(B) eis promoter & eis homoplasy visualization and 2.2.1.1.1.i3 cluster identification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This notebook was created to identify the cluster of sub-lineage 2.2.1.1.1.i3 isolates (444/1893) from a t-SNE (based on pairwise SNP distance) of all isolates and to visualize homoplastic variants in the *eis* promoter and *eis* within the isolate cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import matplotlib.ticker as ticker\n",
    "from pylab import MaxNLocator\n",
    "import seaborn as sns; sns.set()\n",
    "from matplotlib.colors import LogNorm\n",
    "from matplotlib import gridspec\n",
    "import itertools\n",
    "\n",
    "#for exporting to Adobe Illustrator\n",
    "mpl.rcParams['pdf.fonttype'] = 42\n",
    "mpl.rcParams['ps.fonttype'] = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load SNP genotype matrix and Annotation Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load isolate annotation file (columns of Genotype Matrix)\n",
    "isolate_annotation_DF_SNPs = pd.read_pickle('/n/data1/hms/dbmi/farhat/Roger/homoplasy_project/rolling_DB_scrape/Genotypes_Filtered_2/genotypes_isolate_annotation.pkl')\n",
    "\n",
    "#load SNP annotation file (rows of Genotype Matrix) with gene annotation information\n",
    "SNP_annotation_DF = pd.read_pickle('/n/data1/hms/dbmi/farhat/Roger/homoplasy_project/rolling_DB_scrape/Genotypes_Filtered_2/genotypes_SNP_annotation.pkl')\n",
    "\n",
    "#load SNP functional annotation file\n",
    "SNP_func_annotation_DF = pd.read_pickle('/n/data1/hms/dbmi/farhat/Roger/homoplasy_project/rolling_DB_scrape/Genotypes_Filtered_2/genotypes_SNP_functional_annotation.pkl')\n",
    "\n",
    "#load Genotypes Matrix\n",
    "genotypes_array_SNPs =  np.load('/n/data1/hms/dbmi/farhat/Roger/homoplasy_project/rolling_DB_scrape/Genotypes_Filtered_2/genotypes_matrix.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "allele_code_map_dict = {'A':0 , 'C':1 , 'G':2 , 'T':3}\n",
    "allele_code_map_dict_r = {0:'A' , 1:'C' , 2:'G' , 3:'T'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lineage_1</th>\n",
       "      <th>lineage_2</th>\n",
       "      <th>lineage_3</th>\n",
       "      <th>lineage_4</th>\n",
       "      <th>lineage_5</th>\n",
       "      <th>lineage_6</th>\n",
       "      <th>lineage_7</th>\n",
       "      <th>lineage_8</th>\n",
       "      <th>lineage_9</th>\n",
       "      <th>lineage_10</th>\n",
       "      <th>lineage_11</th>\n",
       "      <th>isolate_ID</th>\n",
       "      <th>lineage_call</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>i3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SAMEA3558733</td>\n",
       "      <td>4.2.1.2.1.1.i3.1</td>\n",
       "      <td>4B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SAMN03648641</td>\n",
       "      <td>4.2.1.2.2.1.1</td>\n",
       "      <td>4B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>i1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SAMN03647419</td>\n",
       "      <td>3.1.1.i1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>i1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SAMEA3671418</td>\n",
       "      <td>4.2.1.2.1.1.i1</td>\n",
       "      <td>4B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SAMN07659096</td>\n",
       "      <td>1.1.1.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  lineage_1 lineage_2 lineage_3 lineage_4 lineage_5 lineage_6 lineage_7  \\\n",
       "0         4         2         1         2         1         1        i3   \n",
       "1         4         2         1         2         2         1         1   \n",
       "2         3         1         1        i1       NaN       NaN       NaN   \n",
       "3         4         2         1         2         1         1        i1   \n",
       "4         1         1         1         2       NaN       NaN       NaN   \n",
       "\n",
       "  lineage_8 lineage_9 lineage_10 lineage_11    isolate_ID      lineage_call  \\\n",
       "0         1       NaN        NaN        NaN  SAMEA3558733  4.2.1.2.1.1.i3.1   \n",
       "1       NaN       NaN        NaN        NaN  SAMN03648641     4.2.1.2.2.1.1   \n",
       "2       NaN       NaN        NaN        NaN  SAMN03647419          3.1.1.i1   \n",
       "3       NaN       NaN        NaN        NaN  SAMEA3671418    4.2.1.2.1.1.i1   \n",
       "4       NaN       NaN        NaN        NaN  SAMN07659096           1.1.1.2   \n",
       "\n",
       "  group  \n",
       "0    4B  \n",
       "1    4B  \n",
       "2     3  \n",
       "3    4B  \n",
       "4     1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isolate_annotation_DF_SNPs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31428, 14)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(isolate_annotation_DF_SNPs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_pos</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48</td>\n",
       "      <td>C</td>\n",
       "      <td>[T]</td>\n",
       "      <td>Rv0001</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Essential</td>\n",
       "      <td>dnaA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>64</td>\n",
       "      <td>G</td>\n",
       "      <td>[C]</td>\n",
       "      <td>Rv0001</td>\n",
       "      <td>63.0</td>\n",
       "      <td>Essential</td>\n",
       "      <td>dnaA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67</td>\n",
       "      <td>G</td>\n",
       "      <td>[A, T]</td>\n",
       "      <td>Rv0001</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Essential</td>\n",
       "      <td>dnaA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>69</td>\n",
       "      <td>C</td>\n",
       "      <td>[T]</td>\n",
       "      <td>Rv0001</td>\n",
       "      <td>68.0</td>\n",
       "      <td>Essential</td>\n",
       "      <td>dnaA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>71</td>\n",
       "      <td>C</td>\n",
       "      <td>[T]</td>\n",
       "      <td>Rv0001</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Essential</td>\n",
       "      <td>dnaA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pos ref     alt gene_id  gene_pos gene_category gene_name\n",
       "0   48   C     [T]  Rv0001      47.0     Essential      dnaA\n",
       "1   64   G     [C]  Rv0001      63.0     Essential      dnaA\n",
       "2   67   G  [A, T]  Rv0001      66.0     Essential      dnaA\n",
       "3   69   C     [T]  Rv0001      68.0     Essential      dnaA\n",
       "4   71   C     [T]  Rv0001      70.0     Essential      dnaA"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SNP_annotation_DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(782565, 7)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(SNP_annotation_DF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(782565, 31428)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(genotypes_array_SNPs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load INDEL genotype matrix and Annotation Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load isolate annotation file (columns of Genotype Matrix)\n",
    "isolate_annotation_DF_indels = pd.read_pickle('/n/data1/hms/dbmi/farhat/Roger/homoplasy_project/rolling_DB_scrape_indels/Genotypes_Filtered_2/genotypes_isolate_annotation.pkl')\n",
    "\n",
    "#load INDEL annotation file (rows of Genotype Matrix) with gene annotation information\n",
    "INDEL_annotation_DF = pd.read_pickle('/n/data1/hms/dbmi/farhat/Roger/homoplasy_project/rolling_DB_scrape_indels/Genotypes_Filtered_2/genotypes_INDEL_annotation.pkl')\n",
    "\n",
    "#load Genotypes Matrix\n",
    "genotypes_array_INDELs =  np.load('/n/data1/hms/dbmi/farhat/Roger/homoplasy_project/rolling_DB_scrape_indels/Genotypes_Filtered_2/genotypes_matrix.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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></th>\n",
       "      <th>isolate_ID</th>\n",
       "      <th>lineage_1</th>\n",
       "      <th>lineage_2</th>\n",
       "      <th>lineage_3</th>\n",
       "      <th>lineage_4</th>\n",
       "      <th>lineage_5</th>\n",
       "      <th>lineage_6</th>\n",
       "      <th>lineage_7</th>\n",
       "      <th>lineage_8</th>\n",
       "      <th>lineage_9</th>\n",
       "      <th>lineage_10</th>\n",
       "      <th>lineage_11</th>\n",
       "      <th>lineage_call</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SAMN13051687</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>i3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.2.1.1.1.i3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SAMN09100245</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>i3</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.2.1.2.1.1.i3.2</td>\n",
       "      <td>4B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SAMN08732238</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.2.1.1.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SAMN07658260</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.1.1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SAMN03648003</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.2.1.1.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     isolate_ID lineage_1 lineage_2 lineage_3 lineage_4 lineage_5 lineage_6  \\\n",
       "0  SAMN13051687         2         2         1         1         1        i3   \n",
       "1  SAMN09100245         4         2         1         2         1         1   \n",
       "2  SAMN08732238         2         2         1         1         1       NaN   \n",
       "3  SAMN07658260         3         1         1       NaN       NaN       NaN   \n",
       "4  SAMN03648003         2         2         1         1         1       NaN   \n",
       "\n",
       "  lineage_7 lineage_8 lineage_9 lineage_10 lineage_11      lineage_call group  \n",
       "0       NaN       NaN       NaN        NaN        NaN      2.2.1.1.1.i3     2  \n",
       "1        i3         2       NaN        NaN        NaN  4.2.1.2.1.1.i3.2    4B  \n",
       "2       NaN       NaN       NaN        NaN        NaN         2.2.1.1.1     2  \n",
       "3       NaN       NaN       NaN        NaN        NaN             3.1.1     3  \n",
       "4       NaN       NaN       NaN        NaN        NaN         2.2.1.1.1     2  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isolate_annotation_DF_indels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31428, 14)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(isolate_annotation_DF_indels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_pos</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACCGACGAAG_313_A</td>\n",
       "      <td>313</td>\n",
       "      <td>ACCGACGAAG</td>\n",
       "      <td>A</td>\n",
       "      <td>Rv0001</td>\n",
       "      <td>312.0</td>\n",
       "      <td>Essential</td>\n",
       "      <td>dnaA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TC_1549_T</td>\n",
       "      <td>1549</td>\n",
       "      <td>TC</td>\n",
       "      <td>T</td>\n",
       "      <td>intergenic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>T_1552_TAA</td>\n",
       "      <td>1552</td>\n",
       "      <td>T</td>\n",
       "      <td>TAA</td>\n",
       "      <td>intergenic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TAA_1552_T</td>\n",
       "      <td>1552</td>\n",
       "      <td>TAA</td>\n",
       "      <td>T</td>\n",
       "      <td>intergenic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>T_1552_TA</td>\n",
       "      <td>1552</td>\n",
       "      <td>T</td>\n",
       "      <td>TA</td>\n",
       "      <td>intergenic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                key   pos         ref  alt     gene_id  gene_pos  \\\n",
       "0  ACCGACGAAG_313_A   313  ACCGACGAAG    A      Rv0001     312.0   \n",
       "1         TC_1549_T  1549          TC    T  intergenic       NaN   \n",
       "2        T_1552_TAA  1552           T  TAA  intergenic       NaN   \n",
       "3        TAA_1552_T  1552         TAA    T  intergenic       NaN   \n",
       "4         T_1552_TA  1552           T   TA  intergenic       NaN   \n",
       "\n",
       "  gene_category gene_name  \n",
       "0     Essential      dnaA  \n",
       "1          None      None  \n",
       "2          None      None  \n",
       "3          None      None  \n",
       "4          None      None  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "INDEL_annotation_DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50260, 8)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(INDEL_annotation_DF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50260, 31428)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(genotypes_array_INDELs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Switch Genoyptes Matrix *columns* & Isolate Annotation *rows* to match isolate ordering for SNP genotypes matrix (used to construct t-SNE embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#this will be the order of the isolates in the t-SNE embeddings\n",
    "isolate_reorder = [isolate_annotation_DF_indels[isolate_annotation_DF_indels.isolate_ID == isolate_ID].index[0] for isolate_ID in isolate_annotation_DF_SNPs.isolate_ID]\n",
    "\n",
    "#re-order rows of isolate annotation df\n",
    "isolate_annotation_DF_indels = isolate_annotation_DF_indels.iloc[isolate_reorder , :]\n",
    "isolate_annotation_DF_indels.reset_index(inplace = True , drop = True)\n",
    "\n",
    "#re-order columns for genotypes matrix\n",
    "genotypes_array_INDELs = genotypes_array_INDELs[: , isolate_reorder]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>isolate_ID</th>\n",
       "      <th>lineage_1</th>\n",
       "      <th>lineage_2</th>\n",
       "      <th>lineage_3</th>\n",
       "      <th>lineage_4</th>\n",
       "      <th>lineage_5</th>\n",
       "      <th>lineage_6</th>\n",
       "      <th>lineage_7</th>\n",
       "      <th>lineage_8</th>\n",
       "      <th>lineage_9</th>\n",
       "      <th>lineage_10</th>\n",
       "      <th>lineage_11</th>\n",
       "      <th>lineage_call</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SAMEA3558733</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>i3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.2.1.2.1.1.i3.1</td>\n",
       "      <td>4B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SAMN03648641</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.2.1.2.2.1.1</td>\n",
       "      <td>4B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SAMN03647419</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>i1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.1.1.i1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SAMEA3671418</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>i1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.2.1.2.1.1.i1</td>\n",
       "      <td>4B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SAMN07659096</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.1.1.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     isolate_ID lineage_1 lineage_2 lineage_3 lineage_4 lineage_5 lineage_6  \\\n",
       "0  SAMEA3558733         4         2         1         2         1         1   \n",
       "1  SAMN03648641         4         2         1         2         2         1   \n",
       "2  SAMN03647419         3         1         1        i1       NaN       NaN   \n",
       "3  SAMEA3671418         4         2         1         2         1         1   \n",
       "4  SAMN07659096         1         1         1         2       NaN       NaN   \n",
       "\n",
       "  lineage_7 lineage_8 lineage_9 lineage_10 lineage_11      lineage_call group  \n",
       "0        i3         1       NaN        NaN        NaN  4.2.1.2.1.1.i3.1    4B  \n",
       "1         1       NaN       NaN        NaN        NaN     4.2.1.2.2.1.1    4B  \n",
       "2       NaN       NaN       NaN        NaN        NaN          3.1.1.i1     3  \n",
       "3        i1       NaN       NaN        NaN        NaN    4.2.1.2.1.1.i1    4B  \n",
       "4       NaN       NaN       NaN        NaN        NaN           1.1.1.2     1  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isolate_annotation_DF_indels.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### t-SNE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import t-SNE embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_SNE_coords = np.loadtxt('/n/data1/hms/dbmi/farhat/Roger/homoplasy_project/rolling_DB_scrape/Genotypes_Filtered_2/pairwise_distance_matrix/tb_output/tSNE_embeddings_from_pairwise_SNP_dist.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 15.77039433, -31.88985825],\n",
       "       [ 47.15923691, -36.95353317],\n",
       "       [ 12.14623451,  71.26150513],\n",
       "       ...,\n",
       "       [-15.47960186,  38.40433884],\n",
       "       [ 16.71702766, -43.72228622],\n",
       "       [ 68.94006348,  35.38974762]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_SNE_coords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31428, 2)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(t_SNE_coords)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define function to plot den-SNE & label isolates with a mutant allele"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('ggplot')\n",
    "plt.rcParams['lines.linewidth']=0\n",
    "plt.rcParams['axes.facecolor']='1.0'\n",
    "plt.rcParams['xtick.color']='black'\n",
    "plt.rcParams['axes.grid']=False\n",
    "plt.rcParams['axes.edgecolor']='black'\n",
    "plt.rcParams['grid.color']= '1.0'\n",
    "plt.rcParams.update({'font.size': 9})\n",
    "plt.rc('font', family='serif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def t_SNE_label_isolates_with_SNP(pos_i , mut_allele , SNP_label , fig , ax):\n",
    "    \n",
    "    #Get Genotypes for SNP\n",
    "    #get row index of genotypes matrix that corresponds to this row\n",
    "    SNP_site_row_index = SNP_annotation_DF[SNP_annotation_DF.pos == pos_i].index.values[0]\n",
    "\n",
    "    #get the genotypes for all isolates\n",
    "    SNP_site_genotypes = genotypes_array_SNPs[SNP_site_row_index , :]\n",
    "\n",
    "    #construct a boolean array that is TRUE if isolate's genotype matches the mutant allele and FALSE otherwise\n",
    "    isolate_mutant_allele_match_bool = SNP_site_genotypes == allele_code_map_dict[mut_allele]\n",
    "\n",
    "    #construct a boolean array that is FALSE if isolate's genotype matches the mutant allele and TRUE otherwise\n",
    "    isolate_mutant_allele_no_match_bool = SNP_site_genotypes != allele_code_map_dict[mut_allele]\n",
    "    \n",
    "    #Plot coordinates for each isolate\n",
    "    \n",
    "    #isolates without mutant allele\n",
    "    other_isolates = ax.scatter( t_SNE_coords[: , 0][isolate_mutant_allele_no_match_bool] , t_SNE_coords[: , 1][isolate_mutant_allele_no_match_bool]  , color = 'xkcd:golden yellow' , edgecolor = 'white' , linewidth = 0.15 , alpha = 0.40 , s = 15)\n",
    "\n",
    "    #isolates with mutant allele\n",
    "    mut_allele_isolates = ax.scatter( t_SNE_coords[: , 0][isolate_mutant_allele_match_bool] ,t_SNE_coords[: , 1][isolate_mutant_allele_match_bool]  ,  color = 'xkcd:black' , edgecolor = 'white' , linewidth = 0.15 , alpha = 1.0 , s = 15)\n",
    "\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    for axis in ['bottom','left']:\n",
    "        ax.spines[axis].set_linewidth(0.5)\n",
    "\n",
