCxY0s2bNsqSerBQtWtQsXbrU6jJuys3Nzbi7uxs3Nzen27U2d3d306xZM3Pq1ClL6vP29jbJycnGGGO6du1q/vWvfxljjDl8+PBd/5lIsPuNr6+v45eTqypYsKDZv3+/McaYwoULm507dxpjjNm2bZspV66chZXlLt7e3uaHH37I1P7DDz8Yb29vCyrKWkJCgilQoIBp2bKlSUtLs7ocJwULFjR79+51fHzt/86mTZvMPffcY2VpxhhjihUrZrZs2WKMMSYsLMzMnTvXGGPM/v37XSJ4/lFGRobJyMiwuoxM/P39He/z9fbs2WP8/f3vfkFZ8PPzM/v27cvUvm/fPuPn52eMMeb77783BQsWvNulOYmJiTFubm4mf/78JiEhwdJa/qhkyZJmz549VpdxU1999ZVp0KCB+eqrr8zZs2fN2bNnzVdffWUaNWpkli5datatW2eqVq1qevbsaUl9ISEhZsGCBeb8+fOmWLFiJj4+3hhz9ffz3f4jiDl2vwkPD9fmzZsVHBxsdSk3VKBAAce8upIlS+rAgQOqWrWqJOnEiROW1ZXb5g3Vrl1b33//vSpVquTU/v3336tmzZqW1HSjeUJeXl46cuSI7r//fkfbli1b7mZpWWratKnmzp2rsWPHSrp6OiQjI0Pjx4+3bC7T9f72t7+pd+/eqlWrlvbu3etYdLJr1y4FBQVZW9x15s6dq1dffdUxf+2+++5TdHS0unbtanFlV3Xt2lUzZszINH931qxZeuqppyyqypm3t7cSEhJUsWJFp/aEhATH9cYzMjIsu/b4lStXNHToUE2fPl3Dhg3TunXr1KFDB7399tsusxgqKipKU6ZM0bRp07L8OeQK+vfvr1mzZqlx48aOtgcffFDe3t565plntGvXLk2ePFk9e/a0pL4BAwboqaeeUsGCBVWuXDm1aNFC0tWpP9WrV7+rteTpYHf9Ofs2bdooOjpau3fvVvXq1ZU/f36nvq4w6b9hw4Zat26dqlSpoocfflhRUVHasWOHFi1apIYNG1pW1+3Ov3Bzc3OJYPf888+rf//+2r9/v+N127Bhg6ZPn65x48bpu+++c/StUaPGXampXbt2d+Xr5JTx48frwQcf1ObNm3X58mUNGTJEu3bt0qlTp/TNN99YXZ6mT5+uESNGKCUlRQsXLlTRokUlXZ1L2aVLF4uru2rSpEkaOXKkIiMjHcF93bp1evbZZ3XixAkNHDjQ4gqvevvtt7V8+XLH/5Vvv/1WycnJ6tatmwYNGuToZ9XirX79+unZZ59VYmKi6tWrJ+nqHLu33npLw4cPlyQtW7ZMYWFhltRXt25dpaWlafXq1WrYsKGMMRo/frw6dOignj176j//+Y8ldV1v3bp1WrVqlb788ktVrVo10+8/q+akXu/AgQPy8/PL1O7n5+eYjxoSEmLZIMc///lP1a9fXykpKfrb3/7mmHseHBysl1566a7WkqcXT+S2Sf8HDx7U+fPnVaNGDV24cEFRUVFKSEhQSEiIJk2aZNlKv9zmVu+7m5ubjDEu8767qjNnzmjatGnavn27zp8/r9q1a6tv374qWbKk1aXlCuXLl9eYMWMyrbh/9913NXr0aB06dMiiyn53u6Ovbm5ulq6c/OCDDzRt2jTt2bNH0tXFM/369dOTTz4p6er+j9cm2d9tvXr10uuvv55p0dPWrVvVtWtX7dy5867X9EcRERE3vX/OnDl3qZIba9KkiQoVKqS5c+eqWLFikqTjx4+rW7duunDhgtauXauvvvpKffv2dXwfWOVarLJq9DNPBzvkTYcPH77tvoTl3MuVt7SRrp5C3LlzZ6ZTiPv27VP16tX1yy+/WFQZ7pZLly7Jy8vL6jJyhT179uixxx7ToUOHVKZMGUlSSkqKgoOD9d///lf33XeflixZonPnzlk2lcFVtgYi2CHHueJee9eLjY1ViRIlMs3FmD17to4fP65//etfFlV2VXp6ul577TV99NFHWb6GrrD1zvWnq693bVSkbNmylv7CWrt2rR599FH5+/u75JY2klStWjU9+eSTjtOF17z00ktasGCBduzYYVFludPly5ez3IOtbNmyFlX0u/fee08zZ87UoUOHtH79epUrV06TJ09W+fLlLdmC5UaOHz/uNOp5bWTMVWRkZGj58uXau3evpKs1Xn/a00qutDUQwe468fHxN9ygcfbs2RZV9bvc8AvfVffau15QUJDmzZvnNAlXujp36IknnrD8FFhMTIzeeustRUVFacSIEXrhhReUlJSkJUuWKCYmxiXmKbq7uztOM2R12iF//vzq3Lmz3njjDUtOf1WvXl2NGjXSjBkz5OHhIenq/59//vOfSkhIcInQtHDhQnXu3FmtWrVyzLH75ptvFB8fr48++kjt27e3pK4OHTrcdl9XmHu1b98+9ezZUwkJCU7trjKdYsaMGYqJidGAAQP08ssva+fOnQoODtY777yjd999V6tWrbK0PunqBuj9+vXT3LlzHb/7PDw81K1bN02dOlW+vr4WV+j6ihUrptdffz3THN4PP/xQ/fr1u6tz/6yPuS5izJgxeuihhxQfH68TJ07o559/drq5gjFjxmjSpEnq3Lmzzpw5o0GDBqlDhw5yd3fX6NGjrS5P0tXNQgcPHqwdO3bI29tbCxcuVEpKipo3b66OHTtaXZ4kKTU1Nct5YMWKFdNPP/1kQUXOPvjgA7355puKiopSvnz51KVLF7311luKiYnRhg0brC5PkrR48WKFhIRo1qxZ2r59u7Zv365Zs2apUqVKmjdvnt5++22tXLlSI0aMsKS+/fv3KyoqyhHqpKu/qAYNGqT9+/dbUtMfPf744/r2228VEBCgJUuWaMmSJQoICNDGjRstC3WS5O/vf9s3V9CjRw+5u7vr888/V2JiorZs2aItW7Zo69atLrGCfOrUqXrzzTf1wgsvOH0/1q1b1yX+wJCkQYMGac2aNfrss890+vRpnT59Wv/973+1Zs0aRUVFWV2epKuL3rLagWHatGkaMGDA3S/oD65cueI4O3C9OnXq6Ndff727xdzVzVVcWGBgoGOvK1cVHBxsPv/8c2OM8552U6ZMMV26dLGyNIfcsNdexYoVzXvvvZepfe7cuaZ8+fIWVOTM19fXsbluYGCgSUxMNMZc3fz32r5cVqtXr56Ji4vL1B4XF2fq1atnjDFm8eLFJjg4+G6XZowxpnHjxmbx4sWZ2hcvXmwaNGhw9wvKpdLS0pw2xj506JB57bXXsnzvreLr62u+//57q8u4IW9vb5OUlGSMcd7zce/evS6zb2bRokXNqlWrMrWvXLnSZTb0LlWqlNm8eXOm9sTERFO6dGkLKnIWGRlpBg4cmKk9KirK/POf/7yrteTp7U6ud/ny5Uyn5lxNamqqYz+cggUL6syZM5KkRx55RCNHjrSyNAdX3Wvven369NGAAQN05coVtWzZUtLVU8hDhgxxib9O7733Xv30008qW7asKlSooOXLl6t27dratGmTy0y03rFjR5YLS8qVK+cYhQgLC7NsBNQVt7TJSkZGhvbv35/l9A9XmAf42GOPqUOHDnr22Wd1+vRpNWzYUPnz59eJEyc0adIkPffcc1aXqNDQUJf52ZKV8uXLa9u2bZn+v8TFxalKlSoWVeUsLS1NJUqUyNRevHhxpaWlWVBRZidPnsxylNjPz8+y9//67X4k6a233rrh