{ "cells": [ { "cell_type": "markdown", "id": "f0ad356c-2f8d-4aea-968f-ebcdb9c8e857", "metadata": { "jp-MarkdownHeadingCollapsed": true }, "source": [ "# Exploring Titanic Dataset: Analyzing Survival Factors\n", "This Jupyter notebook looks into a dataset about the Titanic. The goal is to uncover insights into the factors that influenced survival aboard the Titanic. The dataset comprises passenger information such as age, gender, ticket class, fare, and more, allowing us to investigate correlations and patterns related to survival outcomes.\n", "\n", "## Data Preprocessing\n", "Before embarking on our analysis, it's crucial to understand the journey the dataset has undergone. Initially, the raw data was subjected to a series of Python scripts for preprocessing. These scripts handled tasks such as handling missing ids, separation of variables (Salutation, first name, last name), and ensuring data integrity.\n", "\n", "Following preprocessing, the dataset was loaded into a MySQL Database for efficient storage and retrieval. \n", "\n", "## Objective\n", "Our primary goal is to discern whether certain variables played a significant role in determining the survival of passengers aboard the Titanic. By analyzing features like age, gender, ticket class, and familial relationships, we aim to unravel potential correlations and uncover underlying trends that influenced survival rates.\n", "\n", "## Sources\n", "- [Kaggle - Titanic Data Set](https://www.kaggle.com/datasets/sakshisatre/titanic-dataset/data)\n", "- [ChatGPT](https://chat.openai.com/)\n", "- [IBM - Logistic Regression](https://www.ibm.com/topics/logistic-regression)\n", "\n", "-----" ] }, { "cell_type": "markdown", "id": "98d38553-0bf7-4aaf-a052-8aa6b84a620a", "metadata": {}, "source": [ "# Install needed packages" ] }, { "cell_type": "code", "execution_count": 1, "id": "d45b62de-3b2a-42f6-ac79-fc9adaacc3e2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: sqlalchemy in /opt/conda/lib/python3.11/site-packages (2.0.22)\n", "Requirement already satisfied: mysql-connector-python in /opt/conda/lib/python3.11/site-packages (8.3.0)\n", "Requirement already 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(1.2.0)\n", "Requirement already satisfied: asttokens>=2.1.0 in /opt/conda/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.4.0)\n", "Requirement already satisfied: pure-eval in /opt/conda/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (0.2.2)\n", "Requirement already satisfied: six>=1.12.0 in /opt/conda/lib/python3.11/site-packages (from asttokens>=2.1.0->stack-data->ipython>=6.1.0->ipywidgets) (1.16.0)\n" ] } ], "source": [ "# Needed Packages to comunicate with mySQL from python\n", "!pip install sqlalchemy mysql-connector-python\n", "\n", "!pip install pandas\n", "!pip install matplotlib\n", "!pip install scikit-learn\n", "!pip install ydata_profiling\n", "!pip install ipywidgets" ] }, { "cell_type": "markdown", "id": "c1090b5b-54c2-40f0-9626-dea7d94b9b6d", "metadata": {}, "source": [ "# Load Libraries" ] }, { "cell_type": "code", "execution_count": 2, "id": "d4b2f0d1-fc1c-45ca-92bb-7db94de10749", "metadata": {}, "outputs": [], "source": [ "from sqlalchemy import create_engine, text\n", "from ydata_profiling import ProfileReport\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import OneHotEncoder\n", "from sklearn.compose import ColumnTransformer\n", "from sklearn.metrics import roc_curve, auc, accuracy_score, confusion_matrix, classification_report\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import numpy as np" ] }, { "cell_type": "markdown", "id": "baeb36de-752b-42dd-aa0a-7db5dcef545b", "metadata": {}, "source": [ "# Connect to the Database" ] }, { "cell_type": "code", "execution_count": 3, "id": "9e51ef55-9662-45cf-b5bf-458f37a53d1d", "metadata": {}, "outputs": [], "source": [ "# Connect to MySQL\n", "connection_string = 'mysql+mysqlconnector://root:pw@172.17.0.1:3306/titanic'" ] }, { "cell_type": "code", "execution_count": 4, "id": "d3fa9c72-37c9-4ffd-8e37-6b61279b3f02", "metadata": {}, "outputs": [], "source": [ "engine = create_engine(connection_string)\n", "conn = engine.connect()" ] }, { "cell_type": "markdown", "id": "f5812623-59a6-4bd3-ac39-09daed7cd2f0", "metadata": { "jp-MarkdownHeadingCollapsed": true }, "source": [ "## Test Connection" ] }, { "cell_type": "code", "execution_count": 5, "id": "fc04a792-acf9-48b1-8127-ef5f264afc32", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " salutation first_name last_name\n", "0 Miss Helen Loraine Allison\n", "1 Mr Hudson Joshua Creighton Allison\n", "2 Mrs Hudson J C (Bessie Waldo Daniels) Allison\n", "3 Mr Thomas Jr Andrews\n", "4 Mr Ramon Artagaveytia\n", ".. ... ... ...\n", "792 Miss Hileni Zabour\n", "793 Miss Thamine Zabour\n", "794 Mr Mapriededer Zakarian\n", "795 Mr Ortin Zakarian\n", "796 Mr Leo Zimmerman\n", "\n", "[797 rows x 3 columns]\n" ] } ], "source": [ "query = text(\"SELECT salutation, first_name, last_name FROM passengers WHERE survived = false;\")\n", "result = conn.execute(query)\n", "df = pd.DataFrame(result.fetchall(), columns=result.keys())\n", "\n", "# Display the results\n", "print(df)" ] }, { "cell_type": "markdown", "id": "d477c239-bb05-4f5b-bcd1-c0f353971335", "metadata": {}, "source": [ "# Fetch all the data to pandas" ] }, { "cell_type": "code", "execution_count": 6, "id": "4d272bb4-daa3-4b75-b09c-d100692abe05", "metadata": {}, "outputs": [], "source": [ "query = text(\"SELECT * FROM passengers;\")\n", "result = conn.execute(query)\n", "df = pd.DataFrame(result.fetchall(), columns=result.keys())" ] }, { "cell_type": "markdown", "id": "57e0ecb7-d73b-4a97-b683-d48dcd148ca7", "metadata": {}, "source": [ "# Analyze the dataset distribution aimed towards the survived target class" ] }, { "cell_type": "markdown", "id": "ccb8f573-f5fe-4e62-86fa-3923a2071425", "metadata": {}, "source": [ "## Bin by age and see survived distribution" ] }, { "cell_type": "code", "execution_count": 7, "id": "732bb2e5-be75-4ea7-a55f-f300393d574d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Survived percentage\n", "age_bin \n", "(0, 10] 58.139535\n", "(10, 20] 39.490446\n", "(20, 30] 36.676218\n", "(30, 40] 42.307692\n", "(40, 50] 39.230769\n", "(50, 60] 49.180328\n", "(60, 70] 22.222222\n", "(70, 80] 33.333333\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create bins\n", "min_age = df['age'].min()\n", "max_age = df['age'].max()\n", "bins = range(int(min_age // 10 * 10), int((max_age // 10 + 1) * 10), 10)\n", "\n", "# Cut the 'age' column into bins\n", "df['age_bin'] = pd.cut(df['age'], bins=bins)\n", "\n", "# Count the frequency of each bin for both 'age' and 'survived'\n", "age_counts = df['age_bin'].value_counts().sort_index()\n", "survived_counts = df.groupby('age_bin', observed=False)['survived'].sum()\n", "\n", "# Calculate percentage of survivors per age bin\n", "survival_percentages_age = (survived_counts / age_counts) * 100\n", "\n", "# Plot the results\n", "bar_width = 0.35\n", "fig, ax = plt.subplots()\n", "age_bar = ax.bar(age_counts.index.astype(str), age_counts.values, bar_width, label='Did not survived')\n", "survived_bar = ax.bar(age_counts.index.astype(str), survived_counts, bar_width, label='Survived')\n", "\n", "ax.set_xlabel('Age Bins')\n", "ax.set_ylabel('Frequency')\n", "ax.set_title('Age Distribution and Survival')\n", "ax.legend()\n", "\n", "plt.xticks(rotation=45)\n", "plt.show\n", "\n", "# Create a DataFrame for the table\n", "table_data = pd.DataFrame({'Survived