    "    ax.tick_params(axis='y', which='major', labelsize=12, pad = 0.0, labelcolor = 'k', rotation = 90)\n",
    "    ax.tick_params(axis='x', which='major', labelsize=12, pad = 0.0, labelcolor = 'k')\n",
    "\n",
    "    #add legend\n",
    "    ax.legend((mut_allele_isolates, other_isolates), (SNP_label + ' (N = ' + str(sum(isolate_mutant_allele_match_bool)) + ')', 'isolates without mutant allele'), bbox_to_anchor=(0,0.97,1,0.2) , scatterpoints=1, loc='lower left' , ncol=1,fontsize=8 , mode=\"expand\", borderaxespad=0, frameon=False, handletextpad=0.01)\n",
    "    \n",
    "    #plot parameters\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    for axis in ['bottom','left']:\n",
    "        ax.spines[axis].set_linewidth(0.5)\n",
    "\n",
    "    ax.tick_params(axis='y', which='major', labelsize=7.5, pad = 0.0, labelcolor = 'k', rotation = 90)\n",
    "    ax.tick_params(axis='x', which='major', labelsize=7.5, pad = 0.0, labelcolor = 'k')\n",
    "\n",
    "    #add axis labels\n",
    "    ax.set_xlabel('t-SNE 1' , fontsize = '7.5' , color = 'k')\n",
    "    ax.tick_params(axis='x', pad=0.0)\n",
    "\n",
    "    ax.set_ylabel('t-SNE 2', fontsize = '7.5', color = 'k')\n",
    "    ax.tick_params(axis='y', pad=0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def t_SNE_label_isolates_with_INDEL(mut_key , indel_label , fig , ax):\n",
    "    \n",
    "    #Get Genotypes for INDEL\n",
    "    #get row index of genotypes matrix that corresponds to this row\n",
    "    INDEL_row_index = INDEL_annotation_DF[INDEL_annotation_DF.key == mut_key].index.values[0]\n",
    "\n",
    "    #get the genotypes for all isolates\n",
    "    INDEL_genotypes = genotypes_array_INDELs[INDEL_row_index , :]\n",
    "\n",
    "    #construct a boolean array that is TRUE if isolate's genotype matches the mutant allele and FALSE otherwise\n",
    "    isolate_mutant_allele_match_bool = INDEL_genotypes == 1\n",
    "\n",
    "    #construct a boolean array that is FALSE if isolate's genotype matches the mutant allele and TRUE otherwise\n",
    "    isolate_mutant_allele_no_match_bool = INDEL_genotypes == 0\n",
    "    \n",
    "    #Plot coordinates for each isolate\n",
    "    \n",
    "    #isolates without mutant allele\n",
    "    other_isolates = ax.scatter( t_SNE_coords[: , 0][isolate_mutant_allele_no_match_bool] , t_SNE_coords[: , 1][isolate_mutant_allele_no_match_bool]  , color = 'xkcd:golden yellow' , edgecolor = 'white' , linewidth = 0.15 , alpha = 0.40 , s = 15)\n",
    "\n",
    "    #isolates with mutant allele\n",
    "    mut_allele_isolates = ax.scatter( t_SNE_coords[: , 0][isolate_mutant_allele_match_bool] ,t_SNE_coords[: , 1][isolate_mutant_allele_match_bool]  ,  color = 'xkcd:black' , edgecolor = 'white' , linewidth = 0.15 , alpha = 1.0 , s = 15)\n",
    "\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    for axis in ['bottom','left']:\n",
    "        ax.spines[axis].set_linewidth(0.5)\n",
    "\n",
    "    ax.tick_params(axis='y', which='major', labelsize=7.5, pad = 0.0, labelcolor = 'k', rotation = 90)\n",
    "    ax.tick_params(axis='x', which='major', labelsize=7.5, pad = 0.0, labelcolor = 'k')\n",
    "\n",
    "    #add legend\n",
    "    ax.legend((mut_allele_isolates, other_isolates), (indel_label + ' (n = ' + str(sum(isolate_mutant_allele_match_bool)) + ')', 'isolates without mutant allele'), bbox_to_anchor=(0,0.97,1,0.2) , scatterpoints=1, loc='lower left' , ncol=1,fontsize=8 , mode=\"expand\", borderaxespad=0, frameon=False, handletextpad=0.01)\n",
    "\n",
    "    #add axis labels\n",
    "    ax.set_xlabel('t-SNE 1' , fontsize = '7.5' , color = 'k')\n",
    "    ax.tick_params(axis='x', pad=0.0)\n",
    "\n",
    "    ax.set_ylabel('t-SNE 2', fontsize = '7.5', color = 'k')\n",
    "    ax.tick_params(axis='y', pad=0.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get **promoter SNP** of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_pos</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>490018</th>\n",
       "      <td>2715346</td>\n",
       "      <td>G</td>\n",
       "      <td>[A]</td>\n",
       "      <td>intergenic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            pos ref  alt     gene_id  gene_pos gene_category gene_name\n",
       "490018  2715346   G  [A]  intergenic       NaN          None      None"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SNPs_interest = [2715346]\n",
    "SNP_annotation_DF.loc[[SNP_pos in SNPs_interest for SNP_pos in SNP_annotation_DF.pos]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get **gene body SNP** of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_pos</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>490009</th>\n",
       "      <td>2715330</td>\n",
       "      <td>C</td>\n",
       "      <td>[T]</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>1206.0</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            pos ref  alt  gene_id  gene_pos  gene_category gene_name\n",
       "490009  2715330   C  [T]  Rv2416c    1206.0  Non-Essential       eis"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SNPs_interest = [2715330]\n",
    "SNP_annotation_DF.loc[[SNP_pos in SNPs_interest for SNP_pos in SNP_annotation_DF.pos]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get **gene body indel** of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_pos</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31204</th>\n",
       "      <td>GA_2714885_G</td>\n",
       "      <td>2714885</td>\n",
       "      <td>GA</td>\n",
       "      <td>G</td>\n",
       "      <td>Rv2416c</td>\n",
       "      <td>761.0</td>\n",
       "      <td>Non-Essential</td>\n",
       "      <td>eis</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                key      pos ref alt  gene_id  gene_pos  gene_category  \\\n",
       "31204  GA_2714885_G  2714885  GA   G  Rv2416c     761.0  Non-Essential   \n",
       "\n",
       "      gene_name  \n",
       "31204       eis  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "INDEL_keys_interest = ['GA_2714885_G']\n",
    "INDEL_annotation_DF.loc[[indel_key in INDEL_keys_interest for indel_key in INDEL_annotation_DF.key]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Specific mutations that we're are interested in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2715346, 'A-14A eis-Rv2417c')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2715346, 'A' '-14A eis-Rv2417c'\n",
    "\n",
    "2715330, 'T', 'eis 3C>T V1V'\n",
    "\n",
    "'GA_2714885_G', 'eis 448delA'\n",
    "\n",
    "'A_2714860_AT', 'eis 473insT'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Main Figure 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x630 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figM7 = plt.figure(constrained_layout=True)\n",
    "spec2 = gridspec.GridSpec(ncols=2, nrows=2, figure=figM7 , wspace=0.155, hspace=0.265)\n",
    "figM7_ax1 = figM7.add_subplot(spec2[0, 0])\n",
    "figM7_ax2 = figM7.add_subplot(spec2[0, 1])\n",
    "figM7_ax3 = figM7.add_subplot(spec2[1, 0])\n",
    "figM7_ax4 = figM7.add_subplot(spec2[1, 1])\n",
    "\n",
    "################################################################################################################\n",
    "\n",
    "t_SNE_label_isolates_with_SNP(2715346 , 'A' , '-14A eis-Rv2417c' , figM7 , figM7_ax1)\n",
    "\n",
    "t_SNE_label_isolates_with_SNP(2715330 , 'T' , 'eis 3C>T V1V' , figM7 , figM7_ax2)\n",
    "\n",
    "t_SNE_label_isolates_with_INDEL('GA_2714885_G' , 'eis 448delA' , figM7 , figM7_ax3)\n",
    "\n",
    "t_SNE_label_isolates_with_INDEL('A_2714860_AT' , 'eis 473insT' , figM7 , figM7_ax4)\n",
    "\n",
    "################################################################################################################\n",
    "\n",
    "figM7.text(0.10, 0.89, 'A', fontsize=12 , fontweight='bold' , color = 'k')\n",
    "figM7.text(0.51, 0.89, 'B', fontsize=12 , fontweight='bold' , color = 'k')\n",
    "figM7.text(0.10, 0.47, 'C', fontsize=12 , fontweight='bold' , color = 'k')\n",
    "figM7.text(0.51, 0.47, 'D', fontsize=12 , fontweight='bold' , color = 'k')\n",
    "\n",
    "figM7 = plt.gcf()\n",
    "figM7.set_size_inches(8.5, 8.75)\n",
    "figM7.tight_layout()\n",
    "\n",
    "file_name = '/n/data1/hms/dbmi/farhat/Roger/mmpR_BDQ_mutant_project/figures/eis promoter SNP eis mutations tSNE.png'\n",
    "plt.savefig(file_name, bbox_inches='tight', dpi = 300 , transparent = False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uMdpXZ8M5xdAXhfUtE3OAijFBCYTjFLfbjcfjob29nebm5nFZUMPhMJFIJMZqoGkaVqsVi8WC1Wo1tkUiEaxWKwBWqzWhXkI0GjWCHfX3mMUCMGJLgt/vNz6jUChiEdEo3LsXpjngjmrIs8E9u2Twoc6UTFjuHPyMEDIIMQjs6oVfzIeXWhHXboWXWsEqYNU0tCWFAPLx2kp4oW2cj04xlqRcILz++us0NTWxatUqrrrqKl566aVU7+KkxxwoqD8KIcbckmCz2QwxAIOLup65YI5F0C0JNpstIZuhpaWFuro6Q2yY4xlgsGtfYWHhEefkdrtpbm6mrq6OlhZ196JQJLC+Fa4qhYosiAr4gBNWlsHKLQgj9kfAlk7518B1zBYv5FhgQT78qQ0+UwlPLoWlheCNwoL8wfcCLMqHLOv4HptiTEm5QNiwYQMbN25k9erVPPvss9TV1aV6Fyc95kBB/VHTNBwOx5jvu6qqivLy8oQ7fl0k6JaEaDRqbDcHIEajUXp6egAMC4OmaWiaFvP3SFIk/X6/8QOlCyRlSVAo4qjxwexcyLSATQO7Bc4qgg9Nhk/tQIRC8NxS+KHbEAls6YQf7ofOAEyzw+J8mOWAr+8Gtx/OdMo4hH55jYtwFDZ3wkWTjd2Kvj7EBX9HfPhNxIWbEH19E3QCFMfKmNRBcDgcxmI1HovWyYjL5aKjowOA7OzscT3PgUDACEo010IwByqaMxxsNptRllkXEPHv110STqeT4uLiEc8DiLFCeL1eVf5ZoTAzPxfe7IBzJ0mBYNVgTw+smAxR4O4atJ8vRjyxED62FdAg2yJFQX4h/KgWfrMY7tkN11SgLXci3vHKLIbPT5cWhne7YUMb3DcXkOKAf3sPvrMA7XQnYnMn/Nt7iN8uQMvOntDToRg5KbcgLFy4EIvFQm9vL9u3bzd+xBWpp7i4mOLi4nEXYfn5+YaVQLdemN0dgCEesrOzqaurMwIPdTdIvDiwWCxEIhFyco4ur9q8X03TyM3NTdVhKhQnBleUwtNNsN07KA7e7pZxBOcWQ22fLJlss8GhTrBZ4LwpcFk5nFkEdit8ajNcMUWKAyFA0+C0AvjVAVi1TYqDe2ejDVzTXLYV7pkpgxpBPt4zEy7fNoEnQnG0pNyCsGbNGs455xx27tzJmWeeyZIlS1K9C8UEo8cigLQUJLMm6HEIurVBf29LS4vxfj2YUf+s1WrF7/eTlZU1onkUFRXR2dkZM57dbh+bg1YojlM0iwVx5wz4gRu2eeH8yXBqnhQJ1Q44vRBufxeCfpg6Gb42WwYs7u6BrT64aZoMPmzoQ7zVAS8fgmWFcOM0eLMTDgakleK+/Yj7BkSC3WqIA2MepzsRNosMmlzfKl0fC/PgoyWDwkKRVqT8W5k3bx5f+cpXCAaDPPDAA2zYsCHVu1CkAVVVVcbfetyA/k93F+gBjObARl0s6EGNZnEAHHWKo8vlori4mGnTplFdXZ26A1QoTiQWFcLFk+CtTvhbO4SAGQ658N/tAk8I3vTDzVWD2Qyn5MGyAsiwytc7gtAbhf+cBa4cWNMMp+XD8gIZnHhVKbw4kMUQjEi3ggmxuROCYSOjQvvqbKjMhnv3qvoJaUrKLQjBYJBoNMqKFStYsWIFO3bsSPUuTlp8Pl9amdBdLpcRGJibm0s4HMZmsxEIBAgGgxw+fDgmytlsYYivvqiXaYbBBlBDBSrGt5keacyCQnGyomka4trpsLtbWhH29cJZTvjWHNjlg0oH+KMyQ8EXhtyBpeGUPPhZLbT3wy0zwGEFuwbVORAS0NSPtiAf8UIr2uWliA1tcEUZvLRMxhwMuBnE5oGgx1umwczCmPRIAVJYXFE2YedHkZyUC4SzzjqLw4cPU1JSAsBpp52W6l2cNASDQcNk7na7jUwBl8tFIBDA7/djt9snVDSY4x9sA73hm5qaYuog6Iu/HqAYCoUAYgot6RkQZhERCASwWCwxbgO3200kEqGzs1M1g1IohkEIATs88PVdcDgM+XboD8EkO9w1Uy7s73XDH5vh+mnwvfehrhdKBlx8uTaZ6rjdC2WZMD1bioegkBkRc3Jh4yGERYA/jPjBPghFEdEoWnY24rcL4PJtCJsFwlF4YSn8qBHtU7Hpy9qSwkFhoUgrUi4QPvWpT6V6yJOS2tpa4468sLAwZpHVF0l9IR1pWuB4EN8KWnc1JBMCenCj+T3mlMfm5mbjHEybNo3u7u6ENtPpctwKRTohhIDfN8CmDqgugG9Ng1PzZfrij/bD0wcQEQtMzYa5OTIW4Ufz4LYauHumLKb09zb4UzNcUw47umGrFy6cDIeCoAG1vdAVgt81yWZO50yC1j7pMrhvtsxWePXs2HnNz0Vs9xoWBACx3QsL88f5DClGgmr3nIbobhp9IfV4PDHR+uZAQP3vdHA/6MJFX/B1C4LZMqC7FsyiID5QMb7wUjgc5sCBA1it1oTPJTtufV9erxe73a5SbRUnHzU9UNsHVTlwUQksKZDblxXCB4qk7//SEtl06dkW+JcHzpsETyyCz74NZdnSQrCqAp5vg4N+KM2C931y+1YvvNgKe7rh8zNgbh7k2dDKsxBoQ7sMriiVAoIBy8F2LzzbCvfNHtfToxgZKnQ0DWlsbExopxxfFMm8DZhwcTCU5QAG4wwA45j04EWzcNAFBcRmR+hiIBwOM3369JgSzvHH3dLSwv79+3G73Rw6dMgoQ+3xeFTK7RiiKqimGXV+8EdkXMGSgkHL3B6fFAbz8mQNhGI7nD9JFjna64PMTPjyLEBAYQbk2uF78+FWl0yRjAhY2wzdYWlNKM6CD01Gy88YLHa2pFDWRUiCZrFIMXCwD/Ht9+FgH9w3W2UxpCnKgpBm1NXVxRQZMrsRdHEQ/3o6mNnNKYdmcWA+Bl0MmFMc9e3m4knx4kJ/zSw0zNt1dLdMss93dnbS2SmjqtPhfJ1obNiwgQMHDrB69WocDge/+MUvJnpKJzczHLLSoV2D7V0I3YLg9ktB0NQHeVYosEEwKoVCfxR+WQ87u+ChhdAWlNstmvzM53ZAaSa0B+AvhyEQlnEJmzsRpzsNgXAkl4FmsUjrgoo5SHuUbEsj4psh6RecuZSx/mi+807WKXEi0FMOXS6XkdoYX0BJt4zEt4eG2IJJZiuBfh7sdntCHIJ+7MFg0BAH5jHNY+suC2VJGBvMFVSzVbW8iWV+HlRnQ30v/LYBtnfJu//eELx2WC70lgGrwqF+6AnDO92yQdMN02BbN+Ta0DKtaDaLFBvFNvh9E7jypFXhSy6Z9vhovXQ5wKDL4KMlE3v8ipSgBEIaEQgEki6o8ZgXUsC4M04H9JRDp9OZ4EaAxK6NugCI32b+jL7wV1VVkZubm7RLpN1uj7FCADF1GfSxQQmEsWD58uVGBdUdO3bE9N9QjD+apsE10+Dm6VDXDbfsgEvehEfc8HobbOuC3ojMYnj9sLQaLMxDW+aUduWH6mC/DxhY9J9sBG8YbqmSPRxsmoxjuLoSlhbAkw0xLgM0DfFeN+KFVsR7XYh3u+TfNd2xDZ4UaY1yMaQR+iKnC4B487x54TQLCL0CYToF4yVzOZiPKT5oMT7FMX5x17MWIpEINpvNKDOt4/F4YjI94rtE6s/NFhpF6ujq6mLhwoXccsstZGVlMW/evIme0kmPpmlwWjG8GJdJoFcy/M8amX542RT4Txe8fBix4X3pHvjVQljdgPjefrAC0QhU5kBLANa3yFLLFVkwO0cGJC4oQPv3Kjm+EPBME+RaQSCDIM8ogjOccKAPnmlCfKbCsAwq0hclENIIu92OpmlGJH/8YpcsbVBfBFtaWtLOt643lCouLqa+vp5QKBSTtWD4LAdEgi4K9OfxGRHmrAav14vX6zWOOTc3l/b2diAxpsE8ttnqoEgde/fuJS8vjyeffBKAP/zhDxM8I8VQaBYLXFkOV5ZLsfCnRjh/EwgNsq3wrhc2HZaFkFaWw6EAvNoOHysFdx/MzIHyLPAEpaXBhuzZoLOzE95oh/wMmUZ5wSRZW8Gmwdxc6I3ILIsFKrUx3VECIc0oLS3l4MGDAEnN8mb/vDlGAUg7K0I0GqWgoID6+nqCwWDCYq+LoKFSHiG2qVOyzpGBQICsrCzsdjs2m41wOBwzjj62fq7STUSdKLS1tbFv3z76+vqIRCLU1tZO9JQUR0BEo/Df78l0yKVF8JlKmQa5owueapQ1DubkyoV8mRP+rx1Od8KpBbLbo9MOpwTg9VYZ8wCy6dP/7IFzS2QxpUBECo55U6E7BDXd8Mph2NUj9//9U+C0YmVNSFOUQEgzHA5HzKKmC4N4S4L5DhykiEgncdDS0kJvb29MBcVkFhAYOnMh/n1moaA/mps7VVdXEwwGaWxsNCwG5rbYqiTz2PHAAw8AkJGRwcGDB1mxYsXETkhxZNa3SpP/B4vg7GJYUihTGZcWQlTAPz2QY4WwgEUF8M9O6X5wDLgObBqsmAQ13YOWv6v/DypKINMKZxUBQhZbWtcIL3fIKoz3uOCHC2TRpu/WwqXdiOtmKJGQhiiBkIbMmDGD2trapCmC8bEH2dnZOBwOCgsLhxsyKd1/biD/kmkpnTtAfX094XA4oXzycCIgWayCOY0x2TgWi4X8/Fgzpd1uN3pE6IJJCYOxp7Ky0vh71qxZzJo1awJnoxgRNT7oR2YwnFogayTU+WWK5Kn5sPEw9IahIyQzHIps8j1nFcmMCJsmqyk6M+R4bx4Ef64s27ykQLaV3tEtUyXzsuDni2Rlx42HZWrl+ZPgLpcs8bxsEsxXLod0QwmEccbcX2EoLBYLOTk5+Hw+407YvECaF9e+vj58Ph8ej2dE3Qw9a93gFxR+1U3ewX6i5buxHLwkVYdHOByOcSfEC4NkpZf1Y463GJitDuaARv19NpvN6P8QTzpZUxSKiUSEQnDbO7CnV3ZxtAk4xwmnOeG9Tmk1eKgOPjJFpifu6YGH68AObPbK+IIzi2Sq45MHZQGlmTnyff/okFYEgH/fAw8ugMUDC/3cPJkJMdMJRRnwUhucXQSXlUpXw68b4N+mQlCDWr8SCGmIEgjjiLmN8ZF84ZmZmZSUlOD3+wkGg7S3tyftXaAvoPrCPJz4iFb+GWdfFDpDMNkOFtCa+/H8cj/5l5diq0hNNcZkgZTm4kjmOg5AjGkxWYChEIJQKGSMa7fbqaysHFIcKBQKiQiF4LodMobgFpds07yjS7Z9fr4VKu3wtw64oxoqsqXroDwLsiyw4RB8fjrMzoVAFCoc8NEpsLZJWhy8QZnV8HwrotYvO0Ke7pRVFvNtssBSVIMpWbIw0weL5FiWgW6QmVZ4ugnsQvaCUKQd6hd2nDD3KRBCDNtoyNyxEIj5nLn+gXkxtdlsw4oDz1o3zp6INCcCHA7C1CzojeD8QR3cuovQwhwydn5oVMdpLmSUrEKiWTSYS0kDMSJCtzyY20ALIZg0aZLKQlAoRspdNfCxMphXAPNypbVgWaGMIyjPgn09cIZNioOmAESj8Jd2ead/wRTpCvjVAbimQqY1OifBn9vgmYG6CFPsUDbQo8EXggN+2S1S0+Q++sIy1iAi4NNFcrxgBOwW6cr46m745EDpZ0XaoQoljQN6nwLz4q5vH+q9MNiPQP9br6horhKoVwjUA/WGIv+DJZBvHfQXOjOgOQDLc+CD+ZANtnd7CTf5RnWsRUVFCdUMzXEFusskvpoikLSoERAjLDweD/X19aOao0Jx0tAShGwbzMoB60DlRIcVpjuklWB/LywuhvZ+uXjX98keC6fkQUGG/HdWsXRP9EZkzwZhgdtmwuNL4EszYU4+nFcMi53w4F7oCEJ/RAqJJxvh1/vl+DU9MuCxPShvULZ7IRQCFaCYtigLwjiRLEAv2Z2wucCQHnugf8b82fiYBJ/PF2O2j6f7hVacAlkYpTpL5jhPzoYdfjgk+71Hq7VRuxk8Ho9RRREwMgjMnRohsRV0/Lb4jpDm49cbNykXg0JxBMrs8i5+X6+0INg02cTpgF+6DWya7NS4o1tWRfSH5Z29LwwlmdAZlHEEzzTLoki1PXDddJidJ9MYl2cNWAoi8G/T4L/fgZu2Q5kDmnvh+hKw2uD5JvhrO9xUJYMcd/XAI3Xl6vo7AAAgAElEQVSw/nQlDtIY9Qs7DuiLfnyAYUtLC2VliQ1LXC4XjY2N9PX1xVgL4gsmxac5hsPhId0MlkobNPfLPu4ZFulrDCN7uwO09GO5qZxodxBL/vBBlEPhdrtjCjzFB1eCdIWYgxghsb5BsroPycZRKBRH4Mfz4dod0n0QiSLMMQhbPXB1BRRlwxavDCJ0OWB3j0x5RMi+Cgvz4DunSDfCvzwy5bEnLAMPQdZAeKZJFlXKyYSnT0OLCzgUt4Xg9p3wzd2Qa5PWzKeXoGVmjv85UYwY9Ss7Tpjz8fXHnp4eSkpKEu76o9Eo/f39Cf76oWoH6KJhuEUz2h+BikzojkjrQU9YOpim2KVIKMukLxTCcYziwFzqWD8GPTjRXBRp6tSphMNhDh48mGBBAJJ2sTS7JvTxfT7fhLe4VijSHS0jA/EVl+zS+FIr9AvI1mT/hBWToC0MF+bL2IO2fvhgIezzSatATxgW5UFZNuTYZH2Ej5fB3zvgg07pcsixStdBtUPGGvjD8KcmxHMDwuKjJWgWC1pGBjyybKJPh+IoUTEI44jePyAvTwbk5OXlJXUJ6GmO8XfWZpeD2aefbCzPQ/tjxsxd6JTd2BCQY5ExCFHAYYGZmdJEeMWMUR1f/Pw0TaO6utpwLZgDKeOrQep/R6NRI/3RHIdgFg1Wq1WJA4VipCwugvNL4eHFaP84F36xWPZoOGcStPplEOK/VcEDp0gh0BqQsQKP1MGMHJiUIV0Rdgt8qgJ+fQDe7ZGVEbd4ZUGl/ig80SDdEFVZcNFk2OuDm3bK6oqK4xJlQZgAysrKkloOYDBwsaysjP7+fsLhcELZ4fho/6qqKrofrYdb5RjRij/jbOkn+oAbS9MleH65n6xNh8mo7webRVoMZmbDQgds6oKCDESeFccHS0d9bObFfdIkmR+tVzg0uz/Mx2KuDBmfqRHfh0E/bo/Ho7IZFIojYDRmcvvA0494vwdm5sKny6XV4O5OuNMlrQMFGbKokaZJcZBnk+6GM4tk9gPAu90yIPF/dkNxpizHnG2Bhjw4v1gKixfbYGkx3D0TtnbC599B3DkDGvqlpWFenoo7OE5IuUB4+eWXeeKJJwx/+KpVq7j00ktTvZvjnmTiwOzD7+zsxOVy4Xa7E/zv5jbJOTk5WKa/JgXBt914/9uFs6UfAK21n2jVRpwNAWkx8IVlkROHFXwR6AgS/uwUQl+sGrU4SBZnYV7AzeJALycd35DKHFeRrNYDYFRYVOJAoRgeEY3CvXvhqlK0K8tl2+ZnW+HyUinG17fJgMSziiDLKlMRHTY4t1jGG5xeAD9yS6vCkgJZKGldC9xVLUVAjg0+VgJtIbhgskyb3uaF0wpljAHAvHy4sky+/8Ip8JfD8MxBxDfnoJkqpSrSk5QLhKamJtasWWM8f+yxx1K9ixOSlpaWGB++fpdsvnOG2BLFVquVzGd70XRB0NJP4ettUgx4Q7AkD21bj9xBZ0jGGVyQT9ZDSwm3+rDk2MmoyCUjRccw0r4H4XA4xmUSby3QH+NFQnV1NV1dXap0skIxEta3SnGwRJZh15YUIgCea0K81iGrFxZmwMGArIwYFnJFaOyT2QubvXBeEfylTbZ4npULt0yHbd3wP3MhIGRjpu/Nl4XXMiwygLEkS2ZFZFql8Lh0iiyx3BeVBZm2euE/diN+OE92llSkLSn/dtrb2zlw4AB9fX0cOHDAaMF7spGsxsFQ6E2HILaDYzLMEf5CCPhMEaJ8IBLYmYH2lg9ykUVNtvcgyuRroiwT2vrJ2unHkm/HPrsoZZUTzehxFsOhxyKY3QsQG6AYiURiqjHq3RqVOFAoRkiNzxAHBovz4XfNsl/CS2dIC8C6ZhmYqCFdCi+2yRTHpj4Zc9ARgstK4BPl0NQPrx2WmQtnF8FFU6DILt0R2RbZy2FPj0yrjAoZt7ChTY6zrBB6I2jLnXB5idyPIq1JuUC4+uqreeSRR7jxxhv55S9/yTXXXJPqXaQ9breb9vb2mNLKw2GxWIzeAeZgvNzcXHJyctA0jYyMjJi4A33h9Hq9eL/oglyrtBo098smKM0BENLNwK3laL0RKSAOSLeD/1+thJt8eH6+f7ipjRlVVVWGlSBZUKLZBWO326mqqpqAWSoUxzHzc6VbwcxLbfDZcljulILgE5Xg0GBtsyxy9LcOWTvhmqkwNx/e6IBrKuVC/6dmaRG4phL2+WU21IcmS4uAXYM1zTIAujxLxiz8qgH2dMO/vIPVG31hRG8YPuCU8QyKtCblAmHjxo1ceeWV2Gw22tvbWbt2bap3kdaYKyFGIhEaGhro7j7yhVBWVpZQVdBut+P3+427aXMVRn0xrTpnH86v7ZJWBA0oy4QnW+QdQL4NyjPh+Q5ZH92qQW8YTUD2pduxnv4PnHfsIlrx59SehBFSVVXF9OnTsdlsCZ0rQZ6DyspKJQ4UimPhilJ4ttUQCWK7Fx5vgE9WDL7HApQ7ZKzALTNk+mNvFHpCsKIYXjkkXQun5Mm0yKnZsCBfWhysgIjCHw9KgbEoT7ofcmzwxSq4YBKsaYILJ8lUyL6I7MPQGYL3e2TraEVak3KBsHfvXurr63nyySd57LHHmDJlSqp3kdaYSw1brVaCwSCtra0jsiZUV1czffp0pk2bRnV1NU1NTUkLBoG8w560bsBCUJiBttcP07LhUL8sgtQy8JhnlWZBkNkLFVnQGJAmwdbB2IWJsiTYbDaqq6ux2+0xqZDRaJSMjIwjlpBWKBTJ0SwWuG82HOxDfPt9ONgHpXbY2jX4ppqBrIb5edIKMD9fCoB/dcpqizkavHoYDvTJyonekLQYVDlkwaXn2uDhBTL4sDUIpxXIsaLIzIiLp0jLwZ/bZP0Eh1WmPz7bJjtHKtKalAcptrW1sW/fPvr6+ohEItTV1aV6F2mNLgSSBdsN16BJR4/2j0aj9Pb2xkT4WywWQqHQYMGkMgvMz4H3euWH6/tgUQ7U9UOGJvOUwxkwLQsaAlCZJQOSQHZhu6gIXvcgJmdS9KWZY3ZORkJVVVVM+WRVSlmhGD2axQJXlMl/gPjIJLh+p0xtXF4oyx9fMFnGD3SGoCQT7exixMO1UiTYrbKlc7FdVk7c0wOvt4MmoDQLbq2CwixYNVUKhi/MkLEHFiAopNi47324pQoCEXjsAGRqcN9sFaB4HJDyX+AHHngAgIyMDA4ePMiKFStSvYu0Rc860GMF4vsJDJW/rwc0xm83V1AECIVCgBQPMy+oQ+uPygtVX/jLMuFQSFZAm5YlFf/+PulmmJst05GW58J2H0zOhI0eRFkmlqZLxvrUjAizIFDiQKFIPVpGBuKJU+GeXfDdvTJ26UOTpel/SiZkWxDvdskYhaWF8L9NcvGPAn9qgYZeuK5SWhD+2CJ/d6LIcbZ54e0uKTwCUegNy6DFBXloCwsQe3pk58dPl6s6CMcJKf8VrqysNP6eNWsWs2bNSvUu0pauri4jrz9ZPwEhBJ2dnTFCQG/tbLFYjNoHZuJLKkejUYq2WdD8EVnQJAx0BOQF6jO1c24YcCOADFwsy4POPkRzCO2KKbDuEDDgXnhwP0X/NbEWBIVCMT5oGRnws1OBgUyoNc2y5HpFtjT/1/jgE6VwIACz8mBOnmzyNicX6nph4yG4vAwumyIFwm8b4NQC+PBk2ZOhLwKnO2UWxEtt8KkyxAutskiSEgfHFcrGkyL03P74FseAEamvdyLUgxbNAY16K2fdmqC/HwYzG3Tx4V0OYlGONAnqxY98EWkmLDK1c86zDgYuvi3rIWgH+4mGZe8FkOmPShwoFCcnmqbJqordIfhFnbzBuKpU1izY3QPLCuQbMy3yt6Q8W742xQ55GbLx0pVlaB8thWVF8LmpcoyfuKV4uG8O2sJCtMtL0ebnK3FwnKEEwlESDAaHfV1f1OO7L5rFQn6+jN41BzTq7gTduqCnMuriQt+mF07a/2iZzFwQQK4FFudCBuC0xaY8frhIBhqVZ4EGojwT63PnEvq/5XjunZs27oV05EjftUKRjgghEO91I15oRdR0G78ZQ6FpGnysDEozwWqRC/xeH9T2yt+R/gia1SILKf3TIzs+dodlQaW3u2Ge7C2jzc2D+gDaFWVoX50jH1WcwXGN+vaOgtraWhobG6mtrU1YPGw2GzabLcZaoKtlc7lgvc2zjsvlori4GKfTabgXPB6P0bAJiBEJerVFq3XAOpBrlY1UDoWky6GuT1oRcm1QkYl4rxcREtAYQMzIxnJQCgL77CKK75091qfsuKW2tpb6+npqa2vx+XwTPR2FYkQYLgObhnZ5qUxtXtM8MpHwmQrIssA/O2W9g9tmwJse2NuLaO+XAYqdQZny+GM37O+NcRmIPQNdHRUnDEogjACPx4PH4zHcAOFwmIaGBkMo6G6ByspKo1cAxJZHNt/99/T0xIiEnJwcCgoKiEajMUWW+vv7EULEuCF0y0LhZoHW1A/+iMxbbu6XtdRLM6FnIBYhJNBa+tFsFrCAVtuH5xcTk854PBEMBgkPdKALh8NGmmp3d/dRVchUKMadmh5YnC/v5hm4q1+cD7t6jvhRTdOkG0B3B1gscFWZzHjSRcNnKmXDt+unSrfm+1I8iz09MdYExYmBChU/AnraYrKsBCEEDQ0NaJpmBBjabDbDpaBnNBhpiQxWCOzt7SUajVJXV5fQqVDfHyT2XtDf27E0irMsEw1kzQM5wqA4AFn3wJUN7j6YYkc4LNgXHJ3Cj3YH8W9vx16ehSXHPiblmdMNn8+HzWYjHA4b35cQgtbWViOYVC/wpFCkFXV+aTkwoc3Nk0GC84+hMNGCfBm0OCA6DCHw6XI53q4eFYB4AqN+4YZBv1uMFwf6wq+jb29oaKCqqoqWlhZ6e2VtAr2FMcSWD87JySEYDBqvmWsd6H+b0yPNbgp9/7VvuHDuEDjvqkVr7gchBl0OA82ZqO