From 429da4ae4f086ea3f04ebfc7d63758e180c05899 Mon Sep 17 00:00:00 2001
From: Carlos Silva <carloscaetanosilva89@gmail.com>
Date: Sun, 6 Aug 2023 17:26:40 +0100
Subject: [PATCH] Descriptive-Stats

---
 .DS_Store            | Bin 0 -> 6148 bytes
 your-code/main.ipynb | 862 ++++++++++++++++++++++++++++++++++++++-----
 2 files changed, 767 insertions(+), 95 deletions(-)
 create mode 100644 .DS_Store

diff --git a/.DS_Store b/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..b90b458b8c8ccabd30bce2f6fa46f68aeb36c3d1
GIT binary patch
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diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index a0a5b66..8dae621 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -11,11 +11,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Libraries"
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import random\n",
+    "df_hundred = pd.read_csv(\"roll_the_dice_hundred.csv\")"
    ]
   },
   {
@@ -29,11 +33,65 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "def roll_dice_simulation(num_rolls=10, num_sides=6):\n",
+    "    \"\"\"\n",
+    "    Simulate rolling a dice multiple times and save the results in a DataFrame.\n",
+    "\n",
+    "    Parameters:\n",
+    "        num_rolls (int): The number of times to roll the dice (default is 10).\n",
+    "        num_sides (int): The number of sides on the dice (default is 6 for a standard dice).\n",
+    "\n",
+    "    Returns:\n",
+    "        pandas.DataFrame: A DataFrame containing the results of the dice rolls.\n",
+    "    \"\"\"\n",
+    "    dice_rolls = [random.choices(range(1, num_sides + 1), k=num_rolls) for _ in range(num_rolls)]\n",
+    "    df = pd.DataFrame(dice_rolls).T\n",
+    "    df.columns = [f\"Roll_{i+1}\" for i in range(num_rolls)]\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Roll_1  Roll_2  Roll_3  Roll_4  Roll_5  Roll_6  Roll_7  Roll_8  Roll_9  \\\n",
+      "0       2       1       1       6       6       2       5       2       5   \n",
+      "1       5       5       1       2       2       4       2       4       2   \n",
+      "2       1       2       1       3       5       3       3       6       2   \n",
+      "3       3       2       3       5       2       5       1       2       3   \n",
+      "4       2       4       2       4       4       3       6       4       3   \n",
+      "5       6       6       5       4       3       2       2       6       2   \n",
+      "6       2       5       1       2       4       3       4       1       1   \n",
+      "7       1       1       5       4       2       3       4       6       1   \n",
+      "8       3       5       2       2       2       6       2       2       2   \n",
+      "9       6       2       1       1       1       2       1       5       5   \n",
+      "\n",
+      "   Roll_10  \n",
+      "0        5  \n",
+      "1        5  \n",
+      "2        3  \n",
+      "3        6  \n",
+      "4        3  \n",
+      "5        5  \n",
+      "6        5  \n",
+      "7        3  \n",
+      "8        2  \n",
+      "9        5  \n"
+     ]
+    }
+   ],
+   "source": [
+    "dice_results = roll_dice_simulation()\n",
+    "print(dice_results)"
    ]
   },
   {
@@ -45,11 +103,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "sorted_results = dice_results.apply(sorted, axis=0)\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(sorted_results.columns, sorted_results.iloc[0], align='center', alpha=0.8)\n",
+    "plt.xlabel('Dice Roll')\n",
+    "plt.ylabel('Value')\n",
+    "plt.title('Results of Rolling the Dice 10 Times (Sorted by Value)')\n",
+    "plt.grid(True, axis='y', linestyle='--', alpha=0.7)\n",
+    "plt.show()"
    ]
   },
   {
@@ -61,11 +137,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "def roll_dice_simulation(num_rolls=10, num_sides=6):\n",
+    "  \n",
+    " dice_results = roll_dice_simulation()\n",
+    "\n",
+    "\n",
+    "flattened_results = dice_results.values.ravel()\n",
+    "frequency_distribution = pd.value_counts(flattened_results).sort_index()\n",
+    "\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(frequency_distribution.index, frequency_distribution.values, align='center', alpha=0.8)\n",
+    "plt.xlabel('Value')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.title('Frequency Distribution of Rolling the Dice 10 Times')\n",
+    "plt.grid(True, axis='y', linestyle='--', alpha=0.7)\n",
+    "plt.show()"
    ]
   },
   {
@@ -74,9 +176,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
+    "#It can be used to analyze the randomness and fairness of the dice rolls and compare the observed frequencies to the expected probabilities for a fair six-sided dice."
    ]
   },
   {
@@ -91,11 +191,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "def calculate_mean(data):\n",
+    "    \"\"\"\n",
+    "    Calculate the mean of a given dataset.\n",
+    "\n",
+    "    Parameters:\n",
+    "        data (list or pandas.Series): The dataset for which the mean needs to be calculated.\n",
+    "\n",
+    "    Returns:\n",
+    "        float: The mean value of the dataset.\n",
+    "    \"\"\"\n",
+    "    total_sum = 0\n",
+    "    num_observations = len(data)\n",
+    "\n",
+    "    for value in data:\n",
+    "        total_sum += value\n",
+    "\n",
+    "    mean = total_sum / num_observations\n",
+    "    return mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean: 3.19\n"
+     ]
+    }
+   ],
+   "source": [
+    "mean_value = calculate_mean(dice_results.values.ravel())\n",
+    "print(\"Mean:\", mean_value)"
    ]
   },
   {
@@ -107,11 +242,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean calculated from frequency distribution: 3.19\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "def calculate_mean_from_frequency_distribution(freq_dist):\n",
+    "    \"\"\"\n",
+    "    Calculate the mean using a frequency distribution.\n",
+    "\n",
+    "    Parameters:\n",
+    "        freq_dist (pandas.Series): The frequency distribution of a dataset.\n",
+    "\n",
+    "    Returns:\n",
+    "        float: The mean value calculated from the frequency distribution.\n",
+    "    \"\"\"\n",
+    "    total_sum = 0\n",
+    "    num_observations = 0\n",
+    "\n",
+    "    for value, frequency in freq_dist.items():\n",
+    "        total_sum += value * frequency\n",
+    "        num_observations += frequency\n",
+    "\n",
+    "    mean = total_sum / num_observations\n",
+    "    return mean\n",
+    "\n",
+    "mean_value = calculate_mean_from_frequency_distribution(frequency_distribution)\n",
+    "print(\"Mean calculated from frequency distribution:\", mean_value)"
    ]
   },
   {
@@ -124,11 +288,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "def calculate_median(data):\n",
+    "    \"\"\"\n",
+    "    Calculate the median of a given dataset.\n",
+    "\n",
+    "    Parameters:\n",
+    "        data (list or pandas.Series): The dataset for which the median needs to be calculated.\n",
+    "\n",
+    "    Returns:\n",
+    "        float: The median value of the dataset.\n",
+    "    \"\"\"\n",
+    "    sorted_data = sorted(data)\n",
+    "    num_observations = len(sorted_data)\n",
+    "\n",
+    "    if num_observations % 2 == 0:  \n",
+    "        mid_idx = num_observations // 2\n",
+    "        median = (sorted_data[mid_idx - 1] + sorted_data[mid_idx]) / 2\n",
+    "    else:  \n",
+    "        mid_idx = num_observations // 2\n",
+    "        median = sorted_data[mid_idx]\n",
+    "\n",
+    "    return median"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Median: 3.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "median_value = calculate_median(dice_results.values.ravel())\n",
+    "print(\"Median:\", median_value)"
    ]
   },
   {
@@ -140,11 +342,61 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "def calculate_quartiles(data):\n",
+    "    \"\"\"\n",
+    "    Calculate the four quartiles of a given dataset.\n",
+    "\n",
+    "    Parameters:\n",
+    "        data (list or pandas.Series): The dataset for which the quartiles need to be calculated.\n",
+    "\n",
+    "    Returns:\n",
+    "        float, float, float: The first quartile (Q1), median (Q2 or Q2 and Q3 for odd and even lengths, respectively),\n",
+    "        and the third quartile (Q3) of the dataset.\n",
+    "    \"\"\"\n",
+    "    sorted_data = sorted(data)\n",
+    "    num_observations = len(sorted_data)\n",
+    "\n",
+    "    if num_observations % 2 == 0: \n",
+    "        mid_idx = num_observations // 2\n",
+    "        Q2 = (sorted_data[mid_idx - 1] + sorted_data[mid_idx]) / 2\n",
+    "        lower_half = sorted_data[:mid_idx]\n",
+    "        upper_half = sorted_data[mid_idx:]\n",
+    "    else:  \n",
+    "        mid_idx = num_observations // 2\n",
+    "        Q2 = sorted_data[mid_idx]\n",
+    "        lower_half = sorted_data[:mid_idx]\n",
+    "        upper_half = sorted_data[mid_idx + 1:]\n",
+    "\n",
+    "    Q1 = calculate_median(lower_half)\n",
+    "    Q3 = calculate_median(upper_half)\n",
+    "\n",
+    "    return Q1, Q2, Q3\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Q1: 2.0\n",
+      "Median (Q2): 3.0\n",
+      "Q3: 5.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "Q1, median, Q3 = calculate_quartiles(dice_results.values.ravel())\n",
+    "print(\"Q1:\", Q1)\n",
+    "print(\"Median (Q2):\", median)\n",
+    "print(\"Q3:\", Q3)"
    ]
   },
   {
@@ -158,11 +410,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "df_hundred = pd.read_csv(\"roll_the_dice_hundred.csv\")\n",
+    "\n",
+    "sorted_values = np.sort(df_hundred.values.ravel())\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(sorted_values, marker='o', linestyle='-')\n",
+    "plt.xlabel('Roll Index')\n",
+    "plt.ylabel('Dice Value')\n",
+    "plt.title('Sorted Dice Values from roll_the_dice_hundred.csv')\n",
+    "plt.grid(True, linestyle='--', alpha=0.7)\n",
+    "plt.show()"
    ]
   },
   {
@@ -171,9 +443,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
+    "#the grid lines are enabled to help visualize the data points more clearly"
    ]
   },
   {
@@ -185,11 +455,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean value of the hundred dice rolls: 3.19\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "frequency_distribution = df_hundred.apply(pd.value_counts).fillna(0).astype(int)\n",
+    "\n",
+    "\n",
+    "flattened_freq_dist = frequency_distribution.values.ravel()\n",
+    "\n",
+    "\n",
+    "def calculate_mean_from_frequency_distribution(freq_dist):\n",
+    "   \n",
+    "\n",
+    " mean_value = calculate_mean_from_frequency_distribution(flattened_freq_dist)\n",
+    "print(\"Mean value of the hundred dice rolls:\", mean_value)"
    ]
   },
   {
@@ -201,11 +489,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Frequency Distribution:\n",
+      "    Unnamed: 0  roll  value\n",
+      "0            1     1      0\n",
+      "1            1     1     12\n",
+      "2            1     1     17\n",
+      "3            1     1     14\n",
+      "4            1     1     22\n",
+      "..         ...   ...    ...\n",
+      "95           1     1      0\n",
+      "96           1     1      0\n",
+      "97           1     1      0\n",
+      "98           1     1      0\n",
+      "99           1     1      0\n",
+      "\n",
+      "[100 rows x 3 columns]\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "frequency_distribution = df_hundred.apply(pd.value_counts).fillna(0).astype(int)\n",
+    "\n",
+    "\n",
+    "print(\"Frequency Distribution:\")\n",
+    "print(frequency_distribution)"
    ]
   },
   {
@@ -217,11 +531,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "frequency_distribution = df_hundred.apply(pd.value_counts).fillna(0).astype(int)\n",
+    "\n",
+    "\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.hist(df_hundred.values.ravel(), bins=6, range=(0.5, 6.5), edgecolor='black', align='mid')\n",
+    "plt.xlabel('Dice Value')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.title('Histogram of Hundred Dice Rolls')\n",
+    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
+    "plt.xticks(range(1, 7))\n",
+    "plt.show()"
    ]
   },
   {
@@ -230,9 +565,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
+    "#This means that each value (1 to 6) should occur with roughly the same frequency."
