diff --git a/CodeSprints/CoordinateReferenceSystem.ipynb b/CodeSprints/CoordinateReferenceSystem.ipynb index d8025c5..cf1a4ef 100644 --- a/CodeSprints/CoordinateReferenceSystem.ipynb +++ b/CodeSprints/CoordinateReferenceSystem.ipynb @@ -8,6 +8,44 @@ "source": [ "# Coordinate Reference Systems in Python\n", "### [Lesson 2. GIS in Python: Intro to Coordinate Reference Systems in Python](https://www.earthdatascience.org/courses/use-data-open-source-python/intro-vector-data-python/spatial-data-vector-shapefiles/intro-to-coordinate-reference-systems-python)\n", + "### by Chris Holdgraf, Leah Wasser" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EgVtmhqnqOkj" + }, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/LizCarter492/EnvDatSci22/blob/master/CodeSprints/CoordinateReferenceSystem.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "nwMFy0dXqXOT" + }, + "outputs": [], + "source": [ + "#attach to Google Drive\n", + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Ul3RmsxCqbOK" + }, + "outputs": [], + "source": [ + "# Install packages for Google Colab\n", + "%%capture\n", + "!pip install geopandas\n", + "!pip install rasterio\n", + "!pip install earthpy" "### by Chris Holdgraf, Leah Wasser\n", "\n" ] @@ -527,6 +565,9 @@ "provenance": [] }, "kernelspec": { + "display_name": "geostats_env", + "language": "python", + "name": "geostats_env" "display_name": "waterdig", "language": "python", "name": "myenv" @@ -541,6 +582,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", + "version": "3.8.20" "version": "3.11.6" } }, diff --git a/CodeSprints/FeatureDependence.ipynb b/CodeSprints/FeatureDependence.ipynb index d161e8a..5812a52 100644 --- a/CodeSprints/FeatureDependence.ipynb +++ b/CodeSprints/FeatureDependence.ipynb @@ -1,1152 +1,1152 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "id": "204733d1", - "metadata": { - "id": "204733d1" - }, - "source": [ - "\n", - "## Feature Dependence and Regression Analysis\n", - "In this example, we will be using a spatial dataset of county-level election and demographic statistics for the United States. We'll explore different methods to diagnose and account for multicollinearity in our data in regression analysis. Specifically, we'll calculate variance inflation factor (VIF), and compare parameter estimates and model fit in a multivariate regression predicting 2016 county voting preferences using an OLS model, a ridge regression, a lasso regression, and an elastic net regression.\n", - "\n", - "Objectives:\n", - "* **Calculate a variance inflation factor to diagnose multicollinearity.**\n", - "* **Interpret model summary statistics.**\n", - "* **Describe how multicollinearity impacts stability in parameter esimates.**\n", - "* **Explain the variance/bias tradeoff and describe how to use it to improve models**\n", - "* **Draw a conclusion based on contrasting models.**\n", - "\n" - ] - }, - { - "cell_type": "code", - "source": [ - "%%capture\n", - "!pip install geopandas\n", - "!pip install libpysal" - ], - "metadata": { - "id": "nuph--LrdYaJ" - }, - "id": "nuph--LrdYaJ", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bf668f40", - "metadata": { - "id": "bf668f40" - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import geopandas as gpd\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import statsmodels.api as sm\n", - "from statsmodels.stats.outliers_influence import variance_inflation_factor\n", - "from sklearn.model_selection import cross_val_score\n", - "from sklearn.model_selection import RepeatedKFold\n", - "from sklearn.linear_model import Ridge\n", - "from sklearn.linear_model import Lasso\n", - "from sklearn.linear_model import ElasticNet\n", - "from numpy import mean\n", - "from numpy import std\n", - "from numpy import absolute\n", - "from libpysal.weights.contiguity import Queen\n", - "import libpysal\n", - "from statsmodels.api import OLS\n", - "sns.set_style('white')" - ] - }, - { - "cell_type": "markdown", - "id": "daec4204", - "metadata": { - "id": "daec4204" - }, - "source": [ - "First, we're going to load the 'Elections' dataset from the libpysal library, which is a very easy to use API that accesses the Geodata Center at the University of Chicago.\n", - "\n", - "* More on spatial data science resources from UC: https://spatial.uchicago.edu/\n", - "* A list of datasets available through lipysal: https://geodacenter.github.io/data-and-lab//" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9de38c04", - "metadata": { - "id": "9de38c04" - }, - "outputs": [], - "source": [ - "from libpysal.examples import load_example\n", - "elections = load_example('Elections')\n", - "#note the folder where your data now lives:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8c7fe6d5", - "metadata": { - "id": "8c7fe6d5" - }, - "outputs": [], - "source": [ - "#First, let's see what files are available in the 'Elections' data example\n", - "elections.get_file_list()" - ] - }, - { - "cell_type": "markdown", - "id": "d92c29a3", - "metadata": { - "id": "d92c29a3" - }, - "source": [ - "When you are out in the world doing research, you often will not find a ready-made function to download your data. That's okay! You know how to get this dataset without using pysal! Do a quick internal review of online data formats and automatic data downloads.\n", - "\n", - "### TASK 1: Use urllib functions to download this file directly from the internet a folder (not in your git repository). Extract the zipped file you've downloaded into a subfolder called elections." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1318b96b", - "metadata": { - "id": "1318b96b" - }, - "outputs": [], - "source": [ - "# Task 1 code here:\n" - ] - }, - { - "cell_type": "markdown", - "id": "ea133475", - "metadata": { - "id": "ea133475" - }, - "source": [ - "### TASK 2: Use geopandas to read in this shapefile. Call your geopandas.DataFrame \"votes\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "81455057", - "metadata": { - "id": "81455057" - }, - "outputs": [], - "source": [ - "# TASK 2: Use geopandas to read in this shapefile. Call your geopandas.DataFrame \"votes\"\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8ae67e1e", - "metadata": { - "id": "8ae67e1e" - }, - "outputs": [], - "source": [ - "#Let's view the shapefile to get a general idea of the geometry we're looking at:\n", - "%matplotlib inline\n", - "votes.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7495565e", - "metadata": { - "id": "7495565e" - }, - "outputs": [], - "source": [ - "#View the first few lines of the dataset\n", - "votes.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "813ad89f", - "metadata": { - "id": "813ad89f" - }, - "outputs": [], - "source": [ - "#Since there are too many columns for us to view on a signle page using \"head\", we can just print out the column names so we have them all listed for reference\n", - "for col in votes.columns:\n", - " print(col)" - ] - }, - { - "cell_type": "markdown", - "id": "aa277c42", - "metadata": { - "id": "aa277c42" - }, - "source": [ - "#### You can use pandas summary statistics to get an idea of how county-level data varies across the United States.\n", - "### TASK 3: For example, how did the county mean percent Democratic vote change between 2012 (pct_dem_12) and 2016 (pct_dem_16)?\n", - "\n", - "Look here for more info on pandas dataframes:\n", - "\n", - "https://www.earthdatascience.org/courses/intro-to-earth-data-science/scientific-data-structures-python/pandas-dataframes/run-calculations-summary-statistics-pandas-dataframes/" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "818f70e5", - "metadata": { - "id": "818f70e5" - }, - "outputs": [], - "source": [ - "#Task 3\n" - ] - }, - { - "cell_type": "markdown", - "id": "1b0f15a0", - "metadata": { - "id": "1b0f15a0" - }, - "source": [ - "We can also plot histograms of the data. Below, smoothed histograms from the seaborn package (imported as sns) let us get an idea of the distribution of percent democratic votes in 2012 (left) and 2016 (right)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "57df83a8", - "metadata": { - "id": "57df83a8" - }, - "outputs": [], - "source": [ - "# Plot histograms:\n", - "f,ax = plt.subplots(1,2, figsize=(2*3*1.6, 2))\n", - "for i,col in enumerate(['pct_dem_12','pct_dem_16']):\n", - " sns.kdeplot(votes[col].values, shade=True, color='slategrey', ax=ax[i])\n", - " ax[i].set_title(col.split('_')[1])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7c314d64", - "metadata": { - "id": "7c314d64" - }, - "outputs": [], - "source": [ - "# Plot spatial distribution of # dem vote in 2012 and 2016 with histogram.\n", - "f,ax = plt.subplots(2,2, figsize=(1.6*6 + 1,2.4*3), gridspec_kw=dict(width_ratios=(6,1)))\n", - "for i,col in enumerate(['pct_dem_12','pct_dem_16']):\n", - " votes.plot(col, linewidth=.05, cmap='RdBu', ax=ax[i,0])\n", - " ax[i,0].set_title(['2012','2016'][i] + \" % democratic vote\")\n", - " ax[i,0].set_xticklabels('')\n", - " ax[i,0].set_yticklabels('')\n", - " sns.kdeplot(votes[col].values, ax=ax[i,1], vertical=True, shade=True, color='slategrey')\n", - " ax[i,1].set_xticklabels('')\n", - " ax[i,1].set_ylim(-1,1)\n", - "f.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "e6d6ec1f", - "metadata": { - "id": "e6d6ec1f" - }, - "source": [ - "### TASK 4: Make a new column on your geopandas dataframe called \"pct_dem_change\" and plot it using the syntax above. Explain the plot." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e68ce3ef", - "metadata": { - "id": "e68ce3ef" - }, - "outputs": [], - "source": [ - "# Task 4: add new column pct_dem_change to votes:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c5c7a89d", - "metadata": { - "id": "c5c7a89d" - }, - "outputs": [], - "source": [ - "#Task 4: plot your pct_dem_change variable on a map:\n", - "f,ax = plt.subplots(1,2, figsize=(1.6*6 + 1,1.6*3), gridspec_kw=dict(width_ratios=(6,1)))\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "946cb5c6", - "metadata": { - "id": "946cb5c6" - }, - "source": [ - "Click on this url to learn more about the variables in this dataset: https://geodacenter.github.io/data-and-lab//county_election_2012_2016-variables/\n", - "\n", - "Let's say we want to learn more about what county-level factors influence percent change in democratic vote between (pct_dem_change).\n", - "\n", - "There are two types of multicollinearity:\n", - "\n", - "* *Intrinsic multicollinearity:* is an artifact of how we make observations. Often our measurements serve as proxies for some latent process (for example, we can measure percent silt, percent sand, and percent clay as proxies for the latent variable of soil texture). There will be slight variability in the information content between each proxy measurement, but they will not be independent of one another.\n", - "\n", - "* *Incidental collinearity:* is an artifact of how we sample complex populations. If we collect data from a subsample of the landscape where we don't see all combinations of our predictor variables (do not have good cross replication across our variables). We often induce collinearity in our data just because we are limitted in our ability to sample the environment at the scale of temporal/spatial variability of our process of interest. Incidental collinearity is a model formulation problem.(See here for more info on how to avoid it: https://people.umass.edu/sdestef/NRC%20601/StudyDesignConcepts.pdf)" - ] - }, - { - "cell_type": "markdown", - "id": "da5b59ac", - "metadata": { - "id": "da5b59ac" - }, - "source": [ - "### TASK 5: Looking at the data description, pick two variables that you believe will be intrinsically multicollinear. List and describe these variables. Why do you think they are intrinsically related?" - ] - }, - { - "cell_type": "markdown", - "id": "18207d3e", - "metadata": { - "id": "18207d3e" - }, - "source": [ - "## Multivariate regression in observational data:\n", - "Our next step is to formulate our predictive/diagnostic model. We want to create a subset of the \"votes\" geopandas data frame that contains ten predictor variables and our response variable (pct_pt_16) two variables you selected under TASK 1. First, create a list of the variables you'd like to select.\n", - "\n", - "### TASK 6: Create a subset of votes called \"my_list\" containing only your selected predictor variables. Make sure you use the two variables selected under TASK 5, and eight additional variables" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "65d4c6cb", - "metadata": { - "id": "65d4c6cb" - }, - "outputs": [], - "source": [ - "# Task 4: create a subset of votes called \"my list\" with all your subset variables.\n", - "#my_list = [\"pct_pt_16\", ]\n", - "\n", - "#For example:\n", - "my_list = [\"pct_dem_change\", #The number in diff_2016 expressed as a percent of the total votes. Negative if fewer votes were cast for the Democratic candidate\n", - " \"PST120214\", #Population, percent change - April 1, 2010 to July 1, 2014\n", - " \"SEX255214\", #Female persons, percent, 2014\n", - " \"RHI125214\", #White alone, percent, 2014\n", - " \"RHI225214\", #Black or African American alone, percent, 2014\n", - " \"EDU685213\", #Bachelor’s degree or higher, percent of persons age 25+, 2009-2013\n", - " \"VET605213\", #Veterans, 2009-2013\n", - " \"HSG495213\", #Median value of owner-occupied housing units, 2009-2013\n", - " \"HSD310213\", #Persons per household, 2009-2013\n", - " \"INC910213\", #Per capita money income in past 12 months (2013 dollars), 2009-2013\n", - " \"LND110210\"] #Land area in square miles, 2010\n", - "\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f1835a8d", - "metadata": { - "id": "f1835a8d" - }, - "outputs": [], - "source": [ - "#check to make sure all your columns are there:\n", - "votes[my_list].head()" - ] - }, - { - "cell_type": "markdown", - "id": "d28d9dc4", - "metadata": { - "id": "d28d9dc4" - }, - "source": [ - "### Scatterplot matrix\n", - "We call the process of getting to know your data (ranges and distributions of the data, as well as any relationships between variables) \"exploratory data analysis\". Pairwise plots of your variables, called scatterplots, can provide a lot of insight into the type of relationships you have between variables. A scatterplot matrix is a pairwise comparison of all variables in your dataset." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f3f67ff6", - "metadata": { - "id": "f3f67ff6" - }, - "outputs": [], - "source": [ - "#Use seaborn.pairplot to plot a scatterplot matrix of you 10 variable subset:\n", - "sns.pairplot(votes[my_list])" - ] - }, - { - "cell_type": "markdown", - "id": "e7b1b309", - "metadata": { - "id": "e7b1b309" - }, - "source": [ - "### TASK 7: Do you observe any collinearity in this dataset? How would you describe the relationship between your two \"incidentally collinear\" variables that you selected based on looking at variable descriptions?\n", - "\n", - "\n", - "\n", - "### TASK 8: What is plotted on the diagonal panels of the scatterplot matrix?\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "89b42b91", - "metadata": { - "id": "89b42b91" - }, - "source": [ - "## Diagnosing collinearity globally:\n", - "Variance Inflation Factor describes the magnitude of variance inflation that can be expected in an OLS parameter estimate for a given variable *given pairwise collinearity between that variable and another variable*." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9be64d7b", - "metadata": { - "id": "9be64d7b" - }, - "outputs": [], - "source": [ - "#VIF = 1/(1-R2) of a pairwise OLS regression between two predictor variables\n", - "#We can use a built-in function \"variance_inflation_factor\" from statsmodel.api to calculate VIF\n", - "#Learn more about the function\n", - "?variance_inflation_factor" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a53f51dc", - "metadata": { - "id": "a53f51dc" - }, - "outputs": [], - "source": [ - "#Calculate VIFs on our dataset\n", - "vif = pd.DataFrame()\n", - "vif[\"VIF Factor\"] = [variance_inflation_factor(votes[my_list[1:11]].values, i) for i in range(votes[my_list[1:11]].shape[1])]\n", - "vif[\"features\"] = votes[my_list[1:11]].columns\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d465f95f", - "metadata": { - "id": "d465f95f" - }, - "outputs": [], - "source": [ - "vif.round()" - ] - }, - { - "cell_type": "markdown", - "id": "ae322791", - "metadata": { - "id": "ae322791" - }, - "source": [ - "### Collinearity is always present in observational data. When is it a problem?\n", - "Generally speaking, including any variables with VIF > 10 is considered \"too much\" collinearity. But this value is somewhat arbitrary. The extent to which variance inflation will impact your analysis is highly context dependent. There are two primary contexts where variance inflation is problematic:\n", - "\n", - " 1\\. **You are using your analysis to evaluate variable importance:** If you are using parameter estimates from your model to diagnose which observations have physically important relationships with your response variable, variance inflation can make an important predictor look unimportant, and parameter estimates will be highly leveraged by small changes in the data.\n", - "\n", - " 2\\. **You want to use your model to make predictions in a situation where the specific structure of collinearity between variables may have shifted:** When training a model on collinear data, the model only applies to data with that exact structure of collinearity." - ] - }, - { - "cell_type": "markdown", - "id": "f8a7a711", - "metadata": { - "id": "f8a7a711" - }, - "source": [ - "### Caluculate a linear regression on the global data:\n", - "In this next step, we're going to calculate a linear regression in our data an determine whether there is a statistically significant relationship between per capita income and percent change in democratic vote." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "515911da", - "metadata": { - "id": "515911da" - }, - "outputs": [], - "source": [ - "#first, forumalate the model. See weather_trend.py in \"Git_101\" for a refresher on how.\n", - "\n", - "#extract variable that you want to use to \"predict\"\n", - "X = np.array(votes[my_list[1:11]].values)\n", - "#standardize data to assist in interpretation of coefficients\n", - "X = (X - np.mean(X, axis=0)) / np.std(X, axis=0)\n", - "\n", - "#extract variable that we want to \"predict\"\n", - "Y = np.array(votes['pct_dem_change'].values)\n", - "#standardize data to assist in interpretation of coefficients\n", - "Y = (Y - np.mean(X)) / np.std(Y)\n", - "\n", - "lm = OLS(Y,X)\n", - "lm_results = OLS(Y,X).fit().summary()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "68a265a8", - "metadata": { - "id": "68a265a8" - }, - "outputs": [], - "source": [ - "print(lm_results)" - ] - }, - { - "cell_type": "markdown", - "id": "d760fd73", - "metadata": { - "id": "d760fd73" - }, - "source": [ - "### TASK 9: Answer: which coefficients indicate a statisticall significant relationship between parameter and pct_dem_change? What is your most important predictor variable? How can you tell?\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "1202f4f3", - "metadata": { - "id": "1202f4f3" - }, - "source": [ - "### TASK10: Are any of these parameters subject to variance inflation? How can you tell?\n", - "\n", - "\n", - "**With a cutoff of VIF=10, the following variables are subject to variable inflation.**\n", - "\n", - "\t269.0 \tSEX255214\n", - "\t119.0 \tRHI125214\n", - "\t20.0 \tEDU685213\n", - "\t11.0 \tHSG495213\n", - "\t106.0 \tHSD310213\n", - "\t67.0 \tINC910213" - ] - }, - { - "cell_type": "markdown", - "id": "c25a9235", - "metadata": { - "id": "c25a9235" - }, - "source": [ - "Now, let's plot our residuals to see if there are any spatial patterns in them.\n", - "\n", - "Remember residuals = predicted - fitted values" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0098b6ee", - "metadata": { - "id": "0098b6ee" - }, - "outputs": [], - "source": [ - "#Add model residuals to our \"votes\" geopandas dataframe:\n", - "votes['lm_resid']=OLS(Y,X).fit().resid\n" - ] - }, - { - "cell_type": "code", - "source": [ - "vif['OLS_coef']=OLS(Y,X).fit().params" - ], - "metadata": { - "id": "E_TS1YyLQFA8" - }, - "id": "E_TS1YyLQFA8", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a439b312", - "metadata": { - "id": "a439b312" - }, - "outputs": [], - "source": [ - "sns.kdeplot(votes['lm_resid'].values, shade=True, color='slategrey')\n" - ] - }, - { - "cell_type": "markdown", - "id": "0bc83f6a", - "metadata": { - "id": "0bc83f6a" - }, - "source": [ - "### TASK 11: Are our residuals normally distributed with a mean of zero? What does that mean?\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "66f59dce", - "metadata": { - "id": "66f59dce" - }, - "source": [ - "## Penalized regression: ridge penalty\n", - "In penalized regression, we intentionally bias the parameter estimates to stabilize them given collinearity in the dataset.\n", - "\n", - "From https://www.analyticsvidhya.com/blog/2016/01/ridge-lasso-regression-python-complete-tutorial/\n", - "\n", - ">As mentioned before, ridge regression performs ‘L2 regularization‘, i.e. it adds a factor of sum of squares of coefficients in the optimization objective. Thus, ridge regression optimizes the following:\n", - "\n", - "**Objective = RSS + α * (sum of square of coefficients)**\n", - "\n", - "Here, α (alpha) is the parameter which balances the amount of emphasis given to minimizing RSS vs minimizing sum of square of coefficients. α can take various values:\n", - "\n", - "* **α = 0:** The objective becomes same as simple linear regression. We’ll get the same coefficients as simple linear regression.\n", - "\n", - "* **α = ∞:** The coefficients will approach zero. Why? Because of infinite weightage on square of coefficients, anything less than zero will make the objective infinite.\n", - "\n", - "* **0 < α < ∞:** The magnitude of α will decide the weightage given to different parts of objective. The coefficients will be somewhere between 0 and ones for simple linear regression.\"\n", - "\n", - "In other words, the ridge penalty shrinks coefficients such that collinear coefficients will have more similar coefficient values. It has a \"grouping\" tendency." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cebcc4a3", - "metadata": { - "id": "cebcc4a3" - }, - "outputs": [], - "source": [ - "# when L2=0, Ridge equals OLS\n", - "model = Ridge(alpha=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "60b4d30d", - "metadata": { - "id": "60b4d30d" - }, - "outputs": [], - "source": [ - "# define model evaluation method\n", - "cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)\n", - "# evaluate model\n", - "scores = cross_val_score(model, X, Y, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)\n", - "#force scores to be positive\n", - "scores = absolute(scores)\n", - "print('Mean MAE: %.3f (%.3f)' % (mean(scores), std(scores)))" - ] - }, - { - "cell_type": "code", - "source": [ - "model.fit(X,Y)\n", - "#Print out the model coefficients\n", - "print(model.coef_)\n" - ], - "metadata": { - "id": "mbYodbd4hfkD" - }, - "id": "mbYodbd4hfkD", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Hyperparameter tuning\n", - "The L2 coefficient (called alpha above) is a free parameter (hyperparameter) in the model, meaning we can set it whatever value we feel is best.\n", - "\n", - "In machine learning, we often try multiple options for these hyperparameters, and select the value with the highest model performance." - ], - "metadata": { - "id": "RUmJfpeJhhFE" - }, - "id": "RUmJfpeJhhFE" - }, - { - "cell_type": "code", - "source": [ - "# Train on a range of alphas\n", - "alpha = np.arange(0,20,0.1)\n", - "ridge_tune=pd.DataFrame(alpha, columns=[\"alpha\"])\n", - "ridge_tune['Score']=0\n", - "i=0\n", - "for a in alpha:\n", - " model= Ridge(alpha=a)\n", - " oos_scores = cross_val_score(model, X, Y, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)\n", - " #force scores to be positive\n", - " oos_scores = absolute(oos_scores)\n", - " ridge_tune.Score.loc[i]=mean(oos_scores)\n", - " i=i+1\n" - ], - "metadata": { - "id": "LYlRKrRrC_eG" - }, - "id": "LYlRKrRrC_eG", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Plot model performance as a function of alpha\n", - "plt.plot(ridge_tune.alpha, ridge_tune.Score)\n", - "plt.show()\n", - "# What is the y-axis label? What is the x-axis label?" - ], - "metadata": { - "id": "fWJPA8ifIHSb" - }, - "id": "fWJPA8ifIHSb", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Select the value of alpha which minimizes MAE\n", - "a2 = ridge_tune.alpha.iloc[ridge_tune['Score'].idxmin()]\n", - "a2" - ], - "metadata": { - "id": "dOdAzM_YJxHx" - }, - "id": "dOdAzM_YJxHx", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c68402c6", - "metadata": { - "id": "c68402c6" - }, - "outputs": [], - "source": [ - "# Train model with optimized alpha\n", - "model = Ridge(alpha=a2)\n", - "model.fit(X,Y)\n", - "#Print out the model coefficients\n", - "print(model.coef_)\n", - "vif['Ridge_coef']=model.coef_" - ] - }, - { - "cell_type": "markdown", - "id": "53d94d1f", - "metadata": { - "id": "53d94d1f" - }, - "source": [ - "## Penalized regression: lasso penalty\n", - "\n", - "From https://www.analyticsvidhya.com/blog/2016/01/ridge-lasso-regression-python-complete-tutorial/\n", - "> LASSO stands for Least Absolute Shrinkage and Selection Operator.\n", - "\n", - "There are 2 key words here – **absolute** and **selection**.\n", - "\n", - "Lets consider the former first and worry about the latter later.\n", - ".\n", - "Lasso regression performs L1 regularization, i.e. it adds a factor to the sum of absolute value of coefficients in the optimization objective. Thus, lasso regression optimizes the following:\n", - "\n", - "**Objective = RSS + α * (sum of absolute value of coefficients)**\n", - "\n", - "Here, α (alpha) works similar to that of ridge and provides a trade-off between balancing RSS and magnitude of coefficients. Like that of ridge, α can take various values. Lets iterate it here briefly:\n", - "\n", - "* **α = 0:** Same coefficients as simple linear regression\n", - "* **α = ∞:** All coefficients zero (same logic as before)\n", - "* **0 < α < ∞:** coefficients between 0 and that of simple linear regression\n", - "\n", - "The lasso penalty shrinks unimportant coefficients down towards zero, automatically \"selecting\" important predictor variables. But what if that shrunken coefficient is induced by incidental collinearity (i.e. is a feature of how we sampled our data)?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "17f683d0", - "metadata": { - "id": "17f683d0" - }, - "outputs": [], - "source": [ - "# when L1=0, Lasso equals OLS\n", - "model = Lasso(alpha=0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5eccdbab", - "metadata": { - "id": "5eccdbab" - }, - "outputs": [], - "source": [ - "# define model evaluation method\n", - "cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)\n", - "# evaluate model\n", - "scores = cross_val_score(model, X, Y, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)\n", - "#force scores to be positive\n", - "scores = absolute(scores)\n", - "print('Mean MAE: %.3f (%.3f)' % (mean(scores), std(scores)))" - ] - }, - { - "cell_type": "code", - "source": [ - "model.fit(X,Y)\n", - "#Print out the model coefficients\n", - "print(model.coef_)\n", - "#How do these compare to OLS coefficients?" - ], - "metadata": { - "id": "WtYefDGhh29N" - }, - "id": "WtYefDGhh29N", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# when L1 approaches infinity, coefficients will become exactly zero, and MAE equals the variance of our response variable:\n", - "model = Lasso(alpha=10000000)" - ], - "metadata": { - "id": "PGLGB4Fmh9He" - }, - "id": "PGLGB4Fmh9He", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# define model evaluation method\n", - "cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)\n", - "# evaluate model\n", - "scores = cross_val_score(model, X, Y, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)\n", - "#force scores to be positive\n", - "scores = absolute(scores)\n", - "print('Mean MAE: %.3f (%.3f)' % (mean(scores), std(scores)))" - ], - "metadata": { - "id": "pX1r-KF6iEkD" - }, - "id": "pX1r-KF6iEkD", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d6fd8e0a", - "metadata": { - "id": "d6fd8e0a" - }, - "outputs": [], - "source": [ - "model.fit(X,Y)\n", - "#Print out the model coefficients\n", - "print(model.coef_)\n", - "#How do these compare with OLS coefficients above?" - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Hyperparameter tuning\n", - "Just like with the ridge penalty, we want to select the value of alpha that minimizes error in the lasso model.\n", - "\n", - "We'll use hyperparameter tuning again to do this:" - ], - "metadata": { - "id": "Q8od0fBRiNgc" - }, - "id": "Q8od0fBRiNgc" - }, - { - "cell_type": "code", - "source": [ - "# Define range of alphas:\n", - "alpha = np.arange(0,.02,0.0001)\n", - "lasso_tune=pd.DataFrame(alpha, columns=[\"alpha\"])\n", - "lasso_tune['Score']=0\n", - "i=0\n", - "for a in alpha:\n", - " model= Lasso(alpha=a)\n", - " oos_scores = cross_val_score(model, X, Y, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)\n", - " #force scores to be positive\n", - " oos_scores = absolute(oos_scores)\n", - " lasso_tune.Score.loc[i]=mean(oos_scores)\n", - " i=i+1\n" - ], - "metadata": { - "id": "7HoHTeetPoP5" - }, - "id": "7HoHTeetPoP5", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Plot model performance as a function of alpha:\n", - "plt.plot(lasso_tune.alpha, lasso_tune.Score)\n", - "plt.show()\n", - "# What is the y-axis label? What is the x-axis label?" - ], - "metadata": { - "id": "v3Fr81sEPysW" - }, - "id": "v3Fr81sEPysW", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "a1 = lasso_tune.alpha.iloc[lasso_tune['Score'].idxmin()]\n", - "a1" - ], - "metadata": { - "id": "WyqaEXZsR6uX" - }, - "id": "WyqaEXZsR6uX", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Train model with optimal alpha1\n", - "model= Lasso(alpha=a1)\n", - "model.fit(X,Y)\n", - "#Print out the model coefficients\n", - "print(model.coef_)\n", - "\n", - "vif['LASSO_coef']= model.coef_" - ], - "metadata": { - "id": "5gm-aAixSNI7" - }, - "id": "5gm-aAixSNI7", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "id": "5fe1b7ff", - "metadata": { - "id": "5fe1b7ff" - }, - "source": [ - "### Penalized regression: elastic net penalty\n", - "\n", - "In other words, the lasso penalty shrinks unimportant coefficients down towards zero, automatically \"selecting\" important predictor variables. The ridge penalty shrinks coefficients of collinear predictor variables nearer to each other, effectively partitioning the magnitude of response from the response variable between them, instead of \"arbitrarily\" partitioning it to one group.\n", - "\n", - "We can also run a regression with a linear combination of ridge and lasso, called the elastic net, that has a cool property called \"group selection.\"\n", - "\n", - "The ridge penalty still works to distribute response variance equally between members of \"groups\" of collinear predictor variables. The lasso penalty still works to shrink certain coefficients to exactly zero so they can be ignored in model formulation. The elastic net produces models that are both sparse and stable under collinearity, by shrinking parameters of members of unimportant collinear predictor variables to exactly zero:" - ] - }, - { - "cell_type": "code", - "source": [ - "# Explore the parameters in the ElasticNet fuction.\n", - "# Which variable represents the ridge coefficient? Which parameter represents the lasso coefficient?\n", - "?ElasticNet" - ], - "metadata": { - "id": "RgMVSI0Ri6vR" - }, - "id": "RgMVSI0Ri6vR", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ce6967ae", - "metadata": { - "id": "ce6967ae" - }, - "outputs": [], - "source": [ - "# when L1 approaches infinity, certain coefficients will become exactly zero, and MAE equals the variance of our response variable:\n", - "model = ElasticNet(alpha=.1, l1_ratio=.1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "22fc92a2", - "metadata": { - "id": "22fc92a2" - }, - "outputs": [], - "source": [ - "# define model evaluation method\n", - "cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)\n", - "# evaluate model\n", - "scores = cross_val_score(model, X, Y, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)\n", - "#force scores to be positive\n", - "scores = absolute(scores)\n", - "print('Mean MAE: %.3f (%.3f)' % (mean(scores), std(scores)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0fee71d5", - "metadata": { - "id": "0fee71d5" - }, - "outputs": [], - "source": [ - "model.fit(X,Y)\n", - "#Print out the model coefficients\n", - "print(model.coef_)\n", - "#How do these compare with OLS coefficients above?\n", - "vif[\"ElNet_coef\"]=model.coef_" - ] - }, - { - "cell_type": "markdown", - "id": "ac8a540d", - "metadata": { - "id": "ac8a540d" - }, - "source": [ - "### TASK 12: Match these elastic net coefficients up with your original data. Do you see a logical grouping(s) between variables that have non-zero coefficients?\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6c12c783", - "metadata": { - "id": "6c12c783" - }, - "outputs": [], - "source": [ - "print(vif)" - ] - }, - { - "cell_type": "markdown", - "source": [ - "## Extra Credit:\n", - "We saw how to tune individual parameters above. The elastic net regression here has two free parameters. How can you select the best values for these two free parameters?\n", - "\n", - "Demonstrate in the cell below." - ], - "metadata": { - "id": "iLhCXRVcjEc8" - }, - "id": "iLhCXRVcjEc8" - }, - { - "cell_type": "code", - "source": [ - "# Extra credit scratch cell:" - ], - "metadata": { - "id": "Tjlf88d2jH2C" - }, - "id": "Tjlf88d2jH2C", - "execution_count": null, - "outputs": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "geostats_env", - "language": "python", - "name": "geostats_env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.11" - }, - "colab": { - "provenance": [], - "include_colab_link": true - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file + "cells": [ + { + "cell_type": "markdown", + "id": "f6923b43", + "metadata": { + "colab_type": "text", + "id": "view-in-github" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "id": "204733d1", + "metadata": { + "id": "204733d1" + }, + "source": [ + "\n", + "## Feature Dependence and Regression Analysis\n", + "In this example, we will be using a spatial dataset of county-level election and demographic statistics for the United States. We'll explore different methods to diagnose and account for multicollinearity in our data in regression analysis. Specifically, we'll calculate variance inflation factor (VIF), and compare parameter estimates and model fit in a multivariate regression predicting 2016 county voting preferences using an OLS model, a ridge regression, a lasso regression, and an elastic net regression.\n", + "\n", + "Objectives:\n", + "* **Calculate a variance inflation factor to diagnose multicollinearity.**\n", + "* **Interpret model summary statistics.**\n", + "* **Describe how multicollinearity impacts stability in parameter esimates.**\n", + "* **Explain the variance/bias tradeoff and describe how to use it to improve models**\n", + "* **Draw a conclusion based on contrasting models.**\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "nuph--LrdYaJ", + "metadata": { + "id": "nuph--LrdYaJ" + }, + "outputs": [], + "source": [ + "%%capture\n", + "!pip install geopandas\n", + "!pip install libpysal" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bf668f40", + "metadata": { + "id": "bf668f40" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import geopandas as gpd\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import statsmodels.api as sm\n", + "from statsmodels.stats.outliers_influence import variance_inflation_factor\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.model_selection import RepeatedKFold\n", + "from sklearn.linear_model import Ridge\n", + "from sklearn.linear_model import Lasso\n", + "from sklearn.linear_model import ElasticNet\n", + "from numpy import mean\n", + "from numpy import std\n", + "from numpy import absolute\n", + "from libpysal.weights.contiguity import Queen\n", + "import libpysal\n", + "from statsmodels.api import OLS\n", + "sns.set_style('white')" + ] + }, + { + "cell_type": "markdown", + "id": "daec4204", + "metadata": { + "id": "daec4204" + }, + "source": [ + "First, we're going to load the 'Elections' dataset from the libpysal library, which is a very easy to use API that accesses the Geodata Center at the University of Chicago.\n", + "\n", + "* More on spatial data science resources from UC: https://spatial.uchicago.edu/\n", + "* A list of datasets available through lipysal: https://geodacenter.github.io/data-and-lab//" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9de38c04", + "metadata": { + "id": "9de38c04" + }, + "outputs": [], + "source": [ + "from libpysal.examples import load_example\n", + "elections = load_example('Elections')\n", + "#note the folder where your data now lives:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8c7fe6d5", + "metadata": { + "id": "8c7fe6d5" + }, + "outputs": [], + "source": [ + "#First, let's see what files are available in the 'Elections' data example\n", + "elections.get_file_list()" + ] + }, + { + "cell_type": "markdown", + "id": "d92c29a3", + "metadata": { + "id": "d92c29a3" + }, + "source": [ + "When you are out in the world doing research, you often will not find a ready-made function to download your data. That's okay! You know how to get this dataset without using pysal! Do a quick internal review of online data formats and automatic data downloads.\n", + "\n", + "### TASK 1: Use urllib functions to download this file directly from the internet a folder (not in your git repository). Extract the zipped file you've downloaded into a subfolder called elections." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1318b96b", + "metadata": { + "id": "1318b96b" + }, + "outputs": [], + "source": [ + "# Task 1 code here:\n" + ] + }, + { + "cell_type": "markdown", + "id": "ea133475", + "metadata": { + "id": "ea133475" + }, + "source": [ + "### TASK 2: Use geopandas to read in this shapefile. Call your geopandas.DataFrame \"votes\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "81455057", + "metadata": { + "id": "81455057" + }, + "outputs": [], + "source": [ + "# TASK 2: Use geopandas to read in this shapefile. Call your geopandas.DataFrame \"votes\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8ae67e1e", + "metadata": { + "id": "8ae67e1e" + }, + "outputs": [], + "source": [ + "#Let's view the shapefile to get a general idea of the geometry we're looking at:\n", + "%matplotlib inline\n", + "votes.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7495565e", + "metadata": { + "id": "7495565e" + }, + "outputs": [], + "source": [ + "#View the first few lines of the dataset\n", + "votes.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "813ad89f", + "metadata": { + "id": "813ad89f" + }, + "outputs": [], + "source": [ + "#Since there are too many columns for us to view on a signle page using \"head\", we can just print out the column names so we have them all listed for reference\n", + "for col in votes.columns:\n", + " print(col)" + ] + }, + { + "cell_type": "markdown", + "id": "aa277c42", + "metadata": { + "id": "aa277c42" + }, + "source": [ + "#### You can use pandas summary statistics to get an idea of how county-level data varies across the United States.\n", + "### TASK 3: For example, how did the county mean percent Democratic vote change between 2012 (pct_dem_12) and 2016 (pct_dem_16)?\n", + "\n", + "Look here for more info on pandas dataframes:\n", + "\n", + "https://www.earthdatascience.org/courses/intro-to-earth-data-science/scientific-data-structures-python/pandas-dataframes/run-calculations-summary-statistics-pandas-dataframes/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "818f70e5", + "metadata": { + "id": "818f70e5" + }, + "outputs": [], + "source": [ + "#Task 3\n" + ] + }, + { + "cell_type": "markdown", + "id": "1b0f15a0", + "metadata": { + "id": "1b0f15a0" + }, + "source": [ + "We can also plot histograms of the data. Below, smoothed histograms from the seaborn package (imported as sns) let us get an idea of the distribution of percent democratic votes in 2012 (left) and 2016 (right)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57df83a8", + "metadata": { + "id": "57df83a8" + }, + "outputs": [], + "source": [ + "# Plot histograms:\n", + "f,ax = plt.subplots(1,2, figsize=(2*3*1.6, 2))\n", + "for i,col in enumerate(['pct_dem_12','pct_dem_16']):\n", + " sns.kdeplot(votes[col].values, shade=True, color='slategrey', ax=ax[i])\n", + " ax[i].set_title(col.split('_')[1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c314d64", + "metadata": { + "id": "7c314d64" + }, + "outputs": [], + "source": [ + "# Plot spatial distribution of # dem vote in 2012 and 2016 with histogram.\n", + "f,ax = plt.subplots(2,2, figsize=(1.6*6 + 1,2.4*3), gridspec_kw=dict(width_ratios=(6,1)))\n", + "for i,col in enumerate(['pct_dem_12','pct_dem_16']):\n", + " votes.plot(col, linewidth=.05, cmap='RdBu', ax=ax[i,0])\n", + " ax[i,0].set_title(['2012','2016'][i] + \" % democratic vote\")\n", + " ax[i,0].set_xticklabels('')\n", + " ax[i,0].set_yticklabels('')\n", + " sns.kdeplot(votes[col].values, ax=ax[i,1], vertical=True, shade=True, color='slategrey')\n", + " ax[i,1].set_xticklabels('')\n", + " ax[i,1].set_ylim(-1,1)\n", + "f.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e6d6ec1f", + "metadata": { + "id": "e6d6ec1f" + }, + "source": [ + "### TASK 4: Make a new column on your geopandas dataframe called \"pct_dem_change\" and plot it using the syntax above. Explain the plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e68ce3ef", + "metadata": { + "id": "e68ce3ef" + }, + "outputs": [], + "source": [ + "# Task 4: add new column pct_dem_change to votes:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5c7a89d", + "metadata": { + "id": "c5c7a89d" + }, + "outputs": [], + "source": [ + "#Task 4: plot your pct_dem_change variable on a map:\n", + "f,ax = plt.subplots(1,2, figsize=(1.6*6 + 1,1.6*3), gridspec_kw=dict(width_ratios=(6,1)))\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "946cb5c6", + "metadata": { + "id": "946cb5c6" + }, + "source": [ + "Click on this url to learn more about the variables in this dataset: https://geodacenter.github.io/data-and-lab//county_election_2012_2016-variables/\n", + "\n", + "Let's say we want to learn more about what county-level factors influence percent change in democratic vote between (pct_dem_change).\n", + "\n", + "There are two types of multicollinearity:\n", + "\n", + "* *Intrinsic multicollinearity:* is an artifact of how we make observations. Often our measurements serve as proxies for some latent process (for example, we can measure percent silt, percent sand, and percent clay as proxies for the latent variable of soil texture). There will be slight variability in the information content between each proxy measurement, but they will not be independent of one another.\n", + "\n", + "* *Incidental collinearity:* is an artifact of how we sample complex populations. If we collect data from a subsample of the landscape where we don't see all combinations of our predictor variables (do not have good cross replication across our variables). We often induce collinearity in our data just because we are limitted in our ability to sample the environment at the scale of temporal/spatial variability of our process of interest. Incidental collinearity is a model formulation problem.(See here for more info on how to avoid it: https://people.umass.edu/sdestef/NRC%20601/StudyDesignConcepts.pdf)" + ] + }, + { + "cell_type": "markdown", + "id": "da5b59ac", + "metadata": { + "id": "da5b59ac" + }, + "source": [ + "### TASK 5: Looking at the data description, pick two variables that you believe will be intrinsically multicollinear. List and describe these variables. Why do you think they are intrinsically related?" + ] + }, + { + "cell_type": "markdown", + "id": "18207d3e", + "metadata": { + "id": "18207d3e" + }, + "source": [ + "## Multivariate regression in observational data:\n", + "Our next step is to formulate our predictive/diagnostic model. We want to create a subset of the \"votes\" geopandas data frame that contains ten predictor variables and our response variable (pct_pt_16) two variables you selected under TASK 1. First, create a list of the variables you'd like to select.\n", + "\n", + "### TASK 6: Create a subset of votes called \"my_list\" containing only your selected predictor variables. Make sure you use the two variables selected under TASK 5, and eight additional variables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "65d4c6cb", + "metadata": { + "id": "65d4c6cb" + }, + "outputs": [], + "source": [ + "# Task 4: create a subset of votes called \"my list\" with all your subset variables.\n", + "#my_list = [\"pct_pt_16\", ]\n", + "\n", + "#For example:\n", + "my_list = [\"pct_dem_change\", #The number in diff_2016 expressed as a percent of the total votes. Negative if fewer votes were cast for the Democratic candidate\n", + " \"PST120214\", #Population, percent change - April 1, 2010 to July 1, 2014\n", + " \"SEX255214\", #Female persons, percent, 2014\n", + " \"RHI125214\", #White alone, percent, 2014\n", + " \"RHI225214\", #Black or African American alone, percent, 2014\n", + " \"EDU685213\", #Bachelor’s degree or higher, percent of persons age 25+, 2009-2013\n", + " \"VET605213\", #Veterans, 2009-2013\n", + " \"HSG495213\", #Median value of owner-occupied housing units, 2009-2013\n", + " \"HSD310213\", #Persons per household, 2009-2013\n", + " \"INC910213\", #Per capita money income in past 12 months (2013 dollars), 2009-2013\n", + " \"LND110210\"] #Land area in square miles, 2010\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f1835a8d", + "metadata": { + "id": "f1835a8d" + }, + "outputs": [], + "source": [ + "#check to make sure all your columns are there:\n", + "votes[my_list].head()" + ] + }, + { + "cell_type": "markdown", + "id": "d28d9dc4", + "metadata": { + "id": "d28d9dc4" + }, + "source": [ + "### Scatterplot matrix\n", + "We call the process of getting to know your data (ranges and distributions of the data, as well as any relationships between variables) \"exploratory data analysis\". Pairwise plots of your variables, called scatterplots, can provide a lot of insight into the type of relationships you have between variables. A scatterplot matrix is a pairwise comparison of all variables in your dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f3f67ff6", + "metadata": { + "id": "f3f67ff6" + }, + "outputs": [], + "source": [ + "#Use seaborn.pairplot to plot a scatterplot matrix of you 10 variable subset:\n", + "sns.pairplot(votes[my_list])" + ] + }, + { + "cell_type": "markdown", + "id": "e7b1b309", + "metadata": { + "id": "e7b1b309" + }, + "source": [ + "### TASK 7: Do you observe any collinearity in this dataset? How would you describe the relationship between your two \"incidentally collinear\" variables that you selected based on looking at variable descriptions?\n", + "\n", + "\n", + "\n", + "### TASK 8: What is plotted on the diagonal panels of the scatterplot matrix?\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "89b42b91", + "metadata": { + "id": "89b42b91" + }, + "source": [ + "## Diagnosing collinearity globally:\n", + "Variance Inflation Factor describes the magnitude of variance inflation that can be expected in an OLS parameter estimate for a given variable *given pairwise collinearity between that variable and another variable*." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9be64d7b", + "metadata": { + "id": "9be64d7b" + }, + "outputs": [], + "source": [ + "#VIF = 1/(1-R2) of a pairwise OLS regression between two predictor variables\n", + "#We can use a built-in function \"variance_inflation_factor\" from statsmodel.api to calculate VIF\n", + "#Learn more about the function\n", + "?variance_inflation_factor" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a53f51dc", + "metadata": { + "id": "a53f51dc" + }, + "outputs": [], + "source": [ + "#Calculate VIFs on our dataset\n", + "vif = pd.DataFrame()\n", + "vif[\"VIF Factor\"] = [variance_inflation_factor(votes[my_list[1:11]].values, i) for i in range(votes[my_list[1:11]].shape[1])]\n", + "vif[\"features\"] = votes[my_list[1:11]].columns\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d465f95f", + "metadata": { + "id": "d465f95f" + }, + "outputs": [], + "source": [ + "vif.round()" + ] + }, + { + "cell_type": "markdown", + "id": "ae322791", + "metadata": { + "id": "ae322791" + }, + "source": [ + "### Collinearity is always present in observational data. When is it a problem?\n", + "Generally speaking, including any variables with VIF > 10 is considered \"too much\" collinearity. But this value is somewhat arbitrary. The extent to which variance inflation will impact your analysis is highly context dependent. There are two primary contexts where variance inflation is problematic:\n", + "\n", + " 1\\. **You are using your analysis to evaluate variable importance:** If you are using parameter estimates from your model to diagnose which observations have physically important relationships with your response variable, variance inflation can make an important predictor look unimportant, and parameter estimates will be highly leveraged by small changes in the data.\n", + "\n", + " 2\\. **You want to use your model to make predictions in a situation where the specific structure of collinearity between variables may have shifted:** When training a model on collinear data, the model only applies to data with that exact structure of collinearity." + ] + }, + { + "cell_type": "markdown", + "id": "f8a7a711", + "metadata": { + "id": "f8a7a711" + }, + "source": [ + "### Caluculate a linear regression on the global data:\n", + "In this next step, we're going to calculate a linear regression in our data an determine whether there is a statistically significant relationship between per capita income and percent change in democratic vote." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "515911da", + "metadata": { + "id": "515911da" + }, + "outputs": [], + "source": [ + "#first, forumalate the model. See weather_trend.py in \"Git_101\" for a refresher on how.\n", + "\n", + "#extract variable that you want to use to \"predict\"\n", + "X = np.array(votes[my_list[1:11]].values)\n", + "#standardize data to assist in interpretation of coefficients\n", + "X = (X - np.mean(X, axis=0)) / np.std(X, axis=0)\n", + "\n", + "#extract variable that we want to \"predict\"\n", + "Y = np.array(votes['pct_dem_change'].values)\n", + "#standardize data to assist in interpretation of coefficients\n", + "Y = (Y - np.mean(X)) / np.std(Y)\n", + "\n", + "lm = OLS(Y,X)\n", + "lm_results = OLS(Y,X).fit().summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "68a265a8", + "metadata": { + "id": "68a265a8" + }, + "outputs": [], + "source": [ + "print(lm_results)" + ] + }, + { + "cell_type": "markdown", + "id": "d760fd73", + "metadata": { + "id": "d760fd73" + }, + "source": [ + "### TASK 9: Answer: which coefficients indicate a statisticall significant relationship between parameter and pct_dem_change? What is your most important predictor variable? How can you tell?\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "1202f4f3", + "metadata": { + "id": "1202f4f3" + }, + "source": [ + "### TASK10: Are any of these parameters subject to variance inflation? How can you tell?\n", + "\n", + "\n", + "**With a cutoff of VIF=10, the following variables are subject to variable inflation.**\n", + "\n", + "\t269.0 \tSEX255214\n", + "\t119.0 \tRHI125214\n", + "\t20.0 \tEDU685213\n", + "\t11.0 \tHSG495213\n", + "\t106.0 \tHSD310213\n", + "\t67.0 \tINC910213" + ] + }, + { + "cell_type": "markdown", + "id": "c25a9235", + "metadata": { + "id": "c25a9235" + }, + "source": [ + "Now, let's plot our residuals to see if there are any spatial patterns in them.\n", + "\n", + "Remember residuals = predicted - fitted values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0098b6ee", + "metadata": { + "id": "0098b6ee" + }, + "outputs": [], + "source": [ + "#Add model residuals to our \"votes\" geopandas dataframe:\n", + "votes['lm_resid']=OLS(Y,X).fit().resid\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "E_TS1YyLQFA8", + "metadata": { + "id": "E_TS1YyLQFA8" + }, + "outputs": [], + "source": [ + "vif['OLS_coef']=OLS(Y,X).fit().params" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a439b312", + "metadata": { + "id": "a439b312" + }, + "outputs": [], + "source": [ + "sns.kdeplot(votes['lm_resid'].values, shade=True, color='slategrey')\n" + ] + }, + { + "cell_type": "markdown", + "id": "0bc83f6a", + "metadata": { + "id": "0bc83f6a" + }, + "source": [ + "### TASK 11: Are our residuals normally distributed with a mean of zero? What does that mean?\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "66f59dce", + "metadata": { + "id": "66f59dce" + }, + "source": [ + "## Penalized regression: ridge penalty\n", + "In penalized regression, we intentionally bias the parameter estimates to stabilize them given collinearity in the dataset.\n", + "\n", + "From https://www.analyticsvidhya.com/blog/2016/01/ridge-lasso-regression-python-complete-tutorial/\n", + "\n", + ">As mentioned before, ridge regression performs ‘L2 regularization‘, i.e. it adds a factor of sum of squares of coefficients in the optimization objective. Thus, ridge regression optimizes the following:\n", + "\n", + "**Objective = RSS + α * (sum of square of coefficients)**\n", + "\n", + "Here, α (alpha) is the parameter which balances the amount of emphasis given to minimizing RSS vs minimizing sum of square of coefficients. α can take various values:\n", + "\n", + "* **α = 0:** The objective becomes same as simple linear regression. We’ll get the same coefficients as simple linear regression.\n", + "\n", + "* **α = ∞:** The coefficients will approach zero. Why? Because of infinite weightage on square of coefficients, anything less than zero will make the objective infinite.\n", + "\n", + "* **0 < α < ∞:** The magnitude of α will decide the weightage given to different parts of objective. The coefficients will be somewhere between 0 and ones for simple linear regression.\"\n", + "\n", + "In other words, the ridge penalty shrinks coefficients such that collinear coefficients will have more similar coefficient values. It has a \"grouping\" tendency." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cebcc4a3", + "metadata": { + "id": "cebcc4a3" + }, + "outputs": [], + "source": [ + "# when L2=0, Ridge equals OLS\n", + "model = Ridge(alpha=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "60b4d30d", + "metadata": { + "id": "60b4d30d" + }, + "outputs": [], + "source": [ + "# define model evaluation method\n", + "cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)\n", + "# evaluate model\n", + "scores = cross_val_score(model, X, Y, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)\n", + "#force scores to be positive\n", + "scores = absolute(scores)\n", + "print('Mean MAE: %.3f (%.3f)' % (mean(scores), std(scores)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "mbYodbd4hfkD", + "metadata": { + "id": "mbYodbd4hfkD" + }, + "outputs": [], + "source": [ + "model.fit(X,Y)\n", + "#Print out the model coefficients\n", + "print(model.coef_)\n" + ] + }, + { + "cell_type": "markdown", + "id": "RUmJfpeJhhFE", + "metadata": { + "id": "RUmJfpeJhhFE" + }, + "source": [ + "### Hyperparameter tuning\n", + "The L2 coefficient (called alpha above) is a free parameter (hyperparameter) in the model, meaning we can set it whatever value we feel is best.\n", + "\n", + "In machine learning, we often try multiple options for these hyperparameters, and select the value with the highest model performance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "LYlRKrRrC_eG", + "metadata": { + "id": "LYlRKrRrC_eG" + }, + "outputs": [], + "source": [ + "# Train on a range of alphas\n", + "alpha = np.arange(0,20,0.1)\n", + "ridge_tune=pd.DataFrame(alpha, columns=[\"alpha\"])\n", + "ridge_tune['Score']=0\n", + "i=0\n", + "for a in alpha:\n", + " model= Ridge(alpha=a)\n", + " oos_scores = cross_val_score(model, X, Y, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)\n", + " #force scores to be positive\n", + " oos_scores = absolute(oos_scores)\n", + " ridge_tune.Score.loc[i]=mean(oos_scores)\n", + " i=i+1\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fWJPA8ifIHSb", + "metadata": { + "id": "fWJPA8ifIHSb" + }, + "outputs": [], + "source": [ + "# Plot model performance as a function of alpha\n", + "plt.plot(ridge_tune.alpha, ridge_tune.Score)\n", + "plt.show()\n", + "# What is the y-axis label? What is the x-axis label?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dOdAzM_YJxHx", + "metadata": { + "id": "dOdAzM_YJxHx" + }, + "outputs": [], + "source": [ + "# Select the value of alpha which minimizes MAE\n", + "a2 = ridge_tune.alpha.iloc[ridge_tune['Score'].idxmin()]\n", + "a2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c68402c6", + "metadata": { + "id": "c68402c6" + }, + "outputs": [], + "source": [ + "# Train model with optimized alpha\n", + "model = Ridge(alpha=a2)\n", + "model.fit(X,Y)\n", + "#Print out the model coefficients\n", + "print(model.coef_)\n", + "vif['Ridge_coef']=model.coef_" + ] + }, + { + "cell_type": "markdown", + "id": "53d94d1f", + "metadata": { + "id": "53d94d1f" + }, + "source": [ + "## Penalized regression: lasso penalty\n", + "\n", + "From https://www.analyticsvidhya.com/blog/2016/01/ridge-lasso-regression-python-complete-tutorial/\n", + "> LASSO stands for Least Absolute Shrinkage and Selection Operator.\n", + "\n", + "There are 2 key words here – **absolute** and **selection**.\n", + "\n", + "Lets consider the former first and worry about the latter later.\n", + ".\n", + "Lasso regression performs L1 regularization, i.e. it adds a factor to the sum of absolute value of coefficients in the optimization objective. Thus, lasso regression optimizes the following:\n", + "\n", + "**Objective = RSS + α * (sum of absolute value of coefficients)**\n", + "\n", + "Here, α (alpha) works similar to that of ridge and provides a trade-off between balancing RSS and magnitude of coefficients. Like that of ridge, α can take various values. Lets iterate it here briefly:\n", + "\n", + "* **α = 0:** Same coefficients as simple linear regression\n", + "* **α = ∞:** All coefficients zero (same logic as before)\n", + "* **0 < α < ∞:** coefficients between 0 and that of simple linear regression\n", + "\n", + "The lasso penalty shrinks unimportant coefficients down towards zero, automatically \"selecting\" important predictor variables. But what if that shrunken coefficient is induced by incidental collinearity (i.e. is a feature of how we sampled our data)?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17f683d0", + "metadata": { + "id": "17f683d0" + }, + "outputs": [], + "source": [ + "# when L1=0, Lasso equals OLS\n", + "model = Lasso(alpha=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5eccdbab", + "metadata": { + "id": "5eccdbab" + }, + "outputs": [], + "source": [ + "# define model evaluation method\n", + "cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)\n", + "# evaluate model\n", + "scores = cross_val_score(model, X, Y, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)\n", + "#force scores to be positive\n", + "scores = absolute(scores)\n", + "print('Mean MAE: %.3f (%.3f)' % (mean(scores), std(scores)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "WtYefDGhh29N", + "metadata": { + "id": "WtYefDGhh29N" + }, + "outputs": [], + "source": [ + "model.fit(X,Y)\n", + "#Print out the model coefficients\n", + "print(model.coef_)\n", + "#How do these compare to OLS coefficients?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "PGLGB4Fmh9He", + "metadata": { + "id": "PGLGB4Fmh9He" + }, + "outputs": [], + "source": [ + "# when L1 approaches infinity, coefficients will become exactly zero, and MAE equals the variance of our response variable:\n", + "model = Lasso(alpha=10000000)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "pX1r-KF6iEkD", + "metadata": { + "id": "pX1r-KF6iEkD" + }, + "outputs": [], + "source": [ + "# define model evaluation method\n", + "cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)\n", + "# evaluate model\n", + "scores = cross_val_score(model, X, Y, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)\n", + "#force scores to be positive\n", + "scores = absolute(scores)\n", + "print('Mean MAE: %.3f (%.3f)' % (mean(scores), std(scores)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d6fd8e0a", + "metadata": { + "id": "d6fd8e0a" + }, + "outputs": [], + "source": [ + "model.fit(X,Y)\n", + "#Print out the model coefficients\n", + "print(model.coef_)\n", + "#How do these compare with OLS coefficients above?" + ] + }, + { + "cell_type": "markdown", + "id": "Q8od0fBRiNgc", + "metadata": { + "id": "Q8od0fBRiNgc" + }, + "source": [ + "### Hyperparameter tuning\n", + "Just like with the ridge penalty, we want to select the value of alpha that minimizes error in the lasso model.\n", + "\n", + "We'll use hyperparameter tuning again to do this:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7HoHTeetPoP5", + "metadata": { + "id": "7HoHTeetPoP5" + }, + "outputs": [], + "source": [ + "# Define range of alphas:\n", + "alpha = np.arange(0,.02,0.0001)\n", + "lasso_tune=pd.DataFrame(alpha, columns=[\"alpha\"])\n", + "lasso_tune['Score']=0\n", + "i=0\n", + "for a in alpha:\n", + " model= Lasso(alpha=a)\n", + " oos_scores = cross_val_score(model, X, Y, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)\n", + " #force scores to be positive\n", + " oos_scores = absolute(oos_scores)\n", + " lasso_tune.Score.loc[i]=mean(oos_scores)\n", + " i=i+1\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "v3Fr81sEPysW", + "metadata": { + "id": "v3Fr81sEPysW" + }, + "outputs": [], + "source": [ + "# Plot model performance as a function of alpha:\n", + "plt.plot(lasso_tune.alpha, lasso_tune.Score)\n", + "plt.show()\n", + "# What is the y-axis label? What is the x-axis label?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "WyqaEXZsR6uX", + "metadata": { + "id": "WyqaEXZsR6uX" + }, + "outputs": [], + "source": [ + "a1 = lasso_tune.alpha.iloc[lasso_tune['Score'].idxmin()]\n", + "a1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5gm-aAixSNI7", + "metadata": { + "id": "5gm-aAixSNI7" + }, + "outputs": [], + "source": [ + "# Train model with optimal alpha1\n", + "model= Lasso(alpha=a1)\n", + "model.fit(X,Y)\n", + "#Print out the model coefficients\n", + "print(model.coef_)\n", + "\n", + "vif['LASSO_coef']= model.coef_" + ] + }, + { + "cell_type": "markdown", + "id": "5fe1b7ff", + "metadata": { + "id": "5fe1b7ff" + }, + "source": [ + "### Penalized regression: elastic net penalty\n", + "\n", + "In other words, the lasso penalty shrinks unimportant coefficients down towards zero, automatically \"selecting\" important predictor variables. The ridge penalty shrinks coefficients of collinear predictor variables nearer to each other, effectively partitioning the magnitude of response from the response variable between them, instead of \"arbitrarily\" partitioning it to one group.\n", + "\n", + "We can also run a regression with a linear combination of ridge and lasso, called the elastic net, that has a cool property called \"group selection.\"\n", + "\n", + "The ridge penalty still works to distribute response variance equally between members of \"groups\" of collinear predictor variables. The lasso penalty still works to shrink certain coefficients to exactly zero so they can be ignored in model formulation. The elastic net produces models that are both sparse and stable under collinearity, by shrinking parameters of members of unimportant collinear predictor variables to exactly zero:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "RgMVSI0Ri6vR", + "metadata": { + "id": "RgMVSI0Ri6vR" + }, + "outputs": [], + "source": [ + "# Explore the parameters in the ElasticNet fuction.\n", + "# Which variable represents the ridge coefficient? Which parameter represents the lasso coefficient?\n", + "?ElasticNet" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ce6967ae", + "metadata": { + "id": "ce6967ae" + }, + "outputs": [], + "source": [ + "# when L1 approaches infinity, certain coefficients will become exactly zero, and MAE equals the variance of our response variable:\n", + "model = ElasticNet(alpha=.1, l1_ratio=.1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "22fc92a2", + "metadata": { + "id": "22fc92a2" + }, + "outputs": [], + "source": [ + "# define model evaluation method\n", + "cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)\n", + "# evaluate model\n", + "scores = cross_val_score(model, X, Y, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)\n", + "#force scores to be positive\n", + "scores = absolute(scores)\n", + "print('Mean MAE: %.3f (%.3f)' % (mean(scores), std(scores)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0fee71d5", + "metadata": { + "id": "0fee71d5" + }, + "outputs": [], + "source": [ + "model.fit(X,Y)\n", + "#Print out the model coefficients\n", + "print(model.coef_)\n", + "#How do these compare with OLS coefficients above?\n", + "vif[\"ElNet_coef\"]=model.coef_" + ] + }, + { + "cell_type": "markdown", + "id": "ac8a540d", + "metadata": { + "id": "ac8a540d" + }, + "source": [ + "### TASK 12: Match these elastic net coefficients up with your original data. Do you see a logical grouping(s) between variables that have non-zero coefficients?\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c12c783", + "metadata": { + "id": "6c12c783" + }, + "outputs": [], + "source": [ + "print(vif)" + ] + }, + { + "cell_type": "markdown", + "id": "iLhCXRVcjEc8", + "metadata": { + "id": "iLhCXRVcjEc8" + }, + "source": [ + "## Extra Credit:\n", + "We saw how to tune individual parameters above. The elastic net regression here has two free parameters. How can you select the best values for these two free parameters?\n", + "\n", + "Demonstrate in the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "Tjlf88d2jH2C", + "metadata": { + "id": "Tjlf88d2jH2C" + }, + "outputs": [], + "source": [ + "# Extra credit scratch cell:" + ] + } + ], + "metadata": { + "colab": { + "include_colab_link": true, + "provenance": [] + }, + "kernelspec": { + "display_name": "geostats_env", + "language": "python", + "name": "geostats_env" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.20" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/CodeSprints/FeatureDependence_example.ipynb b/CodeSprints/FeatureDependence_example.ipynb index e11f854..85f4371 100644 --- a/CodeSprints/FeatureDependence_example.ipynb +++ b/CodeSprints/FeatureDependence_example.ipynb @@ -1,1101 +1,1111 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [], - "authorship_tag": "ABX9TyO3TiUtc0J4Bihg6I8GN5HM", - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "view-in-github" + }, + "source": [ + "\"Open" + ] }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "code", - "source": [ - "from google.colab import auth\n", - "auth.authenticate_user()\n", - "\n", - "import gspread\n", - "from google.auth import default\n", - "creds, _ = default()\n", - "\n", - "gc = gspread.authorize(creds)" - ], - "metadata": { - "id": "-Kt5YLtBBrqU" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "#Read in data\n", - "The \"CEE 609 Height Model\" sheet contains your measurements of your wrist circumference, foot length, and your self-reported height entered during class." - ], - "metadata": { - "id": "g0OBpBJsf-uo" - } - }, - { - "cell_type": "code", - "source": [ - "worksheet = gc.open('CEE 609 Height Model').sheet1\n", - "\n", - "# get_all_values gives a list of rows.\n", - "rows = worksheet.get_all_values()\n", - "print(rows)\n", - "\n", - "import pandas as pd\n", - "df = pd.DataFrame.from_records(rows) #convert data to pandas dataframe\n", - "new_header = df.iloc[0] #grab the first row for the header\n", - "df = df[1:] #take the data less the header row\n", - "df.columns = new_header #set the header row as the df header\n", - "df\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 683 - }, - "id": "s0NgBUclAPE4", - "outputId": "24f672a5-e8eb-49dc-b855-50916cbb164d" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "[['NetID', 'Wrist circumference', 'Foot length', 'Heigth'], ['nikohl', '16', '30', '194'], ['msmit130', '12', '26', '100'], ['AHussein87', '19', '32', '176'], ['raruggie', '16', '27', '172.1'], ['bmferro', '18.2', '29.1', '180.3'], ['Cgharvey', '18', '27', '178'], ['lborden', '18', '30', '182'], ['Babak', '13', '27', '100'], ['araghura', '12', '26', '168'], ['amcero', '12.5', '26', '165'], ['baedgert', '15', '26', '170'], ['averma12', '16', '27', '171'], ['hkim139', '16', '30', '183'], ['srijal', '12', '23', '173.7'], ['Nariman', '12', '24', '160'], ['mmkoch', '16', '26', '175'], ['Abmccart', '15', '23', '155'], ['abwebste', '14', '25', '165'], ['emjorgen', '15', '25', '162']]\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "0 NetID Wrist circumference Foot length Heigth\n", - "1 nikohl 16 30 194\n", - "2 msmit130 12 26 100\n", - "3 AHussein87 19 32 176\n", - "4 raruggie 16 27 172.1\n", - "5 bmferro 18.2 29.1 180.3\n", - "6 Cgharvey 18 27 178\n", - "7 lborden 18 30 182\n", - "8 Babak 13 27 100\n", - "9 araghura 12 26 168\n", - "10 amcero 12.5 26 165\n", - "11 baedgert 15 26 170\n", - "12 averma12 16 27 171\n", - "13 hkim139 16 30 183\n", - "14 srijal 12 23 173.7\n", - "15 Nariman 12 24 160\n", - "16 mmkoch 16 26 175\n", - "17 Abmccart 15 23 155\n", - "18 abwebste 14 25 165\n", - "19 emjorgen 15 25 162" - ], - "text/html": [ - "\n", - "
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NetIDWrist circumferenceFoot lengthHeigth
1nikohl1630194
2msmit1301226100
3AHussein871932176
4raruggie1627172.1
5bmferro18.229.1180.3
6Cgharvey1827178
7lborden1830182
8Babak1327100
9araghura1226168
10amcero12.526165
11baedgert1526170
12averma121627171
13hkim1391630183
14srijal1223173.7
15Nariman1224160
16mmkoch1626175
17Abmccart1523155
18abwebste1425165
19emjorgen1525162
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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 3 - } - ] - }, - { - "cell_type": "code", - "source": [ - "list=rows[0][1:3] #create a list of column names of predictors\n", - "print(list)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "pf9iHzXkDDtD", - "outputId": "1400c406-4415-48b9-b036-939e7cc54e33" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "['Wrist circumference', 'Foot length']\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "import numpy as np\n", - "X = np.array(df[list].values) #create numpy array of predictors\n", - "Y = np.array(df['Heigth'].values) #create numpy array of predictand" - ], - "metadata": { - "id": "OCU7KdZOB4Qc" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "#convert to type numeric float\n", - "X = X.astype('float')\n", - "Y = Y.astype('float')" - ], - "metadata": { - "id": "RDN7njrwDstZ" - }, - "execution_count": null, - "outputs": [] + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-Kt5YLtBBrqU" + }, + "outputs": [], + "source": [ + "from google.colab import auth\n", + "auth.authenticate_user()\n", + "\n", + "import gspread\n", + "from google.auth import default\n", + "creds, _ = default()\n", + "\n", + "gc = gspread.authorize(creds)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "g0OBpBJsf-uo" + }, + "source": [ + "#Read in data\n", + "The \"CEE 609 Height Model\" sheet contains your measurements of your wrist circumference, foot length, and your self-reported height entered during class." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 683 }, + "id": "s0NgBUclAPE4", + "outputId": "24f672a5-e8eb-49dc-b855-50916cbb164d" + }, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "#Are my predictors correlated?\n", - "Below we generate a correlation coefficient matrix for our predictors using the numpy corrcoef function." - ], - "metadata": { - "id": "-M0qRPJegwiW" - } + "name": "stdout", + "output_type": "stream", + "text": [ + "[['NetID', 'Wrist circumference', 'Foot length', 'Heigth'], ['nikohl', '16', '30', '194'], ['msmit130', '12', '26', '100'], ['AHussein87', '19', '32', '176'], ['raruggie', '16', '27', '172.1'], ['bmferro', '18.2', '29.1', '180.3'], ['Cgharvey', '18', '27', '178'], ['lborden', '18', '30', '182'], ['Babak', '13', '27', '100'], ['araghura', '12', '26', '168'], ['amcero', '12.5', '26', '165'], ['baedgert', '15', '26', '170'], ['averma12', '16', '27', '171'], ['hkim139', '16', '30', '183'], ['srijal', '12', '23', '173.7'], ['Nariman', '12', '24', '160'], ['mmkoch', '16', '26', '175'], ['Abmccart', '15', '23', '155'], ['abwebste', '14', '25', '165'], ['emjorgen', '15', '25', '162']]\n" + ] }, { - "cell_type": "code", - "source": [ - "np.corrcoef(X[:,0],X[:,1])" + "data": { + "text/html": [ + "\n", + "
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NetIDWrist circumferenceFoot lengthHeigth
1nikohl1630194
2msmit1301226100
3AHussein871932176
4raruggie1627172.1
5bmferro18.229.1180.3
6Cgharvey1827178
7lborden1830182
8Babak1327100
9araghura1226168
10amcero12.526165
11baedgert1526170
12averma121627171
13hkim1391630183
14srijal1223173.7
15Nariman1224160
16mmkoch1626175
17Abmccart1523155
18abwebste1425165
19emjorgen1525162
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"cell_type": "markdown", - "source": [ - "Remember that correlation coefficients > 0.7 are generally considered \"too much\" for model stability.\n", - "\n", - "Next, we'll use the standard formula to estimate the coefficients in this regression:\n", - "\\begin{equation*}\n", - "\\beta = (X^{T}X) ^ { -1 } X^{T}Y\n", - "\\end{equation*}\n", - "\n", - "By first creating the variable:\n", - "\\begin{equation*}\n", - "(X^{T}X) =XTX\n", - "\\end{equation*}\n", - "\n", - "to simplify the calculations.\n" - ], - "metadata": { - "id": "De0p2UfmhIY_" - } - }, - { - "cell_type": "code", - "source": [ - "XTX=np.matmul(X,np.transpose(X))" - ], - "metadata": { - "id": "aOOt7LVdG1EH" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "XTX" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "jNdBcyLcjENw", - "outputId": "a9c1cbe2-6b38-4c8c-faaa-fd353dc7c95c" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - 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" [ 990. , 830. , 1085. , 915. , 1000.5 , 945. , 1020. ,\n", - " 870. , 830. , 837.5 , 875. , 915. , 990. , 755. ,\n", - " 780. , 890. , 800. , 835. , 850. ]])" - ] - }, - "metadata": {}, - "execution_count": 9 - } - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "['Wrist circumference', 'Foot length']\n" + ] + } + ], + "source": [ + "list=rows[0][1:3] #create a list of column names of predictors\n", + "print(list)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "OCU7KdZOB4Qc" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "X = np.array(df[list].values) #create numpy array of predictors\n", + "Y = np.array(df['Heigth'].values) #create numpy array of predictand" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "RDN7njrwDstZ" + }, + "outputs": [], + "source": [ + "#convert to type numeric float\n", + "X = X.astype('float')\n", + "Y = Y.astype('float')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-M0qRPJegwiW" + }, + "source": [ + "#Are my predictors correlated?\n", + "Below we generate a correlation coefficient matrix for our predictors using the numpy corrcoef function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "a9z2t9R9DZ3Q", + "outputId": "1209e25c-5496-4bca-e60f-890fc8549879" + }, + "outputs": [ { - "cell_type": "code", - "source": [ - "XTX.shape" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "brvllPVnJiNi", - "outputId": "2b8fe29f-d236-4797-aa95-c28e5d456a12" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(19, 19)" - ] - }, - "metadata": {}, - "execution_count": 10 - } + "data": { + "text/plain": [ + "array([[1. , 0.70374207],\n", + " [0.70374207, 1. ]])" ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.corrcoef(X[:,0],X[:,1])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "De0p2UfmhIY_" + }, + "source": [ + "Remember that correlation coefficients > 0.7 are generally considered \"too much\" for model stability.\n", + "\n", + "Next, we'll use the standard formula to estimate the coefficients in this regression:\n", + "\\begin{equation*}\n", + "\\beta = (X^{T}X) ^ { -1 } X^{T}Y\n", + "\\end{equation*}\n", + "\n", + "By first creating the variable:\n", + "\\begin{equation*}\n", + "(X^{T}X) =XTX\n", + "\\end{equation*}\n", + "\n", + "to simplify the calculations.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "aOOt7LVdG1EH" + }, + "outputs": [], + "source": [ + "XTX=np.matmul(X,np.transpose(X))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "jNdBcyLcjENw", + "outputId": "a9c1cbe2-6b38-4c8c-faaa-fd353dc7c95c" + }, + "outputs": [ { - "cell_type": "code", - "source": [ - "np.linalg.det(XTX) #no determinant!" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "mycKhEisJ4iy", - "outputId": "bb4c00fe-8816-4dad-bb74-43b0d682722c" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "0.0" - ] - }, - "metadata": {}, - "execution_count": 11 - } + "data": { + "text/plain": [ + "array([[1156. , 972. , 1264. , 1066. , 1164.2 , 1098. , 1188. ,\n", + " 1018. , 972. , 980. , 1020. , 1066. , 1156. , 882. ,\n", + " 912. , 1036. , 930. , 974. , 990. ],\n", + " [ 972. , 820. , 1060. , 894. , 975. , 918. , 996. ,\n", + " 858. , 820. , 826. , 856. , 894. , 972. , 742. ,\n", + " 768. , 868. , 778. , 818. , 830. ],\n", + " [1264. , 1060. , 1385. , 1168. , 1277. , 1206. , 1302. ,\n", + " 1111. , 1060. , 1069.5 , 1117. , 1168. , 1264. , 964. ,\n", + " 996. , 1136. , 1021. , 1066. , 1085. ],\n", + " [1066. , 894. , 1168. , 985. , 1076.9 , 1017. , 1098. ,\n", + " 937. , 894. , 902. , 942. , 985. , 1066. , 813. ,\n", + " 840. , 958. , 861. , 899. , 915. ],\n", + " [1164.2 , 975. , 1277. , 1076.9 , 1178.05, 1113.3 , 1200.6 ,\n", + " 1022.3 , 975. , 984.1 , 1029.6 , 1076.9 , 1164.2 , 887.7 ,\n", + " 916.8 , 1047.8 , 942.3 , 982.3 , 1000.5 ],\n", + " [1098. , 918. , 1206. , 1017. , 1113.3 , 1053. , 1134. ,\n", + " 963. , 918. , 927. , 972. , 1017. , 1098. , 837. ,\n", + " 864. , 990. , 891. , 927. , 945. ],\n", + " [1188. , 996. , 1302. , 1098. , 1200.6 , 1134. , 1224. ,\n", + " 1044. , 996. , 1005. , 1050. , 1098. , 1188. , 906. ,\n", + " 936. , 1068. , 960. , 1002. , 1020. ],\n", + " [1018. , 858. , 1111. , 937. , 1022.3 , 963. , 1044. ,\n", + " 898. , 858. , 864.5 , 897. , 937. , 1018. , 777. ,\n", + " 804. , 910. , 816. , 857. , 870. ],\n", + " [ 972. , 820. , 1060. , 894. , 975. , 918. , 996. ,\n", + " 858. , 820. , 826. , 856. , 894. , 972. , 742. ,\n", + " 768. , 868. , 778. , 818. , 830. ],\n", + " [ 980. , 826. , 1069.5 , 902. , 984.1 , 927. , 1005. ,\n", + " 864.5 , 826. , 832.25, 863.5 , 902. , 980. , 748. ,\n", + " 774. , 876. , 785.5 , 825. , 837.5 ],\n", + " [1020. , 856. , 1117. , 942. , 1029.6 , 972. , 1050. ,\n", + " 897. , 856. , 863.5 , 901. , 942. , 1020. , 778. ,\n", + " 804. , 916. , 823. , 860. , 875. ],\n", + " [1066. , 894. , 1168. , 985. , 1076.9 , 1017. , 1098. ,\n", + " 937. , 894. , 902. , 942. , 985. , 1066. , 813. ,\n", + " 840. , 958. , 861. , 899. , 915. ],\n", + " [1156. , 972. , 1264. , 1066. , 1164.2 , 1098. , 1188. ,\n", + " 1018. , 972. , 980. , 1020. , 1066. , 1156. , 882. ,\n", + " 912. , 1036. , 930. , 974. , 990. ],\n", + " [ 882. , 742. , 964. , 813. , 887.7 , 837. , 906. ,\n", + " 777. , 742. , 748. , 778. , 813. , 882. , 673. ,\n", + " 696. , 790. , 709. , 743. , 755. ],\n", + " [ 912. , 768. , 996. , 840. , 916.8 , 864. , 936. ,\n", + " 804. , 768. , 774. , 804. , 840. , 912. , 696. ,\n", + " 720. , 816. , 732. , 768. , 780. ],\n", + " [1036. , 868. , 1136. , 958. , 1047.8 , 990. , 1068. ,\n", + " 910. , 868. , 876. , 916. , 958. , 1036. , 790. ,\n", + " 816. , 932. , 838. , 874. , 890. ],\n", + " [ 930. , 778. , 1021. , 861. , 942.3 , 891. , 960. ,\n", + " 816. , 778. , 785.5 , 823. , 861. , 930. , 709. ,\n", + " 732. , 838. , 754. , 785. , 800. ],\n", + " [ 974. , 818. , 1066. , 899. , 982.3 , 927. , 1002. ,\n", + " 857. , 818. , 825. , 860. , 899. , 974. , 743. ,\n", + " 768. , 874. , 785. , 821. , 835. ],\n", + " [ 990. , 830. , 1085. , 915. , 1000.5 , 945. , 1020. ,\n", + " 870. , 830. , 837.5 , 875. , 915. , 990. , 755. ,\n", + " 780. , 890. , 800. , 835. , 850. ]])" ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "XTX" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "brvllPVnJiNi", + "outputId": "2b8fe29f-d236-4797-aa95-c28e5d456a12" + }, + "outputs": [ { - "cell_type": "code", - "source": [ - "np.linalg.inv(XTX) #cannot invert!" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 359 - }, - "id": "_GIFwdmAJ1kv", - "outputId": "401523bb-b73f-4796-c917-f0f89d413f05" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "error", - "ename": "LinAlgError", - "evalue": "ignored", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mLinAlgError\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[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXTX\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#cannot invert!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36minv\u001b[0;34m(*args, **kwargs)\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/numpy/linalg/linalg.py\u001b[0m in \u001b[0;36minv\u001b[0;34m(a)\u001b[0m\n\u001b[1;32m 543\u001b[0m \u001b[0msignature\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'D->D'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misComplexType\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'd->d'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 544\u001b[0m \u001b[0mextobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_linalg_error_extobj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_raise_linalgerror_singular\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 545\u001b[0;31m \u001b[0mainv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_umath_linalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msignature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mextobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 546\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mainv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult_t\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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[1;32m 547\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/numpy/linalg/linalg.py\u001b[0m in \u001b[0;36m_raise_linalgerror_singular\u001b[0;34m(err, flag)\u001b[0m\n\u001b[1;32m 86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 87\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_raise_linalgerror_singular\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflag\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---> 88\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mLinAlgError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Singular matrix\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 89\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 90\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_raise_linalgerror_nonposdef\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflag\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;31mLinAlgError\u001b[0m: Singular matrix" - ] - } + "data": { + "text/plain": [ + "(19, 19)" ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "XTX.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "mycKhEisJ4iy", + "outputId": "bb4c00fe-8816-4dad-bb74-43b0d682722c" + }, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "Since XTX matrix is not invertable, we'll use the numpy pinv to calculate the Moore-Penrose pseudo-inverse of a matrix:" - ], - "metadata": { - "id": "q5sFJ5XNjMFB" - } - }, - { - "cell_type": "code", - "source": [ - "betas = np.matmul(np.transpose(np.matmul(np.linalg.pinv(XTX), X)), Y)\n", - "print(\"The estimated coefficient on \" + list[0] + \" is \" + str(betas[0]) + \" and the estimated coefficient on \" + list[1] + \" is \" + str(betas[1]))" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "zSn-HRnOHK6D", - "outputId": "bdb76ff7-f4db-452d-a758-b69c8e6c05ee" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "The estimated coefficient on Wrist circumference is 5.919158133872086 and the estimated coefficient on Foot length is 2.7984188001672425\n" - ] - } + "data": { + "text/plain": [ + "0.0" ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.linalg.det(XTX) #no determinant!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 359 }, + "id": "_GIFwdmAJ1kv", + "outputId": "401523bb-b73f-4796-c917-f0f89d413f05" + }, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "Next, we use the OLS (ordinary least squares) function from statsmodels to calculate the same coefficients. OLS regression works by finding the values of $\\hat{\\beta}$ that minimize the following equation:\n", - "\\begin{equation*}\n", - "S(\\hat{\\beta}) = \\sum _ { i = 1 } ^ { n } {| y _ { i } -\\sum _ { j = 1 } ^ { J } x _ { i,j} {\\beta _ {j}} |^{2}}\n", - "\\end{equation*}" - ], - "metadata": { - "id": "vPrKbUfDk95i" - } - }, - { - "cell_type": "code", - "source": [ - "from statsmodels.api import OLS\n", - "lm = OLS(Y,X).fit().summary()\n", - "print(lm)\n", - "# They match! But we can see that the coeficient on foot lenght is not significant." - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ILcqVvSlFPLE", - "outputId": "b09dc59d-c96f-4fd5-88b1-b113505cd601" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " OLS Regression Results \n", - "=======================================================================================\n", - "Dep. Variable: y R-squared (uncentered): 0.983\n", - "Model: OLS Adj. R-squared (uncentered): 0.981\n", - "Method: Least Squares F-statistic: 492.7\n", - "Date: Wed, 02 Nov 2022 Prob (F-statistic): 8.91e-16\n", - "Time: 20:40:18 Log-Likelihood: -85.410\n", - "No. Observations: 19 AIC: 174.8\n", - "Df Residuals: 17 BIC: 176.7\n", - "Df Model: 2 \n", - "Covariance Type: nonrobust \n", - "==============================================================================\n", - " coef std err t P>|t| [0.025 0.975]\n", - "------------------------------------------------------------------------------\n", - "x1 5.9192 3.249 1.822 0.086 -0.936 12.774\n", - "x2 2.7984 1.836 1.524 0.146 -1.076 6.673\n", - "==============================================================================\n", - "Omnibus: 5.027 Durbin-Watson: 1.637\n", - "Prob(Omnibus): 0.081 Jarque-Bera (JB): 2.887\n", - "Skew: -0.903 Prob(JB): 0.236\n", - "Kurtosis: 3.622 Cond. No. 21.9\n", - "==============================================================================\n", - "\n", - "Notes:\n", - "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n", - "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.7/dist-packages/scipy/stats/stats.py:1542: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=19\n", - " \"anyway, n=%i\" % int(n))\n" - ] - } - ] + "ename": "LinAlgError", + "evalue": "ignored", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mLinAlgError\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[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXTX\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#cannot invert!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36minv\u001b[0;34m(*args, **kwargs)\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/numpy/linalg/linalg.py\u001b[0m in \u001b[0;36minv\u001b[0;34m(a)\u001b[0m\n\u001b[1;32m 543\u001b[0m \u001b[0msignature\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'D->D'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misComplexType\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'd->d'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 544\u001b[0m \u001b[0mextobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_linalg_error_extobj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_raise_linalgerror_singular\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 545\u001b[0;31m \u001b[0mainv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_umath_linalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msignature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mextobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 546\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mainv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult_t\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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[1;32m 547\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/numpy/linalg/linalg.py\u001b[0m in \u001b[0;36m_raise_linalgerror_singular\u001b[0;34m(err, flag)\u001b[0m\n\u001b[1;32m 86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 87\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_raise_linalgerror_singular\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflag\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---> 88\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mLinAlgError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Singular matrix\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 89\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 90\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_raise_linalgerror_nonposdef\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflag\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;31mLinAlgError\u001b[0m: Singular matrix" + ] + } + ], + "source": [ + "np.linalg.inv(XTX) #cannot invert!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "q5sFJ5XNjMFB" + }, + "source": [ + "Since XTX matrix is not invertable, we'll use the numpy pinv to calculate the Moore-Penrose pseudo-inverse of a matrix:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "zSn-HRnOHK6D", + "outputId": "bdb76ff7-f4db-452d-a758-b69c8e6c05ee" + }, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "# Penalized regression\n", - "In penalized regression, we solve the equation:\n", - "\n", - "\\begin{equation*}\n", - "\\beta = (X^{T}X+ \\lambda I) ^ { -1 } X^{T}Y\n", - "\\end{equation*}\n", - "\n", - "Where $\\lambda$ is a tunable coefficient.\n", - "\n", - "This effectively creates a situation where we are finding the values of $\\hat{\\beta}$ that minimize the following equation:\n", - "\\begin{equation*}\n", - "S(\\hat{\\beta}) = \\sum _ { i = 1 } ^ { n } {| y _ { i } -\\sum _ { j = 1 } ^ { J } x _ { i,j} {\\beta _ {j}} |^{2}} +\\sum _ { j = 1 } ^ { J }\\lambda\\beta _ {j}^{2}\n", - "\\end{equation*}\n" - ], - "metadata": { - "id": "jN-F_HMGoNjL" - } + "name": "stdout", + "output_type": "stream", + "text": [ + "The estimated coefficient on Wrist circumference is 5.919158133872086 and the estimated coefficient on Foot length is 2.7984188001672425\n" + ] + } + ], + "source": [ + "betas = np.matmul(np.transpose(np.matmul(np.linalg.pinv(XTX), X)), Y)\n", + "print(\"The estimated coefficient on \" + list[0] + \" is \" + str(betas[0]) + \" and the estimated coefficient on \" + list[1] + \" is \" + str(betas[1]))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vPrKbUfDk95i" + }, + "source": [ + "Next, we use the OLS (ordinary least squares) function from statsmodels to calculate the same coefficients. OLS regression works by finding the values of $\\hat{\\beta}$ that minimize the following equation:\n", + "\\begin{equation*}\n", + "S(\\hat{\\beta}) = \\sum _ { i = 1 } ^ { n } {| y _ { i } -\\sum _ { j = 1 } ^ { J } x _ { i,j} {\\beta _ {j}} |^{2}}\n", + "\\end{equation*}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "ILcqVvSlFPLE", + "outputId": "b09dc59d-c96f-4fd5-88b1-b113505cd601" + }, + "outputs": [ { - "cell_type": "code", - "source": [ - "#When Lambda = 0, we have OLS regression\n", - "L1=0\n", - "betas = np.matmul(np.transpose(np.matmul(np.linalg.pinv(XTX+L1*np.identity(XTX.shape[0])), X)), Y)\n", - "betas" - ], - "metadata": { - "id": "DBO_Ti8uIuM4" - }, - "execution_count": null, - "outputs": [] + "name": "stdout", + "output_type": "stream", + "text": [ + " OLS Regression Results \n", + "=======================================================================================\n", + "Dep. Variable: y R-squared (uncentered): 0.983\n", + "Model: OLS Adj. R-squared (uncentered): 0.981\n", + "Method: Least Squares F-statistic: 492.7\n", + "Date: Wed, 02 Nov 2022 Prob (F-statistic): 8.91e-16\n", + "Time: 20:40:18 Log-Likelihood: -85.410\n", + "No. Observations: 19 AIC: 174.8\n", + "Df Residuals: 17 BIC: 176.7\n", + "Df Model: 2 \n", + "Covariance Type: nonrobust \n", + "==============================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------\n", + "x1 5.9192 3.249 1.822 0.086 -0.936 12.774\n", + "x2 2.7984 1.836 1.524 0.146 -1.076 6.673\n", + "==============================================================================\n", + "Omnibus: 5.027 Durbin-Watson: 1.637\n", + "Prob(Omnibus): 0.081 Jarque-Bera (JB): 2.887\n", + "Skew: -0.903 Prob(JB): 0.236\n", + "Kurtosis: 3.622 Cond. No. 21.9\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n", + "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" + ] }, { - "cell_type": "code", - "source": [ - "#As we increase L1, we decrease the net magnitude of both coefficients\n", - "L1_list=range(1,50000)\n", - "coef_frame=pd.DataFrame(L1_list)\n", - "coef_frame.columns=[\"L1\"]\n", - "coef_frame[\"B_Wrist\"]=np.nan\n", - "coef_frame[\"B_Foot\"]=np.nan\n" - ], - "metadata": { - "id": "QcJB3WElVPXh" - }, - "execution_count": null, - "outputs": [] + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/dist-packages/scipy/stats/stats.py:1542: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=19\n", + " \"anyway, n=%i\" % int(n))\n" + ] + } + ], + "source": [ + "from statsmodels.api import OLS\n", + "lm = OLS(Y,X).fit().summary()\n", + "print(lm)\n", + "# They match! But we can see that the coeficient on foot lenght is not significant." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jN-F_HMGoNjL" + }, + "source": [ + "# Penalized regression\n", + "In penalized regression, we solve the equation:\n", + "\n", + "\\begin{equation*}\n", + "\\beta = (X^{T}X+ \\lambda I) ^ { -1 } X^{T}Y\n", + "\\end{equation*}\n", + "\n", + "Where $\\lambda$ is a tunable coefficient.\n", + "\n", + "This effectively creates a situation where we are finding the values of $\\hat{\\beta}$ that minimize the following equation:\n", + "\\begin{equation*}\n", + "S(\\hat{\\beta}) = \\sum _ { i = 1 } ^ { n } {| y _ { i } -\\sum _ { j = 1 } ^ { J } x _ { i,j} {\\beta _ {j}} |^{2}} +\\sum _ { j = 1 } ^ { J }\\lambda\\beta _ {j}^{2}\n", + "\\end{equation*}\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "DBO_Ti8uIuM4" + }, + "outputs": [], + "source": [ + "#When Lambda = 0, we have OLS regression\n", + "L1=0\n", + "betas = np.matmul(np.transpose(np.matmul(np.linalg.pinv(XTX+L1*np.identity(XTX.shape[0])), X)), Y)\n", + "betas" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "QcJB3WElVPXh" + }, + "outputs": [], + "source": [ + "#As we increase L1, we decrease the net magnitude of both coefficients\n", + "L1_list=range(1,50000)\n", + "coef_frame=pd.DataFrame(L1_list)\n", + "coef_frame.columns=[\"L1\"]\n", + "coef_frame[\"B_Wrist\"]=np.nan\n", + "coef_frame[\"B_Foot\"]=np.nan\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 424 }, + "id": "Z_hY3EjjIcTz", + "outputId": "eb8538db-a6df-43a9-ecf6-b1788ffcc81b" + }, + "outputs": [ { - "cell_type": "code", - "source": [ - "coef_frame" + "data": { + "text/html": [ + "\n", + "
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\n", + "text/plain": [ + "
" ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(coef_frame.L1[1:1000], coef_frame.B_Foot[1:1000], label = \"Foot coefficient\")\n", + "plt.plot(coef_frame.L1[1:1000], coef_frame.B_Wrist[1:1000], label = \"Wrist coefficient\")\n", + "plt.legend()\n", + "plt.show()\n", + "#for L1 = 1:1000" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 266 }, + "id": "YR0o0N4hHLEZ", + "outputId": "24353ffe-909c-499f-b5be-05bd5a50742d" + }, + "outputs": [ { - "cell_type": "code", - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "plt.plot(coef_frame.L1, coef_frame.B_Foot, label = \"Foot coefficient\")\n", - "plt.plot(coef_frame.L1, coef_frame.B_Wrist, label = \"Wrist coefficient\")\n", - "plt.legend()\n", - "plt.show()\n", - "#For L1 = 1:50000" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 266 - }, - "id": "YR0o0N4hHLEZ", - "outputId": "24353ffe-909c-499f-b5be-05bd5a50742d" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n", + "text/plain": [ + "
" ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } - ] -} \ No newline at end of file + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.plot(coef_frame.L1, coef_frame.B_Foot, label = \"Foot coefficient\")\n", + "plt.plot(coef_frame.L1, coef_frame.B_Wrist, label = \"Wrist coefficient\")\n", + "plt.legend()\n", + "plt.show()\n", + "#For L1 = 1:50000" + ] + } + ], + "metadata": { + "colab": { + "authorship_tag": "ABX9TyO3TiUtc0J4Bihg6I8GN5HM", + "include_colab_link": true, + "provenance": [] + }, + "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": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/CodeSprints/ObservationDependence.ipynb b/CodeSprints/ObservationDependence.ipynb index 1561c2f..3fd0fc5 100644 --- a/CodeSprints/ObservationDependence.ipynb +++ b/CodeSprints/ObservationDependence.ipynb @@ -1,895 +1,896 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "code", - "source": [ - "#Additional packages required in Google Collaboratory:\n", - "!pip install pysal\n", - "!pip install libpysal\n", - "!pip insall geopandas" - ], - "metadata": { - "id": "DZ154BOnTp4P" - }, - "id": "DZ154BOnTp4P", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "id": "d31b57d1", - "metadata": { - "id": "d31b57d1" - }, - "source": [ - "
\n", - "

Moran's I for diagnosing spatial autocorrelation in model residuals.\n", - "\n", - "In this example, we calculate a global Moran's I statistic to evaluate spatial autocorrelation in a dataset containing county-level results from the 2012 and 2016 presidential elections and US census data. We will then attempt to fit a linear regression predicting change in 2012-2016 voting patterns using two spatial covariates (county level per-capita income, and land area). We will evaluate the OLS linear regression residuals for spatial autocorrealtion, then reevalute model fit using an autoregressive model.\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "44184ce0", - "metadata": { - "id": "44184ce0" - }, - "outputs": [], - "source": [ - "import pysal as ps\n", - "import numpy as np\n", - "import geopandas as gpd\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "from libpysal.weights.contiguity import Queen\n", - "import libpysal\n", - "from splot.libpysal import plot_spatial_weights\n", - "from splot.esda import moran_scatterplot\n", - "from splot.esda import plot_moran\n", - "from esda.moran import Moran_Local\n", - "from esda.moran import Moran\n", - "from statsmodels.api import OLS\n", - "from pysal.model import spreg\n", - "import urllib.request\n", - "import shutil\n", - "from mlxtend.preprocessing import standardize\n", - "sns.set_style('white')" - ] - }, - { - "cell_type": "markdown", - "id": "8de413cf", - "metadata": { - "id": "8de413cf" - }, - "source": [ - "First, we're going to open 'Elections' dataset from the Geodata Center at the University of Chicago.\n", - "\n", - "* More on spatial data science resources from UC: https://spatial.uchicago.edu/\n", - "* A list of datasets available through lipysal: https://geodacenter.github.io/data-and-lab//\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e9f020cb", - "metadata": { - "id": "e9f020cb" - }, - "outputs": [], - "source": [ - "#define online filepath (aka url):\n", - "url='https://geodacenter.github.io/data-and-lab//data/election.zip'\n", - "\n", - "#define local filepath:\n", - "local = '/elections.zip'\n", - "\n", - "#download elections data:\n", - "urllib.request.urlretrieve(url, local)\n", - "\n", - "#unzip file\n", - "shutil.unpack_archive(local, '../../../')\n", - "\n", - "votes = gpd.read_file('/election/election.shp')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bd3fb59c", - "metadata": { - "id": "bd3fb59c" - }, - "outputs": [], - "source": [ - "#Let's view the shapefile to get a general idea of the geometry we're looking at:\n", - "%matplotlib inline\n", - "votes.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "05895af3", - "metadata": { - "id": "05895af3" - }, - "outputs": [], - "source": [ - "#View the first few line]s of the dataset\n", - "votes.head()" - ] - }, - { - "cell_type": "markdown", - "id": "c2f27a6b", - "metadata": { - "id": "c2f27a6b" - }, - "source": [ - "The goal of our model is to predict a change in county-level voting trends between the 2012 and 2016 US presidential elections. First, we created a new column indicating the percent change in democratic vote. Then, we plotted the percent democratic vote in 2012, the percent democratic vote in 2016, and the percent change in democratic vote as maps and using global histograms." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c9c70a10", - "metadata": { - "id": "c9c70a10" - }, - "outputs": [], - "source": [ - "# Add new column pct_dem_change to votes:\n", - "votes['pct_dem_change'] = votes['pct_dem_12'] - votes['pct_dem_16']\n", - "\n", - "f,ax = plt.subplots(3,2, figsize=(1.6*6 + 1,4.2*3), gridspec_kw=dict(width_ratios=(6,1)))\n", - "for i,col in enumerate(['pct_dem_12','pct_dem_16', 'pct_dem_change']):\n", - " votes.plot(col, linewidth=.05, cmap='RdBu', ax=ax[i,0])\n", - " ax[i,0].set_title(['2012','2016', '2012-2016 change'][i] + \"% democratic vote\")\n", - " ax[i,0].set_xticklabels('')\n", - " ax[i,0].set_yticklabels('')\n", - " sns.kdeplot(votes[col].values, ax=ax[i,1], vertical=True, shade=True, color='slategrey')\n", - " ax[i,1].set_xticklabels('')\n", - " ax[i,1].set_ylim(-1,1)\n", - "f.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "87d4407b", - "metadata": { - "id": "87d4407b" - }, - "source": [ - "### Spatial Autocorrelation\n", - "From https://www.sciencedirect.com/topics/computer-science/spatial-autocorrelation\n", - "\n", - ">\"Spatial autocorrelation is the term used to describe the presence of *systematic* spatial variation in a variable and positive spatial autocorrelation, which is most often encountered in practical situations, is the tendency for areas or sites that are close together to have similar values.\"" - ] - }, - { - "cell_type": "markdown", - "id": "43b4e7c8", - "metadata": { - "id": "43b4e7c8" - }, - "source": [ - "##**Task 1: Is there evidence for *spatial autocorrelation* in the three plots above? Explain why or why not.**\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "bf1bd754", - "metadata": { - "id": "bf1bd754" - }, - "source": [ - "## Was the county wide percent change in democratic vote related to per capita income?\n", - "The next question is how can we use robust statistics to determine whether per capita income or the county land area were related to a change in 2016 voting preferences. To do this, we're going to conduct a linear regression relating our parameters pct_dem_change to INC910213 and ALAND. Then, we're going to use the confidence interval around beta hat (our slope parameter estimate) to determine whether the relationships between INC910213, ALAND, and pct_dem_change are significantly different than zero.\n", - "\n", - "First we're going to visualize how these variables relate in the global data:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "37b7c6e3", - "metadata": { - "id": "37b7c6e3" - }, - "outputs": [], - "source": [ - "votes.dropna(subset=['pct_dem_12','pct_dem_16'], inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "566b6d28", - "metadata": { - "id": "566b6d28" - }, - "outputs": [], - "source": [ - "f,ax = plt.subplots(1,4, figsize=(4*3.1,4))\n", - "\n", - "votes[['pct_dem_12','pct_dem_16']].plot.scatter('pct_dem_12','pct_dem_16', ax=ax[0])\n", - "ax[0].set_xlabel('2012 Two Party Vote (% Dem)')\n", - "ax[0].set_ylabel('2016 Two Party Vote (% Dem)')\n", - "print(np.corrcoef(votes['pct_dem_12'].values, votes['pct_dem_16'].values)[0,1])\n", - "\n", - "\n", - "votes[['INC910213','pct_dem_change']].plot.scatter('INC910213','pct_dem_change', ax=ax[1])\n", - "ax[1].set_xlabel('Per capita income')\n", - "ax[1].set_ylabel('% Change in Democratic Vote')\n", - "print(np.corrcoef(votes['pct_dem_change'].values, votes['INC910213'].values)[0,1])\n", - "\n", - "votes[['ALAND','pct_dem_change']].plot.scatter('ALAND','pct_dem_change', ax=ax[2])\n", - "ax[2].set_xlabel('County land area')\n", - "ax[2].set_ylabel('% Change in Democratic Vote')\n", - "print(np.corrcoef(votes['pct_dem_change'].values, votes['ALAND'].values)[0,1])\n", - "\n", - "votes[['INC910213','ALAND']].plot.scatter('INC910213','ALAND', ax=ax[3])\n", - "ax[3].set_xlabel('Per capita income')\n", - "ax[3].set_ylabel('County land area')\n", - "print(np.corrcoef(votes['INC910213'].values, votes['ALAND'].values)[0,1])\n", - "\n", - "f.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "50d273fd", - "metadata": { - "id": "50d273fd" - }, - "source": [ - "The first scatterplot compares the 2012 percent democratic vote to the 2016 percent democratic vote. The second two plots compare our predictor (2012-2016 % democractic vote, or pct_dem_change) to our two predictor variables (per capita income and county land area). In the fourth plot, we compare our two predictors (per capita income to county land area). We want to establish a trendline in the fourth plot using linear regression, and determine of the slope in that trendline is statistically significant.\n", - "\n", - "###Theret are some features of this data that make it poorly suited for linear regression:\n", - "\n", - "We are told above that we are going to be regressing the 2012-2016 change in percent democratic vote against per capita income and land area. The conditions that we need to meet for linear regression to be applicable are:\n", - "1. Linear relationship: *it appears that the relationship is approximately inverse linear, though a linear fit might not be perfect*\n", - "2. Multivariate normality: *this might be an issue...the variance in percent change democratic vote appears to increase as a function of decreasing income. This might mean that the residuals are non-normal*\n", - "3. No or little multicollinearity: *The correlation coefficient between our two predictors (per capita income and county land area) is below 0.7. For the purposes of this example, paramters have been selected that do not exhibit collinearity. Note: this does not mean the parameters are *independent*, just that the are not linearly related.*\n", - "4. No auto-correlation: *We will evaluate this later*\n", - "5. Homoscedasticity: *We will evaluate this later*\n" - ] - }, - { - "cell_type": "markdown", - "id": "0a5c2d8f", - "metadata": { - "id": "0a5c2d8f" - }, - "source": [ - "## Do we have spatial autocorrelation in our data?\n", - "When we're looking at distributions of voting preferences, remember that we're aggregating these numbers over arbitrary (er...political) geographic regions.\n", - "\n", - "Each column in that dataframe represents a data value summarized over a US county, but US counties have widely different land areas and populations:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "831bc6d0", - "metadata": { - "id": "831bc6d0" - }, - "outputs": [], - "source": [ - "f,ax = plt.subplots(1,2, figsize=(2*3*1.6, 2))\n", - "sns.kdeplot(votes['ALAND'].values, shade=True, color='slategrey', ax=ax[0])\n", - "ax[0].set_xlabel('County Land Area')\n", - "\n", - "sns.kdeplot(votes['PST045214'].values, shade=True, color='slategrey', ax=ax[1])\n", - "ax[1].set_xlabel('2014 County Population')" - ] - }, - { - "cell_type": "markdown", - "id": "ded538fa", - "metadata": { - "id": "ded538fa" - }, - "source": [ - "Our *spatial sampling rate* (by county) may not accurately represent the spatial frequency of variability in voting preferences and/or income.\n", - "\n", - "First, let's focus on the spatial componnet: the fact that these counties are different sizes.\n", - "\n", - "If we want to identify spatial autocorrelation in our data, we need to first understand how this spatial autocorrelation decays as a function of distance.\n", - "\n", - "To do this, we calculate the Moran's I statistic, which you can think of as the \"slope\" that we'd get when we regress data values for all geographic entities with data values that neighbor within a given distance. Lets look at our data in lat/lon space again:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "74aec211", - "metadata": { - "id": "74aec211" - }, - "outputs": [], - "source": [ - "votes.crs" - ] - }, - { - "cell_type": "markdown", - "id": "0640f882", - "metadata": { - "id": "0640f882" - }, - "source": [ - "### The horizontal unit (unit of distance) in our votes data is meters." - ] - }, - { - "cell_type": "markdown", - "id": "2359e7b5", - "metadata": { - "id": "2359e7b5" - }, - "source": [ - "One issue is that we're sampling spatially at a distinct, and heterogenous, granulatiry. The smallest unit of measurement available in our dataset is the county level. Counties are different sizes. How can we evaluate whether this spatial sampling granularity is of sufficient resolution to capture the scale of variability in our dataset?\n", - "\n", - "* If we are sampling at too course of a spatial scale, we run the risk of missing key patterns of variability in our data (**UNDERSAMPLING**)\n", - "\n", - "* If we are sampling at too fine of a sptial scale, we run the risk of violating assumptions of independence between our individual observations (**OVERSAMPLING**)\n", - "\n", - "The county spatial unit represents a method for discretizing population-level opinions. Some counties may contain communities with very different voting patterns, which we won't be able to resolve with the data (undersampling). Especailly in rural areas, some counties may represent very few people, and could therefore represent an incidence of oversampling." - ] - }, - { - "cell_type": "markdown", - "id": "b38ea33a", - "metadata": { - "id": "b38ea33a" - }, - "source": [ - "## Calculating a weights matrix:\n", - "The first thing we want to tackle is a quantification of any spatial autocorrelation in our dataset. Spatial autocorrelation inflates our theoretical number of samples (N), artificially increasing the power in our test statistics. In other words, when we're calculating test statistics, spatial autocorrelation in our data can make it seem like parameters that are unimportant are actually significant.\n", - "\n", - "Since we're dealing with a heterogeneous sampling grid in our data, the first thing we want to do is calculate a weights matrix.\n", - "\n", - "We're going to use the Queen function in pysal to do this. Full documentation here: https://pysal.org/libpysal/generated/libpysal.weights.Queen.html\n", - "\n", - "Or just use the built in help with the function below:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d30c9e6f", - "metadata": { - "id": "d30c9e6f" - }, - "outputs": [], - "source": [ - "#Calculate weights object\n", - "weights = Queen.from_dataframe(votes)\n", - "\n", - "#Use built in plot function to visualize how the weights matrix works\n", - "plot_spatial_weights(weights, votes)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "5f71de98", - "metadata": { - "id": "5f71de98" - }, - "source": [ - "The verticies in this plot represent two things:\n", - "* First, they link \"neigbors\" based on the model parameters we set for defining neighborhood (here we use the defaul settings and consider any contiguous polygons).\n", - "* The length of the verticies indicates the distance between the centers of neighborhing cells.\n", - "\n", - "\"Neighbors\" that are father matter less than \"neighbors\" that are closer in identifying the strength of spatial autocorrelation.\n", - "\n", - "## Calculate Moran's I:\n", - "Moran's I statistic quantifies the spatial autocorrelation in your data. From https://en.wikipedia.org/wiki/Moran%27s_I\n", - ">\"Spatial dependency leads to the spatial autocorrelation problem in statistics since, like temporal autocorrelation, this violates standard statistical techniques that assume independence among observations. For example, regression analyses that do not compensate for spatial dependency can have unstable parameter estimates and yield unreliable significance tests. Spatial regression models (see below) capture these relationships and do not suffer from these weaknesses. It is also appropriate to view spatial dependency as a source of information rather than something to be corrected.\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f5393d4e", - "metadata": { - "id": "f5393d4e" - }, - "outputs": [], - "source": [ - "# calculate Moran and plot\n", - "moran_votes = Moran(votes['pct_dem_change'], w=weights)\n", - "plot_moran(moran_votes, zstandard=True, figsize=(10,4))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "source": [ - "# calculate Moran and plot\n", - "moran_income = Moran(votes['INC910213'], w=weights)\n", - "plot_moran(moran_income, zstandard=True, figsize=(10,4))\n", - "plt.show()" - ], - "metadata": { - "id": "CIfxNKLVm_bw" - }, - "id": "CIfxNKLVm_bw", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# calculate Moran and plot\n", - "moran_land = Moran(votes['ALAND'], w=weights)\n", - "plot_moran(moran_land, zstandard=True, figsize=(10,4))\n", - "plt.show()" - ], - "metadata": { - "id": "v1T9OJxOWkFQ" - }, - "id": "v1T9OJxOWkFQ", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "25ba3cd4", - "metadata": { - "id": "25ba3cd4" - }, - "outputs": [], - "source": [ - "print(moran_votes.p_norm)\n", - "print(moran_income.p_norm)\n", - "print(moran_land.p_norm)" - ] - }, - { - "cell_type": "markdown", - "id": "3d38a701", - "metadata": { - "id": "3d38a701" - }, - "source": [ - "##Task 2: what do the p_norm values tell us? What hypothesis is being tested, what is the null hypothesis, and do we accept or reject the null hypothesis?\n" - ] - }, - { - "cell_type": "markdown", - "source": [ - "## Task 3: is there autocorrelation in the data? If so, which features exhibit autocorrelation?" - ], - "metadata": { - "id": "kv1Ery8clSDU" - }, - "id": "kv1Ery8clSDU" - }, - { - "cell_type": "markdown", - "id": "dedff477", - "metadata": { - "id": "dedff477" - }, - "source": [ - "## Caluculate a linear regression on the global data:\n", - "In this next step, we're going to calculate a linear regression in our data an determine whether that analysis determines a statistically significant relationship between our percent income and percent change in democratic vote." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cc5f4ba0", - "metadata": { - "id": "cc5f4ba0" - }, - "outputs": [], - "source": [ - "#first, forumalate the model.\n", - "#isolate and standardize the preditors:\n", - "X_raw = np.array(votes[['INC910213', 'ALAND']].values)\n", - "X = standardize(X_raw, columns=[0, 1])\n", - "\n", - "\n", - "#isolate and standardize predictand:\n", - "Y = np.array(votes['pct_dem_change'].values)\n", - "Y= standardize(Y)\n", - "\n", - "lm = OLS(Y,X)\n", - "lm_results = OLS(Y,X).fit().summary()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "823fea94", - "metadata": { - "id": "823fea94" - }, - "outputs": [], - "source": [ - "print(lm_results)" - ] - }, - { - "cell_type": "markdown", - "id": "3439ad1a", - "metadata": { - "id": "3439ad1a" - }, - "source": [ - "\n", - "With p-values of effectively zero, we can reject the null hypothesis that btoh the coefficient on INC910213 (x2) and ALAND (x2) are zero, and conclude that there is a statistically significant relationship. Increasing income and increasing land area are both associated with a statistically significant decrease in percent democratic vote. It is notable that the standard error on the standardized coefficient for both variables are nearly an order of magnitude smaller than the coefficient itself" - ] - }, - { - "cell_type": "markdown", - "id": "11690f15", - "metadata": { - "id": "11690f15" - }, - "source": [ - "Now, let's plot our residuals to see if there are any spatial patterns in them.\n", - "\n", - "Remember residuals = predicted - fitted values" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "96eae78c", - "metadata": { - "id": "96eae78c" - }, - "outputs": [], - "source": [ - "#Add model residuals to our \"votes\" geopandas dataframe:\n", - "votes['lm_resid']=lm.fit().resid" - ] - }, - { - "cell_type": "markdown", - "id": "136dafbb", - "metadata": { - "id": "136dafbb" - }, - "source": [ - "Remember, in OLS regression we depend out our residuals being normally distributed:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0270685f", - "metadata": { - "id": "0270685f" - }, - "outputs": [], - "source": [ - "sns.kdeplot(votes['lm_resid'].values, shade=True, color='slategrey')\n", - "ax[1].set_xlabel('OLS residuals')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1448992a", - "metadata": { - "id": "1448992a" - }, - "outputs": [], - "source": [ - "#Plot them in space:\n", - "f, ax1 = plt.subplots(figsize=(10, 6))\n", - "votes.plot('lm_resid', ax=ax1, linewidth=.05, cmap='RdBu',legend = True)" - ] - }, - { - "cell_type": "markdown", - "id": "f73a181e", - "metadata": { - "id": "f73a181e" - }, - "source": [ - "These are very not normal residuals. What's going on?\n", - "\n", - "## Task 5: What does a positive residual mean here (the model overpredicted change in democratic vote, the model underpredicted change in democratic vote)?\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "source": [ - "With spatial data, we also care about spatial autocorrelation of our *residuals*:" - ], - "metadata": { - "id": "2C_H7ZsNlksM" - }, - "id": "2C_H7ZsNlksM" - }, - { - "cell_type": "code", - "execution_count": null, - "id": "15a911ec", - "metadata": { - "id": "15a911ec" - }, - "outputs": [], - "source": [ - "# calculate Moran and plot\n", - "moran = Moran(votes['lm_resid'], w=weights)\n", - "plot_moran(moran, zstandard=True, figsize=(10,4))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "41dc15d9", - "metadata": { - "id": "41dc15d9" - }, - "outputs": [], - "source": [ - "moran.p_norm" - ] - }, - { - "cell_type": "markdown", - "id": "9aba6557", - "metadata": { - "id": "9aba6557" - }, - "source": [ - "## Task 6: Do we have spatially autocorrelated residuals? Provide numbers to back your claim. Why do we care?\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c6d7ccac", - "metadata": { - "id": "c6d7ccac" - }, - "outputs": [], - "source": [ - "f,ax = plt.subplots(1,3, figsize=(3*3*1.6, 2))\n", - "\n", - "votes[['INC910213','lm_resid']].plot.scatter('INC910213','lm_resid', ax=ax[0])\n", - "ax[0].set_xlabel('county income')\n", - "ax[0].set_ylabel('linear model residuals')\n", - "\n", - "votes[['ALAND','lm_resid']].plot.scatter('ALAND','lm_resid', ax=ax[1])\n", - "ax[1].set_xlabel('county land area')\n", - "ax[1].set_ylabel('linear model residuals')\n", - "\n", - "votes[['pct_dem_change','lm_resid']].plot.scatter('pct_dem_change','lm_resid', ax=ax[2])\n", - "ax[2].set_xlabel('2012-2016 change in % democratic vote')\n", - "ax[2].set_ylabel('linear model residuals')\n" - ] - }, - { - "cell_type": "markdown", - "id": "13e3f7df", - "metadata": { - "id": "13e3f7df" - }, - "source": [ - "*The first two plots show heteroskedasticity in our residuals. The second plot shows bias in our model*" - ] - }, - { - "cell_type": "markdown", - "id": "98e86763", - "metadata": { - "id": "98e86763" - }, - "source": [ - "\n", - "## Autocovariate regression: spatial lag model\n", - "Let's see if we can get different answers by accounting for our residuals in our model. First, we'll try a spatial lag model. A spatial lag model is a type of autocovariate model that assumes that dependencies exist directly among the levels of the dependent variable, and models them as an \"autocovariate\". So we create an autocovariate function that describes the degree to which the percent change in democratic vote at one location is affected by the percent change in democratic vote at the nearby locations. The coefficient and p-value for the autocovariate function are interpreted as for the independent variables." - ] - }, - { - "cell_type": "code", - "source": [ - "?spreg.ML_Lag" - ], - "metadata": { - "id": "JIUjJWyDZlY3" - }, - "id": "JIUjJWyDZlY3", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "595a494f", - "metadata": { - "id": "595a494f" - }, - "outputs": [], - "source": [ - "Yl = Y.T\n", - "Yl.shape = (len(Y),1)\n", - "\n", - "lag=spreg.ML_Lag(Yl, X, weights, name_x=[\"income\", \"area\"],name_y=\"vote\", name_w=\"weights\")\n", - "print(lag.summary)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "04b3d5ab", - "metadata": { - "id": "04b3d5ab" - }, - "outputs": [], - "source": [ - "#Add model residuals to our \"votes\" geopandas dataframe:\n", - "votes['slm_resid']=lag.u\n", - "sns.kdeplot(votes['slm_resid'].values, shade=True, color='slategrey')\n", - "ax[1].set_xlabel('SLM residuals')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8876a6c6", - "metadata": { - "id": "8876a6c6" - }, - "outputs": [], - "source": [ - "# calculate Moran and plot\n", - "moran = Moran(votes['slm_resid'], w=weights)\n", - "plot_moran(moran, zstandard=True, figsize=(10,4))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "702c34c3", - "metadata": { - "id": "702c34c3" - }, - "outputs": [], - "source": [ - "#Plot them in space:\n", - "f, ax1 = plt.subplots(figsize=(10, 6))\n", - "votes.plot('slm_resid', ax=ax1, linewidth=.05, cmap='RdBu',legend = True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "42cd97a7", - "metadata": { - "id": "42cd97a7" - }, - "outputs": [], - "source": [ - "# calculate Moran and plot\n", - "moran = Moran(votes['slm_resid'], w=weights)\n", - "plot_moran(moran, zstandard=True, figsize=(10,4))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "source": [ - "moran.p_norm" - ], - "metadata": { - "id": "0bQtu0pOcZDf" - }, - "id": "0bQtu0pOcZDf", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "233ea54c", - "metadata": { - "id": "233ea54c" - }, - "outputs": [], - "source": [ - "f,ax = plt.subplots(1,3, figsize=(3*3*1.6, 2))\n", - "\n", - "votes[['INC910213','slm_resid']].plot.scatter('INC910213','slm_resid', ax=ax[0])\n", - "ax[0].set_xlabel('Per capita income')\n", - "ax[0].set_ylabel('spatial lag model residuals')\n", - "\n", - "votes[['ALAND','slm_resid']].plot.scatter('ALAND','slm_resid', ax=ax[1])\n", - "ax[1].set_xlabel('county land area')\n", - "ax[1].set_ylabel('spatial lag model residuals')\n", - "\n", - "votes[['pct_dem_change','slm_resid']].plot.scatter('pct_dem_change','slm_resid', ax=ax[2])\n", - "ax[2].set_xlabel('2012-2016 change in % democratic vote')\n", - "ax[2].set_ylabel('spatial lag model residuals')" - ] - }, - { - "cell_type": "markdown", - "id": "f740100c", - "metadata": { - "id": "f740100c" - }, - "source": [ - "## Task 7: When we account for spatial autocorrelation in our data using the spatial lag model, do we still see a significant relationship between our response and predictor variable?\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "3106dc34", - "metadata": { - "id": "3106dc34" - }, - "source": [ - "## Task 8: Compare these two models. Do you believe that there is a linear relationship between percent change in democratic vote and income level? What about between change in democratic vote and land area? Explain your interpretation using model parameters and outputs.\n", - "\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "geostats_env", - "language": "python", - "name": "geostats_env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.11" - }, - "colab": { - "provenance": [], - "collapsed_sections": [ - "50d273fd", - "2359e7b5" - ], - "include_colab_link": true - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file + "cells": [ + { + "cell_type": "markdown", + "id": "c43bc5a9", + "metadata": { + "colab_type": "text", + "id": "view-in-github" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "DZ154BOnTp4P", + "metadata": { + "id": "DZ154BOnTp4P" + }, + "outputs": [], + "source": [ + "#Additional packages required in Google Collaboratory:\n", + "!pip install pysal\n", + "!pip install libpysal\n", + "!pip insall geopandas" + ] + }, + { + "cell_type": "markdown", + "id": "d31b57d1", + "metadata": { + "id": "d31b57d1" + }, + "source": [ + "
\n", + "

Moran's I for diagnosing spatial autocorrelation in model residuals.\n", + "\n", + "In this example, we calculate a global Moran's I statistic to evaluate spatial autocorrelation in a dataset containing county-level results from the 2012 and 2016 presidential elections and US census data. We will then attempt to fit a linear regression predicting change in 2012-2016 voting patterns using two spatial covariates (county level per-capita income, and land area). We will evaluate the OLS linear regression residuals for spatial autocorrealtion, then reevalute model fit using an autoregressive model.\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44184ce0", + "metadata": { + "id": "44184ce0" + }, + "outputs": [], + "source": [ + "import pysal as ps\n", + "import numpy as np\n", + "import geopandas as gpd\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from libpysal.weights.contiguity import Queen\n", + "import libpysal\n", + "from splot.libpysal import plot_spatial_weights\n", + "from splot.esda import moran_scatterplot\n", + "from splot.esda import plot_moran\n", + "from esda.moran import Moran_Local\n", + "from esda.moran import Moran\n", + "from statsmodels.api import OLS\n", + "from pysal.model import spreg\n", + "import urllib.request\n", + "import shutil\n", + "from mlxtend.preprocessing import standardize\n", + "sns.set_style('white')" + ] + }, + { + "cell_type": "markdown", + "id": "8de413cf", + "metadata": { + "id": "8de413cf" + }, + "source": [ + "First, we're going to open 'Elections' dataset from the Geodata Center at the University of Chicago.\n", + "\n", + "* More on spatial data science resources from UC: https://spatial.uchicago.edu/\n", + "* A list of datasets available through lipysal: https://geodacenter.github.io/data-and-lab//\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e9f020cb", + "metadata": { + "id": "e9f020cb" + }, + "outputs": [], + "source": [ + "#define online filepath (aka url):\n", + "url='https://geodacenter.github.io/data-and-lab//data/election.zip'\n", + "\n", + "#define local filepath:\n", + "local = '/elections.zip'\n", + "\n", + "#download elections data:\n", + "urllib.request.urlretrieve(url, local)\n", + "\n", + "#unzip file\n", + "shutil.unpack_archive(local, '../../../')\n", + "\n", + "votes = gpd.read_file('/election/election.shp')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bd3fb59c", + "metadata": { + "id": "bd3fb59c" + }, + "outputs": [], + "source": [ + "#Let's view the shapefile to get a general idea of the geometry we're looking at:\n", + "%matplotlib inline\n", + "votes.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "05895af3", + "metadata": { + "id": "05895af3" + }, + "outputs": [], + "source": [ + "#View the first few line]s of the dataset\n", + "votes.head()" + ] + }, + { + "cell_type": "markdown", + "id": "c2f27a6b", + "metadata": { + "id": "c2f27a6b" + }, + "source": [ + "The goal of our model is to predict a change in county-level voting trends between the 2012 and 2016 US presidential elections. First, we created a new column indicating the percent change in democratic vote. Then, we plotted the percent democratic vote in 2012, the percent democratic vote in 2016, and the percent change in democratic vote as maps and using global histograms." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c9c70a10", + "metadata": { + "id": "c9c70a10" + }, + "outputs": [], + "source": [ + "# Add new column pct_dem_change to votes:\n", + "votes['pct_dem_change'] = votes['pct_dem_12'] - votes['pct_dem_16']\n", + "\n", + "f,ax = plt.subplots(3,2, figsize=(1.6*6 + 1,4.2*3), gridspec_kw=dict(width_ratios=(6,1)))\n", + "for i,col in enumerate(['pct_dem_12','pct_dem_16', 'pct_dem_change']):\n", + " votes.plot(col, linewidth=.05, cmap='RdBu', ax=ax[i,0])\n", + " ax[i,0].set_title(['2012','2016', '2012-2016 change'][i] + \"% democratic vote\")\n", + " ax[i,0].set_xticklabels('')\n", + " ax[i,0].set_yticklabels('')\n", + " sns.kdeplot(votes[col].values, ax=ax[i,1], vertical=True, shade=True, color='slategrey')\n", + " ax[i,1].set_xticklabels('')\n", + " ax[i,1].set_ylim(-1,1)\n", + "f.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "87d4407b", + "metadata": { + "id": "87d4407b" + }, + "source": [ + "### Spatial Autocorrelation\n", + "From https://www.sciencedirect.com/topics/computer-science/spatial-autocorrelation\n", + "\n", + ">\"Spatial autocorrelation is the term used to describe the presence of *systematic* spatial variation in a variable and positive spatial autocorrelation, which is most often encountered in practical situations, is the tendency for areas or sites that are close together to have similar values.\"" + ] + }, + { + "cell_type": "markdown", + "id": "43b4e7c8", + "metadata": { + "id": "43b4e7c8" + }, + "source": [ + "##**Task 1: Is there evidence for *spatial autocorrelation* in the three plots above? Explain why or why not.**\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "bf1bd754", + "metadata": { + "id": "bf1bd754" + }, + "source": [ + "## Was the county wide percent change in democratic vote related to per capita income?\n", + "The next question is how can we use robust statistics to determine whether per capita income or the county land area were related to a change in 2016 voting preferences. To do this, we're going to conduct a linear regression relating our parameters pct_dem_change to INC910213 and ALAND. Then, we're going to use the confidence interval around beta hat (our slope parameter estimate) to determine whether the relationships between INC910213, ALAND, and pct_dem_change are significantly different than zero.\n", + "\n", + "First we're going to visualize how these variables relate in the global data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "37b7c6e3", + "metadata": { + "id": "37b7c6e3" + }, + "outputs": [], + "source": [ + "votes.dropna(subset=['pct_dem_12','pct_dem_16'], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "566b6d28", + "metadata": { + "id": "566b6d28" + }, + "outputs": [], + "source": [ + "f,ax = plt.subplots(1,4, figsize=(4*3.1,4))\n", + "\n", + "votes[['pct_dem_12','pct_dem_16']].plot.scatter('pct_dem_12','pct_dem_16', ax=ax[0])\n", + "ax[0].set_xlabel('2012 Two Party Vote (% Dem)')\n", + "ax[0].set_ylabel('2016 Two Party Vote (% Dem)')\n", + "print(np.corrcoef(votes['pct_dem_12'].values, votes['pct_dem_16'].values)[0,1])\n", + "\n", + "\n", + "votes[['INC910213','pct_dem_change']].plot.scatter('INC910213','pct_dem_change', ax=ax[1])\n", + "ax[1].set_xlabel('Per capita income')\n", + "ax[1].set_ylabel('% Change in Democratic Vote')\n", + "print(np.corrcoef(votes['pct_dem_change'].values, votes['INC910213'].values)[0,1])\n", + "\n", + "votes[['ALAND','pct_dem_change']].plot.scatter('ALAND','pct_dem_change', ax=ax[2])\n", + "ax[2].set_xlabel('County land area')\n", + "ax[2].set_ylabel('% Change in Democratic Vote')\n", + "print(np.corrcoef(votes['pct_dem_change'].values, votes['ALAND'].values)[0,1])\n", + "\n", + "votes[['INC910213','ALAND']].plot.scatter('INC910213','ALAND', ax=ax[3])\n", + "ax[3].set_xlabel('Per capita income')\n", + "ax[3].set_ylabel('County land area')\n", + "print(np.corrcoef(votes['INC910213'].values, votes['ALAND'].values)[0,1])\n", + "\n", + "f.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "50d273fd", + "metadata": { + "id": "50d273fd" + }, + "source": [ + "The first scatterplot compares the 2012 percent democratic vote to the 2016 percent democratic vote. The second two plots compare our predictor (2012-2016 % democractic vote, or pct_dem_change) to our two predictor variables (per capita income and county land area). In the fourth plot, we compare our two predictors (per capita income to county land area). We want to establish a trendline in the fourth plot using linear regression, and determine of the slope in that trendline is statistically significant.\n", + "\n", + "###Theret are some features of this data that make it poorly suited for linear regression:\n", + "\n", + "We are told above that we are going to be regressing the 2012-2016 change in percent democratic vote against per capita income and land area. The conditions that we need to meet for linear regression to be applicable are:\n", + "1. Linear relationship: *it appears that the relationship is approximately inverse linear, though a linear fit might not be perfect*\n", + "2. Multivariate normality: *this might be an issue...the variance in percent change democratic vote appears to increase as a function of decreasing income. This might mean that the residuals are non-normal*\n", + "3. No or little multicollinearity: *The correlation coefficient between our two predictors (per capita income and county land area) is below 0.7. For the purposes of this example, paramters have been selected that do not exhibit collinearity. Note: this does not mean the parameters are *independent*, just that the are not linearly related.*\n", + "4. No auto-correlation: *We will evaluate this later*\n", + "5. Homoscedasticity: *We will evaluate this later*\n" + ] + }, + { + "cell_type": "markdown", + "id": "0a5c2d8f", + "metadata": { + "id": "0a5c2d8f" + }, + "source": [ + "## Do we have spatial autocorrelation in our data?\n", + "When we're looking at distributions of voting preferences, remember that we're aggregating these numbers over arbitrary (er...political) geographic regions.\n", + "\n", + "Each column in that dataframe represents a data value summarized over a US county, but US counties have widely different land areas and populations:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "831bc6d0", + "metadata": { + "id": "831bc6d0" + }, + "outputs": [], + "source": [ + "f,ax = plt.subplots(1,2, figsize=(2*3*1.6, 2))\n", + "sns.kdeplot(votes['ALAND'].values, shade=True, color='slategrey', ax=ax[0])\n", + "ax[0].set_xlabel('County Land Area')\n", + "\n", + "sns.kdeplot(votes['PST045214'].values, shade=True, color='slategrey', ax=ax[1])\n", + "ax[1].set_xlabel('2014 County Population')" + ] + }, + { + "cell_type": "markdown", + "id": "ded538fa", + "metadata": { + "id": "ded538fa" + }, + "source": [ + "Our *spatial sampling rate* (by county) may not accurately represent the spatial frequency of variability in voting preferences and/or income.\n", + "\n", + "First, let's focus on the spatial componnet: the fact that these counties are different sizes.\n", + "\n", + "If we want to identify spatial autocorrelation in our data, we need to first understand how this spatial autocorrelation decays as a function of distance.\n", + "\n", + "To do this, we calculate the Moran's I statistic, which you can think of as the \"slope\" that we'd get when we regress data values for all geographic entities with data values that neighbor within a given distance. Lets look at our data in lat/lon space again:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "74aec211", + "metadata": { + "id": "74aec211" + }, + "outputs": [], + "source": [ + "votes.crs" + ] + }, + { + "cell_type": "markdown", + "id": "0640f882", + "metadata": { + "id": "0640f882" + }, + "source": [ + "### The horizontal unit (unit of distance) in our votes data is meters." + ] + }, + { + "cell_type": "markdown", + "id": "2359e7b5", + "metadata": { + "id": "2359e7b5" + }, + "source": [ + "One issue is that we're sampling spatially at a distinct, and heterogenous, granulatiry. The smallest unit of measurement available in our dataset is the county level. Counties are different sizes. How can we evaluate whether this spatial sampling granularity is of sufficient resolution to capture the scale of variability in our dataset?\n", + "\n", + "* If we are sampling at too course of a spatial scale, we run the risk of missing key patterns of variability in our data (**UNDERSAMPLING**)\n", + "\n", + "* If we are sampling at too fine of a sptial scale, we run the risk of violating assumptions of independence between our individual observations (**OVERSAMPLING**)\n", + "\n", + "The county spatial unit represents a method for discretizing population-level opinions. Some counties may contain communities with very different voting patterns, which we won't be able to resolve with the data (undersampling). Especailly in rural areas, some counties may represent very few people, and could therefore represent an incidence of oversampling." + ] + }, + { + "cell_type": "markdown", + "id": "b38ea33a", + "metadata": { + "id": "b38ea33a" + }, + "source": [ + "## Calculating a weights matrix:\n", + "The first thing we want to tackle is a quantification of any spatial autocorrelation in our dataset. Spatial autocorrelation inflates our theoretical number of samples (N), artificially increasing the power in our test statistics. In other words, when we're calculating test statistics, spatial autocorrelation in our data can make it seem like parameters that are unimportant are actually significant.\n", + "\n", + "Since we're dealing with a heterogeneous sampling grid in our data, the first thing we want to do is calculate a weights matrix.\n", + "\n", + "We're going to use the Queen function in pysal to do this. Full documentation here: https://pysal.org/libpysal/generated/libpysal.weights.Queen.html\n", + "\n", + "Or just use the built in help with the function below:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d30c9e6f", + "metadata": { + "id": "d30c9e6f" + }, + "outputs": [], + "source": [ + "#Calculate weights object\n", + "weights = Queen.from_dataframe(votes)\n", + "\n", + "#Use built in plot function to visualize how the weights matrix works\n", + "plot_spatial_weights(weights, votes)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "5f71de98", + "metadata": { + "id": "5f71de98" + }, + "source": [ + "The verticies in this plot represent two things:\n", + "* First, they link \"neigbors\" based on the model parameters we set for defining neighborhood (here we use the defaul settings and consider any contiguous polygons).\n", + "* The length of the verticies indicates the distance between the centers of neighborhing cells.\n", + "\n", + "\"Neighbors\" that are father matter less than \"neighbors\" that are closer in identifying the strength of spatial autocorrelation.\n", + "\n", + "## Calculate Moran's I:\n", + "Moran's I statistic quantifies the spatial autocorrelation in your data. From https://en.wikipedia.org/wiki/Moran%27s_I\n", + ">\"Spatial dependency leads to the spatial autocorrelation problem in statistics since, like temporal autocorrelation, this violates standard statistical techniques that assume independence among observations. For example, regression analyses that do not compensate for spatial dependency can have unstable parameter estimates and yield unreliable significance tests. Spatial regression models (see below) capture these relationships and do not suffer from these weaknesses. It is also appropriate to view spatial dependency as a source of information rather than something to be corrected.\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f5393d4e", + "metadata": { + "id": "f5393d4e" + }, + "outputs": [], + "source": [ + "# calculate Moran and plot\n", + "moran_votes = Moran(votes['pct_dem_change'], w=weights)\n", + "plot_moran(moran_votes, zstandard=True, figsize=(10,4))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "CIfxNKLVm_bw", + "metadata": { + "id": "CIfxNKLVm_bw" + }, + "outputs": [], + "source": [ + "# calculate Moran and plot\n", + "moran_income = Moran(votes['INC910213'], w=weights)\n", + "plot_moran(moran_income, zstandard=True, figsize=(10,4))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "v1T9OJxOWkFQ", + "metadata": { + "id": "v1T9OJxOWkFQ" + }, + "outputs": [], + "source": [ + "# calculate Moran and plot\n", + "moran_land = Moran(votes['ALAND'], w=weights)\n", + "plot_moran(moran_land, zstandard=True, figsize=(10,4))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25ba3cd4", + "metadata": { + "id": "25ba3cd4" + }, + "outputs": [], + "source": [ + "print(moran_votes.p_norm)\n", + "print(moran_income.p_norm)\n", + "print(moran_land.p_norm)" + ] + }, + { + "cell_type": "markdown", + "id": "3d38a701", + "metadata": { + "id": "3d38a701" + }, + "source": [ + "##Task 2: what do the p_norm values tell us? What hypothesis is being tested, what is the null hypothesis, and do we accept or reject the null hypothesis?\n" + ] + }, + { + "cell_type": "markdown", + "id": "kv1Ery8clSDU", + "metadata": { + "id": "kv1Ery8clSDU" + }, + "source": [ + "## Task 3: is there autocorrelation in the data? If so, which features exhibit autocorrelation?" + ] + }, + { + "cell_type": "markdown", + "id": "dedff477", + "metadata": { + "id": "dedff477" + }, + "source": [ + "## Caluculate a linear regression on the global data:\n", + "In this next step, we're going to calculate a linear regression in our data an determine whether that analysis determines a statistically significant relationship between our percent income and percent change in democratic vote." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cc5f4ba0", + "metadata": { + "id": "cc5f4ba0" + }, + "outputs": [], + "source": [ + "#first, forumalate the model.\n", + "#isolate and standardize the preditors:\n", + "X_raw = np.array(votes[['INC910213', 'ALAND']].values)\n", + "X = standardize(X_raw, columns=[0, 1])\n", + "\n", + "\n", + "#isolate and standardize predictand:\n", + "Y = np.array(votes['pct_dem_change'].values)\n", + "Y= standardize(Y)\n", + "\n", + "lm = OLS(Y,X)\n", + "lm_results = OLS(Y,X).fit().summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "823fea94", + "metadata": { + "id": "823fea94" + }, + "outputs": [], + "source": [ + "print(lm_results)" + ] + }, + { + "cell_type": "markdown", + "id": "3439ad1a", + "metadata": { + "id": "3439ad1a" + }, + "source": [ + "\n", + "With p-values of effectively zero, we can reject the null hypothesis that btoh the coefficient on INC910213 (x2) and ALAND (x2) are zero, and conclude that there is a statistically significant relationship. Increasing income and increasing land area are both associated with a statistically significant decrease in percent democratic vote. It is notable that the standard error on the standardized coefficient for both variables are nearly an order of magnitude smaller than the coefficient itself" + ] + }, + { + "cell_type": "markdown", + "id": "11690f15", + "metadata": { + "id": "11690f15" + }, + "source": [ + "Now, let's plot our residuals to see if there are any spatial patterns in them.\n", + "\n", + "Remember residuals = predicted - fitted values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "96eae78c", + "metadata": { + "id": "96eae78c" + }, + "outputs": [], + "source": [ + "#Add model residuals to our \"votes\" geopandas dataframe:\n", + "votes['lm_resid']=lm.fit().resid" + ] + }, + { + "cell_type": "markdown", + "id": "136dafbb", + "metadata": { + "id": "136dafbb" + }, + "source": [ + "Remember, in OLS regression we depend out our residuals being normally distributed:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0270685f", + "metadata": { + "id": "0270685f" + }, + "outputs": [], + "source": [ + "sns.kdeplot(votes['lm_resid'].values, shade=True, color='slategrey')\n", + "ax[1].set_xlabel('OLS residuals')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1448992a", + "metadata": { + "id": "1448992a" + }, + "outputs": [], + "source": [ + "#Plot them in space:\n", + "f, ax1 = plt.subplots(figsize=(10, 6))\n", + "votes.plot('lm_resid', ax=ax1, linewidth=.05, cmap='RdBu',legend = True)" + ] + }, + { + "cell_type": "markdown", + "id": "f73a181e", + "metadata": { + "id": "f73a181e" + }, + "source": [ + "These are very not normal residuals. What's going on?\n", + "\n", + "## Task 5: What does a positive residual mean here (the model overpredicted change in democratic vote, the model underpredicted change in democratic vote)?\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "2C_H7ZsNlksM", + "metadata": { + "id": "2C_H7ZsNlksM" + }, + "source": [ + "With spatial data, we also care about spatial autocorrelation of our *residuals*:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15a911ec", + "metadata": { + "id": "15a911ec" + }, + "outputs": [], + "source": [ + "# calculate Moran and plot\n", + "moran = Moran(votes['lm_resid'], w=weights)\n", + "plot_moran(moran, zstandard=True, figsize=(10,4))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41dc15d9", + "metadata": { + "id": "41dc15d9" + }, + "outputs": [], + "source": [ + "moran.p_norm" + ] + }, + { + "cell_type": "markdown", + "id": "9aba6557", + "metadata": { + "id": "9aba6557" + }, + "source": [ + "## Task 6: Do we have spatially autocorrelated residuals? Provide numbers to back your claim. Why do we care?\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c6d7ccac", + "metadata": { + "id": "c6d7ccac" + }, + "outputs": [], + "source": [ + "f,ax = plt.subplots(1,3, figsize=(3*3*1.6, 2))\n", + "\n", + "votes[['INC910213','lm_resid']].plot.scatter('INC910213','lm_resid', ax=ax[0])\n", + "ax[0].set_xlabel('county income')\n", + "ax[0].set_ylabel('linear model residuals')\n", + "\n", + "votes[['ALAND','lm_resid']].plot.scatter('ALAND','lm_resid', ax=ax[1])\n", + "ax[1].set_xlabel('county land area')\n", + "ax[1].set_ylabel('linear model residuals')\n", + "\n", + "votes[['pct_dem_change','lm_resid']].plot.scatter('pct_dem_change','lm_resid', ax=ax[2])\n", + "ax[2].set_xlabel('2012-2016 change in % democratic vote')\n", + "ax[2].set_ylabel('linear model residuals')\n" + ] + }, + { + "cell_type": "markdown", + "id": "13e3f7df", + "metadata": { + "id": "13e3f7df" + }, + "source": [ + "*The first two plots show heteroskedasticity in our residuals. The second plot shows bias in our model*" + ] + }, + { + "cell_type": "markdown", + "id": "98e86763", + "metadata": { + "id": "98e86763" + }, + "source": [ + "\n", + "## Autocovariate regression: spatial lag model\n", + "Let's see if we can get different answers by accounting for our residuals in our model. First, we'll try a spatial lag model. A spatial lag model is a type of autocovariate model that assumes that dependencies exist directly among the levels of the dependent variable, and models them as an \"autocovariate\". So we create an autocovariate function that describes the degree to which the percent change in democratic vote at one location is affected by the percent change in democratic vote at the nearby locations. The coefficient and p-value for the autocovariate function are interpreted as for the independent variables." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "JIUjJWyDZlY3", + "metadata": { + "id": "JIUjJWyDZlY3" + }, + "outputs": [], + "source": [ + "?spreg.ML_Lag" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "595a494f", + "metadata": { + "id": "595a494f" + }, + "outputs": [], + "source": [ + "Yl = Y.T\n", + "Yl.shape = (len(Y),1)\n", + "\n", + "lag=spreg.ML_Lag(Yl, X, weights, name_x=[\"income\", \"area\"],name_y=\"vote\", name_w=\"weights\")\n", + "print(lag.summary)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "04b3d5ab", + "metadata": { + "id": "04b3d5ab" + }, + "outputs": [], + "source": [ + "#Add model residuals to our \"votes\" geopandas dataframe:\n", + "votes['slm_resid']=lag.u\n", + "sns.kdeplot(votes['slm_resid'].values, shade=True, color='slategrey')\n", + "ax[1].set_xlabel('SLM residuals')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8876a6c6", + "metadata": { + "id": "8876a6c6" + }, + "outputs": [], + "source": [ + "# calculate Moran and plot\n", + "moran = Moran(votes['slm_resid'], w=weights)\n", + "plot_moran(moran, zstandard=True, figsize=(10,4))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "702c34c3", + "metadata": { + "id": "702c34c3" + }, + "outputs": [], + "source": [ + "#Plot them in space:\n", + "f, ax1 = plt.subplots(figsize=(10, 6))\n", + "votes.plot('slm_resid', ax=ax1, linewidth=.05, cmap='RdBu',legend = True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "42cd97a7", + "metadata": { + "id": "42cd97a7" + }, + "outputs": [], + "source": [ + "# calculate Moran and plot\n", + "moran = Moran(votes['slm_resid'], w=weights)\n", + "plot_moran(moran, zstandard=True, figsize=(10,4))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0bQtu0pOcZDf", + "metadata": { + "id": "0bQtu0pOcZDf" + }, + "outputs": [], + "source": [ + "moran.p_norm" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "233ea54c", + "metadata": { + "id": "233ea54c" + }, + "outputs": [], + "source": [ + "f,ax = plt.subplots(1,3, figsize=(3*3*1.6, 2))\n", + "\n", + "votes[['INC910213','slm_resid']].plot.scatter('INC910213','slm_resid', ax=ax[0])\n", + "ax[0].set_xlabel('Per capita income')\n", + "ax[0].set_ylabel('spatial lag model residuals')\n", + "\n", + "votes[['ALAND','slm_resid']].plot.scatter('ALAND','slm_resid', ax=ax[1])\n", + "ax[1].set_xlabel('county land area')\n", + "ax[1].set_ylabel('spatial lag model residuals')\n", + "\n", + "votes[['pct_dem_change','slm_resid']].plot.scatter('pct_dem_change','slm_resid', ax=ax[2])\n", + "ax[2].set_xlabel('2012-2016 change in % democratic vote')\n", + "ax[2].set_ylabel('spatial lag model residuals')" + ] + }, + { + "cell_type": "markdown", + "id": "f740100c", + "metadata": { + "id": "f740100c" + }, + "source": [ + "## Task 7: When we account for spatial autocorrelation in our data using the spatial lag model, do we still see a significant relationship between our response and predictor variable?\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "3106dc34", + "metadata": { + "id": "3106dc34" + }, + "source": [ + "## Task 8: Compare these two models. Do you believe that there is a linear relationship between percent change in democratic vote and income level? What about between change in democratic vote and land area? Explain your interpretation using model parameters and outputs.\n", + "\n" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [ + "50d273fd", + "2359e7b5" + ], + "include_colab_link": true, + "provenance": [] + }, + "kernelspec": { + "display_name": "geostats_env", + "language": "python", + "name": "geostats_env" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.20" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/CodeSprints/Python101.ipynb b/CodeSprints/Python101.ipynb index 965b074..dd698e0 100644 --- a/CodeSprints/Python101.ipynb +++ b/CodeSprints/Python101.ipynb @@ -59,15 +59,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "id": "dDbAs3T32YBO" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "My name is Taki. I am 28 years old. \n", + "I am a data scientist! \n", + "It's kind of like being a wizard. \n", + "To see if I can tell you something about your environmental past or future, visit me at Oulu Finland.\n" + ] + } + ], "source": [ - "name = \"Gandolf\" #type your name before the pound sign, make sure you put it in quotes!\n", - "age = \"135\" #type your age before the pound sign, make sure you put it in quotes! More on this later.\n", - "address = \"The Shire\" #type your address before the pound sign, make sure you put it in quotes!\n", + "name = \"Taki\" #type your name before the pound sign, make sure you put it in quotes!\n", + "age = \"28\" #type your age before the pound sign, make sure you put it in quotes! More on this later.\n", + "address = \"Oulu Finland\" #type your address before the pound sign, make sure you put it in quotes!\n", "story = \"My name is \" + name + \". I am \" + age + \" years old. \\nI am a data scientist! \\nIt's kind of like being a wizard. \\nTo see if I can tell you something about your environmental past or future, visit me at \" + address + \".\"\n", "print(story)\n" ] @@ -94,11 +105,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "id": "NuHC8qRs2YBR" }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from IPython.display import Image\n", "Image(\"https://cdn.mindmajix.com/blog/images/Screenshot_12-460x385.png\")" @@ -106,11 +129,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "id": "DQ9O8ML12YBS" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n" + ] + } + ], "source": [ "print(type(name))\n", "print(type(age))\n", @@ -120,11 +154,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "id": "trgnWajj2YBT" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "28.0\n", + "\n" + ] + } + ], "source": [ "#We can convert numeric strings to numeric formats using either float() or int()\n", "age = float(age)\n", @@ -134,11 +177,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "id": "fZbHn4Nq2YBU" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "28\n", + "\n" + ] + } + ], "source": [ "age=int(age)\n", "print(age)\n", @@ -147,11 +199,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "id": "c250Qu6C2YBV" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "My name is Taki. I am age years old. Find me at Oulu Finland.\n" + ] + } + ], "source": [ "#But we cannot input a numeric value into a string:\n", "#Copy and paste this code into the code cell under task 2. Edit it to complete task 2.\n", @@ -176,13 +236,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "id": "q7WIdjut2YBX" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "My name is Taki. I am age years old. Find me at Oulu Finland.\n" + ] + } + ], "source": [ - "#Type story here\n" + "#Type story herestory = \"My name is \" + name + \". I am \" + 'age' + \" years old. Find me at \" + address + \".\"\n", + "print(story)\n" ] }, { @@ -266,19 +335,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "id": "f3dsSKp_2YBZ" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "33.0 396.0 False\n" + ] + } + ], "source": [ "#first, convert \"age\" back to a numeric (float) variable\n", - "age =\n", + "age =float( age)\n", "\n", "#Complete your equations in python syntax below. Remember to use \"age\" as a variable.\n", - "a =\n", - "b =\n", - "c =\n", + "a =age+5\n", + "b =12*a\n", + "c =age>100\n", "print(a,b,c)" ] }, @@ -294,17 +371,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "id": "OG-b15zX2YBa" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#For example, this function takes one input value, X:\n", "def fun1 (x):\n", "\n", "#The functions job is to produce the square of x\n", - " return x*x # note the indentation\n", + " return x*x # note the indentation\n", "\n", "fun1(2)\n", "# what happens if you delete the indentation?" @@ -312,26 +400,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "id": "wn2chIzn2YBa" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "45" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Functions can have more than one input:\n", "def fun2 (x, y):\n", " return x * y\n", "\n", - "fun2(2,3) #experiment with different values" + "fun2(5,9) #experiment with different values" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "id": "4zTWMDes2YBc" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10\n", + "100\n" + ] + } + ], "source": [ "#And we often store what the function returns as a new variable\n", "#Here, we're creating two variables: x and x_sq\n", @@ -359,13 +467,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "id": "TF2eULfe2YBc" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "49.162499999999994" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#write tip15 function here\n" + "#write tip15 function here\n", + "def tip15(x):\n", + " return x*1.15\n", + "\n", + "tip15(42.75)\n" ] }, { @@ -381,13 +504,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "id": "-zSWJ5Ys2YBd" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "53.4375" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#write tip function here\n" + "#write tip function here\n", + "def tip(x):\n", + " return x*0.25+x\n", + "\n", + "tip(42.75)\n", + " \n", + " \n" ] }, { @@ -408,11 +548,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { "id": "a7PgOjXl2YBd" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Homer', 'Marge', 'Bart', 'Maggie', 'Lisa']\n", + "[40, 39, 11, 1, 9]\n" + ] + }, + { + "data": { + "text/plain": [ + "list" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "names = [\"Homer\", \"Marge\", \"Bart\", \"Maggie\", \"Lisa\"]\n", "ages = [40,39,11,1,9]\n", @@ -433,11 +592,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": { "id": "7u9tWMn42YBe" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[['Homer', 'Marge', 'Bart', 'Maggie', 'Lisa'], [40, 39, 11, 1, 9]]\n" + ] + }, + { + "data": { + "text/plain": [ + "list" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "names_and_ages = [names, ages]\n", "print(names_and_ages)\n", @@ -463,11 +640,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": { "id": "UGUn_S642YBe" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "40\n" + ] + } + ], "source": [ "first = ages[0]\n", "print(first)" @@ -484,11 +669,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { "id": "_iwBxGWW2YBe" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "45" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ages[0]+5" ] @@ -505,13 +701,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": { "id": "s8Ds_LIx2YBf" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "c\n", + "55\n" + ] + } + ], "source": [ - "# type answer here\n" + "# type answer here\n", + "name=['a','b','c','d']\n", + "age=[33,44,55,66]\n", + "name[2]\n", + "age[2]\n", + "\n", + "print(name[2])\n", + "print(age[2])\n" ] }, { @@ -526,13 +738,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": { "id": "ZBFhfRLQ2YBf" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['a', 'b', 'c', 'd', 'Grandpa'] [33, 44, 55, 66, 67]\n" + ] + } + ], "source": [ - "# type answer here\n" + "# type answer here\n", + "name=['a','b','c','d']\n", + "age=[33,44,55,66]\n", + "name.append('Grandpa')\n", + "age.append(67)\n", + "print(name,age)" ] }, { @@ -547,13 +772,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { "id": "TTjJPURM2YBg" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n" + ] + } + ], "source": [ - "# type answer here\n" + "# type answer here\n", + "names = [\"Homer\", \"Marge\", \"Bart\", \"Maggie\", \"Lisa\"]\n", + "ages = [40,39,11,1,9]\n", + "New_age=ages[3]+1\n", + "print(New_age)\n" ] }, { @@ -569,13 +806,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": { "id": "0tXSxws12YBg" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[45, 44, 16, 6, 14]\n" + ] + } + ], "source": [ - "ages + 5\n", + "ages = [40,39,11,1,9]\n", + "new_list=[x + 5 for x in ages]\n", + "print(new_list)\n", "#this returns an error. But hey! Errors are opportunities! Google your error message, see if you can find a workaround!" ] }, @@ -591,11 +838,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": { "id": "y7gV1C3e2YBh" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cat 3\n", + "window 6\n", + "defenestrate 12\n" + ] + } + ], "source": [ "#Measure some strings\n", "words = ['cat', 'window', 'defenestrate']\n", @@ -618,11 +875,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": { "id": "vec-_fC62YBi" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5\n", + "range(0, 5)\n" + ] + } + ], "source": [ "print(len(ages))\n", "# What does len() do?\n", @@ -631,11 +897,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": { "id": "hywN1U-L2YBi" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "40\n", + "39\n", + "11\n", + "1\n", + "9\n" + ] + } + ], "source": [ "for y in range(len(ages)):\n", " print(ages[y])" @@ -653,13 +931,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": { "id": "MWA7Vv_D2YBj" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[['Homer', 'Marge', 'Bart', 'Maggie', 'Lisa'], [40, 39, 11, 1, 9]]\n", + "[40, 39, 11, 1, 9]\n", + "[41, 40, 12, 2, 10]\n" + ] + } + ], "source": [ - "# type your for loop here\n" + "# type your for loop here\n", + "print(names_and_ages)\n", + "print(ages)\n", + "new_ages=[]\n", + "for p in range (len(age)):\n", + " new_ages.append(ages[p]+1)\n", + "\n", + "print(new_ages)" ] }, { @@ -674,11 +969,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": { "id": "r0CobJ0h2YBk" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Homer is an adult\n", + "Marge is an adult\n", + "Bart is an adolescent\n", + "Maggie is a child\n", + "Lisa is a child\n" + ] + } + ], "source": [ "for y in range(len(ages)):\n", " if ages[y]>=18:\n", @@ -712,13 +1019,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "metadata": { "id": "QzxVblRl2YBl" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[40, 39, 11, 1, 9]\n", + "41\n", + "40\n", + "12\n", + "1\n", + "10\n" + ] + } + ], "source": [ - "# Write for loop here\n" + "# Write for loop here\n", + "print(ages)\n", + "for p in range (len(ages)):\n", + " if names[p]!='Maggie':\n", + " print (ages[p]+1)\n", + " else:\n", + " print(ages[p])" ] }, { @@ -752,11 +1078,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "metadata": { "id": "xmHK9Gkt2YBm" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([45, 44, 16, 6, 14])" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import numpy as np #import the numpy package, and call it np\n", "ages = np.array(ages)\n", @@ -766,12 +1103,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "metadata": { "id": "I0Lq8lRs2YBm", "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " names ages\n", + "0 Homer 40\n", + "1 Marge 39\n", + "2 Bart 11\n", + "3 Maggie 1\n", + "4 Lisa 9\n" + ] + }, + { + "data": { + "text/plain": [ + "pandas.core.frame.DataFrame" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import pandas as pd #import the pandas pacakge, and call it pd\n", "names_and_ages = pd.DataFrame({'names':names,'ages':ages})\n", @@ -791,11 +1151,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "metadata": { "id": "zPwvkba02YBn" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " names ages ages_5yr\n", + "0 Homer 40 45\n", + "1 Marge 39 44\n", + "2 Bart 11 16\n", + "3 Maggie 1 6\n", + "4 Lisa 9 14\n" + ] + } + ], "source": [ "names_and_ages['ages_5yr']=names_and_ages.ages + 5\n", "print(names_and_ages)" @@ -814,33 +1187,72 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "metadata": { "id": "hkM74eLc2YBn" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 Homer\n", + "1 Marge\n", + "2 Bart\n", + "3 Maggie\n", + "4 Lisa\n", + "Name: names, dtype: object\n" + ] + } + ], "source": [ "print(names_and_ages['names']) #index by column name" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "metadata": { "id": "HQFTGA5G2YBn" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 Homer\n", + "1 Marge\n", + "2 Bart\n", + "3 Maggie\n", + "4 Lisa\n", + "Name: names, dtype: object\n" + ] + } + ], "source": [ "print(names_and_ages.names) #call column name as an object attribute" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 57, "metadata": { "id": "_Br6mw_j2YBn" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 Homer\n", + "1 Marge\n", + "2 Bart\n", + "3 Maggie\n", + "4 Lisa\n", + "Name: names, dtype: object\n" + ] + } + ], "source": [ "print(names_and_ages.iloc[:,0]) #use pandas built-in indexing function" ] @@ -856,11 +1268,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 58, "metadata": { "id": "Yf0Y58if2YBn" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "45\n" + ] + } + ], "source": [ "print(names_and_ages.iloc[0,2])" ] @@ -877,13 +1297,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 64, "metadata": { "id": "N44cck_Z2YBo" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " names ages ages_5yr age_month\n", + "0 Homer 40 45 480\n", + "1 Marge 39 44 468\n", + "2 Bart 11 16 132\n", + "3 Maggie 1 6 12\n", + "4 Lisa 9 14 108\n" + ] + } + ], "source": [ - "#Complete task eleven in the space below\n" + "#Complete task eleven in the space below\n", + "names_and_ages['age_month']=names_and_ages.ages*12\n", + "print(names_and_ages)" ] }, { @@ -898,16 +1333,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 81, "metadata": { "id": "VUp7Fgs_2YBo" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " names ages ages_5yr age_month maturity\n", + "0 Homer 40 45 480 adult\n", + "1 Marge 39 44 468 adult\n", + "2 Bart 11 16 132 adolescent\n", + "3 Maggie 1 6 12 child\n", + "4 Lisa 9 14 108 child\n" + ] + } + ], "source": [ "# Create new column, fill it with empty values (np.NaN)\n", "names_and_ages['maturity']=np.NaN\n", "\n", - "# Write for loop here\n" + "# Write for loop here\n", + "for y in range(len(names_and_ages)):\n", + " current_age = names_and_ages.loc[y, 'ages']\n", + " if current_age >= 18:\n", + " names_and_ages.loc[y, 'maturity'] = \"adult\"\n", + " elif current_age >= 10:\n", + " names_and_ages.loc[y, 'maturity'] = \"adolescent\"\n", + " else:\n", + " names_and_ages.loc[y, 'maturity'] = \"child\"\n", + "\n", + "print(names_and_ages)" ] }, { @@ -924,11 +1382,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 65, "metadata": { "id": "gLYEHJRh2YBp" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['FeatureDependence_example.ipynb',\n", + " 'regression101.ipynb',\n", + " 'RasterData101.ipynb',\n", + " 'Python101.ipynb',\n", + " '.ipynb_checkpoints',\n", + " 'ObservationDependence.ipynb',\n", + " 'VectorData101.ipynb',\n", + " 'FeatureDependence.ipynb',\n", + " 'AutomaticDataDownloads.ipynb',\n", + " 'CoordinateReferenceSystem.ipynb']" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import os #read in the os package, more info here: https://docs.python.org/3/library/os.html\n", "\n", @@ -944,9 +1422,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "IH2021Q_7jun2024.csv Ode_to_git.txt\n" + ] + } + ], "source": [ "!ls ../ClassData" ] @@ -968,11 +1454,512 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 67, "metadata": { - "id": "0NqPMuNC2YBp" + "id": "0NqPMuNC2YBp", + "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1100/81940165.py:1: FutureWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n", + " stream = pd.read_csv('../ClassData/IH2021Q_7jun2024.csv', infer_datetime_format=True)\n" + ] + }, + { + "data": { + "text/html": [ + "
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If you use data presented in this website, we ask you to add a reference to your publication in the following recommended format:Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5
0Stuefer S.L. and Youcha, E.K., [year of retrie...NaNNaNNaNNaNNaN
1https://ine.uaf.edu/werc/imnavaitNaNNaNNaNNaNNaN
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4Imnavait Creek Weir (IH)NaNNaNNaNNaNNaN
52021 Hydrographic DataNaNNaNNaNNaNNaN
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30Note: 6999 denotes missing or invalid data and...NaNNaNNaNNaNNaN
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" + ], + "text/plain": [ + " If you use data presented in this website, we ask you to add a reference to your publication in the following recommended format: \\\n", + "0 Stuefer S.L. and Youcha, E.K., [year of retrie... \n", + "1 https://ine.uaf.edu/werc/imnavait \n", + "2 Last modified 7jun2024 EKY \n", + "3 NaN \n", + "4 Imnavait Creek Weir (IH) \n", + "5 2021 Hydrographic Data \n", + "6 Lat: 68.616821 \n", + "7 Long:-149.317913 \n", + "8 Elev:875 meters \n", + "9 NaN \n", + "10 Col. Data Type Units \n", + "11 1. Date Time (Alaska Standard Time) \n", + "12 2. Discharge (m3/s) \n", + "13 3. Flag1 \n", + "14 4. Flag2 \n", + "15 5. Pressure Transducer Water Temperature (degr... \n", + "16 NaN \n", + "17 NaN \n", + "18 NaN \n", + "19 NaN \n", + "20 NaN \n", + "21 NaN \n", + "22 NaN \n", + "23 NaN \n", + "24 NaN \n", + "25 NaN \n", + "26 NaN \n", + "27 NaN \n", + "28 NaN \n", + "29 NaN \n", + "30 Note: 6999 denotes missing or invalid data and... \n", + "31 ICE indicates creek is affected by anchor, she... \n", + "32 Note: Manual discharge measurements are made d... \n", + "33 Date Time (AST) \n", + "34 5/24/2021 6:00 \n", + "35 5/24/2021 6:15 \n", + "36 5/24/2021 6:30 \n", + "37 5/24/2021 6:45 \n", + "38 5/24/2021 7:00 \n", + "39 5/24/2021 7:15 \n", + "\n", + " Unnamed: 1 Unnamed: 2 Unnamed: 3 Unnamed: 4 Unnamed: 5 \n", + "0 NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN \n", + "5 NaN NaN NaN NaN NaN \n", + "6 NaN NaN NaN NaN NaN \n", + "7 NaN NaN NaN NaN NaN \n", + "8 NaN NaN NaN NaN NaN \n", + "9 NaN NaN NaN NaN NaN \n", + "10 NaN NaN NaN NaN NaN \n", + "11 NaN NaN NaN NaN NaN \n", + "12 NaN NaN NaN NaN NaN \n", + "13 NaN NaN NaN NaN NaN \n", + "14 NaN NaN NaN NaN NaN \n", + "15 NaN NaN NaN NaN NaN \n", + "16 NaN NaN NaN NaN NaN \n", + "17 NaN NaN NaN NaN NaN \n", + "18 NaN NaN NaN NaN NaN \n", + "19 NaN NaN NaN NaN NaN \n", + "20 NaN NaN NaN NaN NaN \n", + "21 NaN NaN NaN NaN NaN \n", + "22 NaN NaN NaN NaN NaN \n", + "23 NaN NaN NaN NaN NaN \n", + "24 NaN NaN NaN NaN NaN \n", + "25 NaN NaN NaN NaN NaN \n", + "26 NaN NaN NaN NaN NaN \n", + "27 NaN NaN NaN NaN NaN \n", + "28 NaN NaN NaN NaN NaN \n", + "29 NaN NaN NaN NaN NaN \n", + "30 NaN NaN NaN NaN NaN \n", + "31 NaN NaN NaN NaN NaN \n", + "32 NaN NaN NaN NaN NaN \n", + "33 Flow_cms Flag1 Flag2 PT_WaterTemp_DegC NaN \n", + "34 6999 ICE NaN 0.692 NaN \n", + "35 6999 ICE NaN 0.188 NaN \n", + "36 6999 ICE NaN 0.188 NaN \n", + "37 6999 ICE NaN 0.188 NaN \n", + "38 6999 ICE NaN 0.188 NaN \n", + "39 6999 ICE NaN 0.167 NaN " + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "stream = pd.read_csv('../ClassData/IH2021Q_7jun2024.csv', infer_datetime_format=True)\n", "\n", @@ -994,9 +1981,496 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 68, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Date Time (AST)Flow_cmsFlag1Flag2PT_WaterTemp_DegCUnnamed: 5
05/24/2021 6:00NaNICENaN0.692NaN
15/24/2021 6:15NaNICENaN0.188NaN
