synthia.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Synthia's notebook to experiment with the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from helper_code import *\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "NSDUH2021Cols = [\"AGE3\",\"IRSEX\",\"AUINPYR\",\"AURXYR\",\"YEATNDYR\",\"YESCHFLT\",\"YEPRBSLV\",\"DSTNRV30\",\"DSTHOP30\",\"DSTCHR30\",\"DSTNGD30\",\"DSTWORST\",\"DSTNRV12\",\"DSTHOP12\",\"DSTCHR12\",\"DSTNGD12\",\"IMPCONCN\",\"IMPGOUT\",\"IMPPEOP\",\"IMPSOC\",\"IMPSOCM\",\"SUICTHNK\",\"SUIPLANYR\",\"ADDPREV\"]\n",
    "NSDUH201819Cols = [\"AGE2\", \"IRSEX\", \"AUINPYR\",\"AURXYR\",\"YEATNDYR\",\"YESCHFLT\",\"YEPRBSLV\",\"DSTNRV30\",\"DSTHOP30\",\"DSTCHR30\",\"DSTNGD30\",\"DSTWORST\",\"DSTNRV12\",\"DSTHOP12\",\"DSTCHR12\",\"DSTNGD12\",\"IMPCONCN\",\"IMPGOUT\",\"IMPPEOP\",\"IMPSOC\",\"IMPSOCM\",\"SUICTHNK\",\"ADDPREV\"]\n",
    "CoreTrends2021Cols = [\"web1a\", \"web1b\", \"web1c\", \"web1d\", \"web1e\",\"sns2a\", \"sns2b\", \"sns2c\", \"sns2d\", \"sns2e\",\"gender\", \"age\"]\n",
    "CoreTrends2019Cols = [\"web1a\", \"web1b\", \"web1c\", \"web1d\", \"web1e\",\"sns2a\", \"sns2b\", \"sns2c\", \"sns2d\", \"sns2e\",\"sex\", \"age\"]\n",
    "CoreTrends2018Cols = [\"web1a\", \"web1b\", \"web1c\", \"web1d\", \"web1e\",\"sns2a\", \"sns2b\", \"sns2c\", \"sns2d\", \"sns2e\",\"sex\", \"age\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "NSDUH2019_wantedCols = readInAndGetWantedColumns(\"datasets/National Survey on Drug Use and Health 2019/NSDUH_2019_Tab.txt\", \"txt\", NSDUH201819Cols)\n",
    "NSDUH2018_wantedCols = readInAndGetWantedColumns(\"datasets/National Survey on Drug Use and Health 2018/NSDUH_2018_Tab.tsv\", \"tsv\", NSDUH201819Cols)\n",
    "NSDUH2021_wantedCols = readInAndGetWantedColumns(\"datasets/National Survey on Drug Use and Health 2021/NSDUH_2021_Tab.txt\", \"txt\", NSDUH2021Cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "CoreTrends2021_wantedCols = readInAndGetWantedColumns(\"datasets/Jan-25-Feb-8-2021-Core-Trends-Survey/Jan 25-Feb 8, 2021 - Core Trends Survey - CSV.csv\", \"csv\", CoreTrends2021Cols)\n",
    "CoreTrends2019_wantedCols = readInAndGetWantedColumns(\"datasets/January-8-February-7-2019-Core-Trends-Survey-SPSS/January 8-February 7, 2019 - Core Trends Survey - CSV.csv\", \"csv\", CoreTrends2019Cols)\n",
    "CoreTrends2018_wantedCols = readInAndGetWantedColumns(\"datasets/January 3-10, 2018 - Core Trends Survey/January 3-10, 2018 - Core Trends Survey - CSV.csv\", \"csv\", CoreTrends2018Cols)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   web1a  web1b  web1c  web1d  web1e sns2a sns2b sns2c sns2d sns2e  sex  age\n",
      "0      2      2      1      2      1                 1           5    2   75\n",
      "1      2      2      2      2      2                                  1   56\n",
      "2      1      1      1      2      1     2     4     3           5    2   65\n",
      "3      2      2      1      2      1                 2           4    2   67\n",
      "4      2      2      1      2      1                 3           5    1   64\n"
     ]
    }
   ],
   "source": [
    "print(CoreTrends2019_wantedCols.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "CoreTrends2018_wantedCols = CoreTrends2018_wantedCols.rename(columns={\"sex\": \"gender\"})\n",
    "CoreTrends2019_wantedCols = CoreTrends2019_wantedCols.rename(columns={\"sex\": \"gender\"})\n",
    "CT2021_slim = CoreTrends2021_wantedCols[['age', 'gender']]\n",
    "CT2019_slim = CoreTrends2019_wantedCols[['age', 'gender']]\n",
    "CT2018_slim = CoreTrends2018_wantedCols[['age', 'gender']]\n",
    "testCT2 = pd.concat([CT2021_slim, CT2019_slim, CT2018_slim])\n",
    "testCT2 = testCT2[testCT2['gender'] < 4]\n",
    "\n",
    "testCT2['age'] = pd.cut(testCT2['age'], bins=[0, 26, 35, 50, 65, 97],\n",
    "                       labels=[\"Gen Z\", \"Millennials\", \"Gen X\", \"Young Boomers\", \"Older Boomers\"])\n",
    "testCT2['gender'] = pd.cut(testCT2['gender'], bins=[0, 1, 2, 3],\n",
    "                       labels=[\"Woman\", \"Man\", \"Other\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Generation')"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(testCT2, x='age', palette=\"magma\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.title(\"CoreTrends Data\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.xlabel(\"Generation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "NSDUH2021_wantedCols = NSDUH2021_wantedCols.rename(columns={\"AGE3\": \"AGE2\"})\n",
    "NS2021_slim = NSDUH2021_wantedCols[['AGE2', 'IRSEX']]\n",
    "NS2019_slim = NSDUH2019_wantedCols[['AGE2', 'IRSEX']]\n",
    "NS2018_slim = NSDUH2018_wantedCols[['AGE2', 'IRSEX']]\n",
    "\n",
    "NS2021_slim = NS2021_slim[NS2021_slim['AGE2'] < 98]\n",
    "NS2019_slim = NS2019_slim[NS2019_slim['AGE2'] < 98]\n",
    "NS2018_slim = NS2018_slim[NS2018_slim['AGE2'] < 98]\n",
    "\n",
    "NS2019_slim['AGE2'] = pd.cut(NS2019_slim['AGE2'], bins=[0, 13, 15, 16, 17, float('inf')],\n",
    "                       labels=[\"Gen Z\", \"Millennials\", \"Gen X\", \"Young Boomers\", \"Older Boomers\"])\n",
    "NS2018_slim['AGE2'] = pd.cut(NS2018_slim['AGE2'], bins=[0, 13, 15, 16, 17, float('inf')],\n",
    "                       labels=[\"Gen Z\", \"Millennials\", \"Gen X\", \"Young Boomers\", \"Older Boomers\"])\n",
    "NS2021_slim['AGE2'] = pd.cut(NS2021_slim['AGE2'], bins=[0, 7, 9, 10, 11, float('inf')],\n",
    "                       labels=[\"Gen Z\", \"Millennials\", \"Gen X\", \"Young Boomers\", \"Older Boomers\"])\n",
    "testNS2 = pd.concat([NS2021_slim, NS2019_slim, NS2018_slim])\n",
    "\n",
    "testNS2['IRSEX'] = pd.cut(testNS2['IRSEX'], bins=[0, 1, 2],\n",
    "                       labels=[\"Man\", \"Woman\"])\n",
    "testNS2 = testNS2.rename(columns={\"IRSEX\": \"Gender\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(testNS2, x='AGE2', palette=\"magma\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.title(\"NSDUH Data\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.xlabel(\"Generation\")\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(1, 1));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean up data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "###ADMT16 - ADMT21  : How many visits to various types of outpatient facilities for mental health\n",
    "\n",
    "\n",
    "<br>AGE3: age (intervals of 2, e.g. 1 = age 12 or 13, up to age 65)\n",
    "<br>IRSEX: gender 1=male, 2=female\n",
    "\n",
    "<br>AUINPYR : stayed in hospital for mental health\n",
    "<br>ADMT25/ADMTREF25 : AURXYR : taken any medication for mental health\n",
    "\n",
    "<br>YEATNDYR : HAs attended some sort of school\n",
    "<br>YESCHFLT : how much they like going to school (1-4, 4=hated it)\n",
    "<br>YEPRBSLV : participated in self-esteem group\n",
    "\n",
    "<br>DSTNRV30 : How often felt nervous (1-5, 5=never) in past 30 days\n",
    "<br> : DSTHOP30 : How often feel hopeless\n",
    "<br> : DSTCHR30 : How often felt depressed\n",
    "<br> : DSTNGD30 : How often feeling down/worthless\n",
    "<br> : DSTWORST : In past year has there been a worse month than current (1-2, 2=No)\n",
    "<br> : DSTNRV12 : in WORST30 month, how often felt nervous\n",
    "<br> : DSTHOP12 : In WORST30 month, how often felt hopeless\n",
    "<br> : DSTCHR12 : in WORST30 month, how often felt depressed\n",
    "<br> : DSTNGD12 : in WORST30 month, how often felt worthless\n",
    "\n",
    "<br> : IMPCONCN : Difficulty concentrating in their worst month (1-4, 4= severe)\n",
    "<br> : IMPGOUT : Difficulty leaving house\n",
    "<br> : IMPPEOP : Difficulty dealing with less known people\n",
    "<br> : IMPSOC : Difficulty participating in social activites\n",
    "<br> : IMPSOCM : Did mental health problems keep from participating socially\n",
    "\n",
    "<br> : SUICTHNK : Think about suicide in past year\n",
    "<br> : SUIPLANYR :  Plans for suicide\n",
    "\n",
    "<br> : ADDPREV : Long periods of feeling sad\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Core trends variables:\n",
    "\n",
    "web1a    -\tTwitter\n",
    "web1b    -\tInstagram\n",
    "web1c    -\tFacebook \n",
    "web1d    -\tSnapchat\n",
    "web1e    -\tYouTube\n",
    "\n",
    "How often use social media, 1-5, 5=less often\n",
    "\n",
    "sns2a    -\tTwitter\n",
    "sns2b    -\tInstagram\n",
    "sns2c    -\tFacebook \n",
    "sns2d    -\tSnapchat\n",
    "sns2e    -\tYouTube\n",
    "\n",
    "gender  - gender\n",
    "age     - age\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "CoreTrends_id = ['age']\n",
    "CoreTrends_values = [\"web1a\", \"web1b\", \"web1c\", \"web1d\", \"web1e\"]\n",
    "# CoreTrends_values = [\"sns2a\", \"sns2b\", \"sns2c\", \"sns2d\", \"sns2e\"]\n",
    "NSDUH_id=['AGE2']; NSDUH_values = ['DSTCHR12']\n",
    "CoreTrends2018_long = cleanUpCoreTrends(CoreTrends2018_wantedCols, CoreTrends_id, CoreTrends_values)\n",
    "CoreTrends2019_long = cleanUpCoreTrends(CoreTrends2019_wantedCols, CoreTrends_id, CoreTrends_values)\n",
    "CoreTrends2021_long = cleanUpCoreTrends(CoreTrends2021_wantedCols, CoreTrends_id, CoreTrends_values)\n",
    "NSDUH2018_long = cleanUpNSDUH(NSDUH2018_wantedCols,NSDUH_id,NSDUH_values)\n",
    "NSDUH2019_long = cleanUpNSDUH(NSDUH2019_wantedCols,NSDUH_id,NSDUH_values)\n",
    "NSDUH2021_long = cleanUpNSDUH(NSDUH2021_wantedCols,['AGE3'],NSDUH_values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cleanUpCT(df, id, values):\n",
    "    # df = df.drop(columns=['sns2a'])\n",
    "\n",
    "    df = df.apply(pd.to_numeric, errors='coerce')\n",
    "\n",
    "    #Remove refused ages\n",
    "    df = df[df['age'] < 98]\n",
    "\n",
    "    #Put age into bins\n",
    "    df['age'] = pd.cut(df['age'], bins=[0, 26, 35, 50, 65, 97],\n",
    "                       labels=[\"Gen Z\", \"Millennials\", \"Gen X\", \"Young Boomers\", \"Older Boomers\"])\n",
    "\n",
    "    df['totalFreq'] = df[[\"sns2a\", \"sns2b\", \"sns2c\", \"sns2d\", \"sns2e\"]].sum(axis=1)\n",
    "    df = df.rename(columns={\"totalFreq\": \"Social media use\"})\n",
    "    #Convert to long form\n",
    "    df = pd.melt(df, id_vars=id, value_vars=[\"Social media use\"])\n",
    "    df=df.rename(columns={\"value\": \"Frequency of Social Media Use\", \"age\":\"Generations\"})\n",
    "    # print(totalCount)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cleanUpNS(df, id, values, year):\n",
    "    # df = df.drop(columns=['sns2a'])\n",
    "\n",
    "    df = df.apply(pd.to_numeric, errors='coerce')\n",
    "\n",
    "    #Remove refused ages\n",
    "    if year == 2021:\n",
    "        df = df[df['AGE3'] < 85]\n",
    "        df = df[df[values[0]] < 85]\n",
    "        df['AGE3'] = pd.cut(df['AGE3'], bins=[0, 7, 9, 10, 11, float('inf')],\n",
    "                       labels=[\"Gen Z\", \"Millennials\", \"Gen X\", \"Young Boomers\", \"Older Boomers\"])\n",
    "\n",
    "        #Convert to long form\n",
    "        renamed = \"DSTNRV12\"\n",
    "        df = df.rename(columns={renamed: \"Nervousness\"})\n",
    "        df = pd.melt(df, id_vars=['AGE3'], value_vars=[\"Nervousness\"])\n",
    "        df=df.rename(columns={\"value\": \"Nervousness\", \"AGE3\":\"Generations\"})\n",
    "    else:\n",
    "        df = df[df['AGE2'] < 85]\n",
    "        df = df[df[values[0]] < 85]\n",
    "        df['AGE2'] = pd.cut(df['AGE2'], bins=[0, 13, 15, 16, 17, float('inf')],\n",
    "                       labels=[\"Gen Z\", \"Millennials\", \"Gen X\", \"Young Boomers\", \"Older Boomers\"])  \n",
    "\n",
    "        renamed = \"DSTNRV12\"\n",
    "        df = df.rename(columns={renamed: \"Nervousness\"})\n",
    "        #Convert to long form\n",
    "        df = pd.melt(df, id_vars=['AGE2'], value_vars=[\"Nervousness\"])\n",
    "        df=df.rename(columns={\"value\": \"Nervousness\", \"AGE2\":\"Generations\"})\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "id = ['AGE']\n",
    "vals = ['DSTNRV12']\n",
    "\n",
    "NSDUH2018_long = cleanUpNS(NSDUH2018_wantedCols, id, vals, 2018)\n",
    "NSDUH2019_long = cleanUpNS(NSDUH2019_wantedCols, id, vals, 2019)\n",
    "NSDUH2021_long = cleanUpNS(NSDUH2021_wantedCols, id, vals, 2021)\n",
    "expNS1 = pd.concat([NSDUH2018_long,NSDUH2019_long, NSDUH2021_long])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Generations</th>\n",
       "      <th>variable</th>\n",
       "      <th>Nervousness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gen Z</td>\n",
       "      <td>Nervousness</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Millennials</td>\n",
       "      <td>Nervousness</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Millennials</td>\n",
       "      <td>Nervousness</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gen X</td>\n",