2TOzwcRLvISc5X3ifa8S6WfUeOPfCv+ifZr3eRMyVDVk0LCkJTs8h46exRn9t0Rc8qif8u4gXbgQMHsFqtMVUePR4PNpvNiDFRnFwIvx8u3gK5GeAPwyvL0BzjbHKf4UDs6TEsCDA607+maYhPlw8tBObnH5vwUBwXKBfDEYhPM9S7EJoXDP19oVCIlpYWysrKqK6uZsaMGYZVQdM0o9+AzWYzXAogL0J9H5qmUVxcjMPhMMor6zEI5oUKpMjQ9muymmJJhnQ3ZGkwyYpYlgeH+6XLQUM+buyExiBaEHx/bR72uKPdQbKFBbI1aA1BSxhaQtje6yV6yl8IN514fnk9q8T8XcS3m9af664mPSPF7Xbj8XhGXDVTcWIh/H64oQZ+tAjtlTPhBwvhhhq5fTyZnwdvd0tRQGpM/wmuB2UlOGlQAuEIJLt7NHccNJdVhkHXgS4EzOmP+jjhcJiWlhYjDsEcZxCJRPB6vUYPBn1b/DzKysooKyuj6L9mIkozZREkm0UGJLZH0Lb2IEqzoLsfzswfdEO09IPVQvCfR/jhmv9/8H9eCCHbuLb0y4Cn7ghatgXrVVsILXstZec5HfD7/QnfqzkTxRwsqgeh6oIgvlX3SPpvmJtTKY5zLt4Cd82ApoGyxi0B+fzDW8d1GkbaYkTIO/6IUKZ/xTGjXAzDUFRURGdnZ0zKonkhiBcLeqpiQ0MDwWAQu90eYwUwxxDELzjxaZHmEs3xWRBWq5Xc3MFYAMvBS+j41l60qeC8c78Rg6AdDCAuKUJ7t3cwFsGZIeuqFwx93J6H9+MMCRnDUJopzaUa8vnSXGgOQUs/trJMwk2+EyIuQb/rj88WMbsZILaEtjl9FYh5rx7kOBQtLS309PSQl5dHWVnZGB3V+PHyyy/zxBNPEA6HsdvtrFq1iksvvXSipzV+OKzw4iH4SAmcUyyD915sk/UDxhlN05TpX5ESlAVhGAIB2bdAv1s0xx6YLQLmu0rd9KwvIPHWA3NbY/Odqj6mvigBRkaEbo3QUyKT9XMo/sZs7DNyId8qRQDIxX1XLz2/OgURiUJ1NnhDiNJMim4dpjhSP1IMaEDvgEujMANmZEGhVdZdsAIt/Vhuf/dYTm1a4fV6DSuOOfYAYi0IQIIgBBJeg8Sy2Wai0Sg9PdIE3NPTc0JYEpqamlizZg1r167l6aefpqmpaaKnNL40+mW3w8UFaJMyYXGBfN40zi4GhSKFKIEwDHonQd1yYI5BMGMugqSjLxpmK4F5m14ESf+8LhzMAgIGRUJ1dTVut5uOjo4hfdy5K8oRFuCTk2BGJhzuR2vsJ7wjiKX2Ejw3T8fz4yMXRyq6a6YswlSYIa0RVk0WTHFY4N0+2NcnezlMz0TbcBj/v1pHekrTEofDEfMdmftfxFt29FgSswtCf8zMzIypZzEUFouFvDzpE87Ly4uxQhyvtLe3c+DAAfr6+jhw4ADt7e0TPaWkiGgU8Vwz4tt7EetbEKkSZ2VZ8AEnZA/8BmRb5fMS1Y1UcfyiXAxHQBcD8f0QzEGHem0C/f3JWjTHuyji/4ZYE7V5XxaLxYiu18dN1vQJwHLgEnx/bcLxggctEltKufg/R14YKfCnU8k+c7N80hmCOQ7ojEjLAshW0RcUIKZl4vhg6dADjZBoNIrf7ycYDA579z0W2O12I15EdwOZC13FW43M37U5q2TatGkj3mdZWRklJSUnhDgAuPrqq3nkkUc4cOAAVVVVfOELX5joKSUgolH4j23wj15w2uHFZvjrQcQPlo6+4l9fVLruTslFhIW0vtX3ye0KxXHKifHrNEa43W7C4bAsXWoy98cLA3MdA7OZ2XzXabYS6MS7JiA2rsH8uj6e+VEv4BRP7ooKLE2XjKqUsuODpdKKADA1C/b7oX+gBjtARSbs6sN/3+irMba0tFBXV0dbW5tRJGq8KS8vT8he0L8vc9wJkGDhOVYXwYkiDgA2btzIlVdeic1mo729nbVr1070lBJ5ai80AT8/VWYa/PRU+fwPKSgetsAB65pht08WJtvlk88X5xz1UMLrRSz4C+LDbyIW/gXh9Y5+fgrFMXDi/EKlmPhGSjrx7gYYXCSys7NjAhjNWQ/6QhMfc6A/mpszxQsCc3Cjbrno7OzE4/HQ2dk55II62lLKloOX0Pfm6QQ+WwT5GeAJyTTKUxyQb4HWIPby0ZlQdcsBxNZ7GG+RoDfS0ucBJIg9syVBF4Nm18TJnN64d+9e6uvrefLJJ3nssceYMmXKRE8pke82wj0z0U53AsjHe2bCdw6MfuyHT4OaLvh7O+KxetjUDru64OeLj2oY4fXCzfvgN8ukiPn1Mrh5nxIJiglBCYQhSNZIKVlcgS4C8vLyjMCz+CyHeEuBuRSzOZ4hPpVR37c+hr7N6XQaZm09uG6sFifHB0vJ/O9FMv4gxyqbQ/VEYK+szmgrHV0Gg54OaHap6M9HUtY4VQF+DocjIWVVP8fm70n/OxKJJLiHRjrnE5G2tjb27dtHX18fPp+Purq6iZ5SIpUOWF4Yu215IZSNvpiRlpEBTy2RHRBfaZOPTy2R24+Gs7ckFzHnbBn1HBWKo0XFIAyDy+Wio6MDgOLiYiMOQHct6OJgxowZxkLR09NjBCCazdXmlDmIjWmAQSEQv91812pekCC2pfRwcQmjxZJvx39hPtm1Aciwwt+9UJZJ33n5OPLtoxrbO3BnFH+OrFYr7e3teL1eqqurk3421amCLpfLqIXQ29tLd3c3oVAoJrUxPg7BPHer1Tru8RPpwgMPPABARkYGBw8eZMWKFRM7oWQ098GWTsRy52CF0y2d0NaXkuG1jAz42amjG6QyxxAHxrinOxHlR++qUChGixIIR6C4uNj42+VyUVdXRygUwm63U1RUFFNWVw8809MSARoaGgiFQkNaCoCEhdF8t2ru3jht2jSysrKMxlH6Z/U73rFcnByPn0G0WwYohlt92EpzRy0OwuEw4XA4Jp7DXFVSf49eU8JMfKpgqgL+WlpakpbWjk9pNIs1/W/HeJfVTSMqKyuNv2fNmsWsWbMmcDZDsGk5/Pv7cM9MxHKnFAc/3A9/Xz7RMxvkYC9ic2eMSBCbO6G5dwInpThZUQLhKJkxYwbhcHjIRj3mjAaAadOm4Xa7jYC3odwW8U2ZgAQfd1NTEy6XyyjgZG4KlewuO97iMFosA4LAnp8aIRIIBBJcBMksNMmOQU8V1C0IqTjOQCCQULfC/H3EZ5bEu5L8fn/Kz7kidWgFBYhH50hzfZkDWvzw9+VoBcNUDRtvNi2XMQcDbgaxOQ1FjOKkQQmEY+Bou/g5nU7sdjvt7e0xFfbiFxzdxWCxWAiFQkCs+VoIQSAQICsrC5fLRVNTE36/n9zc3IRF6Xio1GdeTOPFkrlORG1tLXa7PaExUqpTBbOyspKmmJozTHS3UXxBK03TcDgceL3ek9bNcDygFRTAOxce02dFfz98docsJJatwe8Xo2VmpnZ+hYWI1bPgnC3SrdDcK0VMYeGRP6xQpJiUC4TXX3+dOXPm8JWvfAWfz8dNN93EZZddlurdpDV6oFpRUVFMd0BzJcb4xTC+Yp/ZzG72e/v9fqOAU0VFRdI71rEyv6ea/Px8Dh8+bARaDhdnEQwGDZdEU1MTkUiEzs5OnE5nShfkiooKGhsbY+pcQGL6qR6oas406e3tpaenx2j9rThxEP39cN078JXZoLsnrnsH8btFYyISePeilI6pUBwLKV81NmzYwMaNG1m9ejXPPvtsekYzjyFut5v29nY8Hk9McaP4rAPzwqL73ZNVXITYegl61T/dEpFs4fd6vcdNpT6Xy5UQyKmfK7OIstvtHDhwwBAHIM9pqusmZGVlJVTMNJ8/sytIn6M5qwFGnoGhOI749Da4xwUDAY4sd8rnV2+f6JkpFGPGmLgYHA6HEbCVnZ09FrtIS+JrJ5gbNMWnxOmP5tfMi2R2djaZmZkxzaKsViudnZ1Gx8h4szsQY7GYOXNmWosDHZfLRUtLC729vQkiSj/u0tJSo75/fPxGqjM4XC4XgUAAm81GV1cXGRkZHD582Nh3fEqm3qDIHFCq3AwnGH1iUBwwEEC83AndkSN8cOwRoRDcVQMtQVnA7Ifzjj69UqFIQsoFwvLly9E0jd7eXt5//32CwWCqd5GWmBfm+DS4+Cp88R0B9e3m9Eifz0cwGCQ7O9tYrMLhsFFUCDDM7vpr3d3dCW2ji4qK8Pv9MRH2Xq+XwjTyaUajsn21fp70bpg2m428vLyEFFNdHBwpkHE06G6c4uJifD5fTMBofKCixWJh0qRJZGVl0dXVFZP5ojhB6AzBFi+YUxC3eI3OqROFCIXg+p1wp2swqPH6nYgnTlUiQTFqUi4QvF4vL774Ig6Hg8WLF7Nx48a0rMueSuIXZt0aEA6HY7IazOJAf69ZUGiaRnZ2Nj6fz/h8MBiMcT+Y71KtVis2m43a2lrjLtYsPvQYCB2Xy2U87+joSAs/ebJgyqqqqphMkaEsM2YhNpaCJzc3N8YNAoNWDP2f3n5biYPjBxEMwo1vw+EwCAE3T4WrKpP3ZbigCH7mRtxuWoh/5pbbJ5K7agxxAAM1E+50wT27Rl+TQXHSk3L7s8ViYf369djtdvbu3cvcuXNTvYu0Iz8/37iL1RcRvfFPfJlmc9qcOYJfN6tnZWVht9sT2kKb4xT0cfQKino8gm6t0cdzu93GuDAYPBmJRBBCGEWKJorh2h7HZ4rEu2VgsKrhWAod3WKjx0qYqyrqAkXfv8/nG7N5KFKLCAZh1U64bhrcOA0WF8L6w/CVnck7PN4/D0QEHq9HXLsVHq+Xz781b9znHkNLMGlhJZoCEzQhxYlEygWCvkhddJGMwq2pqUn1LtIOfSE2dwCMT5cDjLvQ+O3mAkEej8c4h+bSv5FIBKfTmTSY0RzwqC/+8X0d9Kh/876HK1M8HkF20WjUCKa0WCw0NDQkfV9RUVFCuWO9HsRYigO3283Bgwdxu910d3dTVlaWUHJbPw63201zc/NJ3Y/huOJzO+HW6fCGB2blon1vPtxWDR0C1jUmvF2z2eCJpYPtm0uy4ImlcvsREEIgNjUi5o9BA6Yyu7RmmPe3uRMqVJtpxehJuYuhoqKC5uZmysvLOf/8841a+ycqHo8noaBRslx6fXGLr4AY70s3jzNp0iRjUbdareTn59Pe3h7zfhgUHpBYknmoeAd9vGToi9xYpesFAgFaWlqMipS6tcQcUxFPfNlr8+NYUFdXF5Mt0drammARAvk9md97pIBJVUgpTeiKynbMK8vRlkj3lLbciYgI+K8a+MT0hI9oNht88xTjuYhEEL9ww+5emJ8LN09HMzVjA/l/h9W74LVeeHzpYIrkzfsQq2eNvr7Bj+fLmANzDMJP3PCEci8oRk/Kf6k+9rGPUV5eHvP8RKazU6r3+KY98Q2dILZ3gnnhjhcX+uv6Nv2xu7sbu91ONBolYyAAyWxuN7sizJ+N7x+gB0MmW4jNbgndTZFK3G43TU1NMT0O9GOx2+3DFqEqLi4eFx9/OBwmFAoZ5yn++4kvuxzvNhoq9VJva93S0jLmx6A4AgUW2NENpw1WURThKCwpAHvcIh+NIp5rRnx7L2J9i3weicCt70C+Dc4qko+3viO3m3mvGx5uhf+chXb6gGjUUySTNGASoRDitrcRKzcjbt8pgxCHQcvIkGLgqQbEyrfgqQZQAYqKFKFuZUaBuc4BMOxiP1RJ5fiFGzAWeXNHSXPqXHwXSH0x14VAfOU/c8S9freezHqguxXihU2q3A2BQMCYL2AIkUgkQnV1dULK5kRhs9kMIWau6GhOSzWnpsZXWNSfm91FegZKJBLB7/dTW1s7kYeo+PWp8E4XbO6ULoBwFMICtnkhb/BnUUSjcO9emOZA++psqMyWz3+2H6ocsMSJdnWljGGocsDDcXVf/t4BFdmDQYT6dbvcCXENmIyMhOumo609Ha6dJq0DIxAJ2s9ORVv7AQ9QEQMAACAASURBVPmoxIEiRSiBcIzER9abU+8gMagu3nIAgxaF+Lt+IYRhbne5XBQXF+NyuYysBiGEEZgYv5jHxxfk5Az+CJnLOSczgevb4o8hVTn9esCffhduFlPp5oqqqqpiypQpRKPRGDeQ2Z0DiW25zefY6/VSX1+P2+3mwIEDMaJQCIHH41EFlSYIzW6H50+FB/fCPzwQisLOLvhJLfz+tME3rm+Fq0oH3RBLCuGqUhnQeHkZ2gIptLUF+XB5GWw4FLsjqwUQCXECbEnSgClJRgJ6RoJCMQGoXgzHiLlhUnwZXvOdZHyAIiTGCZiLJekLiNncrpvV9TvbYDBoZCoM1TxIHzcQCBifycvLw263D2umd7lcRlyFed+jxRzIab4jNz9PJ8zzhdiumfEdJ/X/A7plp7CwkLy8POrr643x4kWhLhCi0agqzTxBaHl5iDWnwTVvQ18Usi2J/RVqfGhXlsd+bkkhIkOTFgMzVQ5Ai912phOqbPDD/Yh7ZsLyQikOvp+kAdMQGQniu3tHeaSDCL8fLt4CuRngD8Mry9BO4i6kiuFRAmEU6IFzPT09hMNh487RvAiYRYPZypCsY6O+CJWXlxt59fFUVVURDAZpbGw0PmN2N8THG+gLU3V19YibTKW6CmBdXV3McZpLKetCx+PxpM1CqQsksxAwl4DWH+N7aOjnXBdVunCML5wV348j1ZUgFSNHy8yEP31g6DfMz0Vs9xoWBACx3QslNvhHB+KsYrRcG8IXhk0dsCzOdbcgH97rgS9VwL27QGjQ0gd/XZIYoDiQkZDQ6jlFGQnC74cbauBHiwYDGm+oQTw+X4kERVKUi2GUFBcXU1VVRVVVVcJCoWMWByAXdf0OHWLTFIEhxYGO3W7H4XAYi5Q5UFEfJ76OwkSZ8PWAv/hy0ubOlTB4TibaN+92uw2zv764J+uJYRYQ8W4en88XE58S/3lzzIj+uhIH6YWIRhHrmuClVnhgL+J/G+W27V54thV+fRr8/iBs9SJaArC1E/5wEL4xJ2YcTdPgMxXgzIVbXfDDBfD2BWhOZ+JOfzwffuI23BFGRsIPU1Rr4eItMNBGGgZcGPfMhA9vTc34ihMOZUFIEeZqiuZqf6WlpfT29tLR0ZHUHWAOTDwa8vLy6OnpSejhMFT65EQtQOb5xYskc58KfZumaQSDQex2+7jP1ev1xlgJ4qtXQmwa61DBpna73TjO+LHivx+r1ZoWVhPFICIahW/sgUIbXF4KgSi0BeH67bCyHO6bjWaxIH5zKty/D35VDzNz4DenJq2LoGkazM+X/4ZBy8hAPHEq3LNLuhUqshIyEkRwYB7NIenNcFrgmWUj6yiZm5HchZGtlgFFco6r/xmBQAC/309+fv6IzeXjhTkmQV8o9Kj8rKwsvF5vzN0oEHP3bF40AoGA0QtgKMz1EMyLcDJxMHXq1LE56BFgsVjIzMw0givj4y/095jrRUyEOABZrlmvtWCeV7yrIb6uhDldVZ+/3h8DSHBH6CJEF0zKvZBmrG+FBXlwOCiLKJ2Sj3ivG7xBQBilmOPrIhwrIhqV+6zxwcI8+MnCpOWeRTAI174NpxfD10tgaja80Q7Xvo14avGRRYIvlNyF0Tex/SQU6ctx42LQ8+c9Hg/19fUxAWDpgp5xUF5eHnNX6PF4mDFjBqWlpbhcLpxOp7FAJFvUzQ2ZkqFnM5hjG+LvcPVxj2bBHauI+niBYl4cdXN9cXEx06ZNo7q6ekzmMFLM35s5ddR8XuNLZpszE/QFX0/bdDqdxnGavyuz+0KJgzSjxifFwYWT0U4xZSlcVgovxWYpHG3dgniGSqNMWu75czvhqjK4tARtXj5aXgacNwk+P0MGWh6JjctlsKTZhfHD/fDKsqOas+LkIb1uw4dAz583R/tHIpEhq+5NJPFR/3o0vMfjIScnh/z8fMPaYF6AjsYfbc5myMjIoLy8nMbGxoTqjcARF1y906PuMx+rQMFk2RHmDo3p1OSorKyM5ubmGAEX34Ja/97iU1TN35/dbo+xLOnjma07yr0wdggh4G0PPLAfgsD0rJG1Qp6fCzu6YNpg4J4IR6HaATZTjYRQCFbtgKungisHanth1Q7Ek6fFugUiEXikPnnFxSRplALgxTa4oix2Xl1RyLKizRt0VWh5GYhZOTILY6hzUNMDdX45/9/Mg49sk26FvtFlMYhoFH63B77bJM9Vkx9+ORvOmnbULlNFepJeq+sQ6HfU8X5cvSVwumIuw2yxWPD7/cbfegaEbtKORqNHVSkwvuOhPp7Zj36kxUcXL+bYAL0BlNPpTPmdbfx4+pzTSRwARgDoUHES+jaQbogpU6bgcDiGPBb9OAsKCvD7/djtdsLh8BGDURXHjujvh4/+C+wZsKwQZueCTYPr3kb8bvHwIuGKUrlw/9ODOKMIMi3QFYJtXXDJ5MH33bYTPjMVzp0EDitUZEMYuOMdeHipnEckAl98Dz43De02F2JLJ3zxPcRDC6RIiEujFEJI18GP3FIofLRk0N1QYIFABLGr2xAJoicE+3plimb8ORAC1jTD4ny0y0sRe3rgBS+8cc5RLeAiFJI1GlqCUJEpgyatVrhnKzRrskfFcqdsf/3D/bC7D3HTHCUSTgCOCxfDUAV8HMdBao55YcnIyIjxvRcXF8eYqY92oYwXR3pBpcmTJx9RHPj9fkMcwGAwoT6focoFp5p0Ewc6LpeLiooKw00TX0pb/xsG/x8OdyzFxcXYbDby8/PJyspS4mAMEcEgXLcT/usUWH0aXFEu0wtn5UqxcOe7w35es1jg0UXwv03w2mFo6INdPfC3dikedN7zwdkDAgLg/7N35vFxlfUa/56ZySSZZCZrm6RJs01bSndaUgFRQKUuiIDoRbwsXnEBuaIIIleviFzh3osXBURlkSsgXEERBMSiCIjsXSjdaGmbpW22tmkyyWTPzLz3j3fek3fOTNbOJJN2ns8nn8lMzpzznpM58z7vb3ketx1OK4BtmgDSLxskOagJdw7U5MFl5XDPXvn3cBslhCf0+l75c9286HTD/cvhyRb48wHEu12SHPy9De6rh0dWRJ/Idr8kBwulIZqx0A0rPPJcxnstR1B35IlGWNcnJaNrZMqUmlzZFfF/hyZ0jBSSFzOCIEC03e5IE2AyKdNZpZLLy8ujttGVEuOF3HEYwDgcjqh+fKtLpEqNjAfJpAoYCoXMsahHlaaaCFwuF5WVlVRUVEQUGqpjJNpqOoVJ4rLNUoFwfjbkpWGsyoVVOWAz4MzZsGP0Gh8AbDb4aCH8Zh9cvRX+dhBuXGCu5kUgAK40aB2ADJv8Jh0U0NwPQtvPjh6THCgYNXly8gZJOJ5slSShbVD+7O6GJe5h1cY/HZDvczrh4RUybXL5O3DWG/C/dTBSgWJ9r0kOzGMvdEPdOM5fYSR1x/9tBE/aMDmAYZKQ4ZjYMVJIWiRvfD4GvF4vXV1do7oQqjx6Xl4eubm5+Hy+aS0CG08YfTpW0UpoyWo9rddDKJW/sa6fijQIIaZd7KilpcWMjqjcv654OZmxORyOiBTOkewrhSlAZ0h6I7QPyskbZCfCE03QHQC7gVj1Evh74K33R2kSmG2OpxVKB8aGXvhTK9y4C3HTQkkSbtwFl5bB3w+BHVjohp3d8OcDkeH+47MQ6zsiSIJY3wGLw6t6mw3xwwWSBPykVkYOLiwd7pRYmYtYO1yPIEnCKMJOOqpciJ3+CJIgdvqh2hUzbRAz7TKSuqPNgI5BWN+BCJMEIYRMM/QHZL1DCjMeMyaCoDASOVDeCHqIvL6+fkKh8rG6ByaLZAujq9oIazskYLYa6oRhtNV3S0uLuS99/9MB1QGi1wxYtQomE0lQUJEe1Y2SQpIixwbv+CDbAf4gIhhCvNsFzx+C49yw9mS4ewW8fw5ctgPRYfFJeLoVPloEJ+djZDtkB8Mni+F4N/zpgJwIt/nhgjLITYM9PfBMK9T3SPfGr2qRwisq4f59khQQJgf31sPv9iM+9jriI6/CwADGp0rgMyUQEBEtjuJtHywdXT9hRCx2wztdkhQQJgfvdMH8jPGbQoXVHXWIdR1QnQmrM+G2WkkSFDm4bQ98fhYsckfvK4UZhxlHEGKhtraWtra2iGpyqznSWJNWbW0tLS0tU5J3n244HI6Ywj16QZ5+/UbSZFATslVJcLoiNj6fz/xd6Q1YtQvG0pcYD0YiqSkkCe5fLlfj27qgLwgbfPDrffCFubAiB+yGXNF/rQqq3PABi5Lg9m5YkIWRPRxgNZZ4ZIpia5dMD1S75GT7mTmygl8gvQ0O9MF5ZcPvs9vh50vkJHrlZvjVHvAF4Nalkqjcsgi+sA3R1xeZboBh1cZPFk3qMhiGARfMgaBAPNMq3SoXZ8Mlm+Ab1TIdwBimUCOqOy6G206ET2TBv2yENa/BFzfA12dDqkDxqMGoBOHee+/lnHPO4dlnn6WpqYnzzjtvqsY1bqjIgT4hKKje9LFaB9WqU73/aCcJHo8nImKgr7B1rwHDMMiLJQkbhs1mw+VyRZCJ6VxZ63bYEFkgqs4zWeokkhkz4b4fDYbTCQ8thW9vhS++Dd/dDq+1wV/b4LmDoAzUavKgKwClkbbLLM6GnX7EY42Im99DPNWC2OKDkJCr+fpe+MECuG8vbPXLlfpsJzzeAv+1MErkyLDb4ctzZcpj/xBcJQv7APl4jRfO3ijf98MF0NiHuPk9aOwzVRsnfS0MA2OxR5KMbX5Is0lSdGIuDIlhmfbVedAUHV0z0tLgweXw8D7E+W/Bw/tMdUfDZsO4dBHG9jMxnj8VY9uZGKdWpMjBUYRRP3k2m42nnnoKp9PJrl27WLhw4VSNa1zQIwd6SDmWPv5o6QMVdtbD6kf7ROL1eiOEf1wuV5QQkM1mGzMaUFJSQnV1ddwLLScDRRatXQa6dkGHNZx8FKC/vz+un9fJ3PePP/44O3fu5DOf+Qyf/vSneeCBB+I2nsnAyMyEl06GoQAUZsB/L4V/my9bCG/chQgGZbjf44Ami+3yWbPg7npJCBa5ZU79J7WwzScn2ioXNA/AjfNlF8GVW+CpFqjJgRXR6UQRDMK/bIarvXDuHFiZAwcHhjeoyTP1FQybDeNTJRjfO04+HgE5iIDe0TA/S0Y/7MhzZHRTKCMtDePO5Rh/eJ98HEtHIoWjBqN++obCOakzzzwTgO3bt49rp42Njbz55ps0NTUd4fBGhpoMgIjaA6t73nhaIpWR0bFmnqM6KKqrqykpKcHr9ZKRkYEQgoyMjHFP+MkidKS6RnRDKL3zRZc2PlqgK4zGK/I1mfv+rbfeYt26dfz+97/niSeeMMn2tOLmWqjOlq13p+bDQAiWeWRnwM/q4Rf1UO+HVyxKgr9ogBPyYHkOnFsCS3NgkQdK0uWEvdgNm/3QGZRpiovK5GT7Xg98dj3iB+8iwu6uYluXDN1fNFcSgfJMGYHIdkBPONq5vgMCCb5eekfDpWXw20ZJEoIJMIVK4ajBqF0Mc+bMobm5mTlz5nDGGWeMyxHwjjvuoLGxkcLCQtra2pg7dy5XXXVV3AYMw90KVpMcqxmQSjuEQiF8Ph+5ublRuvdKtli9Pysri9LS0riON5lhndjLy8ujvBJmErxeL7W1tTGtmI82aWOrwqjdbo+Lr8Nk7vuenh78fr95jTs7O49oDHFBXR8gzCp84bLLSXl+Nly9DVo6Y3Yx8JdD8D9L4Hi3TLkdH55Yv70NrlogX7tgjuz1f+GQLE7McMDV5TK68EY7fPEdGSmoyZek5MRcKbZ0ZiH8ci9QCMXp8G6XLPR7ZlVir4XW0WDY7Yj/Ph5u3QM/3AmVmVGmUCmkAGMQhHPOOWfU57FQXFzMN77xDfP5Y489NsmhxYYeOdCFfvTiOn2CUzUI2dnZEW2QJSUlZv48LS2NoaEh0tLSjilyMBJmKjlQsJIEGC7GnO40SDyhd2woAjSettSxMJn7/uKLL8YwDAKBABs3bqSiouKIxhAXVGdCa79pUGQYBiLLLlfslZkYL38g9vtCwNzMiP5+MTcTAgJxw7uSeMxzwXe98NIhmbs/OR8WZYNhyELIvlJ42ydNnwZC0DIAeWmy0PGKCrh3L/yhWRZRPl8jUyKJxGI3PNaMIKyFsLsXqrLh+gWpmoEURkTcdRB27drFo48+yqxZszh06BB79uyJ6/6t2vax7HR1kqBqEnQ7ZiEEjY2NEVX7ypo5hamBvtJNhKeG1+vF5/Nx+PDho163wOosOdXukA8//LD5+549e9iwYQNCCD796U9P2Rhi4oYFcOlGuLMWcZVXVu2v75D1BA8tH/l9s+zwjzbEBwul14F/SLomCgGfLJE6AG+1S7XGr1TCP5XB4QH4vyb4SCG40+AjsyVxeOoAfCgfnj4IHwhH69b74eXD8D+LYHnelEzQetRDPNMquzAumJMiBymMirgThO985zusXbuWnTt3UlZWxnXXXRfvQ0T4GFgL0fS0g55y0A2SrOkHkF+sqmbiaJ1IphuBQIBAIEBTU5O52lWeGk6n07THjhdyc3NNC+cjqZFI1pSL+kxbW1WnOoWybt06+vr6WLNmDUII0tLSzOr46YThcCAeXAU/eBeu2gw5TihywEMrZKeDhgjL5Zo8eGS/7Caw26C5D97qgC9WwIk58twy7fAtLxQ4wUC2OgaA/f1wQrrsGDghB3LSpBjRP5fCY03wejuszoNHVg4bNk3V9TAMWOyRPyNABIPwk53wyAEozoBAEJ45MfERjhSSEqMShGuuuYbTTz+diooKSkpKuP7667n//vtH3aHT6RxXSPJIUVBQYBoT6Va6yiFR/4JSrYvqC1SPMFhTEkpMp7e396jJVU83AoEAjY2NBAKBKGMofZtEuXMeCTloaWnB7/fjdrspKSkZ+w1TDF2pM17GVxO97++88062bt3Kc889ZxY2nn766Uc8jnjAcDjg5mVw88jbmJbL5xVjnDsHsaEd3uiEedlSMnhNEXy6VBYiPtYstQXqe6VuwXvdMBSC9pDskHipTbYSru+As4tkNOHFNll/MBCCX69M2lW7CAbhi+ug3wH3rggbMHVInYYHlqRIwjGIUZdFJ554ItXV1Tz//PPMmjWLmpqaqRrXuFBaWmpq5Kt6hFAoFLXis4ZgFVT0QJELtW1TU5OpwJgodcVjBQ0NDTQ0NDA4OBhxjYGIAsJQKMTevXunc6hRCIVC+P1Shc7v9ydHZX4MKFIQr06Sydz3S5cu5dvf/jYHDx6kq6uL2bNnx2UsUwKL5TKN/XD1PBlxOCVfTpRlmbLzYIUH3u2WxYjrOyDNALshl1r/aJM1CZ1DcHEZFKbDTj+sa4c/NsOVVZMiByIYRNxVi7hyC+IXdfJ5Xx/ifc8hVryIWPMa4rjnEAcPHtl1+EU97BqAa+ZhrA4vjjSdhhSOPYxKEILBIPPnzyc7O5vBwcGk+4LUVfH0G0/VFuitbda2R/13fVULRNUqzBThJNWNMZ1ob28nEAgAMioQDAajPjdW/wc9RF5fXz/lYx4JNpsNt1tWsLvdbmw221HVIjkSjuS+X7NmDV/60pcSOLoEYHv3MDlQz1fnwd4eKa9sA3Ic0DEEeU74fRNs6oAf75F6BgVO6fj4aBPUeGTh4ZCQ3gzPHwJ/EO5YOilNA9Mu+n35cNdS2XL5+Q1w4UaYXwj3ngB/OQV+sxqubDgykrCuEwoyho2Z1HeqptOQwrEFQ4ySLPzgBz9IUVGRGa4/dOgQL7/8ctwHceONN3LjjTdO+v39/f0EAgGcTid+v9/My6o6BCBCJVAJAql0hA5rV4RCWVlZ0tpLd3d3c/DgQfOcq6urJ7UfffKbTHqltrbWJGZ2u52ysjIzKqA+Q/o1tbapqr9VV1cnJNUwWajrqp/fROpU9KLB7u7upLd6nin3fbwg/tgM5S6TJIgnm6VokGHIWoPFbvAHYG+vNHvqD0kFxl1+WYz4XjcUpEkSsaFTFiM+3QqHB6X989nFkxY8EnfVSnJwYq4kI1kO2Vr56D64eoEplwzIiMalGzF2nDm5Y127VZ7jtxcMt4YKIfd77Vb4bKmUmrbb4KOzIJ5CTikkJUb9Fv7Vr37FggULzOe7du1K+IAmg4yMDAKBAHv37o3qaNDD2eq5Hl3QX9NJhSIP6jW1Kk42WKMbQghqa2snXGhp1ZaYqCujMoDSW+727t1r7lP9T6w1H7oxlLr2yVYQqCIH6nOjbLDz8/PH7BhQpEIpOKrrkZeXl7Q1LjPlvo8bPlUMP9glXZpPyIFsO/yyAb5SAe90Q09AFhv2BeHNDqk+KIDjsmWB4vlzoD8oWxgfbMT4WjUsDRczbjoMn3wTYbNBgQPuW2YWSAohpMJhfa/sKljkjk5B7OjB+FcvojsgyUGWHaqzYJAIcmAYBqImD8otstETwcVzYU8n3LYHcc08uf91HVI46vOl0CvgX71Qmgb/uhXu3oto6IaflsLHFiVtbUUKk8eoBKG5uTnii0L/PZnQ0NBgSuxaCw91wqDnvK3Fcvoq1m63mwQiWSctkKtRK7FR5zIRkhBLlXIy7XIjETP1qPs8WN0W9fEPDg5GpI+mum1PR3t7e0Q0Q43VqqsR61pbSZP+mWxra5t2a+yRMFPu+3ghwnL54b3QJ0CEpNxyYRq80wmdg9IJMsMGH5otuxY2hs2UlnpkCP7lNliUjdjcAffvkykKhx2+f5yMRLxyGM5/G/H7FZCeDg/UwmMtMGDAYAjEEOKFUyKLAcN20VS5IN8p76G6HnAi3RPDJMFc6e/riX2S48GyHElY/qUEbt0F3UGp9/DVMjgYkISoMh2+sm04erG+Qwo9bV+HuGZ1iiQcZRiVIDz44INs2rQJgG3btlFXV8dLL700JQMbL9rb2xkcHIwgBrE6FSDavMca4oZo8yI10TU3NyddL72eH9Yn5Ymq6nk8HlNbQj//3NzcMd+r0NHREVO9UI3HSs701I+K4OiOi6qjQV+BT/W116Mq1shUY2PjiNoD7e3tEddT/3xZiel0kp+RkEz3vejrg7M2gNMu5YifWWVOoNJ2uQte75AGTB8ogMWeSU1Shs2GOGs2PNki5ZNX5UoC8LM6OWE+uB/mZshJ9LiwsuKKHAgKadg0GJQFijYB/9sD/1wW7ngolZ0QKzxwZTW8Lw8ueAf+rRzWtsOaYvh4EZRmytTBxZsRD6/AUAT5ikq4MhzeX5iNaOqDll55vDtrw8ZP4Yn6x3vg5VH0Hca6BoaBuLBMKkRe6oCWfvhyhaytcNkxjvcgrtoMXx92gpRFjPPg0g3wcf+oLZQpzDyMShBuu+028vPzeeqpp3j33Xd5+umnp2pc44I1LK5EkdTv1hSCPoGpL2p9G/29sXwegsFg0uWQdZJjJTvjtSV2OBxRaZaJ9LHrEQg98gKRBYj6cyDqGqv3qK4Hh8MRMaapnEzVZ0sfn36tVS2LVXtAEZqR7LT1Ak2bzYbP50s6gpAs973o64MvbIP/WiLFidYNt9yRng531cOCbDi/BA4NwAttsNWP+FzpiCRBtLTAGdugMkuu8F9cgqHaV3/ZAFdUyXy/AFblwGfnQNMAfKoIfGGZZgNpnZxuk6vq/9wFJRkyDP/XQ/CZ2bKw0W6Ayy7JgfJBODFP+jJ8dQd8dwEsy8E4Xt6n4sOz5Dbnvw3PniJ/t9ngg/lysm7okZGIzDT4/gL44nr4ahcUOmFfN7yyEuMIu0cMw0Ascsvz398Lb3eCNwua+xA7uuDAYGTdA9IJUpRnSSKUIghHFUYlCPn5+dxyyy0EAgHuueeepAqzj2TWNJKBkzXPrX7XV7dqktK/xNU+AFOyOVmghHKs4Ws1eU2k0K+yspJAIMCBAwfo7e0lOzt7XP9vK0lTxEufFHVyppOFWNcfhrsxVMuq+n2qoIo1Y6WqdL0Mu91OQUGB+XnSP5OxhLtikVTlBeK0CPdMJ5Lmvj9rg0kOIDwRXTNPttx9vEROnCtzZNFgfjp8uBBqe+UKOMZEJVpa4JuN8NCJw4Tjm3sQtyNJwo4eucoXAAJ29UjDpt81SbMn+4AUQJqfBQ5DHndrp3R8vKJKpii6g3JMT7XCSXkyrXBF1fAg/OFQfXEmZDpMcgCA2yEJiP5R3+6HlXkYC92StPcEZV3AU4fgoVMmHTEZCUIIGflY4ZHj/nm9TKG0DULdAUgnIrUBYbOnfT2yjiKFowqj3vkXX3wx6enpXHbZZbS0tPDII49M1bjGhHLug+G0AET22AMRX9h6a51qv4tFBHQCod4/Z86cpEovKBQVFUVMorouxERb8hwOB6WlpXi93nGJAqkCSZ0M6NdXn1hjpR7Ue/X3AOZkqSIbarupXGmrz4dezArD11ht4/P5TM2MtrY2M3Wlb6d/ttRzRaQcDkdSkQNIovveaTfJgYKxOk9W0XvssDwHw27DyHLIwsJyl5zc60bQLjljW7jHf5hwcM08+NA2+ffjs2CDT0YI6nqhKF0WKM7PlnUIB/qlvfOeHlkz8OphOZnaBdy/F9oGIMsWXnW74LgsmQLZ4JP79wdgf5/USTjYD30BuSonPDH7A7C7O/JbWXNhNAwDI9uB8aFZkjQsyYkgByIQQNzwLuKijYgbdyAmU1it2UIbhgFXVslxBUIyYtLfCz/eLVMaIMnCbXvgMo8kSikcVRiVIMyePZu5c+fy+uuv89prr/H2229P1bjGBa/Xi8PhIBQK4XA4zAncWoSovqz1iUj/0lYTl16lrt6ntk8GjQErAoEA2dnZEaQoVjvhRDGeFaNSsdTttVXkQu1Dj9rE0j0IhUIR/wv1f5gzZw7V1dVUVlaaltRTSc508qmTRf0RogW49PZZnQQo6P8Ttf1kW1ITiaS57weDcnWqQazrgK5BaY60qxsRDIudZTlgU6ec3EdayVZmxSYcqvL/ikpZXPjCIZidLr0U1ndKRcRTC2FFrmxnvH03fG4D+Abh2nlw0/1GigAAIABJREFUQr7sMFiUDRs74cG9UjVxix8uCkss/6lVthC29sk0xD9q4PfNsPaAJAn+APztEDyyD/6wcniAYRfGiGuw0x91jiIQgMu2SK+Ih1fBJ4rhsi0TJwm6LTRhUvLJYih1YVzpxfjdafA/RdLjYs3rsvbgCy5IFSgelRhVB6Gvr49MraLW+jxeONJ+aBWibW9vx+Px0NDQYIZzrStWa04YiBkWVmThSI1+2tvbycjIiLuGgsrTW0P5QNQ5JGJy7erq4mBYlEUnAoBJsqxum4qk6QTO+r+A5PHCOHz4MCBVFAOBgPmZ8nq9dHV14fF4YlqP6xEGiCRL6jnI61ZYWJh0NQjJct+bNQjhVb9Y1yFXq9eWQ3amJASFTqh0yRX/ug652h+hBkEsfN5ML5ivreuASzeY2gEiGITzN8BFZXJfRU4pl+yyy2MfGIDGHrioHKqypMKiyy7TGgMhaej00F6wO2SUwInM4R8OgNOAU4d1EURPD3zoTchIk8JK2QKePmm4QJHIkL+x0C3JwTtdUUZL4oZ3TSOpiHP7cyvGjceP+38itnWBw4ggCWKnH4ICI1VfcMxh1CT1L37xC84991zq6+u5+eabOeuss7j22munamzjhtPpjGg5czgco076aoVnzSvHijAcCStWIfh4T9Qqb20tULSuaPVUQ7wnIY/Hw6FDhyLEpqz1BDr31K1zgSgCp7tqJos5ki5hHAgE6OzsNF9TBaDKCyE9PZ2WlhYgsugyVjunOvfpMFYaD5LlvjcyM2VB4tkbEQ6b2cVARgb8337pcfDR2bCjG/rDQkbfnTfyPfvSEllzYCUcLy4ZPqbdjvhIoZz4lYpifwhePARvtsvUxpJc+FiRJCXZDhm1WOqBp1sxzilB3FmH8fAJY59fVha89eHRt1EujNv9iDt3wm9bIdcJv9mL+MPKYTJR1xczOiLunKAKrMUWWickKRx7GPVbOD8/H6/Xy+OPP87atWsn1PY2lbAWLKofNUHpE6d6zVqDoLaDyJSDCqH7fL5xjUXtq76+3nyvQrx8HVTe2toZYA3Xq9z/wMBAXI6rQ+/xB8zaB2sNgvqbFXrKx5r+Ge+1nko4HI4RvQ4KCgrMok79PPR2Tr1AUb2WLJESK5LpvjcyMzH+dirGc6fIx8xMef0O9EEaUrHwbR98uhgur4J79428r5ISuL0MLt2A+Gg4PH572XAXg8IVlXDvXll3YADbu2TR4ZfLoCBD1iK83gEeh1RWFEjPBW+WJB3zjkCsaCRsPASv+eGOZfD71XDNfLhwM6JL1jBQnWmmY4QQiO4hxAsHYV8vYgLpUcMwJBkICmkLHRQpW+hjGKNGEAzDoKuri8zMTDIyMhISZowHVDW/dRVtrSKP1fIIkQWN1u2AcWsCKOe/7OzsqAp8u91Of39/3FIN5eXl1NXVRay29SiJgt1up7e3NyGrcquuhE4S9LoDPZqjEKtTxNoyONPg9XojUg46dDJkt9upqqqKtYukQLLf92JoCN7qikw9fGkL/GoZPDAyQQDVrTB6Aa5htyPuXQrf3Aa/tcMpeZIc/KRBFu0dly2r+7sGZfviDr/snvC6pOrg/cvid7IgCwf/cAD+fSFUuCRpOaNQFmaetwWxdjXcsEDWHCDkNnt7pRfErYvhks2Ih5ZHWVyPeP7jsIVO4djAqDNGa2sr5557Lp///OfZtGkTmzdvnqpxTRherzdqFR3ruV6Zrrs/6pObvt3s2bNjfpm3t7dHdAmEQtL5z2az0dfXB0QXtsVz4vP5fFEV9mocsSInSipaH7MqvJxMAaa1i0TXlVA/VVVVVFRURKRrrMWKBQUF01aMmAjo5+H1eqNIkM1mS2pyADPgvr96u3QYDLfaGavzZHviVdtkiDwOMBwO+Nly+FIFZDjgHx1wRYUsJvzLQfhEkYwe/HAH3LEHnpQFh9y/TL43nqjvBWywyAPZDowsh5zEa/LgjFnw5S3ymPcvg2u2wq/2ynbIKyqlj8O3vPDlLfEdUwrHBEb9JF9//fVcf/31ZpHSCSeMnVebLowkF2wlCWp1rQiAXnmvXgsGgxQUFFBQUEBLSwsHDx4kKyuL0tJSYLi2QJfZtdmk819PT0+UYmC8CwXVKtWqSgjRDpWqml7JL6sxqx58dX0cDseEK+pV/h1kmP3w4cMRj4DZXaJeU54Zuo6AQrzsiieCRNRn6Ofh9Xrx+Xzk5uYmpWpiLCT9fd8yKCdHhut7qMmFH/TDXcOr93F5HYwCfSUtfvQe7OyVGgenF8qUQsiABW7IsMtoRqLC8FUuIATrOmSLozq3bX44OU8WZyJJjchJh+8uiCxgrMmDw0OJGVsKRzXGFXO+5ZZbEj2OI4a+olWhdmsRHwxPnNb8uF5YpyYuPSrQ09NDbW2tuQK3yuUC5ntAko25c+dSUlISV3KgEyFrT76eXrCqKyq/Cn1ba4TBGhUZDxSRUr/rj9btYJgwJEO0QGkXJNrOW6WnZgI50JG0932JU/bfh2H6EMzLwNDE0XisWVbkn10sVQ0fa56QQmgEch2wIGs4QrHQLZ9XuMCdltgc/WI3nF8EP6tDrOsYJgf/aJO6C3naOk+EIq4NIJ+nSghSmATGRRDUyjnZoSYehVjiPSC7HkpKSiIiBiC/VAoKCqioqADkajwrKytKU0AnGHrO3Ol0muqFqpAwEcqL1tSFVW3QWvugPBv0MVv3pbwbpmLChOmJFuiwRpwmSoyOBSTtff/TxXB7LayXaTbW++D2OvjJcDeCLvgDsiKfFR7ZjjgZzHVJR0d/uCixOwDFGdLhMcEqn4ZhwCXVcF0Z/Nt2uHmXbKFc5YHb6+E+rebhPxbAHbXDBYvrOuCOWrjp6DbcSiExGJMgvPnmm1x++eUEg0Fuv/32qRjTEUFN+LpErnVCraysxOVymSJLekuaz+ejrq6OhoYGWlpa6O/vj5Bg7ujoMElHLH0EJfCTKAGc/Pz8iNRFrPOzPiofAcMwKCgoiOjOUH/XEQwGqaurS8j4kwXWiNNMW90nGsl83xtpafDgcnh4H+L8t+DhffDgcvm6gkXwB8IkYSSVxbHgzYKeIdjXB839YBjQ1CfbH9+fN+bbhRCIbV2IZ1oR27smHMkwDAPj5FJYuxp2++GOOukdYS0+PKEAPlYA9zcgPrce7m+Qz1ekPt8pTByj1iD87ne/Y926dezfvx8hhNnnnczQOxr0dIKaUIPBoClyU11dzeDgIN3d3bS1tQHDUYdAIGC2S1qjEGlpacyaNWtEM6RES+eqnL4S8IHIVkKHw0EgECAjI4P+/n4gstNAPVePsTQhAoGA6ah4tEKvjUhhGDPhvjfS0uDOUZwLwwqEUYI/k/ULWOyGm96Dj8+WwkzvdMr2ym9UQ30/LAunNTb7pImULwCLs+GqIjjtbfC6pfHTKfnQPACPNiI+Vzbh1IThdMKDJ478d8NAXFQJK/2SDE2i9iKFFBRG/fafO3cura2tlJXJD/L73//+qRrXEaGiosIshINonQB9Ync6nVFtkrqKIkQWPdpsNoaGhsx2RRWanuoVqMr9BwIBGhsbGRoawuFwUF5eTn19PYZhMDAwECU5rQoUrdX11pZFnRQdzUiRg2jM1Ps+AnEW/DEMA2E3wOWQUswVLvhapfSFuGUX4lPFUryprhe+OU8WFr7RDlfUwvcWwIo8aenc2A/L3DJFsa0LlubE9bTVWFNtiinEA6MShJNPPpmTTz55qsYSN4xkX+x0OqmsrIz5Hq/Xa0o1q/C61c1PPU9LS8PhcER0E6huhqmGw+EwnRgdDocpxqRHTvTz0GF1sbRGGZLNRCiFqcFMve91mAqE7/ql4E+1a0zBH7FzJ5y7f9gK+sm5GAsXDm+wxA2DIYxzhnUUxNs+qaK43Q+DAs6bg7EkbN+8Og+cNpmG+Fy5HFMZcHAA3p8Pd9YlhCCkkEK8MP16tglCZWUllZWVzJ49m7y8PPP5aMjPzzeLC3XVO107wW63MzQ0ZJIDGF59T0Vx30hQqYCWlpaocSmSoEcTrOqFeruk2iYZDaqs8Pl8k+q+SOHoh2EYGIs9GGcXy8exyMENXfDQKlh7Mjy4Cm7okq8rfKoYnmyVpIAwOXiyFT5ZJFspQ5jkAJAeDYvc0BuE97rlmDxp0rOhqR8OJ//9lcKxjYQRhL6+PjOvP11wOBzk5uZSUFAwoVx6ZWUl5eXlEaqLgGmBbO0G0F0Uu7u743kKE4KqzNfHp0tO66kS/bz011TawTCMUSMIyTApqzZFNRa9DTWFFCaEc/ZJZ8aavGERomvnwbnDyoyGzQY/XACNfYib34PGPvm8owO+vRU2+xBvtQ8XIPYGZdfEKfmwqROx04/oGoKGXnjuIJyWSm+lkNyIewXafffdx7Jly7jrrrvIzMzk+OOP5+qrr473YRIKJZnsdrtxu904HA4yMjKora2NSFtYlQxVS+F0YSTJ6ZHqKdTfdE0Eu93O3Llzo8iBnqLQ5YStqZXu7u6EtHZaUV9fb/6uzkG1K46W7pmumpEUkhyVWcPkgPBnqiZPtjdqMGw2+FSJ/AHE4cPwtToZeSjPhIcboTeEWJ0Lb3XAvQ1wdbUkHE80w/da4J+KoG0QrkpuRc0UUog7Qdi1axdut5vf/OY3ADzyyCPxPkRCocSRQNr8FhUVYbPZosyJ9JW2vhqf7olHVeYfPnw4op7AagutnusERxc90qEIU0ZGBtnZ2VEOkkohUCcOiazHCAQCEQWUetREEZlYqoWjEZsUjnHs7ZV6Croj4nqfjBKMhlPfll0FNbnQE4DPlcIzrfDdbTA7Hb5ZCRs64aFGeMcHH8mFTCfcNFeSjRRSSGLE/RN64MABdu/eTV9fH93d3RErvZkAJZkM4Ha7Yxb2AVGr8HjLKR8J9JSKLgKlq0eqok2dQMQiBzph6u/vj9CB0Pff3d0dEaVIZKrF4XDEFIlSxZaGYUSRg/r6+ihHz+msGQFS6ZBkwn8WwG17IgWGbtsjXx8NFVmSHBwcAMPAKHPBJeXSkvkqL6yehXGVF25bAjcvghK31FDY0T15VccUUpgixD2CoORZ09LSaGxs5PTTT4/3IRKOWbNmmZEDBRW+11smIbKFcqq19kfTKSguLqaxsRGIbtNUGBwcZN68eXR0dIzY7mez2Uw9BVVrodtk66txPUqR6DRDVVUVtbW1UZ0mqqtDh4o4WKMMoVBo2lIOui9GshDLI8XAwABtbW0UFhaSnp4+3cOZGM49AXa/Ka2gy7NgXw/8SzacM7LmACC7HV49DCtzMbLC92JdL3xkFrgdw6+57LAsG67eCiUZ0NwHGUHEdcfB+eWpaEIKSYm4E4SysjLa2tpwOBzMmjXLNPNJRvT399Pb2xshidzQ0MDg4GDMlkgVvk9LS+PQoUNRZlBTOcno48zNzY0yA1KtnrEssNUqW0VIxtICKC4uZv/+/RG2zWo/6rXe3l68Xm/MGoREESf1//D5fOZ4YnWqdHd3R1lP6+RGTdQlJSUmIUok2dNlntXz6U5NHSkefPBBXnzxRTweD36/nw996ENccskl0z2sccMwDMS3T4KzJigw9OpK+Eot/HAhLPEgtnbB3w/BZ0rgz4dgdfj/OjAEV78LvzpB1iOs98FP9sDmTtj0LuJHi1IkIYWkQ9wJwne/+10OHjzI8ccfz+WXX84DDzzASSedFO/DHDFUeFmF+QzDoKKiIsICOdYXt5pMPR5PTPfCqRq7mvgHBwfNmgPdqVHZWFtJgiIHVVVVUemTkeBwOMwWz1gkwTAMUzhKkQNFXhK9UlZ1E/39/WRkZET9XS8steo9QKTpVktLS4RIVqLG7PF46OjoMAsrOzo68Hg8URLZMwkOh4MHH3zQfP7www9P2bFFXx+ctQGcdgiE4JlVGJmZE97PSAJDIhSCP7bA3w7BkICzZsOnSjBsNoyCAsQ5bfDXA4hXDsMsJ1xeDm91wZ4exLYu2fr4zW1wVbXpQmmszkN8ax78eBecORv+dMAsfEwhhWRB3AnCsmXL+NznPkdTUxNPPPEEeXlj65RPNZT8sFVeuL+/H6fTyeDgIDabjba2tlEnidHcCxMFVSxpTRlYuxT0GgG10p89ezY2m21EiehY0Ds6cnJyaG5uBqKVF2tra83rpBMYfVz19fVUVSWmcjsWOfD5fBGFpboolF5cqn8GrB0f8V7d666aanUqhKChocEcU1ZWltlSO1NQX1/Pq6++yuzZszl48OCU1R6Jvj74wjb4ryVy0l3XAV/YhnhgyYgkYSI20CIUght2wmmFcOsS2aL4p1bYsBNx00K56r9oPvxgF5w+C2NlrtRH+MtBWOmGHX4ZVWgZGiYH6lg1uTAEOGywtStFEFJIOsR9qbJr1y56e3spLS3l5JNPZv369fE+xBGjt7c3qiMB5Oo3Nzc3aoJNlmIy1ecfCoUiig5hWJNB12ZQz9XqOTc3d0LkwNrRkZGRgcPhMHP96jgqdN/f3x8hIBVrHMo7YiqQm5trikSpsajODf3/b7XM1rfv6OiI23jUtRnJKEuNrbe3d8bJXF955ZVs3ryZX//612zatIkvfvGLU3PgszbANfMwVg+vzLlmHpy9MebmQgj47X7ZUXB4EDZ1wm/3j1ww+HQrrJkt1RL9ASjLhE8USanluxtkxCmmPsJx8PlyKZSU7YD3uqQldXgM0qLaB2nIqMfS8d2Xoq8P8cG/I854FXHKy4hTXkD0TtKAKoUUxkDcCcKFF17I0NAQTz75JPPmzeOee+6J9yGOGMoRUZ+81Oo3NzcXiHREVOHz6YQiKVYNBhhOk6hCPavGQVZW1qRC5bE6OiorK6mqqjJD8XoXxODgoBmx0Asa9VX5VJo/qQlZl5sGosih/jddNVMRn3gQRH0f1g4Qq6rlTMPDDz/Ms88+i8fjYdGiRdTW1nLrrbdOzcGddpMcKBir8+SqPBa2+KC+D1bkYnyhHFbkyOdbO2Nvv60LCtPlJF+cDg4DCpxQmAYDAm56D/Gj92SK4JNFGN87DkOlHzQlR149AX68xyQJZg3CcRnQ0CPVGMeA6OuDS7bC9xfBn06Ce0+Aj5fCJVtSJCGFhCDuBGH+/Pnk5OTwxBNPACRthbbX6yUvL4+CgoKoMXq93ghL5Vjh66mGx+MxJ18VQbCGz/UwqS4T3dfXN+kVaUFBAfPmzaOkpMSc5Hp7e6O0ELxeLx6Px9SG0Gsg1Kp8KqMxeiGgXjehYI0q6JEX/VrGq/hU6UfEOlYsy+7eGfSFv27dOl5++WUyMjJIT08nLS2NNN16OZEYDJqtiQpiXYdclcfCrxvhnBJTEtlY4oFzSuCB/bG3b+0H/xBkhj87WXb52q4eOD5LRhPOK5GRhR/skimJGDAKC+GX1XDpBljzGly2ERo6YEUujLdA8awN8PVqOCUfI8shx37eHPh4MXxsw9jvTyGFCSJhy7mZ0OM7Wu2A1+sdsfBtOhCrvVKt1CFyolMTkL5tIBCYsPlSXV2d2UqpF9Tpx9MnNOUDocagT35q/FNVrR9LVVIflzoHvRtDF5aCyMjSkcLpdJo23NZjW23JgaSIWo0Xd955J1u3buW5557jzDPPBJi69uZnT5Q1B+E0g6lf8Myq2Nv7BqXToo4qF7SPoIAaAup6INMuHSJ3dcP2btjkgysqMTxOxDOtGGcXI2DUYkOjsBB2rJnsmcoizIXu4dZJJMERG3yQefTasqcwfUjYp2om+4+rCnxFDpKhDU2fxPSJRS+01ImBIg+hUMg0oJoIBgcHzclMT1tYiyDVxK+TA/UefWIeSaUxkdDbIMciCfq5FBQUkJmZGfdJuri42OyUsHZSqN9VSmimdTIsXbqUpUuX8te//pWuri5mz549Jcc1MjMRDyyBszciHLaYXQwiEICbdkFdH/QNwmttiPcXYmQ5ED0Bacu8Og/R2QmnrodSFzT3wis1sobAmyXFjZ5ohuJMqMyAJR5JDnb6ZaEjyALFtQnsRhgMwk4/IstukgSxrQuCIeibutqeFI4dHNMRhFhQeWuHw0F1dXXSCNqoSd4qj6wm5JEEnNLS0ibVOaCveK2TvZWowDBxsE5+aWlp5OTkTBvB6uzsNMc3kr21NZLg8/kSQmYOHjwIRHeA2Gw28vLySE9Px+VyzThyoGPNmiNYIU8SRmYm/O3UmH8TgQBctgWurApHGNrhtlpItyMWe2BHNzzeDLeUw1d2wf2rhiMRX9kFv/TCd/fDZeXwmVJZQ3BvI/zoOMT6DqjthQvmyGMp62cspGSeC/59PsaR1t88e6KsQfhKJeKUfNmF8WQzbPbBc2MIOqWQwiSQMIKgFBVnEhQZsNlsBAKBqDy2iiSoCMNUo7KyksOHD5s99KoQ0DpJ65PhkURy9GJItS89SqBX/+vV+AoqxTBWu2iiMDg4GBH9sOpC6ITG2pIZ76hRIBAw22et6o/jEatKYZK4aZdJDgCM1fmIa4Bbd0NRhkwb/HwprHjJJAdyuzzENfPg9I2w6Qy4Zy88sE9u/7W58G874OAgLHHDZ4oQW7ql9fMPF8QgJR1w2RbE/cuOiCQYmZmIh5bCx98CHDAQACMIfzsZYwalpFKYOUgYQSgvL0/UrhMCnQyoCcPpdEYpJep5+erq6ikfZ0FBAT6fL8J1DqJXyOpc1MQ0UUKj3qebO6nOBXU866SqUg/WsL1eoDiVkQR1zrFqDfQIl1UTIRG1EioCpP4XbrfsvXc4HBNqPU1hgqjri9HlkI/IsGP8fLn5mih1xeyGECUuDLsdvibvdREIwCUbweuG2RmQYYML1sM/l8EPF2DYbIgbd1pISZ6sT/jRbrjx+CM6HSMzE/5++hHtI4UUxouZG8uMM/Lz86O0BA4cOAAMV5arvLyKMEyX2Y/X6yU/P98UoYoljqTOYTL1B+p9uuOj3pJnJQVAzPQGEDHptrW1Tfk1U9dIjUOlZ/RWRv1vsbpa4oXKykqqq6uprKykoKCA/Pz8FDlINKozY3c5zMuK3K6pN/Z2LZZuku9tg9wMOHcOxn8vlvUGRS7Y0D7ciRCTlOTBnp4xhyuEQGzrQjzTitjeNWNTtSkcHUgRBA1er5eCggKKimRPsgqRq9/379+fNCJKBQUFpKenx5ysFaFRE9JkoNwZrRO/iiZApCiTntu3WknrOfVgMNrpsb29ne7ubtrb2+N+PZXmhRqnihRUVFTg9XopLi42/+/qMZGYSh2IFIAbFsDP6yNdGn9eD/8+P3K7V2tiuzm+UhO53Us+uKwCY5XUSzFW5cr6hBc0cjFeUmKBEAIeawaHIbUT7AY81pwiCSlMG1LfVhYUFBTETDeMVOA2nW2Q2dnZESkQlQJQ+g7x2LdVjlhvybMWJOrPre9RsDo9Kq8EfZt41yuobga/38/Q0BAOh8OcqNUKPlUDcHTCcDgQ9y+DH+1G3FkrJ+kYtQBGTg7i3gXwgfWIEpeMHLxSg5GTE7nD3DRYYYn6rMgBj7a/GxbImgMYrkH4eT3cv2z0wW73y30fly27E+p7IdsO27tkQeU45aFTSCFeSBEEC6w+AnrRn91up6ioiNbWVvPmnG5JXK/XG1FcOZGCN5UuiVU1r1QRIbpYUScKMHIHhSIY6jrOmTMnghwoXwmIbPVTf4tnHYBqsxzNIlsdF0jVBhxFMByOceX+jZwc2PKRMTYKwboOxOo8sBkQErCuQ0oma8cbDymJQn2vVFR8rBlWSAVGsdMPv2uCrX44IWf4tceaERfMSZGEFBKKY5IgtLe34/F46OrqMiehUCiEz+czJzmrIZKa9FRdgsJ0CynFinb4fD5TMnok6KZBLpcryhxIb3PUJZz1a2FtdbQ+15UehRCmU6aCLmakxp/oFr/RyIGqj1CE5tChQ0mrBJrC1EMIARcUw09rpd/DCblSMOn2Wngiss1QJyXKHEqMtfqvckkjqBUeqHIivrARfAGpf/DxWRgLpey5sdAtix7f9Uc5T6aQQjxxzBEEXddAPbpcLnp6eiKkgfXJytoe53A4In6fblgnZn2VHguBQMAUQQqFQqY5kM/ni1i1V1dXMzg4SHd3N21tbeb2ereC1Q/CGmnQr2F7e3tU+sAaAVH7mGrdBBU5sHaDdHV1pSIJRzEm4uzIFh/s7YfeQbhmK6TZ5e8/W4QxwkJBCAF3b4NfHoQ5mdDSB5fPRly+JPo4i93w+2Y4NQe+sBWu9krHx7vqIDMNEQzKjgrCJOGZ1hRBSCGhOKaKFPWJSE8fWN3zrC16OjkATE39ZDDWUUV4unfAWKRFJwcqOlBfXx+zy8DpdEZ0eOj6Cnohorpm6u9q/7qokypytBYiqmLBvLw88vLypm3VHitlMpXukylMLaKKAgnBv25GXLkZ8Ys6hPX+vnsP7OyHGxZjvHoa/PdiqHTDA40jH+Tv9fD3fvjVSoznToH7VsrnL0fbYRuGAZ8pkRoKihwAnF4IvQHY7B8eu6bgmEIKicIxQxD6+/vNtIG1HVBXr9OFh6zSxXq+XD0mgxW0qr6vrKyksrJyzO2bmppiOhyqx5HOSU8vWOWJ9bqEWN0N6vlI0QGPxzMtcswKTqczpq/FdEtsp5BAhIsCjYVuSQZ+0wyXVsCdS6EmD67cFkkS3vDHtpZ+o2vkY1xRG/s9V4zQ7rvEA70hqMkdjjAsdkvFxlrZJil2+uGdLikDnUIKCcT0x8enCBkZGRGhbzWxVVVVmc8bGhrMFaO1CFEvvAOSbgIZ7zjU5B/L8litmjs6Oujq6jLJhnJvhNieELoIkS6sZI3W5ObmJm23QHZ2doSD53SSlRSmCPW94cgB8HgLfHYOxqpcxGAIoyYsbnTPXlMkieIMqMk1u26EEHKVP3uUOqQK13AkQKEmV7o/xoBhGIjuIVjfgajJUy/KaME3tyAy7PL3VIFiClOAYyaCAHKlrefP9ciBzWajuro6QkY4lg1vZWVlRN/8TIMiEiqCAESt/lVoXaUbXC6LtZKMAAAgAElEQVRXxHXRxYbUa3pUxUrE1Os+n29Kz3Wi8Hq9lJSUTIkeQgpJgCqXXI0DvOuX5CAYgrBquFGTJ6MMCgNB6cWA5jWzvgNGSzXu7ZWTvRahY30HNPaN/J6/1ki/iPVhxdT1PvhJLbywGuPsYozFnhQ5mGaIYBBxVy3iyi2x01FHCY4pggDD4fjq6uqoyn397/okWFBQwOzZsyMIwVgTiFX0xyoOlAzQJ3l9ta8mdlWfAfK6lJWVRdQ76AWIOtnQUznWdEMypGRGw1gFnikcRVjshne6JElY5JZ6BUFk+yJIM6bFWhj/zyeGJ+6wCNL6Dvn8mVGMkv6xHP5nzzBJWN8hn7+8fMS3GC4X/HoxXLsV8dHX4dqt8OvFKb+FJIEIBuFrW6DQCR+ZBQVp8LUtRyVJOGZSDDrGmtyV34FaTft8vglFC/RiyI4O+WWiVufTHXWoq6uLMChSY4Pobg0gwvLY5XKZnQ1+v5/Dhw9H1B/oBYmVlZUEAgFCoRAHDhxIupRMCscehBCwdjt8qwUqsmBfD6ydDz0C0g24pwEur5TphfUdcP8++PkS8/26tTQjWEtbYcyejbgLOG0jlIeP+fJyjDHssA2XC/7xwSM/362d8PQBaB+C0/Lh7OJhSegUJoef18P8bFiWA8e7YYcf9vfDL+sRHyyEp1rla247fLwYPjVzr3lCCEJjYyONjY3MnTuX0tLSRBwiobAKD41lNNTd3W2uPPX36m2TMBxmH0ujIFFQ7obWmgGru6DdbqekpCSCHOhwOp0mibKKSQkhTP8H5QGRnZ095UZNKaSgQwgBt70F6+3w0ImyDmB9B3xnD9xqYJxbgTi7GO7Zi1CujT9fYrYVKoxmLT0SjNmzYceZ8TydMSEGB+HSTbAwR1pQZxjw4mH46yHEndHnlUI0RmyBffWw1Lg4Xj4Xx4ejTN/dAfPc8LUqaBmAvx+CPd1ww07ETQtnJEmIO0G44447aGxspLCwkLa2NubOnctVV10V78MkDFbhIVW8N9LkpisvFhUVRbQ+WlfjyrDI5/ONq9sg3lATttWF0eFwRBQtFhYWjkgOFKyKk7EIkY4UOUhhWrHdD7/yw0Mnmh0FoibcUfCxjbCjIsK1cSZChELw+D64vR4KM+F7C2Q3xnY/vH4YLpwjDaNu3o34/nGpOoZRYLbA6oqWjzYhlrihT0QUmRqGgSh0wsoceF8euB0YBekIhwG7u6EvCH86AJ8qkcTtss3QGYI8O9y3DGMSZnpThbgThOLiYr7xjW+Yzx977LF4HyKh0NX9VOh8pLSAMjRSOHDgQFRe3lrNb7PZTFfI6RBZmjVrFq2trRFRg9zcXFP2eDz+EopExSIH6npMZ6QkhRSiUN8LlVkRLouGYUiSUD66idJ0Q4RC8HQrbO+GpW74ZFHUalSEQnDdZtjdDx8ugjNmSXIAw3UU3UPQL+QkllJhHB26L0Z3QNYbzHJCQy98swpeaYMPFCLcDugOwOsdcEIOZNgxHGHBt4VuxO4eKai1tQvxsQK4ZDN8a96wR8clmxEPLU9akhD3mMeuXbt49NFHeeGFF3j00UfZvXt3vA+RcKhCReX0NxKUoZGa/FX7ky66ZLViBkhLS5s2BUaPxxOlhKiLLRmGMWb0QAkn6eempyxUS2MKKSQNqlzQ0BPhsmgWDe4b24Z5uiBCIfj+DnDa4PwSSDPg+zvk6zqeaIJtvfDlSllkuTpXrlwVlrjh1XbId8KqXKiz2FinEIn6XjguGw70w5Yu+HsbZNjgD01weBA2+OD/9sF9e+GR/fDnZnDZoT+ICIS1ZXb6AQFDQZnmuWyYHEBYE+Nb8+DLW6bxREdH3AnCd77zHTIzM9m5cycul4vrrrsu3oeYEhQUFIyror2goCCKEOhuhuo1PdefDAZPSrlQESCv10tpaem4iyj1VkArScjLyxvj3SmkMMVY7IYvuU1LZ5Mc3LYHnlsw3aMbGU80wup8OLVAejG8v0A+/2NT5HYP7ZeFl6cVwjwX7OqWK1tFEjb44N0uKM+AQwMpFcaxUOWCDR2w9iDkOuDsYnizAz5dKiMvNgPmuqS51kI3+EPSAfStDugckm6cfzsI7/klwfhkEXSGIiJYECYJHcmr1hp3guB0OjnnnHNYuXIlMP1uh4lGbm5uxOQPw+ds1VDQCcN0t/sp5UIdY0UOrMgMV29buyBS0YMUkg2GYcA174MvZMKlG+Cjr8OlG+HWIoyKiuke3sj4TTOcUYjhkXaRhicNziiEhywEIc0OBrC/V1bY37sXdvdAUMALh+A/3wPfEPysDn67D4SIWSuUQhiL3XBnPSzzQFUWvO2DDxRIa+/tfvhEMZyUDwEBpxbAVdWwuxdKM+CX9XDLe7ClE+ZlgypQzLFFRLAA+TwveZsJ404Qrr/+eh5//HFee+01Dh8+zH/8x3/E+xBJBaUyqPL3qu5AaQyolIPqDgCOmnY/RSh0IqSLTyn09vbS3t5Of3//lI8xhRQUDMPA+PgSjB1nYvz1/fIxmckBQF8ImiyiSk19kekDkO12JU741X5w2uH2JVDXDResgztq4RteeHw1fKkStvfCtzbDg3UpkjACDMOQs2OeEw4PwLMHpbR1QEgitjIHctLAH4D+IJyYK+sQflEPO7vBmwlFTnj+ENzdIDUS7l8OP9ljkgSxrgN+sgfuWzat5zoa4k5dVDj92muvBeDxxx+P9yGSCkpl0FqIaJUeViH4jIwMysvLp3vYcYPX66W3t5e+vj7y8vKiyEFtba1Zm9He3j5q0WcKKaRgwbxM+PMBBGAc70Hs6II/H4AFlsLKGxfAZ9fDFytlhX3nEGzzw/mlcqVb5ZJOkk19UJ0JB23w7CFYnAU1xdNxZtOK8RR+sioH3umUBYqrcqT/xQqPTCns9EO5S/6tqQ+eaIFvVMPps2Qh470NsKYQziySKYYrt0lNjYeWw5e3IH4UkJGDJC5QhAREEHw+Hxs2bMDv97N7925eeeWVeB8i6aAknPVQOwybPemph6GhoaMu7eJyuSgoKIgZOVDQuz1SkYQUUhgn7lgi6we2dCKeapFh6w0++MniiM0MhwPWzJbEYX8vPNoM58+RPg6L3VII6pd7oTILbl0CX62COS74yvZjLoogCz/flbPfomw58X/iDUTfcKRGCCEn/y1d8G43nFUEv90vJbIXu2XaZqNPbrP2kBRNOq1QFipWZMKlc6EwQxK1lTlwWTncsxfD6cR48ESMp0+Sj0lMDiABEYT77rsPgCeffJI1a9bwgx/8IN6HSDr09vZGGRTBsF67/nqsEPzRisHBQSDa8KqpqSkVRUjhqMaIIjsThJGWhnhoBVzzLjT1yxz3Qysw0tJiHNSA7pCctDb74J9KoScoIwlvd8B5JTKHHhQyJO60ydz6sdby+Ph+ODEfWgfglAI4pwTWz4ILNyMeOwEjPV3+71bmwQWlcHeDLFBcnSuv5e4eWY8QFFJV8a12+Kc5kOUABGzqlPUe+/qgKB1ypa+HeGDfdJ/5hJGwmeqJJ54gKyvrqMi1jwWXyxVhXKQ6FQzDwOFwjOn/cLRCFSta/RhGs5ROIYWZDhEKwQ/fgYs3wF21cNF6uHfHpFfqRloaxp3LMf7wPvkYixyAnLR+1wR/OQhzMuVq9wO5sgZhS5dsbxQCBkJSKnp5DmTaj72Wx3sbIT8NTinAWOLBsBlSM+KKKvjnd+Q29b0YC91SPOvyShm5WeKBdR1QkgHHuaE/BPv74H250o67NwCPNkH7oDT2mpUmIw2GiPb1mCFIGEE41sJWXq/XtJSG4YI9pZgYKwR/LEC1PMbq8kgUjrYUTgozB0IIuHU77ArCPSfAc6fIxxe64dUEryCXhKMAfzkIlZmwuQt+tldOcBUuObkBpNskSVjXAQtdx17LY1DApi6MJVrUpC8oCVRf+LtDc/qUCptVMhrkG5DmWWe/IaMv/3W87HL4ywF4rAnaBmXnwlnFsChH7vcPTdLX46sTL4gVQiC2dSGeaUVs75ryeTVhM9axKONZWlpKVlYWhmGQlZV1TEULRoIeQVIEKZ5dHHokIhAI0NzcTH19PS0tLXHZfwrjw4svvkhTUxMXX3wx5513Hs8+++x0DymhEENDiMs3Ik5/BfGhVxFXvo0YGpKh6c298E0vxuo8+T1YkwfXzoOv7kromAzDgFPyIBiSk+DJuXLluywHLq+Qcr9busBuyDqGx5qgPFtW5x9LSAd2++WqHhA9AegKwHvdkBmeEhe7YVMnYqMP0TYg0wYB4K4lUJ4JdptM6fQEwZsNny+FzZ2y5dHrktd4nkvqVjzcGNPXYyyIQAC+sVUWpealgSHln6eSJCSsAfNYiyAolJSURNgfpyCjK7W1tWYNQrzqD5QfREdHB3a7nUBgWHDE7/fT29ubqnWYIqxdu5a9e/dyzz334HK5uOuuu6Z7SAmDGBqCi96GC8vh1kJZxf7nA3DJO/BPJRCySTOoMExJ59KRXR/jBm82nFMMLx8Gf1AWzG3ulKvjGxbIdr1H9stCxkVu+Jb32FvM/fFEuGgT3NeA8AdkPcaeHvhZLTyyQm4jBDT3y6hBnkN2hgQCcPFmWcvhtMlUwm21cHUVPBxWUlyQLf8WRCovLsiGuVkTJwfBIHxpi4xc1ORK4vn3w7Lj4plWxNnFU/J/SxhBuOWWWxK166RHihxEQ7VDTlSMyQrlYaHIAQx3SBiGEWE6FQwG6e/vH9NbIoX4wOVymf/fzFEskGc8vr4VPlMGHyoEt0O2vQGUZcAfW8EWgvU+RE2u+ZlkfUe0nkEisNgNW3xQlQm7/HJFfHutbMFblQdz0qGxH1Z54NvHzUiHwSOFkZGBePgEOGc9/Pt2KTJ1uA/KXPDpjYjHT4D7wgJVK3NlquGdLqjtkUTLZsCAgFynlF0+bz0sC2skvNwma0HcDhldeL0dPjpr4oP8eQNcVCbJi0CSOYGsczgwICMJF8xJOElI2KfjaOr1TyE+OFJy0NDQQF1dHXV1dRHdIlYHSV24SXVSpJBY1NTUYLPZaG5u5u233z66r/uuXpnvdztMMTQWuiHDIWVzP5wrJ+X1vmFy8OM98MrKhA/NMAy4cC6cVyrJwdZuufK9+T045y24bjt8pwqum5n2w/GCkZGB8ZcPwPM1cuX/hSq4fiFcOx8u3CxdHFfmyv9fWaYkfxs7AUN2fHyqGIozZJSmPAOO88DPlsMb7bL24+AAbPLBP9rktuOEEAKxtVMWNzb3we8aYWsXhMSwn0aGDfb2wg93ykhDApG8Go8ppKAhEAgwODiIzWYjEAhE2Uvr5lNKmMkwDDyeY6h9axrx/PPPs3z5cm666SYKCgpIG6nS/mhAKBx+Ls2AsAQyO/3QH5AFgJctgIX74UsboShDdhO8uhLDIm2eKBiGActy4Y4VcoJ71y87FY6g3fJohAgG4YLN8P3jZGRgd4/UNvhiObQNytqE7iBk2+GUfMhxymjArLB2wfvzJQn74U5Y2yqLEG9aKGs9ftUmDZpuGj8RMy2m57ngPxdJlcYtXTJq8L975Wfr9cNw8Vy4bi681AZXbkNMor5hvEgRhBRmBBwOB2lpaQwNDZmvWT0urLUfQggGBwdxJrkYydGAD3/4w7S0tHD33XcD8Oijj07ziBKIhS7Y2A69AcRp4RqEtQfk6vF78+UEfGo5bJn+KKphhFe8x5LOwXhxV61Unlydh+GwIY7PluTPF4AXDkqthDwHRpYDsbVLahqUZIDLAVnhCXlFjixEdDvgyq2yiLE6SwpUVbvkowWmiuO2LhgSsmZhqUd2nqzwyLh+vlN2Q7gd4B+SBZT5TvhyhTz+T2vhS+Xy7/fsha9VJ+QSHbsxphTGjf7+fkKhEP39/dMaOq6qqooyxrKmGpQGhdqmu7s7pbkwBRgYGODSSy8FYGhoiPr6+mkeUQLxs2Xwdid0DcrV4w074JlW+EQBLB9265vuFrUUxsDaNjgpT7Z8AobdJms4Nvtk7cC+XnDZZch/XQeszpGtjRnhaTMYjs58eJYkFp+dA1/fBg4D4+xi2clg6ToQoRD8YBfMzZSqlh8ogHNLINsBdzVIi+meoPR5cBjy+e9b4PNz4WqvrJNo7INT8yUxWBU2j0oQUhGEFEZFbW2t+bseus/NzZ1yEaz29vaIdIJKMeieFyrNoF5ra2vDZrPR0dGR6mhIIE4//XTz8/DGG2+watWqaR5R4mCkpSF+c4JUN9zbJyeMny6G5Xlm+N4MF6/wYJxdLAvdpqiwLIVxoi8IhwbBMKTXRZZDei9s8IFLyALE/90nScTJeTAnA27ZJVfyK3JkSmJb2J/BbsgOkkEhbbkBY6EbAZFKlU+3wnnFsmWx0Amr86QCY1mmtIZ+qU1GKgZCsjbiz63wiSJY7pGEZLFbLutDgDsNft+UUAGmVARhGjBThHxUp4BiwCq/L4Sgra0tgjxMBfLz8zEMwyQHNpvNlLjWuxeAmN4YqUhC4lChuSJ+8IMfZM2aNdM4msTDVDd86iSMx1ZjrMiPnPi3++XEcVw2YluXnEyy7bC9a/oGnUIkytLg6RZo7YegQLxyGJ5sgQN98LsaWVyYbZfdC2WZ0NIPxenwZDPcv1dO2Cs88GKbXMnv6ZaPOo7LhpfbzCgS2/wYK3MlUTg5H7IdGHYDsmzgCacOnm6Btn5Z5LihU1pOdw5Bmk12Mix0ywjHcjc83jopAabxIhVBmGK0tLTg9/txu91JLaSktxHqBYBqdQ4yvF9bWzulK/O8vDza2trMcVnJgT5WnUgAx4TsdwpJgvpe+GQR3L0dfn0I8tPBNwgnpSF+ekoqipAM+PVKqYdQmg7rfNK2+a12eGI5Rnq6TAds74b3umRrY1UWfL1artozHLChQ07q2Q4ZiXiyBb41XAsgRNir4eR8jFXhdsmgQGxol0TxsSY4OR+x0AU37ZaE5Lcnyr/92w5YnC2LIHf4ZbFrmk0ea5NPkpqydPj8nIQVKEIqgjClCIVC+P0yX+T3+5NW7U/VGejtgooYqDA+DLcXTrU7o9XbQZEBfayASWhSFtMpTDmqXPDANvh7H9y1gv9n777Do6jah49/N430tqGEBEgHpDdRKYIoiBQpKqiI6KuQII8KNn6PyKOChaIooFJEQGooSTQoFgzSAiR0CC1tUwnppJK28/4x7pjNBkLZTQKcz3VxhczuzpyZ7Ozce+ac+1b99hAs7gKXrODXMw3dOgHkSorru8lJpX7JkAta/XI/2P/T6/PRBTnp1INqcLdG1clRzqA4zhMqqmBdMmxKk5NkJZXA8s5wulhJ0cyODDmLZXe5V0HVzkEu6vRlPIxvBUObQYUWPrggBwedHEEFdHOCN32hoErueZofCyfy5R6Mv7Pl9j7oAgfz5UJTJiR6EOqRmZkZDg4OFBYWYm5uTklJSb1/A78RulH/uoJTgEFvgu4irVKp6i0Rka5Xw9zcHLVaTV7eP6lSq42NUKlUuLu7KzkXcnJyUNfT9DJBUHRwgKcy4MeeqO53kb9N9nKGt/zgxSMwrFNDt1DgnyBhbU/l9+pjR7Ayly/ubW3h3XPyOIVeLkiH82B3Drg3QbWpt976pHEt4WwhUngGXCqD4TUyHnraQBs7udBTTKEcDJRp5eBAK8m9BJYqedrliiQ5UVIfV/k2x0oN5FXIaZd9beEj0ye6EgFCA6g5d78xBgm69Mi6WQI1u+t1F+r6uk2Sm5urd2tDVymzoqICCwsLWrduTV5enkEwIIIDoSGoVCqkNrbQy1l/9kIvZ/kiITRO/4wdUbVzQOpQKCc96uQIi+6D1alI3ybIUwtf9ITfswxeXn1aqXSmQJ6e+M+gRUmrhcUJcq9Ebxd58OKOy3CfvTw7oosTWMpfcqRTV+TnjWjx72DX0R5I5wrg13+2+60GKblEnnI5xh06Ohn91pW4xVCPtFotJSUlyv+r/2yMA+h0FSp1qucd0PUg3G52xBvl6uqqbFNX7MnLywtvb2+8vLwwMzMTwYDQuGiK5SyKVCteF50n10EQGqd/yjwDcrd/aIacybBckks7X6mUL8jfJMIs/+uvq4MDnCj495bDtnS5R+A+B7m34D4HeYaEkwWsTJJnUFRISMfy5O0Ob64fsEgSnCyEga7ya5tagrW5PBByWRJsTDH6VFoRINQjMzMzJcNc9XvmjVn1lNm6YKZ6L4JGo6m3tvj6+qJWq/V6WywsRCeY0Ejt8IaFcRCd92/K5YVxEO7d0C0TrqV6mWczM/goACKzIPAUPN4clnWFoS3A3rLWJEjVqVQqGNdSHpgYngHR+TC4mTy9srBKDhK8bOHnTHjWHaJy4ZXj8N5ZGNNUXn/1gEU3M8bTBg7kyeMb3vMHfwfwtZMTdRl5lkzjvjrdhapfcKt3BzXWEfa6qYzVezxcXFyU7v7y8nK9KoqmJnoJhDuFyt8fPnGBF4/C4wfln5+4yMuFxqnmt/6LxXCqCF7xgp7OqJo1kccHPNVSTlRUB5VKhaqDnAuDPq5y2ehmTeQkSJfLYFcWDHaD5HIY1Aze8pV7LhbEw8YU8LL5ty26YOHYFejtDO0c5NwNbWzkXAmVEuzLMerhMNnXr9LSUoqLi3FzczPVJu5Yuvv7usF1jW38gY4uMRGgNwYhLy8PCwsLKisrsbKyEt/iBeEaVP7+cE4EBHcKlUqFNKYp/L+TSFe04GIul35+wEW+GCMnVJIedIX1KTD1xtYrSZKcenlZEjzvKY83OF8kT3V83VtOn6zrIZjmIwchS+Pl6bI2FvLrvW2RjuXLdUAelStESsWVYGMu1wVxt5ZnWRiR0T/ZV65cSefOnVm6dCk2Nja0b9+e6dOnG3szdzxfX1+j1wnQFTMy1gW7+vRG3f91PQkqlQovLy+jtl+UZhYEoSFJFRUw6RS86SfPPonKg4/PQ0IxdKqWBCmxRM6oeCPr1GphaaJcjfHVNrA3B76Oh+ne8GRz+DNbHpDYxRHcm8DaZHn97RzhQWfADP7MknsKkkvlmg2nCsDPTr5dYWsO+3Pk2xU9XOpsz80weoBw8eJFHBwcWLduHQAbNmww9ibuGsa8uCYkJCjf9s3NzfHy8rrtAMTZ2ZmcnByDtMZmZmbY2toarf25ubnk5eU1+h4VQRDuctNj4E1fVPfLF1rV/S5I7/rD90lIr3qh6vjP7ISfLsGkVnWuTqqqgtdPg7+9nCvhaC5EXoGyKnjjDOzoARvT5IGKY9xhfjyMdofnPOXbEauT4f/85NTK7tYw0l0eZxBySZ4i2d5B7ok4VyiXnzZy2mWjBwiXL18mNjaW0tJSqqqq7u6iLY1EeXm5XpEiXYZD4LbrJvj6+pKfn09VVRVqtZqcnBxcXFyMNrhS105dcCBJEomJiXh7i4FcgiDUs0vlSnCg6KeGL+PgxBWkI/ny+AFvG/0ehVpIFRXw7DHo5SInRMoug8xKmOEH7ezljIiB56CgCMIq5ORLT7aQcy+Y/ZMLoVKSxyn0dIH/nJLLi3dxgA/84UKpnOa5SisPfuzgaPRpjkYPED799FMALC0tSU1NZcCAAcbehFBDUVGR3iDC6nkWdAWLbqdYkbOzs/J/Yw4STE5O1munrneiqqqKgoICHB1FiVpBEOqRuxVSVJ5+kBCdD62t5W/sCSXyWIL7HK57MZYqK+U0ztN9QWUGf1yG9FKY4iMHACVVcJ+jnLr5w/NgBxwvhDf85OBAQv55v4vcW+FhCy+0gkmt5dkwr5yCVV1QdTTtZ6TRZzH8/fffqNVqLCws8Pf3p2/fvsbehFBNfHw8ubm5ejkCdDMMdBdb3bLGlGshPj6eiooKvV4PXblmlUpFVlZWvReDEgThHreoA3wVL489APnnV/HwRQdlNoKqjm/qklYLE47CMx7yLAh7c3ikqTwtseM/twBszeWMiK1s5LEMJRLYqeTETNVTGZy6AiWVcLFITtNcUiX3SEz1hhkxJi8hbvQAYdeuXaxYsYJly5YpqXAF06he/lhX1VAXHMC/UxN1F9/GMpXy6tWrSjt1QQH8206QbzlUv1UiCIJgaipLS1jbBdYnI409DOuTYW0XefkNkLRa+PgC3OcMPnbyxTzAXi6u1Fct11ao+ueibq4CTYmcfntWO3mMwvpkOWESwNF8WJsi9yQEeslloUurAEkOMC6VwaIEeZsmYvRbDM2aNeONN94gPz+fTZs2cfXqVTGLwYRq3lqo2YNQPT1y9f83JGtra71bCjXLN1efLaHr+WgswY0gCHc3laWlXFjrJkmSBIsSoY9avqhH58MrbeSESN52cs/AySvy7+0c5P8fuwJv+0KpFt4JgC9iIfwSbEkDtRV0c5R7Hi4Wg6c1WKvk1Mu+dvBlR7mK5DeJSNN8TFIh1OgBQkpKivKBHhQURGlpqbE3IfzD1dWVvLw85UJbPWcB/FtsSRc85OfnN5oLbfVaD/Bv5UVA2R9duxtLmwVBEK4pphAczcHfDtSWcoBw4gp0dZIHNW65BJ0dILYIwjPAwgze8pZfl1Ai33Ioq4JDeXKvQ34FDGkmj3n4Plm+PeFoKQcHDhb/plkuqZLrOnQw/ngEo3+dXLBgAba2toSGhgJgYyMKk5iSr6+v0i2vo5sNoAsadI83tkF/1dtePX2zLjiomVZZEASh0UosgQdd5VwFR65AM0vYnAq7syCjTK6dsCRBnqZYUQWPN4WtGfKsiGHNoYkZmJmDu6W87JU2kFUGOy/LPQlXKmDuBXlKZGtrVE2b/JuuOb7YJLtk9ADB09MTa2trQkJCjL1qoRa6b+G6oECX2EjXc6AbANhYtWrVSmmjrgdE13OgVqsb1cBKQRCEa/K2BZUWfkyRxwhMaiPnNFiVBIviIbcMJnuBi5UcMPyQJPcU+NvLF387CznVchMzSC6B3Ap5cOMTLeTnmankAYuJJSCBVKmFJuaQedVku2SyG9KmHl0pGJZA1l1oq9/T1/00Nzevl5TIJUoh5P4AACAASURBVCUlN3VRrzlFU9dWSZKIj48nLy9PDFQUBKHx6+AA69LhldZyd39aqZy8aIr3Pz0EFvKMhf/XBl5qIxdsOpIn324orQJfW+jrBhmV8FxLuffh6396HDamyL0Kwb0grggO58u1HOKL4KLpqoOaLEAwxYAJQV/1EsjVpwrq7uerVCrUajVeXl54eXmZpA26Qk2VlZXEx8eTnp5OdnY28fHxN1TEqeY+VM/hACg9CyJIEAShMVOpVOBgDt1dwMoMVMjjBNrYwNki6OYMnRzlHoLWtnKQ0N5Rnu3wWxaUa+VZDQ+r4Y8ceZrkK20grwJKJXjQRR57MNlLDijWJMOZAjlxkq+dSfbJZF8pRQ9C/fD29iYhIUFvhkL1XoX8/HyTVUDUaDRUVlbqBYPVL+xJSUlK2ufr8fX1JScnBycnJwoKCsjOzq61N0TMZhAEoVHr4CBftNvZw5VK8LCRxxGUVUHzJvLUxsJKsDOH+53lVMrjPeWv6uGX5UGN493l53V3BiQ5sGhuDSlXoYMlVAFDm8mzIuKKoapQLittAibrQdBlVBRMy8zMDDs7O1QqFU2aNDEY+GeqBEnx8fGUl5frBQe6C7pu25Ik3XA5aF1yLWdnZ73cCLrxCCqVSgQHgiA0biNbQEi6XFzJ0wYulcKJAujtAimlUFwlBwhNzOSBha1t5DTM7tZyHoRW1hCZLw92lCR53IGVSu4xCLsk112wAM4Vyambm6hgXEuT9dibrAehdevWplq1UIO7u7teD0L1KYSmmCao0WgMplaqVCq94EB3gb/ZctC6QlDFxcUG+yQIgtCYqczMkJpZyWMLtMi3F55tKQcGW9Igp1xO2XymQM6aOLy5PC7hQiE86S6v5N0z0NtZ7iEoq4JfLstTHSd7yQMXl2ugQoKcCvi4rUlv55t+1JpQL2omQNJ12xv79kJlZSXl5eXAv4MKa5aErt6TcCszKHQBjy4Tp6lukQiCIBjdhRII8gJzwMsOJBXYWsi3GKJy5W/+5wrhIVc5ODhfKN9aeMpdHmsAsDwJhl6Vcyr424GzFZRXyWMQvO3k3oaP26IyceK7hk+rJ5iMKS6sFhYWercwdAEBoKR81vUg6B67lVscZmZmqNVqERwIgnBnaW8nT0eMviLfRrBUyTUVzhTKtw0yymCcp5wI6at4KKqU6yyUS7AnG0a1gK86yTUcPrkITZvIuQ9QQRsbVMNaQCtbkwcHIAIE4SYVFRXp9RboAgL4N2DQ/b+x1YAQBEEwuSAv+CEFPJrIMxlWJMlTGSe3hs4u8qBEW3Po4iT3CICcSXF/jnzrYVRLOXjo6CQHDjnlco4EcxUUVyGdL5RvOdQDcYtBuCn29vbKVMTqlSKrJ2qqOaNCzD4QBOFeoTI3R/qmI8y5IN9a6OUC41uCvQWoJDk/QnsHKNPKvQW/XJZ7G7KuwpZeUKKFkjJws4IxLWHuRXnWQytbiMqDzHKTzVqoSQQIwk3RzUjQDUKsXkWyeqGo6uMTRHBw9/v1119Zu3YtlZWVWFlZ8cILL/DEE080dLMEoUGozM2R/tdeLqxkZQY2FnIlx1ItJBSD4z9Jk04UQOpVeM1LfvxEgTyI8ZIE1mZymecnW0CZBL9dlvMpfBBQb3mGRIAg3BTdGITqaZ3Nzc1rzVugUqnE7IN7RFpaGsHBwcrvK1eubMDWCELDU6lUSONaws8ZsFIjT3Uc7yE/+MtlCL0EHR3hfX85B0JMAezMhMA2cmrlqDy4UAQTPOXxCQWV0M6hXpMQijEIwk3z8vLSCwjAsOw0YLIcDELjk52dTVJSEqWlpSQlJZGdnd3QTRKEBqdSqeTcCK5W/9w6qJIv+poSeTbCsGbwYSy0tkU1qx1MaQPfauTBizsvy5kXr1TKMx3SSuVETPVI9CAIN62yslIv+VH12wvVAwcxQPHe8dxzz/Hdd9+RkpKCSqUSidIE4R8qlQppvIdc1nlDKlRpYYBartfw0yUY3QJVd2f5uT1ckKaqILUURrSQyzgfzJMHJY73qPcSBqIHQbhpFhYWWFlZ6WU81AUJuh4EFxcXcXvhHjJ37lw8PDxwcHCgTZs2/PDDDw3dJEFoNFQqFaqOjqgme6EK8kHV0Um+2McUKcGB8tzuznC6QH5NB0dUI1rIPxugvpHoQRBuiZeXF5WVlSQlJSnBgamSMwmN36BBg7h06RLLli0DYNOmTQ3cIkG4A3SwRzqWrxckSMfy5VsLjYDoQRBumYWFBb6+vqjVaqW3QAQH96aysjJefPFFACoqKtBoNA3bIEG4E4xsAaEZclDAP8FBaIacgrkREAGCcNtEUCAMGDBAGW9y8OBBevTo0cAtEoTGT2VmBh8FQGop0icX5LEHHwXUS5bEGyFuMQiCcNvatGmj/L9///4N2BJBuLOozMxgpLv8r5ExeoAgEqYIgiAIwp3P6AGCSJgiCIIgCHc+o9/oEAlTBEEQBOHOZ/QeBF3ClLS0NDw9PQkMDDT2JgRBEARBMDGjBwht2rThtddeIzU1ldatW+Ph4WHsTQiCIAiCYGIqSZIkY67w66+/Ji0tDbVaTXZ2Nq1ateL111+/7mteeeUVPD09jdkMQRCuwcvLi0mTJjV0M8R5Lwj17GbPfaMHCMHBwYwbN+6avwuCIAiC0PgZ/RbDxYsX2bx5M02bNiUrK4u4uDhjb0IQBEEQBBMzeg9CeXk5O3fuJDU1FU9PT4YOHYqVlZUxNyEIgiAIgokZPUAQBEEQBOHOZ9Q8COXl5Qb/NmzYYMxNCIIgCIJQD4zag9C3b1/c3d2pvsqsrCz27NljrE0IglCNVqtlw4YNhIaGEhIS0tDNEa4hIiKCDz74gL/++gtra2vRFuGOYNRBinPmzMHLywtvb29l2d69e425iWtKSUnhnXfewdPTk6ysLAYNGsTAgQP573//S/PmzfH392fKlClG3eYrr7yCs7MzZmZmzJ8/n7KyMubNm0dAQAAZGRm8+eabRtvWxYsX2bRpEwEBAZw6dYpPPvmEtLQ0k+2fKfdFJywsjLS0NCwtLZEkCWdnZ6KjowF4/PHHefTRR426vZSUFL3jNWTIEDZu3IharcbT05Nhw4YZbVvBwcEcOHAAKysrzp49y7hx4zh9+jRg3H0rKyvj0UcfVdKbazQag336448/iI+PJz8/n6effho/Pz+jbLuxq+2cqaioMPn7ujZeXl64u/9bjKfme9HYn00305b6ONfrUvOztD41hv3Xacj3RW2Meoth4MCBSnAQGhoK1F9lNzc3N5YuXcrChQvp2rUrTz31FAB+fn50796dzp07G32bTZs25cEHH6Rnz54A/Pbbb/j5+TF+/HhSU1NJTk422raaNWvGW2+9xbPPPktmZqaSwtpU+2fKfdHp1asXr732GpMnT2bHjh0A9OjRg/bt29OuXTujbw/0j9d3333Hs88+y5QpU1i7dq1Rt9O/f3++/PJLFi5cSL9+/bC2tjbJvtnY2Oh92Ne2T99//z1BQUFMnDiRpUuXGm3bjV1t50x9vK9r4+PjY7DMlJ9NN9OWhjom1dX8LK1PjWH/q2uo90VtTFbuOSQkhNGjR5tk3UlJSfz3v//VWzZmzBjGjh1LXl4eTZo0wdbWFktLS9555x1sbW2ZMGECnTt3xs7OzmjbmzlzJk5OTixYsIDIyEhSUlJo27YtIAcsumySxty/1NRU2rVrR7NmzaioqDDK/tXGWPtyPbosm3/88QeTJ09mwIAB2NvbU1JSQmBgIOvWrTPq9lq0aKF3vCoqKmjWrBkAFRUVaLVazIxUh1130d69ezf9+vWjS5cuJt03ndTUVIN9KisrA+QLZmpqqkm229Bu9Jwx9fv6eu2oruZ70Zjn7s22pT7O9braVPOz9KGHHjLJ9mtTn/tfl/p4X9wMkwUIppwc0aZNm2sOfty0aRPjx48HIDExUfmgtrGxobi4+JYOdm3bKyws5NKlSzg5OaFWq8nKyqJVq1ZkZWUBKFkkb8W19i8uLo7IyEgCAwPJy8sjKyvLKPtXG2PtS10iIiKwsLDgvvvu4+LFi/To0QM7OzsKCgqMvq2a7wcPDw8yMzPx9vbGysrKaMFBdQcOHGDWrFkcPXrUpPum4+npabBPuvvMmZmZJvs7NrQbPWdM/b6+3mdTdcb6bDJGW+rrXL9Wm2r7LK1P9bn/damP98XNMP/www8/NMWKQ0NDGTNmjClWfU0lJSUcPHiQQYMGAXD27Fk2bdpESkoKNjY2PPbYY0bbVlFREfPnz6eoqIiYmBhefvll/P39CQ8P59KlS5iZmTFkyBCjbS8uLo4pU6ZgaWnJTz/9hKenJ4WFhSbbP29vb5Pti85PP/3EN998Q0FBAT/++CNNmjTh1KlT7N+/n0GDBilRvbHUfD9MnDiRTZs2cfbsWfr27UtAQIBRt3fs2DHs7e0JCAggNDTUZPu2ZcsWdu/ejbe3N4MHDzbYJycnJ/744w8OHz7MpEmTcHV1Ndq2G7Pazpn+/fub/H1dmz179hAeHo6NjQ1dunQx6WfTzbalPs7166nts9TS0rLett/Q+19dQ74vamOyPAgTJkxg/fr1pli1IAiCIAgmZrIAITk5ucHu4wiCIAiCcHtEJkVBEARBEAwYf1SWIAiCIAh3PBEgCIIgCIJgQAQIgiAIgiAYEAGCYHJarZZ169bV+7RXQRAaljj372wiQLjHRUZGKnn8q/vss89YvXo1P/zwAwsWLKC0tJQZM2YwYcIEtFotubm5TJo0iYiICC5cuEC/fv348ccf2bhxI9OnT9dbl65eQHl5eX3tliAIdRDnvlAXk2VSFO4M0dHRJCUl0apVK730pr///jthYWE4OzsTFxeHjY0NvXv3Jjk5mU8//ZRZs2bRqVMnHnnkEQCaN2/OM888Q1xcnEHmLxsbG2xsbOp1vwRBuD5x7gt1ET0I97hevXrRu3dvHnroIb777jvef/99SkpKmD9/Ph9++CGTJ08mMTFRef748eOprKwkLCzMYF1r1qxh586dtGnTpj53QRCEWyDOfaEuogfhHqdSqQCorKwkKChIWW5hYcFXX31FaWkpQ4cO1Uv5+cEHHxAYGEhlZaXeuiZNmoS1tTWZmZn103hBEG6ZOPeFuogehHtcq1atOHr0KEuWLNFbvnnzZtatW8f69et54oknKC0t5fDhw/z999+Ym5vzxRdfkJCQAMCFCxe4fPky33//PcuWLeO9994z2M6WLVvIzc0lIiKiXvZLEITrE+e+UBeRSVEQBEEQBAOiB0EQBEEQBAMiQBAEQRAEwYAIEARBEARBMCACBEEQBEEQDIgAQRAEQRAEAyJAEARBEATBgAgQBEEQBEEwIAIEQRAEQRAMiABBEAShkTl8+DBjx47lxIkTBo9VVFSwfv16RowYUetrU1JSeOONN1ixYoVR27Ro0SKDrIumUlpayr59+4y6zr179zJ06NCbft2uXbvQarVGbcudQgQIgiA0apIkIR3OQ9qQKv+8RvLXI0eO0LdvX/bv36+3fNGiRfXRTD179uxh0aJFbN26lVmzZhmUO54/fz6bNm1iyZIlbNu2zeD1bm5u+Pv717rukpIShg4dSkVFRa2PV1ZW0r9//1ofq6ioYOnSpQAcPXqU4OBgtm7dSmBgYJ0lmatXfLwRISEhhISE3NRrdBYsWEDnzp1v6bW1KS8v5+jRo3rvneDgYMLCwliyZAnLly8HYM6cOaxZs4YNGzawZs0aAFq3bs2yZcuM1pY7iSjWJAhCoyVJEnyngQdc4OmWcKYAvtMgBXkpxYZ0evbsib29PX379lWWxcbG8ttvvzF9+nRWrFjB3r17GTRoEPv37+frr7/GzMyMJUuW4O/vz9mzZwkKCiImJoaEhATKy8vx8vJi8ODBhISEEB8fj4eHB2vXrmXevHmUl5cTFRVF8+bNyczM5LXXXlO227ZtW/r06YOFhQURERGkp6fj5eWlPP7cc8/h6enJlStXmDJlCk899ZTevvj6+l7zmDg5OV33mHl7e3P8+PFaH4uJiSEiIoJp06bRo0cPWrRoQXBwMJ06dcLKykrvuUuXLqVZs2bExcUxZMgQZXlCQgJBQUFKWehDhw7x+eefs2TJElq0aMHp06eZOnUq0dHRAKjVatq1a8fatWvx9/cnJiaGt99+m08//ZS8vDw6derEpUuX+N///gdATk4OmZmZODk5ceLECWbMmEFQUBCHDh1i6NChPProo0pbcnJyDC7erq6uesWnADZu3Mj48eP5/ffflWXjxo1j37597N27l0mTJgHQrVs3Tpw4QWlpKcnJyUyaNImAgABmz57N1KlTr3vc70YiQBAEofGKyocHXFB1d5Z/7+6MBBCdD/e71Plyf39/mjVrBsATTzzB6dOneemll8jLy+PChQtcvHiRK1euAPKF9+zZs3h4eHD+/Hnc3NwIDg5m8ODBrF69mvDwcAC2b99O165dGT9+PGPHjsXc3Jy0tDTKy8uVi2yLFi0ASExMxNfXVy84APD09AQgLCyMGTNm3OZBunFdu3bFzs5O+d3Dw4MZM2bw8ssvc/nyZZo3bw5AfHw88fHxTJs2jZKSEgoLC9FoNAD4+Pgox7Rnz54cOnQIkG9t2NnZMWHCBNRqNb169QLg4YcfZuHChahUKiRJwsrKioSEBIYNG0ZERASTJ0+mrKxMaVNKSooSBHXt2hUPDw+GDh1Kz549WbVqlV6AoFaref/996+7z2lpaQC4u7sbPNavXz+8vb2ZO3cuy5YtY+3ataxfv54mTZqwa9cu5XkqlYrCwkIcHBxu7EDfJUSAIAhC4xVXLPccVNfREbam1xkgaDQaPDw89JbpLo6WlpZKyeKAgADGjBlDeno65eXlzJs3j9dff52AgABCQ0OvuX6tVstjjz2Gs7Mz7u7uBt/Ajx8/TmxsLIGBgeTk5KBWq5XHJEli8+bNdO/eHWdn5zoPgyns2rVLudi6uLiQm5urBAh1sbCwoLKykqtXryrL3n33XdLT05k/fz6BgYFKQKA7zl27duWxxx7jwoULuLm5UVhYiL29PQBNmjRR1mNnZ2dw+8Te3p78/HyDMtPZ2dl8++23estcXV2ZNm2a8vuRI0ewtrYmJCSEoqIifv75Z0aOHKnsf7NmzcjIyADk2ze6tnzxxRfK8amsrMTa2vqGjs3dRAQIgiA0Xn528m2F7tUuomcKwN/O4KlHjhyhsLCQVatWAXDy5ElefPFFMjMziYyMJCMjA41Gg0ajISYmBoBXXnmFzz77jJCQENLS0nj55ZcZOHAgmzZtom3btmRmZpKQkMDEiRNZsGABPj4+yvbeffddli9fjp+fH1VVVTz44IPKY3v27GHhwoV069aNyMhIhg8fTp8+fZg+fbpSFrmgoIBz586RmJjIunXr2LFjB6WlpTz99NMcPnwYjUZDREQEnp6euLm58frrrzN37lwcHBzYtm0bRUVF7Ny5k6FDh5KSksLy5cuZO3cuCQkJREdHU1RUxLlz52jfvj0rVqygY8eO2NrakpmZyYkTJ0hKSmL58uU4Ojri4uJCu3btlPb7+vri6+vLli1bSEpK4vnnnycyMpILFy6Qn5/PI488wrJly3ByckKj0ZCWlsbKlSvp0qULHh4eNG/eHCsrK4KDg5Xjt3TpUoqKikhPTycwMJCoqChiYmJITU1VelRA7qEoKCgA5NsZmZmZREdHk5aWhkaj0evpcHNzY/bs2dd9Cz355JOA3FsD/waJYWFh5Ofnk5iYqAQUL7zwAitWrKCsrIzHHnsMkMcvqNVqLC0tr7udu5Eo9ywIQqOlNwaho6McHBzKg1rGIJhSYmIi3t7eSJLExIkTWbduXb1t+14UGRlJYWGh3tiHhrJmzRr69et33XEhdysRIAiC0KhJkiSPOYgtlnsOejnXa3AAsHLlSgCuXr1K+/bt9e6DC6ZRfUyHaEfDEAGCIAiCIAgGRB4EQRAEQRAMiABBEARBEAQDIkAQBEEQBMGACBAEQRAEQTAgAgRBEBo1SSsh7dQgfbpb/qmte1x1cXExixcv5tVXX62HFprOggUL9Ob579q1iyFDhpCVldWArWq8brVQVWpqqpIbw1iq/+0yMzOvmf66MTP/8MMPP2zoRgiCINRG0krwdhi0nw3jvoP0w/CNAzzW7rpTHfPy8vD09GT//v1KopyDBw+ybNky8vLy2Lp1K71791aS34wdO5amTZvi7e1dZ5vOnz9PdHT0NYspgZzhb8aMGURFRZGZmcnq1avx8/PD1dXV4Lnbtm1j//79nD9/nvDwcKXQUnx8PH///TeWlpYMHDhQPh6SRHR0NEOGDMHOzo7U1FTefPNNjh49ys6dO9m/f7/y3GuJj49n9uzZXLlyhZiYGDp27FjnPoNcfMnW1va6mR/j4+P59ddfuXjxIt9++y29evXCzs6OrVu3cvz4cS5cuEBoaChVVVV4e3tz7tw5mjZtqreOgoIC3nnnHS5fvszUqVMxNzdn06ZNtG/fHkdHR1JTUwkMDCQjI4P777+f8vJy3nzzTVxdXbG1tcXKyorS0lJ69OjBhQsXeOGFF7C3t6ddu3bk5+fz1ltv0aZNGyVddFlZGV988QXjx4+/oeNwI2r+7XTHoGnTpri41J0ivLEQmRQFQWi8fk+CYd+iGvRP2eNBJ5D4Dv7oBo97XfNlzZs3N0jX6+vrS1FREYWFhfzyyy+8/PLLtGrVikOHDvHkk0+yevVqHnnkEb3XlJSUGBRzOn36NEePHmXo0KF89tln+Pn5ERUVxaeffqoEHG5ubnTo0AEfHx+GDRtGWVkZ0dHRpKamsnDhQr7//nv27dtHWloaY8eOVTIJjhkzhqtXr2Jtbc22bdsYO3YsO3fu1NuH6tzc3Jg/fz5ubm78+OOP9O/fn6qqKhYuXIifnx8nTpzg1VdfpXXr1spr4uLiePDBBxkxYgT29vasXr0aW1tbxo0bpzwnNjaWHTt20KZNG2JjY5k+fbpSfMnR0ZHFixfTrl07NBoN7733nl77XF1d2bx5My1atECtVhMVFYVGo+Gdd95RjunJkycBuaz12rVrlXoLfn5+2Nra8u677+Lp6cnmzZuZNGkSly9fVuozeHp68sYbbxAcHAyAmZkZnTt35oEHHgDQ+6betm1bXn31VeLj45Vlffv21QuKdu/erQR7v/zyC99++y3PPvsse/bs4X//+59elsdjx47p/T1ALvD0xBNP6C2r7W/Xt29ftm7dqne8GjsRIAiC0HgdT4QZZ/SX9TkNixINAoSwsDBOnTrFqFGjai0VHBYWxuDBgxk2bBg+Pj5Kut59+/bxzjvv8Pfff3P69Gk6deqkvOann34yKOb04IMPcvToUUBOBezt7U1QUBBmZmZ8/PHHmJmZMWvWLACioqJITk4mOTmZjz76CCsrK7Zt24aFhQVFRUUEBQUpSXiOHj3K8OHDsba2ZufOnQwaNIiqqqrrHh5ra2usra0pKyvj0qVLeHl5sXfvXszNzRk7diwDBw7EwuLfj/nS0lLWrVvHunXr+Prrrxk+fDgWFhYMGzZMb73ffvstHTp0AORv2Hl5eUrxpfLycuLj43nggQeUKojVubi4EBQUxMyZM4mJieH06dN6gY2tra2Sllr3+tTUVJYuXUpMTAzh4eF6F2XAoEZE7969+eKLL0hLS+P48ePXzbg4fPhwRo8eTWBgIOHh4YwePVrv8eTkZGX9w4YNY9WqVUyYMAFLS0uOHTum15bu3bvTvXv3a24LuObfTq1Wk5ycfN3XNjYiQBAEofHq5g0HOoKuBwHgQCd5eQ2jRo1i1KhR11xVdna2ctE7evQo5eXl+Pj4kJeXR0hICH5+fqxevZovv/xS73U1izlVN3fuXBITE/nvf//L/PnzDeoC3H///QwbNoxp06ah0WgICAjgmWee4ccff0StVivBwZ49eygpKWHUqFEUFRWh0WgoLS0lISGBxMREDh48qFfroaZt27YZlIwGuQqhVqtVftdqtVy5cgWVSkVgYCCvvvoqHTt2VIomVffQQw9x33330bp1a73bCnZ2dnz11VccOnSIadOmsXXrVuWxiIgIHn74YczNzWnWrBmXL1+mU6dO7NmzR3lOVlYWly5donPnziQnJ7Nz5040Gg0+Pj56RZbqMnHiRH788UecnZ0ZPnz4NZ9nZWXFyJEj2bRpEyqVymBfaxaHqq2gl86RI0f49ddf9ZZ169aNESNGKL9f629XXl6uV0nzTiDGIAiC0Hj5OsljDixSoGU27OkCvwRB0APXHYNQVFRESEgIx44dw8PDAy8vL1q1asX27dvJzs7m2LFjTJgwgc8//5xJkyYxYMAAWrZsyddff42Xl5dSlMnPz4/w8HDy8vI4fPgwffv2Zffu3cqH/qJFi3B2dqasrIxBgwYplQCzs7MJDg4mPz+fnj170rVrVz766CPatm1Ljx49+Oyzz5g+fTr29vZs3bqVVatWUVZWRmhoKB06dGDo0KG4u7sTHR1NZmYmAwYMoHnz5uzevZvff/8dKysr2rVrh6WlJeXl5fz++++MHDkSgFatWrF//34yMjLYtWsXvXv3VtplZWWFJEn89ddfFBYW4urqyr59+7CxscHX1xczMzNlv7ds2UJubi5JSUl06dKFDRs2kJ6ejo+PDxs3bsTe3h4LCwtlzATAmTNn+OWXX0hPTyc+Pp6JEyfSpk0bsrKyiI6O5tSpU0RERNC/f3/s7e3ZsGEDQ4cO5amnnqJHjx44Ojoq64qOjiY8PJyAgAC9WyQ6Pj4+zJ07l5deekkp5ZyQkEB4eDipqal4e3sr4xv8/f2ZMWMGs2bNMijZbGdnx759++jXrx+HDx9mx44d9OvXj59//pn09HQGDRqkvNdatmzJww8/rPevbdu2euvr1atXrX+7yMhIPD099YpiNXYi1bIgCI2apJXgjyT5dkM3bxjcBpVZ/dZilDtuiAAAIABJREFUEO5uGzZsoFevXgQEBJhk/Xl5eaxdu5b//Oc/mJubm2QbpiACBEEQBOGeZ8qiTHdqwScRIAiCIAiCYEAkShIEQRAEwYDRZzFER0ezZMkSLl68iKenJ1evXqVDhw6MGzeuzukhgiA0fsHBwXzzzTfEx8djZmZG7969ycnJwdzcnJEjRxIYGHhHdqcKgqDPqD0IO3bs4KmnniI9PZ3ffvuNbdu2sWHDBmJiYli6dKkxN3VPOHz4MGPHjuXEiRMGj1VUVLB+/Xq96TXV3WrK0bosWrSIJUuWGHWd11JaWsq+ffuMus69e/cydOjQm37drl279KaL3cvGjRvHa6+9BqAk9Nm9ezePP/448+bN4/nnnzeYHiYIwp3HaAFCcXEx7733HpWVlbzxxhvKvFknJyfefffd216/FF+MtDIJaXUy0goNUnxxrc87cuQIffv2Zf/+/XrLFy1adNttuFl79uxh0aJFbN26lVmzZhnMoZ4/fz6bNm1iyZIlbNu2zeD1bm5u10znWlJSwtChQw2yxelUVlbqTT+qrqKiQgnYjh49SnBwMFu3biUwMNCgjTU99NBD1328ppCQEEJCQm7qNToLFiyoNeHNrSovL+fo0aNUH3YTHBxMWFgYS5YsYfny5QDMmTOHNWvWsGHDBtasWQNA69atWbZsmdHacjfSnfeRkZFKljtjkLQSxaEZJL15luLQDFGL4Q6oxfDmm28afDmpuS/XUl5ezv/93/9d83Nj69atzJw584bbcv78eZMmKEpISCAuLs5k629IRrvFsGfPHvLz8wHo2bOn3mMPPfQQXl5et7xuKb4YDuTC/2uNykwlf0CsT0UCVL76iSd69uyJvb09ffv2VZbFxsby22+/MX36dFasWMHevXsZNGgQ+/fv5+uvv8bMzMwgnWpMTAwJCQmUl5fj5eXF4MGDCQkJIT4+Hg8PD9auXcu8efMoLy8nKiqK5s2bk5mZqXyzAjnNZ58+fbCwsCAiIoL09HS94/Dcc8/h6enJlStXmDJlikGik5ppVavTpR29Fm9v72sWB4mJiSEiIoJp06bRo0cPWrRoQXBwMJ06dTLoGl66dCnNmjUjLi5OL1tZQkICQUFB/P7774SFhXHo0CE+//xzlixZQosWLTh9+jRTp05V0rOq1WratWvH2rVr8ff3JyYmhrfffptPP/2UvLw8OnXqxKVLl/jf//4HQE5ODpmZmTg5OXHixAlmzJhBUFAQhw4dYujQoTz66KNKW3Jycgwu3q6urgQFBekt27hxI+PHj+f3339Xlo0bN459+/axd+9eJatbt27dOHHiBKWlpSQnJzNp0iQCAgKYPXs2U6dOve5xv5dZWlrSpUsX9uzZw2+//cbzzz9/2+uUtBLnx53g3RRLImxseeRQNvM3XqJdcNfrTnUsKipiyJAhSkpfkGsx7Nixg27duhETE8O7776LjY0NINdieO211wxSLdfm/PnzJCYmXrcnKjs7m1mzZtGsWTM6dOhAVFQUU6dOrfWc3rZtG7m5udjZ2aHRaHj//fcBOZ//5cuXsbW1VZ7r7e2t1BAAOQPh7Nmzldu5NjY2fPTRR9dtf3x8PIsWLaJPnz6oVKobrkEQEhJCjx49aNOmzXWfd/DgQWqOfa+5L9nZ2Xz88cf069ePuLg4nn76afz8/AA5V4Mua2NtqmeyBPnvMWXKFP7880+Dz6+srCx++ukno6U3Li8vZ+3atbi7uxMbG8sLL7yAj48PH3/8MdOmTau11sadzGgBQmJiovL/msU3LCwsak10ccMispXgAJCDhAmesCoZfOvOTOXv76+cVE888QSnT5/mpZdeIi8vjwsXLnDx4kWDdKoeHh6cP38eNzc3goODGTx4MKtXryY8PByA7du307VrV8aPH8/YsWMxNzcnLS1NbzpLixYtlGPj6+trECTpUniGhYUxY8aMWz8+N6lr1656Gb08PDyYMWMGL7/8MpcvX1bSjsbHxxMfH8+0adMoKSmhsLAQjUYDyElKdMe0Z8+eHDp0CJBvbdjZ2TFhwgTUarVyoj/88MMsXLgQlUqFJElYWVmRkJDAsGHDiIiIYPLkyZSVlSltSklJUYKgrl27Krnae/bsyapVq/QCBLVarXyoXktaWhqAklClun79+uHt7c3cuXNZtmwZa9euZf369TRp0oRdu3Ypz1OpVBQWFhokWhH+pVarAYz2ja3kp8u8m2LJzn+y3+20t0eVWsSmny9jN6rFNV8najE0TC2GiooK9u/fz8MPP0x2drayvOa+ODs74+DgQFFREQcOHKBdu3Y0b96cuXPn0q1bN06ePEmvXr3QarV89dVXtGrVisTERMaOHaskfdL5448/ePzxx9mxYwdjxozRe+znn3+mR48eALV+OayeVXH37t1ERkbqvX7AgAH06dNHbz+ysrJwc3OjadOmyvu9W7du7Ny50yhBcWNyZ6RatlAZfFtQmamQLOpOlqLRaPDw8NBbVlsqzZrpVOfNm8frr79OQEAAoaGh11y/Vqvlsccew9nZGXd3d4MI9vjx48TGxhIYGEhOTo7yhgK5MtvmzZvp3r37dSukmdKuXbuUi62Liwu5ubkGec+vxcLCgsrKSq5evaose/fdd0lPT2f+/PkEBgYqAYHuOHft2pXHHnuMCxcu4ObmRmFhoXKSVj/xa6Y/BbC3tyc/P9/g/nZ2djbffvut3jJXV1e9tK1HjhzB2tqakJAQioqK+Pnnnxk5cqSy/82aNSMjIwOQLwq6tnzxxRfK8amsrMTa2vqGjs29SjdO43pZDm9G9p5cImxs9Zb9ZW1Lzp5cgwBB1GJo+FoMmzZtYty4cRw5ckRZVtu+nDlzBnNzc1566SV69eqFWq3m559/Vr506c7x/fv3Exsbi5eXFy4uLpw+fVqvh1pXxOmpp55i6tSpBgGCLgsk1P7lUBc8AAwcOLDOSpgXL16kadOmjB49miVLlvDnn38yePBg1Go1p0+fvu5r70RGCxCql0nNzs5Wvj0bRaWEpJX0ggRJK0Gl4b3II0eOUFhYyKpVqwA4efIkL774IpmZmURGRpKRkYFGo0Gj0Sj1v1955RU+++wzQkJCSEtL4+WXX2bgwIFs2rSJtm3bkpmZSUJCAhMnTmTBggVKGlaQL4jLly/Hz8+PqqoqvXzpe/bsYeHChXTr1o3IyEiGDx9Onz59mD59OsuWLeO9996joKCAc+fOkZiYyLp169ixYwelpaU8/fTTHD58GI1GQ0REBJ6enri5ufH6668zd+5cHBwc2LZtG0VFRezcuZOhQ4eSkpLC8uXLmTt3LgkJCURHR1NUVMS5c+do3749K1asoGPHjtja2pKZmcmJEydISkpi+fLlODo64uLiopcG1NfXF19fX7Zs2UJSUhLPP/88kZGRXLhwgfz8fB555BGWLVuGk5MTGo2GtLQ0Vq5cSZcuXfDw8KB58+ZYWVkRHBysHL+lS5dSVFREeno6gYGBREVFERMTQ2pqql5RFB8fHwoKCgD5QzgzM5Po6GjS0tLQaDR6PR1ubm513tvUlfwNCwsD/g0Sw8LCyM/PJzExUQkoXnjhBVasWEFZWRmPPfYYIH8oqtVq5RuiULucnByA2+sxrMbtYVceOZSt9CAADLpagvphN4PniloMDV+LISsriyNHjnD06FEKCws5efJkrftSXFyslD3Oz88nLCzsmqW2PTw8GDNmjF4Pps727duxt7fn0KFDmJubKymTq7f1Russ/PXXXxw4cEBv2cCBA/XW16xZM6UH0cXFhby8PIA7ss7CDZGMpKioSLrvvvukli1bSuHh4XqPbdu2TZo0adItr1sbVyRp1yZL2iqt/HuVVv49rui22nyzEhIS5O1rtdKECRPqddv3ogMHDki//fZbQzdDkiRJWr16tRQXF9fQzWg0Nm/eLLVs2VLy8/NTlpWVlUnt27eXWrZsKW3cuNEo29FWaaWzTx2Thvc+LdkOiJdGPHBaOvvUMeWz4FoKCwul5cuXS8OGDZP+/vtvSZIkKTExUZo9e7a0ZcsWKSgoSMrJyZGmTZsmnTp1SpIkSYqLi5N69+4t/fnnn8p6SkpKpA8++EDavn27tHjxYqmoqEjasmWLNG7cOCk1NVV65513pJCQEOn999+Xrly5orwuKytLmjJlivTBBx9IBQUFUkJCgjRhwgTpzJkzklarlUaMGCFlZGRIkiRJW7ZskcaMGSN98MEH0iuvvCKdPXtWkiRJysvLkz7//HNpwoQJ0smTJyVJkqSIiAhp8ODB0g8//CCVlJQox/2zzz5Ttl1VVSV9/vnn0tatW6XPP/9cKigo0Ds269evl7788kvpl19+kVasWCE99dRT0s8//yxVVFQoz4mNjZU+/vhjKSQkRFq1apVUVlYmzZw5U5o5c6Z07tw56fPPP5e2bNkizZkzx+DYp6amSjNnzpSmTZumnDM196WsrEx6//33pa1bt0ozZsyQoqOjpStXrkhvv/22tH79emn69OnSzJkzpbKyMmnOnDnSli1bpKVLl0oZGRnK8T98+LA0ZcoUqbS0VJIkSVq+fLn09NNPS0lJSUpb9u7dK61du1aSJEnavn27NG7cOCkxMVGaMmWKtHjx4uu+h2qTk5Mjvf/++1JoaKg0Z84cqahIvgZ99913UlRU1E2vr7EzaibFHTt2MHXqVNq2bcu2bdtwcnIiNzeXUaNGMWvWLAYPHnzrgUx8sTwWwUIl9xw84mYwQNHUVq5cCcDVq1dp37693n1wwTQaS4rSxtKOxiI4OJgZM2Zga2tLbGwsAPPmzWPx4sX06dOHjRs36nVt3w5JK1Hy82Vy9uSiftgV25HNRS0G4YYtX76ckSNH1jr+yBhSUlLYuXMnkydPNsn6G5LRUy1HR0ezePFiYmNj8fT0RJIkXnjhhet2/QmCcOeoLVFSdnY25ubmPPnkk0yZMsVgIJkgNCRRZ+HWiFoMgiAIgiAYELUYBEEQBEEwIAIEQRAEQRAMiABBEARBEAQDd0yAUHEsn/T/nOX8a+dIn3aWimP5db5G5GO/tqqqKv766y9jNO+m7NmzRy9joiDURdJKaLcnUhm4C+32RFGL4Q6txdAQcnNz9ZI23a6ax7+4uNig7s/d5I7IpFhxLJ+jKzIIHd0WyVyFqkpi9IpYekwGy+7XzkAo8rFf29KlSxk9enSd+3mjIiMjSU1NRaVSUVVVxfjx4/nmm2+wtbXFysoKR0dHRowYQceOHVmwYIGSaU4QrkfSSlQO307B7gWUXT1Ok7XdcBz4DhY7xopaDNwZtRhCQkKIiorCysqK5ORknnnmGXr16sUvv/yCo6MjBw4cYPLkyfj4+LB8+XK9DKjXM3PmTNq1a8eOHTvo3r07Tk5OuLu762VTXLBgAR9//PENre9G1Dz+dnZ2pKSkcPjwYXr37m207TQWd0SAkLU6XQkOACRzFaGj/Wm9+gItrxMgiHzstedj12q1REdH88YbbxAfH8+MGTMYPnw4qampBAQE6OUTr6ysZN68eXrbMzc316umJkkSS5Ys4ZlnniEnJ4cBAwYA8Ouvv7Jjxw4AhgwZwvDhw1Gr1SQnJ1NQUICjo+M1/3aCACCFav4JDqIAKLsaRcHfC3EJ64FqTO2Z90Cc+42pFkOfPn0YMWIElpaWfPLJJwwePBgLCwuee+45Nm7ciJmZGd7e3npF5GbPnk1ubi6dO3fm+PHjfPPNNxw7dox9+/bh4uKCtbU1QUFBtGnThvPnz/PEE09w3333kZubq7Tl9OnT2NvbY2lpyd69e/n000+ZOHEi+/fvJzAwUC8dd3x8PJs3b9Y7tt7e3jz33HMG76GaHnnkET755BMRIDSUAq1KCQ50JHMVhVrDbxAiH3vd+dhzcnKU3319fWnfvj39+/fH29ubKVOm6AUIFhYWdRZCys3NVU7SqqoqJk2axJYtW/jggw9Ys2YNzZs3x8zMjOLiYuzt7XF0dCQ9PV0ECEKdtH/GU3ZVvyppWekxtLsSMKsRIIhzv3HWYtAd5/PnzxMQEKC0x8rKikmTJrF48WJ2797NkCFDlHTFw4YNY/fu3UyePJm33nqLrKwsCgoKSElJoU+fPgQEBBjUr7GystJL8Z+cnKykc+7fvz9fffUVzz77LK1bt+bAgQN67xFfX986P+euRfel527UKAKENWvWGLzRqnM0k1BVSXpBgqpKwsHM8F6kyMdedz722goh2dnZYWVlZZCfvLKykk8//VRvWfUPQJC/UTg6OirJcUpLSwE5h/oDDzxAZWUlq1evVvK937V5y4WbUtd5D2D2mC9N1nZTehAAmth0x+xRH4PninO/cdZi0BVLCg0N5e233wbkmjk+Pj64urrSvHlzLl26ZLD96nUTKioq6Ny5M506dSIkJITw8HDmzJlzzeOhe33NOgwqlarWOgzx8fFs2LBBb5mPjw8TJky47jbg7v48axSDFGsW4Kip6UstGR0ai6pKDghUVRKjQ2Np+lLL676uqKiIX3/9lcuXL7Nnzx4AnnvuOf744w+2bt1KXFwcPXr0UCLLMWPGMG7cOCIjI/XK/I4aNYqEhARCQkLYvn07TZs25eDBg0qBoi+++IJLly7h5+endyJlZ2cTExPD4cOHKSws5K233mLOnDnExMTQt29fJWoG2Lp1K4sXL+bgwYO89957pKSkEBQUxCOPPKJ8i9C9CXfv3k1mZqZS2AnkN2lKSorSBda3b1+qqqrYtm0bK1aswNzcXGmXra0tDg4OVFVVcfnyZTQaDUeOHCE6OlopTKVjYWHB7Nmz9f7VHD9gaWnJa6+9xrJly1i5ciVBQUEArF69mjVr1rB8+XK9bsiSkhK9wkzCvamu8x5ANdoLx4Hv0MT6fsCSJja9cRzwNqpRXtd9nTj3az/37ezsGD9+PIsWLSIiIoL+/ftz5MgRwsPD9S6ar732Gtu3byc0NJRTp06hUqmIjo5WbpOsXLmS4uJi5eKv89Zbb9G7d2+0Wi2SJCmBh64io+4WTEVFBd988w2hoaGcOHGCkSNHcuLECaWInK6Im66wXlRUFH///Te//vor5ubm3Hfffcp7SKPRsHfvXoP3QPfu3ZVv9mfOnCEzM5OYmBiio6OJiYmhqKhIea6vr6/B51xtwUFtx//8+fPXDd7uZI0ik+KHH37Ihx9+eN3nVBzLJ2t1OoVaFQ5mEk1fanndAYrC9Z0/f55jx44Z3GMztfDwcFq0aKF0Vwr3rhs570EeqCiFaeTbCo/6oBrlJWoxCDfE1J835eXlLF26lMDAQL3BpHeLRnGL4UZYdne+7oBE4ea0a9dOr2x1fRkyZMhdm7dcMA2VmQrVGG+DMQeCUJcRI0YY3BYyJjMzM6ZPn45KdXcGrI3iFoPQMBriQi2Cg7vTE088wffff09hYWFDN0UQ9JjyM8fCwuKuDQ7gDupBEASh8erQoQOjRo1izZo1AIwbN05vvrggCHce0YMgCMJtU6lUuLm58Z///IeXXnqJn376qaGbJAjCbRI9CIIg3DYHBwfS0tKwtLQkKyvrjk9x3NhIkgRR+RBXDH52cL/zXd21LTQOIkAQBOG2JScnExoaypkzZ1Cr1VhaWt7QDAWhbpIkwXca6O0MQ5rC0SvwvwtIHwagMhOdwILpiABBEITbNmjQIC5dusSyZcsAOaOeYCRR+XJw4GoFZioY6AY2ZvD+eaRP24ueBMFkjB5+itHMgnDvKSsr48UXXwTkJDg3kgRJuEGxRWBrAS6W8j8zoJsTOFtAdN1VbQXhVhm9B0GMZhaEe8+AAQNwdXUF5Gp+PXr0aOAW3UUqtHCxEPIrIKFEHoOgkuQgIbYY7ndp6BYKdymj9yCI0cyCcO+pXgK4f//+DB48uAFbc5exUMG2S3C1Csa6Q1kV/JAClVrwvztrAAiNg9F7EMRoZtMqD88g+/s0CpqY43i1ErdXPbEa0aLuFwqCcGeqkOA5D2jeBDLKoLMTVEqwJQ2+61L36wXhFhk9QBCjmU2nPDyDoxuzCZvaFclchapKYtSac/QAESQIwl1IkiQ4dgXe9we1FeRWwKWr4G4NfV3FAEXBpIweIIjRzKaT+XUSYe/0VMpeS+Yqwia1p/X8I3iIAEEQ7j6H86CTAySVyrMYXCzB2gxOXoEAh1tebV15FSStFtamQEwhdHCAF1uJKZX3IKP/xcVoZtPJ1ZopwYGOZK4iVytOXEG420iSBOtT5XEHR6/AqQIo18oDE0MyoNetFa9T8ipYquDplvLP7zRIVVVIq5OQ3joDo6LAxxbmtgdfO5h6Wg4ahHuK0XsQxGhm07HNvsrj35+jytIM80otJwd4kOHlgFVRRUM3TRAEY4vKh/udIfkqTPWSpzRuTZPHHzznceu3F6Ly4QEXVLrquN2d5aDh2WMwzVvOs5BQIgck/dSo+rshAaxLhRdbG2nnhDuB0QOEmqOZBeMoD88gw9eR319qp4w/eGRTLD13JmFZUdXQzROEe0q9pD6OK4ZxHrAqWf69q5Pc57suFV71ur31Pt1Sf5lbE+jsKAcDcUXwsBs4Wsi3OB5Uy1Mpf8q49W0KdyTRN32HyP4+jfDAjnrjDyKe9cf1cik2juYN3DpBuHdcs4tekoy7IT87eQxAkJfca7A1Hc4V3l7vgW69Zwr0lx3Jl4MCADsLKKyATo5wsVheFpUHnR1vfZvCHUkECHeIK5Kq1vEH5dbmqN/xaaBWCcI9qFoXvcrKTO6qf8DF+FkN73eGQ3lw/Irce9DeHgqrbj8x0j/rlY7lI5VrkY7lw+5s0I0xaNEELpfDgVxoY4u0Nxs2psELnre/T8IdRQQId4gmaaWoqvS/oaiqJJwK/j97bx4fZXn1/7+vmez7npCQBAhhB0F2F4SotS64tE/VuqEVfBTbaneq/nBptat9aquICloK1qW1in5xqYrVWllklX0JSYAskH3PJDNz/f44k0kC2TNrcr9fL16ThMnMNZO57/tc53zO5zT3u8VR59ajXyhAv3Qc/Xw+Ore+w/83LD/AsZlfsOvS7eTN/C8Nyw/0e/0GBoOGo/Uw6Yzd9KQoEQ+6EKVUx+yBVcM9IwZcyuj0cZ+aCK8USTBgscOpRng6D94uhrwGWDHZ6GIYghjDmvyA5ndKqAs1k/PKETZ+O7uDBsEWF9ivx9S