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" + ], + "text/plain": [ + " Date Time (AST) Flow_cms Flag1 Flag2 PT_WaterTemp_DegC Unnamed: 5\n", + "0 5/24/2021 6:00 NaN ICE NaN 0.692 NaN\n", + "1 5/24/2021 6:15 NaN ICE NaN 0.188 NaN\n", + "2 5/24/2021 6:30 NaN ICE NaN 0.188 NaN\n", + "3 5/24/2021 6:45 NaN ICE NaN 0.188 NaN\n", + "4 5/24/2021 7:00 NaN ICE NaN 0.188 NaN" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "stream = pd.read_csv(\"https://arcticdata.io/metacat/d1/mn/v2/object/urn%3Auuid%3A5ac51f92-32c4-4550-a4cb-67ca8971683f\", header=34, na_values=[6999])\n", "\n", @@ -1071,11 +2662,161 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 72, "metadata": { - "id": "aYyEX75Q2YBq" + "id": "aYyEX75Q2YBq", + "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "\u001b[0;31mType:\u001b[0m DataFrame\n", + "\u001b[0;31mString form:\u001b[0m\n", + "Date Time (AST) Flow_cms Flag1 Flag2 PT_WaterTemp_DegC Unnamed: 5\n", + " 0 5/24/2021 6:0 <...> 1 10/1/2021 21:15 NaN ICE NaN 0.121 NaN\n", + " \n", + " [12542 rows x 6 columns]\n", + "\u001b[0;31mLength:\u001b[0m 12542\n", + "\u001b[0;31mFile:\u001b[0m /opt/conda/envs/geostats_env/lib/python3.8/site-packages/pandas/core/frame.py\n", + "\u001b[0;31mDocstring:\u001b[0m \n", + "Two-dimensional, size-mutable, potentially heterogeneous tabular data.\n", + "\n", + "Data structure also contains labeled axes (rows and columns).\n", + "Arithmetic operations align on both row and column labels. Can be\n", + "thought of as a dict-like container for Series objects. The primary\n", + "pandas data structure.\n", + "\n", + "Parameters\n", + "----------\n", + "data : ndarray (structured or homogeneous), Iterable, dict, or DataFrame\n", + " Dict can contain Series, arrays, constants, dataclass or list-like objects. If\n", + " data is a dict, column order follows insertion-order. If a dict contains Series\n", + " which have an index defined, it is aligned by its index. This alignment also\n", + " occurs if data is a Series or a DataFrame itself. Alignment is done on\n", + " Series/DataFrame inputs.\n", + "\n", + " If data is a list of dicts, column order follows insertion-order.\n", + "\n", + "index : Index or array-like\n", + " Index to use for resulting frame. Will default to RangeIndex if\n", + " no indexing information part of input data and no index provided.\n", + "columns : Index or array-like\n", + " Column labels to use for resulting frame when data does not have them,\n", + " defaulting to RangeIndex(0, 1, 2, ..., n). If data contains column labels,\n", + " will perform column selection instead.\n", + "dtype : dtype, default None\n", + " Data type to force. Only a single dtype is allowed. If None, infer.\n", + "copy : bool or None, default None\n", + " Copy data from inputs.\n", + " For dict data, the default of None behaves like ``copy=True``. For DataFrame\n", + " or 2d ndarray input, the default of None behaves like ``copy=False``.\n", + " If data is a dict containing one or more Series (possibly of different dtypes),\n", + " ``copy=False`` will ensure that these inputs are not copied.\n", + "\n", + " .. versionchanged:: 1.3.0\n", + "\n", + "See Also\n", + "--------\n", + "DataFrame.from_records : Constructor from tuples, also record arrays.\n", + "DataFrame.from_dict : From dicts of Series, arrays, or dicts.\n", + "read_csv : Read a comma-separated values (csv) file into DataFrame.\n", + "read_table : Read general delimited file into DataFrame.\n", + "read_clipboard : Read text from clipboard into DataFrame.\n", + "\n", + "Notes\n", + "-----\n", + "Please reference the :ref:`User Guide ` for more information.\n", + "\n", + "Examples\n", + "--------\n", + "Constructing DataFrame from a dictionary.\n", + "\n", + ">>> d = {'col1': [1, 2], 'col2': [3, 4]}\n", + ">>> df = pd.DataFrame(data=d)\n", + ">>> df\n", + " col1 col2\n", + "0 1 3\n", + "1 2 4\n", + "\n", + "Notice that the inferred dtype is int64.\n", + "\n", + ">>> df.dtypes\n", + "col1 int64\n", + "col2 int64\n", + "dtype: object\n", + "\n", + "To enforce a single dtype:\n", + "\n", + ">>> df = pd.DataFrame(data=d, dtype=np.int8)\n", + ">>> df.dtypes\n", + "col1 int8\n", + "col2 int8\n", + "dtype: object\n", + "\n", + "Constructing DataFrame from a dictionary including Series:\n", + "\n", + ">>> d = {'col1': [0, 1, 2, 3], 'col2': pd.Series([2, 3], index=[2, 3])}\n", + ">>> pd.DataFrame(data=d, index=[0, 1, 2, 3])\n", + " col1 col2\n", + "0 0 NaN\n", + "1 1 NaN\n", + "2 2 2.0\n", + "3 3 3.0\n", + "\n", + "Constructing DataFrame from numpy ndarray:\n", + "\n", + ">>> df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),\n", + "... columns=['a', 'b', 'c'])\n", + ">>> df2\n", + " a b c\n", + "0 1 2 3\n", + "1 4 5 6\n", + "2 7 8 9\n", + "\n", + "Constructing DataFrame from a numpy ndarray that has labeled columns:\n", + "\n", + ">>> data = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],\n", + "... dtype=[(\"a\", \"i4\"), (\"b\", \"i4\"), (\"c\", \"i4\")])\n", + ">>> df3 = pd.DataFrame(data, columns=['c', 'a'])\n", + "...\n", + ">>> df3\n", + " c a\n", + "0 3 1\n", + "1 6 4\n", + "2 9 7\n", + "\n", + "Constructing DataFrame from dataclass:\n", + "\n", + ">>> from dataclasses import make_dataclass\n", + ">>> Point = make_dataclass(\"Point\", [(\"x\", int), (\"y\", int)])\n", + ">>> pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)])\n", + " x y\n", + "0 0 0\n", + "1 0 3\n", + "2 2 3\n", + "\n", + "Constructing DataFrame from Series/DataFrame:\n", + "\n", + ">>> ser = pd.Series([1, 2, 3], index=[\"a\", \"b\", \"c\"])\n", + ">>> df = pd.DataFrame(data=ser, index=[\"a\", \"c\"])\n", + ">>> df\n", + " 0\n", + "a 1\n", + "c 3\n", + "\n", + ">>> df1 = pd.DataFrame([1, 2, 3], index=[\"a\", \"b\", \"c\"], columns=[\"x\"])\n", + ">>> df2 = pd.DataFrame(data=df1, index=[\"a\", \"c\"])\n", + ">>> df2\n", + " x\n", + "a 1\n", + "c 3" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "#We can ask a question:\n", "?stream" @@ -1083,11 +2824,11137 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 73, "metadata": { - "id": "V_rQz_6R2YBq" + "id": "V_rQz_6R2YBq", + "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "\u001b[0;31mType:\u001b[0m DataFrame\n", + "\u001b[0;31mString form:\u001b[0m\n", + " Date Time (AST) Flow_cms Flag1 Flag2 PT_WaterTemp_DegC Unnamed: 5\n", + "0 5/24/2021 6:00 NaN ICE NaN 0.692 NaN\n", + "1 5/24/2021 6:15 NaN ICE NaN 0.188 NaN\n", + "2 5/24/2021 6:30 NaN ICE NaN 0.188 NaN\n", + "3 5/24/2021 6:45 NaN ICE NaN 0.188 NaN\n", + "4 5/24/2021 7:00 NaN ICE NaN 0.188 NaN\n", + "... ... ... ... ... ... ...\n", + "12537 10/1/2021 20:15 NaN ICE NaN 0.125 NaN\n", + "12538 10/1/2021 20:30 NaN ICE NaN 0.121 NaN\n", + "12539 10/1/2021 20:45 NaN ICE NaN 0.113 NaN\n", + "12540 10/1/2021 21:00 NaN ICE NaN 0.121 NaN\n", + "12541 10/1/2021 21:15 NaN ICE NaN 0.121 NaN\n", + "\n", + "[12542 rows x 6 columns]\n", + "\u001b[0;31mLength:\u001b[0m 12542\n", + "\u001b[0;31mFile:\u001b[0m /opt/conda/envs/geostats_env/lib/python3.8/site-packages/pandas/core/frame.py\n", + "\u001b[0;31mSource:\u001b[0m \n", + "\u001b[0;32mclass\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mNDFrame\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOpsMixin\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Two-dimensional, size-mutable, potentially heterogeneous tabular data.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Data structure also contains labeled axes (rows and columns).\u001b[0m\n", + "\u001b[0;34m Arithmetic operations align on both row and column labels. Can be\u001b[0m\n", + "\u001b[0;34m thought of as a dict-like container for Series objects. The primary\u001b[0m\n", + "\u001b[0;34m pandas data structure.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m data : ndarray (structured or homogeneous), Iterable, dict, or DataFrame\u001b[0m\n", + "\u001b[0;34m Dict can contain Series, arrays, constants, dataclass or list-like objects. If\u001b[0m\n", + "\u001b[0;34m data is a dict, column order follows insertion-order. If a dict contains Series\u001b[0m\n", + "\u001b[0;34m which have an index defined, it is aligned by its index. This alignment also\u001b[0m\n", + "\u001b[0;34m occurs if data is a Series or a DataFrame itself. Alignment is done on\u001b[0m\n", + "\u001b[0;34m Series/DataFrame inputs.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m If data is a list of dicts, column order follows insertion-order.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m index : Index or array-like\u001b[0m\n", + "\u001b[0;34m Index to use for resulting frame. Will default to RangeIndex if\u001b[0m\n", + "\u001b[0;34m no indexing information part of input data and no index provided.\u001b[0m\n", + "\u001b[0;34m columns : Index or array-like\u001b[0m\n", + "\u001b[0;34m Column labels to use for resulting frame when data does not have them,\u001b[0m\n", + "\u001b[0;34m defaulting to RangeIndex(0, 1, 2, ..., n). If data contains column labels,\u001b[0m\n", + "\u001b[0;34m will perform column selection instead.\u001b[0m\n", + "\u001b[0;34m dtype : dtype, default None\u001b[0m\n", + "\u001b[0;34m Data type to force. Only a single dtype is allowed. If None, infer.\u001b[0m\n", + "\u001b[0;34m copy : bool or None, default None\u001b[0m\n", + "\u001b[0;34m Copy data from inputs.\u001b[0m\n", + "\u001b[0;34m For dict data, the default of None behaves like ``copy=True``. For DataFrame\u001b[0m\n", + "\u001b[0;34m or 2d ndarray input, the default of None behaves like ``copy=False``.\u001b[0m\n", + "\u001b[0;34m If data is a dict containing one or more Series (possibly of different dtypes),\u001b[0m\n", + "\u001b[0;34m ``copy=False`` will ensure that these inputs are not copied.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionchanged:: 1.3.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.from_records : Constructor from tuples, also record arrays.\u001b[0m\n", + "\u001b[0;34m DataFrame.from_dict : From dicts of Series, arrays, or dicts.\u001b[0m\n", + "\u001b[0;34m read_csv : Read a comma-separated values (csv) file into DataFrame.\u001b[0m\n", + "\u001b[0;34m read_table : Read general delimited file into DataFrame.\u001b[0m\n", + "\u001b[0;34m read_clipboard : Read text from clipboard into DataFrame.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Notes\u001b[0m\n", + "\u001b[0;34m -----\u001b[0m\n", + "\u001b[0;34m Please reference the :ref:`User Guide ` for more information.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m Constructing DataFrame from a dictionary.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> d = {'col1': [1, 2], 'col2': [3, 4]}\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame(data=d)\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m col1 col2\u001b[0m\n", + "\u001b[0;34m 0 1 3\u001b[0m\n", + "\u001b[0;34m 1 2 4\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Notice that the inferred dtype is int64.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.dtypes\u001b[0m\n", + "\u001b[0;34m col1 int64\u001b[0m\n", + "\u001b[0;34m col2 int64\u001b[0m\n", + "\u001b[0;34m dtype: object\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m To enforce a single dtype:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame(data=d, dtype=np.int8)\u001b[0m\n", + "\u001b[0;34m >>> df.dtypes\u001b[0m\n", + "\u001b[0;34m col1 int8\u001b[0m\n", + "\u001b[0;34m col2 int8\u001b[0m\n", + "\u001b[0;34m dtype: object\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Constructing DataFrame from a dictionary including Series:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> d = {'col1': [0, 1, 2, 3], 'col2': pd.Series([2, 3], index=[2, 3])}\u001b[0m\n", + "\u001b[0;34m >>> pd.DataFrame(data=d, index=[0, 1, 2, 3])\u001b[0m\n", + "\u001b[0;34m col1 col2\u001b[0m\n", + "\u001b[0;34m 0 0 NaN\u001b[0m\n", + "\u001b[0;34m 1 1 NaN\u001b[0m\n", + "\u001b[0;34m 2 2 2.0\u001b[0m\n", + "\u001b[0;34m 3 3 3.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Constructing DataFrame from numpy ndarray:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),\u001b[0m\n", + "\u001b[0;34m ... columns=['a', 'b', 'c'])\u001b[0m\n", + "\u001b[0;34m >>> df2\u001b[0m\n", + "\u001b[0;34m a b c\u001b[0m\n", + "\u001b[0;34m 0 1 2 3\u001b[0m\n", + "\u001b[0;34m 1 4 5 6\u001b[0m\n", + "\u001b[0;34m 2 7 8 9\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Constructing DataFrame from a numpy ndarray that has labeled columns:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> data = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],\u001b[0m\n", + "\u001b[0;34m ... dtype=[(\"a\", \"i4\"), (\"b\", \"i4\"), (\"c\", \"i4\")])\u001b[0m\n", + "\u001b[0;34m >>> df3 = pd.DataFrame(data, columns=['c', 'a'])\u001b[0m\n", + "\u001b[0;34m ...\u001b[0m\n", + "\u001b[0;34m >>> df3\u001b[0m\n", + "\u001b[0;34m c a\u001b[0m\n", + "\u001b[0;34m 0 3 1\u001b[0m\n", + "\u001b[0;34m 1 6 4\u001b[0m\n", + "\u001b[0;34m 2 9 7\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Constructing DataFrame from dataclass:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> from dataclasses import make_dataclass\u001b[0m\n", + "\u001b[0;34m >>> Point = make_dataclass(\"Point\", [(\"x\", int), (\"y\", int)])\u001b[0m\n", + "\u001b[0;34m >>> pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)])\u001b[0m\n", + "\u001b[0;34m x y\u001b[0m\n", + "\u001b[0;34m 0 0 0\u001b[0m\n", + "\u001b[0;34m 1 0 3\u001b[0m\n", + "\u001b[0;34m 2 2 3\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Constructing DataFrame from Series/DataFrame:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> ser = pd.Series([1, 2, 3], index=[\"a\", \"b\", \"c\"])\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame(data=ser, index=[\"a\", \"c\"])\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m 0\u001b[0m\n", + "\u001b[0;34m a 1\u001b[0m\n", + "\u001b[0;34m c 3\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df1 = pd.DataFrame([1, 2, 3], index=[\"a\", \"b\", \"c\"], columns=[\"x\"])\u001b[0m\n", + "\u001b[0;34m >>> df2 = pd.DataFrame(data=df1, index=[\"a\", \"c\"])\u001b[0m\n", + "\u001b[0;34m >>> df2\u001b[0m\n", + "\u001b[0;34m x\u001b[0m\n", + "\u001b[0;34m a 1\u001b[0m\n", + "\u001b[0;34m c 3\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0m_internal_names_set\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"columns\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"index\"\u001b[0m\u001b[0;34m}\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mNDFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_internal_names_set\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0m_typ\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"dataframe\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0m_HANDLED_TYPES\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mExtensionArray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0m_accessors\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"sparse\"\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0m_hidden_attrs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfrozenset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNDFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hidden_attrs\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mfrozenset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0m_mgr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mBlockManager\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mArrayManager\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mCallable\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0m_constructor_sliced\u001b[0m\u001b[0;34m:\u001b[0m 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\u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAxes\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mDtype\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# if not copying data, ensure to still return a shallow copy\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# to avoid the result sharing the same Manager\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mBlockManager\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mArrayManager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0musing_copy_on_write\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# first check if a Manager is passed without any other arguments\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# -> use fastpath (without checking Manager type)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# GH#33357 fastpath\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mNDFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmanager\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_option\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"mode.data_manager\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# GH47215\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"index cannot be a set\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"columns cannot be a set\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# retain pre-GH#38939 default behavior\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmanager\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"array\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mExtensionArray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# INFO(ArrayManager) by default copy the 2D input array to get\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# contiguous 1D arrays\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0musing_copy_on_write\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mdefault_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mdefault_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mpandas_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mBlockManager\u001b[0m\u001b[0;34m,\u001b[0m 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"\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# GH#38939 de facto copy defaults to False only in non-dict cases\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict_to_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtyp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmanager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMaskedArray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmrecords\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# masked recarray\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmrecords\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMaskedRecords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"MaskedRecords are not supported. Pass \"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"{name: data[name] for name in data.dtype.names} \"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"instead\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# a masked array\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msanitize_masked_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mndarray_to_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mtyp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmanager\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mExtensionArray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# i.e. numpy structured array\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrec_array_to_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m 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into list)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mabc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSequence\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"__array__\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# GH#44616 big perf improvement for e.g. pytorch tensor\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_dataclass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\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:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataclasses_to_dicts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtreat_as_nested\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# exclude ndarray as we may have cast it a few lines above\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnested_data_to_arrays\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# error: Argument 3 to \"nested_data_to_arrays\" has incompatible\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# type \"Optional[Collection[Any]]\"; expected \"Optional[Index]\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# type: ignore[arg-type]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marrays_to_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mtyp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmanager\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m 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"\u001b[0;34m\u001b[0m \u001b[0mtyp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmanager\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# For data is scalar\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"DataFrame constructor not properly called!\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex\u001b[0m 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extension dtype\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mExtensionDtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# TODO(EA2D): special case not needed with 2D EAs\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mconstruct_1d_arraylike_from_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m 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"\u001b[0;34m\u001b[0m \u001b[0marr2d\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marr2d\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mtyp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmanager\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# ensure correct Manager type according to settings\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmgr_to_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmgr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtyp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmanager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mNDFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmgr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# ----------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__dataframe__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnan_as_null\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_copy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrameXchg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Return the dataframe interchange object implementing the interchange protocol.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m nan_as_null : bool, default False\u001b[0m\n", + "\u001b[0;34m Whether to tell the DataFrame to overwrite null values in the data\u001b[0m\n", + "\u001b[0;34m with ``NaN`` (or ``NaT``).\u001b[0m\n", + "\u001b[0;34m allow_copy : bool, default True\u001b[0m\n", + "\u001b[0;34m Whether to allow memory copying when exporting. If set to False\u001b[0m\n", + "\u001b[0;34m it would cause non-zero-copy exports to fail.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame interchange object\u001b[0m\n", + "\u001b[0;34m The object which consuming library can use to ingress the dataframe.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Notes\u001b[0m\n", + "\u001b[0;34m -----\u001b[0m\n", + "\u001b[0;34m Details on the interchange protocol:\u001b[0m\n", + "\u001b[0;34m https://data-apis.org/dataframe-protocol/latest/index.html\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m `nan_as_null` currently has no effect; once support for nullable extension\u001b[0m\n", + "\u001b[0;34m dtypes is added, this value should be propagated to columns.\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterchange\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataframe\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPandasDataFrameXchg\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mPandasDataFrameXchg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnan_as_null\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_copy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# ----------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mIndex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Return a list representing the axes of the DataFrame.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m It has the row axis labels and column axis labels as the only members.\u001b[0m\n", + "\u001b[0;34m They are returned in that order.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})\u001b[0m\n", + "\u001b[0;34m >>> df.axes\u001b[0m\n", + "\u001b[0;34m [RangeIndex(start=0, stop=2, step=1), Index(['col1', 'col2'],\u001b[0m\n", + "\u001b[0;34m dtype='object')]\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Return a tuple representing the dimensionality of the DataFrame.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m ndarray.shape : Tuple of array dimensions.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})\u001b[0m\n", + "\u001b[0;34m >>> df.shape\u001b[0m\n", + "\u001b[0;34m (2, 2)\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4],\u001b[0m\n", + "\u001b[0;34m ... 'col3': [5, 6]})\u001b[0m\n", + "\u001b[0;34m >>> df.shape\u001b[0m\n", + "\u001b[0;34m (2, 3)\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_is_homogeneous_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Whether all the columns in a DataFrame have the same type.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m bool\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m Index._is_homogeneous_type : Whether the object has a single\u001b[0m\n", + "\u001b[0;34m dtype.\u001b[0m\n", + "\u001b[0;34m MultiIndex._is_homogeneous_type : Whether all the levels of a\u001b[0m\n", + "\u001b[0;34m MultiIndex have the same dtype.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> DataFrame({\"A\": [1, 2], \"B\": [3, 4]})._is_homogeneous_type\u001b[0m\n", + "\u001b[0;34m True\u001b[0m\n", + "\u001b[0;34m >>> DataFrame({\"A\": [1, 2], \"B\": [3.0, 4.0]})._is_homogeneous_type\u001b[0m\n", + "\u001b[0;34m False\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Items with the same type but different sizes are considered\u001b[0m\n", + "\u001b[0;34m different types.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> DataFrame({\u001b[0m\n", + "\u001b[0;34m ... \"A\": np.array([1, 2], dtype=np.int32),\u001b[0m\n", + "\u001b[0;34m ... \"B\": np.array([1, 2], dtype=np.int64)})._is_homogeneous_type\u001b[0m\n", + "\u001b[0;34m False\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mArrayManager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many_extension_types\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mblock\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_mixed_type\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_can_fast_transpose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Can we transpose this DataFrame without creating any new array objects.\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mArrayManager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mblocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblocks\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\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mblocks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# TODO(EA2D) special case would be unnecessary with 2D EAs\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_1d_only_ea_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mDatetimeArray\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mTimedeltaArray\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mPeriodArray\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Analogue to ._values that may return a 2D ExtensionArray.\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmgr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mArrayManager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_1d_only_ea_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# error: Item \"ExtensionArray\" of \"Union[ndarray, ExtensionArray]\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# has no attribute \"reshape\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mmgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[union-attr]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mensure_wrapped_if_datetimelike\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mblocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblocks\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\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mensure_wrapped_if_datetimelike\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mblocks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# non-2D ExtensionArray\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# more generally, whatever we allow in NDArrayBackedExtensionBlock\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"np.ndarray | DatetimeArray | TimedeltaArray | PeriodArray\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# ----------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Rendering Methods\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_repr_fits_vertical_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Check length against max_rows.\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmax_rows\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_option\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"display.max_rows\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mmax_rows\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_repr_fits_horizontal_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_width\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Check if full repr fits in horizontal boundaries imposed by the display\u001b[0m\n", + "\u001b[0;34m options width and max_columns.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m In case of non-interactive session, no boundaries apply.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m `ignore_width` is here so ipynb+HTML output can behave the way\u001b[0m\n", + "\u001b[0;34m users expect. display.max_columns remains in effect.\u001b[0m\n", + "\u001b[0;34m GH3541, GH3573\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconsole\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_console_size\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmax_columns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_option\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"display.max_columns\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnb_columns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# exceed max columns\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmax_columns\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mnb_columns\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mmax_columns\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mnot\u001b[0m \u001b[0mignore_width\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mwidth\u001b[0m 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"\u001b[0;34m\u001b[0m \u001b[0;31m# only care about the stuff we'll actually print out\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# and to_string on entire frame may be expensive\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmax_rows\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# unlimited rows\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# min of two, where one may be None\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m 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should show the info view.\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0minfo_repr_option\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_option\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"display.large_repr\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"info\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0minfo_repr_option\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_repr_fits_horizontal_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_repr_fits_vertical_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Return a string representation for a particular DataFrame.\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mbuf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStringIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m 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{val}
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By default, no limit.\u001b[0m\n", + "\u001b[0;34m encoding : str, default \"utf-8\"\u001b[0m\n", + "\u001b[0;34m Set character encoding.\u001b[0m\n", + "\u001b[0;34m %(returns)s\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m to_html : Convert DataFrame to HTML.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> d = {'col1': [1, 2, 3], 'col2': [4, 5, 6]}\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame(d)\u001b[0m\n", + "\u001b[0;34m >>> print(df.to_string())\u001b[0m\n", + "\u001b[0;34m col1 col2\u001b[0m\n", + "\u001b[0;34m 0 1 4\u001b[0m\n", + "\u001b[0;34m 1 2 5\u001b[0m\n", + "\u001b[0;34m 2 3 6\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0moption_context\u001b[0m\u001b[0;34m\u001b[0m\n", + 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+ "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# ----------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mstyle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mStyler\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Returns a Styler object.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Contains methods for building a styled HTML representation of the DataFrame.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m io.formats.style.Styler : Helps style a DataFrame or Series according to the\u001b[0m\n", + "\u001b[0;34m data with HTML and CSS.\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstyle\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mStyler\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mStyler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0m_shared_docs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"items\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mr\"\"\"\u001b[0m\n", + "\u001b[0;34m Iterate over (column name, Series) pairs.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Iterates over the DataFrame columns, returning a tuple with\u001b[0m\n", + "\u001b[0;34m the column name and the content as a Series.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Yields\u001b[0m\n", + "\u001b[0;34m ------\u001b[0m\n", + "\u001b[0;34m label : object\u001b[0m\n", + "\u001b[0;34m The column names for the DataFrame being iterated over.\u001b[0m\n", + "\u001b[0;34m content : Series\u001b[0m\n", + "\u001b[0;34m The column entries belonging to each label, as a Series.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.iterrows : Iterate over DataFrame rows as\u001b[0m\n", + "\u001b[0;34m (index, Series) pairs.\u001b[0m\n", + "\u001b[0;34m DataFrame.itertuples : Iterate over DataFrame rows as namedtuples\u001b[0m\n", + "\u001b[0;34m of the values.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'species': ['bear', 'bear', 'marsupial'],\u001b[0m\n", + "\u001b[0;34m ... 'population': [1864, 22000, 80000]},\u001b[0m\n", + "\u001b[0;34m ... index=['panda', 'polar', 'koala'])\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m species population\u001b[0m\n", + "\u001b[0;34m panda bear 1864\u001b[0m\n", + "\u001b[0;34m polar bear 22000\u001b[0m\n", + "\u001b[0;34m koala marsupial 80000\u001b[0m\n", + "\u001b[0;34m >>> for label, content in df.items():\u001b[0m\n", + "\u001b[0;34m ... print(f'label: {label}')\u001b[0m\n", + "\u001b[0;34m ... print(f'content: {content}', sep='\\n')\u001b[0m\n", + "\u001b[0;34m ...\u001b[0m\n", + "\u001b[0;34m label: species\u001b[0m\n", + "\u001b[0;34m content:\u001b[0m\n", + "\u001b[0;34m panda bear\u001b[0m\n", + "\u001b[0;34m polar bear\u001b[0m\n", + "\u001b[0;34m koala marsupial\u001b[0m\n", + "\u001b[0;34m Name: species, dtype: object\u001b[0m\n", + "\u001b[0;34m label: population\u001b[0m\n", + "\u001b[0;34m content:\u001b[0m\n", + "\u001b[0;34m panda 1864\u001b[0m\n", + "\u001b[0;34m polar 22000\u001b[0m\n", + "\u001b[0;34m koala 80000\u001b[0m\n", + "\u001b[0;34m Name: population, dtype: int64\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mAppender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_shared_docs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"items\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mIterable\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mHashable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"_item_cache\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ixs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0miterrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mIterable\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mHashable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Iterate over DataFrame rows as (index, Series) pairs.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Yields\u001b[0m\n", + "\u001b[0;34m ------\u001b[0m\n", + "\u001b[0;34m index : label or tuple of label\u001b[0m\n", + "\u001b[0;34m The index of the row. A tuple for a `MultiIndex`.\u001b[0m\n", + "\u001b[0;34m data : Series\u001b[0m\n", + "\u001b[0;34m The data of the row as a Series.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.itertuples : Iterate over DataFrame rows as namedtuples of the values.\u001b[0m\n", + "\u001b[0;34m DataFrame.items : Iterate over (column name, Series) pairs.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Notes\u001b[0m\n", + "\u001b[0;34m -----\u001b[0m\n", + "\u001b[0;34m 1. Because ``iterrows`` returns a Series for each row,\u001b[0m\n", + "\u001b[0;34m it does **not** preserve dtypes across the rows (dtypes are\u001b[0m\n", + "\u001b[0;34m preserved across columns for DataFrames). For example,\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame([[1, 1.5]], columns=['int', 'float'])\u001b[0m\n", + "\u001b[0;34m >>> row = next(df.iterrows())[1]\u001b[0m\n", + "\u001b[0;34m >>> row\u001b[0m\n", + "\u001b[0;34m int 1.0\u001b[0m\n", + "\u001b[0;34m float 1.5\u001b[0m\n", + "\u001b[0;34m Name: 0, dtype: float64\u001b[0m\n", + "\u001b[0;34m >>> print(row['int'].dtype)\u001b[0m\n", + "\u001b[0;34m float64\u001b[0m\n", + "\u001b[0;34m >>> print(df['int'].dtype)\u001b[0m\n", + "\u001b[0;34m int64\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m To preserve dtypes while iterating over the rows, it is better\u001b[0m\n", + "\u001b[0;34m to use :meth:`itertuples` which returns namedtuples of the values\u001b[0m\n", + "\u001b[0;34m and which is generally faster than ``iterrows``.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m 2. You should **never modify** something you are iterating over.\u001b[0m\n", + "\u001b[0;34m This is not guaranteed to work in all cases. Depending on the\u001b[0m\n", + "\u001b[0;34m data types, the iterator returns a copy and not a view, and writing\u001b[0m\n", + "\u001b[0;34m to it will have no effect.\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mklass\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor_sliced\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0musing_cow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0musing_copy_on_write\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0musing_cow\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_single_block\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_references\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[arg-type]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mitertuples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Pandas\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mIterable\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Iterate over DataFrame rows as namedtuples.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m index : bool, default True\u001b[0m\n", + "\u001b[0;34m If True, return the index as the first element of the tuple.\u001b[0m\n", + "\u001b[0;34m name : str or None, default \"Pandas\"\u001b[0m\n", + "\u001b[0;34m The name of the returned namedtuples or None to return regular\u001b[0m\n", + "\u001b[0;34m tuples.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m iterator\u001b[0m\n", + "\u001b[0;34m An object to iterate over namedtuples for each row in the\u001b[0m\n", + "\u001b[0;34m DataFrame with the first field possibly being the index and\u001b[0m\n", + "\u001b[0;34m following fields being the column values.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.iterrows : Iterate over DataFrame rows as (index, Series)\u001b[0m\n", + "\u001b[0;34m pairs.\u001b[0m\n", + "\u001b[0;34m DataFrame.items : Iterate over (column name, Series) pairs.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Notes\u001b[0m\n", + "\u001b[0;34m -----\u001b[0m\n", + "\u001b[0;34m The column names will be renamed to positional names if they are\u001b[0m\n", + "\u001b[0;34m invalid Python identifiers, repeated, or start with an underscore.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'num_legs': [4, 2], 'num_wings': [0, 2]},\u001b[0m\n", + "\u001b[0;34m ... index=['dog', 'hawk'])\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m num_legs num_wings\u001b[0m\n", + "\u001b[0;34m dog 4 0\u001b[0m\n", + "\u001b[0;34m hawk 2 2\u001b[0m\n", + "\u001b[0;34m >>> for row in df.itertuples():\u001b[0m\n", + "\u001b[0;34m ... print(row)\u001b[0m\n", + "\u001b[0;34m ...\u001b[0m\n", + "\u001b[0;34m Pandas(Index='dog', num_legs=4, num_wings=0)\u001b[0m\n", + "\u001b[0;34m Pandas(Index='hawk', num_legs=2, num_wings=2)\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m By setting the `index` parameter to False we can remove the index\u001b[0m\n", + "\u001b[0;34m as the first element of the tuple:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> for row in df.itertuples(index=False):\u001b[0m\n", + "\u001b[0;34m ... print(row)\u001b[0m\n", + "\u001b[0;34m ...\u001b[0m\n", + "\u001b[0;34m Pandas(num_legs=4, num_wings=0)\u001b[0m\n", + "\u001b[0;34m Pandas(num_legs=2, num_wings=2)\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m With the `name` parameter set we set a custom name for the yielded\u001b[0m\n", + "\u001b[0;34m namedtuples:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> for row in df.itertuples(name='Animal'):\u001b[0m\n", + "\u001b[0;34m ... print(row)\u001b[0m\n", + "\u001b[0;34m ...\u001b[0m\n", + "\u001b[0;34m Animal(Index='dog', num_legs=4, num_wings=0)\u001b[0m\n", + "\u001b[0;34m Animal(Index='hawk', num_legs=2, num_wings=2)\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0marrays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mfields\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Index\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# use integer indexing because of possible duplicate column names\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# https://github.com/python/mypy/issues/9046\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# error: namedtuple() expects a string literal as the first argument\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mitertuple\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcollections\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnamedtuple\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitertuple\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# fallback to regular tuples\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Returns length of info axis, but here we use the index.\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0moverload\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0moverload\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mDataFrame\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mIndex\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mArrayLike\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAnyArrayLike\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Compute the matrix multiplication between the DataFrame and other.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m This method computes the matrix product between the DataFrame and the\u001b[0m\n", + "\u001b[0;34m values of an other Series, DataFrame or a numpy array.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m It can also be called using ``self @ other`` in Python >= 3.5.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m other : Series, DataFrame or array-like\u001b[0m\n", + "\u001b[0;34m The other object to compute the matrix product with.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m Series or DataFrame\u001b[0m\n", + "\u001b[0;34m If other is a Series, return the matrix product between self and\u001b[0m\n", + "\u001b[0;34m other as a Series. If other is a DataFrame or a numpy.array, return\u001b[0m\n", + "\u001b[0;34m the matrix product of self and other in a DataFrame of a np.array.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m Series.dot: Similar method for Series.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Notes\u001b[0m\n", + "\u001b[0;34m -----\u001b[0m\n", + "\u001b[0;34m The dimensions of DataFrame and other must be compatible in order to\u001b[0m\n", + "\u001b[0;34m compute the matrix multiplication. In addition, the column names of\u001b[0m\n", + "\u001b[0;34m DataFrame and the index of other must contain the same values, as they\u001b[0m\n", + "\u001b[0;34m will be aligned prior to the multiplication.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m The dot method for Series computes the inner product, instead of the\u001b[0m\n", + "\u001b[0;34m matrix product here.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m Here we multiply a DataFrame with a Series.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])\u001b[0m\n", + "\u001b[0;34m >>> s = pd.Series([1, 1, 2, 1])\u001b[0m\n", + "\u001b[0;34m >>> df.dot(s)\u001b[0m\n", + "\u001b[0;34m 0 -4\u001b[0m\n", + "\u001b[0;34m 1 5\u001b[0m\n", + "\u001b[0;34m dtype: int64\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Here we multiply a DataFrame with another DataFrame.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> other = pd.DataFrame([[0, 1], [1, 2], [-1, -1], [2, 0]])\u001b[0m\n", + "\u001b[0;34m >>> df.dot(other)\u001b[0m\n", + "\u001b[0;34m 0 1\u001b[0m\n", + "\u001b[0;34m 0 1 4\u001b[0m\n", + "\u001b[0;34m 1 2 2\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Note that the dot method give the same result as @\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df @ other\u001b[0m\n", + "\u001b[0;34m 0 1\u001b[0m\n", + "\u001b[0;34m 0 1 4\u001b[0m\n", + "\u001b[0;34m 1 2 2\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m The dot method works also if other is an np.array.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> arr = np.array([[0, 1], [1, 2], [-1, -1], [2, 0]])\u001b[0m\n", + "\u001b[0;34m >>> df.dot(arr)\u001b[0m\n", + "\u001b[0;34m 0 1\u001b[0m\n", + "\u001b[0;34m 0 1 4\u001b[0m\n", + "\u001b[0;34m 1 2 2\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Note how shuffling of the objects does not change the result.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> s2 = s.reindex([1, 0, 2, 3])\u001b[0m\n", + "\u001b[0;34m >>> df.dot(s2)\u001b[0m\n", + "\u001b[0;34m 0 -4\u001b[0m\n", + "\u001b[0;34m 1 5\u001b[0m\n", + "\u001b[0;34m dtype: int64\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcommon\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcommon\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcommon\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"matrices are not 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"\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor_sliced\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlvals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrvals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m 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\u001b[0;34m@\u001b[0m\u001b[0moverload\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__matmul__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAnyArrayLike\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__matmul__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAnyArrayLike\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m 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Python>=3.5.\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m\"shape mismatch\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# GH#21581 give exception message for original shapes\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"shapes {np.shape(other)} and {self.shape} not aligned\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# ----------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# IO methods (to / from other formats)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfrom_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0morient\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"columns\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mDtype\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAxes\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Construct DataFrame from dict of array-like or dicts.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Creates DataFrame object from dictionary by columns or by index\u001b[0m\n", + "\u001b[0;34m allowing dtype specification.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m data : dict\u001b[0m\n", + "\u001b[0;34m Of the form {field : array-like} or {field : dict}.\u001b[0m\n", + "\u001b[0;34m orient : {'columns', 'index', 'tight'}, default 'columns'\u001b[0m\n", + "\u001b[0;34m The \"orientation\" of the data. If the keys of the passed dict\u001b[0m\n", + "\u001b[0;34m should be the columns of the resulting DataFrame, pass 'columns'\u001b[0m\n", + "\u001b[0;34m (default). Otherwise if the keys should be rows, pass 'index'.\u001b[0m\n", + "\u001b[0;34m If 'tight', assume a dict with keys ['index', 'columns', 'data',\u001b[0m\n", + "\u001b[0;34m 'index_names', 'column_names'].\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionadded:: 1.4.0\u001b[0m\n", + "\u001b[0;34m 'tight' as an allowed value for the ``orient`` argument\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m dtype : dtype, default None\u001b[0m\n", + "\u001b[0;34m Data type to force after DataFrame construction, otherwise infer.\u001b[0m\n", + "\u001b[0;34m columns : list, default None\u001b[0m\n", + "\u001b[0;34m Column labels to use when ``orient='index'``. Raises a ValueError\u001b[0m\n", + "\u001b[0;34m if used with ``orient='columns'`` or ``orient='tight'``.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.from_records : DataFrame from structured ndarray, sequence\u001b[0m\n", + "\u001b[0;34m of tuples or dicts, or DataFrame.\u001b[0m\n", + "\u001b[0;34m DataFrame : DataFrame object creation using constructor.\u001b[0m\n", + "\u001b[0;34m DataFrame.to_dict : Convert the DataFrame to a dictionary.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m By default the keys of the dict become the DataFrame columns:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> data = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}\u001b[0m\n", + "\u001b[0;34m >>> pd.DataFrame.from_dict(data)\u001b[0m\n", + "\u001b[0;34m col_1 col_2\u001b[0m\n", + "\u001b[0;34m 0 3 a\u001b[0m\n", + "\u001b[0;34m 1 2 b\u001b[0m\n", + "\u001b[0;34m 2 1 c\u001b[0m\n", + "\u001b[0;34m 3 0 d\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Specify ``orient='index'`` to create the DataFrame using dictionary\u001b[0m\n", + "\u001b[0;34m keys as rows:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> data = {'row_1': [3, 2, 1, 0], 'row_2': ['a', 'b', 'c', 'd']}\u001b[0m\n", + "\u001b[0;34m >>> pd.DataFrame.from_dict(data, orient='index')\u001b[0m\n", + "\u001b[0;34m 0 1 2 3\u001b[0m\n", + "\u001b[0;34m row_1 3 2 1 0\u001b[0m\n", + "\u001b[0;34m row_2 a b c d\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m When using the 'index' orientation, the column names can be\u001b[0m\n", + "\u001b[0;34m specified manually:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> pd.DataFrame.from_dict(data, orient='index',\u001b[0m\n", + "\u001b[0;34m ... columns=['A', 'B', 'C', 'D'])\u001b[0m\n", + "\u001b[0;34m A B C D\u001b[0m\n", + "\u001b[0;34m row_1 3 2 1 0\u001b[0m\n", + "\u001b[0;34m row_2 a b c d\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Specify ``orient='tight'`` to create the DataFrame using a 'tight'\u001b[0m\n", + "\u001b[0;34m format:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> data = {'index': [('a', 'b'), ('a', 'c')],\u001b[0m\n", + "\u001b[0;34m ... 'columns': [('x', 1), ('y', 2)],\u001b[0m\n", + "\u001b[0;34m ... 'data': [[1, 3], [2, 4]],\u001b[0m\n", + "\u001b[0;34m ... 'index_names': ['n1', 'n2'],\u001b[0m\n", + "\u001b[0;34m ... 'column_names': ['z1', 'z2']}\u001b[0m\n", + "\u001b[0;34m >>> pd.DataFrame.from_dict(data, orient='tight')\u001b[0m\n", + "\u001b[0;34m z1 x y\u001b[0m\n", + "\u001b[0;34m z2 1 2\u001b[0m\n", + "\u001b[0;34m n1 n2\u001b[0m\n", + "\u001b[0;34m a b 1 3\u001b[0m\n", + "\u001b[0;34m c 2 4\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0morient\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0morient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0morient\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"index\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# TODO speed up Series case\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\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(\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_from_nested_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# error: Incompatible types in assignment (expression has type\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# \"List[Any]\", variable has type \"Dict[Any, Any]\")\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[assignment]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0morient\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"columns\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"tight\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"cannot use columns parameter with orient='{orient}'\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# pragma: no cover\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34mf\"Expected 'index', 'columns' or 'tight' for orient parameter. \"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34mf\"Got '{orient}' instead\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0morient\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"tight\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mrealdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"data\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcreate_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnamelist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnamelist\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\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMultiIndex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_tuples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnamelist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnamelist\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\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"index\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"columns\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"column_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrealdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mto_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnpt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDTypeLike\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mna_value\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mobject\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_default\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Convert the DataFrame to a NumPy array.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m By default, the dtype of the returned array will be the common NumPy\u001b[0m\n", + "\u001b[0;34m dtype of all types in the DataFrame. For example, if the dtypes are\u001b[0m\n", + "\u001b[0;34m ``float16`` and ``float32``, the results dtype will be ``float32``.\u001b[0m\n", + "\u001b[0;34m This may require copying data and coercing values, which may be\u001b[0m\n", + "\u001b[0;34m expensive.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m dtype : str or numpy.dtype, optional\u001b[0m\n", + "\u001b[0;34m The dtype to pass to :meth:`numpy.asarray`.\u001b[0m\n", + "\u001b[0;34m copy : bool, default False\u001b[0m\n", + "\u001b[0;34m Whether to ensure that the returned value is not a view on\u001b[0m\n", + "\u001b[0;34m another array. Note that ``copy=False`` does not *ensure* that\u001b[0m\n", + "\u001b[0;34m ``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that\u001b[0m\n", + "\u001b[0;34m a copy is made, even if not strictly necessary.\u001b[0m\n", + "\u001b[0;34m na_value : Any, optional\u001b[0m\n", + "\u001b[0;34m The value to use for missing values. The default value depends\u001b[0m\n", + "\u001b[0;34m on `dtype` and the dtypes of the DataFrame columns.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionadded:: 1.1.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m numpy.ndarray\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m Series.to_numpy : Similar method for Series.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> pd.DataFrame({\"A\": [1, 2], \"B\": [3, 4]}).to_numpy()\u001b[0m\n", + "\u001b[0;34m array([[1, 3],\u001b[0m\n", + "\u001b[0;34m [2, 4]])\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m With heterogeneous data, the lowest common type will have to\u001b[0m\n", + "\u001b[0;34m be used.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({\"A\": [1, 2], \"B\": [3.0, 4.5]})\u001b[0m\n", + "\u001b[0;34m >>> df.to_numpy()\u001b[0m\n", + "\u001b[0;34m array([[1. , 3. ],\u001b[0m\n", + "\u001b[0;34m [2. , 4.5]])\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m For a mix of numeric and non-numeric types, the output array will\u001b[0m\n", + "\u001b[0;34m have object dtype.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df['C'] = pd.date_range('2000', periods=2)\u001b[0m\n", + "\u001b[0;34m >>> df.to_numpy()\u001b[0m\n", + "\u001b[0;34m array([[1, 3.0, Timestamp('2000-01-01 00:00:00')],\u001b[0m\n", + "\u001b[0;34m [2, 4.5, Timestamp('2000-01-02 00:00:00')]], dtype=object)\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m 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"\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0moverload\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mto_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morient\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mLiteral\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"records\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minto\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m 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\u001b[0mtype\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mdict\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Convert the DataFrame to a dictionary.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m The type of the key-value pairs can be customized with the parameters\u001b[0m\n", + "\u001b[0;34m (see below).\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m orient : str {'dict', 'list', 'series', 'split', 'tight', 'records', 'index'}\u001b[0m\n", + "\u001b[0;34m Determines the type of the values of the dictionary.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m - 'dict' (default) : dict like {column -> {index -> value}}\u001b[0m\n", + "\u001b[0;34m - 'list' : dict like {column -> [values]}\u001b[0m\n", + "\u001b[0;34m - 'series' : dict like {column -> Series(values)}\u001b[0m\n", + "\u001b[0;34m - 'split' : dict like\u001b[0m\n", + "\u001b[0;34m {'index' -> [index], 'columns' -> [columns], 'data' -> [values]}\u001b[0m\n", + "\u001b[0;34m - 'tight' : dict like\u001b[0m\n", + "\u001b[0;34m {'index' -> [index], 'columns' -> [columns], 'data' -> [values],\u001b[0m\n", + "\u001b[0;34m 'index_names' -> [index.names], 'column_names' -> [column.names]}\u001b[0m\n", + "\u001b[0;34m - 'records' : list like\u001b[0m\n", + "\u001b[0;34m [{column -> value}, ... , {column -> value}]\u001b[0m\n", + "\u001b[0;34m - 'index' : dict like {index -> {column -> value}}\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionadded:: 1.4.0\u001b[0m\n", + "\u001b[0;34m 'tight' as an allowed value for the ``orient`` argument\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m into : class, default dict\u001b[0m\n", + "\u001b[0;34m The collections.abc.Mapping subclass used for all Mappings\u001b[0m\n", + "\u001b[0;34m in the return value. Can be the actual class or an empty\u001b[0m\n", + "\u001b[0;34m instance of the mapping type you want. If you want a\u001b[0m\n", + "\u001b[0;34m collections.defaultdict, you must pass it initialized.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m index : bool, default True\u001b[0m\n", + "\u001b[0;34m Whether to include the index item (and index_names item if `orient`\u001b[0m\n", + "\u001b[0;34m is 'tight') in the returned dictionary. Can only be ``False``\u001b[0m\n", + "\u001b[0;34m when `orient` is 'split' or 'tight'.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionadded:: 2.0.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m dict, list or collections.abc.Mapping\u001b[0m\n", + "\u001b[0;34m Return a collections.abc.Mapping object representing the DataFrame.\u001b[0m\n", + "\u001b[0;34m The resulting transformation depends on the `orient` parameter.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.from_dict: Create a DataFrame from a dictionary.\u001b[0m\n", + "\u001b[0;34m DataFrame.to_json: Convert a DataFrame to JSON format.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'col1': [1, 2],\u001b[0m\n", + "\u001b[0;34m ... 'col2': [0.5, 0.75]},\u001b[0m\n", + "\u001b[0;34m ... index=['row1', 'row2'])\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m col1 col2\u001b[0m\n", + "\u001b[0;34m row1 1 0.50\u001b[0m\n", + "\u001b[0;34m row2 2 0.75\u001b[0m\n", + "\u001b[0;34m >>> df.to_dict()\u001b[0m\n", + "\u001b[0;34m {'col1': {'row1': 1, 'row2': 2}, 'col2': {'row1': 0.5, 'row2': 0.75}}\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m You can specify the return orientation.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.to_dict('series')\u001b[0m\n", + "\u001b[0;34m {'col1': row1 1\u001b[0m\n", + "\u001b[0;34m row2 2\u001b[0m\n", + "\u001b[0;34m Name: col1, dtype: int64,\u001b[0m\n", + "\u001b[0;34m 'col2': row1 0.50\u001b[0m\n", + "\u001b[0;34m row2 0.75\u001b[0m\n", + "\u001b[0;34m Name: col2, dtype: float64}\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.to_dict('split')\u001b[0m\n", + "\u001b[0;34m {'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],\u001b[0m\n", + "\u001b[0;34m 'data': [[1, 0.5], [2, 0.75]]}\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.to_dict('records')\u001b[0m\n", + "\u001b[0;34m [{'col1': 1, 'col2': 0.5}, {'col1': 2, 'col2': 0.75}]\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.to_dict('index')\u001b[0m\n", + "\u001b[0;34m {'row1': {'col1': 1, 'col2': 0.5}, 'row2': {'col1': 2, 'col2': 0.75}}\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.to_dict('tight')\u001b[0m\n", + "\u001b[0;34m {'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],\u001b[0m\n", + "\u001b[0;34m 'data': [[1, 0.5], [2, 0.75]], 'index_names': [None], 'column_names': [None]}\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m You can also specify the mapping type.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> from collections import OrderedDict, defaultdict\u001b[0m\n", + "\u001b[0;34m >>> df.to_dict(into=OrderedDict)\u001b[0m\n", + "\u001b[0;34m OrderedDict([('col1', OrderedDict([('row1', 1), ('row2', 2)])),\u001b[0m\n", + "\u001b[0;34m ('col2', OrderedDict([('row1', 0.5), ('row2', 0.75)]))])\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m If you want a `defaultdict`, you need to initialize it:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> dd = defaultdict(list)\u001b[0m\n", + "\u001b[0;34m >>> df.to_dict('records', into=dd)\u001b[0m\n", + "\u001b[0;34m [defaultdict(, {'col1': 1, 'col2': 0.5}),\u001b[0m\n", + "\u001b[0;34m defaultdict(, {'col1': 2, 'col2': 0.75})]\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmethods\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_dict\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mto_dict\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mto_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minto\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mto_gbq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdestination_table\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mproject_id\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mchunksize\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mreauth\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mif_exists\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"fail\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mauth_local_webserver\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mtable_schema\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mlocation\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mprogress_bar\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcredentials\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Write a DataFrame to a Google BigQuery table.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m This function requires the `pandas-gbq package\u001b[0m\n", + "\u001b[0;34m `__.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See the `How to authenticate with Google BigQuery\u001b[0m\n", + "\u001b[0;34m `__\u001b[0m\n", + "\u001b[0;34m guide for authentication instructions.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m destination_table : str\u001b[0m\n", + "\u001b[0;34m Name of table to be written, in the form ``dataset.tablename``.\u001b[0m\n", + "\u001b[0;34m project_id : str, optional\u001b[0m\n", + "\u001b[0;34m Google BigQuery Account project ID. Optional when available from\u001b[0m\n", + "\u001b[0;34m the environment.\u001b[0m\n", + "\u001b[0;34m chunksize : int, optional\u001b[0m\n", + "\u001b[0;34m Number of rows to be inserted in each chunk from the dataframe.\u001b[0m\n", + "\u001b[0;34m Set to ``None`` to load the whole dataframe at once.\u001b[0m\n", + "\u001b[0;34m reauth : bool, default False\u001b[0m\n", + "\u001b[0;34m Force Google BigQuery to re-authenticate the user. This is useful\u001b[0m\n", + "\u001b[0;34m if multiple accounts are used.\u001b[0m\n", + "\u001b[0;34m if_exists : str, default 'fail'\u001b[0m\n", + "\u001b[0;34m Behavior when the destination table exists. Value can be one of:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m ``'fail'``\u001b[0m\n", + "\u001b[0;34m If table exists raise pandas_gbq.gbq.TableCreationError.\u001b[0m\n", + "\u001b[0;34m ``'replace'``\u001b[0m\n", + "\u001b[0;34m If table exists, drop it, recreate it, and insert data.\u001b[0m\n", + "\u001b[0;34m ``'append'``\u001b[0m\n", + "\u001b[0;34m If table exists, insert data. Create if does not exist.\u001b[0m\n", + "\u001b[0;34m auth_local_webserver : bool, default True\u001b[0m\n", + "\u001b[0;34m Use the `local webserver flow`_ instead of the `console flow`_\u001b[0m\n", + "\u001b[0;34m when getting user credentials.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. _local webserver flow:\u001b[0m\n", + "\u001b[0;34m https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_local_server\u001b[0m\n", + "\u001b[0;34m .. _console flow:\u001b[0m\n", + "\u001b[0;34m https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_console\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m *New in version 0.2.0 of pandas-gbq*.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionchanged:: 1.5.0\u001b[0m\n", + "\u001b[0;34m Default value is changed to ``True``. Google has deprecated the\u001b[0m\n", + "\u001b[0;34m ``auth_local_webserver = False`` `\"out of band\" (copy-paste)\u001b[0m\n", + "\u001b[0;34m flow\u001b[0m\n", + "\u001b[0;34m `_.\u001b[0m\n", + "\u001b[0;34m table_schema : list of dicts, optional\u001b[0m\n", + "\u001b[0;34m List of BigQuery table fields to which according DataFrame\u001b[0m\n", + "\u001b[0;34m columns conform to, e.g. ``[{'name': 'col1', 'type':\u001b[0m\n", + "\u001b[0;34m 'STRING'},...]``. If schema is not provided, it will be\u001b[0m\n", + "\u001b[0;34m generated according to dtypes of DataFrame columns. See\u001b[0m\n", + "\u001b[0;34m BigQuery API documentation on available names of a field.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m *New in version 0.3.1 of pandas-gbq*.\u001b[0m\n", + "\u001b[0;34m location : str, optional\u001b[0m\n", + "\u001b[0;34m Location where the load job should run. See the `BigQuery locations\u001b[0m\n", + "\u001b[0;34m documentation\u001b[0m\n", + "\u001b[0;34m `__ for a\u001b[0m\n", + "\u001b[0;34m list of available locations. The location must match that of the\u001b[0m\n", + "\u001b[0;34m target dataset.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m *New in version 0.5.0 of pandas-gbq*.\u001b[0m\n", + "\u001b[0;34m progress_bar : bool, default True\u001b[0m\n", + "\u001b[0;34m Use the library `tqdm` to show the progress bar for the upload,\u001b[0m\n", + "\u001b[0;34m chunk by chunk.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m *New in version 0.5.0 of pandas-gbq*.\u001b[0m\n", + "\u001b[0;34m credentials : google.auth.credentials.Credentials, optional\u001b[0m\n", + "\u001b[0;34m Credentials for accessing Google APIs. Use this parameter to\u001b[0m\n", + "\u001b[0;34m override default credentials, such as to use Compute Engine\u001b[0m\n", + "\u001b[0;34m :class:`google.auth.compute_engine.Credentials` or Service\u001b[0m\n", + "\u001b[0;34m Account :class:`google.oauth2.service_account.Credentials`\u001b[0m\n", + "\u001b[0;34m directly.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m *New in version 0.8.0 of pandas-gbq*.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m pandas_gbq.to_gbq : This function in the pandas-gbq library.\u001b[0m\n", + "\u001b[0;34m read_gbq : Read a DataFrame from Google BigQuery.\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgbq\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mgbq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_gbq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdestination_table\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mproject_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mproject_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mchunksize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mchunksize\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mreauth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreauth\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mif_exists\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mif_exists\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mauth_local_webserver\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mauth_local_webserver\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mtable_schema\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable_schema\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mlocation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlocation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mprogress_bar\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprogress_bar\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcredentials\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcredentials\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfrom_records\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcoerce_float\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnrows\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Convert structured or record ndarray to DataFrame.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Creates a DataFrame object from a structured ndarray, sequence of\u001b[0m\n", + "\u001b[0;34m tuples or dicts, or DataFrame.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m data : structured ndarray, sequence of tuples or dicts, or DataFrame\u001b[0m\n", + "\u001b[0;34m Structured input data.\u001b[0m\n", + "\u001b[0;34m index : str, list of fields, array-like\u001b[0m\n", + "\u001b[0;34m Field of array to use as the index, alternately a specific set of\u001b[0m\n", + "\u001b[0;34m input labels to use.\u001b[0m\n", + "\u001b[0;34m exclude : sequence, default None\u001b[0m\n", + "\u001b[0;34m Columns or fields to exclude.\u001b[0m\n", + "\u001b[0;34m columns : sequence, default None\u001b[0m\n", + "\u001b[0;34m Column names to use. If the passed data do not have names\u001b[0m\n", + "\u001b[0;34m associated with them, this argument provides names for the\u001b[0m\n", + "\u001b[0;34m columns. Otherwise this argument indicates the order of the columns\u001b[0m\n", + "\u001b[0;34m in the result (any names not found in the data will become all-NA\u001b[0m\n", + "\u001b[0;34m columns).\u001b[0m\n", + "\u001b[0;34m coerce_float : bool, default False\u001b[0m\n", + "\u001b[0;34m Attempt to convert values of non-string, non-numeric objects (like\u001b[0m\n", + "\u001b[0;34m decimal.Decimal) to floating point, useful for SQL result sets.\u001b[0m\n", + "\u001b[0;34m nrows : int, default None\u001b[0m\n", + "\u001b[0;34m Number of rows to read if data is an iterator.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.from_dict : DataFrame from dict of array-like or dicts.\u001b[0m\n", + "\u001b[0;34m DataFrame : DataFrame object creation using constructor.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m Data can be provided as a structured ndarray:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> data = np.array([(3, 'a'), (2, 'b'), (1, 'c'), (0, 'd')],\u001b[0m\n", + "\u001b[0;34m ... dtype=[('col_1', 'i4'), ('col_2', 'U1')])\u001b[0m\n", + "\u001b[0;34m >>> pd.DataFrame.from_records(data)\u001b[0m\n", + "\u001b[0;34m col_1 col_2\u001b[0m\n", + "\u001b[0;34m 0 3 a\u001b[0m\n", + "\u001b[0;34m 1 2 b\u001b[0m\n", + "\u001b[0;34m 2 1 c\u001b[0m\n", + "\u001b[0;34m 3 0 d\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Data can be provided as a list of dicts:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> data = [{'col_1': 3, 'col_2': 'a'},\u001b[0m\n", + "\u001b[0;34m ... {'col_1': 2, 'col_2': 'b'},\u001b[0m\n", + "\u001b[0;34m ... {'col_1': 1, 'col_2': 'c'},\u001b[0m\n", + "\u001b[0;34m ... {'col_1': 0, 'col_2': 'd'}]\u001b[0m\n", + "\u001b[0;34m >>> pd.DataFrame.from_records(data)\u001b[0m\n", + "\u001b[0;34m col_1 col_2\u001b[0m\n", + "\u001b[0;34m 0 3 a\u001b[0m\n", + "\u001b[0;34m 1 2 b\u001b[0m\n", + "\u001b[0;34m 2 1 c\u001b[0m\n", + "\u001b[0;34m 3 0 d\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Data can be provided as a list of tuples with corresponding columns:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> data = [(3, 'a'), (2, 'b'), (1, 'c'), (0, 'd')]\u001b[0m\n", + "\u001b[0;34m >>> pd.DataFrame.from_records(data, columns=['col_1', 'col_2'])\u001b[0m\n", + "\u001b[0;34m col_1 col_2\u001b[0m\n", + "\u001b[0;34m 0 3 a\u001b[0m\n", + "\u001b[0;34m 1 2 b\u001b[0m\n", + "\u001b[0;34m 2 1 c\u001b[0m\n", + "\u001b[0;34m 3 0 d\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m 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\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Make a copy of the input columns so we can modify it\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmaybe_reorder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mArrayLike\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marr_columns\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mArrayLike\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndex\u001b[0m \u001b[0;34m|\u001b[0m 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"\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmanager\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_option\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"mode.data_manager\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marrays_to_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtyp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmanager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmgr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mto_records\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumn_dtypes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_dtypes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecarray\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Convert DataFrame to a NumPy record array.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Index will be included as the first field of the record array if\u001b[0m\n", + "\u001b[0;34m requested.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m index : bool, default True\u001b[0m\n", + "\u001b[0;34m Include index in resulting record array, stored in 'index'\u001b[0m\n", + "\u001b[0;34m field or using the index label, if set.\u001b[0m\n", + "\u001b[0;34m column_dtypes : str, type, dict, default None\u001b[0m\n", + "\u001b[0;34m If a string or type, the data type to store all columns. If\u001b[0m\n", + "\u001b[0;34m a dictionary, a mapping of column names and indices (zero-indexed)\u001b[0m\n", + "\u001b[0;34m to specific data types.\u001b[0m\n", + "\u001b[0;34m index_dtypes : str, type, dict, default None\u001b[0m\n", + "\u001b[0;34m If a string or type, the data type to store all index levels. If\u001b[0m\n", + "\u001b[0;34m a dictionary, a mapping of index level names and indices\u001b[0m\n", + "\u001b[0;34m (zero-indexed) to specific data types.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m This mapping is applied only if `index=True`.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m numpy.recarray\u001b[0m\n", + "\u001b[0;34m NumPy ndarray with the DataFrame labels as fields and each row\u001b[0m\n", + "\u001b[0;34m of the DataFrame as entries.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.from_records: Convert structured or record ndarray\u001b[0m\n", + "\u001b[0;34m to DataFrame.\u001b[0m\n", + "\u001b[0;34m numpy.recarray: An ndarray that allows field access using\u001b[0m\n", + "\u001b[0;34m attributes, analogous to typed columns in a\u001b[0m\n", + "\u001b[0;34m spreadsheet.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'A': [1, 2], 'B': [0.5, 0.75]},\u001b[0m\n", + "\u001b[0;34m ... index=['a', 'b'])\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m A B\u001b[0m\n", + "\u001b[0;34m a 1 0.50\u001b[0m\n", + "\u001b[0;34m b 2 0.75\u001b[0m\n", + "\u001b[0;34m >>> df.to_records()\u001b[0m\n", + "\u001b[0;34m rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],\u001b[0m\n", + "\u001b[0;34m dtype=[('index', 'O'), ('A', '>> df.index = df.index.rename(\"I\")\u001b[0m\n", + "\u001b[0;34m >>> df.to_records()\u001b[0m\n", + "\u001b[0;34m rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],\u001b[0m\n", + "\u001b[0;34m dtype=[('I', 'O'), ('A', '>> df.to_records(index=False)\u001b[0m\n", + "\u001b[0;34m rec.array([(1, 0.5 ), (2, 0.75)],\u001b[0m\n", + "\u001b[0;34m dtype=[('A', '>> df.to_records(column_dtypes={\"A\": \"int32\"})\u001b[0m\n", + "\u001b[0;34m rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],\u001b[0m\n", + "\u001b[0;34m dtype=[('I', 'O'), ('A', '>> df.to_records(index_dtypes=\">> index_dtypes = f\">> df.to_records(index_dtypes=index_dtypes)\u001b[0m\n", + "\u001b[0;34m rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],\u001b[0m\n", + "\u001b[0;34m dtype=[('I', 'S1'), ('A', '\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Create DataFrame from a list of arrays corresponding to the columns.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m arrays : list-like of arrays\u001b[0m\n", + "\u001b[0;34m Each array in the list corresponds to one column, in order.\u001b[0m\n", + "\u001b[0;34m columns : list-like, Index\u001b[0m\n", + "\u001b[0;34m The column names for the resulting DataFrame.\u001b[0m\n", + "\u001b[0;34m index : list-like, Index\u001b[0m\n", + "\u001b[0;34m The rows labels for the resulting DataFrame.\u001b[0m\n", + "\u001b[0;34m dtype : dtype, optional\u001b[0m\n", + "\u001b[0;34m Optional dtype to enforce for all arrays.\u001b[0m\n", + "\u001b[0;34m verify_integrity : bool, default True\u001b[0m\n", + "\u001b[0;34m Validate and homogenize all input. If set to False, it is assumed\u001b[0m\n", + "\u001b[0;34m that all elements of `arrays` are actual arrays how they will be\u001b[0m\n", + "\u001b[0;34m stored in a block (numpy ndarray or ExtensionArray), have the same\u001b[0m\n", + "\u001b[0;34m length as and are aligned with the index, and that `columns` and\u001b[0m\n", + "\u001b[0;34m `index` are ensured to be an Index object.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpandas_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmanager\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_option\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"mode.data_manager\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"len(columns) must match len(arrays)\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m 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\u001b[0mcls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmgr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mdoc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mstorage_options\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_shared_docs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"storage_options\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcompression_options\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_shared_docs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"compression_options\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m\"path\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mto_stata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mFilePath\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mWriteBuffer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbytes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mconvert_dates\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mHashable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mwrite_index\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mbyteorder\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mtime_stamp\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdata_label\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mvariable_labels\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mHashable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mversion\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m114\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mconvert_strl\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mHashable\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcompression\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mCompressionOptions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"infer\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mstorage_options\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mStorageOptions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mvalue_labels\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mHashable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Export DataFrame object to Stata dta format.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Writes the DataFrame to a Stata dataset file.\u001b[0m\n", + "\u001b[0;34m \"dta\" files contain a Stata dataset.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m path : str, path object, or buffer\u001b[0m\n", + "\u001b[0;34m String, path object (implementing ``os.PathLike[str]``), or file-like\u001b[0m\n", + "\u001b[0;34m object implementing a binary ``write()`` function.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m convert_dates : dict\u001b[0m\n", + "\u001b[0;34m Dictionary mapping columns containing datetime types to stata\u001b[0m\n", + "\u001b[0;34m internal format to use when writing the dates. Options are 'tc',\u001b[0m\n", + "\u001b[0;34m 'td', 'tm', 'tw', 'th', 'tq', 'ty'. Column can be either an integer\u001b[0m\n", + "\u001b[0;34m or a name. Datetime columns that do not have a conversion type\u001b[0m\n", + "\u001b[0;34m specified will be converted to 'tc'. Raises NotImplementedError if\u001b[0m\n", + "\u001b[0;34m a datetime column has timezone information.\u001b[0m\n", + "\u001b[0;34m write_index : bool\u001b[0m\n", + "\u001b[0;34m Write the index to Stata dataset.\u001b[0m\n", + "\u001b[0;34m byteorder : str\u001b[0m\n", + "\u001b[0;34m Can be \">\", \"<\", \"little\", or \"big\". default is `sys.byteorder`.\u001b[0m\n", + "\u001b[0;34m time_stamp : datetime\u001b[0m\n", + "\u001b[0;34m A datetime to use as file creation date. Default is the current\u001b[0m\n", + "\u001b[0;34m time.\u001b[0m\n", + "\u001b[0;34m data_label : str, optional\u001b[0m\n", + "\u001b[0;34m A label for the data set. Must be 80 characters or smaller.\u001b[0m\n", + "\u001b[0;34m variable_labels : dict\u001b[0m\n", + "\u001b[0;34m Dictionary containing columns as keys and variable labels as\u001b[0m\n", + "\u001b[0;34m values. Each label must be 80 characters or smaller.\u001b[0m\n", + "\u001b[0;34m version : {{114, 117, 118, 119, None}}, default 114\u001b[0m\n", + "\u001b[0;34m Version to use in the output dta file. Set to None to let pandas\u001b[0m\n", + "\u001b[0;34m decide between 118 or 119 formats depending on the number of\u001b[0m\n", + "\u001b[0;34m columns in the frame. Version 114 can be read by Stata 10 and\u001b[0m\n", + "\u001b[0;34m later. Version 117 can be read by Stata 13 or later. Version 118\u001b[0m\n", + "\u001b[0;34m is supported in Stata 14 and later. Version 119 is supported in\u001b[0m\n", + "\u001b[0;34m Stata 15 and later. Version 114 limits string variables to 244\u001b[0m\n", + "\u001b[0;34m characters or fewer while versions 117 and later allow strings\u001b[0m\n", + "\u001b[0;34m with lengths up to 2,000,000 characters. Versions 118 and 119\u001b[0m\n", + "\u001b[0;34m support Unicode characters, and version 119 supports more than\u001b[0m\n", + "\u001b[0;34m 32,767 variables.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Version 119 should usually only be used when the number of\u001b[0m\n", + "\u001b[0;34m variables exceeds the capacity of dta format 118. Exporting\u001b[0m\n", + "\u001b[0;34m smaller datasets in format 119 may have unintended consequences,\u001b[0m\n", + "\u001b[0;34m and, as of November 2020, Stata SE cannot read version 119 files.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m convert_strl : list, optional\u001b[0m\n", + "\u001b[0;34m List of column names to convert to string columns to Stata StrL\u001b[0m\n", + "\u001b[0;34m format. Only available if version is 117. Storing strings in the\u001b[0m\n", + "\u001b[0;34m StrL format can produce smaller dta files if strings have more than\u001b[0m\n", + "\u001b[0;34m 8 characters and values are repeated.\u001b[0m\n", + "\u001b[0;34m {compression_options}\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionadded:: 1.1.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionchanged:: 1.4.0 Zstandard support.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m {storage_options}\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionadded:: 1.2.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m value_labels : dict of dicts\u001b[0m\n", + "\u001b[0;34m Dictionary containing columns as keys and dictionaries of column value\u001b[0m\n", + "\u001b[0;34m to labels as values. Labels for a single variable must be 32,000\u001b[0m\n", + "\u001b[0;34m characters or smaller.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionadded:: 1.4.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Raises\u001b[0m\n", + "\u001b[0;34m ------\u001b[0m\n", + "\u001b[0;34m NotImplementedError\u001b[0m\n", + "\u001b[0;34m * If datetimes contain timezone information\u001b[0m\n", + "\u001b[0;34m * Column dtype is not representable in Stata\u001b[0m\n", + "\u001b[0;34m ValueError\u001b[0m\n", + "\u001b[0;34m * Columns listed in convert_dates are neither datetime64[ns]\u001b[0m\n", + "\u001b[0;34m or datetime.datetime\u001b[0m\n", + "\u001b[0;34m * Column listed in convert_dates is not in DataFrame\u001b[0m\n", + "\u001b[0;34m * Categorical label contains more than 32,000 characters\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m read_stata : Import Stata data files.\u001b[0m\n", + "\u001b[0;34m io.stata.StataWriter : Low-level writer for Stata data files.\u001b[0m\n", + "\u001b[0;34m io.stata.StataWriter117 : Low-level writer for version 117 files.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({{'animal': ['falcon', 'parrot', 'falcon',\u001b[0m\n", + "\u001b[0;34m ... 'parrot'],\u001b[0m\n", + "\u001b[0;34m ... 'speed': [350, 18, 361, 15]}})\u001b[0m\n", + "\u001b[0;34m >>> df.to_stata('animals.dta') # doctest: +SKIP\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mversion\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m114\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m117\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m118\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m119\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Only formats 114, 117, 118 and 119 are supported.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mversion\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m114\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mconvert_strl\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"strl is not supported in format 114\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstata\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mStataWriter\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mstatawriter\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mversion\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m117\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Incompatible import of \"statawriter\" (imported name has type\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# \"Type[StataWriter117]\", local name has type \"Type[StataWriter]\")\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstata\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m(\u001b[0m \u001b[0;31m# type: ignore[assignment]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mStataWriter117\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mstatawriter\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# versions 118 and 119\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Incompatible import of \"statawriter\" (imported name has type\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# \"Type[StataWriter117]\", local name has type \"Type[StataWriter]\")\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstata\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m(\u001b[0m \u001b[0;31m# type: ignore[assignment]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mStataWriterUTF8\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mstatawriter\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mversion\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mversion\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m117\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# strl conversion is only supported >= 117\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"convert_strl\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert_strl\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mversion\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mversion\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m118\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Specifying the version is only supported for UTF8 (118 or 119)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"version\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mversion\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mwriter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstatawriter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mconvert_dates\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconvert_dates\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mbyteorder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbyteorder\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mtime_stamp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtime_stamp\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdata_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata_label\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mwrite_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwrite_index\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mvariable_labels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvariable_labels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcompression\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcompression\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mstorage_options\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstorage_options\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mvalue_labels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalue_labels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mto_feather\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mFilePath\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mWriteBuffer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbytes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Write a DataFrame to the binary Feather format.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m path : str, path object, file-like object\u001b[0m\n", + "\u001b[0;34m String, path object (implementing ``os.PathLike[str]``), or file-like\u001b[0m\n", + "\u001b[0;34m object implementing a binary ``write()`` function. If a string or a path,\u001b[0m\n", + "\u001b[0;34m it will be used as Root Directory path when writing a partitioned dataset.\u001b[0m\n", + "\u001b[0;34m **kwargs :\u001b[0m\n", + "\u001b[0;34m Additional keywords passed to :func:`pyarrow.feather.write_feather`.\u001b[0m\n", + "\u001b[0;34m Starting with pyarrow 0.17, this includes the `compression`,\u001b[0m\n", + "\u001b[0;34m `compression_level`, `chunksize` and `version` keywords.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionadded:: 1.1.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Notes\u001b[0m\n", + "\u001b[0;34m -----\u001b[0m\n", + "\u001b[0;34m This function writes the dataframe as a `feather file\u001b[0m\n", + "\u001b[0;34m `_. Requires a default\u001b[0m\n", + "\u001b[0;34m index. For saving the DataFrame with your custom index use a method that\u001b[0m\n", + "\u001b[0;34m supports custom indices e.g. `to_parquet`.\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeather_format\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mto_feather\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mto_feather\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mdoc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_markdown\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_shared_doc_kwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"klass\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mstorage_options\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_shared_docs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"storage_options\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mexamples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"\"\"Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame(\u001b[0m\n", + "\u001b[0;34m ... data={\"animal_1\": [\"elk\", \"pig\"], \"animal_2\": [\"dog\", \"quetzal\"]}\u001b[0m\n", + "\u001b[0;34m ... )\u001b[0m\n", + "\u001b[0;34m >>> print(df.to_markdown())\u001b[0m\n", + "\u001b[0;34m | | animal_1 | animal_2 |\u001b[0m\n", + "\u001b[0;34m |---:|:-----------|:-----------|\u001b[0m\n", + "\u001b[0;34m | 0 | elk | dog |\u001b[0m\n", + "\u001b[0;34m | 1 | pig | quetzal |\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Output markdown with a tabulate option.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> print(df.to_markdown(tablefmt=\"grid\"))\u001b[0m\n", + "\u001b[0;34m +----+------------+------------+\u001b[0m\n", + "\u001b[0;34m | | animal_1 | animal_2 |\u001b[0m\n", + "\u001b[0;34m +====+============+============+\u001b[0m\n", + "\u001b[0;34m | 0 | elk | dog |\u001b[0m\n", + "\u001b[0;34m +----+------------+------------+\u001b[0m\n", + "\u001b[0;34m | 1 | pig | quetzal |\u001b[0m\n", + "\u001b[0;34m +----+------------+------------+\"\"\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mto_markdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mFilePath\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mWriteBuffer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"wt\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mstorage_options\u001b[0m\u001b[0;34m:\u001b[0m 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You can choose different parquet\u001b[0m\n", + "\u001b[0;34m backends, and have the option of compression. See\u001b[0m\n", + "\u001b[0;34m :ref:`the user guide ` for more details.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m path : str, path object, file-like object, or None, default None\u001b[0m\n", + "\u001b[0;34m String, path object (implementing ``os.PathLike[str]``), or file-like\u001b[0m\n", + "\u001b[0;34m object implementing a binary ``write()`` function. If None, the result is\u001b[0m\n", + "\u001b[0;34m returned as bytes. If a string or path, it will be used as Root Directory\u001b[0m\n", + "\u001b[0;34m path when writing a partitioned dataset.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionchanged:: 1.2.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Previously this was \"fname\"\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m engine : {{'auto', 'pyarrow', 'fastparquet'}}, default 'auto'\u001b[0m\n", + "\u001b[0;34m Parquet library to use. If 'auto', then the option\u001b[0m\n", + "\u001b[0;34m ``io.parquet.engine`` is used. The default ``io.parquet.engine``\u001b[0m\n", + "\u001b[0;34m behavior is to try 'pyarrow', falling back to 'fastparquet' if\u001b[0m\n", + "\u001b[0;34m 'pyarrow' is unavailable.\u001b[0m\n", + "\u001b[0;34m compression : {{'snappy', 'gzip', 'brotli', None}}, default 'snappy'\u001b[0m\n", + "\u001b[0;34m Name of the compression to use. Use ``None`` for no compression.\u001b[0m\n", + "\u001b[0;34m index : bool, default None\u001b[0m\n", + "\u001b[0;34m If ``True``, include the dataframe's index(es) in the file output.\u001b[0m\n", + "\u001b[0;34m If ``False``, they will not be written to the file.\u001b[0m\n", + "\u001b[0;34m If ``None``, similar to ``True`` the dataframe's index(es)\u001b[0m\n", + "\u001b[0;34m will be saved. However, instead of being saved as values,\u001b[0m\n", + "\u001b[0;34m the RangeIndex will be stored as a range in the metadata so it\u001b[0m\n", + "\u001b[0;34m doesn't require much space and is faster. Other indexes will\u001b[0m\n", + "\u001b[0;34m be included as columns in the file output.\u001b[0m\n", + "\u001b[0;34m partition_cols : list, optional, default None\u001b[0m\n", + "\u001b[0;34m Column names by which to partition the dataset.\u001b[0m\n", + "\u001b[0;34m Columns are partitioned in the order they are given.\u001b[0m\n", + "\u001b[0;34m Must be None if path is not a string.\u001b[0m\n", + "\u001b[0;34m {storage_options}\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionadded:: 1.2.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m **kwargs\u001b[0m\n", + "\u001b[0;34m Additional arguments passed to the parquet library. See\u001b[0m\n", + "\u001b[0;34m :ref:`pandas io ` for more details.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m bytes if no path argument is provided else None\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m read_parquet : Read a parquet file.\u001b[0m\n", + "\u001b[0;34m DataFrame.to_orc : Write an orc file.\u001b[0m\n", + "\u001b[0;34m DataFrame.to_csv : Write a csv file.\u001b[0m\n", + "\u001b[0;34m DataFrame.to_sql : Write to a sql table.\u001b[0m\n", + "\u001b[0;34m DataFrame.to_hdf : Write to hdf.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Notes\u001b[0m\n", + "\u001b[0;34m -----\u001b[0m\n", + "\u001b[0;34m This function requires either the `fastparquet\u001b[0m\n", + "\u001b[0;34m `_ or `pyarrow\u001b[0m\n", + "\u001b[0;34m `_ library.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame(data={{'col1': [1, 2], 'col2': [3, 4]}})\u001b[0m\n", + "\u001b[0;34m >>> df.to_parquet('df.parquet.gzip',\u001b[0m\n", + "\u001b[0;34m ... compression='gzip') # doctest: +SKIP\u001b[0m\n", + "\u001b[0;34m >>> pd.read_parquet('df.parquet.gzip') # doctest: +SKIP\u001b[0m\n", + "\u001b[0;34m col1 col2\u001b[0m\n", + "\u001b[0;34m 0 1 3\u001b[0m\n", + "\u001b[0;34m 1 2 4\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m If you want to get a buffer to the parquet content you can use a io.BytesIO\u001b[0m\n", + "\u001b[0;34m object, as long as you don't use partition_cols, which creates multiple files.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> import io\u001b[0m\n", + "\u001b[0;34m >>> f = io.BytesIO()\u001b[0m\n", + "\u001b[0;34m >>> df.to_parquet(f)\u001b[0m\n", + "\u001b[0;34m >>> f.seek(0)\u001b[0m\n", + "\u001b[0;34m 0\u001b[0m\n", + "\u001b[0;34m >>> content = f.read()\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparquet\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mto_parquet\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mto_parquet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcompression\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcompression\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mpartition_cols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpartition_cols\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mstorage_options\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstorage_options\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mto_orc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mFilePath\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mWriteBuffer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbytes\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mLiteral\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"pyarrow\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"pyarrow\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mengine_kwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mbytes\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Write a DataFrame to the ORC format.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionadded:: 1.5.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m path : str, file-like object or None, default None\u001b[0m\n", + "\u001b[0;34m If a string, it will be used as Root Directory path\u001b[0m\n", + "\u001b[0;34m when writing a partitioned dataset. By file-like object,\u001b[0m\n", + "\u001b[0;34m we refer to objects with a write() method, such as a file handle\u001b[0m\n", + "\u001b[0;34m (e.g. via builtin open function). If path is None,\u001b[0m\n", + "\u001b[0;34m a bytes object is returned.\u001b[0m\n", + "\u001b[0;34m engine : str, default 'pyarrow'\u001b[0m\n", + "\u001b[0;34m ORC library to use. Pyarrow must be >= 7.0.0.\u001b[0m\n", + "\u001b[0;34m index : bool, optional\u001b[0m\n", + "\u001b[0;34m If ``True``, include the dataframe's index(es) in the file output.\u001b[0m\n", + "\u001b[0;34m If ``False``, they will not be written to the file.\u001b[0m\n", + "\u001b[0;34m If ``None``, similar to ``infer`` the dataframe's index(es)\u001b[0m\n", + "\u001b[0;34m will be saved. However, instead of being saved as values,\u001b[0m\n", + "\u001b[0;34m the RangeIndex will be stored as a range in the metadata so it\u001b[0m\n", + "\u001b[0;34m doesn't require much space and is faster. Other indexes will\u001b[0m\n", + "\u001b[0;34m be included as columns in the file output.\u001b[0m\n", + "\u001b[0;34m engine_kwargs : dict[str, Any] or None, default None\u001b[0m\n", + "\u001b[0;34m Additional keyword arguments passed to :func:`pyarrow.orc.write_table`.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m bytes if no path argument is provided else None\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Raises\u001b[0m\n", + "\u001b[0;34m ------\u001b[0m\n", + "\u001b[0;34m NotImplementedError\u001b[0m\n", + "\u001b[0;34m Dtype of one or more columns is category, unsigned integers, interval,\u001b[0m\n", + "\u001b[0;34m period or sparse.\u001b[0m\n", + "\u001b[0;34m ValueError\u001b[0m\n", + "\u001b[0;34m engine is not pyarrow.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m read_orc : Read a ORC file.\u001b[0m\n", + "\u001b[0;34m DataFrame.to_parquet : Write a parquet file.\u001b[0m\n", + "\u001b[0;34m DataFrame.to_csv : Write a csv file.\u001b[0m\n", + "\u001b[0;34m DataFrame.to_sql : Write to a sql table.\u001b[0m\n", + "\u001b[0;34m DataFrame.to_hdf : Write to hdf.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Notes\u001b[0m\n", + "\u001b[0;34m -----\u001b[0m\n", + "\u001b[0;34m * Before using this function you should read the :ref:`user guide about\u001b[0m\n", + "\u001b[0;34m ORC ` and :ref:`install optional dependencies `.\u001b[0m\n", + "\u001b[0;34m * This function requires `pyarrow `_\u001b[0m\n", + "\u001b[0;34m library.\u001b[0m\n", + "\u001b[0;34m * For supported dtypes please refer to `supported ORC features in Arrow\u001b[0m\n", + "\u001b[0;34m `__.\u001b[0m\n", + "\u001b[0;34m * Currently timezones in datetime columns are not preserved when a\u001b[0m\n", + "\u001b[0;34m dataframe is converted into ORC files.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame(data={'col1': [1, 2], 'col2': [4, 3]})\u001b[0m\n", + "\u001b[0;34m >>> df.to_orc('df.orc') # doctest: +SKIP\u001b[0m\n", + "\u001b[0;34m >>> pd.read_orc('df.orc') # doctest: +SKIP\u001b[0m\n", + "\u001b[0;34m col1 col2\u001b[0m\n", + "\u001b[0;34m 0 1 4\u001b[0m\n", + "\u001b[0;34m 1 2 3\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m If you want to get a buffer to the orc content you can write it to io.BytesIO\u001b[0m\n", + "\u001b[0;34m >>> import io\u001b[0m\n", + "\u001b[0;34m >>> b = io.BytesIO(df.to_orc()) # doctest: +SKIP\u001b[0m\n", + "\u001b[0;34m >>> b.seek(0) # doctest: +SKIP\u001b[0m\n", + "\u001b[0;34m 0\u001b[0m\n", + "\u001b[0;34m >>> content = b.read() # doctest: +SKIP\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0morc\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mto_orc\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mto_orc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine_kwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mengine_kwargs\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0moverload\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mto_html\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mFilePath\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mWriteBuffer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mLevel\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcol_space\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mColspaceArgType\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m 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"\u001b[0;34m\u001b[0m \u001b[0mtable_id\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mrender_links\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Render a DataFrame as an HTML table.\u001b[0m\n", + "\u001b[0;34m %(shared_params)s\u001b[0m\n", + "\u001b[0;34m bold_rows : bool, default True\u001b[0m\n", + "\u001b[0;34m Make the row labels bold in the output.\u001b[0m\n", + "\u001b[0;34m classes : str or list or tuple, default None\u001b[0m\n", + "\u001b[0;34m CSS class(es) to apply to the resulting html table.\u001b[0m\n", + "\u001b[0;34m escape : bool, default True\u001b[0m\n", + "\u001b[0;34m Convert the characters <, >, and & to HTML-safe sequences.\u001b[0m\n", + "\u001b[0;34m notebook : {True, False}, default False\u001b[0m\n", + "\u001b[0;34m Whether the generated HTML is for IPython Notebook.\u001b[0m\n", + "\u001b[0;34m border : int\u001b[0m\n", + "\u001b[0;34m A ``border=border`` attribute is included in the opening\u001b[0m\n", + "\u001b[0;34m `` tag. Default ``pd.options.display.html.border``.\u001b[0m\n", + "\u001b[0;34m table_id : str, optional\u001b[0m\n", + "\u001b[0;34m A css id is included in the opening `
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"\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid value for justify parameter\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mformatter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfmt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrameFormatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcol_space\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcol_space\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mna_rep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mna_rep\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + 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\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcompression\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mCompressionOptions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"infer\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mstorage_options\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mStorageOptions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Render a DataFrame to an XML document.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionadded:: 1.3.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m path_or_buffer : str, path object, file-like object, or None, default None\u001b[0m\n", + "\u001b[0;34m String, path object (implementing ``os.PathLike[str]``), or file-like\u001b[0m\n", + "\u001b[0;34m object implementing a ``write()`` function. If None, the result is returned\u001b[0m\n", + "\u001b[0;34m as a string.\u001b[0m\n", + "\u001b[0;34m index : bool, default True\u001b[0m\n", + "\u001b[0;34m Whether to include index in XML document.\u001b[0m\n", + "\u001b[0;34m root_name : str, default 'data'\u001b[0m\n", + "\u001b[0;34m The name of root element in XML document.\u001b[0m\n", + "\u001b[0;34m row_name : str, default 'row'\u001b[0m\n", + "\u001b[0;34m The name of row element in XML document.\u001b[0m\n", + "\u001b[0;34m na_rep : str, optional\u001b[0m\n", + "\u001b[0;34m Missing data representation.\u001b[0m\n", + "\u001b[0;34m attr_cols : list-like, optional\u001b[0m\n", + "\u001b[0;34m List of columns to write as attributes in row element.\u001b[0m\n", + "\u001b[0;34m Hierarchical columns will be flattened with underscore\u001b[0m\n", + "\u001b[0;34m delimiting the different levels.\u001b[0m\n", + "\u001b[0;34m elem_cols : list-like, optional\u001b[0m\n", + "\u001b[0;34m List of columns to write as children in row element. By default,\u001b[0m\n", + "\u001b[0;34m all columns output as children of row element. Hierarchical\u001b[0m\n", + "\u001b[0;34m columns will be flattened with underscore delimiting the\u001b[0m\n", + "\u001b[0;34m different levels.\u001b[0m\n", + "\u001b[0;34m namespaces : dict, optional\u001b[0m\n", + "\u001b[0;34m All namespaces to be defined in root element. Keys of dict\u001b[0m\n", + "\u001b[0;34m should be prefix names and values of dict corresponding URIs.\u001b[0m\n", + "\u001b[0;34m Default namespaces should be given empty string key. For\u001b[0m\n", + "\u001b[0;34m example, ::\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m namespaces = {{\"\": \"https://example.com\"}}\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m prefix : str, optional\u001b[0m\n", + "\u001b[0;34m Namespace prefix to be used for every element and/or attribute\u001b[0m\n", + "\u001b[0;34m in document. This should be one of the keys in ``namespaces``\u001b[0m\n", + "\u001b[0;34m dict.\u001b[0m\n", + "\u001b[0;34m encoding : str, default 'utf-8'\u001b[0m\n", + "\u001b[0;34m Encoding of the resulting document.\u001b[0m\n", + "\u001b[0;34m xml_declaration : bool, default True\u001b[0m\n", + "\u001b[0;34m Whether to include the XML declaration at start of document.\u001b[0m\n", + "\u001b[0;34m pretty_print : bool, default True\u001b[0m\n", + "\u001b[0;34m Whether output should be pretty printed with indentation and\u001b[0m\n", + "\u001b[0;34m line breaks.\u001b[0m\n", + "\u001b[0;34m parser : {{'lxml','etree'}}, default 'lxml'\u001b[0m\n", + "\u001b[0;34m Parser module to use for building of tree. Only 'lxml' and\u001b[0m\n", + "\u001b[0;34m 'etree' are supported. With 'lxml', the ability to use XSLT\u001b[0m\n", + "\u001b[0;34m stylesheet is supported.\u001b[0m\n", + "\u001b[0;34m stylesheet : str, path object or file-like object, optional\u001b[0m\n", + "\u001b[0;34m A URL, file-like object, or a raw string containing an XSLT\u001b[0m\n", + "\u001b[0;34m script used to transform the raw XML output. Script should use\u001b[0m\n", + "\u001b[0;34m layout of elements and attributes from original output. This\u001b[0m\n", + "\u001b[0;34m argument requires ``lxml`` to be installed. Only XSLT 1.0\u001b[0m\n", + "\u001b[0;34m scripts and not later versions is currently supported.\u001b[0m\n", + "\u001b[0;34m {compression_options}\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionchanged:: 1.4.0 Zstandard support.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m {storage_options}\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m None or str\u001b[0m\n", + "\u001b[0;34m If ``io`` is None, returns the resulting XML format as a\u001b[0m\n", + "\u001b[0;34m string. Otherwise returns None.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m to_json : Convert the pandas object to a JSON string.\u001b[0m\n", + "\u001b[0;34m to_html : Convert DataFrame to a html.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({{'shape': ['square', 'circle', 'triangle'],\u001b[0m\n", + "\u001b[0;34m ... 'degrees': [360, 360, 180],\u001b[0m\n", + "\u001b[0;34m ... 'sides': [4, np.nan, 3]}})\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.to_xml() # doctest: +SKIP\u001b[0m\n", + "\u001b[0;34m \u001b[0m\n", + "\u001b[0;34m \u001b[0m\n", + "\u001b[0;34m \u001b[0m\n", + "\u001b[0;34m 0\u001b[0m\n", + "\u001b[0;34m square\u001b[0m\n", + "\u001b[0;34m 360\u001b[0m\n", + "\u001b[0;34m 4.0\u001b[0m\n", + "\u001b[0;34m \u001b[0m\n", + "\u001b[0;34m \u001b[0m\n", + "\u001b[0;34m 1\u001b[0m\n", + "\u001b[0;34m circle\u001b[0m\n", + "\u001b[0;34m 360\u001b[0m\n", + "\u001b[0;34m \u001b[0m\n", + "\u001b[0;34m \u001b[0m\n", + "\u001b[0;34m \u001b[0m\n", + "\u001b[0;34m 2\u001b[0m\n", + "\u001b[0;34m triangle\u001b[0m\n", + "\u001b[0;34m 180\u001b[0m\n", + "\u001b[0;34m 3.0\u001b[0m\n", + "\u001b[0;34m \u001b[0m\n", + "\u001b[0;34m \u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.to_xml(attr_cols=[\u001b[0m\n", + "\u001b[0;34m ... 'index', 'shape', 'degrees', 'sides'\u001b[0m\n", + "\u001b[0;34m ... ]) # doctest: +SKIP\u001b[0m\n", + "\u001b[0;34m \u001b[0m\n", + "\u001b[0;34m \u001b[0m\n", + "\u001b[0;34m \u001b[0m\n", + "\u001b[0;34m \u001b[0m\n", + "\u001b[0;34m \u001b[0m\n", + "\u001b[0;34m \u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.to_xml(namespaces={{\"doc\": \"https://example.com\"}},\u001b[0m\n", + "\u001b[0;34m ... prefix=\"doc\") # doctest: +SKIP\u001b[0m\n", + "\u001b[0;34m \u001b[0m\n", + "\u001b[0;34m \u001b[0m\n", + "\u001b[0;34m \u001b[0m\n", + "\u001b[0;34m 0\u001b[0m\n", + "\u001b[0;34m square\u001b[0m\n", + "\u001b[0;34m 360\u001b[0m\n", + "\u001b[0;34m 4.0\u001b[0m\n", + "\u001b[0;34m \u001b[0m\n", + "\u001b[0;34m \u001b[0m\n", + "\u001b[0;34m 1\u001b[0m\n", + "\u001b[0;34m circle\u001b[0m\n", + "\u001b[0;34m 360\u001b[0m\n", + "\u001b[0;34m \u001b[0m\n", + "\u001b[0;34m \u001b[0m\n", + "\u001b[0;34m \u001b[0m\n", + "\u001b[0;34m 2\u001b[0m\n", + "\u001b[0;34m triangle\u001b[0m\n", + "\u001b[0;34m 180\u001b[0m\n", + "\u001b[0;34m 3.0\u001b[0m\n", + "\u001b[0;34m \u001b[0m\n", + "\u001b[0;34m \u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxml\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mEtreeXMLFormatter\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mLxmlXMLFormatter\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mlxml\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimport_optional_dependency\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"lxml.etree\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mTreeBuilder\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mEtreeXMLFormatter\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mLxmlXMLFormatter\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mparser\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"lxml\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlxml\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mTreeBuilder\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLxmlXMLFormatter\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mImportError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"lxml not found, please install or use the etree parser.\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mparser\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"etree\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mTreeBuilder\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mEtreeXMLFormatter\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Values for parser can only be lxml or etree.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mxml_formatter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTreeBuilder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mpath_or_buffer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mroot_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mroot_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mrow_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrow_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mna_rep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mna_rep\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mattr_cols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattr_cols\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0melem_cols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0melem_cols\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m 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contribution of\u001b[0m\n", + "\u001b[0;34m the index and elements of `object` dtype.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m This value is displayed in `DataFrame.info` by default. This can be\u001b[0m\n", + "\u001b[0;34m suppressed by setting ``pandas.options.display.memory_usage`` to False.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m index : bool, default True\u001b[0m\n", + "\u001b[0;34m Specifies whether to include the memory usage of the DataFrame's\u001b[0m\n", + "\u001b[0;34m index in returned Series. If ``index=True``, the memory usage of\u001b[0m\n", + "\u001b[0;34m the index is the first item in the output.\u001b[0m\n", + "\u001b[0;34m deep : bool, default False\u001b[0m\n", + "\u001b[0;34m If True, introspect the data deeply by interrogating\u001b[0m\n", + "\u001b[0;34m `object` dtypes for system-level memory consumption, and include\u001b[0m\n", + "\u001b[0;34m it in the returned values.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m Series\u001b[0m\n", + "\u001b[0;34m A Series whose index is the original column names and whose values\u001b[0m\n", + "\u001b[0;34m is the memory usage of each column in bytes.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m numpy.ndarray.nbytes : Total bytes consumed by the elements of an\u001b[0m\n", + "\u001b[0;34m ndarray.\u001b[0m\n", + "\u001b[0;34m Series.memory_usage : Bytes consumed by a Series.\u001b[0m\n", + "\u001b[0;34m Categorical : Memory-efficient array for string values with\u001b[0m\n", + "\u001b[0;34m many repeated values.\u001b[0m\n", + "\u001b[0;34m DataFrame.info : Concise summary of a DataFrame.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Notes\u001b[0m\n", + "\u001b[0;34m -----\u001b[0m\n", + "\u001b[0;34m See the :ref:`Frequently Asked Questions ` for more\u001b[0m\n", + "\u001b[0;34m details.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> dtypes = ['int64', 'float64', 'complex128', 'object', 'bool']\u001b[0m\n", + "\u001b[0;34m >>> data = dict([(t, np.ones(shape=5000, dtype=int).astype(t))\u001b[0m\n", + "\u001b[0;34m ... for t in dtypes])\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame(data)\u001b[0m\n", + "\u001b[0;34m >>> df.head()\u001b[0m\n", + "\u001b[0;34m int64 float64 complex128 object bool\u001b[0m\n", + "\u001b[0;34m 0 1 1.0 1.0+0.0j 1 True\u001b[0m\n", + "\u001b[0;34m 1 1 1.0 1.0+0.0j 1 True\u001b[0m\n", + "\u001b[0;34m 2 1 1.0 1.0+0.0j 1 True\u001b[0m\n", + "\u001b[0;34m 3 1 1.0 1.0+0.0j 1 True\u001b[0m\n", + "\u001b[0;34m 4 1 1.0 1.0+0.0j 1 True\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.memory_usage()\u001b[0m\n", + "\u001b[0;34m Index 128\u001b[0m\n", + "\u001b[0;34m int64 40000\u001b[0m\n", + "\u001b[0;34m float64 40000\u001b[0m\n", + "\u001b[0;34m complex128 80000\u001b[0m\n", + "\u001b[0;34m object 40000\u001b[0m\n", + "\u001b[0;34m bool 5000\u001b[0m\n", + "\u001b[0;34m dtype: int64\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.memory_usage(index=False)\u001b[0m\n", + "\u001b[0;34m int64 40000\u001b[0m\n", + "\u001b[0;34m float64 40000\u001b[0m\n", + "\u001b[0;34m complex128 80000\u001b[0m\n", + "\u001b[0;34m object 40000\u001b[0m\n", + "\u001b[0;34m bool 5000\u001b[0m\n", + "\u001b[0;34m dtype: int64\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m The memory footprint of `object` dtype columns is ignored by default:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.memory_usage(deep=True)\u001b[0m\n", + "\u001b[0;34m Index 128\u001b[0m\n", + "\u001b[0;34m int64 40000\u001b[0m\n", + "\u001b[0;34m float64 40000\u001b[0m\n", + "\u001b[0;34m complex128 80000\u001b[0m\n", + "\u001b[0;34m object 180000\u001b[0m\n", + "\u001b[0;34m bool 5000\u001b[0m\n", + "\u001b[0;34m dtype: int64\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Use a Categorical for efficient storage of an object-dtype column with\u001b[0m\n", + "\u001b[0;34m many repeated values.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df['object'].astype('category').memory_usage(deep=True)\u001b[0m\n", + "\u001b[0;34m 5244\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m 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"\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindex_memory_usage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_append\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Transpose index and columns.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Reflect the DataFrame over its main diagonal by writing rows as columns\u001b[0m\n", + "\u001b[0;34m and vice-versa. The property :attr:`.T` is an accessor to the method\u001b[0m\n", + "\u001b[0;34m :meth:`transpose`.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m *args : tuple, optional\u001b[0m\n", + "\u001b[0;34m Accepted for compatibility with NumPy.\u001b[0m\n", + "\u001b[0;34m copy : bool, default False\u001b[0m\n", + "\u001b[0;34m Whether to copy the data after transposing, even for DataFrames\u001b[0m\n", + "\u001b[0;34m with a single dtype.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Note that a copy is always required for mixed dtype DataFrames,\u001b[0m\n", + "\u001b[0;34m or for DataFrames with any extension types.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame\u001b[0m\n", + "\u001b[0;34m The transposed DataFrame.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m numpy.transpose : Permute the dimensions of a given array.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Notes\u001b[0m\n", + "\u001b[0;34m -----\u001b[0m\n", + "\u001b[0;34m Transposing a DataFrame with mixed dtypes will result in a homogeneous\u001b[0m\n", + "\u001b[0;34m DataFrame with the `object` dtype. In such a case, a copy of the data\u001b[0m\n", + "\u001b[0;34m is always made.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m **Square DataFrame with homogeneous dtype**\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> d1 = {'col1': [1, 2], 'col2': [3, 4]}\u001b[0m\n", + "\u001b[0;34m >>> df1 = pd.DataFrame(data=d1)\u001b[0m\n", + "\u001b[0;34m >>> df1\u001b[0m\n", + "\u001b[0;34m col1 col2\u001b[0m\n", + "\u001b[0;34m 0 1 3\u001b[0m\n", + "\u001b[0;34m 1 2 4\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df1_transposed = df1.T # or df1.transpose()\u001b[0m\n", + "\u001b[0;34m >>> df1_transposed\u001b[0m\n", + "\u001b[0;34m 0 1\u001b[0m\n", + "\u001b[0;34m col1 1 2\u001b[0m\n", + "\u001b[0;34m col2 3 4\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m When the dtype is homogeneous in the original DataFrame, we get a\u001b[0m\n", + "\u001b[0;34m transposed DataFrame with the same dtype:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df1.dtypes\u001b[0m\n", + "\u001b[0;34m col1 int64\u001b[0m\n", + "\u001b[0;34m col2 int64\u001b[0m\n", + "\u001b[0;34m dtype: object\u001b[0m\n", + "\u001b[0;34m >>> df1_transposed.dtypes\u001b[0m\n", + "\u001b[0;34m 0 int64\u001b[0m\n", + "\u001b[0;34m 1 int64\u001b[0m\n", + "\u001b[0;34m dtype: object\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m **Non-square DataFrame with mixed dtypes**\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> d2 = {'name': ['Alice', 'Bob'],\u001b[0m\n", + "\u001b[0;34m ... 'score': [9.5, 8],\u001b[0m\n", + "\u001b[0;34m ... 'employed': [False, True],\u001b[0m\n", + "\u001b[0;34m ... 'kids': [0, 0]}\u001b[0m\n", + "\u001b[0;34m >>> df2 = pd.DataFrame(data=d2)\u001b[0m\n", + "\u001b[0;34m >>> df2\u001b[0m\n", + "\u001b[0;34m name score employed kids\u001b[0m\n", + "\u001b[0;34m 0 Alice 9.5 False 0\u001b[0m\n", + "\u001b[0;34m 1 Bob 8.0 True 0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df2_transposed = df2.T # or df2.transpose()\u001b[0m\n", + "\u001b[0;34m >>> df2_transposed\u001b[0m\n", + "\u001b[0;34m 0 1\u001b[0m\n", + "\u001b[0;34m name Alice Bob\u001b[0m\n", + "\u001b[0;34m score 9.5 8.0\u001b[0m\n", + "\u001b[0;34m employed False True\u001b[0m\n", + "\u001b[0;34m kids 0 0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m When the DataFrame has mixed dtypes, we get a transposed DataFrame with\u001b[0m\n", + "\u001b[0;34m the `object` dtype:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df2.dtypes\u001b[0m\n", + "\u001b[0;34m name object\u001b[0m\n", + "\u001b[0;34m score float64\u001b[0m\n", + "\u001b[0;34m employed bool\u001b[0m\n", + "\u001b[0;34m kids int64\u001b[0m\n", + "\u001b[0;34m dtype: object\u001b[0m\n", + "\u001b[0;34m >>> df2_transposed.dtypes\u001b[0m\n", + "\u001b[0;34m 0 object\u001b[0m\n", + "\u001b[0;34m 1 object\u001b[0m\n", + "\u001b[0;34m dtype: object\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_transpose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# construct the args\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdtypes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_fast_transpose\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Note: tests pass without this, but this improves perf quite a bit.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnew_vals\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0musing_copy_on_write\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnew_vals\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_vals\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnew_vals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0musing_copy_on_write\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_references\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[arg-type]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_homogeneous_type\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mdtypes\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mis_extension_array_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtypes\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\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# We have EAs with the same dtype. We can preserve that dtype in transpose.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0marr_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconstruct_array_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0marr_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_from_sequence\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m 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"\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.transpose : Transpose index and columns.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m col1 col2\u001b[0m\n", + "\u001b[0;34m 0 1 3\u001b[0m\n", + "\u001b[0;34m 1 2 4\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.T\u001b[0m\n", + "\u001b[0;34m 0 1\u001b[0m\n", + "\u001b[0;34m col1 1 2\u001b[0m\n", + "\u001b[0;34m col2 3 4\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m 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"\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m Series\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# irow\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnew_mgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfast_xs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# if we are a copy, mark as such\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m 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"\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcol_mgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_col_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol_mgr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# this is a cached value, mark it so\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_as_cached\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_get_column_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mArrayLike\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Get the values of the i'th column (ndarray or ExtensionArray, as stored\u001b[0m\n", + "\u001b[0;34m in the Block)\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Warning! The returned array is a view but doesn't handle Copy-on-Write,\u001b[0m\n", + "\u001b[0;34m so this should be used with caution (for read-only purposes).\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miget_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_iter_column_arrays\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mIterator\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mArrayLike\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Iterate over the arrays of all columns in order.\u001b[0m\n", + "\u001b[0;34m This returns the values as stored in the Block (ndarray or ExtensionArray).\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Warning! The returned array is a view but doesn't handle Copy-on-Write,\u001b[0m\n", + "\u001b[0;34m so this should be used with caution (for read-only purposes).\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\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[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_column_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_nocopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Behaves like __getitem__, but returns a view in cases where __getitem__\u001b[0m\n", + "\u001b[0;34m would make a copy.\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# TODO(CoW): can be removed if/when we are always Copy-on-Write\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_indexer_strict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"columns\"\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\n", + "\u001b[0;34m\u001b[0m \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnew_mgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mallow_dups\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0monly_slice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_mgr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcheck_dict_or_set_indexers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem_from_zerodim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_hashable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# is_iterator to exclude generator e.g. test_getitem_listlike\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# shortcut if the key is in columns\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mis_mi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMultiIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# GH#45316 Return view if key is not duplicated\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Only use drop_duplicates with duplicates for performance\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_mi\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop_duplicates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mis_mi\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Do we have a slicer (on rows)?\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_slice_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"getitem\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# reachable with DatetimeIndex\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaybe_indices_to_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# GH#43223 If we can not convert, use take\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Do we have a (boolean) DataFrame?\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_bool_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# We are left with two options: a single key, and a collection of keys,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# We interpret tuples as collections only for non-MultiIndex\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mis_single_key\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_single_key\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_indexer_strict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"columns\"\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\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# take() does not accept boolean indexers\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"dtype\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_take_with_is_copy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_single_key\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# What does looking for a single key in a non-unique index return?\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# The behavior is inconsistent. It returns a Series, except when\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# - the key itself is repeated (test on data.shape, #9519), or\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# - we have a MultiIndex on columns (test on self.columns, #21309)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMultiIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# GH#26490 using data[key] can cause RecursionError\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_bool_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# also raises Exception if object array with NA values\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# warning here just in case -- previously __setitem__ was\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# reindexing but __getitem__ was not; it seems more reasonable to\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# go with the __setitem__ behavior since that is more consistent\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# with all other indexing behavior\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mequals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"Boolean Series key will be reindexed to match DataFrame index.\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mUserWarning\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfind_stack_level\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34mf\"Item wrong length {len(key)} instead of {len(self.index)}.\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# check_bool_indexer will throw exception if Series key cannot\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# be reindexed to match DataFrame rows\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_bool_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_take_with_is_copy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# self.columns is a MultiIndex\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mslice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnew_columns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult_columns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaybe_droplevels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_columns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult_columns\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# If there is only one column being returned, and its name is\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# either an empty string, or a tuple with an empty string as its\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# first element, then treat the empty string as a placeholder\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# and return the column as if the user had provided that empty\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# string in the key. If the result is a Series, exclude the\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# implied empty string from its name.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\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\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# e.g. test_frame_getitem_multicolumn_empty_level,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# test_frame_mixed_depth_get, test_loc_setitem_single_column_slice\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mtop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mtop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtop\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtop\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor_sliced\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_is_copy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# loc is neither a slice nor ndarray, so must be an int\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ixs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_get_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtakeable\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mScalar\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Quickly retrieve single value at passed column and index.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m index : row label\u001b[0m\n", + "\u001b[0;34m col : column label\u001b[0m\n", + "\u001b[0;34m takeable : interpret the index/col as indexers, default False\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m scalar\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Notes\u001b[0m\n", + "\u001b[0;34m -----\u001b[0m\n", + "\u001b[0;34m Assumes that both `self.index._index_as_unique` and\u001b[0m\n", + "\u001b[0;34m `self.columns._index_as_unique`; Caller is responsible for checking.\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtakeable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mseries\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ixs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mseries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mseries\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMultiIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# CategoricalIndex: Trying to use the engine fastpath may give incorrect\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# results if our categories are integers that dont match our codes\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# IntervalIndex: IntervalTree has no get_loc\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mseries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# For MultiIndex going through engine effectively restricts us to\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# same-length tuples; see test_get_set_value_no_partial_indexing\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mseries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0misetitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Set the given value in the column with position `loc`.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m This is a positional analogue to ``__setitem__``.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m loc : int or sequence of ints\u001b[0m\n", + "\u001b[0;34m Index position for the column.\u001b[0m\n", + "\u001b[0;34m value : scalar or arraylike\u001b[0m\n", + "\u001b[0;34m Value(s) for the column.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Notes\u001b[0m\n", + "\u001b[0;34m -----\u001b[0m\n", + "\u001b[0;34m ``frame.isetitem(loc, value)`` is an in-place method as it will\u001b[0m\n", + "\u001b[0;34m modify the DataFrame in place (not returning a new object). In contrast to\u001b[0m\n", + "\u001b[0;34m ``frame.iloc[:, i] = value`` which will try to update the existing values in\u001b[0m\n", + "\u001b[0;34m place, ``frame.isetitem(loc, value)`` will not update the values of the column\u001b[0m\n", + "\u001b[0;34m itself in place, it will instead insert a new array.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m In cases where ``frame.columns`` is unique, this is equivalent to\u001b[0m\n", + "\u001b[0;34m ``frame[frame.columns[i]] = value``.\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m 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rows\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mslc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_slice_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"getitem\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setitem_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mslc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ndim\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setitem_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m,\u001b[0m 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"\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer_for\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Column to set is duplicated\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setitem_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# set column\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_setitem_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# NB: we can't just use self.loc[key] = value because that\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# operates on labels and we need to operate positional for\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# backwards-compat, xref GH#31469\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_setitem_copy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_setitem_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# also raises Exception if object array with NA values\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_bool_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# bool indexer is indexing along rows\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34mf\"Item wrong length {len(key)} instead of {len(self.index)}!\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_bool_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_setitem_copy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# GH#39931 reindex since iloc does not align\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Note: unlike self.iloc[:, indexer] = value, this will\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# never try to overwrite values inplace\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcheck_key_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk2\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iset_not_inplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\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\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# list of lists\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setitem_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iset_not_inplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_iset_not_inplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# GH#39510 when setting with df[key] = obj with a list-like key and\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# list-like value, we iterate over those listlikes and set columns\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# one at a time. This is different from dispatching to\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# `self.loc[:, key]= value` because loc.__setitem__ may overwrite\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# data inplace, whereas this will insert new arrays.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0migetitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Note: we catch DataFrame obj before getting here, but\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# hypothetically would return obj.iloc[:, i]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\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[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Columns must be same length as key\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0migetitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0milocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer_non_unique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0milocs\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# key entries not in self.columns\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\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[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0milocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Columns must be same length as key\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0morig_columns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Using self.iloc[:, i] = ... may set values inplace, which\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# by convention we do not do in __setitem__\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miloc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0milocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0migetitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0morig_columns\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_setitem_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# support boolean setting with DataFrame input, e.g.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# df[df > df2] = 0\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Array conditional must be same shape as self\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_construct_axes_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_bool_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"Must pass DataFrame or 2-d ndarray with boolean values only\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_inplace_setting\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_setitem_copy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_where\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_set_item_frame_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ensure_valid_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# align columns\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mlen_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen_cols\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Columns must be same length as key\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# align right-hand-side columns if self.columns\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# is multi-index and self[key] is a sub-frame\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m 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"\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcols_droplevel\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcols_droplevel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mequals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcols_droplevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol_droplevel\u001b[0m \u001b[0;32min\u001b[0m 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"\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\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\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"Cannot set a DataFrame with multiple columns to the single \"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34mf\"column {key}\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m 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invalid\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# value exception to occur first\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_setitem_copy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_iset_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0marraylike\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sanitize_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iset_item_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marraylike\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# check if we are modifying a copy\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# try to set first as we want an invalid\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# value exception to occur first\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_setitem_copy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Add series to DataFrame in specified column.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m If series is a numpy-array (not a Series/TimeSeries), it must be the\u001b[0m\n", + "\u001b[0;34m same length as the DataFrames index or an error will be thrown.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Series/TimeSeries will be conformed to the DataFrames index to\u001b[0m\n", + "\u001b[0;34m ensure homogeneity.\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sanitize_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_extension_array_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# broadcast across multiple columns if necessary\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMultiIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mexisting_piece\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexisting_piece\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexisting_piece\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\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[0mT\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_set_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIndexLabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mScalar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtakeable\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Put single value at passed column and index.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m index : Label\u001b[0m\n", + "\u001b[0;34m row label\u001b[0m\n", + "\u001b[0;34m col : Label\u001b[0m\n", + "\u001b[0;34m column label\u001b[0m\n", + "\u001b[0;34m value : scalar\u001b[0m\n", + "\u001b[0;34m takeable : bool, default False\u001b[0m\n", + "\u001b[0;34m Sets whether or not index/col interpreted as indexers\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtakeable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0micol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0miindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0micol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0miindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumn_setitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0micol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_clear_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLossySetitemError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# get_loc might raise a KeyError for missing labels (falling back\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# to (i)loc will do expansion of the index)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# column_setitem will do validation that may raise TypeError,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# ValueError, or LossySetitemError\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# set using a non-recursive method & reset the cache\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtakeable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_item_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mInvalidIndexError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mii_err\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# GH48729: Seems like you are trying to assign a value to a\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# row when only scalar options are permitted\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34mf\"You can only assign a scalar value not a {type(value)}\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mii_err\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_ensure_valid_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Ensure that if we don't have an index, that we can create one from the\u001b[0m\n", + "\u001b[0;34m passed value.\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# GH5632, make sure that we are a Series convertible\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"Cannot set a frame with no defined index \"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"and a value that cannot be converted to a Series\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# GH31368 preserve name of index\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex_copy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex_copy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex_copy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_box_col_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mSingleDataManager\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Provide boxed values for a column.\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Lookup in columns so that if e.g. a str datetime was passed\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# we attach the Timestamp object as the name.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mklass\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor_sliced\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# We get index=self.index bc values is a SingleDataManager\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfastpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# ----------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Lookup Caching\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_clear_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_item_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mHashable\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"Return the cached item, item represents a label indexer.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0musing_copy_on_write\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ixs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcache\u001b[0m \u001b[0;34m=\u001b[0m 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of a DataFrame with a boolean expression.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m expr : str\u001b[0m\n", + "\u001b[0;34m The query string to evaluate.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m You can refer to variables\u001b[0m\n", + "\u001b[0;34m in the environment by prefixing them with an '@' character like\u001b[0m\n", + "\u001b[0;34m ``@a + b``.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m You can refer to column names that are not valid Python variable names\u001b[0m\n", + "\u001b[0;34m by surrounding them in backticks. Thus, column names containing spaces\u001b[0m\n", + "\u001b[0;34m or punctuations (besides underscores) or starting with digits must be\u001b[0m\n", + "\u001b[0;34m surrounded by backticks. (For example, a column named \"Area (cm^2)\" would\u001b[0m\n", + "\u001b[0;34m be referenced as ```Area (cm^2)```). Column names which are Python keywords\u001b[0m\n", + "\u001b[0;34m (like \"list\", \"for\", \"import\", etc) cannot be used.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m For example, if one of your columns is called ``a a`` and you want\u001b[0m\n", + "\u001b[0;34m to sum it with ``b``, your query should be ```a a` + b``.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m inplace : bool\u001b[0m\n", + "\u001b[0;34m Whether to modify the DataFrame rather than creating a new one.\u001b[0m\n", + "\u001b[0;34m **kwargs\u001b[0m\n", + "\u001b[0;34m See the documentation for :func:`eval` for complete details\u001b[0m\n", + "\u001b[0;34m on the keyword arguments accepted by :meth:`DataFrame.query`.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame or None\u001b[0m\n", + "\u001b[0;34m DataFrame resulting from the provided query expression or\u001b[0m\n", + "\u001b[0;34m None if ``inplace=True``.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m eval : Evaluate a string describing operations on\u001b[0m\n", + "\u001b[0;34m DataFrame columns.\u001b[0m\n", + "\u001b[0;34m DataFrame.eval : Evaluate a string describing operations on\u001b[0m\n", + "\u001b[0;34m DataFrame columns.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Notes\u001b[0m\n", + "\u001b[0;34m -----\u001b[0m\n", + "\u001b[0;34m The result of the evaluation of this expression is first passed to\u001b[0m\n", + "\u001b[0;34m :attr:`DataFrame.loc` and if that fails because of a\u001b[0m\n", + "\u001b[0;34m multidimensional key (e.g., a DataFrame) then the result will be passed\u001b[0m\n", + "\u001b[0;34m to :meth:`DataFrame.__getitem__`.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m This method uses the top-level :func:`eval` function to\u001b[0m\n", + "\u001b[0;34m evaluate the passed query.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m The :meth:`~pandas.DataFrame.query` method uses a slightly\u001b[0m\n", + "\u001b[0;34m modified Python syntax by default. For example, the ``&`` and ``|``\u001b[0m\n", + "\u001b[0;34m (bitwise) operators have the precedence of their boolean cousins,\u001b[0m\n", + "\u001b[0;34m :keyword:`and` and :keyword:`or`. This *is* syntactically valid Python,\u001b[0m\n", + "\u001b[0;34m however the semantics are different.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m You can change the semantics of the expression by passing the keyword\u001b[0m\n", + "\u001b[0;34m argument ``parser='python'``. This enforces the same semantics as\u001b[0m\n", + "\u001b[0;34m evaluation in Python space. Likewise, you can pass ``engine='python'``\u001b[0m\n", + "\u001b[0;34m to evaluate an expression using Python itself as a backend. This is not\u001b[0m\n", + "\u001b[0;34m recommended as it is inefficient compared to using ``numexpr`` as the\u001b[0m\n", + "\u001b[0;34m engine.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m The :attr:`DataFrame.index` and\u001b[0m\n", + "\u001b[0;34m :attr:`DataFrame.columns` attributes of the\u001b[0m\n", + "\u001b[0;34m :class:`~pandas.DataFrame` instance are placed in the query namespace\u001b[0m\n", + "\u001b[0;34m by default, which allows you to treat both the index and columns of the\u001b[0m\n", + "\u001b[0;34m frame as a column in the frame.\u001b[0m\n", + "\u001b[0;34m The identifier ``index`` is used for the frame index; you can also\u001b[0m\n", + "\u001b[0;34m use the name of the index to identify it in a query. Please note that\u001b[0m\n", + "\u001b[0;34m Python keywords may not be used as identifiers.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m For further details and examples see the ``query`` documentation in\u001b[0m\n", + "\u001b[0;34m :ref:`indexing `.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m *Backtick quoted variables*\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Backtick quoted variables are parsed as literal Python code and\u001b[0m\n", + "\u001b[0;34m are converted internally to a Python valid identifier.\u001b[0m\n", + "\u001b[0;34m This can lead to the following problems.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m During parsing a number of disallowed characters inside the backtick\u001b[0m\n", + "\u001b[0;34m quoted string are replaced by strings that are allowed as a Python identifier.\u001b[0m\n", + "\u001b[0;34m These characters include all operators in Python, the space character, the\u001b[0m\n", + "\u001b[0;34m question mark, the exclamation mark, the dollar sign, and the euro sign.\u001b[0m\n", + "\u001b[0;34m For other characters that fall outside the ASCII range (U+0001..U+007F)\u001b[0m\n", + "\u001b[0;34m and those that are not further specified in PEP 3131,\u001b[0m\n", + "\u001b[0;34m the query parser will raise an error.\u001b[0m\n", + "\u001b[0;34m This excludes whitespace different than the space character,\u001b[0m\n", + "\u001b[0;34m but also the hashtag (as it is used for comments) and the backtick\u001b[0m\n", + "\u001b[0;34m itself (backtick can also not be escaped).\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m In a special case, quotes that make a pair around a backtick can\u001b[0m\n", + "\u001b[0;34m confuse the parser.\u001b[0m\n", + "\u001b[0;34m For example, ```it's` > `that's``` will raise an error,\u001b[0m\n", + "\u001b[0;34m as it forms a quoted string (``'s > `that'``) with a backtick inside.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See also the Python documentation about lexical analysis\u001b[0m\n", + "\u001b[0;34m (https://docs.python.org/3/reference/lexical_analysis.html)\u001b[0m\n", + "\u001b[0;34m in combination with the source code in :mod:`pandas.core.computation.parsing`.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'A': range(1, 6),\u001b[0m\n", + "\u001b[0;34m ... 'B': range(10, 0, -2),\u001b[0m\n", + "\u001b[0;34m ... 'C C': range(10, 5, -1)})\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m A B C C\u001b[0m\n", + "\u001b[0;34m 0 1 10 10\u001b[0m\n", + "\u001b[0;34m 1 2 8 9\u001b[0m\n", + "\u001b[0;34m 2 3 6 8\u001b[0m\n", + "\u001b[0;34m 3 4 4 7\u001b[0m\n", + "\u001b[0;34m 4 5 2 6\u001b[0m\n", + "\u001b[0;34m >>> df.query('A > B')\u001b[0m\n", + "\u001b[0;34m A B C C\u001b[0m\n", + "\u001b[0;34m 4 5 2 6\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m The previous expression is equivalent to\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df[df.A > df.B]\u001b[0m\n", + "\u001b[0;34m A B C C\u001b[0m\n", + "\u001b[0;34m 4 5 2 6\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m For columns with spaces in their name, you can use backtick quoting.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.query('B == `C C`')\u001b[0m\n", + "\u001b[0;34m A B C C\u001b[0m\n", + "\u001b[0;34m 0 1 10 10\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m The previous expression is equivalent to\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df[df.B == df['C C']]\u001b[0m\n", + "\u001b[0;34m A B C C\u001b[0m\n", + "\u001b[0;34m 0 1 10 10\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0minplace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalidate_bool_kwarg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"inplace\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"expr must be a string to be evaluated, {type(expr)} given\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"level\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"level\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"target\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# when res is multi-dimensional loc raises, but this is sometimes a\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# valid query\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_inplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0moverload\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mLiteral\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0moverload\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mLiteral\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mAny\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Evaluate a string describing operations on DataFrame columns.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Operates on columns only, not specific rows or elements. This allows\u001b[0m\n", + "\u001b[0;34m `eval` to run arbitrary code, which can make you vulnerable to code\u001b[0m\n", + "\u001b[0;34m injection if you pass user input to this function.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m expr : str\u001b[0m\n", + "\u001b[0;34m The expression string to evaluate.\u001b[0m\n", + "\u001b[0;34m inplace : bool, default False\u001b[0m\n", + "\u001b[0;34m If the expression contains an assignment, whether to perform the\u001b[0m\n", + "\u001b[0;34m operation inplace and mutate the existing DataFrame. Otherwise,\u001b[0m\n", + "\u001b[0;34m a new DataFrame is returned.\u001b[0m\n", + "\u001b[0;34m **kwargs\u001b[0m\n", + "\u001b[0;34m See the documentation for :func:`eval` for complete details\u001b[0m\n", + "\u001b[0;34m on the keyword arguments accepted by\u001b[0m\n", + "\u001b[0;34m :meth:`~pandas.DataFrame.query`.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m ndarray, scalar, pandas object, or None\u001b[0m\n", + "\u001b[0;34m The result of the evaluation or None if ``inplace=True``.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.query : Evaluates a boolean expression to query the columns\u001b[0m\n", + "\u001b[0;34m of a frame.\u001b[0m\n", + "\u001b[0;34m DataFrame.assign : Can evaluate an expression or function to create new\u001b[0m\n", + "\u001b[0;34m values for a column.\u001b[0m\n", + "\u001b[0;34m eval : Evaluate a Python expression as a string using various\u001b[0m\n", + "\u001b[0;34m backends.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Notes\u001b[0m\n", + "\u001b[0;34m -----\u001b[0m\n", + "\u001b[0;34m For more details see the API documentation for :func:`~eval`.\u001b[0m\n", + "\u001b[0;34m For detailed examples see :ref:`enhancing performance with eval\u001b[0m\n", + "\u001b[0;34m `.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2)})\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m A B\u001b[0m\n", + "\u001b[0;34m 0 1 10\u001b[0m\n", + "\u001b[0;34m 1 2 8\u001b[0m\n", + "\u001b[0;34m 2 3 6\u001b[0m\n", + "\u001b[0;34m 3 4 4\u001b[0m\n", + "\u001b[0;34m 4 5 2\u001b[0m\n", + "\u001b[0;34m >>> df.eval('A + B')\u001b[0m\n", + "\u001b[0;34m 0 11\u001b[0m\n", + "\u001b[0;34m 1 10\u001b[0m\n", + "\u001b[0;34m 2 9\u001b[0m\n", + "\u001b[0;34m 3 8\u001b[0m\n", + "\u001b[0;34m 4 7\u001b[0m\n", + "\u001b[0;34m dtype: int64\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Assignment is allowed though by default the original DataFrame is not\u001b[0m\n", + "\u001b[0;34m modified.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.eval('C = A + B')\u001b[0m\n", + "\u001b[0;34m A B C\u001b[0m\n", + "\u001b[0;34m 0 1 10 11\u001b[0m\n", + "\u001b[0;34m 1 2 8 10\u001b[0m\n", + "\u001b[0;34m 2 3 6 9\u001b[0m\n", + "\u001b[0;34m 3 4 4 8\u001b[0m\n", + "\u001b[0;34m 4 5 2 7\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m A B\u001b[0m\n", + "\u001b[0;34m 0 1 10\u001b[0m\n", + "\u001b[0;34m 1 2 8\u001b[0m\n", + "\u001b[0;34m 2 3 6\u001b[0m\n", + "\u001b[0;34m 3 4 4\u001b[0m\n", + "\u001b[0;34m 4 5 2\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Multiple columns can be assigned to using multi-line expressions:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.eval(\u001b[0m\n", + "\u001b[0;34m ... '''\u001b[0m\n", + "\u001b[0;34m ... C = A + B\u001b[0m\n", + "\u001b[0;34m ... D = A - B\u001b[0m\n", + "\u001b[0;34m ... '''\u001b[0m\n", + "\u001b[0;34m ... )\u001b[0m\n", + "\u001b[0;34m A B C D\u001b[0m\n", + "\u001b[0;34m 0 1 10 11 -9\u001b[0m\n", + "\u001b[0;34m 1 2 8 10 -6\u001b[0m\n", + "\u001b[0;34m 2 3 6 9 -3\u001b[0m\n", + "\u001b[0;34m 3 4 4 8 0\u001b[0m\n", + "\u001b[0;34m 4 5 2 7 3\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomputation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0meval\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0m_eval\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0minplace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalidate_bool_kwarg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"inplace\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"level\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"level\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex_resolvers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_index_resolvers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcolumn_resolvers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_cleaned_column_resolvers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresolvers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolumn_resolvers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_resolvers\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m\"target\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"target\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"resolvers\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"resolvers\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mresolvers\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0m_eval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mselect_dtypes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Return a subset of the DataFrame's columns based on the column dtypes.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m include, exclude : scalar or list-like\u001b[0m\n", + "\u001b[0;34m A selection of dtypes or strings to be included/excluded. At least\u001b[0m\n", + "\u001b[0;34m one of these parameters must be supplied.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame\u001b[0m\n", + "\u001b[0;34m The subset of the frame including the dtypes in ``include`` and\u001b[0m\n", + "\u001b[0;34m excluding the dtypes in ``exclude``.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Raises\u001b[0m\n", + "\u001b[0;34m ------\u001b[0m\n", + "\u001b[0;34m ValueError\u001b[0m\n", + "\u001b[0;34m * If both of ``include`` and ``exclude`` are empty\u001b[0m\n", + "\u001b[0;34m * If ``include`` and ``exclude`` have overlapping elements\u001b[0m\n", + "\u001b[0;34m * If any kind of string dtype is passed in.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.dtypes: Return Series with the data type of each column.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Notes\u001b[0m\n", + "\u001b[0;34m -----\u001b[0m\n", + "\u001b[0;34m * To select all *numeric* types, use ``np.number`` or ``'number'``\u001b[0m\n", + "\u001b[0;34m * To select strings you must use the ``object`` dtype, but note that\u001b[0m\n", + "\u001b[0;34m this will return *all* object dtype columns\u001b[0m\n", + "\u001b[0;34m * See the `numpy dtype hierarchy\u001b[0m\n", + "\u001b[0;34m `__\u001b[0m\n", + "\u001b[0;34m * To select datetimes, use ``np.datetime64``, ``'datetime'`` or\u001b[0m\n", + "\u001b[0;34m ``'datetime64'``\u001b[0m\n", + "\u001b[0;34m * To select timedeltas, use ``np.timedelta64``, ``'timedelta'`` or\u001b[0m\n", + "\u001b[0;34m ``'timedelta64'``\u001b[0m\n", + "\u001b[0;34m * To select Pandas categorical dtypes, use ``'category'``\u001b[0m\n", + "\u001b[0;34m * To select Pandas datetimetz dtypes, use ``'datetimetz'`` (new in\u001b[0m\n", + "\u001b[0;34m 0.20.0) or ``'datetime64[ns, tz]'``\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'a': [1, 2] * 3,\u001b[0m\n", + "\u001b[0;34m ... 'b': [True, False] * 3,\u001b[0m\n", + "\u001b[0;34m ... 'c': [1.0, 2.0] * 3})\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m a b c\u001b[0m\n", + "\u001b[0;34m 0 1 True 1.0\u001b[0m\n", + "\u001b[0;34m 1 2 False 2.0\u001b[0m\n", + "\u001b[0;34m 2 1 True 1.0\u001b[0m\n", + "\u001b[0;34m 3 2 False 2.0\u001b[0m\n", + "\u001b[0;34m 4 1 True 1.0\u001b[0m\n", + "\u001b[0;34m 5 2 False 2.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.select_dtypes(include='bool')\u001b[0m\n", + "\u001b[0;34m b\u001b[0m\n", + "\u001b[0;34m 0 True\u001b[0m\n", + "\u001b[0;34m 1 False\u001b[0m\n", + "\u001b[0;34m 2 True\u001b[0m\n", + "\u001b[0;34m 3 False\u001b[0m\n", + "\u001b[0;34m 4 True\u001b[0m\n", + "\u001b[0;34m 5 False\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.select_dtypes(include=['float64'])\u001b[0m\n", + "\u001b[0;34m c\u001b[0m\n", + "\u001b[0;34m 0 1.0\u001b[0m\n", + "\u001b[0;34m 1 2.0\u001b[0m\n", + "\u001b[0;34m 2 1.0\u001b[0m\n", + "\u001b[0;34m 3 2.0\u001b[0m\n", + "\u001b[0;34m 4 1.0\u001b[0m\n", + "\u001b[0;34m 5 2.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.select_dtypes(exclude=['int64'])\u001b[0m\n", + "\u001b[0;34m b c\u001b[0m\n", + "\u001b[0;34m 0 True 1.0\u001b[0m\n", + "\u001b[0;34m 1 False 2.0\u001b[0m\n", + "\u001b[0;34m 2 True 1.0\u001b[0m\n", + "\u001b[0;34m 3 False 2.0\u001b[0m\n", + "\u001b[0;34m 4 True 1.0\u001b[0m\n", + "\u001b[0;34m 5 False 2.0\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0minclude\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0minclude\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mexclude\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mexclude\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mexclude\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mselection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mfrozenset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfrozenset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselection\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"at least one of include or exclude must be nonempty\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# convert the myriad valid dtypes object to a single representation\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcheck_int_infer_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mconverted_dtypes\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Numpy maps int to different types (int32, in64) on Windows and Linux\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# see https://github.com/numpy/numpy/issues/9464\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"int\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mconverted_dtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mconverted_dtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"float\"\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# GH#42452 : np.dtype(\"float\") coerces to np.float64 from Numpy 1.20\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mconverted_dtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mconverted_dtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minfer_dtype_from_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mfrozenset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconverted_dtypes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0minclude\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_int_infer_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mexclude\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_int_infer_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdtypes\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0minvalidate_string_dtypes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# can't both include AND exclude!\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0minclude\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misdisjoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"include and exclude overlap on {(include & exclude)}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdtype_predicate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mDtypeObj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtypes_set\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# GH 46870: BooleanDtype._is_numeric == True but should be excluded\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mArrowDtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy_dtype\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0missubclass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtypes_set\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumber\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdtypes_set\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"_is_numeric\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_bool_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mpredicate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mArrayLike\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0minclude\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mdtype_predicate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdtype_predicate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_data_subset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredicate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmgr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mHashable\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mScalar\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mAnyArrayLike\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mallow_duplicates\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNoDefault\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_default\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Insert column into DataFrame at specified location.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Raises a ValueError if `column` is already contained in the DataFrame,\u001b[0m\n", + "\u001b[0;34m unless `allow_duplicates` is set to True.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m loc : int\u001b[0m\n", + "\u001b[0;34m Insertion index. Must verify 0 <= loc <= len(columns).\u001b[0m\n", + "\u001b[0;34m column : str, number, or hashable object\u001b[0m\n", + "\u001b[0;34m Label of the inserted column.\u001b[0m\n", + "\u001b[0;34m value : Scalar, Series, or array-like\u001b[0m\n", + "\u001b[0;34m allow_duplicates : bool, optional, default lib.no_default\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m Index.insert : Insert new item by index.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m col1 col2\u001b[0m\n", + "\u001b[0;34m 0 1 3\u001b[0m\n", + "\u001b[0;34m 1 2 4\u001b[0m\n", + "\u001b[0;34m >>> df.insert(1, \"newcol\", [99, 99])\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m col1 newcol col2\u001b[0m\n", + "\u001b[0;34m 0 1 99 3\u001b[0m\n", + "\u001b[0;34m 1 2 99 4\u001b[0m\n", + "\u001b[0;34m >>> df.insert(0, \"col1\", [100, 100], allow_duplicates=True)\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m col1 col1 newcol col2\u001b[0m\n", + "\u001b[0;34m 0 100 1 99 3\u001b[0m\n", + "\u001b[0;34m 1 100 2 99 4\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Notice that pandas uses index alignment in case of `value` from type `Series`:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.insert(0, \"col0\", pd.Series([5, 6], index=[1, 2]))\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m col0 col1 col1 newcol col2\u001b[0m\n", + "\u001b[0;34m 0 NaN 100 1 99 3\u001b[0m\n", + "\u001b[0;34m 1 5.0 100 2 99 4\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mallow_duplicates\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_default\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mallow_duplicates\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mallow_duplicates\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflags\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mallows_duplicate_labels\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"Cannot specify 'allow_duplicates=True' when \"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"'self.flags.allows_duplicate_labels' is False.\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mallow_duplicates\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Should this be a different kind of error??\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"cannot insert {column}, already exists\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"loc must be int\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sanitize_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0massign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34mr\"\"\"\u001b[0m\n", + "\u001b[0;34m Assign new columns to a DataFrame.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns a new object with all original columns in addition to new ones.\u001b[0m\n", + "\u001b[0;34m Existing columns that are re-assigned will be overwritten.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m **kwargs : dict of {str: callable or Series}\u001b[0m\n", + "\u001b[0;34m The column names are keywords. If the values are\u001b[0m\n", + "\u001b[0;34m callable, they are computed on the DataFrame and\u001b[0m\n", + "\u001b[0;34m assigned to the new columns. The callable must not\u001b[0m\n", + "\u001b[0;34m change input DataFrame (though pandas doesn't check it).\u001b[0m\n", + "\u001b[0;34m If the values are not callable, (e.g. a Series, scalar, or array),\u001b[0m\n", + "\u001b[0;34m they are simply assigned.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame\u001b[0m\n", + "\u001b[0;34m A new DataFrame with the new columns in addition to\u001b[0m\n", + "\u001b[0;34m all the existing columns.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Notes\u001b[0m\n", + "\u001b[0;34m -----\u001b[0m\n", + "\u001b[0;34m Assigning multiple columns within the same ``assign`` is possible.\u001b[0m\n", + "\u001b[0;34m Later items in '\\*\\*kwargs' may refer to newly created or modified\u001b[0m\n", + "\u001b[0;34m columns in 'df'; items are computed and assigned into 'df' in order.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'temp_c': [17.0, 25.0]},\u001b[0m\n", + "\u001b[0;34m ... index=['Portland', 'Berkeley'])\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m temp_c\u001b[0m\n", + "\u001b[0;34m Portland 17.0\u001b[0m\n", + "\u001b[0;34m Berkeley 25.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Where the value is a callable, evaluated on `df`:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)\u001b[0m\n", + "\u001b[0;34m temp_c temp_f\u001b[0m\n", + "\u001b[0;34m Portland 17.0 62.6\u001b[0m\n", + "\u001b[0;34m Berkeley 25.0 77.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Alternatively, the same behavior can be achieved by directly\u001b[0m\n", + "\u001b[0;34m referencing an existing Series or sequence:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.assign(temp_f=df['temp_c'] * 9 / 5 + 32)\u001b[0m\n", + "\u001b[0;34m temp_c temp_f\u001b[0m\n", + "\u001b[0;34m Portland 17.0 62.6\u001b[0m\n", + "\u001b[0;34m Berkeley 25.0 77.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m You can create multiple columns within the same assign where one\u001b[0m\n", + "\u001b[0;34m of the columns depends on another one defined within the same assign:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32,\u001b[0m\n", + "\u001b[0;34m ... temp_k=lambda x: (x['temp_f'] + 459.67) * 5 / 9)\u001b[0m\n", + "\u001b[0;34m temp_c temp_f temp_k\u001b[0m\n", + "\u001b[0;34m Portland 17.0 62.6 290.15\u001b[0m\n", + "\u001b[0;34m Berkeley 25.0 77.0 298.15\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_sanitize_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mArrayLike\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Ensures new columns (which go into the BlockManager as new blocks) are\u001b[0m\n", + "\u001b[0;34m always copied and converted into an array.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m value : scalar, Series, or array-like\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m numpy.ndarray or ExtensionArray\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m 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By doing two 'take's at once we avoid making an\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# unnecessary copy.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# We only get here with `not self._is_mixed_type`, which (almost)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# ensures that self.values is cheap. 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the column labels.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.set_axis(['I', 'II'], axis='columns')\u001b[0m\n", + "\u001b[0;34m I II\u001b[0m\n", + "\u001b[0;34m 0 1 4\u001b[0m\n", + "\u001b[0;34m 1 2 5\u001b[0m\n", + "\u001b[0;34m 2 3 6\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mSubstitution\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0m_shared_doc_kwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mextended_summary_sub\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\" column or\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maxis_description_sub\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\", and 1 identifies the 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\u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIndexLabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIndexLabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mLevel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIgnoreRaise\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"raise\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Drop specified labels from rows or columns.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Remove rows or columns by specifying label names and corresponding\u001b[0m\n", + "\u001b[0;34m axis, or by specifying directly index or column names. When using a\u001b[0m\n", + "\u001b[0;34m multi-index, labels on different levels can be removed by specifying\u001b[0m\n", + "\u001b[0;34m the level. See the :ref:`user guide `\u001b[0m\n", + "\u001b[0;34m for more information about the now unused levels.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m labels : single label or list-like\u001b[0m\n", + "\u001b[0;34m Index or column labels to drop. A tuple will be used as a single\u001b[0m\n", + "\u001b[0;34m label and not treated as a list-like.\u001b[0m\n", + "\u001b[0;34m axis : {0 or 'index', 1 or 'columns'}, default 0\u001b[0m\n", + "\u001b[0;34m Whether to drop labels from the index (0 or 'index') or\u001b[0m\n", + "\u001b[0;34m columns (1 or 'columns').\u001b[0m\n", + "\u001b[0;34m index : single label or list-like\u001b[0m\n", + "\u001b[0;34m Alternative to specifying axis (``labels, axis=0``\u001b[0m\n", + "\u001b[0;34m is equivalent to ``index=labels``).\u001b[0m\n", + "\u001b[0;34m columns : single label or list-like\u001b[0m\n", + "\u001b[0;34m Alternative to specifying axis (``labels, axis=1``\u001b[0m\n", + "\u001b[0;34m is equivalent to ``columns=labels``).\u001b[0m\n", + "\u001b[0;34m level : int or level name, optional\u001b[0m\n", + "\u001b[0;34m For MultiIndex, level from which the labels will be removed.\u001b[0m\n", + "\u001b[0;34m inplace : bool, default False\u001b[0m\n", + "\u001b[0;34m If False, return a copy. Otherwise, do operation\u001b[0m\n", + "\u001b[0;34m inplace and return None.\u001b[0m\n", + "\u001b[0;34m errors : {'ignore', 'raise'}, default 'raise'\u001b[0m\n", + "\u001b[0;34m If 'ignore', suppress error and only existing labels are\u001b[0m\n", + "\u001b[0;34m dropped.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame or None\u001b[0m\n", + "\u001b[0;34m DataFrame without the removed index or column labels or\u001b[0m\n", + "\u001b[0;34m None if ``inplace=True``.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Raises\u001b[0m\n", + "\u001b[0;34m ------\u001b[0m\n", + "\u001b[0;34m KeyError\u001b[0m\n", + "\u001b[0;34m If any of the labels is not found in the selected axis.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.loc : Label-location based indexer for selection by label.\u001b[0m\n", + "\u001b[0;34m DataFrame.dropna : Return DataFrame with labels on given axis omitted\u001b[0m\n", + "\u001b[0;34m where (all or any) data are missing.\u001b[0m\n", + "\u001b[0;34m DataFrame.drop_duplicates : Return DataFrame with duplicate rows\u001b[0m\n", + "\u001b[0;34m removed, optionally only considering certain columns.\u001b[0m\n", + "\u001b[0;34m Series.drop : Return Series with specified index labels removed.