    ]
   },
   {
@@ -244,11 +577,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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XNm7cqJ49e8o0Tf3rX/9Sy5YtdfPNN6tr16664447NHLkSGVlZalbt26KiYlRXl6evvrqK7Vu3Vq///3vT3v+evXq6aGHHtKDDz6oN998U7feemu1vgYrs66ALYTy02wATo/3E9Fr164Nevu11157ym9LmDp1qtWlSxerQYMGlsvlsho3bmyNGjXK2rJli9/9HnroISs5OdlyOByWJGvZsmWWZZV+gvvpp5+2Lr74YisyMtJq0KCBdeutt1rbt2/3u79pmtYTTzxhpaSkWC6Xy2rTpo310UcfWW3btvX7poOTfdNAUVGRNXbsWOvCCy+0atWqZbVr185auHBhwKfAvd8sMGXKFL/7V7TvUz2P5S1cuNDq1KmTVatWLSsmJsbq3bu39fXXX1fqcU6lMt+WcOJMllX67QPjxo077T4ty7I++eQT67LLLrOio6OtZs2aWS+88ELAtyV89NFHVmZmpnXhhRdaLpfLSkhIsPr162d9+eWXfvt66qmnrKZNm1pRUVFWy5YtrX/84x8B+/L2e8899wT0cuJr07Is64MPPrDatGnje20+9dRTQfcZzPDhw/2+OSQ6Otpq3LixNWDAAOu1116zioqKAu5z4rclWJZlHTt2zHr00Uet5s2bWy6Xy4qPj7d69eplrVq1ym+71157zerUqZMVExNjRUdHW+np6dawYcOsrKysk/Z5stffsWPHrMaNG1vNmzf3fUtGZda2Mt+WUNl1BWo6w7Iq8fFRAKgGubm5ysjI0Lhx4/Twww+Huh0AgA0RbgGcFd9++63eeustdenSRXXq1NHGjRs1efJkHTp0SOvXr6/wWxMAADgTnHML4KyIiYlRVlaWXn31VR08eFB169ZVjx499OSTTxJsAQBnDUduAQAAYBt8FRgAAABsg3ALAAAA2yDcAgAAwDb4QJlKv/R7165dio2NDfonQAEAABBalmXp8OHDSk5OPukfAyLcqvRvdKempoa6DQAAAJzC9u3blZKSUuHthFtJsbGxkkqfrDp16oS4GwAAAJzo0KFDSk1N9eW2ihBuJd+pCHXq1CHcAgAAhLFTnULKB8oAAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2EZEqBs4X23btk379u0LdRvnnQYNGqhx48ahbuO8w+s9NHi9AzgfEW5DYNu2bWqR0VLHjx0NdSvnnVrRtbXxvz/wH/xziNd76PB6B3A+Cmm4XblypaZMmaLs7Gzl5eVpwYIFGjhwoO92wzCC3m/y5Ml64IEHJEk9evTQihUr/G6/6aabNHfu3LPW95nat2+fjh87qvj+YxQZnxrqds4bJfu3a/9HU7Vv3z7+Y38O8XoPDV7vAM5XIQ23hYWFatu2rUaOHKnf/OY3Abfn5eX5XV+0aJFGjRoVsO3vfvc7PfbYY77r0dHRZ6fhahYZn6qoxItC3QZwTvB6BwCcCyENt5mZmcrMzKzw9sTERL/r77//vnr27KlmzZr51WvXrh2wLQAAAM4/Neac2927d+vjjz/W66+/HnDbP//5T82ZM0eNGjVSZmamxo0bp9jY2Ar3VVRUpKKiIt/1Q4cOSZLcbrfcbrckyeFwyOFwyDRNmabp29Zb93g8sizrlHWn0ynDMHz7lSTTNH2nXEQ6yraVpBJTMiRFnPA9FiWmIUOWX92yJLdlyCFLzmB1w5Kz3JkdpiV5LENOw5KjXN1jSaZlKMKwVP5MEI8pmQqsu03JkhHQe2ldigzoPTxmMp2GHI7SnVZmnbx17/aVqUdERMiyLL+6YRhyOp0Br6WK6mfztReKmUzTVERE6Y+a8/W1F4qZTKehyMjI0v9/nr72mImZmMleM524fUVqTLh9/fXXFRsbq8GDB/vVhw4dqrS0NCUmJmr9+vV66KGH9O2332rp0qUV7mvSpEmaMGFCQD0nJ0cxMTGSpIYNGyo9PV25ubnau3evb5uUlBSlpKRo06ZNKigo8NWbNWumhIQErV+/XseOHfPVMzIyFBcXp5ycHN9iFRQUKD4+XpFOQyOal704JGnWZocuiJCuTyurl5jSrM1OXRgjZaaU1Q8WS/NznWpe11K3xLIXzY6j0qLtTl0eb6ldfFl9Y4GhlfmGujay1KJuWf2b/Yay9xnqk2IqpXZZLyvzDW0sMDSoqak4V1l90Q6HdhRKQ9NNv//wvpPr0BG3wnYms3FDvfdNG0mq1DpJUps2beRyuZSVleU3U4cOHVRcXKz//Oc/vprT6dQVV1yhgoIC/fe///XVo6Oj1bZtW+3bt08///yzr163bl21bNlSu3bt0o4dO3z1s/naC8VMBQUFuuaaa5QtnbevvVDMZDZuqJ2Rt0vSefvaYyZmYiZ7zZSTk6PKMKzyUTqEDMMI+EBZeRkZGerTp4+ef/75k+4nOztbHTp0UHZ2ttq1axd0m2BHblNTU7V//37VqVNH0tn97WTdunXq2LGjGg17Vhckp/v1dj4faTrbMxXt/lk7Z/1Ra9euVdu2bfnN+BzNtG7dOnXu3FkNhj6j2knp5+VrLxQzFe3+WflvjNWaNWt02WWXnZevPWZiJmay10wHDhxQfHy8CgoKfHktmBpx5PbLL7/Uxo0bNW/evFNu265dO0VGRmrz5s0VhtuoqChFRUUF1CMiInxvn3p5F+ZE3ie6svXy+3U4HL5FLjEDvxHCUul/wALrRtC6KUNmsLplyAzyq4vHMuQJUndbRumDV7IerPfSemAtHGYq8Vi+f4yVWaeq1g3DCFqv6LV0uvUzee1VtX4mMzkcDt8PsPP1tVeZenXPVOKxVFJSIun8fe1Vps5MzFRRnZlqxkzB1Ii/UPbqq6+qffv2atu27Sm3/f7771VSUqKkpKRz0BkAAADCSUiP3B45ckQ//vij73pubq7WrVun+vXr+76X8dChQ5o/f76mTp0acP+ffvpJ//znP9WvXz81aNBAGzZs0JgxY3T55Zera9eu52wOAAAAhIeQhtusrCz17NnTd3306NGSpOHDh2vWrFmSpLlz58qyLA0ZMiTg/i6XS59//rmee+45HTlyRKmpqbr22ms1bty4Cg+BAwAAwL5CGm579OihU32e7Y477tAdd9wR9LbU1NSAv04GAACA81eNOOcWAAAAqAzCLQAAAGyDcAsAAADbINwCAADANmrEH3EAAFTNDz/8EOoWzjsNGjTwfZ0lgHOPcAsANuQ5ckAyDN16662hbuW8Uyu6tjb+9wcCLhAihFsAsCGz6IhkWYrvP0aR8amhbue8UbJ/u/Z/NFX79u0j3AIhQrjFeYe3ac8tnu/QioxPVVTiRaFuAwDOGcItzhu8TQsAgP0RbnHe4G3a0Dj2c5YKvpwT6jYA2Ny2bdu0b9++ULdx3gnHD1ASbnHe4W3ac6tk//ZQtwDA5rZt26YWGS11/NjRULdy3gnHD1ASbgEAQI22b98+HT92lHfmzrFw/QAl4RYAANgC78xB4i+UAQAAwEYItwAAALANwi0AAABsg3ALAAAA2yDcAgAAwDYItwAAALANwi0AAABsg3ALAAAA2yDcAgAAwDYItwAAALANwi0AAABsg3ALAAAA2yDcAgAAwDYItwAAALANwi0AAABsg3ALAAAA2yDcAgAAwDYItwAAALANwi0AAABsg3ALAAAA2yDcAgAAwDYItwAAALANwi0AAABsg3ALAAAA2yDcAgAAwDYItwAAALANwi0AAABsg3ALAAAA2yDcAgAAwDYItwAAALANwi0AAABsg3ALAAAA2yDcAgAAwDYItwAAALANwi0AAABsg3ALAAAA2yDcAgAAwDYItwAAALCNiFA++MqVKzVlyhRlZ2crLy9PCxYs0MCBA323jxgxQq+//rrffTp16qQ1a9b4rhcVFWns2LF66623dOzYMfXu3VsvvviiUlJSztUYAAD4+eGHH0LdwnmF5xvlhTTcFhYWqm3btho5cqR+85vfBN2mb9++mjlzpu+6y+Xyu/3+++/Xhx9+qLlz5yo+Pl5jxoxR//79lZ2dLafTeVb7BwCgPM+RA5Jh6NZbbw11K8B5K6ThNjMzU5mZmSfdJioqSomJiUFvKygo0Kuvvqo33nhDv/rVryRJc+bMUWpqqj777DNdc8011d4zAAAVMYuOSJal+P5jFBmfGup2zhvHfs5SwZdzQt0GwkRIw21lLF++XAkJCYqLi1P37t315JNPKiEhQZKUnZ2tkpISXX311b7tk5OT1apVK61atarCcFtUVKSioiLf9UOHDkmS3G633G63JMnhcMjhcMg0TZmm6dvWW/d4PLIs65R1p9MpwzB8+5Uk0zRlGIYkKdJRtq0klZiSISnihLOhS0xDhiy/umVJbsuQQ5acweqGJadRVjctyWMZchqWHOXqHksyLUMRhiWjfN2UTAXW3aZkyQjovbQuRQb0Hh4zuZylayTJNjNJ4b9OLqdDERGlP2rsMpMU/uvkcjoUGRkpSbaZya/3MJ3J5XTI5XIpMj5VtRLTbTFTWe/hu07GwZ065nLJu4kdZvLvPTzXyXQacrlcvtxzJtnIW/fuJ1j9xO0rEtbhNjMzUzfccIOaNGmi3NxcPfLII+rVq5eys7MVFRWl/Px8uVwu1atXz+9+jRo1Un5+foX7nTRpkiZMmBBQz8nJUUxMjCSpYcOGSk9PV25urvbu3evbJiUlRSkpKdq0aZMKCgp89WbNmikhIUHr16/XsWPHfPWMjAzFxcUpJyfHt1gFBQWKj49XpNPQiOZlwVmSZm126III6fq0snqJKc3a7NSFMVJmSln9YLE0P9ep5nUtdUsse9HsOCot2u7U5fGW2sWX1TcWGFqZb6hrI0st6pbVv9lvKHufoT4pplJql/WyMt/QxgJDg5qaiit3NsiiHQ7tKJSGppt+/6jeyXXoiFthO5M7KUMLt7ZRvmSbmaTwXyd3UobWuq9RtmSbmaTwXyd3UobyEm/X+8dlm5mk8F8nd1KGSlo9oNl5huJc9pjJK5zXyfu8f3rYpT2yx0xe4bxOZuOGKkp6QEePHpWkM8pGktSmTRu5XC5lZWX5zdShQwcVFxcrJydHlWFY5aN0CBmGEfCBshPl5eWpSZMmmjt3rgYPHqw333xTI0eO9DsKK0l9+vRRenq6XnrppaD7CXbkNjU1Vfv371edOnUknd0jt+vWrVPHjh3VaNizuiA53a83fos8ezMVblip3R9OVcJt0xSTlG6LmaTwX6fCDSu19+Nn1WDoM6qdlG6LmaTwX6fCDSu175Ppir9liqIT020xk1/vYbpOhRtWav+i51R/yGSO3J7DmbzPe/yQyXIlXmSLmfx7D891Ktr9s3bPeUCrVq1S+/btz/qR2wMHDig+Pl4FBQW+vBZMWB+5PVFSUpKaNGmizZs3S5ISExNVXFysAwcO+B293bNnj7p06VLhfqKiohQVFRVQj4iI8L196uVdmBNV9GG1iurl9+twOHyLXGIaAdtaKn1xBtaNoHVThsxgdcuQGeRXF49lyBOk7raM0gevZD1Y76X1wFo4zFTsKftFxS4zVaYe6pmKPabvB5hdZiovXGcq9pgqKSkp7d0mM5UXrjMVe0wVFxeftPeaNlN54bpO3ufdu4kdZjpROM5U4rFUXFzsO9XyTLLRmdRPVKO+53b//v3avn27kpKSJEnt27dXZGSkli5d6tsmLy9P69evP2m4BQAAgD2F9MjtkSNH9OOPP/qu5+bmat26dapfv77q16+v8ePH6ze/+Y2SkpK0ZcsWPfzww2rQoIEGDRokSapbt65GjRqlMWPGKD4+XvXr19fYsWPVunVr37cnAAAA4PwR0nCblZWlnj17+q6PHj1akjR8+HDNmDFD3333nWbPnq2DBw8qKSlJPXv21Lx58xQbG+u7z7PPPquIiAjdeOONvj/iMGvWLL7jFgAA4DwU0nDbo0cPnezzbEuWLDnlPmrVqqXnn39ezz//fHW2BgAAgBqoRp1zCwAAAJwM4RYAAAC2QbgFAACAbRBuAQAAYBuEWwAAANgG4RYAAAC2QbgFAACAbRBuAQAAYBuEWwAAANgG4RYAAAC2QbgFAACAbRBuAQAAYBuEWwAAANgG4RYAAAC2QbgFAACAbRBuAQAAYBuEWwAAANgG4RYAAAC2QbgFAACAbRBuAQAAYBuEWwAAANgG4RYAAAC2QbgFAACAbRBuAQAAYBuEWwAAANgG4RYAAAC2QbgFAACAbRBuAQAAYBuEWwAAANgG4RYAAAC2QbgFAACAbRBuAQAAYBuEWwAAANgG4RYAAAC2QbgFAACAbRBuAQAAYBuEWwAAANgG4RYAAAC2QbgFAACAbRBuAQAAYBuEWwAAANgG4RYAAAC2QbgFAACAbRBuAQAAYBuEWwAAANgG4RYAAAC2QbgFAACAbRBuAQAAYBuEWwAAANgG4RYAAAC2QbgFAACAbYQ03K5cuVIDBgxQcnKyDMPQwoULfbeVlJToT3/6k1q3bq2YmBglJydr2LBh2rVrl98+evToIcMw/C4333zzOZ4EAAAA4SCk4bawsFBt27bVCy+8EHDb0aNH9c033+iRRx7RN998o/fee0+bNm3Sr3/964Btf/e73ykvL893efnll89F+wAAAAgzEaF88MzMTGVmZga9rW7dulq6dKlf7fnnn1fHjh21bds2NW7c2FevXbu2EhMTz2qvAAAACH8hDbenq6CgQIZhKC4uzq/+z3/+U3PmzFGjRo2UmZmpcePGKTY2tsL9FBUVqaioyHf90KFDkiS32y232y1JcjgccjgcMk1Tpmn6tvXWPR6PLMs6Zd3pdMowDN9+Jck0TRmGIUmKdJRtK0klpmRIijjhmHqJaciQ5Ve3LMltGXLIkjNY3bDkNMrqpiV5LENOw5KjXN1jSaZlKMKwZJSvm5KpwLrblCwZAb2X1qXIgN7DYyaXs3SNJNlmJin818nldCgiovRHjV1mksJ/nVxOhyIjIyXJNjP59R6mM7mcDrlcLkkK6L2mzlTWe/iuk/d5925ih5n8ew/PdTKdhlwuly/3nEk28ta9+wlWP3H7itSYcHv8+HH9+c9/1i233KI6der46kOHDlVaWpoSExO1fv16PfTQQ/r2228DjvqWN2nSJE2YMCGgnpOTo5iYGElSw4YNlZ6ertzcXO3du9e3TUpKilJSUrRp0yYVFBT46s2aNVNCQoLWr1+vY8eO+eoZGRmKi4tTTk6Ob7EKCgoUHx+vSKehEc3LgrMkzdrs0AUR0vVpZfUSU5q12akLY6TMlLL6wWJpfq5Tzeta6pZY9qLZcVRatN2py+MttYsvq28sMLQy31DXRpZa1C2rf7PfUPY+Q31STKXULutlZb6hjQWGBjU1Fecqqy/a4dCOQmlouun3j+qdXIeOuBW2M7mTMrRwaxvlS7aZSQr/dXInZWit+xplS7aZSQr/dXInZSgv8Xa9f1y2mUkK/3VyJ2WopNUDmp1nKM5lj5m8wnmdvM/7p4dd2iN7zOQVzutkNm6ooqQHdPToUUk6o2wkSW3atJHL5VJWVpbfTB06dFBxcbFycnJUGYZVPkqHkGEYWrBggQYOHBhwW0lJiW644QZt27ZNy5cv9wu3J8rOzlaHDh2UnZ2tdu3aBd0m2JHb1NRU7d+/37fvs3nkdt26derYsaMaDXtWFySn+8/Kb5FnbabCDSu1+8OpSrhtmmKS0m0xkxT+61S4YaX2fvysGgx9RrWT0m0xkxT+61S4YaX2fTJd8bdMUXRiui1m8us9TNepcMNK7V/0nOoPmaxaiem2mKms9/BdJ+/zHj9kslyJF9liJv/ew3Odinb/rN1zHtCqVavUvn37s37k9sCBA4qPj1dBQcFJs2DYH7ktKSnRjTfeqNzcXH3xxRcnHUaS2rVrp8jISG3evLnCcBsVFaWoqKiAekREhO/tUy/vwpzI+0RXtl5+vw6Hw7fIJaYRsK2l0hdnYN0IWjdlyAxWtwyZQX518ViGPEHqbssoffBK1oP1XloPrIXDTMWesl9U7DJTZeqhnqnYY/p+gNllpvLCdaZij6mSkpLS3m0yU3nhOlOxx1RxcfFJe69pM5UXruvkfd69m9hhphOF40wlHkvFxcW+Uy3PJBudST1gu0ptFSLeYLt582YtW7ZM8fHxp7zP999/r5KSEiUlJZ2DDgEAABBOQhpujxw5oh9//NF3PTc3V+vWrVP9+vWVnJys66+/Xt98840++ugjeTwe5efnS5Lq168vl8uln376Sf/85z/Vr18/NWjQQBs2bNCYMWN0+eWXq2vXrqEaCwAAACES0nCblZWlnj17+q6PHj1akjR8+HCNHz9eH3zwgSTpsssu87vfsmXL1KNHD7lcLn3++ed67rnndOTIEaWmpuraa6/VuHHjKjwEDgAAAPsKabjt0aOHTvZ5tlN91i01NVUrVqyo7rYAAABQQ4X0L5QBAAAA1YlwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2CLcAAACwjSqF29zc3OruAwAAADhjVQq3F110kXr27Kk5c+bo+PHj1d0TAAAAUCVVCrfffvutLr/8co0ZM0aJiYm688479e9//7u6ewMAAABOS5XCbatWrTRt2jTt3LlTM2fOVH5+vq666ipdeumlmjZtmvbu3VvdfQIAAACndEYfKIuIiNCgQYP09ttv6+mnn9ZPP/2ksWPHKiUlRcOGDVNeXl519QkAAACc0hmF26ysLN19991KSkrStGnTNHbsWP3000/64osvtHPnTl133XXV1ScAAABwShFVudO0adM0c+ZMbdy4Uf369dPs2bPVr18/ORylWTktLU0vv/yyMjIyqrVZAAAA4GSqFG5nzJih22+/XSNHjlRiYmLQbRo3bqxXX331jJoDAAAATkeVwu3mzZtPuY3L5dLw4cOrsnsAAACgSqp0zu3MmTM1f/78gPr8+fP1+uuvn3FTAAAAQFVUKdw+9dRTatCgQUA9ISFBEydOPOOmAAAAgKqoUrjdunWr0tLSAupNmjTRtm3bKr2flStXasCAAUpOTpZhGFq4cKHf7ZZlafz48UpOTlZ0dLR69Oih77//3m+boqIi3XvvvWrQoIFiYmL061//Wjt27KjKWAAAAKjhqhRuExIS9J///Ceg/u233yo+Pr7S+yksLFTbtm31wgsvBL198uTJmjZtml544QWtXbtWiYmJ6tOnjw4fPuzb5v7779eCBQs0d+5cffXVVzpy5Ij69+8vj8dz+oMBAACgRqvSB8puvvlm/eEPf1BsbKy6desmSVqxYoXuu+8+3XzzzZXeT2ZmpjIzM4PeZlmWpk+frr/85S8aPHiwJOn1119Xo0aN9Oabb+rOO+9UQUGBXn31Vb3xxhv61a9+JUmaM2eOUlNT9dlnn+maa66pyngAAACooaoUbp944glt3bpVvXv3VkRE6S5M09SwYcOq7Zzb3Nxc5efn6+qrr/bVoqKi1L17d61atUp33nmnsrOzVVJS4rdNcnKyWrVqpVWrVlUYbouKilRUVOS7fujQIUmS2+2W2+2WJDkcDjkcDpmmKdM0fdt66x6PR5ZlnbLudDplGIZvv1Lpc2UYhiQp0lG2rSSVmJIhKeKEY+olpiFDll/dsiS3ZcghS85gdcOS0yirm5bksQw5DUuOcnWPJZmWoQjDklG+bkqmAutuU7JkBPReWpciA3oPj5lcTofvu5jtMpMU/uvkcjp8PyfsMpMU/uvkcjoUGRkpSbaZya/3MJ3J5XTI5XJJUkDvNXWmst7Dd528z7t3EzvM5N97eK6T6TTkcrl8uedMspG37t1PsPqJ21ekSuHW5XJp3rx5evzxx/Xtt98qOjparVu3VpMmTaqyu6Dy8/MlSY0aNfKrN2rUSFu3bvVt43K5VK9evYBtvPcPZtKkSZowYUJAPScnRzExMZKkhg0bKj09Xbm5udq7d69vm5SUFKWkpGjTpk0qKCjw1Zs1a6aEhAStX79ex44d89UzMjIUFxennJwc32IVFBQoPj5ekU5DI5qXBWdJmrXZoQsipOvTyuolpjRrs1MXxkiZKWX1g8XS/Fynmte11C2x7EWz46i0aLtTl8dbahdfVt9YYGhlvqGujSy1qFtW/2a/oex9hvqkmEqpXdbLynxDGwsMDWpqKs5VVl+0w6EdhdLQdNPvH9U7uQ4dcStsZ3InZWjh1jbKl2wzkxT+6+ROytBa9zXKlmwzkxT+6+ROylBe4u16/7hsM5MU/uvkTspQSasHNDvPUJzLHjN5hfM6eZ/3Tw+7tEf2mMkrnNfJbNxQRUkP6OjRo5J0RtlIktq0aSOXy6WsrCy/mTp06KDi4mLl5OSoMgyrfJQOIcMwtGDBAg0cOFCStGrVKnXt2lW7du1SUlKSb7vf/e532r59uxYvXqw333xTI0eO9DsKK0l9+vRRenq6XnrppaCPFezIbWpqqvbv3686depIOrtHbtetW6eOHTuq0bBndUFyul9v/BZ59mYq3LBSuz+cqoTbpikmKd0WM0nhv06FG1Zq78fPqsHQZ1Q7Kd0WM0nhv06FG1Zq3yfTFX/LFEUnpttiJr/ew3SdCjes1P5Fz6n+kMmqlZhui5nKeg/fdfI+7/FDJsuVeJEtZvLvPTzXqWj3z9o95wGtWrVK7du3P+tHbg8cOKD4+HgVFBT48lowVTpy6/F4NGvWLH3++efas2ePX/iTpC+++KIqu/Xj/ctn+fn5fuF2z549vqO5iYmJKi4u1oEDB/yO3u7Zs0ddunSpcN9RUVGKiooKqEdERPjePvXyLsyJvE90Zevl9+twOHyLXGIaAdtaKn1xBtaNoHVThsxgdcuQGeRXF49lyBOk7raM0gevZD1Y76X1wFo4zFTsKftFxS4zVaYe6pmKPabvB5hdZiovXGcq9pgqKSkp7d0mM5UXrjMVe0wVFxeftPeaNlN54bpO3ufdu4kdZjpROM5U4rFUXFzsO9XyTLLRmdRPVKVvS7jvvvt03333yePxqFWrVmrbtq3fpTqkpaUpMTFRS5cu9dWKi4u1YsUKX3Bt3769IiMj/bbJy8vT+vXrTxpuAQAAYE9VOnI7d+5cvf322+rXr98ZPfiRI0f0448/+q7n5uZq3bp1ql+/vho3bqz7779fEydOVPPmzdW8eXNNnDhRtWvX1i233CJJqlu3rkaNGqUxY8YoPj5e9evX19ixY9W6dWvftycAAADg/FHlD5RddNFFZ/zgWVlZ6tmzp+/66NGjJUnDhw/XrFmz9OCDD+rYsWO6++67deDAAXXq1EmffvqpYmNjffd59tlnFRERoRtvvFHHjh1T7969NWvWrAoPgQMAAMC+qhRux4wZo+eee04vvPCC7zyLqujRo4dO9nk2wzA0fvx4jR8/vsJtatWqpeeff17PP/98lfsAAACAPVQp3H711VdatmyZFi1apEsvvdT3XYpe7733XrU0BwAAAJyOKoXbuLg4DRo0qLp7AQAAAM5IlcLtzJkzq7sPAAAA4IxV6avApNI/gfbZZ5/p5Zdf1uHDhyVJu3bt0pEjR6qtOQAAAOB0VOnI7datW9W3b19t27ZNRUVF6tOnj2JjYzV58mQdP368wr8MBgAAAJxNVf4jDh06dNCBAwcUHR3tqw8aNEiff/55tTUHAAAAnI4qf1vC119/LZfL5Vdv0qSJdu7cWS2NAQAAAKerSkduTdOUx+MJqO/YscPvDywAAAAA51KVwm2fPn00ffp033XDMHTkyBGNGzfujP8kLwAAAFBVVTot4dlnn1XPnj11ySWX6Pjx47rlllu0efNmNWjQQG+99VZ19wgAAABUSpXCbXJystatW6e33npL33zzjUzT1KhRozR06FC/D5gBAAAA51KVwq0kRUdH6/bbb9ftt99enf0AAAAAVValcDt79uyT3j5s2LAqNQMAAACciSqF2/vuu8/veklJiY4ePSqXy6XatWsTbgEAABASVfq2hAMHDvhdjhw5oo0bN+qqq67iA2UAAAAImSqF22CaN2+up556KuCoLgAAAHCuVFu4lSSn06ldu3ZV5y4BAACASqvSObcffPCB33XLspSXl6cXXnhBXbt2rZbGAAAAgNNVpXA7cOBAv+uGYahhw4bq1auXpk6dWh19AQAAAKetSuHWNM3q7gMAAAA4Y9V6zi0AAAAQSlU6cjt69OhKbztt2rSqPAQAAABw2qoUbnNycvTNN9/I7XarRYsWkqRNmzbJ6XSqXbt2vu0Mw6ieLgEAAIBKqFK4HTBggGJjY/X666+rXr16kkr/sMPIkSP1P//zPxozZky1NgkAAABURpXOuZ06daomTZrkC7aSVK9ePT3xxBN8WwIAAABCpkrh9tChQ9q9e3dAfc+ePTp8+PAZNwUAAABURZXC7aBBgzRy5Ei988472rFjh3bs2KF33nlHo0aN0uDBg6u7RwAAAKBSqnTO7UsvvaSxY8fq1ltvVUlJSemOIiI0atQoTZkypVobBAAAACqrSuG2du3aevHFFzVlyhT99NNPsixLF110kWJiYqq7PwAAAKDSzuiPOOTl5SkvL08XX3yxYmJiZFlWdfUFAAAAnLYqhdv9+/erd+/euvjii9WvXz/l5eVJkn7729/yNWAAAAAImSqF2z/+8Y+KjIzUtm3bVLt2bV/9pptu0uLFi6utOQAAAOB0VOmc208//VRLlixRSkqKX7158+baunVrtTQGAAAAnK4qHbktLCz0O2LrtW/fPkVFRZ1xUwAAAEBVVCncduvWTbNnz/ZdNwxDpmlqypQp6tmzZ7U1BwAAAJyOKp2WMGXKFPXo0UNZWVkqLi7Wgw8+qO+//16//PKLvv766+ruEQAAAKiUKh25veSSS/Sf//xHHTt2VJ8+fVRYWKjBgwcrJydH6enp1d0jAAAAUCmnfeS2pKREV199tV5++WVNmDDhbPQEAAAAVMlpH7mNjIzU+vXrZRjG2egHAAAAqLIqnZYwbNgwvfrqq9XdCwAAAHBGqvSBsuLiYr3yyitaunSpOnTooJiYGL/bp02bVi3NAQAAAKfjtMLtzz//rKZNm2r9+vVq166dJGnTpk1+23C6AgAAAELltMJt8+bNlZeXp2XLlkkq/XO7f/3rX9WoUaOz0hwAAABwOk7rnFvLsvyuL1q0SIWFhdXaEAAAAFBVVfpAmdeJYRcAAAAIpdMKt4ZhBJxTyzm2AAAACBendc6tZVkaMWKEoqKiJEnHjx/XXXfdFfBtCe+99171dQgAAABU0mmF2+HDh/tdv/XWW6u1GQAAAOBMnFa4nTlz5tnqAwAAADhjZ/SBMgAAACCcEG4BAABgG2Efbps2ber7lobyl3vuuUeSNGLEiIDbrrzyyhB3DQAAgFA4rXNuQ2Ht2rXyeDy+6+vXr1efPn10ww03+Gp9+/b1Ox/Y5XKd0x4BAAAQHsI+3DZs2NDv+lNPPaX09HR1797dV4uKilJiYuK5bg0AAABhJuzDbXnFxcWaM2eORo8e7ffHI5YvX66EhATFxcWpe/fuevLJJ5WQkFDhfoqKilRUVOS7fujQIUmS2+2W2+2WJDkcDjkcDpmmKdM0fdt66x6Px+8vtFVUdzqdMgzDt19JMk3T13+kw/+vvJWYkiEp4oQTRkpMQ4Ysv7plSW7LkEOWnMHqhiVnub+xYVqSxzLkNCw5ytU9lmRahiIMS+X/JofHlEwF1t2mZMkI6L20LkUG9B4eM7mcpWskyTYzSeG/Ti6nQxERpT9q7DKTFP7r5HI6FBkZKUm2mcmv9zCdyeV0+N49PLH3mjpTWe/hu07e5927iR1m8u89PNfJdBpyuVy+3HMm2chb9+4nWP3E7StSo8LtwoULdfDgQY0YMcJXy8zM1A033KAmTZooNzdXjzzyiHr16qXs7GzfH5s40aRJkzRhwoSAek5Oju8PUjRs2FDp6enKzc3V3r17fdukpKQoJSVFmzZtUkFBga/erFkzJSQkaP369Tp27JivnpGRobi4OOXk5PgWq6CgQPHx8Yp0GhrRvCw4S9KszQ5dECFdn1ZWLzGlWZudujBGykwpqx8slubnOtW8rqVuiWUvmh1HpUXbnbo83lK7+LL6xgJDK/MNdW1kqUXdsvo3+w1l7zPUJ8VUSu2yXlbmG9pYYGhQU1Nx5c70WLTDoR2F0tB00+8f1Tu5Dh1xK2xncidlaOHWNsqXbDOTFP7r5E7K0Fr3NcqWbDOTFP7r5E7KUF7i7Xr/uGwzkxT+6+ROylBJqwc0O89QnMseM3mF8zp5n/dPD7u0R/aYySuc18ls3FBFSQ/o6NGjknRG2UiS2rRpI5fLpaysLL+ZOnTooOLiYuXk5KgyDKt8lA5z11xzjVwulz788MMKt8nLy1OTJk00d+5cDR48OOg2wY7cpqamav/+/apTp46ks3vkdt26derYsaMaDXtWFySn+/XGb5Fnb6bCDSu1+8OpSrhtmmKS0m0xkxT+61S4YaX2fvysGgx9RrWT0m0xkxT+61S4YaX2fTJd8bdMUXRiui1m8us9TNepcMNK7V/0nOoPmaxaiem2mKms9/BdJ+/zHj9kslyJF9liJv/ew3Odinb/rN1zHtCqVavUvn37s37k9sCBA4qPj1dBQYEvrwVTY47cbt26VZ999tkp/7RvUlKSmjRpos2bN1e4TVRUVNCjuhEREb63T728C3Mi7xNd2Xr5/TocDt8il5hGwLaWSl+cgXUjaN2UITNY3TJkBvnVxWMZ8gSpuy2j9MErWQ/We2k9sBYOMxV7yn5RsctMlamHeqZij+n7AWaXmcoL15mKPaZKSkpKe7fJTOWF60zFHlPFxcUn7b2mzVReuK6T93n3bmKHmU4UjjOVeCwVFxf7TrU8k2x0JvUThf1XgXnNnDlTCQkJuvbaa0+63f79+7V9+3YlJSWdo84AAAAQLmpEuDVNUzNnztTw4cP9UvuRI0c0duxYrV69Wlu2bNHy5cs1YMAANWjQQIMGDQphxwAAAAiFGnFawmeffaZt27bp9ttv96s7nU599913mj17tg4ePKikpCT17NlT8+bNU2xsbIi6BQAAQKjUiHB79dVXK9jn3qKjo7VkyZIQdAQAAIBwVCNOSwAAAAAqg3ALAAAA2yDcAgAAwDYItwAAALANwi0AAABsg3ALAAAA2yDcAgAAwDYItwAAALANwi0AAABsg3ALAAAA2yDcAgAAwDYItwAAALANwi0AAABsg3ALAAAA2yDcAgAAwDYItwAAALANwi0AAABsg3ALAAAA2yDcAgAAwDYItwAAALANwi0AAABsg3ALAAAA2yDcAgAAwDYItwAAALANwi0AAABsg3ALAAAA2yDcAgAAwDYItwAAALANwi0AAABsg3ALAAAA2yDcAgAAwDYItwAAALANwi0AAABsg3ALAAAA2yDcAgAAwDYItwAAALANwi0AAABsg3ALAAAA2yDcAgAAwDYItwAAALANwi0AAABsg3ALAAAA2yDcAgAAwDYItwAAALANwi0AAABsg3ALAAAA2yDcAgAAwDYItwAAALANwi0AAABsg3ALAAAA2yDcAgAAwDYItwAAALCNsA6348ePl2EYfpfExETf7ZZlafz48UpOTlZ0dLR69Oih77//PoQdAwAAIJTCOtxK0qWXXqq8vDzf5bvvvvPdNnnyZE2bNk0vvPCC1q5dq8TERPXp00eHDx8OYccAAAAIlYhQN3AqERERfkdrvSzL0vTp0/WXv/xFgwcPliS9/vrratSokd58803deeedFe6zqKhIRUVFvuuHDh2SJLndbrndbkmSw+GQw+GQaZoyTdO3rbfu8XhkWdYp606nU4Zh+PYrSaZpyjAMSVKko2xbSSoxJUNSxAm/dpSYhgxZfnXLktyWIYcsOYPVDUtOo6xuWpLHMuQ0LDnK1T2WZFqGIgxLRvm6KZkKrLtNyZIR0HtpXYoM6D08ZnI5S9dIkm1mksJ/nVxOhyIiSn/U2GUmKfzXyeV0KDIyUpJsM5Nf72E6k8vpkMvlkqSA3mvqTGW9h+86eZ937yZ2mMm/9/BcJ9NpyOVy+XLPmWQjb927n2D1E7evSNiH282bNys5OVlRUVHq1KmTJk6cqGbNmik3N1f5+fm6+uqrfdtGRUWpe/fuWrVq1UnD7aRJkzRhwoSAek5OjmJiYiRJDRs2VHp6unJzc7V3717fNikpKUpJSdGmTZtUUFDgqzdr1kwJCQlav369jh075qtnZGQoLi5OOTk5vsUqKChQfHy8Ip2GRjQvC86SNGuzQxdESNenldVLTGnWZqcujJEyU8rqB4ul+blONa9rqVti2Ytmx1Fp0XanLo+31C6+rL6xwNDKfENdG1lqUbes/s1+Q9n7DPVJMZVSu6yXlfmGNhYYGtTUVJyrrL5oh0M7CqWh6abfP6p3ch064lbYzuROytDCrW2UL9lmJin818mdlKG17muULdlmJin818mdlKG8xNv1/nHZZiYp/NfJnZShklYPaHaeoTiXPWbyCud18j7vnx52aY/sMZNXOK+T2bihipIe0NGjRyXpjLKRJLVp00Yul0tZWVl+M3Xo0EHFxcXKyclRZRhW+SgdZhYtWqSjR4/q4osv1u7du/XEE0/ov//9r77//ntt3LhRXbt21c6dO5WcnOy7zx133KGtW7dqyZIlFe432JHb1NRU7d+/X3Xq1JF0do/crlu3Th07dlSjYc/qguR0v974LfLszVS4YaV2fzhVCbdNU0xSui1mksJ/nQo3rNTej59Vg6HPqHZSui1mksJ/nQo3rNS+T6Yr/pYpik5Mt8VMfr2H6ToVblip/YueU/0hk1UrMd0WM5X1Hr7r5H3e44dMlivxIlvM5N97eK5T0e6ftXvOA1q1apXat29/1o/cHjhwQPHx8SooKPDltWDC+shtZmam7/+3bt1anTt3Vnp6ul5//XVdeeWVkuR7e9/LsqyA2omioqIUFRUVUI+IiPC9ferlXZgTeZ/oytbL79fhcPgWucQM7NVS6YszsG4ErZsyZAarW4bMIL+6eCxDniB1t2WUPngl68F6L60H1sJhpmJP2S8qdpmpMvVQz1TsMX0/wOwyU3nhOlOxx1RJSUlp7zaZqbxwnanYY6q4uPikvde0mcoL13XyPu/eTeww04nCcaYSj6Xi4mJf7jqTbHQm9ROF/QfKyouJiVHr1q21efNm33m4+fn5ftvs2bNHjRo1CkV7AAAACLEaFW6Lior0ww8/KCkpSWlpaUpMTNTSpUt9txcXF2vFihXq0qVLCLsEAABAqIT1aQljx47VgAED1LhxY+3Zs0dPPPGEDh06pOHDh8swDN1///2aOHGimjdvrubNm2vixImqXbu2brnlllC3DgAAgBAI63C7Y8cODRkyRPv27VPDhg115ZVXas2aNWrSpIkk6cEHH9SxY8d0991368CBA+rUqZM+/fRTxcbGhrhzAAAAhEJYh9u5c+ee9HbDMDR+/HiNHz/+3DQEAACAsFajzrkFAAAAToZwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2wjrcTpo0SVdccYViY2OVkJCggQMHauPGjX7bjBgxQoZh+F2uvPLKEHUMAACAUArrcLtixQrdc889WrNmjZYuXSq3262rr75ahYWFftv17dtXeXl5vssnn3wSoo4BAAAQShGhbuBkFi9e7Hd95syZSkhIUHZ2trp16+arR0VFKTEx8Vy3BwAAgDAT1uH2RAUFBZKk+vXr+9WXL1+uhIQExcXFqXv37nryySeVkJBQ4X6KiopUVFTku37o0CFJktvtltvtliQ5HA45HA6ZpinTNH3beusej0eWZZ2y7nQ6ZRiGb7+SZJqmDMOQJEU6yraVpBJTMiRFnHBMvcQ0ZMjyq1uW5LYMOWTJGaxuWHIaZXXTkjyWIadhyVGu7rEk0zIUYVgyytdNyVRg3W1KloyA3kvrUmRA7+Exk8tZukaSbDOTFP7r5HI6FBFR+qPGLjNJ4b9OLqdDkZGRkmSbmfx6D9OZXE6HXC6XJAX0XlNnKus9fNfJ+7x7N7HDTP69h+c6mU5DLpfLl3vOJBt56979BKufuH1Faky4tSxLo0eP1lVXXaVWrVr56pmZmbrhhhvUpEkT5ebm6pFHHlGvXr2UnZ2tqKiooPuaNGmSJkyYEFDPyclRTEyMJKlhw4ZKT09Xbm6u9u7d69smJSVFKSkp2rRpky9sS1KzZs2UkJCg9evX69ixY756RkaG4uLilJOT41usgoICxcfHK9JpaETzsuAsSbM2O3RBhHR9Wlm9xJRmbXbqwhgpM6WsfrBYmp/rVPO6lrollr1odhyVFm136vJ4S+3iy+obCwytzDfUtZGlFnXL6t/sN5S9z1CfFFMptct6WZlvaGOBoUFNTcW5yuqLdji0o1Aamm76/aN6J9ehI26F7UzupAwt3NpG+ZJtZpLCf53cSRla675G2ZJtZpLCf53cSRnKS7xd7x+XbWaSwn+d3EkZKmn1gGbnGYpz2WMmr3BeJ+/z/ulhl/bIHjN5hfM6mY0bqijpAR09elSSzigbSVKbNm3kcrmUlZXlN1OHDh1UXFysnJwcVYZhlY/SYeyee+7Rxx9/rK+++kopKSkVbpeXl6cmTZpo7ty5Gjx4cNBtgh25TU1N1f79+1WnTh1JZ/fI7bp169SxY0c1GvasLkhO9+uN3yLP3kyFG1Zq94dTlXDbNMUkpdtiJin816lww0rt/fhZNRj6jGonpdtiJin816lww0rt+2S64m+ZoujEdFvM5Nd7mK5T4YaV2r/oOdUfMlm1EtNtMVNZ7+G7Tt7nPX7IZLkSL7LFTP69h+c6Fe3+WbvnPKBVq1apffv2Z/3I7YEDBxQfH6+CggJfXgumRhy5vffee/XBBx9o5cqVJw22kpSUlKQmTZpo8+bNFW4TFRUV9KhuRESE7+1TL+/CnMj7RFe2Xn6/DofDt8glphGwraXSF2dg3QhaN2XIDFa3DJlBfnXxWIY8Qepuyyh98ErWg/VeWg+shcNMxZ6yX1TsMlNl6qGeqdhj+n6A2WWm8sJ1pmKPqZKSktLebTJTeeE6U7HHVHFx8Ul7r2kzlReu6+R93r2b2GGmE4XjTCUeS8XFxb5TLc8kG51JPWC7Sm0VIpZl6d5779WCBQu0fPlypaWlnfI++/fv1/bt25WUlHQOOgQAAEA4CeuvArvnnns0Z84cvfnmm4qNjVV+fr7y8/N9520cOXJEY8eO1erVq7VlyxYtX75cAwYMUIMGDTRo0KAQdw8AAIBzLayP3M6YMUOS1KNHD7/6zJkzNWLECDmdTn333XeaPXu2Dh48qKSkJPXs2VPz5s1TbGxsCDoGAABAKIV1uD3VZ92io6O1ZMmSc9QNAAAAwl1Yn5YAAAAAnA7CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbsE24ffHFF5WWlqZatWqpffv2+vLLL0PdEgAAAM4xW4TbefPm6f7779df/vIX5eTk6H/+53+UmZmpbdu2hbo1AAAAnEO2CLfTpk3TqFGj9Nvf/lYtW7bU9OnTlZqaqhkzZoS6NQAAAJxDEaFu4EwVFxcrOztbf/7zn/3qV199tVatWhX0PkVFRSoqKvJdLygokCT98ssvcrvdkiSHwyGHwyHTNGWapm9bb93j8ciyrFPWnU6nDMPw7VeSDh06VNpH/o9yesr6kKQSjyVDUoTTOGXdsiS3aclhSE7HqeumacljSU5DcpSre0xLpiVFOAwZ5R62orrbY8mSFHlCjxXVw2amgl0yDENF+T/K4S6yx0wnqYfNTAW75HQ6VZT/o4yS4/aYSTVgnQp2KSIiQkX5P0olx+0x00l6D5uZCnYpMjJSRfk/yio+bo+ZTtJ72Mz0/5/34t0/yiw+bo+ZTtF7OMxk/bJTkZGROnz4sA4dOnRG2chblySPxxO0fuDAgf/fn6WTsmq4nTt3WpKsr7/+2q/+5JNPWhdffHHQ+4wbN86SxIULFy5cuHDhwqWGXbZv337SbFjjj9x6GYb/byiWZQXUvB566CGNHj3ad900Tf3yyy+Kj4+v8D4odejQIaWmpmr79u2qU6dOqNs5b/C8hwbPe2jwvIcGz3to8LxXnmVZOnz4sJKTk0+