       "      <td>Nervousness</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gen Z</td>\n",
       "      <td>Nervousness</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Generations     variable  Nervousness\n",
       "0        Gen Z  Nervousness            2\n",
       "1  Millennials  Nervousness            5\n",
       "2  Millennials  Nervousness            2\n",
       "3        Gen X  Nervousness            2\n",
       "4        Gen Z  Nervousness            1"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expNS1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4]),\n",
       " [Text(0, 0, 'Gen Z'),\n",
       "  Text(1, 0, 'Millennials'),\n",
       "  Text(2, 0, 'Gen X'),\n",
       "  Text(3, 0, 'Young Boomers'),\n",
       "  Text(4, 0, 'Older Boomers')])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SpFG4qVixIpKTk+Hk5IS9e/di9uzZAJ73uuTn5xd5P3/99Rf69++PtLQ0WFpaon79+ti7dy/at28P4HmISkpKUq2fk5ODgIAApKSkwMzMDB4eHoiKioKvr68mb4OIiIgkSKNw89FHH+HTTz9FzZo18fDhQ/j4+AAALly4oLohX1GsXr36tcvDw8PVpgMDAxEYGFjseomIiKjs0Cjc/PDDD3B2dkZycjLmz5+P8uXLA3je0zJq1CitFkhERERUHBqFG2NjYwQEBLw0f8KECSWth4iIiKhENLpaau3atYiKilJNBwYGwsrKCs2bN8edO3e0VhwRERFRcWkUbubOnQszMzMAwMmTJ7F06VLMnz8flStXxsSJE7VaIBEREVFxaHRaKjk5WTVwePv27ejVqxeGDx+OFi1aoE2bNtqsj4iIiKhYNOq5KV++PB4+fAgA2L9/P9q1awcAMDU1RVZWlvaqIyIiIiomjXpu2rdvj6FDh6JRo0b4448/0LlzZwDAlStX4OzsrM36iIiIiIpFo56bZcuWwcvLC/fv30dERASsra0BAOfOnUPfvn21WiARERFRcWjUc2NlZYWlS5e+NH/GjBklLoiIiIioJDTquQGAo0ePol+/fmjevDlSUlIAAOvXr8exY8e0VhwRERFRcWkUbiIiItCxY0eYmZnh/Pnzqqd4P3nyBHPnztVqgURERETFoVG4mT17NkJDQ7Fy5UoYGxur5jdv3hznz5/XWnFERERExaVRuLl+/TpatWr10nwLCws8evSopDURERERaUyjcGNvb4+bN2++NP/YsWOoXr16iYsiIiIi0pRG4WbEiBEYP348Tp8+DZlMhtTUVGzcuBEBAQF8KjgRERHplEaXggcGBuLx48do27YtsrOz0apVK8jlcgQEBGDMmDHarpGIiIioyDQKNwAwZ84cfPXVV7h69SqUSiXc3d1Rvnx5bdZGREREVGwahxsAKFeuHJo0aaKtWoiIiIhKTKNw8/TpU8ybNw+HDh3CvXv3oFQq1Zbfvn1bK8URERERFZdG4Wbo0KGIiYlB//79YW9vD5lMpu26iIiIiDSiUbjZs2cPoqKi0KJFC23XQ0RERFQiGl0KXrFiRVSqVEnbtRARERGVmEbhZtasWZg2bRqePXum7XqIiIiISkSj01Lfffcdbt26BTs7Ozg7O6s9XwoAny9FREREOqNRuOnevbuWyyAiIiLSDo3CzfTp07VdBxEREZFWaDTmJjk5GXfv3lVNnzlzBhMmTMCKFSu0VhgRERGRJjQKN59++imOHDkCAEhPT0e7du1w5swZTJkyBTNnztRqgURERETFoVG4uXz5Mt59910AwM8//4x69erhxIkT2LRpE8LDw7VZHxEREVGxaBRucnNzIZfLAQAHDx5E165dAQC1a9dGWlqa9qojIiIiKiaNwo2HhwdCQ0Nx9OhRHDhwAJ06dQIApKamwtraWqsFEhERERWHRuHm22+/xfLly9GmTRv07dsXDRo0AADs2LFDdbqqKEJCQlC/fn1YWFjAwsICXl5e2LNnz2u3iYmJgaenJ0xNTVG9enWEhoZq8haIiIhIojS6FLxNmzZ48OABMjIyULFiRdX84cOHo1y5ckXej6OjI+bNmwdXV1cAwNq1a9GtWzfEx8fDw8PjpfUTExPh6+uLYcOGYcOGDTh+/DhGjRoFGxsb9OzZU5O3QkRERBKjUbgBAENDQ7VgAwDOzs7F2keXLl3UpufMmYOQkBCcOnWq0HATGhqKatWqYdGiRQCAOnXqIC4uDgsXLtRquBFCIDs7W2v707asrKxCv9ZHpqamJX5qvBBC9XUe8kpaUpny4s/rxZ+jpl7cR46SbVEcL/68tN0WGYpcKPLyS7xPbRJCICf/eY0mhrISfw5okyJfqfpaG21B+kejcOPi4vLa/6i3b98u9j7z8/Pxyy+/4OnTp/Dy8ip0nZMnT6JDhw5q8zp27IjVq1cjNzf3pcdAAIBCoYBCoVBNZ2Rk/Gct2dnZaNmyZTHfgW60b99e1yW81rFjx2BmZlaifbwYNLeViyhpSWVWdnZ2sXpWX7WPArNuHy5pSWWWttti3IHLJS2pzNJGW5D+0SjcTJgwQW06NzcX8fHx2Lt3LyZNmlSsfV26dAleXl7Izs5G+fLlsW3bNri7uxe6bnp6Ouzs7NTm2dnZIS8vDw8ePIC9vf1L2wQHB2PGjBnFqomIiIhKL43Czfjx4wudv2zZMsTFxRVrX25ubrhw4QIePXqEiIgIDBw4EDExMa8MOP/uMSroUnxVT1JQUBD8/f1V0xkZGXBycipyfa4pfWAgND5790YICAjZ8y5omTCEDPrT3QsASlkeblb9WWv7MzU1VX3d41lPGGl+NrXMyUOeqrfrxZ+jpl7cx9TqH8DEgG1RVDnKPFVvlzbawsrKCgcOHCjxft6UrKws1W1CduzYUeIe3DfFyspK1yXQG6DVTyYfHx8EBQUhLCysyNuYmJioBhQ3adIEZ8+exeLFi7F8+fKX1q1SpQrS09PV5t27dw9GRkavvARdLper7smjCQNhBAPx8ukunStDp4lfDK5G//tHxaeNMQ8v7sPEwIjhRkPaaAsDAwNUqlRJC9W8GS+OB6xUqZLehhuSJo0uBX+VX3/9tcQHmxBCbYzMi7y8vF76S2X//v1o0qRJoeNtiIiIqOzR6M+uRo0aqf3lIYRAeno67t+/j59++qnI+5kyZQp8fHzg5OSEJ0+eYMuWLYiOjsbevXsBPD+llJKSgnXr1gEARo4ciaVLl8Lf3x/Dhg3DyZMnsXr1amzevFmTt0FEREQSpFG46d69u9q0gYEBbGxs0KZNG9SuXbvI+/nrr7/Qv39/pKWlwdLSEvXr18fevXtVVwClpaUhKSlJtb6Liwt2796NiRMnYtmyZXBwcMCSJUt4jxsiIiJS0SjcTJ8+XSvffPXq1a9dXthDOFu3bo3z589r5fsTERGR9Gg8GjA/Px/bt2/HtWvXIJPJ4O7ujq5du8LQ0FCb9REREREVi0bh5ubNm/D19UVKSgrc3NwghMAff/wBJycnREVFoUaNGtquk4iIiKhINLpaaty4cahRowaSk5Nx/vx5xMfHIykpCS4uLhg3bpy2ayQiIiIqMo16bmJiYnDq1Cm1y76tra0xb948tGjRQmvFERERERWXRj03crkcT548eWl+ZmYmTExMSlwUERERkaY0Cjcffvghhg8fjtOnT0MIASEETp06hZEjR6put01ERESkCxqFmyVLlqBGjRrw8vKCqakpTE1N0aJFC7i6umLx4sXarpGIiIioyDQac2NlZYXffvsNN27cQEJCAoQQcHd3Vz0jioiIiEhXNB5Q3Lp1a9SsWRM1a9bUdk1EREREGtPotFT79u1RrVo1fPnll7h8+bK2ayIiIiLSmEbhJjU1FYGBgTh69Cjq16+P+vXrY/78+bh796626yMiIiIqFo3CTeXKlTFmzBgcP34ct27dwscff4x169bB2dkZH3zwgbZrJCIiIioyjcLNi1xcXPDll19i3rx5qFevHmJiYrRRFxEREZFGShRujh8/jlGjRsHe3h6ffvopPDw8sGvXLm3VRkRERFRsGl0tFRQUhC1btiA1NRXt2rXDokWL0L17d5QrV07b9REREREVi8aXggcEBODjjz9G5cqVtV0TERERkcaKfVoqNzcXbm5u8PHxYbAhIiIivVPscGNsbIxt27a9iVqIiIiISkyjAcU9evTA9u3btVwKERERUclpNObG1dUVs2bNwokTJ+Dp6Qlzc3O15ePGjdNKcURERETFpVG4WbVqFaysrHDu3DmcO3dObZlMJmO4ISIiIp3RKNwkJiZquw4iIiIirSjRTfxycnJw/fp15OXlaaseIiIiohLRKNw8e/YMn332GcqVKwcPDw8kJSUBeD7WZt68eVotkIiIiKg4NAo3QUFBuHjxIqKjo2Fqaqqa365dO2zdulVrxREREREVl0ZjbrZv346tW7eiWbNmkMlkqvnu7u64deuW1oojIiIiKi6Nem7u378PW1vbl+Y/ffpULewQERERvW0ahZumTZsiKipKNV0QaFauXAkvLy/tVEZERESkAY1OSwUHB6NTp064evUq8vLysHjxYly5cgUnT55ETEyMtmskIiIiKjKNem6aN2+O48eP49mzZ6hRowb2798POzs7nDx5Ep6entqukYiIiKjINOq5AYB69eph7dq12qyFiIiIqMSK1XNjYGAAQ0PD176MjIqel4KDg9G0aVNUqFABtra26N69O65fv/7abaKjoyGTyV56JSQkFOetEBERkUQVq+dm27Ztr1x24sQJ/PjjjxBCFHl/MTExGD16NJo2bYq8vDx89dVX6NChA65evfrSwzj/7fr167CwsFBN29jYFPn7EhERkXQVK9x069btpXkJCQkICgrCzp074efnh1mzZhV5f3v37lWbDgsLg62tLc6dO4dWrVq9dltbW1tYWVkV+XsRERFR2aDxs6VSU1MxbNgw1K9fH3l5ebhw4QLWrl2LatWqaVzM48ePAQCVKlX6z3UbNWoEe3t7eHt748iRI69cT6FQICMjQ+1FRERE0lXscPP48WNMnjwZrq6uuHLlCg4dOoSdO3eibt26JSpECAF/f3+0bNnytfuyt7fHihUrEBERgcjISLi5ucHb2xuxsbGFrh8cHAxLS0vVy8nJqUR1EhERkX4r1mmp+fPn49tvv0WVKlWwefPmQk9TaWrMmDH4/fffcezYsdeu5+bmBjc3N9W0l5cXkpOTsXDhwkJPZQUFBcHf3181nZGRwYBDREQkYcUKN19++SXMzMzg6uqKtWvXvvJS8MjIyGIVMXbsWOzYsQOxsbFwdHQs1rYA0KxZM2zYsKHQZXK5HHK5vNj7JCIiotKpWOFmwIABWn12lBACY8eOxbZt2xAdHQ0XFxeN9hMfHw97e3ut1UVERESlV7HCTXh4uFa/+ejRo7Fp0yb89ttvqFChAtLT0wEAlpaWMDMzA/D8tFJKSgrWrVsHAFi0aBGcnZ3h4eGBnJwcbNiwAREREYiIiNBqbURERFQ6aXyHYm0ICQkBALRp00ZtflhYGAYNGgQASEtLQ1JSkmpZTk4OAgICkJKSAjMzM3h4eCAqKgq+vr5vq2wiIiLSYzoNN0W54d+/e4sCAwMRGBj4hioiIiKi0k7j+9wQERER6SOGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSdhpvg4GA0bdoUFSpUgK2tLbp3747r16//53YxMTHw9PSEqakpqlevjtDQ0LdQLREREZUGOg03MTExGD16NE6dOoUDBw4gLy8PHTp0wNOnT1+5TWJiInx9ffH+++8jPj4eU6ZMwbhx4xAREfEWKyciIiJ9ZaTLb75371616bCwMNja2uLcuXNo1apVoduEhoaiWrVqWLRoEQCgTp06iIuLw8KFC9GzZ0+t1CWEUH2dZ5ANA5Gnlf1qi4CAkOUDAGTCEDLIdFyROqXs/39eL/4stSEP+tcW+XjeFobQv7Z4kz+vHGX+G9u3JoQQyBXPazKWGUIm06+20LefF5GU6TTc/Nvjx48BAJUqVXrlOidPnkSHDh3U5nXs2BGrV69Gbm4ujI2N1ZYpFAooFArVdEZGxn/WkZ2drfr6tkNkkWqnwmVnZ6NcuXJa29+2cuyh0xezbh/SdQlERIXSmwHFQgj4+/ujZcuWqFu37ivXS09Ph52dndo8Ozs75OXl4cGDBy+tHxwcDEtLS9XLyclJ67UTERGR/tCbnpsxY8bg999/x7Fjx/5z3X93Nxec+iisGzooKAj+/v6q6YyMjP8MOFZWVjhw4EBRytaJrKwsdO3aFQCwY8cOmJmZ6biiV7OysirxPkxNTYv0/0IXsrKy0L59ewDAgQMH9LotTE1NtbIPtkXJaaMtiOjV9CLcjB07Fjt27EBsbCwcHR1fu26VKlWQnp6uNu/evXswMjKCtbX1S+vL5XLI5fJi1WNgYPDaU2O6lpWVpfq6UqVKev0hrg0ymaxUvEczM7NSUWdJsC2IqDTQ6WkpIQTGjBmDyMhIHD58GC4uLv+5jZeX10u9Kvv370eTJk1eGm9DREREZY9Ow83o0aOxYcMGbNq0CRUqVEB6ejrS09PVeiaCgoIwYMAA1fTIkSNx584d+Pv749q1a1izZg1Wr16NgIAAXbwFIiIi0jM6DTchISF4/Pgx2rRpA3t7e9Vr69atqnXS0tKQlJSkmnZxccHu3bsRHR2Nhg0bYtasWViyZInWLgMnIiKi0k2nY26Kcg+U8PDwl+a1bt0a58+ffwMVERERUWmnN5eCExEREWkDww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUmKTsNNbGwsunTpAgcHB8hkMmzfvv2160dHR0Mmk730SkhIeDsFExERkd4z0uU3f/r0KRo0aIDBgwejZ8+eRd7u+vXrsLCwUE3b2Ni8ifKIiIioFNJpuPHx8YGPj0+xt7O1tYWVlZX2CyIiIqJSr1SOuWnUqBHs7e3h7e2NI0eOvHZdhUKBjIwMtRcRERFJV6kKN/b29lixYgUiIiIQGRkJNzc3eHt7IzY29pXbBAcHw9LSUvVycnJ6ixUTERHR26bT01LF5ebmBjc3N9W0l5cXkpOTsXDhQrRq1arQbYKCguDv76+azsjIYMAhIiKSsFLVc1OYZs2a4caNG69cLpfLYWFhofYiIiIi6Sr14SY+Ph729va6LoOIiIj0hE5PS2VmZuLmzZuq6cTERFy4cAGVKlVCtWrVEBQUhJSUFKxbtw4AsGjRIjg7O8PDwwM5OTnYsGEDIiIiEBERoau3QERERHpGp+EmLi4Obdu2VU0XjI0ZOHAgwsPDkZaWhqSkJNXynJwcBAQEICUlBWZmZvDw8EBUVBR8fX3feu1ERESkn3Qabtq0aQMhxCuXh4eHq00HBgYiMDDwDVdFREREpVmpH3NDRERE9CKGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSdhpvY2Fh06dIFDg4OkMlk2L59+39uExMTA09PT5iamqJ69eoIDQ1984USERFRqaHTcPP06VM0aNAAS5cuLdL6iYmJ8PX1xfvvv4/4+HhMmTIF48aNQ0RExBuulIiIiEoLI11+cx8fH/j4+BR5/dDQUFSrVg2LFi0CANSpUwdxcXFYuHAhevbs+YaqLDkhBLKzs7W2v6ysrEK/1gZTU1PIZDKt7lOfsC30izbbg21RMqXl2CgLbUElp9NwU1wnT55Ehw4d1OZ17NgRq1evRm5uLoyNjV/aRqFQQKFQqKYzMjLeeJ3/lp2djZYtW76Rfbdv316r+zt27BjMzMy0uk99wrbQL2+qPdgWxVdajo2y0BZUcqVqQHF6ejrs7OzU5tnZ2SEvLw8PHjwodJvg4GBYWlqqXk5OTm+jVCIiItKRUtVzA+Cl7kghRKHzCwQFBcHf3181nZGR8dYDjqmpKY4dO6a1/b3YfaztLlpTU1Ot7UsfsS30izbbg21RMqXl2CgLbUElV6rCTZUqVZCenq427969ezAyMoK1tXWh28jlcsjl8rdR3ivJZDKtd6OWK1dOq/srK9gW+kXb7cG20ByPDZKSUnVaysvLCwcOHFCbt3//fjRp0qTQ8TZERERU9ug03GRmZuLChQu4cOECgOeXel+4cAFJSUkAnp9SGjBggGr9kSNH4s6dO/D398e1a9ewZs0arF69GgEBAboon4iIiPSQTk9LxcXFoW3btqrpgrExAwcORHh4ONLS0lRBBwBcXFywe/duTJw4EcuWLYODgwOWLFmi15eBExER0dslEwUjcsuIjIwMWFpa4vHjx7CwsNB1OURERFQExfn9XarG3BARERH9F4YbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpKUUvVUcG0ouCFzRkaGjishIiKioir4vV2UByuUuXDz5MkTAICTk5OOKyEiIqLievLkCSwtLV+7Tpl7tpRSqURqaioqVKgAmUym63I0lpGRAScnJyQnJ/MZWTrGttAfbAv9wvbQH1JoCyEEnjx5AgcHBxgYvH5UTZnruTEwMICjo6Ouy9AaCwuLUvsfVWrYFvqDbaFf2B76o7S3xX/12BTggGIiIiKSFIYbIiIikhSGm1JKLpdj+vTpkMvlui6lzGNb6A+2hX5he+iPstYWZW5AMREREUkbe26IiIhIUhhuiIiISFIYboiIiEhSGG6IiIhIUhhuiIiISFIYbiTsypUr+Prrr3VdBpFeuX79OpYsWaLrMugtUCqVui6B8P8PuszLy3tr35PhRqIuX76Mli1bYu7cuUhISCjSU1SJpE6pVGLZsmU4cuQIgKI9XZhKJ6VSqXr+UHx8PLKysnRcUdkkhIBMJkNsbCwWLVqEv/766618X4YbCbp48SKaNGmCdu3aoUqVKlAqlaX6IaFlEX/pvhkGBgaoWbMmLly4gIyMDB4XEvVisJk6dSr69euH2NhY5Obm6riysqUg2ERERKBr16549uwZ0tPTVcveJIYbibl48SK8vLwwYcIEbNy4EUII3Lt3T9dlUTEUfCBER0dj3bp1ui5HMvLz8wEAdevWRX5+PgOkhBUEm6+++gqrVq3Cd999h0aNGsHY2FjHlZUtMpkMx48fx7Bhw7BgwQJMmzYNDRo0AAA8e/YMwJsLOQw3ElIQbPz9/TFv3jyYmJjAyMgI169f13VpVEQFwSYyMhK9e/fGiRMnkJiYqOuySrXExERcvnwZKSkpAICmTZvC0NAQJ06cUK3DsCMNL46xuX79OiIjI7FmzRp06tQJcrkcN2/exPLly3H8+HEdVlm2nD17Fp6enhg2bBgyMzMRFRWFTz75BN7e3ti+fTtkMtkbOfaMtL5H0ok7d+7A09MTkydPxuzZs6FUKpGXl4fKlSvj8ePHui6PiqCgKz02NhYDBw7Ejz/+iEGDBhW6bkEIotfLzMzEqFGjcOzYMZQvXx41a9YE8Pxn/ccff6B69epwc3ODoaGhjiulkhJCqHpsUlNTkZ+fj3/++Qd2dnY4evQotmzZgqNHjyI1NRUuLi6YOXMmfHx8dFy1dO3ZswdPnz5FpUqVkJiYiO+//x6HDh2CgYEBZDIZGjdujI8++gi3b9+Gs7Oz1r8/w41EODo6YsOGDfjkk09U80xMTFCnTh3VX6x5eXkwMnre5JGRkWjcuPEb+U9FRbdu3TrVL2ADAwPk5+fj+PHj6NGjBwYNGoTHjx/j7NmzWLt2LWQyGXr37o0PP/yQwaaIypcvj82bN+PBgwd48OABYmJicOfOHcTFxWHixIlYu3YtcnNz4erqik6dOsHQ0BB+fn4wMzPTdelUDC+G/ZEjR+LcuXM4evQonJ2d0bVrV/z9998YOnQo5s6di9atW6Nhw4a4deuWjquWrlOnTuHTTz/FihUrUK9ePXTs2BGhoaFo3bo1+vfvj1atWuHy5cuIi4t7YzUw3EhAfn4+DA0N1YJNwV8w5ubmuHLlCgCo/jqdOnUq5syZg9u3b7/9Yknl4cOH2LZtG1JTU1GuXDkMGjQIhoaGePz4MbZv344TJ07g22+/hUKhgLGxMR4+fIiZM2fi/fffh5WVla7L13sFv/CsrKxgZWUFV1dXNGvWTLX81KlTWLFiBfbs2YPk5GSsXLkS6enp6NixI5ycnHRYORVXQbC5f/8+0tPT8e2338LU1BSnTp3C1q1b4ejoCC8vL9XnYtWqVTn+5g25c+cODh8+jLFjx6J3794AgB9++AFPnjyBtbW1ar1NmzZBoVCgfPnyb6YQQZKkVCqFEELMnDlTvPfee6r506ZNE+bm5uLs2bO6Ko1ecOXKFTFo0CDRvHlzsWrVKiGEEJmZmcLb21vY2NgIPz8/ceDAASGEEL///ruoXbu2SExM1GHFpVteXp4QQoiFCxeKFi1aqC3LzMwUmZmZuiiLtGDp0qWibt26omPHjuLvv/9WtXWBp0+fijt37ghfX1/RoEEDkZubq6NKpUmpVIrk5GTh4OAgKlasKCZPnqy2rMCxY8fEqFGjRMWKFUV8fPwbq4cDiiWq4C8ZV1dX1X0FpkyZgm+//RYxMTFo0qSJLsuj/3F3d0dgYCBq1KiBNWvWYM2aNTA3N8fBgwdx4sQJbNiwAe3atQMAbNiwQdULQZop6L1s0aIFEhIScOPGDdWNxczNzWFubq7L8khDubm5MDc3R25uLhISEmBlZQVDQ0PVpd9CCERERKB3797IzMzE2bNnYWRkpLqCjkpG/K+X1NHREbNmzYJMJsOZM2eQkJAA4P9/H929exe7du1CUlISYmNj0bBhwzdaFEnYjh07RPXq1cX48eOFiYmJiIuL03VJ9D/5+fmqr69cuSL69+8vvLy8xIoVK9TWO3TokJgwYYKwsrJ6o3/plCXXrl0T1tbW4vTp07ouhTTw4rFT4MmTJ2Lr1q2iYsWKokePHqr5BT04f/31l9iyZYtqmj03JVfQI5OTk6M2f82aNaJKlSrC399f3Lp1S21ZWlqa+Oeff954bRxzIxEv3rTqRQ4ODkhMTMTGjRtx8uRJNG7cWAfV0YvEv650EkLA3d0dkyZNwoIFCxAeHg5DQ0MMGTIE6enpOHjwIM6ePYvY2FjUq1dPh5WXPq86LmrXro2qVavixIkTePfdd3VQGWnqxTY9evQo7t27B1tbW7i7u6NPnz4AgIkTJ6Jv377YvHkzDA0NkZeXB1tbW3z88ccAno9TLLi4gjRT8Dl28OBBrF+/HpmZmbCwsMBPP/2EwYMHQwiBadOmQSaTYfTo0XBxcQEAVKlS5a0VSKVIQVK+fPmyOHHihLh48aJq2b/PMRfMa9++vbhw4cJbq5FeraD9jhw5Ivz9/cWgQYPEqlWrxOPHj4UQz8fVDBgwQDRv3lysXbtWCCHE33//LR48eKCzmkuD4h4Xubm5ol+/fuL69etvrUbSrsmTJ4t33nlHNGnSRLi5uYkOHTqIY8eOidzcXLF582ZRrVo14efnp+syJW3btm3C3NxcfPHFF+LHH38Urq6uws3NTfz1119CCCFWrVol3nnnHTFixAjx559/vtXaGG5KoZ9//lnY2dkJW1tbUb9+fREYGKha9uIHeWEf6qR7kZGRonz58mLYsGGiR48eomXLluLTTz9VddX+/vvvYvDgwaJ27dpiw4YNui22FCnqcVEQhF4c5Eily/Lly0WVKlXEsWPHhBBCTJ8+XZQrV07s2bNHCCFEVlaW2Lp1qzAyMhLTp0/XYaXS9fDhQ/Huu++KhQsXCiGEuHv3rnBychIjRoxQW2/x4sXC3d1dFXjeFoabUqLgg/j+/fvivffeE+Hh4eLMmTNi7ty5olatWmL48OGqdRlq9NeZM2dE9erVxcqVK4UQQiQmJoqKFSuKqlWrii5duqgCTnx8vBg5ciSvjPoPPC7KloL2HjFihOpqnMjISGFhYSFCQ0OFEM+vivr777+FQqEQBw8eZLu/IXfv3hW1atUSjx49EqmpqaJq1apqwebXX39Vff02xtj8G8NNKXLixAkxZMgQMXDgQPHkyRMhhBCPHz8WISEhokaNGvwg1zMTJkwQ33//vdq8yMhIVVd5YmKiqFGjhhgyZIhYtmyZsLGxEX379hV///23EEIIhULx1msujXhclB0FA4l79uwptmzZIo4ePSrKly+vCjZ5eXli5cqVYtOmTWrbsd2179GjR8LLy0ssXbpUdeqpYGBxcnKy6Natm4iKihJC6KaXlCOqSgmFQoE9e/Zg9+7dsLGxUd34yMLCAn379gUALF68GH5+fti4cSNvJ68H6tat+9IA7