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame(np.arange(12).reshape(3, 4),\u001b[0m\n", + "\u001b[0;34m ... columns=['A', 'B', 'C', 'D'])\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m A B C D\u001b[0m\n", + "\u001b[0;34m 0 0 1 2 3\u001b[0m\n", + "\u001b[0;34m 1 4 5 6 7\u001b[0m\n", + "\u001b[0;34m 2 8 9 10 11\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Drop columns\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.drop(['B', 'C'], axis=1)\u001b[0m\n", + "\u001b[0;34m A D\u001b[0m\n", + "\u001b[0;34m 0 0 3\u001b[0m\n", + "\u001b[0;34m 1 4 7\u001b[0m\n", + "\u001b[0;34m 2 8 11\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.drop(columns=['B', 'C'])\u001b[0m\n", + "\u001b[0;34m A D\u001b[0m\n", + "\u001b[0;34m 0 0 3\u001b[0m\n", + "\u001b[0;34m 1 4 7\u001b[0m\n", + "\u001b[0;34m 2 8 11\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Drop a row by index\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.drop([0, 1])\u001b[0m\n", + "\u001b[0;34m A B C D\u001b[0m\n", + "\u001b[0;34m 2 8 9 10 11\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Drop columns and/or rows of MultiIndex DataFrame\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> midx = pd.MultiIndex(levels=[['lama', 'cow', 'falcon'],\u001b[0m\n", + "\u001b[0;34m ... ['speed', 'weight', 'length']],\u001b[0m\n", + "\u001b[0;34m ... codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],\u001b[0m\n", + "\u001b[0;34m ... [0, 1, 2, 0, 1, 2, 0, 1, 2]])\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame(index=midx, columns=['big', 'small'],\u001b[0m\n", + "\u001b[0;34m ... data=[[45, 30], [200, 100], [1.5, 1], [30, 20],\u001b[0m\n", + "\u001b[0;34m ... [250, 150], [1.5, 0.8], [320, 250],\u001b[0m\n", + "\u001b[0;34m ... [1, 0.8], [0.3, 0.2]])\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m big small\u001b[0m\n", + "\u001b[0;34m lama speed 45.0 30.0\u001b[0m\n", + "\u001b[0;34m weight 200.0 100.0\u001b[0m\n", + "\u001b[0;34m length 1.5 1.0\u001b[0m\n", + "\u001b[0;34m cow speed 30.0 20.0\u001b[0m\n", + "\u001b[0;34m weight 250.0 150.0\u001b[0m\n", + "\u001b[0;34m length 1.5 0.8\u001b[0m\n", + "\u001b[0;34m falcon speed 320.0 250.0\u001b[0m\n", + "\u001b[0;34m weight 1.0 0.8\u001b[0m\n", + "\u001b[0;34m length 0.3 0.2\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Drop a specific index combination from the MultiIndex\u001b[0m\n", + "\u001b[0;34m DataFrame, i.e., drop the combination ``'falcon'`` and\u001b[0m\n", + "\u001b[0;34m ``'weight'``, which deletes only the corresponding row\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.drop(index=('falcon', 'weight'))\u001b[0m\n", + "\u001b[0;34m big small\u001b[0m\n", + "\u001b[0;34m lama speed 45.0 30.0\u001b[0m\n", + "\u001b[0;34m weight 200.0 100.0\u001b[0m\n", + "\u001b[0;34m length 1.5 1.0\u001b[0m\n", + "\u001b[0;34m cow speed 30.0 20.0\u001b[0m\n", + "\u001b[0;34m weight 250.0 150.0\u001b[0m\n", + "\u001b[0;34m length 1.5 0.8\u001b[0m\n", + "\u001b[0;34m falcon speed 320.0 250.0\u001b[0m\n", + "\u001b[0;34m length 0.3 0.2\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.drop(index='cow', columns='small')\u001b[0m\n", + "\u001b[0;34m big\u001b[0m\n", + "\u001b[0;34m lama speed 45.0\u001b[0m\n", + "\u001b[0;34m weight 200.0\u001b[0m\n", + "\u001b[0;34m length 1.5\u001b[0m\n", + "\u001b[0;34m falcon speed 320.0\u001b[0m\n", + "\u001b[0;34m weight 1.0\u001b[0m\n", + "\u001b[0;34m length 0.3\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.drop(index='length', level=1)\u001b[0m\n", + "\u001b[0;34m big small\u001b[0m\n", + "\u001b[0;34m lama speed 45.0 30.0\u001b[0m\n", + "\u001b[0;34m weight 200.0 100.0\u001b[0m\n", + "\u001b[0;34m cow speed 30.0 20.0\u001b[0m\n", + "\u001b[0;34m weight 250.0 150.0\u001b[0m\n", + "\u001b[0;34m falcon speed 320.0 250.0\u001b[0m\n", + "\u001b[0;34m weight 1.0 0.8\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0moverload\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmapper\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mRenamer\u001b[0m \u001b[0;34m|\u001b[0m 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"\u001b[0;34m\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mLevel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIgnoreRaise\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Rename columns or index labels.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Function / dict values must be unique (1-to-1). Labels not contained in\u001b[0m\n", + "\u001b[0;34m a dict / Series will be left as-is. Extra labels listed don't throw an\u001b[0m\n", + "\u001b[0;34m error.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See the :ref:`user guide ` for more.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m mapper : dict-like or function\u001b[0m\n", + "\u001b[0;34m Dict-like or function transformations to apply to\u001b[0m\n", + "\u001b[0;34m that axis' values. Use either ``mapper`` and ``axis`` to\u001b[0m\n", + "\u001b[0;34m specify the axis to target with ``mapper``, or ``index`` and\u001b[0m\n", + "\u001b[0;34m ``columns``.\u001b[0m\n", + "\u001b[0;34m index : dict-like or function\u001b[0m\n", + "\u001b[0;34m Alternative to specifying axis (``mapper, axis=0``\u001b[0m\n", + "\u001b[0;34m is equivalent to ``index=mapper``).\u001b[0m\n", + "\u001b[0;34m columns : dict-like or function\u001b[0m\n", + "\u001b[0;34m Alternative to specifying axis (``mapper, axis=1``\u001b[0m\n", + "\u001b[0;34m is equivalent to ``columns=mapper``).\u001b[0m\n", + "\u001b[0;34m axis : {0 or 'index', 1 or 'columns'}, default 0\u001b[0m\n", + "\u001b[0;34m Axis to target with ``mapper``. Can be either the axis name\u001b[0m\n", + "\u001b[0;34m ('index', 'columns') or number (0, 1). The default is 'index'.\u001b[0m\n", + "\u001b[0;34m copy : bool, default True\u001b[0m\n", + "\u001b[0;34m Also copy underlying data.\u001b[0m\n", + "\u001b[0;34m inplace : bool, default False\u001b[0m\n", + "\u001b[0;34m Whether to modify the DataFrame rather than creating a new one.\u001b[0m\n", + "\u001b[0;34m If True then value of copy is ignored.\u001b[0m\n", + "\u001b[0;34m level : int or level name, default None\u001b[0m\n", + "\u001b[0;34m In case of a MultiIndex, only rename labels in the specified\u001b[0m\n", + "\u001b[0;34m level.\u001b[0m\n", + "\u001b[0;34m errors : {'ignore', 'raise'}, default 'ignore'\u001b[0m\n", + "\u001b[0;34m If 'raise', raise a `KeyError` when a dict-like `mapper`, `index`,\u001b[0m\n", + "\u001b[0;34m or `columns` contains labels that are not present in the Index\u001b[0m\n", + "\u001b[0;34m being transformed.\u001b[0m\n", + "\u001b[0;34m If 'ignore', existing keys will be renamed and extra keys will be\u001b[0m\n", + "\u001b[0;34m ignored.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame or None\u001b[0m\n", + "\u001b[0;34m DataFrame with the renamed axis labels or None if ``inplace=True``.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Raises\u001b[0m\n", + "\u001b[0;34m ------\u001b[0m\n", + "\u001b[0;34m KeyError\u001b[0m\n", + "\u001b[0;34m If any of the labels is not found in the selected axis and\u001b[0m\n", + "\u001b[0;34m \"errors='raise'\".\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.rename_axis : Set the name of the axis.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m ``DataFrame.rename`` supports two calling conventions\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m * ``(index=index_mapper, columns=columns_mapper, ...)``\u001b[0m\n", + "\u001b[0;34m * ``(mapper, axis={'index', 'columns'}, ...)``\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m We *highly* recommend using keyword arguments to clarify your\u001b[0m\n", + "\u001b[0;34m intent.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Rename columns using a mapping:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({\"A\": [1, 2, 3], \"B\": [4, 5, 6]})\u001b[0m\n", + "\u001b[0;34m >>> df.rename(columns={\"A\": \"a\", \"B\": \"c\"})\u001b[0m\n", + "\u001b[0;34m a c\u001b[0m\n", + "\u001b[0;34m 0 1 4\u001b[0m\n", + "\u001b[0;34m 1 2 5\u001b[0m\n", + "\u001b[0;34m 2 3 6\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Rename index using a mapping:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.rename(index={0: \"x\", 1: \"y\", 2: \"z\"})\u001b[0m\n", + "\u001b[0;34m A B\u001b[0m\n", + "\u001b[0;34m x 1 4\u001b[0m\n", + "\u001b[0;34m y 2 5\u001b[0m\n", + "\u001b[0;34m z 3 6\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Cast index labels to a different type:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.index\u001b[0m\n", + "\u001b[0;34m RangeIndex(start=0, stop=3, step=1)\u001b[0m\n", + "\u001b[0;34m >>> df.rename(index=str).index\u001b[0m\n", + "\u001b[0;34m Index(['0', '1', '2'], dtype='object')\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.rename(columns={\"A\": \"a\", \"B\": \"b\", \"C\": \"c\"}, errors=\"raise\")\u001b[0m\n", + "\u001b[0;34m Traceback (most recent call last):\u001b[0m\n", + "\u001b[0;34m KeyError: ['C'] not found in axis\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Using axis-style parameters:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.rename(str.lower, axis='columns')\u001b[0m\n", + "\u001b[0;34m a b\u001b[0m\n", + "\u001b[0;34m 0 1 4\u001b[0m\n", + "\u001b[0;34m 1 2 5\u001b[0m\n", + "\u001b[0;34m 2 3 6\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.rename({1: 2, 2: 4}, axis='index')\u001b[0m\n", + "\u001b[0;34m A B\u001b[0m\n", + "\u001b[0;34m 0 1 4\u001b[0m\n", + "\u001b[0;34m 2 2 5\u001b[0m\n", + "\u001b[0;34m 4 3 6\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmapper\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmapper\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m 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Raise KeyError if not found.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m item : label\u001b[0m\n", + "\u001b[0;34m Label of column to be popped.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m Series\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame([('falcon', 'bird', 389.0),\u001b[0m\n", + "\u001b[0;34m ... ('parrot', 'bird', 24.0),\u001b[0m\n", + "\u001b[0;34m ... ('lion', 'mammal', 80.5),\u001b[0m\n", + "\u001b[0;34m ... ('monkey', 'mammal', np.nan)],\u001b[0m\n", + "\u001b[0;34m ... columns=('name', 'class', 'max_speed'))\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m name class max_speed\u001b[0m\n", + "\u001b[0;34m 0 falcon bird 389.0\u001b[0m\n", + "\u001b[0;34m 1 parrot bird 24.0\u001b[0m\n", + "\u001b[0;34m 2 lion mammal 80.5\u001b[0m\n", + "\u001b[0;34m 3 monkey mammal NaN\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.pop('class')\u001b[0m\n", + "\u001b[0;34m 0 bird\u001b[0m\n", + "\u001b[0;34m 1 bird\u001b[0m\n", + "\u001b[0;34m 2 mammal\u001b[0m\n", + "\u001b[0;34m 3 mammal\u001b[0m\n", + "\u001b[0;34m Name: class, dtype: object\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m name max_speed\u001b[0m\n", + "\u001b[0;34m 0 falcon 389.0\u001b[0m\n", + "\u001b[0;34m 1 parrot 24.0\u001b[0m\n", + "\u001b[0;34m 2 lion 80.5\u001b[0m\n", + "\u001b[0;34m 3 monkey NaN\u001b[0m\n", + "\u001b[0;34m 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"\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mperiods\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfreq\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfill_value\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_default\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mncols\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# We will infer fill_value to match the closest column\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Use a column that we know is valid for our column's dtype GH#38434\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mperiods\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0;32mdef\u001b[0m \u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdrop\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mappend\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mverify_integrity\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Set the DataFrame index using existing columns.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Set the DataFrame index (row labels) using one or more existing\u001b[0m\n", + "\u001b[0;34m columns or arrays (of the correct length). The index can replace the\u001b[0m\n", + "\u001b[0;34m existing index or expand on it.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m keys : label or array-like or list of labels/arrays\u001b[0m\n", + "\u001b[0;34m This parameter can be either a single column key, a single array of\u001b[0m\n", + "\u001b[0;34m the same length as the calling DataFrame, or a list containing an\u001b[0m\n", + "\u001b[0;34m arbitrary combination of column keys and arrays. Here, \"array\"\u001b[0m\n", + "\u001b[0;34m encompasses :class:`Series`, :class:`Index`, ``np.ndarray``, and\u001b[0m\n", + "\u001b[0;34m instances of :class:`~collections.abc.Iterator`.\u001b[0m\n", + "\u001b[0;34m drop : bool, default True\u001b[0m\n", + "\u001b[0;34m Delete columns to be used as the new index.\u001b[0m\n", + "\u001b[0;34m append : bool, default False\u001b[0m\n", + "\u001b[0;34m Whether to append columns to existing index.\u001b[0m\n", + "\u001b[0;34m inplace : bool, default False\u001b[0m\n", + "\u001b[0;34m Whether to modify the DataFrame rather than creating a new one.\u001b[0m\n", + "\u001b[0;34m verify_integrity : bool, default False\u001b[0m\n", + "\u001b[0;34m Check the new index for duplicates. Otherwise defer the check until\u001b[0m\n", + "\u001b[0;34m necessary. Setting to False will improve the performance of this\u001b[0m\n", + "\u001b[0;34m method.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame or None\u001b[0m\n", + "\u001b[0;34m Changed row labels or None if ``inplace=True``.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.reset_index : Opposite of set_index.\u001b[0m\n", + "\u001b[0;34m DataFrame.reindex : Change to new indices or expand indices.\u001b[0m\n", + "\u001b[0;34m DataFrame.reindex_like : Change to same indices as other DataFrame.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'month': [1, 4, 7, 10],\u001b[0m\n", + "\u001b[0;34m ... 'year': [2012, 2014, 2013, 2014],\u001b[0m\n", + "\u001b[0;34m ... 'sale': [55, 40, 84, 31]})\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m month year sale\u001b[0m\n", + "\u001b[0;34m 0 1 2012 55\u001b[0m\n", + "\u001b[0;34m 1 4 2014 40\u001b[0m\n", + "\u001b[0;34m 2 7 2013 84\u001b[0m\n", + "\u001b[0;34m 3 10 2014 31\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Set the index to become the 'month' column:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.set_index('month')\u001b[0m\n", + "\u001b[0;34m year sale\u001b[0m\n", + "\u001b[0;34m month\u001b[0m\n", + "\u001b[0;34m 1 2012 55\u001b[0m\n", + "\u001b[0;34m 4 2014 40\u001b[0m\n", + "\u001b[0;34m 7 2013 84\u001b[0m\n", + "\u001b[0;34m 10 2014 31\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Create a MultiIndex using columns 'year' and 'month':\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.set_index(['year', 'month'])\u001b[0m\n", + "\u001b[0;34m sale\u001b[0m\n", + "\u001b[0;34m year month\u001b[0m\n", + "\u001b[0;34m 2012 1 55\u001b[0m\n", + "\u001b[0;34m 2014 4 40\u001b[0m\n", + "\u001b[0;34m 2013 7 84\u001b[0m\n", + "\u001b[0;34m 2014 10 31\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Create a MultiIndex using an Index and a column:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.set_index([pd.Index([1, 2, 3, 4]), 'year'])\u001b[0m\n", + "\u001b[0;34m month sale\u001b[0m\n", + "\u001b[0;34m year\u001b[0m\n", + "\u001b[0;34m 1 2012 1 55\u001b[0m\n", + "\u001b[0;34m 2 2014 4 40\u001b[0m\n", + "\u001b[0;34m 3 2013 7 84\u001b[0m\n", + "\u001b[0;34m 4 2014 10 31\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Create a MultiIndex using two Series:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> s = pd.Series([1, 2, 3, 4])\u001b[0m\n", + "\u001b[0;34m >>> df.set_index([s, s**2])\u001b[0m\n", + "\u001b[0;34m month year sale\u001b[0m\n", + "\u001b[0;34m 1 1 1 2012 55\u001b[0m\n", + "\u001b[0;34m 2 4 4 2014 40\u001b[0m\n", + "\u001b[0;34m 3 9 7 2013 84\u001b[0m\n", + "\u001b[0;34m 4 16 10 2014 31\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0minplace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalidate_bool_kwarg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"inplace\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_inplace_and_allows_duplicate_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkeys\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0merr_msg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m'The parameter \"keys\" may be a column key, one-dimensional '\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"array, or a list containing only valid column keys and \"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"one-dimensional arrays.\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmissing\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mHashable\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mabc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# arrays are fine as long as they are one-dimensional\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# iterators get converted to list below\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ndim\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\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\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr_msg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# everything else gets tried as a key; see GH 24969\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mfound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34mf\"{err_msg}. Received column of type {type(col)}\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mfound\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmissing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmissing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"None of {missing} are in the columns\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mframe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# GH 49473 Use \"lazy copy\" with Copy-on-Write\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mframe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0marrays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mHashable\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mappend\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMultiIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\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[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_level_values\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\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mto_remove\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mHashable\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMultiIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_level_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# if Index then not MultiIndex (treated above)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# error: Argument 1 to \"append\" of \"list\" has incompatible type\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# \"Union[Index, Series]\"; expected \"Index\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type:ignore[arg-type]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# error: Argument 1 to \"append\" of \"list\" has incompatible type\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# \"Union[List[Any], ndarray]\"; expected \"Index\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[arg-type]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mabc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# error: Argument 1 to \"append\" of \"list\" has incompatible type\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# \"List[Any]\"; expected \"Index\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[arg-type]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# from here, col can only be a column label\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdrop\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mto_remove\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\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[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# check newest element against length of calling frame, since\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# ensure_index_from_sequences would not raise for append=False.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34mf\"Length mismatch: Expected {len(self)} rows, \"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34mf\"received array of length {len(arrays[-1])}\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mensure_index_from_sequences\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mverify_integrity\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mduplicates\u001b[0m \u001b[0;34m=\u001b[0m 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"\u001b[0;34m\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdrop\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mLiteral\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcol_level\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mHashable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcol_fill\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mHashable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mallow_duplicates\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m 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"\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIndexLabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdrop\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcol_level\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mHashable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcol_fill\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mHashable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mallow_duplicates\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNoDefault\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mHashable\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mHashable\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mreset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIndexLabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdrop\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcol_level\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mHashable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcol_fill\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mHashable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mallow_duplicates\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNoDefault\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_default\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mHashable\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mHashable\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Reset the index, or a level of it.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Reset the index of the DataFrame, and use the default one instead.\u001b[0m\n", + "\u001b[0;34m If the DataFrame has a MultiIndex, this method can remove one or more\u001b[0m\n", + "\u001b[0;34m levels.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m level : int, str, tuple, or list, default None\u001b[0m\n", + "\u001b[0;34m Only remove the given levels from the index. Removes all levels by\u001b[0m\n", + "\u001b[0;34m default.\u001b[0m\n", + "\u001b[0;34m drop : bool, default False\u001b[0m\n", + "\u001b[0;34m Do not try to insert index into dataframe columns. This resets\u001b[0m\n", + "\u001b[0;34m the index to the default integer index.\u001b[0m\n", + "\u001b[0;34m inplace : bool, default False\u001b[0m\n", + "\u001b[0;34m Whether to modify the DataFrame rather than creating a new one.\u001b[0m\n", + "\u001b[0;34m col_level : int or str, default 0\u001b[0m\n", + "\u001b[0;34m If the columns have multiple levels, determines which level the\u001b[0m\n", + "\u001b[0;34m labels are inserted into. By default it is inserted into the first\u001b[0m\n", + "\u001b[0;34m level.\u001b[0m\n", + "\u001b[0;34m col_fill : object, default ''\u001b[0m\n", + "\u001b[0;34m If the columns have multiple levels, determines how the other\u001b[0m\n", + "\u001b[0;34m levels are named. If None then the index name is repeated.\u001b[0m\n", + "\u001b[0;34m allow_duplicates : bool, optional, default lib.no_default\u001b[0m\n", + "\u001b[0;34m Allow duplicate column labels to be created.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionadded:: 1.5.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m names : int, str or 1-dimensional list, default None\u001b[0m\n", + "\u001b[0;34m Using the given string, rename the DataFrame column which contains the\u001b[0m\n", + "\u001b[0;34m index data. If the DataFrame has a MultiIndex, this has to be a list or\u001b[0m\n", + "\u001b[0;34m tuple with length equal to the number of levels.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionadded:: 1.5.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame or None\u001b[0m\n", + "\u001b[0;34m DataFrame with the new index or None if ``inplace=True``.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.set_index : Opposite of reset_index.\u001b[0m\n", + "\u001b[0;34m DataFrame.reindex : Change to new indices or expand indices.\u001b[0m\n", + "\u001b[0;34m DataFrame.reindex_like : Change to same indices as other DataFrame.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame([('bird', 389.0),\u001b[0m\n", + "\u001b[0;34m ... ('bird', 24.0),\u001b[0m\n", + "\u001b[0;34m ... ('mammal', 80.5),\u001b[0m\n", + "\u001b[0;34m ... ('mammal', np.nan)],\u001b[0m\n", + "\u001b[0;34m ... index=['falcon', 'parrot', 'lion', 'monkey'],\u001b[0m\n", + "\u001b[0;34m ... columns=('class', 'max_speed'))\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m class max_speed\u001b[0m\n", + "\u001b[0;34m falcon bird 389.0\u001b[0m\n", + "\u001b[0;34m parrot bird 24.0\u001b[0m\n", + "\u001b[0;34m lion mammal 80.5\u001b[0m\n", + "\u001b[0;34m monkey mammal NaN\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m When we reset the index, the old index is added as a column, and a\u001b[0m\n", + "\u001b[0;34m new sequential index is used:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.reset_index()\u001b[0m\n", + "\u001b[0;34m index class max_speed\u001b[0m\n", + "\u001b[0;34m 0 falcon bird 389.0\u001b[0m\n", + "\u001b[0;34m 1 parrot bird 24.0\u001b[0m\n", + "\u001b[0;34m 2 lion mammal 80.5\u001b[0m\n", + "\u001b[0;34m 3 monkey mammal NaN\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m We can use the `drop` parameter to avoid the old index being added as\u001b[0m\n", + "\u001b[0;34m a column:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.reset_index(drop=True)\u001b[0m\n", + "\u001b[0;34m class max_speed\u001b[0m\n", + "\u001b[0;34m 0 bird 389.0\u001b[0m\n", + "\u001b[0;34m 1 bird 24.0\u001b[0m\n", + "\u001b[0;34m 2 mammal 80.5\u001b[0m\n", + "\u001b[0;34m 3 mammal NaN\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m You can also use `reset_index` with `MultiIndex`.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> index = pd.MultiIndex.from_tuples([('bird', 'falcon'),\u001b[0m\n", + "\u001b[0;34m ... ('bird', 'parrot'),\u001b[0m\n", + "\u001b[0;34m ... ('mammal', 'lion'),\u001b[0m\n", + "\u001b[0;34m ... ('mammal', 'monkey')],\u001b[0m\n", + "\u001b[0;34m ... names=['class', 'name'])\u001b[0m\n", + "\u001b[0;34m >>> columns = pd.MultiIndex.from_tuples([('speed', 'max'),\u001b[0m\n", + "\u001b[0;34m ... ('species', 'type')])\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame([(389.0, 'fly'),\u001b[0m\n", + "\u001b[0;34m ... (24.0, 'fly'),\u001b[0m\n", + "\u001b[0;34m ... (80.5, 'run'),\u001b[0m\n", + "\u001b[0;34m ... (np.nan, 'jump')],\u001b[0m\n", + "\u001b[0;34m ... index=index,\u001b[0m\n", + "\u001b[0;34m ... columns=columns)\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m speed species\u001b[0m\n", + "\u001b[0;34m max type\u001b[0m\n", + "\u001b[0;34m class name\u001b[0m\n", + "\u001b[0;34m bird falcon 389.0 fly\u001b[0m\n", + "\u001b[0;34m parrot 24.0 fly\u001b[0m\n", + "\u001b[0;34m mammal lion 80.5 run\u001b[0m\n", + "\u001b[0;34m monkey NaN jump\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Using the `names` parameter, choose a name for the index column:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.reset_index(names=['classes', 'names'])\u001b[0m\n", + "\u001b[0;34m classes names speed species\u001b[0m\n", + "\u001b[0;34m max type\u001b[0m\n", + "\u001b[0;34m 0 bird falcon 389.0 fly\u001b[0m\n", + "\u001b[0;34m 1 bird parrot 24.0 fly\u001b[0m\n", + "\u001b[0;34m 2 mammal lion 80.5 run\u001b[0m\n", + "\u001b[0;34m 3 mammal monkey NaN jump\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m If the index has multiple levels, we can reset a subset of them:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.reset_index(level='class')\u001b[0m\n", + "\u001b[0;34m class speed species\u001b[0m\n", + "\u001b[0;34m max type\u001b[0m\n", + "\u001b[0;34m name\u001b[0m\n", + "\u001b[0;34m falcon bird 389.0 fly\u001b[0m\n", + "\u001b[0;34m parrot bird 24.0 fly\u001b[0m\n", + "\u001b[0;34m lion mammal 80.5 run\u001b[0m\n", + "\u001b[0;34m monkey mammal NaN jump\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m If we are not dropping the index, by default, it is placed in the top\u001b[0m\n", + "\u001b[0;34m level. We can place it in another level:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.reset_index(level='class', col_level=1)\u001b[0m\n", + "\u001b[0;34m speed species\u001b[0m\n", + "\u001b[0;34m class max type\u001b[0m\n", + "\u001b[0;34m name\u001b[0m\n", + "\u001b[0;34m falcon bird 389.0 fly\u001b[0m\n", + "\u001b[0;34m parrot bird 24.0 fly\u001b[0m\n", + "\u001b[0;34m lion mammal 80.5 run\u001b[0m\n", + "\u001b[0;34m monkey mammal NaN jump\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m When the index is inserted under another level, we can specify under\u001b[0m\n", + "\u001b[0;34m which one with the parameter `col_fill`:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.reset_index(level='class', col_level=1, col_fill='species')\u001b[0m\n", + "\u001b[0;34m species speed species\u001b[0m\n", + "\u001b[0;34m class max type\u001b[0m\n", + "\u001b[0;34m name\u001b[0m\n", + "\u001b[0;34m falcon bird 389.0 fly\u001b[0m\n", + "\u001b[0;34m parrot bird 24.0 fly\u001b[0m\n", + "\u001b[0;34m lion mammal 80.5 run\u001b[0m\n", + "\u001b[0;34m monkey mammal NaN jump\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m If we specify a nonexistent level for `col_fill`, it is created:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.reset_index(level='class', col_level=1, col_fill='genus')\u001b[0m\n", + "\u001b[0;34m genus speed species\u001b[0m\n", + "\u001b[0;34m class max type\u001b[0m\n", + "\u001b[0;34m name\u001b[0m\n", + "\u001b[0;34m falcon bird 389.0 fly\u001b[0m\n", + "\u001b[0;34m parrot bird 24.0 fly\u001b[0m\n", + "\u001b[0;34m lion mammal 80.5 run\u001b[0m\n", + "\u001b[0;34m monkey mammal NaN jump\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0minplace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalidate_bool_kwarg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"inplace\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_inplace_and_allows_duplicate_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnew_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnew_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m 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axes.\u001b[0m\n", + "\u001b[0;34m Only a single axis is allowed.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m how : {'any', 'all'}, default 'any'\u001b[0m\n", + "\u001b[0;34m Determine if row or column is removed from DataFrame, when we have\u001b[0m\n", + "\u001b[0;34m at least one NA or all NA.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m * 'any' : If any NA values are present, drop that row or column.\u001b[0m\n", + "\u001b[0;34m * 'all' : If all values are NA, drop that row or column.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m thresh : int, optional\u001b[0m\n", + "\u001b[0;34m Require that many non-NA values. Cannot be combined with how.\u001b[0m\n", + "\u001b[0;34m subset : column label or sequence of labels, optional\u001b[0m\n", + "\u001b[0;34m Labels along other axis to consider, e.g. if you are dropping rows\u001b[0m\n", + "\u001b[0;34m these would be a list of columns to include.\u001b[0m\n", + "\u001b[0;34m inplace : bool, default False\u001b[0m\n", + "\u001b[0;34m Whether to modify the DataFrame rather than creating a new one.\u001b[0m\n", + "\u001b[0;34m ignore_index : bool, default ``False``\u001b[0m\n", + "\u001b[0;34m If ``True``, the resulting axis will be labeled 0, 1, …, n - 1.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionadded:: 2.0.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame or None\u001b[0m\n", + "\u001b[0;34m DataFrame with NA entries dropped from it or None if ``inplace=True``.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.isna: Indicate missing values.\u001b[0m\n", + "\u001b[0;34m DataFrame.notna : Indicate existing (non-missing) values.\u001b[0m\n", + "\u001b[0;34m DataFrame.fillna : Replace missing values.\u001b[0m\n", + "\u001b[0;34m Series.dropna : Drop missing values.\u001b[0m\n", + "\u001b[0;34m Index.dropna : Drop missing indices.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({\"name\": ['Alfred', 'Batman', 'Catwoman'],\u001b[0m\n", + "\u001b[0;34m ... \"toy\": [np.nan, 'Batmobile', 'Bullwhip'],\u001b[0m\n", + "\u001b[0;34m ... \"born\": [pd.NaT, pd.Timestamp(\"1940-04-25\"),\u001b[0m\n", + "\u001b[0;34m ... pd.NaT]})\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m name toy born\u001b[0m\n", + "\u001b[0;34m 0 Alfred NaN NaT\u001b[0m\n", + "\u001b[0;34m 1 Batman Batmobile 1940-04-25\u001b[0m\n", + "\u001b[0;34m 2 Catwoman Bullwhip NaT\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Drop the rows where at least one element is missing.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.dropna()\u001b[0m\n", + "\u001b[0;34m name toy born\u001b[0m\n", + "\u001b[0;34m 1 Batman Batmobile 1940-04-25\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Drop the columns where at least one element is missing.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.dropna(axis='columns')\u001b[0m\n", + "\u001b[0;34m name\u001b[0m\n", + "\u001b[0;34m 0 Alfred\u001b[0m\n", + "\u001b[0;34m 1 Batman\u001b[0m\n", + "\u001b[0;34m 2 Catwoman\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Drop the rows where all elements are missing.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.dropna(how='all')\u001b[0m\n", + "\u001b[0;34m name toy born\u001b[0m\n", + "\u001b[0;34m 0 Alfred NaN NaT\u001b[0m\n", + "\u001b[0;34m 1 Batman Batmobile 1940-04-25\u001b[0m\n", + "\u001b[0;34m 2 Catwoman Bullwhip NaT\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Keep only the rows with at least 2 non-NA values.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.dropna(thresh=2)\u001b[0m\n", + "\u001b[0;34m name toy born\u001b[0m\n", + "\u001b[0;34m 1 Batman Batmobile 1940-04-25\u001b[0m\n", + "\u001b[0;34m 2 Catwoman Bullwhip NaT\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Define in which columns to look for missing values.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.dropna(subset=['name', 'toy'])\u001b[0m\n", + "\u001b[0;34m name toy born\u001b[0m\n", + "\u001b[0;34m 1 Batman Batmobile 1940-04-25\u001b[0m\n", + "\u001b[0;34m 2 Catwoman Bullwhip NaT\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhow\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mno_default\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mthresh\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mno_default\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"You cannot set both the how and thresh arguments at the same time.\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhow\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mno_default\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mhow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"any\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0minplace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalidate_bool_kwarg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"inplace\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# GH20987\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"supplying multiple axes to axis is no longer supported.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0magg_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0magg_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0msubset\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# subset needs to be list\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m 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"\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcheck\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcheck\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0magg_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0magg_axis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mthresh\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mno_default\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcount\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0magg_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0magg_axis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcount\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mthresh\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mhow\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"any\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# faster equivalent to 'agg_obj.count(agg_axis) == self.shape[agg_axis]'\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnotna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0magg_obj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0magg_axis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbool_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mhow\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"all\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# faster equivalent to 'agg_obj.count(agg_axis) > 0'\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Return DataFrame with duplicate rows removed.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Considering certain columns is optional. Indexes, including time indexes\u001b[0m\n", + "\u001b[0;34m are ignored.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m subset : column label or sequence of labels, optional\u001b[0m\n", + "\u001b[0;34m Only consider certain columns for identifying duplicates, by\u001b[0m\n", + "\u001b[0;34m default use all of the columns.\u001b[0m\n", + "\u001b[0;34m keep : {'first', 'last', ``False``}, default 'first'\u001b[0m\n", + "\u001b[0;34m Determines which duplicates (if any) to keep.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m - 'first' : Drop duplicates except for the first occurrence.\u001b[0m\n", + "\u001b[0;34m - 'last' : Drop duplicates except for the last occurrence.\u001b[0m\n", + "\u001b[0;34m - ``False`` : Drop all duplicates.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m inplace : bool, default ``False``\u001b[0m\n", + "\u001b[0;34m Whether to modify the DataFrame rather than creating a new one.\u001b[0m\n", + "\u001b[0;34m ignore_index : bool, default ``False``\u001b[0m\n", + "\u001b[0;34m If ``True``, the resulting axis will be labeled 0, 1, …, n - 1.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame or None\u001b[0m\n", + "\u001b[0;34m DataFrame with duplicates removed or None if ``inplace=True``.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.value_counts: Count unique combinations of columns.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m Consider dataset containing ramen rating.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({\u001b[0m\n", + "\u001b[0;34m ... 'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],\u001b[0m\n", + "\u001b[0;34m ... 'style': ['cup', 'cup', 'cup', 'pack', 'pack'],\u001b[0m\n", + "\u001b[0;34m ... 'rating': [4, 4, 3.5, 15, 5]\u001b[0m\n", + "\u001b[0;34m ... })\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m brand style rating\u001b[0m\n", + "\u001b[0;34m 0 Yum Yum cup 4.0\u001b[0m\n", + "\u001b[0;34m 1 Yum Yum cup 4.0\u001b[0m\n", + "\u001b[0;34m 2 Indomie cup 3.5\u001b[0m\n", + "\u001b[0;34m 3 Indomie pack 15.0\u001b[0m\n", + "\u001b[0;34m 4 Indomie pack 5.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m By default, it removes duplicate rows based on all columns.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.drop_duplicates()\u001b[0m\n", + "\u001b[0;34m brand style rating\u001b[0m\n", + "\u001b[0;34m 0 Yum Yum cup 4.0\u001b[0m\n", + "\u001b[0;34m 2 Indomie cup 3.5\u001b[0m\n", + "\u001b[0;34m 3 Indomie pack 15.0\u001b[0m\n", + "\u001b[0;34m 4 Indomie pack 5.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m To remove duplicates on specific column(s), use ``subset``.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.drop_duplicates(subset=['brand'])\u001b[0m\n", + "\u001b[0;34m brand style rating\u001b[0m\n", + "\u001b[0;34m 0 Yum Yum cup 4.0\u001b[0m\n", + "\u001b[0;34m 2 Indomie cup 3.5\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m To remove duplicates and keep last occurrences, use ``keep``.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.drop_duplicates(subset=['brand', 'style'], keep='last')\u001b[0m\n", + "\u001b[0;34m brand style rating\u001b[0m\n", + "\u001b[0;34m 1 Yum Yum cup 4.0\u001b[0m\n", + "\u001b[0;34m 2 Indomie cup 3.5\u001b[0m\n", + "\u001b[0;34m 4 Indomie pack 5.0\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0minplace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalidate_bool_kwarg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"inplace\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mignore_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalidate_bool_kwarg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mignore_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ignore_index\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mduplicated\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdefault_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_inplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mduplicated\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0msubset\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mHashable\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mHashable\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkeep\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mDropKeep\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"first\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Return boolean Series denoting duplicate rows.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Considering certain columns is optional.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m subset : column label or sequence of labels, optional\u001b[0m\n", + "\u001b[0;34m Only consider certain columns for identifying duplicates, by\u001b[0m\n", + "\u001b[0;34m default use all of the columns.\u001b[0m\n", + "\u001b[0;34m keep : {'first', 'last', False}, default 'first'\u001b[0m\n", + "\u001b[0;34m Determines which duplicates (if any) to mark.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m - ``first`` : Mark duplicates as ``True`` except for the first occurrence.\u001b[0m\n", + "\u001b[0;34m - ``last`` : Mark duplicates as ``True`` except for the last occurrence.\u001b[0m\n", + "\u001b[0;34m - False : Mark all duplicates as ``True``.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m Series\u001b[0m\n", + "\u001b[0;34m Boolean series for each duplicated rows.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m Index.duplicated : Equivalent method on index.\u001b[0m\n", + "\u001b[0;34m Series.duplicated : Equivalent method on Series.\u001b[0m\n", + "\u001b[0;34m Series.drop_duplicates : Remove duplicate values from Series.\u001b[0m\n", + "\u001b[0;34m DataFrame.drop_duplicates : Remove duplicate values from DataFrame.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m Consider dataset containing ramen rating.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({\u001b[0m\n", + "\u001b[0;34m ... 'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],\u001b[0m\n", + "\u001b[0;34m ... 'style': ['cup', 'cup', 'cup', 'pack', 'pack'],\u001b[0m\n", + "\u001b[0;34m ... 'rating': [4, 4, 3.5, 15, 5]\u001b[0m\n", + "\u001b[0;34m ... })\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m brand style rating\u001b[0m\n", + "\u001b[0;34m 0 Yum Yum cup 4.0\u001b[0m\n", + "\u001b[0;34m 1 Yum Yum cup 4.0\u001b[0m\n", + "\u001b[0;34m 2 Indomie cup 3.5\u001b[0m\n", + "\u001b[0;34m 3 Indomie pack 15.0\u001b[0m\n", + "\u001b[0;34m 4 Indomie pack 5.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m By default, for each set of duplicated values, the first occurrence\u001b[0m\n", + "\u001b[0;34m is set on False and all others on True.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.duplicated()\u001b[0m\n", + "\u001b[0;34m 0 False\u001b[0m\n", + "\u001b[0;34m 1 True\u001b[0m\n", + "\u001b[0;34m 2 False\u001b[0m\n", + "\u001b[0;34m 3 False\u001b[0m\n", + "\u001b[0;34m 4 False\u001b[0m\n", + "\u001b[0;34m dtype: bool\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m By using 'last', the last occurrence of each set of duplicated values\u001b[0m\n", + "\u001b[0;34m is set on False and all others on True.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.duplicated(keep='last')\u001b[0m\n", + "\u001b[0;34m 0 True\u001b[0m\n", + "\u001b[0;34m 1 False\u001b[0m\n", + "\u001b[0;34m 2 False\u001b[0m\n", + "\u001b[0;34m 3 False\u001b[0m\n", + "\u001b[0;34m 4 False\u001b[0m\n", + "\u001b[0;34m dtype: bool\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m By setting ``keep`` on False, all duplicates are True.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.duplicated(keep=False)\u001b[0m\n", + "\u001b[0;34m 0 True\u001b[0m\n", + "\u001b[0;34m 1 True\u001b[0m\n", + "\u001b[0;34m 2 False\u001b[0m\n", + "\u001b[0;34m 3 False\u001b[0m\n", + "\u001b[0;34m 4 False\u001b[0m\n", + "\u001b[0;34m dtype: bool\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m To find duplicates on specific column(s), use ``subset``.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.duplicated(subset=['brand'])\u001b[0m\n", + "\u001b[0;34m 0 False\u001b[0m\n", + "\u001b[0;34m 1 True\u001b[0m\n", + "\u001b[0;34m 2 False\u001b[0m\n", + "\u001b[0;34m 3 True\u001b[0m\n", + "\u001b[0;34m 4 True\u001b[0m\n", + "\u001b[0;34m dtype: bool\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor_sliced\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvals\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0malgorithms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfactorize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize_hint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"i8\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0msubset\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# https://github.com/pandas-dev/pandas/issues/28770\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Incompatible types in assignment (expression has type \"Index\", variable\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# has type \"Sequence[Any]\")\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0msubset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;31m# type: ignore[assignment]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0msubset\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0msubset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0msubset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# needed for mypy since can't narrow types using np.iterable\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0msubset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Verify all columns in subset exist in the queried dataframe\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Otherwise, raise a KeyError, same as if you try to __getitem__ with a\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# key that doesn't exist.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdiff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubset\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdiff\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdiff\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubset\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# GH#45236 This is faster than get_group_index below\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msubset\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.\u001b[0m\u001b[0mduplicated\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mvals\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msubset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_group_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# error: Argument 1 to \"tuple\" has incompatible type \"List[_T]\";\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# expected \"Iterable[int]\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# type: ignore[arg-type]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mxnull\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor_sliced\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mduplicated\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"duplicated\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# ----------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Sorting\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# error: Signature of \"sort_values\" incompatible with supertype \"NDFrame\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0moverload\u001b[0m \u001b[0;31m# type: ignore[override]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mby\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIndexLabel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mascending\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mLiteral\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m 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\u001b[0mLiteral\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mna_position\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mValueKeyFunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m 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\u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"quicksort\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mna_position\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"last\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mValueKeyFunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0minplace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalidate_bool_kwarg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"inplace\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mascending\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalidate_ascending\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mascending\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mby\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# error: Argument 1 to \"len\" has incompatible type \"Union[bool, List[bool]]\";\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# expected \"Sized\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_sequence\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mascending\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mascending\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[arg-type]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# error: Argument 1 to \"len\" has incompatible type \"Union[bool,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# List[bool]]\"; expected \"Sized\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34mf\"Length of ascending ({len(ascending)})\"\u001b[0m \u001b[0;31m# type: ignore[arg-type]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34mf\" != length of by ({len(by)})\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\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\n", + "\u001b[0;34m\u001b[0m \u001b[0mkeys\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_label_or_level_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mby\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# need to rewrap columns in Series to apply key function\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# error: List comprehension has incompatible type List[Series];\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# expected List[ndarray]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkeys\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mby\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlexsort_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mascending\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_position\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mna_position\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# len(by) == 1\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mby\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mby\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_label_or_level_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# need to rewrap column in Series to apply key function\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# error: Incompatible types in assignment (expression has type\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# \"Series\", variable has type \"ndarray\")\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[assignment]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mascending\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mascending\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mascending\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnargsort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mascending\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mascending\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_position\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mna_position\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_inplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_range_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mnot\u001b[0m \u001b[0minplace\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0musing_copy_on_write\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdefault_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_inplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIndexLabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mascending\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mSortKind\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"quicksort\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mna_position\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mNaPosition\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"last\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0msort_remaining\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIndexKeyFunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Sort object by labels (along an axis).\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns a new DataFrame sorted by label if `inplace` argument is\u001b[0m\n", + "\u001b[0;34m ``False``, otherwise updates the original DataFrame and returns None.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m axis : {0 or 'index', 1 or 'columns'}, default 0\u001b[0m\n", + "\u001b[0;34m The axis along which to sort. The value 0 identifies the rows,\u001b[0m\n", + "\u001b[0;34m and 1 identifies the columns.\u001b[0m\n", + "\u001b[0;34m level : int or level name or list of ints or list of level names\u001b[0m\n", + "\u001b[0;34m If not None, sort on values in specified index level(s).\u001b[0m\n", + "\u001b[0;34m ascending : bool or list-like of bools, default True\u001b[0m\n", + "\u001b[0;34m Sort ascending vs. descending. When the index is a MultiIndex the\u001b[0m\n", + "\u001b[0;34m sort direction can be controlled for each level individually.\u001b[0m\n", + "\u001b[0;34m inplace : bool, default False\u001b[0m\n", + "\u001b[0;34m Whether to modify the DataFrame rather than creating a new one.\u001b[0m\n", + "\u001b[0;34m kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort'\u001b[0m\n", + "\u001b[0;34m Choice of sorting algorithm. See also :func:`numpy.sort` for more\u001b[0m\n", + "\u001b[0;34m information. `mergesort` and `stable` are the only stable algorithms. For\u001b[0m\n", + "\u001b[0;34m DataFrames, this option is only applied when sorting on a single\u001b[0m\n", + "\u001b[0;34m column or label.\u001b[0m\n", + "\u001b[0;34m na_position : {'first', 'last'}, default 'last'\u001b[0m\n", + "\u001b[0;34m Puts NaNs at the beginning if `first`; `last` puts NaNs at the end.\u001b[0m\n", + "\u001b[0;34m Not implemented for MultiIndex.\u001b[0m\n", + "\u001b[0;34m sort_remaining : bool, default True\u001b[0m\n", + "\u001b[0;34m If True and sorting by level and index is multilevel, sort by other\u001b[0m\n", + "\u001b[0;34m levels too (in order) after sorting by specified level.\u001b[0m\n", + "\u001b[0;34m ignore_index : bool, default False\u001b[0m\n", + "\u001b[0;34m If True, the resulting axis will be labeled 0, 1, …, n - 1.\u001b[0m\n", + "\u001b[0;34m key : callable, optional\u001b[0m\n", + "\u001b[0;34m If not None, apply the key function to the index values\u001b[0m\n", + "\u001b[0;34m before sorting. This is similar to the `key` argument in the\u001b[0m\n", + "\u001b[0;34m builtin :meth:`sorted` function, with the notable difference that\u001b[0m\n", + "\u001b[0;34m this `key` function should be *vectorized*. It should expect an\u001b[0m\n", + "\u001b[0;34m ``Index`` and return an ``Index`` of the same shape. For MultiIndex\u001b[0m\n", + "\u001b[0;34m inputs, the key is applied *per level*.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionadded:: 1.1.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame or None\u001b[0m\n", + "\u001b[0;34m The original DataFrame sorted by the labels or None if ``inplace=True``.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m Series.sort_index : Sort Series by the index.\u001b[0m\n", + "\u001b[0;34m DataFrame.sort_values : Sort DataFrame by the value.\u001b[0m\n", + "\u001b[0;34m Series.sort_values : Sort Series by the value.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame([1, 2, 3, 4, 5], index=[100, 29, 234, 1, 150],\u001b[0m\n", + "\u001b[0;34m ... columns=['A'])\u001b[0m\n", + "\u001b[0;34m >>> df.sort_index()\u001b[0m\n", + "\u001b[0;34m A\u001b[0m\n", + "\u001b[0;34m 1 4\u001b[0m\n", + "\u001b[0;34m 29 2\u001b[0m\n", + "\u001b[0;34m 100 1\u001b[0m\n", + "\u001b[0;34m 150 5\u001b[0m\n", + "\u001b[0;34m 234 3\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m By default, it sorts in ascending order, to sort in descending order,\u001b[0m\n", + "\u001b[0;34m use ``ascending=False``\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.sort_index(ascending=False)\u001b[0m\n", + "\u001b[0;34m A\u001b[0m\n", + "\u001b[0;34m 234 3\u001b[0m\n", + "\u001b[0;34m 150 5\u001b[0m\n", + "\u001b[0;34m 100 1\u001b[0m\n", + "\u001b[0;34m 29 2\u001b[0m\n", + "\u001b[0;34m 1 4\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m A key function can be specified which is applied to the index before\u001b[0m\n", + "\u001b[0;34m sorting. For a ``MultiIndex`` this is applied to each level separately.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({\"a\": [1, 2, 3, 4]}, index=['A', 'b', 'C', 'd'])\u001b[0m\n", + "\u001b[0;34m >>> df.sort_index(key=lambda x: x.str.lower())\u001b[0m\n", + "\u001b[0;34m a\u001b[0m\n", + "\u001b[0;34m A 1\u001b[0m\n", + "\u001b[0;34m b 2\u001b[0m\n", + "\u001b[0;34m C 3\u001b[0m\n", + "\u001b[0;34m d 4\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mascending\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mascending\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mna_position\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mna_position\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0msort_remaining\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort_remaining\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mignore_index\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0msubset\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mHashable\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnormalize\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mascending\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdropna\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Return a Series containing counts of unique rows in the DataFrame.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionadded:: 1.1.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m subset : label or list of labels, optional\u001b[0m\n", + "\u001b[0;34m Columns to use when counting unique combinations.\u001b[0m\n", + "\u001b[0;34m normalize : bool, default False\u001b[0m\n", + "\u001b[0;34m Return proportions rather than frequencies.\u001b[0m\n", + "\u001b[0;34m sort : bool, default True\u001b[0m\n", + "\u001b[0;34m Sort by frequencies.\u001b[0m\n", + "\u001b[0;34m ascending : bool, default False\u001b[0m\n", + "\u001b[0;34m Sort in ascending order.\u001b[0m\n", + "\u001b[0;34m dropna : bool, default True\u001b[0m\n", + "\u001b[0;34m Don’t include counts of rows that contain NA values.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionadded:: 1.3.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m Series\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m Series.value_counts: Equivalent method on Series.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Notes\u001b[0m\n", + "\u001b[0;34m -----\u001b[0m\n", + "\u001b[0;34m The returned Series will have a MultiIndex with one level per input\u001b[0m\n", + "\u001b[0;34m column but an Index (non-multi) for a single label. By default, rows\u001b[0m\n", + "\u001b[0;34m that contain any NA values are omitted from the result. By default,\u001b[0m\n", + "\u001b[0;34m the resulting Series will be in descending order so that the first\u001b[0m\n", + "\u001b[0;34m element is the most frequently-occurring row.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'num_legs': [2, 4, 4, 6],\u001b[0m\n", + "\u001b[0;34m ... 'num_wings': [2, 0, 0, 0]},\u001b[0m\n", + "\u001b[0;34m ... index=['falcon', 'dog', 'cat', 'ant'])\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m num_legs num_wings\u001b[0m\n", + "\u001b[0;34m falcon 2 2\u001b[0m\n", + "\u001b[0;34m dog 4 0\u001b[0m\n", + "\u001b[0;34m cat 4 0\u001b[0m\n", + "\u001b[0;34m ant 6 0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.value_counts()\u001b[0m\n", + "\u001b[0;34m num_legs num_wings\u001b[0m\n", + "\u001b[0;34m 4 0 2\u001b[0m\n", + "\u001b[0;34m 2 2 1\u001b[0m\n", + "\u001b[0;34m 6 0 1\u001b[0m\n", + "\u001b[0;34m Name: count, dtype: int64\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.value_counts(sort=False)\u001b[0m\n", + "\u001b[0;34m num_legs num_wings\u001b[0m\n", + "\u001b[0;34m 2 2 1\u001b[0m\n", + "\u001b[0;34m 4 0 2\u001b[0m\n", + "\u001b[0;34m 6 0 1\u001b[0m\n", + "\u001b[0;34m Name: count, dtype: int64\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.value_counts(ascending=True)\u001b[0m\n", + "\u001b[0;34m num_legs num_wings\u001b[0m\n", + "\u001b[0;34m 2 2 1\u001b[0m\n", + "\u001b[0;34m 6 0 1\u001b[0m\n", + "\u001b[0;34m 4 0 2\u001b[0m\n", + "\u001b[0;34m Name: count, dtype: int64\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.value_counts(normalize=True)\u001b[0m\n", + "\u001b[0;34m num_legs num_wings\u001b[0m\n", + "\u001b[0;34m 4 0 0.50\u001b[0m\n", + "\u001b[0;34m 2 2 0.25\u001b[0m\n", + "\u001b[0;34m 6 0 0.25\u001b[0m\n", + "\u001b[0;34m Name: proportion, dtype: float64\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m With `dropna` set to `False` we can also count rows with NA values.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'first_name': ['John', 'Anne', 'John', 'Beth'],\u001b[0m\n", + "\u001b[0;34m ... 'middle_name': ['Smith', pd.NA, pd.NA, 'Louise']})\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m first_name middle_name\u001b[0m\n", + "\u001b[0;34m 0 John Smith\u001b[0m\n", + "\u001b[0;34m 1 Anne \u001b[0m\n", + "\u001b[0;34m 2 John \u001b[0m\n", + "\u001b[0;34m 3 Beth Louise\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.value_counts()\u001b[0m\n", + "\u001b[0;34m first_name middle_name\u001b[0m\n", + "\u001b[0;34m Beth Louise 1\u001b[0m\n", + "\u001b[0;34m John Smith 1\u001b[0m\n", + "\u001b[0;34m Name: count, dtype: int64\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.value_counts(dropna=False)\u001b[0m\n", + "\u001b[0;34m first_name middle_name\u001b[0m\n", + "\u001b[0;34m Anne NaN 1\u001b[0m\n", + "\u001b[0;34m Beth Louise 1\u001b[0m\n", + "\u001b[0;34m John Smith 1\u001b[0m\n", + "\u001b[0;34m NaN 1\u001b[0m\n", + "\u001b[0;34m Name: count, dtype: int64\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.value_counts(\"first_name\")\u001b[0m\n", + "\u001b[0;34m first_name\u001b[0m\n", + "\u001b[0;34m John 2\u001b[0m\n", + "\u001b[0;34m Anne 1\u001b[0m\n", + "\u001b[0;34m Beth 1\u001b[0m\n", + "\u001b[0;34m Name: count, dtype: int64\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0msubset\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0msubset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"proportion\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnormalize\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"count\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcounts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdropna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcounts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcounts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcounts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mascending\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mascending\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnormalize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcounts\u001b[0m \u001b[0;34m/=\u001b[0m \u001b[0mcounts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Force MultiIndex for single column\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubset\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubset\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\n", + "\u001b[0;34m\u001b[0m \u001b[0mcounts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMultiIndex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_arrays\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcounts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcounts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mcounts\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mnlargest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIndexLabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeep\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"first\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Return the first `n` rows ordered by `columns` in descending order.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Return the first `n` rows with the largest values in `columns`, in\u001b[0m\n", + "\u001b[0;34m descending order. The columns that are not specified are returned as\u001b[0m\n", + "\u001b[0;34m well, but not used for ordering.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m This method is equivalent to\u001b[0m\n", + "\u001b[0;34m ``df.sort_values(columns, ascending=False).head(n)``, but more\u001b[0m\n", + "\u001b[0;34m performant.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m n : int\u001b[0m\n", + "\u001b[0;34m Number of rows to return.\u001b[0m\n", + "\u001b[0;34m columns : label or list of labels\u001b[0m\n", + "\u001b[0;34m Column label(s) to order by.\u001b[0m\n", + "\u001b[0;34m keep : {'first', 'last', 'all'}, default 'first'\u001b[0m\n", + "\u001b[0;34m Where there are duplicate values:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m - ``first`` : prioritize the first occurrence(s)\u001b[0m\n", + "\u001b[0;34m - ``last`` : prioritize the last occurrence(s)\u001b[0m\n", + "\u001b[0;34m - ``all`` : do not drop any duplicates, even it means\u001b[0m\n", + "\u001b[0;34m selecting more than `n` items.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame\u001b[0m\n", + "\u001b[0;34m The first `n` rows ordered by the given columns in descending\u001b[0m\n", + "\u001b[0;34m order.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.nsmallest : Return the first `n` rows ordered by `columns` in\u001b[0m\n", + "\u001b[0;34m ascending order.\u001b[0m\n", + "\u001b[0;34m DataFrame.sort_values : Sort DataFrame by the values.\u001b[0m\n", + "\u001b[0;34m DataFrame.head : Return the first `n` rows without re-ordering.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Notes\u001b[0m\n", + "\u001b[0;34m -----\u001b[0m\n", + "\u001b[0;34m This function cannot be used with all column types. For example, when\u001b[0m\n", + "\u001b[0;34m specifying columns with `object` or `category` dtypes, ``TypeError`` is\u001b[0m\n", + "\u001b[0;34m raised.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,\u001b[0m\n", + "\u001b[0;34m ... 434000, 434000, 337000, 11300,\u001b[0m\n", + "\u001b[0;34m ... 11300, 11300],\u001b[0m\n", + "\u001b[0;34m ... 'GDP': [1937894, 2583560 , 12011, 4520, 12128,\u001b[0m\n", + "\u001b[0;34m ... 17036, 182, 38, 311],\u001b[0m\n", + "\u001b[0;34m ... 'alpha-2': [\"IT\", \"FR\", \"MT\", \"MV\", \"BN\",\u001b[0m\n", + "\u001b[0;34m ... \"IS\", \"NR\", \"TV\", \"AI\"]},\u001b[0m\n", + "\u001b[0;34m ... index=[\"Italy\", \"France\", \"Malta\",\u001b[0m\n", + "\u001b[0;34m ... \"Maldives\", \"Brunei\", \"Iceland\",\u001b[0m\n", + "\u001b[0;34m ... \"Nauru\", \"Tuvalu\", \"Anguilla\"])\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m population GDP alpha-2\u001b[0m\n", + "\u001b[0;34m Italy 59000000 1937894 IT\u001b[0m\n", + "\u001b[0;34m France 65000000 2583560 FR\u001b[0m\n", + "\u001b[0;34m Malta 434000 12011 MT\u001b[0m\n", + "\u001b[0;34m Maldives 434000 4520 MV\u001b[0m\n", + "\u001b[0;34m Brunei 434000 12128 BN\u001b[0m\n", + "\u001b[0;34m Iceland 337000 17036 IS\u001b[0m\n", + "\u001b[0;34m Nauru 11300 182 NR\u001b[0m\n", + "\u001b[0;34m Tuvalu 11300 38 TV\u001b[0m\n", + "\u001b[0;34m Anguilla 11300 311 AI\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m In the following example, we will use ``nlargest`` to select the three\u001b[0m\n", + "\u001b[0;34m rows having the largest values in column \"population\".\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.nlargest(3, 'population')\u001b[0m\n", + "\u001b[0;34m population GDP alpha-2\u001b[0m\n", + "\u001b[0;34m France 65000000 2583560 FR\u001b[0m\n", + "\u001b[0;34m Italy 59000000 1937894 IT\u001b[0m\n", + "\u001b[0;34m Malta 434000 12011 MT\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m When using ``keep='last'``, ties are resolved in reverse order:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.nlargest(3, 'population', keep='last')\u001b[0m\n", + "\u001b[0;34m population GDP alpha-2\u001b[0m\n", + "\u001b[0;34m France 65000000 2583560 FR\u001b[0m\n", + "\u001b[0;34m Italy 59000000 1937894 IT\u001b[0m\n", + "\u001b[0;34m Brunei 434000 12128 BN\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m When using ``keep='all'``, all duplicate items are maintained:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.nlargest(3, 'population', keep='all')\u001b[0m\n", + "\u001b[0;34m population GDP alpha-2\u001b[0m\n", + "\u001b[0;34m France 65000000 2583560 FR\u001b[0m\n", + "\u001b[0;34m Italy 59000000 1937894 IT\u001b[0m\n", + "\u001b[0;34m Malta 434000 12011 MT\u001b[0m\n", + "\u001b[0;34m Maldives 434000 4520 MV\u001b[0m\n", + "\u001b[0;34m Brunei 434000 12128 BN\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m To order by the largest values in column \"population\" and then \"GDP\",\u001b[0m\n", + "\u001b[0;34m we can specify multiple columns like in the next example.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.nlargest(3, ['population', 'GDP'])\u001b[0m\n", + "\u001b[0;34m population GDP alpha-2\u001b[0m\n", + "\u001b[0;34m France 65000000 2583560 FR\u001b[0m\n", + "\u001b[0;34m Italy 59000000 1937894 IT\u001b[0m\n", + "\u001b[0;34m Brunei 434000 12128 BN\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mselectn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSelectNFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlargest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mnsmallest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIndexLabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeep\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"first\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Return the first `n` rows ordered by `columns` in ascending order.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Return the first `n` rows with the smallest values in `columns`, in\u001b[0m\n", + "\u001b[0;34m ascending order. The columns that are not specified are returned as\u001b[0m\n", + "\u001b[0;34m well, but not used for ordering.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m This method is equivalent to\u001b[0m\n", + "\u001b[0;34m ``df.sort_values(columns, ascending=True).head(n)``, but more\u001b[0m\n", + "\u001b[0;34m performant.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m n : int\u001b[0m\n", + "\u001b[0;34m Number of items to retrieve.\u001b[0m\n", + "\u001b[0;34m columns : list or str\u001b[0m\n", + "\u001b[0;34m Column name or names to order by.\u001b[0m\n", + "\u001b[0;34m keep : {'first', 'last', 'all'}, default 'first'\u001b[0m\n", + "\u001b[0;34m Where there are duplicate values:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m - ``first`` : take the first occurrence.\u001b[0m\n", + "\u001b[0;34m - ``last`` : take the last occurrence.\u001b[0m\n", + "\u001b[0;34m - ``all`` : do not drop any duplicates, even it means\u001b[0m\n", + "\u001b[0;34m selecting more than `n` items.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.nlargest : Return the first `n` rows ordered by `columns` in\u001b[0m\n", + "\u001b[0;34m descending order.\u001b[0m\n", + "\u001b[0;34m DataFrame.sort_values : Sort DataFrame by the values.\u001b[0m\n", + "\u001b[0;34m DataFrame.head : Return the first `n` rows without re-ordering.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,\u001b[0m\n", + "\u001b[0;34m ... 434000, 434000, 337000, 337000,\u001b[0m\n", + "\u001b[0;34m ... 11300, 11300],\u001b[0m\n", + "\u001b[0;34m ... 'GDP': [1937894, 2583560 , 12011, 4520, 12128,\u001b[0m\n", + "\u001b[0;34m ... 17036, 182, 38, 311],\u001b[0m\n", + "\u001b[0;34m ... 'alpha-2': [\"IT\", \"FR\", \"MT\", \"MV\", \"BN\",\u001b[0m\n", + "\u001b[0;34m ... \"IS\", \"NR\", \"TV\", \"AI\"]},\u001b[0m\n", + "\u001b[0;34m ... index=[\"Italy\", \"France\", \"Malta\",\u001b[0m\n", + "\u001b[0;34m ... \"Maldives\", \"Brunei\", \"Iceland\",\u001b[0m\n", + "\u001b[0;34m ... \"Nauru\", \"Tuvalu\", \"Anguilla\"])\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m population GDP alpha-2\u001b[0m\n", + "\u001b[0;34m Italy 59000000 1937894 IT\u001b[0m\n", + "\u001b[0;34m France 65000000 2583560 FR\u001b[0m\n", + "\u001b[0;34m Malta 434000 12011 MT\u001b[0m\n", + "\u001b[0;34m Maldives 434000 4520 MV\u001b[0m\n", + "\u001b[0;34m Brunei 434000 12128 BN\u001b[0m\n", + "\u001b[0;34m Iceland 337000 17036 IS\u001b[0m\n", + "\u001b[0;34m Nauru 337000 182 NR\u001b[0m\n", + "\u001b[0;34m Tuvalu 11300 38 TV\u001b[0m\n", + "\u001b[0;34m Anguilla 11300 311 AI\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m In the following example, we will use ``nsmallest`` to select the\u001b[0m\n", + "\u001b[0;34m three rows having the smallest values in column \"population\".\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.nsmallest(3, 'population')\u001b[0m\n", + "\u001b[0;34m population GDP alpha-2\u001b[0m\n", + "\u001b[0;34m Tuvalu 11300 38 TV\u001b[0m\n", + "\u001b[0;34m Anguilla 11300 311 AI\u001b[0m\n", + "\u001b[0;34m Iceland 337000 17036 IS\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m When using ``keep='last'``, ties are resolved in reverse order:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.nsmallest(3, 'population', keep='last')\u001b[0m\n", + "\u001b[0;34m population GDP alpha-2\u001b[0m\n", + "\u001b[0;34m Anguilla 11300 311 AI\u001b[0m\n", + "\u001b[0;34m Tuvalu 11300 38 TV\u001b[0m\n", + "\u001b[0;34m Nauru 337000 182 NR\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m When using ``keep='all'``, all duplicate items are maintained:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.nsmallest(3, 'population', keep='all')\u001b[0m\n", + "\u001b[0;34m population GDP alpha-2\u001b[0m\n", + "\u001b[0;34m Tuvalu 11300 38 TV\u001b[0m\n", + "\u001b[0;34m Anguilla 11300 311 AI\u001b[0m\n", + "\u001b[0;34m Iceland 337000 17036 IS\u001b[0m\n", + "\u001b[0;34m Nauru 337000 182 NR\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m To order by the smallest values in column \"population\" and then \"GDP\", we can\u001b[0m\n", + "\u001b[0;34m specify multiple columns like in the next example.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.nsmallest(3, ['population', 'GDP'])\u001b[0m\n", + "\u001b[0;34m population GDP alpha-2\u001b[0m\n", + "\u001b[0;34m Tuvalu 11300 38 TV\u001b[0m\n", + "\u001b[0;34m Anguilla 11300 311 AI\u001b[0m\n", + "\u001b[0;34m Nauru 337000 182 NR\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mselectn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSelectNFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnsmallest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mdoc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mswaplevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_shared_doc_kwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"klass\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mextra_params\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdedent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"axis : {0 or 'index', 1 or 'columns'}, default 0\u001b[0m\n", + "\u001b[0;34m The axis to swap levels on. 0 or 'index' for row-wise, 1 or\u001b[0m\n", + "\u001b[0;34m 'columns' for column-wise.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mexamples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdedent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\\\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame(\u001b[0m\n", + "\u001b[0;34m ... {\"Grade\": [\"A\", \"B\", \"A\", \"C\"]},\u001b[0m\n", + "\u001b[0;34m ... index=[\u001b[0m\n", + "\u001b[0;34m ... [\"Final exam\", \"Final exam\", \"Coursework\", \"Coursework\"],\u001b[0m\n", + "\u001b[0;34m ... [\"History\", \"Geography\", \"History\", \"Geography\"],\u001b[0m\n", + "\u001b[0;34m ... [\"January\", \"February\", \"March\", \"April\"],\u001b[0m\n", + "\u001b[0;34m ... ],\u001b[0m\n", + "\u001b[0;34m ... )\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m Grade\u001b[0m\n", + "\u001b[0;34m Final exam History January A\u001b[0m\n", + "\u001b[0;34m Geography February B\u001b[0m\n", + "\u001b[0;34m Coursework History March A\u001b[0m\n", + "\u001b[0;34m Geography April C\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m In the following example, we will swap the levels of the indices.\u001b[0m\n", + "\u001b[0;34m Here, we will swap the levels column-wise, but levels can be swapped row-wise\u001b[0m\n", + "\u001b[0;34m in a similar manner. Note that column-wise is the default behaviour.\u001b[0m\n", + "\u001b[0;34m By not supplying any arguments for i and j, we swap the last and second to\u001b[0m\n", + "\u001b[0;34m last indices.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.swaplevel()\u001b[0m\n", + "\u001b[0;34m Grade\u001b[0m\n", + "\u001b[0;34m Final exam January History A\u001b[0m\n", + "\u001b[0;34m February Geography B\u001b[0m\n", + "\u001b[0;34m Coursework March History A\u001b[0m\n", + "\u001b[0;34m April Geography C\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m By supplying one argument, we can choose which index to swap the last\u001b[0m\n", + "\u001b[0;34m index with. We can for example swap the first index with the last one as\u001b[0m\n", + "\u001b[0;34m follows.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.swaplevel(0)\u001b[0m\n", + "\u001b[0;34m Grade\u001b[0m\n", + "\u001b[0;34m January History Final exam A\u001b[0m\n", + "\u001b[0;34m February Geography Final exam B\u001b[0m\n", + "\u001b[0;34m March History Coursework A\u001b[0m\n", + "\u001b[0;34m April Geography Coursework C\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m We can also define explicitly which indices we want to swap by supplying values\u001b[0m\n", + "\u001b[0;34m for both i and j. Here, we for example swap the first and second indices.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.swaplevel(0, 1)\u001b[0m\n", + "\u001b[0;34m Grade\u001b[0m\n", + "\u001b[0;34m History Final exam January A\u001b[0m\n", + "\u001b[0;34m Geography Final exam February B\u001b[0m\n", + "\u001b[0;34m History Coursework March A\u001b[0m\n", + "\u001b[0;34m Geography Coursework April C\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mswaplevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMultiIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# pragma: no cover\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Can only swap levels on a hierarchical axis.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMultiIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mswaplevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMultiIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mswaplevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mreorder_levels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Rearrange index levels using input order. May not drop or duplicate levels.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m order : list of int or list of str\u001b[0m\n", + "\u001b[0;34m List representing new level order. Reference level by number\u001b[0m\n", + "\u001b[0;34m (position) or by key (label).\u001b[0m\n", + "\u001b[0;34m axis : {0 or 'index', 1 or 'columns'}, default 0\u001b[0m\n", + "\u001b[0;34m Where to reorder levels.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> data = {\u001b[0m\n", + "\u001b[0;34m ... \"class\": [\"Mammals\", \"Mammals\", \"Reptiles\"],\u001b[0m\n", + "\u001b[0;34m ... \"diet\": [\"Omnivore\", \"Carnivore\", \"Carnivore\"],\u001b[0m\n", + "\u001b[0;34m ... \"species\": [\"Humans\", \"Dogs\", \"Snakes\"],\u001b[0m\n", + "\u001b[0;34m ... }\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame(data, columns=[\"class\", \"diet\", \"species\"])\u001b[0m\n", + "\u001b[0;34m >>> df = df.set_index([\"class\", \"diet\"])\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m species\u001b[0m\n", + "\u001b[0;34m class diet\u001b[0m\n", + "\u001b[0;34m Mammals Omnivore Humans\u001b[0m\n", + "\u001b[0;34m Carnivore Dogs\u001b[0m\n", + "\u001b[0;34m Reptiles Carnivore Snakes\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Let's reorder the levels of the index:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.reorder_levels([\"diet\", \"class\"])\u001b[0m\n", + "\u001b[0;34m species\u001b[0m\n", + "\u001b[0;34m diet class\u001b[0m\n", + "\u001b[0;34m Omnivore Mammals Humans\u001b[0m\n", + "\u001b[0;34m Carnivore Mammals Dogs\u001b[0m\n", + "\u001b[0;34m Reptiles Snakes\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMultiIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# pragma: no cover\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Can only reorder levels on a hierarchical axis.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMultiIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreorder_levels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMultiIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreorder_levels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# ----------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Arithmetic Methods\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_cmp_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mLiteral\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;31m# only relevant for Series other case\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malign_method_FRAME\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# See GH#4537 for discussion of scalar op behavior\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch_frame_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_construct_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_arith_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_reindex_frame_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframe_arith_method_with_reindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mLiteral\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;31m# only relevant for Series other case\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mother\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaybe_prepare_scalar_for_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch_frame_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_construct_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0m_logical_method\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_arith_method\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_dispatch_frame_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mCallable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAxisInt\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Evaluate the frame operation func(left, right) by evaluating\u001b[0m\n", + "\u001b[0;34m column-by-column, dispatching to the Series implementation.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m right : scalar, Series, or DataFrame\u001b[0m\n", + "\u001b[0;34m func : arithmetic or comparison operator\u001b[0m\n", + "\u001b[0;34m axis : {None, 0, 1}\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Get the appropriate array-op to apply to each column/block's values.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0marray_op\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_array_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem_from_zerodim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# i.e. scalar, faster than checking np.ndim(right) == 0\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mbm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray_op\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mequals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mequals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# TODO: The previous assertion `assert right._indexed_same(self)`\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# fails in cases with empty columns reached via\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# _frame_arith_method_with_reindex\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# TODO operate_blockwise expects a manager of the same type\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mbm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperate_blockwise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# error: Argument 1 to \"operate_blockwise\" of \"ArrayManager\" has\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# incompatible type \"Union[ArrayManager, BlockManager]\"; expected\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# \"ArrayManager\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# error: Argument 1 to \"operate_blockwise\" of \"BlockManager\" has\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# incompatible type \"Union[ArrayManager, BlockManager]\"; expected\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# \"BlockManager\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# type: ignore[arg-type]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0marray_op\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# axis=1 means we want to operate row-by-row\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mequals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# maybe_align_as_frame ensures we do not have an ndarray here\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0marrays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0marray_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_left\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_right\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_left\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_right\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iter_column_arrays\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mequals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Handle other cases later\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0marrays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0marray_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mleft\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iter_column_arrays\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Remaining cases have less-obvious dispatch rules\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_from_arrays\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverify_integrity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_combine_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# at this point we have `self._indexed_same(other)`\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfill_value\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# since _arith_op may be called in a loop, avoid function call\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# overhead if possible by doing this check once\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0m_arith_op\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_arith_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# for the mixed_type case where we iterate over columns,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# _arith_op(left, right) is equivalent to\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# left._binop(right, func, fill_value=fill_value)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfill_binop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch_frame_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_arith_op\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mnew_data\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_construct_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Wrap the result of an arithmetic, comparison, or logical operation.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m result : DataFrame\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Pin columns instead of passing to constructor for compat with\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# non-unique columns case\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__divmod__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Naive implementation, room for optimization\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdiv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mdiv\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdiv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmod\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__rdivmod__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Naive implementation, room for optimization\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdiv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mother\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mother\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mdiv\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdiv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmod\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# ----------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Combination-Related\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mdoc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0m_shared_docs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"compare\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34mReturns\u001b[0m\n", + "\u001b[0;34m-------\u001b[0m\n", + "\u001b[0;34mDataFrame\u001b[0m\n", + "\u001b[0;34m DataFrame that shows the differences stacked side by side.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m The resulting index will be a MultiIndex with 'self' and 'other'\u001b[0m\n", + "\u001b[0;34m stacked alternately at the inner level.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34mRaises\u001b[0m\n", + "\u001b[0;34m------\u001b[0m\n", + "\u001b[0;34mValueError\u001b[0m\n", + "\u001b[0;34m When the two DataFrames don't have identical labels or shape.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34mSee Also\u001b[0m\n", + "\u001b[0;34m--------\u001b[0m\n", + "\u001b[0;34mSeries.compare : Compare with another Series and show differences.\u001b[0m\n", + "\u001b[0;34mDataFrame.equals : Test whether two objects contain the same elements.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34mNotes\u001b[0m\n", + "\u001b[0;34m-----\u001b[0m\n", + "\u001b[0;34mMatching NaNs will not appear as a difference.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34mCan only compare identically-labeled\u001b[0m\n", + "\u001b[0;34m(i.e. same shape, identical row and column labels) DataFrames\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34mExamples\u001b[0m\n", + "\u001b[0;34m--------\u001b[0m\n", + "\u001b[0;34m>>> df = pd.DataFrame(\u001b[0m\n", + "\u001b[0;34m... {{\u001b[0m\n", + "\u001b[0;34m... \"col1\": [\"a\", \"a\", \"b\", \"b\", \"a\"],\u001b[0m\n", + "\u001b[0;34m... \"col2\": [1.0, 2.0, 3.0, np.nan, 5.0],\u001b[0m\n", + "\u001b[0;34m... \"col3\": [1.0, 2.0, 3.0, 4.0, 5.0]\u001b[0m\n", + "\u001b[0;34m... }},\u001b[0m\n", + "\u001b[0;34m... columns=[\"col1\", \"col2\", \"col3\"],\u001b[0m\n", + "\u001b[0;34m... )\u001b[0m\n", + "\u001b[0;34m>>> df\u001b[0m\n", + "\u001b[0;34m col1 col2 col3\u001b[0m\n", + "\u001b[0;34m0 a 1.0 1.0\u001b[0m\n", + "\u001b[0;34m1 a 2.0 2.0\u001b[0m\n", + "\u001b[0;34m2 b 3.0 3.0\u001b[0m\n", + "\u001b[0;34m3 b NaN 4.0\u001b[0m\n", + "\u001b[0;34m4 a 5.0 5.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m>>> df2 = df.copy()\u001b[0m\n", + "\u001b[0;34m>>> df2.loc[0, 'col1'] = 'c'\u001b[0m\n", + "\u001b[0;34m>>> df2.loc[2, 'col3'] = 4.0\u001b[0m\n", + "\u001b[0;34m>>> df2\u001b[0m\n", + "\u001b[0;34m col1 col2 col3\u001b[0m\n", + "\u001b[0;34m0 c 1.0 1.0\u001b[0m\n", + "\u001b[0;34m1 a 2.0 2.0\u001b[0m\n", + "\u001b[0;34m2 b 3.0 4.0\u001b[0m\n", + "\u001b[0;34m3 b NaN 4.0\u001b[0m\n", + "\u001b[0;34m4 a 5.0 5.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34mAlign the differences on columns\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m>>> df.compare(df2)\u001b[0m\n", + "\u001b[0;34m col1 col3\u001b[0m\n", + "\u001b[0;34m self other self other\u001b[0m\n", + "\u001b[0;34m0 a c NaN NaN\u001b[0m\n", + "\u001b[0;34m2 NaN NaN 3.0 4.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34mAssign result_names\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m>>> df.compare(df2, result_names=(\"left\", \"right\"))\u001b[0m\n", + "\u001b[0;34m col1 col3\u001b[0m\n", + "\u001b[0;34m left right left right\u001b[0m\n", + "\u001b[0;34m0 a c NaN NaN\u001b[0m\n", + "\u001b[0;34m2 NaN NaN 3.0 4.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34mStack the differences on rows\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m>>> df.compare(df2, align_axis=0)\u001b[0m\n", + "\u001b[0;34m col1 col3\u001b[0m\n", + "\u001b[0;34m0 self a NaN\u001b[0m\n", + "\u001b[0;34m other c NaN\u001b[0m\n", + "\u001b[0;34m2 self NaN 3.0\u001b[0m\n", + "\u001b[0;34m other NaN 4.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34mKeep the equal values\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m>>> df.compare(df2, keep_equal=True)\u001b[0m\n", + "\u001b[0;34m col1 col3\u001b[0m\n", + "\u001b[0;34m self other self other\u001b[0m\n", + "\u001b[0;34m0 a c 1.0 1.0\u001b[0m\n", + "\u001b[0;34m2 b b 3.0 4.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34mKeep all original rows and columns\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m>>> df.compare(df2, keep_shape=True)\u001b[0m\n", + "\u001b[0;34m col1 col2 col3\u001b[0m\n", + "\u001b[0;34m self other self other self other\u001b[0m\n", + "\u001b[0;34m0 a c NaN NaN NaN NaN\u001b[0m\n", + "\u001b[0;34m1 NaN NaN NaN NaN NaN NaN\u001b[0m\n", + "\u001b[0;34m2 NaN NaN NaN NaN 3.0 4.0\u001b[0m\n", + "\u001b[0;34m3 NaN NaN NaN NaN NaN NaN\u001b[0m\n", + "\u001b[0;34m4 NaN NaN NaN NaN NaN NaN\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34mKeep all original rows and columns and also all original values\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m>>> df.compare(df2, keep_shape=True, keep_equal=True)\u001b[0m\n", + "\u001b[0;34m col1 col2 col3\u001b[0m\n", + "\u001b[0;34m self other self other self other\u001b[0m\n", + "\u001b[0;34m0 a c 1.0 1.0 1.0 1.0\u001b[0m\n", + "\u001b[0;34m1 a a 2.0 2.0 2.0 2.0\u001b[0m\n", + "\u001b[0;34m2 b b 3.0 3.0 3.0 4.0\u001b[0m\n", + "\u001b[0;34m3 b b NaN NaN 4.0 4.0\u001b[0m\n", + "\u001b[0;34m4 a a 5.0 5.0 5.0 5.0\u001b[0m\n", + "\u001b[0;34m\"\"\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_shared_doc_kwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"klass\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcompare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0malign_axis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkeep_shape\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkeep_equal\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult_names\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mSuffixes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"self\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"other\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0malign_axis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malign_axis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkeep_shape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeep_shape\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkeep_equal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeep_equal\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult_names\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresult_names\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcombine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mCallable\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mHashable\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Perform column-wise combine with another DataFrame.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Combines a DataFrame with `other` DataFrame using `func`\u001b[0m\n", + "\u001b[0;34m to element-wise combine columns. The row and column indexes of the\u001b[0m\n", + "\u001b[0;34m resulting DataFrame will be the union of the two.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m other : DataFrame\u001b[0m\n", + "\u001b[0;34m The DataFrame to merge column-wise.\u001b[0m\n", + "\u001b[0;34m func : function\u001b[0m\n", + "\u001b[0;34m Function that takes two series as inputs and return a Series or a\u001b[0m\n", + "\u001b[0;34m scalar. Used to merge the two dataframes column by columns.\u001b[0m\n", + "\u001b[0;34m fill_value : scalar value, default None\u001b[0m\n", + "\u001b[0;34m The value to fill NaNs with prior to passing any column to the\u001b[0m\n", + "\u001b[0;34m merge func.\u001b[0m\n", + "\u001b[0;34m overwrite : bool, default True\u001b[0m\n", + "\u001b[0;34m If True, columns in `self` that do not exist in `other` will be\u001b[0m\n", + "\u001b[0;34m overwritten with NaNs.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame\u001b[0m\n", + "\u001b[0;34m Combination of the provided DataFrames.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.combine_first : Combine two DataFrame objects and default to\u001b[0m\n", + "\u001b[0;34m non-null values in frame calling the method.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m Combine using a simple function that chooses the smaller column.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})\u001b[0m\n", + "\u001b[0;34m >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})\u001b[0m\n", + "\u001b[0;34m >>> take_smaller = lambda s1, s2: s1 if s1.sum() < s2.sum() else s2\u001b[0m\n", + "\u001b[0;34m >>> df1.combine(df2, take_smaller)\u001b[0m\n", + "\u001b[0;34m A B\u001b[0m\n", + "\u001b[0;34m 0 0 3\u001b[0m\n", + "\u001b[0;34m 1 0 3\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Example using a true element-wise combine function.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df1 = pd.DataFrame({'A': [5, 0], 'B': [2, 4]})\u001b[0m\n", + "\u001b[0;34m >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})\u001b[0m\n", + "\u001b[0;34m >>> df1.combine(df2, np.minimum)\u001b[0m\n", + "\u001b[0;34m A B\u001b[0m\n", + "\u001b[0;34m 0 1 2\u001b[0m\n", + "\u001b[0;34m 1 0 3\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Using `fill_value` fills Nones prior to passing the column to the\u001b[0m\n", + "\u001b[0;34m merge function.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})\u001b[0m\n", + "\u001b[0;34m >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})\u001b[0m\n", + "\u001b[0;34m >>> df1.combine(df2, take_smaller, fill_value=-5)\u001b[0m\n", + "\u001b[0;34m A B\u001b[0m\n", + "\u001b[0;34m 0 0 -5.0\u001b[0m\n", + "\u001b[0;34m 1 0 4.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m However, if the same element in both dataframes is None, that None\u001b[0m\n", + "\u001b[0;34m is preserved\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})\u001b[0m\n", + "\u001b[0;34m >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [None, 3]})\u001b[0m\n", + "\u001b[0;34m >>> df1.combine(df2, take_smaller, fill_value=-5)\u001b[0m\n", + "\u001b[0;34m A B\u001b[0m\n", + "\u001b[0;34m 0 0 -5.0\u001b[0m\n", + "\u001b[0;34m 1 0 3.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Example that demonstrates the use of `overwrite` and behavior when\u001b[0m\n", + "\u001b[0;34m the axis differ between the dataframes.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})\u001b[0m\n", + "\u001b[0;34m >>> df2 = pd.DataFrame({'B': [3, 3], 'C': [-10, 1], }, index=[1, 2])\u001b[0m\n", + "\u001b[0;34m >>> df1.combine(df2, take_smaller)\u001b[0m\n", + "\u001b[0;34m A B C\u001b[0m\n", + "\u001b[0;34m 0 NaN NaN NaN\u001b[0m\n", + "\u001b[0;34m 1 NaN 3.0 -10.0\u001b[0m\n", + "\u001b[0;34m 2 NaN 3.0 1.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df1.combine(df2, take_smaller, overwrite=False)\u001b[0m\n", + "\u001b[0;34m A B C\u001b[0m\n", + "\u001b[0;34m 0 0.0 NaN NaN\u001b[0m\n", + "\u001b[0;34m 1 0.0 3.0 -10.0\u001b[0m\n", + "\u001b[0;34m 2 NaN 3.0 1.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Demonstrating the preference of the passed in dataframe.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1], }, index=[1, 2])\u001b[0m\n", + "\u001b[0;34m >>> df2.combine(df1, take_smaller)\u001b[0m\n", + "\u001b[0;34m A B C\u001b[0m\n", + "\u001b[0;34m 0 0.0 NaN NaN\u001b[0m\n", + "\u001b[0;34m 1 0.0 3.0 NaN\u001b[0m\n", + "\u001b[0;34m 2 NaN 3.0 NaN\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df2.combine(df1, take_smaller, overwrite=False)\u001b[0m\n", + "\u001b[0;34m A B C\u001b[0m\n", + "\u001b[0;34m 0 0.0 NaN NaN\u001b[0m\n", + "\u001b[0;34m 1 0.0 3.0 1.0\u001b[0m\n", + "\u001b[0;34m 2 NaN 3.0 1.0\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mother_idxlen\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# save for compare\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mthis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnew_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mthis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_index\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mother_idxlen\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# sorts if possible; otherwise align above ensures that these are set-equal\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnew_columns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mthis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdo_fill\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfill_value\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnew_columns\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mseries\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mthis\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mother_series\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mthis_dtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mseries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mother_dtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mother_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mthis_mask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mother_mask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother_series\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# don't overwrite columns unnecessarily\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# DO propagate if this column is not in the intersection\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0moverwrite\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mother_mask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mthis\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdo_fill\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mseries\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mseries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mother_series\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mother_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mseries\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mthis_mask\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mother_series\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mother_mask\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# If self DataFrame does not have col in other DataFrame,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# try to promote series, which is all NaN, as other_dtype.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnew_dtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mother_dtype\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mseries\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mseries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_dtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# e.g. new_dtype is integer types\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# if we have different dtypes, possibly promote\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnew_dtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfind_common_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mthis_dtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother_dtype\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mseries\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mseries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_dtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mother_series\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mother_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_dtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother_series\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_dtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# if new_dtype is an EA Dtype, then `func` is expected to return\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# the correct dtype without any additional casting\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# error: No overload variant of \"maybe_downcast_to_dtype\" matches\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# argument types \"Union[Series, Hashable]\", \"dtype[Any]\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaybe_downcast_to_dtype\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;31m# type: ignore[call-overload]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_dtype\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# convert_objects just in case\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnew_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnew_columns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcombine_first\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Update null elements with value in the same location in `other`.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Combine two DataFrame objects by filling null values in one DataFrame\u001b[0m\n", + "\u001b[0;34m with non-null values from other DataFrame. The row and column indexes\u001b[0m\n", + "\u001b[0;34m of the resulting DataFrame will be the union of the two. The resulting\u001b[0m\n", + "\u001b[0;34m dataframe contains the 'first' dataframe values and overrides the\u001b[0m\n", + "\u001b[0;34m second one values where both first.loc[index, col] and\u001b[0m\n", + "\u001b[0;34m second.loc[index, col] are not missing values, upon calling\u001b[0m\n", + "\u001b[0;34m first.combine_first(second).\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m other : DataFrame\u001b[0m\n", + "\u001b[0;34m Provided DataFrame to use to fill null values.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame\u001b[0m\n", + "\u001b[0;34m The result of combining the provided DataFrame with the other object.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.combine : Perform series-wise operation on two DataFrames\u001b[0m\n", + "\u001b[0;34m using a given function.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df1 = pd.DataFrame({'A': [None, 0], 'B': [None, 4]})\u001b[0m\n", + "\u001b[0;34m >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})\u001b[0m\n", + "\u001b[0;34m >>> df1.combine_first(df2)\u001b[0m\n", + "\u001b[0;34m A B\u001b[0m\n", + "\u001b[0;34m 0 1.0 3.0\u001b[0m\n", + "\u001b[0;34m 1 0.0 4.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Null values still persist if the location of that null value\u001b[0m\n", + "\u001b[0;34m does not exist in `other`\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df1 = pd.DataFrame({'A': [None, 0], 'B': [4, None]})\u001b[0m\n", + "\u001b[0;34m >>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1]}, index=[1, 2])\u001b[0m\n", + "\u001b[0;34m >>> df1.combine_first(df2)\u001b[0m\n", + "\u001b[0;34m A B C\u001b[0m\n", + "\u001b[0;34m 0 NaN 4.0 NaN\u001b[0m\n", + "\u001b[0;34m 1 0.0 3.0 1.0\u001b[0m\n", + "\u001b[0;34m 2 NaN 3.0 1.0\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomputation\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mexpressions\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcombiner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mx_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextract_numpy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0my_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextract_numpy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# If the column y in other DataFrame is not in first DataFrame,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# just return y_values.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0my_values\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mexpressions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcombined\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcombine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcombiner\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdtypes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfind_common_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintersection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_dtype_equal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcombined\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcombined\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcombined\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mcombined\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mjoin\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"left\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mfilter_func\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Modify in place using non-NA values from another DataFrame.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Aligns on indices. There is no return value.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m other : DataFrame, or object coercible into a DataFrame\u001b[0m\n", + "\u001b[0;34m Should have at least one matching index/column label\u001b[0m\n", + "\u001b[0;34m with the original DataFrame. If a Series is passed,\u001b[0m\n", + "\u001b[0;34m its name attribute must be set, and that will be\u001b[0m\n", + "\u001b[0;34m used as the column name to align with the original DataFrame.\u001b[0m\n", + "\u001b[0;34m join : {'left'}, default 'left'\u001b[0m\n", + "\u001b[0;34m Only left join is implemented, keeping the index and columns of the\u001b[0m\n", + "\u001b[0;34m original object.\u001b[0m\n", + "\u001b[0;34m overwrite : bool, default True\u001b[0m\n", + "\u001b[0;34m How to handle non-NA values for overlapping keys:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m * True: overwrite original DataFrame's values\u001b[0m\n", + "\u001b[0;34m with values from `other`.\u001b[0m\n", + "\u001b[0;34m * False: only update values that are NA in\u001b[0m\n", + "\u001b[0;34m the original DataFrame.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m filter_func : callable(1d-array) -> bool 1d-array, optional\u001b[0m\n", + "\u001b[0;34m Can choose to replace values other than NA. Return True for values\u001b[0m\n", + "\u001b[0;34m that should be updated.\u001b[0m\n", + "\u001b[0;34m errors : {'raise', 'ignore'}, default 'ignore'\u001b[0m\n", + "\u001b[0;34m If 'raise', will raise a ValueError if the DataFrame and `other`\u001b[0m\n", + "\u001b[0;34m both contain non-NA data in the same place.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m None\u001b[0m\n", + "\u001b[0;34m This method directly changes calling object.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Raises\u001b[0m\n", + "\u001b[0;34m ------\u001b[0m\n", + "\u001b[0;34m ValueError\u001b[0m\n", + "\u001b[0;34m * When `errors='raise'` and there's overlapping non-NA data.\u001b[0m\n", + "\u001b[0;34m * When `errors` is not either `'ignore'` or `'raise'`\u001b[0m\n", + "\u001b[0;34m NotImplementedError\u001b[0m\n", + "\u001b[0;34m * If `join != 'left'`\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m dict.update : Similar method for dictionaries.\u001b[0m\n", + "\u001b[0;34m DataFrame.merge : For column(s)-on-column(s) operations.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'A': [1, 2, 3],\u001b[0m\n", + "\u001b[0;34m ... 'B': [400, 500, 600]})\u001b[0m\n", + "\u001b[0;34m >>> new_df = pd.DataFrame({'B': [4, 5, 6],\u001b[0m\n", + "\u001b[0;34m ... 'C': [7, 8, 9]})\u001b[0m\n", + "\u001b[0;34m >>> df.update(new_df)\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m A B\u001b[0m\n", + "\u001b[0;34m 0 1 4\u001b[0m\n", + "\u001b[0;34m 1 2 5\u001b[0m\n", + "\u001b[0;34m 2 3 6\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m The DataFrame's length does not increase as a result of the update,\u001b[0m\n", + "\u001b[0;34m only values at matching index/column labels are updated.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'A': ['a', 'b', 'c'],\u001b[0m\n", + "\u001b[0;34m ... 'B': ['x', 'y', 'z']})\u001b[0m\n", + "\u001b[0;34m >>> new_df = pd.DataFrame({'B': ['d', 'e', 'f', 'g', 'h', 'i']})\u001b[0m\n", + "\u001b[0;34m >>> df.update(new_df)\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m A B\u001b[0m\n", + "\u001b[0;34m 0 a d\u001b[0m\n", + "\u001b[0;34m 1 b e\u001b[0m\n", + "\u001b[0;34m 2 c f\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m For Series, its name attribute must be set.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'A': ['a', 'b', 'c'],\u001b[0m\n", + "\u001b[0;34m ... 'B': ['x', 'y', 'z']})\u001b[0m\n", + "\u001b[0;34m >>> new_column = pd.Series(['d', 'e'], name='B', index=[0, 2])\u001b[0m\n", + "\u001b[0;34m >>> df.update(new_column)\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m A B\u001b[0m\n", + "\u001b[0;34m 0 a d\u001b[0m\n", + "\u001b[0;34m 1 b y\u001b[0m\n", + "\u001b[0;34m 2 c e\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'A': ['a', 'b', 'c'],\u001b[0m\n", + "\u001b[0;34m ... 'B': ['x', 'y', 'z']})\u001b[0m\n", + "\u001b[0;34m >>> new_df = pd.DataFrame({'B': ['d', 'e']}, index=[1, 2])\u001b[0m\n", + "\u001b[0;34m >>> df.update(new_df)\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m A B\u001b[0m\n", + "\u001b[0;34m 0 a x\u001b[0m\n", + "\u001b[0;34m 1 b d\u001b[0m\n", + "\u001b[0;34m 2 c e\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m If `other` contains NaNs the corresponding values are not updated\u001b[0m\n", + "\u001b[0;34m in the original dataframe.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'A': [1, 2, 3],\u001b[0m\n", + "\u001b[0;34m ... 'B': [400, 500, 600]})\u001b[0m\n", + "\u001b[0;34m >>> new_df = pd.DataFrame({'B': [4, np.nan, 6]})\u001b[0m\n", + "\u001b[0;34m >>> df.update(new_df)\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m A B\u001b[0m\n", + "\u001b[0;34m 0 1 4\u001b[0m\n", + "\u001b[0;34m 1 2 500\u001b[0m\n", + "\u001b[0;34m 2 3 6\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomputation\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mexpressions\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# TODO: Support other joins\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mjoin\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"left\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# pragma: no cover\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Only left join is supported\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"raise\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"The parameter errors must be either 'ignore' or 'raise'\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mother\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mother\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintersection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mthis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mthat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfilter_func\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m~\u001b[0m\u001b[0mfilter_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"raise\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmask_this\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnotna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmask_that\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnotna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmask_this\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0mmask_that\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Data overlaps.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnotna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# don't overwrite columns unnecessarily\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexpressions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# ----------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Data reshaping\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mAppender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34mExamples\u001b[0m\n", + "\u001b[0;34m--------\u001b[0m\n", + "\u001b[0;34m>>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',\u001b[0m\n", + "\u001b[0;34m... 'Parrot', 'Parrot'],\u001b[0m\n", + "\u001b[0;34m... 'Max Speed': [380., 370., 24., 26.]})\u001b[0m\n", + "\u001b[0;34m>>> df\u001b[0m\n", + "\u001b[0;34m Animal Max Speed\u001b[0m\n", + "\u001b[0;34m0 Falcon 380.0\u001b[0m\n", + "\u001b[0;34m1 Falcon 370.0\u001b[0m\n", + "\u001b[0;34m2 Parrot 24.0\u001b[0m\n", + "\u001b[0;34m3 Parrot 26.0\u001b[0m\n", + "\u001b[0;34m>>> df.groupby(['Animal']).mean()\u001b[0m\n", + "\u001b[0;34m Max Speed\u001b[0m\n", + "\u001b[0;34mAnimal\u001b[0m\n", + "\u001b[0;34mFalcon 375.0\u001b[0m\n", + "\u001b[0;34mParrot 25.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m**Hierarchical Indexes**\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34mWe can groupby different levels of a hierarchical index\u001b[0m\n", + "\u001b[0;34musing the `level` parameter:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m>>> arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],\u001b[0m\n", + "\u001b[0;34m... ['Captive', 'Wild', 'Captive', 'Wild']]\u001b[0m\n", + "\u001b[0;34m>>> index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))\u001b[0m\n", + "\u001b[0;34m>>> df = pd.DataFrame({'Max Speed': [390., 350., 30., 20.]},\u001b[0m\n", + "\u001b[0;34m... index=index)\u001b[0m\n", + "\u001b[0;34m>>> df\u001b[0m\n", + "\u001b[0;34m Max Speed\u001b[0m\n", + "\u001b[0;34mAnimal Type\u001b[0m\n", + "\u001b[0;34mFalcon Captive 390.0\u001b[0m\n", + "\u001b[0;34m Wild 350.0\u001b[0m\n", + "\u001b[0;34mParrot Captive 30.0\u001b[0m\n", + "\u001b[0;34m Wild 20.0\u001b[0m\n", + "\u001b[0;34m>>> df.groupby(level=0).mean()\u001b[0m\n", + "\u001b[0;34m Max Speed\u001b[0m\n", + "\u001b[0;34mAnimal\u001b[0m\n", + "\u001b[0;34mFalcon 370.0\u001b[0m\n", + "\u001b[0;34mParrot 25.0\u001b[0m\n", + "\u001b[0;34m>>> df.groupby(level=\"Type\").mean()\u001b[0m\n", + "\u001b[0;34m Max Speed\u001b[0m\n", + "\u001b[0;34mType\u001b[0m\n", + "\u001b[0;34mCaptive 210.0\u001b[0m\n", + "\u001b[0;34mWild 185.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34mWe can also choose to include NA in group keys or not by setting\u001b[0m\n", + "\u001b[0;34m`dropna` parameter, the default setting is `True`.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m>>> l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]\u001b[0m\n", + "\u001b[0;34m>>> df = pd.DataFrame(l, columns=[\"a\", \"b\", \"c\"])\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m>>> df.groupby(by=[\"b\"]).sum()\u001b[0m\n", + "\u001b[0;34m a c\u001b[0m\n", + "\u001b[0;34mb\u001b[0m\n", + "\u001b[0;34m1.0 2 3\u001b[0m\n", + "\u001b[0;34m2.0 2 5\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m>>> df.groupby(by=[\"b\"], dropna=False).sum()\u001b[0m\n", + "\u001b[0;34m a c\u001b[0m\n", + "\u001b[0;34mb\u001b[0m\n", + "\u001b[0;34m1.0 2 3\u001b[0m\n", + "\u001b[0;34m2.0 2 5\u001b[0m\n", + "\u001b[0;34mNaN 1 4\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m>>> l = [[\"a\", 12, 12], [None, 12.3, 33.], [\"b\", 12.3, 123], [\"a\", 1, 1]]\u001b[0m\n", + "\u001b[0;34m>>> df = pd.DataFrame(l, columns=[\"a\", \"b\", \"c\"])\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m>>> df.groupby(by=\"a\").sum()\u001b[0m\n", + "\u001b[0;34m b c\u001b[0m\n", + "\u001b[0;34ma\u001b[0m\n", + "\u001b[0;34ma 13.0 13.0\u001b[0m\n", + "\u001b[0;34mb 12.3 123.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m>>> df.groupby(by=\"a\", dropna=False).sum()\u001b[0m\n", + "\u001b[0;34m b c\u001b[0m\n", + "\u001b[0;34ma\u001b[0m\n", + "\u001b[0;34ma 13.0 13.0\u001b[0m\n", + "\u001b[0;34mb 12.3 123.0\u001b[0m\n", + "\u001b[0;34mNaN 12.3 33.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34mWhen using ``.apply()``, use ``group_keys`` to include or exclude the group keys.\u001b[0m\n", + "\u001b[0;34mThe ``group_keys`` argument defaults to ``True`` (include).\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m>>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',\u001b[0m\n", + "\u001b[0;34m... 'Parrot', 'Parrot'],\u001b[0m\n", + "\u001b[0;34m... 'Max Speed': [380., 370., 24., 26.]})\u001b[0m\n", + "\u001b[0;34m>>> df.groupby(\"Animal\", group_keys=True).apply(lambda x: x)\u001b[0m\n", + "\u001b[0;34m Animal Max Speed\u001b[0m\n", + "\u001b[0;34mAnimal\u001b[0m\n", + "\u001b[0;34mFalcon 0 Falcon 380.0\u001b[0m\n", + "\u001b[0;34m 1 Falcon 370.0\u001b[0m\n", + "\u001b[0;34mParrot 2 Parrot 24.0\u001b[0m\n", + "\u001b[0;34m 3 Parrot 26.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m>>> df.groupby(\"Animal\", group_keys=False).apply(lambda x: x)\u001b[0m\n", + "\u001b[0;34m Animal Max Speed\u001b[0m\n", + "\u001b[0;34m0 Falcon 380.0\u001b[0m\n", + "\u001b[0;34m1 Falcon 370.0\u001b[0m\n", + "\u001b[0;34m2 Parrot 24.0\u001b[0m\n", + "\u001b[0;34m3 Parrot 26.0\u001b[0m\n", + "\u001b[0;34m\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mAppender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_shared_docs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"groupby\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0m_shared_doc_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mby\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIndexLabel\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mas_index\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mgroup_keys\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mobserved\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdropna\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrameGroupBy\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeneric\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDataFrameGroupBy\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlevel\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mby\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"You have to supply one of 'by' and 'level'\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mDataFrameGroupBy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mas_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mas_index\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mgroup_keys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgroup_keys\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mobserved\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobserved\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdropna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0m_shared_docs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"pivot\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Return reshaped DataFrame organized by given index / column values.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Reshape data (produce a \"pivot\" table) based on column values. Uses\u001b[0m\n", + "\u001b[0;34m unique values from specified `index` / `columns` to form axes of the\u001b[0m\n", + "\u001b[0;34m resulting DataFrame. This function does not support data\u001b[0m\n", + "\u001b[0;34m aggregation, multiple values will result in a MultiIndex in the\u001b[0m\n", + "\u001b[0;34m columns. See the :ref:`User Guide ` for more on reshaping.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------%s\u001b[0m\n", + "\u001b[0;34m columns : str or object or a list of str\u001b[0m\n", + "\u001b[0;34m Column to use to make new frame's columns.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionchanged:: 1.1.0\u001b[0m\n", + "\u001b[0;34m Also accept list of columns names.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m index : str or object or a list of str, optional\u001b[0m\n", + "\u001b[0;34m Column to use to make new frame's index. If not given, uses existing index.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionchanged:: 1.1.0\u001b[0m\n", + "\u001b[0;34m Also accept list of index names.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m values : str, object or a list of the previous, optional\u001b[0m\n", + "\u001b[0;34m Column(s) to use for populating new frame's values. If not\u001b[0m\n", + "\u001b[0;34m specified, all remaining columns will be used and the result will\u001b[0m\n", + "\u001b[0;34m have hierarchically indexed columns.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame\u001b[0m\n", + "\u001b[0;34m Returns reshaped DataFrame.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Raises\u001b[0m\n", + "\u001b[0;34m ------\u001b[0m\n", + "\u001b[0;34m ValueError:\u001b[0m\n", + "\u001b[0;34m When there are any `index`, `columns` combinations with multiple\u001b[0m\n", + "\u001b[0;34m values. `DataFrame.pivot_table` when you need to aggregate.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.pivot_table : Generalization of pivot that can handle\u001b[0m\n", + "\u001b[0;34m duplicate values for one index/column pair.\u001b[0m\n", + "\u001b[0;34m DataFrame.unstack : Pivot based on the index values instead of a\u001b[0m\n", + "\u001b[0;34m column.\u001b[0m\n", + "\u001b[0;34m wide_to_long : Wide panel to long format. Less flexible but more\u001b[0m\n", + "\u001b[0;34m user-friendly than melt.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Notes\u001b[0m\n", + "\u001b[0;34m -----\u001b[0m\n", + "\u001b[0;34m For finer-tuned control, see hierarchical indexing documentation along\u001b[0m\n", + "\u001b[0;34m with the related stack/unstack methods.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Reference :ref:`the user guide ` for more examples.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two',\u001b[0m\n", + "\u001b[0;34m ... 'two'],\u001b[0m\n", + "\u001b[0;34m ... 'bar': ['A', 'B', 'C', 'A', 'B', 'C'],\u001b[0m\n", + "\u001b[0;34m ... 'baz': [1, 2, 3, 4, 5, 6],\u001b[0m\n", + "\u001b[0;34m ... 'zoo': ['x', 'y', 'z', 'q', 'w', 't']})\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m foo bar baz zoo\u001b[0m\n", + "\u001b[0;34m 0 one A 1 x\u001b[0m\n", + "\u001b[0;34m 1 one B 2 y\u001b[0m\n", + "\u001b[0;34m 2 one C 3 z\u001b[0m\n", + "\u001b[0;34m 3 two A 4 q\u001b[0m\n", + "\u001b[0;34m 4 two B 5 w\u001b[0m\n", + "\u001b[0;34m 5 two C 6 t\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.pivot(index='foo', columns='bar', values='baz')\u001b[0m\n", + "\u001b[0;34m bar A B C\u001b[0m\n", + "\u001b[0;34m foo\u001b[0m\n", + "\u001b[0;34m one 1 2 3\u001b[0m\n", + "\u001b[0;34m two 4 5 6\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.pivot(index='foo', columns='bar')['baz']\u001b[0m\n", + "\u001b[0;34m bar A B C\u001b[0m\n", + "\u001b[0;34m foo\u001b[0m\n", + "\u001b[0;34m one 1 2 3\u001b[0m\n", + "\u001b[0;34m two 4 5 6\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.pivot(index='foo', columns='bar', values=['baz', 'zoo'])\u001b[0m\n", + "\u001b[0;34m baz zoo\u001b[0m\n", + "\u001b[0;34m bar A B C A B C\u001b[0m\n", + "\u001b[0;34m foo\u001b[0m\n", + "\u001b[0;34m one 1 2 3 x y z\u001b[0m\n", + "\u001b[0;34m two 4 5 6 q w t\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m You could also assign a list of column names or a list of index names.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({\u001b[0m\n", + "\u001b[0;34m ... \"lev1\": [1, 1, 1, 2, 2, 2],\u001b[0m\n", + "\u001b[0;34m ... \"lev2\": [1, 1, 2, 1, 1, 2],\u001b[0m\n", + "\u001b[0;34m ... \"lev3\": [1, 2, 1, 2, 1, 2],\u001b[0m\n", + "\u001b[0;34m ... \"lev4\": [1, 2, 3, 4, 5, 6],\u001b[0m\n", + "\u001b[0;34m ... \"values\": [0, 1, 2, 3, 4, 5]})\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m lev1 lev2 lev3 lev4 values\u001b[0m\n", + "\u001b[0;34m 0 1 1 1 1 0\u001b[0m\n", + "\u001b[0;34m 1 1 1 2 2 1\u001b[0m\n", + "\u001b[0;34m 2 1 2 1 3 2\u001b[0m\n", + "\u001b[0;34m 3 2 1 2 4 3\u001b[0m\n", + "\u001b[0;34m 4 2 1 1 5 4\u001b[0m\n", + "\u001b[0;34m 5 2 2 2 6 5\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.pivot(index=\"lev1\", columns=[\"lev2\", \"lev3\"], values=\"values\")\u001b[0m\n", + "\u001b[0;34m lev2 1 2\u001b[0m\n", + "\u001b[0;34m lev3 1 2 1 2\u001b[0m\n", + "\u001b[0;34m lev1\u001b[0m\n", + "\u001b[0;34m 1 0.0 1.0 2.0 NaN\u001b[0m\n", + "\u001b[0;34m 2 4.0 3.0 NaN 5.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.pivot(index=[\"lev1\", \"lev2\"], columns=[\"lev3\"], values=\"values\")\u001b[0m\n", + "\u001b[0;34m lev3 1 2\u001b[0m\n", + "\u001b[0;34m lev1 lev2\u001b[0m\n", + "\u001b[0;34m 1 1 0.0 1.0\u001b[0m\n", + "\u001b[0;34m 2 2.0 NaN\u001b[0m\n", + "\u001b[0;34m 2 1 4.0 3.0\u001b[0m\n", + "\u001b[0;34m 2 NaN 5.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m A ValueError is raised if there are any duplicates.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({\"foo\": ['one', 'one', 'two', 'two'],\u001b[0m\n", + "\u001b[0;34m ... \"bar\": ['A', 'A', 'B', 'C'],\u001b[0m\n", + "\u001b[0;34m ... \"baz\": [1, 2, 3, 4]})\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m foo bar baz\u001b[0m\n", + "\u001b[0;34m 0 one A 1\u001b[0m\n", + "\u001b[0;34m 1 one A 2\u001b[0m\n", + "\u001b[0;34m 2 two B 3\u001b[0m\n", + "\u001b[0;34m 3 two C 4\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Notice that the first two rows are the same for our `index`\u001b[0m\n", + "\u001b[0;34m and `columns` arguments.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.pivot(index='foo', columns='bar', values='baz')\u001b[0m\n", + "\u001b[0;34m Traceback (most recent call last):\u001b[0m\n", + "\u001b[0;34m ...\u001b[0m\n", + "\u001b[0;34m ValueError: Index contains duplicate entries, cannot reshape\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mSubstitution\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mAppender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_shared_docs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"pivot\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mpivot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNoDefault\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNoDefault\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpivot\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpivot\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mpivot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0m_shared_docs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"pivot_table\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Create a spreadsheet-style pivot table as a DataFrame.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m The levels in the pivot table will be stored in MultiIndex objects\u001b[0m\n", + "\u001b[0;34m (hierarchical indexes) on the index and columns of the result DataFrame.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------%s\u001b[0m\n", + "\u001b[0;34m values : list-like or scalar, optional\u001b[0m\n", + "\u001b[0;34m Column or columns to aggregate.\u001b[0m\n", + "\u001b[0;34m index : column, Grouper, array, or list of the previous\u001b[0m\n", + "\u001b[0;34m If an array is passed, it must be the same length as the data. The\u001b[0m\n", + "\u001b[0;34m list can contain any of the other types (except list).\u001b[0m\n", + "\u001b[0;34m Keys to group by on the pivot table index. If an array is passed,\u001b[0m\n", + "\u001b[0;34m it is being used as the same manner as column values.\u001b[0m\n", + "\u001b[0;34m columns : column, Grouper, array, or list of the previous\u001b[0m\n", + "\u001b[0;34m If an array is passed, it must be the same length as the data. The\u001b[0m\n", + "\u001b[0;34m list can contain any of the other types (except list).\u001b[0m\n", + "\u001b[0;34m Keys to group by on the pivot table column. If an array is passed,\u001b[0m\n", + "\u001b[0;34m it is being used as the same manner as column values.\u001b[0m\n", + "\u001b[0;34m aggfunc : function, list of functions, dict, default numpy.mean\u001b[0m\n", + "\u001b[0;34m If list of functions passed, the resulting pivot table will have\u001b[0m\n", + "\u001b[0;34m hierarchical columns whose top level are the function names\u001b[0m\n", + "\u001b[0;34m (inferred from the function objects themselves)\u001b[0m\n", + "\u001b[0;34m If dict is passed, the key is column to aggregate and value\u001b[0m\n", + "\u001b[0;34m is function or list of functions. If ``margin=True``,\u001b[0m\n", + "\u001b[0;34m aggfunc will be used to calculate the partial aggregates.\u001b[0m\n", + "\u001b[0;34m fill_value : scalar, default None\u001b[0m\n", + "\u001b[0;34m Value to replace missing values with (in the resulting pivot table,\u001b[0m\n", + "\u001b[0;34m after aggregation).\u001b[0m\n", + "\u001b[0;34m margins : bool, default False\u001b[0m\n", + "\u001b[0;34m If ``margins=True``, special ``All`` columns and rows\u001b[0m\n", + "\u001b[0;34m will be added with partial group aggregates across the categories\u001b[0m\n", + "\u001b[0;34m on the rows and columns.\u001b[0m\n", + "\u001b[0;34m dropna : bool, default True\u001b[0m\n", + "\u001b[0;34m Do not include columns whose entries are all NaN. If True,\u001b[0m\n", + "\u001b[0;34m rows with a NaN value in any column will be omitted before\u001b[0m\n", + "\u001b[0;34m computing margins.\u001b[0m\n", + "\u001b[0;34m margins_name : str, default 'All'\u001b[0m\n", + "\u001b[0;34m Name of the row / column that will contain the totals\u001b[0m\n", + "\u001b[0;34m when margins is True.\u001b[0m\n", + "\u001b[0;34m observed : bool, default False\u001b[0m\n", + "\u001b[0;34m This only applies if any of the groupers are Categoricals.\u001b[0m\n", + "\u001b[0;34m If True: only show observed values for categorical groupers.\u001b[0m\n", + "\u001b[0;34m If False: show all values for categorical groupers.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m sort : bool, default True\u001b[0m\n", + "\u001b[0;34m Specifies if the result should be sorted.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionadded:: 1.3.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame\u001b[0m\n", + "\u001b[0;34m An Excel style pivot table.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.pivot : Pivot without aggregation that can handle\u001b[0m\n", + "\u001b[0;34m non-numeric data.\u001b[0m\n", + "\u001b[0;34m DataFrame.melt: Unpivot a DataFrame from wide to long format,\u001b[0m\n", + "\u001b[0;34m optionally leaving identifiers set.\u001b[0m\n", + "\u001b[0;34m wide_to_long : Wide panel to long format. Less flexible but more\u001b[0m\n", + "\u001b[0;34m user-friendly than melt.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Notes\u001b[0m\n", + "\u001b[0;34m -----\u001b[0m\n", + "\u001b[0;34m Reference :ref:`the user guide ` for more examples.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({\"A\": [\"foo\", \"foo\", \"foo\", \"foo\", \"foo\",\u001b[0m\n", + "\u001b[0;34m ... \"bar\", \"bar\", \"bar\", \"bar\"],\u001b[0m\n", + "\u001b[0;34m ... \"B\": [\"one\", \"one\", \"one\", \"two\", \"two\",\u001b[0m\n", + "\u001b[0;34m ... \"one\", \"one\", \"two\", \"two\"],\u001b[0m\n", + "\u001b[0;34m ... \"C\": [\"small\", \"large\", \"large\", \"small\",\u001b[0m\n", + "\u001b[0;34m ... \"small\", \"large\", \"small\", \"small\",\u001b[0m\n", + "\u001b[0;34m ... \"large\"],\u001b[0m\n", + "\u001b[0;34m ... \"D\": [1, 2, 2, 3, 3, 4, 5, 6, 7],\u001b[0m\n", + "\u001b[0;34m ... \"E\": [2, 4, 5, 5, 6, 6, 8, 9, 9]})\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m A B C D E\u001b[0m\n", + "\u001b[0;34m 0 foo one small 1 2\u001b[0m\n", + "\u001b[0;34m 1 foo one large 2 4\u001b[0m\n", + "\u001b[0;34m 2 foo one large 2 5\u001b[0m\n", + "\u001b[0;34m 3 foo two small 3 5\u001b[0m\n", + "\u001b[0;34m 4 foo two small 3 6\u001b[0m\n", + "\u001b[0;34m 5 bar one large 4 6\u001b[0m\n", + "\u001b[0;34m 6 bar one small 5 8\u001b[0m\n", + "\u001b[0;34m 7 bar two small 6 9\u001b[0m\n", + "\u001b[0;34m 8 bar two large 7 9\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m This first example aggregates values by taking the sum.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> table = pd.pivot_table(df, values='D', index=['A', 'B'],\u001b[0m\n", + "\u001b[0;34m ... columns=['C'], aggfunc=np.sum)\u001b[0m\n", + "\u001b[0;34m >>> table\u001b[0m\n", + "\u001b[0;34m C large small\u001b[0m\n", + "\u001b[0;34m A B\u001b[0m\n", + "\u001b[0;34m bar one 4.0 5.0\u001b[0m\n", + "\u001b[0;34m two 7.0 6.0\u001b[0m\n", + "\u001b[0;34m foo one 4.0 1.0\u001b[0m\n", + "\u001b[0;34m two NaN 6.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m We can also fill missing values using the `fill_value` parameter.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> table = pd.pivot_table(df, values='D', index=['A', 'B'],\u001b[0m\n", + "\u001b[0;34m ... columns=['C'], aggfunc=np.sum, fill_value=0)\u001b[0m\n", + "\u001b[0;34m >>> table\u001b[0m\n", + "\u001b[0;34m C large small\u001b[0m\n", + "\u001b[0;34m A B\u001b[0m\n", + "\u001b[0;34m bar one 4 5\u001b[0m\n", + "\u001b[0;34m two 7 6\u001b[0m\n", + "\u001b[0;34m foo one 4 1\u001b[0m\n", + "\u001b[0;34m two 0 6\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m The next example aggregates by taking the mean across multiple columns.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],\u001b[0m\n", + "\u001b[0;34m ... aggfunc={'D': np.mean, 'E': np.mean})\u001b[0m\n", + "\u001b[0;34m >>> table\u001b[0m\n", + "\u001b[0;34m D E\u001b[0m\n", + "\u001b[0;34m A C\u001b[0m\n", + "\u001b[0;34m bar large 5.500000 7.500000\u001b[0m\n", + "\u001b[0;34m small 5.500000 8.500000\u001b[0m\n", + "\u001b[0;34m foo large 2.000000 4.500000\u001b[0m\n", + "\u001b[0;34m small 2.333333 4.333333\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m We can also calculate multiple types of aggregations for any given\u001b[0m\n", + "\u001b[0;34m value column.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],\u001b[0m\n", + "\u001b[0;34m ... aggfunc={'D': np.mean,\u001b[0m\n", + "\u001b[0;34m ... 'E': [min, max, np.mean]})\u001b[0m\n", + "\u001b[0;34m >>> table\u001b[0m\n", + "\u001b[0;34m D E\u001b[0m\n", + "\u001b[0;34m mean max mean min\u001b[0m\n", + "\u001b[0;34m A C\u001b[0m\n", + "\u001b[0;34m bar large 5.500000 9 7.500000 6\u001b[0m\n", + "\u001b[0;34m small 5.500000 9 8.500000 8\u001b[0m\n", + "\u001b[0;34m foo large 2.000000 5 4.500000 4\u001b[0m\n", + "\u001b[0;34m small 2.333333 6 4.333333 2\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mSubstitution\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mAppender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_shared_docs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"pivot_table\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mpivot_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maggfunc\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAggFuncType\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"mean\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmargins\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdropna\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmargins_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mLevel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"All\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mobserved\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpivot\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpivot_table\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mpivot_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maggfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maggfunc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmargins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmargins\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdropna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmargins_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmargins_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mobserved\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobserved\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mLevel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdropna\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Stack the prescribed level(s) from columns to index.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Return a reshaped DataFrame or Series having a multi-level\u001b[0m\n", + "\u001b[0;34m index with one or more new inner-most levels compared to the current\u001b[0m\n", + "\u001b[0;34m DataFrame. The new inner-most levels are created by pivoting the\u001b[0m\n", + "\u001b[0;34m columns of the current dataframe:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m - if the columns have a single level, the output is a Series;\u001b[0m\n", + "\u001b[0;34m - if the columns have multiple levels, the new index\u001b[0m\n", + "\u001b[0;34m level(s) is (are) taken from the prescribed level(s) and\u001b[0m\n", + "\u001b[0;34m the output is a DataFrame.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m level : int, str, list, default -1\u001b[0m\n", + "\u001b[0;34m Level(s) to stack from the column axis onto the index\u001b[0m\n", + "\u001b[0;34m axis, defined as one index or label, or a list of indices\u001b[0m\n", + "\u001b[0;34m or labels.\u001b[0m\n", + "\u001b[0;34m dropna : bool, default True\u001b[0m\n", + "\u001b[0;34m Whether to drop rows in the resulting Frame/Series with\u001b[0m\n", + "\u001b[0;34m missing values. Stacking a column level onto the index\u001b[0m\n", + "\u001b[0;34m axis can create combinations of index and column values\u001b[0m\n", + "\u001b[0;34m that are missing from the original dataframe. See Examples\u001b[0m\n", + "\u001b[0;34m section.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame or Series\u001b[0m\n", + "\u001b[0;34m Stacked dataframe or series.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.unstack : Unstack prescribed level(s) from index axis\u001b[0m\n", + "\u001b[0;34m onto column axis.\u001b[0m\n", + "\u001b[0;34m DataFrame.pivot : Reshape dataframe from long format to wide\u001b[0m\n", + "\u001b[0;34m format.\u001b[0m\n", + "\u001b[0;34m DataFrame.pivot_table : Create a spreadsheet-style pivot table\u001b[0m\n", + "\u001b[0;34m as a DataFrame.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Notes\u001b[0m\n", + "\u001b[0;34m -----\u001b[0m\n", + "\u001b[0;34m The function is named by analogy with a collection of books\u001b[0m\n", + "\u001b[0;34m being reorganized from being side by side on a horizontal\u001b[0m\n", + "\u001b[0;34m position (the columns of the dataframe) to being stacked\u001b[0m\n", + "\u001b[0;34m vertically on top of each other (in the index of the\u001b[0m\n", + "\u001b[0;34m dataframe).\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Reference :ref:`the user guide ` for more examples.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m **Single level columns**\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df_single_level_cols = pd.DataFrame([[0, 1], [2, 3]],\u001b[0m\n", + "\u001b[0;34m ... index=['cat', 'dog'],\u001b[0m\n", + "\u001b[0;34m ... columns=['weight', 'height'])\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Stacking a dataframe with a single level column axis returns a Series:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df_single_level_cols\u001b[0m\n", + "\u001b[0;34m weight height\u001b[0m\n", + "\u001b[0;34m cat 0 1\u001b[0m\n", + "\u001b[0;34m dog 2 3\u001b[0m\n", + "\u001b[0;34m >>> df_single_level_cols.stack()\u001b[0m\n", + "\u001b[0;34m cat weight 0\u001b[0m\n", + "\u001b[0;34m height 1\u001b[0m\n", + "\u001b[0;34m dog weight 2\u001b[0m\n", + "\u001b[0;34m height 3\u001b[0m\n", + "\u001b[0;34m dtype: int64\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m **Multi level columns: simple case**\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> multicol1 = pd.MultiIndex.from_tuples([('weight', 'kg'),\u001b[0m\n", + "\u001b[0;34m ... ('weight', 'pounds')])\u001b[0m\n", + "\u001b[0;34m >>> df_multi_level_cols1 = pd.DataFrame([[1, 2], [2, 4]],\u001b[0m\n", + "\u001b[0;34m ... index=['cat', 'dog'],\u001b[0m\n", + "\u001b[0;34m ... columns=multicol1)\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Stacking a dataframe with a multi-level column axis:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df_multi_level_cols1\u001b[0m\n", + "\u001b[0;34m weight\u001b[0m\n", + "\u001b[0;34m kg pounds\u001b[0m\n", + "\u001b[0;34m cat 1 2\u001b[0m\n", + "\u001b[0;34m dog 2 4\u001b[0m\n", + "\u001b[0;34m >>> df_multi_level_cols1.stack()\u001b[0m\n", + "\u001b[0;34m weight\u001b[0m\n", + "\u001b[0;34m cat kg 1\u001b[0m\n", + "\u001b[0;34m pounds 2\u001b[0m\n", + "\u001b[0;34m dog kg 2\u001b[0m\n", + "\u001b[0;34m pounds 4\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m **Missing values**\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> multicol2 = pd.MultiIndex.from_tuples([('weight', 'kg'),\u001b[0m\n", + "\u001b[0;34m ... ('height', 'm')])\u001b[0m\n", + "\u001b[0;34m >>> df_multi_level_cols2 = pd.DataFrame([[1.0, 2.0], [3.0, 4.0]],\u001b[0m\n", + "\u001b[0;34m ... index=['cat', 'dog'],\u001b[0m\n", + "\u001b[0;34m ... columns=multicol2)\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m It is common to have missing values when stacking a dataframe\u001b[0m\n", + "\u001b[0;34m with multi-level columns, as the stacked dataframe typically\u001b[0m\n", + "\u001b[0;34m has more values than the original dataframe. Missing values\u001b[0m\n", + "\u001b[0;34m are filled with NaNs:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df_multi_level_cols2\u001b[0m\n", + "\u001b[0;34m weight height\u001b[0m\n", + "\u001b[0;34m kg m\u001b[0m\n", + "\u001b[0;34m cat 1.0 2.0\u001b[0m\n", + "\u001b[0;34m dog 3.0 4.0\u001b[0m\n", + "\u001b[0;34m >>> df_multi_level_cols2.stack()\u001b[0m\n", + "\u001b[0;34m height weight\u001b[0m\n", + "\u001b[0;34m cat kg NaN 1.0\u001b[0m\n", + "\u001b[0;34m m 2.0 NaN\u001b[0m\n", + "\u001b[0;34m dog kg NaN 3.0\u001b[0m\n", + "\u001b[0;34m m 4.0 NaN\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m **Prescribing the level(s) to be stacked**\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m The first parameter controls which level or levels are stacked:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df_multi_level_cols2.stack(0)\u001b[0m\n", + "\u001b[0;34m kg m\u001b[0m\n", + "\u001b[0;34m cat height NaN 2.0\u001b[0m\n", + "\u001b[0;34m weight 1.0 NaN\u001b[0m\n", + "\u001b[0;34m dog height NaN 4.0\u001b[0m\n", + "\u001b[0;34m weight 3.0 NaN\u001b[0m\n", + "\u001b[0;34m >>> df_multi_level_cols2.stack([0, 1])\u001b[0m\n", + "\u001b[0;34m cat height m 2.0\u001b[0m\n", + "\u001b[0;34m weight kg 1.0\u001b[0m\n", + "\u001b[0;34m dog height m 4.0\u001b[0m\n", + "\u001b[0;34m weight kg 3.0\u001b[0m\n", + "\u001b[0;34m dtype: float64\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m **Dropping missing values**\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df_multi_level_cols3 = pd.DataFrame([[None, 1.0], [2.0, 3.0]],\u001b[0m\n", + "\u001b[0;34m ... index=['cat', 'dog'],\u001b[0m\n", + "\u001b[0;34m ... columns=multicol2)\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Note that rows where all values are missing are dropped by\u001b[0m\n", + "\u001b[0;34m default but this behaviour can be controlled via the dropna\u001b[0m\n", + "\u001b[0;34m keyword parameter:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df_multi_level_cols3\u001b[0m\n", + "\u001b[0;34m weight height\u001b[0m\n", + "\u001b[0;34m kg m\u001b[0m\n", + "\u001b[0;34m cat NaN 1.0\u001b[0m\n", + "\u001b[0;34m dog 2.0 3.0\u001b[0m\n", + "\u001b[0;34m >>> df_multi_level_cols3.stack(dropna=False)\u001b[0m\n", + "\u001b[0;34m height weight\u001b[0m\n", + "\u001b[0;34m cat kg NaN NaN\u001b[0m\n", + "\u001b[0;34m m 1.0 NaN\u001b[0m\n", + "\u001b[0;34m dog kg NaN 2.0\u001b[0m\n", + "\u001b[0;34m m 3.0 NaN\u001b[0m\n", + "\u001b[0;34m >>> df_multi_level_cols3.stack(dropna=True)\u001b[0m\n", + "\u001b[0;34m height weight\u001b[0m\n", + "\u001b[0;34m cat m 1.0 NaN\u001b[0m\n", + "\u001b[0;34m dog kg NaN 2.0\u001b[0m\n", + "\u001b[0;34m m 3.0 NaN\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mstack\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mstack_multiple\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstack_multiple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdropna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdropna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"stack\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mexplode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIndexLabel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Transform each element of a list-like to a row, replicating index values.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m column : IndexLabel\u001b[0m\n", + "\u001b[0;34m Column(s) to explode.\u001b[0m\n", + "\u001b[0;34m For multiple columns, specify a non-empty list with each element\u001b[0m\n", + "\u001b[0;34m be str or tuple, and all specified columns their list-like data\u001b[0m\n", + "\u001b[0;34m on same row of the frame must have matching length.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionadded:: 1.3.0\u001b[0m\n", + "\u001b[0;34m Multi-column explode\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m ignore_index : bool, default False\u001b[0m\n", + "\u001b[0;34m If True, the resulting index will be labeled 0, 1, …, n - 1.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionadded:: 1.1.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame\u001b[0m\n", + "\u001b[0;34m Exploded lists to rows of the subset columns;\u001b[0m\n", + "\u001b[0;34m index will be duplicated for these rows.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Raises\u001b[0m\n", + "\u001b[0;34m ------\u001b[0m\n", + "\u001b[0;34m ValueError :\u001b[0m\n", + "\u001b[0;34m * If columns of the frame are not unique.\u001b[0m\n", + "\u001b[0;34m * If specified columns to explode is empty list.\u001b[0m\n", + "\u001b[0;34m * If specified columns to explode have not matching count of\u001b[0m\n", + "\u001b[0;34m elements rowwise in the frame.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.unstack : Pivot a level of the (necessarily hierarchical)\u001b[0m\n", + "\u001b[0;34m index labels.\u001b[0m\n", + "\u001b[0;34m DataFrame.melt : Unpivot a DataFrame from wide format to long format.\u001b[0m\n", + "\u001b[0;34m Series.explode : Explode a DataFrame from list-like columns to long format.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Notes\u001b[0m\n", + "\u001b[0;34m -----\u001b[0m\n", + "\u001b[0;34m This routine will explode list-likes including lists, tuples, sets,\u001b[0m\n", + "\u001b[0;34m Series, and np.ndarray. The result dtype of the subset rows will\u001b[0m\n", + "\u001b[0;34m be object. Scalars will be returned unchanged, and empty list-likes will\u001b[0m\n", + "\u001b[0;34m result in a np.nan for that row. In addition, the ordering of rows in the\u001b[0m\n", + "\u001b[0;34m output will be non-deterministic when exploding sets.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Reference :ref:`the user guide ` for more examples.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'A': [[0, 1, 2], 'foo', [], [3, 4]],\u001b[0m\n", + "\u001b[0;34m ... 'B': 1,\u001b[0m\n", + "\u001b[0;34m ... 'C': [['a', 'b', 'c'], np.nan, [], ['d', 'e']]})\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m A B C\u001b[0m\n", + "\u001b[0;34m 0 [0, 1, 2] 1 [a, b, c]\u001b[0m\n", + "\u001b[0;34m 1 foo 1 NaN\u001b[0m\n", + "\u001b[0;34m 2 [] 1 []\u001b[0m\n", + "\u001b[0;34m 3 [3, 4] 1 [d, e]\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Single-column explode.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.explode('A')\u001b[0m\n", + "\u001b[0;34m A B C\u001b[0m\n", + "\u001b[0;34m 0 0 1 [a, b, c]\u001b[0m\n", + "\u001b[0;34m 0 1 1 [a, b, c]\u001b[0m\n", + "\u001b[0;34m 0 2 1 [a, b, c]\u001b[0m\n", + "\u001b[0;34m 1 foo 1 NaN\u001b[0m\n", + "\u001b[0;34m 2 NaN 1 []\u001b[0m\n", + "\u001b[0;34m 3 3 1 [d, e]\u001b[0m\n", + "\u001b[0;34m 3 4 1 [d, e]\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Multi-column explode.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.explode(list('AC'))\u001b[0m\n", + "\u001b[0;34m A B C\u001b[0m\n", + "\u001b[0;34m 0 0 1 a\u001b[0m\n", + "\u001b[0;34m 0 1 1 b\u001b[0m\n", + "\u001b[0;34m 0 2 1 c\u001b[0m\n", + "\u001b[0;34m 1 foo 1 NaN\u001b[0m\n", + "\u001b[0;34m 2 NaN 1 NaN\u001b[0m\n", + "\u001b[0;34m 3 3 1 d\u001b[0m\n", + "\u001b[0;34m 3 4 1 e\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mduplicate_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mduplicated\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34mf\"DataFrame columns must be unique. Duplicate columns: {duplicate_cols}\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mHashable\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"column must be nonempty\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"column must be unique\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"column must be a scalar, tuple, or list thereof\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\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\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumns\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.\u001b[0m\u001b[0mexplode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmylen\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mis_list_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcounts0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumns\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.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmylen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcolumns\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\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcounts0\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmylen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"columns must have matching element counts\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexplode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m 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"\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m If the index is not a MultiIndex, the output will be a Series\u001b[0m\n", + "\u001b[0;34m (the analogue of stack when the columns are not a MultiIndex).\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m level : int, str, or list of these, default -1 (last level)\u001b[0m\n", + "\u001b[0;34m Level(s) of index to unstack, can pass level name.\u001b[0m\n", + "\u001b[0;34m fill_value : int, str or dict\u001b[0m\n", + "\u001b[0;34m Replace NaN with this value if the unstack produces missing values.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m Series or DataFrame\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.pivot : Pivot a table based on column values.\u001b[0m\n", + "\u001b[0;34m DataFrame.stack : Pivot a level of the column labels (inverse operation\u001b[0m\n", + "\u001b[0;34m from `unstack`).\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Notes\u001b[0m\n", + "\u001b[0;34m -----\u001b[0m\n", + "\u001b[0;34m Reference :ref:`the user guide ` for more examples.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> index = pd.MultiIndex.from_tuples([('one', 'a'), ('one', 'b'),\u001b[0m\n", + "\u001b[0;34m ... ('two', 'a'), ('two', 'b')])\u001b[0m\n", + "\u001b[0;34m >>> s = pd.Series(np.arange(1.0, 5.0), index=index)\u001b[0m\n", + "\u001b[0;34m >>> s\u001b[0m\n", + "\u001b[0;34m one a 1.0\u001b[0m\n", + "\u001b[0;34m b 2.0\u001b[0m\n", + "\u001b[0;34m two a 3.0\u001b[0m\n", + "\u001b[0;34m b 4.0\u001b[0m\n", + "\u001b[0;34m dtype: float64\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> s.unstack(level=-1)\u001b[0m\n", + "\u001b[0;34m a b\u001b[0m\n", + "\u001b[0;34m one 1.0 2.0\u001b[0m\n", + "\u001b[0;34m two 3.0 4.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> s.unstack(level=0)\u001b[0m\n", + "\u001b[0;34m one two\u001b[0m\n", + "\u001b[0;34m a 1.0 3.0\u001b[0m\n", + "\u001b[0;34m b 2.0 4.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df = s.unstack(level=0)\u001b[0m\n", + "\u001b[0;34m >>> df.unstack()\u001b[0m\n", + "\u001b[0;34m one a 1.0\u001b[0m\n", + "\u001b[0;34m b 2.0\u001b[0m\n", + "\u001b[0;34m two a 3.0\u001b[0m\n", + "\u001b[0;34m b 4.0\u001b[0m\n", + "\u001b[0;34m dtype: float64\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0munstack\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"unstack\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mAppender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_shared_docs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"melt\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"caller\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"df.melt(\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"other\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"melt\"\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmelt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m 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series-related\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mdoc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiff\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"DataFrame\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mextra_params\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"axis : {0 or 'index', 1 or 'columns'}, default 0\\n \"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"Take difference over rows (0) or columns (1).\\n\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mother_klass\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Series\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mexamples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdedent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Difference with previous row\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'a': [1, 2, 3, 4, 5, 6],\u001b[0m\n", + "\u001b[0;34m ... 'b': [1, 1, 2, 3, 5, 8],\u001b[0m\n", + "\u001b[0;34m ... 'c': [1, 4, 9, 16, 25, 36]})\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m a b c\u001b[0m\n", + "\u001b[0;34m 0 1 1 1\u001b[0m\n", + "\u001b[0;34m 1 2 1 4\u001b[0m\n", + "\u001b[0;34m 2 3 2 9\u001b[0m\n", + "\u001b[0;34m 3 4 3 16\u001b[0m\n", + "\u001b[0;34m 4 5 5 25\u001b[0m\n", + "\u001b[0;34m 5 6 8 36\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.diff()\u001b[0m\n", + "\u001b[0;34m a b c\u001b[0m\n", + "\u001b[0;34m 0 NaN NaN NaN\u001b[0m\n", + "\u001b[0;34m 1 1.0 0.0 3.0\u001b[0m\n", + "\u001b[0;34m 2 1.0 1.0 5.0\u001b[0m\n", + "\u001b[0;34m 3 1.0 1.0 7.0\u001b[0m\n", + "\u001b[0;34m 4 1.0 2.0 9.0\u001b[0m\n", + "\u001b[0;34m 5 1.0 3.0 11.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Difference with previous column\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.diff(axis=1)\u001b[0m\n", + "\u001b[0;34m a b c\u001b[0m\n", + "\u001b[0;34m 0 NaN 0 0\u001b[0m\n", + "\u001b[0;34m 1 NaN -1 3\u001b[0m\n", + "\u001b[0;34m 2 NaN -1 7\u001b[0m\n", + "\u001b[0;34m 3 NaN -1 13\u001b[0m\n", + "\u001b[0;34m 4 NaN 0 20\u001b[0m\n", + "\u001b[0;34m 5 NaN 2 28\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Difference with 3rd previous row\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.diff(periods=3)\u001b[0m\n", + "\u001b[0;34m a b c\u001b[0m\n", + "\u001b[0;34m 0 NaN NaN NaN\u001b[0m\n", + "\u001b[0;34m 1 NaN NaN NaN\u001b[0m\n", + "\u001b[0;34m 2 NaN NaN NaN\u001b[0m\n", + "\u001b[0;34m 3 3.0 2.0 15.0\u001b[0m\n", + "\u001b[0;34m 4 3.0 4.0 21.0\u001b[0m\n", + "\u001b[0;34m 5 3.0 6.0 27.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Difference with following row\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.diff(periods=-1)\u001b[0m\n", + "\u001b[0;34m a b c\u001b[0m\n", + "\u001b[0;34m 0 -1.0 0.0 -3.0\u001b[0m\n", + "\u001b[0;34m 1 -1.0 -1.0 -5.0\u001b[0m\n", + "\u001b[0;34m 2 -1.0 -1.0 -7.0\u001b[0m\n", + "\u001b[0;34m 3 -1.0 -2.0 -9.0\u001b[0m\n", + "\u001b[0;34m 4 -1.0 -3.0 -11.0\u001b[0m\n", + "\u001b[0;34m 5 NaN NaN NaN\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Overflow in input dtype\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'a': [1, 0]}, dtype=np.uint8)\u001b[0m\n", + "\u001b[0;34m >>> df.diff()\u001b[0m\n", + "\u001b[0;34m a\u001b[0m\n", + "\u001b[0;34m 0 NaN\u001b[0m\n", + "\u001b[0;34m 1 255.0\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mperiods\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mperiods\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mis_float\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mperiods\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# error: \"int\" has no attribute \"is_integer\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mperiods\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[attr-defined]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"periods must be an integer\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mperiods\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mperiods\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mperiods\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# in the periods == 0 case, this is equivalent diff of 0 periods\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# along axis=0, and the Manager method may be somewhat more\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# performant, so we dispatch in that case.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshift\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mperiods\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# With periods=0 this is equivalent to a diff with axis=0\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mperiods\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"diff\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# ----------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Function application\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_gotitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIndexLabel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mndim\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0msubset\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mDataFrame\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mSeries\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Sub-classes to define. Return a sliced object.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m key : string / list of selections\u001b[0m\n", + "\u001b[0;34m ndim : {1, 2}\u001b[0m\n", + "\u001b[0;34m requested ndim of result\u001b[0m\n", + "\u001b[0;34m subset : object, default None\u001b[0m\n", + "\u001b[0;34m subset to act on\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0msubset\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0msubset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0msubset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# is Series\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msubset\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# TODO: _shallow_copy(subset)?\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msubset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0m_agg_summary_and_see_also_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdedent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m The aggregation operations are always performed over an axis, either the\u001b[0m\n", + "\u001b[0;34m index (default) or the column axis. This behavior is different from\u001b[0m\n", + "\u001b[0;34m `numpy` aggregation functions (`mean`, `median`, `prod`, `sum`, `std`,\u001b[0m\n", + "\u001b[0;34m `var`), where the default is to compute the aggregation of the flattened\u001b[0m\n", + "\u001b[0;34m array, e.g., ``numpy.mean(arr_2d)`` as opposed to\u001b[0m\n", + "\u001b[0;34m ``numpy.mean(arr_2d, axis=0)``.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m `agg` is an alias for `aggregate`. Use the alias.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.apply : Perform any type of operations.\u001b[0m\n", + "\u001b[0;34m DataFrame.transform : Perform transformation type operations.\u001b[0m\n", + "\u001b[0;34m core.groupby.GroupBy : Perform operations over groups.\u001b[0m\n", + "\u001b[0;34m core.resample.Resampler : Perform operations over resampled bins.\u001b[0m\n", + "\u001b[0;34m core.window.Rolling : Perform operations over rolling window.\u001b[0m\n", + "\u001b[0;34m core.window.Expanding : Perform operations over expanding window.\u001b[0m\n", + "\u001b[0;34m core.window.ExponentialMovingWindow : Perform operation over exponential weighted\u001b[0m\n", + "\u001b[0;34m window.\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0m_agg_examples_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdedent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame([[1, 2, 3],\u001b[0m\n", + "\u001b[0;34m ... [4, 5, 6],\u001b[0m\n", + "\u001b[0;34m ... [7, 8, 9],\u001b[0m\n", + "\u001b[0;34m ... [np.nan, np.nan, np.nan]],\u001b[0m\n", + "\u001b[0;34m ... columns=['A', 'B', 'C'])\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Aggregate these functions over the rows.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.agg(['sum', 'min'])\u001b[0m\n", + "\u001b[0;34m A B C\u001b[0m\n", + "\u001b[0;34m sum 12.0 15.0 18.0\u001b[0m\n", + "\u001b[0;34m min 1.0 2.0 3.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Different aggregations per column.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']})\u001b[0m\n", + "\u001b[0;34m A B\u001b[0m\n", + "\u001b[0;34m sum 12.0 NaN\u001b[0m\n", + "\u001b[0;34m min 1.0 2.0\u001b[0m\n", + "\u001b[0;34m max NaN 8.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Aggregate different functions over the columns and rename the index of the resulting\u001b[0m\n", + "\u001b[0;34m DataFrame.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.agg(x=('A', max), y=('B', 'min'), z=('C', np.mean))\u001b[0m\n", + "\u001b[0;34m A B C\u001b[0m\n", + "\u001b[0;34m x 7.0 NaN NaN\u001b[0m\n", + "\u001b[0;34m y NaN 2.0 NaN\u001b[0m\n", + "\u001b[0;34m z NaN NaN 6.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Aggregate over the columns.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.agg(\"mean\", axis=\"columns\")\u001b[0m\n", + "\u001b[0;34m 0 2.0\u001b[0m\n", + "\u001b[0;34m 1 5.0\u001b[0m\n", + "\u001b[0;34m 2 8.0\u001b[0m\n", + "\u001b[0;34m 3 NaN\u001b[0m\n", + "\u001b[0;34m dtype: float64\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mdoc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m 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"\u001b[0;34m\u001b[0m \u001b[0;31m# For the return values of reconstruct_func, if relabeling is\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# False, columns and order will be None.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0morder\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult_in_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrelabel_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult_in_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0magg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maggregate\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# error: Signature of \"any\" incompatible with supertype \"NDFrame\" [override]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0moverload\u001b[0m \u001b[0;31m# type: ignore[override]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m 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"\u001b[0;34m\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# error: Missing return statement\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mdoc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mNDFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0m_shared_doc_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;31m# type: ignore[empty-body]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m 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"\u001b[0;34m either the DataFrame's index (``axis=0``) or the DataFrame's columns\u001b[0m\n", + "\u001b[0;34m (``axis=1``). By default (``result_type=None``), the final return type\u001b[0m\n", + "\u001b[0;34m is inferred from the return type of the applied function. Otherwise,\u001b[0m\n", + "\u001b[0;34m it depends on the `result_type` argument.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m func : function\u001b[0m\n", + "\u001b[0;34m Function to apply to each column or row.\u001b[0m\n", + "\u001b[0;34m axis : {0 or 'index', 1 or 'columns'}, default 0\u001b[0m\n", + "\u001b[0;34m Axis along which the function is applied:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m * 0 or 'index': apply function to each column.\u001b[0m\n", + "\u001b[0;34m * 1 or 'columns': apply function to each row.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m raw : bool, default False\u001b[0m\n", + "\u001b[0;34m Determines if row or column is passed as a Series or ndarray object:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m * ``False`` : passes each row or column as a Series to the\u001b[0m\n", + "\u001b[0;34m function.\u001b[0m\n", + "\u001b[0;34m * ``True`` : the passed function will receive ndarray objects\u001b[0m\n", + "\u001b[0;34m instead.\u001b[0m\n", + "\u001b[0;34m If you are just applying a NumPy reduction function this will\u001b[0m\n", + "\u001b[0;34m achieve much better performance.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m result_type : {'expand', 'reduce', 'broadcast', None}, default None\u001b[0m\n", + "\u001b[0;34m These only act when ``axis=1`` (columns):\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m * 'expand' : list-like results will be turned into columns.\u001b[0m\n", + "\u001b[0;34m * 'reduce' : returns a Series if possible rather than expanding\u001b[0m\n", + "\u001b[0;34m list-like results. This is the opposite of 'expand'.\u001b[0m\n", + "\u001b[0;34m * 'broadcast' : results will be broadcast to the original shape\u001b[0m\n", + "\u001b[0;34m of the DataFrame, the original index and columns will be\u001b[0m\n", + "\u001b[0;34m retained.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m The default behaviour (None) depends on the return value of the\u001b[0m\n", + "\u001b[0;34m applied function: list-like results will be returned as a Series\u001b[0m\n", + "\u001b[0;34m of those. However if the apply function returns a Series these\u001b[0m\n", + "\u001b[0;34m are expanded to columns.\u001b[0m\n", + "\u001b[0;34m args : tuple\u001b[0m\n", + "\u001b[0;34m Positional arguments to pass to `func` in addition to the\u001b[0m\n", + "\u001b[0;34m array/series.\u001b[0m\n", + "\u001b[0;34m **kwargs\u001b[0m\n", + "\u001b[0;34m Additional keyword arguments to pass as keywords arguments to\u001b[0m\n", + "\u001b[0;34m `func`.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m Series or DataFrame\u001b[0m\n", + "\u001b[0;34m Result of applying ``func`` along the given axis of the\u001b[0m\n", + "\u001b[0;34m DataFrame.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.applymap: For elementwise operations.\u001b[0m\n", + "\u001b[0;34m DataFrame.aggregate: Only perform aggregating type operations.\u001b[0m\n", + "\u001b[0;34m DataFrame.transform: Only perform transforming type operations.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Notes\u001b[0m\n", + "\u001b[0;34m -----\u001b[0m\n", + "\u001b[0;34m Functions that mutate the passed object can produce unexpected\u001b[0m\n", + "\u001b[0;34m behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`\u001b[0m\n", + "\u001b[0;34m for more details.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame([[4, 9]] * 3, columns=['A', 'B'])\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m A B\u001b[0m\n", + "\u001b[0;34m 0 4 9\u001b[0m\n", + "\u001b[0;34m 1 4 9\u001b[0m\n", + "\u001b[0;34m 2 4 9\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Using a numpy universal function (in this case the same as\u001b[0m\n", + "\u001b[0;34m ``np.sqrt(df)``):\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.apply(np.sqrt)\u001b[0m\n", + "\u001b[0;34m A B\u001b[0m\n", + "\u001b[0;34m 0 2.0 3.0\u001b[0m\n", + "\u001b[0;34m 1 2.0 3.0\u001b[0m\n", + "\u001b[0;34m 2 2.0 3.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Using a reducing function on either axis\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.apply(np.sum, axis=0)\u001b[0m\n", + "\u001b[0;34m A 12\u001b[0m\n", + "\u001b[0;34m B 27\u001b[0m\n", + "\u001b[0;34m dtype: int64\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.apply(np.sum, axis=1)\u001b[0m\n", + "\u001b[0;34m 0 13\u001b[0m\n", + "\u001b[0;34m 1 13\u001b[0m\n", + "\u001b[0;34m 2 13\u001b[0m\n", + "\u001b[0;34m dtype: int64\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returning a list-like will result in a Series\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.apply(lambda x: [1, 2], axis=1)\u001b[0m\n", + "\u001b[0;34m 0 [1, 2]\u001b[0m\n", + "\u001b[0;34m 1 [1, 2]\u001b[0m\n", + "\u001b[0;34m 2 [1, 2]\u001b[0m\n", + "\u001b[0;34m dtype: object\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Passing ``result_type='expand'`` will expand list-like results\u001b[0m\n", + "\u001b[0;34m to columns of a Dataframe\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.apply(lambda x: [1, 2], axis=1, result_type='expand')\u001b[0m\n", + "\u001b[0;34m 0 1\u001b[0m\n", + "\u001b[0;34m 0 1 2\u001b[0m\n", + "\u001b[0;34m 1 1 2\u001b[0m\n", + "\u001b[0;34m 2 1 2\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returning a Series inside the function is similar to passing\u001b[0m\n", + "\u001b[0;34m ``result_type='expand'``. The resulting column names\u001b[0m\n", + "\u001b[0;34m will be the Series index.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.apply(lambda x: pd.Series([1, 2], index=['foo', 'bar']), axis=1)\u001b[0m\n", + "\u001b[0;34m foo bar\u001b[0m\n", + "\u001b[0;34m 0 1 2\u001b[0m\n", + "\u001b[0;34m 1 1 2\u001b[0m\n", + "\u001b[0;34m 2 1 2\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Passing ``result_type='broadcast'`` will ensure the same shape\u001b[0m\n", + "\u001b[0;34m result, whether list-like or scalar is returned by the function,\u001b[0m\n", + "\u001b[0;34m and broadcast it along the axis. The resulting column names will\u001b[0m\n", + "\u001b[0;34m be the originals.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.apply(lambda x: [1, 2], axis=1, result_type='broadcast')\u001b[0m\n", + "\u001b[0;34m A B\u001b[0m\n", + "\u001b[0;34m 0 1 2\u001b[0m\n", + "\u001b[0;34m 1 1 2\u001b[0m\n", + "\u001b[0;34m 2 1 2\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mframe_apply\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mframe_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mraw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mraw\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresult_type\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"apply\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mapplymap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mPythonFuncType\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_action\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Apply a function to a Dataframe elementwise.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m This method applies a function that accepts and returns a scalar\u001b[0m\n", + "\u001b[0;34m to every element of a DataFrame.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m func : callable\u001b[0m\n", + "\u001b[0;34m Python function, returns a single value from a single value.\u001b[0m\n", + "\u001b[0;34m na_action : {None, 'ignore'}, default None\u001b[0m\n", + "\u001b[0;34m If ‘ignore’, propagate NaN values, without passing them to func.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionadded:: 1.2\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m **kwargs\u001b[0m\n", + "\u001b[0;34m Additional keyword arguments to pass as keywords arguments to\u001b[0m\n", + "\u001b[0;34m `func`.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionadded:: 1.3.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame\u001b[0m\n", + "\u001b[0;34m Transformed DataFrame.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.apply : Apply a function along input axis of DataFrame.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame([[1, 2.12], [3.356, 4.567]])\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m 0 1\u001b[0m\n", + "\u001b[0;34m 0 1.000 2.120\u001b[0m\n", + "\u001b[0;34m 1 3.356 4.567\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.applymap(lambda x: len(str(x)))\u001b[0m\n", + "\u001b[0;34m 0 1\u001b[0m\n", + "\u001b[0;34m 0 3 4\u001b[0m\n", + "\u001b[0;34m 1 5 5\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Like Series.map, NA values can be ignored:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df_copy = df.copy()\u001b[0m\n", + "\u001b[0;34m >>> df_copy.iloc[0, 0] = pd.NA\u001b[0m\n", + "\u001b[0;34m >>> df_copy.applymap(lambda x: len(str(x)), na_action='ignore')\u001b[0m\n", + "\u001b[0;34m 0 1\u001b[0m\n", + "\u001b[0;34m 0 NaN 4\u001b[0m\n", + "\u001b[0;34m 1 5.0 5\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Note that a vectorized version of `func` often exists, which will\u001b[0m\n", + "\u001b[0;34m be much faster. You could square each number elementwise.