NB05_GTExWorkup.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.formula.api as smf\n",
    "from glob import glob\n",
    "from scipy import stats\n",
    "from sklearn.preprocessing import StandardScaler,MinMaxScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cross_decomposition import PLSRegression\n",
    "from matplotlib.colors import Normalize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Instructions\n",
    "\n",
    "In order to run this notebook, you will need to do the following steps:\n",
    "\n",
    "1) Download the following (version 8) GTEx data from https://gtexportal.org/home/datasets:\n",
    "* GTEx_Analysis_v8_Annotations_SampleAttributesDS.txt\n",
    "* GTEx_Analysis_v8_Annotations_SubjectPhenotypesDS.txt\n",
    "* GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_tpm.gct.gz\n",
    "\n",
    "Unzip and move into `./data/`\n",
    "\n",
    "2) Preprocess the data. To do so, run the R script found in `code/GTEx_v8_process.R`. Note that you will first need to ensure several packages and their dependencies are installed: WGCNA, SNFtool, flashClust, CePa, plyr. ***IMPORTANT! This script has not been successfully run on R version 4.x. Please us version 3.x***\n",
    "\n",
    "***NOTE*** The publicly available GTEx data has binned ages, rather than true ages. For the manuscript, we used \"Protected Data\" which has the true ages of donors. Therefore, this is perhaps the only manuscript analysis that you will not be able to run yourself with online data. We will include the exact code we ran, and you can substitute our (true) age variable with the binned age variable. But note there will be differences in the results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The section below can be used to download and process the raw data. However, this requires R and some R libraries. Thus, this is commented out, and we suggest downloading the outputs we have prepared from this snippet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# os.system('wget https://storage.googleapis.com/gtex_analysis_v8/annotations/GTEx_Analysis_v8_Annotations_SampleAttributesDS.txt')\n",
    "# os.system('wget https://storage.googleapis.com/gtex_analysis_v8/annotations/GTEx_Analysis_v8_Annotations_SubjectPhenotypesDS.txt')\n",
    "# os.system('wget https://storage.googleapis.com/gtex_analysis_v8/rna_seq_data/GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_tpm.gct.gz')\n",
    "# os.system('mv *GTEx* data')\n",
    "# os.system('R code/GTEx_v8_process.R')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code will simply load the pre-run data. you can look at code/GTEx_v8_process.R for details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.system('wget https://www.dropbox.com/sh/zx0va2j8tbbyhux/AAC0oTSl0Of_mazw3fZGeqpua/GTEx_brain_norm_noregress.csv?dl=1')\n",
    "os.system('wget https://www.dropbox.com/sh/zx0va2j8tbbyhux/AACb8ZPPb495vnIUKCfrQnSBa/GTEx_brain_norm_noregress_meta.csv?dl=1')\n",
    "pths = glob('*norm_noregress*')\n",
    "for pth in pths:\n",
    "    os.system('mv %s data/%s'%(pth,pth.split('?')[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prep data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pandas.read_csv('data/GTEx_brain_norm_noregress_meta.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>SUBJID</th>\n",
       "      <th>RIN</th>\n",
       "      <th>BATCH</th>\n",
       "      <th>GBATCH</th>\n",
       "      <th>AGE</th>\n",
       "      <th>SEX</th>\n",
       "      <th>SMTSD</th>\n",
       "      <th>SeqPC1</th>\n",
       "      <th>SeqPC2</th>\n",
       "      <th>SeqPC3</th>\n",
       "      <th>SeqPC4</th>\n",
       "      <th>SeqPC5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GTEX-1117F-3226-SM-5N9CT</td>\n",
       "      <td>GTEX-1117F</td>\n",
       "      <td>6.2</td>\n",
       "      <td>BP-43956</td>\n",
       "      <td>LCSET-4904</td>\n",
       "      <td>65</td>\n",
       "      <td>2</td>\n",
       "      <td>Brain - Cortex</td>\n",
       "      <td>0.730214</td>\n",
       "      <td>1.373728</td>\n",
       "      <td>-0.032430</td>\n",
       "      <td>-1.243091</td>\n",
       "      <td>-0.269011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GTEX-111FC-3126-SM-5GZZ2</td>\n",
       "      <td>GTEX-111FC</td>\n",
       "      <td>6.1</td>\n",
       "      <td>BP-43693</td>\n",
       "      <td>LCSET-4804</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>Brain - Cortex</td>\n",
       "      <td>-0.877245</td>\n",
       "      <td>0.217794</td>\n",
       "      <td>-0.722488</td>\n",
       "      <td>-2.620600</td>\n",
       "      <td>0.325254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GTEX-111FC-3326-SM-5GZYV</td>\n",
       "      <td>GTEX-111FC</td>\n",
       "      <td>7.1</td>\n",
       "      <td>BP-43507</td>\n",
       "      <td>LCSET-4804</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>Brain - Cerebellum</td>\n",
       "      <td>2.723758</td>\n",
       "      <td>-1.893202</td>\n",
       "      <td>-3.023749</td>\n",
       "      <td>-1.072355</td>\n",
       "      <td>-0.128278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GTEX-1128S-2726-SM-5H12C</td>\n",
       "      <td>GTEX-1128S</td>\n",
       "      <td>6.3</td>\n",
       "      <td>BP-43783</td>\n",
       "      <td>LCSET-4805</td>\n",
       "      <td>65</td>\n",
       "      <td>2</td>\n",
       "      <td>Brain - Cortex</td>\n",
       "      <td>-0.511056</td>\n",
       "      <td>0.888433</td>\n",
       "      <td>0.002476</td>\n",
       "      <td>-1.154150</td>\n",
       "      <td>0.026175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GTEX-1128S-2826-SM-5N9DI</td>\n",
       "      <td>GTEX-1128S</td>\n",
       "      <td>6.3</td>\n",
       "      <td>BP-43956</td>\n",
       "      <td>LCSET-4904</td>\n",
       "      <td>65</td>\n",
       "      <td>2</td>\n",
       "      <td>Brain - Cerebellum</td>\n",
       "      <td>4.048402</td>\n",
       "      <td>-1.947349</td>\n",
       "      <td>-3.737918</td>\n",
       "      <td>1.363319</td>\n",
       "      <td>-0.354989</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Unnamed: 0      SUBJID  RIN     BATCH      GBATCH  AGE  SEX  \\\n",
       "0  GTEX-1117F-3226-SM-5N9CT  GTEX-1117F  6.2  BP-43956  LCSET-4904   65    2   \n",
       "1  GTEX-111FC-3126-SM-5GZZ2  GTEX-111FC  6.1  BP-43693  LCSET-4804   65    1   \n",
       "2  GTEX-111FC-3326-SM-5GZYV  GTEX-111FC  7.1  BP-43507  LCSET-4804   65    1   \n",
       "3  GTEX-1128S-2726-SM-5H12C  GTEX-1128S  6.3  BP-43783  LCSET-4805   65    2   \n",
       "4  GTEX-1128S-2826-SM-5N9DI  GTEX-1128S  6.3  BP-43956  LCSET-4904   65    2   \n",
       "\n",
       "                SMTSD    SeqPC1    SeqPC2    SeqPC3    SeqPC4    SeqPC5  \n",
       "0      Brain - Cortex  0.730214  1.373728 -0.032430 -1.243091 -0.269011  \n",
       "1      Brain - Cortex -0.877245  0.217794 -0.722488 -2.620600  0.325254  \n",
       "2  Brain - Cerebellum  2.723758 -1.893202 -3.023749 -1.072355 -0.128278  \n",
       "3      Brain - Cortex -0.511056  0.888433  0.002476 -1.154150  0.026175  \n",