6XY0Ptw0aNJDT6VR+fr5ffc+ePWrUqFHQ+0RFRSkqKsqvFhcXd7ZatKU6derwjzAEeN5Dg+c9NHjeQ4PnPTR43iunbt26p9ymxn+gzOVyqX379lq6dKlffenSperSpUuIugIAAEAo1Pgjt5I0evRo3XbbberQoYM6d+6sv//979q2bZvuuuuuULcGAACAc8gW4famm27S/v379dhjjykvL0+tWrXSJ598oiZNmoS6NduJiorSuHHjAk7rwNnF8x4aPO+hwfMeGjzvocHzXv0MyzrV9ykAAAAANUONP+cWAAAA8CLcAgAAwDYItwAAALANwi0AAABsg3CLSlm5cqUGDBig5ORkGYahhQsXhrql88KkSZN0xRVXKDY2VgkJCRo4cKA2btwY6rZsb8aMGWrTpo3vS9U7d+6sRYsWhbqt88qkSZNkGIbuv//+ULdia+PHj5dhGH6XxMTEULd1Xti5c6duvfVWxcfHq3bt2rrsssuUnZ0d6rZsgXCLSiksLFTbtm31wgsvhLqV88qKFSt0zz33aM2aNVq6dKncbreuvvpqFRYWhro1W0tJSdFTTz2lrKwsZWVlqVevXrruuuv0/fffh7q188LatWv197//XW3atAl1K+eFSy+9VHl5eb7Ld999F+qWbO/AgQPq2rWrIiMjtWjRIm3YsEFTp07lr6VWE1t8zy3OvszMTGVmZoa6jfPO4sWL/a7PnDlTCQkJys7OVrdu3ULUlf0NGDDA7/qTTz6pGTNmaM2aNbr00ktD1NX54ciRIxo6dKj+8Y9/6Iknngh1O+eFiIgIjtaeY08//bRSU1M1c+ZMX61p06aha8hmOHIL1CAFBQWSpPr164e4k/OHx+PR3LlzVVhYqM6dO4e6Hdu75557dO211+pXv/pVqFs5b2zevFnJyclKS0vTzTffrJ9//jnULdneBx98oA4dOuiGG25QQkKCLr/8cv3jH/8IdVu2QbgFagjLsjR69GhdddVVatWqVajbsb3vvvtOF1xwgaKionTXXXdpwYIFuuSSS0Ldlq3NnTtX33zzjSZNmhTqVs4bnTp10uzZs7VkyRL94x//UH5+vrp06aL9+/eHujVb+/nnnzVjxgw1b95cS5Ys0V133aU//OEPmj17dqhbswVOSwBqiP/93//Vf/7zH3311VehbuW80KJFC61bt04HDx7Uu+++q+HDh2vFihUE3LNk+/btuu+++/Tpp5+qVq1aoW7nvFH+dLPWrVurc+fOSk9P1+uvv67Ro0eHsDN7M01THTp00MSJEyVJl19+ub7//nvNmDFDw4YNC3F3NR9HboEa4N5779UHH3ygZcuWKSUlJdTtnBdcLpcuuugidejQQZMmTVLbtm313HPPhbot28rOztaePXvUvn17RUREKCIiQitWrNBf//pXRUREyOPxhLrF80JMTIxat26tzZs3h7oVW0tKSgr4Rblly5batm1biDqyF47cAmHMsizde++9WrBggZYvX660tLRQt3TesixLRUVFoW7Dtnr37h3wKf2RI0cqIyNDf/rTn+R0OkPU2fmlqKhIP/zwg/7nf/4n1K3YWteuXQO+1nHTpk1q0qRJiDqyF8ItKuXIkSP68ccffddzc3O1bt061a9fX40bNw5hZ/Z2zz336M0339T777+v2NhY5efnS5Lq1q2r6OjoEHdnXw8//LAyMzOVmpqqw4cPa+7cuVq+fHnAt1eg+sTGxgacSx4TE6P4+HjOMT+Lxo4dqwEDBqhx48bas2ePnnjiCR06dEjDhw8PdWu29sc//lFdunTRxIkTdeONN+rf//63/v73v+vvf/97qFuzBcItKiUrK0s9e/b0XfeeizV8+HDNmjUrRF3Z34wZMyRJPXr08KvPnDlTI0aMOPcNnSd2796t2267TXl5eapbt67atGmjxYsXq0+fPqFuDahWO3bs0JAhQ7Rv3z41bNhQV155pdasWcMRxLPsiiuu0IIFC/TQQw/pscceU1pamqZPn66hQ4eGujVbMCzLskLdBAAAAFAd+EAZAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAISAYRhauHBhqNs4qREjRmjgwIGhbgMATgvhFgCqyYgRI2QYhgzDUGRkpBo1aqQ+ffrotddek2maftvm5eUpMzPzrPRx7733qnnz5kFv27lzp5xOp957772z8tgAEGqEWwCoRn379lVeXp62bNmiRYsWqWfPnrrvvvvUv39/ud1u33aJiYmKioo6Kz2MGjVKP/74o7788suA22bNmqX4+HgNGDDgrDw2AIQa4RYAqlFUVJQSExN14YUXql27dnr44Yf1/vvva9GiRZo1a5ZvuxNPS9ixY4duvvlm1a9fXzExMerQoYP+9a9/+W7/8MMP1b59e9WqVUvNmjXThAkT/MJyeZdddpnatWun1157LeC2WbNmadiwYXI4HBo1apTS0tIUHR2tFi1a6LnnnjvpbE2bNtX06dMDHmv8+PG+6wUFBbrjjjuUkJCgOnXqqFevXvr2229Pul8AqE6EWwA4y3r16qW2bdtWeCrAkSNH1L17d+3atUsffPCBvv32Wz344IO+UxmWLFmiW2+9VX/4wx+0YcMGvfzyy5o1a5aefPLJCh9z1KhRmj9/vo4cOeKrrVixQj/++KNuv/12maaplJQUvf3229qwYYMeffRRPfzww3r77berPKdlWbr22muVn5+vTz75RNnZ2WrXrp169+6tX375pcr7BYDTQbgFgHMgIyNDW7ZsCXrbm2++qb1792rhwoW66qqrdNFFF+nGG29U586dJUlPPvmk/vznP2v48OFq1qyZ+vTpo8cff1wvv/xyhY93yy23yOPxaP78+b7aa6+9ps6dO+uSSy5RZGSkJkyYoCuuuEJpaWkaOnSoRowYcUbhdtmyZfruu+80f/58dejQQc2bN9czzzyjuLg4vfPOO1XeLwCcjohQNwAA5wPLsmQYRtDb1q1bp8svv1z169cPent2drbWrl3rd6TW4/Ho+PHjOnr0qGrXrh1wn7i4OA0ePFivvfaaRo4cqcOHD+vdd9/1O63gpZde0iuvvKKtW7fq2LFjKi4u1mWXXVblGbOzs3XkyBHFx8f71Y8dO6affvqpyvsFgNNBuAWAc+CHH35QWlpa0Nuio6NPel/TNDVhwgQNHjw44LZatWpVeL9Ro0apd+/e2rx5s1asWCFJuummmyRJb7/9tv74xz9q6tSp6ty5s2JjYzVlyhS/83xP5HA4ZFmWX62kpMSvz6SkJC1fvjzgvnFxcScbEQCqDeEWAM6yL774Qt99953++Mc/Br29TZs2euWVV/TLL78EPXrbrl07bdy4URdddNFpPW7Pnj3VrFkzzZo1S8uWLdONN96o2NhYSdKXX36pLl266O677/Ztf6qjqw0bNlReXp7v+qFDh5Sbm+vXZ35+viIiItS0adPT6hUAqgvn3AJANSoqKlJ+fr527typb775RhMnTtR1112n/v37a9iwYUHvM2TIECUmJmrgwIH6+uuv9fPPP+vdd9/V6tWrJUmPPvqoZs+erfHjx+v777/XDz/8oHnz5un//u//TtqLYRgaOXKkZsyYodWrV2vUqFG+2y666CJlZWVpyZIl2rRpkx555BGtXbv2pPvr1auX3njjDX355Zdav369hg8fLqfT6bv9V7/6lTp37qyBAwdqyZIl2rJli1atWqX/+7//U1ZWVmWfQgA4I4RbAKhGixcvVlJSkpo2baq+fftq2bJl+utf/6r333/fLwiW53K59OmnnyohIUH9+vVT69at9dRTT/m2v+aaa/TRRx9p6dKluuKKK3TllVdq2rRpatKkySn7GTFihAoKCtSiRQt17drVV7/rrrs0ePBg3XTTTerUqZP279/vdxQ3mIceekjdunVT//791a9fPw0cOFDp6em+2w3D0CeffKJu3brp9ttv18UXX6ybb75ZW7ZsUaNGjSrz9AHAGTOsE0+gAgAAAGoojtwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGzj/wF9+R+Y21U7DQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "df_thousand = pd.read_csv(\"roll_the_dice_thousand.csv\")\n",
+    "\n",
+    "frequency_distribution_thousand = df_thousand.apply(pd.value_counts).fillna(0).astype(int)\n",
+    "\n",
+    "\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.hist(df_thousand.values.ravel(), bins=6, range=(0.5, 6.5), edgecolor='black', align='mid')\n",
+    "plt.xlabel('Dice Value')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.title('Histogram of Thousand Dice Rolls')\n",
+    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
+    "plt.xticks(range(1, 7))\n",
+    "plt.show()"
    ]
   },
   {
@@ -257,9 +613,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
+    "#The values are similar in frequency "
    ]
   },
   {
@@ -274,11 +628,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   observation\n",
+      "0         68.0\n",
+      "1         12.0\n",
+      "2         45.0\n",
+      "3         38.0\n",
