notebooks/4-analysis-note.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b2ac4557",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import researchpy as rp\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "337ab7c4",
   "metadata": {},
   "source": [
    "# `4-analysis-note.ipynb`\n",
    "\n",
    "This notebook takes data/assessments.csv and performs everything done in [`notebooks/1-analysis.ipynb`](../notebooks/1-analysis.ipynb), but instead of using `TOTAL_SCORE` (including to calculate `Acuity`), it uses `CALC_TOTAL_SCORE`. It outputs several .csv files, all of which are available in the [`output/calc_total_score_note folder`](../output/calc_total_score_note)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a7966882",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_folder = Path('../data/')\n",
    "output_folder = Path('../output/calc_total_score_note')\n",
    "output_folder.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "\n",
    "fn = Path(data_folder, 'assessments.csv')\n",
    "df = pd.read_csv(fn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ca7acb7",
   "metadata": {},
   "source": [
    "Instead of using `TOTAL_SCORE` as the basis for acuity groups, use `CALC_TOTAL_SCORE`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "be0fc0ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "def acuity(row, total_score='TOTAL_SCORE'):\n",
    "    score = row[total_score]\n",
    "    assessment = row['AssessmentName']\n",
    "    if ('Individuals' in assessment and score > 11) or\\\n",
    "       (\"Families\" in assessment and score > 8) or\\\n",
    "       ('Youth' in assessment and score > 7):\n",
    "        return '3/High Acuity'\n",
    "    elif ('Individuals' in assessment and 11 >= score and score > 3) or\\\n",
    "       (\"Families\" in assessment and 8 >= score and score > 3) or\\\n",
    "       (\"Youth\" in assessment and 7 >= score and score > 3):\n",
    "        return '2/Mid Acuity'\n",
    "    elif ('Individuals' in assessment and score <= 3) or\\\n",
    "       (\"Families\" in assessment and score <= 3) or\\\n",
    "       (\"Youth\" in assessment and score <= 3):\n",
    "        return '1/Low Acuity'\n",
    "    \n",
    "df['Acuity'] = df.apply(acuity,total_score='CALC_TOTAL_SCORE',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "be9ca270",
   "metadata": {},
   "outputs": [],
   "source": [
    "individuals = df[df['AssessmentName'].str.contains('Individuals')].copy()\n",
    "\n",
    "# Drop any completely empty columns\n",
    "# e.g. columns that are youth survey-specific and not used for adults\n",
    "individuals.dropna(axis=1, how='all', inplace=True)\n",
    "\n",
    "#Convert subscores to an int for math purposes.\n",
    "for col in [c for c in individuals.columns if c.startswith('CALC_')]:\n",
    "    individuals[col] = individuals[col].apply(int)\n",
    "\n",
    "youth = df[df['AssessmentName'].str.contains('Youth')].copy()\n",
    "\n",
    "# Drop any completely empty columns\n",
    "# e.g. columns that are adult survey-specific and not used for youth\n",
    "youth.dropna(axis=1, how='all', inplace=True)\n",
    "\n",
    "# Convert subscores to an int for math purposes.\n",
    "for col in [c for c in youth.columns if c.startswith('CALC_')]:\n",
    "    youth[col] = youth[col].apply(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ef68597",
   "metadata": {},
   "source": [
    "## Analysis: Chi-square tests\n",
    "\n",
    "**NOTE:** For each year and assessment type, do two chi-square tests: one that compares Black and White people, and another that compares Latino and White people. In addition to individual years, we also perform chi-square tests on data across all years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8d229e1d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "chidata = []\n",
    "\n",
    "years = dict()\n",
    "for year in df['AssessmentYear'].unique():\n",
    "    years[year] = [year]\n",
    "years['Overall'] = df['AssessmentYear'].unique()\n",
    "\n",
    "for y in years.keys():\n",
    "    year = years[y]\n",
    "    for assessment in df['AssessmentName'].unique():\n",
    "        for group in ['Black', 'Latino']:\n",
    "            af = df[\n",
    "                df['AssessmentName'].eq(assessment) & \n",
    "                df['AssessmentYear'].isin(year)].copy()\n",
    "            af['ChiRE'] = df['Race/Ethnicity'].apply(lambda r: r if r in ['White', group] else np.nan)\n",
    "            af['ChiAcuity'] = df['Acuity'].str.contains('High')\n",
    "            \n",
    "            crosstab, test_results, expected = rp.crosstab(af['ChiRE'], af['ChiAcuity'],\n",
    "                                                       test= \"chi-square\",\n",
    "                                                       expected_freqs= True,\n",
    "                                                       prop= \"cell\")        \n",
    "            row = [y, assessment, group]\n",
    "            row.extend(test_results['results'])\n",
    "            chidata.append(row)\n",
    "            \n",
    "chidf = pd.DataFrame(chidata, columns=['year','assessment','group','pearson chi-square','p-value','cramers phi'])            \n",
    "chidf['sig'] = chidf['p-value'].lt(0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a9027289",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>assessment</th>\n",
       "      <th>group</th>\n",
       "      <th>pearson chi-square</th>\n",
       "      <th>p-value</th>\n",
       "      <th>cramers phi</th>\n",
       "      <th>sig</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2016</td>\n",
       "      <td>CES for Individuals - CES Survey Part 1</td>\n",
       "      <td>Black</td>\n",
       "      <td>46.9035</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0763</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017</td>\n",
       "      <td>CES for Individuals - CES Survey Part 1</td>\n",
       "      <td>Black</td>\n",
       "      <td>56.0208</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0680</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018</td>\n",
       "      <td>CES for Individuals - CES Survey Part 1</td>\n",
       "      <td>Black</td>\n",
       "      <td>144.7830</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0967</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>CES for Individuals - CES Survey Part 1</td>\n",
       "      <td>Black</td>\n",
       "      <td>55.5969</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0645</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2020</td>\n",
       "      <td>CES for Individuals - CES Survey Part 1</td>\n",
       "      <td>Black</td>\n",
       "      <td>15.0065</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0369</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2021</td>\n",
       "      <td>CES for Individuals - CES Survey Part 1</td>\n",
       "      <td>Black</td>\n",
       "      <td>45.3943</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0623</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2022</td>\n",
       "      <td>CES for Individuals - CES Survey Part 1</td>\n",
       "      <td>Black</td>\n",
       "      <td>6.3298</td>\n",
       "      <td>0.0119</td>\n",
       "      <td>0.0269</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Overall</td>\n",
       "      <td>CES for Individuals - CES Survey Part 1</td>\n",
       "      <td>Black</td>\n",
       "      <td>374.1620</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0682</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2016</td>\n",
       "      <td>CES for Individuals - CES Survey Part 1</td>\n",
       "      <td>Latino</td>\n",
       "      <td>17.2393</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0570</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>CES for Individuals - CES Survey Part 1</td>\n",
       "      <td>Latino</td>\n",
       "      <td>24.3811</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0518</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018</td>\n",
       "      <td>CES for Individuals - CES Survey Part 1</td>\n",
       "      <td>Latino</td>\n",
       "      <td>117.1682</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0982</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019</td>\n",
       "      <td>CES for Individuals - CES Survey Part 1</td>\n",
       "      <td>Latino</td>\n",
       "      <td>31.0955</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0530</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2020</td>\n",
       "      <td>CES for Individuals - CES Survey Part 1</td>\n",
       "      <td>Latino</td>\n",
       "      <td>16.6221</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0412</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2021</td>\n",
       "      <td>CES for Individuals - CES Survey Part 1</td>\n",
       "      <td>Latino</td>\n",
       "      <td>19.0458</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0409</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2022</td>\n",
       "      <td>CES for Individuals - CES Survey Part 1</td>\n",
       "      <td>Latino</td>\n",
       "      <td>3.6135</td>\n",
       "      <td>0.0573</td>\n",
       "      <td>0.0206</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Overall</td>\n",
       "      <td>CES for Individuals - CES Survey Part 1</td>\n",
       "      <td>Latino</td>\n",
       "      <td>122.5931</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0427</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2016</td>\n",
       "      <td>CES for Youth - Next Step Tool</td>\n",
       "      <td>Black</td>\n",
       "      <td>5.0850</td>\n",
       "      <td>0.0241</td>\n",
       "      <td>0.1308</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>CES for Youth - Next Step Tool</td>\n",
       "      <td>Black</td>\n",
       "      <td>30.2113</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1781</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018</td>\n",
       "      <td>CES for Youth - Next Step Tool</td>\n",
       "      <td>Black</td>\n",
       "      <td>8.6309</td>\n",
       "      <td>0.0033</td>\n",
       "      <td>0.0689</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019</td>\n",
       "      <td>CES for Youth - Next Step Tool</td>\n",
       "      <td>Black</td>\n",
       "      <td>21.2462</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1273</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2020</td>\n",
       "      <td>CES for Youth - Next Step Tool</td>\n",
       "      <td>Black</td>\n",
       "      <td>4.5966</td>\n",
       "      <td>0.0320</td>\n",
       "      <td>0.0727</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2021</td>\n",
       "      <td>CES for Youth - Next Step Tool</td>\n",
       "      <td>Black</td>\n",
       "      <td>25.3899</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1724</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2022</td>\n",
       "      <td>CES for Youth - Next Step Tool</td>\n",
       "      <td>Black</td>\n",
       "      <td>21.3817</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1552</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Overall</td>\n",
       "      <td>CES for Youth - Next Step Tool</td>\n",
       "      <td>Black</td>\n",
       "      <td>86.0766</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1110</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2016</td>\n",
       "      <td>CES for Youth - Next Step Tool</td>\n",
       "      <td>Latino</td>\n",
       "      <td>0.3845</td>\n",
       "      <td>0.5352</td>\n",
       "      <td>0.0379</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>CES for Youth - Next Step Tool</td>\n",
       "      <td>Latino</td>\n",
       "      <td>11.5437</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.1141</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2018</td>\n",
       "      <td>CES for Youth - Next Step Tool</td>\n",
       "      <td>Latino</td>\n",
       "      <td>5.4637</td>\n",
       "      <td>0.0194</td>\n",
       "      <td>0.0632</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019</td>\n",
       "      <td>CES for Youth - Next Step Tool</td>\n",
       "      <td>Latino</td>\n",
       "      <td>11.5949</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.1001</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2020</td>\n",
       "      <td>CES for Youth - Next Step Tool</td>\n",
       "      <td>Latino</td>\n",
       "      <td>1.5182</td>\n",
       "      <td>0.2179</td>\n",
       "      <td>0.0456</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2021</td>\n",
       "      <td>CES for Youth - Next Step Tool</td>\n",
       "      <td>Latino</td>\n",
       "      <td>6.3314</td>\n",
       "      <td>0.0119</td>\n",
       "      <td>0.0947</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2022</td>\n",
       "      <td>CES for Youth - Next Step Tool</td>\n",
       "      <td>Latino</td>\n",
       "      <td>8.6024</td>\n",
       "      <td>0.0034</td>\n",
       "      <td>0.1133</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Overall</td>\n",
       "      <td>CES for Youth - Next Step Tool</td>\n",
       "      <td>Latino</td>\n",
       "      <td>33.7851</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0764</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year                               assessment   group  \\\n",
       "20     2016  CES for Individuals - CES Survey Part 1   Black   \n",
       "0      2017  CES for Individuals - CES Survey Part 1   Black   \n",
       "8      2018  CES for Individuals - CES Survey Part 1   Black   \n",
       "4      2019  CES for Individuals - CES Survey Part 1   Black   \n",
       "16     2020  CES for Individuals - CES Survey Part 1   Black   \n",
       "12     2021  CES for Individuals - CES Survey Part 1   Black   \n",
       "24     2022  CES for Individuals - CES Survey Part 1   Black   \n",
       "28  Overall  CES for Individuals - CES Survey Part 1   Black   \n",
       "21     2016  CES for Individuals - CES Survey Part 1  Latino   \n",
       "1      2017  CES for Individuals - CES Survey Part 1  Latino   \n",
       "9      2018  CES for Individuals - CES Survey Part 1  Latino   \n",
       "5      2019  CES for Individuals - CES Survey Part 1  Latino   \n",
       "17     2020  CES for Individuals - CES Survey Part 1  Latino   \n",
       "13     2021  CES for Individuals - CES Survey Part 1  Latino   \n",
       "25     2022  CES for Individuals - CES Survey Part 1  Latino   \n",
       "29  Overall  CES for Individuals - CES Survey Part 1  Latino   \n",
       "22     2016           CES for Youth - Next Step Tool   Black   \n",
       "2      2017           CES for Youth - Next Step Tool   Black   \n",
       "10     2018           CES for Youth - Next Step Tool   Black   \n",
       "6      2019           CES for Youth - Next Step Tool   Black   \n",
       "18     2020           CES for Youth - Next Step Tool   Black   \n",
       "14     2021           CES for Youth - Next Step Tool   Black   \n",
       "26     2022           CES for Youth - Next Step Tool   Black   \n",
       "30  Overall           CES for Youth - Next Step Tool   Black   \n",
       "23     2016           CES for Youth - Next Step Tool  Latino   \n",
