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survival-analysis-for-data-analysis-introduction_2024-06.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.2"
},
"colab": {
"provenance": [],
"include_colab_link": true
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/alonsosilvaallende/da90587f4a543dc115e3d61dfc46e3f0/survival-analysis-for-data-analysis-introduction_2024-06.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aEj365e4uTvD"
},
"source": [
"# Survival Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"id": "0Z1wEfUFuTvG"
},
"source": [
"* Historically, survival analysis was developed and used by actuaries\n",
"and medical researchers to measure the lifetime of populations.\n",
"* What's the expected lifetime of patients that were given drug A? drug B?\n",
"* What's the life-expectancy of a baby born today in France?\n",
"\n",
"These researchers wanted to measure the duration between *Birth* and *Death*\n",
"\n",
"\n",
"Source: [Lifelines: Survival Analysis in Python](https://www.youtube.com/watch?v=XQfxndJH4UA)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JxSbfi7QuTvI"
},
"source": [
"# Survival function and hazard function"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fXfTmltSuTvK"
},
"source": [
"**Definition:** Let $T$ be a random variable called failure time.\n",
"\n",
"- $f(t)$ be its probability density function\n",
"- $F(t):=\\mathcal{P}(T\\le t)$ its cumulative distribution function\n",
"\n",
"Then we define\n",
"\n",
"- The *survival function* $S(t):=\\mathcal{P}(T>t)=1-F(t)$.\n",
"- The *hazard function* (probability of failure between $t$ and $t+\\delta t$ knowing that it was working at time $t$):\n",
"$$\n",
"h(t):=\\lim_{\\delta t\\to0}\\frac{\\mathcal{P}(T<t+\\delta t|T>t)}{\\delta t}=\n",
"\\lim_{\\delta t\\to0}\\frac{F(t+\\delta t)-F(t)}{\\delta t}\\times\\frac{1}{1-F(t)}=\\frac{f(t)}{1-F(t)}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZBY8J3sxuTvM"
},
"source": [
"**Properties:**\n",
"- $S(t)=\\exp(-\\int_0^t h(s)\\,ds)$.\n",
"- $h(t)=-\\frac{d}{dt}\\ln(S(t))$."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1VYNZWpmuTvO"
},
"source": [
"# Right censoring\n",
"\n",
"By the end of the study, the event of interest (for example, in medicine \"death of a patient\" or \"churn of a customer\") has only occurred for a subset of the observations.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vao5BhTYuTvP"
},
"source": [
"# Modern Survival Analysis\n",
"\n",
"+ **Birth:** Customer joins Netflix \n",
"**Death:** Customer leaves Netflix \n",
"**Censorship:** At the current time, I cannot see all cancelations \n",
" \n",
" \n",
"+ **Birth:** Leader forms government \n",
"**Death:** Government dissolves \n",
"**Censorship:** Death of leader or current time do not allow me to see all dissolvements \n",
"\n",
"\n",
"+ **Birth:** Couple starts dating \n",
"**Death:** Couple breaks-up \n",
"**Censorship:** Some couples never break-up (partner's death comes first) "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EVUDoqXOuTvU"
},
"source": [
"First, let's take a dataset from lifelines to see what does it mean in practice."
]
},
{
"cell_type": "code",
"metadata": {
"id": "IcqsnDunuZ3x",
"outputId": "adf5e948-8f03-4c09-9d85-c85c79631e8d",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"%pip install --quiet lifelines"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/349.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.7/349.2 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m348.2/349.2 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m349.2/349.2 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m94.2/94.2 kB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Building wheel for autograd-gamma (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-01-09T22:37:10.331529Z",
"start_time": "2020-01-09T22:37:03.811697Z"
},
"id": "A5qmUxSxuTvW"
},
"source": [
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import numpy as np\n",
"plt.style.use('seaborn-v0_8-bright')"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "-Hz_oM2IuTve"
},
"source": [
"from lifelines.datasets import load_dd\n",
"\n",
"df = load_dd()\n",
"df = df[['ctryname', 'un_region_name', 'un_continent_name', 'ehead',\\\n",
" 'democracy', 'regime', 'start_year', 'duration', 'observed']]"
],
"execution_count": 3,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "xCN-nZXDuTvk"
},
"source": [
"# Democracy and dictatorship\n",
"\n",
"This dataset contains a classification of political regimes as democracy and dictatorship.\n",
"* Classification of democracies as\n",
" + parliamentary,\n",
" + semi-presidential (mixed), and\n",
" + presidential.\n",
" \n",
"* Classification of dictatorships as\n",
" + military,\n",
" + civilian, and\n",
" + royal.\n",
" \n",
"Coverage: 202 countries, from 1946 or year of independence to 2008.\n",
"\n",
"**References**\n",
"\n",
"José Antonio Cheibub, Jennifer Gandhi, and James Raymond Vreeland. [\"Democracy and Dictatorship Revisited.\"](https://doi.org/10.1007/s11127-009-9491-2) Public Choice, vol. 143, no. 2-1, pp. 67-101, 2010."
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-01-09T22:37:10.515360Z",
"start_time": "2020-01-09T22:37:10.466697Z"
},
"id": "9b51kQDjuTvm",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 362
},
"outputId": "6aa06da9-25e2-463f-ef7c-3477fbef68b4"
},
"source": [
"df.tail(10).style.hide(axis=\"index\")"
],
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<pandas.io.formats.style.Styler at 0x797bffa46800>"
],
"text/html": [
"<style type=\"text/css\">\n",
"</style>\n",
"<table id=\"T_1c4c6\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th id=\"T_1c4c6_level0_col0\" class=\"col_heading level0 col0\" >ctryname</th>\n",
" <th id=\"T_1c4c6_level0_col1\" class=\"col_heading level0 col1\" >un_region_name</th>\n",
" <th id=\"T_1c4c6_level0_col2\" class=\"col_heading level0 col2\" >un_continent_name</th>\n",
" <th id=\"T_1c4c6_level0_col3\" class=\"col_heading level0 col3\" >ehead</th>\n",
" <th id=\"T_1c4c6_level0_col4\" class=\"col_heading level0 col4\" >democracy</th>\n",
" <th id=\"T_1c4c6_level0_col5\" class=\"col_heading level0 col5\" >regime</th>\n",
" <th id=\"T_1c4c6_level0_col6\" class=\"col_heading level0 col6\" >start_year</th>\n",
" <th id=\"T_1c4c6_level0_col7\" class=\"col_heading level0 col7\" >duration</th>\n",
" <th id=\"T_1c4c6_level0_col8\" class=\"col_heading level0 col8\" >observed</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td id=\"T_1c4c6_row0_col0\" class=\"data row0 col0\" >Yugoslavia</td>\n",
" <td id=\"T_1c4c6_row0_col1\" class=\"data row0 col1\" >Southern Europe</td>\n",
" <td id=\"T_1c4c6_row0_col2\" class=\"data row0 col2\" >Europe</td>\n",
" <td id=\"T_1c4c6_row0_col3\" class=\"data row0 col3\" >Stipe Suvar</td>\n",
" <td id=\"T_1c4c6_row0_col4\" class=\"data row0 col4\" >Non-democracy</td>\n",
" <td id=\"T_1c4c6_row0_col5\" class=\"data row0 col5\" >Civilian Dict</td>\n",
" <td id=\"T_1c4c6_row0_col6\" class=\"data row0 col6\" >1988</td>\n",
" <td id=\"T_1c4c6_row0_col7\" class=\"data row0 col7\" >1</td>\n",
" <td id=\"T_1c4c6_row0_col8\" class=\"data row0 col8\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_1c4c6_row1_col0\" class=\"data row1 col0\" >Yugoslavia</td>\n",
" <td id=\"T_1c4c6_row1_col1\" class=\"data row1 col1\" >Southern Europe</td>\n",
" <td id=\"T_1c4c6_row1_col2\" class=\"data row1 col2\" >Europe</td>\n",
" <td id=\"T_1c4c6_row1_col3\" class=\"data row1 col3\" >Milan Pancevski</td>\n",
" <td id=\"T_1c4c6_row1_col4\" class=\"data row1 col4\" >Non-democracy</td>\n",
" <td id=\"T_1c4c6_row1_col5\" class=\"data row1 col5\" >Civilian Dict</td>\n",
" <td id=\"T_1c4c6_row1_col6\" class=\"data row1 col6\" >1989</td>\n",
" <td id=\"T_1c4c6_row1_col7\" class=\"data row1 col7\" >1</td>\n",
" <td id=\"T_1c4c6_row1_col8\" class=\"data row1 col8\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_1c4c6_row2_col0\" class=\"data row2 col0\" >Yugoslavia</td>\n",
" <td id=\"T_1c4c6_row2_col1\" class=\"data row2 col1\" >Southern Europe</td>\n",
" <td id=\"T_1c4c6_row2_col2\" class=\"data row2 col2\" >Europe</td>\n",
" <td id=\"T_1c4c6_row2_col3\" class=\"data row2 col3\" >Borisav Jovic</td>\n",
" <td id=\"T_1c4c6_row2_col4\" class=\"data row2 col4\" >Non-democracy</td>\n",
" <td id=\"T_1c4c6_row2_col5\" class=\"data row2 col5\" >Civilian Dict</td>\n",
" <td id=\"T_1c4c6_row2_col6\" class=\"data row2 col6\" >1990</td>\n",
" <td id=\"T_1c4c6_row2_col7\" class=\"data row2 col7\" >1</td>\n",
" <td id=\"T_1c4c6_row2_col8\" class=\"data row2 col8\" >0</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_1c4c6_row3_col0\" class=\"data row3 col0\" >Zambia</td>\n",
" <td id=\"T_1c4c6_row3_col1\" class=\"data row3 col1\" >Eastern Africa</td>\n",
" <td id=\"T_1c4c6_row3_col2\" class=\"data row3 col2\" >Africa</td>\n",
" <td id=\"T_1c4c6_row3_col3\" class=\"data row3 col3\" >Kenneth Kaunda</td>\n",
" <td id=\"T_1c4c6_row3_col4\" class=\"data row3 col4\" >Non-democracy</td>\n",
" <td id=\"T_1c4c6_row3_col5\" class=\"data row3 col5\" >Civilian Dict</td>\n",
" <td id=\"T_1c4c6_row3_col6\" class=\"data row3 col6\" >1964</td>\n",
" <td id=\"T_1c4c6_row3_col7\" class=\"data row3 col7\" >27</td>\n",
" <td id=\"T_1c4c6_row3_col8\" class=\"data row3 col8\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_1c4c6_row4_col0\" class=\"data row4 col0\" >Zambia</td>\n",
" <td id=\"T_1c4c6_row4_col1\" class=\"data row4 col1\" >Eastern Africa</td>\n",
" <td id=\"T_1c4c6_row4_col2\" class=\"data row4 col2\" >Africa</td>\n",
" <td id=\"T_1c4c6_row4_col3\" class=\"data row4 col3\" >Frederick Chiluba</td>\n",
" <td id=\"T_1c4c6_row4_col4\" class=\"data row4 col4\" >Non-democracy</td>\n",
" <td id=\"T_1c4c6_row4_col5\" class=\"data row4 col5\" >Civilian Dict</td>\n",
" <td id=\"T_1c4c6_row4_col6\" class=\"data row4 col6\" >1991</td>\n",
" <td id=\"T_1c4c6_row4_col7\" class=\"data row4 col7\" >11</td>\n",
" <td id=\"T_1c4c6_row4_col8\" class=\"data row4 col8\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_1c4c6_row5_col0\" class=\"data row5 col0\" >Zambia</td>\n",
" <td id=\"T_1c4c6_row5_col1\" class=\"data row5 col1\" >Eastern Africa</td>\n",
" <td id=\"T_1c4c6_row5_col2\" class=\"data row5 col2\" >Africa</td>\n",
" <td id=\"T_1c4c6_row5_col3\" class=\"data row5 col3\" >Levy Patrick Mwanawasa</td>\n",
" <td id=\"T_1c4c6_row5_col4\" class=\"data row5 col4\" >Non-democracy</td>\n",
" <td id=\"T_1c4c6_row5_col5\" class=\"data row5 col5\" >Civilian Dict</td>\n",
" <td id=\"T_1c4c6_row5_col6\" class=\"data row5 col6\" >2002</td>\n",
" <td id=\"T_1c4c6_row5_col7\" class=\"data row5 col7\" >6</td>\n",
" <td id=\"T_1c4c6_row5_col8\" class=\"data row5 col8\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_1c4c6_row6_col0\" class=\"data row6 col0\" >Zambia</td>\n",
" <td id=\"T_1c4c6_row6_col1\" class=\"data row6 col1\" >Eastern Africa</td>\n",
" <td id=\"T_1c4c6_row6_col2\" class=\"data row6 col2\" >Africa</td>\n",
" <td id=\"T_1c4c6_row6_col3\" class=\"data row6 col3\" >Rupiah Bwezani Banda</td>\n",
" <td id=\"T_1c4c6_row6_col4\" class=\"data row6 col4\" >Non-democracy</td>\n",
" <td id=\"T_1c4c6_row6_col5\" class=\"data row6 col5\" >Civilian Dict</td>\n",
" <td id=\"T_1c4c6_row6_col6\" class=\"data row6 col6\" >2008</td>\n",
" <td id=\"T_1c4c6_row6_col7\" class=\"data row6 col7\" >1</td>\n",
" <td id=\"T_1c4c6_row6_col8\" class=\"data row6 col8\" >0</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_1c4c6_row7_col0\" class=\"data row7 col0\" >Zimbabwe</td>\n",
" <td id=\"T_1c4c6_row7_col1\" class=\"data row7 col1\" >Eastern Africa</td>\n",
" <td id=\"T_1c4c6_row7_col2\" class=\"data row7 col2\" >Africa</td>\n",
" <td id=\"T_1c4c6_row7_col3\" class=\"data row7 col3\" >Ian Smith</td>\n",
" <td id=\"T_1c4c6_row7_col4\" class=\"data row7 col4\" >Non-democracy</td>\n",
" <td id=\"T_1c4c6_row7_col5\" class=\"data row7 col5\" >Civilian Dict</td>\n",
" <td id=\"T_1c4c6_row7_col6\" class=\"data row7 col6\" >1965</td>\n",
" <td id=\"T_1c4c6_row7_col7\" class=\"data row7 col7\" >14</td>\n",
" <td id=\"T_1c4c6_row7_col8\" class=\"data row7 col8\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_1c4c6_row8_col0\" class=\"data row8 col0\" >Zimbabwe</td>\n",
" <td id=\"T_1c4c6_row8_col1\" class=\"data row8 col1\" >Eastern Africa</td>\n",
" <td id=\"T_1c4c6_row8_col2\" class=\"data row8 col2\" >Africa</td>\n",
" <td id=\"T_1c4c6_row8_col3\" class=\"data row8 col3\" >Abel Muzorewa</td>\n",
" <td id=\"T_1c4c6_row8_col4\" class=\"data row8 col4\" >Non-democracy</td>\n",
" <td id=\"T_1c4c6_row8_col5\" class=\"data row8 col5\" >Civilian Dict</td>\n",
" <td id=\"T_1c4c6_row8_col6\" class=\"data row8 col6\" >1979</td>\n",
" <td id=\"T_1c4c6_row8_col7\" class=\"data row8 col7\" >1</td>\n",
" <td id=\"T_1c4c6_row8_col8\" class=\"data row8 col8\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_1c4c6_row9_col0\" class=\"data row9 col0\" >Zimbabwe</td>\n",
" <td id=\"T_1c4c6_row9_col1\" class=\"data row9 col1\" >Eastern Africa</td>\n",
" <td id=\"T_1c4c6_row9_col2\" class=\"data row9 col2\" >Africa</td>\n",
" <td id=\"T_1c4c6_row9_col3\" class=\"data row9 col3\" >Robert Mugabe</td>\n",
" <td id=\"T_1c4c6_row9_col4\" class=\"data row9 col4\" >Non-democracy</td>\n",
" <td id=\"T_1c4c6_row9_col5\" class=\"data row9 col5\" >Civilian Dict</td>\n",
" <td id=\"T_1c4c6_row9_col6\" class=\"data row9 col6\" >1980</td>\n",
" <td id=\"T_1c4c6_row9_col7\" class=\"data row9 col7\" >29</td>\n",
" <td id=\"T_1c4c6_row9_col8\" class=\"data row9 col8\" >0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
]
},
"metadata": {},
"execution_count": 4
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-o57OZxvuTvu"
},
"source": [
"Let's look at right-censored samples."
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-01-09T22:37:12.185066Z",
"start_time": "2020-01-09T22:37:10.519270Z"
},
"scrolled": false,
"id": "vzaXN9QWuTvv",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "891c0743-f298-4f0b-a92b-bfe99081f8d7"
},
"source": [
"format_dict = {'ehead':'{}','duration':'{}', 'observed':'{}'}\n",
"(df.query('ctryname == \"France\"')[['ehead', 'duration', 'observed']].style.format(format_dict)\n",
" .hide(axis=\"index\")\n",
" .highlight_min('observed', color='lightgreen'))"
],
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<pandas.io.formats.style.Styler at 0x797bc89ebdc0>"
],
"text/html": [
"<style type=\"text/css\">\n",
"#T_4dbbd_row31_col2 {\n",
" background-color: lightgreen;\n",
"}\n",
"</style>\n",
"<table id=\"T_4dbbd\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th id=\"T_4dbbd_level0_col0\" class=\"col_heading level0 col0\" >ehead</th>\n",
" <th id=\"T_4dbbd_level0_col1\" class=\"col_heading level0 col1\" >duration</th>\n",
" <th id=\"T_4dbbd_level0_col2\" class=\"col_heading level0 col2\" >observed</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td id=\"T_4dbbd_row0_col0\" class=\"data row0 col0\" >Leon Blum</td>\n",
" <td id=\"T_4dbbd_row0_col1\" class=\"data row0 col1\" >1</td>\n",
" <td id=\"T_4dbbd_row0_col2\" class=\"data row0 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_4dbbd_row1_col0\" class=\"data row1 col0\" >Robert Schuman</td>\n",
" <td id=\"T_4dbbd_row1_col1\" class=\"data row1 col1\" >1</td>\n",
" <td id=\"T_4dbbd_row1_col2\" class=\"data row1 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_4dbbd_row2_col0\" class=\"data row2 col0\" >Henri Queuille</td>\n",
" <td id=\"T_4dbbd_row2_col1\" class=\"data row2 col1\" >1</td>\n",
" <td id=\"T_4dbbd_row2_col2\" class=\"data row2 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_4dbbd_row3_col0\" class=\"data row3 col0\" >Georges Bidault</td>\n",
" <td id=\"T_4dbbd_row3_col1\" class=\"data row3 col1\" >1</td>\n",
" <td id=\"T_4dbbd_row3_col2\" class=\"data row3 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_4dbbd_row4_col0\" class=\"data row4 col0\" >Rene Pleven</td>\n",
" <td id=\"T_4dbbd_row4_col1\" class=\"data row4 col1\" >2</td>\n",
" <td id=\"T_4dbbd_row4_col2\" class=\"data row4 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_4dbbd_row5_col0\" class=\"data row5 col0\" >Antoine Pinay</td>\n",
" <td id=\"T_4dbbd_row5_col1\" class=\"data row5 col1\" >1</td>\n",
" <td id=\"T_4dbbd_row5_col2\" class=\"data row5 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_4dbbd_row6_col0\" class=\"data row6 col0\" >Joseph Laniel</td>\n",
" <td id=\"T_4dbbd_row6_col1\" class=\"data row6 col1\" >1</td>\n",
" <td id=\"T_4dbbd_row6_col2\" class=\"data row6 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_4dbbd_row7_col0\" class=\"data row7 col0\" >Pierre Mendes-France</td>\n",
" <td id=\"T_4dbbd_row7_col1\" class=\"data row7 col1\" >1</td>\n",
" <td id=\"T_4dbbd_row7_col2\" class=\"data row7 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_4dbbd_row8_col0\" class=\"data row8 col0\" >Edgar Faure</td>\n",
" <td id=\"T_4dbbd_row8_col1\" class=\"data row8 col1\" >1</td>\n",
" <td id=\"T_4dbbd_row8_col2\" class=\"data row8 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_4dbbd_row9_col0\" class=\"data row9 col0\" >Guy Mollet</td>\n",
" <td id=\"T_4dbbd_row9_col1\" class=\"data row9 col1\" >1</td>\n",
" <td id=\"T_4dbbd_row9_col2\" class=\"data row9 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_4dbbd_row10_col0\" class=\"data row10 col0\" >Felix Gaillard</td>\n",
" <td id=\"T_4dbbd_row10_col1\" class=\"data row10 col1\" >1</td>\n",
" <td id=\"T_4dbbd_row10_col2\" class=\"data row10 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_4dbbd_row11_col0\" class=\"data row11 col0\" >Charles de Gaulle</td>\n",
" <td id=\"T_4dbbd_row11_col1\" class=\"data row11 col1\" >1</td>\n",
" <td id=\"T_4dbbd_row11_col2\" class=\"data row11 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_4dbbd_row12_col0\" class=\"data row12 col0\" >Michel Debre</td>\n",
" <td id=\"T_4dbbd_row12_col1\" class=\"data row12 col1\" >3</td>\n",
" <td id=\"T_4dbbd_row12_col2\" class=\"data row12 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_4dbbd_row13_col0\" class=\"data row13 col0\" >Georges Pompidou</td>\n",
" <td id=\"T_4dbbd_row13_col1\" class=\"data row13 col1\" >3</td>\n",
" <td id=\"T_4dbbd_row13_col2\" class=\"data row13 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_4dbbd_row14_col0\" class=\"data row14 col0\" >Georges Pompidou</td>\n",
" <td id=\"T_4dbbd_row14_col1\" class=\"data row14 col1\" >3</td>\n",
" <td id=\"T_4dbbd_row14_col2\" class=\"data row14 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_4dbbd_row15_col0\" class=\"data row15 col0\" >Maurice Couve de Murville</td>\n",
" <td id=\"T_4dbbd_row15_col1\" class=\"data row15 col1\" >1</td>\n",
" <td id=\"T_4dbbd_row15_col2\" class=\"data row15 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_4dbbd_row16_col0\" class=\"data row16 col0\" >Jacques Chaban-Delmas</td>\n",
" <td id=\"T_4dbbd_row16_col1\" class=\"data row16 col1\" >3</td>\n",
" <td id=\"T_4dbbd_row16_col2\" class=\"data row16 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_4dbbd_row17_col0\" class=\"data row17 col0\" >Pierre Messmer</td>\n",
" <td id=\"T_4dbbd_row17_col1\" class=\"data row17 col1\" >2</td>\n",
" <td id=\"T_4dbbd_row17_col2\" class=\"data row17 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_4dbbd_row18_col0\" class=\"data row18 col0\" >Jacques Chirac</td>\n",
" <td id=\"T_4dbbd_row18_col1\" class=\"data row18 col1\" >2</td>\n",
" <td id=\"T_4dbbd_row18_col2\" class=\"data row18 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_4dbbd_row19_col0\" class=\"data row19 col0\" >Raymond Barre</td>\n",
" <td id=\"T_4dbbd_row19_col1\" class=\"data row19 col1\" >5</td>\n",
" <td id=\"T_4dbbd_row19_col2\" class=\"data row19 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_4dbbd_row20_col0\" class=\"data row20 col0\" >Pierre Mauroy</td>\n",
" <td id=\"T_4dbbd_row20_col1\" class=\"data row20 col1\" >3</td>\n",
" <td id=\"T_4dbbd_row20_col2\" class=\"data row20 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_4dbbd_row21_col0\" class=\"data row21 col0\" >Laurent Fabius</td>\n",
" <td id=\"T_4dbbd_row21_col1\" class=\"data row21 col1\" >2</td>\n",
" <td id=\"T_4dbbd_row21_col2\" class=\"data row21 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_4dbbd_row22_col0\" class=\"data row22 col0\" >Jacques Chirac</td>\n",
" <td id=\"T_4dbbd_row22_col1\" class=\"data row22 col1\" >2</td>\n",
" <td id=\"T_4dbbd_row22_col2\" class=\"data row22 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_4dbbd_row23_col0\" class=\"data row23 col0\" >Michel Rocard</td>\n",
" <td id=\"T_4dbbd_row23_col1\" class=\"data row23 col1\" >3</td>\n",
" <td id=\"T_4dbbd_row23_col2\" class=\"data row23 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_4dbbd_row24_col0\" class=\"data row24 col0\" >Edith Cresson</td>\n",
" <td id=\"T_4dbbd_row24_col1\" class=\"data row24 col1\" >1</td>\n",
" <td id=\"T_4dbbd_row24_col2\" class=\"data row24 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_4dbbd_row25_col0\" class=\"data row25 col0\" >Pierre Beregovoy</td>\n",
" <td id=\"T_4dbbd_row25_col1\" class=\"data row25 col1\" >1</td>\n",
" <td id=\"T_4dbbd_row25_col2\" class=\"data row25 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_4dbbd_row26_col0\" class=\"data row26 col0\" >Edouard Balladur</td>\n",
" <td id=\"T_4dbbd_row26_col1\" class=\"data row26 col1\" >2</td>\n",
" <td id=\"T_4dbbd_row26_col2\" class=\"data row26 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_4dbbd_row27_col0\" class=\"data row27 col0\" >Alain Juppe</td>\n",
" <td id=\"T_4dbbd_row27_col1\" class=\"data row27 col1\" >2</td>\n",
" <td id=\"T_4dbbd_row27_col2\" class=\"data row27 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_4dbbd_row28_col0\" class=\"data row28 col0\" >Lionel Jospin</td>\n",
" <td id=\"T_4dbbd_row28_col1\" class=\"data row28 col1\" >5</td>\n",
" <td id=\"T_4dbbd_row28_col2\" class=\"data row28 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_4dbbd_row29_col0\" class=\"data row29 col0\" >Jean-Pierre Raffarin</td>\n",
" <td id=\"T_4dbbd_row29_col1\" class=\"data row29 col1\" >3</td>\n",
" <td id=\"T_4dbbd_row29_col2\" class=\"data row29 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_4dbbd_row30_col0\" class=\"data row30 col0\" >Dominique de Villepin</td>\n",
" <td id=\"T_4dbbd_row30_col1\" class=\"data row30 col1\" >2</td>\n",
" <td id=\"T_4dbbd_row30_col2\" class=\"data row30 col2\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_4dbbd_row31_col0\" class=\"data row31 col0\" >Fran�ois Fillon</td>\n",
" <td id=\"T_4dbbd_row31_col1\" class=\"data row31 col1\" >2</td>\n",
" <td id=\"T_4dbbd_row31_col2\" class=\"data row31 col2\" >0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
]
},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-01-09T22:37:12.216114Z",
"start_time": "2020-01-09T22:37:12.189453Z"
},
"id": "AVHDxLziuTv2",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "87ba3741-093e-42a7-8cb3-28017590ce2e"
},
"source": [
"print(f'samples: {len(df)}\\n')\n",
"print(f'right censored samples: {len(df.query(\"observed == 0\"))}')\n",
"print(f'right censored samples (%): {100*len(df.query(\"observed == 0\"))/len(df):.1f}%')"
],
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"samples: 1808\n",
"\n",
"right censored samples: 340\n",
"right censored samples (%): 18.8%\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3v0OGKU_uTv9"
},
"source": [
"# How can we estimate the probability of a government survival?"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "J0UzmdpeuTv9"
},
"source": [
"**Example:** I want to estimate the survival function of a new machine and I have 100 of these new machines. After the first year:\n",
"\n",
"Samples | I\n",
"--- | ---\n",
"Initial numbers | 100\n",
"Deaths in first year of age | 70\n",
"One-year survivors | `30`\n",
"\n",
"Therefore, a reasonable estimate of the survival probability of 1 year is 0.3.\n",
"\n",
"I have increased my production. So now I have 1000 new machines.\n",
"\n",
"Samples | I | II\n",
"--- | --- | ---\n",
"Initial numbers | 100 | 1000\n",
"Deaths in first year of age | 70 | 750\n",
"One-year survivors | `30` | `250`\n",
"Deaths in second year of age | 15 |\n",
"Two-year survivors | `15` |\n",
"\n",
"What would be a good estimate of the survival probability of 1 year? and 2 years?"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RVevvzzCuTv-"
},
"source": [
"The estimate of the probability of survival of 1 year would be\n",
"$\\hat{P}(1)=(30+250)/(100+1000) \\sim 0.255$"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BlBS5dveuTwA"
},
"source": [
"$\\hat{P}(2|1) = 15/30 = 0.5$"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zVja8-P5uTwA"
},
"source": [
"$\\hat{P}(2)=0.255\\times0.5=0.127$"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pG5yhITUuTwB"
},
"source": [
"# Kaplan-Meier estimator"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "E6hn2l6PuTwC"
},
"source": [
"**Definition:** Kaplan-Meier estimator of the survival function is given by\n",
"\n",
"$$\n",
"\\hat{S}(t):=\\prod_{i:t_i\\le t}\\left(1-\\frac{d_i}{n_i}\\right)\n",
"$$\n",
"where $t_i$ is a time where at least one event happened, $d_i$ the number of events that happened at time $t_i$, and $n_i$ the individuals known to have survived up to time $t_i$."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cojEUEjsuTwD"
},
"source": [
"We use the [Kaplan-Meier estimator](https://en.wikipedia.org/wiki/Kaplan?Meier_estimator) to estimate the probability of a government survival."
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-01-09T22:37:16.155051Z",
"start_time": "2020-01-09T22:37:12.219801Z"
},
"scrolled": false,
"id": "LCGcjoexuTwE",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 641
},
"outputId": "822995b7-6729-48a9-c376-2496bdd75577"
},
"source": [
"from lifelines import KaplanMeierFitter\n",
"\n",
"kmf = KaplanMeierFitter()\n",
"kmf.fit(df['duration'],df['observed'], label='Estimate for average government')\n",
"\n",
"fig, ax = plt.subplots(figsize=(10,7))\n",
"kmf.plot(ax=ax)\n",
"plt.title('Estimated probability of government survival vs number of years')\n",
"plt.xlabel('Time (in years)')\n",
"plt.ylabel('Estimated probability of government survival')\n",
"plt.show()"
],