h49esDNzQ1KpRJjx45FixYtsHr1auTl5SEkJATbt29HXl4etmzZwpuMFQGPC2n794Dwgq9dXV0xYMAAGBgYYM2aNaq2fvLkCbZs2YK2bduq7YftXjLihYsgCr42MDCAs7MzvvjiC9UdiAssXboUSUlJqku9dXJH9bcep0hjd+7cETNnzhTm5ubiq6++Ulv2+PFj8cMPP4jGjRuL1NRUHVVIhdm9e7eYO3eu2l+PycnJwt3dXezevVsI8fz8dd++fcWPP/4okpOTdVVqqcTjQppevNw7OjpaREdHq07TPn36VHTt2lVUqlRJ3LlzR/z9998iOTlZdOrUSTRt2pSXeWtRQa/Lnj17xOeffy6GDh2quiXF9evXRf369UXr1q1FcHCw2Lp1qxgxYoSwsLDQ+UUsDDd6quA/VGpqqkhMTBTp6elCCCEyMjLE9OnTRe3atV8aKJeRkaGTc5v0esuWLRMymUzMnz9f1a737t0TDRo0EJ9//rlISkoSU6ZMEe+9995bH3RX2vC4KHsCAgJElSpVRLly5YS3t7dYvny5EOL5uLQ2bdoIc3Nz4erqKjw9PUWzZs1Up0Z4Kkp7Dhw4IMqXLy+6du0q3n33XSGXy8XmzZuFEM/v0TV48GBRq1Yt0aBBA9G5c2fx+++/67hinpbSS+J/3X7bt2/HV199BZlMhn/++QcDBgzAiBEjMHbsWADA1q1bYWhoiKlTpwIAKlSooMuy6RUKHoo5evRoKJVKTJ48GRUrVsSnn36KNWvWYPv27TAwMMCOHTtga2ur63L1Fo+LskG8cArk0qVLiImJQVRUFHJycrB8+XKsWbMG2dnZGDduHI4cOYKtW7ciPz8fFStWRIcOHVT3teF9bLTn1q1bmD17NsaPH4/s7GxMmzYNAwYMQH5+Pvz8/LB8+XLk5uYiPz8fxsbGMDU11XXJPC2lrw4dOiTKly8vFi9eLLKyssSsWbOEgYGB+Pnnn4UQz/9ynTlzprC1tRXz5s3TcbVUoKBnISkpSVy6dEmte3zp0qXCwMBAzJ07VwghRHZ2trh06ZLYt28fT0UVEY8LaXvxVFRubq64dOmS6Nevn6oXJjk5WXz++edqlyD/G3tsSq7gc+zq1avi+PHjYujQoWLjxo1q60yaNEkYGxurenD0DcONnik4uMeMGSM+//xzIcTzA9rV1fWl+wekpqaKefPmiZs3b771OunVfv31V+Hi4iJsbW2Fl5eXiIyMFFlZWUKI/w84/MVbPDwuypZZs2aJZs2aiVatWolOnTqpLUtOThajRo0SzZs35z1s3qCIiAhhZmYmPDw8hEwmE+PGjROPHj1SW+fLL78UMplMRERE6KjKV2O40VPdunUTGzZsEM+ePRMODg5i+PDhqjS9detWcejQISEE/0rRN1euXBG1atUSCxcuFEeOHBHt27cXnp6eYuXKlaqA89NPPwmZTCYWLVqk42pLHx4X0vRij01ISIiwsrISU6dOFe3btxfm5uYiKChIbf3k5GTRt29fMWzYMN6MUYsKfpaJiYmidevW4qeffhIXL14UX331lTAwMBDLli1T3W6hwPTp08XVq1d1Ue5ryYQQQtenxsqylJQUxMTE4NmzZ+jUqRMcHR0BAKNHj8aRI0eQmZmJ7t2747vvvoOxsTFyc3MxYMAA1KxZE9OmTeN5ZT1y8eJFHD58GMnJyfj+++8BAHl5eejXrx9u3ryJkSNHol+/fjA1NcWqVavQokUL1KlTR8dV6yceF2XTgQMHkJiYCHt7e3Tp0gX37t1DaGgotm7dio8++gizZs1SrXv//n1YW1vDwMDgpee1keaOHTuG3bt3488//8SqVatQrlw5AMA333yD2bNnY/HixRg4cKDqtgt6S8fhqky7fPmyaNiwofDz81O7VbwQz891Nm3aVDg5OYmnT58KIZ7/NTplyhTh5OQk/vjjD12UTK+Qm5srmjVrJmQymWjfvr3aMoVCIT7++GPRrFkz8eOPP4rs7GwdVVk68Lgom86fPy/kcrkwNTUVO3fuVM1PS0sTM2bMEO7u7mLatGkvbVfYE8JJczNnzhQymUw4Ojq+9Oy16dOnC1NTU7FgwQKRmZmpowqLhuFGRy5fviwqVqwoJk2aJO7du6eav2PHDtVdHTdu3Cjq1asnnJ2dRe/evYWvr6+wsbER58+f11XZ9Bp///238PHxEc7OziIiIkJtMLFCoRA+Pj6ibdu2vCz5NXhclF0PHjwQISEhwtbWVnz22Wdqy9LT08Xs2bNFpUqVxIoVK3RUYdmxePFiYW1tLaZMmSJSUlLUlk2aNElYW1ur7qSurxhudODhw4eiVatWYuzYsWrni+fNmydkMplo06aNOHDggBBCiBs3boiAgAAxdOhQMWfOHHHjxg1dlU0vePGZRgqFQvVXzMOHD0XLli1F8+bNxc6dO9X+qlQoFOLu3bs6qbc04HFRdryqtyUjI0OEhIQICwsLMX78eLVlKSkpIiwsjOOptKjgOPvrr7/E3bt31e6zNXPmTOHo6Chmzpz50g0w79+//1br1ATDjQ5cvXpV1KhRQxw+fFh1kIeEhAhjY2OxbNky0b59e9GpUyexa9cuHVdKr7N9+3bx7rvvinr16omAgAARFxcnhHj+F2iLFi1E8+bNRVRUFLvNi4jHRdnw4vGwatUqERgYKPr27SsOHTokHj9+LPLy8kRISIiwtrYWEyZMKHQfDDglVxBstm3bJpo0aSLs7e1F8+bN1a4+nDFjhnB0dBRz5sxR+8OsNAziZrjRgfXr1wtDQ0O1/yDJyckiNjZWCCHEpUuXhLe3t2jatKm4ePGiap3S8B+qrLh48aKwsLAQc+fOFWPHjhUffPCB+OCDD8SxY8eEEM8DTuvWrYW7u7vYt2+fjqstHXhclC0BAQGicuXKonfv3qJly5bC2tpaBAQEiOTkZJGTkyNCQ0OFnZ2dGDRokK5Llaz9+/cLU1NT8f3334uff/5ZzJ8/Xzg4OAhfX1/VOnPmzBFmZmZi/vz5pSpUMtzowNGjR4VcLlfdG+DFD+eCv2pWrFghmjZtKtLS0nRSI73a5cuXxbx588SMGTNU83bv3i0+/PBD0bp1a1XAuXfvnujUqZPqeTj0ejwuyo5Dhw4JR0dHce7cOdW8JUuWiLp164pvvvlGCPF8DNt3330nfH192fv5BuTn54tx48aJoUOHqubl5uaKQ4cOCXt7e7XTgt9//32pG6xv8N/XU5G2OTs7w9LSEmvXrsWdO3fULmEseOrt9evX4ezsDHNzc12VSYVISUnB+PHj8d133yErK0s138fHB6NGjUKFChUwbdo0REdHw8bGBlFRUXB2dtZdwaUIj4uyIysrCyYmJrCxsUF+fj4AYOzYsejfvz8WLVqElJQUVKxYESNGjMCuXbtgYGAApVKp46qlxcDAALdu3UJycrJqnpGREVq1aoXhw4fj0qVLePz4MQBg4sSJqFmzpq5K1QjDjQ44Ojrip59+wt69ezF16lRcvXpVtSwjIwOBgYFYs2YNpk+fzufi6JmqVavi448/hqOjI3bu3IkbN26olvn4+GDMmDHIzc3FwoUL1cIP/TceF2WHQqHA33//DSEEDA0NVcfK2LFjYWJighMnTgAAzM3NIZPJIIRQBVzSng8//BD//PMPjh49qppnZGQEJycnJCUlqYJnacSb+OlIfn4+Vq1ahTFjxsDV1RXNmzeHsbExUlJSEBcXh927d6NRo0a6LrPME/+7OVh+fj7y8/NhYmICAPj555+xaNEiODk5YdasWahVq5Zqm4MHD8LNzQ1OTk66KrvU4nEhLUql8pWhpFmzZlAoFIiLi4OhoSEAICkpCe3atUNISAi8vb3fZqmSVvA59uDBAwghYGFhAblcjoSEBPj5+cHDwwNDhw5Fq1atADzvqbl48SJ27Nih/zfrewWGGx07ffo05s+fj1u3bqFChQpo2bIlPvvsM7i6uuq6tDKv4ANh3759CAsLQ1paGlxdXREQEIA6depgw4YNWLVqFWxsbDB37txS122rz3hclH4vBpu1a9ciPj4e5cqVQ+3atTFgwABcvnwZffr0QX5+PubOnQshBMLDw5Geno7Tp0+rAg+VTMHn2Pbt2/HNN9/g2bNnePr0KSZOnIjPP/8cCQkJGDlyJPLy8iCXy1GlShUcOXIEMTExaNiwoa7L1xjDjR7Iz8/ngaynduzYAT8/PwwfPhxeXl744osvYG1tjV9//RXVq1fH2rVrsX79ehgZGWHZsmWoUaOGrkuWDB4X0hAYGIjw8HB4e3vj7t27+OOPP9ClSxesWrUKSUlJGDt2LH7//XeUL18e1apVw/bt22FsbMz216JDhw6hc+fOmDlzJurWrYuTJ09i06ZN8PX1xfz583H37l3ExcVh3759cHFxwccff4zatWvruuyS0cUoZlL34lUhvKxVd168IkOpVIqHDx8KLy8vsWDBAiGEEFlZWaJq1aov3WQuNDRUdO7cmTfo0zIeF6VfTEyMcHBwUF3O/+TJExERESGsrKzEqFGjVOslJSWJ+/fvq9r5xbt7k+aUSqVQKpViyJAhon///mrLVq5cKapVqyaWLl2qo+reLI7Q0gMvXhXCh7/pxg8//IBt27YhNzcXwPN2MDAwgEKhwIABA5CcnIwaNWrA19cXS5YsUZ2uEkJgxIgR2LhxI6pWrarjdyEtPC5KF/HCSYCCK5vS0tIgl8vh6ekJAChfvjw6d+6MH374AQcPHkRcXByA5wP1K1euDJlMBqVSyQefaqCwq8lkMhlkMhmePXummpeTkwMAGDp0KHr37o3vv/8eeXl5au0nBQw3RACioqIwZMgQ7N+/XxVwAODx48dYt24d2rZtiw8//BDLli0DACQnJ2PRokXYs2cPAMDS0lIndRPpi8zMTOTm5qqNtalevTqysrJUVz8BgFwux7vvvou0tDQ8ePAAANQGHfOqqOIr+Jmnp6fjyJEjiI6ORnZ2tmp5jRo1sG/fPmRkZMDExET1GdegQQNYWloiJydHcn9A8H8RlWn37t0D8PwKp44dO2LAgAHYu3cvsrKyYGVlhb59+2LGjBl45513sHz5chgbGwMAli9fjtTUVNSrV0+X5RPphV9++QW9evVC27ZtMWTIECgUCgCAnZ0datWqhfXr1yM+Pl61vrW1NVxcXCT3C1UXCoLNpUuX8P7772PChAn44IMPMHHiRFU7fPHFF3jnnXfQunVrPHr0SPU5dubMGZQvX15yvTYABxRTGbZ06VIcP34ckydPVl0V0LNnT0RHRyM8PBxdunTBlStXMHXqVCQkJKBfv36wtbXFuXPnsGnTJsTGxqJBgwa6fRNEOrZ8+XL4+/vjyy+/xKNHj3D48GF06dIFM2fOBADs3LkTkydPRq1atdC2bVu4u7tjwYIFePjwIc6cOcNBwyVQEGwuXLiAFi1aYNy4cRg7diwuXLiADz/8EHFxcWjcuDEA4Ny5cxg+fDiSkpLQrFkzKJVKHD16FEePHpXk5xjDDZVZW7ZswcSJE9GtWzd8/vnnqgO8Z8+eOHz4MNatW4cuXbrg8uXL+OWXXxAWFgZ7e3s4ODhg1qxZqFu3ro7fAZFuhYWFYdiwYdi2bRu6dOkCAPj000/h4eGBIUOGwNLSEuXKlUNMTAzWrFmDqKgovPPOO6hcuTJ27drFq6K0ICEhAfXr18ecOXMwadIkVeBp1aoV/Pz88PTpU3h4eKBjx47Iy8vDvHnz8Ndff8HU1BSfffZZ6b8q6lV0N5aZSDdiY2NFTk6OEEKIHTt2CCcnJzFs2DBx4cIF1TofffSRsLKyEjt27FDNe/r0qcjPzxdZWVlvvWYifXPw4EEhk8nE9OnT1ea3bNlSVK9eXdjb2wtHR0exdu1aIYQQCoVCPHz4UKSmpvKqKC1RKBRiwoQJQiaTqX1+zZkzR8hkMtG5c2fh4OAgbG1txZw5c3RY6dvHcENlyrp168QHH3wg7t27p5oXGRn52oCza9cu8ezZM9V8XpZMJMSdO3dE7dq1xYcffiiOHz8uhBCiV69ewtXVVURFRYnDhw+Ljz/+WFSsWFHcvHnzpe35MEztuHDhgvj000+FjY2NuH37tggJCREVK1ZU/WF29+5d0bVrV9GgQQORnJys2k7qn2MMN1QmFHyQZmRkiJSUFCGEEImJiaoenFcFnD59+giZTCb27Nnz9osm0lN5eXlCiOfHUIMGDYSPj49o1aqVqFu3rtr9nq5evSpMTExEWFiYjiotG65cuSL69OkjypUrJ+RyuTh//rwQ4v/b6fvvvxfVq1cXDx480GWZbxVvJkCSV3AO+tatW0hISEDnzp1x7do19O/fH3379sW4cePQo0cPAMD48eMBAKNHj0aDBg2wdetWmJqawsXFRZdvgUivGBoaIj8/H87Ozti2bRs+/vhj/P777wgLC1O735MQAi4uLnB0dNRhtdLn7u6OadOmoUKFCvj1119V88X/htTevn0btWrVUj0bryxguCHJMzAwQGpqKpo1awZbW1s8ffoU3bt3R82aNREZGQkTExOMHDlSLeAYGhpi6NCh8PT0xNq1a3X8Doj0T0HAcXFxQUREBLp164bw8HDY2tqqHno5efJk2Nvbo23btjquVlrE/54X9SIPDw9MnDgRmZmZ6NixI3bu3In33nsPU6dOxdq1a3H8+HFUqFBBRxW/fQw3VCZcv34dDx8+hIuLC9avXw8zMzOsXbsWI0eOxIYNGwBAFXBkMhn8/Pwgl8tRt25dyOVyHVdPpJ8KAo6TkxMiIiLw0UcfYcGCBTAwMMCCBQtw8+ZNXLlyRbUer4oqGfHC071tbGxeWu7h4YFp06YBAHr16oVWrVph27ZtOHr0aJm7JxcvBacy47PPPsO5c+fg6uqK+/fvIzAwEO3bt8fIkSNx5coV9OvXDyNHjoSxsTF27doFNzc3Pumbyrx/9xK8eAfiAgXB5c8//0SvXr1w/vx51K1bF+fOnYOxsTHy8vL4SAUt+fnnnxEQEIDjx4/Dycmp0HWuXbuGKVOm4NChQ4iOjlbd66YsYbghyfn3h69CoYBcLsfu3bvxyy+/oG/fvli+fDnS09Px1VdfoV27dvj888+RkJCA7t27Y8KECao7eBKVZS8Gm/nz5+Pdd99FmzZtCj0tUhBwEhMTMW/ePCxbtgxGRkYMNlpQ8PNOTU3F6NGj0bFjR4wcOfK121y9ehWVKlVClSpV3lKV+oWPXyBJKQg2ycnJ2L59OwCoTis1bdoUp06dwo0bNxAaGooqVaogODgYBw8eREhICKpWrYp9+/YhMzNTh++ASD8olUpVgLl9+zZ+++03dOvWDWfOnIFMJnvplv0vjsFZvnw5g40WyWQynDt3DgEBAXj27Bm6deuG/Pz8QtctaBd3d/cyG2wAhhuSmIJg06hRI3z00Ufo3Lkzfv75Z/zxxx+wsbHB/PnzsXXrVgDA7NmzYWtriwULFiAqKgobN27E+vXrUbFiRR2/CyLdK+j9nDJlCgYNGoQKFSpAqVTC29sbx44de2XAeRGDjfbs378fZ8+eRXx8PExMTFRh8t/4vK7nGG5IcpRKJVxcXNCsWTP89ddfOHDgADp06IDly5cjKysLlpaWiIuLQ506dTBr1iwYGRlh5cqVyMnJgb29va7LJ9IbYWFhWLJkCebPn49NmzYhJiYGPj4+6NixI44fP15owKE3Y/LkyRg3bhzkcjnGjBmDBw8ewNDQEEqlUtel6SWOuSFJunHjBr788ksolUoMGDAABgYGWLRoEaysrPDbb7+hadOmOHr0KExMTHD9+nWYm5vzXhxE//LVV1/h8uXL+O2331Tz7t69ixEjRuD48eM4cOAAmjZtWuggY9JcwRibmzdvQqFQ4J9//kHLli0BAIsXL8bmzZvRsGFDBAcHo2LFivz5F4I/DZKkmjVrYu7cuVAoFAgNDUWdOnWwa9cuTJ48GZ07d8aYMWNgYmICIQTc3NwYbIgKIZfLERcXB4VCAeD5L11HR0d8+umnyMjIQLt27XD+/HkYGBiwB0FLCoLNtm3b4OPjg379+qFz587o3bs3Ll68iHHjxqFnz574/fff8fXXX+Phw4cMNoXgT4Qky83NDYsXLwYAjB07FhcuXECzZs2wc+dO9O/fHwDPTxMBeGUw8fHxgZ2dHWbOnIl//vlHdbxUq1YNw4cPR7du3eDn54d79+7xF6yWyGQyxMbGYvDgwQgKCkJ8fDx++eUXRERE4OLFi5DJZPjiiy/Qq1cvHD58GHPmzGGwLARPS5Hk3bhxA+PGjYMQAl9//bWqe5eI1C/33rhxI5KSklClShUMGDAAhoaGmD59Ovbv3w9PT09MmDABMpkMEyZMgIODA3r16oUBAwbg559/xvvvv6/jd1I6paamokqVKjAwMFC1xdy5c3H9+nWsXbsWN27cQOfOndG6dWusXLlStZ1SqcTSpUvRtWtXODs76+4N6ClGbZK8mjVrYsmSJTA2NsakSZNw6tQpXZdEpBdeDDZff/01hg0bhv379+Ozzz5D79698eeff+Kbb75B9+7dcf78edSqVQudOnXCnTt3sHz5ctSuXRvm5ua8KkpDa9asQaNGjXD69Gm1tvj999/h6OgIIQTatm2Ltm3bYsWKFQCA8PBwbN68GQYGBhg3bhyDzSsw3FCZULNmTSxYsACOjo5wcHDQdTlEeqHgl+nNmzcRFxeH2NhYHDlyBJcvX8apU6cwZswYJCYmYvLkyThy5Aj27NmDTZs24cKFCwCARYsWwdzcHDVq1NDhuyi9Bg8eDDs7OwwfPhynT59WnV7q0KEDtm7dChsbG/To0QMhISGqtjp69ChiYmKQnZ2ty9L1Hk9LUZmSk5NTpp6MS/Rf5s2bh/3798PCwgLr1q2DhYUFAODSpUvo1KkTPD09MWfOHLVnEx09ehRbtmzBpk2bcOTIETRs2FBH1ZdeL34WeXp6IicnB6GhofDy8sIff/yBiRMn4vr161i/fj1atGiBJ0+eYN68eQgLC8ORI0fg5uam43eg39hzQ2UKgw2Vdf8efNq4cWPExsbixIkTuHv3LoDnp6vq1auHffv24eLFixg1ahRu3ryptl1+fj6OHz/OYKOhgke8/Pnnn5g7dy6uXLmCyZMn4+zZs6hduzbGjx+POnXqoEuXLmjevDk6d+6M8PBwREVFMdgUAXtuiIjKoHPnzqFGjRqwsrLCiRMn0KZNG3zyySeYN28eHBwcVGNA4uPjMWPGDERGRhb6zDbS3Pbt29G3b18EBATg5s2bOH36NExMTLB+/Xo0bdoUiYmJOH36NM6dOwd3d3e0adMGLi4uui67VGC4ISIqY3bt2oUxY8bgiy++wMCBA2FhYYEjR46gQ4cO6NevH+bMmaMWcArwZnGae/r0KczNzQE87xl7+PAh3n//ffj5+eHrr78GAPz999/w9vZGVlYW1q5diyZNmrz0SAsqGv4vJSIqYz788EO0atUKa9euxfr16/H48WO0bdsWBw4cwKZNmzB16lQkJSW9dB8oBhvNLF26FMHBwUhPTwfwfCB3wY0PCwZj5+bmolKlSjh48CCePXuGoKAgHD16lPew0RD/pxIRSdiLnfMvfr1u3TrUq1cPq1atwoYNG5CRkYE2bdpg//79CAsLw/r163VRriTdvXsXq1evRnh4uCrgVKxYEQYGBjh8+DCA52Nw8vLyULFiRdSrVw/R0dEICgpCTk6OLksvtRhuiIgkrKD3JSwsDFu2bEFubq5qWVhYGBo0aIDvv/8emzZtQkZGBlq3bo1z585h8uTJuipZcubNm4fRo0dj2bJlWLNmDe7evQuZTIapU6diz549CA4OBvD8KeoGBgaoXbs2jh07hs2bN8PU1FTH1ZdOHHNDRCRx+fn58PLyQm5uLqZPn47OnTurrtYBgCZNmkChUMDPzw9jxoxB+fLlAQB5eXm8QV8JvfgznD59OsLCwjBy5EiMGDECcrkc33//PUJCQtC+fXt4eXnh4sWL2LhxIxISElC1alUdV1968X8tEZHE/Hvgr6GhIaKjo9GjRw/Mnj0bSqUSH374oerWCHXr1sXhw4eRmJioGvQKgMFGAwU/+6ysLBgYGOCvv/6CtbU1zM3NMWPGDADATz/9BCEExo0bhy+++AIeHh6YPXs2rl69CiMjIxw9epTBpoT4P5eISELy8/NVV9gkJibCzMwMubm5cHJywrZt29CtWzfMnTsXSqUSvr6+KFeuHGQyGTZu3IgWLVpAJpO9dJUUFU1BsLl27Rq+/vpr/PHHH0hISEDdunXRrl07LFiwADNmzIBMJkNISAgAYMSIEejZsyd69uyJrKwsKJVKtYBJmuFpKSIiibh27Rrq1KkDAJg6dSp27NiBhw8fwsLCAqNGjcKYMWOQlZWFXr16ITU1FaamppDJZHj06BEuXboEQ0NDXu6toYJAeOnSJbz//vvo168fGjVqhEqVKmHt2rXYu3cv2rZtiz179gAApk2bhrCwMIwZMwZ9+/ZFtWrVdPwOpIU9N0REEnDx4kW0bNkSt27dwurVq/HTTz8hPDwcz549w7Vr1zBhwgQ8ePAA33zzDSIiIrB8+XIkJibCwMAA8+fPZ7ApIZlMhvv372PgwIH4/PPPVYOEAaBly5b4+eefERgYiF69euHXX3/FzJkzYWJigpkzZ8LY2Bjjx4/nPW20iOGGiEgC8vPzUa5cOTx48ACnT5/G1KlT0aVLF9VyZ2dnDBkyBO7u7ujTpw/Gjx+vtj0HD5fc3bt3kZubCz8/P9XpQaVSCRsbG/Tr1w///PMPgoOD8euvv6JXr174+uuvIZfL0aVLFwYbLWNEJyIqxQpGFjRu3BiVK1fGoUOHcPbsWbWnRiuVSvTv3x99+vTBnj17kJeXh/z8fLX9MNiU3MWLF3Hz5k3UrVsXhoaGEEKoesIsLS3h5+cHY2Nj/Pnnn6ptJk2ahJo1a+qoYuliuCEiKsUKBv4qlUpUrFgRSqUSI0aMwO7du3H9+nXVOoaGhqhQoQIePXoEIyMj9hS8Aa6urgCAiIgIAHhpULaLiwuqV6+OlJSUt15bWcNwQ0RUCu3evRtfffUVYmNjce3aNRgYGKBXr164ePEi6tevj5ycHCxevBg3b96ETCbDs2fPcPv2bQ5cfYOcnZ1hYWGBdevWISkpSTW/4BEK//zzD8zMzODp6amrEssMXi1FRFTKPHnyBIMGDcKtW7eQm5uLxMRE1K9fHxcvXkSVKlVw9epV7NmzBwsWLMDdu3fh4eGBBw8eIDs7GxcuXICRkREv935DIiMj0bdvX3zyyScIDAyEh4eHatnUqVOxYcMGREdH45133tFhldLHcENEVAoVXNn08OFDXLt2DQ8fPsTevXtx/vx5ODg4YP369bh//z52796NhIQEODk5wd/fH0ZGRhw8/Abl5+dj1apVGDNmDGrUqIEWLVrA3t4ef/75J/bs2YODBw+iUaNGui5T8hhuiIhKocIu2xZCYNu2bViwYAGsra0RFhYGGxsbtXVfvMkfvTmnT5/G/Pnzcf36dVhZWaFhw4YYM2YMateurevSygSGGyIiCSg4zaRUKvHrr7/ip59+QmZmJg4ePAgrKytdl1cm5efnw8DAQNUuvIfQ28N+SSIiCSh4bIKBgQF69+6NZ8+e4cyZM7CwsNB1aWVWQbABXr5yit4s9twQEUlIYQOF2WtAZQ3DDRGRxPBKKCrrGOWJiCSGwYbKOoYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiov8JDw/nQyaJJIDhhoiKLT09HePHj4erqytMTU1hZ2eHli1bIjQ0FM+ePdN1eUXi7OyMRYsWqc37+OOP8ccff+imICLSGj4VnIiK5fbt22jRogWsrKwwd+5c1KtXD3l5efjjjz+wZs0aODg4oGvXrjqpTQiB/Px8GBlp9tFmZmYGMzMzLVdFRG8be26IqFhGjRoFIyMjxMXFoU+fPqhTpw7q1auHnj17IioqCl26dAEAPH78GMOHD4etrS0sLCzwwQcf4OLFi6r9fPPNN2jYsCHWr18PZ2dnWFpa4pNPPsGTJ09U6wghMH/+fFSvXh1mZmZo0KABfv31V9Xy6OhoyGQy7Nu3D02aNIFcLsfRo0dx69YtdOvWDXZ2dihfvjyaNm2KgwcPqrZr06YN7ty5g4kTJ0Imk6mexVTYaamQkBDUqFEDJiYmcHNzw/r169WWy2QyrFq1Cj169EC5cuVQs2ZN7NixQ7X8n3/+gZ+fH2xsbGBmZoaaNWsiLCys5A1BRK/EcENERfbw4UPs378fo0ePhrm5eaHryGQyCCHQuXNnpKenY/fu3Th37hwaN24Mb29v/P3336p1b926he3bt2PXrl3YtWsXYmJiMG/ePNXyr7/+GmFhYQgJCcGVK1cwceJE9OvXDzExMWrfMzAwEMHBwbh27Rrq16+PzMxM+Pr64uDBg4iPj0fHjh3RpUsXJCUlAQAiIyPh6OiImTNnIi0tDWlpaYW+l23btmH8+PH44osvcPnyZYwYMQKDBw/GkSNH1NabMWMG+vTpg99//x2+vr7w8/NTvc+pU6fi6tWr2LNnD65du4aQkBBUrly5+D98Iio6QURURKdOnRIARGRkpNp8a2trYW5uLszNzUVgYKA4dOiQsLCwENnZ2Wrr1ahRQyxfvlwIIcT06dNFuXLlREZGhmr5pEmTxHvvvSeEECIzM1OYmpqKEydOqO3js88+E3379hVCCHHkyBEBQGzfvv0/a3d3dxc//vijavqdd94RP/zwg9o6YWFhwtLSUjXdvHlzMWzYMLV1evfuLXx9fVXTAMTXX3+tms7MzBQymUzs2bNHCCFEly5dxODBg/+zPiLSHo65IaJiKziNU+DMmTNQKpXw8/ODQqHAuXPnkJmZCWtra7X1srKycOvWLdW0s7MzKlSooJq2t7fHvXv3AABXr15FdnY22rdvr7aPnJwcNGrUSG1ekyZN1KafPn2KGTNmYNeuXUhNTUVeXh6ysrJUPTdFde3aNQwfPlxtXosWLbB48WK1efXr11d9bW5ujgoVKqjex+eff46ePXvi/Pnz6NChA7p3747mzZsXqw4iKh6GGyIqMldXV8hkMiQkJKjNr169OgCoBuMqlUrY29sjOjr6pX28OKbF2NhYbZlMJoNSqVTtAwCioqJQtWpVtfXkcrna9L9PkU2aNAn79u3DwoUL4erqCjMzM/Tq1Qs5OTlFfKfqNb1ICPHSvNe9Dx8fH9y5cwdRUVE4ePAgvL29MXr0aCxcuLDYtRBR0TDcEFGRWVtbo3379li6dCnGjh37ynE3jRs3Rnp6OoyMjODs7KzR93J3d4dcLkdSUhJat25drG2PHj2KQYMGoUePHgCAzMxM/Pnnn2rrmJiYID8//7X7qVOnDo4dO4YBAwao5p04cQJ16tQpVj02NjYYNGgQBg0ahPfffx+TJk1iuCF6gxhuiKhYfvrpJ7Ro0QJNmjTBN998g/r168PAwABnz55FQkICPD090a5dO3h5eaF79+749ttv4ebmhtTUVOzevRvdu3d/6TRSYSpUqICAgABMnDgRSqUSLVu2REZGBk6cOIHy5ctj4MCBr9zW1dUVkZGR6NKlC2QyGaZOnarqSSng7OyM2NhYfPLJJ5DL5YUO8p00aRL69OmjGgy9c+dOREZGql159V+mTZsGT09PeHh4QKFQYNeuXcUOR0RUPAw3RFQsNWrUQHx8PObOnYugoCDcvXsXcrkc7u7uCAgIwKhRoyCTybB792589dVXGDJkCO7fv48qVaqgVatWsLOzK/L3mjVrFmxtbREcHIzbt2/DysoKjRs3xpQpU1673Q8//IAhQ4agefPmqFy5MiZPnoyMjAy1dWbOnIkRI0agRo0aUCgUEEK8tJ/u3btj8eLFWLBgAcaNGwcXFxeEhYWhTZs2RX4PJiYmCAoKwp9//gkzMzO8//772LJlS5G3J6Lik4nCjmgiIiKiUor3uSEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSfk/hZD8IId2vUkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=expNS1, x='Generations', y=\"Nervousness\", whis=2, palette=\"plasma\")\n",