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.applymap(lambda x: x**2)\u001b[0m\n", + "\u001b[0;34m 0 1\u001b[0m\n", + "\u001b[0;34m 0 1.000000 4.494400\u001b[0m\n", + "\u001b[0;34m 1 11.262736 20.857489\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m But it's better to avoid applymap in that case.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df ** 2\u001b[0m\n", + "\u001b[0;34m 0 1\u001b[0m\n", + "\u001b[0;34m 0 1.000000 4.494400\u001b[0m\n", + "\u001b[0;34m 1 11.262736 20.857489\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mna_action\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34mf\"na_action must be 'ignore' or None. Got {repr(na_action)}\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mignore_na\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mna_action\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunctools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# if we have a dtype == 'M8[ns]', provide boxed values\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minfer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_na\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mignore_na\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_na\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mignore_na\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minfer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"applymap\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# ----------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Merging / joining methods\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_append\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mverify_integrity\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Can only append a dict if ignore_index=True\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mother\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"Can only append a Series if ignore_index=True \"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"or if the Series has a name\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMultiIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mrow_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# infer_objects is needed for\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# test_append_empty_frame_to_series_with_dateutil_tz\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mother\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrow_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfer_objects\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mother\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mconcat\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mto_concat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m 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\u001b[0mIterable\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mDataFrame\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mon\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIndexLabel\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mMergeHow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"left\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mlsuffix\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mrsuffix\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mvalidate\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Join columns of another DataFrame.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Join columns with `other` DataFrame either on index or on a key\u001b[0m\n", + "\u001b[0;34m column. Efficiently join multiple DataFrame objects by index at once by\u001b[0m\n", + "\u001b[0;34m passing a list.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m other : DataFrame, Series, or a list containing any combination of them\u001b[0m\n", + "\u001b[0;34m Index should be similar to one of the columns in this one. If a\u001b[0m\n", + "\u001b[0;34m Series is passed, its name attribute must be set, and that will be\u001b[0m\n", + "\u001b[0;34m used as the column name in the resulting joined DataFrame.\u001b[0m\n", + "\u001b[0;34m on : str, list of str, or array-like, optional\u001b[0m\n", + "\u001b[0;34m Column or index level name(s) in the caller to join on the index\u001b[0m\n", + "\u001b[0;34m in `other`, otherwise joins index-on-index. If multiple\u001b[0m\n", + "\u001b[0;34m values given, the `other` DataFrame must have a MultiIndex. Can\u001b[0m\n", + "\u001b[0;34m pass an array as the join key if it is not already contained in\u001b[0m\n", + "\u001b[0;34m the calling DataFrame. Like an Excel VLOOKUP operation.\u001b[0m\n", + "\u001b[0;34m how : {'left', 'right', 'outer', 'inner', 'cross'}, default 'left'\u001b[0m\n", + "\u001b[0;34m How to handle the operation of the two objects.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m * left: use calling frame's index (or column if on is specified)\u001b[0m\n", + "\u001b[0;34m * right: use `other`'s index.\u001b[0m\n", + "\u001b[0;34m * outer: form union of calling frame's index (or column if on is\u001b[0m\n", + "\u001b[0;34m specified) with `other`'s index, and sort it.\u001b[0m\n", + "\u001b[0;34m lexicographically.\u001b[0m\n", + "\u001b[0;34m * inner: form intersection of calling frame's index (or column if\u001b[0m\n", + "\u001b[0;34m on is specified) with `other`'s index, preserving the order\u001b[0m\n", + "\u001b[0;34m of the calling's one.\u001b[0m\n", + "\u001b[0;34m * cross: creates the cartesian product from both frames, preserves the order\u001b[0m\n", + "\u001b[0;34m of the left keys.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionadded:: 1.2.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m lsuffix : str, default ''\u001b[0m\n", + "\u001b[0;34m Suffix to use from left frame's overlapping columns.\u001b[0m\n", + "\u001b[0;34m rsuffix : str, default ''\u001b[0m\n", + "\u001b[0;34m Suffix to use from right frame's overlapping columns.\u001b[0m\n", + "\u001b[0;34m sort : bool, default False\u001b[0m\n", + "\u001b[0;34m Order result DataFrame lexicographically by the join key. If False,\u001b[0m\n", + "\u001b[0;34m the order of the join key depends on the join type (how keyword).\u001b[0m\n", + "\u001b[0;34m validate : str, optional\u001b[0m\n", + "\u001b[0;34m If specified, checks if join is of specified type.\u001b[0m\n", + "\u001b[0;34m * \"one_to_one\" or \"1:1\": check if join keys are unique in both left\u001b[0m\n", + "\u001b[0;34m and right datasets.\u001b[0m\n", + "\u001b[0;34m * \"one_to_many\" or \"1:m\": check if join keys are unique in left dataset.\u001b[0m\n", + "\u001b[0;34m * \"many_to_one\" or \"m:1\": check if join keys are unique in right dataset.\u001b[0m\n", + "\u001b[0;34m * \"many_to_many\" or \"m:m\": allowed, but does not result in checks.\u001b[0m\n", + "\u001b[0;34m .. versionadded:: 1.5.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame\u001b[0m\n", + "\u001b[0;34m A dataframe containing columns from both the caller and `other`.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.merge : For column(s)-on-column(s) operations.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Notes\u001b[0m\n", + "\u001b[0;34m -----\u001b[0m\n", + "\u001b[0;34m Parameters `on`, `lsuffix`, and `rsuffix` are not supported when\u001b[0m\n", + "\u001b[0;34m passing a list of `DataFrame` objects.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Support for specifying index levels as the `on` parameter was added\u001b[0m\n", + "\u001b[0;34m in version 0.23.0.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],\u001b[0m\n", + "\u001b[0;34m ... 'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m key A\u001b[0m\n", + "\u001b[0;34m 0 K0 A0\u001b[0m\n", + "\u001b[0;34m 1 K1 A1\u001b[0m\n", + "\u001b[0;34m 2 K2 A2\u001b[0m\n", + "\u001b[0;34m 3 K3 A3\u001b[0m\n", + "\u001b[0;34m 4 K4 A4\u001b[0m\n", + "\u001b[0;34m 5 K5 A5\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> other = pd.DataFrame({'key': ['K0', 'K1', 'K2'],\u001b[0m\n", + "\u001b[0;34m ... 'B': ['B0', 'B1', 'B2']})\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> other\u001b[0m\n", + "\u001b[0;34m key B\u001b[0m\n", + "\u001b[0;34m 0 K0 B0\u001b[0m\n", + "\u001b[0;34m 1 K1 B1\u001b[0m\n", + "\u001b[0;34m 2 K2 B2\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Join DataFrames using their indexes.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.join(other, lsuffix='_caller', rsuffix='_other')\u001b[0m\n", + "\u001b[0;34m key_caller A key_other B\u001b[0m\n", + "\u001b[0;34m 0 K0 A0 K0 B0\u001b[0m\n", + "\u001b[0;34m 1 K1 A1 K1 B1\u001b[0m\n", + "\u001b[0;34m 2 K2 A2 K2 B2\u001b[0m\n", + "\u001b[0;34m 3 K3 A3 NaN NaN\u001b[0m\n", + "\u001b[0;34m 4 K4 A4 NaN NaN\u001b[0m\n", + "\u001b[0;34m 5 K5 A5 NaN NaN\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m If we want to join using the key columns, we need to set key to be\u001b[0m\n", + "\u001b[0;34m the index in both `df` and `other`. The joined DataFrame will have\u001b[0m\n", + "\u001b[0;34m key as its index.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.set_index('key').join(other.set_index('key'))\u001b[0m\n", + "\u001b[0;34m A B\u001b[0m\n", + "\u001b[0;34m key\u001b[0m\n", + "\u001b[0;34m K0 A0 B0\u001b[0m\n", + "\u001b[0;34m K1 A1 B1\u001b[0m\n", + "\u001b[0;34m K2 A2 B2\u001b[0m\n", + "\u001b[0;34m K3 A3 NaN\u001b[0m\n", + "\u001b[0;34m K4 A4 NaN\u001b[0m\n", + "\u001b[0;34m K5 A5 NaN\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Another option to join using the key columns is to use the `on`\u001b[0m\n", + "\u001b[0;34m parameter. DataFrame.join always uses `other`'s index but we can use\u001b[0m\n", + "\u001b[0;34m any column in `df`. This method preserves the original DataFrame's\u001b[0m\n", + "\u001b[0;34m index in the result.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.join(other.set_index('key'), on='key')\u001b[0m\n", + "\u001b[0;34m key A B\u001b[0m\n", + "\u001b[0;34m 0 K0 A0 B0\u001b[0m\n", + "\u001b[0;34m 1 K1 A1 B1\u001b[0m\n", + "\u001b[0;34m 2 K2 A2 B2\u001b[0m\n", + "\u001b[0;34m 3 K3 A3 NaN\u001b[0m\n", + "\u001b[0;34m 4 K4 A4 NaN\u001b[0m\n", + "\u001b[0;34m 5 K5 A5 NaN\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Using non-unique key values shows how they are matched.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'key': ['K0', 'K1', 'K1', 'K3', 'K0', 'K1'],\u001b[0m\n", + "\u001b[0;34m ... 'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m key A\u001b[0m\n", + "\u001b[0;34m 0 K0 A0\u001b[0m\n", + "\u001b[0;34m 1 K1 A1\u001b[0m\n", + "\u001b[0;34m 2 K1 A2\u001b[0m\n", + "\u001b[0;34m 3 K3 A3\u001b[0m\n", + "\u001b[0;34m 4 K0 A4\u001b[0m\n", + "\u001b[0;34m 5 K1 A5\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.join(other.set_index('key'), on='key', validate='m:1')\u001b[0m\n", + "\u001b[0;34m key A B\u001b[0m\n", + "\u001b[0;34m 0 K0 A0 B0\u001b[0m\n", + "\u001b[0;34m 1 K1 A1 B1\u001b[0m\n", + "\u001b[0;34m 2 K1 A2 B1\u001b[0m\n", + "\u001b[0;34m 3 K3 A3 NaN\u001b[0m\n", + "\u001b[0;34m 4 K0 A4 B0\u001b[0m\n", + "\u001b[0;34m 5 K1 A5 B1\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_join_compat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mon\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhow\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mlsuffix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlsuffix\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mrsuffix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrsuffix\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m 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"\u001b[0;34m\u001b[0m \u001b[0mframes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"DataFrame | Series\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcan_concat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mframes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# join indexes only using concat\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcan_concat\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhow\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"left\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mframes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjoin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"outer\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverify_integrity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m 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"\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mjoined\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mframes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mframe\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mframes\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\n", + "\u001b[0;34m\u001b[0m \u001b[0mjoined\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mjoined\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mframe\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m 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"\u001b[0;34m\u001b[0m \u001b[0mindicator\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mvalidate\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmerge\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m 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+ "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimals\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mIndexLabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mSeries\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Round a DataFrame to a variable number of decimal places.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m decimals : int, dict, Series\u001b[0m\n", + "\u001b[0;34m Number of decimal places to round each column to. If an int is\u001b[0m\n", + "\u001b[0;34m given, round each column to the same number of places.\u001b[0m\n", + "\u001b[0;34m Otherwise dict and Series round to variable numbers of places.\u001b[0m\n", + "\u001b[0;34m Column names should be in the keys if `decimals` is a\u001b[0m\n", + "\u001b[0;34m dict-like, or in the index if `decimals` is a Series. Any\u001b[0m\n", + "\u001b[0;34m columns not included in `decimals` will be left as is. Elements\u001b[0m\n", + "\u001b[0;34m of `decimals` which are not columns of the input will be\u001b[0m\n", + "\u001b[0;34m ignored.\u001b[0m\n", + "\u001b[0;34m *args\u001b[0m\n", + "\u001b[0;34m Additional keywords have no effect but might be accepted for\u001b[0m\n", + "\u001b[0;34m compatibility with numpy.\u001b[0m\n", + "\u001b[0;34m **kwargs\u001b[0m\n", + "\u001b[0;34m Additional keywords have no effect but might be accepted for\u001b[0m\n", + "\u001b[0;34m compatibility with numpy.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame\u001b[0m\n", + "\u001b[0;34m A DataFrame with the affected columns rounded to the specified\u001b[0m\n", + "\u001b[0;34m number of decimal places.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m numpy.around : Round a numpy array to the given number of decimals.\u001b[0m\n", + "\u001b[0;34m Series.round : Round a Series to the given number of decimals.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame([(.21, .32), (.01, .67), (.66, .03), (.21, .18)],\u001b[0m\n", + "\u001b[0;34m ... columns=['dogs', 'cats'])\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m dogs cats\u001b[0m\n", + "\u001b[0;34m 0 0.21 0.32\u001b[0m\n", + "\u001b[0;34m 1 0.01 0.67\u001b[0m\n", + "\u001b[0;34m 2 0.66 0.03\u001b[0m\n", + "\u001b[0;34m 3 0.21 0.18\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m By providing an integer each column is rounded to the same number\u001b[0m\n", + "\u001b[0;34m of decimal places\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.round(1)\u001b[0m\n", + "\u001b[0;34m dogs cats\u001b[0m\n", + "\u001b[0;34m 0 0.2 0.3\u001b[0m\n", + "\u001b[0;34m 1 0.0 0.7\u001b[0m\n", + "\u001b[0;34m 2 0.7 0.0\u001b[0m\n", + "\u001b[0;34m 3 0.2 0.2\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m With a dict, the number of places for specific columns can be\u001b[0m\n", + "\u001b[0;34m specified with the column names as key and the number of decimal\u001b[0m\n", + "\u001b[0;34m places as value\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.round({'dogs': 1, 'cats': 0})\u001b[0m\n", + "\u001b[0;34m dogs cats\u001b[0m\n", + "\u001b[0;34m 0 0.2 0.0\u001b[0m\n", + "\u001b[0;34m 1 0.0 1.0\u001b[0m\n", + "\u001b[0;34m 2 0.7 0.0\u001b[0m\n", + "\u001b[0;34m 3 0.2 0.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Using a Series, the number of places for specific columns can be\u001b[0m\n", + "\u001b[0;34m specified with the column names as index and the number of\u001b[0m\n", + "\u001b[0;34m decimal places as value\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> decimals = pd.Series([0, 1], index=['cats', 'dogs'])\u001b[0m\n", + "\u001b[0;34m >>> df.round(decimals)\u001b[0m\n", + "\u001b[0;34m dogs cats\u001b[0m\n", + "\u001b[0;34m 0 0.2 0.0\u001b[0m\n", + "\u001b[0;34m 1 0.0 1.0\u001b[0m\n", + "\u001b[0;34m 2 0.7 0.0\u001b[0m\n", + "\u001b[0;34m 3 0.2 0.0\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mconcat\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_dict_round\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfor\u001b[0m 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\u001b[0m_series_round\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mser\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimals\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_integer_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_float_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdecimals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mser\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_round\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdecimals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdecimals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mdecimals\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Index of decimals must be unique\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_dict_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdecimals\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdecimals\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Values in decimals must be integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnew_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_dict_round\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdecimals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Dispatch to Block.round\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdecimals\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdecimals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0musing_cow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0musing_copy_on_write\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"round\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"decimals must be an integer, a dict-like or a Series\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnew_cols\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_cols\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_cols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"round\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# ----------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Statistical methods, etc.\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcorr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mCorrelationMethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"pearson\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmin_periods\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnumeric_only\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Compute pairwise correlation of columns, excluding NA/null values.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m method : {'pearson', 'kendall', 'spearman'} or callable\u001b[0m\n", + "\u001b[0;34m Method of correlation:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m * pearson : standard correlation coefficient\u001b[0m\n", + "\u001b[0;34m * kendall : Kendall Tau correlation coefficient\u001b[0m\n", + "\u001b[0;34m * spearman : Spearman rank correlation\u001b[0m\n", + "\u001b[0;34m * callable: callable with input two 1d ndarrays\u001b[0m\n", + "\u001b[0;34m and returning a float. Note that the returned matrix from corr\u001b[0m\n", + "\u001b[0;34m will have 1 along the diagonals and will be symmetric\u001b[0m\n", + "\u001b[0;34m regardless of the callable's behavior.\u001b[0m\n", + "\u001b[0;34m min_periods : int, optional\u001b[0m\n", + "\u001b[0;34m Minimum number of observations required per pair of columns\u001b[0m\n", + "\u001b[0;34m to have a valid result. Currently only available for Pearson\u001b[0m\n", + "\u001b[0;34m and Spearman correlation.\u001b[0m\n", + "\u001b[0;34m numeric_only : bool, default False\u001b[0m\n", + "\u001b[0;34m Include only `float`, `int` or `boolean` data.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionadded:: 1.5.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionchanged:: 2.0.0\u001b[0m\n", + "\u001b[0;34m The default value of ``numeric_only`` is now ``False``.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame\u001b[0m\n", + "\u001b[0;34m Correlation matrix.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.corrwith : Compute pairwise correlation with another\u001b[0m\n", + "\u001b[0;34m DataFrame or Series.\u001b[0m\n", + "\u001b[0;34m Series.corr : Compute the correlation between two Series.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Notes\u001b[0m\n", + "\u001b[0;34m -----\u001b[0m\n", + "\u001b[0;34m Pearson, Kendall and Spearman correlation are currently computed using pairwise complete observations.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m * `Pearson correlation coefficient `_\u001b[0m\n", + "\u001b[0;34m * `Kendall rank correlation coefficient `_\u001b[0m\n", + "\u001b[0;34m * `Spearman's rank correlation coefficient `_\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> def histogram_intersection(a, b):\u001b[0m\n", + "\u001b[0;34m ... v = np.minimum(a, b).sum().round(decimals=1)\u001b[0m\n", + "\u001b[0;34m ... return v\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],\u001b[0m\n", + "\u001b[0;34m ... columns=['dogs', 'cats'])\u001b[0m\n", + "\u001b[0;34m >>> df.corr(method=histogram_intersection)\u001b[0m\n", + "\u001b[0;34m dogs cats\u001b[0m\n", + "\u001b[0;34m dogs 1.0 0.3\u001b[0m\n", + "\u001b[0;34m cats 0.3 1.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame([(1, 1), (2, np.nan), (np.nan, 3), (4, 4)],\u001b[0m\n", + "\u001b[0;34m ... columns=['dogs', 'cats'])\u001b[0m\n", + "\u001b[0;34m >>> df.corr(min_periods=3)\u001b[0m\n", + "\u001b[0;34m dogs cats\u001b[0m\n", + "\u001b[0;34m dogs 1.0 NaN\u001b[0m\n", + "\u001b[0;34m cats NaN 1.0\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m \u001b[0;31m# noqa:E501\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_numeric_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnumeric_only\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0midx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcols\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"pearson\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcorrel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlibalgos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnancorr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mminp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmin_periods\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"spearman\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcorrel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlibalgos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnancorr_spearman\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mminp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmin_periods\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m 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"\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mvalid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mmin_periods\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mvalid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mddof\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnumeric_only\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Compute pairwise covariance of columns, excluding NA/null values.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Compute the pairwise covariance among the series of a DataFrame.\u001b[0m\n", + "\u001b[0;34m The returned data frame is the `covariance matrix\u001b[0m\n", + "\u001b[0;34m `__ of the columns\u001b[0m\n", + "\u001b[0;34m of the DataFrame.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Both NA and null values are automatically excluded from the\u001b[0m\n", + "\u001b[0;34m calculation. (See the note below about bias from missing values.)\u001b[0m\n", + "\u001b[0;34m A threshold can be set for the minimum number of\u001b[0m\n", + "\u001b[0;34m observations for each value created. Comparisons with observations\u001b[0m\n", + "\u001b[0;34m below this threshold will be returned as ``NaN``.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m This method is generally used for the analysis of time series data to\u001b[0m\n", + "\u001b[0;34m understand the relationship between different measures\u001b[0m\n", + "\u001b[0;34m across time.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m min_periods : int, optional\u001b[0m\n", + "\u001b[0;34m Minimum number of observations required per pair of columns\u001b[0m\n", + "\u001b[0;34m to have a valid result.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m ddof : int, default 1\u001b[0m\n", + "\u001b[0;34m Delta degrees of freedom. The divisor used in calculations\u001b[0m\n", + "\u001b[0;34m is ``N - ddof``, where ``N`` represents the number of elements.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionadded:: 1.1.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m numeric_only : bool, default False\u001b[0m\n", + "\u001b[0;34m Include only `float`, `int` or `boolean` data.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionadded:: 1.5.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionchanged:: 2.0.0\u001b[0m\n", + "\u001b[0;34m The default value of ``numeric_only`` is now ``False``.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame\u001b[0m\n", + "\u001b[0;34m The covariance matrix of the series of the DataFrame.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m Series.cov : Compute covariance with another Series.\u001b[0m\n", + "\u001b[0;34m core.window.ewm.ExponentialMovingWindow.cov : Exponential weighted sample\u001b[0m\n", + "\u001b[0;34m covariance.\u001b[0m\n", + "\u001b[0;34m core.window.expanding.Expanding.cov : Expanding sample covariance.\u001b[0m\n", + "\u001b[0;34m core.window.rolling.Rolling.cov : Rolling sample covariance.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Notes\u001b[0m\n", + "\u001b[0;34m -----\u001b[0m\n", + "\u001b[0;34m Returns the covariance matrix of the DataFrame's time series.\u001b[0m\n", + "\u001b[0;34m The covariance is normalized by N-ddof.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m For DataFrames that have Series that are missing data (assuming that\u001b[0m\n", + "\u001b[0;34m data is `missing at random\u001b[0m\n", + "\u001b[0;34m `__)\u001b[0m\n", + "\u001b[0;34m the returned covariance matrix will be an unbiased estimate\u001b[0m\n", + "\u001b[0;34m of the variance and covariance between the member Series.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m However, for many applications this estimate may not be acceptable\u001b[0m\n", + "\u001b[0;34m because the estimate covariance matrix is not guaranteed to be positive\u001b[0m\n", + "\u001b[0;34m semi-definite. This could lead to estimate correlations having\u001b[0m\n", + "\u001b[0;34m absolute values which are greater than one, and/or a non-invertible\u001b[0m\n", + "\u001b[0;34m covariance matrix. See `Estimation of covariance matrices\u001b[0m\n", + "\u001b[0;34m `__ for more details.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame([(1, 2), (0, 3), (2, 0), (1, 1)],\u001b[0m\n", + "\u001b[0;34m ... columns=['dogs', 'cats'])\u001b[0m\n", + "\u001b[0;34m >>> df.cov()\u001b[0m\n", + "\u001b[0;34m dogs cats\u001b[0m\n", + "\u001b[0;34m dogs 0.666667 -1.000000\u001b[0m\n", + "\u001b[0;34m cats -1.000000 1.666667\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> np.random.seed(42)\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame(np.random.randn(1000, 5),\u001b[0m\n", + "\u001b[0;34m ... columns=['a', 'b', 'c', 'd', 'e'])\u001b[0m\n", + "\u001b[0;34m >>> df.cov()\u001b[0m\n", + "\u001b[0;34m a b c d e\u001b[0m\n", + "\u001b[0;34m a 0.998438 -0.020161 0.059277 -0.008943 0.014144\u001b[0m\n", + "\u001b[0;34m b -0.020161 1.059352 -0.008543 -0.024738 0.009826\u001b[0m\n", + "\u001b[0;34m c 0.059277 -0.008543 1.010670 -0.001486 -0.000271\u001b[0m\n", + "\u001b[0;34m d -0.008943 -0.024738 -0.001486 0.921297 -0.013692\u001b[0m\n", + "\u001b[0;34m e 0.014144 0.009826 -0.000271 -0.013692 0.977795\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m **Minimum number of periods**\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m This method also supports an optional ``min_periods`` keyword\u001b[0m\n", + "\u001b[0;34m that specifies the required minimum number of non-NA observations for\u001b[0m\n", + "\u001b[0;34m each column pair in order to have a valid result:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> np.random.seed(42)\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame(np.random.randn(20, 3),\u001b[0m\n", + "\u001b[0;34m ... columns=['a', 'b', 'c'])\u001b[0m\n", + "\u001b[0;34m >>> df.loc[df.index[:5], 'a'] = np.nan\u001b[0m\n", + "\u001b[0;34m >>> df.loc[df.index[5:10], 'b'] = np.nan\u001b[0m\n", + "\u001b[0;34m >>> df.cov(min_periods=12)\u001b[0m\n", + "\u001b[0;34m a b c\u001b[0m\n", + "\u001b[0;34m a 0.316741 NaN -0.150812\u001b[0m\n", + "\u001b[0;34m b NaN 1.248003 0.191417\u001b[0m\n", + "\u001b[0;34m c -0.150812 0.191417 0.895202\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_numeric_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnumeric_only\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0midx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcols\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnotna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmin_periods\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mmin_periods\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mbase_cov\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\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\n", + "\u001b[0;34m\u001b[0m \u001b[0mbase_cov\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfill\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mbase_cov\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcov\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mddof\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mddof\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mbase_cov\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbase_cov\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mbase_cov\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlibalgos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnancorr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcov\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mminp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmin_periods\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbase_cov\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"cov\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcorrwith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mDataFrame\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdrop\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mCorrelationMethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"pearson\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnumeric_only\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Compute pairwise correlation.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Pairwise correlation is computed between rows or columns of\u001b[0m\n", + "\u001b[0;34m DataFrame with rows or columns of Series or DataFrame. DataFrames\u001b[0m\n", + "\u001b[0;34m are first aligned along both axes before computing the\u001b[0m\n", + "\u001b[0;34m correlations.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m other : DataFrame, Series\u001b[0m\n", + "\u001b[0;34m Object with which to compute correlations.\u001b[0m\n", + "\u001b[0;34m axis : {0 or 'index', 1 or 'columns'}, default 0\u001b[0m\n", + "\u001b[0;34m The axis to use. 0 or 'index' to compute row-wise, 1 or 'columns' for\u001b[0m\n", + "\u001b[0;34m column-wise.\u001b[0m\n", + "\u001b[0;34m drop : bool, default False\u001b[0m\n", + "\u001b[0;34m Drop missing indices from result.\u001b[0m\n", + "\u001b[0;34m method : {'pearson', 'kendall', 'spearman'} or callable\u001b[0m\n", + "\u001b[0;34m Method of correlation:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m * pearson : standard correlation coefficient\u001b[0m\n", + "\u001b[0;34m * kendall : Kendall Tau correlation coefficient\u001b[0m\n", + "\u001b[0;34m * spearman : Spearman rank correlation\u001b[0m\n", + "\u001b[0;34m * callable: callable with input two 1d ndarrays\u001b[0m\n", + "\u001b[0;34m and returning a float.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m numeric_only : bool, default False\u001b[0m\n", + "\u001b[0;34m Include only `float`, `int` or `boolean` data.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionadded:: 1.5.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionchanged:: 2.0.0\u001b[0m\n", + "\u001b[0;34m The default value of ``numeric_only`` is now ``False``.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m Series\u001b[0m\n", + "\u001b[0;34m Pairwise correlations.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.corr : Compute pairwise correlation of columns.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> index = [\"a\", \"b\", \"c\", \"d\", \"e\"]\u001b[0m\n", + "\u001b[0;34m >>> columns = [\"one\", \"two\", \"three\", \"four\"]\u001b[0m\n", + "\u001b[0;34m >>> df1 = pd.DataFrame(np.arange(20).reshape(5, 4), index=index, columns=columns)\u001b[0m\n", + "\u001b[0;34m >>> df2 = pd.DataFrame(np.arange(16).reshape(4, 4), index=index[:4], columns=columns)\u001b[0m\n", + "\u001b[0;34m >>> df1.corrwith(df2)\u001b[0m\n", + "\u001b[0;34m one 1.0\u001b[0m\n", + "\u001b[0;34m two 1.0\u001b[0m\n", + "\u001b[0;34m three 1.0\u001b[0m\n", + "\u001b[0;34m four 1.0\u001b[0m\n", + "\u001b[0;34m dtype: float64\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df2.corrwith(df1, axis=1)\u001b[0m\n", + "\u001b[0;34m a 1.0\u001b[0m\n", + "\u001b[0;34m b 1.0\u001b[0m\n", + "\u001b[0;34m c 1.0\u001b[0m\n", + "\u001b[0;34m d 1.0\u001b[0m\n", + "\u001b[0;34m e NaN\u001b[0m\n", + "\u001b[0;34m dtype: float64\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m \u001b[0;31m# noqa:E501\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mthis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_numeric_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnumeric_only\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m 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"\u001b[0;34m\u001b[0m \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"pearson\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# mask missing values\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mleft\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mright\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mright\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mleft\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# demeaned data\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mldem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mleft\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumeric_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnumeric_only\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mrdem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mright\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumeric_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnumeric_only\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mldem\u001b[0m \u001b[0;34m*\u001b[0m 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"\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mnanops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnancorr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcorrel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor_sliced\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m 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numeric_only : bool, default False\u001b[0m\n", + "\u001b[0;34m Include only `float`, `int` or `boolean` data.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m Series or DataFrame\u001b[0m\n", + "\u001b[0;34m For each column/row the number of non-NA/null entries.\u001b[0m\n", + "\u001b[0;34m If `level` is specified returns a `DataFrame`.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m Series.count: Number of non-NA elements in a Series.\u001b[0m\n", + "\u001b[0;34m DataFrame.value_counts: Count unique combinations of columns.\u001b[0m\n", + "\u001b[0;34m DataFrame.shape: Number of DataFrame rows and columns (including NA\u001b[0m\n", + "\u001b[0;34m elements).\u001b[0m\n", + "\u001b[0;34m DataFrame.isna: Boolean same-sized DataFrame showing places of NA\u001b[0m\n", + "\u001b[0;34m elements.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m Constructing DataFrame from a dictionary:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({\"Person\":\u001b[0m\n", + "\u001b[0;34m ... [\"John\", \"Myla\", \"Lewis\", \"John\", \"Myla\"],\u001b[0m\n", + "\u001b[0;34m ... \"Age\": [24., np.nan, 21., 33, 26],\u001b[0m\n", + "\u001b[0;34m ... \"Single\": [False, True, True, True, False]})\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m Person Age Single\u001b[0m\n", + "\u001b[0;34m 0 John 24.0 False\u001b[0m\n", + "\u001b[0;34m 1 Myla NaN True\u001b[0m\n", + "\u001b[0;34m 2 Lewis 21.0 True\u001b[0m\n", + "\u001b[0;34m 3 John 33.0 True\u001b[0m\n", + "\u001b[0;34m 4 Myla 26.0 False\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Notice the uncounted NA values:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.count()\u001b[0m\n", + "\u001b[0;34m Person 5\u001b[0m\n", + "\u001b[0;34m Age 4\u001b[0m\n", + "\u001b[0;34m Single 5\u001b[0m\n", + "\u001b[0;34m dtype: int64\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Counts for each **row**:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.count(axis='columns')\u001b[0m\n", + "\u001b[0;34m 0 3\u001b[0m\n", + "\u001b[0;34m 1 2\u001b[0m\n", + "\u001b[0;34m 2 3\u001b[0m\n", + "\u001b[0;34m 3 3\u001b[0m\n", + "\u001b[0;34m 4 3\u001b[0m\n", + "\u001b[0;34m dtype: int64\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnumeric_only\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mframe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_numeric_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m 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+ "\u001b[0;34m\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskipna\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnumeric_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mfilter_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilter_type\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m 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"\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_dtypes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"sum\"\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Special case for _reduce to try to avoid a potentially-expensive transpose.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Apply the reduction block-wise along axis=1 and then reduce the resulting\u001b[0m\n", + "\u001b[0;34m 1D arrays.\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"all\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mufunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogical_and\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"any\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# error: Incompatible types in assignment\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# (expression has type \"_UFunc_Nin2_Nout1[Literal['logical_or'],\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Literal[20], Literal[False]]\", variable has type\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# \"_UFunc_Nin2_Nout1[Literal['logical_and'], Literal[20],\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Literal[True]]\")\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mufunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogical_or\u001b[0m \u001b[0;31m# type: ignore[assignment]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmiddle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskipna\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m 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Can ignore NaN\u001b[0m\n", + "\u001b[0;34m values.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m axis : {0 or 'index', 1 or 'columns'}, default 0\u001b[0m\n", + "\u001b[0;34m The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for\u001b[0m\n", + "\u001b[0;34m column-wise.\u001b[0m\n", + "\u001b[0;34m dropna : bool, default True\u001b[0m\n", + "\u001b[0;34m Don't include NaN in the counts.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m Series\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m Series.nunique: Method nunique for Series.\u001b[0m\n", + "\u001b[0;34m DataFrame.count: Count non-NA cells for each column or row.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'A': [4, 5, 6], 'B': [4, 1, 1]})\u001b[0m\n", + "\u001b[0;34m >>> df.nunique()\u001b[0m\n", + "\u001b[0;34m A 3\u001b[0m\n", + "\u001b[0;34m B 2\u001b[0m\n", + "\u001b[0;34m dtype: int64\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.nunique(axis=1)\u001b[0m\n", + "\u001b[0;34m 0 1\u001b[0m\n", + "\u001b[0;34m 1 2\u001b[0m\n", + "\u001b[0;34m 2 2\u001b[0m\n", + "\u001b[0;34m dtype: int64\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnunique\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdropna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mdoc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_shared_docs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"idxmin\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumeric_only_default\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"False\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0midxmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumeric_only\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m 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+ "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reduce\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnanops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnanargmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"argmin\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskipna\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumeric_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# indices will always be np.ndarray since axis is not None and\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# values is a 2d array for DataFrame\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# error: Item \"int\" of \"Union[int, Any]\" has no attribute \"__iter__\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# for mypy\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_numeric_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reduce\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnanops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnanargmax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"argmax\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskipna\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumeric_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# indices will always be np.ndarray since axis is not None and\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# values is a 2d array for DataFrame\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# error: Item \"int\" of \"Union[int, Any]\" has no attribute \"__iter__\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# for mypy\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mfinal_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor_sliced\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_agg_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mfinal_result\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"idxmax\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_get_agg_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis_num\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Let's be explicit about this.\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0maxis_num\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0maxis_num\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Axis must be 0 or 1 (got {repr(axis_num)})\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumeric_only\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdropna\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Get the mode(s) of each element along the selected axis.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m The mode of a set of values is the value that appears most often.\u001b[0m\n", + "\u001b[0;34m It can be multiple values.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m axis : {0 or 'index', 1 or 'columns'}, default 0\u001b[0m\n", + "\u001b[0;34m The axis to iterate over while searching for the mode:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m * 0 or 'index' : get mode of each column\u001b[0m\n", + "\u001b[0;34m * 1 or 'columns' : get mode of each row.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m numeric_only : bool, default False\u001b[0m\n", + "\u001b[0;34m If True, only apply to numeric columns.\u001b[0m\n", + "\u001b[0;34m dropna : bool, default True\u001b[0m\n", + "\u001b[0;34m Don't consider counts of NaN/NaT.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame\u001b[0m\n", + "\u001b[0;34m The modes of each column or row.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m Series.mode : Return the highest frequency value in a Series.\u001b[0m\n", + "\u001b[0;34m Series.value_counts : Return the counts of values in a Series.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame([('bird', 2, 2),\u001b[0m\n", + "\u001b[0;34m ... ('mammal', 4, np.nan),\u001b[0m\n", + "\u001b[0;34m ... ('arthropod', 8, 0),\u001b[0m\n", + "\u001b[0;34m ... ('bird', 2, np.nan)],\u001b[0m\n", + "\u001b[0;34m ... index=('falcon', 'horse', 'spider', 'ostrich'),\u001b[0m\n", + "\u001b[0;34m ... columns=('species', 'legs', 'wings'))\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m species legs wings\u001b[0m\n", + "\u001b[0;34m falcon bird 2 2.0\u001b[0m\n", + "\u001b[0;34m horse mammal 4 NaN\u001b[0m\n", + "\u001b[0;34m spider arthropod 8 0.0\u001b[0m\n", + "\u001b[0;34m ostrich bird 2 NaN\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m By default, missing values are not considered, and the mode of wings\u001b[0m\n", + "\u001b[0;34m are both 0 and 2. Because the resulting DataFrame has two rows,\u001b[0m\n", + "\u001b[0;34m the second row of ``species`` and ``legs`` contains ``NaN``.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.mode()\u001b[0m\n", + "\u001b[0;34m species legs wings\u001b[0m\n", + "\u001b[0;34m 0 bird 2.0 0.0\u001b[0m\n", + "\u001b[0;34m 1 NaN NaN 2.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Setting ``dropna=False`` ``NaN`` values are considered and they can be\u001b[0m\n", + "\u001b[0;34m the mode (like for wings).\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.mode(dropna=False)\u001b[0m\n", + "\u001b[0;34m species legs wings\u001b[0m\n", + "\u001b[0;34m 0 bird 2 NaN\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Setting ``numeric_only=True``, only the mode of numeric columns is\u001b[0m\n", + "\u001b[0;34m computed, and columns of other types are ignored.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.mode(numeric_only=True)\u001b[0m\n", + "\u001b[0;34m legs wings\u001b[0m\n", + "\u001b[0;34m 0 2.0 0.0\u001b[0m\n", + "\u001b[0;34m 1 NaN 2.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m To compute the mode over columns and not rows, use the axis parameter:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.mode(axis='columns', numeric_only=True)\u001b[0m\n", + "\u001b[0;34m 0 1\u001b[0m\n", + "\u001b[0;34m falcon 2.0 NaN\u001b[0m\n", + "\u001b[0;34m horse 4.0 NaN\u001b[0m\n", + "\u001b[0;34m spider 0.0 8.0\u001b[0m\n", + "\u001b[0;34m ostrich 2.0 NaN\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnumeric_only\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_numeric_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Ensure index is type stable (should always use int index)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdefault_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0moverload\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mquantile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnumeric_only\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mQuantileInterpolation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0moverload\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mquantile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAnyArrayLike\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnumeric_only\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mQuantileInterpolation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mSeries\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0moverload\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mquantile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m 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\u001b[0;34m->\u001b[0m \u001b[0mSeries\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mquantile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mAnyArrayLike\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnumeric_only\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mQuantileInterpolation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"linear\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mLiteral\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"single\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"table\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"single\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mSeries\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Return values at the given quantile over requested axis.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m q : float or array-like, default 0.5 (50% quantile)\u001b[0m\n", + "\u001b[0;34m Value between 0 <= q <= 1, the quantile(s) to compute.\u001b[0m\n", + "\u001b[0;34m axis : {0 or 'index', 1 or 'columns'}, default 0\u001b[0m\n", + "\u001b[0;34m Equals 0 or 'index' for row-wise, 1 or 'columns' for column-wise.\u001b[0m\n", + "\u001b[0;34m numeric_only : bool, default False\u001b[0m\n", + "\u001b[0;34m Include only `float`, `int` or `boolean` data.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m .. versionchanged:: 2.0.0\u001b[0m\n", + "\u001b[0;34m The default value of ``numeric_only`` is now ``False``.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}\u001b[0m\n", + "\u001b[0;34m This optional parameter specifies the interpolation method to use,\u001b[0m\n", + "\u001b[0;34m when the desired quantile lies between two data points `i` and `j`:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m * linear: `i + (j - i) * fraction`, where `fraction` is the\u001b[0m\n", + "\u001b[0;34m fractional part of the index surrounded by `i` and `j`.\u001b[0m\n", + "\u001b[0;34m * lower: `i`.\u001b[0m\n", + "\u001b[0;34m * higher: `j`.\u001b[0m\n", + "\u001b[0;34m * nearest: `i` or `j` whichever is nearest.\u001b[0m\n", + "\u001b[0;34m * midpoint: (`i` + `j`) / 2.\u001b[0m\n", + "\u001b[0;34m method : {'single', 'table'}, default 'single'\u001b[0m\n", + "\u001b[0;34m Whether to compute quantiles per-column ('single') or over all columns\u001b[0m\n", + "\u001b[0;34m ('table'). When 'table', the only allowed interpolation methods are\u001b[0m\n", + "\u001b[0;34m 'nearest', 'lower', and 'higher'.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m Series or DataFrame\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m If ``q`` is an array, a DataFrame will be returned where the\u001b[0m\n", + "\u001b[0;34m index is ``q``, the columns are the columns of self, and the\u001b[0m\n", + "\u001b[0;34m values are the quantiles.\u001b[0m\n", + "\u001b[0;34m If ``q`` is a float, a Series will be returned where the\u001b[0m\n", + "\u001b[0;34m index is the columns of self and the values are the quantiles.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m core.window.rolling.Rolling.quantile: Rolling quantile.\u001b[0m\n", + "\u001b[0;34m numpy.percentile: Numpy function to compute the percentile.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]),\u001b[0m\n", + "\u001b[0;34m ... columns=['a', 'b'])\u001b[0m\n", + "\u001b[0;34m >>> df.quantile(.1)\u001b[0m\n", + "\u001b[0;34m a 1.3\u001b[0m\n", + "\u001b[0;34m b 3.7\u001b[0m\n", + "\u001b[0;34m Name: 0.1, dtype: float64\u001b[0m\n", + "\u001b[0;34m >>> df.quantile([.1, .5])\u001b[0m\n", + "\u001b[0;34m a b\u001b[0m\n", + "\u001b[0;34m 0.1 1.3 3.7\u001b[0m\n", + "\u001b[0;34m 0.5 2.5 55.0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Specifying `method='table'` will compute the quantile over all columns.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.quantile(.1, method=\"table\", interpolation=\"nearest\")\u001b[0m\n", + "\u001b[0;34m a 1\u001b[0m\n", + "\u001b[0;34m b 1\u001b[0m\n", + "\u001b[0;34m Name: 0.1, dtype: int64\u001b[0m\n", + "\u001b[0;34m >>> df.quantile([.1, .5], method=\"table\", interpolation=\"nearest\")\u001b[0m\n", + "\u001b[0;34m a b\u001b[0m\n", + "\u001b[0;34m 0.1 1 1\u001b[0m\n", + "\u001b[0;34m 0.5 3 100\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Specifying `numeric_only=False` will also compute the quantile of\u001b[0m\n", + "\u001b[0;34m datetime and timedelta data.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'A': [1, 2],\u001b[0m\n", + "\u001b[0;34m ... 'B': [pd.Timestamp('2010'),\u001b[0m\n", + "\u001b[0;34m ... pd.Timestamp('2011')],\u001b[0m\n", + "\u001b[0;34m ... 'C': [pd.Timedelta('1 days'),\u001b[0m\n", + "\u001b[0;34m ... pd.Timedelta('2 days')]})\u001b[0m\n", + "\u001b[0;34m >>> df.quantile(0.5, numeric_only=False)\u001b[0m\n", + "\u001b[0;34m A 1.5\u001b[0m\n", + "\u001b[0;34m B 2010-07-02 12:00:00\u001b[0m\n", + "\u001b[0;34m C 1 days 12:00:00\u001b[0m\n", + "\u001b[0;34m Name: 0.5, dtype: object\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mvalidate_percentile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# BlockManager.quantile expects listlike, so we wrap and unwrap here\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# error: List item 0 has incompatible type \"Union[float, Union[Union[\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# ExtensionArray, ndarray[Any, Any]], Index, Series], Sequence[float]]\";\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# expected \"float\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mres_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantile\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;31m# type: ignore[call-overload]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnumeric_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnumeric_only\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"single\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# cannot directly iloc over sparse arrays\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# GH#41544 try to get an appropriate dtype\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfind_common_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mneeds_i8_conversion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_numeric_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnumeric_only\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# GH#23925 _get_numeric_data may have dropped all columns\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# GH#41544 try to get an appropriate dtype\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfind_common_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mneeds_i8_conversion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcdtype\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"quantile\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mvalid_method\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"single\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"table\"\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mvalid_method\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34mf\"Invalid method: {method}. Method must be in {valid_method}.\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"single\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mqs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"table\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mvalid_interpolation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"nearest\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"lower\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"higher\"\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0minterpolation\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mvalid_interpolation\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34mf\"Invalid interpolation: {interpolation}. \"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34mf\"Interpolation must be in {valid_interpolation}\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m 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"\u001b[0;34m\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mq_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantile\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;31m# type: 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"\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mto_timestamp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mFrequency\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"start\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Cast to DatetimeIndex of timestamps, at *beginning* of period.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m freq : str, default frequency of PeriodIndex\u001b[0m\n", + "\u001b[0;34m Desired frequency.\u001b[0m\n", + "\u001b[0;34m how : {'s', 'e', 'start', 'end'}\u001b[0m\n", + "\u001b[0;34m Convention for converting period to timestamp; start of period\u001b[0m\n", + "\u001b[0;34m vs. end.\u001b[0m\n", + "\u001b[0;34m axis : {0 or 'index', 1 or 'columns'}, default 0\u001b[0m\n", + "\u001b[0;34m The axis to convert (the index by default).\u001b[0m\n", + "\u001b[0;34m copy : bool, default True\u001b[0m\n", + "\u001b[0;34m If False then underlying input data is not copied.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame\u001b[0m\n", + "\u001b[0;34m The DataFrame has a DatetimeIndex.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> idx = pd.PeriodIndex(['2023', '2024'], freq='Y')\u001b[0m\n", + "\u001b[0;34m >>> d = {'col1': [1, 2], 'col2': [3, 4]}\u001b[0m\n", + "\u001b[0;34m >>> df1 = pd.DataFrame(data=d, index=idx)\u001b[0m\n", + "\u001b[0;34m >>> df1\u001b[0m\n", + "\u001b[0;34m col1 col2\u001b[0m\n", + "\u001b[0;34m 2023 1 3\u001b[0m\n", + "\u001b[0;34m 2024 2 4\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m The resulting timestamps will be at the beginning of the year in this case\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df1 = df1.to_timestamp()\u001b[0m\n", + "\u001b[0;34m >>> df1\u001b[0m\n", + "\u001b[0;34m col1 col2\u001b[0m\n", + "\u001b[0;34m 2023-01-01 1 3\u001b[0m\n", + "\u001b[0;34m 2024-01-01 2 4\u001b[0m\n", + "\u001b[0;34m >>> df1.index\u001b[0m\n", + "\u001b[0;34m DatetimeIndex(['2023-01-01', '2024-01-01'], dtype='datetime64[ns]', freq=None)\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Using `freq` which is the offset that the Timestamps will have\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df2 = pd.DataFrame(data=d, index=idx)\u001b[0m\n", + "\u001b[0;34m >>> df2 = df2.to_timestamp(freq='M')\u001b[0m\n", + "\u001b[0;34m >>> df2\u001b[0m\n", + "\u001b[0;34m col1 col2\u001b[0m\n", + "\u001b[0;34m 2023-01-31 1 3\u001b[0m\n", + "\u001b[0;34m 2024-01-31 2 4\u001b[0m\n", + "\u001b[0;34m >>> df2.index\u001b[0m\n", + "\u001b[0;34m DatetimeIndex(['2023-01-31', '2024-01-31'], dtype='datetime64[ns]', freq=None)\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnew_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0musing_copy_on_write\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maxis_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mold_ax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mold_ax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPeriodIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"unsupported Type {type(old_ax).__name__}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnew_ax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mold_ax\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_timestamp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0msetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_ax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mnew_obj\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mto_period\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mFrequency\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Convert DataFrame from DatetimeIndex to PeriodIndex.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Convert DataFrame from DatetimeIndex to PeriodIndex with desired\u001b[0m\n", + "\u001b[0;34m frequency (inferred from index if not passed).\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m freq : str, default\u001b[0m\n", + "\u001b[0;34m Frequency of the PeriodIndex.\u001b[0m\n", + "\u001b[0;34m axis : {0 or 'index', 1 or 'columns'}, default 0\u001b[0m\n", + "\u001b[0;34m The axis to convert (the index by default).\u001b[0m\n", + "\u001b[0;34m copy : bool, default True\u001b[0m\n", + "\u001b[0;34m If False then underlying input data is not copied.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame\u001b[0m\n", + "\u001b[0;34m The DataFrame has a PeriodIndex.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> idx = pd.to_datetime(\u001b[0m\n", + "\u001b[0;34m ... [\u001b[0m\n", + "\u001b[0;34m ... \"2001-03-31 00:00:00\",\u001b[0m\n", + "\u001b[0;34m ... \"2002-05-31 00:00:00\",\u001b[0m\n", + "\u001b[0;34m ... \"2003-08-31 00:00:00\",\u001b[0m\n", + "\u001b[0;34m ... ]\u001b[0m\n", + "\u001b[0;34m ... )\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> idx\u001b[0m\n", + "\u001b[0;34m DatetimeIndex(['2001-03-31', '2002-05-31', '2003-08-31'],\u001b[0m\n", + "\u001b[0;34m dtype='datetime64[ns]', freq=None)\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> idx.to_period(\"M\")\u001b[0m\n", + "\u001b[0;34m PeriodIndex(['2001-03', '2002-05', '2003-08'], dtype='period[M]')\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m For the yearly frequency\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> idx.to_period(\"Y\")\u001b[0m\n", + "\u001b[0;34m PeriodIndex(['2001', '2002', '2003'], dtype='period[A-DEC]')\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnew_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0musing_copy_on_write\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maxis_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mold_ax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mold_ax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDatetimeIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"unsupported Type {type(old_ax).__name__}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mnew_ax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mold_ax\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_period\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0msetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_ax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mnew_obj\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0misin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mSeries\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mDataFrame\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mSequence\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mMapping\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", + "\u001b[0;34m Whether each element in the DataFrame is contained in values.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Parameters\u001b[0m\n", + "\u001b[0;34m ----------\u001b[0m\n", + "\u001b[0;34m values : iterable, Series, DataFrame or dict\u001b[0m\n", + "\u001b[0;34m The result will only be true at a location if all the\u001b[0m\n", + "\u001b[0;34m labels match. If `values` is a Series, that's the index. If\u001b[0m\n", + "\u001b[0;34m `values` is a dict, the keys must be the column names,\u001b[0m\n", + "\u001b[0;34m which must match. If `values` is a DataFrame,\u001b[0m\n", + "\u001b[0;34m then both the index and column labels must match.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m DataFrame\u001b[0m\n", + "\u001b[0;34m DataFrame of booleans showing whether each element in the DataFrame\u001b[0m\n", + "\u001b[0;34m is contained in values.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.eq: Equality test for DataFrame.\u001b[0m\n", + "\u001b[0;34m Series.isin: Equivalent method on Series.\u001b[0m\n", + "\u001b[0;34m Series.str.contains: Test if pattern or regex is contained within a\u001b[0m\n", + "\u001b[0;34m string of a Series or Index.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'num_legs': [2, 4], 'num_wings': [2, 0]},\u001b[0m\n", + "\u001b[0;34m ... index=['falcon', 'dog'])\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m num_legs num_wings\u001b[0m\n", + "\u001b[0;34m falcon 2 2\u001b[0m\n", + "\u001b[0;34m dog 4 0\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m When ``values`` is a list check whether every value in the DataFrame\u001b[0m\n", + "\u001b[0;34m is present in the list (which animals have 0 or 2 legs or wings)\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.isin([0, 2])\u001b[0m\n", + "\u001b[0;34m num_legs num_wings\u001b[0m\n", + "\u001b[0;34m falcon True True\u001b[0m\n", + "\u001b[0;34m dog False True\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m To check if ``values`` is *not* in the DataFrame, use the ``~`` operator:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> ~df.isin([0, 2])\u001b[0m\n", + "\u001b[0;34m num_legs num_wings\u001b[0m\n", + "\u001b[0;34m falcon False False\u001b[0m\n", + "\u001b[0;34m dog True False\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m When ``values`` is a dict, we can pass values to check for each\u001b[0m\n", + "\u001b[0;34m column separately:\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df.isin({'num_wings': [0, 3]})\u001b[0m\n", + "\u001b[0;34m num_legs num_wings\u001b[0m\n", + "\u001b[0;34m falcon False False\u001b[0m\n", + "\u001b[0;34m dog False True\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m When ``values`` is a Series or DataFrame the index and column must\u001b[0m\n", + "\u001b[0;34m match. Note that 'falcon' does not match based on the number of legs\u001b[0m\n", + "\u001b[0;34m in other.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> other = pd.DataFrame({'num_legs': [8, 3], 'num_wings': [0, 2]},\u001b[0m\n", + "\u001b[0;34m ... index=['spider', 'falcon'])\u001b[0m\n", + "\u001b[0;34m >>> df.isin(other)\u001b[0m\n", + "\u001b[0;34m num_legs num_wings\u001b[0m\n", + "\u001b[0;34m falcon False True\u001b[0m\n", + "\u001b[0;34m dog False False\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m 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axis.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"only list-like or dict-like objects are allowed \"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m\"to be passed to DataFrame.isin(), \"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34mf\"you passed a '{type(values).__name__}'\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# error: Argument 2 to \"isin\" has incompatible type \"Union[Sequence[Any],\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Mapping[Any, Any]]\"; expected \"Union[Union[ExtensionArray,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# ndarray[Any, Any]], Index, Series]\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m 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\u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"isin\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# ----------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Add index and columns\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0m_AXIS_ORDERS\u001b[0m\u001b[0;34m:\u001b[0m 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+ "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproperties\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAxisProperty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"The index (row labels) of the DataFrame.\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproperties\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAxisProperty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"The column labels of the DataFrame.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# ----------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;31m# Add plotting methods to DataFrame\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mplot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCachedAccessor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"plot\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPlotAccessor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mhist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotting\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist_frame\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mboxplot\u001b[0m \u001b[0;34m=\u001b[0m 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+ "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m We recommend using :meth:`DataFrame.to_numpy` instead.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Only the values in the DataFrame will be returned, the axes labels\u001b[0m\n", + "\u001b[0;34m will be removed.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Returns\u001b[0m\n", + "\u001b[0;34m -------\u001b[0m\n", + "\u001b[0;34m numpy.ndarray\u001b[0m\n", + "\u001b[0;34m The values of the DataFrame.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m See Also\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m DataFrame.to_numpy : Recommended alternative to this method.\u001b[0m\n", + "\u001b[0;34m DataFrame.index : Retrieve the index labels.\u001b[0m\n", + "\u001b[0;34m DataFrame.columns : Retrieving the column names.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Notes\u001b[0m\n", + "\u001b[0;34m -----\u001b[0m\n", + "\u001b[0;34m The dtype will be a lower-common-denominator dtype (implicit\u001b[0m\n", + "\u001b[0;34m upcasting); that is to say if the dtypes (even of numeric types)\u001b[0m\n", + "\u001b[0;34m are mixed, the one that accommodates all will be chosen. Use this\u001b[0m\n", + "\u001b[0;34m with care if you are not dealing with the blocks.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m e.g. If the dtypes are float16 and float32, dtype will be upcast to\u001b[0m\n", + "\u001b[0;34m float32. If dtypes are int32 and uint8, dtype will be upcast to\u001b[0m\n", + "\u001b[0;34m int32. By :func:`numpy.find_common_type` convention, mixing int64\u001b[0m\n", + "\u001b[0;34m and uint64 will result in a float64 dtype.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m Examples\u001b[0m\n", + "\u001b[0;34m --------\u001b[0m\n", + "\u001b[0;34m A DataFrame where all columns are the same type (e.g., int64) results\u001b[0m\n", + "\u001b[0;34m in an array of the same type.\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df = pd.DataFrame({'age': [ 3, 29],\u001b[0m\n", + "\u001b[0;34m ... 'height': [94, 170],\u001b[0m\n", + "\u001b[0;34m ... 'weight': [31, 115]})\u001b[0m\n", + "\u001b[0;34m >>> df\u001b[0m\n", + "\u001b[0;34m age height weight\u001b[0m\n", + "\u001b[0;34m 0 3 94 31\u001b[0m\n", + "\u001b[0;34m 1 29 170 115\u001b[0m\n", + "\u001b[0;34m >>> df.dtypes\u001b[0m\n", + "\u001b[0;34m age int64\u001b[0m\n", + "\u001b[0;34m height int64\u001b[0m\n", + "\u001b[0;34m weight int64\u001b[0m\n", + "\u001b[0;34m dtype: object\u001b[0m\n", + "\u001b[0;34m >>> df.values\u001b[0m\n", + "\u001b[0;34m array([[ 3, 94, 31],\u001b[0m\n", + "\u001b[0;34m [ 29, 170, 115]])\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m A DataFrame with mixed type columns(e.g., str/object, int64, float32)\u001b[0m\n", + "\u001b[0;34m results in an ndarray of the broadest type that accommodates these\u001b[0m\n", + "\u001b[0;34m mixed types (e.g., object).\u001b[0m\n", + "\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m >>> df2 = pd.DataFrame([('parrot', 24.0, 'second'),\u001b[0m\n", + "\u001b[0;34m ... ('lion', 80.5, 1),\u001b[0m\n", + "\u001b[0;34m ... ('monkey', np.nan, None)],\u001b[0m\n", + "\u001b[0;34m ... columns=('name', 'max_speed', 'rank'))\u001b[0m\n", + "\u001b[0;34m >>> df2.dtypes\u001b[0m\n", + "\u001b[0;34m name object\u001b[0m\n", + "\u001b[0;34m max_speed float64\u001b[0m\n", + "\u001b[0;34m rank object\u001b[0m\n", + "\u001b[0;34m dtype: object\u001b[0m\n", + "\u001b[0;34m >>> df2.values\u001b[0m\n", + "\u001b[0;34m array([['parrot', 24.0, 'second'],\u001b[0m\n", + "\u001b[0;34m ['lion', 80.5, 1],\u001b[0m\n", + "\u001b[0;34m ['monkey', nan, None]], dtype=object)\u001b[0m\n", + "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0moverload\u001b[0m\u001b[0;34m\u001b[0m\n", + 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\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "#We can ask a deeper queston:\n", "??stream\n", @@ -1096,11 +13963,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 75, "metadata": { "id": "itDzoLUP2YBr" }, - "outputs": [], + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (1574573130.py, line 2)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m Cell \u001b[0;32mIn[75], line 2\u001b[0;36m\u001b[0m\n\u001b[0;31m stream.\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + ] + } + ], "source": [ "#Or we can use the 'dot' 'tab' magic.\n", "stream.\n", @@ -1131,16 +14007,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 76, "metadata": { "id": "0wlzj7Cb2YBr" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.03632e-05 1.226011963 0.044215262492604775\n" + ] + } + ], "source": [ "#use pandas.DataFrame functions to assign appropriate values to the variables here.\n", - "min_dis =\n", - "max_dis =\n", - "mean_dis =\n", + "stream = pd.read_csv('../ClassData/IH2021Q_7jun2024.csv', header = 34, na_values=[6999])\n", + "min_dis =stream.Flow_cms.min()\n", + "max_dis =stream.Flow_cms.max()\n", + "mean_dis =stream.Flow_cms.mean()\n", "print(min_dis, max_dis, mean_dis)" ] }, @@ -1155,13 +14040,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 79, "metadata": { "id": "T37eb3cW2YBs" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Date Time (AST) Flow_cms Flag1 Flag2 PT_WaterTemp_DegC Unnamed: 5 \\\n", + "12537 10/1/2021 20:15 NaN ICE NaN 0.125 NaN \n", + "12538 10/1/2021 20:30 NaN ICE NaN 0.121 NaN \n", + "12539 10/1/2021 20:45 NaN ICE NaN 0.113 NaN \n", + "12540 10/1/2021 21:00 NaN ICE NaN 0.121 NaN \n", + "12541 10/1/2021 21:15 NaN ICE NaN 0.121 NaN \n", + "\n", + " Discharge_total \n", + "12537 NaN \n", + "12538 NaN \n", + "12539 NaN \n", + "12540 NaN \n", + "12541 NaN \n" + ] + } + ], "source": [ - "#enter calculations here\n" + "#enter calculations here\n", + "\n", + "stream = pd.read_csv('../ClassData/IH2021Q_7jun2024.csv', header = 34, na_values=[6999])\n", + "stream['Discharge_total']=stream['Flow_cms']*24*60*60\n", + "stream['Discharge_total']= stream['Discharge_total'].cumsum()\n", + "print(stream.tail())" ] }, { @@ -1178,14 +14088,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 80, "metadata": { "id": "K8HNETRl2YBs" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'6/5/2021 17:00'" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Type your answer here, see if you can get it in one line of code!\n", - "\n" + "stream.loc[stream['Flow_cms'].idxmax(), 'Date Time (AST)']\n" ] }, { diff --git a/CodeSprints/RasterData101.ipynb b/CodeSprints/RasterData101.ipynb index f1f4a0b..51c84a2 100644 --- a/CodeSprints/RasterData101.ipynb +++ b/CodeSprints/RasterData101.ipynb @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "id": "JZafxE79SwOt" }, @@ -55,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "id": "rjA2wv1OSwOu" }, @@ -68,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "id": "3Ww2uVzmSwOw" }, @@ -103,11 +103,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "id": "yFQjaH-2SwOy" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.ndarray" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#What object type did we create with that call?\n", "type(dtm_pre_arr)" @@ -134,11 +145,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "id": "AZ7p2E4OSwOy" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(2000, 4000)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#For eample, using numpy commands, we can determine the dimensions of our raster:\n", "dtm_pre_arr.shape" @@ -146,17 +168,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "id": "1NpNB6iHSwO0" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the minimum raster value is: -3.4028235e+38\n", + "the maximum raster value is: 2087.43\n" + ] + } + ], "source": [ "# We can determine the minimum and maximum values of our raster dataset as well.\n", "print(\"the minimum raster value is: \", dtm_pre_arr.min())\n", "print(\"the maximum raster value is: \", dtm_pre_arr.max())" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": { @@ -165,7 +203,7 @@ "source": [ "### TASK 1: In your own words, describe what physical (real world) values the outputs of the `shape`, `min`, and `max` functions correspond to.\n", "\n", - "*Double click on this cell to type answer here*" + "*Shapes referes to number of rows and column of the grid, min and max value means highest or lowest elevation value*" ] }, { @@ -181,11 +219,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "id": "WYl_tnXKSwOz" }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot your data using earthpy\n", "ep.plot_bands(dtm_pre_arr,\n", @@ -208,11 +257,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { "id": "H5xx_YUqSwO1" }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# A histogram can also be helpful to look at the range of values in your data\n", "# What do you notice about the histogram below?\n", @@ -246,7 +306,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": { "id": "DwhCBPzUSwO3" }, @@ -269,11 +329,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": { "id": "JMjRDws3SwO3" }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# A histogram can also be helpful to look at the range of values in your data\n", "ep.hist(dtm_pre_arr,\n", @@ -293,11 +364,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { "id": "cQVMVv-zSwO4" }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot data using earthpy\n", "ep.plot_bands(dtm_pre_arr,\n", @@ -315,16 +397,27 @@ "source": [ "### TASK 2: Look closely at the plot above. What do you think the colors and numbers represent in the plot? What units do the numbers represent? What does the abrupt white line on the left side of the plot represent?\n", "\n", - "*Double click on the text in this box to enter your answer.*" + "*Black means higher elevation where is white means lower that we can confirm from the color grid legend added. This numbers are the elevation value often described in meter unit. The abrupt white line can be the no value line that were masked*" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": { "id": "MiuOp5m2dBL7" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.ma.MaskedArray" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "type(dtm_pre_arr)" ] @@ -341,7 +434,7 @@ "\n", "### TASK 3: What is a no data value? Why is this an important data format for handling raster data?\n", "\n", - "*Double click on the text in this box to enter your answer.*" + "*No data values are placeholder values used to fill the pixels with no measurement. It is important as it prevents miscalculating unmeasured pixels as 0 or negative elevation that can mess our analysis.*" ] }, { @@ -386,11 +479,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": { "id": "_-5UTDm9SwO7" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'colorado-flood/spatial/boulder-leehill-rd/pre-flood/lidar/pre_DTM.tif'" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Look at the path to your dem_pre file\n", "dem_pre_path" @@ -409,11 +513,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { "id": "--g72HBVSwO7" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], "source": [ "# Opening the file with the dem_pre_path\n", "# Notice here the src object is printed and returns an \"open\" DatasetReader object\n", @@ -441,7 +553,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": { "id": "lW4nxdIOSwO8" }, @@ -454,11 +566,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": { "id": "Ae0tXVt9SwO8" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-3.4028235e+38, -3.4028235e+38, -3.4028235e+38, ...,\n", + " 1.6956300e+03, 1.6954199e+03, 1.6954299e+03],\n", + " [-3.4028235e+38, -3.4028235e+38, -3.4028235e+38, ...,\n", + " 1.6956000e+03, 1.6955399e+03, 1.6953600e+03],\n", + " [-3.4028235e+38, -3.4028235e+38, -3.4028235e+38, ...,\n", + " 1.6953800e+03, 1.6954399e+03, 1.6953700e+03],\n", + " ...,\n", + " [-3.4028235e+38, -3.4028235e+38, -3.4028235e+38, ...,\n", + " 1.6814500e+03, 1.6813900e+03, 1.6812500e+03],\n", + " [-3.4028235e+38, -3.4028235e+38, -3.4028235e+38, ...,\n", + " 1.6817200e+03, 1.6815699e+03, 1.6815599e+03],\n", + " [-3.4028235e+38, -3.4028235e+38, -3.4028235e+38, ...,\n", + " 1.6818900e+03, 1.6818099e+03, 1.6817400e+03]],\n", + " shape=(2000, 4000), dtype=float32)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# View numpy array of your data\n", "dtm_pre_arr" @@ -484,11 +620,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": { "id": "YpaTj8DSSwO9" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'driver': 'GTiff', 'dtype': 'float32', 'nodata': -3.4028234663852886e+38, 'width': 4000, 'height': 2000, 'count': 1, 'crs': CRS.from_wkt('PROJCS[\"WGS 84 / UTM zone 13N\",GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AUTHORITY[\"EPSG\",\"4326\"]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",-105],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",500000],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH],AUTHORITY[\"EPSG\",\"32613\"]]'), 'transform': Affine(1.0, 0.0, 472000.0,\n", + " 0.0, -1.0, 4436000.0), 'blockxsize': 128, 'blockysize': 128, 'tiled': True, 'compress': 'lzw', 'interleave': 'band'}" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "with rio.open(dem_pre_path) as dem_src:\n", " # Create an object called lidar_dem_meta that contains the spatial metadata\n", @@ -530,11 +678,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": { "id": "TbDODgIqSwO5" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Numpy Array Shape: (2000, 4000)\n", + "Object type: \n" + ] + } + ], "source": [ "with rio.open(dem_pre_path) as dem_src:\n", " lidar_dem_im = dem_src.read(1, masked=True)\n", @@ -571,7 +728,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": { "id": "vTyRKj1sSwO-" }, @@ -605,14 +762,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": { "id": "PabxvvquSwO-" }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_81/4249809259.py:2: RuntimeWarning: overflow encountered in multiply\n", + " dem_feet = dtm_pre_arr * 3.28084 #enter number of feet per meters here to convert units\n" + ] + } + ], "source": [ "#Task 4:\n", - "dem_feet = #enter number of feet per meters here to convert units" + "dem_feet = dtm_pre_arr * 3.28084 #enter number of feet per meters here to convert units" ] }, { @@ -626,11 +792,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": { "id": "DdL9jqhHSwO_" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float32(-inf), np.float32(6848.5234))" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#View values:\n", "dem_feet.min(), dem_feet.max()" @@ -647,11 +824,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": { "id": "hgFpnCK3SwPA" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "masked_array(\n", + " data=[[--, --, --, ..., 5563.0908203125, 5562.4013671875,\n", + " 5562.43408203125],\n", + " [--, --, --, ..., 5562.9921875, 5562.794921875, 5562.20458984375],\n", + " [--, --, --, ..., 5562.2705078125, 5562.46728515625,\n", + " 5562.23779296875],\n", + " ...,\n", + " [--, --, --, ..., 5516.568359375, 5516.37158203125,\n", + " 5515.912109375],\n", + " [--, --, --, ..., 5517.4541015625, 5516.9619140625,\n", + " 5516.92919921875],\n", + " [--, --, --, ..., 5518.01171875, 5517.7490234375, 5517.51953125]],\n", + " mask=[[ True, True, True, ..., False, False, False],\n", + " [ True, True, True, ..., False, False, False],\n", + " [ True, True, True, ..., False, False, False],\n", + " ...,\n", + " [ True, True, True, ..., False, False, False],\n", + " [ True, True, True, ..., False, False, False],\n", + " [ True, True, True, ..., False, False, False]],\n", + " fill_value=np.float64(1e+20),\n", + " dtype=float32)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Mask out negative infinity\n", "dem_feet_ma = np.ma.masked_where(dem_feet == -np.inf,\n", @@ -673,7 +881,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "id": "swb2CEZPSwPA" }, @@ -691,11 +899,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": { "id": "7pN2Bki9SwPA" }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# plot the masked data to make sure it makes sense:\n", "fig, ax = plt.subplots(figsize=(6, 6))\n", @@ -724,7 +943,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": { "id": "_yndMglDSwPB" }, @@ -736,7 +955,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": { "id": "i0S9i60LSwPB" }, @@ -748,11 +967,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": { "id": "-lOuZAE0SwPB" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0 1 2]\n" + ] + } + ], "source": [ "# Note that you have an extra class in the data (0)\n", "print(np.unique(dem_waterline))" @@ -760,11 +987,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": { "id": "dtTGTFbSSwPC" }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot newly classified and masked raster\n", "fig, ax = plt.subplots(figsize=(6,6))\n", @@ -776,11 +1014,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": { "id": "-CfR5WQJRsNM" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0 0 0 ... 1 1 1]\n", + " [0 0 0 ... 1 1 1]\n", + " [0 0 0 ... 1 1 1]\n", + " ...\n", + " [0 0 0 ... 1 1 1]\n", + " [0 0 0 ... 1 1 1]\n", + " [0 0 0 ... 1 1 1]]\n" + ] + } + ], "source": [ "print(dem_waterline)" ] @@ -810,24 +1062,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": { "id": "09mSVYWUSwPC" }, "outputs": [], "source": [ - "# Task 6: create water_mask\n" + "# Task 6: create water_mask\n", + "water_mask = np.where(dem_waterline == 2, 1, np.nan)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": { "id": "4ZYu30OlSwPD" }, - "outputs": [], - "source": [ - "# Task 6: plot water_mask\n" + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Task 6: plot water_mask\n", + "ep.plot_bands(water_mask, \n", + " title=\"Water Mask (Land = 1, Water/NoData = NaN)\", \n", + " cmap=\"Greys\")\n", + "plt.show()" ] }, { @@ -841,35 +1109,63 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": { "id": "W31avYkHSwPD" }, "outputs": [], "source": [ - "#Task 7 create water_masked_DEM:\n" + "#Task 7 create water_masked_DEM:\n", + "water_masked_DEM = np.ma.masked_where(dem_waterline != 2, dem_feet_ma)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": { "id": "K2jaC99jSwPD" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Min elevation on land: 5642.0273\n", + "Max elevation on land: 6848.5234\n" + ] + } + ], "source": [ - "#Task 7 print min and max:\n" + "#Task 7 print min and max:\n", + "print(\"Min elevation on land:\", water_masked_DEM.min())\n", + "print(\"Max elevation on land:\", water_masked_DEM.max())" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "39M_ieEnSwPE" - }, - "outputs": [], - "source": [ - "#Task 7 plot water_masked_DEM\n" + "execution_count": 43, + "metadata": { + "id": "39M_ieEnSwPE", + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Task 7 plot water_masked_DEM\n", + "ep.plot_bands(water_masked_DEM, \n", + " title=\"DEM with Water Regions Masked\", \n", + " cmap='terrain')\n", + "plt.show()" ] }, { @@ -888,11 +1184,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": { "id": "jH8l49WdSwPE" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading from https://ndownloader.figshare.com/files/23070791\n", + "Extracted output to /home/jovyan/earth-analytics/data/earthpy-downloads/naip-before-after\n" + ] + }, + { + "data": { + "text/plain": [ + "'/home/jovyan/earth-analytics/data/earthpy-downloads/naip-before-after'" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Download NAIP data\n", "et.data.get_data(url=\"https://ndownloader.figshare.com/files/23070791\")\n" @@ -900,11 +1215,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": { "id": "WxL5BdyNSwPE" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'earthpy-downloads/naip-before-after/pre-fire/crop/m_3910505_nw_13_1_20150919_crop.tif'" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create a path for the NAIP imagery collected before the fire - notice it is a .tif file\n", "naip_pre_fire_path = os.path.join(\"earthpy-downloads\",\n", @@ -918,11 +1244,53 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": { "id": "6Z9DRBaRSwPF" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[113, 117, 137, ..., 54, 51, 74],\n", + " [113, 117, 131, ..., 63, 54, 54],\n", + " [111, 117, 120, ..., 78, 76, 52],\n", + " ...,\n", + " [191, 192, 193, ..., 58, 69, 76],\n", + " [192, 192, 193, ..., 53, 62, 71],\n", + " [193, 193, 193, ..., 51, 59, 66]],\n", + "\n", + " [[114, 114, 126, ..., 58, 54, 72],\n", + " [114, 112, 120, ..., 70, 60, 58],\n", + " [111, 114, 115, ..., 85, 87, 58],\n", + " ...,\n", + " [183, 184, 185, ..., 61, 75, 84],\n", + " [184, 185, 185, ..., 56, 66, 78],\n", + " [186, 186, 186, ..., 52, 58, 65]],\n", + "\n", + " [[ 80, 87, 95, ..., 55, 54, 63],\n", + " [ 79, 83, 90, ..., 57, 55, 55],\n", + " [ 81, 84, 87, ..., 62, 65, 55],\n", + " ...,\n", + " [161, 161, 163, ..., 54, 58, 64],\n", + " [162, 164, 165, ..., 53, 58, 62],\n", + " [165, 166, 166, ..., 51, 54, 57]],\n", + "\n", + " [[145, 143, 139, ..., 74, 47, 65],\n", + " [145, 146, 139, ..., 98, 59, 57],\n", + " [142, 144, 144, ..., 119, 107, 54],\n", + " ...,\n", + " [167, 169, 172, ..., 76, 105, 119],\n", + " [170, 171, 171, ..., 60, 81, 105],\n", + " [173, 173, 174, ..., 44, 55, 67]]],\n", + " shape=(4, 2312, 4377), dtype=int16)" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Open the data using rasterio\n", "with rio.open(naip_pre_fire_path) as naip_prefire_src:\n", @@ -933,11 +1301,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": { "id": "gKoZUINWSJBU" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(4, 2312, 4377)" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Check dimenstions of the np.array\n", "naip_pre_fire.shape" @@ -951,7 +1330,9 @@ "source": [ "### TASK 8: In your own words, describe the physical quantities that are represented by the three values outputted by the `shape` function above.\n", "\n", - "*Double click to type answer here*" + "*Bands(4): (Red, Green, Blue, and Near-Infrared),\n", + "Rows (2312): vertical axis (height).\n", + "Columns (4377): horizontal axis (width).*" ] }, { @@ -965,13 +1346,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": { "id": "81cuPDQZSwPF" }, - "outputs": [], - "source": [ - "# Task 9:" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'driver': 'GTiff', 'dtype': 'int16', 'nodata': -32768.0, 'width': 4377, 'height': 2312, 'count': 4, 'crs': CRS.from_wkt('PROJCS[\"UTM Zone 13, Northern Hemisphere\",GEOGCS[\"GRS 1980(IUGG, 1980)\",DATUM[\"unknown\",SPHEROID[\"GRS80\",6378137,298.257222101],TOWGS84[0,0,0,0,0,0,0]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",-105],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",500000],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH]]'), 'transform': Affine(1.0, 0.0, 457163.0,\n", + " 0.0, -1.0, 4426952.0), 'blockxsize': 4377, 'blockysize': 1, 'tiled': False, 'compress': 'lzw', 'interleave': 'band'}\n" + ] + } + ], + "source": [ + "# Task 9:\n", + "with rio.open(naip_pre_fire_path) as naip_prefire_src:\n", + " naip_meta = naip_prefire_src.profile\n", + "\n", + "print(naip_meta)" ] }, { @@ -998,11 +1392,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": { "id": "FCmw7r7RSwPG" }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot each layer or band of the image separately\n", "ep.plot_bands(naip_pre_fire, figsize=(10, 5))\n", @@ -1011,11 +1416,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": { "id": "NYqYvso7SwPH" }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot of all NAIP Data Bands using earthpy plot_bands()\n", "# Plot color image\n", @@ -1038,11 +1454,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "metadata": { "id": "FTVvZ5tbW_49" }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from IPython import display\n", "display.Image(\"https://www.gim-international.com/cache/1/d/a/c/6/1dac666728a9f8d07521a0094ff1dea58187ffa6.png\", embed=True)" @@ -1083,7 +1511,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "metadata": { "id": "DiF44DykSwPH" }, @@ -1092,18 +1520,34 @@ "red_pre_fire=naip_pre_fire[0]\n", "NIR_pre_fire=naip_pre_fire[3]\n", "\n", - "# Task 11: create NDVI_pre\n" + "# Task 11: create NDVI_pre\n", + "\n", + "NDVI_pre = (NIR_pre_fire - red_pre_fire) / (NIR_pre_fire + red_pre_fire)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 65, "metadata": { "id": "3g3nxNwwSwPH" }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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xcCwtLeHuu+/Ge9/7Xvzcz/3c1/Q7Ll26BOBkoRYOh/Ed3/Ed+PjHP47f/u3fxp//+Z+j3W6fMAX7WsLhcDyuAlhdNAOQOeJ33303XvnKV97wZzgyLRaLnfj6Bz7wATEO+1rjRu9Hp9Phk5/8JAwGw2O+/6vNkw4EAphMJmi32yfAD8bdd9+Nn/u5n8P999+PZDKJz3zmM3jd614n3eyv9/VvFLPZDGfPnsVv/MZv3PDfU6nUV/0db3jDG/CBD3wAP/ETP4FnPvOZ8Hg8Mof9qZgFD+CG1wPACUDgGxH8PPfcc88NmRRq5sFLXvISfP7zn8dP//RP4+abb4bT6cRsNsN3fdd3fd3X5Yl83kajgWAw+HW9nhZaaKGFFv82QyvCtdBCCy1U8aY3vQkf/vCH8fa3v/0J/2yn08EnPvEJpFKpxxhjvfzlL8df/dVf4ZOf/CTuvfdeuN1ufM/3fM836m0/oQiFQnC5XJhOp1+1iP/Upz514u+nT5/+hr+f5eVlKIqCxcVFrK2tPeGf39jYAHDskn6jeesve9nL8PM///O49957MT8/j+l0egIAebyvPz8/DwDY2dnB8573PPn6ZDJBOp0+8drLy8s4f/48vu3bvu2rdku/3L//8R//MV75ylfif/2v/yVfGwwGaDQaj+vnv9xnuHDhAmaz2Ylu+NbWlvz7NyNoRhcOh7/imqzX6/ibv/kbvPWtbz0x9o+d9K8W36jOdS6Xw2g0+rIGeFpooYUWWmjxlULThGuhhRZaqGJ5eRl33303fvd3f/cxVOyvFP1+H694xStQq9Xwi7/4i49J9l/4whfCbrfjd37nd/DJT34SL3rRi75hs7qfaBgMBvzH//gf8fGPf1w69+pQj2T69m//9hN/Ht0Z/0bEi170IhgMBrz1rW99TMdRUZSvOnrqmc98JoBjzfaNYm5uDnfeeSf+6I/+CB/+8IexuLiIZz3rWU/49Z/2tKchEAjgfe97HyaTiXzPH/zBHzyGrvySl7wEuVwO73vf+x7zfvr9Prrdrvzd4XA8prAGju/To9/Pu9/9bkyn0xNf45z1G/2OR8e///f/HoVC4YTb92Qywbvf/W44nU4897nP/aq/48mI5z//+XC73fjVX/1VjMfjx/w71yQ71Y++Lu985zsf1+t8uWv9ROOBBx4AgBPrSAsttNBCCy0eb2idcC200EKLR8Uv/uIv4p577sG1a9du2PnN5XKiae10Orhy5Qo+9rGPoVAo4L/+1/+K173udY/5GafTiRe+8IViEPb1UtG/3njb296Gz3zmM3jGM56B17zmNdjc3EStVsODDz6IT3/606jVak/Ze1leXsYv//Iv4+d//udl5JfL5cL+/j4+8YlP4LWvfS3+23/7b1/255eWlnDmzBl8+tOfxg//8A/f8HvuvvtuvPa1r0U+n5fxVE/09c1mM97ylrfgDW94A771W78VL3nJS5BOp/HBD34Qy8vLJ4CXV7ziFfjoRz+KH/3RH8VnPvMZfMu3fAum0ym2trbw0Y9+FP/v//0/PO1pTwNwPO7q05/+NH7jN34D8Xgci4uLeMYznoHv/u7vxj333AOPx4PNzU184QtfwKc//WkEAoET7//mm2+GwWDA29/+djSbTVgsFnzrt37rDf0BXvva1+J3f/d38apXvQoPPPAAFhYW8Md//Mf43Oc+h3e+8503pPM/FeF2u/Ge97wHr3jFK3DrrbfipS99KUKhEDKZDP7iL/4C3/It34Lf+q3fgtvtxnOe8xy84x3vwHg8RiKRwF//9V9jf3//cb3Obbfdhve85z345V/+ZaysrCAcDn9ZHfpXik996lOYm5vTxpNpoYUWWmjxtcVTb8iuhRZaaPEvI9Qjyh4dr3zlKxUANxxRhodHLOl0OsXtdiunT59WXvOa1yj//M///BVf7y/+4i8UAEosFjsxruzRv//xjih79Hu7UQBQfvzHf/yG/1YsFpUf//EfV1KplGIymZRoNKp827d9m/Le9773q/5ejqD62Mc+9hVf+0Yjysrl8g2//+Mf/7jy7Gc/W3E4HIrD4VA2NjaUH//xHz8xXuzLxW/8xm8oTqfzy45Wq9VqisViUQAoV65c+bpe/13vepcyPz+vWCwW5elPf7ryuc99TrntttuU7/qu7zrxfaPRSHn729+unD59WrFYLIrP51Nuu+025a1vfavSbDbl+7a2tpTnPOc5is1mUwDIuLJ6va780A/9kBIMBhWn06k8//nPV7a2tpT5+fnHjDR73/vepywtLSkGg+HEuLJHjyhTlOP7zt9rNpuVs2fPKh/4wAdOfA9HlP36r//6Y67To+/rjeKrrY9XvvKVJ0aUqX/u+c9/vuLxeBSr1aosLy8rr3rVq5T7779fvufw8FD5vu/7PsXr9Soej0d58YtfrOTz+ce8rxuNKCsUCsoLXvACxeVynRgr9+X2An4O9fi36XSqxGIx5U1vetNXvAZaaKGFFlpo8eVCpyjfYHcVLbTQQgsttHiKo9lsYmlpCe94xzvw6le/+il97dlshlAohBe96EU3pJ9r8a8r/vRP/xQ/8AM/gN3d3SdFnqGFFlpoocW//tA04VpooYUWWvz/PjweD37mZ34Gv/7rv/6kOocPBoPH6JE/9KEPoVar4a677nrSXleLfznx9re/Ha9//eu1AlwLLbTQQouvObROuBZaaKGFFlo8zvi7v/s7/ORP/iRe/OIXIxAI4MEHH8Tv/d7v4dSpU3jggQdgNpu/2W9RCy200EILLbT4Fx6aMZsWWmihhRZaPM5YWFhAKpXCu971LtRqNfj9fvzgD/4g3va2t2kFuBZaaKGFFlpo8bhC64RroYUWWmihhRZaaKGFFlpoocVTFJomXAsttNBCCy200EILLbTQQgstnqLQinAttNBCCy200EILLbTQQgsttHiK4nFpwmezGfL5PFwuF3Q63ZP9nrTQQgsttNBCCy200EILLbR4ikNRFLTbbcTjcej1X75fOxgMMBqNnpL3ZDabYbVan5LXeqricRXh+XweqVTqyX4vWmihhRZaaKGFFlpooYUWWnyTI5vNIplM3vDfBoMBFhcXUSgUnpL3Eo1Gsb+//6+qEH9cRbjL5QJwPBvTYrHAbrfD7/djfn4eer0ejUYDNpsNR0dHuHz5MqbTKcxmMxRFgcFggM1mw3A4xHA4RCKRwNzcHC5fvgyXy4VAIIBer4dOp4PJZAKfz4fRaIRisSjzWPV6Pex2O86dO4dutwuj0YhSqQQAiMViGI1GKJVK8Pl8sFqtGA6H8Hq90Ol0sNlsaLfbMJvNKJVKaLfbmEwmWFpawsHBAWKxGPR6Pba3t2E0GhGJRGA2m1EoFHD69GmYTCY0Gg30ej24XC6YzWa0223odDp0Oh00Gg1Mp1MAwHg8htF4fEmNRiMsFgsURUG328VkMpHPAgAGgwGTyQQmkwmdTgdutxt+vx+dTgdOp1OQpVKphMlkAqvVitFohNlsBqPRiMFggI2NDRiNRuh0OvR6PRgMBjidTuTzeUQiEaRSKej1ejSbTeRyORQKBcTjccxmM1y9ehXpdBrj8RgWiwWRSAR2ux3JZBLhcBi9Xg+z2Qztdhvj8RiKomAymWA2m0Gn02E0GkFRFNhsNtjtdlitVphMJuh0OhweHqLf7yMYDMLv96NSqUCn08mDY7fbkclk4Pf74ff7YbPZ5Lo+9NBDyOVyaDQasFqtWFxcxLOe9SyUSiVMp1M0m00oioLl5WV4PB7U63Vks1m0221sbGzAYDAgFothMBig3++jUCjAbrejXC4jFovBbrfjwQcfhMFgwOrqKlqtFprNJvr9PsxmM6LRKHQ6HabTKfR6PQaDAabTKfr9vswe9vv9MBgMmE6nqNVqOH36NAqFAvr9PhRFQSqVwqVLl+Reud1umEwmBAIBhMNh6PV6zGYzWcvZbBZutxvdbhfdbhcmkwnRaBR6vR71eh3D4RCVSgUAMJlMZP04HA5YrVYYDAYAQDgcRi6XQ7/fx6lTp7C3twdFUWA2m3FwcAC32w0A6PV6sh51Oh3MZjMsFguazSZCoRDW19cxm81Qr9dRrVbhdrthNpvR7/dlvYzHYywsLKBQKGB+fh6VSgWZTAaj0Qij0QherxeKoiAWi8HpdMLj8ch13N3dxXA4RDgchs1mAwAMh0M0m0243W64XC44nU60Wi14PB4Ui0UcHh4iGo0imUzi4OAA0+kULpcLBoMBBwcH6PV6CAQCSCaTKBaL8vPtdhtGoxHD4RCTyQQ6nQ4mkwlWqxXr6+sYDAYol8sYDAYIh8Py/PH9ORwO7OzsoNfrYXV1FQaDAdVqFaPRCIFAAPl8Ho1GA91uF6FQCJFIRJ7dvb09jEYjnD59GjqdDrPZDNlsFvPz8zCZTEin02i32zAYDPB4PBgOh+j3+xgMBtDr9bDZbDAYDOj1eohEIvD5fIjH45hOp5jNZuj1ejg8PEQgEEAoFMJ0OoXH44HBYMBsNoPBYECr1cJoNILD4UCn00Gn00G73YZer4der4fVaoVer4fb7ZY1sbe3BwA4OjqC0WiE0WiEx+NBp9NBOByGoihotVrodrvodDq44447oNfrsb+/D5PJhGAwiO3tbcxmM6ytrWE2m2Fra0vOBe4T4XAY6XQag8EAOp1O3jfPDa5Pg8EAi8UCAPD5fBgOh/J1PovBYFDOK6fTiVKphNFohFwuh2azCY/Hg9OnT8NoNKLb7cJqtWJ7extLS0sYDoeoVqtwOBxIp9Pwer3o9XpotVqwWCwwGo1YWVmBTqfDZDKBXq9Hq9WC1WqF3W6HoihoNBpIJBIol8uwWq2wWCxot9tyr0ajkVzrXC4Hq9WKyWQCl8uF2Wwm+4PFYkGr1cL29jYAyHNkMplkT55MJrLn1mo1uadGoxGxWAzZbBbj8RjxeBwulwv7+/sIh8Oo1+s4d+6cnD21Wg2ZTAYejwcul0s+g8vlgsViwYULFzAcDrG2tobd3V0oioJeryf32G634/DwELPZDHq9XtYPz7lH/z/PJz47h4eHACDnJJ85r9eLSqUie2mpVEKj0YDdbke325W9l3u00WjEdDqFzWZDv9+HwWBAp9ORXMVqtcJoNKJWq0Gv10On08FoNMr75u9aXV2V85s5QL/fR71eh6IoCAaDMBgMcqZarVZMp1PZI+PxONxuNxwOB6bTqazTVqsFRVEwm83gcDgwm81gNpuFVcg99ejoCD6fD0ajEcViEYqioFwuY3NzE7lcDsPhEI1GA263G06nE36/H7FYDJ1OB+VyGU6nE6FQCOPxGNlsFv1+HzqdDqFQCE6nE3q9HleuXEGxWMSzn/1s2V94zmSzWdnnw+EwdDodXC6X5BOTyUT29k6ng2q1CpvNBkVR5Bq7XC5Uq1XZ7/R6PcxmM4xGIxqNBsbjMYbDIXZ3d9HpdGC1WqEoCtbX16XjZjab0e12oSgKqtUqFEVBMpnE7u4u/H4/AoEAptMp8vm85A/1eh2j0UjOm0ajIddZr9fDZDJhOBzKGhkMBnC5XOj1ehiPx3C5XJLP6HQ6yTN4/3gGMvg15kjVahWxWAzT6VRystFoBJPJBL/fj2q1iul0Cp/Ph/F4LGthMplgMBhgPB6j3W4jEAigUqmgVCrB4XBAURRMp1PJnbxeL4bDIQAgEAjg6OgIvV4P0+kUg8EA8/PzuHLlCur1OgwGA77lW74F/X4fJpNJnnmHwwGv1yvPDnDcaLPb7dDpdOj3+/D7/XC73Wi1WjAYDHId+Wz0ej0Ui0X4fD65LuPxGN1uFzqdTp5zu92O6XQqn7nZbMo1ACBrkNeMZwD3UL1ej2q1im63i2q1iosXL8q1W15eRi6Xw+bmJgwGAwqFAnw+n3z/3NwcIpEIBoMBAMBkMmE2m2E2m8k+3e12USqVYDQasb6+LvkY13iv18PR0RF0Op3sJ4PBAMPhUNYXn/1+vw+r1YpLly7B7Xaj2Wxic3MTmUxG7hFwXEhWq1XMzc3B5/PB6XSiVqvJpA3u9/V6HUdHR7LOA4GAPK98VpjX8Jzi/eGeZjKZMBqN5F6yTnI4HFKTdbtdBINBOYecTiem0ykymQxe/OIXS/13oxiNRigUCpLDPpnRarWQSqXkLP3XEo+rCOdhwcRBURTMzc3JgggEAmg2m8hkMjAYDFLMdToddLtdBAIBOBwOtNttFItF1Go1DAYDzM3NwWQyoVarScHc6XQQCATQarUQDAbRaDQwmUxgNBpxeHgoBXkkEoHNZkM0GpVkXa/Xo9PpYG5uDgDQ7XbR7/fRaDQwHA4lGS0UCsjlcnA6nahUKjAYDFhYWIDL5cLi4qJ8v8/nQ7vdlk2JB2i/38doNILNZkO1WoXRaJREQ6/Xw2KxYDqdSvHMg7FWq8HtdmM6naLb7cJsNsvCv+mmm6DT6XBwcCAHWzKZhNfrlYSXrwEcJy5utxuDwQAWiwXBYBAOh0MOQB4ALAIjkQguX758omDnw84Nj5sBk5xAIIBgMAiPx4NsNotKpSKFOBN4t9uN8Xgsn3U8HsvrWiwWmEwmxONxVKtVdDodAUVCoRBOnz6NVqsFh8MBl8sFt9uN4XAom4uiKBiNRuh0OjAajQJaWK1W9Pt9OBwOAJBDql6vy3rgRhmPx9HtduFwONBoNFCr1eD1eqUwW1pawnQ6RS6XkwNvbW0N5XIZjUYDHo9Hilaj0Qiz2YxAIIBMJiNF+Wg0QjgchtVqRbPZhNFohNfrxfLyMoxGowAtFosFZrMZo9FIkrBSqYSbbroJRqMRDz74oNxjSkDa7TZsNhvi8ThisRi2trYwm80wGAzk8Lbb7TAYDPB6vej3+6hUKmg2mxgMBlIARKNRBAIBKIqCXC4nhR6LIv7/+vo6fD4fjo6OZNNTFAXj8RjRaBS1Wg35fF4OZb1ej3K5DI/Hg1OnTkkBwHvJ+3V4eIhQKIRisQiHwyHrtdFoIBaLod1uY3FxEUdHR3Ko8wAZDodYWFiQfcFut8NiscjeYDAY4PP5AACVSgWj0QiTyUTAD51OJ9eKwF4gEIBOp4Ner0csFhNAKpFIIJFICNDGRJOFWSKRgNvtRr1eh8lkkiLXZDJJIdFoNCSBi0ajsNvtCAaDspa5h/BZWV1dRaFQkITEZDIBOE5qxuMxYrEYAoGAABwExAhick90u90n1g+fJYJ2drsdw+EQq6ur0Ol0AkiwgGJyx73a6/Xilltuwc7ODsbjMXQ6nSQJk8kE3W5XikyDwYC1tTWMx2OcP38eqVQKwWAQRqMRuVwO0+lUAB8mCaVSSUAk7p18pvh3nj38WUVRZH91uVy46aabZJ+YTCaSjE6nUxgMBozHY1itVjztaU+Dw+FAJpNBIBCA1WqFy+XCYDCA0+mUvfjs2bMC8JpMJiQSCfneRqOBQqEAj8cjz1UqlZI9s1QqwW63IxQKoVQqIR6Po1QqYTabweVyodlsolarYTabIRKJyP7C/ZfJKBNXj8eDWq2GbDYLm80m5yXBLhbTTqdTQE6TyYS5uTmEQiEEAgF0u13UajUEAgGYTCaYzWaMx2MpRAKBABKJBHQ6HbxeL0KhENrtNnq9HvR6PXw+n9zfyWSCc+fOSVHK5F591vBsMBqNGI1G8ozxeSCIUa1W5d4bDAZZSzqdDrlcTs6jVqslZ5HdbsdsNpN7y7WhKIokoE6nU54dgjc8m+x2u6x1vV4vz5rdbkej0UA+n8fi4iK8Xi+m0ykSiYQUjbPZDBaLBT6fT9YrASO/34/hcIhSqSRnJ898vV6PZDIpZz4LEu6NLpdLzgOPxyMgucfjwaVLlyTZXlxcxLVr1yT3SSQS8h54FnJPM5lMsmfxTDYajej1eojFYiiXyyiXy1hZWYHL5ZKz1uv1SiOAuUGz2cR4PIbZbJbPPhgMYDQaMTc3B7vdjnq9LnkKcFxksEAxm83odDoolUrw+/2wWCzy3HM/4bnOddztduHxeNBut7G2tgYA6HQ6iEajcDgcMBqNst97vV40m00pKn0+nxRWwWAQR0dHCIVC0Ov1GI1G8Pl88Hg8ACA5gtfrRbVaPbHWCTZ2Oh24XC5pAPC8IcgSiURkPTgcDimgTSaTgMws+JmL1Wo1DIdDHBwcSKOi3W5jMBgIyDQcDmXPdzqdUgjZbDb4fD7U63VYLBZYLBZYrVYkEglUKhUBStlUIIja7XZlzbBZwDyCxXI8HoeiKFK08nnkZ2ORzBwXADweD8bjMUajESwWi+Qa/IzhcBiFQgHBYFA+E4vf0Wgk+91kMsF4PJbnlwCew+GQPMNgMODs2bPwer0AHgFkg8GgnOMGgwGRSESA9clkIsBFu90WgH1hYQGtVgsHBwcYj8e47bbbEAqFJHeaTqeYm5tDt9uV3HBjYwMWiwXXrl2D1+tFLBaTRpDD4UC5XEalUsEtt9yCbreLZDKJWCyGSqUi5zEAxONxAZJarRZqtRpsNhusVitisRiq1ao8Vx6PR2qU0WgkwCOvudfrhdvtxpUrV+D3++VccrvdsNlsknva7XZpMvK9OJ1O7O7u4tSpU7Db7cjlcpjNZpJncU99PBJkt9v9pBfh/1rjCc0JJzJqNpulcBqPx6jX67h06RJ6vR6GwyGuXbsGt9uN9fV1KT5brRacTic2NjYkkd/b24PX60UwGMRkMpEicmdnRzZop9MpCQFRxkQiIYup1WrJf0ejkaC6jUZD0EAmZkTbPB4P5ubm0O/30Wq14HK5EI1GJUFWFxeDwQAej0cKvnK5jFarJRsB3xc3b3ZViKiySGZRcPr0aWxtbaFYLMLlcsnnns1mKBaL0pllwbC5uYlQKIRcLofxeAyv14vDw0OUy2Vsb28jHA4jFotJx95utyORSMgmzw3O7XZL4U7knh3UyWSCfr9/4qAol8vSlZxMJvI7M5kMTCaTJF3RaFQ6pjzMCTQwaSKCyMNsNpvB7XZLggYA/X4fer0ekUgER0dHsNls6Ha7aLfbqNfrSCQS0gGtVCrIZrMIh8NIJBIIBoPSHWD3u1wuw2KxoFAoSBJos9ngdDqxtrYmwIbZbD4BHDHhYNekWq1KAW40GqW4JbILAO12WxJndi/6/T7sdjvG4zFsNhssFgsODg4kwZhMJjg8PESlUhGAaDAYwOFwyM+RfcFiggAMkUrg2K/BZrOhVqvh6OgITqdTugt8P8PhEJubm9Dr9bh+/bp0aCwWixRjk8kEm5ub8Pl8wiIwm80oFovw+/2SrDmdTlkL/X4fw+EQvV4PwWAQiqIgHA4jm83C5/NJgcdDptvtolAoCMDEDrLRaEQ4HJY1mclkEI/Hsb+/LwcXD1GCJwS52ImKRCKS6AJAKpWCxWLBzs4OhsOhdDWZnGezWUlWmVAeHh7C7/ej1WqhXq8jEAjAZrPBZrNhMplgZ2dHng2u+WvXrsFoNGJjY0MKa51Oh0AgAL/fD+CRDkGz2US1WkWlUpGkMBwOw2w2S3LHDhC7vLPZDJ1ORxLAfD4PAFKM9vt9LCwsSHeBiT8ZEEajUZ6tw8NDKcSIJDcaDQwGAykAG40GOp0OzGYzUqkU2u02fD4f8vm8IOtkEyWTSUSjUSl2uF46nQ48Hg98Ph8uX76MeDyOo6MjWbNMjgDI++Gey2Kaa537Ktcou4bhcBinTp2SbjD3sm63i0qlIh2vRqMBh8MhXRsm/NVqFcFgUIqBer2O6XSKdDqN6XSKs2fPot1unzgDjEYjNjc30Wg0sLu7i/X1dbRaLezu7sLtdmNzc1M6gGqgr9vtwmKxSNFLMNPlcsna4ro1Go0CWFYqFXm2VlZWJFFnV6XdbgtAEAwGpePP/SmTyQigxY4Ln0Ov1wun04nDw0Ok0+kTe06tVpOzlAU5waBSqYREIgEAuHbtGvr9PjwejxRPZErx/vJ6qDviBFnZgVQzhhYWFpDNZrGwsHDiM5BR9ugin6/BtcIzhYVLMBhEOp0WMJwdPJ5LDAJgwWBQwHACm/wMOp0OqVQKTqcTBoMBR0dHJ8Cj8XiMvb096ZpOp1NcvXoVq6ur8Hg8wi5YXFxEIBBAvV6XIohnEM9tm82GUCiEaDQKRVGkMGAXK5vNCuC7srKCTqeDXC6Hvb09RCIRWQcEGhwOB/x+vwCEbFDo9Xo4HA4sLi6e6GwzX1Lvf71eT/IZi8UCr9cr3btarSZF1HA4FACJ5wZB51ardeL84RkajUbljB2NRtje3pZ8YTweC0vDaDQinU5jeXkZZrMZlUrlBFCezWYFLJ/NZpifn5e8jIUci5J2uw3gmG0YCoVgtVpRLpcRCoVgNBrlzCXrrNfrCQjAcz2TycBut8ufSCSC69evIxKJSJ5E0IgNCN5ztceS0WhEIBCQhgbvAcEZnlU2mw29Xg9er1cYjAcHB8jlcgAgYF8ymZQ8hJ+jXC4LK5P7jcPhQDwel+vHbjJBGxbCBOLtdrvkjA6HA6PRCPfddx/m5+fRbDZhMpnQ7XaRyWQEeA6Hw9Kt5ZmlZht2u100Gg3JT3kOEsjh+V0oFDAcDmGxWKQjmkwmZV2bTCaMx2MBbOr1+gnWSaFQEACnXq/D5XLB6/WeAIVtNhuWlpaE+UUwx+FwoFQqSU7wtKc9DeVyGfl8Hk6nE9VqFf1+H+VyGZFIRBhNBMCYC6dSKbhcLvT7fWFd3nfffYjH44hGo6hUKgLGzM3NIZfLYX5+Hk6nE4PBQPax4XAozSnmvg6HAxaLRdgIXKdOp1NyJeYOZCcTuGCThew9nU73hIpqgiZPZvxrnab9hIpwHpiklfIQ29vbk8R1f38fOp0On/3sZ3H58mWcOXNGEJxyuSwJWygUEoo4CyIirW63G8ViUSiCRNh6vR5GoxGuXr2Kw8ND9Ho9WbxXr1490QlhEcKDn53UTqcDg8GAK1euIJFIIB6PYzAYwO12C82dB8f29jZWV1dl82Tni92Lo6MjOBwOQRbZneHf9Xq9dCe73a5skOrOVb/fRzKZhE6nw9HRkRQuRGEHg4H8NxqNwufzIRgM4sKFC5ifn5drwoSbXWbgOME+OjqCx+NBt9tFLBYTxM5sNsPr9aLb7cLtdmNxcVGoQEzWefjWajVUKhUUi0VJlDKZDDqdDtLpNNxuN8LhsKBzkUhEqJjqLi0fdofDgW63KxsYAClCrFardJCYKFQqFZw6dUo6Elx/VqtVKMosZm02G65fvy6mEqRo9vt9rKysCKWRhdz+/j6m0ynq9bokW4VCAX6/XzZnsiBIC240GtJRMhqNkjju7e0Jih4KhTAYDHB0dCSMAKfTKeuQxSc7Do1GA8FgUChYpHxyI2c3mYkK8AgDwOl0ol6vC72UHT4m5izm0um0rBUW0p1OBzqdDpFIRJITJmZEWIm4+v1+WRcs4Ov1OtrtNg4PD+FyuYQ2WygUsLS0BLPZDJvNJsyJZrMpa5DdZgBClWYyw2SEzxMPm5WVFVy/fh2Hh4dy0Nx+++0wmUzY398XKtve3h4CgQAWFhZw7do1eDweLC0toVwuYzQaoVarCdIfjUYf08GORCLSfWXixnXKBImgGcG2+fl5YcdMp1OEw2GMRiP5nOl0Wgqo9fV16HQ6Ac4CgYA8vz6fT4oJotIskJjAsGBlktHr9VCpVKTjyWKFSQ8LDNJNWYwyic5kMtDr9SgWi1LsWq1WVCoVRKNR6Tax6C4UCvLak8kEly5dkoSZ65PJ0pe+9CWhUDPYYeHPA5BOFgAp/AwGgwBRLpcLiUQCdrtdOjDcI7jXcK9lkmc0GnH27FmEQiFMJhOhXpPRw2KQbAHefxYS7XYbiURC6JCkn4fDYfn5UCiEZrMpbA+uk0qlImA1cMyYSSQSsq+SydRut2GxWNDr9aSAIGjCs6HdbiOfz8s+q+70N5tN6YiRdg1A1hPPtkajAb/fLzRqvg6ZO2RIUZpBaZPf70ev10MoFMLFixflWer3+wIU1ut1tFot9Ho96WjyPqsLdCb0Op0O8/PzsNvtKBQKcDqdklCzOOV+z062mmZJcIbrhvea4BwAOUsjkQgikYgk3qTYkoljtVoF3Ein0wISN5tNkYsQJOW6JrBE0EsNaPMsSqVSiMfjmEwmwrwjU4JJLgsLMlXYyeNzkc/n5T4+4xnPEGbVaDQSNgPPIN7T3d1dHBwcYDKZIBgMIplMSjfY7Xbjec97HnZ2dqTjzYKZ7IRerydSNp7p1WpVZEKUD1Cmwi46WUgs9CjZs1gsInGoVqsIhULCNtjd3UUwGEQmk5G8kOuGlOyFhQWYTCYp0txu9wmghgCtmiVI1kgulxOJI58Z5lZGoxEOh0MkRpTFcM2oryGBIl4TsutcLhdKpZIwIoxGI5LJpNC0CZYSDCXbjdeO74n3js2kU6dOwe/3I5vNnujmcq/KZrPQ6XQCJjDXrVar8pnvuOMODAYD7O7uIh6PS7HKnJrymGw2K8+3x+MR+V6325U1SxCKZ/FkMhHQ1ev1ytl/cHAgDBLmpqPRCM1mUwpnnqekhvN3M88lIOp2uwX85dlweHiIq1evolwuYzabYXV1VYBK7hmRSET2c6672WyGarWKf/7nf8bKygqAYymrTqdDIpFAp9PBcDhENBqFxWJBp9NBvV5HKBTC9vY2ms0mXC6XsLouXLiASCSCQqEgQMbKygri8bgwm+r1Oi5fvgyHwwGHwyEsIzZd9vb2BKhnEa0GGXu9Hubm5uDxeOQsoqyMMlCTyYRr165hcXFRwAo20wjCEeDmnkw5Fd/TZDIRxkg0GpV1z9xciyc3nlARDkC6nSxKTSaT6HWr1ao8UIPBAIeHh8jn84KSeDweJJNJeahZeBFdpA6HujAeBJlMRoouPmikqcViMSlAAEiCwYebhwGTPjXlkQmV1+tFoVAQank+n5dN2mq1in5X/f9cpNPpVBJ0JskOhwMrKytoNBpYXl6WjWQwGCCdTmNpaQnhcBh+vx9Xr14VdNhqtWJubg7FYlHoudRY63Q62fy5eQIQsGJ1dVU2vvF4jMXFRfR6PQAQOt9gMJDrFAqFTlDJNjc3JdGj1v3q1atybzqdjnTn+fmNRqN0QN1ut+hNAAiKy46bz+cTJI7MAEoQmDxyA4rFYtje3hbqYKfTwYULF0SPqNfrsba2hvX1dUn6SAdmokfAJBQKwefzyQFN5oTT6UQ6nUYgEMBkMpHOtMFgQDQahdVqlQJpOp3i4OAAfr9fkn3KLXK5HEqlEgaDAWw2m7AyqJlkccvOC5M5fh+Ty1gsBq/Xi3a7jW63C5vNhlgsJhrDnZ0d0SFy7U8mEwGwSLEmcsxO/GQyQalUQqFQEMqq1+uFx+MRLRTRdlJBzWazXAuLxYJkMonr169LN5DAwXg8RigUOtFVrNVqiMViCIVCKBQK0jVjcp9MJmGz2RAIBLC3t4dGoyFJRKPRQCQSEXpeLBaD1WoVRofJZEKlUhFmhdlslo5Xq9VCJBJBtVqF1+sVOiGBMgByP7lv0c+i1+sJ/ZUJgprpQmAwHA7j8uXLSCaTOH36tCRPas0uu8hMetlxPzo6EgmAx+NBIBAQKh07Tkyw2GGuVquS6DMJtNvtcDgciEQi2NnZgV6vRzAYRK/Xk3vP7j5wnMxS75dIJETrX6/XJUlst9sCLnFP5vXt9/s4f/68FPa5XA7Xr18X+u7GxgYqlQoajcYJ2mCr1ZL9lHs8iyjS2dVaQAKXBoNBtNGk7PK6nD17Vgp5NV2d38trTG+AXq8nyRoLM64P6qjZVedrEJRkUUZfAFL7eN3JKKKHRqFQwO7uLiqVChKJBAaDAbxer4CPzWZTknUynbrdrjBveJ1YoPE+GwwGNBoNNJtNkUCwq879bjKZPKbLyyJJrVNWU8KLxSIWFxflDJxMJmi329K1WlpagsFgEHrmZDLB9evXhcK6vb0tus/Dw0MBtFZWVjCbzYSazffIvYKAoc1mw8LCggB57ByRCVGpVCT5JsXVarWiWq1KkkpAlfmFzWZDq9WSfbhSqSCXy0lxQto5QTgmy7VaTa5BJpPBZDLBwsIC7HY7stmssHG63S6uXbsmHSy+H+4n7BbSl4QsBYPBgFwuJ+ua+tzpdCqd40AgIB0to9GIz33uc1hYWEAkEpFuls1mQ7FYlAYG2R5shhDYXVxcxGAwENp3OBwWWioA0VBTVpjL5aTYJ7WZoAnP81QqJQAgu6CkNvN3zs3NoVQqQafToVgswmQyCSWeZxXPwWAwiHw+L+DTeDxGJBKRs8nr9UoHnPmf1+tFvV6X3OP69euIx+PS2Mhmszh37twJpgg799Sjl0ol2f9isZiwOekpoiiKyD3YfQSAQqEg8jwWdO12W3xHWATRqIr7Nfc2yqLi8TiMRqPo1sla0+l0IoHj+TabzYRqrSiKnHEs5AiisElkt9tFVsnuP6VhuVxOZJ8EWMmg47nucDgwHo+Rz+cFdOczSRYZ9yzKkugB0Ov1JDc1GAy47bbbxMOChTHzcOZsPF+Zq/R6PflvPp8/we4kkMHmFbXe3Id5//l1SrrS6bSAAgT5q9UqlpeXkUql4PV6xZeGeVYsFhO9tNlsxtLSkuT7lK1RvsN8MBqNIp1Ow+l0otvtYnd3V5hJPEvpczGZTBAIBER7TuCY7Aqu89FoJMA6gUgAAhIyqtWq0NZrtRoSiQQWFxelmdJsNiWXI9DEPZPvx+v1ngDm1R4AWjy58YSK8Hg8LlRvFjhEVKkj5IPGQpeHMwDUajU0Gg2srKxId06drJA+yETCZrMhm83KJsCHiEmXXq/H+fPn5fBj0cbXY7eDi1iv10vywqKfplzsnDDpo1abnSAmR2pKHQEI9eekOQq7KaSQsLNnMpmwt7eHYDAIp9MpxRg1ub1eD81mE8CxvpXoZKVSkQSU6DwBh36/j1wuJ0kidS7U5RKJ1Ol0cDgcmJ+fRzgcRrlcxrVr15BKpSRxpqQgEAgIpdLv9yORSGBnZ0fMItgV54ZEsOXw8FAMPrhpzM3NCe2VKGq/30csFhMGAQDZ6JkMUIdIRHV9fR3RaBTXrl2TQrbRaODatWsiKSBd3GQywefzSbcgGo2i0Whge3tb0EEmVwBEQ0M6K7v1vE40eTKZTGLIwsIhn8/DarXC7/dLV4ZUaFJS+bkpuXi0KcrGxoYcSAQNaNpCmiI7mlzHLHpSqZRsrtSDGY1GSWS5Xpm40ZgEOO4WMdGnOdry8jLm5uaEKUDUvVgsCjMgn8+L9pVA2draGqbTqSDtFosFuVwOsVhMDlZq4ug1QGoYD89MJiP0YpfLJWAfZS1kCrAAs1qtyGazsn5IqyOttNlsipyFwIpOd2xWxKKP8hp2O0mzJCWca4X0QdIfg8Egzp07JxTteDwuZlW7u7tyXxqNhoAQpOKT2k/wh8ZolMmwoC8Wi4jFYtLxsVqtJxgTBHu4H5Dqye6jurvK7pLdbhfZAq8PfRZIP/R6vbh8+TKq1SrMZjNOnz4tST87zSzsx+Mxjo6OTnQ1aASpLr4ByMHOezoajWRvZOJar9dPrE3urey6EDAi1dZms4lmk8k82S1kbHDN+Xw+eRaCwSAKhQJKpZIU5mRGHB4eotlsio6SyTG7++y6MvEiU4PsF71eLx4IBHTZCRoOhyLPyWQyQivmmUGJDWmrZLzwHDKZTOKPwuvKAp/Mjel0KnpVl8slzzufoXa7LUk5/SfUTK9er4dsNotgMChaQaPx2Hhrfn5evo97iVrX2+v1TgDfinJsanbzzTcL4wSAsNd4HlM6w7ObwASp33Nzc9DpdCc01Owm0zhT3QUHIBIuPgOlUgn9fh9OpxNnz55FJpOR85vFkMfjQblclk4884nxeIxqtSoGp/Pz8zCbzWg2mwgEAnKNSXl9dJHM7t5sdmzmRS0uNfnU81Jjz/2fHVgm/KTwulyuEyDKeDwWg0LSS3O5HMrlslCpWbxxnblcLuzt7Unhyz2EeybBCu7pZJ30+31hwVGHPZ1OEY1GRdrmcrkE6CTQzU4ez+VOp4NarSbPE8EDaqqZH1osFpFFsZOnKIrs17yW/F2Hh4dy/dnF5N5IhhELE7V+nVI9teY/l8tJocu1QBCV+whwLKmjwXC1WsXKyoq8h2w2KxR+7ocEkJm/8O+k/ZO5wvvmdDrR6XSEmba0tCRMAwL3k8kE8XhcKN1slPDMo6cM12SxWBQvCYfDgVarJcD0/Pw8LBaL7APch8xms+RCiUQCS0tLuHbtGhKJhEj6WNQbjUa5V9z7uAfx+nJvoZzM7XYLNZ/SJebnBEVoDs3uOYFkh8OB/f190aAPh0MBE91uN2KxmEgfmD/QZ4Vge7vdlqbFeDzG0tKSSFsJjpNZFAwGsbCwIOB6t9vFAw88gKWlJaRSKezv74vnwOrqquzpBNLY5KCfEesc5lk8cwDgS1/6ktwDsqDIHGTtVKvVxAyQTUDuF9xri8UilpaWhF3MHId68rm5Oezu7speqsWTG0+oCA+FQqLfdrlcksSx48TuBvAI3RCAFK5Exok2pVIpMWlot9vils3OGPWCRFyZ5PGg5sGq1t7QwIwHRzQaRTQaxe7uriTcqVRKNFZ8bwBEC8HExmg0YmdnB/1+XxJUImP5fF4KcLpc0uWbzqbT6RSXL18WrSZfg4cwdd7UQfGh6HQ6SCQSGI1GuHTpklA5eV3YpQEgBwt1h8ViUYoGmlowlpaW5PDn9yuKgt3dXTHrIQCRSCSQSqWkgGRysri4KEmZ2viEyZ/f7xfEWa/XY35+Xjp9TChJe2SCw7VBBgMBFlK+2clXFAXRaBQ2mw1f+MIX5LMHg0EpkCwWi1BV2XmhkRgZCYqiSDJLLTC1TDygqHMjxZldO6KJwCMUIh5kLGCpSaX7LynkDocDHo9H6Oz1el0KoGw2C0V5xGW2VqvJ+2EBQXS81+sJ3Y1ddP5O9ZrgOuZ7p08Bk0uyD3g4EhyhEUk8HkcikcB0OsVoNEK5XBZNPgtxnU6Hzc1N0eARiMpmsyiXy/LZadxDt2I+L+wyUqdarVYFhKrX68L6oJHL0tKS0CrpfE+GDPXCer0epVJJijKdTicsBhZ0Pp9PElB2UkhbJC2N5nfsMBeLRSn8fD4frl27JiZMTNxokMS1TOZCPB4/oaMnnY+mj1z/3F9zuZywVObn57G7u4tAICAdBT6npL9xXzCZTALCMPEhJXZvb08oj0zeWcSSnk+ZAl3fjUYjlpeXJaH6p3/6pxPXcWtrSwou7hMsCtSFt5oOR1CINF/uify7+mcURZEiV931JI0POC44CoUCptMpksmkuFMTvGg2mwgGg5KI7O/vC4hK6h5pkmqjMe4jFotFOqOkEVKG5HA4pCinxIKdPhrq0Gzp6tWrUlzw2tN8jGBfsVjEcDiE3+/H7u6uFEecGEJ6+crKCg4ODuRMZBLIs42sm0qlIiAHWSDs4NCYkvpNdu2vXbsmU0dIaUwmk2K2tL29jWq1isXFRSwvL6PdbiOTyUBRFOzv72NzcxOtVgutVkvOheFwiL29PUmoCSTynOc+SKql2uiNTKxSqYRIJCK+NEdHR8LMo/Qkm80Ky4dgx8rKiniWpFIpkb5RqhQMBnH58mWYTCbp2uVyOQSDQeka0p2cWnkm7txHfD4fms0mZrPjiQA0SSMLh51PArS1Wg3JZFLWNp/Z6XSKK1euIBwOy++YzWZCGw2FQrLXEzSPxWICWrBjbLPZBESidp9nqt/vx8rKCkajEdLp9AndOP0XWDxyzbdarRNgaLlclmeeFHnubc1mU8AJynKYF7LDxnwiEolgb28PdrtdjEBJUadXAQ0n2dXjNacMgoD0YDAQTTzlWNRQMw9Ss5BoapVOp4Xpx2Ke9N+joyN4vV4BIWjkyPtPoJiGizybyFzo9Xpwu93wer0YDAbCYuPPV6tVAMcGc2QSch2w6UJwmF1nnkeklhOA5ZnAPCCXywmDi133pz3taQKibG1tSZFLzwlq73u9njSL6PxN1mKlUpHpAQRVFhYWBJRj15aSP+ZcPNfZZGEBznOapq2Us9Atng7c1WpV2Lbz8/MAIHsdm3b0sOE5sbS0hL29Pfh8Ppw9e/YEE4sMDDInqdkulUpYWFgQyRANlUnPLxQKOHPmjLCoCDpzrZDu3u/3EY/HhdVLtgxzFb7+qVOn8MUvflHWJT0tCB7Rpb1er8vzTebS5uYmwuEwtra2xJCYE3aMRiPOnz8vLBues8lkUsB4NrLIQGK+w8bM4w1NE/61xxMqwkndZlLPYpJdCG7gRJR50dgVBx4xUcnn85jNZjIiRlEUKTZoakEUjg7LRHLY1SYiRH0zdSztdhsej0c2Ci5A6qmDweAJTaper0c0GhXKULVaFdfsL3zhC9KNZKG2v78vxhrsLNKYhAk9N1r+frXWm4YJPBiJCNJ0IhKJIB6PC8uAG9toNML8/LwcyKTvApDuDwApmBwOh9ApmfCzUzEcDnF4eCgGHdycGOVyWRzwc7kcPB4PWq2WdAg8Hg/m5+dx6dIlZLNZnDlzRmhNRA2px+EBzzXCzgHpOTRMI6Di8XiksKZ+dW9vD6lUSqh5fr8f+/v7YhAFQBBr9UHH4oqunEzyqdVyOp3wer1CmaQpWTabFf05i28ig0TFyf4gfYldcXWhRdo4NX6kF8/NzYmTu9/vF516PB4XJJpFFItKdTLNYoPdCtIuibTyGWQxxjXHtcaNnIAaPzfXY71eFxfPTqcDn88nxiRqWQQP5kwmI5rqfD4viRKpjTwUmHioKWA09eL7JZ06Ho+L0duVK1dknCA1nfF4HMlkEiaTSbTp9J7gs8cEjU6jNItRJ5iNRgNra2uIx+Pi9D4cHo/R8fl8UqiQ2nx4eIiVlRXpatXrdayvr0NRFFQqFfF+oBnNrbfeKt32RqOBaDSKcrmM3d1dJBIJ+P1+lEolHBwcoNVqiQyBhRlHoqn3ECZCvK8EUGisROCCCRlwDGosLy/j8PAQ1Wr1RAFvsViQTqelazIcDk+s0Uwmg/n5eQEUub74rLPzqKa5EXRVU/b5PSzieTYwMSd4we8bjUaSOLOLzXXLn2eSvba2Jv4DLJi4vh0Oh3TY2fkm4MGRbaQ9kyVwdHQkjAh2+2jSyddmZ4uFEjteBFBo/MYOBvcdFvGJREJMCdnlJUPGbDbLFBCO7JzNZgI4qkdD8lzW6x+ZVkAKJZM/MkQ6nY7oXgeD4ykl3Muy2ayAqdSNqin+hUIB+/v78Pl8CIfDuHbtmmg4yVJjt5nv/+DgQNgIasCV65SgIEFs7g0EaieTCWKxGGKxmLAhCMju7u4K46pQKCCVSgGA+GdwfZhMJgEF+dqUl5HWGgqFZJQlu/7s+vEsiMfjKBaLsNlsklfo9XrR6+v1erkXXCM8v0wmk8hUer0e7r//fsTjcczNzcm1LhaL+NKXviSsL4JCBEScTqcAfmRGARDTVHrc0Amaa5fgJT0m+D0ECrmmOaKRn/fKlSsCFLD7RmCK94nnutFoFKNJglF8f6PRSIBtRVHEU2JtbU008HT41uuPPXNolgdAzh3uC8yrCK5RRuR0OmV0F8+d6XQq0i82cMguo7EmC0iyeiwWiwB5oVBITPho4EZgieuIdH2yA5iv0WGeYCLBNE62MJkeGVWp0x2bh0UiEQFlKFVkl55nJaniVqtVaPcEMmh4SMkh2RgsdFkQEzzV6/WYm5sTthelTQR7ufesra3BZDIJQM11TBkc7wvPRZPJJM8W9z2yJMl8YjeYnWz6jQwGAzEbZd5GSQEZTZTBcd8YjUbCsCP4SHkjmzh8lsg25Nool8uYn58XIIX5OPM4v98vBSp9NfjvNGgLh8PiZ5TNZtFoNPCsZz1LgAVKCvgczc/PQ6fT4fz583IfCYCpZTpW6/E4TYJAzC1Yj1SrVTG/9Xg8GI1GyGaziMfj2NvbE3CTa4bnLXMNtbzD7/cjGo2KX5UWT348oSKciRMTFiLj/X5fRrGouyFqKheAx/w/RxeQKkS0JxAIIJVKwWw248KFC6LhZWE3HA7hdruRSqVkXmMul5OFyffHQgY4NmGIRCJotVq4fPmydLa5cRPVojP29evXRdO1uroqI5uo7ySlk5Q+Xgcmm9PpVJIFXje6WrKrx89DQyMmc7VaTQoKAJIoTKdTcXikFm11dVWokkx0SFGhCR0Lk6OjI9Gq0XCDhQ+TeBaijUYDly5dEnff6XR6woSFtDiTyXQCsaUpzXg8RjKZlCKAmwvfV7VaFbQPgGiwiEhyQ2m1WsKwIL3eYDDIaCC9Xi+bO5Ncdmx5+LGjZbfbsba2JgZ4TLA4/5UaPzIkSGMkVdtsNovOmiNoiKCrgSkmluruIP/QuM1isYgGjAkWrx+LVW7A/Byk53KDbjab8vPsiHGtsUPBA49Fqfp+8GukU7M7SQTW7XZjf39f7hEPRM7TphkOdfvhcBj5fB7xeBzAI8ZINBxR08yYZLG7yYKV3WjqWEejkayFXq+Hq1evyrz1g4MDNBoNlMtl6aCRLk2qIQEI4BEDJxZFlHgYDAY5+Ai20Eyo8fBYKiaV/O9wOBSNLt1Teb25X+3t7UGv12NpaUlMhTgjnXOo6TYPPDIDNpPJIJlMSpfl+vXrAjYSJMzn8zIKiJ0xJkWklqslNqSfsihkAc6C12QyYWNjAw888ACi0Sji8TgWFxelW9t42DWd+ylpwlz3BDCZvKgZOLwP3P/JNuA+yeCZwd/P+86En+uUHVYW3Oq9kgU5uzB8zvx+vwAw1Kty7atndRPQUTu2k+ZJczruIclkEltbWwAgNEZeLxpZEog7ffo0gEdAJ8oLSBflOqHHA7t/Ho8H+/v7J4oeUv95bUKhkOh8Kb/qdrsyn5jUaD5n5XJZRuexWzsej8VcVVEU3HfffQKw8T4TrO50OgKW0uzR5/Nhc3NTnlEAMu6wUCjI2cwiiAW4ojwye1xRjudF+/1+HB4eCrAGQEaFkmLLgprXUD37GQAWFxcFSCRLJBqNYjKZyDhTmlaSWaGWn9AvhaACwVqODON5TmPAYrGI5eVl6WSqNeL5fB6VSkX8cMhW47jD6XSKS5cuwev1CuDNXIBFChlfnU4HhUJBzlaeN6QGc7pFLBYDAMlZSGsmYNfr9QT8JjBAdhzPBV5zyomotW42m3C73WLKx2YAnxsAkhPwHtGzgTp+AoR8ZgiG0QhRPW7VYDAIwM9chQU2O89ks/GsUhRFniNOeaFr/f7+PqrVKhKJhLxnjv+kMR67/ixivQ+bjwHHHU7S9NkcIguC+xHdpyuVCtrtttw70ozppbO7uyuGX9FoVJiEbEpwLyXrhuemWs7EM433kd3pq1evYmNjQyQXk8kE9Xodq6urAtIQHGdOMRgMUK1Wce7cOZFoLS4uyvnI/J/yGD5zbFAwB+E95R5KgJjrinkkmaBs1tRqNYRCIQEabTYbGo2GaMitVqtQqe12u3i37O/vCyCvZn0AEMPnyWQieRxzagJT6rGTjUZD6gwW3hw1x6kDBJEqlcqJBhLZXNToc9+7fv26ALysi9gUYZfcbrefMFPrdDr4/Oc/j1tvvVWkG7zGrJGcTqcAST6fD7fddptMwKEWfTQa4cyZM2KKq2ZwMOerVCrikUDTwX+tned/afGEinD13Dmad1F7x+RJ3QHnAwuc7H7wawaDAVtbW9LdJcWGnbnt7W3ppNE8IBaLySYEHFPkmYxzQ2Snd2VlBePxGKdPnxZdBzXDamoZO+6lUkkSbSawa2trWFhYkG4fN5B6vS6dXCbuRACJwFHLTNRvNpvJRswNm/R5FoVEFPV6vaDO3ECoQ65UKjKKiwX9wsKCJK7s5LBbyYIwFovJJk5t3/Xr1+Ve8MBhp6lQKMi9p1nPdDoVF1UapqgPEyaeTqdTrgPNe3hNmJA7HA7RBJMi2O/3xSmaxQQ3/KWlJbn37CySokNtEJPqaDQq1G+ahpEpwcSN4yBIQa/VaphMJkItdDqdAi4BEPCBhSHNX0jBpl6LyK3BcDx/nqZVpGvytTn+bDabie6LdHR2tZk8cxzb5cuX5RrW63UBWth91Ov10rVg0s1uF0EWaqepNaLUg0kND6iDgwMpKkg98/v9grYykdvZ2REKGpFauq3m83nMzc2JCR7wyHzPbDYrX1NTADc2NlAoFHBwcACz2Sxu8+rRQuyI0pyMnUTqEjm7l07GZAzQ5Z4dKiZuRLxZ6KXTaUkg6/U6ksmk7EOZTEbAkKOjIwSDwRPXkFTQhYUFGI1GofsS+KC5i16vF8YFjVnYRSFrhTIGSjNoZsd1y2eV+4lOpxPHdlILKfFwu91SsDcaDZGRsCtEGQbp1JRd0O/g/PnzkhQyGWaiRTo5dXbc//l8PLozyC6n2gCGch92oSKRCJaXl0W2ox4JM5lMhH1Ed21eS3aHl5aWxKyMshkWJKVSScbh8NlhZ2o4HIrBF88tAMI6Ur+PUCgkxT67V+zCcjpBr9eT6877Chwzd+bm5gSA5f4LQBLjVquFhYUFKYzoYcI9olaryZQQgpJMXJnUUUdLCjoN4I6OjoRdpAbv+D08S0+fPi37OamKlPgQ4Or3+zLWi+uw8fDYoJtuukkkU9evX5e9glTVaDSKSCQihVuv1xPna54FW1tbAo7SsZ1+BCysWPAThCagwhyFYNf8/LzQcXletNttjMdjcb0naM3PZ7VaJanl+EOei1xbLGoIAlFmlMlkZLwUgR6O12u329jb2xNttNfrxU033STrRE3fZhFKMI17AruNLBL5vviMxuNx+YyUbVB7zrV4eHj4GDYdu2s8hwKBgIxsosEV9yaCRqPRSOR5ZvPxjPByuYxUKiX0236/j7W1tROSNprW0SiNoC0pvhx/xT2a+z1pvXwfwWBQfGtYONfrdaHeKsqxiSzBjFgsJsUuPwOlf3a7HZVKBfF4XHIJaqVJ2WYBQ8YFAVzOj2b+yvyDDDGuMYJAlBmw+69mIPI5oVkjcxh6CBGE2t3dRb1el+4sjYLp8cPJEhzTCkCo0TxHCaixuHS5XMK6IPOOAC8BWLIF6dXAPZ6gqlrGxwKUvhHM5wjIhsNhYapQGscG2crKCorFolxTSj0I7rL5Y7FYsL+/L1IGRTnW9atZAvS64LXnWrJYLDIKjxICj8cjcgBKcdhMASByFOCYjUNPkM3NTRSLRWk8sUbhc6coxyaA4XD4hHkh82BOM3K73bjllluQyWQQCoWEqUMvIjYjp9Mp/vEf/xHz8/MiUWQOSs8QtbzL6XRKnmk2m+FyuYQlSLM3LZ78eEJFODt00+n0hJ5xZ2dHqIZqiiELcjV9CHiETgQcozrUXVPDt7q6Kk6FRM+MxuMxHPv7+2JwxAfeZDLhlltuwdWrV2UBA5ADj/pqtWHMbDaTIv/222+XAomJDCmWTKA6nQ4CgQCWl5clQWOXWK03Jlrc6XSkQGaSrKZq8vqoO6TcrIh48jBjccUinCZc1I/TsZNjIHioMGEyGo0nRu1Q28L7QSBAnSirN8jJZIJUKiUzt4koF4tF+RykwBPUmJubw3333SfmW7wmTDaJIpJyxO4E0WtqnbhWKpUKDg4OcMstt8jnt1gsQs0hIsyDmlRkjm5iV5sgCWn11LsxSQQg6yeXy8Fut4vukRR3dsA5lgPACf0x1zXXlLqwIAWpXC4LfSyRSAgtjUUiDzQio6PRCOfPn5dudDKZRDqdFpYC1wgdf9n95rNqt9sRDAaFuUHAgbPVWTwNBgNJCHK5nOibWFxyLAtpumQz9Ho9nD9/HvPz84hEIrh48SKy2awwLkgz7/V68Hg8Qh/kXPhMJoMzZ87I/aZUQK8/djZW7zs03+HzTNCA155JfalUQiaTkeSBmjwWrDRDoWRge3sbRuPxvFYWOkxaiIB7vV7k83kBvNjlYZeVxSzHy9Gp1mq1YmtrSxgkTEgJWpAeyKKiWCzK1wHI/jWdTqUrTtR+Op3KzFOyWLwPz4Fut9tCqyfrJxgMyroEIAmtoijY2NgQo00mUXyufD6fACFq0xauMxZg7AKyaOJ+wmeHAIR6f+LzQaofX4PPFVkfTEqoMWYxwY4nZ87GYjHZjxVFERmIy+WS8YMsJABI4k19IYEztWHUYDDA/v6+aPRYUJIZweeS3WQ+l/zMlOrwtchqIPOHhRN1kM1mE+vr65LQszPJa8n9nt1kAsJ8Ttn1bbVayOfzWFpaEsowJSb9fh9utxuZTEb26W63i3PnzknRSMYX99t6vQ632414PC7eFt1uVyiQBBwpTzEYDNjY2IDb7cbBwYHQqgkoJBIJLCwsCACu1+tx/fp1cXAnE4jAA0ErjpI0mUy4+eab4ff7ce3aNVQqFQGQ6dqt0x2PCFxcXBT/A7Ukp91ui7Tg6tWrUjCwS8oijOuDAIfT6cTm5qYAXewKjsdjpNNp2St8Ph8ikYg4QLMoeeCBBzCdTrG0tCQgBLuwBKAJhqhZYjTzY37B/YwdQppQshPZbDYFVKCsx/vwNA7KGei6TTdxfh7uuTQE4z3gnsrXpwEu5UaUTq2trYnpIQts3pujoyPpkrJ7xwkh6tyIkwj4vJDSSwCcgAZwzHhKJpNQFEU8cGg6Np0ez1on4BsIBDCbzaRIMRqNMtaV+lmHwyH5HZkSNOfie+BzQMMwUvQJyhgMBtEek61HujbZj+yO93o9VKtV7O7uCquC86k5RYPmbgRP+JwROGQhqSjHumiyQCgpotcK2Wi8zwTbeAbQgJefDYC8NmUk3M/JLlLL+Lh+eb+YrxMIJfvV5/OJLI+SQgIxhUIB+XxePIlKpZIYO2ezWSQSCZnukc/ncdNNNwlVn3sfn8/Dw0M8/elPF7Ya7wcZSPQaYKOBeTCbVDQe3tzclHw2EAjA4XCgVquJPp4yw93dXVy4cEGM90h739rags1mw8rKCvb29iQPIROCLGHKH9hEoz/I5cuXBUyiLFZRFMzPz4svUafTQTKZhF6vh9/vl2aVmnnJpht9DIBj885IJCKeRo8n1HXNkxX/WjvzT6gIL5fLYmZBU6nNzU2Uy2UZTaGmmTH5Uhd2wMlCnX/PZDKYzWZYWFgQRBGAFFTz8/MYjUYyv89sNmNhYUE6lkzIV1dXsbGxgaOjI3kId3Z24H14DBkXGjd+Fj38GpFgFv7T6VRGafABMBqNoj/mLNFutyvjKrg5qTuTRP7VSSepMzwoWNTTEIkFOTUvTOz5/liktlotHB4eIpFISAeDdMfpdCp0m1KphHK5LIkr0TAeCGr6+9zcnNA+SZ0tl8vSZeLr8L+ky505c0YQcoIVOzs7YlTCTj8Pk/39fTQaDaE80aiIs9j5WRXl2NVxPB5LMZfL5aRTwa7deHw8no2b8tzcnNDGuIZ5X9hlYZcoFArJCBRq7/jasVhMOulMhKjXUusaWThTD0ZwgA7J7CpzzA07aXQQ5ZoHHhkHyLXPBGQ0GuH69evCqiDwQUTVaDSKMZHZbMba2poUlERlWQwzeSd9keOm2FHkdALeh/F4LCOGVldXxRF4bW1NRm4QZOGhnk6nxeSD64zdXGqnaGBFuiyToJ2dHTER4X12Op04ODgAANHPUffEA53mM0yqOQuXRX6r1UKxWBQwkcUeCyDuQ+wkUjfJAvfatWsCUGUyGVljvJccqaIoiozJYcHN7jxBLx6a7F5y/jiZAEx4iVYbDAYxZGKByGdePcKHRSDnjFLvODc3B7vdjt3dXczNzUmSTr06k1q9Xi+jpkKhkDwvwCMHIj8jAQUaBDHp4vNFmitlAkzG2IFlYjcejyVJ4zPF9bS2tib76WAwQD6fF+01pQvck2q1GprNpqx57vE0tGo2m6jVapLcTiYTmbW8u7uLfr8vsgiO3SN45Xa7BVykARk15u12W1633W5LkU5zKnY/DAYD5ubmhHlDQIU0ZRqEeTweFItFlEolASjZYSbt0fuwszsLAu5ZNMeq1WoypiqbzQoDZ319HYVCAV6vV7r86oKIbr/Upo7HYwECCF7yPDIYDFhaWsLKygoeeOABKUJ4phiNx6P36LzLPYSO7+qRedzDJpPjyQHUPpM9wcKFQAR1oGR5zc3NCVOBYx0pNSPD5dSpUwJCkYZNEIt+LC6XC8vLy0L95n3hNWYHkmcmdfg0kaO2luPVMpkMnE6njIHb29vDbDbD+vo6QqGQAFGDwUDAKF7/TqcjexC7WQR3aHqq1z9iLsnOJYvq/f19OaNZGNL8k8+HWpLFCQw0hppMjsfGceIDKfykUtOHhHkLpQw8yzjGlp05PqutVgvz8/Podrsic6Chm7qos1qtAjIwp1EURaj+0WhU5sD7/X7prrvdbvHP4D5HJlQsFoPFYpFpJZFIBMPhEOFwWMZLkWXR7/exuroKr9crzxcZm0ajUfYSdXeWez6ZmfQQoKcGJUyKoog8j2cTgRl205nfME8gI4yFFO/DxsYGAEi+RTnao3MKyrcoo4xEIqLhZzG6v78vZxQle2rmx3Q6lbXH+8SzgIAR7znzzdFoJOuc6y0ajQr7hrp/SmkcDgfOnj0r+RObFWzUqEER4Nh47+rVq4jFYmKcnM/nsbm5iVqtJo282267TdYTC+/9/X1hpBFAKZVKoiPna8TjcclvKfVhjmw2mzE3N4dms4lMJoP19XW02+3HGAhy//Q+PBlKp9NJocwOPXMm3mf1uGfOEGcT68477xRghPuG2pNke3tb3OQJcFPWEwqFZH05HA7ZA8j20eLJjSdUhNNRl5stjbvUFEO1FlBddD86+DW1xpCjkGgkoS7W0+n0CSozEWdqg1hIshvo8/mEWspN2u/3S1eUYwoURZGOLDcTavZ4sDgcDqHndrtdofCQgsROADdXu90uCevq6qpQiba2tqAoirwXJjmkdTMRY8LDz8fibTw+HrcSDoeRTqclUaYOicgckz2acBAU4cxqugP7fD7RBao3QZrG8ODixrm0tCQbALvITqdTDJz8fr8k1DwY7HY7zpw5I1p/btDsSrNoisViaLfbouk0GAwnEjzS19ktGQ6HsjHzYGZyVKvVpBCi/ooFBZkLpIQBkE2YII3RaJSRZVwP1WpVEt3JZCIdQWp31KZTZC+ou4X8TCyKSNNSFEXWLX/XcDiUooLdNz57AKRLAkA6VXR5JoDDoo3mhKRc8WChcUuxWEStVkM+nxcwQVEUSRbUdETqTjlGg3p9dgRo4sJO2fz8vKzt4XCIUqkkXREyMmjsotbQc7Qa1+nCwgLC4TAymYw8f+yGkMXAgoc0Pib/1LGyg0M9F8fQsRtIUCwcDssaTCaTyGaz2N/fFzYEAKGSca8i6hyNRmVmN/WXjUZDRpYBx1T8aDQqewrXjqIoIimhdovJLF2CSXljoUb0m4UJDQvtdrsAHcAx/ZbdWdLNdTqdjBjc2trC8vKyFMIEDvr9Psrlsvxete8BQ1EUkXfw+nKd8b1xn+T38/dwv+ReysSR5jDsCNIgifeUawk4TlCi0egJIDORSAiFVV3kMvnmDGzKF8h2IluLXThqrRVFkT2FzwPNJrn+aS7FvYjO/qTlsrjq9/vCKiGgQzAimUwK7ZZj2GhQxC4h5REEYvj8+v1++Zx0VOce7PP5ZC2Mx8cj3FjY+P1+AZKazSZGoxGSyaQUWOwgJRIJTCYT0R/GYjGYTCYxS2LxxeeAtF9FUVAqlXB4eCjAJM+zubk5ATp1Op24eJ8+fRo+n0+YOblcTozs6KPi8/lkogTvO/+NHiGHh4fS1aR29PTp03J2cH+nSSOTZHbwOaeb949FBdcwizpKN7i/EwDnNeM6Y0eWZyfzkkePlCJARVkR6d4sqAiKAhDWRjgclm495WYcX0UgwuPxiIyqXC4jl8tJHsW1ptPpBJDhfkrpEoFs5hikPCvKsdFao9HA0tISvA+777NDOplMhP5rMBhwcHAgDQNqyrn2QqGQMJeojeZrzc/PQ6/Xo1AoYHt7W8Cj2WyGg4MD6b7y7H70XsUCYzQaIRwOo1aroVarwefzydql9rzb7SKZTEruNj8/j0ajIbkH8zGelzabDcFgEGaz+YRMkec986JWqyXruFKpnDgTBoOBsLyWl5dFTkEJRq1Wk4I1mUwKmGuz2YTazkIMOPZwKhaLwkIj1Z6NAu5f/Dnu5VxXzL+3trYQiUSk8CbATdCA4DO/n/IJ5sks+nlv2Oz60pe+hNXVVXn2KA8gU4DyAOZfkUgE4/FYqPQsyimdrFar4oCeyWTEN4pSBwLIZCHxWWQzzuFwoFwuY319XdhGw+EQX/ziF6Vpk0wmRdbE0XQ8SyqVCubn52UfYGOAUgnu92yQEDTvdrtIJBIol8uo1+uyl1PmoNPpxBjU7/cLuMfxYnwe6aFQr9cF4CFDg672as8E3i+a4nk8Hly6dAmnTp2S8YBaPPnxhIpwg8EgCDY3c4PBIKZh3HC5KBl8MIFHaAtqKjYXmtFolNFGdEBkcsffy8NvNpthb29PECsWY1zUpG4vLy9LMU2UioUlE0WbzSZUMR6m6m4mu0qNRkPoGxznwC4URxPF43GcOnUKDocD165dk+5PuVyWA5UGJEyu2NUm/ZY0mclkIoZzpMSTfry2toZms3miM57L5USrZzAY0O/3hZbFgxGA6KE4QocPW6/XEx0YNWY0KolEIkIDrdVq2N7exrlz5wRJZzLHz6OmYbZaLdGYUaPObjqLy16vh3Q6DQBYWVnB0dER6vW6rAsWiMViEcViEZFIBCsrK0LJ4giJfr8vhlmk4jCBZiHCziOLZf4/k24yHhoPz10kLZbUYyaXRHsJXrBrqabFc+Mn3YvFM+nLRLa5vhk0weLaVxv98Lljt4Ez0EmBVXfQh8Mh2u02XC6XsFW4QasphkwAptNHXJxJa2T3jbRQrmPSuKlzttlsUjiSHt1qtbCxsYHr16/D4XBIwsouZzAYxPz8vFwrMjs4aiSZTIr8gbO9aWgWiURkpBM9FabT4zFfiUQC169fF7CJ9HZ2SQ8ODnD27FmEQiEAx+67mUxG9Kpkv4xGI5RKJaysrMBkMmFlZQVWqxU7OzsYjUZIpVICnuTzeQESWQgzGSWNmuwDmrOx80H9IcGAeDwOt9sNu90uySjXxGw2k6KYtDsWeerCj/tJqVSSBIGFInBMBz08PEQ8Hkc2m4XVasXm5qaAR9Pp8cgyJpM0n1Gb8LBg4B7PvZlrVE3L5trhWcF/5x5rt9uxvr5+ImFiYcBCBoC8xvr6ulB76apMM7dutytmTR6PRzorLMYpN2CSS7CBfgn0g1haWkIgEBA20Xg8xuXLlwX4VEsVms0mCoUCFOXYw4HPFVkcPp9PXHm53zJZ5eghyoz4vAMQLxPSXCk3MRiO59ZS78vzj4ZC4/EYqVRKDIwo+yFb5ejoSMAiylQ8Hg9CoZC8d8p0WEDw3nNcEP9OR97h8HjsGem1lCgBkPOG99xiseDw8PAEmEda9oMPPijrBoCAn6Ts0piNZoa8HtTYsrBkcas2YCwWi8J24brudrvijJ7L5TA/Py/PFX8X92OCTrPZ8YhAFlyz2QzpdBpbW1tS6AeDQdGZsziZzWZotVpYXFyUThbZMgSo+L3MHehs3Gw2hX7Mgpk5Rq1WE68UegWok38+s7u7u3ImsCvP4t3tdguYGwqFZAZ7MplEJBKRLjW76GQjzmYzrKysCJWdBlRkRI3HY9G4ptPpE67kBwcHmJubk0kzBMkBSMFL40NKggieUhrFJgOp4/SN4F7M/Yz3kCypQCAg3Wyfzyc5QSqVEqYJcyjgGJjwPjxNpd/vS8OEQCJzWafTKWunUqnA4/FIzsPCh2BUsViUbjML+cFgIMy/QCCAYDCIbDaLbDaLjY0NNJtN6Vi3Wi0ZpwdAGJbMQ9gQIqOIXX4yTnhtCGbE43HJS2gcTIBVbfiZy+WEicZGAt3bWYQStGSzh++xVqvB6XQK44emrNxP9vb2cOrUKblmdJanKd54PJauP/NImu9Fo1GZqsRzmWaZOp0OCwsLwn4AgPPnzwN4BKQpFosnwIbl5WXR5odCITF0Vd9j0rtZyO/s7ODmm28WZgynAZGuznWbSCRkwhBZSIFAQCSJxWIR4XAYR0dHwio0m804d+6cXCs2ZcgSu3btGiaTCaLRKHK5HPr9vjBlFxYW0O12hb3CfJFeWYuLi2Lsx8bB4wmNjv61xxMqwqn/oLaw1Wqh0+ngjjvuwPXr16VDRVSMwU4scFInzr/zayy4aOTF8WKkfDAZBCCbGAChcfJQIULJGY2kjvAPKeikXRJNpvspAFy9elX0Fizs+KCRlszPRhSa3W0mZPPz85LocENbXl5GOp2WDYsmPkz4+X7pRE7DuuvXrwt6TgBAPQ9RzVDgxsBueKPRkHndRA9JeSFiTNRueXlZDjkaF9Htk9eOXVJ2HNiJY+dKfX85souJ0dLSEjqdDubn52UjJF1Grz8eOcJil7Q3JiVMUJj4MFHl6CAiqg6HQzZLXvtcLicGFDyA2fVSU1XZiWy1WsjlcieKYd5vJj1qzRA7tASK2EFXSwj29vaE6qTT6WSWut/vF0fjRztQ8/+p/eXB6nK5sLm5KS6+TJbVmmEeXNS8kfZNt1AmPBaLRZgD6pnM1EafOXNGHGXZWSQTgO8HAJaXlwXgYYHMBOjcuXMn9H+pVAqdTkeMcdRGNTQ2IdDE7hTBsn6/j16vh8PDQ9GJU18HHBcIg8EAFy5ckOdtbW0N7XYb29vbODw8FMQ/m83KKComNuwIbG1tCYuA6LVam6Wee85EjAciNd5kJLD7TIAQOD5UiEwnEgkcHh4KmJXNZqVrOTc3J4Uu6cDFYlGYHtxDC4WC6Hh530klZoc8m83KM8J7TIMZFoAswOgQm8lksLCwIAktP6da+801re6oEQwgjZF7D39GfU7QHZZ7vLqIZ0eCY7SYbPAa8lmkfq5UKp0AbZjckbq+v7+PSqWCxcVFAdQ4Eo4SHuqNj46OxByQ+wDpzXq9HgcHB/LMkCYfiURER9zpdET7TMCLBolMCp1Op8ikeH1YlLGY4jk1Ho+xsrICg8GAvb09OcMCgQCuXr0qM4sB4GlPe5qsZwKNLL5PnTolI4GGw6EYN3JPVifLs9nxKDZ2brPZrMxr5nnA/ZqTPXhO8Z4T+OHZUK/XZU9jgUDJDynDzDlYAAAQEJxj1chaowyATsbcU5aWlsTEtFQqodlsIhwOC8BM5g33YYKDBCLITCMdl3lKqVQSE1KO96L0YWFhQejyFy5ckGL61KlTsmdms1l0Oh2kUiksLCxIscH9TC2xIe2dgC8AAURJf+Y5Sv222ockEAhAr9dLHsSiiaZpZBmQuqoeKcl9Tq/Xo1KpiFN/s9kUHwZ2EnnG8/856svj8YjJ3GRyPFL0ypUrIv2w2WwIh8PodrsCNun1ehwdHclnJcAGHJvIqbuvHDlIoFI9BovAMnMsysN4djMXVINM/HxGo1F08WQLUiLIPIeUXTJa+IwR8KLRLBsRZFaoAaVcLiej9WgkSHmRmj0IQJhklGzSCV8NvPC54XWlYSWNCcmM4SQdsuXUgBFwPGEgn8+fAPHn5uZQr9cFpFYXmXy+SVmnZw5BGO4rJpNJJnuEQiH0ej3pfBOI4eisdrst74naec6C535Mxil9aq5cuQK/34+9vb0TgD+d1dXyS1K86dlwdHQEt9uNvb09lEolpFIp8Y44OjqCwWBAIBAQ0IuUdAKelDvyHhcKBZHH8XmhJCsQCAhYQFZDIBAQwNBqtSKdTgtATh8LPgObm5s4OjqSM5H5L5m+0+kUR0dHSKfTMt2J5pk8a9lIoOSE54F6zWnx5MYTNmajtpIaxkajgdOnT+O2227DP/7jP8oDzMKbhwYLbf7/o0NNT1cXMtwseJDR4IBzFvmzTKppOsHDm+g3D55AICD6cBZcpEnR7dXn88n7pRM5ndsrlYoUYbFYTBIoovjUOrGbE4lEUK1WJXliApzP54VuRu2TxWJBMpkUvS7pYna7HTfffDOq1aok0blcTg4sUo3YKeImwIJxNBphd3cXnU5HUFl2zkhzJa2M6KJa70azJ26mTN4bjYYYOlAbTUCDVHrSsUiVJ1WVxe90euyOqza8MRgM0vlnYsGkhtRoMgqsVivi8fgJczmOlWPhTF0hNYhMGsgC4Egzo/HYyZqFMA86FheUKLCrzYRMTYUnsEH3WhY71GuSmmSz2RCPx1Gv11EqlURfznXPZEEt9wAgPxuNRrG4uChoKdcV0U12i9SUcnbFLBYLDg4O5PMFg0H4/X7pXpDKSW08UVXOlSXKziKTQBHHb/H5KRaLInkYj8cIhUKSmJNeRaokQS7eW1LbCbDRnZgFOq+3uqjjNf3c5z4H4FgvTkrt3t6e7COkMBJQi8fj8ndSzrnfUCO+u7srPgNcS2fOnAEA7O7uSlLHopv3jOuXSQjXKAsTNZiUSCQQCASwt7cn0oidnR0Z7caEgmBlvV5HtVoV+i8TdnYjTCaT0C19Pp+YCwYCAeRyOQyHQ9x8880itSEdmv4Pq6ur2N/fl64UAQgyhPhfteyIoBTwyEhL0ljVXU01GMu9mtRv7iP8vWazWWZbU+sGHBeIGxsb0skCIHtztVoVmcN9990njCU6hZM+GAqFMDc3h6OjI+nOEThj1zyVSompz9zcnLj+cx/Z29uTBJbMCv7+Uqkk4O3a2prsC61WS+iDHBlpNBqFjaW+jtwPSNkPBALSaWaCyYKUOkpSnjmTns63fr9fGCdqZ+L5+XkoioIHH3wQtVoN1WpVwFCyK/g+d3d3ZS/j2ByTyYRIJAKdTidsrI2NDQEyeY/Y7WVRz0KYZxqZPvzDs5SaUpo1kfHGoppTK/gcUKNN7xqfz4e5uTn5eZ79PJtomEgJDJPQfD6PQqGAjY0NOS+YXA+HQ1SrVWxuborHRywWQzgcFpO/paUlZDIZlEolfOELX5COKKmtTMy5ntk9VHfuee4QQKVRYb/fF5YPgYDpdCqMMHYb+btYyBPMIihEdhHvHz+f3+/HaDRCIBCQ9cgmAYATTt70H6BztMfjEQM4gptkgVitViwvL8PhcCCTycBsNuOBBx7A8vKymHjyjCC1lkAaQUayL/k1Pvt+v19yB0q1CPLn83lEIhHJB1kkV6tVeL1eVKtVkehwz2ODoVariQSFI/dmsxlcLhcikYiMTuQaVYMn9JzodDrC2GMXnmDpYDBAJpPBdHrsEE5pCaUt1IbTzI7FKbXsHJ/IHI97OceeNptNoWXTv6jVaiEQCEihSGo28zpe32q1irm5uRNspmQyKbR6MlHI2qPpJXPQRzfcmF+S7dVut+VZ5dx3grF8z+rXZVOO42W5jzLnqVQq0qhgXsVC+OLFizJhaW9vD8997nPh9XpRLBYF4E6n06jX61hbWxMmDf0BCG6xkKZ/zmw2O2Hk63K5sLu7K0xM1h3ZbBbdblcMUDnJR6/Xo1gsir7cbDaL4SvXKc0zKeHIZDLI5XICJhPcHQyOJ0vxvCO4dnBwgHA4LCxM7nGlUgk33XSTNNGYX/As0+LJjSdUhHNepsViwRe/+EUZlXH58mXcfPPNKJVKuHTpkjys6gcQOOmKDpzUjKuTMXXnUe26zq4nUeD5+Xn5Huo01b/HaDRif38fOzs7cgh0u12hi/GAIxWHdDJ20ti9oAaR1COPx4O5uTmh3ZF+GA6HcfHiRSlevV4vMpmMINK7u7tiREPDK3aJCBxwlqbNZpPO4mAwEC27xWJBoVAQFI6dF7VmGICMrmAyw+91Op2Ix+OoVCq46aabJGlhEcxCgcYu1AwRHeOM09XVVZhMJun0E80kCBMIBEQfpS682u22oPVEbev1OiKRiAAJHHnFopRJI816OD6DBzYPKo6eACCdeo5x4PVlQkwUmNQcvV4vAAfXkVoyoabsMylUr2O1TILaPgCi+6IzKg9vuh0TUOIBxo4U0VpulgAkUbTb7VIQTqfH45Hq9ToASDeF/0/UnoBHNpsVnRCT1m63i1QqJQcArxe7F3QsVZsQpVIpMfijy3s2m8V0euzUTUYJ3ZkbD4/Lok9DJpORbozNZhNNuvdhh+LJZCIdbI54IQOEejxOBchms6jX61hcXJTrtrm5KWwOjv8hKHbrrbcinU6LSRIZBLFYTCQuBHPIwGEnn9eHXUrS8Xlf1bpRHmbck2ieRLkJC3SLxYK9vT0sLi6KnwU7IATSut0uQqGQMIXU2nHeGxoB0ZWdoCk7qExiqH3k85jL5VAsFuXQzWazSCaTMBqN4njMLhBlLfv7+9L1IgKv9gbh+tPpdFhbW4PT6cSlS5ekwOQ+wU6p2rmVIAYBKBYOfK64hlhs5vN5GRm0t7eH1dVVYU7weVSUYylHLpeTLhG7HyysaNJGqq3D4RAJQ7FYRCAQkM7NdDpFKpUS3R69LWjOxUSXSfX+/r4AmAaDQcCkpaUlkfiQIkqKIN1pCcJwT6fTNRk0vV4PBwcHov07deqUyDTIQOD+R1Mj+nwwySTASSkEzRS5r7FbTACOIwXJciKjgkWYwWAQU7tgMIhSqSSa5MPDQ2ErGI1G6eKxu8g9zOv1ChU4l8sJUM1km/swAZtSqSQyGxp00nyNdFk1LZvTRQKBAIrFotxLt9stiSu1lxyVRABhY2NDjC+5RrhvKYoi5zH1lkyG6evAZFrNXFGDryymCQSx88d8hMUdO4+kbAOQ9QxACvRut4tisSgdwFKphPF4LJr0UCgkLAPKhAgQ0TBqOp3K/HXKGkiXn06n4opOoyed7tj9n9IZFu06nU7A0UQiIeuMpq88k2OxmID9BIJms5lIdlhU8JryOSRDQO2pQ0kGzTXJful2uwiHwyJP4fXnXkqQi+7iAESqRuCDFG2yashopKyEemJ29L0PT2oh2DEYDGSdWCwW+XzMGXjmc29h04OFLoFD5rMLCwvI5/PY29sTMCSZTEo+yiLVaDRie3tb7gXPMgLDPLvOnj0LAALakR0ZDAZhNB4bLhoMBmmaUNZC+jhrBjaHCCrR2Hh7e1t01jw3CfSQDcLfz+ui0+nk2W+324hGozAYDLhw4QKi0aj4yrBxwKYZc6zd3V2cPn0aoVBICnXuKR6PBy6XC6lUSvxtyBokOM49h7l1OBzG8vKyrJ10Oi3yJRbX1OYT6B4Oh0in07LGGg9PJOh2uzIpipKDRqOBzc1NAaLD4bD4mLAp2mq1EAqFkEql8OCDD8q1VDcimQtybfMaOxwOrK+vA4BQ5smo0uLJjSdszMZN3O/3I5fL4VnPepYUqC9/+cvxwQ9+EFevXhVETN3hVtPOGexicbGwkLzR9wGPuJaz0wY84iZNZJnJLtFfFpCkirATxU6V+nckEgl5uCaTiZjfMJFbWFiAz+cTLdra2hqq1SrK5bIU6Ux6L168iMFggIWFBXi9XtTrdaTTaaEB8kGm3o3IL4txvtfDw0NxLeZBZ7fbZQPP5XLS7WIhy0SXGja3241bb71VNngmmXTVJPWYGzk7J8Ax+MJNkpoogiNM6OjqTs0pEXoWz7y3DodDdI2BQEA2yqOjI0QiETFsIoWb6CNwnFCwO0qjGBbl6gIEgCD9RMzZ2WZyxfvOA5CJAkEIAHLQ8WBlt4s6fVKPeVhxnTJRYsHALjuLEyYHer1e7oXasZb3kTRzFm402jh37hwAyGHKA4sGSdTxE2AiSMSChDovaqi73a442JN2SbRUfejNz88LnZau/kTcqYtnF5IHCI1GSB8Ph8MiZYlGozLqiYUq0WMeEFwvs9mxQYzf70c8Hsfe3h56vR663S62t7cxGAxkBA/XKZkBfBbYeWDXn0DI0dGRjDlh8ULd/HQ6FXolNa4Gg0E6nEy8WVRynXPdM7Fmh4QHIpN10mFTqZQkhaRNDwYDGSvINU2WhqIoAt4AEBo5E1m1NpsGioVCATqd7oSGkE7MnU4H7XYby8vLssdyHQ2Hx3OzeX35zFutVtkz1IAVrwULaM6YV2vD1SAFu/bqf6PMQW0+SRYAgR+CLNwf6GJOkIpdMJPJhIWFBdhsNuzu7iIcDouvidFolBF4dKBlcsf948EHH0QsFhPGDc8NmqCdOXNGkkpef5ofUs4EQPZJ3kM+S9x3mLSaTMcu5gTRCHLu7++L0dD169flTCNjjPp9XhsmwqS6q83i9vf3kUwmYTKZpPhUG5gtLi4KOOz3+xGJRGSSAbtRNHRit5Ju9SxwL126JEAj/SBIbWehcnR0JAUb9zDueZTFuN1uAVe5V1LPStYUu15MMKkPpq8B74/BYBAtPoFGTkYAjoFzmitR9x6NRpFKpcRgi4U+x2pOJhPJFdil5rlCqQD9TgiMUSYynU7hdruFkcR9lWuda5y5QL1el8KGnWqePwQv2EWlKZTFYpFRdCz4OapKURRks1lUKhUZtcazgYD0ZDIRrTip6aQ2k53B90owXg0osBtOoIrAg5r1SJ05QT5q2ektQjkPGQg8a5xOp0gGCLoFg0Ghovt8PjE3pHSD4BT9IAi0RiIR6eiyi0t/CbIphsOheJjwbCSoXSgUMBqNZEwffw8BeYJWapov8y6CafT4YKd1PB4jm82iVqthbW0Ni4uLAgTxvLl+/bpIzAiuqmWhyWQStVoNDz30EIbDIQqFAlKplDjycz3SW4LnJmn+3H9YZBNoYc7FvJ7sQjKNCMiSNUhwkf45JpMJw+EQZ86ckXxYURTRWpOJqvZE4TrsdDrCYgiHwycYhgTrGg+b1JrNZtn/+QySdZZOp3HlyhXRpk8mx2MdyW5Ip9M4c+YMPB6PTBZiITsajRCLxcSDB4A07ux2O9LpNEqlkjSsrl69CrPZLM9ttVoV6Y7D4UA4HMaXvvQlccVfXl5GPp9HJpNBPB4X4IdACPczngv07yHgxGYIz3myMarVqpyT9M8ZDAbY29vD0tKSPDNkGj+e4Ln9ZMaT/fu/WfGEinBqnSeTCVZXV/E3f/M3Mhbh8uXLmEwmuPvuu/H+979fZqgyGQS+ciccgHwfQ11489/URT2pdGrXQxbfRNE5I1BdCLG7yt/Lgkmn02FnZ0c2Wyb2dMtNJpNIJBIIh8PSqSJKRQobwQRuKtwYSfllcBNxOByCtjHpZxJDJJkPXDweF0op3WPz+bxQjgaDgVC7uRkS8aUui+g+TZpI7eLGob5f1IXWajUkk0l530T+mXyRNg1A2AaDwQCHh4fQ6/VYXl4WTXav10Ov15PuuNqshsADNbYc02KxWISuozZXYUynU6Egco2QVsy/K4oixlXqazMYHI+kYQeKBx9RYW7cy8vLok1m948di0Qigf39/RM6dibxXBMsKvm+WbA0HnYt5n3vdDrSEaQWn8ZQvPe5XE5osXS5ZRJJnRoTBI7HIJ2XxT019EzACRxVq1U5HOnCTBdkzq7n4ZvP5+FyuYRlwrVDJ975+XmYTCbRgO3u7mJ3dxe33347XC4X9vb2TjjisiBhckZjGhbd7IIAkOdhMBjg2rVron8mBZJFFdFfyiZ4T8bjsYzfonTBYrFgYWEBu7u7shboHqzWdtHYSW0ISfaBWovNz0I9N4E+0nC59oFHGBZ7e3tyv9jFJkuDzxXdg6m3IxDEgpPrTK8/dhKu1+s4deqUTBYgOMYE4eDgQJKPSqUiM0K5Tkhv6/V6uOWWW+T6sjumBq647zEB5NohFVa9r5PqbDAYZK4t17zaWInPBhOswWCA+fl52R/JpCANmGPEuAdRdkDTtUwmIwaM3DvYXaSmkzpkdkbdbveJUZzUcBLsCYVCQoMlTTmVSsmIG3bsOI+YewhlPKFQCAbDsbad4y+ZPO3v7wuINJvNZFQkk1UCepQZcL8k3Zd0R9K2x+MxdnZ25PliR5TA1NWrV2G1WrG2tgbvw+NzSKHmteF5zS4Qk24C4G63W+jNdPClLCiRSIiJIQ1eua+T9cKuH7s8nBEdCATEF4Jnd61WE2Oi2ezYKHBubk7OGLP5eLwndY+5XE7W2dzcnMh2HA4HTp8+DZPJhO3tbelIEdxSNxEIQJXLZRmX1e12ZZ9X5ybXrl2DyXQ8y5wjIknN5+/hmUcvAOrLWUBzFCsBVe4vZDgQCDCbzWLexP2qVCoJ4M8uvV6vly4hwXA2Ebxer3jmkLnGOc08e8kGIYuI8jtKC1goqfdEFmJ8nnnGHR0dSTHF55ydT/6deyy7uaPRSFyy6QVAaVOpVBL3fjIN+b65VtRnAllufH8EswmwUoJJ0J0jM0OhkNC1aWgbCARkD+bnbzabiMVicq7TS4SaaXoFZLNZ+Hw+YRdyzW5sbAjtPp1Oi4Qvk8mIgR4BCOaVBCXG4zEuXbokBVoikZDf73AczwE/f/48PB6PSC7IeOGaMZvNyOVy6Ha7AhTxOQPwGCkQAUqCQ2y6sMNdKpWE9s9nlmeBGsyklI5SUI5gpWcEWRQE3re2tgTg5PjNSCQiwD8AAXUHgwEuXrwoObDP58PGxgam02MD5mq1ipWVFczNzcnaZq47mUxw6dIl0V7zd5IVcf78eSl6Ofe78fAY3kqlgkwmIzki11gmk5G59QRy2dAhU0in0yEQCGBnZ0eaAhyLRlYux/syX+fzTk8YGi0SdH36058ue0i5XMb58+exuroqHlJaPPnxhK6yx+MBAOnCLC8v4+LFiwgEAlhfX8e1a9cQCATwnOc8RzTjPLSYrKlpvlyI6sJcXVypqeVqarv696gLdRY8RI/VhRWTYdKuWQCqC3Rustyk8/m8UGHW1tYEIQKOk006jDORYiFqs9nQ6XSkw0QKCfVtLIxjsZjovIxGI9LpNFKplIyVYNduaWlJCh+apTSbTfmMuVxONHLqrjmvoZo6ORgMhFpL+jFHg1EPTuR7Mpng4OBAuid0Xx8MBtI1YYfKYrEgkUiIAyQ3aFIleW3sdruYylFDFQ6HUa/XhWlBN2e1TpQHKa+9Ovh3rhNuiCzmuJlRq8NRRVwry8vL2NnZkc9O+i3da3k4EnlnIk4DQIID6g7NYDA40eFR0wzV610d6g4hACwtLWFtbQ3pdBq1Wu0ELc1ut2N/f190SNR68Tkj3b7dbkuBToAIgGiMaZzFw8BgODbtyOVykngwwTaZjmd0Li4uIhgMSgI5mx27qrbbbcRiMRkx1e12cfHiRRiNRtH7tVotXLx4UZ4PUrSIRI/HY6TTaTlMSPtm0VIoFORZZaeEBVAsFsMzn/lMKIqC+++/H+PxWNYUqbbdbhelUgn1el3eo91uF/o7ATX1/eCzyANeDeJwRBrwyAxWIv9cr+rOKYsUNVDmdrtlji6ZE2R4kFXCIn06nSISicge4HQ6BSQsFAq47bbb5JnlPWVCYDQa4ff7T3Sn1T4LLC5YXJXLZUSjURQKBTGRoiaYwNu5c+cETDGZTOI/oH4mucbYeeP+b7VacfbsWZTLZRkTRZr0o58JMnwILDidTnFHVtOUCVJQ41csFk/MiOc6YNdJ7QIei8WELs5RYT6fT94fnxt2Q2liRv0oi30+ozrdselauVxGsVhEr9dDKBSSa0W2DtdHJBKRaQcGg0E6g6PRSJyRyUrhuE26OFN3Px6PkclkcPPNN0sRQhoyqY58rikTIWWS741SrEKhAAAyQ5fFMeVEpFGSccbu5Pr6ujyvAAT85P7S6XSE0r66uord3V0xLKT8hdIH+jTkcjmYzWbRjlKmxXW0vr4u3g0EnOnxQTo1rz/1lkxu4/G4/G6LxYJsNgvg2ASLrCGCNUxMWUQDEINVAu12u10Mn8ia4T7EjvloNJJJLbx3R0dH0Ol0AsiwuKZmFoB0kln8GY1GeRbIlOPZRwCGzxY7lZSmlUolzM/PiwcA2Q1klvBrLMSq1SoSiYR0NAEI2MumCOnUlUoFVqtVzBJ5b7m3ME8iQHnu3DnJmyhjIyBO9iCbQJRk0MiTRonUdGezWRSLxRNUeQJ8lMWxIz2bzQQYUfvosBOdTqfhcrlkbdCojecSGy5Wq1U69iygWDC6XC7p/nLfHwwGAvaz0cHRtjT1pOac4DuLUqPRKD4PCwsLwvCj4SyN6yixYc4zmUwEBOQZn8vl4PV6sby8LGuI+anL5UK1WpXzhcAngXiyuwDIvs09gdIZUt3JGDGZTEgkEtDr9Uin0zAYDDIumLkcgUSeT5RGVCoVMbOjeR8BQp7NCwsLyGazSKVS8nyoJzqNRiMBAPv9Pubm5gQg2draEskWmwvBYFCAa4JEfMYoG+U+ydGmAKRAJ7OF8hOa29GklmCO2seBMoTZ7JFZ9pcvX5Y1vLGxIYwYAg/NZhNra2sCkrBjTvDV5/PBbDbj2rVrsvfz7GLTil4ZvDbRaPSGeaoW39h4QkU4i1YihaRp3HffffjO7/xOxGIx7O7uIplM4nnPex7+8i//8kTBweL30d3wG9HO1YXUl+ueq82x1MU4UWkWbnzwl5aWJDHh4Uekbjgc4sEHH0S5XBazLi7UtbU1POMZzxB6TLFYlA6MWltHI652u41UKiWjo6bTKa5fvy7jlmh6UqlUxChhf38fc3NzQu2lhu7s2bOygTUaDUngaGih1+slieB7Z1eIdNXZbIZLly7J4ZHP50/oRE6dOiXOsTx82HmuVqu45ZZbpPPLbhpRSW5G3ODpHE8nUBYBNI+jIQoNcXgY8XfRJIqdBB7spNjycOLhxwSc75koJ/BIUUSKE5MLzmWkFo0br9VqxdWrVyURot6IGiYeBqQ70nCDCSR1kDSP8vv9SKfTUgCRnngj4EltisEkX30AsPBkgc3DkQUvJSD8WTIO1BpOtcyAxj7sutARlgkQD1Emgyw61Kj1xsaGzOENBoOCVqt/hnTVlZUV0S0WCgUBLOhgffnyZUHueXgwqVYj73Trpsar2WzKbOJUKiWUSRZQLAgpJ/H5fGLIxPEw1Jvy0Fbr17gO+X7UewqTZP6XSTf3IYIfwCP6aO5xLFjUa4xrlUwdatCq1SpCoRDC4TC2t7elSKfum0Y/NGojTZjrTM3+YMcxmUxiNptJ4kXqM+mqnC5BYybOfy+VSohEIic0ttyfmGwR/GT3i2AqmQB8jrmn8DyhhAWAJM5M8mgiQ08QAALi8Pnb29uDz+dDOp3GaDQSuunc3BwuXbok1HoaVrErRIplIpEQCmutVkO5XBZPD9KdWbTyfVksFjE54qztW2+9VZ4zmlqyo0Z/DXYo7XY74vG43DPSUdmR7Pf7Yr45NzcnlGeyjGjw5H14BJlOpxPKJB2H9/f3xU+gWq3K85LJZESHSkYVE1Y+PwcHBzK6hwAju+yk5lqtVmxtbck+S0aC2imY+kKPx4OjoyPxRCD7iec26bHz8/MnDA9ZYJRKJZRKJZkjT18SdrL9fj8SiQSuXr0qOk/uRaTTu91uzM/Pi5aYNG0yPqrVKhYWFkT7Wa/XhUba7XaFkcJ5vhyXRrBrOp1iYWEBnU5HpDakDxNQVp//7M6azcejuViE8T2xQOHv537Igox07nK5jL29PWFJGAwGkaLRCI+vY7VaEQwGJennvGkAIiGgWzSLYEVRZLwqHdfZtWYRzvutHgVbq9XQaDTg8XjE2Ew9Y3pzc1Mo+JSfsehVa/fJAEqlUvIc0UCKtH5Sl+kOzb2Rzw3p6NTAM5gXAhAdvsfjQTgcFiNTNnc47pTAaigUOtF5V1P6acR3eHgIj8cjnXnmUcViEUtLSyLjOzo6Evdtysco+aH2nF4VXHPXr18/YebHHC4YDIounSN/K5WKgBTZbFa05JQRzM/Py5mqNjRWN8QoHeAkD+YcBKv1er1I0GazY8My5qjMn3gWq80h+Vpk1Fy/fh1zc3NC6b969So2NzcRCARQKpWQz+dx9uxZuT8E29SMuNOnTwvTc39/X9bBysqKAOD5fB6BQABXrlxBJBJBKpUSAIIyLOraybTiPeh0OiiVSjg8PMSpU6cQj8cRDAZx/fp1yclpYOnz+ZBMJjEcDpFKpXDfffdJI2p+fl7AXD47BNbJ2CCoS5aJoiiIx+Oo1WpYWVnBLbfcgmq1Kk2hSCQizS3K0hYXFxEIBHBwcAAA0hBjPsbn7sqVKyiXyzcqA7X4BscTKsKpJyWaaTabZTTA1tYWzp07J5Sm22+/HdlsFhcuXJBNmoUKH2Z1Aa6mQbPQUiet/K+6MFdrCPlz6s4n3b9NJhOWl5elcwzgRBJMt1l2qFiQAsc05Oc973mIx+PQ6XRYXV1FPp/H9vY26vW6gAo8HIi89Xo9VCoVQa2j0SgWFhZweHgoHQ7qvjguZzwe4+joSGhMZ8+elRFgnKPNJJeoIjVLRNpIny8UCqK/UxeuzWZTqDGkHpMSyC5ROp3G3Nwc7HY7lpaWhCrMmcg0NVLT+Llx89qGw2GsrKzgoYceOkEFByDzPnW645mNauMXbiJcI2tra1hdXRVTJx5wnIFMDRY3cH5uFrb8nqWlJUGeWfh7vV60220BVLa3t2W8ycLCAgaD4/m9TMJZWKVSKUE9w+Ew9vb2AECoTAQNSHNk0k3TI2rmmCRR33d4eCidfxZENFLj7wQg5lXsqvNzASeTCRZRfB0mI0xK+VkV5dicbzweyygjdnpv9FzyM6ppUTabDevr62g0GpIsMmkn+MMkr1wu4+abb5aEVK/XIxqNCrhB1J4jBvmZmUj4/X5kMhkpumh2aLVapZAmXZImbtVqFbu7u1haWsLCwgL29/dF08sklpIS7j3sxPA6k67Me8piXV1A8/9J8VfvX7yGZBaw4Cdt9ejoCJPJBLFYDIlEAq1WS4yt3G633JtKpSJUcSZbNGlhZ2c6ncqYGHpNFAoFJBIJAcBIzydIwXXKbubu7q6MYGEHgF06SgHIdppOp1hbWxO3WPV6oXMv1x2Lh9lsJjNyuVaYqDFZVTOM+PPs/qtHuZAppCgKlpeXsbu7K13JYDAoyeNsdmzyRooxE0eaDBaLRWEBERjjMxWLxWA0Hpt98nk2Go04PDyU9+Z2u+WZ0uuPdc1zc3Mn5CosVpjYESCr1+tSxLHDzfVXLpdhs9mEguj3+8UvgeaLs9lMOr3U+MfjcQyHQxwcHAgLie9Lr9fLM5VKpTAYDPClL31J1i5NLXmOsLPNIpA06bm5OXmfLpcL999/P+x2O86ePSt7/2AwkLFflIFMp1MxHKX8iFINJsA0EmVxfnBwAEVRRLJgNBpF38sia3t7W97XqVOnoNfrcf36daH78+fpi0L2A4Gb06dPi4SoVqvJ+mADgns/1yHPQZ7DlHJQttZoNISSq9Ztk9Z9eHiI0WiEU6dOCYhPI0C+T7pb05+BgAPHxDI/U+ueGw+PXSVVmTI99T6fTqcRCoUQDAbFP4XgI1kIagCeJrVcDzwjXS6XyO0IjnCP4zPPc4y5jxrQ5HNAkJDXymg0ypzr2ezYyI1deALlinI88q5Wq4mGnF4+1DvrdDrJX7nHqPXa3D9yuZyYUhI41+v1wmwBHvGpoTkowQnmWmp2IPcQTvgg8E9mgNlsxv7+vmj02QGmzp0AKnMCNTWeHXW1gSGBOYKCk8kEiUQChUJBzjpS0Zl/8jnkeEGCADyLhsOhmEByn2GuyLOSRR4ZJmoTO6PRiN3dXWFM8Pkjq4cMBEq/mLN3u13Jf9bW1pBIJOSZa7Va2NjYEEnPo0cllstl6QrrdDrs7++j0WjIWMVkMon7778fFy9elNyC5wld0vn3RCKBhx56SJh6ZBMEAgG43W6sr6+fmLTSbDZx6tQp+VmuQ71eL4Dt+vo6zpw5A4vl5Fx1XiOj0ShdcxrwsanC53RxcVG0+aPRSEz2hsOhGNORERsIBCTHIwjAdU1AiWeP3+/H6dOn8eCDDz7OylDThH898YSKcCLt0+lUiiaTyYSNjQ3cd9994mjJcROvfOUr8YlPfAL/9E//JEmTOjFVUxP5dYa62OaDrqak3yhYfDEptFqtuO222wT5VVP/uFHS7KFQKAhFh+Yy4XAYt956q6CqLOo4l3F3dxeFQkHeUy6XO+EgTa02u0A02ZjNZrKRkhLGQ4PaD459Ij2ID/Te3p4UOJubm9DpdNja2oLFYsHZs2dx9uxZfPKTn5RZwpPJBOfOnUM4HIbb7Zb5nEwW+bDSVVNRjg3MSLNmF4yJH92u2dUlKMNDVW2CxCLa6XQik8kIO4FFjcPhED0dkxOabDF5jcfjYvTF11teXhZzIVLgR6PjcSsc4UN9PFFfbqo0CqMpHTVpLAg9Ho8AFexyMSHlxqXWkR4eHoqbMNctkyvSLpkoJBIJ+S83PoPBgJWVFaH/0CiPiCvXNBMYHjJMvKiPJOUXgIwnoqkSqU/UTpEVwGeP7uiUV3BMDotUjq9TdxZISWSH5PLly2KCOD8/j0wmg8FgALfbLS7/BAVIiW40GoL8cxQfu+DsytNZNJVKiVkaqWG5XE7QWxqYsYOxuroqOl92NoLBIKbTKba2tlCr1YSOT20i7wcTcH5uPr9MiliE8JlmwsHrqaYkqvcbXkN2DhKJBDKZjEwJUHcTWAD4/X4ZrcSxcdwrqYMlI2AymeDw8BChUEiMkAh8ABCaNKnh/Fl2Sklv55qj74PdbsfKygrS6fSJwpWF18LCAnZ2dpDJZE7IQABIIrC8vCySBXbh1KNfgsGgJO5qXbsaaCUjhXv57u6udMIIFvDZJ+uDzwWpdhzhxf2Z1EoWud1uV34vQdZUKoXZ7NhZPBqNIhaLiQnkwcGBOIMfHBwIZRSAuNRzHebzeflcBPVYxHO/ZpeOen+elfQeASCfR83sIKhNnal6lB+Nd0qlkpiUORwOuN1uAZp0uuPRYuxQs4gzm804e/asfA6eaeFwWNzO9Xq9mLyRlUDdcbPZlOtJY7y5uTkBjtLptNBdCcw2Hh59GolEZCY69xwCH3wv4/EY8/Pz8Pv94pbN60K2CEcfkXFBvTkLab3+eN771tYWbr/9dllDBCm4/nguEtQcjUaiyeW8a3bIWJzyzCJ4zXtCNg/3RLJH+OwXCgUUi0UxWGKBQpkJ9+1arSbStF6vJ0BJsViUr3Nf4rrn2cVns1AowGaziWEUTa2ouybQMxwOsbOzg2aziVKphHQ6jUqlIjI27jcsDkkfV09v4fvgPu52u2XP53Umbb3X64npKPdY7qHcfwkwML/hnkRwgoUm36PNZpOJJVyjfF/q99Pr9QToYZHXbrcFNOCzS+YQZS0EaTh1g/uPWlLJzu9sNsPc3JwU3MxJuQbI4KSMiaZ/ZCoQTOXYOgLtXGc0/8rn82KUWi6XBdjl6MV8Po9oNCra/9XVVQFoeX3JnmRzgJM1uJ8RiOLZTvCSRqYsrPf393HzzTejUqlIPUDGAxtQzWYTqVRK8qXZ7NidngBaOp2WXOGhhx6S86lcLgtLy+fzIRgM4gtf+IJ4aCwvL4vB5Gc/+1kBF+ldQfDp8PBQDJUJFHP9LiwsoFAoiBZ9MpmI3wjHOnK6xmQykSk+gUAAW1tbyGazwrhih3s8HkvhzwaWevY3c1er1Xoix+F6yufzwtLkv3P6gaIo4v80Gh0bLXe7XWHdUjqqZgcbDMfGvBod/amJJ6y8J4LPogg4pnMtLS3h4sWLmJubg8FwPCrg7NmzeM5zniNjEdSUlkdTch9NSefBoaajE+lTd775dSZ+RJHsdjsWFhZkvAB/jgkcADEyIT3X6XQKQux0OrG2toZ4PC40WFJAOC/z3LlzMBqNYv5GjSq7yxy7s76+jlwuJ6geD3TgOJGmYy9126SluVwuVCoVocaoi3V2R9jBX19fFwpiKpWSWZKVSgU7OzuCbq6vr+Pw8BBHR0dYWFiQWZOkTQ0GA9HyApCuCWmEqVRKumAsNrl56PXHrqnsaJBiRtoPjYuSyaSgtZwP7fV6cfXqVeTzeUynU+muUne5uLgo6DIPGaLnTFgNBoN0qQ0GgxhOLS0tidHN0dGRJB+kfRLdJSgCQJygueaovWcyqe4gulyuEzRiAKLP5t9J4SJLI5FIIBgM4uLFi8hmswKABAIBcQzmJs3DngkGKV/U0NGQhgZLg8EAxWJRumYs2tTaPSYyfDY4skxNW+bzd+rUKTnE2+02FhYWhD3A8Us0naGmkgAA7/UXv/hFJJNJOQQLhYIY1pBWTz0vi4r5+XnR9gUCAWSzWdFMs8DliCEyIfg+HA4Htra2RLNNdJ7Po8FwPCaKVH8WjWoTHyZ+6lE17Faqu70ssrn/cHoADVv4e9hB4PeQHk2zHhZ2/X4f2WxWDuF0Oi1uyyy+SAlmp5lrolqt4urVq8I2MZvNWFlZwWAwwNbWFiaTiXToqPPU649HZe3t7QmK3u/3sbKyIpRuOm9Ty08naqL0p06dwvnz52U9qfcOOhM/miFAwzDq9QjEqT0f1GyJXq8nY1xIhR8OhwKcMRm9du0awuGw0EAXFhbEwdxqteLo6EjG2JBKbLfbcXh4KMU393Gz2Sx7F0G+bDaLdruN1dVVjEYjAYi4d5ACyoKfen9FOdbpkiLPdZfP5+F0OhGLxU4YRbGzR1ZVJBKRZ9ThcCCXy8nnZsJEwImj1ra3t8UcyG63S1HmdrvFAZ3JL40RWQxzVA+BUiZ/AATQZIfU4/Egm82iVCrB5/OJ3IfGdK1WCwaDQeRXBD/y+bwU1DTfojSp2+0KyMGkk69Lx2s1841dLo73OTg4kJFs1H+z40nwkEDrhQsXBFwvlUoCnnGiAgChF6vHcna7XWQyGVmrs9lMupo0RaTmeG9vT0AEr9cr14EynVarJZ4rfA5YlLI7SsM0vV4vOualpSWRHLGYILOCYDod3ynP8fl8wqSjj0Gn04HVahXN/dzcnOyZOp0Oh4eHQoEtFAoolUpyLvG5Vntd8NlkwUxgkgA4O/yUkFEWxaYA5YIAJDdhXmU2m8VTgR1lm80mum/6D0wmE3EUZ/HPSShkUPBMt1qtUpiPRiORi3DdM9/V6/UCaPF6MjfkvkxPBY7FYpHK/ZDNFv48n3syOigDYp5IgI3yIUoAacJmNBqxs7Mj3it8jtQ09VqtJk7h9DyiJCybzYp5WrlcFpZgp9MRkJ7zq7m/EyydzWbw+/3i9k1PBwLbXL+Hh4cC9hOcomTLZDIhFouh0WggHo+j1+vh2rVrMgM7n8/LVI61tTVYLBZ89rOfhdfrFY05QddYLIZ2u43z58/L+Ub5yWQyka73dDqVDn0oFJJ868qVK/A+PH6PoDRHmiqKIiOKuX8RnF1eXsaVK1fkvGMOFwqF0Ol0BNAi2/OZz3ymGNQS7GARzGaJWvbEQl091pY1wPr6ujyTBHQoBWm1WnjOc54j7u5kqvj9fmENcVzx3t4eVlZWUCwWBQTW4smNJ1SEUxNKTTIRR248RG340FDzwC4q8EghfaNgUa2m8z3ayIoFNxFlNTUdOO4msUjkRjWZTKTIZsLHg4GJmcFgwGc/+1nZlJ/97Gfj1ltvFYT60e+N9M+5uTl0Oh35OaLfdDq12WyIRCJIp9OiIWKCzsNraWlJNjEeIDQWisViov8uFAoIhUJSeHNMEs13mKScO3cOZ8+exd///d+jUqlIMXrmzBkkEgm4XC7k83m0Wi0sLCzAYDDImAQi79z0Y7EYer2ebILsQjAJ4D2h/oSUuWq1KiNJiJCziOYhQ4oXEfPd3V2hOQUCAczPz4vBBjvBo9EjsydnsxmuX78uxQ5nQdOAjqALAR9242ketr+/LwkP7wOLImoRmeC1Wi2ROBgMBvm8RIlDoRD0er3oP5l0qCUY1GCTQkzHazUVi0UgEX/vw3OzPR4P9vb2RCs4Pz8vc87ZgeaacrlcMuLIbrdjfn5eNF4c8dJut8VUh50ndjm5SRuNx6N4+H2kji4sLMBut6PZbOLq1atSAK6urgqizSKzWCwKkOBwOOS+tFotRCIRGXdCCjk7lNQ4DwYD3HTTTRgMjicVzM3NiYneeDwWtgE1im63G9VqVTqrFotFHI5JnyczhteYv4taWIvleO46pTVMJtSAIA9KAkIE16iJZWfa7/eL8cr29rZcb53ueFTY0tKSFC+k8W9ubgoaTw0mC/1Op3Oi0043VQI8BOf4fg8PD9FqtaRrzzE6TLjZfea67/f7MqaJ94bJrdPpFJNFUr3ZqVJr5wlsERA9OjoSCinBRs4Sbjab0vkFHmEzUXZCs0wm9gCkOKbWL51Oo1AoiOETTYV2dnYwGo3EfKhWqyEajaJer2Nvb098F+g7wDmvpPxx32m323K/t7a2pONFev5gMEA2mxUN99HRkYAjLGA4EqZarQrtme7VBoNBQMZ4PC6yBOpeuU/RFIoSJ4IH7OBzdi9ZAdQrc73G43Ghs6q7/aREElhlJ4TTOHidqU8MBAIyEogaaRauer1eRrkRnFtaWoJOpxO/CAACUKs9FQiWra6uYnt7G8PhEMPhUGildIWmjlHNEGIHM5fLCWVazY4xmUySOHP9ENCk0SAlQSzmq9WqaHnVZwFBNnpPmEwmARcI5BKYoDki6dikcROgIk2fLvjcN2azY1d++gHwGQAeMcHiaxOAJGWbHXv+LAtJdljpB8PXINOC3U6anRFw5JlCid7c3JwU8dwfDg4OpANJ2Q7BALIbuGYIklqtVmEbcT3wWqgnWRDMVrvP2+120b0DEEkBgRXuhdS9l0olLC8vn2DdseFAIFjtL+HxeITtxbFP3N/0ej0ikYg8VzqdTtz2FUWRopcO1aPRSJg47JTOZjP4fD6Z1EBQlOwTThahk3qn05GRdhwfWqlUUK1WxVSNM8E5QnUymYi5Id3yycih5IsgDBtLlINdvnwZvV5P8gz+IbjC54drjYwDSiGTyaTMyaYMkkZ6dPcHHpm2QwCLABPZMQSaCYSdP38eS0tLuHDhAlwuF86ePQubzSZjN4Fj8PK+++6D1+vFrbfeKg04ghiXLl1CKpUSMLLZbCKTychzz2bZwsICotEofD6fmNzW63UAx8Zrly9fhl6vF7POcDgs+whNLg8PD2G325HJZEQq5HQ6Ja+jrDUUCiGfz4tTPhm6BBFJpaeciv++srKCvb097OzsiBzJ7/djd3dX1qfdbse1a9eEjRyJRJDJZJBKpWQPVBRFAAuaW/J6avHkxhMqwmlKw4eEWuJsNgu73Y5oNApFUbC9vS2Jnc/nw4/+6I9ia2sL//AP/3CiMwec7GarQ11YqwtvdZLGpIzfRzoYNxvSN5jocLOgHo5oGIukarWK6XSK9fV1nD59WopzFuF8XSKo0WgUxWJRCgsihf1+Xw4F6p3VHXxuTHq9XroY3Iw3Njaws7MjhwHpbuyocawJi0T+XtJ24/G4dISe9axn4W//9m/RbreF8lir1YRu1Gw2ceXKFTkgIpGIbOA8bNh5pGaMmiYmFUQySUFsNBpigqXXH49WImpHQ43hcCjodLFYhMViwYMPPihO8ktLSzh37tyJgpz6es5TZsHNQ52HPTt9lUpFGBsEEAggFYtF2cR4761WqyDPpI3y99psthMu0dz06Wo/Gj0yi1ftSFmpVGTGNdc0C352EslMoAO7TqfD8vIy9vb20Gg0pHBbWlpCtVoFADnUAEg3o9lsCpJJfTRNiHi4ARD6Ngslrm+uS6PRKAkvgSSOMmJil8/nJfEgpZgADdHT6XQqRjpcwzSMYQLIwmM6nYpRHClilHmww8dniKZ0LBb4HHGWPWmCFy5cEEMas9ksjp+TyQQPPfSQ0PO5Jgg+TKfHc+upbWfCwYSaQBoLIP4b5zLzHnY6HRwcHODKlStSmJbLZVkzfHYymQz6/b44tDqdTiwtLYnEo9PpiNcDqe9kcnAPIMio1mWRlUGtK5M+AGJOY7fbsby8DAACtpCFQ1M9/uzly5fFWIl7Kd1/+T4JdlHjzZ+n7CUUCsnrsevJJJ7IvVpTz59nt4xjfUgPZPJ2eHgohRZZKfw7adadTgeVSuVEsk0GCN83ZSKcG885qbFYDACQz+dlpjQNmziLm3pgdloJOJGuqn7+nE6naBtp/MYkm9edzwpp4Rwvxs4j91y+N7KRSMPlc2M2m2VEjV6vly4i2RMchUTAhNRE7pErKytot9sim6L2ncUOz4h6vY5EIiEdSBZ71OvzNaifZ8FvMpng9XphNBpx8803y96g9hOguWkul8P6+jo2NzfRbDaxs7MjchcyeWgwSe00u/kEbr1e7wlmSjqdxs7ODjY2NkQyQ2kQzxK/3y9jkdj5J2DI7jML8+vXr8Pv90sxRkMlXhOyRwh+EeQjtXc8Hks3j14hzKW4p3KPY67Boo4FNzXnPp9PCjcabtKPgWZ5NJfjFBi17nl7e1sM6Qi4m81mkR1Go1EBUvn5BoMBMpkMYrEYptPpibGCtVpNikteS3a8+dpcg/ydR0dHQrWnzINnVr1eP9HQ0OmOx8hWKhX4fD4sLi6i1WoJGLa1tQWz2Sw6XavVKqMRWVSS6UO2APdudrcf7fnBPZeFGsEOAls8e9UNH659yiMJWhPEJcODAJTT6ZRnjO+JRTOLf8oJSqWSmAFubm6Kz9D6+roAHTQIpryF5wLp4hzTSdCZruTMQQmqUM5CajabRgCEpUR2Go0nCWzSi4FNCwKODPpfDIdDYcxdvXpVGDDMG9mZN5vNODo6EvZHMBjEbbfdJntivV6XRhMbUWRC8ZnkmUUzt2QyKew0sgRcLpecezzLOD2C/gUEUi5evChnxOHhoawv5tnsmC8tLYmZL58V3pN6vS6+DhxFywYCZWXxeBz5fF7ydqvVisXFRWGasKutdm/nmU95Bc9zdubZGHq8oc49nqx4sn//NyueUBGu0z3ihM1CjAj4bDaTcUm7u7twu92C9A8GA9x2222Yn5/Hxz72MaHtqQttdajp5ixc1P9VF+f8O2mLTDypIwMgSQ3pjDz0SQubzY5H9tAUIhaLyagdNU2SCbhOp8Pa2poUgDQEYzKo3qwAIJvNStHIh0CtHb506RICgQAWFxcFXaSpxng8FvMIFgn8TExi+Jm5YfZ6PdF3nTp1CteuXUOz2RQtVLfbRTKZxObmJtLpNI6OjkTXzIKXhxy13tSRp1Ip6bRx0+d1IghBRJCFCqnwtVoNVqsVyWRSOnbdblccxKfTqVBlo9GoGJUQ4aZDarPZFAq22+3G3t6e0MV4EKhRdjUlmEkx7zdpV6T2hEIhAI+MPSPlmrpxJmSkNkWjUaE9Ao90BMmIIJrNBMNoPHY5z+VyUjRwxArpaDxcWBDrdDrptLndbuzu7iKTycDr9Z4wiWNxSJMpPnvU1LPDv76+jqWlJezu7op7MDdp0tWpLSStfHd3FxaLRTRsNLvhNSUtSm06Qxf97e1tKa53dnbEmIoJPWmNZEnQqIpoMR1K+VzTzZfjZ3Q6Her1OiKRiGihM5mMgCrsUrODyMOeOtfZbCZME4KMTJ7+P/b+LLa1NF3vwx9SFDXPFCdR87zHmrq6c9KdwPEBnAQIbF8Yhm14guE7Xxm+sAEDdnLjCwOGgyCAAcO+TXKVIICB+OIk8enTp4+7usY9aGuWKFIkxUGzREoU+b9g/Z69VLbhU47Lzj9oAo2q3qVNkWt96/ve95neVCrl8Cr2ENZ2PB732CPUGFdXV1aBpFIptVot7e7uKhTqJHA/ffpUNzc3SqVSKhaLevPmjZaXl/XTn/7U1oZGo6Hl5WUXXkglCQ6r1+tmv05PT92McjDTwKCaYP1TqNNwwejikS0Wi/aq4bunmZA6BcHs7KzC4bA2NzftVwymYLMGg/56mjasNMxbBRAKWo8ITsSfx77Xbr/3hLPWkQXDyk1PTxvsuLu7UyaTeXTtaYAJrtra2lIoFNKLFy8cwIXqg5RgzjqkhbFYTPl83sxKLpfznockGo8n6zGTySgcDjthGZYQIAnAFdl3s9mZj316eqrh4WGfY3ja7+/vPdsZPydKNJoGgLORkRF70gGxkDATiifJLA3gHYFAZC9QQDL9AbDx9PTUADBy856eHgcookYiY+Pk5MSzp1++fOl7DEhEU8I9mpubkyT7fgEqsGtgRcH3zXi6m5sbFYtFrayseL56UNp5dXXlBODV1VWPP8RnPjMz4/R91jINLDUAgD7SU84A7BrZbNYp+UG7EvebRp5AMMAVGneaNIBGRh3xvEQiEdsI8FhfXl76urOfENKIbJr/jhWnWq2qUqloamrK78vvmp+ft2qH4MHe3l4Vi0WFw2EDHsGg1kQiYQaTLIKDgwONj48/GhsIe0ttR7YDNRk1DoCaJHvMWUMoMiWZ6ceqBiHA2kU92dXV5UCrrq4uj68DoG+1Wg5no8aCNUf2jueZfRbZL2oNrgVANywkDT9+daTXAwMDtgX09PR4FjoqoXC4k18QJB8SiYSv8cDAgHNmpqamTBZhb2CtcW03NjZcnwF2Ayahwry8vNTMzIxBLM4gasEgQIHVhdGX7NdBe0Qk0glpRa2G5Y+sB2oJyCZUXn19ffZ7cwbD+KKiODw8NMgOMLa3t2fbEWGbAwMDisfj2tzctLc8Fov5uSQnCKKHPebu7k79/f06OjrS8PCwrq6ulMvl/O/41lE+ksS+sLBgMA2gJhLppLcDDKImJHsERXEkEtHc3JxKpZJmZmY0Ojqq09NTHRwc+LklMHlmZsYefqwUfX19+q3f+i2dnJw8mrQTCoWsMOHeoVYk3DE4OeA3rx/29b2a8IeHBxegpFEia0FmVyqVHNryk5/8RBsbG/riiy80PT2tDz74QH/qT/0p/U//0/9kFO5fx3gH2XEOnGChFnzxM2zaFDg3NzePvJqSjCLDkEiy9Ht3d9fMwvz8/CO5OP/ju7OR81DTrLIhIEficACl4nfCXvOesVhMQ0NDOjw81PT0tKanpx2mQTE3Pj6uo6Mje6yCQXFcGw4hUK2pqSnLzff39x28RWiK1GF58I5Eo1EfTtfX10okEpqfn1er1dJnn33mzYEmtFKpqFKpOFisr6/PvjXWy8jIiBPLg2PAksmkLi4utLOz41mKJGwDPiBBwqModQ7hfD7vgkKSZ4gCDiDFodClQJHkzSeTyTicgpETjUbDciNkhBxShCwhXwJMKRQKPsCRkgdVGrBFknw4wkggCwYNRYYKi8aLOdEUfqyh6elph5oEGWzkmsfHx06rpggIFj2ZTMYz6QlcKZfLbjzxy3Z1dT3yyA4ODjptGMYO+R7XAlCk0Wgok8moVqvp7du3luTjS8MLSmETjUYfSSuRJqbTaacqS1Iul/P15btTKAOA8BxyPwBzuI48YzAayNlhexm/BFve1dXlBpW9Cgm71GEjf/WrXxkE+vzzzzU/P69oNKqNjQ1J8pxOpJRLS0suUpeWltTd3f1I5o3KYXl52c3pxcWFLQUwPUHWES8bWRKSHsnmJT1ao6enp7q+vtbk5KSazaYDiCiogwEzwcR71ubExIQl08H9Oygrp2DmvlLQcu1pkChq8TqithgbG/P36+3tdcZI8DPQ9N/f35sxkuQ5qfgxKQgHBwddbPb09Gh/f99qB7y8jUbDYGA4HPaYKhhHmGaAkUql4uJckvdoGs1g5gfPD6N6YGd4fgjnGhsbs9+SgvDg4MBABdd7dnbWYEez2bScGdAR9gypMcF0SPA5o6LRqJO5Yb2Ym84eQiARtgmCqzhbWT/lclmXl5cOSKNBm5iY8IhAGgoYy4ODAwMvgJA0ccxUnpiYcCglijPANsL/BgcHDYCSho8F4vXr10qlUnrx4oUeHh60ubmpRqOhvr4+z+bl/AiGF2EzAvCnQW23277eV1dXDllFQYQ8mdrk/Pzc9RRMPyoNZO1IqkOhTmhYcH44eziKHRpLGj++M88fzz/ZGKxbGlWYNxqwubk5zczM2BLDTOpisfiv7LnBcEqeCVQshKwmEgnNzc2pt7fX94RzlznXrEkAAgLqZmdnNTg4aJaZZpP7iwy3u7tb+XxesVhMxWLR65L6EHCXugzgBvCN/YFGOBgiixef64lEmn2U2hDrBmC8JBM/gOAw+ISTMeZUkn8XdRns9dnZmfM5YMKpBVjzPGOw/XzPbDZr20qpVNLGxoai0U7ieDKZ9NQdnvtvvvlG0WjUjd319bWq1aqfXc46mnDu9fDwsMPxrq+vXS/c3NzYtoo/uq+vzzYVlEIAU4VCwfc8m816n2dCAT7rZrOpfD7viQk//vGPrbjCLoRFcnh42ERRPp/35BrUJOl0Ws1m0yAcwbLhcFhPnjxRKpVyADBnLcAL4NGrV6881aKvr0/z8/M+C8j24L8B6kajUYdacr8hEADDAFuoAXd2dnyOZzIZ7e/vm7xCxs85BaDHe/DfGbcH6F8sFq12HR4eVr1e99Sf37x+2Nf3asLZeHiwJJlJQqI+Pj6uTz/91AtudnZWFxcX9mo9ffpUf+kv/SX97//7/66joyMfNhRvNKjBZpsi47uNOP8/+O8UyBTSwUIFSRAPIwXd69evjULOzMwok8k8kp/TUAfRbBgZWNmdnR2PbaNQpykKNgEcCMHGvru729eOg3d5eVlv377V7OyskVWYX1iFIOvEZkjBV61W7QeCgZOkYrGoy8tLM068Pz7iX/7yl5aIEu4C8k/SNEwnwTBB2ejNzY0PdEKeJiYmdHp66jnDBwcHDrVgM+zr69Pz588dWsIBXSgULFMldZK5hvhSJycndXJyoqGhIb17985yv3q9biaXgxF2Kx6PP0qep9miEKLQodmh4MP3BsLf19enJ0+eqFQqqVwua2VlxX4svLBBJhJPIQ0m4Si5XM65BTT7zWZTy8vLZnqCBR3gC0EjSKawh5Csi1yJYikSiVjOVC6XFY1GnWBK8QPjQ3MEY4rUDFQVpm54eNjFdHd3t58HmBq8gABgzWbTUwL4nhxUb9++tVT+5ubGPj4KSSwmgD5IpEHWURTAXPN80WQQbkJmRCgUMpsKSETYHdJAZF40YxzcBACCxLfbbX300UdqtztjmdbX1zU+Pq5QKKStrS0zErOzs/YTZzIZN4OVSkWRSMQhaEEpOczr2NiY9vb2dHJy4oKNNYYvGOYFxpIGkvuA1DHIKEYiESekLy4u6uTkRPl83oV/IpFQOp3W9va2feNIUbmfyJt5dtrtTogNnnZYaEn2B9PkoObg2qJMCKpYgmwWzCtSTOT4pAUnEgnt7OxoZmbGKcKsD9Ydqf0UoVhspqenzUbzfYKee0LVwuGwU74JZuKZpEkBOOnp6dHU1JROTk4eBVPR6HZ3d3ueMkFTZBPMz88rkUhYPcRziJQSuSagBUwuWQCApuxzZ2dnyufzBkE4F9kzg6m4eGaxokWjnTA/zrLp6WmHS4ZCIa2vryuXy1mR8Pz5c01OTiqbzSqfz3u6BYwa6xrJ7cjIiD788EM/U4ASsHWEB559O2otmUz6XhQKBTNxU1NT6urqsj+7q6tLZ2dn+v3f/32rJKhNkK339vYqnU4bHEUuHWTAOXOpOWD9S6WSqtWq8x6wqTDJg6kTV1dXyufzPv8pmFF4ocoBcJJkKXm73VY8Hnd2DI1bkBxA9k+D1mp1skgYE8naYk+BXYUxz2QyVibxfFK/0FjAoBGwFQqF3LxQl2DXGx4edoI19gwsI0tLS1basHaZ78w94/pRa/BsRqNRA7HDw8M+i2jqCQ6FMCGQMBKJ6OzszCAo15/mlsRumtqgRD9oT0Mdg3yeOih4VjWbnYka8Xjc9wlwodVqOXwW68Ts7KwJknK5bOCTGgG7Ae9xenrqmvD4+NhAcrlc9rMhdaY0lEolpdNpHR0daWZmxoAcbDg+YADYp0+feiIDYZYbGxtWiT48PGhhYUFDQ0N+hur1usPRisWihoaGFIvFrBikJma/It8EH3lvb69D1VDzIPXn2ZuYmFB3d2eMLkGHU1NTVt2yN7bbnfBTrFuseVSxKysr2tjYsKIBNSW1M9MjAG44k8rlso6OjjQ5OelwzLm5OWcRcQ4tLy/b6sT9haw6Pj62ha9arSqfz2t2dta11VdffaXDw0O9ePHCNQAWPMA0xltGIhE/R/QfUqc5Pzg48HhQlDGEEwIQwegzOnBoaMhKZT7TH+b1Gzn6v/vrezfht7e3j8aTITFqt9v68MMPLe1CJsVD0G53jP8EK/yFv/AXtLOzo3/2z/6ZarXao98TlJl/lymXHiepB6WXsDSwC7B7FBc0V0iZ8L69fv3acihmfVM8gYaDLnMYc0ghX0ay9+zZM49L4eAINt5BIIEGsN1ua3JyUvv7+078hLFkE8jn857TCGMRbJC6urosV4clzmQyymazyuVyevbsmdbX1xWLxbSxseGAEA6ZpaUlhcNhvXr1Sm/fvlVfX589K5IcdlcsFnV7e6tqtap4PG7ZytnZmVODaQSQLE1PT+vw8NAS7K6uLr1588byrcHBQb18+dJNz9TUlBnK8/NzB2Qh2WHjTKVSbjiQceJnRQLIZrm/v+/mbm5uzsU3460ABThEkVDB0qTTaX399dcOdKJQorkNhs1wbUiJpxiPx+PKZrNGL7u7u+09PDs7swQf1YAky5tDoZBnwlOgV6tVXw98hOVy2X4pZHt81tnZWR0eHroZkeQGGpnw6uqqUXrYCdgElBjRaGekFNJs6b1yAHanXq8/8nbyLAEYIe8LKg0obmER2+3O+I1IpDMmCksDnrJ0Om3Jf61W09HRkZk5fJvYKXjmUUUkEgnNzs76vnV3dzsjADUFElAKN7yt+PRgPnO5nH71q195Fn2r1UlIRlWSTCbNnF5fX+vdu3caGxvT06dP1d3d7UL44uLCHkWAHIAkvHWRSGd0H3smhykWm2CY2/n5ua8pBUa7/T5cij2N/ePw8PCRbBG5HHkV5XJZp6enevnypXp6evT69Wvfc4rp2dlZ3d7eKpfLaXx8XCsrKzo6OvJnR1URTCgGXOrt7bVih/UEU0USMJLd8fFxF1ykUIP+c9+wepTLZQ0NDbmAZ0wR4xaDa5nPQvN9enqqubk53wua52q16jUG0wL7Gwy7AegFxARoJbAQKw9MKmxXUJWTzWY1Nzeno6MjNxAzMzOKRqOeFz88POw9bnx8XGdnZ95rycYol8tOdOZ5ZARTs9lUJpMxkMceeHd3p8PDQzcMeBKZlQvATcBYo9FwOjnNAucD4UaAwDyLW1tbnpHNnsHPSHoEasBWw8bzfBACNzIyYnk+SiyAu6+//lr1el0rKyuanZ114Y/0mEIfSTRgWnCMJoU9JMTx8bEb5P7+fqXTaaumWq2WwWPm9NKcMcaKPahWqymRSPhMQW0F8I1a4+joyGfy8PCwry2he7DiXBvk3DTZwXOLa8esYFQW7EdM2Li9vdXk5KQODg50f3/vbICenh69efPmUQ0D8IMiCvl4vV43G8wzRzI9ird0Oi1J3v9ubm50enpqEFnqNJQQETxTwXObz8soN55/yAn+nEwXgs9Qk9GgwEwCUkG+NBoNT1hJp9MGCQlJ5Z7gnQf8oWZBYYhl8OHhwWuaejZoLWm1OrPRg6n429vbbqT5vCjfAKBRRdCYzs/P+79XKhWD3GTdPDw8GCBcXV11A/711187XBfwnpyUg4MD17FMP2BOdqVS0SeffCJJ9kNLncYuEumMZWw0OuNM0+m0LTxkI7B3EQharVZtL8vn89rb2/MZikriu5Yi8h+4j+zv7N0w9nyHTCZjawWBZ9FoVOl0WldXV+rv79f6+rq++uor526goGTdb2xsGMwgM0GSPvjgA3311Vceu3twcODzNB6P6+TkROfn5wZ+2CtZS5xRqH5g2Mvlsm0NpVJJQ0NDSiQSurq60vr6umsgng+CQnnO+X3UXPV63cFy/P7fvH7Y1/dqwrmRMBBIS5PJpObm5jyHFykKBUCr1fLYotvbW3399deam5vTyMiIfvazn+n3fu/37JEJBpgF/50XTex3ZYywLmygQS8xKHEk8j6BGTTvs88+U6FQcNInM8FhrimGpcfNAQcHbDsJl6lUyvLBIFMPIw4yG2Ta8/m8ksmkR54QNBUKhbS5uen3gdEYHR1VtVp140kxjUQNlPzNmzcuYGCkYaNgDbLZ7CP/IamRyF9IGMYHNDIyosPDQyf9IvMkYGZyctLfH19TsVhUtVrV8+fPVS6XValUbEcYGRlRJpPxBswICw4+ijG+I55ppJevX782sk4zASJMujOSa3yQMPUwwDQ5XLfr62v7n5DxEGxHYYk/9OjoyGnIkjwaDo8VSHVPT4/nddfrda2ururdu3caHBy0j7Fer7tJhtlk3UpyE0NhBgNxc3Pje8DPwERQvDOeJ+hFTyaT2tnZ8YF0dXWl4+NjpdNpM8ccNgROoSRgDiVeWNYzcjxJBkVgL4Py3VQqpUajoc3NTUnvvefIPXm2r66unDEQRHmHh4cdSlStVlUsFiXJChzQXNbB1NSU5eWoZAgou7+/V6VS0cHBgRUTJPUiDQd9f/XqlVZWVrS0tOQiEtCBtcHnpYg/OzvTJ598olarEwBIMilF3+TkpItS9gT2OhQJQeCup6dHa2tr+uqrrxSJRJTJZDQ5OWl5I+/DAUxDyLqhwUCiSHje9fW1YrGYKpWK/fGlUkmSnN7PGhgdHdXV1ZXD9Qi0Ij2/2Wx6hvV35eGwTuxf7K00mUjAkRASQgcgGQp10sa59/hc0+m0w4q4xw8PD06kDobahMOdFNho9H1qMAXV1taWGTkYaGS0hAs1Gg2trq6axYdZC/rzGb/FGUbomNSRc2YyGSt9bm9vNTEx4XtydHTk54LPS8I6YFFXV5cZX8Ij8UIfHR1Z4cSzWalU1Gq1XCijQOG+FItF70cAcQBnqIkIUwIQazabDjoiV4BmkPdoNBoaHBxUIpGw8o359MFxkoODg3p46MydZ341+wChV6haUAThD729vXUjCPiLFeDg4EA3Nzeam5vTxMSEisWiwaCBgQGfG+xRkpx9A1iFvx4JOGcB/lX2e5gmPOabm5uWuENi3N3daXx83I0Uv4fnEkabfR3vNEos9ojb21vl83k35dwflB4DAwPOKSAIC68pIAINS7lcVrlc1sjIiGctDw4Oant72/s8YCR7EDaY5eVlhUIhA1ThcNgBgZOTk4rH43r37p29uaSvE7BIA8qzHQyfhHUmOwR2FCVOo9FQIpGwqgSACRa43W4bUKcxI/OEaRQAYUHVYpAtBIxqtVoGwThnqRUgHTiDec45szjbIpGILUyMAMMiEAS02PNIlWc04P39vebn5/13UOwQKgZR8/DwoDdv3ujk5EQvX770XsI6efHihTY2NiyVj8fjOjg4cIYBoNCLFy88JrTdbhv8293dVbVaNSlAGvjbt28Vi8X07Nkzs8L9/f3a2tryXs2eyhjDoCKNcXr5fN5hjzSd7KeVSkUfffSRQ2d5fmOxmMEk7DHUkMEsoWw2q5/+9KePQs8ymYxtEWT6cEZTf7IuAfDwglPfhMOdsZlMSIlGozo5OdHIyIhre8iUVquTAVIqlRxomc/nVa/Xtby8rFQqpWq1qqOjI2WzWSWTSSfOU6/H43ErHs/OzpTJZLwvAa4fHx9rYmLCajQsKADFBPoVi0WVy2UD3+122xlSv3n9sK/v1YQzFoNGLpVKaX19/VGRDQuChGNkZMSp3zQzsOL4x//kn/yTur6+1j//5//cPl/pPfPNhsbvAZUO/owkF51IGgECeMBhYwhrury81M7Ojous0dFRs5kcvCBrLGw8ISxyCuuVlRUjU0jTQFfx0nHIgMhx4N7c3Oizzz575O8DnQK56unp0dDQkNFkmDASw0dGRvx7eV8KEyRsBN5MT08rmUzq//6//29dXFzYxx2Pxz1bfWlpyf7kra0tZbNZHR8fa3R09JHkPRqNOrwFWSUzbUFYafD39vacUg2iNzs76+TXn/zkJ2at8BwiAUNOy3xZvGAEn1EkB6X+HCwUBrVaTZlMxsAMs9hBMlkn6XTaaZSZTMYFCiFBgDTIV1utzkgTxsy1223l83l/zrm5OTP6HLJkKiClpDgLqj2ClgXGwIyOjkrqFE/IgZFmUzxwYJ6fnysej/t3Iu+9u7vTzs6OEomEmxDGO3EoMgOYxgHpUzBUjaY7yFLBCiwuLrrApqFDLpxIJDQyMuJr1NfXp+PjY/vKeE5gQGH3YEjz+bzy+bxqtZrtAuwPgFDDw8Oampoy+02Tz/pD2gwbCQvEd6ao4h4jBRwdHdXnn3+uu7s7/ehHP/Js0MXFRSUSCXuXKa7IZshkMm4KM5mMU2FR78C4cO8pACORiFkx9oTr62urYYLFPI1fOBz2KCuk54ziOz09dSGZSCSUSCQMNiCrpAEmZIsUZWaIAvgwYYGRfzc3N5bxzs3NqVwuu2inUajX62YHWNtnZ2cqlUo6Pj7W8vKyGQw8otL79O9YLKbr62uPXCHFnHuKFenh4UH7+/v2MVJAIRFETYFMkyAniujT01OH4cAoSnIxPzs7q6GhoUcJ2DTLwUCmsbExr/P+/n6VSiUznlgy0um0arWai7qHhwePhOK+3t3dKZ1Ou5iHyUIBAcPMGUFgH15yJLeVSsXXApA5+BxnMhk/85eXl1bNkIVRLBY1MTFhaTPFf6PRcHI9fmTUcoeHhzo5OXHoH38P5juRSOjNmzeW/7MfBlksGsvV1VVls1kXkpFIRJubm+rt7dWTJ08MWhUKBVUqFV1dXWlxcVGpVEpDQ0P2mPJ8393dPSqQycfgWvJZHh4e7DO+ublxk0tR3d/f7/OVQp/zE4b49vZWs7OzfsbI/Tj7dnoEex5sMT8nyfkWrCsARMgGQNRYLPZoL6EJpfGktnl4eND29raWlpY8fYNzLtiEwspz9iAdD8qd2fcBSMi2ISguEumEWCIz5/MhsSaxP3itAVEAX7EFAooANhLyyr6GChPwkfdE6XJ72xmh293drZOTEzfy9/f3Oj8/t3IieMbz/LVaLS0uLury8tITVHj2qfUgWS4vLx/l9wBmEZIIGwvjzaQd7itgNaP3qG/v7+8NgJMgjqUnnU47sAtworu7M30AZSb2ruvra4fwJpNJdXV1PZosRCI6tgpeAN/k7wwPD+vly5fq7+9XpVLRzs6Ozs/P/fxQR0cinRDkpaUlDQ0NuRYHMAYkxt7RbDa9Z21vb6tWq+nh4UFra2tuclHgcs2pH2DmT05OXD/TRCONx24WBOE4Q6anp608Y+1g5yAlnxGtNzc3BhxZK+VyWU+ePNHV1ZV2dnY0Pj6u6elpy9Hz+bxr5lQq5Xu9vLysdrv9aO8ZHx+3wgtyAZC3VCpZEZJOpzX67WxzgCHW+tzcnCdJBMMeT09P3ctMTk6aJCT7CVXHb14/7Ot7NeEECHBwr6ys+MCGJT89PXWBS1Ixjdvy8rI3/dPTU+XzeTOgyWRS/9V/9V/p/v5ev/71r1Wr1XR+fv5v9IkHEXyKU1CxUChk+QgbY1AqiKwnm806NAzGANYRZpUFi4yRZpMQLhpEgh2Q5M3Pz6tSqXg8ViKR8Bxs5E8geZJ8+FAcUchxYN3f3ztIC38f32d0dFQLCwtOwqV4YqOmEP3666/dGI2Ojmp+ft6eEBJaKbiR24JWg2hySCFXPT091fn5uTenw8NDS1cpJsvlslngdruTlptMJi2JDoU6Y7lgW0ZGRsw24J/i/sNIXF1d2TvEgcxmRqJ0PB63jJWmBbRbkoPDYABRH0xPT0vqSMFrtZqbPw7qsbExswp8P1IwWYenp6dqt9sOVCHUQ5LXfDqd9gbIlAHY89vbWxceUucAzmQyisfjOjw8tOSUUCAaNwozmKNsNmsmCo9hX1+fTk9PVa1WzRwiVXr+/Ln9fjMzM2Zug95WAnH+dUoMbB4w8RSaHFwDAwNKpVIGtphle39/7+C6zc1NN6xcy7dv32pwcFBn3840B5wBOInH47Ye0IQjPby76yRp//KXv7S1YHp62qoEgsfw6uNdC/rAJblYhfGR5JA5RvIwxo17AoAWCoXsBcvn8/r1r3/tIJy1tTUXtEEgAKkjCcR4uilQOSQBN2DgACuRt62urrr4Q5IISo6SgsYVDx6sA6xFPp83I8M+iSIHGTC5CdVq1ZkJgG0UQAR+saaPj4+1u7vrYj2bzSoWi7nRo8kElKOQR3WARYORfxSlMAi7u7taWVnx+DHOCvbHTCZjJpJ7DwDCaK7R0VE3Vnx+EoCDidRSR54cfM4eHjoTH46Pjy3ZfHh4cFOFXLVcLlvSjQWF86RWq3nvhAVFgg+LF/S/DgwMeIbw+Pi4RkdHrZLAa4zKBzAaa0gQZGb9jI+Pa2try03A5OSkCoWCWTrUH+SCLCwsKJfLKRwOe/IDEnsUCgTL4V1st9vOtoDB4qyo1+tm29mj4/G4ffqcH6lUyiPNtra27PvH2nBxceGUYgBPGh5AM86u7zbp5XLZSdzsgb29vbYfAYIg3T09PdXq6qoBpuXlZbPeQYYV8AKlFEpC/MnskzzTqI7GxsZ0cnJiBm9vb0+ZTMa5I+zFNH6Dg4Pa3993c1OpVPwz/L52u+0gRtjJSCSiZ8+e6fj4WJubmz57URV1d3eb/ICQYASm1FFDEQAlyWv/7NsJG/wdajruE+w85Anhn7yi0aibH/Z7Gl6AbWwBWMq++eYbN8S5XM4EAHkEWNlQASJzRvob9MnDYCLPpg6G0eb5R8VBk0mjxhnBqELqQs5batKjoyMtLCw8sregPry8vNTh4aEVDzw/kUjE7Ce1JjlHpHkDOPC+fAbCNRcXF22lASTY29vT7//+7ysUCvmakdOCvbHVaunZs2eeKIP94aOPPjIQBECUy+U0NTXl+pDPT91fKBT8z/HxcS0vL3vfLZVKZv6xh0B0oXji3JientbExITtWWNjYz7nWG/sK0zUSKVSBjRQbvT39yufzxs0isfjOjo60suXLw0sDA0NKZ/PO3gxkUhYOYTlKKh25fnt6enR0tKSstmsgaP+/s7Y56urK5XLZdeESOd3d3f1W7/1W34erq+vlcvlPLnp4OBAi4uLqlarVjhQ0zBl4ezszPYI6giuJ2qZ37x+2Nf3asL7+/st9aHxCTIz9/f3SiaTmpiYsKcHhD+RSOgP/uAPFI/HNT097QcAX3G5XNbFxYVmZmb0x//4H1elUtH/9X/9Xy7opPfJuRy8NOI0r5IeNdocQjB3sHEwrUHGIplMamZmxoyf9N57zsYMOgqSHJSXg2yDikvvwYGJiQnLSEj6JgSGjbKnp8fycJpFAAi+49bWlpkjvm+z2dTh4aGRfpoawIX7+86cyLm5Oc3Ozhqp7Onp0crKigulk5MTM08034x2waNSr9e1v7/vlNerqyuDEMjBKGRJpsRHFmSfZ2ZmNDY2ZjkWBVqtVlMoFHL4DmtraWnJTAXsNgUpYEBQoshIja+++krz8/NWRVAEcSCyydIgsB7x8kvyXFvksrFYzP57WB980rB56XTarBfy1pmZGaPj6+vrZsfYzJvNpj8LjSz2Aa4NCgjWAyARwUZs/JOTk5qcnFQul3OxT6FEU8/YjOPjYxdcBJEwtoyGkJn3HABB6Sb3iIJWeh8kBPtAo4AvMRKJ6O3bt5ZTXVxcWBXAmoZ5kGQfXjALgZCy7u5uzc/Pu0CmyaBYDoJz1WpVL1++fFRMB73VNPQ03oT1BS0RfHfYDoCVq6srI9WFQkEnJyfOOsB/eXp6atnxhx9+qI2NDYXDYXux8dIhD6TgojFEPgywAMP09u1bTUxM2E4zPDxsxhOJNA013nrUEa9evdLr16/tI6TYPDs7cyN8cnLyCOgCuAqqhA4ODjQ/P+99j5CppaUlTUxM2NsflGvf399rb29Pl5eX9kAH9wU886R9U2yzFxAARZI6hV8oFNLJyYmL+VCoE4yHzYFwKVg4nveRkRE3uV1dXcrlcgaRJXkcYDQa1Zs3bxwABwiACohJAoC9oVBIExMTDoaEOcSHzESLZDKp0dFRy4LZp9PptHZ3dzU4OOiE8ampKcViMdVqNY2NjXnePGcGgYDYiEa/nUGLNQcfOQnS+IODFohWq+VzIBKJeD+7uLjQy5cvrRLo7u5249HV1aV8Pq/x8XFfR6T35+fnSqfTLlg5D/GxzszM6O3bt9rb29Pc3JyePn1q8I1ng1wQwj6Z6c69JPhzYmJC6+vrur299XguACWkmRMTE2bJAKMAoAHPOJNgCDm3Ly4uDGJhOenv7zeYmkwm1dfX5waCNGWuMbUD63B/f98NOWBAq9UyyEHdgaqOvYDvsrCw4EAvwrI469jH+R59fX3ORcEu0t/f7/VNHRCNRjU3N+epGfPz8/bHExTFdA9Yy7u7O83Pzzt0F6UZ5wLnliRVq9VH1kH2OpQtgLzX19dWApIAfnV1pVQqZQ+8JK9Frg8NON50EvNLpZIbHQBSzjbWMNYDQGAacM7CWCxmxpxaV5K9+zScgAhMDohEIq6Jg+PHDg8PFY1Gbedhv2evpw7l+eRzYx3hjMW+ghI1Eol4vBdrCdAAdQNNK3Ur1491wPWPxWL67LPP9OGHH7qu2tnZ0fb2toPZxsfHff7w78xob7fbWl1dNWh4cXGhP/iDP9DDQyeIOJlMamlpSTc3Nzo5OVE2m1U02hmnyLlFDY66DcUDmSsAyTyDQSacsDhqoVQqZSJwaWnJoaOQWZubm66lGMUYrC+7u7v19OlTq1AkebwvoNrQ0JDW1taswgBoIxxya2tLsVjMe7GkRzZb1GlI2gm7GxkZ0f7+vs7Pz50Z8PDwoPHxce3v7/tzh0Ih5fN5HR8f6+XLlwaBUJCiMsEeEuxpAAt/8/phX9+rCcf/RTFD8x1M8AW14dAkLCiXy6larTqEIZ/PG4kfGBhQMplUNptVNpuVJE1MTOjP/tk/q0qlol/84hfa3d19JNcN+iRZRIQM4DfGl8hGF41GfSAyRiT4d0dGRlxcI0vj90gyqsxGCKoNC0nxwoGJTxQp5tramiS5kAYRZBQUvslSqfRoDBYb8v39vZaXly0h55AgnRJ/OpI7GvKzszMVCgWtr6+rVCo5KZhE7UgkYjlLpVIxw0xBiyyLB7Ner2tra8sNTl9fn66vr3V+fu65nBQJkvzdRkdHNTIy4jRZZJ7z8/MugIeHhy23grFHgkpxQnPEtQQMCIVCvnfIw7AURCKd2ZpHR0dutilAaEpgrAYHB32Q0HTyfbq6uh6lniPhAqjALkByMf41RnvAWpAOil+fDa+/v18LCws6ODhwsNDl5aUlpuQesLbwczN6jnvS29urmZkZo7Zcn1gs5rRVZMfBtQpyDbjBgQaSSwHG2uXFc0hz1Gq13PSHQiHL0E5OTrS5uWklDAXZzc2NGTBJLhqRzvLMzM/PO7yPZhtVCAwfieAARDTVNBsAPNgXKMZpdoPvE7SU8NnYF371q189Yqqx58BGsn6ljlQeUAtfGTJfrj/zQC8uLqxOaTQaBsuQxuMXxEqA561WqykWi6m7u1vpdFrFYtHMoNTx/K6trTmI6uHhQR9//LHevHljPzajhPC4ItXl/qNiAlQkbOru7s57AoVK8BoGGXzAFu47f97V1Qm6HBwcVKVScUAWjRwS1svLS0t4aSrK5bL29vZ8lsAGLS4umpVHsXR/f++z5+joSJVK5RGzPTIyouPjY38e7jvBQEGPM6n2+OFHvx2FRfowDWS9Xnezjy0FJpGEYJ5VCkuejfHxcdu8YN6QM97f39vuA6sDw06RGlwfWDCC+zKqFoC34EhIPMapVMreatYOY8WmpqZsZ4GNBbzhs01OTnrszcLCgsG4m5sbPX/+XJVKRYODg5qfn9fZtwnxMP1Pnz71GRYKhTx6p9VqeXwhKoqzb2ecT05O+szHd4xaD/b/5OTEoWcA15eXlx6xim+eBoc55I1GQ9ls1uGbAMBYl4LMNnsiQAPgFUB1Mpl0KGMwuTgIIlJ70MRWq1UnutNss0dDMDAxAPUaBAE1C0w7gCQ2JoKpaCR7e3tde+ATTafTBkHOvh2BSSYCZxAAIgw7KrJQKGQwHDUgCgz2VnJfAASxpwS94uypnM8PDw+an593Ng3NMI0Z+yeSfdYWyh7UTzTUk5OT3g8AuwFsE4mEFhYWfC/JvsACJ8nrNRaLuVkGsMaiSf2CQoR12tPT40DUiYmJR98T9QnKNOT3gCPUEyMjIyaE+Pz8kzqdsX67u7uanp623QBAq1qtOn8lHA5rc3NTz5490yeffKKtrS0DG+w9KJ4ODw9NFLCmaTD39/f15MkTW5vIJGAM69XVlXZ3d1UoFNTX16fl5WXfi0aj4WdjYmJCZ2dnDpDl+abmYj0yLWV3d9cJ/lNTU65ZOD8ODw9VLBYNnqCEu7u70/HxsY6Pj/XRRx9Z5bexsWHrzsjIiIkdJmcEA4xR2VJLkOBfqVS0sLDgsXpM/gFMLJVKVpUQvsi+Tv21sbGhYrHokW3j4+PeQ5i6cnR0pKmpKVWrVU1NTZkpZ03ynQYHBx3o9/nnn9ty8Yd5fVep/EO8fuj3/4/1+l5NOMm7NFpIR2jkYKJAuWnauEG3t7f6l//yX+rTTz9VPB7X5uamxsbGNDc3Z9/l2tqa8vm8PeOpVEp/+k//af2Lf/Ev7MUEWWVjGh8fd2IxSOP5+blub2/1/Plzy19pnJvNzmzw7e1to0IsbhorZEeknOIboiE7Pz83Wyp10NOenp5HaCobQZCtZ9PhMOIhDqJ4jIzAg8sB2N/fb68IUvWLiwuPbdjc3PRmR9NDg3x9fa1SqaT5+XnlcjmdfTv7Fek1EhQ8oldXV/axNZvNR159ivrg/6doA7Sg2BsfH/cBMT4+rlqtpu3tbUnS5OSkhoaGHJjGxsKhQrONV7FQKGh7e1utVkszMzPK5XKenQu6iCwNWdbe3p69syC7zE/f39/3gY/MFMaXUJBIJKJ8Pm9Ze71e18LCghOfBwcHNTIy4hE9rVbLrBTBWwBVNEYU7Ug7JdlLjrcJdj+dTiuRSGhra0vJZNJNOQUJ1xt5GkU8jR0NKAcl0igkd11dXVpbW9PFxYV96svLy+rq6rIsnCInKFXlmYY55P9TdAaBI0nOiUBKDpoNCzIyMqKVlRU3+VdXV5YyUryNjIz44O7t7XXhjkIHxgEfJDNTOZiljlyYdYi3jO8TRNqZyRkE+WCVCOJZW1vzHNvb21slEglLxEG/KUzZVyiUaGqRjtJUEs4FaFCpVPTLX/5SZ2dn+i//y//SRRc2kFKp5LwNwp8uLy+1u7urr7/+WrFYTD/96U/9XP3qV79So9HQ0tKSi8NgEEs2m7UiiaIHtcbFxYXevXtnlg5gC+vP8fHxI6BjcXHRgUrsgUgypU4RDaJPWjcFHEnd9Xrd0wZowLiXfNd2u21AI5VKaW9vz2MWaW4Jy+vq6oSAJZNJN/8zMzM+H/Avsp/hAWdtVKtVzc3NafTb8Y94JFFewfRxTl1cXKhQKFiBRZjZj370I/tCb29vPQYRWerZ2Zmv097enpuy+fl5FQoFbW1taW1tTe1228USTRj7QCKR8CgyCk6AYpptJLQ8yzw3khwqxHnPs5xOp31mEjJFUCdNCb7GkZERvX37VsfHx0okErq7u1M+n9fMzIxH6ABehsNhj5A7PDz050DeCRiC+oFwvKGhIbNCMzMzBliOj4/V19dnqwEgB3JlVAETExMu0q+vr22lIdMjEok4lTuXyymVShlAASCEiQZEgsFlhCZqL+wU7Cu3t7cOs6MmgInixdpkv5fkWgd/M5kR2IxQUQVJAqTJ7AWscfZS9jUk9oeHh2YF+X1YnrB80QgC4MCsYQcKkjOS3Gzgh+XzBIkLmgxk+AQi1mo1B+dy7bErsD9LclAVa517NDExoXK57PqCM5/wKpQ4ANCwq6hWOBtqtZq9wEweYM+jyQI05zzHIkmzyVqp1+sOGQ3W2CiAJNnqh22GXAJqGT4De3UqlfJ5xuhdwL9GozMTenFx0YnnNIKcQ9SYTJfAKola85//83+udDptBpdaF2UPew6WuaB/fHV11ecq+xQZTkx3uL6+1urqqvdw1KI8C5yhp6en/oxB8DdoFYWAIAsJ25D0Hvhvt9va3983GMAZg722u7vbZB42laWlJd8/QJ2xsTE1Gg2nlFObDA8Pa2dnx1YNmPtCoaBaraa1tTXd39/r8PDQPv54PG5LBkAgyjum1DQaDWfZcL6gyNrZ2bGCBbKGqTlkAdCf9fb2es8EeKDe/c3rh399ryZ8e3vbKD0FR7lc9sOCxIfmjQOeQw954+TkpGXViUTC84olea4gI7aQNQ8MDOi/+C/+C0v6SBKcnJy0p4iRSjTh7XZbOzs7CofDbnak9/OreQBJig6Ol5D0qKGQZG8Q8kiYCIq24KaMFwgJLoxisHGnecL7icScMSqRSGeOLyzDzc2NXr16ZTkujGMkEnG4GvLHoOyIkCXQ2uXlZW1sbNg7HvT3wCTTXHK4IrujGWOToqiAncOXihSJA5sE5Ugk4uCLTCZj2VSr1VKlUrH/MRaLSZILV77TxcWFZ0kipQQ0QB1Aw4+/ZX5+3smXbM6np6dmgPC/Y4mQ5CTdUCjkueUw7cGRaDQCuVzOXrpwuJOqDDILU05xAMAhyXJ7Ci/WBywCYT/kMEh6JBelwaMoXFxc1Pn5ucrlsqQOQDIyMuIiAgQdUKPdbnvWdldXJ0Rtd3fXY+Hw8EkyoIRvDM8zBTzrF2YEtgHJLxKy2dlZsxmwhOwbs7OzisViBqBAqZHEstZRf/B84dnFR0zAEQn+FApnZ2f2T7LG+Nx8J5p2EHzYI/a6+/t7J+Q3Gg2PDCPNF8krzTwS1GDGA409Mlj8aEEpLEGEt7e3+vLLL/Uv/+W/NNPNwd3b22u2BFbv7OxMr1+/Vm9vr9bX180mf/TRR+ru7tYf/MEf2KcJS724uKhMJqOjoyOdnJxoe3vbTRvyaQq/gYEBHR4eanJyUolEQqVSyVJoDnlAga6uLgeQYQsAcOT+dnd3u7i6u7tzA8Esdcbq4MGEEacpwjt7e3ur7e3tRyoPRiOxf1BcUNzzDMKs4v8OWoFQSjFuicaB53JxcdEZFtg3AJgIB2OkGiwVrBQqiru7OyvH8Ox2d3dra2vL49VIGuf3I4/+8MMP9fbtW9sEGIl1eXmparWqh4cHra6u6vDwUDMzMy4MkfKy5kKhzvQJFDPkjLAnIFnGU9hut30mEIjaaDSsAuIMmJubsyLo9PRUJycnj/I++LwATJLs5axWq5aI0kTxzIyOjmpmZsZyyxcvXiiZTHotMOGC4pjPB+PL+DvGi7KXcV8SiYRisZizZZDQ1ut1fzbUBIxSQuE2MDDg743Ch4BAahB8rIza489p/gAosbtRjFNzoAygQaBpm56e1tm3I+poSNjLCADlbEepAcgUBFUGBwftjcVeRH4JzcfU1JRBY8gYzgPqK/4MmXM4HLYvP3iuUFNyptIkYD9jTbNvA8JKckMGSBCPxw0ewPLlcjknZwcbaYL4+G9kG1AncJZzRrBeqTsBqwAbSqWSayQCCAnDxNaHvY0MIeTbnA/cf35H0IJGo819g0iCnMJSRWNOKj3rCtAmqExaWlrSxsaG83EAZvgs+/v7JgqePXtmELNQKNjmEZxPDhDJOpyenrZCrVAo+PMTrLe5uekgyrW1NcViMdeG3Ff89px7+JqxdaAYoEbALkHw6sTEhFV81KtMAGAvnJmZcb3abredIbO7u2tgg3MceTh1eLlc9ujNfD5vAPPrr7/W/v6+1tbWvO5RqM3MzOj8/Fx7e3smKVZXV9Xb2+u6h32UHAyUMwAo7DH4uCGY9vf3LYkvFose6Qb58vDw4EA/6m5srwAPv3n98K/v1YSD0LE5FotFDQwMOEUY9vny8tKFThDB7evrU6PR0O7urpaXl41+cqjkcjmjyBQBqVTKwQmE29Bk8XnwHsF61eudGcTJZPJRGimNMy8OgWARSHMZbDJolqVOccABBsCA/ArJIQ8qfwd0GEkycj82VhrdVuv9eAzkUwsLC8pmsz7wOcSRSvPvbJD4kGB97+/v3VDj245Go/bb0cgBOMTjcY8UwReGP1l6n1APYk8TRxFAYBXSKVhy5G8ffvihWRDmD9frdc8eJcGTQxN2mhFUi4uLRsXxke3v7/v6gkjzeQj24POsrKyoXq/r3bt3Vj4wuoJrEJQ8EtgCKIQXCLYOFQMsGmwWY4ZgskulkjY3N31gPjw8uEjLZrP2Zg0MDLhZxO8VlMcdHx/7cGXTRXKGJIv7Isn+dFKhJT2ah00wCEUiI6gAjABdaG5BqkGeAQMkudFqt9uWeiJXptAkyZ8Ghus8NTVlFQ2MDOsIlBbkmutPwV2v15XNZtXd/T4x/vr62jK/eDxuRUA6nfYEAFQtNH+k9lMQBX3wPPt48bq7u80GkT7e19fn/QdFCnYQlCZB8I6womq1atken4XryV7HupI6Y/DYVxll1NPTo6urK/3u7/6uQcf5+Xkf6NwvfLJffvmlfbMEBIbDYc3MzNgysbGxoaurKzeDNM14xGHBkHkzF50ALPYqikf2LywLZCRQXLMvnp+f+x6PjY1pfHz8USos0tB2u+1QSDx2MKuEUNGAo1yiOaMJ5ztw/S8vLz3HGK83klRUA4CGjUbDwBZ7EUVef3+/2WFArGw263v85s0be+oBcS8uLjzqB6UM4Ch7L+w7wWAALHjlS6WSQYOBgQGHTZGYjHyYMXOcB5zFzCmHzeRM4Zzr6+szGEaWARMn7u7ufC9OTk50c3Oj9fV1sz7s45wpgHK5XM5MsKRHAW7kGQTnyiPNXlxcVKVS0du3b7WwsOAznDMVSTlNYVdXlwP8aMq536hx4vG4g/eQMwe9tKPfjhEL5obQ0ATfkzNckp8R9iT+Hnk42CFSqZRrCuk92Mq/s1/iE8abSn0CUy3JI/w4n/Gtco6zv3D2TU5OOkOH3IPg9WS0GHO7UZuxRiAzIpGIQWCmINBIUYPBzvf29npiByAY5w33G5Yb5h5QmWeA9UDTDbuKx7ndbptxR4HE/tjV1eX7COtO7UDgKnUl+zzWL8BfntNaraaRkREDJ6i57u7uLKcHOEHezT5YqVScb3Nzc2MbjiSDluQnsP4ACx8eHjQ2Nqb7+05qeiQSsaXp4eHBakjUHM1m89E4rmAIGcDL0dGR2u32o1wQAIXnz5+rr6/PKhI+I0Fo+NoPDg4cIMszgGICMKK7u9sycGr+lZUV18iAL3Nzcz7zqtWqUqmUVaTBhhJ5dblcVnd3J/0epVG5XLY8m32E82l4eNhBZ3jXw+GwFhYWbC9idC8WBNQO7CVYbQGdAUOZNY5dJhaLeYoKKpmNjQ3vZ3d3d9rf39fc3JyT66ltg6AfICeANgGe1B7U5KxRft/NzY3zSer1uu0TgD3BqQvBveLf9vqNHP3f/fW9mvBWq+V0bqS3+IdBxUmGBdFGks3BA1v95ZdfOkzg8PBQY2NjblLW1tYepaNzEEQiEa2srLiZ/d3f/V2zsKQ/7uzsWCbKoUejQIPCJk3hSQGAhJjmgo2SnwuilPiLYJKk9+FOIGQg7jQ0QeaS64f0A4aRQ5qHgmCKcDjs2cew7BSDoPgUc/jDABMoPPidknydb25utLGxYf8vjCQFUjQadVPGgQDrRvF2cnJiWfmrV6/ceLMpB9m21dVVNw0cDrDT5XLZKOz+/r4k2W7AZ+nr67O0mf/GRkgAC5ssDDnXA992b2/vo7E0NAk0mbAzY2NjZgsXFhY8do71DguJbHJ2dtaeNcLNqtWqi2QONa5vPB7XycmJsxVAzXO5nOdllsvlR0AHTAXrkHuBBJuwIIqOiYkJjY2N+b/xe8le4Nm6v7/XwcGB1yxFKD8DKITkjoYb4ILxUPw8zP74+Lg+/fRTe+kpFmFKYrGYm8CTkxMXvBRAvB/sMWFcsVhMxWJRPT2dGb0036w5nlHYMA4n1izrOQi08f6sSVgnJIXMYCUciHRulCPMyoZJrFarVvKMjIyYxYZpR+oYBPn4u/f391a2RKNRj4vp6urS9PS0E8XxgDEGbmJiwix3cM4pz3A4HNaLFy9UqVT09ddfO0iJ7/rw0BkDs76+7vGEMHusqVAopOXlZZVKJd3f3+v58+d6/fq1RkdHPTaIIh7LCCx5X1+farWawwBJhS0UCt7/UqmUAwMBPSm6ARsHBgY0Pj6ugYEBj5ksFAqamJhQoVAw8BQOhz0hYGJiwoUsTT/gVb1et2w0ODUheK4FgdmLiwt77KLRqNPCAbdobuLxuEe18TzAzNBwIBMNjm8K7tMoZ7g2yWTSigfYndnZWedCxOPxR17Q+fl5S0Kz2awLSJowEtRhenK5nF69eqVnz56p2Wx6D+E9CO5ErUN4KmBMNBp1UjI+WPY6AvQmJiaUz+edfo8nmyaTMNe+vj7t7e2ZlcPKMj8/r8vLS/3617/WysrKI/sS1y6o6IAxxXbDvcCOgBpgYmLiUXPO80NOBLUEdpBMJuMzgpFFKFPISQGQAtzr7+/X9fW1n1lYSkBzvLo8bzR/1CHsT+yx7Cko9VhDMNOoOQYGBtyIBZOk2W8A/0iEZ/rB1NSUbm9vrfA4OzvT8PCwJiYmbAGKRDoBYNhIYPyQZ1Mf0bCihuS7oUqhEeacKJVKHvXY19fnCSrYq9rttsO1mI7BGR6sier1uu8TpAnn59HRkdOzw+GwQ+aC4Z9B5RIqEmTDjOajZuvp6dH09LSlxNiqcrmcJNn/DTFAeB0ETjKZ9L1gwsTIyIjBD6kDCM/NzT1a71NTU1biAByyx8C8xuNxgw/IvPles7Oz3m8vLy81MzOjUKgzsmptbc3g+OvXrw069vb2mu2Ox+PeM0kKn5+f18nJiUPoIFr29/e9JxK0GJRDkzmBSo37EAwpPTk58RlArYjsvVgsam9vT6Ojo1pfX1e5XNbm5qaDdfnZSqXiSTbPnz/3HhwOhzU7O6utrS3L8Ov1umq1mkZHR50Z1Gp1RkxyduDpD4c7o9UIS45Go5qZmfE6YN46dS5rv9Vq+dl4+vSp65JGo6F8Pu//DrCPIgXlGXJ3xgMztnF2dlbz8/O2rADkJJNJN+TY0NbX152X8pvXD//6Xk04SBQboCRLPthISenG/7i2tqbt7W1vRhxihKFcXV05kXNkZMR+FNBCQgy6urqUTqctRyM0BK/o7Oysjo+PtbS0pFgspnK5bBR+e3vbnnEAhNPTU3vcksmkDyIOPpgrPCY0dhT07XbbrBiSbKSrPMj8E3kqciBJjyQ0HPQgXjDkMCm9vb1aXFw0UIFvBY8jBw7eP4JO2LRAf2kS+H3ItZaXl1UoFCwNplnB4yPJTefd3Z3T8UGRz8/PvdEj98NL1N/fb4kPSByqCCRX2AZodgFnkIFdXFy4eGezy2QyVkfQqOC5WV1d1cXFhYrFotFdGuu9vT0tLy9rfn5eu7u7VmyMj49rfX1dX3/9tQttPN80fRwOMGgEe5Goi0eNIocgnGKxqOHhYXvCkVfBnqbTaVWrVUkd/yegDY0e1x+ke3Bw0KgugA6MflCxgUR2eXnZxWs02pmzPT097QOPsUD1et1g1tramsPBgl644HqgiKMQmJqasmw/mFbK/aSQ6O7utr+c64a8irUDc8t3+24YEx4/Rr309/f7PW5ubrz+S6WSmQia2Waz6QC6YHMVlPAhlaVY4zvDlgJ0NJtN229o+ik6UBXwHFJwAbhwwMK8seZgatkj2GuQ0GLn+Oqrr2yZaDabSqVS9uY9PDw8stUgF0ylUhobG/P4JbxmFFKNRkN7e3v2pX/66afKZrPa29tzsjnSdBRB0WjUXtj+/v5HwCfrjr3z/v7ehTXgBiMcuebYWGC2hoaGzLghJ8eewIx4ApDwzbFf53I5h9SQYp1IJJRKpXzdaMJhZxlLQ/OBDxUp/snJiT3cXV1dZpVvb291dHTkAhvQmgYqnU5b4UOhDBAA839zc2OZsdRhJmZmZhQOh/Xu3TtVKhUHfn3wwQf+TkdHR95PkGhTRAEQ0CDB3rFPBS1XsP9XV1ceZ7e6uqonT544D6BYLGp+fl7RaNQABTLcL774wudrLBbTwcGBhoeHVSqVDHrC4DF6qru7W4VCQaOjo6rVapqfn7dMNxqNanh4WPv7+wac5ufn1Ww2badJp9NeY6g1glJ/rmF/f7/X9tTUlIM+YfZgR7HPcD6xn8KstlotA6T4pyU54V6S1QBBpVE6nZYkS6GRGxMeVSgUzOhDGlSr1UdKPhoVQhE5BznnaNqpo4IKO2owiBEmOKAERH3Fc8S1YV0EFRNYevClozqR5HMJ0CJYB5EvAeCGDQE2FZDn+vpau7u7GhgYUDqd1vb2tlUvABRcp6dPnz7yAUPc0MzzewHPaeKoi1DBzM7Oqqenx/+Ns/f+/t5BYTCesOfMJE+n02o0GlZHYSWixiRdnHt+fHysUCjkcEfUMsj6Ab6npqYMTPD5ARZgRgFsSUDHBsDZTqM9MTGhXC5nvzJWra2tLV1fX7vpBlwjpwGrG5YLnlNGiwL0MDYRcJqgvHg87npwY2PD0xNGRkacZ4IiJBaLWeJOLcU+gxIONcHU1NSjhhrQhTr6yZMnOjg4cDbUZ599ZtaYnx/9dlQv+8bW1pbm5+ftoW42m94bsDxgl8JyROYJ/wNI39vb83djb76+vlY+nzdhNDY25vrv7OzMLDdz0elvUBGQdbC6uvqo/2IPQ9VFJtTHH39s4Itkdt57cHBQ09PTymazBlV4nlGf/Ob1w7++dxMO8hhE5dj8mNXJoiP0C++FJG+QsJo8QENDQ/rkk0/U399vBvzh4UEnJycql8s6PT3V4uKibm5uPILkj/yRP+LE1Xfv3lmecXR0ZHRtZ2dH8Xjcksq1tTX19/cbqefz3dzcqFgsevQFkja8PLBWwcaZQgtpJYcZbBjIMTJ1mnYKOkluHik+kShxmFBkDg0N2ROSy+XcDHFogJbhIaW4AV2DpcS3D4rGAUUQ0/X1tdlXgmSCf7/Vatl7y2HF+yJrj8fjThgn6KRYLOrm5sZJkKC+U1NT9kkeHR1pY2NDL1++9CYB08K4itXVVR8uUgcpfvLkiYrFov892GAxPkySZU/cYw4GCgtYMBo5kv4HBwedjMqIEzxggB8w6blczmtneHhYsVjsUcgSch+ac+7t3Nyci99Go6G5uTk1Gg0dHR2ZZaBwR17N/QXQwLdPA9vb26u5uTmDMnNzc7q8vFQ2m3UYx8jIiIOvhoeHdXR05FEaSP4pRGmmYWx4npvNpmZmZpROpw3gwPR2d3erVqtpaWlJknxoEhaHvxN/GvsDhwCNGt6rIAvPf2cdk39AQ8xavLq60tHRkeX9fAbuO2uYQo4X8n5mCEsdhQ8yQ5B82CimHCADA+VHFQN4B1rOfGFGeCFHZV+CZSDMjtRiZGfsd729vcpkMg4pQurHdQJs47NKHVYKaTXp0q1WSycnJ2asYNr7+vq0srKi8/Nz5XI59fb2qlAomI2+v7932j/jxqrVqosini32xlqt5rmsrO1Wq2VGEgn+zc2N0um0WcSzszM1m01bZmhm2+32IxldKBSyL5PiZ2VlxTJTpJo8hxRtyMmDgBU5IWQMIJ2F3WQ9IcP+8MMPdXl5aWaMfZ/vxT5Iw8/9QOmFMiqo/kIWzNirWCzmIC18k4RltlotvX37VpeXl2q1WlpbW1MkElEsFnPiPR5I/KVdXV2+jjRtrVbLqbrIoAHNRkZGPGedYhSQFUAWIJ5pHHi8Ly8vDfbyXfb29nR9fe1Z5jCFb9++NZMH+EbeQy6XUyaTcaozIEpPT49VX5FIxAA7zXXw2UayDsDHXoCHVpL9vNgv8KcTusa/U+PweyqVihOLOScA4/Fux2Ix27qKxaLBRe4t1ykIDvBcT0xMOIWdhhs1x3cbTpp5ABiAQNKwUSsEazs+G6o2xs4GAXxA02CAIxYy1gP7Jk0PtpCenh4rDrq7u217iUQiHsfUbDYttY5Go54TzjmZyWRsswIU7evr8zg5fL/UqJwvWBN4/tkPg3UMoCm1WVBujI2GdHhsXAB2qD0HBwetKCOfgdnkzJTnOSQpGwUjezj2S2TFEAE0xclk0ooxvPsoT6kfqYPI2iG1XZLH/n3yySdqNpva2trS5OSkk7pZ841Gw8/87Oys91xsYVgvmJPNWuQ5qtfr+vLLLy2xfvnypTMzLi4u/Pxwz7F/oFChbgNspR4L+uTZo6g/mCLB/eFZnpubMzgTDLUMTlAZHh7W8fGxstmsFhYWPNqMOd79/f0qFouKxWLa3t42KIy18vDw0JaFwcFB12EE+KJuYN/hz2q1mjMqsDeilBwfH3eINWuetdhut3VwcGA5P3stYZShUEj7+/taXFx0NhLnPfcP9St7LuTDb14/7Ot7NeFIXPAMsqlRNDJXlQAUUrBBlQcGBvTw8GAvGlJ22G42PNjk4EZ+fHyszz//XHNzc3r27JmGhoYclrC6umomnc21Xq/rs88+U7FYNKInSa9fv/ZIGF61Wk37+/saHh7W7/7u73osQjgc1tnZmZ48eeJmNBQKma1uNBr2kfLZQa/4cwo8rl3QX0MzTIEnyYUVSFlwrq7UCQxDTvLxxx8rHA7r7du3j2Y1d3d36/r62ocanjeYLg5OgAVQwI8++kh7e3uW07BpwfLk83lJslQrHO4EkFEk9/X1WYJEUUV6vvReVry1teWC6ODgwA8/wXYkg7P5XF1dKR6PG2E/OTlxs4DaAdQajzSNIug1YEAoFNLR0ZHHqWUyGTebkoxWc0/Y7DkkOPC4zvxsKpUyK/vw8KBSqaTh4WH7MoPgCGoAVBcc5DQ0yWRS0WjUXkESRCm6YNF5D1gFCnjYyenpaSs8CCoJWh8ARXgf1t7NzY0ODw99eN/f3/t60YjgE4XFaLValksNDw+bWX/27JkliRSiNGZYB0Dx2TcIPwJVpyg4Pj52UyvJ1wslCp+f0T+wUaVSSSsrK953KAhpNoNMNxJomqvgzHQOR3zqBAvCFuCdhH2DkR0aGrKf++rqygEysJ002ICBNDbNZieIie8CeNlodMYIEfSELJnwO+714OCgrwvAIJYT/K/SeyDl+vraDBbBLDyb5+fnztc4OTnR+fm5VUzkGJyfn/t5kWQAJSi3x//IXgV7MDY25mvQarWcektxW6/XHeYD60LSeLvdNro/MDCg+fl5FYtFJwMvLy87ER+mHFsB0vZg4A9Bo4A3jBqDAUIlxL8jlSZ5GeCDn2MPA9ABOKPppZDNZDLK5/Mueru7uz3S8ezb9HxsD1zb754zNMSw9jD2fNegvL7ZbJpp5bMMDg5qYWFB29vbisfjDln6/PPPtbS05EYb8Obg4ECXl5daWFjQxMSEDg8PVa1WH51bnHswtkH2qlgsGoAJgsGvXr2ydJq1PDk5qc8//1wffPCBfvKTn2hoaMhNIlLry8tLg3soTmg+CZ1jigH3l+CjoJ2Azw4AR6DW5eWlgUjYZFR7+OQJNsOTyt/lu6L44zljL+O5oPYB5A6CkTTV7GEojLB4cN7DWtMY8FyzDhhnSLPCmU/9EhyvSc2CeqGrqxNgxbNHU9But+2hxmbAuDCa/bGxMQftsg5pVLhnfFZk0pw3/Bwy4NvbWz8LgCTca2ow9mvWeCgU8rMlyUAKew3TUvb29jQ7O6tcLqejoyPfBwgGAlfz+bxBFaxiSOcJRaMJx8vfbrc9Jgu7Ans53z04Oi6Xy2lhYcH3qd1u6/T0VBMTEwbQuGco4gAv8U9/93wjEHBwcNDTF7q7u5VMJrWzs6NYLGbPfzgcdsDk+fm5nj9/7joJwJ+m8uTkRKOjowZYyOdAdp1KpTx9JfhMYodh2s/+/r5mZ2cNlqFqCFo4yuWyzzDqVdLMYXPZe6gFUcUBlKPe4rmENLi+vtb6+rparZZWVlYc2kvGSrDf6O3tdYbV4eGhTk5OTOTRd7B2CBWG4SZJnTFo4XBnQsTY2Jji8bju7jpTNhKJhDY2NqxgiEQiur6+tgJqcnJSMzMzDvIlCJTrRqOPNRGlUjB3KJ/Pe03zOf6wr2CP8kO9fuj3/4/1+l5N+PPnz3V4eGg5HOxDcCFTcCGjRPY3ODiopaUlzyQEsS0Wi0qn0zo6OlK5XLbsh4aJTRbp3+LiosLhsA4ODtTX12dJCsFOhMu8e/fODxvFG00P8jwODhDCwcFB/exnPzPzDQBwc3PjBpsmisaIQwi0H38bxQdea1hl0L52u+2DB+klskbpvb8FVBu2s6ury+zexsaG0U8808FrRmI9LBfSHuSXjKjo6+vT6uqqSqWSH3AkSD09PZqamnLgUTQataQ/n8+7WF1eXnb6OawEiC9FGQcZc3K5pqCQSPeq1artDRTnhJNRiPD9kDvTTL17986gB5I1wgBh5huNhqXVoOELCwva39/34Q1YAbOIhaLdbmt+ft7+6UKh4IORTWtqaspeUUI68GdfXV3ZEoDtgt/F/QOYoWAEmKDJ5r9xTymekPOyZq6vrz2ijURxSfbIMYv89PRUmUxGiUTCgEs8HnfzEPTHEk7V1dVlD+fKyooLTQ7zSqWixcVFjY6O2vfOM31xcaGDgwOz4QA9yLCZnYusKxi+iNSSgKru7m4XfCgU4vG4KpWKvvzySzdhSNCCeQcU20F1DocVnmK8lkH/F/8d4IJnisKFEXKwjNgWCEaESUYqSFgY/nDY5Hg87uYI5hBwKhaLGfThs1GwYKNotd7nF1CYAnrABvF8oETgxT7CteH6LiwsaG5uzmvr/Pxcn332mUEmSb6v3w27ZE+g0WAf4VklTR+GCAURFhgmJbTbbT93ZBwgySboiSaFEYn43qQOkwZAgdcavyj3hH05nU47AIg51DAPnHE8T2NjYyqVSorFYlbDIIsPBmaiPqIhx25xdHSkUqnk9wxmF8D40QDAcCFLHh4eVjKZdJhRLBbT5OSkwxuvrq7sNZyamvKeA5BBwSt1WEHYHBjP6elpJRIJA07tdlvlctms8cjIiPb29gwE0WwAdqGQ4Txk76XgDEqtC4WCSqWS1wbX+dWrV14PwfwH6g7Aw6dPn3o9w/Zw9mND47/hhebzsja5tsGwJ+TZ5LyQMXBzc+Omi3tODUQ4JfLzoaEhh2yy3wQVFzQnhMBRQAfBC85vsi9oVFkfQbYX9V5XV5ezEO7v780ws9/xM6imkNry/jSEfX19bix5xur1uhUb/G6es5OTE6XT6UfjlyAcIDIgDGDTCfUkqwdb3tjYmBU0KCIozAFoScNGEs7v414EszeQOePzxbZC4CHqAdY7lp3u7m7nw3R3d9sTTB2Dvers7MwEB4QIeyQ2hkgkYmCE+pV9iL0aKw7PIvcIMgRVBrUqgAW+ddRS1CmLi4tO/SeUT5LDxLBUBKXgXFOeO0iLwcFB7e/vq7+/37JzatdWq+WAtlgs5uAx6ibUsUxAoDG9uLhQIpGw3QTZOzUC9253d/cRsUTdC9jB+qTOhAQJsublclkvX750UyvJvupKpWIgGv87tQxjd4MJ/GT5QJKtrq7q+vpa33zzjWvs09NTkxQ3NzfOkeC6cF4BEmIhoa6jIS+VSj738IVjrcIudnV1ZevawsKCgWgmVDDK7urqSgcHByZRarWanjx54oyD37x+2Nf3asJ5yFg4MERIlXhYeBDOz88lSZlMRpLcQHFo4JNjcdCEsnHwoHAIg5ThwZ2YmLDvlp+nkR8YGPDcUEZ2wGwnEgmFQiEdHh7aH7q5uamRkRH19fVpfn7ev48Gp1qtWtJTKBScbI2kFWQXZjlY5J6dnZkxpfCS3kukgpJYwAm+ExLKWq2mb7755pG8laYqnU5rYmLCkm0ONw4dDgekTRR+yAsBFvh7HLYUp4QMMYqHcKTr62tLrefn512c9/X16e7uTi9evFCpVNLOzo4kPQIEaAjYxM7Pz5VMJlWtVnV0dOSCBiCFjbVarVq+RKEUlPB2d3drdXXVUiOaVw4nGgoQfZpLxurwHkHWmIKbew27Pzw87MYB2Tz+HTz2sDn4SHluQqGQ7QdkIkxMTCgej7txTyaTZpCR90rye8CUUIzSzGI5YM2REsx1gg2gwB8dHfU4wGq1qu7ubq+nYFI6o7448Or1usNTKHrxzyHzg7mHyaHIgI0olUpOaKawoJBHJo1cr9lsOmG6q6szx5xrhDLg9PTUc9qnpqbsocbbxcEJ4PDdtQgLy/MPcARri1SNIgd2L5lM+rpQVJIIjxRekt8HTyrFbiQS8X2lwCZQERCATAuQ+Gq1qqGhIaPrMB18Rz4neyPWF/aO4P5DWFhXV5cODg68xwKk0ZTBuBL+RuEGcMZ+CXAI4AhYQbEFWo9s8ebmxsV3tVp1AGOtVlOj0dCLFy/clGA9IFX+zZs33uNoaChSAAtzuZwikYjnzKPeabfbDgnjWcJzC5tEanu1WvVagimEdZ6enva9i8ViyufzPoOCGSOw4zBfsIHDw8NOEOcF0F0oFCTJ4NXY2JgLNIK1eFZYsxTlnNEoXABmsdmwb3ImMAouHo/r8PBQUmfuMtNIUATwjPf09GhlZcXMFyAsoAYFeXBCA2urUCh4LjjJ7qgJ2IMIm3p4eNCrV68sqa9WqwYFKHx5vovFou0RqKSCYDfBcJeXl2aLULJJ8v4Sj8d9DtDAsFc1m02r5/B6RqNRh8kVCgWD7iR7s35pClF1EFoF63V9fW01BtYJnlfOZGokGgzuPY0fdVEoFLKsnCwdEv8ZiUVdxp7dbDb99/GTkx2AjJ1cAWTihULBtQ1MZ19fn+W2ZAUhbZdkIoMaDMUjzSi+5Ha7rZmZGW1ubkqSEomE83r4OdZRMGvj/r6TCE8zT93Fs4WCimeZcx6A9/b2VpOTk5bmDw8Pe7+GUa1UKiqVSrq8vNTc3JzVHTzXBM1iTyFkDg9wf3+/SqWSA+cAle/u7qwqxS6Bb5yJC81m0+osAFlyDlDI8JkBXCYmJpRKpR7ViZIc8kuKdrlcdgI7qh4Ue2dnZ7q8vNT8/LwKhYIDdcmyaLc7c7epl/r6+vTkyROVy2UVi0UTdQDio6OjKpfLDg7j/vFMXlxcqFarWRLe09PjrAnqOBQE3GvIPggcbApIxMk3Aihn7wYkbLfbfj5rtZrK5bLDeSV5nb17906ZTMYqwWg0atUCQM309LT29/edWXN2dmbbwvz8vPL5vKampmxFQbEHaDg7O2s7RLlc1sjIiMk6njeeddSBr1+/9mQBLLUXFxfK5XJqtzuJ82dnZz4De3t7NTMzY3UxgZi/ef3wr+/VhL97906SXNxLst8MSQOBKOfn5x7zIr2XPBJC0Gg0FAqFXLSn02k3EDxUoIQEHyGrZBB9PB43sil1pMRIgPv6+rS/v6+ZmRm9fftWvb29WlpasucF7wwMKkmmhIHB9tPMUMSk02lNT08/8qBRJIRCITdbjNaJRCL2D1JgIf+lcQHRDfq08ROB/OdyOc8yRybGfPWpqSmzb0gq8UMFA6Q4mGAYnjx5ounpaTMFbMzhcNisNpuVJM+pRQ5EATA5Oen7AJsC8EDxzXVGvgObgXR9Z2fHMtnp6Wkzp8EUVIKG+IxBhnR5ednrs6ury6O8kFHTrOzv7/tQoaFgE6VQ4wAA/CF8hAO/XC5bmoiagqI6Go26iUB9gTwyKBkFYMESABDENWKUGiw6aC9sJY0acsdGozPGZ35+3tIvPMcAQ/xdCq9wuJMASgHJ9+/u7tbm5qYPJBomwsGQkI2NjTkED1CEQpzk4WCAFeObKEb6+/sfjXqhESWF/Orqyh6sarWqxcVFe+Xv7u5ctF1dXWn023EhXV2dcXoUtgASFJ4Aebe3t2YOCLrhGqGsuL6+ViqVMgDCvkTRS7ARxTvFHExaLpezDBkPPswLNgOaYjxfJycnikajDjHE84WtgEYjl8vp9evXSiQSmpqachhXIpFwwM7Z2ZkVN6hD8ACyBiic6/W6me6JiQkj6QCpZFUcHh7qgw8+cCON33l2dlb5fF6Hh4cuYAmM4XtSqAA8Ba0qKFJgrL/66qtHz2M+n1ej0Zmn++TJEzcCuVzO+2rQY45nFsYczzZMGVLJ+/t77e/v2wrDWYAUPJhrEIlENDk56dCqSqViJRiNXKPR0MHBgRtfZn4DQrHvE7CDugg5pKRHihfWIeAlgXiwoNFoVDs7O25cJicnXfxhW+DvI9fn7Lq4uPBnZI/h52CIsR6kUiklEgltb287UZdGE8UU3myefZ4TVDKwMYTlzc3NWWHF7xsfHzfQRODaw8ODi/fl5WWPC+Kej42NuXnjfOrp6fF0DBoDWCgmVACYcE2QGLNfY58j0JBzkWI66OXEloDvm/2MGgmpa5AxLpfLPqOpj2DL+/r6dHx8bIaMOmR8fNw+z4GBAYPIAFyo8QAcmGtPYwHwiHKKOfLs8fzd74bPYmNA9RMOh3V6eqpisajJyUmPJ8xmsw7ZYzQS4CANVdCTCiiwtbXlcVa9vb22gMAkl0ol58wgOY9EIn4uqBMATWGpAQE4cwjLDJ5RsIgo35BWj46OKhaLuZkkWwZ2MhzuTNYBpEgkEo/yL4ITcrAwohgCxKRZRjVJfQ3IRo2czWaVSqUMcEE+sF6xfFKDUbtR+/X39/vPuffUnHd3d55GgYx6fHzc4Y4//vGPrVKExCA3YnR01GAftQ8KuVarpbm5OfX09Ghzc1MbGxsaGhpSMpn080j9OPrtqEVqlGazaUvS1dWVx5ECVLCGUT4BWl1fXz9qUs/Pz012kPdDo48FkHqKPB32LFS24XDYNWkqlbLC6+HhwbUN5BbP+dbWluLxuF68eKFisegamnt/c3NjBSs5WgCn1CmoX7jfqC4YnxcOd6ZEAGggc7+9vdXq6qqnWZCU3mq19OzZM42PjxsY5RnifKtWq6pUKvrFL37hIMnfvH7Y1/dqwj///HN98sknWllZUavV0s7Ojs7OzrSzs6PJyUnPacVfeH9/r1qYyioAAQAASURBVJGREa2vr3usBfH6FB1sPhT5+HM5AIKbhiSzzGtra0b8aPZAgynu0um0vXSkV46NjTnQJ+itZUNcW1vT7u6uvZm9vb1mYwjtWV1dNaqKDIzQIhpoJGx4hIKeK9gKmHOacIoZGBJYYAolGj3QNRjUzc1NHwo0FQAYNzc3DnaC9ePQHR4etqR+f3/fh2uQHYEdbDQ6IxLwDVJgURhOTU2Z6aFZQOb09OlTHR8fK5/Pu9hgM2XTJUxlaWnJyCVM3ubmppskDgkOUppCNu3x8XEdHBzo+vrasst0Om0kmHsgyZszyevInLATBL2rbNw0qZJcoNMwMpN2amrKgRqEOAUBGLyMgC7cEw7ScDjsPAWeBf7J9wDlluRGOx6PO9CK9FWekUQiYXSdNTQzM2Pg6vLy0g0jByiACdc5yLog7T08PDRwRGE6MjLiJrhUKnmNSXLzANqLQqVQKFjlcXt7q/39fbN1o6Oj+vGPf+x57RQtAD5IwUHLpfcztvEFwsxJckENsEETShFB3oEkg28wzNgqKF4pEvf3932PCDNDvtjT05lQwLgkQLtyuezk7YuLCx0fH2twcNBKHZoqGAP2jvHxcfudATQpsre2tpxqCpsIU4M3mDA4CgGS9pHcIgdvNpsGDorFon8X4S/s34BkCwsLSqVSymazVqEAjLBeaExYR0HFEEwebCGqC0BR7iOsFkAZQB/fF78bqg7YgWKx6PfBBkCBzWe5uLjQxcWFlpaW1G63LRmFXQQ8zufzTnpGKXF6emrJaTgctteYhoU9j+BA9k8aByxAgHd8X3zh29vburu70+7urtkbVDA8E2RzcIZw/klyECRNINcX3zqqJtQIFNow4EjHORM5s3Z3d53OTagm4A0ziLe2tuxfJl8Emw3szvz8vKanp/2+ZCsUi0UrpXiOkYwGpfqtVusRuwg4CVADwAvQQoEPG8uYKhoLggaxwZRKJU1NTVnmPzs7K0l+VtmPAHjIOuBnOCOYtw1wwd+H2eUMZZ/GcxpUsXFf2BfwAjPKCSCetG5AViw5sO74jwER+LsAWtRJd3d33m8A3oLBblIHPEokEopEIm6gAd1gumFsUU2Gw50AxL29PR0cHNh+RaYIzw7rGk9+X1+f3r175+d+f39f3d3dZslh8jkDdnd3HXxGbRSUpj88PFjay3sCwnDWUjfwfSR5fB81IFk1o6OjBjh5TgHVCXqFpa7X6waJORdo2hl/itKl2Wza1kLKOe/P/QKU4WyTZIIGwI/fhQ8ccHBoaMh7PXkQuVzOLDjrDdUEDDTfgzGj4+Pj/nwo6lCJ4IfnXEEpW6lUlE6nH4UYk10EqHd315mjje0iGo06wBe1CdasSCSi3d1dLS8vW912fn5uFQz1LrXz8PCwg4/HxsY0MjJiMml4eFgbGxteF8FQRQLc1tfXFQqFtLGxYZAll8upWq1qfn5eX375pTM3Xr9+7XvJPoI9F6CD3ujw8FDRaNTBgHwGgvL6+/udLM81Zw3f3t7qo48+8l46Ozurs7MzFQoFz7Xv7+93oGIikdDx8bGOjo6stP3DvKg/fsjXD/3+/7Fe39sTPjw8rIWFBYVCnUTz3//931e9XtfR0dEjdBA0mPRQ5iVXKhWPpcAbTBDJ2NiYEWCK1cPDQ42MjGhmZsbel2BhGwyaYtMBgRwdHTXCBjtJOitIqSSj57FYTKVSSaVSybIjpF9IEAleYcQTfihYgF//+td6/vy5Cz9YH74rDz5MJAgd8mAABQp1mkLQK1gSqSPzh0FDTok3ETaAwwbUHQTv6upKb968cYpi0Ff/5s0bH45IH1E0jI+PK5FIaHd312wE0jQ8lCS5I73kQIKdDioItra2lEwmbQUgNILimsaejQkrBNeKJhvPEocBRQ9N9/X1teesg4KOjY2pUqk8mmU8PDzsGZCg9UFPK2uGw0ySEXa8ozSy9/f3Wlpa8pgqml1Yd9YvTCxrmc9O407jiseJBgT5FKn5yI1AyFnjbJB8R8CF8/NzF8ChUMjeP55JwAieEYpAindUL2tray6E+/v7LTPGhx4OhxWLxVwoMCaFQvH+/l6ZTMYNAADE6LfjQ3gGkaIhISS4kaBFDlpJZhHwGyP1Dgab4XeDte3v7/fhS5Oez+fdoHGNABnx2IJcU7AC2FEo0IBOTEyou7vbclXsPTBoQU89IFksFlMulzMoSEMQjUb1k5/85FF4oCSPikLGeXZ29ug6oVziOiCp3NvbsyVgeXnZCbI0RJJc+CE/ByhDYorSY25uzo0lvm6eaVhDbDo81zTdXNP19XUzSIBDyCi5r0j1X7x4odPTU7NHmUzG44FQJFB80yQDJlIYz8zMGMCkCYpEIpqamjKryJobHx/3bGwyDZgXjCQxk8loeHjY9qlarfaIJQuqnPh7MHj8HGuZ9UNmQhAUYv3t7u5qamrKElhANc4twqdoFGdmZnR/f//IBsae8fXXX3vPaLfbbtwI2sT2BMvGe7AGZmZmzFQiY+c5xCsLY4yP+dmzZ27yYLF2d3fdGK6vr5uhglFmzRwcHDhkDgUaSjqk1YyMoomn2ZLk92LyASoZgu941rlnhMwBsLA3hMNhh4cixaZh4QzH8kSTyj1mT6Yp4OwHqECVx+fn+uFLZ49FVRCU4OINxiKIKu34+FgrKysGAbm2NDzftcsBUgEcYglA5RGJRKzU41wNhzuz42nsy+Wy1wTneFdXlz766CPviX19fW7uuVc8xz09PVb6MFWk1WqpUCgoEolob2/PAYbUVmSHwIoeHx97PTQaDVuJzr4dIXl+fu4zgGtHTcj+Ozk56fMVtRLKCs5zrhk+d0CLWq2mWq1m0I19EZacuge1IM8OIV5I7CGmWD/UvuzXNN2cQVyHdrvtGhKAor+/X+l0Wq9fv1Y4HLbC4fa2Mw7y7u5Oo6OjVtMA5r59+9ZrgnormUxabXJ8fOzwRu5VNBr1OZLJZDQ+Pq6vvvrKOQgoggj93N7e9vqV5PsxNjZmgHpgYMDztyFkjo+Pbc25ubnR69ev1Wq19OGHH3oCEE0rzDM+dbzgKGJQPbFHo/QF0Hj69KkODw+VSqU0PT2t4eFhnZ+fa2Njw+AFAa19fZ0xktVq1eOUUUQRxgyIxN6K2od8Jq7H7e2t1XPcn+C14/nH2kKN9/LlS11dXTnsenl5Wd98843Gxsb0X//X/7X29vb0O7/zO3+ovvA3r/9nr+/VhL98+dLBJiQ14w9rt9v24SEbmpiY0PLysm5vb3V8fGy2pdFoaHFx0WxLNBpVNpt1gUPwBcU5yA7MCwVIkBXjELy4uHCjVi6XH0mgg/IL/kySZVz4MO/vO+OXkFPSpIMCHh0dqa+vz5JHDs6hoSH96Ec/eiRZRQKDDJSgq9FvU0dpvnh4aHLYKGkm+Oxs7hQ53d3dHnvw8PCgubk5zc/P64svvngkV4IFDQZWxONxLS8va3t7W5VKRdls1uoAkHCUCQSwgWTzvSikkLycn5/ryZMn6u/v1+7uriYmJlzcc2jByPPnJycnGhkZUSqV8rWHfbq+vvbn5l6jUBgfH9fOzo5TJZmpDhLfbHZGZ7FW8MPQzOA/5NoHg7nwJfHiM3MoB/8cPz33sVwu6/j4WM1mU8fHxx7DxXVFzQHDHWSj+J004Kwj5EzIlXgPrhMWEaTTNOnIeWnUWJutVsty9ePjYzdTeLi5T0FfF3kOXB+eARh6Zs7CyhBCFAzs6+7u9ixy/NN437hvL1++1ODgoMrlsnp6elyEhcNhs/S3t7ceRYdiBOYKWd/Q0JAPYIohchAokGBjkHGNjo66OUSyOjo6au8zYShcc9Bwfl9vb692d3ddXNAwE5ZDU8z0BQJ4mHlOUQbwRUOBzK1YLGpkZMRFH+Dfzc2NPXU0AbBAXOtcLmegAYk+3lzkwIw/q1Qqmpubk9RhvAElCT9i/8L6E1R6sFdjOyEYDfCp0Wj4c8E0cI247rxXT0+P081hYEulkq8bFoh2u62xsTGVy2VlMhl/hoGBAX399ddqNpuPAJ92u21vbk9Pj1KplHZ2dhQOhzUxMaGZmRkzCcfHx/aa5/N5Pw8UZsHZ0DS5AB4U2rDgSIhJ4n54eDB7NjExYTaaPTuTyejdu3f2M+ZyOe9f+Ex50ZxT8AfVKi9evDCbGo1GnZ1CYUgmQDabfWRXYH/CBgbI8OGHHyoUCmlvb89+zr6+PidJk1pO8Y5KhnTjqakpDQ0N6ejoSFdXV9re3tbz58/tLecZSaVSzmngbAKAwUOJzYEEfc4mGhj+F1x33Icg6xvca6kD+vv7Pbee9YivHUCEZg4gD691o9EwQMP+BPDCMwUjx1lIPUKOBpMHWL/cUwAmPhOSf9QBPHvULsG9j2cxEok41JHJB81m03L3oDqPsyiTydg+JMkKqeB5RRO1uLjohvXm5saWjaCcG58zvnGuA3YJAGdyMLDxAGayDwMsci2RdEO4AAbGYjFPtaDZ5TxLpVIOQgNM4PrS1HOGsj8B1B8fH/uzwHLzDAcVjicnJwYiyY6p1WqanJz0iMuglYjGPMh0j4yMqNVq2RYGOcB+BvEFk896AqBBIRCNRi315u9MTEyoVqtpeXnZ4XbxeFwbGxt6eHjwiKsgOIVdqlwuK5FI2H5J3s8HH3zgIFuuW7lc1qtXr/TixQu9fv1apVLJCtN2u231KZLvcrmsbDbrhpnnl0wHlDJYSq6vrx8RI+zf1PE9PT0GH1G0ATxRC7LuAbhYX1xDVLTRaFTj4+MqFAoepQkoQUAm5Br9U29vr2ZnZzU2NqZf/epXzqqimWaPoOeZmZnRxcWFtre3NTg46L5Iku2EnIfYSQDyAAQAKYPgFs8lCg3q8k8//VTRaFT/5J/8k3+1EfzN69/r63s14TAVIKggNvV6/VFDwyZECjDMw9jYmOU5ILh7e3t+sEh5JSExKL9ls4e1k+TCHPaAh4jii/AYijcWP5svbDqLEElQPB7XysqKdnd3/TnY7KTOIbmxsaHp6Wl7MjgsaN6CYyGCvnAksCBoNMUcgJJcBEnvWRCkKwR18ZAuLy/r1atXbiJKpZKRamTowTAgmkXpvTeYDYLk1Ewmo8vLS62srPizwRYfHh6qq6tLq6ur6urq0tu3bw0ksAl0d3crl8tpYmLCs9eRj9MktNvv50xy70GbCf8gCIp7GJSvcj+4vjSXQY8+8h0sAhxiHNDBlFbWFIwKB1pQAsM9ASBhPXAoU4wE088ZmRFMt5dkpisohaU55h7xM6wpfi/PBEgoDDfMJWvs/PzcyDX3PMhu8f5LS0tukgljoXggJR1/GTKrarWq1dVV5fN5MwHBEVCSLJMibJGik+cAD3w4HLaXGWkk85Svrq7cNJ59O7cVJQmHGymmAHGs2dHRUV8rkl2DzxYsYTweVz6ft6wYNcPg4KAnOVDc5HI5vx9rBlaNg7nVahn9pwCHSTw9PdXc3JxHH9XrdYcKISMfHBzUycmJnxfYW4KoQOyR3y4tLembb76xv3ZmZsbgGSAMBy4FCGwVI9jIRUD+e3fXSYz+6KOP7DkbHx9XLBbz/GhGMMHe0AhJsnSQZhfpJ/cX9QH+7OBzDMPc39+v9fV1eymDXsnDw0PLJSkqAZLK5bLy+bymp6ctS+bzEIw0NDTk+xEMmsOvyHgvFFrMLuZ+kh8Q9HIjHQ2FQr427XZb6XRas7OzZnVRm9BwwJpizZibm/N6phmkKL+/v3fq7uTkpBtzGLmrqyvLImFtWUfcWxiuqakp7wH4KYvFonNYsFikUimP44GtBYimYQIIvb+/d3YHoDbj42C9V1ZWzCSiRpE6drf7+3vNz8+7MQDYpYkAjG40Gtrf31ehUFAmk9HV1ZXS6bTi8bgBdrywwbORUDhkuz09PbaW0KwDDFGUovrgTIeRC1oVOEdRGKGaCTKRFOsAATTVeDdRnnFPaFCDa5drTiNKXcREBdRIfN+JiQmD/ZLcCFP3AP4AQFMrcL4CemKd4HlG0UdgF8Amipd6ve4GslKpeLpJJBKxqgf2NhLpBFDhvZekbDbr4EdqvtXVVVUqFVuOOCexV7B3olxEQr2/v++1tr297Rqzt7fXgViSvLcQNMy1Rz0wMDCgiYkJN7VkdiBX7+npseKRa4PihxBJyCuAG87YkZERK0yCcmRqi6BlgP2D9HcIhqmpKatIAMOD9Q1kA2AWeTkAtkNDQ1ZOotDh76dSKdfsFxcXOjw8tCXh7u7OCtNkMqm3b98qnU57+g7fZ3Jy0uoPQskikYh2dnY0PDys8fFxK1F2d3dVLBbV29vrwFBAKYiDoHqvXq9rdXXV9o/9/X29ePHCisNWq6Xnz5/7fKZ3QfGAyqnZbDoHANAZUoFzhjBZai32k9XVVdXrde3s7GhmZkZXV1de+5FIxEGP2FOj0ajVIIuLiyYngon3wdyVXC7ntcXZf3V15fGOKNUODg50f9+ZPNHf369sNvvIb44qIplM6qOPPvK0j7W1tUfTnNh//jCv38jR/91f36sJl+TwDVBVJJZdXV2WBOGlg5XjZgZZXg4SNrW+vj6PEcLnHQ6HzdKFw2GPf6FgQqaLtAmmgUKsq6tL6XTayYA0SDA+HNbX19eWPA4PD2t0dFR7e3vevILyLBBzRspI0uzsrL83Mt16vW42nwOV5gHZLIc5h3RQbkzDRfMGegVbyt+PRCJKJpMqFov+/icnJz4EYGCDhyiHY6vVcqI8TR/ev2q1qv39fTf5BEPA2NJ88f0I1+CesUkHk4PxU/L9pPfBfmdnZ27KJfnQvL+/1/r6uo6Pj3VxcaGhoSE3T7VazU0rhfqzZ8+Uz+c9JqJWq2lwcNAIZigU8qHK/eQQJowJMInrxQEfbJAlPTrUkORxUCSTSTO8NOzhcNgMAsw6hwsFFGua5pRsAdYfcihesDff3aD4zHj0kGbB4iN3SqVSmpub0+XlpRk5ZJTDw8NKpVKWkJJLwOfd29tTsVj0GgTlJ+Tjiy++8HqBMcBXHI1GlUwm3VhSHEod5q63t9fp1hSDsHsgvYR43d7e6vz83HYKmAZCt0gV7+vrc9FALgEeYvybFEocujTbhHcxr35kZMRMhyQ3Mq1WS/F43GqcjY0NqyVQ41CAAfAR0EKDgcUEZuvi4sIgx3fXJfkLtVpNz549M/AIG4kkjaRXCkXsJTMzMy66mHnLM3Z8fKyhoSGtrq46fAfp6tu3b1WtVrW8vOz9iwIKBr7VajkdGCAVBUUw8DFo+UCuS2HFPg5owV4fDocdSHZ6eqq1tTWv7+PjYzcS3d3dlihS1LE3UxBSxONFxToFEMD6RTnCZAyAPs4NmiEUH1iDbm9vnZjMWqM5jEY7wZqsgUgkonw+r0KhYJCBJoWmsbe319LLi4sLN/FkUuzs7Ph8RPZaKBQsN6bRADwdGxvTzs6OqtWqTk5O9PTp00c+e6SP7E+RSGe6BFMyAPnw/7NvUQBjxcHTzaQArgOqhS+//NLeW4CNZrOpyclJNZtNF5kA7tQJmUzGjC6fjzOBoluSm2USx3t7ew2882fRaNT+UGwn0ntgnKYVUgEAinvJHj86OupxchThwbMc0BUPNwAta4e1HlS2sO+ivmOe8djYmM85nidyD/gMQUUTzR3hZ8h7g+uDa0m6OwAfnx2Wnec1Eol4T5Q6zXCpVFIqlXKTDugIG392duaRoZyDWABgHoN2o3A4rJmZGf8cZyLfF/ACFhBAjdA0aqhQKGSAbW5uzqqC4LhQQFEaEjIbHh4e3AgznYc6p1arOWODn2u1Wjo8PLSCAlkwezyKMbINAI6D88jZA3huqZfHxsb09u1bDQ4OuukjzIuagH8CpkjvQaSxsTGNjo7q3bt3DkWkjqC5r9Vqmp2dVSKR8JmAzQzAqFQqaXh4WM+ePfNz+fnnnyudTmt4eFi5XO6RZYBRnuxLJycnthi02237klECUccB5s/NzalYLDpVPJlM+jmC4QYM456VSiXbZKjRABbJsQnu9ex3w8PDThknUI0g5FqtpvHxcSuLGLX45MkT3d3daXp6WrFYzID29PS0CUHUYQClKMxOTk60vLysh4cH5fN5K844TwHoUZLc33em2+RyOQMw1OGjo6Me/cz1pXcplUr65JNP/FygtGAPlOR9+jevH/b1vZpwCuXZ2Vnt7+9rfn5eqVTKcy8zmYxHFZycnLhJ4iADvaLxxYeGn5wNlUNckjfhrq4ulUol3d93xiywmECvYRyDLDlsPAw4fxdPEvJMPIsEaiBtpilm1h6jo2iQr6+vtbW1ZfkJYyR4gElWhmXg4echpwiF6cR7BdMLgs01keSmGunz6Oiopqen3TQ8ffrU12xwcNDXnCCd4eFhTU1NaWdnx75wmAjuzc7Ojov4L774wof2d8drSHLRwRgjJG4UFBTLIN6AFDRXBN5QxDCmhkaGdNubmxvNzc05dRy5d5CNjEQ6M+BhW4ObVbVa1cTEhKanp3VxcaFsNvuIKYJlqlQqj+RmgAZcH5gNivAgqIDfisM5OAeYEB0Kf+4rRRmMHjJLGhOAF0CVUCjkg59NOPg+sI8AXlNTU/aKIWs8Pz/3e7G2QqFOyjBNN4zK0dGRGwtGbQGAUBDOzs6q2Wzq008/tT8Vtcn19bXS6bSbd8bTwKSwF6TTaTd4QV8b3vrJyUltbGw4UARZP5kDDw8PDs2D6by8vHThDxjCd2bNEgwVZCODihkAOtYU+0TQL01WAYUywAhTEg4PD10QAbIgyURSyRqAVeAAZB8Msl0U6UxrwNfY09OjhYUFF6Lj4+Neo4lEwnI8AsQIzQLI5BAPhTqjV/b395XP5zU5OalEIuFwsqmpKX9/ru/Ozo73ztvbW7169UqHh4c6PDz05AsS8YMptag4YArZQ+/u7lQul7W9va1YLKaxsTEXZVJHvsr1uLi4sEy1p6dHyWTS9zqXy3nMEM0blheaGxjkRCKho6MjF5IUXfx/gi4PDw81NjbmsYwUk6xTxq2hGAFQYM/Ht0zxSgFMSBYSQRrF6elpM/EAqIlEwsoJQB7YINQ2o6OjzuY4Pj7WycmJbm5ulEwmfUaFw52k9u3tbQeawlTOz8+7uAVwDa5JQI6RkRGH4BGESJ7B0NCQlpaWfG7U63UVCgUrWrq6ujxC8f7+Xh9//PEjhp2mTpIBdjIX8Kty35HSPjw8eL+r1WqKRjuhZPF43Gc3QCcyXtYzlpdms2nPNeAV/+TcQpZ7cnJiS0IwOR2AAmKBwhoygL2cM4PzSOqo7WiAYWHZJ6rV6qPrwn5FA8WegrSfz4HslmaPgErOaxoR7CD39++niwQ91ZwdgIDB8wk2nTWAtYXaAd9rvV73CLwgw4/dhucClSINE3sXgCesIfsYIDnWSGwqPDdjY2Pe64LACiF+3GeImoeHTghcMpn0GkHNAEvJvh4kKQB1ksmkMpmMx1zhD8YuSRNFo8czDlEFoEz9DWDNPQZMYV8FsMNmApiCJJmGkz1GkpUB4XBYr1+/9ni/Wq3maSQnJycGWIeHhzU7O+u6ZmZmRpK8zywsLOjXv/61LZ7tdtvkmSSfu3d3d74+3G/OzXA47PHF7KGTk5MeN4wygvdiBB7hekylWFpaUijUCWzDs313d6dCoWDpOvslvcTt7a1rbJj1dvv9+D9JPoO5HsVi0UoX9u5SqeTaNKiM5dyiXqAm5byu1+vKZrO2reZyOc3Pz3sKAdkAhGQysYK57zyfxWLR9gCsO6FQyN5y9m9svdFoZ4oJwHeQ7PnN64d7fa8mnA0H2UupVDKzu7CwoGw265CHFy9eaGNjwx7oi4sLbzogyAMDA1pZWTFyRzPR3d1taQiHLuhnd3e3N3UOJlBJUC/QXtgG3hPZSXd3t/0/SIfZYPr6+sw64J9cWFiQ9B5EwH8BElsul3V+fm5/DLI5EkWDhwZ+Hj53UE7FQSnJ8uBgYjkSdoovDneC0HK5nCqVigtuZhOyEUajnTCm3d1dhx4FpelBRjXYKCJ1jsVi/s7B4JaBgQEHtOBBk2QUP9h48/2Cjebw8LDl04SXcSA9PDyY2YpGow6zYlRTf3+/RkdHLSE/OjrS+vq6bm5utL+/700llUrZ74Zvht/FAUozCmOOaoPrEZR10YjzHbkOpVJJxWLRVgC8SxQqwSIwWAwG5eI07nhDYVqQioFAI2c8PT31M4YEHVCBwDRSXCkiJiYmFIlEHFDCqDieNVQU/f39mpubU61WUz6fNzs3Ozvr4n9hYUG9vb1O9qQJIkyEcB+uc7PZdLNRLpft66TAJTWWhhMvdLFYdDGXSCT881hgms2mZW+AMtFo1CwufnY8uTzPFGlBqX5wXFu9XncQDqw8VgfWAwxH0NONzFmSQRiKq0Qi4e/C3+dZQy3B6Bmka3jmYVdoZDmAuVZYP1iXgI+AHqyvSqViEINRLVKHhZ6amvIeQsL93d3dI08yeQDRaGc2KgXi3d2d/rP/7D9ToVDQF198oVwup4uLC62vrzvwTZIDZ+7u7nR2dmYGH2YKpjIob2VMI+xso9GwFBQP4+XlpaamppxTgec5Fospm80qkUjYcoFKi+cOVnp/f1/39/dOW8ZPfnFxYfCHJoeUeZ53mCr2wXK5bLYLVorrTGOHfJN7FMym4Dy5urpSoVDQ/Py8qtWqVWLJZNKezK6uLnvVCZ5kpBnj/thbYVTxdOPfxjtarVYfNZVShxGm4YO1bDQaVjlcXV3pJz/5iUOLaLyCeycJ4YAPV1dXOjs787QGACqKYZ7HeDzuZrbdbmt6etpgMwoZGB1At2BjyDPEPkc9QCYMDDf7OvJy1EF8z6DqLRKJWP7d29vrZrFSqSgejzuzAP8tgaw826wDpjJwHtzf35v9xYLEz7bbbY9yRDmAvx0VysnJicEubAHsK1zPYCNK8833p/6gTujr63sUbkutxnUhGJd7x+/p6+vT6uqqMwUWFhacCE+jzrMlybJ2gj3J9UCtwxq7v7+3xQ8CB0VDb29nvB2MN2cOIDl5MNRx7P2wgKh68O2z7wZtSKjlmD4A0YJtEMtNtVp10zQ7O2sVDrk2gMoQJ9i+gt54atJCoaCFhQXb9SYnJ7W0tOR6BHUpUvuDgwNJHTCOZzB4rWm0eKb4HtL7AF6AfmoH9h9A0rm5OZ9xDw8Pmp+f19XVlRYWFry3Erh4d3enN2/eaHR0VAsLC865WFpa0sHBger1uhKJhF69euWxm6xpLDJff/215ufntbq66tGYgNkASUy7IUSPPRbFAteIe/TmzRtlMhlNTU25npTkdca1YuZ4uVw26BkOhx+FkkK8kGfA2mEEMvXUl19+qYWFBQP/h4eHrnvy+bwqlYrGx8c1Pj5uBQDed/YO5P8EzVJ/ECRHTdhsNj3xaXl5Wevr664PqROC4N/t7a2mp6eVy+X+7U3hb17/j1/fqwmnUGm1OuPJstms/WX9/f2anp6WJNVqNW+YhIYE/c946arVqj777DM1m00XURwqbCogT8i3YOUmJiZcMMC+4Gml0EQGBTtLAYvPjk2LTe309NQsMIdptVrV27dvNT4+bsQVGTufDQSNzRvvNYxY0P+WTqc9QiqTyfggD0rfKcSRQoGwgtiBxHLo0NCHw2Fls1l/pq6uLi0sLDjNONi4sckDECDPpfHlsKcJDYVCLuKQesLSPjw8qFAo2C8Es873ChYRNADSe8kpv5NiJxwOa3Nz03MxCZAi2GZgYMBplCDIyKHxlOEfhtkpl8tWI1CIxuNx/xmfJ8iC8z/uDd8ZhpqGmnVEgBwHOTIkDmyKBV40+myCNDSkvNPwYvug0KM5RLYJcj8+Pq5sNmuGa3Jy0mwNXvnBwcFHo4EYp0QqKQjwq1evFI/HPR+0u7vbORCACTyL+NcajYaKxaLtB6hm+H6s7UajocPDQ8+6xIYSDBihmAwCNxz4PMcwrzSHNB+pVMrXk6aO+xgMrovFYmZoWfMUwjwbQaAE2evS0pJBG7IkaCy5PwSgEJ5VqVR0e3trhD4Wi0mScrmcR8RQaAIOtdvvR6LB+gCIJRIJS04l+XrX6/VHIY7IP2lGeP4YW0aKLdLScLgTAjc7O2upZLAwXlhYsByZpqZYLDrRmOckGo16nvf/8X/8HwbjmGfK88y+v76+bvCVNZ9MJnV31xnJRWIxjfXR0ZHBDNYUTCyjbpCcHx0dOcWfdTs1NaXd3V0DN7VazeFErFXWjfQ+PwB/IsUSAKckJZNJjY+Pa29vzzL14PQBlE7sh6SY4+8b/Xa2MmcL9wR2A5UHoAw+SsblsAYl6eTkRJubm1Z89PX1aX5+3nsTewgSx2BaOGqjdDqtaDT6yFaF1QnGkL2fcTfd3Z25y8Hk60ajoY2NDRfUsVjMEnXWC/J4wEf83Pjs37x5o729PfX29jpslO9L8UnzTXMdCoXclLGGCSSEKaaRwv9KYT06OupzG/sVn5nfSVbG8PCwTk5OVCgUDLzxDN/d3fnZRwVBgjbP/fn5+aMgLs5Y9lUYTuoBJK2NRsOz1AluBCSgEbu9vTWYGgqFzIKzpwJEDQwM2B4TPKNhaQ8PDzU9Pa3T01NVq1WHqAEwQIgQgMesdZoJ1htFPkq7RCLhe8L+Rt1IDdbb22s7DUoZQBr2TCw77JWsB1Qr1FOcX8jlYdLZV1nPqKmazfeBeNwPag7C1mC49/f3Jcmfr1gsKpVKebb03t6elZRMdJmcnHSjPTw8/ChrBJUBdSTjWalZCOFDcYPEf2pqShcXF272eS6432Qw1Wo1jY2NPQIsJiYm3Izf39/bm0+uBiTD7Oysa4/T01MrFCYnJ52wDjFULBa1sLCg7e1tN8dItzmnsJAmEgnF43HNzMyYaWdv6enp0VdffaWrqyvXJQAVgEcPD51sB9R07CXsEUNDQx4PiBqIGeSo3djfsb/S80ACSu+BVdhrSElJj2oYck9QNfFPJjGhIBsYGNDm5qYGBgZsZUkmk48S7WGt2evJVZmYmNDh4aH30K2tLdeS7IONRkOJREJra2uPVJMAwtSH7XZbo99OlMIe+Id5BXuFH+r1Q7//f6zX9/aE0yhOTk56lBBsEc2M1EGRuru7PfexWCyaDf3P//P/XFdXV8pmszo6OjJLhR9qZGREiURCl5eXbuqQZEmyNDqYDEyhQ7gNSBEMM2wGjFPwgcEDiuQO2RZFA/IXfDjDw8P++zRW7XbbPtve3l6dnJw8alRpCGDwCKmg6Ae5R9bFpkJBhiSbjYWNFZY0lUo52ZjRIDD7SMcpbtkcghJ4AqyCnrUgCw7iDLvGphj0qcOEgrYG/daSHqkdKKASiYTm5ua0t7en0dFRh51EIp0wKOS/19fXury81NnZmVKplNnTSCTiwpCGgAKfxo5Ci0IYtpMgFEnezCU9YjgpfoJNN5sVzwPfic2Wf3LfKS6xGtDUU7zwvqC4qATYrGn48PzB5kmdpg9WMBwOm5Wp1+tO62Z0DUxlvd6Z5wnbkk6nzRQ0m01fF0LZUG7ANsDQsBZo3HlGLi4u3FjjYTo7O9P09LSOj499T5EZI6W7v783s1Yuly2T4wAKBrPAhnINUqmUGW7mi3M/S6WS/z3oneR7s+5hjPhdXV1dHg+GX47RV4BK+JthLBuNhkcRjY6OOjQOFBxJLoUj+2fwOaEAhnlH4kaDjayQIoa9h9wLbAewA4QUkYPBvGRUNNhx2CfwRpLMzOxswq4ikYitMt8t4nluARUSiYTW19eVzWYNzkjSzs6OLi8vFYvFFIvFNDMzY7YbfyzFYLPZ1Obmptlr3oNQPmZpc88GBweVz+fVaDQM5OEBxaPL9VtcXFQ2m30UJkoyOveBRgbmGNUToMfExISVBnt7e94zDw4OnEhOPgTSbfZj9hmksuyrME08w+fn5+rq6rLCij0lqHpgjfGcoAABFB8YGNDk5KQODw/NaMKY8H6AChT4SJKxXGUyGYMNWIIYD8cZh9SVRogUZ+wdm5ubWl5e1uLioorFolqtTqDlycmJZmdnzfrDYFMnMP4nFovZJhK0gOCh5nfd3t56HwJMyOVyPvMB0ZCZMtqUPIZGo6GJiQmzxsFwTVLTARkgIyKRiM7OzqwS4V5QF0xNTenq6upRDgNj8Mh3YMpBMOGYmgBrEdJfrkG9XndBjUqFGoI9Jch4397eWprc3d2tcrmsqakp/ztFON8X5jDI2LJHsCc+PDw48JbzD6AB3zI1A6A9rDKeapp7VH+c19wvzlqaZ9Rw2HX4PswVRyV0eXlpUOTh4cEWAqS7gJIoDTlXsSsEmULUPuy7fG9UZgRm0VxK0tu3b7WwsKCLiwvP0iYzgnUCmAX4CAjQar0P1mQqA1MDpPcWCEAaGkzUbexHnNfNZicYFVCYcE6mGgFsU3Nks1mNjo7ap41FpaurS1999ZXW1tYs8+ZzArgODAxYrn57e6v19XVJ8l4ftJthi+CcoKkGECWTZWdnR7FYzPUJZzP7F4QYVgasotQtKEz7+/tN1gCKUyNy/ySZNa5Wqw5XW15e1vT0tPr6+vT69WsHQxLAx37N9cbqxPWhpsIumEqldHJy4rXcbrd1eHjoc4fPEo/Htbm5aY8595JalfoY0B7bAqBSkFCKRCIe1wcxhc2W5v03rx/+9b2a8OvrayPQeL546BkvRkAMC6PRaBjVplmAWerp6fHMvnw+r3a7re3tbT08PFiuKcmFOGgq7C0sKxI2Dhc8kTRBFIVs+sHwBeRDSEmq1aqePn0qSR5tgIStv79fs7OzkuSkQkIhzs7OHvnKKOAogmDuQPtGR0dd3CJnoTCUZISQw42ikO8cZGBhJpBcU3DF43GjabAZsJ9BhjDYfJPe2dvbGQGWTqdVLpd1cHBgZQKoPswHm000GnVASK1WM1NCc0ajdXl5Kem9P2ZnZ0f1et0Sc+T1eHVRAVCsMqM8eH3YXEgwJek02Nxwb1g7vCcbDg0z/2StBL3ENOTcW64bv4v/BijA7+F9gwcLAADyXw56GnAKe2SCFOwEnvB+jFWh4Ovt7TX7xcZKYS3JSb+Xl5cueO/u7lzQY+W4v7936jGJrkiFz8/PLXmESRoaGvK8X5oI1kJPT4+++eYb+3onJibMzAZHCRGURMIz95dikOYdgIiDf3x83Og9IAT3lxdNbbFY1MzMjMfX4DUEZGAWL4m+vFdQthhslJF8BpsqnmXADElOzJ6enjazeXNz4zRmSW7uR0dHDRDgy2QefHd3t8bHx81IF4tFszLYDHhWmDiBbx1Qh2f4zZs3lq/SYLdaLa9HUPRwOOzZqaxvGCdAKViGoIf14uJCU1NT/izMiv35z3+u8/NzXVxcaHV11deDvQ/wrNlsamlpSefn5wbY2IdPT09VqVTMJpNcjlz+8PBQb9680c9+9jODvR9//LGVN7u7u+rp6dHq6qpOT0/NXFUqFUujOb/4niTP04w2Gg0HNcEyxWIx1Wo1g8ILCwtmWAjLQ74KYDwwMKDj42PbZE5OTtxUw7aQkQEj/l0WWeqATLCjAHKpVMpyRIpWitqgJJ8zslqtOkOEdT8yMqK5uTlFo1GDeliJGo2Gg92CDCWhcPl83vcPRVgwRZ91EovFnE5+fHxsYKJer+tHP/qRG18AWFgq1idAGOwT/lJJHlWINQOGMJvN+rlBps91YZ8J1i+wb7FYzIq1aDTqYL3geLmenh6fgygokL7zPtQghULBaevcY/bp4P7Hsw3xwF5ME8a5DgvKPgY7B3h59u0ovFqt5vVYLpetJkT5FwqFDE6x9z08dELHeLFPBOsIsh4IzaTZlToNGFkWb968cT2Hr56aDRk1Kg3qH+oKFI8AQOyjkCWsw8vLS58DhFiSF4C8l0DLZDKpqakpq8IA0Aj2DCab8/vYGwihrNVquri4MLPN7wAoRXFDdsT4+Litj3xn1kI4HPYZDNA8MjLiZ56Qze7ubmWzWQehMT0BSTnnNA0yTRkZKAQSY2mDFLi6utL09LSzDQ4PD5XJZPzMJhIJW2MSiYT30Hg8bkBpd3fXI4oBkFCHoBh48eKF93Seh2fPntnGwQgtQLGrqyvt7e1JksGSyclJ9wcEZUYiEbPc1GdjY2MmZpCRsz4gK5rNptcVa4FxZ4uLi66db25uXEvMzs4afKB+isVi2tvbM8jHvSUQcHR01Gcz6pmjoyPbuyYnJ3V9fa1sNqvFxUW9e/fO5y/ZTEtLSxr9dowq9d329rbm5uas5kXNwDNJjchzhDUGAIJ8pv8vvP7H//F/1N//+39fxWJRL1++1P/wP/wP+vTTT/+tf+9//p//Z/2ZP/Nn9Mf/+B/X//a//W8/2Of7Xk04GzAHPPInSQ7BgJ0OIrfIP1gE+/v7qtVqj4LdEomEuru79erVK5XLZZVKJXszLi4ulE6n1d/fb58HM/lA8ZlJXKlULEulyKf5CkqskXiwIcFenZ+fW4LTarV8AFEQ7+3t2UdGkiVAAOgyYyponAkggumn+UJyiySYQwqUCiT4ux506T0Dz/fq6urS7OysN8pWq5OqXa1WzSDhH93a2jLqxvXp7u5WMpnUysqKC+rgOAPQeA4+PjeMB/J3ktZp7rnGFO1s3sj/YI4leS0x2oHvG2xkgwcxhwvNAKnXyOaDTS6NNNcOcID/T1PH+giy5nyOoI89+HN8FhoJ2OxgI/HdnwVkYROEyUM6DlsLWy29z2QgAZ+NPugthl2t1+u2dhBMBFPE/eKZy2azDju8uroykwKqDGCFZBJwBzni0tKSFR0E7BCQBYDAGp6amnKTwAggrjUgEEwVzxmHIfcboIDmqFAo6OHhwcwte0a73bbihHEp3d3dSqfTXtvcq/HxcR0cHFgijWSfBGJYMtYC6hyaBJiLaDRqyR3hUUxzgNUEuOT/o1pgb2i334e4wd6AbsNuUljT1AFmsGfCaDGbe2hoSIlEwhaJXC7nYrXRaBiBl2Tgi/U/Nzen3d1dHR4eeiYs+xT7LM0BEuL+/n4ngsdiMT158kRHR0cORorFYg5KZLY1AB9Syt7eXvseX7x4oWKxqHA4rKWlJQeksW+y/1A0s+fc399rY2NDq6urarfbbnawSXBtAfDYQ0gABlxhjyT4Z2trywAMrO3Y2JgymYzC4bD29/e1sLBgUCgWi5ktgcUMZk6wDyIfv7u7cyox4Bi+P5hybAfVatXztgF+YAMB2GjmUZzhM+R1fX2tg4MDA7QkPI+MjLgpi8VizkQAcCB3Ynx8XPl83o1tT0/PozA9AF3USTS0yGm/+OIL/exnP7OCDQsPSgcmHCAZJQ8BywqsMeo31h95GADseHE5g3iuY7GYwyNRxoXDHb/8xcWFAe5msxP4OTU15ef26upK4+PjVp0AzgJewqyj6oO0IFjp4uLCIxlp3DnLuQaZTOaRMknqkBN49IN1EGw+5zbgKywgliRJBm+bzabPfCTy1AzIYAEhQ6GQGVyk0aiIHh4e7LemMUCBR0gbcvm5uTltb297Mg7PP2TG6emp92HONM4RQK2gfWBoaEilUsmBZDDCyGrZnzlXBwYGVKvV1NfXp5WVFTcfAwMDVmtxtkKOoGKIRCJWW2ANQSVGYjbnAjaFvb09Z3AA0rHXwIje33fGEXZ1daaMnJ6eanFxUT09PSqXyw7rom7jWafJxOqUSqUsK2bcJYw5ez0KpnQ67XMa3zlnM6pTLA/cc8Y1AgYAsmAdHBwcdEbP7u6uw+9QRKDaenh40H/6n/6nuri40O7u7qOzCrveyMiIlVqLi4t6/vy5R6tRM8LgAsIGay5JBhqxpZ6dnen169daXV11UvnR0ZEzhyAYggpDyAkadq47VkTUHJIcQMtIPO4PdRFWL0BHspRQy8B+E4Lb09OjL774QiMjI/r000+9f15dXeno6Mj7RSqVMvAIsUDIIOoynp+gKvDu7s7hnZB4/194/S//y/+iv/7X/7r+0T/6R/rxj3+sf/gP/6H+2B/7Y9rc3DQ58q97HRwc6G/8jb+hn/3sZz/4Z/xeTTgeaxBWpBBs1DzYBMUcHR2ZdeCAYZO5urrSzs6OWSkOrKmpKb18+dISrr29PW1tbXmzJgSMWY63t7c6ODhQKBTy5oQsEQ+qJCPmwUaLAgjkenh4WGdnZ/YKcXCA3iP3wKu3vLzs5oZwHhpoUEwYA4qf4+NjH8xIy9hskN3QsAYbczZdAqsajYb9TXwvNjoKezzeSAtbrZbevn1rdgfWi6ZMkiVPNInS+xFcksxKXF9f24dbq9UsUwt6WGhakQqyRrhG1WrV3sKgLx40n8abACBe/H2S6Dnwe3p6lM/nveHi/Zfes9Yg9fwz2FQH5TdBRjx4L1hfFLA0TTDXNCY9PT2eTyq9D0GR3qscYLxJm6bphcGGUeAADN4XGk7QXkZgSfI1pXBA/op0HDQU7+bo6KildIxVYd3Oz88bKKDg5vCgAMY7STOG5I9mmlEs7B1cMwqVoN0CLyPNXTDDgaITtP76+trsfDC8B3kZ+06j0TCrGXzm7u/vLTUH9W21Wpa4wjJy8CL95Ppz/wGiKOwJ9CGPAA80BQOSetj0QqGgUChkJoW9FWvN8PCwqtWqxsfHzUzTUFHg09BxuPP/g/sWVpFvvvlGCwsL9mPOzc25sOA5Hx4e1unpqcdl9fX1OfODwgRPGUAK7FCpVHKRyvXjeXz9+rUikYh++7d/23+X9Fu8daOjo8pkMurr69PGxoZ+9atfWUoHm7Ozs+O9giKZQoi07d7eXv3kJz9xs8ec1GAAUqFQUCKRsGSV8TaFQkE7OzsGxT755BM34fF43OsQGWZvb69WVlbM+sI80MhzjgBQAVYBxMIi9vX1OQyOtU2xTuATDV44HHYOBs8qYVyoHlBOzM3NGWCgWIQl29/fNys1ODhoPy4gSFdXl1N6a7WalTcAX+SpoJRDovr8+XNJ8ug3gDRCDgnXAszd3Nz0cwrTj7c+WENw5mExCjYigFsnJyeWaGIngFlCRo1nEvCFFGhYX4BV2KpKpaLJyUmlUik3i/iDeX5gtGiopI5qDga6r6/P+6YkP0MofCqViubn5+0TRdodtP7ATgdtDJxjKBx4PlHN0IBXq1UdHBwYhGGCA2PgaNJRYMEAU3ugaru8vHSzjV2GBplGMTg6j32Tz0X9EZT5F4tFLS0tPZrbzt4Cwwr4z37AeUdNQIguKi/2QZ4j9nT+LqPoIpGIrSM8IzSpQ0NDbt4AQQCBJBloOzk58dpm3VxeXj4KAWayycnJiTMdICrwLJOVQj1APcKZ3Gq19O7dO3vNeSYApYaGhuzJp064vb1100Fden5+7jV4cHCg4eFhra6uGlwoFote+6zTxcVFy8IBpABBCFQDyEe1uL6+rpGREZVKJdf98/Pzkt4TItvb26pWqxoZGVEmk1GhUDCRQWNIvQIZx3MDeML5jbqtUCgonU4bnOCZQumEiofPcHZ2pnQ6rZOTE21tbWlpaUkXFxdOxue9sdudnZ09su5tbGyo3W7ro48+cp0EmUAqfrvd1uzsrAOsz76dTIG6obu7WycnJ8rlclazEur3q1/9Smtra2q1Wjo+PpbUIa5mZ2etHiFQFXsM14v9CFUKmVis24uLC+3t7Skajeq3fuu3HmVe/WFewX7qh3r9u7z/P/gH/0B/9a/+Vf3lv/yXJUn/6B/9I/2zf/bP9E//6T/V3/ybf/Nf+3ceHh705/7cn9N/+9/+t/r5z39um94P9fpeTfi7d+8UiXSGzuOLymazWllZMYpDQwL6iswH1pxkTA79er3uMDQQVtjygYEB+8Dy+bzu7u5UKpW0u7vrGXogdkiMOZBZaCA9XV1d2tjYMCtHAQerQOGOlDWXy2lxcdEbLQUUm/fMzIwSiYR2dnYsOUMCF2yWCI2gCbu4uPBoBJompPPM7GRT5mCHiaGBpfDgFZSY4FtdW1tTNpvV1taWWd9otDMCiCZhcXFRX331lRGz4AzeoKx+bGzMhxLesGq1qkQiYTYP5oEDKOhFC4U6Hr25uTl7zxhrBEoY9NyC3k9OTvra397euuFkpF2tVjMSiawO5ocmm0aO6wzYQ0EQDL0ISs6D4AeFEM0iTQgbGfcGqTGs1ncZfNYE+QUUpgAfSJYAJABI+vv7VavVFI/HLf2m+evt7XXC+czMjN8btBkfJnIyEH7WMk0psq1QqOOVJ7GVtFSeb1hsikZyCFjLAC7IPWE9+D0AcRS9NLY0v4AHKACOj4/18NBJbqWZ5NrAXgEGUqgA3FBQ8cxwDWDzeOay2az9y6D+hUJBXV1dZiGPj48dbAUYga8Puff4+LjzCygQOKw5+GmKkbjhb6cR5LshkUXdAHAUjUYfeX+R4ActFsjmkB9j56AIpxFCjcAzwL2g4YGVrdfrDuVDvnh1dWU5Ik0dzwyzoAEv2ZuWlpaUSqX0i1/8Qr/61a98bZ8+faqlpSWrethr7u7u9MEHH+jXv/61fv7zn/vsYFZtq9XS/Py8mzGUFqFQZ8Ra0ANHsxiNRrW2tmarBGcQQBPBcUFAEBAVVoH1j3cR0Perr76yjBMmutFomF1hbjFKJRRA0WhUiURCp6enisfjbnqQFQMaZbNZz52F8cW/zloKhTrjKMn8YD3jl2WueHd3t/b3953jMTIyYnABr2Imk3GRuLW1JalTNE9NTamvr88gWywWMzh9eHiowcFB7ezsqL+/Xz/60Y/ccNMQA4IAvLx69Ur9/f1mrGFW2eM4Txl5CEAabLCx+GDLIESN5oL9iuYZpQTPAHY2mh5Yv+9OYEFlEQqFLEFn9B+zzb/r48R/SqYJtg72LhrRaDSqd+/eKRaLeR+nkaAJIH0fK0O73VapVPIeSp3F92B9B+uQcDjs68hUBIACzl7WJFLZh4cHJ9ofHR25mY1EIs4ueHjoTDJJp9OS9EjxAEiKz5nvE8xViMfjurq6Urlc1tnZmZ49e2aVTjBfBiAKUCYYmMWL64A8GUIFLzIAWTKZtEKRzB2UUJyrXH/AG9RhnOnkIFSrVTPy1FGAsUdHR5qbm7M6oL+/3w0iyk782SgpAQiY/AGzTQ2AZBrQhGsDkYOMGxWH1GGC2+22UqmUz45cLucxvDDnNIQ00yjnsCPBWOfzeb1+/dr7db1e19zcnI6Pj9Xb26tnz56pXC6rVqvp/r4zVSVYH/b39yuXyymdTlv9srW1peXlZZ/1zWbToaxBQAUGWpL3MJRe2JOoA1n/hBlS7wSVnbFYTK9fv9bg4KA++eQTNRoN18/T09MGrScnJ/0M8V6jo6OeQ84ewfN2eHiotbU179Mo1JrNztz1oaEhZTIZn82RSMRZFtls1gq+YIbBw8ODz4vz83OVSiV1d3ebiWd9UfvTJGOTqFarvi93d3f66KOPlM/n9fTpU9vVglbO/ze9UHvyAlj/7uvu7k6ff/65/tbf+lv+s3A4rN/+7d/WL3/5y3/j+/93/91/p3g8rr/yV/6Kfv7zn//7++D/htf3asI58GG1nj59qv/1f/1f9fnnn3uGH40jjcnBwYEmJye1uLhoiePg4KAmJyeVzWYty6LRQS5I4UNxi7yNcI9CoaBcLqdCoaBIpDM2jVEYbOg0SsfHx/ZNSrLsFQnp7OysKpWKNjc3zVZfXFzo6OhIU1NTlqEFZeS1Wk3ffPONvXNsgtJ7WTWyH1hb5FbZbNYsFcABxSoF1PT0tJs2WCYKF5p9EDxQWcKYbm9vdXR05MNycnLSTS7fY3p62of/2NiYJiYmtLOzY9YQ9gH1AYF0bM5sAg8PD3rx4oUGBwe1ubnphp4CRZLR8rW1NfvhDw8P7Y1jE5ycnLTNITjjnRA+Ci9QdbxGbK4EgdB40rRwDfv7+7W0tOR5ucHwOFhl5HMU3xTeFF7S+0CloAccJqunp8dzQfH7UWDBXsLeUeCA+tJ4sYGzNrBF0JjCHnGAzs3NeeMEgIIRkvRornZfX59GRkY0Pj7u1GCkW4AAMzMzLqyQnTJ6A8CFNUjjTkEULARZP0G2g98DUEID09XVZXR4dHRUhUJBk5OTikajymQyarfb2tzcdOBaqVTyiCKADNQxpB+32203pMjU+P34K2EoLy8vtb6+rkql4gCvsbExRaNRvXnzxsUMqHaQWef6IXEH5AFcQhpOASPJRR/sNnI3WCKkl9hRACcpxghMIw0ZlRJeL1g3qXNooVbhnsBGMCeVEUewI/39/YrH4zo9PVUymdTNzY0bh7OzM+3u7jplGaCK+fDIbQHP2AsAsmZnZ5VMJvXu3Tvd3t4azIAd5vtfXl5qbm7O3l8KaJpu9suHhwermJi32m53kskvLy/10UcfubgcHR210oORNOyj/F6KntXVVe/rrEFG7dDIYpXiXlKI5vN5N8CsC/yOyMEBm1A9sEY4u/jZi4sLBwXxDJ6dnZkxfXh4PxqoVCrZ9xwOh3VwcKBIJGI5M3J/ABZJ9tDDnj958kRdXV3a2dnxiMH5+Xn737ECLCwsGHTCq18sFj3y7+TkRL/3e7+nlZUVA2aoJDgXWZvMrCa4lAAozjsyMWApUQ5RbwCGSfIeDpDGPthoNAzacn5OTEz4POHcBrTZ3t42sEmjBYtIU0NDyQg6RinBgvOZmRvPmFbWCg3O6emp9zQYuqGhIc9sjsfjKhaLHu3I/gW4GdxXUADAkCITD+4n+Xxe8XjcDDIAOU0rABZFPA0hBfDMzIxrDpql7u5uT3zh/CRklecI6x2gKcDu+fm5yuWyQqGQEomEPfcAZdg++Du8F00U3xkCg/9xVhMaiQKG7yS9t3nxAsxC7UUtSwI43nVqDkipeDyuarWqyclJA76cQRMTE97notGozyqaV1jqfD6vVCqlTCZjIHH024k/jG5FSbqwsOC6gLqViSqcnxcXF9rf33coMUngNzc3Xk9cP54n6ho+I+c85ASADH7u3d1dtdsdK+pHH32kiYkJPweMbmS/rNfrDra8vb31mu/v738UbshzNjY2ZvUAWVMjIyPuI5DRoz4cHR312X99fe26CmUc/71cLuvw8FA9PT1WMKKe4WwBrCZDA1XpwMCADg4OdHl5qfn5eQN1zOMul8sqFou6u7vTysqKEomE8zHevn2rxcVFj3Ht+jaIjnOCWvT6+tpBttFoVCcnJ55yhEcfJUU+n9fe3p5WVlY8Cm12dtZqPQCPVqulra0tffnllwb2AcofHh7005/+1HUk5/r/G18o8nj9nb/zd/R3/+7f/Vd+DksrIca8EomEvfXfff3e7/2e/sk/+Sf66quv/n193H/r63s14QQyUGgMDAzoww8/1Jdffqk/+kf/qBGnVqulbDarcrlsv8n/+X/+n1pZWdHy8rKTyldWVlQul50kiiS8p6cz5oKgFxptCrXBwUEtLi6qv79fOzs7ur+/Vz6fd7AXGxXSpHQ67cKMQwiWAWQTOTNBCBRF4XDYnkEeUIoIEEqaPF74hgjGoYi5u7vzGCvGH0gyEhtEaPf29rS0tOSNHB8oTSZNIOghkkwSIff29ixFe/r0qQsbPOKSLPdjk69UKi7egtJJ7gnspvS+sePAxivM3MpKpaL+/n772Qg54TojU8XXCiMRHFNC08nYIJp2mg98QWwYHISDg4OWYDcaDTcDrBskmDAJMC+oN1KplM7Pz3VzcyNJPvQ5HECaKUIkWQaWSqUcFMiBPzk5acQaOScsQXd3t/8O47M4OCio+I5IVWHreRai0agbLtYm9gQOV5reZDKpWq32KHSIa02jmkgk9ObNG/3iF7/Q+Pi4Pv7440fhRKx9fE7BoCmuOdJEpGL8DkkGCQhJQ1lB8cxnD/pzz75NZ4Y9g5VmnZIQTL5DNBr16Bga9Eikk9TMtQKIiUajZqp4huLxuIaHh51Ezn0hmIdnlgMbRkSSASxUOBQYSMF4ZiggeTZhtGB8h4eHXQQiheZac3gC7gECsI/yeSnyKIjJhbi8vFSxWHQzw3tS7AK0BhlSDuhQKKR4PG6ZOd95fHzcDR5r7+c//7mazab+yB/5I2q1Wn6/VqtlOw+hRXiXsRfwjI+MjOg/+U/+ExcyR0dHDvFi/wsqCWBdSN+/u+vMIYf54BlBds3nLxQKajQ6M1f57hSbBEriiyUNGcDx/v5eU1NTisfjDkhi3QaVEq9fv5bUKRCWl5eVy+Us5wbMReI5Ozur3t5eywSbzaaq1aqeP3/ukC0aEVRDjUbD43u4JyhO9vf3/e/B6RFBxpkgwsPDQ11dXWlxcdHre2try8xcu902o0sIJOueZvq3fuu39Mtf/lL/4l/8C/32b/+2i2rOvOvra3311VdWhaEWCI4XQ43D+Q8gyr5FAxOLxby/3N/fW+lAtgv7eDARGdAW1Vmz2dTl5aWmp6fdSGMtYN/C6oHkE3CIhpHAKywnp6ennsLA8zs+Pm6Qgr2KVH2UYKgH+EzBDB5+jt/H2cD7s1aCWS5B21VfX59mZmb8jKGQglGlkcATy94GEYAyjnMQYBQigbORFw01lh6UCzDg1BEzMzM6OzszsHx0dKT+/n7l83m/B3VSs9n0uEq+K8AVZzL7H+AvoC/gKfed70xtU6vVFIlElE6nnWhdq9Wc0I9yBgATFQTMIr+H64dKraurS/v7+4rFYq6ZSMpuNpva2dlRq9UyM8z+SIjmw8OD9xl849hiIG8uLy/VaHTmSZP3wrxtsknOz88tgY5GowbYgtkiwecNFRZnOPUFXnjqXEiq4+Nj7e/va3V11WA29ocgOBYKdYKN0+m0vv76a4MV9Xr9kTWBz8x5x/6N1x0LAAAd+zkEAiQBddDh4aGzKhh/Sf1Ms16v1zU9PW3gHtUWYMTw8LCfA0JNh4aGtLm5qfX1dXV1denNmzfKZrN6/vy5jo6OJMk1efB+Aj5BNAI8IS9HpYYKJpvNanZ29lE4IJaTnp4eLS0teQxivV43OFsul20pmpiY0MuXL3V2duYzhAwragFA2v+3vY6OjgygSfrXsuD/Lq/Ly0v9+T//5/WP//E/dm/2H+L1vZrw8/NzN0/Idkg1PD4+1uDgoAqFglOFP/74Y8sw9/b29PXXX+vy8lIvX75Ub2+vSqWShoeH7ZdAHgpzAiNLocymUK1WFY12Ulrn5+fdDFxdXeng4EBnZ2fa3Ny0JGZqasoIO0wCmy4bL4dNf3+/UeF8Pv9IkgSSB9tDQQAqHWRGKQAIqBgbG3PIDhtkNBq1LKVarXrDmp6edgAQDTiFRzC4hEMJZk+SkbBQqDMeCoQVaRaJifiEZmZmXMTPzc1pcHDw0YbF9wqOTsAjTcosSOz5+bnZQ0K/8OFwSEqyl5Din6YVKTkNLCwyTRCyMgp8JLcULKSNcn8vLi582MJeME4i6BXnMyHlXVxcdMFJ4YKMb25uTvl83uFErC8CTpBNIrvn85fLZaOZy8vLPnDwxeMlpChgPfJcgOTTVFMIB2cQS/IhgfTo5uZGp6enTsWWOtYMxtBIemTnoKCiGU2n07ZD4BMCDMEjxt7A+wWfKzb5WCzmNQdLTLHI2qFBgj2BMSDYsauryynK3F8UE0dHR0okEpaQA4qxlvEcUlCefTvNgGYMHxyyL1hPZqkizUNGDFLPd6VJQ3qKzBCJN4UChSj3gj2lWCw68Zxn/Pj42PsfAUqM1kG2Fkz2x94ACEnjzN7Z1dVl5g8pX61W834XZFDPz881OjrqBomxjKVSyfcDNc/o6KgSiYQSiYQbQzIc8Np9+eWX+vGPf2zQgjUGC0IRDwDHmr2/v3fa7ldffaWRkRGniFPIURAx1g4wEzn+6empVROHh4fe71HFBEeJwZ5gjULhwvVDntnd3e39gc9xfHzsgDT2Yqkzjk3So+yFarVqAAuGlb306urK4XMoHZaWlnR3d6fNzU3vX93d3apWqyqVSgb4AGkuLy81MzPj8aA8A0iGg+ApQNbExISVIENDQ3rx4oXX/9bWlg4PDzU0NGSmcHR0VAsLC1byJBIJHRwcOPn4/v5ei4uLnnSRz+dtQ8Ni1mw29fz5c3tUC4WCQTkKQ/yzV1dXbgTZO4LjkvCt8ueALAAM7MXxeNz3Znh42L59VGLFYtFrkiYL8EWSG2AUNjTBkAfY8Zi60mg0lM1mJclgCUFJoVAnQX95edmKDpoHwEVSnwEm8XEPDw/bDgSwyFlOw4KlgkZkYmLCZxOBl9Vq1Uw/loCg2omzGZCQqQBnZ2cGyQG82FsAkZn8wrkAs9lqtXR4eKharWY7H4ARzc3U1JTBAFheAHH2FuoaPm/Q7sB5BJtLPQNgEsyTYF89PT1VKpX6V1RSBwcHrr+C6gvC31j/nAnsjzT80WjUDVClUvH1aTabymQyOjk5UTQa1erqqgqFwiOFKPcdlSM2FoIXUYJKHYXC3d2dR2MSRsgaOjw8NODFaMXLy0vlcjllMhnbxai3AHl4FgA6ANq51thw2u1O7gjreX19XZubm84Menh4cN18cXGhubk5XV5eKp/Pq16va21tzV78m5sbZbNZ2yuvr6+Vz+cNhO/u7lqxAbCD6q5UKtlPnc/nNTw8rMPDQ6sUJiYmrIxiBOPTp08VCoWUz+e9xrF24I0HxD86OjIQ1W639fHHH6vVamlubs5r4oMPPlA4HFapVNL19bVmZ2d9L6WOTQU1Sr1e18HBgV68eGFFCfU/1446jT2cvgLyoFKpaG1tzbUS89tzuZwuLy8Vj8c1OTmpsbExPXv2zOoI+gjqa0g61tkf5vUf0hM+PDz8qAn/N70IEv1uynupVLKaJfja3d3VwcGB/pv/5r/xnwX3rs3NTde7/z5f36sJv7y8VCKRcFNMs4M88u7uztH6ND80fAsLC1pYWNDu7q6++OILTU1N+SIFZdkcXM1m07KldrvtDXFyctLoW5CdgsXGC5vP581mv3371vJJwj2QU3/00Ufq7e3V6empfRkEHRA2g4+G2bAURrxAMjmEKQiQBk5OTrp5BFWngJZkKRd+SOTHqAqQqdJ8s5HB9tCEIdfm/UDcQYaD8hR8j8FFBpPE4f5d3znvQ6HM4czmHPSjceghxc3lckqlUjo+Pva8zIeHBzc2ABlBDysMHp+Jw47rhjSUvz81NWUGlOKd1Fq+K4nV09PTRnVJ46VY+eqrr7yRwpBI8sEBsh4OhzU/P2/kk82RBgkZIx4v3r9cLiuTyTgtGYaQjVCS13+Qxecz4l+mWKPZazab9noBTHB/uUah0PsETQpGJF4UsScnJzo6OrLEFASYdGoQdEYV4dsj24DwKp6JiYkJXxcKfgJvkOpOTk6qWq3au8lnPjs7s/QbBQ0gDQ0shzqfH2kayeozMzNO3+7t7dXW1pZZPPYcpH6kAgNKwBiOjIy4IeE6ghyDliPJ5+8Ek3TxWfKdaMgB02ZnZ72W+R6w2MywRj7LHoEqAVYGDyDJzlKHFWQ2LVJPbDjBGe4g5uzDKAto9pBeMtKJ+wOYubOzo3g87gRsSZZ0f/75524M4/G4GReaTph+wgVh+Sn02RuHh4f15s0bjYyMqFKpaGBgQC9fvrTfk6aYCRoATQsLC1bQIA0MjvsCGEkmk5qdnVWhULACAX8fxbukR/Yf9uGHhwfPgOfneIZjsZiBu9nZWcuPscXAzvX392t6elpbW1veMwBFYrGYDg4OHJzGBBEAxvv7ezUaDbN2FKRcX9LasXrx/MBoLy8vO4RvfX1d6XTaEu92u21bVj6ff6Rswf/M2TM0NKRisajT01Pt7e3p6dOnHgHEmXhwcGDgABl6Pp934jbXmLORPROAh++fSqUMGgbHqoXDYTepALXcT57Ty8tLg8fUM5eXl54JTvPHMwgw09PTo2KxaJXN3Nycrq+vXehNTk7an47FA4sE97Svr08HBweW/BKelc1mHU5JrYAiMJlM2m8/MjJiOwHeVxQN5+fnisfjOj4+9rojk6DZbPqsub6+VqFQUDKZNOAEuIg3lf0YmSx2DwCKYJgr+xbNJaB3EKC6ublRMpnU6OiobYM0c5wreGGZbkETCjmCgkySwbBg/QDRwv6IlQP1zdHRkRtPWGWuCWwwzXEoFNLKyooajYbH8wEAUeOWy2XLpYOAD8A6AAJqQ86KnZ0dA+6pVMrnSqVS0ezsrDMdGGkWVPSFw2FnpAC88cwBwjEajbFeAC40MLD3ANPs2zSEl5eXqlQqzv3B3gTZBXCBdx+iamVlxZlKsPjh8PswPsiNN2/eaG1tzXvfzc2NYrGYyuWy84COj48N0GCrYS8gqJTngDV4eXlpW2Mul9PR0ZFGRkb8eZntDaAKsTg4OKh37945O+PLL7/U+vq6LYSoxNrttskUztNKpeLJIRCR7NPkwjCac35+XsVi0fawoaEhPX36VPv7+waw9vf3NT09bQvc7e2tdnZ23Lyzd6Pu/S7wTK+2vb1tEvTly5dWlQQBJTzrgGj39/cqFovOcPrDvv5DNuF/2Fc0GtXHH3+s3/md39Gf+BN/QlKnZvmd3/kd/bW/9tf+lZ9fW1vTq1evHv3Z3/7bf1uXl5f67//7//5fkcH/+3p9ryac4hsZJowoBR8HMSggjSvS1GazqZ/85Cf67LPP9NlnnymdTuvHP/6xN002bJqxQqHgMSN445Dk0qA2m00XW7C4XV2dKP719XUXJicnJ8rn8w7PQR61v7+vJ0+eaGhoyL69QqGg8/Nzh5bQiDebTSNpMHqg8JJ8AHFgI3nnkAsmo8JcBsOdCDdh80J2ThPEtUYyAjBB04HcHdkxqbkoDSiaAQq4j5FIxBLFZDLpBkaSvV00cyDxjHNCoslB0d/f7wKjVqtpcnLSCbvn5+fa2tpSKpVyijDhRgQf0ZTg8+ZQPT8/9wGDNxJ/Jx7Bnp4eHRwceIZvUGbIWsULTyOAvBLJ2+npqec9vn792gwY/vRSqaRoNKp0Oq10Om3ElCaAonVxcVG9vb365ptv1Gq19NFHHz0q2gcHB/WLX/xCP/3pT/3ZYZNp3CisWAcoCBijFfT0SLLUn0IbKV4kEjFjwFohqZxDstXqBKNtbm5qY2PDs8gpZmDuOPhAyMvlstl51j1p6WyapVJJ4+PjDqXh0OSZhiFkbVP0UoSQAzA8PGz2geZQkptzGjCaX0neE/CXkmDO88TnxtLB+8EIch0pmAnRw3cdVMJ0dXVZ8k0DOTIyokKhYJUDzxDWD4o2JFbBIhAp9vLysgtsPnNQHogsFG85QESj0bAPGUBzY2NDCwsLLsoHBwe1u7urTCbjAoX7CcNfLBZt94FBDMrcpfeKh2q1qmfPntkj+6Mf/UjxeNxKpGw2a2CKwgFQlO/Dcw8Sf/ZtsjuAHew3PtLb21slEgmlUinv3el02rOf2U9GRkY8W/n29tb38cMPP/QMWwon1iJAD3kPNBb8HEAXQGkmk5EkF2LsH4AkKEhGR0ctKWX9XV1dmZFrNBouninCSLDO5XLa3d318/jkyROFw2GH4ZHKfHp6qqmpKQdr7e3tWXK5urrqz05g4PX1tZ49e6ZwOKzDw0OvBUKjYrGYA8E415BfY1Hjebq+vtbCwoLtADQ9sKGZTEa5XM57OSof9hXuP80Y6wT5djKZdD4ADSINLGnnhI+xlhlTlk6nNT8/b4/n0dGRlV08X2Q5SO9VUki3u7u7HW7KrHqmkpA6HwyRQ2KLNBagBkCKvR7bB5al6elp3d52RqHW63UrrSTZfw5YBaCYTCYNpmEhCk5gCe4FQbnxxcWFuru7DQyj8qJhi0ajWlhYeKQAjEajHkdXr9e9J/Ce0WjU4aBjY2Meh0gTB5FCcCjfi/uPmrBUKlmaHQz/ZR/hfA4GNDFudnBw0OfOw8OD65pYLKZcLufPgTWKZ6qrqzMRgPuP3arVaimfz2t2dlaXl5cmKwBux8fHfW4E94uHhwcHygZtG7DdwWtHc0TjTdMXHIOHooR6MhKJaHZ21tkUk5OTarc7oX2oLFmHTJbY29tTV1eXVldXH50pnGnkTQRrbYit8/Nz1Wo1ffDBBxoYGDDLDBjz8PCg5eVlbW5uGtCGpEI9JEl7e3va2dmxBxrQAuCur69Ps7Ozur6+1tu3bzUzM+P7OzMzo6OjI4PZ3Kvz83OrLcbHx20tA9wBTLi7uzN4TKOOvB97RxAoZr8j6PHm5kZzc3POSQJ4A1ChJ2Lt7e7uam5uzlZeFCMQHl1dXc45ARys1+uamprS8+fPtbW15TozmUwaZMS7zzmCwmR6elpra2uedFGr1bSxsWEF8sLCggke9qarqyutr6/b1vX/76+//tf/uv7iX/yL+uSTT/Tpp5/qH/7Df6jr62unpf+Fv/AXNDU1pb/39/6egwSDL/aA7/75v8/X92rCkaDhVWIz4WAOopAgeXhWabDb7c4olfHxcQ0NDSmbzerq6sq+EZi47P+PvT/tbTxP73vhr7iIErVRJEWKiyRqr726a7qnx2fGYzsJEtgIYiBPAhjIO8hryGvJW0iAIE5sxzP2TGap3mov7dz3VdRKcTkP2J+r/mznPve0z5k75w6awMDt7iqJ/PO3XNd3u7JZffzxx+bhhcl6+fKlTk5OrOhtNBrGijx79sx8KI1GQ6enp9ra2jJEc29vT/V6Xaenp8rn8+r1ejo6OrIFW61WVa1W1Wq1ND09rb29PZVKJWUyGStqkLqRgun0FrFRnY0zTd7Z2ZkkTUhwUQ/AkuH9gdWj2eHikTQhh6Wxpvgi/RhpFBI1Ck5JJjfh0IUxpaFFCUCQHswK3hw2MQDL1taWFRKAJ+Fw2JjSWCxm6Y8UZ+l0WltbW/r5z39uSC9IKU3qt/37l5eXur29NZm70zMtyRKiU6mUlpaWJi6U0Whk6xX5O3JAJESgsMlk0ublAnYgK+N3eDwera2taXl52UZJUERvbGzY2jw5OdHZ2Zl+9KMf2bOHAWXWLJ8DsIRmirE9BLSRBeDxeOyyazabqlarmp+ft/FiSKL57igOYQ343MjkkM5eXV2p3W6rUChoa2vLUFUaUxBXCi7UIxTygCyhUMgAAmaGAs6BsNIUdLtdAwnYG7CurCXODuRY/HeAiH6/P6HwgIVCqs9nL5fLJsWmSSEocWpqyhq829tbk7jTmHA5BYNBzc/PGxiCRxlJGfuJyzQWixnrSa4Bkj2KVJ4/Z8loNLKwMj4j0l6Qe/aYJPt9qA5QfSD9BZChmEZJ0m63LSwnHo+rUqlYwwrQ1el0dHJyYv5dJPcUragZYBtpVl+9emXnAgwHRQ9AA2AFe5AihkKiUqlYIwBY6HK59Ad/8Af6m7/5G/Pb5/N5raysqFAoaHt7277Tubk5A/Zubm4mGCZGWbVaLXU6HSUSCQOkYJFGo5ExyiiE+Fl4yzlbCJRjTXB+odxCKdZoNNRoNMwHC6Ccz+dtZjp/j2IPbzMJ5NfX1xYgyp18dnamvb09A2Cc1pFyuWxrgyR+nnWlUtHjx48VCAT05s0bhcNhyyNAKYU6h6kK29vbFhLoDFty2nsongEHO52Ostms7Zu1tTXzsN7c3NgoH75nACdySnq9nnk1uWc8Ho+FIzlZfSTNTtaIQpOZ0BTq7XZbyWTSbA/OUT7Ovcga5fumscMWADiIKsTpG+V+Aiiem5sztpq8Ae4pfj+NJPXAxcWF5dKQsUHtgFWCMWWsl+XlZVWrVWMHObu5V0kG57NxBhWLxQk2FyCQc9Wpguv1esakp9NpO5+q1ari8bjC4bDZcWZnZydICp9vPA+dRHpAWEkGnLOXe72egeR3d3cWOJtIJKwZxUI0GAxUKBQscwNQmkaXwEieI2sXpSEJ9oCI3/bhU3sB5nKno05A4YLUu9Fo2PrAy91qtWxvtdttFYtFhUIhbW5uTrwf1jDfTyAQUKVSsRwjp90B6xbgC1YPlAyXl5c6PDy0++/y8lIXFxfa29uzutXtdhvITdYBsuZsNmtnAkAJmUXOEZOMEkYFCHvf7XZVKpVsFFcul9OLFy9MXcb5mEqlzO8ci8VsRjj+ZFQlW1tbtka4SwGXnJJ37CTsC0aVAc6zLw4ODibOztnZWWWzWZVKJT18+NBGV5LdEw6HVSqVrOlmzQIidDodpVIpAx0ajYZKpZJSqZSF5x0dHVmNQbP/7t07u/vj8bjNe/d4xuP32u22nV8AJO1vRmgR7uh2u/Xs2TObUkAzDzHg9/u1u7trABHgHiQYqsbvIkf/f+vr3/ybf6NaraZ//+//vcrlsj766CP95V/+pYW1ZbNZO3f+V72+UxNOkiILngsGVlySof34ogimoFHlssGntre3Z4diNptVoVDQ/fv3tbOzYzP9CGxZX183KfXU1JR+85vfqNlsKplMKhqN2kHIAfzVV1/p9vbWgqgomJEkvnjxwhr2UqlkDSBycAoDvISNRsOKdwpOZOMwABTE0ofxVlwi+IeR1IGIg9DSVDt9niDXgBukQVJw01z2+33zw/d6PZXLZWMuYD/xxBGSwUbj4JZk6CkblMIFlqj9zZifH//4xxoMBuZJ5iBwXoilUsnYIjb4w4cPVavV9Otf/9oadAq6u7s7S+9Fwut8bySA4p2loGadkSgLgyfJAAynFHF+ft4sDUicaCr6/XHiJ17VXC6nWq1maCp+bEL5aNRhRtgHmUzGJFcw1MPh0LyPgAHPnz/XYDDQp59+ap8VKdlgMA6443sh+AkQhWIdZhm5tXMus7PIAoD66quvFAwGFY/H9fr1a0NJQ6GQ7t+/bwUJRcZwOFQsFjMLB6wARQdBM3zXXJj4EPk+nO8bRh0fKqAcFgf8SYQBUhDDUjqlh3zXNL9OeThsI7kDeO8ByihGpTEQQXHW7/dNQggjg/dsamo82ieTyUx4K/G8cZ7xvvDmOUE7QBAKXAo+0vNZp0jXGeXH34Upp+jnWfAzKJh6vZ55gLE+NBoNA2eQYJLoyugxCkE+Bww4rAHKl93dXQOMyuWygZVv3rwxexDj2GAFyG2AMYSdcoZkccewRwFkpqentbS0ZMU9vtT5+XkVCgVjK5htjp/36upKb968sUYV7z4eYCSvnDmcy4BLvV5vInmXtY48l2adZoVwtsvLS1NWsCedmRnIjrn/QqGQJBnQyH7hTCF1mTVONsbZ2ZnlLJC+DsiFFD8QCNgoIAKbQqGQ3r59q9FoZPkLHo/H9jQMcT6fN48lz4Z1GwgE1Gw2DfSRxt5UmiY83dxB19fXxjIhpWe/EpBIQjgKCBh3VDCtVsvuTNb+8vKynUler1eRSMRADVRmc3NzKhQKFpBKtgAgGQAo1g8aTj7nt5ukhYUFs0UtLCxYcQcI2v5mJBkAFN8N3n/OC8BxGDRprABZWVlRKBRSNptVNBo1MJ4GizoElQ+KQNL3sYRRt3AWAlycnJwYQfLy5UuNRiNFo1FLY6cpAZRAzQDLjfog8M34rMFgHJLI2UVoHo0eZw/1C/cwJAG15M3NjYGo09PTKhaLxloTOsjfr1arFlzl94/HqP7PwMHBYGCs5dTUOBSsXC6bVVDShHeZvUQD7zw33G637RfWMOMc4/G4+bpRxgWDQQ0GAzWbTfsepqentbGxYbk9xWLRngUNIZ9pOBwHtjkBA85FiBqmKxA4Gg6Hlc1mFQgE7DlLY+CWAFbsP3w/WLggwlg/KAiRt/N8aEI5F5hkcX39YXQga46zlb3LiK+nT5/aPdButy23iOykXq9n4DN1H40zigLqOO5u7u9Op6OzszMLIPZ4POYz5zmSW8J5gqpndXVV9Xpd1WpVyWRSXq9X+Xx+YiwcAArPHVuXNPZ8V6tVxWIxzc3NWVo8gXaQRvPz8/ryyy+NPFlfX9fJyYnt36OjI5udDlAOIeD1enV8fGwWEsKvqZuYyw7I0Ol09Cd/8ieW27G8vGw1jd/vN/UW9+r/Dq9/9+/+3f9Ufi5JP/vZz/4v/+5/+A//4f/5N/St13dqwpF+E5zAJgSNo8HG1wGjQvNUr9eNQSRFm4bSGRgAMkHRzTw8n89nXm+Px6PFxUWTXVO08fsCgYCSyaSeP39uhRvyUQ7Fzz77TP1+XycnJ3r//v0EInhxcWGIKmzG2dmZstmsbm9vbQYsKJ7TdwXaDkjBwVAqlSxIBs8Uz8kpFeNgQS6KDApZD4EwPHPYr+XlZSs+Yef4904ZMIUNoSk0JqgWuOCRQ/v9fvn9fn3++efq9/t6+PChFYsUI0jN+N4AC46Pj7W8vGzyKJ/Pp3g8rufPn1tYEp+Zz0+ISKVSMe8oBblTbnR+fm6hNATncBk6lQIwGxS2XKyg2NKHuaJcDI1GQ4VCQUtLS3r8+LGlBuOdAx3nUnaG2BSLRX3xxRdyuVxaX1+3w9nJnhEiQ9Dg2dmZyckoVqvVqny+8XzOdrs9sf/43UghkdsuLy/L7/fbvNV6vW4/r16v2/eOV/sHP/iBRqORcrmcrRlnmB6gEGEti4uLE54mfFA0vKFQyIrNTCZj4wXxTMHMSTIpIjYQGhPOjWq1qnA4PAEwMCObog3Wst/vTzRCgB74afFYO4ut0WhkhSuXjt/vn1ALIOPngqb5RGYKWzEajazh3dnZmVDC0HT1ej3zs1Hc0jTCIDr99fgjASecYB97AKSfc0uSKZWcYNHt7a2NO4EtgCEC1KQgARCRpBcvXpjFxe/3KxaLGYpOI8MZ9+zZMwM0a7WacrmcqtWqHjx4oEgkYkAD1h/GQh4fH+vJkyd69OiRNfjBYND2FMUtBXokElE+n7fvmqyE+fl5Y1HS6bTOz8/V74/DGJ0WF+fnh12UPoDMpCNjk2i321bI8qwAXGFBWJMU4dFoVNls1tYzEt2lpSULsYMJ4f47OzszFY7X6zUpuSSbptHvj0PNnFJ7RvYgc4Qh7ffHicv9fl+RSMRGGuFNTiQSevXqlZrNph48eGBnB8UgdgYUTcjhaV65a2GtYRG5v5xqpNXVVV1fX5tyiMYFeTrKC5o0viOsP8Ph0PyrZLQgQ8YTS8FOwy7J/P2sVWqWjz/+WNlsVvPz80qn06Ycgx1bXl42pnZ9fd0C35yAH0Ai9/Jg8CH8kTtekoGOLpfLzmCARUgNLC2cBUivW62WnX0orFDIYSljjyDnx0suyYBlAHeaZ0ZdRqNRU7dQ1LOeCIkD+Eaxdn5+biGQBGHF43F7j4Bb1WpV9XrdRic6wwABBVAdce+5XC6zJGD9ub0dT6sg6A3JfbFYVCKRMD9zr9czgIqGHb9xNBqd8HwDKvCZUN3RYDrXealU0vr6utUnzpoF9ZYk+2wEg/KseLaj0Ug7OzuWpdDv95XNZu25NJtNY6mZrrC0tGRNLdLrZDJpZ7rPNx4NCXkUCoWMGGHdM3e7VquZ1YLakroonU6r0Wgon8+bEsLv9+vBgwfyer22H1B7YAUBkHQqf1BFjEYjra2tqdfrWR00HA61urqqe/fuTQRPknnTbrf19ddfa39/X8fHx3r48KHcbrc2NjYsLJQ7htBVCBYk2ewNfN9O4uD29sOYws3NTQPb0um0dnd37Uwpl8taWVkxayK2Cj7/7e2ttra2VC6XTYrvJOz4GajrRqORTa1Ip9P64Q9/qKdPn9rZjvLk5cuXevDggU1eKJVKkmQZJtR5nDWcSffu3bMAU2kMtJyenurzzz/XwcHBxChUbD7YOwEhWWuou1AY/i6v/zd6wv//5fWdmnASoS8uLrSysmIIoROJkmTeD6TSU1NTVph1Oh2TKlL0cKDgqcSfhMyWoo3GlsLiwYMHdjmATjk9dgRwgE47vaIkK+M1Bb2t1+t24ZJkiddnd3dXw+HQJFv40AKBgHq9nqLRqBXOyMSQ+FDUv3//3uSZFD1OhpZiajAYWBAcxYzTf0YhyGXu8/nskAsEAkokEnbB0+w5velsPCSzhJdQ3CCToxA9ODhQoVDQT37yEyuQQbNnZ2dNWgi6SIEAOwIwAmCzuLho0jAuJZfLZamHoJ2ALfwzxQp/h6KcMBqKJNaiszmh6EEZ4FQR0ODAzL5588akzvv7+6ZoAICh2OayY4wQsvBkMmkXOt8ZHtTp6WmT7uGbzOfzWltbUywWM8UDkjMaQGdYmCTzjSLX4rPiVXM2Cvz9Xq9nSD7FYqfTUSwWs+fEBQoIQzI2exwZMs0+IAPPslarKRwOa2lpSX6/X9Vq1SYaoKxgTiUFJois8xIdDoeW8krqPsw86D3MI+uD7xTfLcwBnw15PcFKt7e3yuVyWlhYsAA41iDSQVLB8Veur6/bHE4UPwQirq+vW4PCBYnkFikmZwvMV7/fN9bn5ubGJlBwySKD5HlfXFwY+8TagmGloI/FYjbqCgk9gVQg3P1+Xzs7Oxbyw2fBd01IHmnonPfFYtHWiMvlUi6XMxmw3+/X3t6eFTb1el3v3r0zqwLzY2EcDg8PdX19rc8//1xer1fPnj2zQhn/G+oG5IaMZQFMxIfJmRAIBGytAGzRuBcKBWNwGVFJIU3TQJNDdoPP57MATM5x1tP8/LxarZa2trbU6XR0c3OjV69emeLJKT+FZUPh5XK5VC6XlUgkjEUqFotyu91KpVLWUNVqNd3e3mpnZ8cyKebm5iayQGg+Wc9TU1Oq1+smNyT8C5bn/v375vEMBAJWuEsfAEn2FZJamLxut6t4PC6Xy2UF5Pn5uQ4ODmyvDQYDHRwcyOfzWUPRarUs7AuVSS6XmwgxW1paUrfbtTMP1RoBeRTuzjOREXGRSETT09PGwKFGkMZg3fHxsZLJpLFxKEk4p6anp82KRk4BaplcLmdsGnuNBvjm5saK31KpZHcxLCDAq8vlsrDUi4sLY0nxSNMcOUF61hp1COo7PgNAM2oTvPM8T9h7zm0CJvHHQnBUq1X5/X6zrbC+qJfy+bx2dnasaUK+T20BYMa9QIPOOFF8qIFAQJlMxvZDq9Uy5lqSea6dSkBqCUBPmnrG2QFSoqSDDAFcZe0g6WfdQz44R41xZwJ2U+NdX19bvQgwB4CKKkmSMaOkyPOMhsOhKpWKlpaWLHQL68rMzIwpVAgXRBWEMgRAcXV1VQ8ePLBgPRhcgJN4PG6p4qhEyRwBYGV6hdfrVb1et3oaED8cDpsSEJtluVxWp9PR3t6e1becZVgJ3e7xCDa+Z5QA3EtOVnw0GqlYLKrdbqtUKikQCKharcrtduvx48cTZ+njx4/NjkWYJtlPqDgA3Kkn2b/xeFyffPKJ1R6obzgTuKv39/cNhIakq9VqWlhYsHUG2UXd8Ytf/ELValWj0Uh/9md/ZurWxcVFG7EHK0/+A0QYM8+5446Pjw3ch6mnxr6+vrb9A5NPj/LHf/zH2tjYsHXNOTM1NaU3b97o7du3dk4C4P32t79VLBbTn/3Zn2lqaspCiLF3Qt79Lgnk37/+77++UxPOC08mUhWkD8jmKPaRr+L5omkkpMUp76RQarfbOj4+ls83Hn2wublpY3IIM3ny5ImKxaKkMQOD5BSWBUbb7/frk08+UaFQ0KtXrzQ9Pa21tTUNBgNr7BuNhi4uLrS5uWl+D5/Pp4WFBfPIlstlSzqnub+8vLQLplarmTz35ubGQogIO6DQpjGv1Wp2aSF5c7lclvzJIUEhiqyUhoNURBhQNqAk+zMc7oRCgFiS3sohjMcLxoYxMN/23r1+/Vo7Ozs2JxO02emFAYl3+ucAFJxjjW5vb7W2tqZ0Oq23b9+amkCSvRdk5BzuqCM4mGhiS6WS8vm8HfzIoDh4AEAAe/AP8cxp9kCbSeSkIUMBASNEwY8nCaklEr3Ly0vt7e2p2+2q0WjY5ckBzJ+loQHZ/vrrrw2IqNVqxgqzD/CPseZhngjGg4HmYoF5kWQHf7lcls/ns3EajAaBKYdxQB7K+7u9vdXq6qoBcP1+39huvicKRJphCgrADdYqFzcWB4pMwAzODhpGQhUJc2J9k0XBmA2aMGwBvG9YOgoCACxCamBubm5uVCgUFAgEbL9J0ubmpo3FwpdIwUD2AY0+oBT/zPkIE8UFDot5d3dnDQRhTLFYzKRlSG/xZlFor66umk97ampKt7fj5HL8gi6XS6urqxa0howdmWc8Htenn35qGQeSJkKSKJZgVil2YMKZdrC0tKTnz5/r3bt3Wl5e1j//5//cWC3+LN7UUqlkgCjNqMvlUjKZVLFYtLnLyPrYn6iqnGdZrVaTy+WyZnB3d9fupGw2q1qtZiBbr9czC0e1WrXvBPCSApsinv+GLJPvgeRYGjACmUhvR7KPfYDcDAoiwiJvb8fjqpaXl5VKpfT1119b+nWlUrH9jgSY72w4HCqdTpuUFPCA+9Tlcll6NQoar3c8knJzc9OCLcvl8gRTdnt7q83NTTv/AM0p2vL5vH1mRtrFYjH7npF5Hx0d2Uxl0rpJKaZoZ+2fn5/r/fv3NsYIjzAAqcfjUTKZtDsFdjEWixlwNT09baGvZE5w/tdqNWPJLi8vtbq6aqAMDT8SfcYZ0ZCjTOBMWlhY0NnZmf09Saa6ovmA+Uf27jwDKW65n8mEuLu7M9UAIWE0dKzFSqWiTqejZDJpTCpAlNO/iSSe+or7GQkzvxP2GC8xZIcku29gQWk6yR5ZX1+3gp3kbJocrFOFQsHYRJhlWDsnq400m7vMmYpfLpe1urpqZzFNDXWEMzMD3zJALh50muNer6d4PK61tTX7ngAwAfokGeHAmQD5Eg6HDdxmjyENJ4UawJiMCcgp9hC2y06no2KxqGg0qrm5Of3kJz/R119/rXw+r+vra3355Zfm4S0Wi7q4uNDa2prd3Q8ePFClUjElHiF6KBIIh0PSvrq6quXlZQt35D06weFCoWCjrp48eTIRZsu+pibBh46dDqAF0IiA0KWlJQPGPB6PMpmMMbXYXJw2p2AwaIpT7nKfz6disWhr8IsvvjD/9Wg00v37981+gFWTc4TvGXUe4DiAK70Ka53vG/UdSlGyMQC/2GvtdlvRaFT9fl/v379XpVLR5uam1S23t+Oxrufn59re3jZggVr49vZWT548scBJLIrO6QntdluJRMKUVLDYl5eXOjo6Mtvlw4cPDTSj76KeGo1GpkALh8N68eKF5QRQQ9EvALrRC1CHf1ev9P+uTPXv+/Wd54RHIhFr4rh4JE1scGQgJN0iDQLBRwZGgUmh8f79e5MA8WL0Bz5gr3c8x5f5xTB7NG74rdmUwWBQwWBQf/d3f6d3794pm80qmUxaWBgMszPMAQ+g3+83RpMLjdELXEjn5+eGPF9fX1vgy8rKirFfyDpAvbjIyuWyJBmr4gxEoeFnYyN9gq0GKUfuStNCIQDTCepbq9WsMYNhjMViBoZIHxJFkeWVy2WTED58+NCkOdKH0TGwe5LsvbAekLyRXl8ul7W9vW3MgzT2Ob5+/Voul0s/+tGPTPKI38uZQg+iz8G7srJiYzJyuZw181xQHGrOQ4f3TRFDwQAzVq/X9ejRI1tbqBx4RoxhAnFljjwIa6/XszRs1mwkEpnIEsBeAHMgSVtbW7ZGYrGYsdUwzLlcTn6/X5lMxsAUmm787EjvkDrTTOOBh83i919fX9ueZK/B7nY6HWMsYYP4uTTd7XbbipDBYGCjWChQKYKz2axubm50dTWen05jy2XPfifgjrUeCoUUj8dVr9dVKpX05s0bGwsEKIfqBuCIZgJgBRWFcx0R3EQAEs+JzAnCs2D5Y7GYHjx4oHfv3ikQCGh9fV2RSMS+S5BkmjYYLSTP5AZwmcNQIVsbjUYW9gjDNRgMrDlyhvjwfTotLLBP7KtEImFydo/HY/+/NPaqMkuUhpOmFvQ9GAwaqIJvmUaYYoBJCnjTkBPyTH0+n7a2tixg5+joyAK6NjY2jG2PxWKKxWI6Pz/Xb3/7WwPPUCohXQZsY0Qg6d7MLd/c3NTu7q52d3dVLpd1eHhoQYXIJTn7AJdgQQBKnUUHPnVpbLlhtBEMGAATTbMTzGM9OC1G3J+ckwDFzKstFovyer1aX19XsVg0IBp2ijVKEBaFVqFQsLFxCwsL2t3dtekhnL2AW5eXl5Y5QANOIewMXAWMYF1ubW1pNBopn8/bOVqr1SxU8/r6WplMRu122xoJmhrUZ+12Wy6Xy8B4ACwn+Mx5wj4FuAVodrs/JFYD4AP43t7emkXB6/VqcXFRkUjE1EMkxRN4B1NG4J0ky+kANOZ9ouBiFFkwGLTkcpfLZWOwJJln2eVyKR6PazQajzS6vr5WJBKx9wroDNAgydR8o9FImUxGl5eXpsBA5UCyM3uzUqlobW3N5o/D3gG8AQaRYI9yEb8zwMzGxoZmZ2ctDA8ABlafIt3r9RoAQhPB1Ainn93j8UyAhCgfnT54aiPqGyxS/H1+N0nzWBjYn5AzZAA4w0sJ1KNmpJai2YK9D3yTAO4EOKhNAUZ4T+xnAFWsCyhCkJgTCFetVq1hRg5Pk7S6uqqPPvpIi4uL+uqrr7S5uWmy9VarpUKhoGAwqFAoZKPmWLulUklut1tLS0uKxWKWWdJoNEwtKMlUfYAMvB/2LP/e5/Pp9PRU5XLZQn6Hw6H29vZMbTMYDAzkou6UZOc4th72G2pJPOLdbtdyD5DpAw6EQiGNRiOlUin7+8lkUt1u15R+jCqjBgQw4XzlvuVMB+T/dvYR9SokEn/u6upKuVxOq6urWlpaUj6fN3Dp8PDQajRqOp5xv983MmhxcVGnp6fa3t6297a2tmZnSSgUMsWYJFNEZrNZ89T7fD5VKhVVq9WJO+Ps7GzCEgfZUi6XzQoLqcYZTZ2CAgz1FdNWyDNwAv4Aodxn379+/6/v1IQTzMKhKo0PQ5BoGBMKTjYMzMdwONTp6aklDW5vb9s80kKhIEkWWIQvigMTeTbycWexDuqJVJPRYpIswv9P//RP9atf/UpXV1fmhTs9PdXjx4/Nk0YTSpojhZmzmUkkElYk40eGkeRwIeyNwDint4cDbDQaWZFBcBeXiFP6TBFOAj0XDHIwSSYDB5HnEuFQdM4dxccXjUbNi8NB6jzguDxrtZoePHigra0t+365kJ0BaDS7XK4gpEi0GduBLHVmZkbb29vmhYSB5lknEgljNEmrBPF0or/379+3UCnShJ3+IMAZ/i5s/MXFhaGdSLxJwqSp5PfDArIWZ2ZmDHxh3VDkENLBd8GBiM8VJp6/iwSZ74EQJydoBVLr9/v1+PFjk2Lz3VOQw+aQTOw8TPk8tVpNhULBinkaneFwPMOSYtfJ9jAmp9FoGLPslOgBsDBChdEWgCHsY7yDINK3t7eW4MvfHQwGJtdlb9FQwkBWq1VjtgaDgcmTWRuVSsVGCfK9zc3NWcGFV7Ber6ter9sIPWlc3FNssJ5hjB88eCCfbzyjmM9D8AoXOs0NiDLjfihSPB6P7YV+/8Ncd6/Xq1KpZGcpqDvrnvOV4oHGGSa03+9rY2PDMh04HyiUo9GoEomEjeKamvowQpEZuny37BUaUQLGLi4u9Pr1a83PzxuzEY/HzaeO/47kY55Ho9EwyWe1WlU6nTY/KqPGYA/z+bylx2KpGA6HOjw8NFYqHA4rmUzaHjw8PNTt7a0++ugjAyRQsBDihWVCkhWQvG8nc8azY38hz6vValbsc2aQDu33+82iAuiGfJlANtQmhF0x4YBzo9lsToRKXl9fa2Njw85yRu54vePZx8Vi0bIUZmdn9fTpU2v+aahnZmbszJmamrI7yek75s6lwYaRYg+zhgaDgTWR2GXYn8w1n5ubs7m2yGhhrQHdUFqhWCM0DHBjbW3NwACnkoqARIrG4XBoqjasJzRS6+vrdgajcvH7P0yJoOnzeMYTG5wgDE1KJpMxz3AwGNTMzIyBvrxf6YN1h7WEOqjdblt2DbUD+59CGmk9owelsWqpWq3amD1C0qSxPQYGHWYShQkjMQmnc7lclhsRiUSshkCijDUNUBBiAOuP8+6IRCKqVCp2ZvC5Jdn7KJfLBhCgMCPkCXUB4/YAIfHoYnVhn9zd3dkkA9YcYLkzvNEZQsYeRRFRq9W0srJi+Q408JAVeLVZwyj6nD+TvBPuYsANwBueEWnYPPNisWgA287Ojv3c+/fvm8oHAGt3d9ek/mS4UCti3XG5XDo6OrLxVqh+2FOcZ1tbW9ZQ0eBOT0+bvYPkbNY7jXKj0bB6Dbb97u5Ox8fHpmhqNpuWJ8GZ4na7LcPBGTZGfQLZs7m5qW63a8AMgY/UVkdHRzo/P9dHH31kDLbTS06GAHcLKgTeA4049TJrCCCZO2U4HOrs7Eyj0UhbW1tmibq9vTW1HkQWGRRra2va2dkxMJp1OjU1ZfbXq6srHR0d6fHjx9bgAnqiWCITizsaRQGTMdjf1F5nZ2emWsWytbGxoWg0qpubDxOLUGtIsrqd75J/LpVKBl4vLS3pRz/6kVl6UBc7QyqpV5xA9Pev39/rOzXhpG5SKNKMctFQeDq9mZLskCThcHl5WZ1OR0dHR9rb2zO/D826k43m53L4wLgTFoakUJKxvgRbgPyC6Dx9+lQvX740dr1QKMjj8Wh3d1exWEyS7LKn6aXZl2RSOGeKLJ4igql4361WSxcXF8pkMpYomUql7GfCwFOMUzDxzDhAUQuwoblkYG85MJzAAWww30swGLQRJpLMi4bUic8Nezo1NR454nK59Id/+IfmAYYRvrq6MpZYksnD8KzCtAA6IOtDEcGFDzL88OFDJRIJlUolS8j3+/3a3t7W/v6+HbR4lCl88ELFYjFtbm6q1+sZMw6IEPhm9BoHNWgfh6M0Pszfvn2rbDarJ0+eWCMDms3lxqXR7/cN8KEoZt23Wi2FQiE7wCg42CewPyRlsk+cqd7pdNoOUoLU8IwhswT5pZAMhUIms0cKnkwmjakdDAYTKc+SLGjOiepfXl7aXFFAmpWVFUtXhm0+OzvT0tKShaUgU/72fiGkhlRhwBzClfDc1ut1Y7AGg4ExvUjbkA8mk0kL30un0woEAgp8kwRMEc4zpkggONEpl+Iyj8fjWl5eNtuAJHu/5Cfgw5yenjZ5dD6ftwKI2dehUMiaJpQaTn8zxePl5aVevXqlWq2mf/Ev/oXi8bj5+5E9IgckfRZZKM3s7OyszQfN5/N28aOGYP1IsrOSxozn5Pd/mA7gcn1Il+YippAJBoMm2XW73VpbW7OmjqkBNF7hcNjYHOTg0WjUGic+R7lcNo8ibEQ4HNbR0ZFJppEWO5OwZ2ZmlEwmDSRh1FOpVNLp6an5Jefm5qyIWV9ft7sH9q/b7Rqb63a7jY3lfQJG0Myx1/h3NJBkJXg847GFyH1pBLAkwVSwF/P5vFKplOUJcGYAzMCM93o9A6VoSFmrJCvTeKPswbIDw+d2j8clwsi2Wi3t7e3ZOmM6QjAYtCbPCUBSpAIg08D1ej29f/9eZ2dnxjLjW0UlwPNEsooElCLVycSgWGH0H6AC5yj7imZodnZWjUZDbrfbmOJqtWrpyk65Maw86ihUbyg/kLZeXFxYSCWWFAA6fOQ0F/jVV1ZWbN841X+kOmPVW15etrMH+wIM38rKijUqKAl5hihgaPDn5uaMsacWAACSZGAkd22xWNTa2pqWlpYmxqZJsjwd7kjn50DZRjhc4JvpANRngL28t1gsZo0e89ypBQHO7+7uLBi03W7bpAruKbKEvh0MxT2wurpqjTIglsfjMW8uQXS93nhCzNbWlnq9niliSqWSFhYWbDILwLzT7re0tKRUKmUqLfa3k/UlO6NQKKhUKpnSa2pqymwnhNVCSLGuuXNRSKVSKZ2dnVlaezweNxVFu9024CqbzaparVrdubCwYE37cDg0lRHrdDQa2Vk5NTVlagnWE0D86urqhGf8008/NaUAhA/3KXcjwBjnBecPijRAatYBNkasZgCiCwsLFv4GwINliEkafEdYPZz2FkAfXvwccgN4xqwNfgb3LD9/YWHBlJrHx8e25kkvRxl5fT0eEUlqOhJ9iBpISWpD7pnFxUUdHx/b3sXiBBhLCPFwONQnn3yiFy9eWLDb8vKy+v2+fSbudkL5pqamlEqlrOaHgGI9Li8v65/9s3+mXC4nj8ejYDCoZrNpzxIyA1Wy8/l+//r9v75zOrpz7AISIyeLxCbAT7K0tGRstcfj0cOHDy2IgGI2Ho/L7XZbCjnStHK5bE0hTaMkW4AgbhyQfr/fUhuHww9jggiGaDabymQy8vv9BgQg42y1WuatRR5zc3OjSqVi4xzevn1riDozIPEphsNhu0ibzaYFesDWcOg4U0gZ43F+fq7NzU3FYjEr2PGSIUem4G9/MyIM5J3ClAOJ76HT6VgAHAdov983RGw0Gtl3B1oMG+b0CkoymS0ycUkTDSyeFSS+FLHI80EskcdeXl4ql8up0+mYZ295ednYNZjtTCZjY84ikYgxaHi8+F3SBxAB0IVLjzAXimgQQIoRAAOkWFw0pLYGg0ELAqFAYEao3+83HycNUbVaNTk/IAlp9oQRcchRKMMmcJksLCxYimomkzHPrxPhHI1G5kMDpcWDjhIEgAeFAb8fEAMkHRChUqmYJPTq6sqSzbkAWFuwxew99jpqBAoyQrJghmnyuFCRhTuLH5hAVBQE8QC80fwAEOTzeWu8G42GZmZmDFCjYARAgbGcm5szdov9R6M7PT2tTCaj5eVl5XI5JZNJnZ6eWgFPgA1NA2ui3+8rl8vp4uJCMzPjsTqsjXg8bhkR9Xpdv/71r+3yf/v2rSKRiF2ei4uLyuVyur6+Nv8gRSwgB0wvhTJefhRFBH2xNigSkMw6md7Ly0uzGNHYoHJgrzKCBoCzUqlofX3d5JHD4XCi8YABu76+tkKX8wbwAOWL2+3W06dP5fV6rTAg1Iq1i8ccaa6z4F1aWjIfJgVjMBi0jJFGozExRtHr9er169dKJBLa3t7W0dGR5Uk4LUJYMQDzJJmX1mltcblcpn6RZLYPVAyw2qQfLy0tWfZIvV63Z8e5OhqNFI/HtbCwYHdWp9NRq9WyRou7hvAfmghUPktLS5LGYVqoANgzv/3tb209Pn/+XKurq2p/Mzs2GAyq3W6bkoTJBChHnL5Sr9erd+/e6ezsTLOzs1pbW1M2m1UwGLQxZX6/X6FQyFQNSGuRzhPW5ASgAQCoJQCsp6am/oGEEmUEkmbuNlgzpO+oLJwyWexS09PTyufzqtVqCgQCNsIKywKBVKurqxZKt7u7a2OxqHE47wm9kmThqNKHgFAAjUKhYGAwyhamU6CqY+12u11ruvv9vtl6/H6/Se55xoQBUmPAQsNicz7xXRJ+y2xvgFzuUPYiwBW1xPLysknmE4nEhE2iXC7L5XJN3HXIp8lEQR0FcQBIT93C98UZxmfLZrPa2tpSJBIxFUGtVjM2Efk39gF89wTAbW5uTrD/gDEoh1DELC0tWY3B3ry4uFCj0dDm5qY14NxhqB25D9fW1kzyDwDBHoQFByDj2fK9lctlsyJBHqysrGhtbc0aWaTdjBrl2XEOcbdjK2M/Il2nnkFt8ejRI1NYcAewflDkURc5rVGRSMSSw9mng8HAWGnyS7hvyG6h5oEVpv7I5/MGNHAePXjwwAAHwFRAAhpX6lHA5VgsZhkBMNaArITZzc7OKhqNWp2FagoVC3k09ClYCqanpy2XanZ2Vk+ePNHs7KzZBCBeTk5OJuqM1dVV9ft9/Y//8T+0vLxsWSXD4dDOPYB/rDHkQfzmN7+xsDrWgsfj0dHRkSX3l0olqyUgLwFF3G63Aa8Ea8/NzZkqmfOWvC5n6v/v8sLe+ft8/e8KCnynJpwwL5AkkCYeDgWpE7F2zmBkgeZyObndblvUsHvIy6VxwfPo0SONRiMLeZBk/iQKazYkaC7yZ5DAfD5vjHs0GtXHH3+st2/fmqyDYDbSd4fDoSVSk3BJs7SzsyOfz6c3b96o1+uZdwopEY1RNBrVcDjU8fGxqtXqRJJ6NBo1ORjNgyRLYF5ZWbG0UGalNhoNQ6/xxRGoBDvN53f+TORvHKyZTEY///nPJY2L74cPH1poTavVUjAYtFRlkGAaVtBgCh8uVp4ZlzwXGg3i/Py8IcIgm4Rs8DMYowW773a7tb29rQcPHqhUKundu3eq1Wra3t7W2dmZ6vW6PU/SL5PJpDXRyGkY1wHCinRI0oTPlUvj008/tYbQ6fEESAHtpAFzyuKQ666vrxuYw8/gmSH3Z40hf+L9nJ+f22xOgmG2trbsd7daLZ2fn1vaP2FLIOGAAbA1uVzODneaZsARClZnUAnKFjyiU1NTKhaLlqrMuLxvWxu63a6y2ayBNYlEQuVyWbVaTalUylBxfheXAkoD1gqSe+TJNDnD4VC93ni8FynigFjlctlYVawYMF+cPaxXgAVY3YODA3k84wRmZGH4QGGGUbhQbIfDYRs3BrDY6XSUy+UMwXcGdV1cXKjT6VjxxxmFhA+rgLP4dSLR7G/yBLAEOYtH3g8s3Lt374wpwRdHwY99AsAEjySMWTKZtLRtivbt7W2TiAOgMf8bEICz2fmZuMxpGED/OVtJ8H39+rVSqZSlYK+vryudTqvT6eju7k6JREKRSGSCNaWABMRJJBL2+c/OzkwxA1iB7BDmDXArkUgoHA7bXmTiwenpqYVXUUxLYxbJqbqA1WL29Gg0UqlUMuCKIKVCoWDKLMCDfD4vt9utra0tA5QbjYbdo8fHx8aOI2Hf3Ny0IprCh8R/WGZAOponlEpzc3NKJBLGjq2vr5uih8KNYhWZM0FqFGle7zjA8+XLlzo6OtLy8rKi0ail/WKJoUmiCaW5QRXFnuYcRaUB0wsY52S7BoPxDOrLy0udnZ1ZQby+vm5hRtxRADIAjux/1Bj8zqOjI/MGcy5yTtEMbGxsmAqAPBCUJy6Xy/I6AIEBtp0ZKewT/g52sNFopEqlooWFBVMGkMZ8c3NjvmSaRM4iZ8YH+5SGHtCy3W6r2+1a1gQhoqwPVCnSuHllYgUhaoCNKHJQUaGGIMsBFhqVCBYA5hqjNuB7YI3RBAQCAcsjIU+DBhNFFBkrEBSoJK+urizRG5UDTU0wGLS6CL9uMBi0pgU1mVMJAyB1dXVlYAU2FRQchBLz3FEdIJ3mHqPmIL+Hzz0cDi08EpVNMplUJpOx/A6eK1NT1tbWJhjMhYUFY/VRXKDyIXyT2gVL3tzcnHnPyV9aX1+3uwElGo1Yq9VSPB63+uDbakJALGoF1gV/DiIEJZnP59P6+rol6qPO4uchBefzYZ2CXWZv0TMAaDjl6KhUisWi7t27N5Htsr6+rmazaZ8TsIHclkajoYODAwObZmdnVSgULJeADA3O4ocPH+rs7Ey7u7taXl62+oyzPJVKmeoWaXuxWFQ2m7VRjQSVAsJT0+RyOVN5lkolC+qrVCpW/0QiEZPwn5+fW+3MZAvqMhQ/GxsbE+QJ9xA1HCw7ViuUzb/L6/sm/B//+k5NuMvlMjQKhodkSv47zSVfoNNz52RyOQxgC0HLODBgwwaDgXlKaGo4aJ2NEs0ODOXCwoLW1tYUjUYnLseNjQ37PT/4wQ/MF5fJZOR2u/Xw4UMNBgMbYYQsjMsbGdL8/Lw++eQTHR0dqdfrKZFI2GZFdvyDH/zAZouDlBcKBZs9Sjrs8vKyNfeM5yBgiffNM6b5gdGFKaBwpghGJsZhmE6n9fnnn2tmZkbPnj3T27dvValU7L0cHx9bYTEYDLS7u2sHEYcwTIwThaQ4ozHl/SBlReqMlIqG18nckybsVFQMBgMLsvin//SfanFx0YogZJ6vXr1SPp/Xf/2v/1XhcFjb29sWcIZMCESdn03jzPqk8Y3H40omk/bdcZByUfP7uYx51hTVFLDSh2RJCmRnEQOLLskuPqT709PTSiQSdmGz3ii2kNbDjNDIw74g0261Wjb+Ds83Tazf79fl5aU1X4AsV1dXlkvAs0HG5gzvk2R7gGeAPM55qVUqFfOuIcFCdu/z+XRwcGDSZmTprGmyEfie8bxzUVE4FYtFnZ+fKxaLGctZKBR0d3enlZUVk8wuLy/buA2Cd0qlkjULeL9ZF6gpCOTCS7i9vW1sBGGIAGYfffSReVYBK7766itjQUhJDYVC+vM//3NlMhm9f/9eg8FgAkQMBoOm6Dk7O7PmlVFQNP6E3sFUM7KGZp6xVhT6rFkuV1QVLpfLLnZkk5IMpBwMBqpUKkqlUvL7/Xr16pWur69VrVZtNmomkzEwCL8jewcL0ezsrFkTlpaWVK/XrXhjlFo4HNb+/r4ajYZarZYkaXt72+Y0M2OZNdVut+0sAmi8vr7WV199ZcU2KqREImFs7h/90R+p1+spnU4rHA6bKgO5utfrNfkuHkZYqmKxaJJzGq2vvvrKmnw+N5JdxszBEkoyoBj/OXOYfT6f7R2KU5jvVCplAAHg8du3b2380PT0tFZXV+2emJ6eVjKZ1HA4VKPRsHsQcJpg0i+//FK7u7v23fl8PpMVAwCRfcL3WCqVdHJyopWVFfMbV6tVO9eQC3PW0uTD+tGc02DRvFGAUysARmHLYu+XSiVTvSCBRUpNKBr3HzJLngvndbfb1bt377SxsWGBnpeXl7bmYb9jsZg8Ho+NnSOF3GkJAuAgZwLVE+es2+2eaBic6wElBXYOUuDxxgIqct/CcBK6ReAqXmvGbqFi4CwdDAbmR+UMRsK8srJiSfYAZ6jssOhQAHs8HhUKBbMRUb/xdwF3kdBCnnCnUz9QV/DdEOhHI8FdB7iK4o958JzjAHHMZwd02NvbsyDIUCiktbU1A1iwR3CPcd6xLsn44c51AqQANFidaF4I9EJuDYjs8Xgm8kWYZkKjh50RRpRnd3p6apMIsNpRV2NvIyuDwFAAkqWlJZ2enk6E/q2trRnY3e12tbKyonfv3imVSk1Y2/jcgCIo43y+cQgxShGn+oZ6GbsazTmKCNYN9yr2U85MgED2NPcKoWRYc7CYom4A/GWPAxAsLi6aEgEABEUAhADTLwBUUL4C6LNvsUYwk55zqN/vK5VKKZvN2shi8p3YDz6fz0YWj0Yjffnll6pUKhNe67u7O3300Uem8hiNRjo8PDTyrFKpqNlsqtls6vT0VHt7eza3nrsQ8E6SkZKQT9R/2CA4c/hzEELObAT+Ozld379+v6/v1IRTjHIZcMiyAEH5KGadfiEKdRg3JJtO2QSNLjOSaYScPk8aaFgn/huSQC4FNjPsIxsBSQ2bC+8JzT/z90CMnA3J3/3d35k0fXl52RjBbrdrEsfd3V3d3Nzo7OxMkhSLxWze95s3b2w2IiOs1tfXja35NgNL+BTsC7I6kphpgiju+v2+qQS4BM7Pzy0dMRwOa29vz1J0OeSur6/NUwIyxyHPd3p5eWlyVaTPACdcuKSDw4hxyRaLRVWr1YnmksuBohVvJE0zrBCKCX5mt9u1YiIWixnAcX5+rnK5rEKhYI1lIpGYYAQpLBhtVS6X9e7dOxsbAdNDEcnadaoOCMfhwuF501zgy+VgR57GWByCPWgkSTynEAkGg+ZF5CIC2cTLDaNCzgIyY1BMLmnm+yJN5gJaXl62YBfeI3Jlig08ljTASFKR6cLK0ASvrKyYT5BLfTgc2jgPQAoK7J2dHQsOREILOwVTQFMAqkuDxAxcmFWaJmwYPp/PZI1IS/Ec4usCdcbnDHNKs9xut3V6emrWmrW1NW1ubqrVaimdTuuzzz6zS8wpe0eWzjzYTqdjkuKVlRX7fgGtaCIY/YZSYzQaqVar2USC9+/fG6NE0U7BBqD1/v17SdJHH31kRQ6ADCFyfOcAo4PBOKyFOe7ICpEmIhMlIwPG5uXLl1pdXdW9e/eMwUIWSxgZFgEaFtYsigXsCkg7i8WiBoOBJR9vbm6a75q1QBNJPsfW1pY1FhSHFE07Ozs2oQEVA2sIQJafKcmk8yRO49NnLfv9ft27d8/AkJmZGVMMIXmF5UGlxUQM/kej0mg0bO03Gg0bLcRZD8MLa3d5eWlNPTJRpMUbGxsWloTqjHObO4j7O5fLaWdnR6PRSC9fvjRmt1qt2r3K3buwsGBzySlos9ms3YNLS0uq1Wr2uZLJpAKBgMrlsqngqBmCwaDC4bDq9boxjKhyvF6v3eu3t7fmnaXxYv9iHQIQikajds+yrp3p39yZnU7H8jYAkBKJhDY2NiwbBBVQp9NRNBo1PzBzw5kxLY1Tp1GfcWdJMiDp+vpa79+/t+kjyPdR4QyHQ5MQQ2J4vV7z5WIp4vmg6llaWlI2m7UJLEiumWWMxFuSWeJmZmZs3ztrr/PzcyMtnLYLGlrOGCS9PBeyS/jd2LcAU1HvsV8BUZwTGLg/UBOxflh/gE/X19c2zoz7iDPdWScB7hACiZd3eXnZsgqazaYSiYQkmXeaRh72mlqGc5TGRZLtpVarZbUn04ICgYABoc1m0zIcotGosaNYvACVV1dXTTFFrcF6xH9PMr/L5VK5XLYcnVKppEQiYYBwuVw2mxUAMaqpw8NDZbNZI7M6nY6dDdwLAC+o0er1utUI3FGkcDOqc3Nz085dLKTlctkUe3d3d1pbWzN1IiBbqVQy5RKgBWOB6RGwfQB4cB5AZJElhPeZ3AzqjlQqZSBYNpvVzs6Obm5ujFhDKTg9Pa16vW6KOeonaqLp6Wmdn59bI+28u8gsQCU2NTVl53Gr1TLlFrXftxURPBOIFIJpDw8PbSrCzMyMZYFgMUqlUhYyNzs7qzdv3phFE9UGqgBUS36/33JW+PfI9rvdri4vL+2cYoIBQPrv+vqeCf/Hv75TEw5KTdop800lmeRP0gR7iDSZxgtvsLPBAxl2hmYR/sUhD4rGxuNwdyKsXEBIR/AJ0jzCKBLSUq/XrcCA0eIAw9cIS4/c59GjRzaeAwYzGo2q2+3qN7/5jZ4+faq1tTVLmITtxI92cnKid+/eqf3NDPJqtapsNqvNzU3zWN7e3lrhyGXMhUbhz2bGP+JkX0iLhRk4PDy0JgLEmpAHkF7UBBQ+fG48SMwY3d/ft4YZ5JHAFQpcLho84cxFd37P0gefHC8KV9YQByJybhgR1gVIPAUVxe/Z2ZlJmrhc3r59q8vLS/Muzs/PW5oqKDFFM7IjQCOKALfbPTH7Ha84xcP8/LytN/xhzkKcLAEuDoCnaDRqskUaOud8TpBl0jNpwEBy8dC9ePHCGifsIH6/X81m08K5ZmdnzfdEA8dl55wNikQJYIs1hR+ZAg2ZWb/fV7lctuJ4Y2PDZG0wHwsLC+ajQkIIQyuN5fV4fVHDMCOcBh12CUBje3vb7AlckiD4/X5fkUjELjpk+c1mc2JMG40LzDLeUL/fb+g5ctr2N+P+Dg4OFAqFtLKyYgVkOBy2lOt8Pq/NzU3LS6jX6yoUCjaJIRAIKBKJGNhwd3eneDxujP5oNDKZJp5g1jaN52j0YT7saDROfC2Xy5bYDrhHMcDeZ3QVChm+j8FgoC+++MJYUgJ+SH8fjUba2dkxOX+tVtPd3Z3W19etAWbdOUM7OacIMKJgabVaSiaTtjYIYrt3756i0agx80jssFa43W6tr6/r6OjIWAFnsu3Tp0/1y1/+Ul999ZV+/OMfG6iDdJeRiTxnpOJ7e3tm0YAxTafTGgwG9pwkGatJoUSThfIHlQJnLGcpIA1ABWdWOp22wEHO8sXFRQvyZP9K40IEtpGfubCwYP5d553r8/l0c3NjgU+wu6VSSdI4uX9xcVHlclnHx8e2r5AZr6+vm4rk8vJSh4eHevHihd2VsVjMrGIkjt/e3hpbW6lUtLq6qkAgoNXV1YliGb8vz5NE9pubG7NQINUlJZ+7BR/3y5cv1W63FYlELC3fObljamrK1ALkCfCMzs/P7X7jzkkmk9Z0zM/P6/Dw0CZ7kDTOWQeLiUID/yuN1fb2ttVF3JN8Vs62Wq2mWCxm8k+Ytrm5OVOH3N7e2vhN1pzb7VYulzMlAMoImmDqLeezZu02m03F43ELz4OpBOjkzuWeQ3HIuUrgIizv9PS0ASftbxLjAdZovGZnZ02RhEKqWCzaHGsk8ICAgKnO+wsQ6Pr62sgZp+2CMYnUCwAQ1Cr9ft/sRagJeY80YDQwAERYTbgTAXBpBubn581+RgbG5eWl3efOhgdLGc0NdTN3Fp7nk5MTG6ELcILCiqas1WpZSB2gUzabVTqdVqPR0EcffaSpqSm9efPGgPVarabhcJx0H41GjTRC0v/u3Ts7i6kTqGEAZckA8Hq9No6UPKVqtap6vW41OOdZJpOZkKcDngDuHRwcqFwu6+OPP54IskPmPjs7a/c19TZqWJpZyCjAIGfOBWsxEomoXq+b8gNSAsVIOp1WNps1QMKZqQAogk1gZ2dH3W7XmPlyuaxYLGZ7eDAYKJfLGVjLHcp+YMLCZ599plAopNPTU7s3yuWy5ufnzQbLOmcaBvsX5SBEl9M2ybl/e3trbPrV1ZWx5qzr6+tru6tQyBUKBatznbX596/f3+s7y9HxO4MC03zhleEilGSHJF5ppzQHRpCmgkKZpo7ixsn0OmVlNDIcphzMSIrw+SDBJNCBQgxPy9TUODWyXC7riy++0Pb29oT8p9Vq6Te/+Y1qtZp2d3eNyWKcB83v9va2Li4u9PXXX0+k4+JDozmhOMLv0+12VSwW1Wg0FI/H9fDhQ2vyKe5g+blwke22v0ljpylCZprL5bS4uKiDgwNLu6RgpJEIhUJ2MTtl1iC+FBc0kScnJ4ZMIzuk8Eqn09a0gOgRdnZycmLJ2FxerAmnfJt/z/91yq/4H58xl8vZM33y5IkVnviMHj16ZMU6jS4AjN/v1+bmpiGkIIi8fD6f+ddQZTilhxz+hITR+AOS8HJ615xsBhJL9g3SVD6by+UyBoRmGbRaGnuLOEiRRlK8T01N2RgTFCSktCJvRRLH70HqJX2QnzebTUPgKb5oJubm5uwCdipPCEIKBAI2YoeCigKPACYuUp7xxcWFoc8ACLAS7CHWG8qDXC6nra2tCQ+fNGb9YDg4Y5iWAEND4Qm7PxqNA6EajYax5CR+O20F/f44+fnNmzf62c9+prW1NX322Wd2cSNVwzMGs8kYsnQ6rUqloo2NDdvHJJlLH6Y18F1LMrCQC/fs7Ezv3r0z5Qag5MLCgvnSKCoA2HhmeLEp2AG3er2ejo+PzS5EUNkXX3xh31+/3zdmZG9vz1ifer1u3vdQKKRYLCa3220Fb6PRUK/XMzBlNBqZHxvVicfjMek05wHrFGY8l8tZINzCwoJOTk7UaDTsXMA2hAJqenpa//E//ke9e/dOu7u7pmBBzn1zc6NUKmVhRgRgeTzjOeq9Xk+np6fK5/PGEBBw1e12TZKLB5y7aHZ2Vru7u6buYN1zhgSDQftuAPnu7u70+PFjzc3NKZfLTYw/pNlDqQKgIo0ltISCvX79Wul0Wuvr68YkAgKwH91ut7rdrp05Ozs7SqfTdj/iU2TtwlohAz46OtLl5aX29vYsvR5fNgoqmhHUWKizCCylsWDPEAAEu4jygrVKQ00IF1LSmZkZhcNhYyORsjMiDRsUz/76+lrFYlEzMzNaWlpSpVIxNRje11qtZpJ0Mi1oYmCR8ZsnEgkdHBwY8BAOhyWN1R/OwFNAA3zFzvPX6Y3nGaBOGwzGCfrLy8tmXXD+rqmpKVN5AAbHYjENBgMLveV9z8zMmN0Ff6gzSI6mC+JhY2PDxocBtKLSAWTBfjQ/P28WO5QHsKiQLNSHsMjcsfyzJFOpcC6jYEI5ks1mFQ6HrWZBNcFe4K5aW1tTrVbT0dGRqXPC4bBarZaKxaLC4bCNEKTZwyufy+UUCoUs5BJCwxnG6AQgkGgDFgEqsG8JafV6vXY2JxIJe+8QCdzhgD2ouxjf2O12jVRIJBKq1+t25lGD4L3Hhra1taV8Pm9SbJ5pOp2WJBuBh50HEgGiDK8wlp9SqWR12tTUOFF9d3dXmUxGw+FQ29vb6na7dg+sr6+r0Wio2WxazURNgsWiUqmoVCqp1WqpUqmYJeHg4ECBQMCyaHZ2dvT8+fOJcWgAWz6fz7479h2hbpxFFxcXmp6eNgDLaR/lXsM7T8+xu7trfczt7a3V7nd3dzbFhJHGePAXFxdtjc/MzOjs7MzC1ySZhfX4+NgyFagPfvnLX1pQ6dbWlubm5iw7KpfLKZPJWBP9F3/xF4pGo/J6vUqlUlZnAWBQn19fX0/kTKG+Zb9gXQAgovZ22kS/f/3+X9+5CQfFhSns98cjctbX19XtdpXJZPTgwQNjs2GkCHDhcuaLxmdDA8bm4uJybmAKXdBLkCWkVIxywHMB24dcfW5uzlhbWMuLiwtVq1VjzP7mb/7Gij7Ysmq1qv39ffOB0iBKsgZzcXFRT58+VSgU0vPnz/WTn/xEo9HIGBFYnbu7O+3v7+vx48fq9Xr6+uuv9fr1a11eXur09NSkkGtrayaJg2WCKSZkg0CGVqtlXhZYJopAGHUn0wpjw6HI/yRZ8ipsKIg9UiRSIldWVpTNZhWJRAzl49DG50h6KN8toAy/j++OppyLjiYVuZlTMs2fw9PqLORh3nhmyOhub8dJ7wSacZkNBgMrhJAdo35ApkpTyLO4uLgw8IUix6lQgB0AFHD6w5GA8++cDTTrst//kGB/cXGhwWBgXliaNjxTXq/XEuclaX19fUJmTKNDMYQECznf+fn5RHDKzc3NPxjVhM2g2+0acwuwxZ5EDeByuWy8Gcm7FOHsY8ATCl2Kdwplj8djIYekAyM1ZAQYTaQkk7Qxauni4mLCEwmiTeo/8mZJVtDD2jqTm5vN5oS0a2pqyph+gMFIJGI+fOTmrEH2mNvttu+OsCcucSYzACQgDUf50Ol0LKeBJnNzc9N+NgyFpInUaoqLarVquQDxeNxYDthGLlx8sDTDBFFtb2/b6MC5uTlbD2trayZPn5mZUaVS0enpqQqFgk5OTiYaB5gAngEAyfb2tqXHV6tVC8NbW1vT119/rXQ6bXcIPk3Ot/Pzc33xxReWgH55eamPPvpIkUjE1nYkElEqldLx8bH29/e1ublpjDfAAz5SQFikq6gxCNkEvPvlL39pd9jKyoqSyaSx6iT9Li0tWSGONw9JNVLQlZUVC60COD05OdH29rb5tWEqKYr5fguFgoVyUTidnZ2p2+1qf3/flBewSkwwodlAWsmZOTs7q0QiYXuTcxrp7vX1tRVw3GU0BvV6XWtra+aVJ3NiMBgoFouZzSAcDisYDJpknvA29hKWJIAi9qZTJYfNR5LVAKwJAIb5+XnLcMAux90CGLyxsWHJzpLs7FhYWFAul7PmjD3BGRaLxSyL4/r6WoVCYSK4iwwLrATUNbVazVKRh8NxaFQoFDLFizNUD9see4S8ioWFBUUikYmmD5sWd30gEDB7D6OhIBqQvXLG8kydvmvnn4H9AwwkRBWLz8rKiuWb3N3dGcsIcEmAJlYArEa5XM5GssEkIjtmrTlJBRQQnCdIkAG0UKTg36Uu8Pl8xuLS5LMWaMTZW86zCfAG5h+rgPPfAcZ2Oh0LNSN3ggRskvPn5ubs2cKmQhJRa9DEwWAGAgH7OdSzACD8PILzqDeWlpa0s7OjFy9eKJlMGsAiyZLcV1dXrbYuFou2zwChAKQXFxdN/p5IJJTL5VQulyesXNRhrVbL7IAAP05FFGsslUrZ9BmmX+DXJxiw3++bPSKVSml9fd2mR8zMzGhtbU3b29tWw7tcLr18+dLeM6DdxsaGybQ531DTMBViNBrZSLiFhQUDhXd3d82+BaMOcMf64L5eXl62sDXu45mZGTuTAIcODg6MoeeZkt5OPd9oNPTw4UN9/vnnun//vpGA1D2/+MUvJoDFTCajpaUls2wBmAGuut1uVatVmwLB2ck5Bjjq/PeA7ZxpeMO/f/3+X985Hd1ZdDuDlKrVqubn53Xv3j1jJguFgsn06vW6Scq5eJFm0ZQTLgXSBYrobOJYIGxE6cNoNLycNOkUiqurq9ZkcLDyO0Cs8Bp/8skn5m/KZrOKxWL6yU9+ounp8VgRCmaaMqRi+Ec8nvHM1y+//NIOI9BkZ6NGQAnjaDKZjNLptMn+yuWyKpWKpQXj33G73cacgbJ7PB5tb2/L5/PZnN2f//zn+uSTTxSNRu2ZcUFxwTo9+RwafC7QtFKpZMEmSPNo5IbDoV2wMPUej0eNRsNGO9AUA5jwPbpcH0b7II/kczkDWfj/aVBp9iga+I6do0Rub29tzAuSpLZjLAXyaKdUkANrfn7eRhVhEQAQAGW+ubkxdBV2ge+TgBJ8YARXUYwjk8MbRxFAcYtMCtldKBQyBgJGF2YZeTVNMQw6F//09PQE685n5vuu1+u6urpSOBxWuVyeSDkdjUbm52KdwISSmAq7BeCDXI4Al8XFRa2srEwc5igT8MDzfrAY8O+bzaaBEzROINMUbc4QHBpizgJAPFhPfL0oMs7OznR9fW0z0GFeQqGQfv3rXxt7MBiM0+Lx901NTWl/f9/ULhQwjG2s1Wp2pnFenZ+fKxKJqFqtqlqtKpPJ2Ag66YN/lz13c3Nj74X0+Hg8rt3dXQtzZE/xfJD2plIp1et1lUolY6IJzXr06JEVX3zujY0Nk9wzc5qfiTzRacuANUEtsLq6at5DLnPGZq2srNh5jIwVNrhYLBoocXd3N8Hk8BnwdNMcAbQQvoREnZAqpzVnNBrp448/liT97Gc/009/+lNr9Pv9voXYpdNpa/Z4rs4grUwmo3A4bE0t9hfkmox3BAgjpGg0GhlLSWYDa3ZlZUX5fF5v3rwx3zR3I2Mu29/M3kWOK8kASN4jgIvb7daPf/xjyzph79OAOu0RvAeKLbzvAIyAgTB7eGJPT0/l8XhM+n15ealGo2GhPoAQyWTSRiLClNKoYPUA6EAVQcMPYAsrDAjLMyY8sNvtKpfLaW5uTru7u7q6ulK1Wp0ITry+vracCJ7/zMx4PCOp++REcC4AmDExhGbv8PBQp6enevbsmUqlkubn5xUIBGxGs9frNbVYt9vVzs6O3G63Dg4OzBKD/YIGGKsL66bZbGpjY8MaNvYDsmWsIX6/3zy75XLZgAUA7E6no7W1NftM7FvqBMamlUolUzsBzBQKBcvGgGnl++Oc4syjCeQOBPxFgXh0dGSsrzMfBY839x/qPkBjMkwgSahTAI5QVTYaDS0sLGhubs581dz/2PpoOAeD8di6ZDIpv99v4HatVtPs7Hhk4vT0tIHbgD7sMZR9gOn5fN5mbqOsAWwsl8uamZmx8x7AZXV11YDsq6srmzMNUcJ+p4kGgGUfcm82Gg1TuHBO8F24XC6l02kDAvmMU1PjTBgULthbWGsAtNgNSKYH+CYBPhAIGOhUqVRUr9ctOMztdpu9jDDKxcVFs3iQs3N4eGjfIbWKM4fn6urKAAgsCgBJhFdC3Hz88ceWFdNut23Kye7uroEGKPfIRKEfIISX82ZpaUmffPKJTk9Pdf/+fcvDQJ2CqhLQiLoD7/779+8VCARsMo4zA4g6cGlpSeVy2UDY+/fvW49zdHRkdzRsPPkQ2WzWAI14PG4Thhi7x95C7Uc9SEPOXkPpwF6CXAIgpG7iDuBM+V1e33vC//Gv79SE44NGjoqkjnCPbrdrhUG1WlWxWNTGxoZcLpeNamExk+JJYYZfiwWBF9z5u1lsTgaHSwCZC7IekORgMGgsLfNvka7iGcbLRVPjco3HLBGKAxq6s7NjqJgkC91oNpvK5/Pa2NiwMSnn5+cWmhCLxeT3+y0UBRkfYRler1eJRMLmvpbLZbXbbb19+1alUknZbFbr6+va3Nw0rwY+J9BRacyEvnz5Um632+bQMiJKkl3iILtIzXmeyADxRCJ/YXOCIEciEZ2enpqcHwkO32+hULCCnPdGQwJbR9gO36sTCMAfI8nQOYoJSVZUIJvl+4XxBAlG1oWsDHYGtptmDg/NcDi0ERY0uhyIXOoEBrFGaaBY1yDAFKFer9dGAdE0gkACDnH5dDodUwnw+QmNorB2WhSKxaLJJyke8BcCUsGs4x1nfTcaDYXDYSt88HYRzIEEjj0JqyLJlC14SAFIvg2IcNk7JeDIL/F64Ql0+hDxADLCBsYQuwmyVWSpPp9PFxcXdh7wPmDRuagBxfi+2MtLS0vKZDL6/PPPdXFxoWg0qs8++0wul0tv3rzR+fm5nR3M0qUR//Wvf61IJKLt7W1j95mrDcBDaFIgEDB5Noh+vV6fuFwo6pEOJxIJ83VR4GIJgpmUZCqCwDcJuel02hh+WKObmxs9e/bMVCWAh5JMkjocDo21wQ/IGQvDCLuEHWJubk7379/Xq1ev7KzP5XIqFAoKBoMWHkiRSzPFmXBxcWFJ8JwN+F0ZTcQov+FwqHv37ikQCOjly5eqVCpmHzk/P5+Q4fZ6PT148EBffvmlcrmc7X+PZzx2qtVqaW1tzYL3CAgCMIVZ5AylWXFO9eDcczL9l5eXCoVCBhqQA0CT3+l0lE6nbYzS//F//B/mfwVIcsoD+R8qFL6HUqmklZUVbWxsGGPvlH0C5kiyUTWpVMqKLM6HXq9nZztNO0UjDQ97Cvb05uZGW1tbBlKyt2gKCJiiwXGqimD9yLiAOYKB5QzjTgZUZE0gqWdvUHDGYjEDz9g/NDrxeNymSGCZIDiQsC+UC4Shsrc4p2GEaToBYZjhLclkrsi1SY9n6snOzo7m5+dNPfLkyRP7XL1ezwL4pPHkDgK/GMXF9AdCpRh1BhuLuoTGlLUMS4znfHl52dhrznfsM36/31Q9hOyh7qA5Z845tRcALkypM4MHADibzWp/f9/CtwaDgT7//HPt7OxYLem0fDhrjtPTUwM/mErAXUDgFIql6elpRSIRA7JRAmLLKRQKqlQqNre+2+3aXgKEpz4AiHE24NQiw+FQBwcHZseAEaWhWV1dNcCCJpXxU7e3t3r06JEpjJzqPRopgisBNyTZP6O04w4EzOZMIaj25cuXdl9Sb9Hc5nI5A1qxZJVKJasP0um0AXWcLc668vT01IB21FuAeRAmrGdAdOpOFB00qZLs+yNngvsuGAxaLcg56sxWuL6+1t7enk05gKXnjgI8X19ft9R01C6AGuSjsLZ3d3etuUYVtru7a7UKShCADL/fr9evX2t2djzWeH9/35SJjUZD/X5f4XDYzg7nqM5araZMJmMZIJzLc3Nzisfj+lf/6l/p/Pxc7XZb29vbpjA6Pz/Xo0ePbHwoZMvy8rL1LADsAK1+v1+np6c2QQrlAoQqZ3A6nbZ7/vvX7/f1nZpw5J7Mr2OxgxpfX4/nbCJ9ITmWg75QKEzIlJB7ZTKZCV+N05MLc4VEmEYONlSSST0Hg4EhXufn55JkXhGaLCL7a7WaJJk3eWVlxZB4pD+3t+MZ3alUStIHOTXpjTQOg8FA1WpVe3t7hsLTuMLas1HxSiGDDYfDlny4urpqaPuvf/1rm/NMKi6+ymAwaPJ4CrSbmxsdHh5aQuPc3JyxOBsbG1Y0OIs8Dn4KephjmoDl5WXdv3/fDmD8cJLsoK/X65buPT09rdevXxvaxu9xFi6j0cikbQAufA8csqB0qCj4u6DteLlg0mGxYA+5lCkQCDVjlA+FCSoOilbYdwKAWFNO+Tz/TAoyiDMeZGTsXDgul8sKRZpVmgxAj0ajoUajYUgqTagT4fZ6vRPvtVAo6ObmRtVq1QofCj/kq5Ks4KX5If2bEDYkolyIyJ5pDGdnZ03NMD8/bxfa6emp/Q6eAd5z1tFwOFSn07E9FQ6HJ9QbeMl9vvF8c5oZmExJE8wfLCrF8vn5uTVxAFOE+xCWBIORzWaNZcd/RpFYq9VULpd1enqqqakp3bt3Tz7feCwYAXVI4KvVqtrttvb29tRsNvXo0SNLUSfVOBKJGFiI+oCCFk/o0dGR1tfX7WxlbSAPAzhLJpNWVN7cjGfvMsYIcKrf79vaubm50eLiotbX11WpVCbkmyhNAEaurq5ULBZt3XIuYXFBHg+YAujCOck+crvdSiQSqtVqyufz2tnZUbvd1tHRkTFrnIEETeJXJ6Cw2+0qGAxaUiu2DZjtVCplzLvf79eDBw80Ozurv/u7v7N1TOHNHs3lcopGo3r27JkBBPv7+2q32xoOh/bdOAFeUv5nZsajHGG6MpmMhSHhwyYjA5YTgLPX65mSgwKP+wP/I3aHVqtlCfKsj1gsZkwU5z9nCdYRZPlIDHm+fP8U7RSsDx48MIAHUAZVEWccLPz8/LyxKHd3d0qn02o2m5bRQCYGnmyaJdaxc+449i1n8+C0Q8HeMO7PWQiy78mjwLuLYoDvjfVfKpWMKabxouB1u90maaauIKG7UqkYo8R54HaPRyB2u11Tk9XrdVOIIFOn6ctms5JkPvSrqyslk0mTNQMUOqcx8FkKhYIV/6FQSP1+385U7DU8W5qybDZrax7gbDQaKRaL2Rx5Gi/pw8hOMhDu7u60s7Nj0lSnpa1QKGhlZcXWKOGTnIFYCWZnZ22/AqwS+sTvoA64u7szr2s+n7eAtoWFBfN5s3f52dQn3OHpdFr379831REKKpQ+oVDI6g3UKRcXFzo5ObEwNs4i/tvBwYGpViA05ufnTRVCA876Y82zv1qtllmEuD9brZbVUDwHprbkcjmtrKxYgrfX67Vziprg/PzcyA/k087mHjl/oVBQNBq1Zh+QAeaYc5Zal7GVnKle7zjRHH+y0wZ6d3dnAMX19bWOjo7k8XjsrCkUCrq4uLCzlvOJde0EKlZWVuxuIm3c4xmP/fN4xsG4+/v7BgoNBgNTBgAyobBEBYDt0uVyaXt72/JJqNux5C0uLmpmZsasG05wk+94dnbWwpR7vZ42NjZsrvze3p41sUxWiMViVn+igN3a2tLV1ZWRcL/97W/14x//2NYEBCYJ6aVSyYCsdDqtra0tffTRR1bzUf+MRiM9ffpUbrfbRolKsjuZnCfua+4kVMGAYNSVgEPUGktLSxNZP/zdRCLxnZrw75nwf/zrOzXhhBex6KPRqPl6OOBI80VujEyHMTZ47WKxmDXGpAXiM8bnySJARg46hCQGtGx6etrQN5o0GEISpJ1sMB5LpMoU8nhICBXyer1KJpNWDNEsc7kUi0XzriApYmbg6uqqfvKTn1hgDr470PV+v2+pwhyMfK7hcKh/8k/+iSTp8PBQv/jFL+y5JpNJk5uRaPz27VsLj/jzP/9z9ft9HR0dqdVq6Ze//KUymYx2d3dtlIKTtQWEwAtEQ86/m54ep4vDckciEWNaw+GwsQCMTqEocUrLAUn4bDTUgCswVzRmFIZTU1O2lvh5kqz5QF3R643TtfFyEnxzeXmp8/Nz89IgQ1xaWtLS0pJJykma5MDmgJ6enjaEX5K9P9YlHjrGt4Fq0wQPBuM0a3xKTs9Ou91WvV43dgxGkjUO89FoNBQKhSSNwSYKUVhsmCsu7pubGyvgeIYUNBQFl5eXhhQ7G31JJn1dWVkx9h7/Muw4BR0NHgUsHnHnvs/n8zZWAwQZlgN2wAlSkNo9Go0UiURUqVSsUHOCKuxVEs0BTVBnBByzVC8vL23tO4PueI+sUYAKxvNw6G9sbCiZTBoYdXZ2ZsE/GxsbmpmZ0cnJiflGyZigIGQ8CN8jzS/MIbJ8LlbCbJzsItkFyWRSFxcXOj4+ttRpgCi+z5mZGVM18Ll4voCiNNiwZjwHQncozPGQttttVSoVDQbjIMZ+v2/zhVHPwELQFPn9flWrVTtPkMwhC5c0YTVgPcPU4ZFdW1vTxsaGFTEUaVNTU/rkk09Mhg6bijwYr2IqldL9+/f19ddf6/r6Wo8fPzZA1snWsc5I7gVoc7Kv1WpVv/rVr0wJwjx65xgiGMGTkxMrqGD0h8Nx2GM0GjXWNplMamZmxhokzlC8fYBsNKOBQMDmXP/oRz+y8wSAz/kzaAJp+JzKEvYT1gSaeJobUncrlYo1RjCR+MSx5wCAU9w7z8lOp2Nzdp3nD8+Y0NCrqyu702FysaVhgyuVSkomk1YU47FmT8FaEcSFCoH6gPOaMEK8sL1ez75TinVsJgApZLCgYAG8cKqTUCQg/2w0Grq7u9Pm5qbta8KS2Mvkk+CDxs/tzCPB5oQ8H0afF8Fns7OzFvgKoEoQJGfj0dGRvR98pxTq7FnOd75T0uR5loxgwr6GLByVJPsQBQ5gCIAn+wrwlT3COS/JpM2Bb6Y8oBpkvBrPemZmxuZ/A1w4g9T4uQBKMORO9SPkA6AcQDIebBpZ6gO32603b96Y97bVaml/f9/+PCQGUuOTkxOFQiE9evRIw+HQrCHOxvPNmzcTeTeQFdwDPAsIHVQbrEPUjAAOHo/HZnV/m9zK5/PyeDza39+33BSABOckCEBYLFs0rKenpzo8PNSTJ09MicE9gFqGJvHu7k6xWMwADyxK3JcPHjwwOxl3MM+RsaEbGxsGpvV647FmJycn2tzctPuAdSpJ9+/fNxVkOBy28+vi4kK1Ws1k8uw3wAJYaWToTKnAvkT+EYDd+fm5Tk5OtLW1pd3dXfn9fh0dHZkSgZoMFQIkFpYIcglisZjZ7ailCH9jD0E2cT8ywpd/h/qPsGDOznq9rsA3SfZkTPC7yQoCTCOTwVl3fv/6/b6+85xwxuMgcUXqBBNG0A7+RkbDUOA5Q4vweyN3WV5eNnb7/PzcELXp6WkLsZBksj3QNzwm/F0CzSj+uEhgXWCdYDmQjSCnp9jDC4QXCgZlenraLmgkM6DM+FORcz5+/Fj/6T/9J11cXGhra8tmSXPwAzCQlplOp03qR1Ito51evXplqcDxeFyfffaZhf5sbm6a99zj8Sgej+urr75SoVBQOp02pn5vb09u93i0g7NBdib4SrI0VZpdVAj4WpEnnpycSNLExqcpptDjOTuRLIpyQAG82jTqPHt+Dt81YAyzWkFhkfmCZF9eXlqz8ODBA2NIhsNxGjeXLQcPaw4Gl4blwYMHE35zXk4gAbYUvxtFKaExgAr42FiDXF4oMXj+yDu51CgKb29vjZFCIr28vGwXHg0b8icaXzzpTv8qTQfMM9IlLBrValWBQMAKABpvvOihUEij0ciaYFBl/JTseYoF0FaYMcJi+v2+zZWmuGC+NkoDFBLf3tM0JiSxIiWnqETayLlD08NnBDwKhUJaXV3VgwcPtLCwYKwIfn5AltXVVSt+t7e3Lb2VMDVyCliXyDAl2TNDttdoNJTP581XxvqqVCoGAHC+kDqLXJ99jie/2+3afpmbm7MAnGQyaQBjJpOxvVIsFjU1NTUxshH/JU2k3+83vy/qo+np8bzfUqmkbrer4+NjJRIJPXnyRK1Wa8K3iBqHppB8g0gkYgzA+vq6BoOBMQqcO04LC/cO84oB1ChQ4vG4rT0aGYpnGNZms6nFxUU9ePBAmUxGuVzO5twSzifJZJhYhmgIf/Ob31hhT4Po9/vtTAGgxEPr9XpVrVZtXKAzDIuA0FQqZcnCqBI4D5k0IMlALafMXJL5DfEWs65gTbFdATDSVKBAm5mZUTqd1unpqba2toydIiiR38Oc4fX1dQO8yXlg9jPZJoRR8b5h5S8uLhSPxyesXKhWotGoyUIl2bOnaLy4uLAmbGVlxcBxQArO09FopMePHyuTySgQCOjo6MgUNQACnJf4JJ0J1YAI+JFhS/nZqN4AFrgnnIoC9iGNGusUlReBTtznfE+hUEj5fN78uSgOAadQt1FrQRTAnALCIS/Ff+/1evXkyZMJ0I/0cH4vGRaj0UipVErD4VDlctnuU6xJ1Auw7cjhAbskWZ0AK87PcuaYBAIBUx02m00bf+j0XlNXonABGEUhh08dsI+mnTrD6d8GhIb1BQTnu+YZI4umERyNRgZ6ZTIZzc/PKxQK6fb21tLJG42Gfc5CoaC1tTXbl7Cf6XRapVLJaohgMGh3PzXImzdv1O12TYXiBDSdNg2AJ6TG1B80wKjZrq+v7Tt0uVz2/AjU484qlUq6d++enfdYBMm7II+jUqmY6qlSqeji4kKvXr3Szc2N3r9/r729PblcLrNWeb3j1O5+v2+1GY0u53m9Xjfw7dWrV8rlcvrkk09Uq9UMxAM4o77iDAQgW15etgBGRoCSfu9yjb3f1GOcKzStgG7Hx8dmP3z9+rW2t7eNcYdJDgaDEwoBsnsajYbVOdjr2Hsej0enp6f2d7AazM/Pa39/X9VqVYVCwUimubk5I4ycKgIUmwSvUQPwP0gNahGfz2d9zOXlpU0UQeVBICLqUM4UAEYyQBYWFvTy5cv/j73g96//517fqQkHXWy32yYvQR7CYpPG4yYajYZ50vA9IFPn4qDZoKAnUbff7+vs7GzC/31+fm4jSpCHcOCz8dm0q6urCgaDKpVKxnAjCeRgw5dOQAwHz9TUh2AqihUOLi440p6npqZUrVaNzV5YWDB/Gf99dnZWn332mf7Lf/kvuri4sNR0kGIk2aDHbrfbxofRFO7u7mpnZ0der1fPnz+3zQ7D8+zZMztckDB5vV59/PHHCoVCNlrm5cuXxt7D4FHYUSg7g7somG5vb00qSRiSs6CnqeHi4/9SYDpfHKJcurAIzkuP/zkl7RSoNAVOzzrvl8IHHxI/J51O2/sBLUeWD3PnTIim0KFBpbiEIW+32yZ75vILh8PmE0U2yEWGFBEvILJ6vl+KOAoP1h0XCGmsvDeYKVhwfNsg2VxW/X5fzWZTrVZrAvkHMOC5871QYHKoAy4BBODPo0GGldnb25PP51M+nzf5OecFcjoKZ/ajJAMc+LNcjOwlWNLj42Otr6+bhxxbAYFEKDRAn/GW4cd2NuNzc3NqNBrmnWRdbG9vW8FHscMFSbNOIimFDUxhq9X6B1MI+HxXV1cTigQKUBiy0WikXC6n9fV1y7F4+fKlPB6PFVQU7sy1Z+wVaeDI1AeDgQKBgNbX1y3MkRAgxpxR0MNC4sNk/+At40InA8EZcvfkyRO9fv3aAgyZ602a8eXlpSKRiOLxuD2rnZ0dSTI1CCoXmG9AVT4b4CDhUwTelEolY0sAQ1E1XFxcWPOMdYGGwTlG5vXr19ZsMz7O6TlGpsd4yUgkYoUdjQbhPPiEAXJoVtjTS0tLtoYAGObn543lxF8NewbY43K5TJ3Ee6JpJOARJRojyEajkQ4ODowlvH//vjqdjiljOEeHw/Hc9C+//FLFYlF+v9/GAw2HQ1svzWZT79+/t4YC/20gENDx8bGdb0tLS8pms+r1xuOE+PwUkdK46ebeQgLNe3Yqa5CtI2Emb4Yzhfn1Nzc3pkyTxqDE+/fv5Xa7VavVzPrCyDePx2OqLfJYAMJhxHmfAAT8PVg5gquYv818d9Y1BTFFP/5MCmiscNzzhHSORiP5/X6lUilrfGnqWBvIw51sOdM/pqamtL29rX6/byCez+ezKR58XicreXd3p6OjIzs/h8Ohjo6OzBpCXeacVw6DC7sHszY7OzsRGOcEZUulkjY3N01lBkjJfUUAFyOxqKckGaiM6lGSqSoJAqShhlGUZM/89vZWKysrVsMhyY3H4wp8E2jKuYe6ikaf/8s5CGB5enpqTRwqAJQarJWpqfGMbpon51ndaDTk9Xr18uVLU85Rc3LOB4NBu3f5bvic7AvAIxQk7DnYX+70UCikVCploA2KnN3dXR0dHanT6dh3ivqJs4p9h3QfQBX1FMTA+fm5At+MJmWPeL3eiTuAc4TaGSVIqVSyZ3RwcKCzszOrn1HbcNY6cyI4j0ajcRYIcn8C48j/4GdRx5FMzl1Hb8Bee/HihdWl9Xpd7Xbb1iDkCRMn2AP7+/u6u7tTNBpVqVSS2+22wGUAVcBVv98vn89n/vRf/OIXtvYBizgTnIQgn4OJBC7XeFw0NYTTlnd7e2thopxd9DDkD3HnUesCylM7ApZ9//r9v75TE86ligSGzUAoE2EHMzPjecAkRfp8Pq2trdmfpzHBE4sPCrkF8w0pWBggPxqNU43X19fN1yhpwpNEMQOaSDCBU4rM7F0kIoAFKysrthnxk8FkORGt0WhkTR7IP8/n9vZ2IrW7Wq0qGo3qL/7iL/T+/Xv95//8n/WTn/zEZEWg8lzePCNkszTlpVJJ0WhU//pf/2s9f/5c6XRaf/3Xf610Oq0nT54oHo8bys/mWlpaMg8ViGaxWFS5XFYmk9Hm5qaWl5fNs09DxGdGMUAAHQczslnAA7zmPAP+B5vybek5DBMXKcgx6ZEEvUkyNBs2Y35+fmJ0Ec8cOeXl5aWpF6QPFznPBZYNsIG1Q/PL76PBxB/Jgd1qtYwl5qKm0CEt+ObmxqYB4B/jAkilUuarJjWXw5oDlsuYRgRpNiElTja41Wopl8tZAQcLC2M4NTWlp0+f2noiHBDWnUYSDyto62g0siIL5QRsLVJhAhBp3Ej9JASJhh17xu3traW08vMA45yXAZcgDSOSbrfbbXJ9SWY7oHhCnk4YI4FSBIvBGiCB5DlGo1HzrQKO0Cz2++PxUSDT0ocQtOfPn6vVaunZs2cW3MdnBsiDZWACAHaOZrNpbFOhUJAkGzuIP+7t27eKRqMTjR/rCzYEdgUgs91u2yiq+fl5Gw3Je3j16pV2dna0s7NjjCdMCywS87wHg4Eqlco/AIlghSgUNjY2NBwOJ2a8z8/PKxqNKhQKKZlMyuv1Kp/PKxwOTzCPAKDNZtMaAPYpoBeNX6VSsUAbmmOYS0nG/MDsE/bEHUU4049+9CNlMhm9f/9e6XTabEf7+/s6OTnR+fm53R/Ly8sGkFAAMxGCdTocDo0lHY3GY5BIteYcA6gleEmSjZej6Ka4W1hYULfbNekso7wAIs/OznR7e6udnR2NRiMlEgk9f/5c5XLZsgmwfJECzJnGmQm7G41GNRqNJ4EgPQdYIFma3BLCtvL5vPkfWeepVErNZlO1Ws2UNNwrAIU0T4DpnJ/4gKkHAFVgRePxuN27rVbLilnAehL6Aa34vBABhORRCNMAUotgNwNIBIzDjsQUidPTUwPvNzY2JsaeotTjbiuVStZ4wUzBRrF2VldXdX5+bgnWqMH43nO5nPb39w3MQ5EEEMiYtPX1dZ2enlpGDJYhnoH0YSwnthNJpnIiJRwPMTORaa6ovY6OjlStVm0mOe+52+2aiqnRaCibzerm5kapVMr2sRMkpw7gO+a8LpfL9jmxEdBkUG/STOGN50VjfHd3Z5kJfC800qguuN9IxkftADvIz3Wq1LCL8Gw8Ho+y2awqlYqxmF999ZURQ9fX19rc3DSGGDVHMplUt9u1cbT7+/umHtvc3Jzwrfv9fgNxAfoALqgNnPYs5xQFVDicV4SbwfB6vV599tln+tu//VtbtycnJ0YwzM3NaWlpSc+ePVO9XtebN29M2TYcDvXpp5/a+qnX68Ye53I5u8NGo3GYZLfbNYaVHJXRaGThfLe3t6pUKtbsLi8vK5FIaGtra+Ledd793J+Hh4fa29tTNBpVrVZTvV7X3d14bOjnn3+u/f19JRIJvX//3qYWcD/f3d3p/v37JrfHdjIcDvXmzRuFw2GbDFQoFOwcgvQhC2l1dVVnZ2c2Ucd5f8ViMRvZFwgEtLy8rHa7baF7f/AHf6CTkxNjrgF8CEsDkKQm51nMzIxHIlJ34BdHLcaZMTU1pVqtZtZX9hCWNezFzj6IvRkIBP4vusHJF/fJ7/P1+/75/6te36kJh70FRcZTJMlkEqB2Xu84FZqCA5YQGQ0FPbIrGGkKOKeciMsF5phNAAuKtBS2GzYShJaf6ZSLLS0t2SxwZBpcpBRUHMrIgglYYlMMh0Nb6EhRKSQo/Clc8Mi/fv1aL168kCQ9fvxYsVjM/DZc3iSSXl5e2sHM5mSG4l//9V+rWq3q9PRUzWZTP/zhD3X//n2TpTjDLLLZ7ESYDQn3gCTRaNRYRRB8mkIOk/Y3878lWeOEPJEiXtJEs8t64JnBjnCQ8QI0gamkOGJN8GdJGkZSRrHllHtyWTt9ZTQReKZGo5EhgxTSPC/QZZ4L7JRz/u7V1ZWBBQBPhP1dXFxofX3dkm7xL5XLZW1tbRnqS7CPJOXzeUvyxsPGJYwKBDks+wUp0tLSkjE8fH/4LlmnpOTCMMMgsG/4DmAxGUVGQ8O+4vLCX0pz6JR8Sx/C4Dqdjur1uoFyjP8BcGs2m8Z4O0fWXFxcWOhfLBYzGR1FPQWjE0yg6EWSSSHEhUxBSZ4AgS/Ii2E2nPsV2SeNbyaTkcfjsakPjUbDQAcae5Boni3+W4p5bB6pVEorKyvKZDI6Pz/X6empIeHRaNRYRvygFIkojwCaOp2O+ZFHo5HZE2AHSL91u936wQ9+oJubGxWLRR0fHysWixk7g58YtgPJLGsANQWFClkA+Oqr1aqd7/1+32RvFJ9OthNvrFPtARCDIoi9uLW1ZecyChPG5tDYUrhzp+B3r9VqpgTASgVYTIo1Sqq5uTkL81tfXzewDVsUABHNW6vVsrFosHyVSsW845x9kgwcA5ytVqs2BhMAhQwKSdao0IzQNE9NTVlY1g9+8AOTVP/t3/6tBXWxP2u1ms7Ozuzu5JzjHob5evDggQLfJOq/fftWkUhE9+/f1+eff26jBmF4g8GgAeKMJW00Gtra2tLd3Z2dyzSaNGc0RTTfkUjE8gbW19fNcwsAQUAYjA4gSrFY1NXVlTUn7BUaQVhzGjKAfOTDU1PjhOROp2P+/6urK62vr9tajEajisfjymQyFh7m8XisMUcxwb4AHCgWi8Z09/vjsWQ0307FF/sFewyWM+5a1j33JDa9wWCc4A8L1uv1tLa2prOzMwOw7t27Z2Bxu91Ws9k0uTaNP2erx+MxEIUzamVlxZhQ7hM8441GQ6urq5aEzX0JO8kdgQ2GfYgKgzNhdnbWrD7cFXzWcDisWCymUqlk7DVnEXudppNzHABF+qC8AgwKhUL/AJjx+XwW4Ae7zdlH8jtZB0574OLiop2N7CkyMebn51WtVlWv120Pc05juXC5xhk1b968MX8764/aDPICWxiMJXUM3wv1KWuRs5l/B/CDYpA8ET6Tz+ez8wwfNDO4yarAigYwzD5GFSfJQI2zszOVSiUtLS3p/fv3+sM//EM7iwBDUTp+/fXXlv1C/Q9QdnNzo2g0qq2tLSWTSd3d3RlIQx3nnE2PcvLZs2fmH79//74ODg4MrKX2RbVEE059SibE+fm5qtWqbm9vlUgklEwmLefJ7/fr3r17WlhYUC6XswySVCplIGbgm9n27H9qUUnWJ2Hnur4eT1lBCbmwsKAvvvhCP/jBD+yOdNo0qWGwtGBh7fV6Jukn68Ln8xl5QrMPSE0NjmKYkbvkc9DzsO7evXv3O/eG3zfh//jXd2rCnWwRBQZIlVNeThPQ74/nUiOXDIVCFlYBMr+8vKyLiwtVKhVjNmAaYB5gExidNRgMbC45BY4ToQIsYHNxgLrdblvA19fXSiQSqlQqFrYCaoo/FnkGxTEJgjQsgAUsZEl2oXDIcIBRhD148EAPHjzQ3//93+vo6Ehv3ryRyzUeiXZ9fa2DgwO7sN1ut96/f6+5uTlDpiiA/+W//Jc6Pz/XX/3VXymTyejnP/+5zs/P9fjxYwtu6HQ6evfunRqNxgRqSvFGYVsoFOxi+nYhSPGHzNwJtIBSoyj4dmP9bTk5BQS/g0OexgVpJuyYy+UyWZsTRUf6Too4cmykNhSwXLwUcU6fDe/FWbzDxgYCAd27d0/X19fm6bu6urJiGSCHC4/C9/b21oLckHFR+MAsOAOIGPHE+iGkyInoAxzQoOJthMknkI/kfC4+ijoO4EwmY7O7YeVhAQgznJ6etouPy4Qii/fAPwMyAZRcXFwoFotpbW1N9XrdUOvt7W2T/1P8UcRzMYPW8r3TEPPeb29vrRmg+F5aWjL2iv+OQgfLAMU4AUWdTmdiXErgmzGLU1NTCgaDdvnwjFmD29vbxgJRoPX7fW1uburJkyc2Oog9gQer1Wrp7u7Omorr62tFo1HzDLtcLm1tben4+NiSY1EAABo5n+Xs7KyN62I9I70HbAQ0oxAnNNJZJNLIVatV20ukIANgcXbDTiPp83q9WllZ0erqqur1utLptA4ODrS2tmZgmHO/EUJGEU7jS1gUe4HxX7e3t8ZU9Xo9S1fn7MaHxwtbUi6XM4kvScOMCMvn8zZKEpCA0VKwi5zlAFpI8c7OzsyKwBkHwzozM6NUKqW7uzvl83nl83mNRiPF43G7s6anp202MOcYyhfYdo9nHA5VKBTU7Xa1sbFhNiWPx2PScFQrz549swL+5uZGuVxO29vbyufzBj6jZigWiyoWi+apRZkDu8ld2Gq1lEgkFI/HdXBwoEwmY03l6uqqsSKcAefn58bk+f1+FQoFm0E9NzenXC5nTQvBo6FQyGYbA6bhhXUGiyFDRaUFoJnL5bS2tmZsMAAOACYMeq1WM0kyQCdrl3R/7htAZIAYCr379+/bJILhcGjrKhAI2M/gvbHvAP6oSxhVRyPMewHIWVhYsPnA5BFsbm7q/v37Vog7pf2wtKVSydQT4XBY19fXWllZsUYYyS9MojTOOymVSgaectZXKhUlk8mJswNvaKfTsdwHQCuICMAJQrP4npGyk78RCAQsHJe6AYb55ubGWGayTqamprSxsSFpTNwwHSAQCPyDAEYyQlDAoHa4vr62IE6saSjFCHSEiOGscrvdlhRO8w+AR8PGHcQ+pt7l/KYxh3xxju7lbJNkChbu6Gg0at+1U11K/eu056EgBeianp42n78kA5Jhy7e3t22CARJ81HaomF69eqVIJKJUKmVWU+54QArn+QzIXK1WDfT57LPP1Gq11G631e127dkCPHU6Hcs6+fLLLzUajbS7u6unT59qOByaxS0Wi1m+AgFirE3WD3Xf1NSUEomEff+E40ajUQWDQQtAPDg4kNvtViAQ0NbWlgU6smcYGelyufTTn/7UwhJRIM7MzNidC8HoBIRYu9gBuFOOj4/l8/nMm44vHvYaAoFMm7OzM1Mv8Oc4j1ChFQoF++4bjYampqYUjUbt/pqfn7fnjN2BPUQYNiF4ZKLMzs7q5ORE8XhcPp/PlD9Ou8/3r9/f6zs14SDps7OzxqLSeDl9rBx0TrkKs4iRMnOIwfglEgnbWPwuisBwOGzNr/TBm85iJ4RJGksS9/b27JJtt9tWpEsfmFFpfGDt7e1ZsA2Xktc7TsmE4eQwckrX8GRQJN3c3BhbjLx1enpa+XxerVZLoVDILrDBYKCHDx8ai18sFo1V+qM/+iPbfHhcaYQo9Hq9np4+faq1tTX98Ic/NDTyN7/5ja6urrSxsWGFDMmkgUDARtrQXAFa8FydyD0FHyynpAklAxeTJDuQUAPgzQJMANXj+4U9pfimieZ3Epbk9DzhqSPki0O53W5bXgAXBp+Jwgcm3lmQ8X74PDTjeGhyuZwFuHE5cvk4VRb8DAI7QCFhXrBjEF7EGoFJ5OfhFWOtwhY2m0253W6T/IPad7vdCbQTRJx9BTtCsrZTIoVv0uPx2F7E80RDTtMAI8H34WScKHo5sGHpnYgqDRWFLqg9kj+akeXlZaXTafv3zMMkwAfwiAuZMC1nMBFSMrzOBI0BrnEB8X2cn58rEokY20NTwpxSSRYqRPGbzWZNJhaNRo09orkG+KP593g8Wl1dNeXF2dmZ5ubmtLa2ZmsPQIfAMwIekSJSvMHuAUBtb2/r4uJCpVLJZIR8J5ImmnqeC5e5U0HCGuZSZ73RVPH84vG4BfqhrnC73ZZXgVKGnwGzTvEGC8neAwi9uRnPuWctoLTiXMhms9bw0NDROHAnOdOaAYVgJ/Dae71em4PLTNnr62vz5CG55i6YmZnRkydPdHt7qzdv3mhzc9PG7fB+sM40Gg2dn5/r/v37lg/Bd8bZ0+/3NT8/b0AbYA7nFTJjmP1QKGQNOCGRnLHkmZTLZV1cXOgP/uAPrFCmAQXgRsLLsx+NRibRPjw8tJF2sVhMh4eHevHihSmOGN0DAAJoQLNdKpXse7i6urJ8BEBqVAicE/l83hL98Q/DljpDAPlnPMOHh4f2Hjiv+Huo1QjBC4fDFkyWSqUsuIp1cXl5qWQyqWazaWcEgEA+n7cmEHCOuiEYDKperyubzZpC4/z8XOFwWFtbW2o2mwZKE+44OzurV69eaW9vz/YIZ/X8/LylLTebTWtWSCgn4wC1HKo7mGAk/QAQp6enBibyPfV6PdXrdasxaM6npqZ0cHCgxcVFq9ecwNHl5aUx6dRSMM+FQkHz8/MWApjNZm16xOzsrEnTWY80SOVy2f47TTVJ7dwT3W5XkuwuZW9yb/H+aJjxAadSKbOhAKSSC8HoTJhfbE4A8kzkYGIOTD1e3E6nY80162VqakonJycGkBLISGNFY3l6eqqbmxsFg0Elk0nLjUA14/P5jKEldG15ednGS6Le4g51yvJp+Dg7UHsGg0H7biCzyEnibsS+FQ6H9fHHH5uy5erqykBMVJP8LCTTpVLJQNd8Pm9N4NTUeLzn8+fPdX19bWoQaptAIKBPP/3UcjwAP0k0TyaT2tzctN/JHeKsL533C9ZUCA2eD7Uh3n/yKl6/fq1er6dSqaSpqSkDLVGVPX36VKPRSFtbW6b0IGvlq6++0ubmpoGZksy6ls/nNTU1Zfk99DSw8ViKqMXb30xaYp/k83nLunj58qXJw6nR+ewAaOSTMF4UsmJ6etqACO4kfg/nPwQYwB776t69e1b/ADSQ4/K7vL5nwv/xr+/UhGcyGe3v70uSyXCRREgfPMSFQsEuMkaswGAvLCzYXFXmulLQ0ETRoDEWg4tlbW3NijDmY1Igs7inpqaUzWatkZufn7emQvrQuMfjcWsimZ9K4xyJRGxWKJcvfxaJMQWhc2YlyCzzcdlIrVZLpVLJGiEae8ADp+SIg5xgHsADCp+Liws9f/5chULBfmcoFFKhUNDl5aW++uor3d7eTgRTkHr5bZbKCaCw0QEKWPDf3lzf9np/m/0GraRZQKWABBpElwOIRh121hlEgYeFwsPj8ZiygGYFptAJ6gAScBg7pek04rycHmre483NjfL5vLEbMLSJRMIkWfiGecZIi7BIsD76/b7W1tbUbreVzWYtTGowGFiAEIczxSlsIHJI1joMLkwWISF8RopkmlxkfDRShJIRkIKkz4lYU0jiXSKMhAIu8M3oLxgWfMUu14fkekKO+H5peLGu8PkJM6IhowDCUw/YAltFaBdBd4AGgCBOzyCKHH4+n6nT6VgRmUgklE6nrZDl+ZDrwPvz+/3a2dmxGePX19eq1WpqtVpWtFcqFQvc4mLjPHAG68AqIgcEGKBpJ1jNqeRgtirPEcDC7/crFArZqBqnaoh9Oz09bbOpB4OBpdWvrq7q4uJCxWLRWHv8u0je2u32RDGA1No52g8AATkfIAzfBf+bnZ1VtVq1wi+fz5tcFKZZGvujs9msBXqyv0h3f/LkifnbkVYC+mQyGa2trRmQSP7B3t6eeZ0pBpH9UrAThOlyueweQor48OFDvX//3oor2GA8jAsLC9rY2NDCwoIBjM7MCfZss9m0PX54eKi5uTlrIHu9njU8jJ4hXIcxQNg2OOewWPz0pz+Vy+XSw4cP7bNxvr97987OKdg9vstgMGjFOczNixcvNDs7a03R3NycBUEmk0mVy2Wdn59PFOcAfdPT06aQoFlG3YayYnV11fIjkNMCDsDiAZ42Gg2Fw2E7h/r9vmWs9Pt9Y6kBj+/u7sxmQQAY0nsCK8k+4MxoNpvGEkkywAwAOxgMmiqHZgAJPIA8YDJ3McAhNZJzYoMT0IURxvrEPUbNAQnBuUl2yvT0tHK5nEajkX0HePTdbrf5+wGE2SPYNIrForGwNHVTU1MqFArW1EAckI5ONgP+bexijHVzgjTcL5xb1AJLS0uWKE7I5sLCgo6PjyfOlY2NDZvsQfNAUwQg72SkqR+xRzA2EOk0DTb3O8GH1CyxWMzqD6c/HLUUwHG1WlW5XNbKyopZGRnzCDC8vLyser2uk5MTq1lSqZTVpyi9yLVhggprgzOH74l7ANuNM4sGyybKMvzr3DfknmCr49ygoQPIS6fTevjwoe0ZcpX4OaiNAN5isdhE4ChrZ3V1VWtra3rz5o1l9DizKQAj6ROkMWnWbDb11Vdf6Q//8A/N2oCSDUCCdUVTCvjebDaVSCQsiBkbLPsPwIGznzMFdabb7bb/xl4nm6nb7dq0hSdPnkwwyJxHTOjg71BPSTIbAPdLOp3W4uKiLi8vJ0gIbDihUEh///d/L6/Xq729PQNjSTPvdDpaXV219byxsWHnOTUjajP2Y7fbNQsWFgyXy2UhjWRGxGIxeyb5fN485N+/fv+v79SEw4zg33rx4oXJfiSZZI3NwlgtUsOdlwwXBhsKFpL/IfsEyUG2hOyCg0mSofA0FsVi0S53fL80UuFw2BhZZ/gam6RYLGp6etrSJJG3MvYBTwp/v9Pp2AYkTEuSNVbOhoVCvP3NqDMKM+RLMzMzOj4+1uLioqVKwl7jbwVtK5fLVnw8evRIm5ub+u1vf6vT01O9e/fOnvH6+rpJ7ik8OCRo7JxMMEwjsigOe/68U5bu/O+gpvxsJHcUP4Q04XMFlQMUgRFgRIXTX0cBdHV1ZQUrIRy9Xm+i0eZ9O9l7adyU8M+sH2fYESggxRxqAY9nPKcYVQO/Dw8UzLCkiUAwLlMYWfw3jK2p1Wo2womCGFSdwx3pHaAIRZnL5bLkVQ5+aVykIc3i0uKQZdQX0mPSzwlNm52dtcYXO4hT3QKgRfIpFz+MP8Ue77lQKNg5gHyc4oGRH2QnSDKfGcx+NBq1feTxjNNZy+WyjYEhwIvvjWYfiSkIfDKZVL1eN8lnvV7XwcGB7u7u9OjRI3300UfK5/NWGBJOkkwmJ3yvAD6cXzQKl5eXOj091XA4tDAu1AUU3ly8zEQHDOKC5lx0uVwWajcajWfkgu7TADJ9Ad9vLpczVmF1ddVABKwIeL0kmccZ+wuFKvsF5tjn86nVapmygX+XyWSs2GPPo1ZwsvR3d3cmVWSMDMWLz+czgBPgE7UMTD77GPvG1NSUFfB4PQnWRIY6MzOjbDarWq2m+/fvm+eXu4Szi7BEfi5qltFoZCy80yPKdxUOh3V4eKiFhQULSXSeM7Da2BucUtTPP/9cvV5Pq6urajabOj09VTab1crKijVbnLsE6XGPwkihTGHtk+nBGrq5GY/a5Dtxnol4oPlcHs94Xjvy76mpKb19+9ZmGXNPwRpyZ5IoTxNLTgkKEFQgjGCjqEddAmuN5LXVatn4xHg8buwtIzVvbm5sBCb+XZKFOaedXloaMu5e5O8ogiKRiI3EIjgOH3EikZA0VtZQ0FL8MnK1UChoZ2dHwWBQnU5HXq9Xa2trBhzC1GJ5QWpMGBaS1VqtZncGyc+Li4tKp9NGNnAWAKACHpBhA0MMaAQhQKFOjRGNRifGK9KEbWxsmGLNaTPh815cXGhnZ8dAWaZ7kENSLBZ1dnZmf6dWq6nX61lqO80rQX2EQ1EvUjegGGCvUNdNTU3Zd7SwsGDncrlctjOT+xeShXnQ2Pa4I51jyZhmQp3CucNdg22xUqlYAN3i4qJOTk5ULBYnGFwsZJKsFkTNRZbI0tKS3YPUFPitCQ0NhUL291DhsNYB86lXqAGpGVn3WPlQ67HfuWcApDwej/maAQuurq6UTCbNW8yYLULV4vG4KfeYtnJzc6NYLKZKpWKhyHxfT548MXAIkuDu7k6tVsvsEyh2Op2O3SsEuwLk8llPT0+VTqf1+PFjU5B2u11TgtFU0ugDEHA/SNKjR4+USqVsv7ndbtXrdSUSCVt3S0tLE2pRp4qVsbAAJvQtMMqcGT7feM66kxwBcIPBPj8/1/HxsXZ2dlQul+3PAjBdXFyoWq3anqVOguDBEujz+aymk6RcLmeAECAvo0whUaQPgbZkMX0boEwmk/Zdf//6/b++UxMOc1ur1TQcDrW7u2uBJQcHB3aAsljn5uYUj8e1vb1tPm2YjXK5bGgWxV+/37ciIBQKGaNH0wATRvI0hTWN1OHhodbX12380srKihV6TrQd2ZRTMk/jgiychp7PTCiVU0IKa0TwB8g8DIiz2eWyBD1E0sJ/73Q6JnMnEASU38lGs5FIpIaVhVEjYfT4+NhYHtgY5wxe5ICwdU5UHJABaSBBMdIkaw6ayZ/1er3GwCNLh6VincAuIzftdrsTgXesEwpT6YN8HJbp/PzcZKB8JxTVXEi896mpKWuYnJJ43jdMPagtgU5IoRKJhFKplGq1mn0GkOxKpWJMIzI7fLsU9oykoACiCUL9kM1mTXaPWgJlAOwgIVTT09PGQlWrVW1ublqz7vS64f3NZDJWMLDeKDKZ2QqoNhqN/aNILik2ZmbGY03YnxTWyDrxkrMGbm9vjfGcn583vxQAD4xRp9MxZrXT6WhxcXEizAiAJhKJ2Lpjr3ARBb4Zi+L1em2cC6i2JGMmw+GwrQE8/9I4GLFUKqlSqajVauknP/mJSbtAiufm5vTq1Sv5fD7t7Ozo4cOHuru706tXr6x5BuhDPUHImFNBATuGpIw1gbQcUMNpS8BHjX+fzwPYAJNZrVb1+vVr5fN5A3Hw1tGkAhKQ//D1119renpaH330kTV7MH4oOl69emWN8MrKitrttk1IAKjCskDxxzmAAgppI2cW62Q0GpmHle96fn7ewrvwgwIgwK5x7vn9fntf5DvwnL/44gvdv3/fGL3j42MtLy9bEUajTdHj9KoTSIU3j+Ya5iGdTpuNYXl52YCbUqmkcDisZrOpZrNpLBnNJGdmp9NRrVZTJBIxvzC/kwYW4K7X6+nNmzeW8h8Oh80vjlphcXFRjx49mmCvkakPBgN7X0jDUWIwbg8/eLVa1f7+vjVb+Cqvrq5UKpXsnAsEArZOkVtzbnMWE/RGEX1+fq5ut2tBgDDlNDyXl5cmSXU26syPp6GiMaEIp8En4wCwl7OBGdiJREJv375VoVCwol2SPe+zszNjiLgTnZkwhHEi0WZtAO5wLmGVotkGxEWBwt9BSp1IJDQYDNRqtazmAagEYOaenJoaTwHAsoJ6h/BKGCwYLZhkZrkD4gaDQWvaqWWopyjWpQ/STyf45wyAJKwQtRNjz5aWlvTll19asw7ww7lNAwY7T7GPbcNpFXKGvpEvMRqNzMcOgAbI6rTIAPyRHYCCke89FAoZIE1d4MwYYI/R+BISORwOrRFl3Bf1JVN3ksmk9vb2VCgU7H3QYDsVMbFYzGxl1MXtdtvuYQAIAHBqJ2oDp9Sfsxj/PDUVVkum/lxeXmplZUVnZ2f64osvdHFxof39fQv4SyQSFhz34MEDs7rRxDsbfwAMZnO/f/9ea2trpnDi2ZH74XK5dHp6ankVz58/169+9SsFAgFLKW+32zZKkjvl8ePHcrvdVh+9efNGV1dX+pM/+RNrjtlzt7e3dvZ6vV69efNGMzMz2t3dtfqE+pesBgA9FCStVsusMk+ePLG7i5oU9QFAB5N9YNF9Pp+KxaKp16jLAdcAjwBDAX8AlAB9IQCDwaCdoTTr8XjczhPnqLfV1VW9e/fOgA8Ullj+UEweHBzo448/NrCQDAsnIeScPvD96/f3+k5NeC6Xs4IFdKjZbNosVHwhTtkUzDWMBPLWSqViIxecshpnKAGIHymhNJSJRMIOZC4sDgQKN5BGEhwJWMhkMsaqU0hyMdA4s/ho+mHXkHxSNOKrdcr/nMwyBxdyX0lWAPC7+bMwLgAFBJLxZyRZYQ+Sy6GLTOnTTz/V9PS0fvWrX+ny8tIYv4WFBWskeXm9XpM5w9yCkiP14vfy3dAoUJA5GX4uUBgg6UNzzmXnlCI5G2ZnmAVsmRPwQDonjT2W+J1pxpzPj5ez0eZ/SNZpkN1ut66urrS6umpzgSVZkB/J45LsffG7CHZiHYJW8s805UgOFxYWrCmmSeFw3dnZMbQcdj4Wi5lsiTWG4qNSqZh3FbDDKfdfX1+3tSvJggWRZuL7oXDnOSwtLWllZcW8RJLsn3n+rVbLQCnkhPitAVIIhkJKieyL34P03emHpfmk8KSxAwmmqENBsbi4aEFBNH7ICFFP0FhhfRiNxqFZGxsbcrvdSiQSKhaLxhw7ZVjFYtFAjcePH2s4HI/jw2rRaDTs3OGz4w17/PixpTqzjiRZMnaz2TSJImnPsD1OMAyPI4wj4B1ofCaTkSQLpUMWjAoIBh4fcqPRsJE4xWJRlUpFJycnSqVS8nq9Nj4HUGVjY8MY+ZWVFaVSqYlCgPcHEEgRybpoNps6ODjQ48eP7Xt2puwCPlLME4woyRL12XcAMycnJ3rz5o08Ho8ePnw4AcokEgnNzMzo5z//ucrlsjUGiUTCpP6wE6D/eOcATCgyAUHwGvN3lpaWlMlkNBgMrJniPEWOzR2IFBIFCxYbRn4xsooJAsiOURF4vV6TN9MgDofjJGWYYYAtwjZh430+nykpVldXdXh4aKO18Oq222199dVXuru70+rqqilWwuGwZmdnLUwV+ebd3Z15pF0ul1ZWVqwBcrvHoXh4+y8uLixELZvNajQaz4xuNpvWmEUiEfP31mo1xeNxRaNRnZ6eGvjBi6kTsVjMmkYaavba0dGRMdiwoLOzsyoUCsaAM46Lu4YGku8fiwt3IKw24CwZLYCF2N36/fEoT8AW1lelUrGQJ85DUslRIwUCgQn5O0AIkxH4XlhzSKivrq7UbrfVaDR0d3dn0m180OVy2cAUQCcYNO4oxr1lMhm53W6Tfg8GAxt7BLAHUzoYjMfWBQIBY+U43/L5vN3/ZEL0euP58Vgtut2uNRnD4dDGP3EuoC5EYYAs/vLy0mwzpGE7JflYkmjkCeJDPRgKhezvs58BUQExkIsPh0PFYjEFAgGzFF1cXBjLGY/HVS6XDUxKJpN6+fKlRqORAQKpVEqLi4s2ho76CGUfoAUqMAIHg8HghDWFPB/AHEgR8jw4/0ith+WllsP7DnDHs3r48KFev35tYOTjx481MzOjarWqdDpt93C73bYzhSknoVBoQrFWKpW0vr6uUChkxAf1JGvj8vLSsjmq1apevXplIYrX19c6OzuzsX+sVe67+/fv6ze/+Y2Gw6EePXqkm5vxbG/qEu5/53MhtG99fd2yJwqFgiKRiAG9/HtIQT5jPp/X5uamWQk534vFogF1PGunNYr6D6a+Wq0a0Ig6cjgc2nMiBJfadGNjQ/l83lhozgHu8H6/byHOlUrF1k69XrdJSVgGqH9DoZCRHni9UTFyt6HUcIaiMm/+d3k5icLf1+v3/fP/V72+UxNeKpXsAKH4orjH+1UsFtXv9/X+/XtrsmFGkXCxMDhAKHZhIljMFEmgNYVCQYFvxh3k83nt7+9bo0LRQ8gPwVBsNgosPFOBQEALCwvK5/MmO5PG3ghn8jVNL/+dgCO8bYSwcOk7fcmSJrzGNJywyBwgFG3OMDHkLt+WhPMeaDqdcnDCvCKRiAUFZTIZra+vW/oj3xeNz+LiojY3N/X27Vsr2gEPeG/flnvzvfJn8RxSnDhlyDS/eHNpjDj4ebb4jWi+kE5RaILeU6zwPNiYNIC8Z4AP1h6BazMzM/Z8kKPyu0OhkPmZ8QIjXYpGoyYJJYyHdPm5uTkrCFAvMGYEyR85BOQowJi6XC5TQczMzExIfZ3+J7/fr2w2a4UCYTaEHsI6IQOUZPsLhB+ZHJ4m1B3MfAbNpXlgvfAenGN9SJa+ubkx1g6gYDgc2sgkSSbDg8kHoONCgnHA5oLnD2kZTTsqG/x6LpdLm5ubJsOD1YE1JXiLcKrp6ekJdQxscrPZtEbp5OTEQr5AqllP/AxGoBAi8/XXX0/YXur1uuLxuO1lWAn8rMj5kOjTCDpZUQpr1BecEaD2MD5nZ2c6OjpSp9PRaDSe1wyY1+/3bSY7aygQCFhQ2cnJibLZrO7u7rS2tmZ2HSSRt7e3WltbUzAYtFmmsVjMFA6EfwFuLi4uWoK1JAMDfvvb3+qHP/yhPR+KaIAZilzYSEn/IHHWqWhhrf/yl7/U3t6ejU1aXFw0ZpizP5lMmrwQb2cymdTt7a2l1yMVJHuAc9vlGs9i5tkGg0HNz89rdXXVZH+oSSSZjx32FFYERcP8/LyN26FYY31KMsCaYgklGKxgtVpV+5tkceSunEesDVQK8/Pz2tnZMUC32+3qo48+sruq3W7r888/t1A7p68bOTfFMPsxHA7b9AnyJGiyAJNgumGiKaqRc8fjcfX7fTsHSECv1+uqVCqWEI+0mvub75ninCadsEhA8NPTU0UiEQW+mRHP94kMnGKXcxmAFAsGGSycJ5ydMKHValVzc3NmPWJtcfazZjmD8Z0D0lKrkPxMzbK+vq52u23jgmg+AoGAjULE80szDJDCneLxjMeOkbkDII56amZmxqx1sMOj0YeZ0jSaq6ur9vwhMbjnAUFpOjqdjikSYB9hXbm3Ad/6/b5yuZzVeoCW0WjUxprlcjmz9TAulmYcWyKZN0h7OUdQS9K0cL8iyQWMZf2i5Op0Osrlcrq+vrYJK5z7jG/kzwcCAVt7AGOBQEAnJyfGjmM38HrHoW5MZQDE5a7ge+CcXlhYsOdNrQIIgHqGz4Z/l7qH75EaDUKAteYENX/xi1+YOvXi4kKbm5tW//A9ArwUCgXV63WrN8hbwZZEMj4KKc7NXq9nRBi1A3UHjPv6+rqGw6G2t7ft50NuYc3hbGMiwczMjLa2toyIAHThvOeOYs9zx7NWnAGevEesDXwP/X5fq6ururq6UiaTMTAXAJlzge8XwAiP/+XlpeLxuNVDhJKy7iVZ+C61K2cZahsUFdQe3J3sMQjK4XCov/qrv7JE83fv3pnqC7CWs4mzsFQqGbBD/YualjNxenpahULh/2tP+P3r//7rOzXhoVBI8XjcxuRIMmnp+vq6Li4uzIeKbKlcLmtpaWmiSQU1Ixjg/v37xgIiy2WEBEmfoPWgp8hLOIw4MDm02u22zSRGloQ0gw3d6/VsrBPFcLFYNI8LUilkIjBboNH4RgkkQWpEMeSUUzt91chEYIrZhE7WH3TZ2ZABSEgfpNQUp1y0/X7fCspSqWThRBxIvK979+5JGl90XC4AJU722CmfH41GVriC1sLUIfORJtPraaK54JHc0+hTjNCQD4dDO+CcTDwHCH4fnqfT38Wz5DmSXA3gcnFxYbKbSCRi8zu5rPx+v2q1mhXTMBwwfr1eT5lMxppLWEYKOPywg8HAfD6gvBQEMJIU8sgKQSFBMmGsPZ5xgjlsHUxKIpGwIoXmu9/vq1KpmJQedhRWFEACFBe2GPklicMg2OyJ29tb+544zHkmTgsF6ws5KM+Cwj0ajSocDk8ku+Mvn5+fn0g+dipOsLfQRDcaDVOKoByhIHZaTPDdA8JwFrhcLrOQ+P1+/fSnPzUw7dvya2TTyMg4vwBk7t27ZzI0Luhms6n3799rb29vwndJKipMdjgcVrlcViQS0Wg0MiAGgIJ1CMsFYII3jOCZWCym9+/fK5/Pq1Kp6OnTpzaxgWKmVqtNzPXu9/uKx+Nyu90qFos6PDy0s4QC3ZmKHQgElMlkdHZ2ZtI2p9oCUImGBivK4uKiNU9HR0fa3983GxF+ykgkIrd7HKSGnNDZsCPPlMZSUtKNOQOdUk8AhGKxaO+PIDyKOIIBo9GoScfZ36gc8EFzjzUaDUu0BoyhKKZJdf439pIk81Wj/nCOXQSApsEHSKVgPTs7sxRcZ4YFoCRSQuTBzv05HA61sbGh//7f/7vu3bs30ei8ePFCbrdbn376qdLp9MT4TxQ+7Lmrqytls1m741AKcU/yd/Ba0yByl/A++R7Y+9yDy8vLymQyurm5MYCKPSuNwQnGK7bbbQPO8JEisw0EAsrn81peXlaj0ZhQkNAwMWWFu4UzgPoCyxYKHsBk/MDhcHiCPXIGwLFO4vG4gbL8WRqBXC6n9jfjzpzAOtkdKIicydywqVjokCPzmTkbqI9YtzTdPD/qA4BVzkBIhUqlYooKQFrk7QTQUUtwLqGOc94PqN0ajYY2NjbMcjM3Nx7Ren19bUrKV69eTeTmkEcCkMQdvLi4qEqlopubG1WrVbsLsS3i+ecMwGIHWLm4uGg1FeeHc8JGOBy2feesqSTZJKDp6emJkDOyAcg2wK88Gn2YGw34gmoEoId7gGfp9E3TCKNQpJ5kDCh1IzUWf99JzsCwo47o9cbjhV++fKlisajHjx+bmol8Amo47uCzszNTZ3g8HhvRGYvF9Pr1aw0GA0t7x1rC/qZ2QIHGc8/n83K73YpEItrY2FAymVQgEDDFE/ch9xBrPhKJ6OHDh/of/+N/6IsvvtDMzIyOjo7U7/d17949U5ugpPX5fKpWq2ZZ4N/xfdN8Y0eC/GMdUo/zTPg7qNec+wc/PAy3cz05Jy0ASLFGuN+73a6l4aPqKBaL5nGHgAA04ixYWFiwv8dehDjhd0C0AB5CdAHWZrNZxeNxXVxcTJy5SPt/19f3TPg//vWdmvDHjx9bOiNelaOjI/MuLC4uanV11YrEXC5naDlo4mAw0MbGhtrttnkm3r9/r8FgYNH8IJEcIhyebHAQM9DVdrttARoU4jRBBCbAkMPmcRG0Wi1DEFutlqGic3NzNrqiWq1a8TY/P68nT54Y2wGaHwgEVC6XzWvSbDbtOTEnkmLamcAJy0hj6WSduaRpLlnoNLE0yvx9Qo18Pp/W1taM9a9Wq/L5fPrhD3+oYrFohSoJ4zBRzC52SuqlD8wyFzyXFZec8yKBdaMglWRFEiAMTRdNusvlsoYWnyyXCYdxv/9hhBlsIM0fBRTgC8094xooppxz3wE1Li4urLHJ5/NWNDnRQzzZyPOazaaCwaA1BhSGXKBYM/i+GItFUuXCwoKF/QBi0OzTmOMBJAyHhpZgskwmo4WFBWueLy4uLNzP5/MZ4+FcG/je+Q5hSUqlkjE6+NCksRoAbykXqvN75YKdmZkxFhtpOJfazc2Nya+58Gjc8IahfOHiYu3BeJMizSx0ZHvsIxBowp3YQ7BSkmwMiVNBQlEFwEPRjM/RKYnFt1qtVk2qGolEFA6H9eTJE5OvkwoLiry7u2tBNzAOhAySM8BZ5vF4LEQIiS9Be4xjQnnAe4Ide//+vVwul46Ojow9RzYHyzMzM2NZCnxefq/b7darV68Uj8fNW727uytJFkjJn2fiA/Pp4/G4jYWEjZJkZ8RoNNLBwYFarZadJTTW/X7fCgzUK85m8/Dw0KSv79+/N38lIALNA8/TGRLH/UCR4vP5jKGdm5tTt9u15pmxYZwTBHwiI49Go+YRHw6HxlY5RwPRTNA8cdbAHvn9fvOMS2OfO0F8NO5+v1+xWMwYab/fbw0brD0sF2nSzukXFG0oTxYWFrS/v6+dnR2zTLx8+VL9fl8fffSRNavILlEhkXzPWZxKpezvc8fU63Wdn5+bz5hGksIXX6fP59P29rYODw9VrVa1tbWlR48e6fb2Vul0Wl6vd2LUGYUsPn+UCtxb2WzWgMtcLqfp6Wm7f5D6EyAJaw4xQKM6GAwMHOU9I3OnCZmamprIU6A5ZU2jOOE7dwaTzc7OqlgsWqPFHYKPnucCaEAzjC1Fkq0pzg2aIdY3QbVY+mByGYWJhN9ZT3Cu1Wo1Uy/AXCaTSUuo52wGoMrn8wqHw/YeOFedYwFRL8EYAiRWKhWrbW5vxyM4CTlNJpMT6gpYXEgB3jvJ417veEwaWSooT1AuUFfBOBPmyhSVSCQycYfB5AOmO+8tFDv37t3T9fW1KXHK5bLNDAeMYt1xBx4eHloWCxkz6XTaFBLYKZB60+ySwwKQ7TxnFhYWVK/XTU2DQoP3DLhCgz4cDnV0dGSNbb/f14sXL8xelc/ndXZ2Zqw4YAOfk8wE5wjHQqFgZ08sFlMsFlM6nVa5XNaTJ08sBJK7AqAcQC4ej6tUKun8/Fxra2tmSUJOH41GJ+TZ3O+9Xk8rKyv65JNP9NVXXymXy+nly5emuvu3//bfmrpgOBzK7x+P+et0Oqa6QcXhHLEKwQcoTvAy7x8FJFY6ahcIQOwcgFWj0chqWywf1LDkY7AH+F7m5+dNJUQ4LlkQPAeUx1ikIH1QoH5beYqqq9VqaXV11WwG9+7dszsikUgol8vp/PzcQumY7y7JAIHvX7/f13dqwkHnKe4DgYCi0ail6DKea3l52Qq1+/fv20zEra0tuVzjmYUw0Lu7u4ZazszMWOo3skl8HXi4mbdNmrFTpspCpBiksYEB5JLg8oSRmJqaUqlU0sXFhSKRiPlpfD6fNV7b29sKBoM6ODiwg5NiFkaxWq2aTIomD4SM5gg/ltOHTkPE5Q7S+T9ryp1oEJe/U86C6qDX62l9fd2K00qlotPTU2MA8vm8SVr4mRS+FEBOTw/NFWmvIO0c+ISAIB/lkuPz4XGkgObSoNGmYeUCnZqaslA6mBEaUVh5wAiv12tBGqTp4+EHaUTGw9gvwsKQ9A2HQ/NEIxe7vb21sWKxWMyKAN4TrCuoOofnq1ev5HK5LD2UDIOFhQUrDmkieU4wx07v29bWlnmbkOVJMkSW9G1CArmwSKJlHdDkIrni+YGYExbHd35+fm6F1tramvkgndYKZzM7M/NhxA7nBMABLD1FL349iifYE4pS6YPdgrVD0UyRIsnCpJxsKM3txcWFotGolpaW1Ov1TP3g8/nsswaDQQNynF5C9kS/37f55EgJG42Grq6u9OzZswmwYG5uTh9//LFOT09VLBbl9Y6DBXO5nDUSsHj8zuFwaCws4AOIOuGGw+HQLnNkqsygJpl3c3PTPj8zld+/f6+nT59a8weoAvPBRS3JZiWTyl2pVGx0XjabVSKRMJ8aIZQ+n0/tdlv5fN7OQZpjZkTXajVlMhkrqig8CLNhDZGQS2gdTQG2Cp5/vV5XJBKx761er2t9fd1yLjj3eBYez4f57GSJOD2qWFsolNkXFKG3t7fa2NgwmR7gKpYrlCSMNsLeQCHIPYSXEyuCx+PRzs6OzW4HpEZuy89g7+B9dMrjUZ2QTA0bzz3CucLaJpOgWq3qxYsXqtVqSqVSBsBSePFZAMooNAHYKDaxIeHfR0ECA8Tejcfjev/+val2OJdp1KamxnO4mfcryfz2brfbPOKNRsP2HioVzgTUbNwPhJjxngA6kSXX63XNzc3p9PRU8XjcPPkPHjww0AP1DsovwIjl5WV99dVXphK5urqyUXuwSEg5pQ9Btnd34xnejx49mph3L8maI4BqADga0MvLS52dnSkej+vy8tLk4nhCCf+KRqOKRCL2XFCfcKY7QW7ATUkGZgyHQ8sF4V4kXX56elq7u7t2xjqtdDSA2IBg1Pr98eQIQj4J/MLqJMnqvHa7rbOzM2tky+WyNZQoMSqVikn7+R3b29sGWpD3AFNJI8RoQ6ftwJmhwp7hPGJOO4AJv5efPxgMbC3v7++rWCzq4uJCqVTKLEiStLOzY3t/e3tblUrFaruDgwNFIhEjCgBcYeC581izfE/USJ1Ox9ReNI1Ycfj31Mher1e5XE65XM7ulPX1dQW+CTVNpVKm8GIEIeq47e1tayQ5zx49emTPeDQa6Wc/+5levnxpqedLS0t69uyZnbWouyBuTk9PlUgkTDGGQjYajZp9hdqPugplB7XTn/7pn+q//bf/NhFIx10JkUDI22effWbnqdfrNdATFhsQlloaRR6WDiTxZEowhen58+dqtVra3t42gJV7kXXL+c9a5M5lHeC573Q6NuceCx6qLZjvk5MTPXnyxPqczc1NS3c/PDxUJBLR7u6uAU0QQvF4XHNz47n3jObDioXSyRmwS+0LUP27vr5nwv/xr+/UhLM5YIhGo5F2d3et0eVS55BbWVnR7OysUqmUSTv5c41GQ2tra7q8vLQi5u5uPHJkZWXFAuBgGfCSIC9D/umUwtLE0fDC3IKAwY7wfqXxF8vcYPy88XjciuhvF+sU1Xg/KeZ8Pp82NjYsPAppIU0GQAUIFaACwAEMPg08Mkj8w8jCWYg00zSlMzPjEQ2xWEwHBwd2Wd67d8/eYyaT0ePHjy3FlVAIngWqAlQDNBOw3TS2INCSbLYmMj4KUBohUHtUDITTIL+CGSaUBbacRFxJxnwD/vAMYJ19Pp/NTXe5XHrw4IGNIur1elpbWzMEMhqNWqMJsID0nPUAoggyOxwOjeVB/sx36GT3mWm7tbVlciguWNQGPGNCVigibm9vrWgj/I71ClrLAUnxTjHsZP4IK+J5BYNBY/4rlYpZLLxerzFYzlwHvhtnwAsNPgFS7EGkcqg7JJlsz8nus+9hkdxut6Ubc9DzeZCOAdbwnfI72TM0ezwLvk98kfhSQbHZT/jynDI+vlcaBH7u559/rkKhYAXZ6uqqBa3hTacY7Xa7Oj09tfOFz8ys10gkYug7vsarqytjg9nbzWbTGkPeN5c6CbyAYJIsQMXr9erp06dqt9vGQG5vb9t6peGgWSyXy3Z+ACQlEgmFQiFrxFutlkajkUnqlpaW7ByXZIFDKKFmZ2cNrBwOx0m6sVhM7XbbRlIiWWfNXV+P58OTMrywsKCDg4MJv6UzF8Tr9dozcs7+pTglYIz8AfzT5F/ARiA3n54ez1xeWFiwxgV5N8+XxHyauaurK62srFhj7PRKwi46A0dR5sBCotTifOd8Zh7zysqKJCmdThsDzfnP+uV7A5TibnGyYEgzAVbT6bSlmnOvJBIJ+3zkRQDoICEngZ8il+8OsAR1DZYXSXaPEHTIxAbOVsb6PXnyxNb3aDRSJpOxs6TZbGpjY0ODwTgPZnt723ygyGydvnCfz6dUKmXTBrrdrqLRqMla2+22Xrx4oUgkYt9fPp+3Ypk7mXsF1QjWuV6vZ6PMAoGABWBy/ra/mVCxsLCgcrlsIwIBZur1ut0zFNxMWMnlctrc3DRwGYCO9cP9DDDDGR0Oh435Rh3CCEwY2tPTU7MrwdLyfdVqNctUqFarZmd6/PjxBBuLyhCgA+sQQW7s7Wq1Kkm6d++eSZq3t7d1eXmpUqmkeDyu0WhkjWA6nVYwGLTArkKhYIovVCT4fwndQ669uLioQqFgisNOp2OqI5K0JU0whpwJrDcAMAB+zmPu542NDbtLWQ/Ly8tKJBJ29iYSCQOBUM9RP1ADr62t6d69ezo8PFQ+n7c/S+ih05YHyIj6BrVKv983FZazQUPphm2SOobG/vr62sAFpuRkMhlL1j45ObGgvKWlJZVKJWvWGNOJbYTzam5uTuFwWH/5l39pWQaMOEylUhNqMmqqZrOpZDJp7xF1Fknf7969M1WPJAslxFrY6/WUTqcVj8f1x3/8xwqHwxa67FSi9HrjqRLBYFArKyvWkLNfm82mIpGIgZso9LBR0FBLY7VSqVQyguXm5ka//e1vTQ3B+oGIazQaBrZiK6TZ7/V6ikajpi6BiOh2u0YiUXvv7OzYvX51dWXnKERWo9FQsVi0AMBPPvnE7LvYVUjh50zB582ZBlFYr9etHuKM8Hq9Bt5+//r9vr5TE45UAkRRGjNSIEWlUskKW1KhGSHEodFut1Uul81XzqUPelosFuX3+1UoFGyEB6PDKPYoZEFgGYcmyYogZvpS7NAEIaGmMGHuJCnPyICRS+LVpJiJRqPmHcOTSXHc6/WUy+XsIm42m9rc3DRmiJAc6YPUHEYRmQwNmrPZxBMEA0pCMYU77JbTW0ShtLW1pX6/b8hdtVpVMpm0wvv2dpyUHfgmjfP09NQCRijykKZJsu+d3wEYAHtPA4L/j2Ya9oBLlGAXp4eIS45il98Fg8mlSmFMuAQF6dramkmJaBxRYTDDHWAkHo+r0WhYMcJKEAlhAAEAAElEQVS64vuk4QM5p9EkkA+/G6w4F2MwGDQJGaANYUIEtBDGhIWCJFcOT1Bsp/eTBh7klBA8pP+w4u12W2tra1Zg0HAiW6ZgRqaH95s10el0bNoBEjkaXSdzDeAFYk3zie2AYoT1y/fo9XqtCLm8vNT29rZJ/7rdrk0x4HnW6/UJBh8Ard1u6/z8XIFvxo3BnCCHo1gGpZZk+58mjOKMRv/6+tokWQBHfAd8h8j/YQNmZ2ctmXQwGGh/f1+bm5vGYF1fX+v09FQul8vkboVCwVgnWBV8o4AyPDuYOooaZ2DX6uqqnb9ut9vG2tCg82INSDJLEGAJBePV1ZVarZYh+dgujo+PjVmbnZ3Vs2fP9ObNGwMtKZh6vZ4ePXpk9gm+j263q3a7bcwmY68oNpkpPDU1ZSwq3w1nS7/fN3AIFo+imOfCvmd9oiyCCb29vTUAgfflbJDZV0gUUZWQlszaYzIIUj9kjwCFNCSS7HNTzNC0c3Y6G3fuhampKZPIwvAC9PDeuc8AG2C/+I6//d0DPqB6WFxcNIYEpp9nAugB48MZ4fQbc3fyu50js0hWPjk5kc/n0+PHj7WxsWHS3Xg8bg0KjLnH41GpVNLi4qJ++MMfql6v6+zszBjr5eVlSbL7hLWI6on1TwYCZABnNa9sNqtkMmlJ+Zwvs7OzBk4A3vd6PZMRNxoN7e7umlzU5XLZmbK0tKRsNmuSdb57pmrQSDKH+ebmxsK3AKy73a6FnbK3wuGwCoWC/H6/tre37dnzvTFXGhksZyZzliUplUoZ+En9RL2Bb53PQUjT1taWMpmM2emwdLEGw+GwKpWKVlZWJuwj7W/GymHfABS4d++e1QZOiTmquU8++cTYXJ/PZ0AEJMLU1Di9HuUEtZvT60ytBLtHwngwGDSQHLCaeoP9zTg+5MIQPDTtMJIoeFBXVCoVpdNpra2taXFx0aw2nDcEg87Nzdnnh42cnp5WNpu18/jevXum2MOLTzaLpImka0gEahAyCch/4XcD+HLner1eLS4uam9vzwBulI18jwQi4rXH0gaQQvNHcNvV1ZV+9KMfmZ0Qdaf0QQXiJE5okOv1upFrKysrVjegBAPcKxQKZgeBFWcSCIpa7grOienp8WjWu7s7bW1t2XeNpN4JoCKX5+yGqKFGSiaTNsLN7/fr8PBQL168UK83TvtHscZaTCaTBuKxXwEVsAdRZzYaDZuS4AybpNbn7KG2B6Sl5uI+q1Qq8vv9Oj4+tuye2dlZ7ezsWLgsdyjnI3VHqVSy2ewEAwJS+Hy+7+eE///o9Z2acL5gSbbRl5eXValU1Gg0bM4ljQgS3Xq9btJJNpDTe4rcjQXt8XhM1t3pdIwxp7BCEsx8Tf5HMQsyGPgmuZZQNadEhz/LuALY5PY3Sc1Iw2ZnZye8WE4JEOEZNzc3Wl1dNQTe6/Xq+fPnCgQC5mUjGMHj8ahQKFiwCQ0p/9eJwiHvlWQXLWgfgAQvUH2YUljGmZkZffLJJyoWi8rlclaI3L9/3y4sCqr5+XmdnZ1NBGyQ2n57e2vPkcMF5hBUn2dE6mcul7NGcGNjwwpOwnBoGpHnSDI/aK/Xs/9GQ4tULhQKmaz44cOH5u2jGJXG0ksYDZ4HUh4AB/xbsNDSB3SWJGX+PgwwMisSyUEzQX0pNr/tT3MGGREmw/cKm8PzBcBC7k5R4HaPR94wt1eSsfgU191uV8ViUaFQyCRUoLC3t7cKBoMqFovmbWYtMfKIi46ChKaXywNwA1YeuShBQTTq+OWd48VovPHVMnuXFN54PG6eXiRTNzc3dhkx3xWAYXZ2VuVy2dYq3sN+v2/sz/Lysp0BX3/9tfnmKdxhGyiskc55PB47kwAg2u22er2e/v7v/16SDC1mDvXW1pax5ATwUbyenp7aWYbnlhwB/MgUCKxtnhMAR61Ws2KZ9YjsF9Cg1+uZZBj5O3uKc25qasoAGc512LhWq6Vms2nF0cXFhXl58bc/efLEFAKokWDxXS6XnU2oBngGgW9GMQG4co8ALgIoARo5bU7OgpJiD+AV1QP7mTOZM5TvEPbJ4/HYfcXvYG2ybmks2btXV1fmKceSlUgkdHl5aQn+ZBcggUfR0m637d6hkIehwu+JsgS7BpJNgAVAMQARmgoAD2ejjhTd6/UaO1Wv100psLOzY0FqsDVknjD9AICZe4miEnAUcIrCFua/0+no3bt3xmKtr6/bPoB15fwvFAoKhULK5XKmJnC5XJYuTfODrQTVxN3deFZ8pVIx8JYATBR4sLusMb6v+fl5VSoVSWM/MAqRg4MDSbImkIL30aNH9pza7bY9q2azaRJlGiFGhZKmD3uH7aLf76ter+vy8lJbW1vyer0W1MW+YW/f3NwYmAhBwLkLcAVQiXKgUqkYCOfxeEztAijLfHPCvS4uLlQsFu0Z8P9TP5FxwfO/uLjQzc2NAZzsyYODA8XjcS0vL1sjNDU1Zd58Z8ATShPWKXcLai9YYLfbbfUS94rL5bJwMOe4MaxTPCvuT84+fj8NNbVHOBy2exiAHiDJaW9x1kMo4rze8UjHy8tL8+4jredMDAaDRuogv768vDR7DpMlONNGo5Gq1ao8Ho+BGFhOqCXw+k9PT6vRaNh4wm63q2q1avcj58HKyop+/OMfm4ceSxzfkbNZ7na7KhQKpiaCPacmoZEEBF9cXNTjx48tEO/q6sqUsgDBnC+csaxRbCHYRpeWlvTkyRNVq1X7/YTHoWyFfUeBhsLl/Pxcb9++tSwBl8ul7e3tiXqLnCmn0o01LMm+K/JJsKW0223LVSIYEAUWlhAmZgyH4xya9jeTJgCSGRWJBe3hw4cTACRnFc91c3NTS0tLCoVCljeUyWRsJCZgFLkErBdqt52dHRUKBbOMAqZRpzcaDSPLwuGwbm/Ho/B6vZ4prUh5/11f38vR//Gv79SEO1OHQdgJj2LxOMO0kBlfX1+b9xumgctIkoVOUITNzc3p4OBAKysrGg6HOjg4MFmt3+83dA0GEOR1OBxaYUqAXPub2Xyzs+O5pyBnNPQwCBT7eIe5tGCfLi4uzEfMgoPxZ3wEi7rb7Zokp91uq1QqKRKJSJJevnwpj8ejjY0Nm1vIgXd1daWNjQ2TwBcKBfNgclkRUEF4zbd9JsPhhxm4HMaBQEA7Ozt2CZydnWltbc2KTJhqChpkg5KsuKAppCGkCYSRgaGD0QRgkGSfD4aNy51DiguXgBbn5+DiT6VS9pmvr68Vj8fte+SihkkD5YWlDQaDajQappiAnaABBkzi8mbEGBf53NzcRNAPUltYD+fnpwmEGSJVFOYeLxWNHXI+Cj3+udlsWmPLxQFLDdIKC0VDhnSdopnv4OpqPP8cAOTu7s4mEBC2VigUbA4xnxUPJ8+UAx8VA4AHawFFCy+Kc9YJ65E9hoQ2HA5bYeJkz7m4+v2+yfrxQSIPh6FDjsoFPzs7a/kONO8Uo8lkUrlcTrOzs7bv7u7uDCiCDe73+/rpT39qPnvAvqWlJQN5CoWCVlZWlEwmDfXm3Nrc3NT29rYajYaOjo7sokskEhYwBwvDSC4YIICVu7s7ra+vq9frWTMByIPtxeVyaWtrS51OR4FvZvciGcTvy/OlaZNkHmwK7pWVFfODI83O5XKmVCAJPhaLKRgM6vT01PzSyEVREeBdR7YqfQBbURIAWC0uLpoM2RnKhFIFcIbPTEGMegF2AMUVvwO1B1kOzBYGMMMDjAIFULfdblueBEwwTTxMlVN6TaHjLNA5C/CPc8ewBii4+/1xcu3KyoqWlpaswIQx5wyiYMO3xxqB0eG7BNwAvKFZPDg40GAw0MOHD+2cRbJPwjPPjfuB99lqtSzsDxYGeTJ+Qzz9nONOJRx7k6bl7m6cXj8ajVSr1Uy9QsME2D0YjFPXsWc47x2+11arZUA64C/NQrvdNgktDbAkC12lmO33+0qlUmYx6XQ6Fqx4cXGhjz/+2CxtoVDIQuS4t7A+SR9GZebzeTvrUPuhnkKFMhwOJ5hNAizx0XJncj7B/mO9gh1EdeL3+1UqlbS9vW1hsBAn1DkAWNxjzgJ9bW3N7hyAQcBY7FEoWNLptO2VVCqlVCpldiIaaRrBSCRiv5vmyQnEQgBgN8Qnj0KJMxmvrPP+5KwdDj+EJcKeSrJ7HDYUgB+5N4AW2Qzcj6wFrBShUMhURKxJ8geosbCvUR8CGgwGAwt+xZaDapFpL0yiuLu7swYclQ2hntSQqK8AIpBDw6KiGmQ/JRIJkyNTy5L2D8Dhdru1urqqlZUVVSoVU5K0v0mdZ7Y0jV+lUrGAMyTuOzs7VpOyPwBPOANRIAWDQS0tLRn5wndKXUqdxB3R7/dtVBghqalUyoCsR48e6Ve/+pXa7bY++ugjU3mxHqirqEuwgiHB5nyhtqN2WV1dVb1eV61Wsz14e3uro6MjJZNJW1ucaVi/CJRmH5PuH4lEVCwWjWDCDokaE8UsZ9jLly+1sbFhRE0sFrPARe617e1tdTodW1/cu5ubm9ab8PnIuSEjolAoaHp62u4JQm45d75//f5f36kJB8VbX1+3Q2o4HCoej6tWq5kUmi+PAmJ/f9+QIw5pxtzAmFPo1et1pdPp/5O9/+iRNc/Oe9EnbGZkpInIiMiIDJPebV+mqy3ZFECQBDjQXBMBGugLaKRPIEAQIAgQBBCQoIEADTjQjJpIJEjqtGF1V3WZvXeanTa8d+kz7BlE/Va9WboXt6vv6YtzDyoBgRRr79wR7/s3az1uGZsAkgSiiJSPQBq8pEgO8bch30auyiEEqsxh4ZTO0oxls1mTAjIOgKRhDjaaCvw+sIrSpAjb2dkxBo8Denp6Wj/84Q9tpm4gENCTJ08MnfR4vp51iV8H1pxCs9frmW8Kn5Az1IPvClMCkvbs2TN5PB793d/9nQqFgkqlkjFHoa/GadRqNfu+g8HAGhTkmUjwKfhgDJ0X3f39vQWhgYwzLgnvFhcskkeKPYo7fCvBYNAuNdgzpLTOixg5O0UwDTw+JxoP2FgYZqdvl+9GM044jhOogAmCMQZUgMGBweBz0ajTKAIO0FT7/X77M16v1yS2oa9mkcIk4uEFvAJgIgzFCSrhIUZGDtrO76dxx4cXj8eNEYWxY98ieadAJrAE6TSNEdJqmBYAIdDgfr9vhTagAs+sVCqZX9E5w5sfkPiHhwcdHh4a2Abj6AyOIy0elY3f7zf5H9kFBG8hQ3S73TYLFbXH3Nyczs7OtLOzY8Ut659GiuIB2wXFzPX1tS4uLoyhgAnGb4pX9smTJ1b0gUDj20eS72SGYBdgXgaDgVKplIEZXPixWEx3d3dWRFO0MtscNvri4sJYaFhkUHnGEDK/njONIgklxtzcnLa3t42ZGY0mibvOADTSn53p7kjnUaqcnp5qZmbGRgLBfqAq4BwKhUKqVqv2edvtthX6hFUBFHEHwA5wJlSrVWvMOA/4N0nK57PACACm8oxQjzjPWoAIzmSklwB9AG7ck8HgZM5uJpOx3IVIJGJ/zhkoB8jIfdPtdh+pzcrlslll8EfC0lxeXqparapSqViRjSKA9GAakX6/r0KhYHez86wkSwC1BUUy8m18uIDesOAffvihfvWrX1m4GkzNysqK2TVQxGH9gXXn9/p8k6kmt7e3Ojs7s4yNlZUVUw+hKAAszOVypliA9WFUF+AGtrVgMKhcLmcEAyD/7u6uJYNPT09rdXXVxnGxHwBsIATw77LmALAXFhYMQOXeIQfH6/UauAX732q1bGTb+vq61SmoYgBBuZ84/x4eHoxl5WxhckS73dbV1ZWpI+bm5mw0HfWZ094hycAowJFEIqFAIGCj2lAqnJ2dGUg+Go0MfGi326aAI2PGOe+d/Um453A4tGk0gBEANqFQyNYvaxwffuersFwnIE6z7bQ40aAGg0HzPkejUVMJARJcX19rdXXVgBDqGmo57spAYJKCv76+bjUkDQzScJ4vwBkN03g8NnIKtSi1JfcLaq2rqysDcQCP2KesKe5bFEzcwRAV2GKy2aw9UwBJANe7uzttbm5qZmbGQtpYAzSnf/VXf6Uvv/xSW1tbWlpaMlUSPmOACZrTwWBg7xoFwfz8vCqVis7OzqxhnJqaegT2eb1eA6e5wzwej5aXl006ncvlHk1GyWQySiaTZrNgj+GH5r6DQKHWkWS1C4AkQGosFtPLly8N0AeMazQaCofDBsLR8BIe+OLFC+uZBoOBVlZWbJoHxCPvxnmOU2Mw3vPXv/61TbignqvVasZ+v379Wnt7e3r16pXti3g8bs+A+3Q0GpnSbmlpSfPz8zbuj7Ds/f19pVIp3d7efis5+ndM+O/+862acOS4sBUkfztlk5KMfcnn8+bfZmQL7Ofnn3+u1dVVK3BB/TY3N83bMzU1pePjY7148cKYE9DZZDIpSSbrgwVhxEa9XjcPGahYIpEwiR8oKxcyiCTfz5kMKumRvJG0bubu8vtgSygwYZiR8JZKJWUyGWu+4vG4MV8cdAQHwfr3+33F43FjqJEUlUolra2tmZSIZpMClKId1pJZo7Ozs5a2yO+dn583mRgeZCfCx0XHoU5BzWWDVNkpT+fvO4s3/GAABk5EHnSQecaAHU6/MsgqhQ4XFj5NWGvk6axNVBk0xxT/MEJcxDQSsLk0ok7/PrImfg9ILf5YikdYcwI4uOBh3/hzXNyAV4SpRSIR+/00vtJEXsasW6R37BUa/6urK7XbbZM3I4VlbSDr4jPhsccCgr8NbxByV1h1CmP2Ls8DJBmZInuKdUkDCfrLvw8bySVIU0cYFQwoIVusqY2NDWuuuPSxc7RaLXW7Xe3u7lojhEoG8IjzIx6PG3ssfe3Tq9VqqtfrJjGtVqsms4aV4TMD0sDEt9ttCxVKJBJ6+vSpXZqVSkVv375VOp22gmc8Huv8/NyeU7/ff5TUThHr8UxmrHJuOYMmUR3wbvD7Ou0keG+ZGU0TQ6YE0w/wfa6trdnfIU2cfSPJCmX2NPuuVCoZU0/BzxkBswULR0OMrB5wBkkjYUpkM7DenIFcyDg5c5Airq2t2d5lbznfGTJPp1wUySHp77e3t8bOwQQxzhLW3ynZ5nNhRfF4PAYsY7fih2A7ZMXI2p1p6QB8WHtarZYBSjRPn376qT766CPNz8/bOcxzbTab1gDw7+HHJ98BUHB+ft7k2OwnbFCsd34X3xmAHKDx+fPn+uCDD6y54e6hURiPJ1kdnU5Hq6urdqYw7g7QjaT8fr9vjDd+Zu6mhYUFVatV3d3d6enTpwZgu91us5ZBGgwGA2WzWS0tLenu7s6SswFVU6mUvbtAIKBkMmnJ7ff393r9+rVNMoBh48xGBci6dDLyNOMwqTQzWMyYW833RK7PPUHTB8jPeuWeJiMGJpW7JBQKWY4PlqqlpSXLPEmlUmq32yoUCnbHAhyVy+VH46lQXGCl4PxmpjNMM15yMiOYmsF7ZwwWdxbNIIGv3D3YwmDCk8mkvF6vfWemQ8Cm0iSS0M45Qdgoqg/AaZha9g9nIJaP0WgS0ovawefz6fXr1xbKS+0GWMJ7dDKP7FXmVbNuUWjW63Wrpev1us7Pz+1ZAAZQ2zH67t27d3rx4oUuLy+NOECef319bT5/6myaLwCQ4XAy8k6SPvnkEwOpyVJIp9N2nzCdCFvjcDjU8fGxPv/8cz08PKhQKKhcLqvf7+v999+3CSQ0S877kXuGKSF+v98k3jMzM/riiy+0uLhozTzqsP39fRUKBSMjyK0hW6VSqWh9fV1ffvmlms2mnj59qlqtJr/fbz527iTqVmoa5PPcnShOAVI///xzyzFJpVKqVCp6/vy5+ae//PJLXV1d6c2bN1bvoOyTZOQKQE2/3zflbCKRsFnx0kRxA4kUj8cViUQeedOZQT8ajUypQNYDgDXKPM64er1uQP/JyYntIdZ2q9VSvV5XOp1+ZEGhv9nY2Pg27eF3P7/jz7dqwmEHWET4Z0AakaWDLiO5dkqjBoOBSTtBPmGlYeQI3yH5kMsZ9LFQKJgPDIkpklmaWcJcOAQohmgMkMYgb4cpHw6H5rG5v783dJ30Sg4nGj1CGVKplBVYMDLI+ziUSCMF+Za+Hu0F6ofkkea+VCpZkRkOh22G7Pz8vH1HWEH8rkhMGG+DNHBmZkavXr3Sz3/+cxUKBV1cXNgmxf8C80ky+cLCgjVJMC8UmEh88JnSrCPvAgGlqXZeoh7PJEinUqnYO3I2SDDgzBzmgIMlhaGRZCmZXABOKRIHLA3wcDh8JAdDbgcTSyGEFJ6GCxYXpJt1SoFCyBLNMJJb/uw3pas0koBXhNIhowSk4AKkGB8OhxZYMhgMrJlyuVwqFApyu91WrDq9TbxPLgK83J1Oxw5n9iESPNht7AMrKyv27pCpU5w50W/Y2UAgYM3wysqKHh4eHhUqSO46X4XJUQSy9rFfcPmA3ALmYWfxer3a2dmxd4tHsd1u6x/+4R8eyd7j8bjy+fyjIpjPXSgUlM1mLQ0aL2uj0TAQMJ1OKxQKqVwuq1qtWrM9PT2t/f19u1RZ04B2NFhISrPZrLEAjAajobu4uDCwDEYI6SggkzMsie+H95B/F/Cp1WpZQBqNHe+On36/b4DI7OyszbZHlsfkAlRJvHeAT9h51AXsYYp0njWFPCADTUQ0GrX3jhcRNl2Sjc9JpVKPsjiQ6cGEwnCgYuFeodgCjABMo/hAEQSjKMlYB5orpIUAafxOAAWAmGazaSFM3DlMDSF/wOVyWRMIu+r3+81Ti/QRMAJlDqogPNOcmeFw2CwMrDneERLElZUV+f2TMUHMvQa8dIIi+HkBkWHXaTQAgGkSCUMFwMSmxR3rlEDv7+8rm81qenpaGxsbCgaDxmDiveRchFmemZlRKpUyewKhac7kZfzX3Cej0UjpdFpra2va39+Xx+OxrAsA2GQyqVKppHa7rdnZWbOozMzMKJPJWIPI93C73apUKopGo0qlUrae8fkDPPNOHx4eVCqVtLGxYXcRn41GACCHZn1xcdHWGD55aaISAABjSgFsJw0r5wDguxMkodlEek9YFEoeJ6PK82Qdochg3QEiQRisrKxYKBVMOnenNFFQwiBzR6JglKRms6lWq2VTRRKJhEm9qaO47zuOySIEmrVaLVWrVWUyGatNAPywQPX7fXtGp6enWllZeWTFQSUmTYBJZigjOabGIgwNuTGBetfX10aeVCoVA6WdQWDYvBqNhnq9ntUtyIi3t7c1HA5NAXl7e6tGo2F3VSKRMAa4UqnoyZMndv9RHwDaNptNswHwTiGUOE/r9bqWl5eNuEIpkUwmlc1mrXZChYZSBzAQ1RrvAvAM0Btgj6av0WhYnRQMBlUqlayOevHihX0vt9uteDyuUqlkZyLgFzJwJNpkRSCbJ6cEht7tdltWDHcy9wM1kbO3QYXAWXt0dKS1tTU1Gg01m03d3Nzoxz/+sYrFop3RgUBAr1690tu3b7WxsWFSb4BhiDnGEM7MTKaB+Hw+1Wo1hb4af8r+IGvk5ORE09PT+oM/+AO1Wi1dXFxoMBhodXXV/P9kQ9D7oKSDWGJNo0ildqDJ58+itEokEva/f5sRZd/9/O4/36oJr1arur291cXFhYWSIImlCcvlcopEIobIM+aG5g7ZEagu0iouimAwqEajYeEcsGJITK+urvT8+XMLyCFs7Pnz5zbzlEb68vJSsVjMEhf5NziUaWKdcyNpzmgQaKhoXpyJ4siq2Qj8u07/ptfrVbVa1XA4tNRTNmSn01Gr1TKprFOaxaE4GAxsvh8+1bm5OUWjUbVaLTsMxuOxnjx5YnNNkXxSiMIYxeNxYyYKhYLW1tZ0eHhoMprp6Wk9efJEuVzOGDVka8jNB4OBHaYUepKMBXD6sGmK8bQDiPCZePbz8/Mm66dYuru7s6AZGmDCxWhMkBDe3t4qkUhIkqH+IIkUvhRIsF1OFufw8FCzs7M2c9Tr9VqqMH8H+S7NNwCKk2mn+UV2jh+Pf4sGCHaZJiIej1sjy2GJ793tnqSFIk0ejSZhhzBijJDjf2+32yajBjSg4YJBh/1mHyK5p1EkNIxwD94/jQYXP5+fZtYJyiB/wqOEVDUWi1mBRogJOQxOJBYWBZ8S7AvA3yeffGLoei6XM0tIu93WwsKCWTuQObPmAdMuLi7suVHkwaQvLCxYfoSTmYQZrFQqhs6Tb4FnnXVIcU3OBesPqeP5+bl5YjmfTk5ObG+NRiMrehuNhmq1moVqwYIAYvBvA2wCvEkyUIxmjOAovg+M3ng8NjsGwVqwzjx/Sfa++Mx4uWEFx+OxlpeX7QyuVCqKxWKmXqrVao983ZlMxpgcfgAZaMLJQ3AyoBTr/PusRyR5MOmkmMMG9ft9Awe63a6d1YA7SEcpXFut1qO9y38H9IBpAlyjIYbN4s4rlUrGbPH5CCWLxWI2eYNpC8jSYYho4MheyGazGgwGCoVC+pM/+RM1Gg2dnJwok8mYzLJWq5mkFIknrCOgF2cDth1StZ1zp+v1usliUTawfwEFu92usa2tVkvj8djulG63a0AkAXG8m0AgYBJbvMTInGlemJyARSESiSiVSunu7s4S+Ulmn5+fNzVRvV63d1UqlXR1dWXybtYR97HTNnNycmJqkmKxaJMiuG/K5bKx3+RyELAUCATs3AOUIQfD7XYrn8+bJJbGCK9rLpez2iQQCGhjY0OtVkuBQMBUJ/V63ZRD7OVOp6Pd3V0bt0cTvry8bIALGQ545QFOsLNNT0+bDBVLBYAI8m5AaZRqgUDA8laQjiOx5d7jnARku7+/t3Rw6qhQKGRAP97VTCZjdyKsMtkV4/FYzWbzEWFB8+B2u7W8vGx/D1UE6+nJkyfGUqI2A5BHiUM4IOsfywEsJV7tXq+npaUlU8+gwqpWq/L5fMaK82d5hi9evLCwRAIax+OxlpaWdHx8bNLytbU187q/e/dOH3zwgYGX2DBTqdQjNaoTlIORp2lG9RIIBLS9vW13yGg0yTDBkrCzs2O1miQbezccDvWHf/iHevfunSnlfvjDH5qVjPsU5ZzL5TL1G5MNOp2O/v7v//6RFYh3jf3D7Xbr1atXKhQKqtVqluHj8XhUqVRMVfD8+XOdnZ2p3+/rj//4jw0wPTs7s2kh3CXUJdfX12o0GrafnTZQrBhkNc3MTFLR37x5Y0AOqiS/329EIxZbwHL2PYQdoITP5zO1GXVIpVLRxcWFJJn03uVy2eQDZ5Duzs6OqVe5A7ivqVeoY+kj2AfkBkQiETuLUHq43W4jCVFMYO397uf3+/OtmnCYbySufr/fijWPx2Pjr0BdQdhIlgUN5rIHqWIxwJJxqeLPw59GESV9LRW+ubnR3t6efT4QS5qNer2uh4cHO1RptKWvEUSCipAp0yAhL/f7/TbyDPkbh5rL5bLmn0AJGr7RaGRJySx2ZHGlUslGlcA2cijwOZC1sCE8Ho+lV9IwBYNBm2kL+xAKhSx0otFo2AWPj3h3d1e//vWvVa1WtbGxYe+BBnhxcdGksTSWTmkl35kD5OrqSsvLy/bekJjie6fRpnBj3rTL5dLW1pZCoZD5/xjjVC6Xtbu7K5fLpXK5rJmZGZOhV6tVa8CR6zG6gkaA/07TyCWLXLTdbltoDWw4gA/ri+IDOSjS+9BX8yKRFtKE0lzTQLGuQNu50JyBerAKMJTsJcIEYTu73a6i0ag1gTAi/C5nbgGIPUUG6xLQCMni1NSUWRRubm6MIcD3mUqlTNoaCARMseFk+LlcYYok2UVEg4aFAj808ywrlYo2NzfNWoD0nO9NMYcckAKGoLzhcKitrS25XC41m02l02kriN++fWugG3uRpsvJeKytrZk/l0vL7XYrmUxac4wPjNmwrDtACgLZ8E8zjo8CjLMEgOH6+lrBYFDPnj2zqQW8w7m5OT19+lSj0UjHx8e2fij6YHyQpHMOUFSSGI2/ltnTFHXkMMAQ39zcmB0FTyesG02sJJOo3t/fa319XeFw2DyVNMecVyQik7oaDofNf7yysqJkMmlnJmc/rAZeaN4XgBhrhIaK4hkGGhac7AkKTopyGk7YR9Y1MlWaQM4LmmuYRYouCnbOWkbKUZwRYoaiaTQa2R72+/3GACKrRPLNuwGYADQplUoKBoPGTHJ+IbklxO7+/l4HBwfa3Nw0JUIoFDLAEE8ygWP5fN7OBhpR9gAgAw0OntVoNKqlpSU7IzmHQ1+N+2I6yN3dnYrFogWmdb5Kfcc6xR5ZX19Xq9VSq9VSKpWy54Uayuv1GvvGlAPktLe3tyqVSnbGw1Du7OyYlxGgaXV11QAtRlQCbO3u7hq5sLCwoOvra5u4wBrDY41CjsDHcrlsQCZyW57T+fm5SV9p+FH/kNLM3p2amjIpaiwWM18+vuCZmRm1Wi3d3t6aOo87jRA2msSVlRWNx2Or025vb83THgqFFPpqrjuj1sgBKJVKSqfT9g6Wl5etYUbtx/9kbz08PDy6C/m+FPTO94k1hkaHxgiVHaoTzjSCbp0hgNwPPHsCryKRiI3NYn9hD6xWq5qenra54ajKnFkXAFPc+YCTnU5HPp/P9gZBWIzA41yn9ry5uTEFEva7N2/e6MWLF5Jk9QeqHactC4ALeTb1td8/GWsFM+5yuWzyiSQdHx9bOrvTTgkgwTNDhQEAyN3IGpNkdSrPHVANFQkA0GAw0A9+8AN9/vnnptDkd3GeQ15Ry15eXppCdmFhQalUSuVy2UC1er2uZDKp5eVls8LwDBYXF61WYQb3eDy2kYGoWqiHAVwk6fz83N4nCrR4PG5ZCTMzM5bfAHhPLU6IMUAqBMx4PBkFiH2PELvRaKTz83Nby9SXyWRS1WpVfr/fckd4X36/394hJA+KE+pRQgtXVlYeBT+jXtre3rY/g3Xj008/NYUZExi4S7CMMOkAFZvTnklN89v+fOcJ/91/vlUT7vFMQmN2dnY0GAzskOISwWcKY8BBzIGIJw5mlaIJFpmGLxQKmbQChDAQCJjEhGKW8JdwOGzIG+wMTQDNoMfj0enpqTyeSRhOLpezggIZD8inU7pC8czG5hL85iYG1WJMD78bz+7FxYXJR2q1ml1yFMF8H/x2bAaClChuaOac6FYoFNLbt28VjUYViUTsckfKy8iWWq1mG5HPdnh4aAAJEvvT01M7uGu1mikY+H94xwnZogF2yr6dCDOIYjAYVD6f1/X1tYWvcDHxzq6vr63QwX5AIc1zQmoKkrq0tGTFcLlcViKRMMYGUARmi0LWGVzjdrv1/PlzK3KQqcHs87xZ56g6YJMBTijSuYxI46RIkr5uyEn85DAulUrGXBPmcnh4aAXL/f29Tk5OLJxGkp49e2ZAFYg3zCyJwqwrwk4o/mCYkE0tLCxofn7eGH/YbmfWAXJXkqUJwOH7AKgh7767uzMptNc7Sd+lYUH+hy8L2weHOWsHIIGGDRadJvvm5kbpdFqxWMy8c8gWscC8fv1a9/f3j5oeMiq4KLm0b25uzFPV7/dVr9f17Nkza/woUnZ2drS4uGhKFOd8+UQiofX1dWNseOeg9DB0sVhMGxsbmp6e1tHRkbHpfEaS1ZEWMkILKwLsrjS5oAhlw97glCSm02kdHBxY+A0BMFhmCMkETKIhYy3hwUylUhoOh5YwSwPP+2O90vjiwwdcY334/X4DMwnpmZ+ft/Az1jNp6KhZWMuSjFXm3OZzE6rjZCCYUIDUkfc/HA5t5KEkUyVxrzGrmaaHdTs7O2vFFOuJ6QMwapVKxewkkoz9ADxDfpxKpfTs2TOVy2UriGZmZrS6uqpKpaKDgwMLqwyHw/roo48M2ELFAPDbbrcfgWcffPCBPv74Y/MSwqLxHSh4SabmrMEyQg4EZ+JwOLQgJZQCNPMnJycmLyeAiykCKGQ4tzl3ac4555eXl+0uckqreV8UnFhVOI+ePn1qzTd3F6Dd+fm5rVHYIoDaqakpeb1eJZNJA7jH40lq++rqqvmeu92uAQHktLD3YVqj0ahWVlZMcpxOp401BkyG+Sc8ioaHtcEewSoBiENd1O/3bXwQSiEaKoA4JOfcYdzfw+EkcIw7OpfL2ffiDGP9Ue8BUvX7fVO3cJahOKGu4+/QTN/c3KjZbNo+xd4E20f9guJPkimPnKoVlBIAn9w7rHeC8LCNoTrzeichik6/P8Awa5k9z3PEW7uwsGBgDOuI5pLRdqwf/OcoJwA2AMzevn1rxNCrV6/UbretAXVKtmdmZkylgaKu2+2aIghSxxn0OjMzY88EcIcah9oVoJ86G6aas599jKwZMB2GHJUQKkTC00KhkA4PD3VycqJ6va4nT57Ynef3+22KCGo0wG9Uj+zps7MzA5KxJsBUM0aOfInRaGT2BtQ1Pp9P29vbtuYAmZvNptrtttLptP1dZOCAZ5xfnN2tVstUStTcfr/fxqYNBpPA4uXlZS0tLZldixoC1Q/PtVAoWE0H2ExNeXt7q/Pzc2uSNzc3rW5jj/T7fVNVwKpDWlUqFe3t7ZlNCHXy5eWlhcpSH/I/ARQBFnw+n87Pz3V8fKwnT54YWMW58d3P7//nWzXhyGWSyaRJaCjoKOKcmwBJeSAQsEMbuTizaDk4newzPmAONtK7y+WyySiQNz08POj8/NwaKxr2fr+vo6Mja9poCjc3N+1CCYVCury8VOerebrX19fGOPAdKDq4FJEn49nCwwtTz4bkUqH4hEV3+p4plJCy0JjybyMVIyyHwwevliQDGgApOED4d+bn5+2iCIfDVhiFQiFVKhWTfN/f39vvdMpuQXdBUrnIYHNgDBhLxngQPI5c/v1+3/xagDAUtDCOFKwwN0hZKbpo9PCpAbjQ4MJWwsgw/os16WxKuYyRq5VKJUv9daLQpJXD/N/f31uQ1zcbaxpIJxLtbI75705vIIUXTSCIJExhpVIxNoDGH1ke6/T6+tqKEIrzarWqtbU1a1woYiQ9UmjQRME6cVE6U5SxG2CdgAFkP0xNTVkDR3FAAYiygjXg9/vNbw1TBnCHxA5gRpKxeihqWDOrq6vK5XLGJCO1ghkkdJDiNRwOW2EUi8X0+vVr3dzc6OOPP9bGxoYikYikCXJerVZtriqMMecHwZAUTWQXNJtNs6UAQiHtJMQHWW06nTYPJoXB7u6uSqWSbm9vdXJyYimyL168UKlU0vn5udbX1w0YkWRsNYAdxRf/3eudjBzM5/MWaFWtVnV+fq5kMmnvDdAIBYtzf7RaLTWbTdvT0tfqB9QZNBqsaxgL1g+gEusS1hLlSq1WM9uJJJO0k58AAk7jRrNFSBpNPdJkJKxut1uNRkM+n8/OHKdXmvOXAvb6+tqKQRQy+JWx0wBK0QhR3HDWsNbxvcJuwFpMTU1Z/gHMtyRTCmSzWY3HY21sbCidTmt9fV3Hx8eWDH5zc6M3b97oH//jf2xAK3vvgw8+0PHxsd6+fauVlRWbye3z+Uz5s76+rqurK1NE8JxgUZwSXvysWH0A3zirYG8uLy/12Wef6ezszAAqGGlAF9Q4sEmXl5c6Ojoy6TZNNSosQECAWKTHmUxGt7e31twROsk+PT4+ViwWU7lcNtUPzRVF9/39ZEb06emp3f+cqayL6elpk70j/3QG2wHmACbBWgE0oAArFotaX1+3rAGyKZw+ZOTcjP2CmXNOkFhZWbGGwAl2ASIAvCNzpyZA4QVYxX5iLjK2QWTmeL+xxJDLQ1gczRrnPuq6y8tLu5tQP0EYAA4yYotmZnZ21thWwDUa+0AgYM0M9wPNNsBYNBp9FOx2e3v7yN5EbUDeDQ0dYCj3J4GPpFijyMNH61RkATbwbAHyp6endXZ2puPjYz1//lwvX740Fp669t27dzo9PbWRnPivHx4elMvljImGmMJSdX9/r2KxqEwmo36/bwqQ4XBoaw8V2C9+8QuNx2O9fPlSGxsbBrRyJsLsI/1/eHiwHA+AMt6RJFPvcZ+x/52gQqlUUjab1a9+9SutrKzYvHbsXRBX2MqoY8g2oI4/ODhQJpOxsDmCOlFPhEIhtVot6wGGw6EODw8NWPD7/cpms2Z74i44OzuzaSGNRsMsntTJ9DaA/nxOnmMul9NoNFIikTCVyvPnz1Wv11WpVPT06VN7ZvRB1KF7e3tG0jWbzUfWj3a7rVarpbm5OX344Yf2zKjtONMCgYDW19fVbreNSKRf4j7jTiWTgQwjFLgAzvwAfgKOBYNB/fVf/7UKhYKi0ajS6bRZwX6bn++Y8N/951s14UjDkRqC6IN+UkiDnLGgOExjsZgdMDQCSFxpeJ0zJbk88WsyrgC5YzgcVqvVMo8bqCHNYiAQsJmONAYUqfhU3W63EomE6vW6HZgwIUhbka+AYtEo4VPCO8VngnFkw3W7XWuQQdkJ64C9Bml0Bn/B7tDYUlBJMokXYRTRaNTUCalUyhjKbDZrkqZ4PC6PZxKExDxIgAeYlEwmox/84AfqdDrK5/N2SbNhafqR1uCnJK2R706zDdoJOELhhjcW5pnDHk8dB0gsFrOCheIN+TQNGn8XKe7c3JwpM5wSVuQ1qDPwSrrdbhv90flqRjSgC+gmhxhFOv5GmEeacEAVnhF7g89Ic0rDAtvglNTSbHBxwuAiiSLIgzUBWwwj5vP5LNmy1WppaWnJ0HqarHA4rIWFBXk8HgMZeF4wDDwD1pvb7TY2iEIQGwOsICCEU6kwGAyMDXH6ywn9o1AG1Wat0SDieaTQpEg9OTmx86ZarWp2dtbkz/gh7+/vtb29bWO+kOV+9NFHurm50fHxsbLZrIUy4cev1WpyuSYJwicnJ1pdXbVUV1g7fNLb29taX1+3+b2w6DDFgBiwA/wOxkOVSiU9PDxoZWVFnU5HlUpFkgyASiaTikQi9kxRliDBp6FD+o4F5f7+3mZRExoZi8UsIAf/K+m83W5X3W5Xz58/N0CNvIaTkxMLWoQhpSEEJHr69OkjJopmZjQa2dp0WkdQKTklq6zlVqtlVhsCzWDVKZZJsaYg4/yApUeVwt1Fwe6U83Kewe4zEgpJKeuaZg1/LeAC5wJJt+wJGgDAztFoZEwgACEAKUX81NSU/VnS1AnRYf2iwLq4uNDTp08NwOV7xONx3d3d6fj4WJ1Ox8J1YB4puAAFo9Gorf1oNGpyU2bB4y1nDaNwcdpaaD5gdDc2Nqxgwhbj9DjW63ULKEomkzabOpVKPQq6JD8FpQ9gKsx2v9+3BrRYLNrYJo/H88iDj0JnZmZGy8vLKpVKGg4ns5mdc3kJ/EPRh3QThpHz32klArwMh8Om7GDWcbVafeQ7RaWE5B+fKzY/QBuXy2XKECdJcXx8bEAOsvNSqWRZM9yd2PT44UwmMJDmW5qQBJx1nNsEYsHwp9NpU1I5/xyhaEjIYfmxQtAYwajSXPCZ8OE75bmcE+wv1gbBkrFYTIlEwjIiID0ANAATURh4vV6bCe8MfOQcBUjDDsN9huqEPcL763w1w3s8HluwL/seIJZ6MZ1OW7YDadOcoyiwWq2WAZxOKyKfp1gsmtILBVgikbDwtbOzM9VqNR0eHloorySbCMBzcZJXNMbY3qjPqAElGcAHgEnYmMczSabnDHnz5o1mZmb0wx/+0PzTf/u3f6snT55oY2PDVAaATVivsFjBIOMbPz8/19TUlNbX1y3zARsWa3pnZ8fW0M3NjT766COtr69bg+okHOgXisWilpeXTV1EvQ05SJYT87J5F5eXl3r27JkikYjc7kmeAOuGfQXIDbjFOQ+xxHfgz/d6k9Gdh4eHWlhYMOIAZQx9FIQG/Qs1AEACGUGA2NhkYrGYKpWKjT5mLxJmeXFxYQCJNElj39zc1Pn5uYHoBM5+9/P7//lWTTgjI5CzzMzMWPIgfkrYQopnikGnTJoClksM73in07HF/vDwYJchl0K9XrcCjPRemnNkeSDYMKcgt04PFp5Nxv7gOabgQFZNU0ahRKFL00Fh6wx2ooAOh8Pma+SigJFYXV015Bbk8fT01IJdkODD4rF5YrGYhSvh96Gxh52gSKIZREKPHJH3uLKyYoUTz8rj8WhjY8NQYEKqGJuC9wtJzNXVlV10HGisA94tnhYC/e7u7pROp23MBE2wk6VFMeD0h6IGALG/vr628BUuGJoDDj8OUZpQCmQQfi5WimDeD5LmXq9nKge/32/MA2OryuWyJNkldnl5qeXlZWuWXC6XATkUITTJgFf4pmiUAFkoeABYXC6XNcZcsBSZ7AWKB7ycsAcUvPhlYbkp6ClsOIABzJiHzfPjc3wz1wFJJsyHU4I9Hk8SRhcXF00GzGfg+yM/pzFyWhgIN5yZmbHCkUv9/fff19HRkT1Xig72NXOJr6+vtbCwYM07TfTt7a329vY0HA6Vz+eN2frkk08MIHG73crlcqZCATBknQIO8N4pAGgSRqORrfezszMrZJADNxoN+f1+ra2tKRQKqVAo6OHhwTybFxcX1rzgI6RQmpqaUjabVTQaNcawXq+bhA0/nd/v15MnT4zlWVtbUz6fl9vtNpk94CqzUGkiYII5M2gK8IQTTEjKM98NW43TG+3zTUJp+L/DBlGkIs0EaMKj67Ru4FunQeW+kWSgMCwdYA7+TpQeADzOlH6aIWwPKHwIISVYi3+LphAgEAUJY798Pp+l5jMnnZFEyEgpglirp6enlt1BQFEsFtPm5qY19f3+ZOTn8vKyKSCkr/Na/H6/lpeXbX/C8F1dXSmfz9s7QN3k8XhMNg0zOBgMLA04GAya7PPi4sJyERg9R+G2srKii4uLR3YbwJCbmxvzSOIJh5GG+SQsjKwQ7mFktcjOubMAL1F7cH9xDgB8BgIBk3Pe3NyY4ikYDBrAhhw3FAqp0+nYevR4JoGEnMmoA1C8cNYCcMBWw1wvLy/bWuVswOuZy+Vsli/ziyORiLHFNISoqfC2A5wSuAfYh/0GzzYALaAnzRhSb+4hQBxqmW63q36/r0QiYXcv9ZjX6zUFCCAVdrrp6cmYJNhx7nPqJUlGjvA52Kesa+51ag0Y1H6/bxkgfDZyF5wKCyS+NC5+v1+lUknlclnpdPrRdBPUaoAriUTCchYAjWleqcX4jgB7SKOnpqb07t07G9+KlREJOXJygP4vv/zSJpJQG7NPUOKhJCKYjbPe6UOH+Lm/vzelFEHJKF2o2bkzmPZA0CDAgnP/UEOhBOPswW8NuHt6eqr7+3tVq1W5XC49f/5ce3t7SiQSOjw8VK1Ws4kM9XrdMn2wWCWTSV1fX1t48/b2tvb39y0UEFXOzMxkYgej1ah19vf3zb99fn5u65A8FeoV6g1JyuVyisViCofDajabajQaZt+B5SfbxO+fzBan5nbmFPj9k0R3gMiHh8kkhIODAzt7naPmIKA4V7PZrKampvTs2TMDtDnvUBk4QSl6q2QyqbW1NbXbbR0dHVkNt7OzY3YjvOrUIRBG9CSxWEwnJyfyer169uyZJKlYLCoWi1m91O/3DTD7bX6+Y8J/959v1YSzKSVZowpqVKlUTApFw+QsVEg2Hw4nCeLJZFLNZlOJRMKaFlBTxs8gR+Mg39zctAsPXyT+CbxQ4/FkBmm325U0KczS6bTm5uZsjAmFBewJbBpsPOwBzQUzgpFKs4FhZpAfEvDhZLWR4OPhQtJJ8wETROOLdHFpackCVfBb4jXisr++vn6UWg16TGFCgvLW1pYuLy+1v78vv9+vSqVicjMKJb5zqVQyT7Xf77dRbc7kYzyENMkg/Vxa/O88Qw5fvFU0vMwO5VIfj8e2jh4eHiwRmMOE4oJgPS6thYUFQ7lh1BYXF61YBCmn2EYuRwFIwQVzhtzU2dwhT4V5pyigkMWqAbLKxTccDnV1dWVrBYmts3kloVSaMHaE+xDIwvN0+qklPWKIt7e3LWEdGRPybhotmNJyuWy+IpgTQCf27ng8tvfA76NgobjlOdJ0A7Y4VQNer9fG+fBZSX1HHYC0kgadZ8PvAMCAVXMWSVtbWzo5ObGGst1uW4GFbMuZ0E7RNhgMLBgRL2U+n7fQEtizfr+vvb09lctl8+0jsQcUgl2kmMFHjXSWYh2Ze6/X0/b2thXkNApIgtfX15VMJvXu3Tvd3t6qXC7r+fPnFm4XjUbl8/mseQP17nQ6pphIp9PKZrOSJoUXgT8+n88YelJV7+7ujPWmSHdaZiRZGCTPbHZ2Vq9evVKxWDQg8f7+3mTC29vbxrRTaKKSoUjlzzYaDfu7W1tbxoDD3MHaoqSg8SbUEmkpoZ7j8dgaDKd0liYKRmJ9fd3AI6ThNPYUzjSlTjk5bJjH8/UECu4ezhy87JxhKDMogFEdeDyTwDvuUoBElAq1Wk2StLm5qePjY0UiEVMScTay/1B1wewA4C0tLel//a//pX6//8jLSvHo9/tN+YDf8f7+XmdnZ6pUKiafJ5zU4/FYdkcwGNTLly+NhQJs3djYsGfi9/v1/Plz3d/fq1Qq2RmLKqRcLtssbtLB2c8ej0f5fP5Rwjj3JyBDMpm0po7nxzoJh8OmvhiPx5ZxQvEKiEJKNRNGbm5uDJDd3t5Ws9nU27dvDUjo9/tmzXO7J4nnLpfLAKZKpaLV1VV7N9xJNBWZTMb86ZFIRDMzMyZJ5kwAHCAczRkCGI/HbS1zdnGPsf7xJ9PksM69Xu+j4DXOCUlmW8Gq4Ayo4w7CkgQpwp3EOerz+UxNyLryeDyWfE8AFncp34FzACks+xclIVkLqBKR9TqVN9xHADWAvU4m23lH+nw+y/JwTk3B3gQbiOIy9NXsdfaAy+XSxcWFms2mvv/975sihbsS2xW2AKxsBPRhC0B14CQSuK8gmH7605/qV7/6lakbydzgPNzd3dXp6anZcMbjseW7OBsqyAlqAIAWp6UJoJt1Qy03MzOjra0t5XI5ffrpp6pWq3aWn52dWc7Je++9p06nYyAIdwnhgJVKRd1u12aAz83NmZLm+vpa5+fnZp+YnZ19BNABgnMf4YnGOoOdhT+/urqqi4sLvXv3zkCkk5MTs7AOBgOdnZ0pn8+b6gHL7NzcnL0nQBWaWoAuLIB4rTudjqrVqk5PTxUMBg0AJxOJqSwffPCBAQxO2xW1KGnrkCjcSdwL9FdPnjyx3AdA4l6vZ2dpq9XSwsKC2eE2NjZsXTPBaHp6Wi9evNDs7Ky9W3qC735+vz/fqgmX9MivAEo9Gk3GmYDocnAydoBDGhatWCyqUChod3fXZoY7w21YmHjKaLoWFhb09u1bJZNJ5fN51Wo1pVIpk1eORpOEQ5qJYDBoDDuBXSQz12o1k/oReASTWCgUrJGC/eSgbLfbevfundbW1kxijVzYORsTFJnLF78ZjQXFUzAYNAmsM2V6ampKGxsbtukJhtve3tbCwoKxoPiluOgoEEBZOfyz2axubm4eebxpHGBiAVMY+cBB7my+QIJB7zikWq3Wo5RgDu5AIKCzszPzcPGcuXjr9bpSqZR8Pp9KpZJCoZAdtDAlKBwk2efhgpdkc5Xb7baxQMVi8RGzEAgErEjgBwacSwxZDww/oANMoMvlUqlUsgLcKQ8GweQ5UUjy59gvSHr57Mj6WF9I6gGsUJs4PeONRkMrKyuanp62NE5UERRwSJicCfsUUul02hoTvO+g/Tx3Lgb2HuCFUybvvNhZ78gYAaYIMUK5gB2EICwuPEAC1g1/F+AGCwgFIOwrXl9mCEciEQMH+J6ocSSZF549RpHLOKapqSm7mDkXgsGgNbXIMu/v77W6umqfETsGXkyah0gkYgxYKpXS2dmZeQspeFD/4Jefnp62ZiqbzarT6eizzz7TkydPlEql7PlzeZIODStKBgT7lDEqPD+v16vz83NTORDYBEre6/VUrVa1ublpahDOJTzwPPvhcKjl5WWTVn4z1A8FD2udZnowGNh6Ys9mMhmTji4uLlozS3GD/FaaZBYAbDiDKGH0g8GgqtWqpqamrPEE7CNckr19f39vihmaZEAOfgDVYHoJ4IKt4Pey37Bs8Vmwb8GUZrNZxWIxA1l7vZ6NB8JDXCgU1G63ValUbL2dnp4a+Azg3el0DOiE5WdsIUBCLBazZoHzRZKx4ABToVBIkmzuMQFzgOjIJ1E6AIA8PDxoY2NDzWbTms1Go2E1Q7FYtDwGnkGxWDSWnf0AMDsYDHRwcGD2F8BBt9ttnlSKc5pqinHOM879nZ0ds+D8wz/8gyl9yDQ4PT01FQ5SYpq40WikZrOpy8tLy6FBEUNmCgGyAMxLS0va39831tGpEOR89HgmIVSDwUCZTEYPD5M06nK5bI0+axQWFrmqEwQGUMU/zPolr4eGD5WEy+XSzs6ORqNJWGG9Xre9mkwmDUBAOQXwDIONYoqzEjk3DOrd3Z2dXYCyAPowcewj1E7tdtvASvIDJFndxF1EzcIzQeYOKdJut63h5c+jyOMZDQaTyTSnp6fa2dmxiRTYCiRZ/gvMPrlCsIoXFxcaj8fGBC8vL+vly5c2GpTxezTjgOY0vTRXnM9YxarVqmq1mra3tw08bzQapiQCzOf+uL291e7urmq1mo3qjUajdiYDTrDenOo0PNGoCLg7nLYw/j53Moo0zoqlpSVbPwTaZbNZNZtNvXnzRn6/X1tbW3r16pXK5bLZSHgm4XD4kb0UNQTrEbItlUpZnQD4jnQ8EomoXC5bY43Fs91uq9FoKJFI6K//+q9VLpfNpoUyDoCJ82tzc1ONRsMsSfjx+X1OSya1DjYlfP7UXsvLy+p2uzo5OdHr16+1s7Ojfr+vcrmsYDCoH/zgB2ZHYs2jusD6Q42CF7/b7do971QM5XI5tVotvf/++5a7cnZ2ZrVoIpFQq9XSu3fv7N+7ubnRs2fPTNVKww4gvLy8bDXndz+/359v1YQj85Fk0iCaT0KQiPbf2dmxQ5wQAlBJUKFcLqd8Pq9kMqnvf//7FtQEAyJNZHZnZ2fa3Nw0zxXNAY2nzzdJIYzH4wqHwzbGAF8XzVSv19Ph4aHJokgLRLpE4e8M9qhUKhbqQBE5MzNj3r2trS3zllHssTm4uCm2kAZyiXEpOP30SGVpOmCp8MlxWHU6Ha2urloBgrx2enraigbYpN/85jeSZBcr7CeycVBn2Im1tTVr8JEz5fN5JRIJCyvh80ejUZOwcQFIMhm706eM9DyXy5l3hgaNESmBQMAOZcZOwNRwGbH2QHCTyaQuLi60ublpYMbCwoL5KJEuSnrUQPKeuFzS6bQh3oFAwDzRFA0AAaxf3qOkR82n05fEBeb0XSEv5OIDWSW0h5miNJ8wOASsYBPgEmJMB00dHlzYK5pfPgd7EkScWeHIiAEdaJh4bhSlPBP+PMya8/vT6HBWOLMYCDZC2kwj6/QbAkhNTU2ZJJQLkYIP2fXs7Kzq9brG47HJ5uLxuAE+kuz7sAY9nsm4P8ZrIXdHjULAEIUsknN8/jRevHe/328SzsPDw0d/FoBrYWFB6+vrur+/t1nOg8HAzjXOJZLQYfE6nY5ub291enqq8XiseDwuSRaq6HK5rHnHmzkej5VKpezMgUkDGCQzI5fLWeGCtI53UywWtbOzY5c275si6vj4WJlMxqS+FL3D4dDkdjwrUoqxx8BqwRyxL7FA4NEFSOR/h2VDbUATT4FGUBkZCTSPMOUrKys2dxmghYkD7E3ku6zhcDhsTSIMLfuV5svp2Xb60H0+nzVpp6enBr5tbm6aDxxWGcsJqppnz55ZYwaISkH/7NkzKwhhoriH8DAD/FGIvXv3TuVy2UZ6ctbyDPAjA+oCYAJiIdmfnZ1VsVi0MZLNZlO9Xs/GVTHOivONs87r9Wpra0vv3r2zz0Rx3ev1tLy8bLLb8XjiuWV0GXu21WrZqEZk+yi8tre37Y7GN9lsNg0Y9vv92tzctMR0ZOXr6+sKBALKZrMGbjOGqt//OnEZ2TThUvjQkdK3222NRiNLh8dfD1jFu4VlBuBDIUD9QgN0dHRkMl3u06mpKVOSeb1eC8zjTgdAQE7KJA8aD5qHdrut+/t7C6KloQYkJrwV2wR7FoVYKBTS/Py8eU4JZ0Qiz3dAEUedgecW8CISiRhwCgDEucD3AIjjzL28vDSAFbVkNps1gLJUKpl0nCadWs/lmszQfvHihanweAewtexLAA+3221zt2HKkVMvLS1ZuFu/39fq6qqxyxA+TCxg5BUWPd4VSjiUEzRinNMoSVutls3yBrj+/PPPVa/XlclkLJcDi4Bz3+IHd5IYgEqAdYAfNO7USdSo0tcBZsPh0NLB9/b2FI1GTd59e3urXC73CAj1eDxWH+/v72tjY8OyGzgrlpaWDCySpPX1dY3Hk3wa7lbWkdvttndBwCMWVkDvwWCgjz/+WO/evTNgAeJwdnbWGnyAE79/kpSOumxnZ8dUCDyTXC5n9wFAp8czmby0uLholgPsY7u7u/r8889tQkUkEtGPfvQjA2IgowCOnPUT+4f79/z83BRjPp/Pwjz/6I/+SIFAQF988YW83sk0B8gsaj1UOjT28/Pzdv5SK3g8Hssc2NzcNDXxb/PznRz9d//51k04Dxt53ng81sXFhcLhsCE7kh7NuHt4eDDGxemvpchxsnz4IUBwkcPe3NxYCBqsK0jqxcWFdnd3tbi4qEKhYA0CLGIgMJkHjEyvUChYsU3zTiPqlLIRhkAxAzM8OztrjHC321W9Xlc6nbYLiUMHhAs2ghAHxmdJE+8vm7FYLBqT4gzjQiYUCAQMTSZ86ubmxgovJIRzc3OKx+PGijDPmGZJkqHLIL2w2jSXBIc4UU9ns+3xeGw0GDJOUjf5nbzvTCZjMk8uPRoDmsa1tTXzX8IewthQuPp8PgtqIvH1+vravKrItpFWra6uGkOLZJ9CU5KBNBR49/f3arVa2tnZsREZFC8caFyYXGIUQ6xbLij8Vs4mzPl9CCDjcoTBoJGRpNvbW0mT8JxQKKRer6dKpWJeWaTBFIKgu5KsOHe73SajoslyBhU6w+C4lCnGYL2d6hfp63A56WvwgQuEZ4F6hEbBGcBWLpftUnc27pLMvoIShSZ/fn5e29vbBh6hfKAg2t3d1c3NjaX/IxGmQEY5ge+evARJJumDhaPocrvdjwARLkvYZZpVpMP4vzc3N803TwMBwEX4Uz6ft1EvV1dXFraHF5uQKfZxMBjUxcWF3r59q5ubG/ME00yyPgETmOEdiURUqVQ0PT2tQqFgANXGxoby+bxJXOfn53V0dKREImGsGCwWXsr19XVjoDi3WR/ZbNYk0uQWjMdje6b39/fmVWQ2KWE8NM14p29vb7W6uqqrqysLGoPtLhQKxpojC2cdAQZwVgJ4MOIRIBZWD08lAAPsmVOOT1PBf6dxx2cO84unk9Ak/IBXV1f68ssvTdlCyE+/37eGOhqNWjGML5WkegCl6elpHR8f212IbxkP//b2tv19JPlO5Q82GBoWkoc5ywFWeD7v3r2zefScQ5VKxdbjw8ODEomE5ubmdH5+rtnZWbtvG42GWTQoIG9ubqxABUAnR4F3OD8/r9/85jemimE6CgCbc33xPFG40GAx3nI0GlnSLwBNpVKxYp7A1mq1auSB1+vVwcGBYrGYksmkAoGA5cUwuojzslgsmnKB8Wjj8dgyGVwul3Z3d60ZLZfLpnThnGLv3t3d2Rxm1HLtdtuACr/fb1Ydpw+djJDBYDI2CQZ8PJ4ErLJWATNmZmZsbBjNaCQS0cnJiSqViikOnO+JdHBJj1hA0uyRuY5GIxstFQgETC1A0Cn3HewrVg7C566urmzk0sPDJC2ccx67QCKRMOucc7LKl19+aYAB4Dyp/dQX4XDYLHvIygH4nNJrmhyaOJ/P98jONhwO9fTpU1M8YFckQZ56jrptfX3dyIJSqWQjsyqVijY2NsxCgD0SO4NzYgzvoVQqKRqNqlwuWwL4+++/b4q5RCJhYZ2o6wC+uR+kCUOPsgQCwMl6sx9pMDknuf9dLpfNZscOmkqllE6nNRqNVC6XrZZ77733rI6gxjw7O9NgMDAlAWoKMotYG/V63epQng9r4ZNPPlGv19N7771n4Azqqevra2PFU6mUnj9/rlarZbUEpAqjgjnra7WanSk+n0/FYlGDwUDf//73rT+gpllfX7eAVaxpFxcXOj8/N6sH0nHY5Q8++EC7u7tWR0AMsK6c9QJ3OnVrvV63M5JJUdVqVQ8PD/r444/1J3/yJ7q4uDDyjlwXakxyfXK5nAVJUmOR2O/xePT8+XP97Gc/s73w3c/v/+dbjyhjw4OukD5MEY2ciiaFtD6aAWS70iQogHFnBPv0ej21223t7OyYfwMEB09YrVYzCTvFRTgc1uLiovL5vHlDRqORlpeXjVWHKYzH4yZdJ7jk9evXikajltjMgU0hRdND2A5BSIxvIZH73bt3Wl1dtWKYyxqPFtJ9mlc2oMfjMfklz4/mBvkJRQ5FHB5bUG8apVKpZH5Jr9erp0+fWnFHsYJkj+96c3Ojly9fmoWAJkKSMX00Wvx9n89nCcVcomx8mE9CWDh4er2ehY7g83bKgPGO1ut1Y7DW1tZMbSHJWD2KBphsUn5J/mY9SbLnSFGIRJCiB5CE0RL8HWZBRyIRW3dOZpDgkU6nYwwyBT/PkIKfBgZ2mX+f90TBxGWHFx1mzplgvLy8bNIw0Hj8/pubm+ZNcoYh8j3x78EqOQ99Qv1Y5zBpTlScgx1AjkuWItH545T9sj4p+J0BM3zGbyLWFGywAyDINOMULviX+P5Iuihiab5TqZQVp7e3twb4cDYBTsByU6yQcg5oIsku63g8rk6nYzORedYofzjH8Bw6m0/8ZIQ3BgIB1Wo13d/f22fa3Ny0Qvrk5ESnp6dqNBra29szfy2gVq/XM7CQ4h1J+fHxsfk4O52OZmdnbbQNDQaMG1MXAJd2d3dNGVCv182Th7R6enpa6+vrBgzOzMwoGAyqXC6r2+0a4OCcmAALND8/b9Jnimn89WQ+8P/PZDJWrACAcVayrzmbCZvhrMBLSiAkkm4kkPz/sQABUhEWhW+TBhvACAbG7/ebggjLxfHxsflyZ2dnVSgUjClB7grwxp3mzNNgVOH8/Lw8Ho+xIefn59rd3bVZ5HxvCn6K8JOTEwscnJ6etkaJkYe3t7fWWJfLZZOBA9aMx2MLNNza2pL09ahCmGHsCXz/WCxmPnH82ARGAQy4XC6dnp5qe3vb7D3Oub/SpNjP5/MG3LM2QqGQJY8nEgnVajXzNZONQHAcTRLgSKPRsLoDa0Or1bKmJxqN6smTJ8Zgwqrh0aWZI4OEtHNCyqhf4vG47Xdk1aPRyOTDBwcHCoVCWltbs8BJSbZ2acBp1FkTAFZY5gAClpaWLPwLABQ1DcU+Aa+3t7fKZrPa3Ny0fQMbyPp3uVyPxjTSXPI5nCoMRmymUimrt9jjo9FI9XpdW1tbVoMAFjMiCosRhMdwOHw0mQNG+/r62jJFUPuhhIPUGI/HNtEEgBj1D8qMTqdj5/v09LTy+bx5r1nvNDO8W0BY2HIaPwBiziPk8u1224CTVCqlSqWiYrGolZUVJZNJ7e/vWz3oDNbkbiJUkzstEomYIpDEfaySe3t7ajabliy+srKiwWCgdruty8tLe74o47CxcFahMIT8csrRacQAJlGIMG1mb29Pr1+/NsItFAopmUwqnU4bYMr0Bs7cjY0NA2aYRML3LhaLBtxABNAz0CMQbkYjv7q6qkQiYQ1tu91Wr9d7NAI1nU6r0WiYoqXX6+nk5EQHBwf2HHu9npLJpJE729vbOjg4MJBlNBpZaC/gIXYjj8ejtbU1HRwc6PT01M6Qd+/e6erqSn/0R3+k3d1ds6gALHc6HatFCEZz9lsA2QAw4/HYiL58Pm/37cHBgdbX15XNZm1Er9vtNtIHix62Dc72bDarQqFg+8nr9VpWF/vlt/35fypT/fv++VZNOBtmb2/PGppms2nza/EO0liura1ZAUDDVa/XFY1Gtby8bFH4+/v7WllZMV8FPhkOOVIDLy8v1Wg0Ho0xw2/kbETPzs60urr66DKnmMQzy3gJmA5CemAkT09PtbGxYU0laDBFEuwVzCMIG5+XTQZrcH5+rs5XI47evn2rvb09+/cZ9cUIBuf81Uaj8Whjkl69trYmj8djBTvSXdJdo9GoSXMIJllZWdGbN2/sAASNA92jcKFBdhajNL6hUMhCrpBlD4dDK9RAjGlekHDhlyLFkqL14uJCiURC+XzepDfD4dCSY0ECkSQdHR3Zs6LpxSt1fX2t1dVVQzR5/i7XZDwOIVeMkAGtpCHggoWZkGTsIhcSCCJjqJDH4gvqdDrGTlC44zuiIMO/KMn8f0g6UV/AwtEwkEKNJLtYLFpzC5ONd5NLhKaHIh3GApSfyxnkneR+0PjxeGyFFpf0cDg0potwI0A2mliyCdg7SMX4+zDxyDrJUWCvSrI/TwFJASTJZGM8X6dvHdkzgVMoCAAZKF54187LnneGB5BL/fLy0v5vvE+n3N4pLy0UClYUFQoFs5SEQiGNRiML2AL8Yw/C0DC2iMBK/OcU1M+ePZPf79fZ2ZnevHmjJ0+eGNvB36EAhnHDO43SotPpaH9/32TtABrJZNLYMtYryew8f9Q5NHC1Wk1XV1e2TlEgoTbAHsL7zOfzJi+lCIc9A8BiRJizsOVdoxRAkkphybtxjtYDOALAIUSP74ECBjkqYZNLS0tWkMKmcd44U/LJ26CRcDZcKLecTOxoNNJ7771noBuSfGwCsCEAqNyFjKEKh8OKxWI6PT1VpVLR4eGhFhcX1e12dXR0ZNYkFGIPDw86OztTuVxWOBw29Qj3MGwg0leaVcCA1dVV1et1Y8JpxAAlOPNQRGQyGVMLMX40EAhodXXVJJ3sJb/fb++HgL5YLGZ7neLdaWVhNBKsFKAXzS7roN1um0oCtv/u7s4UA9zDKAcAuUmlJkOGswp5Pc0Y1jJsTjQQAC0bGxtGULTbbVNtkFUSjUYNVKEJZv4wFhPWfaPRsOYKdcjV1ZVWVlYeNcQoMijcUbDxmfl83NsU6FiLCLfl2RNGm0ql7N0nk0ldXl4a204eh8fjsWKfQLlQKKTr62vV63VTGaBs4G4AoGUP8kNd5JwwA0jVarXMvwoAsbOzY4ozGDyyTrArwcCj4gKs4ix3hshRE1EjwcAyK56ak3sO6x/7AHC3Uqk8CkhdXl42m9L6+rrOz8+tYea+uru7M0sV/3dAb9Y+dQ33RygU0srKihqNhnq9nkqlktkjfT6fqZ0Y87a+vm73Ence8mcIDdYRMn0A76mpKTuXE4mEer2ePvjgA/NPA8j/8R//sTWD5JNQr/GsCcZjTZAbw70yPT1tKpiZmRnF43HNzs5qc3NTL1680P/8n/9T7XZbu7u7plBDJYsa9t27d5YozihQehKAuPn5eSWTSf3mN78xu8TOzo7lCKDcRRkhyZ4b9QrranNzU7/4xS9UKBSUy+XUaDQsaJC0+KmpKRsP/Jd/+Ze2n/78z/9cy8vL1jSjHCMgDmJlZ2dHJycndoctLS3pJz/5iamxlpeXFQqF7F5otVpqt9v65S9/qaurK7XbbT158sRsUrFYTNfX12axDYfDev78uU16+u7n9/vzrZpw/IlffPGFNTvtdlvhcNhSVSORiHm1Ly4uHiWCErTARY40NhaLmQRqfn7e5lZS8HB5M+KHIgb/8/b2tlqtlknxaMA5wGCunAwuDQyeWtCw+/t7ffbZZxqNRjo9PVUikbAikN8pfR0MdHl5aQ2o1+s1TxBFQyqVsgYzk8nYhqa5BJmCsUHy7fP57O9xocDiOwEKGki85zBbHHSE/iA5HQ6HlvbKwc9Fk81mTVYF++iUFDOrkuAWZgvPz8+bfPbu7s5+J4c/nx2knnRM2HyCsShM5ubmdHFxoe3tbbtwBoOBySEJqvB4PCoUCsbQX11dqVAo2KHPe4Jh4bLEa+b3+20GJj5ll8v1qJGr1+smo1teXtb19bVdChSQwWDQLg8Yay4sGGDYDJpBp2+Li5QUXND7SCSiy8tLXV1dPSr6E4mEQqGQWq2WWq2WIal4ZyuViu07r9dr6hCaaZ4X4NZgMLDRTDQ7IM6kgcPc9no9s0xIExCBfYNXiyIPnxky17u7O2WzWdt3eL8opAELALQIPEOSjL+JjAfnaMLFxUVTzODrx8ZAoUvwFo0iRRtya/IoKEaRNQIiEDwGyIZPGRkdfiyYwdBX4wVJas9kMioUCkqn06YSwRLDmYlUUJLZB+bm5nR5eWksD4j4559/rs8++8yYPWdgz9TUlBWfNBBPnjxRv9835ocAwkajIZ/PZ3kEAD7RaNS8jRTMeL2dPlskorAMTvaJYouQJsALPLuwWw8PD4pEInr16pWazaYCgYCd3U57AkFUNMQwQ4BKrA+AkaWlJeVyOZMgAnIx9hL2CVBGkjEp/D6K6/F4bAUOiiJnUw+ohPcVUA6WBF8pn43sCTIcYIZQbnD+IG9FjcQapniLRCImb+b3UPSSR8J3hPli/TvtXwsLCwYiuFwutVqtR6wae58QQkA81Gz4nROJhC4uLvTw8KBMJqNQKKS7uztjrVwulwEfx8fHNoYKDzXPmnssFAqZVBvGX5owmp1Ox5ghZzYF0tdqtapMJmPqlouLC6tFuLN8Pp9ZphqNhu1d7AK8a5/P9yh/IxKJ2DOpVCqKxWLKZDKPVEKcU5wLrVZLPp9Pi4uLSqVS6na7Vvxi1eAspz7a3Nw0exdBtKhoUL84iQruEfbgaDQZT0qI6sLCgvmN5+fnLbDK6UdFjo6igrNlZmZGc3NzRmzUajXLheEcJRdgcXFRa2trxuzTfM/Pz9tnRaLPfuf+ASQA8MXeAOjK/Y8VBCCAWoP7AiAVIIn7CbAKkIexqNgkeQecdSikyO3hfoNRZO9yzkxNTRnjS0r4y5cvLXvICVxfX19bajjKl16vp2azaSoCgFj+fUkGdjl995JsD3z55ZeKRCKan5838oR3CVEGmUXjDxPOucv34/9hK+QdUbs4wX1qP+4eGFXn3UAgKs8fKyGMOjUlZxMgqPMs/NM//VOdn58bIci5x6hY9ogkm5/OvwtISdAkCgvYc+TZzn7BqXCkNubfdNr3gsGgfvnLX2o4HOonP/mJFhYW9POf/1wHBwf6yU9+Ymfy2dmZvU9Ud/xQQxMEeXt7q6OjIwN9eM6rq6t6+fKlAf3cWYDY1DW//vWvHwWzbm1tPSI2me4wPz+v999/386I735+/z/fqgknyAMUmwCLbDZr0i1ePLIvGjECa7g4YAtZSJ1OR9fX1wqHw3ZIgFB7vV4VCgU9ffrU5LIgsrCGJGXu7OzI75/MJeSQY8PASOLr5qKBLQa5JBBpdnZWZ2dnWl9ft2ILNgkPFMwnlx/sEgBCp9NRIpGwAgFAgs+RTqftfycshQNse3vbGluaJ0ZcwKYuLCxYM5JKpYyJkGSyF5jqbrdrkrFWq/XI/8ghSlNGk89B7WRmCbCjweL30MjB6Hi9XpuxiA94dXXVZNjdblfJZNLYXYKFJJkH1ufzWSNLkA22BC52vtvS0pJJ3fnesPo0gRQs2Aei0aixnM5mbHp62sbZIMMiqIixDshnUQTMz88rEonY5wKdB+WFUSchkxA+VAY+n8+SoCmOCSWDfXPOZo9Go/ZekNNdX19ra2vLPFBcpHjoKK6RuTsvL2dYHE2PU34NY0EjgF+OS5lLiv0myfyNIPlI3tlzjUbDFCXsLy4mGixJVrxR/E1NTRlwEY/HbW/BRCBdC4fDkmRrwOPxKJvN2kgR2CIaXmRp0tdFDqoNSXZ5I9kHOcaL6HK51Ol0rCElQZ1LFgAyHo/r6upK+/v7ZkXp9/sGYpIUvLCwoHq9bv5FLurl5WVrbCqVyqNUakaYkXNAgYt/7eXLl3K73To5OVEikdDy8rKKxaJOTk7seS8sLBgzgmSbsxHwjtRun8+nTqejk5MTPXv2TNFo1NgMzljGuw2HQ1UqFTUaDSuokOXd3U1m3u/t7dm/55S4sf545kdHRxYoheoB9Q2MojPMrVwumxSW7wFryDx1JLP4uxn7QnMM846qgswTFEvIqjmrvF6vyf+RrpKPwu+FjfT7JyMhj4+PbeTO6uqqMZytVstY95mZGZthOxpNUr1pDNjLsKdu9yRQCHksxS/vDUZ0b29PxWJRlUrF1g7AEBYqAODl5WUL3pQmUsSzszNrBLgzwuGwMWo8Gz4jOSr5fN4kxJx97LVkMml2H+5JABeaYSxcNJmMdmRPnJ6eWpIzyiOvdzKmC3vL9PS0NWCtVsssZ4SePTw8mPWKQnttbU0nJyf69NNPbbQe0wwSiYSi0ahNG6D5Y71KsqYPUoGASgr96+tr5fN5jcdjLS4uam5uTqenp0qn0+Z1BiQlHRrPPBM1aP5ROHG3owLC606TzQx5wHOXy6XDw0OFw2FTFDhtErCz5GMkEgkDSwDL8d/TTDnVDABqkuxuYE35/X4Dbfj9PC9UBKgxuP9YG+yn6elpk9ni0+YsBSxnT/R6PV1fX9s9D0jqXHc8K9YMdjJnxgPfkbWPJQMFJzWg2+22MwzyA5KA2oJ90Wq17J4HnEe2zzsNBCajLgm4e/78uUqlkj7++GP99Kc/Va1W0/X1tQWjeb1eU8RwHzvvb84l/neIDlK0mdfOfueepj5+eHiwvY5dlX+j2WxqZmbGGlhIIwAOckd4vkxLwprAXTk1NaVPPvlEm5ubplrzeCZjH6+vr7W5uannz5+r0+mYNZYxocxqJ/OBfxtbAuAKtTc2OOwXkGV8N+4BAEvuk2azqWKxqIeHBwMn2ZcfffTRo8A5wCH6DM4NVGYojAhcfXh40C9/+UtNT0/rxz/+sX1+aQJSTk9Pmx0IwHB9fd3sQOVyWZeXlwoGg9re3rZa5eDgwH7Pdz+/359v1YTjz6ORuL29NU8u0gc827u7u4pGozo5OdH09LQxx0hsuAho7JHPIlkFjUVqDAN8d3dnvgxnwA0NOw1pJBJ51EBSlLDYYVc5ECWZJzIQCKhQKFjAQq1Ws8uz2WzaIcf/zqVD+IE0aT5qtZoVjARW4FX2+/2WsHl3d2fNNSxtr9fT3NycjXDzeDzK5XJWtMKU0JByQFKIVSoVra2tKRgMGmMNe9HpdMxf7Qx+a7VaNuqGYuP29tYOp8FgoHw+r16vZwm70tdSHMJ5aMrZ0M1m00be4JVHEkTjns/nDViBFb+9vVUoFLIwm0AgYOAK6CDMCwcSs5Y5PP1+v9LptIbDobGSLtdktifFVSqVeuSlBEFPpVIW4sd34ADt9XrWMHs8Hntu32zIYLCZgynJii5+AIokGfrI58fjB6MBq8MlHIlErOno9/uKxWLGwgOSIElEzhSNRh+pASgwGHXG+8Mr6vS2OyX8/L3BYGAHPmwFTQv2FAAZQA0OfgooLmK89sivncw9YVRIVikSaRK5TPG88nuCwaA2Njas8ePMolnmu/HcYbhglWu1mhVNFI9OkBB5HiBPLBYzINE5xxWbBN5Mvtfa2ppJ0ZH7YxPggqeYJ3hqMBgonU5rdnZWn332mbFS4/HYfMKZTEalUsmC4kDKmWFNqvjV1ZWeP3+uzz//XIuLiwqFQiqVShb0wzqBWc/n85qfn9fGxoaFZBGWiArj6upKmUzG3r8kC0j7Zl4BTdf9/b1++ctfqlKp2HmKJ559C2uUz+e1vLxs508wGLSZ2siWnQoiGni8ujD3MPoAPOQRpNNp+914CDm3aRYLhYJZCfC8w6ABbAG2zc/P25SQSqVi4CLhUjAyxWLRwE+YHM5iRn6l02kDMbkLzs/PTWExMzNjSjPOoXg8bqw6DCPKqIeHySi1n/3sZ+YBxSrw61//Wj/+8Y91d3dn/mfuX+wO+HhdLpcKhYKdPUiZUT3QLMIUM96IsE3ATYAUVBasmampKcXjcWsK4vG4KYVmZmZUq9VshA8gYyAQ0Pn5udrttgKBgIFc5Jngfec+YEoAUk8aNe4H2E7OYT6v3+9XoVAw8H0wGCibzVrgYiaT0dnZmTH0WNoA1t+8eWN3+c3NjY2hHA6HZtdCHSXJmmB8/JzRADCRSMSCz87Pzy23Buk+FgnAWnzvpVLJ7nruhKmpKQMBeB9YADmLUR9Qu9GEMDWHs5J9iGoEoNrj8ajz1RQI1jxAEXkaV1dXltGA/Yc9QnMLiIEdkIYZCxONJTVTMBi0BH3UepzvSOshHfjf+XOAD07v7vT0tJEufLZUKqXQV+PJaJrv7+9Vr9fNGkLeBkoU6jxsDYBFTOsAJHdaw3q9nu1v52ztfD6vbDZrDS2NPnco/zYABWA0wBnPD2AdVhqwCGaYmgAFmiQVCgUtLS3ZWqNWQ40GMIdV6JsAgJMkok6nlkeR+L3vfU9///d/rxcvXpjSdm5uzjIjrq+vTa2KbQjQADYYcod9QL2Blx7yEEUCXnC89ZyhgH/RaFRPnz6VJOXzeQMAGDH3ySefGDGyuLioZrMpSarValpcXLRneHl5aaB3MplUu91Wq9XSq1evTGlAKv1f/dVfmbQeCTzgyYcffqhQKGRqhJ///OempCWngSDQX/ziFxYs/Nv+sAd+nz//T/Wcf6sm3Ov1WvPDIkYSzPgB0slzuZxdfplMxqTAsE5v3rzR+vq6XdgU0DBCjB1YXl5WqVSywIRIJGKoFcnEa2trVvw4EzJJRuYwJ1DG6/WazxbJ6Wj0dUL1zMyMNjY21O12FYlEHo2EAlklAGN/f/8Rm4GnjCZXmjBq6XRa+/v7JgGjmKRYp5BiRizPBDnM27dvzTdKYBXIaLPZtIJqNJoEzuVyOblcLvNxpVIpk0nOzs5qbW1N5XLZRuNIsguIlFUuIZBWGuL7+3uVy2VtbGzY90QGw2XY6XSsWURqT4GGb5NGm+dGgVav1807NB6Pbb745eWlsdOdr9LApUkYoCSTXz48PBgKj+f43bt3BsoEAgGT9XKJ8JwJGMILxQWfTCZNMsWFCwNTLBZNxkTxRhGL35qGHMmyJDsEUWDQMIAgd7tdm9XMn6fpZB05Q1S4yCguYQV8Pp/a7bapFpB5UggCPKCg6Pe/nnEOMISKhf9Gwcf35bPwLJGNYi9gnc3MzOjo6MiCvwBXGHEzNzendrtt7AIsBBJZACn+GyPWkBjCdCF3pcmjKGEs4rNnzwwwAiTo9/vmQadYdLlcj7z2sVjM3iVeaBhr1CkUgjRlAIo0wDDWo9HI2NZKpWLFFzaC8XgyhSKTyRhIORqNlM/nrbCZmprS+vq6jSthH6MGQNaLx5LngioiGAyqVCpZ+OTR0ZEWFhZsZi1qBD7PzMxk7jcMMCAjyfUXFxcqFAo2OolC2ZkajWWm2Ww+aoiRob98+VL1ev0R8EOhiJWDMwfgiaYeWw7KI0AjLCN4OgH7WK8wN07WA/CLdQErVq1Wbe2TScEzBYSZnZ01/9309LS63a6Fa8Ko3d7eWg4KjNDCwoIl6jYaDR0eHuoP//APtbW19Wjkntvt1vPnz1Uul5XL5ezZEYbH2UhgGOceYXbD4dCYa8Ayml3sJxRnPMeVlRU7O3g+qM5gX2lUacpLpZJZZJzBbJyrkUjEnt3Dw2Q8EFJxp+0BS4ATKGV9pFIppVIpG+FXLBatiK3VaqbSIvEcMBeVDA0YWTAPDw82TowRftQ6FKa5XM7O4VgsZnfdeDy2d8ezHo1GyuVyBtiSrULz8vDwYPUG5x7AKTJ75zSE+/t7C72SJuGTTsbYqTzkfJyenrZzF1XFysqK1XPUL+yBbrerra0t22ucRUwNQRWCJ5x7nBBFSbaWaH6x1VDroAbirgTsAjTjHSDnjsViBqY4rTLUo8iuacCxbKCQobbhruceI5/l+vpa0WjUQAHODWpf/u88Z9hn7kaAZ+5bwMupqSnVajWdnZ3pgw8+kMfj0cHBgaSvJw6VSiU1m01tbGzY3ezMVnCOUORe4zvCtvv9fmv0yExAQVAoFPTDH/7QFFrsT9YDIAOfn1qS5gr2GaYVixB/jr/Pc8UmQX1PXc1ZxP7n7qSO4e502uIk/W+qVZSq3GF7e3s6OzvT7u6uASgosm5ubkxNxBQPpnC43ZPgxpWVFfNiX1xc2Lg4J6DAJArOP2bSdzodjcdjVatVvX79Wj/96U8fZR88efLEbISEtXo8k/Fs3KmtVkter9ck5yhPUESg5Pje976nVqtlGR9+/yR89eLiQrlczmTrjAEOh8M6PT01VRYWp6urK/3kJz+Ry+XS//pf/0u9Xk8HBwfWR62trX2b1vC7n/8vfr5VE55Op1Wv161gYGEgJWasxvr6uskPCTApFApqtVpaXl62iwI09ubmxhYsBXAmk3nEnBMWNBqNrGCloUS+JOlR4jlFa61WUyaTsU3JRcIs4FQqZRIQJO4gzfg7OWhhY5aXl43l4bC4uLgwOWOvN5l7ysUKE4YvnfRgZPsnJyfmOydJE483h0o4HDavYaPRUCaTkfS1151Ubwq94XCok5MTjUYjHR4eKp1OWyPIJiMtU5LJBrk8+Dww4YTFYElAeoPyAFTYeVkRpiJJOzs79v2mpr4OkVtZWTH/LwEi4XBYOzs7JtfG5+XxeMzTiQIBVLVer9sMX6R3IPZ4Gb/JfnGxILsOhUJ2yD88TMbw0PBJMjaLot0poXQGHwUCAZMWgYzf3NwYW7O6uqpyuWwBdDS7gBOSDP2mGeZ7+f1+Y2tpomOxmBVMrD8uHgogii8UBDSGjKcBSLm5uTFmJRgMGgINA0IRwu9GXeIM76JYJvAJBN9p+YBddIaz8f/nwqDYp+nhnfMZUd6AcAeDQQvNoyhyhn8lk0k1Gg0VCgVrlhjphEoAORnF0Gg0mfKwublp6DxSVYJcYDppcPCWezwe1et1AwJR1VCQU0R6vV4b9xMKhUwdQnEHig9qD4MtTQrEdDqtRCKhg4MDPTw8KJlMmpJIkoVB0VQAanAm4Qul4PX5fJYo3Gq15HK5LFmeM5x9AfNGkXR+fq5ms2me13q9rpcvXxrINTU1pR/+8Idqt9s6Pz9XMBjUy5cvdXFxoUgkoq2tLZPGHR4e6vLy0gCdYrH4aAwcuQzsC54dFgjkzzTBKD+Q7/Z6PUUiEa2srDzyOcICseeR0SNxd0qsY7GYJP1vkx0AEWEHOW9gTmmSYL5Z+0z5oGn48ssvbWrF7Oysms2mhRsC4mBLQPYKIwQ7yVqEvQMwQ1bJ+U0zhcw7GAzq4OBAL1++tNR91inrC1BpMBhYYQq4CKuFB5qRoTDQqG8ANmC/YQKd2S4U8b1ezzyuNBMokUKhkNrttt3R+HkJsuR85J1JMlB9YWHBVCgEQR0cHBjoDUDD+8e6xYhIn28S2FmtVo1NhOWjGWdN0DSx/zlTi8WipqenFY/Hlc1mdXd3p0QiYXcMSdKAD2TkSDLwA3UBPnD2srMB4tkBiNRqNS0sLNj9SU10dXVl+x1ZPPcJdSBsG6A67CWKNWdjT0AjTTap1s6ml4aawFNUN7VaTfl83kL0UCXgaea+5/tDBlAj0chRV1IfklERiURMOcbvdWZG0LQhd8cCBRjH2U/9xFnj9/tNVfezn/1MXq9XmUxG6XTaPPUw/CcnJ0acMIZMkt2vzuBFalTOcfJDABUhoQD7sEQR/IvixNnoQshQ76Lew86FHQalI6Ax56YkUzr1+31TeCaTSctIAVzjbuPf4i6DlOLscCrgAI273a42NjYMWEwmkzo7O3uUVQIgwDmILYgagT2L4g6lDwQQ9x81DHc/tQq/c3Z2VqVSSaenp6YYQzkHCPz06dNHeRHkVkGMYZGFYFhaWlI0GjW7H/kJ4/FYsVjMvOGhUEhLS0umtGm1WioUCpImcnSeLfsfhRT3RqFQsHGG3H+Xl5f6+OOPTWn22/x8x4T/7j/fqgmvVCoaDofm6cYPMhgMVK/XDcGheUFuhcQOeSNFycbGhsrlsh2+NFySjLk5Pz9XKpWyVG2K3Wg0qqurKwtCG41GdpnByM7MzKhcLpuUE2Sawpt/B08uFwwNM7Ix5/xgksdhWRmBhAR0fX1dknRxcaFarWaFKJ7itbU1k4ngCSRYp9Vq6fDw0L57r9ezecLPnj0zmXokEjGZI/JZQklcLpd5rZCmMfoLCQ3AR7fbfeT95TJFIg7zCnPNKCOfz2cbmM+DTxL0DoCGueE0rXzeSqWiarVqjS8FhHN+JSwpTCg+GRLVYY1o0DnA8YqBnBPY53a7Va1WH12+sGYwHM7Qu/F4bAoODmSePUFCeD8ZC4ddgxAVkomRzDJvHTbdaY0YDoe6vLw0xozCFgDEGRDF30Gej4cQ/xANwunpqTKZjAEoFK2g//ibuYiur68N6MEziyoDlsIpF/N6vRbm9fDwoFgsZgw7PnDWFGnbNCNI5GENQL+55IvFonkMQdklWfOHH5yMCsYHud1ua1Ro/F2uybijRCJhYZAg5NPT06pWq2YRoJCTJuoK0vRvb29Vr9dNSSFNmlvWFwU33m2kvRT8Nzc32t3dtXUmyUAP5G4oacguQCYIICbJpIK8R4onn89n1gs8rJyVABAoeshF6PV6Oj8/N5YfOwOMarlctv1IuA2gWiKR0Obm5iMZNwqHer2ufD5vhfQnn3xi72lpaUmLi4taXV1VMpnUF198oVAopO3tbc3NzSmfz1uqeCaTsdAgWFbOaYAYpwIDhQJMLswjScowcePxxMPMOXp4eKiHhwc9e/bMvJ23t7fqfDVCBqsFjStFKHcZ7x5ZKEAIe4pMAawr3J3IyslE4Dzmub59+1br6+t2bg+HQyvWaAp7vZ5ZHAD4AJVpAgixS6VSymazdp9x3iSTSZNWnpycmKqCxpK0bAIVyYK5urpSIpFQp9OxZhZbBs0JewoGD2vO3d1k3i2g49nZmXw+n837xYIAG0QT5Nz/KHY4p/1+v51l2MZgyLPZrLHWvV5PqVTKRvHQcFInLCwsGEgW+irpG7BycXHR1FYoi1D7ON89vv2HhwebCMMUEwBF9hdqgGq1qqWlJeXzeWtoYdFpQlKp1KMgKxo/FGKAHMFg0IiCy8vLR2oiztjBYGB399HRkY2+5HNSG0CcsCac5yP/czwe2/gjZL5ut9s+IyRHMBg0UJb7g+YcRR85ETCF2BlY8wDufr//UVAoDTDvlMYawIt16ATb8BazlrhvCoWCKYe4YzmPnd+Z5pNmFFUa4Jo0ybh59eqVCoWCNadYLKixsCZwtgG40EBzr/C+AXP7/b5lXTjB542NDR0fH1vNyr0BgMLvBDjnHGWt8W+Q3QQwLslUczRfPDNnWCTBzfF43PoCSBCnhYX/R/3pVO1gaWBfdbtd/fznP5fb7VYmk9Ht7a2Wl5dtXxDAyrri7qQOJZT45uZG5XLZGk1YZGwsDw8PyufzZkWkPkGVwnevVqtmaSMkstlsGmiByiscDptiRJIWFxdVKpUe5R9QN2M3QnnCXQz7jSwehcfZ2ZlZ10qlkq1R1hVrmnBm1vLx8bHVkwS7ce7mcjkb+/jb/HzXhP/uP9+qCae439ra0sPDgyUTElbEiCtCgzwej371q18pGAyaN4jLIpFI6OTkxGZRzs3NWWAABQkIFOmYhFQkEgnd398rmUxqeXnZZt+yoPDjzMxM5srSVHFx5XI5VatV7e3tPUJlueBBJ/m8NAuEnszOzqpYLGpubk7b29saDod69+6doaxsgLm5OcViMX3xxReWHM8lQHNVKBSUz+ftkK7X6zYDFYb88vJSR0dHdpFRRKysrCgQmMwV5nCjAVpdXVU8Hlcul9NoNLLmudvtWiFK4crFwWfisEFOzeUC6wpjxriXdrutWq1mzCI/tVrNvLo0SMiD+v2+0um07u7uDLlEypbJZKwIJPRsMBhodXVVl5eXKhaLhkZy2AIAjEYjlUolC2nhB2sBaCgFCHJFGB6YGBBimktnQBNrgkDBWq1m0lbWEmj93NzcozFz+OouLi5Mlg6AAnAB++P0QYGYgtTCHqTTafORU1y5XJMgOYomAp0AASjOkTCvrKzI6/Wa/K/dbluqKsoR5HF4rGA6mAkNaADwMzs7a6EvsG0UhzAYAGo8536/r1wuZ/4smDe3261UKqVms2lrBqBKkhUzw+HQEswJ/PH5fJqdnVUul1MqlbJiC0CPogaAhkYEtQprw+2ejLMhdZWgvUajYaPBCFGEXUIZgHwNFhjlDGcBoAsgEkUAsmDOJpg0/h6ZEEwTQJ5H04/aiD06OzurZDKp2dlZLS0tGToO4LexsWHTBFiPq6urlsJPUY3UtVAoGJvFuoCJ5vzCjoH6AJbj8PDQmPN+v69f/OIXxkbCvGcyGVNToYgIhUIGLlBkALpy/nNPOQs6bDC8k16vZ81ovV7X0tKSFfk+n88KKVhAp38dCxOyX9gKchGwlDiLJYCqqakp+7sUthScNHKskfX1dYVCIRuLiB8R0INnQuDmxsaGzs7O9OWXX1pAHk0s6wXwBxCBJoIziEZla2vLPO94YKWJ9eeb6cz4uV0ulynlYrGY3VUU7xTFKBhQwGGfcLsn43ZisZg1a07QD1ULwAcWM0mKx+OKxWIql8tGBjjlroQpUexSj1B4QjDs7u7a8yfICDUcknCPZzLus16vGyAMWMyYt2g0aueJk+kDaL2/v1cikZDL9XWQJGqO8/Nz+XyTME6sZBTS3MeQDjxTSY+krbDRNBVO5ZHL5bIml9ArQEyyEAAWaFTxiDMyCTCN94wUH/sYfx6FgiQ7V2lq3W633r17Z8nqP/rRj9RoNGyvkaUAaIOiyvkcnOclzxeFJD5smgwACGeWBOA7iob7+3u7N1lDZKuwhtkT5ATw7zqVM5IeAebcv4wXJd+BmpJ9wj5fW1vTeDzW+fm5AoGAWq2WqSKpQbj3+v2+rq6urBEvFAqmCGGSDXue0EmsK5Ls7nSq2wCGYL/Jd+B7S19PNWE98ju46zOZjKkbadK5o5wKAmp2J+mAFYOaiXuqWCzq9vZW3//+9zUajezcWVpasvfFGkPd4fF4bLRtOp1Ws9nU/f29isWiqtWqhaeSa4QlJJ1OmxX27u5O1WpVMzMzplqVZPUkIC52NBRHAAjPnj0z+0KlUrE9CYFEzQYp0Gq1VCqVTDbPfuJudYL43W7XPOXU6agynFaR3d1dBYNB5fN5U/vOz8/b50kmkzo/P7faBDD6u5/f78+3asLL5bJWVlZMzgPaDGtBsUNjSwGAxHtubk61Ws0CI6rVqnw+n16+fGnSibu7OxvZg8cFaRyyD6fc5OrqSpVKxQ4gfgdyrG63q7W1NZvRR2PTbDZ1dHRkMlOkXrCdeES9Xq9OTk6sSONwQnLK9/3ggw+sMKJAz2QympqaUiKRsEVP08i4K1gwJI93d5PZ51tbW/L5fNrd3dXt7a2lizvHBdVqtUfzrGkAKDQoaghvccrpuYCcxSvyZCe66QxuoNja3Ny0RsIptaZ4gonGpxmJRDQ7O2t+ZQ7N9fV1K0jwaRLSA3vHBcYlA8NLE41PnWdJGiQXIocaLPHU1JSSyaQl2y8sLKhUKunm5kbPnj2zz4mPDWS9WCzamvP5fOa7BOnk8CeMD3kdABXSUgAOl8tlNgHADp/Pp52dHQUCAVObgLJTtEgyKSPvmkaxWq2q0WhodXXVDuFwOGzgidfr1enpqTFN0WhUkUjEUGKnr7zT6VgYj9M6QHEEMEMxD/ONDJbgHQp4WAbYaxQHrDl8gAsLCyoUCuZFpSE7OjqywoMRbXhwncyaU74qfW1P2d3dNZUF4AHyLjIIkNXib0caRugTjVar1dLW1pYGg4FSqZT6/b6xsuxPPPYU2YTZ8d9mZmasIHH6ILEH8IyRoXJuIBUejUYGyMAOw5ghM0XiSI7GycmJNXbn5+eanp5WsVg0NtTlmiQhc/GzRwD07u/vValUrNGhmbq+vjbWiIDE+fl5XVxcWMFNqCZnIzJFmBv+G+PTkFhyZtFQEIwWjUZtrVKk8R5YyzCHjUbDfIw0xsfHx7YnLy8vlcvltLu7q0wmY+wnzA5MG2cKv5/Rh9g42BvYJViDNJHBYPBR1gZ+Q4BL0sv5OTs704sXLyzvAPkhQag0C+12W5ubm6bKyeVyOjw8NMAYOT4TA2iEuTe4Rzudjv039gZAELJz1jhn8Hg8tpRx/K38XmfD7fTe8ywBMGhIZmdnzZpEIwwwyXkJyM1IQknWlFNcE06EcoAzPxAIGOgfi8V0dnZmrC/ZBwAW9/f3Zg0h7TyVSpkntF6vGyjgPPOdQArMo1NFxO8n+4acBOeIrNvbWwtlY5wRU0bwT9PYY1WD2eR7ALy3223t7+/r6dOnxvLzTGZmZowpY69jC8hkMnaewrDVajU7KwhCPDo6ssDTubk5ffTRRwa6hMNhC85jz8TjcWuYOdcImkRVh8KA+5yznbqLZon/O7Ua5wP1UalUsgwCrFtYifjd/E6AMd4Byk2v16tyuWwgGZ+Du4I7DMYbNRf7gLOLc5t3hxya0C0InGAwaBYe5jU79yr3KtkWBKzBYAImuN1uZbNZRaNRZTIZW5cof6hBsNgA6tNEB4PBRwD07e2t5aKwF6lLqPfZ45zZKOhgYZHs0+hLMjUdpA/nI+Abga3kf4RCIX3/+983mw1N55s3bwyEwlrjnKwAOfL555+rVqup89W8beZyLy0tWX19d3eni4sLjcdjm3bUaDRMzk0NSn0DyUhvQFApIAd102g0yWuC6OGZ806w2UDwkRsxNTVl1kwn2NJsNnV+fq5SqWRgCfcyZ2w0Gn0EGtVqNZuOlE6n1Wq1ND8/r2fPnpmNplQqPfo9v83Pd0z47/7zrZpw2B2kdaC/HP6lUkmj0cjmO+PNDn01AxGWBMkGXo+TkxM1Gg15vV7t7u5qbW1N1WpVvV5P6+vr6nQ6ikQiJrVy+sE4FGOxmCVYg5Aj58TPyGEMGjU/P29+K8KX9vb2VK1W9cUXX+jFixeanp5+FCLFoU9DSUIp/rZGo6FqtarhcGjhaZKscatWqxYy53K5TP7FOIter6cnT55oenpaX375pR3aBNXQ6MBWjcdjC2ajQQiHw0okEnaBUnjBztNcwJDywyGMNE6ShUt1Oh15vV69fPlSkqyApRmYn59XrzcZA8a8WSQ4kqwopamkIQWscQYfUdBdXFwYco3kHR8TAEKj0TBUkIKSzYq3m4JK0qM0cRr5TCZjFwMhFlwESF/JOaAQ5LLgMKZBxhPp9XpVrVbl8Xjsz8EgwgDTHK+srFgKN6wwF54kY50p3p2SMJgo/N8UE4xDKZfLj5I1udBohrgEKeY8Hs+jdFcuVZQWo9Fk5iyNibOhRP3S+WqcEI0jxQcMO3vis88+UzweVyKReFTAezyTSQBcRIR2YVe5vb21xpOzx+P5ek4sjNTDw4MuLy8VDoctY4Lv4wS9sHMgEabhu7y8tP/uPP/4+6D7gE6sW5oC5NCSDOSCyb67u1OpVLLnz/tC0XF7e/tI6kbhCSNIgUIhCohDgQZDweSJ8Xis169fy+fz6aOPPlI6nbZE7Xfv3lmIGAwERQGNLqACUrd3797ZWMTj42PLdOBc+2aQUa/XM8lzOp22dGlYg3q9rtevX2tpaUmJRELtdtv2GYnMBOe43ZORWxR5zuKQPJHZ2Vk1Gg0tLS2ZhC8QCKjb7er6+lobGxuTC/ArqWKpVLLPzOeCpYathUni/kMe2G63TSbOusfnLE2aDacFQ5JJwfmzMPBIUr1er16/fq3PPvtMT58+tSL28vLSsiIAtwi+9HonY5oAQgqFgk5OTkzthMIEiTO/A3CH85q7FKCZtO5YLGaJ5sFg0IJO+Z4UpK9fv37kDX54eLDRkb1eT+l02s4RJpygVsM6wj1N+jpnBs0m+wovP2diOp22UFJsFs7ml31MqBiKjlarpfPzc2uYAI7xb+fzeasBeIaRSEQffPCBhsOhKdZubm4Uj8cNHOTZEtRH6CZhl8iWO52O+TORIvMuSXznPUqys4XzjH3EWcAzxO5EDUXIF8obnqff77e7/fj4WPF43GTqgLAADexLmFaeq8fj0dramlyuSV4PtRcgNI2UU3pOQC73pNvtNmKDWoR9zZ0HwOx2u+3v8zudoC55N6x7/h5qMZLYuQfZTzDShULBmn7WAbah8XhsgBjNCnJuFBic+TTlTgUYnwXvOv/m1NRkXFm73TYFEVZJ6k/scJxNABxYUfx+v7744gvd3d3pRz/6kcnKWTPOYE5CLclUAegB3AcQ4d3QlPO8qB9RorIPITw4A5wqA85CJ0DHu+P381x5TtwpEBMnJyfmi6cmb7Vaurq6UrVatXODvQbY9ubNGwOpuS9RvVxdXZkl9Pj42BjqlZUVJZNJWwM8i36/r2KxqLW1NbOChEIhU0gCfkuyjCcULzTsgAiowJxWDDKfOFc4Yxkjyag1WHKnWgyfN6MZyQxYWVkxwMzj8dh4116vp3a7rcXFRRUKhUeZKN/9/H5/vlUT3mg0HqGtyHoODw+1trZmrClhM/jJOITz+bxubm4sjAN2uVKpWFE7MzPzSE6MP5DGgSAXmolOp6P19XXzlQ4GAz179syYXKS3hUJBo9Ekyff+/l7r6+tWXF9cXJiXFaZqaWlJP//5z40ZcYYPFQoFk+biV6JozOVy+t73vqeTkxPl83m5XC5jFH2+SXo3Pq1IJKJEIqFGo2HphPhnOHQuLy+NvRoOhybpwdPt9XqNrcB7X6/X7TKEmabRRZYHkkyjJ8kuUw4+0NtAIKB4PK7b21sL1uKy7PV6xtpeXl6ahwlFAvNrYZSvr68NyCmVSnZB0lCAtEuTQjWRSOji4sKaKBozJNKsHdJDadL5fVyYNJFIyxYWFqyxA9iQ9MiXLk3YGYozWImrq6tH1gJSVUHgKVgAMrgsaPY6nY75+UhzR37daDS0trZmF6f0dROOtIj3SVPJhZlKpayYpeCF+ahWqzZi0MkEO9k7p3eP706hwvuF+XO5XJbA6fQdXV5eqlKpSJr4pfGzUaAgPyYjAQ/vwsKCarWaMV+AdxTom5ubOjs7szWNdJhgw36/b++ahhRkGdQZDzQsJPYU5gfzGWFHOKNoqJ3hb4CMd3d3ZpmABYGZoRmnYMQLCmPg9DlTLDBmDNCSvQKAyNqFIWDEEMwNIFcmkzFmB8kfBSzFBZLSm5sbHR8f6/T0VE+ePDHLBBaVfD5vhVo0GjUmlIJnaWlJ6XRa/f5k6gXrlbMJL20ikVC321U2m7Vi4+HhQWtra8pkMmo0GqrVaua7T6fTxgo59wH7HFUPCfXj8dhCaGhIZ2dnrbHt9ycBZNPT0xYGSNr7r371K33ve99TNBpVqVSyNGak54FAQBcXF3b/OWeQS19PloBtIEl3e3vbik0CPllT3HfD4dCAVfbC0tKSnj59akAeRSnBioQBXl1dmWTa6amcnp622dAwQ0jKfT6fgdcAkeQ/ZDIZawA4S969eydpUlgzzmtxcdHOkqOjI/NW/+Y3v7Gkdc6KwWCgxcVFVatVjceT9PD19XWtr69bUjlnG6AuZ4XL5TL2mXOH9+4MlwJwBgzhHAEM6Pf7dm663W5LI242m4/yNhqNhqLRqLa3tw3AxS7X7/e1vLxs9yb7m3BBrCypVErtdlv1et0AaeS0nGl8B9inTqejVCqlpaUlZbNZA9No6ni/8/PzBtZglyHXhhwTFFCoM+r1up2L7CP2ts/nM9krIB+5DBToBCPyXGHYYXJRRcDAA0zS9HGXQkxAGDAOjXPu4eHBQLbRaGRyeEI1UT5QG0kymwyMH2oY5ioDWBEIxlqgAcJXXiqVjKwhKHN2dtbUmtRczjwfPqfTn+0MdnNK1JnWcnV1pXg8bs8JMIZai/2Mherw8FAul0t/8Ad/YA07wAkTLebn520vAhQ1m00bQUlzTUZKLpdTIpGwc4s6AnDJ4/FYDUY48TeBBepEzj+ASWo9Mgg4+7BBOP3gnGtOsoG8C84eSBX+bUkGOkqy6RuSVCqV9Nd//dd6/fq1Pvroo0fngNfrVSwWM3vVwsKCTe9Agef1em26R7FY/N/u8u3tbeVyOZuRzthHZn+7XJOASGr9bDZrvQS1HQA6yjFydLjHsF4yPaTX62lpaUnn5+c2yg6rIn0Q3zEYDGp1ddXGwO3v7+vzzz83i8jGxoaBIOzXs7Mzqy8SiYSpdAFKftuf75jw3/3nWzXhLpfLJEbNZlNbW1vqdDomZyUF0uVy2SFNmisSHxgHLtbz83ObBzs9Pa1SqaR6va4nT55oZmZG2WzWUGVQv3K5rM3NTUOWuUBB3EGRJVnwyc3NjTKZjBXApKfDvtPkVCoVS3EHaOh2uwqHw6pWq7q8vFQgELBRGTS1y8vLNmJibm5Oy8vL5v+p1+sm9ZNko9pAt6anpxUOh22MRavVUiaTMQn75eWlyuWyXbqJRELJZFKVSkWFQkF7e3vqdruKx+Oam5uzJogij7FKbBQ+N0y2JCtgnd4cGgs2ODJIv9+varWq+/t7Gw+DL7nX66lQKNi8VZhip2QXYII1wH9DQuvxePTZZ59pfX3dGNJqtWqFtCSTw4e+mskKU8CBzmH48PBg0tJyuWyeYj4X6CrhMFz6rDnePTM9SYaHEScgjs/TbDbt0un3+1YgAyo5pWBceFxgZAgg8WV+uVMaTUgesmeQZj4/jBkMKaAH4UqwDhSQoLozMzO6vb21RpuGtVqtand3174DHqdoNKrd3d1HwVKg7MvLy/bO7+/vVSqVrNghORpwqVarmdxUks1gv7y8VKPR0PLysiqViqLR6CMbAUVDMBhUpVKxQCAuNBpAiobh8Os0ccKjYNhospzeR84JQA8KTRgm3gcXNWg9e6vVapnvGhaStf3wMBmBxB4hpAh2A+bR2XjQ8MP60tRzscMIMqebd4wcFClfJBJRuVy285Rihwbl9PRU2WxWi4uLj0Ika7WayaZHo5GSyaQlrb/33nuSJgXxn/3Zn1nmxN3dnQVnVqtVffrpp49UP0hwWeOkPR8cHFhKtN/v14sXLxQIBEymB7PNWcznI+GdZPpms2ljETc3NzU1NaUvvvhCKysr6nQ6+tWvfmXr7u7uzhLXCTNENtxut21U0PT0tDVS5XJZU1NTpgrJ5XIKh8Om2CJvAHUIRTvPnTOQEERAIaxFhClVq1VTByChhR1tNBp6/vy5fQ/ORwBCpIuw10igyTzAW4warNVqWeFMc8q5TTNIwynJ7ppAIGBrNRqNqlKp2B7q9Xo6PDy0RgNfPvkPsLLfPJNcLpdZqWA82RM0bbC8hLLR+HMeOn3z3IE8GxQtNzc3pkbq9XoGPrKn0+m0jYfD14wyABUEeTXc14QptVot5fN5ra+vq16vq9VqGXALqD4ej7W1taXNzU0bZZTJZEyCznqoVqsWLof8PhwO27viHHdKr4PBoFmcXr9+beua98I5TXYJapebm8lsbkKfyEiYnp5Wo9Gw50mmh9s9ybf5H//jf+jP//zPjWWHzSVnAHYelhMCI5VKPbobqeeco/OcFjm806xpzlKnVxxGHIUUTTjvuNvtqlQq2d7+6KOPDJDivZK7gkoIFQZgG75kSCjuGD4H98XV1ZU17Kg1IYhQACJP5n2urKwoFouZ3Q7yAxUU940TSMAydHt7q83NTavXpK8D9civ4V6jdue9AHQiM6c+RJ3grBcBtVArQL7wHqmL+I5O2b6z2eb7s+f4N7i/g8Hgo8/CXkciDuAEqON8T6zPRCKhP/zDPzQVZCgU0uLiovb39+X1eu05Y4Xj2VSrVbsHBoOBfvCDH5jiAo84ZwugEJZPQC7UcoA78/PzKhaL1h9BzJXLZVPLFItFbW1t6eOPPzbbAc/5e9/7njXPpVJJlUrF1hRMfyqVUqVSMSsmQanT09PqdDp67733tLm5+ShriAlM9B7f/fz+f75VE76xsaHxeKydnR1D/W5ubuwi4ZAaDAZ68uSJBYdg+qcp2NjYUKvVUqVSsRA3pGdIlmDIVldX9fr1a52dnVkzBZqOxIymDrk1fmSKMQ5kQm1A+pCv00S2221dXV1pe3vbQnSOjo7sQJFk0mhCbZDF0DxTICBlw7O8s7Ojh4cHvXnzRtvb20qlUjo6OtLq6qoajYbOzs6M7YVtRdZ2eHhoowhouIbDoc1kL5fLJrcnwRgkGlS1UCjI5/NZQBqy/nK5bOwDckqQX9D+wWBgibnb29sGhCQSCYXDYTvokXzSyK2srFjgFoUNLBvvmfA98gQWFhZszBIFF2PC+PPI+2jAer2eydlhFpH9Iv0nsItxJp1OR/V6XalUSrVaTZKMDUWKw8VEMzEafZ12iofL6Y0GTW21WqpWq5aXUKvVjCnqdrt2WTl91yCmyKlhXSkUeS+sPZ4PTAOSJbyHMPV435CbcpkBPHAZwhhxsTabTZOW0ZzhvwVdZw/gY8Q3vri4aGnR7Hefz2e+qUwmo+PjY0OMOUO49Hq9ntkAYF87nY4luyJzRsmAd8opzeQyJzyG8BWYP4AumGnYDNQ8TksGVhm+N5+BUCSKA4qdb74z55gRJGIwCBSWPHsKFgqUarWqfD5vYFoymVQ2m9VgMFAsFrP5pqD1kUjEfGdXV1fa3983j/yPfvQjKzKR9xJg0+12zY+GF5tLemZmxgIZUbowqvDdu3dmCUmlUo8CH2HMV1dXtbq6qlwup0KhYIEvc3Nz1hSwt5ijynQJmop4PG6WFN5BOp22vUpAVK/Xs5AtWGOfz6f9/f1HDD2Nudfr1dnZmZ3xnAVer9fm69I0STK1C2MK2QNOyfvi4qJJJGGDkD4TCokaBtn/YDB4JF8GTBuPx1ZcYvngLkMmCrAzHA5NLSbJmEEYbywxkmw0FhYZmgSASBplgEbuY6f1A+WK1+tVPp9XIBDQ9va2nS3Mt2efcofCdLMOUJrQ8I/HX89f73Q6BqCh3EFxQsNCQY4nkucKC48lhv8bhWqvN5k+srKyYp/j+fPn9t8A2ZyZG8Ph0AJADw8PTemysLCgXC5nKcM04rzzN2/eWEPNO1heXtbKyopOTk7UbDbNq4xKDdaS9+fz+XRycmKTHJDVz83N6cmTJ3Ze8U5Qz8Bev//++8YaEnqHoqBQKFiqPMnsNP9+v9/SrTtfjfz6/ve/r7m5Of385z+3DB5UV9xB/I7V1VX7PvhaeQdOBQNNTiwWsxwCJwAKEMMZNhhMkt1hliFEONewQwGeAuKxfhYWFlStVnV9fa21tbVHQLwkG2uKUolzkP1JCK7f79fp6al9Tr53LBaT1+s1Gx1TeQjvcobYsqcl2VnORBe+H5MLPB6PnfvOXBEm7DCyEaXkzMyMCoWC1dqAyNjhAHWxcbGfnBJxZ6OO7xywGpUF5AHPkHOdv0ftSogrzTs1Ov8W/zcafvYhYC11ILULe4Pmczye5LxwP/j9fgObCFTMZDKq1+s6PT3VYDDQ8fHxo0woSDDOPJ4n9lXUEqSRLyws6O3bt6aeIbl9Y2PjETiPdQ+AY25uTsVi0VQAkIeA/9Ik84PQ1Ha7bX0NdxTvHzWuMyeFeq5UKtl3QT2JzJ69FAhMpoSwPr7zhP//5udbNeHNZlO7u7u2IGu1moUYjUYjRaNRC1aDVWLcUafTsYVFGiqLaG5uTpeXl8aSdb6ab0iBziFOwILf7zdvyNnZmbLZrE5PT+Xz+fTkyRNjuiuVikm8SqWSJJkUHrkHc66RAlIAwMI4fTz5fN4218XFhUl6SaIEdUOWHg6HzW90f3+vcDistbU1LS4u6vj42AKorq6udHBwoOFwqGfPnimfz+vk5EQ+n88QZ4JhXC6XstmsSftB6iiW2Oh8NqRJXEp4QTwejwULwU7QGNzf3xua2Gq1rNmgUSCNnSITyXIkEjGmixFFhKTRgOGz5CDg0AdRJKCNMXAAAbwzvOeoEfCMDwaTpHOk6Fxoo9FI8/Pz1gze398/CpwajSZBerAINLBchPPz8+p2u+ZzpuGjeYKNg5Xg71G44LmsVquW7nl9fW1hNLDLHHq8J4oeGk4SRmEJbm9vrSiRZKEuPGuaRqRmoMFOr6bT4jEejy211ev12lguGntSqWdmZuwiv7y81PLysm5ublSv1zUYDLSxsaFer6dSqaR4PG7KFC4yLt5MJmPWkGKxqHA4bMnjADKSzCtIoecManl4eFCr1VK321UymVSn07FmglCUubk5hcNhA64oWvBrIbcjvIr1Q7F2eXlpRSWyPAoGbCrtdvvR++I7IK10SjJB773eyZgdzrFqtWq/r1AomPcMFrVYLCqfz+vo6MgasPF4EorF+4Mdf/funTV+i4uLNvvYGZbEvkH+iJwdzyFF7dHRkZaWlvS9733Pfme5XLYxQ41GQ6enp1pdXTUFDI0j49tQGuB7f/PmjSlGmHAxHk9mEDP6MhwOq1wu21nDf6fJcblcBriQz3B7eyu/3690Oq3p6WnVajU7OwjpHI/HOj09lTRputLptGWY3NzcqFQqaXt724rAVqtlBSRnAMAP+/H29taajF6vZzLdra0t5fN5AyOc3lKeszPEiAIWnyrfB2kx4CW/B1a82+2at5H7dTQa2Wx0GgPWGvkSyWTSbE2cL6xTwAOUN87xhsh7neoYCuNIJGKMdigUspF3zO1uNBomE+U8ICCTcz0UCtl75bz7porAOb0EIIN7mrwIbEUAhYPBQOl02lL9KTybzeajed0zMzPG0J+dnZm9Ctk4TTB7COaYdzI3N2epzc6kdL5LOp22NdtoNMybT4NM0879/L3vfe+RbBdfN2BgNpu1CSyooJweap/Pp1qtZntmMBioUqkoHo9bnbO6uipJFuAn6dF5J03k5PV6XSsrKxoMBvr0009NJYe0WJrYE/k7SKg5d5Bg09Ci8ANkBSQlJR3FxdXVlbrdrvr9vmUKECCGzWdmZsbk0IBY7Fe/32+qOCTG3HM+n8/C71jHTlkwSjdqWvI56vW6bm9vtbi4qE6no4uLC+3t7RkhwHri93u9XjvrCOtifaIGQzVKTcXYOwD6+fl5tdttTU1N6ezszNYBxBKqmg8++EChUMjOLwAs9tXy8rI96/v7e5Pro17lLqHRpyGXJrYUFA2AYKhcuLMhb1Dh8N85s7m/qV04H53supNNl77OIQJc554G1Or1etrd3dXm5qYBfePx2KYhoUy7urpSvV43wLjb7dr3oWHe3NzU0tKSqW1dLpc2NjZMqTQ9Pa18Pq9Wq2XAOJ73ubk5ffDBBwYqUJulUimrww4ODixThXdBng+KovF4rN/85jfa3NxUOBw2+9H29rZOTk4MxERNxnrvdDpKJpOam5szyx/3IKCFNCEXuLcgSAG8Z2ZmdHx8/G3aw+9+fsefb9WEr66umpRzbm5OyWTSFi4bgbl2NLV4fkGjx+PJ2IWHh8ksTZgNj8djKcQU3sjaQP0IxUEu8fDwoHQ6bT7Ch4cH/epXv9La2poikYjJzAlqGI1GFt/PGKNIJGLNU6fT0e7urq6vr3V8fGwXKywAlxnJwRSbIMYUIMgTOfTw/eLbbTabJg3Z39/X7e2t0um0ksmkjo+PtbS0ZDJCmpF8Pm/s4uLiotLptG1wDlEaQBpmWAEOvc3NTTsEWq2WHaxcaMgfATwoymKxmKH7NKI0cuVy2RK28UkR5kTxyIFCM4c8C4ABpHN6etoYv/F4kv4OCuz3+5XL5azJp/mRZAzawsKCBX+Fw2ENh0Nj+tfW1szTSMOL3w2k9O7uTvV63dg45NO5XE4bGxvWIHFo0mBRfBDswQgoLhIuIzxzXNwUx9Fo1OTNd3d3JguUZAU3hQwefDxbPFcOXhBXpLCBwCRpnVF/TuUERRYsWuircWIc0j7f1+E04XDYWAZpgtQmk0mbkc0FSxopTSX/JjYIkshvbm50enpqTDdMIkUwvlcuXdhkwCVJxpizppCEomLBC05xzzMjpAYpI0Uka4K/g6KFswuLRrPZtAueCw5AirEfHo/HmkVUA5KMqUYFQLEFoMJ0hP/j//g/Hvk4PR6PzbxF1geKDSsA+x2LxSyEiYI/m83qF7/4hRKJhJ4+fapsNvsoMBA5qs83SeiXZOxIMpk0BhJmE9/Y+++/r08++USvX79WKpWyM49CG9CErICpqcm0CJqCZrNp6xbJO5J8fIgE+rE/FxYWLFgTdZPX6zVAh8/MiDP22Pn5uY1Aozg9ODhQIBCwO2ppacnGuNze3prKgKA3FDQ/+MEPrOCFAf+mDD2fz5tMd3193Zpl9hOfG4mvx+MxwBqJYuirGdWohGji+bsU8nz+QqHwaJyQ2+02dp8kXGxN7CFJ9iy585A/wmw6WVrAIqwB3AvS18FZkh6x2DRf5FbAOjEtI5lMqtvt2lhEJ5DG3qVJYg2yLyiMGV3H2UkTgYqANONSqWRsOXUIBAHz1iORiHkyAUwWFxfV7/cth4SRUU7lG+cmtqX5+XkDktgHfPaFhQUDL9lvKEIYA7a5uWkKLiZVvP/++wbAu92TYL7z83ObIDEzM6OnT5+aLBbQm+Axgi2pi7Dr3N3d2R1LyFetVrNmCzUZOQCcq4TGQsYAqhCOSWPFOwUgHw6Hll0SDAYNBHSCVSi6WOcAQIxhxHqD9UKSgaAAmPzZ9fV1Ayeoc8jH4DMtLS0ZS4y9jJRt/i4ZFYA+BJ5SH5PvQq4Edy+ZHQBQTuUJOS35fF5ra2u6uLjQ8fGxsbxkiKBaQgX16aef2t1Pk0ytCaGAfcy5H1B+0diyV6emprS8vGxnH4AswB6fx8l8ojArFos6OztTJBKx3BJqeWpSbFc8d4BGzm/eMc+aepXGnwacM5Y7m/Nte3vb1J7j8Vhffvml7YGFhQVTNHGuVCoVeb1eA/GxB3k8Ht3d3WljY0Onp6c2TYn8pWg0qrOzM9VqNZOxA/ZsbGxodnZW+XxeS0tLtv6d9sx0Om2ZWpzVfHdn/d1qtaz5B9igxwLUo57O5XIW6ge5ApjlDNzjrodVB5RF1eHMPfnu5/f/862acFBP2DGaBQ4HmkCYArxL/N87X40hgzXBnwLzTGOHL21/f19ra2vmO8MT7PF4VCwWdXp6qvfee88SymEn8OXE43EVCgU7cPDW0TRIMhSfDVcsFm3sGZ6gZDKpqakphcNh5XI5850i9zk6OtLe3p4VSfygEkAeiw+KQwfJ7WAwsFnNsJY0xsjV8Xa43W5jDsfjsUmvU6mUybcI4AKR5BBALo9vZm5uzkKSABQocO/v7w3JnZ6etn8X4IKLHASfRq7f76tQKNh3lGTPGxm1UzmBCgLGxeWaJF+urKxoe3tb3W7XEPF0Oq2LiwuTqhFghuSX0Irr62tdX18rmUxaEYoHs1KpWIgbz5vCJ5vNGttaqVQMvV1fXzefNKALzS1IKQc6jBMHGHMmaRQ54C8uLoxt43mARiLtTiaTthYoGmDK+b6AHPij2+227u7ujKF3ziRGKoWnk+YEJJjUc5o/1Bt4i2HbkLwhc0qn05qamlKhUDBVBgXv0dGRVlZWTHpHYx0OhzU3N2chjFzWrP/p6WmbM06D6yx4e72eMpmMMf/D4dB8mDRHNDA08RS3+J2wyNBwc7ExN5MxQpJMXglLQmFAOjeFDag/jTdqGgKCJNnZQ/PNZ0eiR8NJg0FBzx7zeDx6//33jQXg4gQUwHtGATs1NWXBXqFQyELxwuGw2u22zT8FxKAJ4nmTxIrEjUaHRmBtbU31el3dbldnZ2dKJpMWvuf0scEmpdNpA5MAfZDmYlchhZ2U+Ovra7148cJsDTCOi4uL1rBmMhnFYjGVSiU1m031+32tr6+bcmJtbc2efzQatcC5wWBgZx1zZGFKsSA4z3B8rDDP6XRafr/fPjMgEI01c8tRSwHwce4SYoQyLBqNWqPBmnKySYBHnNOosg4ODnR8fKxnz57po48+kiQrrEajkZ31AIgU9j6fT41Gwz4PxTCsoFPRIcnAhNnZWQNzYc6QQ7J/AaQk2bO4uLgwVQrrGUaNNF9yH2C0er2eqZKc6dkABNyjgBC8J84d/JZOqwl3ps/ns4IZmTazu8PhsKamplQsFu2unZub08rKioVVzszM2Hx0SaaYCwaDqtfrKhQKxmo77zpYSNQo7C23220KHhRIo9HXAXkACgSkEea6uLiow8NDWxPFYtESyFGjSHrk+6ZR9Xq9evPmjXmKK5WK+v2+Xr58afOUUSKSS8OzANBBZcS9SHNHOOQ3zzXONpRpAHAw1cxRZu9BvFCvARJhr6PRX1xctJGiBO7SZAM4YDFBhbS5ufkot4V3mUgkLNSUugl1B3aRu7u7R/PVaTCpCwaDgY26ciaU83khSqhDXr9+rWg0qlarpU8++URzc3P68Y9/rOnpae3s7FhdAIBUr9e1tbWlN2/e2H5kzxNIS7PGd2ctEuC4sLCg/f19m1TCfYbHGyIFmxF3mPMcQ5H46aefajAY6MWLF6rX69rZ2TGgl98N+EBtzh1BDYfai7uG+x8JPYA8FkLqUVRT3GFffvmlqtWqtre3ba59s9m0ZhSrKDVbvV5XIpHQ2dmZjo6O7L2jZGg0GhbqyJkuyfKj8Ns7gSNq71gsZkDn/Py8Dg8Ptb29bRYHVHc3NzeWgA7Y75SnoxhhOgHPFpJyaWlJ+XzePjPACsQVf87n8+mLL76wOxibGvkznNvf/fx+f75VE46Ut9lsKhqNWiPHJTMYDLS1tSWPx6N8Pm8HPZ4kZLg0IxTDzjEL+XzeAqhoQO/v77WxsWGFkjTx6xD+hldib29Pb9++Vbvd1hdffKGpqSljJre3t80rfnt7q6WlJUPo5ubmbD4gLG80GrXmF78hMjYu/nQ6bePJGBEFc8A8dUnWCFMswABzoTCKhKLz7OxMe3t7JjOBXX/16pV6vZ7Ozs4MqR4OhwaKwFSAuIOGwiZ3Oh2TpqIE4PNR8IC4gzxSHBH8kEwmVa1WDZQg4ALGAFkN6LjzEI/H4/Z5STdH+sclgWcU1QTeKQL9QPph2ZDXgdxSQIGmU7ABGsH8cADjqYMJZh0S9udyuayhpeGXpP39fd3d3en58+fG2s/Pz2tpaUm1Ws0CZaanp+3SxQ/V709GW8TjcZMCd74aL3d9fW1IJWwq8meAC35Xu922ZmpmZsZkiqCyyAphGrhkkIINBgOVSiVDqAkqQRWCz6tSqejDDz+09YBkCmn5mzdvrNFmpAqf2ev1KpvNKh6P2/PkOYRCIVN7XF9fW1CVs5mlKYCF7XQ6lvzPj8vlMkvJzMyMybb4e/jFJNm/EQ6HH/nhAE8AsGAJ7u7uVKlUTN7FzFjQZdB+GmKYLhh1imrOHed8cALkut2uNjY27N86PT21YEfUKjDD6XRaL1++NLAR5pRAqtFoZMVrpVIxpcbDw4NWVlYeNRk07YQO0STx3mG1r66uzMvo8Xi0uLhoXtj7+8nM3B//+Me6vb3VmzdvbB3m83mlUilLesaGQNL47OysYrGYyWJ554C6gcBkfB/jLF+/fq3xeGx/HoBqNBo9CnEku+T+/l4nJyfWePF9afaHw0nibbvd1tnZmTwejw4ODiygZnV1Vd1u15qfZDJpKcxut1vtdtsabEAQ5L80FaFQSHd3d8rn8woGg4/AQ85GzhzOK5QSrEsAapKQObN5Tvv7+zYnF+koAAEsMuuDyRg0s0hlSdiWZD585PHIL5F+ow6guWE/YEOCRcEjKsma4kKhIJfLZeF2Tq8rjTufATsV5/zCwoIBWb3e41BK55xymkvAWUAa5PQu12TsEOqL+fl5axgATQlYo9FhL4/HkxA1r9er09NTBQIBZbNZeTyTcT+lUslqHhp5FFidTscIB5oul8ul7e1tLS4u6uDgwBQnd3d3uru7U7FY1LNnz4yJDgQC+tu//Vu9fPnS1BScN/f393ry5IkB+IyTq1QqJtln/vjl5aVSqZTu7+8tZNXv96tQKNjIwdevX2t3d9fUJJyzkiyfgEYYxeFwOJkFTk3C3wFISaVSj9QoV1dXZkVyKoucSfj8nlKpZMAR+98JMrKnkK0DPBI6CHDBvw8oyChD7HGEmmFxGA4nUySQcXOeEsBJU4hqExKHsxbbFx59lFyAB9S91AsPDw8Wtubz+Wxs1j/6R//IAAufz2dS6rW1NQUCkwk2+XxeyWTykW2FRo4mmjMY4AgQl/MNxefNzWT0KWw16ibn3huNRtYculyTCTKJREIHBwfa39/XH/zBH1jWAJ57zic+D2QN75Am1lk/u92TcavU1c6zjvXJGiLPpt1ua21tzWp3vNtYVpiycnt7a/se0KRYLJoSEHYaayTAEDklqVTK1sv8/Lz9WZQkhAZSfz958kQ+n+9R7+A8o/1+v05OTnR+fq5nz56ZaovexNkcY2tYXl7Wj370I7O9UGczE5x7PZ1OG2DJ/6Q+ZBzl9va2KVV/25/vPOG/+8+3asJBryk8Li4uNDU1pcXFRe3s7Mjn8+n4+NhkrhSIhGrQqIJm8fK73a7K5bKFd+GxcSbWDgYDVatVC3Aj0e/h4UGNRkODwUDn5+fm8+ASXV1dVTQatUuRsTjhcNiCzFwul/b397WwsKDd3V3ztlcqFUtcRmJEMUoiOgxCLpdTIBAwJkeSNbRI1ZDhcSg1Gg1TF+zv7yuTySiTyajZbJoMbHV11aRvBPEsLCzY+Bn8acxrJXmTJhWmgYAz2ADYObzaFMTIbikisR+cn5/be8cbXiqVDPlDEsRB4na71Ww2TTqTTCbtwmu1Wpqbm7OihkKBRgd0nWR51AsPDw9aXl6Wy+Uyr1SlUrHQM4pDGlWanpmZGbXbbbsUmWsr6VFyO39vPB6bv56GgvCy+fl5vX371tBXfi9y1KmpKWM1CZGLRqPmywd8gGHhknWqSvAQIp8GXKBo4PIuFou2v0B3+c4w9TDHNOYUvaFQyJhxmkQaT/49p6Sdfxuv5NLSkhU6WD0IY+p8FThHMwjDg5LCObOVAtcZmOSUfjsLFWdwl7NJRGWB/BsfH9I7SY+kxdKEkacQoBi+u7szOwVePhoivgcZFtLjES0g3vgBaVL5PWRHwBa6XC5rxmFCpqentba2Jp/PZ4VCq9WSy+UyCwrS2dFoZGNWUCXAsuIVxt4DcEWxyOzxh4fJ2EVmPq+srBhwQoFDDgPqAuw/V1dXJh8FKAoGg3rx4oWOj491fHxsjDihmaFQSPF4XNlsVqurq1bs8o5goWj4kELSNKAk+vnPf65UKmWSe4K1YD4pFvmujETiuTN2KPTV2EfGzQF0OM+zUCikV69e2Zi1drutUChk9gXk2ahRuPuQ9VMkRSIR8wzCljMiZnd315pS5OAADChE2IfdbtfOm263q3w+b6yFz+fT9va29vb27B0DqJCTApBCs4qVAuUUAB/sjCQDWBcWFrS+vq7T01NjySmQE4mEnj17ZiwOIDCgJw3VYDAw1VipVNLbt2/t2TtlkCiVUFfh2WeU3e3trQGdkuwu5p3l83lJMjWGx+PRzs6O+v2+renT01NTjGSzWZ2dndn53/lq5i8AIaoSzlsaAZdrktwciURUr9cNnKtWq7q7u9PS0pIuLi6sfuKdcj4h10fFhuQX1hKWvdlsKpFIaG5uzkJtWWcwcjTASE3xKTuDFQkg6/V6JpOHIb+8vDQQuVwuq9vt6h/+4R8MEFpeXla/31c8HrdaAABckrG7pGN7PB5tbW1ZLg6WK6dXGvAJMBM2DnCBM5ZRfrDXnNnO/eb04AJCOZt6mk2Px2N2LNYp9zmBbZ1OR7VazaTZMPTfzDQg84YaDOXezc2Ntra2rEl1ZooAFsCU1ut1a2w3NzctC8ntduvZs2d2j2DD4z7I5XKKRCJGZuzu7lrNCjhBww8wxVQcQDSUcjRoAMvc+dQwrF8ATYAQzgeAlmAwqFevXlnmUqfTMQAekoc7kH6CM95pF+XPx+NxtVotffzxx/rZz36mvb09/fSnP7U7lvu43++bHcrZ0CYSCVPpUTfQe/DdqG1ev36tfr+vbDZr+VAAaqgJ1tbWDMhEtURfQ+1A9gh2IhQZEC13d3eWZD81NaX9/X3Nzc0pGo1a4OXx8bFub2/VbDYVCoXM7w8gwl5FZUJuBdkdKBuXl5fte8bjceVyOVPIAvpyR0CcwaYDjnz38/v9+dYjypAvgaB1u12b441kFzbp6upK6XTaLjRnE4x0hM0myTYmDRFjQaRJ2AdyIOSq/NvMbK1Wq4b+c3DBbn3yySfWfOF5JFyGw5eQKRBd0Mjz83Otra0pnU7r8vJStVpNfr/f2H6v12t+O+TEyLR5Zkhol5eXTdKNl5qiEA/c2tqawuGwCoWCySspcikaZmZmLOTrww8/lKRHckhk3rCVSEtjsZh5tfEwO98rhTryXACR6elpQ/mCwaB9H6/Xaz5p/hvfA9m8y+V6lLiKlA1J9cXFhbrdrtxut1ZWVtTtdm1OYyKRsHWAsuKLL77Q9PS0FhcXbSYrIVAgjW6320Zc4Uclcd4pOYNxRtqZSCSMmaZxRSLvdrstxIgEV1iLSCRiksNIJGJoJFKkRqOhTCZjAA+pnBReFHn8HgpRigJ8QmQssMYoYJ2ghpPh4vIlKGk4HBpoBVtYKpWsKCFBlssPv3a73VaxWFQymdTi4qKkyaXsnEWKpzYajZofkdAj1AgE0mWzWWuESXMtlUrmWyIYDnQctUEymTSZGgUOBRaAlN/vN3YXrxNyWqe6AJmnJGtAAPaQGdLsEmjH3GcaRUACAnVgAJ2NDAAKDArSSD4L35P1sLq6ajL53d1dpVIpA7ZQKMA8kqcBAMN5hy+TWducL/gXeVd8LtZKIpGwRGMsKACfFGv8WYpJWCakzoySg91cW1szHytWBUZAtdtt3d7eand312Z3A1C2Wi2Fw2EDBz2eSXJ9uVxWLpdTKBTS06dPzQsPC+20pPDZU6mUSqWSqtWqKpWKsRAU0RsbGzbmTJrYlL788kv5fD6bRYuC6P7+Xuvr69YMoA4BlAL8wePOGQKoeXt7a8AhwWbYJ2B9kbLi02ev0CykUik9PDxYMY4aifUGE+ecFMEUBtQYyEJZf9iBkDnT3FB439zcmCLK6aelgD45OdH8/LwymYxcLpcuLi6sKRoOh/Y/aeph0bmzX758qTdv3hgwzSSJh4cHqwOw8ABS8PsajcajSRd+v9/sWzwngqi4T9jn7Xbb/h45DaFQSDs7O6pUKkqn05a1cX19bQA34/4AECm4pQkYDeDEWcMzZ43QmNFYIs8NhUJWG11eXqpYLMrr9Vq+Cd8lGAyq0+kYqw7jj5SX8wQ2muK9UqnY+1lfXzdbCiwlo2dhKQOBgFKplOr1ukajke2lnZ0dud1uGxHpZNcAU2ZnZy0ottfrqV6vG5DLeseywz3KOoaV5P9B+FDPAMwCAIdCIWv2nbUjti3WiyQjLW5vb5XP5/Xq1SsD653gLCAi9yWNPuzy3d2dhXHRbM/MzFjTxlpFbQMZhfry6OjI1iTAMuqXvb099Xo9u1uazaamp6eVTCYtqAtZOuoKt9ttd7kkA4UAMUjOZt8DjABO4OFfXFy0howaGbAfUD2dTtvzwIPNPmfk39/8zd8oGo1qaWnJCCGAc+cdzPMCFCT/gPPh5OREvd5kmgG1I98duxmKCOp/FIycfxcXFyqXy6YcZOweqsDhcGjjgwGICPN99eqVZTexFvisTlUhdTS5O9x5qLnW19d1c3Ojd+/e2R2YSCSUzWZNZQVgGAqFdHZ2ppmZGT1//ty+K+MYm82mIpGIZU7BXFPrMZIZRQ41M4QL9eXKyorZF1DPBYNBC7P+bX6+Y8J/959v1YTDKkoyKSwHDDMtM5mMyuWy4vG4XRSgTl7vZCA83iK8c8hf8GzghaLpcbvdymQyFmTCJU1z98EHH9iBSRPo803Ge3z22WeGLrvdbiWTSW1sbNilAbrslE/BfnPIVyoV7e/vKxaLqVwuG/qPdzSTySgej9vscOT0Pp9P8XjcWDgKBiQ3i4uLhmSWy2UdHR2ZV5yLmrEIwWBQl5eXJmk/OTnR3NycMeI0kHNzc8aONBoNSxNfW1uzz8cFTajEYDCwBGmK/I2NDQNZuIgSiYQkmbQWyS22AlB6mjsad4o2kF4no85FAPvIRc9lTvMFa7ewsGDhPuVy2ZJduTyQCnHQwxBwAMEiSjKLAKNEQJphJ0hFpWlifn04HDY5Hs9DknmjaVBgv5jTyKXgTEalEKWAdbsnYVRcuJ2v5guDul9fX5uFY21tzfYXUl7kuQArJPUi54rH48pkMiaxW1paUrFYNHlVq9WykB1kdDALsVhMhUJBW1tbxryz7/Bu3d/fP0K3C4WCZTrQvPHf8GzXarVHoAEBdHgHaVwJl4HppTHjAoehoBhjv2CfcQaR8T6QvAUCAZO0IuVDTuv04UoyjxhrnN+LvQQ/LQqEb+YysG+wpDj9s5wtm5ubCoVC5pO8u7szxJpwQi7daDRqafSSLPSqVqvZuQngBsOOn5bCY3Nz0z4P/lJY/fPzc0tKnZ6e1ubmpvb399VsNo0xZ82zzym87+/v9e7dO3m9Xu3t7ZndAMk/wAJhV7VazZgxlBShUMi898idCedhD3F2t1otSTI1iyRjmJFyFotFY7Tj8bj5b7e2tqxRA4Rzu90GiBCGCcsBc4NyB3YbhgT1FWoHmlnW9Xg8tmaa89TtdlvOBOAY5wGAJvuF77C2tmb5GtgHksmkSUppsplIQoPEmuI+QOLOeuUdEYbKpAzG03E/0lBTFHO+o36gwaUpef36tYHJNNXYS8g06Hw16QAAi73A2ezz+Wze++HhoZrNpjwejwUROfMNCOokUDGZTMrr9er58+fqdrvm0w2Hw5YnMxxOMmw4A9lT8XjcgCmAdrzPBDJ98865urpSJBKxWe/UG7CdnClIwp3TCcgWuLubjMl0ypCd65J7nrua8/nDDz80UoRnhfqMhv3q6sqse/w31BTUM2/evFE0GtXW1pax32/fvrU9iiIIQmZlZcXqCQAgn89ndgPqk0gkYkANjKITYOVsgayBPSTgi+dB0B6NHO8aKweqFZQm3G8ezyThnmZzPJ5MUshkMlZL3N3dKR6PW3Pu/ByxWEzHx8c26xwLFIG1WFfYKzRRnI+xWMykxnh8yeZxAm8ANBAkrBMaQUkql8uSZKAGzTXhd3x+7iueCwpB7l/OUoARGnh+DzkL/F1qUs5y7tdyuWznBhkikkxWjYJiMBjYlBxn0CqKFJhk8puwYfFuUW06w82cdQnkHIGb9Crc35wtKJaCwaCePHmid+/eaW9vzyyy1EUoIrBOUXuur6/bqDMa5X6/r6OjI4VCIa2urloeDnbeQqFgdctoNLLQX9ZKLpfT2dmZJbrv7Ozovffes/t/fn5eH3zwgc7Pz+1MkSaqlo8//tjuNH4n78/n86lSqSj01Rhd6hNCQlFe/LY//3duwv/Df/gP+jf/5t+oUqno1atX+vf//t/r+9///v/LP/sf/+N/1H/5L/9Fb968kSR9+OGH+lf/6l/9v/3z/1f8fKsmPJfLWRONrGR6etoCWqanp833jfwvkUgon88bE4tEi9ASiiiSTRlXMTc3Z6EDCwsL1jSHQiE1Gg2bU+3001K4sZnwLmcymUf+H8bj0OyfnJwom80qGAxqY2ND09PTOj09VafT0YsXL7SysqLT01MbwUMYA2goaNfi4qLK5bLJG5eXl62YYJF7vV4bK4WsG3YW2cjl5aWx3+vr63bhv3v3TsFg0P48vsWbmxvlcjn780ik8WGDFBLUAmpWKpXM1wUCKMnmk1MYccA6L7dWq2WHJcWuNJEFMv4BBouij5FgNKEUe7FYzELX8vm8PvroIwslcrlc5t3BW4rVYWZmxqR8NNM0/CB/SKgk2RgcklTH47EVrsjfSL8EqQdpHw6H1sAXi0UlEgmToXIZ0NTAYiEz5l3QnCId4iLEo7y2tmZFGWh5r9cz33qr1VKxWNSTJ0/s4KaQkmRMG78D9gZWBp82AWMU0olEQvV63dgS/OVcXFzezWZTW1tbhrp7vV5TlwA6OWV2BBjhYwIkA2xBmri+vm6SNlB3GmRmud7f32tlZcUaIKcnnDA2knJHo5GFuiHzw37BvnOm5PIeaYAoRGHHOc9YDxQoNIKsL1gBMiVgDEDNb25ubOoBjSSNO8AdqppEImEgBeuKfYAcF+ns6uqqjT8ZDAZKpVJmtQE09Pl8xsY5JZT9ft+CfsgTgJ1GsgYIk0qlzDsM60eWBvsavyX+N9B2mPlut6tIJKLXr19reXnZ5OcE0bDeKMIptAGeYKBhcur1uoFiAIKAtwAi3wTCSJ52NpB4oJ2zgAFTOAOj0ajcbrdKpZIxDIFAwIK2pqamrACjqabJJEEcRQ/g3vT0tDEoFPAUY5xZMKJMHMCeAzOD8gbpOkqM0WikdDptzSwMF2ux89VYKGfIGawqnxugdGVlRaurq5b3wvfE+49KiyYZ2STgLqOB+D6pVMoCVmE3C4XCI5k3jBefATCKPX1wcGC+aWe4HX8WvzbP1Ol1Pz4+1ubmphqNhhKJhDVDb968sZBXmg7sN5J0enqq0Whk2RCcIaghxuNJKC0z6mG4l5aWLJV+ZmZG5+fnptCBScSewh1CACgNCLUL6hLUF6xfJ5PH2sLCx5lF4S5Jm5ubpsBBcedkisfjsRqNhgEsw+HQ6rJQKKTl5WVT7fCsWE803fxO9hoTQpCGoxx4ePh6lCZ1CGcq4X58Js5+7hlUQoAYKCudjQe+Xn435wn7iDOcPQLxQ6YMo9vS6bQFn0IUSNLTp081NTVl90c4HLbmj9ybly9fWu4LnwurA/cBeRROFhjVm8/n0/n5ucLhsFZWVoxMoBaF8SRwjbnQ4XDYRmLF43GrEQAcUQ/AwmJRpP4ggBOFAaAHdyCfzakUREUoTSwhi4uLppTkTEUWjc0nGAzaPnOqXR4eHrSxsWFqRu54wFrsBpzTqFzIs2FGNmCOxzOZQgEjjlUHFQSjSgmCBByHYKDRz+fzCofDSqVSCgQCOj4+1uLiojY3N40AIGWdNYPqlbnfFxcXFirK+gGoounvdDpqt9vWlG9tbWlqajJlhL0xNTVlNRXPIR6PmzWBfgJl5OzsrJaXl81uAUnKZ/1myPT/v/785V/+pf7Fv/gX+ou/+Av94Ac/0L/7d/9Of/Znf2ajV7/583d/93f6J//kn1gQ4r/+1/9af/qnf6q3b9/a1JH/q3++VRMO8w1bTMPNvDmSlAeDgRWhoNpIRAimIoDr5ubGirfd3V1jumn4FxYWdHp6qtvbW62trZnUcjichFnggZL0qIl++/atXC7XI8QMeSdJ1hRmMA7b29uSJswJbPLBwYGFTjmlvMjP2CiglVNTU+ZThlFhdEa/3zcf+OLiojEa+Arn5ubkcrnUbreN9b+/vzcvCKz5ysqKQqGQNdGHh4d2UBaLRa2srBjTzOxlGgMaFo/HYyw44RAEanEAc0BKX/tf8Q/SSMNYwtI5x8uQnOksoGCknB4XUOylpSU1m019+eWX1mTDLJ2fnyudTsvn8ykcDptMG5aDi4NnTkMGawJKzPOjQabJdblcevnypQEebrfbmB9k8+l02hj7h4cHlctlY6iRw9brdZXLZW1sbBjqHggETPaENJt3zSx1Ck5kggQRIoEENaagpOCjsHRKsWDoKTyRY1FslMtlY8aQH97e3lpegVNaSNEI83d8fGxp8c4gL9gq51QBpK+cEexd2DuaBOSYyCEZt0RomSRjeQkq499E2su5gkQ49NU8dxhuZ7I44A/TFpyhTTTNFK2MrOF73tzcWAATFhskphSRzITn73H+YeGhgKCQKRQKNpqPLAO3220JwjCYWEBgEJ0ef9aYkyVCITIcTkZicRHjk6VAl2QMq8fjsURhvKb9fl9bW1vKZDKm/gFIohm+uroyJgv1EoVZLBbT3t6eJBnIsry8rEgkYqFR8Xjc5KmsHRpx5wikWq2mubk5+yw0HTCdqHd4LkxjoAGmYacgTKVSxsLAdHKG0YRT9B8eHmp3d9eKQz4XRTZKl7m5OXW+Sl3njEQuy/oDHKNxkmTNDewg441YC8jXB4OByUyRQzpzQAByADd4387ilDONwp/zLBQKWfPQ6/X09OlTywogs+LJkyc6ODjQ6empzs/PDXxCgYYaIZVKWUYEz4bPKE0UROl0Wjs7OwaMplIpvX792tYka//y8tKaIIKOKpWKbm5utLOzY2q3QCBgCfd3d5OZ4kg34/G4MWDZbNbApqurK3uveCkfHibj1FKplKrVqubn5806ghWOO5VsBtYJyjPqgr29Pc3Pzyubzeru7k7ZbPbR2S1N2EveCWfbcDg0KTrhrYzOqlQquru706tXrwycpREFuAL0HI8ns5KHw6E+/PBDdbtdFQqFR3uN86fZbNpZyJ3mvNez2ax5bV0ul4rFomq1mlZWViz5HSkx34t6LZ1Oy+v1mmqGJtT5Of1+v969e6d4PP6omafRB/Dgjna73TbCj7McWxf1C+AjNYr0OMmdJphaBosAoMNgMDAFE8+CLCIaf/JMYO6dYXQoMqjRAB48Ho/Zz/gdkA1OlRk1MveaJDuD+BmPx8ZAS7Kzj3cB+IZaAoAC0ApQEOsC+ScAMICNgJTkgdBs8zvv7+9trBtn6+npqaWTA9I4G2hqOhr1y8tLO6M5h1KplE1bAuwDJEIhBKhfq9WUy+WMiaf+RZ4tyUIunXJ67CMonGDwOZN5n5ApKLsY3Ue4HSo4CIONjQ1J0sHBgXK5nLxer7a3t43AwErS7XYVj8ftrIxEItrY2DBbFHVcoVDQ6uqqTW1yZo1AoozHY+s7UKLNz88bMB36KuUfAs/lctm7IRgZcue3+fm/KxP+b//tv9U//+f/XP/sn/0zSdJf/MVf6L//9/+u//yf/7P+5b/8l//bn/+v//W/Pvr//6f/9J/03/7bf9Pf/M3f6J/+03/6u33w/w8/36oJb7fb1jDh48WjivwcafL5+bldVhSnq6urxhLgT6JxuLq6UqlUso0RDAbNO8g8Yi5jmnVnABeHyLt37/T+++/L6/XqyZMnxkAii11aWrKNUCgUbJxIIpEw9mh+fl5ra2smg2a0DJIcmgeSFfG7cFAyp5cDlWai0WhY+vT8/LyOjo40NTVlSeeE3fT7fS0tLdmBiP9O0iNpoCS7PLa2thQIBGxWKBcQKCCo2Xg81snJiRU1bFYK6V6vZyySJEucTiaTJrOD0bu6utLS0pI1aMFgUI1Gw+SjBHg4L0cKKJ6jE8VfXl7Wzs6OpRFzONEIn52dmWQS6TCXPu+Ci4UGh3BAAAxk5xzgMK1I0xqNhjVUXq/XUlOR8oIwUqi4XC5TQtDEZTIZa5opqG5ubpROp60pALBBPkjDQ66C0x/McyILgGfNBdhoNCwsh89BgROLxexCwUJBYGAmk1G327W9ysWM5/vt27cmu6d5QRoZ+mrUFfsT9pl1SkGZz+eteGEcoSRjR9xut7Fl/DvkEcDISXoElnCBs2b9fr8h3HgHya1wStFRfdCQOC+aYDBohSPJ0TBZMAQAGq1Wy0YTIZevVCpaXV21747tA9sJZwBgAc+hWCwqGo3q3bt3FrgD6EEaNKx8pVJRKpWy98vzi8ViNjqNwCuaV5grvJcAh41GQ7lczhhOwCunDLHX61nh1ev1dH5+LkkWjkdDgJ+RQqDf7yuTyZiXFjXJ1dWVKZ0SiYSxtYSrIfGUZKnqgE3OwDv2EwqSZrNpDRGfndR2GlQKykAgoGfPnqlWq2l6ejJTPpFIaG9vT9ls1gryXC6nYrGo29tb8/Le3Nzo+vpaqVRKR0dHSqfTisfj6na7Np8d5Q2KCAAQ2EnOJBo2GlvOEYpuFAIUN/hgO1/N6yavg7OYO5Pnhc2kXq/L55uk/MKOMSUBj/fV1ZXK5bI1NpJsfU5NTdmaA5gNBAJ6//33FYvF9Jvf/EbFYtGanG63a6AeCjdYaponr9erfD4vl8ulDz74wNiZ09NTA1UYldjtdi2klGeYSqWUzWa1vr6u0FcjvDi3er2estmsgUPhcNieL8+b+xJGm+9Pc5HL5Sy/xhmMhLw/mUxqe3vb1AUPDw+WeM/cYexS6+vrJq9eWlqy6QkUlABzyN+np6dtvwJiASwCHmLFcblc+vTTT62xYHQUaem8O5R7SMBdLpfW19dVKpUe1UfceSj3sGPReNFsxONx+/wAy6gVnYAoSkcAaWokFB/sU75PrVZ7NE0BuTTKO1QCztwa2Hf8xTR1SGxRucFi8myxTCD5Rh0FSMV6cYLc2MGazaZyuZza7ba2t7et5qG2oxnn3+WeIBAXpSWTTVDEJZPJR2sVZRNnBL5rcg5gp2nAAbH5IcSXWgVZNw0839c5/laa2Jm2trasFqepAyhjRBd5ET6fz+p85zSAH//4x4pEIqaG4275P9n7k+fW83zPC35rsCx5tiTLljzK4/GZMrMya7h9oaMDLhEsWRDBDv6BXrEh2DRsCBqCBTv+A4I9RHAbuHCj4XbVrcrK4YyebXmQrMnzJMmSnoXO650/V0NQWQ9F9PNEKiKjqk7lsaWfvsPn854+nGncxZLMyvN98z3QB1CvMnebemlzc1N/8Rd/4SC4vb09K2OkHtjy6tUrh7ch0a/VapaHYzsBDIahZroA9QrricDUubm5J+ckCgCUHpyJ5H0MDg7qZz/7mf6P/+P/ULlcVjKZ9ASNx8dHk4nUiNFo1BOiqP9QXIyNjdkGyR2KFTifzz9RgAD8APo0Gg3X+sHsFNYDWSYQH/+qvfhueXHW/eGr2Wzq97//vf7j//g/9p+Fw2H91V/9lX7961//Ub8LKzE18Z/j9aOacJhtJDdclMlk0gFj5XLZg+gJ/zk7O1Or1dLZ2Zmq1ary+bwRepgYkJ69vT0jc+jy+/r6jCwj9eAwDqK5LKjz83Mv8J2dnSfS49vbWx0dHen169f6/PPPHY5EUXJ3d+fRGYwdwQtLyFxfX9+TmdUUvZOTky7OCJYZGRmxDwWP5NTUlFlHiko8bIODg0bj8AQTdvP999+rVCoZxSZdd2VlRblcTu/fv9f9/b0ODw+1vr6ubrerd+/eqd1umxmB4cU7KMlp7RykXOqtVkvZbFajo6M6Ojqy//H09FSzs7Pa29szug37Q7HAARD0UJISHpwziZydQx4fPWmzlUrFXnACL0irl+QQFFgugI7gRctlDtPORTA4OGiLAwxVKpUyW0izzEVYLpct2aU4QjaIPQKgY+xT+jjsL54l6QeUutFo+Oexl7Bi0ACx7i8uLnR8fOx9RyOHx1GSfyZFA1JiAnu4VEGxYRXxUQcD6gCiYC4ByrjMkf3jS6MQodmloGMv0Dzf3d254cULTbN0e9sbuwZyDPOJvxLFBQXA1dWVarWa13E2m7WHjTUHMxQMRkMBgNw+6APl+RH2RFAZjSR/f2ZmRnt7e24yaaYp2CngsVuQVUHR8vXXXysSiaherztsBrYOiWpQTklRwgUJm5rP582esR6Ql/LzADnwPrIvBwcHncbL5+N9ALAiC+bZ4rsLvh/YRj7bxMSEGRuKbUAaZiuD6hPGxhrEVyzJvspEopf0DwNxeXmpoaEh5fN5N54AUDwH2GS8bXwn8XjcoWwE6eG1hcnn8xK+OTQ0ZMCBaRqsSRpjvMOAOwB1FFNnZ2cqlUoGKWCgKd6YChGNRp/IjFmjFK3ID/EcUqCNj4/r6urK4AiFOw0FTDrA2dbWlhlf7BKSDDaxf7m7V1ZWHMSG5zQej2t5edkTQlDQfPz40ZaCcrn8xMtKAUgjtb+/b4CK4pWaAACLySmcj5w5l5eXDgzb39/X+vq6nxnKJYBTZPJff/2178ujoyMD67e3t85GqNfrqlar9hCjZmm325qdnVUul9PV1ZXPMtYsXmhUJajLPn78+CQjAlAIMJHPBghDQ0jzRKAkbBv+dJovzlbk7dFoL6n7/v5eBwcHBiwBQWiYAapJRJ+amnKNs7297XuODAzuJuotguKwh4XDYVWrVTPy2O1g2yj0x8fHfX8HvfuFQsENIFYZ2FIsjKxVml3AY6TlPM/ge+AZsne5h1AWVatVpVIp27k4S7jbWZvURZubm9rb29PV1ZVzE46OjjyNp91uO/wumOpOTdjX16f19XWzxqxz9j9Kt6DFgT2yu7urgYEBLS4uOh2b5pVGhPMSZhNWtVQq6fb21pM+AJOxLHJfBnM+Li8vlcvltLe35zySYrFoxc/NzY2mpqYMnlMDoeLkXqjX6xoeHjYRwRQXFB9BRRcNI/ktjArudrt68eKF1RXv3r1TtVrVb3/7WzWbvUk5d3d3mpub0/T0tNUr7BHUGzc3N9rb29PZ2ZkymYxub299B01NTTnglHnzjUZDs7OzvgeQzQfl5kjJqU8eHh5sqZudnXV6fSgU0u7urn73u99pcXFRL168cO2DWoHGHmXIV1995VFn+Onv7+/dSGN1gUDiuUKILC4u+rut1WoekQzoQqMJEdhoNLS8vPyjmOf/N5nw2dnZJ3/+n/wn/4n+0//0P/2X/n3upT8EEyYnJ7WxsfFH/c7/6D/6j5TL5fRXf/VXf9qb/iNeP6oJ59JAlkFYDh67eDyuer2uYrHo8Cck6MxopMFEvpVMJlUul5XJZHR+fq6Hhwetra15o+fzed3d3WlnZ0ezs7PKZDK6vLz0mCBYNor0Z8+eaXx8XIVCQdvb22q329rc3FQqlfLDh5WZmZmxbxjkd2hoSN98843i8bjW1tZ0c3OjQqEgqVfkDw4O6v37906BLhaLbgDxj4IMTkxMeHHGYjEHX9F0tFq9kSEc9mNjY2o0emO4mHtMsQWI0Gg0zJLDaCFzxzcCAzk9Pe2AHjYXElYCt5CBE0qGNJECkfdBgYdM6+PHjy6YMpmMJSxzc3OqVqtmgEDrms2mR2pIsmeNzwpIADKPHw7PDUgpbA4S3ng87r/Hszs9PbVECdUAF8Xg4KBHuc3MzOj8/NzhHIBESARpTtrttuVrzCzH4wkrSPEFeFKv1yXJqgDkU5LMzHMRcTEMDAw8UYqQm8Dnohm/vLw0WxYMEsNWEQxi4mctLS3Z9jE0NGQwBNk2hWur1TIwhffq4tO4FuSgeOTYMzCRKAuQ+AE8XFxc2P8LW8Hly8/CcwsjBwNL0BMMKnIrCk4AGgphQm4o+IJjEfEdUqxRsI6Pjzu7IOiDhZXhkuVZxGIxz4SPRntz0BcWFtRsNnVwcGDGHwXQ2dmZQUZ8valUyrOdE4mEXr58aRCLRpm9jaqE9x2Px3V2dqZsNmv7C0Up4Bx+VWwANLBc1KOjo/riiy/s5QVogEnu6+tzWBAM7sjIiNVKPGNkoxR6kmxpwduGIoq7AKCKYBzWDnYn2IZ0Ou3Ue2SJeGNRRaDiQS3E3QSAwNk5ODioFy9e6OrqypJCmpyjoyPL+dk7/f39XrPB+fCw/c1mUzs7O045p/CB9T05OZEkp9EeHx877bvVamlqakonJyc+v3jegMt49m9ubhzWxFnBuUfgFnuCoEqkw8PDw7Y9HR8f+9ykYKTxhtElCAuLVC6X0+joqDNKaGr7+vrcUDQavSTkZ8+ePbGaFQoFhUK9mc3NZtPpygAjrVZv5jj7SepNLZmcnNTBwcGTELhIJKJ8Pm9GHwDg8PBQf/u3f6tKpeLvMZfL2SZwf3+vUqmk2dlZJ/0CDo2MjJhdhFGkIWHSR61W8/0VDEvjfOPzAazEYjEX1JwRGxsbPlcBdmhSkCLzzA8PD5+whADV/Cc+fJgtWNFgU0roEqFiKAKCQXmcrQBO3JVTU1NuFgEp8eZi77q6utLMzIxZO97ft99+a6k+64imgmky7BX2JjUcDS5sefC+5fcDBqIWQx7MPwSdoaSAdUVdQ32KZZJRrUyIgH1Evs45wCxtGu1cLmfbQ6vV0vfff69f/epXri9R5qCQJCNoamrKzRX2FGosgBy+Zz4rTDrAMlYhbBobGxsG78j4ofkD7KDBZY2jTgTAiUajHmvGM93d3XWKPdkIkUhE8/PzKhQK+uf//J9b3VCpVPTy5Uv9/Oc/N+iOmhNSic92eHhosiqTyVgGTrYK6w0FSBC8w8YWDod1dHSko6MjHR8fe2oD9/rQ0JCePXtmyyUSdXqQ09NTnZycaHV1Vd99951+8YtfWOXBuUetSg7OwMCATk9PdXZ2pp/97GeKRqP25m9vbysajdoqC5hBpg4ALxlZ7XZb/8a/8W9of3/fOSjn5+dWHLIPz8/PTTZiSUVlDMAxNTXlerevrxduvbq66ulE1PmSnNOEahPgL0hQoPx9fHx0YNy/iq+joyNbuCT9n7Lg/0+8/uk//af67/67/05/+7d/673753j96EFwQclouVz2aCiKVBgHEpFPTk60uLjoABUKcdC+QqGgWKw3nJ6D8vj42HLKYDrm1dWVDyQaHVgZEKhms+lRMyzCbrerZ8+e6ebmxmgV7AUX0O7urpLJpAYHB/Xs2TPPH6QYp2HF/0TDzGibi4sLH1wg3vhHQdivrq4855qDLZFI6Pj42BI5knqvrq4cfESICwnRHMR4wwj7oCnn8GZEGPJmnjuyfopY2G6kYXicucQIE6IBuL+/N0MfRI1vbm78vgnsYT44FwuyTtBWqTdfFPYL1hU5F0UDzT6NBp5K5shzscE2ErQxMzNj0AIlAs/g+PjYzx/vJ2zE8vKyRySdnJxoYGBA6XTaMmDmNeO3knqXK94uimUKVuS9gBMUfPjGy+Wynj9/blkUrANNbPBA5qKamZlxARb0FD48PNjPBDhFMZNOp/X4+KjPPvtM0WjUrCB/H1UEElHQUrxleChpFgnbYe+iJpiamjJQx75sNpuelw4DiE2gWq1qe3tbKysrDlkB5ODyohDm5wI0SbJsnIKL7xn/rCQHccCmBINhWIesL1h+EPlIJKJSqaRSqaSlpSVJMsoO4AAbxNnG+QVAhHdY6jVnKBoAdmjykK1zpvJsYb9arZbS6bRZ6NPTU83NzbmY4Ww4OztTPp83SIEUlHF4+L0p1rD/TE5OqlQq+f+ngGR8CfsExoBGAGsR7I0kW284K0DwkcXRDMDOwIbQpB8eHioej7tgD4fDZlP4nIybkWQfPoAJstZgcb2ysqK7uzu9efPGCcaAMXi4+/v7XczWajUDtiih9vb2/Iy+/PJL+05ZM6x55jVPT097L8IOIZ/md/H8CayiKcjlcrq9vfX6ODs7ezL2D/XC0dGRpqamvG9hQyksj46OfD4AxnKWUMiwh549e+a50Ci1KPbJYiDgdH19XbOzsy4mAbDZX2dnZw4+DYd7Y+/wu5JMDvvO+sDPincasJtCmnBAALbT01O9efNGV1dX+qu/+isNDw/r5OTEwFCtVtPq6qqSyaRqtZp92TRYKysrKhaLziugtvjss8/U39+v7e1t53gkk0mHKJJ9c35+bqUY3xcMMuwVgVuhUC+YDkVhuVz2FIRgw4m0GstZt9v1nQ67zXolP4CfD1FBVgrWKYA+6jDAW+5PfifS5k7nh/nZL1++1N/93d9pd3fXICONFLJogJ1Go6Fqteo1iEcb69fKyooeHh60u7urdrutfD7vUCvWNWc1FoJg+C9e5SArGAqFfB4BvKEqQYnDxAisI5y7ZBXw9wDU+vr6/L1zrq+vr7vpevPmjS4vLz1SimeazWbdcALOIIkuFAq2d1EjoLoDVMBaxZ1eq9WcYQGoQpYKYXSAXEFrWbvdVrVa1cTEhFl6VKlXV1daWVlRvV53YC93CyAIwEcw7fsv/uIv9Nd//dc+8z98+KAvv/zSygP+E5UF6/D58+f6/e9/r+vrayuwOHuDICD1K+d9s9mbrnR72xtPy6zteDzuCS/pdFqzs7POc2o2m0qn0zo7O/Pni0Qi2tzctEqW99Nu90aRIbWHTb+5udHh4aHq9bo+++wzDQ0NqVQqaWZmRpIchAZAACsNOLm2tqZarebpCtz/ZEDd3Nzof/1f/1dFo1H9g3/wDzQxMeExq5wnAwMDDjtGXUE9idUACyygA3sfcopGn36iUqm4BoN0AzxAAn9ycmJw9I95/b/JhFNP/N+9AGLw3vMql8tPwqT/z17/1X/1X+mf/tN/qv/lf/lf9Pr16z/9Tf8Rrx/VhBNew0ZjPEG9XrcshouR8Aqp9zA4gEns29/fVyQS0czMjKVfqVTKMm8ugY2NDc/mPT4+1vj4uMeBhUIhffjwwZ70er2ut2/fPkkwBJ2nAeCAA0hoNps6OTlxs4m8GmSUw75ardpvFfRw0twjRYR9jEQiOjg40MnJib1VSPyQ9R8cHGh8fFytVstFJqwMnh+CUmDrFxYWfHm3223Nz88brcOrMzY2ZlnNzc2NFhcXVa/XzTrAbIfDYSWTSQfLkKZKQw+zX6/XXeCn02nPBacw4MBA6kWjLck+U9B5GmyaPwJXRkZGVKlULEdCpnpwcOADC9kr3ikaf6RDlUrFITqhUEgTExPqdHoplniPpZ7smGY/Ho+bxebwp/GX5AZ3dHTUa5wCBKaX5hvfIvvj/v7eie3INZHrBhsDWDR+H+8NH1m9XncQVfC72dra8nNif3JxcyEH7QHhcNgF+eDgoJl/PN6EYyG1x1MuyTJGgBoOeIppgsaazaa/S2wgyG2Pj481MjLi8BJSlUlmJTGZcBbmo+ONRYkRBOBubm4sXwuFQpbisXck2bfJSC0ufJ5zvV73vux0Os47oAnme4lGo1pcXLRXtdFoqFAo6IsvvrAXncYGXyhKHZgZfG4AcLDKMOUUkuQFwKzhCycADZBgZGRE6+vrKhaLajQaTjqnufnw4cOTsS9c9JJsF6K5whrw+PjoBmhyctIgCOcio8KQ4rFnWAc0q1gtTk9PtbCw4GKZpgCml/3T6XSc+s93BnsO+wMDy0QOWPJEImEwDB9fNpt1oc46Anx4fHw0ONpoNJyZMDY2puHhYcvP+/v7zdyTa4AS5fnz586XoCAj6RuGNRaLaW5uTufn527uyuWyQVx+FoAvhd7S0pKBIpp6zlLkg7BxKFTm5+f9c9nHsHzSD4wjr+B5TSOczWbNJl98Go/I3XR9fa2NjQ0NDg4azJudnfX3SI4I3lGAOsINKZoJDGy1Wvrf//f/3c/ru+++UyaT8fmN7zIajWp3d9eKr76+3ggnVGS1Wk0DAwMaHR11sCSZHJxlk5OTrlXIlSGLg0YLaShhaDSkktw0T01N2UcLeHFycmJlEWQBz4JRVzROfCdYL2hw2BN83wA6qHtOT0/V39/vpoMcmf39fdcl2GVQB3HG06gF1wFsEvfFwsKCTk5OfFdwZvF3mfsMY//q1St/Rhr/IICZTCbd6BJIJknz8/O+O2gkaFRpJPjdQVCPuy84po19OzQ0pO3tbTO7jKBjDdAg9Pf3W0mD0ou8Dhps8kRQ/mAro1mcmJhQpVKxRxzWk8ZakutgiBtqA54rqqFIJPJEDUeAYhDEAzABOAMA39racjAfoBU1H+Fr1FrUOTRjyWTSJAwyaOqckZER18jBiQPValWxWMx1H2ox/vPi4sJqUBhp1BI0jgCD3NXsE9RYANiSPDoQJSrSYu7/Z8+e2eoGKMUZlkj0JgF9/fXXJleq1aqBEZ5RMpnUwsKC3r9/r2KxqJubGyt/qO8GBweVz+e1tramqakp/fN//s/1N3/zNwZsCQ/e3Nz0vG1GrJbLZXW7Xf8cMkju7u4Mkg8MDOjk5ET/7J/9M/3FX/yFfvnLX+ru7s55H+12L4Cae/niU9A1c9wZn4bak8kkgF4Al5lMxu+H8x4VJOBdPN6b/c4Zigro/1dfsVhvROPf/M3f6N/5d/4dSb2z72/+5m/0j//xP/6//Hv/5X/5X+o/+8/+M/2zf/bP9NVXX/3Z3+ePasLv7u60uLhoNLndbvvg7O/vzZlMpVL2LKXTaRd3c3NzWl1dVbvd1t7eni4vL/XVV1/p/v7ejDo+oP7+Xvz+1dWVjo6O9OHDB7OmtVrN7AGXAMgyzUckEtH+/r4beTwTs7OzT1IMz8/PXZjSiNXrdXvcQS6ZqQqqvby87OJyc3PTCxppGd49QqPGxsb0u9/9ToODg/rss88MPuBFW1xcNMtPKFWtVrPkOTiShIKTSxvJLg32ixcvNDU1pb6+Pr17987MEs08zQjhGYRq8fspsmkGQqGQ/UTIo4eHh7W/v+/fjZ2AS4fLngIIRPro6MgSfRonCneeHawa7Pr4+LhOT0/NnMViMSfnJxIJlUolszPhcFiLi4sO8eMAIg0dVgeGHZQV1lnqSdC5dBuNxhOkPeglpzgFeAnKLEOhkObn5y0PoxDh5wV9ybAqqVTK42ckaW9vzx7IcLiXAIs1I8img6wyFige743529ra8mVIoQcbXywWtbi46CRhpGq1Ws1p+jBtSCPxtpPsz/eMhC7YQBH0ga0AFj4ej5thxwPGz4xEIvbXkRLNXgYZDgIOyCORHsIIcTFSuPX19blwhk2hUULNQ3E5MTHhDAmQZBohCm4mA1C8SL2xRfPz87q4uPAFn06nDb4AmlC0IAd79uyZi8vl5WX7yPFeE9jEz+A9AMARyvXw8OBZ9xR1FOL45mHWOT+wDjAahvOAolaSizqKWJ4tcnD2Aw0zIFsQeMDLWiwWdXh4qOfPnz+xJk1OTvp9g8Yj02Vma7fbdYYGIOfNzY2KxaImJiZ8XrHOeGbHx8fOmADohE3b399XX1+f0X++L84kgFOK30Qi4WK92+3q+fPnlvXzfdTrdZ93MKUwwah5WAenp6dWiTA+sN1uezIA36MkqwQAkcjMAKRoNpsqlUpaWFjwXmLcJWFy/LucU/zcZDJpJRTSXAIc8dZfXFyoWCxaLcCYJnJC2PuXl5caHx+3/Ji7hYKcpoak7263N15vcnJS3333nZVKFNUU4LOzs5qfn9fh4aHP0L6+Pm1ubtoK1tfXmxuezWZ1fX1thRuy/P7+3rz7TCbjKRuxWEzT09Pa3d3V6emp12K323UAJc8Gjz12ITy6Hz9+9P0/ODiou7s7KwKYGw/Ayj1L+BnSaj4P6rhwOKxSqWTJNu+h1WqpXq9bOffmzRsX1ZwtrOGLT2NbWY9YTZDuTk5OKpFI6PDwUAMDA5YgA74BJqIUKJfLViBJvcRlABik8zTiUo8lZD1Eo1HfjfV6Xdls1oAqzR3N+enpqdLptHNDALMZjQvQS+Dg2NiY9vf3DRJzfrF/ub8AcbknYFYBhAht3d7e1uLioplhagTuXYLpzs/Pfc9dXl6qUChoaGjIakpk94QJUg+Rts/dCsMMIMbeJAQPcBgVHsGqyJBJvMb3CmgwNjZmFQznM2CsJDfo1O2ARATrYWWgfoXYIiPi5z//uUqlkkdjvn//Xi9fvnQ+ysuXLzU2NqY3b974Z4yPj1t1xUx6wn4Bu05PTx3EuLe3ZyUDQZTMymYd8HyoiSuVisbGxmzDTCaT2tnZcVPP3giHw/rlL3+pcDiser2ura0tq45Q9rVaLT179sz35N/8zd94gggTCSKRiP7yL/9S09PTrjvGx8dd/0PmZTIZh7Yxti+dTptcZN9cXl76WVWrVdet5DOwv1FRARygOmKqEH58lHdkEbCvqREANfmusZdlMhmDWH/M6/9NJvzHvP7D//A/1H/wH/wH+uqrr/SLX/xC//V//V/r9vbWaen//r//72t6elr/+X/+n0uS/ov/4r/QP/kn/0T/7X/732phYcGKbmrpP8frRzXhMJL4+2BCKFYpzGGUkbQ0Gg1tbW35wOMioPhPJpP28hAosbu7q3Q6rRcvXpghoth49uyZZZjIt/G6NptN/frXv7bUU+oh82/fvtX8/LybnXq9bl8zoxO4LGHlYEOGh4f18uXLJz5VfHeRSMRhFBSOXKYwiSTKB2UUSLAALKSnGyUWi/lnIVHEx0hzefFpfiAIFkU8jBnF18bGhkc9kSqJFJ1CkDAmSWaiGUvR7fbG5WAzyGQySqVSHgNBeBLeGuT4oL1Sz8bASAWKI6nHlFMAw3wiNadwpoEMhsQA1vB7kUqDRMZiMReiePRZY4RDcSnjEwU8Gfs0iub9+/eSes0IoX78HpK1uUiD3i4uNVKFKRxh2GFed3Z2LOfvdDouPmFMQbPZWyhRgog/hV25XHaK6MTEhBUOwcIulUpZllSr1exrxEd6dnbmi4H3THpot/vDyAv8qBSAKE5ogike2H80KgQ9MXaOtYx8GACCoBi+8+DlQXAR3ytySMKcCPJj7cPM0Mjc3NyoVqtpaWlJpVLJ85gB97CPEM6I35bAx5OTEz1//tzodyQS8RlVLpefZCoAnlDQsH+5MPv6+jxeCTY4aKmZnJw0es7PpWnGFhAsjM/Pz82wUUAjVQR0uL+/1+DgoAYGBuyjxLMH0wvgAdgJwwfTBasTDHiDfUZ1BOsTHDHY6XQ8xgpGg5GK9/f3BgITicSTsEGKEUY65vN5N8+oPyqViiddMCXi5ubGo2/4nZJ0eHioi4sLraysOMEWyS3hdjwvCulOp2PrDzJDzh/kfNhLWKt4+sc+zVQHlCD1PRgOtbq66kRx3gNgQrfbdQgWzSBNCAUqSgT+DswQzzDYgANY5PN5LS8vOzQQaT/KL9hBitilpSW1Wi29ffvWz4qxQdxZFNnlctlgniSftYVCwWAOZzH5LA8PD5YMB9ft9va2BgYGND8/74wCAtfwkf/ud7+zIgOQ8OLiQpOTk2o2m27AKWq51+r1uiWjxWJRa2trT1QYKGDYdwC57A8yP7BVwGLt7u6qv79fm5ubVj1xD/Lds0+5XyWZ/aU24PxFYizJ+44XhT9AMk0l/w7qOUmuaQDlYePZ69ls1nZA6hRJVr0RynlxcaHDw0Nls9knqiv+Hk3l2dmZ1SfUaKjfeL+hUMjWl5GRETeEKD/4dwDpeIazs7NmNAEzpR/GT9EYsD6RJJ+cnGhmZsbBgYyTvb291cLCgs8V7jCaMM7pVqvlgK5IJKK1tTVVKhXNz8+7Iby7u1O1WlUul3tiNZBkgJTvhzuMIDRqTkgsCBFJtltVq1V99tlnBnF4/jzfkZERW0tI+g8mzsP6w/riO5ZkFSsecYgRzvXh4WFNTU1pbW1N19fX2t/f18XFhUF8QAtqog8fPrj+YKQfKkL22M7OjkqlkpUqyWRSy8vLbkqZ4PHu3TuFQiEtLS1Z1QkIhKcb8o3pSJJsHWL/kDVCfbKysqL379879R6AnTvy5ubG/u+vvvpKtVrNku3r62v98pe/9HfMPY8Vlhyd8fFxraysqFqtam9vz8D/wMCAJicnzZZT27L/ALXpg9rttg4PD5VMJr3v2acE+gXrIrIaFhYWvEcAQRjTzNqs1WoaHh5WpVLR/z+8/r1/799TtVrVP/kn/0Snp6f6/PPP9dd//dcGrcgp4PXf/Df/jZrNpv7df/ffffJz/q/C3/6feP2oJhyN/f7+vsrlsvL5vJvyoG8GyRTBM8lkUhsbGyoUCrq7u9PKyorn91F84suGOeRw42Dudrtuqvb29jQ/P69EIqGjoyNvRFDHwcFBLS8v2xsyPT2tm5sb7e/vGw17/fq1Lz7CZkgQJpgol8vp8PDQPuFOp5fWzCFWqVSUy+WcOLu+vm5kslgsqtVqqVQqqdvtKp/PP5GV4XMnFZtGCKaN5hqmhEtbkhvIk5MT3d/fa3Fx0QX62dmZvbA0b0EJKCmvw8PDGh0d9QUE6xRE5/8QZYfRCYfDmpubc+hZLpdzMzYxMeFQDqSkNAqwWBxweJTxhjLCA98/hxi5AwRxwDBx2EkyeCPJviwaWAJ6aCqDknIOHXx5weZS6o1j4rsJ7gMKf2SkfHcUMxT3HKJI7wiEwROP1A+7BGwU3xcywk6n41mwBGRhIaAhhRm/uLhwgj0HMD5j/EuXl5dmXZPJpNkGnjtyUS7WoK/88fHRFyiAD+AAnmWQeL7bh4cH7e3t+RkTFsfaYL2SOdDX1+eGDzCCBofCAFDw4eHBMjeKFBLnASaQbJHM3Gg0rJ6BWYSFZZQP0sfLy0vvY0CNq6srLSws+HOenJw4XZQGigaeVHCeIZJiwAcKeyTWMN6lUsmNA+sJcIaRkDBq+PlpKGENYNIonnhfhJ81Gg1Lh2GLKPhB7mkaaWAoPoOACMWiJIOjIyMjBi9yuZz3qiTnMgC4AFYB3sKC9fX1ecYyTCnyVUb2MW6M3AvYbpQGNJX8XZgiMiy63a6mp6etVunv7/eaHRgYcBgXwY7NZtP3FHcfDSwKG9juTCbjhpBGjTPt4uLCclzUFai6CoWC03eRrdIUEHaGiqher6tSqWh9fd3WFqwRIyMjenh40Jdffunn2O12tba25hDNoFyXc4EMAYADlEvRaNTSSNhEzrhIJOIUZFgZ1AOEFSKbBsSen5/XwMCA76zj42Ntb287kRoQjaYCJg+AATkwYZxYjaReAV0ul9Xf3+8wMYLd+vv79eWXX7qx5c75+PGj7u7u9OzZM4eaIXMFUBsaGlImk3kyqmtsbEx3d3dmLJ8/f65ut6vNzU2zh+xfnjVKhcHBQYenAgCxrwA2UUUhZ767u9PDw4NBlHA47LufBpI79uPHj9533IFBEI3fG1S30NTBhLMOUDBRqB8dHTkgMgiuc5ZwN/EPlhIA5bFPo5aC4YSw6KlUynLnTqeXIE5t0mg0tLe3p6mpKfX392t5edlnMd8nfmbOF/YMTWksFtP+/r7PkqAVhnwKwnATiYStjRAdd3d3DuTEy8t93Gg0nBHCM41EIjo/P3fdGFQIYOHqdnsZCNPT0+rv73c9xVnId00Tz/qjZgBIjcfjVjzRkN3d3Tks9+zszOon8iqKxaLy+bwSiYQthfyOYGYDNsa7uzttb29buQbJwoz0h4cHLS4uPrHSBCfjbG1tqVgsmnxqNBr64osvXEdOTU2pVCr5TmJaCaPDLi8vnwR+kooPSYElDYIDAIr3ihqJz7OysqLt7W3ba1l31BHPnj1TMpnU/Py8/fxYELmraXKTyaQWFxcN2EAwEiLJ93Bzc6Mvv/zS+zCoopmdnTVpWKvVTHrynXG/drtdq33JteBem5iYcJ8TVB8ClLJPsAOgDCX76499/avKhEvSP/7H//j/Un7+t3/7t0/+98HBwZ/0O/6/ef2oJhw5KYV1Pp+3nAkZA5fK+fm5/T34NvGddjodTU9P2wfI/MxkMqmDgwN7EfGFUXzv7+8rlUppdXXVjW6z2fT4ADx8zOtNpVL6/vvvnYxN8jl/N5/Pq16v682bNy4wkBbynwSekFw4PDys7e1tewVJbWWj3d7emrGfmZnx4UxxggQ9HA674aLJxdcMwgU6eXh4aHlUp9NLOIbJoPAtFouWTHChzszM+ELA681FnU6nzbRRWCMRlORglGw268acoLKZmRk1m01FIhEHlFxdXSmVSunq6sqXDw1tMCwJ7xxFAGmux8fHlqAhx0yn0y6omIHc6XTMXgZHydEwEMh1cXHhUQaXl5cuHLFLELw3OztrCRNsFkme4+PjKpVKljPi+1xeXlZfX2+MEfJjZFE0S3isGo2GQ/WQFGezWReMNJ/YIGCPuQhBoZlL3Wq1nJTOpUiBjgSPtUKBQ/YASOz9/b0RQJoN0PVsNuu1/38WlIKPjGdC2BcFDs0mYYFcfjQeeBUJ9QKMY4wWxT5ACAwywAP/H2yVJKtTCGHieTFzGzmX1BvHx/MDmGGP8PyHhoa0vr4uSd5rBJ/h0x4cHPQMdgJ48OpiK0FSTtoy6xgmisaWgBrGBmEp4Dy7vb31P4yl4TxGAg7oBbuGbz8ej/v7RWFCM4EEE7UB77/b7Xo6AcUR4AvPir8LEMN7ikR6aff4746Pj+075xziWTWbTasfuA+CKfrBiz2bzerk5MTpsIwSpGDC/1ooFOynJk8C4KLVajmXIJvNqlAo+LxCzYFKBeVHtVq193pnZ0eff/651ysFNmNuYDDw0o19Sjtvt9taWlpyVgG2EKwujUZDpVLJLCDfO0y71FM5UPyhoqAxajabTxpmvgf2LYn0X3zxhT777DM/L5h0/NI0XJOTkwYt9vf31Wq19OrVKzcpFItXV1eWhcJmw2Iii2w0Gtre3rbXmzMZoAzABWB5dXVV2WxWHz588Ngn7qjT01PnacAMcZ+yvyQZqM/n87YqcedKMnhE8BKZNZxPu7u7Wl5e1ps3b/w9S3KuCGqVkZERF62o4wifDSrJIBk4P0OhkIPVEomEVTmw7bCDMJSA/LCYNLbDw8MunK+vr+0FDQYV0kwHLWCcS9QC7P+gTJjPy3+nmSB0jBRuVDuovtrttj8jTTwNbqvVsl8W4AYbGqwtoCIkAQ3o3t6estms71nyE+r1uhKJ3nz329tbffPNNxoa6s1eJqiL8aY0+SMjIw4gJVSOZxgkkx4feyPCeGYAkpy1NIjkIRDMG2wWsF9JMpjHNBmk8QMDA56BPTw8bOUEZwNqH85K1i9gqqQnajHWGncoAXOs3Ugkolwu5/qaWuL29lbffvutfvazn3nPABazloM+YsgixuGSE5LL5WwJJFQzkUjom2++scLm7du3Pj+++OIL39OoSiA+GBHKz4lGoyYB8fXzewCdURSQ3VCv121X4G75/e9/b8AfMA1l4e7uri4vL63Eub6+Vl9fn5aWltTtdp1nxZ0FaXF1dWV12vX1tUGhZrPp6UWc21hSCLJkXY99mtJzcnLi+eYoAJHCz83NuRZmMhX3c/C7yWazZrpp4LFGUDdCDsbjcStYAbVZRz+9/ryvH9WEg6LMzs66QIJ9KxQKT/yoNLCEriGpAJlDJsEmwFPJppqbm9PW1paLOwLRVldXLV+Kx+N68eKFLyVYJOTRDw8P9ubOz8+rVCqZsSCt/PT01J7FXC6ncrnsYpXDF5kawUSk8yJVRA2AbOjxsTeqi4CDarWqFy9eqL+/X8fHx0qn0z5cKGYJGUHOH/RSZbNZhzHRSOL3xvuWy+UUi8UcbHdycmLvK5chP39oaEjDw8MOTiKQCWk6KZgUrzRed3d3BiVAc4eGhrS7u+vgJwoILiJks8Vi0Qg/LC4MEemWlUrFTT0I4ODgoH0ZXKCDg4M6ODjw55+enrZsEilSsMEg0IZgC5pTJFbIGGm+8WaTfI+HGsUFPqp0Om05PA0rFwBMRzqdVrlcdujJwMCAx+d1Oh2nCg8ODjogC8aQC5VLiIb98vJSJycnZhvn5uZcICHjDoVC2tvb854CNUUmlkqlPL8SdiAej1s+GEzPRdGC9B4PJoF9pHRK8lqjIDg+Ptbc3JxardYTtpHGgSKStQ2KLslyNhrJoMwdSSOsBonEXCjhcNjBQMh1f/vb32p2dlYLCwva2trSxMSETk5OHFII8j01NWUwDBkgrBjvh+Lr+vral//m5qY9+vxuiiSeE1J0GBzOGeTQNLmDg7355YeHh/ZyY09BLjoxMaGzszMz1MjnsQgRYsZ3EZT4kuEAo0a+xO3trRPDYeKQtaFwIRCR5hvwr9FoGH3nDMG3hv0IPyusDpMezs7O7Nkm/IxzN1gs5nI5zzylkZ+fn9fu7q4LHsJpkL/DQs/PzzvUDbQfGw/PCAaMUMWrqyt7/7BpdLtdS2nJHgiFQn4GSMMpYur1uqanp3V6eurEWc545L25XM4ARrVa1dinefOATgDJpIIT8DYxMeFilTGThApiYeF+CqbzwxLCqgKqSnLa/tHRke7u7jQzM+Omt1gsqtPpaGxszInQ3PEAHTRUSJ2xOHS7XYOuBFkBYFO8Ar4z0/fo6MhKEyTUnK3kqtAcp9NpAwl42UklpkkH5AJQAwDnrgl6X3k2MHjITWmImTN+fX2t3d1dN1QDA70RiijVUN4Ek4gBNDg/ULShyKERefPmjdldwF6ANkBqCu3R0dEn0mkyBgC0ACqZ5R1s1iAegmc4L5qLeDyuyclJTUxMKBwO2/N6cXGho6MjB/BxlvLa3d3VwsKCgfhQKOQsj263a3UDFoBcLmeQnkyGmZkZn198dzTx4XAvcX9jY0M7OzsaHBzUL37xC+8V7k3W4sTEhAqFgmZmZkwewaTCnPM8AYKZlx0Ohw3qQ1AcHBw4d4a6mMYIn3sQdALgpg4O2pYA7oKAEYAP76vZbFreT+OGvYuAXoB4gG32BWB5s9m0rYKGfmlpSe/evdP+/r4DzwBssACQr4JagSyJ4+Nj/d3f/Z2Wl5ct2x8eHtbOzo6tXnjxOSPGx8c1NzenmZkZDQwMWGXJ2iJYF5tKu932dIiDgwONjY3ZPsBZz3Pje7m8vDQAc3V1pX/r3/q3nPcA0MLZj5oHUIhwMtRZ9BidTi+89eXLl9rc3DTgDxiAhYyASBj4TqdjtV0oFNLc3JwSicQT1rlWq/1LmRYAEORvoN7EdnF/f++ak5oDr/sfWqGi0agymYzrVxRb1OqcRUw1+On153/9qCacwhCUkvExIJ8cxA8PD5qZmTFCSCFQLpd9MSBNRjIC2h2Pxz1mYmhoyHMHm82mZx1vbW3p+fPnmpqaUrfb1bfffuuk1KGhIbMv8Xhcy8vLbgxBcAEAYPC4vEhhx3dyd3enb7/91v6zg4MDAwakU+NdYoQVSZKwPbAq+EWRuoKura2tuWniUi0UCi7Y+f9AjGOxmGXLIP6RSESLi4t6ePhhZiJsYy6Xc0FKknAqlVIul1M6nTYI0e32Qi3wZ9NwIUsEVEG2RPF8fHyshYUFz4tmBirPgiAa8gEIsQk2BTDskjxzur+/3wEZkhw+RcMU9D4Wi0V1u12trq66MUBWTGAHqDJSTklu2pBpM0ZnZWVF4XBYlUrF/stUKqWXL1/q4eFBm5ubRihJhSdsqt1uK5vN2uuPaoKiHUQUxr5Wq/nA3djYcHI4+4LPQMDO3d2dZ9CDqtJY8ELyfnNzo2w2a+AC2djd3Z0b8EKh4GZ7cXFR0g++QZ4l6DJqEw58ZOkguhzi/E4YOBgfWC0sGYQAIcOlGEGqjOQLPzmhJoSRUSggZ729vTWrAHAE4zU4OOgzgVT2drvt2ZuDg4NaW1uzL4yGl6ICRppGF9Ds8fFRuVzOBS5FBoVuLpdTqVRy2jvFMKwGRQeFNUUSsstKpeIANpoAGC0uXVhuZJ4EG3KJcz4BSgGw0OyjViGfIxQK2RpCc0VhRZHJmY7cn2ZmaGjoCUvMXgIADSoEgmAqaH+n03GC8eDgoHZ3dw2ALCwseIwXgU5nZ2ee6To5OekgLKZBbG5uKp/Pa35+3rJKmgasQNg6OGORBqI+IYek2+36/gNw5IwDdGG9wogi/4XpwvdNMwiAUqvVFI/HbR2gKaYgxq9JKBV+aqknfaSJR42CUoU0fdQjrAGKUAAgQCfuxnfv3pndHhsbsyqgVCrZTweIjsIL7y3nLB52nglBV2QrYG87OTl5kq5MeCefZXNz06Dpzs6O+vv77QflZy8sLHiMmCQ3XKiONjY2tLS0ZEvV7OyswuGwzxD++9XVlZ8vlrfb21snaqNWIYfh6upK8/Pz9j1L0ocPH9z4Q1TA2tN8ceYBQAI0NZtNg/nkB5DPwfclyecaf5fz8fT01ED04OCgQU/OFgLfWF9IgyUZuOAskuTaaWBgQDMzM/ZScw4i7wUUQmWEaiZYO/KzsP7B0gclxNRL5O9wr6CAYWRlt9vV9va2WW68sVNTU0qn01pZWbGViMkcAGbPnz/3ecAZyHujtuCuRimG/SwWi3m9Io0G1JyYmLCiD2aTYEjCa2nweL6olFAqNhoNA1Cc2SiTOB+Roz8+Pvp3wdpzdks9NR3PCzUEZxSgWNAKJkkvXrzQx48fvY95r6RwHx0dWbXJGOCTkxPfIdVq1cAMhAD5IBAK4+PjevXqlebn55/UtAC1KF/57lANVCoVs7xMWUFlSrAi52NQncL9PzQ0pHfv3rlWL5VKTiGHUMMeuLy8rLdv37qWYJIPvQ9p4/xs7KD0FI+Pj1peXpbUG4kVtP1gx2Xkaq1Wc01fr9cN/FDbA9YzZQNAIhQKub+5urqSJCs2+P/JQTo9PbU1oFwua2ZmRqlUysQVQB1nC73UH/v6V1mO/q/660c14SBIzP0LhUJujB4eHozskO6Np4eCq91ua2Fhwazgt99+a38ls+++/PJLHR4e6s2bNz5AgozWhw8ffECVy2Vtb29rbGzM0g8uReQhjKPp6+tTOp22hxtvDpc2zHkkEtGzZ8+0v7/vZE6KGgodNvrl5aU3Ls19rVYzeosMB+k3TWW329XBwYFarZYODw8tg8F/SzAYyCpp5NIPHmUQZ4KoSJWmgaNIQ9bDiJfR0VElk0lLtUDFuLRJVEYiCBLHgYa8DikxhQkHaTjcS9aFsSSVGSlZX1+f1RCEp8GIwybj4yEFlBdAzcjIiI6OjowIUqDRDDMei+RSPLUUqaVSSfPz8z54YQ4pBkDBKYLwzSMnxT4RRJdhzxhlhwyUQvTq6soKAj4782UjkYgPdCRz19fXbkSQHtLMwn4GE8oZgdRqtVx0E5z0+Piok5MTI6Qc6qhLKJxgFYK+SkJWeP7BwKLBwUHLzqLRqIaHh1UqlVQul63k4BWJRJ6oMLhoYKjwkZL+ydgXphbgRSMICSkf6C4M2fn5uY6OjlyILC8va2hoSAcHB0bS+S5gtGg8YGmDATYgxjSlNM9MLkCODUCCZxxwB+BhYmLC418oWFA4sJc5Q1AClEqlJ/51qVcU397eamlpyecVe5ZGi/0Emp/NZj3CjFR5WBUsI7C3FMmsS3zYKDDy+bwuLi7s/4M9phAhsIwQq/39fTMMuVzOe2FyclIPDw9eg+Pj4yoWiw5ZpCne2trS6uqq8vm8A9oeH3uzqQk5BKwChAQ04rzCHjA/P+8iD2YCUAMAoVwuG6h7+fKlksmk1tfXdXV15aZ8fHxc3333nZrNpj777DMlk0kdHx/7jCfrolwuq1wu+44ol8u2GnF/xmIxlUolA1MwF7BOyHMBV7F3cCawdvB7ZrNZv3/UEJxdyKhhtrrdroGQy8tLTU5OelzS/v6+lpeXlUwmtb29bbZ7enraklRCsWheUBfA6MHq0UReXFzo/fv3Ghoa8j0XDoc1OzvrtGeK2m63q1Qq5b2NtJSzHqXW7OysRkZGdHJyYiUTKfE0JEhyP3z4YBaQhhVQGoVKMIWYzJBgRgdnKABuLNablX15eanBwUHt7OxIkkHh7e1tgzCc4TRDwcA2znCa80ajoXw+/ySkFO8m1iNJKhQKkuTzEPUazOzp6anW1tb05s0btdttqyXI5SBk8+HhwQolVByQIQAS6+vr2tjY8FqiWcdTi3cVmx3p6DMzM/4OCPsCaMXiGAQhOVMB/crlslKplME75PbUVre3t9rY2DC4BUBD8/n27VvXKKRGNxoN28ni8bjzRTgTBgYGlMvlrLgCFOp2u0/GGgJoAxSMj4+bOKH+IK8Diwl3HCxvvV5XPp/3BAEUcQAZ5AxxJ8FQUldTp1IzYtWCPQfYRH0TzCJiTUuy9BwCBfkySoZWq6VvvvnGqgR8/oDLk5OTOj4+dtYGMmuYXz73wsLCv/T5UcWgwAiG015eXlr9VCqVDDRvb2+7N4jFYtra2vLdRdMPuQN4xQjg8fFxzc/Pa3x83KAKe/Lzzz832w3IQf/R7Xad4UCIJnUldT/7B+COOimotkK9groTsJSMG3qrg4MD25NmZmYsP7+5ubGvndHBBOhypiwvLxvE4PsEyCdH5PT01HsxEon4+2Sqyk+vP//rR8vRJTmUhZFBSNKQGdLksolHRkY0Pz+v6+trI278ObKUer2u1dVVo2wEXXEpU3Dd3NxoYWFBj4+9Oa8HBwcOJeFSJ0Xx9vZWMzMzmp2dNVrPhtnY2FAqlXKCaz6fV7FY9BiS4MKEDaKYBJEhUROPIVLIq6sr1et1pxHOzMx4NAUXEaFBBCkEvd00c0iOLi4uDCgAbsBsw+ywyZDk03zt7OyoXC6rVqspFospn897ZA+INmPSCNni8APtnpmZ0d7enpaWlvTx40dLU0dHR+0fgZXgWYKKIuOhsaBwYR2QlAujjN8vGJJG4BRyctgPnhmII2ND8BcGmXSkUFgmCFOTZPkj8kTAAUCBTCajsbExnZ6e+gCdnp72+4ORBDWHDYQp7Ovr8yx6kEp+rySzTMjqC4WCOp2OPvvsMyPgeIBo9Ah0oUjgcsZnf3l5aVVDpVLRxae5zYz6KRQKfg78XGTnXFi5XM7+N+kHVoQiiotoZmZGJycnDniBUeNSx0uGBxYPGkEy+JPxNdEgcqnA1uTzeRccSNSR1N3d/TBbvb+/34E7SABZ+zAwWArW1tZ0e3uro6MjHR8f68svv/Sz3tvb0/v37zU1NSVJtigcHBxoe3tbU1NTLmR3dnYsKSdoBh8+6DgFWdBLDbueSCQ825P1R6gUI4pIFCdUDPYxWMBylpDojHqBJhOQhbMStoOwKnyGAB2wfig7KEaCM1WRp+7v75sZf3x8dMPPvcB/zs3NudBlDSL55nylSKQppeGDOWe/Aewi4dvf3zeoSPMC+PX4+KipqSnL4/HzE7LZarWerHUUS8zVxY6F8mtpacmFGY0woWaAsTDP2FQmJyddRCExBCwlyRj5IUGFFG/JZNLPAlYQVufi03g8imrAELzu5XLZ7xumg1Ay1iRKBcCuWCymg4MDdTq9ALVwuJf4vru76xm97XZbF59m1wJo4SGFBSb5em9vz/N2KeIBsrib2S8w8319vRGDrPuTkxNVKhWDjx8+fLAsnXv6/Pzcqh5COQH0yamQ5CkBkUhvbBHhYuyf+/t778FoNKqBgQHPAqZZIwgQ9Vg02ptaMTU15T3OXiLPYXh42CPfYICxBbXbvcDRSqXis4tGlaYlFosZ7GWdo8hhHRSLRYP1+J4ZSbq3t2dAnKC0TCajdrtt3ylsGk02Y7UuLi4si+Xfk+Sfz5g+VIpY7QBmWONBsAUCJJvNqlwuP5mbjj2KPXt6eupz5eDgwOnzBFAy2g9Ag5pzaWlJu7u7Ojg40LNnz7S8vKy7uzuvc9YFQD/7IjhCDUUO+51mCT892SOAjICJgJ7ce5yjAB3UQyRiS7LMmPAvvgvuuaCygiA+QAJsjKgsrq6u9Lvf/U7dbleff/65UqnUE/VB0HdOI8lZR60FUz32af45ZBgWFWwV3EWvX792VhRrhaYclenOzo6Gh4e1vLzs+gx1BPZBeg6sZ5ALTJXg2WEJQk3Az2GvQixNTExodHRUc3Nz2t3dlSS9evVKHz588PSiSCSit2/fWmG5u7ur6elpDQ0NGXxbXV3VxcWF6vW6iTLsW0zf4d9NpVKefMP+keS9QJgeqkEsG+SVoNxjTwIcd7tdHR4eanBw0Fkbw8PDWlxcdDA0wZbY5wYHB10Hkb/Fncue4/Owt/+Y109M+J/++lFN+Pz8vBF0iohisahMJiOpV7gUi0VfCLAKsI34mi8uLlx0Uvhz8YEAgqw2Gg03qjTqJIgTAAM6D8vXaDQ0Oztr+fP29rbRN4JUOLgYL8Us5eHhYVWrVS/YoH8JmR0X+8jIiBYXF3V+fq7j42NJPWkgibjIgROJhN68eaObmxt99tlnbioIkkLixaFDA400lg2HrwnZJAczHpRyuWxJblCCxHtDho5EUpKZN6TkfK8waldXVy4MkO/QNDMmCHtBt9vV0dGRi8WJiQmVSiXF43FLD0HyKShgfHa5EQABAABJREFU01gDrVbL74nvEjnQ5eWlut2uCoWCGo2G7u/vtbOzo0wmY4SUVGSeJXJLmnJmBwOocHm1222zrjDOxWJR33//vZLJpIaGhuyVffv2rb2uhPrgd6OAAH0MHqAUZIAEsVjM41Vgq1CMIBtHto0PDvaEUV/MckYSzKG7sLCger3u0UYgohcXF37vc3NzkuT1QWE+PT3tC4W8hVAoZDad4L1KpaJsNuvGmckHMFqgwkiig9YCpLiABzC8MCYDAwOanZ31hUQRirID/zhSS2T6fEfPnj1zU4zcdnx83EUKoZKRSET5fN7feV9fn0qlkkdkNZu9iQbdbtcjYZBdrq+v+3dgo6FAvL+/1+7uri//s7Mzz/IOKjxogAEbkPYjk8UvCcjJmQQj3263LR1MJBIu+FD5UCxSdFHEoBA5Pz/3+EGADdB1zjyAVppHWGyKHZD1TCZj713QDgCLixSOQB3SWWdmZqy+4FwgiOmLL77wOYffjb0DE4MfDzsDn5l9zRgd6YdwLWbR0uACqOIRHRkZ0fLysr9L1kYqldL09LS9ywAfWJaOjo6e2G0oWhuNhr766qsn5x/nGywZ7DyedYBT7gd8erw/wG/WD801BT/PqlAoqFqtSpLXIt8hyjUAO9bS1NSU1Wk0BCiBWAuAuJLMSAEcIKuuVCoqlUq2jwRZPe5TfLsw39QAnH/sEZipVCqlcrmso6Mjg/1IJwnIS6fTSqfTymQyTzyZKKCQ+rOes9msqtWqi9dOp6OtrS01m02tra05AT84AigcDtuDK/XGI0ajUS0uLqq/v1+1Wk2hUMj3odTzW2ezWYd68Vyw+gEcoeRKpVLqdrtev9yR+Os575vNphtvztWgnYJzj2KZGoQ9TvMZlKQGvc3cEygJaCqk3gxdmrfJyUkHXsbjcWfVwNpefErmRrmBnQPVB6GpAF+APExEODk5MUHw8PCgm5sb/fznP9fc3JyBtEwmo8HBQT08POjrr79WIpHQ6uqqz2ZG2nIfA/7SuHF+8YxphkOhXkgX9kkUac1m00Atfn7OZuow8iKoUbgz+Xc4ZyQ9mVZBHYM/HXCLph3lCYFxfAbsHhcXF/r48aMzZDqdjskRAMD+/n4Vi0WP9cWWMTo6amCN1GiaTUba8v5piOfn5zU1NeU59LxHwK5wOKyNjQ2vncPDQ62trVmdMTIy4rqYz8/3gMUEdrrRaOj4+NiKFuxrQaLi5OTEqiMULVIv9wLCAACQc/Ty8lLv37/3dxeNRvXhwwelUinbHLECkI2CzSYSiZiJpw5sNpvK5/MqFAruQUZGRhw8yL8Pwfjx40c1Gg1VKhU9e/ZMpVJJU1NTKhaLzg/CfoeqqVaraXZ21nkH1MCdTi+Emake8Xhcx8fHVvigbuSOv7+/d8hicCLQT68/3+tHNeH4GEKhkIfTUxQjjWMxIMPNZrMO3oJ5OD8/9wX8+vVrB14h7xgdHdVvf/tbH0BImmA97u/vtbe3p9evX/tAZRwM7DthMpFIRDs7OyoUCr6AkeMEi0lJZoCHh4c1NjamqampJ0ENBOcgd6QoqFarBhMWFhZ0dnamnZ0dH2InJydmKfCv0EAhMalUKi7SSTHnoudAobjifeIVoviOxWLeQPv7+y4KYWdJvob5pPiTekU+EjdJTw79drvtlPp0Oq2TkxPLlvDewDzg20F2zCUaZNmCMjjWFO8fqTuFSdBfxvuNx+NmmgCAkF5SGNJcw46QXwCaSGJ4LpdzswgAgNczl8sZ6EEJgbIB3yb2DJjOzc1NS5PGx8c1Ozurvb09N5kUr7CxoO6dTseFw8DAgBYXF82+85y4BK6vrz27+fr62qFkjUbDYTXHx8d6fHy0nBm5MAg81gkKgru7O3+PQT8XDR9WDy5aGI9SqaRms2nJM94nGGqap9vbW194XMgwREhVUaZQpAOWwA7SUCPRRyrN/pT0JPOBoga/GEXfr3/9a8/BzGazLkbwfbXbbW1sbCidTvt5sI5HRka0tramSCSiYrHowjuVSlmmSGPLHp2ZmfH5US6XzUAGrQwoJgjbAZBg+gSFIYU7TW1fX5/ZI9QkPDOKg2AuAmuYswTQAklqJBKxZI2CBa8bgZeANa1WywwvbFRfX58zM5DF0UxwHrPWCNDqdrseYYZSYWVlxYAYthyyLjqdju0GQ0NDOjo6UjQataWFPRwMR2OUFaOlgkFcNzc3Hn9EocZ/p4gaGBjQZ5995rV6fn6u6elph0zCWiJXZtwfDSU5GjA6QbaP7ynImnPvoCDAcoTkGZCEVPiBgQHvZ/4c5QnqH0AeVGHIl8fHx+1b5E5nLB22JZp/Ggbm5AJOYsdg33InsjZWV1etfuBMADjgv2Nf4cxGfh1sNEZHR5XNZpXNZn2PbGxsPGEpIQdgDcfGxlzsU2giBb29vdXh4aFZeYDM9+/fG1jj3Aa8rtVqDm0E3Lu/v9fg4KCOj4/t8U0kEspkMup0OrbtVSoVbW1tOecGwIZ9ghoKCwyqKMDIoH95ZmZGR0dHkvSEMUVejN+VJHAAirFPk0CQyIZCIZ2ennqd3dzcGPiigQx+v9i4ABjD4bDrE6w3Z2dnevHihQYGBpyPQH3G/ofdpgFjH8AY0gi3222NjIxYSXh1deXQMILOOBNplrGSYSVhnTL1giZKkusSlH+sEerCSqXi5oewN0n+/oeGhrS/v6/Z2VllMhmzmPF4XNfX1w4yZn3zPVFnULNizQG0414K1muoJ/4wWIx7MxKJ6PT0VIeHh5qdnXXIY7PZdAgl6yOVSrkB5G6m8arVagb8CXgjiwP1GkB9LBbTysqKUqmUx9WVSiWP2sKSiOplYWFBtVrNdevvf/97n+1YXWC4sdbRyD979sxjzVi/3CPsa85V6ulQKKTZ2VlPfcCWSo4BcvTh4WFbRviMNO9MM+Ec3Nra8j5iokbQzkhNGlQbYC3hjAUoBdDB6079lE6nVavVlE6n/ftevXrlugsr4dnZmQE/gt+o9/iOWCO1Ws3gSVDhA+s9Ojr6ZD/8sa+fmPA//fWjm/Dz83M3wqA+IPQwCZeXl5bdcOFGIhEj3rB30WjUkj+82qenp5qZmbGUj2CkSCTiES2JRMKpufF43HJtWA4uBSTHXNr4wOLx3kxKEhbx96RSKTMT6XTaRRXS8OnpaUvBLy8vPSIrKEkqFAoOoWODwT4RtjUxMWGvHr4M/FvNZtMFeLAgDHo9KCy5nCmuKTjYfEdHR25u8CJSCE1MTKhWq9nLhBwNuSSHJ9LYu7s7ra6uWno3MDCgTCbjw49gB2ShfL+dTseFGYV3u922h5ORJYABHAo0hufn50+yBZAWT0xMOLTi4eFByWRSF5+SwJHoX11dOZQO6RYsEMFBIIL8zoODAyeLgpY/Pj6qVCppenpao6OjPlxRfiBPYmYqaCqAARJegqU4ECliaBZANUGPedbBMD72GWFSrDOeLyx7q9UbB1OtVu23CjIvFMe3t7eW6HKB8ExgGWna8THR9ExOTurw8PCJ75P3PD09rbOzM83NzTmlE+UBag6+L1gsmm2kgTRsQcnnzc2Nx/1dXFyYZWq1Wk6Lvrm5sU8ZL/Ld3Z19riDDt7e3+u6778yYnZycGDgBiZ6cnNT09LTXKMj3+fm59vf3HRhJQXZ9fa37+3vl83mze1yurH0+G+FtgF+tVsvoe39/vz5+/Gh2jBcXY6VS0dramgqFggsOSU7XJoSSZqZSqXgETrVa9UxTvIN4xJaWlpwrQNEtyWsFFRNSTsLoJLkwZJ8DsMEe4nfF53hxceGRZdwR5+fnWl5eNrtCoYVcksaA8xb1E+xsX1+f8yoAxiS5OaXwwuM+MDCg58+fG1Tl9xQKBWdc7OzsKJVKGXzrdrtO5U+n04rH4w5ompmZMfu0srLivBRAguA5BZNNKNjAwIB/jiTNzs76nIA1k2TlAqGgSHo5z2CwDw8P3XBT2AdVBcivWdNIREkZvr6+9r3JcyWoCNsORTPNCaBDrVazqooXEwFQwnB/8XxopphWQUN5e3trNRpN2vj4uL766iufw8z/hllFKSVJmUzGCcxYxpDxU4+g3Egmk3r37p0ODg6UTqetjjs7O3MxjfWN94n1hEwJ6h/GMeHxDc6khs1j76Fu4Lzgrru8vDRQG432ZrQXi0W1222DuzR3rF2aEABXkt65A6Qeozk9Pa3379+7bkBGz9r6+PGjx4EB8nW7XQMxhNbBIK6trVmhgqqAc57aEYsf1jWCvLgDAWYA+VE6FItFSdL6+rpT/vf29tzcFYtFNzD4x8PhsFUk3E80w9RSNPk0WdPT03p87GXCcE6wPgYHB5VMJtVsNj0BiCBgLASLi4tet7FYzEHFNJXUPgCFnPXNZlNzc3MmXQDlgwAdNSCfB1BMktVI1WpVhUJB/f39+u677xSJRLSysqKxsTGNj49rb29PuVzOOUxM+zg5OTGAGfTBc6fRyPFesR8Azvb39/s5QbBR53IvoUhbWlryWLDXr1+7Bp6envb9RK0AMM0aYd9Wq1WHjnEmANqvrq4aXHj16pUBBf7O+fm59vb23FQHrSvRaNQqNHITOBuodVGenp2d+SzkHqL3oE5nHx4cHFgZwCuYucT5DSgWjUY1NjamRCKhra0tr1uITOxYZFuhImIEGVbKoA2UJh3LG2cKE3oeHx81OTlp1SHBnz+9/ryvH9WE0xzs7u4qn8+r0+lYisVFSYG/vr7uIDdJ+vjxoz10+EtYEMVi0dI7pFfLy8v2b+zt7T0JLAqFQqpWq1pdXX3iuwOtrNVqlsAlEgnl83lLrkByQaTq9bpTnJE0If2kUMKTXigUlEj05rbSjLfbbftFisWiA0MWFhYs/aaYReoWTGln45IEWqlUnvit2MhBZgrJKYgvrNb19bXevXunbrfrMT2wJyCqSDXx6Y2Ojjq5nEuKxh+P2Pb2tgtkvIk8O0luMkCPy+WyCyWQ1cPDQzfYKAr6+nqjYZCtDg8PW5aHxB1fMjJLZG/415Eg87lgrQEcOp3eTPrt7W2rDiguKRhisZgLFbze+GcpBjgoaZ4IZyMF//r62jPdSdCPRqPa3t52wwdKfn9/b8XEhw8fzOju7e25mWb+cSgU8kzPzc1NgxaFQsFoKtJy2C/GlQCUIXFmdBHqAbxIMACsbaS0SJKQ0YVCIYcYwUyxBgll4tI5OzszQ8/vPTk5MSLNPgNMoPmGLcXXS6J7Op12+B1FUyqVsjID5Qq+qnfv3rkZAaiYnJxUNNpLXc9kMopGo3r//r1RfthQVBQAb/iiATcikYgmJiaeBArBmvb393uKAyj+ycmJUqnUEw8w5xZBcKha3r9/7yadC5XCWZJtJJL0/fffmyUCHa/VamYAGE9E8Y7Ul4R6iihAldnZWQOHgCU0A7w/zh3OPz4zIBKsJon7WBmCZxWNQzD8isIzGo2qVCp5bdDU0qyhFCCckf+NXy8ajWpmZkbZbFarq6va2NiwXJbGHoal2Wwa2EKOyXuDoWS6AewY7BMMEX505qLX63UdHByYwYedA/DgrhoeHnaRjjWGUT7pdNr+yKOjI42Pj1uOyXqA+UCSTCgVDAppxig1ODuRPY6Ojmp8fNzPHqtBPp9384ZcHrUEss98Pu+mlGC5wcFBbW9vWwF2eXlpAD0U6o3ZpAnhPoW1RDJ9dnbmXAlAWOSwiUTCifcU95yVr1690vT0tP7Fv/gX3iPBkEGA206n48DVtbU1N8mRSC8MFP8xyrFnz555JCd7gbWIh3R1ddXnSyqV0sTEhFnaer1uiwWgL9YsgEMUHdlsVvV6XXt7e1pYWPCdgcy5UCjo8fHxiRecO5vX6OiowUqAsFgs9mTsHsB+NBo1UEUGAOAle41agOZHkpm1m5sbDQwM6Je//KVTs2HHOSuCgFKQUMDGgXIDpQHNB7UM9WF/f79HY7bbvek6NLmoXwg3fHzspVL/w3/4Dw108KxY99RLvLfb21s3gtjSaBpJ66d5Ip17enpanU4vF2Vubk7FYtHBi1guUExiSwO8m5qa8vMA7OLZNBoNHRwc2MOLOmlgYEDJZNKTCQBneI+s9VgspoWFBW1ubqpUKumLL75Qf3+/gXiCyFAWcm7yTPnZKNxarZbPsFevXimbzWpnZ8drE+90UJHZ7Xb17Nkzy8EvPiXJAx5iNapWq5b0r62tWSHEJAhCNIeHhw3qfffdd2Zq+/t70x+Wl5ctn+dMmZqasmWPLIlcLucZ9GRmYPuLRqM+k1utlp49e+bngmqGdYNSqdvtan5+3rWC9EOyOKAiYA+/I5lMuk7i/se6cXt7q2w2q0KhYMXK2dmZVZjUbExfenh4cEhnq9XS6empG3MC3Ki5u92u1TcQcRCfgDxMjIB4Avz7Y1///8pU/7lfP6oJBw2v1Wq+UPGAJRIJxeNxpxDjUWYRMAeaA0iSZW8wAKQYz83NGaVmkTHiheLs4eHBqZmgVwQENRoNpdNpNZtNo2iS3HRWq1UjSvhpU6mUDg4O1Gw27WPGfxWPxzUzM+NmiUuJEBlQbFiJycnJJ2xM0IOI7JKgn4eHBy0tLanb7br5l2Q5FQUtISZcjhS2eFJBtmiAjo+PjWbNzs5qaGjIhefjYy9tkWfEc0KSRzhFKNQbQQY7eH9/72aMVHOAEbz+pPMSuhdE5GlyJLn4xR9zdnbmdG8uXrxBhMWk02kXA1zQHDQEnNBw8j0RrJLNZi075Wc8Pj664GO9USy3Wr251gReIKMELaUBwwNbLBY1PT39BAQgTIrxYIODgw4xubq60qtXr4zkUjwjNwfgWFlZcchPtVr1GqaZYybs4eGhZb9jY2NPZl+DpgfXCIXG6empvVTHx8fK5XIOQIMNpjlHVgazTSL0wMCAPYsUE3izUHlwgXGZRqNRLS0tuQnDB7+/v2+2FbCFxhHPL00yRRX7olQqaWlpSel0WgsLC/rw4YMZg+HhYTOupVJJ9/f3ev36tZ4/f/6kuCUkCPli0EcJg0azyNxz5L6hUC+si4saYK5arZoBZfwS/7DnmDmOr/zNmzdPQMc/fMHgSD0E/+3btwb4aJpZn8h2mfFMc8SaAPRh/RCsxsgzGhX2Pmde0EIRPGeRuZEEfHx8bBtBqVSSJAOhjC6jQECBsbq6qmg0qp2dHTPFKGoo+PBiItMkjZgskUQiYSaYJj8Y4hcK9cYC5vN5n3/I4e/u7vTx40d1u11nJ/C8adQmJiZULpfdvNHQUiDt7u76v6PKQplE4Q3Dhu2E/RRsQgG7kDYzq/b9+/dW1wDukWVA0QYIA5BL4V6r1bSxsWH1zr/9b//bZpJubm5sSYANQcXAnYFK7PLyUoVCQdPT0w462t/fd/gndxcNKPaJ0dFRAzGNRsN2NgAnwDJk8zwTsjdQEQEWpFIpffnll34u+MBjsdgThRJrkjN/d3dXm5ubT95TJBLR559/rmazqY8fPyqfzxu0ZT9UKhWNjIzo8PDQAU4kGHOms59oGFlzMKAEN8GKBhUMyWTSobW1Ws0SV9Y86oggYEEoWTabtXWIdQubybmDjB1QicwAAFnqDAAiinXWEIzx2dmZay4Ad5oPRp6NjY35Z01NTalcLrtmINMjGAwGuzo1NaVoNGqLIkopQGBY0qOjIxUKBTPtNH6oZQAj+P9RXnEnweiT7I2aiTqIzx1s3lnXY2Nj9r7f3Nw4cO/6+tqgOQACVgnqV8CH09NTPTw8aHl52WtkYmLiic+bRho1G814Op3Ww8ODJes0djc3N9rc3PQ9ATgzPz/vGpo9cnV1pWw2a4INdcLj46NBc3JMIpGIR0cGJ8d88803+s1vfqNYLKZf/vKXikZ700ZisZjVC9Sj29vbzo0BVMG+xDhYsggGBga0tbVly0mxWFQ4HLayjOR98ixgiDk3qB2DY99qtZrHjLFGaOpHRkZUKpV8nmC35efRA7G+mAQECcTabLfb3tfUaOFw2PsIoGtgYEDLy8uu/fv7+62AgehgDjvMOzUxYARqJAKhO52O8vm86vW6dnZ2tLKy8kTmTq4GtoqrqytdX1+bpMCGQA360+vP+/pRTTiF1NXVlWXPIOTIqGBhKO5g2rggLi4uXOSCGErypTQ5OalUKqV3795paWnJTVYsFtPe3p5Zn/n5eReMjN8ZGRlx00tKK8UXErm5uTktLCxoZ2fHzFAymfR7XFtbUyKRcLOGjAqpGIsdRJjmgE2JxP7o6EgnJydaXV1VNpu1x4efwXOBJcEnD5OExDc4cmJoaEgrKyva3d21vISkRxKwr6+vXdgiMxseHvZYCdisbrerUqnk5o9Z3X19fW5wC4WC7u7u9Pnnnxt4oBDFD47MFbnb4eGhmQBmURJCdXp66qIimJ4K2wszje8tFArp8PBQ4XDYDToyP8JK8DKFQiGnVQIMTUxM+MLl4IvH4y4OsBIQ+IEvqVAomIUEseWApvmmsAIAkGRAIxrtza7k/fX19WllZUXNZlPz8/MeU1atVv3/7e7u6vXr14pEIj5wSY6u1WpaWFhQNBq1lBqPtiTP/L69vbVnDm8pINL5+bnS6bTloGdnZ2agCcVBCQETSqOBVPbm5sazK/8wEE6S1z8XBWwJsnya1iAgQ8MHgEXRNTk5qfPzc0u2kIRSkOCbBtXHy1itVi25n5ub8yhA0mphRpEsjo6OqlAoWJqWz+d1d3enQqFgqSjgHgzo4+OjyuWy93FfX58vduR4+MomJyddLNLccbkyNgamhaYrCFxRNP9fNeKwSvf395aUMo6QRhhpH+EssJBYfxiNJ/XCl0DOkdHyPC8uLtzgY1epVCpmkPF7jo+P6+joSIuLi/adHx4e2sMGe40S4927dy5wLi4utLCwYIYGZomfDQCC9aBYLJoBZiwazxIQlO+N57+8vOz1zRkZlNnyXliL19fXWlpaUqFQUDKZVDab9TgZEneZzAAAWyqVNDAwoO3tbaVSKa2urppB4eymOMZjS6gpTRqZFhTeeCNhGJeWlnxms64AJ/ieWScPD71RQN9//71zQoLeRYCJ09NT9fX1+R4nhBBgkaIVe0osFjOIcXFxYUsHuR6cm3iKw+Gw9zZ+WLIYmIgxNDTk8CsaKL6boOeZcxjmnZ/16tUrtVotFQoFXV5eWhadSCQ0OzvrXAMCCtljCwsL3geJREL7+/vqdDpuJrBfcFcz3xeJeTAYr9vtOkSKAM+RkRGfdUxEAFg8OjrS8PCwUqmUwRzOAr4jVBPYKPB6w6bS1ML2oU4ZGhoy0IpUH6UVhAjBVAS9sSavr6+VyWQ8M5u6AkXQ7u6ulpeXbXkhz4TQ0d/85jcGxFhPSHux3vBd0kjOzc1pYGDACgimo2AnYJQh39vg4KCDqCYmJgy+dbtdWy06nY7Gx8cNmHE+t9ttvX37Vr/+9a99fyQSCT1//vxJMOHjY2/MJ2A/EmdAvtHRUc3MzGhnZ0d7e3vOC6JxxSpD6C2kAkA9gWjX19daXFy0yobJGoBTJF4DqEA2YMWEmCH8D5UYEwU4Gy4vL51PQv17cnJiOxsAbC6Xc25Oo9HQ9va2Zmdn9fOf/1yxWEzHx8ceD0gjzHvmDhr7NEeeHiCZTFqF8sUXX/j8ZAoNitZ37955dCE1AM18sVhUX1+fdnd31Wg0bE8EeELVgeKTehPF2IsXL1xX89nT6bRD5d68eWMVKQpKFE6cfcGQPACOUqn0xFPe39+v5eVl/10AYc5lAqRRHKJs431zF0ejvUBPVLR7e3u+q/v6+hxyzR1J3/Ly5UurW7izCOIEWOIf7k7OkaCV6P/u9ZMn/E9//agmHCYlm81qa2vLc7AJYKAgB03mQqKYKxQK9oWzIQleY5RJUDZL4U+jzNy8SCSi6elp7e/va3t728js4eGhzs/P1dfXp3w+7/Eqz58/9wFEITM3N2ck8OXLl+rv71elUvEIj4ODA0vir6+vdXp6qkgkYq8k/jAkaHjl2EilUskjiprNpr03FBHValWVSkWrq6tmimCBkbpw+HJgX15emq2PxWI+mHO5nGq1mq6urpweSoAWc2yR2VDUwWaCWDN6Zmpqyg3K/v6+5ufn3ZiD5pPySAGKggGv29DQkKamphwqw3OZm5vzZ7q/v7e6AcaWQwdAIxhGgt8wGo0aGeSyRZrNz8G/BnNWKBScWItMFlYFkObDhw8aHBzU6uqqlpaWtL29bbkc0sCNjQ0NDw/bw0dwEYXH4eGhWSIKP5BTkudLpZIbwtPTUzUaDW1sbGhsbMzBX0jDHh4etL29rXA4rPX1dV/sSLgo9ggCAu0GmYVlCMpgAXmQGeLfwhaAL+7y8lJXV1eWTgdHvzWbTe3s7Bj5jUajBpe63a4uPo3JkWSZORJ5wrGOjo4Ui8Uc4jc1NaVqteowJJjVoaEhnZ2dqVwue1a01LOD8O+gkAFFrlarRnf5rFtbW2o0Gpqfn3dhShFGc4F8t9FoqFQqaWFhQZeXlxodHXUIFYAEBdrp6akkaWlpSUdHRwqFQlYABYN8gqwgYYY0JLx3QIZoNOqwLC7KP3wF/4wCKRwOW20Ey4wsDoCA53t9fW3FA00RWQd4AQm8g1WlkHl4ePDehYkEvCGoDNCLfULGBqomwDCkdwBpkUhEq6urDmoKhsyhZABMw7sMEwmCXywW3Tyzv/G6jY+Pm8H69ttvDSCxL5hZOzc3p0QioW+++caSewC5brdrdUE8HvdYNCZwvHnzRoODg3r58qU+fvyo8fFxj6Mc+zQSCVYG5gqgd2hoSP39/To6OvI5ifRcktcVTTeBgTT2BDTyXfNca7Wa/qf/6X9ygi+FIPfE9fW1FV80p6g9gv5ZwiuROI+OjtqSA1s0MzOjpaUlN7sEELEW8RAjweRs4D2jRCLngDsE4A6wEMaSumN8fFwTExNWVKC22tzcVLVadaE7NDSkpaUlg+isEdK4aYji8bh9tn+oElhcXFSpVHIhTsMIswrgw50Lg4zFiWYCyxBAEBMskLMjSwWIgj2j0aFBZf43Sg6CcKk3AAKRvUvSxcXFk4aAYC3AzZGREX322WcaGRmx1Jvxbvjzx8bGDDRWq1VFo1EdHh5qZmZGfX19Do9Dks9zAviDxaXmwS4G4QBIODw8bLseigT2D3UJHmruK545IADWhLOzM59d4XDYoZ6oYai3kIFjkySjAfAQ0oG1irw5FotpbW3NKjB83kFGHdKor6/viRWARr1SqajdbluiTeMMmMgZ0ul0HE4H4MAZT81xe3vr33FxcaGZmRkdHx97j8RiMb1//95AHJNJGM8HIdFutzUxMaFcLqe///u/1/v3700EzM/P61e/+pV2dnZcl87Pz7smoB9YWVmRJI/fnZubc73KmYa6qtlsWs02NDTkuujjx48qlUrONWi32w4F5S64vb3V6empfvaznznfg2cfjUZVKBS0urrqzJH5+XkDP0EAGlsnFhJAs0wm43uJhpsai3XFWcD3EovFfBfwOxKJhD5+/Kjz83Nnxtze3mplZcW1Qjgc1qtXr3RxceH9x7lVLpe997kfgrZN9s34+LhB4KClEEVwJBIxuQqY8mPl6D+9/rTXj2rCR0dHzWIQaY9XCnkYiCyMxd3dnWZmZiT1ErgJhZiYmPDhR0hCMCxncXHRwTh4TrvdrmVjU1NTHkmBnD0Sibhp29ra0sBAb07ht99+q8nJSeVyOUuFSD4nERXfCr+LogdE+uHhQZubm0bLT05O/Ew4JPDyMcP38vLSzGEoFNLy8rIDWgAaaFAIUwuHw573iQ+83W47xZnNlsvl3MR98803Oj099QipeDzukRx3d3eWP9JgwTrQ6OHr5yJhdjjp4DBnkUhEh4eHRvkBXJB1cwkhbcK/SqgHBSKMHDJN5m9PT087qAvZGKOWWq2WGWwKOJgCvjN8vQAXeEefPXtmBcDFxYWlSMjKYTE7nY5Dk9LptKLRXlI96OHU1JS9lDRVFL4EKFH0gAizNyjuuRz4fTQ+IyMj2tvbczMO00UR/v79e+VyORcb/F1JDoGbnp5Wo9GwpI/nyHxi5lfCYNH4kTTK94VULJPJPGHuYEqCDFQwiAdVCPaK7e3tJ0m2p6enBpooVvb29pw2XKvVfJmSlxAEQQBj8PoRjFUsFu2BOj4+tj8cefrp6amq1arK5bKLbz4zNhTWCgwX8mgKbMIaURhwWRM8c3Jy8mSe+PT0tJvtw8NDB7WhcGm1Ws4QIBiOc5Eiulwuq9VqWT1A2jznUhApD4VCWlxcdGBYMPDs8PDQbCFrNGitYN/AytMQwUCyziRZnUCgI2thampK2WzWgCoFIPsDqTieftZV0JNMUwFLjGqGhhVGHpkijdHt7a1l7pzlhN21272wScCOSqWier1uYBIv9fj4uL755htLrvF+ohKp1+v+fQTTnZ+fmyV6eHhwqFCn09Hc3Jyq1aoL4HA4bL8woCbN8O7urgHNYrFoiSOJ2oxfXF1dtTUkEono5OTEloCgmgEV1ujoqFmVnZ0dg4Iob8LhsFZWVvTq1SufYdx7NEWnp6dmDiW5qKSgo7k6PT1VpVKx3JtzGbAaH3N/f7+/H5giWErYNJgZmi+aNP6MZ8A9gmw+HA777IWpHR0d1dLSkhVP3HUUoJzx7PG7uzudnJxYvs0LDzBADoAcnwn5LOuerBXph4AmQFOaOprDVquler1utvDx8dGgPudJMplUIpFQpVLxOiQPhOeBr5WcjWDwXrVa1d7enubm5ixVZ6433/fj46PX9OnpqYPmcrmc7+bBwUHXXaQsv337Vs+fP3eTure356BH7rlCoaDPP//cDNvx8bFVCIR/NhoNjY2NeewtGRN810j2UWoBxnBO8Sw4Y2D2aGZhAyFUAD5Qg8GY/uxnP3Nzwu/gO+E5Hx8f+zzO5/Pa3NxUo9HQ6uqqyZZUKuWGiJqZGgilJQqeoAyYOwc71/b2ttLptM/DbrfraTcEBQJM8PuoETc2Nry2Iceo7bjfNjY2HIjJ87+8vHR+0+zsrEHcvb097e3taWNjw9/f3Nyc1tbWDAYATpOCLslWwZOTE/1v/9v/puPjY/3lX/6lg2NJhgc4BjyhoYdMQNlJdkB/f79HfXEftlotn8dSL8V+cnLS9zEMPQGxEDuAlfF43GtvYWHBCg3qarKuqH0Bzqn1UejikSdMNQhEsmdZq0ESAOvqs2fPDASWy2XNzs7aGjI8POweBH87nnP6L6YJ8TvPzs5s+8GeBHjPfsYqQgjdH/v6iQn/018/qgkHZeQSBRkPhUJmzPgHRnV7e9sFaLPZ9IxAEHXSM/mziYkJLS8v6/HxUaenp1pZWfHFQ2IjPtyBgQHl8/knzNvU1JTRvJubG+3u7joAhwMKNgO2mdRffBCMIGLzsIEZdUHjPT09bc8sDQgjRm5ubuzHLpfLDuyqVCpOoqax5wKfmprS1NSUMpmMdnd3jYpubW3p48ePPpC63a4ODg4sJ2QcDM90ZmbGozkYxRS8EJLJpI6OjlSr1ezdTiaTury8dIImoyYYM3Z5eenvAQ8KIXwEBfH/0SjDsDBegmeNRIsibWZmxgfjHwZDgDzSKCO7xW9DM4gnjhmyhK1RtCMVpIkiLTWdTnuUy8PDg4OtmHMNqonc6eTkxI1tPp93aA/ILZLLVqtl5n94eFhHR0e+cDKZjJt9CtdisWg5OV7GTCaj8/Nz1et13d/fq6+vl0oP44EEaW5uziPlkIgTRIaa4Pj42EwshTEIciKR8PuR5FTvoEwVawbsAN75fD7v7zgWi2lyctJBUWRF0MSDisOehkIhjxg8Pj7W4OCgVR3MV5+YmHCmBAFxNFoUK2dnZ3p8fNT8/LzOz89ddLFPFhYWjEZvbm6q0+loZmZGu7u7KpVKKpfL9tyiAhgbG9PMzMwT5QlFAYAUrC6SVApqmGGYDbzAAJBBvy1WloeHB+Xzed3e3tqXCPAQjUbN0nD5g7jHYjGvjVKpZN855yBFFE0toIUk//nFpxFseF0XFhZ0dXXlSQGM8+PfBeWvVCpPFCV8h6RHs/9IKL67u9PR0ZFWV1ftywXIpelpNBpm1Agdwo97cXEhSfZGA2jAmCLNHB8fd3o+ScdI/lh3Z2dn+uKLL8xY/t3f/Z1VK4RYkVMBMMS6JWxQkpUvhFvOzMw44CcUCun58+f68OGDJHm++N7ens8pzr/x8XHt7Oz4TB8fH1cymTS4BGiB75tGimfPHbK/v+9mAaXQ+fm5R2chXby/v1cymdS/9q/9a/7cS0tLlnCSX5FMJs3m0YygJOC8QkVDkOb09LSbiKGhIcXjcQOuNF0kMAM+BQFamnvsCTDJSMKbzaYuLi40ODho/yznS1AGi4KDECISjK+uruxfBSAhCwafblDGzP8Xi8X04sUL27oIKcUGQeGPJJZGm3uHGormgTvp4eFBJycn9ojje6e2Ojk5sWKF58n9BHPI3+X3UGNQM0mytQlvbtAr3ul0rO5gwkk8Htfa2pqbSEZUATqhQsIXDGCNmoPX3d2dMpmMvvvuO9uG8LwCLmAHpHbBRgUgh3KPs5PagefAhJaHhwdPHOl2u5qYmPDZDQOYzWa1v7/vkZiFQkHLy8uanZ31/Yk3m3oAMLJer7u55Pd/++23lpgXi0XNzMxYbQQQw3qMRnthoAMDA2aaqRewJ6KwoN7G4z8yMqJCoaByueyG9eHhQc+ePXsCKAIUsj54/jSgyPaPjo50cHBghRup/JwnQXKIYDL+E8UqCpSLiwsVCgW1222tr6/7e2YUJPv+v//v/3sVi0Xv6Vwup8fHxyd2Q8BCfOvsGZRN2MOWl5d9XqNmkWTLIqxzKpV6YuthDV18CllkRBh3Gd85tb7UA8K+++47hcO9xP39/X1/54DXpVJJoVDIHnoAiSD4Rl0k9ZQo09PTHl0JaIUKilppYGDADXIul7OamHyKhYUFWwO4F+mHUGPQf6XTab8f8iRQdwwODrruYnToH/v6qQn/018/qgmX5KAZCh4QStC/eDyu2dlZFxDNZtOjfPD5Sb0HCoJOsT4yMqIvvvjClwVBOcjBpqam9Nlnn9m/wEWNT+fm5sbNAPJAgmqQuheLRY2Pj7swRz56e3tr2TthCvl83gUv0hoCIVZWViz3AIWkYWm328pkMva/9Pf3q1wuP5mRSrOJ1I75wyMjI9rY2PClGExi5EDsdDpPpL+krg8NDWl5edlzelEtEOJByAj+wFQq5Sa81Wo5XITLotFoOMQMPx2II0Ugcqj+/n7t7OxocnLSnx1/Logyl0NfX9+T+eBcUKCSHF4g2xS4MO74qkC3aY4zmYwP8MnJSYMmFFKw5xS1+A0J5EEqF2TjYZn4HcEC6/b2VkdHR+rv79fs7KyOj489S5nGem5uzjNjYcCRNwcBHEL+YKPa7bY+fvwoSVpeXtb5+bmmpqa0u7treXOr1dLy8rKfDeg9UklkjJVKxRcNMiWKfdKQw+Gw0zFbrZZHViwuLlqKT6Df2tqams3emBbQ2KA3FSUBaxKgYnx8XOfn55ZeUbDTuJHui6yTEJV6va7nz5+7IMefiGQSUA5JLQ0cRT+NzOLiohKJhN6+favT01MNDw97DM3a2pr29vYMVA0MDBgIGxsbM4MX9BImEgmH6bEXt7a2FA73xo4BJhIiiTeUvYYvtr+/N44M6SfJwPgqBwcHvYcBkYLSS5gS0mFR2JRKJUtAG42Gi0zGEEUiETdLxWLRSHuz2XT+BpLVsbExFQoFzc7OKh6Pq1AouCmmYUgmkzo5OfEYPRQ3FMThcFgLCwtqtVoezRWNRvXFF1/om2++0c3NjQuSer3u5mB3d9f3A4xEqVQyk5jL5dzcIsdFgv/4+OgQqlevXmlvb083NzeamprS+fm5Li4uDIL85V/+pcrlsuXR+Xxe4XDYdwdKEOSSIyMjOj4+1srKiv29ABobGxuanZ31WCPYG5h59gD2Cp5Ru932XQb7zKtWqxk8g7WjMQ+GmMKKA6wwBhTQ/P7+Xi9fvnSiOzJJVDY8w4WFBY2NjRmshZkF+KGRYm1kMhmP+0HpRYAYQAYBXgB7ktyocPaiEkGREYlE/DxggYL5CygkgqnENBM0U/ggYeBZ87VazXcQygLAFlgtAGwAXRpMMjYoeMmewVbCz4adA6QjaA4Ws1wuKx6PO+cGILuvr8/+/ePjY6vQaCgSiYQZXL5XmmisCkhKqbHIQqhWqz73uQ/u7u6Uz+etcry4uLDdBlUXsn3qD6wRGxsbmpqa8p4BeAfcHvuUUM29xJgu1ht3GrLYbrerlZUVKypRrgHMo65gYgGeaFSIWJ/6+vqcRQJgMT4+rs3NTf3mN7+R1CNuIAISiYRTu6PRqAM38RKXy2VVKhUlk0nt7OxYar2+vq7f/va3VqlROzFRhnwUwK50Om3vLv5mPler1dI333xj5p7P/rvf/c7A4snJiY6OjrS0tORxanznnCOcmZFIRPv7+1ZHYFti9C7r4vr62mwuwFZ/f78ODg40OTmpcrlsafSvfvUrPTw8aGNjw78Tm8X4+LjevXtn1Z0kjY2NGVxgTncsFvMYzlwup4mJCecwAAKdn58/AfD5riGJbm9vTQ4AlqNegLAD4MM+BGEDK0yNAEjLnpybm9P5+bnW1tZUKpX0/PlzW7KC9yoWhkjkhwA4JOVkrwCKBT8LippKpeJzbmdnR0tLS5qenvZ3w2SJqakpK/XIeQK4PD8/d8AtYazI2cl3iEQiKpVKmp+f991MuJsk1wT0KSirfnr9eV8/qgm/vr7W7u6uFy7NGkxNp9MxEwWrKPXCfmZnZ5XP583okqpJKBcMUKlUUiQScSJto9HQ+vq6UVaaRDxiFEiMbGAua7PZ1Pb2tp4/f+7mC6QMJgfGA/aKg+nFixdaWlqSJPttafhZ8NIP6Zr47Dh8SWikmWCcGuh30IdIYUMwFGOMGo3GE9ZLklEuSfYvIvGdmJjQs2fPlM1mHYSBXAmJOUEuzPgNJo2CnrXbbc3Pz1viTcAYKBSAQSgUcgpxrVZzyjvhLbAjNAvtdtuFLIFhKApI6ESmJ8mhSDxXDioKGfzefBeZTEa3t7cOwGm3e6Pjjo+PzegfHBw4LAcJE2jy3Nyc5cy1Ws0jNgi5ImiJtTT2ae717OysQ86Qi/GZYZnv7u7MpF9cXOjDhw9qNptmuvHYUuAFPbKgmFy8NLkUsniOJRlwoYgB/GJtsSa52IaHhy3nZIxdq9WyPFmSNjY2nqhA+AyZTEb5fN4yM0ANfJ4gvvjUKSR4P8jTa7WaZd4E1gHmwbwQCCPJPlT8S7FYTHNzc1bOUDDTwAVtEQ8PD5bGd7tdyy6RIwOmpVIpvXjxQt99952bh1ar5UYVZUg6ndb29rb97AAS3W4vuOy3v/2ti9KjoyMH8ATzFTY2NjQ6OuqiEaR9eHhYc3Nz2t3ddeEBcyjJioZ6va5f/epXBos4G1E3wMqPjo56LSUSCX9vtVrNqgoKWJhGmov+/n6PSEKKvri4qEikFyLYaPQmIyBppyFAAnzxKSEZKSpsHiOnYBabzd4MZ5pxGofLy0ttbGxobW1N3W4vbKlcLj9RbsCm0sC+efPGzR2KhN3dXe+Nvb09Jw9LvbnJGxsbHiGVSqV8TgXnxtdqNa2srFgWyh3EqETk63g+GREG+Mg8XhoOCjl857wnwGHpqfUAL2wwqToo/SZIigKUfcF5yT34+vVrj81Cqk/OCEoG/j7NDHfn+Pi4LQ+M6Gq328rlcgZuaIb5OTRGrN3gmg4CBjS77GEabRQ1+NVRGkQiEcs1KVK5Ox4eHhw8hYIKdUkoFFI+n1etVtPBwYHZau5n1hZnPTO+kak2m03XI5yZnU5vSgXeXYCWxcVFZ9kEPe2EkQ0MDDgoCosUqiICsljzSIgvPs3eLpVKBre5c4LMZaPRGxmKbSYUCtkf3m633ZQFQ7uGh4etOqJGC86b55wK+krZ29Rf1DWJRMIJ/hMTE3r37p3X8sTEhEZGRpTJZCyjb7fb+uu//mslk0nlcjnNzMz4TkAhB5NJ0xCsiZgcAolBLcr5jnyY73Vubk6Li4ueWMJ3LPXGwnGPDgwMaGFhweP/zs/P9erVK5+1qAm5+xmPRdgaTQ8ZSGdnZyZBUP9AXhH8eXp6qlAopJ2dnSfWhHw+7+89l8vZitPpdLS1teU6tV6vm82HgCGtH1/1+Pi4fvGLX+jh4UGHh4cO66VJ5Vlub2/7PkGZNDY2ZssL4HKlUtG7d++smuQ5np+fa319XYuLi3r//r0tbP39vSk9e3t7Ojw8tAQaCyQNIkRMNBo1IID15+Hh4Ylqoq+vz3UGQWfDw8MmU7AT9vX1mQ3OZrMGk4Pefmp4gg4BbFHwAtCg8iTgOJvNGgBptVomGYLKwG63l1VDrbKwsOAa4/7+XqVSyT8HwqfZbFoVwc84Pz9/ooTFggEQSu3DXmESDPcStrJIJKJMJqPJyUmv659ef/7Xj2rCWeSwN/iTQdtZ5Pf3925MufCYMQriJ/UOTcY3ENCBN3h/f9/sMQXfmzdvNDc35yIabyesCQUnwS93d3c6Pj52U7m9vW3ZEgVLtVrVxMSEXr9+rbu7O9XrdXuA8aNKPc8nDXY2m3UThFyFNFk8XWwODiO8qfV6/UljTWgKFwrBLJLsC8QbyjOjsSe8Y2RkRP/mv/lvKpVKGX2FGZienpbUS/luNpv2c0nyyItCoeDGJpPJGAXGNyX1vO+1Ws0hfBRBSDthDjjYQCRnZmZ0enrq+c2oHFKplL83UHCkZKwR/NJIbCORiC/Sk5MTF2CwYbFYTBsbG5qYmHBabbvd9ozI6elpo/AoMJCzsn6Gh4f9HQCy8D5oEDKZjBHFcDhsqTbyJYq+6+trvX371jMsCbKhiIMlhGkFVaU4A0y4ubnR9PS0vWioPfBhg0Djh4/H49rb21M6nbbCA6ksxRXy6+3tbafbNhoNHR0debwWlwvrgGcD8ATbTAgOhQhFZLPZ1IsXL1QoFAwu4FtEZUCAUDabNYt6f39vD/vQ0JA2NjYsEYzH46pUKnp4eFAul3NxjDcMFBq2jouKP//uu+90dnamzz77zCDXyMiIdnd39fj4qBcvXiidTmtzc/NJ8A/jYmDDgiPyPnz44EafxkDqWUBev36tcrnshOjd3V1VKhV/Horc+fl5JRIJN2koB7LZrC9K1EU824uLCyskYEBZp+xJcgwoqFiXxWLRcsFQKOTgKgpwqTe6JJlMusENrt2dnR2//1Qq5T3JGQnz3Wg0LEeHbYEtvL6+tq9UkoFBQtsoTgF5pB8C1Pr7+/3M8bYBKHEuIRlH3UBDjM1nbW3NBSIS7uBYIOxJ/BySorGBdDodnZ6e+gxLJBJmkhYXF3V/f+8zrl6v69mzZxoaGtLNzY3BvP7+fnvj+b3BLBCaZtjFeDxu5pSmlXuBwqrb7bqhocB79uyZv88XL15YxhmUy05OTmp8fNzNKsUsAFo8HtfFp4kYxWLRUt+TkxM3FKenpz6LkZBS7AEyMGqHnw87y51Fk0dtweej6aPo5hnwHdO8BxuWYCEuyesmkUjYtjE1NaWdnR2n0/PM4/G47u/v9f333xtgJwgOfymEBNNe6vW6pqenlUwmValUNDQ0pFwu57wIlEg0qpFIxEGS8/Pzkn4gCVCXkI+CjJX7gUBX8gvI04HNxUJBgxyJRBzAiDe82+26meH3MkkDFpiiHTANyw4EBt8vigWABX4ewBu2saOjI+/DoFd+c3NT9XrdTcf/8D/8D5qYmNA/+kf/yCwuZw4gI0qOq6srqzEBOYJNBHUH8umpqSmDelgG8Eq3Wi2dnJxocXHRdy/5A2QCUD8FM1ImJyd1enqq+/t7K8iCknIAD7ztqATx8pLvQXAgYBaKvcXFRWWzWfX19Tlgj7R7zgvUAgR8MQp3fX3dSkYa8HA4rH/wD/7Bk5FgBOyiNg2He9Nz5ufntbe3Z8sGNhSpp9RjDxJOOjo6qrFPo1JRxhEgxtkTj8e1uLjo70aSWeVYLKbp6WnPyaZuabfbBs5RYAIMo85Fco9qqlarWT3XarV08SngrFqtWmFDBtDU1JTPB+5HfgZgDoHS3EnxeNzB1OQvMC2K7BLC3wjy5FwGEMrn85bj8xkAemmMAbvPzs6Uz+d9BicSCYNWJycnfu/tdlvJZNJZE5wF4+PjVr1iK7m/v1c4HNb+/r6Oj489Vu6n15//9aPl6FyuHL5ccMhkKHgpsinkJZkBprnhZ+3v75sVoHkGmWc8wcrKilZXV41skhrJJRhExtksjUZDU1NT9mzhUcPvGkwADsqduQg4IPF88N5gaMLhsH2YFIIcrIx2gkH58OGDpXkUHxw4ABDFYtE+FdJTSbGk+Hx8fHS4UKfT0ezsrL788kuPTeHgm5iYsH8ffwuFBWylJDPpNNgAHBT+NJeEORCOhSQe2f/19bUTLzlgRkZGVC6XfZlwWSCbJVAMGRE+Fy5UDjlGK/AdBVkELl6CtvDQISfHL8rFhI+VAx3vZyTSS9ynmOPyYn2jKLi/v9fW1pZarZY9ekjTz87O3PhyQOZyOcXjcVWrVTMioMesN1QgfD4u66WlJcupkN3zO/f29swgwBD19/drb2/PPljsHjTzIJ6jo6MGSoaHhy2DB9TguaOa6HQ6evHihUPlGEEEGk8xHmymYKsIdqKJGxsb0/X1tT5+/OgQkYWFBXuma7WaPXxBsAIwAOXE8PCwi5+LTxkVNPJIb/lZMFAbGxsGiiRZ2opdhMKoXq+7gcWryAzY1dVVhyCen58/CVZJpVIqlUpP9lGQvSInIpPJeEbx7e2tk1sZUUbxnkqlNDk56XGKFGDX19cOmII5A7grFotWOuBvvL291dramo6Pj10kEhQ0OjrqhhE1A6ACY3dYU/V63UqnZrPpgj6YhE5hzRkHSAVISwNMIBtnG98RgBXfd7lcNisi/ZDoTKENU8R+DTK2FPg0qzRnMMj8PZoUAOPDw8Mns2ABJWDrWYOS/N02Gg0dHBw4W4GGkUCkoaEhHR0daXJy0mc/jSZnKkU79ypnLmAh+xRPIEUq0n/uZ+7bZDJp/yjrM5PJaHZ21qP3SM5Fos25js2I/AdJDjIk3A/7QbPZtA0nGJjFXU0wJM8ryFLC6EtyinYmk/Hfh6EOZoAwc5lnzz+Ak+RB8J0j/abBpgZh/ZEtE4vFVC6X3VwC0vE8AFO4E168eGEL28LCguLxuL799lvnPUxNTbnQRe3X399vJQDPtdlsOvCV8x/QDGAJ3yZjpYaHhzUyMqJEIuH7+Pj4WJKsEMN7DoBzc3NjZRLjZvmd+Hb7+vq0t7fnvAueFXcuTKAkK2vw2wO0o2ThWQczDS4uLp5MxMEeR44QtQjPD0CV84Q9wc/DNoU9EXsZdzLz1mGCyeD57LPPbEFgHzSbTZXLZQNw/f39mpubU6PRsN8XuTFACuDe5OSk2VLCXvHdBidTQDSEw2FnkfCdUaccHR1pbm5O3W5XL168cK0LgInKYXp62iAWZw7rnMC3zc1NXVxceKTjzs6Ojo6OlMvl/Dlo3KPRqFZXV5XP57W/v28gh58HMIOCoFgsam9vT+/evdPYp/GYSLJfvHhhlSRK2ZubG9veIC4AWpFnc8Y+PDxYmh4Mo8R6hmojGJzG94/FQuqpbg4PD30HjH0a1waAD3iGgggv+vb2tmtY3h8s+f39vfMQrq+vHXSLxebu7s7EQFAlAogZtKGmUiktLCyo2+3qu+++c+YFobaAP8FRvNTLd3d32tra0urqqkPoeH8QUKxbzki+H57D2KfUfO5m9h1E4x/7+skT/qe/flQTHvSislharZb29vZ8KfLlIxni4KSp5GLlMEXGwmXLIc0i4MvlgHvz5o3DtAhHIA2Qwokil4C0q6srjwVYW1vz4kQCd3l5qW+++UatVksvX75UNBr1Jr28vNTIyIgRpU6nl/RJAMnZ2Znu7+/9Z4x04ZKs1WrK5XJO+ZbkS2ZsbEyHh4dm20AnJTnIBgkJhR5NRqfTGxX35Zdfej4lfvZwOGwAgSICNgjWkjFthJ0NDw97Xvj9/b0ZEJq0x8dH+xf5/29vb40c8p0jlaaYjMfjyuVyTr/s7+/3c+FCIQiLwgj5HawF2QOlUkkzMzMulpG7U+Agxy6Xy/bscHkwZ5riA+kgrK8kp/FSvOJfPj09VT6f19DQkAv3yclJrzHQWqnXBCFVJDE/FosZ6QXVrtVqlv0QMsghGPRXSb0LmiZ5dnZWOzs76u/vf5J+zcxImg6+r/v7e08n4MKmqCBQCq8hhQcBPfj22u22R4/QRJ6dnfm7CBapNKCAN/xMkHb8prwHwldQJLCGANW49JG0Pj4+ujFAHkfuAs0ytgLWAJ8H0KHdbuu3v/2tms2mFhcXnzRqm5ub6na7nijA3HsKze+//96psMg5Q6GQstmsJVzBoL9g+A7FL5c1hXc02pvmwGiZoCIAeW06nTbQBsvNrOFgKB8KD1iRYGNSqVR0fHys0dFRpdNpLS8vW25HoUNzx97hcgY8Q7EyOjqq1dVVZTIZ7e/vm/ENKnZoVjgTJHliA2e79ENWBecxYAEBYqFQL+GZOd0wacjKKby5U/Dsc5ZKetJcBu1IyMFpBCnkgpkC7BNky5VKxYUbZzMsIaomzozgTGO+E5qKoASbtGuaHf4/2F4ALv6c0U1YmgAPg9JdGlOsCKOjo35PjNakuaHwZQQjqhlJ/q4A+1B2JBIJHR4e2p/I+YKdRJIVOqwtsl5YWzRINI1MZMAjj2VJksFyFHPsP6adBJ8nvmuCMslZQCGFHSBYr8zOzjonAoYZYASpZywWc4hXMCgS7+/a2ppOTk5s57i/v3cgGJYsJMqsfeT9gCfMXCeoiX3c6XRs0zs5OVG329XS0pLC4bBtMWTW7O/vO0tid3dX5XLZ9rFwOOwzhKYFKxrP4+3bt0qn015DgH/sZ+ToNNHRaNSWG2o/8jOwu3HmYwmDlW40Gtrf3zdLCTny+eefm3ghCBQmkqaWGoNzGPsLCifuL9Q3Nzc3Bh25WwHPQ6GQLRKsOSb0XF1daXV1VUNDQz7XUbxFIhF7zo+OjgxWrays+HcDRF5fX3tEKc+Ehp73Q8p8JpMxAMeavr6+VjKZNDEStDZSa7NW+P5R9m1sbGh/f1+5XM7BvYVCwSnvwfVM6C6ZE4xa5Dmen59bcfPw8KBisajDw0M3sQAMsVjMICBAQTKZdNYCykyIPYAj6gnWJ4HC1H2c0yMjI86R4f6B/GN8Gc8PYO7u7k67u7tW4QLq3t/f6/37964Xm82m70fOm6OjIzPbgGNkLwCkB8m5mZkZTxR49+6d8vm83wuqk1AopMvLS1svCNElMwEA4PHx0aNL3759q5mZGfv6qZmCaefcAdxl9G6Al5BkkBFzc3MG+vksP73+/K8f1YSzwEhGpNljwSBxIlgFqd7c3JwuLi7cVFxfXzthkiKRhotRGMiwQUur1apKpZJWVlZUrVb19u1bM7OSPF4ECSts0c3NjV6/fq3Z2VldXV3p+PhY09PTLvzxU9frda2vr3vM2M7OjoaHh7W8vOwijYYF+dbq6qobKpr1UqmkxcVFe1VA8ZG8UaBwkONJgRWlOIlEeimeNHhI62lws9msR0fc3t5qdXXVCDCbZ2trS4uLiz5QYevxYvX19flzIsmORqOam5tzs48iICjTbrfbTii+uLhwWuvAwIBn9OJlazQabqgpDi8uLvxnNGYERlE4wxSD0OJjR26LVxTvJrItimMOFg59EGhYWekHHznSeJQKBBadn5/7ZyPJW1hY0OTkpKXBML7I1wnzoNDngsfjDAP0+PjoueKsf4pwDtKjoyM/a5o11iHFGGoSPEfJZFLhcNihZSDiJMcCRuBN5HezbmhUaUSxPPC8pF6hQFo2zCesUqlU8hg3PIIktEsymxKNRrW0tGSWHhBgfn7eScQE2U1PT9trSFFFc8g81+vra52ennpfw/zf3t46rO7nP/+5keWvv/5aU1NTlstJMkMcjUb1/fffu0kEeW80Gr40g4wMlgdJWltbUyqV0vv3772n+Tkg/+QQUGAFUXjk+uQ6tNttB8XQKAOktFotM5AwMqDrFBHv37/3uUPew7NnzxwSF0z2h+lbWFhQJpMxG8j3yLrGN8+ZgvIHeSi2CuRvh4eH/v1v3741aAALJMnfP88qmCtAMUFiPMFzBCdRRAIcSTIAgBUjqCZiPeN15nsg04H8DmSXMDg00Nit2u22FTwApjT5FKrSDxJoSZYh0yzC0lKgMltakhtZSU4Tfnx89Hx39kmr1fI9Iv0Q3pdMJi1BbTR6Y5EKhYJZTkKDgsAZ6xnWkudFkyLJ1qByuey7nO+X93x4eGimlf3OXHesSyjKACfINUin02atAbMATlmnNzc3HsFDkY/Ni6RjQBQATElungqFgu0wQYXexMSEG+zt7W3vM/Yk5ymsFGAprCzFcLvdtiWAJo37nbXOLGfUN0EQiSkZgLXMSGcsJqATDZAkj4YMWpWazaZWVlassEG2yx1ETcBeprlBJg8rlslkdHR0ZDCRphywEB86wXBBK0gkEnHtxX1G5gP+6KGhITUaDc3NzZlMubm5sa++r6/P9q2LiwtL/wlGxZ/MXonH485QYE0fHx8rHo9rYWHBDRufn6yH4eFhN6qSVCqVlEqlnDqOlS4oW+a+Iezz4uLCHnpqJM4ZlDwAptQ8Y2NjPs8gSbDk5HI55xIBBkOqAD5wnrTbbe3t7WlwcND2O1SNOzs7GhgY0Orqqtfy9fW19vf3dX9/r83NTeXzeRM1nLvUBkHLGzUxADf1H2GTgAgAujTz7FlJtiihQgKII9A0Eok41CwWizkVvVqtOmPk48ePzgkYGBhwvRGJRKxM+kOCCHDj4lPm0dnZmUc5Unf+4X3DHQLT3O12nVrPc0gkEiqXy/rFL37hQEOmtxAuOPYp6BJikgae8xsiCcYccBMgh3ttbm7OClFqCmpnLFmSfE4BtlLnoL4dHh72fkTNwfnDDPg/5vUTE/6nv35UE35725v3ip9Rkr0+2WxW29vbRic5UBuNXipvsPjJ5XJaXV2VJIcawbyS1syYHjb4hw8f9Pz5cy0sLLgYhhENhr7AnOLNhlEDCWIm9dXVlRvO9fV17ezs2HeLL4KmlvR0DlUOyqB0buzTfGwKGVBKDodEIqG1tTU3BbyPIJNB4xaLxTQ/P6/FxUW9e/dO+/v79lvGYjEtLCwon88rkUi46SV5Hi96sGnFvzY3N2dvb71e9+ihqakpe+soWrPZrOXL+OEIUIKxnJubU6lUsk/5+fPnkuSkUWbI09hxCALksCZALkmB5eAZ+zQTmmKX4BoYKMY18LtgjTjgGOlQqVQMtNCQDg4O+qIkHCMSiZgp5WKH/cF7DmILIwgTjAyUWcyvX7/2AclFc35+rvn5eRdAjNwAze/r600AIF23XC47TA+5O0wFaCbJrp1OL2AJmwUS5I8fP3qsGsUCwND4+Liur6/ddHPIhUK98D3QeywZ+I9pzq6urixrY/YquQPT09NOGwWAQA4Go0QDzO8L5grgzwTsoEkKhULa3993cApF7uHhoS8h5joHvaEE2FCsUUTBzCHFnJ+ft1WCPw8CCEi6+N+wlkjZKcCCewZPGQxgo9Hw2RYKhWx/ARkfGhpSPp83YJJMJjU9Pf2kKahUKhocHDRrTDPH2dLtdg3gzczMaG5uznJBRkxhx8BScHR0pFgs5vMPJB7ljSSzCkjgyc+gOU+n06pUKm6KYdSLxaL3AoGJsKsodv7Qwz83N6d6vf6kQafgJGmYcDHuAFjjwcFBzc7OPgnPYa0BQNE44jGnUNvZ2TGQgd8PxisUCpmNoJANzg8eHBw0EwUTRAAgfntGsGUyGYeH0TyxZvisrBdYl06nF5IEyxOUrbM+AVnD4bAlk41GQ4VCwexjq9WyGiuomJDke5LnQtGNj3NwcNBS1yDgCsAZZPIJo2K2OHsJRQiFIewLgBIAltSbyBKUn9PM8n0BBJGjAFMKoE2IFJ7bIJN1enqqTCZj9pZiGKsX749Cvr+/37UHjTGfHfYJewu2GQCdwcFBF/TtdtsBjaztRCKhQqHgcxL1HUU8+1XqKQyGhoac48B5dHBwYGCARoXQWQK7GP1I2r4kKwJZu9yxxWJRX3zxhcLhsLMnUIkBkktyCCngCmcjLObl5aUODg6UTCY1OTmpVCrlzBwUAuvr614vBwcHur6+1ujoqPNBWO93d3d6//69/uf/+X92CN4/+kf/yEo79iw1Q19fn46Pjw2gMu6UZ/j4+PjE9jc+Pm4wlrUXjUYN0l9eXhocRNVHEzo9Pa2LiwsdHBw4Rf38/PxJHgojLWdnZ23hoHHiDITBBUwaHR21fPj09NTEAvUva5S7icYLQgxQZGFhwb+ThPevv/7a72lmZsaqTgCbvr4+TUxMKJ1Oe52x/6iBYY1JP8fmCAPO+UGTh0+ZxpbzErUB6lZqpVAo5FnZKMvS6bTr3KA1jP/kOczNzZkN5vdgAcJeV6lUfDYdHx97/6KCYQJGNBpVqVRSq9XSwcGBG/LZ2VnVajVls1kTFChPAdOpCbh/UM5dXl7aPtrpdByGjEqSkbmAtcx4v7y89LQdpPtBSX2lUtHS0pLtdYVCwTUGn5+ME85+3jc9zU+vP//rRwezjX2a/4ocicClQqFg1pFGjkV4enrq2bMEAn399ddKpVKWXjEyBDkkMiaKxbOzM+3s7Oj+vjff9PDw0CNZpB5LR0rr6OioD1dmBSLlaTabqlarnoX38PCg8/NzZTIZbW1tuflkU+zt7VlORXBNp9Nx0APjiYKHe6VSUSaTMXpKgcSFiMcYwECSG0jkSaFQbyTG4eGhk4mRE87OzmpkZEQTExM6PDx0wnEw3CcWi2lqakp7e3v2he/v72tyctJSXmSC9/f3HmeA9J8mhOKbBhAlAqFD0WhUy8vLfh4AA7AaU1NTBmaQWku9zY4EB2k7TTcIbafTsTScQh0ZGiw4BXDQD4/fLBqNmh1EjkxQGRdurVZzsTo0NKTj42MVi0VNTU05fTSRSGh/f98IMWOHLi8vDTThe8zlcvrw4YO2t7e1vr7u30txjCSWcV2pVMqyMfyRsG0zMzOKxWIqFAr+jpDHUnR2u12dnp6q0Wjo9PTUciIabjzDINwc9JLcNEajUaVSKTeQAALhcC90CwYGNnBkZMTSc+a0Ei6Hfwqg7u7uztYSJIg0Kni3x8bG1O12VSgUlE6nnSSMLxLWDkAN+WGxWDT7Fo1Gtb6+rr29PWcNcOnzPs7Oznzp1mo1FyowC8hR8fEFPckUILChKysr9uPy7wNksF/HxsaUTqc9dxR/2cnJidrttuWh+BcpjKrVqgO+KL4JsxobG7MvGIUIZxvy9cHBQb8HAD0aYC5kvLupVMqgD746GGwAPBpV9l/QQ8jeowGEhWR/c2/AfiC3azabllAH2WFASdQ5/f29ETnSU583xT2yS/z0rLObmxu9e/fOZw0ADcAMzRVn5dzcnIvvy8tLjymi2EZdhIyQEKogQFCtVt0UsudhyGgeCC6ioeD8wVYAmALQhpQd/zDeSn4Oe4J1GJTjY+Hi3Lu5udGLFy/UbDYdxHlxceF7DSYZTzPnCGodGjnWxP39vROdCbci5Rp1RafTUTqdtvQc6TPNKc0wEt54PO70bKYooMAAbEDxxs+PRqM+02nWWSfz8/P+eYDvnE8ESfIdUvwDuAMMjI+Pq1gsWnLMWg3WQM1mU7lcTu12z+qWz+fNzD48PPhuBHzge4XxB7zknqAewM4Eu51IJJxwzbOi6aHxXVhYkCQDx0HlAQw2iiDYLnJ0UqmU5ubmfE7e3d3p5OTEazooBeYZAHowIpYsCECW09NTj+yjaUCdALjU19cbxwZwR/MxMzNjZo7PQi0VbKhqtZry+byVcclk0v5hGsKvvvpKNzc3+vjxo54/f+7fD5jS19enRCLh/cS0kKOjI93d3bnWq1Qqmpub09DQkNrtthYWFhxs1Wr1xnKWSiVtbm5qcHBQMzMzymazbtqlnm0tHA67YWMdQCYA9vIPexFZNow69zeeYc5pzmLuf6Y8oLwgLX56elrff/+9zyNAWVjtdrtt4Jr1T0PLeYiiCOVCkAzB4391daVSqWSbCIqOiYkJjY6OWk14fn5uxQJBowAr19fX2tjYMBCKUoY7kHMP5j8U6oXP8r1Qe6FEHfs0wpWQVeo77pShoSGvuVAo5JDQeDyuzz77TIeHh5aQA3gdHx/r48ePJjmazaby+bxOT089IpbnR23CyD/uM1Ry2WxW3W7XQBzqHxQujDhDws85hxqDOw0FWiwW0y9/+UuP/uWMR9HM3cG64c7+Y14/MeF/+utHNeGRSMSznEEA2RxBLxZF2dnZmaVMHE5I2y4+zaBkHApSktPTU118GltDUwY7wixgPDrtdtvSvOHhYaNiQWliu932bNv9/X03KiRbv3371nKudrut3d1do76xWEybm5sudPgzkD0QM1DCWq2m9+/fm2EsFov2XiB/ymQyPvgpwCieOUjC4V6ACB7EeDyusbExra6uanJy0hcZSC2SUhJtaUKLxaJ/9tHRkX7+859raGhIHz58sBRxYWHBiefDw8NG1SORiAOiCIK5vr7WwcGBmSYakvPzc4dLgBbXajXPVGy1Wrq9vTUaSzhcIpHQ/f29D9FqteqwKElPCjkkofwOGkCkSTQFINZ8h3j7zs/PVS6XFYvFlMlkzOjVajWtra25oerr69PLly91enqqcrmshYUFo/YUn/F43CmhPKPFxUW/p1arNwcamScXBYclbAN+vqOjIxfDNNkoJpCfj32axYtaAyCM0RStVsufD8aL4hLmlPT8arXqIhhf8rt37zQ1NaVSqeQiB1sEjJkkF4h8V3weZKIjIyMOIEP2eHd35xGGqFQSiYQWFxftuySbgD0TCoUMFBGUR/5DONwbZ4Y3u9lsPpH/ESiGPBR/PrJvGl6KG0KBKF55jicnJxobGzM4E2xS4/G4R6shmeViB7waHx93aFylUlGpVDITxzmCTBkmt16vq9Vq2bdLMBqqC2wjMHfkHFDYdLtd+6aRZMKKMlGA50jTSOAe8koUL5xNkiyHg6nBdgQISnOKuoFikLOTohKpJQU8hRO+U6S+4XDYoCuNTxAUhPEBUPjVr36lWq1mQPXt27cuLoIybf5+MHCNfQIT8+7dO7MBgKQ0qHzX/Dz+Ls0kxTR3BKwiBT4N7fz8vCXWgLUwpjyrYMAfhej4+LgODw99/8FGIo/kPVGw8HwJq+x2ezNu2ZN4eJH4I+3nzB0bG/O6D9oMfv/73ysa7aUJk/eB8kWSizzuVRo67g7uOoIpaShRTdHoAvSOjIy4OWEPcq8EpwOwhgCOuD9QiPFcmL3LzyPFPfj9sd4AyycnJ3VwcGAliSS/P+wvAMeNRi/JmKaOnBPqJ1hb7ivqplwu52yLbrfrhpW0cQLEeFZ4WVFy0DjBiuGZB0SkkYfk4HykZltbW/PPPzo60tDQkBUB4XBvWsD+/r7XCM0uAAtnMQrAcrlsBdLy8rL3MyxoNps1Aw84h/IN4AQWnKyGd+/eKZFI6B/+w3+ob775RuPj4/ryyy+VTCZ93sRiMV1eXtorHw6HTWL85je/ceOP8idorby8vLS1JBaLeT0B9nLu3N/f+xmxdiKRXhL4t99+6xqGM5QRjuQjYX+SfpiSE5Q3Q9qgIpuentb4+LjrX9Y/9su7uzvNzc1penra43YJmwMUwcoAKD05Oan19XX9+te/9uQhLAOoh8g24IzkfuAc5uziPgiek+/evfP+kOSaF3UQz7dararValkaPjU1pf39fdsQAAZ5JlhCuTs5Y6PRqOs08keCdiTW7e3trTKZjMMEDw8PFY/HNT097VG0p6enVlSwbwk1C4KcPGfOdya2/PKXvzRYHg6HtbGxYcUk3zn3+sWnxHYAae5Y9kuwvgVIQOXDxBQATsIfeT8EjTLxBtKAs5x/uK9YYz95wv/fef3odHSKF8JEuFgpqijGWZQrKytupKvVqsbHx7W2tqbd3V0XpbCgXCp9fX2+5EB82GAE48Aw8vuRvLHp8FZ0u103/aFQSK9fv1Z/f79TgiW5eES6hNSDQIVMJqO5ubknKCwXV6vVcroxnh2KQX42F58kH/A0SUh4ueRAUQuFggvh+fl5/eVf/qX/N4is1JOlLS0tKZPJeEQBhS4BHniLIpGIdnd3n0gdkRXDZIE8Dw0N2QvKBREOh32IBtOjuZiKxaIDqkDGOYBB9/BTgubF43GH4VBwEtQGe5fL5XR2dmaUk8AmgANka3x/zOrEswfK2Wg09OzZMw0MDOjg4EDRaFT5fN6S2FKpZLY6kUjY67i3t+f1j6cdFqTb7XpcEY3Cq1ev7NXF483oEy5DUF4uRfZWMB2UpnJ1ddVp0qwXUEvWsNSzdvC8UqmU2RsubaTBeMBhERghFon0RpHhx2WtRiIR5fN5I7ZIqycmJnR+fu60a4pGGl5Jbo55DsjogjK+y8tLVSoVe+fS6bRZfM4A9nuxWHSDzjrj8oY9ZM/MzMxod3dXw8PDmp6eNgrN/gtKxs/Pz7W9vW3GEakw3rarqysDBhSqFKEEDZVKJeVyOR0fH/u7xGsJYyHJTBj2FYAFvluKf2bXAm7CLHD+sMdCoZBVLfjdADYodpAOck7f399rf3/fVoigPzQYzCfJLAlyTopTChveG8BCsFGiIeT3sscBHViXMzMzOjs7MzMOsJrNZrW1tWUGm+KZ9wXLA9JPsCcSTljUoMyd/y7JIBUzcmGOeM8U1/xdJPDsZZpeSd5PrFlJPuexr6RSKcuMpZ6Pl7VIRgrFIgoCfOesKRQxgFqwRQRl0QwkEgmrqbLZrCXEjK0jl4NGLXi20CzTuPIMGPPz+PhopcLt7a2fM4m+NBQ8F+6//v5+M2fkPEiy7BYZLs8WICeRSOj8/NysJXuY0T3sNbzPBH0yBeDx8dHPCFABAIPAJ9YnLC3fK/cBTQxnVjab1eTkpPr6+uz75xmydmimudfv7u6Uy+V0cXFhqwd3wtzcnJuVer2uyclJgwF3d3e2VPGeOKMfHx+VyWSs6ODM5jMxZvDy8tJNDOACXl1YQwAEPhf/m2DZiYkJ3ymo8JDIjoyMPEmmp7m+vb11XhCKLBpGppp0Or0JONfX1xoYGNDg4KBGR0d9dmHxCTJ3uVxO3W5Xi4uLarVa2t3d9Wx1AlNnZmbc9DIJYWRkxP5nLAsAWmSR9Pf3u0kldBEQivuGsEMkvoTWjY+P61//1/91/d3f/Z06nY4tgJwJAPA8R0ZlwVpKco3BpCAUOVgYh4aGnHtzfn6uubk5JRK9EZfDw8NKpVKq1WqWThPkFswuwG4wNjamX/ziF95/rPFEIuG1nU6nPU6SMxRwCwUOwCvAfbfbdaJ78H5jjC5+aIJI2X+NRkO7u7uuy1CvLC0t6fLy0o0r4CoADfcXdXSn0/PjM3KNzzc7O2u5OUDs/Py8w3eDoBwNK+qbs7MzWzlCoZAODg58Dj8+PuqLL77Q73//e/cjR0dHWllZ8dkjyfUrrDr2LL4zzqog0IECEwsKFgfu7sHBQYe+/aFNic+P6icS6U3BYZQk9wXPkz3GXfbHvH5iwv/0149qwoPSB1IUuTyD7A4oVjKZVKlU0sjIiMPQkGBSNCP5AmVMJBJmX0GtotHeiBdYImQsrVbL7Hi5XPYBSLHBBYNnmgZ9c3PTAVb4cLkI8MbhSxv7NBcznU476ZhEQTbV9fW1arWapWMcUBRCNEsUMqDUMKT9/f16/vy5jo+Ptbm5afliMplUMpnUxMSE530jo+NAHB0dtVeF4AY2IIz8yMiIDg4OnG6O9PDi4sKND0VOoVCwzx22EekShyTfM5IfCkcSvknPRZpK+A3fI94+DvRwOKyFhQUXmBcXF27+YW8opCh0KG7xB9E0AORQEEoy455KpYzuDQ8Pa3x8/Im0C/SeggPGe3JyUolEwoFEjGpCrgaDBBvAWg6Hw74oq9Wq9w1NJOmZXPIUbZOTk1pcXHSTEgzmGB0d9dg3CmGKOAoICqegVzUUCrlQHh8ft/+J54+kkM8I0BUMMeSS7+vrs7qD7xxZFcg8zBGXBU0LAAOXN35zJG+8Zy7jYANExkBQGkwTji+MAJtg0UJ40MrKinZ3d5/47oIF/eHhoc8zUnEHBwfd0B4fHyuZTPq74jMjI2MPjo2N2bLB5R+JRLS6uqrj42OrfA4PDw3c8ArKWx8feyOJADFganheDw8PlmDi82QGKeFiNzc32t3d9azX4+Njy+LS6bSmpqY0NDSk3/3ud5bGo1agEWSd8FkABfie8FdTJPNMudT5njj7+Jz8bOmHsBiyPEDmUVNwAZOWjYogHO6NvEGuB0MYLCCCDRXvB1CAouTh4cFrHICQaRNIoHmf/AxJLt45iyQ9WbPcYRQpnJWwgUiQYZ4BbMkIIE8AKTPnHY0Unv9Op+M0bRhRFED5fN4NLKnnQRUI4OX5+bmneaAAQAqfSqWsXCFokVA11jkserAuAGzhu+Fu6nQ6Ps9YW4yH4vtij8Io88xGR0d9z0my95yfjdST+52mibUXVOXg5ebOpphGAcC9Qw4AhAJrJbhHaFxgoljTqKEIQaL4JhMErzrNBM8b2SpybNhwmEPk5QMDA268s9msGUnWHcW61Gvuq9Wq9zes19jYmJaWllQul1WpVFwn7O7uqr+/32rGaDSqZ8+eWfKN/P7g4ED5fF79/f1WMrLHYOeR9d/f3zvks1qteg1wnwEIdTodZ3TwHbE+IG6Wl5c1NDSkb7/91jbCdrs3inR2dlaSrCbsdru2PlITJJNJLS4uOqcjlUp5wk273fZzHvs0Bg5yie80FOpllBBiSl5OIpHQ8+fPHfyKYpI6kwaPYD3k7fV6XYVCwesQWxyKu3q9rvHxcYPl1JLz8/POguGsAWBgbwJaAhIFcxsmJiasxqQmou4i6Jbzn7oS69Pc3JzK5bLVigC51Kfs/Xg87sabWpI6Kshan52daXh42HU49ydAJkA7zX00GvUdDBjMWU1NFg6HfV4HiTrA/mg06nBXehxCh1GZkJNDzUE2AMHH1GHT09NmxwkzjkQiWlxcNBBHX4Ail7p5aGjI9SgqNtQZ3Bsw2PQm2JqwGRGwS7+AvWBkZMT5TaiHuFth1fkd3Ed/7OunJvxPf/2oJpxLanBw0LIRQg24dIOeZhBI0iy5QKQfxp0FiyDpB/8eqD4MTFCmweHEwcBIIBpB/pxxEY1GQ8vLy6pUKjo6OlK321Uul3PyLd5SmoQgY4LvhMCter1uKS9McVD+dXl5aTkMTSfFc9D7WKvVzOyGQr3Ra/v7+7r4NO93dHRUr169UjKZtD9u7FOyIocaFwMbAHAjmUxaPdDtdg1QBA8zGjUaKi5CSapUKmZHRkdHnSI+MDBgby4JnBefxkSFw2FL2QcGBlSv13V9fa21tTXLBDkQg8FxrVbLqCPzgbnk+NkXFxceIxaJRHy4plKpJ5JQmnCKV9L4kdlKPyRUJxIJy+MpmGDVkQzX63V7CkmpRVlB41Qul/XixQsNDQ05VIsLgvR0mOLr62stLy+bBQ8qFpAfvXjxwmnSIMEUkY+Pj/r22281Pj7uAgNmF+8szWuxWPyXAAtAIZqKZrNp1hlEnQIM+RK+JvYGzx5VCMXcwcGBpV0g4iQ9o44gI6LT+WGuJ9JF/i6FtCQX3fhk8Y4FGyEuWJo3ig4al1gspqWlJW1ubmpzc9NnC6FmrFcucc4cLjr2bbFY1IsXL5xovLW19SQMjUKPObTRaNQSOQoqxlUtLi6q2+06UI1zkAs2Go1qdnbWF6Ykf87g2KLp6WmPZgr6x4NKHd4LTerc3JwVSbe3tzo6OtLMzIwbHmTUeMJglfleaUqDAUCcl6iBut2uGzUacc4WLn2eM8Ux3x97mAv3/Pzcz4WCkGeBmmBqasrFU9DbiOWEF+ckfwaAAKBAsQKbx+9Cvs/nD8rceQ4UeTTgvHiusMmwbjRKKF6YZwxQNzAwoJOTEzfJFMaor1hXNHwAgUF2p1wua3R0VI+Pj27CmetN44fEEwCaMED2IyFBfI/Dw8Pa3Nx0Q/j4+GjGLJjQDlgGYNtqtVQqlTQ9PW1Al3oBKfr9/b2D2VAHZDIZW0HGx8f9vDnnKOK5++PxuObn531/43sGIKLYJnAyk8k4yXhwcNBrkLVJXTEyMmK/O3c+mRGEeAJsEDyFMoimYuzTfHcA/k6nN44PdRZnCTUIdxfWF9YlKr3z83MNDg7a2iX1Gk6AA/YBM5lRxaGG4y6emZnR9fW1zs/PXXjTXGD3gh3EmhZUtQEmtNtth6txvpMRAmBRLpc1Nzen4eFhHR0dKZ1Oa2hoSJVKRYlEwk0yDQL5Hqg/wuFeINfW1pb3Ccongth4PihJaOCDkn7CrGhcDg8P9fr1a0voUWJSM8HEA7qjfkTBB7AKCJ9KpTQ7O6uTkxPbTrC7XVxc6Pb2VgsLC25OyUHhPaMCury8dI5HJBJxpg/EFXcUtQbKCnItsDKwNzi7kTlzRnEO0IxR21ODRCIRA+o0sdTykDvsL543ZzH3MPXm9fW1MpmMQz0BXJBC00s0m02rOLHU0CDzrOgxgsQO+QcAbPw5PQxgHNYg3i+N8+XlpafgcL9yP11fX6tSqdhOws8HUH/x4oUJScDJer3usZbUXLe3t855oJ7kHOauCmZeAS5Qa0xOTnq9A8pzTjLek/uAuot6gffHHUdfE4/HXVeiuPvp9ed//agmnEXLAR+JRCwfoliEAWQBzM7O2oNMo7S1teULhU0xMTHhQgUpKAVQcFO3Wr0k7J2dnSfIerChZ44wf4cmEe9zJBLR3t6emUd8JyTg8j673V76NOxnf3+/UqnUk/RHgtra7bbH9ASlmEhRWdQg1AASXMQU3MjNnz9/rvX1dc3MzBikoEFl1iiFN6w/KYw0/RQAyKxjsZiTMg8ODhweR4EEc8z7R2JGwVupVNyIMaqCw4GDQOo1cMisUUmQjsn4KmTLXGYc/hQNFFVI2pEx0tiwNqrVqiqVitbX1/1dwBwEi2yKZC5BDu+7uzsfQMyb5t+n+OZCQPJIGiiqBAI7eO/VatXeRhKmJTl4BE8kAA/p9Kenp/aS0cwj4aZRqtVqvhwpAAFcRkZGNDk5aZ9Z8MIhOAw/PsVCoVBwQY6EnREs+LpouCV5hiksEgqDlZUVo7Z/mArPxQmr9fj46Pnz3W7XygzGwFF4Ao7gs4N1oREJNmtIsgjKowDvdrva2NgwY8le5fN88cUXajabKhaLBhgpMigCw+Gw8vm8G4hCoaDj4+MnChVGFBJExLrjH4BLgiiPj48tMUZNgiT16OhI1WrVjQQNLqqWtbU1NyUAgJyDExMT+vDhg+0WsVjMDRwSuVKpZKae84fzLgjM0ZBS9ARZCYotLBPIfGHoFxcXNT09rbdv37rY44yGNQ4WTaDvMKQU0jBeqFEKhYK/12g0amk4EkPunuB9ECzCgqw8bC95IhSrSH9pUgBoggwTZx+s1h+y7ZzxwaYUWTJ3JQn+KMxisR8Sk5lPfXp66tExoVDI4+1oYGm6gpkAFFbZbFYPDw/+86GhIY9wRKpP3gBJudyJFNnsteXlZUUiEd+7jA7EagQQBmAIqMCZ29fX51GLPBu+h2KxqOHhYU1NTTkTAJbt/v5e2WzW4CqNPcwwDDnfDd8z92VQ7k3qdiwWe2IPGh8fN/DKOoRRQtkDKE2TDKNN83t3d2cFBUw9DbMkfyepVMqBT8i0C4WCg5ywJACg40dmv3C2oBzi/gbUQGZMzcRnotkYGBhQNpvV6Oio2TzODSTRgPD39/cOzoJNBXQBZKnVav7uGo3eyCj2E0oWXu1227kbJycnT6TFhBL29/c/kXaT+8F+BNROp9M6PDw00E7zxvNGzQejxwi4YJ7Os2fP9PXXXxvoW1pacojc2tqapqenLccOKnioj66vrzU2Nua1BJDFury9vVU6nVYikfC4TZpa6oJQKKSXL1+qXC4baMQmxD2Al7dSqej4+Nh1ABL0SqXi+4t9kslktLu7K0lPgis5v8g9CKpNJRmMYSQtdRu1Dmcb7DC9AKO8gix00C8O0I3NAqsFfnMASZRBgJ40gtRcgACAMjTHnOPBf4d6EGYe8ALra6VSefLZOUtoRlEixmIxLS8v6/DwUN1uV/Pz81YZYQXiuWxtbTnHhvOQUEy+H+6hbrer/f1918CcOTT9nPEAmaijIEwgapg8cXV15VGmAH0jIyMmnCqVyhPb2O3trW2vTGiZmppyr/ZjmvCfmPA//fWjmvDHx0c9e/bMlwlIOY0PPjLQVIoYNvvExISi0ajTrrvdH2ZLdjodJ2WPj48bXQsynchTQNjS6bR9fKBiFLSgPyMjI1pfX7fMJjjqKxQKWZLZ7fa8vUhzQT0ZyRSc6c0oH7yxSH0+fPhg5BGJCswixTLFNgjbu3fvXJBHIhGlUimtra1peXlZuVzOAAIHEkz0w8ODwzlgtgi/qVQqur6+1tbWlhseGte9vT3PMr6+vtbR0ZGZDBhxgBYOEUlOjZ2cnFS9Xrf6YGxsTFtbW5qYmHBDgS8VTxiHNWg4Yzu4GCl0isWifwbfZSKRcPI3flDk0pOTk04wh228vr7W0NCQ0um0D2SYZ35PJpNxsNrq6qpRYoo4Cn6aZJpwkErCsEiplWQpEWAPDQIXMkDT2NiYvX3FYtHrPJfLOVArn89bwcDPvLq60ujoqBYXFx3kRgHCrN+RkRF7iGFlYIZgb5BSX15e6uzszAVlONwL3ZmZmbEdBPYoKJGkgQj6PQHeaK6QI0o/eHjZT+Fw+EmoEGAF4UxBNP3x8dEjbZrNptOu+b6y2ay9Y8wW5kwCNQ56IylCKa4BXpDuMxc+Fou5gSF0EAAPuVw2m9X6+robE34vDU9QsoeVgIZlc3PTbCyNJ9I65Oew8SDh5HAsLCz42eLF5vum4ZJ6DPL6+roBLgrHk5MTxeNxTUxMqFKpWGIaiUQ0Pz/vVHaaDfILgrJzni1eQ4CrxcVFJZNJff3117q7u/N3w8U/OzvrYEeku8HCBDk/dwWeyUgkounpaY8jg/mDFWq3275TCNHhwqbhQx4fvMgBLGFkUG90u13PrIX552cFC1eAPZ4tjVewUZd+KNwluRgDoOWOg7nh7pKkdDqtsbExXVxcOIuDn0GDz9g+bF7ckzQ0fObx8XFNTk4+kV7C9jIjlqITxrlYLGpgYEDpdNoe4v39fQN5JycnljOjLmKvBUECGkjOdvyOFLBBaTiNbjDwEUCCfY+c/u7uznYlzmf+PnJ5mpJms2m/MCN68NWS3H5+fm51BQ0s6qhms2kAg7GMWHoIyex0Or73qQk4/wHQ8KEDQvC52P/1el2pVMrnIMFL5NgcHR1pYWHBah7AKMAl7uugimd8fFzDw8MqlUrqdDqam5vzeU1YGCBCf3+/vvzySz0+PjrMLwi00GDT/M/Nzen29lbz8/NWV2E15Mzg+9nb23NtxDlLQ1oul70vgwndgE/4tcPhsK1dt7e3KpVKWlpaUl9fny4vL7W7u+vxtzQiWJNY87e3t563vbS0pP/xf/wf1Wq19C/+xb8wc7+2tubvBzCPu487hLMWEDtYr3DGsb45d8/OzvTFF19YwUYtB8CCZJ593263NT8/7zufdHTY72w2a9IhqHALBhxTM3LWAxbxvcBSDwwMKJPJ2LoBS8/dxR6dnJw0gBsEtDnjsJkFFVqsBZrug4MD12coIjlHID/4vgFMyS1h70OWUP+jNMU+gKoFlntgYEDj4+O+H1ClYschz4JJMScnJ7q7u9Pnn3/uGp1wPNYC+xRlF+cb4G3wnH/27JnBBs6keDyuyclJXV1daXp6Ws1m06oQ1DUAHAD9yPKpRyHdCBxmQs7x8bHnh/f19XkN8l0NDw87BJi9CNjMc//p9ed//agmnMApGMFQKKTFxUVVq1UzwBQ0Y5/GReGPge3IZDJPRhxks1ldXFyoVCoZDa1Wq0a0JTmoAPZwcnJSS0tLTjgFQYxGozo+PnaxFI/HtbKyoqGhIcu4kZiw8MPhsEcoUIxSsEUivfnDQZna8vKydnZ21Gg0nNo4PT2tw8ND3d/fK51OK5fL6bvvvnORJ8mfZ2RkRAsLC+p0OvrNb35jHypMweLion75y18+AQKQlHLAn5+fO4Ds9vbWLBBFAhd+JBJxYncqlVK73dbS0pIPK1htgphoZAhimZqakiR78rjw8cJ++PDBARzNZlMbGxtaXl52AcsFFmRE7+/vPQoFm0IwTZWDGZkX/18sFlO5XHYCMCwtskz8WclkUjc3N84KALwIrifYQdgAnhG/k3EcpJ+nUilNTk66oH14eNDc3JxZYmRal5eXKhQKDpsjrRWpWCqVMktMQYAXC4sDQBAJ5kjZ7+7ujMryOUFbGb1FMTk/P+/PkkgkbGkIAhVcBjTA7EkyAgCtKP5QEAR9wxQdxWLRBSkXQ/AixspAoCAIOTJS5Iogu1wCBNpIP4y5wvPGfpV6l/7IyIjnU3/11VfKZDJ68+aNwaROp6Pp6Wm9fv1aZ2dn+vu//3t1u129ffvWaxgQACn+9fW1JiYm7H0EYQ9KW2GcCRAjA2FmZkYfP370mch+6Ovr0/LysmXBfX19VsAsLCw4KyAej2tmZsbe0dPTU4VCIR0fHyuXy7kZITeBRGWKcP6s1WoplUp5RAwewOnpadtXKAb4jkqlkiRpbm7OAUF8BzCenPEwb7C1fX19LmCZNQ44wp1BU4tigaIk2Ki+fPnSFqC7uzttbGyo0+k4wZ99B8h7e3trPx7AMLJxvlsYXoqLoOUJ9VOQuet2u7YcUUgCOlFYST9I0WnAaVLv7++fnN9BMJUX64J1gtIkmUx6/5RKJY/fhGFCaTIxMWH1SKlUUjqd9hlPEcv5JvVm4FLERqNRzc/P+2ykMYWdhL2H1dzZ2dHZ2Zl+9rOfuXEkZbivr8/TFABwgh5UJJf39/dWvABMck52u13LsrFBcKbxjBgvOjY2puvra3utUU1hwaFBwBPJeUVTRk4KvspWqxd6mUwmdXp6qrm5OWfacM4QikZzAuhALgaKOWoY/h73DXsAMJ/PS20UzFaBwOA+Pjw8lCStr68b3OOcJK+Az8m9yH1KojvAI0A1d2cQUKBmQynDdBGafhQy3GOdTkcLCws+75eWllwLsg7IrCBPgL9HQB8ANCBUp9N5oszr7+/X8vKy5bcA4Sgar6+vNTU1pWKxaKCFO5bak5wJmrdWq2X2L5VK6fT01HJn7DtIjgGPsBqgFsrlctrd3dXW1pYVLNjMAKRRypVKJX322WdWCjGbmUYVVeT+/r7a7bb+/u//3k0XtqWgslKSAQ+yXiQZeGftoGTg+wBIpV4+Pz/3uiFhHRCStTkwMGCQrtPpeO8BfHHmATDxzKkxuYuw1NFso5L8wyyjIGOPcuj09PTJ1ATOmUqlouXlZTWbzSd2QCwoNMbUn51Ox/56vlPO2YeHB62vr3vkL/cdOSrYgCYmJrS4uOjxx5xBWCmOj4/VarXcMAfPeALdsP7wD5/34eFB09PTJn86nY4mJydtiQzWT61WSxMTE5LkcZrYJ/n+2c83Nzee0hLMyaD2Yswwe5I74o99/cSE/+mvH9WEVyoVXV1daX9/XysrK09CPSTZ50yxQrgCM5ApEGOxmAO6CA3iIh0YGFCtVpMkB44F5bBsvlKppOHhYc3Pzzvg4sOHDw6CQBJGwEm1WnUaJwg9BdHMzIyurq50cHBgtoZDjeaKlE8Qu6GhIRUKBY2OjiqVSung4MA/u9Vq6dmzZ77ggtJ1ZFTv3793sUhRMDk5qVQq5XCVi4uLJ3KXer2u3d1dzc7OOkCMohIGqFQqGXHlvdHM0pDAWuBjgskBgaRobLfbHhnFYVmpVLS3t6fZ2VmtrKz4UEin0y50+Vy1Wk0vXrwwsn1zc6OzszNLtkZGRozmoyBAzgd7cn9/b1kiUmuAF6SDhEyBIHIJ0+iDxtI0BplGvmNGOiC94t/h+wQ9BoSg2Jqfn9fExIRl/4zto1mTfhg/gic5mUxqdnbWTSnrCyluEInExw3QcXd3p6WlJSfo4ytDQh0KhczAM0cdeROjNXhuXK57e3vqdDqanZ315bOwsGDmhkaZSxmAgyDC+/t7yxI5wGkSj46OnNxbrVYVDvdGxyF1AxEeHBy0BO7x8dHNQF9fn5PhQe2Rmq2srOjdu3dPWAQKYcaLcKkHfVAk1/O5CAICEOF8abVaThwNNmh4RX//+98b0GLNRCIR+/8ZTzM6OupgR0bIhEIh7e7uep2FQiG9f//ehdb8/Lyl7uPj46rX61peXtb+/r7lmsjLZmdntbOz4+KD/VqtVjU7O2s/Yjgc1hdffKFyuaxvv/3WvjKaYNKF7+7urDKSfrj8KGRRIRwdHWlqaspFPMBVq9VyrgFMA6wUhSDPDQbx/8PenzU3ml/pvegDApwnzDM4M+eszNLcbu2IdjvC4Qhf+d4f0xd9YUeH292Wpe4uqVRVmcnkDA6YCBCcQWLaF8jfky/VPueoeu86vilEKCRVZZIA3v+w1jMtVFSwnuwxlCZjY2NObz44ODA7AIDJ/VIulx/JnSU9AkJ4BZtxlCpBMIA9yHumgeP9whzAdnA3ATryXKXPFi3Y8qB/PuiHZmwla5/CimII1ofPyrjCk5MTnZ6eqtfr+ZkyxpGGYzgcWspKMBAjxLAVACyjquCuRgl0cnLi/8+6wxPMvsPyBODC3QbDx/8GIAkyLqjXkOiHQiGDx9PT0268uLdh99jHoVDIBSsgOmc/vmwsTLDtnD/cE6iFuO/39vY0Pz9vj2xw/2NXIaeB5O1gkxBk4gHyYa4Ac7DmEEx6dHTkvQYISc3EswEIZQ2R/I19geZ8bGzMYX28NwAYQBdegLKc7zS0hHTNz8/r5OREmUxGMzMzKhaLZsXZw4eHhwaZWNe8D0AL7lvyRQAG2u22SqWSMpmMLUnj4+NuhrlXWDc0+MfHx7q4uNDLly+tKJmcnPSdT/M3HA4dYthut60GC54h1EKVSkVzc3NWZ2KTgLgBHE+n015jNFqoJqhfYrGYlpeXLXknKJTpF/l8XtlsVrFYTEdHRxoMBs7NQda+uLjogOHFxUXNzs56Pb548UL39/e6vb3V6empG/epqSk9e/bMzRjqxXA47L/baDQsuS8UCjo5OXGeCs8L1pQ7mwwF/r0k14xBdpz9V6/XnVlBBgP1EQAoo/nm5+cfKXoAmoNgalA1B3vPOovH46rX6z6nLi4ufCYCZK6vr1vJw9kXtA2yxwEVqUOwRHKXoKy7ubmx2g21AbUlgEK/39fx8bGVMYVCwWd/pVLRxMSEQ9sGg4FJqVarpXa7reXlZYOjNPL461OplGKxmOv3o6Mj15g3NzcmVqgZII8WFxedRUCgLGcnNfuPrx/+9b2acAqM1dVVLS8vWyI8OztrCS8SNtgiFiW+aA6TWq3m4nV5edkLAQkaDVMQxZyfn3cRUqlUHOuPzHxqakpra2v20knS73//e21ubtoPcnNzo3K57CCF29tb5XI5JzrDUCERjMfjRrYociW5sX327JmDOST5787Ozur09FSlUumRV+njx4/a2toyoxePxy2Hn52d1fPnz+1jCXpser2eDg4O7IcCELm7u9PKyoqazaa2trYsDwt6ngnd6Ha7boYpBJPJpC8RmCXYMhDwRCJhBPT8/FyLi4tGQxmxQzEPS724uKhMJiNJRgUzmYzS6bTBBS7iRCKhyclJnZ2dORug2+1ayo8kEGl+MCUYiSKsP8g1lynjv2huKIKRn9JgwahFo1FFo1HbD1ZXVz3jmbwCDsrBYDTHNvppNjT+YS5fEo0fHkazU/Es8/kkufmTRhJiQKNEIiFJljimUilf3NKIRQT8Yu9Q2LXbbSfdAhZIo4sS/xG+SH7mYDDQ3t6em33W6/T0aMzg4eGhCxB810jgYAfu7++dOUCxBHABMHZ6emqUHWCA0VdBxQTAEiM77u7uHCxEkXF0dCTp8dgqmmV8rqFQyIEz+M5prtjHAAz41vH7850zShHZOEwknztok+EFS9Dv9/X06VPLxdLptPMlaCK4qLnwYfpgkClOJHn8U71e94XcarW8lglq6vf7WlpaUrVa9fcLEMI+5ftbXl725c15eH19raurK38/eOkouFGkMI4tmKbPZ+PsSaVSBjZhcwH9+E5pilkHQRVROp3W2dmZqtWqRyJRMLNWmE/PZ2MfBiXivJCJ82cAAXkv/NnhcGhQiuePRJX3HFQs8XNhY3mmvJ+gdYAmHKB5dnbWPmNYU0ZYSfIzZb+trKwoFAr5OTE6D5CMJO/hcKhWq6WZmRlls1mzlNls1pJYzkAaU+TafB6kjGNjoyR6lDXtdlsfP35ULpezsoXvIhqNqtVqPQqfAwyRRiAI9qDz83PFYjErbbAuAczCoLNmUFfwvlnbsDcwl71ez008lpZ+v69cLmcGiLV9dXWl+fl5F/DcmazfoDqItcl9whQTgE2K6+PjY7NcnD+MNwrKT/lzwaYDZQmhogBL7U+jDlnbfH80a8Vi0eAQrC6quOCccpQjFNqc7dwNTCtBKUYzfnBwoEwmY0ALq4Ik/+znz5/r8PDQwHA+n/c0EoBR/LuA3uwvmDhAcWoDaslgg5DNZlUsFpXP583scmdSA6C+Gx8fV6FQ8MQO7CzYOCKRiGsTJpJQywaBcXy23AODwUDJZNL7m993enpq8AOpPwngkFRBtphz//Xr12aEj4+Pvd43NjYegcmMZ+N9sdew5tzf3+snP/mJVldXbdFg/QXtBW/evNH8/LzvNJ4XAAH7kDXJGcvdxXccBJoAQWjCJSmVSvk84m5gv9zdjeatw4ZTt0FecBYHQRLu13A4rEql4rq72Wy6LpmYmHB2EoQKafso3XZ3d93In56eSpIVAS9evFCtVnOgXrfbdQ/Cd4xih/uE30k9yb5lLXAfYPPgOwtaGcrlsl68eOF7FFsB1rIge399fW3lXVBdR87O0tKS/vCHP3gNFwoFg/8AtSgYZmdnvTY5l358/fCv79WEU8CurKyo0WhYBkPTKMkID4UsLA7hZ9LnIoVNTYNbq9WM+oNa/6mcmyacxYMU9Pb2VqVSyWjxzMyMvvzyS/32t781M1Eul5XJZOzxYOzI3NycSqWSg8e49Lrdrhk20C0OHwoEgr1g70HakfnMzs567uT+/r6Ojo6M9G1ubuqnP/2pEomE9vf3XYTi352YmHBIBpIkJKtcXE+ePFG5XLYkmHAFGCd8UzwTEH+QQJouNigJpXjGOUyRK93f3/tABey4u7tTsVh0o4C/JuiRCabBc6AhVep0OvalIB9kDeBzb7VaWlpacrGFNJkCDgsD1gE8if1+/1F4HnIu2HjWFgUiDSk+umaz6TRr1mmj0TCDzAEGagqiC9p8cXHhYDVYqfPzcyWTSc3OzrooAZiAiQg2oaDwBNz1+32zQmNjo9AwmGT2IY1pMpl0uB7FBnK0s7Mzh9Ih36LYx58J042CgNRfwnRQCdBAs4dJ5ywUCo/mzXPxIMWDNQLZDeY5BJ9VPp+XJK/tjY0NVatVtVotXV1duSGS5MIJMOXo6MhNEt63tbU1nZycmCmHxScsSJKLekJpKGph8GnWULyw/2E9B4OBk4APDw+VSqX03XffuSlD/XN8fOyGjbNqcXFRZ2dnHssGCk7zBohJdoL0mdEF5CCgj2A2SQ72o+hOJpPe63x/NH7BUKajoyN76XjusVjMEk7Oe4JlWOuxWMznHU0uhQnMN+AgCo5QKKSvv/7akmfYM9RLwYIcdhm0n6Iv2EijEKGACypNOP+C/vvgmUAxB5ssyQAOzTsZEL1e75HXkuYEHx7POBQKKZvNPlJEBNcvQAkyZebDomhhNjQKlHa7rXw+b1UTktKHhwePo+JeJGyLe3Y4HKrRaBgA+PDhg5vGwWDgpu74+Nh7HrUP+S3pdNoNKV5ZPidMH0DT0dGRFhYWHoVXcuYgsYT9p4EPeoRhkefm5jQ3N2eGDukxnkr8jpw5+EkBPymUgw3V5eWl/x7fI758FFlkGTAiam5uzpajeDwuSc4nAFBmvQK+3NzcKBaLaWpqyt7TiYkJxWIxZbNZLS0t+U5hb5AJsrCwYNA/qNriOaZSKfvcSf1+9eqVpe4oBG9ubhykCat7f3+vpaUlfx8TExNWonA3Eqxbr9f1s5/9zGAT4Eo6nVYqlXJ+QCaT0dLSkkOneLZHR0e2U1GPYNEit4RzKAh+Aryn02k3nAAYnM80XWQCcdagPEPRRbNP6Kc0kvUeHR1pZmZGq6urbhYBVgCAaJ4AEwBwYRwBwq6urvTTn/7UZzuqAJq3iYkJ28GwQ0xNTVk6jIqMEDfOFABN7iosJ6FQSKenpyoUClpfX7ciAGDr+vpaMzOjOd0EdQZrNfYi488uLi58Lubzed3c3Bgoi0ajymazVjHwrGCXkdlzHwCIc7dwx9NQAwawR2Bs+Wx8DjJqAKSpvbg76StmZmZMNGC7CzLGAFpLS0sKhUJ+3kGJPHXJ9fW119PFxYWOj49dE7C+AFVvb2/V6XS0ubmpZrPpe5j6EYVI0KrFPsLGQO3PSD/WHrUGgB01DWn65BAwvYi8oC+//NJrinXQ6/X09u1blctlZ7U8ffrUQDkE4I+vH/b1vZrwUqmk29tbffPNN/a7cDFyuCCLIbyNwyuY9kzxSbLh1taWer2eZz7G43Gj1DT4zWbTP4MmLBqNamlpyT5rYvqvrq4czvHLX/7SBdzKyoqLwEKhoHg87nEmNCFBSTBstfRZCkjoA0wUskEOdUn26HW7XaNr29vbnv8tSU+ePNFf/dVfaWVlxcj9d99954KI8BH8Ob1ez2j77e2t8vm8Q2sYd5FKpXRycmKEnGfCd4z8mHnMNEKFQsHelunpaaPgZ2dnHotGoU/gDHIZiprLy0tLU2lqCPiAqQ7Kvaenp13Mc9lz0HKZISs/ODjw4QNTdnh4qHw+r3q9braE9yZ9vrgJeCIrgJEo+NOQPXJhkNgeTOXmdX9/r1qtpkwm48RmmBTGmQX95BRFFGdccjBLXJCgtBQyMGDssWB+AZ51gq5IDYWZBtEcGxtz04TMCZaS4uf09FThcFgrKyue0cs4IhgcCk0KWS5tQnOQYaH2IF+AfRCPx138UkAgK+O5Ey5zfn5uqWmj0fA+p1ACGGCd7u3t2WOKHG59fd2J53jCCRwKhUKWcJPsjN+QQp5nkc1m9fLlSxd0rPGLiwt7xoKSVs4JinUatUajoZubm0fzhScnJ5XJZOyjg7XjO+r3+/rjH/+opaUlhz5RXFNkAEaRExD0OtIAwSDF43Hd3Y1mvXLxA+jgt2u1WpJkloAww0qlonA4rGKx6HUcBEQBKnjffD72d7FYVLvdduot5wPPmc+/tLRkvzLFN98p48dYP3zf/LwgGELhFJSUUyxRBAFQIIcH6OWc5/dw3gQbcNidYKMCG8vPojCDsaSZ53tjHVJIw+LwnEntBdzimdLYM30i6JWEMaRIRVECoAcrc3FxYbsPvuVgZgbNN4A7IOHt7a2DhWAdWYtYpwg5JGU7GEZI0Y8CifGVodAoE2BhYcFACAX13d2dm2SkuXzPKBwuLy89cYLikkaCPYgiB/vccDh0sY2VrVAoGDxAWg97xYvALP4+EwzYb9wFWEE4c/Dto9aoVCrO1mm1Wp6KALCG/D8IUC4uLury8tJFO4o0iuVIJGLlGXcIoBUhrpJMhAAsAHpms1lLbQlTZC9il4JhRMLaaDS0srLihhRrYvA7gUyhTkBhQE4A2SaAJYDJ7H+esySfZ9Fo1J7xtbU11zjBtXF9fe33ApB0dXWlSqXidOxwOOzQOxqoh4fRlJdcLuemHmCNBGqARkJW9/f3nWxfq9X08PCgQqFgVVi5XLZakbMVFVKv13NjCBsO8AJIB4AWPD/4boLp4NQh9/f3+ulPf/qoXqQuQCaPSgFbD3sH8AeyhP3S7XZ1dHRktR1gSr8/GiEsfSbXOGeDwaFBWxG1XFBFJMlKk2BOCe+ffbiwsOB1wHsMng945DmnAGgkqVgsmkkGsCBMlLn3Ozs7fk8ARdR5T548saqP/gSbzZMnTx7ZHtib7AvOSxRrkUjE8+O5G6gpJOm7774zmMEZRc3Nec15mclkbCPodrt6+fKlFRecDwD3s7OzevHihba2thxqikWyXq/r5ORExWLx0Vnx57z+VG32Q7x+6J//f+r1vZpwSfaVxmIxy1ZIeO71epalTU9P6/z8XOVy2ejo1dWVms2mJbLIkpAtJpNJMxzBBglGlKIkyCwgied3f/z40ay6JKPwuVxO7969c1N1cHCg6+trjxWjkAnOcKaACjKmQe8Wfhcuby42Ng5o+97enhuMmZkZPX/+XH/913/ti5LPNDc3ZxSYppRCEo8Um5FLBR98OBw2KoyckVnRPLdEIuHDfHZ2Vtls1vOOKXIJIqOwgYElWAwQhWKAdNrBYGAv0fj4uFNekUATcoNUm2cICx4Oh43Ow27AdFMwIzOTPo+s4BCEKTw/P1c2m1UkEvG4FRJiT05OVK1WHQITTL0lbAVZEN85qorhcGilRLVatTJicXHRaglYPIAjGN+Liwuj3TQnHKK93mh+NGs7FAq5UEamxuEOctvtdvXkyRMtLi66EOTwhmEdDAYeFwZYRuOL34zv7/j42M0SIFOn0/ElS6MA+sw/r1QqTuQ8OzuzZSEajTqddXV11TOqKXymp0dj9giTA5whiGthYUHLy8v2BfNnKZpg1Cgkggx7u902mMHFxu+QZFYZOR6eK/yygEKJREL1et17ifVKMUsxTuNHIwboyEULGEeTz6QG/Ig8F9gRihayHNbW1lSr1dwo0Oig0uDZg4xThK6treni4sI2m/HxcX3xxRdqNpsutghjJAwomUz6O6A5I4SJucqkxgebUppVLEnNZtPA7Ndff/0v/NEUHZwbhD0BytBUBNd1vz9KCWZEF9LLoKcdoJBX0DvKe/3T4pD/5u9TdAZ/Fg0wdw5NNtLTIDtF88Lno/mlUOa8R7HACB8mYcCq8Z/2p5F+nBc0Z91u12wyo+qCICbvJ5PJ2GrAaEi+T5ik+/t7nZ6eGjyENWb6Bc/+6OhIL168cFDdcDg0U8aeQ/4syaqSu7s7M8eANQQShUIhg3Ch0Cg/BZvH6uqqQ0GpDbDn8FkpXEOhkCXzt7e3DotDKQUgANg3OTlpWwjKKywglUpF7XZbuVxOV1dXj3IBAPsmJyetAKDZhhGH+YIogK3iTC6VSpqdndXe3p663a7W1tb8rKvVqh4eHnwuoPjiXEqlUpZTcwfzmdLptPcujBp7FRCPGobA016vZyYeRQxNLnfo3NycZygDYlGjoNSBCeZ7oNFlrXLX8P0xiYX3f3d35zsUZQr7E4UU5+r9/b0nsgAyYO2T5HuAu7DX66lcLltmfHt7a0KCu55/TsMaiUSsEpuYmPAdPzs765C5nZ0dM8AnJydKpVIKhUIGaVGnsFdhJk9PT+3JR4GXTqft3wUEZw8FGX2+U2onQB/OroODA8ViMUWjUYPj2Ih4/9gD+XuEn0GSJZNJ1x/UT6w7zicAeawJg8EoZBHibXt727UV9yJ7f2ZmRqVSSXt7e488/IPBwLaucDjsMZU8/2ANHgyFQ1EA+URPwVkGgEK2wMHBwSPQBq84gbrBc4HsB8Y3BkkfyLZYLGabCTkBnAUTExO2oVGrcW9xrs/MjMYGcgajOFxfX9fCwoLPNYBf7uh4PG4VKGrDTqejcrmsh4cHra6uetzz1taWWq2Wnj59qpmZGdsNsQdSLwQnSgTB6R9fP9zrezXheDr+VPK3tLSkcrnsWYeMspKkTCZj/xoe1larpWg06guciwwZ8NHRkQ8MmhYKDQpeCgWK9+FwqN/+9rc6Pz83I8mmyWQyWlhY8EHWarV0dnbmjUuAWDweVzQatcSSYgq0iKKL/897R05FUU5D3Ww29eHDB1/KiURCa2trev36tZttUC0Ow36/r52dHbPw+ERyuZzi8bhqtZoPqJ2dHUuCBoOBL1YOIGbFBguddrttgILvkKKU/97e3vaz4CIul8sOBOF742JF2UCqKqAALFoqlTKqR5MGg0mzSzO9uLhomTDFWzKZNOtOkc5lx+9FpkhDQIEAEwbowLoNMjxc+sECm8tW+ow843cfDodKp9MGdCgokWuRhnt0dGTJF6wnATlcyNfX1w5Bo0ihsIHRajQabpyQPwYDBgmoCU4uaDablpxSCB0fH7tZg53lOW5vb2thYcHgTrB4IMiHYlL6nGyN9YD9xedFQsc6w0ZCUYWPCsS5VCppOBw6aJGil0uSi79QKOi7776zHQR/Gmvo4eFB3333neXNeKRhW6ampgzscRGzTqPRqNlBRqkgNea7y2azLmoikYgDka6urlSr1Yzcw+LTLKIwCTZOyCBR8OAdJ6gFTyAXMEh2IpF4BPYEG8hg9kRQ3pbP5x9lc1DoAz5RgMFQIG0nxHEwGOjw8FBXV1daWlrS/v6+wUqAB5oynjFrE5/o8fGxi9ygbLHb7fr9UrBxNlEc0ZCwfzkz8c2yFlEV0HAH9y/sOGc7EkD+OQ1EUL7O2c66Hww+z7wPnt2AYzwH3gfNPP8u+inxH/UGe1qSm9T2p9FEsMY05yjB+v2+Dg4OND4+mqiB/JX7FikmDDRWo4WFBXU6HYPGgERBiSyAAcAfLPzi4qJKpZKZdSxIAJPBlGRYZjyQg8HgUWYLqhuaOhqwYKggYAgFPgBvKBSy9BNVEJLPoLKGv0u2Bq+Hhwerler1unq9nq1Bx8fHSqVSSqfTDm4E+EAhAJvNJBSePfcD5EM8Hn9kKcKGxOg0WP7Z2VnNzMzo8vJSKysrBokAoTlT8XUD6gGs0UjTjLKfOXMgL/h7AC21Wk2bm5tmXKl1YE/xi2MFisfjbuTJLWCNwzoy3gs/McATIBzvC6KlVqvZOrOysuImlDN3bm7OZwhr4vr6Wtvb285JeXh40OHhoRKJhGtKQAfuOQLFsADyHlAOoYgIh8Mew0n+RLFY9HpBlcG9TJAuIOXs7Kyfwebmpg4ODmxTvLq6UqFQsB0QKxDj2W5vbx9NWUmlUlbs7O/vK5VKuY6GoOIs4M5BuVgoFFzfoKhhP0DiFItFfycEdpEHkkqlVK1WbSPFY0xzxqhIRsMxw50z+vb21jUfgFo+n3d/EA6HDYxjd8JONDY2plwu5wDAQqFgJWjwXGEvcx4DknLPUz+xdlD3oNwN2nUk+TlAEHD30MzSZ6BmnZ2dVbFYtCqEAGNC1/Cpp9NpZbNZfwbOhLu7O5XLZSUSCWdinZ+fq1arqVgsKhwO6/nz57bbFQoFg1+NRkPLy8tKJpPqdDr69ttvnY1Fncg9jyrv6dOn+s1vfqObmxvbSicnJz2GkBoCkjWoAPpzXkGl2g/1+qF//v+p1/dqwkEHKX6QmMG2Ir9hBqMke4zv7+8Vj8fdPNBUhUKjGX5cSBRdIIQUC2NjYw4N4wDE24GsiksmkUhYgkSzfHp6arYGefXh4aGy2aw3CR6cjY0Nlctl+ybwV/2pnJKmJOhRRDKyt7eng4MDs6mlUklffvmlotGoGw6KPPxWFBcwdefn53rz5o09cRThoVBIHz580P39vQ9H0MP9/X1L00Cju92uUUdk2zRoxWLRKDcFWSaTMXPMQbe4uOgwimQyaf85TDaHHcmmoVBI5+fnevr0qYsn2AwOTICXhYUFB4uAasI8UmQHmxEud0L+aLgo5OPxuJlECm0kffxdLl2KUgrqoIx+YWHBf25yclKJRMJjImj4QZFBx2FbSE6ncLi/v7dEjaaUJGoaUrxLFOIg9ZOTkyqVSgZQDg4OLOUlnZV1jkxwMBilnbOuKJamp6ctq97c3FQ0GtXW1paDa/Duw96CJrMGaLZoskkqRf1CiAhsFYhyPB534dDpdHR6euoCsFwu28PEQcv3SEHG/qLhhxnqdrsei0NDlEqlnP65srKiarXqNPdcLqevvvpKlUrFfmUQewogGhka2Ha7rcPDQ62tranb7ZpVGxsbszfx5OTEYYxYQYbDoS+4Tqfj0Vqg4K1Wy8Am6zuRSDg8Eik+3k0k4zSxgAywB8HiDImfJIe9BEdmgdRTBMNm8rwA54Je9FBoNI8aTzFTCdjPgI18vmC4F1LGQqGgTqfzyCvHn+H38BxZ15wP29vb3sucw6gHAIR7vZ59ijBc/PngmUtzHGT0+W5ZbzSkNOA0EZxBQTaFJoyfSWHIecN7YG0CEAZVETCrSHhpBFHdBC0OyM9J4UfeDzjV6/U8WjCYn9Ltdu3/x1uJogxFCYU7rNbV1ZXB0VevXmkwGCifz3uN8KwANAEpyC5hn87MzFjBEv0U7MkozGg06rwRclmCCgWCwYJnJYwQuSWxWOxR9gMNNOAMzRb7Ci804A7NO/JRRqZyVnFvwrBSX+DLj8Vi/h0A6oSCkX4NcNrtdv1ZkX1fXl56ykjQNx7MqCG4jCBJAC+UBvhEkcYzioxsgX5/NPGEQMmNjQ0X72NjY86eAUhm0kkoFLI0nWY1Go0+Uq5AEpycnDi1mfUZJCk4g46Pj13X1et15XI5pdNpKw9h3flM3K/Ib+v1uvr9vkM09/f3zfJzl3POEUALSIL/H0834DShszSSnBdYM9iz1Gg0j1j+FhcXlc1mzaYSgAh7z3pgcgv1ECwp9fTh4aH3Pd/9+/fvtbu7q16vp1//+te+0zudjqrVqnZ2dlQqlawwgey6vLy02jOTyXgvUGuh0AMIY09hVaRplGTLHjJ21jLkBD8ftQx1Jio71ijfdTDXgH3Jc5+entbFxYV7hk6no1KpZOBuYmI0gjGbzWp7e9vqHYBUbLFzc3Pa39/X2tqaHh4elM/nTUpQg7ZaLXU6Hb169Uqzs7O2JJK83mq1Ho3nQzkyMzOjWq3mNH0CK9vtts8tRgZfX1+rXq/7M3L/TU9PK51OG1DI5XLKZrM+x5LJpFZXV63sghzgvnn58qXtHahW+N5YI4D9jFJD+s+z2N7etsqEz0cN/ue8fpSj/+tf36sJr1QqDjyiYMMDgrSGjTw+Pm6p55MnTxzABWJerVYtwcH/GwxGkWQ0iwYpHo9bYk2wFpJumhJpxAbg66Jxqtfrvpjxj3Mxzc/P6+DgwMgxaBnI0PT0tNbW1vThw4d/kb4KOkhhOzExoY8fP1ryMj4+ridPnujVq1fKZrNOFYZJQVpDAU5QSiaT8cUcDof18eNHX0CkoiNhpNFrNpuWHDabTc/JBoVGjnVzc6NcLqeFhQVLxbe2tpTL5dw8hkIho/BssLu7O+3s7PhwRZKPHDQYRpROp0cL7JOHBpQToCCbzVo1AKPNgc3M+LOzM+VyORcpQQ8uFzuF8vT0tN68eaOTkxN7HimSuPQp7pGKUhCQgi19HleGf5A1G41GPWqEuZGgtBMTE2Z9kOLTUAYviYeHBxddrHGS3xlj1ev1XAz2ej0HZpCgDpKdTCZ1eHj4yAPLSKlkMmngiAb46OjIKoTp6Wmzi+yBxcVFzc/PW0KKVy2Tybj5obGmyeCyoqEJyqlBlClEWSMk3lPsciFRDJAcXCwWfWkg46TgKJVKj5LoJWl1ddW5BIVCwSAKXngCyWq1mq6vr7W+vu7LGWSbwLxEImE/KBYH3ivsAsn+NBgk89M49Xo9HR0ducADdCRA5fb29lHqKYAO6xbJHixnEMHGvkEyMSBcMJQQ5Q5NK3uT30XjFpRz831eXV0pkUhY5hpU9oRCo+yKwWCUkA5YFwxE4tLnPQBS8j6QztHAcjbQ+KKE6HQ6Wl5e9h6RZJ9msOnm73JO4fsPKlj4jmD7KXC5j9hHvKegAor1GvTvByXofE7+HI0sZ01Q9s5cee4Mfj5FMWsuGIgYlH8+PDyoWCzaGjQcjtLPGbN5dHSkzc1N3yPFYlEXFxc6Pz+3jYZpHPyHtYS1amJiQrlcTqurq3p4GKUi//73v9fMzIy+/vprg9mcjVi5aMKlz+nnSManp6d1dXWlbrerpaUlTU1N6ejoyPYlkrXHxsYs7QXkCwZnBcEDLCUoFbhHWXMoOYIACfsVxQX3N7J3ni+1DbPuaZi5f7gXkceisGl/GsNG/UE9Qw1ze3trW8/FxYUeHkazng8PDz1LPRwOW07/8PDgmgOvLWfi4eGhAU88/uS9EGgFgNTv97W/v6+9vT2rOzjLOCcARbHw7Ozs2E8LsM69hn0K8GpsbMyAIyA2QDWgChknNMjdbtf2g9nZWeXzeT8D/h73NEA3MnLqLMgAZMYoKXj21EMoAbBAAlTh7Qc0yOVyj+5UbCM0P6xr/kyn07H/P5VKqVKpqNlsqtPp6Kc//anXxMnJiX7/+9/7rPz5z3+u1dVVxWKxR80FoBX3Bt91r9fTb37zG83Ozmp1ddU5GYB91Io8J+ptmH7k1HyXNFvFYtHAK4QQjTJ3AGAiVsVIZDT66/T0VBsbG74/BoOBjo+PFY1GH9VmwbshCGYA5tA803SiNAR84A4LKgEuLy9VKpW0uLio/f19tT+N8QJUYVIT6ohCoaBYLOaeJBaLuRbHepFOpxWLxXR1deVsJcZk9nqfM5lYw9w5kUjEpCTnXz6fV6vVcn4WQXSLi4sOXiTklsk29D9B2xRqBc5ziBFUsMVi8dHdhY2LiQSADXNzc3r79q2BqOvray0tLUmSFSE3NzcOkc7lcgb9f3z9sK/v1YRTCMFAwICyAJrNpgvQdDptyeLR0ZGSyaTlxQSZUYhsbGxoZmbGM16RxYJyEphxfX3thY5UOngpTk5OOnyBgoKNtbq6qr/9279Vo9FQLpdzc4ycc2VlRcPhKLBiZ2fHY1pIv/3fyfA5JEESe72evv76azORU1NT2tjY0C9+8QslEgmDA91u1/6jRqNhBI1DNJVKmVE/PT01g8nnPjw81MzMaE4n8uNYLGaJGCw+zR4br9vtKpFI6N27d5qdnVWlUrFCAZ9asVi0VzooC+SZzc3NaWNjQ2dnZ/r666+1vr7uwodmN51O+7nBbNGwtNttFQoFy5z6/b5RYS5cZDbFYtGNBfIYLhYaIpDkUCjkkBcAES4GQn4oOmOxmMc2kQTPhcp75jujKMMzBTtUrVb13Xff2fM3Pz9vrxfrCBlWsDAPpvJGo1F/DtBWvnO8jkgj8aIjwyOpFgkoxSzFIOw7/ji8UkgmabB5TtgYaK4rlYr3X6/XUz6f18TEhC8lpM+MfKGIwVOEbYNibG1tzR5TpIs8Hy4XrA/dbtdhTcgJQWlpDCkWpVHBXywWXTAPh0OdnZ2pUqkolUoZcEDums/njeZfXFwom806PKlUKmlmZsZBYpVKxandNFY0kYACMKy9Xs8eOYr8w8NDhcNhqyNgWQBHGo2Gut2uWVAKpenpaYfaARo9PDw8Qs8luQkKNtTI3ihegrJvip8/bTxpZll3wUAwwjY5+4M2BMLsuPzxodKAwsjzHZHCDNgEy8F5z96hKWBmsiQDozQ0ExMT/l4kmSXlXOHn8T54f4BlNNX8uWCRGixq+Pe8uAv47yAYFGTYg355WH4+B6BO0OrB34lEIga88JKyVjudjp49e+Y/j8dzampKp6enSqfTlkWSZ8B4Rc4RGtpms6lweJTI/PDwYBsUQEq9Xtfc3JxnbKfTaed6nJ6eOvyHQnB6+vOs3aCSAHZ3bGxM9XrdTUCj0fBdIMnAGnc765n6gfMrKL8O1iQ0iTTcNJu8YEy73c8z0RcWFtTv9x2OdXFx4fMCdgw5MqFFrEOAseFwaO8rli1UKZJs2Tg7O9P29rZWVlYMwO/u7mpubk4rKys+S7vdrvM2hsOharWa1tbW7P3kGbRaLStn8EfTpPN9T09PW60Gu4c9B4sKFhnufeoS8k7S6bRZ6p2dHTP+3APUZvwOWHVyYGgkqA+Gw6FDof7iL/7C3zdgSLvddg4MBM/y8vIjZQFhmgAx7U+j95CIc4awHwFHsFmw3iYnJ82S8/O4A29ubrS1taXnz5/73KzVaiqVSgZDAdzxzofDYfuo8a2fnp7q5OTEwZqo9bj/CRKDhOH+y+fzOj09dT3y85//3PcudpD2p3nneNZZ06zFu7s7HR8fK5fLaWpqypaTcDishYUFkwKAoajAgjUXACNgGc8dVQ02r3g8/sjKdnh4qPHx0YQWVB79fl+lUkmJRMLEEGcv+5bvCUshUuvgXUKA8/j4uPMFIGsA4Qg9jsfjVqROT0/r8PBQ3e4oVJJ1yvMF+EHtitoB4pDfIcnp9yiSuDewEgStmQcHBxoMRpk59XrdP//6+tqgFVk7X331lQqFglWvgO+oyIbDoUqlkvr9vt69e6d6va5Xr14pHA5ra2tLFxcXev36tQ4PD93fYLdhpB93O+sfRWMqldLR0ZHOz8+1u7v7/6slfHQn/siE/+te39sTDoJ6fn6uZrNpRhPmsNvtOqmPppuiksaLgoxiGs8SY1LwIsHyIlvr9/ueAw4iTpPA+wJV45IFCUYC9/DwYE80TUqxWFShUNDe3p4ODw8feQKlUbHSarX8OWBLKOwnJ0eztz98+OCgjng87qTmlZUVF4i9Xs/zCTkQFxcXndaYSCTMstVqNY/24DAAXKDhRsZEMQyzS2EOC/T+/Xt72NbW1szY0pAxCoz3L8lSVDxW4+PjRuVpmC4uLhSNRo2EIy9irBMyZQpSABYOPBoSkHU8XTS7koxaU+hTVCC1o1Dm4uAyxfcpyUUDsklpVKQBvhBENzU19Wh8DtJjvkvkojQDBNTAXk5MTDhg4+XLlw4XfPnypX8uYWtBFoXPx3xRUNeLiwuFQqFHQYas8/X1dSfSo0xBilsqlXRycqKFhQUtLS15vjLf1dTUlCqVitOVSSMHcAoWaABm+Nmmp6f14sULffz40c+Z+d0UZXxv5+fnymQy9pGRNIpkENkb3k2YOjxcNOrsVZQcJOteXFx4UgFWk3q9ridPnujjx48OieGMCIaAUcwzTx1bzcTEhFmdPw2MKxQKZrJoMoLNM8y0JF/CQYAgWHByHiJ7pdGiiKIAa7ValpbRwCE/pnhkDdJo3d7euhgH3KPJC4fDZoaD8m4afi5u9ixMI4AWDSCfl+fIuQ4oFlTHwCTzLMfGRl7/INNMQwxjEmTxgmE4FHIEIHEeA8oFm2t+PqAL7yUY6gPzzfsDRIG9CYIV/LugXJ7/DRDJP+e84Gewtni/FLPYZAAMsBax1lm3zWbTwDNNIKGPTGeYm5vT6empUqmUwuGwyuWyOp2OVldX/Xw4x8lLgQG5u7vTz372M9/DKG0Gg88TQyhC7+/vPdro8vJS8XjchX4waIlGnTUS/TRGrVaraWNjQ8lkUs1mUxMTE1pfX/ezQbkGmLi0tKSrqysdHx+bwSE/YXp6Wqurq4/ubO4F5LLc14A3nDt4j/FRIu1utVrOdiAwjbGL7CEav1AopFgs9shWAeh1eXmpVqvlbIXV1VXfrbC7SLCD8mtJZrAk+WcUCgWdnp5qfn5e2WzWTTaZFJOTk2YMqcd4/9RN0qjR+eqrr9yQvn371qpBFHa7u7uPgiap91AaETZJ08KdxR6g0SZYCiCT9/vq1SvXetVq1V5orH8QCZwVkBE0i5ubm/rmm2882YY9NRgMXHPAOAanX5ABAPiPPL9UKun9+/eWGaNaombFm394eKh4PK50Ou3Q03a7besXNj1G1/7DP/yD6zssbt99953Ozs6UyWSUyWRULBYVCoVc/2SzWZ9jPEsAF9Yu424JPKbZhPDgbuOc4E4ZDAba3Nz0ucs+4B5j/F/QonN7e6ulpSUDAPf394/UdA8PD8pms57a0+12VSqVNDEx4THAsVjMNgv2IVJ0so/Yv6T/c44BENMTMLYWJrnT6ejk5ORRvcbaZmQeZ/H6+vojgBVA9PLyUnt7e/4OYOxTqZTy+bwBFeogAis51yV5VG+/33dYZXBP1Ov1R2rA6elpW0ipJ3nmZCxwbu/s7FjNi10GP//5+bmOj49dZ+3v77tOWF9fV6vV0tTUlJVYgKyXl5fKZDJaW1vznh4fH7eq8sfXD//6Xk344uKixsZGc5J5eKQcshglGbnL5XLa2NjwwguFRgFhSOmCDenNzY2ePn3qwC42MAcBTbgkH+Y04EEGgYIB78bDw4MPaQIW8L3G43GVy2V7K0Gk2ARsVAo3DvVwOOwAg7m5OVUqFW1vb1syncvl9Jd/+ZcqFosO0gL1Oz8/1/n5uYMRaPouLy/1xRdfeA7h9va2D5ePHz/aV0/B12w2LVXGXwOTOzk56b8L24uvjMC6bneUrgiLCfIIA408hQuIlGpSM0nF5nsP+qRoFvHA0fQimQXtTiaTOj09tUpiZmZG2WzWoR1IoQjw48BHsskoHOlzEc8FjFUAtjiYgkthi9Wh2+1apk3TSZgaBzQMJIciTI30OfgJsIDClsRtGn28vXh4pc/MPhaC8fFxN4ik9L58+VLX19f+Tnd2dpyo2v40NoeG8Pr62sUQckpSuGHdCIEJSrBI5YSZPzs709bWlvcpxSABcpK0vLzsz813QUAUjD4NGQE1sKzM9UXKD3hC80Q+AWcA3wnBLzs7Oy4WKKIvLi7Ubrd1e3trCT2N3hdffGFWa29vT7u7u2YiOCfu70ejECWZ+WcEDH53lA+oKwALudD5Z4A1fCfRaNRnAcXxzc2NQRPWEeAULAZKF5pGGmGKLBplCg/G19GABz3lNFRBKTc/q/1pHnuwKec/SB6DjTVs02AwCughCJJmP8hCARBwBnCG0swjPc3n835+wffKOgk25ngdKW5hI4KhbpLMevNeaMaDDTL7l7tEkptz3l+wqQ6i/kGwIggkcFbQ1Euf1QvBIpF1wxg5wDz8kygvarWaG+GdnR1tbGwYqCRTBTaTPbW3t2f1CdYL7qF4PG6Al0K8VCppY2PDbLgke6hh6Xh/3A0EN8Kwl0olP1fOa0AoAssqlYpKpZIzZCh6UVHwHXHWs/cZ/8Sf4czudruq1+uKxWIGjXgekjzZg/XDcwSg4pkC9LA+uU8vLi60trZmiStBZvF43HsLbzl3E4qKer2uZDL56L5kXU1NTfkZHBwceI41nyGZTOro6MiWmJmZGYMtzWZTHz9+1NLSkr3qt7e3VhIBfNRqtUeWvMnJSf30pz91c/3+/Xs3+AAlqKJWV1eVzWadDs7zQyHA2uQ8Q2nEOjg6OjKYAvh5cHCgi4sL/exnP3MOxe7urm5vb113oBKgRkGRCOPK+qAZR0kxHA4tMQ6FQgaJUGPwM2mK8MXS7KAowCOPDYz3GZQgD4dDnZycmGRCARKJRJw98u233zotm3yet2/fGkzA1jk2NsoVuLq6smUKLzNkSr1eV6FQUKFQMPgNiYOC7le/+pVT98vlspluFGz4+Gn0sFFh56SGCJJLhItiLQuqDJmpTh2BR5wxprFYTLOzs8rlcvZN8zMYEQs5gnQ8EokolUppd3fXwCqhiCieBoOBCR/OPO4+akb2ERL0qakplctl3d/fa21tzfdBs9lUtVp1OjhAPnXF9fW1SqWS6xmAInze2EHIq0C5FbRgcVYQhIbaF7IB6Tv5Ks1mU1tbW17n2WxW8/Pzevfune/FdDqt29tbHR8fe+2ibJJk0ApPeqPRULPZVDQaVblcViQS0cuXL9VqtbS3t6d+fzRJBkUu4PCf+/qRCf/Xv75XE97tdl28E3oBWxVsLIKHIKMhkA2Nj49rdXVVJycnllDC4n377bcuyJHVzM3N6erqSnNzc6rX63r37p1ev37tAgdWDwaKBglmhkObZpCiiZCySCSiarXqz0GxJX1uroIPH/aKhoYxbBRQ6+vr+uKLL3zoEizFpj87O3PYz9ramnZ3dz3PeG5uTul02iMTJiYmVK/XVa1WzZLDTk9PTyuTyWhubs7NXjwed7NFSi0IezKZdEAVgWagjUH5cig0ClSbn583I8dlxPtgtmmn07G3bWFhwcUgLAigxfj4uL2lsA1IwBlfR+hbUNrK93xycuICBS8paDnrjqIWCV2tVvPIEgLhWCs0R7Crd3d3ZmI5QLkYkWqOj487pIwmH/ae5ouCkIuDEC6YGH7ncDhKgo/H42p/GqUCgMEaxnPF2DOsGqDPNHF4oGC48GxzYfHnk8mki0/Y7Jubm0dSUlio+/vRbHaANppCGtSrqyu/by5K9gtnRFAt0u/33RwcHBw4aRev6+XlpRPBYbSRgcPuU9B3Oh1f7v1+X61Wy9MQhsOR1eCbb76xZz+fz2tra0t7e3sOVwJF7vV6LvpgmZ89e2YpKeE8BBVSqOzs7Ghzc9PsDuAL5w15Cew9GmDAgmDYGc+Hf8Y4k+npaaslAPd4rqwJmFo855FIxM0pwBjNM01s0C8pyaBcJBIxsxAMW2K9BKXewbm+wdwF1v6fhpsF5drBplfSo/9GxgtbAlP86tUrHR0d+fsYDod69+7dI3AGcOKLL77Q3t6eQ5wAKpBY8pz4XMHmOejt5v8H/32wyWbN03DyOQAqYFj47Ow9QD0CEoO5KhS8sNx4ZXkuodDIj18qlTQYfA5eDDaqvV7PEmvCffAlsiZzuZzPq9XVVdVqNTOG7E/uUe5z2G+KXUA5xtGxrmgwaNKlz7JN5I7cG6STT0yMpj1wVk5NTalQKEiS2UeaFeTrgONnZ2eeMd/+NN6J85T3iI+Vs5XAuGw268Rr1gQKEpR0AIM0+c1m06N82G/z8/NmvbAtcZ8SVHVzc+OmvtPp6Pr6WolEwt/Z3Nyc9x13chAcnpmZUavVUjab9T6Znp5WuVzWq1evFI/H9cc//tHgfnAEXLlcfjQrGrAW1cji4qIBXXJlWAPUXwS1xeNxy1i73a4bJHIkADXv7u6cq4H6C7/006dPHSCFajL6aab37e2tqtWqiROAFbI2AFE4B2dnZ3V0dKREIqHDw0NFo1Gtr69rf39f9Xpdz549s6oBIB0VYbfbVblcdjp1p9MxWBH9NMlnbGxMHz58UDKZtPqHZw3YA2nUbDbd8C8sLGhjY8P5AN1uV8vLy3r16pUk2YcMUJNIJNyYAnBwrnAWSPKkFJoxFKe5XM5rfGNjw7YxZOITExMql8vK5/OamZlx7ccZzs9DlUCDiRWFupeMknA4bEUB6eich1iYaOqCdshkMml1ZaPRMOkyNTWlpaUlDYdDHR4eeu8CjhFESG3PdBPunHa77UR8LFrcjySgo3YAKH7//r1mZmaUTCbVaDR8J/C5FxcXrdzjuZPbEFSCUIdgRcGSsby8bNtuIpEwsIDikroRULtYLNrrzfs/PDy06hi12rNnz9zU397eqlKpmEDkrpmZmfEEFvajJAOI6XTahAifFzIgEokY3P3x9cO/vlcTTpNAYRwKhfxwQfa43FjUSLooHINjrq6vr1WtVi0xAkGjyDs6OjKLTPEdlBfDVEmfiyGYbKSLFBOwHiw8EmmDBVSQTQ02LBRTFGYUsDc3N5b3LC4u6vXr11peXvblAVvOXHSQO9KYFxYW9PHjR7OQeGDu7u50fn6uTqdjBcHc3JxKpZIajYYDvJCvgPbhz6GA5/KMxWK6u7szqkvCtzS6kM7Ozlyw5/N5f/6gr49QLy5wnhmhJDwDCmLmfXLYIKHEv458HbYtkUh4NN3k5KTnTofDYS0tLflw5IBot9suYGlKgym5fG7kcBRDPEcaOgAlGn6QWlhnwuyQgPH5adRgcSj+SK6leSd4h4KU5zY+Pu4ETf4eDEa32/UIIwIyaCQAr/ApUhCCWvPe+F6fPHmi8/NzNw7BMJFer2cZdvTT2C2Yc1gILlJk2ag3eLY0nHib7+7uXEjwzGCuUC0A3KAowecNm860APY9c9SRf5G432w2XbTf3d3p4OBAs7OzDiwDyYa1RcI2NzdnhJjm8fb2Vj//+c8ViUQst6RRx/+Nv573gjIINpzLmqTncrlsTzqzk0ke5TyiGA7uh+DUAEbYYSeAWaXYgb0ZDAYGKwCb8GlOT0+7waQZGhsbs1UE0HN+ft4Sx2BjSzPIeRiUc1M0AUD8aXMdZLxpzAmfkmQpOOAGnta3b9/qu+++0+3trYN3YCoBU2EWaQoorAB8pc9zZfn/qBS4J3gF3y97h89HYcOL9x+8b/4UpQ825UFgl/sDwJPvkkYDFooReTzLeDyu3/3ud/qf//N/anZ2Vn/1V3/lZw/YQ0DSzc2Nzs/PdXNz4+IMhiYWiymbzVphxNQQshdgd1EL8T1zHiLFxCIDaMy+u7+/d/r0ysqKlQjMsO73+/ad0twDigWll8iUCehkX/EMgz93MBioWCz6fqXgD55BsKt3d3e+f4KAEZ/n5uZGu7u7zk8BSGc/7+/v23e+ubnpcUIotQB/YIh5fsz2RnmA/YsmAj84DDZnE3YxQuHIu+l2u1pZWdH8/LwnLORyOQdycU5NTEzYJz02NmYbB5kUp6enbiIeHh48Jo26DsVh0OPOOQ37TY0wHA7tyaXRmJub0/b2tr777jv1ej29fPnSYVyXl5c6Pz/X3NycUqmU7+qTkxMzkIB83JH39/fO6GANX15emkkGcLi/v7cfGnsfYAk2LWoLzuDBYOBagKAsQqpQMabTaa2vr1vVFwTC2a+QCViR1tfXDYBwdktyIG2/33cQX3BihiTfXUjJUWZFIqNJRTDmT58+teoRJcLJyYnv9rGxMe3s7FheT77Ss2fPXHsE6w9ALMB59hIqHeoiQCIaa9YBigQAC8gL7g1qNWZWo+AIhUJeO2S68F0sLCzo/v7evQTvgf3E/+d+wyIQVL7w3/f392q1WlpaWlI0GtXOzo7VFljhABJQHHW7owkPqFWxjkxPTyubzfp/N5tNBwrf3t4qkUjo22+/dbMNID45OamPHz9a/XZ+fq6zszMHz3JmXV9f68mTJ+p2u1YU/eM//qPrDbKtkKujhEON0m63tb6+buXx9PS0ew+sPjwf+jYUZn/ui3P7h3z9yIRrJOtKJpMed4D3Gk8KSD4IFcjmwsKC1tbWtLe3p1qt5jCJbDardDqti4sLnZycKJ1Oa35+3iwyix35BYzq7u6uUqmUG9q5uTlvPBYDBxkMM0UoBZYkX4K81+BCotgNFl+wSwRSIGtfWFjQmzdv3CTDtsLC1Ot1jY2NeVTE5eWlXr16pUaj4e8wEolob2/P6Di+56mpKX3xxReSRihoPp83gsiBz+FPo0DxEmTvkC8xw71SqWhtbU3v37/3v19ZWdHHjx+NxBMSs7m56dAdAAaYXrx4FD9Bhq7T6TiUA7aPcCr82bCmg8HAwRPSqHglEZziCpYhKFXkO6IJhy2m+aPY4Du6vr42ugnTgfyai4aQLFgLin1+JuuNIm5iYsIerl6vZ6S10+k4mIQmkLm9HPYUdDR0hGlga8hkMmb9UqmUGVo8bzxTLkGkpkjDgiAJjTM5Ag8PDzo7O3sE7BC8gvIABQCfiYaRsJJ4PK7Dw0OPskHqx2VI0xkOh528CXPAaBqaIhhXLkoYL9gYmiG8ggThAQqlUikj5TSck5OTlsjncjmzWgBp2WzW2Qs8A54d6woABeAMMIhiiqKL9YicdGVlRZOTk05kXVhYeBQahI0AVg+5G4Un3ycX/9ra2iObTXC0I6ADTSznIXuBIDUYOgpb/gzSRNhVkP/7+3szHvjq8PCiPpLks58iPNiYAtwg2yPYk+YbxhoQ9ObmRt9+++2jc1r6PJIwk8lof3/fdgWKQBrRoAebJphnFZSqB73e/C7OouDZD/vN3wuy4pK8ZlifweIlCN7ynvB6IvXmvfBZOd8ACvkZ3377rRufv//7v9e/+3f/zgFbsHLVatX+zXfv3vkMQc2zv7/vPJJYLGYADCAU+xGFPvaezc1N2z0IFiKpmTBCGDUKwampKUWjUYNUweaD3wdQ3+uNRqrl83ldXl5qZmbG6d9nZ2d6/vy59wIzd4NrG7aNOxyAMhwOa3V11cU/ay0UGlnNzs7OfM4w+kcajdSTRk0QOQvIphOJhJ49e+ZzDfnrcDj0vciZSa5I0FoDmMgewCJBUjjKs8XFRbNTBPABgqBS4OdWq1Wv88nJyUc5LzzDL7/8Ut1u157Vf/qnf9La2prvvUQiYZkr2TA0Xeyp4Hrk73E3HB0dKRQKqVQqmdkHyAmFQlpaWnqkDgMsCtZyQWsDNhbWZL/f91lwcnJiYJ/GJwjMwYx2Oh03PtJoakuj0TCwAWjP3YwKApKo1Wppf3/fa5f1ybmG/SwcDjvQlGkwNP2AWoSgMvXh4ODAYyuPjo4enQ/Uc9Qz2LMgR46Pj3V2dmbVCFaEm5sbM98kyXPG/uxnP3v0O2imYZkBj7FiMVc7Ho/7+dN4YlujfkVVAWjP3c8Zxn0NkJHNZrW1taVwOKx8Pm+bQnD6DSnrAGSQa7FYzGcB/nTqokgkonw+7/WJ1/rm5saTCajH2CtYdggZph4HtOJOkEYBf4uLi8rn89rd3X0k0YdxxtqVy+UUjUaVy+XUbrd9piwtLenm5sb1PPUE+Tl46PkOAFzI3SCcEaUwgHoikXhU5wMIApJxBr548UL1et3/vFKpaH193eQWvnBq8R9fP+zrezXh+B8mJiacjA5C+Pz5c4c2EL3PATsYDPT+/Xsf3ozgWl5etn85yI5IchEEQsZ/FhcX1Wq19O7dO8+DHgwGTqnc3t72YHuKe/yZIN4gYxRVLDpQPFAqGgPeN56gs7MznZ+fSxoxyT//+c9VKBRUqVSUSCQcagV6SfAHXqqFhQXF43H9/d//vRtFNlEkEtHx8bEikYg2NjaUyWTMLlK8EryFMuH4+NieKxjM2dlZHRwcaG1tTZIcnIE1gDRvSU4H5ftA4hQ8wLlIuQQ4uDk4ObSQyMDC8Ax4vqRD09BSvCFxQkoj6ZFPics/eNEEkVW8ind3d5ZxA8QA3uC/Q1oJMorMD6YFCe/9/b2TelEokFYcLLoZl4OcmCZ1bGzMhygBLrFYzPYHQB0kaUgJx8fHH4XfTE9Pq1gsGkHmxRrmvQKOjY2NWVZJg46sEpYE/y5NJowIF/jl5aWRXQ5knj17hu8WeRxM28XFhWd309QzigWpP80a64rLnSZsY2PDz+rq6spF4vLysnq9nr8XSZbVZTIZHR0d6eLiQpLsjwfogG0+Pz9XOBx2ounExIR+8YtfKJfLGUSA6UQGio0Dli4cDj/y4vIckIDSTPHdwHCGQqOAo8XFRTM7vDcuvaDck32JbQCkmgApRrkErRyAYcH3RmotqhfORxQlQbk5LM7d3Z0KhYLS6bT9jzRNsAmMiKT5x6fIz4DBy2Qy3nPBxgWQjKafhozxKOwxWEa8d53OaIrEwsKC1Uj8TJhagBLAsiADjqqAP8/vCf43yikAiOC/C6pqKNCCTXuwEUclw/9mH/Dngmcc9xwAHYXY/v6+79V+fxR4+Lvf/U6Li4suolAaXF1d6Q9/+IOf/3A49Fxv1EH1et0e83a7rc3NTSuumHKCgury8tJFG+N2JDnjgPMCVpTmklm1w+HQ5xl2rG6369no2IMojmu1mtO4Dw4OrIQiMKjZbGp9fV2zs7Pa3983e4ZlhsaTOwJrDt8/exylEUFhND4rKyseExkE+UulkoPgbm9vdXFxYbCdZ0ijjfKFpjscHoVekvy8sLCgdrvthg1ZLGNb5+fnXRvMz887cZvzGTYcKTBWg06nYx839dn8/LxarZYajYZ2dnbU7/f1n/7Tf9KzZ888opXmgxqOzw2wQv2DvY+gSMCCXq/nud/4hJn1TL0IQAqwG4vFtLGx4cYcpSTr5+rqynuHMxXpMvcpdzQWPuq8QqHgJhmrzdTUlLa2tix9plYhYwirAw3LH/7wBzf3Y2NjevHihba2tjQcDm0buLm5sXwaFp3aDzWBNFIN4Ktn/CzseCwW83g1rACArKj6AFGlEUBUqVQ0Pz+vX/ziFz4DUbDt7OxoYWFB3W7XTT5AB/dPrVbT+vq6x1UNh0Pfp5zp2EYIJQWc5p7gDKX2hlG/vLx8lCFC3cEazefz9vMDCnBuQJJAKN3fj8bDco5AkAyHQ0/aGA5HyeXUc8ioK5WKlpeXFYmMxiSWSiWPB7u9vdXbt28tryfsELCUWoDgOGqfh4cHlctl18bj4+Oq1Wqanp5+pJ7odDra29vT+vq6E/VDoZGV8/T01Mz5wsKCx0cGFUOhUEjJZNJKiZubG93c3JiYRMHZ6XS0tbWlL774wn/34WE0dpbMiGw2q2+++UYPDw/a2NgwcIPijewI9hfPF6Liz3kFFcU/1OtHJlwy+kXSIt6ctbU1e2aq1arDiwhlmJmZcdInEiPks8hJCbjggoTJQ+ZIk0fxDSp2dHSk4+Njo1hsOhAhgtnw/CJz45JA2ssccBgDZOuSfJBQOOCNzeVy+g//4T84nO36+trj1kKhkSd+Z2dHrVbLiB2+NgI8arWaBoPRzN21tTX95je/UaFQ0PLysiXae3t7Hq2STCYVjUa9sWmI+V7an2Yg1mo1b3Dkt8y7ROpK2mkQBKHwoBG4vr52kx8Oh7W4uOiCttVq+SILWgkoUphTTZo0DSAHFgUhv5uCgcbl6upK2WzWaDhsAMUrKgR8vRRG+XzegA6FNqnseNFpTiiwaGBoimFyYJeDQTSoP/CxXV9fq1wuP/LMgVByqRPcQsAakiRAFYqF4XDoFF7CNHivd3d3lmXynJBiB20ZMAlnZ2dqt9su1mZnZ7W7u2up0XA4dNLw0dGRfz5FLd8rP5dDMAhWoQahgOcCZl2jAKFwATgCcefw5yxhP0YiEa2trTmhOViAAs4QtlMul83mDYdDJZNJjxpqNBr64osvnAYKQAOg1mw2PTUAEAqJKu8Xb3jQG8YzRVEAgESTSmEHc8V6vL29fSTNTSQSqtfrLgAoOIOSWxpZwAjWFz7J4IWJ7QArCmoMvJqMQ0QdQlPCz2bNI/sjLAZwBACVkCXeF88VmeZwOPQ6BwjZ2tpygQ1ogvoA2fzFxYXS6bTRfJg23hteUs5ymtI/Zb5hgnjxjPiOWMuc8xSMyWTSEu6gxJb1HvRJ8/v4WUGWNSg/D8raAQCCTXoQxHx4GI2h4xnQcP/d3/2d70N+1/Hxsf7mb/5Gf/3Xf+3Ckvewvr7u4KdMJiPpM9jMM6IJlD5nldBE5vN5XV9fexxW+9OYQ+5rQAKk33xewjthp5hEwmisSCTiwpfiD5UVXlLyIrDT0CDgXef+Yc/T1HEGw17DmHEmBiW3ADMAC7CN7C/WmyT7aZPJpJaXlxWPxzU/P++zGX9/cHII9yw+9ampKecaBAFegAtUYoDIy8vLjywlMJb4wamXgiG3Y2Njlp1TXPPsAX1Q6gGk4ennOcJsEsZIDYXaCKCHcwayZW5uTrlcTuVyWbOzsw7Yur+/1/LysiXOhIYCBC4sLPj8QfXEs+KsD2bqwBqy9/L5vL8zGHYAQ2oY7vPDw0MDcxAd1CLYw/A9c66SjYOagLosmC+E5YyGDXUV9QJn7OTkpJrNplKp1L8AzBcWFpyVs7Ozo+npac3Pz2t/f9+ecGyKTB36xS9+oadPn9q6h0IsEolYUYAqZGpqNMFke3tbxWLREzm4S66vr13XYtfIZDL+TlASoBahxgAk4txptVqekhIEuzhPuS/i8bgKhYKOj4/dgLJvuFORknOGAqoAiA4GA6sdqMmo00OhkBtpQAzOLRRFqL4gGIL1WC6XU6VS0djYmEFiMh2obVA3AiRTJ1FbBK1/7Xbb4w1vbm5Ur9e1tLSkvb0920ODagfWLWAYUnksrihVqZm2t7cNThOuyPNCtUYttrW15ZGThERjewXg4q748fXDv75XE76wsGBpGV7nYrFoluLi4kK5XE4vXrzwOBEKYpohGlSKouFw6OIfZI15dsiTNjY2LLumOBsfH38UKgQrhXQJn3UwDIkXiwz5Vrvd1snJiSVybAL+HqggATNjY2PKZrP61a9+ZZ/r+Pi4G1IYIA5M/jMYjBLa3759q9nZWfvvotGoFhYWzPzRDLZaLTNCiUTCRWev19OTJ0+8ETOZjE5PT110IJmbmZlRtVp1o8EmjERGKY4UtEjoSFaenp52iBvoHPPKScKmQUMihl8nKOelSQw2dIAGzGXlUkwmk6pUKm5Agqz3/Py83wsXH74dsgg4mJmtSuMAixaUviP/5iDn4ENWND4+bh9/ME0UlB4GHrkjMk684YSzVKtVbW9vm7WhcIEFAZlGos0zA+ih4MWPiGcUdJ5sAWwVfO8USxysZAGcnJz4e5HkZ1GpVKxI4AILSiUpeoMMJlJVJGlPnz61XJlmlZ/5+vVrzc3NaWdnx5cWuRGcBWNjY8rn894DkpxQCpINU460PZlMeo/xzBlhIsmMNQXUn8rjT05OFIlE9OLFC2WzWQMKoOmbm5v27cPegX4jm0eCTVHZ/jTyZmpqSsvLy5qfn7dXnmaCNTg2NmawjKKPc4fijLMA/yTsJKw4ElxYlX6/78KetQMoQFE2OTn5CBxANhgsjJGis++y2ewj9nZyclJPnz5VtVr1vmFd5XI5WwM428m4oCFhHVBsNZtNF7yMh6OIw1P3k5/8RDMzM56UwSz3IKsNK93vfw69ocHm8wafnaRHzXE0GvXzpFGj0eOZBS1LQdab9yF9bs65n/60Oed9s68AFVFrwHasrq7qv/7X/2q2n58tyRLuP/7xj1bhLC8vW6VFwwcgAyjK/QgQTcIwZz5J4BTYQXUSoYgwqJzrjEUjCAi7EMm+sLckeQfvhFgs5iYYUJV7pFqtanl52e8BqxR7p9vtekoL47+CBAEgFlkIKAUmJiacYUPzzxpiH5BtkUwmPX0EtRQgrCSPiUNeD0lBEF2/37d9Z3Jy0qwdz3t+ft5sI+ud9TcYDKwmyufzXh/cuzBfuVxOl5eXrsMY4TgcPp4l/2//7b/12YLlDwCauz8IxsK+oqgAGGR9wXCyrzKZjObn5622Ys2iADs7O3Mtw74M+nw5p8fHx7W2tmY5MmQMNRb7jnWxvLzskLb9/X3t7OxoampKGxsbSqVS6vV6njUOEItNLghgUStMT0/rP/7H/6gPHz7o+vraqi7AbiTuw+Eoufz29lbxeNzPlbuCWgkVFVM6IAbC4bAT7Ov1ui0PrFnOsPan2enhcNjqpIWFBX348MHWKZ4rI0F7vZ5WVla0u7urdrutRqOhh4cHffjwwc8JAAUQYjD4PDIXMgXFImcFgDoqDuoemNqlpSXd39/bIsHn4cxAUYhChbXDfgBQgh1HKcG5DrCIKpf9Bbk0Pj6uarWqbDZrqTl7VBpJyl+8eGEr293dndLptGsGngW1I/c9AA/nHWSiNGLdsR7ynRweHroGIrcJphywlbXAeT4YDKzuDeYWcebwXKh7qFewu3EeAOQyhrbTGc2dLxQK+vbbb61i4v2zrlHFQlT9ua8fmfB//et7NeFHR0d68eKFfaQrKyse78WFw8IljRUZ1Pr6uiVrMEigncjz8E+TWs0ByULEvwWyU6vVzHxTgHH5Bz27BHPU63Wj28jFCENhA4PGSXLR2umMZhBycb1+/dozGz98+KB8Pq+VlZVHo5I6nY5/dpDh+/LLL5VKpVQul9Xv9/XFF184hGIwGOj58+ceQXJwcKBweDTLGhSPz0MBA9LNgVYqlbS9ve3QFSTFXKobGxu+/DjMQNjwEFHcsfmRwoGU8132ej0VCgX/7kgkYlkgYEhw7ilIN40izIE0kp4jH+T5EKZCwQyzIMleO4oxvKutVssMNZc3BXAymXTxCTKI5In1wMzpoKce9hlAgd+BGgCQYzgcjS2Bpdzb2/NcVWaVJhIJN79cxDAOBGjhmYfBDM6pD4a+kBtAYxgEHSiu7u7uzHqhNAjKTbkMgxcBTSeAD004bCMXJs3fzc2NTk5OFI/HfXlykaIEgK0kzAhmLZ1O+/ujIMc7RTgV7M/Y2JhZSj7v/f29NjY2tLu766IS9onikNA45Io0PPPz8/rLv/xLF88UCKg2gnJ8mAKsJJFIxMg7XjpJZndhsmG4WLswkTDqnD2ci3zHyM9isZiZPponxh0h86SIYR9iMeAZEGD58DAaycOljcx3MBg4ECjIlKJCQVUAYELQT6PRUDweVzabtfQSJhIFDLYe1j6e+VarZRAkkUhofX3dgYXIEklXrtfr+vrrr5VIJJxKj7cSX3OQ1QfY/dOmmTOBJjbYuMPAMNqOtUvjQVMYBPekz800ZyqSTH4PBSxNPmex9Ln4hDlEMcLvxz7EKMo/fQEOwLQ8f/7c9wTyYgDkXq+nfD5v5QGj62jwQqGQNjc37f9kZFAikTArQqOSzWZ9JszOzjrDheYZhkySgVvUONz7FLew6TQDkUhEHz58UDwetxWN53p0dKR8Pq9EIqG9vT2fU5zjAJTcP9zffM88HxKfW62Wzs7OtLa25rM4eL4F7x2aESwVkUjEYBuKLMAdgMD7+3vLp2log98pqifqFdYEYB7EQ6FQcEHMHuFMYWQSe0aSVldXvS5ozCEYAN9YowADwXWHlWl8fNzrAwVccLxn8EwAcOK9BAPOYJcZUwW4Mz4+7vt/b2/PoWEoDRgNFbR7cJ7R9HFmZrNZnZyc+L7CukL9wZ3Y6/Xsq0XiTDgYDQjTP+bm5vTy5Us1Gg1tb2975jsgE9ZIRjpRN0Ae8T2hHq1UKnr79q2azabzkTY3NxWJRBwMxlnDd0rNjDp0fX3dhAcqMs4VABICOqnTqDGOj48fJYufnZ09Cq0joIv3IckEAfsMBhtbBd59SBVSvVEvkm9wcnKibDarZDKper3ucEAYY4AS6hDORcg+slWQUhMUyvlLvg9eaeTq7P9er2f118rKyr+ofe7u7jwyDDIj+P0RwhzMJwn2CqFQyCRSoVCwogww9eHhwdbNQqGgnZ0dffjw4dH+oy5MpVJOt6fOgMDhvoZ0Cb6i0ahOT0+Vz+dtT6ROm5qa0tramtPs2c+w+Iwtq1Qqtrglk8l/cef8+Pp///W9mnD8TVNTU5Yx93o9zzXc3Nz0JRL0EYGMtVotI2igulzcpDVKo/AMJD/IdEFtKHgYCYD3uFAo6PT0VL1e79H8VNBzDn+KIWQjvFdJZnMIckCKWC6XjTY+f/5cb9688WeWZOTx66+/tn8XJhwmbWFhQc+ePTMD+PXXX3sjnZ+fq1AoKJFIqNFo6Ntvv1Umk9HU1JTHXYCK5fN5N5UwTSDJ+FOQ/sEw07Tf3d3p48ePWl5edmHGDEtS2hcXFx32RNFJ0YTMiYKN75cCh+8d1JRGCc8Qcl9sBIynQD4TDofdjF5fXysajXq2IeFcMIk0S/gJCazCOwWjHXwht6S5hKWam5szEARiTdFJgcohi+ScpoMwLhhNvifCi5A8HRwcKJvN2i6QTqc9O3tmZsbjcgCKaGhZqxRdsKLD4dApx0igYa4pXPBhI2ekKIdlBOwBTKChQyqPhJkLle+P74SL42c/+5m/fy60YLAiI+2y2azBoe3tbb18+VLj4+P29sJ2s7YosrnsAD5gbvb29twgLy0taXd3V5OTk5ZJsyZDoZDev3+vn/3sZwqHw7q8vNT79++1urpqBo60Zgo1fheN7vj4uJ4+feo1AbNF8U62BV5/SV7DQZXI/f299vf3XShRuOPvQl2BXUf6LH0eHx93U4qag6IzaOdAUoZCiKYVALPT6SiXy5n95vcEfeiMLEKaGlQvwVwjmaQwgM2H+YZNhKW9u7vT06dPHV4zMzOjly9f2n4Cy48ksNvtqlgsKpfL6T//5/+sWq2mTCbjCRyh0CjsqdVq6fj4WDc3N2bwX7x4ofv7e4dF8f5opmm0gp7E4Iv1zWejcacxp9Flf9J8AnhR/HBnBbMrAAhoMrgbaQy73a5D/X7729+6YQsCCKi1VlZW9MUXX3gvI4OlKMYOQK5BKBSyrYM9vrm5KenzGE/WKfLm8/Nze3kpAGHx+C5g1a+vr13M8jOCI9awOABwcaeEw2E1m03Nzc3p7du3ur6+1vX1tWKxmCqVikPgAEDj8biBqImJCctgATf5rmmcJiYmHMbF2u90Oh7Zw3PgTAPIQj1D8xYEgw8PD+2rpUGnAUdZh09bkvcW2ToU9Zwf9Xrds7I5e7lbYL4JJwSYACAfGxvT+fm5ksmkmXXO82DxHpxM0Wq13PygTLm/v/e5AjHA6DGyJaidAO2wvJAF8787u8ihAXDl2fPeAdDn5uZsX6E5CVo3CF7jDid1vtvtuv7DbhiLxfx7+v1R8jrKg3K57D3CnUxDTp4LLGs+n/ddfXl5qWQyqYmJCZ2enloqDtgLKcU5cX5+bmbz9PTU3/GrV69UqVSc3g6IxJnLf7jrYGiHw+GjXBok9YAijUZD33zzjc8jJiigCkVtFJwwAfsMcRa07iQSCYPFhHbVajUDBN1u12QFdRSgK0QJe4IUeZpzQGmIFxRxgEzYVDhDkY7PzMzYIgQxhuQdGwq1K8GOML9I7wFNIPpQDvDcCaDmzuQMDqoAJbkO7PVGo/5QQG5ubiqdTqtcLqvZbOrFixfucyBKAIEAdVn3kAB3d6NpFdS0Nzc3j/KZuKdYaxAh8XhcFxcX/o5QLnC2R6NR2xIgF1CUkluAcunPff3IhP/rX99bjg4CVK1W/aXgnfjw4YNRFpoBDqzt7W17VrhI8FSwOLvdrpaWlpzauL+/70XIpUCxClpXLBYdvJFIJOwvOTs78+Y5PT01WkYTHpRGB2WFFGukKtOABxslpCgcLASnvHv3Tm/evHEDzAH3xRdfWA5Yq9WchIl3GxRqY2PDUhokn2xAQi3wCoJgwrTA0uKh3dnZMdKOJJHDGnlKsVhUuVz29wDAMTc3p2KxaKkyYEWz2dT5+bl9XczbpChAlorkb35+3gcjjUCj0VAmk1E8Hn8kmwQ5xBMOioi3+vz8XBcXF0bJKbaR93CY8yxptqXPvpggS0WBBpPBpc4hh/+ICwQpE6w1FwGhU1z8HLC9Xk8vXrwwAw5YQEAK0nhk8RTFFC/IJ3lvQRkzRRsgUb/ft29pMBhYBhaLxbSwsKBSqaTb29G8dYq24+NjMzlBKSqXINJwnmew0aA5Rb3C3pXkNXp9fa2zszN70ZGLVSoVLS4u+rKgiOHzA+wtLS3Zv0w2APJqCp5SqWTgDNb44OBA+/v7Lqa3t7dtV6CYwCoxPz+vra0tlUolsywUmLALAErIcykaCWTq9XqP2C6+A8AD2CrWBcw94CMyNzzrZAYgi8ZCwu+n6RgOh49Yc2wOrKVggQXYybmJnzToTaYwgkG7uLjwPxsbG3s0j5bmBY8ijAUsAixX0IMN+8/e4xyncYlEIm6CWBcw6r3eKJgumUy6KEUFw15FkYN6AmsOhTW2G/YIRVTwYg8WNqwf/j2MSlAlwt/nO0ISyjkyMzNjGTfAIg0L5xuN73A4dOjc6uqqYrGYtra27J3m3mKc0/39vVZXV/XkyRO9fPnSYBG/Q5KZDjIgeP+Aozx3lCsUavv7+/Zlo0IbGxszkIJ6CD8tzBq/l2KRM79areri4kKZTMbgNkGToVDIDSCFHHJN6g1yLUi6xlI1GAy0tLRkOSlrMRQKufBm7BjsIqOTYE87nY7v12BTiMIiGJAFqUDDzjq/ublxBkGz2TQLv7S0ZECD7JxgU4z1SJKBuHa7rXg8roODA98b8Xhct7e3Ojs7U61W0/HxsZaWlnx28FmRMG9vb1vdc3Z2ZpUWACKN5+XlpdncbrfrJhj1X71e19u3bxUKhVzUB60SQQBGkv87qDLjHCIU6vT0VOVy2dkq7CPIANYsZw8NbpChHA6H/rww0vPz8/rlL3+pb7/91sRLJpNRoVB4NApsbm7OQCxqo8nJSYfqBpn7sbExh3ySM8MewUp4f3+vra0tdbtd25dIvw+CWjSK/HysdgSf4d3GhsbejEQiVkJwr6M4RR4OMA8xARlBPQaBwXriTKQGB7BnL3a7XTeggD2ooVB81et1pVIps76np6dO+eduIWiYZ0r9Hg6HFY/HVa1W3SgTWgqojDWg0+nYfjc3N2eyANVC0PpCLQyRQy3f6XS0srKiSCTioEcabn5GENgKeqoB5DgjOOOpQ6iLkM2z1ugvmLZAICr1/czMjNbW1gyScx5QU0SjUYP+9XrdHn7myTNhAmUA6pDJyUnn+0xOTtoq0OmMRr0Gwdzd3V3vYZSB2Db5jH/u68cm/F//+l5NeCQyChPDsw26+Itf/MIyQ+Q3HOT4wXK53L9AdmCrJiYmLNO9vr5WrVbTwcGBXr165bnb+PQmJiaUTqeNZOPvwv9MCFvQ/wdKG2QSut2um0Muai4LxlXs7++7AWfsU1AOxXdCGMLy8rI9YIR6gLbd33+e44fvG0b31atXKhQKRlIbjYbK5bLBjGDwE58XRpMDj3mhyJQZj0AaKIdupzMa4bO2tuYRKKlUStFo9JF/BtlpJpOxeiEWi2l/f9/eEwpkGhZJ/v8Umu12+1FIG+mUSLVoJGgg+E6RW0WjUbNe4+OjueRnZ2eWDiKVRO728PDgBE0aKp45F07Q4x0M46H5orlA+hsKfZ6/GRzHwzPjAOv3+/Yqt1otVatVX/rIQClUKTy5pCjWCd6gOOQQp2mgKGYt0rTQ0GDpkGSpJ+gnDHutVtPe3p6Ojo60srJi1JjvH/S3Wq0qHo/bg0hxiXSXJpIE85WVFXs0UaTQYGJBmJz8PAqFPYeaArCF9UyRxnMiqZkCGH9qt9vV4eGhx35wXnzzzTf+PBsbG5bnozYISrso6AAXYLqXl5cNBgG0BC8DgqSC6gxYXl5caMjSaDIPDg48FYGmFa8ezBffF+uZ86ZWqykejzvxnFRkmGTOvaCdgz0ZlM3y+ZHcElKFhDioQqB5A4QL/jzAQWwkCwsL3rOoNGA7YVax87x588Z7E1BqcnJS6XTaLFa9Xve4w6CH9Pb21sUujH0mk3HxlM/n3bhfXV3p5OTkkeeNRoHzXJILoSBLAUALyPenMneABlhuVDWE7gT3C78jWLDx75aWljQxMRoPhNKLvx+NRvXmzRu9fv3a99ZgMEocx2bDXQvrjXVlb2/PqcPS5wkQQfCJop+GABvOcDi0tYPGGoCFtckdenV19YgJxHKGUoQ7DFXP0dGR2TQALRoHfh/3fa1W87qA8eRsBNwg5JJzs9PpGPRnrc/Pz7s4leS5uWdnZ26Ez8/P/Tz5LqXP4/YkaXl52YBXvV63wgAlGfctLDKhjLDDy8vLVnbgUYaRW1paMnhO8xlk/wB/qUcuLi5cqPNZeU7I0WkgsbhwDofDozFdh4eHlsZzfjw8PGh/f/8R2wqgyrO/vr727+P7oMaSZL97KBSynWtnZ0fPnz/3+c3eBzBhj3LWwn4vLCw8srNxnkYiEUWjUdXrdZXLZQPVk5OjyTOZTMZnNM0QzQcg68PDgxUMnAsoIcgnmZqa0sePH9Xv900MQHacnZ25ueccHAwGOjk5edTEXV9f6/e//70bY+5B6grAI+5d6gXUmDMzMyoWiyYvGFt7enr6qD7hGcGM4n1/eHjQs2fPDGrBhuLbptbh/OJsYR2hWJmenrbFb2lpSZeXl76HC4WCMyM4PwALsENgjUIJhSX09PRUyWTSk0yYcsKdhGWOO4XGHbCZIFXGCLMWLy4udH5+7v2JfB3bWKvVssVWkvc56iJsJADSqBN43qhN7+7urIjA0kUjTBYFfRJqTLIy+v3RiMdcLveo4c/n8zo6OlIsFnONxIvPFPTgc65B7HCfofjk3IcskGRCJ5/P+5z88fXDv763HJ2UzvHxUdAT8oqlpSU/RBpNFmypVHKT8+233yoej5tRBx2HaaARhylrt9va39/X5eWl508SqBAKhSyd4OJZWVmxD4UiniKfwpbioNFoKJfLaW5uTvV63cgooXL39/cei4QXCxSUGXwwO7/97W+dOknBTDrt7u6uJdWVSkUvXrxwszg1NWVEnQL38PDQjXtQYrK/v/9IukRYErJJDmUKLUaj4PUivA4Z4unpqXK53CNmB8QTKSlM4+LiohqNhgsjmoageoDvIlg0wBrS3ADW4CeEuQF9RK4VPJjOzs4cLpVIJBzOUalUPO6CQBvWEcwb6zEIykhyo0txQvEDsvvdd985ICcSiWhlZcVSMVB5PK2wj8iPHx4eHEwFGg3Dwjrm+4CVobDi+eIvo+Ch+AVM4jKDIZfkIivoKafAAcWnsPnJT35i1QrPsFwuG50PIveM+Or3+//bkWWAWwcHB2aB8ScTwtdoNBSNRn0xkIw6NjamYrFoNjuVSun8/NyXbzabVTgcdlFEkwWDQ+o8Seuw2ryHoC/83bt3SqfTSqfTmpub0+7urtkq2CwKZC5J5JmS3ITRdJDwL8nS2GBzCsB4cnLiJHNST5Fg0uTCjsBE0FDxXoIJ3aDm2BIoOJvNpg4PD62ooRjDsgD4EPRxcn6TiEuwHI0U3kJJZlFolPC9w8xTRFMYBf242HMYBQhKv7Ozo4ODA0sXAaloMmnoaQYHg4GePn3qIhe/Il5/vs9wOOyiFxaYEYU0RBTJ0meUPdiYBmXnnFm8N84AXkHZLH8+ODmA9cM9Apu/sLDgcVjMsv3mm2+UyWSUTqe9hhYXF/XmzRv96le/st0AOSyWEaxfyF5hhS8uLjzjHpYLUIWzhnMM1Q1NMAABYAKzrF++fGmVDp+NpovPHDyzs9msPZGwVARWsfcIEmMd8vzOz881Pj6uYrFo0JQGi+abM4nQWO5amGR87oQ5AgQEFWDSZ8vS1NSUFQEkWc/Ozj6aq4wtDrsR58P8/LwDziAFFhcX7RcG6Gfv9nqj8V7YzU5PT810s54mJiZUKpV0fn6uy8tLTwhgLGOn09Hq6qrPgX6/r3Q67ewWmqW7uzulUinNzMw4oIy6jXAqZi5PTU3ZnpVKpZytQXgigD6AJt9JMJ8BIBD5NAxotVrVYDDQq1evfIew18g+ASxnfUmfLTnsbyTb3OWpVMphgsjBaZ6xxgAicq7CIsPyv3z5UtPT02q1Wr6reA9MwKEWkuSg1unpaZVKJdXrdWfXpFIpkzqMhUJdwbnKd4cVkIYNNSRAJ8wyCrvghIxWq6V+v69KpWLiBwUV9RNnGIAfdQRAH3YDQCwC5wAwyDGCwLq6utLh4aFZdPYUZwhKglgs5vWCrJvvjjuK/INIJKJsNmtJOXY/7hzq/KOjI5MFNzc3zpehjr+/v3fezPj4uD33ExMT2tnZsd0VkAbA5erqyiowaiPOW747bJCorYJ3PwAWORE0+Lwf2GZAawBiVCiAg8j9Hx4enPtEgCWhzHj6qV2p2+lBgmq8YE0CeDExMWESCFUl631qakrHx8f/X/vB4CuoLvuhXj/0z/8/9fpeTTg+ZZCybDbri4QRHQyJX15e1vT0tBFyxgjhe2u32w4J4SLAh0GDSyPcaDT0+vVrhzY1m01LdV+8eKGLiwvt7u4qFBqFvjSbTaXTaftqJycnXYg8PDxYkksYGQzWwsKCfv/73/tCTCaTev78ueXtFE8UflxA09PTWlpaUi6Xs8cZhJck6FQqpcPDQ62srGh6etqodTKZtGQwHo9rZ2fnkZeZguru7s6yHD4zIAHFExLA8fFxHR8f2ysV9Dzf3NxoeXlZ7XbbfnEuVwoYmkX8rDR3U1NT9v8EJY1zc3M6Pj62zAhPVSwWczgcgAvSHS4PpFAgeNfX1y5Og0qKcDjsi5vZ3cEZ6MjFs9msvzvQUT4XqgFJLtwAdPj99XpdhULBAVM0RPif8AdS1BPiEvQzxWIxj19jjE0qldLBwYGZTD4TDRIp17e3tx79BjuFNQJQKcjSYTOg2YbhANCg6Ibt5SLDCrKwsKDXr1/7AqnVaioWi5Ys3tzcPJrhjqRZ0qPCncuU38X3XyqVHIoTHHEIaryxsSHps6SaJgg0GO8YXjwKBtYJ3jkK1mDxen9/ry+//FLj4+OWXGLFSKfT2tjYUL1e18HBgbrdrn75y1+q1+vp8PBQuVzODT57jeTVSCRi+TeedGnUwNVqNXug+eeMUYlEImaCms2mCoWC2Q3WL+uAM4QXYWj39/cuDADj+B4IP7u/HyX3AyIRgEhwztjYmIs4ZJq93mg2LuuVwhrW+urq6lGiMvsKwIuiLhIZBWhyxrGHYRKQ1E9PT3sfnZ2d6dtvv/UIKAoc9h7AF8UqLPjU1JRzKJDeIsVkLREAxOff2NhwAXRxcaFarWaJK41A8AUoEwQeWfcAU0HfZrDRZm8EJcgUWqgfaNhpwika5+bmtLW1pdvbWxWLRX355Zd68uSJwWWanG53NBKJ8ZT8OwrAiYkJHR0dOYshqAyLxWJuQiX57McuAhDG8zo/P/deD2aFANahXuF8wpJSLBadok/hHGRSW62WIpFRKBAFO0oNnv3l5aXBFs4ainQKXfydmUzG7FMwCBTADDaUglr6rOAKWmMKhYJOTk48q5nnilUI5nxhYcF/hpGP1WpV09PTrkGw4lHsBuWrALgovmD+Ly8v/WcvLi48GYA7f3t728oyvMzsZ+qJ8fFxjyeNxWJ68uSJJiYmVKlUnGWD5QiwggkVqHlQIaDsA9ylDmQNUPsAxPZ6Pd/tNICtVsvj71qtls7Pz5VOpw1K0BjWajWDpazP4D5hDXL3U9sAmGNVPDw8NHmEFJxmCik7awAvMk1qNBpVOp12sjZnRa/X8z7N5XJu2JlR//TpU4OmPLvhcOggMO5i6kYyhPieqFNubm6Uy+VUr9f18PCgbDar4+NjA9IoE6PR6KNU7tvbWzUaDRUKBYe3QTaEQqORqo1Gw4FyKIskeX0CBABuAbRDDuRyOd3c3Ojh4cGTZMjv6ff7vgO4rx4eHqzq5PdOT0/7bgyO/aWpBwQIEhi5XM77hgyI6+trk2Psz83NTTfCED1Y0FDMXV5e6uc//7nVV4Bm8XhcjUbDo8o4WyHpXrx4YZIiKDtPp9M6Pj5WOp3W4eGhbQmzs7OeW89+R51Tq9UMiqIOoSepVCpu8NfX171/UK1BdnH3Ui/SP/HeAKWDTfnt7a3r3A8fPlhFR/+Fl/7H1w//+l5NeLvdNqr97bffqlar6cmTJz7UQI8IUykWi/YR0mAlEgnlcjkH95TLZe3u7rpp6Pf7KpVKOjw81Pv3791ssYAlGe2jSOciREI8PT1t1PDm5kbVatUelvn5ecszOLiZnYyfttMZzbN8+/atxsbGzDDDstze3vp30HiDllIM0GwsLi4qGo1qf3/f4zmQKIHIUvjs7++rVqs98t/S6J6cnGhlZcUMV9B3xSaEacvlcgqFQk40Bz2EIQ2FQj60GXMA20mjBcrHqAeYu1qt5g1K0RD0HiEtAqFDHgdjfXx87AsbuTXs/eXlpc7Ozlz44pun2YQpREYMgw04AJKL9B3kGwYV+Tdeehoi1BVcOJVKxSqGy8tLF/BjY2OPZp7CptGwgnrzM2l6YT4J6ZNkpQFMH2FyQWk7TBUedDx8/NwgskxDhOVDGsmUkP3v7e2p3++7wY7FYmp/mhMci8X0xRdf6Pj42EXPcDjUX/zFX6harfqZseZAeoMXLEURTSGeTgJdHh4eXOAgv1xdXbUFAw84nz8ozZqYmFA2m33EaMCaw/DiM2V9vH79Ws1mU6urq5aPoc7IZrOq1+vK5/NmdSgC8PDBzMP+M/mACw1EGS8bawTfMl5nSW48WcfYGkhxRu2Rz+fV/jRqhGeOf5FmD9aZBpT9j+UDa0G/39fBwYHS6bRBE6SmsEM0yYAMU1OfJ1ywfvi8NDy9Xs9+QVhyPj/jBFkL/L3b21s3/cjCOWvGx8edeE7jgtqCZ8H3HgT3+DNBtQeKJwKA/lSFwnlI8zUxMWHJJKOdgoUKxRUjeyhOg4w44CZnNucMDCDPi/cFEyLp0R6GMcbq9NVXX6lcLmtqakq//vWvtbS0ZC9yOBw2OxNUzQAO8d64A8gT4ftAHdBoNCyX5Dnf3t56rZfLZaXTaYNYKLcAoDudjs8Q1hRsNHJPGkwaYQp5xpbe3d2ZccLyxZnO++U94Gfm3EFhAVOG5QegMpvN+i6CgWQ/cUYGAVEAAsBT7hSyTC4uLrS+vu5G8e7uTtVqVevr615LjUbDOSYw57xfgA+sQdLIW1uv17032XtHR0ePgLTFxUXt7Ozo4eFBy8vLJgUAxZvNpq0x3MUkp4+NjWlvb89gOt8F9w6sIxktNAmQHbxX/OI0ZChXCCrFk4v6jedA4wcYJ41AxePjY+3s7DxKlge44pziFcx14WwAXOQsQTFATcrvoUkLhUIGp2hKAN4A4jjnUHmgguBeombCTtNqtbyu2I9BJcDu7q7Oz88NckJavXr1SvPz8/YFUzsB5gyHQ+3u7rrWWFxc1NLSkm5vby1th+CZmprSycmJQWFmv9frdSsGWOeATOVyWZK87smsCCoJeX6AyQTrpdNpHR0dmXVlHzA1AQADSxc5Bblczu8R4oe7ZHFxUU+fPjWQWa1Wtb+/75Fc9BhYrwBZ2+22wWzyZXK5nEH+XC6nP/7xj14PU1NTikajHvnaarVcQzx58kSVSkWtVsuAG0Gr1N3YCZC8A/ijAEElHKxvOQeq1apqtdoj5RlqFEifcDisvb09f55oNOoA2fn5ee3s7Bgo7vf7Bm9QoSwvL+urr756lLfAMw8SFlNTU4+AIywXqOmazaZl+X/O60dP+L/+9b2a8Lu7OzegyCIYT4RvgbRRGjWKnVgsZva53+9bjsNGR64UCoW0srJiBA9ki0OEzbuxsaHZ2VnVajVJIxRzbW1N4+OjhPCTkxNvGMI3KBwpAk9PT50mTvAbi/HXv/61FhcXdXR0ZO8HsiIalSCDS5AVhQIF/MXFhba3txUOh72ZSGsNNjyMrVhfXzcTXqvVND4+StjGW0ezD2oHoMDhEolEHIK3vLysd+/emQUguAX/y2Aw0MuXL1UqlXR0dKREIiFJPjCR3cFuYDNglBggA0g0Px/5biwWs2wYhhB/HKg+hXEsFtPq6qq9fDSVzFIEcY/FYv7cR0dH/txIkCmkCdhgbRAmA9gjjeQtFGgcpqC3II348mkKWVf8XDy7sVjMqavX19f66quvlMlk7F1HWkQRhk+KJgDmrNlsKhwOK5vNuiEIXvLsEZohwBQuCtJgg6EasAC9Xs9oblDWdXBw4FT/Wq1m1gRGCIlcMDCFYhYPGywl3w2MCqyKJEusV1ZWDP5wMUcio7Tz2dlZs2E0WjTE9Xrd4YpIufhMtVpNP/3pT52Am0qlLNVkj/H+KfYvLy9VrVbN3jCeAzBwfHyU4v/kyRMzb2RRID/k8kPhwffOc200Gj4b8KLSGKDAYS3BFmCzwWOKwuH+/l7n5+fa2Njw8ycNndRiPHuw1s1m0/OZ8b7SZDJOD4Yc5Jz9wRgfSWZout2uPbn4KdlzMOYU+AAHrG9Y4iCLTIO4vLzsLJCVlRU1Gg1Lh3u9nu8DAEjk3ABNNKDBqQyoTGjykOGhrmg0GkokEt6/FKGwQ+fn52ZrOddhsWDlKCBRK5HZgQwaebAkF2QUyMFJBuy1VCql//E//odDDX/6059qfX3dTQPnQKPR0MTEhP+Dhw8VytTUlOXfNA/xeNx7gVBKzj4+G+uP9cP+5XcSUsQ9zz4BUCK8DQA2Ho/7zpiYGAVSHh8f+wxoNptmYYKyScAOQA0aWdZS8P9PTU2pXC4b6KLpA8Ah7ArfJN8XZyuqCKxZqFxub289xu/q6kqFQkGXl5dqfxpfNT4+yprAE8oZC3iOdxPVG2oy5PdB9QhqluPjYwdulUqlR9JSzo6gsgxQljnhvBcUTChQYrGYbTmhUEinp6e+bzlfYcfJv0AGS0OGFefi4sIzpovF4qPvjGIcFhXbC/cCzXcoNBrl1Gq1tL+/r9XVVa8n1JTSKCsmaIHid7GOWVeSTHxga6QRQyECOBT0ENPYoThBwcj7B9jvdrsOo+z3+1pfX9dvfvMb9Xo920bYIx8+fHg0yiwajbrRj8fjevPmjZaXl20pYEoCzDKqEWmkwiqVSlpdXfUZCOjOfTYcDh/VFJyH1GvUV6lUSh8+fNBgMFCxWLRKhnP78vLSAXQon7AFATyPjY2pUqk8ku9vbm4+UnjkcjklEgkdHBx4H5IRwz4AEEEpxV2OJL3ZbDr4lvMb6TRNaKVSMckB0NlqtTw5gWePGonsBhh0gMmrqystLy97nXIOEr53enpq73yr1dI333zj/mYwGKhUKnmM5OTkpMrlsoHtL774QhcXF9rf33fDjFQf8oLJSLVazcAOE3Q4JzjvOc9RHnAucP/weyR5zRBMh22F7xnlE2rQg4MD22///9FU//gavb5XE16tVt0Yr66u2j8K+pxIJHRycqJudzSiJhKJKJ/Pe84xrHKj0XB4AE3E69evVa/X7YfCp0AxUKvVnNDJZV6pVFwcPXnyRJ3OaJ53sVhUs9nU8fGxstmsUSBYGiTFHHTIi9rttiYnJ/WrX/1KmUxGlUrFxQQszNLSkj2NSOqDo6A4TCgYkfziwyI8gQ1GQbizs+MwBsLAlpaW1Gw2HZAwOzurm5sbS0ZAFZH439/fOwGYy2M4HGppacnhG4VCwVJRZrLjWQ2Hw76IYb+5oBcWFrz5g6OQaOpo0ufn53VycuK0WgopAA1QdpgQ0FMKN2TGKBUkmbnH935xceGZk0EPPKE9eGVB3kEAAYK4tLls8RcysoWGfWpqyiFPwQBAmPxkMun/T/OQyWT8XimQkQTTwE5MjMY/hEIhg0/BggWgCtaCw/fm5sZFB3NNYUKCKfnB4vzw8NAJvA8PDzo8PPTlx0VYq9Vc4A0GA6+/o6MjS8doAjY3N3V2duZmKtiAw8jiA0cVQC4DRdbJyYk/J7YJiqurq6tHHufBYGB5KkwqXnBGVsGII9VjX8Fs4ZGEVZNGINb29rZOTk70y1/+0msCgAzPG749vt+gD5XnSWANf6fZbFpiyHMF3Q/aQrg4JyYmnBYdDofth4UlaDabWl5edsNOGCGZACDueL5pgjmDCbVBvQH7hS/4/Pzcs5mxuFDowmjg46XQBugK7nvyHmgskchxjsDM0nDAXnKeEaoDQAhjB4NDFgeSebyxiURCpVLJRdrd3ecRjXgzAUD4WTwHwFD2DUw9o2Io2IvFov2CAGGRSMRea3yIsVjMNh+ARNRPFDZIhIPMNVJDwM9YLKZf//rX/t4BvpGSsrbm5+ctT+ZeQ8rPnsWOxFnMXgBI4CxgrY+NjWlubs7ZApxfrJ/5+XktLi76rkONwEjCSqWiJ0+eaH5+XmdnZwYU5ufnnQnAvZNIJCxDx94DgALACbDDmid8cW5uTu1PY5MAlm5ubrzWAYDv7++dLQEDfnt7q+XlZVUqFd3e3qpcLvtuQxkAw0ROA98bkyja7baVPygxYNzIC0FpUKlUDBDNz8+7yUkmk0qlUgacORcBmKldJD3a6/l8XpVKxc90amrKowv7/b6TsLm3ksmkGo2Gzs/Plc/n3bRjN1lcXNTa2prK5bKOj481GAz07NkzK2Q6nc6jcYck0aMQ4f6g0aR2A5xDpdXtdj0xQ5I+fPigcrmsUCikUqmk4XCoVCpl1dy7d++0sLCgfD7vc53aMfh9By0agOM0WoAtKCwajYbrHO5WLFw07+xv2FYmtkBglEolZTIZnZ2d+Q4+PT11MjV7PZlM6vXr19ra2tLd3Z2ePXvmxjiZTNruQB1BBgl5DJx7d3d32traUrFY9J5Alg/gQl3H+QuYwnm2vb1tkL5arZqYodEGeIdMCn5vm5ubj0gfwGHGStJEkr3QbDat7pJGAAnBf+QooAyCrOIs5YwhYJJ7l3Mb0ADVLXXK8+fP/fwh+lDt0aOgqqVmok7iPVWrVU84wnpFOF08Hle9XjcQgKqNST2Ag4Bw1DMo8bi7CT7jHCfcN7iuU6mUqtWqw+DYX9haURdSU9FUY9kcGxuF3HJuHhwc+JznzHj27JkODg5sZV1cXHSGwPX1tdbX1//s3vBHJvxf//rewWxIvlOplC+PqakpffPNNx5nFY/HHbBEk3Z6eqqXL18avcEv9erVK3311Vf6+PGj/93vfvc7S4m73a4XBU1bu932XFV8tTs7O3rz5o2i0ahmZ2fN7pVKJV1fX/uAhJmBGcOjQuT/L37xC2WzWftqQBTHxsaUzWb17NkzS/VmZ2eNriG1jcViLhSPj48Vj8e1vr5utJ1LZnZ21hLhk5MTLS0tOVWb+eNIrfiM1WrVxTGXVDQatYyV8V3j4+Pa29uztJARLkjkLi4u3ODBFDHGIhqNKpfLGW2ELQ76zmm0KKijn5LV8a9zAJJmfHNzo5cvXxp9hVlCjkghQ8NGeisBLTTIc3NzLlrIFVhdXVWn07GCgeaGy4ZDmMugWq0adcfTBSNDI4HyodPpuHE6OzvzGCSYCmT7IKg0anw2DlOkdlghWE9IeclZoGgDBODy6Ha7ns0ZlGKBAtP4UGBxsUqy/K5Wq1kxgZQSCRRoPT7AjY0NRSIRnZ+f6+DgwCAGDQvhhalUSl9++aXOz89Vq9WUSqXMPqJ6QFY1OTlp1gkvGEh5oVDQ3t6e2UPWBEUeCotQKKSTkxNL+CmoeX/tdlvr6+ve19fX1yqXywbcAMoqlYpVBSTrnp+f6/b2Vtls1mvx4ODAQS6k8CJ5RRYPy893Xq1WLdul+QqeBfxdni9qARJ68YfBAOJvk2QwC+sGaw5WB+AhGo0q+mksG2n4lUrFYBmpzjBEnMV4D5Gg4jWliWMUGXPYk8nkoyA1/NexWMznKZ+HpgYgkrvk9PRUpVLJGRQogjY2NnR/f69KpWL5KT5MPnuhUFA6nfZkBs4WGsrhcGjWk/eO9x75c9BnD6hEEU7RjyWhWCyqUqno9PTUjDysa7/f1/Pnzx/tW5psAAiaKSSvMLkwf//0T/+k169fKxQK6enTp4pGo5LkPwvzwnnMZ8MbSx4ATCmNNqDwxMRo4gG+SpQPrAuKNJhb3i9yRawb7E9A5nA47PBVLBec39Fo1E3y0dGRrR0AN6HQKGiRyRaANnx3hInxeYPA7P39/SMWk2dKQ0E+AHv84eHBXtLBYKA//vGPenh40JMnT3zOJpNJg2+wotIomwU7EKopQrNYc6enpw4yY/3Mz89rd3fXIGM2m7XShAYAWwuAGUox/nk4HH7E4KE8YV+yTphTjOydDA7k6uRqXF9f+7PR9MD6shcADAADqf/wnQe9y6w/MoA4k2HLAcRgVlEiMoYQFpaRXwAdsVjM5A1jQA8PDzU3N2dQDeUaAA3vDYBEkr3hpHdzJ42PjyuTyVgdcH9/r3q9bmUIto3r62vV63Urx6gXsA/84Q9/kDRiZqnbsMwARFAv0dBjsQp6pclyYAINaimmOpydndl/DRFVKBQe2Qh4DuQecA7UajXXdMF9AzgYVA3d39/7zgZAqlarevHihU5OTgzSUDdeX1/bNw+5U6/XrWqDWBkMBj7Pl5aWHu1p1iBAV/TTuDrWMI0uyrOgBQdVDmfF8fGxNjc37fFHQYuVlj1BHUZgJSAFah+ePUAeIAmNPrYB9gt1AGcCz4QsEAAD+iQ+x/n5ue1xlUpFpVLJ7wnCBDvWycmJksmkKpWKzxiAPXIzAHYBl2ZnZ/X111+7JuZsh1TZ3983OEbDTq314+uHf32vJhzvBVILpJ+kcN/d3Wlzc9OXGM0mTe/29rZ/FtJwDj0u2rGxMV9ikswEBKWWeH+Q8FEo/e53vzMrFolEzMbBpiABgd2lYQLdKhaLmpubMzNICiQpy3i/fve737mITyaTLpiRvh0dHanX+zzrr9/vm6XC23p0dKTt7W3Nz887SIRQMFDGg4MDy89JmaSRTyaTjxKOHx4eHHQH+gwyR7NLYXRycuKGEmkczS+p4wScUcgxM51wG8CVfr/vZhvZ4uXlpWVq+Fl3dnZ8KQZlo8Ph0GmUNNfVatUXHN8z6DBFEbOXQUdBBPFuo1CAvaL4SSaT6na79nLB8COzAwGl0OEy4DDs9XpqNBqanZ31eIrgGJx2u20f1fX1tdrttkNB4vG4Z4Ken58bxGq325bwI7uenJx0UzszM2PEnQYaVhKgIhwOex/hA6MInJycfDRDV5IlSY1G49FsT1i0sbExxeNxPTyMxrY8e/ZM3377rT58+OAmZ2FhweFAT58+dWMHU44NIBQKuQja2NiwVBnmEBVNJBIxuw2az/vhvcPcSJ9neBL4GI/HDW7x3YRCIa2vr+vDhw9GUilsKLzr9bru7u4MzkxPT2t/f98FDJ44ik38afhjQ6GQfZ1ra2tqNpuePUqTAuDB2gAZJyEdYApPJ00ThVBQAkpTxhkFQxFEz0nLpfBeWFjQhw8f3ECQR4DKh98Fm4LMl30ljTzifF+bm5u6vr5WtVpVPp83YwYri4SUc4e9CctPY8CIHpD7xcVFvXjxQuPj49rf33fzHgqFlM1m3TjCcMGQsBYAWXmOwfVOkQZLIcl2ilAo5BE2qFiwAAUTZVFBwYYEGzWYc/6Dn5HPT4NJsToxMeHJH0dHRwZUV1dXXUxTeAaDhSjysUFgEaN4RmrN+u12uz5fOHsp1rD/BO0qgMuSXKgCpPCzaFooSFG3kKqPqgFJP819cNRoJBKxKoNQsPPzc+VyOZ9l7FO8orBXjALiPu/1ejo5ObHsFWsV6qWdnR03jsHCGJtGKpXS8fGxEomE5dJIyR8eHpRMJnV4eOisAABl/PKAGkxrARRAjjs9Pe2MAPz1SF2npqb8+bA+Mc8cwApyY3p6Wh8+fPBYqOnpaR0eHlpGDFvO9wPQODk5qZWVlUd3JmqTbDZr4iKXy9nW1Gq13GRzn2PLoQnp9Xq+x/i+aQa4tyqVigEOMgCQ0HLOE7hLqB5MJUGEqM6wXbCeUe5wd1JropA7OTlxgBxe/4WFBS0vL3s/sK45k4JjPFFqUsMFg99oxgHHop/GgVF/ocZjDGj707hWyAwAitPTU2WzWVWrVTOklUpFS0tLWl5e9vP6xS9+oT/+8Y+q1Wr27KM2HB8f93cKwcA9jiUOebokZ1BwH0LOVCoVK9Bgjzc2NhSNRlWpVDzCjfUc/TTSFjaXZ0B9xH1Mg4mqh7teksM1K5WK1yqqN6yBALRYrzhXuacAfvH201hib43FYlbk4m1PJBIaGxsluHPvYVnk/XU6HTPyh4eHSqVSBo/IOZmcHCWsHx0dPfregtkKAC4TExPK5/MGnqjXUZVFIhHfx6h2kagfHx+r1Wo5sI6ab3JyNK4Q6T21ASNqsWgAEK+trdkGwrjUTqfjfUuNs7+//+e0hZJ+ZML/n7y+VxNOSFUqldLk5KQbjadPn/qQSafTqlarmp+fVzqd1v7+vllASWa0xsdHQ+sZIwJzJ42+7PX1dftvw+GwksmkiySkZySUUvwxc5QDMZ/PuyAKoqrIbofDoX3Ji4uL+tWvfmWECRYAmWomk7GceXV11b4OipZer6fd3V0nIpZKJWWzWUuFuFwWFxf1/v17HRwcaGpqSuvr64/CSJCqkowJi4n/JpimTNOBGuHs7ExXV1cuijnMSDclhAQ5DCw7slAklDAiNMmkZ/Z6Pe3t7WlmZsayZkkuEFEDkAAP+0rwEahvsVi0Z4cQG9gi1A3BLAB+LlJK5JIUnoAJMEAUTRQcBJkggYPxBdgJjsDhIKWg4TunaOT7o9k9OTkxO4pagsMZNpFwQAqWINsIqPL+/XuPscGXzBoLShJhOprNpnZ2dgwCUfBL8nMmYCYSiejVq1eq1Wq+wGdnZ10wEIIDG8+agaXlQH/9+rW2t7c9w/j58+cuAijWULA0m003SMFRUYzyWVpacoJ8tVq175z5oMF9yu8Ior6dTkdnZ2duvCjykaQvLi7q4ODA5wxMJ+uR7wkJHGw5PjPG/1FYs99Yp6DQp6enlofCbAPKEcgH8szfkUYhl8x0Jb0YdQ5F1e3trS0cKF/wbMNAsIeDjNPu7u4jywjMy+bmpj5+/KibmxutrKw4NBGQjqY1OC2AYhw2AaZAko6Pj+0nI+ODsC+sM8heKd5gWtiLMCR4KNPptPb29nwOYXuAASeJmrnDFLQ0ZEhisR7BNLJ+UGQQtERTHw6HffYA0nLmYE1hncOgBwPo/jSsUJLXGhYBijPk9OyN4XCoN2/e2FcLG0TmAB5y7Ef8DsA32FF+ftDjHZwzjf2A/cKZDkBHc4uViZ/Dd0tYGum7FMLn5+fa399XOBzWmzdvHFAWVBYMh0P7pvn3NGEAotPT02b4sAhxjgEQ4KsG9MHDTLMFezgcDp0uPDk56aYLhmo4HKpcLtsnf3d3p/Pzc6vt+F4lWdpdKpU8NWVqasoABs0bKrCgKoU9iHUF0I5kcOTHANgAVIQLxuNxAzSA1QDJTFLg+8BTjJKLQp78jkajYfUQzzkYKLu6uqrZ2VltbW15agqfl3uQ+4d1D6scnPLC50W9hoWAZHcCHaWR/Qer2jfffOMmkjuee5v7HdCcs1KSAUu8sHzP3CfUJoRmTk9PW7qPhSYej7tBu7u7U6FQcDMUzJJhHxwfH+vw8ND3JypEGsfhcKh0Oq12u63vvvvOIVpkFwGmos4YHx830wkwRJ2HQguF4t7ent69e6fx8XElEgmDMagAqEVo2F68eGHSCasHigRqTkBoamjWH3fRcDjU4eHho7oQayUqHPY2LPv6+rotFnt7e2bXIWbYCyjUGPvGtAfunmq16oYVEg7GHaBndnZWT548seIVxSlgaKfTUalUcp7J2NiYFSG1Ws0WNyb9YJ0ZHx9NRmq323r58qW/QzzagMtYDIINMI1sq9VSoVBwDVEoFHR4eGgJOOAE3+fz589tTQIQPjk5MTiL/aNYLFp1enJyoo2NDVUqFdVqNb1588bWX+wZDw+jZP6DgwPXMvv7+763qD3oRzgrf3z9sK/v1YQzBuDZs2d+cNVqVQcHB9rc3FSpVLIsj+RN6fP84iDjDWPSaDQcYMVBhUeJmZiSPCOTRcglR7DBwsKCyuWypXQg8zCUjExAHtrr9ezpjkQiHm2EzItCVBohdfh/YOs5yAgMaX8ao8Qmi8ViOjg4MIoHu1oul818UqyCfoEIc2lQoARlRiDDPI+PHz9aXrq0tGRGZ2pqykFMoKsLCwvKZrM+2N6+fetLBbkjB3i9XjcivrOz40s1n89rfX3dlwiFMpc7hx7PiPcAk0dBBavDn6MgI0SKkXEUaEEWCAluJDIaBdVqtR6N2uHCpjEslUqqVCpu8IJF7sTEhIGFarXqPAG8xBRkMMfBYLmxsTErFwjlCkqlUFBI0suXL3V5eant7W2PYAOZzufz9j1TQKD0QFlAqEyweL+8vLQPFB96UBFAAUuBlkgkHo31oPnr90czYqPRqPdLs9m0dLTZbDrAjGYDkGRiYhRQQrhVJpOxYmB+fl77+/suDCRZqs84GzIOUHHk83nPtmUPkFxNM48vGaUDgVbYSmgq8d4H2VTp86gb/lOtVvXw8KDj4/pGFvAAAQAASURBVGN/LtgMpPeSdHZ25nXAaBWYVRhnpLCAQYAISNDxeaO6Cc74hCkBNUeZEBzJNTk5aeAGxQMBihT/nGEg+BR7ePAYGTk9Pa1kMqn5+XnLLTc2NszwVCoV5fP5R+ciQWUwmlz0ACYAtABwFxcXarVaBi6q1arm5uac8oqMMBwO6+XLl56Ysba25ueHfYnGiD0Fu8yewQ4Ci0KAG1YEnnuwSZbkZ8geRMkACDEzM+OzBzaVIhXfKJ8lEolY8g7QScMAoMoe5jxhHw2HQ8uSAd8oFmkq8ICyD7BYETwE68yfpUhl3CLWJ2SdKCn4rhYWFvx9olIi/bhQKBgYBUxB2jkYDMxgt1otJRIJM1MAihTHx8fHZgl3dnYUjUadvJ1IJKxGoLnCpkFTwhkKkCjJzbgkK+zOzs60ubn5iHEmYDUI3gCyPnnyxIGFePkleV3DyB8eHtoeBwscbJLC4bCZZkYIoRrM5/PO3Li/v1c0GjUAxPPijiQ5GYXBxcWFp6qg+AB4p35CYhy04ExNTenw8NAWKvYTifXkMOTzeauIAPqmp0djtMhi4flgeSsWiwYTz8/PdXJy4kko33zzjWZmZpTJZLwu2XfstyDgRVL17OysFQCo85DcAywgJ+b8Zx2QJ8H75+y8uLhQsVh0xsmzZ89cD1KHNptNhUKj3Ird3V2fl9gjYOLfv39vUmNlZUX5fN53Mefg8vKyQ/y++uorxePxR5kUAKbUDHi8aZaoFwHSAcbD4VHAL9/VcDhUq9XS06dPneWB1Jjzp1wuuxYJNlew7LVazaQIyjOYfPZ7OBz2HUNY3mAwMNj2+vVr79dGo+HxckG7IaQIz4/cIECaUCjkjCnAHUZ4AaRB3AEqPHnyxIos9h0KQMB8VFVMD4EcCCrJSqXSo++MfXN1daXT01NJo4kFTHoC8CkWiw5rJjcEywfqVtSOS0tL2tra0u9//3uHVgbXIJ+HHgkCBEYdSTtWKNQ81B4QQOz3y8tLvXr1St99953zGFClApZwHgTrCfb/92GeeU8/5OuH/vn/p17fqwlnWHz706gyChUQVDxfzWZTT58+1dTUlC+2nZ0dPXv2TLu7u2q1WlpdXdXHjx/14sULLS8vm70gXfD9+/dKpVIqlUp6eHgwkkbh/OTJE8ViMb1//16tVsszO1OplJndTCZjJhdWhGYX/89wODSSuLW1ZdaJzUjBTOoryZX4LSnEadyYQ1ur1RyIwebg962urhqFRnZ1d3dnOROStqOjI8v7KMjwRbJJaG7wzyNB4XPjGYRBJ3ES9JFLHqkVjTPoniQz59KoiUJiiMxobm7OATf8blgm0qEzmYyRc8LjUFbQcBK2wRqbm5tTKpUy0gkwQrNxeXnpkCzeM4csRRaNG+sStgvGEi8qiD3qDtLOSYA/PT11g3N9fa3V1VUXO1z6FDmg4Y1Gw+BAJBLx7766urJHGDAKHx9AVDDxmuaDA31iYsIpuoz9ozALjtKiAOZn8kJmyXd5f3+vubk5r3E8nXxPSJbPz899UbJOQNZpVrrdrk5PT618QZoZfO5BrxLsfaVSecReM/pkamrKTNH19bWKxaL38mAwCpEbDoc6PT1VrzfyOx8cHFg6SuMVLIyDTVkQ7QVsaTabHseHHBF5LcEz6XTaKgMsOWQ4SDL7iQQVdQTSMPycklysS3IeBT4wSQ5smZiY8CxcQAZYOhhKfIUUNhTZ8/PzLiZI3qdwDFpMrq6udH5+rvPzc4cn0vD1ej0HCsISzczMmE2S9Gi2MX5SGAIYCGRvMCowEMvLy/4OkeshC2bdcV4F1x9FPf+ehjWoppDk9c7nmp2d9dlIgdjtdp0vwVkUZBTwMR8cHBgsYw0QTISsENtGcFZ0+9MIuuAoRhjRiYkJnwE8W851PJMUl6grYA0Z+0eQEUoG7oqbmxszveSTMN2Dgp+1wHlBQTk2NqbV1VWz3rCkFLsTExMuYrGfUPDRUMzMzLiZC4Z0JhIJs6icDdje2ON8z9w9gCKoN2DXkEIDVkciEZXLZZ/NgPGw7DDbNDsrKyv2xwdHDWJBgJ179eqVC+dwOKxUKiVJXqs8V/Y3ai387Pv7+w5uhEXm56GA4ZkyexkFIN5owtFgt1Cj0Tjjn+a7RR6PGoR1V6lUXHvRlJOvAavdarWsesNCMj8/r2Kx6HPh8vLSjTF33MzMjP/M9fW1FX0w2Xz+dDrtuc00+IyTJCX7yZMnj/IaJKlcLmtubk4rKysGdubm5rS1tWVg8MWLF1pZWVEul5Mkh+phs+D8r1Qqan+aNoE9DfUFjdbu7q5qtZrXaSwW08uXLzU2NqZEIqHT01ODr8ViUcfHxyZ5sIUAAHEvbW5u6ve//73v9WCAF9J9vvdcLudw3VgsppubG719+9aTFqiNUT5QQ3C3sBdh0IPWHlQ2U1NTXptYPYrFop48eaLvvvvOawwlCyAqqevYVeLxuFWli4uLznKh5iVfBfAURSf1E4AtFiUaaOwikG+ADrOzs6rX607V39jY8B3I+L7d3V3l83kDn+SroDiErCgWi7ZBUre1221Vq1WrgCRZRQTJwN4MWhLT6bQBvfHxcYM5wRFl1JAATolEQh8/fvSd87Of/UwPDw969+6dFcbHx8dWP62trT3KAuEsmZ2d1fHxsT8boBV1JTUeWRSpVEpPnz41qE8WxJ/z+lGO/q9/fa8mHAkesq+LiwvNzc0pkUgoFBqlW7LgScimiSiVSk4ah70Ih8NmekFG8Wd2u10dHh5aPosPBQlzLpezLwVmNRaL6fj4WJIsq+LAi8fjOj4+9p8F/cvlch75wyxLmhhYTWS7eDlhhZF6gqByYDKDcX19XTMzM9re3tbl5aWlQXweNgcIJIUasvfl5WUjkBQ/0iiABSkd7Jsk++KCPnwCkmBmSW2lkCd8RRr5709PT9XpdOx14XC/v79XoVBwsjkSf4ppilG86UEvPjPapc/zsUkhp7GAZeXzcsBTeHFYzM3NuWgISoOHw6El4nNzcz4cKTpzuZz/DEULknCKEwrhpaUl3dzcuMCD+aR547LAW8iFDlABukniKqE3SGDxUuE9zOVyjxQRqD9YS8lk0hc0rBpqCQp8GjWAGLICYOX4nLDkNKRITmEqIpGIjo+PPdVgcnLSe5oZ6e1PM9EXFxcf5SdQaCE93d/f93xjWFRSgkOhkPb29h5ddvj2aDC4LBuNhgqFwqOGAp9fo9Hw5cnZAniAUgbfMn5GVBScDRRjeMsAt5aXl7W1tWWE+P7+XisrK1pYWHDCMwg3I4sASgimoxHk1ev1HBQFAxqNRnV2duZzJVjAUrxwBtE0vH79WtKokL6+vlY8Hrf9gTFkgEikWrdarUf+ephUciHi8bg+fvyofr9vRQNqCYob2EYue0KB2CeocPB4w0zA5MP40hDDCN/c3Ghvb0+lUslMAuopfhdNGfJwGCQYmqC0HuUEBRbrgs/AmuFMwqPJ+YBfEOCv3W7ryZMnDt4K2j9gAQgrwtYTbKTD4bAbU2SqknxWUpjymUkAp0hMJpPe64AFNE18TlRJTOQAEKWhQEkBo4zaBZASEBGrUT6ft3QYAJxmGTYc+ThgCN/hwsKC59AfHh7q4uLCexiGajgcWm4di8X8nXP2Ukz2+32vjeXlZQM0ALGAoMFAMuqL8/NzA7WSHDDH/Y0igHvp7u5Or1+/1uXlpWq1mpaWlmyho7nh/qZpRoIOoM7+A4Th/UnS4eGhs1F6vVHmCMnbeKaj0aiTvclt2N/f98gwmLdIJGJ5OiPUuPdRbgBKUGuhysOqQDL5ysqKrVL428mvYW3T/Af9q5y1AMWoCFFaAeZPT09rfX3dCgLOAtYtY0iZ04w3GNYSaS+TZgCdaBY4U2jcqQsgR7BtwRJzB8KMQyCwnlmjg8HAifHtdluhUMjjvlB9BPNZGNu7t7en58+fW+1YLBa1v7+vVCrlJo1xpHd3dwbl+v2+yRgUaKhcJFmNAnAeCoU8AYi8Ds4MFEbsOaTtZDVxLwHuRSKj6SYAVbwP6rBUKqWPHz+6dqCeCNYmgO+1Wk2zs7N6+vSp82y4O4NKVHIPNjc3rQKgLkWRQwDp1NRoVvby8rK/w/anyR0ofph+Ui6X1el03FBLcpAuE1nIToBkAcCkrsX+gdrx7OzMagSAoFqtplKppGq16mBTSV5/5Cbl83nt7u56rdJHcE6gAIF04+d3Oh394Q9/ULFYVCqVsrqA2jJIYPH+ID6urq60s7NjAgOgjBpweXnZRMZf/MVfGJABsP3jH//4/7EX/PH1/97rezXh0mdp+W9+8xulUikX5rARjGu4ubnRzs6O5eEUDIeHh5Lk8CY8W8Vi0aMNuOS4xPAoU9heXl5qd3fXjA6epVwup/fv3+vu7s5prRRuzArlfyPxXVtbUz6ft58q2CjMzs7q9vZWHz58sJyUAgZkGcku0rhKpWKkstvt6uPHj079JtwEprPVarnoQuZMM0jji6x4MBhYJsthgdeVDYt0hYuYRuTu7s4eS0alIaci0Cj6aZYlBSBSGA4wClw2KmE9XAgcNM1m00nZHOhra2uKxWJObCcBnEMIiRbSq7OzMzcnFMQk2iJTJkyC4j5YrHPIXF9fKxQKOVSIP08hRiPC5+JS4nnRdCNbJLyDYiOIbOP9paBgPAf5CDMzM75gpFGjzhxrgAu89GQCwG7DwEoyO0HTxLM5OTmx8iPIUN/d3WlpaclNDJcEDCGNSRCBZ90jQ+PykuR/TtEAIEDaKcU5/kFGqiF35rusVCpO4w+uIfY8IVx3d6N5tQB9NJn4tGmaJPncYT/QZAMyUTTc3t4aMJPksDIKKWTu8Xhca2tr3m+Li4tOWUX2L8m/k8/P90xRT0NEsi9hL0jD2X8oSpCeAYKwrpCYETg5MzPj74ECE3ABoII9lUgk3CifnJz4OYyNjblYJpBnampK+/v7yufzymazfj6cKbBugG8UkgsLC57tLckFC/sD9hrlT5CpfngYjWppf0pTB+CBZaYA4xzCDgQQe3d3ZwYb8AMGb3x83N5L6TMjzj+HuZudnfXoLc5cWEyCmSjWs9msJdn4a3mGQdltEBAi3wL2FJkhMlksGoz9mZ+f1/HxsW5ubnw+w+JhDcMWwFnJWpJG7BnWGNZJLpcz6NL+lI7PaDpSjOfm5vw9ohIACAZQYSoFjDohWDCqZKUQ9IUSBrUJjcL19bXzIS4vL33Wca8BwPLdsha73a6bUgBzGn+CSufm5vTy5UuHN3KuAi7B9sMKopwiQ4EiNRQK6cOHD/4cNLU0aTTZeDo5w8gwQIGFjS14l97f3zsICjUa5/r6+rrPcWxtNKtI19l/AF58DkBxzjaeB8GZgCkQIoVCwQAlwAYBjQsLC4/kzOQyfPjwwTasRCJhsKjf7/vvcGZwT9Bo0IhQC7bbbQMJ3K0wkHy/fE+oTQDXSLyfm5vT6empAypZB53OaHQtezeXyxnMAZxZWVlxAw+wjjJsd3fXqr2VlRVJUjqdVqczGldGUwiICqP/3XffKZPJaH193TlBHz580NHRkV6/fu06jDOCc+7y8lKJREKrq6u+w6iX4vG4/vCHP2hyclL5fF7D4dAqCQDc2dlZK1Ti8bjBaGoh1BKoAFgzwXOv0+no7du3rr9Z64QoBu+by8tLWwt574VCwaNiUXLQ2AIuEVCM6gFAcmpqSvl83oD48fGxcwGC3xMKL9YLZxfvjRF+yWRS0WhU6XTa+Q2EsLbbbfcGfNeQI9gQqJGoj6mLYLC5NwE0OccjkYhWV1dVqVQ8CSnohX/27JmTyyFg8NWzV2CvAZPan0Yy3t/fa21tTcPhUAcHB6pWq1b8zs7O6vT0VLu7u1431Ho07aurq/r1r39tq1zwjN/a2rI17899/ciE/+tf36sJp0CjAIpGoxoORzOAkQ3Nz887/e/w8NCBPTc3N/rpT3+qYrGoer1u3y4bnIYU9gGpW7/ftyyaBlPSoyAV/KoXFxeKRqNaW1vzQRKLxYw+cVAR7rC5ualf/vKXljUjxWNDwfa9f/9ew+FQhULBxSwHGEgkhwWLGa8eG3JpaUkLCwtWEdRqNc/iBqEGaaVAIKwDmRVoPKgzUmPkgUg/o9Gow0KQH11eXiqbzdozSQFEUyeNFnk6nTYLdnV1ZSUCz53kViS5gAmkj5O22Gg0lEqltL6+roeHBwfQ0IjNzs569EM8HtdgMHBIH4cP0n/WXvBgouhnTSLBDMpxKHw4YGHZOeQeHh48+mJiYsK+K9j/yclJNRoNI8L45ikGYF+QKSJzxR+EdA6Ahfc/OTlpBQjjgvD2wVTV63WvHdDNo6MjLS0tWSJ5f/85XZzDFaCJQCYucBjB4PcTDo/C52BQ2HesRxjpiYkJyxaR7xcKBW1vbz9Kc+31ep51T+AWM+hPTk5c9AVD9bh0S6WSQQ2YJJBlmm8SoZl1DUMFQIdsnu+d5g95NkDLysqKJiYmVC6XzWaFQqME942NjUeeXr7nTCbzaM/s7OxYPo3vmeArvOOkTJPHAKNNY0yBiGIAUJB1dHR05KBLfL8rKyuKfpojjI0EdVI2m3WC+t7entUYFC2vX79WvV7Xu3fvDPIgN65Wq2YmYFAajYZOT099LtOEwMBhkRkOh8pkMlpaWrICCSCL4k0aNeUUZuxX/v74+Liy2axqtZqfO8UPXl4ARYpyWH0UAIPBwOsTexQjcTqdjhky7hAYJs70ubk5g3OEH8LsECjFfoQt4P1xZgff32AwUK1W87rF2lKr1Wy9wsKBvYDzEfVS+1PYKXclKh9UIpzd3J8AABRkjPwj0I0QL85F1i//G88h3nYYXopN8lcIHOVcg6Gh0ET6jWIHNgv5O8+Ke501wufh51BfMOcaQBmPOyGRMJgkeiOp5d4CqFpfX7eaYGJiwvsa7+nExIQnDCAPxY4RiUSUSCQevTeAvW63a2aXegJlAX5v1jyNMxJvnif3INkGfB8EnKHGY9/wPQVtaTQ2s7Oztubx2XkOKNloNABCYfqxxEijKSQAPUxyyOVy6nQ6BugAEyR53bNXb29vnffC+0X9Qo1Dc0CzPDY2CmVj3jj2v2AWCnc52SpHR0eeqUzTTwODVW5yctKjWgGemNXM+w4G9UEksTcLhYInwdRqNVsV+d5Ru+VyOWWzWW1tbRloxjL13//7fzfD+vz5czfDBFwCTDLdgnsIlVk8Htfbt2/9vWEfQsW5srLiYD9UkKxLWHbsVUjWsfCgaFhdXfWZhLKF/cXvIRcG4K79aWxwsVjU/Py8KpWK1xOKrNPTU88mZ5Qq+5vng0ceAkWSx8gyLnZyclLPnj3T3d1oHBnnNMTe3d1oStP6+rpBAYguAFRYdsAwQtAODg78vkKhkJaXl71+UINwf0E+osLFhohKC2XQ+fm5xsbGtLGxIUmu7eLxuE5PT/2/8WN//PhRktx4U98R2vuP//iPGgwG+vDhg/cTwCR1M8AxQAxgHKoPwngBDPnevv76a11cXOj/+r/+L5/FP75+2Nf3nhOOvPbq6kpbW1sqlUpaWlrS119/rdXVVfX7fRe3zK3kckQSdHt767mCFGFzc3MqFApOKgQ5/dMQC6Q/IFTz8/OqVquP2GTm6El6NIIJFujh4cFBVMh/2XStVkvS53Ez29vbRrMpFpED0mTDbMzPzxvRPzs70+XlpZaXl81GEBp1e3ur3d3dR34UvJbB5HnY5fPzc1+SsHh4R9hAwU3I88lmsy5okW9XKhXNzc0pm83q7OzMfhu85re3tw5aQZJI0Ya07ebmxgFfIP5IaJHM4OfkwMVfyIXO90gQEawmKaXI3vizMJ7X19d+1lzEoOJ4k1lXQbSfw4qiJZggjLViMBhYlsXPwGfJ94RPkCaXwxiJL/JESQZzYOCDMnCCQUBUM5mMfyZ5AuPj4740SYaFhQyy3aztdrvt2c0U0rwnChj+PvYPfLkUAlhMGOtzcXHh9cCzy+Vyur+/d/rocDj0hRcKhcxgJpNJWyv6/dFoDJjJYAPD7EtkxXhHYd5gX9lDNBugu7Au7BnWA/+ez8elTtEsjQLzGo2Gi8nop5n3+Fjfv3+vfr+vUqnk4hfAg+AVpP8AK+vr625S7+/vnaZ8e3urarXqYhaWh/RdQoa4PFEfYFfJ5XI6Pj7W+/fvlc/n/WwkGb2/ubnxmJmf/OQnSiQS+uMf/+h53BQTNI6cjbBfQS8vZyJeNgoC9hxMW7fb9ZiaRqOhVqvlf05xDihCcc99gkKFlPrj42Otrq66sCKkkzUMeMRzRnlCwciYRwpkJIunp6cGZweDgYtcihMY94uLCwdnAV7iJ6Y5Dno3B4PBo1Rq8izwriN37vf7TmoGnOHMxU4ULORpFGEokTMDlHHmBJmVoJQYxpE7FGYMry9sJgU1ewIZKaoqFBMAtDxjmFLsWzS4ZBPACvMM2SewGZzhWLxgmAHWb25uPAM5eN5xtzw8jBKZGRkU9DvyXQE+MYM8l8s5ZZ6ATp476/D29lbJZNJqFwBWGlbOS94TP4MGm5nqZMYEJbYLCwsaGxvlugAecdfz3cBK0rQCVAbPHjzWTCcg5JHnx+fm7ADc5P0SbJpMJnVwcGA1w8zMjPM3YPwAa7hvUYegyMnn82o0GlaoAUJRQwFw0AhCCgBsAmTv7e1Ztg0Q3Gg0VCwWdXd3p0Qi4fBMbAso6LC0BbMCqLVggNkf3EVbW1vOrUHpxPNAUcVznJ6etvyXOgu2FdUHoYaQPsju7+/vtbOzow8fPlgWTI20vLzsGeSQFPPz81pYWFC1WrXyERUHXm7uBe59lF4oNwDdaG5RDGDxo47iHIe9vbi40NOnTxWNRr0nJT0ai3h8fKzLy0vn9aBuur29ValU0pdffmmiotVqedY0IGk8Hlf7Uwo8pA13MVYdlATs0/HxcTerhIbVajVFo1GDpAR9hkIh19/SaJrM3t6ewWNCAmu1mmvOn/zkJwY5qO0nJib0+vVrpdNpBwWiWiXMFL896t1UKuVRvDMzMzo4OPA420KhYEvOcDjKocICJclScfqPwWBgOwggfqfTsY02eK5yf3N28H2nUinF43HlcjlbTVDzAl5BHu7v72t7e1uDwUBffPGFOp2O/u7v/u7P7Ax/ZML/n7y+VxNOMTszM2OpTrlcNrNMU9Pv982eUgTd399rf39f6+vrDrEgBOLZs2f2aVJUUWzAvnJpSnLzSvOJDIQxS/1+378ziFRXq1X7zUhyrVarj6TnbMBIJKLf/va3GgwG+tWvfuWgDHxE+HuPj48t94UF/vjxowaDUVpsMplUq9VyU0cBlUwmlclkdHl56cRgks9hayniYYKQzyDzpjgE2SK8AiQ3eGE1Gg1v1tnZ0exGmkCkQjCGBAVxyMOg4xk6PT11kXB3d+fRCYzAwpLASDh+Lwjd4uKivWk0BRz8XJjBUDKKE0n2E0tyM0kTROALvnWkazC/KAlQMdCQAypwgeHVRVYHa4NfGRCH90TDiGyUpoDGRZIv7kql4qRWGnIKES6AVCqllZUVffz4UQcHB56tPTU1ZRsGbEO5XFY0GnUyKY28JPsGpc8MBYwXrDjMMcwA8kzC8Jir2ul0VKlUtLy8bEABhpvgovX1de+/UCik3d1drxvkquxJWFEAAwLLrq+vPR8a5oFUUi5cnv/e3p6LO8Z3AepJn2WqfM/8JyhLrtfr/s7Yn4Al4+OjcTKcGa1Wy+E74+PjHgUmfZ6/TCOXzWZ1fHxsAAEpKBMCKNZZa//8z//spHyAS6RnSJH39vY8gi0SiTgJNRqNepZruVw2G7O7u2sW7OrqStvb24+aLhpRzljWFABn8LuEeeEeCH6vMJPhcFinp6cGAoONM00MYBmXNudI+1NCfq/X891A2Bd7kUaHPTs7O2uWmd8Vi8WcG4D1YHx8NFYTNQLrlyIZQAJPNf7DycnR7HVY/SB7GAy5xMdH8CCsBZJbGjfYvHg8rsvLS48iJOiJ0TOsE+Ts/BlAFsZEccbCzqEIojHgDOCeADgi42BqasoJ3Pwz9iJSYEBWFCEoPvge+UycJ4DwGxsb3msAmYBy7HuanHQ6bTat/Sm4juaf38XZT0Iw8n3uy6mpKS0vL9seFAzaRDpMY1QoFNw8c5fReNNAYt+hiUeGHQy9enh48J0B2wuwdXZ25rqg2Wy6oeU+Y20AdnNHEnhFTgF+aGoSzhlJziw5PT1VPB53EwroxMSYYMGPImF8fNzPMpVKWd3FuqZpgukNsoYoEADl+v2+arWa7u7uPAIKxQlMMHY4QBSsJajkCHi7ublxrUeW0OXlpSqVilKplLa3t/Vf/st/sark3//7f291XjKZdMNE00guEbPKM5mMzxzCNFH0wGRSh7JX0+m0FSoAxTSEgHTJZFKpVMo2AeyRgMv//M//rG63q3Q6rc3NTRUKBSvs7u9HIcHJZNJ7hLUHoQKzGlRrsra4UwjMJQAUIPri4sK5MmRZsP+paQELsUABdJOfcX19bTIkn88b7CBgDZ90s9nU1taWFhcXNTk5qWw2q8PDQwNZhMBtb2/b4od6bGNjQ7lcTh8+fHCGEnkr1A2zs7NaW1vz88Haxv4CfODZ8meCYy2xWaByxMoD0F4ulw26kpsRi8WsOgR0w0JJIwuzTI5CUAnDi7r99vZW3377rc9pzh0ARNht6qOvvvrKBBAkBmpV7lhA73g8rn/zb/6NCoWCc5oIJm6321Y+npyc6Pz83MBdJpNRJpPRP/3TPxlY+PH1w7++VxMOogL7i38LTyjBX4Q4jY2N6ejoyAuPEAMQYeTCBwcHlgPjh0SyAXPGiBCYXJhyGE2aIgp8Aim4TJDLIFVLp9Nm6iUZFeOyHRsbzbtG/oykmcaSUKZSqeTL8Pb2Vtvb2+r3+9rc3HToCh7Z6elps/Zra2uP5Nh4GweDgeUreL7X1tbMkHChILfG74nPM5fL6eDgQIVCwUUbMibkbgAIfHdcvjTwXPQcYoPBwH5LmLPV1VU3eJJcrCEdnJmZ0cbGhuX2s7OzlmXhexofH7fUqFarKZ/Pq1wuu1CC7UQmvru7q4eHB21ubvr98r3ATnFJEiCFZB9/IQUvzxyVBQyXNEJPg8oCkPDgmiJshvUSlJ0BEgU9ccg+aeCDQUBcgisrK5YZSzLLzXrAB1StVu0XQ85MkMvU1JS94VwAQVVC0AOPQoLmJhQaJZJyQcHGfffddy4maFxg97n83r9/r5cvX3q8UzKZ1Nu3b9X+NHrs+vpaR0dHOjw8dBGEp4sCA6ZEksN5JFl2C8Key+Us5X/37t2jJpvGi8td+szCwgpSzNIk0MTxTJhTSvEsyWg964DGkPVGM8ef/eabb1QoFLx3ZmdnlUgkvIfGxsYUi8VULBY1Pj6aZZ7NZg2MMPEhm80aDMNv2Ov19OHDB0lykTY9Pa2PHz/6/XIu8zy5pAlM5DlygXMGUCxJnwvvP23KOSP4D+Dc7Oysx87AtvF9U7ACZLF++N3S58ZnfHxcmUzGjbEkvy987Yw+Cq4dvOTI0/EoBqc94IGlMWbtIMG/vLxUsVj078Zv+adJ7RSge3t7WllZMRCNhYP1gIoK9RasXTQadSMZBFiDidiJRMJySt4LEzlOTk6UzWYl6RGYATNFQB5sBw0Qqi0sHJIe2QpCoZDHeEmjopHGl/MSlVjQMwkz1Ov1HIZ0cHDg6QL4qAnFIuiIs+rh4cGhhtgCOJ+mpqZsESLEMxwOG2ydnp5WPB53s4EaqdvtPrJSEcKFPJ1RhyivgtkRBLeFQiH/7KC6A9k5AEZQIcJeoxYaGxsFhqXTaX9fQWUUeQbksSQSCfs6mQjB2QvThwoFfyrhV3d3d57usLe390iGSqN1e3trOT+/S5IBekn2+wMG4cfNZDJujlhv0miE7NHRkRUg7MtyuazXr18/8tFjlyMXAoIDu9OTJ08cyEpDR6NGc81aA3i4ubnxxBHOWpqPh4fRvHXuPs43gK18Pq+ZmRkdHh663guuUxpSWGPWIrYqyJyLiwu9fPnS5w8WvZ///Oe+Kwhgvby81FdffeV1NBiM7HikgcN693o9LS0tWQlarVY9cYYsCjIhgtNLGGvY6XT8XQCYjI2NgibJh2GNUBMi9ydTg7NwMBiN0CLQDMCx1+t5Jnn70/hdAPH9/X1/59TMgFmoANh7eMZ5j1NTo1Gp0kgRks1mnYfCtBFqJO6R9qcRoNfX13rz5o3tF7wfVE7B/JmdnR01m02POSTcDSsYClHqYSZJEIjHM4SE29/f936lFiXXgsR4QAzUTKhNyMZiZF3QogCgElSXAdJJn21byWTSqiQIAMBjRg12u12f57/85S+VyWT0u9/9Tv/wD/+giYkJhwr+ua8fmfB//et7NeFI7bj0WdSpVErNZtOSGcZ0ff311/bhIV39+PGjD8fFxUVdXl6aORoOh0qlUp5hy6IMzr4MzrQNBhHU63VdXl7ql7/8pRlXmrTr62tVKhX1+6PUX1jwhYUFzwINh8MOlkGaBSPFZoFZnJubc5pxs9k0s4+c9Pnz5z7MQJ7xuJKCzoU9OTmpTCZjVrbb7T4q1PFfsRFhxTKZjGWgqVTKIAjhZ7xPflculzOaCTIXRPSGw6ElXDQwQXk1hy4N9vj4uIELigFSGQkNYcQCniMugWDzH4lE3IhRoHMpUWzMzs6qUqm4EOx0OgY4JD0qBoMsOeuMA5pmZ21tTY1Gw2wX7431hAcK9q/VarlZrdVqGgxGiZI0afjzYfE58KXPs4JJmV5cXHQQEHJpmAMO08vLS93c3CibzfpiqFarVj3w/QHo5PN5I+E0CyRMw6LV63UnbPO98x5pRGgsgwwqUrRWq2XmrlqtelIBCDsXLZJOUGfCwSYnJ/WTn/zEDcnZ2Zlubm60tLSkcrnsfQqT1+l0LM2/u7vT0dGRzyFAPWalwjDx3GdnZ/2eaBphusfHx5VOpx+x4bC2sAxBiTVFP3NJsQlcXFw4VI9gE2SmrAGkY/wzArIANChWadI7nY59r4z5KpfLRrNplgAsJTn1lDwJ9jRNFFJX1iasYij0OdAQ2SisVDBDAUkuHjP2MMUHDXin03nE4ILa87s4i3kmklykBFlUAA7eK6FCgARBoCuYGtxut11wIMtD0kwGBSqm+/t7F1KsByS+pVLJ4wC5G2Dd+V08XwBdCihkrJyJrEUKYlhPmqZyuayVlRUdHh5aJso67vV6Pl/4zkgBxo9KsYpth30AEBCNRr2WScEvlUq2DiHNvb+/19nZmQqFgm0RrNlsNmsGDHY1FovZi80YPCwsFMUw9pwzCwsLDpLjO7y8vHT+CDYyEue5G2lOg2nCMLnshX6/b78osn++bxrLTqdj37/02TPPrGSsOBTcBHAC6gHqcFbc3987KA51FzLyUCikjY0NPyPO4Waz6RoEUoLGDBXX8+fPNRiM8iA4x7nb2BeATnzXNL7IsMPhsCX4TEVh3fV6PaXTaTPWnBNYsQDikLuenZ1ZYSFJ7969s1WGvIJoNKpWq2XJKywrTDnsMkApYCVn0szMjIO8lpeX7buHdCGzAWXamzdv3MC3220tLy87bJca4fb21p+T9U69gBWMcWk0k41Gw+GLk5OjdPBsNqvd3V3fH0z9AdBLJpNaXFx0kKgkZyHB4N7d3ent27e2h8Tjce3s7Fi9BZOdTCbVbDZ1e3vraQRYwQCM//Zv/1bD4Wgud6FQ8D0G+1qv15XP55XP561C4V7jfGUmdzCHiKkO2A6ur6/97wHG+v2+JfXD4VArKyuPzi3215dffmn1CAGhrLFqtWq7KecV9wkqwUajIWkUTAjghU0zmPiNYgYlBQFsMzMz+vDhg2sY6l/Ow6OjI9ccnG3c39S++LMfHh60s7Ojk5MTAzmc6Q8PD1pdXdX09LT29vZMKm5sbOj4+FjValXxeFz7+/saDod6+fKlz575+XkD7EyjQSbfaDQUiUSUzWYfjf5DkRAkm9hHMzMz+vLLL13PhkIhnZ6eql6v682bNyYiqanC4bCePn2qFy9eqFar6X/9r/+ljx8/6vXr186VYT3/+PphX9+rCWc2aj6f1/j4uBYWFhyAcnx8rF/+8pdqNBpKJpOq1+vq9/ue47i+vq5yuaydnR3lcjk3yUhZCCtDhn50dGQPB8USzCjFsCQf9oTvVKtVh1zRqB8fH3uj5vN5S4BAuxcXFy3vQ5qFnJCLfG1tTdls1u8BKXkmk1Gj0XAxnM1mVSqVHCQBUhlM2F1aWrJsheYDppz5zYPBQCsrK0ZZKQaurq707NkzTUxM+MAmiGU4HFrGRGPI74VlQUJcLBY1PT2tdrttn19QIh2Pxy3Vn5ubezQrPBKJOEGeJigWi1naA8I6Nzdn1LJUKhnJg3VBsofvm2eItQA/XvtTYjI/m+8RFi7IyNEoSDLIg3WBS7per6vVatmHHQzfCfqspVEg08TEhEP/WFeSfHkijQNYQKZKcBqX6uXlpb/Hm5sbJwnDzPDz+fOvXr0ykouNgfmn4+PjvkwBLkC+x8fHtb+/73m7MAzILfmeYCFoKmBQUEwcHR15b/Bd4qMjuAY2BRl7NpvV9fW1Tk5O3Kjxc2GdULAQFEQIY71elyRfnIBvoLgUbQAxgBiAL6gmKDb5O8EcAEn2yUkySAQCHpSM0iAeHR1ZCYJ8kVF+MBSwc4SbIU1fWFiwhQMlBIz92NiY8vm8KpWKpqen/WdgiZGH0+TzvcDoAxQCwNAk0AzxOQHNAHP4TsldgM1AasozRmrOfuKcvru783uSZJAQiT0FN88LoIHvFNBramrKQACgC802zwyFCjYUGKXgM4dJyGQylopSRCHNRPnBORFMxGYsJLYmSVYiESQkfZ4xTLAVagNSqmEDYLxpljm3CNji/MOqQ4NFs4OXD68zoaelUsmeaexbeFVheycnJ3VycuKzGXZfkjKZjM7Pz3V8fKy5uTllMhmHZbIWYH9RFuFDDDaANzc3WlxctEyfeyyYy0L2wcTE5xF8QXZxMBjo6dOntlaRfYEfGVCT+4CfUy6X/axDoZAVFqHQKDSTu50QxeAZy8/hXoaBJbMFVVRQrs1ZG4vFbL0BCKKpIdBpdnZW+Xze6gcUHdls9hFgy7qNfkqxx3ZGbVOpVOw9hQEbHx83yzgcDs0YYoU4Pj5+NJ4L73hQ6cfZwNlFtsft7a1tN5LcYBJ4x1lFM8a4Ue4SGolEIuFZyTRgNHTBkXmQDcH9TeiUJIP9NFnValWFQkHSCGwDzE6n0w4eZI8FmyqmqpBBBGOcy+X8u3q90egzlBkAUKhTotGoSqWSTk5OvOebzaaazeajzBJAYcC/y8tL7ezsPLLiYBtMJBK6uLhQIpEwmLWysuIzGmsWLOrk5Ci1+m/+5m+0vb3t5p5U+6WlJe9XLHqAIUELKZ+X/7RaLZ9dQdKAnBWUSzTJkvx7Ua3VajVtbW1ZIbi0tPQIDELpCNlDfgagE9N6kHNDnBDszNkLcE1QIMDPxcWFlpeX9ZOf/MS2GQihWq0mSfrJT35iBQyBxb1ezyqffr+vbDarSqXi2gdbGd8FJNbDw4OVqaurq/bSkzfAOh0bG3P9iuoKsBUW/osvvtDHjx81Nzen58+fKxwOm6QEiGdiBPcp9wS9QrfbVT6fVyKRcAo/ACRsPLbeh4cHjzedn5/XycmJ/tt/+2/a3t5WPB7Xixcv9OzZM+3t7Tlb5c99cXb9kK8f+uf/n3p9ryY8GCDEpfnNN99Y3kRKuiTFYjG9evVKp6enPqRhlHO5nGq1miWFIDcszna7rUgkYsnzzc2NL3AuSS4AXnhZ9vb2/m/2/iw22jTN64T/4di8O7zEHrbD67vnm1mZZHc1jRBiRCM29UkLccJoDuYMiVFLIw1oNmmQEEKMGLGoxQGHiE2CEySgKYG6e6qoJdd3tf16jc2OCNvh3eFwOL6DyN8/H2fDR2V9Uwx8qpBKlfmm33DE89zPfV/Xf7t8yCHbwweD33J2dla7u7u6vb11yBlSNaS2jP+h0EBOw3+j+CNhc2pqyn7Era0tswP8XVI7kZUGNzK8SBTZ+LJ4qGFAKWC//PJLPXjwQNFof2TGN5HEhYUF+5iQ/8Do4ZsKhfr+5Lu7fsAORRTIOMVmJNIfeVSv1zU7O2tWKBgwRKIuDzthc7e3t5ZdwnYFPanJZFK1Wk3pdFpjY2NudBqNhtcFoScAILAQNGP4mWGQKPCQ+jMSqlarqVarKRaLOdCHA5XCmYY0ePjzuaanp9VqtVyoUdB1u12P6EH9IMkHKNe90WiYoYhEIvZWI2NkvXIYnJ6eOvhwbW1NuVzOa5U1RKomjQ/rk2KJa4LsEp83mznNFWjrzs6OUWC8/gA/3F9YHT5rPB7Xzs6O/Yurq6sebUUhTlFJ1gFsF6NBYKE4nFlfQWYQaXOwQaZ44JBHikcDBwPDdeYgJpGWhozPxXMdDELicN3b23Ohwnel2YSFAsCanZ21lxhGCxno7e2tw1QAK5Ego6ShaYZFnJiY8DiS9fV1bW5uSpKbdTICFhcXXdBTENLwjY2NaXV1Vfl8Xuvr6/fCsJDLIrmT5OIMOw+e106n42I62FwHg3yCdiBJvkY8m+wp7OUoM2AIAFWDAMH+/r6/G/smzyFAJ0WoJO+9gI/4+WAsg018PB63zQigaXR01M8qCebkHdzc3LgR4cyiKbu4uPC4JsZUcj9QdzF2a2VlxeuXa8I6YS2Rck0zxkxdmtBms+l9h3swMzNzz0eJuor1zTmASuv6+trvBzMWDIALKrfYl/k98Xh/EggNweHhoQO6QqGQAQtew8PDPrc5g5mPLfWLfBQOAC6Dg4Pa2tryNA7+O0xXqVRSoVBwo5zJZNT6atzP/Py8ZaUEGfHc0rCmUimnPF9fX1tpx/PX7fbnkWezWZ2cnBiMIHwNADvx1SQCAF++a7fbVaFQsLqNRglPLHYSAOuTkxPLSXkGAFwpyoPPRTab1fHxsYGk09NTs7s060jJg+OZaFqDIC55LfV63TULa519k+Yml8uZEEE5AKATvMbZbPYey0gQHD8D4FGpVMw+RqPRe8BmPp93GjsWsKWlJVsi8fxyxp2dnenzzz/Xhx9+6MaL5xblHYDp9PS0fvKTnziU9MGDB1b/nZ2dqdvtWsUFMZFMJtXpdDQ4OOjRkY8fPzYQzPOF4rLX69kutr+/b58yexlMf6PRMCj8zT1zcXFRc3NzlmXDWlObYg2gZmG/lGTlCI0veSScj5yHnHHcl0QiYRAh6J2Px+NWaQVBVPaUwcFBs+CPHj1Sq9VyGDEA8dDQkK187Lf82dDQkOr1urNnUMxS+1NnEWbHvkwQXLFYdA3LNUVBwvjPUCikx48fq1KpmOQjSyqRSHj/IRz54OBADx48cN4IWUCsyeCkiqOjI4VCIasGm82mCoWCZmZmHH6JMmpoaEivXr0yuEbWBeAygCSgKflAgHhBOyy2KFj5cDisL7/80gDEr/zKr2hiYkL/9t/+W62trVm1c3PTHxH6+vVrEz3BENVfvH5+r2/VhJ+fn+vJkydmuefn53VycqKnT58aSSfFl0ODNONoNKrHjx9rcXHRUlQkgEjtZmdnHc7CRkZ4yMnJiY6Pj73RBOVNIFoUY6B/jUbDkuZMJuPxUEhLY7GY09KD0l0e3qurK9XrdWUyGW+QMLYU9bAlbBb4hpA/0zxQCOMpvbm5cREei8WUy+XskYGVxcPBIXJ8fKxGo2FpCijp+vq6ZmZmfLjSQEajUdsEmLWMjwkW/ezszEwEBef5+bm2trascqCI5VCG9YYFIiCCQgJWmOJycHDQh/rIyIj9PldXV0omk5ZUUbjSCLIB0bRT4CANZKNnYyYUB7Yp6A2kmeGFfBRUGqkVzDWJlqRX8t9hhPAd8bk7nY6bmru7O6sjcrmcmTEkcJubm2YgGOWG3H1nZ8ef/fLyUu/evXNTEGQOgw0gYAQ+slQqpYuLC7PTSIRPT0+tDKCIBDXFL0/xT2Db+fm5stmsiwJmyFMAU+zR4BCAJMmywunpaR0cHKjT6aeP397eOhmc9QJLVygU3PwgQbu+vtbm5qaRaL43659CjOYBNJg1w37B4cQhzLNKMyfJhR/N/8DAgIEO2ATYDw5jSZb2Pnz40KAVDc3AwIDZOBpUmq/h4WENDw+rUCjo6OjII7Vgo2lYsLZQACWTScv2Dg8PPW4MWWlwVnck0g9xY/wK13BkZEQrKyseY/Tw4UONjo468I6AIeTfwUINJoXmk6aaw57GlOdF+hrJ5t+5b0NDQ7aIBGcfo2ABUIAlogFDeooXkPUGE8MZQ+MYlNOzf/PsSvLeWavVfK3L5bJmZ2cN/sH6M9IKBpXRdXw/QMBOp2PbRDQaNQCD9DAIiFFsXVxcWFba+mqkJXkIhGwxmojUXUKbkCDC7ME8I6FEOZNKpQxCoiZi/fP8UFyjoGm1Wjo4OLBtCC83+xe2BO4DexkSTtQpvV5PlUpFh4eHKhQKrhVgiynUAWrxeaJA4f5zdvCeMNFB9RA2qlKpZMXKwcGB5ufn7eeGPMDWASAJU8w1vLq60snJieLxfmI/Evlg8BIgx8rKirNbyBgBqMPaxvmEVJwgJxLBqVNaX41OS6VSVjsEm6pgbkK5XLbNin+nrjk9PbVnnr0H3zc+5lAopHq97vwBFA+cxWSMoDaBRMjlcgZMpK+ZdxR3gGdYwoKy/qBFhv281+vZ3w1TCkifyWScM4MvGHa3UCg4VArQH6CD/SCZTPpe3t3dWYGUSCT09OlTg/qMjsvlcrq8vNTw8LDrVPZmLCLb29v3bAPkCZF/A+NdrVaVyWR0fHyseDyu+fn5e88ezCt5DliEwuGw3nvvPZMgnNsoK1AkHB0dGeggMyGbzRoQxZqBCo0/5zmhtmKfYG9ir0a5SmhjLBbTw4cPneFEknc+n9fOzo7VNdPT09re3lYmk/E+zyhb9ilUZciuAWM556vVqpUUKMDa7baOj4/15s0bN9Hz8/Oan5/Xzc2NPv30U4OEyWRS5XJZqVTK4155TnZ3d73PoIQAEKUuA2TD1hOLxewF7/V6zrZiX6dewxaxurpqZVMs1h+RSkhlMEeFFxlZJMLPz88rkUh4LRG0Njc3d28cIeQjtRejSwcGBvTFF19oc3PTkvehoa/HpY6MjLgfQmX1077+S/aE/52/83f01//6X9f+/r6eP3+uv/W3/pY+/vjj/+jP/5N/8k/0v/wv/4t2dna0srKiv/bX/pr+xJ/4Ez/rx/5Pvr5VE574KqkU7xQPMoXy9PS05ufnXaAXi0X7PGEONzY2jD6CHgUl2UiVcrmcJbu7u7vemPHEkthM07WwsKCtrS1JfTS9Vqs5dCmTyWh8fNzNLouOJpywM2ZkIx0CKaQgBkG9uLjQzs6OIpGI8vm8JiYmXFzBoiI9gnVh5q/0dZLy7u6u0dRgUyXJjC3fOyiDD44QodElVAo2gGIUCeXV1ZXHB3GAU0gh4QmHw2bjUqmUgQPSbWnWCfXAywabhYyHQwP/EQ0QQAJNIJstjQibSzKZtKcShQQFAtcfNhJklnRdGjsKWvwzw8PDBhoo0C8uLpTNZo1Sc+ghA+S6s+4IIOJ3I9sLMmHItfBHIU2jUJfkhgaWDBBhYmJCc3NzLgSDjQwHddDPjXy+XC5rZmbGUr3gJsp9GRoa8ixhQCEarEgkYhCK4C4kfOFw2GFl5CDc3t4qmUxqYmLCTQyfuVKpaGJiQuVyWcVi0aoZigQaE5QpHOqgzgBl6XTaxRDMNGw3MnQkp6wNmkNAF/4dUGN8fFzHx8dqNpvq9XpaXV2VJLNTXGOKCw5SCk2KaDy3FFwklna7XRd2QSlr8PCYnJw040QQG8/j7OysTk5O7KFDMtxoNHw4tlotFYtFA0Tsrexh0teBakG2N8hgUbTCgg8M9JPoHzx4YCYT8IvCEqYguGaDB+83bQ5cG0AE7h/sHmAP351xa6zdoOcXWwsMAwXN9fW1mTZGJvJnyPYIJRodHbUVguaF5gSJP+FvNOx8Z55timOaSEZu0kiz12Jt6nQ6qtVqvte9Xk97e3tW/qDqCKo+UF7wHJJfcHvbT8iOxfoTEAgZ3dnZsew+Ho8rn89bbi19PcMcqxZ7AawTgAlBVwCYsM0UhTQ4ACkUeIOD/RGDPH/4gqkHAGt4XvGVr6+vu8GjwUFafXNzY+BvYKAf3Bqc9Ts1NWWWPR6Pex+8urpy+vbV1ZULZRoF5OdI9Pk97PtDQ0MOK6IJCdpb+LsoR1CO0bjyfHN+FYtFg5JDQ0Nm6AGYOU84Z2gSYSe5ztxD1gTAT7fbdY2Fmi3YzOCl/aZqhPuKZJ5MgGQy6XAyCISBgYF79sFSqfT7gnV5ZpjIQi4MoAyZKJyhPKtYESYnJ1Wr1Ux6hMNhe+oBFskSSafTPi+4j9SYeIZpKPP5vGq1muLxuFqtlq0CnDXj4+P60Y9+ZJVKLpdToVDw+3P+YL1hr4/H45Yt00QBvmOBTKVS9mJzn7HwZbNZ1yQ8N6enp5qYmLDVkwYbdQ1nfvAVBJipKZnJTdZFOBz2c8G6Zv9FPcQzRx3HmpC+Dn0MstyVSsWMNfUFOUrsr5FIxMpJADnAUlhcakoYc+5n66spGYya5M+DfQN2QgKbIVhIVeeaYJ0hzLXT6Whvb8+BhMfHxwYlqUWnpqZs2cRiRcO/v7+vx48fq1QquYFnf4NgAURnfCjrvtPp+HleW1vTwcHBvfwbFEecvZOTk7ZljI2NuS4DjOA8p8bEVhoKhVQoFPT48WO/X71e17/+1/9a7969sxIqm81anZPJZLSysqKtrS33QFgQ/mt+/aN/9I/0m7/5m/qt3/ot/dIv/ZL+5t/8m/q1X/s1ra2tuR8Lvr7//e/rz/25P6e/+lf/qv7Un/pT+gf/4B/o13/91/Xpp5/q6dOnP5fP+K2ZcNhgCrN0Ou3DHGkp4VOxWEzpdNpI6vr6uorFonK5nOWxsODMJEYuTfACzBZNIotZkuW5mUxGOzs7ZsqYkzswMOBiXpJlkzSRzLemsMhms8pms2bXCT2juIN9oehAtkSSM95eEGECxNgwYM5XVlbUarUUj8cdOsJGz3gQilQkwDR8MEewCEhGkI5PTU1pYWHhXgpiJpNxcwmyin8uiK5LsjQT/xSeSR7KoCUACSvyYgqLYHgOXmDAFQpfGm+uHSgg94TrRTEC+0xjQAgSMkpY3cRX47qQT3Lw8GftdtvXgaKXxpn1y0G1s7NjlofvvLe354OaGdiwSnd3/YAk1BGMnCIkh6KegxvFB0U+f0YxPD4+bnkSTHM+n3fTWKvVXBTA6FP00jxwkBO6EiyuRkb689FJZZ2amlImk9HAwIA+/fRTg1jJZFLVatXpqRxWIyMj9mNyqONzPTk5sU8R8IoAMLIUOPwJARofH/dzQSPAmgRUAUCjYIYF4nmRvm74+GdJZk0YF5hMJp06zZ4l6Z6EmsaS5puCjDVCY1+v1/3d6/W6G1vkxzTJNNcwddx3GldCG9k7YJNQHSEZRcJO08venE6nPS+cBpcikLFWAFW9Xn+s39ramptOwDr2WJRFFIp4lr/JbHOvYEjZK8imwOe8vb1txcLg4KBZKkKQgo0Of35+fm45MM0odh3W2vn5uUOvUNdUq1Vls1k/t/hTWTuM9kEaTq4BjAlAMrYCUqdpxLEAAJQODg4a5FxYWPDzBbgm9W0hZGAgYZycnLR/EjCQ+clcP+TFQUsScmRYmOAZjT+V8MqJiQkHpR0eHqrRaCiVSpnJIQslEolYAppIJOwRBgiG9UMJEgqFPJ+X5hQPMIAsDDL3je+ezWY1OTlp7zF1BQ1acPY3gMmXX37pvRaQATkvYBQsOWAJoZTMXx4dHXUzjs1Lkp+TZDJp6wINCs8zdQksNtYg2DqaO/YLACQA51arZbse14pziDMeCTt2Fd4L5jGVSrmhI8Wb/WRkZMRzvq+vr1WtVu3TBrBD5so15nuiDAsSK1h7OD858wBDCHpCyhoKhTx7PJPJOPkaNpXk9W63Py4NUIhagDqGkDPAMsDscDis/f19y+BLpZJub2/tO764uLBseGxsTHt7e7YGJBIJFYtF2wt4nlBpptNpzc/P35NncxZwvvV6PYM25BQBYCLphbiQZLA7GHDF88o6po6mySaDg4YtCG7idedZ5HOhAuCZ41wlQRymnOc0aEXg7wUzO2jm+Tn2gZOTE1ulotGo9xVAWybeAKpWq1WHqgJOBRUVZEAEswW4P4CF1NVHR0ceWdpsNg3k8xlQMRJY+ObNGxN1T548USgU0s7Ozj3SiGsJIcFZenh4qGKxqGazqc3NTT158kQjIyMql8sGObCPpVIp18sQUxBIqE6Hh4f15Zdf6sc//rHu7vqZD9T51Bycm3ye+fl5PXr0yHUuoa+ARairOp2Otra2tL6+rpGREX3nO9/x9B3sKLe3t9rY2DAQ8vz5c4M7Q0NDzlagvme06rd9/byZcF5YEXhRH/6HXv/n//l/6r//7/97/Xf/3X8nSfqt3/ot/Yt/8S/09//+39f/9D/9T7/v5/+v/+v/0h//439c/+P/+D9Kkv6P/+P/0G//9m/rb//tv63f+q3f+n/4m/Rf36oJn52dVSQS8bxJ0EeSl9k8JGl9fd2HvPT1PGlkNpI8T5rAAkZFBOdME4bCJsyGcnvbT0rH/3J8fKzLy0uPigiHw0qlUpqZmXHxvLy87PR1JMMcOOPj407GbTQa2t7eduAccxs5cI6PjzU3N2fWV5IPNdBoGi5QUtgWZGwcthwQh4eHlnfDPMOsg0ghPSZw4/r62kwiLCQeHuTE/Dys1+DgoBu88/Nze6hOTk4sfWcTHB8f/31eddBEijCaWoLvkO8gmYI5o+iGIeKzkPaO5yUUCpn5ohm7u7uzZG1g4OsRJ6CKwfvA4SLpnp81m81qYGDAkh02nIGB/ii6arWqyclJKzBarZYWFxd9LbgXbPow0ngCmWN6d3dnPz4zfCk+CB/kQKQwLJVKLvJIC3779u29cSZscO122w15UOYIyLK3t+eG8fDwUD/+8Y+Vy+W0urqqWCymer3u/ITR0VF99tlnnuGbSqX07t07F1qS3AhLMjvM88BsdNbK6empWampqSlLygnPQc6Ml5SDEKQZWwjyeNgIbBAUNIA9ABc0iaFQyCBQ0DrA7GU8gIVCQc+ePXMKPdJFCiDuN2P3JJnFRdUwPT3ttVitVt10s65ZGzQY2Eco1Hk2eK5JYqeJBGjhnnPgB8MOkQ9+s0iEccEGQEFNA07Rw/5E8XV0dGQZuiSzO+x7FKs0hTxfSNCHhvpjiIKKhmQyqUqlYk89+w8WHvZLABa8nYAf7DMwJQC7AA1nZ2fOWwBUqdVqtvYQwIZvNlgsUaQXi0VdXl46uV6S2TEKZJifg4MDWyhoTLiHBCDe3NzYn9poNDzmjEBOQCcKToAHJLiwo+zdNKYUrTRLNBNMB+FsIcPi5OTERSxN3+Xlpc8uAj1zuZytHPv7+5L6DQQAMYU54HAwFwVbBQ0CQOX8/Lw2Nzc1MzPjlHoUbYzm4/rTfDOaCaUGDRRqJSxljN1bWlrS3d2dP080GnXAJc0NfneYMRojGoxyuax0Ou01xzWlieY5BJRhT0FBBDCJso408dZXkwIIOwVsw8YiSaurq873SHw1Xg9AAdscY4qoUWj8ye8APKEp44xdWFjwdYeNDoVCZvS4V3iYWdu7u7tmPVE+oDZkNCxBa5FIxEFS/Pfp6WltbGx4fjYkzNnZmQqFgtchZzIAVj6fvzdtAjCLzIN2u22Z/M3NjZrNpq6urrS4uOjzD3UCKja+F0AoYOPV1ZXW19cNnqJSCZ7NqHgA7gHDE4mEvdgAFpK8f/EcB4MMYYZRjpHXwD6Exx8GmkaZzADuGXsNABTflakQgPdBNQRnEfJ5QGuAJZ5vfPxTU1M+BwC6mQmPRXNmZsZ7/zfrL0BvzhXqfK5ZPp/3efPkyRO9ffvWbDSgc7vddsI7MnQacmw41FUXFxcOwBwaGjJjHA6H9fTpUx0dHblnYQ/jOTg/P7+n+Hv27JmzlSSZqELNOT09rcnJSW1vb1sp9OLFC39PSA9AStYSxFKz2fQzx7XnPMTyViwWnUXwySef+KygvsZmS0o/k5iw85CrgCLoH//jf2z1I7J1bFGS7MnHIkAfBXj7077+c8rRZ2dn7/35//a//W/63//3//33/fzNzY0++eQT/aW/9Jf8ZwMDA/pv/pv/Rj/4wQ/+g7/jBz/4gX7zN3/z3p/92q/9mv75P//n/799+P8vr2/VhFOIzMzM3PMKctiBTLVaLQfpsCjxPSKbmJ2d1eDgoOfdLi8vG2lutVq6vLzU6OioGweKdpB1DtvLy0udnp7q+PjYkvBIJKJUKmUJG5IqZHmwPNvb2xoeHtajR4+MCl5eXmp7e9soL3IRmCZC5JDKc/hRCEhycUXhHQ73A7kYUURhi18aidb+/r6L9qOjI/v/KLJggQn4CPqFaEyRlzDeoVKpGCWHxYR9ofDi0IpGo8rn85YHwhoWCgUXtLBQzJKkSEW+B3N2cXGhk5MTe5MIDQpK0iT5e0iyLJLCB4+cJAM8HFZBrzjeW1Jr2eRoDPn7zWbTBSrrVfoamcdfGY1GNTU15QMWaR+HDc0Uhz+hN9LXVoNut2t0HGVFOBz2IQdy22g03MjhRZ+cnDQzx6EKgwPqBwiBRJFCMMgCU1hQ5OPpXFpaciM/Pz9v//KbN290dnamer3uwKTl5WWzzxyst7e3ll3DGAAU4QcFYKBgk+SZvsHmAV8aLCdgCxkMFM3B4ojiGOSa78zhHgRkAI14DwpWQqakvuwb1hLW9Pb21iADgYPsQahAgt75jz76yPJjGpigLYVnjcAe2GZABQpvPGSbm5sGEyKRiLa3t31Acg+wpxSLRW1tbZm5IgMAdpgsiSAzzrPLNZXkpGzW+9TUlAtVQI/Dw0NbYLjGFIpcY6SWSFJDoa9H4FCgApRxTfmdNN9YggA+SH/lXsfj/fFJi4uL2tvb87kAc8dez73g/rH+YBa5JkhD19fXnTbb+mq8HoxuOBy2UoRJGKiX2Hfn5uYMsNze3urhw4e2PCEZhoGNxWIGsbjXgGrMaAX8wQoE8MgaRz1EiCWS2Tdv3jgjhPBJzkyAXdZlrVZzwzc9Pa18Pq9IJGKWlAKc4p7njkYHwAyWiTDOTCaj6elpJRIJA04DAwMGkmic8PsT5MiZ2u12DQ5i5bm7u3OSN8UpDDKgAnsk5xTPPd+BYCRUN+wRwXsRtACMj4/f8y+zxoPNGqDO/v6+CoWC/fwAkzRbWL9I3cdeQP2RzWYlyawV+3Y6ndbg4KD29vZsEcvn866VyGEB0ASIIOQpqIwBPGffIeU7FOrPiJ+amjLjy8/Q3MCYkyKOlDUYpkkdCKhJsCBrf2hoyAQIHmDOYp6tfD5vBc3x8bHBBby0sVjM59j6+rrvN2uE/Yf1dn5+rhcvXhgoA9ienJzU4OCgPblBpQ5AE/kEPDs8BzTYPKPBIFn+Hknd+PF5fmjCAFhoMAAmAXxYX/j42U8544PqoU6n45qQukySAXDC8ajJWevsTZeXlwYZqS+4n9TiPIvs29Q3fB8AbK7/7e2txsfHVS6XFQ6H3TdwzSFbUFvC8jP2d2BgwNNDUCaNjY1pampKb9++Va/XUz6f9/zv3d1dq1QnJyc1NTWlUqmkw8NDffzxx2q1Wvr888+1v79v5cj19bVzrfgc9XpdhULBKltqe/oTMo1QdkIKYocKhUJ68eKF09y555A/gByArNyDoCXm4uJCS0tLVjYeHh5qfX1dkUhEf+yP/TENDg6qVqvp5cuX2tnZMaD3/e9/X8+ePbN0HTUszxbnU6/Xcw7P3t7evRGUt7e3ev78+U/VF/7nfmGJ4fUfY8GxT1Ln8Uqn0+47v/na39//D/484PTP4/WtmvCLiwv7T4PeMeSmkvTq1SvLRWgOYIGRqZISjuxiZ2fHTCIHZKFQMGL1xRdfeJMHVb67u1OlUlG73dbBwYE31ZGREc9u5PCdnJzU/Py8Wq2Wtra2ND4+rrdv33rzIBgoEumPA6N4ZWP98Y9/rGw26/mjFxcXKhaLGhwc1Pb2tjcwZGy7u7sOQbu9vVW1WvXoFND+cLjvE19ZWXEhdXPTT3fsdPqJ33gMadoAO5DxXV1dqd3uj8ECpUTSGvQxXl1d6cWLF2bmKIb5ucnJScu8mNGN1JONNVi84jemKaaxxevKvU0mk/ZM8jmazaYWFhYsCQJxQ91Ac82DBbIKuhj0K4Jsc2BxeNHYcxgEWbtMJmO56+npqccGURzQ4AW93pFIxAUuwACHlySzCbCMNA80IRx+SBXz+bxev35tX9L5+bny+bwlSazHd+/e+RAfGxvzLFXWFbI22E4sABRNgBWjo6N6+/at2u22CoWCizd8m5OTk1pfX3d42Pn5uVZXV/XmzRt7mwFOKEa5TkgKUZvQyHU6HbOAFOAUAFhNuN6RSH/kHUVQr9ezR5WCGACAMKXJyUlbToJqCZQYXB/2CkluHk5PT52iHCzCaQpIB6WIAmyhaKFZ5j0JK6IohZVDjhkMw8IOEPT6DgwMWGVUrVbVbDb18OFDHR0d+Z6iiAhKCoeGhpw8HAQRQqGQPy/PMk2NpHsZFNPT08rlch4JyeeTZH8lwBjPKM0b9wf5PM18UJHQ7XadwyHpXhFK40GRSAGCrYRrTXaAJMvkmOdLg8M9hI1PJBKq1Wpm/Gj8guzPwsKCC+BqtapoNKpcLuf9BYBZkoOUaBgoQlEPNZtN1et1S8m5BmR+nJyceNYsMmDOFxpinj1J9+4FgBO/DxUBTBeeTECSdrut2dlZNZtNNZtNjY+Pe+rC3d2dDg4OPJcWZrHdbiuTydjrymcjRA5JMmc664EGnjDIXq+nXC5nRQKAEJMc8Ic+efLkXvo2bDhF8Pr6ugYG+iP8YKqQlPJ9CXJLJBKWZrO+ORNgX5Eew7TiLaUwjcX6ky7Y38fGxuzRJqiKQhigNZh+zn7LvjY1NWXGE7UP4BIgB3XQ0FB/DCfzp/GtMjYxnU6rVqtpe3vb9jHk/8lk0nXPxcWF/amsH/Y/iAuaWr5bu92f7oKyB08/exPrmSwgmioaNcKbaEgikX5ODqwdxAZp8Tz/sOpXV1dWf2Hx4fl98+aN8x4gOnK5nOr1upnFcrlsFg5F3cDAgAMKe72e975gwB8/BwnBOd5sNt0MYe0hLRtpNzkk1KSsTfYgQEZII5pilI7UKpFIxKFzkDUwy8jRyeHgnACs4HdxLYNgtSRbuwAmUY/hl0fNxvrl3EI1xJ4OaA3gFtyP2JMAUwBTa7WaJicnVSwWvZ8zy52zkPeenp52gDCKmng87n9nD1peXtbx8fG9PBveO2hja7fbyufzrkfZu6Wv58OTzdBqtZw0f3Z2pr29vXtkG8qUubk5bWxsmLVeXFxUOBxWuVzWxx9/7D1wd3f3Xt1APSVJ8/Pzfmbi8biTztl7ydVhj85kMs7bQf7ebrc1MjJim9EPfvAD/fCHP/SeVigUHNi6vr6uaLSfbo/FttVq6cGDBw5JnpycVKlUUq/X0/LysicOvHjxwiDJT/ui1vh5vnj/8fHxe034f+2vb9WEgwbiF+l2u0bo2AifPHmiN2/e6PLy0ogVqaY0o2xQNzc3qlarZnNBYk9OTvT27VuHmVDI0uhL/QcKVh0GcHx83J4pECeYRZpn/HzNZtNMX7FYtOTx3bt3RhvZXI6Pj5XL5bS5uambmxul02ldXvbnc3OwIHE7OTnR8vKyer2eAyDS6bSbD1gjUhU3NjYkyR5ovLt4foNMOe9Bs4YSgAMFtiMej2ttbc0e4pcvX/ozwlSDZuPJnZyc9HemgQKV5zCh8cADxyazv7+vi4sLgwbM/8Y3hIeKBkKS2TxkyByWoJEghfy9iYkJsy7BxPparaZ8Pu+il+/2zfXCdaQgochPp9MGPVBR4EHkmkoyu0ezxXU4Pj5Wq9WyR5omIdgQUeiQL0CRwcaKrBwEn2LgwYMHnl0qSdVq1SwfzJnUHxXDfUyn00okEk6556D57LPPlEgklE6nXRSQotzpdNwQcchvbGzo4uJCr169sqwQSwkN0OTkpBYXF80iwJyRYo38e29vT8Vi0VIvQLxer+dC9OrqyhJeDq6pqSmHzQFqUOxQrLEX3NzceHwQzQlrCGAiWKSRngxLSuGJvxh2QeoHUk5OTjotl98nyZ8FwIHm9+Tk5B5L0ul0nMxOIUTjRU7F9fW1Q1P29/ctXQMgGh4etqIGFnVra8vWBBK5r66uzJYEm/AgA05DjZwYv/j8/LzBo1qtZhk6+33iq9GUKHpo5kmCBdACeAUoo5Bjn6RoluRQIs4TJiTQxI6OjtpfiM+b30Vjg2STvYnngWcaYEWSzyYacNQTsHc0jd8EZyiekcXf3t7aP47cnIYS0DQejxt4laTNzU2lUinvXyh9bm76oXs0zSiNAEzHxsasJqKJHhoacoAomQ6cE7BvNJ/Dw8Oan583sHN0dKRGo6HR0VHNzs6qVqvdm5BBM4HSh7OQ9Y9MemDg61Ck1lcp3ijH8E5TkNZqNdXrdedoLCwsaHNzU8PDwwa6ut2uNjY2dHZ2pmKx6LUSZB+RrqMcCIVCDhwlFLJQKNwDmzlDAJRpgmBiAcKQkbP3s/dRJAMKDA0NqVaraXx83M9ZPp93bfDmzRuNjo7avzkwMGCGBVvGycmJdnd39eDBA69pnmGY7vPzc83Pz+v29lalUskZOQSroRrDAsWexKg9lGjZbFbVatVWpFwupy+//NJKG6TWnJXcR57hy8tLTU1NeQ+mWUQVxl4JaApgypkA4Nrp9MfVUgfBLkOYECALSPHq1SvNz88btKpWq1ae8CyTDI1qkKYbtQnfZ35+Xt1u995awd4FsATpQU1LI4PKjhpFkoEXao3gGEAaaNhPfh/7JXvn7e2tAzqDQEJQzt7pdO6B0HxOgFhUggCkKKz4+9RBsVjMOS6MiaOhprmGleW9+TzI57nGQfCUcxeiBhCIepazPWj7QrkFORMk5iTZSkGQKuqnvb09LS0t6fr6+p4FslaraX5+3t+fNQtTfXR0pFevXjmvilwgiCqAIs5gfgd+7EgkouXlZa8dbCZkS6AkginHdsezTsgfIBeAQaVSMaFDxk0kEtHW1pZVaZeXl95brq6u9Nlnn+kHP/iBa4xnz54plUrp8ePH+uEPf2iVXDabNRATVEOsrq6q2Wwa9CuXy96nt7e3NTTUn1JULpf/003hf8EvlCcE2PE6ODiw0uybr0wm861+/v+J17cOZpubm3OIAwEUFFytr2Zz3t7e6oMPPnBj9vbtW3u7kLyQdnh7e6ulpSVLrWAv8Y7wcJOgyUzv/f19Nzx4D2Gfx8bG7kl/ONxooFmAS0tLKhQKur3tJ13v7Ow4MZTClU342bNn2traMkKWzWaN/lOQHRwcWM7AYgfllmSPG402TR2NfiKR0PT0tC4uLizbJtSNIjUoYeZ3I/8LhuDE43HVajWFw2EVi0X7ckDWkBVFo/0ZnaDiNNrMZOWwRF4FAg8iieyYppNrxwYG8MBm1Ov1ND097Y2NYjvom0Mqfn5+btYBjw33pVKpWBYcjUZ93YKzMym6kScGGdJqteqCgrUCotj6KukblhVvWaVSuReOQmgGUigAAz4DBXaxWDSLyX3heuJRYj1wHSgYZmdnLZ+DAQo2GQBZjE9JJpNKJpOWiZ+enmpzc1PLy8tOHpXkZ5ExU0F2YHV1VRMTE/q93/s9HRwcuGgYGxszcIAUutvtpyTv7e25UYRhJcSKhpeROKDzFEBIo2Aq8ToClnFIwSqw11xfX9sbSEFHqN/MzIxHMkn3E+mx1XC92ag3Njb8mZLJpNNCWaPz8/OamJjQu3fvzDzx/hSQ33w+aILZM/FU5nI5F0uSzErwPsfHx35vnrugD4+MClh12C3SaPm7NL6w9zTFAC8UPygCaLBR2eAzZa0lvpohjI2ArAcSdPGf8rxTDLP2KRD4Wd6fGcAXFxcujmBjz87OzHpmMhmzqtg+YErxdgNQ4ffnWiEpZB+U5EwMrlez2bT0d2BgwIwJ64fiCSCOphygF4aWwCLAYsAwJLQ85xTfp6en6nb7adeMK5Tk8EZkwBSXMPWEnHLP8W8ir08mk5Z33tzcuBhkrF0ikTAzc3Nzo5cvX6pQKBh4pTDk+1DYs85p6Pg8MHHNZlPDw8OanZ11sYqfdGhoSCcnJxocHLRqjVwZrAuzs7O+l+zzTAuoVCpqNpsGnFBXkV2CBQPVGAq329tbg6UAV5yhBwcHGh0dlST7aff29ry3UzjzDFKXUJQj76UpCgbKwkATokmWQbvd9noAtEQpMDU15XUdZFu73a7Hg1FDYaNibjM1DKA7ZyZASrVa9fUBROXcQm6PCm5oaMhKJBQFwXsCMx+0gPB8AnjDLDcaDZ+d7AHUPVwnPOh7e3uumZD/opIi8I6cl8ePH3vfo1Zg2g6kD8/iT37yE8uUsYjxHHJ9aXI5z5Doci8AkXmxx6PaoykP2qdocoO/AzUJ/w8gBGgWCoV8P3i+sYcAtAZZZYBh7juNHbJnzlbOAc5N1giAJ2cUa/qbVrTg9+Dvst5Y+4Q1s1dwjQBtaGTx2gNkUksDUkh9Fpn9AJUi9e7c3JwODg68N7Ln8LyyR6ESIs9qYWFBkkzYzc7OanJy0iBprVazimRhYcEgIXXh0dGRWq2Wjo6OtLW1ZZk3dRL5U1tbW1paWrJ9rVgsSurbTTizmX/O58HbPjs7ayWe1FcZ7+zsuBacmJhQIpHQBx984PyE8/Nz250ODw81Pz+voaEhz0Jvt9u2J3U6HZN0H3/8sd6+fWsAgVryp3395/SE/7SvWCymDz/8UN/73vf067/+65L6z+r3vvc9/YW/8Bf+g3/nu9/9rr73ve/pf/gf/gf/2W//9m/ru9/97s/6sf+Tr2/VhCPzSiQSnvUI6nV+fq5yuWyG9vj42P5a/BI3NzcqFAr234IaNRoNoz6Mb3n9+rX9WBcXF9rf33cxxmFF2AUya1gQfGRIg0Fz+X9J+uijj9w8Hh0dGSgA2eSwoDmtVquSZEnP7u6uHyge7pmZGft78TeyiUkyy4Ov8+rqyjNBaYTv7u6USCQUi8UsUaMgBL0HJSYgg6KPzQ2WiKKRUBc22FAoZJkwMwTZ6Dnc8bzg72bmJM1QMICCoBWYG5o/iiJmkRLex2F8d3fnDSXIRIPIcw845EEtYeGDDRDFOzIxGsUg08XGE2yUg4XD9PS0faJsWLw/bBsFO+xLLBaztItgLZq9IDrNtRgZGVEqlfI6ZJYsjAboe7vdH4dBs8r8W67DxMSEms2mU2BpMih2GQ/z5s0bra6umkXFJ87hLfWDLmCaFxYW3ECurq6q1WppfHxci4uLPvxRl5yfn+vg4MBSRoKnkDriJc1kMjo5OfEzUKvVvIYJcGNPoXABlMCWEmQXyXXgniI9i8Vi+uCDD1QsFq2SQCpMaBMe7mAjeHx8rK2tLV1eXrqpIfwLJuXq6srrdXR01M9LkL1gr6MooTHBKkDhk8lkFI1GzerQYHPfYcmRG5KoDFvGJATW2NbWlhsFilRJbjRZaxRErBXUQoBnFOkU9sG5zbDbWCN4AZoG/fZcX8C3b2YWUNRdXl7akkHTDjOPlJWCkN9zd9cPAWQfw8IQiUTuyRVhX5Br3931E4AJPaMYwduKbxWAC2YQZhhlCUUbzyFecBKpUUEE1TIwmxSJnENByTpMIg3kzc2N3nvvPdXrde3t7Vl+F4vFtLCwYOYPEIpzl9AprCbhcNjAM9/x7du3urm5cWIxElLOA8CpXq93b640BRqACWtqf3/fairGjtFY0TwzRgfZO2c+Xl6eRxLB6/W6A7Hy+bz9fdgvAKUjkYg2NzdtZWi32z5LYG4J3UulUkomk9rf3783MxtyAYUKYEo+n7fEOpgfwvrDm93pdAw2d7tdVSoVzc7OWsmDEo5agvUtyeAGwGI6ndbq6qqBrouLCxfXgG3sLwAXu7u7Vh0A0lPH3N7e6t27d3r8+LGq1aozKWADUXIRzAaYC2McTEk+Pj42mBMKhdycMAYsFot5fBVNEpaFgYEBNxhky8Dw0aACsqL2I5iLfQ+wCzCO9S7JFiD2N84j1ByxWEyNRkOlUkmFQsHA2jebbwA65Nl8bvYn9n3YYlhv9ntqA8A1Sf48EAwAbDC13CfIl4GBAascsObEYv0g1MPDQ4PchHlxNtB0cj8luSYKBpbRsFJ7SbLXnTqc+hVigefs9rY/qnBtbU2np6e2Ft7e3mp5edng3MTExD2CgbMnGBzJ76L2YP+HkYY5bzabKpVKkvq5JYQ3oyYkZAwf/+HhofcaSbY8zs7OOlW8Xq+r2WxqaGhIT548se0Ge2i1WvUccWwdkEuA9cEw1V6vZ3UTZ9Hp6anJgU6nP+0AWyD2U9bM3V0/0Pf999/3c1CtVrW+vu69Fzn9H/7Df9ikCqpHRvwVi0VnkFADDQ8Pm4Gnjul2u9ra2rIiEaUOChjsR/+1v37zN39T/+1/+9/qo48+0scff6y/+Tf/pi4uLpyW/uf//J9XPp/XX/2rf1WS9Bf/4l/UH/7Df1h/42/8Df3JP/kn9Q//4T/UT37yE/29v/f3fm6f8Vs14VdXV/rRj37kcT88XEjNSOHjRlNswUCwmObm5rSzs2MPHYztw4cPjSayGKrVqlEnJEj4vInip0Bk1l2n0zHDEww84PMsLy+b8T07O7P3lQKOA2xoaMjBVi9evNAHH3zgJo0DkQ2YDYz04NnZWd3d3Wl3d9eMEshtMpk0GgUbDfuyvr5+b57oycmJSqWSHj9+bBkdRRIHFoXrxMSEx1pJsi8LmTMHFg3L6uqqNz9QVoorGiMeWEAGCv3b21u1Wi3PLD06OrrXGCIx4wClSWBjpjlngwfxbTabPpgpUmGwg4Uq8lOkZPi12FAkmeWUZDaSzwKiGmS2T09PDZIEWetveklpJChYg/LMu7s7o6146ZCksrlxgA8MDGhlZcWqAIoA5Pv4wNfW1rSzs6Pl5WU/W4zUoCgghGNpacmNOCPWMpmMmQUYe4JJ7u7utLe3Z0aT+4DPtlgs6tGjR5qdndXBwYG2trbsSWJeJSwT8j9Q+7OzMyfeHh8fuxinKGPNBgOAkAVTgI6MjHhO7e7urpso1nc+n9fCwoK2t7fNjlUqFQNdNFAcNtwjSZaYNhoNnZ+fK5lM+jmMRPphaKQr0+yfnJxobm5O1WpVo6OjtnTQROOR4/ugnOEQB71uNptOzYap/qbMne8gyYUTAWHsZxSn7AkAU+zDFHKs56CflWIayT5gFQDH2NiY93iacz5DkAHhfWB9er2eC32UD9fX1/euS6fT0fT0tPdEknFpgm9ubjyKbWVlxSwJBTDzhGmub29vtbu7a+aTohm7DmmxrFEAkpGREReolUrFORbcy6D/kbCo6+vre6AS14CiGkAYpmhhYUHRaFRbW1uWG+PrZs/CGgOQc3Nz42vCrGQ84icnJ36WKLaDapWpqSkVCgU/KwBPSPIBpkniJV14dnbWRXNQgg/QBDOIPJ01RRMD6N7r9Ty1g0IfFvzm5sbFPGsskUgok8nYClav13VxcaEHDx6YMeaacA8JDO10+inpyD9prmBeuW4PHjywwoBMC5htQFGUWtw7ClmUOsHQLJ7bSCSicrnsedyAUZVKxTPt2c+DtVEqlfLz9umnnyqbzfqZxmMO4EadQY20v7/vQps1w30B8CQkinu5sbHh7ArG1WF9ofEGAOUa8OeorlAVkr9TKpXU6XSslhoe7o+2QvYPc3h3d+cMoTdv3mh+fl7hcNhFPl5kALVsNquFhQUVi0XXZKjT8NwDyAGaSDJBQC4NTTlN7s3Njd5//33NzMxYzgxYhvooaGvhGgDEBi1QKLeo3/5D4VCsr2DjS13DHhNktpFEY4sYHBxUvV73ejk8PFQqlXJNwfVlH2S/ajabPjMk2d9NbQJgQG0czDmgbmYNEixGzdPpdPTy5UsNDg5qcXHRZxBnSzjcn1IBGM/vR60DAMnZQSMN+YN9g88M6Le1taVCoWB1ZrlcViaT0ebmpjqdjorFotWzBwcHVgRdX18rn89Lkmdjo15BuVsqlTwh4ujoSLVazfcU5erg4KAODw+1sbGhRqNxb5rRzMyMIpGIGW/sqG/evFG329Xnn39uoIH1GqxBsd0+e/ZM5+fnqlQq+uSTT5yxUSwW9cf/+B+3HS6ZTCoej3vf2tzc9DmHlJrfBbA6OTmpra0ttdtt7ezs6MMPP1Q6ndbBwYGGhoZUrVb9PRk9TJ3007z+S2TCJenP/tk/q0ajof/1f/1ftb+/r/fff1//8l/+S1uDmNrC61d+5Vf0D/7BP9D//D//z/rLf/kva2VlRf/8n//zn9uMcOlbNuH4l2Fk7+7ujMQzo29vb08LCwvq9Xoql8seEZDP5y3ByOfzur29tW/47OxMZ2dnnqOHvxM/W9AnMzY2pmQyaRldNBpVsVi8V2TDokQiEfvXJfnA6na7Dh3a29tzsQrjQ3Lq2dmZ00UHBwe1trZmP+7FxYUqlYpubm60srLiDYxGn/9//fq1otGo/sAf+AMaGxuzdJhFAKIGg8QYounpaTMio6OjOjw81PT0tIOHDg8PdXBwYAQ0m82q0+mnMxP2RcAGDR+MFBJQEMHJyUkXQxRWNCI0LTMzMy4EYFEJrRkcHNTk5KTl0kGPHywChwazGwkcoRGTZK/05eWlPZQULvj7ONSQN+Gr+mbDzibH90WeBNuHZI+wMTZEEsq73a5DY9rtthkARkPxeWnOKVSRLW9tbdkfuLm5qZWVFTflSMW5B3hXafaDsrb19XUNDQ1Zeh30eNF0LS8va29vT81m817QSSwWcwOLyiEcDttnjRyRwhYJcjKZ9MH56NEjy5IAjzjckeZKX0836Ha7KhQKajQaGhsb8+zVoO98enpapVJJ8XhcGxsb/r4oIShmafYikYjGx8eVTCaVSqW0vb3tZ5biB7k3oCAMJWmsjAljL4hGozo4OLBs+fa2P3OZxFaKsZWVFTMhnU7HDdbMzIxmZmZ0cHCg169fu3DN5XLeg/C/4b0D6NrY2LBPlBeMBp+NhpeCDaaIF/4ypOmAiqwVGJSJiQmPIsSTznNH8cCzgzpB+rpwomikSWFdUsDBJAZZ7+AeAZsfj8ftJ+e7TUxMuBFBpYCMEVYonU7r4uLCs4eDTSHMEnkDBGpRPAAy9Xo9Pw/Bv884Mphs1jPgHoc+9z8UCnleOfsT51KhUFC5XFaj0TAYCjsJWCrJ00NOT0/t8UMGSkN7c3Ojd+/euXEBNEDCDeCF2oTC+vr6Wvv7+27E8MwTGDQ7O+vzBjsTMljYXQAHmif2YP6HD51gRvZ0AN5Op2Obxvj4uNkjmsFUKqXJyUmPudvZ2VE+n9fR0ZGy2ayZZdLFk8mkzwXuGxMm2EvYU5HfAgYAsMDsAVoWCgWDYjw/d3d32tzcNFh+dXWlSqUiSb6enF00fzB1Q0NDOj4+tuIBAJO9hefq+vraPs1yuewmK3jecs/I01heXlapVFLrq0BURqqdnJyoWCx6PncQ5CdPJpPJ2FtLk40HfGBgwJk2gOYocCQ5u4OQ2dvbW7PxyMHfvXunmZkZgyFjY2POBahUKlZvYRU7Ojq6pxShfkMJSOPMNQyGgwEeo55iD+H+ItFnz0QpAjP64x//WNVqVb/6q79qcBFAhHs6PT3t9cf+wj8fHR259mOvaTQa3m9h/gEpe72eEomEPycgHWsxSCKgSAu+t6R7asednR3LqgFQCJyE1QbkhFkNevU3NzddM5AIf3Z2Zk8yzz2qOvZ0aiJqnuCeOjMz44BZwGiC0ZDV8x6c7cEEce7lxMSEqtWqgXbsqOyd2FCprwDXpL6itVQqWXVHrXZ3d3evZoZA4XqQh8E4ZEg+cndoolGcoDhtNpve3yD9GOWIsnJmZsaed/aPoKoLwJTvMTo66hruiy++cA5LKpXSysqKnj17ZuUYdSLrjUY7Go1qe3vb9ylY/2YyGXvhr66u9Hu/93va2trS3Nycz9irqyvXVvQ1KFL/a3/9hb/wF/6j8vN/9+/+3e/7s9/4jd/Qb/zGb/ycP9XXr2/VhBNARaGLP2lpackFOuh9qVTyATM0NKQXL14YAXv79q3lHyBKIKEgZTTe+I45KNhACdbhoIcZhHXHpwUrQ6OEVPrVq1dmk3nY+bloNOqkWHzqbNSrq6saHBzUZ5995hRrmEf82dPT03rz5o2mpqZs6CeQa3p6WrVaTbOzsw6xQn4UiUQ8GoQii4OdUWMTExNOOD8+PlY6nTZgQGGENHJkZEQ7OzsaGxtTLpdzYUIxySHH9ZXkuYhcF7wnNDg0cWweMHfS1+OK8OtyQNHM0wwStBcO90d2UbTAItBIc4jgB2MTZm1IXwe8UYjwP0lmMPAWwdLAjnDIctDih+V3Uhjhy+HzkNZKYvLOzo4ZJHxjSPwHBgb0/vvvG5iAdQ+OSwPMoqiTZKaGoCsConq9nmq1mlKplIt25OQUOKFQSNVqVR9++KFGR0dVKpV8UBBkt729bTSfQwEAiiIgHA6bAaFwIszj9vbWAFs0GvUIB2wGNAqAQ7u7uz6wKBoojGnyASxIFacgpUgeHu7PEuXQCYVCqtfrLkSQjOKVAxQDrKDBwXfJyLDHjx9bjsUBhxc2OKOXwzd4oFMgDg0N2ds6Pz9vn1dQmQLDzPNDU833gx0J2iAAmVBwcI1ZmxSGfIag3Jy9jL0HBgm/YyQScWI1BWEsFrPMkf0UH1/ws9BAg75TFMCy8R147rAZUcDxbAQTt9lner2ek6FTqZSOjo4MhqB2wDOZSqXMNLFnYSOCReMesN+yvoP/jywYP+HJyck9FiscDt9TKlDMsR9jsXj8+LGbiqBKhgA0mgbUXcGmA4CW71+pVMzusj9TRFNow0QBGOC5Jk8DFhgQ6fz8XLOzs07rRenB9yFE9e7uzmcUa//w8NDMIM3F+fm51xqA2PBwf7zl0dGRZmZmlMlkbBXodPrJzKgcut3uPT84ozO5X0g3FxcXFQqFPD0kaCngcx0fHyubzSoajXoEFnUKzQ7N39HRkY6Pj83a4kVHEUE4U6fTuaeQaDQaKhQKbvwodmG819bW1Ol0tLCw4CBOAGPqHNYV64/9E0YUkOzw8FCTk5NWjRAsOzAwYNk+cvRarWbwExAMQBfAGzBtfHzcY06p1/C0srYIRwRsPDs7c23CWYZtiLMUm8z29rZrHNYse9H777/vc6Pb7XraDvslYZq5XM5efUn2gKOMgIVkrwM0B/zc3t52UNfFxYW2t7etgOA6BBshGHNABEAk9r3T01Pv++wNNJYAt9QKw8PDWlxcdKPIGYTdELUe/2MPPD8/9yjaVqulyclJSbrX4LF+IIKoSdhbYZtrtZqWlpZ0c9MPzg2Hwx4DCOCIv50wUqYIlUolq1QIMG59NQY22OShcmu1WlpeXlaj0bAqgr12cLA/Rgsyg1orm83q4ODAgNn8/LxHDTOFiPvP2QQBQvgsz/bU1NS9BH4AobOzMyWTSYNbZ2dnZnw7nY7PumazqVgs5toGGwaKF+pxJO7xeFyLi4v6+OOPrdLg3CmXyzo5OVG5XNbs7KyOj4/14sULrx9qfIg4Etl/53d+xxbR0dFR/ek//ac1NjamL774Qq9evbKyCfsUSfOnp6fa2dlx/cf9oV5AYcC5jOIpmGGCguzs7Mzn2OTkpHK53H+8GfzG679UJvy/hte3bsJBDwcGBlQul9XtdvXu3TujMiA9LIpQKKR3797d8+hKX8fNc/OCsjw24uHhYU1NTWllZeWeZBYvBWNnrq6u/LPMuUYumfhqfjbN197enr73ve85yA0mZ2BgwONpCBW5u7vTe++9p9PTU+3u7iqTybiwQKJK487np3BdWVnxgUFjTtMzOzvrg4/NnDEwFFmwP2dnZ5qdndXq6qpBBGYRF4tFX1ceHJh/ZqbiSWm3275eBwcHLjRB9zkUYRAlWfpOYxkKfT1fFC8hiG6w2ebAx1OaSqWM/iILJxEZVpyNGyaedcDaCMq47u7u7DlH1knzzoHabrfd0BLQhswVmTqNAwUSDDWM99TUlNdoLBbzoRPcwGjeCC5hUwYNxvNMAQYaTmGDrBZJMJI/An4AHYrFovb29rSzs6Nnz545yblSqTgZMxKJaG1tTfV63QAObAxBSRxQqEouLy9dAHU6nXteMiRNrAfWGXJ0nqGhoSGtr69rfHzc15iU/mq16gOA+5lOp21DQQUSDFIJFjOEut3d3ZlNoRG8vOyPgtvY2DCIAsDDemCdjY+Pe+QGxVcymdSjR4/cCLEGKMDfvXtnTyO+TyS8jKS7vLy0VWRzc9MSNvITkACidgAECIJISL0nJiZUKBTMTuCXCza0wTXPMwpyjk+fMStBJUg6nfZ78pzR5PACiKFgxv/Os0/OBwng7BUg7J1OxwwG6yYYLASbhKycPYjvRaMrycWGJHt8YVX57Iz2A8yIRvtBczwbrFf216OjI+3s7GhyclLValXJZNIScz4TABPPHwU/e9XNzY3y+bwLaNgj/hwAKplMqlqt6vr6WjMzMz5bCMWCJSMkkO8XlKLDfMGkErQTDoe9hwIQjI6OqtFomEWenp7Wl19+qWQy6fMMLyUAGMA1IOzx8bF9v4A5e3t7mp+fl6R7zHzQ44qiZ2hoyP5uFFo0fJIMUtbrdQdCrq6u2qvI/sg0EpLn+W/BMwGFC40S0m1GdB0eHhpQYYQj6rP9/X37hGmIaJZpeCT5XC+XyyqVSvqDf/APmh1ChYD/neYY9vfurj9ClXFHqNxoXMkzwMMLGB2UwQdrkaOjI4/mqtfrLuKj0aiD2bAt7O/vG9gPh8PeM7Ew8J25ru1229JW9iXIDOTPNIAU9zRMPI9jY2Oup6g59vf3/dmoxfhZVBesJz7j0NCQmzAUG6T/Hx0dKZfLueHETkZTHVSsfPzxx94vpqen9Uf/6B/V1dWVQWD+PiACTS31As9YKBS6Z92S5HOViRTdblcLCws+D3gPVAeoZSAgYN+D0m8k0uxVBL9xJnE+nZ2d3Wv8OCtbX43oQsWJFZAzEGXlN0EUwNuBgQFVKhWNj4+rVCr5+qTTaYMmt7e3XmMHBwdeU51OxxNTqA3Ozs48nQc7BfYqni8aaRQOCwsLGhkZUblc1vT0tEFifi/sfaPR8GhKRrtx/lMrYjEDTOB5hsTjPIjFYsrn897DUJ8C4F1dXdn7HYlElEwm9Uf+yB9xLcL9bjQaajQaisfjevTokVqtlj755BMDu9yHarWq169fWxVA/Q9gOjIyomfPnhn8ZsxhvV7X6uqqZ57znLHfdTod713dbn+KST6fV6lU0vr6+j07ydVVf1zhwcGBLi4u9N5773kfePDggTM+gkq9X7x+fq9v1YQTxhZ8fVMaS5M0MTHhwycor5Vk6XeQTYIxx6+Dr4cU7+HhYU1PT5vFoUgi7AsvEuwhElSKrUgkopOTE/3gBz/Q9PS0GxAeYJpN2HtQWbzC1WrVBxUbKs0S4UowqSDahEcUi0WnTdK8h0IhjyCB0W632wYbQOspmvCw5HI5KxFgF0gAvbu78wx0DlpYrG63ayYbqV8sFnOyLUWR9PVoKAIlkK7CehGIwsaHdAmGgsMFiXA+n3eKLUUFgEW1WjVbypz1YCEOkEM4CesoyMJzqFHs8zOsFZpjpLL8OwcvEnUONTY0UM9er+eEW5oRQkAAg2jCKbDw/gQT6YP+KQJfaDwGBwfVaDS0t7fnQgN2CbCDrADAmsvLS+3v7+vq6krLy8tKpVJKJBLa3t7W/v6+JY/BYpv7OT09rXw+71GA/L4gAEOTG/QFB5UGBFcFJfWwc9wTWEYKWgpNmnlmuZK/gFyUw4Tr9PLlSzdZ3D8yH7gfQQYYAGFwcNBsaTQaNcOWzWb1/vvvu2hgdM/4+Lj29/cNVvA8Dw8Pu4gmhZhxRTS4JHHv7e25UcNS802FR3DNsCYJcwl6yClSYDsAf/guwYZRktcuhRrrHGk0DQeHLsxRMIuDSRK8H3tfIpGwfJDJFzyfmUzGaxm5IQ0sieEwEwAlAKPBwhB5Ot+fZ5h/5j4yBQO7DSwI85i5ZrAbQTZkdHTUliaabgp/gFXWbSwWUzKZvAdAAaIEA/K4DldXVw6gYmwYSqaJiQkzp/jCCTBiPfF7sfgAKI+MjGhubs7ZEzyrrHumCExNTdnCxJ6JR5l7n8/nbaUhl0OSbUCc2ZxRpElzDThr7u7u7gV5RqNRT0ro9frzwkm8BlDhXMLDjaIrlUp5zRI4xHxumnRAbYA7ErBhtBqNhp/thw8f+ixBjhpU8uVyOTcw1AAo+5BkB1nry8tLffHFF1pcXDS4e3l56eaH8xNGlWY7Fov57A56lwFcsV8AkNIcY+fY2Njwunj48KFub29t9YFB5uyS+s3A/Py8otGoNjc37QMPKh2C1gfyV4I2I6wNKNk46yEKkMUCGhH6RPNPeBrZCXiZARw49whkBKDGWoUCCrUNM9sBDWjo+edgBgkqABptmv1MJqNOp58BQn4MwELQnwtbnEgkzNICFjJjnaki5HvQEHFmoaKjwUIej72KIK92u+2zJigRRyGCx/rw8FCjo6NaW1tTt9vVkydPtLGxYQUANSX5C/jU37x5o9nZWeVyOe3t7ZkAYiQvqrq1tTWNjY1paWlJp6en3qPr9bry+bwtIRcXF5qenvYehUqm2Wx6f+FnBwcHXfeijgOwATAEKOV701yS8YCE+uzszKAtitHd3V2zw5FIxEpQFCWscxQaw8PDvq6lUknFYtF7HZbKy8tLPXv2TOVy2YntjEAeHBw0GHlwcGALQigU8vr75V/+ZdXrdX3ve9/T8fGxZmdn9ezZMzPQb9680fLysra3t63CJJA5k8lobm5OhULB/nRIn6WlJZVKJX3yySfONiFnJR6Pa3Z2VoeHhw6Pplebnp7WH/gDf0ClUslAw+bmplPd6cP4PsEpQfV63SOKf5rXL5jwn/31rZpwpGhIMtlEgzLgIJvN4mQz54VUnMOf9wh63EDhnj9/7qRImNhwOGwW8sc//rEZ+kQioV/91V81Yh+P90eXwUCR5EzIBUUf/kCYAiRB8/Pz9iZ95zvfUblc1tjYmCV9bIp4oqQ+cplMJrW+vu6gEBgjiiceAECD6elpJ6qDRgd9Q2dnZw6hgnUG3cPXGAx8gKWDJWfDIJUZ2evc3Ny9dOGg/JXGC+kvXj+abQpuAAVYHdBamhfC866urjz6huY2FOrPhScNkqYE1gkGKBwOe6wc6wvmWPras8j6QKobbLgpkmGaYGqC1ofBwUF7t9jI6/W6CwE+S7fb1f7+vue3BhsF3r/X6zn0Ynp6+p7Xkt9DgRkMV2GzJBiGzwngtLy87GL+5uZGz54907t377S/v+9RKnibaD4lmZEE2R8cHFS1WnVB8fjxY83NzVmOxfibYHja+Pi4qtWqUeggoxq8V7BUNBCw4LFYzIUCUnTANppzbCAkpMJUEFQWlFVyH589e+aRY5FIxH7Kqakpz5VG4gdjOzIyou3tbT158kTdbtdg1O7urkEimthUKnXPJsDzIMnjBm9ubryWa7WaJXKw75LuZRlcXFy4yAqCPUF7BM0pXkP+PMj60NSz9nl+aFiCEvDgiBeCZw4ODsxusI/g5aNw5H4DjrIfIYkjHR/ZbzBcjSwHikPYUIpYZHySvKYnJiYMyPGZaBphiQglo8Hpdrs+P4Ig2tnZmXZ3d/0dAIuQ+ZILwb2R5OaPIo4GEAk/hTaKC+4Z7Nfw8LBHaI6OjvqsYx9BSsteCEhF8BEBRTSXgGh8RoovQCnOVCYKNJtNrxXON+4nzcHe3p6mp6e9h9FITE5O2t8dbEaDGQEAYKivUFcFxxIhlWXf39nZsXqAM4+sAlQ5TDPAfhQMD0Q1xmixvb09N0AoxBYWFjy6ErAuEol4H4SRR5XAteGZ6Ha7boaur/szc9PptJ4+fWqmb3t7W9Ho1zOfJTmzYmBgwDOu2ReobwBMUJhwrlBDkZY8PDysRCLhWdp4UgECueYAh9RL8Xhcu7u73scIHDo9PbUaKxKJmNg4PT3152Rv4axsNBrKZrPOZQgCyZwvnU7HSgPqFvy+BJxyBvMMsFfRFF1d9Wcsb25u+j6zp9Ec4Cnn/pClElTncf/r9boikYjBWMgFyATWRDB8DkCCsEzOMIghmncyV2ZnZ32fURpBUFCz0LDyLHS7XQfxcp4jhy4Wi34GYrGYGe5oNOqcBEItV1ZWdHR0ZAvJ9va2hoeHtbu76+b64uJCxWLRCfWlUsls8cjIiDKZjNUHnU7H7x+Lxe5Z46iTk8mk70kwRA5iBmCaCR3Y4VZWVjQyMuJziIaP8xdbzt3dna11MLrcm1AopFqtpnq9rlwu57Gd1APYwwBgyBVhwgnn+fLysmsgxv1CMHS7XX9vbG0QRvi/5+bm/F3ZP8k3QMGXTqf15s0bra+v6+zsTKurq/qlX/oln8f7+/sm5R49eqTh4WG1Wi394Ac/cP+xurqqyclJS/zZS969e+c9f2pqSgcHBw6v5SzjugctC5ubm86Oqtfrevjwoba2tmwx+EN/6A/p6OhI6+vrJqS4to1GQ5999tlP3RsCyv48Xz/v9/9/6/WtmvCFhQUfKKRUUuDBOLHZschhsA8PD40E05iA4krywxoKhTQ/P69UKiVJZn+QtG9ubiqXy9n/+U//6T/1Jv7kyRO99957bghp9CS5EKHZgy0DOQVhYhZptVr1giwUCjo9PdXBwYFmZ2d1e9tP4oU5WFtb0/z8vMdHkGzOiByKsdHRUctdCAxhc0I+fHx8rLm5OfsB2WSSyaRnmc7NzWlkZESff/65mwI29yAzReIm7BnNArYB7g9eqW82msgd8/m806BROKAIQNYnyfJESZb5vnv3zr47vNSwKfjBYPp5T4oOGBgQR1BDGn/YRQoqDkWaAb4HTRvNDywELwrWk5MTrwmQXlhiGEMaMCTd3W7Xs4UJo+Nwh4UulUr2B9LYUYDVajU3HqlUyv896H1vNptKJBJKJpNGsW9ubhwMMzs7a+kcyghQ5NZXc46R7Um6l2bKyB/kra1WSwcHBz7IJaler9+biw1zw2ckhJAiuVarqdFo6OHDhz4saXozmYxtETCeWCIIEJHk56fdbqvRaGhlZcXXF/9cKBTSs2fP7D3kOZD6cziLxaI9+Nvb2yqVSkbMo9GopaOMP4rFYpqZmTHgRzowxQPIPwd8rVbT2NiYdnd3NTg4qGQyqd3dXV1dXTnkieAqikWKcZikIBPOZwCw42cGBgaUzWaVy+W0vr5uuSAhPgAVyP6CDDvNPb8LywjeUKnfZBH8k8lkzC6T2AzYNTg4qFarZZCFYrbb7Y9lQm4KwImEE78uaz4cDru4Zf9BqUPTPjAwcK/RCrLUR0dHv0+uTfPPvwOMbWxsaGlpyczg3NycwcLb21sDNuzDfKYg6ANrQYMKyAZ7CutJXsTY2JjHRAFSMS4LljoSifi7B9cXzxfFJioRQBmaSRRG/F1+BnVIMLTsxYsXZsFpZil4USewN2AZoAEn0Zx/pxBG/QSrSPMXj8c9PoexZPV63Z5pxhDC0l9cXHguPaAtLCHNOGwT9QPrLjgS8uDgQOPj48rlct7n8fLybHBN2VfD4bBqtZoePHjgvRyQj7FbsEQoPWgcm82mwRKYez5/0LaHim9goJ9aPTg4qGw264aLWiUc7gd6bm9vKxQKWeVBcjH3DvCOBGee89nZWSsEUHAEbVIAPgDaWP5QVJyeniqbzXr9ocQgHZtn6s2bNwbvWSs0dTCagDV3d3cGJmnuAfyoiSAbuK/BujIWi3kt83cAVWHYqWtOT0+Vz+d1enpqQAQQEhAHGwbsLJks/Bn7PY0m2RisaxQnBI6y1wHAsj/RFLJG8RcPDg5qa2vL5w7XiyyCcDishYUF21OoLwH2AAYikYgDINPptPcDwD4sPoQbkxcAUYDKiL9DTYEFiDqfeocpHuRNcN4AKLLnFAoFgzrsS9fX19rb21OhUFC9XlcikdDt7a0qlYrXl/T19BrCZ8lugFwhKwWpNXY22O12u590z/i7UCik1dVVr10yllBHcF8JqATgQUGIRa9YLFpZwnP77t0752Gk02nbVH/v935Pq6urev78uR4+fGif9dnZmcrlsrMdksmkFQ946S8vL/XmzRsNDvbHCz98+NA10O/8zu+o3W7r0aNHBhmLxaLK5bJVsORXEQDKOdput/XRRx+pVqtpe3tbjx8/9potl8vuYy4vL1UsFjU1NaX19XXb8n7x+vm/vlUTzgGXSCR0c3NjCRwPNAwyni5Sh6vVqueHctDBikj9ZOCFhQU3PDApa2trqtVqCoVCWltbU7lc1tXVlf7Vv/pXRu1pGgcHBz1yK5FIuNiQdO8gSqVSLohB99k0JLkxxM96c3OjXC7nNHVJ9n7BbARTO3noScKmKadRRdIfZGNDoZClrPgKacyRrjIHe2NjQ9ls1mghmxqbITMB+f4EFDGSA1kxqc2g/xT8yGSj0agbCxKWaZAkOckUBpOQlUKhoEKh4JAlZJ74ommAYEFh4sPhsBMqkdfA2I2Pj9v7gtyPzZQNmsab4iood5V0DywCySY9loMNSR6yUwLEDg8P7XmDRQ0monONQf0Z38XBQPI8hxDI8vn5ucbHx90oEzYFmn51dWW50dHR0T12E7nt/v6+m1BGfSCBZRwSTS5/h+KO9b+4uKhSqWTWpNfrWXI2PT1tfxpNNowjawAvJw0JkjQS9Qn/Qn6KXQG/N0oRii8KAFQCJK9OT09rdnZWu7u7CoVCXmtffvmllTmAeABgkUhEe3t72t3d1dnZmQqFgm5vbzU7O6uRkREfQolEQtVq1SnMZFIkEgkdHx9rc3PTyf9Im4PPQqVS0f7+vtmkdDptNQjFZFCySqMbBIWQsQYZfzzkNL6wCpubmyoWi2Z8KIbxfLLeYSdoiGk4AePYd6amppT4auYogXJBQAv5ezwet1wOZUylUnFhCzCGvDboMWc/Q5KKuob9EhsQvnpYfKSxKCsIFBsYGFC9Xtfc3JwZDDzfyWTSrB9KKIAwQKGhoSFP6gBMAqyrVqsGDQAsjo6ObI3p9XouWmkOO53+OJjT01NJMgANyAvTx3ViH+Mc4DuRhQDLQsbA+fm5IpH+KLN4PO5RYQMDA6rVamq1WlpdXb0H5gGcwfbA4mQyGbPwPNOw7lwjGDsK+2AYEioz1h+qB55Z7AHs2YAb7HEbGxt69uyZEomEVRYU2Xx/mlTsPXx/Er/JZ8HOI8myf/Zanqfj42Pb0rD+hEL9rAQYUxh8mgxAAPIW2Hupd0hf5lzlHmH5obahoeZ8xZaFqohz8PLy0s0cfmuYSoDHqakptdtt1wDlctl7EYAYShC88Miq+Uw869hGYrGY5d13d3dmzqmfSEUnoA+WHyC79dVcaqxua2trur6+tnUOGwM2G1R7kizZ57njDADgwI+NOpD98PLyUmdnZ7bDsU6npqbu+cQhecjCoE7iXOb7AdZQkwKyo/bhvvH8npycqNlsOoSVUVaSVCqV9MUXX2hubk7ZbFZTU1N+BiKRiMPRPvzwQ0nydwJIbLfb2t3dNcjRbrddB0PSJJNJ9Xo9nykXFxf3/P2sNwAhAsH4HMFpFDxTgBCoHfCr53I5N7qEulHfDg4Oamlpyc0v/nUm5+DN3tzcVK/Xs7UgFusHzzLZIqjYCPrpeTbW19cNCLLXEkhLLgJrCyCeBpg6pl6veyIIliUsDKFQP+SVAGRUaO+9955mZmb04sULDQ0N6erqSuVyWe12W3/0j/5RSdK/+Tf/xgHVH3zwgScFbW1tuSZDvXh3d+cZ5Twb1M8AhYx4Y4+nn8FeSB2CPYl9ZHx83AA7NXXwn6n7UAJ9//vf14MHD/T27VtJfRINZh7r2k/7+oUc/Wd/fasmfGBgQAsLC/amsti4uTTm2WxWhUJBGxsbZr35OTxYzO5GGlEoFBSLxRziwcHaarX0ve99Ty9fvlSz2dTq6qree+89B+H86T/9p/W7v/u7urm50S/90i+ZCQqHwx4xhiychoxGk00x6HMHoeKw63Q6Wl5edgF1fn5+70BuNBp69OiRGo2GgxoIshgaGlKtVtP09LSazaYLzEQioVqt5oCvRqOhxcVFNRoNFyqjo6OqVqsO4iHhN6hEoLDGw4mkhs2Q5pAmmKAKPFxBrypsL5sscl4afxo2EOzJyUm1Wi0dHh660cKbFI/H76VKp1Ip3xcksRTmBJGQ4M06ChZlbLSg/MGgPV4UDMjukO3ChnNYSvKIoaCX7u7uzgUPQRzYHpAx4XEH/IHVhJ3qdvthhEieyuWy0VwO6aD8nWL+/PzcwBMMCc8WxTp+Vpp2gvbi8bhldBwsrNVSqaT5+Xk3SxQ6MCgUQAQeIrubnJzU+Pi41wfMA6w1AAdMHp9zbGzMazcYFkMAIIATDSa2FcL5WLfBkDskfhRn3A/8+5LueU8peNLptBkQvKOSDDrg9ZJk/zbN5fX1tUf3ET7JWBw+x+XlpTY2NpRKpbyX4I2F0SFFF5YvmEnAumDt0uhSJHEPJTkVFckX+yN5CnjtYJG4PrDafG9+z8DAgNH7g4MD5fN5ZTIZXw8aP6kPGlKocc+RW5OkPDIyYn98s9n0dYShHB0ddQovz3G9XjdLRlEejUbt6w7mRwB4IVE8OjrS1NSU5ZgExhGAdn5+bhlqLBZTs9k0S4g6iP0ToAQ5H4UMDUOQMT45OVEmkzEzSSOIh5WGC5aHYpj9CMZQkqWxFI43Nzcew4bMns9MkCMed3yPAKecb3jtKSzv7u40NzdnIInPiepLkqanp++NJ4MVjcfjZnxhlghtA+BhLQfl9tVqVQcHByoUClafFYtFA/DMQV5YWPB8ctY1wPfi4qIzEACXKpWKGUsUPqOjoxofH9fm5qYZVfYeAtSQawPw39zceBwWNhmeX8Bpml72p06no2azaXaWz0R6PIANMn2mTMB+Q1qgsFhbW9Pg4KCbCIApmuGjoyMlk0nV63Wtr687h4CApuXlZavCuMZ7e3u6ve2P/Zufn1er1TLbd3Z2pkwm44kRU1NTDmoiOI4UcfaOXq/nZ6Lb7erNmzdaWlrS1dWVfvzjHztIjqAn1h6hsdfX154JTsOFpL7ZbHr9MIMaBQsMHMqGQqHgcx0JMHbD4F7KGgQco4kAcMSnDhED24plgjqRswxPbVBtE4v1w1mpV2nG7+7uDBbQkB8eHjogFTDi+PhYOzs7/nMS9LFedTodlctlxWL9VHCeR2pRgsgA1O/u7rSysqLb21sHcM3Pz/s87PW+ngxBPdPp9CeVQCSwH1GHUKNyjvFcUEtSZ1YqFdeSWMHIU6E+I/yLEFeeAyw0PM/Hx8cOA2Ov51ne2dnxuXR1deWmHYCS+plamjPq7OxMX3zxhebn5+/ZI7Hh5PN5ffrppzo8PFQ6ndba2poT0AcGBtysB8MHt7e3bYskcHBnZ0c7Ozv6Y3/sj1nJh4cfm8rg4KCvC/Yp1hiqWcAh7kW9Xtf5+bkKhYKy2az3a9bcwcGBAdDNzU0TI9QL3/3ud9VoNPTixQtPjGDO+tXVlVVzX375pc8Lggmnp6e1sLCg3d3db9Me/uL1M76+VRP+d//u39Vf+St/xcUATQUFEo2V1GflaLQHBvoBQ3hTmKEKIzA0NKR/9s/+mSKRiH71V3/VjewXX3yher2u6+tr/cqv/IoymYwWFhY0Ozurs7MzvX79Wh999JFmZmZ0eHiohYUFz/SD+WYEE40rDzqHH7IlUF3YpLOzMyUSCb17904//OEP9fz5cy0tLZkhZZMBUZ6YmPDYKaQ7Uh+BTSQSGhsbMyrG4Ud69tnZmTY3N90wc/BOTEyYDcbTQQpnPB5Xo9Hw5oVHN5FIKJFI6Ec/+pH29vaUzWbNEvK9sA4cHR35QSN0gvuFDBjpCyE0wRAomkMKSoocimWa6dZXI5lojCl0QfDxnnY6/WRfxmjh75VkpoWCJRjOF8wj4PewJilwYbgBBmja+e+wB9w7xtfU63XNzs66UYzH4yqXyy7gCRTkcEJyzsFN0RAOh12ocN1zuZxRcJrsTqfjghs2IjgTEjaQz8v6mJubs5dbkpt5ik/QXQ4v6esAnna7rbm5OR8K7XZbExMTljrxzAaTrgFkuKeAP/xZJpNR4quEXIJVaCSHhoYsTyUEZGVlxQ0izSdMnyQ3jOl02o02Y++4rkhGeQ/Aj2AoXqVSMTPdaDS0tLRkRQJgRSKRsAx0bm7ORQeN7OHhoZNkQ6GQfW40XJIscWedsbfAfCIvBghkbVK8BwEkSQ7dwprS6XR0fHysu7s7F0JBOwbXB8CJBh10HJkhHjo+K+AHDevZ2ZlniQLKIM1G/gzAA9BKEBTft9lsWgHR6/V8XZHXwr7TABGIg+qKhotnIJlManJy0r8DwI9nhf2Q34sFAsAok8k4zCc4yg21QTQadVAfoBFSXp511B3BbAdsN7FYP/AS1rfb7dpHK/UZBzyRUj8rIOijhWm+vLz0GgfsuLm58bx0silgj2kIUbmk02mvL+5JMJn94uJC5XLZwFO1WvV7I9XknMRTyp5HoBxF6ujoqEMjGVFHYVkqlXR5eWkJLWAHzBSJzszu3dra8r4Ti8W0s7OjwcFBPXr0SIeHhwa2UXlQMOLFPDs7M/CNhJb7wBkHsIvVY2dnx3siWQWwWSiZgiDK+Pi4p6a0220DA6hLqClQT2FPePTokYNh9/f37akdGxuzKu/29tZAU7lc9vOPt31vb88WgsXFRb19+9aTUXZ2djwxA1DhwYMHzljY3t62lSmoPGBvwy+L5QBZOyF2kUh/7jDjHWnEuP6AguxF1WpVuVzOIAyqOsbiYScISrn5XJ999pkWFxcd7EgYJMwlakpyOAhE5LkE5MM6MTs7a+ktzwvqo2w26/XB3omFsNPp+HwuFos+e3nOrq6ulMlktLOzo+PjY5VKpXsAa9BKFY/H9f7775vtZh0kk0m1Wi2VSiX1ev0ckmw2q3q97uvDKD9qU/Y8akBUTdvb28rlcgb2OU95pubm5mx7AvhOJpPa2dnR9PS0aztUi5BnKLIIUKapJ3QQEImzDWIDJUar1XIOBcrHjY0NTU1N+fOWy2WDsYAjnOeoWLkOyN1brZbK5bLy+byGhoY8km1yclI7OzvOFlhZWXGt9erVK52enqrX62lzc/NeAx581qlhGWW3sLCgRCKhSqWizz//XO12W0+fPrW6YG1tzb8b9VQqlfJoYpRHmUzGtROEBGovfOSoJ9+9eydJHtcmyY09DDxhvj/+8Y+9r+dyOc3MzGh/f9/PG6qgn/zkJ+4jADm4j1dXV3r79u095et/6vULJvxnf32rJnxvb0/VatVM3ze9hnioYR2vrq7cKNZqNQ0PD6tQKCiVSumTTz4xGry2tqZ/9s/+mR4/fmypBM3sr/3ar2liYsJpuHiBCEp59+6ddnd39eGHH2pwcPCehweZLLJSZOCSdHFxYSkoxSRNy/DwsBtJEDAkLyDbzBG+u+uHP7CRI60jTZsDjBC4vb09h1ENDw+rXq/r4OBAsVhMCwsL3nBpWoINJNISZL9IdWiSaDiQqhC8gmyTETRsvrCXsMIcWBQXZ2dnRlphAdm0kdaxqREKBRINAHJ8fKxMJnNvFNfV1ZVHXBCORJHP4YnMHCAHlrHValn2DaqaSCTcsMCeBn3fFDmS3CRyPVmzsVg/FRo5E2E0JEEPDvbHzhwfH7voZ1RUpVLxfYe55qBmDfHZpL4Eb2VlRaVSycEfwTC4oGcViRvKC0CHmZkZAwgE9jGjs91uK5fL+TnBgwxKDLsdj8cdptNut51K3mq1tLW1peXlZVUqFcueSXXl/nNQ442jyJFkmwpNbblctiQK4IYCg0Ap1jcKD5QZ4+Pj9w73eDxudotmlzAWvK1Iw1+9emV2hfyHVqul9957T+VyWXd3d0bsO52O5ufnva+dnZ1pe3tbiURCi4uLZsN2dnYsweU70IhSTCIbZ/1w3VgLwbVPU41UjTUEqHR9fe29pF6vq9FomL2moRsYGPA9RkVDFgTsB8UWNgaeR7yWWC3Yb/Bu06zgY0TWzOi7YAAVtiMAjng87kYrGJpHYUbQUbVa9d5I4VWr1fy+gFzdbtcZHSiW8PTRrLIGCP8CqLi+vvY6AzgBTAgm8CLpA9hE4tnt9kcJweiTNhxUmkSjUQcB4UsPggX7+/se/8S+TGL71NSUm7zd3V1dXFxoZWVFOzs7vneoMI6OjuzpZQ87PDx0oBseYJhJbC3IqJEOw/awxqW+l/T09FSXl5daWloy44IPFIVH0DrB92SPqtVqPlcB52A/r6+vdXBw4IAx7g2MWjQaVbVaNesEYEz+weLiolPwadpQWNRqNctR2XtZP81m02oAJP2AiSg9CC1DnUExTePHSEf+R13Q6fRHkG1sbDhDI51OO7Aq8VXaO5Jyrgdy2SC4Wa/XfQbQODElBvsYZ2+lUlEmk9HJyYlnhPd6PVUqFS0sLEiSVUMoRlCxMK+YQpzfh2oNQoV9DfYsHA77zAmO7uP5x8aHgiqXyxnw5RxHmYStRdI9JU8mk/HvJvWZe8HewMg5GF+uGY04SgueKSTa+/v72t3dVTTaT+IOhfpTXxiFKMn7Hmo76kaYaEkG19jPBwcH9dlnnzl8FCvf7u6ugeAnT55oYWHBdi783ABu1AiNRsO1FXutJG1ubirx1ZQKzjMCSDkfI5GIyuWyG2Pu87t379yAX19f6+zs7N44XsgxanmARwC7oNw7Ho9ra2tLrVbLocaSDPJCWhDutra2ZqUk63NnZ8fkEBJ3rAfkAgEE8t7cC1SjQX874PXr16/t46auOT8/1/r6uj788EMdHx/bow34hIefcxAr0/DwsD3SExMTWlhY0L/9t//We+3z58893z1oV2CvA0xtNBrOl7q9vdX+/r6q1arOz881NzfnULh379651un1evdsCWNjY2a819fXDbAi7ycPgc8EwSPJDfXd3Z1yuZwWFxdt6RgaGjLgd3XVn+rEFIBfvH7+r2/VhNMYhcNh+6BevHihJ0+euCGhMaJggnnOZDKWNkpy6NfKyopGR0e1urrqZgff1ne/+137IQ4ODow0BueMMgqg0WhYwshBu7Oz44VKYUWBFmQ3kIHA0FFMNZtNSfJmjDyVZgbZFgUAvjkK4qDkDRQLgKFYLGp/f9+SOgonmKF6ve6iCJktG83x8bGLHIr1w8NDLS0tebPH6wiYgX9/ZmZG8/PzGhwctE+Oz493JxwOWwLFCAgKt+CGyME+OTlpySrIPd9naWnJIUE02WxGHJokxpfLZa2urhqZx4NE88dnoXgZGBgwcit9PSYvGNjGRktTxP+CI804QEG1Wd9Iwcrlsg8+ZK+E6gV9vRQy+CApCJD7EwwDEwtyD7rdarXsZUWSjD8OFhQAAdAEORYFK4wweQUgonhgGTUFoIAygIaPDZgCH88loYNBNph5m8jYLi8vzZAhzUS+mUwmrcAol8s6Ozvz54P5ASiDmTs5OXHyNs8a6yWVSmliYkITExPa29tzwBL3AVDi5uZGxWLRM5tvb29VLBY1OTmp3d1dfy9ADfygjOggTRQfOgc9hx5FWqFQsGeL9YUkG1m81C8EJyYmfO2RajP/k/vOGuJ6ULziA5Sk9957z8UZDSj7MwUcoBRgBc/T2NiYZmdnzbCyrvDy8ixRfJRKJY8xQUmBZxrAFaAGtiwWi9ne0mw2vc/xvjDlr1690tjYmBkj8gBgJPFe0wiyH0gyuEahSnNDhgI+SIoKmgHkwXiBWb9BNo0zDdaca8JaxDaBh5o0W4ABGv6gTxogAHULDS5FNYURBfbt7a3ToMlWQeHDBATWJWcdL+TxsEYnJyf+99evX5tZv7npj/4hPI5GGvYOrym+WWTQ5JRwRlEQA3peXl56DjiJ6LFYzEw3yp5SqaSpqSk3lAAErC1Uc8fHxwZGmEndaDQkyawl593h4aH3UjzAnCHcP0Ja+R7hcNgBgyTk4z/GkkJhOjo6quHhYY2NjWl7e1uxWD9dGrksHl2CILExoQrD/009ABCH1QBQIp1O2+rGSDJqKf4OTWG73TYzxrNHgx9Me8Z6EwxKhf1mPwGYAMgfGRnR69evDbgwIxs5Lb/vhz/8oWsrnh/IEM7qYJJzECxHZca5zCscDmt1ddVNGGACzCoSYKTr38wc4mxD7t1qtVynQrzQwEIy4EGn8SR0j3Ob5wZmnUyGeDyuhYUFq/mmpqasxGg0GlpeXtbw8LDW1tZcH6JiKJVKBhMkeTpPPB7Xq1evVCwWbRVBMs7abH012o8Az7W1NTfVANkAVljoqCmp3aiNpa9HlJIHQD21uLhokCqZTCoejzv7AYBWkkEcZPXYOwhp5IzJZrP3QGNC9pD5U8tgrTo5OTFj3mg0NDs7ayUVCs7EV+PlpqamrJoChCuVSlatsX8D1PI+TAcBiAZAzWaz+vLLLy0tf/jwoRKJhF6/fq1MJuN1Eww6fvXqlQmfyclJHR0dqVarea8GBL64uNDh4aH3w3w+7zXGuck1ubi40NjYmJ48eeIRteztrC+skR9++KHC4bB+9KMfKZvN+txdW1u7F+RcKpVseVhYWNBHH33kWv6nef2CCf/ZX9+qCYdNQt6ytramarWqfD5/b/41hSpybVA0WNVut6tiseiZkMPDw/qLf/EvOnBndnbWhwZNbrvd1qtXr9TtdpVMJr0Qg75cZL+np6c6Pj7WzMyMZaQwHSDayD+QweEBnJ2ddUgWwRR4vJABs8CRPCI9zufzKpVK9xBbkH/8U/hXDg8PdX197RT0Xq9n6XU6nbbEJpPJaGZmxp8FtBngoFqt2ttTqVTMbvd6vXujalqtlorF4r2wKiT6oGFIN9+8eWO2nQaNABqKCORtQQkNBytNJfdOkoscSfcCJKR+kInUR5+RBSHFp/BBqSDJwT2wU6D/jLQ5Ojoywo+EleYRpUaQuQwWv3i58/m8JFlpAFONDA2pNKx/r9dTJpMxU05aejQatcwpGDqSSCSUSqV8kHe7X4+ZohGGmeaZ4v53u/15tgA+SMxgfJCeTk5OqlKpqNPpaHV1Vc1m03JzinFULBQppIDC0AXHkiGBBGCCicYvD6CF5SGoCpiamrLHFBnwwMCAFhcXffizkR8cHNwLJIFtQfKONYGDlMkB+OFhCo+PjzU/P2/JI4fMzMyMU6GTyaQqlYoPvE6nP25qc3NTT58+VS6X08bGhu9BMCAItv758+eamZnRwcGBAZngmuPnhoaGXJzw/e/u7jz5gYYHBQGAEe9BkTE+Pq6HDx+68T4+Pla73b7HjMK+k1PB+7VaLc/fZQwSfx7cU5H5YflgWsXBwYHl9K1WS6lU6l4SOkUU3xHbTtA3j+qk2+3q4cOHBgTwDE9PT+vm5saNLkAZKgjAoOvrayssJHkqAM8YrAxz5Hm+WI9XV1dO756dnbXvdHp6WlNTUwbakOySzB70m3755ZdOFybwin0PJhifO9+DUZmS7NHF1gDgxj64s7OjdDptPyzKCqxVnH14HpG1U1wBTiCNL5VKljryDEciEW1vb9texjVmEgYqJthO7j8/Fw6HLWUNhUJudPk8qIYAgMgaubm5UaFQsPogaFtjj7m5ubHHngR2QpNoYgDRYNQA82j2sA/gT6YB51oEG/hKpeIzHxUP5wAML0A3YCj3lvPw7OzMAPuDBw90cHCgbDbrHJHLy0s31OS5oKxgj+z1emYF4/G4921JBmbevXvn9YniD1AKuT17OfeNz42EHdsXwBr+d7zip6enDtuSZLVeq9Xy2QiQwTPHswzoDhiLypAXbGylUtHt7a2bM0Atqd9w5/N5XV1dWV04ODiohYUFNZtNKzIZmUjYZz6f9zVDCdZsNq0qkOQkaeS/pVJJ6XTaIDnqPsYpco4fHh46YZ68nUwmo0wm44T5kZERgye8FzXEv/pX/0ovX75Ut9vVBx98IKkPJj5//lz7+/tulNgvWVfYHnq9ntbX17W4uGhQbGBgwOczuUxYdvb29nRwcKBMJmNgifW+u7urdDrt5wdyhWyJYOjX9va22WrOXwICIeV4f2TdWP/i8bjHFNJsLi4uan5+3swxNTqe9tHRUS0sLBiEDQJi19fXnsIA2EIIZTab1d7envb29jQ+Pq69vT3XODwLkC2oeoL5SpFIRMViUa1WS7u7uwZOqe15xl6+fOn7y76BzDwcDmtra0tXV1d6/vy5ut2up7wE7Qd8Z7I4wuGwlWNkBywuLurly5euI6Q+IcrIxVQqZTvC0dGRSqWSwuGw1tbWNDo66rXIhKNUKmXiEpKE+g0lJFbMX7x+/q+faU54JNIfr4KcDf8RDAsbI0X5yMiI1tbWLMuKx/vz7vBbBWWcQ0P9OdRLS0va3t722LCtrS3P2UOqNDIyouXlZR+GUn80ERsvh8vS0pIPUQACGuOVlRU3VXjAFhYWPKIMpvn8/NyBCjDssLnMLN/Y2DBTQwFL4ccLCVWw6AWBTiQSOjw8tKwd1DUYaEJAxt7enpaXl60+YONESgTrAjIJSokc5vT0VCsrK25UaSj39/f9HhTFAC9ci1wu500Xtq3ZbFrqKslSPxQLFIa9Xs9I6uDgoOr1uhvuoBeWYgmpaC6XczNFc04BQCEKSzU6OnovbA9GMejPhd0BXCGwI8iEw0jAhm1ubioWi92zBxCaBejEOuZA4plg42d9Y08IyjVBwKvVqgtr5Nu93tdhXowmo7BC5kxQE4w5wUFra2s6OTnR6uqq5c0kl/OCnaGAZhYvzTcFN9eTZ3xoaMgSYoqh4L0h5AyvKuwpjDXeVhgtGmsKIBq4TCZjqSae1M3NTTNUzOLkmtfrdTOuZ2dnevTokRtZ6WvpI4ceMkWC6brdrmq1mkEegCPWKcAPSoGNjQ3/GeufJpk1RbEh6V6hc3JyYsYUoJO1APtPwQDjyJQFGKG5uTnvexTvMG0UBdxnAg5ZN8jHr66utLS0ZHUAI1lgKAAVeHYHBvoBi4AwNFyEGAVlgsH9grWEwoLCPBgMBMg4NjbmgoUkZNZPcO8hX4J9IxaLeV+GscOry7qikUSlgsWHMwlVByDj3V1/pi3XnABLGqZOp2NLCzJPGkwK0ampKaVSKRddjBnDC4zaJ5lM+ozjGnBN+I7xeNxe2enpaWcwwHayH8Aks3cz75liMhaLWXXFPnZxcaG9vT2l02mzz6i2ut2uFT00paxbVBjdblc7OzsG0BnrhCKBrIVoNKqNjQ1nSPBcJJNJy4NfvXrlRogGmbXMGY/CAqAEhQHSadguilescaT4876sGe4Hc+ZzuZyVPqRH4/NHUgtrSXPIfcGKEQTa2u22lT4A2rCbY2NjOjo6svebfSUUCnkUWa1WM9OJAjB4v9l/OC9o7Nhju92uQSYkuIBn09PTvsfcD9R1NF+8t9RvpguFgpUFgMEAVsfHx/cUZYC3nLHsVdPT0wbAse8sLy/72QqH+0FuMJjBqS4DAwMmOFBn0Zhy1gwMDJjgIb/g7OzMGQPMq+ZcYp1Fo1Ht7OwY3CFDBcAzm836niLxXlpa0qNHj2y94HwLWpNYZ5wRKCkJN4SdHRkZsYoEEDSoZMQmRQ0Jw5/NZh122+3207ej0ai2t7d9jUulkiKRfnI6o9J4HsgYoobF2sh65QwAiEBWDyCJ5ardbqtardpjLsk1fKlUspWsUCi4IUWlhe8deTVsNfvX8fGxxsfHPUaN+kyS110qldLa2poVn5A51CfUi2SCoBxhL6VGg91mX8dexRlB/fn06VPncxA8l8/n9eWXXxpEbLVa91Q3y8vLOjs70/vvv6+hoSFtbW3ds6glvgoZ5XthwUHpAOl1c9OfMX54eOi6OBLpT9R4/fq1ayEUiJKUSCR0cHCgp0+fes9j7/ppX1gafp6vn/f7/7/1+lZNOLIIUv6WlpYcLAKKjpySAh/0D29FLBbT48ePXUB//PHHfoBfv36tyclJbW9ve/Yxs6mfPn1qv8rZ2ZmePXtmRkqSA3w4QPCuEbIDWBCNRp1oy4HHuJJgM0AxyO+A3Q4GFdXrdT158sQsNdcB1oVmslwuO7CEYhKGKeiPw5dDswayJ8leadKml5eXvaE3Gg0VCgX7QUulkkKhkCqViiWryGXwm5GWSpGNnBI5ExsBhRoMEoEQMLeS/D35Hr1ez4UYB+z19bW9h4Q7BWWa8Xjc8joAHYpTrhXNCht4UCrGv8OmwhAEPyPrkcOUYCsODUn2pJGiLMnXj6RLmmaSR2kuuU/I1WFmGTsHa3h+fq6TkxMXQzAggFuEO8GKckhzGOJxIqmezwPTAZt4cXHhe7m7u6tXr14pkUhoYWHBrDzsN9eQ73x3d6d8Pq/r6/48WBqpfD5v/1I4HPa1JC0W9oED9+TkRMfHxzo8PFQ+n9fy8rKVBUNDQ2aAbm9vtbW1dY+hZK7s5eWlGZAguwqQQFFMQ4a8FO8ds0ixmYyOjururp9o22w2je4jyc1ms9rc3DQSDKsZ9PeynhYXFy1XI2Ge4p/rAMsUCoVsY8H+AbKP9I31gN9+cHDQhSE2AQpBbA7BcLRoNGpJJ359JIXIcBuNhmW+vV5PyWTSvluKoMnJSbNXFC0U8xTmS0tL9ndz/WEtAAsppILS206no1qt5ikLeFZJcT49PdXV1ZWOjo40OzvrUD2aeIAawK0g+IhCgaZiZGTEifqspWg0qmg0ar80xSXAGpJ4ZPFYc2BTSCsfHR3Vu3fvtLOzY+80Y8wAoUdHR1Wv113053I5n1FB/zoNBk0uHnBeZCggR2fNsZ65/6gtOIvwrbIn0FjU63VP27i8vNT8/LxZ2eAMXQpzmCKCUlEGYEkJ5mrw8+znZHowDxwlCtkJFLeoNgAmpD4I8/DhQ9tCCEFFCbC/v+9kf54b0s/x9TKnlyY7+AwBzAZBGppMLBiwhrCS7Jmc86FQf8Ro66txUJeXl86t4Jxjf018NQoMppdryZqFXT49PVU6ndb+/r73oHfv3jk7hbGTMN4wrTMzMzo9PfVaJtDw+vrac69R6RQKBa+rRCKhmZkZ1et111kU8DDf2MYAPsrlstdUrVa7B0ZRRwGOQwLk83mtra0plUoZQCAAjkC9crnsjBvO+qCKjrqJUaDJZFKjo6MOvGu1WqpWqyoUCtra2vLfofGkgeSzMQkDtQIEyf7+vg4PDx36yxSNaPTrEGKe27dv3/o7XV1daX9/X9lsVqVSyb5o1BvFYtFe8MnJSa+rt2/f6sGDB64dg/kHlUrFZ3mtVlM2m/WeKfUzJLAOIPlmzaVSKV1eXmpnZ8chidgtEomEZ56TKk+WUzqd9llJLRoERIIhoICbgOw0uNfX17q6ujKYj+WP+obmmmeY2hCFK9kM7Oe5XM6gBlYHLKLT09M6Pj72qNFIJGIFJ+n/AAzUvJyTgEooJXZ2dgxUk2/z8OFDE3jcexSYhKhdXFzohz/8oZ/F0dFRW8+SyaRHvmITxKONrQIJO+GikGlkS52fn+v8/FyZTMb7KwqQWq12z0L75MkTnz0Ey7HuyYw6OTlRsVjU9va2lRD5fP73kTS/eP38Xt+qCUfeQqF4e3vrDY4N7fr6WrVaTbVazeFpeDhBw/FpEpXPPz9//txjUEC8SOUE9Ubmgr+TTZF/T6fTajabDm5YWFhQt9s1C9HpdOwzwb8mybIowmE4bCkUQSZDoZBlwhzanU7H0sBWq6X19XUNDQ1pZWXFDxFSLQ51ikeSjYNJjPg7aF7ZGJGGMfYLGdDFxYUePXpkiSENDk0eDBosPAVyEA2HgcF71273Z5ECCuDHQ0a/srJiySkSS4oIimp8bUdHR96EYHOQfbJhI8dnY4MJ5LCgKQpKVymGJLmohfX+pjRKkt+TQhzZOCwLfjw+M0UswALqABQeFMOANdwbGnOp33jhYzo7O3MIH7I5/F0UVLAJsE6RSH+UBY0cuQETExOq1Wq+zsHnhecQJcfY2Jg+/vhjnZ6e6tWrV5JkKTpNEwhwtVo10wirhId5YGDA0lTuy/n5uYtIPMU8DwSGwKpwqORyObMukUjE48tgMCVpa2tLpVJJx8fHnl/KIRWJRBzUiMSzUqmY4eDQPj4+dmro9va2Hj9+bNBpb2/PSbTBsSeXl5d6/fr1vf2GNYbfFsDt/PxcGxsb/rNgkBGNOzkQFNlBjxp/BqqNdYImDlk1TBT7AL8P8GFyctK/F4aSwntmZsbvTSNIgYL6gWIIlm99fd3F0OXlpQFWrEI8X6gfYGCCqgyYREkuoGjcKFCw1cCkwqzwgp0F3GFMHHah4CxyXvwe1of0dT4FPv1er2dfH0oLmOvgezFy7ezsTPl83s0zlgtUSqTyEuaDXBWZMx491hDNL8AlEnQKTvbw4eFhqzSQwjLO5+HDhw7E5NnpdrvONmC/4jk4OjrS9va2Hj586PcbGhoyeHZ6euo8iUwm45GRMCyAbVNTU260AXE5s6W+V/ro6EixWMw2KkLhJLmpPz4+dpNIAF80GvXUEEnOCAmHw1pYWDD4RXEeDoc1PT2tw8NDM3UAWoSI4cMkwIpnGnsDACrqDNQFjDwiyJLrw57B3gM49+TJE9tA2N+wPQWl5yRsDw0NeW+lUJdkcJERYGQWoDRgT+h0Oh4bieKOOglVSNDONDs7a0CE74nFAfZsZWXFdUWj0XDCPIqFvb09dbtdh7JFIhGHccICorQCOKfOYS/67LPP9ODBAz/7gDpXV1d6+fKlz4NUKuX8iW+muGNV2d7e9vrHA0wD9/DhQwf1SjJr3Ol0tLa2psXFReVyOf3whz+07BoA+fj42ApFnuugv5xrCNGADJk9F0UE4ALXIKjSYYwjdeT4+Likr5WSADuSvG/u7Oz4Wbq7u7MyDxACNcD4+Ljy+bwb9YGB/rhSFDBDQ0PK5/MOlAzuqTT8nDEAOoy3hejhjCXXhGlIqDCZkoG1ByCKWikUCt3LQ0JFgIqrUqkoGo1qZWXF+zcKu9nZWYe2FQoFHR4eOi8KIAI1BM00/QH7/9XV1b2mlLouWFtOTEyY8JqdnbVM/Cc/+YmOj4993rGnIYUfHh7WmzdvNDk5qaWlJVWrVecjXF9fW3VJ/8BZzEQQ0t8TiYQ2NzfVbDZ1dXWlhw8fanh4WMvLy67dCZLE0omlcm5uTp1OP5A1aG0ZHx/X559/rsHBQc3MzCiTyajT6ThFfWpqyuMOt7e39dO+fuEJ/9lf36oJn5+f1/T0tCKRiLa2tnR8fGxZGYXg3t6eJRg0B9PT08rlckomk2Yfi8WiZcCFQsHSPg6/8/Nzp44imZRkFp7imKaFDZB5zaDgeBFJtYQNoqmR5I0+mUxajgGixyYcCoU83uPRo0dmXmDrWq2WEVZmJsJ6It0loA7vETK1brerly9f2lMM60pIw/7+vj9PsVi0dJR0Ujbls7MzF39B9ohihcIBjzWHSVC2ggyf4hEJ3c7Ojh4+fGhJ+d7enqQ+sg/aLsneKub4Pn782I1StVr1IUsqtyR7o4P3SZILrZGREQfRcbASUsX/w+AGEW4OOYoIUEuaBBpF1iWsOswaTSQNFxs1Xk9CACngmIfd7XbtGePwJsiGAml6etryJ9YOXl2yDTgUkaINDAy4EYDB4VAMhfrhXTQrbPgc5rC9kvTJJ5/o3bt3mp6edigdoVZTU1MGgkCv8RJxCO/u7nqWMYxANBo1gyDJbAmF6srKihscvEy7u7vKZDJWIcBOTk9Pa3V11c3q4OCg5eyoTrjO3It0Oq1qtWqpXzwed7E7NTWlzc1Ny++Oj4/tCZfkUVkUk6w7mH4AR+RQ7FVcW+TiwfAuGlTWIn5tEqGr1ap/HgWD9PVoGPY01B2M+OL9Op1+sjeHJ/shckU87CcnJ2YvaIi+mTOAegdPbqFQuJelwPovFApWLQFEwJjSlKFsuLm50ebm5j0P9eTkpMdwMV8dzz9FQiwWc4EEEMo1APBELkcjzPq7vb31OMFSqeRwN86MYBo2+xb7QlABQkF0fn6uBw8emE0HUOF5gpEkg4PrSpODV5OzMVgIYsECNKAZAvziegAYAuIB/tGIsde3Wq17e2oQKGGt3tzc6PXr12Zap6amHKzFGJ+zszNNTk4qmUx6H5NknzCTCnjOLy4unE8Be4qkm+IdwAH2vtPpOLBpYWHBwXCEd/Is4juGIbu7u/Pn5DoAJuLjRT4qyYA6QDXMIcGn+XzewDiM4u3trU5OTpxiPjk56fMa1UqxWPSzhXKAZ+v4+Ng2g9PTU9+zdrttRdPe3p5VNSiyUBvhhwckByDlfiOLpTmmdkHuDoCez+fN8qJMabfbDg0EoN3f37cNDlVIp9NRIpG4p27sdDp6/vy5G2LsHGdnZ7YMAshxNjFFhOd8bGzM7DtZKgRCFQoFLS4uOpzyk08+car6kydPdH5+roODAxWLRd/nhYUFq9YIgEUFyRnGGpdkryz3F+CG4MObmxvNz89bvgvDmU6nNTY2pi+//NK5OYDnnDeocMjbIZANtUc4HFa5XNbe3t49sgHg9MGDB6pWqz6LaKABDMkFAFSG9ADUJBcFVR/+egCLXq+nBw8euKFvt9v3prqgvmOtoGS7vb3V5uam6xauH3suORoAmbFYzE03/nT2YGo6muCBgQF7xqlHCWu9urry5AyuFWDdzMyMlQXs5cVi0ay2JKevj46OejwXSi9Yb0BEUsU5x46OjvTo0SPPU3/27JktoxABgHIoWfFaU3MODw87HBJ1IoBFMDPj4uLCgZvUdr/8y79shdTCwoLBoXa7ra2tLbPh2WxWqVTKZyR2WdLaqZmDQY9YDguFgu2XrJuFhQVnTTSbzXv5Db94/fxe36oJX1pakiSb/E9OTtzY7u7u2ltAIi4zHoeGhhwsQXIhjQtBNkgH2TCQgSNby+VykuRDgAaZxo6DF6YH/xZMIEUuzVVQBghDFYv1o//ZyAmw4AFAHj8+Pm7vEpISmNNggA1IODKwcDhstg7PHkga6cmrq6tG8XmwUqmUN5JqtapyuayHDx96vi9edg6LXq/nAwuJ483NjXZ3dy0np3gZGxvTixcvNDc3p+HhYUuT9vb2LH9CUt5sNnV7e6uFhQW9ffvWD//x8bFH7CAvQooUjUaVSqWcVA0zDavHBkvTRgMFa0chOz8/r7m5OUsFT05OXPTC+kjyZn5ycuKmNihNh+EESUUCGY1GnSiP4oHRWaFQyA0hnwvGhHnwHKjIK5FCUSTUajUNDQ25yIIxoMlHjk6zQMHK2CZ8jYeHh/Z2Dw8PG7QZGBi4x3riY+b6cE0TiYQePnyo3d1dlctlI7/7+/sqlUoqFouS5ECiWq2m7e1tJZNJS5SQdyG9ozEimAulDOuI8BbuDUAQhYb0tQ0ArzbsDd6moHcOVcbZ2ZlevHjh9wBcGhgYUD6fVyKRUK1W87ggSS7gJfl5LZVKPry5pgBe+/v79/xO+CRpiLPZrEcPIWGm6UTdACh3d3enarXqJoOfY0+DOaeZQQp3c3Pjwhlw4PLy0uNqqtWqWS5mkLJvwb4gjQ765LlusFgoOGgw8fgDWuFzZf9mBBgHPCFokrS+vm6GAWCVPTEoG6eYAsQaGBhwMRb0UsPa8XsuLy8d9Hl9fe0gJpJ1ARnIpGBd0lTwGUj2hWGMx+Mu9mjSeE/kpuwz0WjUCgAAwoGBAa2srFiNRCNL+Kcky7pnZmYMjAKSBXMiaNKwSfE78TLj5UcxwDoKqh0kmdWjASV7hYYNOxbA9MHBgaS+8o3rKMkNG/7pkZH+fO/R0VFtbGzo4cOHnnKAxJbrF2zCAT+xJxwdHWl+fl61Wk2xWEyjo6P3AtGQPO/s7NgiQTgdzT5Kh/39fe8DNzc3tjPgl+/1egZDOdcnJyeVyWTs804kEpqenlalUvH5wbkpyYqZwcH+LPaXL1/q7du3Wl5eVqFQ0NHRkdU9m5ubOjs705MnT+wF5XNTzJNxwnN+dXXla09j1+v1DAoQBoW6gvOW5omcGkIByS0ByIlEIq6RsNkBJGCTQTEEkAXoxTMo9RsEpLYEb8ZiMTUaDWeBUMgDNpC4DbiCZYuzK5lMan5+XltbW1pZWdHnn3+uN2/eeNQmagnAYnJTqBkBtHm+yuWyKpWK97ighQq7BWcu9w3QjQZmenpan3/+uRURu7u7CofDWllZ0cjIiFl2CJ+pqal7DczAwIC2tras4GBPRpERrAnIJKHBA+TudDp6+vSpPfWwsozLHBgY0IMHDww2AfJBRAHkczYDfkp9xQ9SfObZ85kODg4ctktdwXgulB/kIrRaLa87zt14PG4VD/cnmHkSVLmenJxod3dX+Xze/nwsN8F6DrsXZ7AkK9k441utlm1LBLkBdKIY/Oa4ueHhYfcTk5OTevnypcbHx60YoDlNJBKanJzU/Py8x0fS0O/v7xswPzw8vDfekFHHqCBYo/Qo5DfMzMzohz/8oQPxBgYGbF/IZrNWjwH8oZ7jLAL0vLy81KNHj7yns4+gPmo0GgZgs9msQU96LVQkP83rF0z4z/76Vk34ysqK0ZhoNKqRkRH7ZGDGmC/KA9FsNt1wraysWL48MDBgjxeFG40sPjoOqmBIF0woQECpVLK0CFkTTTpsDQ03BQ+NKmgfgEAymVQmk1GlUvHijkajDn5Ip9NmWvAtd7v9pGpCUth8YIRGR0cdpNVqtdRqtZTP582ONBoN/16888h5eKAzmYxlNldXV25e8ENFo1EflI1GQ1NTU5qentbr168tHWLu4tjYmP1fExMTOjg4MLMjyewphySH8uLioscokKZOc/zq1SttbW0pl8sZAWQ03e7urpHp0dFRB/5Q+ICYImMEUecgonEGSAk27IAdQRknhx/SpSBSzbqDScQnSDFDI8bfwX4hfR3uxpiM4Cgu2Iirq6t7EjwOKA5fDitJZpKC4UI0p7lcziwXh9/m5qaGh4f16aefWjYKC0shhxQOpoCRKpI8Kgdf6dOnT/X27Vvt7OxYKn59fa319XU9e/bMsj8CzrrdrlOKFxYWXBzQ2DQaDSdxIrM8OjqyPDMW66f3c4gDkuEl5qAETebwpdlAWkwDEQqF1Poqqb/X6zlohMOU3zM1NWXpFuwe9wcZHs8wAXOwHI1Gw4UMB5gkF/43NzeWbgezIGDt2HsA8IIhUPw3mmsKBPYl1gPfmaIKGWQwpRlmEoaAUBxJfoY6nY5VN9gjTk9P7zVrSG9JO8ZvTpGJEgSpcSTSnzBB0U3AIgXx8PCwx3YBQMHMA6bBRMAEsibYr3n+Ab+QJl9cXDhwDdUVQOHFxYVZe7IvgmE9PKfB54VnkfyAXC5nUDCZTDpcDLCY+wvoCjiC6iYYhgbQe3t76+KX54lrA/gXDNeTZEYYcBLlCoA24B0FNhYHninsYgC0nJFMlpiamlK1WrX3ln2KHAlCk1gTQalykLmTZObo5ORE7Xbbs6zJv8DahZKJBF4ySrhOsGsTExM+E2n+qAd4HiYnJ9VoNLS/v38vfJGCPbiug2F0p6enfuZh95Bhzs3NaXt7295o7EgArmRcUOzT6DJaCNk73nMaIaTro6OjWl9fdyI1SrpWq2Xv58nJifM3JicnDcID4MGqDQ4Oep9if0qn0/dyEWgKsXyg/qCxZy2fnp46aJLRkOy/kCmAWOxBgLAAV5L838bHxzUxMaGVlRUzjc1m04wkfxeQcn5+/p6vfnNz05JeVGFv3rzxKK/b21sz659//rn37EKhYDtAu912I8IeHBxTh+qs3W57ws/o6Ki2t7etbkqn05b4cnbX63Xt7++7USFNfnJy0tNbyCxAMk0jFbS8DA4OOnxvbm5OMzMzTsivVqvONSkUCj5bxsbGlEql7P0Oh8NWoGIVIWGc+oi9m/C6YLYTdRnNIrUgGTzUquQhUeOQXwCADjMNcMT4YOoC9j6UpgChTHYIWtpCoZDnvBOGhrqw0+kon8/77yCZJ6gtHA576gKhiZyV7GHpdFrPnz/Xp59+qlgspo8++kjValVra2vKZDJ6+fKlLaXX19fa2toyuDc/P28w7+7uTk+fPrWFhfWfz+f19u1bjY2NaWZmRpubmwbvSOCnjlhcXHRy/8XFhUeoDg/3Z7szpYF7jH1mcHBQa2trrv+63a6ePHmiSCSiiYkJ7ezs2PaIgiQYAHp4eOh1y2hmlIeovH7a1y+a8J/99a094SDf8/PzZimk/ga3v7+vcrmsBw8e2LuJpC4cDrvhazQa90Kndnd376U9IztEkkfxQrFYr9ftgwlKXYKpoBT1PBg0WMiygqESFNSDg4MqFouWqZ6dnXmzY2QUEnvYsKGhIc/yPDs709zcnAuzeDxuSdDBwYHZDnxUNCkU76Rst1otHR0dWR6PpwUfCYc6RQszSRl9A4MP20gDdHx8rNXVVYVCIb18+VLZbFbJZFLZbNb3gvFoHG4jIyPe7AhOCWYBwLZ2u10H4gBOgK5SzOFvleSwMtgjCpSg/5X/l74OhqPZxBvIpsKmBtpNAUbhziZBoYr0ldAYCnEaIcATRo2RQElhQbF9cnKier2uQqGghYUFHRwcmPFD2QFYQAGIH44glGBz0uv1A+gorEdGRrS/v29QI5PJONCLxoXvCxMGKBb8dxoRPGIkjG9tbent27d69uyZlpaW7LG7urqyn4hwE0lmoClceZ45LGHHKbCQphLGQjM1OTmpvb09zc/Pu1inqWZ0RiTSnzsP+MRnA1jC00qACoqWfD5vBpdnFFUCHtvt7W1tbGy46C0Wi0okEnr16pW+853v6ODgwDJA1hQAFX/GPhNsfL7ZyHa7XcutKTK4XxTUQTsI6xLmmvelmcZTR1M3Ozvre4Q6CBUK4BCfW5K9YN98ThjVgqoCsOPk5ESLi4v22vL5Wbs00hTrAE8Ap3wOQFdYUN6L9ysWi7q9vTUwRgMuyfYlrEaHh4f3nv/5+XkzHhQ2Qe8/ICPNIgwsjSuKFCwn8XjcrC+BmOwf8XjcPlwYKhL7UbUg9eXe9Xo9B2cCwPCesBjkHtzc3HhEFUU315b1EFSdsC5gbGmoyCjg952enpqJ5VlcXl526FIwQwK1GkwM/w0J9tDQkIE0zoSgxPLq6sphqGNjY1paWrL6Cwk1uRic0TQpt7e3DqNi1jL7FsqLRqNh6fzv/d7vuSCtVCqKxWJaWlpyrfLmzRstLS2ZYYIJI4ir3W5rfX1dR0dHSqfTzu1g7+h2u1brRSIR1Wo11wpcD86giYkJ58osLCwYlJCkZDKp169fe32wp7LGCCXkO9KwLi8vu3lEwUYYE+Fk7Xbb+SHshdzrTqdjhhI2mwaZbJfLy0sVi0Xt7u6qVCopkUhoe3vbzQohadRsLh4jES0sLHj/2Nvb88glWLuPPvrItRKMOaouWHlABoBDGvagegKwiUk5x8fHbmoajYbBTJpL7Ij4pE9PT5VKpZxvcnFx4WsJGCr1SYhEIuF7DsiN2oT9LDgBpFQqKRqNanFxUb1ez2Fz7KkAZ8jj9/f3Dcbc3d1pe3tbS0tLViMsLCxYVUB9AvjK9UDFAZA4NjZmYCKo9mJPYV0QLst75HI521ZQe1FXUvsBHtdqNY2NjTmAFGIDoJF9Ansq3y8YcEqGCapQ7B88J+l02vsCawwbyuzsrOsfphG0Wi2dn587twkQMZfLmUx49eqVA4AXFxcNZk5MTOiXf/mXPXXo3//7f6/x8XEVi0XPZec5of7iHh8eHloyv7W1ZWafKSBjY2NaXV1VNNoPgg4GETJKEfUV2U8ocG9vb/X48WNbWVhLrAFsEOFwWM+ePfOUhYODA08QKhQKVhMBzDOumb2a3mR0dFSvX7822AEo8/+vTe9/aa9v1YQjF0NiMTAw4E0wHA47FZoHE2nU0NCQmTjQV1DXTqdjORoBMRy4hIbd3d2ZEQwynqRNSrJ3OViU4GmS5KaJhzQo68S7c3NzY0kynrV4PK7V1VV7xHZ2doz24Vnf3d010nx0dGS/FzNsAQxmZmbupVPi4yLNHEkIG3A8Htfy8rLliTQabNywCqDxpHBK8sZUq9X06NEjHxqdTkf7+/s6OTmxv4jE0+vra62tranRaNhvhV+OtNtGo+Gwoo8++kjv3r0zw/zkyROtr6/byw7CS4PAtWHdEFgRnFctyU0/DXjw4KcRgQkkEA8QgYIouFFxuNDksKnBBpJ4HgztCRbZrJXh4WH7cx48eCBJZoBpiNlUkaLCKlAcc+CBbvL72BATiYSvJ5kHV1dXWlxcNFgDC0ihDKpPocXzwPcm+Iv35HMADDDTlkArSf5MAAIgsIQBBYGxeDzuA+Pu7s6yecaHIEkeGhrS2tqaRkZGDHCQmk2A4Zs3bzz6kGtHYV+r1SyXB+GW+gxct9t1kZXNZhWP9+eS8ownk0m9e/fOErXb21vlcjnNzs6qVCrp9PTUzQ8qGySMkqzsQVYflIwTBsS+FLQ00Hjh/eV+o0qhGUbyjsKIe4+NRZIlrjThhGBOTEzo8vLSbD33GqkmdgzYWcZ8ASAg0UQmiuKj0WjYQ59Op81awZiEw2H70AgfOzo60szMjP1nT58+tW8UnyRAK0U2DDRBMScnJy6aKQQAEmjiCaBDssj1Tnw1Uoc537Vazd5OGHUsBcgR2ZMABWj6gl5vlE3sPzTQANE8v+wZ8Xjc7wW7zfNGGjMZCuxLwSTiYFAm6w4QivAegGXeHyk6510QqIXBYuQR7PLV1ZVZJfyABM0lk0lneNAAwFpVKhVbPiiG8ZfDFgE8AWweHx9rYmJCQ0NDevbs2T12kABG9gkCSynqCXpif0XeTP4L929ubs5BTahc8HEiQ+12uwZLYXwBW/HAo/gJntGcA/l83gwTMmlGBpJuXSqV1Gw2vd8EAzFR193c3HjiAOtja2vLwCKfG+AAex0kBiRCPB6/B8RgO8BChaoPGTYsNLPbOY9RIAD4IG9nrWEl4tkcGRlRu902gHR+fu69G1VjJBIxSCv1FQjZbFbdbld7e3s+e1lDkrSxsWELUa1WU7VaVTablSRtb29renraYzO5X4D67XZbb9++NYO7tLTkBhVmj9wCPLk8Pzz32D4IVoQBhngAYKYZh20mo6Jer/u6zszM3COASK8PWlCGh4e1tbXl7wGxFQ6HfZ84a8l+wdYh6R4gzKhCwDdJVi8C3lOTAdRwrTjjADsHBgac3bG5ualUKqXBwUFfh+B+h1WsUqno7u7OYXMAxaFQyHYj6krqE8BKwGiYfEg75OwwzzS9d3d3Dl5mL0WdgkJ3bW3N13psbEx/5s/8GYVCIb169UqlUsn353d/93dNduzt7en58+d+jgm0m52d9bmyuLjo/QcgvF6va319XblcTqOjo6pUKqpWq5qZmbEtplgsesoJpBf7HZJ9aqdyuewpCRBdPG+EHHe7XRUKBYPWOzs7ymQyOjs7sxoMixyZENVq9R7BBiBwcHCg+fl5KzxSqdR/uBH8j7x+0bT/bK9v1YTn83kjNZLsv8HHCbIqfe29LJfLmpqaMsOJrAj2qNvtWuZ1fX1tRg8/JMUehznS4qDsU/o6XI0GTpIZUAq1IFoXfB8efDaSILND09RqtZwMXKlUzIbS/LRaLWUyGSdWjo6OanZ21ggoD2U0GnWDPDQ05FAlRvUEJVJswqBbZ2dnmpmZ0cHBgXK5nENjQKpnZmbuFRo3Nzf2D1JQUMgiISKdeWBgQOvr6/bARiL9NEoSG5EsNhoNs7yEvhGAw+YJy8d3efTokdli2IVyuexCA4kR4WxssMg5+WcYQhBdkp35HMH7CTIuyWuPA5Qinv9GkSLJvlACBIMFHIXS3t6eZylTOLM5c11owPAY0kBzMAYLSxoUGjGQf0Y1wZpR6IGkw8icnJw4FCvx1eSAb3qDKOBRskQiEc3Pz2t5eVnv3r1zQfzd737X14jmKplM6vT01Egs0nV8n/h/KR5pbihuQViDTTVMFiwFBTsFB6AH9welBKDbycmJGo2GE3LxeDJiB8Ztf3/fKHHrq/GANFOg2UHmFsYJhhd2VJIZHYotZtIGJXnRaNTPCE01RXLQvsCzHmQw2TcBEllHBIiR7k4z0m63zRrgp4/FYgaEAJJotHm+2CsJbWJPhqGE9YrH4y5y8JNT/Esyk0fxS0Iuagg8pyTqc07AYrCGgmFBzFRtNpsuOsLhsH2a7G8wiJwJXDcUOIeHh6pUKvadAloEvdYAbARSBQOnCNaCoSGwin1KksHR4JnCcxe0LgDIwcbDnsKQS3JgUzwe18zMjOWFwXwNQu0AW4I5F1hoAK9YVxTorVZLCwsLury8NDiMtYB040QiYYB9aGjIrChqsmg06jTdBw8e6PT01KMYgxMS2H/39vacZAz4Wa/XzSYBThNmdXBwoMPDQ4/zDAYqMR+a9XBwcOCwOBhbaghko4Qltb6ayRsEVaanp7020um0vc+RSMSA4t7enqLRqGZmZlzQXlxcaHh4WM1m0zaFcrmsiYkJFQoFT7/ASgQxgSKCZ5MwsMHBQQej7e/va2JiQtvb2xoYGDDoz55Lngzy8lKp5GYLYBTlIXsz7BpAHMopmGLGObJGCccjSA0ADfUWUy3Y7+r1uqWuS0tLajabTjYPh8N68+aNwdbR0VGHQJFxwKzvV69eWZXHfo6kH7UjSr9MJuP9AlCDs7PX+zp8s1QqaXR0VKurq/f24Fisn+gf9M1T07C+mO6DmisS+XqsKkxysL5YW1tTs9nU48ePlc/nJcl7KUqTxcVF7e/vW01JICkN7djYmK+z1M9gCir0ODPD4bCVqKOjo9rb29Pp6akePHhwT1UIMInq4P333/fZTs11eXlpC0673dbR0ZHa7bbtbr1ez88DOTqRSMS1P5aSq6srhy9jz+IMxa4DAw3QMTk5qUqlYsk6+0Cv19POzs69fQfQnfUQDGomhK/X6ymTyaharaparWpgYMD1LEo6GtytrS29//77Oj091c7Ojur1uubm5tRu98dPTk9P6/vf/76fFaZI7O7u3ptJTlC11J/mIPUDrMnZgHAsl8taWFiwgomfm5ycNCGFVWJ9fd22U2q2kZERra6u6vDw0PaEL774wsD6mzdvDHLQB9Ej3dz0xy2iUEJ5yQjEmZkZfy7UmDs7Oz7nfvH6+b6+VRMeDAnioKOwJeCDzRs5F7IifCuwIWycFLoUsNVq1SPKkD7TiEkyosUmHCygkfvQ7ARDO/Crf3NcDMUZRRTFHGz3+vq6w3BAU2noJNnbhm90cXHRXkgaRWSDSMUpxK6urpwOfXx87Ifm+vpa1WrVEjNCnwgfosGAYRwY6AdR7e/vuwllE2IsB6wVDCyMOA849w5GmgaTzXxjY0P5fN5hb5lMxgnbqB8IYqFxAJ3EV8nGPzs7a4aS74dEh4aLIpT/p8Dj3sF2U1jQYIPqI3GW5O+EimBiYsJAUlDaz/pi/SAzZY11Oh17fPD2IInDO8b3Qk6ayWSMNoNiUjCBpNMAkXQc9FvTxAQlaGNjY2q323r69Kn/LvdO+nr8G+hyKpXS2dmZ6vW68xnIQBgZGdGjR490eHhojziAFQ0KzSLeLtihgYEB7ezsKJ1O2y/HYY28mrVNABIMw8bGhuXw6XRak5OT9s7DlAP4Ucywd0h9RmVzc9OJ7viXUUQwIofwnWq16nuJvzTok4RZJQmdcYmEkAGIwYqQ2MuBR/I3gBUNL9dQkhlzimbW9Tf/x7XnEATkC873JmEegJKGimaXBhWW6eTkRK1WS3Nzc96DkDfzjKC2gZ3nesJwX19fW/KbTCYt9SZ8kntIs8UIlkKhYAZ8b29Pi4uL/r1kQOB/RxnBM0qhiecOFQLMU1DKzzrlvMDvjTUHgLDT6VjZwfgX7AgAdsG9gaKTvYZzb25uzv7HRqOhTCajdrutvb09M1qcKUdHR/apFotFF+IAAoRpAXaR3h8EIVAAtb5KQqexhz2H2YO14xqxv5NqDCA8NzdnJhwgB6//ycmJqtWqbQicj7CssImjo6OeqJFOp/1zFJrYT2ZmZtTr9QNDCe9ElUHzwZQNbGXsaYxA+6YVBClrKpUyUE7IGAFb4+Pj2t3dtVc4GPQE2MUejrKO+8peznQO1EUAc81mU/F4XHNzc54RjGoH/zS5LpFIxB5nLAyJROKenzwIXnc6HTfj+XzerOPQ0JAWFxdtW4OFvr6+9mg4lCX8M00VMmnmtHMmU7DzedjH9/b2DADRnACcAfrl83k/j61Wy+GdNAPs/zQtMOoozx48eKDj42MzbzR3n3zyiYaGhtzgs8+SXs4aikajKpVKDsYC1EA1BHCRTqeVy+VcZ7IeAfp4ngC9eOaYmkKQFrOop6amPEmAvYfPiSefurjX6/meVSoVZbNZJRIJz26Gcc7n87bHoKQ5PT31WMv9/X2trKwYTCDUDgIAQgWyhf0byX+r1dLOzo5yuZxD8oI2GBrBYrHo5g4b5fPnz702UCHe3d151GMQMIZVxZvMXsfeFKztY7F+0v/BwYEWFxettgEM5LymjgHAPTk50cXFhXZ2dtTr9Qxg0hDv7+9raWnJawVg49WrV845aTQaqlQqOjk50Xe+8x2l02n99m//tgH7u7s7PXz40CxzJNKf6oF/emtrS5VKxZky7F83NzeampoyQfT8+XPnE4RC/cDfH/7wh/rwww8NXKfTaT9fZIagemw2m16D7XZba2trur29daYSgA7WLOxkKAcJcsvlcraoHh8fa29vz2t1cnLS+Risb0Cmn+b1C0/4z/76Vk04zAksBBs3hXMwIAZkGdkFxRTpw0hm2PSQ4jJSAPmMJI+RarfbTg+lsGXjg0GQ5IOHBxnkjcOCwwBQgeIVYICAArx3+Cba7bbnldLQMIoMZokCpdfr2c9KUQ1qh3xXkg95wIugNKTVaumjjz7SysqK6vW6A5AACq6vr1UulzU/P6/h4WGdnZ3p8ePH3pBWV1fdcNGc4KfMZrPa29vT8fGxPvjgA21vb+vi4sLyvFQqZZl6LpfTo0ePNDEx4fERSJxApwnZk6TZ2Vm1Wi0j7K1W656vHDaHogX2me/ONefaUZSQrEqxC5vIfaRhj8ViLphohtnQKXTYWPHipFIpfy4CvlijhH0sLi66+MTrhTfq8vJS19fXDtiT5EIUICPoQSWVknAkwjBYH2xq2CBgIKenp52Yie8YaTgoOSPhkISur6+7IUV+x4YbbFo/+OAD3d3154/CunMPAJkuLy/9/ZBW8554oQBLxsfH9fr1a4+T4VqFQiG9//77RpaRX4dCITfLJMDTCHPA8zMvXrzQ3d2dn5ngGEBAET5Tr9dTLpfzYby4uOixfkj70+m07wHf482bN07/hcGPx+P68MMPNTMzY3CDAxogCEAHJoeDmX2QBiLo9ebPYRJ5xpFX4oVkn8XTDLNNoA9hSaFQyMwzMvP5+XlLZ1ElcP9g8IKKm16vZ0YjGo1qc3PTa5DvxvNJwE6v1zOTLn1thYBdhUVMJBKWVjOqTZKvBe+BVQElCHJ8Qojwt1OMUGwFvfXI3uPxuNUqqCVSqZQLZmS6fGfWHN+BIuvly5dm0gEteE52dnbcAMBKwWSyTqLRqBkSrlNwL2u1Wh5Bhf2LnInLy0vt7++r0+mYEYJh5zkCFCZ4CdAXm0IkErHlCXZtenra60KSgYxMJuN9CWAruMZPTk5UKBQMdHCtUFzB5FxfX7sZicfjSiQSlq9iHRsfH7dnPejBp8iGjT48PNSTJ0/05ZdfWr1CvZBIJDzis91u682bN2q1Wnr48KGmp6e1tramm5sby91RL6DcwTOM7LzRaBjAQo1ByGy329VPfvIT2zQABXq9nlKp1D1lCqoifJqoAKkl+G+oyd6+fWv59/r6utcACr1wuB9QyCx7JKtByxrnMEnfyJGRfQfJA5QMnLVDQ/1Z0i9fvvRM+bW1NcvaYS5pxoOsJ40W1kNkr7DO9Xpdm5ubViuwZ1B3sb5Q1NRqNWWzWQMPnD+w4yS912o1Jb4aKYZycW5uTuVy2eF1c3NzfqYJIyPbhIYT0oAEftYuQANqjXq9rpubG5XLZR0dHRnwDNp1WFfI88fGxpzuDvjBM0luC0AcOUEEChNa2e12fY+lPnHw6NEjA2Y8h5wnWBHxVV9fX+v169eanJzUs2fPXGNns1nv6ag3CA5GIg+TDWBwcnJixQzP/dHRkY6Pj1UsFr038ZzBbmOXAURDJUINxV4GCFGpVFSr1fT06VOrHJ8+fapSqaRUKqXEV5OZgtkTb968cWJ9t9sPbN3Z2fGaffXqlRPC5+fnPb5xYWFBR0dH+uCDD8wMv3z5Uo1GQ7lczv59gG5AeOqo09NTraysmOiAoOK5QdUE4YKy6+LiQv/3//1/q9lsmh2nxyFHAaJvfHzc1huCjvGWx+NxVSoVAx+oMlE1ZLNZg2UQbplMxnJ8rGf0KL94/Xxf36oJZ8YdXklQPthbNiEYcAI6KDSDUjlQvMvLS+XzeY9CAGkEqcWzS8ouiGU0GvVDSvEIC4N3CoaFTY5miMIINoYDCqSv2+26SOG7Sf2Dk2Aair5IpJ9SDooMExxkBCj+JN07nCjwpb7U/+rqypt5KpXS7Oysms2mZa+3t7eWe1F0kBwdCoXMpNzc3OjZs2f3WCkYBhInYQhB5W9ubvT++++b3T08PLR09OjoSC9fvjTbBWgCs8DvhH06PDxUKBRSKpVyejLy4YcPH94L2Ws2m5qfn5ck/05e9XrdhxWKBmRuyOe4nzBW3Fu8zxTYrA/UADCnSO4k+T7zu2EVkYUx65E1AtNJYU0hQcGLrYFwF4r11ldjOrhfh4eHnqdMOjjMFNYEChz85Kg6aJaCwTU3NzeeawyLTnIo2QEUwTRBPJtXV1eWXtEMcV+4hiC/oLbIiZlfSbIxB0woFLJiZnFxUXd3d5a9kWIKizc4OGhJJAdnu912Y/SjH/3IDNDAwIDH6NEQnZycaG5uzrNrmS8bbL4oLqLRqCVfgIlIMbnHSLdbrZaSyaQTspnRjbwzGo2qXC5rZmZGz54908bGhj8T8ncAx2A4IKoCsgy4lzBJFL2PHj3yc8R9xGe3tLTkghpGCAkeaDaFHEwxqgNe+CVZK4AC2FGGhoaUzWbdJKPcKJfL90LGCKeiWbq5uXHiMkXM6empQ6aQ1/GM3t3dqVarGTAki+L29tZhX0g38SbCrAE2csagMrm+vtbe3p4BJ8A4SU78l+Q0YZoBAF72B+wzSPiDXnjAvaB/fHp6WpK0tbVlVigSiahSqbi4puGEvUSOi5cVFmNsbMyg3vLystVYjBmSZPARHytFIbN6KeqCQYWAAKRwswaDbCXg5ubmpubn532eHx0d+fm7vb21+gmbQlBlxjlKmBjnw9jYmJPAeVYIXMX7yt4P+ITFhLwUSABqj16v57C9u7s7K5TevXtn9Q8WBYBXJKiA5xS2JJ0zMgkml9yY1dVV+2yxojFbvt1ueyza+fm5VlZW3HRTFLP3jY6O2oYBE1UsFrW5uamZmRnnl5CJwHlFOnZQSsrzQ9OeSCS0s7NjYuL6+tqyYYCqYIgn8vNarWbVFs9AkLGF0CiVSlZpQGKwR9DUNptNM/CXl5cOaby9vVU6nTah8ODBA+8tk5OTDrIlsBAwHaXExcWFarWaUqmUASBk+mSxPHr0SJ1ORy9evNDv/M7vKJVK6fHjx36GotGo1WowyLD31GuQD0HVF3lIsOkAASg1Ly4uVKlUnJsQifSD7EKh/vxs9gck/1hepqamnL1CRguM6O3trRKJhEdVQSgAZnPmP3r0SCMjI94H2WOoU5DGs+dhryAPZ3d3115m9rYgKXJzc+PrXiwWrYzFRjQ7O+t8CBQHQfCDM3Bubk57e3sG4SQpm81a3QUQOzMz42fx7u7O9TDkGmfM0dGRFhcX9cUXX/h3o9pjjaIu+eyzz9Rut/Wrv/qrOj8/187OjpaXl/XkyRN98sknrgEajYZSqZRubm5UqVQ0ODjoYEeINYAzLDu1Ws11/qeffqper2dAlbyGZDLp3oMshna7reXlZa2trUnqT6QCfIYQQ/lL7c/373T6E394niBqCBTe3Nz02gqO10WSTr1wdXWlzz77zPkiP83rF0z4z/76Vk04jAZSRoqdUCjksAEaBpgF0NWjoyPLP/FFI51BRoP/GqSe0Uk0BN1u14f04OCgizIaMuTCsK+8kITyPjCHPASSnNYai8Use4WhZg42ssxYLKZcLqd0Ou2DCwYt6JPM5/Oq1+v2CHGg4feF2QP5ZK4gqZ3I0ZFoRSIRFQoF5XI5o41sqgAUBBzBUHNwX11dGehYWloyoIAUhkaHWawHBwduihjzMz097c2HojadTns0Gw0aYRLj4+MOp+G+VSoVp7xzKMRiMY9fANGnySINN5FI2NsWlO1yzSnCkbUj/QERRmoMu3R2duZxcjQMUn8+NkxDp9PxwXd2duZxPQAxJESHw2EXljSuSM1hcrAFMJLk4ODAjBk/E4/H7RdlNm5QOonscHBw0ONA+Bk2adD3kZERffHFF/eCVPg9Ur/Z4hDjOQwyWTC4oPg03MjQgtLfbrerarVquX0kEvEYmmCj0u12tbu7a7AJhQjvGwqFLJEkjIUiaHd3V5VKxY0QHlC8u0FmmWcUxQaJ9kga8adx6M/MzKjdbtvniJ+Y4ornjiaPgEEYEsBE/h4sLEU+6xzgjH+n8AC84D6Cir///vuW0YK246VMp9N69+6dcrmcvYWhUEi1Ws2NN6qCy8tLe9XZv7kOrFHUH3yGbrdr5pNCiP0ThgqLAsU1KbJXV1cu/CjSeUYrlYp2dnYMtoZCIc8lZf9nXQBy4WuNRL5O9keKTuAMdhsmZSC1A7Ti+iBrJ/GX5+P2tj9vHEkgfsVwOOxZ5njCSXK+vLw0+59Opx2yQ/MKGMv+QIEdDveTgdl3CIbDxhQKhTQxMeHvA2DJsy/JUlXOF35PsHCCQd7e3raVKuhvBfDlvS8vL33NAToARmBUABMBcJrNpoaGhlSv151qfXV1pdevX3vEFv5NGlDux8bGhjKZjBsfFBGbm5s+c5BHTkxM6MWLF/cAStRWWHtI5mcmdqvVcohbs9k0EEbGwvDwsCqVijKZjBlXrCeMcIvH49rY2NDt7a3y+bzrDho41B2ANqurqwaiaTZpdvn90WjU8lvYqt3dXe9jFPVnZ2eanZ21XBtFws3NjZlhZLCAz4RQER43NTVlPyhjq9gfOCcAH9m/YTR5FvCa0rByRrIOUVrwXMH6cr4wkxjLwaeffqrz83OP5EL+fXZ2ps8++0zj4+Nmi9mbmFbC+YisnrV6d3fnBHMa49HRUavmbm9v9eTJE/3kJz9RrVazR5050HwvwjABOjkrUZixl6FMYN9GPYgiEAIll8u5Htzd3dXNzY2WlpZ0dXXlMXooDbBTtNv9MNZSqWT7X71ed8ggKdsAr2dnZ9rf33cC9+rqqpUpg4ODevnypTNMyNORvlaM3tzcaG5uzrU8TR8hjNTrS0tLfvaxXACeA2QAPrImqbsODg58JgwPD5u4kqRMJqPb21tLy5FIA5QS4MueS5YLddzh4aEymYzS6bQ92+SvEFgHGFWtVr2Psye3222TfNQeyORLpZIGBwf1B//gH9TAwIBDz1Ad9no9bW1taWdnx3vK0NCQxzdCLJJ3cHFxYQCaaUjs26lUSoeHh1bZdjodA9HlctnW1eHhYf3gBz/Qw4cPDRZBtqHwI8sI4P36+tpp75xr+/v7ymazZsFRZyK5/wUT/p/n9a2acGRGeHcokPEnwEgjuYCNPjk5UTgctlyQhg/5N9J1EE5+loKCAp4H6u6un3KLX5NNGEYMGTsHDMEYpOLW63X7etl4gmE6sHAUBYlEQvPz8+p2u/ryyy+dHgrjt7CwoHa7bb8Pyat48xiBwMbQ+mp0CIherVYzwwATwnzj4KG9v7+v73znOy7mOSgTX43UIIQDySdIea1W83xcEhXZzC8uLrSxseFQDlgsJIoUn8Vi0cCE1Gf08/n8PXat1+tZRnl0dGS/brVadRGPJxYEcGpqyigqXrtsNmv/MU0USCcNAtI7SX7fXq9nFo9DE+aatFKyBPCSElAxMTHh1PhsNuu5ksFQKoqKyclJHwwgpbCsfIegUoP50xzGQY8k70sRTNBXLBZziBqyp0Qi4XnNIODHx8dOq6X5g5VMp9MGh1gbNFKg8/iyuD40lM1m0ywvMrjDw0Nff5qT4+NjPzPBECwYxVAo5CZA0j0vK6me/DyeYtjww8NDs9JYMWhWGEEWbJ4pEilY33//fS0vL7twprgFjAjuPzc3NwYbeC4BWzi0UTCcnp5qe3tb6XTas7YbjYZZaPYeDn1sOMPDw1pbW/PIr06no+PjYyt1Li8vNTY2ZmlotVq1P5fnjsL8+vra1hjAn+CYHkmW+SGLRTKfz+fNzsFwsBZhE/ESYyHg/VEkgJ6jWgDF59nb3d01kIoXnGCfwcFBj4uioOYsIU8kFAqZYWGvBBwKZkbglyMUCMY9Ho9b7jw9Pe3iHbBSkkdj7uzsKJlMKplM6vDw0FMBYChvb2/1+vVr5wfQxLA3AvKxXgkEY5+amZm5BzoyhSAUCml2dlZSP+OAedrlctl7y8bGhubn563aASzBt84+SvHIMw+AW6lUrNwaGhq6t3+iPqhWq1pcXDQgSXEWi8WsdOB8j8fjBthoYrCD0RSwFyMTptDnGWB0JYV20EOby+VUq9VULpfV6/Uc2oRFLRKJqFQqqVKpqFQqGbAHrEd9BDB4fn6ujz/+2AoYrES1Ws1yb74vzf319bWtB1tbWw6Qo0COxWKeKsEYv7u7/hg95u8ODPSTsQHryL1YW1vT0NCQGw/Oy2w2q3Q6rdevX99rIAgGA0AL+nlR66GiQME3Pj5+b7yRJIPA3A/WN/J31iU2Ayx/kAbUL6enpz7PYY8hM8hLQDGEB51zDGAqCEwE/d4Acoxlu7i4sDcXe8XY2Ni9YFsaV657Mpm0/x0LByQF7DO+Y8gbAH/OL94TBR7WrKdPn6rZbBocGB8f9zg7vPrDw/3Zzj/+8Y/1/PlzA8xcQ9YXTS6ZHkHQEw/1wsKC2Xx+dnJy0s1qLpdzkvWzZ89cJwLMUUNCCDHdCMsKa/zw8FDv3r2zyod7DUhJBlHQo401A1D49vbWNSFAOIoTnvmhoSETR5BnKFk4WwnvZWIS+xNAK8TLwcGBQVwAHJRmEG0AyJIM/AECUINzzaiD8IkHlYHtdlvf//73NT097f+hyELRJMlTGtbX15VKpazKYS+mXoGoa7fbqtVq3nvJnUDhJ/UzNXK5nHZ2djQ3N+ezB8Cn0Wjo6dOnrtPIwuL5mZyc9OcDIKYmZ7oFkxy63a5WV1c1NDSk1dVVffHFFz91b/gLJvxnf32rJhwZJoUvTBc+XaS5oMKwcsiLaMSCkkwQfP6c9w+mnLbbbcunKB6Hhvqjz9jYgv4R/HxsbqC7wcONQ+f8/NwbH8gcPjk+O9J6glgonur1umcgMsqJMCSY7QcPHrg4o1DgO9PsSnJRVKlUdHp6ai8eTSqo793dnfb29ixLYXwM0nZShy8uLtRoNPTkyROPK6DZZvRBoVBwyEO327Xfampqygmn5XL596Vj9nr9sJxms+nEdCR2IHDZbNbBRgTqbG1tOb2Se8IaouhOJBKampry38tmsw6CgfUKMmJ4lHkfQCE8tUhp2bxo5EGikYpy+BYKBe3u7rrJRD4VDHOicAENJggGHzvfh3m2JJwDFjFmBdCCg5GNGcaCBgavJA0y4AlAQq/XH48BM1mtVlUoFBzWAXt5cXGh2dlZsycoJ2q1mu7u7nzo0qByuFIsweTwPNOkMxWAYDJkbg8ePFA43B/Vhz8N6SYgAHJalB6Hh4f2KhMwt729bcaQ9c1ByaGKl47DAIaJQ2lhYcHABc8R/8OzGg6HlU6nnVsxNzenTCZj726z2fTvAOmOxWIe10MTSDPJWkPOiK+PBPbr62tLpgmLQzUUtNS8e/dOY2Nj9rKTRcF+cXR0ZNkgv5N1CbPPvQKkQbkSZE93d3cNVrLflMtlRaNRM4r/H/b+q7fVPE3vRi+JFKkcKFFMEkVlrVSpu6dnjLFf2/CBAX9VH/nAMHxiYDzT7pnu6korKCcmMYlUliiG94D1u+rRGNi7q4He+6QENGaqai2Jep5/uO8r3QAwPDsk6TTphBlubGw4cAe/3PDwsPMqaASwtcTjcTNOFHA08ZK8fylsgjJ5ACjAL9gIsghg0Vutlq0zpP9yzjJl4Pr6WpeXl1pfX/d5wV5EWh0EhyloaRBhVhjTh2SR/AKKxHg87owLZJ74y6enp3V6euqzhucMo0emCaACwECv11OhUHAzRpgW5zIWBVRhNGK5XM5NAJYWGHksQTwXAB/WB8ojzgN+T1hZ7GStVssAEiAgDC2fa2VlxUBYqVTSl19+qVgs5uKaegH2EJYtGo16nZGADpvKOCwYcnyRhKSy3mmkAH4BVDc3Nw1M4Bl/fHx0g8o5nM/nHbBGM8XoUQCLUCikbDZrJpb7IRhMe3t7a5Du/v7+haoPdcbU1JTD6FD1ULwDsofDYSf+AxovLS1ZhTQ/P6+1tTWDIahBSPwHcKGRxwKGdJYwzOfnZ52fn7s5wdPNmme6Bs3k7u6ubm5ulM1mPXmB/BDuAFQ2gNMAUgDqgEfsR8ZL5nI5A/PBe4t9SYbR4+Oj9vf3rexj9FWhUFA2m1U6nfa0Dny9WOwId5QGCiqaF8AcPMOXl5fa29sz2HBycmJWlH3LOT8yMmLr5enpqWKxmGZmZrSwsGAv8fr6uvfJ7e2tlpaWvM8fHx+t8EkmkwZfb25uDD4yyq1Wq1k5RYP8/DwYs0ttubOzo8XFRddd1N40t9SrPB9GkPE74eeH2AEUQ/EJoARRgnoT7z4gFT+Dmr/ZbKpcLhtYoobhDgNsJaWcWpDPjqpRkvb29qzuC4fDVpCRoQBgRa0nDVQffL6RkRF9/PjRqpq1tTUVi0U1m03t7OyYtGAM8NTUlI6OjhSNRpVMJvXDDz9obW3N5B7BigQIwrSjVkNFzLQdzgXsoxBuqIYAHDl/uR94B9THe3t7KpVK2tnZMXFIf4ES5Jevv+7Xz2rCE4mECxU8arCLyNbwniDxAEVG+htszKSfms+rqyt7aoIHIB4smiFYT8Ih8DIhuWz9mBwL64v/F4lzu932/Dsudg4QGHMK4E6no1wuZ69NPB73Z6XAL5fLLnxRCjDupdFoWDGAtB3/GmmXrR9HjuBlp7i6u7szukfi5+TkpE5PT3V8fKxGo2FP6/Pzs+c5EnIDY1EoFJTL5VSv183YIOvhsgWcQA7H98a7iDS6WCyq0WhY9lSr1XR0dGSfKoxJrVZTrVaTJLOWpB8jdw/6rUAgFxYWLL1Bfo5kjmaSnAGKClg8UmVheiW56fjX/lcQ0Eaj4fTVUqlkP+rc3JyR8mazaZ8UaCzrkoah0+n4MMeDyjtoNptmKJHlDw8Pu+hdXl72ODA8hxQQhGsguyQ8ixTRx8dHM8wczhQLhULBvxNzpBcWFnzxo/SQZNCMtE0CvZ6fn1UqlbxPYLgYn4FXCokX65fnBDvKpQyzQ3HMZUZjAzhFQZnJZHR6eqpisaitrS0tLy97Dqs0SDrFZ4rPGZk+bMfNzY1BHZhdPG6AKzTNSBsl2dPGKDr8iX/4wx/cIKJAQSpO40/q+PHxsZrNplZWVgw2jI0NZoImEgk1m02HwQSZIfYGc+DxmD48PFg2FyyU8dnisQWUAszkTMb/X61WvZ+w78AmkcjMe6ZRRykQi8VccBOGg/ycfc25hy2Deeh41xYXFx3+1Gw2nVNBM0ADFGStJPkZzM3NaWVlxYDO4+Ojwz5RPUSjUQN4BKzh7Q2qA2CuOWtQl1Cg8jPw6cO0Ihnl/uD35PyFradJ5QxDyox/kv9Owvzd3Z3PQQrcu7s7g8XDw8Nm/WhKUDHA5CWTSa8LfndCsSKRiJ8j7wulC+n7QYlur9dzYBt3FEoqSb4rYNVoaGhSJZnBRCHFPQXIwoSHdrvtObeJREKXl5c6OTnR2NiYdnZ2NDMz4/nG/F4fP340uMf0g+npafslYZyxkKAyAaCam5tTNBp1+BkN49nZmRs3MgAARBgVNDs7axUCPlfeNecnFoegNJfpD0xIAZSkoQcYCGZcSHIzRdo+9Q57krXMObuwsKCzszM3aygkeH+ACti2yD9ATYO0GAUjDQ0e3rm5OdXrdTf33I2AKLCjjLF6fHzU7u6uRkdHPW0GQLjb7dqCcHl5qbu7O2UyGcXjcWcQEDwpyez6yMiI1yy+8pmZGW1tbRk4PT091dbWlvfp5uamRkdH9cMPP7gexFYRrEtY/+RhcFcFsw44L5LJpK6urrS/v683b95oe3tbFxcX+vTpk1ZWVgyk8f1YF5FIxFJi1m7wnVxeXpow+PTpk0ZGRrS8vKyVlRWf75ylsO7UU4B1MzMzDswrl8tWKlAroVLa2toyQYYaDhtW0HoVrBkgRLCqBJl9niFnB/+Oe436DJsn4yHZV6VSyfkP2NOen58N6JADQL7S+fm5ARw+c6/X8/7Dcsj++OKLL7S4uOi8BPqA6elpjyldWFiw5DubzRq0J5cBbzo1FnUYABoTJFDKAdxigz04ONCrV688Ynh8fFyfPn0yuz82Nqb9/X0NDw/bIre6umplBWoDVImcobyfer2uVCrlRHmsT5BAk5OTWllZMTDDbHZq0D/36xcm/C//+llNOAU1jDHzr2FR8TmSfouknEsWHy1eKpAqfFqkeFK8wzbe3NyYweGiQ4oiyeyS9JOUHKkLLHEw2IWGm+Y16GunIabYohhFUgcziaQqHo9b4glTEQ6HHeRxfHysdDrtwqpcLruo5XvhCwbBwm92eXmper3uIvz29tYSJS5rQlh4Po1GwxK3ra0tVatVj5oJIpkjIyNuNNmcPEPGS3CIr6ys2Pd3dXWlo6Mjo580OTMzM6rVavruu++Mkt/e3hpICHrJgxcA75F3ijcllUpJ+ml0EusCWSRjj4LBG8giQZvxJweT0vGT8jNZe0tLS14DMNLVatVBOENDQy7cUQ7A+JCYi8ohGOgHgsn7pACB3eD34vPwvWZmZpTJZGx94PkgCYRJhv2mSGTfASYEvbPkF7D+SDcl2AdLCHOuI5GIcw2C8q1ut6vj42OvidHRUQMIvd5gZnqr1VKz2dS7d+/c6ILskgZKU0HDAltFE1AqlXR0dKSNjQ2nwnc6HctZ//7v/942CyRsvHfyF2KxmNLptC0yXBbX19duuqLRqBOWo9Gozx0K63K5rLu7O+VyOaVSKW1sbFiuXa/XXZAzTYH3zXujCSWIjERoijpkoUEmEUUFz4ymD7AG/1e/P5iNKslSUkCdUCjkArVWqzl5+u3bt+p2uzo5OfF7fPXqld8LctagJxcWD48o6iB8yqSzc0ZTUEoy83p9fW3PLIGThC1VKhWzz/j5KTwkWSY/PDyso6MjLS0tuWlIJBJaWlqyhJK9QVHIs4eNB+BlXRK8xz2F7I+9ReHIiBz+Pmc1FgIad0ASQJPZH5Pgn5+fna1BKCGhTEiyKS5hWynqKHI5KwC0YaC5s/A3owDjHqNxCN45gExYCHiPZAkgYU8mk9rd3bWsHusV/khYL9YfIxSDVgn2wMHBgX8OEx1gvbD1jI6OOkWdc5K7IplM+vsDnkAATE5OWqGHPJOzjgA21uenT5/U7/c975w7m3XIWUPRimopCHYA3l1eXmr2x1TuarWqk5MTr89cLqdisejANYCHkZERg9l45QHoqZOoJZrNpu9/7iisGjc3N54/XKlUDF5AEPT7fdsHWZ9ra2sGlfCkopbC6kRgIsoC7iXeJSMZ8ebDoh8dHSmbzVrdwe/bbre1u7vrUDjOa1R6SHupkyRZVQWwSO2CxBg/8P39vd6+fStJymazZu2Hh4etvmPsFWfR6uqqbm9v9Y//+I/KZrMe08c5DNlEPgDnGLUE5zfjBqmBPnz4oL29PaXTaaVSKWdgPDw82Jq0vb1tgIN9SaDW7I9zuKmLURSRGr60tGTrCuqrlZUVlUollctlJRIJFQoFgyoo9FADEf7X7XYd5oWtkHunXC7r6urKYF6wdqIORh0RzFUqFAo+v87OzgzKV6tVTU5OmozDM354eOi6v1KpeK1GIhHt7e0pm83q4uLCPcfY2Jh2d3d9dkMaDQ0N2R6InZP7HXk9wD0qV2p18iJmf0x3px588+aN3r9/r6+++spEwKdPnyw5z2QyWl5edp2HxLzZbGpvb0/X19eanp62EoZ9Va1Wtbu7q7W1Nd3d3TmJHMCIvcBEKO6i6elp/e53v3ONRJgo+6JYLHrSx9TUlMk7auFut6tGo6Fut6v19XVbtiR5kke/37dyCPDkz/36pQn/y79+VhOOP5egKhq/Tmcw0qfVanmx1+t1z0Zk0TA+gIaYREmQZgokxsTg66FxCBZQSENggyimaBwrlYqDx0B5gpsZeS8M+/T0tMdUIRcFBZbkdFKaR9A8EDYkyDQB9XrdRVqxWNT9/b2WlpZcgOHNQxYiycwFB4Q0UB/AiiJ3A8ktlUpufAhiIzSFi5givVqt6v7+XslkUnNzczo+Ptbh4eELD93t7a3S6bSZEbxxsHIzMzNaW1tTPp/3hR2LxTz79/Hx0aOcaFpJI8c7wwxKQIqbmxstLi5qbW3NKcAkf+Ld/vbbb+1nRHVAARBkXEB8YawomLlcWEN4dGq1mpvUZrNpHxKHN0E1HDCgg6ge8BPTWLK+pUG6ejKZdOMLw5/NZl3sI6UHlAi+e94JcnPWIIgyyelcAKCWsBt8b9Ls8aHe3t6qXC4rm81aBYG/DaUBFyefI5FI6O7uzjYP9i5yRRqGw8NDZbNZB7q8fftWkUhEGxsbfp7YJgDEWCeMObu7u3OzenR0ZNYcRg7/Lwg6RS7ZCKS5A4QQakijSqgR0sCRkRFdXFy4uKKZoimYnJxULpezNQEWl2wHgAvWFwoLCkG8rIwGopk9OTnRw8ODWT8KXN47TTcNDs9dklOjCbIjG4P1R2Mf9PNxQdOUIH3F/lGv1w3q8DyRrQH6dbtdN0mANwQzEiLGHmM8IpkFSAp/+OEHbW1tuakCgKQpg5lClfL8/KzV1VVJct4AGQbz8/O6vb3V8fGxVlZWvFdocmq1mqanp9VsNp2Nkc1mXxSUyNeHhobsnQP4Q2VFGCBMvSQH2BHMRDPD/n94GIyJY3a2JLOXNCaNRuNFuObDw4PZHeSr/H2aTNYDZwBWlG63a9ZqbGzMVgZJBh+ws7DnCWdkXzBqiRR8zp/7+3uvYxgdFAjkCwwNDZL6YZzJXLm8vDTTSLAV9yx/vtvtWsUzNTXlUKm5uTmD5vzMm5sb+9THx8f1q1/9Sh8+fLDCBpn6yMggpbhSqbggJV0ZuwPAWzqdVigU8n3AVI+gZxngtt1uO32aZ4o9DpUDzyxopcpmsw7Te3h4sK2HM4Z9yTshxK/T6ejs7Ey/+tWvvB8pqBkpipSV5qPb/WmMILUS0nFJzrsAnEWVQF3GGqZW4ywJhUKWQp+enhqgYS2Sr8I5xV11eXmpUqnkWo/PQiZDtVp9EcSG9ajf79u/SmgU8tpcLmcVTtDvy544Pz/3FABqP+ohLAB4mAkChIns9wdBqg8PDy9IA9h+Ankl2TK4v7/vHBACEPv9vtUUKJjOzs50d3enzz//3E0/IB1J6CgV+bmAkqyrkZER7w0+L88A4Kzb7SqTyXgay9jYYKIKEnyUoiiUCD5DKUZTSp2DuoXpB5wzkUjEIb1jY2MGmLkXeIYEcWKhBFQsl8uSBsAKdobz83OHuHLmYh384osvzNLH43GVSiUdHh6q3x9Mzkkmk2aMJdnzHo1G9ebNGzUaDf3pT39ylhOMNrUMocmAe//7f/9vA6SQSdTjFxcXrkOYlLK4uOia6+rqShcXF8rlcrq7u1M6nVY6nVY+n9fS0pLHwAHSfPHFF97bWEqZtAPxgnoSYASFFef56Oio55hjLwWkRM0BWEp/Qk83NDRkHzkgzi9ff/2vn9WEBwNKQGdgbQnQCHqC8HRwaFFwgYq3222zZsh6GBPFiBEaiHK57KAJUETYQzxQML0sSgpmFh1sKQ0AzRVSDVgUpGGksMPs8H9HR0eVyWT88zh8QJ1GRkbMIEYiEc3Pz3tD0fDjAQqGHdFEHB8fu8mhuEYSiwQNuWA2m/V/R5pNQxZsGs7Pz5XNZrWysmJmd2RkRK9evVKz2VQ+n7cvkVAHmLf379/7OYE2BkNrJHlmryT7fDjsQdYYacPnRZ6Ev/vu7s4eTVQJHP4wrefn52aTg0wcsjaYWphcmhuaEw5nmP5ms6lGo2GvLb9z8O/DHhE4EovFNDw8rHK57MuHz0fzJemFDBVU/ejoyMU0BTTfH6YvGOACE4rnFDArKBkiGIgvmAveBZ5D1m2z2XQ4Ie8cq0TQdz80NOQmlgYOJglpM00F64a9Ozw8rEaj4csYtHdxcdH7OOg7pBBvt9s6ODjQhw8fDFCggKA4eXh4UDQatc8NGfL8/LxKpZJGR0c9Q5nGrVarOTH49PRUmUxGvV7PxTVFGvK+6+trM+LBEBXW9MnJiV6/fm0ma2RkxAw7rBrp3EgXUZuEw2G9efPGTE+1WrXEGzkvygPsOLB52Go4f3gXnA8wh6xt1g92g2q16ucXDBx8fHz0ug+Cj/86h4HGg32Cj4wCCxaTMVNPT0/64osv9PDw4BEuqIiQGlPk8X05ezOZjD58+PBCHg4r1mq1VCqVDI5RQE5MTJj1Qm2RTCZ9zsI8DQ0NWSnC8wAMfXp6UqVSUalU0vb2tvc7e4v/0cBz7w0PDzs1GnUUaqGJiQnfBxTWlUrFM6TX1tZeWFE2NjY0NDTku5Y7k2YfzzbsnPSTzQblF55u2H3OiWq1ah87OQ6oX2BgV1ZWrKagiSWrgsA7Pgt3Vz6fVzKZ1PPzs0f5AJwGWXL+Pl5KAID19fUX4G3wjoYNHh4efjF7Gl91NBpVPB43SM7/kO8D9FFk8k5DoZD3DAAwjczIyIgODw/NqhGIyPNgvwB68DPX1tasMJPkmeGsETJCYDslOcmcsz8IrNOYsi9CoZD3J1YFPtfj46PZQIBNQEbYWd4rZznPmOeBxx4lAWcGwMvNzY2azaZOTk6UyWQsXSeHgprr8fFRlUpF+/v7BnLm5uZ0fn7u84g/f3V1ZcUGKhx+Zjg8GM10dHSkoaEhB2nd3Nxoa2vLgCv+ZwgEakwYWRpSgA78to+Pj/r666+1tLRkxQrPCLvf9fW1stmsmxUY03Q67RRtzhqAVOq91dVV1Wo1B5ldXl7q66+/Vi6X09LS0ouMjdvbW7PWgLGo8paWliTJ5w2NGHsCdVuxWHSjODk56TUEyCANgDmeUTBcjIYefzjp8gA6SK4nJiZcH5BfEbRGMVZPkqXnqC+CU2q4w3Z2djQ5Oelxd2tra4rH49rf39fNzY3PFAIXAU1TqZSOj48Nlu3u7urq6sq1CHvsP/2n/6TZ2Vmdn5/b6jAxMaEPHz4oFAp5agQKvImJCf3bf/tv9fDwoIuLC0v4qYcgOCAj8/m83rx5o+npaYPiy8vLOjk5cQ1L0zs2NhgbCQDCWQ1ANzY2ptevX7vWxDpCnYAVJ0iUEcL56tUrgxjh8GB07snJiWevU5eTNbO6uuoQ0W63q3Q6bTAvOP7y/9sX6/ev+fXX/v7///r6WU14u912gcMLZdQYqB0SGJpX/h4sCqw1CZ8UsPf390ZskeohV4MlwbeJh4bwFYoHmssgg8BlxOeg6KdRZeOBKIJqs1m4JLkQSFKm0UOGHvRnIRHf2Nhwei7hIpVKxeEgwYA2QiUODg40Njam7e1tF4UEXeEBrlQqurm5US6Xc+GJF52fhU+l0+lod3fXRSxFO+wKY44ikYgWFhYsbbu+vtbBwYHnIm9ubr6QqvR6PcspFxYWNDc3p3K5bOaUzzIzM+MEUeRhQfAkGh2M8jo5OTGDjnWBtUUgGig7iCAIPl8c0olEwsoIgpxoIsfGxqxqgAG+ublxyBzrZHh42B7qZDLp5qzX61kVEAwN4XDkguHdMbJqfHz8RTI7ewP5HZ9Tkgs1LspoNOqwPti2VqvlixHZKYcUoYfSoBmLx+Mu2lmrNE1I30DY8Zfx79gDoVDInwmZLmwslysXCQ1Lq9VyCjbyafylvEO+B01Ks9nU7u6uIpGI/v2///dmpgELaEZIA0bpgOcV0Gp2dtYySYob0o1XVlaUTCbNtuAFRFnB+hsaGjKIRZorhRsM88PDg6SfCiNChmDvYZtpLnu9QdAPsrirqyv1+31dXFwYMIpEBmFvMKY0TkHVEM+BRoP3Tf4BZ6kks528Y2wrsVjsRbgghRL2BMJwABpo8s7Pz9XpdMzCS7Lk+/T01GEz3W7X3n/2F41kv9/Xl19+KUn+GbBETE0gzZfnen9/r4ODA21vb78IgSJh/OnpyXYcGDfObVQHJLh3Oh03909PTy4qOd9GRka0vb1tcJf1wfekAYZZo/HAj8z84nfv3hlgpDEjpX9lZUWzs7Pa3d31XkM1AUNI8xIEvqamptw8dDodzc3N6ezsTF9//bWDPCX5fqTQxZ7BpAnG5FQqFadS7+3tqdFoaHNzU/f3g/n2SDABq7hbsZh0OoMEYzyJy8vLvs9QlKGQYU4u7B/3KwX9/Py85bcwvDTZKHGWl5d1dHRkIJSxb6i/SqWSFQ8AU0hXObdh9GKxmCX+jD7ES7q/v/+icYclm5yctBIE8Iamg6aIfZFIJHRxcaGzszM3I9xhjGvc3t5WsVj0GYZnm8/Pfo1EIvYVU2thwQHsHxoa0unpqSXS/HlsdzTH3AWcYwBsMONkXDw8PFgVwz7HapbJZNwASQMQY2FhwVNGTk9PzeqtrKzo17/+tZ8hktyNjQ0Vi0UVi0UtLy87a+Pm5sa1FZktsIkE/iGj5/2hoKFpv7i4+L+mLgDEoUoKh8P64osv9Pvf/94hraurq6pUKpqamlI8Hre1oVwuK5lMOvQuKOenYeHu4GfSiGJ7evPmjWq1mur1un744Qc9Pz9rZ2fnRTAd98bc3Jzy+bzm5+e1tLSkcDis09NTSXLgJLXR09OT+v2+Gd7x8XGHNAIOEQ6MRZO6G3YaaX+QhKIOluQpLAT7LiwsuN7ivjs/Pzcpx3nV7/c96ovxu71ezxk07CnCQlHBfPz40YRbrVazOqrdbisej9snj0UNIoGGHJXn9va25ubmdHJyoo8fP2p4eFi5XM53RTKZ1N/93d/pX/7lX7SysmJ1yezsrGtvVB9nZ2eqVCr6u7/7uxeZH2R69PuDvCJsM7e3t/r2228NwqFSXV1d9RmOIhT1SqVS8ajWyclJkxNDMSPVAAEAAElEQVQA+WQj4I1PJBLev58+fbLMnDF58/PzDipFrQohkE6nbcsDYKP+RfX1y9df9+tnNeEUiTSZFIQ0Pb1eTysrKxofHzdiCMrDBcGmBGlBfg5ijj8XzwvSRYoL2LFer+cGDbkgbDaFAZcI0ul2ezASJxiaFfSrEnYQLKw4MAiUgzlAnkuoEcgbjKM0OHiLxaLH9SSTSW1sbDhNfHl5WWdnZ9rc3NTe3p6Z6dHRUe3v75sd4eBj5MnU1JSOj491f3+vUqnkDS7JzEe9Xtf4+LgeHx/dcIKsAjTgn4cFu7+/14cPHxSPxy2rIRQFHw4+adhnDluSHQmD6PcHYWmEWvD8KeZAAEHBCWAiKOj+/l7Hx8fa2NjQ5OSk0W8KWRKOYTB7vZ4vQopBpHIUj7C1IJlBNDkY3sWhC3oNU4v0lOaR5puCY35+XpVKxc+awg4ZGQ2VJDMQSMco6PFAIQWkoeSSQ4ofRFYBeoJ+NQoOFA8oV/A8t1otBwRxCAOUBVF4WFRJL/4voAHhMHiVufRh1yR5LE6r1dLW1pb3MsFXQcXLp0+fFA6H9atf/UqhUMjFLI0yBTIsBSAIku25uTkdHR3Zb/brX//aoBENB8UyoXs8L9YFDQ8NGO8SKTCFHn8XJgPQK5jgTLNNgx8Khcys0TTAgHY6HTNheOeQ9kYiER0eHjp7g9FQMLLI9WkCAVh4f4Q8kQDNGUzhTUEBAISsl7XP/o9Goy8CXmAaYW9QeRAkyJqAAQZkYyRUOBx2Ir0ks7PB2fYUaewXQAJAPNYafkL28fDwTwE0SPEo8JAiIy/sdDr605/+pJubG71588bKDdRQAGvsCc6Py8tL5fN5s7j4fgl3RAXCGUPoD4DMxcWF3r17599raGhIZ2dnurq6UqvVUiaTeRGMhc+Xop5EXCwQgJqwIMF9+fQ0mIxBgB9NLUBTp9PRwsKC5Y/8M8oOCrbJyUkDspLsEw0y+TCo6XRa8/Pzzid4fHy0vJNzDqk6oXw0ejSLqO3wvuOjJ7jy4uJCc3NznpcejUZVLBaVSCT8TlDGAB4Vi0U9Pj56bjnAfL8/yO6Ix+OWR4fDYafcw5xTRxC8ikweYGxjY8OKGSZa4IHmjMXCUigUDCTwMwBXqbOwgOzv76vf7zvdfXp62ufp0NBgHCSKAMIWeeYQEjx77HmwrciWOcMIDWNSzNzcnNc+/vuxsTGD+Dc3N07DJpTy4eFB6+vr2trasuJmaGjIdyHKHxpD6admD9ULzCvrOJPJWB1GaBVhdajkzs7OzNrS9KIqClpQsC2m02nVajVVKhXXn/V6Xa9evfIdHYlEHCzIOllZWbGkl/BOVAl3d3cGCrLZrG5ubpxXkUql9N133+n4+NhNMzXk09OTR4glEgnfzZ1OxwFwZNWQrUGQLmBRo9HwyLHLy0src7ArQPxAKjHaDEUqvxN3GcG30kDpxnojIPT8/NxNWyaTsRWMIEASxyEguAsA87HC4FPnTIBVJkF9YmLCashisajb21tbkQA/ALYZ88bd8/3336vX61k5x/20sLCgb7/9VldXV+5VyuWyyuWyHh8f9dlnnxkkX1xcVDQa1dnZmdbX163oYkLH+Pi4crmcsxLIv6COg4nn/A0CBfjvt7e3lUwmXStiScFWxfnDWfn8/NPc9nA4rLdv3+rx8VHff/+96vW6/vZv/9aKS97f0tKSc4TIncDmxN0BmfLnfP3iCf/Lv352E04jA5qLVwwEE/8bHqFgWiksWigUUiaT8caGhcETCLJHOATeFfxlQSQXaWnQn4bPjEN9fHzcAVKEglAsUmBJP71kZibDUIIEIklGbotnDokec1CR5l1cXGhyclJra2sv5KgADsfHx5qYmLAcnQOKlFRQ20Kh4KKeomB+fl4zMzNqNBpaWFiwB2ppaUnlclm5XM7vCL/9wcGB5T5cVDTCXHB8rsnJSW1sbOibb75x8Xp0dKS7uzsfljwbJPaEAXGJ5HI5vX//XtlsVnt7e248KTRQTCCTBSShCGI01NbWljY3N9VsNi3zhaGnwIbtm5ycdBL88/OzvXmtVsuXcjQaNQqLugKQBTQaRpIGHwkTIx4AnmBpaHph5WjcWj+OvKBABdUkWO/09FT39/eKx+NGU2kU8EwyaoLfGZQeaSJpqzDtgDJ4skCtCdTjs8LiYiNg7Nz29raWl5dVKpUcHhP0KcNMIh8E1KGZI1Co2+0anSZYZmZmRsVi0WcCMqqFhQWP2/r888/d5IH8wuLC0qByIf2aSy+dTjvwKDhlgYaLZ8Y5xp8BiENmzc8KAhuVSsXFMhIwrDRI7Amko6mkeYhEIk45BqQAkFxdXfVlSHMXj8dfyK9B22H2YGexmaAieX5+1vLyskObKDiQflMsE7jE56HRphC8uLhwsbS1teWGGECBwpSfyf5hpBtpvRQQNA58H5hEwE68nABrQQZ+fn7ePxNgl9mxzGNdXl42K0n+AMUovlCmOKyvr7sAogGB4UWK//HjRwdEARoCaAEAo2YCDKJZ4w4JjpXhfpAG7CjMLmO1YMA+fvzoc+1v//ZvXfh3Oh2P+kOSCdMxOjqqbDZrsBSP7u3trV6/fu3POvvjPHu8iIDZnJOcbTDM8XjcYDOyXO7q+/t7MzacP6urq1a6HBwc6Pr62msxGo16NBbrBSknxSVZKljRULw8Pz97XdRqNT09PVlNsra25kAq7i9CxjKZzAsVHowfjR//fWRkxFNOSGfHz0wzzqxoah3UP3g2OZe4G2FuU6nUixwZAKrd3d0X9hJCE2u1mlKplJvAdrutlZUV3zE0R3xurGnIY1Hb0eBwrsGCDg0NWbGyv7+vmZmZF6Ax9RfgDVNc2AehUEgXFxfOuOB3QOKN9J3JHJ9//rnevXtnSe4//dM/aW9vz/VMpVJRLpfTxcWF1ZEkx5fLZW1sbFihxqhFmph4PG7AjSRp0qxZx6T2482GCEJtA6hECnu/33d+D7VIpVLxn+fddzodN0SocagTUGlxXpBBwF7m3X3++ef68OGDvvvuO/3qV7/6v+xDo6OjDvpLpVK+d8jz4OzlPkPhAkAvySoyQPr9/X2trq76Xnt4eLBS7unpSalUypNuhoeHbYkjFJEz7vDw0Iq6YrHou2VnZ0cTExPODUJ9wvkYTGi/v79Xp9OxSmxra0v39/d+z4AmnIezP07joJlst9vK5XLa2dnR+fm5wuGw3600GOm3u7ur+fl5ra+vu2YnYLJYLCqVSimVSmlra0vFYlF7e3tm84+Pj02+sC7GxsasAIEg2Nzc1NnZmccYxuNxFQoFXV1dmUS6u7szcEeNwVl9fX2tarWqzc1NZ+tQV0xNTTnQcHl52WAh9yb1D9Nv0un0ix4im82q0Wi8UG5AkB0dHWlqasogKXZMiEbyvX75+ut+/awmHNQUvx+yIS5WDtF0Ou1LI51OG21Dah5MToXRgOXgUmPTff/991paWtLCwoLlF/hZWfAUE0iUYLZAPJEPIvErFose60FqaqFQcHNIo80FToEa9LcRSgajxwHf7XZVrVadRLi5uenZoiDKjLMiiImGj3E4NOsHBwd+9kin6/W6ZYZ3d3fa2dlROBzWysqKL0ASQu/u7iy7kqTPP/9c3e4gDI4wtYODA/tJkWf2ej0zCchvq9Wq53pymedyOR0dHfl3YtYjifG7u7tKJBKSBvIpZHKAG5ubm7q9vdXFxYWLnNHRUZXLZX3++eeKx+MOxDo8PHRjDXNAMBfPj3AaAm0eHh6MmsOSU/AS9IO1AsUDEnY89zMzM2r9OCKMdQmzy/sGiEDyGmQKkXKjzJAGrC6FKMgv0uZ4PO4xUniHkeii6CCZnuAjkqlp1JgxyR7g0IY9nZ+f9wzM4M+mgIHR4R2hdmCE0u3trf++JBd6+GP5HYMNzvj4uEdRMSaKn8P6rFQqevfunRF6ACJAJC5wUplBl6vVquX+FN+EAxHywzuS5IKM8wYJKGwg0nOYG5hXCgcYd5oZgncajYaDfLgEZ38cV8KaJE0aHzCzSxmxw5/lMwcZd9g1pkicnp6aJW82mx6hyDmA9Bd2CmYbKSJjxWC8QO0JxAyFQg5lq1QqtmXQwNDoMRaHDARCNWmKycBAPh3MFcAWATCBzJuGg3uEBPtut6vT01On+dfrdYMuQdae/8HsIOHDkw5rjM9vaGjITGqpVHJQFMAg64FmgsJm9scRdRQ2wXE0wbAcbA6oF25ublQulz0+EysGAT0PDw9aWlryvnv//r2b7kqlordv32pra8tMZb1e1+rqqgOdCC2tVCovggMXFxddlGPvoGnFgwxjBYAAo8e7o6FeWFhwkBl7DYXH+vq6VQkAdoR/1et1JzJzlxNwKMkjrcbHxxWLxSwLlWT5OAoI7nJUMjRKnHUEtpGpQe0Rj8cNKpDNwpnBZ+h2u8rn8252c7mcGxPsDPiNG42G33E4HNbS0pK9se122+sBVQe2B851MkCkQb4KBT4g1ujoqBVokBWoWqgZIC0AN2DLAeyYQ01ez+joqJaXl9VoNFzDEDAXDg9mJ1N7kT2Sz+f19PSk9fV1g1RMlZAGIDjnz9///d87BI5zg98Rzzv7E5CYM3phYUEbGxtep0wGaDabVnIR1Enjtbq6qmKxqF6v51C2k5MTK8VmZ2d9t/A5mGJQLpft8d/f3/fdHI1GTaCg3qSeaLfbTvSnUYP4oTaMRqNef1jKqCGmpgbz3o+OjpxLwD4g+2RkZMTjMvf393V1deUzmXoUxZ00sFciQQ4qXwCVstmsx4sSXEkjmM1mbYUgIT8Wi2lubs6qQWpsrDoo9gANOENqtZrVVLFYzEw2zx+ZfTwe19ramk5PT92kjo4ORo2WSiUTYkH2mRGNNP3cD4x87Ha7DuMlf+Tbb791kDE1GtlE4+PjrskAv8bHxx1YRpZQrVbTl19+6bqTmeWpVErpdFqdTsdhr9QAqBWxMF5dXSkej5s0qtVq6vf7WlpasmoOG0alUnFOElM1AHfoCZLJpJVvIyMj7qcIZcM6F7w7yJgg8BSgkykdnBfs7z/n6xcm/C//+llNOL7YdDqtarWqi4sLo6rJZFKtH2c3glaB1pCaTGgRjSf/QzqDXxfmDEQH1ptmgSaCQAEaAsafcFDC3I+PjzuoCkYFJqJSqbgwYfYicxO53ChAgmwGGwxmBOk3/kcAChg8gkPS6bTl+oQKDQ0NmXHFn0cBGZQHwfDAeFxdXbnpHh4eduhTqVQySEIxDNBxf3+vT58+2cefyWR8cTKCp98fpIzu7e35cKcJovkHsabxzWazury8VLVaNSqezWZdgN7f3yubzerw8NCSbwJbJJllg308Pj7W3t6eD20YJw5PGgtSyEl1pxHknRFM8/z80yx11h/ILIU/0jcCfyYnJ7W3t6fXr19bzgto8e7dO83MzOjo6MiBczCohIEgQaURoYBHHgV7yIWMf4+GjAtkeHjY++P09FS5XM5yYrxDoVDI/47UaHxUyC1BPSn6CCva29vTw8NgdBUNDWxmuVzW5eWlwYrp6Wl7glGCSPLzZK1xGQKK8TNbrZYBLthQQDCYcopWQl5IGu12u1pZWbF6JRjqQ6o8+52CGkCCJo+CE0Dl7u7OI102Nzd1c3PjsT/sGQKOAOBoELEl0ITi3yJ9P9jIAhTRyBaLRbPDfFbABr4vhTqeUC5fihsQbBq1ubk5lUolF6CElyHdjMfjL7ytKD8APvmdxsbGtLq6qufnwUxWJLsUmjw/WBieN+cYUsyxsTGdnZ2Z+WWfoY66v7+3x3F6etrWDgAqpIjMgYWJozFvNpsvWGXOKRgRVBSc3RMTE2bGKNx4lxT6vV7PXmkAGKSzfH58/oAH7GE+B4ombCD8d8A+Zv8mEgm1Wi3l83lbAba2tjQzM6N4PG6glAZxe3vbdxdhn4Bo+BhRiADkArDwuQiQhLHDR8s0BBLK2S8jIyNmCYO2Is60UCik8/NzRaNRh73RvM/MzFhKm8/nfU4C3mAfwNsPUEIuBf7R4LMl+IyJHSiUUMGMjIxoZ2fnhfQaRpJRUFg+aOgPDg5sp8PzXyqVPPaNzwZ7m8lkVKlUbO0K1kSsJSwIwRAwFDucmQA0gP3MLGctsHZpBgqFgrNiCNmjKG82m5agw2oRwIfqizucdRyLxbS3t+fzgIkvKysrVtTQFNOAhcNhq/SYu0zT0el0VKlU1Gw29dvf/laff/651xaqPd5Rs9lUKpVy00nTIA0YzDdv3qjVaml3d1e5XM57jowSmF1GpFKTke8jycw+knVJWllZ8b7kjqxWq74nOMfT6bQajYbtjBsbG2q1Wm6ikDxDSoyNjXlGOecL+4R6gGezurpqSxl2ku+++06vX782ocH9SiYAY8MWFxeVTqd9rlPTkkdE7QkRtbOzo8PDQ83NzRm0YZ0DUgVtg7M/jkgDrOKsD8qruePz+bwBTWTfrVZL5+fnkgZAPLU17CprFLsgQPvU1JTK5bJrBybIMAEGG125XDYIOD4+rt3dXdfLgLDU1Pl83mqBjx8/Wh1F0jh1+PPzs37/+997P83MzGh7e1tPT0/69OmTz59cLqdEIqGjoyPfF6iZUBkBLCPvbjabzrqQBiooMjaodVknEJWoJCUZwIC4wgqH/YLzipoIBQh1Jz0T5zlKCkjP6+trjYyMaGlpSd99952zZlBw/PL11//6WU04m6XVavnwx7/T6QwSVRkvUigUdHFxYdYODwTyn7u7OyPQbEaYyPX1dS/gX//61y5yx8fHPaoimDi+vr6uaDSqd+/eWeY1NTX1gnGGVVlYWPDhAwIlDRBIvOmM/ggilhSfBMDN/ji7FXYkGP5FcQHAQLpiLpfzxn18fLQEr9PpGMCQZLkwBRQbhYMH3wboOg0Q/j2+//X1tYsVGrFiseggosfHR2WzWTeM4XBYOzs7lseEw2FNT0+7SUFeR9AUzMXc3JwlR6gkGo2GC7rz83M9PDzos88+01dffaVGo2HGOxwOvwBTSMIkrIhDEf8hbEq/33cz124PxsAxWxsvMcnkNEUnJydGEzmwYBmDTYUkJ8Cvra3p+PjYSH88Hn+RCBt8V91u1yzi1NSUjo6OzJSS+ItviksG7xPMzP39vRtwJL2EkcAoUjAUi0U3scjqaUqRjeFdB9FvNpsGGtrttuf0Bp8DEuDJyUkVi0UnffK7JJNJ1et1Ny0AKMEAKPYDF8bs7KwmJyd9QcFY4Qd78+aNVRMUpbA/NHXIyWEVYMnZw41Gw1734IUKk0dwEO8dtQNz21FBAJjhvaNwCBYqQWXC2dmZNjY2FIvF7O2jUOFCYxTL+Pi4isWinp+f9ebNGwNlgH1Bb/bMzIyl/hQjWFLYz8zDplGnmR4aGowbAaBjjSB/hY1g4gUNM+w56P/JyYkBERhWmEEKVBq44eFhraysWIodDoedyrq3t2dpLkAhrEOQGR4bG7P8ttPpuLjgnWcyGc3Pz1sJ024PwkIBEgjko5G8urrSw8ODC0oKIhLFUQKwj5ghi8SapGjO1mAxKsnrnfPj+PjY0lHATNYy9yD5AXwPik3uRe7B0dFR23gAnGjkAUbwKAJGIuXmLGMtkSOAeocxNOxZSS+UQ0FrAnc+afcwjzT0U1NTllqzhyORiC4uLnR+fu7f9e3btwZP9vf3HZ4GeM2av7m5MRjH+6LIRgFBlgm2BM4WgDYS1LnHYNLwtiNhPj4+drgVv48k7zkSkE9PTx0UyNxj7mqUMouLi/5+hExxV7GuaNgAwRnfBXPP2oN5XllZMeuLegS5Lwwt4aLc1xTmgMDVatVKPMBcQiEJZ2q3225mYc2pLRg7h+T5+vpa5+fnBnaQixOqOT09rVevXnn9Ac5FIhH9+te/VigU0h//+McXeS8A2Pze3333ne9cziJABMJgs9msdnd39fr1a93c3Ojs7EyS3IRi1cAGeXNzo4ODAy0vLysUChlIgUxhNB/gTzDTADsje1UagKF4zZnGgHcfxRmMJ+AwuQYoPACDpqenPc+cuwuWn3W2vb3tOos7m2aLOy2ouKERB7jjfKE+CIJ2o6OjqtVqthNhheIeZy+trKyYWOKMTKfT9mhjv6vVarZUdbtdkycw8efn52o2m97H1AM3NzdWn05NTZklX19f1+Pjo9bW1nR9fe3ANsB53jlTG4IWrbW1NTWbTa+3SCRikAd7AMqxhYUF9ft9fffdd17bd3d3+uKLL3RxcaFPnz5ZmbKzs6NCoaB+fzASD1KSszgWi9m+hi0gm83q4ODAloCxsTHF43ErJnlvjEtcWFgwaIFClz/PHYlFLXh2oThFUXR8fGz1GgF5APh3d3f68OGDCcCgbeLP/fqFCf/Lv35WEx70FHK4STLLHAqFdHx8bFZmfX3dBWipVFI8Hlc0GjW7BGM3OjqqfD7vsSgg0QS44OOBUUD+R+NZKpUsu6AhpOgmmZsACQ4WEGw8myDdSExhOjj82SD8eYJ7gkFWoMWS7A/iAjg6OrInlCIBFnh4eNiJrqlUSuVy2U0u4ADBLkgeG42Gbm9vPb6AhoIDiQ1KYRpknMfGxiynoSgZGRnR8fGxXr9+7eaHA3t1ddUzxRlLhSTv888/19PTk87Ozizrub+/96gaEjolaX9/X8vLy2YUKAYAGGDAtra2PDsR1BZ0lPCsmZkZh9UFZ2Hig4Vd6ff7mp6e1snJiR4fHx08hEoCtUI4HLaMGeCDRFlCbRqNhgqFgiKRiNbX192wsz5Jw6SYQCoPIIA94unpySE3NKqnp6dKJBIaHR31xUjQFmP8AKZAKxcWFlStVh06dXl56bVJs8q6xV9L88FFSo7D6emplRedTseyPuRJo6OjBrQAWyQ5JIQQOXxXNK5I4bAQwKZHIhEdHBzo9vZWv/nNb8y8ImnmPXC5plIpnZ6eOrAL+RW+Xs4TnikefEAp0GxkV0iJaSooOiQ5oIT3wM+UfpqmwLtnLZbLZbVaLSUSCXvCkboeHh6qVqspFospm81a+g9bNTk56aad9w9DIEnn5+e6ubnR2tqaC1vOAUDN4+NjpdNpX66wLOwHFDX4zmdmZl54nfHnw5LCHsNGA1CsrKyoVqu5WBwZGdH8/LzOz881NDRkmXY0Gn2hAiBskQAuUHhGsxH0h5wZaTgsIkUijTIgA/JoChVYY5gE1BiAHJwNAKeEySHRBxBErYKV4/T01PJwWBSY8larZXk0VgvOYuxONAGAUtFo1OxVr9fz1IigNQaQgvfMO+CcZa8GAyO5e5A13t/fW3KLoghvION0AF1Ip4aNwqMdbNA4O2liUYdhGwqFQpYew6gtLi7q4OBAT08/jToKh8NmWjgT+H1oVCRZHcR+Hxsbs//z5OTkhRVHkhUnsIY09cViUY1Gw6wVQVYodCKRiGXFmUzGqq16vW6Qu9cbpKqj+uOsIUQJL/HMzIzev3/vhgYrBXdtKBQyy4wqK5VKuVnG/vD09KQffvhBmUzG7wUvfr/f952Cjz6VSimZTKrRaFjqTX5Nu93WwsKCpcg8M6xVNEuoxUipp2hn36CqoH7hbI9EIvrDH/6gu7s7vXnzxoAwjSFECh70v//7v9fDw4OTttlnhEk2Gg0HXx0fH/vsbTQayuVyWlhY0NnZmUeeYfNpt9sOsmV/onhB/h1UwpEGHYvFPKb06enJoazhcFj7+/tWdHCHA7Yyfo6AU8iUYN7Ap0+fXsy7z2Qyuri4cA1NRsb8/LwajYYODw/V6/U807vb7fqOos6pVCpaXV11SG0qlVKhUNDS0pJGRkaslgDQJuyMjJjb21tLypvNpi4uLrS9ve0znUkGEEkoPI6Pj830r66umlyYn5/X7OysJJl0IpcJAALFCiQPwCKAF3JuFHKLi4uq1Wq2anz69ElbW1sGeBOJhDY3Nw2M7u7u6unpye8Q20mvNwgA5O7EzhCJRHR9fe3+pfXj+DryWba3t/XVV1+pVCrp5ORE5XLZPnYUdChBUTYMDQ3p3bt3JrtyuZzOzs7ssT8+PnZdUCqVbMdDpXV7e6uTkxNbw2DH+d4AbqhdqMkgtJCmAxoRPggoCiBBz0Q6PPUFOUbUCr98/fW/ftZTpnEiBZLL+urqyqEFbBoOv+npac90hrmDlQDJgzFtNBqKx+MuCDl4ginTNABIh5AcI1+Elefiub29ddGP/F0aMKqMI6FxoehCegtrg+wIn/Tsj/NnKbRubm4sSQ/K6gACaBRaPyYbk1gJ003zyZ9jJFi73XbDQDERDofNSBCMAhs5NDSkYrHoZjAWi9nbi48um826QGWcCbIymHIY2eDzHR8fd/NfLpc1OzurmZkZz6QlyIKgHuS0BH0gdczn84rFYvZvdbtdN0/hcFjxeNyHDwxAKBSyVJjMAaS8pBLT1ODjpaHCltBqtfTq1SuvA/yKwaZxf39f0kAyhB+d4L9wOKxqterU+IODA8/uxP93cXHhtf78/OxE0KenJ338+FE7OzuWrwXDwGh+YQsoxrLZrH05jKZD4dHpdDyjdHR0VPV6XalUShcXF07JRTZIgBAsGEwgzOvx8bHu7u70zTff6O3bt14z6XTaSbt4v5CZo2YA3YeVRBXAumR6QTKZNNsnyeFRhInAarMOYBYfHh5UqVQkDXIFaCgYPYMki/EbhPllMpkXnjkk/viZSX+mwCAwBYAPpJl9WalUVKvVlE6nff71+/0XPuKg+oPQIRpdQBx8yHj3KD5gTVEoBGX9FICwj0gracj6/b42NzctWZN+AhKQqjIqjGfGWQdzjuIBxQVJw9Vq1YFUi4uLZl8LhYID0ShokKZzbuCLpwnf29uzfO/q6sqjXC4uLuyjxS6AV45G8ubmxqMPm82mg/ZmZ2dtG6DQIHAJzzgSUZ4vWQ9I/FERINeE3UaBRAODqqHRaPjnkv1BA825wvtkrXPvtX4c2xeUENL8Pzw8+NyFzZibm/OoInyB09PTKpVKXuvX19f6/PPP/ayxDfE5KPLIRmHt8PNgZyk+aRZQXhEYlclkVCgUrMKgQeCeAtSAUcMyhjSXuzSVSimfz1uS3+12dXh46Kkc7He8yOyh29tbnZ2duUEZHR3V4uKiZmdnVSqV/Hu1220tLS25wWT2+c7OjsP99vf3lclkzLijCIjFYr5r2LfSIPG52+26oUIph9IiGLiKJ526AfUP5wkAJ8/k+vraYOvIyGAEYKlUMngNQ0sjhUIDDzKASyKRUKFQULlcdq2EFBgFjCSH5UEqQBigcMGXCwBLSO7ExITm5ua0vLysSCSi77//XgsLCx7HF9wvPE/S4UdHRy3JBdjg7GVvBucwT01NeU2SwXN+fu6GCvAByT6qBbzjwQyXjY0NN8EXFxe6vLy0YhN2f3Fx0Wc99QFBc4Cs4XBY6+vrnmjD+6TeBYjm8wBwIQ8mYBK/NPXJ7Oysvv32W42Pjyufz6vX6+nNmzcODyMbgX0QBIxhxhuNhoka6gNIC9R/WJC4M/h9FxcXnfoO+EVWBxaLoFIxEhmMs+W+A/RH3UqdwHkJIYMnm0DUYDApoCYqCvYIdi/qRlS2nLnDw8O2TrL3yVG5v7/XZ599plKp5FoDqyEgBc8F1QAKoufnZ+3t7VlJUywWDZAnEgmD09zfnNs8e2TlDw8POjo6egHsINEHsMVCC7iOUmF+ft6qF/IJUEdwT6FK2tjYeAHUAGyT7xCPx/X1118bFEOBwdqAFOL5MVr3z/36hQn/y79+VhOOTC7oNUOqxEtEHnp7e6vvvvtOX3311QufN6xst9v1ZXF3d2dEi0uB75fP5108kqZNs0rQx9jYmNkINidebVghwmCCsq2gDHt0dNRMAgsexgS5D/P4uDRpNPB2SzJ7HmQUhoeHXWySzg3yPj4+rv39fbM9/N92u+2DcWFhwR4w2KmJiQnNz88b4aOwp2lDxgLoMDU15d95ZmbGze309LSur6+1s7PjQhakmMvv5OTEjQef8fz8XCMjI55RS3o3iDeXfr/ftywJZpJUSsIv+v2+DyFG8BB8ReNIMZDJZBy4gcKB0J/9/X03/jQzMJaknXJYcAFzwYRCITNOFODd7k8jkEif5CAmCXNsbEz5fN5heLBwFCJTU1P2QQa9ajB5FIUoKbBEkEybz+eNGMNSwPhNTU1pdnbW8m7YRJo70FmAHBrAu7s7H+woENLptP3t/X7fgX+wo4+Pj6pUKi6agr43mEOeHf5QJOuSXKzjEaxUKup2u1pfX7dFJCifRY1BwQVbhy8RkIcGEglbMDE9nU57L/A+KN5YTzQ8Dw8PL5QtgFpIREns533xeYPvj+eFzwzZf6/X09u3by175ZwhMAyZOMg1Py8YtEcaOWco8kNyABYXF+2Rxe/PuQZqT4gTCgCKB8bLoNKAIeCsB+AYHx/3+0yn08rn8zo4OLC0GTnm4eGh0um0ZZBXV1eanZ11cOPh4aGDeigaz8/P/f4Z4wiyD+CEPB/ZXTwelySrXjiPOp2OZbfz8/Mulmmal5aW/N4J1