percentage': survival_percentages_age})\n", "\n", "print(table_data)" ] }, { "cell_type": "markdown", "id": "edab23bd-faa7-49be-933d-81d110ec58b1", "metadata": {}, "source": [ "## See ticket class and survived distribution" ] }, { "cell_type": "code", "execution_count": 8, "id": "ae056dc8-aea5-4426-aaad-d220a2bcc206", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " Survived percentage\n", "pclass \n", "1 61.919505\n", "2 42.745098\n", "3 25.528914\n" ] } ], "source": [ "# Count the frequency of each category for both 'embarked' and 'survived'\n", "pclass_counts = df['pclass'].value_counts().sort_index()\n", "survived_counts = df.groupby('pclass')['survived'].sum()\n", "\n", "# Calculate percentage of survivors\n", "survival_percentages_class = (survived_counts / pclass_counts) * 100\n", "\n", "# Plot the results\n", "bar_width = 0.35\n", "fig, ax = plt.subplots()\n", "embarked_bar = ax.bar(pclass_counts.index, pclass_counts, bar_width, label='Did not survive')\n", "survived_bar = ax.bar(pclass_counts.index, survived_counts, bar_width, label='Survived')\n", "\n", "ax.set_xlabel('Ticket class')\n", "ax.set_ylabel('Frequency')\n", "ax.set_title('Survival by Ticket Class')\n", "ax.legend()\n", "\n", "# Add labels for each class\n", "labels = {1: 'Upper', 2: 'Middle', 3: 'Lower'}\n", "ax.set_xticks(pclass_counts.index)\n", "ax.set_xticklabels([labels[x] for x in pclass_counts.index])\n", "\n", "plt.show()\n", "\n", "# Create a DataFrame for the table\n", "table_data = pd.DataFrame({'Survived percentage': survival_percentages_class})\n", "print(table_data)\n" ] }, { "cell_type": "markdown", "id": "116f00d1-9b9a-495c-9e51-fa083d892d07", "metadata": {}, "source": [ "## See sex and survived distribution" ] }, { "cell_type": "code", "execution_count": 9, "id": "29f8187d-e298-413d-950a-427c335db207", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " Survived percentage\n", "sex \n", "female 72.149123\n", "male 19.374248\n" ] } ], "source": [ "# Count the frequency of each category for both 'sex' and 'survived'\n", "sex_counts = df['sex'].value_counts()\n", "survived_counts_by_sex = df.groupby('sex')['survived'].sum()\n", "\n", "# Calculate percentage of survivors\n", "survival_percentages_sex = (survived_counts_by_sex / sex_counts) * 100\n", "\n", "# Plot the results\n", "fig, ax = plt.subplots()\n", "bar_width = 0.35\n", "sex_bar = ax.bar(sex_counts.index, sex_counts, bar_width, label='Not Survived')\n", "survived_bar = ax.bar(survived_counts_by_sex.index, survived_counts_by_sex, bar_width, label='Survived')\n", "\n", "ax.set_xlabel('Sex')\n", "ax.set_ylabel('Frequency')\n", "ax.set_title('Survival by Sex')\n", "ax.legend()\n", "\n", "plt.show()\n", "\n", "# Create a DataFrame for the table\n", "table_data = pd.DataFrame({'Survived percentage': survival_percentages_sex})\n", "print(table_data)" ] }, { "cell_type": "markdown", "id": "b0130007-d0cc-4358-9ea1-b9ac8c77b040", "metadata": {}, "source": [ "## See fare and survived distribution" ] }, { "cell_type": "code", "execution_count": 10, "id": "d072465f-bcc1-40ab-9bbf-9b6e604c14d7", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " Survived percentage\n", "fare_bin \n", "(0, 50] 32.038835\n", "(50, 100] 63.225806\n", "(100, 150] 78.787879\n", "(150, 200] 61.538462\n", "(200, 250] 57.142857\n", "(250, 300] 76.923077\n" ] } ], "source": [ "# Create bins for fare\n", "min_fare = df['fare'].min()\n", "max_fare = df['fare'].max()\n", "bins = np.arange(int(min_fare // 50) * 50, int((max_fare // 50 + 1) * 50), 50)\n", "\n", "# Cut the 'fare' column into bins\n", "df['fare_bin'] = pd.cut(df['fare'], bins=bins)\n", "\n", "\n", "# Count the frequency of each bin for both 'fare' and 'survived'\n", "fare_counts = df['fare_bin'].value_counts().sort_index()\n", "survived_counts = df.groupby('fare_bin', observed=False)['survived'].sum()\n", "\n", "# Remove bins with no data\n", "fare_counts = fare_counts[fare_counts > 0]\n", "survived_counts = survived_counts[fare_counts.index] # Update survived counts accordingly\n", "\n", "# Calculate percentage of survivors per fare bin\n", "survival_percentages_fare = (survived_counts / fare_counts) * 100\n", "\n", "# Plot the results\n", "bar_width = 0.35\n", "fig, ax = plt.subplots()\n", "fare_bar = ax.bar(fare_counts.index.astype(str), fare_counts.values, bar_width, label='Did not survived')\n", "survived_bar = ax.bar(fare_counts.index.astype(str), survived_counts, bar_width, label='Survived')\n", "\n", "ax.set_xlabel('Fare Bins')\n", "ax.set_ylabel('Frequency')\n", "ax.set_title('Fare Distribution and Survival')\n", "ax.legend()\n", "\n", "plt.xticks(rotation=45)\n", "plt.show()\n", "\n", "# Create a DataFrame for the table\n", "table_data = pd.DataFrame({'Survived percentage': survival_percentages_fare})\n", "print(table_data)\n" ] }, { "cell_type": "markdown", "id": "38843fc3-81fb-4d4a-9045-eae86ff6bd1e", "metadata": {}, "source": [ "# Correlation Heatmap\n", "A correlation heatmap is a visual representation of the correlation between variables in a dataset. It uses colors to show the strength and direction of these relationships.\n", "\n", "## How to read\n", "- Color Gradient: Colors indicate correlation strength, with red for positive correlation and blue for negative correlation.\n", "- Diagonal Line: Represents perfect correlation (1) of variables with themselves.\n", "- Symmetry: The heatmap is symmetrical around the diagonal." ] }, { "cell_type": "code", "execution_count": 11, "id": "126eb3d9-67a0-4b00-8c57-860d10b5645e", "metadata": {}, "outputs": [ { "data": { "image/png": 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rl7XsiSee0Pnz5xUXF+eUdpERAwAAAJCnpaSk6MKFCzZbSkrKHTv/unXr1LJlS5uyyMhIrVu37o5d458KOO3MAAAAAO4ZSzyqOO3cG4d309ixY23KRo8erTFjxtyR8x8/flyBgYE2ZYGBgbpw4YIuX74sb2/vO3KdGxGIIRNnvojuRu3T9mvqt4zwtdcLHUwa+3FabjcjXxn9pIeS1n+b283Idwo16KArC/+X283IV7y6vqi58bndivyndzPps3WW3G5GvtIlwk0n92zK7WbkOyWq1c3tJrhcTEyMoqOjbcrMZnMutebOIBADAAAA4DCTh8lp5zabzU4NvEqWLKkTJ07YlJ04cUK+vr5OyYZJzBEDAAAAcI+LiIjQihUrbMqWL1+uiIgIp12TjBgAAAAAh7kVcF5GzF5JSUk6ePCg9fGRI0e0bds2FS1aVOXKlVNMTIz++usvffjhh5KkZ555Rm+//bZefPFF9enTRytXrtRnn32mJUuWOK2NZMQAAAAA3FU2bdqkWrVqqVatWpKk6Oho1apVS6NGjZIkHTt2TAkJCdb65cuX15IlS7R8+XKFh4dr4sSJmjVrliIjI53WRjJiAAAAABxm8sg7OZ5mzZrpZl+XPHfu3CyP2bp1qxNbZYtADAAAAIDD8tLQxPwg74StAAAAAHCPICMGAAAAwGHOXL7+bkRGDAAAAABcjIwYAAAAAIcxR8w+ZMQAAAAAwMXIiAEAAABwGHPE7ENGDAAAAABcjIwYAAAAAIcxR8w+BGIAAAAAHGZyJxCzB0MTAQAAAMDFyIgBAAAAcJgbGTG7kBEDAAAAABcjIwYAAADAYSY3MmL2ICMGAAAAAC7mskAsPj5eJpNJ58+fd9UlAQAAALiIyd3NadvdiKGJdoqPj1fz5s117tw5+fv753Zz8qWijesqZEhf+dWuLq/SJbTpX//RiW9W3PyYB+ur2oSXVKhaJV3545gOxr6nPz/80qZO0L+7KyS6r8wli+vCjn3aPWicEjfudOatuJxhGNq4bJr2rP9cKZcvqFT52nqw82j5Fw/O9pi/D23U1vgPdOqv3bp04ZTa9H5bIdVbWvdnZKRpw/dT9fu+n3XhzJ/y9C6k+yo1VES7aPn4BbrgrpyvWQ031a7kJi8P6Y9ThpZsyNDZi9nXb3y/m6qWMynA16T0jKvH/Lg1Q2cuXK/j7iZF1nHT/cFuKuAmHTxmaOmGDCVfcf79ONtnP67Rh0vjdSbxoiqVLaUXn3pU1SuUu+Vxy37dqpffna+mte/XpEFPW8tXbtypRT+t074jfyox+ZIWjBusKkFlnHkLueLT9Xs0b81OnU66rMqBRfVS+wiF3Vc8y7pfb/1No778xabMs4C7No7qbVN2+NR5TflhozYfPaZ0i6EKxf018YkWKuVfyFm34XKGYeiXb9/Stl+uvq/dV6G2IruPUdHA4Jset/mn+Vq//AMlJZ5SifuqqvUTI1W6fA2bOn8e2qpVX0/W30d2yOTmpsD7QtX1hQ/k4enlxDtyPsMwtPLLadr08+e6cumiylWqpUd6jlaxksE3PW79j/O1+vvZSko8rZLlqqr9k8N1X8jVPruUdF4rv3xbB3evUeKZY/IpXFShtVuoRefn5VWwsAvuyrm+WPqDPvlqic6eT1SF4HIa1K+XqlWukGXdn9dt1EeLv9Zfx04oPSND95UKVNeO7dSmWRObOl8v+1H7Dx3VhaQkzZ70miqVD3bR3eBucXeGl/eQ1NTU3G6C3dx9CurCjv3a9fzYHNX3Dr5P9b55X2fi12t13Y46Mm2ewt5/VQGtGlvrlHq8rULHx+jAq+9odf1HdXHHPjVY8oE8ixd11m3kiq0/zdKO1R+p6b/G6F/Pf6YCnt76bmY/paelZHtMWuplBZSuqgcfHZXl/vTUKzr11x7VbfkfPT54sdr0mqbzJ49o6Zz/OOs2XKpRNTc1qOqmJeszNCsuXanp0pMPFdDN/rgWFGjSxv0WfRCXro9+TJeb29VjPNyv12lT102V73PT56syNHd