       "3      2017           CES for Youth - Next Step Tool  Latino   \n",
       "11     2018           CES for Youth - Next Step Tool  Latino   \n",
       "7      2019           CES for Youth - Next Step Tool  Latino   \n",
       "19     2020           CES for Youth - Next Step Tool  Latino   \n",
       "15     2021           CES for Youth - Next Step Tool  Latino   \n",
       "27     2022           CES for Youth - Next Step Tool  Latino   \n",
       "31  Overall           CES for Youth - Next Step Tool  Latino   \n",
       "\n",
       "    pearson chi-square  p-value  cramers phi    sig  \n",
       "20             46.9035   0.0000       0.0763   True  \n",
       "0              56.0208   0.0000       0.0680   True  \n",
       "8             144.7830   0.0000       0.0967   True  \n",
       "4              55.5969   0.0000       0.0645   True  \n",
       "16             15.0065   0.0001       0.0369   True  \n",
       "12             45.3943   0.0000       0.0623   True  \n",
       "24              6.3298   0.0119       0.0269   True  \n",
       "28            374.1620   0.0000       0.0682   True  \n",
       "21             17.2393   0.0000       0.0570   True  \n",
       "1              24.3811   0.0000       0.0518   True  \n",
       "9             117.1682   0.0000       0.0982   True  \n",
       "5              31.0955   0.0000       0.0530   True  \n",
       "17             16.6221   0.0000       0.0412   True  \n",
       "13             19.0458   0.0000       0.0409   True  \n",
       "25              3.6135   0.0573       0.0206  False  \n",
       "29            122.5931   0.0000       0.0427   True  \n",
       "22              5.0850   0.0241       0.1308   True  \n",
       "2              30.2113   0.0000       0.1781   True  \n",
       "10              8.6309   0.0033       0.0689   True  \n",
       "6              21.2462   0.0000       0.1273   True  \n",
       "18              4.5966   0.0320       0.0727   True  \n",
       "14             25.3899   0.0000       0.1724   True  \n",
       "26             21.3817   0.0000       0.1552   True  \n",
       "30             86.0766   0.0000       0.1110   True  \n",
       "23              0.3845   0.5352       0.0379  False  \n",
       "3              11.5437   0.0007       0.1141   True  \n",
       "11              5.4637   0.0194       0.0632   True  \n",
       "7              11.5949   0.0007       0.1001   True  \n",
       "19              1.5182   0.2179       0.0456  False  \n",
       "15              6.3314   0.0119       0.0947   True  \n",
       "27              8.6024   0.0034       0.1133   True  \n",
       "31             33.7851   0.0000       0.0764   True  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cf = chidf.sort_values(by=['assessment','group','year']).copy()\n",
    "\n",
    "fn = Path(output_folder, 'chi-outcomes.csv')\n",
    "cf.to_csv(fn, index=False)\n",
    "cf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27122524",
   "metadata": {},
   "source": [
    "## Table: Acuity group distribution by race/ethnicity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "52dce7cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "acuity_group_folder = Path(output_folder, \"acuity_group_distribution\")\n",
    "acuity_group_folder.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "acuity_group_distribution_tables = dict()\n",
    "\n",
    "for assessment in df['AssessmentName'].unique():\n",
    "    af = df[df['AssessmentName'].eq(assessment)].copy()\n",
    "    cross_tab_prop = pd.crosstab(af['Race/Ethnicity'], af['Acuity'], normalize='index').mul(100).round(1)\n",
    "    cross_tab = pd.crosstab(af['Race/Ethnicity'], af['Acuity'])\n",
    "    \n",
    "    cross_tab.loc['Overall'] = af.groupby('Acuity').count()['Unique_client_ID']\n",
    "    \n",
    "    cross_tab_prop.loc['Overall'] = cross_tab.loc['Overall'].div(cross_tab.loc['Overall'].sum()).mul(100).round(1)\n",
    "    \n",
    "    cf = cross_tab.merge(cross_tab_prop, left_index=True, right_index=True,suffixes=(' (count)', \" (%)\"))\n",
    "    \n",
    "    cols = list(cf.columns)\n",
    "    cols.sort()\n",
    "    cols.reverse()\n",
    "    acuity_group_distribution_tables[assessment] = cf[cols].copy()\n",
    "    \n",
    "    fn = Path(acuity_group_folder,f'{assessment}.csv')\n",
    "    acuity_group_distribution_tables[assessment].to_csv(fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f8d9308e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Acuity</th>\n",
       "      <th>3/High Acuity (count)</th>\n",
       "      <th>3/High Acuity (%)</th>\n",
       "      <th>2/Mid Acuity (count)</th>\n",
       "      <th>2/Mid Acuity (%)</th>\n",
       "      <th>1/Low Acuity (count)</th>\n",
       "      <th>1/Low Acuity (%)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Race/Ethnicity</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Asian</th>\n",
       "      <td>468</td>\n",
       "      <td>24.6</td>\n",
       "      <td>1246</td>\n",
       "      <td>65.4</td>\n",
       "      <td>190</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>13026</td>\n",
       "      <td>25.7</td>\n",
       "      <td>32300</td>\n",
       "      <td>63.8</td>\n",
       "      <td>5324</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>10533</td>\n",
       "      <td>28.1</td>\n",
       "      <td>24143</td>\n",
       "      <td>64.5</td>\n",
       "      <td>2772</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>989</td>\n",
       "      <td>27.6</td>\n",
       "      <td>2309</td>\n",
       "      <td>64.5</td>\n",
       "      <td>283</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>935</td>\n",
       "      <td>23.5</td>\n",
       "      <td>2688</td>\n",
       "      <td>67.5</td>\n",
       "      <td>359</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>9578</td>\n",
       "      <td>32.1</td>\n",
       "      <td>18556</td>\n",
       "      <td>62.1</td>\n",
       "      <td>1743</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Overall</th>\n",
       "      <td>35529</td>\n",
       "      <td>27.9</td>\n",
       "      <td>81242</td>\n",
       "      <td>63.7</td>\n",
       "      <td>10671</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Acuity          3/High Acuity (count)  3/High Acuity (%)  \\\n",
       "Race/Ethnicity                                             \n",
       "Asian                             468               24.6   \n",
       "Black                           13026               25.7   \n",
       "Latino                          10533               28.1   \n",
       "Other                             989               27.6   \n",
       "Unknown                           935               23.5   \n",
       "White                            9578               32.1   \n",
       "Overall                         35529               27.9   \n",
       "\n",
       "Acuity          2/Mid Acuity (count)  2/Mid Acuity (%)  1/Low Acuity (count)  \\\n",
       "Race/Ethnicity                                                                 \n",
       "Asian                           1246              65.4                   190   \n",
       "Black                          32300              63.8                  5324   \n",
       "Latino                         24143              64.5                  2772   \n",
       "Other                           2309              64.5                   283   \n",
       "Unknown                         2688              67.5                   359   \n",
       "White                          18556              62.1                  1743   \n",
       "Overall                        81242              63.7                 10671   \n",
       "\n",
       "Acuity          1/Low Acuity (%)  \n",
       "Race/Ethnicity                    \n",
       "Asian                       10.0  \n",
       "Black                       10.5  \n",
       "Latino                       7.4  \n",
       "Other                        7.9  \n",
       "Unknown                      9.0  \n",
       "White                        5.8  \n",
       "Overall                      8.4  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acuity_group_distribution_tables['CES for Individuals - CES Survey Part 1']#.to_clipboard()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c3c5e7e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Acuity</th>\n",
       "      <th>3/High Acuity (count)</th>\n",
       "      <th>3/High Acuity (%)</th>\n",
       "      <th>2/Mid Acuity (count)</th>\n",
       "      <th>2/Mid Acuity (%)</th>\n",
       "      <th>1/Low Acuity (count)</th>\n",
       "      <th>1/Low Acuity (%)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Race/Ethnicity</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Asian</th>\n",
       "      <td>44</td>\n",
       "      <td>36.1</td>\n",
       "      <td>58</td>\n",
       "      <td>47.5</td>\n",
       "      <td>20</td>\n",
       "      <td>16.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>2312</td>\n",
       "      <td>41.3</td>\n",
       "      <td>2509</td>\n",
       "      <td>44.8</td>\n",
       "      <td>781</td>\n",
       "      <td>13.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>2030</td>\n",
       "      <td>46.1</td>\n",
       "      <td>1859</td>\n",
       "      <td>42.2</td>\n",
       "      <td>511</td>\n",
       "      <td>11.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>266</td>\n",
       "      <td>53.2</td>\n",
       "      <td>185</td>\n",
       "      <td>37.0</td>\n",
       "      <td>49</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>201</td>\n",
       "      <td>42.2</td>\n",
       "      <td>200</td>\n",
       "      <td>42.0</td>\n",
       "      <td>75</td>\n",
       "      <td>15.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>765</td>\n",
       "      <td>55.1</td>\n",
       "      <td>499</td>\n",
       "      <td>35.9</td>\n",
       "      <td>125</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Overall</th>\n",
       "      <td>5618</td>\n",
       "      <td>45.0</td>\n",
       "      <td>5310</td>\n",
       "      <td>42.5</td>\n",
       "      <td>1561</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Acuity          3/High Acuity (count)  3/High Acuity (%)  \\\n",
       "Race/Ethnicity                                             \n",
       "Asian                              44               36.1   \n",
       "Black                            2312               41.3   \n",
       "Latino                           2030               46.1   \n",
       "Other                             266               53.2   \n",
       "Unknown                           201               42.2   \n",
       "White                             765               55.1   \n",
       "Overall                          5618               45.0   \n",
       "\n",
       "Acuity          2/Mid Acuity (count)  2/Mid Acuity (%)  1/Low Acuity (count)  \\\n",
       "Race/Ethnicity                                                                 \n",
       "Asian                             58              47.5                    20   \n",
       "Black                           2509              44.8                   781   \n",
       "Latino                          1859              42.2                   511   \n",
       "Other                            185              37.0                    49   \n",
       "Unknown                          200              42.0                    75   \n",
       "White                            499              35.9                   125   \n",
       "Overall                         5310              42.5                  1561   \n",
       "\n",
       "Acuity          1/Low Acuity (%)  \n",
       "Race/Ethnicity                    \n",
       "Asian                       16.4  \n",
       "Black                       13.9  \n",
       "Latino                      11.6  \n",
       "Other                        9.8  \n",
       "Unknown                     15.8  \n",
       "White                        9.0  \n",
       "Overall                     12.5  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acuity_group_distribution_tables['CES for Youth - Next Step Tool']#.to_clipboard()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6964daf3",
   "metadata": {},
   "source": [
    "## Chart: high acuity frequency over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e32d9946",
   "metadata": {},
   "outputs": [],
   "source": [
    "high_acuity_freq_folder = Path(output_folder, \"high_acuity_freq\")\n",
    "high_acuity_freq_folder.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "high_acuity_freq_charts = dict()\n",
    "\n",
    "for assessment in df['AssessmentName'].unique():\n",
    "    yf = pd.DataFrame()\n",
    "    for year in df['AssessmentYear'].unique():\n",
    "        af = df[df['AssessmentName'].eq(assessment) & df['AssessmentYear'].eq(year)].copy()\n",
    "        cross_tab_prop = pd.crosstab(af['Race/Ethnicity'], af['Acuity'], normalize='index').mul(100).round(1)   \n",
    "        yf[year] = cross_tab_prop['3/High Acuity'].copy()\n",
    "    high_acuity_freq_charts[assessment] = yf[range(2016, 2023)]\n",
    "    \n",
    "    fn = Path(high_acuity_freq_folder, f'{assessment}.csv')\n",
    "    high_acuity_freq_charts[assessment].to_csv(fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1fb57df3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Race/Ethnicity</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Asian</th>\n",
       "      <td>14.0</td>\n",
       "      <td>11.2</td>\n",
       "      <td>22.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>33.5</td>\n",
       "      <td>32.7</td>\n",
       "      <td>29.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>11.9</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.4</td>\n",
       "      <td>29.1</td>\n",
       "      <td>38.3</td>\n",
       "      <td>32.9</td>\n",
       "      <td>32.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>13.3</td>\n",
       "      <td>16.3</td>\n",
       "      <td>21.5</td>\n",
       "      <td>30.3</td>\n",
       "      <td>38.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>32.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>10.5</td>\n",
       "      <td>17.8</td>\n",
       "      <td>26.4</td>\n",
       "      <td>32.3</td>\n",
       "      <td>40.0</td>\n",
       "      <td>30.5</td>\n",
       "      <td>35.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>12.6</td>\n",
       "      <td>19.1</td>\n",
       "      <td>22.4</td>\n",
       "      <td>20.2</td>\n",
       "      <td>35.6</td>\n",
       "      <td>29.8</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>17.5</td>\n",
       "      <td>20.3</td>\n",
       "      <td>30.1</td>\n",
       "      <td>35.3</td>\n",
       "      <td>42.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>34.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                2016  2017  2018  2019  2020  2021  2022\n",
       "Race/Ethnicity                                          \n",
       "Asian           14.0  11.2  22.0  26.0  33.5  32.7  29.3\n",
       "Black           11.9  15.0  21.4  29.1  38.3  32.9  32.3\n",
       "Latino          13.3  16.3  21.5  30.3  38.0  35.0  32.9\n",