    "plt.xticks(rotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "CoreTrends_id = ['age']\n",
    "# CoreTrends_values = [\"sns2a\", \"sns2b\", \"sns2c\", \"sns2d\", \"sns2e\"]\n",
    "CoreTrends_values = [\"totalFreq\"]\n",
    "CoreTrends2018_long = cleanUpCT(CoreTrends2018_wantedCols, CoreTrends_id, CoreTrends_values)\n",
    "CoreTrends2019_long = cleanUpCT(CoreTrends2019_wantedCols, CoreTrends_id, CoreTrends_values)\n",
    "CoreTrends2021_long = cleanUpCT(CoreTrends2019_wantedCols, CoreTrends_id, CoreTrends_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "expCT1 = pd.concat([CoreTrends2018_long,CoreTrends2019_long, CoreTrends2021_long])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Generations</th>\n",
       "      <th>variable</th>\n",
       "      <th>Frequency of Social Media Use</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Millennials</td>\n",
       "      <td>Social media use</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Older Boomers</td>\n",
       "      <td>Social media use</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Young Boomers</td>\n",
       "      <td>Social media use</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Young Boomers</td>\n",
       "      <td>Social media use</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Young Boomers</td>\n",
       "      <td>Social media use</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Generations          variable  Frequency of Social Media Use\n",
       "0    Millennials  Social media use                            8.0\n",
       "1  Older Boomers  Social media use                            0.0\n",
       "2  Young Boomers  Social media use                            3.0\n",
       "3  Young Boomers  Social media use                            5.0\n",
       "4  Young Boomers  Social media use                            6.0"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expCT1.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4]),\n",
       " [Text(0, 0, 'Gen Z'),\n",
       "  Text(1, 0, 'Millennials'),\n",
       "  Text(2, 0, 'Gen X'),\n",
       "  Text(3, 0, 'Young Boomers'),\n",
       "  Text(4, 0, 'Older Boomers')])"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=expCT1, x='Generations', y=\"Frequency of Social Media Use\", whis=2, palette=\"plasma\")\n",
    "plt.xticks(rotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Generations</th>\n",
       "      <th>variable</th>\n",
       "      <th>Frequency of Social Media Use</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Millennials</td>\n",
       "      <td>totalFreq</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Older Boomers</td>\n",
       "      <td>totalFreq</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Young Boomers</td>\n",
       "      <td>totalFreq</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Young Boomers</td>\n",
       "      <td>totalFreq</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Young Boomers</td>\n",
       "      <td>totalFreq</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Generations   variable  Frequency of Social Media Use\n",
       "0    Millennials  totalFreq                            8.0\n",
       "1  Older Boomers  totalFreq                            0.0\n",
       "2  Young Boomers  totalFreq                            3.0\n",
       "3  Young Boomers  totalFreq                            5.0\n",
       "4  Young Boomers  totalFreq                            6.0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expCT1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "CT_t = expCT1.rename(columns={\"Frequency of Social Media Use\": \"Frequency (lower = More frequent)\"})\n",
    "NS_t = expNS1.rename(columns={\"Nervousness\": \"Frequency (lower = More frequent)\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Generations          variable  Frequency (lower = More frequent)\n",
      "0    Millennials  Social media use                                8.0\n",
      "1  Older Boomers  Social media use                                0.0\n",
      "2  Young Boomers  Social media use                                3.0\n",
      "3  Young Boomers  Social media use                                5.0\n",
      "4  Young Boomers  Social media use                                6.0\n",
      "   Generations     variable  Frequency (lower = More frequent)\n",
      "0        Gen Z  Nervousness                                  2\n",
      "1  Millennials  Nervousness                                  5\n",
      "2  Millennials  Nervousness                                  2\n",
      "3        Gen X  Nervousness                                  2\n",
      "4        Gen Z  Nervousness                                  1\n"
     ]
    }
   ],
   "source": [
    "print(CT_t.head())\n",
    "print(NS_t.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "CT_NS = pd.concat([NS_t, CT_t])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x15cc7f310>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SpUpYv379B/NPmjQJ3t7eWssWLFgAd3d3reepVasWbG1t4eDggHr16uHevXua9X/88QeqV68OpVKJkiVLYvLkyUhPT8/ib/D9WIyboEGDBuHUqVMYNGiQ3FGIiMjA+fr6YteuXfD19ZU7ilGws7ODnZ0dtm3bhpSUlEy3EUKgXbt2ePbsGSIiIrBv3z7cvn0bXbp00Wyzc+dOdOjQAa1atcLZs2cRHh6OGjVqvPd5FQoFfvzxR1y6dAlhYWE4cOAARo0apXP+iRMn4rvvvsOZM2dgbm6Orl27YtSoUVi4cCGOHDmC27dvY8KECZrtly9fjnHjxmH69Om4evUqZsyYgfHjxyMsLAwAkJiYiNatW6NcuXI4ffo0Jk2ahJEjR34wQ3JyMqpXr44dO3bg0qVL6Nu3L7788stsXTclPT0d7dq1g6+vLy5cuIBjx46hb9++mvMj9uzZg+7du2PIkCG4cuUKgoODERoaiunTp3/0c2bgMBUiIiKiXGJubo7Q0FD06dMHy5YtQ7Vq1eDr64svvvgClStXBgDs378fFy5cQHR0tGaM/urVq1GxYkWcPHkSNWvWxPTp0/HFF19g8uTJmn1XqVLlvc87bNgwzW0PDw9MnToVAwYMeKeX+r+MHDkSzZo1AwAMHToUXbt2RXh4OOrVqwcA6NWrF0JDQzXbT506FfPmzdP02nt4eGiK2a+++gpr166FSqXCypUrYWNjg4oVKyI2NhYDBgx4b4ZixYppFexff/01/vzzT/z222+oXbu2Tq8nQ0JCAl68eIHWrVujVKlSAIAKFSpo1k+fPh2jR4/GV199BQAoWbIkpk6dilGjRmHixIkf9ZwZ2DNORERElIs6duyIBw8eYPv27WjWrBkOHTqEatWqaYrYq1evwtXVVetkWU9PTzg4OODq1asAgHPnzsHPzy/Lz3nw4EE0bdoUxYoVg729PQICAvD06VMkJibqlD3jCwMAFClSBABQqVIlrWWPHj0CADx+/Bj3799Hr169NEcE7OzsMG3aNNy+fVvzWqtUqQIbGxvNPv7rJFaVSoXp06ejcuXKKFiwIOzs7LB3795sXR22QIEC6NGjB5o1a4Y2bdpg4cKFiIuL06w/ffo0pkyZovU6+vTpg7i4OLx+/fqjnxdgMU5ERESU65RKJZo2bYoJEyYgKioKPXr00PSwZsxe829vL9dl/P69e/fQsmVLeHl5YfPmzTh9+jQWL14MAEhLS9Mpt4WFheZ2RpZ/L8u4qnfG/5cvX45z585pfi5duoS//vpL85p0NW/ePPzwww8YNWoUDhw4gHPnzqFZs2ZITU1972MUCsU7z/Xv1x4SEoJjx47Bx8cHGzZsQNmyZTU51Wo1Jk+erPU6Ll68iJs3b0KpVOr8GrSyZevRRERERJRtnp6eml5qT09PxMTE4P79+5r1V65cwYsXLzRDJypXrozw8PAs7fvUqVNIT0/HvHnzUKdOHZQtWxYPHjzQ/4v4lyJFiqBYsWK4c+cOSpcurfWTceEoT09PnD9/HklJSZrHZRTA73PkyBG0bdsW3bt3R5UqVVCyZEncvHnzg48pXLgwHj58qFWQvz3Vc4aqVatizJgxiIqKgpeXF9atWwcAqFatGq5fv/7O6yhdunS2r9XCYtwELV68GDVq1NB8KyaiN/jeMBwRERFo2bIlIiIi5I5i8tgW+vX06VN88sknWLNmjWZc+G+//YY5c+agbdu2AIAmTZqgcuXK8Pf3x5kzZ3DixAkEBATA19dXc5LmxIkTsX79ekycOBFXr17FxYsXMWfOnEyfs1SpUkhPT8dPP/2EO3fuYPXq1Vi2bFmuvN5JkyZh5syZWLhwIW7cuIGLFy8iJCQE8+fPBwB069YNCoUCvXr1wpUrV7Br1y58//33H9xn6dKlsW/fPkRFReHq1avo168fHj58+MHHNGrUCI8fP8acOXNw+/ZtLF68GLt379asj46OxpgxY3Ds2DHcu3cPe/fuxY0bNzRffiZMmIBVq1Zh0qRJuHz5Mq5evYoNGzbgu+++y+ZvSOZifObMmahZsybs7e3h5OSEdu3a4fr161rbCCEwadIkuLi4wNraGo0aNcLly5dlSpz3xcfHY+XKlZqTJeLj4+WORGQQ+N4wHElJSZgxYwbi4uIwY8YMrR4zyl15uS1UQiBdrc7xH5WOwyzs7OxQu3Zt/PDDD2jYsCG8vLwwfvx49OnTB4sWLQLwZqjHtm3b4OjoiIYNG6JJkyYoWbIkNmzYoNlPo0aN8Ntvv2H79u3w9vbGJ5988t7ZRLy9vTF//nzMnj0bXl5eWLt2LWbOnPnxv1wd9O7dGytWrEBoaCgqVaoEX19fhIaGanrG7ezs8Mcff+DKlSuoWrUqxo0b994rhmcYP348qlWrhmbNmqFRo0YoWrQo2rVr98HHVKhQAUuWLMHixYtRpUoVnDhxQuskUBsbG1y7dg0dO3ZE2bJl0bdvXwwePBj9+vUDADRr1gw7duzAvn37ULNmTdSpUwfz58+Hm5tb9n5BACTxMYN19KR58+b44osvULNmTaSnp2PcuHG4ePEirly5orlq1OzZszF9+nSEhoaibNmymDZtGg4fPozr16/D3t7+P58jISEB+fPnx4sXL5AvX76cfkkGLzAw8J0rcK5cuVK+QEQGgu8Nw7F48WKtqz727t0bAwcOlDuWSZKzLbLy+Z2cnIzo6Gh4eHhoxu3mhStwkmnI7N9nZmQtxv/t8ePHcHJyQkREBBo2bAghBFxcXDBs2DAEBQUBAFJSUlCkSBHMnj1b823lQ1iM/7/jx4+jf//+7yxftmzZR08FRGQM+N4wHDExMejQoQNUKpVmmbm5OTZv3owSJUrImMz0yN0WH1uMA28K8oyTB3ODQqFgIU7vyGoxblBjxl+8eAHgzfQywJvxOw8fPsSnn36q2cbKygq+vr6IiorKdB8pKSlISEjQ+qE3ZwFnfKH5t6CgoFz9o0VkSPjeMBxCCMyaNeu9yw2o78jo5fW2MDMzg4WFRa79sBCn7DCYYlwIgW+++Qb169eHl5cXAGgG42fMY5mhSJEi7x2oP3PmTOTPn1/z8/YcnaYsMjJS82Xn3168eIHIyMhcTkRkGPjeMBzR0dE4duyYVk8s8KaX89ixY4iOjpYpmelhWxDlHoMpxgcPHowLFy5g/fr176z791yb75t/EwDGjBmDFy9eaH7enhbIlNWvXx/58+fPdJ2DgwPq16+fy4mIDAPfG4bDw8MDdevWfaeX0czMDD4+PpoTvijnsS2Ico9BFONff/01tm/fjoMHD6J48eKa5UWLFgWAd3rBHz169E5veQYrKyvky5dP64fejGd739nJs2fPzvYcmUR5Fd8bhkOSJIwePfq9y9/XCUP6x7Ygyj2yfsoIITB48GBs2bIFBw4ceOebtoeHB4oWLYp9+/ZplqWmpiIiIgI+Pj65HTfPq127Nry9vbWWVa1aFbVq1ZInEJGB4HvDcJQoUQKBgYGaYk+SJAQGBnLIoQzyUlvw3A4yRFn9dynrbCoDBw7EunXr8Pvvv6NcuXKa5fnz59dc5nX27NmYOXMmQkJCUKZMGcyYMQOHDh3i1IYfKT4+Hk2aNIFarYZCocD+/fvh6Ogodywi2fG9YTiSkpLQrl07zVHQrVu36nTpb9IfOdsiK5/farUaN2/ehJmZGQoXLgxLS0v22pPshBBITU3F48ePoVKpUKZMmQ8eZTXPxWzvWLp0KYA3E9e/LSQkBD169AAAjBo1CklJSRg4cCDi4+NRu3Zt7N27N0uFOL3L0dERPXv2REhICAIDA1lsEP0P3xuGw9raGmPHjsXs2bMRFBTEQlxGht4WCoUCHh4eiIuLy5XLuxPpwsbGBiVKlPjP4Y4GNc94TmDPOBERUd6jy+e3EALp6envzP5CJBczMzOYm5tn6UiNrD3jRERERNklSZJmzm+ivIbTBBARERERyYTFOBERERGRTFiMExH9T0REBFq2bImIiAi5o5g8tgURmQqewElEBO0p3JycnLBt2zaDmznCVLAtCODnN5kO9owTEQFYuXIlHj9+DAB4/PgxQkJCZE5kutgWRGRKWIwTkcmLiYlBSEgIMg4UCiEQEhKCmJgYmZOZHrYFEZkaFuNEZNKEEJg1a9Z7lxv5SD6DwrYgIlPEYpyITFp0dDSOHTv2zsVCVCoVjh07hujoaJmSmR62BRGZIhbjRGTSPDw8ULduXZiZmWktNzMzg4+PDzw8PGRKZnrYFkRkiliME5FJkyQJo0ePfu/yrFzKmPSDbUFEpshc1wfcvXsXR44cwd27d/H69WsULlwYVatWRd26daFUKnMiIxFRjipRogQCAwPxyy+/QAgBSZIQGBgIV1dXuaOZHLYFEZmaLM8zvm7dOvz44484ceIEnJycUKxYMVhbW+PZs2e4ffs2lEol/P39ERQUBDc3t5zOnWWcp5SIsuLtua2LFCmCrVu3cm5rmbAtCODnN5mOLA1TqVatGubPn4/u3bvj7t27ePjwIU6fPo3IyEhcuXIFCQkJ+P3336FWq1GjRg389ttvOZ2biEivrK2tMXbsWDg7O2PMmDEs/mTEtiAiU5KlnvGdO3eiVatWWdrhkydPEB0djZo1a2Y7nD7wmzUREVHew89vMhVZGjOe1UIcAAoVKoRChQp9dCAiIiIiIlOh82wqZmZmePTo0TvLnz59+s50VERERERE9H46F+PvG9WSkpICS0vLbAciIiIiIjIVWZ7a8McffwTwZr7XFStWwM7OTrNOpVLh8OHDKF++vP4TEhGRyQkMDMS5c+fg7e2NkJAQueOYtIiICMyePRtBQUHw9fWVOw6R0cny1IYZVz67d+8eihcvrjUkxdLSEu7u7pgyZQpq166dM0k/Ek8AISLKW6Kjo9GhQwfN/S1btvDqmzJ5e5pJJycnbNu2Lddmt+HnN5mKLPeMR0dHAwAaN26MLVu2wNHRMcdCERGR6fryyy/fuR8ZGSlTGtO2cuVKPH78GADw+PFjhISEYODAgTKnIjIuOl+B8+DBgzmRg/RACIHk5OQsb6NUKrN0eemsbkf/Lytt8e/tsvJ7ZlvoLqfaQpftKOtCQ0ORmJiotSwxMRGhoaHo0aOHPKFMVExMDEJCQjTnigkhEBISgtatW6NEiRIypyMyHlkeppJBpVIhNDQU4eHhePToEdRqtdb6AwcO6DVgdpnSYa6kpCT4+Pjofb9RUVG86IaO2BaGI6faAmB76Ft6evoHr1Fx8uRJmJvr3IdEH0EIgUGDBuHEiRNQqVSa5WZmZqhVqxYWL16c419ETenzm0ybzrOpDB06FEOHDoVKpYKXlxeqVKmi9UNERPQxfv7552ytJ/2Jjo7GsWPHtApx4E2H3LFjxzRDV4ko+3TuGS9UqBBWrVqFli1b5lQmvTKlb9ZZORyflJQEPz8/AEB4eHiWevV4KF53WR0aoWt7sC10l1NtAbA99I0944bjQz3jtWvXxqJFi9gzTqQnOv9Vs7S0ROnSpXMiC2WTJEk6HTK3trbmIfYcomtbAGyPnMK2yDvMzc0xdOhQLFy48J11w4cPZyGeiyRJwujRo7VmtXl7Ob+EEumPzsNURowYgYULF7734j9EREQfq0ePHrC1tdVaZmtri4CAAJkSma4SJUogMDBQU3hLkoTAwEC4urrKnIzIuOjczRAZGYmDBw9i9+7dqFixIiwsLLTWb9myRW/hiIjI9KxevVqrR3b16tUypjFtPXv2xPbt2zXzjAcGBsodicjo6FyMOzg4oH379jmRhYiICB4eHvD29tZcgZMX/JGPtbU1xo4dq7kCJ4dvEemfzsU4L0tMREQ5jZ81hsPX1xe+vr5yxyAyWjqPGQfenPG+f/9+BAcH4+XLlwCABw8e4NWrV3oNR0RERERkzHTuGb937x6aN2+OmJgYpKSkoGnTprC3t8ecOXOQnJyMZcuW5UROIiIiIiKj81EX/alRowbi4+O1xo61b98e4eHheg1HRERERGTMPmo2laNHj8LS0lJruZubG/7++2+9BSMiIiIiMnY694yr1ep3Lo8LALGxsbC3t9dLKCIiIiIiU6BzMd60aVMsWLBAc1+SJLx69QoTJ05Ey5Yt9ZmNiIiIiMio6TxM5YcffkDjxo3h6emJ5ORkdOvWDTdv3kShQoWwfv36nMhIRERERGSUdC7GXVxccO7cOaxfvx5nzpyBWq1Gr1694O/vz4sBEBHRewkhkJycrNN2SqVSczn2D8nqdkREhkbnYhx4c0Wunj17omfPnvrOQ0RERio5ORk+Pj45su+oqCh2CBFRnqRzMb5q1aoPrg8ICPjoMEREREREpkTnYnzo0KFa99PS0vD69WtYWlrCxsaGxTgREWVKqVQiKirqP7dLSkqCn58fACA8PDxLPd5KpTLb+YiI5KBzMR4fH//Osps3b2LAgAH49ttv9RKKiIiMjyRJOg8lsba25vATIjJqOk9tmJkyZcpg1qxZ7/SaExERERHR++mlGAcAMzMzPHjwQF+7IyIiIiIyejoPU9m+fbvWfSEE4uLisGjRItSrV09vwYiIiIiIjJ3OxXi7du207kuShMKFC+OTTz7BvHnz9JWLiIiIiMjo6VyMq9XqnMhBRERERGRy9DZmnIiIiIiIdKNzz/g333yT5W3nz5+v6+6JiIiIiEyGzsX42bNncebMGaSnp6NcuXIAgBs3bsDMzAzVqlXTbCdJkv5SEhERkSwCAwNx7tw5eHt7IyQkRO44REZH52K8TZs2sLe3R1hYGBwdHQG8uRBQYGAgGjRogBEjRug9JBEREeW+6OhonDt3DgBw7tw5REdHw8PDQ95QREZG5zHj8+bNw8yZMzWFOAA4Ojpi2rRpnE2FiIjIiHz55ZcfvE9E2adzMZ6QkIB//vnnneWPHj3Cy5cv9RKKiIiI5BUaGorExEStZYmJiQgNDZUnEJGR0rkYb9++PQIDA7Fp0ybExsYiNjYWmzZtQq9evdChQ4ecyEhERES5KD09HQsXLsx03cKFC5Genp7LiYiMl87F+LJly9CqVSt0794dbm5ucHNzg7+/P1q0aIElS5bkREYiIiLKRT///HO21hNR1ulcjNvY2GDJkiV4+vSpZmaVZ8+eYcmSJbC1tc2JjERERJSL+vbtm631RJR1H33Rn7i4OMTFxaFs2bKwtbWFEEKfuYiIiEgm5ubmGDp0aKbrhg8fDnNznSdjI6L30LkYf/r0Kfz8/FC2bFm0bNkScXFxAIDevXtzWkMiIiIj0aNHj3eOeNva2iIgIECmRETGSedifPjw4bCwsEBMTAxsbGw0y7t06YI///xTr+GIiIhIPqtXr/7gfSLKPp2L8b1792L27NkoXry41vIyZcrg3r17egtGRERE8vLw8IC3tzcAwNvbmxf8IcoBOg/6SkxM1OoRz/DkyRNYWVnpJRQREREZhpCQELkjEBk1nXvGGzZsiFWrVmnuS5IEtVqNuXPnonHjxnoNR0RERERkzHTuGZ87dy4aNWqEU6dOITU1FaNGjcLly5fx7NkzHD16NCcyEhEREREZJZ17xj09PXHhwgXUqlULTZs2RWJiIjp06ICzZ8+iVKlSOZGRiIiIiMgo6dQznpaWhk8//RTBwcGYPHlyTmUiIiIiIjIJOvWMW1hY4NKlS5AkSS9PfvjwYbRp0wYuLi6QJAnbtm3TWt+jRw9IkqT1U6dOHb08NxERERGR3HQephIQEIBffvlFL0+emJiIKlWqYNGiRe/dpnnz5pqrfcbFxWHXrl16eW4iIiIiIrnpfAJnamoqVqxYgX379qFGjRrvXJ1r/vz5Wd5XixYt0KJFiw9uY2VlhaJFi+oak4iIiD5ACIHk5OQsb6NUKrN0ZDyr2xHRGzoX45cuXUK1atUAADdu3NBalxNvvkOHDsHJyQkODg7w9fXF9OnT4eTk9N7tU1JSkJKSormfkJCg90xERER5XXJyMnx8fPS+36ioKFhbW+t9v0TGKkvF+IULF+Dl5QWFQoGDBw/mdCaNFi1aoHPnznBzc0N0dDTGjx+PTz75BKdPn37vBYZmzpzJk0uJiIiIKE/IUjFetWpVxMXFwcnJCSVLlsTJkydRsGDBnM6GLl26aG57eXmhRo0acHNzw86dO9GhQ4dMHzNmzBh88803mvsJCQlwdXXN8axERER5iVKpRFRU1Ae3SUpKgp+fHwAgPDw8Sz3eSqVSL/mITEWWinEHBwdER0fDyckJd+/ehVqtzulcmXJ2doabmxtu3rz53m2srKze22tOREREb0iSpNNwEmtraw4/IcoBWSrGO3bsCF9fXzg7O0OSJNSoUQNmZmaZbnvnzh29Bnzb06dPcf/+fTg7O+fYcxARERER5ZYsFeM///wzOnTogFu3bmHIkCHo06cP7O3ts/3kr169wq1btzT3o6Ojce7cORQoUAAFChTApEmT0LFjRzg7O+Pu3bsYO3YsChUqhPbt22f7uYmIiIiI5Jbl2VSaN28OADh9+jSGDh2ql2L81KlTaNy4seZ+xljvr776CkuXLsXFixexatUqPH/+HM7OzmjcuDE2bNigl+cmIiIiIpKbzlMbhoSE6O3JGzVqBCHEe9fv2bNHb89FRERERGRodL4CJxERERER6QeLcSIiIiIimbAYJyIiIiKSCYtxIiIiIiKZfFQxvnr1atSrVw8uLi64d+8eAGDBggX4/fff9RqOiIiIiMiY6VyML126FN988w1atmyJ58+fQ6VSAXhzlc4FCxboOx8RERERkdHSuRj/6aefsHz5cowbN07rKpw1atTAxYsX9RqOiIiIiMiY6VyMR0dHo2rVqu8st7KyQmJiol5CERERERGZAp2LcQ8PD5w7d+6d5bt374anp6c+MhERERERmQSdr8D57bffYtCgQUhOToYQAidOnMD69esxc+ZMrFixIicyEhEREREZJZ2L8cDAQKSnp2PUqFF4/fo1unXrhmLFimHhwoX44osvciIjEREREZFR0qkYT09Px9q1a9GmTRv06dMHT548gVqthpOTU07lIyIiIiIyWjqNGTc3N8eAAQOQkpICAChUqBALcSIiIiKij6TzCZy1a9fG2bNncyILEREREZFJ0XnM+MCBAzFixAjExsaievXqsLW11VpfuXJlvYUjIiIiIjJmOhfjXbp0AQAMGTJEs0ySJAghIEmS5oqcRERERET0YToX49HR0TmRg4iIiIjI5OhcjLu5ueVEDiIiIiIik6NzMQ4At2/fxoIFC3D16lVIkoQKFSpg6NChKFWqlL7zEREREREZLZ1nU9mzZw88PT1x4sQJVK5cGV5eXjh+/DgqVqyIffv25URGIiIiIiKjpHPP+OjRozF8+HDMmjXrneVBQUFo2rSp3sIRERERERkznXvGr169il69er2zvGfPnrhy5YpeQhERERERmQKdi/HChQvj3Llz7yw/d+4cr8ZJRERERKQDnYep9OnTB3379sWdO3fg4+MDSZIQGRmJ2bNnY8SIETmRkYiIiIjIKOlcjI8fPx729vaYN28exowZAwBwcXHBpEmTtC4EREREREREH6ZzMS5JEoYPH47hw4fj5cuXAAB7e3u9ByMiIiIiMnYfNc94BhbhREREREQfL8vF+CeffJKl7Q4cOPDRYYiIiIiITEmWi/FDhw7Bzc0NrVq1goWFRU5mIiIiIiIyCVkuxmfNmoXQ0FD89ttv8Pf3R8+ePeHl5ZWT2YiIiIiIjFqW5xkfNWoUrly5gm3btuHly5eoV68eatWqhWXLliEhISEnMxIRERERGSWdL/pTt25dLF++HHFxcRg0aBBWrlwJFxcXFuRERERERDrSuRjPcObMGURERODq1avw8vLiOHIiIiIiIh3pVIw/ePAAM2bMQNmyZdGpUycUKFAAx48fx19//QVra+ucykhEREREZJSyfAJny5YtcfDgQXz66aeYO3cuWrVqBXPzbE1TTkRERERk0rJcTf/5559wdnZGTEwMJk+ejMmTJ2e63ZkzZ/QWjoiIiIjImGW5GJ84cWJO5iAiIiIiMjksxomIiIiIZPLRs6kQEREREVH2sBgnIiIiIpIJi3EiIiIiIpmwGCciIiIikgmLcSIiIiIimXxUMX706FGkpKS8c5uIiIiIiLLuo4rxFi1a4O+//37nNhERERERZd1HFeNCiExvExERERFR1nHMOBERERGRTFiMExERERHJhMU4EREREZFMWIwTEREREcmExTgRERERkUxYjBMRERERyeSjivGxY8eiQIEC79wmIiIiIqKsM/+YB40ZMybT20RERERElHUcpkJEREREJBMW40REREREMmExTkREREQkExbjREREREQy0akYT0tLQ2BgIO7cuZNTeYiIiIiITIZOxbiFhQW2bt2aU1mIiIiIiEyKzsNU2rdvj23btuVAFCIiIiIi06LzPOOlS5fG1KlTERUVherVq8PW1lZr/ZAhQ/QWjoiIiIjImOlcjK9YsQIODg44ffo0Tp8+rbVOkiQW40REREREWaRzMR4dHZ0TOYiIiIiITM5HT22YmpqK69evIz09XZ95iIiIiIhMhs7F+OvXr9GrVy/Y2NigYsWKiImJAfBmrPisWbP0HpCIiIiIyFjpXIyPGTMG58+fx6FDh6BUKjXLmzRpgg0bNui0r8OHD6NNmzZwcXGBJEnvzNIihMCkSZPg4uICa2trNGrUCJcvX9Y1MhERERGRQdK5GN+2bRsWLVqE+vXrQ5IkzXJPT0/cvn1bp30lJiaiSpUqWLRoUabr58yZg/nz52PRokU4efIkihYtiqZNm+Lly5e6xiYiIiIiMjg6n8D5+PFjODk5vbM8MTFRqzjPihYtWqBFixaZrhNCYMGCBRg3bhw6dOgAAAgLC0ORIkWwbt069OvXL9PHpaSkICUlRXM/ISFBp0xERERERLlF557xmjVrYufOnZr7GQX48uXLUbduXb0Fi46OxsOHD/Hpp59qlllZWcHX1xdRUVHvfdzMmTORP39+zY+rq6veMhERERER6ZPOPeMzZ85E8+bNceXKFaSnp2PhwoW4fPkyjh07hoiICL0Fe/jwIQCgSJEiWsuLFCmCe/fuvfdxY8aMwTfffKO5n5CQwIKciIiIiAySzj3jPj4+OHr0KF6/fo1SpUph7969KFKkCI4dO4bq1avrPeC/h74IIT44HMbKygr58uXT+iEiIiIiMkQ694wDQKVKlRAWFqbvLFqKFi0K4E0PubOzs2b5o0eP3uktJyIiIiLKi3TuGff398fy5ctx8+bNnMij4eHhgaJFi2Lfvn2aZampqYiIiICPj0+OPjcRERERUW7QuWfczs4O8+bNQ79+/VC0aFH4+vrC19cXjRo1Qvny5XXa16tXr3Dr1i3N/ejoaJw7dw4FChRAiRIlMGzYMMyYMQNlypRBmTJlMGPGDNjY2KBbt266xiYiIiIiMjg6F+PBwcEA3gwfOXToEA4dOoSFCxdi0KBBcHJyQlxcXJb3derUKTRu3FhzP+PEy6+++gqhoaEYNWoUkpKSMHDgQMTHx6N27drYu3cv7O3tdY1NRERERGRwPmrMOADY29vD0dERjo6OcHBwgLm5uWacd1Y1atQIQoj3rpckCZMmTcKkSZM+NiYRERERkcHSecx4UFAQ6tSpg0KFCuG7775DamoqxowZg3/++Qdnz57NiYxEREREREZJ557xuXPnonDhwpg4cSLatm2LChUq5EQuIiIiIiKjp3MxfvbsWURERODQoUOYN28ezMzMNCdwNmrUiMU5EREREVEW6VyMV6lSBVWqVMGQIUMAAOfPn8eCBQswZMgQqNVqqFQqvYckIiIiIjJGH3UC59mzZzUzqRw5cgQJCQnw9vbWmhmFiIiIiIg+TOdi3NHREa9evUKVKlXQqFEj9OnTBw0bNuRl54mIiIiIdKRzMb569WoW37lMCIHk5GS97CspKSnT2/qgVCohSZJe90lERERkzHQuxlu3bq25HRsbC0mSUKxYMb2GIm3Jycnw8fHR+379/Pz0ur+oqChYW1vrdZ9ERERExkznecbVajWmTJmC/Pnzw83NDSVKlICDgwOmTp0KtVqdExmJiIiIiIySzj3j48aNwy+//IJZs2ahXr16EELg6NGjmDRpEpKTkzF9+vScyEn/s7K+D5RmZh/9eCEEUv73pclKocj2sJJklQo9I6OytQ8iIiIiU6VzMR4WFoYVK1bgs88+0yyrUqUKihUrhoEDB7IYz2FKM7NsFeMAwIEkRERERIZB52Eqz549Q/ny5d9ZXr58eTx79kwvoYiIiIiITIHOxXiVKlWwaNGid5YvWrQIVapU0UsoIiIiIiJToPMwlTlz5qBVq1bYv38/6tatC0mSEBUVhfv372PXrl05kZGIiIiIyCjp3DPu6+uLGzduoH379nj+/DmePXuGDh064Pr162jQoEFOZCQiIiIiMko694wDgIuLC0/UJCIiIiLKpiwV4xcuXMjyDitXrvzRYYiIiIiITEmWinFvb29IkgQhxAe3kyQJKpVKL8GIiIiIiIxdlorx6OjonM5BRERERGRyslSMu7m55XQOIiIiIiKTk6XZVI4dO5blHSYmJuLy5csfHYiIiIiIyFRkqWc8ICAA7u7u6NOnD1q2bAk7O7t3trly5QrWrFmDkJAQzJkzBxUrVtR7WCK5CSGQnJyst/0lJSVleju7lEolJEnS2/4MEduCiIiMQZaK8StXriA4OBgTJkyAv78/ypYtCxcXFyiVSsTHx+PatWtITExEhw4dsG/fPnh5eeV0biJZJCcnw8fHJ0f27efnp7d9RUVFwdraWm/7M0RsCyIiMgZZKsYtLCwwePBgDB48GGfOnMGRI0dw9+5dJCUloUqVKhg+fDgaN26MAgUK5HReIiIiIiKjofNFf6pVq4Zq1arlRBaiPGVlfR8ozcyytQ8hBFLUagCAlUKRreEMySoVekZGZStPXsW2ICKivOqjrsBJRIDSzCzbBSAAcABD9rEtiIgor8rSbCpERERERKR/LMaJiIiIiGTCYpyIiIiISCY6F+PR0dE5kYOIiIiIyOToXIyXLl0ajRs3xpo1a/R6wQ0iIiIiIlOjczF+/vx5VK1aFSNGjEDRokXRr18/nDhxIieyEREREREZNZ2LcS8vL8yfPx9///03QkJC8PDhQ9SvXx8VK1bE/Pnz8fjx45zISURERERkdD76BE5zc3O0b98eGzduxOzZs3H79m2MHDkSxYsXR0BAAOLi4vSZk4iIiIjI6Hx0MX7q1CkMHDgQzs7OmD9/PkaOHInbt2/jwIED+Pvvv9G2bVt95iQiIiIiMjo6X4Fz/vz5CAkJwfXr19GyZUusWrUKLVu2hELxpq738PBAcHAwypcvr/ewRERERETGROdifOnSpejZsycCAwNRtGjRTLcpUaIEfvnll2yHIyKivEEIobcZtpKSkjK9rQ9KpRKSJOl1n0RE2aFzMX7z5s3/3MbS0hJfffXVRwUiIqK8Jzk5GT4+Pnrfr5+fn173FxUVBWtra73uk4goO3QeMx4SEoLffvvtneW//fYbwsLC9BKKiIiIiMgU6NwzPmvWLCxbtuyd5U5OTujbty97xImITNzK+j5Qmpl99OOFEEhRqwEAVgpFtoeVJKtU6BkZla19EBHlFJ2L8Xv37sHDw+Od5W5uboiJidFLKCIiyruUZmbZKsYBgANJiMhU6DxMxcnJCRcuXHhn+fnz51GwYEG9hCIiIiIiMgU6F+NffPEFhgwZgoMHD0KlUkGlUuHAgQMYOnQovvjii5zISERERERklHQepjJt2jTcu3cPfn5+MDd/83C1Wo2AgADMmDFD7wGJiIiIiIyVzsW4paUlNmzYgKlTp+L8+fOwtrZGpUqV4ObmlhP5iIiIiIiMls7FeIayZcuibNmy+sxCRERERGRSdC7GVSoVQkNDER4ejkePHkH9v+mnMhw4cEBv4YiIiIiIjJnOxfjQoUMRGhqKVq1awcvLi5cVJiIiIiL6SDoX47/++is2btyIli1b5kQeIiIiIiKTofPUhpaWlihdunROZCEiIiIiMik6F+MjRozAwoULIYTIiTxERERERCZD52EqkZGROHjwIHbv3o2KFSvCwsJCa/2WLVv0Fo6IiIiIyJjpXIw7ODigffv2OZGF3uPtoxDJKpWMSd71dh5TOFrCtiAiIiJ90rkYDwkJyYkc9AHJycma2z0jo2RM8mHJycmwsbGRO0aOYlsQERGRPuk8ZhwA0tPTsX//fgQHB+Ply5cAgAcPHuDVq1d6DUdEREREZMx07hm/d+8emjdvjpiYGKSkpKBp06awt7fHnDlzkJycjGXLluVETpOmVCo1t1fW94HSzEzGNNqSVSpND/HbOY0V24KIiIj06aMu+lOjRg2cP38eBQsW1Cxv3749evfurddw9MbbF1ZSmpkZVAH4NlO4ABTbgoiIiPTpo2ZTOXr0KCwtLbWWu7m54e+//9ZbMCIiIiIiY6fzmHG1Wg1VJrNIxMbGwt7eXi+hiIiIiIhMgc7FeNOmTbFgwQLNfUmS8OrVK0ycOBEtW7bUZzYiIiIiIqOm8zCVH374AY0bN4anpyeSk5PRrVs33Lx5E4UKFcL69etzIiMRERERkVHSuRh3cXHBuXPnsH79epw5cwZqtRq9evWCv78/rK2tcyIjEREREZFR0rkYBwBra2v07NkTPXv21HceIiIiIiKToXMxvmrVqg+uDwgI+OgwRERERESm5KPmGX9bWloaXr9+DUtLS9jY2LAYJyIiIiLKIp1nU4mPj9f6efXqFa5fv4769evr/QTOSZMmQZIkrZ+iRYvq9TmIiIiIiOTyUWPG/61MmTKYNWsWunfvjmvXruljlxoVK1bE/v37NffNDPSKh0REREREutJLMQ68KZIfPHigr91pmJubG0xvuBACycnJOm2nVCqzdGnyrG5Hb7AtDIdcbSGE0NxOzuRCZHJ6O8/bOXNDVtojJ94XbA/DkdX3ZFYkJSVlelsf+LeW6A2di/Ht27dr3RdCIC4uDosWLUK9evX0FizDzZs34eLiAisrK9SuXRszZsxAyZIl37t9SkoKUlJSNPcTEhL0liU5ORk+Pj5629/boqKiODWkDtgWhkOutni72OgZGZUjz68PycnJsLGxydXny4n2+K/3BdvDcOTUvwE/Pz+97o9/a4ne0LkYb9eundZ9SZJQuHBhfPLJJ5g3b56+cgEAateujVWrVqFs2bL4559/MG3aNPj4+ODy5csoWLBgpo+ZOXMmJk+erNccREREREQ5QediXK1W50SOTLVo0UJzu1KlSqhbty5KlSqFsLAwfPPNN5k+ZsyYMVrrEhIS4Orqqpc8SqUSUVH/3eOTlJSk6UEIDw/P0jd/pVKZ7XymhG1hOORqi7fXrazvA6UBnU+SrFJpeodz+99TVtojJ94XbA/DlN22EEIg5X+f+1YKRbaHlbzdFkT0ht7GjOcGW1tbVKpUCTdv3nzvNlZWVrCyssqR55ckSedDatbW1jwMlwPYFoZDrrZ4uyhQmpkZVPH3ttweE6tre+jrfcH2MEz6aAv+1STKWToX4+/rkc7M/Pnzdd39B6WkpODq1ato0KCBXvdLRERERCQHnYvxs2fP4syZM0hPT0e5cuUAADdu3ICZmRmqVaum2U4fvQ8jR45EmzZtUKJECTx69AjTpk1DQkICvvrqq2zvm4iIiIhIbjoX423atIG9vT3CwsLg6OgI4M2FgAIDA9GgQQOMGDFCb+FiY2PRtWtXPHnyBIULF0adOnXw119/wc3NTW/PQUREREQkF52L8Xnz5mHv3r2aQhwAHB0dMW3aNHz66ad6LcZ//fVXve2LiIiIiMjQKHR9QEJCAv755593lj969AgvX77USygiIiIiIlOgczHevn17BAYGYtOmTYiNjUVsbCw2bdqEXr16oUOHDjmRkYiIiIjIKOk8TGXZsmUYOXIkunfvjrS0tDc7MTdHr169MHfuXL0HJCIiIiIyVjoX4zY2NliyZAnmzp2L27dvQwiB0qVLw9bWNifyEREREREZLZ2HqWSIi4tDXFwcypYtC1tbWwgh9JmLiIiIiMjo6VyMP336FH5+fihbtixatmyJuLg4AEDv3r31OpMKEREREZGx03mYyvDhw2FhYYGYmBhUqFBBs7xLly4YPnw45s2bp9eApC1ZpcrW44UQSFGrAQBWCkW2L86U3TyZEUIgOTlZL/tKSkrK9LY+9mcK2BaGw5DbQp/7ISIyNToX43v37sWePXtQvHhxreVlypTBvXv39BaMMtczMkruCDkuOTkZPj4+et+vn5+f3vdp7NgWhoNtQURknHQeppKYmAgbG5t3lj958gRWVlZ6CUVEREREZAp07hlv2LAhVq1ahalTpwIAJEmCWq3G3Llz0bhxY70HJECpVCIqSj894klJSZqesPDwcFhbW+tlv8CbnPpWI90XCph99OMFBNR4MyxHAQUkfPywHDVUOGUe8dGPz+sMqS0AIA2pOGsema195FWG1ham/t4gIsoOnYvxuXPnolGjRjh16hRSU1MxatQoXL58Gc+ePcPRo0dzIqPJkyRJr0VzBmtr6xzZrz4pYAazbBQdpD+G1hYqA8qS2wytLYiI6OPpPEzF09MTFy5cQK1atdC0aVMkJiaiQ4cOOHv2LEqVKpUTGYmIiIiIjJJOPeNpaWn49NNPERwcjMmTJ+dUJiIiIiIik6BTz7iFhQUuXbqU7enwiIiIiIjoI4apBAQE4JdffsmJLEREREREJkXnEzhTU1OxYsUK7Nu3DzVq1ICtra3W+vnz5+stHBERERGRMdO5GL906RKqVasGALhx44bWOg5fISIiIiLKuiwX43fu3IGHhwcOHjyYk3mIiIiIiExGlseMlylTBo8fP9bc79KlC/75558cCUVEREREZAqyXIwLIbTu79q1C4mJiXoPRERERERkKnSeTYWIiIiIiPQjy2PGJUl65wRNYzlhUwiB5ORkve0vKSkp09vZpVQqjeZ3/iFvH4VRQSVjEm3/zpKsyn42IQRS1GoAgJVCka321UeefzPUtgC08ySlp2d7f3mpLdKQChXMPn5fEFDjzWtVQAEJ2fu7ojawfxtERHlJlotxIQR69OgBKysrAEBycjL69+//ztSGW7Zs0W/CXJCcnAwfH58c2befn5/e9hUVFQVra2u97c9Qvf3F6LR5hIxJPqxnZJTcEXJcXmmLXkePyR0hx73dFmfNI2VMQkRE+pTlYvyrr77Sut+9e3e9hyEiIiIiMiVZLsZDQkJyMofBqJHuC0U2Dv8C+j0ErIYKpwy4RzInKJVKze3q6b4wy2Z76IsKKk3v8P79+2FjY5PtfSYlJWmOnoSHh+vtyMfbv0N97ceQ2gIA0pGOM+aHAeinPQy9LRwcHBAeHq6XfSUlJaF169YAgB07dujltb69TyIiyjqdL/pj7BQwM6iCwxS9PVbXzEDbw8bGRu9DhqytrQ1uGFJeaAtA/+1hiG2hUChQoEABvezr7XNZChQooLdinIiIdMfZVIiIiIiIZMJinIiIiIhIJizGiYiIiIhkwmKciIiIiEgmLMaJiIiIiGTCYpyIiIiISCYsxomIiIiIZMJinIiIiIhIJizGiYiIiIhkwmKciIiIiEgmLMaJiIiIiGRiLncAQyCE0NxOQypUMMve/iCghhoAoIACEqSP3pcaqv/f71s5TcXbr/9j5FRbmCJDagt95KGck6zK5r8VIZCifvNvxUqhgCRl799KdvPkNW9/Vhjaa387jyl+phFlhsU4gOTkZM3ts+aRMib5sOTkZNjY2MgdI1edMo+QOwL9D9uCsqpnZJTcEUza259phtwWpviZRpQZDlMhIiIiIpIJe8YBODg4IDw8XG/7S0pKQuvWrQEAO3bsgLW1tV726+DgoJf9GDqlUomoKP305iQlJcHPzw8AEB4erre2UCqVetmPocsLbQGYTnsYMv5bMRxvv8aV9X2gNMve0Et9SlapNL31ptAWRFnBYhyAQqFAgQIF9La/pKQkze0CBQro9YPEFEiSlCO/M2tra7aFjtgWlFX8t2I43h5jrzQzM6hi/G3ZPReAyFhwmAoRERERkUxYjBMRERERyYTFOBERERGRTFiMExERERHJhMU4EREREZFMWIwTEREREcmExTgRERERkUxYjBMRERERyYTFOBERERGRTFiMExERERHJxFzuAHmJEALJycn/uV1SUlKmtz9EqVTy0sA6YFsYDraFYclKe7AtiIgMB4txHSQnJ8PHx0enx/j5+WVpu6ioKFhbW39MLJPEtjAcbAvDomt7sC2IiOTFYSpERERERDJhz7gOlEoloqKi/nO7tw8TZ/XQrlKpzHY+U8K2MBxsC8OSlfZgWxARGQ4W4zqQJCnLh2ltbGxyOI1pY1sYDraFYclqe7AtiIgMA4epEBERERHJhMU4EREREZFMWIwTEREREcmExTgRERERkUxYjBMRERERyYTFOBERERGRTFiMExERERHJhMU4EREREZFM8kQxvmTJEnh4eECpVKJ69eo4cuSI3JGIiIiIiLLN4IvxDRs2YNiwYRg3bhzOnj2LBg0aoEWLFoiJiZE7GhERERFRtpjLHeC/zJ8/H7169ULv3r0BAAsWLMCePXuwdOlSzJw5U+Z0hkUIgeTk5A9uk5SUlOntD1EqlZAkKVvZTE1W2gLQvT3YFrrLqbYA2B66YlvkvmSV6r3rhBBIUav1/pxWCsV72+JDeYhMlUEX46mpqTh9+jRGjx6ttfzTTz9FVFRUpo9JSUlBSkqK5n5CQkKOZjQkycnJ8PHxyfL2fn5+WdouKioK1tbWHxvLJOnaFkDW2oNtobucaguA7aErtkXu6xmZ+WclERkOgx6m8uTJE6hUKhQpUkRreZEiRfDw4cNMHzNz5kzkz59f8+Pq6pobUYmIiIiIdGbQPeMZ/n24Swjx3kNgY8aMwTfffKO5n5CQYDIFuVKpfO8RgwxvHybO6mFdpVKpl3ymJCttAejeHmwL3eVUW2RsR1nHtsgdH/N71vfzs82Iss6gi/FChQrBzMzsnV7wR48evdNbnsHKygpWVla5Ec/gSJKUpcO0NjY2uZDGtGW1LQC2R05jWxgOtkXu4O+ZKG8x6GEqlpaWqF69Ovbt26e1fN++fTqPOyQiIiIiMjQG3TMOAN988w2+/PJL1KhRA3Xr1sXPP/+MmJgY9O/fX+5oRERERETZYvDFeJcuXfD06VNMmTIFcXFx8PLywq5du+Dm5iZ3NCIiIiKibJGEEELuEDkpISEB+fPnx4sXL5AvXz654xAREVEW8PObTIVBjxknIiIiIjJmLMaJiIiIiGTCYpyIiIiISCYsxomIiIiIZMJinIiIiIhIJizGiYiIiIhkwmKciIiIiEgmLMaJiIiIiGTCYpyIiIiISCbmcgfIaRkXGE1ISJA5CREREWVVxue2kV8onMj4i/GXL18CAFxdXWVOQkRERLp6+fIl8ufPL3cMohwjCSP/yqlWq/HgwQPY29tDkiS543y0hIQEuLq64v79+8iXL5/ccUwa28JwsC0MB9vCcBhLWwgh8PLlS7i4uECh4KhaMl5G3zOuUChQvHhxuWPoTb58+fL0H1djwrYwHGwLw8G2MBzG0BbsESdTwK+aREREREQyYTFORERERCQTFuN5hJWVFSZOnAgrKyu5o5g8toXhYFsYDraF4WBbEOUtRn8CJxERERGRoWLPOBERERGRTFiMExERERHJhMU4EREREZFMWIwTEREREcmExTgRERERkUxYjBuRy5cv47vvvpM7BpFBuX79On788Ue5Y1AOU6vVckeg/8mYpC09PV3mJER5A4txI3Hp0iXUr18fM2bMwLVr18AZK4neFGiLFy/GwYMHAYDvCyOlVquhULz5ODt79iySkpJkTmS6hBCQJAmHDx/GggUL8M8//8gdicjgsRg3AufPn0eNGjXQpEkTFC1aFGq1GpIkyR2LsogFYs5RKBQoU6YMzp07h4SEBL4vjNDbhfj48ePRvXt3HD58GGlpaTInMz0ZhfjmzZvx2Wef4fXr13j48KFmHRFljsV4Hnf+/HnUrVsXw4YNw9q1ayGEwKNHj+SORVmU8eF16NAhrFq1Su44RkWlUgEAvLy8oFKpWAwYqYxCfNy4cVixYgXmzZuHqlWrwsLCQuZkpkeSJBw9ehR9+vTB3LlzMWHCBFSpUgUA8Pr1awAsyokyw2I8D8soxL/55hvMmjULlpaWMDc3x/Xr1+WORlmQUYhv2bIFnTt3RlRUFKKjo+WOledFR0fj0qVL+PvvvwEANWvWhJmZGaKiojTbsDjP+94eI379+nVs2bIFK1euRPPmzWFlZYVbt24hODgYR48elTGl6Tl58iSqV6+OPn364NWrV9i5cye++OIL+Pn5Ydu2bZAkie89on8xlzsAfZx79+6hevXqCAoKwrRp06BWq5Geno5ChQrhxYsXcsej/5BxaP3w4cP46quv8NNPP6FHjx6ZbptRtNN/e/XqFQYOHIjIyEjY2dmhTJkyAN78vm/cuIGSJUuiXLlyMDMzkzkpZYcQQtMj/uDBA6hUKsTHx6NIkSI4cuQIfv31Vxw5cgQPHjyAh4cHpkyZghYtWsic2rjt3r0biYmJKFCgAKKjozF//nyEh4dDoVBAkiRUq1YNHTp0wJ07d+Du7i53XCKDwmI8jypevDjWrFmDL774QrPM0tISFSpU0PQIpqenw9z8TRNv2bIF1apV4x9BGa1atUpTLCoUCqhUKhw9ehTt27dHjx498OLFC5w8eRJhYWGQJAmdO3dG69atWYjrwM7ODuvXr8eTJ0/w5MkTRERE4N69ezh16hSGDx+OsLAwpKWloXTp0mjevDnMzMzg7+8Pa2truaNTFr395bR///44ffo0jhw5And3d3z22Wd49uwZevfujRkzZsDX1xfe3t64ffu2zKmN219//YVu3brh559/RqVKldCsWTMsW7YMvr6++PLLL9GwYUNcunQJp06dkjsqkUFiMZ4HqVQqmJmZaRXiGb1Etra2uHz5MgBoev/Gjx+P6dOn486dO7kflgAAT58+xdatW/HgwQPY2NigR48eMDMzw4sXL7Bt2zZERUVh9uzZSElJgYWFBZ4+fYopU6agQYMGcHBwkDt+npBRpDk4OMDBwQGlS5dGnTp1NOv/+usv/Pzzz9i9ezfu37+P5cuX4+HDh2jWrBlcXV1lTE66yCjEHz9+jIcPH2L27NlQKpX466+/sGHDBhQvXhx169bV/E0sVqwYx4/noHv37uHAgQP4+uuv0blzZwDADz/8gJcvX6JgwYKa7datW4eUlBTY2dnJFZXIYLEYz4MyO8SeUYiUKFECFy9eBPDmQ2vixIn44YcfcOLECfaKy6hgwYKYPn065s6di+XLl0OlUqFXr14YP348Tp06hXbt2uHTTz/F119/jSZNmuDixYv4/PPP8fz5cxbjWZTZEYSML66lSpXChQsXUKNGDdSoUQMAkJiYCODNF1jKWxYvXoxly5ahWLFiqFq1qqadu3TpAuDNyYJPnjzBgAEDkJCQgF69esmc2PgIIfD333/Dx8cHSUlJ6Nu3r2adhYWFphA/evQo1q1bh/Xr1+PAgQMoVKiQXJGJDBZP4DQSGYVI6dKlNfO6jh07FrNnz0ZERISmACH5eHp6YtSoUShVqhRWrlyJlStXwtbWFvv370dUVBTWrFmDJk2aAADWrFmj6eGlj5fxxbVevXq4du0abt68qbkQia2tLQvxPCgtLQ22trZIS0vDtWvX4ODgADMzM81UhkIIbN68GZ07d8arV69w8uRJmJuba2bXoezL6PwpXrw4pk6dCkmScOLECVy7dg3A/38excbGYseOHYiJicHhw4fh7e0tY2oiw8WecSNjZ2cHhUKBYcOGYenSpYiKikL16tXljmXyMk7YrFChAkaPHo1Zs2ZhxYoVUKlU6NOnD0qXLg0AOHDgAP744w+Ehobi4MGDLMb1JOP3GB8frzmpk/KGt+cRB970unbq1Ak2Njbo378/OnbsiC1btsDCwkLTQ96sWTNYWlqiU6dOMDMz0zp/hj5eRhGenp6uGfrTs2dPSJKEsWPHYvny5Rg0aBBKliwJ4M25TUOHDoVSqeTfMqIP4F+nPOrfH1AZXFxcEB0djbVr1+LYsWOoVq2aDOkow79nQhFCwNPTE99++y3mzp2L0NBQmJmZoWfPnnj48CH279+PkydP4vDhw6hUqZKMyfOm970vypcvj2LFiiEqKgq1atWSIRl9jLfb88iRI3j06BGcnJzg6emJzz//HAAwfPhwdO3aFevXr9cU3k5OTpohKyqVioW4HmT8Ldu/fz9Wr16NV69eIV++fFiyZAkCAwMhhMCECRMgSRIGDRoEDw8PAEDRokVlTk5k+CTBCT8NWsYfwMuXLyMhIQG2traoXLkygP8fD/s2lUqFFi1aYO7cuZqLLZA83r6gzx9//IFnz56hfv366Ny5M/Lly4eLFy/i+++/x61bt9CvXz8EBAQgPj4earVa68Qnepeu74v09HQEBgZi/PjxKFu2rByRKRtGjx6NX3/9FYULF8bLly/h5uaGCRMmoHbt2ti0aROCgoLQoEEDrFmzRu6oRm3btm3o3r07+vfvD3d3dyxcuBBmZmY4fPgwnJyc8Msvv2Dq1Klo3rw5xowZAzc3N7kjE+UNggzexo0bRZEiRYSTk5OoXLmyGDVqlGZdenp6prfJMGzZskXY2dmJPn36iPbt24v69euLbt26ifj4eCGEEBcuXBCBgYGifPnyYs2aNfKGzWOy+r5Qq9Va/6e8JTg4WBQtWlRERkYKIYSYOHGisLGxEbt37xZCCJGUlCQ2bNggzM3NxcSJE2VMatyePn0qatWqJb7//nshhBCxsbHC1dVV9OvXT2u7hQsXCk9PT/HPP//IEZMoT2IxbqAyCofHjx+L2rVri9DQUHHixAkxY8YMUbZsWdG3b1/NtizCDdOJEydEyZIlxfLly4UQQkRHRwtHR0dRrFgx0aZNG01BfvbsWdG/f38RHR0tX9g8gu8L05HR1v369RNBQUFCiDdfbvPlyyeWLVsmhBAiMTFRPHv2TKSkpIj9+/ezzXNQbGysKFu2rHj+/Ll48OCBKFasmFYhvmnTJs3tjL9tRJQ1nE3FQEmShGPHjiEoKAjly5dHx44dUbNmTQwaNAjDhw9HeHg4+vXrB+DNjBGcKUBew4cPxw8//KC1LDY2FnXr1kXv3r1x9+5dNGnSBO3bt8fYsWPx119/YeDAgYiPj4e3tzcWLlzIqSezgO8L0yH+N4LyyZMnqFq1KiIjIxEQEIA5c+agX79+UKlUWLduHf78809YWlrCz8+PbZ6D7OzsULBgQaxZswZ169ZF69at8dNPPwF487du9erV2LVrFwAgf/78ckYlynN4VouBSklJwe7du7Fr1y4ULlxYc6GEfPnyoWvXrgCAhQsXwt/fH2vXruXlvWXm5eX1zsmy7du3R7ly5aBWq/H111+jXr16+OWXX5Ceno6lS5di27ZtSE9Px6+//sqLkmQR3xfG698n32bcLl26NAICAqBQKLBy5UpNO798+RK//vorGjdurLUftnn2ibdOPM+4rVAo4O7ujhEjRmiusJlh0aJFiImJ0UxdyKsGE+mGJ3AasJiYGISFhWH27NkYNmwYpk2bplmXkJCAlStXYvXq1dixYwecnZ1lTEoZdu/ejXPnzmHUqFGaoiA2NhbNmjXD999/jxYtWuDZs2cYPHgwfHx80K5dOxQvXlzm1HkL3xfG5+1CPCIiAgDg5uYGd3d3vH79Gl27dkVkZCTOnj0Le3t7JCYmok+fPnj69CmioqI4W4oeZRTff/75J7Zv3460tDQMGjQI3t7euHHjBjp37gxHR0c0b94cJUuWxIEDB7B+/XocPnyYkwYQfSw5x8jQ/8sYH/ngwQMRHR0tHj58KIQQIiEhQUycOFGUL1/+nZOTEhISODbPwCxevFhIkiTmzJmjadNHjx6JKlWqiAEDBoiYmBgxduxYUbt2bZ7glAV8X5iWkSNHiqJFiwobGxvh5+cngoODhRBvzqto1KiRsLW1FaVLlxbVq1cXderUEampqUIInh+gb/v27RN2dnbis88+E7Vq1RJWVlZi/fr1QgghLl++LAIDA0XZsmVFlSpVRKtWrcSFCxdkTkyUt7E7wQCI//VEbNu2DePGjYMkSYiPj0dAQAD69euHr7/+GgCwYcMGmJmZYfz48QAAe3t7OWNTJgYOHAiFQoFBgwZBrVYjKCgIjo6O6NatG1auXIlt27ZBoVBg+/btcHJykjuuQeP7wviJt4ZDXLx4EREREdi5cydSU1MRHByMlStXIjk5GUOGDMHBgwexYcMGqFQqODo64tNPP+UFfXLI7du3MW3aNAwdOhTJycmYMGECAgICoFKp4O/vj+DgYKSlpUGlUsHCwgJKpVLuyER5m8xfBuh/wsPDhZ2dnVi4cKFISkoSU6dOFQqFQmzcuFEI8aZncMqUKcLJyUnMmjVL5rQkxP/32sbExIiLFy+KtLQ0zbpFixYJhUIhZsyYIYQQIjk5WVy8eFHs2bNH3L9/X5a8eRHfF8ZLpVJpbqelpYmLFy+K7t27a3q579+/LwYMGKA1nd6/sUdcPzL+ll25ckUcPXpU9O7dW6xdu1Zrm2+//VZYWFhoesiJSH9YjMss4wNp8ODBYsCAAUKINx9CpUuXfmf+1gcPHohZs2aJW7du5XpOytymTZuEh4eHcHJyEnXr1hVbtmwRSUlJQoj/L8hZJOqO7wvTMXXqVFGnTh3RsGFD0bx5c6119+/fFwMHDhQ+Pj6cQzyHbd68WVhbW4uKFSsKSZLEkCFDxPPnz7W2GT16tJAkSWzevFmmlETGicW4gWjbtq1Ys2aNeP36tXBxcRF9+/bV9FZs2LBBhIeHCyHYE2RILl++LMqWLSu+//57cfDgQdG0aVNRvXp1sXz5ck1BvmTJEiFJkliwYIHMafMmvi+Mz9s94kuXLhUODg5i/PjxomnTpsLW1laMGTNGa/v79++Lrl27ij59+vDCTXqW8fuMjo4Wvr6+YsmSJeL8+fNi3LhxQqFQiMWLF4uXL19qPWbixIniypUrcsQlMlqcTSWX/f3334iIiMDr16/RvHlzzUwagwYNwsGDB/Hq1Su0a9cO8+bNg4WFBdLS0hAQEIAyZcpgwoQJHBtpIM6fP48DBw7g/v37mD9/PoA3l1zv3r07bt26hf79+6N79+5QKpVYsWIF6tWrhwoVKsic2nDxfWF69u3bh+joaDg7O6NNmzZ49OgRli1bhg0bNqBDhw6YOnWqZtvHjx+jYMGCUCgUWuPMKfsiIyOxa9cu3L17FytWrICNjQ0AYNKkSZg2bRoWLlyIr776SjONKBHlAJm/DJiUS5cuCW9vb+Hv76916W4h3ozVq1mzpnB1dRWJiYlCiDe9fWPHjhWurq7ixo0bckSmTKSlpYk6deoISZJE06ZNtdalpKSILl26iDp16oiffvpJJCcny5Qy7+D7wvScOXNGWFlZCaVSKf744w/N8ri4ODF58mTh6ekpJkyY8M7j3u5VJ/2YMmWKkCRJFC9eXFy/fl1r3cSJE4VSqRRz584Vr169kikhkfFjMZ5LLl26JBwdHcW3334rHj16pFm+fft2sXPnTiGEEGvXrhWVKlUS7u7uonPnzqJly5aicOHC4syZM3LFpvd49uyZaNGihXB3dxebN2/WOnkzJSVFtGjRQjRu3JhT7P0Hvi9M05MnT8TSpUuFk5OT6NWrl9a6hw8fimnTpokCBQqIn3/+WaaEpmXhwoWiYMGCYuzYseLvv//WWvftt9+KggULimfPnsmUjsj4cZhKLnj27Bnat2+PKlWqYOHChZpDrLNnz8aYMWPg6+uLcePGoUmTJrh16xaCg4Px/PlzeHh44PPPP0fp0qVlfgWmTfzvsPiTJ0+QL18+pKWlwdbWFs+ePUPbtm2hVqsxZswYtGzZUnPhktTUVDx+/BjFihWTOb3h4vvCNPz7ypoZXr58ibVr1yIoKAiBgYFYsGCBZt2DBw+wd+9efPnll7yiph5l/C179OgR0tLSYGFhoZliderUqfj555/Rt29f9O7dW+uCWU+ePEGhQoXkik1k/OT9LmAarly5IkqVKiUOHDigOcy6dOlSYWFhIRYvXiyaNm0qmjdvLnbs2CFzUnqfbdu2iVq1aolKlSqJkSNHilOnTgkh3vTw1atXT/j4+IidO3fyMLoO+L4wfm+/H1asWCFGjRolunbtKsLDw8WLFy9Eenq6WLp0qShYsKAYNmxYpvvgybn6kXGy5tatW0WNGjWEs7Oz8PHx0ZqdaPLkyaJ48eJi+vTpIjY29p3HElHOYDGeC1avXi3MzMy0/qDdv39fHD58WAghxMWLF4Wfn5+oWbOmOH/+vGYb/gE0DOfPnxf58uUTM2bMEF9//bX45JNPxCeffCIiIyOFEG8Kcl9fX+Hp6Sn27Nkjc9q8g+8L0zFy5EhRqFAh0blzZ1G/fn1RsGBBMXLkSHH//n2Rmpoqli1bJooUKSJ69Oghd1SjtnfvXqFUKsX8+fPFxo0bxZw5c4SLi4to2bKlZpvp06cLa2trMWfOHH4RIsolLMZzwZEjR4SVlZVmbta3i4mMnqOff/5Z1KxZU8TFxcmSkTJ36dIlMWvWLDF58mTNsl27donWrVsLX19fTUH+6NEj0bx5cxEdHS1T0ryH7wvTEB4eLooXLy5Onz6tWfbjjz8KLy8vMWnSJCHEm3Mw5s2bJ1q2bMmjSzlEpVKJIUOGiN69e2uWpaWlifDwcOHs7CyGDh2qWT5//nyeHE2Ui94dyEd65+7ujvz58yMsLAz37t3TmpYrYyzl9evX4e7uDltbW7li0r/8/fffGDp0KObNm4ekpCTN8hYtWmDgwIGwt7fHhAkTcOjQIRQuXBg7d+6Eu7u7fIHzGL4vTENSUhIsLS1RuHBhqFQqAMDXX3+NL7/8EgsWLMDff/8NR0dH9OvXDzt27IBCoYBarZY5tfFRKBS4ffs27t+/r1lmbm6Ohg0bom/fvrh48SJevHgBABg+fDjKlCkjV1Qik8NiPBcUL14cS5YswZ9//onx48fjypUrmnUJCQkYNWoUVq5ciYkTJ8Le3l7GpPS2YsWKoUuXLihevDj++OMP3Lx5U7OuRYsWGDx4MNLS0vD9999rFeuUNXxfmIaUlBQ8e/YMQgiYmZlp3itff/01LC0tERUVBQCwtbWFJEkQQmR6widlX+vWrREfH48jR45olpmbm8PV1RUxMTGaL0tElLs4m0ouUalUWLFiBQYPHozSpUvDx8cHFhYW+Pvvv3Hq1Cns2rULVatWlTumSRP/m2lApVJBpVLB0tISALBx40YsWLAArq6umDp1KsqWLat5zP79+1GuXDm4urrKFTtP4/vCeLxv1hQAqFOnDlJSUnDq1CnN7CgxMTFo0qQJli5dCj8/v9yMavTEWzNACSGQL18+WFlZ4dq1a/D390fFihXRu3dvNGzYEMCbnvDz589j+/btvLgPkQxYjOey48ePY86cObh9+zbs7e1Rv3599OrVi9O0ySzjw2vPnj0ICQlBXFwcSpcujZEjR6JChQpYs2YNVqxYgcKFC2PGjBk8hKtnfF/kbW8X4mFhYTh79ixsbGxQvnx5BAQE4NKlS/j888+hUqkwY8YMCCEQGhqKhw8f4vjx45y+UI8y/pZt27YNkyZNwuvXr5GYmIjhw4djwIABuHbtGvr374/09HRYWVmhaNGiOHjwICIiIuDt7S13fCKTxGJcBiqVih8+Bmj79u3w9/dH3759UbduXYwYMQIFCxbEpk2bULJkSYSFhWH16tUwNzfH4sWLUapUKbkjGxW+L/K+UaNGITQ0FH5+foiNjcWNGzfQpk0brFixAjExMfj6669x4cIF2NnZoUSJEti2bRssLCzY9noWHh6OVq1aYcqUKfDy8sKxY8ewbt06tGzZEnPmzEFsbCxOnTqFPXv2wMPDA126dEH58uXljk1ksliMyyCj5+Lftyn3vN2TJ4RAfHw8WrdujQ4dOmDkyJFITk5G6dKl0aFDB60L0gQHB+OPP/5AcHAwL+ijZ3xf5G2HDx9G165d8euvv6JBgwZ49eoV9u7di169eqFbt25YvHgxAOD+/fuwtrZGwYIFIUkS0tPTYW5uLnN645Dxcd67d2+kpaVh1apVmnUrVqzA1KlTMWrUKAwaNEiuiESUCZ4lI4O3iwwWHLnvhx9+wNatW5GWlgbgTRsoFAqkpKQgICAA9+/fR6lSpdCyZUv8+OOPmuErQgj069cPa9euZSGeA/i+yDve7sPJmPkkLi4OVlZWqF69OgDAzs4OrVq1wg8//ID9+/fj1KlTAN6cGF2oUCFIkgS1Ws1C/CNlNuOMJEmQJAmvX7/WLEtNTQXwpkDv3Lkz5s+fj/T0dLAfjshwsBgnk7Nz50707NkTe/fu1RTkAPDixQusWrUKjRs3RuvWrbV68hYsWIDdu3cDAPLnzy9LbiJD8erVK6SlpWkdYSpZsiSSkpI0s6MAgJWVFWrVqoW4uDg8efIEALRO8uSsKR8n4/f+8OFDHDx4EIcOHUJycrJmfalSpbBnzx4kJCTA0tJS83euSpUqyJ8/P1JTU/mFl8iA8C8hmYxHjx4BeDMDSrNmzRAQEIA///wTSUlJcHBwQNeuXTF58mS4ubkhODgYFhYWAN4MTXnw4AEqVaokZ3wig/Dbb7+hU6dOaNy4MXr27ImUlBQAQJEiRVC2bFmsXr0aZ8+e1WxfsGBBeHh4sPjTk4xC/OLFi2jQoAGGDRuGTz75BMOHD9e0xYgRI+Dm5gZfX188f/5c87fsxIkTsLOzY684kYHhmHEyCYsWLcLRo0cRFBSkmTGgY8eOOHToEEJDQ9GmTRtcvnwZ48ePx7Vr19C9e3c4OTnh9OnTWLduHQ4fPowqVarI+yKIZBYcHIxvvvkGo0ePxvPnz3HgwAG0adMGU6ZMAQD88ccfCAoKQtmyZdG4cWN4enpi7ty5ePr0KU6cOMGTNLMpoxA/d+4c6tWrhyFDhuDrr7/GuXPn0Lp1a5w6dQrVqlUDAJw+fRp9+/ZFTEwM6tSpA7VajSNHjuDIkSP8W0ZkYFiMk0n49ddfMXz4cLRt2xYDBgzQfBh17NgRBw4cwKpVq9CmTRtcunQJv/32G0JCQuDs7AwXFxdMnToVXl5eMr8CInmFhISgT58+2Lp1K9q0aQMA6NatGypWrIiePXsif/78sLGxQUREBFauXImdO3fCzc0NhQoVwo4dOzhrip5cu3YNlStXxvTp0/Htt99qCvSGDRvC398fiYmJqFixIpo1a4b09HTMmjUL//zzD5RKJXr16sVZU4gMkSAyYocPHxapqalCCCG2b98uXF1dRZ8+fcS5c+c023To0EE4ODiI7du3a5YlJiYKlUolkpKScj0zkaHZv3+/kCRJTJw4UWt5/fr1RcmSJYWzs7MoXry4CAsLE0IIkZKSIp4+fSoePHgg1Gq1EEKItLS03I5tdFJSUsSwYcOEJElaf8OmT58uJEkSrVq1Ei4uLsLJyUlMnz5dxqREpAuOGSejtXr1akyaNAnPnz8HALRp0wYLFy7En3/+icWLF+P8+fMAgM2bN+OTTz5BQEAAdu7ciaSkJNjY2EChUMDKykrGV0BkGMqUKYNy5crh9OnTmhM0O3fujIcPH+Knn37C2rVrUa9ePQwbNgy3b9+GpaUlChQoAGdnZ86aokeWlpbo0aMHunbtiqZNmyI6OhrLli3D999/j99//x07duzAiRMnUKdOHWzcuBGxsbGaxwoeBCcyWBymQkYn47Dty5cv8fLlS7i4uODu3bsoVqwYLCwssHXrVgwdOhTNmzfHoEGDNENWunTpgt9++w27du1C8+bNZX4VRIYhY2jJ3bt30a5dO7i4uCAxMRHPnj3Dn3/+qZnm8+rVq/D29kZwcDB69Oghb2gjd+XKFUyePBk7duyASqXCsWPHULVqVU1b/fDDD1i0aBFOnDiBggULyh2XiP4DuyrIqGQU4rdv38a1a9fQqlUrXL16FV9++SW6du2KIUOGoH379gCAoUOHAoCmIN+wYQOUSiU8PDzkfAlEBsXMzAwqlQru7u7YunUrunTpggsXLiAkJERrvn0hBDw8PFC8eHEZ05oGT09PTJgwAfb29ti0aZNmeUbf2p07d1C2bFlYWlrKFZGIdMBinIyKQqHAgwcPUKdOHTg5OSExMRHt2rVDmTJlsGXLFlhaWqJ///5aBbmZmRl69+6N6tWrIywsTOZXQGR4MgpyDw8PbN68GW3btkVoaCicnJzg5+cHAAgKCoKzszMaN24sc1rjIzK5Im3FihUxfPhwvHr1Cs2aNcMff/yB2rVrY/z48QgLC8PRo0dhb28vU2Ii0gWLcTI6169fx9OnT+Hh4YHVq1fD2toaYWFh6N+/P9asWQMAmoJckiT4+/vDysoKXl5eHCNO9B4ZBbmrqys2b96MDh06YO7cuVAoFJg7dy5u3bqFy5cva7bjrCnZl1GEP3nyBIULF35nfcWKFTFhwgQAQKdOndCwYUNs3boVR44c4XURiPIQjhkno9SrVy+cPn0apUuXxuPHjzFq1Cg0bdoU/fv3x+XLl9G9e3f0798fFhYW2LFjB8qVK4cyZcrIHZtIVv/ugX37CpsZ3h5D3qlTJ5w5cwZeXl44ffo0LCwskJ6ezpM19Wjjxo0YOXIkjh49CldX10y3uXr1KsaOHYvw8HAcOnRIM9c4EeUNLMYpT/t3sZCSkgIrKyvs2rULv/32G7p27Yrg4GA8fPgQ48aNQ5MmTTBgwABcu3YN7dq1w7BhwzRXpyMyZW8X4nPmzEGtWrXQqFGjTIdIZBTk0dHRmDVrFhYvXgxzc3MW4nqS8Tt/8OABBg0ahGbNmqF///4ffMyVK1dQoEABFC1aNJdSEpG+cGpDyrMyCvH79+9j27ZtAKAZZlKzZk389ddfuHnzJpYtW4aiRYti5syZ2L9/P5YuXYpixYphz549ePXqlYyvgMgwqNVqTcF9584d/P7772jbti1OnDgBSZLemRbv7THkwcHBLMT1TJIknD59GiNHjsTr16/Rtm1bqFSqTLfNaBtPT08W4kR5FItxyrMyCvGqVauiQ4cOaNWqFTZu3IgbN26gcOHCmDNnDjZs2AAAmDZtGpycnDB37lzs3LkTa9euxerVq+Ho6CjzqyCSX8bRpbFjx6JHjx6wt7eHWq2Gn58fIiMj31uQv42FuH7t3bsXJ0+exNmzZ2Fpaan5AvRv/z5qQUR5D4txytPUajU8PDxQp04d/PPPP9i3bx8+/fRTBAcHIykpCfnz58epU6dQoUIFTJ06Febm5li+fDlSU1Ph7Owsd3wigxESEoIff/wRc+bMwbp16xAREYEWLVqgWbNmOHr0aKYFOeWcoKAgDBkyBFZWVhg8eDCePHkCMzMzqNVquaMRkZ5xzDjleTdv3sTo0aOhVqsREBAAhUKBBQsWwMHBAb///jtq1qyJI0eOwNLSEtevX4etrS3nQib6l3HjxuHSpUv4/fffNctiY2PRr18/HD16FPv27UPNmjUzPamTsidjjPitW7eQkpKC+Ph41K9fHwCwcOFCrF+/Ht7e3pg5cyYcHR3ZBkRGhu9myvPKlCmDGTNmICUlBcuWLUOFChWwY8cOBAUFoVWrVhg8eDAsLS0hhEC5cuVYiBNlwsrKCqdOnUJKSgqANwVi8eLF0a1bNyQkJKBJkyY4c+YMFAoFe2f1KKMQ37p1K1q0aIHu3bujVatW6Ny5M86fP48hQ4agY8eOuHDhAr777js8ffqUhTiRkeE7moxCuXLlsHDhQgDA119/jXPnzqFOnTr4448/8OWXXwLg2EoiAO8tpFu0aIEiRYpgypQpiI+P17xfSpQogb59+6Jt27bw9/fHo0ePWAzqkSRJOHz4MAIDAzFmzBicPXsWv/32GzZv3ozz589DkiSMGDECnTp1woEDBzB9+nR+GSIyMhymQkbl5s2bGDJkCIQQ+O677zSHeolIe/rCtWvXIiYmBkWLFkVAQADMzMwwceJE7N27F9WrV8ewYcMgSRKGDRsGFxcXdOrUCQEBAdi4cSMaNGgg8yvJux48eICiRYtCoVBo2mPGjBm4fv06wsLCcPPmTbRq1Qq+vr5Yvny55nFqtRqLFi3CZ599Bnd3d/leABHpHbs3yKiUKVMGP/74IywsLPDtt9/ir7/+kjsSkUF4uxD/7rvv0KdPH+zduxe9evVC586dcffuXUyaNAnt2rXDmTNnULZsWTRv3hz37t1DcHAwypcvD1tbW86akg0rV65E1apVcfz4ca32uHDhAooXLw4hBBo3bozGjRvj559/BgCEhoZi/fr1UCgUGDJkCAtxIiPEYpyMTpkyZTB37lwUL14cLi4ucschMggZhd+tW7dw6tQpHD58GAcPHsSlS5fw119/YfDgwYiOjkZQUBAOHjyI3bt3Y926dTh37hwAYMGCBbC1tUWpUqVkfBV5W2BgIIoUKYK+ffvi+PHjmuEmn376KTZs2IDChQujffv2WLp0qaa9jhw5goiICCQnJ8sZnYhyEIepkNFKTU2FpaWl3DGIDMasWbOwd+9e5MuXD6tWrUK+fPkAABcvXkTz5s1RvXp1TJ8+HZUqVdI85siRI/j111+xbt06HDx4EN7e3jKlz9ve/ntUvXp1pKamYtmyZahbty5u3LiB4cOH4/r161i9ejXq1auHly9fYtasWQgJCcHBgwdRrlw5mV8BEeUU9oyT0WIhTqbu3yf6VatWDYcPH0ZUVBRiY2MBvBm+UqlSJezZswfnz5/HwIEDcevWLa3HqVQqHD16lIV4NlhYWAAA7t69ixkzZuDy5csICgrCyZMnUb58eQwdOhQVKlRAmzZt4OPjg1atWiE0NBQ7d+5kIU5k5NgzTkRk5E6fPo1SpUrBwcEBUVFRaNSoEb744gvMmjULLi4umvHLZ8+exeTJk7FlyxatGVNSUlJgZWUl4yswDtu2bUPXrl0xcuRI3Lp1C8ePH4elpSVWr16NmjVrIjo6GsePH8fp06fh6emJRo0awcPDQ+7YRJTDWIwTERmxHTt2YPDgwRgxYgS++uor5MuXDwcPHsSnn36K7t27Y/r06VoFeQZeWCZ7EhMTYWtrC+DN0YenT5+iQYMG8Pf3x3fffQcAePbsGfz8/JCUlISwsDDUqFEDZmZmcsYmIhnwLy0RkRFr3bo1GjZsiLCwMKxevRovXrxA48aNsW/fPqxbtw7jx49HTEzMO/PwsxD/eIsWLcLMmTPx8OFDAG9Ons24WFLGCbBpaWkoUKAA9u/fj9evX2PMmDE4cuQI5xAnMkH8a0tEZCTePtD59u1Vq1ahUqVKWLFiBdasWYOEhAQ0atQIe/fuRUhICFavXi1HXKMVGxuLX375BaGhoZqC3NHREQqFAgcOHADwZgx5eno6HB0dUalSJRw6dAhjxoxBamqqnNGJSAYsxomIjERG73ZISAh+/fVXpKWladaFhISgSpUqmD9/PtatW4eEhAT4+vri9OnTCAoKkiuyUZo1axYGDRqExYsXY+XKlYiNjYUkSRg/fjx2796NmTNnAgDMzc2hUChQvnx5REZGYv369VAqlTKnJ6LcxjHjRERGRKVSoW7dukhLS8PEiRPRqlUrzUweAFCjRg2kpKTA398fgwcPhp2dHQAgPT2dF/TRg7d/jxMnTkRISAj69++Pfv36wcrKCvPnz8fSpUvRtGlT1K1bF+fPn8fatWtx7do1FCtWTOb0RCQH/uUlIsrD/n2ipZmZGQ4dOoT27dtj2rRpUKvVaN26tWaqTy8vLxw4cADR0dGaEwwBsBD/SBm//6SkJCgUCvzzzz8oWLAgbG1tMXnyZADAkiVLIITAkCFDMGLECFSsWBHTpk3DlStXYG5ujiNHjrAQJzJh/OtLRJRHqVQqzewb0dHRsLa2RlpaGlxdXbF161a0bdsWM2bMgFqtRsuWLWFjYwNJkrB27VrUq1cPkiS9M4sKZV1GIX716lV89913uHHjBq5duwYvLy80adIEc+fOxeTJkyFJEpYuXQoA6NevHzp27IiOHTsiKSkJarVa60sREZkeDlMhIsqDrl69igoVKgAAxo8fj+3bt+Pp06fIly8fBg4ciMGDByMpKQmdOnXCgwcPoFQqIUkSnj9/josXL8LMzIzTF2ZDxpeYixcvokGDBujevTuqVq2KAgUKICwsDH/++ScaN26M3bt3AwAmTJiAkJAQDB48GF27dkWJEiVkfgVEZCjYM05ElMecP38e9evXx+3bt/HLL79gyZIlCA0NxevXr3H16lUMGzYMT548waRJk7B582YEBwcjOjoaCoUCc+bMYSGuB5Ik4fHjx/jqq68wYMAAzUmZAFC/fn1s3LgRo0aNQqdOnbBp0yZMmTIFlpaWmDJlCiwsLDB06FDOKU5EAFiMExHlOSqVCjY2Nnjy5AmOHz+O8ePHo02bNpr17u7u6NmzJzw9PfH5559j6NChWo/nyZr6ERsbi7S0NPj7+2uGDKnVahQuXBjdu3dHfHw8Zs6ciU2bNqFTp0747rvvYGVlhTZt2rAQJyINdosQEeURGaMKq1WrhkKFCiE8PBwnT55EcnKyZhu1Wo0vv/wSn3/+OXbv3o309HSoVCqt/bAQ14/z58/j1q1b8PLygpmZGYQQmqMN+fPnh7+/PywsLHD37l3NY7799luUKVNGpsREZIhYjBMR5REZJ1qq1Wo4OjpCrVajX79+2LVrF65fv67ZxszMDPb29nj+/DnMzc3ZC5tDSpcuDQDYvHkzALxzIqyHhwdKliyJv//+O9ezEVHewWKciMjA7dq1C+PGjcPhw4dx9epVKBQKdOrUCefPn0flypWRmpqKhQsX4tatW5AkCa9fv8adO3d4kmAOc3d3R758+bBq1SrExMRolmdc0j4+Ph7W1taoXr26XBGJKA/gbCpERAbs5cuX6NGjB27fvo20tDRER0ejcuXKOH/+PIoWLYorV65g9+7dmDt3LmJjY1GxYkU8efIEycnJOHfuHMzNzTl9YQ7asmULunbtii+++AKjRo1CxYoVNevGjx+PNWvW4NChQ3Bzc5MxJREZMhbjREQGLmPmk6dPn+Lq1at4+vQp/vzzT5w5cwYuLi5YvXo1Hj9+jF27duHatWtwdXXFN998A3Nzc56smcNUKhVWrFiBwYMHo1SpUqhXrx6cnZ1x9+5d7N69G/v370fVqlXljklEBozFOBGRgctsGkIhBLZu3Yq5c+eiYMGCCAkJQeHChbW2ffuiQJSzjh8/jjlz5uD69etwcHCAt7c3Bg8ejPLly8sdjYgMHItxIqI8JmPYiVqtxqZNm7BkyRK8evUK+/fvh4ODg9zxTJZKpYJCodC0DedxJ6Ks4LFLIqI8JuMy9gqFAp07d8br169x4sQJ5MuXT+5oJi2jEAfenVmFiOh92DNORJRHZXZiJntkiYjyFhbjRER5GGdKISLK29h9QkSUh7EQJyLK21iMExERERHJhMU4EREREZFMWIwTEREREcmExTgRERERkUxYjBMRERERyYTFOBERERGRTFiMExERERHJhMU4EREREZFMWIwTkUkKDQ2Fg4OD3DGIiMjEsRgnog96+PAhhg4ditKlS0OpVKJIkSKoX78+li1bhtevX8sdL0vc3d2xYMECrWVdunTBjRs35AlERET0P+ZyByAiw3Xnzh3Uq1cPDg4OmDFjBipVqoT09HTcuHEDK1euhIuLCz777DNZsgkhoFKpYG7+cX/GrK2tYW1tredUREREumHPOBG918CBA2Fubo5Tp07h888/R4UKFVCpUiV07NgRO3fuRJs2bQAAL168QN++feHk5IR8+fLhk08+wfnz5zX7mTRpEry9vbF69Wq4u7sjf/78+OKLL/Dy5UvNNkIIzJkzByVLloS1tTWqVKmCTZs2adYfOnQIkiRhz549qFGjBqysrHDkyBHcvn0bbdu2RZEiRWBnZ4eaNWti//79msc1atQI9+7dw/DhwyFJEiRJApD5MJWlS5eiVKlSsLS0RLly5bB69Wqt9ZIkYcWKFWjfvj1sbGxQpkwZbN++XbM+Pj4e/v7+KFy4MKytrVGmTBmEhIRkvyGIiMhosRgnokw9ffoUe/fuxaBBg2Bra5vpNpIkQQiBVq1a4eHDh9i1axdOnz6NatWqwc/PD8+ePdNse/v2bWzbtg07duzAjh07EBERgVmzZmnWf/fddwgJCcHSpUtx+fJlDB8+HN27d0dERITWc44aNQozZ87E1atXUblyZbx69QotW7bE/v37cfbsWTRr1gxt2rRBTEwMAGDLli0oXrw4pkyZgri4OMTFxWX6WrZu3YqhQ4dixIgRuHTpEvr164fAwEAcPHhQa7vJkyfj888/x4ULF9CyZUv4+/trXuf48eNx5coV7N69G1evXsXSpUtRqFAh3X/5RERkOgQRUSb++usvAUBs2bJFa3nBggWFra2tsLW1FaNGjRLh4eEiX758Ijk5WWu7UqVKieDgYCGEEBMnThQ2NjYiISFBs/7bb78VtWvXFkII8erVK6FUKkVUVJTWPnr16iW6du0qhBDi4MGDAoDYtm3bf2b39PQUP/30k+a+m5ub+OGHH7S2CQkJEfnz59fc9/HxEX369NHapnPnzqJly5aa+wDEd999p7n/6tUrIUmS2L17txBCiDZt2ojAwMD/zEdERJSBY8aJ6IMyhnVkOHHiBNRqNfz9/ZGSkoLTp0/j1atXKFiwoNZ2SUlJuH37tua+u7s77O3tNfednZ3x6NEjAMCVK1eQnJyMpk2bau0jNTUVVatW1VpWo0YNrfuJiYmYPHkyduzYgQcPHiA9PR1JSUmanvGsunr1Kvr27au1rF69eli4cKHWssqVK2tu29rawt7eXvM6BgwYgI4dO+LMmTP49NNP0a5dO/j4+OiUg4iITAuLcSLKVOnSpSFJEq5du6a1vGTJkgCgOflRrVbD2dkZhw4demcfb4/JtrCw0FonSRLUarVmHwCwc+dOFCtWTGs7Kysrrfv/HjLz7bffYs+ePfj+++9RunRpWFtbo1OnTkhNTc3iK9XO9DYhxDvLPvQ6WrRogXv37mHnzp3Yv38//Pz8MGjQIHz//fc6ZyEiItPAYpyIMlWwYEE0bdoUixYtwtdff/3ecePVqlXDw4cPYW5uDnd39496Lk9PT1hZWSEmJga+vr46PfbIkSPo0aMH2rdvDwB49eoV7t69q7WNpaUlVCrVB/dToUIFREZGIiAgQLMsKioKFSpU0ClP4cKF0aNHD/To0QMNGjTAt99+y2KciIjei8U4Eb3XkiVLUK9ePdSoUQOTJk1C5cqVoVAocPLkSVy7dg3Vq1dHkyZNULduXbRr1w6zZ89GuXLl8ODBA+zatQvt2rV7Z1hJZuzt7TFy5EgMHz4carUa9evXR0JCAqKiomBnZ4evvvrqvY8tXbo0tmzZgjZt2kCSJIwfP17TU53B3d0dhw8fxhdffAErK6tMT6r89ttv8fnnn2tOPv3jjz+wZcsWrZlZ/suECRNQvXp1VKxYESkpKdixY4fOxTwREZkWFuNE9F6lSpXC2bNnMWPGDIwZMwaxsbGwsrKCp6cnRo4ciYEDB0KSJOzatQvjxo1Dz5498fjxYxQtWhQNGzZEkSJFsvxcU6dOhZOTE2bOnIk7d+7AwcEB1apVw9ixYz/4uB9++AE9e/aEj48PChUqhKCgICQkJGhtM2XKFPTr1w+lSpVCSkoKhBDv7Kddu3ZYuHAh5s6diyFDhsDDwwMhISFo1KhRll+DpaUlxowZg7t378La2hoNGjTAr7/+muXHExGR6ZFEZp9KRERERESU4zjPOBERERGRTFiMExERERHJhMU4EREREZFMWIwTEREREcmExTgRERERkUxYjBMRERERyYTFOBERERGRTFiMExERERHJhMU4EREREZFMWIwTEREREcmExTgRERERkUz+D7wLMDREt+iZAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=CT_NS, x='Generations', y=\"Frequency (lower = More frequent)\", hue=\"variable\", whis=2, palette=\"magma\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(1.37, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "percDepressed_NSDUH2018 = getPercentage(NSDUH2018_long, \"Depressed\", \"age\", 2018)\n",
    "percDepressed_NSDUH2019 = getPercentage(NSDUH2019_long, \"Depressed\", \"age\", 2019)\n",
    "percDepressed_NSDUH2021 = getPercentage(NSDUH2021_long, \"Depressed\", \"age\", 2021)\n",
    "SMUse_sum_CoreTrends2018 = getPercentage(CoreTrends2018_long, \"Uses Social Media\", \"age\", 2018)\n",
    "SMUse_sum_CoreTrends2019 = getPercentage(CoreTrends2019_long, \"Uses Social Media\", \"age\", 2019)\n",
    "SMUse_sum_CoreTrends2021 = getPercentage(CoreTrends2021_long, \"Uses Social Media\", \"age\", 2021)\n",
    "\n",
    "CoreTrends_SMUseSum_concat = pd.concat([SMUse_sum_CoreTrends2018,SMUse_sum_CoreTrends2019, SMUse_sum_CoreTrends2021])\n",
    "NSDUH_concat = pd.concat([percDepressed_NSDUH2018,percDepressed_NSDUH2019,percDepressed_NSDUH2021])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='age', ylabel='percentage'>"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=CoreTrends_SMUseSum_concat, x='age', y='percentage', hue='dataset' ,palette='viridis')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "id=['AGE2']\n",
    "id2=['AGE3']\n",
    "# values = ['IRSEX', 'AUINPYR', 'AURXYR', 'YEATNDYR', 'YESCHFLT',\n",
    "#             'YEPRBSLV', 'DSTNRV30', 'DSTHOP30', 'DSTCHR30', 'DSTNGD30', 'DSTWORST',\n",
    "#             'DSTNRV12', 'DSTHOP12', 'DSTCHR12', 'DSTNGD12', 'IMPCONCN', 'IMPGOUT',\n",
    "#             'IMPPEOP', 'IMPSOC', 'IMPSOCM', 'SUICTHNK', 'ADDPREV']\n",
    "values = ['DSTCHR12']\n",
    "\n",
    "NSDUH2018_long = cleanUpNSDUH(NSDUH2018_wantedCols,id,values)\n",
    "NSDUH2019_long = cleanUpNSDUH(NSDUH2019_wantedCols,id,values)\n",
    "NSDUH2021_long = cleanUpNSDUH(NSDUH2021_wantedCols,id2,values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "percDepressed_NSDUH2018 = getPercentage(NSDUH2018_long, \"Depressed\", \"age\", 2018)\n",
    "percDepressed_NSDUH2019 = getPercentage(NSDUH2019_long, \"Depressed\", \"age\", 2019)\n",
    "percDepressed_NSDUH2021 = getPercentage(NSDUH2021_long, \"Depressed\", \"age\", 2021)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "NSDUH_concat = pd.concat([percDepressed_NSDUH2018,percDepressed_NSDUH2019,percDepressed_NSDUH2021])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x160370410>"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=NSDUH_concat, x='age', y='percentage',hue='dataset', palette='bright')\n",
    "# sns.barplot(data=CoreTrends_SMUseSum_concat, x='age', y='percentage', hue='dataset' ,palette='dark')\n",
    "\n",
    "plt.title(\"As social media use increases for Millenials & up, so does depression. Inversely so for Gen Z.\")\n",
    "plt.ylabel(\"Percentage\")\n",
    "plt.xlabel(\"Age Group\")\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(1.5, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from helper_code import *\n",
    "import plotly.express as px\n",
    "from helper_code import readInAndGetWantedColumns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CoreTrends2021Cols = [\"web1a\", \"web1b\", \"web1c\", \"web1d\", \"web1e\",\"sns2a\", \"sns2b\", \"sns2c\", \"sns2d\", \"sns2e\",\"gender\", \"age\"]\n",
    "CoreTrends2019Cols = [\"web1a\", \"web1b\", \"web1c\", \"web1d\", \"web1e\",\"sns2a\", \"sns2b\", \"sns2c\", \"sns2d\", \"sns2e\",\"sex\", \"age\"]\n",
    "CoreTrends2018Cols = [\"web1a\", \"web1b\", \"web1c\", \"web1d\", \"web1e\",\"sns2a\", \"sns2b\", \"sns2c\", \"sns2d\", \"sns2e\",\"sex\", \"age\"]\n",
    "\n",
    "CoreTrends2021_wantedCols = readInAndGetWantedColumns(\"datasets/Jan-25-Feb-8-2021-Core-Trends-Survey/Jan 25-Feb 8, 2021 - Core Trends Survey - CSV.csv\", \"csv\", CoreTrends2021Cols)\n",
    "CoreTrends2019_wantedCols = readInAndGetWantedColumns(\"datasets/January-8-February-7-2019-Core-Trends-Survey-SPSS/January 8-February 7, 2019 - Core Trends Survey - CSV.csv\", \"csv\", CoreTrends2019Cols)\n",
    "CoreTrends2018_wantedCols = readInAndGetWantedColumns(\"datasets/January 3-10, 2018 - Core Trends Survey/January 3-10, 2018 - Core Trends Survey - CSV.csv\", \"csv\", CoreTrends2018Cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1502, 12)\n",
      "(1502, 12)\n",
      "(2002, 12)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(CoreTrends2021_wantedCols.shape)\n",
    "print(CoreTrends2019_wantedCols.shape)\n",
    "print(CoreTrends2018_wantedCols.shape)\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CoreTrends2021_wantedCols['year'] = 2021\n",
    "# CoreTrends2019_wantedCols['year'] = 2019\n",
    "# CoreTrends2018_wantedCols['year'] = 2018\n",
    "\n",
    "CoreTrends2021_wantedCols.rename(columns={'gender': 'sex'}, inplace=True)\n",
    "\n",
    "#Concat all dataframes and convert answers to numeric\n",
    "CoreTrends_all = pd.concat([CoreTrends2018_wantedCols,CoreTrends2019_wantedCols, CoreTrends2021_wantedCols], ignore_index=True)\n",
    "\n",
    "#code cleanup\n",
    "CoreTrends_all = CoreTrends_all.apply(pd.to_numeric, errors='coerce')\n",
    "CoreTrends_all.fillna(0, inplace=True)\n",
    "\n",
    "web1_SMused = [\"web1a\", \"web1b\", \"web1c\", \"web1d\", \"web1e\"]\n",
    "sns_SMfrequency = [\"sns2a\", \"sns2b\", \"sns2c\", \"sns2d\", \"sns2e\"]\n",
    "\n",
    "CoreTrends_all = CoreTrends_all[CoreTrends_all['age'] < 98]\n",
    "CoreTrends_all = CoreTrends_all[CoreTrends_all['sex'] < 98]\n",
    "mask = CoreTrends_all[[\"sns2a\", \"sns2b\", \"sns2c\", \"sns2d\", \"sns2e\",\"web1a\", \"web1b\", \"web1c\", \"web1d\", \"web1e\"]] >= 8\n",
    "CoreTrends_all[~mask.any(axis=1)]\n",
    "CoreTrends_all[web1_SMused] = CoreTrends_all[web1_SMused].replace(2,0)\n",
    "\n",
    "#create sum columns\n",
    "CoreTrends_all['SM_usedTotal'] = (CoreTrends_all[[\"web1a\", \"web1b\", \"web1c\", \"web1d\", \"web1e\"]]==1).sum(axis=1)\n",
    "CoreTrends_all['SM_frequencySum'] = CoreTrends_all[[\"sns2a\", \"sns2b\", \"sns2c\", \"sns2d\", \"sns2e\"]].sum(axis=1)\n",
    "# CoreTrends_all.drop([\"web1a\", \"web1b\", \"web1c\", \"web1d\", \"web1e\",\"sns2a\", \"sns2b\", \"sns2c\", \"sns2d\", \"sns2e\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   web1a  web1b  web1c  web1d  web1e  sns2a  sns2b  sns2c  sns2d  sns2e  sex  \\\n",
      "0    2.0    1.0    1.0    1.0    1.0    0.0    1.0    1.0    3.0    3.0    2   \n",
      "1    2.0    2.0    2.0    2.0    2.0    0.0    0.0    0.0    0.0    0.0    2   \n",