       "4  Brain - Cerebellum  4.048402 -1.947349 -3.737918  1.363319 -0.354989  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that here I am getting the true age information from an internal spreadsheet. You will skip this step (unless you have access to the GTEx Protect Data, in which case you may change the path below so it points to your own spreadsheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bwdf = pandas.read_csv('/Users/jacobv/Dropbox/Work/Projects/PLS_ABA/PLS_GXP_CollabSpace/GTEx_meta_forJake.csv')\n",
    "# bwcols = ['AGE']\n",
    "# df.loc[:,'merger'] = ['%s_%s'%(df.loc[x,'SUBJID'],\n",
    "#                                df.loc[x,'SMTSD']) for x in df.index]\n",
    "# bwdf.loc[:,'merger'] = ['%s_%s'%(bwdf.loc[x,'SUBJID'],\n",
    "#                                bwdf.loc[x,'SMTSD']) for x in bwdf.index]\n",
    "# jnk = pandas.DataFrame(df,copy=True)\n",
    "# jnk.loc[:,'old_ind'] = jnk.index.values\n",
    "# jnk.index = jnk.merger.values\n",
    "# bwdf.index = bwdf.merger.values\n",
    "# shared = list(set(bwdf.index) & set(jnk.index))\n",
    "# for col in bwcols:\n",
    "#     jnk.loc[shared,col] = bwdf.loc[shared,col].values\n",
    "#     df.loc[:,col] = jnk.loc[:,col].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "376"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of samples\n",
    "len(df.SUBJID.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also want to make sure that our results aren't driven by individuals with brain diseases. Another recent paper (10.1038/s41593-021-00887-5) recommended an extreme processing stream for the GTEx that, among other rather heavy-handed steps, excluded all individuals who died of/with brain disease. \n",
    "\n",
    "Attached is a spreadsheet of GTEx donors remaining after this preprocessing was done. By cross-referencing our spreadsheet (with everyone) with this list of donors (without individuals with brain disease), we can ID who is who"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "excludf = pandas.read_csv('data/GTEx_healthy.csv',index_col=0)\n",
    "excludf.loc[:,'Healthy'] = [x.replace('.','-') for x in excludf.Healthy.values]\n",
    "dx_exclude = list(set(df.SUBJID.values) - set(excludf.Healthy.values))\n",
    "df.loc[df.SUBJID.isin(dx_exclude),'Excl_Dx'] = 1\n",
    "df.loc[df.Excl_Dx!=1,'Excl_Dx'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "151"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dx_exclude)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note, to run the next line, you will have needed to run the GTEx processing script first (see instructions above)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load processed expression data \n",
    "exp = pandas.read_csv('data/GTEx_brain_norm_noregress.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15758, 2484)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp.columns = ['ENSGID'] + exp.columns[1:].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hgnc_symbol</th>\n",
       "      <th>ensembl_gene_id</th>\n",
       "      <th>chromosome_name</th>\n",
       "      <th>start_position</th>\n",
       "      <th>end_position</th>\n",
       "      <th>entrezgene_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FGR</td>\n",
       "      <td>ENSG00000000938</td>\n",
       "      <td>1</td>\n",
       "      <td>27612064</td>\n",
       "      <td>27635185</td>\n",
       "      <td>2268.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>STPG1</td>\n",
       "      <td>ENSG00000001460</td>\n",
       "      <td>1</td>\n",
       "      <td>24356999</td>\n",
       "      <td>24416934</td>\n",
       "      <td>90529.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NIPAL3</td>\n",
       "      <td>ENSG00000001461</td>\n",
       "      <td>1</td>\n",
       "      <td>24415802</td>\n",
       "      <td>24472976</td>\n",
       "      <td>57185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AK2</td>\n",
       "      <td>ENSG00000004455</td>\n",
       "      <td>1</td>\n",
       "      <td>33007940</td>\n",
       "      <td>33080996</td>\n",
       "      <td>204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KDM1A</td>\n",
       "      <td>ENSG00000004487</td>\n",
       "      <td>1</td>\n",
       "      <td>23019443</td>\n",
       "      <td>23083689</td>\n",
       "      <td>23028.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  hgnc_symbol  ensembl_gene_id chromosome_name  start_position  end_position  \\\n",
       "0         FGR  ENSG00000000938               1        27612064      27635185   \n",
       "1       STPG1  ENSG00000001460               1        24356999      24416934   \n",
       "2      NIPAL3  ENSG00000001461               1        24415802      24472976   \n",
       "3         AK2  ENSG00000004455               1        33007940      33080996   \n",
       "4       KDM1A  ENSG00000004487               1        23019443      23083689   \n",
       "\n",
       "   entrezgene_id  \n",
       "0         2268.0  \n",
       "1        90529.0  \n",
       "2        57185.0  \n",
       "3          204.0  \n",
       "4        23028.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# spreadsheet to map gene symbol (Allen) to ensemble gene ID (GTEx)\n",
    "genes = pandas.read_csv('data/gene_info.csv')\n",
    "genes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Whole dataset congruence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Preprocess\n",
    "Here, we will match the GTEx genes with the Allen genes in order to derive PLS components for GTEx samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdict = dict(zip(genes.ensembl_gene_id.values,\n",
    "                genes.hgnc_symbol.values))\n",
    "gmatch = [np.nan if x not in gdict.keys() else \\\n",
    "          gdict[x] for x in exp.ENSGID.values]\n",
    "exp.loc[:,'gene_symbol'] = gmatch\n",
    "exp.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_symbol</th>\n",
       "      <th>gene_name</th>\n",
       "      <th>C1</th>\n",
       "      <th>C2</th>\n",
       "      <th>C3</th>\n",
       "      <th>C1_genelist</th>\n",
       "      <th>C2_genelist</th>\n",
       "      <th>C3_genelist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1BG</td>\n",
       "      <td>alpha-1-B glycoprotein</td>\n",
       "      <td>0.002967</td>\n",
       "      <td>0.001055</td>\n",
       "      <td>-0.005412</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1BG-AS1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001287</td>\n",
       "      <td>-0.001796</td>\n",
       "      <td>0.006987</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2M</td>\n",
       "      <td>alpha-2-macroglobulin</td>\n",
       "      <td>0.003203</td>\n",
       "      <td>0.003630</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A2ML1</td>\n",
       "      <td>alpha-2-macroglobulin-like 1</td>\n",
       "      <td>-0.000960</td>\n",
       "      <td>-0.003713</td>\n",
       "      <td>-0.009880</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A3GALT2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.002451</td>\n",
       "      <td>0.001712</td>\n",