59bJDuDMDZVJouyimNdC45jin1pdRlhJGbVY0uxeIGPLKVfuZsfwAYY+Nd+0LNDDwJ1pT9Oe26yLYW+MWV0OllGQMXGyn3Nnj6hWTRd+wvkTKCf+YYQQFQxwjQPADylYVUjY8kv2zk7nsLwexBTi6GOalMvn9uv496MYyZ3AASJBwy3Aab9zONh3Mht+e1yEQAdiweALDH9rMqMdc9coMDPyQWTGiOQDJHOytkVLAPSN69eseETj2A2UyGV0KBh0wwsMB0vxOCUXXbOfg9bsounobze+4XulbE2xm94I0Jmw5xQe3j+PDRWP54PZxzPj4JAmL+1kXDFDO4KAVZVKU5FnYsHjCWWLIc/5diDYramJCBvpyDAz8moGk/j0mcPQztNWKvmsH+qLP0Tmfo1cdM3wXfAAjgzAAmt8pYcfr5bzZzvr4unUHORfXWh9HWWyUjBDXNGcGocXO6NO1BC2c3L8HtWq0XXcIErRdUxMd3KkY0hZgQtk0UVVN/X4dBgaDhX6n/jvzIAAROA7RqYzaaoVrv4Q7MuGJeMhtgNdOwt270SvPMcocXsR45wdA2apC3rxlXIfd9pu3jKNs1UmXPk/C4jSuW3eQkhGRvH/neD66ZQxhgQyseyEnQTQHdtm5tGoQoqosnYohzS12rly1n5TL4wbyUgwMhjYeEjj6FffugbtHwKWJqIRg1OxYuHG4dG2sde251KBvGBmEAVATbO50t10THEBqF7/TH4IWpnAukLFiJzXBAURZrCQsHtgUR5UVLjuX1cfRAUrSpDkJpByt48pV+51lBmXTXPn8PjIOVZL0jSRDoGhgMBA8KHD0GwqbYEIURLS7HJ0TBZsrxM3RwGsYAcIAiLLYUDbdIUhQNk2Uxdrj7/Z1bHPQwhRSF6a4NPBQWeHis96OsMfGM2P5AYY/tJmamBCiqppIuTyOsDcucuEzGxgMUQYocByUpIXA/hpICIIoR1fW7hqw2Q1zJi9jBAgDoDX131pmaNUg9CQc9JR2ob+EPTbe6FQwMOgl2maDxw5LmSA7HJaPQZk7dzdVSqHvGeGYq1Ak93eBt4Ff88xk0SBYNPqieCnDvF4IdVZYmeXt1Q1pjABhAPQ39V+2qtAZHECbdiFjxU5SfSBA8Cb6j0elBTMxGGqt0GKH741C5SR6e2kGBmehbTa4cYcMU1o2RtLiN+5Av3put0GCO7wN/BUVEIB+ayZ8dw/8KRfMCm5OhTtGGAJFL2MECAOkP6l/T2kX/A39x6NQ2gwrpkBaKNi1DKp5uwgNRpDgw6xevZpp06bx+OOPo5Ti6quv5rbbbvP2stzPY4dh6QjUAsdnc0GilA9+eQQeHufNlfkVKiAAVk7z9jIMzsAIz7xAq3ahPb3VLgxqPq+AuzIhLRSlFMpsgjszID3cmCTn40RHR7N7927eeOMN/vGPfwydlPmRepgjnT1aS+sws2NhZ3WP3gZaa/SWSvTLJ+W2k/trqw39o63oC9ehz38F/Yev0DbDH8DAMxgBghdo1S60Bgm91S4MelJCIMDU4eKizCYINkGkkezyZerq6ggKCmLbtm0cPnyYEydOeHtJniE7HDZXyMW99fq+uRKarNLHb+t8FHtvDJO01QaXr4WLl8GHP4GHV8LOV+BbrxhBgoFHMM66XsAdbYuDgpImsNrRWjuDBG2zy/CY2iGeXfFxrr76alasWMH69esBeOKJJ7y8Ig+xfIxoEDQy0XFzJfzpGPw8Gz6tgGu3ot+adbYeoTeGST/9Eu5fg7pir3z/tf1omwl2FsOKqfC9iR5BhzWEAAAgAElEQVR6kQZDFSOD4CKa3ymhcPbnHJr5Bbvmf0netM+7nX4YtDCF1PUzGPf6VFLXzzCCA4AL4uD5AihslPSrzS4ahBP1cI3x/vgyy5YtIyYmhri4OLKzs1m3bp23l+QRlNkMr54rn9sbt8HfT8IlCRARCAsSYFoMXLIJvTqvo3VwbwyT/nMALj7Y8T4LDkJBLOzIc9+LMjBw4PIMwlAUKzW/U8LeFYWUjk/oMG3xyheM6Yd9Qd0/WoSK93wFScHS5tRih+8aXQy+Tk5ODsXFxaxcuRKAV155xcsr8hzKbEb/IAs2lMCliRBmhi1VMDMGHhgj2pp/FsHWr9DPThFlfjvDJK21ZMi+qICWdhm0Zit8ng2XtNtofJ4Np6Lg4pHee8EGQwaXZxCGolipbFUhVdEhzuAApCthw5IJlLxX4eXV+Rfq/tGod+agVk9DvTYT9c/ZRnDgB1gsFhYtWgRAS0sL+fn53l2Qp5kVA6cs8EkZNNgkOJgeAwEK5sXD1SmQGAQ/3o/eUgkzo2FzJXp7JeQ3wK5qyR5MjmzTIozLhL+eh/54PLopAP3xePjreXAyHZYamw4D9+PyDEJ7sVJUVNSQECvVBJuxBZo6bV2sigzm5MVbSLo/s9MyQm8dFRuWH6BkQwXVMcFEVFqIxErco2ON0oSBTzB//nzi4kTNv2nTJqZPn+7lFXkWpRR6xWS4dqu05/40W26tWrJgmWGwswomR8GnZfBWMfxyLKwthNMWmJ8A3x0pj6OUaBHW5kDOYfgyEzaNApMdDqfBf24T8a6BgZtxeYAwFMVKURYbZpO9U9vl8GoLB8bHUvOrfEZ8Wdmh3NBbR8WG5Qf4cr+Vd5+Y47zf5asOkPhkAdn4hvuiwdAmMzPT+fW8efO8uBLvocxm9Fuz4JYd8J9ymJcAVjuUWuA/pbCnBpJD4L5RsKUSHjsCo8NgaiRF39rGqUob6eZGAiIaCBmRQsjG+bDxbrhjHJwogPRM+Gw+KsjQlht4BpeHoUNRrJSwOI2Y6iZyXjnSoXXxyuf3U5EcBkpRkhHJvi/qKZjymVO82NtpkMX/r5x3l0zocL/3Fo8Hi3b55EgDA4P+o8xmeHk6/LcC3iyCMgusLICEEPjjZBlCtPq4lB7sdthQyObr97AkJonIlhO0VP+KxpN3UPnfn1OV+CIohXr5EtRnd8qtERwYeBCXf9qGolgpaGEKk4CyXxwl88EKGiMCiaiyUDIikrfum+Lc9ee8cgRrkJmCA1ZmLD/QtaMiJoY9nw8XJ6KywqmOCen0fk1hgdQEm4e0+6KBga+hTCb0o+NgSwUsPwhfT4ap0ZAQLBbiJRbpYpifyNEVJ3h0ZCoXVJUTbnkB9JfyIPpLGmsg+M5JhP11rkvXp7WWNssjddBsl/Ziu5Lui9mxQ0I3ZtA7XJ5BGKpipaCFKaRuvYCotGAqU8JojAzi/90zqcOuf+O3s4msbsZqNnHkszqC8uo7d1TEDosz4b8V6Nx6Iqosnd4vospiuC8aGPggSinUnHj4egr8T6r4JNS0QI1VnBY1NPyzmFM2E/+JCSenohj0ro4Pondh+Sx/QOvQdjv6pQL0j/fKrc0mBk0BwPlxkBEGIWa4LAHePw0r8jqaNWmN3lSOvvcr9OKd6BcLOrZrGgxqXB4gDHWxUsLiNKIrGrF2IVpsDjKz+6JUyoeFUZwSzlXP7evUUVGZFNwyHDaWEdLQzOWrD3S43+WrDxBgaTHcFw0MfJnR4bCvFkaFQWULVDRDXgNkhlJ2wsKh0GAurKpnY9wwUFM7/q6aSvC8Ef1+am23w9I9MtL9l+PldtEumBUN2REQGwjz40UgqZV0WkQFiEASR6bhmWNSIrkhFZ6aLAHF0j1GkDBEcHmJYaiLlYIWpjD+y0qK1pedJVocdqSakPoWzns7j/roYLZeNQLsmtse/RIVbiak3ILNrsnfV0XT748Tq20kz4gk4foUKj+p5pZfbKMxIpDQuha01iSdE24IFA0MfJlZMbJjB5gYKZNK/10GD2STcH0K75wo54H8U/w6I5EbgpcQbkEyCWoqoVFLCF09q//PveYE3JSGmpcg389LQFe1QEWLtGLGBMrkxElRMnr6W6kipDxSL26OW6ugvAXuzHA+hp4fL9vKtSdhUcZA3hkDP8BQvLiBsMfGk8oBrn5uH2//70S0WTHsSDXnfnySNb+Y5dQkXLL2EBGVFqriQ9ABJr78xmgmbDnVwWzp2lX7mH5bAtlAyXsV2EIDCLdYSbk8zjBgMjDwcZRS6HtGyK78H8XStZARCrkNhH1zGL96oZCfZybwjdPVnArMZKR9GWFJTQQvGEno6lmowM5HRveKfbXw7TMyjHPi4OljcttkE7Hk3hqZKbG3Ri7+2eFy36P1UG3tOJbarGBGDGw41f91GfgNRoDgJsIeG8/Md0oYuWInVRYIKmnihd/M7aBJ+OjWsVz20kFQ8MHt47jsLwf54PaOXQ1vLZ5I5oqdpK6fwajHXLe+9v4LEeUWVF0LFREhBNe3EBmkif/JKCM7YWDQBU6h39F6KSPMiulS3KeUkous40KrZ8VKwPDPEsb9Opt1n1dS/mkL8RelEf7Dka7zOJgYCVsrpd2ylYO14rtwpA7igmBbNXxVDRfGwdunxMxppmM+xOhwOFrX8TFsGrZVwZSos5/PYNBhBAhuJGhhCqkLU0gFDl6/q1NNgi3Q1PZ1QOe6hZrggAF3KjQsP0DJe5XUxAQTXdZIZUoor90/tUOHxf65KZSMiCTnlSMkPV/ERNo8FprfKaHi4cPUYqYuNpjoKgspVxpZDAP30pcLsUfX9Gy+DGf6VqrsvJ/NR98zoldrOzNgiJgbT8RP3LDQRemiFwBHyaAS/lYIz0yG7TXSimm1SybhgzK4PAlmtetimBUDX1bA6uPyGNNjYFMl/KMIVkx2w4INfA0jQPAQURZbp0ZKZqu9w9ed3WegnQoNyw+w7YCVDb+c3SEgSMmvpTgrytlhcdlfDlJ853j5+qWDlK06SerCFJrfKeHIkwWUjo7jvTvHOx/jilX7mWnMmjBwE1prWJEHEWbIb5S2waeOor83CubEeS9Q6M0kRh9AmUzi7rj2JKwvkV3/iskyC6JdgNLl7yuFvneUvO9rC0XTcGFc22MYDHqMv7KHSFicxnXrDnboRMh55Qi756exe36afD0v9SyzpYUr9w24U6HkvUo2LJ5wVsvlOf8udN6nNYPh/DrQRE2wxI9lqwqxW3EGB633eXexMWvCwI1sqZR0eF4jXJEsg4+uSIGfHYCZ/0Y/tE/a9jxNbyYxegBnC+IP9qAXbUe/mH9Wd4EymVCLMlBPTpLbPl7YW9s11TNTZD7K7ZlGcDCEMDIIHiJoYQrnAhkrdlITHEBkuYXTYYFsHBEpF1275tJ1h6mPDOCWX2yjfFgYCUUNZI4wD1gLUBMT3Hl5I6DtQD8rm9Fid2YuaoLNNESozssfMSEDWpuBQZd8VAZZYTAhCiZGQVkzXJksLYNvFMKOGhml/Pg4mBvvuYxCu0mMTlqFfh7CmV1JDIafZ0NBo8x3uKfdxEh8s0Rj4D8YAcIA6e2wJeioSQBIfKeE+9oFDObKBkIag6iNCSbjWI3LavxRDqOls8obLW0BQWs2o/Xr2HoLCXdJ5iLKYqOlztp5+aOqacDrMzDoFDNg0TAuUurk0QEQESBCukobLB8rHQGflMH7B9GPjPXM7tbRuqhBMgd7a2BzJdwzwv3P3crWKhgVDnNjUTFBkORwW91W6WxBHKhWwsDAbwIEnVsvPcQBSqajOWyIvUlvhy11xZkBg7tIuTyWK1ftd5YZlE1z5Qv7GX28mtTnvyKy3IKqbSH1RB0h9S1EnNHFkLA4jconC7h89YGzNAgpl8e5efUGQ5YFCbCpQlT2o8Jlt1zdIueBRcNhZqzzXEBcEDx4EP3EeLdf/Dq0Lv69SDIHnr7oHq2HaVEQGdj2s0lR8FWN/AO/0UoY+C5+ESDo3HpJn1lscKoZgk1iQ5wUCHeP8lqgULaq0BkcQNuwpYwVO0n1cItgdwFU2GPjmbH8AMMf2kxNTAhRVU3io/CPCzoEJ10FKkELU8gG4h8+zPCfb6Y2JpjoaqOLwcDNzI6F7Y6L8JUpMBlpsdtdLSY9doclcGSApM/31Hjs4ndmJ4LHGR0uZYXUEIgJkp/trYFmW1sL4tF6yRy0p9UUyQgQDHqBXwQIvF4IgQpSQ+HekWLe0WQTC9B7dqHDAuFbw1A3p3t0WV0OW3JBW2Jf0BtL5aCPDRSHtKQgeKsYfe0wZ5AQMDOWkN0NNAebCQkNJmBm304QQQtTSDF8EQw8iFIKvXQk/OIg/OYQRARKb/+MGNhdA5MjZcaBTcP+Wpkt0OoCONiZFSMahFqruBu2ahDKmuHZKXKfPmoltN0unQr7auV9XpRuCBKHOP7x1z9tgahAuC4F6m0wPFS8xB/KhlAzxAfCp+Xo7+yQnbSHaG1dbI8r2hL7gs6th88r4KEx8Ph4+MMkSAsFrSWwoq0U8tTSqbx41xSeuncaO14vp/mdEo+t08CgPyil4LIU+NUEWJgEn5dLjf3lE7CzWjYKG8sk0xBi8qhQ0JsopWDpSMgIgV8dEXfE7HBoJ1BkVgxsrkTvqEI329E7qkQrMTPmrMfrdG6DMXNhyOMfAUJYgKTOa6wwLAQU0GiDg3UwNx6emiRR87JseKPIY0FCZ62LrcOWPMbGMhmkkhoiLUkmBbemS0B12gI4SiG3jDurFFK26qTn1mlg0F9mxcDWapgaCx+cB/PiYWQorC+GRw/BF+VwdTJsqer04jdYcbYg/t9k1JrpZ7UgKqVEOGnVkmG06q61Eu3mNqgQs8xeuClNgjGDIYt/lBhiA2VUavvNelWL1BxvSQOzCZSSzMIVyfDPYvjJaLcv68zWxSiLlYTFXXcxuIUABYEd4zxlUugABaHy5/WVUojB4GXjxo2MHTuWZcuWUVdXx+LFi7nyyitd8thnzTO4Iln+40i9lBfMCuzK80JBP6DXWonO5jbMihWDJYMhi38ECNcNg9dOwl9OwAPZYFJyYjjZJOplrduChzCzjFX1EJ7qROgSq5Y8ULMdHWSSk6nNLgFVrCicu3Jx9GQpxGBw895771FQUMBzzz1HWFgYTz/9tEsfv9ML3Wyjg8ZldDa3YWulMXNhiOMXJQaVFQ43DIcvK+Gu3fBMHvzmqKiXQXbRzfY2BX/jELrw5STAB6VSTrDY0Y1WWH1cxIrfGAb0vRTSsPwAx2Z+wa5Lt5M38780LD/gsZdj4L+EhYURFhYGQGhoqJdXY9AbtNbozRVwohF+dxT9fB660Yr+rEzmNtzqwXKpgc/hHxkEJEjQVw+DUCVTxwKQoOCl43BburQ+FjbBe6cgKdjby/UYKitckievFknmpNEqr/+G4c4Ohr6UQjqb23Dlqv3MMGYuGHTDzJkzUUpRVFRESUkJzc3N3l6SQQ843RhLm+GqZBE9biyD7+yCyxKNmQsG/hMgALJb/m8FvDpD6uzvlcDKfDEEiQiAuECICYTr07y9Uo+issJ71Fz0thRS8l6lMzgA0SpsWDyB4Q9tdum4aYPBxYcffsg555zDY489Rnx8PIGBgT3/koF32VolWdi5cU4zJX15EiQHQ6jZCA56wWC3snb5J2Djxo0UFhZy6623ct1117FhwwaXPbbKCpde59XH0S8dh5MWuC8LLoiX3uiMMGjX+2/Qd7qa22DMXDDojosvvpiWlhZWrlzJ448/zrhx47y9JIOeOOro9mo/eCo8AEaGeXzwlD/itLIOVGJIFajEylrrHn/XX3B5BsHtYqWscOnRbU+OS59iSNPV3AZj5oJBd1gsFhYtWgRAS0sLeXl5Xl6RQY+MDodDtR3NlOqtkNcwZPwkBsQQsLJ2Sw7JECv5Hzq3nub79hCoNP/zh118fdUBhuXWODUIxswFg+6YP38+cXHyGdm0aRPTp0/38ooMemRWDNRa4e0S9LZKdLkFPiuXlsch5CfRb47UQUYouqQJXdMimQMvjP12Jy7PIBhiJf9D59bT8mw+OypMvPmLOW3ixOf3k3GkkqRrEw2BokG3ZGZmOr+eN2+eF1di0FucVtZbK+H9UpmemZMAVyQPqjq6O9A2mxh15dbLsLApkVDUBCVNgyr74vIAwRAr+SEbyyg70sibS6d1FCfeNYH7Vuw0goMe8MVJowbuwZ2iNG2zwWOHZQeaHQ7Lx6DMZpc8dlcopcRPwvCU6DXabodFu6TrIzNMWkRfPCHTNTeWwyNjvb1El+HyAOHiiy+muLiYlStXAvDqq6+6+ikMXE2AuCoabot9R+fWS2fNnRnSWWPXsO4kGowgYZCh7XZ48KAYkJ0XJ2fPZ/PRLnBw1BYLzPlC3GBjzGCxwiWb0P+ajTI2Wb7FmhNwfSpclCC2/wlBot3YI4OwBlP2xeUaBEOs5IdYxVXR24On/JI3iyUte8qCLmoEix1uGS4ZBYNBg9YaHjkMlyfB/Vni2LqlCmbHiChtII9ttcLVO+DJSfDqDLhjBOyth28mwze3GQOTfI19tTAnDuqtMg8jKlDKDKebYUyEt1fnUlweIBhiJT8kJ4GE7NCz3RbXenjwlJ+hc+vF0jotBDUsRAaJVbdIkBAweHYRBkhZ4fw4uCAeFWQS5fqcWGiyD1yUdu8e+NlouCgeQswwPwF+mAV762B0BPz1hGteg4FrmBgJB2uhxoqubZGs4RcV4mZ7hrhTa43eUol++aTc+lkLpMtLDENFrDSY6s4qK5zAe0Zw7p+OkfHbbdREBBJltZFwd7pnB0/5GxvLpPd5ZT460OT4HCTIfAyrf50IDHrgaL24C9ZbIdKR8p8UBX/MlQv6QChsgtmOtjjlGLY2Nw6ez4eb02FVAdye2e1DGHiQ24bDHbthUboEC5+UwetFsGZqh/KC0ydhTqz4JOytcVlJylP4l5Oij+DOunP1JV9QWmanJj6EyPImQqotxJwXTeTLM1yz+C5QWeEEPTWZVDA0B73lZAMkBMPVKZAaAnYNfz0J+6rhnlHeXp2BKxkdDgUNkBIive7hAbC5QuzNB9oSmBYCWyqdNW2ttTz22AgxLYoLcsUrMHABWmt47jh8fwTsroFXC+W4XzP1bEHpIPBJMAKE/vB6IcQHwZoTaEf2gFuGy5CkAQQI1Zd8wa6YSN79yQRnq+EVL+xnxKbTWCd8TOz+i134IgwGTG4DLB8LzRqKLSJYuiQB3jvlt9kkgy6YFSO7wdkxEBwG/z0Nmyrh8XED2g1qux1mRMOvDssP5sTC5kpYkQ8rJ8EHZXCDEbL7DO0v+jPi4E7QO6pge83ZF/2j9ZI5aM+kKPh7kd8ECIbZdh/RufWgFCzORN2RAYszJZuQ1zDgunNpmZ13l0zo0Gr47pIJNMWG8NWEJGpv3uaKl2DgKoYFy6CbEBMqNQRSgiHQBJlnm4Pp3Hr0CwXol46jn8+Xz5GBW9F2O/qlAvSP98rtAMR+Sim4ZwTYkOmpcUHwyNgBzSvQdjvcvRuONEBWKNz/FSzcAn87AY9lS3BQ2Wy0IPoSR+s7WlND1+ZIo8OlrNCevTV+5ZNgBAh9ZWOZZAscsYAyKfn+49IB151r4kM6bTWsiw3h3SUTOLXfsDv2KZo1RJih2IIubpIsQoRZft6ODiWpdkGlESS4B601+j+nYManUhs2A+nBsHTPgIMENSsWdfNwuR1oHfnFAnEy/Hoy/GYS3JEJWsNXNfD8ccgKg3tH+U29erCjtYZmG/wxt6PgsKuL/qwY2FyJ3lGFbrZLpmFzpV+5VBoBQl8JUFJeOGVp+4AoxCwjZ2Bipajypk5bDSMqm8STIM4YmOQr6Nx6CFbwh2MQEyAjtpOD4Jl8uOYMYacjqFQmOdE7g0qjFdLlaK3hqcPw6FFYPg5emgaXJcPK43Bdsm91BLxaCHdmwoVx8FyBDJ17bSY8NBaaNLgiCDFwCc7R2BabnO8/L4OH9qO3VXR50XdmnaxaygpWDX4kUARDg9B3rBqCTRAdKDtHBdg0RAX2u+6sc+vhzWISrRaueGG/s8ygbJorVu3n4IxE8SSoMDIIvoAzI/DrifDvMlh2QELtJjtcn4rKSez4CwHKGRy0okwKbbRCup4vyuDVEnhiAsyLhwYbZEeI693G01Dc7LaOgL67LCrRHHxWAQsSZCKtXcP4SPnvxw6hHx6YxsHARWyugPxGuC4FpkaLF8IbRdJpsnJql38jpZToDfxEc3AmRgahr+QkwLqTEOyoOycHy07wG8P69XA6tx4+KoW0YKIuSmDK7lMs+dkmbvjVDr7zwGaq4oL54htZXPHCfpInGBkEn6BdRkDlJKL+NBn+MAmmx5wdHABYtXS6tEPbjVZIV6K1Rm8qh/v3wXnxMDcOzAoiAiAqACZEwp468Rlw0/P3efRvQpAo2o/UwRRHXft4o2QoL00CO4NufLCv0RufAq01/OkYzI+X4C3UjJoZC99MhQAzbKse+DpsNvTDB9A3bZNbm23Aj+kKBnUGQW8shefyISwAGq1Q1SIish9kdX4i7wUqK1xaVVYflx2gVUNOQv9V6xvLINIMxxrgdxOJWVJH8P8dI2hLNZbQINKP1HDXzzaRNDHE7a2OBr2krxkBR1CpHUFFa1vsQEtSBoLWGp45JifqCZGQHgJfVcOMWNkCRZhFI3S6CX4+2j2L6E9L2yUJ0vk0KwZ21UBGKEQGQEwgbK8Stz4T0i21KN3IJLiY3vgUOO8zKlwyUgBFTejUEBgVJrbbh+vQGgn0WuxyjcmO6PWcDm2zwY07YOkIWDZGshU37kC/eq7bZ3H0xKANEPTGUthwCh4eKwecVcM7JZBXD6+cEM+CAQQJA2ln7ECAgo/L4OnJMhnsyypCX5xKulLSd/16EczM7PdaDdzAp6XoD09DYpDYq34tERZldJkRcHlQadCRzRXSk359qpzI8+rh+QL5v3OixeXuz8fgxmHu6wjoT0tbSADMjIIKC/zikBjvXJksQcVHpfDdEWDR8OABOFSHfnzcgLomDM6gN0Gd4z5URoh4dFYsaKDEAl9WwvBgMUoqbBTfitgQ+Kq2w5wO5+N0VXp67DAsHYFa4DjHL0iUdfzyCDw8zgNvRNcM2gCBp3LhdxMdF3IFaLh2GKw5DvV2WF8Cbrjo9tlh0arF1z3YDB+WwvdHgUlBow1atIjZfrYfnRlmXFB8AP3HozAyHB7IhgCT1IwfPwL37IKfjuny91waVBp0ZN0JSclfEA/hZkndN2nYUgH/LIZDtXBeFHw/23278NaWtnPbidV6amnLjoDxEdI6mRUBG0thV7VkFL47Aupssmm4PV10To8eRj8yFux2j099HJT0JqhrvU8A8PujcrxPioJPy2TDWd4MP8+GmbGiS/iyCs6Llb/pnFgZpb29uns3xSP1kjloz5y4tiDXiwy6cFTn1qPv+wpigyAtFFDyKk1K+tRrrKI6j3R9bNSvdracBChuAptdgoTW4KDaCumhqLRQmBtrtMW5meZ3Sii6ZjsHr99F0dXbaH6n5Kz76Nx6+LRc/q6nm0UAp5SkrUubXRLA1d68jaPTPmfHxdvInfa54X3RA1prSc9fGCeBtknJsX9eLHxeAWgIM8EvJrk3Rd+flrZZMTLwKUDBDcPlc1XeIucBi5bgYG+tHP+jw2FcBDx8AG7YLvbOq6fJ7Y07fKZm7Vf0xqeg9T5KwbRoESbevF3+Nlcnww+yID1M9C4zzpjPMSkKPixzZik6zPBoP+ArO1yyYO3ZXCF/by8zqAIEnVsPK4+JuCfMLGM58+olJdRKrQ2qrNJ/7Gr60c6mssKh2Q5PHoMmm0SolS1S2wpQaJtdLkQXJ8BTx4wgwQ00v1PCjtfLeWrpVF68awpP3TuNHa+XdwgSnMHf/AQJ2lJD5DNksaMCzTAsVILT3x9FP3gAff8e9BOH+vT3qr15Gzuag3nh13N59efn8vyv57KjOdgIErrAWR8Oc2h4WrTstBXicnm6WYyG1k53e2q+Py1tZ/2ODVgxWdLXDx6Qi87dmWKkFGqW9tmKFvifVPkchpolLb10hKSjDfpGb4K6WTGwqQIeOSSBXFKIzM2YFCXXkTlxkrVqsMlnb1KUnCdGh8GrJ6GgHjJCO4ofzzRWWj4GVuSjPylFN9rQn5SKk+ZD2R57K7piUAUIrC4QQeJPRsN9WTKa9dNyOFYvf7x1J6HUAlbb2b3qrqAL8VqPDosXxkN1M2yrlA9ig01ODmgRMV2cKBFsVAA8nSf6CgOXUbaqkDdvGdfBwfLNW8ZRtupk251aDbKa7BK0gXgfVDSja1vgZKN8/szAHenS1ZAWCm8VO4OEntwUT+1v4t3FZzhpLvY/g6yqqirsnhhR3FofvjxJ0r17aqDeJif0vxdCohmem+ax9Ht/jJTO+h2TSbQICUFyziqxQFKQtFUfqJWf5yTIz1uZEwcH69z4yjyLKx0wu6M3QZ1SCqZFwahQ+KISpkbBT7NlcuuxBpnqGGqWzHSLhv+WQ7kFNjjKD5lh8M8iKGxsCxL2VMORWufrQyl49Vz4rBy+s1NufUCgCINNg3C0Dn4zEYaHyk683gYXxMHvjkqqqLgJihvh7vHuEf052tnaBwm9ameLCRJ9xL/L4Vgd/Hy/pA7rbCKASw0RQVxmGHwnHX6bi84MkzTUe6flpFHaDFckoW5Od/3rGuTUBJs7dbCsCQ5oG1zlCP70+bGw6jh8J0MyP1Ut0gu9OAMuTJCOlJcL5aC/1TGJb2OZJLF6GPBVE9e5k6avG2Tl5uZ2+P7999/HZrPx/e9/371P3FofPicS7twF2yvl5Ntoh8N1sH6mXyr/lVLox8fBo4dl0uM1KbCzSsoROQlQ0CgZxlYGkI7uu3eDe9F2OyzdAzelwbeHSw1/6R70isluyQL1yqfguQL4VhqMiZQuGatdsgxWDf8ogmPuuY4AACAASURBVEsSYVyk6BI2nIKEQHFTnRcv2YKPSiXTdf8o9PFG+PVR0ZjMjnO+PlZMRnlZkNgZgyZA0Ln1kj0YFiIpRrOS1E9mmIjJjjeKjelfprtP7NffdrachI4XjyN1Iq66MRWCzBIcvFwI06Mlav1GCvx4r/TkrpmGMptkV/v4EfTLJ4wgoY9EWWwom+5wcVY2TZSlXRmq1cvADmwph3+dFk1LWTMsGg7zE+XrYJNzcJdakiljoEEyEI6/LzjaIs8Y8BVV0dT5OnzcIOu2224jMzOTlJQUtNbk5+cDuD9AcNSH1bkx6NVT4el82cG12CQ4CPDf05symUSQ+OhB+EM9fC1JdrtawwMHYHYcOi1UgoMV+bID7SM+OY54zQm4KQ01z3HOnJcgwfXak7AoQwKINSdEEDgxEhalu6185AyejjdKx8JPs+X4PmWR7M7kSHjPBH85DlemwJgI+NNkES1fl4iaLkGHviJZymD/u1t8OL6TDvMS5D0+4/X5Gv57BLXDWR+22CVz0GyHIJMIlswaalokaLg9w62dAP1tZ+v092bGwP8dEzFlZpgEB/FB0iutkR3DkkxJbcUGSZDwYDYs2ikz5A16TcLiNK5bd9BZZlA2zXXrDpKweHjbnXIS4JGD8vmaEy9p7bRQKS18XCbDukaEQWUzKjUUHaDQ9VbYWS1/Q63l8xnaljY80zsheUIIV6za7ywztDpp+rpB1r///W9ee+01Ghsbuemmm9iwYQMTJkxw/xM7JixqkJ3ahfHSg+5ndrZdoZRCLx8rO8wmm3x+tlaKRml3lUyVHRfR/3S0L44j3lcrmYP2zIqF9SUezS50CJ6enQK/y5XS1fmOTpmyZml7/KQUPr2gw/uvzciY7vakhsC0GIgLlI4Hh5eCM4Ox/mxRtC/g1gChqqqKqKgoTO7u3XXszni3BF4phG+nSSeAXUtkVtwotUgPtJn1t52ts9/TmWHwTJ5EnMca2oIDraHBLheko/Wi2gYJEuIHx+x4vbFUDprIAAmCrk1xmxdE0MIUzgUyVuykJjiAKIuVhMXDCVrYTqdS0CBBwKQouCNDSjoK0ZfckgYvHJeTgh3J5jTa4Ok8uDAWylpkVsfKPPTXklATo9CNNihpgo2lkjGKDSLisfGcu/wAacs2URMXQlRFE8kTfN8gKzAwkFtuuYWqqipeeuklNm/ezPXXX+/251VKSZ/5l1VSQ84OHzTBQSvKZEKvmCznsfUl4rj47BTXXBB9cRzxxEi58M9rl3XdWimv25Fd4MJ4EQiPiZDyy19PuN4+u13wpLWG+0bCMwVS9h0fIUZWa0/CJ+edHZwtSBDx47x4CA+A0xY40STl4nqbnEfGRchriApse30+iEsDBK/VIlvrwzcNh8/K4C+ODEKzDQ7XwlUpfukhoLLC0Vclw29zpazQGhy8XCh1Lrt2iBkFbbOLMKYf9Nm/wY3ojaVy0v/DxLbyye9zB2Ru1RNBC1NIXZhCald3WF8i6mVwiE4dupKYQAkWApWUH2qt8EK+tKfdkAp1dvjfEbL7q2iGDafkN8PNYoK1fGybPuadU0Q8Np5IP/ys3nXXXUyZMoW9e/eSmZnJI488wiOPPOL25/V3r/veIMJFN6Sf++Pd4G4WpUtWAORvurUS/lYo3R0/3S+Zg6ImEWwnBUumdelX6EUZrg0M2wVPSin05Gj4wUi4YyeEBkjg//9mdZ65mR0r2Ycd1bJp2FUtG4zvjpLz9+9z5VyREozeVd32+nwQlwYIXqtFtooDrx4mH6x3T4nq91AdzI9H3e8me1UPoHISJZPw8EFJc9uQ21kx8NeT0u8NTg0Clyf1+Tk6+Dd0IaDzKOtLnMEBODIjP86CH+5zmlt5MsPQ/E4JZbvqqMltJspuJ2F2LIETI0VJnhwMoXYJaA7VQXII3JYhSuRqW5vuINSMDjPDeXHwK0dL2s9Go7JFXKaHhYiL3nun/dJQKScnh6KiIlauXAnAK6+84uUV+QbaZvNdU6MzSzR7a6TNr9X9zwt0mjFxlBD0xEgZjnZ+nMzYqLVKADEmXG5d6ZJ5RvCklEI32OGpKagegtEOma3/VMiG67w4CWCUknPZqgJ4rgouine+Pl/EpQGC12qR7cWBVw9DX5Ui4sC7Rvhl5uBMVFY4+jsZ8oH7cVbbrvqFfHi7GJ0RJpmDy/vZxdALAZ1HiQxwBgetKLMJ7TC38mSGodUj4c0HZrXpE546yLn3pUqQcKROTmZFjfCzKc7Pm95YBmac76lutImByuhwCRJCTJAYjG60ST+7UiJO9NMJjxaLhdtvvx2AlpYW5+ZgKOP02L9nBPwsWy6+PuKxD75boukyY7IoHW7cLh0cicFwvEHaO7+eBM/lo2fGuO5CO8DgqX1mq1XPoJVqeywrsLrrKZC+gksDBK/VIoeA173Kcfhz/3CfXChrrXCNi3bNvjaOuNaKttk7BAnaZm8zt3JkGGjW6Mom0QLclAa/OOxy++yyVYW8uXRqR4+EW8eR8YMtDJsQBg1WyVY9NaXj5y0nAZ7Ja3sdlc0iMjUpEdEGKOlOOdUs2QXtMPnx0wmP8+fPJy5OdnCbNm1i+vTpXl6RD/DoYbmgzE+Qz+h8R139F4fhkfHeXJkTfyrRKJMJ/YNR8E6xpPnnJcD3Rkpb63XD4MGD6CfGu+Si68rgyVcDsd7gcpGi12qRPu5174oav8pJdMv8iH77N7iLa1IkI9A+W/L73DZzq8gA6TOubhHb7CqrtLKaFHpjqUuzCF16JEQGM6zBJsOBFp3dHdNeP6JvGy4aBJMSEe2liRLsrDsJlyRKcFDcJD3Ul/jnUK7MzDaR2Lx587y4Eh9iTw38PLvtuFKg58bK6GCD/jEnDlYWwK+zHDM3bHIsXZwoltsu7MBwZfDkT4FYe1weIBi1yLPRufXi4X1FsvTRWuzwRhH6m6m+keXwsXHEPWZLaq3iVhYXJDMrkoNEMzglUmZWuHCwVZceCUEata777gKnfuTd0442yFBnYKgbbRKALdsvXu6xgfCNYb7xeTBwDcEmSUsvaBf0ba6UXniDfqGUQo92aA5mxYrYNzUEdlSJy+GRer+7CPsyLg8QjFpkJzybB0tHwsgw+YBrxwTHZ/LgyUneXp1Plmi6zZZckwIvFMhwm9QQCQ7WnZQUboBJTKZ+4hphaq88Erp7HY7MllMI2tofHWyS1tVHxhlBgR/TrXHP90dKBglk57u5Ap46BtOi0Fsqve5a6Aq84sR4aSL87aQE1XPixGvkPxUSICT7tmeIv+HyAMGoRXZCVYszOABHFDwyTFLkPoKvl2jao3IS0R+VSjfAuTHS7pmTIOs/Wg+nXOc82CuPhN6s2QeDMIOB0aNxz9x4uLhK5kI8lw8hCiZHyGjffbXedy0cIF5zYpwdK3NrXjkp5kXnRstwpP11Xu3AGIx0GyA8//zzbNiwgbvuuoupU6fy3e9+lzfffLPbBzRqkZ1gscsut/0xoxFVu0H/uDMTHj8Md6SLTkFrsUAdFiy3LqRHj4Re4i9BWH+O+yFJD7bASin0d7OkLv7hKTjZBA1adr+LHN1G3nQtHChecmJUSqHvHSUB2YdlsK1aNghXpvhtsOWrdNsTYjKZWL9+PUFBQRw+fJhx43xvmISvoDeWou/bg37oAPr7ezpOXKy3wdoTUtvHocj/6wmoau50qp8/0tOkQlejssLBboffHkUXNkKxRcSLbxRD9KBwEPcaxnHfS/bVnn0hnBUrFrwOlFIwPQq218D/pMGj4yRIXLoHJkR0HPvrbxytl7a99pw5ythNKKVQs+NQD41B/XyMfN1JcKC1Rm+pRK87IZMh152Q77V/dgt5mm7PpC0tkgK/9NJL+eSTT9i3b59HFuVv9NiX/81hYs3ZZJPhPS12MdX59nD5501TIhfgNaOlSdFiFLXhlHjwt9hhUiSc7p+bpIFgHPe9pDtbYAdaa7jrK/juSNHI2HWbI+fvjsJlyR5etAvxRSfGdjhLILNjZH7FOVGQHiqOpn5e3vEU3QYIqampFBUVkZqayoIFC6ipqenu7kOXHpz/1M3pknp777TsckstMDy4zZfbm6ZErsBbRkvXDZNRqlcmi+W0TUuw8I1h7nvOIYBx3PeS7myBW9laJe2t5zlc/lpbHqdGw5/z4KGxnl616/CwE2OfBZGOEggtGs6Pk7kKtS3iR6KUf5d3PES3AcI111zT7fcGDnpw/gPagoSPy2SYR7Md5sbKzhv81kEP8JrRklP4995pef+sGi7p3F/C6UNR1Sy14KxwcTP04swJX8U47ntHd7bATo7Wy4V0a6UMGdLIxWlLJVyZ5Nc7WHcaAJ0VDMyMFv+DvggiW+cp/L2obShV6/Akbw+l8hOMYq0r6Mn5D0cZ4kAdPDdFWvHsGl48Lh/4j0vlpOGveNFoqTfCP2cJZH48bKqEH2bJgCWHZsGfyzsG3qXHQUqjw2Xk/KoT8v2sWFHerzsJL031zCLdiDsMgDrtjnjkMFyT3DdBZGsJpH0ppN4qLeY+VArxZXxzQoS/0er8Z5OuhLOc/0B2GPeMkOBAI6nG72RIcHCi0WumRC6h1WipVYTpRqOlfokhN5ZJGeff5XDLcAnkkoPFZOmW4fL/BgZn4BS4vXyy/8K2WTGwtRoWp8vcju/tkWPjxXN8dkCP12nXHaGCTBIUnBd7dtdXT4LIWTFS8ghADNQ+K4OyZvk7bK6USZAG3dJtBuFHP/oR8+fPJzMzk2HDhrFs2TJWr17tqbX5Db2akxAZ0JYlaJ0WrBSYTBAV6Nc7WHf1+J9lTz06XMoDfRVDto4Db1cKUUqhlZdnTvgoxnHvuh7/Dmn4kABYkgkz/d8gya20G7XsZEaMZFzPj2/7WQ9ZgA7vfWyg6L2ON8KYCL+ZheBtug0QZsyYwahRo3j//ff50Y9+xMyZMz21Lr+jxzkJtVaIMkuPfnKwXJhsdvmQ/yzbcwt1E67u8e+0M+I3R+F/hvVdDOkogbQvhWitQXt55oSPYhz3uLTH3199+L1GZ90RBQ1Q2YLeUdUnQaTx3g+MbnNcNpuN7OxsIiIiaG5uxm4fmsY+LunxvyYFnsmHCDMUW6R3/+FDcFG8X2cP3IajLNA+GOA2R5mgHcrUixHJjhII8+Ml42CzS6AWHeDVmRO+inHc49Ue/yGPozSgd1Shm+0SFGypgsfHSTD/9yK5NbIAbqdHJ8X169ejtebDDz+ktLSUpUuXemptPoGrevydZYgHD0q5wZXjmgcjnXRGoBScMeemNxkAZwmktYvhh/sk4xBqNuyOO8E47vH5Hv/BQlezLLrsjjCyAR6l2wBh1apVjBkzxvn94cOH3b4gn8OFPf5uG9c8GOmkM4LoANhb21Ym6IMY0l9sjn0B47jH4z3+Q5EeZ1n4cTDglSFWbqDbEkNRUVGH79ufNIYMXfT4+7VvgT/QWWfEG8VwVbKIIV86LkHa+XFGBsDFGMe9o3Z9zwgjpe1O2s2yUCFmmWlxU5r4SvgxToFroBKxZaCSYNMP7Z27zSCsWbOGnTt3ArB3716OHTvGJ5984pGF+Qxe7PEfSuiNpdIKGhkANVa4NgXOj+u8MyLH26sd3BjHvWCktN3MvlrJHLRnVqycB/wZLw2xcgfdBghPPvkkcXFxrF+/nv379/P22297al2+Q+tO1iGYc2eP/1Clq1kWAGpJZg+/7duc1arpB86NxnFv4BF6McvCL+msTbMb58aeyhHeLFd0GyDExcXxxBNPYLVaee655zANQWMPd/X4G7Sjh1kW/orXhlgNkP4c948//jjnn38+zz77LBaLheuuu45FixZ5YLUGfktvZln4I30QuPbkt+H8/4mRcGGceDmsyENPj4HcBrcHDN0e+bfeeivBwcHceeedFBcX8/LLL7tlEb6OygpHLclE3ZEhtz58cvdLuphlQaSfO4F31qrpB86N/Tnuk5OT+eCDD1i7di1vvfUWjY2NHlipgT+jTCYJBvIa4MEDjovfZP93mOysTbMr58bOXCPnxEpGFWRmR2YoTIuCwibIrZMOuEO18D/D5PZ7e9CbK9yicej2DJyUlER6ejpffPEFWmt27NjBzTff7PJFGAxxejHLwi/pwxCr3pQiOtNpuKNNtj/HfUxMDIsWLSIoKAiA4OBgl6/LYPDR4ywLP6RPQ6x6Kkd8UgZLMmBdoQQON6XDrmp4Ph/qbDA3TqbZ7q11ywjrbgOEX/7yl4SGhjq/X7hwYY8PaKQaDfpM6yyLH2d11CC0n2Xhj/RS4NqbUkSnOo3fHkW/fwrGR7lU39Cf4z4pKYlx48Zx+vRpXnvtNaxWPw/uDPwWX2gx7LXAtadyhA1495Qzy6DtGiZEwuXJ0GRr+9nYCNk4uFgI2W0uZ8WKFeTm5vLRRx+xYMECnnnmmR4f0Eg1GvQVlZMo6bcf7kM/dEC0BzNj/N9EqrdDrHpTilhfAj/OgvxG9AsF0iIWHQilLTDP4U+/5jj6d0f75/TZjv4c92vXruXpp59m+fLllJSUUF1dPaA1DCa01pIC/tVh9C8Puy0dbNCuph+ApOY/LYMHDojngi/SUznikgTYWA6ZoXL+qLPKwKlzoqH1JbVOqHSD02ePIsWsrCx+97vf8d5777Fu3boeH9BINRr0B3eYSHm7g6DXAtfelCIiAyC/0ZlpAEBrKG6Ct4rhB1kyBKywCTaWDUgI2Z/j/uKLL6aoqIiVK1cC8Oqrr/bruQcbzgtWZijcPBz218LfTsK2SvS9owxfBVeztQpmx4g185xYuG8UrDkOd+yStP/sWJ96z3ssR8yOleP7yyoJChRyLjvWAAp0bYuUG1NDYGe1y50+uw0QlFLU1NQQGhpKSEhIh7RjVxipRgNfwFc6CHrl4NibUkStFT46DUtGtL0ek4Ib0