5ugp7S10edM/+pPnED79u06QF32hAp1aa/8ogVS5XWlHjZ+rshZtErpL+PnVWUz75TrWqlM+073JqqmpWDtZzXds7q9m5Lm7nYU2IW6+BzWrp02c6qkrJovr3h3E6k3Q522MKmT204r/drFtcdFeb/X+cvaDes75T+QA/zerTTouefVQDmtWUZ4H8/zy70a/LZmrTyo/UpscY9XrpM3mYvbXwrb43fV/bs3GpViyKVeP2z6rP8C8VeF9VLXyrr5IvnLHW+fPQVn32Vj+Vr9ZYvWI+V++YRarTvIdMpvz/0eeXpbP06/KP9UivMRo4aqE8zQU1b2J/paVm32c71y/V95++qeadntW/xy5WybJVNG9CfyX9f59dPH9SF8+fVJuuL+q5175R536v68DOX/Tl7BGuui2nWbF6nd6eM1+9u3bWrImvqmJwOQ155Q2dO5+YZX3fwj7q+VhHvffGGM2dHKt2DzXVG9NmaP3WHdY6l1OuKCy0ip7p+YSrbiNfcHM3OW27G9n1btSsWTNFRUUpKipKfn5+CggI0MiRI2UYhiQpJSVFw4YNU9myZWU2m1WxYkV98MEHWZ7rzJkz6tatm8qUKaOCBQsqLCxMn3zyiU2dRYsWKSwsTN7e3ipWrJhatmyp5ORkSVczU/Xr15ePj4/8/f3VqFEj/f777zm6j2+//Vb16tWTl5eXAgIC9Oijj1r3ffTRR6pbt64KFy6skiVLqnv37jp58qQk6ejRo2revLkkqUiRIjKZTOrdu7ckyWKxKDY2VuXLl5e3t7fCw8O1aNEim+t+8803qlSpkry8vNS8eXPNmzcv03DNxYsX6/7775fZbFZwcLAmTpxoc47g4GCNGzdOPXv2lK+vrwYMGKCHHnpIUVFRNvVOnTolT09PrVhx80xTbji1bJV+Gz1FJ77+MUf1gwY8octH/tTeF99U0r7D+v3d+Tq+eJnKv9DbWqf8oKf1xwef6c95Xyhp7yHt/M9oZVy6orK9/+Wku3A9wzC045cPVaflMypfvYUCSldRiyfeVPKFkzqyK/u+DAp9UA3aDlJIWKss95u9C+uRgbNVsWZbFSkRopJBNdXk0ZE69eduXTz3t7Nux2UahLpp1U6L9v9p6OR56au1GSpcUKpaNvs39fkrM7T9sKFTidKJ89LXazPkX8ikUsWuHmP2kGpVcNOyzRk6esLQsbPS1+syVK6Em8oE5O9fFh/H/axHmzXQIw/WV0iZknq597/kZfbQ1z9vzPaYDItFI6Yv0MDOrVWmeLFM+9s3qqMBnVqrwf2VnNn0XPXR2l3qXKeKOtWurAolimhEh0by8iigr7b8lu0xJpNJAYULWrdihbxt9k/7cZMaV75PgyPrK7RUgMoW9VWzqkGZ6uVnhmFo44oP1ajdv1W5ZkuVuK+qHn76f7p4/qR+25b9+9qGH+covHEX1Wj0LwWUrqg2PcaqgKeXdqxdbK2z4vNY1XnoKUW0GaDipSupWMkQhdZtpwIenq64NacxDEPrfvhQTR95RqG1W6hk2Sr6V/83dPHcSe3dkn2frV02T3WbPq7aTTqrRJmK6tBrjDw8vbRl1ReSpMD7Kqvbc2+paq3mKlqinEKqPaCW/xqk/dt+UkZGuqtuzykWfvO9OrRqrvYtmqp82fs09Jk+8jKbtWTFz1nWr1W9mh58oJ6Cy5ZRmVKBerxDG4UEl9POvfutddo0a6Knu3ZW3fDqrrqNfMHkZnLadjey+89C8+bNU4ECBbRhwwZNnTpVkyZN0qxZsyRJPXv21CeffKK33npLe/fu1fvvv69ChbIePnHlyhXVqVNHS5Ys0a5duzRgwAA99dRT2rBhgyTp2LFj6tatm/r06aO9e/cqPj5enTt3lmEYSk9PV6dOndS0aVPt2LFD69at04ABA2Qy3fqHtGTJEj366KNq166dtm7dqhUrVqh+/frW/WlpaRo3bpy2b9+ur776SkePHrUGW2XLltXixVff5Pfv369jx45p6tSpkqTY2Fh9+OGHmj59unbv3q3BgwfrySef1M8/X32RHzlyRI899pg6deqk7du3a+DAgRo+fLhN2zZv3qwuXbroiSee0M6dOzVmzBiNHDlSc+fOtak3YcIEhYeHa+vWrRo5cqT69eunBQsWKCXl+l/CPv74Y5UpU0YPPfTQLfskr/N/oKZOr1xnU3Zq+WoVeaCmJMnk4SG/2vfr9Iq11ysYhk6vXCv/B2q5sKXOdeHsn7p08ZTKVmpoLTN7F1ZguRo6/vu2O3qt1CsXJZNJZm/fO3peV/MvJBX2NunwcYu1LCVN+vO0obLFc/6mbva4+u/llKt/dCpV1CR3d5MOHzOsdc5ckM4nGSqbjwOxtPR07Tv6l+rfX9la5ubmpvrVKmnnwez/0DXzq+Uq4ltInZo2cEUz85y09AztPXZaD1QobS1zczPpgQqltePPk9kedyk1TW0mfqrWEz7VCwuW6+DJc9Z9FouhX377U0HF/PTMvDg1e3O+erz/jVbuPerMW3G586f/VPKFUwoOvf6+5uVdWKXLh+uvw1uzPCYjPVXHE3ar/A3HmNzcFFy1ofWY5Atn9PeR7fIpXEwfvvmEpg5tqI8nPKk/Dm5y7g25wLlTfyop8bQqVIuwlnkVLKz7KtTQH4e2Z3lMenqq/j66WyE3HOPm5qYK90foj0Pbsr3WlcsXZfYuJHf3/DuTJS0tXb8dOqI6NwRMbm5uqlujunbvP3DL4w3D0KYdu/THX8cUXq2qM5uKe5Ddr6yyZctq8uTJMplMqlKlinbu3KnJkyeradOm+uyzz7R8+XK1bHl1/klISEi25ylTpoyGDh1qffzcc89p2bJl+uyzz1S/fn0dO3ZM6enp6ty5s4KCgiRJYWFhkqSzZ88qMTFRDz/8sCpUuDq+NzQ0NEftf+211/TEE09o7Njrw+LCw8Ot/+/Tp4/1/yEhIXrrrbdUr149JSUlqVChQipa9OpQtxIlSljniKWkpOj111/Xjz/+qIiICOuxq1ev1vvvv6+mTZvq/fffV5UqVTR+/HhJUpUqVbRr1y699tpr1utNmjRJLVq00MiRIyVJlStX1p49ezR+/HhrMChJDz30kIYMGWLTl1FRUfr666/VpUsXSdLcuXPVu3fvHAWneZ05MEApJ07blKWcOC0Pv8Jy8zLLo4if3AoUUMrJM/+oc0Y+VbJ/DuY3ly6ekiR5F7bNOHgXCtCli6ezOuS2pKel6NclE1SpZnt5euXveSiFvK4+//85byv5iuTjlfPXRpu67ko4adGp/x/FUshbSs8wlJL2z/Mays/JivMXk5VhsaiYr+3PvZhfYR09lnVAsXX/EX398wYteDXaFU3Mk85duqIMi6FiPrY//GI+3jpyKuuhT8HF/DS2UxNVCiyqpCupmrdmp3rN/FZfRP1LgX4+Opt8WZdS0zT7lx2KalFHg1rX05oDfyr60xWa1bud6pYv5Ypbc7rkC1ff13x8bd/XfHyLKTkx6/e1S0nnZFgyVLBw5mPOHD8sSTp/+g9J0i/fva0W/3pRJcqGatevX+mTyb3Vb9R3t5x/lpcl/X+/FPL75/0HKCnxVJbHXLp4XhZLRqZjCvkW0+ljR7I8JvniOcV/857qNu1yB1qdexIvXlSGxaKifn425UX8ffX7X9mP+khKvqTO/aKUmpYudzc3RQ/orXo1w5zd3Hzvbh1C6Cx2B2IPPPCAzYf7iIgITZw4UVu3bpW7u7uaNm2ao/NkZGTo9ddf12effaa//vpLqampSklJUcGCBSVdDY5atGihsLAwRUZGqnXr1nrsscdUpEgRFS1aVL1791ZkZKRatWqlli1bqkuXLipV6ta/mLZt26b+/ftnu3/z5s0aM2aMtm/frnPnzsliufqX9ISEBFWrVi3LYw4ePKhLly6pVSvboV+pqamqVetqRmb//v2qV6+ezf4bM3GStHfvXnXs2NGmrFGjRpoyZYoyMjLk7n51XkDdunVt6nh5eempp57S7Nmz1aVLF23ZskW7du3SN998k+19SlcDyBuzaJJkNptvegxc57ct3yp+0Wjr4/Z9pzv9mhkZafrho0EyJDX91xinX+9OCws26eEG1+fPLPgpw+Fztq/vphL+Js3+IX8PzXGG5MtXNOr9BRrR5zEVKeyT283JV8LLBSq8XKDN40enLdLnm/YpqkUdWf5/yH/zquX0VMOrf8mvWqqYtv9xUp9v2pdvA7Fd679R3Pzr72tdot53ynUM4+rv7lpNuqpGo6tD1EuWq6aj+9Zpx9rFavbokJsdnqdsX/utvpk3xvr4ycHvOf2aVy4n6ePJz6hE6Yp6qNOzTr9eXlTQ20uzJ72uy1euaPOO3Xp7znyVLllCtapn/VkQuB13LNfs5WXfCkTjx4/X1KlTNWXKFIWFhcnHx0eDBg2yLj7h7u6u5cuXa+3atfrhhx80bdo0DR8+XOvXr1f58uU1Z84cPf/884qLi9PChQs1YsQILV++XA888MBNr+vtnf2fq5OTkxUZGanIyEjNnz9fxYsXV0JCgiIjI2+6KEZSUpKkq8Mey5SxXQnMGYGNj0/mDzz9+vVTzZo19eeff2rOnDl66KGHrJnE7MTGxtpkBiVp9OjRqpdN/dyScuK0zIEBNmXmwAClJV6U5UqKUk+fkyU9XeYSxf5Rp5hSjt+5TJGrBVdrrq7R11cAy0i/+hy8fPGMfHxLWMsvJ51WsdI5ywjfzNUgbLAunvtbHZ+Zmy+zYfv/NPTn6esB07U1DXy8pBvXTPDxkk6cM3Qrbeu5qVIZN839IV0XL10vT7osFXA3yewhm6yYj5dJSZdvfd68yr+wj9zd3HTmQpJN+ZnEiwrwyzxM9c+TZ/T36XMaPHmOtexaAFG/94ta/OaLKvuP1+7dqEhBL7m7mXQm2XZhjjPJlxVQOGcpUg93N1UtVUx/nL1gPWcBN5NCivvb1Ctf3E/bfj9xR9qdGyqFP6TS5a+PQrn2vpZ84YwK+V1/X0u+cEaBZbMeBlawUBGZ3Nx16aLtKIir57j6fCvkd3W1yoBStqviBZSsoMSz+Wvua9VaD+m+Ctd/F6T/f58lJZ5RYf8b++y0SpbL+ndBwcL+cnNzV1KibZ8l3dBn16RcTtaHE/vL06uguj03Te4FPO7UreQKv8KF5e7mprOJttnpc+cvqJi/XzZHXR2+eF+pkpKkSuWDdfTPv/XR4m8IxG7BREbMLnbPEVu/fr3N419//VWVKlVSeHi4LBaLdU7UraxZs0YdO3bUk08+qfDwcIWEhOi332wnNZtMJjVq1Ehjx47V1q1b5enpqS+/vL5kea1atRQTE6O1a9eqevXqWrBgwS2vW6NGjWwXsNi3b5/OnDmjN954Q02aNFHVqlWtC3Vc4+l5dZJvRsb1v7RXq1ZNZrNZCQkJqlixos1WtmxZSVeHIm7aZDs2feNG28nvoaGhWrNmTaZ+qly5sjUblp2wsDDVrVtXM2fO1IIFC2yGWGYnJiZGiYmJNltMTMwtj3O1879uU7GHbAPsgBYNde7XbZIkIy1NiVt2K+Ch62PfZTKpWPMInf816zkG+YGnVyH5BQRZtyKBFVWwcHH9eeD6fLnUK0k6kbBDJYNqOnSta0FY4qnf9cjAOfLyKeJg63NHarp0Lun6dipRunjZUEjJ6291nh7SfQEm/XHq5gFT23puqlrWTR/+mK7zybb7jp01lJFhKKTk9V84xXwl/0Im/XE6/wZiHgUKqGpwGW3cfX3ehMVi0cY9BxVWMfMfdoJLldDC14dowauDrduDtaqpbmgFLXh1sEoW83dh63OPRwF3hZYK0PrDx6xlFouh9Yf/Vo37StzkyOsyLBYdOHFOAf8/ttWjgLvuL1NcR8/Yfnj8/cyFfL10vdmrkIqWCLJuAaUqyse3uI7uu/6+lnI5SX8f2a4yIVnP8XUv4KmS5e7X0b3XjzEsFv2+b531GL9i96mQfwmdOWE77O7syaPyK5q/vjrB7O2jYoFB1q1E6Yoq5Begw3t+tda5cjlJfx7aobIVwrM8R4ECniodfL/NMRaLRYf3/KqyFWranGfehL5yd/dQjxfelYdn/h8l4+FRQJUrlNfmHbutZRaLRZt37tL9VXK+gJBhMZSWxsgI3Fl2Z8QSEhIUHR2tgQMHasuWLZo2bZomTpyo4OBg9erVS3369NFbb72l8PBw/f777zp58qR13tKNKlWqpEWLFmnt2rUqUqSIJk2apBMnTliH/61fv14rVqxQ69atVaJECa1fv16nTp1SaGiojhw5ohkzZuiRRx5R6dKltX//fh04cEA9e/a8ZftHjx6tFi1aqEKFCnriiSeUnp6upUuXatiwYSpXrpw8PT01bdo0PfPMM9q1a5fGjRtnc3xQUJBMJpO+++47tWvXTt7e3ipcuLCGDh2qwYMHy2KxqHHjxkpMTNSaNWvk6+urXr16aeDAgZo0aZKGDRumvn37atu2bdZFOK4N9RwyZIjq1auncePGqWvXrlq3bp3efvttvfvuuzn62fTr109RUVHy8fGxWQkyO2azOVeGIrr7FJRPxevfSVSw/H3yDa+q1LOJuvLHMVV5NVpeZQK1/elhkqTfZ3yqoP/0UNXY/+qPuYsV0PwBlXq8rTY+MtB6jiNT5ih89ps6v3mXEjfuUPDzvVTAx1t/zPvC5ffnLCaTSTWa9NTmFdPlVzxYvkXLaEPcW/LxLaHyN3wv2NfTeyukekuFNX5SkpSWkqzE0wnW/RfP/qnTf+2VuaCfChcprYyMNC378AWd+nOP2vedLsOSoUv/P2/DXNBP7gXy9wpj6/da1KS6m85cNHQ+yVDzcHddvCTt++N6wPRUC3ft+8PQxt+uDmdqV89NYeXd9Gl8hlLSrmbQpKvZr/SMq/9uPWRR6zruupyaoZQ0Q23rueuPUxb9lY8DMUl6sk1TjZ75qULL36fqIeW04IdfdDklVY88eDVXPur9T1S8iJ+e69JOZk8PVbzPdohc4YJXA4kbyxOTLun4mXM6df5qtuf3Y1efX8X8CivAP38vCHPNUw2ra+SXq3R/6QBVv6+4Pl63S5dT09Wp9tWFT4Yv/lklfAvqhVZX+3H6T1tVo2xxlSvqq4tXUjV3zU4dO5+kznWqWM/Zq1GYXvz8J9UJKql65UtrzcE/tWp/gmY93S5X7tEZTCaT6rXoqbVL31PREkHyC7hPq76eqsL+JVS55vX3tQWTeqlyrVaq2/zq+1r9lk/ru7nDVDK4ukoH19DGFfOUlnpZNRp2tp63Qau+Wv3tNAXeV1UlyoZq57ovdeb4YT068K1cudc7xWQyKaJ1T8V/O11FSwapSMB9WvHFWypcpIRCa1/vszlvPq3QOi31QMsekqSGkb30xcwYlSlfXWVCwrTuhw+VmnJZtZtc/bxw5XKS5o3vq7TUK+o+8H9KuZyklMtXs+M+vkXl5pZ/vzah6yNt9fpb76tqhfIKrVRBn38Xp8tXUtSuxdXpNK9OfU8BRYvomaeuLkX/0eKvVbVCiMqUDFRqWpp+3bJNy35erSEDr38/4oWLSTpx+rROnz0vSUr46+ofYor6+6tYEX+X3l9eYnLL/18P4Up2B2I9e/bU5cuXVb9+fbm7u+uFF17QgAEDJEnvvfeeXn75Zf3nP//RmTNnVK5cOb388stZnmfEiBE6fPiwIiMjVbBgQQ0YMECdOnVS4v+njn19fbVq1SpNmTJFFy5cUFBQkCZOnKi2bdvqxIkT2rdvn+bNm6czZ86oVKlSevbZZzVw4MAsr3WjZs2a6fPPP9e4ceP0xhtvyNfXVw8++KAkqXjx4po7d65efvllvfXWW6pdu7YmTJigRx55xHp8mTJlNHbsWL300kt6+umn1bNnT82dO1fjxo1T8eLFFRsbq8OHD8vf31+1a9e23n/58uW1aNEiDRkyRFOnTlVERISGDx+uf//739ZgqHbt2vrss880atQojRs3TqVKldIrr7xis1DHzXTr1k2DBg1St27d7B4q6kp+daorYsVH1sfVJlztoz8+/EI7+sbIXKq4vMte/xB3+eif2vjIQFWbGKPg53rqyp/HtXPgCJ1evtpa59jn38uzeFFVHv381S903r5XGx7up9R/LOCR39Vq3k/pqZcVv2iUUi9fUKnydfRw/5kq4HE9oL5wJkGXk6+vvnbyj136enov6+M137whSapSt5NaPPGGkhNP6OjulZKkzyZ1srlex2fmqUzF/L0S3po9FnkUkDo0cJeXp5Rw0tDHK9OVcX0hRRUtbFJBr+sBVL0qVz9w9G5t+xb51dp0bT98tV7cJosi61z97jB3d+nQ31e/KDq/a/1ATZ27mKTpXyzTmcSLqlyutKb9t5+K+V39QtfjZ87ZvQjQz1t3a+zMhdbHMe9+LEka0KmVBnaOvHONz0VtwkJ07tIVvbtys04nXVaVksX07lOR1qXmjycmye2Gfrt4JUWvfL1ap5Muy9fbrGqlimle/4dVocT1bHSLasEa0aGRZq/arjeX/qrgAD9N7NpCtYNKuvz+nOmByP5KS72s7z8epSuXLqhsxTrq8vwsm/e186f/0OWk6+9r1eq106Wks/rlm7eUfOGUStwXqi7Pz5KP7/VhdvVb9lZGeqp+/DxWV5ITVeK+qnpi0GwVKX7rLyfP65q066e0lMv6Zs5oXbl0QeUq11bPITNsMlhnTybo0sXrfRbWoJ2SL57Tii/fUlLiaZUqF6qeQ2ZYhyYeO7pHfx6++j1Zk1+0fV1Gj/9RRYrnr0zijVo0jtD5Cxf1waeLdPZcoiqWD9KEUcNU9P+HJp44dcbmfe3KlRRNmjFHJ8+cldnTU0FlSmvkoH+rRePrI29Wb9ys2GkzrI/HTHxbkvR0187q88Td89U5cC6Tce1LwHKgWbNmqlmzpqZMmeLEJt07XnvtNU2fPl1//PHHHTnf0aNHVaFCBW3cuFG1a9e+7fMs8ahy60qwap+2X1O/zd9ZkNzwQgeTxn6cduuKsBr9pIeS1n+b283Idwo16KArC/+X283IV7y6vqi58bndivyndzPps3WWW9bDdV0i3HRyT/7/WgFXK1Gt7q0r5YItLRo77dy1V6y+daV8Jv9+MUQ+9O6776pevXoqVqyY1qxZo/Hjx2f6IubbkZaWpjNnzmjEiBF64IEHHArCAAAAgNvB8vX2uesCsfvvv1+//571F4++//776tGjh4tbdN2BAwf06quv6uzZsypXrpyGDBlyRxbHWLNmjZo3b67KlStr0aJFd6ClAAAAAJzJrkAsPj7eSc24c5YuXaq0tKyHOwUGBmZZ7iqTJ0/W5MmT7/h5mzVrJjtGmAIAAAB3nMmNjJg97rqM2K2+OwsAAAAActtdF4gBAAAAcD2Wr7cPvQUAAAAALkZGDAAAAIDDmCNmHzJiAAAAAOBiZMQAAAAAOIzvEbMPgRgAAAAAhzE00T4MTQQAAAAAFyMjBgAAAMBhLF9vH3oLAAAAAFyMjBgAAAAAhzFHzD5kxAAAAADAxciIAQAAAHAYGTH7kBEDAAAAABcjIwYAAADAYWTE7EMgBgAAAMBhLF9vH3oLAAAAAFyMjBgAAAAAh7m5MzTRHmTEAAAAAMDFyIgBAAAAcBiLddiHjBgAAACAu84777yj4OBgeXl5qUGDBtqwYcNN60+ZMkVVqlSRt7e3ypYtq8GDB+vKlStOa5/JMAzDaWcHAAAAcE840ucRp527/Oxv7Kq/cOFC9ezZU9OnT1eDBg00ZcoUff7559q/f79KlCiRqf6CBQvUp08fzZ49Ww0bNtRvv/2m3r1764knntCkSZPu1G3YIBBDJlO/5Slhjxc6mLTEo0puNyPfaZ+2X2v2JOV2M/KVRtUK6YsNltxuRr7Tub6bXpmfntvNyFdG9Sigt5fyu8BeUe1MGjE3Nbebka+82ttTjTv8nNvNyHdWf9s0t5uQpbwUiDVo0ED16tXT22+/LUmyWCwqW7asnnvuOb300kuZ6kdFRWnv3r1asWKFtWzIkCFav369Vq9e7Vjjs8HQRAAAAAAOM7mZnLalpKTowoULNltKSkqW7UhNTdXmzZvVsmVLa5mbm5tatmypdevWZXlMw4YNtXnzZuvwxcOHD2vp0qVq167dne+oa21y2pkBAAAA3DOcGYjFxsbKz8/PZouNjc2yHadPn1ZGRoYCAwNtygMDA3X8+PEsj+nevbteeeUVNW7cWB4eHqpQoYKaNWuml19++Y730zUEYgAAAADytJiYGCUmJtpsMTExd+z88fHxev311/Xuu+9qy5Yt+uKLL7RkyRKNGzfujl3jn1i+HgAAAIDDTG7Oy/GYzWaZzeYc1Q0ICJC7u7tOnDhhU37ixAmVLFkyy2NGjhypp556Sv369ZMkhYWFKTk5WQMGDNDw4cPl5oR7IyMGAAAA4K7h6empOnXq2Cy8YbFYtGLFCkVERGR5zKVLlzIFW+7u7pIkZ61tSEYMAAAAgMPy0hc6R0dHq1evXqpbt67q16+vKVOmKDk5WU8//bQkqWfPnipTpox1nlmHDh00adIk1apVSw0aNNDBgwc1cuRIdejQwRqQ3WkEYgAAAADuKl27dtWpU6c0atQoHT9+XDVr1lRcXJx1AY+EhASbDNiIESNkMpk0YsQI/fXXXypevLg6dOig1157zWltJBADAAAA4DBnzhG7HVFRUYqKispyX3x8vM3jAgUKaPTo0Ro9erQLWnZV3uotAAAAALgHkBEDAAAA4DhT3pkjlh+QEQMAAAAAFyMjBgAAAMBheWnVxPyAQAwAAACAw/LaYh15Hb0FAAAAAC5GRgwAAACAwxiaaB8yYgAAAADgYmTEAAAAADiMOWL2obcAAAAAwMXIiAEAAABwGHPE7ENGDAAAAABcjIwYAAAAAIeREbMPgRgAAAAAx7FYh13oLQAAAABwMQKxPGbRokUKCwuTt7e3ihUrppYtWyo5OVmSNGvWLIWGhsrLy0tVq1bVu+++az2uT58+qlGjhlJSUiRJqampqlWrlnr27Jkr9wEAAIB7i8lkctp2NyIQy0OOHTumbt26qU+fPtq7d6/i4+PVuXNnGYah+fPna9SoUXrttde0d+9evf766xo5cqTmzZsnSXrrrbeUnJysl156SZI0fPhwnT9/Xm+//XZu3hIAAACALDBHLA85duyY0tPT1blzZwUFBUmSwsLCJEmjR4/WxIkT1blzZ0lS+fLltWfPHr3//vvq1auXChUqpI8//lhNmzZV4cKFNWXKFP3000/y9fXNtfsBAADAvYMvdLYPgVgeEh4erhYtWigsLEyRkZFq3bq1HnvsMXl6eurQoUPq27ev+vfvb62fnp4uPz8/6+OIiAgNHTpU48aN07Bhw9S4ceObXi8lJcU6lPEas9ksyfOO3hcAAAAAW4SteYi7u7uWL1+u77//XtWqVdO0adNUpUoV7dq1S5I0c+ZMbdu2zbrt2rVLv/76q/V4i8WiNWvWyN3dXQcPHrzl9WJjY+Xn52ezxcbGOu3+AAAAcPcyuZmctt2NCMTyGJPJpEaNGmns2LHaunWrPD09tWbNGpUuXVqHDx9WxYoVbbby5ctbjx0/frz27dunn3/+WXFxcZozZ85NrxUTE6PExESbLSYmxtm3CAAAANzzGJqYh6xfv14rVqxQ69atVaJECa1fv16nTp1SaGioxo4dq+eff15+fn5q06aNUlJStGnTJp07d07R0dHaunWrRo0apUWLFqlRo0aaNGmSXnjhBTVt2lQhISFZXs9sNv//UMR/Mpx7owAAALj7MEfMLgRieYivr69WrVqlKVOm6MKFCwoKCtLEiRPVtm1bSVLBggU1fvx4/fe//5WPj4/CwsI0aNAgXblyRU8++aR69+6tDh06SJIGDBigJUuW6KmnntKqVavk7u6em7cGAACAu9zdOoTQWQjE8pDQ0FDFxcVlu7979+7q3r17lvt2796dqezrr7++Y20DAAAAcOcQiAEAAABwmMnE0ER70FsAAAAA4GJkxAAAAAA4jjlidiEjBgAAAAAuRkYMAAAAgMNMLF9vF3oLAAAAAFyMjBgAAAAAh/E9YvYhEAMAAADgOJavtwu9BQAAAAAuRkYMAAAAgMMYmmgfMmIAAAAA4GJkxAAAAAA4juXr7UJvAQAAAICLkREDAAAA4DCTiTli9iAjBgAAAAAuRkYMAAAAgOOYI2YXAjEAAAAADmP5evsQtgIAAACAi5ERAwAAAOA4Ezkee9BbAAAAAOBiZMQAAAAAOI45YnYhIwYAAAAALkZGDAAAAIDDTMwRswu9BQAAAAAuZjIMw8jtRgAAAADI35JnjnDauX36v+q0c+cWhiYik7Efp+V2E/KV0U96aM2epNxuRr7TqFohLfGoktvNyFfap+3Xd1vSc7sZ+c7DtQto2IzLud2MfOXNAd5asfNKbjcj32kR5qW4bam53Yx8pU1NT437hPc1e43sljc/wpvc8tZgu3feeUfjx4/X8ePHFR4ermnTpql+/frZ1j9//ryGDx+uL774QmfPnlVQUJCmTJmidu3aOaV9efOnCAAAAAC3aeHChYqOjtb06dPVoEEDTZkyRZGRkdq/f79KlCiRqX5qaqpatWqlEiVKaNGiRSpTpox+//13+fv7O62NBGIAAAAAHGfKO8vXT5o0Sf3799fTTz8tSZo+fbqWLFmi2bNn66WXXspUf/bs2Tp79qzWrl0rDw8PSVJwcLBT25i38ocAAAAA4IDU1FRt3rxZLVu2tJa5ubmpZcuWWrduXZbHfPPNN4qIiNCzzz6rwMBAVa9eXa+//royMjKc1k4yYgAAAAAc58Q5YikpKUpJSbEpM5vNMpvNmeqePn1aGRkZCgwMtCkPDAzUvn37sjz/4cOHtXLlSvXo0UNLly7VwYMH9Z///EdpaWkaPXr0nbuRG5ARAwAAAJCnxcbGys/Pz2aLjY29Y+e3WCwqUaKEZsyYoTp16qhr164aPny4pk+ffseu8U9kxAAAAAA4zolzxGJiXlJ0dLRNWVbZMEkKCAiQu7u7Tpw4YVN+4sQJlSxZMstjSpUqJQ8PD7m7u1vLQkNDdfz4caWmpsrT09PBO8iMjBgAAACAPM1sNsvX19dmyy4Q8/T0VJ06dbRixQprmcVi0YoVKxQREZHlMY0aNdLBgwdlsVisZb/99ptKlSrllCBMIhADAAAAcAeY3NycttkrOjpaM2fO1Lx587R37179+9//VnJysnUVxZ49eyomJsZa/9///rfOnj2rF154Qb/99puWLFmi119/Xc8+++wd659/YmgiAAAAAMeZ8k6Op2vXrjp16pRGjRql48ePq2bNmoqLi7Mu4JGQkCC3GwK8smXLatmyZRo8eLBq1KihMmXK6IUXXtCwYcOc1kYCMQAAAAB3naioKEVFRWW5Lz4+PlNZRESEfv31Vye36joCMQAAAACOc8s7X+icH+Sd/CEAAAAA3CPIiAEAAABwmCkPzRHLD+gtAAAAAHAxMmIAAAAAHMccMbuQEQMAAAAAFyMjBgAAAMBxzBGzC4EYAAAAAMeZGJpoD8JWAAAAAHAxMmIAAAAAHOdGjsce9FYONWvWTIMGDXLKuceMGaOaNWve0XPOnTtX/v7+d/ScAAAAAO4MMmIAAAAAHMdiHXYhEMtFhmEoIyMjt5sBAAAAwMXuyrDVYrEoNjZW5cuXl7e3t8LDw7Vo0SJJUnx8vEwmk5YtW6ZatWrJ29tbDz30kE6ePKnvv/9eoaGh8vX1Vffu3XXp0iWb86anpysqKkp+fn4KCAjQyJEjZRiGdf9HH32kunXrqnDhwipZsqS6d++ukydPWvdfu/b333+vOnXqyGw2a/Xq1Znaf+jQIYWEhCgqKkqGYSglJUVDhw5VmTJl5OPjowYNGig+Pt7mmLlz56pcuXIqWLCgHn30UZ05c+YO9igAAABwC24m5213obsyEIuNjdWHH36o6dOna/fu3Ro8eLCefPJJ/fzzz9Y6Y8aM0dtvv621a9fqjz/+UJcuXTRlyhQtWLBAS5Ys0Q8//KBp06bZnHfevHkqUKCANmzYoKlTp2rSpEmaNWuWdX9aWprGjRun7du366uvvtLRo0fVu3fvTO176aWX9MYbb2jv3r2qUaOGzb4dO3aocePG6t69u95++22ZTCZFRUVp3bp1+vTTT7Vjxw49/vjjatOmjQ4cOCBJWr9+vfr27auoqCht27ZNzZs316uvvnoHexQAAADAnXTXDU1MSUnR66+/rh9//FERERGSpJCQEK1evVrvv/++BgwYIEl69dVX1ahRI0lS3759FRMTY81ESdJjjz2mn376ScOGDbOeu2zZspo8ebJMJpOqVKminTt3avLkyerfv78kqU+fPta6ISEheuutt1SvXj0lJSWpUKFC1n2vvPKKWrVqlanta9eu1cMPP6zhw4dryJAhkqSEhATNmTNHCQkJKl26tCRp6NChiouL05w5c/T6669r6tSpatOmjV588UVJUuXKlbV27VrFxcXdmU4FAAAAboU5Yna56wKxgwcP6tKlS5kCndTUVNWqVcv6+MZMVGBgoAoWLGgNwq6VbdiwweYcDzzwgEw3fFFdRESEJk6cqIyMDLm7u2vz5s0aM2aMtm/frnPnzslisUi6GkxVq1bNelzdunUztTshIUGtWrXSa6+9ZrM6486dO5WRkaHKlSvb1E9JSVGxYsUkSXv37tWjjz5qsz8iIuKWgVhKSopSUlJsysxms+7SRCkAAACQZ9x1gVhSUpIkacmSJSpTpozNPrPZrEOHDkmSPDw8rOUmk8nm8bWya4FUTiQnJysyMlKRkZGaP3++ihcvroSEBEVGRio1NdWmro+PT6bjixcvrtKlS+uTTz5Rnz595Ovra72fa0Geu7u7zTE3ZtluR2xsrMaOHWtTNnr0aJkqDnfovAAAALgHme7OuVzOctcFYtWqVZPZbFZCQoKaNm2aaf+1QOx2rF+/3ubxr7/+qkqVKsnd3V379u3TmTNn9MYbb6hs2bKSpE2bNuX43N7e3vruu+/Url07RUZG6ocfflDhwoVVq1YtZWRk6OTJk2rSpEmWx4aGhmbZtluJiYlRdHS0TZnZbNYbn+e42QAAAMBVfKGzXe66QKxw4cIaOnSoBg8eLIvFosaNGysxMVFr1qyRr6+vgoKCbvvcCQkJio6O1