"execution_count": 8,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1000x700 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "pdrG0N6nuTwL",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "2f108395-d17c-46a6-ae2c-ea1c6be647f5"
},
"source": [
"print(f'The median number of years of government survival is {kmf.median_survival_time_}')"
],
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"The median number of years of government survival is 4.0\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"scrolled": false,
"id": "NQHPG2x6uTwQ",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 641
},
"outputId": "3f7059c5-59ba-4378-d36e-44e849b75250"
},
"source": [
"fig, ax = plt.subplots(figsize=(10,7))\n",
"for r in df['democracy'].unique():\n",
" ix = (df['democracy'] == r)\n",
" kmf.fit(df['duration'].loc[ix], df['observed'].loc[ix], label=r)\n",
" kmf.plot(ax=ax)\n",
"plt.title('Estimated probability of government survival vs number of years')\n",
"plt.xlabel('Time (in years)')\n",
"plt.ylabel('Estimated probability of government survival')\n",
"plt.show()"
],
"execution_count": 10,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1000x700 with 1 Axes>"
],
"image/png": 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z2czrrrvO+X9jmqY5Z84cU5L5+uuvm6aZ8zhWr17dvPnmm12uk/v/9dVXX5mmmfN/HRkZaY4YMcJlvyNHjpgVKlRw2Z77//XII4/kqV+SabPZzN9//93lMZFkzp4927lt4sSJpiTzzjvvdG7LzMw0a9SoYRqGYT7zzDPO7X///bdpt9vNwYMHO7e99dZbZkBAgMvz1TRNc968eaYkc+PGjZZreu655/I8l85n69atBf6fJiQknPd5d+7zOPfxGDhwYJ59O3ToYLZq1cpl2/fff29KMt98803ntsGDB7v8DPvkk09MSebHH3/scmyPHj1cfoZlZmaa6enpLvv8/fffZnR0tDls2LAL1p2fV155xZRk7tixw2V748aNzc6dOzs/btGihdmzZ88Lnis/ud/zXbt2NbOzs53bx44dawYGBpqnTp0qsN5atWq5PJ9yz9mtWzeXc3bo0ME0DMO8++67ndtyn6dn/4zJ/b+22+3mf//7X+f27777zpRkjh071rmtS5cuZrNmzcy0tDTntuzsbLNjx45m/fr189R0xRVXXPD3Yq6ZM2eaksy3337buc3hcJgdOnQww8PDXX5e16pVy63H/tSpU2ZoaKj58MMPu2wfM2aMWa5cOTMpKck0TdP8+uuvTUnmO++847Lf2rVr82zP7/fCXXfdZYaFhbk8Jp06dTIlmfPmzXPZ94MPPjAlmT/88EOB9aPsYKoeSp2XXnpJn332mcvbmjVrnJ+PjIxUcnJygdM0rIqLi3OZ+9+uXTuZpplnWL9du3Y6dOiQMjMzndvOvu8nd8SsU6dO+uOPP5zTcS5k2bJluvLKK1WxYkX99ddfzreuXbsqKytLX331lSRp9erVCgoK0siRI53HBgYGavTo0Za+1jvvvNPlax05cqSCgoK0evVql/1q167tMsUit4a2bdvqiiuucG4LDw/XnXfeqf3792vXrl0u+8fFxal8+fLOj2+55RZdfPHFLtc6+/FLTk7WX3/9pY4dO8o0TW3dujVP/WeP+uROr3I4HPr888/dfQgsi4uL03fffad9+/Y5t73zzjuKiYlxTrHLz+HDh7Vt2zYNGTJElSpVcm5v3ry5rr322jyPubvWrl2rDh06uPyVv1KlSs6pcLk+//xzORwO3X///QoI+OdXxogRIxQREeGcYmkYhvr27avVq1crKSnJud+SJUtUvXp15//3Z599plOnTmngwIEuz9XAwEC1a9cu32lbZz9fz9a1a1fVrVvX+XHz5s0VERGhP/74I8++ZzfLCAwMVOvWrWWapu644w7n9sjISF1yySUuxy9btkyNGjVSw4YNXert3LmzJOWp10pN7sgdUfrkk0/ynQbpqbvvvjvPtv79+2vLli0uz9ElS5YoJCREN9xww3nP1blzZ1WuXNllBObvv//WZ599pv79+zu3BQYGOkeksrOzdfLkSWVmZqp169b66aefLH8NN910k4KCglyu+8svv2jXrl0u142MjNTOnTv122+/Wb6GlPPz7uyR9CuvvFJZWVn5Tmt01x133OFyztzfF2c/H3Ofp/k9d/r06aPq1as7P27btq3atWvn/Hlw8uRJffHFF+rXr5/OnDnjfN6eOHFC3bp102+//aY///zT5ZwjRoxwjgpfyOrVq1W1alUNHDjQuS04OFhjxoxRUlKSNmzY4P4D8f8qVKigG264Qe+9955zamJWVpaWLFmiPn36OO+3WrZsmSpUqKBrr73W5fuxVatWCg8Pd/l+PPv3Qu5jcOWVVyolJUW//vqry/VDQkLyTDfNvVf0P//5jzIyMix/TSidCE4oddq2bauuXbu6vF1zzTXOz99zzz1q0KCBunfvrho1amjYsGF57ovwRM2aNV0+zn3BExMTk2d7dna2SyDauHGjunbt6rx/pUqVKs451u4Ep99++01r165VlSpVXN66du0q6Z+mAQcOHNDFF1+cZy2jSy65xNLXWr9+fZePw8PDdfHFF+eZv55fd8MDBw7ke73cqZDnvhg591qGYahevXou1zp48KAzWISHh6tKlSrOMHLu4xcQEKA6deq4bGvQoIEk+bT9fP/+/RUSEqJ33nnHWdd//vMfDRo0yOUF1LlyH4/zPWZ//fXXBafVXOi8+XWaPHfb+a5vs9lUp04dl/+v/v37KzU11XkPRVJSklavXu2850qS88Vr586d8zxfP/30U+dzNVdQUNB577c493tOkipWrJjnXof89q1QoYJCQ0OdU+XO3n728b/99pt27tyZp9bc58y59VqpyR21a9fWuHHj9Oqrr6py5crq1q2bXnrpJbd+LhR03nP17dtXAQEBziBimqaWLVum7t27KyIi4rznCgoK0s0336wPP/zQed/XihUrlJGR4RJgJGnRokVq3ry5816jKlWqaNWqVR59PZUrV1aXLl20dOlS57YlS5YoKChIN910k3Pb5MmTderUKTVo0EDNmjXTgw8+qJ9//tnt65z7f5o7ZdHT/9P8znmh3xf5Xefcn4tSzs+x3J9hv//+u0zT1BNPPJHnuZs7tfHc525+z4n8HDhwQPXr13f5Q4p0/p/h7oqLi9PBgwf19ddfS8r5o83Ro0ddWpj/9ttvOn36tKKiovJ8XUlJSS5f086dO3XjjTeqQoUKioiIUJUqVXTbbbdJyvt7oXr16nma/XTq1Ek333yz4uPjVblyZd1www1auHBhnnsbUbZwjxPKnKioKG3btk2ffPKJ1qxZozVr1mjhwoWKi4tzubnVqvP9pe5823P/qrZv3z516dJFDRs21PTp0xUTEyObzabVq1drxowZbrX9zc7O1rXXXquHHnoo38/nvsgrakXRQS8rK0vXXnutTp48qYcfflgNGzZUuXLl9Oeff2rIkCHFpm1yxYoVdf311+udd97RhAkTtHz5cqWnpzt/kZcG7du3V2xsrJYuXapbb71VH3/8sVJTU11ePOf+f7z11luqWrVqnnOc2wI+JCQkzwu0XAV9bxW0rzvHZ2dnq1mzZpo+fXq++577QtdKTe564YUXNGTIEH344Yf69NNPNWbMGE2dOlWbN29WjRo1zhu8z70J/mz5fW9Wq1ZNV155pZYuXapHH31Umzdv1sGDB533gl7IgAED9Morr2jNmjXq06ePli5dqoYNG7rc8P/2229ryJAh6tOnjx588EFFRUUpMDBQU6dOdRnlsmLAgAEaOnSotm3bppYtW2rp0qXq0qWLSyC+6qqrtG/fPufj9+qrr2rGjBmaN2+eW237C/N/er7/Ayu/Lzx57uR+nz3wwAN5Rv1znftHEn93PO3WrZuio6P19ttv66qrrtLbb7+tqlWrOv8AKOV8XVFRUc4/QJ0r9x6qU6dOqVOnToqIiNDkyZNVt25dhYaG6qefftLDDz+c5/dCfl977gLsmzdv1scff6xPPvlEw4YN0wsvvKDNmze7vZg6SheCE8okm82mXr16qVevXsrOztY999yjV155RU888YTq1at3wREAb/v444+Vnp6ujz76yOWvkPlNWTpfXXXr1lVSUpLLL5j81KpVS+vWrVNSUpLLD/09e/ZYqvm3335zGcVLSkrS4cOH1aNHjwKPrVWrVr7Xy506ce6CredOrzFNU7///ruaN28uSdqxY4f27t2rRYsWudxAfb6pmNnZ2frjjz9cwuTevXslKc+aXVYV9LyJi4vTDTfcoB9++EHvvPOOLr30UjVp0uSCx+Q+Hud7zCpXruxR2+BatWrp999/z7P93G1nX//skTqHw6GEhIQ8z7l+/frpxRdfVGJiopYsWaLY2Fi1b9/e+fncaWxRUVEFPl+Lg7p162r79u3q0qWL134ueHKeZs2aqVmzZnr88cf17bff6vLLL9e8efP01FNPOUdAzu2O6clf/vv376977rlHe/bs0ZIlSxQWFqZevXoVeNxVV12liy++WEuWLNEVV1yhL774wtnEJtfy5ctVp04drVixwuUxKMwaP3369NFdd93lHCXbu3evxo8fn2e/3M5/Q4cOVVJSkq666ipNmjTJa+udVaxYMc/j73A4dPjwYa+c/1z5TTvcu3ev82dY7vdqcHCw17/PatWqpZ9//lnZ2dkuf9Q4389wdwUGBurWW2/VG2+8oWnTpmnlypV5pg/WrVtXn3/+uS6//PILBr0vv/xSJ06c0IoVK5yNXiQpISHBcl3t27dX+/bt9fTTT+vdd9/VoEGDtHjxYtbKK6OYqocy58SJEy4fBwQEOF+E5w7B574QdbdNd2Hk/lI4+6+Kp0+f1sKFC/PsW65cuXxr6tevnzZt2qRPPvkkz+dOnTrlvJ+qR48eyszMdGl1npWVpdmzZ1uqef78+S5zvufOnavMzEx17969wGN79Oih77//Xps2bXJuS05O1vz58xUbG5tnzY/c7lG5li9frsOHDzuvld/jZ5qms718fnLbFOfuO2fOHAUHB6tLly4F1n8hBT1vunfvrsqVK2vatGnasGGDW6NNF198sVq2bKlFixa5nPeXX37Rp59+6lZYzU+3bt20adMmbdu2zbnt5MmTef6S27VrV9lsNs2aNcvlMX7ttdd0+vRp9ezZ02X//v37Kz09XYsWLdLatWvVr1+/PNeNiIjQlClT8r1vIL+W7f7Ur18//fnnn1qwYEGez6Wmpno0TdLKz5fExESX+yGlnBAVEBDg/HkVERGhypUrO+9lzPXyyy9bru3mm29WYGCg3nvvPS1btkzXX3+9W8E8ICBAt9xyiz7++GO99dZbyszMzDNNL7/v1e+++87lZ4FVkZGR6tatm5YuXarFixfLZrOpT58+Lvuc+zM/PDxc9erV8+qUq7p16+Z5/OfPn3/BUb/CWLlypcs9St9//72+++4758/FqKgoXX311XrllVfyDW+F+T7r0aOHjhw54nJvWWZmpmbPnq3w8PAL3rNZkNtvv11///237rrrLiUlJeX5GdmvXz9lZWXpySefzHNsZmam83sqv+eaw+Gw9D3x999/5xnty70nlOl6ZRcjTih11qxZk+fGTymnLXSdOnU0fPhwnTx5Up07d1aNGjV04MABzZ49Wy1btnTO0W7ZsqUCAwM1bdo0nT59WiEhIc51lrztuuuuc46A5f6yWLBggaKiovL8wmvVqpXmzp2rp556SvXq1VNUVJQ6d+6sBx98UB999JGuv/56DRkyRK1atVJycrJ27Nih5cuXa//+/apcubJ69eqlyy+/XI888oj279+vxo0ba8WKFZbvL3A4HOrSpYv69eunPXv26OWXX9YVV1yh3r17F3jsI488ovfee0/du3fXmDFjVKlSJS1atEgJCQl6//3380zLqlSpkq644goNHTpUR48e1cyZM1WvXj2NGDFCktSwYUPVrVtXDzzwgP78809FRETo/fffP+/9B6GhoVq7dq0GDx6sdu3aac2aNVq1apUeffTR87bKdVfdunUVGRmpefPmqXz58ipXrpzatWvnvHcgODhYAwYM0Jw5cxQYGOhyc/WFPPfcc+revbs6dOigO+64w9mOvEKFCgWuNXU+Dz30kN5++21de+21Gj16tLMdec2aNXXy5EnniECVKlU0fvx4xcfH61//+pd69+7t/D9v06ZNnhc2l112merVq6fHHntM6enpeV48R0REaO7cubr99tt12WWXacCAAapSpYoOHjyoVatW6fLLL3cJtv52++23a+nSpbr77ru1fv16XX755crKytKvv/6qpUuXOtcps6JVq1aSpMcee0wDBgxQcHCwevXqlW9A+eKLLzRq1Cj17dtXDRo0UGZmpt566y0FBgbq5ptvdu43fPhwPfPMMxo+fLhat26tr776yjmSakVUVJSuueYaTZ8+XWfOnMnz/3ch/fv31+zZszVx4kQ1a9YszxIO119/vVasWKEbb7xRPXv2VEJCgubNm6fGjRu7NBSxqn///rrtttv08ssvq1u3bnkWgG7cuLGuvvpqtWrVSpUqVdKPP/6o5cuXuzSJKazhw4fr7rvv1s0336xrr71W27dv1yeffJLnHjpvqVevnq644gqNHDlS6enpmjlzpi666CKX6dovvfSSrrjiCjVr1kwjRoxQnTp1dPToUW3atEn//e9/tX37do+ufeedd+qVV17RkCFDtGXLFsXGxmr58uXauHGjZs6c6dLMx6pLL71UTZs2dTZlueyyy1w+36lTJ911112aOnWqtm3bpuuuu07BwcH67bfftGzZMr344ou65ZZb1LFjR1WsWFGDBw/WmDFjZBiG3nrrLUvTHhctWqSXX35ZN954o+rWraszZ85owYIFioiI8PgPVigFirCDH+BTF2pHrrNa9S5fvty87rrrzKioKNNms5k1a9Y077rrLvPw4cMu51uwYIFZp04dMzAw0KVl9PnakZ/bLji3nnNbmea2Az679fVHH31kNm/e3AwNDTVjY2PNadOmma+//nqelsVHjhwxe/bsaZYvX96U5FLHmTNnzPHjx5v16tUzbTabWblyZbNjx47m888/79I6/MSJE+btt99uRkREmBUqVDBvv/12Z8tjd9uRb9iwwbzzzjvNihUrmuHh4eagQYNcWmWb5oXb0O7bt8+85ZZbzMjISDM0NNRs27at+Z///Mdln9zH9b333jPHjx9vRkVFmXa73ezZs6dLi3HTNM1du3aZXbt2NcPDw83KlSubI0aMcLaBPvtrGjx4sFmuXDlz37595nXXXWeGhYWZ0dHR5sSJE11abZumZ+3ITdM0P/zwQ7Nx48ZmUFBQvo9pbnvn6667Lt/H5nw+//xz8/LLLzftdrsZERFh9urVy9y1a1e+j5k77chNM6fV9ZVXXmmGhISYNWrUMKdOnWrOmjXLlGQeOXLEZd85c+aYDRs2NIODg83o6Ghz5MiR5t9//53veR977DFTklmvXr3zXnv9+vVmt27dzAoVKpihoaFm3bp1zSFDhpg//vijc5/c/6/8SDLvvffePNvPbf+c3/fbhc7dqVMns0mTJi7bHA6HOW3aNLNJkyZmSEiIWbFiRbNVq1ZmfHy8efr0acs1mWbOEgLVq1c3AwICLtia/I8//jCHDRtm1q1b1wwNDTUrVapkXnPNNebnn3/usl9KSop5xx13mBUqVDDLly9v9uvXzzx27Nh525Gf+3icbcGCBaYks3z58mZqamqez5/bjjxXdna2GRMTY0oyn3rqqXw/P2XKFLNWrVpmSEiIeemll5r/+c9/8j3fuXVfSGJiomm32/O0yM711FNPmW3btjUjIyNNu91uNmzY0Hz66addfi7m53w/w3O/z85eRiArK8t8+OGHzcqVK5thYWFmt27dzN9///287cjd+b1gmnmfp7ntyJ977jnzhRdeMGNiYsyQkBDzyiuvNLdv357na9i3b58ZFxdnVq1a1QwODjarV69uXn/99eby5csLrOlCjh49ag4dOtSsXLmyabPZzGbNmuX7+8PdduRny13eYsqUKefdZ/78+WarVq1Mu91uli9f3mzWrJn50EMPmf/73/+c+2zcuNFs3769abfbzWrVqpkPPfSQs3X+2f93+X3Pm6Zp/vTTT+bAgQPNmjVrmiEhIWZUVJR5/fXXu/yMQtljmGYh7lgFUKbkLjr4ww8/WP4rO3Js375dLVu21JtvvunSLaq4uP/++/XKK68oKSnJrdbEAOBNL774osaOHav9+/fn26US8CfucQKAIrRgwQKFh4e7tEv2l9TUVJePT5w4obfeektXXHEFoQlAkTNNU6+99po6depEaEKxxD1OAFAEPv74Y+3atUvz58/XqFGjPOqE520dOnTQ1VdfrUaNGuno0aN67bXXlJiYqCeeeMLfpQEoQ5KTk/XRRx9p/fr12rFjhz788EN/lwTki+AEAEVg9OjROnr0qHr06KH4+Hh/lyMppzvW8uXLNX/+fBmGocsuu0yvvfaaS/teAPC148eP69Zbb1VkZKQeffRRtxoNAf7APU4AAAAAUADucQIAAACAAhCcAAAAAKAAZe4ep+zsbP3vf/9T+fLlnQs8AgAAACh7TNPUmTNnVK1aNQUEXHhMqcwFp//973+KiYnxdxkAAAAAiolDhw6pRo0aF9ynzAWn8uXLS8p5cCIiIvxcDQAAAAB/SUxMVExMjDMjXEiZC0650/MiIiIITgAAAADcuoWH5hAAAAAAUACCEwAAAAAUgOAEAAAAAAUoc/c4AQAAwH+ysrKUkZHh7zJQhgQHByswMLDQ5yE4AQAAoEgkJSXpv//9r0zT9HcpKEMMw1CNGjUUHh5eqPMQnAAAAOBzWVlZ+u9//6uwsDBVqVLFrS5mQGGZpqnjx4/rv//9r+rXr1+okSeCEwAAAHwuIyNDpmmqSpUqstvt/i4HZUiVKlW0f/9+ZWRkFCo40RwCAAAARYaRJhQ1bz3nCE4AAAAAUACCEwAAAAAUgOAEAAAAlBBffvmlDMPQqVOn/F1KmUNwAgAAAM5jyJAhMgxDzzzzjMv2lStXcr9WGUNwAgAAAC4gNDRU06ZN099//+3vUkos0zSVmZnp7zIKheAEAACAImeaUnKqf96srr/btWtXVa1aVVOnTj3vPu+//76aNGmikJAQxcbG6oUXXnD5fGxsrKZMmaJhw4apfPnyqlmzpubPn1/gtVevXq0GDRrIbrfrmmuu0f79+/Ps88033+jKK6+U3W5XTEyMxowZo+TkZJdrP/XUU4qLi1N4eLhq1aqljz76SMePH9cNN9yg8PBwNW/eXD/++KOlryk9PV0PP/ywYmJiFBISonr16um1116T9M+UwjVr1qhVq1YKCQnRN998o3379umGG25QdHS0wsPD1aZNG33++edundc0TdWrV0/PP/+8y/7btm2TYRj6/fffC3w8C8UsY06fPm1KMk+fPu3vUgAAAMqM1NRUc9euXWZqaqppmqaZlGKa6uSft6QU9+sePHiwecMNN5grVqwwQ0NDzUOHDpmmaZoffPCBmftS+scffzQDAgLMyZMnm3v27DEXLlxo2u12c+HChc7z1KpVy6xUqZL50ksvmb/99ps5depUMyAgwPz111/Pe+2DBw+aISEh5rhx48xff/3VfPvtt83o6GhTkvn333+bpmmav//+u1muXDlzxowZ5t69e82NGzeal156qTlkyJA81543b565d+9ec+TIkWZERIT5r3/9y1y6dKm5Z88es0+fPmajRo3M7Oxst7+mfv36mTExMeaKFSvMffv2mZ9//rm5ePFi0zRNc/369aYks3nz5uann35q/v777+aJEyfMbdu2mfPmzTN37Nhh7t2713z88cfN0NBQ88CBA26d9+mnnzYbN27s8jiNGTPGvOqqq877OJ773DublWxAcAIAAIDPlfTgZJqm2b59e3PYsGGmaboGp1tvvdW89tprXY578MEHXV7g16pVy7ztttucH2dnZ5tRUVHm3Llzz3vt8ePH5wkJDz/8sEtwuuOOO8w777zTZZ+vv/7aDAgIcD7W51778OHDpiTziSeecG7btGmTKck8fPiwW1/Tnj17TEnmZ599lm/tucFp5cqV5/36cjVp0sScPXu2W+f9888/zcDAQPO7774zTdM0HQ6HWblyZfONN9447/m9FZyCfDueBQAAAOQVFiolrfHftT0xbdo0de7cWQ888IDL9t27d+uGG25w2Xb55Zdr5syZysrKUmBgoCSpefPmzs8bhqGqVavq2LFjkqTu3bvr66+/liTVqlVLO3fu1O7du9WuXTuX83bo0MHl4+3bt+vnn3/WO++849xmmqays7OVkJCgRo0a5bl2dHS0JKlZs2Z5th07dkxVq1Yt8Gvatm2bAgMD1alTpws+Zq1bt3b5OCkpSZMmTdKqVat0+PBhZWZmKjU1VQcPHpSkAs9brVo19ezZU6+//rratm2rjz/+WOnp6erbt+8F6/AGv97j9NVXX6lXr16qVq2aDMPQypUrCzzmyy+/1GWXXeac7/jGG2/4vE4AAAB4l2FI5ez+efO0Gd5VV12lbt26afz48R4dHxwcfM5jYCg7O1uS9Oqrr2rbtm3atm2bVq9e7fY5k5KSdNdddzmP3bZtm7Zv367ffvtNdevWzffaud0A89uWW09B7Ha7W/uVK1fO5eMHHnhAH3zwgaZMmaKvv/5a27ZtU7NmzeRwONw+7/Dhw7V48WKlpqZq4cKF6t+/v8LCwtyqpzD8OuKUnJysFi1aaNiwYbrpppsK3D8hIUE9e/bU3XffrXfeeUfr1q3T8OHDdfHFF6tbt25FUDEAAADKsmeeeUYtW7bUJZdc4tzWqFEjbdy40WW/jRs3qkGDBs7RpoJUr149z7ZGjRrpo48+ctm2efNml48vu+wy7dq1S/Xq1XP3S3BLQV9Ts2bNlJ2drQ0bNqhr165un3fjxo0aMmSIbrzxRkk5we/shhfunLdHjx4qV66c5s6dq7Vr1+qrr76y/gV6wK/BqXv37urevbvb+8+bN0+1a9d2dvRo1KiRvvnmG82YMaNEBqfsbFN/paV5fHzl0FAFBLB+AAAAQFFp1qyZBg0apFmzZjm3/fvf/1abNm305JNPqn///tq0aZPmzJmjl19+uVDXuvvuu/XCCy/owQcf1PDhw7Vly5Y8s60efvhhtW/fXqNGjdLw4cNVrlw57dq1S5999pnmzJnj8bUL+ppiY2M1ePBgDRs2TLNmzVKLFi104MABHTt2TP369TvveevXr68VK1aoV69eMgxDTzzxhMsolzvnDQwM1JAhQzR+/HjVr18/z/RFXylR7cg3bdqUJ3l269ZNmzZtOu8x6enpSkxMdHkrLv5KS1O1tK4ev0VuG6nsbIv9NAEAAFAokydPdnmxf9lll2np0qVavHixmjZtqgkTJmjy5MkaMmRIoa5Ts2ZNvf/++1q5cqVatGihefPmacqUKS77NG/eXBs2bNDevXt15ZVX6tJLL9WECRNUrVq1Ql3bna9p7ty5uuWWW3TPPfeoYcOGGjFihEsb9PxMnz5dFStWVMeOHdWrVy9169ZNl112mcs+7pz3jjvukMPh0NChQwv1dVphmKbVTva+YRiGPvjgA/Xp0+e8+zRo0EBDhw51mVe6evVq9ezZUykpKfnOiZw0aZLi4+PzbD99+rQiIiK8UrunjqWkqlqa+0Ob+fnGsULtq0Z7qSIAAADfSEtLU0JCgmrXrq3QUA+7MwD/7+uvv1aXLl106NAhZ2OL87nQcy8xMVEVKlRwKxuU+q5648eP17hx45wfJyYmKiYmxo8V/aNyaKj+p891IOmMzmQ43D7ub0e6+leIkySlZGX4qjwAAACgWElPT9fx48c1adIk9e3bt8DQ5E0lKjhVrVpVR48eddl29OhRRUREnLcDR0hIiEJCQoqiPMsCAgxFhdkVFeZeV5Jcx1JSJc9vjQIAAABKpPfee0933HGHWrZsqTfffLNIr12i7nHq0KGD1q1b57Lts88+K7IbwgAAAAD4z5AhQ5SVlaUtW7bk24nQl/wanJKSkpz95qWcduPbtm1zLoA1fvx4xcXFOfe/++679ccff+ihhx7Sr7/+qpdffllLly7V2LFj/VE+AAAAgDLCr8Hpxx9/1KWXXqpLL71UkjRu3DhnJxBJOnz4sDNESVLt2rW1atUqffbZZ2rRooVeeOEFvfrqqyWyFTkAAACAksOv9zhdffXVulBTv3P71Oces3XrVh9WBQAAAACuStQ9TgAAAADgDwQnAAAAACgAwQkAAAAACkBwAgAAAIACEJwAAACA8xgyZIgMw5BhGAoODlZ0dLSuvfZavf7668rOzvZ3eShCBCcAAADgAv71r3/p8OHD2r9/v9asWaNrrrlG9913n66//nplZmb6uzxLHA6Hv0sosQhOAAAAKHKmaSrZTPXL24WWw8lPSEiIqlatqurVq+uyyy7To48+qg8//FBr1qxxLp9z6tQpDR8+XFWqVFFERIQ6d+6s7du3O88xadIktWzZUq+//rpq1qyp8PBw3XPPPcrKytKzzz6rqlWrKioqSk8//bTLtQ8ePKgbbrhB4eHhioiIUL9+/XT06FGXfT7++GO1adNGoaGhqly5sm688Ubn52JjY/Xkk08qLi5OERERuvPOOyVJDz/8sBo0aKCwsDDVqVNHTzzxhDIyMtw67+TJk9W0adM8j1PLli31xBNPWHpsSxK/ruMEAACAsilFaarwd1e/XPt0xc9VTvZCnaNz585q0aKFVqxYoeHDh6tv376y2+1as2aNKlSooFdeeUVdunTR3r17ValSJUnSvn37tGbNGq1du1b79u3TLbfcoj/++EMNGjTQhg0b9O2332rYsGHq2rWr2rVrp+zsbGdo2rBhgzIzM3Xvvfeqf//++vLLLyVJq1at0o033qjHHntMb775phwOh1avXu1S6/PPP68JEyZo4sSJzm3ly5fXG2+8oWrVqmnHjh0aMWKEypcvr4ceeqjA8w4bNkzx8fH64Ycf1KZNG0nS1q1b9fPPP2vFihWFelyLM4ITAAAA4IGGDRvq559/1jfffKPvv/9ex44dU0hIiKScsLJy5UotX77cOcqTnZ2t119/XeXLl1fjxo11zTXXaM+ePVq9erUCAgJ0ySWXaNq0aVq/fr3atWundevWaceOHUpISFBMTIwk6c0331STJk2coeXpp5/WgAEDFB8f76yrRYsWLnV27txZ//73v122Pf744873Y2Nj9cADD2jx4sXO4HSh89aoUUPdunXTwoULncFp4cKF6tSpk+rUqeOVx7Y4IjgBAACgyIUpVKcrfu63a3uDaZoyDEPbt29XUlKSLrroIpfPp6amat++fc6PY2NjVb58eefH0dHRCgwMVEBAgMu2Y8eOSZJ2796tmJgYZ2iSpMaNGysyMlK7d+9WmzZttG3bNo0YMeKCdbZu3TrPtiVLlmjWrFnat2+fkpKSlJmZqYiICOfnCzrviBEjNGzYME2fPl0BAQF69913NWPGjAvWUdIRnAAAAFDkDMMo9HQ5f9u9e7dq166tpKQkXXzxxc7pc2eLjIx0vh8cHOzyudxOfedus9Ktz24v+DEsV66cy8ebNm3SoEGDFB8fr27duqlChQpavHixXnjhBbfP26tXL4WEhOiDDz6QzWZTRkaGbrnlFrfrLokITgAAAIBFX3zxhXbs2KGxY8eqRo0aOnLkiIKCghQbG+u1azRq1EiHDh3SoUOHnKNOu3bt0qlTp9S4cWNJUvPmzbVu3ToNHTrU7fN+++23qlWrlh577DHntgMHDrjsU9B5g4KCNHjwYC1cuFA2m00DBgxwK8SVZAQnAAAA4ALS09N15MgRZWVl6ejRo1q7dq2mTp2q66+/XnFxcQoICFCHDh3Up08fPfvss2rQoIH+97//ORss5DdVzh1du3ZVs2bNNGjQIM2cOVOZmZm655571KlTJ+c5J06cqC5duqhu3boaMGCAMjMztXr1aj388MPnPW/9+vV18OBBLV68WG3atNGqVav0wQcfuOzjznmHDx+uRo0aSZI2btzo0ddYktCOHAAAALiAtWvX6uKLL1ZsbKz+9a9/af369Zo1a5Y+/PBDBQYGyjAMrV69WldddZWGDh2qBg0aaMCAATpw4ICio6M9vq5hGPrwww9VsWJFXXXVVeratavq1KmjJUuWOPe5+uqrtWzZMn300Udq2bKlOnfurO+///6C5+3du7fGjh2rUaNGqWXLlvr222/ztBF357z169dXx44d1bBhQ7Vr187jr7OkMEyrjexLuMTERFWoUEGnT592uQGuJDmWkqpqaTntOz9NXaLO1Wv4uSIAAIALS0tLU0JCgmrXrq3QUO80Z4B/maap+vXr65577tG4ceP8Xc55Xei5ZyUbMFUPAAAAgCXHjx/X4sWLdeTIEUv3V5VkBCcAAAAAlkRFRaly5cqaP3++Klas6O9yigTBCQAAAIAlZexuH0k0hwAAAACAAhGcAAAAUGTK4kgF/MtbzzmCEwAAAHwuMDBQkuRwOPxcCcqa3Odc7nPQU9zjBAAAAJ8LCgpSWFiYjh8/ruDgYAUE8Pd7+F52draOHz+usLAwBQUVLvoQnAAAAOBzhmHo4osvVkJCgg4cOODvclCGBAQEqGbNmjIMo1DnITgBAACgSNhsNtWvX5/peihSNpvNKyOcBCcAAAAUmYCAAIWGhvq7DMAyJpcCAAAAQAEITgAAAABQAIITAAAAABSA4AQAAAAABSA4AQAAAEABCE4AAAAAUACCEwAAAAAUgOAEAAAAAAUgOAEAAABAAQhOAAAAAFAAghMAAAAAFIDgBAAAAAAFIDgBAAAAQAEITgAAAABQAIITAAAAABSA4AQAAAAABSA4AQAAAEABCE4AAAAAUACCEwAAAAAUgOAEAAAAAAUgOAEAAABAAQhOAAAAAFAAghMAAAAAFIDgBAAAAAAFIDgBAAAAQAEITgAAAABQAIITAAAAABSA4AQAAAAABSA4AQAAAEABCE4AAAAAUACCEwAAAAAUgOAEAAAAAAUgOAEAAABAAQhOAAAAAFAAghMAAAAAFIDgBAAAAAAFIDgBAAAAQAEITgAAAABQAIITAAAAABSA4AQAAAAABSA4AQAAAEABCE4AAAAAUACCEwAAAAAUgOAEAAAAAAUgOAEAAABAAQhOAAAAAFAAghMAAAAAFIDgBAAAAAAFIDgBAAAAQAEITgAAAABQAIITAAAAABQgyN8FoHBS06QzKdaPCwqU7CHerwcAAAAojQhOJdwv+6Uz+60fF26XurQiPAEAAADuIDiVcMGBUoTF8JOWISWlSplZvqkJAAAAKG0ITiVcSLAUFmr9OEeG92sBAAAASiuaQwAAAABAAQhOAAAAAFAAghMAAAAAFMCte5xuuukmt0+4YsUKj4sBAAAAgOLIreBUoUIFX9cBAAAAAMWWW8Fp4cKFvq4DAAAAAIot7nECAAAAgAJ4FJyWL1+ufv36qX379rrssstc3qx66aWXFBsbq9DQULVr107ff//9BfefOXOmLrnkEtntdsXExGjs2LFKS0vz5MsAAAAAALdYDk6zZs3S0KFDFR0dra1bt6pt27a66KKL9Mcff6h79+6WzrVkyRKNGzdOEydO1E8//aQWLVqoW7duOnbsWL77v/vuu3rkkUc0ceJE7d69W6+99pqWLFmiRx991OqXAQAAAABusxycXn75Zc2fP1+zZ8+WzWbTQw89pM8++0xjxozR6dOnLZ1r+vTpGjFihIYOHarGjRtr3rx5CgsL0+uvv57v/t9++60uv/xy3XrrrYqNjdV1112ngQMHFjhKBQAAAACFYTk4HTx4UB07dpQk2e12nTlzRpJ0++2367333nP7PA6HQ1u2bFHXrl3/KSYgQF27dtWmTZvyPaZjx47asmWLMyj98ccfWr16tXr06HHe66SnpysxMdHlrTRJzzCUmi5Lb2kOyTT9XTkAAABQcrjVVe9sVatW1cmTJ1WrVi3VrFlTmzdvVosWLZSQkCDTwqvxv/76S1lZWYqOjnbZHh0drV9//TXfY2699Vb99ddfuuKKK2SapjIzM3X33XdfcKre1KlTFR8f73ZdJc2/p1aS0q0fd0mM1LOD9+sBAAAASiPLI06dO3fWRx99JEkaOnSoxo4dq2uvvVb9+/fXjTfe6PUCz/bll19qypQpevnll/XTTz9pxYoVWrVqlZ588snzHjN+/HidPn3a+Xbo0CGf1lgUwkILf449h6QUDwIXAAAAUBZZHnGaP3++srOzJUn33nuvLrroIn377bfq3bu37rrrLrfPU7lyZQUGBuro0aMu248ePaqqVavme8wTTzyh22+/XcOHD5ckNWvWTMnJybrzzjv12GOPKSAgbw4MCQlRSEiI23WVBIbxz/tzJpxQncAabh+b5pBumuCDogAAAIBSzHJwCggIcAkoAwYM0IABAyxf2GazqVWrVlq3bp369OkjScrOzta6des0atSofI9JSUnJE44CAwMlydI0wdIk1CbZLf8vAgAAALDC8lS9evXqadKkSdq7d2+hLz5u3DgtWLBAixYt0u7duzVy5EglJydr6NChkqS4uDiNHz/euX+vXr00d+5cLV68WAkJCfrss8/0xBNPqFevXs4ABQAAAADeZnms4t5779W7776rJ598Updddpluu+029e/f/7zT6y6kf//+On78uCZMmKAjR46oZcuWWrt2rbNhxMGDB11GmB5//HEZhqHHH39cf/75p6pUqaJevXrp6aeftnxtAAAAAHCXYXo4x23v3r1655139N577ykhIUHXXHONbrvtNsXFxXm7Rq9KTExUhQoVdPr0aUVERPi7HI8km6mq8HdOG/dX9i1R3SD373FKTZd6PJLz/uEVUtVKvqgQAAAAKP6sZAPLU/VyNWjQQPHx8dq7d6++/vprHT9+3DnFDgAAAABKk0K1Ffj+++/17rvvasmSJUpMTFTfvn29VRcAAAAAFBuWg9O5U/Q6d+6sadOm6aabblJ4eLgvagQAAAAAv7IcnBo2bKg2bdro3nvv1YABA5yNHAAAAACgtLIcnPbs2aP69ev7ohYAAAAAKJYsN4cgNAEAAAAoa9wacapUqZL27t2rypUrq2LFijIM47z7njx50mvFAQAAAEBx4FZwmjFjhsqXL+98/0LBCQAAAABKG7eC0+DBg53vDxkyxFe1AAAAAECxZPkep65du+qNN95QYmKiL+qBRemZUkqa+2+p6f6uGAAAACh5LAenJk2aaPz48apatar69u2rDz/8UBkZGb6oDW5wZEiJKdbechGiAAAAAPdYbkf+4osvasaMGfr888/17rvvKi4uToGBgbrllls0aNAgderUyRd14jyuuUyqYyH+Jqf98352tvfrAQAAAEojyyNOkhQQEKDrrrtOb7zxho4ePapXXnlF33//vTp37uzt+lCAcLtUPszCm93fFQMAAAAlj+URp7MdOXJEixcv1ttvv62ff/5Zbdu29VZdAAAAAFBsWB5xSkxM1MKFC3XttdcqJiZGc+fOVe/evfXbb79p8+bNvqgRAAAAAPzK8ohTdHS0KlasqP79+2vq1Klq3bq1L+oCAAAAgGLDUnAyTVOzZs3SoEGDFBYW5quaAAAAAKBYsRyc7r33Xl199dWqX7++r2pCEUlJk86kFLzfuYICJXuI9+sBAAAAiitLwSkgIED169fXiRMnCE6lwJa90s791o8Lt0tdWhGeAAAAUHZYbg7xzDPP6MEHH9Qvv/zii3pQhIIDpYgwa2+2YCkpVcrM8nf1AAAAQNGx3BwiLi5OKSkpatGihWw2m+x214WBTp486bXi4Fv2ECks1Ppxjgzv1wIAAAAUZ5aD08yZM31QBgAAAAAUX5aD0+DBg31RBwAAAAAUW5aD08GDBy/4+Zo1a3pcDAAAAAAUR5aDU2xsrAzDOO/ns7LoGgAAAACgdLEcnLZu3erycUZGhrZu3arp06fr6aef9lphAAAAAFBcWA5OLVq0yLOtdevWqlatmp577jnddNNNXikMAAAAAIoLy+s4nc8ll1yiH374wVunAwAAAIBiw/KIU2JiosvHpmnq8OHDmjRpkurXr++1wgAAAACguLAcnCIjI/M0hzBNUzExMVq8eLHXCgMAAACA4sJycFq/fr3LxwEBAapSpYrq1aunoCDLpwMAAACAYs9y0unUqZMv6gAAAACAYstyc4hFixZp1apVzo8feughRUZGqmPHjjpw4IBXiwMAAACA4sBycJoyZYrsdrskadOmTZozZ46effZZVa5cWWPHjvV6gfCdNIeUmm7tLc0hmaa/KwcAAACKluWpeocOHVK9evUkSStXrtQtt9yiO++8U5dffrmuvvpqb9cHHxrk4XrFl8RIPTt4txYAAACgOLM84hQeHq4TJ05Ikj799FNde+21kqTQ0FClpqZ6tzp4XVio1L5x4c6x55CUku6degAAAICSwPKI07XXXqvhw4fr0ksv1d69e9WjRw9J0s6dOxUbG+vt+uBlhiF98py04ispIkyyh7h/bJpDummC72oDAAAAiivLI04vvfSSOnTooOPHj+v999/XRRddJEnasmWLBg4c6PUC4X2GIYXact7sIe6/hdr8XTkAAADgHx4tgDtnzpw82+Pj471SEKxJMdN0xky2dMwZU3IYQZIsDDcBAAAAZRgr1pZwWzL3aGdWgqVj0rKkXeXt6pDVRmGEJwAAAKBABKcSLlhBijDKWTomWw6lB6QqKytTjDoBAAAABSM4lXB2I0RhRqilY3J6H2b4ohwAAACgVLLcHAIAAAAAyhrLwalz5846depUnu2JiYnq3LmzN2oCAAAAgGLF8lS9L7/8Ug6HI8/2tLQ0ff31114pCsVfSpp0JsX6cUGB1taOAgAAAIoDt4PTzz//7Hx/165dOnLkiPPjrKwsrV27VtWrV/dudSi2vtru2bpO4XapSyvCEwAAAEoWt4NTy5YtZRiGDMPId0qe3W7X7NmzvVociq+IMOvBKS1DSkqVMrN8UxMAAADgK24Hp4SEBJmmqTp16uj7779XlSpVnJ+z2WyKiopSYGCgT4pE8WMP8WzUyEEzPwAAAJRAbgenWrVqSZKys7N9VgwAAAAAFEcereP022+/af369Tp27FieIDVhwgSvFAb3pJkOpZrplo8xZfqoIgAAAKD0sRycFixYoJEjR6py5cqqWrWqDMNwfs4wDIJTEYtLiffouIsvjtGVBzt6uRoAAACgdLIcnJ566ik9/fTTevjhh31RD9wQplC1C2yi77J2enyOw6GHlG6kSQr3XmEAAABAKWU5OP3999/q27evL2qBmwzD0Jry07XCsUERRjnZDfe7NKSZDvVNflSS5MiUUizcspaWd/kuAAAAoEywHJz69u2rTz/9VHfffbcv6oGbDMNQqGFTqGGzFJzO5siUEjPd3//s4OTIYC0mAAAAlB2Wg1O9evX0xBNPaPPmzWrWrJmCg4NdPj9mzBivFQff6tpKig5wf//ktH/ez6K5IgAAAMoQy8Fp/vz5Cg8P14YNG7RhwwaXzxmGQXAqQcLDpPIWglOAUfA+AAAAQGlkOTglJCT4og4AAAAAKLYsjDe4cjgc2rNnjzIzLdwkAwAAAAAlkOXglJKSojvuuENhYWFq0qSJDh48KEkaPXq0nnnmGa8XCAAAAAD+Zjk4jR8/Xtu3b9eXX36p0NBQ5/auXbtqyZIlXi0OAAAAAIoDy/c4rVy5UkuWLFH79u1lGP90C2jSpIn27dvn1eIAAAAAoDiwHJyOHz+uqKioPNuTk5NdghRKtzSHlJpu/RjT9E09AAAAgC9ZDk6tW7fWqlWrNHr0aElyhqVXX31VHTp08G51KLYGPe3ZcZfESD15mgAAAKCEsRycpkyZou7du2vXrl3KzMzUiy++qF27dunbb7/Ns64TSpewUKl9Y2nzLs/PseeQlJIuRZTzXl0AAACAr1kOTldccYW2bdumZ555Rs2aNdOnn36qyy67TJs2bVKzZs18USMuIN10WNo/zeL+ZzMM6ZPnpBVfSRFhkj3EwnUd0k0TPL40AAAA4FeWg5Mk1a1bVwsWLPB2LbAgSEEKl11JSlW6meH2cWcHpzQLx+UyDCnUlvNmJTgBAAAAJZlHwUmSjh07pmPHjik7O9tle/PmzQtdFApmN0LUxdZGmbK2AHGymSql5LyfpSwfVAYAAACUPpaD05YtWzR48GDt3r1b5jkt0gzDUFYWL8aLit0IkWRt2CfA+tJdXpeSJp1JsX5cUCCjXAAAAPAPy8Fp2LBhatCggV577TVFR0fTghyWfbU9Z6qfVeF2qUsrwhMAAACKnuXg9Mcff+j9999XvXr1fFEPyoCIMOvBKS1DSkqVMhnQBAAAgB9YDk5dunTR9u3bCU7wmD3Es1Ejh/VeFgAAAIBXWA5Or776qgYPHqxffvlFTZs2VXBwsMvne/fu7bXiAAAAAKA4sBycNm3apI0bN2rNmjV5PkdzCAAAAAClkeUWa6NHj9Ztt92mw4cPKzs72+WN0AQAAACgNLIcnE6cOKGxY8cqOjraF/UAAAAAQLFjOTjddNNNWr9+vS9qAQAAAIBiyfI9Tg0aNND48eP1zTffqFmzZnmaQ4wZM8ZrxQEAAABAceBRV73w8HBt2LBBGzZscPmcYRgEJwAAAACljuXglJCQ4Is6AAAAAKDYsnyPEwAAAACUNZZHnLKysvTGG29o3bp1OnbsmLKzs10+/8UXX3itOAAAAAAoDiwHp/vuu09vvPGGevbsqaZNm8owDF/UhWIuLcPi/g7f1AEAAAAUBcvBafHixVq6dKl69Ojhi3pQzAUFSuF2KSlVclgIT2cHJ0eGZA/xfm0AAACAr1gOTjabTfXq1fNFLSgB7CFSl1ZSZpa145LT/nk/K/v8+wEAAADFkeXg9O9//1svvvii5syZwzS9MsqT0aKAs54qaQ4pNd3a8WkOyTStXxcAAADwBsvB6ZtvvtH69eu1Zs0aNWnSJM8CuCtWrPBacSidBj3t2XGXxEg9O3i3FgAAAMAdloNTZGSkbrzxRl/UglIsLFRq31javMvzc+w5JKWkSxHlvFcXAAAA4A5LwSkzM1PXXHONrrvuOlWtWtUrBbz00kt67rnndOTIEbVo0UKzZ89W27Ztz7v/qVOn9Nhjj2nFihU6efKkatWqpZkzZ9KsopgzDOmT56QVX0kRYdam+6U5pJsm+K42AAAAoCCWglNQUJDuvvtu7d692ysXX7JkicaNG6d58+apXbt2mjlzprp166Y9e/YoKioqz/4Oh0PXXnutoqKitHz5clWvXl0HDhxQZGSkV+qBbxmGFGrLeaOrHgAAAEoSy1P12rZtq61bt6pWrVqFvvj06dM1YsQIDR06VJI0b948rVq1Sq+//roeeeSRPPu//vrrOnnypL799lvnvVWxsbGFrgMAAAAALsRycLrnnnv073//W//973/VqlUrlSvnesNJ8+bN3TqPw+HQli1bNH78eOe2gIAAde3aVZs2bcr3mI8++kgdOnTQvffeqw8//FBVqlTRrbfeqocffliBgYH5HpOenq709H9auCUmJrpVHwAAAADkshycBgwYIEkaM2aMc5thGDJNU4ZhKCvLvQV+/vrrL2VlZSk6Otple3R0tH799dd8j/njjz/0xRdfaNCgQVq9erV+//133XPPPcrIyNDEiRPzPWbq1KmKj493qyYUfylp0pkU68cFBTI9EAAAAJ6zHJwSEhJ8UYdbsrOzFRUVpfnz5yswMFCtWrXSn3/+qeeee+68wWn8+PEaN26c8+PExETFxMQUVcnwsq+259wjZVW4PWfhXsITAAAAPGE5OHnj3iZJqly5sgIDA3X06FGX7UePHj1vx76LL75YwcHBLtPyGjVqpCNHjsjhcMhmy/uKOiQkRCEhvFouLSLCrAentAwpKVXKdG8wFAAAAMgjwJOD3nrrLV1++eWqVq2aDhw4IEmaOXOmPvzwQ7fPYbPZ1KpVK61bt865LTs7W+vWrVOHDvmvcnr55Zfr999/V3Z2tnPb3r17dfHFF+cbmlD62ENy1oSy8hYaXPB5AQAAgAuxHJzmzp2rcePGqUePHjp16pTznqbIyEjNnDnT0rnGjRunBQsWaNGiRdq9e7dGjhyp5ORkZ5e9uLg4l+YRI0eO1MmTJ3Xfffdp7969WrVqlaZMmaJ7773X6pcBAAAAAG6zPFVv9uzZWrBggfr06aNnnnnGub1169Z64IEHLJ2rf//+On78uCZMmKAjR46oZcuWWrt2rbNhxMGDBxUQ8E+2i4mJ0SeffKKxY8eqefPmql69uu677z49/PDDVr8MAAAAAHCbR80hLr300jzbQ0JClJycbLmAUaNGadSoUfl+7ssvv8yzrUOHDtq8ebPl6wAAAACApyxP1atdu7a2bduWZ/vatWvVqFEjb9QEAAAAAMWK5RGncePG6d5771VaWppM09T333+v9957T1OnTtWrr77qixoBAAAAwK8sB6fhw4fLbrfr8ccfV0pKim699VZVq1ZNL774onNxXAAAAAAoTSwHJ0kaNGiQBg0apJSUFCUlJSkqKsrbdQEAAABAsWH5HqennnpKCQkJkqSwsDBCEwAAAIBSz3JwWrZsmerVq6eOHTvq5Zdf1l9//eWLugAAAACg2LAcnLZv366ff/5ZV199tZ5//nlVq1ZNPXv21LvvvquUlBRf1AgAAAAAfmU5OElSkyZNNGXKFP3xxx9av369YmNjdf/996tq1arerg8+lGKm6YyZbPkt1Uwv1HXTMqSUNPffUgt3OQAAAKDQPGoOcbZy5crJbrfLZrPpzJkz3qgJRWRL5h7tzEqwfFy47OpiayO7EWLpuKBAKdwuJaVKjgz3j0tz/PO+I0OyW7ssAAAAUGgeBaeEhAS9++67evfdd7Vnzx516tRJ8fHxuuWWW7xdH3woWEGKMMpZOibddChJqcpUpiRrCcYeInVpJWVmWTpMyWn/vJ+Vbe1YAAAAwBssB6f27dvrhx9+UPPmzTV06FANHDhQ1atX90Vt8DG7EaIwI9TycemmheGic6/pwWhRgOHx5QAAAACvsBycunTpotdff12NGzf2RT0AAAAAUOxYDk5PP/20833TNCVJhsGQAIpGmsN6s4g0h/T/T1UAAADAIx7d4/Tmm2/queee02+//SZJatCggR588EHdfvvtXi0OONegpwveJz+XxEg9O3i3FgAAAJQdloPT9OnT9cQTT2jUqFG6/PLLJUnffPON7r77bv31118aO3as14tE2RYWKrVvLG3e5fk59hySUtKlCGu9MAAAAABJHgSn2bNna+7cuYqLi3Nu6927t5o0aaJJkyYRnOB1hiF98py04ispIsxag4k0h3TTBN/VBgAAgLLBcnA6fPiwOnbsmGd7x44ddfjwYa8UBZzLMKRQW84b6zgBAACgqAVYPaBevXpaunRpnu1LlixR/fr1vVIUAAAAABQnlkec4uPj1b9/f3311VfOe5w2btyodevW5RuoAAAAAKCksxycbr75Zn333XeaMWOGVq5cKUlq1KiRvv/+e1166aXerg8+lGY6lGpa6+2dZjqcbegBAACAssKjduStWrXS22+/7e1aUMTiUuI9Oq5BQIx62jpKLN8FAACAMsJycEpMTMx3u2EYCgkJkc1mK3RR8J0whapdYBN9l7XT43PszT6kFKUpQuFerAwAAAAoviwHp8jISBnG+YcaatSooSFDhmjixIkKCLDcewI+ZhiG1pSfrhWODYowysluuN+iLs10qG/yoz6sDgAAACieLAenN954Q4899piGDBmitm3bSpK+//57LVq0SI8//riOHz+u559/XiEhIXr0UV5kF0eGYSjUsCnUsFkKTgAAAEBZZTk4LVq0SC+88IL69evn3NarVy81a9ZMr7zyitatW6eaNWvq6aefJjgBAAAAKBUsz6X79ttv8+2ed+mll2rTpk2SpCuuuEIHDx4sfHUAAAAAUAxYDk4xMTF67bXX8mx/7bXXFBMTI0k6ceKEKlasWPjqAAAAAKAYsDxV7/nnn1ffvn21Zs0atWnTRpL0448/6tdff9Xy5cslST/88IP69+/v3UoBAAAAwE8sB6fevXtrz549euWVV7Rnzx5JUvfu3bVy5UrFxsZKkkaOHOnVIgEAAADAnzxaADc2NlZTp071di0AAAAAUCx5FJyAkiglTTqTYv24oEDJTtd2AACAMo3ghDLjq+1SqM36ceF2qUsrwhMAAEBZRnBCmRERZj04pWVISalSZpZvagIAAEDJ4FZw+uijj9S9e3cFBwf7uh7ggtIyLO7v+Od9e4hno0YOi9cEAABA6eNWcLrxxht15MgRValSRYGBgTp8+LCioqJ8XRvgFBSYM2UuKdVakDk7ODkymG4HAAAAz7gVnKpUqaLNmzerV69eMk1ThmH4ui4UgXTTUfBOZ0mzuL832UNy7jOyOmUuOe2f97OyvVsTAAAAyg63gtPdd9+tG264QYZhyDAMVa1a9bz7ZmVxM0hxF6QghcuuJKUq3XR/+Obs4JRm4Thv8WS0KICMDwAAAC9wKzhNmjRJAwYM0O+//67evXtr4cKFioyM9HFp8BW7EaIutjbKVKal45LNVOn/23lniYAMAACAssPtrnoNGzZUw4YNNXHiRPXt21dhYWG+rAs+ZjdCJFkbwglQgG+KAQAAAIo5y+3IJ06cKEk6fvy49uzZI0m65JJLVKVKFe9WBgAAAADFhOXglJKSolGjRumtt95y3s8UGBiouLg4zZ49m5EolEopaQXvk5+gQDr5AQAAlAaWg9PYsWO1YcMGffTRR7r88sslSd98843GjBmjf//735o7d67XiwT8JTAgpwX6+q2eHR9uz+kGSHgCAAAo2SwHp/fff1/Lly/X1Vdf7dzWo0cP2e129evXj+CEUiXEJsVU8ayVeVpGTuiy2kIdAAAAxY9HU/Wio6PzbI+KilJKSopXigKKkxCb58daWawXAAAAxZflNmkdOnTQxIkTlZb2z00fqampio+PV4cOHbxaHAAAAAAUB5ZHnF588UV169ZNNWrUUIsWLSRJ27dvV2hoqD755BOvFwgAAAAA/mY5ODVt2lS//fab3nnnHf3666+SpIEDB2rQoEGy2+1eLxAAAAAA/M1ycJKksLAwjRgxwtu1AAAAAECx5FFwAkqiNIeUmm79uFCbZBjerwcAAAAlB8EJZcagpz07rmltadZowhMAAEBZZrmrHlCShIVK7RsX7hy/JOSMVgEAAKDsYsQJpZphSJ88J634SooIk+wh7h+b5pBumuC72gAAAFByWB5xGjx4sL766itf1AL4hGHk3KcUassJTu6+hRZi4VsAAACULpaD0+nTp9W1a1fVr19fU6ZM0Z9//umLugAAAACg2LAcnFauXKk///xTI0eO1JIlSxQbG6vu3btr+fLlysjI8EWNAAAAAOBXHjWHqFKlisaNG6ft27fru+++U7169XT77berWrVqGjt2rH777Tdv1wmUWClp0pkU62+etE4HAACAbxSqOcThw4f12Wef6bPPPlNgYKB69OihHTt2qHHjxnr22Wc1duxYb9UJlDiBAVJSqrR+q2fHh9ulLq2sNbQAAACAb1gOThkZGfroo4+0cOFCffrpp2revLnuv/9+3XrrrYqIiJAkffDBBxo2bBjBCWVaiE2KqSJlZVs/Ni0jJ3RlZnm/LgAAAFhnOThdfPHFys7O1sCBA/X999+rZcuWefa55pprFBkZ6YXygJItpBCd+RzcMggAAFBsWA5OM2bMUN++fRUaGnrefSIjI5WQkFCowoDixNMFcENtOe3QAQAAULJZDk7r169Xnz598gSn5ORkjR49Wq+//rrXikPxlWKm6YyZbPm4IAXJbpS8m3Y8XQi3aW1p1mjCEwAAQElnOTgtWrRIzzzzjMqXL++yPTU1VW+++SbBqYzYkrlHO7OsjyqGy64utjYlIjyF2nKCzy+FGDz9JSFntIoGDwAAACWb28EpMTFRpmnKNE2dOXPGZcQpKytLq1evVlRUlE+KRPETrCBFGOUsHZNuOpSkVGUqU1LxTxKGkTNa5Mk0vTSH56NUAAAAKH7cDk6RkZEyDEOGYahBgwZ5Pm8YhuLj471aHIovuxGiMOP897mdT7pZsjoeGAajRQAAALAQnNavXy/TNNW5c2e9//77qlSpkvNzNptNtWrVUrVq1XxSJFBWpaR5dlxQIIEPAADAm9wOTp06dZIkJSQkqGbNmjK42x3wGRbPBQAAKF7cCk4///yzmjZtqoCAAJ0+fVo7duw4777Nmzf3WnFAWcXiuQAAAMWLW8GpZcuWOnLkiKKiotSyZUsZhiHTNPPsZxiGsrJ4tQZ4A4vnAgAAFB9uBaeEhARVqVLF+T4AAAAAlCVuBadatWrl+z4AAAAAlAVuBaePPvrI7RP27t3b42IAX0rzcPpaYEDhps0BAACg5HMrOPXp08etk3GPE4qjoMCcLnNJqZ7d+5OUmtOogfAEAABQdrkVnLKzPWjtBRQT9pCc1tyedJlLSctpCe5JdzsAAACUHm6v4wScLc10KNVMt3xMft0YiwLrGQEAAKAw3ApOs2bN0p133qnQ0FDNmjXrgvuOGTPGK4WheItLiffouAYBMepp6yiVofWT0xyeHRdqk1hnGgAAoHhwKzjNmDFDgwYNUmhoqGbMmHHe/QzDIDiVYmEKVbvAJvoua6fH59ibfUgpSlOEwr1YWfF20wTPjmtaW5o1mvAEAABQHLi9jlN+76NsMQxDa8pP1wrHBkUY5WQ33J//lmY61Df5UR9WV7yE2nKCzy+F+Hb5JSFntIpphgAAAP5XqHuccu9XMfiTeJlhGIZCDZtCDZul4FTWGEbOaJEn0/TSHJ6PUgEAAMA3Ajw56LXXXlPTpk0VGhqq0NBQNW3aVK+++qq3awNKNMPIGS2y+hbqpbbnKWnSmRTrb6nWen4AAACUCZZHnCZMmKDp06dr9OjR6tChgyRp06ZNGjt2rA4ePKjJkyd7vUgA7gsMyFl7av1Wz44Pt+e0b2eKIAAAwD8sB6e5c+dqwYIFGjhwoHNb79691bx5c40ePZrghFIpzYOFc6WcEFPUC+eG2HIW7PVk7am0jJzQ5cmaVwAAAKWZ5eCUkZGh1q1b59neqlUrZWZmeqUooLgICswZgUlKlRwehKek1JwQ44/w5ClPvk4AAIDSznJwuv322zV37lxNnz7dZfv8+fM1aNAgrxUGFAf2kJxpa56MwKSk5UyX82TkBwAAAMWLW8Fp3LhxzvcNw9Crr76qTz/9VO3bt5ckfffddzp48KDi4uJ8UyXgR/6814fFcwEAAIoHt4LT1q2ud5m3atVKkrRv3z5JUuXKlVW5cmXt3On5wqgA8mLxXAAAgOLBreC0fv16X9cB4P+xeC4AAEDxU6gFcAF4H4vnAgAAFD8eBacff/xRS5cu1cGDB+VwuL66W7FiheXzvfTSS3ruued05MgRtWjRQrNnz1bbtm0LPG7x4sUaOHCgbrjhBq1cudLydYHiKnfxXAAAABQPAVYPWLx4sTp27Kjdu3frgw8+UEZGhnbu3KkvvvhCFSpUsFzAkiVLNG7cOE2cOFE//fSTWrRooW7duunYsWMXPG7//v164IEHdOWVV1q+JgAAAABYYTk4TZkyRTNmzNDHH38sm82mF198Ub/++qv69eunmjVrWi5g+vTpGjFihIYOHarGjRtr3rx5CgsL0+uvv37eY7KysjRo0CDFx8erTp06lq+Jwks3HUox09x+SzXT/V0yAAAA4DHLwWnfvn3q2bOnJMlmsyk5OVmGYWjs2LGaP3++pXM5HA5t2bJFXbt2/aeggAB17dpVmzZtOu9xkydPVlRUlO64444Cr5Genq7ExESXN3guSEEKl13pylCimWzpLVeayQqrAAAAKFks3+NUsWJFnTlzRpJUvXp1/fLLL2rWrJlOnTqllJQUS+f666+/lJWVpejoaJft0dHR+vXXX/M95ptvvtFrr72mbdu2uXWNqVOnKj4+3lJdOD+7EaIutjbKVKal45LNVOn/nx5Z8mA1WRSplDTPjgsK5N4sAABQOlkOTldddZU+++wzNWvWTH379tV9992nL774Qp999pm6dOniixqdzpw5o9tvv10LFixQ5cqV3Tpm/PjxLgv4JiYmKiYmxlcllgl2I0SStVfHAdYHN+EHgQFSUqq0fmvB++Yn3C51aUV4AgAApY/l4DRnzhylpeX8Ofqxxx5TcHCwvv32W9188816/PHHLZ2rcuXKCgwM1NGjR122Hz16VFWrVs2z/759+7R//3716tXLuS07OzvnCwkK0p49e1S3bl2XY0JCQhQSwqs4wB0hNimmipSVbf3YtIyc0JXJgCIAACiFLAenSpUqOd8PCAjQI4884vHFbTabWrVqpXXr1qlPnz6ScoLQunXrNGrUqDz7N2zYUDt27HDZ9vjjj+vMmTN68cUXGUkCvCDE5vmxDm5fAwAApZRH6zhlZWXpgw8+0O7duyVJjRs31g033KCgIOunGzdunAYPHqzWrVurbdu2mjlzppKTkzV06FBJUlxcnKpXr66pU6cqNDRUTZs2dTk+MjJSkvJsB4qLNA/DRGBA4UIMAAAAvMdy0tm5c6d69+6tI0eO6JJLLpEkTZs2TVWqVNHHH39sOcD0799fx48f14QJE3TkyBG1bNlSa9eudTaMOHjwoAICuD8GJU9QYM49P0mpno3EJKXmTJsjPAEAAPifYZqmaeWADh06qEqVKlq0aJEqVqwoSfr77781ZMgQHT9+XN9++61PCvWWxMREVahQQadPn1ZERIS/yykzks1UVfg7p+38ngpLVDewhp8rKhqp6Z7d85OSltOgISJMCgu1dr0e/z97dsVkKdSD0BVqkwzD+nEpaVJiitSzg1Q+zPrxAAAARc1KNrA84rRt2zb9+OOPztAk5bQof/rpp9WmTRvr1QKlmD+7y900wbPjmtaWZo32LDwBAACUVpbnwDVo0CBPFzxJOnbsmOrVq+eVogB4JtSWE3wK45cEKc3hnXoAAABKC7dGnBITE53vT506VWPGjNGkSZPUvn17SdLmzZs1efJkTZs2zTdVAnCLYeSMFnkSfNIcno9SAQAAlHZuBafIyEgZZ83bMU1T/fr1c27LvU2qV69eyspiERfAnwzDv1MEU9I8Oy4okIVzAQBA8eVWcFq/fr2v6wBQwgUG5HQCXL/Vs+PD7VKXVoQnAABQPLkVnDp16uTrOgCUcCG2nPbpWdnWj03LyAldnnQgBAAAKAoeLYB76tQpvfbaa84FcJs0aaJhw4apQoUKXi0OKOtK2uK5hbmmJ2tdAQAAFBXLXfV+/PFH1a1bVzNmzNDJkyd18uRJTZ8+XXXr1tVPP/3kixqBMid38VxHRs7aSFbfDh2X0umMBwAA4DWWR5zGjh2r3r17a8GCBQoKyjk8MzNTw4cP1/3336+vvvrK60UCZY09JOd+n8IsnuvJlDkAAADkz3Jw+vHHH11CkyQFBQXpoYceUuvWrb1aHFCWlcUmCXTkAwAAxZXl4BQREaGDBw+qYcOGLtsPHTqk8uXLe60wAGUHHfkAAEBxZzk49e/fX3fccYeef/55dezYUZK0ceNGPfjggxo4cKDXCwRQ+tGRDwAAFHeWg9Pzzz8vwzAUFxenzMxMSVJwcLBGjhypZ555xusFAih6aR42lgi15SzA6wk68gEAgOLMUnDKysrS5s2bNWnSJE2dOlX79u2TJNWtW1dhYWE+KRBA0btpgmfHNa0tzRrteXgCAAAoriy1Iw8MDNR1112nU6dOKSwsTM2aNVOzZs0ITUApEGrLCT6F8UuC56NVAAAAxZnlqXpNmzbVH3/8odq1C/kKC0CxYhg5o0WeBJ80h+ejVAAAACWB5eD01FNP6YEHHtCTTz6pVq1aqVy5ci6fj4iI8FpxAIqWYdCZDgAAID+Wg1OPHj0kSb1795Zx1o0MpmnKMAxlZdHaCgAAAEDpYjk4rV+/3hd1AAAAAECxZTk4derUyRd1AAAAAECxZTk4SdLff/+t1157Tbt375YkNW7cWEOHDlWlSpW8WhxKpxQzTWfMZMvHBSlIdoMbcNyV5uHaRoEBhVtTyV9S0jw7LiiQ+7oAAEDBLAenr776Sr169VKFChXUunVrSdKsWbM0efJkffzxx7rqqqu8XiRKly2Ze7QzK8HyceGyq4utDeGpAEGBUrhdSkr1bGHYpFQppkrJCU+BATk1r9/q2fHhdqlLK8ITAAC4MMvB6d5771X//v01d+5cBQYGSspZGPeee+7Rvffeqx07dni9SJQuwQpShFGu4B3Pkm46lKRUZSpTEq9wL8QekhMEMj3o05KSlhNAsrI9v76n6ziF2jxbODfElhP0PKk5LSMndHnyWAEAgLLFcnD6/ffftXz5cmdoknIWxh03bpzefPNNrxaH0sluhCjMCLV8XLrp4dyzMsifoyeerufUtHbOOlKehidPeTIqBwAAyp4AqwdcdtllznubzrZ79261aNHCK0UBKFlCbTnBpzB+SfB8tAoAAMDXLI84jRkzRvfdd59+//13tW/fXpK0efNmvfTSS3rmmWf0888/O/dt3ry59yoFUGwZRs5okSfBJ83h+SiVt9BYAgAAFMRycBo4cKAk6aGHHsr3c4ZhsBguUAYZRskLETSWAAAA7rIcnBISrHdDA4DiiMYSAADAXZaDU61atXxRBwAUeUc+qfCNJZjmBwBA2eDRArgA4Av+6MjnKab5AQBQthCcAPhVbke+XwoxCzi3I19RhhCm+QEAULYQnAD4VUnuyMf6UQAAlB0EJwB+VxI78gEAgLKF4IQil2Y6lGqmWz7GNE0fVYRzpXk4GhIYULhRGAAAgOLKreBUsWJFGW7edX3y5MlCFYTSLy4l3qPjGgTEqKeto1SEDQDKmqDAnKYFSameTSVLSs2574fw5B468gEAUHK4FZxmzpzpfP/EiRN66qmn1K1bN3Xo0EGStGnTJn3yySd64oknfFIkSr4whapdYBN9l7XT43PszT6kFKUpQuFerAxns4fkdHrzpGlBSlpOhzlPmiWUNXTkAwCg5HErOA0ePNj5/s0336zJkydr1KhRzm1jxozRnDlz9Pnnn2vs2LHerxIlnmEYWlN+ulY4NijCKCe74f4rvjTTob7Jj/qwOpyNF+O+R0c+AABKHsv3OH3yySeaNm1anu3/+te/9Mgjj3ilKJROhmEo1LAp1LBZCk5AaURHPgAASpYAqwdcdNFF+vDDD/Ns//DDD3XRRRd5pSgAAAAAKE4sjzjFx8dr+PDh+vLLL9WuXTtJ0nfffae1a9dqwYIFXi8QANzhyTpQUs4CvG72vgEAAGWY5eA0ZMgQNWrUSLNmzdKKFSskSY0aNdI333zjDFIAUNQ8XQi3ae2cBXgJTwAA4EI8WsepXbt2euedd7xdCwBYEmrLCT6/JHh+jl8SckaraIoBAAAuxKPgtG/fPi1cuFB//PGHZs6cqaioKK1Zs0Y1a9ZUkyZNvF0jAOTLMHJGizyZppfm8HyUCgAAlD2Wm0Ns2LBBzZo103fffaf3339fSUlJkqTt27dr4sSJXi8QQMmSlpGzppPVt3QP71EyjJzRIqtvoSzSCwAALLA84vTII4/oqaee0rhx41S+fHnn9s6dO2vOnDleLQ5AyREUmLMwa1KqZ+2yk1Jz1jYqTJtuT5XExhIpaZ4dFxTItEQAADxhOTjt2LFD7777bp7tUVFR+uuvv7xSFICSxx4idWnl2cKsKWnS+q2eLQjrDSWpsURgQE7IXL/Vs+PD7Tn/T4QnAACssRycIiMjdfjwYdWuXdtl+9atW1W9enWvFQag5ClJL8ZLamOJEFvOyJwnITMtIyd0eRJuAQAo6ywHpwEDBujhhx/WsmXLZBiGsrOztXHjRj3wwAOKi4vzRY0oZdJNa/Oi0izuD7jDW40l/DHNrzDTGT2ZRgkAADwITlOmTNG9996rmJgYZWVlqXHjxsrKytKtt96qxx9/3Bc1opQIUpDCZVeSUpVuuv/q7ezglGbhOKAguY0lCqMkTfMDAACesxycbDabFixYoAkTJmjHjh1KSkrSpZdeqvr16/uiPpQidiNEXWxtlKlMS8clm6lSSs77WWKOUWmW5mEuDgwo2qYSJXWaHwAA8Jzl4DR58mQ98MADiomJUUxMjHN7amqqnnvuOU2YwMIoOD+7ESLJ2ivFAOtd81HClLSOfKwfBQBA2WOYpmlaOSAwMFCHDx9WVFSUy/YTJ04oKipKWVnFe0QgMTFRFSpU0OnTpxUREeHvcuCGZDNVFf7uKknaU2GJ6gbW8HNF8IXU9MJ15IsIk8JCvV+Xt6WmSz0eyXl/9TNFO+KUkiYlpkjXXOrZY0UrcwBAaWMlG1gecTJNU0Y+k/K3b9+uSpUqWT0dAEjiBXlRoJU5AACeczs4VaxYUYZhyDAMNWjQwCU8ZWVlKSkpSXfffbdPigSA0qioO/LRyhwAAM+5HZxmzpwp0zQ1bNgwxcfHq0KFCs7P2Ww2xcbGqkOHDj4pEgBKI3905CtsK/OUNM+OZZofAKCkczs4DR48WJJUu3ZtdezYUcHBwT4rCgBKq5LakY9pfgCAss7yPU6dOnVyvp+WliaHw3WuCQ0XAOD8SmpHPqb5AQDKOsvBKSUlRQ899JCWLl2qEydO5Pl8ce+qBwD+5o2Fd/2hsNP8AAAoySwvkPPggw/qiy++0Ny5cxUSEqJXX31V8fHxqlatmt58801f1AgAAAAAfmV5xOnjjz/Wm2++qauvvlpDhw7VlVdeqXr16qlWrVp65513NGjQIF/UCQAAAAB+Yzk4nTx5UnXq1JGUcz/TyZMnJUlXXHGFRo4c6d3qAMBNaR5OBQsMKNwUNAAAUDZYDk516tRRQkKCatasqYYNG2rp0qVq27atPv74Y0VGRvqgRAA4v6DAnI5tSame3UeTlJrT9IDw5Hu0MgcAlGSWg9PQoUO1fft2derUSY888oh69eqlOXPmKCMjQ9OnT/dFjQBwXvaQnDbXnnRsS0nLaa/tSac4uI9W5gCA0sBycBo7dqzz/a5du+rXX3/Vli1bVK9ePTVv3tyrxQGAO8riC2pP2plLOetIebJwbmHQyhwAUBpYDk7nqlWrlmrVquWNWgAAbvJ0PaemtXPWkfJHePIUrcwBAMWBR8Hphx9+0Pr163Xs2DFlZ7v+CZHpegBKmpLSWCLUlhN8fknw/By/JOSMVpXFUToAAArDcnCaMmWKHn/8cV1yySWKjo6WcdafLY2i/hMmABRCSWssYRg5o0WeTNNLc3g+SgUAADwITi+++KJef/11DRkyxAflAEDR8UZjieR0z+7d8XS0yjAYLQIAwB8sB6eAgABdfvnlvqgFAIqcpyGkpI1WAQCAwvGoq95LL72kmTNn+qAcACgZaIMOAEDZYjk4PfDAA+rZs6fq1q2rxo0bKzg42OXzK1as8FpxAFCcldQpcyWplXkuFs8FAPib5eA0ZswYrV+/Xtdcc40uuugiGkIAQAlTklqZs3guAKC4sBycFi1apPfff189e/b0RT0AAB8oqa3MWTwXAFBcWA5OlSpVUt26dX1RC1CgFDNNZ8xky8cFKUh2gz85o3gpyvWjvNXK3B/T/Fg8FwBQHFgOTpMmTdLEiRO1cOFChYWF+aIm4Ly2ZO7RzizrfzIPl11dbG0ITygW/NWRzxutzEvSNL9c3B8FAPAGy8Fp1qxZ2rdvn6KjoxUbG5unOcRPP/3kteKAcwUrSBFGOUvHpJsOJSlVmcqUxKsg+F9J68hXUqf5cX8UAMCbLAenPn36+KAMwD12I0RhRqjl49JN5uugeClJL8a9Nc2vqHF/FADAmywHp4kTJ/qiDgBAMeaNaX7+UNj7o5jmBwDIZTk4AQBQ2jHNDwBwLreCU6VKlbR3715VrlxZFStWvODaTSdPnvRacQAA+IM3pvklJns21Y/RKgAontwKTjNmzFD58uWd77PoLQDAKn+0Mi8MT6f5BQZIJxMZrQKA0sat4DR48GDn+0OGDPFVLQCAUqwktjL3BE0pAKB0snyPU2BgoA4fPqyoqCiX7SdOnFBUVJSysvhpDwDIUVJbmRcWi/YCQOljOTiZppnv9vT0dNlshfhNAQAodUpqK3MAAM7ldnCaNWuWJMkwDL366qsKDw93fi4rK0tfffWVGjZs6P0KgbOkmQ6lmumWjzlf4AdKqjQPRyUCAwo3GuKJktrK3J9ogw4AxY/bwWnGjBmSckac5s2bp8DAQOfnbDabYmNjNW/ePO9XCJwlLiXeo+MaBMSop62jVELukQDOJygwp3lAUqpnU7qSUnPuvynq8AT30AYdAIovt4NTQkLOBPVrrrlGK1asUMWKFX1WFHC2MIWqXWATfZe10+Nz7M0+pBSlKULhBe8MFGP2kJwXxp40D0hJy3lB7knTAhQNGksAQPFl+R6n9evXu3yclZWlHTt2qFatWh6HqZdeeknPPfecjhw5ohYtWmj27Nlq27ZtvvsuWLBAb775pn755RdJUqtWrTRlypTz7o+SzzAMrSk/XSscGxRhlJPdcP9PqWmmQ32TH/VhdUDRYzShdKOxBAAUTwFWD7j//vv12muvScoJTVdddZUuu+wyxcTE6Msvv7RcwJIlSzRu3DhNnDhRP/30k1q0aKFu3brp2LFj+e7/5ZdfauDAgVq/fr02bdqkmJgYXXfddfrzzz8tXxslh2EYCjVsCjVsshshbr+FGsxHAlC2pKRJZ1Ksv6Vau30UAMocyyNOy5Yt02233SZJ+vjjj7V//379+uuveuutt/TYY49p48aNls43ffp0jRgxQkOHDpUkzZs3T6tWrdLrr7+uRx55JM/+77zzjsvHr776qt5//32tW7dOcXFxVr8cAEAJUNIWz/UH7o8CAN+yHJxOnDihqlWrSpJWr16tvn37qkGDBho2bJhefPFFS+dyOBzasmWLxo8f79wWEBCgrl27atOmTW6dIyUlRRkZGapUqVK+n09PT1d6+j9/RktMTLRUIwCUNiWpI1+usrJ4bmF44/6oxGTP7pGimx+AssBycIqOjtauXbt08cUXa+3atZo7d66knABzdqc9d/z111/KyspSdHR0nmv8+uuvbp3j4YcfVrVq1dS1a9d8Pz916lTFx3vWiQ0ASpOS1pGvrC6eWxie/t8EBkgnExmtAoALsRychg4dqn79+uniiy+WYRjOwPLdd98V+TpOzzzzjBYvXqwvv/xSoaGh+e4zfvx4jRs3zvlxYmKiYmJiiqpEACg2SlpHPhbPLTp08wOAglkOTpMmTVLTpk116NAh9e3bVyEhOX9eCgwMzPeepAupXLmyAgMDdfToUZftR48edU4HPJ/nn39ezzzzjD7//HM1b978vPuFhIQ4awSAsq6wIwJFPc3PG4vncn+Ue+jmBwAXZjk4SdItt9ySZ9vgwYMtn8dms6lVq1Zat26d+vTpI0nKzs7WunXrNGrUqPMe9+yzz+rpp5/WJ598otatW1u+LsqmVNOhM2ay5eOCFGSpBTpQGpW0aX5n4/4oAIA3uB2cevTooffee08VKlSQlDNN7u6771ZkZKSknKYRV155pXbt2mWpgHHjxmnw4MFq3bq12rZtq5kzZyo5OdnZZS8uLk7Vq1fX1KlTJUnTpk3ThAkT9O677yo2NlZHjhyRJIWHhys8nMVNcX4bMrZ61J48XHZ1sbUhPKFMK2nT/Lg/quilpHl2HI0lAJQUbgenTz75xKU73ZQpU9SvXz9ncMrMzNSePXssF9C/f38dP35cEyZM0JEjR9SyZUutXbvW2TDi4MGDCgj4Z7mpuXPnyuFw5Bn1mjhxoiZNmmT5+ig7IoxyloNTuulQklKVqUxJ/GZH2VaSXtxyf1TRoQ06gLLC7eBkmuYFPy6MUaNGnXdq3rmL6u7fv99r10XJk25aexWUdtb+uYviWr8mk/eBkoj7o4oGjSUAlBUe3eMEFLUgBSlcdiUp1VKQOTs4OUwH0+0AWOLpyFO96jkjXp4oiaGLxhIAygK3g5NhGDLO+Ul+7seAr9iNEHWxtfn/KXPuSzZTpZSc97PkvVFSAKWXN+6P+v1PqYe1RrNONKUAgOLJ0lS9IUOGOFt7p6Wl6e6771a5cuUkyeX+J8AXckaLrI0YBSig4J0AFImibmXuqcLcHyVJY2bnBCdPlcWmFDSWAFASuB2czm03ftttt+XZJy4urvAVAQBKlZLYyrww90fN/zdNKdxFYwkAJYnbwWnhwoW+rAMAUEqVtFbmheWNphRlBY0lAJQkNIcAAPgcQcKastTNr7CNJZjmB6CoEJxQZqSZDqWa1u7FSzMdXm29DwDu8HTKXllqLME0PwBFjeCEMiMuJd6j4xoExKinraNUBl6IAPAfb3TzK0uNJZjmB6CoEZxQqoUpVO0Cm+i7rJ0en2Nv9iGlKE0RCvdiZQDgqjDd/MpiYwmJ9aMAFC2CE0o1wzC0pvx0rXBsUIRRztICuGmmQ32TH5UkpZoOnTGTLV8/SEEsugvAbd5oLFGW7o8CgKJEcEKpZxiGQg2bQg2bxyFmQ8ZWhRrW/7QZLru62NoQngAUGe6PAgDfIDgBbogwylkOTummQ0lKVaYyZXXhXgCuSsriuf7C/VEA4HsEJ8ANdiPEo1GjdJNJ9EBhlMTFc/3BW/dHMc0PAM6P4AQAKLbK2uK5heGN+6OY5gcA50dwAgAUa4VulsA0vwsqy9P8WDwXgBUEJwBAqcQ0P/eUxTboLJ4LwBMEJwBAqcQ0P/d5Y5pfScLiuQA8QXACAJRaZSkM+FtJayzB4rkArCI4AQBwHtwf5T4aSwAo7QhOKDPSTWt/Dk07a/80i8fmHmOapuXjAPgf90e5pyw3lgBQ9hCcUOoFKUjhsitJqZbWVTo7LPVNftSjazcIiFFPW0eJv6QCJYo37o9KTvfsHpqSNFpVFhtLACi7CE4o9exGiLrY2ihTmZaOM01Tr6R/oB+ydnt87b3Zh5SiNEUo3ONzAPAPT0dAytpoVVlrLJHL01bmhUEbdMC/CE4oE+xGiCSLv20M6ZPyM7XCsUERRrn/P4d70kyHx6NUAEo2uvlZV5IaSxS2lXlh0AYd8C+CE3ABhmEo1LAp1LBZCk4AyjZe2FpTkhpLFKaVeWHQBh3wP4ITAADFTFno5leSG0v46zGmDTrgXwQnAACKibJ0f5S3GkuUpGl+AEo2ghMAAMVEWbs/yhuNJUrSND8AJRvBCQCAYoT7owpWkqf5ASi5CE4AAJQiZeH+KNaPAkq+lDQpLNTfVVhDcAJ8LNV06IyZbPm4IAXRyQ+A28rS/VFS2V0/CigN/j4jbf9dalFPqlje39W4j+AE+NiGjK0KNay/EgmXXV1sbQhPANxS1u6PAlBy5Y4We9rcxV8IToAb0k1r39lpZ+0fYZSzHJzSTYeSlKpMZcrywr0AyqzCjsCUhWl+JV1Kmr8rAAqvpD6PCU7ABQQpSOGyK0mpSjfdf0VxdnAKlKEww/okXivXA4DCKGvT/EqiwICcx3n9Vn9XAnhHuD3neV2SEJyAC7AbIepia/P/Iz/uSzZTpZSc97Nk+qAyAPAepvkVfyG2nHDK44zSIDU95w8BJe0+RYITUICce4ysfWcHqIT9CQVAmVfSXsB4Q0lbPJcRPZQmGSn+rsA6ghMAACi0knh/FIvnArCC4AQAADxW0u6PYvFcAJ4iOAEAAI+VtPujWDwXgKcITkAxlmKme3Qci+cCKEolbeSFxXMBeILgBPhYmulQqsUAlGFm6Ex2itZnbPHomiyeCwAA4F0EJ8DH4lLiPTqucUCsptrvkWHx7mMWzwUA3/O0I19h+KubH4AcBCfAB8IUqnaBTfRd1k6Pz7Ere78CjACPRo1YPBdASVKWOvIVBt38AP8iOAE+YBiG1pSfrhWODYowylkKP2mmQ32TH/VhdQBQPJTFjnyFQTc/wL8IToCPGIahUMOmUMPGvUYAkI+y1JGvMOjmBxQPBCcAAOA3hR09KeppfnTkA8oughMAAChxSto0PwAlH8EJAACUOCVtmh+Ako/gBBRjaab1ifRppkOmafqgGgAoXpgyB6AoEZyAYszT7noNAmLU09ZRomUtAACAVxCcAB9LtzhqZJqmGgXEanf2fo+vuTf7kFKUpgiFe3wOAAAA/IPgBPhIkIIULruSlGp5QdpHQwfrpJmoGgFVZDPcv3OZNaAAAAB8g+AE+IjdCFEXWxtlKtPysSlmutZnbFGwEezxGlCppkNnzGTLxwUpiHWnAKCYKuo1pABfSHNIJfF2bIIT4EM5AcQ/IWRDxlaFWhityhUuu7rY2hCeAJR6Rb0GlDewEC5Ki0tipJ4d/F2FNQQnoJSKMMpZDk7ppkNJSv3/UTKCE4DSqaStARVqk5rWln5JKJrrAUVhzyEpJV2KKOfvStxHcAJKKbsR4tGokdX7sQCgpClpa0AZhjRrNNP0UDqkOUruyCnBCQAAlDklbQ0owyh5NQOlTYC/CwAAAACA4o4RJ6AYs7oGVJrF/QEAAOAeghNQDHm6BtTZwclhOuiMBwA+4mlHvsLwZzc/AAQnoFjydA2oZDNVSsl9P10209pv2DTTIbMkLqwAAEWksB35CqOou/kBcEVwAoopT9aACjjrtsW4lHiPrtsgIEbXBLfy6A5IFs8FUNoVpiNfYfijmx8AVwQnoBQJU6jaBTbRd1k7PT7H3uxDWpuxmcVzAeA86G4HlE0EJ6AUMQxDa8pP1wrHBkUY5SwFmDTTob7Jj0pi8VwAAIBzEZyAUsYwDIUaNoUaNo9Hflg8FwAAwBXBCYBXpZjpHh3H/VEAAKA4IzgB8IpABSjJTNX6jC0eHc/9UQAAoDgjOAHwihDDppiAKGXJessn7o8CAADFHcEJQB5nL6RrRahsMgzDo2O5PwoACuaPhXcBb0rz7CVGsUBwAkqpdIvh5+ywlNtdz6omgXU0036/x+EJAJA/fy68C3jT2cEp1bPbov2G4ASUMkEKUrjsSlKqpVEc0zTVICBGe7MPeXztnVl/KE0O2ZluBwBe5a+FdwFvS0775/3sEragM8EJKGXsRoi62Nr8//1C1lwT3EprMzYXag0oT6b5pZkOmaZJRz4AuAAW3kVpEFCCJ6UQnIBSKCdEePAbNkCFXgPK02l+dQOqS5JH0/zoyAcAAHyN4ASg0EJlU5PAOtqZ9YfH59iX/adC/j+0WUFHPgAAUBQITgAKzTAMzbTfrzR5Nk0vd5TKboR4NGpERz4AAEqGsFDp93ekvYdK3vRTghMArzAMg6YQAADgggwjJzzZQ3LeL0kITgDysNrKPFegAhRicaqdt9BYAgAA+BLBCYCTp63McyWZqYoJiPI4PHnSkS/DzNCZ7BStz9ji0TVpLAEAANxBcALgVJhW5ilmutZnbFGKmaYsub8wgzcW3m0cEKup9nssd+SjsQQAAHAXwQmAC09bmftz4d1d2fsVYAR43FiCaX4AAKAgBCcAXuHvhXc9EagAJZmpTPMDAAAFIjgB8Bp/Lrzryf1RklTDqKJsw7R8HNP8AAAoWwhOAEoFT0eemgTW0Uz7/Zbvj5KY5gcAQFlCcAJQbFhtg26aphoFxGp39n6Pr7kz6w+lyWF5DSpvTPNrH9xUIUawR8d7isAGAIBnCE4A/K4wbdAfDR2sk2aiagRUkc1CG/Sz748q6ml+GWamjpgn9XnGDx5dtzD8Fdj8haAIAPAWghMAv/NGG/RgI9jjF8hFPs3PkEJNm6W27d7gz8DmLzTwAAB4C8EJQLHgcWOJ/+evaX6nzCSFyvqCv6GyeXRfVaH4KbD5Cw08AADeRHACUKL5e5qfp6NVdQNqaGbY/R4dWxh+CWx+ZPU5AQDA+RSL4PTSSy/pueee05EjR9SiRQvNnj1bbdu2Pe/+y5Yt0xNPPKH9+/erfv36mjZtmnr06FGEFQMoLrwxzS9bpky5f6+STUGFHq3al/1f9Up6wOPjPeWvwOYPaaZDpmn9HjQAAPLj9+C0ZMkSjRs3TvPmzVO7du00c+ZMdevWTXv27FFUVFSe/b/99lsNHDhQU6dO1fXXX693331Xffr00U8//aSmTZv64SsA4G+eTvPzxmhVZSNSwYb7P0pNmXok9WUlZP/Parle4a/A5i8NAmLU09ZRKjuDbABQ7IXaXP8tKQzTz3+Oa9eundq0aaM5c+ZIkrKzsxUTE6PRo0frkUceybN///79lZycrP/85z/Obe3bt1fLli01b968Aq+XmJioChUq6PTp04qIiPDeFwKgREo10z0arUo3M7Q54xclKdXysaZpehS6CsPfgc2f/hv5oaoGVPZ3GQCAs6SkSWGh/q7CWjbw64iTw+HQli1bNH78eOe2gIAAde3aVZs2bcr3mE2bNmncuHEu27p166aVK1fmu396errS0/9ZoDIxMbHwhQMoNTwdrSpvyOMpgmeHLoeK7h6cyaEjijyw+Uua6VBcSry/ywAAnEdxCE1W+fU3519//aWsrCxFR0e7bI+Ojtavv/6a7zFHjhzJd/8jR47ku//UqVMVH88vTwDe54/QVRj+Cmz+cPbXF+T/WekAgFKg1P82GT9+vMsIVWJiomJiYvxYEQAUvv26J/wV2PzBNE39y9ZOQQrSRUYFf5cDACgF/BqcKleurMDAQB09etRl+9GjR1W1atV8j6lataql/UNCQhQSwvodACD5J7D5hSFFKNzfVQAASpEAf17cZrOpVatWWrdunXNbdna21q1bpw4dOuR7TIcOHVz2l6TPPvvsvPsDAAAAQGH5fareuHHjNHjwYLVu3Vpt27bVzJkzlZycrKFDh0qS4uLiVL16dU2dOlWSdN9996lTp0564YUX1LNnTy1evFg//vij5s+f788vAwAAAEAp5vfg1L9/fx0/flwTJkzQkSNH1LJlS61du9bZAOLgwYMKCPhnYKxjx45699139fjjj+vRRx9V/fr1tXLlStZwAgAAAOAzfl/HqaixjhMAAAAAyVo28Os9TgAAAABQEhCcAAAAAKAABCcAAAAAKADBCQAAAAAKQHACAAAAgAIQnAAAAACgAAQnAAAAACgAwQkAAAAACkBwAgAAAIACEJwAAAAAoAAEJwAAAAAoAMEJAAAAAApAcAIAAACAAhCcAAAAAKAABCcAAAAAKADBCQAAAAAKQHACAAAAgAIE+buAomaapiQpMTHRz5UAAAAA8KfcTJCbES6kzAWnM2fOSJJiYmL8XAkAAACA4uDMmTOqUKHCBfcxTHfiVSmSnZ2t//3vfypfvrwMw/B3OUpMTFRMTIwOHTqkiIgIf5eDUoDnFHyB5xV8gecVfIHnFawwTVNnzpxRtWrVFBBw4buYytyIU0BAgGrUqOHvMvKIiIjgmxtexXMKvsDzCr7A8wq+wPMK7ipopCkXzSEAAAAAoAAEJwAAAAAoAMHJz0JCQjRx4kSFhIT4uxSUEjyn4As8r+ALPK/gCzyv4CtlrjkEAAAAAFjFiBMAAAAAFIDgBAAAAAAFIDgBAAAAQAEITsD/tXfvMVXWfxzA3wcON7kcuehB5rgliiWXQKGjNgkJwkaStzJGeME2O5JAm9UUyCy1mCE2Lw02sE06TOx0HRgjwRRCwQhNYmneWnJbQXJUrt/fH85nngCPPwUf0fdrYzvfz/N9nudzts8O+5zn+zyHiIiIiMgENk4y2rlzJzw9PWFtbY3Q0FAcO3ZM7pRoFDl8+DBiYmLg5uYGhUKBL7/80mi7EALp6emYMGECbGxsEBERgd9//12eZGnU2LJlC2bMmAF7e3uMHz8esbGxaGxsNJpz/fp1aLVaODs7w87ODgsXLkRzc7NMGdNosHv3bvj7+0s/SKrRaFBcXCxtZ03Rvdq6dSsUCgWSk5OlGOuKhhsbJ5kUFhYiNTUVGRkZOHHiBAICAhAVFYWWlha5U6NRwmAwICAgADt37hx0+0cffYQdO3Zgz549qK6uhq2tLaKionD9+vX7nCmNJhUVFdBqtfjpp59QWlqKnp4eREZGwmAwSHNSUlLwzTffYP/+/aioqMBff/2FBQsWyJg1PegmTpyIrVu3ora2FjU1NQgPD8f8+fPx66+/AmBN0b05fvw4Pv30U/j7+xvFWVc07ATJIiQkRGi1Wmnc19cn3NzcxJYtW2TMikYrAEKv10vj/v5+4erqKjIzM6VYe3u7sLKyEp9//rkMGdJo1dLSIgCIiooKIcSNOrKwsBD79++X5jQ0NAgAoqqqSq40aRRydHQUubm5rCm6J1euXBE+Pj6itLRUzJkzR6xdu1YIwc8qGhm84iSD7u5u1NbWIiIiQoqZmZkhIiICVVVVMmZGD4tz586hqanJqMZUKhVCQ0NZY/R/6ejoAAA4OTkBAGpra9HT02NUW76+vnB3d2dt0R3p6+uDTqeDwWCARqNhTdE90Wq1eP75543qB+BnFY0MpdwJPIra2trQ19cHtVptFFer1fjtt99kyooeJk1NTQAwaI3d3EZkSn9/P5KTkzFr1ixMmzYNwI3asrS0xNixY43msrbIlJMnT0Kj0eD69euws7ODXq/H448/jrq6OtYU3RWdTocTJ07g+PHjA7bxs4pGAhsnIiIalFarxalTp3DkyBG5U6GHwJQpU1BXV4eOjg4UFRUhISEBFRUVcqdFo9SlS5ewdu1alJaWwtraWu506BHBpXoycHFxgbm5+YAnuzQ3N8PV1VWmrOhhcrOOWGN0t9asWYNvv/0Whw4dwsSJE6W4q6sruru70d7ebjSftUWmWFpaYtKkSQgODsaWLVsQEBCA7Oxs1hTdldraWrS0tCAoKAhKpRJKpRIVFRXYsWMHlEol1Go164qGHRsnGVhaWiI4OBhlZWVSrL+/H2VlZdBoNDJmRg8LLy8vuLq6GtXYv//+i+rqatYY3ZYQAmvWrIFer8cPP/wALy8vo+3BwcGwsLAwqq3GxkZcvHiRtUX/l/7+fnR1dbGm6K7MnTsXJ0+eRF1dnfQ3ffp0xMXFSa9ZVzTcuFRPJqmpqUhISMD06dMREhKC7du3w2AwYPny5XKnRqNEZ2cnzpw5I43PnTuHuro6ODk5wd3dHcnJyXj//ffh4+MDLy8vpKWlwc3NDbGxsfIlTQ88rVaLgoICfPXVV7C3t5fuBVCpVLCxsYFKpcLKlSuRmpoKJycnODg4ICkpCRqNBk899ZTM2dOD6p133kF0dDTc3d1x5coVFBQUoLy8HAcPHmRN0V2xt7eX7r28ydbWFs7OzlKcdUXDjY2TTF566SW0trYiPT0dTU1NCAwMRElJyYCb+YmGUlNTg2eeeUYap6amAgASEhKQn5+PdevWwWAw4LXXXkN7eztmz56NkpISrgWn29q9ezcAICwszCiel5eHZcuWAQCysrJgZmaGhQsXoqurC1FRUdi1a9d9zpRGk5aWFrz66qu4fPkyVCoV/P39cfDgQTz77LMAWFM0MlhXNNwUQgghdxJEREREREQPMt7jREREREREZAIbJyIiIiIiIhPYOBEREREREZnAxomIiIiIiMgENk5EREREREQmsHEiIiIiIiIygY0TERERERGRCWyciIiIiIiITGDjREREw2rZsmWIjY2V7fzx8fHYvHmzNPb09MT27dtly2ckvf3220hKSpI7DSKiR4JCCCHkToKIiEYHhUJx2+0ZGRlISUmBEAJjx469P0nd4pdffkF4eDguXLgAOzs7AEBraytsbW0xZsyY+57PSGtra4O3tzfq6urg7e0tdzpERA81Nk5ERHTHmpqapNeFhYVIT09HY2OjFLOzs5MaFjkkJiZCqVRiz549suUwXLq7u2FpaWly3uLFi+Hp6YnMzMz7kBUR0aOLS/WIiOiOubq6Sn8qlQoKhcIoZmdnN2CpXlhYGJKSkpCcnAxHR0eo1Wrk5OTAYDBg+fLlsLe3x6RJk1BcXGx0rlOnTiE6Ohp2dnZQq9WIj49HW1vbkLn19fWhqKgIMTExRvH/LtVTKBTIzc3Fiy++iDFjxsDHxwdff/31kMd97733MG3atAHxwMBApKWlSePc3FxMnToV1tbW8PX1xa5du4zmv/XWW5g8eTLGjBkDb29vpKWloaenR9r+7rvvIjAwELm5ufDy8oK1tTUAoKioCH5+frCxsYGzszMiIiJgMBik/WJiYqDT6YbMn4iIhgcbJyIiGnF79+6Fi4sLjh07hqSkJKxevRqLFy/GzJkzceLECURGRiI+Ph5Xr14FALS3tyM8PBxPPvkkampqUFJSgubmZixZsmTIc9TX16OjowPTp083mc/GjRuxZMkS1NfXY968eYiLi8Pff/896NwVK1agoaEBx48fl2I///wz6uvrsXz5cgDAvn37kJ6ejg8++AANDQ3YvHkz0tLSsHfvXmkfe3t75Ofn4/Tp08jOzkZOTg6ysrKMznXmzBkcOHAAX3zxBerq6nD58mUsXbpUyqG8vBwLFizArYtFQkJC8Oeff+L8+fMm3zcREd0DQUREdBfy8vKESqUaEE9ISBDz58+XxnPmzBGzZ8+Wxr29vcLW1lbEx8dLscuXLwsAoqqqSgghxKZNm0RkZKTRcS9duiQAiMbGxkHz0ev1wtzcXPT39xvFPTw8RFZWljQGIDZs2CCNOzs7BQBRXFw85HuNjo4Wq1evlsZJSUkiLCxMGj/22GOioKDAaJ9NmzYJjUYz5DEzMzNFcHCwNM7IyBAWFhaipaVFitXW1goA4vz580Mep6OjQwAQ5eXlQ84hIqJ7p5SzaSMiokeDv7+/9Nrc3BzOzs7w8/OTYmq1GgDQ0tIC4MZDHg4dOjTo/VJnz57F5MmTB8SvXbsGKysrkw+w+G8+tra2cHBwkM49mFWrVmHFihX4+OOPYWZmhoKCAulqkcFgwNmzZ7Fy5UqsWrVK2qe3txcqlUoaFxYWYseOHTh79iw6OzvR29sLBwcHo/N4eHhg3Lhx0jggIABz586Fn58foqKiEBkZiUWLFsHR0VGaY2NjAwDS1ToiIhoZbJyIiGjEWVhYGI0VCoVR7Gaz09/fDwDo7OxETEwMPvzwwwHHmjBhwqDncHFxwdWrV+/ooQqD5XPz3IOJiYmBlZUV9Ho9LC0t0dPTg0WLFkm5AkBOTg5CQ0ON9jM3NwcAVFVVIS4uDhs3bkRUVBRUKhV0Oh22bdtmNN/W1nbA/qWlpaisrMT333+PTz75BOvXr0d1dTW8vLwAQFpieGvDRUREw4+NExERPXCCgoJw4MABeHp6Qqm8s39VgYGBAIDTp09Lr4eLUqlEQkIC8vLyYGlpiZdfflm60qNWq+Hm5oY//vgDcXFxg+5fWVkJDw8PrF+/XopduHDhjs6tUCgwa9YszJo1C+np6fDw8IBer0dqaiqAGw/RsLCwwBNPPHGP75KIiG6HjRMRET1wtFotcnJysHTpUqxbtw5OTk44c+YMdDodcnNzpSs5txo3bhyCgoJw5MiRYW+cgBuPOp86dSoA4OjRo0bbNm7ciDfeeAMqlQrPPfccurq6UFNTg3/++Qepqanw8fHBxYsXodPpMGPGDHz33XfQ6/Umz1ldXY2ysjJERkZi/PjxqK6uRmtrq5QHAPz44494+umnpUaOiIhGBp+qR0REDxw3NzccPXoUfX19iIyMhJ+fH5KTkzF27FiYmQ39rysxMRH79u0bkZx8fHwwc+ZM+Pr6DliSl5iYiNzcXOTl5cHPzw9z5sxBfn6+tJzuhRdeQEpKCtasWYPAwEBUVlYaPcp8KA4ODjh8+DDmzZuHyZMnY8OGDdi2bRuio6OlOTqdzujeKiIiGhn8AVwiInpoXLt2DVOmTEFhYSE0Gs2wHlsIAR8fH7z++uvSMjm5FRcX480330R9ff0dL2kkIqK7w09ZIiJ6aNjY2OCzzz677Q/l3o3W1lbodDo0NTVJv930IDAYDMjLy2PTRER0H/CKExERkQkKhQIuLi7Izs7GK6+8Inc6REQkA35FRUREZAK/YyQiIj4cgoiIiIiIyAQ2TkRERERERCawcSIiIiIiIjKBjRMREREREZEJbJyIiIiIiIhMYONERERERERkAhsnIiIiIiIiE9g4ERERERERmfA/nACrKCxC2bEAAAAASUVORK5CYII=\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "PBKPtOFKuTwV",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "5a32f8b1-88d4-461e-a431-1857860f8177"
},
"source": [
"for r in df['democracy'].unique():\n",
" ix = df['democracy'] == r\n",
" kmf.fit(df['duration'].loc[ix], df['observed'].loc[ix], label=r)\n",
" print(f'The median number of years for a {r} is {kmf.median_survival_time_}')"
],
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"The median number of years for a Non-democracy is 6.0\n",
"The median number of years for a Democracy is 3.0\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZhK-X5-fuTwc"
},
"source": [
"How can we tell if these survival functions are different?"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uL59D8GAuTwc"
},
"source": [
"# Log-rank test (not recommended but still very common statistical test)"
]
},
{
"cell_type": "code",
"metadata": {
"scrolled": false,
"id": "LDq8D5ovuTwe",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "f23cc053-1dfc-4a67-b8a2-8dffac7c0bcd"
},
"source": [
"df['democracy'].unique()"
],
"execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['Non-democracy', 'Democracy'], dtype=object)"
]
},
"metadata": {},
"execution_count": 12
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "kaRMHNtSuTwi"
},
"source": [
"from lifelines.statistics import logrank_test"
],
"execution_count": 13,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "1qrrTDDWuTwn"
},
"source": [
"ix = df['democracy'] == 'Democracy'\n",
"T_democracy, E_democracy = df.loc[ix, 'duration'], df.loc[ix, 'observed']\n",
"T_non_democracy, E_non_democracy = df.loc[~ix, 'duration'], df.loc[~ix, 'observed']"
],
"execution_count": 14,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"scrolled": true,
"id": "Qw20wiiRuTws",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"outputId": "fadab160-bbaa-4f29-b98d-bccb2705dd76"
},
"source": [
"results = logrank_test(T_democracy, T_non_democracy, event_observed_A=E_democracy, event_observed_B=E_non_democracy)\n",
"results.print_summary()"
],
"execution_count": 15,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<lifelines.StatisticalResult: logrank_test>\n",
" t_0 = -1\n",
" null_distribution = chi squared\n",
"degrees_of_freedom = 1\n",
" test_name = logrank_test\n",
"\n",
"---\n",
" test_statistic p -log2(p)\n",
" 260.47 <0.005 192.23"
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <tbody>\n",
" <tr>\n",
" <th>t_0</th>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>null_distribution</th>\n",
" <td>chi squared</td>\n",
" </tr>\n",
" <tr>\n",
" <th>degrees_of_freedom</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>test_name</th>\n",
" <td>logrank_test</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div><table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>test_statistic</th>\n",
" <th>p</th>\n",
" <th>-log2(p)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>260.47</td>\n",
" <td>&lt;0.005</td>\n",
" <td>192.23</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/latex": "\\begin{tabular}{lrrr}\n & test_statistic & p & -log2(p) \\\\\n0 & 260.47 & 0.00 & 192.23 \\\\\n\\end{tabular}\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "1Oms68oOuTww",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "efe21c11-25fc-4c7e-cfbc-41aed7c8ea89"
},
"source": [
"print(results.p_value)\n",
"print(results.test_statistic)"
],
"execution_count": 16,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1.3557143218482446e-58\n",
"260.46953907795944\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qTX47C6-uTw1"
},
"source": [
"# Univariate Cox regression"
]
},
{
"cell_type": "code",
"metadata": {
"id": "meyGKaYcuTw4",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 474
},
"outputId": "a2ebf539-b273-496b-cdc3-e468f6c09082"
},
"source": [
"from lifelines import CoxPHFitter\n",
"\n",
"cph = CoxPHFitter()\n",
"df_Uni_Cox = df.copy()\n",
"df_Uni_Cox['indicator'] = df_Uni_Cox['democracy'] == 'Democracy'\n",
"cph.fit(df_Uni_Cox[['indicator', 'duration', 'observed']], 'duration', 'observed')\n",
"cph.print_summary()"
],
"execution_count": 17,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<lifelines.CoxPHFitter: fitted with 1808 total observations, 340 right-censored observations>\n",
" duration col = 'duration'\n",
" event col = 'observed'\n",
" baseline estimation = breslow\n",
" number of observations = 1808\n",
"number of events observed = 1468\n",
" partial log-likelihood = -9614.27\n",
" time fit was run = 2024-06-05 10:26:05 UTC\n",
"\n",
"---\n",
" coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n",
"covariate \n",
"indicator 0.96 2.62 0.06 0.84 1.09 2.32 2.96\n",
"\n",
" cmp to z p -log2(p)\n",
"covariate \n",
"indicator 0.00 15.40 <0.005 175.43\n",
"---\n",
"Concordance = 0.59\n",
"Partial AIC = 19230.53\n",
"log-likelihood ratio test = 264.03 on 1 df\n",
"-log2(p) of ll-ratio test = 194.81"
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <tbody>\n",
" <tr>\n",
" <th>model</th>\n",
" <td>lifelines.CoxPHFitter</td>\n",
" </tr>\n",
" <tr>\n",
" <th>duration col</th>\n",
" <td>'duration'</td>\n",
" </tr>\n",
" <tr>\n",
" <th>event col</th>\n",
" <td>'observed'</td>\n",
" </tr>\n",
" <tr>\n",
" <th>baseline estimation</th>\n",
" <td>breslow</td>\n",
" </tr>\n",
" <tr>\n",
" <th>number of observations</th>\n",
" <td>1808</td>\n",
" </tr>\n",
" <tr>\n",
" <th>number of events observed</th>\n",
" <td>1468</td>\n",
" </tr>\n",
" <tr>\n",
" <th>partial log-likelihood</th>\n",
" <td>-9614.27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>time fit was run</th>\n",
" <td>2024-06-05 10:26:05 UTC</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div><table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th style=\"min-width: 12px;\"></th>\n",
" <th style=\"min-width: 12px;\">coef</th>\n",
" <th style=\"min-width: 12px;\">exp(coef)</th>\n",
" <th style=\"min-width: 12px;\">se(coef)</th>\n",
" <th style=\"min-width: 12px;\">coef lower 95%</th>\n",