       "Other           10.5  17.8  26.4  32.3  40.0  30.5  35.2\n",
       "Unknown         12.6  19.1  22.4  20.2  35.6  29.8  32.7\n",
       "White           17.5  20.3  30.1  35.3  42.0  39.0  34.9"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_acuity_freq_charts['CES for Individuals - CES Survey Part 1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ba897f9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Race/Ethnicity</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Asian</th>\n",
       "      <td>50.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>30.8</td>\n",
       "      <td>36.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>66.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>30.3</td>\n",
       "      <td>23.1</td>\n",
       "      <td>38.8</td>\n",
       "      <td>45.1</td>\n",
       "      <td>51.0</td>\n",
       "      <td>45.5</td>\n",
       "      <td>48.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>40.2</td>\n",
       "      <td>29.5</td>\n",
       "      <td>40.2</td>\n",
       "      <td>49.1</td>\n",
       "      <td>54.9</td>\n",
       "      <td>56.2</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>47.6</td>\n",
       "      <td>33.3</td>\n",
       "      <td>56.7</td>\n",
       "      <td>52.9</td>\n",
       "      <td>57.1</td>\n",
       "      <td>56.5</td>\n",
       "      <td>69.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>42.6</td>\n",
       "      <td>34.1</td>\n",
       "      <td>30.4</td>\n",
       "      <td>47.9</td>\n",
       "      <td>47.8</td>\n",
       "      <td>47.7</td>\n",
       "      <td>43.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>44.3</td>\n",
       "      <td>41.7</td>\n",
       "      <td>47.4</td>\n",
       "      <td>61.1</td>\n",
       "      <td>60.4</td>\n",
       "      <td>67.1</td>\n",
       "      <td>69.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                2016  2017  2018  2019  2020  2021  2022\n",
       "Race/Ethnicity                                          \n",
       "Asian           50.0  21.1  30.8  36.0  40.0  35.3  66.7\n",
       "Black           30.3  23.1  38.8  45.1  51.0  45.5  48.6\n",
       "Latino          40.2  29.5  40.2  49.1  54.9  56.2  56.0\n",
       "Other           47.6  33.3  56.7  52.9  57.1  56.5  69.2\n",
       "Unknown         42.6  34.1  30.4  47.9  47.8  47.7  43.4\n",
       "White           44.3  41.7  47.4  61.1  60.4  67.1  69.3"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_acuity_freq_charts['CES for Youth - Next Step Tool']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fc56fde",
   "metadata": {},
   "source": [
    "## Chart: subscores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "de463d6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "subscore_folder = Path(output_folder, \"subscore_frequency\")\n",
    "subscore_folder.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "subscore_charts = dict()\n",
    "\n",
    "for assessment in df['AssessmentName'].unique(): \n",
    "    af = df[df['AssessmentName'].eq(assessment)].copy()\n",
    "    af = af.dropna(how='all', axis=1).copy()\n",
    "    \n",
    "    subscores = [c for c in af.columns if c.startswith('CALC_') and not c.endswith('_SCORE')]\n",
    "    subscores.append('GENERAL_SUB_SCORE')\n",
    "    \n",
    "    for subscore in subscores:\n",
    "        af[subscore] = af[subscore].apply(int)\n",
    "    \n",
    "    rates = af[\n",
    "        af['Race/Ethnicity'].isin(['Black','Latino', 'White'])\n",
    "    ].groupby('Race/Ethnicity')[subscores].mean().T\n",
    "    \n",
    "    subscore_charts[assessment] = rates.mul(100).round(1).sort_values(by='Black').copy()\n",
    "    \n",
    "    fn = Path(subscore_folder, f'{assessment}.csv')\n",
    "    subscore_charts[assessment].to_csv(fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fc1c9aa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Race/Ethnicity</th>\n",
       "      <th>Black</th>\n",
       "      <th>Latino</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CALC_WELLNESS_TRIMORBIDITY</th>\n",
       "      <td>17.6</td>\n",
       "      <td>21.7</td>\n",
       "      <td>24.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GENERAL_SUB_SCORE</th>\n",
       "      <td>19.7</td>\n",
       "      <td>13.9</td>\n",
       "      <td>21.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_WELLNESS_SUBSTANCE_USE</th>\n",
       "      <td>23.9</td>\n",
       "      <td>34.1</td>\n",
       "      <td>34.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_RISKS_LEGAL_ISSUES</th>\n",
       "      <td>28.6</td>\n",
       "      <td>35.1</td>\n",
       "      <td>35.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_WELLNESS_MEDICATIONS</th>\n",
       "      <td>35.1</td>\n",
       "      <td>34.5</td>\n",
       "      <td>40.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_SOCIALIZATION_SELFCARE</th>\n",
       "      <td>39.9</td>\n",
       "      <td>40.3</td>\n",
       "      <td>44.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_RISKS_RISK_OF_EXPLOITATION</th>\n",
       "      <td>45.9</td>\n",
       "      <td>46.9</td>\n",
       "      <td>50.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_WELLNESS_MENTAL_HEALTH</th>\n",
       "      <td>49.3</td>\n",
       "      <td>50.8</td>\n",
       "      <td>56.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_RISKS_EMERGENCY_SERVICE_USE</th>\n",
       "      <td>56.3</td>\n",
       "      <td>58.1</td>\n",
       "      <td>63.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_RISKS_RISK_OF_HARM</th>\n",
       "      <td>59.0</td>\n",
       "      <td>60.6</td>\n",
       "      <td>63.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_SOCIALIZATION_MEANINGFUL_DAILY_ACTIVITY</th>\n",
       "      <td>59.9</td>\n",
       "      <td>60.4</td>\n",
       "      <td>63.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_SOCIALIZATION_SOCIAL_RELATIONSHIPS</th>\n",
       "      <td>64.5</td>\n",
       "      <td>68.0</td>\n",
       "      <td>65.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_SOCIALIZATION_MONEY_MANAGEMENT</th>\n",
       "      <td>65.4</td>\n",
       "      <td>73.7</td>\n",
       "      <td>70.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_WELLNESS_ABUSE_AND_TRAUMA</th>\n",
       "      <td>67.2</td>\n",
       "      <td>67.9</td>\n",
       "      <td>70.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_WELLNESS_PHYSICAL_HEALTH</th>\n",
       "      <td>71.9</td>\n",
       "      <td>73.8</td>\n",
       "      <td>79.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_HISTORY_HISTORY_OF_HOMELESSNESS</th>\n",
       "      <td>76.2</td>\n",
       "      <td>74.8</td>\n",
       "      <td>76.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_HISTORY_SLEEPING_LOCATION</th>\n",
       "      <td>81.1</td>\n",
       "      <td>85.5</td>\n",
       "      <td>84.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Race/Ethnicity                                Black  Latino  White\n",
       "CALC_WELLNESS_TRIMORBIDITY                     17.6    21.7   24.1\n",
       "GENERAL_SUB_SCORE                              19.7    13.9   21.8\n",
       "CALC_WELLNESS_SUBSTANCE_USE                    23.9    34.1   34.4\n",
       "CALC_RISKS_LEGAL_ISSUES                        28.6    35.1   35.3\n",
       "CALC_WELLNESS_MEDICATIONS                      35.1    34.5   40.4\n",
       "CALC_SOCIALIZATION_SELFCARE                    39.9    40.3   44.3\n",
       "CALC_RISKS_RISK_OF_EXPLOITATION                45.9    46.9   50.9\n",
       "CALC_WELLNESS_MENTAL_HEALTH                    49.3    50.8   56.5\n",
       "CALC_RISKS_EMERGENCY_SERVICE_USE               56.3    58.1   63.3\n",
       "CALC_RISKS_RISK_OF_HARM                        59.0    60.6   63.5\n",
       "CALC_SOCIALIZATION_MEANINGFUL_DAILY_ACTIVITY   59.9    60.4   63.1\n",
       "CALC_SOCIALIZATION_SOCIAL_RELATIONSHIPS        64.5    68.0   65.5\n",
       "CALC_SOCIALIZATION_MONEY_MANAGEMENT            65.4    73.7   70.2\n",
       "CALC_WELLNESS_ABUSE_AND_TRAUMA                 67.2    67.9   70.5\n",
       "CALC_WELLNESS_PHYSICAL_HEALTH                  71.9    73.8   79.7\n",
       "CALC_HISTORY_HISTORY_OF_HOMELESSNESS           76.2    74.8   76.2\n",
       "CALC_HISTORY_SLEEPING_LOCATION                 81.1    85.5   84.7"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subscore_charts['CES for Individuals - CES Survey Part 1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6bf89604",
   "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>Race/Ethnicity</th>\n",
       "      <th>Black</th>\n",
       "      <th>Latino</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GENERAL_SUB_SCORE</th>\n",
       "      <td>1.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_WELLNESS_TRIMORBIDITY</th>\n",
       "      <td>9.8</td>\n",
       "      <td>13.3</td>\n",
       "      <td>17.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_WELLNESS_MEDICATIONS</th>\n",
       "      <td>18.9</td>\n",
       "      <td>21.1</td>\n",
       "      <td>29.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_WELLNESS_SUBSTANCE_USE</th>\n",
       "      <td>26.7</td>\n",
       "      <td>38.2</td>\n",
       "      <td>39.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_SOCIALIZATION_SELFCARE</th>\n",
       "      <td>29.1</td>\n",
       "      <td>25.9</td>\n",
       "      <td>27.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_RISKS_RISK_OF_EXPLOITATION</th>\n",
       "      <td>30.0</td>\n",
       "      <td>32.9</td>\n",
       "      <td>42.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_WELLNESS_MENTAL_HEALTH</th>\n",
       "      <td>30.3</td>\n",
       "      <td>33.1</td>\n",
       "      <td>45.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_RISKS_LEGAL_ISSUES</th>\n",
       "      <td>33.8</td>\n",
       "      <td>41.0</td>\n",
       "      <td>37.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_RISKS_EMERGENCY_SERVICE_USE</th>\n",
       "      <td>35.7</td>\n",
       "      <td>35.7</td>\n",
       "      <td>44.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_SOCIALIZATION_MEANINGFUL_DAILY_ACTIVITY</th>\n",
       "      <td>38.8</td>\n",
       "      <td>37.2</td>\n",
       "      <td>39.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_RISKS_RISK_OF_HARM</th>\n",
       "      <td>44.6</td>\n",
       "      <td>46.1</td>\n",
       "      <td>55.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_SOCIALIZATION_USE_TRAUMA</th>\n",
       "      <td>57.6</td>\n",
       "      <td>62.0</td>\n",
       "      <td>62.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_HISTORY_HISTORY_OF_HOMELESSNESS</th>\n",
       "      <td>59.2</td>\n",
       "      <td>55.7</td>\n",
       "      <td>57.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_WELLNESS_PHYSICAL_HEALTH</th>\n",
       "      <td>60.2</td>\n",
       "      <td>62.2</td>\n",
       "      <td>65.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_SOCIALIZATION_MONEY_MANAGEMENT</th>\n",
       "      <td>63.0</td>\n",
       "      <td>66.2</td>\n",
       "      <td>69.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_SOCIALIZATION_SOCIAL_RELATIONSHIPS</th>\n",
       "      <td>72.7</td>\n",
       "      <td>73.1</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_HISTORY_SLEEPING_LOCATION</th>\n",
       "      <td>81.5</td>\n",
       "      <td>82.5</td>\n",
       "      <td>82.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Race/Ethnicity                                Black  Latino  White\n",
       "GENERAL_SUB_SCORE                               1.8     3.2    2.3\n",
       "CALC_WELLNESS_TRIMORBIDITY                      9.8    13.3   17.4\n",
       "CALC_WELLNESS_MEDICATIONS                      18.9    21.1   29.7\n",
       "CALC_WELLNESS_SUBSTANCE_USE                    26.7    38.2   39.6\n",
       "CALC_SOCIALIZATION_SELFCARE                    29.1    25.9   27.4\n",
       "CALC_RISKS_RISK_OF_EXPLOITATION                30.0    32.9   42.4\n",
       "CALC_WELLNESS_MENTAL_HEALTH                    30.3    33.1   45.3\n",
       "CALC_RISKS_LEGAL_ISSUES                        33.8    41.0   37.1\n",
       "CALC_RISKS_EMERGENCY_SERVICE_USE               35.7    35.7   44.7\n",
       "CALC_SOCIALIZATION_MEANINGFUL_DAILY_ACTIVITY   38.8    37.2   39.1\n",
       "CALC_RISKS_RISK_OF_HARM                        44.6    46.1   55.4\n",
       "CALC_SOCIALIZATION_USE_TRAUMA                  57.6    62.0   62.5\n",
       "CALC_HISTORY_HISTORY_OF_HOMELESSNESS           59.2    55.7   57.2\n",
       "CALC_WELLNESS_PHYSICAL_HEALTH                  60.2    62.2   65.5\n",
       "CALC_SOCIALIZATION_MONEY_MANAGEMENT            63.0    66.2   69.6\n",
       "CALC_SOCIALIZATION_SOCIAL_RELATIONSHIPS        72.7    73.1   75.0\n",
       "CALC_HISTORY_SLEEPING_LOCATION                 81.5    82.5   82.6"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subscore_charts['CES for Youth - Next Step Tool']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e73b1f4",
   "metadata": {},
   "source": [
    "## Chart: Acuity score distribution by race"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1912fdea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "score_dist_folder = Path(output_folder, \"score_distribution\")\n",
    "score_dist_folder.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "score_dist_charts = dict()\n",
    "\n",
    "for assessment in df['AssessmentName'].unique(): \n",
    "    af = df[df['AssessmentName'].eq(assessment)].copy()\n",
    "    \n",
    "    score_dist = af[\n",
    "        af['Race/Ethnicity'].isin(['Black','White','Latino'])\n",
    "    ].groupby(['TOTAL_SCORE','Race/Ethnicity'])['Unique_client_ID'].count().unstack().copy()\n",
    "    \n",
    "    score_dist_charts[assessment] = score_dist.div(score_dist.sum()).mul(100)\n",
    "    \n",
    "    fn = Path(score_dist_folder, f'{assessment}.csv')\n",
    "    score_dist_charts[assessment].to_csv(fn)\n",
    "    \n",
    "    # Plot for context\n",
    "    score_dist_charts[assessment].plot(title=assessment, ylabel='Percent')\n",
    "    high_cutoff = af[af['Acuity'].str.contains('High')]['TOTAL_SCORE'].min()\n",
    "    plt.xticks(np.arange(0, int(af['CALC_TOTAL_SCORE'].max()) + 1, step=1)) \n",
    "    plt.axvline(high_cutoff, color='red', linestyle = '--') # theoretical \n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87f9648c",
   "metadata": {},
   "source": [
    "## Chart: Rate of people matched, but in yet in, permanent housing by race and score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cec0e28b",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in df['Intervention'].unique():\n",
    "    df[i] = df['Intervention'].eq(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cca5b25a",
   "metadata": {
    "scrolled": false
   },
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Matched to Permanent Housing, Not Yet Housed</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>Total</th>\n",
       "      <th>pct</th>\n",
       "      <th>pctref</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALC_TOTAL_SCORE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>107.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1028.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1045.0</td>\n",