      "3    2.0    2.0    1.0    2.0    2.0    0.0    0.0    3.0    0.0    0.0    2   \n",
      "4    2.0    2.0    1.0    2.0    1.0    0.0    0.0    3.0    0.0    2.0    1   \n",
      "5    1.0    2.0    1.0    2.0    1.0    2.0    0.0    1.0    0.0    3.0    1   \n",
      "\n",
      "   age  SM_usedTotal  SM_frequencySum  \n",
      "0   33             4              8.0  \n",
      "1   76             0              0.0  \n",
      "3   60             1              3.0  \n",
      "4   55             2              5.0  \n",
      "5   58             3              6.0  \n",
      "(4846, 14)\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(CoreTrends_all.head())\n",
    "print(CoreTrends_all.shape)\n",
    "print(type(CoreTrends_all))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PART 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "NSDUH2021Cols = [\"AGE3\",\"IRSEX\",\"AUINPYR\",\"AURXYR\",\"YEATNDYR\",\"YESCHFLT\",\"YEPRBSLV\",\"DSTNRV30\",\"DSTHOP30\",\"DSTCHR30\",\"DSTNGD30\",\"DSTWORST\",\"DSTNRV12\",\"DSTHOP12\",\"DSTCHR12\",\"DSTNGD12\",\"IMPCONCN\",\"IMPGOUT\",\"IMPPEOP\",\"IMPSOC\",\"IMPSOCM\",\"SUICTHNK\",\"SUIPLANYR\",\"ADDPREV\"]\n",
    "NSDUH201819Cols = [\"AGE2\", \"IRSEX\", \"AUINPYR\",\"AURXYR\",\"YEATNDYR\",\"YESCHFLT\",\"YEPRBSLV\",\"DSTNRV30\",\"DSTHOP30\",\"DSTCHR30\",\"DSTNGD30\",\"DSTWORST\",\"DSTNRV12\",\"DSTHOP12\",\"DSTCHR12\",\"DSTNGD12\",\"IMPCONCN\",\"IMPGOUT\",\"IMPPEOP\",\"IMPSOC\",\"IMPSOCM\",\"SUICTHNK\",\"ADDPREV\"]\n",
    "CoreTrends2021Cols = [\"web1a\", \"web1b\", \"web1c\", \"web1d\", \"web1e\",\"sns2a\", \"sns2b\", \"sns2c\", \"sns2d\", \"sns2e\",\"gender\", \"age\"]\n",
    "CoreTrends2019Cols = [\"web1a\", \"web1b\", \"web1c\", \"web1d\", \"web1e\",\"sns2a\", \"sns2b\", \"sns2c\", \"sns2d\", \"sns2e\",\"sex\", \"age\"]\n",
    "CoreTrends2018Cols = [\"web1a\", \"web1b\", \"web1c\", \"web1d\", \"web1e\",\"sns2a\", \"sns2b\", \"sns2c\", \"sns2d\", \"sns2e\",\"sex\", \"age\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "CoreTrends2021_wantedCols = readInAndGetWantedColumns(\"datasets/Jan-25-Feb-8-2021-Core-Trends-Survey/Jan 25-Feb 8, 2021 - Core Trends Survey - CSV.csv\", \"csv\", CoreTrends2021Cols)\n",
    "CoreTrends2019_wantedCols = readInAndGetWantedColumns(\"datasets/January-8-February-7-2019-Core-Trends-Survey-SPSS/January 8-February 7, 2019 - Core Trends Survey - CSV.csv\", \"csv\", CoreTrends2019Cols)\n",
    "CoreTrends2018_wantedCols = readInAndGetWantedColumns(\"datasets/January 3-10, 2018 - Core Trends Survey/January 3-10, 2018 - Core Trends Survey - CSV.csv\", \"csv\", CoreTrends2018Cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "NSDUH2019_wantedCols = readInAndGetWantedColumns(\"datasets/National Survey on Drug Use and Health 2019/NSDUH_2019_Tab.txt\", \"txt\", NSDUH201819Cols)\n",
    "NSDUH2018_wantedCols = readInAndGetWantedColumns(\"datasets/National Survey on Drug Use and Health 2018/NSDUH_2018_Tab.tsv\", \"tsv\", NSDUH201819Cols)\n",
    "NSDUH2021_wantedCols = readInAndGetWantedColumns(\"datasets/National Survey on Drug Use and Health 2021/NSDUH_2021_Tab.txt\", \"txt\", NSDUH2021Cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "CoreTrends_id = ['age']; CoreTrends_values = [\"web1a\", \"web1b\", \"web1c\", \"web1d\", \"web1e\"]\n",
    "NSDUH_id=['AGE2']; NSDUH_values = ['DSTCHR12']\n",
    "CoreTrends2018_long = cleanUpCoreTrends(CoreTrends2018_wantedCols, CoreTrends_id, CoreTrends_values)\n",
    "CoreTrends2019_long = cleanUpCoreTrends(CoreTrends2019_wantedCols, CoreTrends_id, CoreTrends_values)\n",
    "CoreTrends2021_long = cleanUpCoreTrends(CoreTrends2021_wantedCols, CoreTrends_id, CoreTrends_values)\n",
    "NSDUH2018_long = cleanUpNSDUH(NSDUH2018_wantedCols,NSDUH_id,NSDUH_values)\n",
    "NSDUH2019_long = cleanUpNSDUH(NSDUH2019_wantedCols,NSDUH_id,NSDUH_values)\n",
    "NSDUH2021_long = cleanUpNSDUH(NSDUH2021_wantedCols,['AGE3'],NSDUH_values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "percDepressed_NSDUH2018 = getPercentage(NSDUH2018_long, \"Depressed\", \"age\", 2018)\n",
    "percDepressed_NSDUH2019 = getPercentage(NSDUH2019_long, \"Depressed\", \"age\", 2019)\n",
    "percDepressed_NSDUH2021 = getPercentage(NSDUH2021_long, \"Depressed\", \"age\", 2021)\n",
    "SMUse_sum_CoreTrends2018 = getPercentage(CoreTrends2018_long, \"Uses Social Media\", \"age\", 2018)\n",
    "SMUse_sum_CoreTrends2019 = getPercentage(CoreTrends2019_long, \"Uses Social Media\", \"age\", 2019)\n",
    "SMUse_sum_CoreTrends2021 = getPercentage(CoreTrends2021_long, \"Uses Social Media\", \"age\", 2021)\n",
    "\n",
    "CoreTrends_SMUseSum_concat = pd.concat([SMUse_sum_CoreTrends2018,SMUse_sum_CoreTrends2019, SMUse_sum_CoreTrends2021])\n",
    "NSDUH_concat = pd.concat([percDepressed_NSDUH2018,percDepressed_NSDUH2019,percDepressed_NSDUH2021])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 7))\n",
    "sns.barplot(data=NSDUH_concat, x='age', y='percentage',hue='dataset', palette='bright')\n",
    "sns.barplot(data=CoreTrends_SMUseSum_concat, x='age', y='percentage', hue='dataset' ,palette='dark')\n",
    "plt.title(\"Percentage of depression plotted against social media use per generation\", loc='Center')\n",
    "plt.ylabel(\"Percentage\")\n",
    "plt.xlabel(\"Generation\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(1.4, 1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 5))\n",
    "sns.barplot(data=NSDUH_concat, x='age', y='percentage',hue='dataset', palette='bright')\n",
    "plt.title(\"Percentage of depression per age group\")\n",
    "plt.ylabel(\"Percentage\")\n",
    "plt.xlabel(\"Age Group\")\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(1.5, 1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 5))\n",
    "sns.barplot(data=CoreTrends_SMUseSum_concat, x='age', y='percentage', hue='dataset' ,palette='dark')\n",
    "plt.title(\"Percentage of Social Media use per age group\")\n",
    "plt.ylabel(\"Percentage\")\n",
    "plt.xlabel(\"Age Group\")\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(1.6, 1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Part 4: NSDUH cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "NSDUH2021Cols = [\"CATAG6\", \"AGE3\",\"IRSEX\",\"AUINPYR\",\"AURXYR\",\"YEATNDYR\",\"YESCHFLT\",\"YEPRBSLV\",\"DSTNRV30\",\"DSTHOP30\",\"DSTCHR30\",\"DSTNGD30\",\"DSTWORST\",\"DSTNRV12\",\"DSTHOP12\",\"DSTCHR12\",\"DSTNGD12\",\"IMPCONCN\",\"IMPGOUT\",\"IMPPEOP\",\"IMPSOC\",\"IMPSOCM\",\"SUICTHNK\",\"SUIPLANYR\",\"ADDPREV\"]\n",
    "NSDUH201819Cols = [\"CATAG6\", \"AGE2\", \"IRSEX\", \"AUINPYR\",\"AURXYR\",\"YEATNDYR\",\"YESCHFLT\",\"YEPRBSLV\",\"DSTNRV30\",\"DSTHOP30\",\"DSTCHR30\",\"DSTNGD30\",\"DSTWORST\",\"DSTNRV12\",\"DSTHOP12\",\"DSTCHR12\",\"DSTNGD12\",\"IMPCONCN\",\"IMPGOUT\",\"IMPPEOP\",\"IMPSOC\",\"IMPSOCM\",\"SUICTHNK\",\"ADDPREV\"]\n",
    "NSDUH2019_wantedCols = readInAndGetWantedColumns(\"datasets/National Survey on Drug Use and Health 2019/NSDUH_2019_Tab.txt\", \"txt\", NSDUH201819Cols)\n",
    "NSDUH2018_wantedCols = readInAndGetWantedColumns(\"datasets/National Survey on Drug Use and Health 2018/NSDUH_2018_Tab.tsv\", \"tsv\", NSDUH201819Cols)\n",
    "NSDUH2021_wantedCols = readInAndGetWantedColumns(\"datasets/National Survey on Drug Use and Health 2021/NSDUH_2021_Tab.txt\", \"txt\", NSDUH2021Cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NSDUH2021_wantedCols.rename(columns={'AGE3': 'AGE2'}, inplace=True)\n",
    "# #Create buckets for consistent labels for age\n",
    "# NSDUH2018_wantedCols['AGE2'] = pd.cut(NSDUH2018_wantedCols['AGE2'], bins=[0, 13, 15, 16, 17, float('inf')],\n",
    "#                        labels=[0, 1, 2, 3, 4])\n",
    "# NSDUH2019_wantedCols['AGE2'] = pd.cut(NSDUH2019_wantedCols['AGE2'], bins=[0, 13, 15, 16, 17, float('inf')],\n",
    "#                        labels=[0, 1, 2, 3, 4])\n",
    "# NSDUH2021_wantedCols['AGE2'] = pd.cut(NSDUH2021_wantedCols['AGE2'], bins=[0, 7, 9, 10, 11, float('inf')],\n",
    "#                        labels=[0, 1, 2, 3, 4])\n",
    "\n",
    "NSDUH_all = pd.concat([NSDUH2019_wantedCols,NSDUH2018_wantedCols, NSDUH2021_wantedCols], ignore_index=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NSDUH_slim = NSDUH_all[['AGE2', 'IRSEX', 'DSTCHR12', 'IMPSOC']]\n",
    "\n",
    "NSDUH_slim = NSDUH_all[['CATAG6', 'IRSEX', 'DSTCHR12', 'DSTHOP12', 'IMPSOC']]\n",
    "NSDUH_slim = NSDUH_slim.apply(pd.to_numeric, errors='coerce')\n",
    "NSDUH_slim.fillna(0, inplace=True)\n",
    "\n",
    "#Remove refused vals\n",
    "NSDUH_slim = NSDUH_slim[NSDUH_slim['DSTCHR12'] < 85]\n",
    "NSDUH_slim = NSDUH_slim[NSDUH_slim['IMPSOC'] < 85]\n",
    "NSDUH_slim = NSDUH_slim[NSDUH_slim['DSTHOP12'] < 85]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CATAG6</th>\n",
       "      <th>IRSEX</th>\n",
       "      <th>DSTCHR12</th>\n",
       "      <th>DSTHOP12</th>\n",
       "      <th>IMPSOC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CATAG6  IRSEX  DSTCHR12  DSTHOP12  IMPSOC\n",
       "0        4      1         3         3       3\n",
       "1        6      2         5         5       2\n",
       "5        5      1         2         2       2\n",
       "9        4      2         3         2       4\n",
       "10       4      2         4         4       2"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NSDUH_slim.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ML on NSDUH, experimentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler, MaxAbsScaler, RobustScaler, Normalizer, QuantileTransformer, PowerTransformer, StandardScaler\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.svm import SVC, SVR\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_selection import RFE\n",
    "from helper_code import *\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.metrics import classification_report, accuracy_score, confusion_matrix\n",
    "from ml_analysis import feature_selection\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "NSDUH2021Cols = [\"CATAG6\", \"AGE3\",\"IRSEX\",\"AUINPYR\",\"AURXYR\",\"YEATNDYR\",\"YESCHFLT\",\"YEPRBSLV\",\"DSTNRV30\",\"DSTHOP30\",\"DSTCHR30\",\"DSTNGD30\",\"DSTWORST\",\"DSTNRV12\",\"DSTHOP12\",\"DSTCHR12\",\"DSTNGD12\",\"IMPCONCN\",\"IMPGOUT\",\"IMPPEOP\",\"IMPSOC\",\"IMPSOCM\",\"SUICTHNK\",\"SUIPLANYR\",\"ADDPREV\"]\n",
    "NSDUH201819Cols = [\"CATAG6\", \"AGE2\", \"IRSEX\", \"AUINPYR\",\"AURXYR\",\"YEATNDYR\",\"YESCHFLT\",\"YEPRBSLV\",\"DSTNRV30\",\"DSTHOP30\",\"DSTCHR30\",\"DSTNGD30\",\"DSTWORST\",\"DSTNRV12\",\"DSTHOP12\",\"DSTCHR12\",\"DSTNGD12\",\"IMPCONCN\",\"IMPGOUT\",\"IMPPEOP\",\"IMPSOC\",\"IMPSOCM\",\"SUICTHNK\",\"ADDPREV\"]\n",
    "NSDUH2019_wantedCols = readInAndGetWantedColumns(\"datasets/National Survey on Drug Use and Health 2019/NSDUH_2019_Tab.txt\", \"txt\", NSDUH201819Cols)\n",
    "NSDUH2018_wantedCols = readInAndGetWantedColumns(\"datasets/National Survey on Drug Use and Health 2018/NSDUH_2018_Tab.tsv\", \"tsv\", NSDUH201819Cols)\n",
    "NSDUH2021_wantedCols = readInAndGetWantedColumns(\"datasets/National Survey on Drug Use and Health 2021/NSDUH_2021_Tab.txt\", \"txt\", NSDUH2021Cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "NSDUH_all = pd.concat([NSDUH2019_wantedCols,NSDUH2018_wantedCols, NSDUH2021_wantedCols], ignore_index=True)\n",
    "NSDUH_slim = NSDUH_all[['CATAG6', 'IRSEX', 'DSTCHR12', 'DSTHOP12', 'DSTCHR30', 'DSTNGD30', 'DSTNGD12', 'ADDPREV', 'DSTHOP30', 'IMPSOCM']]\n",
    "NSDUH_slim = NSDUH_slim.apply(pd.to_numeric, errors='coerce')\n",
    "NSDUH_slim.fillna(0, inplace=True)\n",
    "\n",
    "#Remove refused vals\n",
    "NSDUH_slim = NSDUH_slim[NSDUH_slim['DSTCHR12'] < 85]\n",
    "NSDUH_slim = NSDUH_slim[NSDUH_slim['DSTHOP12'] < 85]\n",
    "NSDUH_slim = NSDUH_slim[NSDUH_slim['IMPSOCM'] < 85]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = \"IMPSOCM\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['IMPSOCM'], dtype='object')\n",
      "Index(['CATAG6', 'IRSEX', 'DSTCHR12', 'DSTHOP12'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "NSDUH_moreslim = NSDUH_slim[['CATAG6', 'IRSEX', 'DSTCHR12', 'DSTHOP12', target]]\n",
    "y = NSDUH_moreslim[[target]]\n",
    "X = NSDUH_moreslim.drop([target], axis=1)\n",
    "\n",
    "print(y.columns)\n",
    "print(X.columns)\n",
    "y = y.values.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for 10 neighbors: 72.10%\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=555)\n",
    "\n",
    "max_accuracy = float(\"-inf\")\n",
    "best_model = {}\n",
    "knn = KNeighborsClassifier(n_neighbors=9)\n",
    "knn.fit(X_train, y_train)\n",
    "y_pred = knn.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy for {10} neighbors: {accuracy * 100:.2f}%')\n"
   ]
  }
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