Lq7u1M8HjcDFI1G3cTGYjEXl+zdk5MTBymiMAFc5m6cnp7W8PCw07iZaPGvLV2wUwQ3EeyGXL3XG4z+hCEGRGVOL4nlsBeAzzx/Ghy8n0g/8VcGAWrORs6fT58+qV6vO4eCbAuY/VarZXCUhghZKJadcrlsNQ579vb2VsvLy7q9vdWbN298fwBA0rBQeCI1x96AxLPZbGp2dtZqITzGR0dHlrFPTEwokUj480jyfd3v95XNZp2TkkgknDyNegTLR7FYtPIDRhcgmr0JmEJzj40BCS3s/f39vcF/AupyuZzzdAD02WckkhMgxTvCs887BWxAhUf4HAFcAKzI2fkdsOGhDuMM5Z3hJYVVxg40NDQYcbe/v6+HhwdnihQKBasv7u7uXOsALlBv0UxQxwEej44OJjrwjFCmSIMgKZpFVEk0fqiQOEc5KzjPyb0hEJOaDcUHyg0SrJeXl9XtDgLNsJ+RmI76g2aQ+x3LCQqIXC5nUgoFYzwe1z//8z9rcnLSddO//Mu/uOGu1+ueUU6DB9jIfgZIRQlD1gHqQhpnbJzUb2STkMTP2UlwIPsXIofvA8DMmUG9DrAezMYgu6HVahnIhQyC8FpaWrKChHOO0FLOZsA/zjNqQ2r4YrGow8ND13PI1Bkdd38/mOozOTlpK2SlUtHOzo5+//vfv/Blo3jFOnNycqJKpWKwkfu6Xq/7HItEItrd3dW7d+9UKpX04cMHffHFF57URB0CQ850IWpIUtuxNYyMjCidTuvu7k4//PCD3r59q4WFBfcxkUjE2RF8Zu5lAADyVp6fB6PxANWDEzQYwxYM+R0ZGTGA+OWXX/qu/+Xrr/v1s5pwmpJGo+EGYnl52eEW09PTnh86PT2ttbU1PT4+2sdAUQWLPTo6mKcK6hIKhVQqlSwL4zKjoKHIhw3JZDJObhwfH/clRXNMk80BTvonCDWeLS5yPL1IlPB9I5nBqxNM+aSR5VKl2UTWgswL2fjt7e0LGSHIJgcIMhuKTcaocZHhLwNtQ+Z5dnbmGeunp6eel4wkDOnYw8ODdnZ2LB1aWlryZuZ3zOVyGhkZ0cHBgZs35rXze0ajUSfgjoyMaHJyUrlcTtVqVa1WSxsbGwqFQvr22289Y3Vpacnjqdrttl69eqXh4WHl83l/H7xxyFDfvn3r1Gkap3Q6revra62srFhd8fDwoJWVlRfKAuSbyLwlGQnkXSG1Dnq26vW6/z4sC0mSNFq/+tWvfAFTbEvyv6NoAdlkjBrMGxJLPitBLEiyaUzn5uYciMj3xTaBdBvJJpcCsj6KfLzUu7u7RtGRKuIZi8fjDudDOQBCGvTl0mTTpACCsGdQIcByBMNBhoeH9enTJzUaDd3f3+vt27eWxAHQsPZZj8lk0pcuia3IWVHLkNFAqjI/j2dKc04zGwwSe3p6Uj6f9yieVCplJpumNp1OK5fLuRjhPCgWi8pkMpZXwyTBEs/NzTlzIfh7hUIhnZycqNPpKJ/PW35M/gTrn+aey5FkWmwmBK6FQiH7+LAM8d8AFPjMeLqDxTEgEcAOLBF5AZIccsYEB4plwCR8nzc3NwY/eF6ce9iPkD7DssCWsA/xidOM1Go1S3cBBmECJZlF4f22223PjkY1hAyeMwSfNHsP1pPPe3Bw4FA4pOETExMOSOp2B1MKUGVxL7AH+bkwTZw53H2ofwjump6edjgdDZAkrz+UXkiiAZh5/qz3WCzmzAYA0+fnZ9VqNUmDxp3ZyKTwM8+eMwGVCtJgRkwhgaS45HfCR4uc9OjoSLFYTIlEwtJxGm/YtomJCWWzWUtAySngeSMdJ/gJvyiNTrfbtTeSfRwOhy3JRbUB4Pj69WvbLpjSwB6KxWIGkmF77+7udHh46DtjfHzco/oIz8Q+EYlErICqVqsGTZrNpsMxucdXV1dd16DaYsIM57Uk10nn5+cGxxYWFjza9fvvv1ckErGFj/0xOzvrbAjGHtGMQG4MDQ35nINYoZlDbcOsYwBW7itICEAVahNAs/n5eS0vL/tufXh4sO0IDzZAyczMjJnkcDj8YhYyoCkgHI0wbDryaEnOaOCcxEJEc45NoFKpqNFoaGtrSycnJ26Ggw0udxcBgwsLC3p8fPTYLlhfWGbqFdRtwdBKmnrIAkkvvm+xWPRYUJ7zl19+6Rn2nIGTk5M+FwB3SM8vl8svRjJKMvjB78N9XavVPNqQ8waFG2MWr66uDIiiBN3c3NTx8bGmpqbs94ZoKxaLXg8oN7BbAMB2u11tb2+7kUZOT+1wf39vlSjKy6OjIw0PD2ttbU2VSsX3HHJ8VAJBpQU/682bN1peXtbh4aH30+Pjo/7whz9Y+XN8fKzWj4n03AfcN51OR0dHRyZiqtWq6wjOomw269Dap6cnrxHyL1AaYcniczMJAJXw09OT3w9ha6gwyNfodDoO9APgJ7yNmh2LEOcsVh8IBsC0vb0990yxWEwPDw/K5/POtMDCgIrtz/36hQn/y79+VhOOX2NkZJCGyTxpLk5kDjRZMKHISqampnRzc6Nms6nHx0cXXsgyer2efeNE/+NtoPkEURsdHbU8Ggb46upKlUrFLAJssSTLckkhvL+/d7AG8vJqtfrCAxksVAkhQU4Mw4xHnKILRoEGGtaf4ou0w8fHRz8rxinwDAlqggVHlk34HY0ZiBqIOJJlim5+xtXVlbLZrGZnZ51sjueMQC0Y27OzM0uNV1dXNTk5aakfzcHDw4PlhsiIU6mUKpWKJiYmPFrt7du3L1j9ZDKpoaEhy9Zvbm5UKBReMNAUtDTPILBLS0t6fn62TIn3S1I3bCkAC2mkGxsbVjsAuASbumBaJ00uhRWqDdDSu7s7LS8vu4i9vx+MkyK8CR8P7woGhaICORzAB2E9FA9BkAqGDFUHjQfsPQFKFKb49PEes0ZRSRDUEUzqRh1ye3trZQFhixR9IyMjkgbsPWOmAIjw31KM05gj7Q02f7CVNPhLS0uWdFKoBKVknc4gaZfZmFwskUjEPi+KRHxTsHI8Dz4jRTAXMk0qgEiQYeV35O8HwxaRckciERWLRf3www9uoGjgaNaYDIAyRJItJZ1Ox8+dPXVzc2O/V6/3U/I7DA9jZwACkb2yp/Ed89/7/b5lbzCEzH2/urryOuRMBgQjkAgmh/MLFcLo6Kg9ZjAV8XhcyWRS3W7XTVU2m/X+4nxGrcT+QvpPkBEeNBrETqejWq1m5h4GDGsSnwcVQqPRsCKl3++7EaCJJtjm+vpasVhMk5OTDtxLJpP2EtPAS3K6ba/XUz6f933D+Eb2JsAxVgyeG/5rmleaHZoIwA2Cr+r1ukPOFhcXlc/nXyRaE4bIXlpbW/Nsc9QcJycnZhjZf6wzWF6SsDmfkQHjM+SOlqRCoeDzDGtDu91WtVr17O/Hx0efqYAapVJJ4XDY9yufJxQK6dWrV5b4YjfjbkeWSoGKTBT7ECAaDQ3NHkGKFK3cs1dXVzo9PbWcv9frOfiI83RhYcFEwdXVlZvK169fezrC2NiYJfqwhevr62aRCOSs1+tm6QHLOScABrAvwGZS3wASBG13gCmTk5OeZ765uelwLtQt5BbgE52dnfXIUMYPtVotg3UUztw9BIU9Pj7q+PhYoVBIiUTC7Fm/33eCNYDLyMiImxvsYUiln5+fPS8aAkGSvfT8GcAQAEDu8vv7e71580b39/fa29tTuVx27SMNLFowh7Ozs3p8fHQILOz68PCwZciEAv7DP/yDFhcXfR5Eo1ETLoDGwZRymH5JSiaTWl9ft02DppQRetz/jGzN5/PO9+B+rtVq2tvbUzg8CE+F3OBcgyChHgYkQO1GnQCIwH1HPff09PR/7fN6vW4fNuuRepoxh5OTk1peXtbT05PXMEGmZBtQX+/u7vpMSyQSVm1yHufzeQOdvI+bmxsH+3LWZrNZnZ+fuw67vr62omdhYcESe5homleUpdxf1KLBINeFhQX99//+3w1e/P3f/70nHxEAh8qRHgYShfOQeoTnv7Gx4TF9ePLn5+d1cHDgmj+fz+uzzz6zjY89LP1ka8FyR6YJz/vi4sKzviX5d8HWR92JeoaQSZQwnIXU28FwY/ISUM/Qu6AS4uyDfPo5nvBfmvC//OtnNeHIa9jgr1+/duovQWLLy8tOPefSiUaj9opRMEcig3EGeGcJjcDXTaONbJALizmXhOngdyO4Bjbl+++/17t37yybuby8NAIMI/7tt9/60APFAmEPpuPCTNCIkM4OUhksdrisuFi5QLvdri+naDSqUqmkfr/vwDN8zqDSABE0hjB4wYOTUJj7+3sXn8/Pz07lDB521WrVhfbw8LBlibDx4XDYHj0Ow5WVFQdA4TkmDfLx8dGXGKhot9t1cEe1WtXp6emLzdz6ccTHzc2NLypQ0OHhYSsm7u/v1Ww2PcqHIJRisWhgAEYK/wtNEusLCR4HHb8fARk0oiTJSrKvCfawVCo5FCcYTIcvPwjk4JM7PDzU09OTfXccllxOFCDBfAO+aJRpJDlQaUSRjQVnzWYyGUuf8TohoadhYb4qDREMPFMJkDeB6CLfJjAHySWMEH8WBovnAnqO3Aw5NGw7rN/W1pZevXqlhYUF1Wo1sxw03ygrxsbGVK1WLdeenZ1Vp9NRqVQyQ0HjQOI0qbTSYAYnqfEkfRJkxs8BRMLXDgvBOC9Ge8FAs05gmPg819fXWltbs+yaJpGkbEb64LkeHh52+BFScNjzoO2kXq8bBQdkCoVCblp53oTpBM9K1gXASjBQE/kp+xgJJ2sV5uvq6so+NgAP7oF2u+3wQc5gmChYAGT8gAYzMzO6vr62vJb9uby87NFgeOkZn4iHHF8pKgTWOSosvje2C+6l8fFxr3U+F4Ur43RgQGBXYRBYu9guXr165aIM1QfsIXaUoNIKEE2SR3HiA2e/oJjBjjMzM2M59tramgFArEJHR0cGV4aGhuy5pAllvB7hechRkRUD6DD9gIkN5IkAVuF3R13DGQQQD9DBrPZer+dzHeXG8PCwPf/SIFdmaWnJkn+AaAL9YNlHR0dfyM3Hx8c9LhN1F2nNkpy1AihFkB+jIxnnxnlAsCc/h8aHcC3uYJ5ftVq1LBg/PAAMjQJBaaQVJ5NJFQoFg46Ay+wLvNjdbtdn1b/2EBeLRTchrR/HOzI9gPXHPUUDEWzaUYAgqQVQDq5vwun6/b4/M/u+Uqno/v7e0mJGKubzeTNxnOsEdWG7A3yBJODs4g6iuUMNE4vFPNEjFAr592y3237G33zzje/Nra2tFzLs1o/J3LxLlEOMYAUIxgrD+Qn7SrI1Kg5k2xcXFx6fOTk5qdPTU+XzeaVSKYMJ+Xxe6XRaHz9+dD2VzWadHwMYw/vibkXFQSMF+8sZWq/XJcmqUcBa2HHUCdSbMP+A4kE/PWF0qAS4D0ZHR33/cWZyhjHelT03OjpqKwMAFu8Dkol8FED/sbEx586Q1s3zubi4UL8/GJd1eHjovTYzM6P19XUdHh66zpekxcVFLSwsuPmOx+MqFAr+jMi8mWTAXVWpVPTw8KB/82/+jS4uLrS/v+9nh2qQWkKSNjc3VSqVHP6J7Yt6mcb9iy++MHFzfn5uOwZMNGcqE1gAOAHlsBZQ+8Tjcd9zqJ8KhYJ6vZ4+fvyozc1NhxFjyyF8bW5uTpeXl1pcXNTOzo7VElgEwuGwYrGY91uhUPB5Ewwzzmazrn9/+frrf/2sJhw/FM3w+vq6D2eQPJoqGJ1er+eCWRosbg5fDh8kEHinggFaJCWCkiNF2d/f18TEhDKZjBcwl0wikfBGabfbOjw8VCaTUalUcrgWi1iSD6Bgc1Sv1+21GB4e9oKnmAcxxkPaaDSMntIII1MPFl7I/VZXVy1FSqVSfg7IVkj2JM2ZtE18GiCQ9XrdElYKEZgDfFCpVMoHPKnbR0dHisfjnrtMkcuBStFC0VQul+29ffv2rSYmJlSr1fTdd99JkuV0r1690sPDg7755huPRKHJvbu70/n5uZaWlgxQcJEBYIRCIfs0KVIJtYJtQ4L54cMHAyN4iWigYaeQ+cLccdFks1kDJhQFeMiRZp2cnEiSpYzHx8cueLvdroERDkkK83g8bvZWklkH3gcNK5c/kn/QYppKWKrr62tL8NvttpNYE4mE3r9/r+3tbV92knwhIRcMStP29/cVjUYdbETBBjt4eXmplZUVX6K8F8CtRqOhs7Mzgz7NZlPxeNzFJ5eVNBgVQwEGG4fMnL0NgMQlL/0UpsdzgBG7vb31XGkaAGbDj4wMEmGRbGEn6XQ6bmqDskCK/ampKQNL+IFXV1fdyNLoY78JfmYKzpWVFdXrdZ9HeGYpgLkAZ2dnfTHCOE5NTSkej/uyD4fD+qd/+ieHv9AwwsaxvvH/I/HDFsGaxmpAqFooFDLoQdI1qiC8jRT1FCSE8+Db5j2yR5FGM3eVPYfUlz1FA3FxcWHFw87OjllPwozOzs48AgfUO3iOAxYwvQBfJOuJUCnCqAA2CACamZnx7wFLIckp4UjS2b/sTaR+sNFPT09mlRqNhkFj9hmNK/J4WAY+G/JsGBpAYRoTAJShoSHfQxSCNGqhUMhydtYpKd2cnTS1gK404NxDyLJRGFxcXBhMx/4FCEyeQzqddtL4yMiIwyaRtcfjcdVqNVUqFZ97fL5weDCBIpfL6fT01GGNeIkBoQkeIlCIAndtbc37ttfrvQjvenh4eBFmR8PHHgFkARBAmh3MuMASx93EPXp0dKTFxUUr3ZrNptrttkE1gLMgi/ntt986x4H6AACTsVzYB7CPBWsBWGTGDJbLZa/nTqfjQh/WvFgsKhwO69OnT9rY2DADhr0OyxE2jn5/EOYVBE0lGXBJJpMql8va3NzU3t6e1X78eTIGKNRR9JVKJcuQOYO4G9lPs7OzqlarJgsYT0foFe8VhliSVQrcEdR8BIChGAyqg+7u7rS6uqp+v69SqWS7D2F61ImLi4uq1+u6vLxUq9XS4eGh1tfXDSg+Pz8rFoup2WyqUqkYEMNW0PpxygEWvXa7bXsDkxwgo2BUZ2ZmnO9CzYUaglBEasFqterk6vn5eStHANlQ9CA7npmZ8SQHAA0yX/ABM9qOpG4UOdy7jPja399Xvz/I9eFdQ0gRWohXmjUEGXJ2dmYbEvUx9zCWVUkmpFCthEIhK5ao3bg3Wq2WXr9+bfXdhw8fDA4DiHHGrqys2PP+X//rf9X333+v5eVlFYtFj48N5r+g8iTHA4k9ACLqMCxmj4+PHjGbzWaVTqf9vmm2ub+73a7HFkKMQATc3d3ZEtput/2OsRnh4Ub9iBQdAAHbYjCTJAiYM1623W7rD3/4g21IrVbLORd4wZkkAXP+5379woT/5V8/qwl/eHhQs9nU/f1grjHjvAhMabfbqtfrOj099WHw6dMn9fuDedyhUEgHBwdubEGdgvJEmAN8ETQoMJTJZFJPT08OH9nf31cymdTS0pLHtiAfxM8dDC1h0cGQwHZz6FPUs0Ek+VBEIoP8BmR+bGzM0nnCknq9nv1whULB8hOKP7yJBJbQAHDQIocH4USmfHl5qXQ6bea1WCyqVqs57AVPPGw97wYEOBaL6enpSRsbG5JkNgD5JXJ50nsvLi7clN/c3OjNmzdqNBp6enpy4A6jQjY2NpxkiZ+3VqtZ1hYKhZz4fXp66oL28fFRxWLR7+/5+VkbGxtuzPCEUtgmk0mzBDs7O5YuEgBISifsT7//U3Ae3myas1qtZuYUrzGH2MbGhqrVqj58+ODDl2YJefjT02AMBSzQ3d2d5eCSrOQgeAn25/n5WSsrK24q+fk0OazP0dFRX7ZBxJ5AKNYbBQKecQ7jILPPoUohWKvVzMriyUKxglQV/9Hw8LAqlYrtDiTGFwoFSYMxhVyqFL5482A4YQ0uLy/dRH322WcvgBJsH1xINOLsRxqksbHBXOtYLObZnkhUucQ5P8iUoDiUBqoMQnoqlYpmZ2d9Rl1dXbkJSiQS6vf7KhQKymQyisfj9vItLi66SQRJpnhoNptaWVkxw0zIFAUUhQWFCTLdDx8+qFwuK5fLGTgDfAEoQT7ORU4zQYHNpQ6yT5L4/f29ZddI8QmBQ/IcDocdiklAEOCSpBeqGj4PEkDCZVZXV52+DKNOER4M+4FlhAmhuEdKS5YFoC/jzBqNhgNqOKdggWG++LswhoTZBYEJ7gjOVdQE7DEaQXx8MEOEJWUyGQfwADbBjiLhD6pDKLIJ3OIsQbLIPiK5HeUMaobLy0vlcjltbW0ZFJLk4pl3goqGLAm85PhuV1dXNTQ0pLOzM93f31tp8/z87KKdvXN1daVEImFmiZGiNGpIntnv/KyZmRkzTIwrolDmzKCJ4b5j309MTOiHH37Q6uqqQQruQ/Y+5y8ya1gv1B0Aspy/3BsoiVgXqCQgDmZmZiwHJYCKd0jo1cTEhJ8PcuCJiQnn2RBgBYMNg0kTQVPM+kUeDkgFSAIoxp1OAylJJycn9vVK0u3trcFA9jEZEIVCwRNikJ5LcsOHihB1AEBzIpGwhDWofAoGVb1//97gQTQaNbBCw4ndhL2IbFmSz89ut2u1G/5tznKUUpzf1FbUaoAh7D3OC6ascHZwn1CP/OvgRBLsg2pKPOkwr2NjY9rd3VWtVtPy8rLC4bD97oyfzefztrcFFSDFYlELCwuam5sziENjhFIIZcQPP/xgtRLNOSQHBEA0OhjJhxR+enraNhAS67E8QO4w+pU8GUlWrebzeXU6gwDMtbU1VatVVatVjY2N6fXr13r//r3BTABmQuSi0agKhYKtSYQJAgxj+yDbAEl7vz9Iri+VSt5P7ItyueygXUD6h4cH/eY3v7F1dG9vz/dYu932M+X3zuVyVpX8h//wH/R//s//0X/7b//N4DJsPmC5JK8Txu4Fla7B4NJ6va5Go2GrAyDU3t6eKpWKwV5qKoi3fr9vheb4+Lg+ffqkmZkZZbNZA6HYoQBuJFldhAqJM4nfnf6JmjmYPcU6BpQi5BMAc3t723U/ljlGHf7y9df/+tmD4EBhOAQlWR4Jezo6Oqrl5WUj/4+Pjzo7O7PsmMsYZAkEmQRfRkgw/kCS0VL8GrBH0iCpF480m4ZC8/z8XJlMRpIsEyOUgPFZHLwUm7BubC6KHC4BNjnFFwXR2tqai9nn52cfhoQe3d3dWT5HoibFO14xDm4Ohd3dXcuHYIRJ+RwbG1MikVCnM5jDOTY2Zok5wU7I3ig+QM9IeO31epbqkDYJ6wbTdXR0pJmZGWUyGScp0uwxl5ggr6Ojoxehcr1eT0dHR1pdXbVvqVarWTrEu4V5wONNIwcyi30B3xrFDah30C+JpBNvvSQHXbCGqtWqCyjYDz4Psstms6mtrS199913nndNWNbS0pLu7u60u7vr5HiKVeSP+Mw49EGv8ZqTYk5zSiMJa4ZCg8ZEkpOQ8dJzaIKwIsXlEqZJHBoaUqlU8rqhSULuRoNOYineSQpjmBX2xnfffadUKqXXr1+rVCo56Z5miyBAAKtWq2X1CIh5o9FwcikIfZDxYK/ieUW6TSEAIwTgFFSrwNxxacKqgGwjl+31elbeEMIiDWadrq6u6uzszN5AkFj8u6gPvvvuOy0vLxtYDPrfeZ8wl7FYzIm+QeXNw8ODyuWyjo6O9NVXX/miPj8/9wi3ILiBCiAYVomq4fb21gFMMKKci/hFKdJ575yrFMOjo6OW4MIGI42fnZ01aDY6Omqmn31fLBY1Pz+vZrPp/cvPIAtAkuV92IYArvBGck/AYFNc8P6i0agZaeTQsD/Yg1C+YM2AGWcEGoUSvuDh4WFNTk5a2hsMzbu6unLhBUjQarU8axVfNsoV1iCszcPDgycs8PnIp3h4ePC5ig0AxRB3aL1e14cPH7S6uur7MBqNKp/Pq1Ao6M2bN56Y8fj46FE+nHsUkgCaMGGwIpyzAMx4s7EGhMNhK69QeNEgtX4cvcYa5768urp64Q1FhQY4iG/15uZGkizDBqwmpC+RSFjtQqGJwoU6gHohOAKJe3phYcEgJqFlSD55f+xRahpYp7W1NZ/p7XbbTQb3aq/Xs8w2FovZDw8YQ/ASY51gzyORiPL5vC0NADe8IwBG5P7M82WP0LTAZiIPZwQh9x/PaH9/XxsbGyoUCt73/LylpSXvbRpHlIXsEVSEfMXjcWdM0LiiXIM15wxHEYm9LRL5aZQfZyoNFQAS4KUkA2bcHdRNNCMoDPnZKMogBdgHBPwFJ+2gyAMEJuyO+wYZMftzdXXVk0JisZjPLtYq/mTC8pLJpIEgGlJ+N1hIWHeA136/r1wuZ1AedVtwHjXPXJLvmIuLC4+nIusFFjUcDttOQz4Nn4vMCJpAAMLx8XFPs0ACDsgGI41SFWsbn6HdbjtbAkXk4uKiUqmUPzO2StYYHvl+v+/GlLsXJvgf//EffV8HFYjPz8/a3t7W559/bjUPcu9YLKb/+B//o373u9/p/v5ef/d3f6dIZDDbHlIGoE+ScwKen58NznEvUc8RMJfL5VyjvH371qnrEF0E5+Ht7vf7Vh0COJVKJVuLUIZVq1UlEglPEmKtohrmzGWvBa15CwsL+v7775XL5QzMoB6i1kdyjyqK8EGAWHqQP+frFyb8L//6WU04aZ9E3SN5rdfrKpVKbhDevn3rEUQrKyvqdrsO/JqYmNDm5qampqZ0dnbmoAJ8KIwxGB0d9Tgq/D408DSaNGSXl5cqFApmkWFAKQhAvCcnJ+23gYnm4CfxkmCQVCrlgpWNCTtPA0BTAYsTRItBcsfGxpxsSWFLEBTM2dPTk4stSR51Egxeu7m50ezsrIMtKDZWV1c9y5qZigQR5XI5e1EmJye1sLDg9Gh8RBQlSOd5hhT7q6ur2t7eNruM37ler2tlZcWFKAEinU7HY4tqtZpTo5HMgVYSaIGMBhBienraxWM0GrU8L4jgE35B4Uqj2Gq1FAqFLFsHWWdmJ0m7wSRr/n+aEGSvx8fHlvVRXN/e3nrW4/7+vtF0JIiwGgTX4G0l4RnGnjFbFJowHchUKSrwHtJ0cumQhByU5CKHJoCM4p9mHIUJFzoHLSg76blPT0+WVLMvKRKCfvOgLzQaHYy5gaUB1CJ5nGKB35k9DRJNoxssnpCvsRZ59/y7oaHB6A/AIH4/PJb4w8ms4NkzdxpPfSgUUrVaNeNASvy7d+8UDoct+6ThIxQPhcjIyIg2NjZcHKJiSCaTL5QKsHn4bWGGWBuwao+Pg/nw+C4JuQFxp3nm83POBFULS0tLlv7DHEky64M8Epk1SqRkMqlwOKzj42Ntbm6aDQuFQlbbIElH2YF/lhwG1iVNbdAag9+YJgH29fT0VNls9sXUBwC7aDTqsz8ejzsngiaWmdKZTEbFYtEhUOw3GBbeBanhwXRngBrOQgDTUCikcrms7e1tJ96mUinLT9lj+Ku73a4uLy8tbw+FQla+BBkQ7hUaROSTsPX5fN7vmeeM9YWfBeNHQ80Ixj/+8Y/2I8NoPz4+Kp1OWyoMQ0vDUygUzMYThMe7bjQaOjk5UTab1dPTk78vypp6ve7nRSNFAY/El3UdHGNKA0/hSD1B7gns+M3NjZk8GGfuGSwQZAw8Pj76vbCm6/X6Cwn109OTWq2WEomEg46wiTw/P+v8/Fzz8/MO2CJUMxQKeVxVPp/XmzdvnDYdzBuo1WoGH96+fesgWoAWAsGoadLptOLxuA4ODvz8sbRBJGQyGZ//uVxOpVLJ1gvUTu32YAID9yKNH+FX3JV4cDnPx8fHdXp6ajUIdQYFOHPIX7165ZFyNDxBKeyrV6/soW82m5qamlK/39fExITzUbBURSIRM6eM7OOcYiZ3sPkdHR31+//XsuWlpSWNj4/bXgC43O/3rSzAf8vdQTNFUQ/wsLS0pG+++Uazs7O6ublx3dhoNFQoFJRIJAxoBXNOIpGIR5ZGIhElEgknYSPZBkijWWbfomCUBgB4KpVy3RgODwLYqtWq6zKAScBFQlc5cwnepfagZuP5xuNxnZ2dOcsDwgVQC78y6439xahH9jY2KjIgeKbVatVgDvkkV1dXJhgAELFicg8SQouSi7ybXq+ng4MD54d88803HhfJ+YF/nvM8CAKgOJudnfVEinA4rG+++cZWu2CIJvuCe5x7nYBXQO90Oq3V1VXPAD87O1O//1O2CWGIKPuCwDA1VZBUDE426PV6+uabb5TL5TyxgKBrAiXX1tZ8R5HdgL+b8xE1F407wB2K0tnZWdusAO5rtZrzX/jdf/n663797CZ8cXFRw8PDbp7wIc/NzWl1dVW1Ws0oOag/iY4gWSzuxcVFlUolMzjhcNjpjEg8aWgo2pAa4rOCicUrzGGCvzwcDlv+xKGLzLDXG4xCgSnvdrsO2WFjgE4jJ+fgYrNy0eFtwwdH4Rsc84P3CgQbiU3QN0WhfXNz4wN6cXHRlzlyx2CCJuMRkPyxEWmYKHCGhoYcgDU2NmaPSiqV8u+ET4v/S5ONHJgwKsYpEOr1/v17I394lL///nvNzc0pmUzq6upKe3t7RrRBonk+zCLnsD8+Ptby8vKL5pumD7l1KpVSsVi0rx1PLA02zLAky+25FEG9ucBLpZJ9d7AL+IfT6fQLKT/rlxmftVrNfjTWMaw3jcPi4qLlojSsMEEg0RMTEy6u2WMUOySOIwfkz9JY02TiGz47O3vRNFF4EbwGyEGKKFJJFAlPT08uVGgUkGVxueIfRprI8x0bG9PCwoKTpWmQaeZhIBg5RkMPyt5utz3WjbXMOu/1evbBIicHqCoUClpcXLS6BmnoyMiImUnk3fhHebYUvZOTk0qn0wbUSE0GWKDIIy8BSTXrjJyCubk5nZ6e+p0BzFEY8CxbrZYDl2KxmFZXV51UK8nAB5YKgAok8TSrsFkAcJKsuEBZgJQ4qKTAT4/0n2fBucu6gGkKnj/9/k/jH1G2wKZSMBMGA6AnydYYwJ/r62t9/PjRqhQadUAb9tPx8bESiYTi8bg/I+FMT09PTu0Oytxp8LCOJBIJ3yMoQpASPz0Nxh+iuLm4uFAikdDz87POzs4sQycPgOaMkLqgrJZ3AtATZE1REJDuTb4F4CwKI9Y2TC/7BFk0QZMwRwCwWFgAZMbGxjzKB7AZVgWrEXJRfN/X19eeXX15eanDw0ODSQSXco6SA8P34HN3OoNk6ZWVFR0eHmpsbDCeCDsT89d5Tqh1CB6C0QbAAnCT5NnDnJ+wsYDmjNw5PDxUp9MxkMY+wpuLkgrWkDsBZQn3xOXlpe9sRhxxVvM9ZmZm9P79+xdrIFjUp1IpW7iC4BxyaBQCDw8PymQyLuLX19clyU3CzMyMzs7O1G63PTWk9WNoGoFKS0tLvmMY4wgQAMMJOA2T324PRmotLCwYJGXEFc3H7Oys3+379++d40Czy+/aarUcwEv2RqfTcYNA3UcNCUAJoIfNBSn58vKyWVf89YB8knR0dOT7krODvYDih3uVGpF1QM26urpqRc3V1ZU2NjbU7XadWURuA+AA1iwyjqgT8WBDvHA+SvIzBhhbWlrSyMhgHCzrYXp6Wnd3d65B8ayzVwGMqS9RA/G5QqGQ1tfXrTRiAg5+e4IbR0ZGzMwC2AUDJiGFOE+RJ4+OjnoMJQQP5AWj325ubszi0jusr6+7Nu52uy+Ce1kj9XpdsVhMi4uLDkteXl62nePm5saMPF/cx+l02uGm7EnuLWoL1kMw9BjwFfICGTqZKRB/KDjW1tZcGyF5l35q1Dc3NzUyMqK9vT1PBwAsYQITKhDqY2r4YrGo8fFx/c3f/I0kmWjpdAYTHwDluHsItaRuu7u7s70W+ysTHQi9wx5arVYdand9fa23b99qZWXFZzt768/5+oUJ/8u/flYTvra2pna77QOFAJ1isajPP/9cnU5Hp6enikQi2t7e9vw5DgF8Ot9++612d3fV7XbNRIAicdEiGaTJQ3LJnFkOEfyISJWCckCQI+ZeVyoVo/hcaHNzc2ZcaOgJu+BgohggtZsRSSDByF1hWYMNNSnuFN4clsE0d4pkpMdIepDX4wfiWbR+HPVDCjnSlfX1dd3e3qrRaGh6etq+sUQi4eaRAgxJCt8P6TcXMCwa86phXZBP4zvkWdHIgbzjdeE9gR6DynHxStLZ2ZmbvnK5rKGhQRJ2IpHwSCAKQi5rkiBhhQFgQOlp2J+ennR4eOj0ed5DNBq1BUEaXCyvX792kxcMG8Lzf3t7q/Pzc83OzhoECYfDPoxhuZGhIyvF139xceGinOcBKgnIg0wUVBh2nUaQdYbsHfUJzS+XHIciybD8e/48l2wkEtHR0ZGTqMkooImg8QQ5XVxcNEsBSwcww97iEqTxAfWnIDk5OTF7RFhOq9VStVp1tgJyLTyhhIVcX19bKYM8G2XOw8ODfeJcOoAlFBuMEKvVal7nFFezP04TwLZBgcDhT0ENY85ZyPuk6Lq5uVGr1XIYFmOUUBacnp7az4kvMphSSyE9MjJiVD/oe8YPje8MyR6zeBcWFsxIAvwBKiCl57NIAzYF9jYSiWhjY8OFUevHUS0UvfhVOY8Ih6SAC0os6/W60um0gRCSWdljgLQoeyga+eKsCFpo8OMGi2nAUXzZABwzMzM6Pz93cw6DSMEC2yrJ6dUAYsiGI5GI/zvsC0UaCewwrsioKWoJKBwdHbXiQJLBDZobJJPBZycNig6A0ouLC/vP8f3iMYeZYrJIpVLxcyE9OJPJGBSq1+v2S8OOz87OevJH0AbU7/dtNQLMfXx8dHglZzqyZnIHOI8IsaNBohjGf0heC5Lr6+vrF1L2fr9vQJN3G0zuBvAj42F4eFjlclnRaFQnJyduevhZpJPDLnH/8nd5V0HbXa1WMwgLuQBo1u/37QHd29vT8/OzUqmU8vm8FR6oxpiTTROFIgk1UbfbtTpMGkhiYV5hq1knACzIkyEDCMMql8sOjiXhHLVCs9m0PxRAampqysAGljEaD+51lAzYAYJjuSAisJHw+amtpJ9me6POarVaVia8evVK9/f3JjKQ7GPFIe+FfYtqj/eJh358fFzb29s6Pz/3Hcg5Qc0UVF12u12ztd9++62ZX0kGwNivqABQKFH7ATxw/3FH0VAjH2adYwfAIsedzM9lTeDHp45EZXR9fW3QnbMg+H+xCBAuKclgFSF7gKgAI7yv2R9D89jvgLhML+JcqVarJleCoC1NYLs9GGHIXZHNZh1gOz097TyTu7s7vXnzxrUqYzSr1apZ2WBIHPcMMnyAUZQMn332mc8w5OWxWEyZTEa1Ws31Vb1eN2HBGc0/U++yJ1HgkrA/Pz/vJp/3GIvFHGy5tramw8NDraysvAiKA3wrl8sKhUIqlUr66quvVKlUPEqQs4ccqpGREdu3ANFnZmZ8lgO0YX+DfCPAFHUnewO1CBYCAA/eExk29Ba/fP31v35WE85hUalUXHzSELLB2Ayzs7OeqwpCi9QoONaKxOytrS1LL0jYRTooyUgsc0mD6BWNIQwtxVC7PRhXVSwWLcfqdDr66quvdH9/r3w+/8LTBOotDRpD5ug+PT358qOB39/ft9yDglT6KZSKxjwo+wNlvLq68ugUmEXm2OLdYWMgo6JYn5mZsS+r1Wr5zyP9YpxRp9PRycmJ6vW62Xtkzpubm35Wj4+PZtvPzs7UbDbthz0/PzczTICLJEtimDXIxUgDTtHcarWUSqWUTCZVqVSUzWbNHq6tren5+dk+t93dXdXrdYMAqVTKlxyBc/gyGa8WiUS0tbVl79bQ0JDliDCWoKE006R/07Aif0J1QYHAuhsaGnKT2u/33XxjBahUKorFYi8AiSDrCNPI92KNU6TCxIE6ElYUDD/h71GslUolX/78XkgqaTx5rqzBmZkZXz5TU1NmZZnvzmUNMxKUEQMWIO+FyeQSosCgOYIZpDihCeRiC4fDRsj39vYM1CCBJDwHJUiQbQ16wGEG8Z3BYqDSwJs8OzurUqn0giEFeaeZ4eICTODypHjlc8OyIMHD/9ftdtVoNLSxsaHWj9Mistms/Zr8/VgsZr826gE+E1I/FDzJZNIN4+XlpcOPWCu8H94Dlpjj42Pbbm5ubjyzdWZmxsAODRf7BkCVIhj5N8+aLITj42M3uDRE0iB4CPXG1NSUZXXn5+dmL2CNOQeHhobMZnGOoGBaWVkxg80ZAKCLbI/sBM5RCkH8phR8hDTCRAPMHB0dOVCUhhiGPOg17nYHqbu8F/YDOQOE6LDWkXbDpADKwugS0IfqA8YP0JP/xrg8ACWyHmj+Dg4OFA6HDZbBQMViMeeGUNwVi0UXzGtrayqVSvY1LywsaGxsMPaN5pbPSzM0NTWliYkJpyXDinGO8PujPHt8fPRZSlYKdynflyIbOTueWkkGtGgUALMODw/1/PxsYPrq6sr/fXR09MWYRjy0NNMnJyc+I6gX8NqWy2VP6qDwHh7+KVUeC0i73VaxWFQ2m1W/39f333/vu5jmWxpM3yCojZFsAIpHR0eSpI2NDZXLZZ8v2WxW1WpVxWLR4ADrgXOd+5pkb0BkEto3NzeVz+cdknV6eqrb21sVCgUX9fx+x8fHkuTALgAWMlfI8sDihEUwnU7r+PhYFxcXti7wbLi/goAVIDn3a7VafcE4ApAT5hcEkTmTmV/Me728vPT3ZvQtdRz3K/UYdiwUXrxjwkTJqOEuk2R2MZlMWonIGc/ZAigV3PMTExM6OTmxsopcEIDCWq3m2gv7A+dhu932O2m329rY2DDg/Pj4aEkz4DlnB0w+TTJTXVCAEbD29u1bA9XcbzC7BwcHWlhY8Fi4UqnkWeQLCwva3Ny0Nxyv+9XVlZLJpCXwo6OjL0Z70gzzu0OwnZyc6PHxUWtra2o0Gtrd3dXs7Kxrf8BSVBHff/+9RkdHDeZS07IfILOwbZD8TtYEdS3qXe467gvC2Th7Op2O3/vt7a2KxaJ/j36/r0+fPjlwFtC+1WppZ2fHtlsIx6mpKVsC8vm8QTrOtkKhYNBraWnJ6eqobABb2CsoJCBCbm5utLm56TudPU7YHMDy0tKSRkdHdXd3Z3UXFjKm/gDAsQdardaf3RsG1b9/ra9fmHANXsrS0pIRYTwoSOSQUlOQc/AODw9mhUYiEZ2dnTk4BiaSi5yZjGywpaUl+2HC4bC9kqA8hI1xQYB2np+f+0DngG82m/rmm28cdoS38+lpkOB8enpqj9iXX36pbDZrv0q/338x6xkWmd8TBoC5lFxE+HqQrrDpc7mcD2HUADTvMFhsYNI/E4mEpAE4gGcdRBckmkTEx8dH3dzceBQFI2OWlpYsy63X61pdXTUixjuJxWIOraNIKZfLSiaTltDBGnLxwPCConIgwPAiTZuamtLHjx81OTmp2dlZlctlHR8fa2FhQe/evbOXjNA9WOebmxuHdDAblgYPLyesNygpzNrh4aFmZmYUj8fNmvZ6PctxKTCDRRnNlTRotpldzsWNbB0JP0Unvi0aTg5TPlPQN0kC78XFhRqNhgt5kHMk1igYYKSHh4dfWAVoOlkLeO5oyPACdbtdM5YcZqwV/IkzMzP29RJMx3oI7mVComi2uNBhAaWfxnnQsOKxw2eN5IkGHaYRNQZSd84Rnsf5+blVLdlsVqOjo9rb29PY2JjHJwEIEnhEcc4Yu06no/Pzcy0sLJiRxsKAbxNwDNXP+PhgXCGNL740GsTLy0vbTkZHR1+wy0H/a/D3JogxGObGRUj6/dzcnD15sO1BJQABf6gJOJ8KhYKDp1ZWVswWSQMwiDF3qDU6nY6fB+umWq2aeeWcp9jp9/su1FBOvHr1ymolGuler2fGpFQq6fXr1z7LAT5hE+/v77W+vq7Ly0sXbTc3NyqXy96H7C3OXNYPkr27uzvPbL6+vnbAJwAqIZfIEoPMF3cLQVqciwQPUmTzs3g/AMLseZqBoByV/cU5BdvA2ZBKpfTx40ePg3x6GqQIY8egCGdPsN9h2MkDAaRF1gzzAsD59PTkaQvcr/xd7jfuNX4HpObxeNznMMoeUoZZ34uLi2q1Wl5TFN2zs7MeycRIN2YKA7KOjIzYqgP4iD806O0lPIo7l4C7o6MjM+OAxhSunKW5XE7FYtFBTMPDw1Y0ABIhS8ciw/eiwSfACOZrdXVVFxcXZmEBfcn5yGQyBjYA2LvdwexmbDWNRsMsJgGF9/f3tnQBRKZSKTfmgJf1et17n8kh3AWweZyj2JsgLgD3e72ednZ2bEdBCjw5OalEImEGtlKpqNVqWWWASgdLwMrKitljLFC9Xs9y+VqtZpAVEBqWlTsAdR55DtgdkEAD9BA+hoUQTzNKJmyL/K7sD4DRhYUFT3LhzgS45p8/fvxoQP/4+Nj3G6Ax9yw5J0wToBE7Pz/3mQwQxfnPfXR0dORzGNCBhh/PfrPZ9B5BxYFSj/OSsx3rWDCvIpiBsrOzo0gk4noTEKP1Y9AXXu10Ou1MjImJCc9WJ2Q3eAbd3t66tnx6enKY4dnZmeuUXC6n+/t7FQoF21gJBU4mk0okEioWi0qlUpqfn9fj46PBIeoiajRscOxJRsohL2eaD33K4uKi1QoAlel0Wp1OR8fHxy/8z4A2KE8hyLD7cVaWSiX9+te/Vjwe9/QIRpzSDAMwktfDrG+yfQBS2Qurq6u+K6nfsToRasvzmpqaetE7UKdQT3L+UyejmOOsrlarmpiYsG1zcnJSnz59st0rGML4y9df7+tnNeHIxBmJg4cab1oikdDi4qIXaLC4HR4e1tnZmcLhwfw+5mN2u137Z46OjozCwKQx8mF4eNi+2OfnZx+klUpFiURCW1tbRjSRRzcaDYceSYNAr83NTSeA0xgjpyNcg2YO2S/JvKCQNGMUkMhvGPdCsAqBIsGCj8sUlgUZLdJY2CEK9MXFRUu9gyFx/f4gMRZmZ2hoSJVKxY018nxQ9Vwu54AxZDkgjKDQSE5pQmHA19bWJMnsDEnAMLT4e5E/cdFzoRDuEovFtLa2ZmYP9BdGhOYCOTpzyQFluMxagXFaHLiXl5duGChCj4+PHV7B54ENY3QG/nykyiDqIOQoIEgQXlhYcKHE2guOs+j1epqYmLB/KR6Pq/VjYFwQLOLCJtGevxv0ZBPcxBoD2WSP4PMHyYWNCkqLer2emw4aBWSNjF7KZrNqNptubHiurLPLy0srBfD98jm5yILhheVy+QXDSYPOZ5akT58++f0jV6T4AdwjVBF7RbFY9N8hTXx4eNhru1Kp+Jnx2ZnXSnr7wcGBG6rFxUXL8pBYZjIZjY2N6eDgwFL+4N4EcAj+HHxX1WrVF+Pw8LCToZEpYmdB6cA+B5zh4sR3SRPHc239GAjG3wfpZ+0BbqEiYG0g8cNzh985lUpZgk4DS5PKOKz7+3uVy2XNz8+/8Fzf3NxoZWXFSgQknTByY2Nj2t/fN4MpSfV6XdVq1QAmDSlAXafTMdPKOcIZREEQTGwFYIzH47q6unKAJWAR6x/2DKAsFosZSCPLg8a7WCzavgPYOz09rbOzM01PT2t/f19ffPGFU/rJyuAcB1gg6C4Y3Dk8PGxLAF80vtPT0/Z34wuPx+M+h5Avwlo/PT05aOjm5saSZxQikUhEqVRKHz588GcE5CI8Liinvby8dAENSIZCC1XM6empRkdHVS6XbWtgn6HuAMiGxeQZkrvCOYHSiYyC0dFRqxEA+f71rHJJ9ijf3997NjY5MiQUwyjG43HF43GDoZFIxOoCmp54PK5YLGZPLox/EIzknCHgLGhBAagh8I49EGQymVnPOyR8DlaWtcNzRk2VSqUsR0VCL8ngzOzsrL3QKA3JumBdTU1NWWl4dnZmafDKyoouLi6sQOTPwjBvbm5ayTA3N2fABNAY/zGBrIA8hULBlh/8sij1Wq2Wp7vAxM3Nzfl3BCScnp5WKpUyy9/p/BTWyHpHAYS6ghFsqCnGxsa8VgCesG3R0BweHroZppmGsEG1VKvV/O5Yi9gWNjY23GQCOAEWwwYDaJF/APsIyMP9ACixsLDgJrvZbPqewJ++v78vSbbtBb+4t7F6Id3mLsFaxH1FI8oeb7VaHmXF2YX3vVQqOaRzcnLSmQisG8CzWq32wtKASo+13+l0tLS0pFKppJOTE3W7Xdex9/f3TlIfHh5WrVbT09OTtre3dXBw4DFaNOO8S9RS1JuJRML5FwTG0X8AHGxtbelXv/qVPn78qKOjIzf6AAb0BQCXu7u7kmSLKuAf91Sw5kLxd3t7q9PTU21vb6tYLCqXy2lqasrg9djYmMF23vvJyYnvceayn52dKZFIvAA5QqGQASf6AqZKsW/m5+dVr9f9WTizC4WCstmsFhYW3LsAOPV6PTPj/3p9/X/6+sUT/pd//awmvN/vK5/Pu0AB1SUIBsaKjbK+vu4GhcYW5BTErlAoOKgGCcX4+Ljy+bzZFIpIDknSbklTRQpMIwkyh9wU5iqXy2lubk6Tk5M6PDy0RJvgC2RqSHu4lEFKaQKZjYhHeHV11WE6oOt4w9kUeOE4FJEoB/3owYKaQvrm5kaZTMaIPKwnXmHACqT7BLFQcMPYcbiw8Sls9vb29OrVK18QHCiwG0joKODYnCBupCDDllLgcxBwAUxMTGh+fl53d3eWPcZiMUnywbC2tmaJDam6jH4pFAqanZ3VxsaGD1YuN9BlgksI/dnc3FSlUnG+AEnbNDawosx1jUQiRv+5TCcnJ3VwcGCvzv39vZlxkHdUA8geaZ4Iwnh6GiTKn52dGa0NFsJcisFiE9QblBT0kkMYBg35Lc0f88JplJFfITsmaGRkZMTFIgUvIW4AbIyw46AmlRk5Gs0jFxGMID7QyclJo8TST2F4BEnB3NAYBEPO+N407/x7LmhUCIx04aJFnsYonpGRESeVT0xMaGJiQplMxpJCGgmKCdYVDfjm5qbK5fKLgESKMBi20dFRez45bwhaIakfBp3G+OTk5AUDRzOCcgaFRfDMgBGluOIMRUqPNJwzkCDNfD7vcYz4QVkTeLwpQEguhuVBrowEGdkgUnB+X77/9fW1ksmkLQKw63xGWGTYf+xMyEXxqnJ+ED6IvYjia2RkxMUsLBu+1CBLjgWJInNra8uFGg0QX8fHxy7kaB5piPj+SMJRsWCrgtWGlSDDQBooyJgPDNCB3FeSrRHLy8v6+uuv3WQQQtXv97W9ve0RcexzpM54yknv5v388MMPfkfYcrLZrM7OzrS4uKiRkRElEglNTk7qn//5n90Ic16Pj4+rWCz6d4aZIxSTVHXGg6bTadVqNTfd8XhcodAg8ZtGjAbj1atXarVaZiIBi6LRqCYmJnR6eqqHhwdls1ktLS3Zt0pAFYBQs9nU/f29w5Ly+bwWFxddF1CEArAB4gGU8N5prpCTr6+v+wzgziW8jZ8JWAFYhE3v5ORECwsLqtVqLrZhx2ngmbWNyoPPgypjZmbGnulSqaRXr15ZWUegLeFXSOYBY/f39zU9PW2ZcTQade2TSCQUjUadU8Md+PDwoPfv3ysajWpnZ8ce6CCrjRqvUCj4jCRHBBVQv983KEI2Bmfe8PCwdnZ2dHx8/H95jmkOeT8ET0Fm0GTDfAO8Ly0taXl5Wfv7+xobG9PS0pI9r6iD+v2+VldXPXdakr8/AB21JQBB0JrGPzPyitqNQNZ0Om0Qq1AoKBwOa2Njw9J4PLvhcNigAcFcEEooEqkTAaA4Nx8eHjwq9s2bNw7zY80gne/3+1pbW3OC/NzcnI6Pj18A/DR8sKI0nIArAKSAp5xBq6urJi84h4LKqXA4bMsbdWQmk/H+AaRGeYj6j8/EPfPx40eFw4PxZZubm26GUWShTICQyGQy2tra8h5iQgF1K+8EMGtkZET/+I//6LGoqEUmJibsh69Wq/qHf/gH712eB58XEClYp1SrVS0sLDjkl7BGalJUUsVi0fuMUbDSAJD97rvv9MUXXzhfiLGZoVBIHz9+1NDQkL8nMn9IkEwmY3UjazSRSOibb74xyEy9TB4GVg+85vRW4XDYdrk/tzf8pQn/y75+VhMOY3h8fKzp6WllMhnLoWhgYT0pIiVZqsx4BCSN4+Pj9mwyLicejzuEBan5xMSEzs/PNT097YsPBhRAAA8iHjo2z8PDg+UZoVBIJycn9ikXCgVFo1Ef1lwAMDgbGxtmACmsCHeBocMvxSVCI0uRBNsQbBRAvaWf0o/xB4LSRyIRo4gU4jT5x8fH3iTB7zs3N6dSqeSQHi64oC+V5pLLdWlpyQ0KgUSStLKyYok+DUSQcUHOsr29bRYZydfr1691fX3tcBCkcTwb0mvb7bay2ax93l9//bWZ1WDQEcUSqGcQBKIBLJVK2tzctP8KRJWCA9aewzkYmsezj0QiL7xmeGso/PEIFQoFe1QJQWJdcrFJcgAfI3pgsDudjr2tACcEYdA4d7vdFwF8NCndbteNCQUEFzzfe25uzusZdgS59PHxsX37rDeKvbu7Oz9zJHyAAkjZaO44wGmM+DN3d3cv0klXVlascsDaQCiPJL8Tfm+KtYmJCR0cHJjV5HcgrIhRSgQw8TwITkQSi+oCBoYGHLYEgEqS100kErH0mWAXlCvj4+Mu7GB34vG4zzAaO9YF0lVAFN4jM5VpAlkzgB+SXpwLrAeCycgLQBYIIEax2O12nStBMwe7gxUDtu/q6srrCpkrjXOQZeK8rdVqDubK5/MaGRlxCOfd3Z1OT08tdSVJOdiA0/Bztrfbg5Rnxr4Vi0Wtra0ZWCQg7/b21oUBawSmj6KEd8reDBZjjBiMxWL+cw8PD2bjABkAOWAlWZ/4++7u7vTtt98qHo/r5OREo6OjymazL4pY1g5qEfYmTTNsHIAZqe2dTsfNliT/XtVq1SAWz5CAOxKEK5WKwuGwFQz39/cOOQQYImBseXnZ8loyPQDQuW/z+bxmZmasMnp6etLs7KzBhCBrCxMGQ5pKpXwPLS8v2/MNKP2nP/3JYCj7imffarWUy+W8hvHSAgrC9F5fX/t3w6dNgCZrlOaAoFbOSs577CcoFpDcYgNCqsv7Q96JCqPRaGh5eVmNRsO+7pOTE21sbPgMQK3GGCbWGfJ7mlTk3IDATJm5u7tTs9lUv9/3eby8vOxZ95wVMM3IoclWwavL55iZmbFig3MR1VE6nbayg+BEgFX8uyjFSLFvNBoG8CKRiO9bEqx5JzQm8/PzPqMJlmWf0uhyHnKHDQ0NvQi95V4EDEWajkru8fFR5+fnGhoaUiqVMpNLMGEwb4CmGHUUnlsUKJzdWDRgiRuNhs/Nd+/e6dOnT7ZhMLIqWFdwDvV6Pe8PvMiA841Gwynb3AeTk5N6fHxUMpk0KDw/P++sHX7O1NSUJicn/bkJMu33+1pZWdEf/vAHn3mA9Ixd3NvbM8CGxJ3zFEIGBWUkEtHy8rJr3uHhYefUcLagIMKzT2OIogvAfWlpyco37AA8q4WFBS0tLemHH35Qp9Ox0oSkf9Q2m5ubikQiDo0DTL+7u7Pak+Yzm83q+PhYk5OTBtRYa9Q7Dw8PtvhQ99G4kgkzNjamRCKhm5sb3d7eWhVAmPTa2poDWLEESHImxcTEhNbX1w3qU+tFo1GVy2Vtbm76zqT+p68BIOW+J6eq2WyaHGTq0MjIiO/UV69evVAPYB8kiwXbbLCf+eXrr//1s5pwQlqur6+VSCReFIzhcNgyOApHUpWfnwfjEShKer2eLi4u3DDd39/r9PRUr1+/1sXFharVqtOaSXL91a9+5Yuy2+0aCeZQDEqRWJAcxr1ez809hWGpVNLKyorRxF6vp9XVVTfatVpNu7u7SqfTymQyvmDxmNCo4FmGoYSR4iILjnHCy0QqMQseTxZMHd8LiQ0phUj9aN7K5bLnnYJmgkDjo6YhgDlCUjw9PW00lEsO+TCeRCSGsIsTExNuxEBX8b3wZ6RBY7W3t+fZgwRY3N/fq1ar6fXr13p6evK7Xl5etooClgpwAtCDYgQ5Kg0khyghVrFYzJe6JF1eXjotH+SfICcaCmZHLy4uGtUkwbvdblvOfHR05PczPT1tCTFKD6wPgB14kTk0KQopdGFdkdshoaWRB7mmAZZ+GtNG0iYS7iCSz3onhZdG5u7uzkmjPO9KpWKPL3sWCSLyPSTlAGX888TEhBtSUrW5qM7Pz92U0TQi65ZkCSCXM4UolgTWPr4uSb5AUqmUpqen/S5ITo3H42aGdnd3HTJD6jZgCIoN2PggA0LRxdrhvbBemD/e6XQ8RgXmhjVOkcLnpbBA/hyNRt2wxeNxPTw8qN1ua3Z2VrFYzAUGDOb09LTzEqRBSCWN48XFhQqFgouMWq3mAmd8fNznJIXczMyMny0hQfjGAUgJjez3+4rFYi8+H6OxeHc0pnjzIpGIzzFJviv43v1+34FSNDOSHFrDGRKJRCwrZH8ATDJ3GGkxzE+j0TCKDyiGjePu7k7v3r17se9ossjYQG0BIMT+J79jfHxcknR6euqzHgUKTQ/7l6aWz3FwcODcjsXFRZ8Bc3NzltJLcrovCjKKShp4JMmc64SDArphBXl+fraMc2RkxOFsvK9vv/3W5xRsOfu62+369wsCGQQ7kfVB6B0NJODW/Py8PbEoPAiLYt3M/jj94vLy0pYCwGTWAcGfkgwoIDEul8tqt9va2tqytYexluwX7jfuznA4bCUTZ97JyYnrEOws2IZ4L8xIf3p6cjL66emp1R9BIJG0enJQaPAKhYJl7r1ez6ALZwz++nK5rI2NDTUaDeVyOR0eHqparbpG4B3AXAGqYLuSBoFvwbBHSc5XYe9hlaEYR2kIyMFdQ9OFLQlgJpPJeCQiLB/PSJLHYFYqFbPUKOjwCiPffXx8VKlUUiaTMTiHdQ+mjwaBGoHvBTABQD48PBhnNjw8rIODAy0uLtqmAKMLEHt3d2ev+/z8vIrFou+Ey8tLA5D8Po+Pj3r16pUmJia0u7trrz9rCQAZgJaaj72F7SMSiWhpaUnSgOw4Pj42uIeCBw/x8/Oz9vb2FAqFlMvl7MkfGxvT2dmZG9per+cZ9yhUOBsIDGVfVioVzc3NufE6PT2VNGgQSe6ORqNWYj4/PyuXy6nX63k0XiwW09bWlj5+/OiMILI5zs/PPa6W2hCApVQq+Tny+3HGofZcXl5Wq9WyDXJmZkafPn16keOA3z+bzZp8YTIQihiUhPf391pZWbHtqNfrmeCjjgkSOjxP7qhQaDAKjO9FHUnGDnsRywD1JaAbVqTz83ONjo5qdXXVdwyAOaqYXC7n9X13d6darabR0UHYNLZerJHUY2RZAPbwPFEpoQ6hviLYE8sk++r09FTRaFSZTMaf+8/9+oUJ/8u/flYTzmWFPDWbzdrDBrMgSblczozn4uKim6JSqeQxOpLM0OZyOa2srFgai3QEr8T19bUuLi7sw8BvRCP6/DyYk3l7e6tQKOTAE9CkhYUFSbIfgxm1hULBgWMc0O1220xOp9PR999/b7kZB0XQYwvDycUBakWgCIcGiBnj0oLSYLzuQQm3JIfAkMD7/PxsnxlFCggiB2qlUvHFQ+OMzBpQAEaLYr3ValkiHAqFjJ6zaZGVlstlJxSHQiFLNLkMYTPwwcGEoGAIsmM0X9VqVff392o0Gnrz5o39gHh0eJ40S6RHXl1d+e9mMhkXIhxgBI7QJNP84i3DiwzaDyIPMknq/eTkpBFdwITWj3OEkVMS7iXJzQfPkgRcClmKaAq0YEo7vnBYBS6BYOhKpzMISIIxQtaIvI+U9Lm5OUsxp6enDeRwOHOJBZFZSW7+adT5bPf397Y+zMzMOIAPaTtBdr1ez8myFOHsg2QyqcPDQ/vUl5eXtbe350KVNYJ3lpF4sIKbm5tOM+b9YxMAZKHI57KC/f748aMvOjIrcrmc1tfXPYKILAF8WrDM7EeAjLGxMXu5aURodAEeAAiGh4ctveM9ERyGRQCWC2l1kOVGXcLfwetJpgKNAWoTZghzTgwNDWlra8tgyuyPs8MJKeI8lWQlEGAE+wSG4PHx0Qg+QTLslbu7O6VSKSe0It2/vr72GUaxjgwenz2NNefh9fW1C/iHhwf7Vrl/kPTd3t5qY2PDRd74+LjPOc6RlZUVy7uxE3EmworzjmgquOtQXgSZ8aGhQdo3rBXrNJlM6vn52SnszMCdnZ11KODs7KwLKCw4oVDIXktCwZAoAxim02nLBFF4cQezfmj4n56enFQfzG6guAQQQznFqCBAX5hs1FskdLPGKfaCCfI0bhR/qVTKz4/ilpDEoNeZex31CNkr1Aw0bADIyHTxbdMskuD8+Piovb09j9KDJOAuAWyiwMUbCtOPDzUWi/lMQ6KPHBUAgSkckgzELS8vq1qtGlhD4cXvjb+0Vqup2+2aIYtGo1peXjZYyPukeSCHAqUNrC/p3oAHSOTL5bLBCtYyzDQBauxDzsjgVA7ueUmuTZ6enpy0/+7dO62urppUqNVqnnpCU9zpdGzjkeSQRQAiWPLZHycdzM3NaWJiwuc795kkr0WaBXIrIHmw7BGCSvPIuDfICwgZ0uMBsFFYADpsbGwYNAI4ptYNhUIOM0ylUg6jvb29tdICEkr6abY7dUs0OggrPTg4cJ3Az6bxAZhHGUKwJ0na5F4EQ4mp51o/jqTie6AYeP/+vckP9jrjAJkOs7Ozo9/97neuaYNKM85j6ll878lk0uAsI7yQoHc6He3s7Oj8/FyPj48ql8vqdrtWwcLuX15e6vLy0gDc6uqqm91qteq+odlsvqiFWNuo1yqVijqdjjY3N22HQ9mJfSgSiWh1ddV2136/r2QyqUajYTtTEDRBmo5KZXh4EI7KekW1BNiPDeZPf/qTx4wmk0krHzKZjBlzLKgofZhEErx3mSzFnm82m64bIDo2NjYM0heLRU//YV9DkKJaoNZE8SENVJtYqj5+/GgL7C9ff/2vn9WEI1v4194WFiHpjxwo5XLZSduMtRoeHtb6+rrZajyDkUhEx8fHDrShMcOLhoSKxrHT6XhTwq7hT8E7HYlEPJ6HICEuMzxNeFWKxaKbAFBYWGPGEFFUMW8cNhFpHIFboO4XFxdaXl6WJAcjgeSSMAsbBGtJE4RkhEMWxgoWLFhYEhIUDoddfEUiEQMXyKbwuCHFZnSBNLhsKSBg2EqlkuWNhEDRmCBRx9+OPwWvK8UaIUhBxJC51ch+eOf1et3zj5FE0WzAUHS7Xa2trWl2dlZLS0v6+PGjpJczSBuNhjKZjNO9uTy/++479ft9/fa3vzUSGZQ1RaNRVatVM/BB2R1/lgIFVJK1BgAAaMC+CIVCRt7HxsY0MzOjRqPhsWQ0arBAFB94FKWfxgBJg0u9Uqk4fBDZ1NzcnNch3jKKYkaYMFO22WyaFeEzckBzeNMwSj+hnBQJAEKFQsH/ndnbZDrAFpPAmk6nNT8/byQc+WQ8HjcLAPsOY9hut732KO7IOYChYf9QJB8dHbmoYd3AYLM3pqentbe3p/n5eV1fXyufz1sBMDIySJ/lrMO/TwARwYBI3wmwQlIGOIa38+7uzmcJZxONIAwPTQteX/Y7jBeXMag1LAZgw9TUlIuK+/t7W0zOz889OhHWuNfrOeGaIEcAQNZft9t18UL+AIUkzxLvIg3b559/7nNyamrKxSWFAPcFErp+v+8wGxhyGrtoNOo0acAFZMehUEjFYtFFAr7Lw8PDF9Mg2HeE2BBgNT09bbaEIDSk9hRXwX08MjLipohzEsUVZz7gDXsUuTQp1axD5KcAXDS3eLD7/b6VPayrXC7npl0ayLYvLi4cakaTAAsXjUbNpKJMI+ASuxeAOOuQtO6bmxun+8KSwNQAkAAEMkmD5mJhYUHpdNoKhe3tbUsimRe8s7PjZ8P7X11dNaCHogswn6Ah7gyaQc4tQF/OSe4hrFA7OzsO8uJnkgRPqBgAEc0SIAesGk0uXlbAWphG7nEYP3I2ANlphlOplJPHmfLS7w8CUaempuyTZvQZnwE7TSKR8Pqh/kEqDEsNWFatVs1+SrIUnXM3eG6hamCcFLahN2/eWG3xpz/9SdVqVcPDw/qbv/kbbW1tvWCkgwAX0vZ6ve78isnJSZ2dnRmsI8+D3ycowwVsubq6MgPJHqWBQAHGuQ8ryVlPI8s8at4NYXycO1tbW1pZWXEewtramlVKnLPPz4PxrrDRgHI8v0KhYA8ywD13KXdBPB43k8mdz9nPO8GawNQfwEjqlHq9rqWlJY2NjWl3d9fnGp75zz77zPvmj3/8o+9rQuXa7bZOTk60vr7uZ1QoFOy3xv4FAcD5Buu7u7vr8yyTySiZTGp3d9cBfoyzvLq60r/5N//GbDIZPOwbyAzICGmQ81Cr1bSysqKnpyd9/PjRafnDw4PJSlj7WAN83d/fa2FhQcfHx/ZDkzmETHxyclJ/+tOf1G63HYSLgoORX9S0qJ6oybi3aXw5NwhG63Q62tjYsBrg9vbWZ9LCwoLW19etZIJkCY6dwx/Osw7aVAGtOp2Ojo6ObAuhxiVDCaVos9k0YItClTUCMRAOh20rQL0yOjqqiYkJg+cAVbM/juP9c75+YcL/8q+f7QmHvSC0ot1uOxCNZoSLAZYB5HF1dfVF88bhPTk5qVarpdXVVUnyhcKYsYmJCTOdSDSCYzuur6/N8OC9AHFkxm0ymVQ4PJhh3mg0dH19rXg87mKYC353d/cFusjGm5qaslSHJiU4QxQZXlBSxyLmQGKkBOFBFKrd7k/zCnl2FPxcchSXFEOSXESR8k0wBKm2Nzc3Thk9Pj52IUCDSnEFIxuPx30xjoyMKJPJeBwWBz2NM2FT0k+jfb799lt7HQFE+N/c3JwODw/19PSkVCql5eVlFwCXl5ceTYT8B1afg3FiYkJnZ2dGGrkgQYMvLi6UyWT0/PzskB5Gx8zPz/vvVqtV7e3t6c2bNwYLKMJGRkbMrDHW7ObmxhcuyPHS0pKbE1g7CsB6ve61ivSXZwS6SVHEe+ewB8iAFcVPDHPMOmJNI5dkz8FaTE5OvgigW1xc1MXFhYvH+fl5STIDGGy+ubzb7bblUTDSXJyg9Nls1mwH6gUYL86H8fFxj7jBC0czzzqioIMNRzLG3F7k1njQka/C9BDSgwQykUiY0QmHwx5LA7tzdXVli0mhUNDz87NTkqVB8BBeVJp7ZMs0AzTPV1dXtp8giWMe6eXlpfcJAFQ4HDaryBnDpYkVhiKMBpczB/sJwCAhVBR9m5ubDo8KsjeMTyHl//b2Vvl8Xtls1oXy1NSUmzFCmWD3Hx4eHDTGnqP5Z+9TNBOIic9vdnZWyWTSI+FgZev1uoaGhhSPxz1ijWJxZGTEkm1YKhgJ/n673VYikbByCnaUOa1ra2sGnCjmCI8MhrZxlrNvLy4u/HuRq0FgGAAkQBFKlcXFRc/i5UzHmxe0yjBSi3fLc8BnPTIyYjkh3sx+v++9BRjy9PRkyw5FmzQATJrNpk5OTl4AEnd3d9rY2DA4Asi1vr7u/QVDGYlEPMkCeSMN2PPzs379619rbGxMjUZDpVLphQQbifrFxYXOzs40Pz+varWqZrNp/ySMNL5oQAqaL8B5ADjuwvX1dQcLIrF8enryO6IAJkSVbIJ+v++QUppfnh/nciaT8Tu+ublROp32uoAIoPkF3MTKAQM3PT1tEgLggvuYzBImWAAw4yUn4FGSdnd3HQ4Kk76+vu7/jnT7XyvsCNcE9CTsi4BB9idnzejoqAEx6hyyLZDSMh6QM29tbU3ZbNZ3EZ8nkUhobm5OJycnLyxS/DzYRULjFhYW9Pr1azOAsVhMjUbDSiZqqGq1agskoBx5HdyFAK6w59iFUBodHBzYXkXS+dramprNptcDwWk0xeFw2AqptbU1TwDiTKORLJfLJpZg/llTnA23t7dKJpMG3ckTws6Iwoh3QZPYaDScXcH4X8CJYI4ITSGKLEaiBcNbFxcXNTQ05LBDFB53d3dWZ8KgQwARrooKDuufJOc+YfHqdrue2sFkHeqPQqGgpaUlgwwLCwu+iwCa1tbWDI6iTAAoDIVCHhlIfULtQh9SKBQ8exx5Nrk+1G+zs7P6+PGj5fmApM/Pzwar+b3D4bASiYQymYx++OEHA3LBhHEIstevXxt4293ddS2FfWl6elrlctn+ekIDj4+PNTY25qkR1B4fPnzwOcjUA2mgBllcXHQdQv0bCoUcIAvJV6lUDArc3d3p7OzMYaaAGMFARBhvzoqhoSHf+798/fW/flYTTuo0CDhSFyRjhI/QPIK+gtqB6sEEwkLTfBJ2RXMO0kt4CAww35vLCw/iycmJfdCME8F/RaPD5U9qMQf7Z599pnw+r1qtpi+//FLtdlvv37/3iCp8FM1m02na3W7XQS5IR9lAz8/PngvLhUfTwYEO2wlTRlIpvloK3WBhR6AV8nCKJn4evjw2IuwjTB6BESsrK76wkFniGyFBNegLhBEqFot68+aNUVveJ5I/itsgIgzbjnS1XC6braCAplEKyt9hrmg4V1dX9fT05CYP2R2hSITOcODwmWKxmP1n3W7XybcEBUlyIwjoA2IZlCwhRY1EIk4E7vf7ViIgww8GdNVqNWccxGIxX3K8U5BKUFwYG3znhNRQfOMBlH4a6RUc0QQwRdPM9yAICIYVQA25EsgpkjaaZTyDgGKSzNQkEgn1+4Nwrm53EMr2P//n//RojX/37/6dwuGwkebb21s3mdFo1GNpGMfXaDQslev3+zo/P1ez2bSclUA/QtcAvWja+/2+Q1Q6nY7PhXK57MJaGkiv8GTOz8/bCnN4eKhEIqFUKuWCHGUC3tWZmRnNz8+7iULaDBhJ4fP8/GxLDc0ezBH/HmYalQSNFZkAFKIAWjw7SS7WJFlq1ul0nIDc7/cth3t8fNRnn32mcDisUqlkho6RRGR2BFNusfF0Oh0DTUgIHx4edHZ2prW1NYMPSOlhSGk4gmd88HMyyYCzkPGQ5DKgKkJthRIGsADknTUNyAEjxr6C+cMnihWKJgsFDPcBcl6AHd5FLBbzOQNTgtTw+PjY53k8Htfa2poqlYotGbx3gvWmpqbUaDQ8IWF9fd0FPsnZk5OTBu2Wl5cVCoUMNLVaLWWzWSsfHh8fDRil02n7RSnMSQAulUqam5vTwsKCrq6u7O/FknV1daV37955li9rE+AjnU6r3+97dBA/l/AizkEAcAIauT+5g2mQACrW1tbcJCAtDo5LJOAMO8/MzIxKpZKZf9QWZLNQAAM2A2Z2Oh0Vi0XvCSwUrFkUfdPT0/rmm29s/4hGo86CmZ6eVjabtT2HezedTuvi4kKhUEiHh4fa2trS3Nyc5ubm9OnTJ6uK5ubmzGiWy2X7xwlyowYIhpMdHR25/onH42ZJGcOGEokpL+wF8huGh4ddkAfVjKiFkBtXq1XlcjkzdPv7+6rVakokEtre3jaje3Nzo/v7e11eXtoPfXt76/MckIwv9gaAayqV8t+fmpqyIgTwnfMQEgM1EAqgarVq1RS2BPy6gNqAJt1u12D71dWVZfVYHlBm9fuDpPJqtWrwkufAu+Cs4czlPkH+jqKF+5xMB/IyODMgYMhxoAZl5CN3Dk0oU2kIC0ylUmq1Wi8mA3W7g/Fl2Em43wEic7mcQxYBqjlTUS8ASD8+Pmp+ft4BrsH59cfHx7YTdDodffvttxodHdVXX32lZDKpWq2mDx8+qNVqaXt724GqW1tbtu9x9ufzeVtCANfJX6jX6wbY2u22z3uAPsi3q6sr/Y//8T/cfzBtiWYzHA7r5OTEigACnCV5H3CmMK2i1+t5sgHkwdu3b71+eN6AQcjSl5eX9fg4mIIA2EctBGlA/Y595OTkRKFQSCsrK4pEItre3vZanZyc1P7+/oupBoS5QVbQI7RaLc3MzFglRh+Gai+RSKjVatmexTQLal5JVmlRm/7iCf//zdfPasKRvzF/7uTkRIuLi04iZHFPTEx48eH7BuWnmSCpFTQLpDOVSkkaFEkg8IT30LyzoZD2UsQhTcXHAjIUTKPFJ3l0dCRJHuvA2AXQ4JubG33xxRfqdDr68OGDPn365E39/Pzswi7oLQo29cGDAFaQi0+Sm8Th4WFfWDBfHIq9Xu9FiicNO3JnLju8gCBuBFY8PT3ZIw4jNzY2ps3NTRf7hArhb6IYW15efuH3x/MnyUgzDRa+0lAoZPsBYANN4MzMjF69euWLF9lLEAQ4PT1Vs9m094ZgJNLIK5WK0um0vz+s8tDQkBYXF31Ywz6zFguFgtcT6wGPHuBINpt1cRFkHYaGhuyRh2E8OTnx+CIYM2wSNE2oO5gEgHIiGo36gCUgiqabNYSPLhKJmFktFosaGxuzb5TLaHR01EUoxQdAUK/Xs3UC9huvKYDR5OSkm3EYV8AClCw0U8i1WcuwanzedrutYrHoFGWAIBqRoP+INculT6AaKopGo2FQotfruTFKp9NKJpOWDdJMh8NhnZ6eanh42LPsQfphMQnPisfjZv7xVQOMoIpBSRKLxWwV4TKjGaIAYn/jK4VJhQ2hSQ/K8yiGQbORKT4/P1sRArvw8PDgEBiAtmQyaSAQ6wfv//r62o0qTQr+L8Kh8OgBjMKEwNLQ0CPR7XYHYU6AUoThVKvVF4oDQDDUEbFYzOO5JLkJq9frWlxcdPHM8+dZISNGQo3UF5tSJBIxQIm3laRf1h8j6igyANiQ1XMG8/zr9bql30j1WCeoiKampmyD4qx+/fq1jo6OPAN+bGxM2WxWk5OTZvfw7SKxhqVB1ULhwxjFer0uaTA+iyR97l+knwDDi4uLHh2IHxwmZGRkxKoH1C2Tk5Oan5+3ciyoWqhUKlpaWvJoRfYRLDBnaTqd1tnZmdcaDFe1WtXr16/tIaXARFqJl31sbMzNGZkkKAPw9gffHXcOgDpsfLlcdqAXYX6wX4D7vV5PxWLRijjuafJUaLyQ/2LfwpZEfgAKIEY1hsODaQMUwFgauCdnZmYM+kSjUW1ubmphYcEZECjx2u22Dg4OHNxI3ke329Xq6qoSiYTev3/vsxfFz+bmps7PzzU+Pq5ms+kxnHime72e6wXuFu7Ep6cnK1qGhoaUy+Ve7KNqtaqLiwvNzc3pt7/9rZaWlsykAkJXq1WFQiEr6LijyAV5enp6Me1kZmbGDThJ+jB+MzMzqtVqOjg48OQdMiwikcgLSxd31/Pzs9cv9pT5+XldXl56HzQaDSssqUVgqMkQQrFFQ89YVADhcDjsDAGUjNSNsOhBJQnkA+qcUqnkhv3xcZBAPzs7qz/84Q/++c/Pzzo+PjbwyB0N8AjQMTc3Z2IHOTL1KyNgOdM4v6LRqEftotDiDANsxi6ELYbns7Kyouvra1sFYrGY7VY0m4+Pj67NIK3K5bLy+byWl5ctc356elImk1G9XtfDw4N2dnZsEeWuQVkXrI1Zt0NDQ66tYfqpH7h/YMD5XNSYwbsQoAcbIuQG/QvnQ5BUQ83LHHAUYZA52Mx+//vfa3JyUn/zN3+j77//Xnd3d3rz5o3PIICjRqNhCyZkzczMjC25WFN5H8FsD/JVotGoAwkhrwhF5ozk7pqdnbWykPNekhvtYO7VxcWFyc1fvv76Xz+rCSdgpFaruemhsYjFYqpWq5bGIg2mGSC4YmJi4sXQeZoQZi1zgeARp9CgIEWChcwMlpKALtAfCkf8GxyQ4+Pj+vrrrxWNRvXll1/q8vLS8/EoNA8PDx0YI/3kN279ODqFEBykMsHUYRh+6SfkhoRgvhcNDM0XjCOXN80km41UZLy+HLKpVMp+SfwvNJk050jrLy8vXUjOzc051Ob+/l7ZbFb1el2h0GA2OT4qfP5BxG15edmeNGagIp9dWVlR68cxKalUyn7a+/t7rxOYSy44EDl8/ySdBmV/MzMzWl1d1cPDg5s3iiTeMwc1BRFrAGYSLxMp1XhJUV/QhMA+IhXiskKmC3IcDEZhXcKM1ut1B5YQioN/CEkm3i9kSuyTINBTLBYlySAImQQwoygsaNiQtnJxIK8iCInCEeCLdwoAQLNHeicFMI07yD3qgVKpZO/w5eWlzs7ONDIyoi+//NIIdKFQ0H/5L//FTWmpVHLBcnh4qPn5eW1vb5stb7VaOjg4UDg8mLW6v7+v+fl55XI5ffPNNwY2rq6uPK+d5nd2dlbj4+M6Pz93WvL5+bmWlpbc4MFk83een591cXGhWCym1dVVA3cEtTFeDZUF3lH2KL5tgBu8XOxjii8UELA9FG/kCvR6PYeYwWpR0I+NjWl1dVVXV1dmHtm7IO7VatUgGv4ybEKZTEbFYtFSZ5gSftebmxuPWyQXo1gsKp1OG+mHpc1ms2bskITn83m/X0kvvOrz8/Nm3glNI0Wc4hZlAZ5JSZYok2kwPT1t0BZ5PKGS2Wz2BWhYKpUcnojvv9VqOdhzcnLSScKh0CBJFznvyMiIYrGYisWii05CHQkD5ByjkOv1ep6ocX5+7vySm5sbRSIRKwyQAQMe3d0NZqKTWAxrz30FCEuqMLO1aSKengYTJri/IpHByCYUC8iK+fc0xQCHBImxFpCqIvf+9OmTw5gAd2gOaIaTyaTW19cVi8V0dnZmdongS/z5eGQ5e4IFLSwh4UWAfu1220AejGE0GjULBHg7NjamYrHoPAWePew2dgvmW8MAc94SZkngInkjyOIJR4rH4w64nJ2ddVHNNBH8pRTQpCHncjmPTRsbG/O7luRzPBQKObk+6ONlDBos/enpqZUph4eHmp6ednAhQVrMVEapdnh46OeC9UGSKpWKksmkM3lQGDFObGpqSr/61a+c4P/4+KgPHz5oe3vbdrvLy0s364xTovnnzMzn8z6PqPuazabrKYBNFFsTExO6urqyyhDPPyAgd9jU1JTfPxk3nP00Yd1uVycnJ7bSDA39NBaRBpvvz/Not9tmoTmPWDeSPG0B6x7MNY0gGSw0eNi7aCiLxaIuLi5e2Emi0ai2t7f14cMHhxI+PT1pa2tL/X7fI/AAH0OhkP8sz5v6mXVG8CJrj/U0NDRkkI8G7+LiQul0WsvLy37XnJ0AVrDITIqZ/XHCAXPu2+228vm8FYLUU7Dp5G3c3d3p888/N2HFHTYzM6NcLqd8Pm/rIvL/sbExr2Esc9w5gKEQgoysDIVCrtUBy6mveGdB9Sj3F2cG5y8hzGQXBBv4YrFoRWqz2dSbN290dXXl+oDsA9QX+/v7+uGHH6yYAOCEDEEtenl5+cI2CAkHqMOZFIlEVCqVND4+rlgsptnZWZ2entpudH19rT/+8Y969+6dgVdA6GazqUqlYqAL1R7jClkjf87XL0z4X/71s5pwUObLy0v7eblcKQ6CKd+gciB6zAi9vb312I35+Xl7q6VBSANo19LSkqW2HKxsIhjVYGGKJEWSL1oaaBgamsEgKn9zc6NWq+X0SRp8WEAKhOHhYc9HJBGTmZ6gikgY2bDX19dmAIMSD4oyEHz+PcnloN2wcARFrK2tmUE8PT11wAMhaEiwms3mC88oTBrgCIUyjTa+X5oKfgcaQmRSIO6JRMKM2vHxsX2PSIAjkZ/GNeVyOYMCIPM02aDTsIDNZlPX19dKJpMaGxtzI8CByufh4CEBnANTGoAeeNF5djQFvPdgc8zaRYoIek54EXNumYc6MTGh6+trra6uKplM6uzsTLOzs5bLI2c/Pz/X/f29EomE5ufnXZTR4IKCI6HjIAuFQtrd3fUc9pGRQTowckyAAJhpLjyQXtYWwWKwTKDAXLgoWCRZKQFoRYGKD372xxEkFM+Pj4P54peXlzo4OFCj0dDIyIjW1tZUq9UkyeEte3t7evXqlZUL+HMBY/BxIunjcszlcrYufPz40fJa/P5BICQSifgz4AnlMiTYbXp62l7JYD7D8vKyf1/WJaMY+Xzz8/NuBiRZ4RIEU5AZch7wXnmeNPdIQ1lzvINeb5DQzX5Dfo5FhfMIGS9+53a77VnHqG0ojJDL4sunEJuYmLDCA7USDRwoPWcAM55nZ2fNltMEUTwx/gb5I0g9hQNS9FAo5Gb34uJCl5eXevPmjf8bTaMks4LMciYDA+CIFGw87HgmSWiH8eMdMiaPzw0wPDc3Z3CyVqspn897XwIwxGIx2wgAYu7v751lwRrEkzc5OalEImHvIe8Gi8PFxYVVBjAaNAacj4Bv2KR6vZ7P0nQ6rcPDQ9vDJPlzJhIJr7dSqaTNzU0rt2BTC4WCZb0TExNutmkqR0ZG9OrVqxf//Pj4qK2tLY9JkqRXr14pEhmM1uIZMsOWZGoKznA4bGsZewSgIujvRSVCYzo1NaWTkxPFYjHLiZm9jt0B4Knb7ers7ExTU1NWijEdBRAwmGMDO76/v29PLN7IZrOpjY0Nzc7OuoFgPZ6enlodALOEcgrQCvAJwOXq6kqfPn2yIoMzj1wGgpU4b87OzryOM5mMi2vppxF+rGeUaYzdQxGFwgSAXZLfM3sRIOD+/l5//OMfDYb99re/VSqV8u9IMwDAgXeZP09zwBx5Ql5hb4P7GZUgxAcM6Pz8vEEw/jmoYkSZgQJMkusYzjFAxtaPic+hUMiTNBgricVlZWVFKysrqtfrSqVSfs6obVAaZTIZs6GxWMx1A88EaT7NOU0Nd5Ek5/YgX0Yqj/ea70eDiTLy8PBQDw8PajQaHtkJc0wwIGw6+SPBEbSEcSEhh+ii3u52uw5F29vb02effWa5OO8PIIjAY4BIbIlMnonH4757dnZ2XNMCZtL0MT2AOpFz+OPHj6rVagYN6BsYz8g9zR0UVIpi20BJQ83De8RaREMLyUA9BxgwMTGhZDJpKToNOE3q+Pi4ksmkXr9+rd3dXZ9Z8/PzSiQSlqVT511eXurDhw/Oo7q9vXUm1cjIiKdJAYrPzc05u4X7kAwJSSaHsB7ws/v9/ousi1AoZPsoAHylUrFVNxqNamlpSdFo1D8H4gaw9pevv/7Xz3rKwTAamlMaMXyP19fXTgOtVqtKpVLa2Ngweg/KzwWLBxyEr16vuxGneZ2bm9P+/r6li0HkFgaFgjscHoSv0VTwM/FykNKMxASmFj+RNDjUr66u9NlnnxmZbrfb2tjYMINH8fvp0ydtb297jBXSeeQjKysrlsUE2RAKZBIPObQ5fJEkNRoNLSwsWLJI4U4jQ/OJfJMiAJkgvid+NhIyGlZAC1gqZLsEbNEgDQ0NWRKM/A9kbXt72wUkzQkSp+fnwTgpSX4XhFMgAQLBY30lk0kf5NJgxikXOFYB2GwCn0iBnp2d1dHRkVZXV9VqtXRycmL/LI0qUs7JyUltbW058ZjilUMPwOLm5sYMLs82Ho9raWlJlUpFNzc3/p4U1vl8Xq1WS7FYzMg5I91YmyDesEPIivf29twEIHlnTRIEeHNzo1qt5pmagE4c/kjEAcJoHDjso9GopfOAVfx7GDS8wVwmJM2DIh8dHelf/uVfDFLF43GHKSUSCe3u7rqRefv2rY6Pj/X69WvnSaBYwPcVi8WUTCaVz+ctfeVSR1KHsuH8/NzsKIADbAV7HeaBQMd37945dZ3nRaEBa8P3QiZWKBQcsNJuD+aoHx0daWpqysz/4uKiWZRgAxT0W6GQQepH0y7JjTsMBkwNRTTvFfaJhobsjGCWAesG6TnPjncGowdASGNL2Fy/33eWBec64T4wimtra0b68bkFPZh4nWlqgkBIMCF9e3vb5w/S0rOzM69x1Et3d3fa39/355+enrY9ptPpWFpLk41XnSKfdR+8p1jbrA0yLyj6AWn4mYlEQo1Gw4GWKBsODg7MDjEacnJy0uw7DTUSRFQ5eFxhJZGgk8RLA55MJi25pdkl4CkISPB+UWchm7+6ujLISbM1PT2tubk5bW9vez0yRg1GE5+w9NO4Nn7fdruteDyup6cnnZ2dSRr4YrFnBbNKaOI4o1AGsO6QcGPhwOICeBOJDCacYGNjb/Hua7WayuWyVlZWzBL1ej03QoyQpFFeWFiwPatQKGh/f99BjgAhFOswgVh/8MWTKcGdKP3kpyR9ut/va2lpyT50GgaeKepAAqFYB9PT0zo8PFSxWLTFL5VKuV5qNpt+TjRsNBuM5EPSDKgVDodtX2k2mw584g7gbMFaNDw8rN/85jcaHR1VrVbzdJt4PO5sBfYR9g5UgKgUOcfw53Ovttttj/PDux4KDSYZsK5pDhjzR5PA+uLzYnmDtUTFgZQa0oOGG8Xf4+OjFhcXlU6nVa/XValUbOv44YcfHMRKTko2m/W9UKvVDOIBhnLmcp6z5wip7Ha7BnUlWZmWTCZNboTDgwRyGupKpWIgAUUPKpmVlRVNT09rY2NDX3/9tf3XpOJLP40aXVpa0vT0tD5+/OjPmsvlVCgUdHl5qaWlJfvisUFRZ/FZDg4OFI0Opi6cn5+7OUVpkslk9P79e4VCIeeRoFSCKKOGzmazvnsg4w4ODqyKpflmffMesCTx+TgfORcBwBgF2Wg09Pr1a/V6PVWrVRNaBOlR67LnsCDV63V9+eWXSiQS2tvb8wQFwBUUot1uV19//bWBfAAdMicIJCSQGCY+FArpN7/5jSR5jTB9hRA/6qGhoSGDI9yj3Cvj4+M6Pj5+oWyC1Dk8PJQkk5jcN9Vq1eACwZbsH+pasmwAO/7cr1+Y8L/862c14cGiJshqVqtVe0VA1pix2m4P5m6DvoBOszHxJRMQRHOHRCaVSrlAQ2Y7MzNjuej4+Lh/FqwIBTrfLxKJWH6HV+ni4sLjXkCeQI9h7g8ODtTtDhI3l5eX9f3337voIdTm9vZWxWJRvd4gZZZEcTy0p6enLxLUJZmxobkBlACt4mdysQUXONJrxmhQdAGKgHDjx+QAp3ggpAT0ttfrmY0eGxtTqVRyiBVNMA0xnhUOOw5jpM8AGoxo43KU5Kaexi+RSDgNG/Tt4uLCqciEinFIgRYXi8UXXjyAmaenJ7MKoVDIYUSMYOLS5PAGDCJFFzaORgcWDTSb4gqQZGNjw0FhAE4ATRSYMLrlctked3w2NE54EmHmYSeQfNKIDQ8PRl5Q7A0PD9sDhpcKiwR+wsXFRTfV+XxeKysrPsyRxQOwcBHBdAQDj4LPjEkEqEvwZ8PeoKwolUqq1+sGdf7X//pfGhsbs8cZ3x0JqqxDGI7n52e1Wi29fv3aoV2xWMx2Avb74uKifVnDw4OxXNLgYg2GywH+BD2P3W7XzyAcDjuojPVLUAyA1ePjowPkTk9PLeFHgjgxMWFZNGAk+4MGm4RuSfZ/0zBsbGxoeHjYDC2FJU0x7xp5LwnuSOT4nNFo1FMBgkFxQUYSew1KmaDygUYRawNhbDSUyH05K4PTEE5OTnyxYzHCN42f/Orqyudau902mAFjTeHGeYxliOIGVcw333zj4pT0fHzFWHEkWR2CfJrnCjPNvFuYGUAJiniAKkAUmA/2FsFt7XbbAAWNQjD1mcA1moKpqSkdHBzo5uZGq6urtuZMTExoYWFBHz58cBgmyiisWxR12KZQIPHc6/W6VVm80yATRFDa6empqtWqfd35fN6p5zQ5yDGx4UxOTqrZbOr169e+lwmj5NxjjJskh4ICKpPQjWQ6m82+uEcmJycN4BKaRKiVJPtU8cdTfDKOdG1tzWuT37lcLlviy93Neidv4NOnT5qamtLm5qYl/qhBAHAAhYKgJzVQLpfTxcWFQa5ut+uAKRQUwYKYJrbZbBqIPT4+Vq/Xs9IAW8+XX37pux01YKvVshqH73t0dOTGi5ydYE7D8PCwswv4+5L0u9/9zmqY/+f/+X9swyFZGdkv9R/BTwAOV1dX6nQ6Dnjkv1cqFTOTlUpF9XrdDTdnE+cdMtjb21vfq3yfqakpK/RI9ob1ZO1xjhKixpqZn5/X+Pi4Tk5O1Ov1tLOzo+npaZ2dnfnukWRlIbUXtRZnz/X1tUMBgxbHjY0NnZ2dqdfrWepLU8idHlSAHB8fa3R01PPIAaE2NzdVKBRsm8H6iD8Xby8/h7GjT09PajQaDtGF0OI5X1xcGJSIRqP6/vvvDY4MDw+bJMDu9/79e2chzP44dYDaPZfLmcEdGxtTtVpVJBLR5uamz0gk2MjDc7mc/xt7sVgs6urqyuPI2Kec16gVqQ0/fPhgBStKK85DaiTuCpSUhGwCUEpy4jy+c+49ZOnhcFjv37836UaGxHfffWc7IyHCh4eH+vWvf62JiQnl83n98MMPGhkZ0fb2tm0S+/v7ikaj+s//+T97/T89PWlyclInJydaXV1Vp9PR6empwuGw0un0C6Vmv9/Xx48fPcmCcwhFX683CO6k9+h0Ovrss89s3QLED2Y5cDaiakRZS3Ab50kwW+mXr7/e189qwlkckmz6RypJoXt9fa1UKqVKpWJ2ZGpqSpVKxeEZoMcUO8jx8NvBCOKZGR8fN8vCxYpMkgRaGJ/Hx0fP9yRJGpSN0AIKoNnZWbMkIyMj2t/fN0sJe05wzPLyshtJSUZGp6enXdQCIMCoE8IQbLpB8hhfQ0oifjdJ9kuSTk5wWxDxDUonCS45Pz/3xT49Pe0GQ5LZSQ61YJBXvV7Xzs6ONy7sEggcqDnSPuaM0pTil6ZQozGheaP4QNLL4UJxHIlE7H+FGet2u74UKTykQVECkzH2/7L3J7Gt52t+H/zVQGqWSImkSErUPJ6xJte9fX2700gnthPDgIPeBEgW9iKNLJxVNt7ZgJFFAG+SbLw2bMMLGwaSTeAAhp2+XXeoqlNn0tFMTZwpah6ogWQWOp9v/dWxnVv3fdvx+/oSaPStqnMk8s/f8Dzf6enpcYNWr9f9rCj68FKdnJxYGkng1NHRkWWdeM+ur6+NVt/d3RnphdHjIMdnNjY2plQq5TEcXNo0iYyR4DJkPSD959BHnkxgz/X1tYsjCiaYCsZcUURQ9KBu4EKiySexFTad5goJF5I2SV5vFNJB5ooCulAoeO8/ffrUNpJ0Ou3AIg7+kZERKwJYt+vr60qlUpqZmbHvjAKH8Rujo6OPZsmzvwh4IfmdPUTxR4oyDdSzZ888yQHfMVJ5CjhAnEgkYvACMANPJcUUY8FQFSBFJISSdYTSBe/Y8fGxmSCaYOl7GSX7k5/H2kBKCfIP2AFLzpmI1xc7DI3s2dmZ0um0QSH2XNAKAnMF0MMawg8LKCDJwB/ee5Jv8ZSFw2HL+5E/Xl5eGoAElMAi8ebNG718+VLv379XLBbzyJhSqWRPZzQa1fT0tJO3SQQmNAjwkTuI8wUvIw0CLCjnQltbm20/sABkjHD+0fQTnNPd3a1kMulGnsIRaSXWHM5LbFaAJlgFCAJiXbVaLXsgkVWzZre2tnR/f6+pqSm1tT2ES+K5JCulWCyaeWREG2tjYWFBc3NzKhaL/nsEUG1ubtrmBYgKE4PEniaI7ALOWsYQXV5e+p5pNptWBmFd4FzjmQUB6ba2NgPWwQIecOHy8tL7mrGeKHwIc0TpxHQJ0s4lWX47MDDgjA6YwsjH5G0APwBfQMxsNmtJNdYowHz+d7FYNCEA68T74XkEQzZRXtBsSHKAGBafYI4ENYUkra6uuhECNKOJILCMmiObzRrMp2mu1+v2zJMpgRR4ZWXF+xiglvPs4ODA5yCAIec8xTps5eDgoMdJARrS5Gxvb/vfSQ9AyuXlpQqFgs7Pzw3EAFzTJOIl7u7utp0Gix+NAk03TCKMNEogvuPp6WnlcjmVSiWdn58rn88/SukHrAqHw5qYmLBSAfXY2NiYv18IFBQmg4OD2t7eVrPZtPe9Xq+rWq36vp6dnbXq5OLiQh8+fHATSRYIzHQmkzE4g9T74uJC6XTaDRqAG/dhLBbT1taWbRcnH6eccOf19fX5rEQptLW1paGhIX9ugC7+ztbWls+Mjo4ON+DYmqhD5+fnzfhXKhX75ZHZk+C9vr7ulHHOT/YDQBW1SpCN5c/S/FNXNptNr+cguI218fj42PcfvwP7BzUSZ3I8Hnemw9zcnEd+clafnZ1pcXFRm5ubJmEuLi60t7fn5z0zM+MQX6YLpFIp7e/va3t721bOIOAJMcaeRL3H+ms2m49qI1RDkUhEpVLJNqN6ve5xrig+arWapzlgu7u4uNDu7q6Gh4et9OXnU/d3dnYqm83q1339lgn/zV8/qAknIIxCEjSb4vHu7u4Rw4nPgwa0Uqk4mRWpH8w1EuSBgQFfnhR2IL80bDTNFLsU4tKDDJwUXeSjoMw06tFoVKlUyhJY0CqK8XA4rIWFBW1vbxtBJoQK6S2yRXwe/f39mp6edjgaBzuIHb5aGiDYzmCzBnIL88SFmkqlLFGlYOACisfj9pPf3997RA9KAAp6mHkubApP5roiRwzKQGniSKwHKcWfJ8kXCE0bYUeEd4yPj/v7aDab2tvbUzwe9zxeMgIkOUU7FAqZqSCkgqI3qFgI+q2Ghoa0uLio1dVVHR0d6dtvv1U6ndb5+bnBHWTzHPIkq3NAM/Oyu7vbsr+g37RUKnlkWjgc1ps3b/TixQv19fUZIafZxqYRXKsg9hwmeO1oGg8ODvTkyROvZdg1GH6CsCiorq+vdXBwoGQyqampKTMdU1NT9tFhU6CZpwhDIsuFTlPJd88IMPYRlwQyVJr+L7/80oXE9fW1RkdHfSHhFd7d3dX+/r4nD+BNxJMebAwZC3R7e2sAhTNjfHzc4Bh+d/IjwuGwG20a97a2Nu9VZNOoOFBsgAoDspCLQMETBH/YozSTFI3BoMF8Pu8kago1GgFJVq9ggUmlUm78gl5ALBsAjyTvIw9HDkjx0Gw2nercaj2M24nFYg6HkeRZ1JypAFnsa4ANWOKzszNVKhXF43F/f8xEvb29tdIBRRTFaDCxmCRkmAdABGTP+Ovwy9/cPMxwJmwrFAr5/AuGO6KwSCQSDu/E0iHJwYMUXJ9++qmazaZ2d3d9lwG2MkIQD+XTp09VKBQs+Wf9UCgjoYWxJMgMMFL6PgUeQBnfMj8PVghFAQFgjFPkrBgdHXWzhN8vFou5iEUp0dPTo/39fctGAbWur68dLIjEm/e0vb1tbzVgkSSNjIy4qRsfH1c4HHZYV7PZdMIx7FFQ1o+NLDi+B58yKp+gDQbLGmAvYFCj8TD+k7GGgOGEn6VSKZVKJQNcAF7IWZ88eWJfJeoWLDf463n+sLSxWMyNZPAehHxARYf1ifWFH7larXpN82d5AShyP3BekJVyf3+veDyuYrHo+gowt1Qq2S9MLQSQR53EXRxU29GkwMKyhqgxarWalStjY2P68ssvPdqMZo5E+qGhIa2vrzubARsAQaHcxd3d3bbnNBoNq8pYg6ideGYAPIVCwVJ5RiO2tbVpdnbWag5AD1QcQWtHOBw288r9zBnLPmWd5vN5e/Xx1KIiDE5IQblzdHTkaQeE4BI4Wa1W7Xen7opGo3rz5o3fF2f5ysrKI883zd7FxYV2dnZ8l6OwwIfd2dmpfD6vRqOhbDar+/t7j1EDOCZ8E7UT5z7n7uLiokqlkusNQLBwOOxn3d7ebtk/ykHu4c7Oh3nkrElqBGx5ZOZMTU35LERpgicaFjjIYPMeqMkgciT5z6LCaLVazrcAMOEF8M9eg/3nvkXBxefkzwKW3dzcaGlpSaVSSbFYTLOzs6rVanr16pXBZ6Y4dXV16Y//+I+dvcCaw7ZWLpf14cMHBwZyl+ZyOYf+tVotFYtFpVIpz2lH5RmLxZxddHh4qFgspunpaZ+Bu7u7ruWmpqYMhmDxu7q68uQESZ5Fzp13c3PzKKwW++X4+LhyuZz3fTKZ/Nc3gv+a12+b8N/89YPl6MxuZixBKpV61Kzg/bu6unLBz58jeRIJWjabteQ2n89b2oZcmiaVIhWGgAuKxp+iHLaFhU6BS7MB+g1KROJrOp3Wzc2NJiYmXEC3Wi1lMhnFYjH7W/j9eO7wdk5NTXlEA4w/RRJJlhxABO6ARCIt4tAMopSwakGPDA1Ds9lULBZTe3u70XUaYA56ZOuSjJodHh4qnU77wAuHww7T6ezsNOqNh4/AGS5PgBcKM34XoAPfDcF6HECMUWF94IWm4QD9Q7qNjB6mhiRk/GHt7e0eewOgEolEtLy8bPAFFmZ6etq/5+7uzsAALyRtINxc0tvb2y6YQLwZN3Z1deUCFX80P/vdu3e2O+DVJVgN8Aqms6vrYWQZ411gltva2hw+xzipqakpgx74pWElkZGjxEBGz6gvWEAsA9Fo1AAWRV9bW5sLHOmhkINR5EIPhUKPUnxZvzS/JAZHIhGvDQLR8M11dnbq3bt3mpyc1PDwsIt3whBh5E8+zlWmsGg2m0bh8RVyQXOhE0iyubmpcPhh7iaJ+hQSpIKOjIwoHo+7wYZllR6CE08+JrEzuxjWlcwDSW7ekOgih769vfXaC4VCyuVy6ujoUDqdtp91aGjIBQnMI5MLYCRgI7kwYXdgqGmwb25uHJ5HmBMNGOoJmF5AS4rzWCxmlo31jOXh+vragZW3t7daW1vzVIZCoaDp6Wn75zmvCagjaZWLHraevU5xfXl5qUQi4QRgpipQTG5sbLi52draUiaTsVIIGSWyT8JDYaTYLzRIzWbTY5PI0EBuCmB8enqq0dFRn9+MpEskEn7enNM0tbCw/DMSS9bj+Pi4z5tarWa1FkAQapAgiHd8fOwGZ3d3141aPp83kEkwabVatRIDNmNqaso+dBjBVqv1CBBIpVI6PT11Y5rP5y3nvL+/d3YHwBMyXJo1PkM4HFYsFrPyIZ/Pa3R01JMxAA74PQRNsZdgwgnt43kiF+ezU/Sura1pdHTUtqSOjg7bQ5ClAuzBUHH3N5sPAYjValVdXQ8z4nd3d3V0dGT1G9JX1hfnS7PZ9PhTUo0ZiRWPx5VOp63i4lxFRk5NA0gN6wfgXiqVvDfJnGFvTU9PO9SPoh8vuCSfm52dnY8ai4mJCWc2cKeenJzol7/8pb2g8/Pzmp6eNkFAECLqD4C6IGAIWMTvLpfLbuZOPk66oNFFqdTR8TBDnTsAsBRginORs5l6h8+EVByQBkIGtRejJZlOc3Z2ppGREed3IAMOBssNDg466yZoXWNNUD/t7OyYKYY4CjaJZ2dnzjMK5vwMDg7+a5leztq9vT2fFeFw2NNJmPRTLBZtu7y+vvbnSafTOj091d7envMGWFcw0Hh8G42Gc0wmJiYkPShZe3t79fbtW2e9cOYSGknAWH9/vyYmJpTL5SQ9TKC5uLjQ+Pi4A1YB6ABIpQfSplQqaWVlxcnkQVulJGcV8DwIWiPZHvVGtVpVo9HQn/zJn9iCEsxEgQikwUZVCSiLdxrwgX/HfuXu+vGPf6yvvvpK//gf/2PXmpyDQWKDngclCHclGRkAtpAv6+vrvmP39/cN8p6fn2tzc9P7l/RyVEMEQvN9AswCTNbrdYPx5OMcHBwY6O3u7ratj3qG+6+vr0+lUkkTExOKxWKe7IFSlFrot68/29cPlqP39PSY+STZkfEsbH5Ssmkikd1WKhUdHh5qZmbGC3tiYkLr6+uWUSIdQVqN9AJJYLPZtAQcVAsWhiRWZIMweDT2q6urlgdKMrP05/7cn7NHiyIjlUrp5OTEjOju7q79HoeHhyqVSpqenlalUtHx8bH9pt3d3dre3jbSj89xfHxcpVLp0RxemEAk8fi4JZlppJmm2MLLRSMaiUTssx0YGPChT9BQs9m0NJAZgNls1iFCQV8wY046Ojp0fHys9vZ2AyNcdEizCLiAoYdp5Bmw4UGaq9Wq2tvbPWqJy7dQKLhYZm4tBfv19bWLplQqZRQUWeXAwIDGx8eN0pOmura25gOXubuh0MOoCtibYrGoWCzmZFaYG+RpMMMEZsGYwMYATPB5kQUj4W82m1pbW1NHR4dmZ2ftLYK9IlUfLy8pvFgtKBRheWigeC/IvWjGYLH39/c1MTHhhhCWhc8LW4yflUC6jo4Oe6hIcAclZz3i4QZEwRKCJSXItPG9nZ6eOigLVgsfEwnEsLCAQNVq1WN6kMwjowM1B2xCJRAOh/Xdd99pf3/fM087Ojr07bffGkzDm9loPMzYJRyIJh21hST772kqYS4jkYg+fPhgQIGmudVqaX9/30oNWDZChsLhsNPOkSrCmOGhC8o1Ozo6fMkDQMDgU8zxWcgvkOSJAvwO7C4wIDSRgCtDQ0MaGBhQrVZz6Nvt7a0qlYrGx8e1uLiot2/f+vKmIRkfH/faRXZM0CIBesiIKb5QDxBwc3l5qWQyqUqlYiaM7wn5LvstqBDY2dkxmFUoFBw+g6KFgo6wSZhllA40K8w1Z7/h47y//z7BGdktyg5sPtxtAFSASATcSXIIIE0EwZgw7mSAACIWCgWfPZLc0LIfjo6ObA/JZrOWEAZDe4LrGKD8+vpaoVBIsVhMxWJR0WhUJycnisfjBs5gDHl+e3t7mpycdEgngNHm5qaGhoYkyQAPzWBQck/TzZo8+TgiDjsT7BrKCOaoYwNA3k9w2eXlpZl9Uq4BONkjY2Njj8b6UQsEFTUw/q9evfJ9RsEPW8qdRpONyuDi4sLzjgmNRcrPe0YJAtiFcgmrB8oFgMzj42OfaTSlkuw7Dqq/mMeNxP3g4MBBcvxOGnpJXmPkRlxdXendu3c6Pj5Wb2+vFhYWFAqF3IgwN5wzBiBgb2/PVrLLy0utrq4qEolobGzs0ZQYFDKEBoZCITeJnEc02gRJ9fb2+l6SZDtCsJ7gewZg5nkBKgH6YUWi3hkaGnJIK4AQewOLyvLystra2jQzM6Pp6WnV63Xn05TLZc3Pz6tYLBrQ4dwFCIFR5F4PBotJcpgqCh8UcpANNHQDAwN69uyZVldX/bOoX5C0I+kGoB8ZGdHMzMyjvKFIJOLgQiZAMDWEvSrJPuZoNKrR0VHb72C3M5mMrq+vnbfRarXsd49Go66VCR3s7Ox0AHNnZ6f29/f1+vVrK/Z4f9yn5On8p//pf6pQKGQACssNADJ3S7P5MFnl/PzcMmnIQPZx0D9Ps835QsYN/weAg70OwAf16vHxseLxuEeOIUnHNpHP59VqtWz7QKV6cHAg6YGw3NzcVCQSscUNBYEkr13On6B/m4k/AF1k0cRiMefffPXVVwqHw1peXjb7zj3zySefSHqYXLGzs+O1fn9/b/KmWq2aoGBiAxlIKDjpRX6d12+Z8N/89YOa8MPDw0czPhkphF8EXzfsNMiU9NDwzs/PW6LGRba6uqparab5+Xn19/crl8u5ETs/P9fr16/N3HLYU5TCQNzf33tO7t3dnSWH+OUongjUQSIKQwdCD7pKA48cBJkRgRnxeFzlctkSDlhXDikYTTzxNCA8J7x/NOOoCwAIKJqCCCloF7IdmCrkcFzyyG+ClgFYL0n24g4NDWlvb8+NDYcDBxpBXVwwFMAUSRQ6MDBIzUKhh7nDpVJJ8/Pz9uGyDgBaeJ/8PNZFsAi5uLhwqAcN3+DgoBKJhF69emVQ4eDgwCqDTCbjC6zVallKOzo6qsHBQTfWgDcEncD0Hx4e+j0zOiJ4wRM8yKWA1B3vfzabdTHU1vYwu3Jqaspgw5++zA8PDzU/P6/r62vV63WPMWtvb3eYIKzjzc2NLxjAFhjHkZERra6uWtmAHLe9vd2BaDT9TCSggGF/UtCzZpAPBxFgfGWSPNtVki+1np4es4w0FFzUfX19Dt+hSNrZ2bEvF2AjHA678Li/v7cEEnaBPcv5QoDSs2fPDPjAAiSTSTeMhP4w9g71BnsdIIhnzPnC5yXEjGYbbzzhRBSejBPhvQfD4QBi8ILTzANqBa0rvEiJ5pLGLx1UoCBbkx4aeN4bZw7qFaxBa2trGhsbU0dHh8rlsnZ2dvT06VMXMzDpyOUKhYLH60xMTDhoDhn15eWl5ufnVSgUPNudBgVfIAGPwcT+09NTqzJIsaYIJeyK2b+AkJeXl25c29vbNTo66twBzhMk5DQ7MGaMyKvVam56yIzg+aAMYFZyW1ubQS0kxKOjo24UCXjks2ADwHZF0w04h2IDdhqQk+ai1Wo5SC6fzyuZTGpnZ+dRNkir1bJXORqN6v37914XPAcAbMbXtbe369mzZ9re3lYqlVJPT4/ev39v1RPMC3YBZjxztxAW1dHxECJK40iRV6vVnH+B9BepPfdcsVi0tFqS5a4UpSMjI2YBWW+s5+vra42PjzuAikBWnoMkB6/19vbav0pdwv3BKE8Y2bGxMfX29urDhw8ql8uWWSN5pcmbn59/FIB6e3urXC6nu7s7s/mco3iI4/G43r9/r/7+fgN7kmynoWYAeOYsQd6Klay9vV2Tk5MG7thb2FJ4Jlj8eAXHNMLyh8NhvXjxQp2dD1MgstmsIh/nCycSCa9RFICk78OW4s+tVqueZkFILXcuACnFP8o+Ql9ZE7FYzBkP2NYANanzqJd49oAeZKeQbcEdBGBUr9cNXPCz2tvbDfS1Wi1ls1l7aqlPCIKV5AY9m83aj43nFl8xTW5fX58D6mBbX7x4YeCsWq1qb2/Pidqo9riDuX+Ojo60v7//KCCSeoXfQfYCShbuU8ALLEUnH5P8yY7h79DokW1EGCZ3a61WM3jGuRcElDo6HqYg0LwxhpEsh52dHZNANOycIcPDw7YVDAwM6Pr6WicnJ1pZWVFnZ6fK5bIn6tCYt7W1aXFx0Y1sMOsGlSL1NEo69ghEEGsQxVShUPAdiZJsfX3d6/TJkydaWFjQ1taW5ubmdHp6qt3dXZ2enurk5MRneq1W830myfUi7LYkWy4lGcimpq9Wq5qenrZNdmtryxZIlG18L0GbyNLSkpLJpM9NrH30EP39/Zqfn1c2m9XV1ZUGBga0trZmhQrqtra2hykPi4uL2t3dtXecM/W3rz/b1w+Wo9/c3PhLCjJ0wTRVECkkbRxUBLaA8DJ6S9IjeXF7e7vHQxWLRXuFSAOmMKJBxWt2eXmpaDSqdDqtarVqmTYSJ3wO1WrVBxesYbPZtN8ROR/MFZ6f6elp1Wo1M2T9/f1GdBcXF7W/v6/7+3snGR4fHzt0hPnfeE6DY7Z6enoeJa7yLIMyfIpvChsOQlLKkaZwmYHEwz7ir6PplL4PR6EI5ZDioqM5oSjGY0cjxyFYLBaVTCYtnQXBBOUn9IgGhwMxFHqYK42EDW/wxMSEWTdC/Hp6ejQ5OaloNKq1tTU3lbDK6XTaioRUKmVv68DAgDKZjJFGfOgnJye+kEZHR7W1tWWfP2EYFPGRSES5XM4+QQpjwj+YMwpQc3f3kLCOAoLwwGaz6ewDmsqgJHl/f98FCP9/b2/PxRbNAV7lq6srp3TDujAO6O7uTpOTk0ZhQcthECRZrcC+4EAGwABQYTwHOQ80NBQkJDSTc4DUDaCgUCi4ECmXy56icHZ2ZiXM7/3e7/msACQAWcZbh4RW+j60iD1aLpeVy+VUrVbdGOOd39raMvOMXx7PGwAW0kI8aYBOgGkwoaOjoxofHzeIViwW7RPmmfb392tpacmydEk+L2nGo9GoCyGaHpLfyUXAOw7gQEgYTDtgD+Akflpkr3yfFCh8ruvra4+Dq9frlgpSKExOTmptbc3qo+AZBKCIRQQGDglwLBbT7e2tR5o1m01PNhgaGvJZQJHB2kX+yZmHpx0JJE0yZyb7g1GDIyMjzhKB6aKhQRWBPxNWBAaQoo1gOxRXyEv5LNxHJLJjS0IyzBrH7gFAV6vVHBCYy+WUSqUMREhSNpv1iDMYZfzvd3d3toHgbUyn034vKAQYY8Uegb2R5DUIwwe7yuhKkoyxTFHAsx7Ye9VqVSMjI1a5cAbf3d05jJM7h2I4Ho+ru7tbhULBkxUIxNzd3TWAcnl56cK8vb1dExMT3leA6qhEWNOEgdF400xzbuP/JpAUsJezm5A7gAvAE4D009NTpVIpN6JIg/v7+81IARDSLCOfTSQSyufzur6+NtuL8gZpKc0BoDnKI/zqKMZQyPzqV7+yvY2sG0m+S/heJVm9wZ4ANIdNa7VaBlhQwZHbw1m/t7enRCKhsbExgyHUNdwNMO/c/Z2dnd57fDbOnUQi4eY5aLNCBl8qlSTJY9O6u7vtG282mw4nvb6+Nts8PT3t3BuyTQAKGKdIuFnQdkazTpOIgoLMl6dPn+ry8lK5XM4s+tbWloNMad5arZaePn2qlZUVg3+STCawnjc3N91MoWxADRccSbW0tORxYtxtKLCQ43d0PKStn52daWxszLJp1E6QFh0dHR4RCHlAbdLV1WWCAhBBkmsN6jmAf+TP/B6IJ2ot/Oq7u7uP/NoogAAAURZh9ZOkt2/f6t27d55ogo+6WCy6ds7lcvrlL3+p7u5u/eW//JeVz+fNiAMkSLL1iucGucTdGLSn9vb2anR01EqcfD6vYrGowcFBPXv2zDJybBS1Ws0kI6BI0FsfDKvmjuY+Yo8H1VjUtf39/QYFstmsTk9PNTk5qePjY+/per2ujY0NjY+PKxaL+e4MKi+Dah8IF3z8KJuwfUB08Gc6OjpsweVu5v74dV6o3P4sX3+WP//o6Ej/3X/33+l/+9/+N7W3t+sP//AP9T/9T/+T7aH/uj//t/7W39I//+f/3Hk5f/Wv/lX9nb/zd1wL/rqvH9SEN5tNFyQnH0dolMtls5MUBPh9OeDwysIG09BRANGUBROoa7Wa5TbffPONvS09PT1mjbgIKHYJc5Ee0povLy+1tbVl319/f79qtZqKxaILgKDnsqurS9vb21pcXNTJxxnTJJ5yge/t7ZkZRupOcTc0NGSpHamFNGpINQkZ4wCgWIaJ46CAocOTF0T2aIyRdVIMAzwg36I5xxt1cXFhSSoprxSDV1dXBjKQMvFdIM+kECaNeWtry6gwCaZ4k6LRqCKRiD3CoKr4k2HcJT2Sb+FnK5fLLijxVO/v7/v75ju9urpSPp932AXpqI1Gw2Ew+JyDTEMwCZR1Dcp8cHBgJoQLEoktMkSCWTj8aTpp7vL5vFKplJF/0G8ACthBEEwYwmg0as/Y5eWlJicnfYDTvNTrdQeNRCIRHR0d2X/c09NjNorCDm8fnxfJGb5W5Pp8F3zn+P5pANmDKDJg2xkpxPfXaDTM7nZ1dWlmZsay8a+++kq///u/r/7+fr17906Li4saGRnRzs6OJicnzVjjiy0Wi2ZWARE4B2CC9/f39eHDBweAUYgjE65UKp6tend3p9XVVfX09GhmZsb7DRCI75e9B7qM8qSzs1PLy8v65ptvnHvR0fGQBH90dGQPGInVAG+oINgrQTZpbGxMp6enOj8/19zc3CNvOnsdqXXQawwzDtjBWjw6Ono0oosLFmCpvb3d/k6e5fT0tG5ubuzLJVwpGo26uWGv4EUeHR1VNpu1xSOTyejg4MA+QVgTLB6SlMlkLL1mzcHm85l5zhQ4sM0w50EPIP8syewXvn2Ysfv7e2cyMAEhKI/s63uYb8xorouLC88ORtnBCDRyJQC6OAuxT9B4Yl8gb4NCEoCN9USmgCT/rvb2dociAigD1iDBDPpsARFo/mdnZx+pDfCUI7/n7iVRGckqEvLu7m7fBcGchrm5OYNAsGjr6+uKRCKampryegeEo/kFEE2lUjo6OjIATp1AkdrR0WGAB/sJgVTYPu7v7w1E07Sxf66uHkaIzc3NWT4LqAU4LMk5BSS2czcSrlUoFJTP500YsFZPT09txQO4PD8/t1QVgAEwGBsewBIKAL5L/htrHmUJQChrmfuYLBKsOOwPQsQA1NnHAKITExPq6OjQzMyM/+779+8fSWKHh4c1OTmpd+/eOYQQwBl7EcG53HFM+eD7J98DO8nCwoIbHPzs2JFQVMKiNptN2zDGxsYcWEceASBaMOuhWq06GI2Eb+qyYBPUaDSssuHMGxsbM+GztbVlIBSA+fDw8BFZExzxFbwPyMsYGBiwYhIFT7FY1M7Ojl6+fKmpqSnfD319fVZ/tLW1qVar2WoyOzurH/3oRyoWi8pms26sP/30U9fhnFfI2wEDi8Wik9VHR0edJk9dBNONFQjAj322vLysSqXijCTOn729vUcZPpKsGDw7O/P3S8YD9zTfW3d3t8bGxhSNRvX27VsVi0WrRt6+fav29na9fPlSS0tL9rpfXDyM/qWGrtVqevPmjXZ3d7WxsaHnz587IBCVFPdJUC3B/+buYQ1j3USJc3V1pbW1NcvQe3p6fF5Tz7FneW7cQdhqsATSK2DTQ8nKeUweRDqd1ujoqC4uLpTL5TQ5Oan7+3uDhCitIpGI+wsUS5xrgIbUdageIZ0gO2ZmZjy3nBqY74GzOzixiff9H8rrv/qv/isVi0X9H//H/6G7uzv99b/+1/VHf/RH+kf/6B/9a/98oVBQoVDQ3/27f1dPnjzR3t6e/tv/9r9VoVDQP/kn/+QH/e4f1ITjFaLwRPYblHhz8AVTjwkTQuLIYQpKOzY2Zl/I+Pi4stmsQxnOzs40Ozur7e1tN829vb2eEwpzTZOCdIviCn8j3lia16mpKXu0GP8TLH7j8bg2NjZUq9XMIOOXzWQyOjk5cSALQRk9PT1m/AcHB+3ZQ8JJejEykIODA6NYBL0Ex5AgJ0RKRVFE8w9aDHLOqBY8dIlEwgUAAQ4wtRxYFA7MFSa1nOaOsK+g15XfCQs1ODioo6Mj+/eQAMIOEPAGond/f69qtepCJ5VKOSAL9QBsBoUMCgKSsCuVipGxcDhsBuLw8NBjdWBSh4aGDGJIsgwIxQH/DT8sskPeNz6toEQPBJPCnwYeuSQJoxRL7e3t2tvbU0dHhy9/xpHU63UninPpMPrs4uJC09PTHhVCgEZ3d7cODg584FNoM2KIoDFk8RR8SLHwVtJYgLiSxgzTC6rMzyoUCo8CZv60PxmvGxcUjP3o6Kilcswlfvr0qa6ursymsDbC4bAZWsAJABDkVpJctAM4/PSnP9Xr1691c3PjC44iLx6PS3rwu3Z0dHjtAkDAztNwwEqxFzs7O1UqlZRKpRQKhTQ7O6v/8//8P53sTTP9zTffuNmenZ3VyMiI10HQ9gGgAWCAx5Jzk8YTvy+sUzgcNvPDz+FZSPKlTVGLXI/cBxBrmmqaB2wFwVDLZDLpsxX/fqPRUCaTsVwzmUxqZGRExWLR/veNjQ0rI8rlshYWFty8sNc+++wzHRwcWGZMoQLIitUJZgd1EkUja7bVaimXy1k+zQzZarXqEYP4YQcGBpTP5yXp0fqF9ZFkmSMzhyuVihskGk8UDTT3hLpFo1EXvjAjgDE0E4BczLDnvKJAnJ6etiKgq6tLOzs7kuQsAQrCxcVF74tcLmc58OXlpX7+858bKBgdHbWqDCaG7IrR0VEtLS15Li7S3uvrh5nwTOZAQURxx1ph7nnkY0I2oPvh4aE9ijTt8/Pzur9/SHXmXN/a2lI6nTYww9go1iaqDc7vVCqlaDSqbDbr9XR1daVisej8DBQ/rGFqjOvrayWTSYPUFxcPs6nn5+etVDg+PjZzG/Q0A8ixf1hPeOkBxrFA4AumYeOM6u3t1cXFhd/T2dmZmzyAcWoqyAmAee54CAvWMP+bGgGmb3h42AAw6w+whwYBv+rJyYnrN6at1Ot1zczMOECqXq/r5OP4JhhZrBudnZ367rvvzMjCxiMXpwHp6+tTsVh0cwI4TjNAXcOe5Wcgq+bPBWsPPOmArqT9I4PHqw6YQYI6z3Z7e1u7u7vq6enRmzdvXGcQ2EazGI1GrXrjfsjlcgbEZ2dnbUPY29vzPXhycmJ5dbP5kM+TSCQUDoeVzWY1MjLifyawkRoFpcrt7a1+9atf6ac//altID/60Y/8jGDHy+Wy7V7sCeaG02QD1mBv4dydmprS7u6urq8fJpzAjvf391sxOTQ05IY/CJARcEyzi1KE9czsbhSMKEfq9brm5+f16aef2jrC/V8sFl37AXB+8cUXqlarWltbU7lc9rmNSoj7AHAJIgn7ZCKRcONNg99qtQy48n8nJyf66quv1N3drWfPnqm9/SF0bXd399H5gLqV/JdgcB8yekAf7Crk8HR2durg4MCE0tOnT9VqPYzU3dvbc44M4+6i0aiGh4eVSCSs5ODOov7AIhcMaQuCnARPosyJxWI+dwDeLi4ufFYDbv86r/9f9oSvrq7qf//f/3d9/fXX+uKLLyRJ/8v/8r/oP//P/3P93b/7d32mBF/Pnj3TP/2n/9T/PDs7q//hf/gf9F//1/+1gZlf9/WDmnBkHJVKxYXs0dGRDg4O3EyDAjcaDc3NzbmIA/nmwpHky50miosQ33c6ndbR0ZFD0kA/OYTxlkYiEZXLZctvK5WKpfF4fXlRnMK+weDBrnPh4K0lFR0vV1dXlz58+GCp4OTkpC9ILgTGa52fn2tyctIIPaM5giPbgmnqFJz4GIPhPQAPZ2dnHkeAx5RACTy1sNkUrFxAMN7I4KSHJvzk5MSeExg2pC2Xl5fKZrMO58CjRIrixsaGw1CClyI+ymQyaZkj8lHC/WhekWfDOlPMYBfY3983gn94eGip5ujoqA4PD91c4qXs7+9XMpl0IipyR+aLM3JibW1NIyMjmpiYMCo9MzOjk5MTh+R1d3ebSbi7u9PMzIxDZgA9eN8wFsgYw+GwD3kanXg8rq6uLq2srDxqtlqtllkvvtdwOOwLDH9os/kQUnd3d6cnT54Y2KGRPT4+ViQSMaO+v79vSTWWAxj4wcFB/30YMlLPObwpAG9ubiwdZJ4uzByNFEnCh4eHlt8DmiBNfPHihU5PT40+E8ICs0hDzWQFCk0aIH43AAopxKzxaDTq8S2Mm+nr6/OlXa1WnREBQAhbCoBBkRC0JgBy8X01m01NTU0pl8tpampKkhzeRCH95s0bsxsAHk+fPjWTguWDEEQ+N8+Sc4ADndRqCmsueJqnoHc/mLtAvgZFLgqTRqOheDxun/P5+bmBQ4IIOedIwO/v77fc9v7+3qPxQqGQm2Zk3oB9JGTD1gDoMc2B5xuJRDxOb2RkxHNz8eDxfdEAUxjToAVlq6hx2FtYL2jwAKf4rJytbW1t9grCAjNDmHMvyHJfX19rYmLCayOoPCHACE8uLCLNCwVnpVJxQU8wVbPZ1MTEhFU72GEAnavVqovM4HoYGRkxy8kZ2tHRoUwm4/BTms2hoSFbCJDHUkwChBYKBZ8l3LecdQCuFKK3t7duQDlDuP8bjYZZcbJDUqmUZmZmLBkH+JVkmxvgNWoq6gLsChSX/C6CGVFJAT6OjIz4/IM5Gh8fN1OUyWSUTqd9ZvNcG42G741ms2np6MnJicNjsYEBUiGHDlp7JD1iyFjz/DcyMSQZOOfMQD3Gs+f8o54iI4SahaBX0vHxhxKky/dH4zI9PW0PPoweqi4sBuwpiv9qteo/w3lIrsHExITPY2aLUw9B5OALrlQqjyTwY2NjbvgJjWNKSE9Pj169emXAh1ro9vZhksXp6akqlYqBZDIakKhTW1EHATj29/fr6dOnBvp45qTxp1IpB01iQ1leXrbVLZfLeQoJ9xwe7Xg87tnk8Xjcqe/RaFQfPnxQtVrVp59+amCKWpY8Cp4lP4dmnoRs9iANFMAJIYOFQsFAxsbGhvdwq9Xy/QKRdPJxnjf2AGoPJN7kGhDmSm4P7Df1bUdHh89rlBuALj/5yU8MepATRUPLvdXW1qZ4PG45NOf3wMCAfvzjH+uf//N/bqVGOp1W5OMkFhRDEEzBBjQcDmtzc9Pn2MLCgqcFcV5ks1lnk6RSKdXrdX3zzTdKJBK2wqAcIVMgKJHm3g4CZZAGgHLc2fjwUY01m029evVKl5eXtj8SLPjZZ59Zvci5QX1wcnLiGh/ioF6v28qSSqU0PT1tgILv7u7uzpN+SGdvb2/3/YZS49/H19nZ2aN/xlb4m75+/vOfKxKJuAGXpP/kP/lP1N7erl/+8pf6L/6L/+LX+jkEc/6QBlz6gU04kh+YvXQ67RC1m5sbbW9vq6enx4U5B3o8Hrd8HaSPYqjRaGhvb88MLqmmsNostMnJSRUKBUnywkNmhFdudXXViagUgHi6ggg+P3t+ft7oNgwV0uPr62uVy2U9ffpUBwcHGhwcNKslyfOuOXRWV1fdOHV2duro6MhznkHHjo6OLJm/vb31iIBqtarnz5+7KAsyjjR6gAeE3tHQIlkjvAmfKSmfJJnDiEU+jrRiJjDIc0dHh3Z3dx30EfS9crAfHR1paWlJ7e3tbg5gv5FxohYolUpKp9NmDe7u7rxRYBMoTpHYUpTjCSIghFFlIPSAEqFQSJlMxkgmKDHJphwirFHk00iEkR+ffJy1yZ8jYIaLHvkQQXx8DpLax8bGLO9pa2tzuA3hM8w/bzQanrGeTCad8k7jMD09ra6uLn377be6vr7W9PS0walSqWSJP3sniPb29vZ6xuPe3p4KhYKWlpacXQDji4KFBpuZvvV63YqRarWqu7s7y7LII0in0yqVSgZqGBfDM8WfLMl+OSwK2DBgAJHoDg4O6vPPP9fW1pYvNAoMLjRGzXFJgDIfHx/bMsJ6WFpacrop6g7GaOFHJqsgl8v5u6IppQhg7RSLRe9Z0P3e3l7PdCas6MmTJ2o0HkbBcAHznW9tbenHP/6x9yrNIvudy5lm+ObmRolEwqNKKOYpJrD44B3m79FUwrDjGUYlhJezq6vLYBoWFVgcxptFIhGH88D2U/SiNAqi6zSbJycnCocfZvbS2NHgx2IxyyE5j2EGaGphitiLBGYioQdQouCiaGIWNOqbZDKpmZkZra+vm83lTOjr67NtiUkDhULBxWA4HNbU1JSZHM4cFAbILVEa0GAHLUDMm6aw5zu5vr52Qvjt7a0bJIonGkaUDZwdfMcwifw71BpBKxNBYqy/d+/eucBva2vT0dGRPvnkE11eXlrBBHucTCbt1wyFQhoeHlYqlXKTBwBBQ4+/kf3JnUygUCgUUj6fVz6fN/PCXcCkDJQQfC+dnZ1mjkgr3t3dNSNGkBLgGdYKQBJJZl75XTD9FPf7+/vecx0dHR6XSrOA9xlAgnMsHo+7Me/t7dXOzo52dnYMDgI84rXGBgQIjyccKwuN76effqq1tTWDfENDQ5qamvI4VZQPnJmAGVtbW95/qI+CWQXUCshXb29v1Ww2nc3CmoRtk2SQjb2yublpYKm3t9dki/T9rHSaZRh+AIHp6Wlls1nd3d1pdnbW1jGafsCN+/t7K2fa2trMoKNcwHaEGgzLBtM4sFtwZgXrUtZA0PInPRAQ6XRaBwcHbui422h2AeqZhFEoFGwt6ex8GHmJnQM1QSaT8Xsi1Z/vYGtrS4uLi74PFxYWNDAw8CgLBa94Z2enZmdnzaSWSiUr2EqlklWp29vbkh4yH/b29jQ3N6dms6mtrS3XY9SPWEQAbpjuwyQC2PJ6vW4ZOnkqxWJRxWLRGR+Ek7W3t/tsgWSbmZlxVg62pVQqZYk7ZMjm5qYmJyd9/yLBJseGZ4rVdWZmRk+fPnXK/4sXL/Ts2TPt7u66L+A+DwJN1I70HK9fv3Zt/uf//J/X69evNT4+btvE+fm5czJQCwbVvvwOgFb+Hs063ynrj0BZCMl4PK62tjYHvqVSKU+2aWtrM7i/ubmper3uNUiG1MbGhtc0ICYgyNXVlRYXFw2GYoMJhx9GtjYaDVts+vv7tbCwoPPzc6/JSqVi6fuv+/p3yYRnMplH//5v/a2/pb/9t//2b/xzS6WSEonEo38XzDv6dV6Hh4f6O3/n7+iP/uiPfvDv/2Et+8dfRsE/NDSkSCSiubk5FYtF3d3d+TCEPUun048SFfmSOQxWVlbMJiPBRj5GcxVE4q+vr31JkCI9MDCg7u5urays6OTkRKlUSqVSycmxhK+QbgzjADItPTQNHH74mLu7H+YT5nI5LS4uWpp9enpq5PrVq1cekQO6xSgr2Fk8Roypoug6OzvzGBAausXFRUvY8Y4ii+bng7Yi3SPs5P7+/tFYMEJcYIM4TPCFkhxaLBYVDoc9FxIpDEg8ygEYKNC5oHcEdDwcDmt8fNzMNcg9KgrYy+vra83Ozmp2dlZbW1sO4wEBRybFnFuKYJjtVqtl1hUklvdLQ0LRRuHOZ7i6utLp6amWlpbstSWBlYO6r6/PzPPExITq9bqq1aq/O1g2fFHI6fDr7O/vO6WckAuyAUqlkl68eGE/JjJ8GB/GZ7W1tWl0dNTFRmdnp2q1mn2oBMGAtEajUY2Pjxsx7+zstM8T+WAkEjFSF5Qwnnwce0fgHoAFTQrNxd3dneXQJMmyzvv6+sxIIkUl2AKGiayD8fFxffrppw43g8FFKsr+SiaTVtyg8CDQkYKV4L2trS0rNADg+vr63ChGo1EXy6xrEHZS67nkGK1zeHjoMTQEfCEpxlNLqN/l5aV+9KMf6dWrV27eQb45T25ubv5vqLr0gJgzEoTsCLxgFEwoDrhsaRj5XvgesOSgrEDNwxnKWMmTkxNNTEz4cxEMNzo6qr29PWdW1Go1jwLjzMTbGBxHhAqju7vbkmqUQRMTE2o0Gnr16pXlk8EXjBXsM5JpSWYCLy4uPLqJBouwPIpJGh8YJXIhWPc0VY1Gwyy7JDdzoVDIM28BmQ8ODh6BwhT3BDmRLIy9iUYLRung4EB9fX06+Tj3Hvn227dvXWBmMhkXVaiaYO85OyIfRxgiXZ2amtLAwICy2azBL5QMs7OzBtNQQXA+AkCwZmigUblxBpTLZQ0NDT2yJaDiGBsb08DAgAHqo6Mjyz1h5/jZAJ4TExMqFAra2trys+GcoMBCopzP560O4fxgzyKZp8EgNJEm/ujoyPfI/f29hoaGrMahLkBWDKCWy+V87/B7uBtvbh7mbY+Ojqqnp0c7OzsOcaIJ/uKLLzQ9Pe3mmJwOahQCuVCJsef5d4wbi8fjVsHt7e35HuRMpaE8OjpSuVx2QBtNB3UUrKwkK8iSyaSbTkluNqLRqGZmZnRzc6PV1VVnDWxubqq7u9sgPnYPAKZ4PO7gsfv7h0BaAAkky21tbUqlUtrd3dXe3p4bTRoZQJCpqSmz0PF43GAMOUFkgIyNjWlkZMR3OiMtkRujCAPYDKaLo9CEtICouL291dbWlt8XwPJ3333nxmtvb0+Xl5e6vb3V5OSkzs7OlMvlvPfI5wGcDoZdAuqWy2W1t7erUqmYdEilUqrVag6XJH+C/BdY09vbW49Qi0QiWllZsWe82XwIFQyFQpY6cx4PDw9rdnZW6+vrevr0qRl9LDUAWjR/rVbLCeHIys/Pz7WwsKDV1VUDX7VazeDj7Oysn32tVrOHXnoAEVOplM/4YKgfYAjnAKo75L/Uj0zXQWL/V/7KX1E0GtXKyorr346ODge/0WQCVrIPAELY55xH//Sf/lNNTEzoyZMnev36tVU1rOVms6mxsTEVi0WDsjT1hEeimuH+Cto3yRzi7wbVUuVy2ZkL7PVyuayf/vSn2t3dVWdnp9W8qI74PCi0yDkpFAoaGBhQLBbzPsZ2gaoS0BegE+Cyq6tLCwsLzlmp1Wr/t2b335cXdw6vfxML/jf/5t/U//g//o//1p+1urr6//H7OTs701/+y39ZT548+Y3AgB/UhCPT7Ovr0+zsrC82PLh9fX2anJy0D5fxYMlk0qOK2NQ0JhyYSBG57JEHctDf3d1pYmJCZ2dnlpGDbgebNpoj5hyyiPH6NZsPM/4IbqtWq4rH42ZdIx/nbp+dndkLg2QkeHDMzMzo/fv3RpOReiKF4vCnYEFSj9/1+PhYx8fHevbsmXK5nMOUmLdOw02hAMJIUYYfhe+AC4dmhyYYH9Lt7UNi8fb2tkqlktNO8VFnMhnL/RqNhnK5nKVJoPqwkMhYKLaCKbqEAlEE8F3y/M7Pz/19EhxBuBTNMQqD4Ng3ihhky11dXQYbCJtAEomdgQsMEEZ6YFlZM9fX1zo4ONDU1JTl+4ATXJ6dnZ0uYO7vvx+Fx7MfHh7W3t6eSqWSxsbGLPFMJBKP5lL39fW5IIXB4yJHbcAcTzIFKPTv7u4cKsZ3ysFPI04BgNQUsObs7MzjlPjZfC8woQSOTE1NuQGKRqNO467Vav5csBLkNSAVREoG6it9z9Tz/O7uHlKUkRyS2NtsNvXJJ5+4EILxGx0dlfRwkR8dHTm8C5UNMnVAGsLySKXHB7u1taWFhQVLwBm/R1MDO0cw3srKir3PU1NT9nVS3LC2t7e3lUgk1NXVpV/+8pceTxSPxxWNRrW9vW2GmUKcn9NqtXwRk7YdnMeLv+7u7k7RaNRNK8+YGc2ogAhHk+TPw/Nvb283iwLYQWMUCoW0urqq8fFxF7+NRkMLCwu2frx8+dLsN4Ua51EoFDITAxAE4313d+fGmBnPoPqM36GhxU8N65hOp3V//zDfHlCG4gMZMiAgZwxM8tzcnIvnsbExK316e3vNtKDWIVkafzMSe1iSVCrl5ieZTNpyQ4YFzTmACVaV9vZ2e/xJhUamSZEDKBAEFmnogyxvMpnU0NCQYrGYNjY23DgRJEehR8gPYVjch1dXV8rlcmYeORORocOEYsFgdF2z2dTS0pKtQqxL9hyTDmCNCf65urrS3t6eOjsfZgcjUec7GB0dNZOOh5HfQXYGPnGeNWdfMpm0wuXs7Ey1Wk2jo6MGuwG9uN+DzRcBVaSYcz40m01NTk4qmUx6mgIj3Lh7UHqRJbKysuIMm9nZWU1OTjrLg3MbFQNrDJk79yn3wvj4uMGYhYUF76+joyNPx2Ctce/hvSd8Eq8qah/uv9HRUau4KNhRdFxeXlrRs7a25s9J/g3KB1LVscqg4iMHAqtVJBKxYgiADksaYw0nJycN+OJZTSQSBk+4v2h0aKTI2iHrI3huB4Pq2Pv1et2y1WDNhd2EPcV3d35+roODA11eXurZs2cOuZqbm9PW1pbPov7+fm1ubjpHgO95ampK9Xpd6+vrtnsxSWB/f1/Ly8sqFApKJBLOEmJqAMQWIOvk5KTy+bwBIfzDJMHn8/lHgB7gfjqddv19eHhoaTGhYpw1gHGoILAkjoyMGLicmppyXkkqlfLdhlKLMLLnz597T66vr+vJkyeam5t7pIaBEMnn855bD7EUi8VM+GBhKBQKBvsB3VGdYIN4+fKl5ubm9NVXX6lQKGh8fNykDCofziaY++7ubn322Wfa2trS69evbWWD9QdkZA+k02l9++23DujlvAg29kGiCe85Fi5A2nq97vOOuuiLL77wdzk6OmplBZ58SDPujuBIWkkGfwn7i8fjHjvKmEnAhKC6BhVoT0+PyUMUjPQZ7e3tWlhYsDXo13n9u2TCBwcHHzXh/6bXf//f//f6a3/tr/1b/8zMzIySyaQzbnhhBU4mk//Wv39+fq6/9Jf+kgYGBvTP/tk/+0HqAV4/qAmHXSOEirAAmGg26OXlpdLptLLZrKVkyC+QQeOXBtVHgk5Rf35+rqWlJX8ovIL4wdvbH5JyC4WCnj596uKYImd6etoXZ3DWZbVatbR6enras6ZBq0DxSD69urpSLBZzk3N4eGjfs/T9nFP8Mni7Ya5Acrmwa7WaGWNGDfAekHDhxYYJCMqj8/m8kWAYcULXkL/AACBNheVAVUDzAUMIK4YciPRIglZodmDxec4g4AS6MTOUQBSsBRQIsL1PnjyxJ0rSoxFtjHI5Pj5+FMSGD7C9vf1ROAXycC5wChjkrkHWi8ISxppAJIJrkPWBBoLAw2Die4dVZA1eXV0pHo/7wAVlRDInSb/4xS9Ur9c1NTWlqakpra+ve45jX1+f/bxIxGnWkI/y3cB2gpLDViFT39vb08zMjD2KBDaRqAoLwOVOgwvzSqFDEYiXGoSe75NnOTo6aoSWfAXYq9vbW2WzWc3OzrqRwVNM6jrfBcVtJBJRqVQywBb0yN7f3zs1tbOzU7/3e7/n8TmsOdKK+XeM+eBZMFqFcDrCuwgygQ1GzXN4eKjIx/AtCmnWDkVDKpXSr371Ky0sLKinp0dffPGFQ2dQEJBlQWGTz+dt5Tg4OHjETkoPUsmzszPPtgf8o3EaHBz0BUqDii0EQI6GHdbo/PxcY2Nj9hwSFMdnQvnA50fJELQB3N3duUg4PDx0Gv/9/b2DBQ8PD/0sE4mE/zwqI9YtaficYR0dHS5Isd3g9+bzTUxMeFwOoA4Wgbm5uUfeZoA0JMYUMnhKq9WqIh8D8ZASdnV1eS2Wy2VJ8jOg4Id9BUDCB9ZqtVwA4UnFBoGcEKBxdnbWfljky/V63awZ8ksK3q6uLgcDce+enZ2ZGTk4OPC82lgspng8bokh8vhyuaxYLKZkMqmzszOtrq6qs7PTZwdNfrVa9XkLs4/1KagKQ7US+RiuCSPImJzIx4kW3D8oLvBnd3R0aHt72+cIwBI+f/bB9PS0SqWS1UWobWj+goAMQU5I6Ts6Otysk+4O4Mb6Yp9TqANs3d7eanZ21nuBRpY9/Pu///uam5tzkyzJrGUikTCzi6INpQ/ALHYu3iPWH6TIoVDIQFFvb6+Ojo60trZm+SkgDs0D6w/5b6vV8prm/u7s7DR7TvNTq9WsEgwCQ9xjyWTSUu6hoSGHw6JUQ+0SzFdAuRb5OHsZiX8ulzNYw7l8fHz8qMnf3Nw08x20x1AQV6tVVSoVzc7OGpSo1+tWkiGhZi8BtHDez8/PG4hDSURtNz4+bkXB0tKSZb7ffPONAWnWzfLysllF7GekanMO0lDyGbFcMqIym816T5+enmpvb88WhD/35/6cz52JiQnXAzs7Ow6PLRQKJqd2d3c1OzurpaUlj8ejyaQhBVAnAwYQEVl7Lpez+oHaYXh4WNVq1U0wNe4nn3yiRCKhnZ0dr9Pt7W1NTk6qu7vbarDz83Pd3d0ZcMC2iUWDtY7Sj2YWYBBgnu+TfYrlcXt7W1tbW7ZqzMzMqFgsGlyu1+t6+vSpdnd39d133ymbzbqeD4VCBjgBaclVev36tevCd+/e+Q6mGSbngL3/p3N0ACtp1O/v7/Xll1/q7du3ymazHjdG6CyWqKOjI3333Xd6+fKlksmkz858Pm8l39DQkMrlsprNh+kOu7u7bijfv3+vUChkwomxa6gVqR1brYexgKxLgAZIoGCW1v/T699lE/7rvuLxuAN5/22v3/md39HJyYm+/fZbff7555Kkf/Ev/oWazaZ+9KMf/Rv/3tnZmf7iX/yL6urq0v/6v/6vP+h5BV8/qAm/vb31CB0uFEn2w62vryubzaqt7SG1FJ094QT4UTl04vG4USDSTBlPQXFIwASXzfn5uTKZjAYHB5XL5RySQHNCM4uP/PXr10bYt7e37RWk2aGRRRIbRJAkGR2nQQGZJ42QMSeEMtA00Iwhj0bqJ8nvLxKJqF6vu4mjAEB+ieoAyT1sGAFFeB/5/chpuYBIucXHgkeE4hWJ+8jIiMMaRkZGfNDx50hpZKMODQ1pZGTEIWLIYvlsBBYBjBwcHPj3UgiTH0CCNbLmw8ND+4bwGtP0lstlAwv7+/tGcml2OQgIwYCdZZYm4yeazabD2lASSLIqg8sUJmZubs4jf0C1CW5D+glqvbOzo7GxMct5u7u7HdCFf4dnND4+bqSSdYIcOhx+SMGmaEHGBbtNE808YlLA0+m0gSieVUdHh4t1/JwUATDlBJUdHx8rGo1qdXVVmUxGs7Oz2tjYcKMVTJwNhjQFPfeEhlxeXmp8fNxBTKgUbm5unOVAaFS5XPbfYR1RYA0PDyufz+vu7u5RgxgOhxWPxw1MUeAiC0Zux/eEZJOCkWIYtplCmEwFgtJQ3UgPBWAQIALNzmQy+vLLL7W9ve3E48jHeayE2eC9Y58zTYFCGHCMgg5FBzaOSqWitrY2zzOGWaMoRLlAs9Xb26uJiQlJD2ncJEjf3d2pVCo5CLNer2tlZUUzMzP2QKFCAOBCxdPR0eF0fsJ0enp6rGi5vLy0JLfVaunJkye6urrSysqKJiYmfA7QbNCcMH+bhvv29tbZGTznbDarcrnsRhdGDnAjaDG4vb21/QfGke9lYmLCABBBSYA8pB3v7OxYZn9/f+8ARhLPYSkoMmnOg2FAAMA0bZubm2aTCZdEfowkk3UVbCaQmwKkwezDguPTbrVaLpxpiHn/KMyQ4V5eXnpqx/39vbLZrIFZFGCcn+VyWaOjo8rn81ZfkEkSZG9IqL6+vtbIyIgVQwBBKD0kWeJP0jZe2Pv77xN5kdOXy2Xt7e25WYKVq9frzspAmj4+Pv5IFoxqjKAhmt7Ozk4HuaIGwX6CWglp7O3tw/SQ1dVVVatVpVIpvXjxwgUyknOkvZzlBHDxHZClc3NzYxAS+wGA0du3b7W8vGzFRiwWUzab1dramjKZjDKZjBUWNC5kYtDctLW1aXFxURsbG5Lks7+7u/v/Fp6K+icSiWhnZ8eF+sjIiANKNzc3FY/HzZRDCPAzAfiwbLG3JWlyctKWQIAm5LrkDLRaLW1sbHgfUmdwtqCKgHgA4AdsJVCUfJ/b24fpCTQ1ALJYDlBC8R3hkZ+amjJYfHh4qNPTU6+FiYkJE0sXFxf66U9/qt7eXr1+/dpKyc3NTU+BefXqld8T9XIymdSHDx88MopA3tPTU21tbenu7s4/q6enR2/fvtXz58/V19enarXq2laSa8K2toeJLdRsm5ubevr0qaanp/X27Vvfd6hlUBKOj48bEOBuPzg4MIALsIGSqlAoeK40RBjnRDgcNiAxMzPzyDaF6qFYLNq2x302ODjoscHr6+uanZ3188IKQg5TMCsIe8Hc3JyGhoaUyWS0urqqvb09RSIRzc/Puw764z/+Y+XzeVv4qJuwo6AGC+ZQYCPEDgQrTQ0AGMB+6ujocH0dDC9knwBAS3JeQjqdVjQa9ezxmZkZXV1dqVaraXJyUv39/To8PJQkK3NRD+7u7urly5c+xyHDuC+wr9F8owYG9AiFQt7T2Ku4c9ra2jQ+Pm6i5D+E1/Lysv7SX/pL+m/+m/9Gf+/v/T3d3d3pb/yNv6H/8r/8L22NyOfz+oM/+AP9/b//9/Xll1/q7OxMf+Ev/AVdXV3pH/yDf2DltiTPXf91Xz+oCeegp2hFWlUoFIyE4Uk5OTlRIpGwjJXNSzGDP4tDbnh4WMViUYlEwmNhYrGYarWa0R4OXGTksIf4x2A1GIkECkm4BTPCkQ4hfaZYAvmnsELOg6QNJpECamhoyCghM7SRMSH5ImSCYpBGgo1fKpXMwnDZwMTClOMLlb73xpHWHvSB02wHA94I/+GyJACP2aiMfWs0Gj6U2NgEsVBYwP7A3lHUSHLKJiE/+N5OPo7JoaDlZ+E1vrq6stSRECoYORBPmnRGTSHB4TBBPoc/Cu8P7BSNzO3trRFNmFsYf547IWx4p04+zl1FARBkfXd3d9VsNhWPxz2nnO8aZBl0loAMJNOwmCgVWDukRxOQhNQcZpz5zCgOTk9PNTU1pe3tbY+0u7l5mPG5sLBgZuns7My+ra6uLrNowfA0mrjd3V2dnZ09Gt/V0/P9DGMUMTC6BIsgkSN0DuULOQrBJimZTFoyCLOyu7sr6QHUg5mhyR8bG1Mul7PPGbkbzDbNQKvVUqVScROO1YFGGlZXeijy8VNS3NHA7u3tKRQKmWWoVqvq7Oy0hDMSiZg5KBQKWlxc1MXFhbq7uy3N5cwBrEyn01pbW9PNzY0bYkZT4UmnCQfIwOpDujHACoUB0lPWJKoV9g3AAmFdsFOshVwu57WJj4xAyXA4rIODA3V1dXluNRfzyMiIMpmM5eiEY3V2dppFBfxBNi3JqeCcH2SHUKDx/mikAQ5RdiwsLKhYLFoqRhOTz+d9rrS3t2tyctKADY0qjRznZhBouLi4sH0EqS2Bflg4WFucLycnJwZb2dMUybA7AMfYngj6Ye2FQiGflwT8kEaMfJ/zGekwCiVmyyMj7+rq0tOnTz15AAZOklVfe3t7tmxJcvYB99Hg4KCGh4d1cHCgTz75xKAfc8RZm9z1wbwQwEDCTSl6ufsACTlXk8mkVT339/cGwIIFIGFTAGYotWD1Ojq+HyHV3t6unZ0dg2ycb/l83vYPFHfc6blczgAejCmp2MyLr9VqevfunS4vLzU1NaUf/ehH3ocAoxToNHlBvzPPln9HQc4aR4XGnY5Kg7sBpmx8fNzALcBM0D43MjLiUYbsLRhlgCHOSM5v7B8EAlYqFVvAOO9TqZQuLy+tGOzo6HADnEgkdPJxfBj33uDgoGKxmAqFgo6Pjx+p5mBgS6WSAQBJBm5OT0+tQkN9BgMJoYB1IRKJqFqt+n2iRIN5HRgYMOhze3vrM6yz82FqSLlc1szMjFl5gGHIjZOTE62trdlPi7pxdnbWgNvY2JgBJdSWJH9Th42NjWlqasrNfDab1d7enmtNFDL8eWo8QlDn5+clSdlsVtFoVF988YVZZ4JlCdNdXV31uLCxsTE/V4LdkGmz5+v1unZ2dpx1Qq1CzU6YHntbkiYmJpRMJj3f+unTp/rZz36mm5sbbW1t2WIHo7y3t+cgX7z+Z2dnVuBQX+GHPj4+tqKM+518BX5mqVTyXcu6Gx0d1dzcnH75y1/a8vfs2TNFPqaN39w8zLefn5/X7/7u7+rq6krffvut75m2tja9ePHChAvefuo+zn3WIyBUUHHGOcX5w/7HIkovQ55DJBIxoDo9Pa2dnR1/N0zMIMy2Uqno8vJSn332me/Mp0+f6uzsTL/61a+c8A+B097erlqtZlCB+p73e3V1penpacvsAX/oIbgrf53Xv49M+A95/cN/+A/1N/7G39Af/MEfqL29XX/4h3+o//l//p/93+/u7rS+vu4z+tWrV/rlL38pSZqbm3v0s3Z2djwx59d5/aAmPBqNmuHgEru4uNDOzs4jj+fs7Kzu7u7smQwevqDSqVRKe3t7nusKksaoBwoWNh7IMCOvWMT43oI+X1hvpFdsVFgMilKKO+STyIBhg/f3982i4Qm8ublRLpfT6OioUqmUGSCkqfiyYKSR7CHPBNkm5APpHsXa7OysBgcHtbe354aJxhmFAXId5LmE3oEw44/i39OkBxkn0tul72U0wd9FA8OBCpJbr9ctHSV0r1Kp2PcP0o1XnGeKNA65JD8fhBPGg6As/j3WABpcEvKDQEypVPIlAhNE4BJWAMARAuAo5pDUATAxT5WwGP4OyC3oMJK+o6MjS9FBnfFLLS8vS5Llfa1WS+Pj448uvZ2dHUvl8W4HJdEbGxsu0kFPQ6GQpqentb+/r1gs5s9/cHBg8ATfLIwxftRg0xlsrJArUhSQikwDVS6XlUgk3NRR+FDQ00S2Wi3t7+9rcnLSYz4Ag/BE4f/HJ3V+fm7JLGuPiQagw/zdoBKD8JNEIuEL8vz83GOEwuGH2ekjIyMOjCFkDSXI6Oio9xT5D4BnAE8oJRKJhO0iJJzDqAPMIWcmZDHIqt3c3Ojp06ducEFLX716ZZ8jTTRhK+Fw2A0VIWiwE0FZP4AOZxDAHbJeLEJI22EVKHLJxECtQtGA4oZMjWg0asD16OjI5yBgAOg/43UoiC8uLsy48B0hq6/VapqdnbV/Es9yJpPxZ9/d3TWguLu76wTzXC6nfD6v3d3dR+olJIbYo/C51+t17e7u6tWrVxoZGdFPfvIT5z7A6qCe4E67vn4YLXh8fOwGlO80HA5b9kczAwDS2dlpL/zNzY1KpZLZaJ5VR0eHpeWojp48eaJisejvlDuHWegwL0wrub6+VjQadVBivV53I3p0dKTDw0PlcjkHh3GW8r1IshcZ6xhySs4y7jLAn2g0ava7VCppe3vbIVzX1w/TPebm5jQ5OalSqeS/t7+/7z0MqIQklzA1/ju2HdjocDis9vZ27e7umvVua3tIEu7o6PB3gQUJ5gfPMWtjZ2fHwbColbj3GalFnkw2m1WtVtPR0ZFmZmb0/PnzR+F5AHPc5aiCuMOCElXOR+4Q3jPKMexEa2tr3oeTk5O6uLjQd9995/0YDP7Dt07BzH1VrVYlyWcFZ1QoFNL8/Lz29/f9frD3YfEKKuFQHrJmCNujYUP9gv2BNUbzQPNPk3l+fm7QNBR6mMM+ODjouzZIBjC/mrOpUCiYQMlms7aSUY91dnZ6FCK1CfesJHtokc8fHR1pe3tbz58/914ji2ZwcNCgGGpEgDl89pzPk5OTOjo6clo2FhMsVWQesOcvLi5MNAWVbJLsXUc1sL+/r5mZGdffmUzGisFgrg/yb5SNfI9Y8lC2/OQnPzFbCvgLEcQanZ2d9X3dbD6krHNHczbOzMxYlcP9TvhiOp1Ws9nUxsaGCRNUJWNjY0omk/rZz36m9vZ2BxhPTEwoGo2a3Q2Hw55yw/NYXFw0Uz4/P6+1tTUlk0lFIhErKBmfnE6nvU+mpqZ0d3enWq3mUNyLiwvvAdQkiURCIyMjCoVCWllZeQRocLeyRllXgFVBZTDqCgDUYEN+cnKi8fFxn6vt7e3O8WBtl0olvXz50uGYHz580Pn5uZaXl11nsD8B9RYWFrxm+/v7vR+xbtFPYLEcHR11jRUkq7i/OGv/Q3kNDw/rH/2jf/Rv/O9TU1OPQIDf//3f//8aKPCDmvCgL+7q6spMIBfZu3fvLFNMJBJuWjOZjO7u7lQsFi0dHRoash+0Uqno4OBAS0tLury8NFNA4XZ1daX9/X03YzTioC/I0glcmZ6eto+VJFAaqYWFBY9FITTp7u5OlUrFvjjCYfCGgSqzaKenp81wIc3p6+vT3t6eJiYmNDg4qJ2dHUtEBwcHnVR5e3urdDptP3upVPJccTzrMHY02wALktw0Ij0PytIoDnghieTnNBoNM1FsfrzwSFkqlYpZq8vLSyOVNKjIZrnkSqWS7QLM5qb4AHW+ubnx/NlWq+XQnpmZGeXzeQc+EXIFS4+EClkZCfB3d3eKfEw/JsAKedbo6KgDqpDMBRk3ZGPLy8sOW4lEIpasIhGmSeCgRSnBd0qzdXd3p5OTk0fj6gjogTFAHr+wsCBJTplG4sRoM7ye8/PzZh34vCTk5nI5dXV1aXJyUsvLy7Zp4OWCbUJlABiBP59nA6sePHBrtZpBGNZLMBjw5OREFxcXDt7B38llDJtH0jXfd6FQcNHL2kCeyKQFLoKgdzuYBwFrQ5gI+wsVAVMV8vm8+vr6HA6JsoPnwVz0er1uZoOAuqAd4vz8XE+ePLFShNE0sPdIDYNpv/wsmDUQfkkql8u6vn6YKQ1j3d3d/cgHi1KC5ob1g0Q2KN/m+2F0DCoDin8KXdJ0AexgzSqVir755hul02n9Z//Zf2Z7xfr6ukMG8eTB2KFMoGhAFso5yedAco1cenp62kw338HExIS/H5LPyXugoTg9PbVcnPWyublpmfr+/r6Oj4/1/PlzHR8fe3oBQAV/hzMCMBVg9vT0VMvLy2bpEomEQWGk1Ej7KOiDxdfJyckjGSxMMdJ4wCR8kK1WyywsxTKMA+oUGESePet0YmLCICbWnfv7e9ulODuD96z0oF7DO8vepVnh9xFQyZnGXYWEt7+/33Yszjeav0ql4iaKNc3PJ4k+kUg4+yWfzyuVSrmp4dlxNgwPD7vpvr6+dmDg/f29Njc31dbWpoWFBdchgJc0V6zx+/t7z3vHroWNiWfb0dGhsbExbW5u6uTkxGFbFxcXnqZxdnZmyfvz58/NvqHy4dzs7e01g8cdwR1Ew8m5gyoQ5hSlGuwbP5f/jrIHxrWjo8OgiSTbW9gzSNJPT089XpC7iqDKtraHYDcKf5R0SM4BvVqtllV5vC/8zigeke0CIgKeA1hS+MOIS/I+Pzo6Ujgc9h3T39/vQF9yfcgEoZECAENNwpSZarWqcrls9vvk5MSsKyw6zyk47hDg8PLyUuvr6/6MKFDGx8c9SpbzBJUKjTTnx9DQkH72s595v0MCBIH6WCym+fl5bW1t2WYFqHp3d6fl5WVdXl5qd3fXNS9p5Kg9zs/PNTc355wCsgloEnnWzIlHoQOgjOUKK2dQMYpSFU97UI3K2maOOWQbhAYZLdRLoVDIY7gAfADIIGQIpWNPQFrQ/PKdtbW1GUDgXhoZGbGSjueDko79RvI4Y4axMLEuSGBfXl7W3d2dstmsdnd3TT7xXgCng/c6QC2MNsANe4dnS6/S19fn6QO//OUvrfhbXV1VLBbzmLStrS09e/bskaro/PzcqiDAGLKfaPg5t/nOUZRMTk7aFhsKhfTdd99pcnLS9Qt2QpSo1J4/5PVnzYT//+vrBzXhIGuMJkE+WKlULNtDynJwcGCvFBHyBwcHDr25v793MmI0GlUul3PjTYhOd/fDKB4uaw4IWEyCsvDFgEZScOM56+np0ebmpmZmZrxQYrGYNz5FdDabVeTjHFsWObOECe44PDy0T6Ber/t3cOk0Gg+zoPkzNGmgzMhZYUNGR0d1dnbm5Gk2L5c83lhkSrAePA9+N2moNDMgqTTgodDDCA8OfJh1gBVJRsyRxNL4IWEjoRu5KKmypIriGZak7e1tzxPl0uJyxoPCGAlJbriDMmSAlGDYBYw6yeShUMh+w+HhYY2MjNgfODg46NmiNHyAFciuJyYmPB/x9vbW/ujBwUHd3t46BRmUPBT6fgzI/v6+hoaGNDY2pt7eXl/S+H7xZeJhwkbAhUtxAdoOarq+vq6RkRFtb2+7+cpkMtrf3/cYOWSE5XLZ6pNUKmXpGDO8KbaQMdIA8TwZVwXzS9GAmiMUCpmVp0n605Inkt5J6A7OhiZEJOjRl+R0S5jf4FxX5FAUFKhFhoaGlEql7F3r7Ox0ww6gMDg4aO8sASOSDCYw1gMZ2cnJyaM59uRGBBOEg4nChI0R0DQ4OKjZ2Vmtra2p1WopHo87b2B/f1/h8MN8UNYrBTNn6eDgoC/lQqFgL9j4+Lj3GPNradQkeZ/zz4QigYYjv/v0008tg8N7TDbB5eWltra2tLGx4Qb59vZW29vbDpxsa2tzE0RhBSACk81Mc5oowvEI4yyVSrZ9oEhBGkwzxXqigMhkMmbeSPV+9uyZVT6MW4nH48rlcpIeAMqnT5+qWCzqw4cPLsZgutPptIPuMpmM8xGur6+1vLzsfYu8GlYZawHnOcX8/v6+Px/PBYBFehghg92AgDKCxAjfpFmF0Tg8PPTvgJWnqWROfXd3t3MhkGmylwAvGX1FI426jNm/MzMzisVi3reVSkWJRMKBcUglUbJVq1X/eZQw+XxekY/zgWFUWPvRaNQFdbP5kJ7e19f3iMHBUsLkBMY3zszMKBKJWBFEWBiWIUAgfhaeZu7DcrnskaPlctkg6ObmpmeBd3V1aW5uziAy45goyjlnsbm9fPlSrVbLo8Twg1M/SPIoPsZTsT9QLnCPkQPCWSjJd0F7e7tevnypRqPh9OZkMqnx8XGPMmOqBmsYtUlQfh6LxbSzs6Pp6WlbpXgfhDlydm5sbKinp0fz8/OKfJzgwLMGlEE6zNlAAx20YZCADkuLqiAcDmt6elq7u7sG7chGIbGZ7xkLF7YlbDMAWL29vVpbW3tkYwNcQ2FweHioZvNh+g62MpRA1FmVSsUWKXIC+Fnc/9gaqU0YscWdGgzk4nOQrSDJqrVQ6GFyx/X1tarVqseM3t/f+16mvuR5IW9GqQSbj2qEs3hyclIrKytqNpuanZ3Vhw8ftLGxodPTUz158kRLS0s6Pz/X7u6uzs/PXS98+eWXBu4YGcwdjaUEUBxlE+vyu+++0/z8vG2gzebDBB7qUnISmH6AvYExl1dXV54MAvFze3urs7Mz17Gjo6NKp9NKJBIeTVmr1VxPsr+D7/Xm5sZqL+T81MTM/IaEgrw6Pz/X+Pi4stmscrmcQ/ui0ajS6bR+9rOfOWuGvCHIE84HVHz0OJxv1C2ohqk5uFvpcwgU3t3dNRsPmEHzThOOfQJl1cDAgBWH9D805tT1KDsAnbCuUftjxUE5AHjx26b6383rN2rCCahBmgR7u7W15cKUcCkaSEmPZBkw1Iy/oIBHepjJZIyog9YwwmZwcNAMMk09EvhUKmVPDfKnXC5nJoeCCnkOzBCpuvx3/BfhcFhbW1tuVM7OzjxCZnJy0nN2x8bG7FGLRCJG/GCxCbMJhUKqVCqq1WqPihWY74GBAR/wPG/YkNvbWzfRFOBIYNva2iyJQQpDGBYbmffBJqW5JIwDP1gikVCxWHST293d7QtLkiWWAAjxeFzb29uampqyxDk4+mZsbMwFU1tbm4Oh8GuSZhyLxewF5/cAyhAWgpKAObl4QylCCFO5uLhwsUgxjxSzr6/PzTlKBwoOZM7IpGCJ+bOS7BENhR6SsfmeYYeCMn/WOxJB2FAsEGQJpFIpbW5uugDu6+vTZ5995ucAIMV3iheXhqFcLpv9mJyctC8ZNDSYPh+81AEujo6OVKvVzDS8ePHCBSoBZzAV+PuRKMPCIjns7Oz0OuSfKfyQpdIMbm9vq6Ojw8g84TGkHHd0dHiWKsm7FE2seaR/XMg0hI1Gwz55bCMgwdFo1PMmSQC+u7tzIUoDxpmFTWBxcVE9PT0ql8uerco5ls1mVa1WVSwWzZgGA1+SyaQl2YyQoTjH37y3tyfpwbMMWMA6ojgDjAK0wBtJowTwGY1GPQMddUlQwsZz39jY8HkAA4nVp9FoWGWxu7vrs7ivr0+lUknffvutPv30U9szYJA7Ojr07t07e4JbrZZmZ2e9ZpgZDAMGm1upVJROp83601jh5YWZg01uNBqeHT41NeWwvs3NTfX19SmdTj9SYITDYc3Pz2t6elr/6l/9KzWbTX366aeesZpOpx3S2dvbaxYok8nY5pFMJvX69WtLw4NJ/6TaHx0dqdV6CAqFtSKcr7+//xHbi1y1q6tL09PTqlQq/o64O5k+IT2ExKACgcnjvMXPCovEiD1GIsGkA4ATgkk4KCPyYHzPz88ViURUKBTcyHN+I4HnnESNBqMe9JhWKhUrYvBnY39YXl72DGAaKTyjyI2DqourqyuHfdHAoXrgHEEGPDY2pqOjIzfEZMWwL5DQHx8fK5vNamJiwgFL29vbKhQKev78uSXHQbUUgWBI4/Hpcp5LMotHI0chj0yYNQkQiTKIP7ezs+OchUaj4RFoJycnWl5edlPFHUT9RbAgFhuID+6pZrP5CLCkvqrX6yqXy7ZxRCIR3/PUDYCGTKlAZcB3J0mZTMZ7/OjoSDs7O1bwwSpyftfrdb19+9Z1TCaT0bNnzyTJ5xsgDYoiRvnxmTkLYRqxd6COvLu7Uz6fV0dHh+0WjUZDxWJRkqzeApBhrZElERzZGRxtiOplZmbG3njAgXK5bICeqTpI/Lu7uzU5OalEIqHNzc1HwZZv3rwxwM2agzQCkOD9kg9A87e4uOhxhJubm5qdnVVfX58ymYwnuRBESBhYLBbT8fGxisWiU9x5n7DZ09PTisVievfunVU2n376qba3tw1UDQ4OKh6Pq1Qq6cOHD9ra2jILzfjVf/kv/+WjnB8Awvn5eef6UJejJpMePP2FQsHjbYvFosE6GHKaSZQpjEtEoo4ijP0Dq7+wsODzCcvt4uKiyRkUN9SlrGHqHoghUvxLpZL3P30EjfrAwIDevHmjRqNhwqmnp0epVMoqDvopJpiQW4XaKCgTZ4zx6Oio7T3I1QEgpO+VD1i9uP/r9brPe6YaUFv8h+QJ/3/z9YPnhFOMc6Dgiby5uTFbRpAIBfjGxoZ9WRcXF1pbW3N4B81ob2+vLzTGQuH/JfQJxJuwL0IM7u/vHUUfjPNPpVJuehOJhCKRiAu4oE+ZkLClpSVtbm5aSojUFkbj7OxMn3zyiUOAYOk5/I6Pj+31abVampmZ0c3NjZsoWCUQasbKBBcXDXQ8Hndzzsbh77PguSiQvCEfRq7F99Ld3e1CnNATfh6MBGFsY2NjlrXgpYTVp4ij0QQFDYVC9m6BGsLkgdqBkCI9A1iAuQmOHSPpnBFNhJ5wuCG3QqoPug3jQvAFB0wwLIN1jISus7PThyeHai6XezSOC/YKme3ExISLTSSzgC0XFxdG8JEI0TSfnp5qZmbGPxPv4+Hhod69eyfpgWnjckHGBmN4e3vrhNfz83MXgxQqMMMUZIT1gHAjAzs8PFQikXBRfHR0pPv7e2ccAD7RNBH2097ebibz5ubGo8/Y50j2Xrx44Qscloi1yBoHleUZIwlExs9elh4uGhB5xrk1m00jx0hdaVpubm48XYDmlIsd8GF1dVXJZFJv37617BjVQqPRcBAYa5bGsVQqKZFI+NJCLQPqTKFKESk9yCIZPbO6umoQ8sWLF0qlUrYMdHd3u2n4+c9/rpubGz1//lzr6+uPwj94j3w2Ai5Ryuzt7Wl6evoRyj40NORsBHysCwsLvrABDimCKJAJ1CGgcGpqymnv7HUUGpwFpFUzE/YnP/mJdnd3H81Bn5mZ0eDgoP2QyHlhm2B6Wq2WQ5+Q4W9ubhrUC6oyUI1w/wwNDWl7e9t2H3IqOLd+9KMfufmZnZ1VLpfT69evNTAwoGQyqdXVVbPsBCPipW5ra9PMzIxzB16+fOm7gDF8Z2dnti7d3t76/opGo5ayA/rt7++b8SGlGQAWGTEsXFtbm7/Xs7MzM/vZbNZZELBlWCyCwCGyykKh4O8Q1Qds1sHBgUffMDuYdGpsBOQtIJkEOKFpD35fAwMDisfjGh4e1ocPHxQOh3V8fOyke8Zk1Wo1jYyM+OzEHsI+pAhHts09iKx+YGDAzXG5XHaTdHZ29sjmUywWHSCJH5wEfUl68+aNKpWKnjx5YhYtFAoZSIQtw88N2HZ2dqbFxUUDcIQ1ojIB1KZJpO5oa3sYfwUwQrAWSqlyuazFxUWDG4D3zIY/PDx0c0GAW6FQ0OvXr/XFF184UZ007M3NTd+pHR0dKpfLarVaTjNHfUL2Ct/xxMSE1wpnEcGhHR0dVgNyhmKLQa3BGMulpSU1Gg2tr68/kvCPjo4aVETRMDg4qHQ6besi6grYT9QeNEdYC1BrQnDwvCYnJ70+sC5iJ+GeQ4mCXQ4LBgrEm5sbT8k5Pj42aYR//O7uTi9evPBnhLgB3G9vb9eHDx9cF758+dKeZUAQrDJ47u/v77WwsOCacn19XfF4XC9fvtTbt2+t+qDRJgMJa1Wz2VQymVQul7O6FMAIJr67u9thd1gbuaPX1ta0sbGh2dlZJZNJtbe3W3nEWvvkk088xYLzi9qLeru/v992J2TTzWZTi4uLTmCn7ia7BQab1O/p6WlNTk56Zjv7gnsDdR6MOWpUQIF8Pm8L21dffaW+vj49ffpUzWbT2USrq6sGSGHfIRaowbG7cNag0MP2Nz4+9xz6dgABAABJREFUro2NDT8nrJikvn/77bfuiyDgksmkR9ZhEWy1Wp5UkclkFI1GTVoNDQ1pcnLSeQY00MHcHvZ3f3+/EomE9vb2lM1mHfoHUE7vIn0PIP729Wf7+kFN+MnJiT3Zt7e3Ojw8dNNGaAjM7P39w/zow8NDTU5OmkXENwhzxagD0KuDgwM3yzQN29vbXuB4qmBMp6amjDSNj4+bHZbk0Izt7W1Vq1Wn/yJrPz8/d3G5tLTkkWqlUklzc3OWsU9PT6u9vd3NKCwQB1ipVPKoH+amt1oty4J6enocNLe/v+9LDZSTRhk5IZIwJHY0/ARcUIBysHHZB8cr0ZQSdkXCJ5sZnwmoGo0m/0yhhQeUzUmjw2EfZMyQbHZ3P8yVxUMPa0iADZcuSG8mk1Hk4yxm5Kkg9njYkN9eX18biUXSCWLIZctoB0AEmm3k3jCL9frD3NsvvvjCz+329tYzSy8vLw0M9ff3O4jlT/v48ZkiI2Ud4XkCEEFihOeQddJoPMxNjsfjur6+1sbGhgPEQqGHebc3NzeanZ2V9JBEz4VJUAm/kxF6eABReNTrdXtUT09PH3mqkUI9ffrUgBfpxyDYpNjSXPM8KRJAzlGMwJbDUsPEIOPmYsM7jvwVUIhCAMaDEWZ45Eli5zvH5oE0MOiNZDwKjBB7Dl/8wsKCfW4TExMGaJhMADv7k5/8xMXJ3d2dGdfDw0PNzs5qdHTUITLsC+aMs05QLDCbGvVCd/fD3FMYtjdv3jiVmYaVBp+iptlsan5+3j62qakpnZ6e6vj4+NH4l76+Pn348EHT09Nqa2vT5uamksmkfYQHBwePQCEC6WKxmEKhkF6+fKk3b94YrNrZ2fF7pzkGoNvb2zNYRDPx7t07r8ejoyNJstUFpvXy8lLlcllTU1OKfJz/DCiGTYKZxzDluVzONhKUEvxsUqNho2iUmIGNvB/fLZ/h6OhIu7u72t3ddfGPhLJQKNh2xIxzrCXSQ9bD2NiYw+XIxaB5vbm50cHBgQtDSTo+PrYvdmhoSOl02uqd/v5+ra2tufHnmQCcAG7CtDGGBvAa2wFBqCiabm5uPHP8s88+s9VoaGhIuVzOe7bRaGh4eNiAZzKZtKecphxf6OTkpLNVIpGIC+OgJLTRaCifz+vq6kqZTMbjo9bX1w3S0thwt3HuUF8MDAw40R+JNOoWCuSOjoexhyQDo2IiCZ5mnLOCe4czYmNjQ6VSSVNTU1peXjYxQNPAeC3uEZRWqM5gOhuNhuWq3L3MJSe/Afk041oBoUm4v7y89PxoGnNCnWgyANbxdRPe9PTpU11eXtoORJiWJIeLXV1duTEgwRv5NLJzwAruePZyPp/X9PS0FSs0O9RGAGqcDTS77EPuQBKpuTeOj49VLpfV3d1t0gGSAEvS+vq61QLJZNL2I0kev7S8vKw3b954DTAOdXV1VZI0NjZmEBcVHd8P+RJkqVA7MHazXq8rnU57PfC5m82mnj17pqOjI+3t7XksbWdnp549e+a8Gb7XV69eqb+/X0NDQx5XhYR9YWHBSs5Wq6XDw0N9+umnDjcMAj0dHR2PvNsvXrzwWUcdSHbM9fW1FhYWTLBwJy0sLPj8Iy+IhPmrqyttb2+rXq9rYWFB29vbttyhKOM+kGTZNjUA5x8gOTVEJBKR9ABeAzqgagkCUkE12+zsrBVI1ECsf/YG6hLWWlBFwGhG8laurq40MTFhiwJ+96mpKe3u7vrcxfqF75w9TsAseSCffPKJs0/wwpdKJWeL0HSTi4Di8Pj42IAaZwv5UagTqJF2d3dVKBRMqhDGdn9/79F3KA4gS+jTpAdwAJUyCjhIKvoz7qhf5/VbJvw3f/2gJhzpZ7AIR75KQQ3aOjw8bLlDuVw2AhWLxSy5hU0EfaSBn5+ff+SjHhoa8sxQNiXSchJ66/W6ZVlIffi5/f39PihgYYeGhiz5IcVSki+cd+/eKZVKOVV8f39f4+PjTntE+oK07vb21kWq9NDEFotFe2phLyW5GcCjSHhGZ2enAQ7CZCRZbkgxib8ExgNZKWEarVbLPhFYm6DflkuLYh4ZMIcEXh0aWzzi/DvQQaQ0NIsdHQ/pj9gB8BtyiJNiCnMbLEgI8cInyXrj8gNIuL+/t9cMTxu+LlB3GrSbmxulUik3ljc3D6nmsEQTExOPGkGSpJFbElIG8EMxEfQzBsNdQMd5piTbIs+FdWZGOhJeGBVALNY646LYI/iCeN7NZlMTExOP1j0NDEmYyL+RFl1dXXm8EsBDKBTS+Pi4mS1+F8UuBzljxC4vL7W0tCTpoSmHLUBqDWMOC8VlCshDSi/MAesvyDC1Wi2PHrq8vFQ+n9f8/Lza29sVj8eVzWYNJkxPT2tubs4BgMEgQ4AHSU6DTSQSSqVSLlIIcwI8RJL35MkTWzRgiCnOisWiWVzQZy4+lAs070x8QG1B8dTT02Pw7t27d6rX67ZfoCIga6FYLGp+ft7gJ/5zCkjWD6z3/f29Pnz44PnOoVDI7CEqH0bWoETa3t7W5OSkYrGYJiYmzLQyru329lavXr1ywcFnAkSi6JiZmVF3d7fDj05OTjQ4OKh8Pm+/JYok1FWoHk5OTiTpUcgiapn379/7Z3I+MN5nYGDAwAEFO8+JQocJHbAsfA+wgRSx6XTaTB2FYzqdtqe0Uqkon8+7QcU/F8zhkOQCkaAzmllY0EwmYwUYAUmHh4e2HcEkt1otN/rn5+eWWXIuAlAg5+fODE7vCHrhC4WCQSAambGxMX+vAGjImSnWKc7u7+8txS8UCp4FLz00G9jSxsbG/Ez6+vq0tbWl9vbvp1MEA9Ki0ajZ3M7OTodDsZ4rlYqB5aGhIQefZbNZJxpzh46Ojlpaz3Pncy4tLZltAxxnjd3c3OhP/uRPDLARekeBSYPN3r26unItgdommUyqo6PD3+fAwIBzKAjwajQaPpNQpQAOByX9/f39qlar9vP+8pe/1CeffOLmB7aLNPfu7m4dHh7q7OxMl5eXSqVSzrLAb8u5ODMz4wR1slvOzs48tebo6EiRSMTA9NLSkmXG1AVB8L3RaFjOCtsNQNLb22sG9PDw0AAvAAW/b2JiwjUezxuGEBWVJIMxrNHl5WU3YisrK1Z95PN5K5/m5ubs3wdgop6q1Wqep8wdASCC3HtoaMh7o16v215SrVY1OzvroOHV1VUrHFnLmUxGsVjMhMj29rbBXuqHX/ziF86GoJml0QLM47/hsZcegviq1aoGBgZsj8QuhaIRcAhwnTr1/Pxcb968cYMLaAaoPjIyomfPnqm7u1tfffWVyS4mFa2urqqt7WFqSyaT0a9+9St98803VrXRSAOW0xRLcojhycmJzs/PdX9/r52dHauauF9QCUWjUY2Pjz+ywJBFRDgkLDhhqtSDkAAHBwe2GPH9Dg0NWZ14cHDg8aeffvqpurq6dHJyokql4voJMDF4f6DSodaRHkZ4Dg4O6he/+IUkWdkDYHJ/f+8sFv7uF198oYODAzUaD+McUWbc3NzYDsXfz+fzajQaVlBQt5EbQvI6/u9KpeLJCre3D1OEAMVQZ1BvYOH7D2VO+P/brx8sRw+mOs/NzTlNlvFjNOj8H8UQCGI6nfbYK2blDgwM2J+0srKiSqWis7MzBy/kcjlNTEzYu8JoKt4HTSPhDngWmeU8Pz9v31s2mzVTiwdpcHDQ0pmgvL6np8d+ZRKnkSWBBG9ubvp9Tk5O2p8jyWwGUmEkVblczrI3wAbmEDNeC2luLpdzsiHsLUmh4XDYPlcOHUaDkEaOFLtWq1myD8MLG0WhB/NdKpUcBIM/hAseSVdfX59DxmCRKAzD4YeZ16CvFG34CQkB43Mwk5NxWbBLjL8COabI6u/vN5qIHJxCM/iKRCJmRLq6usyQnp6euglEosQ6RN5IiBeBPiDuU1NTRvi5uFn3fX19viiRTeMHBqCoVCqanJz0dIGJiQkHi42Pj2tyctKzR6+vr/0ZgyEf19fXXr8AXagfgon5JycnGhsbswQZhJW9hjxXkplyFCx4dfGL80zm5uY8/g/2Z3x83Mg+vubx8XHL4vF/s6eQqDF2jOKfvYfUHlAJ7yT7Oqiyub+/t9eNOaOTk5MGRQhgAoCCAcW/B8u1s7OjVCql3t5eLS8vuxmU5PcNC0ojRkNJsUrOBEFuMPdDQ0N+5pFIRCsrK2YrCYDB77mzs+OCi2KU/c0e5Xs+OjpyMYPv9/7+3n5mFBcvXrzQ6uqq1tfXDRQdHR09ynmAzXz27Jm2t7cfZSkEZ0fPz88rFovp22+/VTgc1tLS0qNALSTaR0dHHleFVJDiF38bhRaAXbVaVav1MO1icHBQY2NjkmSvKkAe6oi+vj4zUKztubk5W28IBwUE+dNpr8gLh4eHdXp6quHhYWUyGTNu+/v7Ojs70/HxsaWQU1NTam9vt0SbYpagKxqim5sb74GbmxsrGQBsCMght4LGk7MPJgQZ5sDAgD2R7FXACO5BALBWq2VJv/SgCKOQvLt7mPAQnKAAsw6owve0vb1tQCufz6terzvfY25uzow1Um1ANdj5trY252DQ8JZKJf/vQqHgz8yfub6+1szMjAtwzrH7+3tLr2Fwu7q6FI/HLblkfw8MDCifz1tWj70rEomoWCx6nfGdwvKRVM0caCxFlUrFY7rK5bID0Pb39/33UU/RbDACFeAU2wDrkHGU7C3uZdQyk5OTam9v18LCgi0fBFzhU8WOFCycqUU4twBPAe+GhoYkyXJvAkVpTjc2NrxGuf8IxyJ4FwkudzYAJeA4vxuyhFwCfl9fX5/naEPI0KBy3zM54LvvvjMLSEMpPTRDCwsLVicg82efSTL4tbCwYMJlZGTEdZL0vaeWcxM7C2A+LGRwfwTTqLnv+P6551gH1DGAtTDjNHuAbdSzgEs9PT3a3t42s02QLV7uer2ufD5viX4QYGm1Wp4qwdQDzopEImFyAEUdKg/GIgKYJxIJ17xkD0jS+/fvbX/iLt/a2jJIRyMaBGhgZQGb6vW6bV2stWKxqBcvXtivjb0CaTkkCn8PxQnnBtNTGD1GtoQkA3WcYTC/ZE8MDg5qZmZGFxcXev78ufr6+rSzs+O1DWAJaRT0iFOnooZcWVlxY805wN8lE4Na++bmxjVgcC48ZEBvb6+tlQAsEF4oZSD8AKRZb2RqYdENWgMBSag1yG/CpsT9+Ou+fsuE/+avH9SEkw4cCoUseaKoJvAAmSlhRkiDCUAbHR3Vj3/8Yw0NDdkbRYHJ/E+Yunq97oRsAtnYdMhgQfwoPtLptE5OTh55SLlwkWLEYjF73g4PDzUyMuIRU6QEdnZ2Kp/PK5FIqK+vT9vb22aV8Sm1t7dbBooMnUsaCSFyHEJkQH/xqIMCN5tNsyZIhq+urjQ9Pe2QGYoMQIJGo2GpZFtbmw9JmE8aMw4M0iG5HHO5nNLptGchglRTRMD08rv6+/uVzWYtZWfj0bxJD0oCZKM0iMHQB9DPDx8+eJY07/ns7EzJZNLSpq6uLs+YJbCivb3d/nGKBD4n0nQadBKOh4aGtLe3Z78iAEi9XndAGwngFCMwO0dHRz7sCdwC4AlaLLh4SKmE4YYxgm25u7vT2tqaMpmM4vG45bbT09PeWwQawQJzwPM5KTxh4FhvMBs0KPgUAXIoRhuNhi8qpLhHR0f64osvJMkXFFJA1iUHNyE0ABeoSvguyDEIBgbB1JCkenBwYLkzzA/NJgVJOp3WL37xC4/SGRwcNNODcoJnTwIwHjhYxnK5bLZzenra3z/qCWSAqDz4rLCTs7Oz9jZS7DEGbXl52c84Eono1atX+vLLLx3iBvBXLBbdgHN+3dzcqFwuO5CQES+oIJLJpBKJhEqlkj7//HMHftEgdXZ26pNPPlGxWNTNzY02NzcfyUXZk4B0MKZIkYPhMoAo7e3tnljAZU82x/X1tQvSm5sb3dzcaGZmxrJWAIxKpaJyuezmVNKjZg/ZJ80eSfSktS8uLurrr7/Wzc2N3r17Z6++9P1Yo1ar5SYEgLetrU2jo6MOICIlF58p4CgAKkAe6390dNR2BsZHfvjwwcU6Ps96va7f+73fU3d3t9bX1+3XRi0wNjamer1ueTiSRZLwYak4Wwi5AogmHAkrBEqD3d1dy1aRfFJgwXgie8Q+w1phVGgymTTAy3l2eXnpMVUoUGiUkWi/ePFCW1tbPqMZEUcQF2FWWCFQxjG7ORQKObcAlcjbt2/NmnJeA/ru7Oz4TkcyTDCYJAP0BOW1tbU9ul9Q8jDVhDOqUCg4aJLGsNlsKpvNamdnRx0dHVpaWrIsGwUSIB3JzUimIST29vZsNaLJQOpJIF40GnXzzpkK0CjJqrBwOOyRTgBm8/Pzj0A41Hc0FNg5ALU4pzl3pe+D4fD9v3v3zuDRzMyMwT/2JOA5drAguIAyBXWBpEd36MnH0VHcZ0yzAQSHVCHHAHBhbW3NYD5KHFSVGxsbHkUGcYEtD0VivV43iMa6Gxoasu2Q90/CPfuGDAOYx/Pzc7W3t1sJQVPIPdzR0WFCZGlpyfXR9fW1FhcX1dHRoWKx6GcA286c75OTE71588ZNLJN+Li8vHXoMaICXd39/34FkkA6AwiMjI3r69KktYblczmcboXvYePb29pzVw2QhFC3UnG1tDwGb2CJqtZrzngiZnJ2d1fv37w0yoxgBDIDYATCCKPvTgDxnFfaWdDrtpnxvb0/n5+cObWMiRzQatRIjHo8/ur+npqZ0dXVlC2QymbTdpaOjwyAI5yKWyoGBARWLRT+rvb09dXd3a3p62kAcoahBcot1RmYIvvbr62tnxQBcEcKLerOnp0dLS0uesjEyMmL7DXJ2ahz84lgTAfC4O7BHHB8fGyRhGhTWFcibYJYTZBEECIo29vGv+/ptE/6bv35QE44cpF6vWwZDWApeWtAVxhMwdxN0HFkpkhIQZQoc0HtSntnMoGDIfnp7e81ynJycuDGkAYXpxTeCZBb2eHZ21tIPRndRoODNJUV2Y2PDPvJ0Ou0Lj89wc/MwmoJigYMfObskz2jd2NhwiNnJx/m7AA94QoLzmkFva7Wadnd3NTMz44sHKSOHGnI95JAwIgRB4KummKOpZBN3dnZqbGzMbIEkAwBIsZFDXV9f6/379+rr69PIyIjl76DbABr7+/t6+vSpKpWKlpeXlc/njViOjo4apQ/6q/EPwaKSvHt5eWn5MQwhTMLR0ZED2qamprSxsfHI99be3u5LBIkSiH8mk7FMEMbo+vpaiUTC9gOeKc+Lw7pef5hXSQPPc6LBQW5GMU8oEgg4s9UJkYHtPT8/d0FFgU2TFQyYajQaur29VSQScXbC1NSU0425kCW5KWX9Bhvg5eVlr19JBhkongETQKUpIPDII5PlggwivazPcrnswJH7+3u9ffvWRejS0pIbWgoTkH0Ah/X1dcViMQc3SbLnFAl8MACOYpliis9McUjYYjQaNYrMBYRXipRtpI3Hx8f65JNPHAKGAoWxYtVq1Z5psh2wdIDmUzivrq5atYD38NmzZ5b0kUq7v7+vcrnsUUUwUITrEAhJkc6scUA6mFLCKi8uLjy66PT0VGNjY5adg6gfHByYZaFZ4bu7uLjQl19+6eAz5IOwcgQqAjz29PS4mUSaCNJPkzo3N+d9F4vF9Mknn+jt27ce4dVqtbS8vKydnR2rdChmOAtgNgA6+vv77ZNmAsbs7Kzi8bjvnqC9R5IZy56eHkWjUVWrVc3Pz1tBsbW1pVevXjlFm0Iff/bp6akBKpKyOWdh0WBxYKpisZjZaAIhucPIRsCSQMHP+K7R0VHVajUdHR1ZVUGgEY0YZyT7EICRAElYqnw+r9HRUVsqCKI8ODjw3YFE/Pj4WGNjYw7bQwINQzg9Pa3V1VXF43GDhlgzeMbcO0glYaW6uro0Ojpq4ItAsPb2dgNaPBNJbvy7urr82Zg2QEZHpVJRb2+vE+ZZL+/evbNVbX5+3pYsglFhEBn/Mz09bVnv5OSkVUqAFoDosFDBUUoAOVtbW5qbm/O4Tkm+o2gG8PH39vZ6H3Pvcwex31ENAPKipgBsAtDBM76zs2OADgAJIIrnhcUOIAeQADCN/09zxXuS5NGE3Bs8F85QgOpGo6FMJqPLy0u9f//eFoT29vZHthmamuXlZX/f7M1qterUaPzsMJt8DtQNt7e3rhW5G87OzpROp22/wgYYDocd/ItMnDNCelCXsCZhSgma7OzsVLlctmSYsy4UCvnvxONx5fN55xhw12GDAFhoNB6mPyDtbjQabsYymYyT8FEYUYMFp/8wTQRyB3Ds/fv3Dum6v7+395lnx92I4ie4/kimv76+1nfffWclAfZBVEb1et0edEL9IL9OTk70/Plz3dzc2LKICg4ZdTj8ML/66OhIk5OTmpubc2hlKBTS2tqaR0SGw2FVq1XF43Gf3wDBzICH/Z+entbm5qbDQ3t7e52IjpUsmKuAfZO8FDIpgrXe3d2d/sW/+Bfq6emxKkWSM6WGhoa0vr6uUCikFy9eOBOKRhlvOFYz/N/5fF7hcNh36O3trWZnZ11b0cegSkBZc3Bw4HOfs4J11mw2XWej/mBdQ5bQi/329Wf7+kFNOAmmNE34Trns4/G4nj9/buYkmUx6QwWDdpCYkZTLuJRsNutQCphhks0HBga0vb3tUSa7u7u6v7/X0tKSGwdCSvh5NGDX19ean5/3YVmr1STJxThBSfjC8YfxmUno3tvbcyhQo9Hw4cVG5LLKZDIqlUpmBU9OTpTL5bS8vKx0Om05HD7O/f19N/MUKz09PWZmkL0iE5H0KBynVquZeYR1x1dIUQ7KBeAQ9I9woFDQogrgsxHok0wm7b0iPGNmZsZjW0Cxkdb39PRodHTU4TcE8fHMYU9p2PC4ElSF1BxPPEUa/k/Sqblo7+7uNDY2pkKhoJOTk0ehXbA2ktzo41W9uLjQ2NiY3r9/r0wmo+npaZ2fn1um19XV5UAxGvbz83OjhyDpyE8p8GmwaLppwiYmJiRJhUJBb9++VTKZ9CgKfLgoFwBnaFZ4XpFIxB77w8NDZbNZNRoNM5UdHR1OuB8ZGVGlUrFcMplMamtry9kJFC+EiJDcSUHLc76+vjaCPjAwYAUFiaqSbDHg2eNBAjQDvKJh4ZIslUr2qSKdDNpekHpVKhWPHCFlurOz0x49ksPJbEgmk7q+vn4UoAgqDZuD34/cBuTBsKioLBgBBlq9trb2KLm0s7PTVhr8V0hSKcgGBgY877atrU1bW1sOIWpra7NE98c//vGjebgwhnw+6XuZfJD5SSaTisVimpqaUrlc9lqHicLjS9FCUNGnn36qWq2mWCymvr4+vXr1yt7aeDyu9+/fKx6PG9AbGRlRNpv18yM8jwaWYMdqtWpbAUGZNBTlctmKGYpOmoW7uzsNDg7q5cuXKpVK2t7eVq1W8xiZer3usVmE02CjQM3CMw+Hw9rb2zOYIMmScYpWzivSqAuFgpvBkZERs0eff/65tra29O233+rq6kozMzNORub8JnwK/yhMF88GEAowkzMG1gqW+ssvv1RnZ6fzAuLxuKcV3N7e+nMykQMAAACio6NDExMTCofD2tzcNEhLIBmFOY0cc42l75lNAsSi0agtPSioUHrt7OwonU7bZ0wGDHaw3t5eg7QEWtZqNbOzyB5Z47wn8i329/c9r5wsFWSiyPpRR6D8KpVKtqIgQ5Xk+fTkMGxtbWlsbMyqpHQ6rf39fe3v77vZBBCjKQB4YxQmjUYmk9HR0dEj4OT29taBicj6UZKgDCF5nEK/vb3d6o3z83Mna9/d3fm7CcpiaSp57tif+FlkIFCkS3JBT93B861UKjo4OHDzTA0hyXcQlsNUKvUoxI/3ho2pWq3q5OREkUhEu7u7JhyoiQBuc7mc813IMujp6XGQHmFia2trisViXtudnQ/zn6PRqFZXVx2YB0gFeAsocn9/78wdnh13NKAZdjOa+ng87voBRR9eWkI6qWMBVQBwSP/f3d31mcC5SM4DTT/qjYuLC1WrVQNZQVVILBbT8vKyQylpyplYhC2NiRtI7/m8KA4IyCSjYGJiQpubmwZWYrGYRkdH9c033/isQpHKe1hZWXkUtAYQyjmKbYozJNhQ7u/vK5lM2lbU09OjyclJff3117q8vLQk+tmzZ7q8vNSHDx80Nzfnxr6trU3v379XZ2enLQSsm3A4rJ6eHj158sTN+uzsrLNoqOWCmT4fPnwwuHd+fq6NjQ2zruvr60okEn5PpVLJ6g/qY4g2ah5sVOz/lZUVxWIxW2Q4M8/OzgykUwvlcjlb0z7//HOPgjw7O1OhUFAqlbK6JpPJ6MOHDw42hiTBwkQWVTgcNvAftD2RExHMluA+huD5dV+/ZcJ/89cPasL7+vpUKBTUaDyMruDLp+imgA6G0ARTgmdnZ83M4nFF9owngUup1Wrp4ODAQS7lclnj4+OWiZMmfHV1pampKaNTzPcFPa3X6z4kuUxDoZA+fPhgNgvpHumIS0tLKhaL9mWtra3piy++cNhHJBLR1dWVnj9/rqOjIxWLRbMDrVZLmUxGkjwygkAQRtgwLoiwMApkAo7wenCg1et1Hxywr1wi4XDYRcb5+bk9ZEjGGo2GvU2glQcHBw5n48LmksbHjuydQqS/v1/X19cqFAqKRCJKp9P2l6F06O7uNmINMwLThHcOD0rQGwRbiG96ZGTEzRqIPTYAgrcIGqGQwWvGzHIkksgUAQFOT08VjUYtMeaZtlotJRIJJRIJgyYEsLRaLa2srFjeLj2ky4LcIvPBuwj7dHp6qrOzMzfdBPjs7++7USIErr+/34qSaDSqr7/+2gnXBDHhXaOw4TOMjIyYLccyApLLHGxkVDB2PDckl+FwWCMjI5JkawnMIgUxrAeXLcmrBJRtb29rbm5OZ2dnLlhhxSn2KDawfFBMbW1teSQgctPDw0MXGTc3N/b7MjGBNUODTe4El/Xe3p7XeTqdtv+RBgi1Br5CQC+C12joOPwBMACBGF30ySefaH193aBGPp/3DN3j42MzTIQ1XV1d6eTkxKE2iURC2WxW+/v7CofDSiaTymQyzo6Ympoys93f3+9xYHilLy4u9Lu/+7tqb2/3JAi8gMfHx5qYmDArVS6XtbOzo62tLS0uLnrvcuZQOFCAovDgHCIBnrOiUCjo8PDQI90ikYiT+oMF5/r6uq6vr/Xq1StNTU3p6dOnSiQSLipbrZa2trbMpI+NjZkRQuaby+U0NzdnNiuoDOnr6zMoTPAM7Kf00HQvLS2ZeYRNQ9lze3urg4MDff311z4Xnzx5osXFRfX391tVAtszOTmp4+NjjxODMQsCToDB3Gmw4t3d3RoZGbGHEgsTbAd/l3GH9XrdlgWKVnyBe3t7qtfrvhsHBwetSgAUDoVCSqfT/p0U20ikCSrCroX6iTuZdQYo3mw2tbq6qs8++8xe8IODA6vQyIFhRCj2NRKXAakAmtnjJMRjByKAjQaGQE/pe78pc7BpBGksATiQKQN2AAj/yZ/8iRm+Tz75RAMDA1bV4e3F3x8sZpmdjqwzOLaoXC47TK9SqWhsbMzeUJQhIyMjSiQSZnz5vlFlwe6Oj4/7fuzo6HAuCiFnKBPS6bQikYhubh7mLuMzxiYwMzOjYrHodXR3d6cvvvhCvb292tjYsC2nr69PBwcHtkMB5HI+E0RFQ95sPoxlWlpaUldXl2q1mo6Pj712Xr16ZVUSKjMUKthHgioxCBvyJQhgBXgAmGNPE1zZaDS8p4KAKUow5MoA+DDK0oMyDMYbe9bR0ZG/i+npaa2trbl5A0zCUkMAJvcV6dU0rfPz8977rM9gzsfz588Vj8e1tramzc1Ngy6AyAcHB65t+D45AyAh8BNzJtKMUdPd3d05BA9rD6oGnh0qAFjUs7Mz/fznP/dd29nZqU8//dRqqGw2q0Kh4LofwgYAjz4AQPL8/Fxra2sOIm21Wp6fjlWKc5O9BNBK7ZhMJh8loadSKYc5HhwceB2Njo76rkZ1mUgkdHV1ZZsHqgVCzjKZjINamaxBkFuxWFQqlfL9S5AyChOIO+7KeDyuvb09XV5eOoT4iy++UCwW0zfffGOAgzU1OjpqpczAwIDev3/vxhiQACITux61OM8aiw+fm9qG8zxo9wVQYO3z3+jVyJaAWPzt68/+9YOacNDn7u5uLS4umnmKfEy6BuG+vLz0QoYJGhwcNDOzsbHhGdpICgl5oogAOZdkiSEFwcnJiTKZjDo6Oixxxd+CB2V+fl6rq6v+u93d3SqXy/bLXlxceHYiaCkAwvr6un83Mo8/+ZM/0eLiomKxmKrVqvL5vEZGRpyweXBw4HnjQeQdJg50i3FaeDEoZmHcFxYWPL6DAphNQjHIBb6/v28/OtJvZCc8r0ajoZ2dHU1OTiqdTnvMDj9fki/KoKoBtoDDATkwaDOMGtIb6aF5Q85L+AeyMi5VmmXQNqRGsMhdXV0+vJHT3d3deYYuzHwoFNLi4qK2t7e1sLBgdp5Zzvx9fIEcVs1m0+8LOVw+n1exWHRTCRs3PT3t53BycuJ0bvyu1WrV8l7C9XZ3d/XkyRMH1xEyRSpzR0eHZUAAVRMTEzo6OnL4CZK2RCLhIjccDmtra0sTExNmLTo7O11YbG1taXh42Om8mUzGP4/GGbnj7e2tnj175oacwDeUEFyEMKvsZ1hH1CUAJl1dXbZeUOxIeuQlDzKApKbjQ6aY3t7eViqVsmICZp4UWjyBhJGQ9v2rX/1K8Xhcvb29VowMDg5qdHRUOzs7+uyzz3RxcaFQ6GHcWzab1cbGhp48eeJmQZIVKtls1kw5wTWkfvf19ZnVDYcf5pMD0CSTSRci09PTHqfU2dlpmTFKFYrIo6MjjY2NaWxsTNls1s3O3d2d09nJOiDTAQT+8PBQ0WjUKcWACoA7x8fHbhZvbm7MhIRCIc3Nzamjo0NjY2N+joAz1WrVYUDd3d1aWVkxqMVZSejT6empNjc31d7+ELSJlae3t1f7+/sGMtjvgDaJREKff/65Cy/GLPX09GhlZUX5fF7Pnz/32BoKDxitX/ziF7q/v9fU1JRqtZoB3pOTE3sh8SASXkcwFQ3F9va2A/OCzD4M7/39vba2tpRIJGxDAqRA7QJIWSwWPZZxbW1N+Xxe8XhcT548sU0LNhRABrYxyBoGA8pYc4wrRA0yPj6u29tbZbNZr62uri4zTOyn/v5+s8qM6Zufn/eZiyedcVgAY2Q1FAoFyyRTqZQBSmSUKAd2d3dVr9d1fHysdDptnzZscFC5wlpASnt5eek9QtDl6emp378kT0eYnJzU5eWlVU7c+0y2uL29dYjk0NCQawSe++DgoBuejo4Oy5t5PqwT7FN8bjyrgGhnZ2d68uSJz2/mLLO3YM0JaEThgCoNpjadTruRJCCM7ByUIIAGNFXDw8OucWiAaAYAglAu1et1e70BMrDs4f3l82DFoxbgjEbSjGSXhpLsjvPzc6fdd3V12XOLtQRVBsAx9+fFxYVH89EkTUxMuFagiZ6dnVWxWDS73NfXZ4Ay8nEMYa1Wc/1AvQewiL2CvIpYLOb79O7uTtvb2wYsGUE7ODholhJlFOc9aicadekhoT+dTtsmQUNMMzQ+Pq77+3utrKxod3fXd8vExITPEaYGoTRAVYOtgPoY0BWCAi858nNqTlhumlzWCyD49fW1isWiG3jWA4GB1MdY1wC6fvaznz0KCea7pg4k4wZwFPCAphEbGKHABwcHHhM2NTVlQgaSBjUlBBXP9ubmxqPECPEbGRmxNejo6Ej7+/smzDirAMJQedCIr62tqbe3VwsLC2q1WrapzM7O2u7Taj1MR7i8vNT8/LxarZZDe7/44guDE/F43JZJgLpQKKTl5WUrUFdXV63KgyiRHggtgIRarWa/PmsQ1QMg4+eff+6JVJAsTLVAlQJoR/3LWQDhAcCIZRPgZ29v79fuDX/LhP/mrx/UhCOhoelCdkLTBlILAs8Xw0W1ubmpm5uHMQh4dyg4gsgLlzj/HpYOFJHETkkO0SGIAp91LBZzUcTFzwWRzWbV29triTZziWElJGl+fl4jIyNaWVnxTGZCmdhc7969M2PBYYHnotVquUBEmgMqxwgMEpaRPNEsMvucA39qasq+YdJ5OYCZn4wniARkmimSeWH52WCg0bCapHXCLksP7NHe3p7m5+fV3d3tmcSVSkUjIyPa39/3xYPcZ2JiwgUsHkfCzCRpd3dXxWLx0RxX5lqjYqBhwHNKIfPy5UuHvJATkEqlLG+LRqOWlBGmQ7AJjShFTzD4DZ85BQ6yKy4UQKHe3l7LhzjA8ToThLa4uGgEniaaAxN0s1Kp6Pj4WF988YWBGNg7DuWg7K9YLKrRaPiCKZfLGhoacmjgxcWFVRtnZ2eWmk1OTkqSL2guZQCaoDQ8KKOlQEHySCMMsEBxBspP440fi32JHAzg6vXr1/r888/NUHFRIAfET4m8uF6vO1H26uphNOH6+rovFPY2NgTULBQ/x8fHHk0Dc0UDODw8bHUMzW1b28OIm6DfcXR01LLIoaEhHRwcWMKPlC8cfhhZt76+ridPnmhubk47Ozv2q5MaDHM/NjbmppdiJRwOK5VK6fj42MFVY2NjRsv587lcTpKUyWTU39/vUSuFQsHF/szMjFUn5GLApB0cHOju7mGuOKGPZHw0Gg030gcHBzr5mEwL6Mnepkmq1+uWWiYSCcViMX+2N2/emPG5vb3V06dPLQFvtVpaWloysHN9fa3Ly0uHtOXzeZ2cnPg7o3DJ5/MuopD0/vEf/7EuLy/1ySefaGhoyEXm8fGxvcpDQ0MaHx/XwcGBC+54PK7x8XHfL9JD3sOPfvQjHR8fK5FIWAFAQj8puL29vfrqq6/U2dmp3d3dR779qakpy/oIwSMRV5IZqXg8bqUUYGnk4zSH+/t7ZbNZM+lnZ2e2XNHwkaQMgAsLf3p66rP1+PjYUnIaYBpH9h7MKUoe/Pl3d3fKZDIaGhoyUHR9fW3m/+DgQKVSSaOjoxodHfXPBRB9//69xzwGQceBgQEHUN3c3LigZP//6ZwPmDCsS11dXWb4OKOwh6Go4HyB0QmFHsaEfvPNN9rd3VVfX58WFxeVTCatbpFkkD4cDnvMEWcQ1pZCoeCmlH0Qi8UMniJJBjCXvmeryHZh/B4SepRINOpPnjyxdYomaHR01CwnzRnTM2j8kaBzz3G2cqZz9hP4yTpgTwI0Ya8inLNSqRioBDxC9UXKNk0zhTzTNKi3UEPc39/7bKVB4ZxEkQhLODg4qPHxcRWLRQO8yKkBiQFW+S74d6hRstmsz0ICG4N2AJrV4+Nj2xYbjYY+fPhgxQ/AG98F4AegKfXKzMyM71CeLeuPzzIzM+PsGaTU6XRaExMT2tjY8GQKfiefhTA2lG8jIyO2ZD579kySdHh4qFwuZ5D+6OjI4VpBlSPNGon3NGIo0YKZIigi7u/vlcvlrHag0ZZkIIh1zPfP90u4IPUGzSBjCovFotrb200Qtbe3u54ZHh5WqVTyOisWi87+6ezs9Nn39OlTRSIRE3FbW1u2CzYaDeXzeQMe5XJZe3t7isfjWlhYUKFQ8JnJeYTid3R0VO/evbOvPhaL+e8TtEZe0czMjAF5VJ339/daXV213bJQKKhcLhv8ZIwmNTIkx8LCgm2qjNJFTRIOhzUwMKCNjQ0tLy87nwd1A2crNQ0s+u7urpLJpIOv+TyFQsEp+PwfNqTfvv7sXz+oCYeFQKZK6l5XV5fTHKXvI/D7+/u9WQkEo/nb2dnR559/bqY1m806yZok5YmJCQ+vf/funbq7uzU1NWUWDeljLpezzzB4QUqyTJdFd3V1ZYnq1taWfSs9PT16/fq1UT5Q1Wq16lmVFxcXLuJevnypV69e6eTjDFLkzDDrsVhM2WxWY2NjLrLx+lBY4GVpNBp6/vy5isWiE6VplIaHhy13Ry7bbDbN6OAvoVD/+uuv1dPT4wTLWq2mp0+fan193RcEKaiEMuHZRIZHI9rV1eULXZKliIlEwsxGo9EwsAD7jOeITX51deWNHolELA/joF5bW/P6wMsMkozHnCab98TvA6Ht6+tTtVpVd3e3mW6KKwoEmnwCX4KBRsE0YdLZkfFzweRyOc+UXltbU71eV7lc1sjIyKPEeBquIELMMw6FQnry5IlZEuToBAl9+PBBkUhE4+PjZgdJzU4mk6pUKo/GZYHUw06Q+I2MF4AMHyPfJTJskt7JewgCYsiLg80UzSejQprNplNlGZsyNzfnyw8VCCGN2BPwm9/c3BgkuLq60t7enmZnZ404M8qG0Dqkazc3Nwa4wuGwvvrqq0dza5FxUYziX9vd3dXs7KxZtlAo5MCroaEhsyGcUfjpCTQiSIrzAEaCwD/+O6FRBEHW63WVSiUNDw87TZYk35OTEx0eHiqZTGppaUm7u7va2toyEIEHGEsGCbH39w/JzPg+BwYGtLCw4KCVtraHGby1Wk29vQ9zm+fm5pz8ii+TkYNIBfne1tbW1Gg0FI1G7bmnaAdMHR0dVTQa9Uzora0tJxcPDw+rUqmYkQUgIvuAXIaNjQ2HI9VqNSWTSdtdAIGwMwB8jI6Oant7W5IsVyQnA3aY84apEX19fZqennaRzB2BTDwej2tyclK5XM4J4VdXV5qcnPSzODk58f0BSPfdd99Zbj09Pa3f+73fc1jlxsaGG69araaJiQnt7+9rZmbGUwUAY4LSVqSu/FzAm1AopPX1dQ0MDFiZQP5KNBrV4uKiQ0JHR0e9n1FBVCoVhUIhnw8wijT1yBkBgSn+GU9F8/L8+XOrALD/YJHCRsZ3giqBkDykqrDvnGEASjD+WAsA28j+4OwBUOMzUn9g9eC7PTs709dff62joyNNTU3ps88+s3SYn4d6jDMbwIeGEYXE8PCwZaFzc3MGkIrFos9FpNWxWMzjRQEvb28fRsnl83kH1KVSKRMMNPCwh/wz+7nValkJgwQVsBSVV9BqxF5ltF0ikXDQKs8HcANFXa1WU3d3t5V7NHSwwdR12M1yuZzu7+89rxpvP8AgdzrKSX4n0m5UEzD8MMGtVkvffPONOjo6nFMTiUS0v7/v/ApANupK6rWpqSnF43G9evXKRAANST6ffzSmLxaLeb0fHh56EgDjrrgnCRPjOVDXwuaXy2VVq1Wr77hbUDSOjY25OaOBJHfj+vpaY2Njvgsk+SxGGr+zs6NisWiQBjk3xAQADUBts9l0gNfOzo6VVWNjYyqVSr6LJRmsnZqasnIB8LutrU1ra2vKZrNWEaG45P6lxoGJJSywre1hEhCZT7FYTDs7O76/QqGQA3qxjQEQMGUD1Uuj0dDTp0/V19enDx8+eCIB758zFIVgW1ubxyDz8+fm5tywvnz50s84l8upra3NtkxS8HO5nG2LKHDi8bjq9bqy2azJE84uwAvG6rW1tVlphDUNO8X4+Lj6+/u1ubmp3/3d3/XaevbsmbOh7u7utLOz43uCe5REeNYHwA6qt6DtJRiyh8we1UzQ1gEYjZrvh8wJ/y0T/pu/flATTvgUh9D19bWbZoIwgt6rw8NDJRIJ9fb2ql6vW4JNYAgSkd7eXk1MTDzyXgUvLEJKQJb6+vrsZYnFYp4NSRPfaDRUKpW0v7+vzz77zP62crmscDisTz/9VCsrK5afBpli/HX39/d68+aNbm9vfaF+8803ury89IEoyRfS4eGhkXmKGBqem5sbI2s0Zevr605MLBQKZv9qtZqePHliFP3m5sYJz7CXkoxW7e3tmRHF70y4Dcg3yc0AFQcHB04L/vDhg6VesCMnJydm53t6epTL5ey13djYsKwOyR2ypsvLSy0vL+v29taeaRJh8/m8ZmdnHxUaHFDI3/DGcLlzSPPc7u/vrXrAJ81INQJF6vW6crmcEeZWq6WLiwvnF5BOzoxlpHtIlk5OTtxo01CfnJxoamrKHs7d3V3bLfj5BOS0t7fb940sn0N9ZGTEgSnVatWfhcaFC+bu7u5RAwn7Q+FCg0wCMoF7yDtBe1GtIB1jtjmBfq1Wy8F8waICkIgGcGBgwEUtoXkUTQAFBMCMjo46IAavLrJ3wlJqtZotLMxDp1jHooCMGJnZ4OCgJe/BfXDycU40zFx/f7/i8bgbLM4ewm+QxcE2oCCh4B0fH9eHDx/Mol1fX6vZbCqdTpuNTafTBtoGBwe1t7en6+trvXz50msXCWEikdCbN29cYLKm4vG4Li4uDEDRGLW1tXkCQj6ft+1kbm7OIXwwYTQG7LOXL19abra6uuqzg+90bGzM6gQCEuPxuKrVqmq1mgOvOjs7NTMzo5///OeKRCJKpVJmj9ljfX19mpyctKSQ7wOmjACayMdRN7lczmcOgAUFP940Au9GR0ed+ApwQQBc5GPCOEmyy8vLikQiXs94EGEYGeNCgbWysqLp6WlPh6DYBhDBj03AJ2GY3EHRaFQrKytWIRQKBeVyOf/O9fV1AxfBlP9wOOzPQfED80xD1Go9jJ7q6OhwiA4eQQrf58+fW6I8NDSk1dVVT/9A9t7b26toNOqgvGg06nWcyWR0fHzsIKC2tjYNDAwokUh43xEIODMzYxCQMxWlye7urtdyf3+/Ze4A2bOzs5qYmNDBwYHPHUK0OGsJFCWdHyk5o8qoCbq6uvze8FwDfgN4oVDgbEEhlM1mlc/nNTw8rJ/+9Ke+x7a3t9VqtZzKjvybYDnpobAnZIkiGqD/7OzMKodms+nwU6wTFOzsP6w7ME4oiU5OTqzem5ubM6M+PDxsiSnKOxqeIMBLzUE4I556QGqk2EGQGUXEzc2Nm+Dh4WErvMgBgeEDOAFk5/yHbKFOGxgYUCaT0dXVleezw0wHgW7WJKouAEuaedQNWOSwEXAn3t3daXp62vUjxAdgFvsP2xChqtSn7e3t9sbye8hw2draMglEY4EMmVoGxQHASiaTceNJjkFHR4ctDHhwyV3g7uPZdXd3OwxwZmbGWSPByUJff/21rq6uLOUnwI3QyKWlJdtHLi8vbUW7urrSixcvrFBlX3BH1+t110W3t7ee8FEsFi0nJ0uH9S3J9YAk17xkAADYYGX75ptvtLCwoFKpZIABiyoKLPbW8vKyQTjk1FdXV4rH41peXlapVLIsm+dKsFkw3JPv5/b21l5vfh5nL7UN9iLWUavV0n/0H/1HkqSvvvrK9QHASjBsttF4CJfc2NjwWh4eHtbbt2/d1BIMS7hctVrV2tqaM0mazaYVdoAZkB2np6f+nYDQqJvOz89VLpdtKyJgFoKLYGjuFiZOcB9BTjF3nhBIMoyYqvPb15/t6wc14aOjo49CrmiQKKwpTDjYQqHvR+/AjJHy2N7ertXVVWUyGftednd39fz5cx9cwd/FxUKhjwSUcAma6cvLSzPcBBIxizsajTrshzFOxWLRMo4f//jH2t/f18LCggMaarWax6hdX1/r2bNnHvuDj5cDAe9V0NcCYg3axFzE4+Njo+qxWEz7+/sOLuFQQaZL8YsMiAM88nHmMT768fFxH6bRaNRMEA3O7u6uLi4ulEqlHMJ1e3urzc1NM9E9PT2WoWSzWWUyGfutKNqQ33V1PYzT2tnZsXRoY2NDsVjMz4MibWRkRBcXF48uKJ7b/Py8bQWEoV1fX3vEy+DgoD2fMOcUPzzz6elpdXV1GQwB/Q1KsILMEehyKBTS5uamg8AkeZ1K31suaLaRsCFVLBaLDg1BTl0ul3V9fe0QqbOzM79fSQZ8YrGYOjo69PLlS3348EEfPnxwABOFESABslF82SSgYhPAooCEvdVq6Re/+IVGR0c1Ozv7CN1kxj3rhkRRGiS+I4Jg8FxyIbOvaSgoirFrDA0NOdUeNp4AOGSFQ0NDHqXR29tr9J5kXBqBWCymWCymJ0+eOHgRkI8Clj9zdHRk2RoFCucIzDNAFOw0aaIwXhTPzBlFPnt5eWkZLM0dl3PQs0lyfFtbm7MJKLawJ2DxQO6XSqVc2BEqBFP66tUrFyLYIghRQT6K5JTgJVgSFC1BkBNE/ODgwKPZ2D943FAxdXQ8jHLs6+tzETwyMuKAPkb+cNbhcevp6fGc81/96lcGo969e6e2tjbNzs5aaTQxMaGf/exnntowODjoszGRSBikk+R5qfl8XsvLy24m+O88a0k6Pz83O3xwcGA1R0dHh0dA3d3daWJiwhI/lCok4DYaDc3MzFh+ynfIZAH8hpLsYSQ4EwAQsAErF4omgFEUF5lMxsFC/f39Bnlvb2+VyWS8jgmhQhKOp5xQMUbqkdxdKBS0srKigYEB7e/v+72xpm5vb7W2tmaAClkq2Qt8LhgjmC3sWwMDAx4HBGDLGiQ07fz83D721dVVA3U8b1L3+byAV0FFVbPZ9HmDqmh8fNzyZMB8lBC1Wk2lUkmbm5saHR3Vn//zf94J5OQXAF6hAkHNNzw8rHw+78yD29tbj0tlnVOst7W1KZPJ+Fxg5BbACsUtORmoNfjc09PTOj09taqDoCdGNQUBOmSuSNE578PhsN8nDSxALtYtAt5OPoaOYotACYfMVpIzYJDmIk9PpVJqtVpKJpNeezSprCf89qgosIcgSScsDsUEEm/WJGNvATra2tp0cHDg8+ny8tKfaX9/37lCMNr8ndevX1vFNz09rWaz6dC7/v5+N0Hkr0QiER0cHHgvd3V1+ZmiCkT2jvJmZ2dHR0dHDrRra2vT4uKizwpq0uXlZd+pMMtYMqglsHGxNrEiEZJKmvbJyYlevXolSQaRsSORB9FoNLS8vKy2tjbl83mP9mTONqMp2d8wyuw19hjZOKhWAOlRjHAeoDwF6Ojr6zMRhALn/v7e+SQAnoVCwXXX+Pi47xHO0Pv7e+3u7kp6aCKLxaJBlBcvXpj553miAGo2m5qcnPTdAECFyqKnp8dA/eDgoEeTMXpVks/2ZDJpBh9ZdyqV0t7entWovLfb21vnCKHYAESq1Wp6+/at7VZ9fX3OUhgfHzewure35yk8hUJBx8fH+p3f+R1FIhFbIckb4S4/PT21Mg0wG/UjIB53HFlDlUrFYCljnFH6oArkLPp1Xr9lwn/z1w9qwlnMsNXNZtPMGk0ZjQGoIEneeGhhuQlZur29feS5hjUEccW/gkyY4ogAOBgq0PparaZQKKTZ2Vn/XsZgLC8v2xtBCAhhF+l0WqlUyuEwyPVAod69e+fDe39/317k+/t7I76JREIXFxcuCtmAKAZIKe3u7tbw8LAWFxcfjSDA00tKJQj79fW1WT4COkA6YRpPT0/12WefSfregwZahxT76upKn332mS/Xq6srXVxcOISFz4Xsi4IDD0+tVjPin81m3WDDOgOUID0H4STBGiS9VCppcnLSiGawUBkeHrYcHBlkKBRSrVazJLKrq8thFc1mU1NTU/bXHx4eusnEp00YVFdXl8ciSbKfC5kmyH6j0XA4CT8LlgFWAhR8bGzMcnEOSmZzBt935ONIGMAFglwIboOFm5mZUT6fd9AbagMYJHxtkiznR9pOeAiXBRJAiozBwcFHLDZeWxpSguRmZmb82QCC8NlSoNVqNY/1y2azGh4e1sTEhLa2tozsMs+5q6vLgF1QJjk4OGhkm2ae9TAyMqK9vT37ZPlc0WjUygvYve7ubg0NDWlqasrF6fHxsfb391WtVjU0NOSiBt8czCnFcTwe91qbn583G7K/v6+rqyurB/L5vFZWVvQf/8f/sVKplAuSs7MzB8HU63V/F4TiENJ1cXHhxpK9d3t7q3Q6raOjIzPSMLJLS0u6v79XqVSyd6+3t1ebm5uan583ko8ftr+/X7lcziAK3nlyH/iMFGHRaFSZTEbX19d6//69PYB8b+3t7S404vG4wRxCl8rlsp4+feozeX5+3nuUgKV0Ou21d3BwoLa2h1Fsw8PDnve7sLDgEWzkCHR2dj7Ky+Ac5/yAeWAP1Ot1e3ZRRBSLRUlyUwQoxvtB8gdAROEyPT1ttRf7iTA8ilXS3JFos65Jxe/u7rY/j9RZpONIEnt6erS8vKxKpWLLFI0UvmSSx3t6eizbh8GORqPa29szQAKbS5I7qf+ZTEb7+/vq7+9XNpv1n8c3ur+/b/CEYgcZNLOwsaecnp4qlUppYGDA4xGZ504Rjf3q5ubGzQNebZo05L4nJyfq6+vT1NSUzs7OdHFxYZsFsuzOzk4HBaK2OD09dQArGTLIWb/99ls1m00tLi5qYWFBIyMjvvdQj9RqNRedePJRNcHqwRqxB6hhAEWxdcHsc+4HGX1k74VCwYAq1rKtrS3v387Oh8kvBE8hqR0bG/N3AhhOfUHOBOcIjR5ZA9Fo1Huf4poZ3dQPd3d3qlarthRguYB9Y3+hGABUARDiTJ+fn9fZ2dmjM51zA7B3e3tbb9688e+C2WacF+NV8/m8Pv30UzcY4+PjWl9ft1+cOpQxXOSzYDMiDDCTySgWi1luvLe3p3D4YYwYgYqpVEqHh4e+X1iDrIVUKqVkMulG9fr6WisrKw4mu729tdJxeXnZ6ioaaJ4fIF25XNb6+rpmZ2etmOnv7/ddBNiOMvT6+lq7u7s+U3mWKAnT6bT9xpI0Pj6unp4eh55Rc3H/Muaw0WgYVK1UKlpcXFSlUtHGxobBdqTvr1+/9pnQ3t5uEIr7T5L3NeopyICXL1+6Xjk7O3Pg1+HhoeLxuMmoiYmJR4Qaz2F5eVnDw8Pa3NzUxMSEm0zqTYIYUXvScFMXc98RQEd+FPuMQE9sZUi4GYkMgBMKhfTy5UtFo1E3yM+fP1cul1OlUtHExIRD3wirpHZkJFpHR4eeP3+ubDar169fa3p62vUbRADgBfsJMA7AiBwhcrjIqAmqgKllc7mc7xrUm5w32CSCZxhANsHFv3392b9+UBMOSkaoBCmSoGTd3Q/zgsfGxtyc0VAhD6UZbzQa9sF+8803Gh8fN9ILQkaD/6eTebmY1tbWdHFxobm5ObMyJKFHPs5RTiQSOj4+tpQRb+379+8dpkHBB9L+6tUrMxkwFTR0a2trlr7Nzs66cIYZRyITCoXMFjAHmgKPw0+SkVmeFVKg4HzsUCjkwAwOHprBaDSq3d3dRxuKTUzqOKmX29vburq6clO9u7trJLSnp0eFQsHNZLVaNQsMIJJIJLS7u+uC6+zszIwdkjpGgpCkC0vx4cMHe/opVGDGSTFlbYDAUayQOwDCD4vI76QxoDhA6UDQBigngAhMBEUCckEQTBpT2BiKHAo4Gj3SOK+urhxECJMM893e3m4ZME3syMiIotGokV+aF5hLmHsktaVSSd3d3Uomk/aLIikDNAJMIOOAWe0ALIAivCcuLy5bGG8aJzzZsFpYFCQ5jAiWA2lhNBrV9PS0mxXGfJHofX5+rlgs5gR0LsLgSDWaKYJVRkdHVSqVnKHA2B8uN6YvENBD+OHq6qpZOeaD5/N5+/L29vb09OlT3d3dWRrO9wzokM/nVavV1NXV5dF/jJPKZrPq6OjQ4eGhfvzjHz8KXoPVGh8ftyoHbzkSe3xveNSGhobsJQ3KzmZnZx0UB+vHaMadnR0Dl5FIxFJ1AB3WeD6f19bWlhlyJKmcT8fHx2Y5+PswLCsrK2ZISYmGMSANnsBNwEjYaPbZ5uamnjx5Ypbi5uZGz549c+FMSBMznBk/QwF7dnam29tb30ELCws+32GYGH/FqC7OhEql4j/D756YmFAymfR5DBMAgEuzDNBDETU0NKS3b99avr25uekCCWk2I+cY7wN7RxOFDBKJ9fDwsO7v7zUzM6Pb21vt7e1ZYUH4HQALZzae356eHk1NTWlwcFCDg4NWYNDs8ndo1sLhsIELAFGY7fHxcRfHm5ubSiQSymQyj6xANLjYWABuw+GwmxdGIDGeESA9mUw6eG1zc9OjehYWFgyov3nzRpFIxOA+nxF2DzCA0ZN4M/Ec39zcaGVlxTO+f/zjH3uGO6wd9QR3rST/DgrQDx8+6ObmxgGH+DtRZeXzeUky6I76iXTqVuthpjDPHfsHqjySxLu7H2b9hkIhbW9vez1wbzPakGA1Gh5ADvJdUJ+QucM8YsgAwEACzbhnAGRp9MinQCJdLpet/gk2gDT1qBjz+bzvVQLc8vm8rYvZbNbjRbknkcNzd52dnT1SO9BosKawbp2fn6tYLNrXD3gOMwvDiEWQMDsC0pg+gxWCjJH9/X09e/ZMqVRK79+/t2ro5cuXZh85K5FQoybgnibArL+/3+cJ5y21DkApfmDuMc5eGiAUI7OzswbTYD2fPXtmSyF7bW1tTZVKRfPz8z7PuQeo1fj5jBzE9z81NeU1Uy6XDSxip6hUKiZFCJxra2sz4AHYxXjdiYkJ14gAz2TccI8TCkfzzH3fbDad5dPR0aHFxUXXT0wcgQQBcIKwQiHBnmPudm9vr8NYg4w4yguUerVazd89+3dqaspA7meffabBwUFJD9NPsASWSiX7zLm70+m0Qbj7+3v9zu/8jrLZrMrlsr8L1tj09LTziRYXFz1eMpfLOYeAoLp0Om2AuFKp2FKFKogejfVerVZ9l5J8T84UfnbCKCH4qAmDloP/p9dvmfDf/PWDm3AYEFgD5A0UsUNDQ2as8WLjE7m5ufGM05ubh7nCy8vLTk4HaY5Go0okEvZEUazhcW42m48KGA5xEK+BgQGHoF1eXmpnZ0dPnz61bx0PHwVwpVLRZ5995iaesT80DjTc29vblnytra3Zh0vzjceFZmxkZESFQsFFIZ4o/JFsWA63oGQeHxYHLWMYCP7g0ka2zPxImEGaxZ2dHY+coQkdHh7Whw8fdHFxoWfPnjlpEn+jJHtvx8fHHepydHRkqSoydxpGwp6k7xnmyMf5pTDA+LVpBGOxmJtoZk1TSCBTIhmXYCrpgdUCrUb+Fw6HzbrTRPMMJTkYBZ8bVgRUCrAHFOEc9IyOQ+aNpy8cDntGOCgmyDz+OkJiKKBgg4PzuCmCguFxsGYw3cj++F64WEKh0CN/NOF8SMNBx0ms5vKhaO3o6HCxTAFDIdHV1eULnpmYyK2R4gdnkEejUYMuMD4czAQbgtQTRIO0sVQqeSQWBQRBasyXp1k7PT3V1dWVQTq+h+PjY5VKJaXTaTUaDedXTExMeBwITTWNdj6fd0YAqgfG7JyfnzuRnwRqZJucbbu7uzo6OvKs3OCECKTxPT09evLkiRs8UHTWK8+Ts/X+/t7nEAVDd/fDSEgY15OTEwMcsJs0pfx5MgCQdvb09DwKxIK5pVGAGcbPfXNz43Oyre1hnnm5XPacXjy8/FnOYfY7/u5ms6lCoeB1TaAmoCg+R/ZFPp+3XBlWHPvR3t6eisWiZZRffvmlAclSqaREIqFSqWRVBEF2AInIw5eWlrw++V7Pzs60sLCgN2/eaGNjQ8+ePVMsFjNbenFx4f2JAgFQGFkggXMUdZxz7J9qtWqWhZDLQqHgQh4mjL1LZgXND6oB2GFCppAsFwoFJ1/f3d05mRzLEAqs8fFxnZ+fa39/3w3e6empXrx44eJckoEmGGjCq1iDqJaQaNbrdcXjcUu68emenZ15PnkkEtHy8rLOz891cnJiEK23t1e9vb3K5/OamZkxY391dfXIegMwSiPM/fPdd9+pUqno+vraoAIqB7z2KDQ6Ozsd3oZ/G0Xb2NiY7u/vdX19ra2tLQPWsNKSDErwbFFNAb7Q+KAeIoCu0Whofn7eyj+C3Dg3AdoAlFDjBLNmCKikeUNiT3gY9QcNLeF5yP1ptvFvE7aLHYZmJhQKWXLNfqOJw4KRyWQM8qRSKaVSKeVyObPuMKYACXz/yP+R7yLPPT8/1+TkpIMyz8/PTfSQScA9nM/nnVgtyev9+PhYh4eHPjf6+vp8ZtH4wohyjwfHqAGeAdAwUYI6AbBycnLSOQEABwD4yMvZi3jbIRn6+/s1NTXl7yYYYsb5gVcYG9KLFy9ULBZ1cHCgr7/+2gogiCASt1HE0dTBfgJAoQJptVp68eKFmW4YaoB4Pjvv4ebmIa0f0IJzlfBEsjBub2/19u1bTU9PGxxF8RcMDaN2ZUwuWQjcrQDBqFFZ69xrTCXi+YdCIeVyOVuQCN9l9Cw1uSQH9GJZQvEkyeFnkY+hz41Gw6Gj29vbBuzT6bT3LdY0gKVMJqPDw0MDlUNDQ9rc3FQul7P69LPPPrMFBBKQXICOjg4lk0lbRUZGRqxMxZqxu7ury8tLk06rq6u+g4aHh/18l5aWXIOiBMQfTlgoe4FMJqaAQCb89vVn+/rB6ejSw8XX2dlp9JfLJihVhMkh7ZGEVJLU2ViwcIQQnJycWCbFpga9hhX+v9j7r9/W8yvNG31EKkcGiWKWqKydq8pVLk/3hG5gBpj7+VMHczHTwPR0e2yXXWHnrZxIilESqSyKEs+F9ucx1e+Ny4DPOXhRBAy7yntLJH/fsNaTFlIfmGH8W4wjYmEiHaT546IDASKlWJK2trYsH+kO0IEd4P/rlvD99re/1ZMnT3w5U4zR7BLWc3h4qFgsZu9eJpNRs9nU7u6um7pIJOLAGi7K29tbH/pI+xnbw2FNAYc3H/k+bAIszt7enkfdgIQzE52LHG9frVbTF198Ya8V/r5u1J8GGfCjWq06cAQvGsDJ4eGhU8s5uGZmZozWwUzAlHC5EnYmyYULkhkSKylcLi4uNDIyolQq5eKvt7dXs7Oz2tzctIw+Go368OP5T01NqfE5CX1iYsIoJQdoq9Uycs6ovNPTUzOxt7cPKbV4R2EUms2mC3OYKC4ZGn18vFwkNEvMiITFAJjgO6O4IQEZuSxZALDFSOYYZ7OysqKtrS2l02l7mUkTx0/H2iJMimYN2RIFIBaR4eFhZwFQnINY42MFiUbixHrtnqwAYwioQKGK1YFmC4l1d5o/AXHr6+va39+3xJlL8dmzZw7MA8E+Pj5WPB5/pJIpFArK5/O2BtBck9T6/PlzjY2NaWZmRoODg/rhhx88GxSQYGFhQe/fvzf4UavVNDAwoK2tLVtnsKpgjSiVSg4EowDhDOlOfqVRhmkZGhpSNpv1qMOpqSkj4bBhzMhFug57hARQeghhIdiFv8efub29dbFHankgEPAM7WAw+Oh8oDnZ3Nw0Y0fDCwNSKBSUSCR8xmGxgW0l2BJ/IE2AJPtPKQJhnwKBgBv24eFhv5+rqysHzg0MDHi04sDAgFPQQ6GQyuWyfv/739s3C6vOuUshE4vFVCwWPYca/3WtVlM2mzXQwV1JajgJxnw/3Z68Vqv1SHGAjQWwslQqWY7YnQEAeIhdCfYF5cvY2JjDgFAw7O7uutjG7gJQfnx8rIWFBR0cHDh/gXAy9iTqLpLTUVnwvQBUkP3AmTIwMKDT01Mlk0kDZ6jqrq+vtbS0pHg8rvv7hxFDrJ3t7W0/ezz+hHJ2y0hHR0e1vLysxcVFZ7BwzvC/uaM+fvyoZDKpQOAhHBU/5YcPH9TT02MpdjwetzyX8xxAYnd31xJ+mr1UKuW7Dinr2NiY9x82gu7wRjJyAGYAU7FGEdIGqMe65zU0NOQgJvIf8vm8SqWSG1wsZgDWiURCoVBI+/v7rhcA8bn/AEdQx6CCIDR3Z2dHc3NzthVRuwAu0wgwxhG5POomQMa5uTnVajU/x4GBAc3Pz9u3T8jbl19+ab8/FkhqKe5HvkPYQtQkwWDQ1j6Ua7D+19fXymQyrvPS6bSBo729PQPdu7u7BgH4Tmi6IW0AqGms8e0T8Avg0Wg03PhjR8OPTLNPdg4MZrddhakz5XJZoc/TDWA9IbVgtEm+5v7m75OPVCqVDHrSoMOkd98D5LsAfHDPsDdR6HEeodpIpVJqNps6Pj62TbRUKrl2abVazsJBzg3LjoIUJcfV1ZVmZmYcSsY9x3lPUFk6nZb0MGoYsuHo6EhLS0sKh8Pa2tqSJJNXzWbTNQ/7bmJiwqAMfQT7D4samUkTExO2w2L7GBoacsbNTz/95J6CDJidnR2Njo5qb2/PNeXo6KiDQ7e2tlwfvXnzxspXiI9cLmeLXCQSUT6fdzZANBq1wg5iA+sC02hOT09dB3O/Yl/pnmDzl7x+YcL/+tfPasJhyLqZbRLFkRBSHBHYcnp6aiaBi58UcNgfQmUGBwc1MjKiTCZj2TVMzM3NjRYWFny4hEIho1crKytGmsbGxrSwsGBpCfLFy8tLLSws6PT0VNFoVJubmy44isWimXjmXnPQcnlzuGcyGUWjUY8bSKVSDjpZW1vT0tKSfdiDg4NKp9NGnwiEiEQiSiaTBgkIBIOJxENIY4qPk8IN5hx/KYgZaOj4+LjnM3Z7Z2nwCLZZXV1VOp3WDz/8YKR3cXHR4R0g+yMjI/aDMxYtkUhob29PmUzGl0O9XlcqlXIQBd7GZDKpk5MTh5BcXV15jvTBwYGLWVhUAva4OGBAYEEpEGgqBgcHLV/sbvhhgvAa9fb2Wu5Osy/JDSPBSQAJgB2MZlpeXla73XagTXd4Cw08TQ6haxQasADdoAnKDNg0SZasY29IJpOWBnHJI5ukSaVgZA0Q4scFh+x2aGhI6+vrbm5IB+10Oo8kv/j5kE2Srgx4kU6n1Wg0HGxWKBR0dXXl8TR4VvElkv1AQ0thEI1GdX9/r7m5OV+EfI5u/xpzi1FxwB5QLPN9LCws2MdFkY7KY2hoSIuLix57uL+/72AdQCPY5+npaTPegGHIxUulklZXV80McPlSrBYKBUUiESfgExJXKpV0e/swluTZs2duUDhT7+7uXBB3+5m71TOdTudRmBrNEOcoyoRSqWSVUS6Xe9SIYwtijm/3JAWAju69TDFNSi+MIDLlbDZrT2c6nXYDSDI4diXk3/v7+5Zo02xfXFzo06dPZlTj8bhDoGAWKW4IR5uamtLOzo4zHw4ODjQ7O6u+vj4HQ7JWkMtS3KG8mJ+f1+zsrEEA0oWxV1UqFT1//lz1el3lclmSzGjjt0WmjFT96urKhR/r7u7uzhYRpheghIBJaXyeykChTrgn5zfANAUs5yVjZGCvuyX8R0dH9sczUg8mCgUJxRcFWLPZVKVSsTw+FAppampK5XJZjc9jIrmLALWRaHLO3d8/pAUjg2TvEqKVSCRULBYNwADuSw8TWIrFogHQ6+trZwIEgw/p8zR6vKLRqP7dv/t3kh7UQNguUKUgwccTjvWFkYrj4+NKJpM6PT3Vzs6OstmsFWdI7QF3aALxGmM5QHKLrYJEZBopzmvOE84WSQZZLy4uND8/r6GhIQfMwYTz/XYnOSMNx67A/U6mAfYw7GKoZVjbWGMAtqenpy3lRdkCSLC/v+8GlP+mMaVxp1kmFAuyBSD2/v5epVJJs7Ozmp6edoOLYgu/OXdOu922LJgzElCHbIV2u22FQKFQ0NLSktrttsLhsI6Pjz0Bh9GEl5eXZrk5x4eHh7W7u6tms2ngm32E1B1vOPkxTB+BWSbfoVumzmcHPBsaGnIDS4gaP7+n52F6A9ahbnnz0dGRPfm8v3q97ve1uLhoq9D6+rpJIu5R7odqteqz/+uvv1YsFrMtiTuCsw/y5+bmxuc9ZxJKD0KHuTdHR0dd1zF2cGlpyffKxcWFx24S4veP//iPPg+r1aptelhBAWClP4/MOj09VbVa1e3trTKZjNbX163GQlHIfT0wMOCRXIlEwrbW1dVVffjwwWQUXn0ULdPT0+p0Om5gq9WqksmkrQiDg4MmNDqdjvb39x8RHjSwtVrNYAZ1X/daJmOgVqvp5cuX6u/v1/r6umZmZtxob29v+zyhnqPuqVarJqcAGdnPgCuJREJnZ2fa2NhQLpfzd8j5iGKD77nxeYQe9fFf8vqlCf/rXz+rCZfkYvP09FSJRMIbeHp62p5u6YEBuLq6UjabtRQT2QfMyOXlpdbX182MptNpvX371rLiQCDg8Q/ILBnBRYBTs9nUjz/+6KCxYDBo+RBy1ng87kuNRg35ZSAQUC6Xs0Tm7u7O8yxZlNKD3xjJ07fffqs3b97Yg0kRgXSeApTgI8Z14XGCwR4ZGbHcBukoDBCoczwet0/78vLSSF1vb6//LBdxLBYzesf3i3SLkC8kWQsLC4/kqzCaJycnLtCRccEagljCnKbTacuiYRJJ2aWIXl9fd2pos9n050Vyhtf65OREJycnWlhYcLgEhxRNOocfjbD04PmMx+O6vLx0GiXJqgAqhJk1Gg1JctIpBQuyccbpUSD19PS4eGm1WtrY2HAhBqPb7a9DosXFz9gMUnhfvXplNJlLHPaK5u7s7Mwj0pBBwcBwOCIh5uAkwI4GCjChXq9rdHRUr1+/1sLCgrLZrGfrIoOXZHYduTN+POmBeaLwIwEVFQJFfTgcVujzPM+TkxMtLi7q8PDQFzbfF++TUD7AgWaz6QsDzxyXP80Tygaab1QTNAJcXt3p98g9O52O2XRQX5p9wu6Ynzw2NqZIJGJmeGNjQ/l8XsvLy04e5XuvVqtm0WOxmM+94eFhF4jIjL///nsz9BcXFw56A4AZHh5WuVzW/Py8Li4uHOxCSBwjdWCsGFsGQ9DpdFyw7OzsqFar6e///u99TjYaDVUqFbMizP7mHLm6unJjRTjM/f29i+JCoaCbmxvNzs76GQEEUGTBuB0dHVkmeHh4aHYXIAr5OUqNnZ0dNyS1Wk3NZtPpsxSo2FOwRaBI6el5mNbw/PlzZzRwPqA2AGhhRBbKqm4p/tXVler1uv7xH/9Rv//971UqlTQwMGCJZ3//Q3o6yhfW6Pr6usPoarWaLi8v9fz5cwd1AVgDngDuYQFhFBD2pFgs5iK13W67kERCy7mzurqqtbU1pVIpbW9vGzSmqednTkxMaH9/3/kdyMRpdGmWULIxj3dnZ8d3y+vXr9XpPKS6l8tlhx/l83mNjY3pxYsXOjw8dLgcnlDUGXi+Ye9Iq6fQ3NzcVD6f96i7QCDgwvz29tbTCzivRkdHrRIYGhpy7gzAIMzkxcWFQXwUXt1qD0AgWESUAeS/kKCcSCQ8ro6mjxBA7lSAX84GWE1GzhGMmsvlFAgE7O3Hd8/9ic0NwBIZe7f3ORAI+EyGXQOUYJ3S3HLWdfuXaQQymYxnWw8NDTkjR5I2NjYkyd8jTLwks2t9fX1O6O7r6/P9wp/tVpOwNsklIH29UCjY7gArfnBwoHK5rPPzc0uUGYVL9sTCwoJubm60ubnphgTVYTQatSownU773sG6AUCeSqV0cnKitbU11et1Z4SMjIw8quX4fEdHR+rp6THowT05PT2t2dlZSXK2DIDJ7e3D9ALUZAAoKBm4z0nix5M9MzOjcDisd+/e+XlSE9Lwkjezv7+vu7u7R6P/AAGmpqYeAS7Dw8NaWVlxWB51InYHMk8gByT587P2aSpRorGXCIHj3uY7xItfLBbV29urxcVFbW1tPVJ9kWtQrVYVjUYN2GEFJISOdQ+wh0IyFovZWgGIPjo6qru7O2UyGd9XR0dHVmlGo1FFo1H/ue5pG9Tv9AWzs7MaGxtTPp93bwIQKz3I2Pf29pwnwvfDe+PZlUolqxXv7u40PT3ttYotcXJy0vkpjPB8+fLlI/Jzenra6q7+/n41Gg0tLy8/6tXocbqtKKiTCW/DDkD/1J2Bggr4l9ff9vWzm/B6vf7I69Lb2+tRTMHgQ/InyBKH8dbWlr2YXCo0r9fX13r+/LnOz8/1/v17N6oUwgMDAw4qI4SB0CRYxlevXmlgYMBIHV5NijFQfOS6FEyFQsGXIjPxOOy7PYTSg9QYFq/VarlBIjgJ2ezd3Z1DyMLhsMrlsprNpmZnZx38hcSeQ41DlOIXppvNTFFA08j3zsHCeyPU5ujoSNPT0zo6OjKLzaFVq9VULBYVDAZVq9U0PT3t+YuMoUJySCopbDvfPwUerBHNJnODSSmnuSOUZ2pqSoVCwf4pfL/dQRkcrDA/+PppEAOBgOr1utrtthn/s7Mzs9WwxhxMAwMDSiaTPuQ/fvxoxpqGBlbm48ePLriQuDUaDS0uLhr42dra8jqX5Bm33Q0kjU0gEHDa8NTUlJl7CicOXZpEWInu8L1area1CevEd0CT1G63LXuHedra2tLY2JgGBgb08uVLTUxMmOVFWi3JBy9S0Xg8blac/764uNDU1JS/f/xlsDvkMOTzeY/vQ3UAm0VIFx5v/s7Z2Zk2Nzctv4ShHxwc1LNnzxQKhVxcI18juJHsAZQut7e3lp13e8ukhxm05EjAgJycnOjFixf6+PGjmWrApsvLSy0vLysQCGhmZsZS0tHRUY+6mZ+ftw/18PBQ2WzW3xsMPKz2v//3/15PnjyRJMua8/m8lpaWFAwGtbm56WdITgUBi6DmqDQo/igE/vt//+/OJvhv/+2/aX5+3on9NKXdxQfrl4KVc4EGbmdnx0ACkkUaSop4fHnkfMDMUVB1y1k5a1A9wTKA1qdSKYXDYR0dHVkev7m56VA8AFAUHLCxS0tL6unpcdGNHBaFFM0/50skEnFRhN+NJjOVSikejysSiejLL7/Uu3fvNDAwYDkvKhGadmZ0c6fhET48PFSpVLJtCKCxWzmByof9L+lRqBAyZYq1fD5vfzpn+Y8//uhm+/r6WuVy2WAvr1gs5rFA/FwsNLBZWEBGRkb8+QDEuKvv7u70/Plzra2tuVDFu0zzCyuFZ5l7l89OTgO/c2xsTHt7e1pbW1Nv78OYTlh3AJtOp2MvJMAF5z7Pg8Zmf3/fz5vCGNAVgqA7VwM7D2qQg4MDg+a7u7sKfR5Pisz47u5O2WzWdq5AIKDZ2Vl9/PjRSoODgwOfawSKDQ4OujmlKG98HqvI6FZJZsRonJH6c/ZVKhWrYqhNxsfHzfB3s+uoHMiGgFGv1+tWXBF+hjUAkP7k5MSy1ufPn6vdbntCCiO9AEhh5ABZu3NdULmRoE4jf3v7MIecxvL+/l7Hx8eeEQ44CviO+mJ5eVm7u7tqfJ5uEAqFHu1ZnvX5+bnvtC+//FKdTkdbW1s6PDx0EwIwSaPPuXZzc6OZmRmtra2p0WgY8AoEAgZksIzV63WPvMTmRKI8WRTdTRJ7qVKpqKenx80XAAE5B+wBaihyZAAGeF8AKvl8/tF7Za2yvyuViorFogmc0dFR2x/C4bAqlYrevXunvr4+J8QDEFxdPUxP6QZ4aMDx53Mf8T4BxlFUYM/q7e3V0tKSQafZ2VkdHBwYqK9Wq7q8vLQUPxAIeOzx3t6e558fHh46SA6wi4R5LE8APNR/1Ch3d3cOLGaMKiTT/v6+RkZG9OzZM42OjqpUKjkANRwOa25uzmrE6+uH0bTZbFZzc3Pa2Nh4lFnEOF2CHAGtsK2cnJxoaWnJ+Q2tVsvPDNCecXI06vl8XtVq1UGc3aAhSi6mBoXDYecO0cvc3t7qyZMntoGwBlutlhWWAKyQSNxNf8nrFyb8r3/9rCachfLx40cvlt7eh7m/jNaAGUF6C1NDMQ5CNjU1ZXkkYV14c2jUaIaQ0uDHu7+/d3G5u7urer2ulZUVpzqzIWCKYbfwJE9OTtrbNTExYYYIXx8MSrcHCjS3t7fXPp3z83OHYGSzWRcQ79+/d+ADcvKBgQEzMMwgRqJNaBFAQiwWcyI4hzmfmYAPEEFktxz8vb29bsRIYYR1OD099efpDoaDMUcGT9oizTgznWGJJVnyCaDA+8ILNDY2ZuYLhh2PMD54SQ5guby8tIwtEok4tKpcLtv/D0BAKjMoK2FuyWRSm5ubKpVK+uKLLxzAgwKCS4PmBe/OxMSEi0pmfnKwdzcQU1NTDiBqNBr26lOYb25u+rOPjY1pYmJC29vblkZJcrOJnQLfL+8HlmV4eFjX19ceFwbSzZgULvBQKGS5G8/74ODAnxdQAgZxYGBAk5OT9o/xZ/CJIX1l9jYhW3jzkbb9WwUIe4lCjkA7LgYaGpQtrVZLHz9+9J6Hdet0Ovr9739vxpY1yGWJJA9GiKkKyWRStVpNc3Nznn4AEEhiO43u4eGh1z/n0YsXLwwAIevb3993MCEFVavV8gWH5JgCCuayezoBhSSy0EAgYGaM8DuSsLt99u12W7FYTKlUSm/fvvVscfz6hKCRgn97e+swtbm5OXvASFlFdod8luA45HvNZtNSc7zUMEG3tw/jTWZmZvTdd99pbW1NgUBAX331lQEiGiQabJ7Z/f29G7E3b97o6urKxRDF88uXL3V1dfXIZoKvstFoaHV1VZubm24kt7e3lc/nHapHmFd32A/NHj5xRmlls1kHDuERf/r0qX92sVhUNBrVs2fPHOIYDocN8vF9sHYAh6PRqHK5nC0agBWkdlcqFbNxsKo0XDx/9gWKMu4cLAEoPrr34fr6utlgFGcoDAAbOT8AXAjmIciL6QQkXSPDTSaTVqpw7gPY0MAuLCxYVkyaLw0eYaMA163WQ7o0ib6ANV9++aX9+XzGi4sLLSws2FICQEkYJd8zwFgymbSEl8IdCxHs4/DwsBsbQqPS6bTa7baVW4Auk5OT2t3ddQAlEmeArEKhoGw2awY0n887eE/6M5jx6tUrnZ+fG/yQ/gy4DA4OanNz099FMBjUzMyMaxfYSbIf+LzcZ5xJSGbZ25IcWonFoPvM4C5Hln12dqbZ2VklEgm9fv3a2SaMiwqHw05fRuECqAt4S6OGLDyXyymZTGp/f99gcaPRcAYCgbfIYrGsAZhhpQOYZ7Y1z252dtaKoUKhoOPjYxM0gOt7e3u+l7CyEZKHVQivPNa++fl51xZkGQwOPowynJ6edsM1NjamX//617q4uLDipLe3V/F43PUXjW03oELaPmuke6RiLBbTwcGBZezHx8daWlp6BLAMDg56H1M3Y91CEYAClP2IFfFXv/qVXr16ZfUh9ql2+2E6AywvADLkx+3trckgAB/2I/5/1iNkB/YG6obu82R3d1c7OzuamJhQPp/X7OysJ/yQxXN/f69Go6F0Oq1EIuHzq1Kp6ODgQLlczk375OSk9wwEHQRcsVj0z8RaQaMPsExvQdo/wZb40lF7Uidls1kdHBwYeJuenna9z9QAgkwJUCVsFG/97u6uLi8vNTs7q3w+78whAPpuciwejzusDzXRzMyM3r9/79C5SqWi8fFxffz40eNdAcRRC6BqYZxao9Gw8grwAo845/4vr7/962c14SyEiYkJj+8A4Xv69KkODw/NmjGOA9kDiB++ChBFEs1hBhgHNjU15dE3MzMzajabisViRtz6+vr04cMHMxw0W+VyWdlsVqFQyAnip6enWl1dVTKZ1OHhoRccEmakO0+ePNH29rZ9lMfHxyqVSpaLEdJAmEMmk/HcSopW0hQJ4mC+ICADiB8+wkql4sAvNlO37I3gJkkOn2GEF40TwUjIZEDYuYzwojIiaXJyUkNDQ9re3tbJyYn91ozbSSaTlsvt7u7q1atXajQa2t/fdxIzz7Sn52GMA6wWMqZWq6Vnz56pWq1qc3PTfiPmtYPWU8zF43EfUjSjt7e3DrZAjlcsFhWPx13kVyoVXV1dufGYnJw0so5H9P7+XuVy2bL5YDBoRBPWkjEYr1+/1t7ensOdUDdUKhX19fVZ8nV6euqAkE6no9vbW8viKd4JGexO8T8+PjaAwXuFOWD9AAYBGGUyGQMhFOWg0TSFrVbL8nNkcwQUjYyMqF6vPwpL6/YaYocA3Qc8A82FJQNRZf8NDQ2p2Ww6TIW1ikrg4uLCI4wAZAiVqlarOj091dOnT538TyHJRQ5wh2y/Xq9rcnLSUkjYsaGhoUfp5NFoVMlkUpeXl/ZIp9NpByIdHx+b7alWq3r16pWWlpbs7f7yyy+1vb2t8/NzbW9ve/TL/Py8ixTGI01PTzt1lt/N5YWKBIlosVh0uurAwIBmZmY8ZgyAgT0Pa1QsFi21HBoa0qdPnyxHwxbyn/7Tf3KQEGzVxMSEw7VYlxRV5BckEglVKhUj9ihQUqmUGzgYd/xoTMaAIQMMZHQUcru+vj57ySXp6OhI5XLZwMHz588dpofnE/YT+TAZAWNjY/r666+1u7vrUTqvXr0y8LS7u6vNzU2nvo+Ojmp1ddWFDWqG9+/fW13DnuGegNErlUqampoyUAU7QTAeDfHk5KS++OILXVxcaHNzU61WS8+fPzerQ0PY09Ojs7Mzy6GRaOJBhm1CUo6liCadLBKK30Qi4QKYoDtAVUlWAzALmPMDoI+wzf7+fhe9NAv8XGaK1+t1Z71gT0FNk0gk9PHjR9uoqtWq+vv7HZpIQBO5F6h6eM8zMzNaXFy0ugugGuAURRAjz5CZc67w72n6ATnxJWPfKhQKVr8hm0VGypmKz//o6MjgKOGZsGrX19c+QymoqQd6e3vt/WT9IqHd2NhwrgcAK6NTYcGx0ZGZwt8lDIozlSK9218K4NO9Rtrth9FxoVBI6XTatr12u60nT548stMAyFGDMKudOw1WHwsfYCc5B4SkIgduNpsOvuQMpxHhe8IXzBkKiNTf/zBHHCsV64BchydPnigcDjtlO5/Pu2mIx+MegQUICPGB2gyl2uzsrCXxd3d3rpGazabevXtn0ITmtt1ua2dnR8lk0oAcirKTkxOnzKNEZJ2hEqIpBSxG7QQ4Tr7G0NCQlpeXTTgA8rGm+vr6NDc3p8bnsGNS3Y+Pj01aAQbt7e35Z/f29mpmZsbA4uHhoaanp01azc/P64svvtC//uu/6ubmxnUJ9Ri/H8YVFp5zHTsIZNH4+Lj3B3X/6OioDg4ODLpMTU3p5uZhgg4TWFBKplIp1/2QeJA7d3d3SqfTisfjWl9fd24Q/03DOzY2plQqpXw+r83NTX355ZcOfGQKx/39vaampjQzM6PNzU33DigdOp2OZmdnLSe/vb3V9PS0qtWqiS/UoZzfd3d3KhaLmpmZcd3Ubrc1OTmp3/3ud4rFYvryyy9VKpV0cnJiBVksFtPV1ZVtb6xBSc5TWF1dValUevT8Go2G+vv7NTc351GuWIrW1tZs8SAfCaCoO8ej0Wjo8PBQkUjE2T/YeP/S1y9M+F//+llNOMEZ3cUZvjw2K35emAVQXoK/8NzgXU0mkw4e6A6qINCn3W5bJsGCvb6+9iHDpcGBCzJ2f3/vovnHH390s8zBUqlUtLCw4IKIZh2kCG8H0tVoNKoffvhBgUBA5XLZqPPo6Kg95zQ5vb29Wl9ffzRHEfllt7QcloAgrfHxcfuaUqmUarWaf3+3XwWZfj6ftzUAXzf+Z1h3CqijoyNVq1XPVB8dHXUIGz/39vbWzTSIKyOBaKi+/PJL/1mK50aj4ZmmMAb4bJC3LSwsKJPJmPmT5PAHkpI5zA8ODjQ8PKxMJqPb21sHdhB6hqwMFpxCgcAcvEPb29saGBhwqAa/E98qBSkI7/7+vlNma7Waw5+Oj48tJ6cRBhCgmDo/P1cqldLY2JgTvGl2kQJGo1HL1fL5vKanpx1mODY2pvX1dSskEomEFhcXDdJIcoNO8QPCGQwGXRAEg0E9ffpUpVLJ7AqNFwwEvmNJnh+PPwoA5ejoSJL8HFljAC80aayv/f19nZ2daXFxUZLMltF80zQODg5qb29PR0dHTgHF29lqtbS3t6fG59FFfC+oBBgRRUHb39/v1HsQ5lqtplQqpbm5Of2P//E/7JtdWlrSixcvHEInyZf31dWVE0rJV5ibm7Of9uDgQO/evdP6+romJycVDoeVzWa1ublpCTffgSTNzc3p/Pxc5XLZDCJnI0w+OQHJZFK//e1vXcRT3CBh++KLLzQ7O6uNjQ0Xl5wB2H0ojlqtlt69e6dAIKCXL18qlUrp+PjYUwy6k1KDwaDPoqGhIUnS9va29vb2FIlEfNbjRQdc+vWvf62xsTFtbm56zZEujlwcQGJ8fNwjUth/pPmSkDw0NKRKpeL3CoAJMDY6OqqNjQ1ls1kdHx/bUgD7AWofiUS0s7Ojq6srNRoN/dM//ZMmJia0srLyiLVDNkoxz5ghplfMz8/by473mWaZQDH8hjBl+OS7z7/b21v7/wcHH+ZBYy8ifLGn52Eu78DAgH8G75/7kzDQZDKpP/7xjyqXy1blsF7xZlL4A4gFg0FnHdDg0cRh+apUKk75JrEcJpvw1LGxMdu/xsfHlclkvK6mpqYsE8V+ggyaBga5M+Dz4uKinjx54mK3VCpZWXRzc2MQuVKpGBRkr1LsUX+gnuC9Any1Wi3NzMyYoQ2FQr6zABeazaYODw8NNBLsyrMcGxvT3Nyc8wFyuZxOT0+1tram+/t73xcUtePj47q7e5hqAGOcSCQ0MDBgyStqtlKp5DsPoB4QUZJzZZBKdwMDrDOeE/J9GnbqH5oC6hJqKu7SbkUaoVSs3fv7e0UikUe5KjCUAJGsP9QLMPScd4C7MIuRSETlctn3QLdyYH193fuP+gPLkaRHVjTAte4JBE+ePHFDBNBC41sulw3yhEIhs66AKEhwZ2dnnZrN9AvyhcgXmJ+ftw2EhhELFvZF7tDT01MDAICVgJKscUBcJpN0kxCkykMY4RsfHR316Evqnvv7e8vokeaPjIxofn5ew8PDZtQZ7UZNQp1TLpctI7++vjZwD2DTDRz09fW5zkLaTP1OU4f1BeBob29P09PTKpVKvitoNicnJxWNRn0m4QVHdcNnCYfDGhkZ8fMk7BNfOaQTdSPvgQkV3HfBYNB3HE0sKjEmkRDWyrjRvb099fT0aGZmRqlUyn+G5HcAqv7+fttfr6+vTR7wmVCOAFKur6/bajYxMaFqtap2u629vT3FYjHFYjGTIah5O52OCoWCfv3rX+v29lbX19e+U6gRh4aGND8/b3sDexoFL2cDZBF2IqwLZDT98vrbv35WE85Fx0VGI42sCClILpdzGA5yUoJ/aNZhQJFhU6wSPIZ3ZmBgwIc4CYBnZ2c+4GDCmBsIA8DPub6+1srKij5+/KhXr16pp6dHa2trTigvFose2XJycmKZBlJSfKugUQRn0XQODw9re3vb4yZI0QWZI4SIJoLCrnv+NJetJM9IBfkmfR7vMU07EvO7uzvl83mtrq4aaCgWi/5euGRgMM/Pzz2rFOSe2a7ValW1Wk1Pnz5VJBKx0mBwcNB+PjyKMLrT09O2CMDm0hRTcHQn9/b09JjJxjOIf5ACjmYcbxfrDW8Oz390dNS/A1T/6OjIEjRJj2T93fJsQrGQtB8fH/ug293dtWoAJv329tagRzAYdMAPY0Jo5JGE8b1xaXFww7RwQaG4wEsPM8BYmqdPn6pSqfiipeDB44gnMJVKeVQJDd7bt2/tGV1YWHDRTwAUlyYyvVgs5mKDJHRyFmCkKTQormhm8NtyYXNZMqN+b2/PlggCe0Bzy+WyG3w+D2ODKAxoTikq8IvSsEnyxcgzRvGCZw61CH7z6elpSdKf/vQnjY6OKpPJeP9T8OZyOaVSKf3hD39QuVy2cgJVCGza7u6uksmknj59aj8ehXq73bYFgkTq+/uHOeXJZNL7nzBIGjDGHa6vr5vlWllZ0YcPHxxggyQOZP78/Fxzc3NuUiKRiKSHIqbT6VhWOjU1pY2NDfvQsTbQwBDaCKDWnXrb29urubk53d7eWnEAMMX6R4JOLsLt7a2+/vpr+/g5D1DW0FgcHBy4aYZJZ95ztVrV1NSUlpaWDGgS3MQzhe3N5/NqtVqedU3zSeNOpgHsfi6Xs4IBH15/f7+KxaLPfrITAKGDwaBBOIp9LBTIGguFgtVi7XZbs7Oz9q2S/A1rSNgiwAogVKlU8n3H+41EIvaAo3BgL4+PjyuXy/lZ0/RQ+OEH3N7e9rNABszZQmAkDBUgN0AL2QgHBwcu+uLxuAtefK7IiHt7H5Lv0+m01Sx433O5nNPxAcG676XuKQ/dZy9M9e7uriT57EAtgYSUJhdwZWZmRpJsh4OlRSI9MjJikKW/v98ABXcLd3sikVCj0fAIQs5HGLNuJRtWMxoygJN6ve6cA0bE9ff3ezIJBXI2m3UjRJ01MTHhXBTuHM4AGm+a+rGxMdcNWKD6+vr835J8zjKGiXMKlRKKRRQJnFn4XmHQBwcHXcgDghCsR73H74Yx5b5ETs14SVQW9Xpd79+/NwnEmcVdROga0mJYs4uLC4Pa09PT2t3dVa1W08jIiC1z2HYODg58VpMqLckWNexs3cFu1Grsw7OzM9d4kD74plHqcA8BmsGe81wKhYJrIdbG5uamYrGYP3ulUlGj0dDFxYUzUFgb1Ahzc3N69eqV66Hb21sD39fX156Ks7S0pKurK83OzurTp0+6u7vTs2fPrDaYmpqyLYSMCOoAFJesPQinWCxmUInMDJri8fFxM878nbW1Nds/G42Gg01RqobDYY8+PTs7UygU8uft7+9XqVQySAd7joUnnU7r4uLCpN7+/r6ePHliZWo0GlUqlbJljSyHvr4+bWxsuE4HEACMqFQqBnvItcAqASvNzHr2EVkRWAYjkYhSqZS+//57TU1N6eDgQJVKxQAAeUO3t7f2qc/Pz/uOLJfLrp8Aexm/hyUStRQqRGTpa2trWlxcfARuAsKh8vtLX78w4X/962c14dFoVOl0Wru7uw734XAZHR21LBtZJeNGCDTDA9fpdCyz6evrUy6XU71eV7VaNRsuyRucA+rDhw9OhARR4tD+/vvvfQl++PDBAMDS0pIPs++//15HR0eq1Wr65ptvtLe3541PEcMFkclkPBKNJGsKJaSXl5eX2tra0sXFhZrNpuepnp6e6uuvv7bELRwO6/z83OgnIUkgx4SioBIYHR11ER+Px11UzM/Pmwnj8kTKxufqTspeWVkxo3R6euqf32g0dHR05IATiiuCs66urvS73/1OrVZLkUjExRFsC2wxBxWJmkNDQz64+czdGx+fDWERiURCgUDAMmUOcOSxXGp4rJExn5+fW24Iujs8PKxYLObZi8jIkfUx6mRmZsaoNAFX5APgZ8NfxSxLCi+Yd5oklB7YLyhCGM1CUwkrBVDFM4T1mZqa8sVyd3enRqNhAIACixAN/Jkgm6DKND5cPsjO2SP4wPBEEb5RKpUc0oUkH2b538rOkT1SrOPBZZwXfnCaB+TwIOa1Wk3lctkNBL6809NTh4kALLDPo9GoPn36pLGxMUuYaVpheAlgoUiHhWNc0dDQkN68eaNsNvsoMwH7RSqVUi6X83qoVqs+454/f262dXFx0eF8pAoTBIlNYG9vz/u3Wq06wRgwikICpQENF0wdf57gmN3dXXsmCeeB9S+Xy8rn8woEAlpcXLRMEVnnr371K52fnz8CJ5Hu8z2wrpBeE7xIcUPAIoUXa657JjVp7/F43IFLjUbD6wkpL6FRKHYAXinSIpGI5cuNRsMgKBadWCymp0+fGrzi3qHwBzhhdNT5+bkikYg+fvzouwXmjTMA3yzrpNVq2S5Qq9UMJNzd3dl2sLy8rP7+hzT+hYUFS/smJydVrVatimq3H8Zs4W29uLh4lFVAEFMqlbLfnEb6xYsXXg/4xUulkot8bCzBYFBPnjyx9eD8/FzxeNxBRgAO1WrVQGU2m/UZhQwf6Shp5t0JyHi3aUZRHAAghkIhf97r62u9ffvWEyc4H2KxmCWONNr4t7G1cdaRWzE+Pq5isahyuWy5M+uFOgEgeXR01GF+nNH4fNPptLNZOC9QVdCoDwwMaGJiwoq7WCxmlR/nxN7enl6+fGm2s/F5rGomk/F5CqiSSqV8/nZbkwBruQtQ/fBZAcwPDg7M3s7NzTlvgJqBWgMbQSAQ0PHxsaXMkuzZleRmnHsIFppGua+vz4U96gyaFs5L1gK+WBR02HZQJjU+h/S2222Dy4uLi+rr67OKhMYAayLy5LOzM6VSKau7sB82Po/wk+T9CitPcjmfm73dbbeKxWI+mxKJhBtCFAs03tgTbm5u9Pr1ay0uLur58+fa3t52mB4TQsiu4a4FxECVJP05/Zy6r1QqSZISiYSi0agKhYIKhYKBOe4/ahrOgKurK3369MlnMM0w9TG1azwetxwbwJPxbACrxWJRa2trOj091TfffOOgOvJS2BuJRELHx8ePgkFppgkVA1zu9qZTj97d3WlhYcE1AyNwuevoEwjB/eabb5TP562+o7YaGxvT1NSUc49gsskRabUeRnUy+YU9yFmCqm58fNx1ZHcjDAHE/YoVkffA5JKlpSWPP2s2m5qbm9P333+vmZkZZbNZHR4eOoDt+fPnzhLirMWK8dNPP6mnp0dff/213r9/r3K57LA3AICZmRmDyYlEwrYkrCEoiLjTyToaHx93HkYoFHJeA0095+XZ2ZmT30OhkOr1usd8lstlnxe/vP72r5/VhNO4kGSKHL1SqSgej9uXzainWq1m1IgDETby9evXur6+dsIgcpTuMSzd3idYjNPTU01OThoRYzQCG53/SHJwzcuXL7W0tKR/+Zd/8eGLVLTb+wNKTdgThynsLAnhQ0NDev/+vTKZjL0jMJf39w/JqTs7O06yRvJCSAWXDaweC76vr89MOv4WZkUjOaU4omjCnyrJTRDI/sjIiJ9Nd/NLcYEkDEYFnzqeLho8iltSaGmqp6enH8maQboDgYCDk2B5tra21Gq1lMvlnKrMmCUC2KrVqp49e2bmA3SX5hRgYnFxUdfX1zo/P3dS7vT0tJmthYUFsyL8PZoXGgbmjMJMDw4OqlKpmAmhUIbx6Zb98azwKnYzDgRyTU5Oqre314w86Z14E5FBshaOj4+NolLs3NzcaH9/3yNHYC339vbU6XT08uXLR+m4V1dXbupGRkYeSREJ4ZuZmTEYQ2FAoXlwcOCmZmRkxKEd+EBBmZFBIt0CgME/iQKF/6Zh39/ft0QKKRjgAJfy1taWenp6tLCwYEYbjzLKGJ4NzaQk//PJyYkbwCdPniiTyTgABYaO5w2wxvkBY4gEjNFlnD1Im7nAUch0Oh17Ibe2tjQ6OurRJ9JDSjUTI2B8JyYm/P3CJgJysC5RA6XTad3d3Wlra8shhvv7+3r16pXPyUgkorOzMwfanJ2d6ePHj49sPRQv7B9AHSS2Q0ND+vbbbz1vvPF5fBWAK8+ZwhEQigIY0G1oaMhZDufn5zo5OfHZzWx4QD2KKdbj3d2darWax/rQrFcqFa2srNh/DmuVTqeVTCb9+4eGhpxNwrxWRtbQcHBncP7D4PMMYLOxQkhyw885Mzw87JnOFMrNZtPsJucTuR0jIyNOYiYDYnx83JYfzgtGuqHcgeWjgUsmkwoEAmaS2SNnZ2caGRlRNBq1b1H6M9vb+DxqUfrzOJ16ve47CNlxsVjUycmJUqmUbVLd7CDzm6+urhxkVKvV9O7dO8tmsZ5ls1nbHpDQj42N2RvPmDEaCPzskqxOu7+/91xzJL80hMij8cVi7+D/b7VaHvPF2sAHTyAWoZxkABweHurZs2e2hkEeAJbj1YXtJmmaJgVFUSAQcCIzLBh5LTSuBGmOjo5ahkv4F3kpvF8+JyBjpVKxxYD7h7uafcm5inoF5VI4HHaDwu8lFwBQhruBuogzn6aetG1C7QgrpAlDfo4tIBwOP5pNjCz29PRUuVxOsVhMhULBCfMA3qjAkL5LMgAPSBGJRFStVq1eQK3Evuzt7dW7d+8MBkxNTXlMKOec9NAYv3z5Uj/99JPvdWodFDc0eijAyDDoVolhl0LtUq/XdX5+ro2NDdXrdd3d3Wl7e9tMMVLuf8smshf4d9y/kkwKcCey/r755hvXOgCTKO8AVd+8eePRrqjUTk9PNT09rWfPnul//+//rTdv3rhpq9VqBpYZTQqIS56JJJNnpIhHIhFFo1H98Y9/9Fn1q1/9Sru7u1peXvZ5y3N7//69x3F2e+lpkLEhXl5eOmG/O3+EfAJqF1QQZGQg5ef8RCVKMF8kElEikbD6Ban8zs6OCSEUVnNzc2bnsWxhW0UpNzY2pm+++UZ//OMftbOzY/AWoAlVKjUT4XwbGxv6+PGjwuGwEomEksmkbUD//M//7O8aZcbLly+tsOgmFFnXfBfk1QSDD1NLstmsa8VQKGRA6vT0VAcHBwb6/pLXL0z4X//6WU04BzkNMH5MpFTlctnsBZcO6DB/d3V11XJKDnGQbdBt2HWkoyCTIOQc2DSHkizBwVPORQxaTlADMulEImE/DQuIBmdsbMz+junpaSNw3XLRVCqlwcFBI3SgsTSCFBhIyvGd9ff3O1yNz0Yzw9iF4eFhz6Etl8suBsPhsMc+4D2B3UN+zuv29lbr6+suTCgEQBkJueL3kuDJSIuZmRnFYjF9+PBBd3cPsxY/fPigq6srvXz50qwIYAGjtJrNpgtKfGS5XE75fN4SVRDfTCbjNYKsTZIbbz53o9HwqB1mqIIsApoUi0VfEgAlBwcHisViltPOzMzo+PhYjc+zPFk/NBcghzxr/M4085KsIpicnHRxBDvLKIzR0VEXJhTp/C7UH6D5XEL4bwFDkMxtb28rFovp9vZWu7u7foY0KOwtmiz8wjs7O7q8vFQqlbIqZWxszNKqdrtt9pPilrE97FU+gyTP2yUISJIlevwMpgpMTEy4gO3v79f79+9VKpXMtN3f35vJ49KYn5/Xhw8fnAScy+XsZSe5mL0NoADzQc4AsmieCyFKPO/uPcKzkGSfbSaTUbvdVigUcu6C9OfxQXt7e5qamlKr1dKTJ0/U39+vjY0NpdNplctlByqen59rZ2fHTCOzoOPxuCYnJ524fHd3p0QioZWVFW1sbNgOgR+XGeSoSLa2tnyuMobpxYsXfv6ABDwvZrXTQBFYh2eY75yxhbFYTHt7e05hHx4edrrxq1evdHp6qmq1qkqlYokfHkMSX+fn5w1WdXsuCRBk/nkmk1G1WtVPP/2k1dVVgwtra2vKZDLOumCeLOu8WCz67A+Hw+p0Onr27JkajYb3fiqVMnu+ubnpufTBYNDzhfGc9vX16d27d3r79q1Z8m4QhZC0cDispaUl308UNZeXlwYAYJv4rmEsUSMQGgWDCFi9trZmNpICqVAoODek1Wopk8k4PDMej/8/5t0DAjJpgGaX/XF7+5CwTAOCsor7hPfFuYvKhL9L0Y1libR5AL5Wq+VCMRKJKBKJqNlsuhBn5B7sF4ARoBRnw97enm5vb33Okz2BAoBmkDNVkmW9BLUFg0ErCLizO52OQaJCoeAzLplMev8MDAwoGo3q6OhIjUbDDS6y/Lm5OWd7wL4B3qF4iEQiCn2e+Y3iA7sWLKokn0ewr4xFQzYOu4rdC/CdJg9AEdsaLCXnG2chfx7gkzUE6IW0Hh/w2dmZlpeXHVYGwMH6QQaOLYJ7mN9PI8o5jSKFmgUC4OLiwmNeaRhI5seiAuPYbS0gI4iaEPIFthxmmAYRwA8Q7fz83HUCAARgCKGLoVBIh4eHvifz+bwGBgY8svLm5kanp6fa2dnR4uKicwlYx4eHh35fP/74o8E3fp4k18XSn8fsSX+2BNBgo87heQ0NDVm5yJpCKYoyk+eNHQWAC6sCIbVPnjxxLc0YWhSTPT09tnENDg7q6upK6XRaL1680E8//eQ/h6qP+5nP0dPT4ylH3AHX19f2QLMPnz59qlar5WAy9gP1A/UwoD01J8AV4NP19bW2t7f11Vdfqa+vT58+fZIkZ1awF8klID8CkpAzhhqh22pBAHCn09GrV6+cBTA4OKgXL164j1hYWLD9sVqtWgI/MjJiVd6LFy90dHSkRCLhdY1ykZyn4eFh5XI5PX/+3D8HYm1yctLgA5a2T58+KZPJ+PnSY6Do4mf/8Y9/1MDAgL788kuPC2UqSn9/v3K5nHZ3d61q+39r0/v/b6+f1YQjV+bC4tKmSZmYmLB0FU8QCB+biUsDWRO+5Xw+b5krhwsXHL5ULg3QWlBmPLfBYNBhAjC9NBEEzCBr4mK5v783e93f3280Cyav0Wio2WwqHo8bGeKgoAlh4z5//lyFQkGhUMgHN34OCg+KZAoyUHUKfZgMSfb+wQojnyHtFZCCi5Bk2729PV8ET58+dYJ1PB43wk44B6xEIPAw2kp6CCWZmpryHOPb21s/w3Q6rfv7exUKBY2Pj1sqODEx4RE5t7cPaeylUknNZtN+IpooDtHLy0unKXdLlJi9XqlUFIvFDOowagIfFGFJrVZLs7OzBmLK5bKLAlBgUFGQv25v9fX1wxzry8tL+xcp2o6OjoykRiIRS7zwEiF1ikajTpk9OzuzXYDmlO8PjyPfE8WPJF/wZ2dnDsMjRwG/FlJvwo8KhYJHZOzu7lr+hwyLsA+k0/39/ZaKDQwMODCFgDsueQrDRCJhuSBFRLVatccdQI4zgCIT6SshRBTZ0sPM7m4vdL1e9zigm5sbs6gwPzTDADiAGzA79/f3lshypjBiB/8bzADfM4UZXs/T01O9ePHCjCbMb/eIpXq9bkZ7Y2NDq6urHqWVTCaN2Pf19elPf/qT7QkwsGQHED52cnKiQqHgAJ35+XmDPXt7e7q7u3PTzHkQCoUsOdze3la5XFYul/NnZ+QSDDLj1P7pn/7J8nFmt6bTaTcaqH26ffndRft3331nLzYBRzB/+DspnpgAQegga+fy8tKhgGQTSA8KHlKk+Q53d3et/CAYkfAsmku+w1gs5kKUvbS7u+ui9ebmRsViUf/1v/5XM9A0OT09PXrx4oWb1v39fRUKBTWbTc+BJ2GWLAIakHK5bGahO8cD6bkke6opzg4ODtyEdKtjCN+hgN7d3XWAHGP0FhYWHJDJe6dZmpmZscKD0XOANf39/Qp9Hj+Hf5kmg7GENL7dSjKkq4FAwJLrQqFgIIJxg7e3t4pGo3r69KnzO/r7+x9NDWBt0RixnqUHqXS39QHgCYCz286DQgJ1BeowCAFkzvv7+1aoIY+GVSZVWZKB3MvLS6XTaTOnqAVYZ729vQ48RWE2Njam0dFR+5CpcciygBWn5kin07q8vHTwK8oUVBfkV/T39yuRSLhxQX0lyfuNtVKtVh8Boa1WyzkgKI2SyaTte3iXWXuxWMyqAd7D7u6uotGoXrx4oZ6eHufAoI7r6+vT4eGhbSB4xvmeqLv4Xk9PTzU+Pm4PrSRbBJiowOg49g/g7enpqWXs09PT2tnZeaRy456n5uM/TKGgBujr61MikdDR0ZFDttg7nAVkykgywAbTSvYRNQH3OZkPBN9RG1H3AWxTB8Dwj42N2Y7HemZ9ojJj3xeLRTdT5Mb09fWpWCxqb29P0WjUTPHGxoZrEn7GzMyMFhYW9MMPP7gOuLu70/r6ukdh1Wo1NZtNra+vq9PpOGOJWp47eHNz0yGKnBP8bwB2ciji8bi++uor2wqazabev3/vZ03mAx5+wEMS3cl4YY/jpYfVx74TDAb9c2iec7mcFQGw0qgFj4+P9eLFCysVeM49PT1WenbfXUi+ATxIyc/n8w7rZfoH7/v58+cGRvr6+jQzM2OAMBKJ6PT01OA/attMJqM3b97o17/+tYEP6pX+/n69fftWY2NjyuVyVlaRSTQ+Pu5cKiaLkMWVTqcVDocNfFLnhkIhjYyM+C7r7X2YDrK0tORz7y99/cKE//Wvnz2iDLSeFFHkIgQ4cIiSjgw6y6gJJLHdrA5sbHfAA9IW/D1IU87OzvSHP/zBD4Qi/fDw0HJOmuenT58alYrFYg6Vu7+/twyXw5GCALkWvqiZmRkfzDTC8/PzPmjwsyIRX1hYsDcL+cqzZ8881xyJIsVEpVJRpVJxgd1sNrWxsaHBwUEz1RTDtVpNw8PDTu0Oh8OWpUSjUZ2fn6tSqSgSiWhjY0PPnj0zm0kIUHdjOjAw4IatOzSF4hr24uzszAXKzc2NR/2EQiEnKiLhJQiC5jGVSlkdAGNPGA4NH7PAAV4IDuM7I7kSWRdsRSaTcWEGu57P5y3NRE7PxdNoNGxnAOmlUQVQiEajPpDwM/J7d3Z23AywTvFGYtNgXi8eZf4cFxU+agAt1jF+WvYWhefm5qYbAdLB0+m0JboAVIzjoQgqlUou9FhzJycn+vjxoxmJk5MTF68rKyu+MA4PDz1WCVkfwUgEW8E8gfR2y0xpCEHm2+22Za+s4f7+fiPIsVhM7969M9NCiBzfK0yV9GcGm2YQHzdFHnYDgm14VgSi8Bzu7x9C066urhxKBvjHxIDt7W3VajVFIhG9ePHCfr56va5yuaz19XWvAQLQLi8vNTc3ZxYrl8s9KmZOTk60t7dn9oCCh2AY1vfS0pJ9m4AHuVzODHGxWLS3FN/Y1dWVvYsrKyueIT0zM6N8Pm9gq9PpaGNjw+N7UOk8e/bMsvZisWhQJRwO63/9r/9lSV0ymVSpVHL4DkFfnJk0jkwZIJgN0BEADAUVoO7Z2ZlyuZwbI0ZUjoyMaHl5WTs7Owa3pqamtLu7q9vbW/3www9aWVmxBQOmljuDRoz1iWSw28pAXgKptqlUSgcHB7ZTMKd9d3f3Ua4E3+fS0pIZfoKuYLTn5ubUbrc9dgm5PyAdzdvCwoJDqpA50szhVScrIxQKaX9/X43PYww5p5HZEqqH/QVWJhgMenoJEmvuBfYV99Of/vQnN877+/uKRqP66quvFI/HdXFxoXQ6LUmeWMDZDcvXfc62222z9Kenp0qlUr4PYXgDgYBisZg98t05FGQLNJtNN41kmpAwTiNweXmpRCLhjIN6vW61DPJyvisaXM4FWN1uSxLvHbUQKghAbDJuUFgAugNCMl6ze8wSfnTCmhKJhEqlkjMgUqmUQUkURj09Pc7cYOwlElSasNPTU2WzWauB2A+wfIzlQiEH88y5Ojw8bG9qPp+3PYbGgeYSsoK6rlKp2OaETbA7G0X6czgkoZ58j4wcIzxtZGREx8fHbp7m5+d9PgK+I7eX5KA4MiVoYNjjqKIYzShJa2trtiRJ0uzsrKanp21vYyILdzigEkotGjNUbOvr67q4uDA4gIyfJpEw2NXVVQNag4OD+umnnxyMyghNvjsUK4CVNJWsM35Pq9XSF1984VFvhD+Oj49rd3fXAaXT09PKZDIe3UvgIiNRI5GIXr586YkgAPXcAygjqNlHR0dNEAEoYPPr6emx55hxZ9TgEDnIsPf39zU1NeX8Cs6Snp4e9xOdTsc2UWxuKJ9Q00ry2Y4UnxwB7njODzJ49vf3nXbPuddutz3nm+T73t6HiUe5XE6dTsf2hsHBQRUKBRWLRS0sLGhsbEw//vijAylRtnFuQEhB8gEyNZtNzc7O6smTJ5bHU7MEAgHfrYxLQ0nV29vrUFisfCjPUAPxXdCIYylEJcEZhrVjampK+/v7Xnd/yeuXJvyvf/2sJpzCtzuwhMYBjzJIFhsM3xbeJEZndYcT0fBxuIBKgspzQVDcs+EIVOGgBZ08Pz/X1dWVSqWSgyEorGHUaYyQQ1OMt1otfffdd0bc+vr6tLKyYllItwyw1WpZUrWzs6PDw8NHRc/4+LhWV1f93RHi0T2LEA88TAUKg8nJSV+GhFoMDg66OCTYBd8OyafI4LjQJPniJTSCYArCikAEe3p67EXFc9YtyWbmJxLnsbExB5QwUqzT6RiUoFCQHgo1Gg4OHYK/aLhAO2lGuOBgeChqsDUgaa7Vak51PD8/d2gcsjLGnCChotkEBUdCyD8jQ8XDtrS05DnEsAHsgWAwqGg06vRYDnDQWX4fIE+j0VChUNDz58/9WUjblx6ALoqpXC6nhYUFbWxs6P379y5++HlIVFFIfPXVV1YC9PX1WX6M5xFZMowOKDDSboo+1C14TZk/zagipFXNZtMSNHxUNMz9/f366aef3OB3y6RhUSmC7u7u9OrVKys4AM6Gh4cfBaRw4YMMg3zf3Nx41BygHwUaawxQj9TqP/zhD8rn8woGg1pdXfU4HMAaGA8uz+5wKMABVDjNZlPpdFrPnj3T2dmZJOnv/u7vVCgUdHl5qZmZGU8MuLu7U7Va1Zs3b6zMAUXf3t52oB1y8/fv3+sf//EfjWpTlAMI4DMD3KJ5QylDCBO+NcZJ3d4+pK/Pz89re3vbthJJZsoYz5TP561IgsXA77u1taVOp+NGkwKc5ONkMmlUvtt3TRAT7BGprqgt+F4AkNiLnU5HuVzOLAwhOtls1uFkhHzicdve3lY8Hrdyht/JXdD9vQ4PD2tlZcV+yU+fPtnn+unTJ4VCIX+319fXevbsmUdYAWRSSJITgnUENnV0dNTS1W7/JIBIsVh0g1qv15XL5XyOMxIOnyJnPUw8SjQUG7B7+FSReJ+dnWlvb0+VSsXnIu+pr69PjUZDq6urmpubcxGXyWRcmMFCEnrKuTQwMOCMEUb0oIwA3Lu5uXG4KrUCKhnsKaVSyZ5HvLME7sE0wxQBkhIiOjAw4OR8vMTMqKf4JGwxGAwqkUj47iYrhqIYJheQH1nx/f29Dg4ONDk56ZFg3TY4RmmGw2EtLi5qZ2fHwWsUxmdnZz7/UEIAHJGh0dv7MOMZhQ71AlYFxrDd3NyYzeQZAfTijebOJnjr8PDQoXhk77RaLSukCJXiruBOo4Gfn593KBu/LxaLedRZLpfz+cgorL29Pd/R8XjcoWDkayD/xRKEgmJvb88KMeoR2FlqRfYdUz9Q0FUqFUlyE8b6WVlZ0ejoqEekcrZzh19eXj4aQ0m4IPXiycmJtre3rZaiEYHtDIfDVgFBGjBjfnt728AFAAcKBoKzABeSyaQGBwedA8E9mkwmlU6ndXBwoMPDQ0kPgOLr168NwlGP42kHVE8kEj4HyPfAXnl7e6t6ve7anOaHff4f/+N/dJgcUmq+81Qq5Zrt5OTEZ093SOPS0pLP+MvLS6srIKMAhSuVis80QENJtlMxdpd1PTs764aV3gDVZalUclgzExzIXGEsbLVaVS6XM7HA6LqDgwPbeKrVqpaXlyXJoH9/f/+jVHKAGhr6TCZj4BT7KARKPB73vuJ5b21tGXxm7Fx37s78/LzPoK2tLQc7o5o9OTmx3Q2Ai/uX58J3TP0fCoW0vb2tJ0+eeK/+Es72/53Xzw5mg5WdmJhwgUtoGSmieGvxA8Ea0yienp760qUhILyJOcjdiY8UYff39zo/P7cPm+AjmmkQokajoWg06iaM9D8uEklmIkAq8URTiM/MzBitI8SH2X21Ws2MM6mboVBIoc8jfriwl5eXNTExoYODAx9ygAzValXFYtGNfTqddlEBagia270JkeFks1n19fVpfX3dTUgwGFQ8HvcliZyZz0lxATgiybJcCiGkOPl8XpI8jxtvJMBIKBSyJJ2ClcuR7wOfFcwnTUcikdDc3Jxev36tbDbrRjAcDlvqRNMmPRR8SD8nJyd1dHTkwhWAAsaXtcHFDnsL60Hhil/w5OTEDQ6XULlcNlrPhSj9OQyDA46LjouKQvHk5ESDg4M6Pj42+ktDdH19raWlJTdZ+Gx5DvV6XTMzM5qdnbV09fr6WrOzs9rd3VWn09HOzo49+wsLC07rRY6GnJPMAL4nmmTCmmiIlpaW3FT29j7M1ex+fwAnnAGARSDknAv4C1FzME2BfQ/TMj4+rvfv32t+ft7TAPL5vNbX1xUKhTQ0NKRgMPioAODzwRoSrkITzt7m3MCbX6lUvLY4iyhgz87ONDMzY5khjf7g4KC95CDnvb29WllZ0adPn3xBd0vRLi8vVSwWJcnMFYg0ICLS+ffv32t2dlbPnz+XJO3v77v4LxQKVj50+2rxnYdCoUceVr6DgYEB50jg8aTYev/+vTqdh3nWKFVSqZTi8bhH6Lx+/Vo//fSTvv76az19+tTzQrv3BmMZASwPDw81NDSko6Mj7e7uanx8XJOTkw7NmZ2dNbCFBy2ZTFpCm0gkNDMzo2KxaNYTaWihUHBxOzExoaurK33xxRdqt9uKRqNOqMeX1/ickM+UCJ55JBKxrJgpBzDRqF0ODg6USCQsx0UKTZNGLghFGH8XNohRjqgexsbGFI1GXShxvwE+EPTIf2BFK5WK6vW6xwkxsWB8fNyBg4CUZD8AZpLmD+Dd7dOFNeacRUnEbFmKMTy+c3NzSiaTlrOiNELRhgy8e8wl5zTgGc+R0VEEn3HHIXOuVCpu9AlL4h6HRe7v79fy8rJBQdY3nt6LiwvNzs4aWAWog8W+vr5+ZAE6PT21FBq1xuTkpNbX182gA7qjpIGVhGlqt9v27UtSsVh0M5tMJh+NF8O+cXNz4zFn8Xjc02KYpMBn5jMADtM08yza7YfJAIxxuru7cwOTSqX8O3Z2dnymE/YJsM7nIcfm9PRU8Xhcl5eXtihMTExodHTUjTJTSQgX479piGnMAZpRRwKI472mBgt9nrLQrb7jHIQVjEajqtVqyufzbn5ev35tGxlsuyRLjakfUCpSo3InkznDXhwbG3P9OTAwoPfv3zvMtl6v278PEVCpVFQqlcx8Y3cKhUKanp5WOp127k+j0TDQlc1mbY05PT3VP/zDP+jdu3c6Pj7W/Py8/u7v/k6Hh4e2ErJ2s9msw3m7m+zV1VWFw2H96le/UiAQ0ObmptcrAbi3t7f66aefbG9Aur6+vu41MDAwoNnZWRMRqEoWFhYcHMrdmkqlbMnI5/MOWt3c3PQ5A0A3OTlp0gfQeHFx0aPYkE4nk0n99NNPevXqlZnvw8ND5fN55fN5pdNpq284G29ubtwADw4OqlgsKpvNmtUmuJNQssHBQYNtjEgFgCOLaXFx0UoTaort7W2PRIShZn56PB63xbNSqej3v/+9gbTV1VWri1B9cqZcX1+rWCyqWCxqfn7eoMTvfvc7RaNRra6u2ou/t7eny8tLZTIZg63ccZIMJjISEXsd2U/YVKPRqOr1uo6OjjQ3N6dA4M+p9oRX9vX1+ezszsz5S1//b2Wq/9avn9WEN5tNJ0jjv4PhJpiEi52Qmk6nY1kLm5yL+NmzZ7q4uNDGxoZevnypf/3Xf/X8Sw5R6QGNQ3rCJcmm2dracvJp6HP6K16ag4MDjzDAK4LkrtPp2NtxfHys5eVlDQwM6MOHD5qYmNDy8rLy+byLeTx3FJRIokg1hEU8PT3V7Ozso5EusA8wQcims9ms6vW6mZFms+lRSRRnzLOu1+uKxWIOdWAMUafTsW8G0ALmm8YKSS+NOL7l7u+Lz0Wo2uDgoBYWFuw9IaRnZWXFAV0cmHz/HDg0m7BUjGRjhBp+ayRe4+PjOjg4UDQaVSwWMyuO7I7CC2kOBwTPksaJ9Onz83MfTvjBac4Ifdnf3zeDSUgUB2x3MYokimbn8vJSb9++tS2Az413dmJiQjs7O5YODgwMeK9QVMGC8Gdo7EHRYfXxeedyOSWTSTc4oP5MKQAIqFarvqxRStCcHh4eKp1Oq16va29vzx637nA6kNKzszNNT0/r8PDQjTa+X8JMACdA2mGgP3z4YEaXWdKsL9hAUFgYiGg0qu+++071el1ffPGFQRvWLjJ+QAUQ3mKxqFQqZd8r3n4AAUmPij7W/cTEhJaWljQ0NKTFxUWP5sEjRfGaTCbdgAEgxeNxJRIJbW5uqtls6urqyp5smrJms2mVBg08ZxLnBKj98PCwBgcHNTc3ZxlupVLRp0+fnBPxxz/+UR8+fLC3l5Cyr776ytJCAKJwOGwvJuwSYCVKjpWVFSthmI8ejUa1sbGhzc1NX8bdUuX//J//s0ZGRpwbAUDCZ0HmfHV1pcPDQwOOhKh1Oh035uQzYIUBpGl8nm3KaEd8oMgXYURQocAQjYyM2OJC2i1ql5cvX5q5p7lhTefzefslASW5d9rttqanpx3YJ0kbGxuPwiBbrZY+ffpknzXgXK1W8/xV/OqwVQAsgChXV1cGnc/OzsyUHB0dmTV7//69IpGIAaXu+4H1wh1HI04xxbrv6+vTs2fPtL29rfPzcxflmUzG5yQKE84lzlQyTzhnOf9JT+9Wr8HiInW/vLxULpczoIINi3OezIJ2u+0MCOTVKF8ymYx97txbKIui0agZY9Qm3O+JRMLnBrLLy8tLra6uamRkRPv7+7ZSkd6eTCZdwxwfH/u9XV1d+fOhLFpdXXUSP97/7kAyAC6ak25gjvwQVAbdYD0e92w2K+lBRYbaizoBaTtJ3dQjSOjr9bpubm4eTVhAEca5jYUBGS5S43K5rMXFRctmCcQizb9er6vZbDrQjzOdWgLGvtPpaHp6Wvv7+2ZCmW7Bmjw8PFQkEnE44/b2tlU07XZbnz590ujoqH2tV1cPc6339vb8WU9OTmxh+vDhg4HIi4sLraysmGU8ODhwqG40GnXS9cjIiF6/fm0lC58Nrz5gxPn5ufb3992kdgPEnCPctT/++KPtE4QWMucZC+eHDx+0urqq9+/f6/z8XK9fv9bs7KyWl5d9F4+Pj6vRaPgMBewgPBAFIz8TGTphlmRRZDIZEzdDQ0NaX193vhNWtqurK71+/Vq9vb3+flDcYa3B903oGjXQ7u6uwuGwvvjiC1UqFX38+NHAVzwe99pmTXQTRVtbW6rX6wab9vb2NDQ05HsdwKynp8eZMOSpcP5LchAxlgRUJqiDWJsQgPf39yZn0um09vf3LW2PxWJWiLFXU6mULVEoBQl5A9hkvG4mk9HOzo5zhFBjHB4eanJy0uQUa5rvhXsZ0gqFcS6XU7lc1tHRkSqVimZnZzUyMuJMFIKQNzc3PTaTEGryBKampkyCkFfD94SFU5LVtAQl//L6275+VhMOaoy/jeJucnLS6NH09LTRVS5ULhuKfoJFhoeHVSwWLZ/iYkXyiDSL4pImVnqQTP7xj39080mTTiPWnTjY39/vEA6at4mJCSNC/f39nj8oPWxO2BNmJeJZRvKKL4UQmmq1qvn5eU1PTxsxOzg4sCcEVBR5GFIrpLlIzKenpxWJRB4FJHCp9/f3a21tTdFo1MFRi4uLlqDl83nV63Ulk0k1Gg2HG+GNnp6eNpN9dnbmoDPYEHycfI/IjTnEuouycDhsxF2SGWwQZ1htxjyAWhJo1+l0jD6TgI2PjiAdvOwnJydOyIZ9oRDD34aPu16vG+Xc2NjwAYY6gcNsbW3NBw9FIYUePk3kkKDdJLTS4LB+aFzxPDNuDNAAb+Ld3Z2DOC4vL/3dxONxSQ+XeSQSMau+sbGhxcVFj3prtVpusgktYmwQbDfexN3dXQ0PD+vt27dOEUUhAuO/vr7uwpGGHWnV3d2dpWCNzzNuT09PdXPzMDd5enpa0WjUctqzszN9+vRJe3t7GhkZUS6XUzAYNEOJwgTP5PDwsA4ODvTkyRP7MvGE8vmQ3sG2kGSMhIrCDfABkAlmG2YKOSsNWCAQ0Ndff+0EdICa169f6ze/+Y0tBOwNGhyYoJ6eHmUyGQND3Sm2eN5pIvCR0gQAzDBKjnAnWGw+wzfffKO1tTUj3jA2+JrHx8f19u1bvwdYN8BH9g/PCzZ7ZmbGaD0yXdK2SbY+PT11cNHp6amWl5c9sSGVSunw8NBqCTxmSBMZ75NKpZTJZFQul93YMNKPKQWMj8lkMpZBn5+fa3NzU729vXr69KmLeKwoFPyAnFhi+G7C4bAmJibsv+vr69O/+3f/zuGTPGtUQwTQcZ7t7u4+XIyfA9wWFxf9z6TO0lQFAgHlcjn74Jm/m0gkzMoDYnL+4cdDGcGd1G63NTs76yaPpGLAplKp9CiQDjsVBTggK0Dj6uqq/dgU891S+HA47L0QDAYNFNJw0nBwBwEi0VwBLsGEw+jSsAA08P4ikYiy2ay2trY8MYLiFi8se0WSmfGpqSmriAYHBy0pJdMFG1QgEPAs9OXlZYMB9XpdiURCksy2Am5dXFwYRA0EAma8UK3BYE5NTSmdTqvZbGp/f1+SXIhTUOP17nQ6fj7JZNJe7+7U4nq9rlQqZVYU8AAgAk8t3yG2s3Q6bek34Pz9/b3nBqNKATAdGhrSwsKCsy2ur6+1trbmM4azHmUNn7vbUgi4zN22tbVlwBbQOJ1OO0n79vbWNjfpgbWWHhSP1FhMXegOtItEIrYuHB0dOXxwcnLyEfgGkAKARPOBbemLL75wvYaPmIR6silQI5G4DwCPXx1rweXlpYaHh1WtVrWzs2OAl9plenrahAsBdzs7O3r9+rWBKII4/8t/+S+u01hbm5ubfm7I/CFMALponmgipYdQ30Kh4LNkYGDAc8VPTk40OTmpRCLh85C6q5vdDIfD2track3HRBYAffYHoY3UDEyImJmZcS5Rp9PxFIS3b98qlUpZ3t9qtRy8h5wacKWvr89gK0pSANDBwUHXKiTrYw3DMpfP572u8ZSjtCT4l32JMikWixlcQ21HDyFJKysrev/+vRW0EEOVSkV/+tOflMvlVCqVHKCMFXB/f19zc3NWUDWbTU9Y4plyH3HeoQKErOPeury81PLyskqlkoH6t2/fanBwUNvb2x6hSCgsvVej0fDZc3V1pWKxqEQi4T0AOSTJ9zJWNmx4EFbs57/09Ysn/K9//awmnEucQCdJDiBA9rG7u2sGgcOdiwJfBwfR2dmZkSRYNtB+ghLwXYLY4vnpTvXN5XIOo+Fgx3+TSCTs96A4QDJGMQAqf3BwYGk3l2moa9QDskF8PBwWFAt4cff39xUOh82sLy8vmzGQ9GhcC1I5UlFpIsLhsHZ2dtwgElYyODjoVGOQK7wo5XLZBxHoKZ9rfn7eTQEbEc8HF9ju7q79n7BjkiyjBOCgmEY6TaMaDoctRScUBSk8o99KpZLD0PC04Tvr9k7DLJHUig8WCTGM4tjYmNUISPnOzs58aRFYBXtE2BLMAawnhyWoNRcXFzRMGcUPVoCVlRUnF/f09GhjY0NDQ0MOEUEaxHeBDAjpMp5hwIapqSlLkZByAWbREA8PD2tubk7Hx8eanp7W3d2dmXUawZ6eHucvJBIJr3/GXFSrVfvQucxDoZA94DSwyLjY8xQl+IZgn+v1umW9zMCm6CwWi4pEIpaQg4DD9Pf19TlbAhk8KD4Be6hNGKWysLDggoq0fAoS2CX2Jww1/ngYLX4u3xXgSHfjQmNCgcb+7/ZR0jzCUDM/tlvmy3u8v7/Xv//3/161Ws3Bac+fPzcrRBHEMyRnYWxsTKFQSP/yL/9iEIjAKc4lvPrValWZTEb39/eW4M3Ozmp2dtZNHWwGioloNGpVwtHRkdcTnxV29vj4WKHPI00kGWy4uLjwM+e8Ztbo6empm6hqtaqenh6n7neDLXt7ewZwc7mcz5VkMun3ie0I1vLw8FBTU1Nen4AdsVjMqgGkhUtLS6pUKmb0Kdwo9O7u7pzW39PTo7m5OTcNhGfF43ENDAxobW3Nxd/i4qJub2/13XffeU/DujB6kEIQFRCFNvsf+SAF4ubmpo6Pjz1Wi0aPcKvJyUnLngmYoxEGIERGy9re39/3/QnTArjbbeEZGxtzg9M97gawrK+vz2ASewbbGOAFd2Rvb68lm+vr627+URR0hyW22w9jE5mOEI1GNTU1pXq9brsVHl+mVXSzNlh/kMGjwOOMQN2EfQRfMqBZf3+/SqWSnwHPl/Rg1A+cY9jn7u/vzVKj7CJJn2wOCvZKpaJAIKB0Om3GrFwuq1qtepIJe+vo6EiFQsFTZyhCNzc3Pbrt6OhI/f39SqVSBqhpLiVZ4o/djGAnbEndrCA2jU6no3g8rlKp5CI/l8tZyUQIZjdwMz4+rsPDQ3+PNORDQ0NKJBIKBoNWOJIVgScamwL3bCKR8HSZZDLphv3Zs2fO6pmdnVV/f7/rt+5JCyhLWAOhUMhyetYQkwdI/6fuoAbotsUQLAiTj31wfn5eExMTOjk5UbPZ1IcPHxwwyj3+93//9zo9PbXEWpLy+bxl+Pl8Xv39/VpZWbEsmTOuVCp5bUny7z85OVEqldLV1ZWmp6f93gOBgL8XGH/AFmwTfP9YS8jmYLrN6uqq1tbWdHx8rLm5Odf45XJZL1++dIP38eNH1Wo1TU9Pa2BgQN98841zH7AmSQ+ZJhByjIXlnkf1ClBfLBY1MjKi58+fW77e+DwqENC+r6/PmTmolTjjAOlRdFCnkfdBX8Eaoi6Mx+PuOwBEsdagJJyZmdHNzY2Wlpas1CMg9ODgQKFQSI1GQ3Nzc1YnEKDK/gGwzOfzWlxc9BhjmH/UD9R5pVJJ7XbbI3ix2JJ78eOPP3piE2pUQCmAM9YSoAJEEr0SNpmBgQEDDuSlMGnql9ff9vWzmvCRkRFls1k/ZIp66QFBarVa2t3dNdqEb/T09NRzNvE4EHhCU7i+vu5ABBJ32YDdGwbUcGpqyj6wo6MjN5TIZzi4KDa75Yx4Gzc3N42Q0XTt7OxoZ2dHKysrbrDxYcGwEWSAlB25KiOXLi8vfYGdnp561vbLly/NaMJCX11duXHl55M4it+NC2psbMyAAKnYd3d3yuVyli3hs4NxSiaTKhaLno/N9zI5OemDh8uTghcWd2Njw5fP0tKSEWCeBQUVI7loOih4YPhoEPB+jYyM+JAlOIMGBbVArVZTNps1Os4IOr43/NOM7YlEIi4AyCMArMBnRkjP4OCgnj9/rp2dHVsq9vf3NT09rX/4h39QMBjU7u6uAz5IM/+3PrObmxv9+OOPmpqacrIxjf/g4KAliEjOKX4YSUUgFMBUMBi0jJk5pjBopFtyUeBPZKzP1NSUPfpc5u/fv1er1XLRgcwU9pXnhfcZWSKFKd5fknW7RwMxjgommYZrcnLSxSpS6Egk4sJ8enrae2ZyctLgDDMtkZHe3t4aYOlOmw4EAmbBLi8vbXnAa8WzIlwOBQKMJ4EtFO/4/klbbTabBtBgD5G9BYNB1Wo17e3tGSWXHmaRkuRNAceeZT0yMoXCPBQK6eXLl7Y58Gy6x69EIhGdn5/7zxC+x3rFJyzJ0uZIJKJkMqnZ2VkXWezz7e1t+w0B/lBRsHc4e/v6+jzN4OjoSMPDwwZbBwcf0vd5hiSb3909jGahcK9UKlbQgJTDJKGCACzEVkSDDZtBWBZ5C+RpdBcUACOxWMyBbLCahN8gzUaFQ04JQVeDg4P+85IcZAXAQqgh8kdGxB0dHdlCRMOAUqw77ZvCk+KMYJ5UKqVIJGKF087Ojj31eExHR0f17NkzB5PWajUtLS05lZhAPewHNzc3evPmjSKRiCYmJjQ2NubwJVRavEeeJ9YJijyaS0kOnqOwDQQCZqCYEX9ycuI9VK/XzeKTxYAyBS/p+fm5FhYWHPZJ888d0/g8/pS9d3BwoOnpaZ+HTBEAaKHhhiECOOhOGD85OfF7gnkkPwIP/d3dnVKplJthgCAUXISXBoNB7ezs6PLy0kGISI8BoA8PDw2cRyIR1wlXV1e2MMA+9vX1aXd31zYKvLWVSsWhl6ghYA45IwCjSFxGsg74GAqFHExXLBbV0/MwvnViYkKhUMg1CKxgd14MQCcz4UdGRjQxMaFareak7u5pIpubmz7L2UdMO8FGBADUbrc9oYJzhyaUs/ng4MCjuzY3N/XixQufLwD35CQAmk1MTGhzc9PqKxQfqI1QHpBaj2WqG8DtDmtEUh0Oh+0Dvr5+mHkNGHR8fKzd3V319PRoZWVF2WxWnz59ci2EJL5UKvk9RyIR5XI5tVotra6uOgz3/PxcHz58UDabNeiHGrBer3sU4s3NjQmUgYEBJRIJbW1tKfR50sfZ2ZlHHWJzgcXFPsZko3a77WY2EAhobGxMyWRSkqwGmZ6eVm9vr0djXl9fK5/PO6iu0WgY1IFVBQQfHx+3SrbVatnqyXQbfic2sL29Pf3pT3/SP/zDP2hgYEDr6+u+v8lPoqkmj4q90/3M+TutVsukG0AMNeH29rZubm701VdfSZKVWUNDQ1ZxHR4e6uTkRD/99JPVUN99952i0ajD0FChpVIphySSqVIqlbS4uKiBgQEVCgVbsLBvJZNJN/RMKKDWBPANh8Me/QuZIz0wxOSsUAO1Wi0rSbAODA0N6fz83JkBkHHLy8va29vzuQi4iOrmL3lBmP4tX3/rn///q9fPasIpgJGcSXJgSiDwMCeRlNyxsTEjlNFoVOFw2AckkizkW/f39xoeHtbk5KRHM3CwI0/jdyAHBkmncMSXGY1GLaPmz/LPhChtb29bCgozAFPJ+AiQO1gxQiFAECmGmCGODw8Un6TFqakpy9VhypF/MIYKhJHCjFRJLueenh6jVJJcdDIXkO8KxB52RXoozuPxuCUveMloKpAYP3/+3EFHyMkJgqpUKmZgufzS6bSi0ajROqRrpCoiVSRchwsYhI3iGQZU+rM8aGhoyN/pzMyMEfN4PO4LAan54eGh5ufnLXPGEzkxMWG0nrnawWDQoEYwGNTs7KzBhGw2a9YFzxFpqAAgBOngoT4/P7eMiMIVDxOjVg4PD30BdNsaFhYW7KXM5/NmT5ipjcSYucmwVDT1rKFwOKxCoSBJDrkLBh9GJ/X29urVq1eS5CabmbEctBTYsFJI8M7Ozszo4c/CFgHghAyrXq9rdXXVnqOenh5LlrGVdE8xIFQINh1pJ40jCendABwj+GiaGZ+GdB3ACzvBycmJZmdn3eCDblMowlCenJwYWIMhSKfT6u/vd0gO6cMUk2QO1Go1zc7OWhYnyawintlkMmmbiCR/Z5eXl5qdnTXSzoV/fn7uSxOJOY0JNhR8rIzUYl0xAgtWnWLz5ubGheL8/Lzl8M1m01JeGgfpAaCjOFtdXfX3ToHKRY+EFTCH6QukQTMXHek0CD8S1L29PTP+2IdgeynuAANJtaeYxhowPT1tEIW8BKwxCwsLbjJCn0MzCXnLZrMO9UExgnR/eHhY0WjUAVV8p4ODgwZzCIfkzKLhYZ441hMANlQjh4eH6u/vVzKZdOPzf//v/zVQIj0EVv393/+9rSnctTRFMNGXl5fKZrOWU7MnCfQDJL+5uXGg2v7+vgYGBvx+YOyTyaRqtZrVVfjGuQexHLBWBwcHHfpDdgOgO0Ar6rXT01OHRwKs07wjA6Ypl+R/xioUDofdIKTTaT9TEotRmMHunpycKJ1Oa3p62uf5yMiIEomEyuXyI3AfaSsjme7v730Pwm6dnJwon88rGo363Ac05b0iaeWcp/m/vr52CjX+T8DyZrNppU+30gCbF9YyLEObm5sGYvHbQmBgcTs6OvJZScEN04yHOxqNmkFjfXFfA9AdHx8buJH0SOkGqMznJ9m/8TkZGqsSs9RRHBFoBTDK/iEPBcsidpl8Pm8mLxgM6tmzZ/bqo0IaGRnxXvrhhx90f3+vVCpltQAqt3a7rfX1dTegjG3r6XkYuYkPnFqDuyKZTOrZs2eKxWJWq/3ud7/T6Oio3r17p2azqfn5eX3zzTcGX4eGhlyftNttW+Du7u701Vdf6fe//70bKOnBevEv//IvarfbevHixSPlCs0TidkErtLssq8B06+vr/Xx40eNjY0534czHn8zE34A/ZeWlhw0R0gZ+TPUfdJDY12v11WpVDQzM2O58vLysur1ugYGBmxHiMfjevr0qfr6+pybQWbGhw8fXOd1E2tHR0e6u7uzPQY5Ot8B5zhZJfxMzm/sH2Q2kV8A0cKLM5I7EIJkbW1Nk5OTrkkA2FD9fPvtt+p0Ovr48aMikYi97ShuP336pFgsZlCaRHTG315cXHjN1et1j86k9uR57O3tefrPixcv/GyxnHKXk20CK04NOjIyonQ6beUy5/rFxYWVhBBe2O1QXGGJ5J795fW3f/2sJvzm5sboWHcISHcQFJcbnmUOb2S1SMYIt6AZm5qaeoRMU/DhQRsYGNB/+A//weEbpM52j/NhPEJ3guLu7u4jWRleT3w8jG66vb11s8VYDKSSyPdgjpDugUQSEBUIBFSv183eEfAlPfh4kNSNj4979A1hQsFg0B7hSqVivxGSVQKSgsGg1tfX/Xnx4xMsBQsR+jzDm8KCJmJqasqebkIwUqmU5whLspQ6FovZO9Jut5XP5zU9PW12lBAZ5tGWSiUzBhQld3d32tnZ8XcPywjSSrNDAUcglyQ/D4owvLrdCDONMY0/Bz02CGZa01x9+vTJ3kaQ0ZGREc3NzalarVqyyezw3d1d3dzcPBpzQYNCajqNaCwW09TUlANYSK/NZrOeVX97e2u/NjPI8fkAmCDVx9ZB4UJByOXRLddl37AGKYQJjYOl7+vrc7GEP4lmA3b/8vLSzwDmFoaWUA/SlW9vb/X8+XOzpkhSAZCQhNGQDA8PuzGGleT/6x5xRoNLw9g9YgW2Do8chQjFGiwms7u7Q6Ngo87Pz/XTTz89mgWLGmRra0uRSETFYlFzc3OKRCLeH6SaB4NB5fN5+7IZuUTaPvuZUSPff//9IxVBNpt1wjTvD4UAkjhmdm5vbyuTyfhSXFpa0vn5ua0jfDaawUajYeUCqemsl2+//VbHx8c6PDxUJpMx4Imcl1AkJiXwXJDatVotj1/KZrMeJwewksvlbIfJZrOam5vT1taWvWoU2YCOKJgoWPEGS3qUW4HaBjUK7DrFZHfOBKO0sPswr5bildwJSQ5PCgQCWltbU09Pj2ZnZ5VMJi2VRB4MS0SgEYoGlBl4elmbZDtgSTk4OPB7LpfL6nQ6+v777yXJjT+FJ+co2REw9LD4yJ2RDw8ODvr5T09PGxjkffX09DwCxWiOANq6z0P2NSALTDPsTHfeAXkJnU7HgB7ZIwAegcDDCM2zszNNTk7ajlar1azqwkrAC2lmd9o6/mvqkEaj4eKS7xSJOQoGvgdUKs1m0+AN8tCJiQmtrKw4WZgwRsDi8fFxBQIBhyul02mrfQBO8GMD8CLDhpFk7Nbd3cOUGBp37mGARQASZL+MygyFQg4iXFtbs1wfMJV7YGVlRaHPU2EAPzjbpqen1Wq1nGPQ6XQMtAPocwfAJPI7+vv7NT8/79BEwGQaU2qcUCikXC5nsAImnLMdxQ2AVDewlslklMlkHLo1MDBg4HBxcVGnp6euCdbX19VoNJTJZAwKkRNRKBR8t6FQ4IwHTGEKD2GsABaAWbOzs0qlUiZeqtWq3r59q5OTE9VqNZ87KMe691yhUHDjW6/X9e7dO5NBOzs7BpBgiQmuo9lGOh8IBJwwD8NbLpd9TjJlgcA8QDgadPKA9vf3bQHk7OLPUReiBGk0GvrNb36jiYkJFYtFW0nu7+/tf+bPDA0N6Xe/+51++ukn2/8A66vVqutYao+NjQ2TXsHgw9zuk5MTB8V+8cUX2tvbc2r3+Pi46zHAUe4xSa6VyBRAIUBaPaFmBwcHSqfT2tzcdOO6s7NjBSyflTuL7CVILbzbZJgkEgkdHBxYzcHkEqwABHJS1xAqiFKLmoj3/eHDB+3t7RmcZB1CDBCcCSHYaj1MUQkEAp4zT681Njbm7IP9/X2HdzJtaHR01Pue3iAcDmtubk75fN5gPKTIX/r6xRP+179+VhOO145CjcKTAooLFj84SHe73TYSTrgATTtFMP4XwkYouPGCUgheXV05tVv6c4FNcNTQ0JC9Qm/evHkkHSYIpqenR4uLi94IoL+SjCaBYCPVkx7CDCSZ5cAXDoOCvINDQ5Kb3uHhYTf0o6OjevHihba3t9VsNvX999+rr69P4XBY1WpVMzMz2t3d1fr6upl2UHiYMeTf+O4oKPgzpJJ3h4qB+tPEsem53Aljo4nmUmF0BywD7MfY2Jgk+WKSHgrn0dFRB9tRoMViMSPtyGJoFkdHR60sgMUBYeSzIjUCEDg/P7dkjFAeJGxIcwl1oTHZ2tp6lII8OTn5aMzS1dWVZdq3t7ceu0GDS8NIqBbp+p8+fdLg4MMMd4rlnZ0d+/fJE8CvNz8/b1/d73//exUKBZVKJX3zzTePPMwU2xRFPT099kmzv/j8NHMkpOLVAwFm/AwSW4ppLlfGM8H+sL9CodAjLxUMCQF9L1688JrrliDynYXDYdVqNcs/u4N47u7ulM/nDdwxF77RaHg9owwA6GP/jY+P+7Pi66Lp4oV6ZmhoyKxN93NjbSFBXV1dNbN8fHxsZg7m7PLyUh8/fnSzhzVAkhU0MKajo6M6PT3152m3226G+I6YPZ7NZh18RYPCOTo4OKivv/7aa6/dfhjByNkJA01YGSE+ZCBcXT3MBT0+PrZVhSKacxbPG8DC5OSkfdJ4lXt6elQoFB4pDCgQkBTy71EIffvtt+rt7TU7QIMCY0/KfncjLcnJ/LAz7XbbklS8+tw5SNyLxaIVFzxTzkHGtQHmVatVN5KE0N3dPcyqx3c+ODho9RSN7/T0tO7v7y11pUFCssvZkE6nHV4UDAZVLBa1sbHh84qCDtn08vKyk+KxJNCEUmDxGTi7yFUIBAJ+lgDWNH6EbAEI0QCS58F32D2CC/k08kLOHhoqFAd4xQGjAUpgexkVxBnD/cs9SIOLpYNGGRYb/+vd3Z3VA0dHR75Dl5aWvF74+zRteJIJaI3H475rYPwPDg6sxoGRHhgYcH4L40P7+vq0urqqQqGgmZkZ1et1j2wDoEZZhmwZVq/VarmJabfb9n5mMhkVCgWPqsLSAaBRrVYdlortA4k58mjybVijBCptbGy4CYvH425kWNPY3rBG8VywkUFIABIjW8bTjOXg9vZW2WxWR0dHToHnP7xX2PzT01NlMhnF43Grx+LxuF68eOGpAXNzc57+wnQOQEzmXZOezfv89a9/rVKppD/+8Y+anJzUixcv1Gw2JcnPqNv+RmMD8INkGvXW+Pi40um08ygIVtvY2NCHDx/8TNlHoc+zzI+Pj/Xb3/7WNeTd3Z1/TiAQ0KtXr3RxcWEfNfd3vV73/UYtnUwmtbm56RpramrKY994hrVazaA/4amowti7AJGE/ALIQCycnp5qcXFRtVrN383z58/19u1bra2t6euvvzaw/vHjRyu6qNcqlYpCn0OFAX0BNBnPRko/Y+O4x1FRQMTBvK6trWlkZOSR3QyLAJOWCJUEwBoaepiKRPgrBA91pySD+fF4XM1mU8ViUa1WS9ls1tMeFhYWDIRwX/N9YWNZXl42uM8zJosgHo/r8PBQHz9+9HPh+ZOlRE5BIpFQOBzWwcGBrTxkQsTjcedrce6yz/Bul8tl1x/YQqitCdTkbOgOT2Ys6YcPHzQ9Pe3nT6gfdyykC2TfL6+/7etnNeF4D/D4hsNhB5QgESGJFGnjzMyM/8zIyIg2NzeVz+f15MkTj9saGxtziAYN1/X1teXm0kPhsbe3p/n5eWUyGTfUNL/MLuc9Hh0duTBLpVIuBmmUQNdB+mmekKXDpCNvpYhBkguKhEcMpFWS2VI8GrCxFCM06SsrKyqXyx7t0y1LS6fTZvopokKfE8GZIQ3rCtLcPfaHwhE2lIAkZmBTQMGWc4FS6KBqoGiD8R4bG9OnT58kPRxu0WjUDA/eHoptmLpoNKrNzU09e/bMEmtkp1x2pVLJLDuhM1zukhwyAmNAAA5IPWFRR0dHnq2MxIyLk2J1dHRUT548cXO4sbGhFy9euGgaGRnRjz/+aGUCRSNoIgVnp/Mwkg3k8PXr14rFYvbbwDTRoFerVb9vJLQgm1zgoK0DAwNeaxTYBI7c3d2Z0aFoppGBDaaRg/kiCLHb50bgFOgvia6tVssXAb+fdSxJu7u76u/v15MnT8xuUqTf3t6aeaeopJEhfR+/JSxNPp93sjsXI3sZ+SXyWMamEFaFIqfb89QNnCG15s/D9I+OjmppaUknJycGEJHhEagHa0CSd/dFSHjV1dWVLzpmYBO4AjtD2BSAGRcdRXK1WnX6bF9fnyXGNFGAL6enp9rf39eLFy/MCjFdAhYolUoZwYZ9xfv8008/6Z//+Z9dEB0fH2tqaspJzQA7eLZR/BDMdnt76/AbgnRQqqAcolik+crn85ad0/ARMsV6RXUBEFMsFtVut7WwsKBAIKDz83P7bpHd8+/6+voM0mKVYY+giLi5udHt7a0neoRCIbP3NAAEmnHGkrtAojYs8sbGhur1up4+fWp2n9GcpGUvLy/r06dP2tnZMeBAQ8DdkkgklMvlXCDB/uAV5G5Bho/Xl6aX+xSgpa/vz6FsSB2lhxBQgOXuWd3YpAKBgN68eWOwDTUTyqebmxvL+vkd5MFwB9PAz83NOQ8Fqxc2KJh4AJXe3l5dXl4aJAOg4OwCLJyZmbF3k0YCNQYgFGut0+k4KJRmFpvc5OSkmxHYZ/Z9KBTS1taWenp6fH5gKcHeAHjE3hgZGdHY2JjnCNMsYJNDvnx+fu5zgjFt7OV2u22/NqPisH4wDQR7UE9Pj0FpgDXYW9RD7HXpYR7z7u6u75vl5WXt7+/7DoXFvb6+tjSe+wVJNY1NNpv1CCrO797eXkuX8aAiweWOvb6+9vxi2FbAA0lu0CQ5Z6harbreRGkDkAmYzxqVpPn5eV1fX2thYcGS+3Q6rcHBQU1OTnp/Iuflv1ExDA0N6T/9p//khjAQCOiHH34wcI+FIhAIaG5uzrY/CBMYaNYT6rvr64cxsdiWmO0MSNvf3+/zjzMVlRxgxPz8vEZGRlStVtXpdCzFJmMpl8vp/fv3VnN0g5D45avVqkqlklPS6/W6pqenlc1mzVCjGOjt7fWUFcDaQqHgsFYA7mw26/oWUgWrSDAY9ASBJ0+eaGRkRN99951ub29NrCHF71bNoEDpdDo6OjryzyKnoFsiv7e35/nxjHFEdcR4ZM5K1iKM/+DgwzhQ1LPUHoBpNOHpdFqStLCw4BwFQhD/rT3v4ODAjX0mk7EdtPE5YZw79eTkxCq2m5sbFQoFRSIRpdNppVIpbW9vOyS6t7dX2WxWBwcHBgyq1arPZUgFQtS4q6jb2V+cBwAdrPnj42NPnCKoGrXv6empKpWKJicnf1Zv+AsT/te9fvaccDx04+PjLtxIXCY0B9YTRPTo6EjSg2x3bW1Ns7OzmpqaUqPRUCQScZPR09Pjhvn8/FwbGxv21nI54MlDVtr4PD6JA+jy8lL7+/tO1yRQjGAdSW5wKACQNkqyh53GgU16f3+veDzu5onimJ8Nao40GjSJi4mQHP5+t5Tt5cuXnvc7MDCgd+/eaXJyUtls1n+XhGoWeywWc3HPd0dTFQqFtLu7q0wmY680DQvfJbJQUixHRka0vr6uyclJS/wIRaPoWF1ddXr40dGR8vm8WX7m8KJm4PNhG8BrR4IzLFSj0dDCwoJ9Kni02+32oznQ7XZb/f39KhQKXh8wr1zKNOCrq6tqNpuW/uNLBF1OJBJmGjudjjKZjJ8lPrS7uzvNzMxYDry6uqr+/n7t7++78Gk2mwZnSqWSCy3kh0jZCRsk3IWCPpFIaHV1VeFwWM+ePXORDlvHiDMKYLz/HKT4C0ldxid+fX2t+fl5I/80n5IcCghaDZgwNDRkFoE9SMMwPDxsCfOnT5/Ubre1tLRkSSchTjSv7Kvu8MHJyUmdnJxYUkwTPjU1ZdCHMDv2oSSDWzT4t7e3+vjxoxtYLizpz7J0LiGKKlBhnjHFcH9/v+LxuGXi3QBZJBKxPQOQLhwOWykwMzOjarWqRqPhBgmLC2wwzMTTp0/1ww8/mDngbKDISSaTBvJgqVEI8b5QN4RCIZ+FFJjLy8uamZnR69evVSqVlE6n/TtozCkOpQdP3Pv371UoFLS9ve31AKMwNTWleDzuwoWwOdgpitKbmxuHOrLGCHPE2kFg2rNnz+wlD4fDBhy2tra0trbm0Sw0T729vU4RxnLT/XsArsLhsIOFSJ7GG9wNhNZqNaeSA77yQrnFnZBKpQywhcNhMwzpdNqFGEUlste7uzsrKP7pn/5JlUrFjTyhSZzp3HnNZlOlUskNMHJk0r8BuSmkAGZRDcEU09ggYaYRQhp5fn5uUIokeVQqkuyxn5yctMSS946nmMAosjFQ7ADcVCoV76lWq+UpEd17khE8gLqpVOqRNHNoaMhnCrLlbuk94zvPz8+tcukG4rFr4f0nCA6AA7YIf/Lp6amWlpacuoyPvTuj4ezsTJVKxdJXzkkmDSAlxypEg4kCEPtOT8/DTO9iseh7h6aDM5sGlwaPiRVYTvBZUwdJsjqBz8Z4qqurK6XTaQMENMqccQTowbihYAAkJAQVpSBAL8+XPQgA0m0dQRXS19enSqXiwM9CoWDCAIk/EuJOp2O5MqBgo9HQysqKDg4OlM/n7S+GKOB5YAXs9j8TlHV6euoZ1cxZ5q5CPn93d6dnz57p7du32tjYMPA4NDRkFdj09LRevHjh0NTf/va3tvwMDw/r+fPnVkoVi0V1Oh0VCgWf34DMkqzqI/sFhRZWFBRzm5ubzj/I5XK+h/juAG8BVCqViuXTAAFkLzQaDfX29iqdTptRRhXabRFCpfPdd98ZJAOcQrrMmiWEj+bv9vbWk2U6nYdxsqgJecaAi9SefNbb21uP1mRtnp+fu/bd2dkxWcR7QC0CWNSd5E/2UTqdtnoJMII+AEXe0dGRBgYGND8/7z08OzvrdHQyCLrl8NFo1MD+1dWVaybUNihLukfTXl1dWcn54cMHv19yA/DQX19fq1wuOywTJUU0GnWjfnp6ark7svruvYcyYnx8XHNzc7YQEq5drVb15s0bhcNh3zvkZKHwoK765fW3ff2sb/ni4kL5fN6sJF61s7MzJRIJS6Q5vDqdjvb29rS3t+cH+uLFC7N0BPIgaYpGo96EHMZc9sj48HwQIoU0jkvo+PhYlUrF6FK73XYYwfX1tRKJhJrN5iMUn8IbWXK1WvX4H5pLmgMOnO7wHRgrGGAkt4SWwADBFuK1IrW50+lobm5Ot7e3ltNJ0tbWlr1EmUzGCD9oHwgfSbrdfkOKNRhQmCLGRXU6HXs+A4GAms2mw6tA77oLY2SwsGJs0kKhoKGhIftmUDLgx6Ng5ZJgdAfBQPjU6vW65ufnValUzFxzkHNY0/AcHx87zAOEOBgMevTW8PCw6vW64vG42UgKKTxVJOrjc45EIqrX634eSEJBM/Fb4knu6ekxiHR0dKRgMKgvv/xS19fXDuFjVAzPGha1u2BhVA2KkXK5rDdv3ujJkycOlqEgiMfjnlXK+DTSZLtBGsYLjY6OesQbYXt3d3eep9vf3+8xTUjvKFIA02CGLi4u9P3335uRIbCEJoTGh+dcq9W8/7AtIMNqtVoG05AEgkLTRMACBgIBg3tcSjBVAEV4EwFlCEFLJBJuANiD+EIvLy+1sLDgQjqRSHg+LYnSINag8SRSf/z4UcvLyzo9PVWtVtPKyori8biq1apZeuTVnA2ESFH04tXHo3x8fGwPOIAT+xVJMGDO1dWVfdbRaFSZTEbSg1qIZhTASpLZDcaVRaNR/eY3v9EPP/yger1uUJB53KVSSZubm1YZYAOBnb+7u9P6+rqVI+yl29tbra+v21MeiUQ0OzurhYWFR6w31pfz83OPdqTwIWuh3W67cGCkmvRniwH/zP++vb01q4y0//Dw0MFinU7HGSbIFVEsUMxgsaDJAMCh6FxbWzPQiDUJz/Pd3Z2bIQA37qBO52HkE15YmGwYYjyMSBY5p0jV39/fVyKRcMMDq9GtfOnr67OnmVBNFCXcc5ubm5qcnNTs7KwDI8/Pz7W8vKy+vj5LY2H3eN/Yk1AIkP3AP/Od8HwnJiYUDD7Moi6Xy4pEIlZ6weBLchPT399vlpBnCoB4dHSkJ0+eGPBnvWazWTf5AC3sOxpg7AGSLGMHCMeSgz0FNRZec5qru7s7HR0dme2s1WqamZlxYBzyUEIhj4+PzdihzGKPHx4e6urqSktLSzo6OtLs7Owjaakky5Kxj6FwI0Szv7/fDTP3n/TgoQeM3d/f1+joqGsdPof0ELoIe4iXu9uXTXJ3tyJkcHBQ09PTBrZpeGZmZh6FW9Gw8gyxzpHlIskKBz4H3w3n7vz8vK0QqC2YltEdsIl1YXx83NkrfX19ZoALhYKOj4/tVQZ044x49uyZhoaGHGRG6vXq6qpqtZqePn3qEZUHBwcGRKgJJDndH7UPDDI5C9h4JNm+gxIkFotZYYO1ip/F1ARsEjRZkvw8UQvGYjE1m01dX19rcXHRNkjOOQgMxrwGAgHfv4FAQMlkUoFAQOvr6zo6OvLEIgJh2+22AU+URdgkGWd5eXnpRHAAQc7XVCplFQBAJ2dttwKWGh6lVLeyDZIpl8tZ6YQqs9VqOasJqwW9RiQSUaVSsd2VtYZ94+rqSvF4XJOTk1ZMAiBAhExPT9u+xp+nJiclH9AboBiLbiqV0v7+vq2bPT092t7eVigU0urqqvb39w1IXF5eKhKJePrQt99+q4uLC21sbOj9+/c6PT3V8vKypqenbVEChGD/8blRBREAjA1rbm5O0WjUxBkyfPYIYwapc3/OiLJfmPC//vWz09HxOHBA0xiXy2UjnK1Wy34nihVYdAqdYDCoRCJhpHJ5eVn5fF4nJyeamZlx8i9BAoODgyoWi2bcYe8Igmo2mw55AJlEuoGsCDaDWbEEPBFsdnh4aD9SIBBws3dzc+PwqmAwaEkTyB5FEOg0hSENaLcUlwaCgoE5pmxSZPOkuOMfyufzBgTwTBO4QKMCiAFzViqV9OHDB2UyGTec+HKQtXb71/v6+nRwcKDR0dFH3wFFKN4wkjEZqUFQCqqIX//615begMzF43EFg0EVCgVL6xOJhFl1bAJ4hSYmJuxronn/tym5Jycn2t3dNShUKpU8pgSJFyACBQYMZzezsLm5aa87zQEXzt7enoLBh1T1VqtlKVmtVvPlQMEEu84lQtOM55rvj8IY1pzvgODCdDptj1K73dbGxoZniZdKJStBADEkGYUm2ff+/l57e3uamJhQNBq13BPvVjdbCpstPbBiTBwAjOh0Oh7Xtri4qFwu56CQ7oax2WxqbGzMo28I+6FAx9fKBATCZ+7u7vznu9ULQ0NDnrMJCJXJZNT4nBpOYCHFNF5g0vC7E3yxZ/B34/G4Go2GZdLsc2RgZ2dn2tvbM4MIuEgR9z//5/9UMBjUF1984XnnMDNI/ZHxA14RnIgHF/CFApygP/IX+vv7tbW15ZEsNCEohG5uHuaWIi+bn5/X5eWlffak1TNL+v7+3iP9AoGAnj596lyB/v5+7e7u6v3797q4uPB+ODk50f39vRnvpaUlZ3oQYsf58OTJEwNiX3zxxSPGjnOQNTw7O6t6vW7rRTAY1PPnz7W4uGi5K4UsFgmYHfYUzTYAFLJKFDQE1DDVAeCFfYqSYG9vz352GkqsI7CWgJmoiWjqR0dH3YCMjIwolUp5Da6urioUCj3KBBkaGvKzRpkAq819BjBKQ3l/f+8gRFjh7u+Edcu/J1UbzzPALIAk5yHj6WB/7+7unAsCC4YsFFCCe5Q1m8lkNDIyosHBQc88vri4cFEME9S9DmkExsbGVCqVNDg4aFsaigVsYFNTU1YbZTIZbW1tORGYOxYgEkaR9YiViiAmLGjDw8P2iXJ+cA7DTFFT9Pf32wt+eHhoBQvTHnZ3d32/P3v2zIAfv5ucE5pglDJXV1cGyZLJpMEKshK4hwDyABN5ThTTAD+wv5w7gLCTk5MGqbA6AG4+ffpUjUbDig72IQ0vii3uPCwGAJqTk5Nmd5vNpuWr1WrVvxOvdXf+QyqVsmojkUhYuUFjWiwWtbCwoKmpKf8spNLYm/L5vEFNCJJms2kgfXt724FiKIKolVBGTExMqF6v+znMzMzYNjQ9Pe07hLrs7u5O5XJZh4eHDu4jibrRaFidtb6+bkCz0+lYkUJtSB5IMBh0ij+gF78HYGhtbc2MPWoogG/UZlg+mFbAOcCdTANMPUK45cTEhOLxuLa3t+0tRxWWy+XM2Pb392tpacngHncmoCGEGBkMnDHU3dS9nOeo/VgTrGn27enpqV69euWQSWrgjY0NWxkBOKib2u22ySWeOXs9GAzqV7/6lQFA7IDr6+tecwQ0cz9dXFwolUr5/qMhxQ7HSOBCoaDQ53Go7JnuVHlqbiTvhEpjn1xcXPR3BLCObQCr4Pj4uBVTkBaQG4ODg84qwO6GEhfCg2fFeMNUKuV7jn0wMjLy/7DI/Bwm/Jcm/K9//awmnEsK5gBZIkxPd9E+MTGhg4MDs0P4O9+/f690Ou0NTJFAWjaXO2nINzc39viOjo4aZSdVl4VXKBTsx/rmm2/MXuJVPD4+dpNJQcBliE+Z8DP85YQfEWzEmCBkfMiukQx2F2TdUlzeAwwDbCahI/hJSHrkgkNGc3NzY4lOs9k0e9zX12c2jUYQ6VA4HHY6Is18IpHQwsKC3rx548OOVPOxsTFls1kzv6enpx7P9OTJEwehURgMDAxYRknjywF7d3dn9gPJGd9DIpFwEiTFXF9fn5tXivhAIOBxT0iWOHiRdHP5MhszGo2aLSIABBR7YWFBtVpNY2Nj2tnZ8cXX29urvb09VSoVLS0tObE8mUy6yUXed39/bzbk/PzcIR00o3j/q9WqmUBAEw5yiiAAGkkGTdhf2WzWoTg9PT321lJ4ohDoZv6Qz4Y+z49FDoWUt1AoWP5IeifFd3f+AoAPCo7uMLK/+7u/c/hipVJxgjjz4PGKdfsqu+dO82dpYJgEADMGs9v4PAoFlQDfFaF/NP00z3xHSKr4PJ1Ox1I4CjYsBN22CFjXmZkZbW1t6V//9V8NOlFM01AHAgF9++232t7eVrFY1MjIiILBoPb29iwzLpVKGh8fN9MJAAGYgIUEdQd7m8Tgnp4ej93DU3l6eqqdnR1bUBYXF82kUMSen5/r+PhYGxsbjwo6mivWCt5cZKqwgzTRMKrHx8eKxWL2Y8LCI7Em9Au2//nz58pms/rw4YObj9vbW717905LS0v+DmjkYFMSiYTHhVEoDw4O6vz8XDs7O0qlUioUCs6AGBgYUDQadepvPB7X3NycAcW+vj7nFRB8Mz4+rs3NTSdFT0xM+C5LJpP2DM/OzmpnZ0cDAwP6x3/8R7OshUJB6+vrevr0qe86Eqlvbm60v7/v34E95fj4WLOzs5qdnbWVAmaQxHpG+5ADgGwRlgr7Df+O++f8/NwTDrhHkXrCNALqcX/wuw8PD51FALMEMA4YgJqNGfXj4+NWaVD4E8BIw0tg3sHBgWWqhLienp56DCP2L+xXNPGNRkOTk5M+97sLYpQ2rLmLiws3d7DO3FEwud3TB0ZGRjQzM+Pv+OzszOcI4AjKIRRxWDO4V7sBTcA9WGaeB40I9y0gHiAtijXeL2F/rEeyLwi2RKkBOwWQQMOMIowzqre3V7Ozs5bBA7JRV6C+K5fLzu6AveazXl09jHfqBoUgHUZGRrS4uOhxfYTxpVIp3/3FYtEKFCTPi4uL+v77723dgMHleQGS4pNdW1vzXGVGdiEPHhoaUi6XMymxsbGharWq09NT2x24O2OxmDKZjAEU6tZSqaTt7W0D6dQTJGkPDQ05vZ9cFJ4fdzr7682bN5qcnNTW1paOj4/t2Ya1Rz1EAwj4DEhIPdhtE+DnDw8Pe7wVZ1coFFKxWNTKyoouLi707t079fX1aW5uzvklvb29mp6e9j3HOYY9g9qVkX2EsgKCoq5cXFx0s8i/h52vVCpu5lHzUOeiZqQ/uLy81NTUlOsQ9q0kv6d2u+37jBT/n376yQDB/f29dnd39Zvf/MZ3CXa6Vqtl5RssNHcf9SBgJbYkGG/mfjOLfXJy0n0CmREoAnjmw8PDOjg48AQE7lLCLcm7AkBdXl6WJNc8Y2NjSqVSnhwEkEiNg5o1nU57P9Xrdb19+9afkbBH6uKpqSlbXqg5UXvF43HvDYjR/f19TUxM2HJ1dHRkpSiZL7+8/vavn9WEz83NeY4iHq6DgwMdHR05jAoJIX5DGEXpIXiHIA3QRaQyyGVBuWChg8GgstmsJD2aUTk5OekD6u3bt26Enj9/bpk27Fo38wFKRAhJd3AKCB+jHihGCfmKRqMuBgigg7VAxoOEB6RXeriASX8H+ePCorgHCWUT3t7eamlpScPDw8rn8zo8PNTx8bFGRkZUr9ft5QkGg/ZvkkZK0urg4KCWlpbU39/vVOtoNKqzszOPPAKRo5mBWeUzEsgBEwjzQGry2dmZ3r59q3g87mb1zZs3ur+/1/z8vO0DXKCpVMryb5JQY7GYfYUULrCHwWBQkUhEu7u7WlxctEyOdcMc4bGxMasyaCjv7+/17NkzS58BPUKhkL2RrINOp2NWg3nMhKqhfDg7OzM7EovFlEwmtbe3ZzkzrAEyt3K5bOk4xSY+YrziAEHDw8OWSIVCIeXzeR0dHWl4eNgyJdZYu902QkxhVa/XlclkPK4GlodAudnZWbVaLcsjacyQflNY0rhwIfzud79TJBJx4QWLi7SWcCpYXOwSXEaDg4Nm6/r7+31ZSbKdheYWJj0SiTiUhQK52WyqUChobm5O6XTa45AItOG/7+/vNTc3Z6YJwAjrCBfzhw8fLHknHK6bVYF5KBQKKpfLRqbT6bS++uorF/AU8/gn+fmgzvjcOffwfAJGHB8fq1qt6ne/+509rYxJpHg6OjpSpVJxARAKhSTJc8ABg25ubpROp80SYwNB2UHTQLFOUGb36BrAvHb7YbTey5cvDSaMj4/72R0eHnoOM40IDCJFCUnReMc5Q2kIZ2ZmPPOUs5p9inwVkBFWtLe3199tLpfzzHq+Z/IQYKNJc+YOODw81OrqqpsEzvRCoaBWq6V37955TvvU1JTZw97eXk1MTCiXyzm0D9AYhhmwAmm09OdpETTRMCY0FcjLmVzQnYKO1x1ZIxJo9lkkEjEICLvGuULwHmcBzClrYmJiQpubm/ZjU5SibsOOBLPS09NjlQ22CBpl/OOwXQTGAX6wf2OxmK1L5+fnTtsm9ZuGkN9JAJIkS6BpQMky2d3ddYMMS93f369KpWIbEGArwCThQ8xaR2U3PT3t84l1ipJDegikQsVVKBSc1M2dTjjt/f29Q9DGx8cduri9va1Pnz4pHA4bQKBxJ82bzwoTHolEnA0CMN3pdAziATrxZ1Op1CNGkH2Pao26i5ojlUpZXcUEg27QEXktShJYPcCGVqtlQJsRpjQ1nAujo6Ou67gPUB4MDAxYhkymA6nT2OiwPfX09Ojp06eekvDp0yfd3Nxob29Pp6enBtXYJ9yvrF3YeXz7qIuGhob0d3/3d9re3rbiA7acEVYoCUmepmFGaVmpVKzYAIBlrbI+V1ZWDNTy3LBqEdJ6fX3tGhVVGD8HJent7a0ymYzZTRo+gm4Bdnj2yOohSjjDAeBJnefv7O3tufYdGRnR5OSkdnZ21NfXp9nZWa8DmGYsGrVazaGtAIfsybm5OZVKJdsyUEUQJsl3QuPOlA6CCgE8IRLIgmKf8r1Sn7RarUdBtp8+fdJvfvMbh/WyP6h7CJ9LJBJWrcAEo9DljBwYGND29rbS6bQnAcXjcTf4mUzG4AdqCbKhUK9WKhVPueBsC4fDCgQeAgDX1ta0tLSk169f+27mWaDQ4ztjvx0cHHj9BINBPX36VNvb21pcXHQG0x/+8AcTDKi7JD0CsLEs0rf9Ja9fmPC//vWzmnBQXQpPmk7m6bFZDg8PHcgTj8cdLsEhyQxlNn8sFvOmPDo60v7+vtrttuLxuAtNAhmOj48thwL1x/eRTqeNuoGgMioAqTnNBYEloNykRnIpc4iA+HYHOrA4uQwpCvDnERTBYVSv1120cwnigWk2m8pms2Z8uyXONAS5XM7NsiQVi0WznRR4FHeElOAhXl9fd4ELw8DzYKwbhzOyQ8Z33d8/hJgxW5tDFwQdz15PT4/laP39/WalYG7xsh0fH2t9fd1N//HxsZaWlixt77YXjIyM2D+DXHFgYEClUsksLHLbxcVFS0VRCTCajGcC646/CZAlHA4rnU4/kljCVDYaDQcVXV9fW+ZOgUToIEw+qaDhcNgMABL50dFRra+vu1HhEmM2tKRHvh6SgClwkSwh22L9g96D0O/u7iqXy/l5wl6iYKBIkh4C7YaHH+Z2DwwMePxOMBjUu3fv1Gw2lUwmNT8/70YTdB+kH/CNhF1CiGBtbm5udHR0pLu7O1sSKJDb7T9PHejr63NzSaEHMzw9PW2GamxszMndNI+sWyT3WAiYCABoBfuGBJVLWHpIcq1WqwoGg04q5qLs6elRNpu1XJngsKdPn2p5edm5DIw4gYHD87awsGCp3fv3782EABgBAp2fn+vbb7+1BQFmMJfLPQqMYeZysVi0VxEpWSwW09dff61SqWRGjSCbnp4eVatVT0SAcYalOzg48DlWLBYfhbWwFmu1mptcQB3CIM/Ozjw9A/aBIiQcDjtvAybw6urKvvpYLObGEdaEZ0aI4dHRkc8lACC81rCxBwcHZnLPz8+1trbmiQcUe6w5Pn+r1dLu7q4lrwAXBAACmgB0XFxc2JKDQqO3t9ehW8j7aAKZ6fpvC5Xx8XGlUik3hihC8IUTlAjQx75l/9frdRer+JjJAjk5OXEY0/j4uDKZjP34hHQFAgGziTSoNEYEM97d3Wl+ft7KM8A7Gnzure6MDtQ+3VYOZL6w0jSeWEI+fvyoUCj0KLCJZol0dO5rCm4Aiv7+fue8wCoXi0V/9pGREdtaent7DdjyXcOihUIhbW9vO+cB1plahZ8NSMD51dvbq+XlZSuWaCrZd0NDQ8rn805gZmQWTRDnP9kcFMzc+YD6sOJLS0tmfZvNpgv7xcVFkw0ADYRe3d7e+nfig83n855nDLMO+HF3d2evKc15N7EA8NCtXKIZz+VylkAfHx9bMZTNZpVKpfy9c4/iZwdQ5PcT5CnJ0mvW3vv37/3Mues4u3t7e/XVV18pEAjo//yf/6PDw0OrUQgFPjw8VLFYVDKZVF9fn/b393V2dqZcLuc6BEkue5Ozp6enR7Ozszo/PzeYw6QJSbY8oVTD/oWqiwb79PRUa2trvj8AlgBfyUYKBoMGYjl7IERisZi+/fZbq1MAADc3N9Xb+zDFAlUnjSSjRbGIdnu4UflJ8vQJQsBo7tgDZ2dn2t3ddXYQE1FisZhBza2tLYd/VSoVffXVV/rpp5/U6XS0srLiUbasiaurK9cY33//vVqth4kT8/PzajabngxCjXB7e2vWm7Be7hf6Cwg9ninnM9MfqB8hXI6OjvTs2TOPYgMMgGmH/cdHjrqj0Whofn7etRxZKgMDA9rZ2dHNzY3m5uZsFSXANBaL6cmTJw6AQ+WKjWdpacnjKwGj2KcnJyc+Z5ClA1xubW3Z/rK2tmYSbmxszFMkAOwB8TY2NhQKhcz2//L6279+VhN+enpqpBCJE1Kf169fO1k8HA5rdnb2kTcDDyYs5t7eniWIFCIwxhcXF57Zij+Swp9iKhAIaGdnR1tbW+rr61Mul3Njg7f06upKu7u7arVanom3vb2tXC7n8BvGy3Q3wixICnyYIAo2pF7dHnjkYhcXFz7YkI2MjY15I8MwFAoFM171ev3Rz0qlUpafgGoj47u7u1M2m1WtVrNcnEMAUIBkYDYrYS+hUEilUklPnjwx09ztKeYAgDVAroOsngMSlmhoaEi/+tWv1G63PQ5FkpUFXFh8/u5cgE6n4xEkNKOkODOKiYYVtgsZ2/39vYGIqakpp3Ij10I23h0aBPsP6HJ1deWmCqYaf+Le3p6DBVlreMrx85TLZVUqFT8rkpw55MrlslKplK6urpzKSWNCQXJxcWFlCd8XhRnM7ujoqO0c3fIqwmHwnk5MTGhtbU2xWExTU1Oq1Wpuxt+8eaOZmRlfqnw2LgH8tBTmjNyIRCJ6+vSpnzv/IbUYVmhiYsLfUyDw59mUML2SPGrvw4cPljAj2+SihV2DecOHCFiSyWR0cnLipppwQIph2MazszMtLi66YULlgZT1w4cPLsgBpdgHqDNubm40NTXlVHyKwevraxWLRf8d9hvBZfh0e3p6tLW15ffFXGc8ZoB9yDDT6bQBmO6cBtKhv/76a3348MHpyN1KATIl0um0Dg8P1dfXZ2sE6pNqtWpGlGIEkIywn+7zkrXFmQ6zy1lHowuIdn9/bwbgxx9/dEgUMkoClAAeSfxlggD/wW4zNjZmJob1ge3n+vra41fK5bKbKsIMKR5QnIQ+TxegKe/v79eLFy8cEkTDikUjm80a1OQcLxQKDulC6gyrRzNEwBAMMSw049AIocPjyeglmsWJiQltbW3Z24u8EbaHuwNgMxKJuDkA+ABcJcAQ4JvmibOmWq1atry5uelEcRrpm5sbBw+RC4IPEp89TRrgAd817x0bSU9Pj+WYAJ0AYVgQyIAA3GfPUlBLcj1Rq9Usv0RlQyDW2NiYqtWqbm5uvMYBgVCbnZycGCDhO0NO3G63PS2Dwpp7ELUH6oWBgQGvGdZCOp22xWt/f18zMzM6OjoyCNbb26tSqSRJVlVhJ4CBQw3w8uVL3d3daXd318+O/dpsNs2O3t8/hBQCJBEQxX7tDibrtiuQaYD15Pj4WNFo1DUYmQkAi6yBbi8+FkEaE0LksEYVi8VHSfGBQOAR2EDzPzIy4rGYsKWAV5Ic+tlqtbS2tuZgQUD2hYUFN629vb0GBwYHB5XNZvXixQuPmGROufQAECWTSTPIuVzOjSwBavv7+264ud9ghff3980MY8GgjuQ84Nxl0kx3Y02QIzYQ7IDv3r2zd5faFmUANTh1FIFn3Vkq1Fn4/zmDsWNQ07GuCTtbXl7Wzs6O6zjCyc7Pz7WwsOBEdDzI3eqBUCikk5MTq8xYb5xzsP6sZfzRqHG6QVeUf1gbSccHsIQIIksIogug5vb2IT0flUC9XnfQYV9fnz5+/GjmGvIEqw7nAeBXq9VS6PNYS9YFtQ4qXuwDNzcPU10ODw8l6VHAHEnt19fXWllZ8XNLJpMKBoMmZ3Z2drwuqJHL5bLDaSHeyMhiogZkCsBJp9PR2tqa9vf3beX5j//xPxrwwYYRCoX06dMnffjwQeFw2BkqgDF/yesXJvyvf/2sJhyGgPThw8NDy9GRnU1OTprdu7y8dOpjtyeFJgHGkrEKMESkYnI4IPUsl8vKZrOamprS3t6e5+UhLc7lcj4QenoeAlKWlpa0u7vrwIGvv/7aElRJHrlBAvL19bUikcijoANkcjDVyB9pRChmJblRpzkgoOHk5MTFIOEarVbLTQp/dnZ29tHhDdoHOAC7SFAZ/iRCdgjpWF9f18nJiUOjaDaKxaIl6d1sABJ8FADFYlFzc3OWN9brdU1NTTmRkcMMXypzO5H6UKhzIfb39+vVq1eW1/b393sGYiaTMVPD3Ob7+3stLS250YAVCX0eKUJzU6lUFAqFlMlk/L0HAgFf2vhuachBhkFK8RPj+0GCKckNc3ex3j0zmaYVJhLpIQwRLGK1WrVkvt1uWyoHuNMd4gWAwPvsDiCh4ETihreV75T1DIJ6enqqfD5vOTrIJ8oTfOzdacU//PCDisWiAoGAL7hWq6V0Ou1LkveEP5wiHQUDhRNFGpdNp/MwvoPLGHSfooDvmNCyqakpjYyMqFwu+xw4OTnR9PS0C2L88NgPensf0uvD4bD9oNFoVLe3tw5G47khC81mswYV+Z5Q9JRKJYNeSONSqZQLQn4fYXZMiri6utLHjx8tuS8UCspkMkqn0/5+hoaGNDMzo9vbWwfiBAIBHR4e6ubmRs+fP7fMeGlpyUUsUx44mwB0ABQJnaLI4nIFmKTxK5fLLiKCwYfpAj/99JOBPsZ/wfRyOQPCIm9FLo5cnosca8Lx8bELURQP29vb3nvRaNTMMgobbCYAWATHcG5XKhUrW/r7+zU7O2twsFgsKpvNWo7429/+1uANQCYF+ps3b1Sr1czGHR0dqbe31wAayg7Of8Y6MQmk0+mYweWOAwgYGRnx/QJwAGMBYMzPuLm5cXPZ399vcAGVze3trWZmZmyz6HQeRlwR/nN4eKhwOKylpSVbK6LRqNLptKXBgElXV1cGJN+8eaPb21tNT087qJKi8/Dw0IAGygesRfwsnrMkF7UoeghlnJmZMUhAk0+SdbfPlkYPMI3zBMAR9gsLD9adra0tN4Y8Gwpa9g/WNiS/7DGk9aVSyZYLFB8XFxeWlQJAEmpYKBQcxjU1NeUwO6Te8XhcZ2dnKhQKViPR2BKahFy/VCr5fAyHw7aElMtlT58hqI4AUphh0qmx6BDeRE0C+AKLPjAwoIODA6sJA4GAVlZWtLu7a4UZIPz9/b3rDGwSEAt8V+xZ7jHYOiZrhEIhs3yMPyL4DYICcBmgb2tr69GEBKTD2BcAAnt6emxPmp2dfTSJZn9/Xz/88IO++uorvXjxQh8+fPAZBovfHWoXCAR8buMpR0FB5hHMPCTN9fW1G+9uhps1f3x8bLvGwcGBwTosHRcXFwY6Z2ZmFOqaujI4OOiMChRJ8XjcgZh7e3taWlrS4OCgGc94PG4vL9Ji9kwwGLTHlxGtAKKwtigw5ufnncnEaFIk1JwbrEfUKYB41CSEFgIU8OyKxaLC4bAajYbVUKy1bvLqxYsXzvxgOlD32FmA7XA4rEKh8CiEsDsPiDXNZBPqTupXpiv09va6TmH9o9jBAstY0lqtptnZWedpMEsdJhtLVSaTsQowkUh4YgwjzFAi8b/pC8if4vvd2dmxbYW56icnJ0qlUlpdXVVvb682Nzc1OzurWq2mH374wWQTnwnFIko3CIr9/X2TEsPDw1pYWHCOEsGhv7z+tq+f1YSfn5/r3bt3CgaDj+Tdq6ur9jSR/oqnsDuFj0KZBqPRaLgoo6FdXl62f3p2dtZMLIUBfizmai8tLT0KkeLA6h4hwBgUXkhSR0ZGjGh3y0AIKRgaGlIikdDMzIx6enrMZHN50Yh3I0+BQMANRa1Ws+wIdomgJ1A0GoLBwUHVajWdnJyYVeN7Ad0kAV6SpXJ4MHt6enxJNptNX5Dz8/M6PDzUycmJDg4OfNhQ8NM8discQKMZZcXF0e1JqlQqajQampub86VPOjQHKrJE1kOr1bK/Jp1Oa25uTuFwWKVSSaOjo9rY2LCk/enTp7q/vzfTwPM9OTnx2C+KnpGREYfQ4ZnFP8fhyKVIKBpFBb+bwKeZmRkzaDTRBLSQL9A9ZzoUCrkQGh4edmEEa0ahIcnNZq1WcyALBQBFBQU5bDUoO8VPX1+fE3+75fs0RUhrebZcKH/6058MVMCwo/5ot9sql8va29vzelxcXHRQD2w875s1h/wbcA1wBrYLSSsAA5cUnn8CAWFnG42Gmzyk2re3tx5dJz0kCV9eXhr1J4m53W47aReAglTWyclJNxE0iCcnJ4/ktIlEwoUhBQaA1PDwsItRZPDdz5WfgzwYoJDgFRQ/NHiAQXxHjD/iP5lMxo0KYF4wGNSTJ08UCATMjMViMUuxaeI4c8fGxrS1teXAOfxyNLN4xTY3Nw1aojBot9tuAmBc+M5gfAEThoeH9ebNGxWLRZ+VT548ceo27AEeVBQAWBtqtZoWFhYs5wYIOT4+Vi6XU6FQ8PqHQWNN0fQCTuGrJPmftfbp0ycFAgFlMhlNTU2Z7WL0Ec+STALCGikSAcA4Y7EbDQ8Pa25uzuFl3bOW+UwAxcjpT09PXTyWy2XvIQpRPLGlUkn1ev0R+LGxsaH5+Xmrqu7vHyYgZDIZ3w/Io7HLDA8Pa3x83JJzbFjMKkb62263VSgUfNbDWAJCcuZ3261GR0eVTqedgYB6phsQo7HizwD24ufEsoUCChUQ74mzBDAT9RIZMgAz3bPSUZUxSYOQM+620dFR24skWfpdKpUMOE5NTXncHncubBUya/JFuGcmJye1vr5ueTTqKdg51imNPgwoaxAFSDQaVa1WU71eN2PMXmACAGAazSKKMELlqHtIBW82m1YGkJMAeMvoSr5PlFeco7wHQHtAEv4/7igUOqyh9+/fe5IGWR8EaqFiuL29dd0IYYP1iHXAvoaZRBI9NTWlV69e+c44ODgwcPi//tf/Ul9fn+/TqakpJ9xTO3CuoVAKBAIGD2KxmDY3Nz1RhVqVgKt37945BG1yclKtVsvMPMAE3xFjc8mLmZqaMpvJGLmRkRHt7Ozo/v5ei4uLVmvg/SYoDZIFRcfU1JTS6bQmJyd9f2BpRNIfj8fV6XQeBYcyvpGcjWazqVKppKurKwf97u3tqdlsamFhQYHAw8QflDMXFxcelch5BKGEeimZTLp5bHxO4UfxQ63NPQjxwOhVajvWJ9ZM9iMKIMJzyQTAj4+aaG5uzrbLs7MzpdNp221QQ/7www969uyZ3wPqre3tbYP3AJTclf39/RodHdXOzo6q1arBfM4z1EmTk5Pq6enRwcGBIpGIDg8PzaoHg0ED8eQ9YRuABcdCwH5mDOnf//3fO9QZyTo2YUIYR0dHzY5PT0+r0Wjo+PhYHz9+dB1LcKYkq/6mpqa0s7NjcuYvef3ChP/1r5/VhH/33XcuPvDpTk5OuvDmwCRZU5IRbophkKahoSHt7u7aRwUbwUamee1OmBwYGNDh4aG2trZ0e3urL774wg3v4OCgmcr+/n6jU6FQ6BEAwPshNfr29iGxluKEBdw9i5GipRut5eDvTv5utVr2DwEqjI+PO1iFv4v8BtaAopKxE6QY4ovjkmPUBSzwvw1fiUQiKhQKqtfrbqR6e3vtx2NkFd8/DAbFH/9udHTUBf7m5qYikYhllOl02mhwLpfz5YyUdXJy0kUFsjmS75GQhkIh/7nt7W3//svLSx0cHGhgYMAePSTTePKQKZ6fn7uIw7ONNI3n8+nTJ83OzroYRpmBDA/WhbFGNMTBYFC5XM5BR7FYTK1Wy8FdACrIvgKBgC+zzc1NSxNbrZbZeKSGNI4UmygcukPGpAfQgvRy0F0aJNYjzAesiCRLQUFu8UQfHh7q/v5eKysrLt4IlTo/P3coYafTUSqVchoqiDCzSGH2kCPD0sEeI98iZC6RSPjykuQAxHw+b1ai2WyaTeQSwVddrVY1Pz9vuwDFNgnusEZXV1cONuFz0BDQOFFILC4u6s2bN5qfn3c4DnsfVQyfD2SbwhNAgt+DwoZCC6YM7yzhiKx3GCmYjq2tLQ0ODioWi6mnp0epVEpLS0tOxUamhxQxGAwaFOR9IuFmHTCCLxaLKZFI6Pr62mP/tra2LJtlvTAOhzRmWDRAPWwx2WzWoB1prjBztVrN3rRWq6WNjQ1lMhmH5kjyc4pGo/rVr36l7e1tM0VkfRDKREDY8+fPdX9/bzl9MBi0Z62np8dnGonrhBNJD0qLZrOp1dVVgyjBYFDb29s+k588eWK7yujoqPL5vKrVqpsrkrRRPCCV7h6vh3oH+TjrEyaWz48yA595o9HQ/v7+ozE23Xsc6wfqk/Pzc1UqFRf69XrdzQGy5k7nYbY3TX130jLFtfQQNpRIJDQ7O+vQqdPTUyUSCcuv+/r6HKDFnqJIHRgYcCI2/ufb21szKKQdd69dzm32NoGrqGj4PdzLNNywQ925IaiEuHu5m1lP3JcjIyOanp5+FJJ5f39vsApVCBJkxhMeHR15kgCZKpVKRbFYzOAmapRqtWpmvtt/vbKyor6+Pmei8F3T4BLW2Nvb689DqGq3N79arVp+TWOJHzUSiZgtpNElHG9wcFDlclnb29uKxWKKRCKamZnRzs6Ozs/PXYtR27RaD2nnFO2Aod2WNPYgzXe3hQBAiaBYVDuQGZzVgNPcJfzMbiKhWCzaxw8Qg+KO9wfziWqq2Wzq7du3+vTpkwkAUtNpkvv7+/XkyRNPMSDvgSYe4Ov8/NwjvKamplQqlVQqlXR5eamZmRl/T5eXl5qdndXu7q6bY4COaDSqYrHoBgV/cDqd9v2I5ebo6EjT09MGRObm5tTf3+8pEAAYAG2ou2jqeHZ9fX1W8WAvINgXyX13LcqYtu6JAgAxIyMjevHihZtH5lqTkk7KNins6+vrCgaDPpNgdlEWsLbxQ6P2JPsEaTbnAAyuJN+N5EZhIYVco0mlUSMvhHOlUql4jjkzwzlDsanQcEOCoM68urryOE3eA58JUIH7E1vf/f29peME6RFUiE+bMx/rGJbXoaEh2zEJRMzlcr4nyTBCodAdosfeQJ5PX5TP5zU3N6c3b95ob2/PSkjOgOHhYVtCUeKyRn55/W1fP6sJr1arevXqlTcyzBwNAowU6GM3Eg4jTnMJUvX8+XNfpFyMBLHBmF1eXqpWqykej9vDCitEgzU3N2dZNiw3XovNzU0HTlFEgn729/d7SD1yUiS1+LzZoJeXlwqFQh79BGsIE430mQabOZuhz+FiFENcPhyG/H1JLswYjSA9HJSnp6dml9m0fLcUBWxo0mnxKcH447MEleSz0wTDoLN5aQ75vTRA3T4XPLjd6aQUowAFSBsBZ5AFUygiWf/06ZOazaZ9dRT2SMdgJ2Hpu72m0p/9RygLYJsBG8bHxz1/u1AoWDaIj5/vB18yqCpFIwc98iHCrBKJhM7OzlSv1y2lzWazljLx3mGjupULNDIAKhSk7CueDcwQz5xGLvR5JBkyLX4GqChsSCwW8/gUWKHR0VHVajXt7+8b5JidnbWHDBALCTyWkd7eXst6kVBy8eA5RyVwdnamw8NDh7KBfFM0dx/0yPqQufKd1Ot1hcNhMxo0urDW7CsUNDTMSN0oBGkootGo4vG42u22FhcXH70HLkGUDrBO7A/e8+XlpZkcJPcUpFz0AGuEUlJATE9PKxgMmsEg+R07Agm8+GZrtZo6nY5BOmTxBOaNj4+72CPwEIVIJBJ5NAIIuffLly8NnhBQtLi46IsfgAG5OCxFJBKx4gDEfmVlxTLjcDis9fV1N7LIvMn8IP+hWCzqhx9+sPyfAE7yMhYWFvw8YMNhuePxuBP6UQIMDAxobm7ODe/19cMInFQqpWw2q42NDQPHwWDQIYkw1L29vYrFYgadAIRhPthTBCTSNLTb7UcgNNYp7EkUeufn55YH7u7uqlAomNFl3SC9Rr7I/up0HkYS0oTwXgB5AeyQOnO+Y39BLXJwcGCJJRkhFxcXDjKENS8Wiw4fpAkbGBhws3R1dWVLC4Bip9NxRgP3Pb567lUadqwdeC85DykKOUdoQgEAsU0hC0ZOzve9t7fnpjYajVp9MTo6qng87vsKSWo2m1VfX5/lpScnJ24QqG12d3ftKSW8Duk1+4RGZ3R0VCsrKwb9sF0FAgGr9mhUd3Z2zMYCrtFQ0BQTXsn4LEb+cadxttB81Ot1+55p7Kenp93II5MNh8MOe6SZh22mjkHlB3iNfQfwrXuEF7VLOBxWLBbT0dGRPn786FqEz8/6mJqacgYKBAm1097entbX1509QFObTqeVSCQejY6lxqAZrlQqWltb0/39veLxuEF6zsPx8XEdHR09Gh9FbYBKijOgWq36ji6XyyqXy8rn836/fJ99fX3a2tryd0R6OOAL0t9Op6PNzU3bFAjGwpdLSB0MdS6Xc2DW3d2d93kymXQwGUncPBPmtMPQQ2L09fU5k2VgYECFQkH39/dKpVJqNBrOlkBBiuIBsopzsK+vT+/evVPoc7AvCpG7uztPK2G+OCAnZEH3HmZ9Me6P4EfGHDK5gfMxn8/7PoMI4n4nfJlaaWxsTHt7e36u3f53ZPjj4+P6wx/+oLGxMS0uLjpwbWhoyJ8D4gP10unpqaampnx+dNfK2A0A/gFL6GtQ4larVSvGsBVls1mTVePj42o2m64nU6mUSShA2U6no42NDdeskAzxeNwqEs61/v5+h7UxH7xcLtvOwT4GBO7v73cgK2DjX/r6hQn/618/qwmfnJw04gjqRwEaDofdrMF23N7eunHiEqGIJeG3Wq2qUqno5cuXDr6ChZLkRVmtVrW5uWm2pFuCS2o6XnLSTkHfkaYyzxAvIiEZzEKE9bm+vra3ipnmSItAsijEaCYYAcLBSHFF4Erj8yxGZu8il5ekcrlsiRUXDO+ZYCnYhu5CD5YGz1mr1XLxPjY25iIVLxIjWEC98/m8LzkWOKPCKNKQv3R73AhEo1EfGhqyd06Sm038swRr8fuQ4ff09LgxmJycdAokPkkUFFxihJPgBby9vdXFxYU2NjYe2RoIAeFiTiaTBktg25H14e8nMR//JN8vUh0KvWQy6eYZhhEWpNPpmHW7vb3Vzs6Owzq6pZW8F55FMBh8xKTxZ5DNU+DD1vGshoeHnUKK7I1GkSIYBhi5Lej/xcWF6vW6arWa/ac08MwQhukHKIGdIkBrcnLSQVU8J9gc3mNPT4+SyaQVAcjlkIjD3gJ8dANS/f39mpmZ8RoBkKCJgKnkuyddVvrzSCPUEZwDjH959uyZ8vm86vW6UqnUo6A0mjoaMEluYkGeKf54X4VCQfF43Ig5HlAsCpwV3fOakS5XKhXPJQZ4BAw5OTlxo0NCNUqCZDKptbU19fX1OeCSICSUPKwdGLKVlRX96U9/UrFYVDqddup6tVq1Xw0FCEg/aotKpeKCg7MnHo8rl8tpfHxca2trTo0GNEE2CUjBWmPuN+zs4eGh/vmf/1n39/cuylApMN0ik8n4vSYSCa+pmZkZS4l59n19fVYt0PzD7JL7AMiAt77ZbCqXy1kRwHuX5P3L3uffkQzdXVwuLy+bvR4dHdXBwYHXDv5afItjY2OKxWJmPMbGxqzWQVnVarW0sLBgxhPmCGD55ORExWLR67enp8dFG2nv3MM0v7D/tVrNDA3nOeBdu912sjAAEA04c86xXdGkd08egKnGWzo9PW1bE7PpAUvC4bD6+/vtnexWALXbbY+Ko9nvLu7Hx8e1u7vrQEhkm/wzoCJ+fNgizm7Y5eHhYQNGtVpN0gP7Ss7A/4e9P4ltNU3vu+GLg0gNlESJ4iiSmqcz1qnqqu5227ATJ0BgIHACb4JkkwBJVs4iyMa7ZBsgQSYEyDpAvE3gbAw4cAan3XbXdKrOOZpFivNMUbNEcfgWPL9/PWonr7v8fZ28xtcEClV1jgbyee7nvq/rP10ez2jiAnUD6fV4p2Ep+f2wpYQ5MdOXWgOQZHx8XGvd5RpN1IA1jsVilkqlZPkiJGx5eVmKGUY/0XBxTqJYgAmGmbu+vtbPhd3DksToI3J/yHhZWFhQVg4ZD8hnnXu9U12YTqft9vbW3r17Z5988olUIhMTE3Zzc6O0bQDUt2/fKombnzkzM6M9CHsY50Cj0bDPPvvMgsGgeb1eOzk5sUAgYGtra3Z1dSXZN88JFj4Sstkn8TWvra1p1ODy8rLV63VlHiwtLdnCwoIsSM4JMQCkPCN4n51qRmwuML2A3uTR0AChsup0OiJbeH55Xsj2Ye1x/mxtbanmpunDSvD06VPtzQQcI+Pmz6gVySFwWkT8fr9yDAB4ALfJt9jc3LSTkxPluMDwk8sCcMFIWsJqeQ75nZzlPp9PFg+UcNls1j788ENZJlC/BgIBjWabn5+XwqhQKNjFxYXGhfb7fVtaWrKTkxPZ3Vhn7BflctnC4bBdXV1JKeHz+SwajcpehSUFYKndbtvq6qpYdRp2Mk2QvwNIjo+P2/b2tmpxwteoI9zuUf5IIpGwRCIhZRze/z/6oz+yhYUFhaydnJxI8UoPsLCwoJ7o+vraqtWqwiepc9bX1+3+/l5WImaHY5f9+etn//pWTbhzML0zcAap5f39vRgxEkEJW5iamnokX+LwCYfDau6QQiAleXh4UDNPCBcsBUU7KBc+YNI+KWIGg4GaLd4vaBsyXGQqvM9yuWw+32j2Lt4aNgeKEwrwyclJFWakOVerVVtdXVUxhMw1GAxKrkODDGOGTItDcmZmRowoCCGyTBQANFowtchKSLFFXg1aDYLK/GlGgvj9foECg8FA4W34uZFRUky4XC47PDxUcUQhDXJJKAbBWs5gDmb3RqNRSYhJGMXDT7PG4XN5eSnkDnbEzNT04Yuj2A6FQgocoWCm+OE9ENxF0+gMrXny5InYjsHS/GQAAQAASURBVEwmI4aWwDrWqJnpumK3gImi0aXgdjbYFJhm3yCIMPy8VywWSMFdLpctLS3Z1taW7e7uKpwFewibPz8DxoT7BrAD4o+8E/sE0i3k5DSKzkLZzB55PJvNpny98XhcQBU+Lpho1hLX0ZkZ0Ol0BCQhR6cRoIkD4Qad5drxLOEpRR53cXFh9XpdPkmeUaTDMAapVEoZFzBy3BMKd6dfn8YNdQQjQmZnZ61cLtvbt29lP8FPzxg17i/AIE1mt9tVqJ6ZqUCnocOrDhCGX3JlZcUuLy/tyy+/tIeH0egdihfeJwAP9qFyuWwzMzP28uVLzaGHraIBiEQi2p/cbrdUT86xjbx3ipN8Pq8GBWYPAI9gShoBEvrxvCOBrFQqyuGIRqMqVrAgLC4uKkyOpsDMlBxOQY8PGsUQ3lcCymB7KK5ouGlGKAoZichoNbNv5sYCHlarValLCMnhc7DnUnxHo1HNG+a8YQ/BEkNaNhkYznXUaDRUeAYCActms3ruW62WQEWAHkClmZkZy+Vy2rtRISGX7PV6Khjx1VNcxuNxSVqRWwJE8vmw11QqFVtaWjKzUUHLPaeh4nfd3d3Z6uqqdbtdpdKbfZO6z3mN5xxPLvsx7xHbCEoO1iyF9d7engAwmiUaxuFwqMAm5zPPucFnxF6EX3hjY0PNI83PysqK/JNnZ2d6xjm3SUkG2EwkEvLoOj8T4BzgGcAa6wdGmSK72+1arVYzr9drgUDA+v2+GHTAQGoVnm2+j1rp8PBQ/nn2ewA4wJr5+Xmbm5uzm5sb3XNmG0NKABg4GbZUKqX37hwrRVBjt9tVqOhnn32mGsDMlDUxMTFhiURCWQ7Hx8f2+eefS9GEvaHVatny8rItLS2Z3++3nZ0djRecmJiwfD4vgOujjz7S6En2ZDzqpLETdAj7PDY2ZoeHhzqnAY6pbSCOZmZmxFJ6PB5bXV2VSjCZTNr29rY1Gg3L5/NaYwsLC1I6ouDAQ4+yD7YaOXmtVrNwOCxAg3oZ0AqwBaLJ6/UqkJR8Cu4lzSgETzabtX6/b0+ePLFms2mvX7/WZBWXy2W7u7vWbDZtfX3dPv74Y+v3+7J+vn371mZnZ0WmObMRsMLBtHo8o/F2x8fHkn2zjw0Go/GD6+vrNj09bWtra1LfQShBPKEswR+O/QfFQjgcljXm6OhI95lzMhwOS37PpJNwOKxpLc6MF0ilTCYjuya9QjKZlCLQGfqLkhOg7dmzZ2q08ctDOnJ/fD6fra2tWbPZtGKxqJBkiE4UiwAKgLWhUEiALPJ39s6zszPZsAAB/H7/I3CTOeM0++yNP82L/fBn+fpZ//z/W69vPSfcOZd6fHxcMtN2uy3Ui/FfbOawWzzoSLoo+JkBCBtIAcaGVy6Xrdls2tOnTxUmQ8EF+u5Mp+WBplCEhcLbyKGzvLystG8KSbORx4XCrtlsCgHt90dzp9nQa7Wa0g1hVJlTTeFOUUU6bjKZVDgJjBobP9cRuTcbPME/g8FAczWRQt3c3IiJ4WdROHOfAE7w8tCMxWIx+aFItzcz29raEjqMJNfJkIDWg46D/BI+BSvKwQvazCHTaDRUbPl8o7nivV5PfkHYJ0J08FOzNmjkzEzoJgywc8QKSdW8J5BeM1PhMxgMdMgBNoAeOyXXpOdTGCITi0ajQqenp6ft4OBA1wHVhbPRdn4W1gi+/na7baenp0r2ff78uWZKgvLi5eOQo+GB5QaUcgYVIW1HunRxcWGd9/Mw8bpiO4B1Ju8BFQgWBQ4+wBSYSQ4lirxerydwgs9I0jThavjiXC6XkqnxlXJQwLbCKKAamJub054Bq450G9Sa4sPJVtBc8WczMzNialjzTpsNaxBPLcwpIJPL5XrURGNBwbfHC4aK5uH8/PwRG0tK8tu3b21tbU0p+0j6WEP4vrrdroXDYXvx4oWABZiD4XCo0V/4FfGyca9RJOAfnp+fV4NGcTw5OWkbGxtKn2ZvRaZJHgafDVCGSQ0PDw+6ZqzV4XBop6enVq/X7cWLF5JjzszM2IcffqgwGdQsAJ/9ft/29/et3++raKOhxV6APJG1QjOOEoEwPnyTgDuwAGam+caMWqLJ4B6wHziBIJQhzsYStmFyctIikYhNTEzY/Py8/Os8o4PBQI081iqYFJpz2HSux9HRkdbh2dmZra6uCngaGxtTFsLp6an2oXA4LKsU94bPBpgMA0ZhiG8Wxp+wJNLXAZXZO5DVOgEggMdoNCpQ0jmq0hm2BYPnlPM7AwwBbDjPotGojY2Nyb8O2EGAKc8/oDmgBQAwkzXYa8i1wNNLEQ9LhbR+bGxMEy1QkrDnzMzMSL1CgjNBn1wnQly5H6jWuAYAuoChNB7399+MWGKPxKKAZJX7CSgGYLS8vGy9Xs8qlYpdXV3JMkXOCWs7Ho9btVpVMjz3iAwJmnVnhozP5xOrySgl2Dhk7ChR+D5UCa9fv7ZSqaS9jqaf/Ygz/eDgwN6+fSt23+122y/+4i9aKBSyfD5v0WhUgbYwuycnJwLyAS0eHh7s+PhYIZKwszwTWK7IHSgUCjpjXC6XVatVm5+ft0wmowwA2O83b948Oj84n/r9vh0eHtrZ2Znt7OzY4uKi5XI52SqePHlid3d3ssJxj4fDUTYDNQcqPUDfyclJgQXkGrjd34TthsNhjd5kNC+SZ1RgNKIQHd/73vfkJ/Z6vQJjPZ5RGDNSaRQ6nU7Hnjx5Yn6/X8GaqA+mpqa01wMs89xxny4vLwXyAaiSTeHxeCyfz9v4+LhC13w+n+VyOZuYmNB5jYWH5wLSBxWHmal2A3hmf3S5XDY/P29nZ2eyl9RqNV1z1BBkSvB7AfRnZmYsk8noWaJHQMVyeXkpRQw1C++HZ2Vubs7i8bh13k/xcVoXAaXJN2BUJ0piFE3pdFrWULIGmDQD249CE3KH53V8fNwSiYQdHBzY8+fPrVgsmsvlUj7XT/P6uRz9z/76Vk04SdDOghIJMkh+JBIRKjo/P68UPmdyIZIbt9ut0SqEI1GUs9lXKhUtCpBVClSnpwvEHRkhEuR+f5QIy9+DTNOcUOg7A9GQYfL/+GDZeEhkxY8LuoRHDabLzMRUIE/LZDIKV4ChpEFyHtSwTjSOFPY+n08haSBnzDx1sscUM05JNZ5Afh6KBJJJkWuTXMxrfX1dgMn5+fkjWSVSxfPzc0nvw+GwAvG4J8H3Y7wmJyctl8tJAoz0BR8OrByswuLioq43zTENSLlclhWCDYNriOcZewEHCUAA1we5FQxvJBKxbDZrS0tLkohSGFFQUDAvLS3Z3NycUsVh7ZjXDHtK2AzNNqghzBqyZ2ZUwhjs7++b2+22ra0tqRsODw9VhML805wDjlSrVYWlPHv2TD7w9fV1azabdnZ2Ztls1qampuzZs2cqhHu9nu4tRSRFU7lclmrAzCSBN7NHyhRAJNYvBRvjLngG8TJfXl5q/6AQ5tBwfrZAIKB04OFwFEyENQCwiU0a1JlCjyAUkqDJeoDJvbi4sB/96Ef26tUrrWtn4j9FFQckh2Q2m9Vs+e3tbbElb968sY2NDSkmCLnpdkcze2Frz8/PJUv0+XySW+dyORXreNLYR7xer+3v78sO8qu/+qvymhGu4mTYYO1h/wApEomEFQoFC4fDug80KcjF4/G4+Xw+W1xc1D4JCNtut8XKNRoNjeMhLdspN72/v1fjQ0OHRQkgABk2e4CZ6brQgNzd3Smpl2c5EonIN1ytVi2VSul5gr29ublRYd/pdAScOkG/hYUFvR+3260ZqYCd7FE0l6h5aBxmZ2et0WjY0tKSTUxMaFbuYDCQgoz7iBweGa1TOUYBfn9/LyaDAMaZmRlJQ2nOSQDnuSREiOsH4FutVtWAU1Syx8HKPzyMRkfBqpXLZRV3i4uLkmiPjY3ZysqKgBC89XgzYSid+znnFfsh4Z3sCYCe2I6i0ah+DtkUACA01jc3NwInNzc3lVhOJkOtVrNQKCSgGbAca4/f79deE41GpdQbDAZ6rlnvMLgoEtLptEDWUCikfYPfeX19bclkUv5w2K5+v6/7RQPAecf9Q01Ecw9oxrqGiOj1elYqlSwSiWj9sdd2u13L5XICHzkPmXV9fX0tEIAzf3x8XN8Dw48c2hkcBTgEm4/CAeBuZWVFHmf2LEavnZ+f28XFhR0cHCgkstfrCfB79uyZmY0UQc1m0z7//HNl+Xi9XltdXbWXL19qxNza2prt7Oyo7qvVatbv921hYcESiYRqSQCW29tbm52dtVAoZLu7u5ZMJgW2s8cBVPKMMlI3kUhIGeFstgHD2HenpqY0b5uJGyhsUGJSC8Fs4yPHvnd3d2fZbFYWx6mpKQuFQlIUzs/PK2Ee1RbrlIaU60hWULfbtaOjI/mDHx5G420BINizUYSsra1psgyqVNQB5XJZcve9vT2paajzjo6OHpEeDw8Ptr6+bul02vb399VEovBwhvoyP51mkfON5xfJPOAugCVrESk/qgKk7tQJ7EEohQaDgZLwl5aWFJrm8/ksEolYqVSyi4sLAXsoLVZWVhQeiqXC5XJZNBpVHhAqEmaRA3jT/PIe2beOj49tb2/PAoGAPXv2TNYjriv9FOA99b3L5VLYJsAN9jbuS7VaVcMOOMCkIoLoUDHxvT9//exf36oJZ6NxBnQQdkIibblc1ibPyKJUKqXQEx7WRqNhrVZLfh2nzyKRSCj4gdEMbIaEJeAJBpUmeAkUO5/PC62en59XsBvMJzI3JOSkWXY6Hbu6utKDPDY2puCa6enpR4EahNPQLDvl52wQNNc8LHh2nHLX+/t7Be9QBMBUwZDiMScIhs/PoT4+Pm7Ly8sqPAkYAolFxkszxANNIBDFGkwRTGC/37evvvpKYWkwUk+fPrVWq2XZbFYMmtvttg8//FDACPfi4uJCh1Eul5P0HkS8VqvJX4xsD8kkrOH09LTt7OxoJjUhcBQ/SJxh1JHwIaPu90ep7KSYbmxsqAEkFApmHml9IBCwra0tKxaLaigZIxKPx217e1t+LGdQDAUNwIVT8mj2WFZDEe7xeGxra8sODw8tGo1aLBazyclJNQeASDBLgF/kDxDehQqjWCzKA8coilarZeVyWTOWkf9TZMHoAcL0ej3JddnMnbJq5P3c+7OzM2UeOG0rFOoUj8ywJ+wLNhC0mmKOtYxSw+fzydOO0gLFA2w5dgAyEEgwJokU9gZGmiL18PBQ6xy1CjPCYVKRyfl8Pvvud79rhUJBzxJFEKBTNBrVTFQKUgKsbm9H44XW1tbU+KysrKhgOjg4sM8//1zP987Oju4PDe3s7KxVKhXzer2aaVyr1Wx1ddWi0agVi0Xr9/uWy+W0brvdrm1tbT1SSpycnEjGCSCHCoki9v7+XsUUihozk4WDJGcCfwKBgNhNZMUExABGIBuk4YYtu7u7s2KxaPV63VKplPYWZ7E8GIyS22FxarWagC3yLBKJhNhsmOvr62trtVqaSV4qlWRNooFeXFw0M9NMb3yuNPNIC3mO2Q8418hRwJ9HMjTXi2wQJ7vB77m9vRUYDMtMk51KpZTOTEYKgAqy9mq1KqXH9fW1vXjxQmyVE9iguScsjfPRzAQ839/f2+LiokUiETVeJNdzfQjsoyFDcQSYQyAczysKi5ubG/k0aUZ51gHHCEejIKRZxMIBUA0Ivri4KM9mu92WSonfA/A/OzsrRYTH49GZB2t5cXGhhmxjY8MuLi4sn89bKBRSlovL5bJMJmNPnjyx4XAUkodPnGBGZKRML0DyamYiEciKYU9hz2WvJZwU8oFnCnKDnBhUgPxD40FmDs0N6iAa8Hq9rrFoNF+RSEShfCj58AKjxKFeA4wkUI4zjnsaCASsXq8LDLy6urJ3797Z/v6+ahS85d/97ne11rCckHIeCoVsaWnJQqGQZNg0EYB0jDS7urpSk4Elo9vt6gwqFovKnul2u5qHzSgvxrX2+6NwNuyUzkYNib3HMwr9RXngVLeRaI0djnoKUAnryUcffWQ3Nzd6X5lMRqNeu92u5fN5TSpxuVyyMrDuzEzhmzRfZBGhqHj79q1GmAFWpFIpy+VyVigU7Pz8XAGEqVTK0um03idkUaFQsDdv3ojtXVhYsHq9LpKLYLDp6Wnb29uTjx61HAD/ycmJRaNR5Rmcn5+LiDMz1eC5XM62trY07eL58+cCgRYXF6UGZN8sFAq2vb0tGwwSf7fbrX3l7u7O6vX6o9oKoNfj8ShUj8BEv98vYqnRaGj/++qrrxRsSh1DTUjOAPUFtcv4+LgIPc4iwB7UkQArBBSOjY2J6Lq4uLDT01MBKJAkNzc3trGxoYC6drttwWDQrq6upGR89+6dmm+UHQ8PD7aysmKpVMqmpqbs5OREHnasJwCUP83r53L0P/vrWzXhMGZscBwk6XRaGz6Iv7OQxa84Pz+vRvv29tbMRmFvPAw0aARTZLNZ83g8YrzZHClmYauRm7EBTE5OWiqVskAgoMRJn88nGZIzLOb8/Fzja0gdpjBGUsisUQpA2AQOF1Dmzvu52ZeXl4/8yHi1YfGQNhEyNTMzIz8rXmbSeZPJpHzcSNjwJDHCpVarWaVSsVQqpREeuVxOiGgqldLInNvbWxUnyNBo1GlAkduD/DFeZH9/X0AGqBrXFRkqzB4N2/z8vBq7TqcjdDMYDFqj0RA6TYI2xVKpVFKTt7a2JvnowsKCud1uOzg4UNjEs2fPpAig6WVUm5mJkYZ9pUip1Wq2tLRkV1dXWl94kmD58Hq9efNGSCuH+meffWYbGxvmco1GqiANhDWANYPFh8mA7WF9IAVE3cE4GQ4QgpT4HKyb4XAogId7iIyRjfaHP/yhUo7x8d/f39uHH35o6XRagUM03Bz0MNsUWS6XS40I/nvmzuLvhf3hM1N0I2tEDojyBbsCybV+v9/29/ctlUoJyQ++Hw8ES8l4Dho7ADDyEVCcAMhRrHPQce8A0kCRX7x4YYVCwfL5vC0sLEjRc3NzI4kb6DNrECT8xz/+sd3e3try8rKlUinb3d21XC4nySzPHWw2ie5XV1dWrVYV0sXaR9p2fn5u29vbtrOzo9F9eJ3xo8IYMOnB7XbbZ599psKe9FwaHZj/arX6yBLiDDDjWQF0vb8fTWLAX42HPhqNihFnPSChnJmZUQAQYBp2BGd4F3J+vPKwLLyn/f19u7m5sdPTUxW+Y2NjAg4J8+KZo1FjnTFLleeBQoocCcJ9SqWS3d/f28bGhlLgLy4uLBaLSQbP/oG/nIaaRiEWiykbgfDQbrcrlcHU1JT8noSeUbChYvjJoDDOQvZls1FBf3Z2JnZ1fn5ezz97gTNAEbUA+2+j0bBOpyMWF3YMAIlih/AlQFAKUpfLZbVa7VEuQDabtenpaT3/AAHOYEMUQdhK+FmA1qw9ZJaTk5PyLw+HQwXYoWq4ubmR3J7nc3x83FZWVuz169f6jPV6XYwS45gA9QBAPR6PRobi3afJ4dkhQBXvaq1WE6DFtfL5fJbJZASWT01NWalUEoC3sbFh4+Pjtre3p+s8MzMjNR9qQ1Lwnb5uwElnQCYZOdgI2KNQPvFzkGGjQkFRVqlUpH4hbJf9jsA2mEFn3gjTDlBRDIdDgTU8azMzM5qBnMlkrNFoKEm83+/bxsaG+f1+297elh3H6/Xa27dvLZVK2UcffWQ+n89ubm6sVCrZl19+abFYTEpMJ1j88DAaN4cE3zmqbjgcWi6XU61I2jnnFWofAnBpqvD04/cnITuXy9nnn38uuwI/F3Cw3x+lhcMaIynudkezs/P5vLJrnNNHKpWKJMXMQc/lcpbJZGx9fV3gH/eBa4AkmfVEQ/vw8GC5XE6J5y7XaMwX1pxAIGBPnjyxWCxmi4uLAlvISmIdABjMzMyIQCNgzWkHnJycFNgKmWNmAhEODw+t0+nY9va2nZ+f2/LystR+fr9fNSBTImgCqS1ZH5yPzWZTINnY2Jj2fywlAJQQXP1+35aXl83n86khPTk5sY8++kg16djYmCTivJLJpK4VYDb3jTqeGgPpPGCtMxMIdZPHMxqzGQqFtKfQ0LNXHxwcSBHHmmQfnJ2dVSA2gCkjSq+vr+34+FhnBD0Hf47tFLk+KiYIOSZFAMz8/PWzf32rJhwEDil2p9OxQCBgc3NzYiOQQJfLZUsmkwrvAKFi86GoRnJG8ibMDsUqPh8YX4odUE4YH7PRwzIYDGxra8uy2aykxIQ6gYAzmgopMmEUMN3IimE38PjG43E7OjoyMxPDiKeL8QbZbFZMrJkprAuWl00NpNnJ0nHoEshGqA6jgGjiGo2GpVIpjWbi8AAc2djYsHQ6La+KMyiDg9fj8VgkEpFsBuSd901hSOF3fX1t19fXmqfdaDTUFKXTaTFrqBMozpCvIw9yu922t7dn09Oj2eEU/+12W2EyyAtJWk0mk0o1532Tfs/oLXzRkUhE9gLWB3K5arWq0TVYEWhWzEbFbSaTEao8NzenxoMii/UwOztru7u7tru7+yjhm7RkCjpYLtg4mmizbywR5COEw2GN7QAA4ft6vZ6+l5C4Xq+nmY9INUGjAZJoACj6x8dHY81SqdQj+wLWks3NTTEgyKlQLuCjp+BxesO73dFMdORQyLlpcs/Pz+WRzmQyarpZm7DJDw8PVigU9JwC4CFdRdnA/YSxhcGCpYfp4LnDSkAYys3NjVQvFxcXtry8bPPz80pZxqMG0IP6h6KQQnB+ft4++eQTXR/uF+E7fO7Ly0s1rTCnFBHYcmCv7u7u7JNPPrGjoyPZX1wul9LfyT+A8U8kEmJAnUoRvKyBQEAgKY0jexSBXihzhsOhmgrC1JxWAdhs/GWsj6urK3vy5IlkuxRu7CcEFRWLRbu6utL8eEAW1EE8F4wug6m6u7uzH//4x5ZKpSwSiWh8z+Hhob18+VJefGbtAoIhiUcVMz4+Lt87Ek1k2/F43AKBgAojkty73VG6cLVaFTAxMzMjmSiBhE6WikAgGChkxU4GEfUOBRoBQ6wb0vN51hgXdHl5qfE6eGlPT08F5hKeFwqFBGwwRxYGeG5uTgCyU50FmwsjiA+RPQR2HgWTc2wlPkfYH55T7DCwwU5rGU0KZzXMN00wgJATSF1YWBCgR7gokyoAPfBmDgYDFcsoOJCA85xQowC839+P5hUzYYNr7Rz9Nzk5Kb/qcDgUIEk+CKOwFhYWbH19Xevw4OBAtRRhlDTfi4uLWn/47hknxh5MKBmSfYBTwHKYPu4JwXQALChKeA5haMvlsvn9fj0DAGecJZyZKBFhfgHLAXHOzs7klU+n0/qzk5MTKxQKGqu3urpq3/ve96RkrNVqlsvlLBAI2Orqqq2urloul9PeTT3IVACv1/soi2E4HMoqRiO8sLBgmUxGjKbf71fIVzwet263KxskgAx1F4ocPNVLS0vmdrs1DndlZcVKpZKdnp7a2dmZ7HszMzO2srJip6enSsoPBAKSka+urj7KFcEH3ul0BPBFIhEzM+XZsI9mMhndW8bTsXejdMF24fF4dI+pJ8nv4dmAyAEEaDab9vDwIJ95o9GwSqViOzs7NhwOLR6P297enq2vr9uTJ090/b/66iutA87NaDSqeg3rYDKZVN4H+/rV1ZXVajXz+Xz2wQcf2NTUlO3v72vU5Q9+8APr9/t2dHQkRhmQZX5+3qLRqO3t7Sl8jHOAc4q9nHuKrQEbBGud9cszgKVqbGxM0yCYKoB98/Ly0kqlkpLXaZi5xowRo7dBjZrL5SwUCqlGBIQECJycnLT19XWr1Wr29OlT8/v9Wif0WgRIY1sjOPLq6spWV1fVc4RCIdva2rJkMmn5fF6ft1arWb1eV02FpRTrFfXht+0Nf5avn3vCzcSoIR8dDod2dXVlZ2dnKnZpwpGVOucaulwuNd+g+8ht8CExMsnJVnEYIC1EEom/BoSoVCpprMP4+Lglk0mFSCA9DYVC1mw2LZPJKGGYOaIgcoFAwEqlkhJzkUOROE1i7eHhocViMRsfH1c4BOF1zoOPgxmwAEkcknEKXJJib29v5Q+9urrSOBeY6lQq9cjfSnPL2IpCoWCTk5M2Nzen6+FyuTTOCEai1WpZo9EQU8noKLMR+thsNq1cLuuwmp+ft1arZc+fP9d4GGTzgAnlclkyWw5GZF9er/dRoA7Nm9frta2tLRsMBpKBTk1NWbPZlFSOEKZ0Ov2oIXEe4BSw3W7XlpeXdRjj3Q+HwypSYAMAMPCv8bNhChn9BTLr9/ttfX1dqeQ0DkjSYKk5CClcaLRpyikoAR1g55B2Iy3nv/l+QrFYD6DvZqav5VlF/s0LMIoGAoCKgp0RgjRksHesQVgqAA5YFXxY6+vrKkaxQpBiWygUJLNzorYwwVNTUzY7O2svXrxQM8pngcm9urqyYDCoEClktRcXF/Kv0kTAEN7c3NjXX39tr1690nWnOCkUCgpLCYfDtrq6KtYVpJvPNhwOFeJUqVRUpPf7fXmUYQGQ1MFmkpdQq9WUMvv69WupDLa3t211dVWSfmcad6PRsMvLS7EOyIUJikJyxvWamJiwDz74wA4ODsRCr62tqSEgyAyJqpnJWsBoJBJe+WyAQTRtsKWwYDQ9PHuE+BHuZGZWr9ft8vJS6p50Oi1mjvGOyWRSbFY8HrdMJiNwytkEezweq9Vq8ukNBgN79eqVCp+JiQlJYQlCA3AhjKnVatni4qKAv+XlZatUKmK8Ou/HU05NTdl3vvMdzYEFXJqZmbG5uTl79eqV2CCKOvZpQExnw0lTTOgoa4D9YjAYKHgUwAiAGNk8oOTJyYnGZQLocR5lMhmBzRTxBGARDEfRxfWBzUOZwFlFswojjqwbnzggOqoIFEjs7dibaOwHg4HOACcYhj3JyVADVrO/cc4ASMP0ZjIZe/r0qRWLRU3/oHbA9kCKM/kZnEVYsWCo+Hr2SAA4mG6ntQQAY25uzmKxmB0fH0spgrzT6/U+Ahmurq60noPBoNXrdTFjrVZL53q/35ff++zsTI0yDBbqMIB7bC14SMvlstYX1jtARBh59nj2VUgClGFOxRZ7WrPZtPv7e0un0wIlCLRCMswYw/Hxcdvd3VUYVyAQsA8//FB2DBQRpVJJyi5AlYeHBzs5ObGdnR1bX1+3o6MjkQBcD85CnjHsi5ubm2qCIQsATJyz5VGnsP5gzlHpoAJCRXl5eWmbm5uWz+ctn8+LgUbSTMPE9ZyamrKNjQ178+aN1IOwpOxXwWDQlpaWLJFIWCQSsaurKxE8ZiYFGkQSPmO32y1VAf5q9mwCe9mnYZp5fs7OzmxpaUkWjEQioUBVwF0nUMS9/5Vf+RV5vrFabG5uKpR2YmLCNjc3BXYeHBzoGnY6HTs6OpICCaVMr9ezlZUVKxQK9u7dOz2LKPLcbrfsLzTPgMq9Xk+j2VAmcA5ns1n5p8lFAtAmmC4YDMo6C5DBWUbDmslkpIRCyUOgHwocRqWxJ5BuPxwOtVewhy0sLNjS0pJlMhmFyk5PT2tG+dnZmWUyGbu5udFkAEL8ksmklLn8OesDK9bTp0/NbERK0vhD5NAnYOtDEfb8+XM7Pj62ZrNp7Xbb1tbWvk1r+PPX/xevb9WE43Ws1WpKFc9ms3pAMfnDDA6HQy1WZNAUG7B5Ltdo3BWHG+FBeCUoxHw+nz179kyF4MzMjDWbTW3iIPRmpkKBGaj4a0hpvL+/14Hhdo9m5Z2dnWlGabfbVZFwenoq73er1bJkMqkwnidPnkgKjgcQpA2JmJlpLM/9/b1lMhn5uxm/xmfK5/OWSqXM5/NZsVg0v99vT548scnJSSG6U1NTCrkJh8NWqVSsUChokyDIgvmwbNbIqkBQObhBmQmJq1QqZvZNwM/19bUtLy8rGAzJNYU2jWuz2ZQcEzTw/v7eIpGIwijYXDkE3W635Jd4mkH68W4i8+n1erazs/NorBjj0mAeQqGQlUolWQFoFFkP5ASAFsNC12o1zb1mTvrY2Gj2ezQalQQXOeTBwYHkd9zj2dlZBZWQA+CcW+0sJM2+kWciIXM26hwCw+HwkWfc+edm3/hwKJRoyClQyWlA6jk1NSXfEQw515K8ARBNCmZ8o8j+QJd5HzRiHPLOmb+EzEUiEY33AWgIBoN2dnampnl6eloA3tTUlCWTSY2tcbtHIY40eBT2HGR8D+8JJcHExISFw2GrVqu2t7dnCwsLNjc3Z8Ph0N6+fftoxvkPf/hDCwQClk6nFQSJh3d6etoKhYJADfaZzc1NazQakpb+hb/wF9R07+/vi/1l36HBIcSMoiGfz2sG83A4tFgs9kj6++7dOwu+nyWLPNjMlPBbLBbt8PBQAX7T09P25MkThSLd3d1ZJBKxQqGg+wcTsrm5Kbk8o63YR8xMDTjrFoasWCxaOBzWPg/QhYyY0BiYVqwJ2WzWdnZ2bHZ2VvI+RjG5XC6BXp9++qma862tLQU+5XI5W15etkAgYPv7+3Z3d2ezs7PW7/eVqDsxMWHtdtvq9bokwexdR0dHWtcUnCiZsPF0u12BAoQj0dzD4iHVhRW8uLiQP5p7xz7N74bFwTYAYMf+gQ+RTBTe38TEhNXrdQXUUUyPj49GfJ6enqqRZbII5xz3k8aWhgkFABM9uA6oEgjkmp2dtWazqdE4tVpNqcz39/c2Pz9v8XhczwtNfalUErBt9k3KudlILsuZTIYHTQ9qGJfLJZ8v686pqggGg3Z7e/vI0lSpVGx2dla+YrIfuDaEBGKngcFmcgZsIaq7er2uPyeoDzvV9fW1bGkAUQS2sc5JwXYCLhTC5KRgrTEz29/ft16vp9GONLNOeX46nTaPx/NIsbO+vq6zgs9eLpe172MnAxTg2cX/HQgElEFwc3MjUCcSidjDw8Oj7AlSy2nk1tfXH61H6jxkva1WS97qWCxmsVjMlpeXlTNydXVlJycntre3J5B1dXVV4COKLOS64+Pj9u7dO01BQGZMneeUcCOvpZldW1tT03R3dye5MOcS16jzPgB1bGw0mmxlZUXWLJQAAD1+v19jYwGuUP9RBzutG8vLy+b1emWRQDFATXR9fW3ZbFYZSWYm5Ra+4+Pj40e5DhAuyOapK9j7xsfH7fT0VMAo+zn5LWZm2WxW2SWc2c6JQJlMRs/jycmJ3d7e2sbGhm1tbWmsFez5559/bolE4pE9Znx8NCITAIxRfcVi0WZnZx8BFHNzc/b06VO7vLzUuRuJRMzj8djh4aGtrq5aOBwWoG5mqr1zuZwFg0GFIrJmIMW4zlwjMmOwuRAMR8Ptdrv1u6gnXS6XZbNZm52dtXg8LsCCPdfr9Voul1NwLGe9M0+HUYgej8dOT0+llHFmHTBHnLBELJzU5yi/qLErlYr+2+v1WqvVskqlojplampK4yG/973vWbvdltprampKeVfLy8sCm3/aF8DVz/L1cybcTE0SSBv+Zg4Op1QXCSFy5oeHBzVBNHU0Cr1eT8nCyMuQfzmbDiSlyHeQH7148ULMiNvtVriF07OKj4LiiJFqsLsgZfPz8zqUfT6fBYNBMzMFWSErYcyHmQnRZHOfm5uT54L0SdBbvu/m5sa++uor297elreKsQg3Nzdi3AEjkK+y2AlsQJYDckpzydeYmRri7e1tzUeH8W00GlInUJhEo1ELh8OPfGGZTEbsMvKhcDhsqVRKAVkU0mdnZ0r5pOA7PT3VJtftdiWFh10FdCBcjmYKVDH4fu6w2+3WmK6vvvrKVldXrdlsavOJRCJiZyjcKXw4fJCfr6ysiP1CusfmxigogsVcLpfmDpdKJRXTFCAUxzQDNHQnJyePfJE0Y/yb4pINjK+h8Xf6eWmSWXM0zBRgNGk/6T+n4Kchcz6XpPmSWIrsigOIBn9mZkaBWc5JAoPBQKFaSPsAnBYWFuTnI+SL58Pj8YiVJG+AAwywiAYf1QHXg8wEv98v2S/PYqFQkF2GYtLj8VgymXzU5JInwd87R/VxnVlzsIZv3ryxUCgk5gVvKcwErMrk5KT2t729PfnQV1ZWrN8fjUr8wQ9+oOTlo6MjFfPOBp5xfEdHR1apVOz+/l7NC6nZsK0EByUSCavX6/qsyI5B25vNph0dHVm5XNYoGbx8zqIDcMbM1OSQg+EMkkTtwRqjaHOmDIO6f/XVV7a1tSXZHJ+XwgvpJOn0yWRS15+pExsbG1Yul6WsomAD/KIQ9vl8trKyomePfe/09NTS6bTm2K+srKhQxOIDc+bMumD9ko7NueLzjUZTscc7PYA8l840axoG9iQaEfJS+J67u9G8bjIVAKwobslDyOVyuncw6R6Px1KplN3e3trJyYmCTblPg8EooJPRm3g/zUxycZpYJJUUpTw77JXOsTyoekhY5zMhQQaMury8FAhOA+GURjNhgYwOZygZa/T+fjSaiNRo7BcAqwRtInUHVII5BPygUfd4PGKWWcsER9JE1Go1ScSx3fT7fYV6eb2j8W2MaqJRw7LXcUxX4bzG5sR+BHPHc8l4VuxZZMPQUBH6xzl5dHQk8oFQLZqhk5MTm5qaktTVCTJwvvj9fnnkef+o+Px+v3U6HdlBYrGY3d/f28rKitYtjRD5OqggP/74Y3vx4oWNjY3ZwcGB5jofHR1ZoVCwX/zFX7Td3V2FaXJ+jI2NCThAks/9QdpNaOv+/r4dHR2Z3+8XcYL1pdFoWLlc1h5MIPD9/b0lk0m7vr62QqFgwWBQYCXs4+np6aMRZCga2buoe9kLWeMwvsy17na79qMf/eiRJQ5CCFsPNpzhcChVEnsaFhwUUFyHWq1mp6entrW1pXoAppQGmTrM6/VqnrTH41EDyc9DtcL3UWuglup2u7axsSFAeXV1VbaUfn80X/zw8FCNttlIBcUoxcvLS1tcXLSPP/5YhArPJPs21+rm5sYKhYJqXBpZGlefbzTlgz8jlA5LFA08tbnz+UZVBAAdCAQEcl5eXsoGy3qbmpqStx7ADWsSWS6np6eqYdhPnec6QC22vEQioSwj3svi4qIyVpC8Y1FbXFy0w8NDWbi4RlyfyclJ2X5RGMzPz1uhULBarWYffvih+Xw+29nZUT+1tLRkl5eXVq1WZZ3CdkMGwc9fP9vXt2rCOeApNpHnUux5vV4hbhTNNzc3lkgktMkzNopwGgr1QqEgifXy8rJ8ZCCUsMMUOpOTk7a4uChEHJ9wp9ORpA/JGcw9xcf5+blm3JqZAr0I+6Gp7ff7FovFxOZTDITDYRWRNH8UGCRm0vhywN3c3Ei67/V67cmTJ2oo2cDm5ubs5ORECCqpjHi12cSq1eqjOeJOGfbFxYWtrq4q+IqfBQOJl9XMhKpxYIPO4cFBpk4CZqPRUAAHzdTCwoLCeGq1moK+zs/Phc6yUVHMwPBwYCHHvr291cxpWAPeK+g/fsXj42Olh1KokZK6tbWljR2wgLEP+AldLpcYDBglig+PxyOGjYYMcAGPX7FYFDPLRr6wsGDn5+fW6XQkfadQp9iD5TGzRw2183PSeCNH50VhD4vOf3Nw4k/l+/k9Tpad5hl2mwkEHDQwynwuvL0U4oxIAhXf3NwUSw6zNRiMxhulUikBGQA/HLKVSsUGg4FYFVJSfT6fra2tyUdLw02Bh5qm1+tplAtFIaPJuAastYmJCYvFYmY2AtNKpZJUJkwZeHh4EEoejUY1Rg/Qb3JyUkXZ9va2kuW5DzzbrF32R0ZOVSoVOzw8FNDB2Ciatnw+L3Bvfn5e0mzsNzRWzqTzbrdrb9++VRPFz3QWYkhLh8OhvXr1SmzE1NSUbW5uWiwWUzAZMmGeI4AwM9OamZycFOBGCi5FMPs99xWLECPQqtWqffe735W0uVqtquFbWloys9F0DeT+Kysrj0IGAQsBB8/OzjQO6fz83P7rf/2vlkwmFb7DWqD4nJyctFgsZufn57a4uCjwFMkgDTXSf/ImKpWKra+v6+cdHh4qdA5WlcL14eHBFhcXHzGDfB/KI54DnneyR2i+UUcBAgP6wGwDrlKAszc4n30AsXa7LSUA+QQwHwRuZbNZne3sC6lUSjJXZxbB5OSkcjYAwvBu8+yTYfL111+bx+NR8BaNL2nUPBuArV6v12q1mk1MTFin05HCgDMUAIB7SbELA+VyuRR6hb+W3IFEImGNRkPvkWkc/X5fCc7D4VC1ArJRAJTr62sx1l7vKGH87OxM4VrszdFoVE1UIBCQ5QpFD88GapFgMKhnhnvHSEBUW3jwUYmgcEGZBLCNtHh5eVnhY9Fo1MrlshKsITaoC8hSgXFmPwTooOlAkbG0tGQnJyd2fX1tz549E6hLHgx5QACX/X7fFhcXbWNjQ4AAa/nHP/6x7e3tSV3U6XRkO1tfX7d3796pwXLKw1HOYDWAqSbngTMGeXkgEJA03GkrYvQUUmmsRthDANC4hyjRqtWqbB140Pl9Pp9P4wDD4bDOk2azaS6XS+sD4mpjY0P1GwA4zbfT4oK1izOBcGFqa8AYPNGQEMi0NzY2bGJiwlqtll1fX9vW1pYmZ7C/NxoN2QJLpZIdHBxIWXRzc6PadWNjwzye0WSNN2/eCNBnLZBdQf3JPrm+vm5mI988I1IXFhZ0dpqZFBOopzwej3311VdWq9XM6/WK4Lu8vFSdNz09LauG1+tVxgKZLgDW5+fn8m1zzZhJXq1WrVgs2osXL2RfpTdAHQqQ5iR3Op2O1ttwOJQ9iH2U2pDzmzXu8/l0TwH2sZ9y71FyzM/PK5OA6QSMO4bEgSQ5OTlR3ULY53A4tI8//lj2H9YHIxidSgHWKt7zn/ZFzfGzfP2cCbdRyBibLWngeG5JI47H45Jkgkrx8JycnDxKRKa4arfbknZPTEzIm8JGxQaM5CeRSGjjv7q6snq9bqurq4/kWMgfafQIe8JfxfsyMyHMIFQwdgT4tNttOzs7s+3tbW3++IiR3cLKEW5Bsz4xMWGFQkHBT0jvKEJAm2COQFTNzF6+fKlQHiRVhP10Oh0hsJlM5pHfFzYCthTfLYUnXmAaM8K2kDLSXA8Go1EKhEwtLi7a+vq6xhwcHx8LuSfEbDgczYEmJINDcTAYKOHYWUTSZJiZ5JsXFxeSF3EIosKIRCJCMr1er8ASNqPx8XHb39+XlNK53kjsxF+FZ9UZYEQzz0gervHy8rK1220VjtwPUG8KNJJCS6WSGnt+Bn9vZn9C4uv0cvNeuN/8Htg7GF4nq889d3rHKfr42Ujx8GXyc5GmUnBziMBkIPf3+/0K4QM4A0kfGxtTGjkF89TUlJ4VwAh8palUyu7v7+3s7EyBd0tLSzY7O2urq6vaZ7ineBBJ8QWgYEQVAAHP+9nZmRjdQCBgw+FQQBf2hydPnuj5RvkCq358fGz1et2eP38uQJAGioMLKwfy2k6nY6FQSM0J1x8Aan9/3773ve/Zixcv7Pd+7/ckn2WEI8AT75950hTFgHbD4VCji2AHubaTk5Py3yKXRHGxv79vXq/XPvroI+0NpVJJzxdzwc1MY5a8Xq9Fo1G7vr4WixCJRMzn8ymLoVQqycuNtPLy8tJOTk5UCMJK3N3d2enpqZoHbByolqrVqq2trUnhEAgEbHd316ampiwSiVgsFrOvvvpK1//y8tKWlpbs8PDQ2u228gaQ1yPXNDPL5XJ2cnIiFpFnZmFhweLxuIK2KGDHxsak0KFJAyjm7OD+HB8f2/z8vFQhPHckKvf7fWV+IDt3NiU036iRkE0zipH9gmcChhm5pds9StWvVqs6l+PxuLzkhArCWCLZbzabmmtLMUlDyHvweDxikwqFgiaRwCAjnR0bG7NSqWStVstisZgk1WdnZ3Z7e2utVkv7N80GM78B1gmKJDSIZpQX0xBgnEiARsGGYon1Re4Gkl4aNRhKAl6x3Xi93keKNBhVnmMaeRhu2KrBYGD1et22traUO+P1etXUJRIJMdvdbtdcLpfGzKHaYV3x3hqNhkYcFgoFi8ViyhyAjIAVJGSWhgev+c3NjYXDYV1LxvNNTU2phisWi7a4uCibEY03IB7rlWwW0ruxaABiFQoFq9frCgSl3vrBD34gO1qlUrFMJqP8AM5lAFju1aeffipwgpnxXq/XlpaWpEZggkm/31eI5AcffKD9i7qEcxLwz+Vy6fctLS1Zq9VSyCc1Bc/M0tKSJtdg92KPpu4A/AKYgS0vlUq2tbVlR0dH5vF4NJYLDzQ1Sy6Xk7KAMDvS+yFOsCI61TIkXTOxhiaeBoyzj/0D8otgO8aDAkZSA3DN5ubmLBgMKtl9b29P4/hooAHkA4GA6jcskdSRziwRlIo3NzdSgAFkYv/64osvzMwUCkhtSn2MTQ1VgJNM4lklL4M6kf339vbWMpmMLS4u6u/q9brGpTEaFACUtVcoFKT8IkMDaxv9wtXVlfKcarWaQFiAI2qoVqslZVYkErGDgwOlxQMu4jvvvA9SRXFIUObu7q4ISiwq4XBYAXbtdls9DnskYy7JTAoEAlKBMuZ0bW3Nstms1Wo1jWD9+etn+/pWTTheOZoQChYaB6ef0CmlMjM9bDTVoEMXFxfyQYM4O2d8I8UGaUQiAutoZmqEKWoGg4EdHBxozBZjCkg2pMinoaL46ryf3UshyiFNqjHzoOPxuL1580ZjJEiWJthoe3tbzTcbENLs8fFxBTcFAgEFMiAFQ0Y+NjYmRoGH/Pb2VnPUr66uJMenKAJZJ3WSYoZCjk31/PxcDx9fs7y8bBMTEzoYmCXa6XRsfn5eG6GZWTQa1edKp9OP2A+v12tra2tWKpV0ICHjX1xcFDrulK9SQCLpYW41m3m327VYLGbRaNR8Pp9mGyeTSfv8888tFArZ3Nyc7j1eR9BIQkTMzE5OTqzXG6WK4829u7uTHYBiC1AB6TvrZ2pqyvL5vDbAtbU1azabKqLHx8etVCqpQMF/RFNm9njeIQCT2ahJpunmc/CiAOdnOAO0YAjMvpGp8zU0yBxONJIwX/w5svtKpSJFyHA4lAyXYB2kpoBkJDXDNtJUAwRwDWgoaVAIpwHtjsfjSgJFWopXFzkWP3NyclJFlXMuZiAQkIQXqdvY2JiAGCSGLpfLnjx5YtlsVsFhMzMzj/YnZFztdlsFAmxzuVy2SCSiXAbWjVNFwPrGHw/4ND09rWKL7IvBYCB1Dem6eJNh4GiKxsbGBMK1Wi0xsuVyWQnYy8vLkjvf3t7axcWFpdNpOz8/t3Q6baFQyN6+fSs/LusASZrP57ODgwMx+VwzihlyMrhXsVjMQqGQHR8fKwEYyS1hScVi0R4eRjPQATrI3uj1epLrEcDpdo/G88ViMZuenlYAJ8+n1+tVkUSxTFIseytfQ+F8cnKiUVrj4+O2tbUlgAdgqlar2ezsrKSF3W7Xnjx58kih4vP55Bucn5+3TCZjJycnKoSc+xsN32AwkDeZ5iEcDj9qjGGem82mpNY0q+wtznUP24bqCVDYbDQWCGaZnJJ4PK7JIrxHskwqlYoSqFEcUMBT8CLRHhsbs3a7bWajEYKc56VSyYLBoOT+fr9f+xJycwAQp0ILqXOr1bJoNCqPMkBGtVpVGCnKBSTZ5XLZxsbGbGVlReGwrBvku2YmFhKfKLY3JJxYimCP+v2+GDe/3y8Fxfj4uOTzvV7Pdnd3xSh3u12ltvM7mTbBXglh4fP5LBKJWLPZtFqtZsvLy2ZmOuPn5+f1/gFVKPC3t7cFkKLaS6fTj2xGiURCaiy87vf39xqdODY2pjyN7e1tq9fr+nn4XgH6sNE1m02RINRad3d3ssU0m027vb3V+/b7/fbs2TMV/z/84Q+1LxMg6EymNzMp7hg1hX0Hb/NgMJBthGYFdU48Hrfr62s7OTkR4B4OhxWCRs5JPp+XpQPQHVkwmQcLCwuPpOdORQZnP42lx+PR7+PzmJnGTNIcNxoN1TWEO+ZyOVtfX9fPmJyctNnZWcvn8wKBIIOw8zFWkLqFDBGAZpSZTiUGQYPsFYAsnU7HCoWCLS4uWjQaVf3pzDGZmJiQrx0rKQqDfr+vkFFsdADW/X5fNjO8+vzu29tbpabjt5+bm7O7uzulcn/nO9+xy8tLPa9+v18gRiKRkIedM2tyclJgLv0G1zwUCkldBkjI89hsNm1xcVFTlbApEWpMZg3hfYD+nAmAD+Rhcb4RIgdIwehiADnUuKjP7u/vrdFoaMzsYDCwp0+fimV//fq1rCHUHAB49BZPnz7VfeGZ5VqxxkulkmWzWfN6R9NRqtWqgPXT01NbWlqyRqPxSIX5p71+zoT/2V/fqgkHSSY1s9vtKhEQeTlBKGamkQRs8KQxIqmiGOfl841m2IJQLS0tiVFlYyaEpNVqaT45TASBSjs7O0o/nJ+fV6o2Uj6CyJihC5oHMk1DxZ8lEgmbm5uzXC4nmRwFlLO5brfbYitRDTBS4vLy0s7PzxXUxIxgirZEImF7e3t2fn4ulvDLL7+0ubk5+Z4BJWAPzOxRs0KjglwcJAvP3sHBgQXfz12NRCLW6XT0/4VCQenSNHsUAi6Xy05PT4VuLy4uWrfblfeZhn18fNyOj4/lkazX69oIkYGVy2UzMyVfwpaEQiGNBMlms7a6uqqNChAATyeBbSSe0zTDwvv9fjs9PTWXy6WRJshTW62WhcNhecyQG9OEmpl+J7MVASAmJyft5OREhzcoPOjyycmJ9fuj0SiE0uCX48BBWkfIC8U6L1hjGnOnjBWwB3mh2z0Km0OSBlv2v5Kh05yjDqGg4D1sbGzIf0tAYiwWU1Iy3/fw8GBffvmlLS0tScKLvwx/Hs337OysmX3jncN24Ha7pXYBlAFkgiWC6YElhzW5u7uzcrksFhipHL41JGDBYFAsXj6ft7W1NRU9FBS5XM7c7lEwY6VSUYGMXQUpWaFQkJKGg5i1XygUxFpghzEbFZPFYtEGg9H4tampKfulX/ol8/v9tre3Z16v1549eybf6tLSkiStyIiRzb169cq+/vprXatUKmUvXrywcrmsogIQElQbcITGh2cEwJKGhqIOmwFrnAYNmwmM6vX19SMwMB6PS0KI2sXsm9GMjG2C4cWDzH2q1Wq2uLhou7u7NjY2Zh988IFdX19LMg8QVS6XJScfDocaq7i/vy8/3MPDg21vb8vi5MwUYUQcbDYe2m63+yjVvl6vy4/NuUABQ5ODbYHxb7A1yPUZFQlLjA1lbm5OYA1yTRoimnCkgjDPFOwAMShXUFDUajX5xZ2ebNYKfvB2u23X19faQ5D3I2WGsednwLL1ej2xJHhosTiw9yMR5fcBJnCGAn6Nj49mlHu9Xs1rd7lcktw/PDxYo9GQigOvN/XG3t6epdNpjVpEUnp3d2eXl5eSeVIfUFPQWFQqFQuFQpLkAxJ5PKNUa+4jzBWNGl7oXq9n4XBYZy+svs/nE2jGVAkUZmNjY7oXjJ8E3HW73ZZIJKxcLislnutIc42qkPwVp7WJgCcCxq6vrx81nVxDgPX7+3tLpVLWaDRUGywvL0vSy1QCznM8woxZCgaDAh9pIAh/4uxBEu/1eu0HP/iBbG58PmdgKaF1i4uLCvojoIvnlIaG6TTJZFIeWhLv8/n8I7afIFwydLDuELrGWTQzMyO1GspN7FFu92iULs8jhA+/w8x0NnHGcl6xtwFWLSwsWCKRkDXCmTOSzWYtm83axMSEpVIp/ayzszONbON60TiTVZFKpZS2j4qCumJubs6KxaIAGK/Xq0A858QNGl+k2VhemIazv79v5+fnOk94Hv1+vxQNBDBeXl7a/Py81go2T8b6JhIJm5+ft88++0yNNc82VtGVlRV79+6davoPP/zQbm9vrVar2d7enuT9s7OzAg1zuZzt7OxYpVKxRqMhoAbLIGcGoW1bW1tSazCVhTMSAAVVIVOWmG9PGBvKM4gJLJvFYlF1OMoOr9erPRzShfvFPVteXpaNqNFo2Pb2tiZ/9Ho9hbdCFj179szq9brUnO122+LxuM4OVMhOsAwVzeLior1588ampqbs2bNnNhwObX5+3kqlkmUyGfvkk08ESP/89bN/fesm/OLiQgZ+fAw0maR6MgMYJA2EDTmsx+NRA0swF4wpjTiSmFgsZpVKRQUUBT+yNbd7lPh5e3trl5eX9r3vfU8bIyEUpF/joYHdzGQyklZRWBEUQxgOGyRNI37kYDAoH8bc3Jx8gIzN4aDmPc7Pz8tLhA+G4p3xAhShgUDAKpWKNgSkdBxwFGsUekhNkcMyxgC/HU03hyRgCcE6yMPPz8/t6upKgS5ffPGF3d3dyRvOvEOCPLAUcMD3ej1bXl6WdIoCot1uWyqVkjQODyzNLJskzRk+PIL2YKlozgnFAn0j4fbi4sJmZmYUoENxSNowXuSrqysVHIy4oei4vLxUAB2z5lmvjI0gXRwfvsfjkXRwYmJCsu5araYmA4aE58LMpHKgEKYhBpjimjgbdWSK9Xrd/H6/GiqKJw5x/s7M1MzxdxQkTpk61xp1gJkJZEPmGw6H1eydnZ3JYgF7Ho1GlRQLw4hMl+ewWCxqtM39/b3WM3PkaRJ5P0iLYR4ICHMi0zxLNGa9Xs8qlYr94Ac/0LzWy8tLOz4+tvv70Sz0h4cHeclJOH/69KlkexxYKCYAwdjrisWira6uis1BNubxeMQsMJ8bJsntdov983g8ep7cbrcShFE2pFIpC4VC8n1xH2iy5ubmBPihiEHixtQA9ltQ+UajocCyZ8+eaWYxieoul8sqlYrWGvJWpLr46fBMttttFYjOMLBOp2MLCwu2urpq19fX9vbtWxsOh5qBC1jH56TZvbm5scPDQ8llYQCWlpZsb2/PyuWyishSqaQilLnPjJK8uLjQcwdbgRqIJgim0+PxSL7JOgG8mpmZEQPmcrn0vTMzM7q3SAY5Nwg0QhXEtTEzBTwB3KL+QklGiA9hoHw/z+nCwoIUBaFQSPkpqDOYTjAxMWF7e3uy0cCm0riamQAxGEeYtlqtphyMh4cHW1hYkEKKs55GirBVj8eje4t0l5Ah9knGH8JqOotYznuaoLu7O91DJ2hDWCn7KGcrMlrAcKwpjMpkIgG5KPi4ARFRSpiZ8i9Yg4uLizojg8GgCnX2NmTvnBPJZFKKmUKhYNFoVAoy6he8pWSHAH5zHQhfCwQC1mq1LJVK6QxmPwR0Zt8g3dntdmuiDAwhexYBUd1u17a3t7UWOu9nVONhphaamZl5NKIOwI094vLy8tEUB/amh4cHe/bsmX3wwQeWzWbtf/yP/yElF9eDs/vm5saOj4+lIIIBpPZBRcBegze53+8LGHWG1GKJAzSEmCHIi9qVfYrrDSEDAExyPE0i9Qj7iVOlZmaPrgGNDxk6nffjTdkbisWi6jK3261nsNfraYQlU2jYZ+fn56V2oDEHbESFls1mrVqtWj6fly3I4/FodFgqlVI+AcARgHkul1OmDL8Ddc3ExIR+PzY8s5FHGtYVlQZ+7Ha7bblcTvsj+T3su4Ac+NPZG10ul2w1g8FAEvPx8XELBoO2vr4utVAqlbJMJmOrq6uPAhQB0Mk4Yi0DavT7fatWq1IUwGI7lRcEtaHK5NkGeOHaOa1a3EOa+uFwKHaesDnAYs4n6g9yidgrAaKLxaL8/IeHhwqXY404k/BPTk6Ur8I5jTK52WyKFAsGg/bxxx8LWEwmk9ozsPqicP5pXz+p3PxZvH7WP///1utbNeEkFIIQgQKTkEpBwULDs8bICoLLYCByuZzFYjG7uLjQYseDBFtZq9VsaWlJI8lOTk7s5cuXarycScRnZ2eSX7GQnLJv0NtEIiE2OR6PK715fX3d+v2+gnEmJyctlUpJEo1cbmFhwWZnZxU6d3d3p3R354aeTqeFxvIwnZ6ePhqJwcHZbDYfjXdLpVIWj8ft3bt3tr+/LzSL341kjBRFM9NBhRcKHxcP8du3b+3u7s5CodCjkAq8JsiGflI6X6/XbXFx0SKRiO4RzSoBW6TlLi4uKrnW7/dLAo9/tlKpWCwWUxFxd3dn+XzelpeXLZlMWqPRUIHLgw0yv7S0ZNPT05bJZCyVSqmRhLVKp9PKFcBHA8BD8AoboMfjUQF7f38vkIfiz8zUFCN7PTg40HuCOST8CzWDM+2ZAJpSqaQ0VmcwG4FsbKROqbbZN2FLFCysF5QUsHKEn8FwIb1HButswGHZnfIhmi+Qc9j3m5sbMcrIY30+n/zxXu8o1X19fV2WBCRwFMKhUMji8bgNBgOFcb169Uqf6/T0VB5Lp3R8enpa/j0OM64NAUD8g+SMwCPAJ/yY5+fn9l/+y3+xra0t+/jjj1XkwRbx76+++kqFtlM6j6qDw5rJC+12277//e8/OoDxjZXLZbFZAF+AfzTufFbQa0bY8JyPjY1ZMBi0arVqNzc3ChqKRqNiWmjqd3Z2tLZ6vZ6Kdrd7lCJMujoNP6y+0xuNjJa9Hlnnd7/7XRVOrVbLHh4eZMOBPac49fv98memUikrFAqazx2JRPS5zUxsPP794+NjBUvyM/Glp1IpfWbADgDI+fl5FSV7e3s6D2BkGcdEQ9Xtdu3k5ERz4mHrx8fHbW1tTRMDkHcT9sO8XUb30FgyNunTTz+1ly9fysoQDofFesISMy4IKSGBV1w37h3ACeE5sIM0KVgH2EdomrkfgDbFYtE8Ho+tra39iURdikKKSWw3NKF4gGGokfHPzc3JCsP6GwxGYWc0SmamyRMENQE6MyYNNRz3yOVySQ4K0IoHG7YVcI8ArOnpadUT4+PjVq/XZT0xGzWYPCcokNjbkOgiJXY+hxSsyJOxxXC/KMy9Xq/OOHzZ4+PjCpXFskfxzs/gbAFQNTOdI3hkK5WKhcNhu7m5keydGcCDwUDNGbLneDyudQaISMATn92pziIPA+Cb98c+D9BB8w9AiqT/9PRUeyUN9c3NjYJnr6+v7eDgQAGlnBcwmJxP/BsSh7PJmcPg8XhkbYzH43ZwcKCayGxEEFGzdTodq9VqAnnX19fV+CO9bzQaNjs7q/0D24zf75dlj2C86elpC4fDYsOxvZHHQRMMAMy6cJ5dgAVYq/i6iYkJ7VcADNRN5HGcnp5qHjTBfZlMRuuL4E8Ik3q9bqVSyT744APNop+bm3sEzni9XpETqFoJKiRnhTXJVA6zkSKJ85zG2O0eTXnAhheNRq3Vatne3p4IGZQ7BOliz2EKCvtMqVTS752dnbWvv/5a58Mnn3yieguZNVM+3r59K5AIIBowxOfzySM9NjYmYg1AAXsoSpVEIqH6k5olHo8L3GE/cJKIiURCNhPUNJzBEFcoobCrkXdDI089+ObNG0un0xYOh215eVl7PsoMak9qjlqtpnFoZBmg7JmcnBRYxfhg6vXO+5A/nmvGctKPfZum98+7HL3dbts/+Af/wP7zf/7P5na77Td+4zfsX/2rf6Wspz/tff3ar/2a/e7v/q79x//4H+2v/bW/9q1+97dqwpF6XV9fKywFWSsFkdnICwPKFw6HJfM8OTl5NDpsZWXFIpGIlUolyZbw+FWrVaFyx8fHCjq4vLy0bDZrV1dXYgPYQGhKnZIZWEIzk/85HA7b3NycwlOQjoNklstlazQaSuxdW1szt9utsS1sAs1m0548eSJGE5QZTxo/a21tTYmrMP2JRMICgYDVajUlcdMwsiHhm6WQoIBpNBqWTCZ13ZG60IAWi0VtPhS6+LVgCkg95/BcWFjQQYDEiMIuHA7bysrKI69usVgUoIDU6u7uzhKJxKMUbQ5oPMGRSEQbOGEwLpdLIyDu7u5UpKBaQIIEOgnLgNSKA5tDEtae9UMA4MXFhS0uLooZcXq0kZNeXV1ZLpfTtR0bG82jJt10MBhI/kxDeHd3J2AB6T1fi6wTr6mTfeZ9wXg7/eGsRxga/h80GPbg5cuXKtooDijmfrL55v9p8vla3o+ZqUjl5yEvBXzDv7+1taXkTVQn1WrVVlZWJPHDS0WhxbV0euYXFxctm81qjzAzFaJMXqBgJkQJuRw+S5JKnYF/s7Ozsj/AtBM2CIPmZPg+/PBDy+fz9vnnn9sv/dIv2ezsrMDD7e1tIc2RSMRarZadnZ1ZPB6X/YFQR0be/PCHPxQCjxKIOeX5fF6Mk8fjeRS+ZTZiQN+9eyfZHc1WNpsV+zg2NvYoEBDGa2xszDY2NtT4w2p2Oh15+U9OTuTBHBsbzfPlazc2Nuzy8tK+/PJLSYgJjWN/c7LhPDc8C8iWUbw459iyzmD5+FoYFOwOzWbTlpaWZOfx+/0q5vEP+3yjaRl42ZDmohBhHS8sLIgJikQiCvECtEI1wnVPpVI6D9jvaFz5jFxTEvNZ/zROvM8//MM/VOH28ccfPzqveN5hf7j+gNkAUeVyWcn1qE3cbrcCEwk5q9Vq8pq7XC55cofD4SMLFBJfFGoU2eSE0JyjbCONnoadPcI5Jx25PtJ7AAO8hktLS/b27VvJLfmsrB2UHM1m09rttq2vr+t3oR4j+I0ALe4ncnf861jQut2uUo3xdDcaDd3vwWCUAO31ei0cDgtkJL9kYmJCfnsUgNgDUOlcXl5qjBc1CwABe0yhUFC95PF4bGFhQaq4qakpMX6A+4VCQYqfpaUl8/l8AtwZB0udg1e41+up3piYmND9J9wU+TT7IiAWMnUUCWRtHBwcKNSPRG0k77u7u48S+VGkQbogbZ6dnbVcLqd52S6Xy169emWnp6daI7CcNAY8sxAUWDBosKm7UPQAfpqZ7InInGmsaLpQZ15cXNjBwYF+LnXC+fm5xlI557Q3Gg3ZJ9PptNQ4NNzIjlGIOC1mzhAvLAnUKuPj47aysqI6hByg5eVl2SBCoZDY8Hw+b5VKxTY2NnReOPdfmkAae9Yg9d78/LzuO2ADIcLkIiSTScvn849IDaesuVarqe4hn4RgtJcvX9rCwoKdnp6K+Z+enrZPPvnELi4uLJvNWqPRsFQqpck7gDn//b//dwUuulwunY2bm5vaC6kzJicnleqez+dlR8EqRDbF2tqanstyuWwzMzOaXFKtVmU5GA6HUtpiZQAwZ30C+KJovbq6snA4LOsRIy6xIVIHkqUBSOl2uzWOjp8DSMu+ajaysvHsAiA8PDzY06dPpWjDPgAgen19rSR3pkJw31AYYjMFOMAi4vP5LJ1O2+3trX399dcCXVKp1LfoDv98v/7W3/pbVqlU7Pd+7/fs4eHB/s7f+Tv29//+37ff/u3f/lO/91/+y3+puv3P8vpWTTiBC5FIRKEGNFQUJwSxeL1e+W/Ozs6UXhoOh3WABINB+SLw/jgLCLfbbR9++KHQHDxB+F46nY58itfX15ZIJCydTttnn30mpIo00vn5ebu8vJRPhUAFj2cUCkVQAQFHZqbkY8K4zL6ZK0rRTJHVbreF8m1vbyvYJp1O6/d8/fXXZvZNojYPKQFVpHbS6OTzeV0Xv98vKTWFAXJ9PFF4O/Hg4cfv90fzsxmpwcFXq9UehUCw0RLGhbQOJI/GikAqNh6/368kclJrQZFhTEhidaY7IyuD8aPwqdVq1mg0LJ/P2/39vRgnWDP8V/j84/G4dbtdyYApiFE4JBIJi0ajdnh4KMkPh+3l5aUaU4oyJG0UBLVaTV5lik1Cr2BgkNWBcLPpXl9fC13nYKVBxxfEYc4/FPkcEhRdNA4govzDRs994uc6G3p+JuvvJ5FL/szsGxk8uQJO5Uev15Ps3cnY4A9+eHiwTqdj8XhchRaHZDAYVDgijNDJyYkQXPYNJyiCbJbiAtCC/cP5HCUSCctms4+esampKVtbW5Ov7/DwUI0y82hBtFOplGTr+B1hnjnIOBBp+gkIojFg7X/wwQdC5/EA+nw++RmRDWOvmZ+ft/X1dUmCkfECFCSTSQsGg5pVi4Q4Ho9LjQJaTvEAaMH+Nzk5qZnjoPnORGXmGVNsuFwuMW3cE5pR9ii83qxngFJAoqurK1tbW7NEIiG5+ZMnT7QeSOylGEQdYGbyZ757905NIgUm96zdbtuXX34p+aSZifX3eDwCJ53zppH1x+NxOzo6sq2tLTWQNGOAdux5sGM8GzCd2A9oGrE7+P1+e/36tfyX2LguLy9tbW3NqtWqnZyciD12Jk2zf7GPAOYAZMDm83zjycRrDqjbaDRseXnZWq2WAF7YaaaYlEoleUV9vtEM2UwmY2YmAKlQKNj8/LwaCRrM8/NzNTHFYlENXuf9jHfmB2Nx4vrjKUeSenNzY2/fvlXzS1YDXnnYbZfLZZ1Ox168eKF7SHo5azMWi8nCxvtEFQNrPjY2JnYImTHpxHNzcxqFyXQM1HSXl5cChQGiBoOBmGy847CbY2OjOeAEmgFoYnGo1+ta45z/BEZxftLAT09PWzQalcqAcwAZdLlc1vhNAE8nWEQ4F01JMpnUaEreL40lZ72ZSTHUaDTs9PRUygCUYzxr1Amzs7P26tUrZVegusH+RT4QTQHnDXs/xIlTOck6JwALQIiRa07gplAoKPwPVRHTOFBrAWBOT08rw4PpMpAdKIOYxkHgGtf1J4tu6l4APbMR6ME944x2NpQA9tzfwWCg72dsZKfTUePdbrft/Pzc8vm8LDiwmc6pDqjxOp2O7hPKOdY/4W800/V6XeP/YNOz2axqDUBwM7PPP//cPB6PPXv2TE0rzzHz3/lzANKdnR0bDEaBySSwc0aTOwQhwB5TqVRsYWHBPvjgAzs8PNTzcH9/b4eHhxYKhWxxcdEeHh5sbW1NxNLd3Z2dnZ1Zu922crlssVhMZ2K5XJYtJ5vN2tramq7B7e2tzc7O2tHRkblcoykLtVrNVlZWVKcBtKB6mZ6etmQyKSKOPYH+gR4Hco69vFgsqomntpqYmLBms2lbW1uqyWdnZ+3du3d2f39vz58/t1qtZtFoVEGbhMSxRzlHk5HpQ0YDmR+QoYQuEszs9/tta2vL3r59K7Lxp339eZaj7+3t2e/+7u/ap59+at/5znfMzOzf/Jt/Y7/2a79m/+yf/TPNu/9fvV6/fm3//J//c/vss89E9n7b17dqwpH3wPxQDCK/wb/lRAtXV1ft9PRURTRS62AwaLlcTpJymgkCtHhgwuGwzc7O2sbGhrXbbbu8vJR0GnaJUTwsPFIkYR6RhCNHfnh4UPGAFNzj8Qjtpnns9XqaBTocDjVbMRQKWSQSsWw2a8fHxwpXQNZTKBTUDGWzWTHbpL5SrObzeaWJExpB0V0ul+X/QOp2c3Mjxm1ubk6j2JDURiIROzo6UlH88PAgaRqoYygUkj8GLzKBNul0Wqg5gSLT09NiGGCOnfMqCcuCheSQoaBmzuHU1JSQZkKx5ubmFGaHRIYDieRLNi+/3y+pOqgmzSw+OR4C/HFsuMVi0RqNhg4mko0ZKRGPx83rHY38efLkibVaLUsmk3Z6eqrxVoTtwWLhH6Iop9HlGlFYOCV1zB0GkEIyb2aPgjpgsZ0/h2aUAxHWDCYCtJVr4pSx8+dOhh5E1MxUHPD7QLthe1ZWVuSpN/vGY47cCykX84fr9bpYbWbGUmBFIhHNWUbVYjYC+JCT07x5PB79LFLLzUz/DZNhZlI8ILHkmgLk4aX9wz/8Q7u6urLvf//75vF4NKru4uLCAoGAxeNxKxQK1mg0xLJSmG5ubtqPfvQjJYOvrq5qSgMJyzSUeKU///xzW1xctJmZGWu1WvLwIXUjbRgAheaeUYUkTCOHxgNPgwY7cX9/b1dXV2KV+bywwciNA4GAJRIJSyaT2l+5BliDms2mhcNhHdpnZ2c2HA5tfX1dgJOZ2dHRkVQK+OMHg4HSxL/44gv5qt+9eyfknyTY2dlZ8/v9SvKdm5vTZ4URp3DkGiFLZ88nydfMHo1dmp6efhQaRfgaap9oNKrPytpnksb3vvc9Ozs7s4ODA1tZWbHFxUUV7sxUTiQSAkORAqLoikQiuhfsE6xZ2G8nGwxzhL2FF8/hYDCQf5BzAmUV9hT2Gc5k7CLIcpEa00g7i+T5+XlrNpvaE53WJ9Y/ewhBcmRRoCZYXV21t2/fWigU0pQBJpqQhMz4pF6vJxAb68LU1JQlk0l5fZnQwegungnk6GRDUEOgWuBsQsUzGAx0/rN/I5leWVmxq6srnUGxWMxyuZwa8Gq1+ihwD6/r2dmZlctlNWI8x5xhyPZp6ILvx4UBQAPYwESzdlCflctli0aj8tLC8pPe3u12pR6gWb2/v7dsNqtwrGq1ar1eT7YtVGDRaNRWVlbkRaahg1H2+/328uVLu7i40DODVQkl1MbGhp5Nvg85bTweVxozxMXq6qq53W776quvHmUf8CzA6JLs7/S6Ym1ib56amtL5bGbaO1BjfPTRR4/CygBZIFSGw6G9fPnS3r59q/WNQgMFH9JxJPNIdkulkhpwGl+eDWo/wEWCAlGFwELy86iZsPzhVY/H43Z8fCw1KUGFABBOoJXgSp5rghTJGkCJyDVEEUOtQ31Cs4bCj6k2iUTC6vW6hUIhKxQK1nk/hpL0cNY8gXqw+e/evbNerydrIon7n376qQInT09PFS4L0wxxRqPK/ubz+ezZs2eavx4IBATqUIOiMGUvzefzIpKwnQD0kzEC6IIKtdVqWT6ft0gkYv1+Xxkh1KdkJ7jdbj039CD8PcpV1FhmIyUtWR+ob5mwMDY2ZuVy2S4vL2VjzefzApkhO0KhkGWzWVlfGHMGgBaPx+3w8NCi0ahVq1VLJpOyh0A6zMzMaPrQ69ev7e7uzpaWllS7ENb84sULKxaLsn/8v+0F2M6LEYp/1tePfvQjCwaDasDNzP7SX/pL5na77Y//+I/tr//1v/6//L6bmxv7m3/zb9q//bf/1mKx2J/593+rJpx0PnzgFOU0EDQMBI+wiJEKMysSbwvpkc+ePROKha90d3dXc4MJaimVSprNyZ+73W5bWVlRo0oxgUcQyc3i4qLYmfPzc1tZWbHp6Wmr1WpiqTqdjtJS0+m0LS4uWqFQEOqFrwXv7fb2tuXzefmLCLCANQGlnpubU5ibM/iKF5t7532g0e7urs3NzUmezwHl8YxSnwnPMRt53gASkNkhVwbpJJGTIpM5i3g0Ya+ur6/VwNAM4W1E7cAmxXqg2SYNm0IUhpEgMWSl+M45TAnFAwlFUuRyuTQTlDW2uroq8AUpEkm6MKNspDDQjOQ4Pz+3tbU1eYjdbrfYW0IvkG8xzgVmghRXWM7Ly0sdQDCxyHNpnPgMFN7OhgiEFKkrCCqfk6aAUWFm34wy6/dH40ecUnIaZjN71Gjz/zTWzp/D1zl95zTVMLMkRX/99dfm9/slz65UKkJww+GwbW1tKeyKZ4kDiEkDjUbDQqGQvXv3TjLGXC6n9Yln3u0eBS3icRwMRvMxd3d3ze12qwnjeePAw28dCoUUDEeRQtFaLBZtfHzcfumXfkmeZp65k5MTXTOYmK2tLTs4OFDwI0VFv9+3ZDJp6+vruqb4TNkLu92u0ohvb2/VYNXrdXlpzUYFQTwet7OzM8vn8/IDYv0B8Gy1WhoPR+qr0wtMyBlhfJVKRcFUgB4zMzO2s7NjFxcXAvdCoZAkgoyzYt2A6k9NTSk9+fr6Wgg6IBfqCFJsCcnhGSOgCtYNOwupsMgvCUOD7fJ4PLa1tSWW9vb21l68eGHdblc5C6enp/LOoYwCTDAzyZiR7Xs8HsvlcgqXIlkWhYbX67Wvv/5a8kaaNhLyKbZQ3zDrFRCDkLThcGg7OztSQTAWEzsNeyBSZwLwzExNH4FTyFlvbm4ElPK7KbRgwsxMgCHAF40ufkRYHvJAKPZgUwgkYjzS+Pj4I9UIY4OcMmesUMhk2Ztg7e/u7iyZTJrH49H3wwI3Gg2pMrAbsNc/PDzY3t6ezc/P29OnT+3o6EgWIJgxnlenlJhZ3exH2MkuLi5kD+l0OlapVBTgB9i3vb0tH2u73bbb21s7Ojqy+/t7Pf8U/y6XSxkVLpfLVlZWdJ1oLmq1mtbSwsKC5XI5i0QikntDGLjdbiW/Y0tbWFh4pDQEAMNHz3MXDAbl+b26ulKwLE0XUtng+yT6ZrOp4DWsESgxnJL9/f19gRbJZFKBUVhTUAVw75B9cyYzN/3LL7/USDTOORpY/sFbDDnA+p2cnBTjiUwW0Mrv91symVRIJbkihLNubm6KULm/v7d2u231el0KJJQfZOm43W6x/yidyEwAJKVGRRmDeov1ALPpZHc5n2Ek+/2+raysaE2YjcanIosOBAK2sLCgaQJ+v1/1CwoOgAZnJgw2grdv31owGLSFhYVHqlGaO+fYNgBobEWnp6dSMhBUOT8/L2VYPB63k5MTEWiw87FYzJaWlgSQo3gEBHOqG/Dmc028Xq/sNYBtTmDa5XLpnBwbG9NkCMBG5wScp0+fqs9wu91KOsce2+/3ZTvhrAuHwxpH2u+P0uVRCRHyy3UrFAqqnb1er1UqlUeKXKwl2LgAHlgPnE3UcdVqVfePMGEIuUKh8EhFg02KPeD+/l4TnVBLnp+fS5EQCAS0T/d6PTs8PNT4yvHxcavVahohyLlzeXlpi4uLmnTy07z+T3rCf1Im/4//8T+2f/JP/smf+edim3K+vF6vwMz/3esf/sN/aL/wC79gv/7rv/5n/t1m37IJv7q6UmGMr5gNGmaMGd7I45D08ACCSJL6OBgMJMup1+uWSqXk2yP90OfzaawYC4piFdkgmwLJtMPhUN4lUkZdLpeFQiFJ1vb29h7JrkhWpGEhHRZfNOh/Npu1X/iFX5DUKpVKWTgc1igkj8dj19fXaihPTk50wIGKd7tdFW8cogRzxONxpQjDgJFIi6SVMBZYneFwaJlMRps9hRNFHkEzkUhE7BH35OHhQZJ7pEhnZ2eSi+MrJ+2TWZbI7AntAbkE4Zubm7OjoyMxaXiLPR6Pmud+v2/lclmSJgL2CPfDu+fxjNIpGaWAz69Wq1mr1VKzPTY2ZouLixYMBq3RaCjABeR/bm5OMyDv7u7UmLGeYB+C70docYiw8Z+fn6sAwQtEocmB7AyXYS3Bpo6NjelwRfoLWOP8OmSwMNnIzZFp8jtouPkeWHQOL9QeTik6zSZydphHnmGk//wc1jLS/16vZ6VSSej+5OSk2Cma4WQyaZeXl/IUM+prOBzaq1evJO3u9Xq2urqqgpKZ6055E8EsTE/odrt2fHxsKysrkpnB9IKEc91Io+/1esqgINnZ4/FozBeSo2KxKJnfH/zBH6jY4XOzBmA+vF6vmvNms2mrq6vaFzc2NmR/Yf8hjwKWjnvEfSgWizYcDi0Wiyl0Btl5oVCwVqv1qDkC5BgMRkFNNP8TExPWaDSkQoAlhb0GEBsbG5MUGLsQ95F19vTpUxsfH9coNDzeMCoLCwvao5rNplWrVZucnBRDlUwmLZVKCbjl2aAQJoTL4/FYIpHQLGO32y3JIuARKfOxWExZDAQiAryRRNvtduUHhM1gVBnSYTzwoVBIBafP51PzgioDdigYDNry8rKAUEBSGGsC9bBMEc6G+gj/Lkx0IBCwTCYjyTb7iZnJQwxbhseb7Ivp6Wk7PT21+fl5gRqsJUYFIf9lLj0p6KjaKPCRPgIez8/Pq4hcX1+X39CZ0I0dhecUZp3PvrS0JFsLe3gkEtHeenFxIUDOzKSSi0QiCq9LpVLKMmC+LxJhWGp+H7UB6iXUZ6RmM6aNz8Fzwr6NJ5rCFyCGAjeZTFqxWDSfzyf2kGYZALFcLj8Kr8XLzH5B6B9sGyC9s3GHPWfcEiq+SqVi6+vrsqJxrvV6PQERKBVo8GdmZkQ8wAQTvjYcDgVampkkzyi1/H6/LS4uCqReW1uzer1uu7u7j6xOyNEjkYh95zvfkdcdQH5sbOyRTdF55qFM5M/dbreabSc4QNYDowoB81dXV1UHNhoNC4fDCjEbGxuzTz/9VIoMLBCs0U6nI8sfjDM2EJhMsmpQmPC5AVy5BzThV1dXskLQ+DlltCgOIYxoQHnhX0dth3Lp6urKBoOBVIWAyyjBzMzevHmjehHlELJiptUkEgk7Pj5WJhHr/Pz8XGRIMBjUuozFYmLF+dmXl5f2/Plz1eHb29tKo6/X6xopS94BdsWNjQ2Fv7548cK++OILkVKBQECSbOwEL1++tI2NDYFHgD+ABvV63arVqj158kS1RCqVEkBLwDNqIILbsDOQ3M655HK5FAoJMULuAkpGj8ej5puQPVQRZEVQk9zf3yuYmt/LZ+y8T+bHVjAzM6PrBSjDeUOiPiBYsViUajAQCEi1ysx1AFnk7ZCmjUbDotGo7e3tWTwet3a7bcvLy5ZKpTQ2DnCPTID/N74YC8vrf8eC/9Zv/Zb903/6T/8ff9be3t6f6T38zu/8jv3+7/++ffnll3+m73e+vlUTjvSVge8XFxd/wpfCIoBtAWUgcZPGCUad4olxQbCZwWDQstmsFYtF29raUqAJITtLS0tWKBTk15mamrInT55InkmBbGbaiJjN+NFHH1mxWLRWqyX/L14f3kMoFNIcVQoVxuKASPMAIkukAaJAZkOfnJy0eDxuc3NzGm9kZirEGMmEVJIio/N+riZFBSw8aCIHFuE1MJv7+/s6NDiYaeLOzs4kXwH5BPlyooFIyi4uLlRIMFYD4ILRCoPBQPO3KaiWl5eVQA8osb29LZkh4zCazaaADjNTgUHKdK83GttRqVSUat5qtSQ7xQ9H8v2TJ08k14VNQkLK7GF8bIBJgUDgUQGA9K/ZbNrl5aWl02m7vLy0TCajYC68704/N80OCCsNL/+mMcB3RTGIFAu5oplJGscacsrKnWw5RRrMAmvj6urK6vW6xWIxfT6njw0LCUwmrARNIewqoIKZSaLl8Xjs6dOnKqZmZmasVCop+Ih5p91uV001rJbb7bYvvvhCXksKKe4XLDINlNfrtY2NDV1js1Gxj+QQsIb3AYvDgYdXudfrqRFrNptqAN1ut8KVCIoj2Iem78svv5RSBhvOxcWFnZ6eSr5Zq9VkdaBBSSaTStcOh8PWarXkIac4xssKmOk8NHn+T05OLBAI2NramvYPPLkAL4BbqHgAcGALaXzZE2makUuiari/v5fvmz0IsIPwKdbH5uamrEeVSkXPeygUEtCK6qRarVqz2bR+v28ffPCBpdNpOz8/17xXmkOeP1QI7DtnZ2f2/e9/X02DczQODARrg2vc7XblRex2u/b1119brVaz73//+9rXnUU0wPLZ2Znk5rC23/3ud+1HP/qRhcNhSyQStre3Z5lMxjY3Ny2dTqsJhJnBm0fTiuQV8CWXy+l+EFCJ3JFxUvF4XIBfo9FQwwkA68xEoNilmab5XltbU+E6NjamCReNRkOjBaenp5V90Gq1bHV11SKRiGUyGdmQkLjX63VbWFiQLQoLBusMsAYrlZlJ0oriAWaSRjwajcqe9vr1a6XFN5tNASvYcNhzsBQBbKF4c7tH0yxIOQeEIhEbVpMxedFoVPN1zUZWq6mpKSuXy3Z6eqp1TKgiDR4sFvkJMIzUABTx5+fnAmlQ5zFCjnFdyOn5GnILOJMIwYOp4n63221zuVxicZ49e2Y+n0/gczgc1jnidrtl0QCUJFiQ32M2Ih6YhuJkKJeWlqzX6+n/AYSRb7vdbltfX7eZmRnb29uzP/qjP7KHhwcFzW1sbFgwGLTXr18rW4T9z2wEKAH4AvqTnI3KKBqNWiqVsmq1qtydSqUi4PXh4cFev34tAMHsG+Ko1WppHcIsvnz5UonikAn4mlFWoATi1Ww2VYNx7nK+c33wjjvtZpyl19fX9uMf/1g/e2ZmxiqVivYjM1OYHspMiAVILEAWVJswo2Qh4ImORqOPRnxlMhmBnti8gsGgQGsaW66/2SiXIxgMWj6ft8FgoEBIp4qBMW8ALcjsU6mUmHa/369sifv70WhHaj8ymDgrB4OBxitOTk4+yh0AiMHuAZEzPz9vmUzGPv/880eAO0oPCKfO+7Fc5BT0+33L5XJWLBbt7OzM1tfX7eHhQaQXY/guLi6kzOScXltbs0wmY4uLiwqKJnEfawh7P9YC7gkAOKo/pOpra2u6rlw7ACdyPpjogRLq4uLCwuGw6ujJyUnb3d0VoIkdMhAIWKPRUK/E72W0L9lLu7u7FovFpFr5aV7/J5lwQO0/7fWP/tE/sr/9t//2/+PXrK6uWiwWkyqDV6/Xk0Luf/X6/d//fTs5ObHg+3GivH7jN37DfumXfsn+23/7b3/q++P1rZpw5HRmJkkd/p7Z2VlJKgqFgi0tLUkGajZimGZmZhT/T6HR7XatWq3a1dWVvXz50nw+nzb8Uqlk0WhURSljCaLRqKQmBF94PJ5HUmrC3WgmaNJarZblcjkd/Ol02r7++muN8ur3+3qvY2Njls/nlb4cDofNbCQtIuDJmbbIuK2lpSWNgwFBIzmUhpFQKuYJk0TLWKjNzU3L5XJ6CNm8aCbZiJDlIEuicSYtOBaL6XrBjIXDYfnvCRxpt9tigC4vLy2fz9uLFy/EWFWrVdvY2JCX/uDgQL8LloH5pjTzR0dHeviRkMMAUFRy8IyPj9ve3p6AHbyOzGfHQ4VqgtAkpPbMuTQzFYMARjA029vbauza7baYmXa7LZXA27dvBXqANhLwBEvFwYokmOaQ5phiytmgc0jjf0PyVC6XhfLS2LPZcIjz9WamphLmjfdCQQiTcnNzIwkj0l4KWDMT4uyUplNUUGSY2aMm3GxUICwvL1swGNQM7rm5OUnoKEQoEEB9QZ+5L/l8XgALo0JQa1CQBYNBu78fjfTCc0YIIkh/t9sVO1WpVBQyNTExIXuLExjs9/vKVqChubm5Ud5ANBpVYJrTu3V/f68GjeaPBqpcLmsiQSaTsYeHB0skEpoXSsMxPz8vWwiqF9YdAAJNCftfs9m03d1dAT54XQEWKbC+853vqNHAD8b7hj0kB2Bqako+PXz0BAMxahDVwevXr204HIqxgOmHjaChyGQyem9Pnz7VAe6UFcbjcdvd3bWDgwNbW1uT7HB1dVXrnjA7FCvYUjqdju3u7trKyoo82N1u1+r1uiTIFFuET5HGX6lUZH8YHx+33d3dRx5UwBWk7PgoWfvOXJDz83P7nd/5HQGXJO7STLndbtvf3zeXyyXFSyAQEOjMc+r3+1XQtVotqWmQFgJSwLbhkcdvzHNA8BygAYE/qEKwypiZilAAL/4bGSdz4wGdUY4xkYNz9MmTJ5IWw6Sxz52fn1sqldL6MjPZL+7u7jTNwmmlMPsmGZjCenNzUyD73d2dPXv2TEF2yDCx6pBpwr1rNBoKMAWwwipG03h/PxohtLy8rOecxplQrkgkYul02g4ODuzm5sYymYyYdQBAwAczk3yXWeR8RiZyAEzD4BOy5NzXOVdokDwej2wqTPXodrtiqXK5nKaKmI1GCwL4hUIhAbA0dUjfb29vLZ/P28TEhMK3sG+x9rFDkFBdqVTM5RqlnLfbbatUKjpnAAEPDw/t008/Nbfbbb/8y79skUjE9vb25A8nB4Gzy+VyifVGTs7PYj/a2dmxVqtlxWJRYAjjJ7kGBNMiH6cWu7m5sS+//FLgCLac+fl5seMAbR6PRywjSgT23Hw+r+vIWkVh5mxI2X+4p5yj1APYgNLptOpTmtyzs7NHYBp7Ra1WUxDk8vKyxeNx+aMB0DqdjtQmhMyx/5Mz4PV6LRaLmcfjUXCvcyQgjeDLly/1jHm9o6knZBkAwKJ6NBsFkOEfpk4FCN/d3bXr62t79uyZra6uKjNqf39fWQR+v98SiYRqybm5OYHSt7e3trm5qUwfznJCfAGrqJMDgYClUimpBVDfse6cCkFCZz/66CM7PDy0brdrsVjMzs7OBNpzHxOJhCTbg8FAigsUf4FA4JG9CPCsUqno/kQiEZ0p7Ltut/tRYCf1HMCh3++34+NjKZuYLkRfYzaytKG6BTTHFguIwyQbt9tty8vL8vFDCKISwTbHOfnn+UWuzZ/2+v73v2+dTsc+//xz++ijj8xs1GQPBgP77ne/+7/8nt/6rd+yv/t3/+6jP3v+/Ln9i3/xL+yv/tW/+q3e57dqwkFuKQBIWqWA4iAslUpC58LhsFUqFc3qBpHy+XyS2rHx0ZjPzMxYvV7XiA5Y3oWFBaHASOVgYZaXl+XpQgIfDAY1J7XT6dj6+ro2EcIOzEyNMIUu8j8OU4/HYycnJ0p/ZlRIOp3WLORWqyVvRz6ft9XVVbu5uZHnHMkjhRiSp1wup3AmmkVQVGfj32g0zMwkGeWBZrRGq9VSswN7SqGbz+dtYWHB0um0UHkYDSTDLpdLGwbqAOa4xuNxybX5ep/PZ/F43Nxut9Lv8c+VSiUbGxuzlZUVC4VCtru7K2neYDBQ40LBhZSce2tmmrvO5o6fp91uq+Dmz5DywPRfXFzIo8ec4U6nY3/0R3+kRpXGDwAnkUhY5/1IE+RwKysrdnp6Kjbm/v7eWq2Wmkx8T7DTTiba6bemwHIGnrGm5+bmVPQgH6xWq/p6NkcKOjPT5uuUo9P88+cTExMKJXE21bw/p4zeydhzfWgg+Hozk+dtOBzaycmJmY0AL5hIQoCOj48fJaB6PB77zne+Y9PT03Z0dGTNZtM670djMHaIsV8cHnwvxTuHMOsQpozmyRl0hYyaQgCGnWAwnkGXy2Wbm5s6mCuVipVKJQF8c3NzCnNhHTkD/X7Sm4ui5YMPPtAB2Ov1ND+UgBwYKCSG5ClEIhEBVoTvAE7BiMOkAjLAYtP4EexCwefz+Wx9fV3KgGKxKIk4zzH72U9KRmOxmH344YdqwOfm5rR2YIoIwULZgawdsNFsJJPE9zw2NqYxWWYmMHFpaclOT09lNaGgQum0s7Nje3t79vbtWzs6OtLePTMzo2RXwLnhcJTaPDExYV988YU1Gg2pCrD4xGIxm5+ft3K5LGY4mUza8fGxJNOMRrq5ubGTkxMxibBYzn2Ugg5VFsxiMBhU84kUmjXKSCwYTQCiYDAo2T9FUjwelzwy+D5zhIwVlF+MUQSEHQ6HknUj+0ZKeXl5KUsPqoharaZCjDUAAEnCOk03igPGQnEWw3IzS/38/NyWlpbM7/fbmzdvpHjAq835TsOBn/b4+Fggos/ns9PTUzFoAE80Ksg5nZko7McA5UwBGBsbkxSzWCzaYDCw7e1tgWU0gyjSnPs469KZ34EM2ev1WqFQkKwVFp9zHcAW8AxVAsocmFKAD0BbzkACZ/GGclam02kxlHd3d1Jf7OzsSKmHhxmpMdeayQokSAPMnp+fWzabfaRMSiQS1m63rdls2sHBgZRMAGDYZk5PT+3y8tL+8l/+y7a6umrFYlGKJ5hOnkfGmALMoIa8vr62sbExe/LkiZ2dndnr169Vn+FRd9ZsBLaVSiXtQYDAhN0xgYCsoq2tLTMz7d+Ax3Nzc/b69WvZ7WgqWdtmJruRM2OBr3HKz10ul+4zoLXH47FXr17pzKAmZZoCqql2u60aBbXJw8ODZsETsAtAT47L+fm5lFhOQAPwBdCPs2tmZkbBlx6PR5kGXE/CAKlbotGo6nQAZZLmUdvd39/bysqKwJbJyUmxlwAZgGSAbA8Po8lHgG9ra2uP9qvBYJThQN4IY4pJM+/3+/bs2TPtB/V6XfkkkESQVn/8x3+sTCgaYT5boVDQZ3AqurAocj07nY7t7+/b1taWuVwujRuFqUZ1Sfgf14H7CfjAWY4iksab6Q1e72ieO1YZFIbtdlvKOwi029tbndVra2vm9Xptd3dXzT25ApA7jDrd3t62i4sLKxQKen/kWPy0Lych9LN6/ax+/s7Ojv2Vv/JX7O/9vb9n/+7f/Tt7eHiw3/zN37S/8Tf+hmyKpVLJfvVXf9X+/b//9/bJJ5/I0vyTr3Q6bSsrK9/q93+rJpwxWSCI4XBYARQUZBz2IEYgcxxM+Iivrq7EAOCT5bBzIujINQhTMDP78ssvbWZmRuEBBBnNzs6K5eBgojhweic6nY6Vy2WbmJiwTCajJgj5XzQataOjIz3koLX4QJjdS6oyckd8ucFg0AqFghBzGDg2E1B2ZFUg/jBxeEmQurGBEgTCiChSmmErpqenrVqtKmyJ4oh5qoxXajQakpCBzFHE4fWEFWdzRaqEJ3Nubk4H2+TkpEUiEQVO3N3d2crKiq2vryskaH193T799FMx4IwTA5kl+OXh4cFSqZSSVJF3w8ZRuBIuB5sHysf9ubi40D1Gjg+CjZeWa4mHLRaLqWDDw8xapDijkGfTBj0mydftHo3dobDmsyHXSSaT8txOT08rvOv169cq2Gi2OYQpmij++cfp+XZKCtmsotGohcNhOz4+VsOOV9CZbOuUt1N0wpLxHpwedHyPz58/lywM6bbHM5oycHx8bM+fP7dKpSK1AOwviehbW1v28uXLR54/xqTk83nb2NiQFDuVSknuzBqDlcjlcgIrkGMx6YBQIgot1gHZDbe3twpOSyQSViqV1FS+fPlSrB3zh53SPQoS2Pbb21upTpA4MlIFlhZk+uHhQeE4SOqdz2I2m7WxsTH7gz/4A0nXaKwJ6EFaOz8/b0dHR2I0AFDwoR4dHUmlRCHfbrctHo8LEGWUFPJ0LENkHyCjQ77f7/etVCrZ6empLS0tyX7DKDEaYvb4wWCgvYI8iouLC40dI6HbzMQ+kZlAgVYul+3t27fag1ZXV+373/++UH0kt8Ph0La3t63f79u7d++0D5qZ9koaI6dnnuKUMwn7BTL1zc1NpRb/+Mc/tr29PWs0Gvbrv/7rCrxcX1/XdQdwwLpEg1Wr1dQsY4lCZWE2kpUiA6fB4XOhNKJhCwaDYi6QaQOqomDy+/2S2JIijG0klUopVRgJbzabVUgfTRnPF2wjcnnCnpwWJYBZrA21Wk0AFOAJKhnWFWPcuNYAYZwlZBT4fD6xZUxKAShjHKeZCcShiGIdIhclbfv6+trq9boUDYPBQCw7IAD3jfwZgCYaMZRGsLQw85z3NNnOuoamGIUXoAzPw/n5uTIC8MuajUDjpaUl5XYAUjIpAFaQcWrcU851gDbUEEicaQxQsAAGEX6FF5amC2UH5yjnY7FYVCL5H//xH8sW4Pf75eNnH0OhQIPLdSIA7/Xr1wpVDAQCtrW1JTm72+1W1g52AKbmIA/mTMYOubKyYsfHxzqbnaw55zKyYWo/AF7IIthhQJOxsTGBSE5V2djYmO3s7EjNSDgYmUP4f/GF8/+DwUAJ4dgAUbZwLaenp5U1Qn3R7Y5GwaK85GvxJqNSQx0JuBgOh9Wkc2+y2aw9ffrUotGoFYtFOz4+tpubG3v69KnUZ9hKZmZmbGlpSRYKAlsZmQcosLS0pPUBy8wetb6+/ogNT6VSOo/wkJNpAangZLhR4d3c3MimlMvlpNxi/BojeQlLfHh4kAKNaRlMg7m7u7PT01NLJBLKr0HaPxwOLZfLKU8EUJMRtwcHB5ZOp6UwA7Rk6gGqOAilfr+vCRcoPCEaCPxlP7m+vrZsNqtGenZ2VioW1irAAbklZGmxh6AyXFlZEThwfHysZ4E1Bdj4/w+v//Af/oP95m/+pv3qr/6qud1u+43f+A371//6X+vvHx4epIj6//XrWzXh/f4oodo5SoxNI5FIiN1kUVQqFVtdXZVM2MlsMLJlaWnJzs7OlJ6MrHxiYsKq1aqaHrznnU7Hgu8TPpFedDodazabkqNz2ICuBt8nDb57987m5+eF3n7wwQeahVwsFu3i4sI2NjbkM4INMTMxaMyVxmcM6wkCSopxu92WtANUntRSpJ5IRmG9qtWqfFC9Xk8Jt6VSydLptAqPfr8vRBeUkmtDYiVJ8hS0ACQg9TxoyDrxD9KcEdAAUsyGScPGQUx4DPNJaYJhaY6OjsTQk6bO11LA0UAuLy8r0RX2ls3H2ZAjd7q4uFBqNgExl5eXCq/h94EigwZSxMNi8B5Aa1nLBHnALCLLYzxMr9dTuMdPSroBZNxut+6B2/1N+u3c3JxFo1E7Pj62VqslVg65KQcvkk23261RGx7PKNX15OREbDwHEsUwxRiNCevWzBQo5/R84zs3+2ZmOPcF2RwgAJ8R/x6MNXaHd+/eKS2ZwCOuYyaTkRR0bW1Na5FDkHsJEo/UjcYXJpb732639Rysra09Yg8oNAD+KFbGxsasVCopFZkimOJueXnZtra2FPSIpJS59DRN9/f3ViwW7eTk5FHBRIEMOh4KhR6NpcInTECYmam5g9FoNBrWbrdV/AeDQXntFxYWzO/329u3byUZXVhYsGKxKOk5nnAODvyQc3NzNjc3J48fig4OXOTlyWRS43/i8bh13id0I/M8Pz9Xou7V1ZWYSWR22FyQeqPkIRSOYmlqasoymYz9p//0n/TMLiwsiHFkni2f2/l9S0tLSn2mGMO6hGcSEJVk3+3tbQsGg/YHf/AHYpFWV1cfgSIwNky0gBWl6Hl4eFDCdjQa1f7vTJNnrCNrgAkfhAW5XC6pHFAHsQaRg8/NzWk983uRvAIkMgUAVnB6elpgLSoq/LiwlB7PKORyMBgIvKEY41yjoaPJoOjDK0jDApvMvSeMDQYd8AufKRM6AKbYa9jv2euGw6HAn5WVFctkMvKfTk1NaUIAklVAIPyYMMZcI/YggHPnCCfUUIBmKLbc7lHuAmc/TQbAMQ0Jn8/lcgn8cmbKOM8BFCTISQGJAZTx8tP0eTweC4fDGhU6NjYmcHlqaspqtdqj9H/uxe3trSUSCflOO+/nl9MgAmzDsD08PEjpQLCf2z2aPNNsNi2bzUphR93HZ2LtFwoFu7q6sk8++UQZG6FQSDPjscvBAkMCUK+h6nMqjsgTQaWGpaBUKtn09LTAUZpTzhTC2EqlkponbIcTExNmZvLXk8WQzWbN7Xar1ioUClJfhUIhhTVyDgKgUKeYfSPrpeni637SnrW1taURs+xfThUGrCdnK2AYZwG1G2c39w11Bsyq2+22SCRitVrNvF6vJg+1Wi3r9Xr29OlTjRXlGSdTAatgMpmUHYdJDEigAXVOT08tmUza2tqaPovL5bKPP/5YZ+Pu7q4k38zoXlhYkOIAOXWhULCnT5+qbopGo6qfrq6u9MxijaK2GAwGjwIl2TM7nY5NTk5qmkk0GpUKcGxszI6Pj/WMAdLCvGPnwrYYDAbt3bt3GpNLrkY+n7eDgwP7/ve/b0tLS1JQQJ5RC/EMl0ol1dfValWzw7H4AhoAqEJyoBjc2Niw+/vRWEKUngQOo6ikqSdwGa99o9HQhIaDgwOpnDj3UGV9G4/3/0lP+M/iNT8/b7/927/9v/375eXlP/X3/1nf37dqwpGOkTINO0NTtri4qKLr/PzcBoOBJKBXV1dK7DP7RsaKR4tCBMSbRhBp28XFhT158uSRtBbv9/z8vPl8Pkn3YLtmZ2et0+lo/AvSLlDh09PTR+FrExMTkhuOjY1ZKBRSgQlS2+12hcbRDBaLRfmnCToYDAb29OlTu7+/t3w+Lxae7+PAur6+to8//lieoJubG9vc3BSKTbPn9Dk7ZbUk1YJkUpyBVlIIMc6rUCgIQCGkhWKccBpkui6Xy968eSMZz2AwkAyGw7HVaj26pxRw+LA4nNiwp6enlRWAnB4GjwLTyVxRNHDYdLtdedFgGigC2IAZYwNLgGQTQAbfJUgoY3c670cP4XdkFBMBdqwjl8ulJgyWl3+j5KApBkBwytGRAZLaCWgVjUaVxD49PW293igAkFFlgCFer9cuLi5sfn7+kQye4oDimWKAZ3QwGDxSrjiLKTIdOp2OWHanrB4JMY1Av9+XfAlU9/j42A4PD5XG3Ol0bHl5WYEiX331ld3e3trs7KwKCWwoqC14YSXh+aap47PCErKuOeBgXPla/GM0Eqgler2epJkHBwePmBk8x6D6pDID9FxdXWlSALYcCtRisaiUfUAbinuaK5oUUHjYffInABo4GGErs9msra+vK+15bm7OGo2GffnllzY+Pi77QbFY1GdlbEw+n1eBC6qP4gP2fXJyUnYA1kOv17NEImFnZ2dWKpWs8z5NNhKJyJu4srKiorBer9vbt2+lEqDZoMiq1Wrm8/keeeVhzvhat9utAExSb82+saQsLS1ZMpm0RCIhaeTt7a3yBXgvMIVLS0v27t07BdaxLpHNk3WyvLysMZZInGmoCAB6+/atpVIpSTFRfuXzeUun05IKsg53dnbU/CAfrVQqApzxSd7cjOaNw/gQ6jU2NibWiIYcxQKgAwW7E6gjzInnk/MBWwBSac5p1oST1UNminKHfR7mFDUDgCYWCUA+xjjSbKXTaZuamrLT01MbDocqVJEBcx+Q0/IMYglCicH5ChhB7ggMNsAjDDtgF+wZslI8+EjFOUdoZChIsTjwORuNxqMUfBL6IQOobZw/E7AbtRKpyxAUc3NzVq1WpWoAsKFxAmBgXyNHguDR4XAoYJzzGcCLQh4QgGYPBQ4FtNfrtVwup2K/3+/bzs6O3d/fK0S23W5bOp1+pIjhvMKHzrOLQhFixmlh4nxl/dKccHYOh6MRf7VaTWTMD3/4QzWbDw8POhuOj4/N4/EoiIoJLnjcGWXIyLJUKqWG0+fz2eLionIysBsG348QRWI8NjYmqyV1k7NGAYSilsSDzxpARowCkD3l5uZGtr1msylQy+v1SuqczWbN5XJJqYedEvUIE4uoryBanAAX13d2dtYSiYTUeoTYMToSYIb3m06nbW9vTyTR2dmZLS0tPdrrYOQhVcbGxlTzc84Mh0Mll09PT2tu9fj4uMKaK5WKpdNpNZyMFaV29Pv9dnBwYJ1OR7/3k08+eWT3fPfund3e3trc3JwUF9QgFxcXFovFzO0eBYXybLNXdDodBViSG+NyuaxWq0mliaoLBQwgMbVQq9WySqVif/Ev/sVHtc319bUFg0HZJSGXZmZm5CcH5Oa8oz9IpVKyBzFijbObfYf7TE0HcBUKhTR+FTUcqmSnPYYJLEwCgfT8aV9/nuXo/7df36oJJ0iEBjYSiUiK7EQBCoWCkHbCFyhC3G63/AZOj3alUrFMJmOxWExewnA4bCcnJ3Z+fm5+v1/jJEC5QXXwo9NQPTw82LNnzx75tEiV5P0HAgErl8tqjAgrQ0KH/LHVaknaCDLYbDZtaWlJ/ovhcGjValVNIGwOc8jZ3PFWMVMTqSHNzvLyssZhkIxII0ez6yyS8vm8DjEOWBhU5yxy5q0TpAXj42Qe+P5YLKa5hkjkCLUzM0mACXPzekfz9PCkgU6zaVJYUzwgKZ+dnTWPx6O0XgAVGE8YXlB9DhSaR2SHFN0AQXgInU0ybES/37fZ2dlHIV+gkRyoKBaurq7s+PhYBS7sJwmqBE3BBrH5cXDzeSkWzUySssPDQx083F8KSsJSer2eJgrQVN/d3Wm+pvN3AkrBZi8tLUkyCkP/k3LzpaUljSyimKcI5kWBBotMYcoB1O/37eTkRJ7a169fS2Y/Pz8vOXGpVLJSqWTdblfztRmRks1mNXYLpoumlxRPCu3b21vb39+3fn80Ng/feSAQUBHbeR9GxZx79gl861wLt9tt9XpdFg1mS/MZKaZoGDgI+fn41pGU9Xo9u7u7s6OjIxsfH7cPP/xQBx4sjZk9Si2nOQG4iUQiKhDwu/t8PrGHsVjMtra27OHhwUqlkk1MTGhueCQSEfAGq86a6/V62lMpxpj5nc/nVaDwDMdiMe3fHPaxWMwWFxfFIMfjcTs+PrZnz56pScD/iDcTJYGZySIAm3J0dKRZySghACUAQpxhfmYjmXY8Hlehd3V1peCpSqUiZgkVz3A4mlyRz+cV9OZ2u8V8sl933s/QxQbEuEk8lHd3d/Jonp+f2+rqqhKXz87OzOfzWSKRULAZ5xHS5263Kz/jcDi0lZUVzTF2NkAorWBiaKiQcbPuWdt8nTMocHJyUuOzYCNJ+IUpX1tbs0ajYaenp3Z7eyubTCAQsOPjYzUNNJflclk+d0LgsObwfAFCsQ4AKqvVqgL8ZmZm7ObmRns2ygFY6bm5OZ1/7PNut1tNgbOgx3uPDPnu7s6CwaCVy2VbXFzU+RkMBm1sbExWFgL0UFUB7vFsoOKAnSVxmMRk1g0APV9HOCRhQKVSSeuA84fzhb2VesPMxJSh9uh2u4+S4bEZeL3fzERGZWBmAljZJ53sciwWE9iCFZC8DM4qs5HvEZUjNdvp6ak9efLExsZGIxQJiOKs4P6TPj47O6tcC3IJ8vm8nZ6eWjqdtkKhYLVazaampuzZs2eyHJBR4PF4bHx83GZnZ9Wksl5QLH344Yd2eXlphUJB65c11m63BejAGmPxIi3bzARGsd/BenOOXF5eWrlctqWlJYtEIpZMJjXGidoIgJUGBIAcpRhrltrR5/NZJBKxhYUF+/rrr1U7k13gDG+k5sjn8yJe8AZTHwESo7ADcGB/o2l5eHiQ5YrmGiAcSwV2DBQf4+PjUqUCZAF4O4mLUChke3t79vTpU5FArB/2i6mpKWs2m3Z6eqopHFj4YrGY1mMymdT4y4mJCTXme3t7trCwYGtraxqBXKlULB6Pm5lJOQnzu76+Lvvb4uKiZbNZjYGkjonFYiJnqtWqMkFQz21ubkp1cXh4qCk5gF69Xs9ev35td3ejcb/X19eS5jebTdXmTDAhfA/1BErG4PtJAzTbg8FAeyC1Sud9Sj5nIwAae2Oj0bDt7W3V89iEC4WCmHFscdgnAbABO3q9nixE/D97089fP9vXtx5RVigUbHp6WgUQxQCbHui7MxHVzCSFMDN5Uhhyz59T0CDVHAwGtrm5aZeXlwogMDMhWjBGXq9XCwiPBhsVBevt7a2trKyIvcGbnUql1LiWSiUtVgJ0kGEzSuEnm9ybmxuLxWI2Ozv7J3zJIO2rq6sWDAZVZOLfWV5etnq9Lu/T5OSkraysWKFQkEwL+RchI4Sy8BCamQoTAp5IWCaQwpkcTDNLY1AqlXSQ43XGC3l/f29ra2sWCoXE7MViMXl3j4+P1WAy1zybzaqopYEGJOCAMTM13PiUaexh00mcPT8/t8XFRcnG3G63inA2/UQiYRMTE/I80XjTjCDL53csLCzYxsaG1jMFopkpVI5xa0jZKOKQ6fA+pqam/oSPn8bDeTibmdZrr9dTTgBFCMyPzzeaDsA8VFB1riGz4DnszUyNMqwHMjgzU/K+830jHaQhcnrL+T6adSfzzffzrLP2SqWSXV5emplJzp9MJm1jY8N++MMfClRYXl62ly9favJArzea/YscH0luNptVaMh3vvMdvU/mkxIghf+Z63x1dSXGG48m3i9CeSYnJ+VLPj4+1kjCjY0NJRXjI6WhQDLGzwYUCwQCFovF7OXLlyqqkcjynHNNnXYFCl+KYMBBRpgh+WN2NMAgip9sNqvwSVJsj46ObHl5WeAejB9yO8ZwwFayhsjjgGUAbAKYoyExM4El+KNgFAHXSI8lBJL352T+8QbjI4eBJqyMkCUsLFwfxoXgf4WlePPmjYBEpkYAfNFIUMSenJzY0tKSTU5OStGD3YhUa6/XK8sEwCmsnnPkD3sJa9MJrLD3wdo6QxbL5bItLy8LYEABxMQMp32FzA0nE0QBBpjCZ2OEXL1elyqo3W7b6uqqfn6lUtG50mg09Ax/97vfNZ/PpzVOOjGqo4eHBwEtzBPu9/tSBbHfo4KCCUeyznVwAo2cq9xHwAjOUQAH1HIoXZDLB4NBm5+f1/fSjFOYAjzhq2SfhX2fm5vTHn9/fy+gHLsInwUG2e12S5GA35IilXWGWo1Mgevra4vH48qMoOZwyuiR3mMz4tpy3qNsA2BFYstZAkCBYtDlclk8Hrfb21s9a+xb29vb1u12bX9/X+AVzCehbMVi0UKhkBr3y8tL+V8B6QHruP8whzTSNIw0ewCLhF2R33B8fGwbGxuWz+fFumNHI5sllUqZx+Oxo6Mj/Q72bfZzM9P4yXA4bIFAQLkHTt8tNZjP57NkMqlz9+bmxk5PT9XcYZVy1r17e3tWKBQUUnt6eiplkzMvhX9ub291JnPfAZZcLpf8u0xuYG1h32w0GlI0oZThfOU893g8ViqVbG5uTmARAbf4gnk/6XRaCiH2FfJcWFs06oFAwCqVig2HQ4vFYra9vW3ZbFZ7M/PYCRDE9nB2dqa6F3UKI4g7nY7t7OyIWODZ43mBZAJAQHXU7/ctHA4rZ4maEOKnXC4LJJyYmFA4LIrdP/7jP5YFlOeMZHrUPqFQSPPWyVthbBqTWKrVquZuRyIRS6VSNhwO9Rmw3kYiEbu7u1OQMw01QbnkHgCaEbbJDPVOp6OwRXJCIL5QlUSj0UcgBzX04eGhJlagJCGTw7lHo7qoVCqqe968eWP1el3Tp25ubjT68Kd5/XmXo//ffH3rEWXRaFQsBA8vGwQyqnA4bI1GQw1VtVpVo47sAxkMbNmrV6/s5ubGcrmcWFLSkp3zp3moWaTIrsbHxy34fr43hUCxWLRAIKAUcGL6KRwIQYCBYnOC1UKec3d3J/aeptPp/Wo0GpJjBYNBbWAul8sSiYQe1EwmY7OzsxaNRoWsE5SztbWlQBA8PvhFKUYqlYqKTZfrm1AXfHLtdtvC4bDYEwohPPds6By+yKpAafncyClJ24RV5uchYwGZRy4Piu3xeB6xZ1988YVCimDyke0jL0bKxqERiUQsl8uZ2SgZmM2GgxWW/ObmxkqlkjYopDQ0vnw+mpWDgwOpDyqVil1cXGi94avEe4UlgvAYZkRTNAyHQ0un05Inm33DdCAPBYX/yUYMQInPhNwQ1J8RGDS8gDSsfUAVACzG6iClpPmHLeFANjM1N2bfBK7RaPN1SOR5X7woSEGUYUt7vZ4lk0mxOc+fP7dsNmv7+/vm9/stnU7bq1evJLOHBcTTnEgkFB4CgwmgxzVyFio8F9lsVrJX55pFysq1brfbOuj43nA4bOVy2drttn366ae2ublp29vbygxwNh+wlawV9h287F6vV9JSQECnlx62EqkgDESn07GFhQWBd8FgUJLCbrcrVozCjjnkLpdL8stCoWCBQMDW19et2Wxas9l8JNfFB4wHPpvNWrvdtsXFRRWJNOSwm6enp49kvuwxpHnj90Q58vAwmihAQ0wKORYPVDOoR1AgTE1NWfD9yB7eM1I6sjxoOK6urqxer0uGDwMSj8etUChYq9XS73/z5o3Nz8+L3ej1epoPzz3Cl8z/wwg67QvIk5HP49WDMYnH41rPsOk0jYT1bWxsWLvdFvvGGkfeTao1rAZrCU80nkGnnBCmC5AMae/Dw4PAuKmpKT1LrBm8qnzt1NSUvLrkJCwsLNje3p7AQlQWhF7F43EbDod2fHysaxYOh6VCY09iv4OZ5T7QDODVBlzgugPoAdzSSAEowg4RqDY3N2fD4VANBHsSwAX7GQAUDTZql+npaaXbA6YCcjul2MFgUMU44DKAopmpuQTAYC9EAUJzAjMKIIc6zcyk7gFko3YAiObect4ASqGmQ3UBCDkcDuVhxXJFEvrDw4PsMxAG8Xhc6dJ8Nqdcl8kpBLRxnvV6PUulUkpRZ18/Pz/X2Y2Hfnl5WVJ2zl2Px6MxZGam58lJItDwVqtVAUhYp1DB+P1+qSQymYz251AopIwdWErsCgBtPNP39/dSSQE+1Ot1KY1QoJEx4sxq4DylngIwYVoAAC21SyQS0blMk0H9CtA7Pz9vq6urj3IasCogQQcEguXlOXESG4BxZB5whpNf4rQqhMNhy2Qyqqc2NjYEADISDlWjy+WyL774wnw+nz179szq9botLy8LWAewYDoSa7ZWq8mCyf5OXkkikbBUKiUFEOcJTDP7+e3trcXjcVmNAO/C4bBGnhUKBfm67+7uLJvN2vT0tN3c3MiWg9ITkoIAakinm5ubRyGXNN7lctnOz89tfX1dzWsymVRPwP4LoTc7O6scKIDIZDIpYAELBdM4nGQRCoFisWgbGxuazY5f3cwUBEem1mAwEHGImgmVb61Ws2az+Whm/MzMzKPxlT9//exf36oJn5iYUOHGocpDDquAPzkQCFiz2bR0Oq1iDg8L8hSK5LOzM1tdXdXBxYJAplksFiVbpBAhgAapOVL3s7Mz+f04zBhKD/vEvEkK48PDQxWST58+tVarJRadhwO5D40xicMUzLAPFHV8P964Vqtlz58/V5F4fX0tb5mzGKtWqwpbw8vlbIBpJJgNzugbt3s0l5ANCpQX5JlkSaTayI+Zr4hnGskX7AK+VCQzeN1g6UH9p6enJUElzXY4HEpmi8wddpyxdPi3ksmkvNirq6vykBaLRdvd3X3kLaRBojmjaBsMBvb8+XO7v78X8MP6gqn0er325MkTIfOwUSRsUxA6ixtCYQiDqlQqQso5YCcnJ21hYcG63a6VSiWxKDTfTtkfcmgnUv2Tsn2YFwKlKCxZX06pOAUt8neaZkAyZ/PP3/FnvCezb5pdintkqQAJgDf8HQWkmT0CZWZnZ+2LL76wL7/80iYnJy2dTgs5h62k6WLEVK/Xs9PTUzWu6XRaSeP9/ig9FCnV9va20qNdLpea0OXlZWs0GgIPUIggVXUyoz6fz169eqWRcMgoz8/PhcqzxlEi8JzC4AOC0TyTioy0C3UGxR4KFIpPvFvIaQleg4GuVqt2dXVlqVRKzG0+n7fNzU378ssv7YsvvrC1tTXb3Ny0Vqtln332mWYsT09PW6PRkMyOVHwSyBlNRRYEkyuQ6AKiAiZxz/HEkmBLQUcxCnDQeZ+2zb1lPydPA5UOs5xJfKbgzWazdnNzY1tbW3oWKFqPj48VXpVOpwVcUagDbCYSCd1fAu4AEZ12D0BFgF32dQoXmjnYTeTJLpdLKd7s0wBZzhCu3d1d/TmgNHkfXq/XotGowE/2Hv6M2dc0cQQNOT+vc1QKknT2hpubG6VAJ5NJKxaLdnZ2Jhnv1dWV7e7uPtqDKpWK9i1UEaiLeL74PbCRAMXO5pLPjPwb4A1wnfOUlGW8jl6vV+sU2Tvgk9/vfzQnmRGoLpdLPlsUTMiTae5hbPHdNxoN5U04/csoO8Lh8J+YcsAZMjMzI2nzcDiUNx7GCo/m/v6+QHP21UgkomkQXJ9oNKow1cnJSaUoc92oJRh3yJhY5NaEnfGcIsnmlcvlpBAIhUJqwBqNhti5mZkZS6fTj0CTxcVFi0ajls1mJd/udDoavco54vP5LJ1O2/r6up2fnytkyucbJcET6kqS9cbGhu3u7logELBf/uVftk8//dQKhYKaIJfLpfMb4IbsD4AQQGb2VdZwqVSy2dlZyZt7vVEaNU0eikmYSFR1qAwBTWEOmeoxOztrtVpNSkanwtApQacmhPhAhYEqiZFgWHSwMHB+o16AQV1aWtJ5QaPPfQZoofHk2cb6QbMNkM+9w1p3dXWlugULSb1eV/YGaw97zMPDg718+VKqqkajYbFYTORKp9NRHgg5E+RUUMdyTrDvUru1Wi2FVt7d3el8enh4UFN6f39vx8fHZjaaywyTnMlk7OOPP5Yyb2VlRUnrkF4EvALu0Wje3t7a27dvdVZBKKE4RSFFjxMOhwW+ut1uKUxoaklF77wf4TcYDHSGY7mBYInFYnZwcKBAyNvbW9vd3bVXr149Yn6x66FARjGwvr5u1WrVksnkI3VVt9u1UCikwDrqvk6no0YdMIkMkdvb0YjVYrFo/X5fpMJP8/o5E/5nf32rJhyPRLfbFeJIQ+n0n52cnEguzgbJLDvnqIpAIKCNg4aXWa3MrmR8BAcSjDeHEAc4iBpsEMU2i45AKlA6p++TkBuXyyV0jeaSA4GUcLNREuTMzIwFg0GLx+OWz+ft6OjIYrGYNRoNCwaD1nmfrAijzWKn0APRJkWaebXT09OSt9dqNXm5Hh4elKBdKBTkLYetQNpWr9c1F5jAItBXMxNqipyMMCMAEjZjUHsaDQ55ikteoMjIXvDUsaki70P5wPtwpqGGQiEVVEdHR3ZycmKtVsvW1tas8z6VGQYfGZ2zwHOyxT6fz8rlskLLaHgAZQBtUqmUWCdAIkZkMMLJzCTXY7QFwTTRaNTOzs7szZs32mRZ53itCG6iAHPKLs2+aca5zhwQfD0SOmTuFCUUujxbHBhOtpsCmvuJYsHpQYW15xrRpMO00VDiLae45nf0+32x3M5xS4VCwXK5nPn9ftve3rapqSlLJpNiowj9QxLvLMKdPtl3795JDk0iM4cb4BPFDEwDwTaAFD6fz0qlkpmZGguApMvLS7u+vralpSV7+vSpTUxMmM83mkmMdBWGlMYGKS/jBRlBw3WORqM2HA61JzllxPgNh8OhJRIJFXDZbFayeOTJhMqZmZ2cnOjQpGnnuTs/P7e5uTk1LdfX148sHOPj4xqtlcvlVBSj4HCm+y8uLtr+/r4ARWRtw+HQyuWyGgHk0IxzoxAEyERNAAjI2qxUKo8UJzQvNP8wNoxRc86QJYyHyRLhcFhNaKFQ0Nrn3iPlQzrLeQMjxN4BWAarhVeQJp+k2nQ6bdlsVmAX7DzrTAfq+/XS7/dtfX1dUksYMqTPePCd5yfNlMvlsv39fQuHw/KKAurA9LCvULjzZ5zPNMQU+jyzKLB43pjQQZPOdQZkMzMBYdyzXC4nuT7p45lMRnsD+w5nxWAw0H3G8sQ+x3oaDAZ6XvB30ogBEKdSKe2JANYnJyeP5Jqc0wB8KGEAeQh6qtfrWiuciXxGgqpCoZDk1L1ez/L5vDzb1WrVzExnJ9cKhsvMNPPceY+63a4K8LGxMQsGg2LIaDCdDS6gEYDT7e2t1vH8/LzC1biewWBQthhCFFF04FG9ubmxhYUFC4VCku9jC+TZQK6Nn57sDeTOTpk+Bf7R0ZFtbm4KMBsfH5eFplwu66zFJtPv9+3LL79UECZAOzXV27dv7cWLFwIgUYghTw6Hw3Z4eGjlctlOT09tZ2dH5wrhtYxadHq2l5eXNQLS7/eLCSb0Cs/7/Py8GNi1tTUrlUoiP3jOAaV9vlEALV5awAlCfVFjeDwe29zclLcYpSFhXUil0+m0pdNpqQGx4LEWed+c06i3ONNJWQc4R2bPmqJpazabj/YTM7Nnz55JDUmNB8gAccIaoaZfWVmxer2uWgllEmsbNhem16mapI5gz0ZO3263LZPJyCrB+OFwOKyJCAShEcoWjUbt5OTEPvvsMylSksmkADkm3Zyengo8dVpeCoWCra+v28XFha2urgq4y2azNjs7a3d3d7a8vGyTk5MWj8elmPF6vbqHnO/US+Vy2b766itZQmDDGSW3s7Njn3/+uaxbBLvyc6+vr3VNm82mvXr1yjKZjFhrwHzsLJ1Ox87OziwajZrPNwqsJvyv3W7b6empVLXsjcFg8NFY4FQqpbX389fP9vWtmnCQyJubG7u5+WYmH03W2dmZ5fN5Fc5sPLBj+EWcRc/5+bmtra2psZ+bm7OnT5/awcGBFYtFm5mZEZN9fX2ttNy7uzuNGaBJp3h9eHhQvD+pu0jXHx4erNVqSYIFegq6TLOLlBPZELI9PBq8Z6SEyE9BzobDoWZpLy0tqSAgfOf09FTX7OzszAaDgYIm3rx5Iz8G8jFkjAQvuVwuW1lZ0bVBOgQiF4vFVGSYmaVSqUesiMs1CufqvB91RtBKJBKxs7MzzWGnaG02m5K+ExjCi/sQDAYV5Z/L5TSqAe8SRbaZKfwhkUiY3z+a412r1czMJEclnRE5uJmpoABBxUYA0EIze3t7K2UFzBRs6P39vYpsmruZmRklJYMkcv1AQQkRwZdFE+lyuRRyxPulEaHB5Zrz9c4gOlBhWGlYZ4pYDiSYOadvG2sHPkxmIFPwO+Xm/Gynx5tryIFEscLhDWrsLCwovHw+n5gNAhDPz8/ls9/Z2dGBS/FfqVTU3CcSCQEhzsyCQCAgP2c+n7fV1VXzer0a+4dlBbb7+fPn5vf7rVwuC7xyMvdmpgaCdYSKZnV1VeNQKBYozGmCmYWN4sApbR0OR4E/INQLCwvytc7MzNjnn39uw+HQlpeXVTzD9nDwJxIJe/fundXrdfmoASrZFykkO52O5XI5ofQU7BT/3IPg+5Fm5XLZLi4ulHrMWs/lcjrgWZ+ZTEYFPWwPbJ3b7bZ8Pm/Ly8sCx+7u7mxhYcGazaa1Wi0BCKxV3iOe7oWFBTUlWA9Q2tAcsO9tbW1ZuVwWwIcPDqCT/T+fz+v34DFnT2akEHvYwsKCwAQYEJ45Z8o2gGY0GlUSOU0l/ngKajI8yAhxPie5XE7MNsAWtizOC5on5NgEyt3djeYbM46KM9NZ9OHXhrHAxsP95H0APmWzWTX8zJflTGD+LOAGDS0gjFOJhIKIMxdGy9loUJBij0mlUlIjARDycgaLBgIBy+Vyj2wvAO40QNx/9s5QKKRMD+xDhPCxP3J/kW4CpLhcLiuXywJkOM94Vvkc19fXsjeUy2WB9IR5obKDLee54fxxqo5gJwGVq9WqBYNBASZYQ7gu/X5f84pp9i8uLtSA89xT49zf34vE6Pf7+gzlcllEBrULSeFIYguFghQFvDdUhmTBDAYDWe7MvgGTCfLDToF83uv12rNnz2wwGNhnn32m59/r9VqtVpPsfWJiwq6urmxvb88eHh7kT0V9MD8/by9evLCbmxt79+6dZOU0/eSEULOhGmDKAFk9wfcjbiEG8vm83d/f2+npqS0uLlowGDQzk998cXHR3rx5o/vUarVkuaG25HznPrCed3Z2tHYXFhZsYWHBjo6ONEGEprbVatmzZ8+kukMRxnQOwMzJyUkLBoMCwMgp4vpAUhCQamaqYWDPCTxeWFiwzc1NjcalPjIbTeq4vr6W8pXrSSJ3s9m04+PjR++HUE72KwgolK2ZTMamp6dtc3NTcnvyVdiv6/W6LSws6NpjgQkEAnZycmKRSER5GWdnZ1KscJYCjgL6YF9y9g4E9PEsQR7xnmDvGe3H3hwMBm1zc1NqG7zuhD1i25mbmxMgjeUJghBAEUtQo9Ewt9ttz549U23Buq3X63Z2dmZ+v9++/vprSyaTNjs7a5lMRoDbcDgU4ROLxaRK4RzjGSS49NWrV5bNZrUXbW5uyvJE3buxsaEMrJ/25awxf1avnzPh9k0I0/z8vB4spDEkJOODY0GAHuPlwb9GIAON7cLCgiQrLICLiwstSLy6IO0ez2jUE83Y3d2dnZ2dyS9IoYWvg7E6NJ0UfyRW3t/fW7PZlJ+DB3UwGKhIYZGCWMPgMv+U8Us0mox9YBwVfhjCGj744AN7eHhQQJuzwACpB0lbWlqyXq9nKysrmiWOjHRsbEwNRK/Xs0ajIfSQ0Qqnp6dK98UDT7ENa+VyuR41wmwsyHZIwYU5Awzxer22urqqZujt27eWTCa1qSCTm5ycFLNPaM3V1ZXl83k1a06W9/T0VIwBzQGNLkUNc19hstk8zUxKCppRPKSwqRS0jD1hLMXe3p4YKCSSbrfbDg8PJT2jKaIIdblc8rteXFxImkxzBngAgotX1ykDdRbMHOjr6+t2eHho8/Pz1mw25b2FuXY2B1wrNn4ae1BspzQdKwZBTYBpw+FQ65T3RZNOU+v85/7+3jKZjNY8COvLly81QxnZL2oQrh9FJWwCX8v8edYbvzsUClkwGFQqMvsHIE+pVNIegX8N9gCU3+PxKHCQ0Kq7u9G4mkKhoP2Ea4m/mwNxZWXFDg8PxUgSJsazVyqVxMLhhYOF4xqzfilUASxbrZal02k7ODgQcn18fCz5WSAQkMd5amrK9vf37fr62jY2NtTUlctlMzNd87GxMTs4OFCzn8lklIwPc+H0rhN45Qw15P0iccUT2+v1LBAIqKkKBALWarVscXFRABYNMe8bqS2/G6USCbOMHUK1FI1GFdgDS4mEPhwOS3ZJQUdTC5vW6/XULDKqhecMsArGf2xsNFOZ82lsbEzNDmCAs7ll4gRrDebQKX3mRcjf3d2dxgsCAtCUmY2aq4WFBcnCAYM5n8LhsL5+eXlZuRrsq6xXnkfGi3m9Xo0FymazVq1WZScB/JqcnLSnT59q1FWxWLRSqSQQBM89sn6PxyMQlXvA2qA45P0DECO3RpFCk+1sTFGpobgihIjG3OVySepKSN/Dw4OUTs41nUgkpPjApgKITUZCuVwWEAowBdDo9JATuIQU1KmqgAEjiI8xcOw/gBdke3Cm0NCjZmM/Zo0SdEg+BhNdFhcXJQd22hM4E1dWVqRumZubUy4A+So0mpwbfA781CgCE4mE/PhmJlCAzwVgEwgE7KOPPhL4BigP8FcqlQSKoASAFfzoo49UU2BxoTkBlFheXpaSodPpaHY1De729rbyijjPwuGwUtY586ghUAkClPO7GStbLpeV4k3qPGtxcnLSEomE1et1PT+A1dPT0+b1joKCaRw5+5F545UHVOc5QUHKvj0YDNR8o6BkLzo8PLRAIGA7OztWrVY1+SYSiehMxN/Oc0SjRA3ncrlkk5qZmbFMJqNnEnsN5zGg3O3trRrfer0uxYRzTeVyOQEr19fXVq1W9T0/mZFwcnKi6QhmI0JgeXnZBoOBpdNpGw6HFgqFBNYA6K+srNj4+LjIk7m5OVteXra3b98qYGxmZsZWVlYEJiEl51q2Wi0FdsJuT0xM2MnJiV1dXVksFrOxsTF7/vy5TU1N2eHhoRhn6naeU+4j1hX2hJOTE3nbnZZBLDFkX83MzNj+/r72Jc4OgAKXy2V7e3sWj8ctGo1aJpPR51pbW5PSCAB+bGzMGo2Gziv2oDdv3mjG+MbGhkB9aqloNGrpdNra7bZIwZ+/fravbz0nnI2fh4ibiyyWJoUDGZTU6efmwGMEBbItGoNut6s0x1wuZ41GQ7MgkYGTtJtIJLRJsnkihQLxj8fjdnR0pKRkUj5ptAqFgg0Go/FIbEpI7R8eHrSZARYgOy4UCvbw8KDQCjZ05FUwzKCirVZLTTxyomQyKa83fwfimclkhCTe3d1ZOBw2M1OTSXNMKnqz2ZQsCL85hyIyS/zbHBzLy8vy/SHbdTKhl5eXlkgkFG7XaDQsEAgIbMH/Eg6HJfGBMU+n00o6RXJPQ0gxigSTjZmmCKWCM8UTEALfl5kJDKChPT09lfem3W6rOUIKZzZi2UC/Ly4u7Ec/+pESsfGCg1oC5tzf3ytYixTYra0tsZYwF0hZQeNJimZsGwwyoATrniKcTdPMlFdAkNr8/Lzm2AMgAATA7Pxkk+yUwMPG8fOR2eZyOTXzNE9IVVdWViQr40CgkWGdYH1gfZMiDQvV7Xbt4uJCRfLi4qLQYYAJmDSK8uFwaIVCQTOoyUmgEcL3TyEYi8VsfX1dxT2yMn4OgBbP9vr6upj8Uqlk1Wr1EQNF3kW/37fp6WnJ02AdYEspolOplCWTSX2udrtt1WpV4+L8fr8sJBzkTvCM/cbjGc2OdobJIOEFWJudnbVisWjLy8vyWjabTfN4PJZOp+3du3fycNKILi0t2eXlpe3s7FgwGJQvcHNzU6BCMpnUvkwhxz4GaIAChmK3Wq1aIpGwcDis4g0WH4khmQ1YfqrVqmSbzrFTTsUMiD6ySJq/XC5nl5eXtry8rEYQsIsm+OHh4RHaz33neeFZQwpN845yCjCXPQLG9OLiQvcAGxBMNHJap8yaxsgJAJF0TYFLMBPjofr9vlgzM1NTxM9BmlwsFm12dlbADmwthSC/28yUAEyTQNPE/UVdMBgMrFgsigEncIjzlMYIFRv2J855RuOYjWTZjAGlwWG0D/5DZJn4JgeD0QQVzhqyBvr9vqXTaRsfH7dms2mdTscCgYCYw9PTU+1PBFHSLDqBSH7m2tqaHR8fS7XHfkzAI2clbBUAB4FH7H9cA5KY2T+djTeBsqiU+O9QKGSrq6uWzWbVFNzf3ytVnHMC2XmpVNL0GFQFAEfUBQBwExMTWr8XFxfy/fJsDgYDjaILBAK2uLgokCkUCpmZWbFYtJ2dHe3jAPi5XM7W19ft6urKMpmMGrPLy0v7/PPP7Vd+5Vfk3ycDBpb/9PRUioF6vW7D4dCi0ah99tlnAsgXFxf1LA+HQzs8PLSlpSUFak1MTNja2pq1220rl8u2tbVlXq9Xaff1et3i8biYS67b3d2dZlCjAEAFRz3IvkB9ga0CHzmgC4FxZ2dnqj9obgmN9fv9Ul0QSOjxeGxvb08+YgKOsVn1+307Pz+3aDQqZRhgPiQJay6RSFgul7NkMikfOyoylIDUmP1+X6q9ubk5SyQSIiw4t5vNpk1PTysDgb3RCVygeoJlvr+/ty+++EKkSjqdlr2RYEiUVyjGnHsw12x+ft4KhYJ98MEHVigUdKahpqvX63r/kFqZTMZevHghsur09FTBm2QAYC9kdOrs7Kzsjaxp7B1ra2t2eHgoJVs2m1X4WSaTsVevXlkqldLeA5DI98Ow7+7u6nmampqynZ0dycHZlyHtfL7RlJqZmRmF00IQAuyj8HG73ZbJZJTvks/ndcZy/VHfYIHK5XI6J+krhsOhZTIZnU8A2pOTk6p5qbfZV36aFzXmz/L1cybcRumfSK+Q2JXLZS1Wr9erkJFKpWL39/e2tLQkbzApfYSB+Xw++Z9hVGEfkSvBliKVgTEmcRO/EoUWTSkNEQnK+HTZqPlamrx+vy801swk3RkOR3OXnSESFH6g8MViUQU0siaSOtvttrxbzp/B2JLhcOQzxas2NzdnW1tb2myd6bBzc3Oak2v2TcJzsVjUOCNmm6+trakoc7lc1mg0NCO91WpZPB63brdrh4eHtrKy8uhAgaEEYUeRAGpO8TkxMaECjI2Fzf3i4kLpl/1+X8U0VoXx8XGr1+s6OGCEYeMoBmDnASycXlvYeRgSpKZOZgdpJ0wKaOrW1pbt7+9bqVSSxzcej+t3U2BQdFPcEvJF0eVyuaSooAHAL0RgBizi5eWlzc/P2+zsrL17985ub2+1YSNFJODIWUwCeCF9bLfbUi+wRlGYOH3eNLVO36pzsyR51en94e/9fr+trq7a9PS0EqW5H3w/SD0JoRT7vEcaATy8ABkUnXjh+X8kiYQLLiwsyM4AWw7I5/GMZm9Go1FJ6cxMSpMPPvjAZmdn5RdGBnx2dmbpdFryakaTMHqLAwuv7nA4tPn5eRUr3P9kMmlTU1M2MzOj4CieL2a902TDALI/4DeHofD5fBaNRu34+NjevXtnd3d3VigUdEAvLy9r/wXIZA8D9Lq7u1Mzzv5Sq9UU0Fgul9WUYSdiT3ICOiS0OpsImFaCwtxut52fn1s8Htc+QYggsr/JyUnb2dnRtUGhxLhInvtkMimWcn5+Xs1nKBSyVCplZqa5q91uV0nVR0dHZvYN08szQMPLWsZixN7G3s2egOQfpgoWDbktkmOsCM+fP5cijM9BcYPdBtaXZ4+9cnZ21srlsq2srEga2ev1FMjGvgiIwxl2fn6uYolAITzLNDqsNUBjzpWZmRlLpVLKJXCqEc7Pz8UUEaR6cnJiHo9H48oYX8d+SkNLcj1J2KSaE65HE+GUlcbjca1RABuaeAA9rEfRaFQe5FarJeCABtMplSR0EH86tQnnCs0se/3+/r5AAD4PigenvB45OOGIyIwBzWhOKK5pTAiwQ9rLeUr2AcBtNptVLoDzbJyampK9yufz6fkhZwVVDHJqbFiohgAW8bHPzs4qjwLL1cbGhpQqvGfqJhoN9gqKdp71UqkkQGg4HNrq6qpk4efn51r73W7XCoWCagescSio1tbWRNJQjxSLxUf5DYTuraysCFSNxWJqQCYmJmxra8uazab9z//5P7UPTE5OSsURDAaV6eJMreY+zczMKDGeZHzqXMggpM00lqgp+DwoQLCzMc0GFR4jJ81GVjxGZp2dnSnky5n1QbI45zoEFXWn3++3VCol2bOZabqPk7nmHlKfkpzOjGi+l3XHe+L7PR6Pfp/b7db0EWpu9t52u22tVsvW19dlgVpYWNDZBYBBGOPDw4NCxTqdjhQ1gIqc1YzcJYenUCjYhx9+aIVCwT777DNbWFiwZDJptVrN8vm8eb1e29nZ0fo4ODiQj3xiYkLScyYSdDode/nypbndbtUFNzc3NjMzI3VltVr9EypX9ofFxUW7uLiwer1u1WpV3nHqdeo/wnqfPXtmU1NTVqvVbHp6WoFr2ARQj/BsUsO53W7b2dkRuEz4qdfrtWw2KwsJYGMm8/9h7097G0uvNG10kRJJDRQHcZ5EkZoVYzpto+tDAQ30l/4N/UdfoIEGqqpR1WU77cjIUIRCEzVxFCUOoibO7wfldeeWu4Hj9EGePigkASPTGSGK3Hs/z7PWPa2SRSIRy2azGn0KYUEfNJ1OX9TrwWDQms2mCIhfWl7+6+v59bOacBivQCBg8Xhcvpput6tEymAwqBAIPBvOpEceHh7o6+trbSygNcijkMzkcjnJ+5LJpH348EGNOUg0GzlSSqf8Fr8h6cxmJgmhz+ezWCymsVOwJ58/f1YhyKHDuIBGoyEkr1arSW6fTqfFyCKRYyNF1kSRSSNLQbuwsGA3NzcWjUatXq+LLWLDRtparVblGYIZmJ19DotCEgfrDrsPM3t/fy8k//b2VuwfDC+NAt9hZmZG/lp8LhxUNH/BYFCbOkqJXq9nS0tLFggE9H2Q9FNs4lOCYQDBnUwmtr29LUQv9OMoM/xhbNpLS0u2uroqKTQF9ezsrG1ubioQhuLh6enJjo6O9FzB0IIIgpCm02lJjJCVRaNRMQUU8yDP+Xze3G63kj/D4bC+38rKivy0JycnkvgDQrEhLi0taRNmPuvHjx/ld2fqAM/08vKyRtSw4fNM8UxTWJmZmL6/RitbrZZ9+PBBzQsNPIUr7ABMGcUi0mozkw2DxoBilQYRSwYeJSd7iWQQ+TjNst/vt6OjIzEBHo9HY2mKxaIdHBxYo9EQKswhRZOfTqft9PRUAWJut1vhSYQhYqvBE41qpFQqWaPREPPO/oIHPBgMWjwet1qtppEpHo/HSqWSLCr4ogkB7HQ69v79ewXqYBmguWMdIX90u91SDAQCAUulUnZ6emq9Xk/hVE6LAGqCmZkZJavirZ1MJrazsyOLTbVaVcAYSDwFTrlc1np4fHyeyYxKYTAYSMEAg3F5eSl5HwoQpggQLsPaB4ij6IRFQOVD/sb9/b2mWwC6UpSganA21DznFHbsqRSOpGfz92mUAHJYG7DoTgabxhtAAg8o/4ShBBgKhUICmSjSULlQqE6nUzs5OVHTjjefRo3il+cZICSTyWj/AJgEuKZxACDjGpiZmE/+jGtN4zA/P2/Hx8eWSqXUEG5uboppbDabL/IzGBOIHQdprZlpraBcoAHCP9ntdrUGsUExCxvQnXPdOToNkBNFAk1+q9VSzYGNB887RefDw4PyC1C+dH4c4Umzj6eSz0u94AT0WQcAIez/fGeeK2fzz31H9cIzyL0CuFxbW5Nqjt+JOmxzc9PMTPkL+N/Jksnn85LWhkIh8/v9VqlURDZwLlDfhMPhF9YYQHryLFhPt7e3tre3Z7/73e/st7/97Yva4sOHD8o64b6Vy2VNDSFgEum0s3lE6o4veTQayRsNsBiNRq3z43QF/Og04KifUAjc39/b8fGxlEDFYtHa7bbIht///vcKFCN9++rqSuu00+koL4I54F6v1zY3N+309FTn5Hg81jPCmYqqjxf7djAYlF0KXzdKLIL49vb2zO1+DuHa2Niwo6MjS6VSdn5+btfX15rKwB6CEoYm2e12q24jzwRgBCabvwvAyXkIO80+gGWINcXITKZ7EOQI4BmPx2WJwpbkdrsF2AEsItem5m80GgLnAB6LxaKUna1Wyzwej3333Xd6hhihR0AgZJ/ZM/iWz+cFkvCdOz+OxXQmgJOG73a7VW8BCsKIO89TfN7srQQTA4jc3d3J/kBOFLZDQBXUI2RasNdgE+VcA/gD+GUfpEZnugzTHJChA2AD6pGL0mq1BKzW63VzuZ4T+1OplIgECBant9x5n5013t/y+msA/Jd4/UcFBX5WEw5SQ1HKfGa8Vxw8sAc0I6SeM2IF5geWF+YPtN3lckn+hpzp8vJSLKzf77etrS3JX2nukXAyBg00LR6PW7PZlKTG6/VaKpV6EVxAEYeXA0YPmSA+eA4GAnM8Ho/QssXFRSG6T0/Pc8bxDCG9JLk0m83qUEskEtZoNPQ5Q6GQhX6cSUqBTzhbtVoVa4FPGukWIRkAH/igQT/ZrBYXF5UeDvoOm8Z8RbxZXHOay3a7bevr61Yul+36+trG47EVCgU7Ojqyx8dHq9VqFg6H7fHx0c7Pzy2dTlu73bZvv/3WPn78KLUBhS0NL4gqEhiCIr5+/SqJc6VSEVrMIUt4TiAQsNvbW9ve3lbzCvKfTCY1Euf6+lrBXm632+LxuKXTaet0OlYoFLRh04iyEcFKrK+vKyCMGZa1Wu3FdfV6vfblyxc1IzS/gEsU8AR7UMQCRHDIIoFlXTkD3PCIc/ADlCSTSTs5OVGB2el0xOywtsxM4AfFLXJQM3sRQMbByGFMPgMFMaydmSnxG08svkWYJqwngCMkBUciEXnCuP6wsnd3dypQOTBRpsRiMYvH42Ikua6Pj4/KdEBazix3vFLhcFiHIoeXmWmWLAczXmeuP55zmNSZmef52+QuUFQ6Q5vwcIE+8/zD8KM+SSaTVqlUNFoHmTNSzWg0qvRTmAnAOCxA/D5sNalUSo0L94DCGXnwxsaG9ngKJuwhsN1YCgBEHh6eRz0tLS1p5Nbq6qpdXFxYPp8Xi0ImB3Oys9msnguUBrlczs7OzhRQ2ev1LJFI2PX19QugDNkn8mbYIZgEvidFsJnpnrNGaGzZe8n5wAcMK86a4GewQpyfn0udwf7rZEzxBKPaITyKphqPLd+LMNJwOGynp6dSSgA+Z7NZ7bNOYHQwGNjXr19tYWHB8vm87CGsg78ev8aoTRjlp6fnWcQ0ZycnJ1LxwNoDsDntRjQWhH3ROJr9NOJwZuY5gO7169cq+nw+n3W7XTXzhKDRBHJWUcSOx2OpLii6WTsUyRT0TESAVfL5fEr7rtfrWv/UFzQlT09PUo4sLy/r3Lq+vn5RVAOqOMdaOZuj8XisPYt1z7nPPsmz5JTH81ywr41GI4vFYjYcDgVqdjqdF2FRrEUaWywM/AzrgT2Oe1mr1eRxpZEAtAUwhEkLhUIC6WgGnbJbnnfWC7L+crls//iP/2jLy8u6ZqPRyL799luLxWIKnfrjH/9ouVxO6c5er9cajYakyKjW5ufnbXl5WYx1Op2WGoB6qNVqWTabFQDCe56cnEh+DqDvzANAKo5sHMk8lgHqVCbnHB8fq5lBNeNsjrEPAP6wjmickXKvra3JC4w8f2dnx4rFon369EkKCNQdAP8obKg3CRjmc7JHApawjrxer/35z38W28/1GwwGag7xK3OPyelBvRcMBi2fz2vPrNfrmvxg9lP2UTqdFmCI8q1Wq8ne4swUgpW+vLyUsoRwMgLgIHjIpECO7/f7bW9vz5aWluyf//mfLZ/P67xgzeDJd+ZIMGKPCUsej8c2NzfN6/VKDTk7+5zh4vU+p4ovLCzo3Of92bcfHx9FiACckAszGo0smUxarVaz9fV129raEhj55csX293d1edMp9N2dHRknU7HNjY27OnpSYqVb7/9ViAX9/74+FjBtXjZUcQNBgOLxWLy9t/f39urV69sOBwKEMU2BzjJmhmNRhqJG41GZfn89fXLvn5WE86mTaPg8Xg0X7NcLiuZ1Ofz2enpqQqn+/t7Ozw8tHQ6rbAZZin6fL4XMy4JziK8hlAKGkZ+x2Qysa2tLTs+PhYbDVpGwQHbiPSExpPgGJqbXq+nTcPsWf5YqVQkVQdRB0mNRCJWKBTkw3N67DhgHx4erFwuWyqVUqFHo7u4uKjZ4Wyeg8HANjc3bTqdSlrDgceGXqvVLPTj6J67uzshbp0fZzPe3t5atVpV0BFSIg5tguwommjckBsTHEQzxoihu7s7bdzOmeE0/oAASPphzZeWlpRwDMDB80NTSgHBgQbaCWrLhl0qlcTumD0fkow8mkwmL+aqsmmVy2V7enpSCBt/7vf71ajAti4sLNj29rZYMwoB/FI0nrlcTlJiAoIoPB8fH+XVpNjD34a33Pk8Ov3ZZqYxcc6mmGYNJJ21AHpPA0xiZ7VaVTEMQwl44PF4NIuV7ABGevx1sTgajTQSA4Yclo0mknsAOk4B8/T0ZOvr6y/Yzbm5OY0WgdmHVcaa8erVKzs8PFQRQ0OIugKGiAaY3wfyPxwOrd1uy3qBfBDmjSBFmA1ADPIJuG98Jlgh1iG+vLu7O6k6VldXlWBOAUUDCGKeyWQE9FQqFYvH4xaJRKRQMXtWNLB3gspTnB8cHNju7q78nTCKsI38O57VUqkkuSCjySg6mHMPY/nmzRvdX4qler0uQI7iFqUSlo9isajint/vcrkUwMah3+12lRb99u1bSd64R4B9KDHYD/h8MO8ko1Mk4AOlcWdPwVqEuoJ1i8yc9cQeAjvKM0+DDuDE7+fFWUCSLqqkpaUlNTcwxLBSnFfT6VT7Iv8+nU7F9qPSonE4Pz9XeBAJwd1uV5Ls29tbSyaTGnNJE0eCME0Qewz3l4aBQKTHx0c7OTnR2q1UKsoeQHGDJJ59DxAdvy2sMB7FmZmZF0GWj4+PtrKyojF4XK+joyMpD2hguN7kvPCePA+wdFyv29tb+WhJk0bmzhrDtsQ9RC2HygOlXSwW07NdLpeVf4M9ACADIIFnn0Kf9+QZ45r5fD6BSqlUSs8Q5zmqCp4ZwPFarWaVSkU2NJg1J/gxOzurvY2GrF6v29HRkYVCIXv37p398Y9/lC+cuow9D7sTTSrXC8UcthmUf6wL1HCA/3iMqbs4Uwj6q9VqUqMBAmPX+f7771VLUVOidGQP2dvb03dFBdLtdmVbYsxkuVzWM0oT73a7rd1u2/HxsXIqptOpwIevX78KjDw7O5MqZDAYaLLFaDSy9fV1CwQC9unTJ6tUKsoDoEElIJjzHjCfehM2GYtAoVCws7MzSyQSIiBg4JHVz8/PyyqVSCTUpLGeWR9YCXjOGZ3p8/nszZs35nK5RJIQGJfJZNRMw4KTHYFdrdfrCQgmZG08HmvEXTqdFuDE9Bb2Gax2qDZRFc7MzFgkElHeApM+GMM7Pz8v+yuTAfb39zV+jrWHJ75UKml/x+aDLxsZ/MrKihpoxgWSc8Tze35+rsBPVC+5XE7s/tbWlggDFBcLCwtWLBYtGo3acDi0g4MDAc2MB+UaYt/88uWL5r8T8sazl8/nLRwOC2iZTp9HhBKCNxwOLZVKCSSAXEJ9+uXLF7u9vbVIJKJrx8g46kfGfdJTQeZhMyuVShYMBv+PfeD/6fXXKstf4vVLv///rdfPasIXFxcleyZUhdAhpAz40iji8BeYmVgRxlAgO7y5udEsYRBQilw2BVhKHppyufzCS8cCx795eHgoqQcSoLOzM42poBkikX1tbU0zhwEEQOJgFvD84pVjdAGM6tPTk5i1ZDJpnU5HDL7X+zx/GMSdedNmzx4hwj4IDyMJkwKSIpUZ5bFYTMnBnz59svv7e0mZYQLxxJNiyTVFKTAYDBRkNx6PJXUlqTUcDuuwW1hYsGaz+b/5US8uLnQYT6dTW1lZeSHbIlwC3zxofr1eV+gHPj7QcxBzfgdNsdnL0Di8VplMxuLxuMLPms2mNpn19XXb29tTY0Qji3fo7OxMjdbh4aEOs/F4rMP13bt3OrgBUHw+nx0dHVkikRCIAOJ+dnam71KpVFToUWw6LRQU/Xw3nlkKUTxI/HenesDMNElgMpn8b8jv/f29inJYOt6DIrRcLj9vBLM/jffAdnJ7e2vX19eWSCRsY2PDSqWS2FM28tvbW1taWtK6p2FjDjIjYbi/TmaCBgyAirVDOAkghcvlsnQ6LTkzTM3s7HNwYzQaFdMBo4hCA6AHBp9rjhSUIqxQKJjH47FqtSq5Kp5LWDT2JUAwbCAATbCFFBM0NAABZmaFQkHNkZnpXiGlnpmZ0XxyWEQsB6iDkOGi3oFl9fv9mqJAgE2v1xMrC9gAGIeCiXsaCATEhno8Hjs8PLTNzc0XQTB+v988Ho8CF2H9WMMzMzMq2MLhsAJ6eGb4OVQQ7XZbRQDnCI0e/7y7u7OVlRWLRqPKrkClQFPBteT55ZpSBHOd2Dv4+yTIsn7wi9OYOBvpcDhsPp/P6vW67j/3hrRjmGL2N36eAhxlEeuQNHXCJNkvAbwAsGDp8NweHx+LsWGdAFyz9wAMcobQuCG55MygCb+9vbV4PG6FQuGFXev29vYF2MizjNqB7zk/P2/RaFTP3dzcnJoMgLD9/X2tu8nkOQSN8Vhzc3NKEjYzPdPOxg55P6qEzc1NKxaLL7yrSKBZt7FYTP9tMplojKHzexCAx15YrVbVTNFUwPaRj8DZz7o7PT1VvgHAHd8DdQmeefJu6vW6vOiohlhLyWTS2u222LqlpSWtK4B19ufZ2VkBKshu3W63ra2tyX7hcrmkjEHt9Pnz5xeEwsLCgsImM5mMwAb2z/X1dfvjH/+oUXZY73w+n+XzeQH2PHOkSmMjm0wmls1m7ejoyHw+n84fVJLpdNpubm6U/TAcDi2fz9vi4qIdHR1JQdL5cWQoYAkWgGazqXRrAGcmvNDQE64LGBYMBpVuTb2KVW55edn29/dfTKQBiJuZeQ4jJG+D/Q9QimtA04dEn2eQ55X9sdlsWiaTsd3dXYFw1HpLS0taq0yPwOqHeocMCXzXPP+MZgQc8/v99ubNGykYqOOdeU+Pj48vFAbcf/atq6srG41Gls1mX6w9mkHOEYACQg7ZCznXcrmcAM9Wq6XsBOxMztoAVWA2mzWPx2Pb29tiqLmPAKput1tjOz0ej+wWkBzYHBcWFjTpqdVqqRmfTCa2uroq4N3lctnW1pbWKOc7vQnnC9YD6mSmPlWrVfVEBFjS/K+trZnb7bbvv/9ez9xwOLTr62v79OmTzoNarWbZbFaKnaenJ2u1WlYoFCwUCsn2AlHG+gQo5flaXl62r1+/at12u10B+ldXV9b5cewa+/Df8vpVjv73v35WE44UB2keASnIWLiRg8FAMywZA+T0F8POzs7OCu00M6E0zLyj4MNXgscVmSqMC6FKJKfCNoO4XV9fayNhQ6xWq2pEQZIId6GxQerFZpZKpSybzcpDzGIeDocCBJCgz8/PC00LBALWarVsbW1NbHg8HpfcCPQZVA7GisL4/PzcarXaiyTE6XQqpgGJFbNQSRkdDod2fHxs4/HYSqWSeTwezQD0+XwWjUY1eikcDpvf77dSqWRzc3O2urpq3W5XBT8HH4oCRjARoIV3BlYTqTLoJIU+3nCzn1KLYb3MTEw2cmA8PX9dQLPR4qtZXFy0fD6vIBhk3/xePJ2MJyuXywoLcbt/mvnJs2pmSmJdXV1VkBKp0qCl/Gy9XrebmxsBN4wWaTQakk7CxiHxM7MX7D7NBGoKik4ze7HBcS0oDvELIeuiiKB5YAYnja2ZKTCP93B6k7i+BPTA3AK6lMtlAS3kQyCTHAwG8r0zJuTo6EjKBgoinp9AIKCAM+TkjATjQKJhds7mHQwG1mg0dL05dBlPMp1OrVKpCEByyoudhbrP59PIHLfbbfV6XdeRmdUUlowOwdeJvxvA5urqyjwej62urlqv15NcsFariQ2lGMMXSQASElKsKKREX15eSubv8/msUqko4XwyeU45RslDw1ypVBQMx/30er12dHQkT+kf//hHPbedTsd2d3c1o5zfjS2F0CEYBa4nhTNAKsVnIBCQ0mRhYcH29vakeqDgy+VyFo1G7YcfflATw3hGfhcMElJ7wENAM543gCz8bdwPmnr2FHI3sOXQjJiZAFZAIjMToAUzzh6NZJzQUKc3lP2D/YY9CnWUU73BtTL7aSYxKgtC01BHwVgiY4WdpJhFCo1kn6KQzASKaX430tl+vy/1Gdfgw4cPyg8JBAK6dgBpvV5PwA/fnfXHvWMfI2wSgISznDMBO0W9Xtf1GwwGdn19raRsZL2cK/V6XaOr2AvH4+cEaAA70oWxk6HgAZQEuKQpRElWLpfVtMNukuVCQUvdQUYM4BIEAOAH5w576/z8vBhuAj9RK6FqqNVqL0abwbpXq1UxsoDZs7OzyvXweDyyXNFEf/PNN5J6ozwEJACkIJ+E5xDQHQb08fF5Wg0WIKyDzoaK0Vv//b//dysUCvbq1Ss7OzuzYrEo5Ue329UaZdxUoVAQkObz+WSNmJmZsYODA1tbWzOv12v1et02NzdtZWVFzXYgELCbmxvt1/f39xaNRm0ymdh/+k//yer1uk2nU8l2eZ4jkYjOEiyUpVJJ0m3eG1n0wcGB9mxshd9//73l83mpGamlGIE4OzurdUOzRyN3fX1tfr/fUqmUpMisJepYzoREImFmz0n1pNLzd3lukNFjkwwGgxYIBOz6+lrZCACV5XJZdefBwYFAHcaxASDCuJfLZWXSsLcmk0krl8t6/m5vbxU02ul07ODgQLPcOTPJRqpWq3Z2diaLF6pUalhqZ1QM1WrVarWaHRwc2O9//3vZud6/f28nJyeyBhE0CbOPagiVK+dFrVaz4XCoNQ0YzJo9Pz9X3U39h7QfG1Gr1dJ1i0QiCg6kdmHUJipfxg8SAIv8G5UANSW2LzOzk5MTKcr8fr+Fw2FbX1+3p6cngTU09ZCGeMRRP3z77bfW7XYlY3faL8mV+uGHH170YEtLSzrDd3d3dTb++vplXz+rCccbTZNAgwFKS3gS3gtnUi4MBgg9oVXcdLwe1WpVIRx4ZGke8Hkwv9rp2UOGxX9jtMb29rb1+33JoJzJrRySPLjpdFoFCp43DszNzU1rNpuS/lGAcJDDUBAeR1NPKmMkEhGaThgP6BQe7vn5ecvn89ZutyXBRZoNAxyNRtVIkADKeAOAEZI5Qf9AihnHwyaEfIk0UIob0D+zn9AnZJ8g4Iw7c4bVgKYDnDiLbqfEG6Tf6Y8DRHBKRtkEUAc4i2QaSLyRFLr4bMbjsQUCATs+PtZn4ndRROMlpOCCMaNoQhY9HP40psnlcgmAYVPO5/OyTTCSB29ooVCwbDZr+/v78jY7izIKIucoG7yLoLQ804AWHPQ0zSCzeEdB+EejkbXbbTE3FJX8jPP+OhtUWCfYSUCgXC5nDw8PChBJpVKaxdrtdtXMcj+n06mdn59bp9Oxzc1N6/f7kmRmMhn5zpF1wR5QUBJ0hiSVUB+KImTjqEpQK6BQyeVyKjI5+Alr4Xml+Ca4Z3Z2ViOAAJ4ACSgAnYXrYDBQOjB2AKSW4/FYSgCk4aD27Hn8XKfTURgN99rMLJVK2efPn8XW8TPHx8cq2gAVjo6OVAwgF7y5ubFarabCEhUGTTSgAWuJP6/X67L4sBYoEHu9nl1fX9vKyort7e0JHPR4nmemk9TKuC8K9kajYWtra1YqlfRdkHJjp4CV5p8UeIA7mUzGSqXSi3PBCZ7BvHD9AVewQzibc/Z/5Os0lISjEYI2Ho/FgMNkFYtFKxaL1mw2rVQqqfHmfGE/wK+HFcfMVMTxGZmiActMccXUirOzM1taWtKIHMBbkp7Zr9mHnFJrGH72C0beoVTCruNUepk9s7ecwyhaUAZ1u10V+jwf7CkoBGiAWq2Wra+vi7W9urpSbgFJ2F+/ftW9YCqE2bM8lmcc5Qv7ViwW0zOEWo3nk+eHvaRSqVgkErFIJGKZTEaFudvtFoGAvQBvP+AG5wJKlNFopPV1fn6uJpW6AfaWGge7wfz8vNhdvMisGfzxnFkw/7C2ZO9gGwNUJe+DEUMAWFtbW5qkABCBVQbAEqCPGgE/Kco5pniwBgHI5ufnbWNjw758+aIz4/Hx0er1uk0mE4XCkZwfDAY17eVf//Vf5Vl9/fq1aquLiws1fqyLubk5u7+/V7O6v7+v+wwJYGYWCoWsUCgIiJxMJhYIBF6AIrCw+GR3dnZEmkwmEz2LJKh7PB6x9uz7ECzI50k7B+hmzcIAo/6CYLi/v9eIS4JozUz1xGQyETnlJI74c85Kl8tlR0dHYnGReyOfB+CtVqv6/k7VINYk1EdI8r1er1QWnU5Hzxl1A88T9ejDw4NsDYR4ZjIZy+fzmoiRTqeVXcBZCEgESRAIBLT3kj0AID87O2tv3761i4sLOzg40LlSqVQk3ybP4He/+51k/UxLwc44MzNj+Xzerq6uFMxYrVbtzZs3Ci28vLy0YDBo6+vr9unTJwHyTLrBbspzhKKAs4N8IPYqegIC0rBllEole3x8lALg9vZW+22xWFQWUqVSsel0ar///e9FYGBrnUwmGmMJ0bi5uWkej0fEYa1WUw4DQBEj2gAE7u7udD6QlZLL5aQ++Dny71/l6H//62c14WY/MQZut1vjsZCgkbbKQQWqip+42+3a1taW0OvHx0c7OzsTCzsajXTo4oeEMXT+/263q43euXk+PT1pY2Q28NnZmWUyGbFT7XZbsrtWq6Uk93g8rsITCRvFDAc+EmlCifhcMELRaFT+pdFoJBaJpGZC2WATrq+vJb2hGEO2CaBQq9XEQrIZkkDNeDYQUJrPXC5nzWZTB+rGxoY2I4LqGF/GbGDYbqdHKJFI2Hg8ll+NNEwYjlwuJ0YDNJaQGuYfg8A5E2e5BhRLFFc0xNxzFjZJ8rBfzr9jZmp++XcABBgeMxPzgSICWSHMHwgzxRvNSLlclvRnbW1NMizCN5D10lgRlNRoNBTWQTAUxRYhUDS9t7e3ahIJznLK4WlsnbJ/il5+r9lzYUqyNw03BfJkMtFGzlga5/XjhTcIoINRO4PBwL58+SKbBoi73++3UChke3t7KmiDwaBFIhG7uLjQZySMBsmjM93XmW5KwUIxA9jDM8ZoF2S3BLsQXIZkeDKZSPr6zTffWDwet0ajIXZ6ZmZG6a08gxRSy8vLFgqFlLhLo354eChZOuPgYKWY08n9xYe/uroq2WU0GtUhvL29rZTjSqViyWRS/l6Ch2ioSHtntBhIeqlUEiADqg9Yhx2FBGSzZzb8+vpaYXuAjDRIy8vLYh0B7kjkpjnp9/tSuJiZVB7ZbFZsFjYkxjdms1mbm5uzL1++WL1et0KhYLe3tyqyubZOKwVN49zcnBWLRTs5ObF2u60JEihTnGwo7FAmk3lhmwDQwBrk8/kkHXWCT6wp1jPvz14VCARsbW1Niqvvv/9eRS7nWSgU0rV69+6dws9o5tgLx+OxpVIppdbzviSoU/jjvwyFQlYuly0UCum8ZJ2wRjifKczJYwmHwy+aQz6fE7DEVsP3n5+f1/xtj8djp6enyhkAXKYBxRZxf3+vZ5AxQ6PRyM7Pz8WiM2oLCwfgDh51fPUoiwDQUAw5/b3sh+wfHo9HwUwAAoBLMLbYSfBPnp2dSZ3FM816cSrPuM+pVErvubCwYJlMRvYspK5er1dWO96Lpszsp1wB6iL2eEIZObfYf3n+CCzknlMXDIdD1TtcM/b+Wq1m6XTawuHwC1k11w9gnrXD3opaMB6PK52aGcywiHwus+eRgv/wD/9g4/FYNdynT5/s6urK/H6/wr/YT1FHnJycaP4xHlnSni8uLsQk93o9W19fVzAw6x75faFQUGN4c3OjvWJ1ddUCgYCdnJyoJup0Otrv8PwuLCwoPI3nZmZmRgwloAB7883Njfn9fisUCnZ6emqtVst2dnbE8qP24T4S5raysqJaDXsJexrA+/Hxsa2vrwvYe/XqleqhUqkkRSqZSYBXNzc32gs3NjZk5UDtmM/nlQNRLBbl+02lUjYcDq3X66kGdq6thYUF+YMZ+wmYh9UKGw2qG+yYPBuELhYKBYtGo/K5o+rk3AFIcnr+kXFTx1Are73PY2HPz89tZmbGvvnmG4GmfH7yE6hZk8mkzgVqVdYLjDzN9dramuT9JLtjQ3WuSzIm2JcI4+33+1I9xuNxW1pakp3AWf/wu5k8xHk7GAwECAJOoKBLp9Mi5LD38B7pdPqFSow1z5jg29tbOzo6smAwqHO73W5bIBCQje3r16+y6/z6+mVfP9sTTsNCSBAMDQxbs9m0SCSiUUO9Xk9jTUAvQdUIbMCTw+GG7NHpq4EpRY7Kw18sFm1+fl7Jk41GQyzwePw855HfT2ARHrtut2s7OztaaMiInp6ebHV11S4vL61cLuuQdjbpsAAU3Nls1qLRqMXjccm4OeiQDdHU4tmKxWJaGIxj8Pv9mv8I4ksqOwcSCdCJRELXjHvB4nUiv6RBImWFjaZBZUMCUY9EItZsNrUwYf3xquKVu76+licI72Emk7G9vT35ykHKb29v9XudsnI2Mn4/AATMBhuvmak4x2sGO0KxRDGZzWb1vPL52+22nZycSGJFQQZSy4ZOoYwPB1n9ZDKxo6MjNfCwsjMzM/Lhj0YjoeiEr/R6Pcl/AoGAUG+uH88nafeAFGYmpBq0lcPFyRZSJHMYBwIBq1QqlslkdC2dvmgC/lhnoLfOA47v7fTlkmK7tLRk2WxWbE6/37fvvvtO32txcVEhcAA2NG+wtoyeA+12ggzlctnm5ubUOKBaqdVqAr76/b41Gg3tL2/fvrXHx0cdgDDiqBfweGJVmZmZUXKr2+22ZDL5onkFSAyFQno2WId8R4KN2AsymcwLlvLh4UFNnBMgY3oCvtbDw0M1Vg8PDxotBrON7HljY8Pcbrd9+PDhxagUmk+acQJvCDhE0XB6emoul0ujz5aXl+3s7EyeaA5zmLnZ2VmrVCpS4RAuxWcE1Qdou7u7U9I094fvyH4B0EGDRuAUa4zG0vnMU6jzvM7Pz9vq6qpYQb47UlKnf9jMFMCD8gcPu9lzURuNRq3ZbOp+wRLRmKBMgcmOx+P6fk7wioYTdRf7L88MTCSglFOm2uv17OTkxHq9njzUsNtzc3O2trZmPp9P4WPIiJlhjNonnU4rARfAjXMU+xXsfCQSESOPf7Hb7eosTyaTsgoQsvfb3/5WoT97e3t6JmB3sZaQP0IIK+eA099NE8U5gFyZ5ol9x+t9nmQSCoXMzMRAIuXs9XoWj8fVCE8mP402vb+/V0o4IAbABXstjM/i4qIVCgWlElOsl0olZR4QBIgcn5GkrHvYJtLzKWQp7DkrYCEBTtjzkdQnk0kV8IPBwJaXl2WjAzRiBCrFdujHEV8owlZWVqzT6cgSgueen2c6h8fjkQWO70N4lJMcITDt5uZGihX2nnQ6rckuc3Nz9uHDB1tcXLRoNKrnD/UUYViwejStMHCoILCL/eEPfzCXy2Xv3r1T4CBWJUZA7u3t6eex0MBok7XA2T43N6cwSZr4ZrOpz+dyuTTFwJmAjsXRGeoFUwyoVSqV7NWrV5LGc2/I2FhaWrJmsykVKGqV0I+j5ZA1YymDUAoEAgqmhTEPBAIiuzwejxhifMl8rlqtJmCZ9WhmktCj7ERBB2jz9PQkdhjQBCn1+vq6JRKJ/83m8fT0pJGcoVDIYrGYEvwnk4nGETKVgB4AiwNnPns74DbBpmREUC8AsPL5OLdRtSwtLYk973Q6tra2Zr1eTz77u7s7zRk/PT2VxxrQiNr99vbWut2u1Wo1y+fzZmZ2cXFhp6en9u7dO61x9pPhcGjhcFjKDGq18XhsW1tbCoxzuVzax1yu58wqnpeHhwcBkOwb/P18Pv9CGcTZ5wSiAQ9REHKmAeTu7OyYmSm8dnl52S4uLmxubk42jZ/j8f6VCf/7Xz+rCafRgI0ul8u2vb2ttGKao86PMwGfnp6UgHt5eWmhUEh+5aurK6G4FGtut1sMF4cHYU0wQM5Gh/cgfAQJGJsBBRa+UcImbm9vNaMSZB2WDv8SXmfYCFgZ0GGPxyPpNunwLAoalGQyaWY/BQadn58LjOC/Ucg0Gg0dHFxLDpBIJGLn5+d2eXkpqSSIJwwgDTRN1+Liop2dnb2Qwr17987c7ucwIho0CnMAA7zRl5eXGsviDA8Cfc1msyru3G63xqbBAJLsymKmyPhrPyf/n+LA2fyx0dAgO4PqeAHG4IOGeUwmkzYYDHRPyRcwM/vmm29sOBza0dGR3d3dqfEZj5/T3WkS2eAYgedsEmjQ2u22mhDYB4KbuFeMOmHtwLJNp1MlEVOYEtqDFJbN1eynjY4mnGtGUcrGjcSJ55EDazKZaHY0702T6rRpMLeX/8b1pUFHUu3MUxiNRpbL5ey//Jf/YktLS3Z4eCjZNsFpFLIUncViUQ0zqCuJxvw76wPmBNaYsJlIJCJmHmaxXC4rXAr2idR7mHs8VclkUuFwuVzO5ubm7ODgwG5vby2fz9vq6qpAP+4tEnindO7Tp0/yJDpl0HzvyWRiJycn5vF4LJvNamQiABnqA1gMwD0aJPa+8Xhsp6entru7+0JWyDUlZRYmB4UBrAbj3AjpotnFT8fapGnl2SPcBmQfJU06nRZjgVeeIpvnHf/p9va2zc/P2+fPn63f778AaBKJhCUSCTs4ONB+wXpCaokK6ePHj2KcKU4AErDMoJIAlIHdZ7+gKEcG7lRScI08Ho8Vi0VrNBp6lgHvnGw9ShesH6xPAhoBqGFakdBTHDsZzPPzc4FwgKvsUQDezDMmTMrj8Si/gHOFczgYDCrtPhAIvFCMjUYjFZuA2MvLy/b9999LzlssFu3z589iExkpyv1lf0kmk7a5uWm1Ws2CwaCaYhQ57KfYQZyWGBg15Luzs7OWTCYtlUoJBGJ/Z1/j/gCgkRxPM5XP519MxgBIY0+HXfX5fNrLKODZL7iXSLaRuAJwzMzM2MXFhdZTNBoVs7a0tGSJREJNRCgUEkMfDAbFmlMDsM4Gg4FdXl6K2WUfdirjCDcFMAGsRLrNyLtOpyMVw+7uru3s7Oh3MRIVixfjC0ej54BJGHrk3wTytloteVX52UwmY16v1/793//dFhYWbGtrS6Gu7KMQH+SAMBGBPXc6fQ7vYk6xUxVFE5FKpez4+Njq9bplMhkpgFZWVlQThUIhKxaL1u12FZrI1JFEIqGxbV6v105PT6WyGI/HUvrNzMwoGwelGrYSiIREImFnZ2eSOjM9AiAHkB4lRDKZ1L6BL/zu7k4J11hHqNX4d7IPAJJopFARUcNhrUJtwNz4aDSqMaKtVsuKxaJ9/frVbm9v5demFsSaxD6Uy+UEWiJrxi5RLpfFEEejUZ1xTEEgwI7aw+xZIcaeQ63aarXU7D49PcmSRe3C1BvACGo41iv1EjkaHo9Hzyd+bDKsGHk4Ho+VwN9oNGSpBMgAkCZjiL5meXnZ4vG4+Xw+AQ0APe122zY2NlQfc47Qf8DKQ9x4PB6Bf6x9gAGnvcYJbvd6PctkMjpTuLb0C856DZATYo96EtLx4eFBNpXOjyNPIWhub29tbW1NlolfX7/s62c14Q8PD3ZycmKzs7MKfqApwduKzJXFSOGUTCY1dgA5FAfS9fW12MF8Pq8xLGwc8XhcASXT6XNK4NLSkuXzeY0zwzfDWAOXyyWkDknU5uamGurZ2VkFySDHcAbuMO6LZpFQB0KKkCTji8VrBivDZv/161dtjoTFzM3NKShte3tbKdYUxI1Gw1qtllKMw+Gw/EgUmxy+19fXSnTER3R+fm6/+c1vbGZmRkE0BAJxjwgwQiaPxAe/n3N8EEU3FgOkaLFYTGMZRqORHR8f28nJiTZq/H9sUjQRvNhAKSrm5+flE0Ou5Ex5pMjGx+Is0MfjsWYs4u2meaexICQIJHxubs5evXql+8xhMDs7a8Vi0f70pz8poI+Z5AAOrVbrhR8e9o3GjyaD7wmrRvHp9LUTwEORiTyRBpyik6KV4pb/OQGM6fSnMU2wy/iXCBqhsXV6J8fjn0JS1tfXxeaDvvLiwHDK1V6/fm0fP360d+/evWgoJpPnMWpbW1vyjcG0NBoN29zcVCHfaDSkSqD4IQmdAtrZwIOqAxSBLPN9y+Wyzc/PW6FQML/f/2J+Os1pIpGwYDBox8fHYia59qwpUpSfnp7s9PRUKDzJx+RWsC4oXmku2+22lcvlF9f78PDQcrmcQvNg1mF72b9YZzSePC8UfO/fv7f9/X27uroS4wSzBSsOG48kNxAIqOlnRAwSUp5RWBuKB0L4YE+Ojo6sUCjY0dGRCiWsOmam4B4k4xREHz9+lOTUKXN3uVwaBei0dnAtkTvzfZBbOxP0zUwBQHgXQ6GQRlYha4QtIKwG8JEQH2eYJICkU7VzdHRk/X7/RXNIE8P8VRgpM9N7Ob3SgHJOnylgztPT83g/2BnOy1wuJxUQKhdk0uFwWFJIgpV4zgFCUD1RDMKCttttOz09ldczm80qX+T+/l5KDQKZPB6PFQoFW19ft4ODA4HozpGSMJU0rTCJ7IsodtgrYJEBfFk/AN2zs7MqJAGaIpGInh3Y/W63K4Zuf39f441QYtGUMeWDfZBgotPTU01GoAFGgo7FgVFtzuwMCulmsynFF7J07gsADc8fbKrTdgS7TUaEy+USgUCQ3urqqphLcjiwwcTjcY065NnyeDwKxWKCS7vdNpfLpdGUyKYJI+M5BXBDpQIhkslk7PLyUucAuTSFQkHydsLHqCtcrudgr2+//VbqCZQM/X7f9vb2VKel02nz+/32/v17gR4ul0uKFXze7BcEDNJ0Yrs7Pj62dDqtXA7ANLzbTNO5uLjQVAQAoXq9bjs7Oy8IJgCibrdrBwcHNjc3JxBoZ2fnhXoIfy3KgQ8fPthgMFA2AUnXTslzqVSySCTyYn9IJBIKCcVSWCgUZEWkFqW2cSrpyGian59XMCYKk93dXQHn5Dnx7F5dXUkaT8OJMhLmnTXc6XREkDFCl5rU4/HYycmJwBpqwMfHR4FuqH+QZKOem5mZsWq1KgCSZxKVCPYLQJREIqEpS6TId7td83q9CpFD0fL4+CgQam5uzk5OTuzNmzfWbDbt5uZGilXqxMHgeXwwn8vlctnbt2+lemLP4HuitnNmuDgtM2amz0pdDcngcrlkjeWcJ7uJfYbQVfJMANSpL5w1odkz6Is6mGDHy8tL1YLT6VT7N6Gsf133/X96OW2gv9TLqTz7j/T6WU04rOnCwoKdn59bOp0W0nNzc2PhcFjMDE00hTheTN7n8fHRCoWCzc7OKmCEB4FiFjlGo9GwTCajgon5tQSqIXFGugG79PXrVx2SJLMjrXUmglJo4fdio6BBQvbe7XYVvIR8hmRzxjGAGhNkRZAPzEOv15N8xOVyqdnBk0gTim8km80K2W80GpLCEQKDr5Tkd2TmgAvIftnAkTjRRCYSCQU6MOoEhoANDtQaFBzJIH/O9YLNg+kFUODzIiHns9L88f2dEhgKFJpufJ1mJok7DAbWB5QK+Hbxj/H3IpGIJRIJa7Vadnp6qu92c3NjkUhE4z5yuZz5/X7b2NiwnZ0d5QhcXV1pc+d5YvNxoo9mPzVn6XTa1tbWJIem2USFgZQrHo+/kL/TdFAMU9Dx+wjmY3N3Wjc4KJxsEymxNHts1ihBnIfEcDhUc0t+AGg7TR0/jxrkzZs3As3wQ7GG9/f35QODSZ9MJi9mhA4GA43boSiFpeI+IZVjX2FKQqvVsnK5bOl0WjNkzcwqlYqCYmZmZiwYDNrKyop8/TCDNO2El2WzWet0OvouSJAJ3MGDfn9/b5FIRA3H169fxbxxj8yewUvGESFXJ3yRBhzwpNlsCtTinnDdAQgGg4H95S9/saWlJXmV4/G4VSoVNZvO5H+nqoQUWQAXGEvki4QjmZnWfSaTMbfbrWYWgIJGiPFVJLRms1mFzxwfH0sBBJuCNQdAEKsE7MZfS5X5HgsLC2KsKJzxeMMsLS8va5QRxSjNKuAr+4tTqWP23FDAwJOLAdAKyAKwtrKyYo1GQ3s6680pqeW/PT4+qnk6PDxUmjNNFnJ11FvJZFKhQKFQSIAN0vXT01OFamHBQUXGMzUYDKTMABgGsAA8gyUidZvkeXIgYIK3t7ft9PRUMlmuJYoSFEe1Wk2KllevXtnS0pLt7e1pP6FIBYShCOTMdd77Tqdje3t7CqRjX3XaBBhdhArh7u5O6zGZTOrMgKGGbZubmxOJkM1mLR6PW7lctvF4LMYIhQL7PvUGwCAsp9M3CctPdg3XClAD1pMwWifwBzPI2UVIFwwdzxN/l2DByWQixWGlUrFAICBwj/WMmqJarVooFLJIJGK3t7cWDodlGwGMAxij8TYzrQfAnA8fPghkNDMpTra2tuzq6sq+fv0qINMp1a5UKraysiKrxuPjo+YdUwNyzzwej6SzTJ1oNBoWDodtdXVV9STn6MrKirI7/vSnP71QSAFMMeMcSTC5POFw+AV7vby8bGtra1ar1bT3UO9xXobDYSmHaCZdLpf2RFRu1FdMGqBWQNkC4dTtdi0SiVgul7OzszNbWVmxx8dHKZlIKEeRSE4KtSQAN+nvZLdMJhNZGwggZQwhAMPx8bFyUBgtCiEF4cBkE7fbrfvB5JRSqSQJN8nznH3I//P5vMaXARzy2X/44QdzuVxSIgFosebG4+fAwsPDQyWHr62t2f7+vuyjJKWXSiWr1Wo63zY2Nmxubk6y+3q9rolBzCOHoCG4jek21WrV3r59a6VSyZrNpgUCAfUIThsCI8WwcXGGF4tFgSOcuQDr0WjULi4uBLqg3EBBikqq2Wya2+3Wvvb4+ChlD0TTdDqVOunm5kYE5MPDg+yxKK/IkyD/gOf18vLSVldXLRqN6hxwEma/vn65189mwmkcWSBut1sJrcykHQ6HQslAx5+enmxpaUkoDA8PCFC321XEfyaT0Xs7PWVOZAdZEJ42kDlkMTQW9/f3mueMVIWN1ym5gp3O5/OSoIVCIet2u9bpdOy7776zQCBgmUzGKpWK3d7eip3r9/tWr9cl9WOW9MXFhYXDYTP7ib3EPxyLxdTEg5o3Gg2LxWIaNYY3hlAbGBzYmsvLSy10Cj2nrBRGgtEOi4uLtrS0pECHf//3f9d1xpe8srKijej8/Nyur68195XkTtBtvE4UsWxo+MkGg4HYbZqd0Wikhptm9O3bt2ZmCkGDleZ+03zSkDnTy9PptMZKXF5eajPkkCbp0czEBJqZDqePHz8KKR4Oh5J24oPDgxT6cXSU3++3Tqdj8Xj8hc8WRgg0E4VHsVi0h4cHzb+loGFeMj8H+w8rSRHHgRT6cdY9xSqFO9fp7u5OYS/RaNTMTM0AQIkTKSWEDMDE+fdIWU+lUpbP521vb+95s/jxPlIYcF9gJvl5pLobGxuaz06Rx5qan5+3vb09m06ntrOzI3CNZ286fU5XHo/HChPkUODQA4FfXFy009NTS6VSZvacKH50dCQG2+VyKVnbaXcgaZ0cgYuLCz2ny8vLdnNzo2dlOp1qLdMMLSwsKCeARhmJ/f7+vvl8PotEImKekU0igeY6Usg5pb7ValVFMNdjc3PTJpPnQDZUFUgo2VNh/ZHVE2DD+qf4M3sehTKdTsWympkOdmS5brfbWq2W1BXNZtNisZiKo5WVFSkRaAQ5wMPhsAL5SK42e26AA4GAeb1eBWOy/peXl1Ugm5n2FxhD5JaAEU5FyN3dnR0eHup7+Hw+u7y8FBuLesGpOOE543rCKACADIdD2Ut4ljwejxLeQeeHw6GVy2UxGQRGAQ47m2GaqvF4rOYaGWShUBBozGfmO6OQctq3YJlJbWbvoRij0SWwysxegOWoSyj4bm5upAbyeDzKYeC+YL36y1/+Yvl83orFosbkkSTc7Xbt8vJS8+kBqlEa8b0AGwHXudd4nwEMOSO5R5wps7OzVq/Xrd/v2/z8vGUyGdUHk8lEICAec+4TjObm5qYNh0M7OTmx169fK9fi7OzsRTbBcDi0RqNhLpdL+QzHx8dq9gHrqIUIfaPBvrq6kiqN/bHb7QoEYb0w2zmVSmmqDOsEkgNlSr1e176NDzwajVoul7PLy0vZydLptC0tLdn29rb2HEAYAvYAEgDCeI6wiwHan52d6VxhP+B+eTwe++d//mdbWVkR24ttkekvc3Nztr+/L3Y9k8lYsVi09+/fa89j/CLKmouLCzs6OtLkmq9fv+rzc81QTuHvT6VSViwWzeV6zgW4uLiwRCJh79690zQawBxnYwWo6fF47PLy0tbW1uzs7ExsPAAv5yPkx9LSkn369Mny+bzmjxPMSsgZdTDXG2AAINLn81kmk7Hz83NbXV0V6FQuly2Xy+k78/thcbE/oUYCPCGnhzorlUrJM41s+fT01PL5vBL0Y7GYJRIJja4Nh8OqyZ2j5NbW1nSefP78WeAWis9MJmNHR0dSYEIMEabc7/c12cFZtwNEPz09j/CFkV1ZWREQEfpxrjbsMyHF3NNAICA7Wr1et0qlouYYO+rd3Z1lMhldf9QK7Bkw6cFgUDJ4rB/tdlsEHWcMs9adqgMUmDD32Bq4hj6fT0Qb5Bj2AxRBkEXYLBlrSHgjzxXgDnXxdDqVMpizgiwtwOTLy0sbDAYKh8POwz7Pmfm3vpz92S/1+qXf///W62c14RQnFFjdblcJsDDOPChIxEejkeY7+v1+pUqD/pmZ2EGfz6fUSZCeyWRiV1dXlkwmLRAI2MXFhdghHlYWCg0iCC0NGwvYOTJkMBhY58fRSWyMPp9PReNgMNBoAg7OXC4nxh5/DswQxWi73RZqi78DmQ+M7Nu3byUFTaVSViqVzMyUHE2KuNfrtWazqQXc6XQU7ASLSsHk9/ut2WyK8cYXjVwNNBbUkGREfK5ObzwbU6VSkeSTWarRaFTyQw7Bh4cHNZUwJIRhBAIBoZosVAoxwA+8LzR2MK1mpoOee+L8s8nkOYQnHA7b7e2tUEMYe64FgACFJs1WJBKRjeL09NRyuZzkf7CHfr/f6vW6PjPoMr5xfGbHx8dSeiAPAxUFecTrvLi4aOl0WsU9GyZSLwpAmo+trS1bXl6WfNCZyoo/m0IHmTENB2yz3+8Xs0wxCQgA4s76djKQlUrlBVsFU0QhDMuF9N95b0GfaXJoNEhINjMV41dXVxYMBjWJ4PHxUSm5eEgjkYjk/yQPA1Ssra0JAWZt5/N5Gw6HktuxP2FDqVQqaqpY58y7fv36tWwUMNuj0UhBLgTB8QwBIK2urqqBjUajL8ITaSJ4xmG8kELy5zTSFN1mz2FnpMwSOAiwUSgU5I+m4IANR5Hh8/ksnU4LdadAW1tbE8KOvHFpacmSyaT2etgbGsvBYKDxcc6mc3V1VU2R0/vL+ziD35xyN8YbEQpDmjhgAeAP0nzYT5gFQur+Glzie3F9J5OJbDmw74CJnU5H+4szm4HvTYEFaFIsFqV+gNHhXMPHCAAAsOVMraUY47mIRCLWaDQU4IccGUD19PTUut2uFYtFm5ubs0+fPqng59wzMzs/P5e0GFBjYWHBTk5O1CCyzyKfZ4Sd2+0WuIIUFckzKqLLy0spBhgtCOOK95nfBzgAEOEERG9vb1/kJ+CrZ98BtEfdhAWNvYdgOcAX1GY09oBiqIKc/tCZmRmLx+MqODkv6vW6rays2NbWlu3t7enZ5/xlzCdKnYWFBYG9APf8j7qE0CnCjtjTzEw2MM5l1F+McYxEIlK6wVKylpBKM8eaezUzMyNJtMfjsWq1asFg0N69eyfFEOnVfE6sIaw1GtH7+3s7Pj7WcwWDt7u7a6VSSdNEYKOxbW1vbyscEqkwVj0sDXNzc9Zut61Wq0kaTc4MoD/11Gg0kiUGNp77gJTb5/NZtVq18Xhsb968sYeHBzs6OjKfz6eAsG63q2cIz/0PP/wgywfrjmsMcIis38xk4zMzW1tbs+PjY3t8fLTd3V2dr61WS3kjfr9fKficW/zZ5eXlC+sd5yO12MPDg/zrKJEAFDh/ITo4d2u1mm1tbanZJf+g0WhYKBRSLUZDS3hfJpOxSCQi9p7QRjOzQqFg0+lU6+X09NTW19dfMMLkTLBnAybBSkNcAY7gdY7H4+b3+5XlxBxxCAxyL8bjsa2vr2tix9XVlQKMUbc61a4zMzO2vr4u8JRrPDs7q7HF1J/v3r2zP/3pT+ZyPY95vLm5sffv36u2IU/j/v7enp6elIROPQDABKiPCmhmZkYA3GQyUd3x9PQkVauTFABUhiQjcR5FjDNAmr2V9TozMyOVLNN2UFxxtkejURuPx3ZxcWGrq6vKe7i4uNC+1W63LRqNWiwWs48fP/6c9vDX19/5+llNOA0QUifYRA4oxqmASlGEEV51fX0tGTJyTB4UCpx0Om3T6VQze1lQoPp455B6sXlS8FcqFcvlcpJGM9ey2Wza6empTSaTF2MrOp2OJJhzc8+zZNvttoqjSqVi4/Hz2C+8VUiaYeFvbm5UZIKsO8d4OBEct9utAJNgMGiVSkXpnXj1kM1EIhGFgrGQzEwbrsfjsXq9rk0X+VSz2RQriMwc9G46ndrZ2Zn99re/taWlJWu327aysmLVatVyuZx9/vxZjLLf75dEZW1tTVJyQqceHh7EqFCo53I5McYU0gRegB6SUAsLcHR09EKKx8bCiz9zskgkQJo9S79rtdoL6RYKCq/XqzRovKevXr0SkhgMBu3w8FASv7/85S8Wj8ctEolYq9WSP6rf71sul5N0H2lzq9XSfOZ+vy8EliIDNoX56igBksmklUolGwwGGnFCOvHCwoJGTThT00kBBrWE4W21Wvbb3/5WwM7//J//08bjsW1ubprX6xWzwPtMp1P9fyTO3D/kXQA9/B2KHjZ3Cj+YOJ533sflcsnbTKNNUzQcDm1ra0tSKcaeIJPkvVnjKBjIR2BcmdfrVbFEoTQajZR0fHV1ZalUytxutwJ+8IJmMhn78uWLDjX+iSzOOQKm1WopbAWryWQy0XgRrqeZqWjN5/Mag+h2u3UPotGoLCgwnAALrIf7+3s1cm6327a2tuzw8NC+fv2qgomiAjsH69HMxILu7u5aJpOxVqslgIqJCU7pN1JTrCp4yFKplOwDqVTKcrmcimHCDnkmYIVdLpfCbPCmAgKY/ZR34GzC+O/9fl+J0jTHmUxGqbEg88xzRaWzurqqvR1wkPeE6To8PBSrSlELM0iRxxmDZ5GCjn2TtYNfEA82Elr83ozMQbaN7zYcDlupVJLKBPYCKahzpOP8/LzGNNFI9Ho9Oz4+tkgkYhsbG/IK4jGFfaZ4ZixQOBy2QqEgxRhsOaAhhSR7DrLaWq1m9XpdYCMNOp8V2SRrJplM2t3dnR0cHOieI3lutVoWi8VeNL4wqNxvp4XDzLT38dkovl+/fq0iPhqNKnDt5uZGPlrsZjSKt7e3AhAnk4lm5qJQuLi4EJtFLgOhgnzntbU163a7dnJyYmbPDRngstOL67TtEPTFiK1msylAkDMZRhbAymnFOjs7s1arZclkUj/D1A0AsXg8LhDl4eHBtra2BJoi2XW7n0Nc4/G4wDD2S6dygNri4eHBjo+PbX5+3kqlkp6jUCikdctn5yydmXmeb10ulzUTejKZ2OfPn61er5vL5VIuABZCgP6bmxvb2dmx4XBoNzc31mq1FGxHA0+995vf/MZarZZdX19LNcioP2oarD35fF5jyQgma7fbspbd3d3pzL25udEZf3NzYx8+fLCdnR1LJBI2nU7l48aKNBg8j+fDLlGr1TQpA+kv+ywBn2am8DwylZaXl19McuGZxVpJXQAAQWOGnHw0GsmbjfTfzDQyjGdqf39fQBI1+tXVle3u7grEZwQoyeIkunNOVCoVGw6H9vHjR8tkMrazs2NHR0fW6XQEcFxdXQlwYq9j7ZCNsb29LbKjXC6LnONZZjIPNTO5KlhVqEU/ffpk6+vrmonN2UkvALgAQIUVguwU1AmoD7lnzWbTLi8vJaPns/JzpKQDZhNaOBwOBZ457ayQieSqYDV12mnZ37LZrBpt1JHYi8jaoe7BPkJtxdql6Z5Op/bq1Su7vLxURgvTqOghzEzTWWj+eb+/9fWrJ/zvf/1sJnxm5qcUUBgIWAPkFCQJEniE38rpV8KnTLGVTqc1OoZCj9mEjIvg0IPVJajs6elJDUu32zW/369DDQY0FovZycmJnZ6eio0FCcbzRSFNgXlycmLX19cCGi4vLyVLI2jGmfSKJI2CmsbayYLxu29ubuRDwqNDmjteHDy8ND1mpoLXKROl8Oz3+yr6kT7C/tfrdcvn89ZqtSS5p8g3e5Z9/ulPf9JC3d7etmw2K6+l038FesumiowS5Jp7TEBFMpkUkg97h0QH9I/NisA1CgQKaq4ryL3zeTw7O1Px5FyoFIV4VmEcCGUicX44HNq7d++s0+nY69evrVqt2snJifX7fSkGnCOkIpGIraysSD4JsweTgzcT+RF/D+SUIpG5uMhHm82mRmtxbZaXlxVEEwqFND6E6w5AwBxqEFi8nRzYKEcAOWhW2Wg5MGDuzH5SIZg9HzagpfjLKBYBSWjCGeMWj8clkQf95/47/eqsddBzGHXkhRT6zWZT35dDaH5+Xv6qeDz+Ihme0UYg9sPh0BKJhF1dXelZCgaDYpK9Xq99+fJF33swGFgsFpMHdGlpSZMPSJA/OTnR+pufn1eqM2qIaDRqHs/zLFMAx7u7OwuFQnofijpYRNh35szf3d1ZoVBQ0xcKhV6E7mBZmEwmkrIOh0PtZxQsw+FQY3ZA0gErHx8f7fDw0La2tszMFK6DKobmBmk1BaXz2t7c3MgnDSOHZI+Xs5mFFUUeSWFPICU+cJhUCqjxePwijO7w8FC+f0KjUBPgUaSh5qxysvOcKy6XS/sSZw/5A4DLqIxoWCaTiQARZg2Px2PJhWE6ACWRTPLMUmTt7+/bZDKxw8NDNdeTyURgDL53QAQ+E9Mc5ubmrFAoWCQSMTNTEE82m9UZ5ZQaYuVwhhfxWQHlCLajiYTJo0hzBv/d3t7qGefaHh8fm9frlSTXGeLESE8yVgD7kPgSAgpQze8LBoMvQBSac7JBYLu8Xq/OVxob8hfInoFZfnh4kFSchpBzmvU1HD4HvBLsNRgMNEaLIFaaJOe+D3ji8XjkjyfTZnZ2VpktjGciGA3ACGAbJhNbBpNpKOBRIhJehyQVWfje3p5tbW0pjA7bj9frVd01GAzkl+V7bm1taexloVCw+/t7K5fL2jfY7wH6+cylUslWV1eVZg6wzvont4d51Y+Pj/bDDz/o2TEzndmxWEwg4nA4tC9fvtj29rbSrrmG2EFQwb19+9YqlYrUJ4AcbrfbDg4OLJlMSiI9Nzen5xN5MwArEvC5uTk7Pz/XXkq6+uHhoWUyGUskEhaLxZSrUyqVBNqhIuPZJFsm9OPoPUB1zkBnuCKAIHJtQEJAn6urK4tEIgJfmfcMIOrMW0DVhgIQthXbA9cTABUAJhqNWq1Ws06nY2/evBEAzv6cyWRkSwV8Qt1E0Nny8rI898itw+GwbW5umtlztgBAWSAQ0N5AAB72EpfredIFiihsAsFgUJNYDg4OXlhFIRHYTwlqAyjAogPIzZolyZ3AP4hCp8VlMpkoVRy7F1J31KKocVAJY+11uVwvgG32k4eHBz1r1JL0Nl6vV1MtzEx1EsHU1EgQUaenpy/OgUKhYLe3tzpLCFcGlAWEZ9Tv3/L6VY7+979+VhOOnJzAL8Zf1et1ba4sytvbWzVhsFo8UM7AF8Y1VKtVu729tXg8rgU3P/88/9vp64S9oNkHIQ0GgzpgI5GIlctlGwye52fie11cXLRXr15Zt9uVrBMZGlJOFjkbN42gM9yGJp3AHN6LUQ0UWqSeU2CQPuh2u+3r16/yG52enlq1WrWlpSUlRU6nU40B8Xq9CkNbWFiQlIoGlxehJ4+Pj3ZycqKkSBoaZ6F4cnKi+4knptfrWbFYFFrGPYA5IFwCRQPj2ZzJrzBaTnaOkAkaJkZVwIAQhmRmGm1CIcGhSWOJFJJGHVaGItv5ouA8OTmRT3k0GmmMTq1Ws37/efbk1dWVVSoV+ZCY64tn08xeBFHNzs6qOAgEAmrOKPQIPAJtZKQT4A0MaLfblcKB8Xg04vV6XcguzQTMJrJm/Jz5fN7u7u7s/PxcgAfBbsvLy5bNZpXaTBNL+BSHL+w3jKCZSdZLscp4OhgVxu7gPeV+8vu51/h+Ly4u9JxwaIR+HEsCO8h34/PBVAOyIfFut9v29PRk+XxeUmWK8ul0qgKYxgBfF2h8KBSym5sbm5mZsVQqZdfX1wqdIZzGzCRX5XmjUCZcCzmkcxzS6emptdtt29ra0qgeWH+QaBBspGgog1ZXV212dtay2ay1221ZLkiSnZ+fF3vODGIaS/bIfr+vNGAUPuxfvJ/L9ZzvAXhwd3cnxcCbN2/s8fF5POPKyorALgoAs2fGHXCUQ51mDiAObzyAInsxBQR2FuwTw+HQDg8Pte7JXfD5fEpwZ8+qVCpm9pNn3+ynmctcVxKtyasAYEKlwzUwMxVFNBewDhSC7B+MXQqFQlYqlbRH88x6vV4rFotiPYfDoX3//fd6T5h3zi1ADgo2mtJYLGb5fN5++OEHK5VKlslkJG3lOhOiiULk6upKIVUwwKSC852RHl9eXmo0DUoD7C4k+pbLZbu+vhZDj4IGuSprv9vtar0COgJCOy1FTGkIBAJaKwDHpPHf39+rCQLsCIVCshYdHx+b3++XHBPgh4Kfc45gMADSZDKp9c6zALh9dXWl2oNmJplMCvgj2JIahvAlp6qEffL6+to8nueRmoDKlUpFKhOXy2XZbNZubm50LzjPCWWdTJ5D4WjY8ZhXKhVZFZBRk8rO2R+JRCyVSsljDti4sLAgnzpnDxkOZAYQ1Ghmuj9ra2uqwQBMyRdwkg9bW1v23XffSXJ9eXkpu5zznOce09SgzIjH49ZoNAQC0BR0Oh0xuKxJzoGjoyMBZcPh0DY2Nmw8Hivjh1ydb7/9VrXH4eGhDYfPwWUAkijlOp2OZTIZET1cn6WlJQVfFotFKUZIlB+NRvblyxeLRCIa/VYsFl+AlNQkZvaiNuSZxGdPsBkqm263K3Xf4uLiC0k8rOzt7a0VCgXJs8/Pz219fV0ZLKh0AIyxnTHFAvae/Wk6nepebG1tCeBIJpO2tLQkuxNAdSQS0f4CaBCJRCThpsnr9/tWKpXUDB8fH1sul9MIURrjpaUlC4fDdnBwoMDPi4sLe/funXqG3d1dOzo6spOTE1tdXbXFxUVljPR6Pet2uxaNRkVcYKFh8pGZSU3Q7/c1sq3T6djq6uqLaQGoeff397WfOGuddrttqVRKQALkZKVSUR1frVZtc3NT5xPXOJ1Oy7LHGY0vfTKZSAF5cnJiqVRKWVuhH6c6AMhQY6KsIfuKPZhxZeTp0NzncjlrNpvqhwAZeVZ/ff2yr5/VhMP6UBhRBBCkgKeQhcjBAjqGbJvFzqEA8xuJRMSSkiDs9XqtVCqZ2+0WesqhTWGMN4zNY3FxUSnZoIqgU7VaTUwbHiJYXqQ9TrSYZonmd3l52cxMjers7Kx8eg8PD5LbhkIhpQrDIHPNBoOBvXr1St6qs7MzydqRUnEtc7mcvIuEy+HFYySFmYmVhvXEI99oNNTg4lUiaZomajR6HiO0trZmr169erFJcK9pNGA1vF6vVSoVqRxWV1fVrBMMglyOwgSEGV8NjQPgDU05TRKNM0AL7I/z3pDm6GwGQZ5rtdoLpn9xcVGFKocaDR/+bAruUChk79+/t8PDQxXiJIEiYzo5ObGjoyN78+aN5GVmJtnW5uamTadT+chJY2ajRMpNMUtjiC2Bhvz29laMOc8pbCINPzM8GR3E9WEkBQWb8x4AnGEv8HhlD/00AAEAAElEQVQ8Ao8ANrjXMPnT6VTBJxT+ACRmPzUyFDSwnaenp2rMUG94PB6LRqPm9/vt9vbWWq2W2Ck+D3MsCRAB/e92u5pdS6FJ0dtut6UqAChEGgnqTaGGFPX6+lpybCY70BRTIKEAaLfb1uv1tP7x18Now55hQbi/v7ft7W19N1KkSfRHCugcoYNEFHbk48ePtrCwIIkmoVX4gWEPWE+RSERMQTabFTNKgUfjxLWMxWK2urqqvaJarVq73bZXr17pmQ4EAtZoNPTeACOwDKRNU0jB9MDcmpnAGrzbrLc3b97Y/f29NRoNe3h4EHszHA5td3dXclAUGU6QCKAI4AwpLhYHijMyDPDNI5lmFAzAEdkkNORIUDudzgvmmN/F+sA/yPvCEsJI8Bnv7u40g5eMEhr5jY0N63a7NhgMlADs9XqtUChInQODheczl8vJxkCyOtJL9kPenwYMAAK1g5np8yB3Ho+fg/dmZ2ft4uJCShpk/vwOZ4ozDTDPCH5V9nSUYfV6/YVsH7aavATei4aWs53/xnNuZlITUPxSpHKtl5eXze12672c6h+yAtiTsLowMhQ1CvJ8rBRORQj3kOfFKSv3+XxSpAAQIIl3phUDNPX7fbu4uLDl5WVbWlpS8wJQhQUGRRPgASNW4/G4Jm3c398rIZ91PplM7OTkxEKhkN3d3UmWXSgUZLPDX764uGgfPnyQqo252CgQeZHBQcOL6gAw+a/ZYD5Pr9ezP/7xjzpfOXOowQA/AUmdTeJ4PLbDw0Pr9XrWaDQEyvyv//W/rFqtWrFYtGQyaaurq1apVGQhQtI+Pz+viRVYrDh39/f37fb21jwej7179045REtLS5ZOp215edlarZbd3d1pak2lUrH19XXLZDLax1DLIR2nRgHwQhmHZ5swOsAEzlzA986Pc9/b7bbORRh6FKXUM1x/v99v+XzeqtWqCIS7uzv7/PmzLS4u2vr6uoBZgA3AYhhS1GvZbFbPKM8T4A611N3dnQA61jPAMtaDdDptk8lzqOH3339vuVxOIX4QXIA4NJLD4VDWMj7X0dGRtdttjYkjzHAyeQ4jxgqF0pVzn7WDjQxAADIGG867d+/s/PxcBF4ul7P7+3vLZrNSFRHUhkUIwBNGH5UJKjfei3wDAHQ85XjsO52OGnG85uy9fr/fjo+PbX19XfZe7kk8HhdhEggEVKdAhAD89ft9q1arCiWMxWKyyTQaDYECf+vrVyb873/9rCYc6QIPFgnfhF6ADDG+6vr62nZ3d+VRoDGmUcSPgczz/yRd9Hq9Ghk0mUys0Whovh6IGwU7wRJfvnxRY314eKhwBucsW5hTfnZ5eVnFImwayDaBZCBEeMNB2zudjhoOwsBgxcxMhQON1szMjJ2enprZs1T09evXKs5pyM/PzyWLRrpK+BRNNu89M/Oc3g1bhsyHhgAWvlqtiokllTOXy2n0DHJDFjqHp9lPRTgMDOyNc24kIxGurq4sFospOIaCgnsOM1csFu3r169CQJG/JJNJoawUTDSGzjRUAB08xLCGzjApWF1AmkAgYOl02k5PTyUrGw6Hlk6nbXFxUUnygAEAGb1ez87OziwYDNo333xj9XrdPn/+bPf392Jz4/G4WBI2QAK1SKGnWfvrrAAzE3vH96KBPjs7U8hf6McRM84wJgpPWAYKZBpgrgdyK55rj8ej62xmkmg5ZbTOhEyXy6W0VGbDRqNRFXfI/ZFnwu6yXggtJCwGRhxvHutjMBioAGVaAvsDCdtIP5eXl5VkjnTQ6/UqWIxmD7kaQMLNzY18ZARzZbNZC/2YQk+ICpYVZveen5/bePwcEre2tmZ3d3dioRiZxnocj8dC9NkLTk9PBTTipV1YWBDifnd3p1FGFIcUIePx2L5+/aqD2pm6jY8PRiiZTNrT05N1u13laABC+P1+HbSApBT2sFB8F8ZDst6R1bEXU8g75ak0WtxjwALWIcogZMSED8HAIhdFpstYuVAoZKlUSs0yP88ZcHd3Z9Fo1EKhkFQb+XxeEkvuPesPywt7pNv9PCWCtG7GuADEoiBJpVKS/fOM0ZQAHiwuLloqlZJXHXsSjPTXr18tnU7bxsaGAMDRaGR/+ctfdG9PT0+VIwBgA8jLfr2wsGBfv36VX9Lv99v5+bnCQBnPs76+ruAegANyRLCJESbGXhOJRGxubs5WV1d1D1OplMJCOYuGw6G8z+yn+OD7/b4sSlxj/g6ACFYCAErUYoCDKN7G4+exQk6wENCS52g6nQqcox5BQeNyubS/Y7HifBmNRhrNNj8/L6kxwVFmpvM/EonovA6Hw9bpdCRFnU6nau4Ie1xfX1fGwt7enppNwKPl5WVbX1+329tbsXyMy7q+vrZCoaD1A9BNBgbf08wk1cc2QYEP8YAXm8ZveXlZSjz2ZK/Xa2/evBFQRXiax+OxYrEoJQQFOjYkGm6XyyWQCGm5kxhAPcTeSdAfABZrhRqKZ+D09NS2trZsdXXVvvvuOwEiLpfL0um0bW1t2Z/+9CdLJpP27t07AR3YqyqVit3d3dnKyorFYjE19IuLi1JgnJ+fC/hmbyT9OxaL2dbWlsCjWq2mJmg8HturV6+klKM2np19Lq8B0WnISc8+OTkR+LCwsGD1el2TWbg/1LkoD1B7YpHc3d1VDYt6E587dRr2NhQ/WOsAumgCsa4Nhz+lZ8fjcYV6UjOT38QzybPF3sL9brVaFggEbHV1VZac9+/fC6x6fHxUEnu9Xrdisah7Qf7K7373O43cvbm5UeNKXUGtTv2USCQEyhBIHIvFrFqtSmbdarVkxcNKlEwm7eLiQg374+Oj/dM//ZMUe4R7AtjCPlNXTSYT+5d/+Rebn38ejXtycqIQOpQDnM2rq6u2v7+va4faBrUr8n+ChAHMSMlvNBoaR7a6umq1Wk32GFRrADg+n0/ZONxrwGosQpBE2DaxrZEt8Ovrl339rCacA5RwIa/Xa/V6XQFGsMHc0On0OQQMBjwcDtv8/Lzd3NxIBjGdTiVPwfcKesbcTxbs/Py8DkU2c2QT+Ms5TGCGWaBsGPF4XO8biUS0WJeXlyX9icfjdnBwoJRePDOlUklzLfk5QAdQd1B7godAncxMC8fJgBFuRDIwsmJkZ2yiXEuKVK7FwsKCXV1dmdlzY4IszswkpR+NRvozGr9AIGDBYNA2Nze1ucRiMUnGACxisZgSvJ2SotFoJImrmWlERa/XEwqITBLfdKPRUDEKwLKysqLngcMJ1hG/NmEkJNHOz89bOByW58cZIoYMkiLbzPTnZs8NaaVSkWrBzCS5p0imOEd27XK5NGrl4eFBXk48bTRajUZDsvvr62v573legsGgDlwnG+C0WoxGIzWSZs+FXy6Xs9vbW6vVatZsNu3+/t7i8bhkn2YmOfZoNJLX7ePHjy9UDawDfoYGmJ93rvOZmRnL5/MCmGjAzeyFZxPG0OnphS3CG0kIEh5cAADei58jYIn7wPcnOb7ZbEp6ylxamg9YYxJnsTUgqXTOH3W5XC8AMpi3s7Mz++abb2xmZkbp7MwgRpY9OztryWTSxuOxLBioRFDL9Pt9hYYlk0nJJ9nD8Cr3+327vLy0WCxm6XRazLvf77doNCrADDUJTLuzqeOA7/f78j+ytlBawGIgk4SlPzw8FBKO9I7rhf/8+PhYoTAw3BQxMOo8H16vVwUQTDjPKaolWHyeb0AGZqayNwAuUCQha+Ss4XuxZnhmyuWymZmAOkCk5eVlzQmeTp+TfqfT59A5WBAaS6wkMKN4kre3t+38/NwWFxeVMkyAmtPuQlFMaA6Ft1MZxj69t7cngJYGhn0KVv3q6srOzs7Ebnq9XmUokB8wGj0HUv75z3+2VCpliUTC9vf3BZRyPjSbTX13n89nlUpF00dCoZCeH4pMGvNkMinv7OzsrIAvVEWABbDGjFAC+MXWg8yYZwB1DB5WpLNOmTLXigZkdnbWPn/+bOFwWDO+kZw6PfxYslBp8P6cBdQzgLWMAVteXhaI4fV67eLiwpLJpJos/n4gEFDdAJNKDQTQQ3Pa6XQEFAF6AZK2Wi37+PHjiyA59liURnx+/PwU5jTRqIkIF4vFYvKbmj3LlpHGs2+jFCH9/fz83LLZrJ6lWq0mqfTi4qKankqlYtfX12L4naocpuVwTnG2OSX7i4uLVqlUBEZw/4fDoWS/s7OzlsvlVEsOBgO7uroSqOj3+y0cDtvl5aUtLCzY4uKi/e53v9PZFQ6H7fT01J6enmxtbc0qlYrNzs5aOp22RqNh9XpdobasMRSPkAafP3+WQgLVz9HRkc3PzysnhtncTlsJY2AfHh6s2+1aNpuVCooU71KpJLvHzs6O8mSSyaRyHgCgObMBtwChYHQJSOQs4myj7mY9MTllMpnYq1ev7OzsTPsy95EpL8ViUT5oAA2AP/ZtzhjUGNQa7AWob7rd7gvixCnjhtl/9+6dud1uOzw8tHQ6bbOzs7a2tqZ9/ejoyHq9nqVSKZudnRXJhGpkMpnI1ucETMmkoc7DfsW9GY1GVigULBgMSj0HWGj2Uy4B9Y8zg8fv9ysn5eHhwT5+/GgbGxu2tbUl5c3r16+1LiE88IBvb28LzADkJxiP7xSLxez6+tomk4kY8WazaSsrK1I4/uu//qt6FIhAgv6Q29MTkYFFXgh1IPeHtX95eanr9re8qL9+yZezRv2P9PpZTfhgMBArg7eMICT8BaR6mz0fcozIIlCHQotDnEOs8+M4K5puJOocSMgincguqBQF8dnZmYLUCD5hY8Jj+Zvf/MYODg5sbW1Nyeo3NzdWr9ctlUqJCd7c3LTZ2VmNgUIC1e12LZ/P6+9xTfDmEqrgZMVg5hgzwJgO0s9J0kaeTUEXCAQ0A/v09FQJ1DDEhJU4GxeKBFBYigzk7ARKOee5X19fK+iBBRkMBlW0UZhScE2nU9ve3jYz0+9DvgIKitSVAwyJ4NzcnJKh+fxLS0sWj8c135mkfeaaMwcdWTFNG/e4Xq/rwKEhdKLDHEow+wAKMIeEjJH4C9KJX4zNBQsFsiMSRA8ODqzb7cpHhD0BJnAyeR5P0Ww2FZTDxjgYDCyRSCgplWYalgNJFV5svJflctnC4bBmRE4mE7HhFxcXWmv8DgoAvKdIVJ1MN9eVa8jaXlhYsE+fPlk6nbbV1VVbX19XAQT7wf9IEMVTCmMPwwQQxN9Dpsb4MeRSeFWxsfCcTqfPIzx4LwKjkOLl83kdigBFFC00tTS5FHfItmdnZyVlpAEhWyKdTtvDw4Mlk0nJ4mHu8Flms1kBhjw/3BMsNNPp9EW4G2OpnADU/Py8LS4uvpDBA9oxio+gOIIvYS1oiPb29hQCSHpvpVIR24FMMR6P29PTk11dXUmBgSQbFhJrAY0QFomZmRkBbzynHo9HqiAasouLC61VgFAYXRjjaDRql5eXso3gES4UCmb2DPLNzc1pZB5sNawuIG6v1xPw53a7BfARKkiRRVYAn4XvRngOs4BhnbGWrK2tWalU0vcHgIGtp+hhL6aB2tzcFPtBYU9BxTni9XrV2CNjx9JzfHysfX15edlmZ2dVVGWzWTs6OlIRDJsCQNHv9+34+FggMQ0j8lSaDp5JnhVnMJ6ZWTabla2IopxC+9tvv1WmBSADqhEmSSBpZ8wQLLpTkQawTsHPZBDUd04FBUAsQAlrjjMXSxfWj2azKVCHkag0BUiDWc8AbPF4XOcc1xtGDZk+39Hj8cj7SROysLAgZh7fMzaqVqulghl7Euw1exKg3tXVlc5xRptRL3k8Hl2H6+trAS0QAF6vV/tXr9ezw8NDm5ubs3g8bhcXF/b69esXgYSESiUSCQsEAhaLxezh4cE+ffpk2WxWQbqHh4e6dzw76XTajo6OtP8CqAFum/1kWcI+gPKJ7+IMbmMfxKvf6XSsVqvp+mIrRHqPxQn/PMCcM8ANxR7KPqdtiXuA95sw2/39fdvc3LT3798LwFpYWLCLiwszMyuVSlJ/oYBotVoKeGQP63a7Lxpdl+s5cBT13Hfffad7h2d/aWnJzEweZ8Csubk5Oz09FdNMOOnFxYUAHtYq+TIEczErm6A4vnuhUNDnJ9SU89QJeM7NzQkUIBARNhvgib9DGjmgDGf3zc2NEtaxHabTafUAnI+j0UhrYjgc2sXFhS0uLlo4HFagXrvdFpNOTQIZQhr5/f29bWxs2PLysn358kXN7XfffSflIo2e2+3WNANsOag4v3z5IvsG+zsy/NXVValsYrGYVIcA/wCp3Dsz071eX1+3lZUVkU+oGemt6FfoLZxqRjz9c3NzFolENHEoGAxao9GQNW4wGMiewx7abrfV8KMU8Pl8eu5+ff2yr5/VhDOuAA8gCXw89GwSIL6ZTEZSXpi+arUqaSQjECguODBcruf5qD6fT7NWkZviIXt8fLRUKqXGqVarKQBidnb2f0PlaQiGw6Ht7OxIrkFoElJ05CHpdNpWVlaUBBoIBMQkcVANBoMXEm1CPGDzkcnhieNzc8hMp89psLlcTr4big08hhRlzEiE+aI4TCQS9vT0pMAofDFer1eAxOPjo62vr6tBg6lEHk7hS2GJBObh4UH/zgGK7xSgwOwZeUSaF41GFfQWiUS0sbHxcM87nY6FQiG7uLhQgF6325UE7u7uzt69e6d0dhhCGv6HhwfN7+ZzO32CTil6sVhU41kul6VuoPGdTqfyAcMKsrHjzf78+bPCK/7bf/tvFgwGbW5uzg4ODgS6YK/A2wSCCqNFk40vETlZKpWy/f19SZYYCXd0dGTr6+uSw+Xzeet2u1Yul+3w8FA+ZpJMke4RIsOBQkNLg8jLWaTDgMGY4wFECosUNJlMCgCjaUOOBiATDofVxCSTSTV1FNZut9uKxaLSVgGU8FCiKgFhv7u7M7/fL+UFaDIF8uPjo9QFt7e3el4pDmgIJ5OJRtCQgsv6ojGHpcNjBrhCAzkzM6MgQhJsyX5gcgKgFbYKmhXGNJLGzUQEj8ejwhJpKcGGhGYB8nEtYRAvLi4EljQaDfN6vUqpJenc5/NJHksDBAPFvyNrZb9bX19/AQKBoCPPHg6HGluIusGZXfGXv/xFAXU0y06fG1577n0qlbLBYGC1Wk2S9VevXqlhTCaTkr2yB5k9A0eZTEajmwBeGZvnnHBA7gZsPCO5YPVgytiz2SP9fr/2QwA5/plKpbSnYqcip+Dy8tLC4bBdXV3ZwcGBmpvZ2Vkl+rJX0YTgUXU2pbBg7P0XFxf29u1bM3v27tOAx+NxSU4fHh5sZ2dHid4AbtzTq6srjfKZTqdWqVQ0DQOgDtUAjQvNHgw3o4to6ILBoNXrdY1V4/vhzwckeHp6UqbE3t6eGheaY1RPrGlUVSh6KFgB5rCbOJUUqJlY38jrI5GInZ+fawQln8Uprw6Hw0qF5xyjTkH6CQNqZgoxpSZgNBBqDnIHyLXhLABQZm1yttFws59hraBR3t7eFojDM4jXOpfL2devXwWQoaLjHpH/wJkLeN/r9eTVD4VCFovF7P7+3orFooC1mZkZPdNkwZiZzlj2ADzpNFCw4DRWrBPuKd8RGfK7d+9sMBgI4AiFQlIGra6uSuEEGIJy8je/+Y11u107Ojp6cV9CP04VATxDvQjzTi34+Pho2WxWCgeUR+wfnD/O+eWoC0jFTqfTem4YSeVU3PR6Pcvn83ZycmJzc3PaU8liQA35+Pgo8AOygqwj9qiDgwNbX1+XD9xJUKAg6ff7dnJyoutoZvJfx2Ix8/v9tr+/L9aZMDXOOa4hCkie636/L8abvY8gOmTQkFD4jAGYO52ObB2hUMgajYasFLDUqBlR8aAIpE/gvVFyzszMaMIB0vd8Pq99D6UIzW+1WrWdnR2NHOx2u7a1tSXAi5oWwIVmmDqFe+P1em1/f1/fx+1225cvXyyfz9vp6amFQiFbWVkRqfXDDz+IEEA1g1rs8fHRjo+P9czw7LpcLuUZQRxNJhP785//rIBWps/0ej1LJpP26tUr7VG8H/cGMosUeyxJBI52u13VOD/n9R/Vs/1Lv35WE+7z+ZSCiJScB4KClOIOnzCIOw8isiyKMxBpPGkEoyDLQr4OEnV1dSVka39/XyO9QKNhj2HawuGw/MJIEtPptJgZ5OGpVErsCOg0aBQhHjQAeE8JxigWi9q4KJzYSNgA8FiZmVhuED7+Gwxtr9cTWsU4sdPTU4vFYkokJ8xmOp3aysqKEDvQWIpRZuoiX1tdXbWTkxOhz2bPmzIjY0ippKElrGVhYUEJ6SgUCDJDWlQoFOT7b7fbKijx1DWbTcvlcvKlmZkQczywMJ0g7NgM0um0mKXFxUU7Pz8XgschBDhg9lP6Mc/VxcWF5qciGT85OZGHc21tTc0OXvmrqytbW1uzq6sr+/Of/6wi+ODgQFaB4fB57NXGxob98MMPNhwO5aGl6KnVavpu+Juenp5se3vbCoWCPT4+WqfTUcOArzEYDFq5XJYPnGwBJOfILznEs9msTadT29jYULMBIzcejzWaC4kZ14Z/d7LisBiPj48aMfPq1SsVM9fX1woKYn4wEjCKb2RwMIaoYgaDgZgrDkzkfAB6JIHT8FNUJ5NJOzw8tE6nY4lEQoUfYX9YZCiEObTMTIUE35OiBQaNhougoUgkIhAC2TTXgoaBYBsKVfxjHN6NRsNWV1fNzOzw8FAjrI6Ojmxubk5SbUazAcYBwrHuAEj4XRQJ2H/4ful02paWlpRsS8BbKpXSe5NwzH1GYox8GbkoqpnT01NLJBKWyWQUcBQMBiXPxffGvn1ycqLshb29PSsWi3Z6emoLCwuWz+ft4uJC9gSAI4Kl2PuQRzcaDc0JJ/cCiS3S0/v7e7u5ubGNjQ1JUc/OztRIzMw8zy8G8OEz0xBQcGOZAWCEIb29vbWLi4sX8mp+FiAabyPPD37Tq6srm5v7aayXz+cTYwMbhCcWy8B0+hyISa4I5ytKnnw+b/1+X6N/qtWq5pADDjHyCgCQ844RlCiPAFywXwAYr6ysqGBGSsl3fnx8tHg8LkCNJp68Fs5wzkjsG4Abw+FQINPMzPNYuVAoZLVaTfuQkwVn7QMaw3QD+DHZw7m/BYNBSYCXlpY0lYHzlawZwl+x2HG+LiwsKN07nU7LLgW4m8vlzOfzaY43eyhnCGw0lhHUEWYmZj+dTgv8J9/CCf5ms1lNN+GcXF9f13ODigOGt1qtaqQUcnKA5svLS4FZzESnmXp4eDCzZ2sQHmQAI6x2EBlHR0f6vGQ7AE4gxcaWQ2gVORQ0iGYmBWIgELD/+l//q93d3dne3p5UN/v7+zYej+33v/+9ppCQpQNoCXu6s7Nj4XBYtp9er2fxeNw2Nzet0WgIwMPSxDrDV00gKtYb9lcC9vjctVpNfloyBLLZrBjb8fh5jCFNZSwWs+XlZYWH0VBx3pMJQhNGMBz+dM4KzjGv1yvgi7+Pfxtgt1qtqtaA4MDOBSDo8Xgsk8lYuVyWKsPr9Yr55lnhc1GfOW2ErE9Aa/Ypp/qNRvru7k6jBgHtOEPG47Ek5ZylnHVkSaD+JJAukUiY3+9XkDETIjiXUZ4CgJuZwklRt9zc3Fg6nZbNw+12a/78x48fRfzMzc1JJcR5BCC7vr5u5XL5RRhqt9u1N2/eqD7BlkKQJrZJVCooBZx2GaZHsHZQDEMG4lv3+/2yFlF/0BtRK5Dh4NzXg8GglG98r9evXysrI5PJ2NXVlXIIfn39sq+f1YRzoLJ5mpnGUcDwkbYJgzc7+zwz8eLiwtbX1+V9A5FmQbXbbVtdXVUAF+EieDur1aoCUJxBNh6PR6g1sl42XIqo6XQqbw1FKkUyPvXHx0drt9t2dXWlpFwkYnxmGgY2RfxmeBRpOJ3BP81mU95t5ww/Co1qtSqvMEU3SBWKg2AwqFCi4+NjfR6aGDNTEUlhSxGKVOzLly8KIaEpJpCLphufG6FSoK4UTfi3SY5koyQEi43o/v5ehSLeybW1NWs2m3Z5eSnknJEKeFtgASleqtWqzc7OinUmZIcUaRo8mhnQbBhapKpmpvs4Go2sXq9bJpMRelmpVKR+wMOL9PWHH34wj8djhUJBqaYkChMMSIIon9PM1PzgmcZPzjz1p6cnsX94wDOZjIoQZLs0/ITPUSSRlo7cl/m2pPjiM4alIwPA5/MppAQZOy+nr4fnAW8i4AeyMHIVGo2GGCp+x3A4VGFKuicouzNUzBkww3NANgEF+uLiokYYDodDoc3YFJCut9tthW+BzFO4Y2UwM61Tr9er556wOVh4PLyoKmCgCDtyuZ5HtRDec3x8LAaEwxNZJd5CwJ7p9Dm4KRKJqNh0Muyrq6vaW/n7FLGs+YWFBVtbW7OvX79qLFQikbClpSV5vQlqQQnklD0DajDrlPVHQ3h9fW3D4VBScOeEAr/fb4VCwTqdjhihdDqtPTkUCunvcz3Za/GYkdhKYwkajyS4UCjoOpqZWEuuHQAgf35zcyNlwOHh4YvQzslkonFSsMtOpvzo6EisI8wcTTHKDprvb7/9ViOc2FtgNcgP2dzc1Bm1uLgowBd23Ov1is07OjrSPUUGCAsGuIIqBPb/7u7ODg4OtNc+PT3pXnLfzEwjqGggYGBhepHwUgQvLCzY6uqqffz48YXaikR0rjtriEKS8380+ml+7nA4FKOIkoREZrfbbdvb2+Zyuezo6EhqFCxHs7M/jf6DbQSoDgaDKsqxhrDPxGIxFbY0kXx37vvDw4NdXV3ZzMyMxeNxi0QiyopgH4f1osAFsKMpRflBI8Koy7W1Na3/wWAg1o7JLrlcTuutWq3aysqKPT09KcMA5RCp9IS+0oSFw2ERC4BfFNGcfdgA8NUzcgkrE7JgzqlAIGCnp6e6ZoxTZH0AEHz69EnrgYAozkFmOdNouN1uS6fTuqZ/+tOfZOVz2p8gEO7v7+2HH36QDYHMjKOjI4Ehnz59sqOjIwGi1CBbW1uS77InAIAT2AVL7vF4tDflcjmp3miEIAaCwaB1u137+vWrxeNxS6fT9vnzZ5udnZUNCfXg1dWVFD7IfGu1msbVooxkH8RCUq/XZT9B2UR9SCL409OTSBcsCnyHSqUi9c/Z2ZklEgmr1+vKdkAZxr0OBAKWTCZltaLu3NrakgIEGwpkCnscFhZ88w8PD7a1tSWFHwpQnjmX63mGNyG3AHvUndSYT09PUkRyNmMlgrQgowIwCKYbILnVapnX67WtrS3ZtC4uLmx3d1eKm+l0qjqSmhyg/OPHj1IZPTw82P7+vl1eXmoMGCAoexBWRDKenOoa6iKUnDTw0+nUbm9vbX9/X8BapVKxs7Mzy+fzZvZsM4hEIgLyAJbpSx4fHxVyx2fhfEZ163a77bvvvtNzgmJta2tL+Uf0E+yLsPvkU3AmYy9Lp9N/c2/oVFH+Uq//qEz7z2rCQz/O1WWDJY0RlghPBwwxDUkymbR2u22lUsl2dnZeyCzx75EE2W63rdFomM/3PEoJ3+n5+blYDIo6mhACnQjowA+xuLioB5kHGK8XEimannK5LK8eTRLsKgchD+9wOJS/MBqN6sGnUKfgxLuI7C0ej1smkzEzU+NBArHH47FkMikPy8rKiuTkHLrNZlPjRJD24DPBB4W8cGFhQV5vDjeABJ/Pp+C6RCIhxhbkjJm/SImj0ajQewqrer2uYBYkdTCAHJ7OxPf5+Xn5KWn+YbT4uXa7bYlEwhKJhDbCRqMhT3S9Xrd2u63G1cyEYjrBFArHyWSiIJRAICDVBAcvDBxMytu3b+XPmp2dtXfv3lmpVBKCi+oAPzzhM6DvKysrSssfjUZ2cnIiqbzb/Twz3em1RBZHQ4d08P7+XnJPWEYKI5gdGByPxyOm5vr6Wgc815fnlvnBHDr9fl/oOS+eDw5TGmEK3OvrayuVStZqtazZbL5oGgFQeHaQkPt8Pq0nPNqkSuOVdqaYIzdzu91KTfX7/RYIBOzy8tKOjo5sdnZWs8KXl5ft4uJChbvZs2Xi7OxMY/NgoQj24vchAaxUKtq/+N1cBzIFKNJpvjhwnUFcgHLsiew5qVRKSgECkKLRqOXzeTXgNHQg8KlUSkUMah2nJD4YDFoikZCXPpFIWKlUsmq1qsYCVQfsWjKZtPv7ezG/ZqbmhBEsbrfbzs/PNV6HPAyC6zo/pqkSDOQMvKRJAAhhn2+327aysqKmjcYWKSjgCnsmgUU8y4CxAEPOcC3eMxQKqVmBxYb5NjNLp9OS3DJdA1YD6wgha3wv2DCn/BVVBP8N2wdBmZxPzFamyeS59vl89vXrV7E7yN7JLVheXlbQFtMwCPaBtaOhabfbNjMzI1sJ5yKfHWCBZ5TzbnFxUUU745ZmZmYkrySdm3OS74B6gPWA1QyZ6/7+vkWjUUlUabQYwTadTnVek6kwHA5VQJOJwufmrAJgRr5Jc+w8z2D+AAJRlWENISzLzCSVpZlB/u2cJFIul9X0AYoD0pArAKhvZmJQkRyzRrFZoWbw+/0KYWQOMnsn45gADplgwt7BPUW6fXZ2ZsViUVJ15z6EfNkZGlgulzXvORgMWqvVstXVVc1Ovr29lQ0om80KjCE7AxLi5ubGisWitVotM/splRoJOZ8DVRNsYrPZ1DOIL5jG/uHhwXK5nLXbbfv3f/932d+ur6+ltLq8vNTeEolELJvNKmcFhjybzVo+nze/32/1et1qtZq9e/dOjCggtHPcGbWCz+cT+7e6uqpxXdQ9KO6i0aiC1fD9k5Qei8VkYyEfgSR3gPRUKiUlEvbJZrMpaxnnHWuW5gOWdHt725rNpkZOnZ+fS6lJM3h9fW2ZTEZsOrU2kwhQ1sGmw1oDFpJvQC15d3enyQJmJtVIp9OxVqul31OtVkUQTKdTTTXBygc4ChubTCalZsE6hnoVMDOZTFo6nZYlkTo4m80KVEVmTTo64cCktHM+/NM//ZMmZqyurtof//hHNZzOMZyj0cg2NjZkQwV4RKGG8oiw4bu7O0nVOcuoyY+Pj6WwnZubk5oMNQtkDGpYQtnYDwFceIbIOGLtUd9xBmNNJSuDIDuuqTPYl33W5XLZ9fW11BNI1X99/fKvn52OztgWZNmDwcCKxaI1Gg0bDAaWSqWURorsGu8UMhVQLKRpyEbwv8Bi4SeiIXaydmx4u7u7Lz5jqVTSIqPw52BEXk5RhE8E3w/STEIVYCNA2uv1upmZJEhsdshZOSjNTBsUjSrzOsfjsTYLJGvLy8vm9/sVOIfvF4SNRHDCuLi+NHU0yaQts9mzueHtJbGWDejq6ko+cjZ9AAu/3y/0FLkL1wXf7f39vYqFyWSiIJ75+Xnb2tqyQCCggxE//e7urh0cHNjl5aXYBGRyKysr2hQ4FJHZILOk2DF7biAAM5CFBgIBKxQKCtFj88FCQPgSYVEcsG/fvn0RPMVhMxgM7P379yrGSasEbIEFPjs7s0qlopEe9/f3lk6nLZ1O2/HxseSPq6urViqVrNvt2uHhoRWLRTXoNJJIppD/0tA4vc2DwcBWV1cFipHcCaPJYQD4YmYqkFwul8Xjcctms3ZwcPACYWRD5u+x7mDOnQn0ZCIwN5YGiuuUz+cVmEcDi3wyEonY3t6eDn7en+t/f3+vgCaal8lkYplMRl43mLNms6l0ZUAvZ7iYmcnPWy6XNe8TSR9gBZ5p1CT8k9nQ7DlYaNi78KY7pe7sB6z7h4cH7YNMRTB7LvZub2/VFANcXlxcqJlBlQHrD8tBgY/XnN9LYV2v1+UfBTEHZATscxbLNLDOhF1Cx8iroPnAe4x8mML/9PRUfj2auWazaQ8PD7LWLC0tScYHoJXP5wVqOUfsIHdlDSClpfGmgMGOEwgExECwNycSCctms/pZ7i2KAjOTfYNnhYaM/T4UCtnh4aHuEzYGckmWl5ft6OhIqbLr6+vyQE8mExWjhMidnZ0JsAFcgaHBblSr1Ww4HGoOLg0ljTgyUJoIkqxJbXYGAsXjcQHIrCdAE4rIg4MDrd+/ZtLX1tbEdnBfuEeErXHuMFECZrRardrm5qZAa8IxV1dX7fj4WCACijpYNqwrSFudDDl7YDQa1aQB1sbZ2dmLkZA0vzSvhNEBAoVCITs/P5cKCoCdpph9gAZwZ2dH+xMhZTDHzjTxy8tLgdkQDTc3N5JW893w5o9GI7GPxWLRzEwM8Pb2tkATlHZOG9PBwcEL2Te5L6zT4XBoW1tbUpFtbm6KeZ2ZmbFWq2X9ft9isZjt7OzozHXukTwrKysrtrCwYKVSSWcLCkRUWNVqVU19KpWybDYrMNZpkUJlcXBwYIPBwHZ2duyPf/yjQq9mZ59TsofDobJGZmdnJY+ncSIAj2YOVRxsKjYeVC4A4DSfzWbTksmkmj72d7KLyBiaTqdSKNze3trW1pb1ej37/PmzxWIxBX+NRiM7ODiwUChky8vLUoVRizB9gEBeMgGSyaSUkbDs0+lUgax8rqWlJTs9PdV9I0yu3++rxgOoSyaTdnJyYrFYzILBoLXbbWU3ofZCbUKGD1Jx1p0zyBBgG+UHI4A5K5j2Qz/w6dMnNZNfvnyxyWRiGxsbuh+oKqjNDw8PpaRhL6YB5TzASklafy6Xs+FwqPFxgIU0z4Qj0sesr6+LyCkWi5ZOp5VtU6lU9D2CwaBlMhmNlOz8GDoKYB8Oh3UmAjgBZrL3n5+fS0F7dXWlgDj2fZ5Xznck+/REWG2pa8zM6vW65fN52RecgdnURaEfQ1sBEVDCtttt9T7hcNj8fr+S17e3twXI/pwRZb8y4X//62fPCa9UKjqQ8XqA3uDRSKVSanYqlYqKBfxlhHGMRiMFjLG42aBptDjQkIWwyTJ7F8/f7e2txWIxhYrg4XIubhqT6+trOzw81Ht0Oh15Dcfj55mPj4+PSm9Fbri7u2u1Wk2+QaTnFPywKLBFIP8EEcHesYETIAcb0O/31QxTYBPwQSHFYoUlxZdDsQBrig8RNO729lZJ5TTNzgRHmGJYYzykZqZiMRaLqdGemZmx9fV1+QxhfM1M35csAKc0HGlWKpWSVCyfzwvJbbVa1mq1LJlMCs2EwQZh5xmhKMX/NjMzI1aG9+PvUwQxhunLly8Keer3+/aHP/xBDJnb7bZEIqF/mj1veowaYWMbj8cWjUbVABASyMGSTCYlJyO5mkOf5nIwGEjtAZINQgnzATjCRsdoDZif2dlZgRPr6+tiLKfTqVhsrgPXECkbjTYFppnp8wOGMBoPaXEqlZIPkWYTtQvIvsvlkuce7yIyeFhJZ4PvDE7kO8/OzqqAbTab2l8ofqPRqHyEqFVA7mFCEomEnhUzk+QKEGE0GkmRQnFEGiwSTORyt7e3kqBzHVHcsH/xc7B1fB8YCvx5NASseXIkmN1LoArPNz5TGk2edRqQWCym6wxrxHXAf480js+Oj5a905nIju+MKQrsWaS1E9yXSqVseXnZzs/PbW5u7kUy9+rqqhh0wiJ7vd6LZobRZ4C6JDfHYjErFApWqVTM7CemEYCJEKPl5WWBwuRg4BHn+R+Px9ZsNq1erwvo2dra0nmAFQkFCVI9r9drsVhMezvyRBQViURCjT7J6GamvQBQzMmculwuzfFm3XGOlMtlu7m5sWw2a3d3d3Z9fa3inQaN55Zm2MmII/UEDEIZNBgMNDoOvzoFNA0/ORWkoYfDYSlsOE8ICu33+1IPMO4KtheFTy6Xs62tLeVxYOXBj0mBO51Olb9AHgCBSAC/MO+j0XNiM6ynx/M8a9ntdtvKyopSma+vr7VfArBHIhG7ubmxtbU1NTMwQ6xXnn0UAPyPZwMrDNYxMmXIfVhaWlJQ18PDg/n9fru4uNC0jOvra3mRa7WabWxs6HnkflKDcD8BX5+engTo0Rz7/X4rl8tSzL17985isZgdHBwoZInzGU/weDy2eDwuwISzhFrp5uZGqg1GJi4tLQn07Pf7dnR0ZCsrKwKgzUyy9FgsJovhly9f5Ks2M+3xf63IQymXTCbt+++/t9vbW6Vsf/78WSRNtVq1+fl529jYENC9sbFh5+fnUjgBCgFwz83NabIHHmCmDzgBVKxQgAk8y4AOLpdLgAWNM2fv/Py8bW5uWiwWk3WPmd+oOQkWm5+f16hEJPMA68PhUM0V2UWEHV5fX9v79+/l+56fn9ceCLPp9XrVbPv9fu3hqVRKgarT6dQSiYSa2VKpJKUlZzfnMDLmQCBgw+FQ/6TGd1pyUJuxt7jdbvnkd3Z2bG5uTvUgzzDnFbU2Ac5ut1vvSXNdrVZtbm7OSqWS6lNIOUC/x8dHgbyLi4uaIMKY0Xfv3ikkjrW0urqqpr7dbis7CmsoExbi8bgY9vF4bKenp9btdqXqQLGZSCTM4/EIOGDfYJ3Rl2AtevXqlQB57AMoaNn3qC98Pp/88wS9kWTPNeXnqOcZVeYcvbaxsSFgirql1+tJVXN3d6e097/19WsT/ve/flYTjoSXA5VimNFChAxQhLBx85DgM/nrG8Zhw4ZGQNXDw/M8zEQiIc8oaZKwQTTgPIx4tMbj8YvxJDQirVZLchBk1JFIROM1GCWELAWAADkWcuyZmRkdvshJZ2ZmhDgNBgMdlshSQTvNTDJCNqJ6vS7pE7Mi8b0iKa5Wq+Z2u63VatnGxoaFw2E7OjoSGsc1oBki2ObVq1d2dHSkRUgSIkwymyZFRq/XkzeNVHuKcAojCvvV1VWNVGKOLg04GxReKORQjIeDecIDPJlMxKhxf0AVaWqZL0qDiF+bIms6fZ47T8HMdYatRe44M/OcXk2gEsz509OTvXr1SqwG0mKaeJ6tarUqHyDMuN/vV+p3r9dTsYLPNhAICDTioCuVSub3++39+/d2c3OjdUThzzpzsswU7IBHa2trtrGxoTRvRo/AkhNgRiMK2zI3N2dbW1v6TgBRyEg5VPv9vtKIP3/+rIaXop51ylqZmZmRlA+rxXT6PNYOZBpW0Kk+oFij0eDZo2gDtcUzRvPYbDbNzCQZBLxChst74feEFUd6iv/YOSKHfYqxRQAjPHMUzjQ8JNh3u12x/9FoVL+b96MZ5RqwttgT8YLRAPA7GDNkZirYYVVbrZYYC2Ycw/6xpgB1kBheX1/b+fm55XI5BRWenp7a+vq6mZlYXKcMlbV/c3Mj+TrX5Pr6WkE6sKDlclmoO940AEGYCmRwtVrNCoWCLSwsWLvdtoODA3v79q3k0BQ8gAdI7crlshgv9lkCvpARt9tthdpgb+D+n52dWSgUUtM9Hv+U4P/09GQnJyc6q7BBxGIx29zclFcRVo3zZ3FxUXsR3n/AN9hPQJT5+Xnb2dmxUqkkZRLjyPBMAiLCkHBvotGoZP080zzfzmeV/wbbx9pFPUQ4JMwHozZZlwR2AuqghuG7TKfPGR/D4dA+fvxol5eX2u9RWdTrdRWKNCVYkxYXF+3du3fWbDbtw4cPFolErFgsvggIxPsNo0ROAD7gSqWibJbHx0fZKZDeAuoTeAkDRdGMvxVGD4bt9PTU5ubmFNBJUTsYDGQr4T3ICSE3YDwe2+vXr/VMwjjz87e3t8pJwCKFeo3mNxQKid1ut9vW6XTUPOZyOTs+PrZSqWTLy8tWqVTs6OhIYXs+n892d3dtb2/PWq2W/fDDD7a7uyuFISoxUqFpEC8uLl7sg8jBncQEoZCAtYAe7DEoal6/fm13d3d2eHgo8B8Li9Py8O233woUXltbk4pgeXlZ4/9CoZCFfhxbhrWw1WpZpVKxd+/eWTgc1tQQ6hkCx87Ozmx2dtZ2dnZ0rwC5E4mEmt2joyOFYRLcxznCeqNxZ2/Gc4z1p1qtSn3D1AvAvru7O1teXtaUlnA4bJlMRmsR5SdNKNeXrAlqbnIqOGuZ8sN156w8OTmR1B0LESDs5eWlGmoUqNSS3H8m1qC2ZK8DPL29vRVpwnVDNUhd+dcKGogYQsecI34Bl2gE8UN3Oh2ppmiUo9GoakgC53K5nAiYq6sr63Q6Ink4+whBRbGJKhOrTCAQsE6nY8fHx3Z8fCyw7OLiwlKplO3s7Mj3ztnOKGHqbyYwoIx13g+nbRVQnZqPXonPiiWkVqtpzw6Hw3Zzc2O1Wk3fC2ANVQ91KXYtnrG7uzspjABpAcE5QzjnnVbFX1+/3OtnNeGlUskWFhYkJWfjRbb912w2i5D/sQBh+/jvZiYf39LSkgoWNo3RaCSfYzwet3/+538WwsT4BoINmK24t7dnZiZZPJJTHrR8Pm+TyUTsMqhSPB5XI8r3QhbEwoBNQ2LF52VTNDMlHHIw4VVfWloSo0jzT/PMprSwsCBGbG1tzc7Pz+3h4cECgYAORWQ7bJLT6VQIGeOb/jqQClT/7u7O4vG4WF/ugZmp2SBN/cuXLwq4CofDKkKurq6s0Wio2KK4Bmgg6AqvNgyY08cOm8FBTKOIN53PDCLc7/dfjBQZDJ4T3kk2bzQaKr5B9AjIcHrGkWI6R4LhzWZUFam3WBuurq4UJMjzEg6H7fPnz2p0eA5oGgeDgXxzx8fH9v79e4VZOYs9CiykZEj9YSCQ1gGi8DuQ2BcKBcmSAUpSqZSC3Vwul9BuAuAIBGJtkbEQj8cVFuZMqedZRmLcarU0KmVubk5+PdYTbBP+Jmc+AewtjCNyPT6L04/mtL7EYjGBOiT6gpzPzs6KEXK5nkN6uHczMzNqfmloCKRhJinjmSqVipL4AUIAQVgbMJ7YTZADsx7K5bIORjMTO8ALxQMFOwwczeDj4/P4OuYyg45Pp1Md/hRLNFv4qklC7vV6ls1mbTKZKC2cRhobEAAHa5DifzAYKAAI3zv3IRqNWi6Xs5OTE/lPya3odDqWy+Ws0WgoeIf9CsZje3vb2u22RqEhBTUzMaiAh/V63er1uiZxOPcll8slEJR1DVjB83x2dqZsgpWVFTMzrQUUQzSVSMhZnzxH7EkUe91u1+bm5jRhguBFzjp8pdiaeDYBM9zu50DOfr8voIjgK6e9hT0EZo5CFr8ma4r/zrMNQ2tmArj4s5WVFctkMnZxcSGgYjqdKtAR5qZcLluz2bSVlRWpBLB7wKAADhPMhhLACTah1BiPn2cTl8tl+/Lli8ViMRXZHo9HwEvnx8kp6+vr5nK57Pj4WMUr2Rt3d3eyXeF1xAcKuDw3N2fZbFaqkEajYZlMxsLhsBqPxcVFpR0T5Mr3RAmDLYP1OZlM7PT01FZWVsSwMVoJtQ7Pdjablf3F+cyNx89jMgmholEcj8d2fn6uCRfVatXW19dteXlZSoXQjyPFaHCcYbCDwcCy2ayYbuS02LPq9brO0/PzcxEiEAwXFxcC/2ZnZxXIiWIRT/Vk8hwWWCwWXyT2I0vmfHe73ba7u6tpHgBPzjyWzo+TT377298quXt9fd1isZj95S9/sXK5bNVqVZ70YrFoqVRKWQ6QAZyP1DoEhZHST5MEUMn5hOQeq8HJyYmtr6+LjBiNRra0tGQ3NzcvalOaQwCd8Xismnh1dVUZGwTGmZlyS5C2c/az5gC2sDag/OCsJ73f7HkUJeeZx+NRLeDxeMT2IiufnZ1V2jl7LDZSmFKAPQArAGC3261z0RmynM1mZSsA8CK4DLk/6e/Umdx7nlv2DmplAISvX7/q84R+TJMnpyIYDNrm5qZIk8lkovOAjJvpdKp1jDScka2E8jGCjP1pOp3qLDk/P5c6gb6i1+vZ7u6uwI3RaGTZbNZmZmYELPEdeL7MTEpb1jDjfvP5vLKkarWamHzOl4WFBYtEIpoXPhg8h/hRs+GbZ53WajXZYO7v79XvwIhTg5iZgCRqUvZPlJFI11E+/a0vp1X4l3r9/+J3/N94/awmHMSPTZTFjPfSWRDz8JBI6SxESB9lY6PI9/l8eqhhSmComFl4enoq9gSvJhJBn88n2QuyEZgCPJEUQKTKsuBplgiDIDkQ6SUoJHIdCj6+D02TcxwZLCCeJafcE4aRTYZRbshpCIsyM4Ww3dzciP0MBAJWKpXkQYZVpLCh6KdgZfMnTAiZWjweF8ABuwd6hx+MpoRAJJg1Fjss4+3trcXjcSkPKCoJT3FeL5j1bDarQ3FmZkbsLZ8H6ZjZT80M43OQ3TlHuPCdrq6udO1Azin8KBrwXNKkvXv3Tl47POzn5+e2s7Mj0OTk5ER+mr/85S86bCiWy+WypG40OSgqCOm4vr7WJAD8QH6/X3/fyZa1Wq0X4RlmJtYP+ZXf77fLy0tbWVmRDO3m5kasHMmlHo9HlggKAdQqBJPhO4L14rl0Stefnp6EGp+dnakZo1FuNBpiZ0H1eT7YM5wZBPl8XjMuCSBEkk6TweFKqi3PRLPZlM8OJp69CDXKysqK5NzM+qTZB3CDJaXQwgOJFJtQFZ5pvLdcq16vp5FG2WxWTBTgUCQSMb/fL6UNxReWCxQp5XJZ7Ey5XFZDSvPP53LmGcRiMa2pSqUib1ooFBK63u12LRAIWKVSsel0arOzs/ab3/zGPB6Pff782Xw+nyWTSbF2brdbmQSk9cLa88zd39/b0dGRFQoFJS4jqefaAxienp7K+8h9+vz5s4V+nKPKuqPxOT8/t0ajoWKDKRNut1uqFpLGHx8frdFoqLCam5uzi4sLARq9Xs/S6bRkpGam8EQzE4jG1A+KWwo8UqQJyUP1QpGMVBDPXygU0nmBaoJmYGFhQbYeFDaAW1gSaFq4d04fOvYsimxkvTAmsO3OtU/eCYUpZwz7LMAD59tgMJB9ptVqKYgOmxSeVRhCJ/iHvJ0zFDUPrBlAE89vJBKRp3llZUWsHJYCn8+nxGvumcvlskQiISUS0tH5+Xld/16vp5Fpk8lEAZwwRDSu9Xpda8XseYwgcueZmRmxmjQWnLH44J3SagILGRNaqVSk2oGRi0QiVigU7Pvvv7fz83NbW1uTTQrQDhsO5wmhfwC/SLwJmWXPZfYxtimIB6ZYkKZ8dXWlrBeUhwDf1A2A3KPRSE3t7OysAFOaXOw4xWLRRqOR5fN5SVkvLy+tVqtp0oyZCXCHiHh4eLA///nPYhGp6bhfgIhut/tFLkylUpF8GYtYq9WyxcVFZW/QMPv9fsvlclofNzc3dnZ2ZmtrawqHhd1NpVJqFFF5sm4IqSMgDTaTYDOawNXVVYGxztnLWApRCrRaLT3vjMZj/cJ6YmO5vr7WtJp0Oi1Qh/0a1QCjQwHYCPpDqVYul5XjgtUOVQgKUfakcDhs5+fnFggENBmFjAdqDoAzRulBGjhBAiwy5HEwypFnjXqcGgq7C2GenU5H9Q6THZaXl9UjoPJyApnsCZwzKysrAuGcY8kajYbUiuyvnMNkNHGdo9Go1et1Oz09taenJ8vlcrIEUBOhFqHmIxgR9cI//MM/WLVatXK5bCsrKwp5xQ7gtBMxapT6KZlM2uXlpYAzJnBMJhMrFAp6ppCvMwb15uZGShcAU0jOWCxmHo/HNjY2pFZizN1/1Kb3/99eP1uOjn/TKekjuZdmGMlvLpd7gTbOzMxo/iAeWB5OAnzK5bLl83k1nv1+X6NXKDaQ/tB4g0AlEglJOzY2NlRwEMyAz5jwDZDpWq2mA55NmWYF1Ljf79vnz59td3dXkkWkp6CDpGLi/QQ9Zm4uzTeSIArP0WgkGRFhJiCjSFSZE0njgG8SaZGZSUbGYY7Hy+Vy2cbGhvx9HGrMDERW5wy7wjdrZmI2+v2+GqTb21tJ4vEE9/t9+/Lli62vr8vfTTIqhxBABP7MbrcroKZer8szQ2I6wAoyONQDZqYNn0OAJHQkTkiUKAJgAmmQzUzAEGmlINCEcN3f39v+/r6trKwoxRf5udlzwRQMBjXn2szUqICwImeGiSgWi7aysmL7+/t6JvHzD4dDNT1ObzAbJx5hgqH6/b7t7e3ZzMyMVSoVKRJQA+CJpRnFR+v0t1LEFAoFMR1OP6XH85zAXqlUxMIhgSV4cHFx0eLxuFiFyWQiuRXhMWYm1pZ7fHNzY5eXl0p6xY9H8QEwYWa6psjQKMhoaGlyYHECgYDGdSG/Go1GSjzFaoCPlAAyQBGaQPxxc3NzSq6GmQAwgTkgyZQGlFFAzNvtdDoKU9na2lJI2+LiojI2AJUAM2GYACn39/ctl8vpesM+ff36VSz3zs6Ozc/PKxAMPzjXj0INZqTValksFtN+DBtIw0x2BYxk6MdxcI+Pj1o72DTC4bDC4VKplGwO9Xrdjo+P1VguLi6qgZ+ZmbGPHz9qBCPFLb719+/f2+zsrJ2dncnHn8vlrFwuK1SnVCrZ1taWud1uBYSWSiWFO/Js0WRWq1Ul1DImE/a70WhYPB4XQ0uIzeLiooA/muWnpycrFovW7z+nLadSKV3rfr9vmUxGSe8AUABgKK3G47EYYbf7OTjT5/NprjdqJ5qR4XCokEIAJEBn5MGAAKgS2J+cKeicQXxvAC8AZBpozoDLy0u9N57jp6cnMdNmpsYb9QDrFoCXtbqysmI7OzvWbDat2+3KbtFsNsVQcV+4nm/fvjUzU94C16vdbmvEIwArkz4AM71er84XgGJkpLBznBeQBG63W/fbzKQEOT4+tkajIQUflgXUPXgsUbUAoBEUR40xnU6tXq+L2ABw5Twpl8uqmbgGAFnD4VCqk1gsJpsIHnKmw5BBkMlkNEecyQowpM6MCsDDx8dH2ZnMTCqZRCKhyR+cv5wBTILw+Xy2trZmwWDQPn36pPDI+fl5i0QiGmVLnUNI2/b2ts3PzwvEOT8/1ySQVqtle3t7trm5aScnJxqbx57d6/Usl8vJSjg7O6sRgRA7qEwKhYJyHR4eHiTzNzNZXfClwzg+PT29yDUCGEHqy/O1uLhox8fHGpVGHgPP2P39vdXrdUulUspUQBk1HA4FCC0tLdnZ2ZlID6ZpsO9Q18TjcTs/P7fZ2VmRHWamBhkCin3v8+fPVigUFGrHfXfm7QSDQXmiscI5rZw05SgqvF6vpPjUaAQP8xwyzzufz1ur1bJer6e569T/Ho9H+SQul0v5EJBkyNVRqaLwQ+lEbYxHO51OK1Pl+PhY15h6k8YbC+3c3JwdHR1ZPp9XeNrFxYWZmc4LclyYDIGF4Pr6WvecM6DVaok1p0cgAHI8HlsymbTb21vtIeFwWMQOZAbWp2q1and3d1YqlWxzc9MymYwdHR1pv6Nu6fV6mrhDqJtz9rjf75c94+vXr1IJhcNhq1QqImAAqf+W119bjH+J1y/9/v+3Xj+rCWfGIJu0mQn5pPklzIsEcDaxRCIhiZXP57NcLiePM4fWwsKC5fN5LUy8YIx/co4YgQnBIwObhGQUFg1P3czMjNB4ZKZ4/3ghzXEmY9IAzM7OiqXa3d1Vo4qU7e7uzrLZrHW7XY1vYE4nLCHINIcmxQ2bPBsqrB5NwNzcnBiNyWQiOXy/37d0Oq2gpouLC3v//r0CGmDUGDuxtLSkjQ85EPJ6ZLj4eEmPpgnBXsCmg78PEIQxD4xXQUrEoYGEhtAaM5Mni0bt+vra1tfXzeN5nsGJnzSRSAicQFK2tLQkLxuyO6SzsJcceIFAQB4mDhs8oHwGmhCKHXyUjNu4u7tT4wr6joJhZ2dH6crOYDGUAf1+X7JMCvFOp2OpVMpyuZxdXFwIZMA3C+AFG0uRjISW+4lnjOvO3Mz5+Xm7uLiwpaUlKxQK9m//9m+SAL99+1YF3cbGhpmZila8nc754sjzzJ4D75aWluzk5ERACv4q5NCsDVh8Gj1UKsya5jmi2ORA5/4Q8EgxwSbMWqag6vV6Gg9GrgSHEfsLzDsKGKYCOLMhVlZWBNY5G1eabuR22EuQHnMdq9WqVatVhckBrlAI4Z13PhfkBQA6djodW19fV2BaPp+XRw31Dwohp50DVqnb7VqhUFASLR5zFEF4fVGkmJlsLqhz+Hvj8VihMff39xpLxLVmwgBzv2ETbm5u1BABhDlZKKYXRCIROzg4sGAwKInv1dWVJOYnJyc2Ho/FPHJNGOGTSqXs4uJCickUix6PR2FwZs9A29evX83MbG1tTazx2tqawtB4tgDpYKdguMzM3rx5I0kyihrUPdhmzs7OXqx1JliQ0eHMlYDlQTZ+e3srZhUJNUAuPlpY4MvLSwWn0dgCXJqZwGdePCNnZ2diyjudzgsLEEU4vwdGysn0z87OSkZMA8yeSip2PB7Xec/zxfMB686owkqlIkCPkWc06wRMISFnTblcLl1nJh0gZ+V8YRIG9QrfCdabcCSeeX7fw8ODnZ2dWTabFbMN2LaxsSEQFCtXuVy2ZDKpcxa5NfvhxsaGtdtt29/f15xl7BtIXQF0ULHAEgLsmZmdnZ29AMMA6lA/uVwuy2QyFo/H7Q9/+IM1m02NWOt2u/b69Wu7vLy0ZDL5YkQWACzXnXWPYolQWrI8WI8AaQBDf/7zn5XVs729LWtOpVKRRQTLApYkl8slIBk7x5cvX2wwGNi7d+8sGAxqsoLZT4GHBwcHNhqNBPz4fD4ByKiSUHTBTjIaCqXmdDrVXtrv9zUui0YsEokIyKDmYSoNey7g6unpqSbmQCQNBgON38JGhNKl0+lIXdhqtXTm0qRzzgOaAb4yDnFtbU02SO4ZzaPX67Xt7W2t2+Fw+CJP4erqyiKRiJpYr9erEcMoLrBEon6jbgZEIojZ5/PZ73//+xfKkPX1dSkJeVGTocxCncGeWKlU5PFG/Wn2k/WBcMa7uzv74YcfNAoWsHE0GknVSpOJcov+gJrbaYs9Ozuz3/3udxqrxn8jJ6XVasnmQE1DfQ6h1O8/p6kD2rGvrq+vi/h5fHyU+hGQDmCLdb+2tiZCi9/Hez4+Pkq9QgAfZ0w4HFaGVCKREAgHCEYtipJuZmZGNgnAf6T9EJsQnbe3t39DV/jr6//b189qwqfT5/S/UCgklg5pGawXKA+euNFoZK9evXrhbYQRxj/a6/Xs5OTE5ufn7e7uTjISZ8AJs0adqDG+VJ/PZ5VKxVKplJBzGId+v29/+ctf7Ntvv7VwOCyZ+ZcvX+zh4UGeFaRsm5ubGrmAtJjDBnky8hinxJ3wCAqMh4cHq1arOki73a6trq4qQOXs7Mza7bbt7OwopMrMXvjPkF0SrkB4DIUfaZ+EhRwdHYkJe3h4sEwmIwkm7DDJuxRid3d31m63hZrBUrLBwNQi5e33+0J7b25udH2QmDnDtkjohmW/vb1VSitgBEmhoR/HVo3HYwWJAKIQcEWSOwUsDIXX67VSqSQFQTweFztdKpUsFouZmUmOA3uFjzibzQqVLpfL8jMjz+feMx4O5ghGE08nxSwF+Pz8vL1588bK5bLS7oPBoPl8Pms2m7a7u6uNejAYaHzFcDi0lZUVSTCxX3g8HqXdO5s2CjVAJZJjFxYWNJppeXlZSfv/8i//8sKrP5k8p4T2ej27uLjQrHYSySmcxuOxHR8f2/b2tnyYg8HAlpeXX7CGFJXIKvE/xuNxyddh6zOZjCRSSEvn5+flDYZ94KAYjUZWLpetUqkIBa9Wq3Z/f2+rq6tSvQBMIbH2+Xx2eXkp5hzlwXT6PHKGAoPDmLXotLKcnp7aZDJRIxEOh5VJkUql7De/+Y1Vq1U7OTmxzc3NFwndk8lEKpFkMilJM0VZIBDQyBFsA7Ozs3ZyciIZI/NOkek5/emg8wCJ9/f3dn19bel0WhaawWBgd3d32jvv7+9ta2tLzRvPH4V2Pp+XUmQ4HNrBwYEYs2AwaMvLy2Zm8kVyRqBw8fv91ul0bG9vT1MZsGqg6nGm49IIkmdRLBZfFAIUqBQOV1dXls/nFXYDSHB/f2+tVkty5m+++cb29/cFgME+cw1pOAj9qlartrW1pbRkfOOEUXFvsTXguUTdFQgEFN6ztLQk77ZT7kpBjezPyTrgNTw+PpbdC7CLPANk3LBvNCMLCwv6/wB47Mk06AARsNoEi1LU0xAsLS2pGH16etK5jwWDVGsUak6f/dLSksInKZZhY66vr5W1cHh4qO8FI7a4uGhLS0sa30fwFI07iikCiggIQ/UBq16tVgVqwJwvLS1ZtVqVrQMAmeAlwiq5noyxikaj8s8jx4XVvr6+tmQyqX0Y1QHXB4a382MKPOuJuoDm8vHxUWPzyE0BnOXaAeD2+8+TCwjNGgyeU+9RTmSzWdllZmZm7OjoyDKZjCZTmD2HWX39+lUWQOc+YfbTuCqCzmBT6/W6AqDMTOAXoB0y94WFBSXV12o13QeyJabTqb7zwsKCxrJdXl7a7e2tQAmv1yuAlBR+MxOoil0AAIa9ZDwe29evX83lcilskJ8dDod2cnJiq6urdnFxITLD4/EoBZuEe34vexIAAvsJE1Cw3c3Oztrx8bGl02mB+l6vV9kzS0tLYiXNngHwYDAoyX+73VYeCL5n/MnIyyuVinIJIEhcLpesGKw7Z/gWYaA8z+QxXF1dyeII0EA9T7POvkpTC8CN8m00GtnNzY2IsEQioRqOc5q1iOScaR88M9S+qEZmZ2f1O7EoFAoFS6VS6hFQQ3Ie896MaQRgcblclkwm1a/wzEPsAMgAUKEwouYrl8tKvUfpBxBDQOr3339v/X5fKtxerycLLxkrgDynp6fKKaCxhtQieA6VDnZCVB69Xk+AEspkJ3noBIexjNC4DwbPY+P43oxLpH+hNgEI+jm94a9M+N/3+llNeDQa1YLzeDxWrVYtn88LPYXt+Wt5CZIWUBoOUqevDWYXVoNNHRabeH6QZIoPM9OYsel0KuSsUqnY4+OjUqT/7d/+TUFTSGSQk9AUzs/Pa/Gz8c3Nzdn+/r7QWtDhu7s7LTDmfHY6HaGOzjCI09NTSeVB15C+gtIibwaRJtUXZQHykHA4bLVazUKhkOTJp6enktkFg0GxPIxqOzs7E2sOyxCPx+309FRyn2KxqGJsZmbGUqmUmm+Ai4uLC21OJKdyX9nMSZtGAsTsSuZ+4oPFIxsKhYTAwcrArIZ+nLHZbrfFGFDsIG+u1WoKiqlUKjrcut2uJZNJjYfg4KOAJ2Qtm80qxI2kY1hCmHyuP/+OH55DjcPUzPQdnp6ebGVlxe7v79W4UIiHQiE7Pj62s7MzbeKsg8XFRUsmky8K5+l0KumdmYnNrNfrano4FEajkZVKJd0b2MTf//73Vi6XNSuZpF+8vEj6z8/PNVanWCza0dGR2GbWIoEhFNsrKyt6JrEEwNDTgICmw9ZWKhX75ptvVEBnMhkdAsPh0K6urtTg4g00Mx0QHMb4CRllxei44+NjC4VCUkDQPBNcaGbKO2BP6vV6ls/n7ejoyEajkWa4U9w1m02lkZuZCkKYXT4zqgW8rSh+stmsinr8Zqh2YF47nY4AR2TB29vbsvvwDNNIsmadfngACtJ9U6mUnZ+fq9EbDocqgiuVimSsnU5HRTFFivPZpolh3CHMEQnCNFp46PlMqIDMzDY2NuTThqkdjZ7H7XGP9/f3zev12ps3b174/ba3t+0Pf/iD1Wo1e/36tRonxksBmozHY41Hgx3BegNoCuOJoiGTyZjX69Ve2Wq1lJvA3utyuVSMdn6cF3txcWHT6dQODg4sHA6L+aSpeHp6snq9Lt+6z+dT0vDFxYX8qxTSZiaGnPvFugdgq9VqlkwmNWsdAIYmEbkl7LkzayWZTGqv534DgjjDopaWliwSicjeQTo9qg38wzTlk8lEe8Dd3Z32HSYG0FBS/K2urlqpVNL7ojhCrcN1YB8lxGh1ddXS6bQCQin6NzY2JO1Np9MaUQgIQY1BuBLNLKAcBWsqlRJoTT7J+/fv7enpSbOvnYFZy8vLuofIbdmPjo+PLRKJ2MrKipQvMFzkyAB+I2EFtHPOKQeodOZkoByhBqHxPzk5MTPT2EtCl/hu5XJZgCSgpPPsAkzm/zufTfbBWCwmogMmnCYgmUzKCkXQIqo2xli6XC77x3/8R3O73faHP/xBCgEa5mAwaFdXV2KTAbaq1ap5PB79Dr/fb4eHh5bL5SydTiuYikYxnU5bs9m0Tqdj9XpdTOXy8rKY75mZGYvFYmJJWQuJRMLa7bbCdHu9nkUiEZtOp1ITtlotseooPslVwNNrZiI5ADhTqZRVq1UpdajTmNBzeXkpgJv6FLUJYa0ej0fqLQDP3d1d63a7qiH5XzablU2IeoMmm3oGEANlCGQDVlKaWAAj6mvqN+pp1kSn07Hz83NbX1/XucSaQoXiHLGGwuPk5MS2trZscXHRSqWSRtjidcf73O/3rV6vW7FYtGg0qpFjeNaxMDrDxVCwcT4vLi4qq4SGHPANpvjDhw/m9Xptd3fXstmszgeULm63WzUVQMzZ2ZlVKpUX4Drhad1uVyPbyA0ZDoeyrU2nU7HtgFZYYwFlUCdimXh6ehJwjU2AusrM1BvEYjGROoD9j4+Ptru7a2bPardmsymQl97j19cv+/pZTTgH2P39vS0uLlq9XpefGJ9gq9Wy7e1tSbVYLG632w4PD8WScZjwIIDkshAoMs1+GmFm9tOBgFSU9NSZmRmLRqMqOpGYklqJRJvmFYSp1WpJSoY3kfAdUo9B55F8ME6lVqtJUocfp1AomN/vV+Lshw8fJPu7uLhQGuFoNJJ/FMABqR2hKLDUIMrNZlPXBukgYUQ0v6SVX19fi3FFdnt0dGRmzxYCZDvr6+v25csXNZOMGIJxhlUJBoPyyIDYsimY/eQDpMDweDz6c9BPgnhgo2DdGZdFUXp6eqr51sizCe3odrs6nBYWFuzt27c2nU7F+uE35/dsbm7KG2tmmmcPqIPEH4CDICkKQywHFO54pZC6O9MxyS0AyHC73fb//D//jwUCAcvlcjqckSFToKfTaT0LoLtnZ2dCh3nuab6cxZGZiSXCl2VmCjHhQIaZ3tzctEajYc1mU7J7gkeSyaSKsuPjY9kPtre37eDgwBqNhnm9XoUZBYNBy+fz5vP5LB6PK714a2tL/iUOA/IFKIjZA8LhsCYatNttazQakjqiimG9z83NKejK5XKJMaDZvbq6smg0aoFAQOw6EnWXyyVgCYDNmaYe+nGMDbNVKcSdCD5p8Ovr6wLbptOpgovOz8+lgACEgFWPRqNKTKbh39/fVwMOGIQPE9Q8k8moiUYNgtIFbzFWHGwfKHxgQShYAfZgPTlwATQBd1qtluVyOSsUCnZ8fKzCmRCoZrNpx8fHlslkBLTxDK6srNj5+blUTFiGaARRB5ExAGDHmqCAGA6fx129fftW3xeAg0wCCg+A0+3tbctkMgIC2I8oMvf391XAwNT5fD41P4TluN1u+/r1q7VaLRXKKDG8Xq99/frV7u/v1ZzDit7e3mqkF2sM1QKNAaoX9mnmlZdKJRuNRkqnpehtNBoCatlzYaxJp15ZWbFsNmv1et3K5bJtbW2pOcV2w/W9vb21g4MDNcTsJRTSFGk8DycnJ1YoFDQSifchqwOlGs85qrj7+3srFAqSr06nU6vVajqnDg8PtW+yV2PjophvNpvW7/et0WjY8vKynifWO+Ax5wgSY7fbLcAd2SjNvnMvovBnD0XCzhjEp6cnhWzhBeWeA0IiL6VJIDMkk8koqNGZkUEwFaqxs7MzW1lZ0XgmmldmFrMeOENRMI1GIz0bnLkEffn9fvvDH/6gdPHJZGKZTEZS6W+++Ua5GqypaDQqwJM9wswU8MXvIbsAxRYe3bdv32rvMjOd1YlEQvO/Hx4eBEqgCJxOp7a2tiYQ7+LiQsAd14pGPxQK2TfffGNzc3NS8zFekRfNPCodZNSoQmiGSRg3MwWqoRAgE4I060gkYplMRiA3zef9/b1ls1kzM4G1Hz58sK2tLT0XS0tLlsvlVEtRG/K/3d1dKVjwVAOEoaAiGBE7D40js9EhuiBXCOXKZDJSS6VSKbu5ubG7uzspJ7B1ARJQkxNsO51OZeuiFisUCvbp0yeLxWJSuqDcREVD0CcKFkgL7imju7BhOuekkxNDo02mjcvlkvID0PHu7s5qtZpIM5RUgLIQOwQjk4VE1ghe/vn5eatWqxpdxuQIpxqA/ejk5MQymYyk5f/jf/wPBbm9e/dOAAm+f8B26jczE1jw3Xff2fv37y0YDEpyD8EJo4/Fi70GEqFer8tGxpkzHo9llXACfpwXBDajyDk8PLRisajwNmwtMPhY0f6W169M+N//+llNOJJTJFKJREJFEV4on89ne3t7Fo/HtSGAksJ4UqAiL4QhpTBBHsnCpiF3JsVSpJBcyYba7/fly8b3CvrOIc2mzmEci8Xs+vrajo+P5a0iTCyTydji4qIkdL1ez+r1usXjcc3o+/Lli11eXtrFxYX1ej2FAjEnNxwOWzqdtsfHR1tdXX2+8D96TPAa53I5MVs0wBSLtVpN0vjpdCrWEG8KSgMkKozYQVZIM0Ajc3h4aMPhUDNjf/Ob34jp5Ho7pavcZ8AUPIsAIGxqi4uLL9QSNIn4OfkzNkIOGOTnNF2MoiJIg6aG68rmXq/XtbE4A72SyaTSRgE4QL+RT+GTjcfjtrCwYIeHh9bv9wUKULTBVODnXVxcFIqIHAlpMcmVbLgUrWYmHxUhHRSYFF/7+/tquDmQAoGACkeYau6T2U9MLCFySN2RjhLuA2OIT4hikKLbOUKKFGcsBASJOccQMj5vbm5OacSPj4+2vLys5F7kcIARTt8wkijW5MLCgtYO9xBZHKAZTQIyzng8LqY8m81qfMfMzIxtbW3Z0dGRCnuQd54BABwYGw5vgBWYIJoVmsBgMKjgPwCsvb09+/7771W0cg3ZL8xMUkJyB3iesX9sbGzoYKbIAJEvFovy4cI8Ih8koMflctnNzY09Pj4K9Hh8fJ77jEKHgpCGDJYhkUiY3+9XCvNwOLRUKqUxQWRaUETt7+/b7Oys1EP83bOzM029QLkEYIraAoCHJqnb7dqrV6/s8vJSP+/MQQDoxVPN2qHxY02QkfD09KTUY4J3isWiTSYTeUuRVjtDMkmvpnD3eDy2tbWlXI2DgwPJftPptB0fH9vKyoqtrKzY3t6erBo8+6VSybLZrBjCWCxmj4+Pug+EPJmZff/99+ZyubTfm5kanUwmI38z62h1dVUKBvZzmEOfzyf1QKPREKMF08S69Hq9dnl5acvLy/bmzZsX6xsVy3T6HNY0Ho8FEJiZ9g6eRTyyrBEsaD7f83hCAt5Yw8gqsRCQ7cGZc3h4aC6XS9kx9XrdzJ6zKEqlkh0fH2vvwRfKntf5cXIIChasYDRsTquQU4LPnkvWCmOK8PADFgHiARLz3DO5BLk3QBbXGjCMVGg8n5PJxLLZrJjvm5sb7a0LCwuWSCQEUjoVDajObm9vZTehIc7lctbr9Wxzc1OAADUBzxzfeTwey7vL/hUOhwW2Li8vW7FYtPPzc3t8fBTwAfNHc5BIJCyTyVggELAPHz7Yf/7P/9nW1tZUg1A7Yh1E/UOdRiNPpo6ZqX5AcotUmc/Bey4vL8sz7mw0+Hs0zex9nNPBYNAuLi7s/PzcNjc3ZSnJ5/NWKpWU8bK9vS1AF9kyEzk2NjakOimXyxaNRm1nZ0dWDPZT6jD2atjH+/t7jWLFlvbhwwftrcvLy/Licy5fX1/b0tKSra+vKyuIP3PWza9fv1YNw55J7gskFTYsrA4ul0t5D4yXPDs7k4cdwoeak4aRMGXybcjJ8Xq9suQMBgOx/W63287Pzy2dTqsG/P7775WtgG2R2oczi++HwgDlKk0uxNfd3Z1dXFyIVICxdr4fSjMzk9qH/aBWqylA+M2bN9ZsNu3jx4+yleDd//Lli/X7fcm7eW5YhwC9KI6wd8JMDwYDq9VqtrCwYKVSyba3t2XBRNEK0Mn7nJ2dWbPZtM3NTZE1/F0CgAFWIQ8gjVB5sk/7/X6FgUKWLSwsaJKKUyXz6+uXe/2sJrzZbKp4WVpaku/Q6Xscj8fylFKEks7M4QdixgY9HA61YYKaUQjzmkwm8r+yeC4vL8UMEu4CIoicw8xU9FK8dbtdMUXIpfh8ZqbwIeResPVIQI6Pj83n8+lBDYVCdnl5KXYexszv95vX67VUKqVgF7w/vV7PHh8flbJMs8dc8/F4LKkkYT5XV1f2+vVreXRJHfb7/Rp5UygU5F25vLxUsBNNECgjUlLYM2Ti+OCYkwiqiKQX1JYGluac77u4uKiNGH9LsVjURkyx5/Qq8hw4NxAks6B6LpfLLi8vJTMEse31egJPaB4J58JHhDcVJokxUfl8XiPbzJ4DhEhW5dlhJvrMzIwtLy/LJ46FgCIBJQAbH2FgjMei2T46OtLz3m63LRKJWC6XMzMTU0TTgu+J54cGBxCJIns6ndr5+bkk2nd3d0rExT6AguH4+Niy2axCSADDxuOxXV9fa8RLJpOxs7Mze3h4UGI13y0SiSgxdDKZ2OHh4YvD8vLyUhYGkPp8Pq/riCf/6urKgsGg1et1NY98VsZRxeNx7TFut1uBVc7GmawJs+fi7eTkREX/5eXliwZ9OBxKGk0+BUw2f8fr9aqwgclaWlrSZABAGVJyp9OpZJ+oRmBJSDv9+PHj/ya1RBpJocjaOj4+lhS6VCqZ2fOBCrDJembkCyqTubk5hZYtLi6qwDIzFV53d3eSRXP/rq6uVKjDhsFuk5lBo0aWx/b2tsYclctl7Ws3NzcCGviet7e3Nh6PNU2BvRoWHx+71+sVWMm1vr6+NjNTmjPgY6/Xs9PTUz3fBLkRgrW8vKz1+Pnz5xdp+gCMhGwtLi6q6OMsYS42jDKzzHd2dl5ck2w2K0n/aDTSfeasAhzAQwq7RkFE8YMc0sy0H+/t7dlkMhGb2+l0rNPp2MrKygsZpTMfgXAsgtIajYa1222rVCoCnkej53E7SKKdeRTsBWY/WWtohmDAUYixJgEheBa5t4y84T1QlTw+PsqWwJ87w494JjOZjGxMpFDv7+8L3IEx5zliDVDYU/Tz2VhnSEABSlwul4XDYfn6GWWGzczj8Vgmk9HYKRomlF2A1PwO/h1pfqlUkgohnU5LCedcH+xLJNoDkqI+cSYxk/oOA86zNxqNpFIkVAzpPufO09OTvNbr6+sa/4YNgKYYm91kMpEaDjUI68fsuRHe2dkRYP3dd9/Z2tqaxeNxKR2or+7v72URajab2tt4ZgGMsSZ0u11NHUCpRt0YjUat1+vZ+fn5i2bF7/fbN998IxUknvKnpydLp9N2eHgoJnh9fd3MngN5AUh6vZ7FYjHl6VDXMlFnOBza6uqqpVIpgaYoBJCsc0aSf3B/fy8wnvoYIJ5n0DleDcCfhrrRaFgsFrNQKGTxeFzE1dPTk8AGSCieecBPAN2ZmRmpighIpcFHnUM4IuNxI5GIlKCo5qj9SNHmcyGFLhQK1u/35Rt3BuYBRJ+dnVk0GhXjX6vVtE+iGGJ/JcfJ6/XKJoe6DVvc/v6+pdNp5eVUKhX5otmfE4nEi/BKl8slQKDT6YjACofDls/nrVwuy+Ly+PhoW1tbmlwyO/ucKs6UipubG6kF2d8YZctzh3WDoNp/+Zd/sVgsZuFwWNlPMNODwUC5RNixUKN0u12pfAh9I1sGVW6xWFTdTq9Ec8/6JbwQOyeZSNRh7HV/6+tXJvzvf/2sJpxNBw8SDA0bv5lJ2tpqtWwwGAi15gFwhl3BPIFg8sCTUIv8h79LccCLQv3w8FCNBYijswAiJI6GA2YElJtmivmPzMBE+rmxsfGCKe73n+feggbincO7enBwIM8lGxwNvxMZjUajYjIIgEA6zqgJmlQKhUwmY9fX1/rZ/5e9P4lxLU3T+/CHDEYw5iCDMyMYjHm492ZVZlVXlXoAtOiW3NLO0EaANtLC1kYSJBgGWtBC2giCAW1sbbwyYAHWyivbMCzJbUFDo7tUnVl555hHMhhkBMmYJzJIL5i/557Iv2VXFv7dtuEicJGZN2Mgz/nO973vM70EpZycnHhDa7fb+v73v29vbbfbdUFAMYU8GXkpTBnBESD2KB9IFCbVkkR6woiQnZ2cnDiwis9IYjKHcqVS8RxMJMZ4Te/u7hz8lkgkdHl5qfNvUsnxI9FkIIlj5BKJoPf3955JS5Iza4iQEQ5FZtwCELHhZ7NZy+QoVmlqCOaCjUHW/OrVKx0cHLgwpHjE/xOPx7W4uKhQKKT/9X/9X7Wzs+OGiMLw6enJv4fRNLe3ty5og8mpgBf4sNrttra3t51eC9obtHIg14zFYorFYpZyDg8PezQQdgGa1P7+fofWtFotF000ozQc79+/d9oqUiY8+jReAApMOiiXy25I+Cy8UK4gbyVVvFgsmkkGSIJlIPmckBKAOmZyUrxysDH+C9nc4uKiGxbm6wLOkQvBvc3lcvYOLyws6OLiwmg0YAHrneKHPWl2dlbj4+NWuMD8T09PG9BbXl62RSMSiejq6spMgSTLBGEss9ms2YpoNOqJEqTU4y29vLzU8vKy3r9/79wOnmd8uABpFARkfSAt5/eMjo7q3bt3zhcgJOf4+Nj7BPsNbDPp+uRl3N3daW1tzff/4OBA0WjUAT+wHXt7e/a2XV9fKxqNant727YozgfOJvZ2wDcCe/B5428lJIj9/vHxUaVSyUUZjTlrMmivmZub8yxXmtB2u+1Qp0qlopcvX3p8HD5kVGN9fX1uLrg/WLhoPIaHhy2JBLCLxWKampp6FqQnyXYiwrAIcAPIZL00Gg3NzMzo6OhIsVjsGVAXiUTsgSbkDDCNZo/1jAKA5y8oZUemDhMUVOwE5fSokFBwsG8j745Go7bBwdQEn32Kd87xRqNhySYeUj57JBIxWEH6uiTvd8j0YelI60aFRyja0dGRJbytVsu+2YGBAc8vpw4C3GLtITXNZrOWuaPwIZcEVv3x8VH7+/uKxWJ+f0ySubu705s3b9TX1wugjMViOjw8NJA8Nzen6elpdbtdN+WoIgCcUSeSuwKzy3uZmZmxnQmAgVwc8ksAvNfW1rS3t6f/6X/6n/T5559renrajRfNSKFQsE0Oa9Ll5aV2d3d9Rg8ODmp2dtbhgdQpsL7sj5OTk8rlcvrw4YOtY4VCQdVq1aAG34u6ilGQ4+PjZt47nY5DPaUe2TQ2NqZUKmV1D3k7BJbCcnPe8XvYiwlzO/9mNOL4+LhrTrKPYOxpfPr6+lQoFNzQjoyMuI4+PT3VycmJAR/OaJ4VVBKw6OxVgMoAjNTCqARhhWFnJT1Lx19YWLCtLJlM6s2bN5qenrZ9KmiLvL291dDQkG5ubpzCD/l1dnZmkIkpOYRSco4MDg7qyy+/9Pk+NTXlervT6SiVStk+hb8bsBVZOntPtVpVLBbzxJvgBJ2gbRDWG7Dl9PRUU1NTBryoQQEeOPuur6+1vLysFy9eWHE7Pj6uxcVFT/0IArUo0sgHYc/g80m97ID9/X3Nzs568gze9cPDQ1v+YLP39/f1wx/+0PZHptMwMpq1iM1Mkl6/fm2VAwQE1iLuX+ybkcE8D796/cm+vlMTzmbBgiR0YH9/3/I25F4Ufvv7+/aUwWJ+m6menJw0gs3CAelmQ6AJDxbrNBuEZsEWsgHTfMEAUOzzsxqNhmU5FxcXmp6eNkLKg8Ic4Vwup52dHactU8zCEvBzkRZy6AdTd9+8eWO2FmkW/uStrS0z1BQUbJagx/V6Xevr626MaAooiPEUlstlvXv3TvF4/NmGPTU1pWq1qmq16k2EhgUQgoIfxhLUemxszBsvG3gQ3Wu3eyNDUqmU0X3uH6gdvm7YF34PnhgSSNlo8TBlMhmne4Nu4z8jTItrFo1G3SSDRNNknJ+fO1UU5pdMAFijcDisQqFgEAcvOb4ZilzkeyCjkuyxRfrfarXMCOCXJrGTpppk3v7+fjOoq6urDrtjfSFZpinp6+tzEQHgMTo66oaH98gzBVsEMFAqlVy0/sZv/Iaur681OTmpg4MDnZ6e6vj42IcYwWOAFzQh9Xpd2WxWa2tr9jOjRGG0z+3trX3pMF2tVsvsuvTJTweTA+DDdeR7OJDwk0oykwazC/iCtJ+fzc+noEHitbCwoFar5REyXBMKOK4lhT++qeComkajoQ8fPphZGBgYcChkPp/X2dmZ09jZ15iggJSS9xUEh7a2tswQ82wFA4AAtWBSSaEPZkN0u71gnt3dXe3t7XmUzPj4uEOAKJRg4NjPWq2W90jAwFQqpfHxcTdiExMTtuUMDAxoc3PT8lFJ/ndQdp55ijWAT0na29tzCBgFAr8ba8719bUZJKSSzMvlZwNmpdNpJRIJKylGR0edfM2akuTnHDVHqVTyDG6a7xcvXnhPZIQmzRgZIpOTk5qbm7O3FRaV4jMajT4DFUnM5kzFj41V6O7uTvV63ZJi9hyeJyTuXBsUPDSbgJ8wvIysQn2A6mBzc9MsEUo0QDZUHo1Gw+c2RXZwrBXPCgVuLpczYHl9fa1sNqt0Oq1sNqtIJOLpJEG2hlGAi4uL9pKjhACowkLC+8PeBmMmyXYcGL9gUBX7KfcJRp9ziudvfHzcjWlfX582NjYMZk9MTJitJVUYYAE7Gd53JOX4Nfv6+jwmFUYfcAMZKz5Z7jkecD4LGQKMamo0Gk6Cp5mDICHhOZFIuDYgEJI9pFKpPMuU4GfwbFGTcQ0BfFCIHBwceMTSX/gLf0H/6l/9K62vr6u/v1/r6+vqdDpmyY+Pjz3qluT9kZERFQoFXV5eOjej0+no6OjIYzMBjzgX8HYDTMHWnwfmeLO+UUuh7KtWq5qZmXl2NobDYYPRMOcoszjTCfykOcT7K/UaKPbsx8dHTU9Pq1qtqq+vz9klwQyPcDhsgJamB9AGwIl9amFhwWcbz/v+/r7W1tacGcA6hmhg/8UTTNOHeom9lfMN+fX4+LjXP6GL/f39mpubU7vdtkceBdTg4KDtgViKBgcHtbGxoYWFBZ1/M4qVkWqA/wCd9/f3VjNApMzMzPg8wT6GHF2SQfd8Pm+CCoBpfX3dBE+1WrWaktwJnnHqu0QioXfv3jnLhBqKbCHOesgbJgItLCw49I/6Y3l52fsrgXqAuKg4mRJAHYycHGB4eXnZ04lYPwCg1G68/2w2q62tLcViMSenQ0Ki+OJzlkolXxesvAT+3d3dGchE3Ust8Yu+fsWE//Kv79SEc7DhZWw2m4pGo24AkXnjZ3x6evJhh1eCopFGF/kXhTIHIgw5m0pwk5HkvyOdOzjCAqRRkpvwYKHPv8OcDA8Pq9FoaHp6Wtvb2/Ywh0K9maxnZ2f2d1SrVaNjoPokj15eXrpQw/9WKpVUq9WMLHU6vTmPFBo0oNFo1I0MzBxeDkZKBP1oSPj4/snJSW1vb1u2DgOFnHJ6etqeZ2RHPJjj4+OW2+GdRWrD++jv7zcCiFQTqdPExIQTQxlBhTIAaRKyJ0JCkD8hnePeJhIJF9LIypFmUaDjWRkaGlIqlVK9Xtf09LQ2NjbcRINycm3YaCYmJswY/vznP1e327UcFxCDApcgPA7/UCjkwxFVSDBkEBkjxT0/k8Zbkmc0MlqETRpp5dramiVJHJy3t7cqFouWHYLaguIzk/fg4ECRSET5fN6MPGtfkkEhNmqCSyhaYR9XVlb07t07NRoNbW9va3R01InQ3W7XQSoEsoFIA5Bw/fm5HL7dbtdSMZ4TDlvGavG8ss/QdAIqUDTAHFHM0CAQgtPp9GaMvnr1ygwS4UkwtoxtAVzENwdbjkcM9hn2m0ILr2S73TYQgrcUCWO1WvXehdSS+ef1el1ffPGF5yOjMkA1s7GxYVk480Dx+7L/BC0QoVDINhcSynlOu92uTk5OVCgU7CG7vLy0rSGoTmD9wkDQVKDaQSqXSqW0u7vrUVbIJvv7+1Uul13UwyoBhFYqFTeKqCNoGAhaAijDv1mpVBxuSJEA6IGCh2aer0OuHSwoCKkLPpc8h4BQd3e9sVMEvzUaDXW7Xc9pjUQiz0bPJJNJh97BhANELi8v6+PHjz7XIpGIVlZWvJ+wP9NYwvjR2JKCTODQ09OTKpWKQ6soegFJOEfC4bDBX9i/TCbjvTOfz7tAk+R9nHnKNEI0bzD47XbbLCVnRH9/v/cagOSXL196z02lUioWiwaFAT4JDQuOJkMVwGxwnnP2dkCd/v5+ryck8dVq1VMkeJ5REFCY0lDx78G9KAhe7u7umpmcnJy0Z5agsr29PdsE8JF/m/nHg5zNZm1po9FjnbB/cEZzTnLvYcxRrjH9JZ/Pa29vz7kGfAbOQ8IUa7WaAX8C5AggRWVEXYT/HGYeuSuvbrdrbzmqLt5To9HQxMSE0um0/dtBpRh+XVQqZ2dnur+/1/e//33t7e25geEcAECmmUDWXiqV1N/fr1wuZ4JkbW3N9eXNzY1riGDGDfOxw+Gw8vm8x4LBktMQ4o+meWXGdKvVy/4hhwDQlLGqMzMzur299bkFKI7Fj/UImIvdEHYWQI0pDaTjY5sBjEulUg49Y48fGhp65t+l5uF3BmsU2NLNzU0tLy97T6KOJlAMtQbPxcTEhPb29hzoBdhL8y1J3/ve9xxwyJlD7YR1cXh4WHt7e8/qx2KxqHK5bDUCAXOMPEW5xpkMmEKQ6/z8vEm0+fl533OAJUlW5bIWx8fHfa+YuBMOhz1Kmdrtyy+/1MjIiGuL6elpjYyM6PHxUR8/fnTK/tnZmZlrwtPu7+9dT9ze3jq/in2a+95ut5/tJagr6DMAJ1utljMbOFdPT0+1tLTkzzkxMWGlBtONIpGIg13D4bCWl5cNeHC2YBkrlUrOeGDSwi/yghT5k3z9afyO/zte36kJ393dtZSEBQs7ykLm4OHfYUCC4REgbt1uV9vb2974g5I2Ljj/zSvIoLNBPD4+OiwkuNHxu2AF2Awpiikerq+vVSgULAVH0oL0G4aWz9TpdOxVRRYDK82Lhhd/GhsdhyEHFMVqLpczykhwWDgcfubDIWgl9s0sSEn2Ix0cHKjRaDhchJCzZrNpFpINgcRIUOVgYnK32/WsUeYos5HDKsPm0aDu7Ow4BOvh4UGJRML+LjYbDgkS7AlUQcZHMY28Cl8mI4CQNLPBoXz4+PGjcrmcm5XJyUltbm4q9k0CNsVmo9Fw8BEhU1NTU25MYCgIrQB1DCLNNBXcZ5q74JxWlAGsX6TDeIYpDKenp/Xq1SuvM2aCY3WoVCpO0u92u57BCQvDz6JYR1ZJ4n8QtKKJ5f3R9DIuIxQKWR5HAzMyMqI3b96oXC7rpz/9qdLptItXGCSpl6KOTA+2hkKeRigcDnuuPO9BkhH7YMEJmMdhOzQ0ZEsBzAWFMFJH2AwK9unpae3t7VnWiNpjeHhYk5OT3r8InqHZpCEfGRlxgCL+uoeHB8ViMcv3kMizB9HIZrNZzc7O6ujoyGsbNhLZM8U/TRLMwvHxsRv5t2/fujBmXTMaBr80xS4MMIV8MHm2Wq2q0Wh4PCK2FaTXFGdcHw5jPPYAS0j6M5mMDg8PHZJIgcz1lORpFOy5uVxOkrS/v6/9/X09PT15tjiy242NDds+BgYGFI/Hn82hRo5HIwTAS+P4/v17zc3NqVwuq9FouLAALIAxIb8ACS1ZDplMRhsbGzo5OdHo6KguLi7U39/v0WmMd4QdkWQgmrmw1WrVjQoF6NbWlhNz+b6TkxMrcVBH4A3sdrsesUVDzbPD9INglklfX5/W1tY8AhB2MB6Pa25uTru7u17jyPlhp05PT52OjZKEPARYo+vra21ubjqMkrM5WNTx3HIe3N7eWnVBUcmUg7OzM4+/6evre5bAC4B5dHRkhhGWeGZmxvcKEIg9ARUaQbDIQwG+g2cYTV2Q/UaJBEmAVxRQKBKJuOHCrsCoy6+//tpMO+AkP/vh4UE3Nzcem4YslbOQwDWCVZkEAkOF5Yb7gnoMtrCvr++ZBYY9FH8wFoePHz/q1atXmp6e1t3dnY6Ojqy8ury81OLiohYXF5VOpz0PmYL/8PBQlUrFdQzP5/e+9z1fD9SHSPCDwWSsj5WVFY2Pj2t3d1fX19eKx+N68eKFIpFeyCOKsXq9brUafldsVmS30FyXSiVVq1U3QKlUSh8+fHAmBYA0VqtYLGaAltqDfZQ12mq1tLW1pbu7O4dwSbJNYn5+XkdHR89skgDZJPtjQRscHFQymTRgRmOP4iqoBoCFhCWenp72pKGHhwdVKhUtLCz4mQPsuL6+NrHSbrc9DpNRZKwh7CHYSJCeDw4Ouk7herCn9vf3RpyiDNvb21M0GtXnn3/uZ+jk5EQLCwtukFExMVUgkUi4BoRAgRUHZE2lUp5QhAf94uJCx8fHymQytrg2m02PK4SA416ztrvdXl4Izy1ZG5AGl5eXGhsb09nZmccEHx4eqlAoaHFx0fs3WSM/+clPtLe3p/fv32thYUHhcFhHR0e+dsPDwyoWizo5OfEIwP39fQPFNMeSbAeFpATgCubIBBV4WAD5TNfX11pYWPCZT9ggz/re3p5GR0e1tramarXqKUEE2BaLRYcWQ0hgBYUUQ7nDmfSr15/86zs14bxY/CMjIw7HQqKHrGJ7e9sMJEEfsW/GOvAzGKVD8YKElweMQ5niJXgo8fU0XzDvHHBBbwyNAYwr6HeQgcBnwWY1OjqqQqGgmZkZj3UByaZp29ra0unpqQ/HIHhwd3enubk5y9pgzWEymfkLQ0rBTsHMocbGiKSeBEVSKS8vLzU/P6/19XUf1PjpYPMfHx897xLGgTEvQfnd4OCg57dz/UE8o9GokTq8rWwwBFJxf/D5Li8vW+0Qi8W0t7fnUCkkw7xXAn+C0hiaD97j1NSUw7weHx8tz4X5YHNZXl524iPMD+sBBrDb7VryeXp6ahkOjDahJE9PT88asaenJ0tpATPY/FlDNDA0OKzZ/v5+5fN5PT09eezO0dGRLi8v7RWmeSCEjeTR7e1thcNhyxhZ10H5GuBHkL2ABeD98ZxwoElykjfXkAIsk8k8yykgHAtgAcSbBG+AFxrT4eFhN5/1et3NRyj0KewJWSnA0+3trQtY7AUAH/xdJBIxI8kfmvurqyttbm466XhgYEAzMzOWntHQBC0ueOZojkGoaUwfHh6cjkzwGPI92POHhwctLCxoaWnJzxzvgaIRiS3POp+X/W5ubk6np6dmUkh65T6SWhwM/GPtS7K8OxaLaWhoSAcHB55PTCNAhgOKDOSRsFPBDAgYl5GREU8qgPU4Pz83+99q9RLV+/r6nDpPsRWPx83Ydbu9AM+Tk5NnoxcpAGZnZ/1sUmxmMhkDBzRbm5ubHn9FQjWfE4UBnwmW9Obmxoodkq+np6d1eHho1hs1Auoc/LW1Wk3v37+XJC0sLDiIEjA0GPrIzF7OLc4mikuKLN4LgBeeUxg8CmhAM+SRJD1zrev1uj58+KBCoWDPcafT0fHxsRsN2GupBwAgg2U/xB8IU8N6omDEMgEQub+/b/aWvQ7lDiqKarXqbJZ2uzdOa2lpycBuuVw200mRLsnnSnDvBLjkDyABTTWNDT8jeBbRBHJO8dn4fHwGGtirqys3RxMTEz6juQ9ch5ubG5VKJduVuG/8Tv4JGIPCArCFM5QpI5FIb8Qp4VJIq/Hxsib4HKhyCoWC1tfXPVmiWCw6h4bAS9hnnj+CdUnfRx0C0MgUk1KpZMDv/v7en5UsHPy0sIGs03A47DGFk5OTVtkgIedMQnl3cnKi3d1d78HkQ1xcXGhmZkY//elPrcJ68eKFxsfHdXBwoMHBQU1PT+vFixd6enrSu3fv7D2HJdzZ2VGxWHQzQoo9ykA8vqxTvLUEu7GfLS4u2vKGDSFoCyA5vVgsOpQVlR1WL9RvNKOcrUNDQ1pbW7PyIwi8EGqGpPzp6ckA4dDQ0DNQB4kyVkzsPwAa5+fnenp6Ujwedw3IPYPd3d7edl4EIBH7YSaTMUkCIcWeRd1Gzbqzs6Pd3V3Nz8/72mcyGVUqFZ2fn+vVq1dWCzUaDS0sLJjZpz4ms4EJQqhkuF+bm5tKJBJWlrGvN5tNVSoVpVIpNRoN25zwdkOa1Ot1K12CeQKQBcvLy95blpaWVK1W9fnnn7sG5toCZqFUbLVaz/oeSBvUBZ1OR5OTk37WqXfX1tacss8+hAKAfgU1Qjwe1/r6urLZrAkW1hzvP0g+MXaN8Xzn30xkGhgY0MHBgYF4iBRsFL/o61dy9F/+9Z2a8Fwu96yZ5qEgTIRxKoy0kXobNkmsBG7BjCKLjsVi9oGWSiVvHvhTeOhh4IKBVJK8ATBmQOrdMKRAkixNAfWGVaPxoXmGaby5udHu7q4ZrrGxMcu73717p1wuZ+k4rDISbNB1WIzHx0fNz89bgsi8w26363T0Vqvl0WdIMi8vL51ES2M8MjKiSqVijzEptKRN1mo1FwP42UAdKUDOzs4sV8KTfH19bRkaBS/SpZubG83Pzzt8IxgmA/LPIcbv7nR6c8lhKm5ubhQOhy0bo/Hg+yn2WCPz8/NmcJDtkyZPgQzqy/qg8aMRhB0Jetz5XBSUqVRKx8fHLmwkGe0OJtmznr/3ve9Zmtnf369EIuGE2sHBQTf7FOAUeo1Gw8Eh+JTa7bZevnzp4B78pox9gfVhvio+o3A4/IwZpjEqlUp+NoKe6d3dXcv7YU+Z1cpGSyIoibWg+vF43KguDDp/kDLBNLB2gmFqhBlRyMOYco+C6zqYc8D1ofDhee92u5bo4RPkXkqfxjuh5kDmBWuLqiWYa4C/l70lmBLMZ85kMtrd3XW2RbPZVCaT0crKikZGRpymGg6HdXh4qEajYeDg5cuX9tQibafhXVpa0ocPH7S4uGh2WOoF1jGGis+DAuXFixdqNBpuyPnnycmJDg8PVSwW/SxlMhn7GgGUzs7OLFlbXFw0at7tdg2oNJtNjyF6fHzUzMyMm8jDw0OnzMIOHR4euqGCdYzH40qlUtre3vb/A4hBlg+rwNiZWq0mqZevwAzd/v5+7e7u6sOHD57p3Ww2PY4J3zeJ/YCUnFmoJ/A9x2IxS/pDodCz8Tw8W9KnIEPOFN4/xTshc3gWeW5g11KplOWUrBFAJIJM8a3zvEQiEYPS3He8oBThl5eX2tzcNFiFhBJmjAY7GEAEOEhmxv7+vm0vFLCJREJ7e3t+HhmHhUKBF00ZLDbMFQUrUmtATPyUFIqMpKTJ53nH+47FArYIawdFGHsHTA5AAg0FMm/Ydyw93watwuGw99tCoaBQqDfKkcTnVqsXbErTChh+fX2tvb09rxNeABO8t29b7E5PT52VMz8/r/7+fiugaLSRpF9eXlrqzOcJh8Mek5rP5w2IMkkBDzcEQTKZVDqddu3y8PDw7GsBPk9PT3V0dGS5M9cUdSEqN85s0pynpqYUiUQ0PT3t2osxctjaCG5DOTg7O6ubmxtPI3j//r3Gx8d1e3vrHJKDgwPvdbB57KWMphwcHNSLFy90enpqmXqr1dLnn3/ulHNsZ61Wyzkx3W7XzDENSV9fn0F9AKEvvvhCe3t7isVirg+4/0NDQ8rlclaiNJtN5XI5HR8f6/37926iUDkF532PjY2pv783LYSf12q1rDaIxWImH3i+CVMdHBy0ugTLYlCFRM0TVHyyrzCiFOC42+26vkSl+v79e6XTadcH7DeoKMmGIbUboJQMH8CAVquXIE+oJKwu+346nbbqByIFsGRpacl7OPvB/f29a2YIL8bh0sgHCTnAWWoRApFLpZLBLqwx1J5kH6DKY9oMxOLj46Ovb39/v/b39x3CNjAwoEKh4H2YsyCRSGh+fl57e3uqVqu+TuyZZElQb3z8+NFkEIBGu93W1taWLagEGLOuz87OtLKyooGBAW1sbGhmZsaAFs8BmRDNZtN1LplNANfT09MeX3xxcaGFhQWrJX71+pN9facmPJvN2ssCA9ztdv1g8eDjGcJzCoPDgPlut+sihQKJ5iuVSllSSGFMeAeHPt5RigykabA6NEjBBNfBwUGFQiF7MAiwwWsp9R58WBxQJApRZJH44CQ5RXVubk7NZlNHR0dmISmiVldXNTw8bHkhRUnQB81mh6QQSe3o6OgzaV1Q2k4hSPjN0NCQHh4elEqlXFwCiEiyp5x5ucj8Hh4ezHhSFCFZZDwbDRezDmlMg0oDDhik81wHUiFprtgYQHcZr3N2dmYlQjwed0IoDC9NBXJ2fMYgyrxggzhokZsTdlOr1VQulzU3N+d1SDHG+BrAGbzQADGStLW1ZSksY5JgvdjQ8C4HDzwOW+b3wv7jG6Jwm56eflZE4g8irAOGHyaBAo8mi3tCoUkzHlSQ3N7ePmMimHkryRszjCrMIEg6rAkAzMrKigtvivB8Pu8ij+uHn5OvI/kV5UmQ6WFdN5tNJ9cTUMbBys/FrwxzGFRYUKxQhO3s7PhgYz48zTv3iqYgCHIE3/Pq6qqazaZOT09VLpctRex2u1pfX39mH4GJglXLZrMO5frss89Uq9V0d3enfD6v8fFxS+do7hgrCHsPoLe+vu5r9+LFC8XjcecjIDPLZDJm+C4uLsy2gqZzSCOXDYd7o5AAw5LJpKWpNG7JZFLFYlGNRsOhN/Pz85Y0397emqGioDk4OLCtJzitAuAsnU5rfn5eHz580Pv3770OeLZvbm5UKBS0sLCgzc1Ne9BZC5wFFD6SvD92Oh03rxSdQS848mzuMw0bQW+sA3INYJODwE8kEtHGxobPmoWFBdVqNY/8CmZpoAzB/oFXOLiXwgriXcYOQPGZy+WsDAh6mXd3d/2sd7u9MDVksdFo1L5i9l5sPTA8p6enzi8YGxvT0NCQQ+fwQwL2sVaWlpZc2GLJAmDDDvH09GRWbmNjQ7FYzEoCQGDuAdcQRRn7M/uC9AnkCP6hKadwBeCGYSKgNQi8Sb0sGJhOAPKJiQnLQS8uLlStVn1WsQ+htADc4BoH7UrB4LOgTY264ePHj5qfn9fa2pqtGKjQpF5Y7fn5uS4uLsyc85xRfJPIPzQ05Pn1BCeenJxoaWnJIBYMF/v6/f29tra2fE4yzSCZTFoWe3V15XOXGiIajWp1ddVnPSo5nqHNzU3d3d1pfn5eMzMzGhoa8lok62FkZMTKAPaEQqFg8oUGi3P1xYsXqlQqJmjOzs7MMOdyOQ0MDCiVSmlyclJTU1PqdDquFWu1mra2tvTFF18Y1Njd3XVo8MPDg37yk594r+l0OlpfX/f5wrQZiBiYSeTpnNPUTsimCVuj7uvv79fR0ZHPdK4pSkMCs7i/APs844zlpJ6VZBkxNSW1R5Cp5llaWFiwDRB1FWOssIzOz8+7MWS9AIhls9lnKk9qEpo6nlXqamwRqAGoXznPrq+vdXd3p2w2q/fv3zuAr1Qq6enpyVNQYO6r1aoymYxKpZKJPGxTNOCHh4d68eKFbWNMVhgbG9Pbt289QaDZbCqfz6vRaJhkQt3w4cMH9fX1aWpqyqrEiYkJlUol/46hoSF99tlnfjaoD8iGOjw89P9jXGqn0zGpiJVBktlz9jkmOjQaDdtJqPXD4bDevXunm5sbvXz50nZK+gcAHvZ39gmUjdQFEC+lUsmkFfbZTCbj8FNsLb/I61dM+C//+k5NOIhwvV6XJHsG2BiQUcViMR+iMCM06IeHhy7+PvvsM0uLut2uZ9lmMhmPz6HhxTdO+iBFiNTbjGZnZ5XJZJy0uLKy4ob14ODAkjsOuSAjR8gJqB6NDVLXH/zgB2q1Wjo+PrYMlbTFSqXikVlTU1NuGjiYJicnFY/HtbGx4c2JAyYoi2+1Wg7Hopl6enoyw8pnp4manp42Kk3xBNPCiCWQa8aCEJyVSqWUSCQ8huf29tbSu2CwBlJivNJ4VNgwuP8kdsI20AwQHMU1wM/K946Pj5vVx4ePfA7JIQfN4+OjxykAuiC9pwElQOP6+lpHR0eamZnx97N+ARbw8vPZOZzevHnj91CpVPx7yDp4fOzNsaSJpbkMFiIcSjAjNAH8fSgU8hiOy8tL+3VRUlCIYk/o6+tlKBweHrqJQfkBQ8Tv45lA+TAyMqKFhQXV63UXYKCtyP8uLi6cTfD4+GjPHkU7iDvSWzzZMJLxeFyFQsHMQSgUUi6X0+7urg8BmAy8uIODg/53ACiCHvks/I7b21sfnA8PvTFThEEiz8PfDACFcgEwjaIcxQFp0ZOTky5oKXpQywSvJbaH6+trlctl5XK5Z6MXJT0bI8XhCLNEgvHV1ZW9pnjUksmkZ7vHYjGdn59rfn7ekjwYI5qKSKSXXj01NWV578DAgHK5nIPvCC48Pj420NVqtcw6BT16FAhTU1Mql8sOlmLNrq6u6ujoyBI/1BuTk5O6vLxUqVTSysqK7SQkntPYoKLo7+93sdpsNo24M2qQYiOVSjlBmVwC2GJSp9lLKKyxZKCOymazGh0d1dHRkffjvr4+s9bYNlChSLL0Fwk6BfbLly8dfJhOpz16p9lsOlkcVuX169fqdrs+/wALBgYGtLKyYoaLdUkBDBDXaDTMumFF4r1RzMHyU5RHo1EX8bOzs9rY2PDXcY5wbtbrdZ8z5XLZqbrtdltzc3Nm+/f29nwGAiLDGMEESrJUMpjQjOqG98QZ3mw23dRLctMK8woYx7rhGiDrZn8Nql64JpIMOvH8A6jS3BPcJ+lZ8RmPxw1404B0u13LTpnLC9iDTJx8iJGRESv52BNRsFGnIE1FEdbX1+eQV0AeXkyLwTIFAICCI6iUAhxkD2TdEpoGmENtg90kHo+rUqno6OhI3/ve9zQ0NKTNzU03jDD0nOnIqfl9MLA8V2dnZ35G/tyf+3O6ubmx3xcbDN5ZaoJGo6HZ2Vmtr6/rw4cPBlOenp48AnZyclKSnEKfSqW0urrqZyVY/OfzeVsPGXvGWgqFQkomk9rZ2dHFxYWazaampqZcA/A8393debRksVh09kE+n9f9/b3njKfTaTdp1Cejo6MaGxvzNCDOe2xsw8PDKpfLtqTMz88/s5aRM0DmBTYFnn3yCCYmJixVRu0ViUScog/AFbRFhMNhg7Ccd9xPQgNZF4BePP+S7G/GV88+yzpFeQrwyfNBfQ64QxAdzyx1H6PQWPNI4AEreJ6xPbG38JmSyaTl5CjqgkTEixcvrNTlbKfWCofDfhaxmklyAGKj0fDIQsBv9oOgonBgoDcVZXZ21nUt9x+LAvsZ1xbij3yTL7/8Uq9fv3b2Vq1Ws4yc94SNjboaIKVYLHp/SCaT2trastr09PRUyWTSBAFEGTXg9va2SqWSHh8fTcoF98tfvf7kXt+pCafIh0mgIKKoIyQM/wbzhEEPE4mEGVJQRJqa82/GR5Gmvrq6qnQ67aRWikkeEjwxeKMmJyfVarU0Pz8vST7U8/m8arWaJbU0ssh88RL96Ec/8lxU2ICbmxtlMhkNDQ1pZ2dHzWbTDTkIF40yzQDeOORf+Mw4sEARs9msg1soWGBIKchB5Un1BjSAFUPSBLvHXEoYs9HRUR/wFL3dbm9UT6lUcpgVm8/Z2ZkKhYI+fvzoOd1499jwYCLwm3M4kFQJEALLLcmNB5Ivkk1hWGq1mlKplEqlkpkS5mGC6NJ8s3mA9o+OjprBazabSqfT9i8NDPTGnCWTScuECDQBvWY8BEwGhw4WCw4LCi08psiegs03Cgt8t6D+bNwwrXiOYfR4zwBY7XZvpjLKCGaLI2nkkIBt5H0HZfgU/91ubxwJ15VEViRiyMIoDlgPFDRImnO5nN6+fesmgcKOMVx4xSqVihuuRCKhUCikUqkkSUbCAXYIiuE5JAwGdo/iAb8W6bYw5UwT4LOjguEeETpIcUAhDEoc9JUG5YY0L7BqPGOPj49OdOYenZycaHp62oFK4XBYHz9+9PxxvG6kuDIGD7k8hy0FFT+DtbO/v28wcnh4WNlsVmNjY2Zxg9JDpJKMhTs8PPS1qVQqymQy3rtRJVDM3N3daWtr6xlQ0d/fr/Pzc71//97FBCBHp9Ox3BM/6NHRke8N94PiCf8m+yHAFDN9g4mxV1dX+vjxo9kYknnxR9JcPTw8WC0TZF4HBgaUz+fN8OM95vtp6JAYc14AugAy9/f327t3enrqcBzUNYQ2kW3APG/2RFQj3KNkMqlkMmnPO9MHYLaQhbbbbadqB72GAGPFYlGbm5sGt1mbAwMD9gDG43Fbk+7v77W3t2eGTJJZoqurK+VyOUvyefZQIKHoaLfbBpeQLhIWGTzzuK80pEFATJILWJpCSWbmsD5IesaG02izX/BzAeuCTUHQDsW+LMnMGZJO1rYkpw2jnnl8fLR6j4YT4BEWCVCIhhJQi2cEYIb1FXy/kuxFpnhnr2O9ooaiKeYcpME4PT01IEL9NTc3p62tLW1vb1uGnkgklM1m1Wq1zMDime/r69MXX3zhRq3T6ej169e6urqyaiQajRqkGB8ft/+aSRwPDw8ql8v6N//m3ygej+s3f/M3NTc3p42NDZ9F3IeNjQ0dHh4aIAAQ554BHAL6w5QjM45EInrz5o3T3QmFA9ii+SbAstVqObmaZ6darbo+4jnj3GCPLRaLrhFrtZrvxcTEhJLJpK8fPvlMJuP9hUwkAKbj42MNDw8rnU7bZ352dqYf/ehHrovZu3n+SIjHCoYSL3g9AYoBNXlWqPuenp68fthbg899kH1nn+VrqBs4P/ldnCOoqthzUGLC2sPGAuQmEgkTRqx/PNWcY4DPnU4v+ySfz6u/v98KMc5JmmhJzrDgfZAYT3ggikTY/Xg8rqOjo2fZBJKegWuLi4u+HtjXIHqwMu7u7tpqwV6WSCSs/uMzvnz50uNUuc/UEgCVQdVN7Jt56SgUc7mcVQNcZ76X8+b+/t5nEp8ZFWE6nXaIZaFQ8D3qdDo+g3mGAULprVCi/aKvXzHhv/zrOzXhc3NzikQiajQabo7ZMPAk0+ghx0bCzOLsdrva2dkxWpNKpbxhj42NaWdnR0dHR5qdnXXCNWMFWMBB/ygHMyE5SMUeHx8thc5kMpqZmdHm5qYODw/18PCg1dVVSbLk6w/+4A8sT8fTc3d3p6urK3399dean583k9JsNrWysqLHx0ft7+/bv8lDTSLw2tqaZbsccjwM9XrdcwOR997e3mppacnBGgRJPT31grQY4SLJskZk+JIsgWLkT71eV6vVUrlc1tDQkNlqEP7R0dFnrE6lUvFIg9XVVfvQYAy49rDDHG54PzkM8vm8SqWSEomEpqam7EfCu09Bsre3p4WFBTOjNO4wD2x+9XrdgXU00jQDrVbLPljCpMrlsuWfo6Oj9vfCWMPuXF9fW1YYDvdG+vz4xz9WqVRy4vvk5KRVGow8W1pa0sHBgWZnZ90YI4sGWe7r69OLFy+ssIDNgVWNfTOyi8YcjxzFD8E85+fnZgnwAXFYcQAHZdps0BxUwYN7fn7ejT+BfjTJpLK2Wi2HcV1cXGh4eFjLy8u2mNTrdV1dXfneoG4h7f/i4kJTU1M6Pj5WoVBQu93WwsKCm1eKVcKyKC75DNInlopCYGxsTNls1hIvchU4lFCJSLIVhc8sydeIAw8lAzK5YBHD/aCQp6iA9aAo5RoGw6FmZma0vb2tWCzmJHaev6mpKSWTSS0sLGhsbEzn5+e2z+DJQrqHTJCmn4YeO8r5+blubm5cfND0hcNhF7LIBGGGGMvy8PDgJF3Q7uDeDVJP857NZu0zAzxg7aEUIdRpeXnZydbBzAvef1BhxL6NHxYpJcoHGmakpsjmO52OGzmCgShG2Pdvb2+1vr7+rBkEQKbY4HOyvyHrHR0dtZKI93l4eOh1ioWHUDq+DvDn25YMxgsC9OHpZV3c3NxYHowfHt8xzF+lUpHUC6YkzZjmA6khoFW329Xh4aFDt2BCmCHOtQqHw8rlcmo0Gtrf37dlp9vtam5uzveeQEjC4MLhsJlE9gueG54VilA+C38HqBVsknnOAW2+3UwH7w/fiw2OZpXv5+t53d/fW41Eg9Ltdv38AjZRqFcqFduSYLX4zFIPQNje3jbwR4Iz2Q3UI5wFfBZe7EeozwBR+DpqpOA5m0wmHQzHnPfgWVoqldRsNm1DYS9AdVSr1XxuZbNZDQ4O6vj42Kxvo9FQqVTyPsOzh/qKwEDe//X1td6/f693796p3W77Z+ZyOf34xz9WNpt1+Bb7D2Ff3W7XWRjsDzSSsLihUMhAPrVKNBrV0tLSs1oSzzJqlXq9bjXJ5OSkFhYW1N/fS/deWVmxyow64fb2VuffjHCE4fzRj37k0Vg0zIuLi4rH41bOsQ8wb71YLEqS7YoEROITx/N8c3Oj09NTj7nq6+uz7QOA+OrqyoAc+xRNJ1k6jM8KAqVcZ9SA7JHBhp2MG+xOWA2QbvOcXlxc+GcBCE9MTOjo6EiRSMTKzCCAy5oNWnpY15BI/JP9sdFoKBzuBW+efzOestvtWmna6fSybJrNpvr6+jQzM2MS6O7uzmQBQZNYfcgcwerH/gxgsrOzY2AOEIR6JxQKGXSAsZd6AFq5XFYo1Js6sbq6qouLCx0dHalQKDhtvVKpWC04ODio+fl5xeNxT9zhmeQF+B2Px71eAQsk+fzCQgs4wmSbp6cnxWIxZzRRN5LvASCOTQgwmn6B+oZ6kcwQFAu/Skf/03l9pyacudT39/f2MXHwsBmy0ECcQaWYL319fe1ZqOvr62q32x6pg+S7Wq06Xfvy8tJzdBllgFcIT1DQTxYOhz3vMjj3ttPpaHFx0XPJkXmRFg7Dz3gokHok0dvb23p4eNDr1691f9+bfTo7O6vR0VF7tfCZwgwGg8NarZY/P54/RiSwqSDHh63c39/3XEKuLem7fNaLiwvP3CThFi8ID9fNzY2Gh4eNinJAkFqOfz+ZTHpcUq1Ws5QdFj04P3ZhYcEe2EKhoOvra42Pj2tvb88+NhqrUqnk4CAQZAq4q6srXVxceO5sNpt1ony329X4+LilwhSdHNbcs3Q67SYChrq/v9/NOpt6s9lUs9m0BJbih0KVsKD7+958awrhXC6n0dFRS+p3dnZ8mID20jRQLN7d3VmqSvPJpgcKS5FICjesANL/QqFgqT5NC0VjMpl0cAtFHYcKgEa73dbk5KQGBgYc9hQOh+05qtVqRqRnZma0uLioer2uzc1NJ8bf399rfX39md8LtqjT6bgZPzk5sSoAnx/3gr97eOiNs0P5QEPL9YcFk+SGqd1u29YA6BOUz8FsEjY0NzdnsIVxPLwXUGR8fpJsQ6BgaLVazoEIsniRSMSSN+5jJBLR1NSUvZHsPYzf4yCnsWfvAchEjg0rh02FMXPj4+P2YCM7pnGlCAvKqWlmAe6QOY+Ojhodp2jCyvHq1SuFQiGHW9FUn5+fm2ECGJiYmND6+rok2f9NuFo8Hlez2bQ8HPa42+1qdnbW/njWP6g214eGCtbj5cuXvk77+/tmdSlIabwGBwe99nmeKE6y2azevXvnNdVu90b40DSiVqK5ozAFHIFZbjQampqakvTJghWNRn1taYCx01A8MSYpHA4bCGw2mzo4OPBYTKTG7Ec0C6gnkP22Wi2tr6/b/9vf369sNmtm/f379wqFemFmsVjMNpbZ2VnbWPBODgwMOBCU4LHHx0cXlZx53KfBwUHvndInlRnAFXtxEPiSPjEXwcaa7+Fes6/wdUEZelDOjeWNNRQEzfj9QV8uTTx7Cfcalo7fydhKfiZBnUxtqdVqqlarBp5hUAEVeb8AccH3DVMOOEITxJ5I88WewP7Eekbay3UkgwKLE5YPPi8gJp8V1pvAwp2dHQ0MDHg/fHx81Pv3732mcg0GBwc9zqi/v1+1Ws2ZA8wO5hr++Mc/9lmbTqf17//9v3e+C896LpdzzgzjAlEjnZ+fa2xszPeWZpDrlkqlNDU15dGt5+fnnsHMM8O9qFarXp88S4wI+7f/9t8aqJyfn9fS0pIJh8vLS0vQP378aHCSlPZOp2NCKBQK+fmNx+M+P2l2WMNYE5HrPz09eS43ViTOUCZocF9Z8wcHB5qenvY1Ye2enJw4KwPgEUCDDAPqQEBYMkKoI9mjsBKRX0GQZLfbtS8Yex6WCELu2Hupt/msqEAId+W8QBEFM49CkvGmsMobGxueEASrz/pEycnvyOVyz5RsAJ8AtjDUAwMDmpubc9p6PB73/hBk94+Ojqw4Cea0kCj/4cMHzc7OqtFoKJVKeZ95enqyRWd4eFgzMzPOX6L2Y89hz6I5p06lxsRKeXh4aFUSLDmKTGwFsNnBOi+YIcKaZ50DwvD7yQihJri9vdXU1JRzmX7R16+Y8F/+9Z2acApSDvpms+n5h9Fo1M0yA+NTqZSSyaSOj4+djphIJDyahqIHaQtoXzabNQsE6wCqg9zi6enJvloOQH7e5OSkR0fBCBJmw1xa/l+5XHbRyny9Wq2m+/t7h1HgU6W5gSG9u7uzXJeROWx0pVLJG0OtVtP8/LwPGUYdvXv3TktLS8pkMj4UQXv5OTc3Nw5roOAbHx+3FIfGDZQZyTTFPDPb+TqQTa4jwUvIz0HyYNW2t7eVz+e1vLzsUVcUmkF/CwUIs1pjsZgGBwfdQPCz4/G4jo+PjRo/PfVCOHZ3d+1ZkWQpKTLxqampZ+we4RNDQ0N69erVs/FAXF8KmpOTExcwSKTZzGGmQBB5b8jJy+Wy9vb27B+uVqvP7ivFD4cLDRqp0dvb2+rv781vPjo68j1ggwS1pSkA2ELWHZR40nRIveYRBBQPeTKZ1Nrams7OziTJDDPWA8JSbm97owFHR0cdIEVxQFow4BdATLfbtbSOxgRU+PGxN6qFIiIoVyeHgOav0+loeXlZmUxGp6enLjxh0iSZRWw0GsrlckokEnp4eNDk5KRmZ2dVLpdVKpX8bPN78/m8MpmM6vW69vf33RBwENEkEY5G88pBzbrmPjK2j+sOuMh9RK0wODjoxFcYMuSWgGP1el339/e6urrS6uqqzs/PHcoH0/vw8KBcLqdsNuvCLZfLKZ/P205zc3NjJRBFPfsh1gMyKMrlspaWluz3pxlot9vP5r53Oh0/P/F43FJ5GFhJtjPQJKNyGR4edlHW19dnsK3b7VpGB9t+dnb2DECgKKHYphF+eOiNdOJFcBnAKAf+zc2NXr9+7XUFGEtThI80KF/mxbkRlAsHmznpUzEvyaPjUF8wWgjVB1JlZsfCZAIoBhN8kS0fHR2Z3WbWMUwJLODR0ZHfv6RnPnfWAk0PCg+sPME0ZfabSCRiiTN7D1axbrc3TUKS9vb2lM1mrX4ZHx/X+TcjjiiCuS9cO9ReQZZb+pRPgVqAF+w13899pZnEPkKTQ0FMsyjJoGdQEdRqtSzDlGQLA9Y31gjMFxkb7I0ENtXrdftUgyx2cC0CykA60CzglQekjUajWllZseKMENX+/l5gVzqd9h4hyRJorg+gNWuDP4CznBWSnq0X/ju49r/9HEAUwIS9ePFCMzMzuru7089+9jPt7u4auPnxj3+s8fFxh7MuLy9re3vbDdXIyIgODw99joyPj5str9VqqtfrTlPnOtMAwSYC2IbDYVuxqDMBNFFqQGzc3t7qhz/8oZVChJiyriUplUo52yWTyTjTI5/Pe1woQDYp3bzOzs7MBF9dXSmTyVgFls/nvd9xVgGEIEU+OztTt9t1oBv7PWqzaDTqdGxYz/NvQiVDoZBB6w8fPljpFGzYO52OR3ABeFCDB2XpKGeoE4NhwoQjj46OOmxyYWHBdRs1RjDwjCkNMNkA78j0abiDY714vmncyeLg7B0dHfU42vv7e1tK7+7urHij8aVeAwgAGCErheDlbrfrkb48KyS6Pz09GQwiWySdTqvb7VoBQL0C+bK4uChJlqajUCBIrV6v60c/+pG++uornyPsvbwHlAHUXtQprHWuKype7FFDQ0M6PDzUzc2N2u22MpmM1tfXlUwmHUxIthRE293dnSqVigmCm5sbh+MBnlALQf4AoPzq9Sf7+k5NOMzb09OTvaR4FpD/4sOl4Xx4eFA2m7UXp9ls2ovX7XbNgHKIsxho9pB28/CB7AWDJCqVipnbwcFB7ezsGOnE90PAA7Lb4+PjZ/Oug2zMZ599plarl2QNIseIKfzFBCVQoLC5nJycOFn79vZWmUzGjSOFM6Fe0WhUtVrNkvCgf5w0W+nTjGAK1WDY2NjYmPL5vOWpx8fHikQiliLSRCAfR9rD2AekqY+Pj046vb+/1/z8vKLRqBKJhIOQ8D6Njo7q8PBQCwsLHvlEYB9yNtKd6/W6kW6kTwTNgdTDzl9eXioWi6lYLFp6FJQbXl5eOmEaNG9sbEzNZlMTExMOOgHVHR8fdyNwcXHhuY8Eh8B2cc+QmyHZPjs7c7r2wcGBG1mKHVg0Ci3UIFzH2DczV5lVGfTiMYOYOaqS3NDgd2XNEiJCg7C0tKRaraazszOjxKC+Uk+2SkDh4OCgpqam3KzhUQ+OiWGzJ7CDgDvYMvxI0qeAKJiYra0tN10En3BPz8/PrShgPSaTSSfAB5sa1mgkEjELj70A6S6AA4ARByYefQ7NTueTX5l1xfNEYc66eHp6esaoc3/5I+lZ+FBQxdHtds1gw0DAHBNOtrKy4sbsZz/7mYaGhrS7u6vHx0fNzs4qFOqNReLakARLwYHcMRKJeL7n09OTm5LR0VENDw/bwkJjTRbCwcGBpXYoMKLRqKanp9VoNPTx40cXHLFYTLlczinYsIowDOzRBKjh2WSPRGETDoe1vb1tkGl/f9/PGj+XgoSiDsk7DeHJyYkWFxc9hQMwivdDA80fCm0ajru7u2eMOecKadb8N69vNy3Bv6NIZZ+HlULmyHtIJpO6vb21eoUsAholMjsAkM/Pz7W1teXGcGhoyIoCzovgKJt8Pm+Wg8ZrZ2dH7XZbiUTC42suLy9tFeC9cr7xbKJmmZmZMXPOz+V8DYfDzjvguk9OTjo3haKZhhR1VrAhp2Hmv4OsOdeXr5P07NmDUQ4qF4JWiW9nN6BoQAHE+ufrADlgKHnmr6+vzSq122036ayBoF2KRphgUfzW3L+BgQEDP6iNQqGQLi4uVCqVvN+SLE3eSz6fd2E9MDDgIEXqH+aAY9VCaVGr1XR4eKhQ6NNIIlQ/326+g2BJ0KLDP4eHh/XixQvL2re2thyu9uf//J83KdHt9tL3X79+rTdv3tgGtbu7q3g8rs8//9yy6GQy6WtAvURGCu+TRpR6gGebTJLz83Otr687TR5ygDFv+/v7znEoFAoONEOqDMu+vLzshg0whyb99PRUzWbT1hFmoiNxJvMjkUh470Idwn4aj8d1/s2YuP7+fjd5NKCcUTRlNNmo1AjVOj09VTqd1suXL30dAJJI6MZqRzAZE2IISSQMrN1uewwowM/Pf/5zByMXi8Vn7xOQaWpqyhJqnm+sa6lUygQOVho83YBtKMNQe1CTBkMsFxYWbAFpNBra2NhwWB7XHpIEpQKNfLPZ9PhG7DME+rIfPz09OQeHM3NoaEhLS0vq7+933ReLxZ4pxZB7Q9Ll83mDIbDVSPfPzs5c76Ic2djY0PX1tba2ttw7SPL5F4vFNDs7aztZEFhi/yWEbnd31+QkBNPt7a3y+bzPdEYAkn+A7ZTrDgjKuh8aGlJ/f78nCUHUFQoFHR0d+XMFx1L+X71+xYT/8q/v1IQHkyZ5ENlAaLTwiK+urroQyGQybjqbzaZZY9gvCkc82My+JdmUREgCoii0M5mM0um0xsfHvfGB9CH/Gx8fN1rGwUwTQmAbfqT+/n4HT4GSglZRsNdqNY2Njdm/l0gk3JgT0EahOjc3p06nY7Y9Go3q4ODAng3QM9D3QqFgiTkbKaFHeB/52ScnJ2aDGQ/DPUJWzkzNkZERj914+fLlswc/2MwDlMzOznoG4+TkpCqViud9Mh5sampKlUpF1WpVuVzOB+nDw4NT1B8fH41WN5tNjY+PW1JMgAoN0w9+8APLrPCpdLu9wDBQVqk3cgRPH8nR29vbHp8HI0dw29TUlJPEYRYIeqPpR3HA9T05OVGxWPT7jMfj3kwpokChYULwHyHJvrq60pdffqnvfe976uvrs6eWkSr1et0zh8PhsAPZGo2Grq6urO6A4ZQ+jRnb3983swZQw6H+5s0bH4RjY2Oan59XLBazbx21Rr1eN+sW9I8TSLewsKDh4WFVq1X742lKeb+zs7Pa2trS7OysPbILCwtKJBKWW9MgUzQE0feghJJDiMIe73cqlbLPCwki7BwyQWbEUqhznziMU6mUCoWC1tfXvVdwoGHdgF2g8IdV4/3RuKRSKasRSJklTfT8m6C2ubk5TU9P682bN1YHwQqBeOMnpTgYGRmxzzGfzxv0gfHs6+vzvNloNKrd3V2rGQBAUetQLFCEMQ96b2/PgNvGxoYbF9Dxo6Mj77HNZtNrD5aFQz2RSHj9YqHhfhJKheSRe8k9poiPRHqjiLi3hMJxHxihxPzWoIQ3yLICTLFHB5lWMgd4Zvk7VA08YzRcyAO5/4AyQa8xexbXDtk3zQSFZ7fb9R6H1QSfLEotLFbFYtGTBCqVitLptEqlksdUhkIhLSwsqNPp6N27d27OAHr29/cdfhT02cdiMcViMW1ubj4LT+rv7zdbzvoeGBiwl5zn8OjoyIA5QBkNOU0t+zz2miCjHSwug9JyGsHg3sb5GpRsBllnvjYcDpslkj4lrPMzAd0p5GmAKdABhhg/it8y2OjDCHNOsHbD4bBubm40NTXlZPR/9+/+nUPwKMZ5HzR8ZDwA4uI7l+SRpICyWBe2trZ8bTiDms2mjo+PlclkrLSZmJjQD3/4Q6/pvr4+NxNbW1tuemgiOEs5qzkLaLIqlYoqlYpWV1f1ox/9yMo56i4sdhT+w8PD+vrrrzUw0JuVTN1QKpXs497e3lYmk/EeNTIy4gBdVBzsL0GgFCCcue1YvPAeswb4nX19fR61tra29kyu/fj4qNevX2t+ft6ACTJcZP0vX7709WLf6HQ6uri4sGQ4lUrp8fHRrPHa2pp9thcXF9ra2jKbTPNHuFs6nVYoFNLu7q7rP0Y3jo2NPZs7DRAP2EoY7W/+5m/aJofyk1qITAHUSlhK+Myc70zX4fcC6JFjwP7RbDY1OjpqNZMk18bX19fq6+tToVCw+oA58OR74NvGr0x9xd5xdHRk9R8huzMzMwZ/yY1ikgXTUfr7+7W7u+t8IJ5bxixSF9/d3TnMFeXrixcvdHl56ZF00WhUp6enin0zjhWVbKfTUaVS0eDgoFKplDMKJDlbRZLvIVaL+/t7KzwPDg7MXHe7XRMLAN2oK09OTvTu3TuPS6S+lD7NEx8aGnLQ8dzcnHOlIG4k6auvvjIrH7TdAJiEw2EVi0XXAtScsOKAf9SG36Xp/bYV6U/i9asmXHp2AAblx9VqVbFYTBMTEy4M2AgIqbi/v9fR0ZEfZiRIBAFUKhVvSDBzSFgpYEOhkIsiEr1Bk/DgDAwMKJvNamNjQ9ls1tIhSZ7RTCATLAabFz7kg4MDo7DtdtvSKmTOhDQwVgEfyfHxsYM88KVIsrwGJjqTyajRaNj71dfXG3lGSiwFw/HxsT/78fGxstms/XiAIUgMh4eHzVo/PT05WXNtbU0fP340Itnp9EJIAARubm40PT1tFDQIjoBMI5tjHBH+p1arZbYatpQ0WQ5IGvdgSMbl5aVRYaRcQR9mrVYzojk5OalEIqG+vj7t7Ox40+HnU1AcHh660OHwAe3E906hR3MjfWKzCcyCmR0ZGdHi4qJlyqwRmHEYc4o25PcAN6ghCDrh2iJJRm6GN+f6+tqHtNRL1hwcHFS5XFalUnHRcXNzo7u7u2cBM4+Pjz7oKVZpSgC/ksmkQabT01OHMuFxJLcB31YkEjEYgByMogSZ2OHhoZaWluyTBuWvVCq6v79XuVzW8PCwpqen/b0cogTTMfqr1Wq56djY2DAAQdNUr9ddlOGDpklHncG6xTNOE3pxcaG9vT3LA/FNM3MdJQ8NYLDpgn2DUSRrggMcTzcNJAfR8fGxfYmhUG+iQbFYNHgl9YrrpaUlBz4xxpADDYn/09OTG7xgoN/t7e0zqw6WjUqlYhBjdHRU1WpVJycnmp2d9X7F/o3HERkvoCTTCsgj4P/TMPBeSAymQH7//r0Z0aBUNhQKeW9Hgjg9Pa3zbxJlYX9ZZ6xj/tAwUhAC1AYbDEACwFw+I4FqiURCy8vLz/YqgCksKel02kwLyou5uTnl83kNDAzo7du3z2wowdAbvL2sFRpcPNecUzRLhULBzMXIyIjevHnjbINisWjQaXFx0YxZOBzW7Oysw3OYrMA642el02nLSJG7EsCH5/XpqZc/UqvVbON6eHgwqAUjgtWLhptzhMbt28qBIBDCHh1kZgFR+F4a9aA6IQieBNcCxS8v/LAEPAULbsYqsQewP/AHMIVQvuDcZGS1jNzrdDpmiD98+OCzi/yEsbExTUxMaGxszLPXmWZAoQ/B0O32poJcXV05nOvi4sLjWlutllUX7OF8jm/ncTDTmzUFeBQ8XwAk8LLzvJD4HfQz12o1y+hDoZBevnypUKg31/7h4cGKwPv73hxr2OHZ2VkrthjXur+/r2g0qpmZGU1NTenx8dGNOzk3/DxqGIC7oKLk8vJSn332mXMyyPrhuvb19WlpaUkjIyM6Pj7WwMCACSBIjeXlZdcC/D7WH+uY869arWpmZsZAL+qEer1ukB9gPpFIWJV2dnbmNcEscZ4NwM+LiwvNz89rZGREGxsbZtL509fX5+yddDptcoPGNPh+mQDDnsk1Rc0RtLfw/lEFlMtlEytSD4iHQaXOL5VKajQazmYaHx93/cXzCpgEsIQlqdFoOEMEqxbXApaWNPNareYsIK4bzyu5FLFvRp9KPRIimE+BqpMan+Z2YGBAe3t7knpWhG6362cJ6+PR0ZFzUPCwAww8PT1pdXVV3e4nmxeAgdQDgBcXF3V7e2sgn+wWzkd85GNjY977URA8Pj7q66+/9mxuFIOARA8PDyoUClpZWVG73XbeEnksTMLgGSoUCgYCUB9xriGHZ18HDEQtSUj09PS0Qc3vko7+q9cv//pOTTibPwdi8GEneZAxODQEhPJ0Or1kcGLzgwER+O0GBgaMduFVCIV6iYTFYlG3t7dm3ikEFxYWNDk5qbdv39qTg08lGo3abwcjR0osBTrSRdBGHqBIJKIvvvhCj4+POj09lSQjycwARpqKP7tYLBo9hEFnY6rVakYb+/v77WO+vLxUPp+3p48mgveczWadxMmDxSZGkwTrIsmeaq5tqVSypLPdbptBA60lfGJiYsKN7ePjow/wwcFBFx9IrSiGQLcJSEH2jEqApi8IGhA6RygK8utwOKx0Oq1Go+HxbOl0Wq1Wy1YDCluaGKTfHBysEXzgJMIjfaO5xHMTDoedFiz1AjpOTk68EedyOR+2sDhI1jlUsWg8PDxoZmZG6XRaHz58cJALaoarqytNT09rbGxMh4eHurq60vHxsdcSI+s4FJEY0rSCws7MzFjpQOgcBwQqC9QnyJuDjRsFOZsyTSZzs2FtYYNAoIeHh53+GZSRgqSOjo7qw4cP6u/vd9BZPp/XysqKlR7sCaxRCnCKipOTE/83oMPe3p799nyfJDemDw8PBleCaGy32/WBTqAPoN7AwIARaKRZhKlQcDP+Cl89axhZH4ctLA4jD6VeIcC1JYipr6/Ps7bHx8edmvv09OQAM0KMYKknJycNAkYiETPZ/MzNzU2NjY3pxz/+sZPNkeNKMhNTqVS8t01OTlqCNzk5qe3tbRfefd+EKAZZok6n88zSwv5WKBScXdDtfkrU5tpJn8ZCBSWINPP9/f36+c9/bv9n8P6xV9DQYkHAG4yqgnRnfifriP0cRg0rE95AvPawfEE5PaNkdnZ2XOAjF2aONEwDckDk/319fc+af84RGr2ZmRkNDw/r4ODAz0VfX5/29/cVDoe1trbm0VNkSHA9UNIAWl5dXWl3d9cBV2/fvrXqgyaYPXN2dlbNZlP5fF5DQ0Pa3t7W/v6+nwMmGvC5YEIAKmHFaEQ5W/gdQXb72w03L76GtQkoyb0OStj5GulTqGooFLKEHBUcjGsmkzH4QOgi1pFgSBs/B5k/nx+gMZvN6quvvtLW1pYikYgDDZkRnc1mlc/ntb6+rouLC83MzGhtbc0KQIKseBbx5FerVV1eXjqQS5KOjo6s2Mlms/b4cr1pwpGLBpttfhf7Insm5xF7AOAn+zk2KUn68OGDHh4evDb4uZAD9XpdqVRKkpzAnslk9L3vfc8NI2BlMCsnuPfCfsI007Qio+ZMgC0kLwElF0C6JMuCkVdjseG85DxgHjJ5Bm/evFE6nTbJwc8HmICwoL7iPdL8DQ8PW93Z19en+fl5P9fUDYBlZK1wD2juqDHK5bItfKz/+fl5k077+/u27M3NzVl1GPRLU3Mzhg7mE4sFrCnAJdeSM06SVlZWHKhLrkC1WnVdSK0HqYMqhP0kmFfAdaSGZ0wtyhwATSyLrD98ydxDwGQABWrlw8NDjzVF0YDyKxgIjIoSJSb2LUiuvr4+k0oQBYBQfO3s7KwBYmoDznPUqDc3N7q/v3eTX6vVNDMz43VJo8v0FLzpV1dXOjo6MhkyOzvrepp6iokX1WpVL1++tD01nU7r/Pxc2WzWyqjgPWcK083NjWsRQHDqDLKkUFRQJzKesd1u69WrV1ZALS4uKhaL6Rd9/Wmw1L9iwvVJ+oXn6e7uzojU3d2dkTtkm8zABBFiDAoIfbPZ9KK8ubnR0tKSZmZm9Pu///v2ujFOgTFNLFhkSyA4pDQiyZufn9fd3Z1Hd0jyQY7nI+hFQ05HcMfY2JjZoBcvXqhUKnkWHwc4vx9pCx5dGKp4PO4HqNlsanJy0s0fnl/GqBWLRR9q3W4vUAJkGdAChIoGgOY4mLQM60rhy6ZNqBSHVSTyaQ4hL5rhTqdjKTdgA3JbWGUkQiBmIyMjSqVS9jzCPpDkSJhRMDU0GB739PTkcKKXL1/q8vLS8hsYPJpfikF8zXibkRsykm1lZUXn5+f21wKEnJ6eutimQczlchoaGlImk3F6P1J5Gn+QVMZkzM7O+uCUZMn96uqqWdVms6n9/X1vzjDUAAKzs7MOebm4uND+/r43WORCFCI0JqxrUFNYA4q4fD6vFy9eeCOmEUJhUCgUtLe3Zxadg3dzc9PMYi6XM/pNOv78/LzVIhR+5XLZ4/IAt1BXJBIJ3d3dOfyN5ikY6ESiPCAYCawwNOwvPDMU4UEABAUCVgCAJYA9nm/+HbWLJB/wAB3sRaxhDlZYGp4rAMmrqyuzT8iwHx4ejMaTPsr1wUpCA0IhQ5Hx9u1b3dzc2N4jyQnHZBlIcrHPXhZM4h4YGHB4EsxgPB73Mz09Pa1yuax6va56va4XL17o+PjYYSz9/b0Ub8YLwsxho2H0HM3L4eGh9vb29PT0pEwm4xCiYHPAuqIpgpXBkzs2NuYxWDTQeBWDuQncW94rIB/hmJ3Op5F53Dv2MfyLMBEUJ6enp1pYWNDQ0JCOj49Vq9Vc5CPfZE0UCgU3PmNjY2bCAV1JIgdc4Pui0aj29vZsC4lGowarUKSsrKwYsLu7u9Pi4qLnvReLRXu9YSdp7ggxRDnywx/+0CDF3d2dJZ0ASTD3d3d3Dluk8Xh4eHAS/FdffeXwKNYqnxeQgQKXJoFnLejx5t7j3Q6qG/r7+/1zggoUUoWDTDjqA7zz5Lnc3d1pfX3dYV9IbynaePaDYZ40V9QMo6OjevPmjXZ3dzU7O6uZmRlblmZnZx2KCNCPeuPt27eSPoEFvP/p6Wk1m02z8xTsnU5vVGQikVAsFrOcmCkglUrFSdaA7ABhJCQDnNJwkMdSKpWsCHp4ePB5ynQWlBqEXXFdSHXGZ7q6umpfNWA74X7s13wmnnF+F2oaQi7ZNwlvxI7FODHOua+++soScppGkttpggHy8Pbu7e056BdJNFJ57jvKAzzhAALYEVdWVgw83NzcaG5uzrXk0NCQ50oPD/dGTnIOB6cBkT0TjUb1/v175XI5g9eAMEwFikQi2t3dNWkDMzsxMaGlpSVnyZArUiwWrVJkr+LMabfb2tjY8Fqq1+tu/PD7YitKJBLa3t5WMpk06UK9e3Jy4mYb1SINN+Mn8e+ffzM2dXZ21vslQCb3hz3m6urKSgHsk5x3JycnJvIAdbAkULfyfE9NTWl8fFylUsn7FqRAOBz2s9PpdJxzBFnEdB+USDDnWD9ZI8FMCGpk9hZAQQB8egwArru7Ow0NDZmgYMzY7e2tQqGQlpaWHJB3enrqfAOUsq1WS4VCQblcTsPDw/rpT3+qbrfrZH+a/qenJ717907n3wTLUvOFQiGrgNvttpWL7JfcE94PNTR2GVRV1LGTk5NWzv7q9Sf/+s7p6DSJPCQ0YrFYTIVCwZ4Y5iYGfSqgwZ1Ox4c/YR4wsdFoVMvLy0okEk6BDS56EEuK8Pv7e2+YAwMDevfunZkN5G3InmhEQfUluQDAJ4PHs9Fo6M2bN24e8PHhu0Sug7cG2RCFCl46pCETExOWzjK+DBRwfHxcj4+PqlQq/lokJPjynp6ePF82Ho9bUhwKhRyaxudDEhb0SoIeg8AiiUYGCsrIRkwhTtAMxTmFBnOa8f5cXV0ZXYSdh6EfHBy0DPT09NS+Ilh/qYekZrNZFzgg+aVSyaNHkIVHo1GlUilLR/GcSfIGG4lE7OkJjoWamJhw2BWACV4l/LlcRxobVAbIy0DWGSGFLHBgYMDFI8UTwE2hUPDBMzIyYsTzd37ndzyz+vT0VLu7u5Z6xWIxpdNpo+HpdFrb29ve5DkgUFyQepzP5yV9YpppVlqtlsEIACNJDmzh2cavitz+9PRUpVLJjQ8NJYfT3t6eR+qEw2F99tlnisVi2tra8r4BY09Azd3dncEHwoRYrxcXF06pXVhY0NNTL9zr+vpag4ODXlMXFxdmaxYWFvxegkwdDQNrFyADm8XR0ZGKxaJ9tmNjYwZp+D6sKIRQBsdGkdpeKpV8wCOVhhnFJ01hc3/fmwkNA0CiLYdyJpPxbN6DgwODKF988YXva39/v968eeNn8wc/+IHu7u6sNEH5wt6QTCb1/v17exMnJyf14cMHe97IjqAwGx4eNtjaarWcEl0oFJTNZp1WWy6XzWCx3yaTSR0eHhrkoSkKNmZYR7APLS0taXBw0KOQmI/MSC3WPDNZDw8PzYKT7Mz+wJ4E4AqQRFBft9u1L7NcLmtgYEAzMzO6vLzU/v6+AQEaHJ6tYE4JZwGBZBT+WEpgGNgLsZHAsJ2enj4LxgKkxFtJMNfOzo4LpnK5rOvra/3BH/yBz1zWF8zR/Py8951YLGaWCJCKZ4MzD8XL2dmZmUmCHfHZ4rnkHAdURanAGqGoRS3HWg2y3EGPOM9k0MpCbgBSVcZ/okhhf/348aOfN6YVwHYjy+aeAbrwXur1usMnKb7Pz891cHDgQCyadOxWTJiAzSN/IBqNWs0FS4iS7erqStFoVJlMxowY+2ZQWcW9CIJsBC0S7kdwKIFRnLdra2tmRQHnLy4ulMvlND4+buCVACwUDk9PT665aNywqZEfAIDSbvfmgROuRVI3sulCoaDR0VHt7Ozo6enJZybJ5uSJICm+vb1Vs9lUq9XSy5cvbQ8aGhrSycmJvv76a38feTtSz+LzxRdfWHF0/s2IPfI0pqam3MTV63Wr4JrNphsMzubHx970BKyUNzc3yufzOjs78+znZrOp5eVlbWxsWGqOAuDjx48aGBjQ9PS0QQCIDcgRwM+7uzsHjgGujYyMaHBw0Jk61KOESTIq6u7uTm/fvtWrV6+snMnlcj7vsCyyR5IMPjAwoKOjIx0cHBjsnpmZsToKWT3rgGabEWjJZNKqy+npaa8Tnutms6nt7W2rjzhnUD9gM8LiwdoJEi4oPIIhiZBGEBCJRMKAKhNZsEsFyZNGo6GRkRFbNrGotVotE0GVSsU17t7eniYnJ5XNZp+tl93dXcViMZ/VWBRh4EdHR5VKpfTmzRtJ8uSYiYkJ2+9QWNL4B5U9Q0NDVpGxHrhfnBEAF81m07kvgJeMSuPMwTcPGMJeSw/DhJZYLOZaa2RkxD+H60cY3OPjo1l4AuV+kdevmPBf/vWdmfBWq+VwM1AgGKB3794pFOrN7kPCDdobi8Vc0CATbjQafvgoShmvNDIyov/oP/qP9Id/+IcqlUpGc/FrwLwiXS+Xy1peXjYSy2JkM3l6erIUnLnTMMQ8NLOzs94EkYCy0M/Pz1WpVIwQw8gRqkRRwUFDQSvJ0io2ZsayLS4uand3V7VaTdPT05a3gnIzL53ijwRG0HMKzZcvX3okDpI4CpunpycX3Xihg4c788Txd0k9FB8QA7aFMU6wJqVSSWdnZ/YZ3tzcuLHnUKJok+SAKvzfpKESvESzjFfm5ubG4X8TExM+SPGfUpBGo1E3/IRLwBYEUU+Q7maz6VAoXqFQL/UTCWJfX58lrDCZwdFBNGIfP35UNpvVzc2NNjY2zMbx+9+/f6/r62sH+XS7XW1vbxu9Z/Y6ayqVSqlSqRgYYQTZ0tKSrQU8X6DErCmeGcawAFawuWazWYf+sDbS6fQz77MkF7oUx1gpJFnyK30KxILl4DpSUH47rTiYpk06PsxFX19vdCDoc19fn1H/p6feuB/k+hz6AFWJREJnZ2cGkiTZAtLtdl0sc41QJMCYEFTSbrfti1xcXLSqpFQqWeJHM0nzgsWl2+1qZmZG+/v79upTRKLO4BCPRCIGycjDGB4eVrlcdgGLz3p9fV2Xl5eamZnxz0QZ09/fr+XlZd3c3Ojjx48qlUpaXl7W7e2t94Jms2lFBNJlGh+kk4QqplIpjYyM6OPHj88SYYPycA77UqnkNdPtdpVKpZznATpPoR/0c9MkwJrA8NGY8WzOzs5qampKe3t7/twwHiiIyAjA44k/kffMeqAhuru7cyBP7JsZ4hStj4+P2tnZsYormUz6vOF8YDwNfk+uMbJE1s7Y2JhZi3a77SRo2Knj42Ov62+DEuxfTDBgvCMjaVCi0fBJshoin89rd3dXNzc3lrH39/crl8upv7/fsmrSfLlGXLNyuWw5bqlUMkAalKXTDHO/aKx5LlBhoVaT5N8FwAZQAzghyVYhPKrlctlqIH4+DO7j46PK5fKzVHquIcA/8mgYz9nZWS0sLOjt27d6fHzUq1evXOSfnp7q6OjIM9nz+bxisZiZXaxlqDJYp7C8EBOcKYAYQRCYcyMc7iUQM2aKJoRrAjMFEweIyhkdBKGxN5CzQC11fn7ue4XKaXh4WOl0+llaONJWzv1Op2MgPCgJxz9Ltg/Ay+Pjo9WOh4eHPt9opKmdkC0T4Li/v+8znSZnc3PTo/bK5bKbNTzCKGEIPgOIZRoH5yd2EmwX2AfJsYjH4/apE9g6NTWlcrnskWzYO1CV8AxKshIilUopm83q9PTUCiam1HBOM5EH5SLzrLESEGq5uLholh8lGTUUoYsQO0yPYIwZMvZKpaL+/l5YLqrLcLg39hF7KPVBkIABuCQngpBASB8YZoATFJSdTm9aT7vd1szMjGq1mh4eHpylBFmEcgD7IURctVrV8fGxisWi9yHWPHsWe2CxWHQTn8lkFA6HrdADMB8cHFS73bY/O+hrp+YHZOH6I90GOEEmPzc354yJ7e1tA21YVwcGBgxes4Y59xYWFvzcsYaxw6LSeXx8dFZWf39vPOHg4KBKpZKKxaIJD2oV2HvqUdTGgC6hUMgqE/qaer2uubk5j0xEQQNgxf4NYM2643dRK/xqRNmfzus7NeF9fX0+kAnwIW0Y1O/x8VEnJydKp9M6OjqyRA2Umll+sBOSfJjt7e1pf3/f7DJFKQcWEmEOIooXghyOj481NjZmZBNElmaCOcg7OzsudkdHR3V8fKy+vj5Lfaamphy0wmdGYscGeXh46EKKsAWQddhcZKgcpgS8JRIJ7e/vG11nc0SqywNM8nu73Tb7DXLP+CgOcL6G5M54PG5PWzCIBB+ZJEvkS6WS2R/8YGxaBwcHPmxJy6b4gYmh6ecaUIjBJNCkw25jH+CgQdJ2d3dnhLLT6Tgwig1keXnZCDqINFId5Hc0D0j58YBLciOHguP09FTLy8s6OzszGo1PD1SYogjvc6vV0t7entfN3t6ecrmcC0kYjUwmY1R2YWHB6C5FDPPD/+iP/ki/+7u/6+b5L/7Fv6h/8S/+ha0KBHJw2H/xxRdu4mGrUqmUE+m59hS8QRSaualBUGV6eto+JxrXYIDZ3Nyc58Pz//Aq8vODjRr+aTyAkszuEHQzNjYmSVZSEPoX9DNTdPf19VkOS+OFFzQUCjlVFWlasPEPjqIib+L4+FiS/Fmi0ahKpZLtKxzg9Xrdzy7ebu5dp9Px3sWzPzY2ppmZGWcISD1GmPyKoFeW5j8cDmtnZ0f39/fez8LhsCWBw8PDWlpa0vj4uHZ2dpwULMnqCdiixcXFZ4m2iURC0Whv3ODHjx+dpEqRVa1W9dlnnxnBPzw8VKFQ8Oxxsihgmln/jC2hAWMPwqfOWmC9gLijEKDx7nR6I/V4dlE3wSwF7QA0mTDSrHXk63xtsBEMSp9RIsAoEwjKvoksnD0rOOIIEOH6+lrLy8tuxLCU8E8ko8lk0j8PWfH8/LwDHkdGRvTy5UsDUmdnZzo9PbXPEdlwo9HQ2NiYgeGpqSlNTU1pd3fXfm5JVkoxtm9mZkbNZtNySJg9QCuaxmCSOcU3DS37B8U3Zwd2rqDNiVoAUIXnjfOSZj0oQQ8W3lgJaGofHx8d8vdt+wKgY1DKzpkaHMN3fHxskF3qMaYoCgYGBrS/v28wD8sM9UE0GjX4iQ2J94C1JrgGAGn6+3sjBi8vLzU/P+99kjMBOxOsGfsG643GFaCaEYvtdvtZdgdnLXkpNFww7exPPKOSDCTc3NyoXC5bSg7zBiOXSCS0tbWl8fFxXzdyFCqVipUVqNnwdt/e3mp7e9vkA0QB72d0dFQnJyc+8yFjIA1ubm48gnVoaEizs7PK5/OW+ks9sIlU+JOTE01OTjqzhfqBz4vXlmYQJVs4HHYd8fj4+MzLi0KRPRS1FsAvgF5wQkU0GlWxWNTNzY1ev36t5eVle7R5/gAjS6WSAUEaH9YY6wfiCoDg8PBQZ2dnfj5isZhVC8PDw9ra2jLIh6cc1nh8fNxnJ3kGZBBJn7Ic2KuQ/QcVKShGyXEBjME+xRzwsbExp5SzPwCs4FOHAd/a2tLAwIBDzjivaSgBBRuNhtlbnnlmgUMuSZ/GRo6Njdl6SPhoOp1WKpVSvV7X/f29Li4u9OLFC4VCIU8doqbudDra2NgwCcNzCmjBe726ulK5XNav/dqv+blh7VD3o0qC9InFYlb3ARizf3CePz4+amtry2QA6yqVSqlardo+kU6nrRIC+E4kElpZWXHGBWBkpVIx8UbAKIRFMJeH2hzA6/T01IqwX/T1Kyb8l3995yYcFBR5GCgUwRSxWMzMJg0MLBRyWRIsR0ZGHGJEqNf5N6PFKJgfHh4Uj8cd9gGL9fj4qKOjIxUKBR0fHzuEit+FzAX2hMMA6V48HtfS0tKz+ck0NsT3n5+fa2pqSuFw+Nmc7maz6fCu3d1dI8fI1AlrACVDXgRLzeZHkTI5Oan9/X2HJdBoUICSWokXBq8fqgJAh4GB3ogBZH94avF6I2vBwzQxMaHHx0cXx/i8g2EnSPmDvnIalaWlJR0cHKherzuIhXuDvDmVSrl5D/ow2QAvLy9dJKKIIG9A6jVPb9688X2D+WXjDRZf09PTTqhGHg3wQ0InHmqYYNbG9fW1EWmCcrrdrkeGnZ2daXJy8hl4s7i4qEql4nnGc3NzWl9fV61W87x2GkQYCgocZLjb29v6F//iX+g3f/M3NT09rdXVVaVSKf3zf/7PjbDW63VlMhmv5b29PRd9jUZD8/PzLqqQSUsy8lwqlew9x6+LJ/Xdu3e+hpIcQoWcjI0v6CXl3/k5KDeCnlOS/vF5UsgiXwXQglG4u7szC09BDoJLAcnzIMmFJA0caDXPEL5+Ch7pExMAa4lVIBqN6vDw0KwigYLZbNbBRVgOaDbxbeOtC47hSqVSnt8Lis2+eX5+roWFBRWLRX38+FGxWEzFYlH1et2sLAoBGGtsOhz6HPI0foT+FAqFZ7LcarVq9gzfGLYYCuR3796ZDSMUB3lpX1+f1tbW1Gw2DVAG5ZzBoCD+nkkK4XDY42EAqKamplQqlfT4+Oj9qVqtanx83EAW+yFNNEXgzc2N0+Lv7u6cAxGU98L+4s/nPd3f3+vs7MysMqx/ECTmZ8ViMR0cHNgPC1DDvj05OelmiHXPWhweHtbp6amOj4/Vbre9Zw0MDFimjpICefTg4KBmZ2dVq9U0ODionZ2dZ+OaKNyQ0/b39z/z7AEQvH792qwImQ5cK95b0BPJs0qTHFS2AOTxNUEPcNAaFgQ8vg2+SLK8lJ8H48LXwogSQnp4eOg9JgissNfQiIbDYd8HJOIAZbu7u5bt0hANDg4atCZTo6+vz+n8mUzG6cqMVMI7G4lEvBcGmxbqG85CAA+UG9Q3WCXOv5n1DsAFqM06Yb+TZGsI+xCJxfg3kWPT1NLgAIgR6Hh9fe29nL0dCbnU84DDEpPXQzOO8oQiHgUI+wss5+3trfcNZLUTExM6Pj52/UfyM4GUu7u7braoBfD9djodB8GhguJzASIT2go502w23ZyS+5BKpQwgscb47HjokeDj10Wue319rUwmY6UAZJMknx0A5H/0R3/kZPrt7W29ePFCMzMzbrA5lx8eHqyQo6ahvmRU5tDQkCqVivL5vK6vr1UqlTQ5Oelk+KmpKbP/qBRmZmb09NQL34T9JIuGeot6EutosHaGlOG9BnNTsDFMT0+b7Nrd3XWWz9TUlO2T1IzsLdPT08rlcrY0pdNpWzSpCYIqT2afU+ejiJV6oAprkRqRaSOrq6tef+12L3wM8B6lGcoGzknABhLNqV+RpgPEYV2DnEJ9CniD7YjJDADOgJrUllwrrCejo6Ou56mb6H8qlYqBCSxI09PT2t/fN5AM6/342Js2UKlUtLKy4ueR0EPOYc4c9qmJiQnX30wSOjs709zcnHuUYGDxr15/sq/v1IRThDE2CNkKCw4WbGpqSjc3N0omk04wzuVy2tzc1NTUlPL5vOr1ug+HVqulDx8+aGJiQvPz8w7KGBkZ0Y9+9CNJvab65z//uZMH8TYit+Cgh1HJZDI6PT11QUdg2OnpqUdqMKc3n8/r5OTESbgU70jXKWZhR8/OzjyDm2Z8aWnJC1iSCwtCD2BmYbbxWHe7vZCoq6srs3HJZNKS152dHTdWwSacppHiH5kZiCmyTfwwFDRBRhyv0N3dnWXKQabj8PBQ6XTamzgHPSMVzs7OvOngFabRkeQwoEKh4Aa20+k42G18fNwBO/39/f5aPOnJZNKM6tTUlNcafj0KVNgHJJqMh6EBI3OA8XSM6yHAbWlpSX/wB3/gxFvWZaVSUS6XM6J6eHjoxhDJ5J/9s3/WhYakZ95JZGR4qdhE7+/vLbmiMPuf/+f/2enq+/v7RsBRC/zGb/yGmVMUDEgZT05OVCgUrIRgY6eAjMVi2t7eNsJbLpc94g/vKD59GBoKApQr5BXAQlHIcdCwRjickMaPjY3p+PjYe8bLly9dEAXHcbGvcJDiUTs8PLR0lUMwqPoAaaeYCIVCmpubUzKZ1MbGhkfTNJtNy/Z4FpC1I+dkjbCHlEolF2iS7J/FY0VuAj7Q8/NzXV9fGyyYnJzU3d2dstmsJicn7Um7vLzU5uamarWaU0hphg4PD7W8vGxPdaPRUDqddh5AJBKxH/Dw8NDgF4ALB30k0hu3xVg/xrjk83nt7Oxofn5eu7u7ntMdHAPX6XT0/v17AxpI+tjPuAfsF4Co0WhUq6urvmeZTEb1et2gKszh09OTJbD8TNY1Pk7pE5sKEAWgAKDaarVcQNI0os7AZ91qtZROpxX7Zg4s502z2TSrz3PRbrdd5OLJxPO/vLxslQNsPkAd+xohTExKGB0dta8a8Jj7A9CYzWbVbrc93orQIpgMCufXr18bkKXApLjkM1AcDwz0knlpLu/v79VoNPTu3btnKdxBawnNc7CJflYofMPq0mgHA9N47ijw+ayEfrXbvdFYNNjBOcNkOwQbblQ/BF8hK8YSUiwWfc3Hx8ctMYUFhi2Kx+PK5/NW3WF1AgyMx+O+/qQnwyp2u1176QlH4plH1kmzw5kPE4WqC382tjVsKMH9h0KZYp1G/P37995vaZAJHwN05n5gGcjlcjo8PLRFL5PJKBqN6t27d1YEcm/D4bABZMIAd3d3vY5Q+tzc3PgMCIfDPvNpypgWwO9aX1/38wcICWCGJYmMDexZjLQsFovK5/NWd8zNzenP/Jk/o42NDZVKJa9/ahr29+PjY9ch2CRZRzDXd3d3rhP7+z+N40qlUrZyAHy0223b8LCdHB0daW5uTvf391aa/Pqv/7q2trbcQNN4UyezR9IUsReWy2Uzz+z9PK/n5+dWWnz++eeuFxknRZI9QcYA1EjhAW0IakQdJsm1Vbvd1vz8vI6OjmzTSaVSJtTIBgiy4uVy2dcTSfXAwIAODg5ULpdVLBY1PDxs4A+pNaAJexMgDzVGLpfzVJeRkRGz/tgOw+FPIxmxLgCUJxIJXVxcGHC9u+tNywiCEMzZLhaLlt0HLXjYDQC28vm8R3NRd8NS53I5h/GilGB/x2KHMjZINvDsoag4OjpSu912EPDe3p7PC7IV2OcAPQG39vf3tbi4qP7+fk8AaDQaqtVqSqVSVhGQwxAkZe7u7hyWx3uCMCXvZmxszGD6d3n9ign/5V/fqQnHEwT7RTosjXAoFHIBhl+CEVzRaNSbBs0TUidYOQrBYAIoG32n09HMzIyOjo7c6DADnDTJ4MGYTqedwC7JoWQ0mcjJJycnPeqJgxNm/OzszAhU0EMIwhuNRpVOp+0fk3psf7VafTZ/k0XPOC2aMIIgaOJzuZwDWUDACFUjKAhmg2R52ASY81Qq5SRxfGlItmAT8JrALoKqNRoNF0A0rjCTFFmgpwMDA9rZ2bEcDykizBBFE4XV7e2tDg8PPRooWOxQkEiybA8Wi2AbwpCYyc7voBAD4KAYRoLKOul0Ok4qxh9O0Aab4tLSkorFog4PD32v8MoxLiIUCml2dtbF59DQkJkrmP0f/OAHury89L2kQQgikqDXNCCkl3755Zd6enpSsVjUy5cv9ebNGzezABiSlMvl7GN8euqNBpmbmzO6TLOAbDwUCunjx48+HJj/u7S05PfFHFRJfh74/DRErVbLoSU0UUEpK8wXaDTTD0CV19fX7aOmWEXCynOAVG13d9fvBfQYSSlggdRL8wSkwrNK4ch9kuR7wWEMKEfzJskAFJYX2FCeGUKZKKhp2GAOFhYWdHR05AAXrB0UcmRDEH40Pj6up6cn7ezs+BrV63UtLCzo7u5O7969M4hwf3+vmZkZjxlbWlpSJBLRhw8fzOAUCgUXujSzeO7xjDIznGsl9VgyRnLd3t6qWCyqVCqZ+Q+mx8Ksdrtdy0JhpLgfzWbTadAADPv7+y4oWJ/I9vg5NBbIJflefIywtTCSQZkzezvADntKLBZz0jETCCS5ieT+490eGhp6JnMOFqNBppiCM6gQAXAl8wHgFNAgFArp9PTUDTlrhGsRjUYNWGCdIT+B/+b5C0rq+QzT09OanJy0T5/9gPCi4HMa3He5FjwDMOD8PdeJYpLPe3Z2ZoUbDSXX69tWJAADZNYoLAAakF7j4SSQMhaL2aMJoHN0dKS+vj4nV0ciEf385z/X6OiolpaW9L3vfc+1AIA/TBUyXZommgbY3YODA7NAs7OzttvhFScMCV8yPnRyPMbHx90MApRS8zDKjuvH7282m7aGPD09OVCOmgagBBCZ+gL2MhqNPhurBBFyeHhocoGAWc5H9iH2RkZbTUxMeG9G6RIs6vF6E5hGEO7p6akmJibcPBE8h23g6enJDerAwICv5dzcnEqlktLptOr1usLhsObm5rSxsaF6va6VlRUrHpDLPj09WZ7N+2bKwcXFhWcnI2OnVisUCrq/v/eIVNjeoAWF3BuaPmqIVCplm184HHZWUSKR8NQg9mqyeEjfR+VFNg4BfzwTQQ/5ycmJLWb8LOos7jn1AMw+NiTONWpdJPzUTJBlsOkw/NVq1c1pPB63crFYLPr5ZwY1NkvGA2IPZO/AsrCxsaHV1VXXp+fn5w4Ve/XqldcM1h1qJAAV9vNKpaJ3797phz/8oSKRiGZmZpRIJFy30B9QZwwNDXnvpW5H5YHth5qGNQfwwagz5tkTxLm2tuZnHg95q9WbwsT5w1nLdebacz9IUAdwYN8EIGbCATYLQCDOr4eHB62srLhWOT8/1+7urvdAAtYeHh5MQLJOg357AuQgc7h33W7XIB+f4/8rr0ajob/5N/+m/sf/8X9UOBzWX/pLf0n/5X/5X9qL/x96/eEf/qH+3t/7e/rpT3+qvr4+ff755/rn//yfex/+RV7fqQmn+eZA5SGPxWJqNpva3d11AjI+GFLF7+4+zadDbhUOhz2KC09HLpdz0RuUdsS+GY3Fw5/NZvXFF1/4MJmdnTWrRVOGP4bESrwbNKqMLiARG8lTX19vXi6SMpIYSVFlc6dxJdhndXXVjRKBdAsLC6rVakbUYQru7u50cHBg2TryOw5DUhFhFQhpi8ViZuNA//r7+5/5zyTZ58NcweBsTZpCCmmKIgpePCU0njc3N970ms2mBgcHLZsJNvYUaaDB+H8pvvGQJhIJhUIhe5qRKVLIUHCwEfN5ggUCRUEikTBLioQpWFgGUWgaP4phPPdYIGgiQCNpXJE2Pz315i5OTEzohz/8oRMykbhjs6CxkGT5NFkK2DNOTk4kyRI6ZIz4n5vNpv74j/9Y7XZbq6urToBHsgpLxkE0ODioWq3mec339/e2JoyMjKjZbNpzxCiYnZ0dbWxsWFrHaBY+U7DZ5l6zftjs2+22Tk5OzGDDMCHR/vnPf+7rTrDL09OTi7JgpgHNFnPtpV5jPjMzY0lfONybB493n3VCIQ/7yPU8PDx0wwJizl5G6jezXIPMIAUnzwZNGoUxwCGSSWTYTAIgyAamAoAI2TX2lPHxce9z8/PzGhwc1NHRkVk3rgnPJ2uJXAAK5YODA8tBO51eujhMpPTJBoGHlMKAz0p4E3kXJE7jG0VyHgqFPH6OfXdvb08nJydmP2BoAWeenp4MzsK04fmjOEwmk6pUKtrb29Pnn39uGwzrhs8Osh8EVGH4guuJf396etLR0ZGlvdw/ClP2PH7e/f29G37UXlxz7FjB9wAjy7WkCYfBJXCSonBjY0OHh4ceYcez2Wq1HI7zxRdfmA0CQMpms0omkzo6OvLIIFRYNK5BOwdyT9YPHlaUBax/rhcNOAoC5Jc8Y1xTshQodMfGxrS2tmZWjeY1FOr5O1+/fm2bCOuOZxegBdab6wvYzdQCmN+npyfNzs76fpEgjwKhWq0qn89bKfL27VvfV6ZZxONxkwYA7gcHBz4HCKuksGVNEPZFCnE6nfaZShBpt9u1ND5Y6EciEc+Zp6GhmWHf5RzmvBoYGPBoTvzIyI+pjQAR8LuipOA5xXoGSMC5y9xz0o9hvmkuUY9QI3EvaXDu7nrjaGncAP1arZZBDsBeVBsAN4ODg95XLy4ulEwmPTru7u7OoxIBplHncKacnJx4qkatVnOYIc8rVgSYY6mnyOP8hlzJZDKamZnx/4eNr1Qq+v73v29rDEAgih/Oxevrax0fH5uRRClZKpX8bMOwPz4+ampqSgsLCz6/s9msFT1cL5prGFHIiK2tLT/TNMJ4odmPxsbGnBUEawwIzz3t7+9XOp12CDIWp4ODA8XjcZ2dnXk/gF2PRCJeJxALrPFUKqVEIqGNjQ3vg2SvBPfUi4sLnZ+f2y53fHxs///o6KjXJwFjsOH8HnqHzc1Ne6rZuyBCqPcBqp6enqx2vLy89F5K/YraLjidKPjccD8vLy+t3EC9Fw6HVS6XbU/tdDrOE6CmIGQZkoR+AVUWoBGqQXoAJjSxN1ILEfYoyZOeAMG5huwH0Wj0mdd8YWHBoXkoXm9vbw2CoBZmpjgjlwGIfpHX/9uZ8L/yV/6KKpWK/uW//JdqtVr6a3/tr+k//U//U/2zf/bP/oPf84d/+If63d/9Xf3dv/t39U/+yT9RJBLR69evvzN48Z2acJISORRgNUDUh4aGPH4nm80a3cFnXKlUNDIy4k0GeTCbHIcQ6FBQuoJ8rdvt6tWrVw7T4EHlsAsyDfPz8w49Ojs789fd398/Szus1Wr2ftB412o1bz6EVMzOzrpIINAFiWi73fYhgQep2+36mlAIUSziSaK5YdNFLnd/f6+dnR17eSKRiIPlkMaRys7YGTZiJG80Y5K8CSGhDB7e+/v7yufzljADOuBNZWwJs4eRrTBWCpSOQiEoMw2GtLBJXVxcWBFAIc/ILA492BC8oxT2yLMoRAhpAWHk/sH6BJFkfiY/Kxj0FQzgAJWdn583kshGCkMP8tpsNo2e0myWSiU3lWzwNCX8buwGnU5vFjlSLryjMEHpdFpTU1OqVqueB03AxtzcnDKZjDY3N58F6rDhU4BVKhWHDQ0PDztsLZFI2Jc7MTFhX2Sj0XDDHftmTBrseTB4ifA0xkoVCgXNzc0pn89ra2vLBdH5+bmq1aq63a4WFhaUTqc1MzPjwifYcIPEcn37+vocEPbw8PDM1y/J643nCPkjstCzszMz7xSlrAGQ4WQy6RRTQJqnpyffNz4vewZMF8/yyMiI8vm8Eefh4WFlMhkf4rB8c3NzljifnJzo4ODArBayXfy/yAYZb4QdYn9/X6enp576ACDw8PBg1g3ZLQcs8t9gON/GxoZSqZRWVlYctojNBOAABh+wFK/YwsKCA4akHutWqVR8PzgT2M9gjlk7kixvhy2DUQQ4CYfDlozTaAIYsKfCuAXZcIo/gEYArW63a/tH8JAkJI41hJ0DKweBZgBVWF4AOSnIWXcUVrAzNMxce2aYT01NWdnFeST1/MBbW1vKZrMaGxvzXksRRhbA4+OjGzTOnmDDjb0KppTRRGSHkKw9Pj6u3d1dg+ow8PF4XMvLyzo4OPBZxnoHUO12u1pbW9PAwICbSBQhvCdCU1G6UXDDMKOWQcVDoXV6eqqlpaVn1pxcLmfvYyaT8Wemoea8jcVi2tjYcPDR/f29WUmIAs4IEqBpDAB4aXI+fPhgoJ7gz+PjY+3u7lphQGPD/sB5ByAD0ASDCyjAeUmDHI32ZiUfHx9rZmZGp6enZl4hI/jcKMBQ11QqFYMGAFp8ruvra83Ozmp3d9eTGAC0i8WiM2xQnuEjxw5FM3F9fa16va58Pq83b95Y6s9ziHWtXC7bKsjeSzNFw0StFQqFtLGx4UaEZzeZTFoa3O12HV4HK3hxceE534zUhHQAiC2VSl5/sPGwxgBY2PpYB9Q8qBGpidhDn56enOIejUb1G7/xGwatYIUlWS3Ffnx6eurJJzDiWEvwc2N1e3x8NCFA/gVJ2zxbNErHx8daXFxUJpNRtVp13ROLxWyT41wZHR11qvnV1ZX99isrK8/CMIP7Icnni4uLXh+EA9ZqNVWrVWcFkH8StG8NDPRSwWlm2U+p/VFukaNBgCi5Gdynly9fWkFAwzw4OOj9GxUtijGAI7IWOGdevnypWCymUqmkt2/fanFx8RlwixQ7aJlptXqjIlFFId+mPuHs2dzc9OjcYL4L64FAUD4n4AufY3Fx0b0IalFUtP/23/5bq1tQnZGXQ8p7Npt99r5LpZJub2+VTCZdS1AL0wdRdzHRg3OawOT/L7w+fvyo/+V/+V/0s5/9TL/2a78mSfon/+Sf6C/+xb+of/yP/7FH/n779Xf+zt/R3/pbf0u/93u/57/jfPour+90lUEuYexgmAidKBaLlrki6Tg/P9fMzIxn+lEo0SzTOIJinp6eqtvtGoEFXQMxp9gZGhqyvDIY/Pb4+Kj9/X0zpBwYsFlI0EGgpV6BTZAB8m8edNDClZUVPT72xqNw6FGEBiUuoVDo2bzN09NTpdNpra6uukDnIAIF5mHkQD4+Ptbd3Z1+8pOfaH19XZOTk0qn05bPDgwMmA0EcYPBR/oHI3t/f+/wBQAUNh3YDoooJBTb29v2T8G2gvSBtOP3wm9KaFt/f/8zCS8MD/OL+/v7n31vcC2xCQRlzq1WS8PDw5qfn1e9XndwWzabVaVScQBbUF7/bWklr6BsVeo1DMwJ5uCl6IFp4PcD+ASDTAqFgt68eePik/UD+xcMd0KiGwqF7OUpl8tmNfEi48/FZ0gxgfWDAv7Vq1fOLSBYg8At3gvFIywFskTYJRoN0GSKMcJNmEWLdI8ikwaE52NmZsYgEGnceO9ojFE14AsEpABJhlnvdruampqylBlgDbZ/dnZWW1tbBjWC7DwsHUF4sIHBghi0mOc7HO4FFpJMOzIy4oTih4cHryGuM80hXi8kkmdnZw7kgQG/urrS1dWVVTGoVkZGRjQ9Pa3T01M1m01fD56tVCplrz+FMGtrb2/Pz7HUk34vLS3570lkp6lDKbC9ve2QMeRyMHpnZ2f6+uuvn/mKeb8DAwNaXFz0morFYl7vnU7HTQ3Nh9QD/CRpZ2fHzxrsDRkPSA8lmSXtdHrjqMgf6O/vdzFA84sSiyIlGBLG/sF5QSMufRr3wppjf6CQoRiHVcIuA0uXTqet2qGJx3sLw9npdOxvDofDTrMdGhrSV199peHhYb18+VKJRELNZlPn34x8XFpaMgO2u7urN2/eaGNjQ+Pj4/r8888tKwyFQpqfn3dzwf7N+2Hvx5rEc8E655/kcwAO8LlQgiFZ39vb83NIc873cW03NzetUqFBL5fLikQi/lrUYazNiYkJvXz50oUkElWAnFQqpZ2dHf3BH/zBM7sao/OQuwLI7u7uant7WyMjI1pdXfX7mZycVCaT0fHxsZmnXC5ndUO320sG3tvb0+zsrCKRiG0XfX19Ojo6epY3EQTPYdGxPQG6fdt3TUAsDCH2ICxvtVpNyWTSmRh7e3t+Zkl4p3nlfEdO//btW0+A4HdzH87Pz7W3t6dWq6Vut6vNzU0DFZ1Ox+DEV1995XMqkUg4wI5rAFkCS4w8E5UI2SsoK4aHhw0ac+8eHx9tJYpEIjo9PfXXSp9S2M/Pz5VKpTQ5OelaEFk39drAwICBBdZFcM9i9NfR0ZGmpqa0s7Ojubk5q97wPxPOBnh+fn5uUOCrr75SX1+fZmdnbZ/b29vz+Xpzc6M/+2f/rK87TO/p6anzbwhWozbCakIeAYrQ8fFxq55ub2+dmyTJSrShoSEz7MGa7urqSqurqyYIeN6ZssA+D7DIcwHYg3SdOpbG9ebmxuAeNoByuWzAjLN2dHRUmUzGQZ2MHab2APwDmKdeB8CdmZnxnoNP/uXLl540Qp0RzJig5gmCjShPr66uvFdvbm4+Aw4BirCOYnlgXNjQ0JDevHmjlZUVW2AajYZmZmb8HKLYCYKmAM5BwpBzEhAbVR6heZ1Ox6HTyP7Zj+7v7zUxMeE0dNSqNzc3HoEKWYE1lj2b8/jx8dEjeAFh2LP4uRB0sOC8EomERkZG/Mz+oq+g/etP6sW+Sg4WL+rKX/b1h3/4h4rFYm7AJel3fud3FA6H9dOf/lT/8X/8H///fE+tVtNPf/pT/ZW/8lf0G7/xG1ZC/8N/+A/1W7/1W9/p93+nJvzm5kaFQsE3nDAnkBZkhRSooG3dbi/kBFYUHyQBYniDQbaZA0n4EiEjhK0dHBw4cZhCudXqjaLY2tqy/wfpWDQaNXsIq4P3YmhoSH/8x3+sXC5nhocHfG1tTX19fTo7O7M/+v7+Xvv7+y5MeNCHhoacBInPi0TuaDSq7e1tb1QcujQH+JWHh4eVzWZdOOJHz+VyRs4API6Pj52yKvUKQgptDhkOegAPwjSCoUrBBERSKSl4uceSHCjCqIqg74gGEZaKQjAUCml/f9/qgXK57OA2gIfgZhGUQhJowsg27ADRaFSdTsdeSe47zDgIH01Dq9VSoVDQ2NiYdnd3zah1u11v3KCpSCuR4sDS4jlk1AwH8/39vRYXF/X69WtJ8iE1PT2tUqlkRQafSfoEClDswWg0m003N8i1GXHGbFKYXBqFcDhsNgn/c6PRUKVS0cLCgi4vL12EwWhPTU35OgaZ1ImJCZ2cnHhUGMUJsnmaZUASfq4kj3fDY8yhwQzMoaEhe8PxrcFU4eOC3QPoCY6zQjqFt4wX/59rL8lNER7M4ExVPF5IfTls8doyqxz2mqKVQ5LiArYvn88rHo8brQ9et0ajYdCDNST15ODM/aXRr1armp2dtW0ERRDFUK1W84xrfGLBPY/nnQwLZuUiGadIQZp/enqqlZUV+z7JgpBkaT/s0+LiosFV9nTYgOC1pjDDOsP1o7lGLcAeBeAUZLGDf2KxmGZmZlzI4otlb6Lwp8inAac5BCTiawcGBixXZC0FwQaeO6w90WhvljDBYblczoBQq9Uyk8NeRqFFM4Etg1yUtbW1Z6AkLAf7GRLmlZUV9ff3q1KpqFwueyZtLpdTsVjU4OCgJ0kUCgWPWyqXywbLAAfY5/l8QYsFQEcQFKe5DoVCVp+xZ6H8CHrCO53Os/0i2OQRpAmIi22t2+2akSEFmjn3FxcXbpy5161WS7VaTdvb23p6etJPf/pTK6awduAzJoF4f3/fzDshlLxfAsMYifT4+Ggv+M7OjmKxmBYXFzU8PKzd3V2r5YIyVQKTQqGel5VgNBpMPjMAM0GiTCeBiWRPAeAjkZlsERjeoEeePZZQsevra11eXmppacnjjGBLs9ms94tms6l8Pm8wk+dof3/fQAwNLVJ/mD2UQ8jjI5GI5zeXy2UHoVED0phiPSJtHtJlamrKdeHm5qYVYYuLiwbDAIzYc7EHlstlSbK0nhqIUYfn5+daWlryecD7r1arDnzDBkOjCHA2MTGhqakpNRoNh3NdXl46PwaABbb36enJuSyNRkMLCwtqt9s6OjpyPYUiBi83JMzV1ZXm5uZsA6JmAsw+OTkxoAhLf3Z2ZlvKzc2NZmdn3VAz4WJzc1NXV1dWbXLGo7SiZg6+J/ZFPP2cfyjxaCRpPJF+A4jy+avVqubn5xWJRFyzSHJd2Gw2NTDQmxRBUjl7EN7kfD5vIAlggTVFA0wGTyQScc4DqiBG7mJBpXacm5vzPTs6OrKtijULeEGuAjUHezmfsb+/32P2sOMxSozzuN1uW7oOWAWYFrTi0e+glgTwLZVKDoml/ua6jo2N6eLiwuv/5ubGYCrrCgAUZczLly+9R6NiRpHEvsheizoWK+ov+vrTlKMXCoVnf//3//7f1z/4B//gl/65jNQOvlAeU/9++0Ve0T/4B/9A//gf/2N9/vnn+qf/9J/qt3/7t/Xu3TstLS39wr//OzPhFLH9/f3P/HMUGCT+3d/3ZhMiaxwaGtLGxoaSyaSlMolEQjs7O7q+vrbUmgTji4sLb+6Dg4M6OTnR9va2WZ6+vj7t7e2pXq+bZQxKeInbj0Z7sxxp9CnmaGJIIWQUEZt/MpnU2dmZmfajoyNFo1EVCgWdnJxoZGREyWTSch0YmeHhYY2MjHj2K4UYkqRSqaSTkxMtLCw4IRjPIKEuZ2dnlh5PTEzYR93pdCyDgl0DxUwmk6pWqzo+PjbSjLeOop4ClIIZKQzyfvzvwQcRr0+tVtPV1ZXi8bgllJLMcILus8GDonJwIb1lU+l2u88sBRSJQe8vvwP0MBKJGJAAAIGdhem5u7vT8vKyJDk1ErkojAaNHEwUo0zGx8dVLpd1d3fnw4DmlPcIqAHrCyN+dHRkLy6SKwI/grIeDnDQaTxS325wLy4ungX4MMIqnU4/U06wIUuf0j6j0d6IDZ6znZ0dFYtFe/5o2vf29gyGUaBIsqQL+S8KEgpPGidJbpppGhl/t7GxYZT3t37rt3wA8Z6RsMHAcf3x1bGuuUcU3qOjo5qennaeQzA8EIQ6GCrCukahMDQ0pNPTU6VSKUWjUasRkMxy71gjMFSscQ5PggLr9brv78HBgcEJgCJkc5lMxpJVwDqaAMaksPaxRnQ6vdmlQaAxGN4o6Zm3Hrbq5cuXLl6urq4MgKAwmZiYcMIvtgFYXAqDbDZr2X2r1bLUkusCQ4FUOyhtA0wJAigEF4Gw44Xk/vP+KI55RtLptEGC4EFPc4+SAc8o2QQUzoCBhO1xf1jfXG9UG9zXYrGo+fl5h/ewlwEYUbxUKhVls1n19fXStff39zU3N6eHhwc3GJOTk2aw2Q9XV1fV19fnTIVIJGJ1FUnDyWRSW1tb2tzc1O7uriqViiYmJjQ+Pq5kMum9BfXP7OysWchqterrQfEFI4cKATaRIjT4XAe94OzX/EGBwj85I/Bajo2N6Xvf+57q9bp+9rOf6dWrV64V7u/vtbGxYWlru912IB++xKmpKQ0MDFiaS1M6OTmpi4sL7e7uKpPJqN1uK5FI+P4Ui8VnUztWVla8DzDOC9CzUqmYECDHhPwaxhHSeM/MzOj+/t72o2g06okR5KQg0wUgZ08YHh52Qj2sJEwOZ3C9Xrd3tr+/3+uXBHFAX+S17XbbMlu8nolEQqVSyQAt+QEAoNQhFxcXmp6edoOL5YHkdUKj+LxYUGj6ARzZfwBdkdECgDPnHI89jfnY2JjBF0az0QCzl3z48EG5XE43NzdmS/G5YhkCAIRdRoY+ODiot2/fej8gWwJCoq+vTzs7O26OBgYGdHR05HwhVHo0KITfPjw8eJwnlha8809PT1pZWVEo1Av2Avi9ublxjgvqoEwmo1CoN+3g7OxMxWLRjWC329XLly/9s1G2IIfNZDJW0IyMjHjyBSGfqD+oQTnHOJNRGwKGE6jH/VxaWrLCBrUOGTyQBzTd7A14nmmK6/W6JFkCTjM3MTGhvr4+g8/7+/tWp0EwoSSjt+Ae1mo1kxalUknLy8tu0pkOQg2BWpBQRBQeAIv47tnLOB82Nzc1OTnpbCEa0FAo5HOLz1Sv1816B+2LoVAvA2Rzc9NkWuybkXmcFcH+I5hvAQiCugbg/Pr62rU+GSmVSkWZTEbpdFr9/b3Z8zc3N75ffB/34fXr1xodHTX4RB0ciUS0vb1ta9L9/b1VGEGg8P9pr6OjI1svJf0HWfDf+73f03/xX/wX/6c/6+PHj7/Ue0At9tf/+l/XX/trf02S9MUXX+j3f//39d/8N/+N/tE/+ke/8M/6Tk044VMUUASnlMtlS3mDPihYDGTWBAXR/CHhkOQNg/Fhf/zHf/xMTsr4L9AgUhHxI4LUs6A4iEkexeN0e3vrRY7HNZlMqlQqKZFI6OzszMwYHsXh4WFdXl6a5SAEBGkYjTISZqSALGxGL8TjcY8hIRkRSXUul/PItOvra83MzGhkZEQXFxcaGRlRpVJRX19vbm+lUlGpVNL8/LwbLuRbNNBsDrDNBAWx4bN58j6QJU1PT3ukSTqdtgzp8bGXEA8rFrw3zEoGUZbkzYWmPyizYVNvtVpuoilIKaxhowAlYFzwx4OWwm7C6odCIdVqNVsXKAAo+JDZPz4+evMKFtg0JLVazYULWQh40vABw2KwyZdKJYeegIjyWTnwee8wTiCQknwdYFWZW726uuqCLcgQUST863/9r12EwziVy2VLuGAFgjOOORixTKBWoKnkcKM54b6yhmgu2YzY1FmzNHBzc3MqFou6ublRLBbTx48fNTU1pVgspmq16jRsihtGqxA8CBOCz4mihrRYsge4ZjTKNAnsPySHwgww4YADkNTR29tbp6izdikyaN74u/Pzc52fn2t0dNTFLInBXCOAFvIdAANQ+bBfNptNF6n9/Z9GetGA3N31Rheura3pZz/7me/H+fm5i0yQ79HRUY9XCwbzIIcLzi8HRIQRDoVCyuVyLjiKxaKazaZVTIQxwRBKMvvP+ghK0xmZhTR1bGxMuVzOuQE88zTZ3Dukpslk0uMfyQKgcALsQ5EVlF8DdtGEsVZYV5KsApH0LLfhiy++sCc4uFchPeT+sg7Zo7FmvXnzxqPfSLM/OTnxiDUUD+Pj46rVarq4uFAqlbI0nnGdhULBuQVffvmlvvrqK2dbEOjJ3oLVBx8u+x0yfOxUW1tbZlYBGIKsebDZDjbdoVDIa5mA1CBTSdASz/CbN288do06gOczCBoiIYfVYd+/vb11oOfQ0JBWV1ctneRZpQnD0vX09KT9/X3fc4BIGGQAYopLzhMaQdjC/f19xWIxLSws+N8PDw+fvWdkxlzHh4cHF9yoaliPSI/z+bxtedjCyCsAlIhGo87T4UweHBxUo9GwWopzGTarWCxa7gxLjhKOZxslG3spViRAsGQyqZGREWWzWQNtAAYwZ2R4YJmCQUVVh+x/fn7eDT1S2dvbW6XTaUuWaYpptILXivoM5V69XnegGoq1YLgvteP29rbHzHGf8E739fVpbm7Oakbey/39vRYWFrz+vvjiC+3u7hr0oWHBxkRdA9uLPQzvLdk70WhUR0dHzwB/CIfR0VG9evVK7XbbI8eCuSYA9Ovr68+YWrJxAFNRM1DHMTEHBhf/PCAuKhieWxQy7IWMV+Re4Fln38Pqtbi4qNvbW52fn3sf4/wmO6FUKunq6krZbNZj4gj9pK6iBuW5hnhijREShmwaW+LDw4Ptd+xlwfBJACVA3lqt5jMQexNgMM8CORGA7ezvp6enVsIlEglbbU5OTlQsFl0LUCuRyE7qOr1IKBTS0dGRLi8vHdCXTCb9bGxsbPj+dTodHRwceF/jGp+dnRmwxXJwe3vrXoeMBIguzqd4PG41i9RjflG7ABgDyMHqQ4D+oq8/TSYcZcj/1es/+8/+M/3Vv/pX/0+/Zn5+XtlsVrVa7dnft9u9UX3ZbPb/8PsIvH3x4sWzv19bW/NZ8Yu+vlMTPjY25oRZBsg/PDzY800QVFCyQ5GQSqXcOIHec2jQeBKQdXBw4A2QcCUQ783NTUlyUbawsOBwlEgk4vmHoKwgc6B0HFzn5+cuyAiO4EAoFAo6PDz0e0QOCBNKQBiSPWQ/IO2Tk5Nm8DhEkZggnwU5vr29dUgMDBjy0nA47DmcV1dXlk5NT087iOTy8tIhHyD0oIJIYUG1aXonJiZc8LHBsnkHETHkmLBGMPHIcL/88kv/LhrZdrttHxnMCMwfmyFyGZp4mh02D3xnbGpBKTQeMYLYAESYrQ6LDNvD5+MAhQUF2e3r67PXDpbw5uZGpVJJS0tLDsmiyFlZWXFxwe/moGR0DlJkDswg80SoHgUyRS6HKgUkUjT8OYzaoMmBiby6unJ6Negnh8De3p4lcBTohLE0m02PtpqZmdH8/LyGh4dVrVbtT+O54f7DvIHsSp989gAMSONIkaf4g+F+fHx06ioNLH4/JJisVfYDnjHG48H2M8oFdotnmuaQ4hGmkvGGNzc39uMFARP8yKwzPOj9/f22oLBuuG8DAwOWulGUcJ3wl+Gd5XDH34lCp1armUFi3BpgFLPGaZKwXiCNBuCCfSZ0Bnlj0PaSzWYdaMS4FRgbVC9Bq0y73Tb4ODw8rHg8bjYfKwoHO2AazyrP3NzcnMdAwioEnxvuMUBg8BqjUKA5QJ6HFSXYkNMkA5xGIp8C7mC7afSj0d5UD1h3PgvMBV54gh6DY9Q4u1CALCwsaGCgN/Ln/fv3vpcDAwP6/PPP7cVj9BF+f2ShrVbL6zOVSml4eNiWKgCJTCajn/zkJ3r37p3Pyffv37sZn5ubM8DE2QeTC3CHsinon8tms1pdXVW1WnUYHo14UKIeBBJp5g8PD9Xtdp/NF4bl2dnZUTgc1q//+q/r137t156l+wMyIdGk8YLZhfHtdDoOL2OPxksaHIvJZ2JdB9m2crlsoA8pfNDCQgI5Spv7+97oKpL+8REDfrOHvX792unXgB2MW6MJ5LkH+AeklvQs5BSw/Pb2Vvl83qze3d2dQ6hYf5zxFN94sgESqCcIUVteXtb29rbP5aGhIYeujY6OGoCpVCoaGxtTsVjU5eWlJebpdFqlUumZmofZzPwOmPfJyUkHsrF30lBVq1VJvYKVgDkA5L6+PtVqNYfPAe5lMhmNj4/r/fv3WlhYUF9fn0EOZkFjW3h4eHDiOMF0WKKurq40NTWlZDLpeegkzENaMMLs6upKx8fHlhsfHh56z0UGzHWh8aQZoKHkPQLAPTw8WOVEIjfXnP0WYoKxhuffjJ5KJpOq1+u6v++NtP3w4YODPQna4nMyd5tnD+IIEA45NeHIhOFiZ5ucnFStVlOxWPQaYt1w9ktyPfLw8OAw3/7+fnvqeZYTiYSBx6CCDgUciksmRVAbowAbHR21PQ7fOdkrAHCxWEzHx8cGw1DEovgLTvUh/HNiYkJ//Md/7JDUsbEx7ezsaHl5WY1GQ/v7+7bnUdtCuJVKJeXzeVseUbKieqP+RtVFA91qtQxSsGcAfpNSz/vNZrPKZrNWB0DiST0ZNurbvb09K15QAjWbTZVKJdtfgnsIZx17D0AkVrJ4PO59l4yuYrH4XdrD/8e9UqnULzTv/Nd//dd1fn6uL7/8Uj/84Q8lSf/b//a/qdPp6Cc/+cn/4ffMzs4qn89rY2Pj2d9vbm7qL/yFv/Cd3ud3asJBzmlAgwuEDZfAEpotSZZbk8CXSqW8iHjYKTgIdGPk2fT0tDKZjDckZu/V63UHJgQPpv7+fqVSKb19+9aoKs05gVEPDw8O4IJZwFMUCoXsAWfz4cC5vLw0kACDms1mdXh4aHQKmSfevvPzc21sbLihRw6J9ySXy+n6+lqZTMYeYORfT09PDmdg9FQwPAiJ+cDAgJFCHtJoNOpZpRS7AAqMLYM9At2lWSwUChofH9fJyYm/Jp1O6/3797q4uNDS0pJTdvHPUAjQTBNwNzIyorGxMS0sLGhra0uZTMbyUn4nDDAbWlC2l81mjQCziYPkgdiVy2WvDQodUFCaq+PjYzUaDfu9QdOC42UeHh48dgRWC6AJKROgCWg6TQSHC9J02GcaDhLLQWYlmYHhvVK4kXeAZA7/HPcKBuf29tZ2D0ag/OxnPzOTMjAwoGw268M0EomoXC7r/r6Xfk2S6uTkpK8LBSnMNhYASS5oYWZ5nzQ47XbbB0sqlVI4HHYSL6wrjdzj46MPFeawAzZxr/El39/fa3p6Wul0WgcHB34G19bWdH9/7wPx6urK8kcOZUaNgOyGw7005eHhYSf7drtdp1ZTSFGccN8IjqNBhK3ja0DkBwYGXIgzDgWwicJM+qRK2NnZcfp0NptVOBw2i4RHDNUC3nYAFf4d8IZRi5eXl2bkYOFIUw+Hw5bX7uzsPLMeUIwODw97n6xUKpaTE9YDC09jxf5N04bkb3Z21r5UigDe18ePH31dpE8MOsycJDdN7JXsJYCrPNucATRsFBIw4rAgWGQSiYTfDwoLrg1sAGcavkmAHdhU1geS6T/6oz/SwcGBstmsXr58qf39fatKGIPHHt1qtXzdR0ZGbF+ike50Ok62n5mZMcBH414ulx0EVSqVfDYDSEkySwazU61WvT75+v7+fr19+9ZgGfci2ITDxtA88x5gyEZGRtyAJRIJbW1t6fHxUT/4wQ/s10UhBcBTr9cNRHH2whbxc7GOAEZw/wBmWX/lctn+S9YIKhXWauybaQFbW1taWFjwuDL2L1RHAEyTk5PeIxYWFvT4+OgwRoAGPvvx8bFarZazJWBOKMTJvOHz7+3t2ZbFJBXqAj4bVoy9vT1FIhEdHx/7ecRjPD09/SwjAzIDAoBsF9YqUmUkrdInRmx8fNzjwGBE+Z0AFpFIRKVSSfF4XNFoVNVq1TO3yRhAJQLYwhhCzoarqyuzz0GlEGOyLi4uvP5ZexMTEx4HVigU3IRBUADEAUDlcjlnEaXTaXU6HXuvUTsQOAoIxnPHSCtUiZABw8O9+eo0XcFah2eGBpTpP0wjoU5EZXN1dWVlyMLCgps8akqYZPztrKezszNNT0+r0+lY0g5wGg6HPdp3eXlZnU7H4bo0ZNRi3//+953rQd3FeVooFAwsAESjEGItMSklmKbOfaTR57oFpezs6cPDn+ayN5tNTU1N+flgzUKesA+HQiGDXFdXV3r58qXW1tZs7SLzQOpN6mg2mx4dCBEI8TA6Oqq1tTUNDg6qWq1awbC+vu6sKprXm5sbPyP0Kuz5w8PDOjs70+3t7bOg506nN4aX8GhsNoCtkJhk5qTTaX9OiEdA2e3tbeVyOT09PRkYAVhAps7z2e32QqgrlYrVgViA8PkD9DKmERsTgDZAOZ8HUucXef1pMuH//36tra3pd3/3d/Wf/Cf/if7r//q/VqvV0t/4G39Df/kv/2VbQcrlsn77t39b//Sf/lP9+Mc/VigU0n/+n//n+vt//+/r+9//vj7//HP9t//tf6v19XX99//9f/+dfv939oTTrLDIkLty6OPJw4MN6hMK9YJ65ufnjfIxX3hqasqFPdJe0FV8onifkGvAWObzeR0eHpq1uL291d7enj2OHISHh4caGhpSoVCwDAwJM+g4jC0hSjxA1WrV3nFQb7xghNVFIhHPtmTsAgEWMzMzXvB4zUHUQKdhQmEqx8bGVKvVDHjQ3D4+PnoTANUC5SdZHu82jS4FDAc9CoBgsRuUORHGUalUfPARStFuty1pBBVNp9OanZ3V4eGhGQWQeQIpGLWDdD04XghpGxL++fl5XV9fu1mn+WQzRqb+9PTkJiEUCrnhIzUZvyKpqVxjClgYOop7ficzPAkjYcwESDsNM8xcX1+f0cWxsTEtLS35WtCscR2QrLGuOKguLi50dXVlIAWAAM8W4AvoKA1MLBbT9PS0A77m5uYsaQMwq9VqZpBRNoTDYQcZASYE1QasE6TEMBFYNjgMOJg42Bk/gycRvxlrGHAHRQOHbS6X0+TkpPb3981s8gdbyfr6uqLRqFZXVx2MdXZ25sIV9JbGDRAmOJIGOWM+n9fZ2dkzaXMoFNLS0pK63a4Py4eHB8uCUT+wJ8FGXVxcKJ1O69WrVwYeV1ZWNDQ0pI8fP6pYLFrqPzAwYKl3o9Fw5gDJsfF43Nf15ubGM04p/E9PT31os+8AINDM0CDAEgIgcsh3Oh3t7+/r8PDwmSyZ5oeDmj1gYWFB1WrVnjMObpoclBxSrwgqFotuXEHiKXhhZgA/kGFKskxycHDQ6hoQfZiFvr4+75k8C7DwNG3IWwEPsJTgI5Zklgy/KQXtxMSENjY2zA4ALtAEMTu5Xq9bmfH7v//72tjY0MzMjFZXVzU2NmYlFoAvQA9WJNZkPB5/tvexD3GW0Wzg+x0fH9cPf/hD3d3d6V//63+tu7s7ra2t6f379x65s7q6KkkGYPf29gy00EQxk3dqakqzs7N69+6dmyBASuxh8/PzPns4exn3dHt7q1KppHfv3ml2dlblclnxeNyNDzkt7B0AtkGLR/Dfg2F6eJg5JwDAUC7gl6XhluT1Ew6HfQ/Idkmn006u5pryHJ+dnSmdTjs4lHsYDoc91ok9k3W5t7en29tbZbNZJ7yzf9IsdjodnZ2dSepZdghhpEnAp1+tVk1SIKEfHh62qoU9AKaWohqFDJ+F2elTU1OW46OEQo0Vj8f92VB2wJDSIJFL02q1NDk5qU6nY4YOL3R/f78VkAC22Cf4faSl47VFkowXPjh3muyJTqejvb09gzfYLWKxmFni1dVVqwPxwDLmEGA4HA6rUCgok8k48Z30+u2q2QAAOOhJREFU+cPDw2eyedYd4MzAwICSyaQ9s7VaTalUSs1m089Gf39vPOHd3Z2l0bCOkvy8EUJGjfD+/XutrKz4PQdtL9TMBHUGLWWwxGQPNJtNVavVZxlEo6OjHj1J/ce95kxBITUwMOCxfCjDWB8E0QFo0EiWSiVnBw0PD3t6wuDgoAHOu7s7vXv3TmNjY0qn06pUKp5WFA6HNTs7q06nY8CAdYISYmRkxGchtQ/S+t3dXTWbTQOO2E1DodAzqyfqLoIs2+3eiNCglZGJDVtbW+p0Oh4DSbjw+vq6m+ButzeiFYLx5uZG6+vrKhQKvmcotzgrIHcIdoQMYIQznx05PM0+YA7fRxYCKiHUZNibsHrC2HNGc7a0222fx61WS19//bWKxaJVAoTdnZ+fG4AhC+f/K6//7r/77/Q3/sbf0G//9m8rHA7rL/2lv6T/6r/6r/z/W62WNjY2niXG/+2//bd1f3+vv/N3/o4ajYa+//3v61/+y3+phYWF7/S7v7MnnIYNFhTpFCmRoI+gXxT4sLOdTi8pl4WOfC+VSml5eVlnZ2cuhlj8T09P9psAAJByGgqFjH7hixweHtby8rJ2d3ftMb25udHi4qIRRryTbOw0NiC+HOYUxJJckEWj0Wc+GeTkhEuArIOKgUZyCCNDury8VKlUclop6BOSfor9u7s7z9GOx+OW2OMngcnodHqj1vBRSXLhSEGeTqe9CdFcMV7s6enJjMft7a09VHd3dw51AGEm/OPNmzc6OztTvV5/JtPn/fBZmQMJM/zZZ585dIrrySHBuCWKbP6ddYUkmnuCp4VD7NvMHGwvzTrsCOBP0McOEx0KhfTzn//8GUNG0FpQZkcAIYAUMp+5uTmP3aCJkWQpK/cYQAfpKFJyPGBYJSgs8FhK8r8T7Ib0lfeMjw5AAiUF1ySXy1kWSagdzRs+P3y5SPAp3HheotGom2QS/pGkco/YxFqtlsdsXF1d+bnDu01DzXulMEKdIsmoN17o4OiyIKNE2Bns0vX1tQsECk/m3gLwdDqfQhKDQV34NpEqoyAgaR71DcAA+xoH2unpqWZnZ81kESqE745CEmUOhyaeVxKSJdmDSNPQbDZtb+B6IzcGaMNfl81mNTk56XuH2oawL9hR2AEYGpKpaYaCuQVIyFutln7rt35LuVzO7ARBSYSFYUcB3GLNE+wFwIEUPsh+BuX/FNrI5IMjZGCGKHrYJ3n/qECQ5wKOUbh9/PhRFxcXikZ7o/ewenB2BUONotGoXr9+rePjY8ViMb148cKy42Kx6L377u7OklQaPeTOXANJPt/Gx8ddIB4eHuro6MiMPsqueDyuP/Nn/oyLMtYaPvvPP/9ciURCtVpNmUzGgZKFQkEPDw9WhdXrdQemIavn3sRiMcXjcR0cHPhZpAkA0Lq9vfU5jAwXIBFQF0YV8BQwBPD+7u7OmRSo1yQ5zAw5MGuaYCVJLs7r9brHTZKFMD4+rsPDQ11eXhpsIL+GMwMAZmpqSu12WxsbGz4vaHoBUhYWFqy243mbnp42+xy0uqHY63Q69kXjCx8fH7dnFrk79wWmEJXOw8ODisWigTZYUOoWFB3sLYxJBYSCLR8bG3Nd1e12rdaamZmxha1eryuZTCqTyWhsbMwAPOGOjUZD9Xrd5zANxtbWliqVitLptO8tDWa1WjX7vLOz4/2cc5YzHyY0FoupWCzaox4cycV+XywWlclkVKlUDKaxB+ZyOdvzgo0xoD/rbHt72/eJ31GpVEzC5HI5jxxD5UjjMz097SwephzASLIuwuGw2cZSqaRMJqPDw0NbM6nDIGBQUAJGcN+vrq6sYIlEIkokEgaOYdnv7j7NEkfxBJu7ubnpvfj6+lonJyeanp42EDU8PKz379/bGorFUZLrOBp3lHkQC4eHh/79hUJB5+fnBqRnZ2etCCADgj2c/ZZrBEA4Pj5u0L/VanmfIdSZCRR48Knt+/v7PY2hVCq55oJwIaSOn0vwYb1ed58xPj6ut2/fGvAJhULK5/MG2akT5+bm3ATncjklEglVq1VfT/KhAGkg3AYGBnRycuI6l3A/9ttkMmkVWtCeMzg4qP39fXW7vdGtb9++9XQGpunQA1xfX9vONzY2ppGREYOHZNCcfzO6dXd313USSrrp6WmTL/QHv+jr/81MuNRTIfyzf/bP/oP/f3Z29v/w9//e7/3esznhv8zrOzXh5+fnTkakmSZVGIa22+0+YwEGBwedNH17e6ujoyM3Pclk0uwfCAMHGf6c1dVVe35hIDh05+bm9P79e/sP8XiyGGdmZpTJZDwrD3kI0nCYVIocUPKZmRkz+O/evdPExIQWFhbsF81kMpqcnNTu7q6KxaKGhoa0ublpfxAyWjwYoVBIx8fHSqfT9rLAThKmxDWlcQ2m2RI0glcNOQlfGwr1QsFIhJTkYppQGhgfik4eQIp9pJxB6dHY2Ji2trY8FmNjY0MTExOWzYJ6Hx0dGbRAko9sEQScDRfJEYih1FNYAFogEwasCDbWQck63kXsDJKeNd98fsJ+kCsTkoc8CVAFWXcikdDa2prq9bplZzTlFMCSzPxSUCPHwmfX6XT04x//2GPmxsbGND09rQ8fPujy8tL+5+vrawfM9PX1OQgE9oyinpEfrL9ut6vr62vt7e3p6upKP/jBDxwGws+lYczlcvYl8qyCbNNEttttKzhAziUZeDk7O/NhGYvFXJzBQgRTs2EvYASQvj09PTm5HrACGXwwE0LqydYonoIS7IeHB48OgvlhFCBecRgcigp8+EtLS9re3nawD2tqZGREJycnur+/N4MelEYT7HN6emp0msaJYr5arboYxhs/OTnp5woVB2xyPB43KPTixQsDEjBEKCEoAkC9AbV4fzQbrHHYehJuYfA7nY7evXundDrtGbbI/oJzqPFKBsMC7+7urE4hIDEIXBEwh2UGhoqfCbuI2olnKGjHCPrgKZxhPiWZFZJkBh6lCazR/f29VSfIRfl6CkmadRoBgAUADMCvwcFBe+zy+bylzvze4+Njh/hNTEx4vnFQrVIqlazkwasMy8RnoLir1+taXl722gLYpulBmgtIhMcQWWKn0zGgXa/X1e12n9m1UMAgI6VpoWCcmZnx8wULg5R9bGzM+R5IMHm2WcvYzLa3tzU0NKRqtWqAl0R/ABWsY0g7AXPYU1H50Eyyzkjbp3kOh3vTILa3t+11L5fLBntYozRI7969M2u9u7trlp77QOAcSjOAC1RPnFkjIyPa3t5WoVDw1BLAo52dHU1MTFiefHNzo93dXc3OznrvYO9DcXJ9fW1GDFtVt9tVqVTS4uKigR9AJ9YOygEUgwAgAwMDVk7w2Tkvg88u95v8AM4LiJS+vj7fD+orvm9wcNDhVMPDw/aLI7PHtoXK5ujoSGtra65BmA8PmLa3t6epqSkVCgWPsYR4QL24sbGhbrdr8LnRaCiRSGhyclJ7e3uWa+MLh52kiabhwzcN0Mj+MDs763GQ1H+RSMT1IEGphOYFm3heKNxQT8zNzTnM9fj42DZICnrUaQR6NptNq7CQX1PvUOfSeF1eXj6b9EGjDtBerVYdFIZNKZVKeVoKNfnCwoJBOCxUUg/wJnl9YGBABwcHZr2pRegDgkDa0NCQR2bCPgf3cYiTUCjkRhqwk1nssVjM6sVcLqdCoWCCanNzU4VCwWd+o9HwWDbWNmrL4L6HqqPRaJj5Pz8/1+LiogEFVEqPj4/PFCMoEVm7NK/YbQAMrq+vlc/nHXYbDMEFFDs9PdXt7a0BK8hMSbZHUVdAGKGYwFqDtQp7HxJ8QBKUKZxvTNfBxvnw8ODpCdls9hlJwxkdPH9/9fqTe32nJhyPwczMjFm/q6sr1Wo1ff/733chjsSBooUbzVxAikYYBxiTu7s7o1TtdtsH1+DgoI6Pj43Swog8PPRC4fDxMEeQQolAo7GxMTdqhDQ0Gg0VCgWPKSF1GbSMZE7QLSQbkiyhJfGdDTc4Lxi2rVgsmrWjOZV6UlKaMyTOwUAgEkQJdaIRhJlDpggjzwMN0k1jz0YUi8WcsA4bDIuFLJNANdgGguyYhTo/P29mNhTqhXHF43GH9KTTadVqNTdFm5ubymQyRidjsZhBlPv7exf6HDKhUOgZWkchwIumnq+n2UCaCKJHQT40NOTkXr4X6RWNPRsdc3jb7bY2NzdVr9e1trZmKR+IPSn8FGlIAmEraWKenp40NDSktbU1tVotbW5uWsbJdQ4GSZ2cnKivr88jUVhHBK6gHuEApkClKCUQBgkzTUQ6nXYjiu8SKRoWA1DZoEeW64N0D3Q/6BW/uLiwDIqgP9hH1DKErFxeXvpwbjabyuVyHrHCDOyJiQmtrq7q48ePfg+SzIzS0AZTqmER+LxYAoIHLmuHTACUA4S9sC5gW5Bo0zzB/NLUg85HIhFlMhn7QmkosSiUy2Xd3NxodHRU6+vr9uiHw73AtaOjIz08PBgkQOmAFw8JO74zmq5SqWR5Jfdjdnb2WVMRjUadb3B/f+9iZG9vz0Bcp/Mp/Or8mzGINOmAgwR6oSDCygLLxz5ydXWlzc1Ny8T5/xTikUjEIBGeYgBCWIMgEBsOh73nsWdxBrH+g/7fRCKheDzuIDuKMBpi1mXwnvKHZoAiZ3x8XJOTk+p2e7O4eT9c7y+//NJNBDK0ZDL5bMQbzJEkpdNpAyTv37+XpGfPMLang4MDJZNJyzwBdtjnAPsAytjzg+Pc8Df+D//D/6DJyUlPIgg2y5FIxGP+KMxggmGuT05O3LSxNmFoSBFmPT08PDg8qdVqGbQgQRkQPgj8AQLAwiOd53mngKSJYi0yUiafz3u/ozGC5cJuRjEZiUR0dnZm1o5xW2SPwEjBMNOM8Izwc2j6+X5Ab8BhFDLBxpfzCXCYfZAMmsfHRyuWAFxRz9CsnJycWOLNvsX3UDeR5Cz1UpxhACW5EYDVBEzvdrs6Pj62x5GkfuxBqHoYW8QIJZ6XoBqA7BRqDaTtsOGElN3e3mpmZsYsOnkVeKMhSc7Pzx0Kl81m1W63NT097fqr0WhYYbi/v+8g2fv7e62tranZbOrNmzdKJpOampqyHQq58dzcnK1ANMUodLrdrq0BTPrAAkFdVyqVTAbhaebnw/gmk0lVKhWz6H19fZqamnJtw74MWcA6BFRhbjf7BYw4yhqa66GhIR0dHWlpackgICA+tQQ1Few3dQRZCt1uV+ffzMRGTt1ut90ko5ADFDo5ObH8nHVycnJiBcje3p5mZmZcpwWZfvz+weeafRk7DiB5q9VSrVbzHsYardVqfh4AmQFJaLLD4bD29/e1vLxsNQBWEdYfHvxut2vi5/T0VHNzcwZLAcSPj49VLpeVTCZtV9jd3dXY2JjBuJGREZMOzWbTIcrVatXqrJmZGZ/bnC3BPoCzvd1ue8wza5Nnen5+XpVKRQcHB65jADgB8tmn2E8BpxkdSz1VrVYNziaTSa/l79KE/7+dCf+/8/WdmvBoNKp8Pq/d3V3F43FNT08rHo/7BtIcIad8fHw0QoSUikAdvNr8oWlBrgyKiaQUxCkSiRh1hslIJBJm3ykICSQhCViSpqen/TMymYxOT0+9kSG7leS4/6GhIf3gBz/Q4+OjpW54xPBE7e3tOewB2QoN7eTkpBt0GBrm7fJ5KRiRcMEIkVYOK4VMZmhoSJVKxam0FAo8pFwHNnm87Px9PB639/38/Nzf2+l0LKvkvWWzWctokDvV63UHbwRlMkgZmfkK+pdOp+1jAxHlYaIJDgbgsRZoZmh+aITYyGAFJdlTNTQ0ZCCEw7bVaplpgOnHtwXaR9EJs0FQGPcN2dfIyIgZae4ZI5iCTDbFJIxPLBbTzMyMPnz44NyD29tbz5VGJoxEHKAAm4AkKxkeHx8t7Z+cnLQCBT8YPjD8hOfn54pEIvrZz36mSqWiubk5zc3NGZWlYON3cPDx/GD/4MAIhUIuIJvNptH1oP0AKVyr1TJzznNNc4yaA6lZNBrVy5cv3SAcHx+7mYtGe6Otzr8ZqcP9DYJjMOrBgBzABxpmciUYFQTQRJE0OTn5LMgKjxryVA5BxgMx0gW5KMU6+2CtVjNjyPMPy0GTlc/nHdhF00gwHAwYwCWfB7m8JEs12WuxhcRiMauHYHnx2ZbLZcvUut2uEomEIpGIFhcXvY+gfqAQJeOARpA1IsnMNWuI5HCAEho/GnYAFRobGATWD6wM5wHNHTaJYGYBTTny1lgsZiCj1fo0PQC/Ks8p7xmvJYXO/v6+ksmkUqmUYrGYAUtklCcnJ/rw4YN+7dd+TVtbW8rlck6Whfkm54LPgA8fKevw8LCBJu4jexHPFsAQAAWsJSwo14JcAvyXMIFff/21SqWSR4cODQ2ZNUT22Wq1dHR0pOXlZZ/RNKCAUtfX1zo6OtLZ2ZlZPM4X7jN7FCOsarWawYvFxUU3NbCM5InQqAO+ohZgDbBXA47TQFMXjI6O6kc/+pHi8bh2dnZULpf9DFF4xmIxn2OMUAUEJHRqYGDAzxRNz/HxsY6PjzU3NyfpU6I5HllSrhuNhu06ZJ6g+ri/v1cikdDY2JifCzzCl5eXOjs7c1MGe0lQ293dnaXK3W7XyeOsjyDoTdgZieGsv1gsptPTU4dKBZunzz77TJubmzo9PbWqAykxHl7YusHBQW1tben6+lrFYtFnHNY/rICMSuNZHxkZUT6fVzgc9kgxlEVB5QvNIAGh2E2SyaSl/ljiNjc33fCkUilFIhHNzc3ZcrG+vm4FBioByKJ8Pu8zo1Kp6OrqyoolEsZTqZQajYZJJBre0dFRs6Sjo6MeQYd9cWRkxE03Se00PYCiqOTIZUCRdXNzY2UIoxsBDmny2S9pZqempjzakPqFuo4mnzocSySqF0CVSqViMIy9d29vT0tLS67NqLX7+/v16tUrq71Q3/Bsn5+fm4km5AyJNkFk1FOEN7MuJZnkoB5Op9NWlTw9PanRaDyzI42Pj/vsw7opSZeXl5qfn9fjY2+udzqd1ujoqL+fhjkSiWh3d9d7GHaN6elpPyvsszzjy8vLJsYgwyARsSdwniUSCbPvrPXp6WlnvAAw7u/vO3+FswhVKSAI7DuqWqZANJtNZ11xFpArMjg4qEql4ueA9YAV4Pj42COMqdexwtXrddt/fvX6k399pya8r6/P/q9oNKrt7W1lMhnlcrlnTEWtVlM+n3dqJh6LgYEBBzNR9BImMDs7axT27OzMDUVfX5/29vY8zw/fN6mHBORQtATHhjDSg6IB5p1GJhqN6vz83EU8Td709LRR7t3dXctbYYKRYyHLbTQampqa0tXVlQ8gSUaGDw8PjdIGH4bb21sj7ouLi8rlcj6MkYFFo1FLmpDV5HK5ZwUoDAxFcz6fN8OP1JsUcdjc8/NzLSws2D9KsAwSRQqyyclJF6fIryYmJhxQgjyeFGk2XIqsr7/+2sw8DzrIKgUQ15oGCqYRFgs/JhJDmAauB2sTHwtrEdkpAUeMqeD/4ZOlSEZ6xIxrEikZDca1qtVqZo+53xww/f39mp+f91xoki3j8bhi34zUqNVqRqkpwvnvUCiknZ0dpdNpF2tsjuVy2e+XQgNgJh6Pa2RkxMzQxcWF4vG4IpGIPn78+L+3d+4/bZ5ZHj++AQbMzWBsY7Ax5pKkyaTVTDXS/O3z08yomioaaEoIF4ONbbCNcXzBXA32/kA/376sdqXO7LY/VM+RVqtpm4TY7/s853xvR6uzyuWypKUwAPh8kerCjH3+/Nni8bglk0mpGYLBoBDTXq9nnU7nRYOF/5/k2ouLC6lnut2uTU9PywJC0/bq1SsNvHi+uNxZnYRqgsEuGo1KFbO/vy+mCasH9gYvO8Hf8+zsTM8r7EYkErF6vS6mGuk+YA2D+sjI81qjZrOpRGuYQfzWsG6BwPOKrnK5/CIEbH193cbGxmSbeXh4sEqlYul0WoMx2Q9I0pHd8a5iHQgGg2KNRkdHZQ9pNps2Ozur4B3AtbOzMzs+PrbBYKBnkmYWdU6tVpO9Ip/Pv/BS0iQxiGMpYrhCJVSr1TTEeuX9BO0xYPGO9ft92VkY7vnvzX5O+SbfgGcBMAwZJwqC4XCo8COUO71ez+r1uiTVBOXEYjFbW1uzg4MDDQgksvPMDIdDBe2Mj49rc8TT05OVSiWdMQwIyJpRVMH2YlVg4AqFQmq8np6eFCpKU4xyBgCGs5SzDta+VquJHctkMpZKpexvf/ubVpkhSdzY2NB3wZ1K4jHPp1exUqvVbGJiQuwNaf8MxuRWsPGEhhDwzMxefOes4ASc8iahYzegV8CuQf7LxcWFpOs0lrDL/Dvvr49Go5bJZHRO+P1+DZr8d3y3DNUwnT6fz9bW1qTG8ErTYfjxbcI+BYNBgfdmpnVFPL+Ao36/XxtYuKsYFLDm4akny4Zcina7bc1m0zY3N63b7WrdFmvGsK2gOqOp5lmdm5tTTk8wGLS1tTVZigKBgFWrVbGlsO+omng/UclwrzQaDbHNABIM1oQPVqtVfd8oFDnrkCZjzRobG5PHeHJyUiABAE2r1VJA6JcvX+xPf/qTrHqPj4+yYAAaX19fWyqV0rtXqVTk1Y3FYpbP5wVU+Xw+yZS5C1CFsDoXZhnAi+G53W7L7hSLxSwcDkuNxDMUiUTsw4cPtrW1pUEcRcHMzIxIBMghv99vhULBHh8fbW1tzXq9nu3v71symbSVlRUN1tlsVncWwXLezUVYtwqFgnIC+HwgNADd2+229Xo9i8fjYodXV1dlx+Kuenp6sqOjIxFF0Wj0hXqFP4cVkzDUuVxOdgayelDZwuxXKhXdr14yArCLe4Z7Cyaes+f6+lqbgo6Pj+3m5ka5GiMjI5bP52Xto3fk/MGWcnFxoT+LZ49BGSsU/StbefhcUXB65eP0HKenpwI3Xr16JWAZ2y2qLQCkUqmkfptej89rdnbWYrGYFELYAOiRsdDU63V79eqVfnZWDTYaDVtcXLSVlRX1jV615i8tzuJfsxwTbs/+FRgsmAh2NiKNhomgqebgZmiEnWUI5eKjYZmenlZYGezhyMiIvXr1yiYmJrTarFgs2uLiopmZPEePj49aV8KKCYYgfHY0RVxAeDeQod/c3LwIgYABGBt7XmlAkBFsPSxIo9GwfD5vU1NTtrS0JGaZAaDVaqnhYEctQ8nR0ZHd3t7a5uam5HTdblcMHC84nx0HVbvdFhPC5cMlhyxmfn5eEm+Y8NvbW0nFCGVhACfELhwO68+dnZ2VpJAkU0LrWOUG68PPCZMZjUbFhMKcepsukGmaevx+DD5Ic3jBaWpAeWG4eSa3trbs+vr6hTcWBowBHuaYsB1kn4Twob7g13hXwSBL8/l88sITsAS7amZK/5+amlLDPz8/r/UnAFMg8HNzc7a4uCjmNhAIyHfIBY2vNZFI2OTkpCSLKBMAeOr1unbPwrZls1mxbSg1QE9BawGPBoOBmm/SwtPptLVaLbGO8XhcCbo0g/3+c/osz1O5XNblRzAdTE4oFNJ+WAAQPjcYWxqDwWAgtQEoNPvMeW549vBzeX3vgHkzMzPaIY83C0AoHo/b8fGxzgDkqTSL3W7Xbm5ubH193SKRiIDEwWBge3t7kh+yD5b3u1KpqJGGCePSRz5MGJ2XXebifnx8tPPzc4vH40q6n5ycFEPQ7XY1OIdCITs9PTW/32/pdNqGw6FYLb/fb5VKRXJ0kss5M5Gv8gxWKhV5gGmKv/nmGzs8PFRicyKRkPLBK++DNfYOVQx5sNoopWhsAOdgWL1gGs0OzSKXMf+ewFAkwfzeFxcXVq1WBbKyJjOTyWjIwbLS7/cVVAjA2+/3ZU2Y+WmHNecTwPOXL18snU6LwcMKw7mBvBVLC81ZNBqVtx/mGwbVC/wgM+Re8yYiw4yjtuK9QvaZyWS095m8i1gsZqlUyobDoSxDoVBIbA8SdawKZBhwtgyHQ4HNgLzesNBAIGDZbFYbNmCO+f+ki5MNwu/ltQlg+eB8L5fLVi6XX4AQfF67u7sa8AEFCBrEI0yWDecHKjGeI+w5pLmTccDAzzlQLpeVH+KVmBPuRigaZzigiDfskrtzZWVFzyoKNt5ZtmgAWpCpAnCDnJthg97p8fFRa428YBYDA4Nnp9NRvg5rw6anp2UXzGQyFovFtFeaFZibm5s6/8kdYLDnM43H49br9RSyRngkTTpAGft7T05OxFYWCgVZqCKRiEC8zc1NqQdR/aBWxA9NSj0KMTPTuw8whzXO7/fbxsaGlctlkQwMQQTsDYdD3RUoL6anpy2VSlm9XhdwxlYJv9+vbTIzMzOyepHhUSgUbGFhwa6vr21ra0vnCBk+AJKAHD6fT0w59zvvEZ9XIBBQ/gG9ML3X1taWLSwsyOrFmYA0u9vtvvg+sQ3QNzJYYjOrVCpWr9dtaWlJPdjT05PVajVrtVq2sbEh1Q5rNWdmZuzo6Mg2NjZEeKVSKd2NZCnhoY5EIvbx40dLp9M2OTmplGksUGytSKVS8qCz8nZyclKrN1HM0edCOmCr5D6i12YNGZuJAHEJd768vFQAKr0JwY2QL5xvpMbzbmMp4WdFZk8vigUVjz99I+/74uKilKwo+TirUe7Rt9Ov+v1+nX0M3UtLS/bp0ycRPwAU8/PzUiJBGtFf0w/+knJy9P+8/q0hnIeQMA8aXPbJgbjgAwGRDYVCdn5+bmYm+Q8DHQ8lTROSkM+fP2ttx6tXrxScxQ49Grp4PK7F9/wzmi5v4BVDaz6ft2QyKd8QjHEsFrNKpaImib2EDMIg7fz7fr9vZ2dnLy5FfCNc3t6XESkTErlarabdnHhnYKi5aAAHuEjGxsbUhMIYw1ajQJifn9cAwYEyGAys2WxKhmVmkgkhG0MyRaAeTBp2AdBc5HEwEUiTYD3K5bKawoWFBTX6sLs04Lz8sEI0X8gGuRxorvkevQcOz1m329WlxOfCAMdwg5ydIY7DEvR0ZGTEyuWypVIpa/+0ziaZTCogkACW2dlZNTwMXY1GQ8gkCCfNWKVSERrKs43iA6kQlzjsbTQaFRDVarW0biQQCFi5XJbEjUR+LyOFT86bRo20NJPJ6PKikfQ2vPz57M81M8vn82Zm9ubNG3lRAZgA2xiEGdZ5bsLhsG1tbUkdgX90dXVV0v5Go6HE12q1ar1eT4m0eNg3NjYkCby4uNBwdHR0ZGY/M6IE/nn3Hg+HQzVOSLt4RmgiaZ7xv3vD6wKBgOVyOTs/P1eSeT6f1+fI3wN5OoyZN8zt/fv38kojbxsdHbXp6Wmpc0ChkUVfXFyIFcPzjOoFRQeyf4KQGBy9fsaHhwf9LIVCQe8E4TLs8oY9X1tbU5Bfp9Ox5eVlMVGFQsGazabyOUZHR8XOM2TDFjIkeQcBviMUQd4Bx8ykKuh0Okp+npiYeJEfYfbzDmbeOXzNNFaXl5e2v78v6SG748lO4PtixR0MP6AqzTFDTzqdVuODkury8lIrygjNgUVCKgyTCVjE54T9h3uhWq3Ki8efDRvnVahwxyIfhb3F7xkIBBQoOTU1ZcvLy5ZIJOzg4MC+fPlih4eHViqVbGVlxUKh5wRdGrwvX77YxMSEhg2YcMAr1BnI6Hlej4+PBVS0Wi0lMANoeJk+wGi8ncFg0BKJhN5Db4o3zwysqtnz+jtYVN5X7EbcaUtLSwpFRU2GGgqlDBYunk/UTHjeuWewcdCYAtqgGCBUC0sSdw774UOhkO3t7envyLBHH/Dw8KC7jd2/yPC92Se8H7DurF2LxWJ2cnJiZibVUCaTkbJkfHxc5yEWLCSq5OuQo8HwjT0NyTm5Gq1Wy5LJpE1OTmoI5kziM2UfMZ8rnmZk8eVy2ba2tvR3v7q60llMz7S8vCw1BbYI7IJ87jMzM2Zm9s0334iZRGpuZjpTk8mkCKCrqytrNptWKpW0v34wGFi1WlW4XK1W03swOTkpOwbrsDiLsHaQs8HwgiKKNVEAil5rH0zlYDCwnZ0dDZsoRPF305/S/9zc3NinT59eqNFQQr57905Ki+PjY1n8ICbK5bLS5LFGRCIROz09tdnZWSuXy1qTyTmKLcWb+8NObz4bFKsAsoDCT09Ptrm5qd8DUGt5edkajYZ62b29Pc0F2BrGxp5X/zGIcw+hfkFhQaguCkpyhth5j7I1m80K7AMYYmgmJC6TyVitVrPz83PdQY1GQ1se6GfMTFYFr7edswaFI708zHsikdB5Q8I8pBlADYqbp6enF6sx6ddDoZBtbGzYp0+f7P3791av161UKslzHo1GLZFISGUDiEu/c3d3J5CPvydr7rB/oGzl3OD8c/Xr1r81hCNN4uUGPcHvwAFVr9dtcnLSotGonZ+fixFH3heJRIQksmCeQwBZZzqdVsJzr9eTfDMWi2m/MsgVByGXPkEOZs/DNHIVAp5YI5BIJJRYigR6bm7Ojo+P5YucnJy0VColuSG+MhqAbrdrq6urVqvVdFjPzs6amWmYzuVyYprwPtIMgojBBDLM+v1+sV7NZtOSyaSaX15mBlV2NZqZhnQGMQL08JnTnN/d3dnKyoouW/4sfE2JREJrp2hyYRs7nY4CjbgQaMBpnPg9CTbh5+QAGgwGdnp6qhfd25Q2m035p2DFOViRw9IY4e9HBsWgc3Nz8+Iz4b/nGSBtGPknQEM+n7d4PG5LS0ti6fExcZEXi0VbXl42M9OwOTc3p92P+BaXl5flweNn8Pme1wSxDoWUUoaKxcVFybKQRdHY4Rnle0Eu+cMPP1ixWNRzEwo9r8agwaEZ4LOq1WoCEGh2CKBjPQ0S3w8fPlgwGFTABywlO8YJ6js5OTG/32/ffvutRSIRK5VKek+ur69tcXHR7u/vxZ4y8LJ7M5FIWKlUErOHxHJzc1PgEMBDp9NRo4DsHRl9NpvVM87f/eLiQsysV+IMWALbRhPCzweqjNcXSwnSOti70dHndVYwVqQfj4yMvNjbbvbcbCD/ZdD0+sgZKrASFItFnbs+n0/5AXjy2U19dnYmP3iv17NisWgzMzNWLpeFtOMxRq6LD/Ph4cFSqZTt7u4qXGpra8u2t7eV7TAcDi2fz0vZ8NVXX9nBwYGAT/y8BIeRkssww3kKuGNmkkN72WOCY/j9sAlw5sAcMoSNjo5aNpu109NTK5fLtr29LQDt3bt3UrRwvrRaLQUw8l6gUgF89Pl8trKyonepVqtZv9/XMA/oeX5+brlc7oU8FeUXgyoyc4ZnGDZChILBoAKVyIsIhUJiynw+nwYjVtIQfAkggPKJEDZ8mwC+/OwLCwv23XffqUlGdlwul5UYPj09LfUD659ub2+t0+nY+fm5npV//etf1mq1BPQBrP7xj3+0QCBgOzs7tri4aLlcTt8re9KxKAC0odoxMwGGvDPkX9BkJhIJ+/Dhg8I1OQdSqZQC4crlsu4kkp1ppLEFmJmYKLPn0DFCCTnXvX5UfPeoYtLptM71iYkJK5VKAk5zuZwGsWKxaJVKRcMnighS0QGhOp2O1Wo1Sdph4VDzxWIxgYUoT9gsAxuPv3d3d9fm5+cFtpC0j0qRZ/+/B+RisSJ0EPAil8vp3YVlnJqa0vnxww8/iC2fmpqyZrP5QgXHZ4zXHb/xyMiIra+viw0mnwS1Gf0SK1ZjsZg9Pj7qc4PVRFVJf8kZQa9K8j877B8fH3VGwMri593c3BQwwpDJ3mp+P9jWlZUV8/me0+739/d1f+AHR11xenoqZaF39zl5SsFgUPYs7mLuXPqH8/NzC4fDOpcA5wi8I9eFtZn8rNjIIHtQvTDskRnizftgvSar9MiSACBj6wLv/crKitavBYNB6/V6CjBDXUIGgxfkj0QitrS0pB4okUiYmdnh4aHAZb4/s2eCh+c+kUjojPX2zagY+DnD4bD2gKNCw25DiO3d3Z3Nzs7KAkFQm9dOuLy8rGEXdtm7ys2rOs3n87Ie8L2zkQkFIEohtlugPoJswT4zMTEhAA0SjLOq3W7L9gXIDsHHGVUqlWxzc1PfFeq9o6MjEVL8/pB2kFr8/X5JOSb8P69/awinyfCiN8i0IpGIkJi3b9/Ki8SKFdCqZDKpD5MHYmFhQf5LAh/MTF5gGme/3y/ZF5InXmQYXQY1LiZv2iYXI3JQGDwaq2KxKNlTPB43s+cXrNVqybvCzkNSYmm+WNuCpJ4Lm9Tf6+tr/TtYp0wmY+Pj42oU+H1ubp5XuUWjUTUsNFVcOPhw8OYQzIQ/l/AG0H8vKo7M0efzqdEwM3lGaRTwHvf7fV1I+Pi5yPkeYZv5dRzs/DNCeHhu8NQyoHvThhl6AT24tPmzzEyNP0wyTCwXNIgk/xxWk6YERnVsbMwSiYR9/vz5xZ5N9vo2Gg27uLiQXIznl8MNiRg/B+ukGEDT6bQ9PDxIksZnm0wmrVAoKDym2+1atVrVM3lwcGArKytav4Snq9/va3cv/nJ8PHjACoWCpHu3t7diRggNTKVSQlh9Pp9UDgys7EM/OjoSq1OpVNQwBQIBMXAbGxu2vb1tnU7HUqmUfgYCefCjhcNhrZ/h5yXsL5lMSk6dzWZl7/DK/JFM0kDH43GF52DBILSQ/aYAZfwf8uXhcKiB3CsDh932/vdIulOplBUKBbGMKEAY5Pn1yBlBlFFrkI2BvxXZMJ5KQBhkoDybACOwiGamobpWq1kkErFkMmkTExMv1qTBgNMYolyiOUT+DbM+MjJiX3/9tZmZBuk3b94IZCgWiwqV29ra0motPqOFhQXJ3hmuORewPOATHhsbk2eaZxMVkPdsfnx8VPOCZBtGjHMxlUpZo9GwnZ0dm5mZsa+//tpyuZwFAgE1FI1GQ40NK++Q/fLzECZGsxgKhRRyMzExIeXM3d2dUoA5HwBskNCa/RwWiQIG0AMwloaU4RMVCwPp7Oys2BwaRqSetVrNksmkft/5+XntLmfoJtUeZmdvb88WFhYsmUzap0+fbG9vT7kKXqsRyh8sWWbPst6///3vAixgDAkIC4fD9u2338rfSPNLuOTY2JgknYFAQEqH5eVlndOAoKhHeEcB2jj/y+WyBkDAqrm5OVtYWJCXn4bczOQTBvAgtJG7HcaSngSQm+e10+moSebsI0MBuedwOLTLy8sXwA2DICnjtVrNstmsnZ2daQVWqVSyaDQqOTpDGeFW9Bncv+12296+fWvdbtei0ahVq1W9U51ORzJ2vPAwicin+YwIfgwEAiIMkLVyF9zf30v9Q+J1sVgUi7+6uirwiXd5dHRUChRAf4Cber0ujzxDuTdwi/Vmw+FQypdAICBPNd8Tvw628urqSqtlAfrxg8/OzlqlUtHfCRVXv9+3YrFo6+vrOk8BMOifwuGw+ia8/LCK33//vZQ8S0tLCnMFkGEw5M/EM8/PzvkDscDAd3h4aPF43EKhkFUqFYW0wmJ6AUUGMta1wbDC1mNF63Q6Gg75rJaWlqxYLNrR0ZEChiEIONdZOUfuTKVSEZAMg1oul0VoofCampp6kdWAJBsWGgUBwy9Whr29PYtEIpbNZmU/wK8Nqw+QTzgeCkVC6pDOc/cHg0F9L+Pj49qGxHB9dnams4/ARs4bPOfkCQAEmZnlcjnlGPD3m56eVhismb3YeDDz08pZlIasxuO7BECin+U7B4xhnSj5UK1WS+8xGwU4c+j3Dw8P7fT0VL59MnwgenK5nPV6Pdvd3TUzU7YAZABqYle/fv3b6egMMl6WcWxsTDLxYDCowadYLFomk9G+QV4eGD4YWvx219fXQoJYCwDzyP8GteUQxK8NowcrD7vCpc4aDAbZcrksOQhpifgwafYJgMMDwotNyBVsOkxHOBy2UqlkzWZTAwZeGF76k5MTq9frFo1GLZvNKjDF2yg8Pj6vJkA6Q0AD65QYFMxMUmYunsfH5zVJBGs0Gg2xuJOTk1apVBS8wAv39PSkYAYYRSSCIP8+n8++//57e/PmzYtgF9QQNB8MFzDsHM4M/aCM7Nadn5+3o6Mj6/f7knLSYGN9YEDxDjYEkNBM0yTd398LDQUoQN7I4J7L5STNg/HlwEdCBwDy+vVrm5+fFxqKn40LGSSeIAsuAoAIL9CRSCSUK4DP9uTkxLLZrGwb8/Pzdnx8bBMTEzY+Pq7fD+UEbIlXcjYzM2MHBwf21VdfaYBmCEKu/vT0JKUI6Zc0SV7JP58bl9HV1ZVCbfDe0hQCMvDfLS4u6pnyBqcwaCEfI8AJj/b5+blCRkKhkBWLRTEVoPi3t7c2Nzdnb968EZi0s7NjzWZTcsL7+3td9gxwoOVIxmjmkWaZPSPvJLd7PboMlAxnP/74oxi55eVl+fpLpZKGf0ANmPNut6vPEFYHXyqAFL5VACdWABEuw7kbDD6n3F9cXFg6nbbFxUWpLwiZMXtWQ3iVIA8PD/bPf/5TkmCkaGwyCIfD2toAmMF3hnycQQFf5dnZmT6vxcVFm52dtbOzM+01L5VKkoPS1MO20JwBwvIdo+whJ4E7ZzAYWDwet5WVFTUUZs9ewXK5bDs7O5bJZOwvf/mLnnmaW2SHtVrNzEyJsChFaDgAFJA9Ij/HmkCwEkqAiYkJsX78+pmZGfl0zUySRIBhmEAaKLzMSHoZDgnrC4fDkjQOBgPdR6ikaKh4P2neAPrMTHaxZDJpT09P9vXXX9uf//xny+fz9unTJyuVSjY3N2fRaNSenp7XvbHmD/9gu91W8BYWlkwmI4UEzC4AcbPZlMyf7AR2x3M2Z7NZW1paemGvOj8/l40KJpVegIHj8+fPAqZev34tawOybXywSEZnZmbEFPLZHB8fS2lCLsvMT8GFAMU8m3wOsFb0BeRGVCoVi8Vilk6nrd/vKykcFRLS7Wq1KhUUflgyQk5OTqQQRCXFeQX7DZN/dHQk8I9hguccpQgg/Pn5ucDCaDSqLIlut6twM9417nKGXUB3AMRsNmtmz7vNAdfImclkMhYIBGRNSSaT+g44/+v1unowryISwA0wg7AyfkZAJTNTbwP4cXV1pfeWjRfkFqE8wZ7Vbrdtc3PTPn/+rMRzAAbsEZxnvLfewfn09NSurq5eDNm5XE4g6x/+8IcXfmR8895ekiA+VnKReE+vxhAIuIWFkLW9nGXD4dBWV1e1xxrA6/Ly0jY2NqQiAuxnmH94eNAg+PDwoFyCp6cne//+vU1OTtrp6alC0cjkQRXBOU1ODd+Ltxel9+n3+1apVGxzc1NybOwFfr/ftre3NbwS9kwga7lctqWlJREu3W5XShSUTwDIq6uruqN4/gHDYah5xvmuUDvxfsbjcdmaUFKgjhsdHdUd6w3J5HsulUoC1slJAswCkAXk5/kmn8KbMUKoMYGVxWLRIpGILS4uKjsE5pq+hH55ZWVFmytQ0tFTEuzG+UwgHoGOnMc8g2amO4bE+V9av1em+teuXzSE8+HSnHLh9Xo9hUIQkkFCJwEboGckWYbDYa0BYIgABac5YgCrVCq6qGnQwuHnfdWwG8iPvWE/3t2dHMS8wATB0dx5JXfIfxjMkP/gQUWCz3AXDj/vGCwUCpbNZl/I4JvNpk1MTFixWLSzszOledbrdfkwTk5OtGohFApJWeBtzJEyIVODjSAYol6vSxbH8M3FfHh4aDM/BXoBciBpRhblTUTn883n8/LWcRhUKhWbm5uzRqMhlBVQBAkmaCzIsZnZwcGBXV1dKfmUVRR8x3iEUECQstxsNl+sPuNiYp3T27dv7ebmxkqlkj4DEEUGEIYT/FYoHFgrgdT14uJCnycWBJpHmkouhWAwKD8/6HKz2RRy3ev1bGlpSZ7d0dHndRp8RyD/DIf48e/u7qzZbFq9XtczenZ2pj2QyWRSqhH82PxZW1tb9te//tWOjo5kPzAzSYW52Pv9vlj729tbu729FePolesDKCFND4fDCiZLJBIaGJFZbWxs2D/+8Q/77rvvNGzBVtHc0eyOjIxYIpHQfuDx8XEx1+Pj47a/vy/WDKkmKeLNZtNGRkbETj8+Ptrl5aU9PDxYOp1Wk4/H17u67PHx0dLptBpIwDFkf5lMxn788UfJzMfGxrQT/vT01AqFgi0uLiqJGnmoN+UaAAQVCCi1z+dTWj4MrRekGB0dlb8SFo93mUsV3yS+uv39fVtaWpK3n9wFfJ1c1oQTdTqdF6v6YFXGx8dtZGREzx3PQCQSEZjApY8knMA5AEqehe3tbQ1nJycnYvrW1tY0VPD3AAwYHR21brdrvV5Pz5n3juG7m/lpEwXDEr7AfD4vIOHk5ERovldJw9+J98w7ZNzc3GjF0/Hx8QvQkUY6FotZIPCcqk/jBpBFAjVBf0jO+X5pnAeDgVjVwWBglUpFwaAMPnt7exq6AWT4dTRn3D0MIDThPHM0k8hnUQ7c398rQTkQCNjGxoaNjIzYzs6OVatVq1arUodwVw4GA3v9+rUCmLyhizTMzWZTwWcMsIRzARjOzs5q3y3+YoZTQJlarSaZJqFvSGw5p7k3aUBRyeDjJwgQ1RIKu729PfUybFdhtzFnAWorrEAo6SAMOp2ORaNR+/jxo3oa3m02uvBs0LwDKKNY2N3dtfX1dZ3HDDlmpvC28/Nzq9fr1m63RTxUKhVrNpuWSCQUSokl6Pj4WJkxBKlyptKzAJjCGPNMMPTVajXr9Xp2dXWlIZ9sCsD9arUqSTRryjqdjq2vr1s+n9fQ4QVcAKnxrvI8Xl9fi2CAIGFjAcBPrVaTeoQ7CoKmVCoJDJibm1NvGAqFbHl5WeAa9yXZBWdnZzY3N6fwz263a4eHh+pDUJQQ/OclVLD87e7uCmQ5ODjQGY9apV6vKziM1Yes0GW4BCQnO4F3slAoKCT09vZWO645A/Fzt9tt297etsFgYO/evRMBEAw+r/ZrNBrqtQG7JyYmrNVqWbvdtlarJaCmVCrZ+vq6tdtt2bxev34t8PHq6kqr/8h8YtjjXQRQxg5ChgiWFzaGsFYX1Sjhr7DIZAFwbqLeu7+/t0KhIMa7/VMgsdf/jDqMtYy807yXWA4SiYSGWu4wznlCMAEpUBt5SQ9+jZlJoXF/f2/VatXev3+vvAaGfFZ90mfzmXLvdrtdZSox/Jv9DD6jiEN9eHl5acViUax4sVg0v99vHz9+FJgAuIFalrMZpdbJyYnOBUJ+6Y+xFCNr985/rn6d8g1/wSdcqVTkgXXlypUrV65cuXLlypUrV7/fIrD4f6q7uztlYv0WFY/HrVAoSI3ye6hfNIQPBj+nIeOrdOXKlStXrly5cuXKlStXv5/C5kK+z/9WBGP/FoU65vdUv2gId+XKlStXrly5cuXKlStXrlz93+t/hzdcuXLlypUrV65cuXLlypUrV/+v5YZwV65cuXLlypUrV65cuXLl6jcqN4S7cuXKlStXrly5cuXKlStXv1G5IdyVK1euXLly5cqVK1euXLn6jcoN4a5cuXLlypUrV65cuXLlytVvVG4Id+XKlStXrly5cuXKlStXrn6jckO4K1euXLly5cqVK1euXLly9RvVfwGJpH3jok5/QwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "#Task 11: plot NDVI_pre\n" + "#Task 11: plot NDVI_pre\n", + "ep.plot_bands(NDVI_pre, \n", + " title=\"NDVI Pre-Fire (Vegetation Health)\")\n", + "plt.show()\n" ] }, { @@ -1116,7 +1560,10 @@ { "cell_type": "markdown", "metadata": { - "id": "b4CvWugISwPH" + "id": "b4CvWugISwPH", + "jupyter": { + "source_hidden": true + } }, "source": [ "### TASK 11: Plot post-fire data\n", @@ -1130,11 +1577,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 61, "metadata": { "id": "SPDeTTiISwPH" }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Add the code here to open the raster and read the numpy array inside it\n", "# Create a path for the data file - notice it is a .tif file\n", @@ -1145,9 +1603,14 @@ " \"m_3910505_nw_13_1_20170902_crop.tif\")\n", "\n", "# Task 11: open naip_post_fire\n", + "with rio.open(naip_post_fire_path) as naip_post_src:\n", + " naip_post_fire = naip_post_src.read()\n", "\n", - "\n", - "# Task 11: plot naip_post_fire\n" + "# Task 11: plot naip_post_fire\n", + "ep.plot_rgb(naip_post_fire,\n", + " title=\"NAIP Data Post-Fire\",\n", + " figsize=(10, 5))\n", + "plt.show()" ] }, { @@ -1163,15 +1626,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 67, "metadata": { "id": "pDHP9PIcSwPI" }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Task 11: create NDVI_post\n", - "\n", - "# Task 11: map change in NDVI\n" + "red_post = naip_post_fire[0]\n", + "nir_post = naip_post_fire[3]\n", + "NDVI_post = (nir_post - red_post) / (nir_post + red_post)\n", + "# Task 11: map change in NDVI\n", + "NDVI_diff = NDVI_pre - NDVI_post\n", + "\n", + "ep.plot_bands(NDVI_diff, \n", + " \n", + " title=\"Change in NDVI (Post-Fire Damage)\")\n", + "plt.show()" ] }, { @@ -1186,6 +1668,75 @@ "3. Create a mask for fire-imapacted regions (burned area = 1, all other values are no-data).\n", "4. Interpreting the Affine of the NAIP imagery, what was the total area imacted by the forest fire?" ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 1. Histogram of change\n", + "ep.hist(NDVI_diff, \n", + " title=\"Distribution of NDVI Change,\", \n", + " figsize=(8, 5))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 2. Set threshold (e.g., diff > 0.1 indicates significant vegetation loss)" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "#(Burned = 1, Others = NaN)\n", + "burned_mask = np.where(NDVI_diff > 0.1, 1, np.nan)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total Burned Area: 2185921.0 square meters\n" + ] + } + ], + "source": [ + "# 3. Calculate Area (resolution 1m x 1m)\n", + "total_burned_area = np.nansum(burned_mask)\n", + "print(f\"Total Burned Area: {total_burned_area} square meters\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -1194,9 +1745,9 @@ "provenance": [] }, "kernelspec": { - "display_name": "geostats_env", + "display_name": "waterdig", "language": "python", - "name": "geostats_env" + "name": "waterdig" }, "language_info": { "codemirror_mode": { @@ -1208,7 +1759,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.20" + "version": "3.11.6" } }, "nbformat": 4, diff --git a/CodeSprints/VectorData101.ipynb b/CodeSprints/VectorData101.ipynb index abe3d8b..fd61d5e 100644 --- a/CodeSprints/VectorData101.ipynb +++ b/CodeSprints/VectorData101.ipynb @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "id": "fxgQBbU3Oxbl" }, @@ -931,7 +931,7 @@ "kernelspec": { "display_name": "waterdig", "language": "python", - "name": "myenv" + "name": "waterdig" }, "language_info": { "codemirror_mode": {