" <th style=\"min-width: 12px;\">coef upper 95%</th>\n",
" <th style=\"min-width: 12px;\">exp(coef) lower 95%</th>\n",
" <th style=\"min-width: 12px;\">exp(coef) upper 95%</th>\n",
" <th style=\"min-width: 12px;\">cmp to</th>\n",
" <th style=\"min-width: 12px;\">z</th>\n",
" <th style=\"min-width: 12px;\">p</th>\n",
" <th style=\"min-width: 12px;\">-log2(p)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>indicator</th>\n",
" <td>0.96</td>\n",
" <td>2.62</td>\n",
" <td>0.06</td>\n",
" <td>0.84</td>\n",
" <td>1.09</td>\n",
" <td>2.32</td>\n",
" <td>2.96</td>\n",
" <td>0.00</td>\n",
" <td>15.40</td>\n",
" <td>&lt;0.005</td>\n",
" <td>175.43</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><br><div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <tbody>\n",
" <tr>\n",
" <th>Concordance</th>\n",
" <td>0.59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Partial AIC</th>\n",
" <td>19230.53</td>\n",
" </tr>\n",
" <tr>\n",
" <th>log-likelihood ratio test</th>\n",
" <td>264.03 on 1 df</td>\n",
" </tr>\n",
" <tr>\n",
" <th>-log2(p) of ll-ratio test</th>\n",
" <td>194.81</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/latex": "\\begin{tabular}{lrrrrrrrrrrr}\n & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\ncovariate & & & & & & & & & & & \\\\\nindicator & 0.96 & 2.62 & 0.06 & 0.84 & 1.09 & 2.32 & 2.96 & 0.00 & 15.40 & 0.00 & 175.43 \\\\\n\\end{tabular}\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-01-09T22:37:17.131185Z",
"start_time": "2020-01-09T22:37:16.573714Z"
},
"id": "USPOd2fOuTw8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 639
},
"outputId": "66dd051f-4177-4f21-f5ca-1c256a7ca26a"
},
"source": [
"fig, ax = plt.subplots(figsize=(10,7))\n",
"\n",
"for r in df['regime'].unique():\n",
" ix = df['regime'] == r\n",
" kmf.fit(df['duration'].loc[ix], df['observed'].loc[ix], label=r)\n",
" kmf.survival_function_.plot(ax=ax)\n",
"plt.title('Estimated probability of government survival vs number of years')\n",
"plt.xlabel('Time (in years)')\n",
"plt.ylabel('Estimated probability of government survival')\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1000x700 with 1 Axes>"
],
"image/png": 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IZO2+cnJyAjLeV2lq1KhhWraxsaFAgQLExsaa7WPp34klcdWsWdNsHzc3N9PfVVa1aNGC5cuXo9PpaNGiBfXr16dMmTIWnSPNo9epXbt2rFixgn///ZcGDRqQlJTEX3/9xYABAzI9PiAggBkzZhAYGGhqerV161batGmDVqsFjH0zf/rpJ/bu3cvt27fR6/WkpKSQkpKCTqfL0kA2TZo0wdXVlW3btpneiy1bttCgQQOKFi2KoijP/DlZtWpVU6zwsA9hTEwMzs7OTz3+0XOlSfubqFy5coZ19+/fNzsus8+ICxcuANlz36eJi4sjNDSUN954w2y9u7s7pUuX5uzZs099jWnq1KnDq6++yh9//MFrr70GGN8Tf39/nJ2diYuL4/Lly6ZmkmkqV66Mq6srZ8+epX379hZ9lj76+rLzb0GI7CTJkhDZRKPRULZs2Sfuo1Kp+O677/jxxx/ZunUrS5cuxcXFhS5dujBs2DCz/+SfxsHBwey8gNmoVmnr0v6jCgoKok+fPtSpU4cZM2ZQtGhRNBqNWV+UzMTFxZGSkpLhC0BqaipgbH+f9kXz0VG10v7TfxoXFxez52nnSf/FNy3hTB9XZsemfSGKj49/7LFgvH5pfZtWrlzJjBkz6NatG2PGjKFgwYJERkZmem0ePVfa+5CYmPiEV5h1Xbt2ZdCgQYSHh1OsWDECAwOZNGnSY/e35Do8zb179zJ9z9IngVktr23btsyZM4fdu3fTtm1btmzZQpkyZahVq5bpPE+7r9Jiyez9g4z3m0qlMt3vaSz9O7EkrszKt9Snn35KxYoV+f333xk6dCgAzZs3Z9y4cRYPxPHodapWrRrly5dn69atNGjQgN27d5OYmEj79u0zPb5w4cI0aNCAbdu20aVLF4KCgggPD2fGjBmA8Rr17duXGzduMHr0aKpVq4adnR0//fSTRaNn2tra0rp1a7Zt28bQoUO5ffs2R44cYfr06cDzfU4+7j159L7IiszunczWPXruzD4j0j4fsuO+T5P2t5hZEujs7GzR3z7AO++8w9y5cxk/fjzx8fEcPXqUTz/91KysRYsWsWzZMrPjEhMTiYqKAiz7LH30MyQ7/xaEyE6SLAmRy5ycnPjwww/58MMPiYqKYuPGjfzf//0f9vb2DBkyJMfK3bx5M2q1msWLF5v+czUYDE/9JbxAgQLY29uzfv36TLcXL17c9KvpownDo7/yP86j/6mnPU9fw5FZXGD8VTf9F5i0X3nTf9HI7EtDQkKC6UvJhg0bqFmzpllSEh0dnaVY0xKurCaGT+Pr60uxYsXYtGkTnp6eaDQaXn/99cfun/46pJfZdXgarVabaWfq9ElUVssrWrQo9erVY9u2bbRp04bt27ebDVmelfvKGnI7LpVKRadOnejUqRPx8fHs2bOHOXPm8Omnn7Jq1arHfiGPj4/Hxubp/4W3b9+en3/+mYkTJ7Jlyxbq1q37xBHR2rdvz9ixY7l37x5btmyhZMmSpi/2Fy5c4Ny5c0yePNmsNkOn01n8ujt06MDq1as5f/48R48exc7Ozuw+z6nPycwS6swGhHke8fHxZp9J6T9rsvP+Svscf3QglbR1lo5817FjR+bOncuuXbu4d+8eFStWNNUYpyU277//vtmAD2nSklRLPksf9bS/BSGsRUbDEyIXRUZGmo2eVKRIEfr27UujRo0IDg422/dZfgl9kpSUFLRardmvkFu2bCEpKSlDWemf16xZk6SkJBITEylbtqzpYW9vT4ECBdBqtVSsWBHAbNQpgKNHj2YptiNHjpg9P3PmDAAVKlR47DHVq1fPtIxjx46hVqvNmuyEhYWZjdh09epVYmJiePXVVwHjtXm0Cd0ff/wBZHwfHh0h7+zZs2g0GsqXL//4F/gU6cvQaDS89dZbbN68mU2bNtGhQ4cn/pJerVo11Gp1hmt47NgxANNIWFlRtmxZQkNDzRLo06dPc+3atWcqr127dvzzzz/s37+f27dvmzXhycp9ZQ3ZHdeT/o4TExPZvHmz6UcFJycnAgICeO+990yfB2nJZ/rR5mJjY7ly5UqWym/fvj3R0dEcOHCAPXv2ZGhG9ahWrVphY2PD7t272b59Ox06dDAlbGlTGqQfHj8uLo4dO3Y89bU+qnbt2pQoUYLAwEC2bdtGy5YtTV+4LfmctJSLiwuxsbGmmhwwNo3LTuk/IxRF4ezZs6bPmuy8v5ydnXnllVcy/C1GRUURERFh0d8+GO81f39/02fPW2+9Zdrm5OSEp6cnV65cMYu7bNmy6HQ6ChcuDFj2WZpeVv4WhLAWSZaEyCYGg4Fbt2499pGUlERsbCzDhw9n7ty5XLp0iRs3bhAYGMh///1nasOeVpuyZ8+ebJ2stGbNmsTHx7Ny5UquXr3KunXrWLVqFTVr1uTixYum4WQLFChAaGgoQUFB3Lhxg+bNm+Pp6cmIESM4cOAA165dY8+ePfTo0YPx48cDULFiRapWrcrSpUs5ePAgly9fZsGCBYSEhGQpttjYWGbMmEFISAiHDx9m8eLFVK9e3ZSEZaZ69eq89tprzJw5k927dxMREcGff/7JkiVL6NSpk1kfJVdXV8aMGcOZM2c4d+4c48aNw9HRkdatW5uuzeHDhzlw4ABhYWHMmTMHg8GARqPh1KlTZr+MXrx4kWXLlhEaGkpgYCA//vgjLVu2tKgGJ03aMYGBgab+UwBdunQhNDSUnTt3ZvorbnoeHh507tyZZcuWsWnTJiIiIti1axczZsygfv36pqQyK/z9/UlJSWHKlClcunSJf//9l4kTJ5r9Qm1Jea1bt0av1/P111/j4+Nj1kw1K/eVNWRnXAUKFODWrVscPXqUiIiIDNttbGyYPXs2n332GadOneLGjRv8999/bNiwwfR5kNaHbNWqVVy4cIHg4GBGjhyJu7t7lmIoU6aMaahuRVFo06bNE/d3cnLCz8+PH374gWvXrpn6mIHxx4u0WK5cucKJEyf44IMPaNmyJWAcpjqrzVFVKhVt27Zl27Zt/Pfff2blZOVz8llVr16dlJQUlixZQkREBIGBgaYpErLLjz/+yL59+7hy5QqzZs3i2rVrdO7cGcj++75fv378888/LFy4kNDQUE6cOMGQIUNwdXU19Xu0RLdu3di/fz/nzp0ze08ABgwYwK5du0yf7ZcuXWLWrFl07tzZ1D/Kks/S9LLytyCEtUgzPCGySXR0NI0bN37s9hkzZvDGG2+wZMkSvvnmG1atWoVer6dkyZL06dOH999/HzA2w6pVqxYzZ87E09Mz2/4jb9u2LUFBQSxdupT58+dTv3595s2bx7Fjxxg3bhzvv/8+gYGB9O7dm88++4zu3bvz6aef0rt3b1auXMmXX37J8OHDiYmJwd3dnbZt2zJ48GDT+f/v//6PiRMnMmDAABwcHGjTpg2DBw/m888/f2psHTp0wMbGhl69ehEbG4uPjw/Tpk176nGLFi1i9uzZpmZDRYsWpUePHnz88cdm+7366qt07tyZYcOGcf36dcqVK8eiRYtME3IOHTqUW7du8fHHH2NnZ0eHDh2YOHEijo6O/Prrr6hUKj766CMABg0axOnTp+nSpQspKSk0adKEKVOmWPJWmL3ujRs3MnToUJo3b87ChQsBYxO2WrVqkZKSgqen51PPM2nSJNzc3Pjyyy+5desWhQoVolWrVgwfPtyieHx8fJg6dSrffPMNb7zxBq+++iqff/45M2bMMPvFO6vlFShQgGbNmrFjxw4mTJhgtk2r1Wbpvspt2RlXt27d2LdvH++//z7dunVj7NixZtttbW1ZuXIls2fPpl+/fsTHx+Ph4UGTJk1Mc3U5OjoyZ84cZs6cyZtvvknx4sX5+OOP+fvvv81q/J6kffv2TJ06lTZt2mToJ/K4/T/88EO8vb3NakwdHR358ssvmTFjBh07dqRs2bIMHToUHx8fjh8/zuDBg1m8eHGWr0/79u359ttv8fDwoEGDBqb1r7766lM/J59VQEAAJ06c4JdffmH58uX4+PjwxRdf0LZt2+c6bxqNRsOECROYNGkSwcHBuLq68vnnn9O8eXMg++/7Tp06YTAYWLFiBUuWLMHe3p569eoxbdq0Z5rMvHr16hQtWhQfH58MNUTt2rVDrVbz7bffsnTpUmxsbPD29mb58uWmwRos+SxNLyt/C0JYi0rJ7rY+QghhAS8vL/r168eIESOsHUqeERkZSatWrZg9e/ZTawKyW3R0NC4uLtja2gLGjueNGjUiICCAiRMn5mosQojcdfr0ad566y3WrFljcTM+IV5WUrMkhBB5RExMDFevXmXixIlUq1btiQM75ISQkBA6dOhAhw4d+OCDDwD44YcfiI2NNeu/IIR4uURHRxMSEsKYMWNo27atJEpCpCPJkhBC5BFz5sxh48aNNGjQgKlTp6JW52630ooVK7JkyRIWLVpEly5dUKvVvPLKKyxdutRszhkhxMtl+PDhnDp1ipYtW1q1z6AQeZE0wxNCCCGEEEKITMhoeEIIIYQQQgiRCUmWhBBCCCGEECITkiwJIYQQQgghRCbyzQAPqampxMTEYGdnl+udpoUQQgghhBB5h8FgIDk5mYIFC2Jj8/iUKN8kSzExMYSGhlo7DCGEEEIIIUQeUa5cOQoXLvzY7fkmWbKzswOMF8TBwcGqsSQmJhIaGponYhEvD7mvRE6Q+0rkBLmvRHaTe0pYKu2eScsRHiffJEtpTe8cHBxwdHS0cjRGeSkW8fKQ+0rkBLmvRE6Q+0pkN7mnhKWe1j1HOu8IIYQQQgghRCYkWRJCCCGEEEKITEiyJIQQQgghhBCZkGRJCCGEEEIIITIhyZIQQgghhBBCZEKSJSGEEEIIIYTIhCRLQgghhBBCCJEJSZaEEEIIIYQQIhOSLAkhhBBCCCFEJiRZEkIIIYQQQohMSLIkhBBCCCFEHrZgwQLefvtta4eRL0myJIQQQggh8g0/Pz9q1qxJfHx8hm0rV67Ey8uLdevWWSEykRdJsiSEEEIIIfIVR0dHAgMDM6zfuHEjbm5uVohI5FU21g5ACCGEEEK8HGLi4Fx47pZZqQzYWvjzv6+vLxs2bKBjx46mdWFhYdy9e5dXXnnFtO63337jhx9+4Pr16xQvXpzBgwcTEBAAQM+ePWnUqBEhISHs2rULJycnRowYYTpnUFAQM2bM4MKFC2i1Wlq1asW4ceOwtbXl8OHDDBw4kCFDhjB//ny+++47fHx8+P777/npp5+IiYnBx8eHyZMnU6pUKVM8v/76K4sWLSIuLo5u3boxatQoFi1axK5du8xqw44ePUqfPn04cOAAzs7Oz3JZxQOSLAkhhBBCiOcWEwflusK9uNwt19UZzq6w7Bg/Pz9GjhzJ7du3cXd3B4y1Sq1bt+b06dMA/PXXX8yZM4elS5dSo0YNdu7cyciRI6lYsSJeXl4ArFq1iunTpzN9+nSWLFnClClTCAgIwNbWlmHDhtGhQwd++uknIiMj6dq1K6+88go9e/YEICUlhbCwMA4cOICdnR2BgYF8++23rFixggoVKjBlyhRGjBjBb7/9BhiTuZiYGP766y8OHjxI//79adeuHR07dmTBggWEhIRQsWJFALZv307z5s0lUcoG0gxPCCGEEELkKwUKFKBx48Zs2bLFtG7z5s106NDB9Hzt2rW0a9eOOnXqYGtrS0BAAJUrV2b79u2mfXx8fGjSpAm2trb4+/sTFxdHVFQUAOvXr2fgwIFoNBpKlChB3bp1TYkYGJOl7t27Y29vj0ql4vfff6dt27ZUqlQJrVbLsGHDeO+99zAYDADY2NjQv39/tFotvr6+ODs7c+XKFUqVKkWdOnXYuHGj6dyBgYG0b98+x65ffiI1S0IIIYQQ4rkVdIbQ36zXDO+6hcd16tSJJUuW0KtXL86ePYtaraZy5cqm7VevXuW1114zO6Zs2bJcu3bN9Dx9Ezl7e3sAkpKSADh06BCLFi0iNDSU1NRUUlNTadOmjdn5SpQoYVqOiIigfv36pueFCxfG39/fbF+1+mE9h729PTqdDoCOHTuydOlShg4dSlBQEPHx8TRt2tTCKyIyY/Vk6Z9//mHUqFHUr1+fr7/++rH7GQwG/u///o9NmzYRGxtL9erVmTRpEqVLl87FaIUQQgghxOMUdIb6VXK/3IQEy49p2rQpY8eOJTQ0lI0bN2aoiUlLRB6lUqlMy+mTl/RCQkIYMmQIo0aN4u2338be3p6RI0eSmppqtp+NzcOv4iqVCkVRHhtv+nIf5e/vz9SpUzlx4gR///03bdq0QavVPnZ/kXVWbYb37bffMnXqVMqWLfvUfVetWsXGjRtZtmwZf//9N+XKleOjjz564k0lhBBCCCFEZrRaLf7+/mzfvp3t27fTrl07s+1lypTh8uXLZusuX76cpR/qg4OD0Wq19OrVC3t7exRFITg4+InHlC5dmitXrpieR0dH8/3335OSkvLU8pydnWnRogXbtm1j69atZs0JxfOxarJkZ2fH2rVrs5QsrV69mvfff5+KFSvi7OzMsGHDCAkJ4eTJk7kQqRBCCCGEeNl06tSJ1atXU7RoUbMmdWBs2rZx40ZOnDhBSkoK69at4+LFi7Rt2/ap5y1ZsiRJSUkEBwcTExPDnDlz0Gq1REVFPfaH/jfffJPNmzdz8uRJdDodixYtYtu2bdja2mbptXTs2JE1a9aQkpJC7dq1s3SMeDqrNsPr1atXlvZLSkri0qVLVKnysF7X2dmZsmXLEhQURM2aNbNcZmJioqVhZqsDJ07yiaMb5eLu8W30favGIl4uafe2te9x8XKR+0rkBLmvRHaz5J4yGAwkJyeTkJCAp6cnGo2G1q1bk/CgLZ9er0en09GmTRv69OnDiBEjiI6Oply5cixevJgiRYqQkJCAXq8nJSXFdFxaX6WkpCS8vLx455136NGjB/b29vTr14/hw4czZMgQBg8eTJcuXQBM5wFo0KABH330EYMGDSIxMZEaNWowbdo0EhISSElJwWAwmMoCUBQFnU5nWlerVi3s7e1p3bq1/G1lQVavkUrJA+3YRo8eTXJy8mP7LEVGRtK0aVM2bdrEq6++alrfrVs3GjRowODBg59aRkJCwlOrP3PDH6eOMq3BAABGb/6Ft5p5WTkiIYQQQgjxoktISOCTTz5h2rRpFCtWzNrhvDAqV66Mo6PjY7dbfYAHS2RHXleuXDkcHByyIZpncyIy1LR8OzaGYqUqU8jFauGIl0hiYiKhoaFWv8fFy0XuK5ET5L4S2S2/31PJycl88cUXNG7cmObNm1s7nBdC2j3zNC9EsuTq6oparebevXtm6+/du0fhwoUtOpeDg8MTs8ecVqVCVdOyc4EEvvrdkbmDrBaOeAlZ+x4XLye5r0ROkPtKZLf8eE8dPXqUPn36ULduXebMmZPvXn9OeyEmpbWzs+PVV1/lzJkzpnWxsbGEh4dTvXp1K0ZmuQplKpiWbVzULFgHl65aMSAhhBBCCPHCqlOnDqdOneK7777Dzc3N2uG8dPJsshQZGUmbNm2IiIgAjP2TfvzxR0JCQoiLi+PLL7+kcuXKeHt7WzlSy7g7aFErxo58ehc7UlJh9DIrByWEEEIIIYTIwKrN8NISnbQJugIDAwEICgoiJSWFK1eumCYE69q1K7du3aJnz57Ex8dTv359Fi5caJ3An4NGBYX0MdyxcSPB2QmA3/fCP6egyYtVSSaEEEIIIcRLzarJUlBQ0GO3lSpVivPnz5ueq1QqBg8enKWR7/K6Qrp73LFx476zM64e8dy75cTwxXBoMTxmImghhBBCCCFELpOv5lZQSBcPwD1HFwZ1NfbDOnIOft1lzaiEEEIIIYQQ6UmyZAVuqcZJy+7au1Cn+DFeKWlc//m3kJhsxcCEEEIIIYQQJpIsWYGbogLgjo0bcRfOMmegcX1EFHy9xoqBCSGEEEIIIUwkWbICd60dALdUHqjDr9OxMfjWMG6bsQpu3rFicEIIIYQQ+dy1a9fw9vbmypUrT93X29ub/fv3A+Dn58evv/4KQJ8+fZg3b15OhvlER44cwdvb2zRYmng2L8SktC8bD3t7AO6q3XCIjkelgrmDoM4AiEuEiSth6XDrxiiEEEII8bK6fPkyixYt4uDBg8THx1O4cGH8/Pz4+OOPcXV1pWTJkk8ciCy9x+33/fffZ2fIZry8vLC1tUWlUqFSqShatChNmjShX79+FC9eHIC6detm+TXs2LEDLy8vypYtm2Mxv6ikZskKijq7PHyicUBRFGp7Qa/WxlXLN8Ppy9aJTQghhBDiZRYcHMxbb71FsWLF2LBhA//99x+LFi3i/PnzdOvWjaSkJGuHmCWLFy8mKCiII0eOMH/+fOLj4+nYsSMXL160+Fzz588nLCwsB6J88UnNkhUUL+ACD5raGZxcuabcopSqCNP6wprdxkEeRnwD2+ZYNUwhhBBCCIvEGOI4Z8jdL92V1GWxteD3/ylTptC4cWNGjhxpWle5cmW++eYbpk2bRlRUFGq1mhYtWrBlyxYWLFiAg4MDM2bMMO2/cuVKVq9ezdatW/Hy8uLbb7+ladOmZuX07NmTGjVqMGLECBRFYe7cuWzcuJHY2FjKlSvHmDFjqFu3rmnfRo0aERISwq5du3BycmLEiBF07Njxqa/Hzs6OypUrM2vWLIYNG8bkyZP5+eefOXz4ML169eLUqVPY2dkRERHBxIkTOX78OK6urvTu3ZtevXrRoUMHLl68yKBBg2jfvr3Z6xSSLFmFu+3DP+jEQi5cSAmjlF0RShWBEe/AFz/C9iOw7TC0qW/FQIUQQgghsijGEEfFmLe4p9zP1XJdVS4EaX/K0r537tzhv//+46efMu7v7OxsShSuXr1qWt+mTRsmTZqEXq9Ho9EAsHPnTgICArIc459//sn69etZu3YtHh4efPPNNwwePJh9+/aZzrlq1SqmT5/O9OnTWbJkCVOmTCEgIABbW9ssl9O7d2+6dOnC7du3M2z7+OOPqVevHosWLSI0NJR3332XihUrsmHDBry8vFi8eHGGhE9IMzyrcE931e+7OxEeccr0/LOuUMzNuDziG0hNzeXghBBCCCFeUhEREQCUL18+y8c0a9aM5ORkjh07BjxMuCxJltq3b8/WrVspVqwYGo2Gtm3bEh0dzfXr1037+Pj40KRJE2xtbfH39ycuLo6oqKgslwEPX9e1a9fM1p89e5bz58/z0Ucf4eDgQOXKlVm4cCHFihWz6Pz5kdQsWYG7RjEtxxRyIinkFLxifO7sCFP7wgdz4EwofLcFBnSwTpxCCCGEEFlVUO1MSMG11mmGl6Tm+tN3RaUyTt9iMBiyfH57e3t8fX0JDAykXr16/PXXX7z66qtUrFgxy+dITExk+vTp7N27l5iYGNP69CPVlSpVyqxMwOL+U6kPfmVXq83rQ8LDw3F2dsbV1dW0rmHDhhadO7+SZMkK7FTglJpAvI0jsS6OFAgOMdv+fhuYvw5OhcD476FbCyjgZKVghRBCCCGyqKDamfrqqrlebgIJWdqvTJkyAFy8eJGiRYtm+fz+/v7Mnj2bMWPGsGPHDotqlQAmT57M+fPnWbVqFWXLliUiIoJWrVqZ7fNogvMsgoOD0Wg0lCtXjrNnz5qd25IEUTwkzfCsxC01HoAYJ0dUl6+abdNoYO6HxuVb92DmL7kcnBBCCCHES6hQoULUq1ePFStWZNiWmJjIG2+8YWpul56vry/R0dH8999/HDp0yOJk6dSpU3To0IFy5cqhUqk4c+bMM7+GJ1m4cCFNmzbFxcXFbH3p0qWJj483a9YXGBjIv//+myNxvEwkWbKSgvpkAKJtXXELSyJZMZ8wrGUdaPuacfmr/0HYzdyOUAghhBDi5TN27FhOnDjBp59+ys2bNzEYDAQHB/PBBx9gb29P9erVMxxjb29Ps2bNmDt3Lp6enqYaqqwqVaoUQUFB6HQ6Tpw4webNmwEs7pP0OOHh4QwfPpzQ0FDGjh2bYXvlypWpUqUK8+bNIz4+ngsXLjB27FhTMz87OzvCwsKIi4vLlnheJpIsWYmr3tim9Jbag9L3HQgxXMuwz5wPQaOG5BQYszy3IxRCCCGEePlUqlSJ//3vfxgMBjp37oyPjw9Dhw7ltdde4/vvv3/s6HNt2rTh6NGjtG3b1uIyhw8fTkhICPXq1ePrr79m/PjxtGrVikGDBj1zLdOgQYPw9vamWrVqdO/eHXt7e37//XdKly6d6f5Llizh2rVrNGzYkIEDBzJo0CDT6Hddu3Zl9uzZZsOpCyOVoijK03d78SUkJBAcHEzlypVxdHS0eixdT99kY4EKeKeeYsXwflzdOIeO2ozDNX40DxavNy4fWgz1q+RqqOIFkpfucfHykPtK5AS5r0R2k3tKWCqr94zULFmJq8Z46W+r3XF2sOVS3KVM95v0/sPBHT5dDPkjtRVCCCGEEML6JFmyElcb4wRkt1Xu4K7h9uXTme7n4QpjexiXD5yG3/fkUoBCCCGEEELkc5IsWUkh9ACkqLQklHAk8crFx+47+A0o+2B0y8+WQrLusbsKIYQQQgghsokkS1ZSSEk1LccWdYQrEY/d194OZg0wLl+5AQv/yOnohBBCCCGEEJIsWYlr+mTJw4HCofeINsQ+dv+3m8NrDwZ3GLscvvgRkpJzOkohhBBCCCHyL0mWrKSQkmJavu9qR5lQHRcM4Y/dX6WC+YPBztY4lPiE78G7D2yXucSEEEIIIYTIEZIsWUn6mqV4F1tKh6dwUf/4pngAdSvBsWXgW8P4/NI1aPMZvDUBIrJnTjMhhBBCCCHEA5IsWYkTBmwV4yAP0dqCFL/jxPnUsKceV7U8/D0Pfh4LRQsZ1/2+Fyr3gjm/QUrqEw8XQgghhBBCZJEkS1aiAtwMxmHtbqk9sC3gwrVbF7J2rArebQXnfoRP3gC1GuKT4LMlUPMD2H08BwMXQgghhBAin5BkyYrc1QYAbqk80BdxIuFy1pKlNK4uxn5MR5c+HPzhbCg0HwY9psLNO9kcsBBCCCFEPjJu3Dg+++wzABYsWMDbb78NwPr16/Hz87NmaE/l7e3N/v37rR3GC0+SJSvysDVe/ttqdyiiRXU5HINisPg8Pq/C/oWwfCQULmBctyoQvHrB/N8hVZrmCSGEEEIA4OfnR82aNYmPj8+wbeXKlXh5ebFu3ToApk6dyuzZszPs16lTJ/766y/T87Vr1xIdHZ1zQafTs2dPqlSpgre3N97e3jRu3JjBgwdz/Lh506KgoCAaNWr01POdOXOGAwcO5FS4LzxJlqzIXWsDGJvhUVhDsbAEIgzPNlKDWg1928L5n6BfO+O62HgYsgDqDoSDZ7IraiGEEEKIF5ujoyOBgYEZ1m/cuBE3NzeLzqXX65k5cyZ3797NrvCeqk+fPgQFBXHixAl++eUXqlatynvvvcf69estPtfvv/8uydIT2Fg7gPzM40GqekvlgcZdT5kwHecNYZTVFHvmcxYuCMtGQB9/GDQPjl+EE5eg4UfQJwBm9Qd312wJXwghhBDCjCEmBv05y7oVPC9NJU+wtbXoGF9fXzZs2EDHjh1N68LCwrh79y6vvPKKad3o0aNJTk7m66+/Njt+3bp1zJ07l/3791OvXj3i4uLo2LEjAwcO5OOPP2bDhg0sWbKEGzduUKhQIT744AO6d+8OGJvznT59GgcHB/bu3Uvfvn3ZtWuXqTYL4OjRo/Tp04cDBw7g7Oz8+Neu0VCmTBkGDBhAgQIF+OKLL/Dz86NAgQJ4eXnx7bff0rRpUxITE5k+fTo7duxApVLRqlUrxo8fz6xZs/jll19Qq9Vs376dnTt3WnQd8wNJlqzIXaMAxmZ4toWTKbNfx0V9BK/b1n/uc79WFY4sgW/+hHHfQUw8fL8Fdh6Ff7+BYoWfuwghhBBCCBNDTAy3y1VFuXcvV8tVubridPaoRcf4+fkxcuRIbt++jbu7O2CsVWrdujWnT5+26Fx//vknLVq04M8//6RixYpEREQwatQovvvuOxo0aMChQ4fo06cPtWrVolKlSgCcOHGCIUOGMHfuXG7cuMGCBQsICQmhYsWKAGzfvp3mzZs/MVF6VJcuXZgzZw779u0jICDAbNtXX33FpUuX2Lp1KwAffPABixYtYvz48Vy4cIEaNWowYsQIi153fiHN8Kyo8IOrH6dyQSmcQpkwHRf0j5+Y1lIaDXz8hrFpXs/XjesioqDLJNClPPFQIYQQQoiXVoECBWjcuDFbtmwxrdu8eTMdOnR47nOXKlWKQ4cO0bBhQ1QqFQ0aNKBw4cKcOfOwT4RGo6Fbt25oNBpKlSpFnTp12Lhxo2l7YGAg7du3t6hcGxsbypQpw9WrV83WK4rC+vXr6dOnD25ubri5uTF9+vQs9WcSUrNkVWk1SwDx7hpKXtVxMTkUnLK3nKJu8OMYKF4YZv8K+4Jg+GJYMCR7yxFCCCFE/qUuWBD30DNWaYaXZGsL169ZdFynTp1YsmQJvXr14uzZs6jVaipXrvzc8ahUKn799VfWrl1LVFQUiqKg0+nQ6XSmfYoVK4ZKpTI979ixI0uXLmXo0KEEBQURHx9P06ZNLS5br9ej0WjM1t29e5fY2FhKlSplWpdWwyWeTpIlK3JXp0uWXDUUMtgQG3ERLOtXmGXT+sJ/FyDwGCz8A+pWgl6tc6YsIYQQQuQ/6oIFUdevm/sFJyRYfEjTpk0ZO3YsoaGhbNy40eKanMdZs2YNy5YtY/HixdStWxeNRoOvr6/ZPjY25l/B/f39mTp1KidOnODvv/+mTZs2aLVai8qNj48nNDSUChUqmK1Xq41NmQwGy0dcFtIMz6oKp0uW7mkLkFzIDZvL10lUknOkPBsb+G0ClC1qfD5grjF5EkIIIYTIb7RaLf7+/mzfvp3t27fTrl27bDlvUFAQderU4bXXXkOj0XDr1i2iop482rGzszMtWrRg27ZtbN269ZmaAy5btgwXFxcaNGhgtt7V1ZUCBQpw5coV07ozZ87w559/WlxGfiTJkhWlb4Z3S+VBapGClA5P5pL+6hOOej6FC8IfU8FeC0k6eGM83L6XY8UJIYQQQuRZnTp1YvXq1RQtWtSsmZol7O3tAQgNDSUuLo6SJUty+fJlYmJiuHbtGlOnTqVEiRJERkY+8TwdO3ZkzZo1pKSkULt27SyXf/fuXZYuXcqKFSuYMmWKKZ703njjDZYvX05kZCR3797liy++4OLFiwDY2dlx9epVYmJiLHjV+YckS1ZUWA0qjAnTLbUH+iKOlA3VccGQfYM8ZMbnVePw4gBhkdDtC5m4VgghhBD5T82aNbG1tX2uJnju7u60bt2aIUOGMG/ePLp160bZsmXx9fWlf//+9OjRgx49erBixQpWrVr12PM0btwYBwcH2rVrZ9afKTPff/+9aVLaVq1acezYMX744QdatmyZ6f7Dhw+nevXqBAQEEBAQwKuvvsrHH38MGBOpvXv38vrrr6PX65/5OrysVIqiKE/f7cWXkJBAcHAwlStXxtHRMc/EUuaqPXcMasYmTOXdGbv4R3+RO6um8bnDezkex+D5sODBkP6fdYNZA3K8SJGD8tI9Ll4ecl+JnCD3lchuL8M9FRcXh6+vL+vWraNs2bLWDuell9V7RmqWrMzdxvjLwS21B2p3FaWzefjwJ5k7CBp7G5dn/wprdudKsUIIIYQQIp3k5GSmTJlC48aNJVHKYyRZsjIPzYNkSeWBxl1P2bBkLugjcqVsWxtYMwlKGOdio/dMOHPliYcIIYQQQohsdPToUerWrcudO3eYOHGitcMRj5Chw63M48FQ+LfUHmjdknC5o+dqzGWUAspT26tmh2KFYe1k8B0C8UnQeTz8+w24uuR40UIIIYQQ+V6dOnU4deqUtcMQjyE1S1bm/iBZuq1yx84tDoACYdHcVu7lWgwNqsKCwcbli1eh53SQofiFEEIIIUR+J8mSlXk8qNu7pfbAodBdAMqE6nKtKV6a/u2hb4BxedNBmPpTrhYvhBBCCCFEniPJkpWlNcOLVrlhXzCaFJU9ZcJzfvjwR6lUsHAI1K1kfD5pJWw+mKshCCGEEEIIkadIsmRlacmSolITY+tCcmE3yoTm3iAP6dnbwe9TwMMVFAXenWpslieEEEIIIUR+JMmSlaX1WYIHE9MWLWCsWcql4cMfVboI/G8iaNQQEw+dx0FcglVCEUIIIYQQwqokWbIyj3TjEd5SeaAv4kiZUB0Xc7kZXnrNfGDOh8blM6HQd46xpkkIIYQQQoj8RJIlK/N4pGYJDxtKh+u4lHoVvaK3WlxD34LuLY3L//sb5q62WihCCCGEEHnaunXraNSoEQBHjhzB29sbnU5n5ahEdpB5lqwsfTO82yp31O53cUhScI1MJNT1BhU1pawSl0oFy4bD6StwKgRGLQOfV6FFbauEI4QQQgiRLfz8/IiMjEStNtYZaLVavLy8GDp0KPXq1Xvu89etW5egoKDnPk9OWLt2LX5+fri5ueV4WT179uTYsWNoNMYvuwULFqRWrVr07t0bHx+fHC8/u0jNkpU5qMHpwdyzt9QeaNxTASgTlvvDhz/KyQHWTQFXZ+O8Sx3HwuL1MgeTEEIIIV5s48aNIygoiKCgIPbt20fLli3p378/ERHW/e6Vk/R6PTNnzuTu3bu5VmafPn0ICgrixIkT/PLLL1StWpX33nuP9evX51oMz0uSpTzANNeSygOtWxLwIFmyYr+lNBVLwq/jwUYD8Unw0Txo8SmEXLN2ZEIIIYTIa5LvG7h+KjlXH8n3n+9XXAcHB/r06UORIkXYu3cvANHR0QwePJgGDRpQp04d+vXrx40bN0zHeHl5sXLlSho3bsyyZcvMznf48GG8vLxITk4GICgoiO7du1OnTh0aNmzIxIkTSUlJMe1bq1Ytdu3ahZ+fHz4+PsybN4+goCA6dOiAj48PH3/8sWl/g8HA/PnzadmyJTVq1ODNN9/k2LFjprL9/PxYs2YN/fv3x8fHh5YtW7Jv3z4A6tWrx/379+nYsSMLFy4EYMOGDQQEBODj44Ofnx+//PKL6VwLFixgwIABDB06lFq1arFo0SLeeOMNs9d69OhRqlevTlxc3BOvsUajoUyZMgwYMIDPP/+cL774gtjYWADu3bvHiBEjaNy4MT4+Pnz44YdERkYCcPXqVby8vNi9ezf+/v7UqFGDzz//nPDwcLp27UrNmjXp2bMnMTExWXmrn4k0w8sDPDQQmgK31e7Yud0HoExYMhetXLOUpk19OLQYes+CoMuw+wRU7wvTP4BP3gC1pNxCCCFEvpd838DS1tdJjs3dUaHsCqjoud71uc+j1+tNTcbmzJlDfHw8u3btQlEUhg4dyvTp01mwYIFp/8DAQNavX0/hwoX5448/HnveYcOG0aFDB3766SciIyPp2rUrr7zyCj179gQgMTGRgwcPsnnzZrZv387o0aM5f/48K1euJCYmhg4dOvDXX3/RunVrfvjhBzZv3szy5cspUaIEq1ev5sMPP2T37t04OjoC8N133zF79mwqVarEpEmTmD59Olu2bOHPP/+kRYsW/Pnnn1SsWJGIiAhGjRrFd999R4MGDTh06BB9+vShVq1aVKpknHjzxIkTDBkyhLlz53Ljxg0WLFhASEgIFStWBGD79u00b94cZ2fnLF/nLl26MGfOHPbt20dAQACjR4/GxsaGzZs3o9FomDhxIp9//jnff/+96Zj169fzv//9j3PnztGjRw/CwsKYM2cO9vb2dO7cmd9//50+ffpkOQZLyNfcPCCt39ItlQeOrndRgLKhOs5bafjwzNT2gqNLYcJ7xlqmhCQYuhCaDoYLeSOnE0IIIYSwWHx8PN999x3R0dH4+voCMHnyZBYsWICjoyNOTk60bNmS06dPmx3n7++Pu7s7KpXqiedfv349AwcORKPRUKJECerWrWt2LoPBQPfu3XFwcMDPzw9FUWjdujVubm6UL1+eChUqEBYWBhj7HL3//vuUK1cOrVZLz549KVCgALt37zadr3nz5lSvXh2tVkvr1q0JDQ3FkEkfilKlSnHo0CEaNmyISqWiQYMGFC5cmDNnzpj20Wg0dOvWDY1GQ6lSpahTpw4bN240bQ8MDKR9+/ZZv9iAjY0NZcqU4erVq9y5c4e///6bYcOGUbBgQZydnRkxYgT79+/n1q1bpmPefPNNXFxcqFu3Li4uLjRq1IjSpUvj4eFB9erVCQ0NtSgGi+LNsTOLLEsbEe+W2gOngre4p3GndLiOi4a8lYVobWFyb+jcGPrMhuMXYf9pqNEXvugDw7qARvP08wghhBDi5WPnombA9hLcuZKSq+UWLm+LXpNk0TFTp05l+vTpANjb21O5cmVWrlxJ8eLFAQgLC2PmzJmcOnWKpKQkDAYDrq6uZucoUaJElso6dOgQixYtIjQ0lNTUVFJTU2nTpo3ZPmnl2tnZAVC0aFHTNjs7O1OTvvDwcKZNm2aKHYzJVvomgqVKPRwczN7eHr1eb2rGl55KpeLXX39l7dq1REVFoSgKOp3ObBS/YsWKmSWDHTt2ZOnSpQwdOpSgoCDi4+Np2rRplq5Demm1eGl9xDp16mS2XaPRcOPGDdNAFGnXJ+16PHp9cnLkQUmW8oC0Pku3Ve6oNXqSPdwoG3qDq4Yo4pVEnFQO1g3wETVfhcPfwOxfYfIPkKSDkUtg7R74fhRUKWftCIUQQghhDXYuakpUt8v1chMSLNt/3LhxdOvWLdNtBoOBAQMGULt2bbZv346bmxtr1qxh3rx5ZvtpsvALcUhICEOGDGHUqFG8/fbb2NvbM3LkSFJTU832Uz/Sp+HR52ns7e2ZOnUqrVu3fmyZjzv2UWvWrGHZsmUsXryYunXrotFoTDVraWxszFMFf39/pk6dyokTJ/j7779p06YNWq02S+WliY+PJzQ0lAoVKmBvbw/A3r17KVSoUIZ9r169CpCh9i6rrzE7SDO8PMA9Xc2SAqQWcaHYjRS0yQarj4j3OLY2MLYnHP8W6hqbtXI4GHz6wYxV8MhngBBCCCHEC+H27dtcu3aNnj17mmo2zp49+0znCg4ORqvV0qtXL+zt7VEUheDg4GeOrXTp0pw/f95sXVpCYamgoCDq1KnDa6+9hkaj4datW0RFRT3xGGdnZ1q0aMG2bdvYunUrHTp0sLjcZcuW4eLiQoMGDShZsiRqtdrsNaWkpJgGeMgLJFnKA9Ka4elUdtxXuaAUcUCtQKmIFC7moX5LmalaHg4shFkDwM4WdCkw5lt4bZBxMAghhBBCiBeJm5sbjo6OnDhxguTkZDZu3EhwcDBxcXHEx8dbdK6SJUuSlJREcHAwMTExzJkzB61Wa2r2ZqmuXbuyatUqTpw4gV6vZ8uWLbRr147r168/9di0WpzQ0FDi4uIoWbIkly9fJiYmhmvXrjF16lRKlCjx1ESlY8eOrFmzhpSUFGrXzvoEnHfv3mXp0qWsWLGCKVOmYG9vj4uLCwEBAXz55ZfcvHmTpKQkvvrqK/r06fNM1ycnSLKUB3ikq8W9pfJAcTdWeZYJS+Z8Hhg+/GlsbOCzbnBiOTSoalx37ALU7g9TfoAUqWUSQgghxAvCxsaGSZMmsWzZMho2bMiRI0dYsGABxYoV4/XXX7foXD4+Prz77rv06NGDtm3bUrJkScaMGcOFCxcYNmyYxbG99dZbdO/enY8//pjatWuzfPlyFi5cmKX+U+7u7rRu3ZohQ4Ywb948unXrRtmyZfH19aV///706NGDHj16sGLFClatWvXY8zRu3BgHBwfatWv31MEtvv/+e7y9vfH29qZVq1YcO3aMH374gZYtW5r2GT9+PGXLlqVt27Y0adKES5cusXjx4qeeO7eolLyStuWwhIQEgoODqVy5smloxbwSy4EEaPQgJzoUU5+CP7tQaOEhPp9TktSB3fnReaJV47WEXg/z18HY5ZBo7ItIjYow5C3jKHrPokEVeKXU0/fL7/LSPS5eHnJfiZwg95XIbnJP5Z64uDh8fX1Zt24dZcuWtXY4zyyr94wM8JAHeKR7F26pPHAvmggYJ6bdlseb4T1KozGOite+AfSdA3tPwskQ6DPr2c9Z0AmCVkDpItkXpxBCCCGEsExycjJTpkyhcePGL3SiZAlphpcHmDXDU3tgW/hhsnTBEJFn2mxa4pVS8PfXsHCIMdl5HjHxMPAreAEvgxBCCCHES+Ho0aPUrVuXO3fuMHHii9Pq6XlJzVIeUFBtfCNSMQ4fbu/6LwrGZClWiSdSiaaYqrCVo7ScWg0fdYb+7SEu8dnOMeUHmLcWthyCVTuhh2VNhYUQQgghRDaoU6cOp06dsnYYuU6SpTxApTIOH35Tb6xZcixwmzhUlAk1dvo5rw+nmPrFS5bS2NpAIZdnO3baB7DxAIRchyELoVUdKOqWvfEJIYQQQgiRGWmGl0ek9Vu6pTImS8naghSMNVDwXioX8+hcS7nB0R6WjzQuR8fCJ/OtG48QQgghhMg/JFnKIzzSTUyrVhvQeRirT8qE6l6I4cNzUjMfGNDeuLxmN/zxj1XDEUIIIYQQ+YQkS3mE+4Nk6bbKHYBUD2O7tTLhujw/MW1umD0QSnkYlwd9DXfvWzceIYQQQgjx8pNkKY9IX7MEoBRzAIw1SxckWaKAEywdbly+GQ3DF1s3HiGEEEII8fKTZCmPSN9nCUBV2hmAsmE6Lhuuk6KkWiu0PCPgNejRyri8YivsOGLdeIQQQgghxMtNkqU8Iq1m6b66AMloURczvjWlw5JJRc8Vw3UrRpd3zPsYihQyLvf/EuISrBuPEEIIIcSTLF68mB49ejx2+7Bhwxg9enS2l3vkyBG8vb3R6XRP3XfdunU0atQo22N4GUiylEe4p5uY9rbaHZtCxmHDy4YZb/Dz0hQPgMIFjRPdAoRFwuffWjceIYQQQrxY/Pz8qFq1Kt7e3nh7e1O7dm26d+/Ov//+myPlDRo0iJ9//jlHzv2oHTt2EBYWBkDdunUJCgpCq9U+1zmvXr2Kl5cX1apVw9vbmxo1ahAQEMCXX37JvXv3siHqvE2SpTzCI12ydEvlgbZALAAlI1JQ65V8PXz4o97yhc5NjMsL/4B9+W9+NCGEEEI8h3HjxhEUFERQUBD79u2jZcuW9O/fn4iIF/v71vz5803JUnb7888/CQoK4sCBA0yfPp0LFy7wxhtvEBUVlSPl5RWSLOURHummB76l9sDe5Q4A2hSFYjdSuJDPhw9PT6WCRUPB1diti75zIDHZqiEJIYQQAiA1Bu4fzt1Hasxzhezg4ECfPn0oUqQIe/fuBaBnz57MmTOH9u3b079/fwCuXbvGwIEDqV+/PnXr1uWzzz4jLi4OgMTEREaNGkWDBg3w8fGha9eunD59GoAFCxbw9ttvm8r73//+h5+fH7Vr12by5MkYDAazeH7++Wf8/f2pUaMGbdu2JTAw0LStZ8+eLFmyhJEjR1KrVi2aNGnCn3/+CUCHDh24ePEigwYN4vPPP+fw4cN4eXmRnGz8khQUFET37t2pU6cODRs2ZOLEiaSkpFh8vZycnKhZsybffPMNRYsWZe7cuaZtBw8e5J133sHHx4cmTZqwaNEi07YFCxYwcOBAFixYQN26dWncuDGBgYGsW7cOX19f6tatyzfffGNxPDlNkqU8wqwZnsodR+fbGDCuLBuq44LULJkpXhi+/si4fCECpvxg3XiEEEKIfC81Bo6Wg1Ov5e7jaDnQP1/CBKDX69FoHn4h27x5M9OmTWPp0qUoisKgQYMoXrw4u3fvZtu2bURGRjJr1iwAfvjhB27fvs3OnTs5fPgwTZo0Yfz48RnKuHz5MhMmTGDMmDEcPHiQqlWrsmfPHtP2HTt2sHDhQubMmcOxY8cYMmQIQ4cO5fr1h33XV61aRYcOHTh8+DBvv/02U6ZMISUlhQ0bNgDGPlIzZszIUPawYcN47bXXOHz4MGvXruXvv//mt99+e+brpdFo6NmzJzt37kSv13Pz5k0GDRpEt27dOHr0KMuXL+e3335j48aNpmOOHz+Ou7s7+/fvp3nz5kyaNImgoCB27NjB2LFjWbBgAXfu3HnmmHKCJEt5ROH0zfDUHji63CbJzjgxbekwGT48M++1gdZ1jctzfoP/Llg3HiGEEEK8eOLj4/nuu++Ijo7G19fXtL569epUr14dlUpFUFAQFy9eZOTIkTg4OFC4cGE++eQTNmzYgKIoxMbGYmtri729PVqtlkGDBrFu3boMZQUGBlKlShVatmyJVqvlrbfeonTp0qbta9eu5a233qJatWrY2Njw+uuvU7t2bTZt2mTaJ63WxtbWFn9/f+Li4rLUFG79+vUMHDgQjUZDiRIlqFu3rqn261lVqFCB+Ph47t69y6ZNm3j11Vfp1KkTGo0GLy8vunbtaqr5ArC1taVbt25otVp8fX25desW/fv3x87ODj8/P/R6fZ5rCmnz9F1EbrBVQSE13DUY+yyp1Aop5UrA+VuUDUvmpnKHWCWeAiona4eaZ6hUxrmXqvWGuEToMwuOLAVbuauFEEKI3GdTEOqEQuK53C3XoRLobIGsjxw8depUpk+fDoC9vT2VK1dm5cqVFC9e3LRPyZIlTcsRERHo9Xrq169vdh69Xs/du3fp3r07ffv2xdfXlyZNmtCyZUtatGiRodzIyEhKlSpltq5cuXKm5fDwcPbv388PPzxsMqMoCq+88orpefrj7e3tAUhKSnrqaz506BCLFi0iNDSU1NRUUlNTadOmzVOPe5LUVOPUNmq1mvDwcIKCgvD29jaLvXz58qbnxYoVMy2nDTxRtGhRAOzs7ABMzQbzCvlamYe4a4zJ0m21OwCGV0vC+ZOUfjAi3gV9OHVsKlszxDynbDGY2R8+/j84GQKzfoFxvawdlRBCCJFP2RQEl/pP3y+76SybS2TcuHF069btifukb5JnZ2eHo6Mjx48fz3RfNzc3tmzZwuHDh/nrr7+YMGECGzZsYP78+eZh6nSmBCNN+j5L9vb2DB8+nD59+jw2LrXa8oZhISEhDBkyhFGjRvH2229jb2/PyJEjM8RiqeDgYAoVKkShQoWwt7fH19eXJUuWPHb/zGJ/lteTm/J2dPnMoxPTUqYA8HD4cGmKl7kPO0LjBz9ifPETnA21ajhCCCGEeMmUKVOGhIQEsyZicXFx3L17FzA25dPr9TRs2JBx48axZs0atm/fbtqepkiRIty8edNsXUhIiFk558+fN9t+/fp1FEV5rviDg4PRarX06tULe3t7FEUhODj4uc6p0+lYunQp7dq1Q6VSUaZMGS5cuGAW661bt7I0z1NeJslSHpI2fPgt9YNk6cGoD2XDjCOVyCAPmVOrYflIsLMFXYqxOZ5eb+2ohBBCCPGy8PT0xMfHh2nTphEdHU1sbCwTJ07ks88+A2Dw4MHMmjWLuLg4DAYDx48fx9XVlYIFC5qdp2nTppw9e5bdu3ej0+lYtWoVkZGRpu3vvPMOW7ZsYffu3aSmpnLo0CHatWvHyZMnsxSnnZ0dYWFhplH60pQsWZKkpCSCg4OJiYlhzpw5aLVaoqKiLE7EFEXh/PnzfPjhh2g0Gj755BMA2rZty71791i8eDFJSUlERETQp08fsyaFLyJJlvKQtGQpCmPbTXVBY5tNj6gU7BMMMnz4E3iVgcm9jcuHg2F+xj6VQgghhBDPbO7cuSiKQosWLWjVqhV6vZ6ZM2cC8MUXXxAWFkbTpk2pW7cuP//8M4sWLcrQxKxGjRqMGzeOSZMm8dprr3HhwgWzfkONGjVi1KhRTJkyhVq1ajFlyhQmTZpEzZo1sxRj165dmT17NiNHjjRb7+Pjw7vvvkuPHj1o27YtJUuWZMyYMVy4cIFhw4Zl6dwdO3Y0TeQ7cOBAKlasyOrVq00JYaFChVi8eDG7du2ibt269OjRg+bNmz+xSeGLQKU8b73eCyIhIYHg4GAqV66Mo6Njnozl8yiYGQ3uqXe4FePO5dOdcRq4A4Dm+71wqlqdIwVXWCvsPC81FV4bBMcugIMdnF4BFUpYO6rck5fucfHykPtK5AS5r0R2k3tKWCqr94zULOUhaX2WojWuGFChdYg2bSsTZpxrKZ/kts/Exga++wxsNMZJavt9CXK5hBBCCCHEs5JkKQ9Ja4ZnUGmIVrlh7/BwzPwyocnEk8h15baVonsx1HgFPn/XuPzXf7B8s3XjEUIIIYQQLy5JlvIQ93QT095WuePoEomhZFng4Yh452VEvKca2wOqlDMuj/gGrkt+KYQQQgghnoEkS3mIR7pk6ZbaA0eXaHTlqwKYzbUknsxOC99/Zpy0NjYeBs9/+jFCCCGEEEI8SpKlPMQj3RTBacOHp1Y0ztJcMcw4FrYkS1lTvwp88oZx+fe98Oc+68YjhBBCCCFePJIs5SEejzTDAzAUdwGgVHgyKIoMH26BqX2h1IMpqz6aZ6xlEkIIIYQQIqskWcpDHNXgoDIup9UsGQoZ3yL7+FQK307lokxMm2UujrD4wdQB127D2OXWjUcIIYQQQrxYJFnKY9Jql26pHlSJOOpM28qG6rhiuEGyosvkSJGZ9g3hLV/j8qL1cOiMVcMRQgghhBAvEEmW8pi0fks3DSUBUNvFmLaVDtdhwMBlw3VrhPbCmj8YCjoZ51zq9yWkpFo7IiGEEEII8SKwarJ07do1+vfvT/369WnevDlz5szBYDBk2M9gMDB//nz8/Pzw8fGhffv2bNmyxQoR5zx3U81SCQBsbG6BnR1grFkCGeTBUsULw6wBxuXTV+DL1daNRwghhBAvhnHjxvHZZ59l+3mTk5Px8vLi8OHD2X5ukb2smix98sknFC1alMDAQFasWEFgYCA//PBDhv1+/fVX1qxZw/Llyzl69CiffvopI0eO5Ny5c1aIOmeZmuGpiwCgtb2JplwZAMqGpwAy19Kz6NcOGlUzLk9eCRevWjUcIYQQQliJn58fNWvWJD4+48hPK1euxMvLi3Xr1gEwdepUZs+endsh4ufnR9WqVfH29sbb25vmzZszevRoLl26lOux5HdWS5aCgoI4d+4cI0aMwMXFhXLlyvH++++zenXGn/3PnDlD7dq1qVChAhqNhubNm+Pq6sr58+etEHnOSkuW7mjcALCzv4mmQnkAvMKM22SQB8up1bBsBNjaQHIKDPzK2CxPCCGEEPmPo6MjgYGBGdZv3LgRNzc3K0SU0bhx4wgKCuK///5j+fLlFCpUiDfffJODBw9aO7R8xebpu+SMM2fOULJkSQoWLGhaV7VqVa5cuUJcXBzOzs6m9c2aNWPSpEkEBwdTsWJF/vnnHxITE6lXr57F5SYmJmZL/M8jLYbMYilosAG0RGsKoABOBSKJKdEOFVA6NBmA4JQrJCQk5F7AL4lyRWDE27bM+MWWv/6DZRuS6dlKb+2wss2T7ishnpXcVyInyH318ooxwIWU3P0t3tPWgDY56/eUwWCgUaNGrF+/nlatWpnWh4eHEx0dTfny5dHpdCQkJDBhwgSSk5OZOXMmffr0oXr16gwbZhxqd+3atSxfvpzff/8dJycnduzYwXfffUd4eDiFCxemd+/evPnmm6a4Jk+ezP79+3F1dWXw4MGAsTleZt/pDAaDKQaA4sWL88knn2AwGBg7dix//vknGo2G69evM2vWLE6dOoVer6dp06aMHj0aZ2dnjh49ytChQ021YzExMXTv3p1mzZoxefJkrl69SoMGDZg5cya2trbP9ya8gLL6+ZOlZKlx48ZZLnjfvqzN/nnv3j0KFChgti4tcbp7965ZsvT6668THBxMp06dAHBwcGDWrFkUL148y3GlCQ0NtfiYnJJZLCk27mBXliS1LfE44ewUQ7BtAUoCbtfisUlROK8OI/hycK7H+zJoV13FLzurEHbLnlFLNVQoeA43l5drxIe8dI+Ll4fcVyInyH31colDTXtHb+6rcve3eBcllY0J53Ema/dUSkoKFStWZPv27Rw6dMj0/XPdunX4+Phw+fJlrl+/TnBwMDExMeh0Os6dO0f37t2ZMGEC1atXp2DBgsyfP5+BAwcSHh7O5cuX+eKLLxg2bBjVqlXjwoULzJo1CxsbGzw9Pfntt984ffo0M2fORKvV8t133wEQFhaGk5NTpjHevHmT4GDz73v169dn5cqVbNmyhYoVKzJmzBg8PT35+uuvSUpKYuHChUyYMIF+/foRFhZGQkIC27ZtY/r06fz7778sWbKE48ePM3z4cOLj4xk9ejS//PLLM1VA5BdZupvfeecdVCpVtheuZLEd1Pr161m/fj1r1qzBy8uLgwcPMnz4cIoXL0716tUtKrNcuXI4ODg8S7jZJjExkdDQ0ExjuZSggdvG5VtqD5wN8RTwLAeA2qBQ8qqOsPJxuFQqTClVkVyO/OWwbAS0HgUxCTZ8t6cq3498OYZif9J9JcSzkvtK5AS5r15OMQbQXNNALjdz16g1lClTlujwK1m6p2xtbalSpQqNGjXi8uXLdO/eHYBjx44xY8YM5syZQ4kSJahcuTIFCxYkOTmZypUrU7lyZUJCQli3bh1ly5alUaNGpmPXr1+Pr68v77zzDmBsLXXy5ElOnz5Nx44dCQoK4t1336VRo0YAODk58e6771K2bFkqV66caYzFihXLdJuLiwtarRZFUbh69Sq//vqr6TVrtVo++ugj5s6dS3x8PIqiMHDgQMqVK0fFihX55ptv6NSpE/Xr1wegfPny6PX6TMt52aV9Dj1NlpKlTz75JEuFrlmzJkv7Abi5uXHv3j2zdffu3UOlUmVoK/rzzz/zzjvvmBKjZs2a8dprr7FhwwaLkyUHBwccHR0tOianZBZLqXTLt9QelDeEoir0sGq0TJiOsPJ2rFXvYYzDe7kU6cvl9frGAR++3QSr/7aht78NrV+iH1Ty0j0uXh5yX4mcIPfVy8URCH0FzuXyb5CVtCpsk+2JJmv3lFqtxs7OjjfffJMlS5bwwQcfcPbsWTQaDT4+Pmg0GrRaLY6OjtjY2KDX603n/Pjjj+nYsSNnz55l69atpvXXr1/n4MGDpiQEjJUCjRs3xtHRkcjISCpUqGDav1KlSgDY2dllGq9arTbF8Ci9Xo+DgwNRUVHo9XqaNWuWYXtycjJ2D0ZTLl++PA4ODmg0xo7xpUuXNp3XwcEBRVHk7/AJnqmeNCYmhgsXLpCcnGxad+PGDaZOnUqXLl2ydI5q1apx48YNoqOjTclRUFAQr7zySobqSIPBgF5v3rdEp3s5agMelTZ0OMBtlTsABtuHbVlbRRTjH+JZmbyJ0fY9UatkqqxnMWsAbNgPkXeNgz2cXgFO8uOmEEII8VwKaqC+Ff4/fZae3E2bNmXs2LGEhoayceNG2rdv/9Rj4uLiiImJQa/XExUVZfoOa29vT7du3Rg/fnymx6WkpJh9l81q66pHpTWtq1ChAuHh4Tg6OnL8+PEnHqNWq5/4XDyZxVdr//79NGvWjJ49e9KvXz8++OAD+vXrx4QJE8w6yT1NlSpV8Pb2Zu7cucTFxRESEsKKFSvo1q0bAG3atOHo0aOAcfjEtWvXcu7cOVJTU9m3bx8HDx6kRYsWloaf53mkS1+jMDazU/S3UBU2/jG2vFoUgMuG6+xOffIfh3i8Qi7GyWoBQm/C5Iwj1gshhBDiJabVavH392f79u1s376ddu3aPfWYmTNn0qJFCz788EPGjh1rSoDKlCmTYZTmmzdvmrYXKVKEGzdumLY96xDgCxYswNPTE09PT8qUKUNCQgIREQ9HSY6Li+Pu3bvPdG6ROYuTpa+++opevXqxZcsWbGxs2LlzJ7NmzcLPz49x48ZZdK758+cTFRVFo0aN6NWrF506dTK1/bxy5eGIbwMGDKBjx4589NFH1K1bl5kzZzJ16lQaNGhgafh5nqsa0iqXbhjKAaBKvYGmvHH5lXCFQioXAFYkb8z9AF8iXZpB29eMy1/9D45ftGo4QgghhMhlnTp1YvXq1RQtWpRSpUo9cd8DBw6wd+9ePv30U3r16kVSUhIrV64E4K233uK///7j999/R6fTERwcTJcuXdi+fTsATZo04X//+x+3bt0iOjqa5cuXWxRnZGQkM2bMYNeuXUybNg0AT09PfHx8mDZtGtHR0cTGxjJx4sQcmUQ3P7M4WQoNDeWTTz6hQoUKqFQqSpcuTYcOHejatSsTJkyw6FzFihXj22+/5eTJk+zfv59PPvnENJDE+fPnadq0KWDs5DZ06FB27drF8ePH2bRpk2lkvJeNWgWFH2RLkUppAGxUN9BUKAeAcjmc7trXAVin28NdQ6w1wnwpqFSwaCg42YPeAP3mQOrLNTCeEEIIIZ6gZs2a2NraPrUJXlJSEhMmTODTTz+lUKFC2NraMn78eBYsWEB4eDgVK1Zk7ty5LF++nDp16vDJJ5/Qt29fAgICABg5ciTly5enTZs2vPXWW3Tu3Bkbmyf3hpk6dSre3t5Uq1aNDh06EBkZyZo1a8z668+dOxdFUWjRogWtWrVCr9czc+bM578wwkSlWNhosk6dOuzbtw97e3vq1avH9u3bKVSoECkpKTRs2JAjR47kVKzPJSEhgeDgYCpXrmz1TmxPi6XaZTijg+4x21iV6s+1K40ocNqPhFlfoSrsxvWbgdSJ7Q3A/zkO4yP7t3L7JbxU5q2BYYuMy199BMOy1u0uz8lL97h4ech9JXKC3Fciu8k9JSyV1XvG4pqlOnXqMGrUKBITE/Hy8uKbb74hOjqaXbt25csJrXJCWr+l2zbG/kn2DpGoHzTDU+5EUz2+KLU1XgB8n7zxmTsJCqNP3oA6xsvJuO8g9MaT9xdCCCGEEPmDxcnS6NGjuXz5MgCDBg3it99+o1GjRgwbNszU30g8H48HzfDu2BQGwKlAFCnFKpq266+E0dvOWF18Un+J//TnM5xDZJ1GY5x7SaOGhCQYNA8k/xRCCCGEEBYnS+XKlWPjxo04ODjQoEEDNm3axNy5c1mzZg0ff/xxTsSY76QNHx5tUwAAe8dYkp0Lm7brr4TSVdsSB4zj569I3pTrMb5sfF6FT982Lm89DP/727rxCCGEEEII67M4WerRowfr1q0zjVRXpkwZAgICqFatWrYHl1+l1SxFqx+2n0y20cODcfH1l0NxVbvwprY5AL/odpCgJOV6nC+bSe9D+eLG5cEL4O59q4YjhBBCCCGszOJkqWjRonzxxRc0atSI0aNH59kBHV5kaX2WYlRadBj7gaXcv4G6tHFIS/2VUAD6PGiKF6vEs063O7fDfOk42sOST43LUXeh+xew/V9ITH7ycUIIIYQQ4uVkcbI0d+5cDhw4wOTJk4mOjqZ37960bNmShQsXcu3atZyIMd9Ja4YHcEdlbH6nj7tuGj5cfzkUgCY2NXhVbRxe/DuZcylbvF4XejyYW3nbv9DmM3BrD/6fwf+thXNh0p9JCCGEECK/sDhZAnBwcKBDhw4sW7aMvXv30q9fPw4cOMDrr7+e3fHlSx7pkqVIjCPiKcnX0VQoD4D+8hUAVCoV79u1BeCf1BNc0IfnbqAvqQWDjQmTs4PxeZLOmDgNXQiV34PyXWHgXFj/D8TGWzdWIYQQQgiRc54pWUpz//59du/eTWBgIKdPn6ZChQrZFVe+lj5ZupbyCgDq1BtoypcFQB8ajmIwANDLzh8NxgNWJG/O3UBfUq4u8NNYuLMB/v4aRneHmq883B4WCUs3QufxULgDNBsCM1fB8Yvw4G0RQgghhBAvgSdPHZyJ+Ph4du3axZYtW9i/fz8FCxakbdu2fPrpp1SuXDknYsx3PNK9KzcV45DhNqqb2LxiHNCB5GT05y9gU7kSxdXuBNg2YGPKPn5K3soUh37Yqix+W0UmtLbQzMf4mNEfbtyBHUeMtUw7jkJ0LKTqYc9J4+Pzb6FoIWhdD9rUg1a1wd3V2q9CCCGEEEI8K4u/VTdo0ACVSoWfnx8LFy6kcePGaDSapx8osqxwust5S22sTdJqb2DbrIlpffKWHdhUrgQYB3rYmLKPm8odtqYcpIO2CSL7FS8M77UxPvR6OHoetj9Ing4HG2uVIu/Cj9uND5XKONltmwfJU71KYCN5rBBCCCHEC8Pir27jx4/H398fZ2fnnIhHAFoVFFRDjAFu25QEBRwcI1G5F8GmTi1Sj/6Hbst2nIYPBsDf9jWKqwpzQ7nD98kbJVnKBRoN1K9ifEx4z1jLFHjMmDht+9dYC6UocOSc8fHFj+DqDK3qGBOn1nWhpIe1X4UQQgghhHiSLCVLa9eu5a233gLAYDCweXPmfWNUKhVvv/129kWXj3lojMnSHW0xSAbngpEk3DVgF/C6MVnaux9DbCzqAgWwUdnQyy6AWUk/sSXlINcNtyihlm/iucmtALzd3PhQFAi6/LDW6Z9TkJIK9+JgzW7jA6Ba+Ye1To29wU5rzVcghBBCCCEelaVkacqUKaZkaeLEiY/dT5Kl7OOugUspcFdbGJJBax/P3Zv3cGvbhvgpMyE1FV3g39i/0RGA3nbtmJX0EwYM/Ji8ldEOvaz8CvIvlQqqVzQ+RnaFuAT4+4Qxcdp6GK7cMO53+orx8eVq4xxPfj4Pk6eKJa36EoQQQgghBFlMlk6dOmVaPnfuXI4FIx5KG+QhWlvQtC7p9lVsmtZC5eGOcus2yZu3m5KlVzSl8LXxYU/qcVYkb2KUfU9UKpU1QhePcHaE9g2ND0WBS9eMk91u+xf+Om6c9DYhCTYdND4AKpZ4mDg19wEnB+u+BiGEEEKI/MjiocN79OjBunXrSExMzIl4xANpw4ff0TzsG5Zy7xoqtRo7f+N8Vrot21HSzZDax649ACGGa+xJPZ57wYosU6ng1VLw8RuwaSZEb4CdX8Lwt6FquYf7hVyHReuh/Rhw6wAtP4Uvf4PTl2VSXCGEEEKI3GJxslS0aFG++OILGjZsyOjRozly5EhOxJXvmZIlHnZk0cdfB8AuwJgsGW5Gknr8pGn7G9pmFFQZk6sVyZtyKVLxPOztoGUd+HIQnF4J4f+Db0fAW75Q0Mm4jy4Fdv0HI5eAdx8o3QX6zjb2fbp735rRCyGEEEK83CweDW/u3LkkJiayc+dONm3aRO/evSlWrBidOnWic+fOlCwpnS2yg/uDZOm2Xk2qwQYbdSokG5Ml7estjMOx6fUkb9mOba2aADio7OiufZ1vktfxu+5v/s8wDFe1i5VegXgWpYvAB+2Mj9RU45DkaSPsHT1v3Ofabfh+i/GhVsNrVYzN9Xy91TjIpLhCCCGEENnG4polAAcHBzp06MCyZcvYu3cv/fr148CBA7z++uvZHV++ldZnSY+Km0meAKgNxmRJXagQtg3rA6DbvN3suN527QBIQsevup25FK3ICTY20MgbvugLR5ZC5B/w81jo+ToUKWTcx2CAA6dhwvfgO8yePgu8uBVj3biFEEIIIV4Wz5Qspbl//z67d+8mMDCQ06dPU6FCheyKK9/zSDcx7XWDFwA2qpumdXZt2wCQcvgIhtu3Tetr2XjhozEmV98nb8yFSEVuKVII3m0FP46BG7/DsWUw7QNoUh00D/6ST4c70/oze65GWTdWIYQQQoiXgcXJUnx8PBs2bGDgwIE0bNiQr776igoVKrB69Wo2bpQv59nFPV2ydEtdEQA7u4fJkvZBvyUUheRtgWbHptUuHddf4L/U8zkbqLAKtRpqecKYHrB3PtzZAJ90TgHgfISaxp/ApatWDlIIIYQQ4gVncbLUoEEDJkyYgIODAwsXLmTPnj18/vnnVK5cOSfiy7fS1yzd0pYFwMHpJvoU41BoNtWqoi5dCjCOipdeN20r7B8MDCEDPeQPBZ1hxgcpDPK/BkBYJDQZbJwcVwghhBBCPBuLk6VPPvmEffv28fXXX+Pr64tGo3n6QcJiHumG3oi2KwOAs2sk8bdSAeMEwGmj4iVvC0RJTTXtX0hdgDe0zQD4RbeDRCU5d4IWVqVSQZ+WN5n7oQ6Am9HgOwQOn7VyYEIIIYQQLyiLk6XFixfj5OSUE7GIdJxUYPdgTtl7DkUBsNUmER8Vbdonrd+ScvcuKYfNh3BPm3MpRoljnW53zgcs8oyB7VP54XNjP6a796HFp/C3TLslhBBCCGExi5Ol+vXrs3Xr1pyIRaSjUj1sinfX3sO0Pun2NdOy1s8X7OwASH5kVDxfGx8qqo3DuMtAD/lPr9awdjJobSE+Cfw/gw37rR2VEEIIIcSLxeJ5looXL860adNYtmwZZcqUwdbW1mz73Llzsy24/M5DA1dTIdrO1bQuNeYaUAsAlZMT2mZN0G0PNPZbmj7JtJ9KpeJ9u7aMT1zGntTjXNJf5RVNqdx9AcKqOjWBzTOg4zhISII3xhtH0uve0tqRCSGEEEK8GCyuWbp06RIVKlTAxcWFu3fvEhUVZfYQ2Set39IdlbNpnT7+mtk+af2WUk8Gob9qvu09uwDUD95iGeghf2pZBwLngqsz6A3QYxp886e1oxJCCCGEeDFYXLP0008/5UQcIhNpw4ffNtii19ui0aSA7obZPtqA1jDkMwCSt+7AsV9v07YSag/8bRuwOWU/PyZvYbLDB9ioLH7LxQuuQVXYPQ9eHwlRd2HQ1xATB6PftXZkQgghhBB5m8XfnI8cOfLYbSqVijp16jxXQOKhtD5Lt/QqkhKL4eQcgVq5braPzSsV0Xi+iv7CRXRbtpslSwB97dqzOWU/N5Q7bE05RHtt49wKX+QhNV6Bf+ZDy+EQEQWffwsx8TC9n7F/nBBCCCGEyMjiZKlnz56oVCoURTGtU6X7thUcHJw9kYl0yRKkpBYFIrBV38ywn13A6yRcuIhu598oycmoHgz6AOBv24CiKjcilWhWJG+SZCkf8ywN+xZAqxFwIQJm/mJMmBYOMU5yK4QQQgghzFmcLG3ZssXsucFg4PLly/z66698/PHH2RaYeNgML1GBWMriylHs7DImS9q2bUiYtwglPh7dPwewa9nctM1WZUMvuwDmJP3M5pQD3DDcprjaPbdegshjyhQ11jC1HgknLhn7L8XGw4rRYCstNIUQQgghzFj8e3KFChXMHq+88gqvv/46U6ZMYc6cOTkRY76VfmLa21pPABydI9ElGMz20zZpiOrB3Fe6zdsynKe3XVsA9Oj5KTnjdpG/FCkEf38NDasZn68KhLcmQpLMXSyEEEIIYSbbfksuUaIE58+fz67TCR42wwOIdiwPgLNrFPdvpaItqzVtU9nZoW3VnOT1m0jesgOXr2eZncdTU4bGNjXYl3qS75M3Mty+GxqVBpF/ubrAjjnQeTzsPGqcg8mhde7HoVZD9xawfCTYaZ++vxBCCCFEbrK4ZunKlSsZHsHBwXz55ZcUKlQoJ2LMt9InS3edygBgY5tMQtTtDPtqA4zfdPUXLpJ6KSTD9g/sOgBwyXCVRcm/50C04kXj5AAbp8MbTa0Xg8EAP++Ed6dCaqr14hBCCCGEyIzFNUv+/v5mAzoAKIqCra0tEydOzLbAxMM+SwCxzsUh3ricfOcaUMJsX7uA1tx/sKzbsh2bwYPMtnfVtmR+0v/4T3+e8QnL6GjblLKaYjkXvHgh2Glh9QTYeBDCI3O//C2HYccR+H0v9J9rrGGSwSaEEEIIkVdYnCz9+OOPGdbZ29tTunRpqVnKZm4aY9WfAbjn6GFanxJ7Fahrtq+mZAlsaniTejKI5M3bcXwkWbJR2bDEaRQNYvsRTyIfJcxho/OXGRJfkf/Y2EDnJtYpu397CBgFu0/Aiq1Q0Am++kiGMxdCCCFE3mDxb7j16tWjbt261KtXz7Rsa2uLWn4OznZqFRROm5hW7Wpab0i4nun+dm2NTfF0u/9BiY/PsL2WjRfD7LsCsC3lEKt1gdkbsBAWcrCDDdOhbiXj83lr4YuMv8cIIYQQQliFxRnOsWPHaNGiBWAcNrxXr1507twZX19fDh48mO0B5ndpTfFuK/boUx/Mn5RyI9N90/otodORvGt3pvtMcOhDBbWxCd/QhHncNtzLxmiFsJyLI2ydBVXKGZ9PXAH/t9aqIQkhhBBCAM+QLH355Zd07Wqsnfjrr7+4ePEiO3fuZOLEiSxYsCDbA8zvHk5MqyIpqSgAGiXzZMn2tXqo3NwAY7+lzDiq7PnGaRQAt5V7jEyQ90xYX+GCsPNLKF/c+HzoQli51boxCSGEEEJYnCxduHCB999/H4C///6bgIAASpcuTYcOHbh06VJ2x5fvpc21dEsPulTjN0lbTcaJaQFUGg12rY21fsmbt6MoSqb7tbCtw3vaAAB+0m1jZ8q/2Ry1EJYr4Q6Bc6F4YePzvnNg3V7rxiSEEEKI/M3iZEmj0aDRGKs7Dh48SOPGjQFjk7yUlJTsjU6YapZu60GPMVmyt7/52ERI27YNAIar10g9feax553t+DFFVMYBOQbFzyFeSczGqIV4NhVKGGuY3AoYhxXv9oVxHighhBBCCGuwOFmqUqUKCxcu5JtvviE2NpYGDRoAsGPHDsqVK5fd8eV7aX2WbqWCYmvsa+ToEklSrCHT/e1atzANJabbnHlTPIDC6oLMcxwKwBXDdSYnfpd9QQvxHKqWh22zwdkBdCnQaRwcfHzeL4QQQgiRYyxOlkaPHs327dtZtWoVEyZMwMHBgejoaEaNGsVHH32UEzHma2k1S3cNoHItDYBzwSiiziZnur/a3R3b1+oBkPyYfktpumhbEGDbEIB5Sas5lnoum6IW4vnUrWScMNfOFhKSjMOLn5RWvkIIIYTIZRYnS5UqVWLLli3s27ePDh06AODm5sbOnTtp2bJltgeY33mkmwkrqcQrAGhsUoi9dPixx2gDXgcg5cBhDHfvPnY/lUrFQsfhOOOAAQMD4meSoqRmT+BCPKdmPrB2Mtho4F4cvD4SLl61dlRCCCGEyE+ybXKk4sWLZ9epRDppzfAA7rm2JEXnBICH/bTHHmOXNoS4Xo9ux64nnr+MphhTHQcAcEJ/kXlJq58vYCGyUbuG8OMYY8vSqLvQcjhERFk7KiGEEELkFzKTbB7nkS5ZuqVyJeLahwAUK7ELJWZ/psfY+NRAXbwYYBwV72k+tHuD+pqqAExOXM4lvfx8L/KObi1g8VDjcngktBpuTJyEEEIIIXKaJEt5nFmypIckl6EkxRcAQH9pfKbHqFQqtP7GpnjJW3egGDIfDCKNRqVhqdNobLEhCR0fxs9+7Gh7QljDwI4wo59x+XwEtB4J9+5bNyYhhBBCvPwkWcrj0jfDu62HotWKcCSwPwA2SX9DzO5Mj7Nra2yKp9y+Q+rR/55aTjWbCnxm3wOAv1OP8YNuy/MFLkQ2G/0ujOpmXD5xCdp9bhz8QQghhBAip9g8fRdzPXv25Keffsqw/v79+7z77rts2LAhWwITRnZqcFHDfYOxZsmtvA1Bh/tSu8V3ODrfhfDxUG2vabjwNNqWzcHWFlJSSN68Ddt6dZ5a1hiH9/hd9zfnDGGMTFiAv20DiqrdcuqlCWGxGf2Ngz0s3Qj7T4NTG2tHZBm1Gj7sAAuHWjsSIYQQQmRFlmuWIiIi2LdvHydOnGD//v3s27fP7PHHH38QGhqag6HmXx7p5lpSqVUUftWNI9sHGlfG7oN7OzMcoy5QAG0T47DgyVt2ZKkcO5WWJU6jALir3GdYwrznjl2I7KRSwaKhxn5MLyKDARathys3rB2JEEIIIbIiyzVLx48fZ8aMGaSmptK3b99M9+nYsWO2BSYe8tDA5RRjzRJA8Wpa/vvxPeq0Wo5TgVvG2iXXVhlrlwJao/trD6lH/0N/MxJNsaJPLauxbQ0G2HViafJ6/qfbRXdda9ppG+XEyxLimWg08OPn8HYzCIu0djRZp0uFUUtBUeCHbTCpt7UjEkIIIcTTZDlZ6tChA+3bt6d69eps27Ytw3YHBwfc3KTJVk5I67d0Oy1Zqm5His6RQ1sH0eKdyRD3L9zdBG7tzY6za9uauBFjANBt24nD+z2yVN50hw/ZqNvHdeU2Hyd8ia9tTVxUTtn2eoR4XjY20KmJtaOwXOAx2HEEVm6DCe8Zm+UJIYQQIu+y6L9qlUrFwYMHKVmyZIaHJEo5J21iWlPNkrcWgJN730WnL2FcGT4BFPNR7zRenmjKlwMgeXPGBPdxCqqdWeA0HICrhijGJyx79uCFECa9H/SxCouEv49bNxYhhBBCPJ3FAzykpqYyZ84cLl68SFJSxqGofvzxx2wJTDyUvs8SgLOHBpdiGu7ftOfShWFUqTwS4k/AnT/A/U3TcSqVCm3b1iQuXIpux18oKSmobG2zVGZHbVPesG3GupTdLEr+nXe0rWhgWy2bX5kQ+UunxuDqbBykYsVWaFHb2hEJIYQQ4kksbgQyevRo1qxZg1qtxt3dPcNDZL/0zfDSpj9Kq106uu1tsCtrXBkxERS92bF2AQ+GEI+NJWX/QYvK/T+nYRRUOaOgMDBhJjol5dlfhBACe7uHg1P8vhdi4qwbjxBCCCGezOKapSNHjrB+/XpKly6dE/GITKTVLKUCMQZw1RiTpQs7E4m6oCK16DhswvtBwhm4vRo8upuO1TZrAg4OkJhI8pYdaJs1zXK5xdXuzHL4iIEJszijv8LspJ8Z5yC90oV4Hr394Zs/IUkHq/+G/u2ffowQQgghrMPimiUnJyeKFSuWE7GIx/BIl9I+2m9JMcCNm13B/hXjhvBJoKSa9lc5OKD18wUs67eUpo9dO5rY1ARgauIKdqc8fYJbIcTj1fGCquWMyyu2WjUUIYQQQjyFxclSt27d+O2333IiFvEYaTVL8LDfUtGqWlQP3r0bpw1QeqLxSdJFiPrZ7Hi7tsamePqz59CHhllUtlqlZqnTKFxwJBU9b8eN5ZL+6jO9DiGEcYT/3v7G5UNnIdiyP0khhBBC5CKLk6V79+6xdOlS3njjDYYNG8bw4cPNHiL7uadLltKGD9c6qilc0ThYw40gHXh0A4fKxo0RU8CgMx1j5/+6aTl5y3aLy/fUlOEX5ymoUROtxNLx/kjuGmItfyFCCAB6tALNg0/flZZX+AohhBAil1icLJ09e5by5cvj5OTE7du3iYqKMnuI7GdWs5Ru/IYS1Y1N8W4G6UClgTKTjRuSr0DUCtN+mnJl0VSpZNy0ZcczxeCvbcBcx8EAnDeE807ceFLSNfcTQmRdUTdo28C4/ON2SJU/JSGEECJPsniAh59++ikn4hBP4KIGrQp0inmyVMxby6nf44m9oSfuth7nwm+CY3VIOAURU6HIe6C2B8CubRsSzp5Dt2s3hrg41M7OFsfxsd1bnNeHsST5D/5KPcqQhK9Y5DgSlUqVXS9ViHyjjz9s2A83o2H7kYfJkxBCCCHyjmeaP/7evXusW7eOhQsXmtZdu3Yt24IS5lQq8+HD06QN8gBptUtqKDPFuEJ3FW5+a9pu16mdcSEpiaRf1zxjHCq+dhxKC5s6ACxL/pMFyc92LiHyu4DXoEgh47IM9CCEEELkTc/UDK9169bMmDGDJUuWABAREUHbtm05duxYtgcojB6dmBbAvaIttg7GWp0bQQ/6KLl1AGdjMsPV6aBPAMC2QX1sqlUBIPGb5ShpEzZZyFZlw2rnqVRSG+d2GpGwgC26A890LiHyM1sbY98lgA0H4PY9q4YjhBBCiExYnCzNnj2bN954g0OHDqFWGw8vXbo0Q4cO5auvvsr2AIWRKVlKV7OktlFRtIqxdunGqWTjSpXqYe1Syk24ufjBahUOA/sCkHr8JKlHnj2xdVW7sN5lNm6qAhgw8G7cRIJSQ575fELkV73bGP9NSYVfdlk3FiGEEEJkZHGydPLkSQYPHoxGozHrq9K9e3fOnDmTrcGJh9LmWkqfLMHDpng3z+hQDA9qi1zbgEtD4/LVWZB6HwD7nt1QOTkBkPDN8ueK5xVNKdY4T8cWG+6TQKe4z4gy3H2ucwqR31SrYJx3CaQpnhBCCJEXWZwsOTg4ZNqhPy4uTjr656DM+izBw2Qp+b5CdOiDNnoqFZT5wricehtuLABAXaAA9t27AJD021oMd58vufG19eEbp88ACDPc5I240SQpyc91TiHym7Q5l05cguMXrRuLEEIIIcxZnCxVq1aNRYsWma27f/8+06ZNo1atWtkWmDCXWZ8lgOLVHw7yYOq3BODqBwWaGZevzYHUewA4DPzAuC4picQffnnuuN63a8sI+3cBOJR6mn7xM565P5QQ+VG3FmBnnDJNapeEEEKIPMbiZGnEiBGsWbOGhg0botPpaN++PU2aNOHw4cOMHDkyJ2IUPEyW4hVINDxc71JMg2Nh49toliwBlH1Qu6S/B9e/BsC2Vk1s6hkHgEhc8uwDPaQ33WEgHW2bAvCrbifTk3547nMKkV8UcoFOjY3LqwIhWffk/YUQQgiReyxOljw9PdmyZQsDBgzgnXfeoVGjRnz++eds376dSpUq5USMgofN8MC8KZ5KpUo3Oe0jTeAKNAbX143L17+GlDsAOD4Y6EF//iIpe/557tjUKjU/OI+npuZVACYmfsuaZOmtLkRWpTXFi46FjTK4pBBCCJFnPNM8S25ubrz33ntMmjSJ0aNH88477+D0YOAAkTM80k0f/OggD8Wq2QEQdT6F1ORHaorS+i7p78O1LwGwf+dNVK6uwPMP9JDGWeXIepfZFFcVBqB3/FSOpAZny7mFeNm1rA2lPIzLK7ZZNxYhhBBCPGTz9F3MRUVFsWLFCkJCQkhKSsqw/ccff8yWwIQ5j3Q1S4/rt2RIhahzOkrUsHu40aUeFGoPdzfCjflQYhgqxyI4vNedhP9bTPK6DegjI9EULfrcMZZSF+EPl1k0j/2IRJLpfH8UBwt8S2nN859biJeZRgPvtYZpP8O2f+H6bSjhbu2ohBBCCGFxzdLw4cPZvHkzjo6OlCxZMsND5IzHNcMDKFY13SAPpzLp8JA275IhAa7NBMBhQB/jutRUkr7LvgS3jk1lVjqPB+CmcodOcZ8RpyRk2/mFeFm9/2DOJYMBftph3ViEEEIIYWRxzdLp06cJDAykcOHCORGPeIzCGlABChmb4dkXUONW3oboK6ncOJ1JsuRcEwq/CXd+hxuLodhAbCpXwrZZE1J2/0PCshU4jvoUlUaT8dhn8Ka2OVMc+jMhcRkn9ZfoGTeZtc7T0aiy5/xCvIxeKQVNqsM/p4yj4n3WzTgLgBBCCCGsx+KapdKlS2Nra5sTsYgn0KjALW34cH3G7WnzLWVaswTGvksqW1CS4WJvUPSmgR4MYeHotgdma7yf2/fiXW1rADam7GNM4pJsPb8QL6O0gR7OR8Chs9aNRQghhBDPkCyNHz+e8ePHc+TIEa5evcr169fNHiLnPG6uJXjYb+leRCqJ9zLJphwrQ+mJxuX7B+D6fOw6d0BdxNirPLsGekijUqlY6jSKBjbeAMxN+oWDKaeztQwhXjZdfMHJ3rgscy4JIYQQ1mdxsnTt2jX27dtHr169aNWqFS1atKBFixb4+fnRokWLnIhRPJDWb+nRPksAxas9HNThZmZN8QBKfgZODyYODh+DSh+Gfd/3ANBt3oY+LDw7w8VeZcda5+kUVDkDMDThKwyK4SlHCZF/OTtCl2bG5d/+goSMY+gIIYQQIhdZ3Gdp7ty5tG3blhYtWuDg4JATMYnH8HhCMzwPL1s0WtDr4PopHeUbZ/LeqG3h1ZVwsjYYkuBibxz7/UjCzLmgKCR+uwLnqROzNeaiajcmOXzAsIR5HNOfZ6VuM33s2mdrGUK8THr7w8ptcD8B1u2FHq9bOyIhhBAi/7K4ZikhIYFJkybh6+tLvXr1MjxEzkmbaymzZEljq6JI5bTJaR9TswTg5A2lJxiX7+9HY7cBrb/x21ji8h9QUlKyM2QABtp1pqqmPABjE5Zwz3A/28sQ4mXRpDpULGFcljmXhBBCCOuyOFlq1aoVR48ezYlYxFM8qRkeQIm0QR6CdCiKkvlOACVHgZOPcTlsDE6DjGMWGyKjSF6/MbvCNbFV2fCV41AAbin3mJL4fbaXIcTLQqV6OIz4X/9B6A3rxiOEEELkZxY3w6tQoQKfffYZPj4+lCxZErXaPN/69NNPsy04YS6tGV60HvSKcYS89Io9SJYS7xmIuarHtfRj3l6z5niJ2Jb+AXWZkhjCr5G45Dvsu7yR7bG3sK1DZ1tf/kjZw6Lk3/nAvgNVHtQ2CSHMvdcGJqwARYEftsPE960dkRBCCJE/WVyz9Ntvv6FWqzl58iRbtmxh06ZNpsfmzZtzIkbxQJEHyZICRD1h+HCAG0HJTz6ZU3UoZZw8VnV/PwUmeQGg+2sPqecvZEe4Gcxx/AR7tOjRMyx+3pNrv4TIx0oXgVZ1jMsrtxknqhVCCCFE7rO4Zumvv/7KiThEFpRNN73VFR0Uf+Tdcy1tg31BNUkxBm4E6agc4PTkE5b6HKL/gPgTaL12oilrgz5MIXHp97h8NTPb4y+nKc4I+3eZmrSCXalH2ZDyDx21TbO9HCFeBr3bwI4jEHoTdp8Av1rWjkgIIYTIfyyuWUoTHR2dYY4lmWcpZ1V8WHHE5UzGYVCpVE+fnDa9tOZ4KhtUShKusxxBpZC4chVKYmL2BP2Izxx6UFpdFIDhCfNJVJ5SAyZEPtWpMbgaR92XOZeEEEIIK7E4Wdq3bx+NGzemUaNGpjmWZJ6l3FFUA44P+imFPGbQurRkKTJYhz4lC83cnGpAqXEA2JSKwuGNVJS7d0n637rsCDkDR5U9cxw/BiDUcIOvkn7NkXKEeNHZ20G3Bx+pv++FmDjrxiOEEELkRxY3w5s+fTq1a9cmICAAR0fHnIhJPIZKBRW0cDoZQh5TcZSWLOl1cOtCCsWqajPfMT1Tc7yTuAxKRXdQQ+KS73B4791sjP6hN22b42vjw57U48xM/JFeWn9Ka4rmSFlCvMh6+8M3f0JiMvxvN/RrZ+2IhBBCiPzF4mTpxo0brF+/Hq02C1/CRbaraGtMljJrhgcPR8QD4xDiWUqW1Fp4ZSWcqovKLpUCnydzd/BhUk6cwrZm9ewJPB2VSsU8x2HUjn2fRJIZlbiIX5ynZHs5Qrzo6nhB1XJwJtTYFE+SJSGEECJ3WdwMr3z58ty/L5OKWkuFB4M8PK5mybGQhoKljMPmPXVEvPSca0KpsQBofQw4dE4lccny54j0ybxtKjLQrhMA/9PtYm/KiRwrS4gXlUplrF0COHgGzoVZNx4hhBAiv7G4ZmncuHFMnTqVQYMGUaZMGVQq88l+LKlxunbtGpMnT+bkyZM4OjoSEBDA8OHDM8zdBBASEsKkSZM4deoUrq6u9O7dm/fff9/S8F94aYM83NRDggEcM0l3S1S3I+ZqAjeDsjDIQ3qlxsCdPyDhFC4f6rjz4S8Y7k9D7eLy/IFnYpJDP37TBRKtxDI04Wv+LfAdNiqLb0khXmo9WsGopaA3wPBvoIl37pafmmpD6QKOVK6cu+UKIYQQeYHF30w//PBD4uPj2bZtW6bbg4ODs3yuTz75hKpVqxIYGMidO3cYMGAA7u7u9O7d22y/pKQkPvjgA959912WLVvGxYsXGTNmDE2aNKHi/7N353Ey138Ax1/fOXdmLxY5c99n5EiOiHIUioiUKKFCkiu/IlFyVY4kikK5IpWjQrkp5M5t3be9d2bn/v3x3XWEzOzO7Oyu9/PxmMd+5+s73+97+dqd93w+n/e7VClfv4VsreQN5cOPO6Cy8dZjClQxcGCFhavRTmyJbozhXg4gagxQ5ms8u2uhmFxE9I0lZe53mF/t6Z/g/yVKE8FIUw9et4xnj+soX9p+plfI0wG5lhDZVf4oeKIu/LQJVmxVH5nLAFRg5h8u/veC2v/pX5+RCSGEEDmWz8nSkCFD/HLhvXv3cvDgQWbNmkV4eDjh4eF07dqVb7755pZkaeXKlYSFhdG9e3cAqlatyrJly/wSR3ZzU/lw++2TpWvNaT1wYb+dYg+FeH+BsOpqwYczozDUcGOfNxqPp8ctI4j+0t3Ymum2pex2HWWYdTrtDY+SRxMZkGsJkV0N6wJ/H4arCZl/bbvDg8utsH6PlvUD4cGyMOQ5eLoBaLWZH48QQgiRmXxOlkJDQ3n88cczfOH9+/dTuHBhIiOvvzGuVKkS0dHRJCUlERYWdm3/jh07KFu2LG+//TarVq0ib968vPbaa7Ru3drn61oD1D8oPTGkJ5b7PKBgwoPCgWQ7TbXOW44JL+pBowW3C07uSCJfVbdvF8nzFsbDM9Gaz2Fuc5LkP+ajeaiNz7F6a4z2NZq7+hPjSeB/iZ/zsb5vwK6Vk2XkvhJZW4X74dA3wbn2hSspfDzfysJNBbkcr2HHYWj/HpQu5ObNZxx0auLCqL/raYS4ify8Ev4m95Twlbf3is/J0tChQ2nUqFGGq+HFxcURERFx0760xCk2NvamZOnChQts376dkSNHMmzYMH755RcGDx5M6dKlqVixok/XPXHiRIbi9qf0xpLfVJkLGiM7Lsdx4Ozp2x4Ten9uEk/oOfZnHKEP+b4q3GwcQXnXK2jMoD3Rl3/+KQ1KunsY/6fc6Hg834P8FraDmc5lPHKqImXthQNyrXtBVrrHRc7QrQl0aniRn//Ky5y1+TkXY+ToOQ2vTzLy3td2Oj9ykacfukJoiI8fzIh7nvy8Ev4m95TwN5+Tpa5duzJ+/Hh69+59S7LjK4/Hi6apqcdVqlSJVq1aAfD0008zf/58fvnlF5+TpeLFi2MymXyO1Z+sVisnTpxIdyxlL+q4YIP48DxUuC/stsdcejCJfSdsWE6GUL58wXRMo6tAytfTMFXbgan4FSqbV+Eq3s/nWL010fMWD9pewqKkMK3IClYYJgRs6l9OldH7SojbSbuvypcpRvWqJoZ2c/H9ehsTFur556SGywkGPv35fr75owg9Wzl5tbWDvDKTVtyF/LwS/ib3lPBV2j1zNz4nS6tXr+bChQvMnTuX8PBw9Pqb519s3LjRq/NERUURFxd30764uDgURSEqKuqm/fny5bvl2MKFC3P58mVfw8dkMmWZZrrpjaVMCKy3wQm39o6vv7+Gh32LbViuenAlGIko6HuVOcfDX+DYUwt9KQ+GKyNQSj4HIcV9Po83ylCMtzVdeNc6nU2evazQbaW9sUlArpXTZaV7XOQcN95XLz0BXVuoxSZGfweb90FsksJH8/RM+kHPK09A/w5QVHpNi7uQn1fC3+SeEv7m8zvopk2b+uXClStX5vz588TExFxLjvbu3Uvp0qUJDQ296dhSpUoxb948PB7PtdGGs2fP0qBBA7/Ekt2kFXk44QCXB7S3GYApWPmG5rT77OlKlvQPPEj8Bw8S0Xs7is6O5+grKJVXpTfsu3ozpCOzbMs47j7HIOtntDQ8TKginw4JkRVpNPDkw2qlvo17YfS3sPJPsKTAxMXw2VK17PmgTlChWLCjFUIIIdLH50UovXv3/s+HtypWrEiVKlWYMGECSUlJHDt2jFmzZtGpUycAmjdvzvbt2wFo3bo1sbGxTJs2jZSUFJYtW8b+/fvTVeAhJyiVOphn98DZW+s7ABBVQochTM2izu/xsd/SDQyt3sQyT72gEr8a7BfTfa67CVGMjDerxR1Ouy8y1jo3YNcSQviHokCDqrBiDOycAR0fVRMppwu+/gUqdYW278Jf3neVEEIIIbKMdK3Y37JlC0OGDKFLly4AuN1uVqxY4fN5Jk2axKVLl6hXrx5dunThqaee4rnnngMgOjoai8UCQP78+fniiy/45ZdfqFWrFpMnT+azzz6jaNGi6Qk/27ux19KxO+RBikahQOroks/NaW8Q0v5p7PtyX99hi073ubzRSl+fx3S1ARif8h3RrnMBvZ4Qwn8eKAPzhsGh2dCzFRj04PHADxugzqvQpD+s3q7uE0IIIbIDn+dmrVixgkGDBlG/fn127twJqNXqhg0bRnJyMu3bt/f6XAUKFGDGjBm3/bNDhw7d9Lx27dr8+OOPvoabI93Ua8kBje9wXKEqBk5ttXFhvx23y4PmdvP17kIxmdA/0h74DAD3ub/RlHvI96C9vZ6i8HHoG1SP74INOwMtk/k+fHTArieE8L/SRWDaWzC8K3z6PXz+IyRa4Pe/1Yf0ahJCCJFd+DyyNG3aNMaNG8e0adOurR8qVKgQEydOZObMmX4PUNwqtxZypf7LHXPc+bgCVdSOtQ6rh6v/deBdhHR9A0/q4JRj4+J0n8dbFbTF6R2iJt1LHetZ5fgr4NcUQvhfwTwwpiecWgAfdId8udT9ab2aKnaFr5aDPf0/noQQQoiA8jlZOnXq1LWmtDeWdq5bty5nzpzxX2TiP6WNLh3/jxl2BavcUOQhA+uWdGXK4E5SS5S7T/yJJxMavr0b0o37FHX631uWibg90r9FiOwqVzgMfR5OzIfJfaFYapW8w6eh+zgo2Qk+XghJluDGKYQQQvybz8lS7ty5uXr16i37o6Ojb6liJwInbd3Sfw0YheXTEl5AneNyfl/6kyUAJbIcAJooK9ZvF2ToXN6I1ITxvqkHAP+4TvCX65+AX1MIEVjmEOjdFo58C3OGQqXi6v6zV+CtqVCsI7w3C67GBzVMIYQQ4hqfk6WHH36YoUOHcuTIEUDtjbRx40b69etH48Z3Wj0j/C2tIt5/jSzB9dGljIwsASiFagCgLejBOnGq1w2FM6KjsSkhqPF/b/8j4NcTQmQOvQ6efxz2zIQfP4CHUnuLxyTAiG+g6LPw5hQ4fSm4cQohhBA+J0uDBw8mJSWFVq1aYbPZqFu3Lt27d6dQoUIMGTIkEDGK20ibhhfjhjjXnY9LS5auHHVgt6R/KpsSUgIAbQEPzv37sf++Nt3n8laYYqa5Xi0mscT+R6YkaEKIzKPRQOt6sPkzWPspNFcLYWJJUQtDlHoOXhoDB08GNUwhhBD3MJ+r4UVERDB37lwOHjzI8ePHCQkJoUSJEpQoUSIQ8Yk7uKl8uAMevENFqbRkyeOGi//Yub9mSPouaFT/fRU9aPJ6sEz8HGOTwI8kPmN4lKWO9ZxyX2Sb6wC1dRUDfk0hROZSFHjkAfWx8wh89B18vw4cTpi1Uu3X9HQDtYJerfLBjlYIIcS9xOeRpeeff54lS5ZQtGhRWrZsyaOPPiqJUhDcVD78P2bY5a9kQEn9Vz6fgX5LhBS/tqkt6MG+bCXOY8fTfz4vtTQ8jDF1Kt5imYonRI5XvQwsGA4HZ8MrT17v1bRkPdTuBU2lV5MQQohM5HOylD9/fkaOHEm9evUYMmQI27ZtC0Rc4i6K6CBtcOm/ijwYzBrypC5wylCyZLyeEGsLAx4P1snT0n8+L0UooTyuV+fmLLGvlal4QtwjyhSB6QMgeh4MeBbCTOr+NX/DYwPUxGnxOnBLoUwhhBAB5HOyNGHCBDZv3syIESOIiYmhW7duNG3alClTpnD27NlAxChuQ6tAcS+LPBSqqo7MXMhIsqS/DzTquxVDE3UejHXmHNwJCek/p5eeMTwKQLT7HH+7Dt3laCFETlIoL4x7FU4thJEvQd5Idf/2Q/DMcKj4ojpVT3o1CSGECASfkyUAk8lE69atmT59OuvXr+eVV15h8+bN1/ovicyRNhXvbv1mC6SuW0o47yLpyn9Ug/gvigLG4gAYHioCgCcxkZRZc9J3Ph88qa+HIXUcTabiCXFvyh0O73SBkwtgUl8omtqr6dBptQhEqefg00XSq0kIIYR/+Vzg4UaJiYmsXbuW1atXs2/fPkqWLOmvuIQXrvVa8rJ8OKijS6Ubm9J3wZDiYD2AJiIZXa0HcW7bgWXyF5h690LR3qHChB9EasJ4TF+b5Y5NLLb/wQemXjc1RBZC3DvMIdCnLfRqDfPWwJh58M8JOHMZ3vwMRs6BPk8HpxCETquWQY8My/xrCyGECAyfk6Xk5GTWrFnDihUr2LRpE5GRkTzxxBP079+fChUqBCJGcQdpI0unnWD3gOEO+UPeUnr0JgWH1cP5jCRLaRXxbNGY3/iIhOdfxnXsOPYVv2Js1TJ95/RSO0Mjljs2ccx9lt2uIzygKxvQ6wkhsja9Dro0g+cfg583w+hv4c8D13s1BUtUBAx/UU3mDPq7Hy+EECJr83kaXt26dRk2bBgmk4kpU6awbt063n77bUmUgiBtZMkNnPyPqXganUL+SmnNaW3pv2DqNDxspwl5phWaggUAsEycmv5zeqmVvj761NxeGtQKIdJoNNCmPmyZCn98Ao/XCm48MQnwxmSo1BV+2CBV+4QQIrvzeWTp3XffpUWLFoSFyTyDYCt1w6eWx+1QxnDnYwtWNnBmu40L++143B4UTTqmsYWkVcRzoXguYXrtFZLfHYl9zVqc+/ajq1zJ93N6Kbcmgib6mvzi2Mpi+x+MNPWQqXhCiGsUBRpVVx8XrkKSNfNjiL4AQ2eoxSeOnoW270KDqjD+VagtnycKIUS25HOy1L59e7Zv384PP/zAqVOnUBSFEiVK0KFDBypVCtybZXGrkjckR3cr8lAwtSKeLdHD1WgneUulY35IyA39tGwnMPd8ieRRY8FmwzLxcyJmTPH9nD5oZ2jML46tHHGfZp/rOFV0pQJ6PSFE9lQgT3CuW7oINKkB83+Ht2fAqYuwYQ/UeRU6NYEPu0PxgsGJTQghRPr4PA1v+fLlPP/88/zzzz/kz5+ffPnysXPnTp599lnpuZTJQjWQP7WuwvG7JEuFHzBe2z67M51T8dKm4QGkRKPJl4+Q5zoAYJ07H/eVK+k7r5fa6BuiQ/2Gv7f/HtBrCSFEemg08FxTODQbPuoBEaHq/nlroHwXGPwFxCUGN0YhhBDe8zlZ+uKLLxgxYgQ//PAD48ePZ8KECfz0008MHDiQTz75JBAxiv9wrXz4XSrihd2nJbKwmmikO1nSRYE2XN22nQDA/MZr6vOUFKwzvk7feb0UpYmgse5BQG1QK4QQWVWIEQY/B0e/hdefAq0GbA4YOw9Kd4YpS8DhDHaUQggh7sbnZOnUqVO0a9fulv2dOnXi6NGjfglKeO9a+XAvGjIWSh1dOrsznc1pb+i1REo0APpqVdA3agCA5bPpeByB7QzZztAYgAPuE+x3Hg/otYQQIqPy5YIp/WD/19C6nrrvagL0mQSVu8GPG6UIhBBCZGU+J0u5c+fm6tWrt+yPjY0lJCTEL0EJ76UtPTpuv/sv3MLV1WQp7pST5PQ2p01bt5Q6sgTXR5fcZ89hW7w0fef10lOGhmhTp+ItdqwN6LWEEMJfyhWFHz9QK/bVSO18cPg0PPUONO4H2w8GNTwhhBB34HOy9NBDD9G/f3927dpFcnIyycnJ/P3337z55pvUrFkzEDGK/5BW5CHZA5fukv8Urn69IsTZXRlct5Q6sgRgbNUSbQl1v2Xi5+k7r5fyanLxiK46AEukhLgQIptpVB22TYM5Q+H++9R963ZDrV7w/Ci1KIQQQoisw+dkafDgwWg0Gjp27EjNmjWpWbMmnTt3xul08r///S8QMYr/cFP58LvMgMtbWo8hTC23ne6peGkjS/az4FbPoWi1mHr3BMCx9S8cf21P37m99EzqVLx9ruMcdJ0M6LWEEMLfNBp4/nE4NAc+fAXCzer+b1dD2efh7emQkBzcGIUQQqh8TpZy5crFnDlz+Omnn5g8eTITJkxg8eLFLFy4kDx5glSv9R5W6sby4XfJfzRahULVUtctZXRkCQ/YTl3bbXq5C0pq761AN6ltY2iIJvXWXSyjS0KIbMpkhLc7q0UgXm1zvQjER9+pRSCmLpUiEEIIEWw+J0tpypYtS9OmTWnZsiUVK1b0Z0zCB/m1YE7tzepNkYe0qXgX99txpLh9v+C/ei2l0URGEtK1MwApC5fgOnfe93N7Kb8miga6aoAkS0KI7O++3DD1Tdg7C56sq+67HAevfwpVX4KfN0sRCCGECBafk6Xy5ctToUKF2z4qVapEkyZNGDNmDDZbOkcuhE8U5fq6peNezKxLK/LgdsLF/emoXPevXks3MvfppW44nVinTvf93D54xvAoAHtcRznsOnWXo4UQIuurUAx+Hg1rPobqZdR9B09B66HQpD/8fTi48QkhxL3I52TpvffeI3fu3DRt2pTBgwczZMgQHn/8cfLly8fAgQN58sknWbZsGZ9++mkAwhW340v58IJVDCipjWzT1W9JFwm63Or2DSNLALqyZTC0bAaA5YuZeFJSfD+/l54yNERBHVKTnktCiJzk0Rqw/Qv45m0onFfd98dOeLAHdPkQTl8KbnxCCHEv8TlZ2rFjB0OGDGHy5Ml07dqVF198kYkTJzJo0CBOnjzJm2++yYwZM/jtt98CEa+4jWvlw71IlgxmDfeVU1+Q7ua0t6mIl8bc73UAPFeukvLdwvSd3wsFNXmpp6sKyFQ8IUTOo9FAl2ZweC6MehnCTOr+Ob+pRSD+9yUkWoIboxBC3At8TpZ+//13WrZsecv+5s2bs3LlSkCdqhcTE5Px6IRX0oo8nHeCxYtlSGlT8c7usuNxp2Mi/G16LaUxNG2MtmJ5QC304AngRPu0qng7XYc55joTsOsIIUSwmEPgfy/AkbnQs5WaRKXY4cO5ahGIaT+CU4pACCFEwPicLOn1erZs2XLL/u3bt+NM/Ym9detWqYyXiUreUD482qsiD2qylBLvJiY6Hb9l/2NkSVEUzH1fBcC5Zx+OdRt8P7+XnjY0ura9WKbiCSFysAJ5YNpbsOcraPmQuu9SLLz6CVR9GZZvkSIQQggRCD4nS+3atePVV1+lZ8+efPDBB4wdO5Y+ffrQs2dPWrZsid1up3v37nTs2DEQ8Yrb8KV8OPyrOW16puKljSw5LoDLessfm17ohJJbXddk+TRwZcQLa/LxcOpUPGlQK4S4F1QqAcs/glXjoWopdd+Bk/Dk2/DYW7DrSHDjE0KInMbnZGngwIF8+OGHeDwetm7dyvr160lOTmbIkCEMGzYMg8HAp59+Svfu3QMRr7iNYjpSSx14V+QhPL+OiEJqlYd09Vu6sSKe7damsIrZjKlHN/WPf1qO8/itI1D+0i51dGm76yDRrnMBu44QQmQlTWvC39Nh5mAolFoEYs3fUKMHdPsIzl4ObnxCCJFTpKvPUuvWrZk+fTo///wzy5YtY+bMmXTq1AmdTgdA06ZN/Rqk+G9GDdyv/tV7VT4cbli3tNPLF9zoDr2WbmR+vQdoteDxYJ3yhe/X8FJbfaNr21IVTwhxL9FqoVsLODwHRnSD0BB1Kt7Xv0CZ52HYTCkCIYQQGZXuprQia0mbiufNyBJcn4oXe9JJ8lWXbxczFru+fZt1SwDa+4tgbNsaAOtXs3EnJvp2DS/dr81PHW0lQKriCSHuTaEmGPYiHPkWXnlSLQJhtcHI2VCmM8xYJkUghBAivSRZyiFK+lA+HKDwA8Zr2+d8nYqnDQN9PnXbducpduY3XgPAk5BAytdzfbuGD9qlVsX7y/UPp1wXAnYdIYTIygrmgekDYNeX0Ly2uu9iLPQYDw90h5V/ShEIIYTwlSRLOUTayFK0A1xe/DLMW0aPIUxd6ZSuqXjXKuKduOMh+ocfQlezBgCWSdPwuHwcwfJSWrIEsMSxNiDXEEKI7KJKSVg5Fn4dp24D7D8BLQdDs4Gw+2hQwxNCiGzFq2Rp3bp117bXrl0bqFhEBqSNLNk9cNaL6RYarUKhqmnrltJT5CGt19KdR5YURbnWpNZ19Bi2xUt9v44XimkLUFNbAYDvZSqeEEIA8Hgt2DkDvhwIBaLUfau2Q/VX4OWxcO5KcOMTQojswKtkqW/fvlitaonoN954I6ABifQpdUOvJe+LPKjDURf/seO0+Tg3I6S4+vU/RpYAQjq0RVtCPTZ51Fg8bi+65qZD2ujSVuc+zrgvBeQaQgiR3Wi18PITalPb4S+qTW49Hpi5Qi0C8d4ssKQEO0ohhMi6dN4cVLx4cZo3b07BggWx2+3/2UNp/vz5fgtOeK/kjb2WHNDIi9ekVcRzOeDCfjtFahjv8oobpI0sOa+AK0ldx3Qbil6PeUh/Env2xbl3P7aflhPyVCvvr+OldoZGvG1Vezr9YF9Hn5D2fr+GEEJkV2FmeK8b9GgF786EWSvVJGnEN/DnAVgxBhTl7ucRQoh7jVcjS5MmTaJFixaUKFECRVEoUaLEHR8iOKK0kCv1X9PbIg8FqxpQ1HZLnP3bx6l4aSNLcNfRJdOLndEUKQykji4FYIVxSW1hamjLAfC9/Xe/n18IIXKCQnnhq0FqEYjG1dV9v/ylPoQQQtzKq5GlYsWKMWTIEAAuXbrE6NGjAxqUSJ9SBtiRAse8nIZnMGu4r5yei/84fF+3ZLyx11I0hFa+46GK0Ujo4DdJ7DMA546d2H9ZhbHF475dzwvtDI3523qIzc69nHNfppAmn9+vIYQQOUHVUvDzh+pUvPNXYfAX8HhNddqeEEKI63yuhvfVV1/h8XjYvn07P/zwA0uXLmXPnj2BiE34yNfy4XC9hPjZXXY8bh9GfEJu7LV04q6Hm15+EU2B/AAkjxwTkNGltoZGAHjw8IN93X8fLIQQ97hQE7zXVd3eexy+WxPUcIQQIkvyOVk6ffo0zZs35/nnn+ftt99myJAhdOjQgbZt2xIbGxuIGIWX0oo8eDuyBNfXLaXEu4mJ9qFroSYE9AXV7f+oiJdGMZkwD+wHgGPLn9h/X+v9tbxURns/1bSlAVhs9//5hRAip3mpBZS9X91+5ytISUdxVCGEyMl8TpZGjx5N0aJFWbp0Kfv27WPPnj18//33REREMG7cuEDEKLyUVuQhxg1xXrY0SquIB+koIR6SOhXPi5ElAHPPl1Dy5gHU0aVAaGd4FIANzl1ccF8NyDWEECKn0Olg9Cvq9qmL8PlPwY1HCCGyGp+TpW3btjFmzBjKly+PTqfDYDBQuXJlxo4dy+bNmwMRo/DSTeXDvZyKF15AR0RBdZL62V2+rlsqrn71YmQJQAkNJbR/HwAc6zZi37DJt+t5od0NU/GW2tf7/fxCCJHTPN0A6qit6hg1B+KTghuPEEJkJT4nS4qiEBoaesv+XLlykZyc7JegRPqUurF8eDqm4p3d6cOLwOeRJQDT6z1QcucG1Mp4/lZOW4wq2lIALJYGtUIIcVeKAmN6qtsxCTBWOoAIIcQ1PidLpUuXZvbs2bfs//rrrylVqpRfghLpU0QHaYNLvhR5KJQ6FS/2pJPkq17O34PrI0uuOHDGefUSTUQE5jdeBcD+2xocf233/npeSiv0sM65k0tuWUcnhBB388gD8MRD6vYni+DclaCGI4QQWYbPyVL//v2ZOHEizZs3p2/fvvTt25dmzZoxadIkXnvttUDEKLykVaB4Boo8AJzzZSpeyA3lw30YXTL3fRUlPByApACsXWpnaAyAGzc/ylQ8IYTwyuge6iiT1aY2qxVCCJGOZKlmzZosX76cxo0b43K5sFgsNGjQgMWLF9OwYcNAxCh8kFbk4ZgPI0v5yugxhKqt232aipc2sgRer1sC0OTOjbmPOufDvmwljp27vb+mFypqS1BRq8b2rf1X7B4f/jKEEOIeVaUkdEltgffVcjh4MrjxCCFEVuBzsgRqk9rBgwfz2Wef8eWXX/LOO+9Qrlw5f8cm0qFUOnotabQKBauqWZZPFfGM93PtFvJhZAnA/GZvMJsBSB7l/9Gltnp1dGmjczdl4zvwWcr3WD1SE1cIIf7L+y+BUQ8uN/zvq2BHI4QQwZeuZElkXWlFHk45wO5D39e0qXgX/7HjtHn5Qo0BDIXVbR9GlgA0efNifrW7+tIlP+Hct9+n19/NGyEdqKktD8AZ9yXesHxC6bhnGG/9lkSPFCIRQojbKZofej+tbi9ZD1v9+6NZCCGyHUmWcpiSqSNLbtSEyVtpyZLLARf2+zAVLx0V8dKYB7wBISEAJH843ufX/5fcmgg2R8xgSdhH15Kmi54YhlinUiruGUZZZxHnTvTrNYUQIid4uzNEpha9HfQFeHz44E0IIXIaSZZymBt7LflS5KFQVQNK6t3g21S84upXH0eWALQF8mN6pSsAKQsW4zx8xOdz/BeNoqG1oQFbIr5kedjH1NNVBSDGk8B71i8pGd+OdyxfcFkq5gkhxDV5ImHIc+r2hj2wYmtw4xFCiGDyOVk6eVJWfGZlJW7oteTLuiVDqIZ85dRMy6dk6caRpXR8/Bg66E0wGMDt9vvoUhpFUWhmqMO6iM9ZEz6FJrqaACR4kvkoZTal4p5hgGUy591SK1cIIQD6toNCedXtIdPB5UNXCSGEyEl8TpaaNWvGCy+8wM8//4zd7mMTUxFwYRrIr1W3famIB9en4p3bZcfjbeKTNrLkTgLnVd8uCGiLFMbU7XkAUubOx3nc9xEqXzyir86vERPZGPEFT+jrAWAhhU9T5lM6rj19kidw0nUhoDEIIURWZw6BEV3V7X3RMHdVUMMRQoig8TlZmjt3LiVLluSDDz6gQYMGjBw5koMHDwYiNpFO18qH+5jLFn5ATZascW5iop3evSidvZZuFDrkLdBqweXC8tGEdJ3DVw/pKvNj+Fi2R8yinb4xCgo27HxuW0K5+A68kjSa466zmRKLEEJkRV2bQ/mi6va7MyFFCooKIe5B6eqzNGLECDZu3MhHH31EfHw8nTp14plnnmHhwoVYrdZAxCl8kJ7y4QCFq1+fw+f1VLx09lq6kbZ4MUJe6ASA9etvcZ06na7zpMcDurIsCB/F7sg5dDY0Q4MGJy5m2ZfxYHxXVjn+yrRYhBAiK9HpYPQr6vbpS/DZ0qCGI4QQQZHuAg86nY7GjRszevRohg8fzsmTJxk2bBiNGjVi1qxZ3k/jEn6Xliwds/u2jCiioI7wAuocPq+b0xoLg6JTt9M5sgQQOnQAaDTgcJA89pN0nye9KmpL8E3YMA5EzuNlYyt0aEnEQqvEAXxtW57p8QghRFbQpj7UraRufzAX4qSIqBDiHpPuZOmff/7hvffe4+GHH+aDDz6gVatWLF26lHHjxjFv3jzGjh3rzziFD9Km4SV74LKPi3LT1i2d3eXlyJKiA8P96nY6R5YAdGVKE9LxGQCsX36D63xw1g2V0hbhi9AhrAj/hAglFCcuuid/yAjLV/IBgBDinqMoMKanuh2bCGPmBTceIYTIbD4nS/PmzaNt27a0a9eOffv2MWjQINavX8+wYcMoX748DRs25IsvvmDJkiWBiFd44aby4emcihd7woklxstMK23dku2Ebxf7l9D/DVR/M9tsWMZPzNC5MupR/YOsD5/G/Zr8AIxMmUn35A+xe3z8CxVCiGyuQVVo9bC6/en3cPZycOMRQojM5HOyNG7cOCpXrszixYv5/vvvad++PSaT6aZjSpQoQdWqVf0WpPBNyRvKh/tc5CF1ZAng7C5vp+IVV7+mZKySna5iBYzt2gBgmfYV7svB/Y1cWVeSTRFf8IC2DADf2FfQKnEACZ7koMYlhBCZ7cPu6kzpFDu893WwoxFCiMzjc7LUvn173n//fSpWrHjT/uTkZEaOHHnt+YwZMzIenUiXAlowK+q2r0Ue8pXRo099sddFHm4cWcrgVLXQdwarGxYLyR9PydC5/KGQJh9/RHzG4/o6AKxxbqdRwmuccV8KcmRCCJF5KpeEF5up2zNXwgFpuSiEuEd4nSy53W7sdjvz58/H4XBgt9tvepw8eZKFCxcGMlbhJUVJf/lwjU6hUFX1xT5XxHOngOOibxf8F321KhhatQTAOuUL3DExGTqfP4QrofwYNpaXja0A2OM6Sr34Hux2HglyZEIIkXlGdAOjHtxuGCqfhwoh7hFeJ0vTp0+natWq2O12qlatSrVq1W56tGvXjjJlygQyVuGDkuksHw7Xp+Jd3G/HafNipOjGXksZXLcEEPauOrrkSUrCMnFqhs/nD3pFxzTzYN439QDgrOcyjRJek9LiQoh7xv33Qd926vbSjbB5X3DjEUKIzKDz9sBevXrRuHFj2rVrd9N0uzQmk4mHH37Yr8GJ9LtWPjwDyZLLARf22ylSw/jfL7ix11JKNIQ/5PtFb6Cv9SCGZk2x/7oay6RpmPv3QRMZmaFz+oOiKAw1vUhRTX5eSR59rbT4tNDBdDU+EezwhBAi4IY8BzOWQVwSDP4C1k9SZzMIIURO5XWyBFCuXDkmT55M48aNAxWP8JO0aXjnnWBxg9mH1WmFqhlQNOBxqyXE75osGQqCYgCPPcNFHtKEvjsY+6+r8cTFYf1sOqFDB/rlvP7wvLE5hTT5eCbpbRI8yXRP/pCTrgsMM72EIu8ahBA5WFQEvN1ZTZQ27oVlW65XyhNCiJzIq2Rp0qRJ9O3bF4CdO3eyc+fOOx7bv39//0QmMuTG8uHRDqh0l3znRoZQDfnK6rl00ME5b9YtKRowFoOUI36ZhgdgqFcXfeOGOP5YT/LHkzG93iNLjC6lSSst3ippAKfdFxmZMpNT7gt8HjoIg6K/+wmEECKb6tMWJi2Gs1fgpTHw6zioUTbYUQkhRGB4Nd6wYsWKa9vLli2742P58uUBC1T4plQGyofDjc1p7d41Y01bt+SnkSWAsOFvA+C5GkPy6Al+O6+/3Km0eLw7KciRCSFE4JiMMLGPOv3uSjw06gfrdgU7KiGECAyvkqVffvnl2vbvv/9+x8eaNWsCFqjwTTEdpE0IS1+RBzXbssa6iT3hvPsLjP5pTHsjwyMNMDzZAgDLp5/hOpH1atXetrR4opQWF0LkbO0egfnDQK+DRAs0HwQ/bw52VEII4X9eJUvR0dFeP0TWYNTA/amTLDMysgRelhAPKa5+tZ0Ej8v3C95B+LhRoNWCzUbS0Pf8dl5/+ndp8b2uY9SK78ZC22rvRuWEECIb6tAYfv4QzCFqs9qn34G5vwU7KiGE8C+v1iy1aNHirgvXPR4PiqJw4MABvwQmMq6kAU450zeyFFFQR3gBLYkXXJzdaadK27u8IG1kyeMA+3kwFvH9orehK18OU8+XsE6dQcq8RZj7vY6+dk2/nNuf0kqLF9MUZJh1Opc9cTyXPJz59tVMCX2LQpp8wQ5RCCH8rlltWD0BWg5WK+S98CHEJF4vMS6EENmdV8nSN998I1W+sqFSelhL+sqHgzq6dHClhbO7fBhZAnXdkp+SJYCw94aSMncBnoQEEt96m9zrf8uS92NaafHauor0Sh7DCfd5fnJsYF38Tsab+9DV8ESWjFsIITKibiW1hPjjA+BCDLwxGWISYHhXKSsuhMj+vEqW6tSpE+g4RACkNaaNdoDLA1off2kVrm7g4EoLMdFOLLEuzLm1dz7Y+O/GtA18DfeONPnyETp0AElDhuHYuAXbDz8R0raN387vb031tdgVOZt3LF/wmW0x8Z4kXkkezQLbaqaFDqa4tmCwQxRCCL+qUhI2TYGmb0H0eRjxjTrC9Glv0PjQukIIIbIar5KlF154gTlz5gDQsWPH/zx2/vz5GY9K+EVaRTy7B8454X4fK1rfuG7p3C47pRub7nywPh9ozOC2+LUiXhrzG69hmToD96nTJA0ehvHJFigGw91fGCRhiplPQ9+kvaEJPZJHc8h9itXObVSLf4EPzb141dgWjSLvIIQQOUfJQrBxMjQbCPuiYfISiE2EmYPVQhBCCJEdefVurXjx4jdtlyhR4o4PkXXc2GspPUUe8pXRozerw1F3LfKgKGAsrm77sSLetdOHhBA2egQArqPHsEyd4fdrBEI9fVV2RH7NoJDn0aIlGStvWD6hceLrHHJlvep+QgiREYXywrqJ8FBF9fncVdD2XbB6MZtbCCGyIq8+6xk5cuS17Y8++ihgwQj/KnnDwMtxBzTy8fUanUKhqgZObrV5XxHP+k9ARpYAQjo+g+XTz3Bu20Hy+x9h6tIJTVRUQK7lTyGKkQ/Nr/KM4VFeSf6Q3a6jbHLuoUZ8V4abXqZ/SEd0inzsKoTIGaIi1KIPbYfBb9tg2Ra1tPhPH0BkWLCjE0II36RrHtCGDRsYNmwY3bt35/XXX2fUqFHs3r3b37GJDIrSQq7Uf+GMFHkAuLDfjtN+lzLYAei1dCNFoyH849EAeGJjSf5gXECuEyg1dOXYGvEVI0yvYECPDTtDrZ9TL6EHe5xHgx2eEEL4TahJTY7aN1Kfr98Njd+ES7FBDUsIIXzmc7I0e/ZsXnnlFXbt2oXZbEaj0bBlyxY6duzIwoULAxGjyIC0Ig/H0zEND64nSy47XNx/l5Nc67V0GjxeNLJNB0P9hzG2bQ2AZfI0nMeOB+Q6gaJXdPzP1JXtkbOorVXnqexwHaJ2wksMt8zA5knnP5QQQmQxRgPMexdeeVJ9vvMINOgLJy8ENy4hhPCFz8nSnDlzGDduHD/99BOTJk1i8uTJLF++nA8++IAvv/wyEDGKDEgr8pDekaVC1Qyk1SG461S8axXxXGA7k74LeiFszEjQ6cDhIGnIsIBdJ5AqakuwIWIa40x9MGHEiYsPUr6mdsJLbHdKrzIhRM6g1cIXb8GQ59Tnh09D/T5w8JTUFBdCZA8+J0tXrlyhZcuWt+xv3bo1ly5d8ktQwn/Sijykp8ADgCFUQ76y6knO7vJyZAkCtm4JQFe6FObXewBg+34p9k1bAnatQNIqWt40dWRn5Gwe0VUHYL8rmiYJfWRanhAix1AUGN0DxvZSn5+5DI8PCuGf0+bgBiaEEF7wOVmqUaMG+/fvv2X/4cOHqVatml+CEv6TVuQhxg1xrvSdI20q3tmdNjzu/1i3dEuvpcAJHTYEJVcuAJLeGorHc5f1VFlYaW0RVoVPYqp5IHp0JGPl6aTBXHbL5H4hRM4xsCPMGKD2XbqaoNDr87Ks2y0tFIQQWZtXP6U2btx47dG8eXMGDRrE559/zu+//87atWuZMWMGgwYNom3btoGOV/joxvLhx9M5Fa/Ig2qyZI11c3Rtyp0P1OUGbbi6HcCRJQBNVBSh7w4GwPHnNmwLFwf0eoGmUTT0CHmKz0MHAXDSfYFnkoZi96TzH00IIbKg7k/CwuFg0Hmw2LQ8PczI0g3BjkoIIe7Mq3rF3bt3R1GUmz69nzhx4i3HDRkyhDZt2vgvOpFhJW9MluxQI8T3c5R+1ER4fi2JF11smRZP6cYhKMpt5psrijq6ZNkT8JElAPPrPbB+Nh3X8WgShwzH2OZJlJB0fINZSFfjE+xzHefTlPlscu6ht2U8X5iH3P7vWwghsqF2j8BinY0OI/RY7VraDYevBkLXFsGOTAghbuVVsrRmzZpAxyEC5H496AEH6S/yoDMo1H4pnDWj47j4j4PojSmUbGC6/cEhxVOTpcCOLAEoRiNhY94nvv0LuE+cxDJ5GqED+wX8uoE2xvQaB1wn+NWxlZm2ZVTWlqJvSIdghyWEEH7zaHU3n/c6zJszyxObpNBtDMQmwZvtgx2ZEELczKtpeIULF/bqMWTIkEDHK3ykVaB4Bos8AFRpF0poXvV22TIt4c5rhNLWLaWcSP/FfGBs9xT6hx8CIPmDcbivXMmU6waSVtHyXegIymuKATDAMplf7X8GOSohhPCvysUs/DYuhUJ51ef9P4N3voRsvARVCJEDeTWy9G8LFixg165d2O3X331fuHCBw4cP+y0w4T8lDXDEkf41SwD6EA21ukWwdlwc53bbOfWnjWIP3WbKW1pFPPtZcNtAY0z/Rb2gKAphEz4ktu6jeOLjSRoxmojJEwJ6zcwQqQljafhY6iZ0J9aTyHPJw9isnU45bbFghyaEEH5TsZiHjZPhsbfg2Dn4YC7EJMKUN9RCEEIIEWw+/yj6+OOPGT16NNHR0axcuZILFy6wYcMGLBYLH330USBiFBl0rXx4BmsFVGsfiin39dGl27pWEc+jNqfNBIaHamN8th0A1mlf4TyUM5L20toizA8bhRYt8Z4knkocTKz7Dn/vQgiRTZUoCBsnQ9VS6vPPf4TOo8Au9W2EEFmAz8nSsmXLmDt3LvPnz0en0/Htt9/yxx9/ULRoUUKy+eL6nCqtyMMpBzgyML3BYNZQ60W12t3p7TZOb79NZbwbey1lwrqlNOGjR4DBAE4nSYOzZ6Pa22mir8mn5n4AHHGfpmPSuzg9zuAGJYQQflYgD6z7FOpVVp/P/x2eegcs/1GAVQghMoPPydLVq1epXFn9aZZWIS80NJQBAwYwduxYvwcoMq5Uaq8lN3Ayg5/UVe8URkjEf4wuGYtf386kdUsA2hLFMb/xGgC2H5dhX5dzatG+GtKWXsanAVjj3M4Ay+QgRySEEP6XKxx+Gw8t6qjPV/4Jjw+AuMTgxiWEuLf5nCzlypWL48ePAxAZGcnRo0cByJ8/P6dOnfLpXGfPnqVHjx7UqVOHxo0bM27cONxu93++5uLFi1SvXp3Jk+UNo7f80WspjSFUw4MvhAFwcquNc7ttNx+gi1T7LUGmjiwBhA4dgJInCoDE/m/jucu9lJ18Yu5HI10NAKbYvmdGyo9BjkgIIfzPHAJLR0HHR9Xnm/ZBo35w4WpQwxJC3MN8TpaeeuopOnXqREJCAvXq1aNfv3589dVXDBgwgCJFivh0rj59+pA/f35Wr17NrFmzWL16Nd98881/vmbUqFFotVpfw76nlTBc385IRbw0NTqHYwhT+/7cfnQpcyvipdHkykXYe0MBcP69i5RvF2Tq9QNJr+hYEDaKkppCAPSxTGCdY2eQoxJCCP8z6GHu/+DV1LaNu49B/T4QfT64cQkh7k0+J0tvvPEGvXr1IiwsjCFDhpAvXz4mTpxIdHQ077//vtfn2bt3LwcPHmTAgAGEh4dTvHhxunbtyoIFd36Du27dOo4ePUqjRo18DfueFqaB/Kn5ZUaLPACERGh4sLO6dun4hhQu7P9XBpa2bimTR5YATD1fRlu2DABJQ9/DY7FkegyBkkcTydLwsYRjxomLDkn/47jrbLDDEkIIv9Nq4bN+8M4L6vNj59SEaX/m/1oRQtzjfC4drtFo6NatG6BOw/v666/TdeH9+/dTuHBhIiMjr+2rVKkS0dHRJCUlERYWdtPxKSkpvP/++3zwwQcsXbo0XdcEsFqt6X6tv6TFkJmxFNcauejScsTqxGLJ+PBSxXZats8BhwU2To2h5biIa3+m1xZBD3is0ViDkKzoR76D69kXcZ85S9zYTzAOejPTYwiU4uRnpn4oHRzvctUTT5uEQawyfEqEEhqU+0rkfHJfiUDw9r56uxOEhegYMsPAuSvQsK+HJSNs1Cqfc6ZZC/+Qn1XCV97eK+nqs7RhwwZWrVrFuXPnMBqNFCpUiCeffJJq1ap5fY64uDgiIiJu2peWOMXGxt6SLH322Wc88MADPPTQQxlKlk6cOJHu1/pbZsYSZSwOujwcSLJz4MoBv5yzYKNQTq0IJXqdg22/HiasqAuAfE4jRQHFeZGD/+zEo2RylcSSxchX4wGMf+/C9uF4LihgebJ55sYQQEWJoHdEaybn+ZEDnhN0jP0fYy++gjZ1oDgr3eMi55D7SgSCN/dV0/Iw/Nk8jFxYjJhEhRZD9Izvdow6ZaXyg7iV/KwS/uZzsjR79mw+/PBDypYtS/HixfF4PGzevJm5c+cyYsQIOnTo4PW5PF626T569CiLFi3i559/9jXcWxQvXhyTyZTh82SE1WrlxIkTmRrLA3F6VibAeZ2J8uUroCgZP2exvm7mrInFaYOrawtSa7Q6NU8T/xCoNUCoUNKEJ6R8xi/mI9fUT7A0eQIl2ULUe6MpkJCEYcT/UHJIl8NRnvJcdVj4zr2Kjeb9LCq9hSHOzpl+X4mcLxg/r0TO5+t9VaECVChjp8tHBqx2LW9+VYZZg+w8Vd+VCdGK7EB+Vglfpd0zd+NzsjRnzhzGjRtHq1atbtq/ZMkSpk2b5nWyFBUVRVxc3E374uLiUBSFqKioa/s8Hg/vvfceffr0IV++fL6GewuTyYTZbM7wefwhM2Op4AASIMmjEGcwU1h/15fcldkM1To42DEniWO/27Ge15OnlB64nhyZlItgrpHxi/mqTi1CNq0hrnUH3KdOY/94MsqxaCLmfonmX6OW2dUMz1COJ55nq3Mfn7gWUN5QjAcokqXucZFzyH0lAsGX++rZppAvCtr8D5KsCi98ZGT6W/DyEwEOUmQr8rNK+JvPH7NfuXKFli1b3rK/devWXLp0yevzVK5cmfPnzxMTE3Nt3969eyldujShoaHX9p07d45t27YxadIk6tSpQ506dVi+fDlffvklTz/9tK/h37Oq3zATbpsfm/zV7haB1gB4YMv01Mp4xmLXD0gJ3mpcfbUqRP21Fn1dtWmH7cdlxNZ/DNep00GLyZ+MioHFYaO5X5MfgD6OT9hrlNXPQoic69Ea8PsnkCcC3G7oPg7GzQ92VEKInMznZKlGjRrs37//lv2HDx/2ac1SxYoVqVKlChMmTCApKYljx44xa9YsOnXqBEDz5s3Zvn07BQoUYN26dfz444/XHo8++igdO3Zk+vTpvoZ/z6pggNDUqXd/+TFZCrtPS9V26kjNwZUWYk86QBsK+vvUA2wn/HexdNDmz0/u35cT0vlZAJy79xJT+xHsW/4Malz+kl8TxQ9hH2EmBDsOBt33Jac8F4MdlhBCBEyt8rBhMhTOqz4fNA3eng5ezuwXQgifeJUsbdy48dqjefPmDBo0iM8//5zff/+dtWvXMmPGDAYNGkTbtm19uvikSZO4dOkS9erVo0uXLjz11FM899xzAERHR2OxWNBqtRQoUOCmh8lkIiwszC/T8u4VWgVqpo4u/eXnQjG1XwpHowOPG7bOSF1wayyufg3iyFIaJSSEiDlfEvbBcADcFy8R27gl1hzSh+kBXVm+DnsHgBhdIh3s75DgSQ5yVEIIETgVisGmKVAmtb3jR99Br4/BJUuYhBB+pni8qLJQvnx5FEW5a0EGRVE4cMA/ldb8zWKxcODAASpUqBD0uazBimXQJRgXAxEaiC0DGj8UeUjz63sx7Pk+GUUL3ZcXJFfy83BlAYTVhGrb/HehDEpZ8iPxL7wCqSXNQ/83kND3380RhR9GJczkPedXADTTP8SPYWPQKekqeCnENVnpZ6fIOfx1X12KhWYDYddR9Xn7RmpDW4Mf1uWK7EV+VglfeXvPePVOas2aNX4LTARP7dSRpQQ3HLZDeaP/zl3n5Qj2/pCMxwV/fpVAsxeLq3+QBUaWbhTStg3aEsXVwg9nzpL8wTicBw4ROXsGyg1r5bKjN7XPsj12H8vC/+RXx1betExkkrk/ij9KHwohRBZ0X25Y+ym0Ggob9sCitRCfDEveh1ApiCaE8AOvPk4vXLjwLQ+tVsvZs2c5d+4cBoPh2n6RddW+4ReHP9ctAeS6X0fFJ9WsfN8PyVhtRdU/cF4FZ9bqhaGvXo2ov9ahq10TANuSn4hp8DiuM2eDHFnGKIrCkCvP0lDzAACf25Yw2bYouEEJIUSARYbBr+Pgybrq89+2wWMDICYhuHEJIXIGn+cexcTE0LVrVxo3bkyXLl144YUXaNiwIa+//rp0Tc7i7tdBfq267e91SwAPvRKBogG3Ew78ft/1PwhykYfb0RYsQNTalRg7PgOAc+duYmo1xPHX9iBHljF6dMzRD6OcRk1W37JM4mf7xiBHJYQQgWUywpKR0Lmp+nzLfnjkDTh/NbhxCSGyP5+TpdGjR5OQkMCUKVP49ddfWblyJZ9++ilnzpxh4sSJgYhR+ImiXB9d8vfIEkBUcT3lW6ijS7uW3lB8IwsmSwCKyUTkd7MIHfkuAO4LF4l5pDkp87P3aExuJZwfw8eRR4nEg4fnk95jl/NwsMMSQoiA0utg9lDok1pral801OsNx7L3pAEhRJD5nCxt3LiRKVOm0KRJE4oVK0aJEiVo1qwZkyZNYvXq1YGIUfhR2rqlXSlgc/v//A/1iAAF4i4Wur4zi61bupGiKIS9M5jIRXPAZIKUFOI7dSNp+Cg87gD8BWWS0toifB/2IQb0JGOlTeIgzrkvBzssIYQIKI0GJvaB4S+qz6PPQ/0+sPd4cOMSQmRfPidLdrud++6775b9hQsXJjY21i9BicBJG1lyALtt/j9/3lJ6yj5mwuUMISlObZaaVUeWbhTyzNNEbfgNTaGCACS//xEJXV65awXIrKyB/gFmhL4NwFnPZZ5KHEyyR6bKCiFyNkWB97qpSRPAhRho2Bc27wtuXEKI7MnnZKl48eKsXLnylv0rVqzg/vvv90tQInDSei1BYKbiAdTtEQFA/JXUBhhZeGTpRvoHqxO1bT26mjUASPl2AZaPJwc5qozpbGzGOyHdAPjbdYgXkkbg8kgjEiFEzte3nTotT6uBuCS16MOvfwU7KiFEduNzstSrVy8GDRrEyy+/zJgxYxgzZgwvvfQSQ4YMoWvXrgEIUfhTlBbKpPafCESRB4D7yhso3TiE+KtqkQG35URgLhQA2kIFiVr3C7oHqgKQNGQY9q3Z+7frcNPLdDQ8BsBPjg0MsX4e5IiEECJzvPA4/DAKjHqwpKglxhf+EeyohBDZic/J0mOPPcY333xDaGgoW7ZsYe3atRiNRqZNm8ZTTz0VgBCFvwWyyEOauj0jib+qjiy5k7LXZHHFbCZy4WyUsDBwOonv2BV3TEyww0o3RVH4MvRt6uqqAPBJyjympywNblBCCJFJWj2slhYPN4PDCR3fh+k/BzsqIUR24VVT2hvt27eP2rVrU7t27UDEIzJBnRD4NgEO2SHOBbm0/r9GgcoGzuQuCYBOl0BKbAwhuaP8f6EA0ZUpTfj0SSQ89xLuk6dI6PYqkUvnZ9sGryGKkSVho3k4oQfR7nP0sXxMCW0hHtPL/2MhRM73yANq89rmg+ByHPScoPZhGvycusZJCCHuxOeRpS5duuByyZqH7OzG5rTbAzi6VLxxuWvbh37MfitrTZ06YOqhrvex/bQcy8SpQY4oY/JpcvNT+DgilTBcuHg26R3+cWWP9WRCCJFRNcrChklwf2qNqrdnwKBpkI3r+AghMoHPyVLLli35+uuvs3WVsHtdNSOkLlsK2LolgLyVridLZzYfxmnLfvdM+Kdj0VWtDEDSoHeyfdPaCtriLAz7AB1aEjzJtE4cyEV39p1iKIQQvihXFDZNgfLqklrGL4Du48DpDG5cQoisy+dkKTY2lpkzZ1K3bl3atm1Lx44db3qIrC9EA9VSq+IFct0ShvvxpN5iZvMpTm4N5MUCQzGZ1PVLoaHgcBD37Iu4s3mJ/Cb6mkwxDwDghPs8bROHYPUEoI68EEJkQfffB+snwYNl1eczV8Cz74PNHty4hBBZk8/JUkREBA0bNqRx48aUK1eOEiVK3PQQ2UNac9o/rQGcgqDRg0Et8hCR5zRHVmfPHj+6cmUJ/2ISAO4TJ0l46dVsP7LaPaQ1b4U8B8Cfrv28nPwBbk/2bcIrhBC+yJcLfv8EGj2gPl+yHp4YAomWYEYlhMiKfC7wMHr06EDEITJZbRNMjYMLLjjrhCL6u74kXZSQ4mA/RWTe02z51orb6UGjy36raU2dn8WxdgPWL7/GtnQZ1smfY+77WrDDypDRplc55jrDUsd6FtrXkF+J4iPzaxgVQ7BDE0KIgIsIhZVj1FGlnzbBmr+h6Vuw4iPIExns6IQQWYXPI0sAW7ZsYcSIEfTq1YvXXnuNUaNGsWPHDn/HJgKodiY0pwUgRB1tjMxzBmucmzN/Z9/pXuETx6KrXBGAxAH/w7Ete9/zGkXD7LDh1NSWB2CybRE14l/kd0f2/r6EEMJbIUZYPAJebKY+/+sANHwDzl4OblxCiKzD52Rp9uzZdOvWje3bt6PRaPB4PGzdupXnn3+eBQsWBCJGEQDlDBCe+q8fyCIPGIsDEJn3NODhcDadigep/ZcWzbm2fin+2Rdxx8UFO6wMMSshLA0fSz2d2oT3kPsUjyf25YWk9zjvvhLk6IQQIvB0Opg5GPo9oz7/5wTU6w1HzgQ1LCFEFuFzsjR9+nTGjBnDzz//zNSpU/n8889ZtmwZo0ePZvLkyYGIUQSARoFamVHkIUTttWQwWgiNuMSRNVY87uy73kdXvhzhn38KgCv6BAkvv5bt1y8V0OThj/DP+Cp0KHmVXADMs6+iUvxzTElZhNMjZaKEEDmbRgMfvw4jX1Kfn7wI9fvAriPBjUsIEXw+J0tJSUk88cQTt+x/4oknSEpK8ktQInOkTcXbngKuQL3fN1e+tpmv8CGSLrq4sD97lxwyvdCJkG4vAGBb8hPWz74IckQZp1E0vGh8gn8i59HD2AYFhQRPMv0sn1I34RX+dO4PdohCCBFQigLvdIHP+qnbl2KhUT/YuCfYkQkhgsnnZKlKlSocOnTolv1HjhyhWrVqfglKZI605rSJbjgUqPzFXBHQApC/2D8A2XoqXpqIKRPQVqoAQOJbQ3Hs2BnkiPwjShPB1NBBbIz4gupata7uTtdh6if05LXkscS4E4IcoRBCBNZrT8G374BOC/HJ8PhAWLE12FEJIYLF52TpmWeeYeDAgUyfPp3ff/+dVatWMX36dAYOHEjLli3ZuHHjtYfI2m4q8hCo/EUTAia1OW3xBw4DcHiVNdtPXVPMZnItnA1mM9jtxHfogjs+Pthh+U0dXSW2RMzgU3M/IpRQPHiYbvuRivGd+Ma2XMqMCyFytE5N4McPIMQAVhu0+R/MWxPsqIQQweBz6fDBgwcD8PHHH9/yZ8OHD7+2rSgKBw4cyEBoItAK66GQDs451XVLXQN1odCqYP2HfEUOAhB3ysmVow7ylcneJap1FSsQMfUTErr2xHU8moRXehO5YDaKkv1Ko9+OTtHRO6Q97QyNGWSZwjz7Kq544ng5+UNm2pYxxTyAKrpSwQ5TCCECouVDsGo8PPm2OsLUeRTEJqojT0KIe4fPydKaNfLRSk5SOwSWJgW4yENoVbgynxD9IbR6Oy6HgSOrrdk+WQIwvdgZ+x/rSfnmW2yLfsDaaAbm13oEOyy/KqjJy5yw9+jmaEWf5PEccp9ik3MPNRO68UZIB4aZXiJMMQc7TCGE8Lv6VWHtp9BskLqG6fVPISYB/veCuq5JCJHz+TwNr3Dhwl4/RNaXtm5pdwqkBGpmlVktS63goOKjp4GcsW4pTcRnH6OtoE41THxzCI6du4McUWA8qn+QnZGzGWXqiQkjLlx8nDKPSnHP8UXKD1xwXw12iEII4XcPlIGNk6FYfvX5uzOh/2fgltnIQtwT0tWUVuQcaeuWnMCuQPWLDa16bbNcw6MAXD7kIPZUzihJrYSGkmvRHDCZ1PVL7V/AnZAzCyEYFD1DTF3YG/ktrfT1ATjruczrlvHcH9eGevE9GGOdwwHXiWy/Lk0IIdKUKQKbpkDF4urzT7+Hl8aAM2f8GhNC/AdJlu5xNTOjyIOhCGhzAVCo9PVKikfWWAJ0wcynq1SRiCkTAHAdO05855dzbMIEUFxbkB/Cx/BD2EdU0pYAwIOHP137+Z91GlXiO1MxvhODLFPY5NiDy+MKcsRCCJExhfPB+olQWy2Eyje/wjPDISVQHzQKIbIESZbucZFaKJ+6dOjPQK1bUpRro0tG9lGwqnrBI2tyzlQ8gJBuLxDyQicA7MtWcrVKHWxr/ghyVIHVytCA3ZFz2R/5HaNNr1FXVwUFdSL/EfdpPk6ZxyOJr1IkrjWvJI3mZ/sGrB55ZyGEyJ7yRMLqCdCkhvr8x03QcggkJAc3LiFE4EiyJK5NxQvYyBJcn4pn2UOZpupCqXO77CRdzjkjDoqiEDFtIiGd2gPgPnWauKatSOjzFp7knP2btJy2GANNndkQMY0zuX5ieugQntDXIwQ1Mb7siWOWfRlPJw3hvtgWtE0cwje25VxxxwU3cCGE8FG4GZZ/BG0bqs//2AmPvgmX44IalhAiQLyqhvf22297fcLRo0enOxgRHLVNMDsBjjogxgVR2gBcxJzasNh+jnKNklmfWnn+yBor1TuGBeCCwaGYzUR+Nwvj061IeLUfnqsxWKd8gf2X1UR88wWGhx8KdogBl18TxUvGVrxkbEWyx8oqx1/8ZN/AcsdmrnrisWLjJ8cGfnJsQIOGjoamTAkdQIQSGuzQhRDCK0YDLBgGPT+GmStgx2Fo2Bd+Gw/33xfs6IQQ/uRVsnT8+PGbnh8+fBidTsf999+Px+Ph5MmTAFSvXt3/EYqAu7E57TYrNAtE7nJDkYdcUQfIW6YiV444OLLakqOSpTQh7duib1iPxJ5vYPtxGa6jx4it/xjmAW8Q9v47KCEhdz9JDhCqmHjK8AhPGR7B6XGy2bmPH+3r+dmxgePuc7hx8539N7Y7D7Aw7EMq60oGO2QhhPCKTgdfDoSocBi/AA6egnq91d5M5YoGOzohhL94NQ1vwYIF1x4tWrTgmWeeYdOmTSxZsoQffviBjRs30qZNG+rXrx/oeEUAVDWCIbVfRMD6LZkrQepaFix7KNNEnYp3apsNa3zOmYp3I23+/ET+MI+I2TNQIiPB48Ey7lOuPlgfx46dwQ4v0+kUHQ31DzAhtC+HIheyK2IOnQ3NADjsPs3DCa/wne3XIEcphBDeUxQY2wtGv6I+P30JGvSFvw8HNy4hhP/4vGbpm2++oX///hgM1xuKms1m3nrrLb7++mt/xiYyiVEDDxjV7YCtW9KGQkhpdTt5D2VT1y15XHBsbSA74gaXoiiYXuhEnn1/YXi8CQCufw4SU6cRSe99gMfhCHKEwaEoCpV1Jfk69F0+Nw/CgB4LKXRJfp8+yROweezBDlEIIbyiKDCkM0zrr25fjoNG/WDdriAHJoTwC5+TpcTERCyWW0s+22w2EhMT/RKUyHzXijykQMDa46RNxUveQ75yeiKLqIujjqzOOSXE70RbpDC5fllK+BeTUEJDweUiecRoYuo0wrlvf7DDCxpFUXglpA3rIz6nmKYAAJ/bltAo4XVOuS4EOTohhPBez9Yw713Q6yDRAs0Hwc+bgx2VECKjfE6WatSoQa9evVi1ahUHDhzgwIEDrFq1itdff13WLGVjtdWBHi65IGC9Ys2pyZJ1PwouyjY1AxC9KQW7Jee3QlcUBXOPl4jasxV9w3oAOHfu5uqDDUge8zEeV86cjuiNmroK/BUxk2Z6tQDGNtc/1Ep4id8cfwY5MiGE8N6zj8LPH4I5BFLs8PQ7MPe3YEclhMgIn5OlUaNGER4eTp8+fWjbti1t27alT58+OBwORo0aFYgYRSaonRnNadNGltwpYD16rYS4yw7RG3PuVLx/05UsQe4/VhL2yRgICQG7naQhw4ht8DjOw0eCHV7Q5NFE8nPYOIabXkZB4aonnicS32KUdRZuT85PpoUQOUOz2mqRh1xh4HLDCx/CpMXBjkoIkV4+J0v33XcfM2fOZPPmzSxevJjvvvuOtWvXsnjxYgoWLBiIGEUmKGOAyNS7IWBFHm6oiIdlD4WqGgjNp170yOqc1aD2bhSNhtB+r5Nn12b0dWoB4NjyJ1cfeJjkcZ/isd5bfx9pNIqGd00vsSxsAlFKBB48vGf9ktZJA4lxJwQ7PCGE8MrDlWHdRCgQpT5/YzK8NyuA09yFEAGTrqa0TqeTI0eOcODAAapXr06BAgVuu45JZB8aBWoFujmtsThow9Xt5N0oGoUyTdSpeMfWWXHa773fIrpyZcm9cRVhH74Hej1YrSQNeocrZaphmfYlHvu9WeigmaEO2yJmUVNbAYBfHFupldCNHc6DQY5MCCG8U7UUbJwMJVI/Rx7xjZo0uWWgXIhsxedk6fTp07Ro0YIXX3yR4cOHA3D27FmaNm3K0aNH/R6gyDxp65a2p4AzEHmLogFzFXU7eQ/Atap49mQPp7beO1PxbqTodIS+PYCo7RvQP1QbAPfZcyS+2o+r5Wtg/ebbe3I9UzFtAdZFTKWX8WkATrov0CChF1+m/IRHPp4VQmQDpQqrCVPlEurzyUvgxdHgCNTaYCGE3/mcLI0ePZpq1aqxefNmNBr15QULFqRNmzaMGTPG7wGKzJO2bsnigQO2AF0kbSqeRU2WijxoJCR1/t/he2wq3r/pq1Ym9+Y15Pp5EbpqalLpij5BQteeXK1ci5SFi/HcYx9JGhUDU0IH8HXou5gwYsdBL8sYuid/iMVzbybXQojspVBedUreQxXV53NXQdt3wRqo37NCCL/yOVnatm0b7777LlFRUSiK2mRUo9Hw+uuvs2PHDr8HKDJP2sgSBLI5bWqyZDsFzji0eoXSjdUs7egfVtwBGdLKPhRFwfhkC6L+3kTkwtloy5cFwHXwMPHPvkhMjXrYfl5xz42sPG9szuaIGZTR3A/AN/YVNEjoyVHXmSBHJoQQdxcVAasnwOPqElWWbVFLi8cnBTcuIcTd+ZwsaTQaQkNDb9nv8XjuuTdwOU1BHRTRqduZUuQheS/AtXVL1lg3Z/6Wj9pALQAR0r4tefZtI+Kb6WhLFAfAuXsvca07EFv3UWyr/7in/s9V0ZVia8SXPK1/BIDdrqPUiH+RT6zzcXpkTosQImsLNcFPH0D7Rurz9buh8ZtwKTaoYQkh7sLnZKls2bLMmzfvpn0ej4epU6dSvnx5vwUmgqN2oIs8mCtf306dilesrhG9SR2lvNeq4t2NotVi6vIceQ7+Tfi0iWgKFwLA8ec24h5rReyjLbFv3hrkKDNPpCaMhWEfMNbUGx1aLKQw0DqZ+gk92e28d8uuCyGyB6NBbVz7ypPq851HoEFfOCk9uIXIsnxOlvr27cv48eNp164dTqeTXr160bhxY+bNm8ebb74ZiBhFJkqbirfXBgHpE6uLVKviwbUiD/oQDSUbqlnakTXWe2q0xFuKwYC558vkPbqHsE/GoOTLC4Bj7QZi6zUltmVbXDv3BDnKzKEoCv1Nnfgz4qtr1fK2uw5SO+Flhlo+x+qR0UkhRNal1cIXb8GQ59Tnh09D/T5w4GRw4xJC3J7PyVKtWrVYsmQJNWvW5OGHH0av19O6dWtWrlxJ7dq1AxGjyERpI0suYGegp+JZrr+5T5uKl3jRxYV992a5bG8oISGE9nudvMf3EfbBcJRcuQCwr/wNS/2mRA0ahvvwvVGVspquDJsivmCCuS9mQnDhYmzKXKrHd+F3h6yfFEJkXYoCo3vA2F7q8zOXoUEf2C7dEYTIcnxOlr7//ntKlSrF22+/zYwZM5g8eTL9+/cnMjKSL7/8MhAxikz0YAgoqduBK/JQTf2avBc86vBVyYYhaPXqbpmKd3easDBChw4kb/Q+Qt8ZhBIWBoD593Uk12xAQs8+uM6dD3KUgadVtLwR8ix7IufSTP8QAEfdZ3g8sS+vJI2WRrZCiCxtYEeYMQA0GriaoK5h+mNnsKMSQtzI52Rp5MiRt92fmJjIpEmTMhyQCK4ILVQwqNsBW7eUNrLkToaU4wAYwzQUq6sOax1eLVPxvKXJlYuwkcPIe3wv+t498ej14HJhnT6LK6Wrkjj0PdxxccEOM+CKawuyLGw8c0KHk1fJBcAs+zIqxz/HItsauZ+EEFlW9ydh4XAw6CHJCi0GwdINwY5KCJHG62Rp5syZ1K9fH7vdTv369W95NGvWjIIFCwYyVpFJ0tYtZUpFvBum4qU1qI096eTqMalu5gtNvnyEjBnJhSVz0XV+Vp3jYbViGT2eK6WqkjxhEp6UnN2XSFEUOhkfZ1/kt7xgaA7AJU8snZKH8VTSYE67LgY5QiGEuL12j8Dy0RAaAjYHtBsOX68MdlRCCPAhWerYsSPvv/8+Go2Gjh073vLo06ePTMPLIdLWLR13wJVA5CwhpUCTmpElX0+WSjU2oaTekYdXWwJw4ZzPVbAApumTidq9BcOTLQDwxMSQNGAoV8o+gPXruXhcriBHGVh5NbmYFfYuK8I/prhG/QBnuWMTVeI781nK97g8Ofv7F0JkT01rwpqPIXc4uN3QbQx8sijYUQkhdN4eaDabefTRRxk6dCidO3e+7TEbNmzg/vvv91twIjhubE67LQVahPn5AopWLSGetO2mZMmcW8v9NY2c+svGkdVWHu4V6ecL3zv0VSqT++dF2DdsImnwMBxb/sR9+gwJ3XqRPH4i4aNHYHiyxbXG0jnR4/o67I6cw3vWr5iYsoAkrLxh+YTvbL8xPXQIlXQlgx2iEELcpE5F2DAJHh8I565A/8/gajz874Xr64nF7aXYweZQSLGDRhvsaMSd6LSg8zr7yBp8DjctUTp37hw22/USvefPn6dv377s3CkrE7O7KkYwKmDzqFPx/J4sAZirqsmS5eZy12WamDj1l41LBx3EnXaS6/5s9j8qizE0qEfuTaux/bScpLeH4zpwCNf+A8S17oC+Xl3CxryPoV7dYIcZMKGKiXHm3jxraEKv5DHsch3hT9d+aiZ0o42hIVW1pamiLUklbUmKawqiUXxeximEEH5VqQRsnAyPvQXHzsEHc9WHuBszUCPYQYi7CDPBt+9A63rBjsR7Pr8T3bdvH6+//jqXLl265c9q1arll6BEcBkUqG6ErSmZUOQh5Ri4kkCrZmSlHzWxZnQcAEfWWKjVNSJAAdw7FEUhpM2TGJ9oTso335I0/APcZ8/h2LSF2PqPYWz9BGGj30NXsUKwQw2YmroKbIn4kk9S5vO+9StSsPO9/Xe+5/drx4RhoqK2BJV1JamsLUVlbSmqaEuST5M7iJELIe5FJQqqCVOzQbDnWLCjEcJ/kqxqM+YcnSx99NFH1K1blyeeeIJXX32VGTNmsGvXLrZt28bkyZMDEaMIgtqm1GQpBTwetV6AX91U5GEfhKtlnyMK6ihYxcD5vXaOrLFKsuRHik6H6eUXCXmuA5YpX5D84Xg8cXHYflqObdlKTL1eJnz8hygm091Plg3pFR2DTM/T1tCIsSlz2OE8yAHXSew4AEjCyl+uf/jL9c9Nr7tPyZ2aPJW8lkhV15ZFr8iopxAicArkgc1T4OfNkCDLeO/Kbrdx/vwFChYsgMFgDHY44g5yh0OrbDahxeff9ocOHeKrr77CaDSi1WqpW7cudevWpWjRoowZM4b3338/EHGKTJZW5OGKC044oITBzxcw35AsJe++liwBlGlq4vxeO2d32Um67CIsn0w+9ifFZCJ0YD9M3V8kecwnWCZOhZQUrFNn4Fi/iciFs9FVKB/sMAOmtLYI00PfBsDhcXLEfZp9zmPscx1PfRzjuPvcteMveWL53bmd353bIXXmcTFNAd4zdec5w+NoFbk/hRCBEWqCjk2CHUX2YLG4OHDgChUq5MNsDnY0IifxeYK+0+lEq1XfHOj1epKSkgBo0qQJv/32m3+jE0FzY5GHgJQQ10eBoYi6nXzruiUAPHD0d2lQGyia3LkJ/+h98h7ZjbHNkwA49/3D1ZoNsc7+LsjRZQ69oqOitgQdjE1539yDJeEfcTjXIuJyr2JzxAymhw6hr7E9jXUPcp9yfTreSfcFuiWP4sGEriyzb5I+TkIIIUQO5XOyVLVqVcaOHYvdbqdEiRLMmzcPgMOHD8sbhhyktB5ypd4dAV+39K8iD1HF9eQtowekhHhm0BYpTOQP8wifOBb0erBYSHixB/HdeuFJTg52eEERppipravIS8ZWfBzaj1URkziXexnnci3j29ARlNWoVT/3uY7zVNIgGiW+xibHnrucVQghhBDZjc/J0ltvvcVPP/2E3W6nW7dufPzxx9SoUYOOHTvSokWLQMQogkBRMqE5bdpUvOQ96sKoG6SNLp3eZsMaL31xAk1RFMx9XyNq8xq0JUsAkPL1XK7Waohz3/4gR5d13KfJzbPGpuyJnMs082AKKXkB2OTcwyOJr/JU4iD2OY8HOUohhBBC+Eu6RpbWr19PWFgYLVu2ZPbs2fTq1YtPPvmE4cOHByJGESRp65Z2pIAzEIOGaSNLrgSwnbrpj8o2VZMltxOOrQ1Utib+TV+zBlF/b8T4zFMAuA4c4mrtRli/+kZGjm+gU3R0D2nNwVwLGG16jVxKOADLHJuontCFl5JGcdJ1IchRCiGEECKj0tVUxGC4vtq/Vq1a9OjRg2bNmuXoBpf3orRkyeqB/bb/PjZdbqqId/MUpnzl9EQWUdfGHVkj65YykyYyksiFcwif+gkYjWC1ktD9dRJe6I47MTHY4WUpZiWEgabOHIlcyMCQ5wnBgAcPs+0rqRDfkf7JE7nijgt2mEIIIYRIJ5+TpWPHjtG3b1+eeOIJmjRpcstD5By1Al3kIaQsKKmJ97+KPCiKQpkmajmb6A1WLh20ByAAcSeKomB+9RWitv6BtkxpAFK+XUBMzYY4du8NcnRZT25NBKPNr3Io10JeMbZBixY7DibZFlImrj2jrLNI8sj6OyGEECK78TlZGjhwIKdPn6ZRo0Y8/fTTtzxEzlFAB0VTi8sHpMiDRg/miup28q2L46u0DUWrB5cDlva7ImuXgkD/QFWidmwgpFN7AFyHjxBTpxGWaV/KtLzbKKzJx+ehg9gTOYd2+sYAJGLhPeuXlI3rwGcp32P3OIIcpRBCCCG85XOfpejoaDZu3EhoaGgg4hFZTG0TnEoMcJGH5F1g2X3LH+Utpeexd3Pzy7BY4s+4WDYwhnaf50WjlememUkTHk7EtzPRN25IYt+BkJJC4qv9sP+xnogZU9BESOPgfyunLcaC8FFscx5gqOVz/nDu4JInljcsn/Cl7Sfmh42knLZYsMMUQgghxF34PLJUtmzZa72VRM6Xtm5pnw2S3QG4QGg19av1CLhunaZUpW0YD3QMA+DE5hQ2TooPQBDibhRFwfxKN6L+Wou2fFkAbAuXEFOjPo4dO4McXdZVS1eBVRGTWBn+CTW05QDY6zpGnfiXWWhbHeTohBBCCHE3PidLo0aNYtiwYSxdupQ///yTbdu23fQQOUta+XA38HcgRpeuFXlwg+Wf2x7y6OBcFK6urm3686tEDv0qaz+CRV+lMlHb1hPS5TkAXMeOE/NwE5InTMLjkOlld/KYvjZbI75kgrkvOrQkYeW55OH0TZ6AzSPr8YQQQoisyudpeGvXrmXDhg2sW7fulj9TFIUDBw74JTCRNTwYombUbtR1Sw3Mfr6A+V8V8cJr3nKIVq/Q+pO8zOlwkaRLLla+E0NUCR35yhpuOVYEniYsjMhvpmNo3JCE1/uDxULSgKFYZ84mfOI4jE0bBzvELEmjaHgj5FlqayvRKfldzrgvMdW2hL+cB5gfNpLi2oLBDlEIIYQQ/+LzyNKXX35J3759Wb58OWvWrLnpsXq1TCvJacI0UNGobgdk3ZLhPtDnV7dvU+ThWhx5tbT5JA9aPTisHpa+cZWU+EDMCxTeMnV9njzb1qGrWQMA1z8HiXusFXFtO+GKPhHc4LKwuvrKbI+YRXP9QwBsdx2gZkI3frZvDHJkQgghhPi3dPVZ6tGjB6VKlaJw4cK3PETOk7Zu6c9AtTtKm4pnuXOyBFCompGm/8sNQNxpJ8uGXMXtkopswaSrWIGoP9cS8dVUNPflA8D2w89cqfAgScNG4klODnKEWVNeTS5+ChvHSFMPNGiI8yTydNJghlim4vA4gx2eEEIIIVL5nCy1a9eOX375JRCxiCyqTuq6pZNOOB6I5RVpU/GS98BdylFXfSaMau3VSozRG1LY9FlCAAISvlA0GkwvdSHP4V2Y+/cBnQ5sNpJHjuFK+RqkLPheyozfhkbR8LbpRX4N/5T8ShQA41O+pWliH866Lwc5OiGEEEJAOpIlu93OyJEj6dChA2+++SZvvfXWTQ+R87QMhbRi3QsSA3CBtJEl51Wwn7/r4Y++nZtC1dT1SlunJ3B4lRR8yAo0kZGETxhNnj1bMTyuNqh2nzlLfMeuxDZqLs1s76Cx/kG2R86ioa46AJuce6gZ35VVjr+CHJkQQgghfE6WDh06ROnSpTEajVy5coVLly7d9BA5TxE9NEgdXZoXiIGcm4o83Npv6d90BoU2n+YlNJ96+64YGsOVo1KJLavQVShPrl+WErl0PtoSxQFwrN9ETI16JLzWD/fVq8ENMAsqqMnLb+GfMiSkCwCXPXG0TOzP+9aZuDzSjFkIIYQIFp+r4c2ZMycQcYgsrlMErLfCXhvst0Elox9Pbq4Aig48TnUqXu4Wd31JWD4tbT7Oy/xul1ILPlzh+Xn5CYlI1zI84WeKohDS5kmMzZpi+XgySR+MA4sF6+dfkjJ/MWGj3sXU4yUUnc8/gnIsnaJjlLkn9XRVeTH5fWI8Cbxv/YrNjj3MDhvOfZrcwQ5RCCGEuOd49c5yy5Yt17Y3btx4x8emTZsCFqgIrnbhoE3d9vvoksYIpvLq9n9UxPu3wtWNNBmqvoGMPelk+ZCreNyyNiYrUUJCCB06kLyHdhLSqT0AnthYEl/vT0yNetjXrg9yhFlPC0NdtkfMoo62EgCrnduoGd+VjY67j7oKIYQQwr+8+li3Z8+e7Nmjvont3r37HY+TPks5Vz4dPBYKvySrydLIvKAod3+d18xVwbLvrhXx/q1a+1Au7rezZ3Eyx9ensGlqAvV7R/oxMOEP2iKFifxuFqZXu5PYdyDOXXtw7t1PbOOWGDs+Q/gnY9AWyB/sMLOMotoC/BHxGUMsU5lkW8g5zxWaJPahX8izvGpsKz2ZhBBCiEziVbJ0Y/W7NWvWBCwYkbV1ilCTpeMO2JYCtU1+PHloVbjyHVgPgtumjjZ5QVEUmvwvN5ePODi/x86WaQnkr6CnTBN/d88V/mBoUI+o7Ruwfvk1Sf8bgedqDLb532P/ZTVh40ZheqkLikamUgIYFD0fh75BfX1VuiePJsGTzISU7/g4ZR4t9HXpaXya5vo6aBXt3U8mhBBCiHTx6l1JoUKFrm1Pnjz5tv2VIiMjGTlyZMACFcH3VBiEpI4m+X0qXlpFPI9TTZh8oDMotPkkD+Y81ws+XD0mBR+yKkWrxdzzZfIe3oWp50sAeOLiSHylN7GNW+A8eCjIEWYtbQ2N+StiJq309dGgwYOHFY7NtEkaSNn4Dnxknc1Fd0ywwxRCCCFyJK8/wo2LiyM6OpoVK1Zw4sQJoqOjb3ps3bpV1izlcBFaeCJM3V6QCH7tB3tjRTwf1i2lCc+vo80nedHowJ7sYWm/K9gS3X4MUPibJiqKiGmTyL3hN7QVygFq1byr1eqS9P5oPDZbkCPMOkpri/BD+BiORi7i7ZAXr/VlOum+wDvWLyge9zTPJQ1jnWOn9LQSQggh/MjrZGn58uU8+eSTOBwOWrRoQcuWLW969O7dm1q1agUyVpEFdApXv553wgZ/tjcyFAKd+gYwPckSQJEaRh4dkguAmGgny9+Wgg/ZgaH+w+TZuZnQ998BgwHsdpKHf8DVB+pi3yAfwNyoqLYAI809iM61hHmh7/NIam8mB04W2tfQJLE31RKeZ0rKIuLdSUGOVgghhMj+vK7b27lzZ1q1asXDDz/MzJkzb/lzk8lEhQoV/BqcyHpahkG4BhLdMC8RGoX66cSKoo4uJaz1qtfSnTzwbBgX9zvY+0Myx9ZKwYfsQjEaCXt3CCEd2pLQow+O9ZtwHTxMbMNmmHp0I2zMSDS5cgU7zCzDoOhpb2xCe2MTDrhOMD1lKbPtK4n3JPGP6wT9LJ8y1DKNTsbH6Gl8mhq6csEOWQghhMiWfFpJHRERweLFi6ldu/ZNj/Lly1OlShV00jMlxzNp4OnUqXjfJ4DdnwM3odXUr+kcWQK14EPTd3JToLIBgC3TEtg0NV6mJmUTunJlyf3HSiK+/AwlNTmyTp/F1QoPkrJoifw73kYFbXE+Ce3H6Vw/MiP0bWpq1TL8FlL4yvYztRNeom78K8yxrcTmsQc5WiGEECJ78bnslE6n47nnnrv2fODAgdSuXZv69euzf/9+vwYnsqaOEerXGDesSvbjidOKPDgugv1iuk+jMyo89WkeIouoVcI2T03g94/iZEpeNqFoNJhefpE8B3dc683kvnCR+A5diGvdAdep00GOMGsyKyF0Mz7J1siv2BLxJV0NT2BCrSq5zfUP3ZJHUTKuHSMsX3HBfTXI0QohhBDZg8/J0ocffnhtut2WLVtYs2YNs2bNonPnznz88cd+D1BkPU1DIU9qtWK/VsW7sciDZW+GThVeQMdzs/OTt7Q62vn3t0msfCcGt1MSpuxCmz8/kd/NIteKJWiKFQXAvmwlVyvWxDLxMzwuV5AjzLpq6SrwZdhQTuVaysfmNyiruR+Ai54YRqbMpGRcO7omjWSH07fKk0IIIcS9xud5c3v37mXy5MkArFq1imbNmlG3bl0efPBBvvnmG78HKLIevQLtw2FaHCxNBIsbzP5ojWOuiJq/u9WpeLmaZuh0Yfdp6fj1fSx+9Qrn99rZ/5MFW6KbVuPzojP6s6OuCCRji8fJu38bScM/wPLJFDzJyST2G4x17gJMXTqluzuytnQp9HVro4nMuWvacmsi6BvSgd7GZ/jV8SeTbYv4zfEndhzMtf/CXPsv1NNVpW9IB9roG6BTZCq1EEIIcSOffzO63W7MZrXh59atW+ndu7d6Ip0Om5T6vWd0ilCTpWQPLEuCDhF+OKnWDKYyYD0ElvSvW7qRKZeWDl/l44e+Vzi11cbRP1JY/Oplnp6cF0OoND/NLpTQUMLHf0jIcx1IeKU3zr934dz+N4nb/87giRV0VSqhr/cQ+np1MdR7CE2xoijpTMCyKo2ioYWhLi0MdTngOsGUlEXMsf2ChRQ2OfewKWkPRTX5edXYjpeNrYjS+OM/tBBCCJH9+fxusVSpUixatIgff/yRM2fOUL9+fUCdklewYEG/ByiypvomKJKaas8PxFS8DBR5+DeDWUO7qfko09QEwKm/bCx46RKWWJnGld3oazxA1J9rCZswGiUqKuMn9Hhw7tmH9fMvSXj+Za6UqMSVImWJ6/AClklTcezYicfpzPh1spAK2uJ8FjqQk7l+4CPTaxTV5AfglPsib1unUjzuaV5LHss/ruggRyqEEEIEn88jS/369aN3797YbDbefPNNIiIiiI2NpXfv3gwdOjQQMYosSKPAsxEwIQZWJEO8CyK1fjhxaFW4uggs+8HjBD9NC9IZFFqPz8Ov78Wyb2kyF/Y7mP/iJdpPz0d4AZl6lJ0oOh2h/ftgfrM3pLc6nsOBc9ce7Bu34Ni0BcemrbgvXQbAfe48tkU/YFv0g3q90FD0dWpeG33S162NJiL7j7zk1kQwwNSZfiHP8pNjA5NSFrHRuRsLKUy3/ch02488pqtND6U1RQgLdrhCCCFEUCiedNTidTqd2O32a9PxAP7++29q1Kjh1+D8yWKxcODAASpUqHBT3Pd6LBmxIwVqnlC3ZxWArrn8cNKrP8HBNup29X1gruSHk17n8XhYOz6O7d+oDTsjCmnpMCMfuYvp/XqdYMgp91UweDweXMeO49i0FcfGLdg3bcF14NDtD1YUdFUro29YD0ODh9E3qIe2QP7MDThAdjoPMzllIfPtq7HjuLa/Ukoxvo54l+ph0ktP+If8vBL+JveU8JW394zX0/AOHDhwbVun091y0ho1ajBt2rR0hCqyqxpGKJOaY8xL9NNJ03otgV+n4qVRFIVGA3JRv4+6qD/hnIvvXrjExYPSf+ZepigKutKlML3YmYgZU8j7zw7yXTlJrp8XYR7yFvr6dcGoluHG48G5ey/WydOI79CFKwVLcaVMNeJffg3r13NxHjuebftBVdeVZWbYO0TnWsJw08sUUPIAsD/kJPXtrzLe+i0uj0xfFUIIce/wOlnq2LHjTc9ffvnlW475/PPPMx6RyDYURS30ALAmGS75Y2mHsShoU08agGQJ1DfGdXtG0PSdXKCAJcbNgm6XOPO3FCgR12ny5MH4ZAvCR48gasMq7os/R+7NawgbOwpj6ydQcue+dqzr6DFSZs4moVsvrpauypXCZYh7tguWKdNw7NmHx+0O4nfiu/yaKN41vcTxXIsZqXsFg1uHDQdDrFN5JPE1DrlOBjtEIYQQIlN4nSz9+5PS7du33/WYuzl79iw9evSgTp06NG7cmHHjxuG+w5uKefPm0axZM6pXr06bNm1YvXq1T9cSgZHWoNYFLPLH6JKiXG9O66eKeHdSvWM4T3wUhUYHtkQPi3pc5vgGa0CvKbIvxWjEULcOoQP7kevHBeS7cpI8e/8kfOonhHRqj6ZwoWvHus9fwLZwCYl9BhBT7SEu5ylK7JPPkDzmY+xb/8LjcPzHlbIOg6LnDV0HZp8bRA2lHABbnft4ML4rn6bMl1EmIYQQOZ7XyZI3pXR9Lbfbp08f8ufPz+rVq5k1axarV6++ba+mX3/9lQkTJvDhhx/y119/8fzzz9OvXz9Onz7t0/WE/1UwQrXU2Ul+a1AbgIp4d1LxiVCemqj2XXKmePihzxUOrrQE/Loi+1M0GnSVK2F+9RUiv5tF3tOHyHt8HxHfTMfUvSvacmWuHeuJi8O+/BeShgwjtu6jXM5dhNhmbUgePR775q147Fl7GmgJRwFWGybygakXBvSkYGeAZTKPJvbmqOtMsMMTQgghAiZojWb27t3LwYMHGTBgAOHh4RQvXpyuXbuyYMGCW45NSUmhf//+PPjgg+j1etq3b09oaCi7du3K/MDFLdKm4m2ywil/fGCeNrJkPwOOGD+c8L+VesTEM1/kxRCm4HbCz4OusmthUsCvK3IWRVHQliiOqctz6rqngzvJe+EYkd/PxfzGa+hqPAAa9UeuJzkZ+29rSBr6HrH1mnIpdxFiH2tF0gdjsW/cnCWTJ52iZbDpBf6KmEkNrTrKtMm5h+rxXZiSsgi3J3tNNRRCCCG8EbSayfv376dw4cJERkZe21epUiWio6NJSkoiLOx6qdo2bdrc9NqEhASSk5PJn9/3ClRWa/CnWaXFkBVi8Yc2eoUhqD2MZl+10z8iY4uXNNqyhKRup8Rswx3eIIMR3l2eivDU1Ah+fiMBa6yHVe/Hkhxno/rzpoBf219y2n2VI4SHQ4vH0bZ4HBPgSUjEtflPXBs349ywGffO3eBygcWCffUf2Ff/QTKAyYS2dk20DR9GW/9htLVqoKQVmMhk/76vSlKAVbpP+URZwBjnXKzY6Gf5lO+tv/OZfgAlNNJvT9yd/LwS/ib3lPCVt/dK0JKluLg4Iv7VqyQtcYqNjb0pWbqRx+PhnXfeoVq1atSuXdvn6544ccLn1wRKVoolo6qGlGOPNoy5V520OHvg7i/4DxqPluqp25dOrOayLm/GA/RS1cFado3NhS1Gy+bJyTjyniG8WPZqSpqT7qscqVhhKNYeOrdHSbZg2L0X49+7Me7YieGfQyguF1ituNZtwLVuAwAeowFblUpYmzUhuVUL0GX+j+5/31etqEE5Q37ezzuXI8azbPTs4aGU7vSJacPTifXQBG/igshG5OeV8De5p4S/ef0b1+Fw8NZbb93xOaj9l3zha0EIh8PBkCFDOHr0KLNnz/bptWmKFy+OyRTc0QKr1cqJEyeyRCz+8mKijrdi4ZDWjLZ0RcrqM1Y62b2/BBp7NIUiL5O3aCb2dqkAZcu7mNcpDqdV4dyP99FmaoTP6/GCISfeV/eEmg9CanFRT1ISrq3bcG3YjHPjZtzbd4LTiWKzE7J9JyHbdxL13SKMbw9A1/EZFK0/OkH/t/+6rypQgRaeRxjn/I7xru+wauyMzbuIv+47yhT9WxRVckb/KeF/8vNK+JvcU8JXaffM3XidLD344INcunTpjs8Bn5rSRkVFERcXd9O+uLg4FEUhKirqluNTUlJ47bXXsFqtfPvtt+S+oWyvL0wmU5ZpVpaVYsmozgYYGAtuYKndxHuRd33Jfwt7AGKi0dn+QZfJf0fm0lDnZTebpiRwdoeTc39qKPNo9vnBm5Puq3uO2Qytn1AfpK5t2vIXjrXrSVm6DNf+A3iiT5LSow/ajycTNuJ/GJ95GkUT+FGcO91XZuADXqWd81FeSh7FPtdx1rp3UtfWg3HmPrxsbJUtPmwQwSE/r4S/yT0l/M3rZGnOnDl+vXDlypU5f/48MTEx15KjvXv3Urp0aUJDQ2861uPx8Oabb6LT6fj6668xBmnuvriz/DpoYoZVFrVB7fC8ahXwdAutCjE/gGUfeFygBP4T9BvVejGcPYuSSbzoYt2EOEo2CEGrlzd8InMpoaEYmzbG2LQxoe+/i23REpKGf4Dr0BFcBw8T/+yL6KqNJ2zkuxiebBHUpKSGrhx/RnzFSOssxqbMJRELvSxjWOnYwpyw4ZiVkLufRAghhMhigjapvGLFilSpUoUJEyaQlJTEsWPHmDVrFp06dQKgefPm13o5/fzzzxw9epSJEydKopSFpVXFO2yHnRnt75pWEc9thZRjGTyZ7/QmDQ3fTF1Dd9LJzvlSHU8El6LREPLsM+TZt42Ir79AW6I4AM7de4lr3YGYhxpj+22Nz9Ob/cmoGBhl7smmiOlU0Kjx/ehYz+OJb3DVHR+0uIQQQoj0CuoK3EmTJnHp0iXq1atHly5deOqpp3juuecAiI6OxmJR+90sXryYs2fPUrt2bapUqXLt8c477wQzfPEvT4eDIfWD7Qz3XErrtQSZ0m/pdiq0NFOgsgGAzZ/HY42TBpwi+BSdDtOLnclz8G/Cp0281gzX+dd24pq1IfaRZtjXbwxqjLV0FdgWOZP2hkcBtZHtIwmvctJ1IahxCSGEEL4KWjU8gAIFCjBjxozb/tmhQ4eubd+uUa3IenJpoUUo/JgE8xNgTD7QpHdWUEhJ0JjBbVGTpbzP+DVWbygahcaDcjGvyyVsCR42f55Ak7fTt1ZOCH9TDAbMPV/G9GJnrF98RfKH43Ffuoxjw2ZiH2mO4fEmhI18F33tmkGJL0Qx8m3oCAoqeZhkW8RB90nqJ/RgWfgEqunK3P0EQgghRBYgtV2FX6VNxTvjVJvUppuiAXMVddsSnJElgCI1jJRrphZ32LUgiZhof3TdFcJ/lJAQzG+8Tt7j+wj76H2U1OI39t/WEFOnEXFtnsWxe29QYtMoGiaY3+Aj02sAnPdcpXHi6/zh2BGUeIQQQghfSbIk/KpVGISmjibNz+hUvLR1S0Gahpem4Zu50OrB7YS1E+KCGosQd6KEhhI6uD95o/cR+t5QlPBwAGw/LSfmgbpcyls0XY+kYhXI+1p/7FO+wHnU9/WDiqIwwNSZb0KHoUNLgieZJxLfYpFtjb//CoQQQgi/k2RJ+JVZA23U92gsSgRnRtaah1ZTv9qiwXokw7GlV64iOh7son5Tx9amcHJrStBiEeJuNJGRhA0fSt7ofZiHvKWWIwc8V2PS97hylZC/dmAb/C5Xy1TjSvnqJA4Yin3tejwO70daOxub8VP4OEIxYcfBc8nDmZSyMFB/DUIIIYRfBHXNksiZOkXAdwlw2QVrkqFZWDpPFNUGovuBxwlnx0PpL/wZpk8eeiWCfT8kY4lx88fYOLosyo9GK6XERdalyZOH8NEjMPd7jZS5C/DEp68anT0+geTf1mA4eBgA16EjWA4dwTJhEkpkJIbmTTE+2QJj86Zo8ub9z3M9rq/D7xFTaJU4gEueWPpbJnLOfYUPTb3QKPLZnRBCiKxHkiXhd4+HQm4NxLrVqnjpTpaMRSDf83Dpa7j0DRQdAYYC/gzV+1DCNNTrHcmq92O5fNjBvh+SqfpMer8xITKPNn9+Qt/qm+7XWywWort0pFxkLjR/rMe+7Bdsq/8AiwVPfDy2BYuxLVgMGg36unUwPtEMw5PN0VWudNu+Tw/qyrMhYhpPJL7FUfcZxqd8y3n3FWaEvo1B0WfkWxVCCCH8Tj7KE35nUOCZ1Kl4PyRBijsDJys8SP3qscG5iRmOLSOqtg0lbxn1zdyGyfHYkzPyjQmRvWgKFcT8Sjdy/biA+66cJNeKJZhe74Gm6P3qAW43jk1bSBr6HjFVH+JK8YokvP4mtl9X47HbbzpXKW0R1kdMo6a2PADf2n+lTeIgEj3Jmf1tCSGEEP9JkiUREGlV8RLcsCIj73/MFSCqtbp94XNwZrRqRPppdAqNB+YCwHLVzdYZwYtFiGBSTCaMLR4nYsrH5D3xD3n2/knY6BHo69UFjfprxX3qNNapM4hr/hSX8xUn/rlupCxcjDtB/X9znyY3qyMm00z/EACrnH/RNKEPF90xQfu+hBBCiH+TZEkEREMzFEyd5JnhBrWFB6tfXfFwIXjrlgCKPxxCyQYhAGyfnUj8OWdQ4xEi2BRFQVe5EqFD3iJq4yryXTxOxJwvMT7bDiUyEgBPQgIp8xYR/+yLXM5XnNiWbbFMn4npYiJLw8bwgqE5ADtch2iQ0JOjrjPB/JaEEEKIayRZEgGhVaBD6lS8ZUmQ4MrAySIehoj66va5T8Bty3B8GdFoQC4ULbjssP6T9C2aFyKn0uTNi+n5juSa/w35LkWT67efML32CppCBdUD7HbsK38jsWdfrhQqTWL95kyeGsaHZ9SE6bj7HA0SerLNeSCI34UQQgihkgIPImA6RcDEWEjxwI9J8EJkBk5WeDAkbATHebg8F/K/7Lc4fZWnlJ4Hng1j53dJHFxpoUbnMAo/YAxaPEJkVYrBgPGxRzE+9iieyRNwbv+blKXLsC39GdeBQ+Dx4Ni8Fcfmrbw4GJ6qUIhvmrv4paWFptV70ynkcbTp+ExPi4bG+gdprW+AVtEG4DsTQghxr5BkSQRM7RAoqYfjDrVBbYaSpdwtwVwJLPvh7Di4rxsEsdTww69G8M/PydgSPfwxNo7O395328pfQgiVotGgr10Tfe2ahH/4Hs5Dh7H9uBzb0mU4tvwJQOSBc/Q9AH0/ucj5gnqWtz7Gkmdys7u6CXz8/zXVtoSymvt5K+Q5njc2x6gYAvFtCSGEyOFkGp4IGEWBjqmFHn5JhvWWjJxMc70ynvUQxPyY4fgywpxbS91eavZ3fo+dgysz8s0Jce/RlStL6KA3idq8hrznjhI+bSKG5o+BXq04WfC8g+5fXGHFY0f4s84xPhiXQpPoPJTXFLvrI5+SC4DD7tP0tIyhdNwzjLd+S4JU2xNCCOEjSZZEQPXMBZEacAPPnoXzGamHkLcTGFLLFJ8ZAx6PHyJMvxrPhZGrqDo4u+7jeBwZqpEuxL1LW7AA5p4vk3vlD+S7cpLI+V9jfOYpCFGLqRQ5lkzXjw4xu+bvrHv8LH99U4/dKZ+wL9d3t32cyvUj34QOo7K2JADnPVcZYp1Kibi2vGP5QiruCSGE8JokSyKgiuphduq67gsu6HAWHOnNcTR6KNxf3U76ExLW+yXG9NLqFR7pr44uJV5wsWN2UlDjESIn0EREEPLsM+RaNFetrDdrGoamja+VJHdu20Fiv8FcKVyG2GZtsM7+Dndi4k3n0Cs6OhubsTNiNj+GjaOerioA8Z4kPkqZTcm4dvROHs9x19lM//6EEEJkL5IsiYBrHQ5v51G3N1ph8KUMnCx/d9DlVrfPjslwbBlVpomJ+2uqxR22fplA0pWMlP0TQtxIExGBqevz5F71M3nPHCbskzHoatZQ/9Dtxv7bGhJe7MHl+0oQ92wXUn5aflMDXEVReMLwMOsiPmdt+FSe0NcDwIadabYfKB/fkc5Jw9nlPByMb08IIUQ2IAUeRKYYmRf+ssIaC3wSCw+ZoENEOk6kDYMCveHMSIhdCcl7ILSq3+P1lqIoNB6Ui9nPXsRh8bBpcjzNRkQFLR4hciptwQKE9nud0H6v4zx0mJTvFpLy7UJcx45DSgq2hUuwLVyCEhVFyDNt0JYqedPrqwPfUZAL7sasd+xil+swbjzAd3zFd5TVFuURXXVKagujK1UCXfVqaEsUl8ItQghxj5NkSWQKrQLzCkGNE3DGCS+dh8pGqJieituF+sC58eC2wtmxUHauv8P1Sf6KBiq3MbNvqYU9S5Kp3imM+8pL5S0hAkVXrixhI94h9L3/4fxrO9ZvF2BbsBj3pct4YmKwTp91x9eGAS1THzc7D/zJjSUglIgIdA9UQV+9Grq0R4VyKKlFKIQQQuR8Mg1PZJp8Ovi+MOiBZA+0OwuJ6Zm1ps8H972kbl+eDykn/Bhl+jTomwu9SQEP/DEuDk+Qi08IcS9QFAV9nVpETBpP3rNHyPXLUkJe6IQSFQVGo9cPp1GHzaiQYlRw3tCWyZOQgGP9JiwTp5LQtScx1R7iUngBrtZsQMIrvbFMnY59y594kqXKnhBC5FQysiQyVR0TfJofXr8IB+3w0gVYWMjnFipQ+C24MA1wwbmPoeSkQITrtbD7tNR+OZxNUxI49aeNY+tSKN3IFNSYhLiXKDodxmZNMTZrmq7XJ3ksfGX7ma+Sl2I7cphK+6xU3mOl8l71kedq6ic7NhvOHTtx7th5w8UVtOXKXB+BeqAq+upV0eTN64fvTAghRDBJsiQy3au5YIsV5ibA94nqGqb+vi7zCSkBeTvAlXlw8Uu4/111xCmIar0Yzp5FySRedLF6VCyRhbXkKyPT8YTIDsIUM2+EPMsbIc9yKXcsW2rsZaNzD586dvO38yD5zqVQea+VSnuvJ1H3n3aoL/Z4cB08jOvgYZi36No5NUUKo6teDX31qqlfq6Eper+sgxJCiGxEkiWR6RQFvigAu22w1waDLkHNEGho9vFEhQepyZLbCuenQNERAYnXW3qThkfeysWyQVdJvODi2+cu0XxkFOWb+/qNCSGC6T5NbtoYGtLG0BAAiyeFbREH2FR6D5ta7WGmcy8JnmRyxTqpuE9NoirvUROpModT0Ka2XHOfOYv9zFnsP6+4dm4ld+5ryVPaCJS2XFkUnfw6FkKIrEh+OougMGtgcWGoeQIS3GrD2r9LQEFf7siwByBXM4j7VU2WCg8CbWiAIvZOhZZm7BY3az6IxWH18POAq1zYZ6dhv0g0Ovk0WYjsyKyE8Ii+Oo/oqwPg8rjY5zrOJvMeNuffy4pHdjPd0b1a7QAAPxZJREFUrfZECLG6Kf9PyrXpew/udVJmfzL6FLUjtyc2Fvvv67D/vu76BUwmdJUrookIz/TvLVhcWi3mh2vjKV8+2KEIIcR/kmRJBE0Zg9qw9qmz1xvW/l4U9L7kFIUHq8mSM0adjlfojYDF661qz4SRr6yeH9+8StJFF9u+TuTiATutxuXBHKW9+wmEEFmaVtFSTVeGaroyvEY7AE65LrDBuZu1hr/5vdZ25j544frxTg+ljtqotNdK431GHtzrosieS+hiUxtZW604t+0IxrcSVFG/rcG6YQshX01FW6xosMMRQojbkmRJBFWbcBgSBR/FXG9Y+3F+H04Q2QjCakHSNrXQQ4HXQBP8sr6FqhrpsiA/Pw24ypntNk79aWN2h4s89WleClSWdUxC5DRFtQXorC1AZ2MzPB4Px91n+cPxN384d/CHsoPD5WM5XD6EH9qnvsATRtEzLlrvi6LJ/hDKHbSR12lGuUeK1Dr27ccTfRLXmrVcrVybsLEjMfV8GUVzb3z/QojsQ5IlEXQj88FfKfB7ehrWKoo6unToGbCdgivz4b4XAhqvt0LzaukwIx/rPo5jx5wkEi+4+K7LRR57NzdVng4LdnhCiABRFIVS2iKU0hahO63xeDzsd0Xzu3M7vzu2s965iwSSOXW/jin3JzClRQIApTXhfGjuxdP6Rjm+CERyTAzn3nqbiNnz8CQlkfjam6TM/56Ir6aiK10q2OEJIcQ18hGOCDpdasPawqmp+0vn4R+bDyfI8xSElFW3z46FLNTjSKtXeHRwbp4YE4UuRMFlh1/ejeW392NwObJOnEKIwFEUhcq6kvQN6cDS8LFcyrWCTRHTGWXqSRNdTUJQR5uPus/QIekdGia+yhbHviBHHVhKSAgJvXtgXvcLuiqVAHCs38TVqg+RPGESHld6mvAJIYT/SbIksoT7MtKwVtFC4YHqtmUfxK747+ODoOIToXT+9j4ii6hrlnYvTGZe10skXZI3BELca3SKjjq6SgwxdeHXiIlcyf0Lc0KHU0xTAIAtzr00SOzJs4nvcNR1JsjRBpa2xgNEbd9A6HtD4f/t3Xd4FOXax/HvbM9ukk1CgFAFpIYqLTTpiIgogoKgIspRQCwHxS4HVI4e9OBRsaJUwYoFAV8QRIoQ6ULA0Ak1ISE92ZIt8/4xIQgEaUk25f5c11zZnZ3dvQeezOa3zzPPGI3gdJIz4QXSO/fGu/vPQJcnhBASlkTp0SH/grVw9oK1l91JVOU+MFbTbp+YWiz1XasqjUzc91VV6naxAJC4I4+5dyVxfOuVdKMJIcobi2JmmPkmdts/Z2rQOMIUbVa8bz2/0jzzHsbnvs1pf0ZgiyxGislE8KQXiNi6DkPb1gB4Nm4mtXUXcqZMRfV4AlyhEKIik7AkSpWxYXBv/vlKZy5Ye1l0Zqj+T+121jrIii2G6q5dkF3PoPcj6Tha20lHqp+vRiWzbUE2aikaPiiEKHkWxcxTQcPZa/+KJ8xDMWLAg5fp7m9olDmUN50LcKnl98sVY/NmRMSuInjqq2A2Q14euRNfJa19NzzbdwS6PCFEBSVhSZQqZy5Y29ys3X8mGdY6LvPJUaNBn5+0SmnvEoBOr9DlMTsD343EZFPwe+GX1zP46YU0PE5/oMsTQgRYJZ2dabbH2WVfwF2mngBkqjk87/yA6MxhLHAvx6+Wz2OFYjBge2Y8lXbEYuzcEQDvHztJa9eVnJdeRnWX37AohCidJCyJUufMBWtDdeADBhyHGRngv1THi8EOUWO122mLwBFfzJVemwY9g7jvy6pUqqfNbPHnYgef35dMxnFvgCsTQpQG1+tr8kXwq6wPnUFnQwsAjvpPcX/uK3TI+gerPOX32kyGRg0JX7uckHffBKsVfD5y//0mqTd0Iu/3TYEuTwhRgUhYEqVSAxN8Vk1roFl+GJ0EPY/C/rxLPLH6E6DkX8foxJvFXeY1i6hr5N4vqtKwTxAAyXs8fDb0FEd+dwW4MiFEaRFjaMrqkA/4Nvh1GuhqAbDNt5ebsh/ntuyn+dN3OMAVFg9Fp8P62Fgid23C1Ks7AL74vaR36kX2U8+jOi532IEQQlw9CUui1LotBNZfB03zs88aJzQ/DP9JhYvOum2qBlXu126nzAd36Z9JymTTcdtbleg63o6iA1emn29Gp7DlMzmPSQihURSF201d2Wmfz7vWJ4lUwgD4ybOBVpkj6JD5Dx7IeZWpzs9YnLeO/b5jeNXy0Uutr1uHsBWLCfnkPZTQUFBVHG9NJ7VFB/JWrw10eUKIck7CkijVOgTBtrowOVKbVtytwvMp0D4Btl6s86XGBEAB1QMnppVcsddAURRiRoUy+MNIzKEKqg9+nZrB/72UhtctgUkIoTEqBh6xDGav/SuetdyHBRN+/GzxxfNZ3jJedH7EHTnP0STzbkLTe9Mq8z7uzpnIy46ZfO1eyU7vgTI5SYSiKFj/MZJKuzdjurUfAL6Dh0jvcQtZj/wTf3Z2gCsUQpRXEpZEqWdSYFIk/FEXOmqj1fjDrQWmZ5LBcf55zkENodIg7Xbi27CzI6R8Cf7SP/1s3c755zFdr53HtHuRgy9HJpN9qnx8QyyEKBp2XTD/to4h3v4lEy0PcoexG010dTCgL9gmDw+7fIdYmLeKV12zGJ47idZZ9xOa3ptGGUMYmP0Mzzk+YK57KRu9u8lScwO4R5dHX7MGYT9+Tej8mSgREQA4P/yU1GbtcS9fGeDqhBDlkSHQBQhxuaLNsK42fJCu9S7lqvBmGnyXDTOioKftLxvXfhUy14D3NGT/ri2mGhD1CEQ9DMbIgO3HpYTXNnLv51VZ+nwaB1Y5SYzL47Ohp7j9f5HUuMEc6PKEEKVILX1VJllHFdz3qF4O+I+zx5dAvO8I8b7D7PEdYY/vCE60HiU/fg76T3DQf4IlnvXnvF4NpTKN9dfRRF+Hxvo6NMlfKithKIpSovt2MYqiEHTPUEy9u5P92ATc33yP/+gxMm4eiGXkvYS89Tq68PBAlymEKCcUtYKcFOFwOIiPj6dJkyZYrVappYw76oExSfB/f/kidJQd3qwC4We+WPVmQvJsSJwOrkNnN9RZoPK9UO1xsDUv0bqvhOpX2fBhFhs+zAJAZ4A+L4XT4s7gQreXdiWKg7Sr8sGv+jnqP0W8L4F4XwJ7/FqY2uNLIF299BC2CCU0PzzlByldHZrq61JLX/Wq6inKduX69geyxz2J/1QyALqoqoR8+DaWgQOu6XVF2SLHKnGlLrfNSM+SKJNqG2FpTfg8C55IhlQfzMyEpTnwXlUYHIo2lXj1f0K1xyBtCSS+A5m/gt8Fpz7VFntPqPYERPQHRX+pty1Rik6h8zg7lRsZtWswOVSWT07n1B4PPZ8NQ28sHd/yCiFKP52io46+GnX01ehHx4L1qqqSrKbzZ0EP1NkQdVI9XbBdmprFBu9ONnh3nvO6/Ywd+cT2PFG6SiW2L+ezDB6IqUdXssc/h2ve5/iTTpF5xzBcQwcTOv2/6CpXDlhtQoiyT85ZEmWWosA9doivC/fmX4s2yQd3noRBx+HkmVOUFD1Uuh2arYJWO6DKKFDyh7NlroI9t8O2hnDybfBmBWJX/lbD3lbu/bwqYbW07zb++DKHrx9KITfVF+DKhBBlnaIoVNVF0MPYhrGWQbxje5KfQ9/haPgiUsOW81vox3xqe4GnLMPpb+xMPV11FM5+UfN/nlhaZY5gcd66AO4F6CIisM+dQdjSb9HVrAGA+6tvOd2kDc4vvpaZRYUQV03CkijzKhvgs+rwU02ond9X+n0ORB+G+ZnnbWxrAQ0+hXbHoPYUMFXX1rsOweHxsLkGHHoCnAdKdB8uJbK+kXu/rEKdThYAjm9x89nQU5z681IXnhJCiKtj1wXTwdCMkeb+TLWOY1HIG+wL+4as8F/YGjqHJ8xDATitZnBHznM8kvsGuaozoDWbb+lLpd2bCRr9IABqahpZwx8kc+Dd+E4mBrQ2IUTZJGFJlBv9gmFXXXg8HBQg0w8jEmFJTiEbGytDrRehTQI0/ByCY7T1/hxIfFfrado/CkrRdUqC7HoGfxBJu5EhAGQn+fh8RDLxP5X+GayEEOVHkGKmpaEB02yPsyzkbaor2oQ5M9yLaJf5AFu9ewJany40lNCP3iX8lyXo69YBwP3jUlKj2+KcNU96mYQQV0TCkihXQvTwTlXtYraV9aAC95yEvRe7rIjOCJWHQcvfoUUsRN4NigFQIXkWHHwEStEHq86g0H1CGP3/E4HBrOB1qSx5Jo01b2Xg95WeOoUQFUNvYzu22+dxh7EbAPv8x+ic9TBTnZ/hUwM7VNjUszuV4jZifeIRUBTUzEyyRj1CSuXrSL/1TnKmTMW98lf8mecPQRBCiLMkLIlyqWMQLKyhzWCS5YfbT0DmpT63QzpAoy+03qbQ7tq6U5/AscnFWuvViL7VxrB5VQipqk1KsWlWNkufzMaTK5M+CCFKViWdna+D/80ntuexEYQXHy86P6J39uMc9SUFtDbFZiPk7TcI/20F+kYNAG1oXt7SZeROfJWMPgNICa/J6aZtyRz1CI5PZuOJ24Xqk3NChRAaCUui3OpqhbfzZ7Xdmwf3JYL/cjpfzDWgyQ9ga6ndP/YKJH5UXGVetaimJu77qio1WpsAOBrrYcvL4aQdKj1DB4UQFYOiKDxgvpWt9jm000cDsM77Bzdk3c+X7hUBrg5MnTpQ6Y9Y7F/OIejxsRjatwWjUXtQVfH9uQfXrHlkP/wYaS06kBJWg/Re/cl5cTLuxT/hT04O7A4IIQJGwpIo1x4Jgwfs2u3FOfDy6b/d/CyDHaL/D8zXafcPPQKp3xVHidfEFqln6MwqtBqqXZHXecrAwgcz2b8qsCdZCyEqpvr6mqwN/ZAXLSPRoSNTzeHe3Mncn/MKmf7CTiAtOYrFgmXonYS+8yaVNq6mSlYi4Rt+Ifit/2AeMghd7VoF26o5OeStWkPua/8l47YhpFStx+l6zcgc/gCOdz/As2kLap5MsCNERSDXWRLlmqLAB1Vhtxs2ueCVVGhlgTtCLuPJpmoQvRziOoM3FfYOh6Y/g71rsdd9JfRGhT4TIwirB2veyMHjUPjh8dN0HhdKx9GhKDoZmieEKDlGxcDL1ofoY2zPyNxXSfAnsiBvOb95dzDX9i+6GFsGukRAC0+mjjGYOsYUrPMlJuHZuBnP75vx/L4Jz+Zt4HBojx1OwHc4AdcX32gbm80YW7fC2KFd/tIeXa2aKIocc4UoTyQsiXLPooPvaminIp3yaTPkbTRBtPkynmxtBNFLYVdP8Dsg/jZovg5szYu77CvW9A4LWbrjxH8YgTNNZf37WSTv8XDLaxGYbNKJLIQoWV2MLdkaOofHHW+xIG85R/xJ9Mx+lOcs9/GUenegyyuUvloU+oEDsAwcAIDq9eLd9acWnPIDlG/vfm1jtxtP7EY8sRsLnq+rFlUQnIwxbTG2bY1iswViV4QQRUT+ghIVQg0jfFsDjECOH24/DhmXe/5uSAw0XqjNkufLhD9vBteR4iz3qoU19DBkrp2optpY/P2/OFlwzynSj8p5TEKIkmfXBTM3+F8ssL2MXQnGj5/XXHO5KW888aajgS7vkhSDAWOrFljH/AP7nI+J3LOdymnHCFv2A7bJL2C6uQ9KWFjB9v7EJNzfLybn2Ymkd+9Hsr06qTd0ImvsEzjnLsC7dx+q3x+4HRJCXDFFrSAXHHA4HMTHx9OkSROsVqvUUkHNyIDR+ZMz9bPB4pqgv9wRE8nzYP/92u2gxtD8NzBWKo4yr8pf25VRZ+Hnyen8uUQbPmIJ1THgv5UKLmorxOWS45UoKkd9Sdyf+yrrvH8UrGutNGKM9Q6GmnpjU4ICV9w1UP1+fPsPnB269/tmvDt3wUVCkRIervU6nemBat8GXXh4CVdd/sixSlypy20zEpYqeC0V0Zgk+DhDu/18JXit8hU8+fhUOPKcdjukAzT9BfSl4//w/Halqipb5uWwZloGqh8UHXR70k7b+0NkTL24bHK8EkXJp/r4r+tzpjhn4+TsBfBCFRv3mW7mIfNAmhnqBbDCouHPycG7dbsWoDZuxhO7CX/SqYtur2/cEGPM2XOfDM2iUQxypsSVkGOVuFISls5Tmn6JSlMtFVGeCj2Owob8CeO+qg5DQi/zyaoKh8dD4jva/fBbocn3+ReyDayLtauEDS4WT0jFlaV9yxl9q5WbJodjtMgoXHFpcrwSxeFkbjLvnJzP0sgt7FHPHdbcydCCh823c6epBxblck4uLf1UVcV/7Phfzn3ajGfrdrjYjHpWK8Z2rbWep3atUUIuZ1aiis3tcnH02DFq16qF2SKjKIqTLsyOoV2bMv/Fq4Sl85SmD/zSVEtFleiFtglw0gtWBWKvgxaXe2xV/bDvHjj9pXa/yoNQ/1Nt6r0A+rt2lX7Uyw+Pp3D6gHbuUtVoIwPfiSS0WuBDnijd5HglisOZdtW4cWO2mfYzw/0D3+atJg9PwTYRSigjzLfwkPk2GumvC2C1xUN1u/HuiDsboDZuwXfocKDLEuKyBE99Fdsz4wNdxjW53M83+WpZVEjVDNoMeSYFHCoMPAGplzvhg6KDBnPA3ku7nzwLjk4srlKLRHhtA/csqEqDXto5Aaf+9PDZ0FMc3+a+xDOFEKL4KIrCjcZWfBY8maNhPzA1aBz1dTUBSFOzeNv1JU0zh9M76zG+cf9Cnuq5xCuWHYrZjLF9W6yPP4L989lEHoyj8qlDhP34NbYXJmDq2Q0lODjQZQpxIUVBVyki0FWUGPlaWVRYMUHwUVV4MAkOe2DYCfipFhgup4NIZ4bG38Gu7pC7HY7/G0xRUO3R4i77qplsOm7/XyViZ2Sx/r0sHGl+vnowmV4vhNNqiHwgCyECK1IXxlNBwxlvuZtfvduY4fqBRZ61ePGx2ruN1d5tVHGEM9Lcn4fMt1NXXz3QJRc5XZUqmAfcgnnALQCoPh++hCPgKT8hsbg4nS4OHTpIvXrXExQkw/CKk2K3o68WFegySoyEJVGhPRAG21zwXgascMDzKfBmlct8siEUon+CnZ3AfRgOPQ7GqhB5VzFWfG0UnUKnMXYqNzSx9LlUPA6VFa+kkxyfR68XwtEby/b4YyFE2adTdPQytqWXsS1J/lRmu5fwqftHjviTSFbTecM1nzddC+hjbM9o80D6GzthKAXnjRYHRa/HcH3Zn/CiJOgdDrx+L/pGDTDIkGFRhGQYnqjw3qoKXfNnrP1vGnyeeQVPNkVB05/BWBlQYd+9kPFrcZRZpBr0DOLeL6oSVlv7A2PHN7l80i+R5ZPS2LPMgfOyL0IlhBDFJ0pXieeD7mef/WsWB/+XAcYu6NChovKzZyODc56nXsZgJjs+5Zjv4rPNCSHE1ZKwJCo8owLf1ICa+V9MjkrSepsuW1B9aPIT6Gyg5sGegZDzRzFUWrQirzdy3xdVqdNZG66QneRj57e5LJ6Qyns3nmTekCTW/i+DIxtdePMqxDwwQohSSq/o6WfqyPchUzloX8hEy4NUVyIBOKmeZoprNtdn3snA7Gf4KW8DPlW+8BFCFA0JS0IAVQzwQ02wKOBS4Y7jkOK9ghcIaaudw6QYwJcFO2Ng/4OQG1dsNRcFi13H4A8i6T81gib9rVgj8g8JqjYJxMaZ2Xw9KoXpnU7wzegUNs/JInlvHhVkEk0hRClUS1+VSdZRHAr7lu+C/0NfYwcUFPz4WeJZz205T9Mg8y5ec84l0X860OUKIcq48jnIV4ir0MYCM6JgRCIc9ULPY/BiJRgcovU+XVL4TVB/Duy/X+thSp6tLfbeUONJCOurzaRXyuj0CtH9bUT3t6H6VVL2eUiIdXEk1s3xrW68bhWvSyVhvYuE9S4gE2slHdd1sFCnk4U6HS0EV9EHejeEEBWMQTFwm+lGbjPdyGHfST51/8hs9xKS1XSO+k/xL+cMXnHO5DbjjTxkuZ1ehrboSuExWAhRuklYEuIv7rPDdhf8Lx12uWHYSW2a8TFhMDoMql7qN6bKPRDcGk6+DSnzwO+CzJXaEtQYqo+HyveBPqj4d+YqKDqFKo1NVGlsov0D4HWrnNjuJmGDi4RYF8nx2oxMjlQ/8UsdxC91AFDjBhNtR4RQv2cQOr1MEiGEKFl19dX5t3UMk4JGsShvLTPci/jVuxUvPr7zrOY7z2qu19VglPk26gVgFj0zRrobWxOi2Er8vYUQ10bCkhDnebMK1DHC2+nalOKJXph0GqachqGh8Fg4tP+7rGNtAvU/huumQNJHkPg+eE6Bcw8cHA1HXoCosVBtnDZBRClmMCtc18HCdR0sdAMcaT6O/O7mSKwWnrKTtPMCTmzP48T2VMJqGWhzXzDNBtowWeUbXCFEyTIpRu4y9+Iucy/2+Y7yiftH5rqXkqZmcdB/ghecHwastlq6qsywPUcfY/uA1SCEuHLy14wQ59Er8HgE7K8Hi2pA7/wZSD3A/CyIOQIxCTA/E9z+v3khY2WoNRHaHoH6s8HaXFvvTYXjU2BLbdg/EnJ3FO8OFSFrhJ4mt1i5+dUIRq+oxqjFUXR90k5IlDYML+OYl19ey+Dj3omsfTuDnGQ5yVoIERgN9bV50/ooR8N+YK7tX3Q2tAhoPcf8p+iXPZ5Hct8gW80NaC1CiMunqBXkTG2Hw0F8fDxNmjTBGuD590tTLeLyxLvhvXSYmwm5f/mNqaqHh8O0YXrVjZd4EVWFzFVw8i1I/+ncx+w9ofqTEN7vqs9rCmS78nlU9q1wsHlONqf+PHvxRJ0BmvS30nZECFUamUq0JlE05HglikOg2lWaPwsX7hJ7vzNivbt4NPe/pKgZANTRVeNT2wt0N7Yu8VrKKzlWiSt1uW1GhuEJcRmamOH9KHitMszJ1ILTAQ+c8sGrqfB6KtwZog3R6xgESmGn7SgKhPXSFsee/POa5uaf17RKW4IawXWvQ6U7SnoXr4neqNDkFhuN+1k5vsXN5rnZHFztwu+F3Ysc7F7k4LqOZtqNDKFOJwtKof9AQghRvCJ0oQF538GmHnQ1tOLR3Gl86/mVBH8ivbMf41HznfzbOgabUjrPYxVCyDA8Ia6IXQ9PRMDeevBTTbg5/1xdL/BlNnQ+Cm0T4J00+MMFvov121obQ/2PoO0xqD0FjPnnLjn3wp47IWNlCexN0VMUhVrtLAx6rzKjFkfR8i4bBrMWjI7Eulk4+jRzBp0i7vscuXaTEKJCqawL58vgV/nc9jIRihba3nMvpE3mSNZ7dga4OiHExUhYEuIq6BToFwz/V0sLTo+HQ0j+b9M2N/wzGW5IgMj9MOAYvJkKm5zgOT8fGCOh1otawmowFwzhgB/23g2uIyW7U0Usoq6RmyZp5zZ1HhdacA2n0/s9LJuYzoybThL7cRbOTDmvSQhRMSiKwhBzb3ba5zPA2AWAA/7jdM9+hKcd7+FUS36IoBDi70lYEuIaNTTBO1XhxPXwXlVoYT77WIYfluTCMynaxBDh++Cmo/Dv07DO8ZcJInRmqDICGn4OKNokEHsGgc8ZiF0qUtYIPZ3G2hm9ojp9J4dTqZ42+jf3tJ/fpmcyo28iv3+Shcf5d7NlCCFE+RGlq8R3wf9hjm0idiUYFZX/ub6gXeYDbPTuDnR5Qoi/kLAkRBEJ0cO4cNhRF5Lrw7c14IlwuMEMZ87QyVVhhQNeOg1dj4J9P3Q/Av9KgV9yIdd+M9R+NX/jbXBonDYxRDlgMCu0uDOYB36IYvAHkdSO0VJlXo7Kuncy+bR/EjsX5uD3lo/9FUKIv6MoCveab2anfT43GzsAsMd/hBuzxvCi4yPcal6AKxRCgIQlIYpFZQMMCoG3q8K2upDWAJbUhGciIMYC+vzt3CqscWqTRPQ+BmH74DZeYGPEBG2D5NlwakbA9qM4KDqFel2DGDqzCvcsqELNtlpoykn2sXxyOrPvSGL/Lw4qyESdQogKroauMouD/8sM23OEYMWPn6muz4jJGsU2795AlydEhSez4QlRAsL00D9YWwBy/BDrhLUObdno0oKTF1icq7BYeZM+YbczMed5bjz0GNhaQkiHgO5Dcaje0szdsytzeJ2LNf/L5PR+D2mHvfzwRCrVW5ro9lQYNVubL/1CQghRhimKwoPmAfQytOOh3NdZ5d3CLt8hOmU9xHOWEdxsKn/H/6Lm8rtIMCeQ7VexeC0l+t5GDDTT18OsyCUyyiMJS0IEQLAO+ti0BcDlh00uWJoDH2VAlh9W6Luwwr6Orp41TNz/Nr2avoNirhrQuouDomg9TXU6W/hziYPfpmeSneTj5I48vhiRzPXdLXT9ZxiR9S91ISshhCjbrtNHsTzkbT52/8CzjvfJxckU12ymuGYHurSyoTqQl7+UsCDM3GhsRW9DO/oY29NMX08uk1FOyDA8IUoBiw66WmFqFThyPbwcCeH5v51rjd3oY/2STgdPszTLW15OYbqATq/Q7HYb/1haje4T7FhCtX+Ag6tdzBmUxLKJaWQlegNcpRBCFC9FURhjuYPt9rncaGgV6HLEZXLi5mfPRp5xvscNWSOolXE79+e8wmfu/+OkPyXQ5YlrID1LQpQyYXr4VySMD4cPMmBaSg4pBPO7rim3noTWZngpEm4P1qYwL28MZoV2I0NpPiiYTTOz2Do/B69bJe77XOJ/ctD6nmBiRoVisct3PUKI8quevgarQt7jT99hcnEFupxSz+VykXA4gTp162CxlOwwvHR/Nqu92/jFs5ltPu08syQ1lQV5y1mQtxyApvq69DK0o7exHd2MN8iFiMsQCUtClFIheni2EjwaZmHGng94UxlIoq4629ww6AQ0M8OLleCuENCXw9BkCdXRdXwYNwwLZsOHWcR9n4vXrbJpVjY7FubQ4R+h3DA8GKNFQpMQonxSFIWmhnqBLqNMcOgc2PJUmugaYzVYS/z9+5pigLGc9mewyrOFld7NrPRs5qj/FAC7fYfZ7TvMu+6vMWKgo6E5vY1aeGqjb4Re0f/9G4iAkb8yhCjlbHoD4xvexaHcbryf8wi1/McA2OWGYSeh6WGYlwnldcbtkCgDfV+O4IHvo6jfU/smzp2lsuatTN7vepLFE06zd7mDPIdcp0kIIURgRerCGGLuzQzb8xy0f8tu++e8Yx3PAGMXQtBCnAcva73b+ZdzBp2yHiIqoz9Ds1/iE9ciDvtOBngPxPmkZ0mIssBYGUvjL3gkrgv/cH/KvOCneN02hUNePXvz4P5EqKO3cI8hkutVKPnv1IpfpeuN3PFuJCe2u1nzVgYntufhcajsWeZkzzInBrNCnc4WGvYO4vruQQXnPAkhhBCBoCgKjfTX0Uh/HeMsd+JRvWzy/slKz2ZWejezyfsnPnykq9l86/mVbz2/AnC9rkZBr1MPQxvCdCEB3pOKTVEryMVMHA4H8fHxNGnSBKs1sH9KlqZaRBlzahYcGAWAN/xWvqi+iH+n6dj7l5l/aur9PBepY5RdmziiPFJVleNb3Oxd4WT/Sic5yb5zHtcZ4LoOWnCq3zMIa4QMb7hacrwSxUHalShqZbFNZfpzWOPdzkrPJlZ6NrMvf+TIX+nQ0VbfOD88taeDoSkmRWaHLQqX22YkLFXwWkQZdGAMnPpYu11rEr5ak/k2G15J8bPbczYdRenh6UowOgxs5TQ0Aah+lZM789i3wsn+lQ4yT5wbnBQd1GxjpmGfIBr0CiKkqnSoXwk5XoniIO1KFLXy0KaO+JK04OTdzCrPVlLVzAu2CSaIbsbWBT1PjXXXyRTlV0nC0nlK0y9RaapFlEF+N8R1g5yN2v0miyHiVnJyHXy8/yTzQ+rxx19CU6QenoqAR8IgtJx3sKiqSnK8h30rHOxb6STt8IVTjVdvaaJhnyDqdLJQ6XojuvI4O0YRkuOVKA7SrkRRK29tyq/62e7bpw3Z82xmvXcneXgu2K6GUrkgOPUytqOKLjwA1ZZNl9tm5CtWIcoanRkaL4QdbcCTDPvuhZab0Sk16O7LZEyUizWqlVdPw+8uOO2D51PgjVR4IgIeD4fwchqaFEWharSJqtEmujxuJ/Wgl30rHexb4SRlr/Yhc3JHHid35AGZGK0K1ZqZqNbcRLUWJqq1MBNcuZz+4wghhCgzdIqONobGtDE05tmg+3CoLtZ5drDSu5lfPJvZ6TsAwAk1hbl5PzE37ycAWurr0ys/PN1oaEWQYg7kbpQLEpaEKIvMNaHR17CrF/gyYc8gqP8LAIoCt9ignw1WOeDV07DGCel+mHwa3kqDR8O16zhFluMjgKIoRNY3ElnfTqcxdtKPeti30sn+FU4S47STvDwOlaOb3Bzd5C54XkiUnuotzwQoM1WbGDEGleNxjEIIIUo9q2Khrykmf4pySPKnalOUezazwrOJRDUVgB2+A+zwHeAt1xeYMdHZ0II+xvb0Nrajpb4+OkU+z65UOf5TSYhyzt4N6v4XDo8Hxy5MR8eB+mzBw4oCvWzass4BU1Lh51zI8sNrqfB2GowNhwkREFUBjgThtY3EPGgk5sFQck77SNzhJnFnHid35pG0Kw+PUxuRnJ3kY2+Sk73LnQAoeqjc0Ej1/J6nas1NhNc2oDPI8D0hhBCBEaWrxHBzX4ab+6KqKn/6Dhdc22mt5w9yceImj1XeLazybuF5J1RWwuhpbKsN2zO0o5a+aqB3o0yoAH8iCVGOVXsCsjfB6S8wZCykll6PzvEoBHXU/srPd6MVllthoxP+nQqLc8ChwrQ0eD8dHgqDMWHQxKSFrPIuOFJPg15WGvTSxij7fSqpBz0k7swjMU4LUKcPeEAF1QfJ8R6S4z388VUuoE0aYYvUE1JVT3CV/J9V/3I/SvspF8wVQghR3M5cvLipoR5PWIaSp3qI9e4qON9pq28PfvykqBl8lbeSr/JWAtBIV7vgfKduxtaEKrYA70npJBM8VPBaRDngy4WdHcERd3ad3g6hXcHeQ1tsLbS/8PP94dJ6mr7NPvelKuuhqxW6Bmk/W5hBVwHCU2Hycv0k7c4r6H1K3Okm9/SVXfjWYtddEKSsEXqC7DosYTrtp11HUJgOo1UplTMayfFKFAdpV6KoSZu6uHR/Fr96txaEp0P+Cy98a0BPe0NTehvb0cfQnnaGxhiU8t2nIhM8CFFR6G3QZBG+vSPQ5axHQdXOY0pfrC0AhggI7VYQnlpZm7KwhsJut9bT9FUW+IEUnxagzoSoMB10CYJuVi08tbZARRl9ZrLpqN3eQu32FkCbaS87yUfSrjwyT/rISfaSneQjJ9lH9intp/+8yfdcmX5cmX5S9l04g9H5dAYtXJ0fpCz288JVmO7sdnYdxqDSGbKEEEKUDuG6UAaZejDI1AOAg77jBcHpV+82MtRsvPjY4N3JBu9OXmEmdiWY7oazU5TX19WssJ81EpaEKA8sdXE3WM6+PzfSpHoKZtcGyPwVHDu1x71pkPa9tgAYK0NoN5rae/B5WA+mVW7MGqfCWgesccCf+Re5zfDDklxtAbAp0Dm/56mbFdpZwFxBRpopikJoNQOh1Qo/bKp+FUeavyA4ZZ/yknNKC1IF65J8BedGnc/vBUeqH0fqlfVe6Y38JUjpsYTq/jZcnVknQwSFEKJiul5fk+v1NRltuQOf6mOLb09BeIr1xuHFR6aawyLPWhZ51gJwnS6q4Fynnsa2VNLZA7wXJUfCkhDliE8JxRcWA9Yh2grPachcA1mr88PT7vz1KZC6UFuAasYo7rZ35257D6jWgxRDfda5tPC01gF/uEEFclVtkoif88OTRYEOQWeH7XUMAmsF/Rtc0SnYIvXYIvXQ9OLbed0qrkw/zkyf9jPDn3/fX9ATVbAuw48rS7vtdRUesnweyE3xk5viBy68rtTFGMyKFqQuFq5CtduKxUNOqp7cSD+mKBWDuWJ+syiEEOWRXtETY2hKjKEpLwaNJEd1sMbzB7/kXxz3T18CAEf8Scx0L2amezEKCq31DellbEcfY3s6GZpjVkyB3ZFiFNCwdOLECV5++WV27NiB1Wrllltu4amnnkKnu/CvrXnz5rFgwQJSUlJo1KgRL774Is2aNQtA1UKUIcZIiBysLQB5yWeDU+av4NyrrfckwekvtQWobKrBIHsPBtm7Q7UeZBjqst6lsCY/PG1xgQ9wqbDaoS2kageUdkFne546B5X/C+FeKYNZIbiKdv7SlfC4zg1Sfw1Yha/z4crw47vICECvWyXnlI+cU77LePdKbCIdSMcYpGAJvXi4OtPDVTCMMH+d3ighSwghSrtgxUp/Uyf6mzoBcMKfwkrPJlZ6NvOLZwvJajoqKlt9e9nq28sbrvlYsXCjsRW9DdqQvWb6euVqyF5Aw9Jjjz1G06ZNWblyJampqYwePZrIyEgeeOCBc7ZbtWoV06dP59NPP6VRo0bMmzePMWPG8PPPP8tJfEJcCVMViByiLQDuk+eGJ9dBbX3eCUiZry1AmLk2/e096J/f85RjrE2sk4Kep40ucKtav0asU1umpoEOuMFyNjx1sUIlCU9XxWjRhs6FXMFMr6qq4nWpl9175cw429t1/vlXZ3icKh6nNrTwiuq3Kn8zLPAv4eovQwUtoRKyhBAikGroKnO/uT/3m/vjV/3E+Q4WDNlb5/0DF3k4cLHc8zvLPb+DE6KUSvTKn6K8l7Et1XWVA70b1yRgs+HFxcUxdOhQYmNjsdu1cY9ffPEFc+fOZdmyZedsO3r0aOrUqcPzzz8PgN/vp2vXrjz//PP079//st6vNM2SUppqEeVHkbQr9zHIXH02PLkTCt/OUg9CexRMGOEyVmeTi4Kepw1ObWrywjQzQ7f8YXtdrRXjGk9ljaqqeJzacMH0JAf74hKoYq+F6jLmhyvfhYErUwtdFwtZV8tkUwgKOz9IFR6uCtaF6uQ6WKWcfA6KoiZtquS5VDfrvXH5vU6b2ebbW+h2TfV16WU4M0X5DdiUoBKutHClfja83bt3U6NGjYKgBNC0aVMOHz5MTk4OwcHB52x7yy23FNzX6XQ0adKEuLi4yw5LZzidzmsv/hqdqaE01CLKj6JpV5UgeLC21ADFfQRdzhr02WvR5axF5zmhbeY6pC3JMwHQR46mba23aGuFp6zgUWF7no7f3DrWu/RscOvIUrU/Xne5teX9DO2lxgZ7+G/EpWeLEyXPYIdgk4cIv4eadfwEBenQ+gsvMsmFquLJVXFlqbgyVdxZflyZ2n135tnbrkw/7iw1v0dLxZ2lol5kXou8XJW8XB+ZJy6/J0unh/ajrbQZWTo+kMWF5HNQFDVpU4HRkWg6Es1E4/2kGjJZ7d/Or/6t/OrbxjGSAdjtO8xu32HedX+NFQvzjBO5Sd8+wJVfflsJWFjKyMggNDT0nHVnglN6evo5YSkjI+OcUHVm2/T09Ct+34SEhCsvtpiUplpE+VH07aqdtuiexGw6Toh/S8FiJBUAb+rPxOfEn/OsEKBf/uID9uuC2K4LYZs+mG36EDLzr9+wPMvLqFPnPleUPlfVrkK1xYC2XOxyh6ofvE4Fb66CJ1eHJ0eHJ0fBm6PDk6to93N12uP5j3lydHgdCqgX9iD5fbB3dQbWmKuoWZQo+RwURU3aVGBFU5VobuER+nHUmMwmy142Bu1ha9B+HDo3DlwsS15HrYyQQJd62QI6AOZKRgAW1WjBOnXqEBQU2G8bnU4nCQkJpaIWUX6UTLuKBm4CwKOqeN370Tm2g60tTczX/+0zmwF35N/2q3ns8XjY6dHRzgTXG5sUU73iWpXm45XqV3HnXNiL5XOp1O4URnCVaoEuUVxEaW5XomySNlX6RBPNzXQHwKN62aru4biawi1RHbFWswS2OM62mUsJWFiKiIggIyPjnHUZGRkoikJERMQ568PDwwvdtkGDBlf8vkFBQaVmLGtpqkWUHyXarmytIKLVVT21bf4iyobSeryyBQNRga5CXK3S2q5E2SVtqvTqSeCH3l2NgF0RpVmzZiQmJpKWllawLi4ujvr162Oz2S7Ydvfu3QX3fT4ff/75Jy1btiyxeoUQQgghhBAVS8DCUnR0NM2bN2fatGnk5ORw8OBBZs+ezbBhwwC4+eab2bJlCwDDhg3jhx9+4I8//sDpdPLhhx9iMpno3r17oMoXQgghhBBClHMBPWfp3XffZeLEiXTu3Jng4GDuvvtuhg8fDsDhw4dxOBwAdO3alSeffJJ//vOfpKam0rx5c2bMmIHFEvjxjkIIIYQQQojyKaBhKSoqik8++aTQx/buPXeu9uHDhxcEKSGEEEIIIYQobgEbhieEEEIIIYQQpZmEJSGEEEIIIYQohIQlIYQQQgghhCiEhCUhhBBCCCGEKISEJSGEEEIIIYQohIQlIYQQQgghhCiEhCUhhBBCCCGEKISEJSGEEEIIIYQohIQlIYQQQgghhCiEhCUhhBBCCCGEKISEJSGEEEIIIYQohIQlIYQQQgghhCiEhCUhhBBCCCGEKISEJSGEEEIIIYQohIQlIYQQQgghhCiEhCUhhBBCCCGEKISEJSGEEEIIIYQohCHQBZQUv98PgNPpDHAlZ2soDbWI8kPalSgO0q5EcZB2JYqatClxpc60lTMZ4WIUVVXVkigo0FJTU0lISAh0GUIIIYQQQohSok6dOlSqVOmij1eYsOT1esnMzMRsNqPTyehDIYQQQgghKiq/34/b7cZut2MwXHywXYUJS0IIIYQQQghxJaSLRQghhBBCCCEKIWFJCCGEEEIIIQohYUkIIYQQQgghCiFhSQghhBBCCCEKIWFJCCGEEEIIIQohYUkIIYQQQgghCiFhSQghhBBCCCEKIWFJCCGEEEIIIQohYamEnThxgocffpiYmBh69OjBm2++id/vD3RZooxZt24dnTp1Yvz48Rc89tNPPzFgwABuuOEGBg0axG+//RaACkVZdOLECcaNG0dMTAydOnXiueeeIysrC4D4+Hjuvfde2rRpw0033cSsWbMCXK0oC/bs2cP9999PmzZt6NSpE//85z9JSUkBIDY2ljvvvJPWrVvTv39/fvzxxwBXK8qi1157jUaNGhXcl3YlipqEpRL22GOPUbVqVVauXMns2bNZuXIlc+fODXRZogz55JNPmDJlCtddd90Fj8XHx/Pss88yYcIEfv/9d0aOHMmjjz5KUlJSACoVZc2YMWMIDQ1l1apVfPfdd+zfv5+pU6ficrkYPXo0HTp0YN26dfzvf//j448/5ueffw50yaIUy8vL48EHH6R9+/bExsayZMkSUlNTmTx5MsnJyTzyyCPcfffdxMbG8uKLLzJx4kTi4uICXbYoQ+Lj41m0aFHBfWlXojhIWCpBcXFx7NmzhwkTJhASEkKdOnUYOXIkX331VaBLE2WI2Wxm4cKFhYalb775hm7dutGtWzfMZjO33XYbDRs2lG/WxCVlZWXRrFkznnrqKWw2G1FRUdxxxx1s2bKF1atX4/F4GDt2LFarlaZNm3LXXXfJsUv8LafTyfjx4xk9ejQmk4mIiAj69OnD/v37Wbx4MXXq1OHOO+/EbDbTqVMnevbsyTfffBPoskUZ4ff7mTRpEiNHjixYJ+1KFAcJSyVo9+7d1KhRA7vdXrCuadOmHD58mJycnABWJsqSESNGEBISUuhju3fvJjo6+px10dHR8q2auKTQ0FBef/11IiMjC9YlJiZSpUoVdu/eTaNGjdDr9QWPRUdHs2vXrkCUKsoIu93OXXfdhcFgAODQoUN8//339OvX76LHKmlT4nJ9+eWXmM1mBgwYULBO2pUoDhKWSlBGRgahoaHnrDsTnNLT0wNRkihnMjIyzgnjoLUxaV/iSsXFxTF//nzGjh1b6LErLCyMjIwMOedSXNKJEydo1qwZt9xyC82bN+fxxx+/aJuSY5W4HKdPn2b69OlMmjTpnPXSrkRxkLBUwlRVDXQJopyTNiau1datWxk1ahRPPfUUnTp1uuh2iqKUYFWirKpRowZxcXEsW7aMhIQEnnnmmUCXJMq4119/nUGDBlG/fv1AlyIqAAlLJSgiIoKMjIxz1mVkZKAoChEREYEpSpQr4eHhhbYxaV/icq1atYqHH36YF154gREjRgDasev8b2YzMjIICwtDp5OPEXFpiqJQp04dxo8fz5IlSzAYDBccq9LT0+VYJS4pNjaW7du3M27cuAseK+wzUNqVuFbyKVeCmjVrRmJiImlpaQXr4uLiqF+/PjabLYCVifKiWbNmF4zNjouLo2XLlgGqSJQl27Zt49lnn+Wdd95h4MCBBeubNWvG3r178Xq9BeukXYlLiY2NpW/fvucM1TwTrlu0aHHBsWrXrl3SpsQl/fjjj6SmptKjRw9iYmIYNGgQADExMTRs2FDalShyEpZKUHR0NM2bN2fatGnk5ORw8OBBZs+ezbBhwwJdmignhgwZwoYNG1i9ejVut5uFCxeSkJDAbbfdFujSRCnn9Xp56aWXmDBhAl26dDnnsW7duhEcHMyHH36I0+lkx44dLFy4UI5d4m81a9aMnJwc3nzzTZxOJ2lpaUyfPp22bdsybNgwTpw4wTfffIPb7WbNmjWsWbOGIUOGBLpsUco999xzLF++nEWLFrFo0SJmzJgBwKJFixgwYIC0K1HkFFVOcChRSUlJTJw4kU2bNhEcHMzdd9/No48+KmP/xWVr3rw5QMG3/Gdmmjoz493PP//MtGnTOHHiBPXr1+fFF1+kXbt2gSlWlBlbtmzhnnvuwWQyXfDYsmXLyM3NZdKkSezatYvIyEgeeughhg8fHoBKRVmyd+9epkyZws6dO7FarXTo0IHnnnuOqlWrsnnzZqZMmcLBgwepUaMGTz31FDfddFOgSxZlzPHjx+nVqxd79+4FkHYlipyEJSGEEEIIIYQohAzDE0IIIYQQQohCSFgSQgghhBBCiEJIWBJCCCGEEEKIQkhYEkIIIYQQQohCSFgSQgghhBBCiEJIWBJCCCGEEEKIQkhYEkIIIYQQQohCSFgSQgghhBBCiEJIWBJCCFFkNm/eTPPmzTl8+HCJv/fhw4eJiYlh8+bNAa2juMydO5cBAwbgcDgCXYoQQlQYiqqqaqCLEEIIUfq99NJLLFq0qOB+Xl4eBoMBne7s925xcXGBKA23280dd9zBgAEDGDt2bEBqKAljxozBZrMxbdq0QJcihBAVgoQlIYQQV6VRo0ZMnjyZYcOGBboUZs2axaxZs1i5ciUWiyXQ5RSbAwcOMGDAABYsWEDr1q0DXY4QQpR7MgxPCCFEkdm4cSONGjXi4MGDAPTs2ZOPP/6YZ599ltatW9OlSxd++OEHNm3axIABA2jVqhX33nsvp06dKniNffv28dBDD9GxY0datWrFiBEj2L1790XfU1VVZs2axdChQwuCUmF1zJo1iylTptChQwfatWvH008/jdvtLvT1evXqxRtvvHHO+pMnT9K4cWPWr18PwIoVK7jrrrto3bo1MTExPP3006SlpRVsn5CQwJgxY2jTpg033HADgwYN4rfffit4fPr06dx+++1Mnz6d1q1bs2zZMvLy8njllVe48cYbadmyJT179uSjjz7izPea9evX58Ybb2TmzJlX9P8ihBDi6khYEkIIUawWLFjAwIED2bRpEz169OCVV15h/vz5zJs3jxUrVnDkyBFmz54NQFpaGvfddx8NGjRg5cqVrF+/nujoaB544IFzgshfxcfHk5KSQteuXf+2jtmzZ9OmTRvWrVvHzJkzWbp0KQsXLrxgO0VRGDJkCD/88AMej6dg/ZIlS6hevTqdOnUiNjaWJ598kpEjR7Jp0yYWLVpEcnIyjz76aMH2jz32GEajkbVr17Jx40a6dOnCY489Rnp6esE2SUlJZGZmsmHDBvr27cucOXPYvHkz3333HTt27OCdd95h3rx5rFu3ruA5Xbp0ITY29pzahBBCFA8JS0IIIYrVDTfcQMeOHTEYDPTu3Zvc3FyGDx9OeHg4lStXpnXr1hw4cACAxYsXoygKTz/9NDabDZvNxtNPP43f72fVqlWFvn58fDwATZo0+ds6WrZsSb9+/TAajbRo0YJ69eqxb9++QrcdPHgwWVlZ57zn4sWLGTx4MIqiMH/+fLp3707//v0xGAxERUUxYcIEtm7dyrFjxwD48ssvmTp1KjabDZPJxMCBA3E4HOe8Z2ZmJuPGjcNisaAoCllZWeh0OoKCggBo3rw569evPycINm7cmNzcXI4ePXqpf3ohhBDXyBDoAoQQQpRvNWrUKLh9ZpjcX9cFBQUV9BodOnSIjIwMWrRocc5r+P1+Tpw4Uejrp6WlFQSSv1O7du1z7lut1kKH4QFERkbSs2dPvv76a/r27cv+/fs5ePAggwcPLqjzyJEjNG/e/Jzn6fV6jh8/Tq1atdi5cyfvv/8+e/fuxel0Fmzz1/cMDQ0lPDy84P4999zDunXr6NKlC+3ataNz584MGDCASpUqFWwTEREBcE4PlRBCiOIhYUkIIUSx+utseWcoilLothaLheuvv56lS5eWSB1/5+6772bUqFGcOnWKJUuW0KVLF6KiogrqHDp0KJMmTSr0uUeOHOHhhx9m6NChvPvuu0RERHD06FH69OlzznZGo/Gc+9WqVWPRokXs3LmTDRs2sGjRIqZPn86cOXMuCGZCCCGKnwzDE0IIUWrUrVuXY8eOkZmZec76vxtyFhERQW5uLnl5eUVaS8eOHalVqxZLlizhxx9/ZMiQIefUef6kE06nk+TkZAB27dpFXl4eY8eOLegJ+uOPPy75ng6HA5fLRYsWLRgzZgzfffcdTZo0OWfK9jO9cH/tkRJCCFE8JCwJIYQoNW699VZCQkKYPHkyaWlp5OXlMWfOHG699daCc4HO17hxYwD27NlTpLWcmejhww8/xOPx0L1794LHRo4cyc6dO5k1axYOh4P09HReeuklRo4cid/vLxjyt2nTJvLy8li7di3Lli0DIDEx8aLvOW7cOF544QVSU1MBrYcqMTGRunXrFmyzZ88erFbrBcMKhRBCFD0JS0IIIUqN4OBgPv30UzIzM+nVqxdt27Zl+fLlzJw5k1q1ahX6nOjoaCpXrszatWuLvJ5BgwbhcrkYOHAgBsPZkestWrTg7bffZtGiRcTExNCrVy88Hg+ffPIJOp2O5s2b8+ijj/Lyyy/TsWNHvvnmG6ZMmcKtt97KlClT+Oqrrwp9v//85z/k5eXRr18/WrZsyT/+8Q9uu+22c65l9dtvv9GpU6cLhvAJIYQoenJRWiGEEGXezJkzmT17Nr/88gtms7nIXvfAgQMMHDiQ5cuXnzMpRaAcPHiQAQMG8Nlnn9GmTZtAlyOEEOWe9CwJIYQo8+69916Cg4OZM2dOkb3mqVOneOGFF7jrrrtKRVACmDZtGn379pWgJIQQJUTCkhBCiDLPbDbzwQcfMGvWLLZs2XLNr/evf/2Lm2++mTp16vDMM88UQYXXbt68eRw7dowpU6YEuhQhhKgwZBieEEIIIYQQQhRCepaEEEIIIYQQohASloQQQgghhBCiEBKWhBBCCCGEEKIQEpaEEEIIIYQQohASloQQQgghhBCiEBKWhBBCCCGEEKIQEpaEEEIIIYQQohASloQQQgghhBCiEP8PdCVeCJOk42AAAAAASUVORK5CYII=\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-01-09T22:37:17.690930Z",