       "      <td>0.003116</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gene_symbol                     gene_name        C1        C2        C3  \\\n",
       "0        A1BG        alpha-1-B glycoprotein  0.002967  0.001055 -0.005412   \n",
       "1    A1BG-AS1                           NaN  0.001287 -0.001796  0.006987   \n",
       "2         A2M         alpha-2-macroglobulin  0.003203  0.003630  0.000001   \n",
       "3       A2ML1  alpha-2-macroglobulin-like 1 -0.000960 -0.003713 -0.009880   \n",
       "4     A3GALT2                           NaN -0.002451  0.001712  0.003116   \n",
       "\n",
       "   C1_genelist  C2_genelist  C3_genelist  \n",
       "0          0.0          0.0          0.0  \n",
       "1          0.0          0.0          0.0  \n",
       "2          0.0          0.0          0.0  \n",
       "3          0.0          0.0          0.0  \n",
       "4          0.0          0.0          0.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NOTE: This spreadsheet was generated from the ModelExploration notebook \n",
    "comps = pandas.read_csv('tables/all_comps.csv',index_col = 0)\n",
    "comps.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Match genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12647"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shared_genes = list(set(comps.gene_symbol) & set(exp.gene_symbol))\n",
    "len(shared_genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Harmonizing expression data with genes shared between both datasets\n",
    "shared_genes = list(set(comps.gene_symbol) & set(exp.gene_symbol))\n",
    "matched_comps = pandas.DataFrame(comps[comps.gene_symbol.isin(shared_genes)],\n",
    "                                 copy=True)\n",
    "exp = pandas.DataFrame(exp[exp.gene_symbol.isin(shared_genes)],\n",
    "                                copy=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving the index for a moment\n",
    "egenes = exp.gene_symbol.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Are we harmonized?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12647, 2485)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12647, 8)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matched_comps.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yep!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### regress covariates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we are going to regress out the first five \"Sequence PCs\" from the data. These are PCs derived from confounding covariates (see processing script). ***Note:*** This took a few minutes on my computer\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "# Make empty dataframe\n",
    "nexp = np.zeros_like(exp.iloc[:,1:-1].values)\n",
    "# get small table with only relevant covariates\n",
    "dat = pandas.DataFrame(df[df.columns[8:13]],copy=True)\n",
    "\n",
    "# regress covariates\n",
    "count = 0\n",
    "# for each gene\n",
    "for i,row in exp.iterrows():\n",
    "    # fit model regressing the PCs\n",
    "    dat.loc[:,'exp'] = row.values[1:-1].astype(float)\n",
    "    mod = smf.ols('exp ~ SeqPC1 + SeqPC2 + SeqPC3 + SeqPC4 + SeqPC5',\n",
    "                  data=dat).fit()\n",
    "    # save residuals\n",
    "    nexp[count,:] = mod.resid.values\n",
    "    # keep track\n",
    "    if count%100==0:\n",
    "        print('finished %s of %s'%(count+1,len(nexp)))\n",
    "    count+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# update the expression dataset with the residuals\n",
    "exp = pandas.DataFrame(nexp,index=exp.index,columns=exp.columns[1:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp.loc[:,'gene_symbol'] = egenes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp.sort_values('gene_symbol',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You might want to save this so you dont have to run the regression again\n",
    "# exp.to_csv('data/GTex_harmed_GExp.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyses -- replication"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The objective here is to see if the PLS Components derived in AHBA \"reproduce\" in GTEx. \n",
    "\n",
    "The way we're doing this is taking the PLS Model trained on Allen data and applying it to the GTEx data. We compare the \"transformed\" GTEx data based on gene expression to the transformed Allen data by correlating the transformed values of both datasets across regions. The reason this works is because Y is spatial location, and regions are spatially embedded.\n",
    "\n",
    "So, what we are essentially doing here is just ensuring that the genes expressed in the Allen PLS Components are expressed in a similar regional pattern in GTEx as they are in AHBA. Another way to think about it is we are looking to see how similar the two are datasets with a specific focus on the expression of genes contributing to the AHBA PLS Components. See the XSpecies.ipynb for different null models.\n",
    "\n",
    "This is an important step because a) we see how robust the regional expression of PLS Components in a separate dataset, and b) it is necessary to show a very high correlation across datasets if we want to look at how the PLS Components vary over age in the GTEx dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Load the data if it was already saved\n",
    "#exp = pandas.read_csv('data/GTex_harmed_GExp.csv',index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to be able to test how robust the reproducibility is to age of the sample (everyone vs only those in the AHBA age range), cognitive status (everyone vs only those w/o brain disorder at death), and the interaction of these features.\n",
    "\n",
    "So, we'll create separate subsamples that include:\n",
    "* full -- Everyone\n",
    "* younger -- everyone <= 60\n",
    "* ctrl -- all \"controls\" (weren't excluded by stringent pipeline)\n",
    "* yctrl -- all \"controls\" <= 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create these \"datasets\"\n",
    "younger = df[df.AGE<61][df.columns[0]].values\n",
    "cntrl = df[df.Excl_Dx==0][df.columns[0]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change this to the location you saved your abagen data\n",
    "allendir = '/Users/jacobv/abagen-data/microarray/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the Allen Human Brain data in order to refit the PLS model\n",
    "\n",
    "# Generated in NB1\n",
    "hbigdf = pandas.read_csv('data/expression.csv') \n",
    "probes = pandas.read_csv(os.path.join(allendir,'normalized_microarray_donor10021/Probes.csv'))\n",
    "# Generated in NB2\n",
    "hgdf = pandas.read_csv('data/PLS_GXp_gdf.csv',index_col=0) # Generated in NB2\n",
    "hbigdf.index = hgdf.index\n",
    "hbigdf.drop('well_id',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reduce the genes included in the PLS to those included in both\n",
    "# the Allen Human Brain Atlas and GTEx datasets\n",
    "hbigdf = hbigdf[exp.gene_symbol.values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit the PCA to this reduced set of AHBA genes\n",
    "pcamod = PCA(n_components=100, random_state=123).fit(hbigdf)\n",