sCBA7VlyxZNmzZNEydOlCSVK1dOnp6emjZtmp555hnt2rVL48aNs+v8Pj4+WrJkidq2bau2bdsqLi5OlStXVo8ePdSzZ09NnDhRtWrV0qlTp7RixQrVqFFD7du31/PPP69GjRppwoQJ6tixo5YtW5aj+WFms/n/hyL+U5pd7QYAAABgn7sybB03bpxGjhyp2NhYhYaGqk2bNlqyZInKly/v0Hl79uypy5cvq379+nr22Wf1wgsvWBf/KF68uObOnavPP/9c1apV0xtvvKEJEybYfY1ChQrp+++/l2EYat++vZKTkzVnzhz17NlTQ4YMUZUqVdSpUydt3LhR5cqVk3R17trMmTM1depUhYeH64cfftCIESMculcAAADALiaT87a7kMm48YuwAEljPyYjZo/RT3pozZ6k3G5GvtOoWiEt8aiS283IV9qn7dd3W9Jzuxn5zsO1C2jYjMu53Yx85c0B3lqx80puNyPfaRHmpbhtqbeuCKs2NT017hPe1+w1slveHNR2Zcl0p53bq/0zTjt3bsmbP0UAAAAA+QvL19uF3gIAAAAAFyMjBgAAAMBxrJpoF3oLAAAAAFyMjBgAAAAAx92lqxs6C4EYAAAAAMexWIdd6C0AAAAAcDEyYgAAAAAcx9BEu5ARAwAAAAAXIyMGAAAAwHEsX28XegsAAAAAXIyMGAAAAACHGcwRswsZMQAAAABwMTJiAAAAABzH94jZhUAMAAAAgOMIxOxCbwEAAACAi5ERAwAAAOAwFuuwDxkxAAAAAHAxMmIAAAAAHMccMbvQWwAAAADgYmTEAAAAADiOOWJ2ISMGAAAAAC5GRgwAAACA49zI8djDZBiGkduNAAAAAJC/Ja/9wmnn9mnY2Wnnzi1kxJBJ0vpvc7sJ+UqhBh30xQZLbjcj3+lc303fbUnP7WbkKw/XLqAlHlVyuxn5Tvu0/frtUEJuNyNfqVyhnKZ8w99p7TXoEZMWrKbf7NG9sUlLt6TldjPynXa1PXK7CbgDCMQAAAAAOI7l6+1CbwEAAACAi5ERAwAAAOAwg4yYXegtAAAAAHedd955R8HBwfLy8lKDBg20YcOGHB336aefymQyqVOnTk5tH4EYAAAAAMeZTM7b7LRw4UJFR0dr9OjR2rJli8LDwxUZGamTJ0/e9LijR49q6NChatKkye32Qo4RiAEAAAC4q0yaNEn9+/fX008/rWrVqmn69OkqWLCgZs+ene0xGRkZ6tGjh8aOHauQkBCnt5FADAAAAIDDDJOb0zZ7pKamavPmzWrZsqW1zM3NTS1bttS6deuyPe6VV15RiRIl1Ldv39vuA3uwWAcAAAAAx93GEMKcSklJUUpKik2Z2WyW2WzOVPf06dPKyMhQYGCgTXlgYKD27duX5flXr16tDz74QNu2bbtjbb4VMmIAAAAA8rTY2Fj5+fnZbLGxsXfk3BcvXtRTTz2lmTNnKiAg4I6cMyfIiAEAAABwnBOXr4+JiVF0dLRNWVbZMEkKCAiQu7u7Tpw4YVN+4sQJlSxZMlP9Q4cO6ejRo+rQoYO1zGKxSJIKFCig/fv3q0KFCo7eQiYEYgAAAADytOyGIWbF09NTderU0YoVK6xL0FssFq1YsUJRUVGZ6letWlU7d+60KRsxYoQuXryoqVOnqmzZsg63PysEYgAAAAAcZjhxjpi9oqOj1atXL9WtW1f169fXlClTlJycrKefflqS1LNnT5UpU0axsbHy8vJS9erVbY739/eXpEzldxKBGAAAAIC7SteuXXXq1CmNGjVKx48fV82aNRUXF2ddwCMhIUFubrm7XAaBGAAAAADHOXGO2O2IiorKciiiJMXHx9/02Llz5975Bv1D3uotAAAAALgHkBEDAAAA4DBDeWeOWH5AIAYAAADAYUYeG5qY19FbAAAAAOBiZMQAAAAAOI6MmF3oLQAAAABwMTJiAAAAAByWl77QOT8gIwYAAAAALkZGDAAAAIDDWDXRPvRWHhEXF6fGjRvL399fxYoV08MPP6xDhw5Z969du1Y1a9aUl5eX6tatq6+++komk0nbtm2z1tm1a5fatm2rQoUKKTAwUE899ZROnz6dC3cDAAAA4GYIxPKI5ORkRUdHa9OmTVqxYoXc3Nz06KOPymKx6MKFC+rQoYPCwsK0ZcsWjRs3TsOGDbM5/vz583rooYdUq1Ytbdq0SXFxcTpx4oS6dOmSS3cEAACAe4rJ5LztLsTQxDziX//6l83j2bNnq3jx4tqzZ49Wr14tk8mkmTNnysvLS9WqVdNff/2l/v37W+u//fbbqlWrll5//XWbc5QtW1a//fabKleu7LJ7AQAAwL2HoYn2IRDLIw4cOKBRo0Zp/fr1On36tCwWiyQpISFB+/fvV40aNeTl5WWtX79+fZvjt2/frp9++kmFChXKdO5Dhw5lGYilpKQoJSXFpsxsNt+J2wEAAABwEwRieUSHDh0UFBSkmTNnqnTp0rJYLKpevbpSU1NzdHxSUpI6dOigN998M9O+UqVKZXlMbGysxo4da1M2evRoDW1bx/4bAAAAwD3N0N05hNBZCMTygDNnzmj//v2aOXOmmjRpIklavXq1dX+VKlX08ccfKyUlxZqx2rhxo805ateurcWLFys4OFgFCuTsxxoTE6Po6GibMrPZrLRtPzhyOwAAAABugYGceUCRIkVUrFgxzZgxQwcPHtTKlSttAqTu3bvLYrFowIAB2rt3r5YtW6YJEyZIkkz/P3nx2Wef1dmzZ9WtWzdt3LhRhw4d0rJly/T0008rIyMjy+uazWb5+vrabAxNBAAAwO0wTG5O2+5Gd+dd5TNubm769NNPtXnzZlWvXl2DBw/W+PHjrft9fX317bffatu2bapZs6aGDx+uUaNGSZJ13ljp0qW1Zs0aZWRkqHXr1goLC9OgQYPk7+8vNzd+zAAAAEBewtDEPKJly5bas2ePTZlhGNb/N2zYUNu3b7c+nj9/vjw8PFSuXDlrWaVKlfTFF184v7EAAADAP92ly8w7C4FYPvHhhx8qJCREZcqU0fbt2zVs2DB16dJF3t7eud00AAAAAHYiEMsnjh8/rlGjRun48eMqVaqUHn/8cb322mu53SwAAABAkmQw68kuBGL5xIsvvqgXX3wxt5sBAAAAZMlgaKJdCFsBAAAAwMXIiAEAAABw2N26zLyz0FsAAAAA4GJkxAAAAAA4zBBzxOxBRgwAAAAAXIyMGAAAAACHMUfMPvQWAAAAALgYGTEAAAAADuN7xOxDRgwAAAAAXIyMGAAAAACHsWqifQjEAAAAADiMxTrsQ28BAAAAgIuREQMAAADgMIYm2oeMGAAAAAC4GBkxAAAAAA5jjph96C0AAAAAcDEyYgAAAAAcxhwx+5ARAwAAAAAXIyMGAAAAwGHMEbMPgRgAAAAAhzE00T6ErQAAAADgYibDMIzcbgQAAACA/O3Q4cNOO3eFkBCnnTu3MDQRmVxZ+L/cbkK+4tX1Rb0yPz23m5HvjOpRQMNmXM7tZuQrbw7w1m+HEnK7GflO5QrltMSjSm43I19pn7ZfWw+czu1m5Du1KgXwGrVT5QrldOTQwdxuRr5TvkLF3G4C7gACMQAAAAAOMwzmiNmDOWIAAAAA4GJkxAAAAAA4zCDHYxd6CwAAAABcjIwYAAAAAIfxPWL2IRADAAAA4DACMfswNBEAAADAXeedd95RcHCwvLy81KBBA23YsCHbujNnzlSTJk1UpEgRFSlSRC1btrxp/TuBQAwAAACAwwyZnLbZa+HChYqOjtbo0aO1ZcsWhYeHKzIyUidPnsyyfnx8vLp166affvpJ69atU9myZdW6dWv99ddfjnZLtgjEAAAAANxVJk2apP79++vpp59WtWrVNH36dBUsWFCzZ8/Osv78+fP1n//8RzVr1lTVqlU1a9YsWSwWrVixwmltZI4YAAAAAIc5c45YSkqKUlJSbMrMZrPMZnOmuqmpqdq8ebNiYmKsZW5ubmrZsqXWrVuXo+tdunRJaWlpKlq0qGMNvwkyYgAAAADytNjYWPn5+dlssbGxWdY9ffq0MjIyFBgYaFMeGBio48eP5+h6w4YNU+nSpdWyZUuH254dMmIAAAAAHGYYzsuIxcTEKDo62qYsq2zYnfDGG2/o008/VXx8vLy8vJxyDYlADAAAAEAel90wxKwEBATI3d1dJ06csCk/ceKESpYsedNjJ0yYoDfeeEM//vijatSocdvtzQmGJgIAAABwWF5ZNdHT01N16tSxWWjj2sIbERER2R73v//9T+PGjVNcXJzq1q172/2QU2TEAAAAADgsL32hc3R0tHr16qW6deuqfv36mjJlipKTk/X0009Lknr27KkyZcpY55m9+eabGjVqlBYsWKDg4GDrXLJChQqpUKFCTmkjgRgAAACAu0rXrl116tQpjRo1SsePH1fNmjUVFxdnXcAjISFBbm7XBwe+9957Sk1N1WOPPWZzntGjR2vMmDFOaSOBGAAAAACH5aWMmCRFRUUpKioqy33x8fE2j48ePer8Bv0Dc8QAAAAAwMXIiAEAAABwmDOXr78bkREDAAAAABcjEMtlhmFowIABKlq0qEwmk7Zt25bbTQIAAADsZpHJadvdiKGJuSwuLk5z585VfHy8QkJCFBAQkNtNAgAAAOBkBGK57NChQypVqpQaNmx42+dIS0uTh4fHHWwVAAAAYJ+8tmpiXsfQxFzUu3dvPffcc0pISJDJZFJwcLDi4uLUuHFj+fv7q1ixYnr44Yd16NAh6zFHjx6VyWTSwoUL1bRpU3l5eWn+/PmSpFmzZik0NFReXl6qWrWq3n333dy6NQAAANxjDMPktO1uREYsF02dOlUVKlTQjBkztHHjRrm7u2vVqlWKjo5WjRo1lJSUpFGjRunRRx/Vtm3bbL507qWXXtLEiRNVq1YtazA2atQovf3226pVq5a2bt2q/v37y8fHR7169crFuwQAAADwTwRiucjPz0+FCxeWu7u7SpYsKUn617/+ZVNn9uzZKl68uPbs2aPq1atbywcNGqTOnTtbH48ePVoTJ060lpUvX1579uzR+++/TyAGAAAAp2Noon0IxPKYAwcOaNSoUVq/fr1Onz4ti8UiSUpISLAJxOrWrWv9f3Jysg4dOqS+ffuqf//+1vL09HT5+flle62UlBSlpKTYlJnN5jt1KwAAAACyQSCWx3To0EFBQUGaOXOmSpcuLYvFourVqys1NdWmno+Pj/X/SUlJkqSZM2eqQYMGNvXc3d2zvVZsbKzGjh1rUzZ69Gi9FFrQ0dsAAADAPeZuncvlLARieciZM2e0f/9+zZw5U02aNJEkrV69+pbHBQYGqnTp0jp8+LB69OiR4+vFxMQoOjrapsxsNsv4aqp9DQcAAABgFwKxPKRIkSIqVqyYZsyYoVKlSikhIUEvvfRSjo4dO3asnn/+efn5+alNmzZKSUnRpk2bdO7cuUzB1jVmsznLoYhXHLoLAAAA3IuYI2Yflq/PQ9zc3PTpp59q8+bNql69ugYPHqzx48fn6Nh+/fpp1qxZmjNnjsLCwtS0aVPNnTtX5cuXd3KrAQAAANiLjFguGzRokAYNGmR93LJlS+3Zs8emjmEY1v8HBwfbPL5R9+7d1b17d6e0EwAAALgZ5ojZh0AMAAAAgMMsud2AfIahiQAAAADgYmTEAAAAADiMoYn2ISMGAAAAAC5GRgwAAACAw1i+3j5kxAAAAADAxciIAQAAAHAYc8TsQ0YMAAAAAFyMjBgAAAAAhzFHzD4EYgAAAAAcZjFyuwX5C0MTAQAAAMDFyIgBAAAAcBhDE+1DRgwAAAAAXIyMGAAAAACHsXy9fciIAQAAAICLkREDAAAA4DCDVRPtQkYMAAAAAFyMjBgAAAAAh1lYNdEuBGIAAAAAHMZiHfZhaCIAAAAAuBgZMQAAAAAOY7EO+5ARAwAAAAAXIyMGAAAAwGEGi3XYhYwYAAAAALiYyTAYzQkAAADAMXHbUp127jY1PZ127tzC0ERkMjc+t1uQv/RuJr29lL9n2CuqnUkrdl7J7WbkKy3CvDTlG55r9hr0iElbD5zO7WbkK7UqBWiJR5Xcbka+0z5tvxK3/JjbzchX/Gq31KdreV+z1xMNGQJ4NyAQAwAAAOAwvkfMPgRiAAAAABzGhCf7sFgHAAAAALgYGTEAAAAADrOwfL1dyIgBAAAAgIuREQMAAADgMOaI2YeMGAAAAAC4GBkxAAAAAA5j+Xr7kBEDAAAAABcjEAMAAADgMIvhvO12vPPOOwoODpaXl5caNGigDRs23LT+559/rqpVq8rLy0thYWFaunTp7V04hwjEAAAAANxVFi5cqOjoaI0ePVpbtmxReHi4IiMjdfLkySzrr127Vt26dVPfvn21detWderUSZ06ddKuXbuc1kYCMQAAAAAOMwznbfaaNGmS+vfvr6efflrVqlXT9OnTVbBgQc2ePTvL+lOnTlWbNm303//+V6GhoRo3bpxq166tt99+28FeyR6BGAAAAACHGTI5bUtJSdGFCxdstpSUlCzbkZqaqs2bN6tly5bWMjc3N7Vs2VLr1q3L8ph169bZ1JekyMjIbOvfCQRiAAAAAPK02NhY+fn52WyxsbFZ1j19+rQyMjIUGBhoUx4YGKjjx49neczx48ftqn8nsHw9AAAAAIfd7qIaORETE6Po6GibMrPZ7LwLugCBGAAAAIA8zWw25zjwCggIkLu7u06cOGFTfuLECZUsWTLLY0qWLGlX/TuBoYkAAAAAHJZXFuvw9PRUnTp1tGLFCmuZxWLRihUrFBERkeUxERERNvUlafny5dnWvxPIiAEAAAC4q0RHR6tXr16qW7eu6tevrylTpig5OVlPP/20JKlnz54qU6aMdZ7ZCy+8oKZNm2rixIlq3769Pv30U23atEkzZsxwWhsJxAAAAAA47HaWmXeWrl276tSpUxo1apSOHz+umjVrKi4uzrogR0JCgtzcrg8ObNiwoRYsWKARI0bo5ZdfVqVKlfTVV1+pevXqTmsjgRgAAACAu05UVJSioqKy3BcfH5+p7PHHH9fjjz/u5FZdxxyxXNK7d2916tTJ+rhZs2YaNGhQrrUHAAAAcITFMDltuxuREcslU6dOlZGX8rcAAACAA/hoax8CsVzi5+eX200AAAAAkEsYmuhkixYtUlhYmLy9vVWsWDG1bNlSycnJmYYmSlJ6erqioqLk5+engIAAjRw50iZr9u6776pSpUry8vJSYGCgHnvsMeu+Zs2aWcfBZnc8AAAA4Cx5Zfn6/IJAzImOHTumbt26qU+fPtq7d6/i4+PVuXPnbIOjefPmqUCBAtqwYYOmTp2qSZMmadasWZKkTZs26fnnn9crr7yi/fv3Ky4uTg8++GCOjwcAAACQdzA00YmOHTum9PR0de7cWUFBQZKksLCwbOuXLVtWkydPlslkUpUqVbRz505NnjxZ/fv3V0JCgnx8fPTwww+rcOHCCgoKUq1atXJ8PAAAAOBMlrs0c+UsZMScKDw8XC1atFBYWJgef/xxzZw5U+fOncu2/gMPPCCT6fqqMBERETpw4IAyMjLUqlUrBQUFKSQkRE899ZTmz5+vS5cu5fj4rKSkpOjChQs2W0pKioN3DQAAAOBWCMScyN3dXcuXL9f333+vatWqadq0aapSpYqOHDli97kKFy6sLVu26JNPPlGpUqU0atQohYeH6/z587fdvtjYWPn5+dls175dHAAAALCHYZictt2NCMSczGQyqVGjRho7dqy2bt0qT09Pffnll1nWXb9+vc3jX3/9VZUqVZK7u7skqUCBAmrZsqX+97//aceOHTp69KhWrlyZ4+P/KSYmRomJiTZbTEyMI7cLAAAAIAeYI+ZE69ev14oVK9S6dWuVKFFC69ev16lTpxQaGqodO3Zkqp+QkKDo6GgNHDhQW7Zs0bRp0zRx4kRJ0nfffafDhw/rwQcfVJEiRbR06VJZLBZVqVIlR8dnxWw2y2w23/kbBwAAwD3nbl3d0FkIxJzI19dXq1at0pQpU3ThwgUFBQVp4sSJatu2rRYuXJipfs+ePXX58mXVr19f7u7ueuGFFzRgwABJkr+/v7744guNGTNGV65cUaVKlfTJJ5/o/vvvz9HxAAAAgDOxWId9CMScKDQ0VHFxcVnumzt3rs3j+Ph46//fe++9TPUbN25sUycrHh4emjJlSpbHAwAAAMg7CMQAAAAAOIyhifZhsQ4AAAAAcDEyYneJWw1bBAAAAJyJjJh9yIgBAAAAgIuREQMAAADgMFZNtA8ZMQAAAABwMTJiAAAAABzGHDH7EIgBAAAAcJjFktstyF8YmggAAAAALkZGDAAAAIDDGJpoHzJiAAAAAOBiZMQAAAAAOIyMmH3IiAEAAACAi5ERAwAAAOAwvtDZPmTEAAAAAMDFyIgBAAAAcJjh1EliJieeO3cQiAEAAABwGIt12IehiQAAAADgYmTEAAAAADjMYsntFuQvZMQAAAAAwMXIiAEAAABwGHPE7ENGDAAAAABcjIwYAAAAAIfxhc72ISMGAAAAAC5mMpz7zWsAAAAA7gETv3JeWDGkE1/ojHvAZ+tYe9QeXSLcNGJuam43I995tben4rbRb/ZoU9NTC1bztzN7dW9s0m+HEnK7GflK5QrllLjlx9xuRr7jV7ullnhUye1m5Cvt0/Yrftfl3G5GvtOsunduNyFLhlPHJt59gRhDEwEAAADAxciIAQAAAHAYi3XYh4wYAAAAALgYGTEAAAAADmMJQPuQEQMAAAAAFyMjBgAAAMBhFiaJ2YWMGAAAAAC4GBkxAAAAAA5jjph9CMQAAAAAOIxAzD4MTQQAAAAAFyMjBgAAAMBhFlJidiEjBgAAAAAuRiAGAAAAwGGGxXmbM509e1Y9evSQr6+v/P391bdvXyUlJd20/nPPPacqVarI29tb5cqV0/PPP6/ExES7rksgBgAAAOCe1aNHD+3evVvLly/Xd999p1WrVmnAgAHZ1v/777/1999/a8KECdq1a5fmzp2ruLg49e3b167rMkcMAAAAgMOMfDhHbO/evYqLi9PGjRtVt25dSdK0adPUrl07TZgwQaVLl850TPXq1bV48WLr4woVKui1117Tk08+qfT0dBUokLMQi4wYAAAAgDwtJSVFFy5csNlSUlIcPu+6devk7+9vDcIkqWXLlnJzc9P69etzfJ7ExET5+vrmOAiTCMQAAAAA3AEWi/O22NhY+fn52WyxsbEOt/n48eMqUaKETVmBAgVUtGhRHT9+PEfnOH36tMaNG3fT4YxZIRADAAAA4DDDMJy2xcTEKDEx0WaLiYnJti0vvfSSTCbTTbd9+/Y5fM8XLlxQ+/btVa1aNY0ZM8auY5kjBgAAACBPM5vNMpvNOa4/ZMgQ9e7d+6Z1QkJCVLJkSZ08edKmPD09XWfPnlXJkiVvevzFixfVpk0bFS5cWF9++aU8PDxy3D6JQAwAAADAHWDJQ2t1FC9eXMWLF79lvYiICJ0/f16bN29WnTp1JEkrV66UxWJRgwYNsj3uwoULioyMlNls1jfffCMvLy+728jQxLvA0aNHZTKZtG3bttxuCgAAAJBvhIaGqk2bNurfv782bNigNWvWKCoqSk888YR1xcS//vpLVatW1YYNGyRdDcJat26t5ORkffDBB7pw4YKOHz+u48ePKyMjI8fXJiMGAAAAwGFGXkqJ2WH+/PmKiopSixYt5Obmpn/961966623rPvT0tK0f/9+Xbp0SZK0ZcsW64qKFStWtDnXkSNHFBwcnKPrEojlcYZhKCMjw66lMAEAAADkTNGiRbVgwYJs9wcHB9t8R1qzZs3uyHemMTTxDmvWrJmioqIUFRUlPz8/BQQEaOTIkdYf1kcffaS6deuqcOHCKlmypLp3724zQTA+Pl4mk0nff/+96tSpI7PZrNWrV8tiseh///ufKlasKLPZrHLlyum1116zufbhw4fVvHlzFSxYUOHh4Vq3bp1L7x0AAAD3LsNw3nY3IhBzgnnz5qlAgQLasGGDpk6dqkmTJmnWrFmSrqY2x40bp+3bt+urr77S0aNHs1zR5aWXXtIbb7yhvXv3qkaNGoqJidEbb7yhkSNHas+ePVqwYIECAwNtjhk+fLiGDh2qbdu2qXLlyurWrZvS09NdccsAAAAA7MB4NycoW7asJk+eLJPJpCpVqmjnzp2aPHmy+vfvrz59+ljrhYSE6K233lK9evWUlJSkQoUKWfe98soratWqlaSrS2NOnTpVb7/9tnr16iVJqlChgho3bmxz3aFDh6p9+/aSpLFjx+r+++/XwYMHVbVqVWffMgAAAO5xlnw6Ryy3kBFzggceeEAmk8n6OCIiQgcOHFBGRoY2b96sDh06qFy5cipcuLCaNm0qSUpISLA5R926da3/37t3r1JSUtSiRYubXrdGjRrW/5cqVUqSMn0vwo1SUlJ04cIFmy0lJSXnNwoAAADgthCIudCVK1cUGRkpX19fzZ8/Xxs3btSXX34pSUpNTbWp6+PjY/2/t7d3js5/45fIXQsELRZLtvVjY2Pl5+dns8XGxub4fgAAAIBrDMNw2nY3IhBzgmvLWV7z66+/qlKlStq3b5/OnDmjN954Q02aNFHVqlVvmrG6plKlSvL29taKFSvuaDtjYmKUmJhos8XExNzRawAAAODeYFict92NmCPmBAkJCYqOjtbAgQO1ZcsWTZs2TRMnTlS5cuXk6empadOm6ZlnntGuXbs0bty4W57Py8tLw4YN04svvihPT081atRIp06d0u7du9W3b9/bbqfZbJbZbM5iz136bAcAAADyCAIxJ+jZs6cuX76s+vXry93dXS+88IIGDBggk8mkuXPn6uWXX9Zbb72l2rVra8KECXrkkUduec6RI0eqQIECGjVqlP7++2+VKlVKzzzzjAvuBgAAALg1y106hNBZCMScwMPDQ1OmTNF7772XaV+3bt3UrVs3m7KcfEGcm5ubhg8fruHDh2fa988vmZMkf3//u3Y8LQAAAJDfEYgBAAAAcBhJAPuwWAcAAAAAuBgZsTssPj4+t5sAAAAAuBxf6GwfMmIAAAAA4GJkxAAAAAA4jCli9iEQAwAAAOAwg6GJdmFoIgAAAAC4GBkxAAAAAA7jC53tQ0YMAAAAAFyMjBgAAAAAhzFHzD5kxAAAAADAxciIAQAAAHAYGTH7kBEDAAAAABcjIwYAAADAYSTE7EMgBgAAAMBhDE20D0MTAQAAAMDFyIgBAAAAcJjBFzrbhYwYAAAAALgYGTEAAAAADrMwR8wuZMQAAAAAwMXIiAEAAABwGHPE7ENGDAAAAABcjIwYAAAAAIfxPWL2MRnkEAEAAAA4qM/Yk0479+zRJZx27txCRgyZnNyzKbebkK+UqFZXjTv8nNvNyHdWf9tU4z5Jz+1m5CsjuxXQ0i1pud2MfKddbQ8dOXQwt5uRr5SvUFGfruXvtPZ6oqFJ8bsu53Yz8pVm1b21xKNKbjcj32mftj+3m4A7gEAMAAAAgMMsDLSzC4t1AAAAAICLkREDAAAA4DAW67APGTEAAAAAcDEyYgAAAAAcxmLs9iEjBgAAAAAuRkYMAAAAgMMszBGzC4EYAAAAAIexWId9GJoIAAAAAC5GRgwAAACAw1iswz5kxAAAAADAxciIAQAAAHCYYbHkdhPyFTJiAAAAAOBiZMQAAAAAOIzl6+1DRgwAAAAAXIxADAAAAIDDDMNw2uZMZ8+eVY8ePeTr6yt/f3/17dtXSUlJOb7ntm3bymQy6auvvrLrugRiAAAAABxmWAynbc7Uo0cP7d69W8uXL9d3332nVatWacCAATk6dsqUKTKZTLd1XeaIAQAAALgn7d27V3Fxcdq4caPq1q0rSZo2bZratWunCRMmqHTp0tkeu23bNk2cOFGbNm1SqVKl7L42GTEAAAAADsuPGbF169bJ39/fGoRJUsuWLeXm5qb169dne9ylS5fUvXt3vfPOOypZsuRtXZuMGAAAAIA8LSUlRSkpKTZlZrNZZrPZofMeP35cJUqUsCkrUKCAihYtquPHj2d73ODBg9WwYUN17Njxtq9NRiwXBAcHa8qUKU69Rnx8vEwmk86fP+/U6wAAAACSZDEsTttiY2Pl5+dns8XGxmbblpdeekkmk+mm2759+27rPr/55hutXLnS4c/zZMRywcaNG+Xj45PbzQAAAADyhZiYGEVHR9uU3SwbNmTIEPXu3fum5wwJCVHJkiV18uRJm/L09HSdPXs22yGHK1eu1KFDh+Tv729T/q9//UtNmjRRfHz8Ta97DYHYHZSamipPT89b1itevLgLWgMAAAC4jjPnctk7DLF48eI5+swdERGh8+fPa/PmzapTp46kq4GWxWJRgwYNsjzmpZdeUr9+/WzKwsLCNHnyZHXo0CHHbbznhyYuWrRIYWFh8vb2VrFixdSyZUslJyerWbNmGjRokE3dTp062UTWwcHBGjdunHr27ClfX18NGDBADRs21LBhw2yOO3XqlDw8PLRq1SrrcddSmd27d1fXrl1t6qelpSkgIEAffvihJMliuZqOLV++vLy9vRUeHq5FixbZHLN06VJVrlxZ3t7eat68uY4ePep45wAAAAB3sdDQULVp00b9+/fXhg0btGbNGkVFRemJJ56wrpj4119/qWrVqtqwYYMkqWTJkqpevbrNJknlypVT+fLlc3ztezoQO3bsmLp166Y+ffpo7969io+PV+fOne360rgJEyYoPDxcW7du1ciRI9WjRw99+umnNudYuHChSpcurSZNmmQ6vkePHvr2229tvjRu2bJlunTpkh599FFJUmxsrD788ENNnz5du3fv1uDBg/Xkk0/q559/liT98ccf6ty5szp06KBt27apX79+eumll263WwAAAAC75cdVEyVp/vz5qlq1qlq0aKF27dqpcePGmjFjhnV/Wlqa9u/fr0uXLt3R697TQxOPHTum9PR0de7cWUFBQZKuphXt8dBDD2nIkCHWx126dNGgQYO0evVqa+C1YMECdevWLcsve4uMjJSPj4++/PJLPfXUU9b6jzzyiAoXLqyUlBS9/vrr+vHHHxURESHp6njW1atX6/3331fTpk313nvvqUKFCpo4caIkqUqVKtq5c6fefPNN+zsFAAAAuA32JDPykqJFi2rBggXZ7g8ODr7lvd3Ovd/TGbHw8HC1aNFCYWFhevzxxzVz5kydO3fOrnPc+J0D0tXxqK1bt9b8+fMlSUeOHNG6devUo0ePLI8vUKCAunTpYq2fnJysr7/+2lr/4MGDunTpklq1aqVChQpZtw8//FCHDh2SdPWL6P45hvVa0HYzKSkpunDhgs32z2VBAQAAANx593Qg5u7uruXLl+v7779XtWrVNG3aNFWpUkVHjhyRm5tbpsg2LS0t0zmyWv2wR48eWrRokdLS0rRgwQKFhYXdNNPWo0cPrVixQidPntRXX30lb29vtWnTRpKsQxaXLFmibdu2Wbc9e/ZkmidmL3uXAQUAAACyY7FYnLbdje7pQEySTCaTGjVqpLFjx2rr1q3y9PTUl19+qeLFi+vYsWPWehkZGdq1a1eOztmxY0dduXJFcXFxWrBgQbbZsGsaNmyosmXLauHChZo/f74ef/xxeXh4SJKqVasms9mshIQEVaxY0WYrW7aspKuTDK9NHrzm119/vWU7Y2JilJiYaLPFxMTk6B4BAAAA3L57eo7Y+vXrtWLFCrVu3VolSpTQ+vXrderUKYWGhsrHx0fR0dFasmSJKlSooEmTJuX4y5F9fHzUqVMnjRw5Unv37lW3bt1ueUz37t01ffp0/fbbb/rpp5+s5YULF9bQoUM1ePBgWSwWNW7cWImJiVqzZo18fX3Vq1cvPfPMM5o4caL++9//ql+/ftq8ebPmzp17y2veiW8jBwAAACTnLl9/N7qnAzFfX1+tWrVKU6ZM0YULFxQUFKSJEyeqbdu2SktL0/bt29WzZ08VKFBAgwcPVvPmzXN87h49eqhdu3Z68MEHVa5cuRzVf+211xQUFKRGjRrZ7Bs3bpyKFy+u2NhYHT58WP7+/qpdu7ZefvllSVeXyly8eLEGDx6sadOmqX79+nr99dfVp08f+zoEAAAAgEuYjPy6vAmc5uSeTbndhHylRLW6atzh59xuRr6z+tumGvdJem43I18Z2a2Alm7JPFcVN9eutoeOHDqY283IV8pXqKhP1/LxwF5PNDQpftfl3G5GvtKsureWeFTJ7WbkO+3T9ud2E7LUvl/OpvHcjiWzqjvt3Lnlnp8jBgAAAACudk8PTQQAAABwZzBHzD4EYgAAAAAcRiBmH4YmAgAAAICLkREDAAAA4DCLcXd+8bKzkBEDAAAAABcjIwYAAADAYcwRsw8ZMQAAAABwMTJiAAAAABxmWJgjZg8yYgAAAADgYmTEAAAAADiMOWL2ISMGAAAAAC5GRgwAAACAwwy+R8wuBGIAAAAAHGZhaKJdGJoIAAAAAC5GRgwAAACAw1i+3j5kxAAAAADAxciIAQAAAHAYy9fbh4wYAAAAALgYGTEAAAAADmP5evuQEQMAAAAAFyMjBgAAAMBhzBGzD4EYAAAAAIexfL19GJoIAAAAAC5mMgyDHCLytJSUFMXGxiomJkZmszm3m5Nv0G/2o89uD/1mP/rs9tBv9qPPbg/9BlcgEEOed+HCBfn5+SkxMVG+vr653Zx8g36zH312e+g3+9Fnt4d+sx99dnvoN7gCQxMBAAAAwMUIxAAAAADAxQjEAAAAAMDFCMSQ55nNZo0ePZrJsnai3+xHn90e+s1+9Nntod/sR5/dHvoNrsBiHQAAAADgYmTEAAAAAMDFCMQAAAAAwMUIxAAAAADAxQjEYBUfHy+TyaTz58/ndlOAO6ZZs2YaNGiQU849ZswY1axZ846ec+7cufL397+j54RrGIahAQMGqGjRojKZTNq2bVtuNylf6d27tzp16mR97MzXLrJ39OhRnr83CA4O1pQpU5x6DT5/3bsK5HYDgDstPj5ezZs317lz5/hAC8Bl4uLiNHfuXMXHxyskJEQBAQG53aR8ZerUqWL9MOQ1GzdulI+PT243A3cpAjHAyVJTU+Xp6ZnbzcAdZBiGMjIycrsZyGMOHTqkUqVKqWHDhrd9jrS0NHl4eNzBVuUffn5+