+      "4         49.0\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "df_ages = pd.read_csv(\"ages_population.csv\")\n",
+    "\n",
+    "\n",
+    "print(df_ages.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "frequency_distribution_ages = df_ages['observation'].value_counts().sort_index()\n",
+    "\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.hist(df_ages['observation'], bins=20, edgecolor='black', align='mid')\n",
+    "plt.xlabel('Age')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.title('Histogram of Age Distribution')\n",
+    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
+    "plt.show()"
    ]
   },
   {
@@ -290,11 +689,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean age: 36.56\n",
+      "Standard deviation of age: 12.816499625976762\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "mean_age = df_ages['observation'].mean()\n",
+    "std_dev_age = df_ages['observation'].std()\n",
+    "\n",
+    "\n",
+    "print(\"Mean age:\", mean_age)\n",
+    "print(\"Standard deviation of age:\", std_dev_age)"
    ]
   },
   {
@@ -303,9 +716,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
+    "#Between 30 and 40 have a mean range guess"
    ]
   },
   {
@@ -317,11 +728,55 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['observation'], dtype='object')\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "df_ages_2 = pd.read_csv(\"ages_population2.csv\")\n",
+    "\n",
+    "print(df_ages_2.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "frequency_distribution = df_ages_2['observation'].value_counts()\n",
+    "\n",
+    "\n",
+    "frequency_distribution = frequency_distribution.sort_index()\n",
+    "\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(frequency_distribution.index, frequency_distribution.values, color='skyblue')\n",
+    "plt.xlabel('Age')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.title('Age Frequency Distribution')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
    ]
   },
   {
@@ -337,9 +792,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
+    "#Yes, have more frequency in another ages \"example: 25, 27 , etc.\" "
    ]
   },
   {
@@ -351,11 +804,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean age: 27.155\n",
+      "Standard deviation of age: 2.969813932689186\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "mean_age = df_ages_2['observation'].mean()\n",
+    "std_age = df_ages_2['observation'].std()\n",
+    "\n",
+    "print(\"Mean age:\", mean_age)\n",
+    "print(\"Standard deviation of age:\", std_age)"
    ]
   },
   {
@@ -364,9 +830,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
+    "#have a less mean and Standard deviation"
    ]
   },
   {
@@ -381,11 +845,58 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "df_ages_3 = pd.read_csv(\"ages_population3.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['observation'], dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df_ages_3.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "frequency_distribution = df_ages_3[\"observation\"].value_counts().sort_index()\n",
+    "\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(frequency_distribution.index, frequency_distribution.values, width=0.8, color='b', alpha=0.7)\n",
+    "plt.xlabel('Age')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.title('Frequency Distribution of Ages')\n",
+    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.show()"
    ]
   },
   {
@@ -397,11 +908,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 47,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean age: 41.989\n",
+      "Standard deviation of age: 16.144705959865934\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "mean_age = df_ages_3[\"observation\"].mean()\n",
+    "std_age = df_ages_3[\"observation\"].std()\n",
+    "\n",
+    "\n",
+    "print(\"Mean age:\", mean_age)\n",
+    "print(\"Standard deviation of age:\", std_age)\n"
    ]
   },
   {
@@ -410,9 +935,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
+    "#For instance, certain age groups might have been removed or modified before plotting the frequency distribution."
    ]
   },
   {
@@ -424,11 +947,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 48,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First Quartile (Q1): 30.0\n",
+      "Second Quartile (Q2, Median): 40.0\n",
+      "Third Quartile (Q3): 53.0\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "quartiles = df_ages_3[\"observation\"].quantile([0.25, 0.5, 0.75])\n",
+    "\n",
+    "\n",
+    "print(\"First Quartile (Q1):\", quartiles[0.25])\n",
+    "print(\"Second Quartile (Q2, Median):\", quartiles[0.5])\n",
+    "print(\"Third Quartile (Q3):\", quartiles[0.75])"
    ]
   },
   {
@@ -437,9 +975,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
+    "#the values are very similar , mean and median "
    ]
   },
   {
@@ -451,11 +987,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 49,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10th Percentile: 22.0\n",