       "      <td>0.016268</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2418.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>2466.0</td>\n",
       "      <td>0.019465</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3813.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>3912.0</td>\n",
       "      <td>0.025307</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5141.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>5279.0</td>\n",
       "      <td>0.026141</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5661.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>5861.0</td>\n",
       "      <td>0.034124</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5457.0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>5695.0</td>\n",
       "      <td>0.041791</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5337.0</td>\n",
       "      <td>268.0</td>\n",
       "      <td>5605.0</td>\n",
       "      <td>0.047814</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6461.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>6870.0</td>\n",
       "      <td>0.059534</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6250.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>6692.0</td>\n",
       "      <td>0.066049</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5764.0</td>\n",
       "      <td>458.0</td>\n",
       "      <td>6222.0</td>\n",
       "      <td>0.073610</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5306.0</td>\n",
       "      <td>497.0</td>\n",
       "      <td>5803.0</td>\n",
       "      <td>0.085645</td>\n",
       "      <td>8.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4710.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>5310.0</td>\n",
       "      <td>0.112994</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3940.0</td>\n",
       "      <td>597.0</td>\n",
       "      <td>4537.0</td>\n",
       "      <td>0.131585</td>\n",
       "      <td>13.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3074.0</td>\n",
       "      <td>603.0</td>\n",
       "      <td>3677.0</td>\n",
       "      <td>0.163992</td>\n",
       "      <td>16.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2377.0</td>\n",
       "      <td>639.0</td>\n",
       "      <td>3016.0</td>\n",
       "      <td>0.211870</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1513.0</td>\n",
       "      <td>509.0</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>0.251731</td>\n",
       "      <td>25.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>177.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>0.329545</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Matched to Permanent Housing, Not Yet Housed   False   True   Total       pct  \\\n",
       "CALC_TOTAL_SCORE                                                                \n",
       "0                                              107.0    NaN   107.0       NaN   \n",
       "1                                             1028.0   17.0  1045.0  0.016268   \n",
       "2                                             2418.0   48.0  2466.0  0.019465   \n",
       "3                                             3813.0   99.0  3912.0  0.025307   \n",
       "4                                             5141.0  138.0  5279.0  0.026141   \n",
       "5                                             5661.0  200.0  5861.0  0.034124   \n",
       "6                                             5457.0  238.0  5695.0  0.041791   \n",
       "7                                             5337.0  268.0  5605.0  0.047814   \n",
       "8                                             6461.0  409.0  6870.0  0.059534   \n",
       "9                                             6250.0  442.0  6692.0  0.066049   \n",
       "10                                            5764.0  458.0  6222.0  0.073610   \n",
       "11                                            5306.0  497.0  5803.0  0.085645   \n",
       "12                                            4710.0  600.0  5310.0  0.112994   \n",
       "13                                            3940.0  597.0  4537.0  0.131585   \n",
       "14                                            3074.0  603.0  3677.0  0.163992   \n",
       "15                                            2377.0  639.0  3016.0  0.211870   \n",
       "16                                            1513.0  509.0  2022.0  0.251731   \n",
       "17                                             177.0   87.0   264.0  0.329545   \n",
       "\n",
       "Matched to Permanent Housing, Not Yet Housed  pctref  \n",
       "CALC_TOTAL_SCORE                                      \n",
       "0                                                NaN  \n",
       "1                                                1.6  \n",
       "2                                                1.9  \n",
       "3                                                2.5  \n",
       "4                                                2.6  \n",
       "5                                                3.4  \n",
       "6                                                4.2  \n",
       "7                                                4.8  \n",
       "8                                                6.0  \n",
       "9                                                6.6  \n",
       "10                                               7.4  \n",
       "11                                               8.6  \n",
       "12                                              11.3  \n",
       "13                                              13.2  \n",
       "14                                              16.4  \n",
       "15                                              21.2  \n",
       "16                                              25.2  \n",
       "17                                              33.0  "
      ]
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       "      <th>Matched to Permanent Housing, Not Yet Housed</th>\n",
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       "      <th>4</th>\n",
       "      <td>780</td>\n",
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       "      <td>0.047619</td>\n",
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       "      <th>5</th>\n",
       "      <td>854</td>\n",
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       "      <td>909</td>\n",
       "      <td>0.060506</td>\n",
       "      <td>6.1</td>\n",
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       "      <th>6</th>\n",
       "      <td>837</td>\n",
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       "      <th>8</th>\n",
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       "      <th>9</th>\n",
       "      <td>643</td>\n",
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       "      <td>722</td>\n",
       "      <td>0.109418</td>\n",
       "      <td>10.9</td>\n",
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       "      <th>10</th>\n",
       "      <td>486</td>\n",
       "      <td>86</td>\n",
       "      <td>572</td>\n",
       "      <td>0.150350</td>\n",
       "      <td>15.0</td>\n",
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       "      <th>11</th>\n",
       "      <td>342</td>\n",
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       "      <td>408</td>\n",
       "      <td>0.161765</td>\n",
       "      <td>16.2</td>\n",
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       "      <th>12</th>\n",
       "      <td>219</td>\n",
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       "      <td>0.206522</td>\n",
       "      <td>20.7</td>\n",
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       "      <td>106</td>\n",
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       "      <td>56</td>\n",
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       "      <th>16</th>\n",
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      "text/plain": [
       "Matched to Permanent Housing, Not Yet Housed  False  True  Total       pct  \\\n",
       "CALC_TOTAL_SCORE                                                             \n",
       "0                                                12     1     13  0.076923   \n",
       "1                                               103     5    108  0.046296   \n",
       "2                                               305    12    317  0.037855   \n",
       "3                                               553    23    576  0.039931   \n",
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       "6                                               837    50    887  0.056370   \n",
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       "8                                               818    91    909  0.100110   \n",
       "9                                               643    79    722  0.109418   \n",
       "10                                              486    86    572  0.150350   \n",
       "11                                              342    66    408  0.161765   \n",
       "12                                              219    57    276  0.206522   \n",
       "13                                              176    32    208  0.153846   \n",
       "14                                              106    34    140  0.242857   \n",
       "15                                               56    13     69  0.188406   \n",
       "16                                               31    11     42  0.261905   \n",
       "\n",
       "Matched to Permanent Housing, Not Yet Housed  pctref  \n",
       "CALC_TOTAL_SCORE                                      \n",
       "0                                                7.7  \n",
       "1                                                4.6  \n",
       "2                                                3.8  \n",
       "3                                                4.0  \n",
       "4                                                4.8  \n",
       "5                                                6.1  \n",
       "6                                                5.6  \n",
       "7                                                8.7  \n",
       "8                                               10.0  \n",
       "9                                               10.9  \n",
       "10                                              15.0  \n",
       "11                                              16.2  \n",
       "12                                              20.7  \n",
       "13                                              15.4  \n",
       "14                                              24.3  \n",
       "15                                              18.8  \n",
       "16                                              26.2  "
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YsKHq3Lmz2XFXV1c1fPjwXHUPGzZMabVasy8sI71eb9a+rl27mo4ppdT48eOVTqdTcXFxSimlEhMTlZeXl3r88cfNyrl8+bLy9PQ0O960aVMVEBBguq1SNwOCgoJSpZRq0KBBnk/K888/rwC1ZcsW07HExEQVGhqqQkJCVHZ29m3LDQ4OVvfee6/pw/fAgQNq8ODBClDPPPOMUkqp0aNHq4CAAHX16lWz2w4ePFh5enqanhPjC71GjRq5nifji++9994zHYuNjVXOzs5Ko9GoH374wXT8+PHjClBvv/226VhaWlqu+3L27Fnl6OiopkyZYjpmbEO9evVUenq66fgnn3yiANMbXq/Xq7CwMNW9e3ez5zglJUWFhoaqbt26mY4ZP9xGjRplVv8DDzygfHx8zI7l97rLizEoLejHGJRmZGQoPz8/1bBhQ5WammoqZ+XKlQpQb731lulYYYPS5557Tnl4eKisrKx825lfUAqYfRmmp6crf39/NWDAANOxjz/+WAFqxYoVpmOpqamqbt26FglKlVIqPDxc+fv7m15zeQWlTZs2VX5+furatWumYwcOHFBarVYNGzbMdMwYYN4alN1OZmamatq0qfL29laA+uKLL5RSRft8uPWDvSDGx/92PzmD0sJ+ThQ2KN23b58C1NKlS/NtY0REhNLpdOrdd981O37o0CFlZ2dnOm58XTdt2tTsPfvll18qoNhBabNmzZSnp2eB5xkZP6Py+unevbvpvMOHDytnZ2cFqKZNm6rnnntOrVixQiUnJxeqnrxen0V5bxvbOWHChELfN6WUevrpp5W9vb1yc3NTQ4YMMR0v7HujsEGpUvl/Dhbls7SkcgalmzdvVoCaPn266fpb44stW7YoQC1evNisHGPAmfP4oUOHlIODg3rsscdUbGysCgwMVHfffbfKzMw0nbNnzx5Tp0thrF27Vul0OqXT6VTr1q3VK6+8otasWWMKhnPK7/Et6nd1YGCgKbBWSqmffvpJAeqTTz4pVJuVyv3ZtWLFCgWoqVOnmp03cOBApdFo1KlTp5RSSu3fv18B6rHHHjM776WXXlKA+uuvv0zHihOUFnvC4KBBg0hNTWXlypUkJiaycuXKfIfuAbM5IbGxscTHx9O+fXv+/fffAuvS6/WsWLGC3r17c/fdd+e6/tYhszFjxpgda9++PdnZ2Zw7dw6AdevWERcXx5AhQ7h69arpR6fT0apVKzZu3AhAVFQU+/fvZ/jw4Xh6eprK69atG/Xr1y+w3bfz559/0rJlS7PhKTc3N8aMGUNERARHjx4tsIy1a9fi6+uLr68vTZo0YenSpTz66KN8+OGHKKVYtmwZvXv3Rilldj+7d+9OfHx8rsd++PDh+c7deeyxx0yXvby8qFOnDq6urgwaNMh0vE6dOnh5eXHmzBnTMUdHR9O81OzsbK5du4abmxt16tTJ87kfOXKk2cKc9u3bA5jK3L9/PydPnuThhx/m2rVrpvuUnJxMly5d+Pvvv3OtSL11fm379u25du0aCQkJ+T+4hTBmzBjWrVuX6+fRRx81O++ff/4hJiaGsWPHms3x7NWrF3Xr1i3WlA0vLy+Sk5PNptAUlpubm9k8IgcHB1q2bGn2vK1evZrAwED69OljOubk5MTjjz9e5PryM2nSJC5fvszcuXPzvN74/hsxYgTe3t6m440bN6Zbt278+eefJarfzs6OL7/8kuvXr3PPPfeY7lthPx+KKyQkJM/XzaJFi3Kda4nPiZyMn2Nr1qzJd9h1+fLl6PV6Bg0aZHb//f39CQsLM91/4+v6ySefNHvPjhgxwuzz0mjTpk15Zjy4VUJCAu7u7kW6X05OTnk+ph988IHpnAYNGrB//36GDh1KREQEn3zyCf369aNKlSp89dVXRarPqDjv7aeeeqpIdbz77rv4+Pig1WqZMWMGYP33Rn6s9Vmanw4dOhAeHn7bqT5Lly7F09OTbt26mb1emzdvjpubm9n7tWHDhkyePJmvv/6a7t27c/XqVb777juztTFF1a1bN3bs2EGfPn04cOAA06ZNo3v37gQGBvLbb78VePvifFcPGzbM7D0ycOBAAgICSvS8//nnn+h0Op599lmz4y+++CJKKVatWmU6D8i1HsC4IK0kUxChmAudAHx9fenatStLliwhJSWF7OxsBg4cmO/5K1euZOrUqezfv99s3kFh5mBduXKFhISEfFOp3Kp69epmf1eqVAnANL/k5MmTAHTu3DnP23t4eACYgtiwsLBc5+QXVBXWuXPnaNWqVa7jxpVv586dK/D+tmrViqlTp6LRaHBxcaFevXqmeS0xMTHExcXx5Zdf8uWXX+Z5+1snYYeGhuZ5npOTE76+vmbHPD09CQoKyvX8eXp6ms3j0ev1fPLJJ8yePZuzZ8+SnZ1tus7HxydXXYV97ozzu/ISHx9vul1BZRqf6+IICwvLcxXurenSjK+jOnXq5Dq3bt26eaZXK8jYsWP56aef6NmzJ4GBgdx7770MGjSIHj16FHjbvJ63SpUqcfDgQbM216xZM9d5tWrVKnJb85PzCyevhXm3e9zq1avHmjVr8l0oUljG+WPNmzc33dfCfj4Ul6ura56vm7zmwFricyKn0NBQXnjhBaZPn87ixYtp3749ffr0YejQoaZA8uTJkyil8vzcA7C3tzfVDbk/H+3t7alRo0ah23QrDw8Ps3+QCkOn0xVqRXzt2rVZuHAh2dnZHD16lJUrVzJt2jTGjBlDaGhokVfVF/W9bWdnR1BQUJHq8PDwoE6dOly9etW0KK803ht5Kc5naVJSEklJSaa/dTpdru+T25k0aRIdO3Zk7ty5jB8/Ptf1J0+eJD4+Pt95wbd+z7388sv88MMP7N69m/fee6/EHUxg+BxZvnw5GRkZHDhwgF9++YUZM2YwcOBA9u/ff9s6rly5UuTv6lvfcxqNhlq1at12Hn1Bzp07R9WqVXP9Q5jzs8b4W6vV5vou8Pf3x8vLy3RecRX/3wPg4Ycf5vHHH+fy5cv07Nkz31WJW7ZsoU+fPnTo0IHZs2cTEBCAvb098+bNy7UwxhJypgHJyfhfurEnbeHChfj7++c6ryT/NZWmypUr5/sharyPQ4cOzTeAa9y4sdnf+fWS5vd4FvQ4gyEFxptvvsmoUaN455138Pb2RqvV8vzzz+eZY6+wz91HH32Ub7oONze3IrezrNBoNHm2K2cwD4YJ9Pv372fNmjWsWrWKVatWMW/ePIYNG8Z333132zrK0uPx9ttv06lTJ7744guLrmouifL4+ZDfP/e3vm4APv74Y0aMGMGvv/7K2rVrefbZZ3n//ffZuXMnQUFB6PV6NBoNq1atyvO1cuv7y9Lq1q3Lvn37uHDhAtWqVbNKHTqdjkaNGtGoUSNat25NeHg4ixcvtnqqp5wjR6WlKK+NghTns+N///ufWbqt4ODgIgVPHTp0oFOnTvn+86rX6/Hz82Px4sV53v7WAPjMmTOmfzwPHTpU6HYUhoODAy1atKBFixbUrl2bkSNHsnTp0tvmwS3Od3VZYI1FnVDCoPSBBx7giSeeYOfOnfz444/5nrds2TKcnJxYs2aNWQ7TefPm5To3rzvq6+uLh4cHhw8fLklzTWrWrAkYvthv9yFkXKVpfAHndOLEiULVld8TFxwcnGcZxtWpea0QLQpfX1/c3d3Jzs62+gft7fz888+Eh4fzzTffmB2Pi4ujcuXKRS7P+Nx5eHhY9H5Z6w0GN5/LEydO5Op9O3HihNlzXalSpTx7ifL679PBwYHevXvTu3dv9Ho9Y8eO5YsvvuDNN98scY9mcHAwR48eRSll9tjklQmhJDp27EinTp348MMPeeutt3K1AfJ+rx0/fpzKlSubeoIs+fwV9vPB0vXmpbCfE8Yeq1s3D8iv18IYkL3xxhts376dtm3bMnfuXKZOnUrNmjVRShEaGkrt2rVv2zYwfD7mfF1nZmZy9uzZYqcs6t27N99//z2LFi1i4sSJxSqjKIxTwqKioop826K8ty2pKO+NSpUq5bmpRF6vDWu8nocNG2Y2/aQ46Z0mTZpk+uf1VjVr1mT9+vW0bdu2wLL1ej0jRozAw8OD559/3pQ3tH///qZzLPUY5PW6yi++Kep39a0xiVKKU6dOlSh4DQ4OZv369SQmJpr1lt76WRMcHIxer+fkyZNmOU2jo6OJi4sr8Wu+RP+yubm5MWfOHCZNmkTv3r3zPU+n06HRaMz+M4uIiMhzZwNXV9dcbyCtVku/fv34/fff89ztpag9PN27d8fDw4P33nuPzMzMXNcb00cFBATQtGlTvvvuO7N0KevWrSv0XK687g8Ycr3u3r2bHTt2mI4lJyfz5ZdfEhISUuIhBZ1Ox4ABA1i2bFmewXxeKbKsQafT5Xp+li5dmitdSmE1b96cmjVr8r///c9sSMiouPcrv+fJEu6++278/PyYO3eu2dSVVatWcezYMXr16mU6VrNmTY4fP252Pw4cOJAr3dW1a9fM/tZqtaYPJEuk5ejevTuRkZFmc6LS0tKKPffudoxzS28dusr5/sv53Bw+fJi1a9dy3333mY4Zv4At8RwW9vPB0vXmpbCfE8ZA+u+//zadl52dnesxTUhIICsry+xYo0aN0Gq1ptdN//790el0TJ48Odd7Vylleu3dfffd+Pr6MnfuXLO0fPPnz8/z8ShsSqiBAwfSqFEj3n33XbP7bZSYmMjrr79eYDm32rJlS57Pp3GOXF5D4QUpynvbkory3qhZsybx8fFm03OioqLyTG1ojc/BGjVq0LVrV9NP27Zti1xGzn9e09LSzK4bNGgQ2dnZvPPOO7lul5WVZXZ/pk+fzvbt2/nyyy955513aNOmDU899ZRZbvWivqc3btyYZwyS1+sqr8e3ON/VCxYsIDEx0fT3zz//TFRUlCmdXXHcd999ZGdn8/nnn5sdnzFjBhqNxlS28bU1c+ZMs/OmT58OUOLXfInHoW43t8+oV69eTJ8+nR49evDwww8TExPDrFmzqFWrltkbBQxBx/r165k+fTpVq1YlNDSUVq1a8d5777F27Vo6duzImDFjqFevHlFRUSxdupStW7cWaejPw8ODOXPm8Oijj3LXXXcxePBgfH19OX/+PH/88Qdt27Y1PTHvv/8+vXr1ol27dowaNYrr16/z2Wef0aBBgzyDols1b96cOXPmMHXqVGrVqoWfnx+dO3dmwoQJfP/99/Ts2ZNnn30Wb29vvvvuO86ePcuyZcssMsTzwQcfsHHjRlq1asXjjz9O/fr1uX79Ov/++y/r16/n+vXrJa6jIPfffz9Tpkxh5MiRtGnThkOHDrF48eJizznTarV8/fXX9OzZkwYNGjBy5EgCAwOJjIxk48aNeHh48Pvvvxe53Pxed5Zgb2/Phx9+yMiRI+nYsSNDhgwhOjqaTz75hJCQELN5UqNGjWL69Ol0796d0aNHExMTw9y5c2nQoIHZYoLHHnuM69ev07lzZ4KCgjh37hyfffYZTZs2tciOHE888QSff/45Q4YM4bnnniMgIIDFixebFnPk/I9/06ZNhIeH8/bbbxdrZ6OOHTvSsWNHNm/enOu6jz76iJ49e9K6dWtGjx5Namoqn332GZ6enmZ1NW/eHIDXX3+dwYMHY29vT+/evYs1p64onw/Gep999lm6d++OTqdj8ODBRa4zP4X9nGjQoAH33HMPEydO5Pr163h7e/PDDz/kCkD/+usvnn76aR588EFq165NVlYWCxcuNH0xgiGImTp1KhMnTiQiIoJ+/frh7u7O2bNn+eWXXxgzZgwvvfQS9vb2TJ06lSeeeILOnTvz0EMPcfbsWebNm5fn+3vYsGFs3ry5wE4Ee3t7li9fTteuXenQoQODBg2ibdu22Nvbc+TIEZYsWUKlSpXMcpVmZWXluVAMDCN6rq6ufPjhh+zdu5f+/fub/oH7999/WbBgAd7e3jz//POFfl5ytrWw721LK+x7Y/Dgwbz66qs88MADPPvss6SkpDBnzhxq166da12ENT8HS+rtt98mPDw81/GOHTvyxBNP8P7777N//37uvfde7O3tOXnyJEuXLuWTTz5h4MCBHDt2jDfffJMRI0aYOtHmz59P06ZNTXP0wfD69/LyYu7cubi7u+Pq6kqrVq3yXXPxzDPPkJKSwgMPPEDdunXJyMhg+/bt/Pjjj4SEhDBy5EjTufk9vkX9rvb29qZdu3aMHDmS6OhoZs6cSa1atUq0ELV3796Eh4fz+uuvExERQZMmTVi7di2//vorzz//vOkf3yZNmjB8+HC+/PJL4uLi6NixI7t37+a7776jX79+eT5HRVKUpfp5pcTIS14pob755hsVFhamHB0dVd26ddW8efPyTElx/Phx1aFDB1PqjpzpE86dO6eGDRumfH19laOjo6pRo4YaN26cKR1Jfu3LKzWO8Xj37t2Vp6encnJyUjVr1lQjRoxQ//zzj9l5y5YtU/Xq1VOOjo6qfv36avny5fmm2bjV5cuXVa9evZS7u3uuNCmnT59WAwcOVF5eXsrJyUm1bNnSLG/r7eT1GOclOjpajRs3TlWrVk3Z29srf39/1aVLF/Xll1+aPQ7kkybGmAPwVh07dlQNGjQosF1paWnqxRdfVAEBAcrZ2Vm1bdtW7dixI1eqiPzaYEy/dGt6jn379qn+/fsrHx8f5ejoqIKDg9WgQYPMchDmTC2SU17pc273uruVsU05U/fklF+9P/74o2rWrJlydHRU3t7e6pFHHlEXL17MdftFixapGjVqKAcHB9W0aVO1Zs2aXK+3n3/+Wd17773Kz89POTg4qOrVq6snnnjClGdTqfxTQuX1vOX1ej5z5ozq1auXcnZ2Vr6+vurFF19Uy5YtU4DauXOn6bzff/9dAWru3Ln5PmZG5EgJlZOxrXm9f9evX6/atm2rnJ2dlYeHh+rdu7c6evRorjLeeecdFRgYqLRabZHSQ92uTQV9PmRlZalnnnlG+fr6Ko1GU2DKo/wef6Xyf10V9nPi9OnTqmvXrsrR0VFVqVJFvfbaa2rdunVmr4EzZ86oUaNGqZo1ayonJyfl7e2twsPD1fr163OVt2zZMtWuXTvl6uqqXF1dVd26ddW4cePUiRMnzM6bPXu2Cg0NVY6Ojuruu+9Wf//9d56pYAqbEsooNjZWvfXWW6pRo0bKxcVFOTk5qYYNG6qJEyeavc5vlxIq5+tg27Ztaty4caphw4bK09NT2dvbq+rVq6sRI0bcNg+l0e2+/wrz3s7vs7Qw8nvdFPa9sXbtWtWwYUPl4OCg6tSpoxYtWlSk79+ifJaWVH51KXXzNZTXd9+XX36pmjdvrpydnZW7u7tq1KiReuWVV9SlS5dUVlaWatGihQoKCjJL76jUzbSDP/74o+nYr7/+qurXr6/s7OwKTA+1atUqNWrUKFW3bl3l5uamHBwcVK1atdQzzzyjoqOjzc693fdMUb6rv//+ezVx4kTl5+ennJ2dVa9evdS5c+cKemjN5JXOLjExUY0fP15VrVpV2dvbq7CwMPXRRx+ZpV9UypBOb/LkySo0NFTZ29uratWqqYkTJ6q0tDSz84qTEkqjVBlc7SGEKJNmzpzJ+PHjuXjxIoGBgQC88sorfP/995w6dcpszrgQQgjLMY5KLV269LbZjsoz2dhcCJGnW/MCpqWl8cUXXxAWFmYKSMEwp+rNN9+UgFQIIUSJlL3cJkKIMqF///5Ur16dpk2bEh8fz6JFizh+/Hiu1Ct79uyxUQuFEELcSSQoFULkqXv37nz99dcsXryY7Oxs6tevzw8//MBDDz1k66YJIYS4A8mcUiGEEEIIYXMyp1QIIYQQQticBKVCCCGEEMLmZE5pKdHr9Vy6dAl3d3erb00ohBBCCMtQSpGYmEjVqlUtsrGNyJ8EpaXk0qVLVKtWzdbNEEIIIUQxXLhwgaCgIFs3444mQWkpcXd3Bwwvag8PDxu3RgghhBCFkZCQQLVq1Uzf48J6JCgtJcYhew8PDwlKhRBCiHJGpt5Zn0yOEEIIIYQQNidBqRBCCCGEsDkJSoUQQgghhM3JnNIyRK/Xk5GRYetmiEJycHCQ9CBCCCGEhUhQWkZkZGRw9uxZ9Hq9rZsiCkmr1RIaGoqDg4OtmyKEEEKUexKUlgFKKaKiotDpdFSrVk1638oB42YIUVFRVK9eXVZlCiGEECUkQWkZkJWVRUpKClWrVsXFxcXWzRGF5Ovry6VLl8jKysLe3t7WzRFCCCHKNemSKwOys7MBZBi4nDE+X8bnTwghhBDFJ0FpGSJDwOWLPF9CCCGE5UhQKoQQQgghbE6CUlGqOnXqxPPPP1+iMjZt2oRGoyEuLq7U6hRCCCGEdUlQWsaNGDECjUaDRqPB3t6e0NBQXnnlFdLS0kq9LZMnT2bo0KEAhISEmNqV8+eDDz4Aih44FkWbNm2IiorC09OzUOcvX76cd955x/R3SEgIM2fOtHi7hBBCCFF8svq+HOjRowfz5s0jMzOTvXv3Mnz4cDQaDR9++GGptuPXX39lwoQJpr+nTJnC448/bnaOu7u71dvh4OCAv79/oc/39va2YmuEEEKUioQoyM4A9wCwk4XBdyLpKS0HHB0d8ff3p1q1avTr14+uXbuybt06AK5du8aQIUMIDAzExcWFRo0a8f3335vdXq/XM23aNGrVqoWjoyPVq1fn3XffNV1/4cIFBg0ahJeXF97e3vTt25eIiAizMi5cuMCRI0fo0aOH6Zi7uzv+/v5mP66urkRERBAeHg5ApUqV0Gg0jBgxwqw9r7zyCt7e3vj7+zNp0iSzujQaDV9//TUPPPAALi4uhIWF8dtvv5muz6sXdtu2bXTq1AkXFxcqVapE9+7diY2NBcyH7zt16sS5c+cYP368qXc3OTkZDw8Pfv75Z7N2rFixAldXVxITEwt+koQQQljX9k/hk8bw1zsFnyvKJQlKy5nDhw+zfft2UzqitLQ0mjdvzh9//MHhw4cZM2YMjz76KLt37zbdZuLEiXzwwQe8+eabHD16lCVLllClShUAMjMz6d69O+7u7mzZsoVt27bh5uZGjx49zLY8/e233+jUqRMeHh4FtrFatWosW7YMgBMnThAVFcUnn3xiuv67777D1dWVXbt2MW3aNKZMmWIKso0mT57MoEGDOHjwIPfddx+PPPII169fz7O+/fv306VLF+rXr8+OHTvYunUrvXv3zjNV0/LlywkKCmLKlClERUURFRWFq6srgwcPZt68eWbnzps3j4EDB5ZK768QQogCJFwy/Paoatt2COtRolTEx8crQMXHx+e6LjU1VR09elSlpqbmum748OFKp9MpV1dX5ejoqACl1WrVzz//nG9dvXr1Ui+++KJSSqmEhATl6OiovvrqqzzPXbhwoapTp47S6/WmY+np6crZ2VmtWbPGdKxbt27q888/N/0dHBysHBwclKurq9nP33//rZRSauPGjQpQsbGxZvV17NhRtWvXzuxYixYt1Kuvvmr6G1BvvPGG6e+kpCQFqFWrVuVZ9pAhQ1Tbtm3zfTw6duyonnvuObO2z5gxw+ycXbt2KZ1Opy5duqSUUio6OlrZ2dmpTZs25Vvu7Z43IYQQFvZ1N6Xe9lDq8C+lWu3tvr+FZcmc0nIgPDycOXPmkJyczIwZM7Czs2PAgAGAIXH7e++9x08//URkZCQZGRmkp6ebdoY6duwY6enpdOnSJc+yDxw4wKlTp3L1BqalpXH69GkAEhIS2Lx5M998843ZOS+//LLZsDxAYGBggfencePGZn8HBAQQExOT7zmurq54eHjkOsdo//79PPjggwXWezstW7akQYMGfPfdd0yYMIFFixYRHBxMhw4dSlSuEEIIC0mIMvyWntI7lgSl5YCrqyu1atUC4Ntvv6VJkyZ88803jB49mo8++ohPPvmEmTNn0qhRI1xdXXn++edNQ+/Ozs63LTspKYnmzZuzePHiXNf5+voCsGrVKurXr0+1atXMrq9cubKpXUVx65acGo0GvV5f5HOMCrqPhfXYY48xa9YsJkyYwLx58xg5cqQkyBdCiLJAr4fEG0Gpe4Bt2yKsRuaUljNarZbXXnuNN954g9TUVLZt20bfvn0ZOnQoTZo0oUaNGvz333+m88PCwnB2dmbDhg15lnfXXXdx8uRJ/Pz8qFWrltmPMeXSr7/+St++fYvUztLcgrNx48b53r+8ODg45NmuoUOHcu7cOT799FOOHj3K8OHDLdlMIYQQxZVyDfSZgAbcC599RZQvEpSWQw8++CA6nY5Zs2YRFhbGunXr2L59O8eOHeOJJ54gOjradK6TkxOvvvoqr7zyCgsWLOD06dPs3LnTNBT/yCOPULlyZfr27cuWLVs4e/YsmzZt4tlnn+XixYtkZWWxatUq+vTpk6sdiYmJXL582ewnISEBgODgYDQaDStXruTKlSskJSVZ7fGYOHEie/bsYezYsRw8eJDjx48zZ84crl69muf5ISEh/P3330RGRpqdU6lSJfr378/LL7/MvffeS1BQkNXaLIQQoggSbyxycvUFnf3tzxXllgSl5ZCdnR1PP/0006ZN48UXX+Suu+6ie/fudOrUCX9/f/r162d2/ptvvsmLL77IW2+9Rb169XjooYdM8zNdXFz4+++/qV69Ov3796devXqMHj2atLQ0PDw82Lx5M25ubtx111252vHWW28REBBg9vPKK68AhrmlkydPZsKECVSpUoWnn37aao9H7dq1Wbt2LQcOHKBly5a0bt2aX3/9FTu7vGenTJkyhYiICGrWrGmaomA0evRoMjIyGDVqlNXaK4QQoohM80ll6P5OplFKKVs3oiJISEjA09OT+Pj4XGmV0tLSOHv2LKGhoTg5OdmohXl79tlnycrKYvbs2bZuSqlYuHAh48eP59KlS6YpCPkpy8+bEELcUf75FlaOh9o94eEfSrXq231/C8uShU7itho2bEjr1q1t3QyrS0lJISoqig8++IAnnniiwIBUCCFEKZKe0gpBhu/FbY0ZM4ZGjRrZuhlWN23aNOrWrYu/vz8TJ060dXOEEELkZJxT6i7poO5kEpQKAUyaNInMzEw2bNiAm5ubrZsjhBAiJ+kprRAkKBVCCCFE2SY5SisECUqFEEIIUbbJvvcVggSlQgghhCi7MlMhLc5wWYLSO5oEpUIIIYQou4y9pPau4Cgpme5kEpQKIYQQouwyDd0HgEZj27YIq5KgVAghhBBllyxyqjAkKBU2FRERgUajYf/+/RYrU6PRsGLFCouVJ4QQwoZkkVOFIUGpsKoRI0ag0WhMPz4+PvTo0YODBw/aumlCCCHKA+kprTAkKBVW16NHD6KiooiKimLDhg3Y2dlx//3327pZQgghygPpKa0wJCgVVufo6Ii/vz/+/v40bdqUCRMmcOHCBa5cuZLr3OzsbEaPHk1oaCjOzs7UqVOHTz75JNd53377LQ0aNMDR0ZGAgACefvrpfOt/++23CQgIkN5ZIYQoj6SntMKws3UDRPEopUjNzLZJ3c72OjTFXAGZlJTEokWLqFWrFj4+PiQnJ5tdr9frCQoKYunSpfj4+LB9+3bGjBlDQEAAgwYNAmDOnDm88MILfPDBB/Ts2ZP4+Hi2bduWqy6lFM8++ywrV65ky5Yt1KpVq1htFkIIYUOmLUalp/ROJ0FpOZWamU39t9bYpO6jU7rj4lD4l87KlStN+8knJycTEBDAypUr0Wpzd9Tb29szefJk09+hoaHs2LGDn376yRSUTp06lRdffJHnnnvOdF6LFi3MysnKymLo0KHs27ePrVu3EhgYWKT7KIQQogzQ6yHpsuGy9JTe8SQoFVYXHh7OnDlzAIiNjWX27Nn07NmT3bt353n+rFmz+Pbbbzl//jypqalkZGTQtGlTAGJiYrh06RJdunS5bZ3jx4/H0dGRnTt3UrlyZYveHyGEEKUk+Qros0CjBbcqtm6NsDIJSsspZ3sdR6d0t1ndReHq6mo2dP7111/j6enJV199xWOPPWZ27g8//MBLL73Exx9/TOvWrXF3d+ejjz5i165dhrqdnQtVZ7du3fj+++9Zs2YNjzzySJHaK4QQooxIvLHIydUPdBKy3OnkGS6nNBpNkYbQyxKNRoNWqyU1NTXXddu2baNNmzaMHTvWdOz06dOmy+7u7oSEhLBhwwbCw8PzraNPnz707t2bhx9+GJ1Ox+DBgy17J4QQQlifaT6pDN1XBOUzqhHlSnp6OpcvG+YExcbG8vnnn5OUlETv3r1znRsWFsaCBQtYs2YNoaGhLFy4kD179hAaGmo6Z9KkSTz55JP4+fnRs2dPEhMT2bZtG88884xZWQ888AALFy7k0Ucfxc7OjoEDB1r3jgohhLAsY0+puyxyqggkKBVWt3r1agICDP/luru7U7duXZYuXUqnTp2IiIgwO/eJJ55g3759PPTQQ2g0GoYMGcLYsWNZtWqV6Zzhw4eTlpbGjBkzeOmll6hcuXK+AefAgQPR6/U8+uijaLVa+vfvb7X7KYQQwsKkp7RC0SillK0bUREkJCTg6elJfHw8Hh4eZtelpaVx9uxZQkNDcXJyslELRVHJ8yaEEFa2YizsXwyd34QOL9mkCbf7/haWJcnzhRBCCFE2yW5OFYoEpUIIIYQomxIlcX5FIkGpEEIIIcom45xSWehUIUhQeosPPvgAjUbD888/bzqWlpbGuHHj8PHxwc3NjQEDBhAdHW27RgohhBB3uvQkSI83XJaFThWCBKU57Nmzhy+++ILGjRubHR8/fjy///47S5cuZfPmzVy6dElWcQshhBDWZBy6d3AHR3fbtkWUCglKb0hKSuKRRx7hq6++olKlSqbj8fHxfPPNN0yfPp3OnTvTvHlz5s2bx/bt29m5c6cNWyyEEELcwUyLnKSXtKKQoPSGcePG0atXL7p27Wp2fO/evWRmZpodr1u3LtWrV2fHjh35lpeenk5CQoLZjxBCCCEKydhT6i5BaUUhyfMx7Lf+77//smfPnlzXXb58GQcHB7y8vMyOV6lSxbRLUV7ef/99Jk+ebOmmCiGEEBWDpIOqcCp8T+mFCxd47rnnWLx4sUUToE+cOJH4+HjTz4ULFyxWthBCCHHHk57SCqfCB6V79+4lJiaGu+66Czs7O+zs7Ni8eTOffvopdnZ2VKlShYyMDOLi4sxuFx0djb+/f77lOjo64uHhYfYjLCMkJISZM2fauhlCCCGsSXpKK5wKH5R26dKFQ4cOsX//ftPP3XffzSOPPGK6bG9vz4YNG0y3OXHiBOfPn6d169Y2bHn5MGLECPr161es286fPz/XtAkwZEkYM2ZMyRomhBCibJOe0gqnws8pdXd3p2HDhmbHXF1d8fHxMR0fPXo0L7zwAt7e3nh4ePDMM8/QunVr7rnnHls0ucLz9fW1dROEEEJYmzFxvqy+rzAqfE9pYcyYMYP777+fAQMG0KFDB/z9/Vm+fLmtm1XuTZ8+nUaNGuHq6kq1atUYO3YsSUlJAGzatImRI0cSHx+PRqNBo9EwadIkIPfwvUaj4euvv+aBBx7AxcWFsLAwfvvtN7O6Nm/eTMuWLXF0dCQgIIAJEyaQlZVVWndVCCFEUeizIenGJjWym1OFUeF7SvOyadMms7+dnJyYNWsWs2bNsk2D8qIUZKbYpm57F9BoSlyMVqvl008/JTQ0lDNnzjB27FheeeUVZs+eTZs2bZg5cyZvvfUWJ06cAMDNzS3fsiZPnsy0adP46KOP+Oyzz3jkkUc4d+4c3t7eREZGct999zFixAgWLFjA8ePHefzxx3FycjIFukIIIcqQpBhQ2aDRgZufrVsjSokEpeVVZgq8Z6P/Hl+7BA6uJS4m51auISEhTJ06lSeffJLZs2fj4OCAp6cnGo3mtgvKjEaMGMGQIUMAeO+99/j000/ZvXs3PXr0YPbs2VSrVo3PP/8cjUZD3bp1uXTpEq+++ipvvfUWWq0MGAghRJmSeGORk1sV0Ops2xZRauTbWNjM+vXr6dKlC4GBgbi7u/Poo49y7do1UlKK3gOcc2tYV1dXPDw8iImJAeDYsWO0bt0aTY7e3bZt25KUlMTFixdLfkeEEEJYlswnrZCkp7S8sncx9Fjaqu4SioiI4P777+epp57i3Xffxdvbm61btzJ69GgyMjJwcSlaHfb29mZ/azQa9Hp9idsphBDCBmTlfYUkQWl5pdFYZAjdVvbu3Yter+fjjz82DZ//9NNPZuc4ODiQnZ1d4rrq1avHsmXLUEqZeku3bduGu7s7QUFBJS5fCCGEhUmO0gpJhu+F1cXHx5vlgd2/fz+VK1cmMzOTzz77jDNnzrBw4ULmzp1rdruQkBCSkpLYsGEDV69eLdawPsDYsWO5cOECzzzzDMePH+fXX3/l7bff5oUXXpD5pEIIURYZe0olKK1Q5BtZWN2mTZto1qyZ2c/ChQuZPn06H374IQ0bNmTx4sW8//77Zrdr06YNTz75JA899BC+vr5MmzatWPUHBgby559/snv3bpo0acKTTz7J6NGjeeONNyxx94QQQlhaQqTht6SDqlA0Sill60ZUBAkJCXh6ehIfH59ry9G0tDTOnj1LaGgoTk5ONmqhKCp53oQQwko+uxuunYThv0NoB5s25Xbf38KypKdUCCGEEGWLaaGT9JRWJBKUCiGEEKLsSEuADMPufpISqmKRoFQIIYQQZYexl9TRs1xnmRFFJ0GpEEIIIcoOUzoo6SWtaCQoFUIIIUTZIYnzKywJSoUQQghRdkji/ApLglIhhBBClB3SU1phSVAqhBBCiLIjwbibkwSlFY0EpUIIIYQoOxJvDN9LjtIKR4JSUeZoNBpWrFiR7/WbNm1Co9EQFxdXam0SQghRSqSntMKSoFRYzdy5c3F3dycrK8t0LCkpCXt7ezp16mR2rjHQPH36dIHltmnThqioKDw9PQGYP38+Xl5elmy6EEIIW8jOguQYw2XpKa1wJCgVVhMeHk5SUhL//POP6diWLVvw9/dn165dpKWlmY5v3LiR6tWrU7NmzQLLdXBwwN/fH41GY5V2CyGEsJGkaFB60NqBq6+tWyNKmQSlwmrq1KlDQEAAmzZtMh3btGkTffv2JTQ0lJ07d5odDw8PN/199epVHnjgAVxcXAgLC+O3334zO9c4fL9p0yZGjhxJfHw8Go0GjUbDpEmTAEhPT+ell14iMDAQV1dXWrVqZdYWIYQQZYxx5b2bP2glRKlo5Bkvp5RSpGSm2ORHKVXodoaHh7Nx40bT3xs3bqRTp0507NjRdDw1NZVdu3aZBaWTJ09m0KBBHDx4kPvuu49HHnmE69ev5yq/TZs2zJw5Ew8PD6KiooiKiuKll14C4Omnn2bHjh388MMPHDx4kAcffJAePXpw8uTJ4j7sQgghrElylFZodrZugCie1KxUWi1pZZO6dz28Cxd7l0KdGx4ezvPPP09WVhapqans27ePjh07kpmZydy5cwHYsWMH6enpZkHpiBEjGDJkCADvvfcen376Kbt376ZHjx5m5Ts4OODp6YlGo8Hf3990/Pz588ybN4/z589Ttarhw+2ll15i9erVzJs3j/fee69Ej4EQQggrSJRFThWZBKXCqjp16kRycjJ79uwhNjaW2rVr4+vrS8eOHRk5ciRpaWls2rSJGjVqUL16ddPtGjdubLrs6uqKh4cHMTExha730KFDZGdnU7t2bbPj6enp+Pj4lPyOCSGEsLyESMNvWeRUIUlQWk452zmz6+FdNqu7sGrVqkVQUBAbN24kNjaWjh07AlC1alWqVavG9u3b2bhxI507dza7nb29vdnfGo0GvV5f6HqTkpLQ6XTs3bsXnU5ndp2bm1uhyxFCCFGKJB1UhSZBaTml0WgKPYRua+Hh4WzatInY2Fhefvll0/EOHTqwatUqdu/ezVNPPVXs8h0cHMjOzjY71qxZM7Kzs4mJiaF9+/bFLlsIIUQpMm0xKj2lFZEsdBJWFx4eztatW9m/f7+ppxSgY8eOfPHFF2RkZJjNJy2qkJAQkpKS2LBhA1evXiUlJYXatWvzyCOPMGzYMJYvX87Zs2fZvXs377//Pn/88Ycl7pYQQghLMy10kp7SikiCUmF14eHhpKamUqtWLapUqWI63rFjRxITE02po4qrTZs2PPnkkzz00EP4+voybdo0AObNm8ewYcN48cUXqVOnDv369WPPnj1mc1eFEEKUEUrl6CmVoLQi0qii5PcRxZaQkICnpyfx8fF4eHiYXZeWlsbZs2cJDQ3FycnJRi0URSXPmxBCWFBqHHwYbLj8+mWwL/z6BWu63fe3sCzpKRVCCCGE7Rl7SZ28ykxAKkqXBKVCCCGEsD1JnF/hSVAqhBBCCNuT+aQVngSlQgghhLA9yVFa4UlQWobImrPyRZ4vIYSwoMQbw/eSo7TCkqC0DDDuOJSRkWHjloiiMD5ft+4YJYQQohikp7TCkx2dygA7OztcXFy4cuUK9vb2aLXyv0JZp9fruXLlCi4uLtjZydtICCFKTHpKKzz5Ni0DNBoNAQEBnD17lnPnztm6OaKQtFot1atXR6PR2LopQghR/klPaYUnQWkZ4eDgQFhYmAzhlyMODg7Sqy2EEJaQnQnJVwyXpae0wpKgtAzRarWyM5AQQoiKJ/EyoEDnAC4+tm6NsBHp5hFCCCGEbRkT57v7g4xAVVjyzAshhBDCtmSRk0CCUiGEEELYmixyEkhQKoQQQghbk55SgQSlQgghhLA16SkVSFAqhBBCCFtLvBGUuktQWpFJUCqEEEII2zKuvveQ4fuKTIJSIYQQQtiOUtJTKgAJSoUQQghhS6mxkJVmuCxBaYUmQakQQgghbMfYS+rsDfayq2FFJkGpEEIIIWzHtPJe5pNWdBKUCiGEEMJ2TDlKZei+opOgVAghhBC2IzlKxQ0SlAohhBDCdmQ3J3GDBKVCCCGEsB3pKRU3SFAqhBBCCNuRnlJxgwSlQgghhLAdWX0vbpCgVAghhBC2kZUOKVcNlyUorfAkKBVCCCGEbRgT5+scwbmSbdsibE6CUiGEEELYRs5FThqNbdsibE6CUiGEEELYhixyEjlIUCqEEEII25B0UCIHCUqFEEIIYRvGOaWyxahAglIhhBBC2ErCjeF7WXkvkKBUCCGEELYiPaUiBwlKhRBCCGEb0lMqcpCgVAghhBClTylIvGy4LD2lAglKhRBCCGELKdchO91wWYJSgQSlQgghhLAFY45Sl8pg52DbtogyQYJSIYQQQpQ+yVEqbiFBqRBCCCFKn+zmJG4hQakQQgghSp/0lIpbVPigdM6cOTRu3BgPDw88PDxo3bo1q1atMl2flpbGuHHj8PHxwc3NjQEDBhAdHW3DFgshhBB3AGNPqUegbdshyowKH5QGBQXxwQcfsHfvXv755x86d+5M3759OXLkCADjx4/n999/Z+nSpWzevJlLly7Rv39/G7daCCGEKOeMOUpl5b24QaOUUrZuRFnj7e3NRx99xMCBA/H19WXJkiUMHDgQgOPHj1OvXj127NjBPffcU+gyExIS8PT0JD4+Hg8PD2s1XQghhCgfZreBmCMwdBnU6mrr1uRLvr9LT4XvKc0pOzubH374geTkZFq3bs3evXvJzMyka9ebb5a6detSvXp1duzYcduy0tPTSUhIMPsRQgghxA2y0EncQoJS4NChQ7i5ueHo6MiTTz7JL7/8Qv369bl8+TIODg54eXmZnV+lShUuX7582zLff/99PD09TT/VqlWz4j0QQgghypHMVEiNNVyWhU7iBglKgTp16rB//3527drFU089xfDhwzl69GiJypw4cSLx8fGmnwsXLliotUIIIUQ5l3hj5b2dMzh52bQpouyws3UDygIHBwdq1aoFQPPmzdmzZw+ffPIJDz30EBkZGcTFxZn1lkZHR+Pv73/bMh0dHXF0dLRms4UQQojyKWc6KI3Gtm0RZYb0lOZBr9eTnp5O8+bNsbe3Z8OGDabrTpw4wfnz52ndurUNWyiEEEKUY8aeUplPKnKo8D2lEydOpGfPnlSvXp3ExESWLFnCpk2bWLNmDZ6enowePZoXXngBb29vPDw8eOaZZ2jdunWRVt4LIYQQIgdjOiiZTypyqPBBaUxMDMOGDSMqKgpPT08aN27MmjVr6NatGwAzZsxAq9UyYMAA0tPT6d69O7Nnz7Zxq4UQQohyzNRTKkGpuEnylJYSyXMmhBBC3PDTcDi6Anp8APc8ZevW3JZ8f5cemVMqhBBCiNIlPaUiDxKUCiGEEKJ0mVbfy0IncZMEpUIIIYQoPXq99JSKPElQKoQQQojSk3IN9JmABtxvn/NbVCwSlAohhBCi9Bj3vHf1BZ29bdsiyhQJSoUQQghRenLu5iREDhKUCiGEEKL0JEQafnsE2rYdosyRoFQIIYQQpUcWOYl8SFAqhBBCiNIjw/ciHxKUCiGEEKL0GBc6uUuOUmFOglIhhBBClB7pKRX5kKBUCCGEEKVHekpFPiQoFUIIIUTpyEiBtHjDZekpFbeQoFQIIYQQpcO48t7eFRw9bNsWUeZIUCqEEEKI0pFwY+jeIwA0Gtu2RZQ5EpQKIYQQonRIjlJxGxKUCiGEEKJ0mHpKZZGTyE2CUiGEEEKUDukpFbchQakQQgghSof0lIrbkKBUCCGEEKVDekrFbUhQKoQQQojSYdrNSXpKRW7lOiitUaMG165dy3U8Li6OGjVq2KBFQgghhMiTXg9Jlw2XpadU5KFcB6URERFkZ2fnOp6enk5kZKQNWiSEEEKIPCVfAX0WaLTgVsXWrRFlkJ2tG1Acv/32m+nymjVr8PT0NP2dnZ3Nhg0bCAkJsUHLhBBCCJGnhBudRW5VQFcuww9hZeXyVdGvXz8ANBoNw4cPN7vO3t6ekJAQPv74Yxu0TAghhBB5kkVOogDlMijV6/UAhIaGsmfPHipXrmzjFgkhhBDitiQdlChAuQxKjc6ePWvrJgghhBCiMKSnVBSgXAelABs2bGDDhg3ExMSYelCNvv32Wxu1SgghhBBmTOmgJCgVeSvXQenkyZOZMmUKd999NwEBAWg0Gls3SQghhBB5SbwxfO8uw/cib+U6KJ07dy7z58/n0UcftXVThBBCCHE70lMqClCu85RmZGTQpk0bWzdDCCGEEAUxzSmVnlKRt3IdlD722GMsWbLE1s0QQgghxO2kJ0F6guGy9JSKfJTr4fu0tDS+/PJL1q9fT+PGjbG3tze7fvr06TZqmRBCCFFKdn9l6IXs/CaUwtqKbL1i0c5zHLwYz5S+DXB1LEQoYewldXAHR3frNlCUW+U6KD148CBNmzYF4PDhw2bXyaInIYQQd7yLe+HPlwyX69wHQXdbtbrDkfFMXH6IQ5HxALSu6cPA5kEF39CUo1R6SUX+ynVQunHjRls3QQghhLANpWDNazf/jthqtaA0JSOLGev+45utZ9Grm8cPXYwrXFAqOUpFIZTrOaVGp06dYs2aNaSmpgKglCrgFkIIIUQ5d/RXuLDz5t/ntlulmo3HY+g2/W++2mIISHs1DuCNXvUATD2mBZLdnEQhlOug9Nq1a3Tp0oXatWtz3333ERVl+E9s9OjRvPjiizZunRBCCGElWemw/m3D5Tr3GX6f3wH6bItVEZOYxtNL/mXk/D1ExqUS6OXMtyPuZtbDd9G5rh8ARy4lkJWtL6AkpKdUFEq5DkrHjx+Pvb0958+fx8XFxXT8oYceYvXq1TZsmRBCCGFFu7+E2Ahw84cHvgBHT8Pq9suHSly0Xq/4fvd5un68mZUHo9Bq4LF2oawd34HOdasAEOLjirujHelZek7GJBVcqPSUikIo13NK165dy5o1awgKMp/PEhYWxrlz52zUKiGEEMKKkq/B5o8Ml7u8CU4eUP0eOLkGzm2Dqk2LXfSpmEQmLj/EnohYABoGevD+A41pFORpdp5Wq6FBoAc7z1znUGQ89QI8bl+w9JSKQijXPaXJyclmPaRG169fx9HR0QYtEkIIIaxs84eQHg9VGkGTIYZjIW0NvyO2FavItMxspq/7j56fbGFPRCzO9jre6FWPFWPb5gpIjRoHeQFw6GIh5pVKT6kohHIdlLZv354FCxaY/tZoNOj1eqZNm0Z4eLgNWyaEEEJYwdWT8M83hsvdp4JWZ7gc3M7w+/x20BdijmcOO89c475Pt/DphpNkZis61/Vj3QsdeKx9Dex0+YcJjQINwerBghY7ZWdBUrThsgSl4jbK9fD9tGnT6NKlC//88w8ZGRm88sorHDlyhOvXr7NtW/H+WxRCCCHKrHVvgT4LaveAGp1uHg9oAg5ukBoLMUfBv2GBRcWlZPDen8f46Z+LAFR2c2RSn/r0ahRQqFzfxqD0WFQCmdl67PMLYJNjQOlBowNX3wLLFRVXue4pbdiwIf/99x/t2rWjb9++JCcn079/f/bt20fNmjVt3TwhhBDCcs7+DSf+NAR33d4xv05nB9VaGS6fu32njFKKX/dH0nX6ZlNA+nCr6mx4sSP3N65a6M1ngn1ccHeyIyNLz3/RifmfmGCcT+p/s2dXiDyU655SAE9PT15//XVbN0MIIYSwHn32zUT5d48C39q5zwlpC6c3GJLot3oiz2IuXE/h9RWH+fu/KwDU8nPj/f6NaBHiXeQmaTQaGgd5su3UNQ5djKdB1bznnpJ4Yz6pLHISBSjXPaXz5s1j6dKluY4vXbqU7777zgYtEkIIUSZcPQlLHoK9d8h3wYEfDOmeHD2h0wSzq746+BVvbXuLzGr3GA6c227Y7SmHzGw9X2w+TbcZm/n7vys46LS80K02fzzbrlgBqVHDG0P4t02ib+wplS1GRQHKdVD6/vvvU7ly5VzH/fz8eO+992zQIiGEEDZ3YQ98cy/8txp+fw7ObrF1i0omIxn+ujFc3+ElcL35vZeYkchn+z7jl1O/sEmfAHbOkHIVrpwwnXPgQhx9Pt/G+6uOk5ap554a3qx+vj3PdgnD0a5kw+mNA72AAoJSU0+pLHISt1eug9Lz588TGhqa63hwcDDnz5+3QYuEEELY1InV8F1vSL1uWPiDguWPG3J7llfbPzPk+fQKzjUsf+DKARSGXtFlZ36Fai0NV5zbSlJ6FpN+O8IDs7dxLCoBLxd7pg1szPeP30MNXzeLNK1x0M3FTulZ+ewmJT2lopDKdVDq5+fHwYMHcx0/cOAAPj4+NmiREEIIm/l3AfzwMGSlQq1u8Ox+8AkzBHS/PZ1rSLtcSIiCbZ8YLnebDHbmObj/jf7XdHl75HaigpoBcPngBrpN38z87RHoFfRrWpX1L3Rk0N3VCr2QqTCCKjnj6WxPZrbiv8v57OwkPaWikMp1UDpkyBCeffZZNm7cSHZ2NtnZ2fz1118899xzDB482NbNE0IIURqUgs3T4LdnQGVD00dgyPfg5gsDvwGdg2HV+p6vbd3SovtrKmSmGFbW1++X6+q90XsBsNPaoVB8r08FQHt+O1HxqVTzdmbBqJbMHNyMym6W31TGuNgJbjOELz2lopDKdVD6zjvv0KpVK7p06YKzszPOzs7ce++9dO7cWeaUCiFERaDPhpXjYeO7hr/bvwR9Z4HO3vB3QBPoNsVwec3rcPmwbdpZHFEHYf9iw+V734VbejgzsjM4fNVwf0Y3HA3AvHM7SFH2+GnimNjSgbXPd6RDbevmBm1kWuwUl/cJpi1GpadU3F65DUqVUly+fJn58+dz4sQJFi9ezPLlyzl9+jTffvstDg4Otm6iEEIIa8pMhR8fhb3zAA3c9z/DXvC3Dk+3ehLCukN2Ovw8CjJSbNLcIlEK1r4OKGg4AKq1yHXKkWtHyNBn4OlQifU7GqCyncA+jhXuhjzdTwRfwtnB+nlBTTs75bXdaFoCZNwY1peeUlGAch2U1qpVi4sXLxIWFsaDDz7I/fffT3BwsK2bJoQQwtpSrsOCvnDiD9A5wqAF0PLxvM/VaKDfbHCrAldPwJqJpdvW4vhvtSFZvs4Ruryd5ynGofv42CD2n0+BpOaG49VvrKmIKJ2dDRvdGL7/LzqRtMxbFjsZe0kdPcHBtVTaI8qvchuUarVawsLCuHatHK+oFEIIUXRxF+DbHnBhFzh5wrAVUL/P7W/jWhn6fwloYO98OPJLKTS0mLIzYe2bhsv3PAWV8u5sMS5ySkuqTl1/d+b2ewqAv9IucV2rNezsVAqLuwK9nPF2dSAzW3Hi8i07OyXcWOQkvaSiEMptUArwwQcf8PLLL3P4cDmaIySEEKL4oo/AN90MPZ4egTBqDQS3Kdxta3SCduMNl397DuLKaOrAf+bBtZPgUhnav5DnKXqlZ0+UISjVpNVg5uCmtKvehAY+DchS2ax094CESIiNsHpzNRqNKYn+wVsXO5nmk0pQKgpWroPSYcOGsXv3bpo0aYKzszPe3t5mP0IIIe4gZ7cYekgTo8C3HoxeC371ilZG+GsQeDekx8OyxyA7yzptLa7UONj0vuFy+ERDT3Aedl08Qpo+GZXtwLg2Hajr7wFA/7D+ACyv5G3IXnqudIbwG98ISg/fOq/U1FNa8kVOUUlRrDu3jmx9PvlQRblnZ+sGlMTMmTNt3QQhhBCl4cgvsHwMZGdA9TYwZAk4Vyp6OTp7Q5qoue0Nw/+bP4TOr1u+vcW15X+GxP+V68BdI/I8RSnFu3/9AYCzqsHY8Nqm6+4LvY///fM/TmelcsDRgaYR26DZUKs32ziv1Jo9pQuOLmDRsUX0qdmHd9u9W+LyRNlTroPS4cOH27oJQgghrG3XF7DqVUBBvT7Q/yuwdyp+eZVC4P4ZsGw0/P0RhHaA0PaWam3xXT9ruK8A3d8FXd5f0b/si+RU/CHsPaFPnbbY624Oero5uNEtuBu/nf6N5e5uND23tTRablqBb1zs5GR/Y9W/hXpK49PjWXZyGQC9avQqUVmi7CrXw/cAp0+f5o033mDIkCHExMQAsGrVKo4cOWLjlgkhhCgRpWDd27DqFUBBi8fhwfklC0iNGg2EpkMN5S4fY1jNb2vrJxl6gmuEQ62ueZ4SnZDG278dRucSAUD3mrnn0w4IGwDAalcXkuMvGBaGWVmApxOV3RzI1iuORSXcvMJCQenS/5aSmpVK7Uq1aR3QukRlibKrXAelmzdvplGjRuzatYvly5eTlGTIhXbgwAHefjvvFBpCCCHKgexM+OVJ2DbT8HfnN+G+j0BrwbybPT8En1qGbTB/tfE2pOd3wtEVoNEaeknz2ApUKcVryw+RlH0FrX0Cdlo7Gvk2ynVeM79mhHiEkKrVstrVpVTmlWo0mhxJ9HMM4Vtg+D4jO4PFxwybCIxoMMKi26SKsqVcB6UTJkxg6tSprFu3zixZfufOndm5c6cNWyaEEKLY0hNhySA4+ANodNB3NnR4Kc9ArUQc3WDgtze2If3DdtuQ6vWw5jXD5WZDoUqDPE9b/m8kG47H4Oh6DoD6PvVxtnPOdZ5GozH1li53d4OI0h3CNyXRz86EJMMIZkl6Sv848wdXU6/i5+JHj5AeJW2mKMPKdVB66NAhHnjggVzH/fz8uHr1qg1aJIQQokSSYmD+/XD6L7B3gYd/hGaPWK++gCbQdbLh8prXDSmnStuR5RC5F+xdIfyNPE+JTkhj8u+GtjWqZcjPfZffXfkW2btmb+w0Wg46OfLfhVIKSoO8ADhs7ClNigYUaO0N6a2KQa/0zD8yH4BH6z2KvXH7WHFHKtdBqZeXF1FRUbmO79u3j8DAQBu0SAghRLFdO23IQRq13xDEjFgJYd2sX+89T9luG9LMVMNcUjDkUHWvkusUpRQTlx8iIS2LxkGepOtOA7cPSn2cfegUaFi89Ys+FhJyf1daWuMcOzulZmTfrNPdH7TFCze2Rm7lTPwZXO1dGVB7gKWaKsqoch2UDh48mFdffZXLly+j0WjQ6/Vs27aNl156iWHDhtm6eUIIIQor8l/45l5DsvdKIYYcpIHNS6funNuQXjleutuQ7pwD8RcMGwG0HpfnKcv+jeSv4zE46LS82bc6ZxPOAoa5o7fTv85DAPzu5kr62c2WbXceqng44evuiF7B0ah4w1xdKNF80u+OfAfAwLCBuDu4W6KZogwr10Hpe++9R7169ahevTpJSUnUr1+fDh060KZNG954I+8hECGEEGXMyfWGIfuUq4bh9NHrwKdm6bYh1zakK6xfZ9IV2DLdcLnLW+DgkuuUy/E3h+2f7xZGgv4kADU9a+Ll5HXb4ttUbUMVrRPxOh1/nVxhyZbny5hE/9DF+Js9pcXcYvTItSPsvrwbO40dQ+tbP9eqsL1yGZTq9Xo+/PBDwsPD2bdvH48++igrV65k0aJFHD9+nIULF6LTWXCFphBCCOvYvwS+fwgykw2pkEb8AW5+tmlLjU7Q7nnD5d+ftf42pJveg4xECGgKjQblutowbH+QxLQsmlTzYkz7GvwbY9ha9K4q+Q/dG+m0Oh4IaAvA8vhjFm16fsyS6Jt6Sou3yOm7w4Ze0h6hPfB39bdI+0TZVi6D0nfffZfXXnsNNzc3AgMDWbJkCT///DODBg0iLCzM1s0TQghREKUMvYQrngJ9FjR+CB7+CRxtPEQb/rph2kBaPCx73HrbkMYcM/TIAnR/L885lz/vvcjGE1dw0Gn538DG2Om0/Btd+KAUoF+Tx9EoxU47PRejD1iq9flqZKGe0sikSNaeWwvA8AayUU5FUS6D0gULFjB79mzWrFnDihUr+P3331m8eDF6vd7WTRNCCFEQfbYhIf6GG6ve2z4H/eaCncPtb1cadPYw4BtwcIcLO+HvadapZ+2boPRQ934IaZvr6svxaUxZeRSA8d1qE1bFnZTMFI5dN/R4Nvcr3HzbQN8GtNYbdob65cBXFmp8/oxB6ekrSWTHF7+ndNHRRWSrbO4JuIe63nUt2URRhpXLoPT8+fPcd999pr+7du2KRqPh0qVLNmyVEEKIAmWmwc8jYfeN+Zs9PoRuU4q9OtsqvEOh90zD5b8/snyez1Mb4NQ60NoZ7vstlFJMyDFs/3j7UAAOXDlAtsrG39WfALfC9z4+4GVIsL8ieidZeiv1/N7g5+GEv4cTegWZcRcNB4vYU5pzS9GRDUZauomiDCtDnwKFl5WVhZOT+TZz9vb2ZGZm2qhFQgghCpQaB4v6w9FfDQnrB34L9zxp61blrdFAaPqIoTfTktuQ6rNh7Y2FuC3H5Lmga+k/F9l04goOdlo+ftAwbA+wL2YfcPtUUHnpXLsfXtnZxOjT2X5pe8naXwgNAz0BhS7psuFAEVffG7cUDasURuuqsqVoRVIug1KlFCNGjKB///6mn7S0NJ588kmzY4Xx/vvv06JFC9zd3fHz86Nfv36cOHHC7Jy0tDTGjRuHj48Pbm5uDBgwgOjoaGvcNSGEuDPFR8K8noYtLx09YOgyaFi4z2mb6TnNsA1pQiT89oxltiHdtxBijoKTF3R4OdfVl+JSeefGsP0L3WpTy+/mHFvjfNLmVYqWKsshtCO9k5IBWH7sh2I2vPAaB3niQQr2+jTDgSLs5pSRncGSY0sA2VK0IiqXQenw4cPx8/PD09PT9DN06FCqVq1qdqwwNm/ezLhx49i5cyfr1q0jMzOTe++9l+TkZNM548eP5/fff2fp0qVs3ryZS5cuFTroFUKICi9iqyEHacxRcPOHkasgtIOtW1WwnNuQHl8J/3xTsvLSE+Gvdw2XO74KLt5mVxuG7Q+RmJ5F02pePN6+hum6TH0mB68eBIreU4prZfrbGzIabI7axtVU6+542CjIkyqaWMMfTl5gn3sr1Pz8efZPrqRewc/Fj54hPa3TQFFm2dm6AcUxb948i5W1evVqs7/nz5+Pn58fe/fupUOHDsTHx/PNN9+wZMkSOnfubKq/Xr167Ny5k3vuucdibRFCiDvKtdOw7i1DQAdQubahh9Srum3bVRTGbUjXTITVr0H11vnuTV+grTMhOQa8a0CLx3Jd/dM/F/j7P8Ow/f8ebIJOe7OX8Pi146RmpeLh4EENrxq5bluQWtXa0yRyJQecHPnt9G+MajiqePehEBoFeuKvMUx3yHYPoLAJGpVSpmT5Q+sNlS1FK6By2VNqTfHxhj17vb0N/8Hu3buXzMxMunbtajqnbt26VK9enR07dtikjUIIUaalxhr2kZ/VyhCQanSGIGz02vIVkBrd8xSE3VuybUjjL8KOzw2Xu03JlWngUlwqU1caVta/2K02tfzczK435Sf1uwutphhf3SFt6Z+YBMDyk8tRlpiKkI/Kbo7UdUkEINHet9C32xq5lVNxp3C1d2Vg7YHWap4owyQozUGv1/P888/Ttm1bGjZsCMDly5dxcHDAy8vL7NwqVapw+fLlfMtKT08nISHB7EcIIe5o2Zmw60v49C5DAKbPhFrd4Knt0OtjcK5k6xYWj0YDfXNuQ/pa0cvYMAWy0iC4rSENVA45h+2bVffisfa5e0L3Ru8FCp+fNJfgdvRITsFFr+dcwjlTedbS0N0wBS4a7wLOvMnYSzogbIBsKVpBSVCaw7hx4zh8+DA//FDyieDvv/++2fzWatWqWaCFQghRBikF/62BOW1g1cuQeh186xqG6of+DH53QJ5JN1944AsM25DOM2QQKKzIf+Hgj4bL9041BLk5/Ljn5rD9RwPNh+0B9EpvWnlf0H73+XKvgot3TXomG3p5l59cXrxyCqmmk6Ej5kx64dZ3HL12lF2Xd6HT6BhaT7YUragkKL3h6aefZuXKlWzcuJGgoCDTcX9/fzIyMoiLizM7Pzo6Gn///Lc9mzhxIvHx8aafCxcuWKvpQghhO9FHYOEDsGQQXP0PXCpDr+nw5Dao1bXg25cnNcMNif7BsBo/rhCf60oZpjIANB4MgeY9nZFxqUz9wzBs//K9dXIN2wNExEcQlx6Hk86JBj7FnM8KEHxzCH/duXUkZFhvBC/gxkKno0muhTp//pH5gGFL0aLkYBV3lgoflCqlePrpp/nll1/466+/CA0NNbu+efPm2Nvbs2HDBtOxEydOcP78eVq3zj9/mqOjIx4eHmY/Qghxx0iKgd+fg7nt4MxGwwr1ts/Bs/9Ci9GgK5fraAvW+Y2b25AuL8Q2pMd+h/Pbwc4JurxpdpVSignLDpKUnkXz4EqMaheaZxF7YwxD7Y18G5Vs8U9IOxqlZ1BLryMtO41VZ1YVv6wCeGYZVvgfTnQlIe32OcQvJV1ibcSNLUXry5aiFVmFD0rHjRvHokWLWLJkCe7u7ly+fJnLly+TmpoKgKenJ6NHj+aFF15g48aN7N27l5EjR9K6dWtZeS+EqHgy0wx71n96l2HvdqWH+n1h3G7DAh6nvIdrrbmwplTl3Ib0/A7Djk/5ycowZB8AaPMMeAaZXf3DngtsOXkVRzstHw1snGvY3si0331RU0HdKrgNGmBA7DUA065J1mCXbFhzEa0qcTgy/rbnLjpm2FK0VUAr6vnUs1qbRNlX4YPSOXPmEB8fT6dOnQgICDD9/Pjjj6ZzZsyYwf3338+AAQPo0KED/v7+LF9u3fk4QghRpigFh5fB5y0Me9ZnJELVZjByNQxaYNiaMx9bLm6h1ZJWfHXQ+nuvlwqzbUinQcS2vM/b8xXEngVXv5vD/jdExqXyrnHYvnsdavjmHrY3slhQ6hkEXsHcn5SEvUbHsevHOHrtaMnKzEtWBiRfAeCy8r5tUJqQkcCy/2RLUWFQ4YNSpVSePyNGjDCd4+TkxKxZs7h+/TrJycksX778tvNJhRDijnLxH0Py+59HQfx58AiEB76Ex/6C4NtvA6mU4pN/PyE1K5VP931q9QU2pcZsG9LHc29DmnIdNk8zXO78BjjeXE2ec9j+7uBKjGybf0B/Ofkyl5IvodVoaeLXpOTtDmmHl15PFyfDvE2rPB83thfN1thzHXcOXsw/KF16YikpWSnU8qpFm6ptLN8WUa5U+KBUCCFEPuIuwLLH4OsucHE32LtA+Ovw9D/Q5CHQFvwVsv3Sdk7E3ty6ecqOKWyN3GrNVgOQma1nwY4INhyLRq+30tSBntPAu2be25BungZpceDXAJqZryb/fvfNYftptxm2h5u9pHW96+JqX7hFQ7cV3BaA/omGlE1/nvmTtKy0kpebU0IUAJkuVQANh/LpKc3MzmTxscWAbCkqDCQoFUIIYS49CTa8A5/fDYeWAhpDr+Az/0LHV8DBpdBFfXv4W8CwQ0+fmn3IVtm8sOkF6wwb5zB302ne+vUIo7/7h56fbOHX/ZFkZestW4lxG1Ktvfk2pFdPGYbuAbpPBe3NPY0uxqbw7h+G+17QsD2YJ823iBBDUNoq8giBrgEkZiay7tw6y5RtlHgJADsvw573566lEJ+Se7GTaUtRZz/uC73Psm0Q5ZIEpUIIIQz02fDvAvjsLtjyvxvJ3tvBmE3QbzZ4FC1Vz+Grh9l9eTd2GjuG1R/GpNaTuCfgHlKzUhm3YRyRSZFWuRsXrqfw+cZTADjaaTkRnchzP+yn88ebWbzrHGmZ2ZarrGpT6DbZcHn1a4YUWevfBn2WYReomp1NpxqG7Q+RnJFd4LC9kSkoLW7S/Ft5BYNHEFp9Fg/4GMq0+BD+jZ5SO69Aqnsb/oE5fMm8t1QpZUoD9Uj9R2RLUQFIUCqEEALgzGb4oqNhGDop2rA/+0OLYcRKQ+BVDMZe0p6hPQlwC8BeZ8/0TtMJqxTG1dSrjF0/lvj026/MLo5Jvx0hPUtPm5o+7H6tKy/dWxtvVwfOX0/h9V8O02HaRr76+wzJ6QWkcyqsVk8Zdq7KTodFA29urdrtHbPTluw+z9ZTV3Gy1/LRg7mT5N8qPj2eU7GG4LrYSfNvpdGYekv7ZmrRarT8E/0PEfERlikfTD2luFelUaAhG8Ot80q3XdrGqbhTuNi5yJaiwkSCUiGEqMiunoLvh8CCPhB9yJDSqft7MHYX1Ls/1+5DhXUu4Rzrz60HYGTDm6uq3R3cmd1lNn4ufpyJP8NzG58jIzvDIncFYN3RaDYcj8Fep2FK34Z4utjzdOcwtr4aztu96xPg6URMYjrv/nmMNh/8xYx1/xGbXML6tVroN8ewDakxIGs+wmwnqwvXU3jPtNq+LqGVC54fuj9mPwpFiEcIlZ0rl6yNOd2YV+p/cR9tqxou/3LqF8uVf6OnFI8AGgUZgtJbV+Abe0kH1B6Ah4Pk8RYGEpQKIURFlHIdVk2A2a3gxJ+Gnr2WY+CZfdB6HNg5lKj4+Ufmo1B0COpAWKUws+v8Xf2Z03UObvZu7I3ey+tbX0evSj7fMyUji0m/HQHg8fY1zHZHcnGwY2TbUDa/HM60AY0JrexKfGomn2w4SdsP/+LdP44SnVCCBT85tyF19IROE01X6fWKV5cdJDkjmxYhlRjZJqRQRRqT5lusl9QopJ3hd+Q/DKhxPwC/nvqVTP3tk9wXWuKNoNQ9gMbGntLIONPVx64dY1eUbCkqcpOgVAghKpLsTNg5Bz5tBrvm3Jj72B3G7oT7PgJXnxJXcTX1Kr+d+g3IP/dk7Uq1mRk+EzutHasjVjNz78wS1/v5X6eIjEsl0MuZpzvXyvMcBzstg1pUY/0LHZn18F3UD/AgJSObr7acpf2HG3ntl0Ocv5ZSvAbUDIfH1ht+3HxNhxfvPs/209cMw/YDm6AtYNjeaF+0Yb97i80nNfKuAW7+kJ1BB+WCt5M319KuseXiFsuUn3Cjt9ijKg1uBKUXrqeaeqSNvaTdQ7pT1a2qZeoUdwQJSoUQoqJQCpaNhtUTbqYrevQXeOQn8K1tsWoWH1tMhj6Dxr6NaV6leb7ntQpoxZQ2UwCYd2Qe3x//vth1nopJ4qstZwB4u3d9XBxuv82pTquhV+MA/ni2HfNGtqBFSCUysvUs2XWeTv/byHM/7OP45WLsDR90t9ljeeF6Cu//aRi2f7VHXUIKMWwPkJaVxuFrhwFo7pf/Y1gsOeaV2l/YSd9afQELLXhSyqyn1NPZnhAfw2KnQ5HxRCVFsSZiDQDDG8iWosKcBKVCCFFRbJ0BR3817FN//wx4covZ6nBLSMpI4sfjhh3xRjUcVWDuyd41e/Nss2cB+GD3B/x1/q8i16mU4q1fD5OZrehc149u9asU+rYajYbwOn4sfbINPz3Rmk51fNEr+HX/JXrM3MJj3/3Dv+dji9wmMAzbv/LzQVIysmkZ6s3w1iGFvu2hq4fI0mfh6+xLkHtQwTcoqhvzSonYSv9a/QHYErmF6OTokpWbGmvI2gDgbsjW0CjICzAEpaYtRf1bUd+nfsnqEnccCUqFEKIiOLUB/rqxGvy+j+DuUWb5My1l2cllJGYmEuIRQni18ELd5rFGjzGw9kD0Ss+rf7/KwSsHi1Tn7wej2H76Go52Wib1blDsJOwtQ72ZP7IlK59pR69GAWg0sP5YNP1nb2fIlzvZevIqShU+Ef/iXefYceYazvY6PhrYuNDD9nAzaX4zv2bWSSpvnFd6cQ8hrgHc5XcXeqXn19O/lqxc49C9iw/YOwHQKNCwkGnfhUv8/N/PAIxoOKJk9Yg7kgSlQghxp4uNMGwRqvRw13DDynAryMzOZMHRBYBhxb1WU7ivGI1Gw+utXqdDUAfSstN4esPTnE84X6jbJqZlMnWlIRn9uPBaVPcpfGL//DQM9GTWI3ex/oWODLo7CDuthh1nrjH0m130m7WN1YcvF7hL1IXrKby/6jgAr/aoQ7BP0XZj2hdjpfmkRpVrg6uvoVcz8l8G1B4AGIbwS7TozDR0f3OuaKNALwD2x68xbSlqXPUvRE4SlAohxJ0sIwV+HGqYQxrY3NBLaiUrz6wkJiUGX2df7r+xqruw7LR2fNThI+r71Cc2PZan1j/F9bTrBd5uxrqTxCSmE1rZlTEdahS36Xmq6evGtIFN2PxKOCPahOBkr+XAxXieXLSX7jP/Zvm/F8nMY5covV7x8s8HSMnIplWoN8OKMGwPkK3PZv+V/QC3nZNbIhoNBN/Ya/7cVroFd8PN3o3IpEj2XN5T/HJNi5xubrTQMNADyCLVeRNgmEsqW4qKvEhQKoQQdyqlYOXzcPkQuFSGQQvBztEqVemV3rSqemj9oTjoip5SysXehVldZhHoFsj5xPM889czpGal5nv+0UsJzN9+FoDJfRrgZG/56QgAgV7OTOrTgG2vdubp8Fq4O9lxMiaJF346QPj/NrFwp/kuUYt2nWPnmes3hu0Lv9re6ETsCZIzk3GzdyPMK6zgGxRX8I0h/IhtONs506tGL8AwBaPYcixyMnJ3sqdq4HG09gl42PvIlqIiXxKUCiHEnWr3l3DwR0MO0kHfgWeg1arafGEzZ+LP4GbvxoO1Hyx2OZWdKzO762w8HDw4eOUgE/6eQLY+97ager3izV8Po1fQq1EAHWr75lGaZfm4OfJS9zpsm9CZV3rUobKbAxdjU3lzxWHafbiRuZtPc/RSAu//aRi2n9CzbrGmExiH7pv4NUFnhXm/JjdW4HNhN2Rn0j/MsOBp/bn1xKXFFa/MHOmgjJRS6D03A1DXuWex/mERFYMEpUIIcSc6tx3WvGa4fO/UmwtbrMS4peigOoNwd3AvUVk1PGvwWefPcNA68NeFv5i2Z1quBUY/773I3nOxuDjoeOP+eiWqr6g8nOwZ26kWW1/tzJS+DQj0cuZqUjofrDrOfZ9uITUzm3tqePPoPcHFKn9vtCFpvsVTQd3Ktx44V4LMZLi0n/o+9annXY9MfSZ/nP2jeGXm0VO6/dJ2ktUFVLYDKqG1BRou7lQSlAohxJ0m4RL8NMyQGL/Rg3DPU1atbl/MPvZf2Y+91t5iO/TcVeUu3mv/Hho0LDm+xLSACiA2OYP3Vxlyf47vWpsAT2eL1FlUTvY6hrUOYdPLnfjfg02o6WtYzOTioGPagKIP24OhV9G48t5qi5yMtNqbqaHObQXggbAHAMMQflEyDZiYthi92VNqnNaRGdeCY5cstGuUuCNJUCqEEHeSrHRDQJp8Bao0hN6fFnv/+sL69pChl7RPzT74ulhuGL17SHdevPtFAP73z/9YHbEagGlrThCbkkntKm6MaBtisfqKy16nZWDzINaN78jC0S35ZWzbYmcBOJ94nmtp17DX2tOwckMLtzQPpnyl2wC4L/Q+HHWOnIw9yZFrR4peXuKN4fsbPaXHrx9nZ9ROdBodmbHtiIpP40piuiVaLu5AEpQKIcSdZNWrcHEPOHnCQ4vAoeQpkm7nVOwpNl3chAaNVXboGVZ/GI/UewSA17a8xg8HN/HDHkO6qKn9GmGvKztfY1qthvZhvtTxL/70BWMvacPKDXHUWWdRmhnjvNLzOyE7C09HT7oFdwOKseApKx1Srhku3+gpNfaS3htyLzUqVQPgcGR8iZst7kxl590shBCiZP5dAHvnARoY8C14h1q9SmPQ0bl6Z0I9LV+fRqPh5btfpkv1LmTqM3l/76to7GMYcFcQLUO9LV6frf0bc2Po3s/KQ/dGVRqCoydkJMJlw6YFxgVPf575k5TMlMKXZZxPqnME50pcTr7M6rOG3u3hDYbTONATgIMXJSgVeZOgVAgh7gSRe+EPw1A3nV+HsK5Wr/Jy8mXTgphRDUdZrR6dVscH7T+gqlNd9NoUXKvP44nOla1Wny2V2nxSI60Ogm8sPjpnGMK/u8rdVHevTkpWimmf+kIxzScNAI2GRUcNW4q29G9JA58GNLwRlB6KjLPgHRB3EglKhRCivEu6Aj8+CtkZUKcXtHuxVKpdeHQhWfos7q5yN419G1u1roRUuHRiCPr0ymAfyxs7xhetF68cuJp6lfOJ59Ggoalf09Kr+JZ5pRqNxtRbuvzk8sKXY5pPWpXEjER+PnljS9EGIwBoHGQMSqWnVORNgtJSVqzVjEIIkZ/sLPh5JCREgk8teGCuYVW1lcWnx5v2MR/ZcKTV63v/z+MkpjhSPfNZKjlW4tj1Y7y0+SWy9FlWr7u0GHtJwyqF4eHgUXoVm+aVbocbOWH71uqLTqNj/5X9nIk7U7hycvSU/vzfzyRnJlPTsybtAg3pyOpX9UCrgeiEdKIT0ix9L8QdQILSUnYqJsnWTRBC3EnWvw0RW8DBDQYvAafSCWZ+OvETKVkphFUKo31ge6vWtfPMNX7ZF4lGAx/0CWdWl1k46ZzYErmFqTun3jH/7Jf6fFIj/ybg4A5p8RBtWHFf2bkyHYI6AEXoLb0xpzTTrQqLji0CzLcUdXGwo5afGwCHZF6pyIMEpaVs++mrtm6CEOJOcehn2PG54XK/OeBbp1SqTctKMwUdIxuMtOo+5pnZet5ccRiAh1tWp0k1Lxr5NmJah2loNVqWnVzGV4e+slr9pcnYU2q1/e7zo7OD6q0Ml2/MKwUYEDYAgN9O/0ZmdiHyi97YzWm1JoWYlBh8nX1NW5caNQr0AmQIX+RNgtJStv30NVs3QQhxJ7h8GH57xnC53Xio36fUqv7t9G9cT7tOgGsAPUJ7WLWub7ee5WRMEj6uDrzSva7peHj1cF5radix6rN9n/H76d+t2g5rS8pI4kTsCQCa+TUr/QaY5pVuNR1qG9gWP2c/YtNj2XhhY8FlJEahgHnxht7Wh+s9nGtLUZlXKm5HgtJS9s+5WNIyc+/jLIQQhZYaCz8OhcwUqNkZOr9ZalVn67P57sh3gCGHqL3W3mp1XYpLZeb6k4BhH3lPF/O6Hqr7kGnV/1vb3mJn1E6rtcXaDlw5gF7pCXQLpIprldJvgHEb2nPbQa8HwE5rR99afYFCDuEnRLLD2YmTqdE42znzYO0Hc53SMEdaqDtl2oWwHAlKS1l6pp5/z8XauhlCiPJKr4flYyD2LHhVhwHfGNL6lJL159dzPvE8no6ephXa1vLOyqOkZmbTIqQSA+4KyvOc5+56jp6hPclSWYzfOJ7/Yv+zapusxbTffWkP3RsFNAV7F0i9DldPmA4btx3dfmk7l5Iu5X97pSDxMvM9DRsHDAgbgKejZ67T6gd4oNNquJqUTnSC7OwkzElQagN/n5R5pUKIYtr0PpxcC3ZOhh2bXEovgbxSim8PG7YUHVxnMC721tstauOJGFYdvoxOq+Gdfg3z3Udeq9Eyte1UWvi3ICkziafWP8Xl5MtWa5e1GBc52WToHsDOAYJaGC7nGMKv5l6NVv6tUCh+PfVr/rdPucYJnWKHszM6jY6h9YfmeZqzg46wG4udDl6Ms1TrxR1CglIb2Hrqiq2bIIQoj47/CX9PM1zu/SkENCnV6ndf3s3Ra0dx0jnxcL2HrVZPWmY2k34zzEsc2SaEuv63zyjgoHNgZvhManrWJCYlhrEbxpKYkWi19llaRnYGh68aFnOVWtL8vJiG8LeZHTb2iP9y6hey9flMP0u4xHxPw/N0b/C9BLoF5ltNo0CZVyryJkGpDRyOTOBakgxbCCGK4OpJ+OUJw+WWT0CTh0q9CcZe0n61+uHtZL0e2rmbT3PuWgpVPBx5vlvtQt3Gw8GDOV3n4Ovsy8nYk4zfNL5wK8bLgKPXjpKenY63kzehHtbfGjZfOZPo55jv2SW4Cx4OHkQlR+U7b/fy1WOsdjX0nA9vMPy21chiJ5EfCUpLWR1/w3ybbbIKXwhRWOmJhoVN6QlQvQ10f7fUm3D8+nG2X9qOVqNlWINhVqvn3LVkZm86DcCb99fHzdGu0LcNcAtgdtfZuNi5sCtqF5N2TCoXi2mM80mb+TWzanqtAgU2N+xbnxwD106ZDjvqHOldszcAy04uy/Omi8+tIkujoYXGhQaVG9y2mkZBXoAhV2l5eH5E6ZGgtJS1rukDwJb/ZAhfCFEISsGv4+DKcXAPgAfng856K97zY+wl7R7cnWru1axSh1KKt349QkaWnna1KtOrUUCRy6jrXZfpnaaj0+j47fRvfHHwCyu01LL2xewDbDif1MjeKc95pQAP1DIseNp4YSPXUs07VRIzEll6/QAAI1zDCqymrr87dloN15IzuBQvOzuJmyQoLWVtbgSlW09dlf8QhRAF2zYTjv4KWnsYtADcSz9d0MXEi6yJWANYd0vRNUcus/m/KzjotEzp26DYvYZtA9vyVuu3APjq4FdleuGTXulNQanNVt7nZNxy9JZ5pXW869DQpyFZ+ixWnllpdt2y/5aRrLKomZFBO59GBVbhZK+jdhXDqKHs7CRykqC0lN1VvRIOdlqi4tM4fUW2HBVC3Mbpv2DDFMPlnh9CtZY2acaCowvQKz2tA1pTz6eeVepITs9iyu9HARjToQY1fN1KVN4DtR6geZXmZOgzynRv6am4UyRkJOBs50xd77oF38Da8plXCtC/tmHB0/KTy02dKpnZmSw8thCA4fGJaD2qFqqam/NK4yzQaHGnkKC0lDnZ62gValggsEVSQwkh8hN7Dn4eBUoPzYbC3aNs0ozradf55eQvAIxqZL02fPrXSS7FpxFUyZlx4bVKXJ5Go+HZZs8CsOLkCi4kXChxmdZg3Fq0iW8T7LSFnz9rNUEtDL3yiZcMuXBz6BnSE2c7Z87En+HAFcNw/eqI1cSkxFBZr6FXUjJ4FG7KRc4k+kIYSVBqA+1qVQYkKBVC5CMz1bCwKTUWqjaD+z4GGy2A+f7496Rlp1Hfpz6t/FtZpY6T0Yl8s8UQAE3u0wBnB8tsBnBXlbtoF9iOLJXF7AOzLVKmpRnzk97lZ8NUUDk5uBgWPIGhtzQHNwc3uod0BwwLnpRSpt29HklOwwHAvWg9pYcjZbGTuEmCUhtoH+YLwM4z18jI0tu4NUKIMkUpWDkeLh8EFx8YtNCwAMUGUjJT+P7494BhLqk1VoYrpXhjxWGy9Iqu9arQpZ5l58w+0+wZAP448wcnY09atOySUkqZekptmp/0VvnMKwXDTk0AayLWsP78ek7EnjBsKXr9xuLdQvaU1vF3x16nITYlk4uxqRZptij/JCi1gbr+7lR2cyAlI5t/z8uWo0KIHPZ8DQe+B43WsNLeyzor3Qvjl1O/EJ8eTzX3anSr3s0qdfy6/xK7zl7HyV7L273rW7z8+j716RbcDYVi1v5ZFi+/JC4lXyI6JRo7jR2NKhe8QKjU5JxXeosmvk2o4VmD1KxU3tj6BgD9q3XBU68HO2dw8ipUFY52OlOKRMlXKowkKLUBrVZjGsLfKkP4Qgijcztg9QTD5W5TILSDzZqSqc80Dc2OaDACndYyQ+o5xadmMvWPYwA80zmMat7W2bZ0XNNxaDVaNpzfYNo5qSww9pLW86ln1S1bi6xaK9DoIP48xJ03u0qj0Zh2eErJSkGr0TLU9x7DlR4BRZpm0ijQC5CgVNwkQamNtLsxhL/lpOQrFUIACVGwdDjos6BBf2j9tE2bsyZiDVHJUXg7edOnZh+r1DF97QmuJqVTw9eVx9pbbyejml41ub/G/QB8tu8zq9VTVGVuPqmRo5thLjPk2Vt6f437TYuy7g2+l6CsGztnFXI+qZFpBb4sdhI3SFBqI+3DDD2lByPjiUvJsHFrhBA2lZUBPw2DpGjwqw99P7fZwiYwzHWcd3geAI/UewQnO8vPaT0cGc/CnecAeKdvQxztLN8Tm9OTTZ7ETmPH9kvb+efyP1atq7DK5HxSI9O80q25rvJx9mFQ7UG42rvyeOPHIeGS4YpCzic1ahR4c7tRWewkQIJSm6ni4UTtKm4oBdtOyZajQlRoqyfAxd3g6AkPLQIHV5s2Z2vkVv6L/Q9nO2ceqvOQxcvX6w2Lm/QKejepStsb05msqZp7NQbUNizS+WzfZzYPgmLTYjkTfwYoAzs55SW4neF3Hj2lABNbTWTHkB3UrlT7ZlDqXrSgtHYVdxx0WuJTM7lwXRY7CQlKbcq4Cn/rKRnCF6LC2rcI/vkG0MCAr8Cnpq1bxLwjhl7SgbUH4unoafHyf/znAvsvxOHmaMcbvayTjD8vYxqPwVHnyL8x/7I1MncPYGky7uJUw7MGlZwq2bQteap+j2GxXezZm0HnLUzZGBKNPaVFG753sNNSL8Cw2OmgJNEXSFBqU+1uDOH//Z9sOSpEhRT5L6x8wXC500So3d227QEOXTnEnst7sNPYMaz+MIuXfz05gw9XHwdgfLfaVPEovXRXfi5+DK4zGLB9b2mZHroHcPIA/8aGy/n0lpokRBl+FzEohZtJ9GVeqQAJSm2qVag3DjotkXGpRFxLsXVzhBClRSnYvwS+6wPZ6VC7J3R42datAuDbw98CcF+N+/B39bd4+R+uOk5cSiZ1/d0Z3jrY4uUXZHSj0bjYuXDs+jHWn19f6vUbldlFTjmF3BjCz2NeqZnEG0FpERc6wc3FTrKzkwAJSm3KxcGO5sGGYRtZhS9EBZFy3bCoacVTkJEI1dvAA3NBa/uP44j4CDac3wDAyAYjLV7+3nPX+fEfw3afU/s1xE5X+ve5klMlhjUw9AB/vu9zsvXZpd6GlMwUjl0zpMJqXqV5qddfaLfJV2qi198MSou40AlupoU6fCkevV5GDCs6238KVnDta8uWo0JUGKc2wOzWcOw30NpBl7dhxEpw9rJ1ywCYf2Q+CkXHoI7UqlTy/edzysrW88aKIwA82DyIu0O8LVp+UQyrPwwPBw/OxJ/hj7N/lHr9h64eIktlUcWlCgGuRQ/kSk1wa0AD105CYnTe56RcNaQxQwNuRd+NK6yKGw52WhLTsjh3XUYMKzoJSm2sfS3DYqcdp6+RmS1bjgpxR8pMhVWvwqL+kHQZKteGxzZA+xfACknpbycpPYvtp66y/fRV9p67zsGLcRy/nMA/FyL49fRvADxY61GS07PIzNZbbN7lgh3nOBaVgKezPRN61rVImcXl7uDOqIajAJi9fzaZ2ZmlWn/O+aTW2LrVYpwrQZWGhst5bDkK3FwE5eYHOvsiV2Gv01I/wAOAgxfjitFIcSexs3UDKroGVT2o5GJPbEomBy7E2bT3QAhhBVEHYfnjcMWwuIcWjxt2a3Io3R18zl1L5rvt51j6zwUS07NyXe/guxrHyplkp1Rn+JwrwJqb19lpcdBpcbDTYq/TmP6212lxtDP8drAzXq+9eb5Oi72dBgedjp9uDNu/0qMOPm6OpXW38zWk7hAWHVtEZFIky08u56G6lk99lZ+9MXsBaO5XhofujULaQvQhQ1DasH/u603zSYvf49s4yJP9F+I4HBlP36aBxS5HlH8SlNqYVquhba3KrDwYxd8nr0pQKsSdQp8NOz6HDe+APhNc/aDfbAizzh7yeVFKsf30NeZtO8uG4zEYOz0DPJ1wdbQjM1tPRpae9OwUMivtBCDjekfAvPcuI8twHukla0+Tal4MblG9ZIVYiIu9C483epz3d7/Plwe/pG+tvlbZJOBWmfpMDl45CECzKmUwP+mtgtvCrrn5zytNKF46qJyMK/BlsZOQoLQM6BDmy8qDUWw9eYUXutW2dXOEECUVd8GwkClii+HvuvdD70/A1fpJ4gFSM7L5ZV8k87ef5b/oJNPxjrV9GdE2hI5hvmi1NwPPeYfnMX1vGqGeoawY9hJ6PWRk68nMUqRnZ5OZrUyBaWa2nvQbv3MeyzD+nZ3jWJaejBu3BXikVXV02rIzXD2w9kDmH5lPVHIUP574keENhlu9zhPXT5CalYqHgwe1vCw7b9cqjIudrhyD5Gvg6mN+vYV6SgGOXEpAr1dmr01RsUhQWgYY85XuvxBHfGomns5Fn5cjhCgjDi6FP16E9Hiwd4WeH0CzR0tl29DIuFQW7Ijgh90XiE81zJN0cdAxsHkQw1qHUMvPLddtMrIzWHR0EWBYca/VaNHqMKyMdwC4cz+PHHQOPNXkKd7a/hZfH/qaAWEDcHPI/RhZ0t5ow9B9M79maDXlYFmHqw/41jMEpee2Qf0+5tcnFH/lvVEtXzec7LUkpWdx9loyNX2t+xyIsqscvCPufFW9nKnp64peGRY8CSHKodRY+Hk0LH/MEJAGtYAnt8Bdw6wakCql2H32Ok8t2kv7D//ii81niE/NpJq3M2/0qseOiV2Y0rdhngEpwB9n/iAmNQY/Zz961ehltXaWVb1r9ibEI4S49DgWHlto9frKfNL8vITc6C3Na7GTcTenYuQoNbLLsdhJkuhXbBKUlhHGLUclX6kQ5dDZv2FOOzj8M2h00Ok1GLnaqluGpmVms/SfC9z/2VYGfbGDVYcvo1fQpqYPXw27m00vhfNY+xq3HXnRK70pWf6j9R/FQedgtfaWVXZaO8Y1HQfAgiMLiE+3XlCklDJtL1qmk+bf6nb5Si3QUwrQOMgLkHmlFZ0M35cR7cMqM397hOQrFaI8yUqHv96B7Z8DCrxrQP+vIOhuq1UZnZDGop3nWLLrPNeSMwBwtNPS/65ARrQJpY6/e6HL2nRhExEJEbjbuzOw9kArtbjsuzfkXr4+9DUnYk/w7eFvGd98vFXqOZtwltj0WBx1jjTwaWCVOqzCGJRGHzaMCDhXunmdBXpKARrdWOx0OFKC0opMgtIyolUNH+y0Gs5fT+HctWSCfVxt3SQhxO1EHzWkeoo+bPj7ruHQ/T1wtM58uH3nY5m3LYI/D0WRdWPnm6qeTjzaOoTBLapRybVovZxKKVMv6aA6g6w+l7Is02q0PNPsGZ7+62mWHFvC0HpD8XXxtXg9xqH7RpUbYV+MnJ42414FfMIMSfTP7YC69xmOZ6RA2o0gsoQ9pY1uLHY6fCmebL0qUwviROmRoLSMcHO0467gSuw+e50tJ69KUCpEWaXXG1LkrJ9k2LfexQf6fH7zi9qCMrL0rDocxbfbIjhwIc50vEVIJUa2DeXe+lWKvVXnvzH/cuDKAey19gytP9RCLS6/OgR1oLFvYw5eOchXh77itVavWbwOY1DazK8cpIK6VXCbG0HptpuvdePKe3tXcPQoUfE1fd1wtteRkpHNmStJhFUpfI+/uHPInNIypH0twyr8rTKEL0TZlHAJFj0AayYaAtKwe+GpHRYPSK8mpfPphpO0+/AvnvthPwcuxOGg0zLgriBWPtOOpU+24b5GASXaO37e4XkA9KnZh8rOpZOqqizTaDQ81+w5AJb+t5RLSZcsXse/MYagtEzvd5+fkHaG3xFbbx4z5SgNKPFiPp1WQ8PAG4udZAi/wpKgtAxpX9swXLTt9FWyZMtRIcqWIysM+9af2QR2ztDrY3j4J8PQpoUcjoznxZ8O0Ob9v5i+7j9iEtPxdXfkhW612T6xMx8PamJKNF4SJ2NPsvniZjRoGNFgRMkbfodoGdCSVgGtyNJnMefAHIuWHZ0cTWRSJFqNlia+TSxadqkwziu9fPDmkL0xKC1BjtKcJIm+kOH7MqRRoCeezvbEp2ZyMDKeu6pXKvhGQgjrSksw7Ft/YInh74CmMOBrqBxmkeKzsvWsPRrNvG1n2RMRazrepJoXo9qG0LNhAA52lu0/mH9kPgBdg7sS4hli0bLLu2ebPcsjUY/w2+nfGNVwFKGeoRYp19hLWqdSnfI5f9czECqFQGwEnN8Fte+9ucjJwzJbgxqT6EtPacUlPaVliE6roW0tw24ZW/6TIXwhbO7cDpjb1hCQarTQ/iUYvc4iAWlmtp75287SYdpGxi7+lz0RsdhpNfRpUpVfxrbh13Ft6ds00OIB6eXky/x55k/AkCxfmGvs25hO1TqhV3pm759tsXLLZX7SWwXfGMI/d2MI30LpoIwaBXoBcPRSgowWVlASlJYx7WoZhvC3npJ8pULYTFYGbJgC8++DuPPgVR1G/Ald3gS7kuXyVEqx/mg03Wf8zaTfj3IpPg0fVwee6VyLbRM68+mQZjSz4ijJgqMLyFJZtPBvQSPfRlarpzx7uunTaNCwOmI1x68ft0iZxp7ScpWf9FYht+QrtVA6KKMalV1xddCRmpnN6SvJFilTlC8yfF/GtL+x5ei/5+NITMvE3akcpQ0R4k5w5T9Dqqeo/Ya/mzwMPT8Ep5KtLgY4FpXA1D+Osu2UYee2ym4OPN+1NgObB+Fkrytx+QW5nnadn//7GYBRDUdZvb7yqo53HXqE9GBVxCo+3/c5n3f5vETlJWQkcDL2JFDee0pvBKVR+yE9yeI9pVqthgaBnuw+e52DF+OKlHNX3Bmkp7SMqebtQoiPC9l6xc4z123dHCEqDqVgz9fwRQfDl66TFzz4HTwwp8QB6ZXEdCYuP0ivT7ew7dQ1HHRanuxYk40vdWLoPcGlEpACLDq6iNSsVOr71Kdt1balUmd5NbbpWHQaHZsvbmZ/zP4SlbU/Zj8KRbBHcPnOdFApGDyrgT4LLu6+mRLKQj2lAI0liX6FJkFpGSRbjgpRyhIuweIH4Y8XISsVaoTD2B3QoF+Jik3LzGb2plOE/28T3+++gF5Br0YBbHixIxN61i3VkZD49HiWHDcs1hrTeAyaEqbwudOFeIbQt1ZfAD7b91mJyirX+UlvZewtPbsFEi8bLluopxRuJtE/KEFphSRBaRnULkzylQpRKpSC/d/D7Hvg1DrQOUKPD2DocvAofu+PUorfD1yiy8ebmbb6BEnpWTQO8mTpk62Z9chdVPN2seCdKJzvj39PcmYytbxqEV4tvNTrL4+ebPwk9lp7dl/ezc6oncUu546YT2pknFd6dAWobMMCQFc/ixVv3G5UFjtVTDKntAxqXdMHnVbDmavJXIxNIahS6X+BCXHHS7wMvz8P/60y/F21GfSbA371SlTs/gtxvLPyKHvPGdI7+Xs48UqPOvRrGojWRlsnpmSmsOjYIsDQS6rVSH9EYQS4BTCoziAWH1vMZ/9+Rqv7WhW5hzk9O53DVw1b0ZbLpPm3MvaUXj9j+O1WBXSWCyVCfFxxd7QjMT2LkzFJ1Aso+VxuUX7IJ1MZ5OFkT9NqXoD0lgphcUrBwZ9gVitDQKq1hy5vwej1JQpIL8Wl8vwP++g3axt7z8XibK9jfNfa/PVSR/rfFWSzgBTgpxM/EZ8eT7BHMPcG32uzdpRHjzV6DGc7Zw5ePcjmi5uLfPtDVw6Rqc/Ex8mHau7VrNDCUuZdwzxZvoUS5xsZFjvd2NlJkuhXOBKUllHGVfhbJCgVwnKSYuDHoYbV9WlxENAEntgM7V8sdm9PcnoW09eeoPPHm1ix35AiZ8BdQWx8qRPPdQ3DxcG2A1JpWWmmZPmjG45Gpy2dRVV3isrOlXm47sOAYW6pXhVtSHlfzD7AsOr+jpjHq9Hc7C2FEk1zyU/jIC8ADkbGWbxsUbZJUFpGGYPSbaevkq1XNm6NEHeAw8sMvaPHVxp6R8PfgMc2QJUGxSpOr1cs/ecC4f/bxKd/nSItU0/LEG9+f7odHw9qgr+nk4XvQPEsP7mca2nXCHAN4P6a99u6OeXSyIYjcbd357/Y/1gTsaZIt90bsxe4Q4bujUJyBKUW7imFm/NKD0UmWLxsUbZJUAr8/fff9O7dm6pVq6LRaFixYoXZ9Uop3nrrLQICAnB2dqZr166cPHnSqm1qEuSFu6MdcSmZkhpDiJJIvgo/DYOfR0HqdajSCMZshI4vg654q993nrlGn1lbefnng8QkplPN25k5j9zFj0/cY1o9XBZkZmfy7eFvAUMvqb1W8h4Xh6ejJ8MbDAdg1v5ZZOmzCnW7bH02/2/vvsOjqLoHjn83vSckIZWEkhB6AOldBAVEkCJFAUGsiCCowGvhpy8WuvVFKdIURBAEQSRIC0U6GFoCIaGEkkII6W3L/P5YEoyEms1Oyvk8zz7ZnZ2dOTfZbE7u3HvusaRjQAWZ5FSgYGUnMOnM+wIFSWlUfDr5OpnsVJlIUgpkZWXRuHFj5syZU+zzM2bM4Ouvv2bu3LkcOHAAR0dHunXrRm5ubqnFZGVpQZsg45Kje2LkEr4QDyXyN2PvaORvYGEFnf4DL28Hn4dbyeji9Sxe/fEwg+fv5+SVdJxtrXi3R122vtWJHo18y9zl2Q3nNpCYnUhV+6r0qd1H7XDKtaH1h1LFtgoX0y+yPnb9fb3mbOpZMrWZOFo7ElIlpJQjNCPP2rdm3JuwRmmB6h4OONtZka8zEJ2YYfLji7JLklKgR48efPLJJ/Tt2/e25xRF4csvv+SDDz7g6aefJjQ0lB9++IGrV6/e1qNqah1CjPVKd0VLvVIhHkjWdfjlBWMPaXYyeDUwXqrv/O5DLROalqPl042RdP18J5tPJWKhgSGtAtkx4VFe7RSErVXZG6epM+j4/sT3AAxvMBxbS1uVIyrfHK0debHRiwDMPTaXfH3+PV9zJNF46b5J1SYVayyvRgNtx4BXfQjuUgqH1xBaTYroV0aSlN7D+fPnSUhIoGvXroXbXF1dadWqFfv27SvVc3cILlhy9AZZefd3uUiISi/qd/i2FZz6FTSW0OEd4+V6vyYPfCid3sCP+y7QeVY4C3afR6tX6FDbk01vduTTvo3wdCq7iV7YhTAuZVzCzdaNASED1A6nQhhUZxBeDl7EZ8XzS/Qv99y/oGh+uV5a9E7ajTUuMOFkuhql/9TQX4roV0ZSp/QeEhKMK1Z4e3sX2e7t7V34XHHy8vLIy8srfJye/uADtqt7OBDgbs+llBwOnL/OY3W97/0iISqr7BTYNAlOrDI+rlrXWHfU/+ESgvAzSXy6MYqzSZkABFV15IOe9Xm0TtUyd5n+3wyKge+PG3tJn6//PA7WUuvYFOys7Hg19FU+3v8x84/Pp29w3zt+bxVFqVhF880s1N8NkLJQlY30lJaSqVOn4urqWngLCHjw+nQajYb2wQVLjsq4UiHu6Mwm46pMJ1YZV5hpPx5e2flQCenZxAyGLzrIiMWHOJuUiZuDNf/t3YCwcR3pXNerzCekANvjthObFouztTOD6w5WO5wKpW/tvlRzqkZKbkrhsq3FuZxxmeScZKwsrGjo2dCMEVYMBZfvTyekk6fTqxyNMBdJSu/Bx8cHgMTExCLbExMTC58rzrvvvktaWlrh7dKlSw91/o5Sr1SIO8tJhbWjYMVgyEwEzxB4cQt0/Qis778kk8GgcCklm8nrTtL9q93sjL6GtaWGF9vXZOc7nRnetgbWluXj41JRFOYfnw/As/WexdnGWeWIKhZrC2teb/I6AItPLiY9v/irYAWloBp6NMTOqmyUBytPqlWxx9XeGq1eITohU+1whJnI5ft7qFmzJj4+Pmzbto0mTZoAxkvxBw4cYNSoUXd8na2tLba2JR9v1jbIEwsNxCRlEp+Wg6+rfYmPKUSFEP0nbBgLGfHAzYkXnd8D69t/R7R6AwlpuVy+kcOV1Bwu38jmSuH9HOLTctDqb9UDfqK+N+8+WY+ano5mbJBp7L6ym6iUKOyt7Blab6ja4VRIT9Z8koUnFhKbFsvSU0sZ03TMbftU6PGkZlAw2Wn32WSOX0ktU6XWROmRpBTIzMwkJiam8PH58+eJiIjA3d2dwMBAxo0bxyeffELt2rWpWbMmkydPxs/Pjz59+pR6bK4O1oRWcyPiUiq7zyYzsHkFWKZOiJLITYPN78HfxrXccQ8ir9f/uOIUypULmVy+ce0fCacx+UxIz+Vea1BYWmho5O/KxO51aBvkWfrtKAWKojDv+DzAOCmnil0VlSOqmCwtLHmj6RuMDx/PsshlDKk3BHc79yL7FK7kJONJH1ojf2NSKjPwKw9JSoHDhw/TuXPnwsdvvfUWAMOHD2fJkiVMnDiRrKwsXnnlFVJTU2nfvj1hYWHY2ZnnkkyH2p5EXEpljySlopLKytNxJTWH7Mg/qX3gXRxzEzGgYYP908xMH8jleTeAu69LbmNlgb+bPf5u9lSrYvzqX8WealUc8K9ij7ezLVbl5BL9nRxMOMjxa8exsbApLPYuSkeXwC7U96hP5PVIFp5YyIQWEwqfS85J5kL6BTRoaOLVRL0gy7mCIvrHZbJTpSFJKfDoo4+iKHfuRtFoNEyZMoUpU6aYMapbOtSuyjfbY9gTk4zBoGBhUfYnWgjxsHR6Awv3nOdo3I3Cy+u67DTes1rOc1Y7ALhg8OYd7asczq1b+DoHG8tbCWcVe/zdHArvV3Ozx9PJtsL/7iw4vgCAfrX74WlfPnt7ywuNRsPYpmN5betr/Hz6Z4bVH4aPo3GeQUEvaXCVYFxt5bLzwyq4ZH8mIYNcrR476wpU61UUS5LScqBpoBuONpakZOUTGZ9eWL9NiIomT6dn7Iq/2Xzq1sTCthYnmWE7n2oa42S/rS59OVDzDbp7uvPSzeTTv4o9VRysy8XM+NISkRTBgYQDWGmsGNlwpNrhVApt/dryiNcjHE06yvzj8/m/Nv8H/GM8qVy6LxF/N3vcHW1IycrndEIGTQLc1A5JlLLyfa2qkrD+x5KjMgtfVFS5Wj2v/HCEzacSsbG04L2uAexu8Ds/2XxmTEjdqsOIjXR9awnv923OSx1q0b2hL42queLuaFOpE1KgcMZ97+De+DqZfj1ycTuNRsPYR8YCsPbsWi6lG6usSH1S09BoNIWX8E/IuNJKQZLScqL9zdWd9sTIkqOi4snM0zF80UF2Rl/D3tqSNT30vHJyCAGxN+tAtngJRu2FGu3VDbSMirweye4ru7HQWPBiwxfVDqdSaebdjHb+7dApOr499i1Z2ixOp5wGZOa9KRQmpZdT1Q1EmIVcvi8nOoQYi+gfOn+DnHw99jYytkZUDGnZWoYvPkjEpVScbK1Y80QOdba+AIoBXAPh6f9BrU5qh1mmFaxx371GdwJdAlWOpvIZ03QMf135i43nNlLfoz4GxYC/k3/hGFPx8ArGlcpkp8pBekrLiVqejvi52pGvN3DwQora4QhhEsmZeQxesJ+IS6m4OVizepAPdXa/aUxI6/eB1/dKQnoPMTdi2HJxCwAvN3pZ5WgqpwYeDXi8+uMoKHx+5HNALt2bSsHKTmeTMsnVyspOFZ0kpeWERqOhQ+2bS45GyyV8Uf4lpOUyaN4+ouLT8XSyZeULodQNHwW5qeDfHPrOA1tZjehevj9p7CXtGtiV4CrBKkdTeY1uMhoNGnQGHQBNvZuqHFHF4ONih6eTDXqDQmR88atniYpDktJypH3tgnGlMtlJlG+XUrIZOG8fsdey8HW1Y9UrraizbxIknQInbxj04wMtE1pZxaXHsen8JgBeDpVeUjUFuQXxVK2nCh8382qmYjQVR5HJTnIJv8KTpLQcaRfsiUYDpxMySErPVTucSm151HIGbhjIrsu71A6l3Dl3LZOB8/YRl5JNoLsDq15tQ60zCyByHVhYw8AfwMVP7TDLhYUnF2JQDHTw70B9j/pqh1PpjWoyCgcrB2q41KCma021w6kwGlVzA2QGfmUgSWk54u5oQ0M/43+M0luqnt/P/c60g9OISoli9LbRzDw0E61eq3ZY5cLphHQGzttPfFouwV5O/PJaGwKu74VtNxem6DEdAlubPS6dQcfeq3sLi56XB/GZ8ayPXQ/AK6GvqByNAAhwDmB9n/Use3JZpS9RZkrSU1p5SFJaznS4eQlf6pWq42jiUf7vL2OB7EaejQD4IfIHnt/0PJcyLqkZWpl3/HIqg+fvJzkzj/q+Lqx8pTXe2iuwZiSgwCPDobl5i75fSLvAl0e+pNvqbry65VWGbxrOH+f+MGsMD2vxqcXoDDpa+rSUpSzLEG9Hb1nFycRuTXbKIDtfp3I0ojRJUlrOtP9HUnq3pVGF6cWlx/HmjjfRGrR0DezKsieX8XXnr3GxceHk9ZMM3DCQsAthaodZJh26kMJzCw6Qmq2lSYAbK15ujYe1Fn4eArlpUK0lPDkTzNC7lJmfyZroNQz7Yxi91vVi4cmFJOUkYWdph4LC+3veZ8+VPaUeR0kk5ySzJnoNIL2kouLzdrHDy9kWgwJRMtmpQpOktJxpVr0K9taWJGfmcTohQ+1wKo20vDRGbxtNal4qDTwa8FmHz7DQWNA5sDNreq+hqVdTMrWZTNg5gSn7ppCrkzG/BXafvcawhQfIzNPRupY7y15qhau9FawbBdeijBObBv4AVralFoNBMXAo4RDv73mfx355jI/2fUTEtQgsNBZ0rNaRzx/9nD3P7qFHzR7oFB3jd4wnIimi1OIpqaWnlpJvyKdx1ca09GmpdjhClLqCS/hSr7Rik+L55YytlSWtarkTfuYau89eo56vi9ohVXhavZbx4eO5kH4BH0cfvnnsG+yt7Auf93H0YVG3RXwb8S3fn/ieX6J/IeJaBLM6zqKWWy0VI1ff1shEXl9+lHy9gUfrVGXu0GbYWVvCrlkQtf7mxKYfwaV0lsWMz4znt9jfWBezjiuZVwq313CpQZ/gPvQK6oWXg1fh9k/bfUp6fjp/XfmL17e9ztLuS6ldpXapxPawUnNTWXlmJWDsJZWxi6IyaFTNlW2nk2RcaQUnPaXlUGG9UhlXWuoUReG/+/7LoYRDOFo7MqfLHKo6VL1tPysLK8Y+MpZ5j8/Dw86DszfOMnjjYNaeXVtph1lsOHaV15YdIV9voHsDH+YNu5mQRv8J2z8x7vTkTAhsZdLz5upy2XhuIy//+TLd1nRjTsQcrmRewdHakf61+/Njjx9Z32c9LzZ6sUhCCmBtac3nnT6ncdXGZORn8OqWV7mccdmk8ZXUsqhl5OhyqOtelw7+HdQORwizKBhXKjPwKzbpKS2HCiY7HTyfQq5Wb/xDL0rFwpML+S32Nyw0FszqNIuQKiF33b+NXxtW917Ne7vfY1/8Pv5v7/9xIOEAk1tPxtHa0UxRq2/V4Uv8Z81xDAr0berPzGdCsbK0gOuxsOYlQIFmL0DzF0xyPkVROJl8knUx69h0fhMZ2ltDW1r6tKRPcB+6BHbBwdrhnsdysHZgTpc5jAgbQUxqDK9ueZWlPZbiae9pklhLIiM/g5+ifgKkl1RULg1vXr6PuZZJVp4OR1tJXyoi6Skth2p7OeHtYkuezsDhCzfUDqfCCrsQxldHvwLg3Zbv0t6//X29ztPek7mPz+XNR97EUmPJxnMbGfT7IKKuR5VmuGXGD/suMHG1MSF9tmUgswc0NiakeRnw83OQlwYBraDHjBKfKzknmSUnl9D3t74898dzrIpeRYY2A19HX15r/Bp/9PuDhd0W0iuo130lpAVcbV2Z9/g8/J38icuIY9TWUWTkqz+G++fTP5OhzaCWay26BHZROxwhzMbL2Q4fFzsUBU5dlclOFZUkpeWQRqOhfXDBJXxZcrQ0HLt2jPd3vw/A0HpDGVx38AO93kJjwUuNXmJx98X4OPpwMf0iQ/4YwvKo5RX6cv534bH832+nABjZriaf9W2IhYUGDAZY+xpcOw3OvjcnNtk81Dm0Bi3b4rYxZvsYuv7SldlHZhObFoutpS1P1nySBU8sIKx/GKObjCbAOeCh2+Ll4MX8x+fjbufO6ZTTjNk+RtUJbNnabH6I/AEwrt5koZGPb1G5NJJL+BWefKqVUx1D7qNeafRmSI83U0QVx+WMy4zdPpZ8Qz6PBjzKO83feehjNfVqyupeq3k04FG0Bi3TDk5j3I5xpOVVrA9VRVGY/ecZpoedBmDMY8FMfqrercvLu2fD6d/B0sY4scnZ54HPcfbGWWYemknXX7oybsc4wi+Fo1f0hHqGMrn1ZLYP3M70jtNp7dvaZAlboEsg8x6fh5O1E0cSjzBh54TCtc3NbXX0alLzUqnmVI3uNbqrEoMQamrk74qnky16g0HtUEQp0SgVudumDElPT8fV1ZW0tDRcXEo+Yz45M4/mn2wF4ND7Xanq/K9yOqfWweoXwDMERvwBjh4lPmdlkJ6fzvN/PE9sWiz13OuxpPuSB7rseyeKovDT6Z+YfXg2WoMWX0dfZnScUSGKniuKwicbo1i45zwAE7vX4fVHg2/tEL0ZfhoEKNDra2g2/L6PnZaXxqbzm1gXs45T108Vbvew86BXUC/6BPchyC3IVE25o8MJh3lt62vk6fPoHdSbj9t9bNaeyjx9Hj3W9OBazjU+avMR/UP6m+3cQpQVWr0BKwuN2cdSm/rvt7gzGSlcTnk62VLf14XI+HT2xibzdBP/ojv4NQEnH+Pl0mX9YPh6sJNVRu5Ga9DydvjbxKbF4uXgxTePfXPnhDThJJz6FYr0mv3jg7LIh6YGDTAEaOLZiYkp+4nLimfEpud5w6UhI13qYVG4/52PAYC1PTQaAK7/+nmrxGBQeH/dSVYcjAPgv70bMLxtjVs7JMfcmtjU/MX7SkgNioH98ftZd3Yd2+K2kW/IB8BKY0XHah3pW7sv7fzbYW1hXQotKl5zn+bM7DiT8eHjWR+7HjdbN95p/o7Z/jiuO7uOaznX8HbwpndQb7OcU4iyxtpSLu5WdJKUlmMdansSGZ/OruhiktIqNeD532Bxd4iPMPZUDV0DNpVnBviDUBSFT/d/yv74/dhb2TOnyxy8Hb2L3/nIUvhjAujzHvg8DYCVGg1TPN3Z5OTIV+knOJRwkE+vXcfzfi9J7f4cekyDxs+aZQWkO9HpDUxYfZy1f1/BQgPT+ocysPk/xnDmpt+c2JQOgW2g+7S7Hu96znXWxaxjdfRqLmfeKsNUu0pt+gT1oWetnnjYq9fj3zmwM1PaTeH9Pe/zQ+QPVLGrwkuNXir182oNWhadXATAyIYjsbY0XzIuhBDmJElpOdahdlXm7TrHnphrKIpye69N1RAYthaW9IK4fcYlHZ9bWaor55RXS08tZc3ZNVhoLJjZcSZ13evevpM2B/54B/5eZnxc61Hwbmi8f9somH88LvKcghMwXVFonXOJqZmR7HWwZ0DNIKY6h9LaxuPux7p82PhPxrpREPkbPPVlqRWev5t8nYGxK/4m7FQCVhYavhjUhF6N/W7tUDCxKfkMOPvBgKXFTmxSFIXDiYdZdWYVW+O2Fo7XdLJ2ometnvQN7kt9j/plpvRR76DepOamMvPwTL46+hVutm48E/JMqZ5z47mNXM26ioedB/1q9yvVcwkhhJokKS3Hmteogq2VBYnpeZxNyiTE2/n2nXwbw9DV8EMfOLcDVo80JgiW8qMvsO3iNj4/8jkAE5pPoFNAp9t3unEBVg6DhOOgsYDHPoB248Hi4S4naYB+QOiNGCbsmkBMagyvpB3mpUYv8XqT17GyuMPPR6+DvV9D+FSIDoNvWxsL0DcaYLZe01ytnteWHSH8zDVsLC2YM+QRHq//r17lXTPhzEbjxKZBP4Jz0efT8tL4LeY3fon+hQvpFwq3N/RoyMA6A+lWo5tJxvKWhucbPE9qXioLTizg4/0f42LjwhM1niiVc+kNer4/8T0AwxsMx87KrlTOI4QQZYFMdDKT0hooPWzhAXafTWbyU/V5sX3NO+94LhyWDzRecg4dBH3mPnRCVZGcSj7FiLAR5OpzGVxnMO+1eu/2XrnoP+HXlyE3FRw84JlFxl5SE8nR5TD94HTWnF0DwCNejzC943R8HO8yQz0xEta9BvHHjI/rPgVPfQFOXnd+jQlk5ul4aekh9p9Lwc7aggXPNy9cYazQmU2w4mYJrafnQNOhgLFX9Ni1Y6w6s4rNFzYXjhW1t7KnZ62eDAgZQH2P+qUav6koisKU/VNYHb0aawtr5nSZQxu/NiY/z6bzm5i4ayKutq5s7r+5Ui3AIERZIROdzEeyknKuYHWne9YrrfUoDFwKFlZwfCX88XYxl4krl/jMeN7Y/ga5+lza+7dnUstJRRNSgx52fAY/DTAmpP7N4dVdJk1IwZiUfdT2I2Z0nIGjtSNHk47yzIZnCL8UfucXedeHl7ZB5w+M68ef/h3mtIKTv5o0tn9Ky9YybOEB9p9LwcnWih9Gtro9Ib0WDb++Yrzf4mVoOpSM/AxWnF5B/w39GbZpGBvObSDfkE+dKnWMpZwGbOfDNh+Wm4QUjLWCP2j1AY9XfxytQcubO97kxLUTJj2HQTEw//h8wFgrVxJSIURFJz2lZlJa/2lFxafT46vd2FtbEvHh49ha3WPJ0ROrb82GbjsWHp+i6mQZtWTmZ/J82POcvXGW2lVq82OPH4v+0c9OMX6fYrcZH7d4Cbp9VurjcePS45iwawKR1yMBYzLyVrO37j65JeEErB0FiTeTovp9oOdscDTdspjXM/MYtvAgkfHpuNpb88PIljQOcCu6U24aLOgC189CYFtOPTWNVTG/sun8JnJ0OQDYWdrRvWZ3BoQMoJFnozIzVvRh5evzGb1tNPvj9+Nm68bS7kup5VbLJMfeFreNcTvG4WTtxOZnNuNiIz00QqhBekrNR3pKy7m6Ps54OtmSo9Vz5OJ9LDna6BnoZVw6k71fG8f+VTI6g44JuyZw9sZZPO09mfPYnKIJ6ZWjMK+TMSG1soe+84xJnhkmiAW6BPJjjx8ZWs94yXtZ1DKGbRrGpfRLd36RTyN4eTt0mgQaS4hcZ+w1jVxvkpgS03MZNH8/kfHpeDrZ8PMrrW9PSA0G+PVVslNiWF21GoM8HBi8aSi/nv2VHF0OQa5B/Kflf9g2cBsft/uY0Kqh5T4hBbCxtOGrzl/R0KMhqXmpvLLlFeIzS75ghaIoLDi+AIDBdQdLQiqEqBSkp9RMSvM/rfErI1j79xVefzSIid2LmTVenH3fwuZ3jfe7TYU2r5s0prJKURQ+O/AZP5/5GTtLO5Z0X0IDzwYFT8LRgnJP+eBey7j6kE9DVWINvxTOB399QFpeGo7WjnzU5iO617zHSj5X/4Z1r0OSsaeVRgOgxwx0tm5k5evJzteRlXfra1aejqx8Hdn5N+8XPJevIztPT1a+juOX04hPy8XX1Y5lL7UiqKrTbac9s3kiv0Sv4ncnR7JujlW2trDmiRpPMDBkIE29mlaIJPRObuTeYHjYcM6nnaeGSw2W9liKu537Qx/vryt/8drW17CztGPzM5tLdCwhRMlIT6n5SFJqJqX5pl5z5DJv/3KMRv6ubBjT/v5fuHMG7PjUeL/3N/DI8yaNqyxaFrmM6Yemo0HDF52/oEtgF+MT2hzY+A5E3Cz3VKcn9PkW7N1UixUgISuBibsm8nfS3wC08uxBsEMHLAyuaAwuaLU25OQbiiSXebk59EtfxoC8NVhiIElx4z3ti2w1NHvoOALdHVj+UisC3G/NiM/V5bL5wmZ+OTafY5lxhduru1RnQMgAegf1popdlYdvfDmTkJXAsE3DSMhKoIFHAxZ2W/hQ40AVRWF42HD+TvqbYfWHMbHFxFKIVghxvyQpNR9JSs2kNN/USem5tPxsGxoNHPngcdwdb68HWSxFgS2TYe83gAaeWQgNK+7yheGXwhm7fSwKCm83e5sRDUcYn0g5D6ue/0e5p8nQblyZqU6gM+j45K8vWRP7A2iK/roqBisUnQuKzhmDzqnwvqJzpoY+kwlspKmSgLvewDp9Bz7VP4/e1g1HG0scbK1wtLHE0dYKBxsrHG0tcbCxwunm14LHLvbWdAqpiqu9cVzrudRz/BL9C7/F/kZGfgYAVorCY3a+DOz0CS19WlboXtG7OZd2jhGbRnAj7watfFrxbddvsbG8z9/Hmw4lHGLk5pFYW1gT1j8ML4fSragghLg7SUrNR4pVVgBeLnbU8XbmTGIGf8UkFy1ifjcaDTz+MeRnweFFxlnT1g5Qp0fpBqyCqOtRTNw1EQWFZ0KeYXiDm8tdRm++We4pDRw8jYm5iWfXl4SiKKw8dIUVmxugs3kRR69d2NqnoyUNPTloLHRobFLAJoV/T3FLAN7GCqiGhaLgrj9HEFPw9KxHVc96eNp74mnvSVWHqlS1r4qHvQdV7asWWwszX5/PH+f+4JfoXziceLhwu78BnklNpY9bAzwHbIBKvtpQLddafNf1O0ZuHsmBhANM2jWJWZ1mYWlxjwmI/1AwlrRvcF9JSIUQlYr0lJpJaf+n9cnvkXy/5zyDmgcw/ZnQB3uxwQBrX4UTq8DSFoasKlOJWUklZiXy3MbnSMpJoo1vG+Z0nYM1FhA+DXbNMO7k3xwG/lBm1pQHSM3O5z9rThB2KgGATiFVmT2wMZ5OxglXObocknOSC2/Xsq/dup9z635KbgoG5T6XMAWcrZ2NCapDVTztPbG3smdH3A5u5Bkn0llqLOlUrSMDrsbSNmYPFi7V4JVwcKp69wNXIgfiDzBq6yi0Bi39a/fnwzYf3lfv8fFrxxnyxxAsNZZs7LcRf6ey834UorKSnlLzkZ7SCqJ9bU++33Oe3WfvsOTo3VhYQJ/vQJttrHe54jl4fh0EtCy1eM0lW5vNmO1jSMpJIsg1iNmPzsY6NwPWvAix2407manc04M4eD6FcT//zdW0XKwtNUzqXpeR7WpiYXHr52pvZU+AcwABzgF3OZLx8v+N3Bskp1/m2oFvSD67mWRLC645uJLs35hkC4vCJDZPn0eGNoMMbUaRlZYAvB286R/Sn37B/fA+8D3ELAUrOxi8TBLSf2nl24oZHWfw9s63WXN2DW62boxrNu6eryvoJX2q1lOSkAohKh3pKTWT0v5PKydfT+P//km+3sC2tzsVO0P6nnR58NMg43Kktq4w4nfwfcBe1zJEb9Azbsc4wi+H427nzk89f8I/LQFWDYe0S8ZyT72+gsaD1A61kN6g8L/tMXy1LRqDAjU8HPjm2UdoVM3VdCe58Bf89rpx6VSAZi/AEx+j2DiRqc00JqjZt3pcU/NSCfUMpUO1DsblT6M2wEpjySr6zoPGg00XWwWzJnoNH+37CIB3mr9za9hIMU6nnGbAhgFo0LC+z3pquNYwT5BCiLuSnlLzkZ7SCsLexpLmNaqwN/Y6u6OvPVxSamULg5fDsv4Qtw9+7AsvbIKqIaYP2AxmHZ5F+OVwbC1t+abz1/if/hM2TbxV7mnQMvBuoHaYha6m5jBuZQQHz6cA0O8Rf6Y83RAnWxP/mtZoB6P2wtaP4OB8OLIYYreheXoOzjU74mzjTC3XOxSATzoNa18z3m81ShLSe+gf0p/UvFS+PPolsw7PwtXWlT7BfYrdt6CXtHuN7pKQCiEqpbIxvViYRMGSj3tikh/+IDaO8NxK8G0C2cnww9O3etTKkZ9P/8yyKGN5p0/bfEjo3nnw+zhjQlqnp3EMZBlKSMNOJtDjq90cPJ+Co40lXwxqzOcDm5g+IS1g4whPzoTn14NrIKTGwdJexhqt+VnFvyYnFX5+DvIzoUYHeOLj0omtghnZcCQjGowA4KO9H7E9bvtt+5xLO8eWi1sAeCn0JXOGJ4QQZYYkpRVIh9rGZSX3xV5Hq7//iS23sXOFob9C1bqQcdWYmKaXfJUaU1EUhaw8HVdTc4iKT2f/uetEXk2nYCTKnit7mHpwKgBv1h1Gty3TIWK5sdxTlw+NPaR2JrwcXgK5Wj0frDvBa8uOkJajJbSaKxvHdqBv02rmCaBWJ3h9r/ESPhh7Tr9rCxf3Ft3PoDdWKUiJBdcAGLCk0s+0v18ajYa3mr1Fn+A+6BU9E3ZO4FDCoSL7LDyxEAWFzgGdCalSPq9MCCFEScmYUjMxx5gUg0Gh+adbScnKZ9WrbWhZs4SrwKTHw+IecOM8eNYxXsp39DBJrIqikJWvJy1HS1q21vg1R0t6zq37/7798zmd4fa3bZMAN3q10LAg5i2ytFn0rdqC/x7fiqaMlnuKTsxgzE9/cybRWOvz1Y61ePuJOthYqfS/Ysw2WD8G0q8AGmj9Ojz2Adg4wLaPYfcs48SmkZvBr4k6MZZjOoOOt8LfYselHThaO7K422LqedTjUsYleq3thV7Rs6LnChp6qrOCmBCieDKm1HwkKTUTc72px6z4mw3HrjLmsWDefqJOyQ944yIs6m7sMfVtDMM33LGXMVer52jcDS6lZP8rodTdllim3yGxfBBWFhpc7a1xtbfm8o0ctJpUHGrMwcI6jfq4s+x8BNYA1VrAgKVlptyToij8dDCOKRsiydMZ8HSy5fOBjekYUgZmsOemweb34O+bK1t5BEPoYNjxifFxvwUQOlC9+Mq5PH0er215jcOJh3G3c2dp96UsjVzK6ujVtPNrx9zH56odohDiXyQpNR9JSs3EXG/qVYcvMXH1cZoEuLFudDvTHPRatLHHNDsZAlrDsF/BxhGt3sDxy6nsjbnO3tjrHIm7Qb7uwYYN2Fha4GJvjau9VWGCWXBz+dfXf98cbCwLS19dupHG85uGk6yNxTvfgjXxF3E1KBz1foZqg7/Aq0rZ+CD5d+3RjiFVmT2gMVWdy045KgCi/4QNYyHjH8M2Wo+G7p+pF1MFkZmfycjNI4lKicLX0ZfknGS0Bi1Lui+hmffDLwUrhCgdkpSajySlZmKuN3V8Wg5tpm7HQgN/T34CVwcTjftLOIGypCea3DQuV2nNf50n89eFTLLz9UV283axpb6vC24ONndNKAtudtYWJV6S0qAYeDv8bbbGbaWKQWH5lXiq6iyYlP8SvxnaY22p4ekm/rzcoRZ1fJxLdK6S+Hft0Ynd6vJi+6K1R8uUnBsQ9i4cWwFBj8Fzv4ClFOwwhes51xkeNpyL6RcBaObdjCXdl6gblBCiWJKUmo8kpWZizjd11893EpOUyXdDHqFHI9+HPo6iKMRey2Rv7HX2xlwn69w+5hqm4KjJ4099M17Xvomzgz1tgjxoG+RJ2yAPano6ltq654qikJKbQkJWAlezrhKfGU98VjxnUs5wKPEQ1orCwvhEmjpWQz/gR7ameLJg1zkOX7xReIyOIVV5pUMt2gV7mG199uJqj379bFNCq7mZ5fwllnYZnH3hAZbKFPd2NfMqwzYNIyk7iQVPLKC1b2u1QxJCFEOSUvORpNRMzPmm/mj9KZbsvcCzLQOZ2q/RA732Uko2+2Kv81dsMntjr3MtI6/I84/ZnmaeZirWaEkL7oPzs4uwsDRNsqLVa0nITihMNq9mXSUh69bj+Kx48vR5d3z99KRkngx4DPp8W2Tc69G4G3y/+xxhJxMoGMZa18eZlzvUoldjv1KdWGS22qOiXErJTeFKxhUaVX2w31MhhPlIUmo+kpSaiTnf1NtPJzJyyWGqVbFn98TOd+0RTErPZd85Y0/o3nPJXErJKfK8rZUFzWtUKewJbeTvilXMn7ByCBh0xlJCT30B9+h1VBSFDG3GrYQz8+qtHs+seBIyE7iWcw2Fu78dNUBVA/jk5+Gn0+Gr0+Or09E4X0v9Du9Cu3F3jCXuejaL/jrPqsOXCocdeLvYMqJtTZ5rFYirvWlLHIWdTGDSmuOk5WhxtLHkk74NzVfqSQghhElIUmo+kpSaiTnf1Fl5OppM+ROtXiH8nUep4elY+Fxqdj77zxknJu2NvU5MUmaR11pZaGgS4EbbIA/aBHnSNNANO+tiekJProE1L4FigDZvwBOfkGfIJ/J6JFczjYnmP3s447PiydLeoSj7P9ha2uJr74WvhR2+Oi2+WTfwvXEFv7wsfHQ6fHR6ClNHj9rGmfUBLaBmJ/AIuq/vT2p2PssPxLF07wWSbvYEO9pYMrBFACPb1STA3eG+jnMnuVo9n2yMZNn+OABCq7ny9eCmRX4OQgghygdJSs1HklIzMfebetC8fRw4n8K7PeoS4uPMvtjr7I1N5tTVdP75E9dooIGfC22DPGkT5EGLGu73f2n56I+w/g3j/UffI7HFCLqu7nrXl7jbuePj6IOfo5/xq4M3vtp8fNMS8U2OpcqVo2huXLz9hbYu4N/sZhLa0njfoWR1WPN0etZHXOX73ecLa4VaaKBHI19e6VCLxgFuD3zMMld7VAghRIlIUmo+kpSaibnf1P/bfpZZf0YX+1ywlxNtb05Oal3LHTcHm4c/0f7vIOw/ABie+JSnEzdT1aEqvo6+t25Oxq8+jj7Y56TD5YNw+RBcOgRX/wZdzu3HrVoXqjWHai2NiWjVOqU20UZRFHadTeb73efYffbWEq0ta7jzcsdadKnrdc8Z8rfXHrVh9sAmdCoLtUeFEEI8NElKzUeSUjMx95s6OjGDJ7/ajc6gEOBuT9tanrQN9qBNLQ+8XOxMe7KdM28VV+/1NTQbbryvy4eEE0WT0LS4219v52pMPAtu/s3A3s20Md6nyKvpfL/nHOsjrhYW96/l6ciLHWrS/5FqxQ5lKDe1R4UQQjwwSUrNR5JSM1HjTX0hOQtLC02Jx0jek6LA1g/hr68ADTQZAtfPwtUIuG22vAa86hvHgVZrYewJ9QgGi7J1eTshLZcley+w/MBFMnJ1ALg72jCsdXWGtamOp5Mx4Sx3tUeFEEI8EElKzUeSUjOp8G9qRYGNb8PhhUW327vfmoxU0Atqq14B+weVmadj1aFLLNxzniupxmEGNlYW9H+kGu6O1nwXHls+a48KIYS4LxX+73cZIkmpmVSKN7XBALtnQ2bCrUvx7rXuWS6qPNDpDYSdSmDBrnMcu5xW5Ll+Tf2Z0kdqjwohREVUKf5+lxGSlJqJvKkrBkVROHThBgt2n+PUlTTe6VaHfo9I7VEhhKio5O+3+UjXjhAPQKPR0LKmOy1rlqwclRBCCCGKKluzS4QQQgghRKUkSakQQgghhFCdJKVCCCGEEEJ1kpQKIYQQQgjVSVIqhBBCCCFUJ0mpEEIIIYRQnSSlQgghhBBCdZKUCiGEEEII1UlSKoQQQgghVCdJqRBCCCGEUJ0kpUIIIYQQQnWSlAohhBBCCNVJUiqEEEIIIVQnSakQQgghhFCdldoBVBaKogCQnp6uciRCCCGEuF8Ff7cL/o6L0iNJqZlcv34dgICAAJUjEUIIIcSDysjIwNXVVe0wKjRJSs3E3d0dgLi4uAr9pk5PTycgIIBLly7h4uKidjilprK0EypPW6WdFU9laau0s3QpikJGRgZ+fn5mO2dlJUmpmVhYGIfvurq6VugPjQIuLi7SzgqmsrRV2lnxVJa2SjtLT0XuTCpLZKKTEEIIIYRQnSSlQgghhBBCdZKUmomtrS0ffvghtra2aodSqqSdFU9laau0s+KpLG2VdoqKQqNIjQMhhBBCCKEy6SkVQgghhBCqk6RUCCGEEEKoTpJSIYQQQgihOklKhRBCCCGE6iQpNYM5c+ZQo0YN7OzsaNWqFQcPHlQ7JJObOnUqLVq0wNnZGS8vL/r06cOZM2fUDqvUTZs2DY1Gw7hx49QOxeSuXLnC0KFD8fDwwN7enkaNGnH48GG1wzI5vV7P5MmTqVmzJvb29gQFBfHxxx+X+3Wud+3aRa9evfDz80Oj0bBu3boizyuKwv/93//h6+uLvb09Xbt25ezZs+oEWwJ3a6dWq2XSpEk0atQIR0dH/Pz8eP7557l69ap6AZfAvX6m//Taa6+h0Wj48ssvzRafqdxPO6Oioujduzeurq44OjrSokUL4uLizB+sMClJSkvZypUreeutt/jwww85evQojRs3plu3biQlJakdmknt3LmT0aNHs3//frZs2YJWq+WJJ54gKytL7dBKzaFDh5g3bx6hoaFqh2JyN27coF27dlhbW7Np0yYiIyOZPXs2VapUUTs0k5s+fTrfffcd//vf/4iKimL69OnMmDGDb775Ru3QSiQrK4vGjRszZ86cYp+fMWMGX3/9NXPnzuXAgQM4OjrSrVs3cnNzzRxpydytndnZ2Rw9epTJkydz9OhRfv31V86cOUPv3r1ViLTk7vUzLbB27Vr2799fbpfFvFc7Y2Njad++PXXr1iU8PJzjx48zefJk7OzszBypMDlFlKqWLVsqo0ePLnys1+sVPz8/ZerUqSpGVfqSkpIUQNm5c6faoZSKjIwMpXbt2sqWLVuUTp06KW+++abaIZnUpEmTlPbt26sdhln07NlTGTlyZJFt/fr1U4YMGaJSRKYHKGvXri18bDAYFB8fH2XmzJmF21JTUxVbW1tlxYoVKkRoGv9uZ3EOHjyoAMrFixfNE1QpuVNbL1++rPj7+ysnT55UqlevrnzxxRdmj82UimvnoEGDlKFDh6oTkChV0lNaivLz8zly5Ahdu3Yt3GZhYUHXrl3Zt2+fipGVvrS0NADc3d1VjqR0jB49mp49exb52VYk69evp3nz5gwYMAAvLy+aNm3KggUL1A6rVLRt25Zt27YRHR0NwLFjx9izZw89evRQObLSc/78eRISEoq8f11dXWnVqlWl+GzSaDS4ubmpHYrJGQwGhg0bxoQJE2jQoIHa4ZQKg8HAxo0bCQkJoVu3bnh5edGqVau7DmUQ5YckpaUoOTkZvV6Pt7d3ke3e3t4kJCSoFFXpMxgMjBs3jnbt2tGwYUO1wzG5n3/+maNHjzJ16lS1Qyk1586d47vvvqN27dps3ryZUaNGMXbsWJYuXap2aCb3n//8h8GDB1O3bl2sra1p2rQp48aNY8iQIWqHVmoKPn8q22dTbm4ukyZN4tlnn8XFxUXtcExu+vTpWFlZMXbsWLVDKTVJSUlkZmYybdo0unfvzp9//knfvn3p168fO3fuVDs8UUJWagcgKp7Ro0dz8uRJ9uzZo3YoJnfp0iXefPNNtmzZUqHHLxkMBpo3b85nn30GQNOmTTl58iRz585l+PDhKkdnWqtWrWL58uX89NNPNGjQgIiICMaNG4efn1+Fa2tlptVqGThwIIqi8N1336kdjskdOXKEr776iqNHj6LRaNQOp9QYDAYAnn76acaPHw9AkyZN2Lt3L3PnzqVTp05qhidKSHpKS5GnpyeWlpYkJiYW2Z6YmIiPj49KUZWuN954g99//50dO3ZQrVo1tcMxuSNHjpCUlMQjjzyClZUVVlZW7Ny5k6+//horKyv0er3aIZqEr68v9evXL7KtXr16FXJ264QJEwp7Sxs1asSwYcMYP358he4JL/j8qSyfTQUJ6cWLF9myZUuF7CXdvXs3SUlJBAYGFn42Xbx4kbfffpsaNWqoHZ7JeHp6YmVlVWk+nyobSUpLkY2NDc2aNWPbtm2F2wwGA9u2baNNmzYqRmZ6iqLwxhtvsHbtWrZv307NmjXVDqlUdOnShRMnThAREVF4a968OUOGDCEiIgJLS0u1QzSJdu3a3VbSKzo6murVq6sUUenJzs7GwqLoR6GlpWVhj0xFVLNmTXx8fIp8NqWnp3PgwIEK99lUkJCePXuWrVu34uHhoXZIpWLYsGEcP368yGeTn58fEyZMYPPmzWqHZzI2Nja0aNGi0nw+VTZy+b6UvfXWWwwfPpzmzZvTsmVLvvzyS7KysnjhhRfUDs2kRo8ezU8//cRvv/2Gs7Nz4bg0V1dX7O3tVY7OdJydnW8bJ+vo6IiHh0eFGj87fvx42rZty2effcbAgQM5ePAg8+fPZ/78+WqHZnK9evXi008/JTAwkAYNGvD333/z+eefM3LkSLVDK5HMzExiYmIKH58/f56IiAjc3d0JDAxk3LhxfPLJJ9SuXZuaNWsyefJk/Pz86NOnj3pBP4S7tdPX15dnnnmGo0eP8vvvv6PX6ws/m9zd3bGxsVEr7Idyr5/pvxNua2trfHx8qFOnjrlDLZF7tXPChAkMGjSIjh070rlzZ8LCwtiwYQPh4eHqBS1MQ+3p/5XBN998owQGBio2NjZKy5Ytlf3796sdkskBxd4WL16sdmilriKWhFIURdmwYYPSsGFDxdbWVqlbt64yf/58tUMqFenp6cqbb76pBAYGKnZ2dkqtWrWU999/X8nLy1M7tBLZsWNHsb+Tw4cPVxTFWBZq8uTJire3t2Jra6t06dJFOXPmjLpBP4S7tfP8+fN3/GzasWOH2qE/sHv9TP+tvJaEup92Lly4UAkODlbs7OyUxo0bK+vWrVMvYGEyGkUp58uWCCGEEEKIck/GlAohhBBCCNVJUiqEEEIIIVQnSakQQgghhFCdJKVCCCGEEEJ1kpQKIYQQQgjVSVIqhBBCCCFUJ0mpEEIIIYRQnSSlQgghhBBCdZKUCiEeWEJCAmPGjKFWrVrY2toSEBBAr169iqylDjB16lQsLS2ZOXPmbcdYsmQJbm5uJjnPv4WHh6PRaO56K1iSMCcnhw8//JCQkBBsbW3x9PRkwIABnDp1qvB4NWrUuOuxRowYUbhvt27dsLS05NChQ7fFNWLEiIdexvPYsWP07t0bLy8v7OzsqFGjBoMGDSIpKanIfmvWrOHRRx/F1dUVJycnQkNDmTJlCikpKYX73E+bAT766KPCNlpaWhIQEMArr7xS5Fh3+/5MmzbtodoqhKicJCkVQjyQCxcu0KxZM7Zv387MmTM5ceIEYWFhdO7cmdGjRxfZd9GiRUycOJFFixaV6nn+rW3btsTHxxfeBg4cSPfu3Ytsa9u2LXl5eXTt2pVFixbxySefEB0dzR9//IFOp6NVq1bs378fgEOHDhW+bs2aNQCcOXOmcNtXX30FQFxcHHv37uWNN954qDbfybVr1+jSpQvu7u5s3ryZqKgoFi9ejJ+fH1lZWYX7vf/++wwaNIgWLVqwadMmTp48yezZszl27Bg//vgjwH23uUCDBg2Ij48nLi6OxYsXExYWxqhRo26LccqUKUW+v/Hx8YwZM8Zk3wMhRCWg9jqnQojypUePHoq/v7+SmZl523M3btwovB8eHq74+/sr+fn5ip+fn/LXX38V2Xfx4sWKq6tric9zP4YPH648/fTTt22fNm2aotFolIiIiCLb9Xq90rx5c6V+/fqKwWAo8lzButzFxfDRRx8pgwcPVqKiohRXV1clOzv7vuK4l7Vr1ypWVlaKVqu94z4HDhxQAOXLL78s9vmCeB+kzR9++KHSuHHjIvu99dZbSpUqVYpsK69rrAshyhbpKRVC3LeUlBTCwsIYPXo0jo6Otz3/z8vxCxcu5Nlnn8Xa2ppnn32WhQsXlsp5SuKnn37i8ccfp3HjxkW2W1hYMH78eCIjIzl27Nh9HUtRFBYvXszQoUOpW7cuwcHBrF692iRx+vj4oNPpWLt2LYqiFLvP8uXLcXJy4vXXXy/2+YLvWUnafOHCBTZv3oyNjc3DN0YIIe5AklIhxH2LiYlBURTq1q171/3S09NZvXo1Q4cOBWDo0KGsWrWKzMxMk56npKKjo6lXr16xzxVsj46Ovq9jbd26lezsbLp16wYY2/wgifjdtG7dmvfee4/nnnsOT09PevTowcyZM0lMTCzc5+zZs9SqVQtra+u7HutB23zixAmcnJywt7enZs2anDp1ikmTJt322kmTJuHk5FTktnv37odprhCikpKkVAhx3+7US/dvK1asICgoqLA3rkmTJlSvXp2VK1ea9DymYKpzLVq0iEGDBmFlZQXAs88+y19//UVsbKxJjv/pp5+SkJDA3LlzadCgAXPnzqVu3bqcOHECeLB2PMi+derUISIigkOHDjFp0iS6detW7FjRCRMmEBERUeTWvHnz+z6PEEJIUiqEuG+1a9dGo9Fw+vTpu+63cOFCTp06hZWVVeEtMjLyvif/3O95SiokJISoqKhinyvYHhIScs/jpKSksHbtWr799tvC9vr7+6PT6Uw64cnDw4MBAwYwa9YsoqKi8PPzY9asWYVxnjt3Dq1We9djPGibbWxsCA4OpmHDhkybNg1LS0v++9//3vZaT09PgoODi9zs7e0ftqlCiEpIklIhxH1zd3enW7duzJkzp8is7wKpqamcOHGCw4cPEx4eXqTXLDw8nH379t1Xonk/5zGFwYMHs3Xr1tvGUBoMBr744gvq169/29jL4ixfvpxq1apx7NixIm2ePXs2S5YsQa/XmyTef7KxsSEoKKjw+/Pcc8+RmZnJt99+W+z+Bd+zkrb5gw8+YNasWVy9etU0DRFCiJus1A5ACFG+zJkzh3bt2tGyZUumTJlCaGgoOp2OLVu28N1339GtWzdatmxJx44db3ttixYtWLhwYWHdUr1eT0RERJF9bG1tqVev3j3Pc6fevgcxfvx4fvvtN3r16sXs2bNp1aoViYmJfPbZZ0RFRbF161Y0Gs09j7Nw4UKeeeYZGjZsWGR7QEAA7777LmFhYfTs2ROAtLS029rs4eFBQEDAHY//+++/8/PPPzN48GBCQkJQFIUNGzbwxx9/sHjxYgBatWrFxIkTefvtt7ly5Qp9+/bFz8+PmJgY5s6dS/v27XnzzTdL3OY2bdoQGhrKZ599xv/+97/C7RkZGSQkJBTZ18HBARcXl3t+/4QQApCSUEKIB3f16lVl9OjRSvXq1RUbGxvF399f6d27t7J582bFw8NDmTFjRrGvmz59uuLl5aXk5+crixcvVoDbbkFBQfc8z44dOx4o3ruVYsrKylLef/99JTg4WLG2tlbc3d2V/v37KydOnCh2/3+XhDp8+LACKAcPHix2/x49eih9+/YtjKO4Nr/44ot3jT82NlZ5+eWXlZCQEMXe3l5xc3NTWrRooSxevPi2fVeuXKl07NhRcXZ2VhwdHZXQ0FBlypQpRUpY3W+biysJpSiKsmLFCsXW1laJi4tTFMVYEqq4dr366qt3bZcQQvyTRlHMOKNACCGEEEKIYsiYUiGEEEIIoTpJSoUQ5VJBsfjibg0aNFA7vAdSkdoihBAPSy7fCyHKpYyMjCLF4//J2tqa6tWrmzmih1eR2iKEEA9LklIhhBBCCKE6uXwvhBBCCCFUJ0mpEEIIIYRQnSSlQgghhBBCdZKUCiGEEEII1UlSKoQQQgghVCdJqRBCCCGEUJ0kpUIIIYQQQnWSlAohhBBCCNX9P8Dg9jmxX0UAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matched_rate_folder = Path(output_folder, \"matched_rate\")\n",
    "matched_rate_folder.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "perm_charts = dict()\n",
    "perm_overall_chart = dict()\n",
    "for assessment in df['AssessmentName'].unique():\n",
    "    intervention = 'Matched to Permanent Housing, Not Yet Housed'\n",
    "    filters = df['AssessmentName'].eq(assessment) & \\\n",
    "              df['AssessmentPredatesIntervention']\n",
    "    re = ['Black', 'White', 'Latino']\n",
    "    \n",
    "    permdf = df[filters & df['Race/Ethnicity'].isin(re)].groupby(['CALC_TOTAL_SCORE','Race/Ethnicity',intervention]).count()['Unique_client_ID'].unstack()\n",
    "    permdf['Total'] = permdf.sum(axis=1)\n",
    "    permdf['pct'] = permdf[True].div(permdf['Total'])\n",
    "\n",
    "    perm_charts[assessment] = permdf['pct'].mul(100).copy()\n",
    "\n",
    "    fn = Path(matched_rate_folder, f'{assessment}.csv')\n",
    "    perm_charts[assessment].to_csv(fn)\n",
    "    \n",
    "    # Overall numbers\n",
    "    permoveralldf = df[filters].groupby(['CALC_TOTAL_SCORE', intervention]).count()['Unique_client_ID'].unstack()\n",
    "    permoveralldf['Total'] = permoveralldf.sum(axis=1)\n",
    "    permoveralldf['pct'] = permoveralldf[True].div(permoveralldf['Total'])\n",
    "    permoveralldf['pctref'] = permoveralldf['pct'].mul(100).round(1)\n",
    "    display(permoveralldf)\n",
    "    \n",
    "    # Plot for context\n",
    "    perm_charts[assessment].unstack().plot(xlim=(0,17),title=f'{intervention}: {assessment}', ylabel=\"Percent\")\n",
    "pass   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e8ea06f",
   "metadata": {},
   "source": [
    "## Chart: 2021 Youth Acuity Group Breakdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "63e3e611",
   "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>Acuity</th>\n",
       "      <th>3/High Acuity</th>\n",
       "      <th>2/Mid Acuity</th>\n",
       "      <th>1/Low Acuity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Race/Ethnicity</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>45.5</td>\n",
       "      <td>45.8</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino</th>\n",
       "      <td>56.2</td>\n",
       "      <td>36.6</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>67.1</td>\n",
       "      <td>27.6</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Acuity          3/High Acuity  2/Mid Acuity  1/Low Acuity\n",
       "Race/Ethnicity                                           \n",
       "Black                    45.5          45.8           8.8\n",
       "Latino                   56.2          36.6           7.3\n",
       "White                    67.1          27.6           5.3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "af = df[\n",
    "    df['AssessmentName'].str.contains('Youth') & \n",
    "    df['Race/Ethnicity'].isin(['Black','White','Latino']) &\n",
    "    df['AssessmentYear'].eq(2021)\n",
    "].copy()\n",
    "\n",
    "cross_tab_prop = pd.crosstab(af['Race/Ethnicity'], af['Acuity'], normalize='index').mul(100).round(1)\n",
    "\n",
    "cols = list(cross_tab_prop.columns)\n",
    "cols.reverse()\n",
    "\n",
    "display(cross_tab_prop[cols])\n",
    "\n",
    "cross_tab_prop[cols].to_csv(Path(output_folder, '2021-youth-acuity-breakdown.csv'))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}