1kB31V2NkS5syJAhWV6WxpXcd999ZsOGDcYYY+6//34TGxtrjDFm/vz5plixYlaW5vDYY485LpUTFRVlKlasaF566SVTu3Zt8+CDD1pc3VUZGRlmyJAhxtvb27i7uxt3d3fj6+trxowZY3Vpxhhj/vWvf5mXX37ZGHP1vc2XL5+pWLGi8fT0dFymxmphYWGme/fu5tKlS462y5cvm+7du5uwsDBjjDHr1q0zQUFBltT3x8sO3ewyRFZZv369KV++/A0vk+QKihYt6hh1f/PNN02NGjVMenq6+eijj0zlypUtru6q+Ph406hRI7Nq1Spz4sQJc+bMGaeb1d58801TunRpM3/+fFOgQAHz4YcfmpdeesnxsSto2bKl6dixo7l48aKjLS0tzXTs2NFlfm5XrVrVTJ06NVP766+/bqpUqWJBRVfPSjVp0sS0aNHCPPDAA6ZFixZZ3h544IG7WleeXjxxfdrOyMjQu+++qxo1aqhGjRqZNmh0hdWcQ4cOlZ+fn4YPH64FCxbo6aefVlBQkJKTkzVw4ECNGzfO6hJz1V5758+f1/fffy8fHx+FhIS4zGjYH61fv17r169XSEiIHn30UavLkXR1V/+2bdvK3d3dMeK1Y8cOpaen6/PPP1fDhg313nvvKTU1VdHR0Xe9vtywpU1YWJjuu+8+jRkzJssLr7vCHDZfX1/98MMPKlu2rDp16qSqVatq1KhRSklJUaVKlVxiNOfaisg/vn7GRRZPSFfnzY4ePVoHDhyQJJUuXVqjR49Wr169LK7sqp07dyo8PFyXLl1yXH1n+/bt8vb21rJlyxxnXaw0e/ZsRUZGKjo62ulMy8SJEzV58mT16dPnrtfk7u6u1NRUFS9eXMHBwdq0aZNjM3Qr5elgl1s237yRDRs2OEKTq/zCR95x7tw5ffDBB05bDzz55JMqVKiQxZXlDgUKFND27dsz7WPnSmrUqKHevXurffv2qlatmuLi4tSoUSMlJiaqTZs2Sk1NtbpErVmz5qb3N2/e/C5VkrWLFy/KGCNfX1+lpaVp586d+uabbxQaGqrw8HBLa7teWlqaPvjgA/3www+Sru77+NRTT8nHx8fiyn43Y8YMvfzyyzpy5IikqzscjB49+u6f6vxN0aJF9cUXX6hBgwZyd3fX0aNHXWKLmDwd7HIbV99/7XquvKdUbpBb9r3avXt3llvvuMIl+Pbs2aOpU6c6bVDcr1+/TNcItkrLli01ZMgQtW7d2upSbuiTTz7Rk08+qfT0dD344INavny5pKs/i9auXasvv/zS4gpd30MPPeQ0T7Fy5couN08xtzl+/Lh8fHxUsGBBS+t45pln9O6776pUqVJKTk7Wvffe67Ty+XrXLnt2NxDsfnPmzBmlp6dn2pX+1KlTypcvX5bXqLvbXH3/NUnau3evevXq5bJ7SuUGuWHfq4MHD6p9+/basWOH0yXYrrH6fV64cKGeeOIJ1a1b17FZ6IYNG7Rp0ybNnz9fjz/+uCV1Xb9o48CBAxoxYoSio6OzvD61lYs6rpeamqqffvpJNWvWdJz23Lhxo/z8/FS5cmWLq5NGjx6tmJiYTJvUnjlzRs8++6w+/PBDiyq7KiAgQGvWrFHVqlX11ltvaerUqdq6dasWLlyomJgYxx8ed9unn36qv//978qfP7/TddOz4gp/qEnSr7/+qtWrV+vAgQOOswNHjhyRn5+fZSEvLi5O+/fv1/PPP68XX3zxhmcs+vfvf/eKuqsz+lxY69atzfTp0zO1z5gxw/z973+3oKLMvLy8zMGDBzO1HzhwwHh5eVlQUWaNGzc2zZo1M1988YXZunWr2bZtm9MNt1alShXHVh3Xb4+wY8cOU7RoUQsr+90jjzxiHnvsMXP8+HFTsGBBs2vXLvP111+b+vXrm7Vr11pdngkODjYjR47M1B4TE2PZFizGGKeFG666qCO3uffee02jRo0c/0+MMWbVqlWmTJkyjq13rOTj4+PYvqhjx45m9OjRxhhjkpOTjY+Pj2V1ubm5maNHjzo+vtn3pCtISkoylStXNr6+vsbDw8Pxfj///PPmH//4h8XVGdOjRw9z9uxZq8swxhhDsPtNkSJFzO7duzO1f//99+aee+6xoKLMXH3/NWNcf0+p3CC37Hu1fft2Y4wxfn5+5ocffjDGXF2heG1VrJV8fHzMvn37MrXv3bvX0l+mSUlJt33D7Tl16pTp2LGjKVSokJk1a5YZPHiwyZ8/vxk+fLi5cuWK1eWZ6tWrmylTppjk5GTj5+dnEhISjDHGbN682ZQoUcLi6nKPxx57zDz99NPm0qVLTj8XV61aZSpWrGhxda6FK0/85tKlS1nuDn3lyhVdvHjRgooyu7b/2pw5c3T48GEdPnxYs2fP1sCBAy1ZEZQVV99TKje4tu/VH7nSvlfp6emOUw4BAQGOyczlypVzXGvSSi1atNDXX3+dqX3dunVq2rSpBRVdVa5cOcdt3rx5io+Pd2orV66c4uPjNX/+fMtqzG2KFCmijz76SJGRkfrHP/6hKVOm6Msvv9TLL7+sfPms36o1JiZGgwcPVlBQkBo0aOCYGrB8+XLVqlXL4uqumjt3ri5dupSp/fLly5o7d64FFWX29ddfa8SIEfL09HRqDwoK0o8//mhRVS7K6mTpKlq0aGEiIyMztf/zn/80TZo0saCizFx1/7Xr94xy9T2lcoPcsO9VkyZNHKeLu3TpYlq3bm3WrVtnunXrZqpWrWpJTf/9738dtxkzZphixYqZvn37mvfee8+89957pm/fvqZ48eJmxowZltT3R+XKlTPffPNNpvYNGzZYtv9fbvX6668bX19f8+STT5pKlSqZ0NBQl5r68dNPP5ktW7aY9PR0R9u3337rMmc33N3dHadlr3fixAmXORVbuHBhs2vXLmOM85mMr7/+2hQvXtzK0lwOwe4369atM97e3qZp06Zm9OjRZvTo0aZp06bG29vbJeYMXe/cuXNm48aNZseOHeaXX36xuhzHPIxrtz9+zryh7Hv//fdNxYoVHfNcSpcubd566y2ry3KIi4szCxcuNMYYs2/fPlOpUiXj5uZmAgICTHx8vCU13WpTYlebM5Qb5szmBuHh4eaee+4xH3/8sTHm6sa6zz77rPH29javvPKKxdXlDm5ububYsWOZ2rdt22aKFCliQUWZderUyfTp08cYczXYHTx40Jw7d860bNnS9OjRw+LqXAurYq+zbds2vfrqq9q2bZt8fHxUo0YNDRs2TCEhIVaX5tKu30cqKSlJZcqUybTkOyMjQ8nJyerevfvdLi9XS0tL0/nz51W8eHGrS7mlU6dOqUiRIpk2ikXWQkJCNGrUKD399NNO7e+9955GjRp1V7dHyM3+9re/ObacuN7SpUvVu3dvyy5rlxvUqlVLbm5u2r59u6pWrep06jo9PV2HDh1S69at9dFHH1lY5VX/+9//FB4eLmOM9u3bp7p162rfvn0KCAjQ2rVrc8XPyLuFYIcc5eHhoZ9++inTf7KTJ0+qePHilm+DkZscO3bMMV+tcuXKLrHxZW4xd+5cde7cOdPVRC5fvqz58+dbtqHp9caPH6/x48fr1VdfzfKaxcOGDbO4wtzj66+/1htvvKEDBw7ok08+UenSpfXee+8pKCjI0jmVrm7MmDGOf6Oiopy2DPH09FRQUJAef/zxTPParPLrr79qwYIF2r59u86fP6/atWu73CbKLsHS8UIXdfHiReaG3aEbDeknJSUZX19fCyrKfc6ePWuefvpp4+Hh4Th9mC9fPvPUU0+Z06dPW11erpAb5gy56pzZ3OaTTz4xPj4+pnfv3sbLy8sx92rq1Kkus1WVq3vnnXecrhPritasWZPlKucrV66YNWvWWFCR62LE7jdpaWkaMmSIPvroI508eTLT/Yw03dy16+5OmTJFffr0ka+vr+O+9PR0ffvtt/Lw8NA333xjVYm5RufOnbV161ZNnTrVsYJu/fr16t+/v8LCwlgxeRtudHmf7du364EHHtCpU6csqiyz3HLNYldVq1YtDRw4UN26dVOhQoW0fft2BQcHa+vWrfr73//uEpc9w5/H2aDbZ/1acBcRHR2tVatWacaMGerataumT5+uH3/8UW+88YbGjRtndXkub+vWrZKuXmFix44dTkP3np6eqlmzpgYPHmxVebnK559/rmXLlqlJkyaOtvDwcL355psuffkpV3BtzpCbm5sefPDBG84ZciUFCxZUvXr1rC4j19qzZ4+aNWuWqd3f31+nT5+++wXlQunp6Xrttdf00UcfZXmJQFf4Q8j84eo215w8eVIFChSwoCLXRbD7zWeffaa5c+eqRYsWioiIUNOmTVWxYkWVK1dOH3zwgZ566imrS3Rp1y5zFRERoSlTprjEJdhyq6JFi8rf3z9Tu7+/v4oUKWJBRblHu3btJF1dCBUeHn7DOUOwj8DAQO3fv19BQUFO7evWrVNwcLA1ReUyY8aM0VtvvaWoqCiNGDFCL7zwgpKSkrRkyRLFxMRYWluHDh0kSW5uburRo4fTiHZ6erq+++67TJfZzOsIdr85deqU44eAn5+f4y+UJk2acJHmbJgzZ47VJeR6I0aM0KBBg/Tee+8pMDBQ0tXrdUZHR2vkyJEWV+faRo0apfT0dAUFBemhhx5SyZIlrS4Jf7E+ffqof//+mj17ttzc3HTkyBGtX79egwcP5v/Lbfrggw/05ptvqk2bNho9erS6dOmiChUqqEaNGtqwYYOef/55y2q79keuMUaFChVyWijh6emphg0buswG/a6CYPeb4OBgHTp0SGXLllXlypX10UcfqX79+vrss89UuHBhq8tDHjJjxgzt379fZcuWVdmyZSVJycnJ8vLy0vHjx/XGG284+m7ZssWqMl2Wh4eH/vGPf1h2cXXcXUOHDlVGRoYefPBBpaWlqVmzZvLy8tLgwYPVr18/q8vLFVJTU1W9enVJV6cGnDlzRpL0yCOPWB6Orw0WBAUFafDgwZx2vQ0Eu99ERERo+/btat68uYYOHapHH31U06ZN05UrVzRp0iSry0Mecu10Iu5ctWrVdPDgQZUvX97qUvAXc3Nz0wsvvKDo6Gjt379f58+fV2hoqNNpeNzcvffeq59++klly5ZVhQoVtHz5ctWuXVubNm1ymcU8Q4YM0fVrPQ8fPqzFixcrNDRUDz30kIWVuR5Wxd7A4cOHlZiYqIoVK6pGjRpWlwMgG+Li4jRs2DCNHTtWderUyfRXPnNAgd8NHTpUfn5+Gj58uBYsWKCnn35aQUFBSk5O1sCBA11iAeFDDz2kDh066Nlnn9Xp06dVqVIleXp66sSJE5o0aRJTpq5DsANgO+7u7o6Pr19Jd21lHVsjADe2YcMGJSQkKCQkRI8++qjV5UiSAgICtGbNGlWtWlVvvfWWpk6dqq1bt2rhwoWKiYlh6sV18vSp2Ndff/22+1o5eRT2l51LcbnC1gOubs6cOTe9tB2Aq65cuaJ//OMfGjlypGPqQsOGDdWwYUOLK3OWlpamQoUKSZKWL1+uDh06yN3dXQ0bNtThw4ctrs615OkRu9udf+Pm5sZ1G/GXevfddx0fnzx5Ui+99JLCw8OdNihetmyZRo4cqYEDB1pVZq7BZqbA7fP399e2bdtcek5qjRo11Lt3b7Vv317VqlVTXFycGjVqpMTERLVp04aNqK+Tp4Md4Ioef/xxPfDAA4qMjHRqnzZtmr766istWbLEmsJykRtdeeLw4cMKDQ3VhQsXLKoMcD3du3dXWFiYS//R+Mknn+jJJ59Uenq6WrZsqRUrVkiSYmNjtXbtWn355ZcWV+g6CHaAiylYsKC2bdumihUrOrXv379fYWFhOn/+vEWVuT4ubQdk30svvaSJEyfqwQcfzHKxkatMRUpNTdVPP/2kmjVrOubRbty4UX5+fqpcubLF1bkOgt11/ve//+nTTz/N8pIqbHmCu6VcuXJ6/vnnFRUV5dQ+ceJEvf7668wnuYkHHnhAkrRmzRo1atQo06Xtru2FFRISYlWJgMu52SlYV5uKtH//fh04cEDNmjWTj4/PDS81lpfl6cUT14uPj1fbtm0VHBysH374QdWqVVNSUpKMMapdu7bV5SEPGTNmjHr37q3Vq1erQYMGkqRvv/1WcXFxevPNNy2uzrVxaTsg+w4dOmR1Cbd08uRJderUSatWrZKbm5v27dun4OBg9erVS0WKFNHEiROtLtFluN+6S94wbNgwDR48WDt27JC3t7cWLlyolJQUNW/eXB07drS6POQhPXr00DfffCM/Pz8tWrRIixYtkp+fn9atW6cePXpYXV6uMGfOHEIdYCMDBw5U/vz5lZyc7DTFonPnzoqLi7OwMtfDqdjfFCpUSNu2bVOFChVUpEgRrVu3TlWrVtX27dv12GOPKSkpyeoSAQDIcT179rzp/bNnz75LldxYYGCgli1bppo1a6pQoULavn27goODdfDgQdWoUYO5x9fhVOxvChQo4JhXV7JkSR04cEBVq1aVJJ04ccLK0pAHnD179rb7MhIFICf9/PPPTp9fuXJFO3fu1OnTp9WyZUuLqnJ24cIFp5G6a06dOuUylz1zFQS73zRs2FDr1q1TlSpV9PDDDysqKko7duzQokWLXG6jRthP4cKFbzkBmKsmAPgrLF68OFNbRkaGnnvuOVWoUMGCijJr2rSp5s6dq7Fjx0q6uqgjIyND48ePdyyawlWciv3NwYMHdf78edWoUUMXLlxQVFSU45IqkyZNUrly5awuETa2Zs2a2+7bvHnzv7ASALhqz549atGihX766SerS9GuXbvUsmVL1a5dWytXrlTbtm21a9cunTp1St98843LBFBXQLADXNDp06f19ttvO65/GBoaql69esnf39/iygDkFV988YW6d++u48ePW1rHlStX1Lp1a8XGxmrFihXavn27zp8/r9q1a6tv374qWbKkpfW5GoLdH1y+fFnHjh1TRkaGU3vZsmUtqgh5zebNm9W6dWt5e3urfv36kqRNmzbp4sWLWr58OdvvAMhR1zb2vsYYo59++klLly5V9+7dNW3aNIsq+12xYsUcZ9FwcwS73+zdu1e9evVSQkKCUzvzmnC3NW3aVBUrVtSbb76pfPmuToP99ddf1bt3bx08eFBr1661uEIAdvLHOWru7u4qVqyYWrZsqZ49ezp+Dllp4MCB8vLy0rhx46wuxeUR7H5z//33K1++fBo6dKhKliyZaSJ7zZo1LaoMeY2Pj4+2bt2a6RI5u3fvVt26dZWWlmZRZQBgjX79+mnu3LkKCQnJ8rJnXB3qd9bHcBexbds2JSYmcr05WM7Pz0/JycmZvhdTUlJUqFAhi6oCYFctW7bUokWLVLhwYaf2s2fPql27dlq5cqU1hV1n586djmkoe/fudbqPS4o5I9j