    "pca_tfm = pandas.DataFrame(pcamod.transform(hbigdf),index = hgdf.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PLSRegression(n_components=3)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and then the AHBA PLS model\n",
    "full_y = hgdf[['mni_nlin_y','mni_nlin_z','abs_mni_nlin_x']]\n",
    "\n",
    "pls_mod = PLSRegression(n_components=3)\n",
    "pls_mod.fit(pca_tfm, full_y)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the components ends up being \"inverted\" comparing to the original PLS fit with all genes. In other words, the component is very similar, but the weights are inverted. This happens a lot and is not a big deal. To prevent confusion, we'll just invert the component loadings. This is as simple as multiplying them by -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# deal with reversed component\n",
    "multiplier = np.array([np.ones(pls_mod.x_rotations_.shape[0]),\n",
    "          np.ones(pls_mod.x_rotations_.shape[0]),\n",
    "          np.ones(pls_mod.x_rotations_.shape[0])]).T\n",
    "multiplier[:,2] = -1\n",
    "\n",
    "pls_mod.x_rotations_ *= multiplier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to successfully replicate this analysis, we need to try to get the GTEx genes into a similar data space as the AHBA data. This means we want the values to be between 0 and 1, so we'll sue a min-max scaler. In addition, since the AHBA subjects are between the ages of 20 and 60, we want to make sure we train the MinMax scaler specifically on donors within this age range, and who don't have other issues that may affect the signal. Accordingly, we'll  go through the following steps for replication:\n",
    "\n",
    "1) 0-1 Transform the GTEx data using \"young controls\"\n",
    "\n",
    "2) Use the PCA model trained on the Allen data to transform the GTEx data into components\n",
    "\n",
    "3) Use the PLS model trained on the Allen data to transform the GTEx components to LVS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dataframe with the important columns\n",
    "scols = exp.columns[:-1]\n",
    "pred = pandas.DataFrame()\n",
    "pred.loc[:,'sample'] = scols\n",
    "pred.loc[:,'Region'] = df.loc[:,'SMTSD'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalize by young controls\n",
    "n_comps=3\n",
    "# min-max scale by young controls\n",
    "mmmod = MinMaxScaler().fit(exp[list(set(cntrl) & set(younger))].T)\n",
    "# apply AHBA PCA model to GTEx data\n",
    "hb_pcatfm = pcamod.transform(mmmod.transform(exp[exp.columns[:-1]].T))\n",
    "# apply AHBA PLS to GTEx components\n",
    "xtfm = pls_mod.transform(hb_pcatfm)\n",
    "# store transformations\n",
    "for c in range(n_comps):\n",
    "    pred.loc[:,'C%s'%(c+1)] = xtfm[:,c]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a mapping between GTEx brain regions and AHBA divisions\n",
    "rkey = dict(zip(pred.Region.unique(),\n",
    "               ['Frontal','Cerebellum','Frontal','Caudate','Accumbens',\n",
    "               'Putamen','Hypothalamus','Hippocampus','AntCing',\n",
    "               'Cerebellum','Nigra','Amygdala']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add to new dataframe\n",
    "for i,row in pred.iterrows():\n",
    "    pred.loc[i,'Div'] = rkey[row['Region']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample</th>\n",
       "      <th>Region</th>\n",
       "      <th>C1</th>\n",
       "      <th>C2</th>\n",
       "      <th>C3</th>\n",
       "      <th>Div</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GTEX-1117F-3226-SM-5N9CT</td>\n",
       "      <td>Brain - Cortex</td>\n",
       "      <td>-0.000865</td>\n",
       "      <td>-0.295222</td>\n",
       "      <td>1.000281</td>\n",
       "      <td>Frontal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GTEX-111FC-3126-SM-5GZZ2</td>\n",
       "      <td>Brain - Cortex</td>\n",
       "      <td>-0.246158</td>\n",
       "      <td>-0.153416</td>\n",
       "      <td>0.329235</td>\n",
       "      <td>Frontal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GTEX-111FC-3326-SM-5GZYV</td>\n",
       "      <td>Brain - Cerebellum</td>\n",
       "      <td>0.433798</td>\n",
       "      <td>-0.037611</td>\n",
       "      <td>0.253396</td>\n",
       "      <td>Cerebellum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GTEX-1128S-2726-SM-5H12C</td>\n",
       "      <td>Brain - Cortex</td>\n",
       "      <td>-0.394155</td>\n",
       "      <td>-0.200781</td>\n",
       "      <td>1.161674</td>\n",
       "      <td>Frontal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GTEX-1128S-2826-SM-5N9DI</td>\n",
       "      <td>Brain - Cerebellum</td>\n",
       "      <td>0.310180</td>\n",
       "      <td>-0.153596</td>\n",
       "      <td>0.764623</td>\n",
       "      <td>Cerebellum</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     sample              Region        C1        C2        C3  \\\n",
       "0  GTEX-1117F-3226-SM-5N9CT      Brain - Cortex -0.000865 -0.295222  1.000281   \n",
       "1  GTEX-111FC-3126-SM-5GZZ2      Brain - Cortex -0.246158 -0.153416  0.329235   \n",
       "2  GTEX-111FC-3326-SM-5GZYV  Brain - Cerebellum  0.433798 -0.037611  0.253396   \n",
       "3  GTEX-1128S-2726-SM-5H12C      Brain - Cortex -0.394155 -0.200781  1.161674   \n",
       "4  GTEX-1128S-2826-SM-5N9DI  Brain - Cerebellum  0.310180 -0.153596  0.764623   \n",
       "\n",
       "          Div  \n",
       "0     Frontal  \n",
       "1     Frontal  \n",
       "2  Cerebellum  \n",
       "3     Frontal  \n",
       "4  Cerebellum  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How are we doing\n",
    "pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C1</th>\n",
       "      <th>C2</th>\n",
       "      <th>C3</th>\n",
       "      <th>GTeX_div</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Div</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accumbens</th>\n",
       "      <td>-0.002636</td>\n",
       "      <td>0.363786</td>\n",
       "      <td>0.563030</td>\n",
       "      <td>Accumbens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amygdala</th>\n",
       "      <td>0.205626</td>\n",
       "      <td>0.065847</td>\n",
       "      <td>0.173163</td>\n",
       "      <td>Amygdala</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AntCing</th>\n",
       "      <td>-0.248048</td>\n",
       "      <td>0.455739</td>\n",
       "      <td>1.088403</td>\n",
       "      <td>AntCing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caudate</th>\n",
       "      <td>-0.188800</td>\n",
       "      <td>0.231241</td>\n",
       "      <td>0.518303</td>\n",
       "      <td>Caudate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cerebellum</th>\n",
       "      <td>0.482063</td>\n",
       "      <td>-0.146904</td>\n",
       "      <td>0.554027</td>\n",
       "      <td>Cerebellum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frontal</th>\n",
       "      <td>-0.317494</td>\n",
       "      <td>-0.059244</td>\n",
       "      <td>0.842851</td>\n",
       "      <td>Frontal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hippocampus</th>\n",
       "      <td>0.145826</td>\n",
       "      <td>-0.005940</td>\n",