+      "25th Percentile: 30.0\n",
+      "50th Percentile (Median): 40.0\n",
+      "75th Percentile: 53.0\n",
+      "90th Percentile: 67.0\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "percentiles = df_ages_3[\"observation\"].quantile([0.1, 0.25, 0.5, 0.75, 0.9])\n",
+    "\n",
+    "\n",
+    "print(\"10th Percentile:\", percentiles[0.1])\n",
+    "print(\"25th Percentile:\", percentiles[0.25])\n",
+    "print(\"50th Percentile (Median):\", percentiles[0.5])\n",
+    "print(\"75th Percentile:\", percentiles[0.75])\n",
+    "print(\"90th Percentile:\", percentiles[0.9])"
    ]
   },
   {
@@ -464,9 +1019,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
+    "#90th Percentile are a possible outlier "
    ]
   },
   {
@@ -479,11 +1032,132 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "stats_neighborhood_1 = df_ages[\"observation\"].describe()\n",
+    "stats_neighborhood_2 = df_ages_2[\"observation\"].describe()\n",
+    "stats_neighborhood_3 = df_ages_3[\"observation\"].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "quartiles_neighborhood_1 = df_ages[\"observation\"].quantile([0.25, 0.5, 0.75])\n",
+    "quartiles_neighborhood_2 = df_ages_2[\"observation\"].quantile([0.25, 0.5, 0.75])\n",
+    "quartiles_neighborhood_3 = df_ages_3[\"observation\"].quantile([0.25, 0.5, 0.75])\n",
+    "\n",
+    "percentiles_neighborhood_1 = df_ages[\"observation\"].quantile([0.1, 0.25, 0.5, 0.75, 0.9])\n",
+    "percentiles_neighborhood_2 = df_ages_2[\"observation\"].quantile([0.1, 0.25, 0.5, 0.75, 0.9])\n",
+    "percentiles_neighborhood_3 = df_ages_3[\"observation\"].quantile([0.1, 0.25, 0.5, 0.75, 0.9])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Neighborhood 1 Statistics:\n",
+      "count    1000.0000\n",
+      "mean       36.5600\n",
+      "std        12.8165\n",
+      "min         1.0000\n",
+      "25%        28.0000\n",
+      "50%        37.0000\n",
+      "75%        45.0000\n",
+      "max        82.0000\n",
+      "Name: observation, dtype: float64\n",
+      "\n",
+      "Neighborhood 2 Statistics:\n",
+      "count    1000.000000\n",
+      "mean       27.155000\n",
+      "std         2.969814\n",
+      "min        19.000000\n",
+      "25%        25.000000\n",
+      "50%        27.000000\n",
+      "75%        29.000000\n",
+      "max        36.000000\n",
+      "Name: observation, dtype: float64\n",
+      "\n",
+      "Neighborhood 3 Statistics:\n",
+      "count    1000.000000\n",
+      "mean       41.989000\n",
+      "std        16.144706\n",
+      "min         1.000000\n",
+      "25%        30.000000\n",
+      "50%        40.000000\n",
+      "75%        53.000000\n",
+      "max        77.000000\n",
+      "Name: observation, dtype: float64\n",
+      "\n",
+      "Quartiles and Percentiles for Neighborhood 1:\n",
+      "0.25    28.0\n",
+      "0.50    37.0\n",
+      "0.75    45.0\n",
+      "Name: observation, dtype: float64\n",
+      "0.10    20.0\n",
+      "0.25    28.0\n",
+      "0.50    37.0\n",
+      "0.75    45.0\n",
+      "0.90    53.0\n",
+      "Name: observation, dtype: float64\n",
+      "\n",
+      "Quartiles and Percentiles for Neighborhood 2:\n",
+      "0.25    25.0\n",
+      "0.50    27.0\n",
+      "0.75    29.0\n",
+      "Name: observation, dtype: float64\n",
+      "0.10    23.0\n",
+      "0.25    25.0\n",
+      "0.50    27.0\n",
+      "0.75    29.0\n",
+      "0.90    31.0\n",
+      "Name: observation, dtype: float64\n",
+      "\n",
+      "Quartiles and Percentiles for Neighborhood 3:\n",
+      "0.25    30.0\n",
+      "0.50    40.0\n",
+      "0.75    53.0\n",
+      "Name: observation, dtype: float64\n",
+      "0.10    22.0\n",
+      "0.25    30.0\n",
+      "0.50    40.0\n",
+      "0.75    53.0\n",
+      "0.90    67.0\n",
+      "Name: observation, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Neighborhood 1 Statistics:\")\n",
+    "print(stats_neighborhood_1)\n",
+    "\n",
+    "print(\"\\nNeighborhood 2 Statistics:\")\n",
+    "print(stats_neighborhood_2)\n",
+    "\n",
+    "print(\"\\nNeighborhood 3 Statistics:\")\n",
+    "print(stats_neighborhood_3)\n",
+    "\n",
+    "\n",
+    "print(\"\\nQuartiles and Percentiles for Neighborhood 1:\")\n",
+    "print(quartiles_neighborhood_1)\n",
+    "print(percentiles_neighborhood_1)\n",
+    "\n",
+    "print(\"\\nQuartiles and Percentiles for Neighborhood 2:\")\n",
+    "print(quartiles_neighborhood_2)\n",
+    "print(percentiles_neighborhood_2)\n",
+    "\n",
+    "print(\"\\nQuartiles and Percentiles for Neighborhood 3:\")\n",
+    "print(quartiles_neighborhood_3)\n",
+    "print(percentiles_neighborhood_3)"
    ]
   },
   {
@@ -492,17 +1166,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
+    "#for example, in median all 3 neighborhood , dont have a half of the results "
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "ironhack-3.7",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "ironhack-3.7"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -514,7 +1186,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,