9JjQ0lP3q4BI6d+6sXr16acKECWrcuLEk6ZtvvlF0dLS6dOlicXUA7Gb16tWZro8uSb/88ou+/vprCyrK7NrlAnFreTrYXb8p7CuvvKIhQ4bo3//+t6pXr678+fM79WVTWNwtEyZMkJubm7p166Zff/1VkpQ/f34999xzzC8BkGO+++47x8e7d+9Wamqq4/P09HTFxcWpdOnSVpSGPyFPz7Fzd3d3GsK9tlDieiyegFXS0tJ04MABSVKFChWy3HUdAO7U9b8Ds4oCPj4+mjp16i0vOQbXkqdH7K4f2k1KSlKZMmXk4eHh1CcjI0PJycl3uzRAvr6+ql69utVlALCpQ4cOyRij4OBgbdy4UcWKFXPc5+npqeLFi2f6nQjXl6dH7K7n4eGhn376ScWLF3dqP3nypIoXL86IHQAAcHl5esTuelmdhpWk8+fPy9vb24KKAAC4e3bv3q3k5ORMCynatm1rUUW4E3k+2F3bcdvNzU0jR450mseUnp6ub7/9VmFhYRZVBwDAX+vgwYNq3769duzYITc3N8d8u2uDHZyxyl3yfLDbunWrpKsjdjt27JCnp6fjPk9PT9WsWVODBw+2qjwAAP5S/fv3V/ny5RUfH6/y5ctr48aNOnnypKKiojRhwgSry0M2McfuNxEREZoyZQrbmgAA8pSAgACtXLlSNWrUkL+/vzZu3KhKlSpp5cqVioqKcgyAIHdwt7oAVzFnzhxCHQAgz0lPT3dc1SYgIEBHjhyRJJUrV0579uyxsjTcgTx/KhYAgLysWrVq2r59u8qXL68GDRpo/Pjx8vT01KxZsxQcHGx1ecgmTsUCAJCHLVu2TBcuXFCHDh20f/9+PfLII9q7d6+KFi2qBQsWqGXLllaXiGwg2AEAACenTp1SkSJFstwGDK6NOXYAAMDh8OHDSk1NzfIyY3B9BDsAAPKg2bNna9KkSU5tzzzzjIKDg1W9enVVq1ZNKSkpFlWHO0WwAwAgD5o1a5aKFCni+DwuLk5z5szR3LlztWnTJhUuXFhjxoyxsELcCebYAQCQBxUtWlSrV69W9erVJUnPPfecjh8/rk8++USStHr1akVEROjQoUNWlolsYsQOAIA86OLFi077tyYkJKhZs2aOz4ODg5WammpFafgTCHYAAORB5cqVU2JioiTpxIkT2rVrl+6//37H/ampqfL397eqPNwhNigGACAP6t69u/r27atdu3Zp5cqVqly5surUqeO4PyEhQdWqVbOwQtwJgh0AAHnQkCFDlJaWpkWLFikwMFAff/yx0/3ffPONunTpYlF1uFMsngAAALf04Ycfqm3btipQoIDVpeAmCHYAAOCW/Pz8tG3bNq4f6+JYPAEAAG6JcaDcgWAHAABgEwQ7AAAAmyDYAQAA2ATBDgAAwCYIdgAA4JbKlSun/PnzW10GboHtTgAAAGyCETsAAPKo//znP2rVqpU6deqk+Ph4p/tOnDjBnnW5EMEOAIA86PXXX1d0dLQqV64sLy8vPfzww4qNjXXcn56ersOHD1tYIe4E14oFACAPeuONN/Tmm2/qySeflCQ999xzateunS5evKgXX3zR4upwpwh2AADkQYcOHVLjxo0dnzdu3FgrV65Uq1atdOXKFQ0YMMC64nDHCHYAAORBAQEBSklJUVBQkKOtWrVqWrlypVq2bKkjR45YVxzuGHPsAADIg5o0aaJFixZlag8NDVV8fLy+/PJLC6rCn8WIHQAAedDQoUOVmJiY5X1Vq1bVypUrtXDhwrtcFf4s9rEDACCPO336tN5++219//33kq4Gu549e8rf39/iypBdBDsAAPKwzZs3Kzw8XD4+Pqpfv74kadOmTbp48aKWL1+u2rVrW1whsoNgBwBAHta0aVNVrFhRb775pvLluzpD69dff1Xv3r118OBBrV271uIKkR0EOwAA8jAfHx9t3bpVlStXdmrfvXu36tatq7S0NIsqw51gVSwAAHmYn5+fkpOTM7WnpKSoUKFCFlSEP4NgBwBAHta5c2f16tVLCxYsUEpKilJSUjR//nz17t1bXbp0sbo8ZBPbnQAAkIdNmDBBbm5u6tatm3799VdJUv78+fXcc89p3LhxFleH7GKOHQAAUFpamg4cOCBJqlChgnx9fS2uCHeCYAcAAGATzLEDAACwCYIdAACATRDsAAAAbIJgBwAAYBMEOwAAAJsg2AEAANgEwQ4AAMAmCHYAAAA28f+gHH6SqRl3YQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "forest_importances = pd.Series(importances, index = X.columns)\n", + "forest_importances = forest_importances.sort_values(ascending=False)\n", + "fig, ax = plt.subplots()\n", + "forest_importances.plot.bar(ax=ax)\n", + "ax.set_title(\"Feature importances\")\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "1526680d-a94a-4335-8644-902d6a5751b4", + "metadata": {}, + "outputs": [], + "source": [ + "# Drop the least important feature and compare the result with model 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f57a68eb-efd5-41e8-8348-bffa40f1c2c2", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Seminar5/heart.csv b/Seminar5/heart.csv new file mode 100644 index 0000000..0966e67 --- /dev/null +++ b/Seminar5/heart.csv @@ -0,0 +1,304 @@ +age,sex,cp,trtbps,chol,fbs,restecg,thalachh,exng,oldpeak,slp,caa,thall,output +63,1,3,145,233,1,0,150,0,2.3,0,0,1,1 +37,1,2,130,250,0,1,187,0,3.5,0,0,2,1 +41,0,1,130,204,0,0,172,0,1.4,2,0,2,1 +56,1,1,120,236,0,1,178,0,0.8,2,0,2,1 +57,0,0,120,354,0,1,163,1,0.6,2,0,2,1 +57,1,0,140,192,0,1,148,0,0.4,1,0,1,1 +56,0,1,140,294,0,0,153,0,1.3,1,0,2,1 +44,1,1,120,263,0,1,173,0,0,2,0,3,1 +52,1,2,172,199,1,1,162,0,0.5,2,0,3,1 +57,1,2,150,168,0,1,174,0,1.6,2,0,2,1 +54,1,0,140,239,0,1,160,0,1.2,2,0,2,1 +48,0,2,130,275,0,1,139,0,0.2,2,0,2,1 +49,1,1,130,266,0,1,171,0,0.6,2,0,2,1 +64,1,3,110,211,0,0,144,1,1.8,1,0,2,1 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