"start_time": "2020-01-09T22:37:17.134877Z"
},
"scrolled": false,
"id": "ydmaYBqnuTxA",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 639
},
"outputId": "4aa50651-ba32-4508-ee5b-18519b001cd8"
},
"source": [
"fig, ax = plt.subplots(figsize=(10,7))\n",
"for r in df['un_continent_name'].unique():\n",
" ix = df['un_continent_name'] == r\n",
" kmf.fit(df['duration'].loc[ix], df['observed'].loc[ix], label=r)\n",
" kmf.plot(ax=ax)\n",
"plt.title('Estimated probability of government survival vs number of years')\n",
"plt.xlabel('Time (in years)')\n",
"plt.ylabel('Estimated probability of government survival')\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1000x700 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-01-09T22:37:17.723699Z",
"start_time": "2020-01-09T22:37:17.694880Z"
},
"scrolled": false,
"id": "NXIl1sBfuTxF",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 143
},
"outputId": "f46070d3-8f0a-47ea-9c6d-ceb655377129"
},
"source": [
"df.query('ctryname == \"United States of America\"').tail(3)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" ctryname un_region_name un_continent_name \\\n",
"1721 United States of America Northern America Americas \n",
"1722 United States of America Northern America Americas \n",
"1723 United States of America Northern America Americas \n",
"\n",
" ehead democracy regime start_year duration \\\n",
"1721 George Bush Democracy Presidential Dem 1989 4 \n",
"1722 Bill Clinton Democracy Presidential Dem 1993 8 \n",
"1723 George W. Bush Democracy Presidential Dem 2001 8 \n",
"\n",
" observed \n",
"1721 1 \n",
"1722 1 \n",
"1723 0 "
],
"text/html": [
"\n",
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" <div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ctryname</th>\n",
" <th>un_region_name</th>\n",
" <th>un_continent_name</th>\n",
" <th>ehead</th>\n",
" <th>democracy</th>\n",
" <th>regime</th>\n",
" <th>start_year</th>\n",
" <th>duration</th>\n",
" <th>observed</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1721</th>\n",
" <td>United States of America</td>\n",
" <td>Northern America</td>\n",
" <td>Americas</td>\n",
" <td>George Bush</td>\n",
" <td>Democracy</td>\n",
" <td>Presidential Dem</td>\n",
" <td>1989</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1722</th>\n",
" <td>United States of America</td>\n",
" <td>Northern America</td>\n",
" <td>Americas</td>\n",
" <td>Bill Clinton</td>\n",
" <td>Democracy</td>\n",
" <td>Presidential Dem</td>\n",
" <td>1993</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1723</th>\n",
" <td>United States of America</td>\n",
" <td>Northern America</td>\n",
" <td>Americas</td>\n",
" <td>George W. Bush</td>\n",
" <td>Democracy</td>\n",
" <td>Presidential Dem</td>\n",
" <td>2001</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
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" </tbody>\n",
"</table>\n",
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}
},
"metadata": {},
"execution_count": 23
}
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-01-09T22:37:17.748036Z",
"start_time": "2020-01-09T22:37:17.725349Z"
},
"scrolled": false,
"id": "Qzy7VJ_luTxI",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 143
},
"outputId": "ce908664-3461-4057-dc22-9a248bb57081"
},
"source": [
"df.query('ctryname == \"United Kingdom\"').tail(3)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" ctryname un_region_name un_continent_name ehead \\\n",
"1710 United Kingdom Northern Europe Europe John Major \n",
"1711 United Kingdom Northern Europe Europe Tony Blair \n",
"1712 United Kingdom Northern Europe Europe Gordon Brown \n",
"\n",
" democracy regime start_year duration observed \n",
"1710 Democracy Parliamentary Dem 1990 7 1 \n",
"1711 Democracy Parliamentary Dem 1997 10 1 \n",
"1712 Democracy Parliamentary Dem 2007 2 0 "
],
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"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"summary": "{\n \"name\": \"df\",\n \"rows\": 3,\n \"fields\": [\n {\n \"column\": \"ctryname\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"United Kingdom\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"un_region_name\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Northern Europe\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"un_continent_name\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Europe\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ehead\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"John Major\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"democracy\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Democracy\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"regime\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Parliamentary Dem\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"start_year\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 8,\n \"min\": 1990,\n \"max\": 2007,\n \"num_unique_values\": 3,\n \"samples\": [\n 1990\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"duration\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4,\n \"min\": 2,\n \"max\": 10,\n \"num_unique_values\": 3,\n \"samples\": [\n 7\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"observed\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 24
}
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-01-09T22:37:18.129226Z",
"start_time": "2020-01-09T22:37:17.749639Z"
},
"id": "010659VeuTxM",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 639
},
"outputId": "395cdd51-2d31-4faf-a595-0696d4210119"
},
"source": [
"ix_US = df['ctryname'] == 'United States of America'\n",
"ix_UK = df['ctryname'] == 'United Kingdom'\n",
"\n",
"kmf_US = KaplanMeierFitter()\n",
"kmf_US.fit(df['duration'].loc[ix_US], df['observed'].loc[ix_US], label='USA')\n",
"\n",
"kmf_UK = KaplanMeierFitter()\n",
"kmf_UK.fit(df['duration'].loc[ix_UK], df['observed'].loc[ix_UK], label='UK')\n",
"\n",
"plt.figure(figsize=(10,7))\n",
"ax = plt.subplot(111)\n",
"kmf_US.plot(ax=ax)\n",
"kmf_UK.plot(ax=ax)\n",
"plt.title('Estimated probability of government survival vs number of years')\n",
"plt.xlabel('Time (in years)')\n",
"plt.ylabel('Estimated probability of government survival')\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1000x700 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VaQCN1ChuTxs"
},
"source": [
"# Multivariate Cox regression"
]
},
{
"cell_type": "code",
"metadata": {
"scrolled": false,
"id": "nCPi7YY1uTxQ",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "b7f2f61a-6fe3-4c44-beb5-86fb413123fe"
},
"source": [
"df.columns"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Index(['ctryname', 'un_region_name', 'un_continent_name', 'ehead', 'democracy',\n",
" 'regime', 'start_year', 'duration', 'observed'],\n",
" dtype='object')"
]
},
"metadata": {},
"execution_count": 26
}
]
},
{
"cell_type": "code",
"metadata": {
"scrolled": true,
"id": "xphf-zpauTxU"
},
"source": [
"df = df.drop(columns=['ctryname', 'un_region_name', 'ehead', 'regime', 'start_year'])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "NOuRvPJXuTxZ",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "cdd2837c-e592-4c46-8d0d-9f6253935a9c"
},
"source": [
"df.columns"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Index(['un_continent_name', 'democracy', 'duration', 'observed'], dtype='object')"
]
},
"metadata": {},
"execution_count": 28
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "tzcg35DtuTxd"
},
"source": [
"# df_hazard = pd.get_dummies(df, drop_first=True, columns=df.columns.drop(['duration', 'observed']))\n",
"df_hazard = pd.get_dummies(df, columns=df.columns.drop(['duration', 'observed']))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "MGxRvM20uTxg",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "80645479-4fef-4f6c-e13a-2095c93c07c5"
},
"source": [
"df_hazard.columns"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Index(['duration', 'observed', 'un_continent_name_Africa',\n",
" 'un_continent_name_Americas', 'un_continent_name_Asia',\n",
" 'un_continent_name_Europe', 'un_continent_name_Oceania',\n",
" 'democracy_Democracy', 'democracy_Non-democracy'],\n",
" dtype='object')"
]
},
"metadata": {},
"execution_count": 30
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "i9b7PSd2uTxl"
},
"source": [
"df_hazard = df_hazard.drop(columns=['un_continent_name_Americas', 'democracy_Democracy'])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "3-O2MTLPuTxo",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "714ea350-2dc0-4e07-9468-b091758b7132"
},
"source": [
"df_hazard.columns"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Index(['duration', 'observed', 'un_continent_name_Africa',\n",
" 'un_continent_name_Asia', 'un_continent_name_Europe',\n",
" 'un_continent_name_Oceania', 'democracy_Non-democracy'],\n",
" dtype='object')"
]
},
"metadata": {},
"execution_count": 32
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "IUKzqcFvuTxs",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 679
},
"outputId": "078be668-ad26-45a6-ff43-f3ea39d7f9e9"
},
"source": [
"from lifelines import CoxPHFitter\n",
"\n",
"cph = CoxPHFitter(penalizer=0.1)\n",
"cph.fit(df_hazard, 'duration', 'observed')\n",
"cph.print_summary()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<lifelines.CoxPHFitter: fitted with 1808 total observations, 340 right-censored observations>\n",
" duration col = 'duration'\n",
" event col = 'observed'\n",
" penalizer = 0.1\n",
" l1 ratio = 0.0\n",
" baseline estimation = breslow\n",
" number of observations = 1808\n",
"number of events observed = 1468\n",
" partial log-likelihood = -9613.12\n",
" time fit was run = 2024-06-04 21:24:34 UTC\n",
"\n",
"---\n",
" coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n",
"covariate \n",
"un_continent_name_Africa -0.20 0.82 0.08 -0.36 -0.04 0.70 0.96\n",
"un_continent_name_Asia -0.06 0.95 0.07 -0.20 0.09 0.82 1.09\n",
"un_continent_name_Europe 0.23 1.26 0.06 0.11 0.35 1.12 1.43\n",
"un_continent_name_Oceania -0.12 0.89 0.11 -0.33 0.10 0.72 1.10\n",
"democracy_Non-democracy -0.72 0.49 0.06 -0.84 -0.60 0.43 0.55\n",
"\n",
" cmp to z p -log2(p)\n",
"covariate \n",
"un_continent_name_Africa 0.00 -2.48 0.01 6.25\n",
"un_continent_name_Asia 0.00 -0.77 0.44 1.19\n",
"un_continent_name_Europe 0.00 3.79 <0.005 12.70\n",
"un_continent_name_Oceania 0.00 -1.07 0.29 1.80\n",
"democracy_Non-democracy 0.00 -11.48 <0.005 98.97\n",
"---\n",
"Concordance = 0.62\n",
"Partial AIC = 19236.25\n",
"log-likelihood ratio test = 266.31 on 5 df\n",
"-log2(p) of ll-ratio test = 181.91"
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <tbody>\n",
" <tr>\n",
" <th>model</th>\n",
" <td>lifelines.CoxPHFitter</td>\n",
" </tr>\n",
" <tr>\n",
" <th>duration col</th>\n",
" <td>'duration'</td>\n",
" </tr>\n",
" <tr>\n",
" <th>event col</th>\n",
" <td>'observed'</td>\n",
" </tr>\n",
" <tr>\n",
" <th>penalizer</th>\n",
" <td>0.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>l1 ratio</th>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>baseline estimation</th>\n",
" <td>breslow</td>\n",
" </tr>\n",
" <tr>\n",
" <th>number of observations</th>\n",
" <td>1808</td>\n",
" </tr>\n",
" <tr>\n",
" <th>number of events observed</th>\n",
" <td>1468</td>\n",
" </tr>\n",
" <tr>\n",
" <th>partial log-likelihood</th>\n",
" <td>-9613.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>time fit was run</th>\n",
" <td>2024-06-04 21:24:34 UTC</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div><table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th style=\"min-width: 12px;\"></th>\n",
" <th style=\"min-width: 12px;\">coef</th>\n",
" <th style=\"min-width: 12px;\">exp(coef)</th>\n",
" <th style=\"min-width: 12px;\">se(coef)</th>\n",
" <th style=\"min-width: 12px;\">coef lower 95%</th>\n",
" <th style=\"min-width: 12px;\">coef upper 95%</th>\n",
" <th style=\"min-width: 12px;\">exp(coef) lower 95%</th>\n",
" <th style=\"min-width: 12px;\">exp(coef) upper 95%</th>\n",
" <th style=\"min-width: 12px;\">cmp to</th>\n",
" <th style=\"min-width: 12px;\">z</th>\n",
" <th style=\"min-width: 12px;\">p</th>\n",
" <th style=\"min-width: 12px;\">-log2(p)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>un_continent_name_Africa</th>\n",
" <td>-0.20</td>\n",
" <td>0.82</td>\n",
" <td>0.08</td>\n",
" <td>-0.36</td>\n",
" <td>-0.04</td>\n",
" <td>0.70</td>\n",
" <td>0.96</td>\n",
" <td>0.00</td>\n",
" <td>-2.48</td>\n",
" <td>0.01</td>\n",
" <td>6.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>un_continent_name_Asia</th>\n",
" <td>-0.06</td>\n",
" <td>0.95</td>\n",
" <td>0.07</td>\n",
" <td>-0.20</td>\n",
" <td>0.09</td>\n",
" <td>0.82</td>\n",
" <td>1.09</td>\n",
" <td>0.00</td>\n",
" <td>-0.77</td>\n",
" <td>0.44</td>\n",
" <td>1.19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>un_continent_name_Europe</th>\n",
" <td>0.23</td>\n",
" <td>1.26</td>\n",
" <td>0.06</td>\n",
" <td>0.11</td>\n",
" <td>0.35</td>\n",
" <td>1.12</td>\n",
" <td>1.43</td>\n",
" <td>0.00</td>\n",
" <td>3.79</td>\n",
" <td>&lt;0.005</td>\n",
" <td>12.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>un_continent_name_Oceania</th>\n",
" <td>-0.12</td>\n",
" <td>0.89</td>\n",
" <td>0.11</td>\n",
" <td>-0.33</td>\n",
" <td>0.10</td>\n",
" <td>0.72</td>\n",
" <td>1.10</td>\n",
" <td>0.00</td>\n",
" <td>-1.07</td>\n",
" <td>0.29</td>\n",
" <td>1.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>democracy_Non-democracy</th>\n",
" <td>-0.72</td>\n",
" <td>0.49</td>\n",
" <td>0.06</td>\n",
" <td>-0.84</td>\n",
" <td>-0.60</td>\n",
" <td>0.43</td>\n",
" <td>0.55</td>\n",
" <td>0.00</td>\n",
" <td>-11.48</td>\n",
" <td>&lt;0.005</td>\n",
" <td>98.97</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><br><div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <tbody>\n",
" <tr>\n",
" <th>Concordance</th>\n",
" <td>0.62</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Partial AIC</th>\n",
" <td>19236.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>log-likelihood ratio test</th>\n",
" <td>266.31 on 5 df</td>\n",
" </tr>\n",
" <tr>\n",
" <th>-log2(p) of ll-ratio test</th>\n",
" <td>181.91</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/latex": "\\begin{tabular}{lrrrrrrrrrrr}\n & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\ncovariate & & & & & & & & & & & \\\\\nun_continent_name_Africa & -0.20 & 0.82 & 0.08 & -0.36 & -0.04 & 0.70 & 0.96 & 0.00 & -2.48 & 0.01 & 6.25 \\\\\nun_continent_name_Asia & -0.06 & 0.95 & 0.07 & -0.20 & 0.09 & 0.82 & 1.09 & 0.00 & -0.77 & 0.44 & 1.19 \\\\\nun_continent_name_Europe & 0.23 & 1.26 & 0.06 & 0.11 & 0.35 & 1.12 & 1.43 & 0.00 & 3.79 & 0.00 & 12.70 \\\\\nun_continent_name_Oceania & -0.12 & 0.89 & 0.11 & -0.33 & 0.10 & 0.72 & 1.10 & 0.00 & -1.07 & 0.29 & 1.80 \\\\\ndemocracy_Non-democracy & -0.72 & 0.49 & 0.06 & -0.84 & -0.60 & 0.43 & 0.55 & 0.00 & -11.48 & 0.00 & 98.97 \\\\\n\\end{tabular}\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "iYkJ1coruTxx",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 639
},
"outputId": "d4149397-c1cd-426a-b73e-b8300c369045"
},
"source": [
"fig_coef, ax_coef = plt.subplots(figsize=(12,7))\n",
"ax_coef.set_title('Survival Regression: Coefficients and Confident Intervals')\n",
"cph.plot(ax=ax_coef)\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1200x700 with 1 Axes>"
],
"image/png": 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hQenp6Xna5x6j3H3NyclRvXr18iyXk5OjhIQEy7/9/Pwk/d9xufpGz25ubpbtx8TE5Kk9t/7Y2FjFxMTo8uXLeWoJCQnJ07Zs2bJ5/r1u3TrNnz9fsbGxunz5srKyslS7dm1L7bl9k9sPuf3ZsmVLjR49Wr/99pvq1KmjDRs2qH///vn20Y1UqFBBHh4eVi93rxQsWFD+/v46cODAPd1uWlqaTpw4cd/3j1mVK1c23dYW/f0guHrM5H4PBwcH27gq3O8etHMN7j7GDKx16NAhzZ07V+Hh4apUqZKty4Ed4DwDa1kzZnLb3swdC3UyMzPz3F9GujKrQ5Lc3d3VuHFjRUREXHd5Z+drb++T32u5YUt+nJyc5OzsbNnu9Vwd+MycOVPr1q1TRESEKlWqJMMw9K9//SvPOq+3vnr16snHx0ebN29WSkqK6tWrp+LFi99w2/mpXbu2GjdurAkTJuj111+3vH6zfc3v/6/Wq1cv7dq1S5LUvn17vfbaa5KU5zhdPSvBzc1Nnp6e+t///nfDev95XPI7TjeqP/c4Sbrhsbr6OO3YsUMjRozQxIkT1aJFCxUsWFDPP/98nnVeb4aFl5eXmjdvru+//14lSpRQXFycmjZtet3tXo+Hh4c8PT2tXu5eyR0Htqrxfu8fs6y5hMzJyemB2Gdb8fDwsPm4hf15UM41uHcYMzDL3d3d8l/GDKzBeQbWupNj5o7dKLlkyZJKTk7WpUuXLK8dPXpU0pXZGX///XeeD93nzp27YWhxPbmzZK5+/G3u/wcEBMjf31+xsbF51r1ixYrrTu3ft2+fmjdvrn/9619ydna+5q/u/v7+eW58fPLkSS1cuFDSlTAjNDRU69ev17p166y+9OpqgwcP1qZNm/THH3+Y3teb+fzzz7Vv3z7t27dPY8aMka+vrwoWLKjTp09b2hw5csTy/7n3romOjra8lpycnGeWjjX++Vjo7OxsnTx5Uv7+/ipbtqycnZ3z9O0vv/yizZs357uuvXv36qGHHlKbNm1UsGBBZWRkWMaXdO1xyszM1Ny5cy21d+jQQRs2bNDq1av15JNPMqUWAAAAAGD37lioU716dfn4+GjOnDnKzMzUb7/9ph9//FGS1LZtWyUmJmrWrFlKT09XdHS0evXqpS+++MLq7RQrVkyNGjXS1KlTlZiYqIsXL+rjjz9WvXr15Ofnp8cff1yenp6KiIhQRkaGfv31Vw0fPjzPrI+rlS1bVocOHVJaWpqOHDmiOXPmqHDhwjpz5owk6ZlnntGiRYt07NgxpaSkaMKECfrtt98sy3fo0EE///yz9u7dqxYtWtxCz11RqlQpvfrqqxo/fvz/a+/eo6qq8/+Pv7ioCGiCmij6zcQb3tCUEU1RIIWhEHN0kspFmak1Y4qjQhqVDpGgTIVZLStRXEbmLdQsCy9Y6TDqWJJJo2SKoqwSERWTyzm/P1ycnwQYh/AckOdjLZaLvT977/c5672A8/KzP7vGr9VcTZo0kY+Pj5KTk3X58mWdPXvWFFBJUvfu3TVgwAC98sorys/PV2FhoV566SXNmzevVq8pNDRUH3zwgbKzs1VcXKx33nlHZWVl8vf3V6tWrRQQEKDly5eroKBAubm5io6ONr3vv+Xu7q7z58/r3Llz+uWXX/Tyyy/r7rvvNo0fN26cMjIytHv3bpWUlGjVqlVKTk42rZ00dOhQ2dnZKSkp6Q+Fb/XZjz/+WCFEQ+2dPn1aXbp0ueXX6dOnrV3mHYG+BQAAAGqvzkIdBwcHLV++XDt37pS3t7fefPNNTZ48WZLk4uKit956y7Tv8ccfl5+fn2m/ueLi4uTo6Kg///nPCg4OlrOzs2ktm6ZNmyopKUnp6eny9vZWdHS0YmNj1b179yrPNW3aNJWVlcnHx0dRUVGaMWOGHn74YcXExGjnzp2aNGmSxo4dq7CwMPn5+cnOzk7R0dGm4z08POTh4aERI0bIycmpVq+nXHh4eIU1iX7vtdbGK6+8Ikny9fXV008/rfDw8Ar7ExISZDQaFRAQoFGjRqmsrEyLFy+u1bUmT56soKAgPf300xo6dKgyMjKUnJxsWpT61VdflaOjo/z8/PTII48oKChIjzzySJXnCgwMlK+vr4KDg/XII49o5MiReuaZZ5SWlqYlS5bI09NTS5cu1T//+U95e3tr165devvtt9WkSRNJN27lCgkJkaOjY6U1g4CbderUqUYz4cpnBgIAgDuDvb29WrZsWeUTaQGgvrIxmvOoF1RSVlam0aNHKyYmRkOGDLF2ObiFyMhItW/fXrNmzTLruKKiIh07dkyenp7cK1sF3h+Yi55BbdA3MBc9A3PRMzAXPQNzmdMzNR1LDP0HlJaWKjExUa6urqZHaaN+2rlzp/bs2aNt27ZZuxQAAAAAAOoEoU4t5ebmKjAwUJ6enkpISKjwxJwxY8ZUWLT3t1auXClvb29LlAlJQUFBKi4uVnx8vNq2bWvtcgAAAFAPnTlzRnFxcYqJial26QYAqG8IdWqpQ4cOyszMrHLfli1bLFwNbuWzzz6zdgkAAACo50pLS1VYWKjS0lJrlwIANVZnCyUDAAAAAADAcgh1AAAAAAAAGiBCHQAAAAAAgAaIUAcAAABAo+fm5qannnpKbm5u1i4FAGqMUAcAAABAo+fg4KAuXbrIwcHB2qUAQI0R6gAAAABo9PLz87Vjxw7l5+dbuxQAqDFCHQAAAACNXmFhofbu3avCwkJrlwIANUaoAwAAAAAA0AAR6gAAAAAAADRAhDoAAAAAAAANEKEOAAAAgEbP2dlZAwcOlLOzs7VLAYAaI9QBAAAA0Oi1adNG48aNU5s2baxdCgDUGKEOAAAAgEavuLhYeXl5Ki4utnYpAFBjhDoAAAAAGr3c3FwlJiYqNzfX2qUAQI3ZW7sAoL4zGAySpGvXrlm5kvqp/H3h/UFN0TOoDfoG5qJnYC6DwaAOHTrIYDCoqKjI2uWgAeDnDMxlTs+Ujyn/PFodG6PRaPzjpQF3rgsXLuinn36ydhkAAAAAgEamc+fOat26dbX7CXWA31FaWqpLly6pWbNmsrXljkUAAAAAwO1lMBh0/fp13XXXXbK3r/4mK0IdAAAAAACABohpBwAAAAAAAA0QoQ4AAAAAAEADRKgDAAAAAADQABHqAAAAAAAANECEOgAAAAAAAA0QoQ4AAAAAAEADRKgDAAAAAADQABHqAAAAAAAANECEOgDMUlBQoFmzZmno0KEaNmyYFixYoF9//bXa8WvXrlVgYKAGDBigwMBArVmzxoLVoj4wt2fy8vL0zDPPqH///ho6dKgSEhJkMBgsWDHqA3P7ptzVq1c1cuRIRUVFWaBK1Cfm9sznn3+uMWPGmH4/ffTRRxasFtZy9uxZTZ06VYMHD5afn5+WLFlS7e+Y5ORkBQYG6r777lNYWJi+++47C1eL+sCcnklJSTH93RsaGqq0tDQLV4v6wJyeKZeXl6cBAwZo2bJlZl+PUAeAWaKjo3Xt2jVt27ZNGzduVHZ2tpYuXVrl2PT0dC1ZskTx8fE6dOiQ4uPjlZCQoD179li2aFiVOT1jNBr197//Xe7u7vrqq6+0Zs0a7d+/XxkZGRauGtZmTt/cbNmyZbpy5YoFKkR9Y07PHDlyRHPmzNFzzz2nAwcOaP78+Vq0aJEOHjxo4aphaTNmzFC7du2UlpampKQkpaWlafXq1ZXG7dq1S8uWLVN8fLz27dsnPz8/TZ8+XUVFRVaoGtZU057ZsWOHEhISFBsbq//85z96/PHHNWvWLOXk5FihalhTTXvmZjExMbKzs6vV9Qh1ANTYL7/8orS0NEVERMjV1VXt2rXTs88+q40bN6qkpKTS+O+++07dunWTl5eXbG1t5eXlpe7du+v777+3QvWwBnN75sCBA8rJydG8efPk7OwsDw8PbdiwQUOGDLFC9bAWc/umXFZWlrZt26aHH37YgtWiPjC3ZwoKCjRt2jQ98MADsre314gRI9S9e3dCnTtcZmamsrKyNGfOHLVo0UKdO3fWE088oXXr1lUau27dOo0bN05eXl5ycHDQlClTJEm7d++2dNmwInN65tdff9Xs2bM1cOBANWnSRBMmTJCTk5O++eYbyxcOqzGnZ8qlp6frxIkTGjlyZK2uSagDoMaOHTsmOzs79ejRw7Std+/eKioq0o8//lhp/PDhw3XixAllZGSouLhYhw8fVnZ2toYNG2bJsmFF5vbMoUOH1L17d7322msaPHiwAgICtHLlSkuWjHrA3L6RbszyevnllxUREaGWLVtaqlTUE+b2jK+vr/72t7+Zvi8tLdXPP/+sdu3aWaReWMfRo0fl7u6uu+66y7Std+/eOnnyZKUZfkePHlWvXr1M39va2srT01OZmZkWqxfWZ07PhIaG6tFHHzV9X1hYqKtXr/JzpZExp2ekG2HgokWL9NJLL8ne3r5W1yTUAVBjBQUFcnZ2lo2NjWlb+Q+sixcvVhrfr18/Pf/885o8ebL69u1rmobar18/i9UM6zK3Z86fP69vvvlGrVu31p49e/Tiiy/qtdde4570RsbcvpFu/K+6jY2Nxo0bZ5EaUb/UpmdutnTpUjk6Oio4OPi21QjrKygoqBT6VtcnBQUFFT6UlY+tST/hzmFOz9zMaDTqhRdekJeXl/70pz/d1hpRv5jbM8uXL1f//v3l4+NT62vWLgoCcMdKTU3VvHnzqtwXEREho9FY43P9+9//VkJCgt577z3dd999yszM1MyZM9W+fXs98MADdVUyrKwue8ZoNMrV1dU0zX3EiBEaNWqUPv30U3rmDlOXfXPhwgW98cYbWrVqVYUP9biz1GXPlDMajVq6dKm2bdum5ORkNWvW7I+WiXrO3N9JgLl9UFJSoqioKJ04cULJycm3qSrUZzXtmRMnTmj9+vXaunXrH7oeoQ6ACkJDQxUaGlrlvq+//lpXrlxRWVmZaSGvgoICSVLr1q0rjU9JSdHo0aNN66EMGjRIDz74oDZs2MAH9DtIXfZM27Zt1aJFiwrb3N3d9e2339Zt0bC6uuybxYsXa+zYsRVuvcGdpy57RpIMBoOef/55HTlyRCkpKerUqdNtqRv1h6urq6kvyhUUFMjGxkaurq4Vtru4uFQ5tlu3bre5StQn5vSMdONWmmeffVbXrl3T2rVr5eLiYqFKUV/UtGfKbxufMWOG2rZt+4euSagDoMY8PT1lNBqVlZWl3r17S7qxGFjLli117733VhpvMBhUVlZWYVtxcbFFakX9YG7PeHh4KCcnR1evXpWTk5OkG4+FdHd3t2jdsC5z+2bLli1q2bKlNm3aJOnGH9UGg0G7d+/myWmNhLk9I0mxsbE6fvy4UlJS1KpVKwtWC2vp06ePzp07p/z8fNOHq8zMTHXt2tX0O+fmsUePHjUtvF5WVqbvv/9e48ePt3jdsB5zesZoNCoiIkL29vZatWoVM/8aqZr2TG5urg4cOKDjx48rMTFRklRUVCRbW1vt2rVLmzdvrvE1WVMHQI25uroqMDBQr7/+uvLz83X+/HktX75c48ePNy3sFR4eru3bt0uS/P39tWPHDh08eFClpaU6cuSIPv30U40aNcqaLwMWVJueadmypeLj41VUVKT9+/crLS2NdVIaGXP7Jj09XVu3blVqaqpSU1M1ceJE+fv7KzU11ZovAxZkbs8cOnRIW7Zs0YoVKwh0GpFevXqpb9++SkhI0JUrV5Sdna2kpCSFhYVJkoKCgkxPQAsLC9PHH3+sb775RteuXdPbb7+tpk2b1vrpNGiYzOmZrVu36sSJE3rjjTcIdBqxmvaMm5ub0tPTTX+7pKamyt/fXxMnTtSKFSvMuiYzdQCYpXx19oCAADVp0kQPPfSQIiIiTPtzcnJ06dIlSdLDDz+swsJCLViwQHl5eWrXrp2mTp3KB/RGxpyecXBw0HvvvaeXXnpJPj4+cnV11cKFC+Xt7W2t8mEl5vSNm5tbhWOdnZ3VvHnzSttxZzOnZzZu3KjLly/Lz8+vwjm8vb154t4dLjExUdHR0br//vvl7OysiRMnmp5YdPLkSRUVFUm68YS02bNna9asWbpw4YL69u2rFStWyMHBwZrlwwpq2jMbN27U2bNnKy2MHBoaqpiYGIvXDeupSc/Y2dlV+julefPmcnZ2Nvt2LBsjK4ABAAAAAAA0ONx+BQAAAAAA0AAR6gAAAAAAADRAhDoAAAAAAAANEKEOAAAAAABAA0SoAwAAAAAA0AAR6gAAAAAAADRAhDoAAAAAAAANEKEOAAAAAABAA0SoAwAAYAU9evRQSkpKnZ93+/btGjFihPLz8+v83JJ05swZ+fj4aP/+/TUabzAYNG3aNEVHR9+Weqxh9erVCgkJUVFRkbVLAQA0coQ6AAAAd4js7GwtWLBACQkJcnV1VUZGhnr06KHs7Owqx/bo0UMZGRmSZBrbp08f9e3b1/Tl6+urmTNn6scff5QkdezYUQsXLlRERITy8vJ+t6a33npLubm5euGFFyRJly5dUkxMjPz9/eXl5aUxY8YoPT3dNL68jptr6Nu3r0aNGmUak5SUpCFDhmjw4MFKSkqqdM0XX3xRsbGxNXrPcnJy9OKLL8rf31/9+vXT4MGDNWnSJG3ZsqXCuEmTJikiIkKSFB4eLnd39zsqqAIANEyEOgAAAHeIJUuWaMiQIRo0aFCtz5GamqrMzEzT14cffqiysjI9+eSTunz5siQpMDBQ99xzj958881bnisvL08rVqzQzJkz1axZM0lSZGSk9u3bp3fffVcHDhzQ9OnTNXPmTGVlZVU49uYaMjMz9cUXX0iSTp8+rcTERKWkpGjDhg1KTEzU6dOnTcft3btXhw4d0j/+8Y/ffa1HjhzR2LFjdf36da1cuVLffvutPvvsMwUFBSk6OlqvvvpqtcfOmTNH27dv13//+9/fvQ4AALcLoQ4AAEA9sG7dOoWEhKh///4aNmyYFi5cqGvXrpn2b926VQEBAerXr58mTpyo/fv3V5hpk52drd27dys8PLxO6+rQoYPmz5+v8+fPV7jl6oknntCmTZt08eLFao9ds2aN2rRpo4CAAElSUVGR0tPTNXXqVHl4eKhp06YKDg5WQECAPvjggxrVk5mZKQ8PD3Xu3FmdOnWSh4eHMjMzJUkFBQV6+eWXFRcXZwqRqmMwGBQVFaX+/fsrLi5OnTt3lo2NjVxcXPTYY48pLi5Otra2KikpqfL4rl27avjw4Xr//fdrVDcAALcDoQ4AAICVbd68WbGxsZo7d64OHjyo999/X+np6Vq0aJEk6ezZs4qMjNTYsWN14MABzZ8/v9LtRV9++aWcnZ1133331Xl95cHGzUHJ0KFDZTAYtG/fvmqP27t3r4YPHy4bG5sK241GY4XvXVxcTMFMuaioKPn6+srHx0fTp0/XyZMnJanSuQwGg2nbwoULNW7cOG3fvl1jxozRY489VmkGULljx44pOztbU6ZMqXJ/UFCQIiMj1aRJk2pf37Bhw7R///5qgx8AAG43Qh0AAAArW7NmjUJCQuTr6yt7e3v16NFD4eHh2rZtm4qLi7V9+3Y5Ojpq+vTpatasmfr166cJEyZUOMexY8fUrVu3KkOI0NDQSmvUhIaG/m5dRqNRp06d0sKFC9WxY0cNGTLEtO+uu+5S+/btdezYsSqPLSsr0//+9z/16tXLtM3R0VHDhw/XihUr9MMPP6ikpERff/21Pv/8c9PCzk5OTurXr598fX2VlpamjRs3qrS0VE888YSuXLkiLy8vZWdn6+TJk6Z/vby89MknnygnJ0fdu3dXenq61q9fr7/85S+mtXx+69SpU5JuzLiprZ49e+rq1asVbv8CAMCS7K1dAAAAQGN3+vRpjR07tsK2rl27qri4WHl5eTp//rzat29fIbAZOHBghfH5+flq1apVledPTU2Vh4dHhW3Z2dkKDg6uNDY0NNQ086V8FkxISIhiY2PVtGnTCmNdXV2rfcpWQUGBjEajXFxcKmyPi4tTfHy8nnrqKZWUlMjX11d//etftXnzZklSnz59tH79etN4d3d3LV68WPfff7/27Nmjhx56SHPnztWkSZNkY2OjqKgo2dvbKz4+XklJSfroo480bNgwNWvWTKNHj9b8+fN19epVOTk5VVnnrWbi/B5XV1dJuuUtaAAA3E6EOgAAAFZ2/fr1StsMBoOkG7cbGQyGSuGDre3tmXB9cwB0+PBhPfbYYwoMDJSbm1udnN/FxaXSAsSxsbHq0KFDtce0adNGTk5OpqdtPfroo3r00UdN+6dMmaIpU6aoS5cuKiwsVLt27SRJzZs3l9Fo1OXLlyuFOl26dJF0Y42e4cOH18lrAwDA0rj9CgAAwMo6d+6sH374ocK248ePq3nz5nJzc9Pdd9+t3NxclZWVmfb/9qlLrq6uKigoqNO6BgwYoMmTJ+v555/Xzz//XGl/fn5+pZk45Vq1aiUbG5tKs1i+/PJLHT582PS90WjU3r17Tbd2bd++XStXrqxwzNmzZ3X16lXdc889la6TkpKisrIyPf7445KkFi1aqLCwUNL/n0Hj7Oxc6biePXuqZ8+eSkxMNAVoN0tPT9eDDz5oeuJXVcpnKVX3HgAAcLsR6gAAAFhZWFiYtm7dqq+++kplZWU6evSoVq9erfHjx8ve3l6jRo3SxYsXtXLlShUXFyszM1MbN26scI6ePXvq+PHjKi0trdPannvuObVp00ZRUVEVFji+dOmSzp07J09PzyqPs7OzU7du3SqtubNnzx7NnTtXOTk5un79uhISEnTp0iWFhYVJurEY89KlS/Xxxx+ruLhY586dU3R0tDp27Khhw4ZVONfp06f1zjvv6NVXXzXdMubt7a309HRdunRJW7dulaenZ5WhjnTjVrBTp04pPDxcWVlZMhqNKigo0Nq1azVz5kyFhoaqRYsW1b43WVlZcnR01P/93//9/hsJAMBtQKgDAABgZWFhYXruuecUGxurQYMGafbs2ZowYYIiIyMl3Vhf54UXXtDq1as1ePBgvf7665o1a5akG+GJJPn6+urKlSuVZvD8UU2bNlVcXJwyMjK0evVq0/Z9+/bJ1tZW999/f7XHjhgxQl9++WWFbXPmzNGgQYM0fvx4+fj46LvvvtPq1atNs10CAgK0ePFiJSUlafDgwQoNDVXbtm314YcfysHBwXQeg8GgyMhIzZ49u8KtYQEBAfL19VVAQIA2bdpU6SlhN+vZs6c2b96se++9V88++6y8vLwUHBys3bt3a9myZZo6deot35uvvvpKQ4cO/UPr8gAA8EfYGH/7TEkAAADUO8XFxbK3tzetpbNv3z49+eST2rlzpzp27ChJmjZtmpo0aaI333zzttczceJEde3aVTExMdWOOX/+vEaPHq3XXntNAQEBt70mS8rOzlZISIjWrFlTadFqAAAshZk6AAAA9dzPP/+sgQMH6t1331VJSYl++eUXvfPOO+rdu7fc3d1N4+bMmaOvv/66zmfr/NYXX3yhkydPasaMGbcc5+bmpilTpuj1119XcXHxba3J0hISEhQYGEigAwCwKmbqAAAANAC7du3SsmXL9NNPP6l58+YaOHCgIiMjTbN0yn3yySdasmSJNm3aZHrkdl06c+aMJkyYoH/961+mxY1vxWAwaPr06XJzc9OiRYvqvB5rSE5O1vr16/Xhhx9W+6h0AAAsgVAHAAAAAACgAeL2KwAAAAAAgAaIUAcAAAAAAKABItQBAAAAAABogAh1AAAAAAAAGiBCHQAAAAAAgAaIUAcAAAAAAKABItQBAAAAAABogAh1AAAAAAAAGqD/B8DxLV/od6DoAAAAAElFTkSuQmCC\n"
},
"metadata": {}
}
]
}
]
}
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