ud2Eu961964CBfj4l9PfzcWLF3dBa3CvYmjiXaZZs2aKiopSVFSU/Pz8FBAQoJEjR1r/ypiSkqJhw4apbNmyMpvNqlixoj744IMsz3XmzBl169ZNZcqUUcGCBRUWFqZPPvnEps6iRYsUFhYmb29vFStWTC1btlRycrKkq5mp+vXry8fHR/7+/mrUqJF+//33HN3Ht99+q3r16snLy0sBAQF69NFHrfs++ugj1a1bV4ULF1bJkiXVvXt3nTx5UtLVIRXNmzeXJBUpUkQmk0m9e/eWJFksFsXGxqp8+fLy9vZWeHi4Fi1aZHPdb775RpUqVZKXl5eaN2+uefPmZRousHjxYt1///0ym80KDg7WxIkTbc4RHByscePGqWfPnvL19dWAAQP00EMPKSoqyqbeqVOn5OnpqRUrVuSoT250s36fNWuWQkND5eXlpapVq+rdd9+1HtenTx/VqFFDKSkpkq7+IqpVq5Z69uxpdxuc4WY/o2tDN5YtW6ZatWrJ29tbDz30kE6ePKnvv/9eoaGh8vX1Vffu3XXp0iWb86anp2f7mpBu/py68drff/+96tSpI7PZrNWrV2dq/6FDhxQSEqKoqCgZhqGUlBQNHTpUZcqUkY+Pjxo0aKD4+HibY+bOnaty5cqpYMGCevTRR3XmzJk72KOOiYuLU+PGjeXv769ixYrp4Ycf1qFDh6z7165dq5o1a8rLy0t169bVV199lWlI065du9S2bVsVKlRIgYGBeuqpp3T69OlcuBvn6t27t5577jklJCTIZDIpODj4lv13bQjYwoUL1bRpU3l5eWn+/PmSbv46zu+ye//659BE6dav3Xfffdf6nh0YGKjHHnvMuu9Wvw/zg1vdw+2+d1ksFv3vf/9TxYoVZTabVa5cOb322ms21z58+LCaN2+uggULKjw8XOvWrXPpvWclu+dOVsNYO3XqZP39L2X9u7lhw4YaNmyYzXGnTp2Sh4eHVq1aZT3u2tDE7t27q2vXrjb109LSFBAQoA8//FBSzj5rLF26VJUrV5a3t7eaN2+uo0ePOt45yJ8M3FWaNm1qFCpUyHjhhReMffv2GR9//LFRsGBBY8aMGYZhGEaXLl2MsmXLGl988YVx6NAh48cffzQ+/fRTwzAM46effjIkGefOnTMMwzD+/PNPY/z48cbWrVuNQ4cOGW+99Zbh7u5urF+/3jAMw/j777+NAgUKGJMmTTKOHDli7Nixw3jnnXeMixcvGmlpaYafn58xdOhQ4+DBg8aePXuMuXPnGr///vst7+G7774z3N3djVGjRhl79uwxtm3bZrz++uvW/R988IGxdOlS49ChQ8a6deuMiIgIo23btoZhGEZ6erqxePFiQ5Kxf/9+49ixY8b58+cNwzCMV1991ahataoRFxdnHDp0yJgzZ45hNpuN+Ph4wzAM4/Dhw4aHh4cxdOhQY9++fcYnn3xilClTxqZPNm3aZLi5uRmvvPKKsX//fmPOnDmGt7e3MWfOHGv7goKCDF9fX2PChAnGwYMHjYMHDxrz5883ihQpYly5csVab9KkSUZwcLBhsVjs+hnfrN8//vhjo1SpUsbixYuNw4cPG4sXLzaKFi1qzJ071zAMw7h48aIREhJiDBo0yDAMwxg6dKgRHBxsJCYm2tUGZ7nZz+ja8/OBBx4wVq9ebWzZssWoWLGi0bRpU6N169bGli1bjFWrVhnFihUz3njjDes5b/WaMIybP6cM4/pro0aNGsYPP/xgHDx40Dhz5owxevRoIzw83DAMw9i+fbtRsmRJY/jw4dbj+vXrZzRs2NBYtWqVcfDgQWP8+PGG2Ww2fvvtN8MwDOPXX3813NzcjDfffNPYv3+/MXXqVMPf39/w8/Nzbkfn0KJFi4zFixcbBw4cMLZu3Wp06NDBCAsLMzIyMozExESjaNGixpNPPmns3r3bWLp0qVG5cmVDkrF161bDMAzj3LlzRvHixY2YmBhj7969xpYtW4xWrVoZzZs3z90bc4Lz588br7zyinHfffcZx44dM06ePHnT/jMMwzhy5IghyQgODra+Zv/+++9bvo7zs5u9f/Xq1cvo2LGjte6tXrsbN2403N3djQULFhhHjx41tmzZYkydOjXHx+cHt7qH233vevHFF40iRYoYc+fONQ4ePGj88ssvxsyZMw3DuP68rFq1qvHdd98Z+/fvNx577DEjKCjISEtLy5V+MIybP3eaNm1qvPDCCzb1O3bsaPTq1cv6OKvfzW+//bZRrlw5m9/D06ZNsykLCgoyJk+ebBjG1c8n3t7exsWLF631v/32W8Pb29u4cOGCYRi3/qyRkJBgmM1mIzo62vozDQwMtPmsgXsHgdhdpmnTpkZoaKjNm8qwYcOM0NBQY//+/YYkY/ny5Vke+89ALCvt27c3hgwZYhiGYWzevNmQZBw9ejRTvTNnzhiSrG889oiIiDB69OiR4/obN240JFnfGLO6jytXrhgFCxY01q5da3Ns3759jW7duhmGcbWfqlevbrN/+PDhNufq3r270apVK5s6//3vf41q1apZHwcFBRmdOnWyqXP58mWjSJEixsKFC61lNWrUMMaMGZPj+7zmZv1eoUIFY8GCBTZl48aNMyIiIqyP165da3h4eBgjR440ChQoYPzyyy92t8EZbvUzuvZz/fHHH637YmNjDUnGoUOHrGUDBw40IiMjrY9v9prITnbPqa+++sqm3rVAbM2aNUaRIkWMCRMmWPf9/vvvhru7u/HXX3/ZHNOiRQsjJibGMAzD6Natm9GuXTub/V27ds0zgdg/nTp1ypBk7Ny503jvvfeMYsWKGZcvX7bunzlzpk0gNm7cOKN169Y25/jjjz+sfyi520yePNkICgrKdv+N/WcY1z/wTpkyxaZeTl7H+dXN3r+yCsRu9tpdvHix4evra/0A/E+389rPa+y9h5y8d124cMEwm83WwOufrj0vZ82aZS3bvXu3IcnYu3fvnbit23Kz505OA7F//m4+efKkUaBAAWPVqlXWsoiICGPYsGE2x10LxNLS0oyAgADjww8/tO7v1q2b0bVrV8MwcvZZIyYmxuYzg2Fc/ZkSiN2bGJp4F3rggQdkMpmsjyMiInTgwAFt3bpV7u7uatq0aY7Ok5GRoXHjxiksLExFixZVoUKFtGzZMiUkJEiSwsPD1aJFC4WFhenxxx/XzJkzde7cOUlS0aJF1bt3b0VGRqpDhw6aOnWqjh07lqPrbtu2TS1atMh2/+bNm9WhQweVK1dOhQsXtt7PtXZl5eDBg7p06ZJatWqlQoUKWbcPP/zQOlRo//79qlevns1x9evXt3m8d+9eNWrUyKasUaNGOnDggM2cobp169rU8fLy0lNPPaXZs2dLkrZs2aJdu3bZDJvIqez6PTk5WYcOHVLfvn1t7vHVV1+1GQ4VERGhoUOHaty4cRoyZIgaN25sdxucISc/I0mqUaOG9f+BgYEqWLCgQkJCbMpuHJojZf+auPYzy+lz6p8/12t1WrVqpVGjRmnIkCHW8p07dyojI0OVK1e2uZ+ff/7Zej979+5VgwYNbM4XERGRsw5zgQMHDqhbt24KCQmRr6+vgoODJV295/3796tGjRry8vKy1v/n62X79u366aefbO6/atWqkmTzM71b3az/bnTj8yqnr+P86ma/N7Jys9duq1atFBQUpJCQED311FOaP39+pmHJt3rt5wc3u4fbee/au3evUlJSbvp7VrJ9ry1VqpQkZXpvdSV7nztZ+ed7ePHixdW6dWvrkOAjR45o3bp16tGjR5bHFyhQQF26dLHWT05O1tdff22tn5PfY3n9fR+uxWzNe8iNH5hyYvz48Zo6daqmTJmisLAw+fj4aNCgQUpNTZUkubu7a/ny5Vq7dq1++OEHTZs2TcOHD9f69etVvnx5zZkzR88//7zi4uK0cOFCjRgxQsuXL9cDDzxw0+t6e3tnuy85OVmRkZGKjIzU/PnzVbx4cSUkJCgyMtLarqwkJSVJkpYsWaIyZcrY7DObzTntkhzLaoWlfv36qWbNmvrzzz81Z84cPfTQQwoKCrL73Nn1+7fffitJmjlzZqY3eXd3d+v/LRaL1qxZI3d3dx08eNDu6zvLrX5G136J3biQgclkyrSwgclkksViyfF17XlOZfVzLV68uEqXLq1PPvlEffr0ka+vr/V+3N3dtXnzZpv+l6RChQrluH25qUOHDgoKCtLMmTNVunRpWSwWVa9e/aavtRslJSWpQ4cOevPNNzPtu/bB7m6W0/678Xl17XVwq9dxfnWz3xv2Kly4sLZs2aL4+Hj98MMPGjVqlMaMGaONGzfeEyvmXrly5bbeu272O/ZG/3yvlWTXe+uddrPnjpubW6a5f2lpaZnOkdV7eI8ePfT8889r2rRpWrBggcLCwhQWFpZtO3r06KGmTZvq5MmTWr58uby9vdWmTRtJrv+sgfyPjNhd6J+/0H799VdVqlRJ4eHhslgs+vnnn3N0njVr1qhjx4568sknFR4erpCQEP322282dUwmkxo1aqSxY8dq69at8vT01JdffmndX6tWLcXExGjt2rWqXr26FixYcMvr1qhRI9sFLPbt26czZ87ojTfeUJMmTVS1atVMf6G7tgrSjX/xrFatmsxmsxISElSxYkWbrWzZspKkKlWqaNOmTTbn2rhxo83j0NBQrVmzJlM/Va5c+ZYfksLCwlS3bl3NnDlTCxYsUJ8+fW5a/2ay6vc1a9aodOnSOnz4cKZ7LF++vPXY8ePHa9++ffr5558VFxenOXPm3HY77qSc/IxuV3avCXd39xw9p27G29tb3333nby8vBQZGamLFy9Kuvrcz8jI0MmTJzPdT8mSJSVdfT5l1ba84MyZM9q/f79GjBihFi1aKDQ01Oavz1WqVNHOnTutC79ImV8vtWvX1u7duxUcHJypD+725aBv1X/ZCQwMzNHrOD+71e+NG93stStdzVC0bNlS//vf/7Rjxw4dPXpUK1euzPHx+UF293C7712VKlWSt7f3bS0Ulduye+4UL17cZtRNRkaGdu3alaNzduzYUVeuXFFcXJwWLFiQbTbsmoYNG6ps2bJauHCh5s+fr8cff9watObk91hoaKg2bNhgc8688r4P1yMjdhdKSEhQdHS0Bg4cqC1btmjatGmaOHGigoOD1atXL/Xp00dvvfWWwsPD9fvvv+vkyZPq0qVLpvNUqlRJixYt0tq1a1WkSBFNmjRJJ06cULVq1SRd/eWwYsUKtW7dWiVKlND69et16tQphYaG6siRI5oxY4YeeeQRlS5dWvv379eBAwdytDrf6NGj1aJFC1WoUEFPPPGE0tPTtXTpUg0bNkzlypWTp6enpk2bpmeeeUa7du3SuHHjbI4PCgqSyWTSd999p3bt2snb21uFCxfW0KFDNXjwYFksFjVu3FiJiYlas2aNfH191atXLw0cOFCTJk3SsGHD1LdvX23btk1z586VdP2vgUOGDFG9evU0btw4de3aVevWrdPbb7+d4xXN+vXrp6ioKPn4+NisBGmPm/X72LFj9fzzz8vPz09t2rRRSkqKNm3apHPnzik6Olpbt27VqFGjtGjRIjVq1EiTJk3SCy+8oKZNm9oM78sNt/oZ3U728JrsXhOScvScuhUfHx8tWbJEbdu2Vdu2bRUXF6fKlSurR48e6tmzpyZOnKhatWrp1KlTWrFihWrUqKH27dvr+eefV6NGjTRhwgR17NhRy5YtU1xc3G3f551UpEgRFStWTDNmzFCpUqWUkJCgl156ybq/e/fuGj58uAYMGKCXXnpJCQkJmjBhgqTrr5dnn31WM2fOVLdu3fTiiy+qaNGiOnjwoD799FPNmjUrX30Yttet+u9mbvU6zs9u9v61Y8eOTPVv9tr97rvvdPjwYT344IMqUqSIli5dKovFoipVquTo+Pwiu3u43fcuLy8vDRs2TC+++KI8PT3VqFEjnTp1Srt371bfvn1dcEe352bPHR8fH0VHR2vJkiWqUKGCJk2alOMvR/bx8VGnTp00cuRI7d27V926dbvlMd27d9f06dP122+/6aeffrKW5+SzxjPPPKOJEyfqv//9r/r166fNmzdbP2vgHpTbk9RwZzVt2tT4z3/+YzzzzDOGr6+vUaRIEePll1+2TvS9fPmyMXjwYKNUqVKGp6enUbFiRWP27NmGYWRe5OLMmTNGx44djUKFChklSpQwRowYYfTs2dM6mXrPnj1GZGSkUbx4ccNsNhuVK1c2pk2bZhiGYRw/ftzo1KmT9TpBQUHGqFGjrKuF3crixYuNmjVrGp6enkZAQIDRuXNn674FCxYYwcHBhtlsNiIiIoxvvvnGZoEAwzCMV155xShZsqRhMpmsk3UtFosxZcoUo0qVKoaHh4dRvHhxIzIy0vj555+tx3399ddGxYoVDbPZbDRr1sx47733DEk2CxIsWrTIqFatmuHh4WGUK1fOGD9+vE3bb5zY+08XL140ChYsaPznP//JUT9k5Wb9bhiGMX/+fGvfFSlSxHjwwQeNL774wrh8+bJRrVo1Y8CAATbne+SRR4yGDRsa6enpt92mO+VmP6OsFmGZM2dOpoUtblzJ0DBu/ZowjFs/p7JbyOaf17p48aLRsGFD48EHHzSSkpKM1NRUY9SoUUZwcLDh4eFhlCpVynj00UeNHTt2WI/54IMPjPvuu8/w9vY2OnToYEyYMCHPLNaxfPlyIzQ01DCbzUaNGjWM+Ph4Q//X3v27pBbGcRz/3EVwCCohiEChIIJAIglcLP0TGmpvbAsaQogmpaE9dHEIbBIcBNeGOJOTS0QtSotLRWsh34aL0r2d0lv2lN73az348Bx5fpwPnPN9JCuXy2Zm5nmeRaNRCwQCFovF7PT01CTZ5eVlt42rqytbX1+38fFxCwaDtrCwYDs7O/9cLXQY/F2so9f/1ymK8HLt6nhrHg+799Yvv2Id783d8/NzW1tbs4mJCQsGgxaNRv8oiNTP3P/pet3DR9eudrttmUzGIpFIdy/rVCf2G5f39/cmyc7Ozhzctb/3xs7j46Ntb2/b5OSkTU1N2eHhoW+xjrf25mq1apJsdXX11TW/311cXJgki0Qir8ZTP88alUql+6yRSCSsUChQrOM/9ctsiA7UQE/JZFJLS0vdMy/wOdlsVrlcTjc3NwNpr9FoaG5uTrVaTcvLywNpE/gpisWitra29PDw0Pd3KMBXGYX9cBTuAcDbeDUReOH4+FgrKysKhULyPE9HR0evDmL+iKenJ93e3mp/f1/xeJwQhpFwcnKi2dlZzczMqF6va29vT5ubm4QwAAD6QBCDc4uLi2o2m77X8vl8zw9lv9L19bUymYzu7u4UDoe1u7urdDr96XY9z1MqldL8/LxKpdIAegp8v1arpYODA7VaLU1PT2tjY0PZbPa7uwUAwFDg1UQ412w2fcvKSr8rho2NjTnuEQAAAOAWQQwAAAAAHOMcMQAAAABwjCAGAAAAAI4RxAAAAADAMYIYAAAAADhGEAMAAAAAxwhiAAAAAOAYQQwAAAAAHCOIAQAAAIBjz/978EjUvwvoAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Categorize 