uCLCufvarMj+9APp0/nmvtx3FssFm6//XYAWlpayMvL69dzDzq2VsHECMgIgyATTI2WSa7ri0S1PzvO2yscXBytl2BsTixMi5bgbHYMXJkC26rg4UPoR8b4lBCyu3KEUgr9xHh49LBYzU+PlizCpgqoaoHMMHmtO6vd4vTZ7btUUlLCtddey0033cTOnTvZvXt3jw9opBq7RufWo5cfRN/7FfqOHeg7dkpt2cD1ONL25DW0peQbbfB6obdXdja9KUVckyLpw9YYQmsZXRtqhkCT40cyoXKgTp/9Oe7nz59PXJxc7DZt2sT06dP7/fyDiqP1MMwRYIWbISlY0sqNGv5lHPsuZ3Q4fFEh6fbWbMKEKEgMkgvwJQnw4EG/KvEokwkeGSsjqz8oFV3Co+Pgqcly/P+j2G1On91mEJYtW8ayZctobGwkNDSUadOm9fiARqqxc3RuPbxTAnekw4gw2UWsPQH/Oi27Wn+vt/saAUraplpT8gdq4V+n4bQFvWQXfDvNZ97z3pQiVE4i+t/lcLgOHWiCFi0WrsFSPtFay4TK2MABO33257jPzGwztJo3b16/n3vQMTocvqqGryWhIgMB0AWNsCAedhibJ5czK0ZS8psrJIC+JkXOBU02CdBmx8Kn5fD4EfSliX4zI6HLLIObnT57lWd54oknev2ARqqxCzaWweVJkBwMxRY5mV+cAHFB/m8t6otYNXxcKlmEA7Wyq/j+KPj9RHh4DHxZ5VPZm155bSxKl9rqiDDU2Aj5LL14AnLrRXsQHQjBJpc5ffbluDfoglkx8O5p2FeLbrKht1fKZ/FriTDM0Ge5GqWUjIX+uAwqm+GrGskc1lhFx7OjGubEQHqIX89I8BS9cqJJS0vr9QMaqcYuCFBSQ6qxwrDgNpesWiv4qsLWn8lJEHGSAj4she+NlPdfAWaTdAT4mVNjp5mGmx1e9u+dls+YC50++3LcG3SOUgq9JAO2VMC+GhgbCTenwReVfvXZ8yeUyYR+ZKxoPJ4rgOtTYUGCCP22VIojYXSQaBT21sBvjqAX+E82wZP0GCBs3ryZu+++G5vNxp///Gfuv//+bu9vpBq7wKqh2Q7JwW0fQg0MD5FUuIFLUVnh6KhA2VmbVVtwAPK+O77RLx33mxkJ0I3o0cVr7+txb9ANc+JgZw1kR4iA7qtaKGiEK5K9vbJBi1IKZsehZ0TLSOkvKqQL4LxYycLdnSkCxlkxMD9BWgfdYDTk73QbILz++uts3bqVEydOoLWmuLjYU+safOQkwMsnYVQYOtTcpkEYHwkVLd5e3eDkumFS2rHaRdSnlJR2ogOg2SaCvjsy/GZGgqcwjnvX0qGV7T8V3TvrGbgMrTU8d1ycCHfVwN8LRbNzSaKIlmfHwJgIOQ9khPntxEV30m2AkJ6eTklJCcOHD0cpxfnnn++pdQ06VFY4OlDB6gKwO/ryp0XJEI5DrjW3MBCcKflVBfDYIViSCfHBEiz8KU9GSoNkcJps8OJxdGao32QT3IVx3CPGOmtOyEjiiZGwKH1ArXHG6GgvOBy290SYHSNahHGRkB4K64shIgCqrZAWIvfvZuLiUKXbAGHu3LnMnTvXU2sZ/FyfJqp6Y3S0x1BZ4fCrCeg/HoX790JyCBQ1wtUyx8Dpl7A4U7ILycFDPpsw1I97bbfDPV+Jne13MuBwPdzzFfrZKT7VP+9P9DS10C2090Ro0XB+vOgOTltgeKh0OcxqZ5zUzuLYF+yafQHj0+5BVFY4nB8nIrOXjouZzflxQ/ZC5Cn0xlKwaHhtBuqZKfCjLNhVI0HDn47BN4eJJKHVQ8APJi4auJGXCuCiePk3JgLmx4vIbc1xb6/Mf+lpauEZaK3RWyrRL5+U2/50GrT3RDhaL5mgBpv4CSxIgN01sKEEbbF1sDh2BjOByiMTE30ZP5+n6150br2UBEos4nVtsQ+4f75XznqDGK/YH68vaRtylFsPRxrg1+NFmPRhqXSWlDdDorSdtbc59rZds4Fn0VrDK0Xw8nSICRStUFSgZPnu3QN3jPDyCv2UnqYWtsNl2Yb2ngijwmB7FcyIgc/L4fMKKLPAwRoJUm5Mg3Oj4W+Fok+aHdPmxDgn1q0TE30ZI0DoAp1bD6vyIT0MHhwjO8xSC6w9aRgb9ROv2R9HBrSNkt5YBrenS1dJvU2ChAgzlNlQoWZZp8NoqP16yWsQX4U1x9GRgfCNYUagMBjZWgVRZjjeiEoOEb97u5Y5GEY7cv/pbGrhnmox+Xr5ZMc0frtsAwDnxvRLQKiUQj8+TmyK58bC5nL46wkxqksMFu1BfoNYMD9XIKZKX0uUQOKLSilLtJ+iOCJMdAxrTqAXpQ+JIMEoMXTFmhMiaPvfTFR4gFhaJoVIpGkYG/WP3kwtPAOdW49+oQD90nH08/n9m1RYa5XxyK3YtHzyy5ulw+HpfGiwoSubO+pC2tk1O3UKj46Db4uWZKBTEw18kKP1cFkSvFWM3l6JtthgZxX8swiuMtoS+0L7MgFay9TC7ZXoQ7XodSfgoYOQGSJp/PamRUfrJbvQnn5OKuxgU7y5SsySchLhjgwYGQb76uCiBPl+XrzMOcgME+3C307CxEhZU2WzmJDNTxANwxAxWDIyCF1R3gwjQp07T6UUOsQkIzYjjbetX3Q1tbDRJvMSzkjjuyzjcE0K/D4X/eMsGY1q1/DScak5ZoZBYQO8ZJEdQ7kFwgJkEMqxegkONpY51wCuGYhk4KOMDgdzGOw4LjvJr2qkw6WwEZ4Y7+3V+Q2dlglsNnjggOzmU0LgJ1my6SptlnQ+SJags2xDOwFhX1FKoVEwNkJan2fEyBRUlEx73FsDkyOlq6FFQ7BdgoWPSuXfeXFShhwRJkOR5ifIuWoItEQaV7quaLRJ+stmb0tPg1zAao0Jlf3CMbUQix0qW+RCXd0iqcaHx0rGxmEVrOGsC7MyKXQ/LswqJ1Ee74f75G+3aAdMiIQXp0KzhopmePIInGiSNqhF6TIAyYT4tlc1twUHNrvsMgDy6tG59UapYTAxK0YubEsyRMT2RaV8Tv8yzehg6AudlQkKGqW1+DuZ8GohnBcvwVdFixyXrZqEm9Jkhw7ys701A59UeLReBmWNDpdzTlQAFDWJQLnWKp0qk6PEI6WsGQ7WyVo/KBVh48KUjhMTW/SQaIk0PvHdUWuVi5XNMQynqEl2ntekeHtlfkVrmYCqZnj8MJxqkrq/1S6ioOvTpIRT3SLBQ2vZoYuMQ38mFaqcRNRTk2UXYlbws2w5yGus0GSHabHSBvXQGAk+TArqbHBBHOyuQdc7yhQnm6R2mRwsOwqj1DCoUErJBcCuICRAvDNWTUWZzd5ems+j7Xb0SwXoH++FdSdgQkTHO4wMlWNve5WcS18/KQOUrHaobYbv7YKV+XDBf2DjKdhZKdNXXTGpcHS4zF/4sko2J2FmES5urpTswSdlsK1SzvkH6+T7+Qnw2Dj4T7l0O7VfxwAyGv6EESB0xS3DJZIsbIAf7YMH9sOy/TA8xBAo9oEOZYKfZEt70ZO58MhBmR8QaZZ6f41VUnyVLW1BQGvGof3jDXBSIYEmubCblJQRUoLlZHDLcMleBJuclthEBsCIcNlZPJkrJ47EIAgxi07h4kSjJXIQopRCzYpF3TxcboeAGG2gaLtdLI2zwuGX40X1//8dkp/jKDnsqILPyuVYX+wwLVt+CGotcM8eWJAEb8yAP06G0ZHw9imotMBMF3gQzIqBWpsECAdqZWBesw2ezhPb66WZkjG6Y6fk1f83E443SQBxU5qcmwIUtOgOLZGDHaPE0AXOtPT6ErlQ1FrhjgwjOOgrZ5QJyAoXUdI/iuViXGSRaYTDQ8WoaHsV+rUiSesmBMF7p9BLMlCXp7jGWCo+CI43SFdKq24xQEnA0Oz4gVIQYpLdjl3LxM358eLGmJN41kAk3Y+MhoHBoGLNCbgpDTVPjk19WZJk5lYVoBdlwIenxWr+Z2PkWGqyi818VQvcugt+OUGO6+hA6TBQyEV4f41Lav1KKfTSkTLA6V+nJSMwmHWHGAAAIABJREFUPRp+Olq6mdYWStkxKQhyG2BGLBxwDHdKDJKs0rZqKSsMIatsI0DoBpWTaExc6yN6Y2lbUFVjhdiAjmWCz8pEjXzfKCkrXJMCq47DVUlyEtlSJQri0eHimT4sGH59FP3BKZgYM/BJhVYtz/nHY9KNYNeiK8mrlz7o1cfl5zYlyuvVBTLkJdwsWYVF6R1ez4AzGgYGg4F9tfDt4c5vlVLoryfB0q+gvEWOsQvjpWSnkaA8KgBmx8KwEBEQRga2Pd7kKKn55zfCv0rRLsgitA5wYnYceu3xNl1CRABcEA9m4ESjtEI+kyfH+8NjZR3bqlFD0CrbCBAMXIbeWCrRfqspkc0ODx9C76lGTY5G73OIvn43QU4SQSYxoVqULuWbhCB4eJxjgIojW3B+HPxkNNy1C/XHzB7X0CM5CVLymBENjxwSM5wmq5STlmXLSWHtCdlFhJrgyhTU3Dh5Lc0a/i8X/YMswyrbwKA9EyNldz6v3bGwtVLcKANMYLNLW+GuGkn3BzkydO+eglHhIhJMC5EMAsCeGgkqLk2Ac2NFtHhutByXrrA+HhMp56DKFik5NtulBHGkXr6vtUrQ8rdC2bDkN3QIDrqzYj7r/2ZEwV9Pumyuhyfxj1Ua+AfrS+DHWW2toWYTLBsNf86Ti+mHpXBOFNgQLwKNpPRad+CLM+XEoRy/206sSGxQ/30Q2uG0uz7WCFOjoaABzCZpa/rTMXFYMyv4epIMdckMk7W/XAjfGyllCcMq+yw+//xzHnjgAfLy8igsLORHP/qRt5dk4EbaCxL1SwVwaxr8rRD9WRm6yYb+rEwurrcOl4skiDBwVzVsrxZB8vP58E4JnB8D647D+6fFjG5bFfyjSAKMrHDZ1VtsYnl8WaKUJR5u0zf0i1kxEgS8XSKiyTorfFIu3gfaLmXGIJO0aNo1HKl3+h50sGL+VmoHDwdts8H39khb7AVxYNZww3YJdiZFyXPesA29qdwvfBSMDIKB62jvWOhARQSiowPkYprfING5Qg7AFscB3mCVi7JCAodW2ncsaCSAcIHzYnu7aw3yHDNj5V+wSdKjBQ3wRjEcbRCVdWIQmOIgIQh1R4bzsZwdGp1YMXewaT5cJ94aScFSerk2ZVDpWT799FNuvvlm3n77ba666iqCgoK8vSQDN+EcZjU9Wi56zTa4dy88MwleLpKNwpQoWDEZZTKhZ8VIh8A7p+DKJBEefu8YXJcK60aI18DESNhbBSvzpHtobrQEFFGBcGECHKiDFwvg2mFwaaJk9x48iH5ifL8yCR00Ce+XSnkhJwFWT4UHD8LlSbKBqLfKuSLc3KaF6MrpcUsFPHkMlo6QTEptC/y3XjKi02JgXIQIncua4atq2Fnj87bNLg8QPv/8c959912WLFlCUND/396Zh8dZlvv/+8xMJpnsW7MvbZOU0g1ooVWWWltQQBRERKxwWo5SBdzY5Ce4cI4CB4TicoQei2ApUkBB8KBQPAREEWgpIt0oTZql2ZNmbfbJPL8/vu9smUzWdzLJ9P5cFxdkZjLvM0Oe976fe/nedmzevBn333+/2ZcRZgh+NQcfdEF3DkIlenOJ7tC8uqYQ+lt7gQszgKfrgCtyeBovOw7cW0bjajUKBat7oQsc9Nz7XMBPDgMOxUKoniHghx9AF8Zy82bYgS/kTd5hcFiZzrBb6C0MuLjZOwYBuwLuWeRNJ2w/CjT0eT/bKEJOeKsVOHAcuDqfYdFBF5X43u8EChWw/Sj0H+uARUkRMd9h7ty5WLx4MRYvXozHH38cTU1N4V6SECp+U810YHE80wYVPYzE/ehDYEEi25Z9Qu4eY/zWMeCBIyxG/upc4MwU7u+z03gw2NkM/Nci4M9NQF4MhZKK47kXlyQA6/OAl5uBy3KAj6SyiHEKBYy+NQm+6MUJLFhsMmbw5MSwcNKtexBsrsR39jNCelYalFUByXbof3VyvUsSgSgFdUYK7w9/qOfjM1xsyfQUg+9Joq+vT04SEYwubWZF8KJ4etnLk4AfHoTuHOTzQy7gvnKvbsTFWcAfGoCPJAPbaoCflzMM+V+LgO+dxHbDOEMPYW8npVhfqKd+wjeLgM9kcY7CwngKnGxeAuQ6KIs72dTD2nSG/bYfZQTDbuFJ/5k6ymq7nXsFnlxsPlsmmHT0tmr2Tv/nSVDF8azEXp3GG8uGfODf8oGHTwXSY9j2GQFaCo2Njairq0NTUxNOPfVUZGaKLHHE8moLiwtPT6YS6enJrPo/0MVag0NdwIMV/iF0rYFttWwfPCmOtQnxNkbnWgeAT2Qw1/98I+8TTlC0LNHGf+r7qGjYPgjU9FJc7azUSckvj0lJPGdxZMVAJUYF6h64lR592ddJOebVaf7POSxAgYP3D6txM5kXyy6pScpHTyemRxDkJHEC8XgNw4znZxq6BS5GBy7fDX1SAg3tBRmeULqndfSuw0wftA6wZ7rPRS99YTx//4PjfC43Brgom6p2FsWTh8NCJ+GpOm60q/KBh6torCdxCldFcdCXZFOQ5aZ9gMNG3fZ4G8OC9f3QxihopNh5cnITTDr62CDnzLvTLdFW9n9/uYBCUHYjjbEhn+HNLxfMetnmgwcP4tlnn8W+ffuQlpYmB4MIw6/wrmUAWJXiiRTqeCujZJ/LBS41ZJX/2MDwvft0brRBItrK039tH9MK0VF0yt9uozDRay10OgYN3YRVKTw0QHMMd0UPaxK6+oH1+UBxLPTbbSMWC04aQ00zqJJjsOc/luadOeF+LsPOFMr5mYBFQfc5OYL6E3NmhdiS6REEOUmcQPQ66e0XOKByHUBBLHXWE6OYvzsnDWjoDzwdZ0VzM30mizeJrGigwwmsSKaY0lmpwLw4DkZyaToHFsUOg9o+Q7TE6IAo66aDMQUtAlUUB/XdBVAPLIW662SoW4qBZDvrEXJioLJjoHJiqNvg29IYTMipdwgYgve5AZdXa0EpOgdaA1BeJ2OWaymsW7cOAwMD2LJlC+68804sXLgw3EsSTCKgKC/OApR1QzsNhdm32tgVdEoilN3C3PxFmcBffATE9nd5w/NfyOXvvNvBQ8Wh48Cz9RQeOjOVRYyrUpi+e6+TEcUtVexwuGYu8/tOC7D5MLC7bcRiwangUdN0GnLKw5Qcgz2PjQUcFb44ntoK9x5m4WPbAJ2l/Z0cNV3VQ8G2WSC2ZHoEQU4SkY1f4V20FchxeD328m6eJO44CWpRojdv/3Qt8N0FfM3zDUxJXJXPU7MGQ295MUDTAHSMlV0OSxKAfk3j2jPEU8aQZqjx/5op1/zXFuYx93YA6dFBiwUnxdp01hMYKYQRWxqDvQYAPpbqeQ7nzQF+VUXHwWI4B/V9QLLN62TMci2F/v5+bNy4EQAwODiIioqK8C5IMI9d7cCqZO/8gUWJwAuNdNIXxXNeySXZbBl2My+WhX9u3G2QxXF0Fq6dC7zYyP0SY6HIklLQDgu7nebYgRvmA/eWUzvl6/O532KtjEzmxnCOQ4JtymOhR0IpxfdYmeKJnujhUYrhughKQT+4FNhew1qjZYnAbSUsyN7dzs89pBmJHMKsEFsy3UFYt24d6urqsGXLFgDAk08+afYlhDARUJRX2gz0D0HHWPmHXtrCorzDPQCMcPtV+Qzdu0mwAXYrnzMMLD6fzZuN1v6jlrVmOPK3NexgKDvOTfaZLKYB7BbWOJyRDPyzc+pTH31QRXG82fy6mkqJw9QTR3sN8mJYa/HZLDpBVnAy5B2HgK8U0CnKNl6zJi0itBTWrFmD1FSGk998802sWLEizCsSTOOwkfJzOwBFsZRBfjeGssXHnRyJfZGhdtrt5OnY9296Qz6lmL+YA+wyogL5Du6F3e3A5bmUMH67HfjhAqoW/r4e2N3K0/iyRCDWQkd7ZRIjDV1OYGsV9BVZUNHRvI574JNJhX8jTqV8qDJo94GyWIANBYFvNIrI0miaCuHGdAdBThIRzP9U8FT/4XHoTidPxDtqgStyOQJZAWgZ9Bb2Afxvh8+fWZcTGBiCdmmvgd1SybTBvi7gm/P5uEtTXOSb89gD/UA5CxdvLaEhfq+TbUVpUeybTrJNeerjcPzaIY3IiX79mF+Ewvc1Htyf65eVXpnuG4qYWy1t4fdW3k1H4rVjU1eHnAEUFnpFrFavXh3GlQhm4Ge0Puzinjs7zZtzP9TNVr3YKA47ah2g4qCnq6EXuNCbXlYWi/d0XdHDCY5FsSwIvCCDzoCvhPHKFGgN4I6FwLf3AQc7gZ2NQOcgnZNPZQE3FwMfSwcu2A29cxVUVJT5eX2jpRGnJXEf58R4oyHDuh8mw0QdkOnGdAdBThKRiS5tBuKjgLtLvCqJ3z0AvN/OwrsYKw34x9JYUwB4Q+kpPqHHi7PY+bD9KKML82OBi7KAp2qBr8/zOgdvtjGs+UQt0GKIp8RZWdHcO8RTzacyaWCXJgK72qCvfw/ql6cCMJwEk/L6o7UzBjPsQWW6R3EE/NI3ZqRJBGESBBitF63cf0sT+Le5LJHh/dIWqO+d5P2d3e3A31qDzioIeroGRja27nbCnSuAz74HfHUe0xsA7zGJNt5vmgqBK/ZA375w6mOhR1rDZdmcPploo45JlKJQkxmDvIJpKsyQ9kfTHQQ5SUQWHqP1Xgd7gyt6gOJ4Ogl3LwI2vAu0tFIA6fgQ8FwDsD4Puq+P+bY/NQKXZnvez9PJ8HgNc5cOK6MAa9KB145B/73VP5y/Fqwt+MVS4Gv/Yqj+6XqgMIanjm/MY/Hfv+UD/3kI+tEqqKsLzc3r+wyc0r1DlGddlw7cUwb9rfmmGPHJOCGCYCaeqMFfmoFzUoHTklgXcGoSDfI77dQmsCrKEP/1mOd3R8zJT3UtA0NsqYxSwI8XAlBcw8YCOtDdQ3zxWalsmTZjLPRwiuPodCxPgjJmReiqXh5ezDDiwTQVflcHfUZy2FMPoqQoBMXXaKGyh33K2w2jVRxPw5weDXxjvr964ItNnnHNODfwFBz0dL02yBoqe1jokx9NY/2dYuClRuDLhRQzSbDxn02FwM37OT3OzLy+0Wmge4eYO82ONm6cidQwgAlGfNjUS980iXY/L5EFIUT4RQ0KYlgkXNcHnRPD2gO7YlHhqtSRawzMXIvTCVy+hz+4NPVVvlXEDqZUI42wPAmo6aOz0tRP3QQzxkIPZ2Uy8K29gEtDF8Uxcvm/DZxEubNp6kOk3JoK7ggCwJ+LY2dE6kEcBCEAT9TgQCdwUxFTCENGu+FVecCvqqDzHXyszxVQuGdWP7/HQSl0sMXxlBSGFLuc3tbHrBimMeJs/CfNztoDM/P67k6Dpn6eZhr6mWI5NsjvxwwNg2CaCu0DElkQQo9PqFsPatYQlMRxryXa2J7n1BQpGqHGwCz00BBw2R7gunksPn6nHfivw0wtfCydwmLbaihXPj+WyqbPNQBrU0NnQJOi6BgcOQ78qwv4ZAbX9ve2qV8zmKbC8qQZkXqQYU2CH35Rg2VJVCrsGOQJvbqXRjnGytPsLyqAy3PGftPJcss+4A91TG9c8jYdlgQbi/s6nYDWNKoWxRtFoo0tkWYX/a1NB7ZW8TSTHUON9fvL2Wr5o0NATc/UrxFMU6Gmb2S1xtKWkd5FECZHWTcNFECj9VYb9QnaB1l8WNXLsed/a2VaMFQn2f84BFw7j45AspF6vKWII5gPd/PeUxLL1unXmoE3jgH1vcA9i3na3t1u7np2tQOXZAHnZQCFcezGOCuVkx/PS2fB4hSuGVRTobzH+//DTRiUF8VBEPzxlQ92arYhZUZzbkKijUOH/nYMuHE/Q3ohGjikf3CAN6TfnwH14CnAsytZ49AyQJGR5UkMv/c62WblsHK4UpQyXbpYFcWxSvsP9ZwL8ft64EcLgZ8sBv69gKJIpc1Tu4hbU8FwEjyaCkVxI0YWZruwkjDD8JEP9hitg11sMXbn9lelQn0pD8qM4rxhaK2piPi3VuCsFO/ft02xe6J9ENjXQYXGBCtrJMp6gPp+4NenQlmtoTGgZd3A0iTeX6p6OHgp1saC6TgbuzameE2lFL9T3+82mJzzNCsvioMg+OMb6nbrFBiif0iKYp/y7QugfrY0JM6BLu+GvvtDtjHevsAjLKRsFtYebDvKF56aRJGW2w5yuMuj1VQquzI/NCfsrBiebKwW4NZiqHijM2NQc6T18w1TenvPGOpho6ThsI4cWZjlwkrCDMOIGuh326EHXIwadA0B/68kJA6BL576BxvYobSnw/8FezpYe+MEC6XPmQPkx/KgcO9iOgdAaAyo21DHWoH5cXQG+of4c1M/o5uDLvNHNw/7/6HfbQ+L8qLUIAj+GKFuZVFenYKHq4DKXnrLIezZ1+XdwHP17DXOc3iHm8C4iZTEU175uwfZcnW0l8VTVT2UM/1CLtT5zIua1eLowakptWzILWvnENDrooPQNcTHyrtNG0Ptxt3xMaqioyBMEaUU9LVzGS7/XV3QVkWz0UNDwHXvcy8d7mLYfmsVDyQrU6g3sLUS+HIesCSJh4GdTcDH06i2+M8O6JHmJZiFUSMArVkL9etq4KOp/H6O9vHec6b59Q/h+v8xHHEQBH+GyQdz8piVRXKhLoorbWGNwVX5dBTccxjcDGnm/5ckcHKcwwq0OYFbiqFK4j0vC8kJ2x1NGTTmKvS7WJtRHMvni2LN62jwYTyKjoJgBma3KvoyklogXC7ginepLuqwcoz08iQWHe9sBJ6t40m9fwjoV1RO/U6xvzPwTkdIDaifof5HKy3m1kqmPz+VCXx9Pl/jllM2+7vTw/49zYiDIPgRDoOky7uBB8rYzphs52nCYQF++AEHNlkUnYMfHwLSo4BbS7yn6a1VwB8boG8oCukJ2y+a8p+HOJu+2PhOHqlmZfPJCaGfymh2KFMQQoCfQ1AUy8FMw1r20NALfK0QOHicz92zmGJr8+OBJBvD+Q4rcEEmcGpyYEX/Ox1QvvMS3m6DfrWFcw7OTefESRMcBqUUNQneaQcuzgGK4ukc9Lm8LwqzxHOoEAdBCMDMVsXhBKgFFscBLzbwpPC5XG7sXAdPFbfvBy7eRc322l7eLHac7q8VcE0hOwrcDk1DH3OTrx+DfqXZVM0AVRQH3L0I+rdHWW390VQqyq2bA7WYFcdmpzZEQEmYbQQYtydrGJ43hJewPJmv+U4t+/9XGlLGSrEIcNBF9dSr8lnzZLeOXNFvGGTP9Qod1EKp6OEwqT3t0NfNM8egjtoGGsWBcYMu6N/WmCNqNEMUFsVBEEKKn0NQ3wukRQPXFNLYdTuBuw4DlX0cE/2lXOb0G/vZOfHjRcD/VLEW4eZi4PVjI2sFpNuhri7wGtPPZXN8tNbAY0ehV6WYVlCpy7t5QlmTzgFSCkBjP0WUoi1Ay4C5UyVHEVAKaaRCEHyY0EChYcZNWy10FgxjqrWm/HqanaJDdy5iys5hZXdAvgN4spZFidfO5fuNJCbkLkjc1c52Q7faYVo0tEWxaNksg+qreOiuS3BpICMa+vBxYGs1cE0BOx7MOO2PorAoOgjCjESXd0NvrYJ+tBr6V5VjthL6nX6vLqDq4qczgX4XDWqXE1ifwxP/sUEWIA66WIdQ3wfU9fMk0TtEIxtMK8Bdb1DaQuegy0m1w1wHc5YvNJrX9mi0gWLdHG+HR2Y0R9BureKOcn/erxROveUyiICStDkK04XnhB6laLSiFA1gsHSXr6YCwBTDnxqAJ2rYytgxQAd301yKLR3sYrvikW4KMbUPUiXVZjHqIsao6C/rZq1UnA26fwi6x0nnwQXz2h5HawPd2wVcUwC1IgXKbqFjNFVNhhnS5igRBGFcTCrUPez0i45Bhv6cmieGUxKAqj7gjpOA11qo917XD0Bz/POAi/lItzEcVkAZUG9gU4wcZNqBAZe3rufkeK7FjBO322D71GrApjjZLiPaUx8BmHTa9+kqcSNtjsK0Mjwi4J5seOeH0NnRbInscQFnpwIb8/3kg7XWrD/IiQHWJTP8f9MB4OJMKiNaNHBvOR3tkjiG7p+sBTYVAMWxdCjKulm8OOgauSCxOI7ORYzVOwZ+dzsPGWvSzPkORlI8NNpA8UQtIwe+TPW0H0xh0ewujTEQB0EYHyOFuj+XDdx2EDorGtjfBaTYAGUBLsliSN/n9KvLuxky3JBvCDBp4OcVwGezGJo/OZ4aB1cXsL840caq5o+mAEd6ec2xCiidGnAarYdue+rSvJnZTAqWDWsDhXv65K91cLnkqZz2x3KKBCFEeNIKv6sFLsn2Rgzq+thJ9K92ihV9MoP1OH9vBa7bC/xyCfA/xgyRniFgSTyQGs09WRQHnDeH9QYWRfGz6+dS9+QnZcD5mVRnfaaeWge+RXqGgQwI269MBr7xPrDOCaxOpRPy50YOe/rgOPCRqTkJnu8h0caDwIEu1koYa9HB5ilM4bQvbY7C7GKY8fOkCE6OZ5Vx/xCwpYqFQk/VQtf3+Z9+S1uADXkMJRY4eBrYkE/J1E1z+aZpduBXVUB1D2sVLs/he16UOb68/tp0hkJ/sACItvJUsv0osCrFK7A0VUYz2KUtpp/2pc1RCAeetMLiBOCrc4Hybv78pVwayoPHuR8/l83JjhqccBpjAR6v5Ul3dzvbla8pBDKjoZLtfO91c4D7yqgf8FYb03NOzfTj3DgjAtA27iI9pRR0tIUdTr+qYo3QBRkcS/29D8z5Hj6SAlye63VUfIc0jfO07+60wDg7LULZdjpexEEQxkSXNgOvNDMK0OmkNvnCBIbyu5w02m+1Az9ZBDQO8PEfHOINwzCmsCmOZ80xhitp8L0cVt5U7BYgK46e8sPVLAKMj6IYSk3fuFIbqigOurKb6opLE41iwjT2L7smb6Q934O74LJ3CPj2Puj8GLZlGgY7VKJGoewqEYQRebuNzv7yJEoKp0axre+vLUCOA3j9GA3y0kTWEDX1M8V2cgLwl2aPcdMajC4We3VKUN0DDLnYCXRZDnC6MTr5nQ7eV5r6gSE1audCAEsSAQ2o7y7wPKRfb6GTMBXG0U0wntP+tHRahABxEIRR0aXN/MN/9FQW/dgUcF85cKwf6NXc3K8e8xpwBSirBfrqfD6+Np05+AOdwJkpQKwVKo5/dvpYP284nU6e+KMV5yqsTGE9QlIUCxcnktefHw+4r21TwGvHqLp2aGrFSsFqMHBWqsdRGe9pX5d3c65D2yBH52bYgS/kSVRAmDm82gJsKmRXAACdauf+/fpenvSvKeB9YW8nIwixVv4tH+wCliV6w/KHjwN/aQJSoqDPSuPp+u12CoxlxQKFsUDTAKN8c+y87vEhnq4nErbfkA9ct5ctiMuTKM/8u1pgY/7UWg99ugm0NtKV6XbgtzV+o57HPO1PR6dFCJAuBmF0nm8Abi6CirIyj+/UwDfnAU/VsT4gzwFYDcOtOdzJ/W+/SvucGBr1bicAI/T+GyP8f34GcP4cYHUafyfeRufg8RogL2ZiVfwpUTzRfCkXuCIX+GgyP8Oga1ydF0HxGWKle4fouKxLB352xO89VVEc1DWFUFcX8N8jOQf/1wyszwV+vBDYvIS6D8/VmzpgKtwcOHAg3EsQpsIQeMo1UEpxmuuyRBbnlXVz3PIz9SwwHnDx7/rX1bQqtx3k8fPyXOAb84B7yoB7PmQUcXE88JdjwAUZUIWxrGeItnBSam0fxZMAYNtR6L+2sDNhPLMIXC7g1WYKmb3axNSF3TK+zotgGPUFWmvWXigwwpEXM7H38+200Bra6aKz0+viALwZSsgjCAcOHMCiRYtCfRkhVCTYoKz0I5VVAcZgFF0UB7zZBnwqA2gbhB5yAc0DQGs/sKOG/czvdXIKolvl8J02huaXJnIi2pAGLs0BkqOgUuw0vJflAveX8QRQFMsQXG0vN2aKHco9vChYXj87Gvh1FfOeNX084VySxTBpRvTkRYaMGgzdO8RujOxohhZPTZyYxHJpC1XYsmMYkXG6WGvxUCXwbD1wS/HE1jUDuPvuuwMeO3jwIB577LEwrEYwhXPT2R5sUd68+guNzO2vNFr4/tEGLIgFXm7i3268jYPLCo3IwNvtwPJkqNNToe9cyN9/rp5Oxk3zgbdaoefG8TCREgU09zPiuKWKOf97F7FG6Xd1wFV5oxfpbTsKXJkPtZrpPP12G9DjBHIcUHbL5IWG3JoHnYOss/pzE1uyr59L52NXGzTU2PoQxXF0uNxdFlZlOAYa2N/FgXQzMM1gqoMgN4oIpMsJPeTyOAkA6Az0uTht8NkGoLGPA5TOncN2o6/PY/j8kxnMVVb0cGzx6SnQ98QC39oHzI3la2yK4kJtg3QY0u3AsiRGE95o5Xu90sKIQPMAnYNn6oPn9Xe1c/O+1My0xk3FzJ/W9kFlxUy+7dCtwfDPDs6hbx6Adlg5fvqWYuDpWuj06LELKW2KNwe3c2Bnr7dOjgIqeqY88CkcdHd349xzz0V0dLTnsaGhoTCuSJgyq1KAPe0Mge/tZFRgjh1wT3b0DadfbRjkKEWlwYY+jmiOt3kN8tIk4INuqC/l8dT9YAUHwF2Ww4PAqy3AS00sZPZtqbwynw7Eqy0AFHQwA7y/C/hinvfnsm7g0mwqsLqZROuhUgr6a4XAv7/HA8b6fKZH3mrjve5wN7CxYGw5ZLej0TbIVE11Lx2mOXbgsuwZm2Yw1UGQG0UEcnEWcF859M1FrC0YcrEG4eIsv+I5Xd4N/PwIhYmODdJT1qBcqo9BVmnR0OvmUPnwBx8wElAcx42oNecxVHTzcXe+36KAe8sY2hvQdC6CkWCDWpAALEiAfrQaKs/B9Vl9ahgm03a4Nh14oJyniFwHcGsJ19jUT4elfdB/RkSwSIVT0xEadAHRhnPgjojkO8zTa5hGbr31Vhw/fhzZ2dloampCc3MzrrvuunAvS5gCSino6+bRcB3uZjj8jJGNs9YaKG0GvpQH3Tkpv3u5AAAYGklEQVTIwuNup79BHq58uCSRUYEPuoGdzUwPnJkM/PUYcGOx930fqqRxXZNOZzyYAV6cwMmPRgQBxXHAW60senQz2dbD3R1Mpy5PhjqDRlyvTmM7dbRl/J0W184F7vyQTpdW3mjMoJ52hcTxYqqDIDeKyEOtncM/+hv3QyfYWKRzcVaAdLEqimPaQSmmA9oGvHoEPgbZLz1Q08sIQ2EsX6fBn2v6gHlx/rUHbqPslmEOZoB9Ix7uU78CjfLw60/keyiKY0W2zcKUgNMQc8pzcKP/qXF8hZRr04E/NQKfmAPMjeV6th/l93pJNj//LCMhIQE33ngjTjnlFOzbtw9paWmIiorCHXfcEe6lCVNgPG12HiOeG0NnOSWKf8sawJEOYK7DWz/gbvsr6wbOSQXio7zDlgZcTE064S1ONDoIUMI0hCqIDZ4mcBcpAnyuzwk8WAn8v2LoNPvUhIbKDBGmhfGsHbAq/lzoYLrRl1GiFEop6PMyPJEWz3e4r2PaFRLHi6kOgtwoIhO1dg4wxiwDXd7N03+mnZEGd1FPVQ9Py0Bg219ODEORw6r+cbg7UEPhywUeJ2JUA+wT8cDadBrf1WlAun3qbYdpdq7RorypAW38bFR7e76zIJEKT6fD3YeZTukdYujy8lwWMZW2TG5tYWbdunWoq6vDli1bAAA7duwI84qEacFtxE9LoqNgUTSc+zqB0mM0fD6iQgCMfHw3YLdwL8Ra2e1gAXBeOmWVAeBQF+t1Op28VwABBthvRsSGPODQcRYlL0sEnlwO7OmcutBQcRzX8uFxjnkecHHfNvQHlvmPFaWYIQqJ48X0IkW5UZyglLbwj/y3tV4dgKxoDmOyKehHqwPb/roMWeRPZRrGX1OEpcvpJ0gEm/IMRUIKDfFwA+w3FOq4E7jmX7yp1PVR5GVe3NRFhtwKkBbllXLWPo/7MFqkQhXFQd9awhqLCFFI7O/vx8aNGwEAg4ODqKysDOt6hGnCaAP00wJ4oRE4Sgn1EQ3yGUlUXLwsh6H7Pe109s9I8kYsdrWxyDE5ipLMbnwMcMDUyL0dbGc+LQkoiQcsFk+EYkqsTAbeaQP+t5H7fZ5Rg1DJLg/9bvu4jf1MUUgcL6Y7CHKjOEGxKaiSeNYL+EYEYixQt5SM/Dsrk4HNR4DbS7z1DXceZiVwURx0VQ9wwz46DGemABnRUA6ji8LHAAdoFBw+zhtHRjRVG80a+bw2nVXYT9SwtkKBok87m4Ahb8RjPMY+0hQS16xZg9TUVADAm2++iRUrVoR5RUKo0VpTzvin5dAfS+e+XZnCv+eTE4Ibvd0d1FFwao6CznFQu8Rh9Ubl9nQA/57PTol/dQD5DujWAToNbgPsO4JZaz53QQawKIFFgFOdqGjgqcfY1cbiZyu4ty/MND7PxIz9TFBIHC+mOwhyozjx0OXdwHsd0I9UcdMbBpkzCqqD/2KPi7MYflEBbTeGM302C3irne9Z0wc8sIRdEH8/Bpw3BzrGGKvsa4B95kTo8m62X7pVHbOm0No4DFUUB31JNluvbtoHOGyMaFyazRdM0NhHkkJiYWGh579Xr14dxpUI04Hn9L4qGUi1A0d7gf+ugD4zxd+Ij4Q76mC3AKtoK/SAi51AUCx4zI1hHcLyZK8Bzoz2N8C+I5HdqY5Tk4CmfjoNgGndAUoprtVYrx+zxNhPBtMdBLlRnFh4Tu93ncyT/hw70wwuTUM9WsjcpqCWJgVMQtMvNbEj4pRE4OEqjlY+O42hy8pehvh8DbDvnAhfZ0GZNFHRB1UUB/jIufoRIcZeEIbjl+svjmNKzff0nmbnYKZ9XWOfootigSdroK0Wj3YA9naw9mhRAvClPBY8PlTJ91qZAn1qEvC7Ov/3LY4D9nZAO+EZKIXjg6xpAKY+UXG0zz8ZVcZZiEgtCxNClzazCCjBxuKh5CjmGt3tiI0DVBi8twz45vzRT9ENfdC/qqTyoaEdAJdmJfN3iqFyHf6SxpvmsnXx6gL/93FqphVeO8aiyIeroNemU4wJJkxUdH923zqH0bQOBCGC8IsWfHIOw/9P1AC/OgWAcbpOjIJeOyfQiI/0Xu92sJDxIymMDv7HIaBviFGF5clslUyJYq2POwIwUvGfu5ZhfS7wnRK2D26pAm4u4vNTnKgY8Pl9J0ualL6Y6YiDIIwbz1yGzYu9NQO3HmCrj6FyCHeNwKlJoxpPXd7N0ORFhqqgBjsO3moD7lsMdDipLuYTAdDzYkcu/CuKpZLbzUXeYVGPVHNaXE7MlCcqetY7wiwGM1IXgjCj2dXuTSVYFPDxdKCqG/hTE/Rns7xGcjwG2bfrocvJNuGPp3OgmjuSmGBjcXGhg2qONjVy8Z+7lqEkntGLubFsQXyzjSJmZnUHjGNgU6QisxiE8fN8A3DjfJ7Y+4dodEviGA70YVwGubSFcsgpdo6AbuxnBCHexmFOKVFAY7/HSYAVwQv/ynuouNg4QCGjql5gfR7wlybobqc53QE+sxgAYx7ElXmzti1REMbN4eOMxqVEGSd7cM7JnxqA149BD7jGNysBYIh+SSKUUlCJUVBZMcBHUzn7YV8nACMikRPDdMVRjo0fMW1R1g0sTYJKjGJ9QqodyIrh4WDQZV53gLFmP5YkMiUyDK019Ntt0L+t4b8nOvthhiEOgjB+YiyUCLZboKKt1BRfncaTtMtHiGg8BtmoG1AOK1RODFR2DFUPnS52AzisHNh0tBe67DilVmt6gr+X26lwWFmY2OEEWgaB73/AgU9TPeX71jkYjDY0Spd3Q2+tgn60empDogQh3Ay66CQkRtGw2yxsJ7w8G/hbK/P8wYz4cIzhR37s6/TqH7zbzoLFd9spsT7Xweiiga8BxsAQaxDcmisWcGbCmjSmQcwi2JqHRUs8qYgoNbUBUTMISTEI46e2j4bSZ8SpXhBPHfSJtuv5CiEZaJf2DlS60tBVtyng1VbgjoUsTjTC+gC89QDvdTBS0OH0DlGKsVKh8csFLFBcO8XPHmy9I0RKJB0hRBRRFobZk6OgP5JK47irHZjvAD4xh1oD42U0oSD3EKina3lPuSybaQd3zv9rhd5BTm7dg63VwBed3hP9LqOD4p8d5qUAxituFIGpCIkgCOMnP4YqhUOGMqJ7LsNJo484HhG3ENLwyMPluRwC9etq4EcfsnPh7DSOUXaH9Z+t9xrgqwuYXvjvCsDp8s42MKIYo46GngjB1jtSpETSEUIkURIPXJIJvNgE/LScKoJL4oG32sdOKQxDKUXD6tQBkQelFJ2NkngWLK5IgbIbsw4+kgI8VuMxwMpugVqRwhqEZ+p5n/CNYgRJAUyG0dbsxwRSEbMFiSAI42d+PE8NvnMZPpMJHOkd+3eHMaZQUFHciB0LyqI4+fGmIq8BLomHvjQbuO0guxeG4HkvMwoUx7VeX4KkI8zopBCEacc9ifCybE5xfaOVUwnvXDipHP+YQkG++gZuliQCv61hbZEvS5OAjGZgaaLffAOzOhjGvWbAm4oI4TqmG3EQhPGzNp03hweWmCIPPKZQULCwfq8z0ACXxEOfksgBShtDI1/scRLcqY1XmoNObBxvOkIQZjp+8sCVvZysGGSyoykEM7RLE0d+3Hd+QwjnG4yphTDL5iyMB3EQhHEznlO0qVoBxohlnWBjHnTAxTkLGdEjG+Bkuyc9EQr54nHXFvjMkYiEOQuCMO7JjmaICa1MBh6sgPYtDOxy0tBuqRq9fiFE8w3Go4Uw2+YsjAdxEIQJMdqpPyTFefE2ap5bFUc2/6mRGy+IAQ6pfLGPSiMQXKUx0uYsCMJYmC4mpBSLjOfFUkxpfxcfG80Ah1LyeJwFiLNpzsJ4EAdBMI9xGtAJvd81hf6RgmsKja6E9Ok3wBOoLYikOQuCMCZmVvAPf6+CWEYRd7d7pzNOtwEOVhdhkpTzTEW6GATzmKBWwFTeTxVNonNiqhi1Bb5IbYEgwNwK/jB0A4wpcDROLYRIQxwEwTzMNqBhMMijChxNpNVREE4kzDSg02yMxyVwtDLZK+TUPwT9WDUVG116VgshjYU4CIJ5mG1Ap9kg+9VQXF0AfKUQeKPV4ySoojhvEeSj1cA9hykz/foxUUsUTmx8DehEpJdD/V7jwXc+xD87gIPHgXgrsKvN8xKPFsKgC/jOAdZH3L+YarKzXC1xNKQGQTANs4vzpr3Yz6ihQEUPtLsTw5hT7x7x7K4t8DgTwwolRS1ROBGZSAX/WN0O094NUNZNjQffIsv3O4DtNdArU/y7FKCADfkRpZY4GuIgCKYykeK88bRETmuxn02xYnp4J8a9ZdDl3f5r8ynI9HwOK4CfHYH+1hhjrgUhAglWwe/nEBTFAnvaOaBplG6Hae0GKI4Dnqr1K4zUJ8UDl2YHGn6fYkXP5zrUBVT3QYdSGyJMiIMgTBt+DkHLABNcNxTNnBO4UwOvNANf8emcUKBM8otN/o6KUUAZ0NpZ2wu80nLCRRL+/Oc/Y9u2bXA6nbDb7bjqqqtw4YUXhntZQpgJaH98sxVoHgBOS6IxnQkn8JXJHDX/qUzeh7qdQKeTa/59vf+6jPoIfVqS93N9KpOTJ6fS1jlDMd1BkBuFMBIBhrSml8a4ogcw5ixMqSXSDNamA9uq6RTAuLk19gNp9sBODHcBpW8kQWv2aof7c4SB2tpaPPXUU56ft27dGsbVCDOGYS2L+qR4IM7q7xCEuV1QKePes68LOCkeiLVy3PQ/OwILI92y0/s6+d8lcXQmVqcBRitmJKUaTC9SdN8onnnmGezYsQO1tbVmX0KYjQwbYASrAq7K9xtgZNpgpUmiiuKAxCigtg+6vg+o7+fI6WhLYOeEUUAJK7zOQWM/kBIV9s8RDlpaWlBVVYXe3l5UVVWhpUUGUwkIbFmMtVL8yLdlcSa0C65KoRhTXR8QY6VzMEJhpKdYsa6Pk2c1gJwY0wdEzRRMjyC4bxQZGRloamqSG4VAhmsaaPCk7mNIZ4SmwGez/ZyZYJ0TngLKnx9hWkEpOgcO68z4HNPM+vXr8dBDD+Ho0aNQSuGuu+4K95KEmcDwuQoJNuDPjYDLBT3gmjHzCiZSGKmUgv74HKBlAKog1vvETHB0TMZ0B0FuFMKIDB9glBIF1PexOBEzR1NgIp0TqigO+pvzgVfGdiginR//+MdYtmwZ2tvbkZaWhkceeQR33HFHuJclhJuRBhhV9QIrkmbcvIIJFUZG4GCmkTDdQZAbhTAiwwYYIdoC/G8j0DpATYEZNK9gIp0TMneBrFu3DvX19diyZQsAYMeOHWFekTATGPVkvio13MubNJE4mGkkTHcQ5EYhjMSIhvTcKUx6nEHI3AWgv78fGzZsAAAMDg6isrIyvAsSZgyRNsDITaR+Ll9MdxDkRiEEQwxp5LJmzRqkpvJE+Oabb2LFihVhXpEgCFPFdAdBbhSCcOJRWFjo+e/Vq1eHcSWCIJiF6Q6C3CgEQRAEYfYjw5oEQRAEQQhAHARBEARBEAIQB0EQBEEQhABkWJMQ8ejSZuD5Bqq4dTqBS7Kg1s4J97IEQQgh2uUCth2lhPLiBI5ptsiZeCKIgyBENLq0mWImmxdDWS3QQy7gvnIqPYuTIAgRiXa5gOv2AutzgS/mAbvagOv2Qj+4VJyECSDflBDZPN8A3FwEZeWfurJagJuL+LggCJHJtqPA+lyo1elQMVao1el0FrbXhHtlswpxEITIJsHmcQ7cKKuF6QZBECKT/V2BCocrU4D3O8OznlmKOAhCZNPlZFrBBz3kArqcYVqQIAghZ3EC0wq+7GoDliWO/HphRMRBECKbi7NYc2A4Ce4aBFycFeaFCYIQMjbkA0/UQr/eAt03BP16C/BELXBVXrhXNquQOKsQ0ai1czgk6sb90Ak2Rg4uli4GQYhklMUC/eBS1hw838DIgRQoThhxEISIR62dA4hDIAgnFMpiATYUhHsZsxpxpwRBEARBCEAcBEEQBEEQAhAHQRAEQRCEAMRBEARBEAQhAHEQBEEQBEEIQBwEQRAEQRACUFprbeYbPv/883jwwQfhdDqRmZmJG2+8Eaeffvqov/OVr3wFeXkiYCEI08HcuXOxceNGU99T9r0gzHwmuvdN10Ho6urCzp078fTTT+Pzn/88HnzwwTFvFA8//LDZyxAEYRqRfS8IkYfpKYampia0traitbUVSikcP37c7EsIgjDDkH0vCJGH6Q7CSSedhFtvvRWf/OQnUVtbi3nz5pl9CUEQZhiy7wUh8jDdQaitrUVjYyMOHDgAANixY4fZlxAEYYYh+14QIg/THQSLxYI//vGPsNvt+PDDD7Fw4UKzLyEIwgxD9r0gRB6mOwgDAwMAgPPOOw8AsH//frMvMaNxuVzYvn07Lr300nAvJSL4xje+gcceeyzcyxDGQPa97Huzkb0ffkzvYsjNzUVdXR1ycnLw8Y9/HJ2dnWZfYkL09/fjnnvuwYIFC9DQ0IBvf/vbIb/eueeei6eeeiqk1wGA5557DrW1tYiKioLWGl/96ldDer3Kykq89tpriI6ORmlpKbZu3RrS673//vvo7e0N6TXcHD16FLfddhsyMzNRUlIS8u9Sa41t27YhOTkZe/fuxfe///2QXu+pp57CG2+8AbvdjgMHDuBnP/sZSkpKTHt/2ffTt++B6d37073vgcjd+7Nt35seQbj44ouRk5Pj93M4eemll1BcXIwrrrgCNTU1qK6uDun1HA4HsrOzQ3oNN2eccQauv/56bNq0CS+88ELIrzd37lx8+tOfRl1dHZYuXRry67300kueE+l0UFxcjOXLl2PZsmUhv9Yrr7yCI0eOoL+/H4sWLQr59VavXo3NmzfjvvvuwznnnGOqcwDIvp/OfQ9M796f7n0PRO7en237PuKVFI8ePYo5c+YAANLT01FTUxPmFZlHbm4uAODll1/Gpk2bpuWaaWlpuOmmm7B//340NDSE7Dovv/wyzj333JC9/3CysrJwyy23YP369fjlL3+J7u7ukF7v0KFDSEtLwxe+8AW88cYbKC8vD+n1srOzYbPZ8Oqrr+Kcc84J6bVmApG874Hp3/vTte+ByN77s23fR7yDkJ+fj+bmZgBAS0sL8vPzw7wicyktLYXNZsNpp50W8mu9/vrrnlxzWloaWlpaQnatyspKHD58GLt378aePXtw6NChkF0LACoqKjA0NASAp8FQOwgZGRlITEwEACQnJ6OjoyOk13Pzxhtv4Oyzz56Wa4WTSN/3wPTt/enc90Bk7/3Ztu8j3kE4//zzUVZWhieffBJ5eXnTcqN4+umn0draitLS0pBe5/nnn8fmzZuxc+dOXH/99SG9FgD09fXhvvvuwxNPPAGbzYbFixeH7FqbNm3CmjVr4HK54HK5EB8fH7JrAUBjYyMeeOABPP7441i4cCEyMjJCer2LLroIFRUVePrpp6GUmhYH7913352W68wEInnfA9O796dz3wORvfdn2743fRaDIAiCIAizn4iPIAiCIAiCMHHEQRAEQRAEIQBxEARBEARBCEAcBEEQBEEQAhAHQRAEQRCEAMRBEEKO6NQLwomJ7P3ZjTgIJzj/+Mc/RtSPv/vuu/Hoo4/ikUcewU9+8hP09vbixhtvxJVXXgmXy4XW1lZs3LgRpaWlOHToEM455xw89thjeOKJJ3DDDTf4vZdbp94ttiIIQviRvS+MhenDmoTZxe7du1FVVYX8/HyceeaZnsd37tyJ5557DsnJySgrK4PD4cCqVatQXV2Nu+66C9/73vewdOlSrF27FgCQmZmJyy+/HGVlZYiLi/O7hsPhgMPhmNbPJQjC6MjeF8ZCIggnOGeccQZWrVqFM888Ew899BBuv/129PT04N5778Udd9yBTZs2oaKiwvP6K664Ak6nE88991zAe/3mN7/Biy++iMLCwun8CIIgTALZ+8JYSAThBEcpBQBwOp249tprPY/bbDb89Kc/RW9vLy644AK/yWrf//738bWvfQ1Op9PvvTZu3IiYmBg0NTVNz+IFQZg0sveFsZAIwglOfn4+9uzZg1/84hd+jz/55JPYvn07Hn/8cVx44YXo7e3F22+/jddeew1WqxX3338/jhw5AoATyhobG/Hwww9jy5YtuPXWWwOuM5069YIgjI3sfWEsZBaDIAiCIAgBSARBEARBEIQAxEEQBEEQBCEAcRAEQRAEQQhAHARBEARBEAIQB0EQBEEQhADEQRAEQRAEIQBxEARBEARBCEAcBEEQBEEQAvj/uIx/yaokLVwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 612x673.2 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figM7 = plt.figure(constrained_layout=True)\n",
    "spec2 = gridspec.GridSpec(ncols=2, nrows=2, figure=figM7 , wspace=0.155, hspace=0.34)\n",
    "figM7_ax1 = figM7.add_subplot(spec2[0, 0])\n",
    "figM7_ax2 = figM7.add_subplot(spec2[0, 1])\n",
    "figM7_ax3 = figM7.add_subplot(spec2[1, 0])\n",
    "figM7_ax4 = figM7.add_subplot(spec2[1, 1])\n",
    "\n",
    "#A: sublineage 2.2.1.1.1.i3 highlighted in context of all isolates\n",
    "################################################################################################################\n",
    "circ_size = 10\n",
    "alpha = 1.0\n",
    "linewidth = 0.05\n",
    "\n",
    "#Plot coordinates for each isolate\n",
    "all_isolates = figM7_ax1.scatter( t_SNE_coords[: , 0] , t_SNE_coords[: , 1] ,  color = '0.85' , edgecolor = 'white' , linewidth = linewidth , alpha = alpha , s = circ_size)\n",
    "\n",
    "#lineage 2.2.1.1.1.i3\n",
    "isolate_sublineage_filter = [isolate_i_lineage_call[0:13] == '2.2.1.1.1.i3' for isolate_i_lineage_call in isolate_annotation_DF_indels.lineage_call]\n",
    "\n",
    "#plot points for all isolates that belong to this sub-lineage\n",
    "L_22111i3 = figM7_ax1.scatter( t_SNE_coords[: , 0][isolate_sublineage_filter] , t_SNE_coords[: , 1][isolate_sublineage_filter]  , color = 'xkcd:bright pink' , edgecolor = 'xkcd:white' , linewidth = linewidth , alpha = alpha , s = circ_size)\n",
    "\n",
    "figM7_ax1.spines['right'].set_visible(False)\n",
    "figM7_ax1.spines['top'].set_visible(False)\n",
    "for axis in ['bottom','left']:\n",
    "    figM7_ax1.spines[axis].set_linewidth(0.5)\n",
    "\n",
    "figM7_ax1.tick_params(axis='y', which='major', labelsize=7.5, pad = 0.0, labelcolor = 'k', rotation = 90)\n",
    "figM7_ax1.tick_params(axis='x', which='major', labelsize=7.5, pad = 0.0, labelcolor = 'k')\n",
    "\n",
    "#add legend\n",
    "figM7_ax1.legend((L_22111i3, all_isolates), ('Lineage 2.2.1.1.1.i3' + ' (n = ' + str(sum(isolate_sublineage_filter)) + ')', 'other'), bbox_to_anchor=(0,0.97,1,0.2) , scatterpoints=1 , loc='lower left', ncol=1, fontsize=8 ,mode=\"expand\", borderaxespad=0 , frameon=False , handletextpad=0.01)\n",
    "\n",
    "#add axis labels\n",
    "figM7_ax1.set_xlabel('t-SNE 1' , fontsize = '7.5' , color = 'k')\n",
    "figM7_ax1.tick_params(axis='x', pad=0.0)\n",
    "\n",
    "figM7_ax1.set_ylabel('t-SNE 2', fontsize = '7.5', color = 'k')\n",
    "figM7_ax1.tick_params(axis='y', pad=0.0)\n",
    "################################################################################################################\n",
    "\n",
    "#B: only isolates belonging to sublineage 2.2.1.1.1.i3\n",
    "################################################################################################################\n",
    "\n",
    "#lineage 2.2.1.1.1.i3\n",
    "isolate_sublineage_filter = [isolate_i_lineage_call[0:12] == '2.2.1.1.1.i3' for isolate_i_lineage_call in isolate_annotation_DF_indels.lineage_call]\n",
    "\n",
    "#plot points for all isolates that belong to this sub-lineage\n",
    "L_22111i3 = figM7_ax2.scatter( t_SNE_coords[: , 0][isolate_sublineage_filter] , t_SNE_coords[: , 1][isolate_sublineage_filter]  , color = 'xkcd:white' , edgecolor = 'xkcd:bright pink' , linewidth = 0.4 , alpha = 0.75 , s = 30)\n",
    "\n",
    "figM7_ax2.spines['right'].set_visible(False)\n",
    "figM7_ax2.spines['top'].set_visible(False)\n",
    "for axis in ['bottom','left']:\n",
    "    figM7_ax2.spines[axis].set_linewidth(0.5)\n",
    "\n",
    "figM7_ax2.tick_params(axis='y', which='major', labelsize=7.5, pad = 0.0, labelcolor = 'k', rotation = 90)\n",
    "figM7_ax2.tick_params(axis='x', which='major', labelsize=7.5, pad = 0.0, labelcolor = 'k')\n",
    "\n",
    "#add legend\n",
    "figM7_ax2.legend((L_22111i3, all_isolates), ('Lineage 2.2.1.1.1.i3' + ' (n = ' + str(sum(isolate_sublineage_filter)) + ')', 'other'), bbox_to_anchor=(0,0.97,1,0.2) , scatterpoints=1 , loc='lower left', ncol=1, fontsize=8 ,mode=\"expand\", borderaxespad=0 , frameon=False , handletextpad=0.01)\n",
    "\n",
    "#add axis labels\n",
    "figM7_ax2.set_xlabel('t-SNE 1' , fontsize = '7.5' , color = 'k')\n",
    "figM7_ax2.tick_params(axis='x', pad=0.0)\n",
    "\n",
    "figM7_ax2.set_ylabel('t-SNE 2', fontsize = '7.5', color = 'k')\n",
    "figM7_ax2.tick_params(axis='y', pad=0.0)\n",
    "################################################################################################################\n",
    "\n",
    "#C: only isolates belonging to sublineage 2.2.1.1.1.i3 cluster\n",
    "################################################################################################################\n",
    "\n",
    "#lineage 2.2.1.1.1.i3 (SUBSET this sublineage to cluster of isolates with -2 <= t-SNE_1 <= 8 , 26 <= t-SNE_2 <= 38)\n",
    "isolate_sublineage_filter = [isolate_i_lineage_call[0:12] == '2.2.1.1.1.i3' for isolate_i_lineage_call in isolate_annotation_DF_indels.lineage_call] # n = 1887\n",
    "isolate_cluster_filter = [( -2 <= tSNE_1 <= 8 ) and ( 26 <= tSNE_2 <= 38 ) for tSNE_1, tSNE_2 in zip(t_SNE_coords[: , 0], t_SNE_coords[: , 1])] #n = 439\n",
    "\n",
    "#create filter based off of isolates belonging to cluster within sublineage\n",
    "isolate_sublineage_cluster_filter = [(isolate_in_sublineage and isolate_in_cluster) for isolate_in_sublineage, isolate_in_cluster in zip(isolate_sublineage_filter, isolate_cluster_filter)] #n = 438\n",
    "\n",
    "#plot points for all isolates that belong to this sub-lineage & cluster with double mutants\n",
    "L_22111i3_cluster = figM7_ax3.scatter( t_SNE_coords[: , 0][isolate_sublineage_cluster_filter] , t_SNE_coords[: , 1][isolate_sublineage_cluster_filter]  , color = 'xkcd:white' , edgecolor = 'xkcd:bright pink' , linewidth = 0.5 , alpha = 0.75 , s = 38)\n",
    "\n",
    "#plot isolates with promoter SNP\n",
    "\n",
    "#isolates have eis promoter SNP + eis indel\n",
    "eis_promoter_SNP_row_index = SNP_annotation_DF[SNP_annotation_DF.pos == 2715346].index.values[0]\n",
    "\n",
    "#get the genotypes for all isolates\n",
    "eis_promoter_SNP_genotypes = genotypes_array_SNPs[eis_promoter_SNP_row_index , :]\n",
    "\n",
    "#construct a boolean array that is TRUE if isolate's genotype matches the mutant allele and FALSE otherwise\n",
    "isolate_mutant_allele_match_bool = [(isolate_with_eis_promoter_SNP and isolate_belong_to_sublineage_cluster) for isolate_with_eis_promoter_SNP, isolate_belong_to_sublineage_cluster in zip(eis_promoter_SNP_genotypes == allele_code_map_dict['A'], isolate_sublineage_cluster_filter)]\n",
    "\n",
    "#Plot coordinates for each isolate with both mutant allele\n",
    "mut_allele_isolates = figM7_ax3.scatter( t_SNE_coords[: , 0][isolate_mutant_allele_match_bool] ,t_SNE_coords[: , 1][isolate_mutant_allele_match_bool]  ,  color = 'xkcd:sky blue' , edgecolor = 'xkcd:bright pink' , linewidth = 0.5 , alpha = 1.0 , s = 38)\n",
    "\n",
    "figM7_ax3.spines['right'].set_visible(False)\n",
    "figM7_ax3.spines['top'].set_visible(False)\n",
    "for axis in ['bottom','left']:\n",
    "    figM7_ax3.spines[axis].set_linewidth(0.5)\n",
    "\n",
    "figM7_ax3.tick_params(axis='y', which='major', labelsize=7.5, pad = 0.0, labelcolor = 'k', rotation = 90)\n",
    "figM7_ax3.tick_params(axis='x', which='major', labelsize=7.5, pad = 0.0, labelcolor = 'k')\n",
    "\n",
    "#add legend\n",
    "figM7_ax3.legend((L_22111i3_cluster, mut_allele_isolates), ('Lineage 2.2.1.1.1.i3 cluster' + ' (n = ' + str(sum(isolate_sublineage_cluster_filter)) + ')', '-18A eis-Rv2417c' + ' (n = ' + str(sum(isolate_mutant_allele_match_bool)) + ')'), bbox_to_anchor=(0,0.97,1,0.2) , scatterpoints=1 , loc='lower left', ncol=1, fontsize=8 ,mode=\"expand\", borderaxespad=0 , frameon=False , handletextpad=0.01)\n",
    "\n",
    "#add axis labels\n",
    "figM7_ax3.set_xlabel('t-SNE 1' , fontsize = '7.5' , color = 'k')\n",
    "figM7_ax3.tick_params(axis='x', pad=0.0)\n",
    "\n",
    "figM7_ax3.set_ylabel('t-SNE 2', fontsize = '7.5', color = 'k')\n",
    "figM7_ax3.tick_params(axis='y', pad=0.0)\n",
    "################################################################################################################\n",
    "\n",
    "\n",
    "#D: only isolates belonging to sublineage 2.2.1.1.1.i3 cluster\n",
    "################################################################################################################\n",
    "\n",
    "#lineage 2.2.1.1.1.i3 (SUBSET this sublineage to cluster of isolates with -2 <= t-SNE_1 <= 8 , 26 <= t-SNE_2 <= 38)\n",
    "isolate_sublineage_filter = [isolate_i_lineage_call[0:12] == '2.2.1.1.1.i3' for isolate_i_lineage_call in isolate_annotation_DF_indels.lineage_call] # n = 1887\n",
    "isolate_cluster_filter = [( -2 <= tSNE_1 <= 8 ) and ( 26 <= tSNE_2 <= 38 ) for tSNE_1, tSNE_2 in zip(t_SNE_coords[: , 0], t_SNE_coords[: , 1])] #n = 439\n",
    "\n",
    "#create filter based off of isolates belonging to cluster within sublineage\n",
    "isolate_sublineage_cluster_filter = [(isolate_in_sublineage and isolate_in_cluster) for isolate_in_sublineage, isolate_in_cluster in zip(isolate_sublineage_filter, isolate_cluster_filter)] #n = 438\n",
    "\n",
    "#plot points for all isolates that belong to this sub-lineage & cluster with double mutants\n",
    "L_22111i3_cluster = figM7_ax4.scatter( t_SNE_coords[: , 0][isolate_sublineage_cluster_filter] , t_SNE_coords[: , 1][isolate_sublineage_cluster_filter]  , color = 'xkcd:white' , edgecolor = 'xkcd:bright pink' , linewidth = 0.5 , alpha = 0.75 , s = 30)\n",
    "\n",
    "#get the isolates with the promoter SNP\n",
    "eis_promoter_SNP_row_index = SNP_annotation_DF[SNP_annotation_DF.pos == 2715346].index.values[0]\n",
    "eis_promoter_SNP_genotypes = genotypes_array_SNPs[eis_promoter_SNP_row_index , :]\n",
    "\n",
    "\n",
    "####### plot isolates with promoter SNP & (eis 3C>T V1V) #######\n",
    "#isolates that belong to 2.2.1.1.1.i3 & have eis promoter SNP + eis SNP\n",
    "eis_gene_SNP_row_index = SNP_annotation_DF[SNP_annotation_DF.pos == 2715330].index.values[0]\n",
    "eis_gene_SNP_genotypes = genotypes_array_SNPs[eis_gene_SNP_row_index, :] #get the genotypes for all isolates\n",
    "\n",
    "#construct a boolean array that is TRUE if isolate's genotype matches the mutant allele and FALSE otherwise\n",
    "isolate_mutant_allele_match_bool_eis_SNP = [(isolate_with_eis_promoter_SNP and isolate_with_eis_SNP and isolate_belong_to_sublineage_cluster) for isolate_with_eis_promoter_SNP, isolate_with_eis_SNP, isolate_belong_to_sublineage_cluster in zip(eis_promoter_SNP_genotypes == allele_code_map_dict['A'], eis_gene_SNP_genotypes == allele_code_map_dict['T'], isolate_sublineage_cluster_filter)]\n",
    "#Plot coordinates for each isolate with both mutant allele\n",
    "mut_allele_isolates_eis_SNP = figM7_ax4.scatter( t_SNE_coords[: , 0][isolate_mutant_allele_match_bool_eis_SNP], t_SNE_coords[: , 1][isolate_mutant_allele_match_bool_eis_SNP]  ,  color = 'xkcd:yellow' , edgecolor = 'xkcd:bright pink' , linewidth = 0.5 , alpha = 1.0 , s = 30)\n",
    "\n",
    "\n",
    "####### plot isolates with promoter SNP & (eis 448delA) #######\n",
    "eis_del_row_index = INDEL_annotation_DF[INDEL_annotation_DF.key == 'GA_2714885_G'].index.values[0]\n",
    "eis_del_genotypes = genotypes_array_INDELs[eis_del_row_index, :] #get the genotypes for all isolates\n",
    "\n",
    "#construct a boolean array that is TRUE if isolate's genotype matches the mutant allele and FALSE otherwise\n",
    "isolate_mutant_allele_match_bool_eis_del = [(isolate_with_eis_promoter_SNP and isolate_with_eis_del and isolate_belong_to_sublineage_cluster) for isolate_with_eis_promoter_SNP, isolate_with_eis_del, isolate_belong_to_sublineage_cluster in zip(eis_promoter_SNP_genotypes == allele_code_map_dict['A'], eis_del_genotypes == 1, isolate_sublineage_cluster_filter)]\n",
    "#Plot coordinates for each isolate with both mutant allele\n",
    "mut_allele_isolates_eis_del = figM7_ax4.scatter( t_SNE_coords[: , 0][isolate_mutant_allele_match_bool_eis_del] ,t_SNE_coords[: , 1][isolate_mutant_allele_match_bool_eis_del]  ,  color = 'xkcd:blue' , edgecolor = 'xkcd:bright pink' , linewidth = 0.5 , alpha = 1.0 , s = 30)\n",
    "\n",
    "\n",
    "####### plot isolates with promoter SNP & (eis 473insT) #######\n",
    "eis_ins_row_index = INDEL_annotation_DF[INDEL_annotation_DF.key == 'A_2714860_AT'].index.values[0]\n",
    "eis_ins_genotypes = genotypes_array_INDELs[eis_ins_row_index, :] #get the genotypes for all isolates\n",
    "\n",
    "#construct a boolean array that is TRUE if isolate's genotype matches the mutant allele and FALSE otherwise\n",
    "isolate_mutant_allele_match_bool_eis_ins = [(isolate_with_eis_promoter_SNP and isolate_with_eis_ins and isolate_belong_to_sublineage_cluster) for isolate_with_eis_promoter_SNP, isolate_with_eis_ins, isolate_belong_to_sublineage_cluster in zip(eis_promoter_SNP_genotypes == allele_code_map_dict['A'], eis_ins_genotypes == 1, isolate_sublineage_cluster_filter)]\n",
    "#Plot coordinates for each isolate with both mutant allele\n",
    "mut_allele_isolates_eis_ins = figM7_ax4.scatter( t_SNE_coords[: , 0][isolate_mutant_allele_match_bool_eis_ins] ,t_SNE_coords[: , 1][isolate_mutant_allele_match_bool_eis_ins]  ,  color = 'xkcd:red' , edgecolor = 'xkcd:bright pink' , linewidth = 0.5 , alpha = 1.0 , s = 30)\n",
    "\n",
    "\n",
    "figM7_ax4.spines['right'].set_visible(False)\n",
    "figM7_ax4.spines['top'].set_visible(False)\n",
    "for axis in ['bottom','left']:\n",
    "    figM7_ax4.spines[axis].set_linewidth(0.5)\n",
    "\n",
    "figM7_ax4.tick_params(axis='y', which='major', labelsize=7.5, pad = 0.0, labelcolor = 'k', rotation = 90)\n",
    "figM7_ax4.tick_params(axis='x', which='major', labelsize=7.5, pad = 0.0, labelcolor = 'k')\n",
    "\n",
    "eis_SNP_label = '-18A eis-Rv2417c & eis 3C>T V1V' + ' (n = ' + str(sum(isolate_mutant_allele_match_bool_eis_SNP)) + ')'\n",
    "eis_del_label = '-18A eis-Rv2417c & eis 448delA' + ' (n = ' + str(sum(isolate_mutant_allele_match_bool_eis_del)) + ')'\n",
    "eis_ins_label = '-18A eis-Rv2417c & eis 473insT' + ' (n = ' + str(sum(isolate_mutant_allele_match_bool_eis_ins)) + ')'\n",
    "\n",
    "#add legend\n",
    "figM7_ax4.legend((L_22111i3_cluster, mut_allele_isolates_eis_SNP, mut_allele_isolates_eis_del, mut_allele_isolates_eis_ins), ('Lineage 2.2.1.1.1.i3 cluster' + ' (n = ' + str(sum(isolate_sublineage_cluster_filter)) + ')', eis_SNP_label, eis_del_label, eis_ins_label), bbox_to_anchor=(0,0.97,1,0.2) , scatterpoints=1 , loc='lower left', ncol=1, fontsize=8 ,mode=\"expand\", borderaxespad=0 , frameon=False , handletextpad=0.01)\n",
    "\n",
    "#add axis labels\n",
    "figM7_ax4.set_xlabel('t-SNE 1' , fontsize = '7.5' , color = 'k')\n",
    "figM7_ax4.tick_params(axis='x', pad=0.0)\n",
    "\n",
    "figM7_ax4.set_ylabel('t-SNE 2', fontsize = '7.5', color = 'k')\n",
    "figM7_ax4.tick_params(axis='y', pad=0.0)\n",
    "################################################################################################################\n",
    "\n",
    "figM7.text(0.10, 0.89, 'A', fontsize=12 , fontweight='bold' , color = 'k')\n",
    "figM7.text(0.51, 0.89, 'B', fontsize=12 , fontweight='bold' , color = 'k')\n",
    "figM7.text(0.10, 0.46, 'C', fontsize=12 , fontweight='bold' , color = 'k')\n",
    "figM7.text(0.51, 0.46, 'D', fontsize=12 , fontweight='bold' , color = 'k')\n",
    "\n",
    "figM7 = plt.gcf()\n",
    "figM7.set_size_inches(8.5, 9.35)\n",
    "figM7.tight_layout()\n",
    "\n",
    "file_name = '/n/data1/hms/dbmi/farhat/Roger/mmpR_BDQ_mutant_project/figures/eis promoter SNP eis mutations 22111i3 tSNE.png'\n",
    "plt.savefig(file_name, bbox_inches='tight', dpi = 300 , transparent = False)\n",
    "\n",
    "plt.show()"
   ]
  }
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