'pclass'\n", "pclass_mapping = {1: 'Upper', 2: 'Middle', 3: 'Lower'}\n", "df['pclass_category'] = df['pclass'].map(pclass_mapping)\n", "\n", "# Calculate correlation coefficients for categorical parameters\n", "categorical_params = ['pclass_category', 'sex', 'embarked']\n", "correlation_data = pd.DataFrame(columns=categorical_params)\n", "for param in categorical_params:\n", " if df[param].dtype == 'O': # If parameter is categorical\n", " df[param + '_encoded'] = pd.Categorical(df[param]).codes\n", " correlation_data[param] = df[param + '_encoded']\n", "\n", "# Add 'age', 'fare', 'sibsp', 'parch', and 'survived' to correlation data\n", "correlation_data['age'] = df['age']\n", "correlation_data['fare'] = df['fare']\n", "correlation_data['sibsp'] = df['sibsp']\n", "correlation_data['parch'] = df['parch']\n", "correlation_data['survived'] = df['survived']\n", "\n", "# Calculate correlation matrix\n", "correlation_matrix = correlation_data.corr()\n", "\n", "# Plot correlation heatmap\n", "plt.figure(figsize=(10, 6))\n", "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\", linewidths=0.5)\n", "plt.title('Correlation Heatmap')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "a722f2a8-f219-4aee-9324-5af2f4ff6750", "metadata": {}, "source": [ "# Generate general report" ] }, { "cell_type": "code", "execution_count": 12, "id": "6d7d55c5-16db-4b46-abc4-bc9b00b6066a", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "963786f49b904d95b40120ed1b3ecdf7", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Summarize dataset: 0%| | 0/5 [00:00" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.drop(columns=['age_bin', 'fare_bin'], inplace=True)\n", "profile = ProfileReport(df, title=\"Profiling Report\")\n", "\n", "profile.to_notebook_iframe()" ] }, { "cell_type": "markdown", "id": "400e2a71-2d6b-41d5-a553-618e302e5f68", "metadata": {}, "source": [ "# Multivariable logistic Regression\n", "A multivariable logistic regression with a binary target variable aims to predict a binary outcome based on multiple predictor variables.\n", "\n", "- Objective: The goal is to predict the probability of a binary outcome (e.g., Yes/No, 0/1) based on the values of multiple predictor variables.\n", "- Model: It uses a regression model (e.g., logistic regression) to estimate the relationship between the predictors and the probability of the binary outcome.\n", "- Prediction: Given values for the predictor variables, the model calculates the predicted probability of the binary outcome. This probability can then be converted into a binary decision based on a chosen threshold.\n", "\n", "## Interpretation of regressions metrics\n", "- Accuracy\n", " - The accuracy score measures the proportion of correct predictions made by the model out of the total number of predictions.\n", " - It is calculated as the number of correct predictions divided by the total number of predictions.\n", "- Confusion Matrix\n", " - A confusion matrix is a table that is often used to describe the performance of a classification model on a set of test data for which the true values are known.\n", " - It's a matrix with four different combinations of predicted and actual classes: True Positive (TP), False Positive (FP), True Negative (TN), and False Negative (FN).\n", " - It provides a more detailed breakdown of the model's performance than just the accuracy score.\n", "- Classification Report\n", " - Precision: Out of all predicted positive instances, how many were actually positive.\n", " - Recall (Sensitivity): Out of all actual positive instances, how many were predicted correctly.\n", " - F1-score: Harmonic mean of precision and recall. It gives a balance between precision and recall.\n", " - Support: Number of actual occurrences of the class in the dataset.\n", "\n", "## ROC Curve (Receiver Operating Characteristic Curve)\n", "The ROC curve is a graphical representation of the performance of a binary classification model across different thresholds. It plots the True Positive Rate (sensitivity) against the False Positive Rate (1 - specificity) for various threshold values. Each point on the curve represents a sensitivity/specificity pair corresponding to a particular decision threshold.\n", "\n", "### Interpretation of ROC Area (AUC):\n", "The Area Under the ROC Curve (AUC) quantifies the overall performance of a binary classification model. It ranges from 0 to 1, where:\n", "\n", "- AUC = 1 indicates a perfect classifier that perfectly separates the classes.\n", "- AUC = 0.5 indicates a classifier that performs no better than random guessing.\n", "- AUC < 0.5 indicates a classifier that performs worse than random guessing (inverted predictions).\n", "\n", "Typically, an AUC above 0.7 is considered acceptable, while an AUC above 0.8 is considered good discrimination." ] }, { "cell_type": "code", "execution_count": 13, "id": "8f122f3d-bfbf-4270-b394-e36f1bc8998e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.7622739018087855\n", "Confusion Matrix:\n", " [[193 40]\n", " [ 52 102]]\n", "Classification Report:\n", " precision recall f1-score support\n", "\n", " 0 0.79 0.83 0.81 233\n", " 1 0.72 0.66 0.69 154\n", "\n", " accuracy 0.76 387\n", " macro avg 0.75 0.75 0.75 387\n", "weighted avg 0.76 0.76 0.76 387\n", "\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Select independent variables (X) and dependent variable (y)\n", "independent_variables = df[['age', 'fare', 'pclass', 'sex']]\n", "dependent_variable = df['survived']\n", "\n", "# Perform one-hot encoding for the 'sex' column\n", "# Convert categorical variables into a numerical format\n", "column_transformer = ColumnTransformer(\n", " [('one_hot_encoder', OneHotEncoder(), ['sex'])], \n", " remainder='passthrough'\n", ")\n", "independent_variables_encoded = column_transformer.fit_transform(independent_variables)\n", "\n", "# Split data into training and testing sets\n", "# Changing test_size to increment/decrease the sample size\n", "X_train, X_test, y_train, y_test = train_test_split(independent_variables_encoded, dependent_variable, test_size=0.3, random_state=42)\n", "\n", "# Initialize the logistic regression model\n", "model = LogisticRegression()\n", "\n", "# Fit the model to the training data\n", "model.fit(X_train, y_train)\n", "\n", "# Predict probabilities on the test set\n", "#1: to predict survivors\n", "#0: to predict non survivors\n", "y_pred_proba = model.predict_proba(X_test)[:, 1]\n", "\n", "# Compute ROC curve and ROC area for each class\n", "fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n", "roc_auc = auc(fpr, tpr)\n", "\n", "# Make predictions on the testing data\n", "y_pred = model.predict(X_test)\n", "\n", "# Evaluate the model\n", "accuracy = accuracy_score(y_test, y_pred)\n", "conf_matrix = confusion_matrix(y_test, y_pred)\n", "class_report = classification_report(y_test, y_pred)\n", "\n", "print(\"Accuracy:\", accuracy)\n", "print(\"Confusion Matrix:\\n\", conf_matrix)\n", "print(\"Classification Report:\\n\", class_report)\n", "\n", "# Plot ROC curve\n", "plt.figure()\n", "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n", "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n", "plt.xlim([0.0, 1.0])\n", "plt.ylim([0.0, 1.05])\n", "plt.xlabel('False Positive Rate')\n", "plt.ylabel('True Positive Rate')\n", "plt.title('Receiver Operating Characteristic (ROC) Curve')\n", "plt.legend(loc=\"lower right\")\n", "plt.show()\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.6" } }, "nbformat": 4, "nbformat_minor": 5 }