       "      <td>0.416169</td>\n",
       "      <td>Hippocampus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hypothalamus</th>\n",
       "      <td>0.241812</td>\n",
       "      <td>0.272148</td>\n",
       "      <td>0.620508</td>\n",
       "      <td>Hypothalamus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nigra</th>\n",
       "      <td>0.356851</td>\n",
       "      <td>0.010105</td>\n",
       "      <td>0.427258</td>\n",
       "      <td>Nigra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Putamen</th>\n",
       "      <td>-0.102038</td>\n",
       "      <td>-0.035461</td>\n",
       "      <td>0.313743</td>\n",
       "      <td>Putamen</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    C1        C2        C3      GTeX_div\n",
       "Div                                                     \n",
       "Accumbens    -0.002636  0.363786  0.563030     Accumbens\n",
       "Amygdala      0.205626  0.065847  0.173163      Amygdala\n",
       "AntCing      -0.248048  0.455739  1.088403       AntCing\n",
       "Caudate      -0.188800  0.231241  0.518303       Caudate\n",
       "Cerebellum    0.482063 -0.146904  0.554027    Cerebellum\n",
       "Frontal      -0.317494 -0.059244  0.842851       Frontal\n",
       "Hippocampus   0.145826 -0.005940  0.416169   Hippocampus\n",
       "Hypothalamus  0.241812  0.272148  0.620508  Hypothalamus\n",
       "Nigra         0.356851  0.010105  0.427258         Nigra\n",
       "Putamen      -0.102038 -0.035461  0.313743       Putamen"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create separate subsets according to \"young\" or \"control\" status\n",
    "datasets = {'full': pred.groupby('Div')[['C1','C2','C3']].mean().sort_index(),\n",
    "            'younger': pred[pred['sample'].isin(younger)\n",
    "                                 ].groupby('Div')[['C1','C2','C3']].mean().sort_index(),\n",
    "            'ctrl': pred[pred['sample'].isin(cntrl)\n",
    "                        ].groupby('Div')[['C1','C2','C3']].mean().sort_index(),\n",
    "            'yctrl': pred[(pred['sample'].isin(cntrl)) & (pred['sample'].isin(younger))\n",
    "                        ].groupby('Div')[['C1','C2','C3']].mean().sort_index()\n",
    "           }\n",
    "for nm,gtC in datasets.items():\n",
    "    gtC.loc[:,'GTeX_div'] = gtC.index\n",
    "    \n",
    "# show an example\n",
    "datasets['full']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== full ==========\n",
      "r = 0.949, p = 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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LKdUSYyXiZhhB60ugB9AYOKSUmqK1fr/QaimEKFbJyclcvXrVOiqvJLtx4wZvv/02a9euxWKxAPDggw8ybdo0/Pz8iuy6np6+ePn2w878LSB/rxe3fAcvpdQUYDZGCpJg4L9a6x+UUtX5d5Thu6l5ucbktNijEKLkSUxM5OrVqyQnJ9u6KjmyWCxs2LCBxYsXExkZCUDdunWZOXMmXbp0KbLrurq64uPjg5OTEzp5KIT/gASv4pev4JX6nKsbRjfhBmBc6iAOtNZBQA+l1ESM+V0PAceVUuO01hsLtdZCiCIRGxtLcHCwtQVTUh07dozZs2dz/LiRFs/NzY0JEyYwfPjw/CxVki9OTk54e3tnGKWo7Guj3f4DsZ8BCUVyXZG1/La87sJIZ/+M1vrTrAporZcppX4CVmMkjvy6ANcRQhSzGzducP369RI9+Tg8PJzFixezYcMG67Z+/frxwgsv4Oubay7XAnF0dMTLywt3d/esC7iNAnRqAEtGWmHFI79BZS8wUmt9KadCWutTSqmOwKvAiwWsmxCimISFhZXodbiSk5NZu3Ytb7/9tnXkY5MmTZg5cyZ33pndYhG3x97eHi8vLzw8PHIcjKGUggqj0U7dIO5LSNhpDKEXRSq/GTZU5owaeTimk9a6zOQ3lAwboiwpDetwHThwgDlz5nD27FkAKlasyOTJkxk8eDB2doU/n8rOzo7KlSvj6elZoBGE2hILKQEoh8Yy/LAI5XeScr77E8pS4BKiLCnp63CFhISwcOFCtm/fDhgtnMcee4zJkyfneY2t/DCZTHh6elK5cuV8JefNTJncwNS4EGsmspJj8FJKeWCk6b9l+eq8SO06dNRa/1qQ44UQRcNisXD16tUSuQ6X2Wxm1apVLF++3DrHrHXr1syaNYvmzZsX+vWUUlSsWBEvL68iacmJopFbyysKYzj8LZMllFIfAZ5a6wE5HP8N4JOH6wghiklKSgqBgYElcg7XL7/8wrx587h06RIA3t7eTJkyhb59+95Wayg7Hh4eeHl54eCQ1bqJoiTLS1DJrt/2YfKwVHMOxwshipnZbObq1aslbh2ugIAA5s2bx65dxur1aWtsTZgwIftRfrehQoUKeHt7F9mwelH0pEUkRDmRkJDA1atXSUkpOUO54+PjWbFiBStXrsRsNhLcduzYkZkzZ9KgQYNCv56rqyve3t5Fky5KFCsJXkKUA7GxsQQFBZWYOVxaa3788Udef/11goKCAKhatSpTp069rTW2suPs7Iy3tzeurq6Fel5hOxK8hCiFtLZwNeA8Rw/9wvWQy6SkJGNnZ49PFX9a3XE3NWo2RCnjGVFJW0DywoULzJ07l99//x0w1tgaM2YM48aNK/TgkusEY1FqSfASopSJigzluy0fkZgQS1LSv2tJJScnEXz1H8KuB+Lo5ELPvmOwaPsSs4BkTEwM7777Lp999pk1b+I999zDjBkzqFWrVqFeK22CccWKFQv1vKLkkOAlRCkSFXmdTeveIclsJm0tqcySksyYzYl89tECOt01AM9KPsVbyUy01mzZsoU33niD0NBQAGrWrMmMGTO49957C/VatzvBWJQeEryEKCUslhS2b/owx8BllNPcuBmD2ZzELz99Te9Hx2FnZ5v/6qdOnWL27NkcOnQIABcXF8aNG8fo0aNxcnIqtOuYTCYqVapEpUqVimRIvSh58vIv2kUpNTKr7QBKqRFkPxzepaAVE0JkdOXSaZLMieQcuCxERsVYu+WSU5K4GnCemrWLN+NDVFQUS5cu5auvvrJmqO/ZsydTp06lWrVqhXYdmWBcfuUleHlgrNOVnVU57EtbYVkIcZuOHtpNUlL2E4uTU1KIiowmJd1yJinJSZw++UexBa+UlBTWrVvHkiVLiIqKAqB+/frMnDmTTp06Feq13N3d8fb2lgnG5dTtTFIWQhQTrS2EXgvIdn9SUjKRUdFZDoWPigzFYknBZCralsnhw4eZM2cOJ0+eBIyJwBMnTmTYsGGFGmDc3Nzw9vYu1G5HUfrkGLy01tJ5LEQJkJyURHYdGYnmJG7ciMl2DpdSiuQkM45ORdOLHxoayuLFi/nmm2+s2x599FGee+45fHwKb7CIi4sL3t7euLjI0wghAzaEKBXs7B3Q+tbVjRMSErlxM+es8Fpr7Oz/bfkEBgbSvXt31q9fT4sWLQpcp6SkJNasWcOyZcuIiTFydzdr1oxZs2bRpk2bAp83s6xWMBYit6zyHwNRWuvniqk+QogsmEwmKnr6cCMq1LotNjaemNiMWeE3bv6Fw8fOcd/dbbn3LiOAuFXw4K+/DjJy5Ej27dtHtWrV2Lt3L5UqVSpwffbv38+cOXM4f/48AJ6enjz77LMMGjSo0AZOODg44O3tLROMRZZy6xYcBQwphnoIIXLR6o67sXcwEsnejI69JXClsbe347d9x4iNjcfOzoFGTdpn2G9nZ4ePjw/29vnveAkODmby5Mn85z//4fz585hMJh5//HF27NjBkCFDCiVw2dvb4+vrS+3atSVwiWzJMy0hSol6DVoBEHUjmvj47Ecd1qldDU/PCuzecxgU1KrTNMP+wMBAGjVqxPHjx63bdu/eTY8ePWjRogXDhg1j+/btNGrUiMDAQAC+/vprmjdvzoMPPsh3330HQJMmTZg/fz5XrlzhoYce4o477uDxxx/n8OHDBbo/k8mEl5cXtWvXlknGIlcSvIQoJZTJjuZtHiQpOefZJ0opHryvPX8ePE3teu1wyGXZj6CgICZMmMA999zD5s2bGTFiBG+88YZ1/+7du1myZAlJSUmYzWY8PT2ZMmUKq1evxtfXl759+7J27VrWrVtHkyZNePLJJ4mMjMz7fSmFp6cnderUwcvLSyYZizyRARtClAJp63BV9KxCt3sf5bfdm9DaQkpK8i1lFYomTerSomVzPl+7nrbtuuR47i+++AJ/f3+mT58OQN26dbl06RJLlixhxowZ/PHHH9ayffv25eWXX6ZChQoAt8zdmjVrFj/88AO//vor/fr1y/W+ZF0tUVASvIQo4WJjYwkODrZmqqhStRa9Hx3HxQsnOPP3n5gT41EmE9piwWRnj7uHG336j6NB0/sYPHgwY8aMyfH8//zzT4ZRh3FxcZw7dw7AGrjq16/PxYsXmT9/fobnWuHh4bz11lv88ccfhIWFYbFYSEhIIDg4OMdruri44OPjI+tqiQLLS/CqoJT6v9u5iNZ69u0cL0R5FRUVxfXr12/Z7ujoTKMmd9KwcVsSE+JISjJj7+DAn0evERUVhaOTCy1btuTBBx/kjTfeYPz48bleS2vNjh07mD9/PiEhIQD4+Pgwa9YsYmJimDt37i0DMqZOnUp4eDjTp0/Hz88PR0dHRo0ale1KzY6Ojnh7e1tbbkIUVF6Clxvw8m1eR4KXEPl0/fp1a4ql7CilcHZxw9nFzfo5veeee45evXqxZ8+ebM9Rt25dvvvuO0aNGsX+/fsBY0RiSkoKq1aton79+mzcuDHLYw8ePMjMmTO55557AAgLC7Nmjk8vbYkSDw8PGYghCkVegpcFCCzqigghDBaLheDgYGJjc558nBe1atXiscce47PPPstyf3R0NBEREQQEBBAQYKSfat68OaGhoVy7di3XbBZ16tRhy5YttGrViri4ON54440MqaAk27soKnkJXqFa6zpFXhMhBImJiQQFBWXb7VYQTz/9dIbUTWl2797Nl19+SVhYGIB13peTkxOTJk1ixowZueYPnDdvHrNmzeLRRx/F19eXCRMmWEcaenp6SrZ3UWRUdvnQAJRSFiBEa129+KpUsimlagMXL168SO3atW1cG1GWREdHc+3aNevAjKJy4sQJ5syZw5EjRwBj8MT48eMZNWqUddTfp59+yttvv81ff/2V724+d3d3vLy8ZAShJDUvUjLaUIjbcOPGDf48sJ+/T54gISEBe3t7qvv50blLN/z9a+b5F394eDjh4eFFWtfIyEiWLFnC119/bU3i26tXL1588UV27tzJ6dOnqVSpEkePHuW9996jf//++Qpcrq6ueHt7ywhCUSwkeAlRAGazmW82ruPsmdNorUlJSbHuu3nzBufPncPdw50hjw+nSpWq2Z5Ha01ISAjR0dGFWr8dO3YwadIkzpw5Q0pKCl999RVLly7lxo0bADRs2JCZM2fSoUMHAC5fvszrr79OSkoKNWrUYMiQITz99NN5upaTkxM+Pj64uroW6j0IkRMJXkLkw6FDh2jXrh21a9di1Mhh1hWL09Nak5RkJiI8nJUrljNq9Fj8/GrcUi45OZmgoCASEhKKrL4HDx5kzpw5nDp1CjC69CZNmsTjjz+eIbfhjBkzOHXqFA0aNOD//i9vM2McHBysIwiFKG4y/EeIfFi5ciX339+dq1eDCE6dC5UTs9nMZ6s+Jj4+YxLdhIQErly5UmSBK62FNXToUGvgGjBgAN9//z0jRowoUFLeNCaTCW9vb2rXri2BS9hMbsGrDtA+lzJClAvx8fGsWbOGBvXq0KRxIw4fOWbdFxUVxey58zlx8m9WfbaGefMXseLDj7l27TpBQUG0b98ONzc3unbtyvHjxwkICODSpUs0btw4Q4JcMJLgdujQAbPZDOSeNBdg06ZN3HvvvbRq1YqHH36YuXPnWvc1b96ct99+m8jISPr27Uvr1q3p378/P//8c473u3nzZgYMGECbNm3o1KkTkyZNIj4+njp16lC5cmWZryVsKsfgpbW+DDgqpfoqpbpn3q+UWq+U2pXD151FVnMhitn69evx8vLC19eHli2acezYiQzPugB++WUvXTp35Mkn/ouTszMbv9nC1u3f0aVzR/bv309MTAyTJk1Ca02NGjXo0qULGzZsyHCODRs20K9fPxwdHXNNmgtw9OhRpk2bRseOHfHx8eHChQvWwDd37lzWrVtHrVq1uOuuu/j444/ZvHkzDz74IBMnTuTChQvZ3m9SUhLPPPMMW7ZsYfXq1cTHxzNx4kQZ+i5KhLz0HXwB3AlMAnZm2tcF8CXrIaEaWAp0vY36CWETgYGBnD17jri4OJycnPD3r8FHH31Es2ZNSElJoVatmjg42HPm7DmaNmlsPa5jh3Y0qF8PgE4d2/PlV+sZdE9/qlerhgKGDBnCnDlzrOUHDRrErFmzmD59Ok5OTly4cIEjR45Yy+SUNDfN+++/T+XKla1ZMEwmE3Xq1OHChQsMGjQIgMaNG9O48b/1fOqpp/j555/5/vvvs00dNXDgwAw5COvWrUuTJk0IDAykRo1bn+EJUZxyW0m5I9AOOKu1fieHoq9lsW0E0Ekp1U5r/edt1FGIYnP06DF++OEHIiMjSUlJsc65io2N5ddf9/D8sxMBIw1Ti+bNOHzkWIbg5VvF1/o+bdl6by8voqOjCQ8Pw8vLi7i4OOLj43FxcaF79+7Mnj2bH374gT59+rB+/XpatmxJw4YNgVuT5gK0amWs62U2m3n33Xf5+eefrUPf77zzTmbNmsXx48eZOXOm9Zi4uDjeeecddu/eTWhoKMnJySQmJtKoUaMsvw8ODg5cvXqVN954gyNHjhAREWG9xpUrVyR4CZvLreU1EKMFtTynQlrrWZm3KaWuAu8BgwEJXqJE01qzdetW9u//I8vsFgcP/oXWFhYvefuWfTdu3LS+t0uXAimtOyI2Ng5nZ2fs7O2tz4nSgqKDgwP9+vVjw4YN9OzZky1btvDMM8/kWleA0aNHW7O3u7q6Mnv2bHr37o1S6pbnaAsWLGDPnj1MnToVZ2dnnnzySRo1anTLvSql8PX1xd7envbt23P//fdb1+0KCwujW7du1i7JV155hfXr13PixIkc6ytEUcgteHVMfd1RgHNvwwhenXIrKISt7dq1K9vAZbFYOHnyJN263UXjRvUxKUXaWIVNm7dy5OgxWrVsbi2/ecs24uLiadeuLYB1Hlhg4FXGjh17y/kHDRpEr169WLt2LbGxsfTq1cu6r27duuzc+W9v/cWLF3n11VcBCA4OxsHBAX9/f3x8fOjTp4+13NGjRwGYNm0akZGRBAUF8cgjj9CjRw/rQI+QkBCaNjVWWVZKYW9vj4eHB56enhw8eJCwsDDmzZtHnTpGdrjskvMKYQu5Ba8GQIrW+kw2+7MdbqS1DlRKxQP1Clo5IYpDXFwcP/20M8s5WwD//HOB+Ph4WrZsiZubC04OJmsLqlmzphw8eJiWLf4NXsY8r2SSzEYgVArqN2iQIWFtenXr1qVt27YsXLiQXr16ZVguZMiQIXzyySfMnTuXxMRENmzYYB0kcueddzJ79myio6MZMmQIH3zwAT169ODAgQP8+OOPGa5Ru3ZtfvzxR7p3727N5JHWgqpYsSJeXl44ODhY76tmzZo4OTnxzjvv8PTTT3Pq1Clmzbqlg0UIm8ltqLwncCOH/VOAnBYKik09hxAl1p9//pnjsO/jx4/j7++Pi4sLFovRj56maZPGRN24wT8XLwHGCD2zOQmdrpTJZEebNhkH3vbt25ePPvrI+nngwIEkJSWxadMmGjVqxPHjx2nUqBG7du2iSpUqrF69mq+//pqUlBQ8PT0BWLp0KcnJySxduhQ7OzuWLFlCz549+fbbb5k40Xg2980337B7925++OEHzp49y5AhQ6yDP+rXr8++ffuoU6cOLVu2JCIiwlqfypUr06lTJ95++23q1avHgAEDblk1Oavv44MPPoi3tzceHh507dqVffv2ZSijlGL58uX069cPV1dXGjZsyM8//0xgYCA9evTAzc2N1q1bc+jQIesxq1atumX9r927d6OUsiYVvnHjBiNGjMDX19c6uGTp0qU51leUbrkFr3gg21XjtNartdYf5HC8B1B06QOEKAS//74vxyzu/fs/yqBBj1k/m80W63OnSpU8+b+Z02h7R2teeO6ZDL9kfX19mDRxPHfdfTe+VarwzjvGmKedO3fy2GOPWbvhjh8/ztSpU3F3d6dx48bs3buXJk2aAPD6669bu/nShqj36dOHChUq4OrqypgxY3B1dWXjxo189tln1KxZE3d3d4YPH86hQ4fo2bMnnTt3Zu/evezdu5eDBw+yadOm1Psws2LFCo4ePUq7du24evUq8+fPB4yu0m7duvH7779z8eJFPvvsM9avX8/KlSuta3dlFh0dzYgRI9izZw8HDhygdevWPPzww7fkbJw7dy5Dhgzh6NGj3HnnnQwZMoQxY8Ywfvx4Dh8+TPXq1Rk1alRefnRWM2fO5Pjx42zbto0zZ87w8ccf4+fnl69ziNIlt27DUKCuUqqm1vpKfk6slKoFOAL5Ok6I4pbfvIIWDYlJFpwc7LC3tyMpKYmYmBiSkv7tdrxyJYAVKz/GZLKzBp302TQeffRR3n77bfbt22ddQsRisTBo0CCcnJxYsGABgLWLsH379jzzzDO8+uqrfPnllwwZMoTt27cTHx/PwoULrUFz9uzZjBw5ksuXL1OrVi2cnZ2Jj4/Hx8cHBwcHvL29rcHk2WeftT4nmzdvHp999hlHjhyha9euODg4MHv2v2vI1q5dm0OHDvHFF18wZsyYLL8v9913X4bPy5YtY8OGDXz33XcMHz7cun3kyJE8/vjjgJGW6osvvqBHjx7069cPgBdffJF7772XsLAwvL298/QzuXz5MnfccQft2xs5FWrVqpWn40TplVvL64/U194FOHff1NcDBThWiBLNYgELdjRv0ZKExETAhKOjo7EMiFLUq1eXVatWsXXrVjZt2sSmTZto166d9XgfHx/uueceXnnlFV544QXA6HKMjIykXbt21sUj09IvnTlzhhEjRnDu3DksFgu9evXiwoULNGrUiB9++IE2bdqwa9cu63Opp59+2rq4ZNoIwu+//542bdpYh+FfvHjRWp/q1Y1Vj+bMmUO1atVwdnamWrVq1K1bFx8fH9zc3HjjjTf45ZdfcHFxoVmzZhw+fDjD96RLly40bdqUSpUqoZTCZDJx7do1/vnnH55++mlrd2da6iow1i8DCA0NpWvXrjg7OzN69GjAWEka4PTp08TGxlq7CMEYbAJYl3UZO3Ysq1evxsHBAXt7e3x9fZk2bVq+f66i9MgteG3DGJQxTSnlmdeTKqUqAS9iPB7YWuDaCVEM0uZj5VdMTCwNGjZh9JhxDBj4GL369OOR/gNo2LARNWr4c+ed7ahVq5b1K/OqxIMGDSIsLMwaqNIGSKS9ByMfIRjPoD788EMee+wxXF1dmThxYoYBJmazmXfeeYc5c+aglEJrzYQJE3BwcMDFxYWff/6ZiRMnMnnyZHbsMAYPL1q0iK1bM/73PHnyJJ988gkLFy4kLCyM7t278/333/P999/TuXNnfHx8OHnyJJMmTWLbtm3ExMRYjz116hRnzpyhZ8+ebN++nWnTpqG1ZvXq1TRs2JC//voLgA8//NA6xD/tWePKlSt55plnOHLkCJ07dwa4pUx6mbt5z549i6+vLy+++CKPPPII0dHR7N27N28/SFEq5Ra8vgYuAH7ANqVU9ms7pFJKVcMIen7AeWDd7VZSiKLUsWNHHBzynqhWa01MTAxeXpVxcnLCzs4O3ypV8PevSdVq1a3dhFprzGYziYkJaP3vApO9evWiTZs2TJ48mdjYWGuXWvruy2XLlgH/to7Gjx9Pt27dCAwMpEuXLiQkJBAdHc3Zs2dJSEggOTmZl156yXrdhQsXcvbsWWJiYrBYLCxatIgRI0YwYcIE6tatC8BDDz1k7Z786aefAHjhhRd46KGHOHfuHJ07d+bDDz/kjjvuoGvXrlSqVAlHR0fq1q3Lk08+SZMmTTK0om7cuEGdOnVYu3YtDz/8MJMmTQKMjB+TJk2ifv361vr99ttvGb6n/fr147HHHqNx48bMmDEDgK+++grA2mJL//35+++/Mxx/+fJlGjduzNy5c1m/fj2rVq3i999/t7bsRNmT4/9YrbVFKTUC+BljvtYppdQnwLfAMSAytWgloCXQCxgFVMQYqDFCp/9fK0QJ1KFDe+sv79wkJycTExODnZ2d9flKZklJZiIjw1m54l2Sk5MwOi80168bXV0ffPABPj4+bN++nRUrVlhbEXXq1OGZZ57h2WefpXLlyoAxgg+MbsYPPviAffv28fXXXxMaGoq7uzvOzs6sW7cOk8mE2Wxm9uzZPPLII/Tv35/q1aujlOLEiRNER0czaNCgDC2W1q1b89577wFYuwDTMmc0bNiQVatW8d1331G/fn3Wrl3LDz/8gMViwcvLi8TERGuWkDSurq7Ex8fz999/Exsby4svvmjtskyvQoUK1i7BNGnzzcAIdmBkFwGsg1fmzJnDjBkzOHr0KKtXr85wfHx8PAcOHKBOnTp07tyZgIAA6tSpY23BirIn1yVRtNb7gf5AFEZQmgR8DwRjBKiE1PffA8+klokE+mut5XmXKPEqVKhAt25ds52HlSattZP2CznzoACtNQf+2Mc/F85z88ZNzOZELBYLFouRZiouNg6AHd9tY/Lkybz77rsZgskHH3xAy5YtM5zz0UcfBeDJJ5/kiy++4PXXX7eWsbe356OPPiIhIQGLxcKkSZPo1q0bq1atsh7fuXNnawvp2WefvaXFk90UgXHjxvHYY48xdOhQ2rVrx7Zt2zCZTHh4eLBz506OHDlC48aNraMuARo1akRycjJt27ZlyJAhjB49GkdHxywT+aZlGMmLtJbX7t27adGiBStWrGDy5MkZytSuXRs/Pz+Cg4PZsGEDf/zxBz4+Pvm6jihd8rSel9Z6B9AKeB9j+LzK5iseI5VUK63190VRYSGKQs+ePWnVqmWWASytmzAuLg47OxNeXl4MGjTQ2kJIs+/3Xzl8+E/u7343fXr3uOU89vb2uLm58sWX6zh79ixKKbp37249T1a/5CtWrAgYowF3795N//79iYuL49y5c9SrV4+mTZvy/PPPA/Dtt9+yatUqKlasyJUrVwgKCqJdu3b88MMPdO7cmf/+97/cc8891K5dG601gYGB1hZPmzZtUErRrFkzABwdHfnoo4+IjIwkKiqKqlWrWp/RtW7dmnr16uHg4JBhEEqFChUYNGgQ8fHxXLhwgREjRlC/fv0MQ+u11jg7O1s/p7Uw04/EbNSoEQ0bNrSu8uzj4wMYy77Ex8fz/fffW/9wSAtsL730EqdPnyYxMZHExET27NnDH3/8wfnz52/5noqyIc8d/VrrQGC8UmoSxhpfTYDKqbsjgFPAAa119hNmhCihlFI89thj1KpVi507dxIXF4/ZbCYlJYWYmBhMJmM04R13tKFz5863BLkrVy5x/PhRkrOYL5aSksKRoye48M9Fa0uliq8Pjw54hGPH/qZ9+/bs378/x/otX76cypUr4+vry7vvvouDgwMjRozA398fV1dX7O3tmTx5Mm+99RYuLi48++yzNGvWjPvvvx+AKVOmMGjQINq2bcuDDz7Ijh07WLNmjXWuWffu3enQoQMDBgxgyZIlNGzYkPPnzxMbG8sjjzxCw4YN+eqrr9i7dy/e3t4sW7aMixcv0qZNm8L49rN8+XIaNmxIixYteO+997h8+TJPPfUUYEym9vf355VXXmH+/PlcunQpw3plAG+++SbVqlWjdevWODg4sHbtWjw8PCSBcBmW7+VUU4PTb6lfQpQZSik6duxIhw4d+Oeffzh48JA1f6CfX3UaNGiQ7QrEB//8I8vAdflKIL/++huRUcbABpPJxH33dAPg/fdX0qhRIyZPnpxr8Hr++eeZP38+Fy9epFGjRmzdujVDt6WTkxMvvfQSI0eO5MqVK3Ts2JGNGzdauwUfeeQRli1bxqJFi5g8eTK1atXivffes87zMplMfPfdd0yZMoXhw4cTHR1N3bp1eeWVVwBjEvDFixfp2bMnLi4ujBo1imHDht0ycKKg5s+fz5tvvsmhQ4eoVasW33zzjTXwODg48OWXXzJ+/HhatWpF69atmTdvHr17/zuDx93dnTfeeINz586hlKJNmzZ89913uLq6Fkr9RMmj0vdZi9wppWoDFy9evEjt2rVtXBtRFLTWXL9+PcNIupzcuBHFl2s/zTB0/cbNm+zZu59//rkEGIGxVctmdGjf1jqIwN7BgY4du9Cqddtsz/3HH38wcuRI9u3bh7+/P97e3rcMQli1ahUTJkzIMGy9tLh06RJ16tThzz//5M47y9zatbLUdBHKd8tLiLIsMTGR4OBga9LavAgJCUalPrdKTk7mr4NHOHjoqDU7hl/1atx9Vxe8vStnOC45KYkrly/lGLzS+Pn5SbojIdKR4CVEqhs3bnD9+nXy2xuRZDZjSbFw4Z9L7Nmzj5up85Hc3Nzo1rUjDerXzXZUX05B0tHR0ZoeKfMEZyHKOwleotxLSUkhJCSE2NjYAh1/7XooGzdt5fJlIx2TyWSiTeuWtLuzDY6OOQ+/d8xiHpK9vT1eXl5UrFjROjIwJ6NGjcp3ItuSIi/3J0RWJHiJci02NpaQkBBrF19+xMTEsHz5clatWmV93lWrpj93detEpUqeuR5v7+BIrXTPTU0mE5UrV7bmBhRCZE+ClyiXtNaEhYVZM7rn99ht27axYMECQkNDAahcuRKdO7WjTu1aeQ882kLjxs1QSuHp6UnlypWznOslhLiVBC9R7iQlJREcHJxhYmxenT59mjlz5liTzDo5OfHkk0/y0EMPsvOnHVkOl8+Kvb09DRs1xcvLG29v71yzewghMpLgJcqVqKgoQkND8/2c5caNG7z99tusXbvWmnKoR48eTJ061ToKMCz0OkePHso1gNnZ2eHj48PQYSNxd3cv2I0IUc5J8BLlQlJSEteuXSMuLi5fx1ksFjZs2MDixYutXYx169Zl1qxZ1qU70nTo2AUHBwf+/HM/CjLM+wJjrpejoyP1GzRk1H/H4ugoSWOFKCgJXqLMi4qKIiwsLN9JWo8dO8bs2bM5fvw4YAx9nzBhAsOHDzcWncxEKUXbOzvQuEkzTp44xvHjR0iIT0ApMNnZ0apla+5/4CH8avgXyn0JUZ7lO8OGUmpX6lutte5+u+VKG8mwUXokJycTEhKS79ZWeHg4ixcvZsOGDdZtffv2ZcqUKbcs75GblJQUXF1dqFatepYBT5RpMmS0CBWk5XVP6mtuUS+v5YqFUmoo8BTGumN2wGngE2C5rDlW9sTExHDt2rV8DYFPTk5m7dq1vP3229aFD5s0acKsWbNo2zb3LBiZOTk54ePjI/n1hCgCBQlev5K3gJTXckVOKfUuMB5j7bGdQBLQHXgH6K6UGigBrGxISUkhNDSUmzdv5uu4AwcOMGfOHM6ePQsYS5FMnjyZwYMH53v4evpJxkKIolGQrPL3FGa5oqaUGoARuEKAu7TW51K3V8FYIbo/MBF4y2aVFIXi5s2bhIaG5qu1FRISwsKFC9m+fTvw79IokydPtq41lVcyyViI4lMeBmxMT32dmha4ALTW15RSTwG7gWlKqWXS+iqdzGYz169fz9ezLbPZzKpVq1i+fLn1uNatWzNr1iyaN2+er+srpahYsSJeXl4yyViIYlKmg5dSqgbQFjAD6zLv11r/opS6CvgBHYHfi7eG4nZorYmMjCQ8PDxf87Z+/fVXXnvtNS5dugSAt7c3L7zwAv369btldeTcuLu7yyRjIWygTAcvIG2Z15Na6/hsyvyJEbzaIMGr1IiLi+P69ev5WrokICCAefPmsWuXMRDWzs6OESNGMGHChHxPFnZ2dsbHx0eyvQthI0UWvJRS9kBnAK31r0V1nVzUSX29nEOZK5nKWimlPAHPTJtlXXEbslgsXL9+PV8DMuLj41mxYgUrV660BrsOHTowa9YsGjRokK/r29vb4+3tjYeHR76OE0IUrqJseVXEeJ5kKeLr5KRC6mtOa12kLT+b1Z/ek4GXC7NCouBiYmK4fv36LZkrsqO15ocffmD+/PkEBQUBULVqVaZNm8ZDDz2Ur0EVMhhDiJKlOIJKaf6fvhRYlWlbDWBPsdekHDObzYSGhuZrva0LFy4wd+5cfv/d6Al2cHBgzJgxjBs3Ll/zrmQwhhAlU1l/5pXWqnLLoUxa6yw68w6tdRQQlX6b/NVdfCwWCxEREURGRuZ5QEZMTAzLli1j9erV1iHzd999Ny+99BK1atXK1/VlMIYQJVeOwUsp9eBtnLskPBS4lPqa02+ttERzl3IoI4pZdHQ0oaGhee4itFgsrPr0U9595x1iYoy/WSpUcKdt2w489HBPnJ3zPrDCxcUFHx8fnJ2dC1R3IUTRy63ltYMSkiWjgA6nvjZTSrlkM+KwXaaywobi4uIICwvL11pbR48e5bnnnicwMAAAOzt7mjRtQcPGzbCzs+PI0ROcOP43zVs0pV+fntl2/zk4OODj40OFChWy3C+EKDny2m1YKvvKtNYBSqlDwB3AIOCz9PuVUndjPMMKAfYVfw1FmsTERMLCwvL1XCsyMpI331zC119/Zd1Ww78WrVq3w9Xt355ii8WC2WLh+PG/SUxMZMhjj2bo/rWzs6Ny5cp4enpKt7AQpURuwSsYqAo8qrXelJ8TK6W8gesFrFdheh1jgvICpdTvWuvzAEopX+C91DLzJbuGbZjNZsLDw62JcPMiJSWFdevWsWTJEqKiogDw8KhIm7Yd8K1SLdvjkpKTOXfuHw4eOsqdbVujlMLT0xMvL698T04WQthWbsHrT6APcCewKZ/nLhHdjVrr9Uqp5RgZ5Y8rpX7i38S8Hhj39Y7talg+paSkEBERQVRUVL6yYxw+fJg5c+Zw8uRJwOjqa9q8FfUbNMlTADInJfHrnt+5q1tnfH19ZZkSIUqpvASvvkD7YqhLkdFaj1dK7QWeBu7m3yVRPkaWRClWWmuioqKIiIjIVwLd0NBQFi9ezDfffGPddvfd9+DlWx07u7yPBrS3swNlItGcLIFLiFIsL8ELjPyA+ZWCkb2iRAQGrfVaYK2t61Feaa25ceMGEREReR5BCJCUlMSaNWtYtmyZdRRhs2bNmDVrFpevBPHXoaN5Oo+dyYSziwsODo4oZeKffy5Rr27tgtyKEKIEyC147ebf/ID5kjpHqnZBjhVlS0xMDKGhoSQlJeXruP379zNnzhzOnz8PgKenJ88++yyDBg3Czs6OM+cu5noOk1I4OTvj6OhkHYyRkpKSr9GMQoiSJ8fgpbU2A3n701aITOLj4wkPD8/XUiUAQUFBLFiwgB07dgDGxPDBgwczefJkKlWqZC3n4pL9PCwFODk54+TsfMsIQnt7e5xzOLYkeOWVV1i/fj0nTpywdVWEKJHKeoYNYQMFDVqJiYl8/PHHfPDBB8THG1Py7rjjDmbNmkXTpk1vKd+gfl2OHfubxEyZ5R0dHXF2dskwgCMs9DqbNn6Br29VBg4eTuOG+UvIK4QoWXLLsPE7Rm6/r7TWN4qlRqLUKmjQAti9ezevvfYaV64YSf59fHysa2xlN/eqUcMG2Nn9G6Ds7OxwcXHF3v7Wf9anT52gSbOWnDt7CnQK/v5++a6jEKLkyG1scUdgORCslPpSKfWwUkomxIgM4uPjCQwMJCAgIN+B6/Lly4wbN45x48Zx5coV7O3tGT16NDt27OCRRx7JcdKwnZ0dXTp3xNHBHkdHRypUcM8ycCUnJ3Ph/GmaNGlB/fqNiIoMzbB///793Hfffbi5uVGxYkXuu+8+axZ6rTWLFy+mQYMGODk5UaNGDaZPNxbnvnTpEkop/vrrrwznU0qxfv36DGW+/PJL7r77blxcXGjTpg3Hjh3jxIkTdO7cGTc3N7p27crFi7c+w1u5ciU1a9bExcWFRx55hLCwsAz7P/nkE5o2bYqzszMNGzZkyZIlWCz/jpFSSrFixQoGDRqEm5sbdevW5fPPP89wjtmzZ1OrVi2cnJyoWrUqI0eOzPZ7LkRJkVsg2oCxCrEzRoaKrcBVpdQbSqkWRV05UbLdTtCKi4tjyZIl9OrVi927dwPQuXNntmzZwtSpU/Ocoumubp1o0aJFjtkxLv5zjgruHlStVp3+jw7gh+93WAePHD16lHvvvZf69evz22+/sX//fgYPHmwdETljxgzmzJnD9OnTOXnyJOvWrcPf3z/L6+Tk5ZdfZurUqRw+fBhPT08ef/xxJk6cyGuvvcaBAwdISEjgmWeeyXDMpUuX+Pzzz9m8eTM//fQT586dY/To0db9H374ITNmzGD27NmcOnWKxYsXs2DBAt57770M55k9ezb9+vXj6NGjDB48mNGjR1tbuBs2bGDRokW89957nDt3jm3bttG+fameGSPKC611jl8Y63I9CezFGPZuwRgGn4KRD3AS4JPbecrKF8YISn3x4kVdXsXFxemAgAB95syZfH+dPn1aL126VFetWlVjTGTX1atX12+//bY+ffp0vs515coVHR8fr5OSk/XHq9bo8RNf0P97+nk99n+TM3xVr15Dd+p8t/5m83adkpKia9WqpdetW6e11nro0KG6Y8eOWd5ndHS0dnJy0suXL89y/8WLFzWg//zzzwzbAev508q8//771v1bt27VgN6wYYN12yeffKLd3Nysn19++WVtMpn05cuXrdv27NmjAX327Fmttdb+/v76s88+y3DtJUuW6CZNmmSoy7Rp06yfk5KStIuLi169erXWWuvFixfrhg0barPZnOU9itti899XZfkr1y5ArfUNrfUKrXVXoD4wB7iIMaCrFfAmEKiU2qyUelQpJetHlFEJCQkFbmkBnDt3jv/85z9MnjyZkJAQHB0dGT9+PN9++y09evTIc15BR0dHqlevjr+/P87Oztjb2fHf/wzl5VlTueeuLri4uKCUQilFcrKZkJAgPv7oAx7p+zAmk4lhw4bx0UcfAUbGjvvuuy/L6/z9t5ELsXv37vm+18xatmxpfV+lShUAWrRokWFbbGxshu+rn58fNWvWtH7u0KEDJpOJU6dOERoaSkBAAOPGjaNChQrWr2nTpnHhwoVsr21vb4+Pjw/XrxuZ2wYNGkRCQgJ16tRhzJgxrFu3jsTExNu+XyGKWr5GG2qt/8FYWfhlpVRX4D/AQIzWWR+gNxCplPoS+ExrfaCQ6ytsICkpibCwsHzlH0wvOjqad955J8MaW/feey8vvfRSvrrg7Ozs8PLyomLFilkGOl8fbwY/1p/Bj/XHYrGglGL69OlYLBaaN29mLaeNFjQBAQEFup80aaMZ084HZDuXLf2aYGl1z2pb+udVOUkr9/7779O5c+ccy2Zej0wpZT3e39+fM2fOsHPnTn766Seef/55Xn31Vf744w/c3HJaBk8I2yrwUHmt9V5gr1JqAtAPI5A9AFTGyCP4lFLqLPCp1np+YVRWFK+C5h9MY7FY2Lx5M4sWLbIONKhduzYzZszg7rvvzte53N3dqVKlSp4T6JpMJpKTk/n00095/fXX6d27d4b9I0aM4JNPPqFNmzbs2rUry3M0adIEJycndu7cSYMGtw6t9/HxASA4ONi67ciRI3m8o9xdvXqVgIAAa4A/cOAAFouFJk2aUKVKFapXr86FCxdue4CFs7MzvXr1olevXkybNo2qVavy22+/8eCDt7OcnxBF67bneWmtE4Gvga9TM7UPBUYCrYFGwGuABK9SROuC5R9Mk5Bg5uuNm1m54n2uBQcC4OjoxJNPjmXcuHH5yimolMLHxwdPT89812P79u2EhYUxduxYvLy8MuwbMmQI77//Phs3bqRTp048+eSTPP300zg7O7Nnzx4efPBBatasyaRJk5g+fTpOTk7cddddhIeHc/DgQZ566ilcXFzo2LEjCxYsoF69ety4ccM6ErEwuLi48J///Ic333yT+Ph4/ve//9GrVy9rIH311VeZOHEinp6ePPzwwyQlJXHo0CGuXr2a53qsWrWK5ORkOnToQIUKFfjqq69wcHDIMlgLUZIU9rD3KCAIYymV/P/WEzZ38+ZNLl26RGhoaL4DV1JSMh99up6H+wzgtVdnWgOXT9VatO/Wl9MXo/l49SZiY7NaE/RWjo6O1KxZs0CBC+Cjjz7i3nvvvSVwgfGsJ+0+f/rpJ06fPk3Hjh3p0KEDX375pbWr7fXXX2fq1KnMmTOHJk2aMGDAAAIDA63n+fjjjwFo164d48aNY+7cuQWqa1Zq167NkCFD6NOnD/fddx9169blk08+se5/4okn+Pjjj1m9ejWtWrWiW7durFixgjp16uT5Gp6ennz00Ud069aN5s2bs2HDBjZu3JivcwhhC6og3UG3nESpbhitrYEYy4yAMaAjFFirtX72ti9SQiilagMXL168SO3atW1cm8ITGxtLWFhYgR/Wx8cn8NSEKfz5x26Sk4yMF24VKlK/STs8K1exlrO3s8PDowLTnx9DRY/sh8NXqlQJb29vWRxSlGbyj7cIFTh4KaXqYwSs4UCttM0Y88K2AZ8C32mt855CvBQoa8ErNjaW8PDw20pUe/DgQSZNfp7Q68azHzt7B2rXb4mff0NUFs+o7OxMVPH1ZtaLT2bIkGHss6NatWq4uroWuD5ClBASvIpQvp55KaUqAUOAEUCHtM2pr39iBKwvtNaRhVZDUSQKI2hdv36dRYsWsXnzZuu2qn71qNOgNY5O2Se+TUmxEBERxclT52nZvKF1u4uLC9WqVcsyS4YQQqSX628JpZQ9xhD4EcDDgCP/BqwgYDXGiMLTRVVJUXgKI2glJSWxevVq3nnnHWJjYwFwr+hF/cZ34uHpnadzJCSa2fHTb7Rs3hCTyYSXl1eGjPFCCJGT3BLzvgMMxhj+DkbQigc2YbSyftLZrEKcGvQ6A2itfy2k+ooCiouLIzw83JqtvaB+//135s6da50I6+npiX+d5lTyqZnv51MXLwdhZ2dHrVq1pLUlhMiX3H5jjE/3fi9GwPpaa52X2aoVMRaztOThOqKIxMfHExYWdttB6+rVqyxYsIDvv/8eMOZRDR06lGeeeYZprywjJSX/C2a7ubrhVqGiBC4hRL7l9lvjMkbA+iw1u0ZByENLG7id5UnSS0xMZOXKlaxYscLa1XjnnXcya9YsGjduDIBJmUgh78FLKYWTsysOjs7Y29vdVv2EEOVTbispy2SPUqawgpbWml27djFv3jzrvCYfHx+mTp1K7969M3QRVvJ053pYzmN0Th/fx7Ug4+8fpUw4u1bAz78B9nZ5+9tGVhYWQqQna3OVEQkJCdZ0QrcbuC5evMjYsWMZP348gYGBODg48MQTT7Bjxw769Olzy7Ot7vd0zDVrhgK8fPzo3vu/3NtzBE2ad+LyheOFmpEiJ+vXr5c5Y0KUIRK8SrnExESCgoK4cuWKdeRfQcXGxrJ48WL69OnDnj17AOjatSubN29mypQp2a6x1bFdixxzH5pMdtjZO2BnZ49C8c/Zw5w5uZ+UlGRWrFhBgwYNqFWrVoZjVq1aZb3eqlWrePXVVzl58qQ1W/yqVasAePPNN2nZsiVubm74+fnxxBNPEBUVdct5Dh8+DICrqyt9+/blxo0brF+/ngYNGlCxYkVGjBiR4bmg1prKlSvj5eWFi4sLLVq0yLCIY9oikxs2bOCBBx7A1dWVpk2b8uOPP+b9Gy6EKDAJXqVUWtC6fPkyMTExt3UurTXbt2+nZ8+erFixgqSkJPz8/HjnnXdYuXIl9erVy/F4FxdnhgzsgaPDravh2Nk54OLqjlKKlORk9vz0FWHXA3h8+H8ZOnQo7u7uNGjQIENy28wGDx7M888/T6NGjQgODiY4OJjBgwcDxsCRpUuXcvLkSdauXcuBAweYOHFihuMTExPZsmULADt37uSvv/5iwIABfPrpp2zYsIFNmzaxbdu2DIs4zpw5k+joaPr378/ff//N9OnTGTduHNu3b89w7pdeeolnnnmGo0eP0q5dO4YMGXLbPw8hRO4keJUyZrOZ4ODgQglaAGfOnGHkyJE899xzXLt2DScnJyZOnMi3337LAw88kOeutm6d7qBf73txdHCwjtCxt3fA2cXNeo7om+EoBf/3yiLG/Gcw3333HT169KB9+/bWAJnWkho3bhxxcXE88cQTJCYmUqFCBezt7dmxYwf169fHxcUFgMmTJ2MymahTpw7NmjVj4cKFfP3116xatYpatWoxbtw4kpOT6d+/PwCdOnVi6NCh7Nq1i+TkZB588EF69+6NyWTiyy+/BIwW6Ouvv05ycjIfffQRdevWZdiwYYwdO5Z3332X33//3Ro8Q0JC+Pbbb6lSpQrz5s0jIiKiUDPLCyGyJsGrlEgLWpcuXSrwulrp3bx5k7lz59K/f38OHDCWXbv//vvZvn07EyZMwNk5+wwZ2Xng3o48+/RwmjdrgLOzC56VKuPs5ISLsxNaa8zmBJKTEpk1fTxdunThrrvuYtmyZYCRFgr+bUm99tprODk5ZdmSSm/Xrl08//zzgJHI9tFHH8VsNjN69GiefPJJXn31Vezt7Xn33Xetx1SpUgVvb2/69evHjz/+yNGjR2nWrBkHDx7k9OnT/P3332itUUrh4OCAi4sLLi4uLF++nL///psHH3yQ+++/H4BFixZx5MgRRo8eTfXq1QGsCz0KIYpObpOUs17oKG9kReVCkJSURHh4ODdv3iyU81ksFjZu3MjixYuJiIgAjF/6M2fOpFu3brd9/np1/ZnxwpPYOTjx7fd7uBIQTHKKhdPH3Lh6BZYuXUrv3r2pXr26NXO7yWSyPjObPHkyABcuXMDOzo6FCxfSr18/pk2bdsu1Ll++TK9evejZsyeHDh3ip59+4tKlSzz++ON07tyZl156iVWrVuHk5ET//v2tqycrpXB1deV///uf9VydO3fm0KFDrF+/ngceeAAAX19fRo0axRNPPGEtN3XqVDw9PRk7dizz5s2jdevWLF++nDZt2hAaGmr9HgshilZu87zuATQyV6vYmc1mIiIiiI6OLtBCkFk5duwYc+bM4dixY4AxeGH8+PH85z//ydcaWzlxd3fn9wMn+WbrTszmJOITjCz110LDAfhm+680atY2w/L2Pj4+XLt2Da01P//8M6+//jr79u0jNjbW2pJKTEy8ZYmWv/76C7PZzNNPP80333xD/fr1+f333wG44447MpTt1KmTNXiBEWBefPFFtm3bRnBwMLGxsSQlJXHlyhWaNm2Kk5MTycnJeHt7U79+fetxp0+f5vz589YuxrvuusvaLZqWdUQIUfRyC16fYQQvUUzSglZhtbQAIiIiePPNN1m/fr01EPbu3ZsXX3yRKlWq5HJ03nl4VGTZiq85c/YiieakDPsc7IzgGBhwhfc/WsfJvy/wzFNDUUpxzz33EBERwZQpU3jnnXfo0qULjo6OWCwWPv74Yx5//HGqVavG5cuXuXLlClprEhMTadCgARaLha+//hqADRs2sHTp0jzVNTIyknXr1rFo0SIaNGjAu+++y5o1azCbzbi7u/PCCy/w+uuvc+DAAc6fP09MTAz79+8nIiKCJ554gkGDBnHvvfeyZs0aWrRoAYCfn1+hfS+FEDnLbZLyqGKqR7mXmJhIeHh4oY5US05O5ssvv+Stt96yBsNGjRoxa9Ys2rVrV2jXAfD29ua9les5ffYi5kyBC8BkZ4ejkzOXzh+jToOW7Nl3CA+PCvx3eD+aNGnC4sWLmTdvHomJiXh5efHyyy/z0ksvERQUBMBDDz3Enj17WLhwIXFxcXz88cc89dRTvPXWW8yaNQuAzz//nEWLFjF48GDr0Pg0+/fvz/A5MTGRkSNHMmDAAMBoMaZfy2zOnDmsWLGC3bt306xZMzw8PGjdujVNmjTh5MmTTJkyBQB/f/8MLTMhRPGQpHI2lpCQQHh4+G3P0crsr7/+Yvbs2Zw5cwYADw8PJk+ezODBgws9l6CPjw/Xw25w5PiZLAMXQOv29xMbc4Pff97Anp++plGzjqyNCqNpQz8OHfyTJUuWsHPnTlq1akWHDh3o27cvvr6+TJ06FQAnJyfWr19PREQENWvW5MSJE5w/fx4/Pz8qV67MzZs3+eabb/D29qZmzZp07tyZ119/nYEDB7JkyZIMk6FfeOEF9u3bxzfffEO/fv1wcHDg0qVLGdYQU0rRunVrHB0d+eCDD3BycsLb25tjx47RsWNH5s+fz6FDh3B3d2fbtm1s3bqVDz74oNC6eIUQOZPRhjYSFxdHYGBgoUwuTu/atWs8//zzDBs2jDNnzqCU4rHHHuP7779n2LBhuQau++67L8Ozodz4+PhQqVIl6zOunLhVqEi3+wfjU8Wf08d/Z9e3a3jooR5s2bKFFStW0LJlS9566y3efPNNmjZtysqVK1m0aFGGc1SuXJk1a9bw448/0qJFC1asWMGcOXMylOnYsSMfffQRy5cvp2XLlmzcuJFXXnklQ5k333wTX19funXrRs+ePenYseMtA1Zmz55NQEAA9erVw8fHB4CWLVvy66+/cunSJe6++25atWrF9OnTC7X7VQiRuwKvpFxe3e5KyrGxsUREROSY5f3kyZMMHDiQVq1aWQcG5MZsNvPpp5+ybNkya/dXy5YtmTVrFi1btgQgLCyM999/n927dxMSEkKlSpVo1KgRI0aM4O677waM52NpQ8Nz4+vri6enJ2ZzEoNHTSE5OSXXYzLzrOjO6g/n5fs4IUoBGehWhKTbsJjExMQQERGRp0Ug161bx9ChQ9m0aRMXLlzINcPFnj17mDt3LpcuXbJue+mllxg+fDgmk9G4DgwM5PHHH8fNzY3nnnuOxo0bo7Vm3759vPzyy+zevRswWjZ5UbVqVTw8PAC4cTMGO5OJZPIfvG5GF253qRCifJCWVz7lt+UVHR1NREREhsEAOUlISKBr166sWbOGTz/9lIoVK1qf+wQGBtK9e3fefvttvvzySw4ePIi9vb2129FkMt0yx6h///7Mnz+fsWPHcvr0aXbs2IGbm1uGMjdv3rQGovvuu49hw4YxZswYwBjgMXv2bH777Td+/fVXvLy8+L//+z/Gjh1rPX7H9z/y+OPDuXkjnAoelWjUvCN/7t1Gx7sfwdu3Ro73azKZ2PzlW3n63ghRykjLqwjJM68iEh0dzaVLlwgODs5z4ALYsWMH1atXp1GjRvTr149NmzaRlJTxWdKbb75JpUqVsFgs1sDVtm1bNmzYYM1YsX37dvbu3ctLL71EVFQUe/bsYdiwYbcELsAauLLz7rvv0r17d7Zs2cLgwYN5+umnuXLlCmC0KEcMH0oFD0+63f8YTVp25tSx3/N8vx7ut9ZHCCFyI8GrkMXExHD58mWCg4Mxm825lr8WHs3pi9f4JzCMuAQzGzZsoF+/fgC0b98eFxcXdu7cCWAdyRYVFcX27dtJSkrC29sbgOeee46mTZtSsWJFwOj+8/Hxwd3d3To3Krfux+z069eP/v3707lzZ+bPn4+9vT2//vorAGvWrCElJYUnxk3Cw9Mbnyo1adCkbZ7O6+BgT4/unQtUJyFE+SbPvApJbGwsYWFheWplJZqT+G7vKVZs3MeVkEgcHexAgzkuiuSzf/G/Z4xuQqUUffr0Yf369TRs2NA6nykqKgoHBwdr/r62bdtaUz1l5Xa7hps0aUKNGjWs+Q59fHys+ftOnz5N8+bNGTygJyf+vkBCohnPylXzfO6eD95+SiohRPkjwes2xcXFERYWlqeBGABXgiMZ/tJqouMSiIs3ugOTklIHOoSeAm1h9PCBKKUwmRRaa7TW9OnTh+TkZABat27N/PnzqVPn34Wuc8qnV6tWLZRSXLhwwZq3Lz+qVKmSIVGvUuqW6zVqUJtmTetz/MRZ8jIEw8nJkYfu74JX5Yr5ro8QQki3YQElJCQQEBBAYGBgngNXSNhNHnvxE65HxlgDl5W2wI1/wKcN1OmNfYO+NOvYk4oVK6K1Jjk5mapVjRbNSy+9lCFwpZeW7DZ9cPH09KRr1658/vnnWc4pyy4VVdqcMIcs1ulK07hxY06cOEFCQgLTnx9D/Xq1iL0Zlv03ASNwdWrfktEjHsmxnBBCZEeCVwEFBQXlOFcrK8+/uZkbsYloSxbdeDGBkJIAng1AQ1LA7xz7bRuRkZHY2dnh4eGRISt6dvz8/FBKsXv3biIiIqzB6uWXXwZgwIABfPfdd/zzzz9cuHCBtWvX0rdv31vO4+DggL+/f673NHToUOzs7Bg7diwXzp/nvi5NCA44BYCzU8Zkv87OTnh7efLEyP48N2GkdRi/EELkl3QbFpMrwZEcO3sVS0o23Xs3zoOrL4QehaizpOVDruzXmLcXzGT48OEcPHgw1+tUqVKFiRMnsnTpUmbOnMkjjzzC/Pnz8ff3Z+PGjXzwwQcsWrSIa9eu4enpSePGjZk9e3aGc9jZ2eHv75+nNFLu7u5s3bqVp556ijZt2tC0aVOWvLmIgQMHMnxIH+wc3ImNi8fd3Y3WLRrRolmDPC9wKYQQ2ZF5XvmUNs9r586d1KiR8xym9Oat/JE1Ow6SnJTFRF5tMYJX6GFISR3w4VgRqrTD0dOfH5Y/RTWf4nk25OTkRI0aNayLQxbE5s2b6d+/P9evX7eOhhSiHJK/0oqQtLyKyV+nArIOXPGhcO0AJBjrXWFyAK+WULkxKDscHe05dfFasQQvZ2dn/Pz88h24Pv30U+rWrYu/vz8nTpxg8uTJ9OnTRwKXEKLISPAqJgmJmQZoJMdD6CG4kW4BQ4+64HsH2P+b3VxrTXzmY4uAq6sr1atXL9BzqGvXrvHyyy8THBxM1apV6dWrFwsWLCiCWgohhEGCVzHxcHMy3mgLRJ6BsCNgSQ1KTpWgSgfjmVdmSuHh5nzr9kLk5uZG9erVC/ws6sUXX+TFF18s5FoJIUT2JHgVkx6dGnPy+BHMgfsgMcrYaHIEn9bg2RBU1i2e5KQU2jTO+7O1/KpQoQLVqlWTQRRCiFJFglcxCAkJ4dCuLzBf2PHvRs+G4N0a7LNvVZnsTPTq1pQKrk5FUi8PDw/r3DEhhChNJHgVIbPZzKpVq1i+fDlxcXEAKFdftG87cPbK9XhHexNjHulYJHXz9PTE1zeLbkohhCgFZKh8PqUNlXesdRf9etzNqAH3Ub929VvK/frrr7z22mvWNba8vb2ZPPlZvjpwk7OXQkk0J+d4HSdHe+ZN7EXvu5oX+j1UqlTJujKwEKLISF98EZLglU9pwYuqnbBzcsPe3o57OzbnjWn/xdHRgYCAAObNm8euXbsAY8Lv8OHDmThxIu7u7iSak5i6dCs7D5xFky6vYSo3F0cwKRZN7st97RsWev29vLzw8sq91SeEuG0SvIqQBK98Sh+8sHcBwMnJgeYN/GhX24mPP/7IuhRKx44dmTlzJg0aNLjlPIHXIlnz7UG2/nKSmPhE7O3s8K/qyROPdOSBTo1wdCj8Hl0fHx8qVapU6OcVQmRJglcRkuCVT7cEL62NicY3z0OykaC3atWqTJs2jYceeqjEjOKrUqWKda0vIUSxKBn/+csoGbBxO5JijTyEiZHGZ2Vi7BNPMH78U7i6uuZ8bDFRSlGlSpVcV0sWQojSRIJXQd28BHEhpCXQxdkL56rNaNL27hIVuKpVq0aFChVsXRUhhChUsiZFQcUFAxrsXIxchN6tSEi25/eDp2xdM8AIXH5+fhK4hBBlkrS8CkyBRx1w9wf1byLbqOg4G9bJYDKZ8PPzw8XFxdZVEUKIIiEtr4LybgMetTMELgCPCrYNGHZ2dtSoUSND4Bo1ahS9e/e2Ya2EEKJwScuroGICIOzQLZtr+dxXZJe87777GDZsGGPGjMlyf1rgcnIqmnRSQghRUkjwuh1OlaByU+tHF1dnRj6WsYVjNptxdHQs8qrY29tTo0aNYrmWEELYmszzyifrPC9nH8AC3q0AY6Lyfx69jyO/bKBu3bq4urryzTff4Ofnx4YNG/jzzz9ZuHAhp0+fxt3dnd69e/PCCy9Yg82IESOoV68eHh4efPXVV5hMJh555BGmTJmCyWRixIgRHDhwIENdzpw5Q2RkJHPnzuXw4cOEh4dTt25dXnjhBf773/9ay40aNYqwsDC2bdtWTN8lIQQyz6tIyTOvQuDo6EDjejWYOLIXAFu2bEFrzZo1a1i4cCHXrl1j7NixNGnShE2bNvHaa6+xfft23nzzzQzn2bp1K3Z2dnz55ZfMmjWLTz/9lG+//RaAZcuWUbVqVZ5++mn27t3L3r17AWOxyq5du7Jt2zZOnjzJpEmTGDduHDt37izeb4IQQhQj6Ta8HQkRcPUXkk0mzgbbM378WQBq1KjBtGnTrMWWLFmCr68vr7zyCiaTiXr16vH888/zf//3f0yaNMk6uKJ+/fpMmjQJgDp16rBu3Tr27dtH79698fT0xM7ODjc3N2tSXScnJ9q1a0fHjv9mnn/yySfZtWsXX3zxBd27dy+u74QQQhQraXkVkDIpvKr6887ylez4bjubN2/mtddeA6B584yZ4C9cuECrVq0wmf79drdt25akpCQuX75s3daoUaMMx/n6+hIeHp7l9Z2dnfH39wfgtddeo2XLlnh5eVGhQgU2btzIlStXCuU+hSjNbt68ycyZM2nSpAmurq54eXnRvXt31q9ff9vn3r9/P4MHD8bPzw8nJyeqVq1K3759+fHHH/N9LqXUI0opnfaVS1kHpdRTSqm9SqkopVS8UuqcUmqpUqpagW+olJGWVwE90LU1ZrOZB+7tfMu+/MyvSp/70N7e/pZ9WT2TdHV1pXr16phMJhYsWMDixYt56623aNGiBRUqVGDGjBlcv349H3cjRNkTGBjIXXfdxcWLFwFj1fCbN2+ya9cudu3axVNPPcV7771XoHMvWLCA6dOno7VGKYWnpydhYWFs3bqVrVu3MmPGDOsfs7lRSlUAluWxrCewHUj7xWMG4oD6wCRgpFLqIa31gazPUHZIy6sY1KtXj6NHj2KxWKzbDh48iIODAzVr1szzeRwcHLCzs8PPz8/aitu7dy99+vRhxIgRtG7dmnr16nH27NlCvwchShOtNQMHDuTixYvUrl2b3377jejoaKKjo1m4cCEmk4nly5fz4Ycf5vvcW7ZsYdq0aWitGTNmDCEhIURERBAZGcns2bNRSjFv3jyUUo/n8ZRzgBrAH3ko+zFG4IoBhgEVtNaVgMbAr0AlYGtqkCvTJHgVg6FDh3L9+nVeeeUVLly4wO7du1m8eDHDhw/PVyutZs2aHDt2jKCgIMLCwgBo2LAhO3fuZO/evZw+fZoJEyZY/9IUorzavHkzf/zxByaTiW+++YbOnY2GirOzM1OmTOGZZ54B4P/+7/+sSxjl1csvvwxAp06dWLlypXVFcnd3d2bNmsWoUaPSis5XSuXYu6WUugOYCBwEVuRStjXQP/Xj81rrtVrrJACt9RmgHxAB+AJT8nVTpZAEr2JQpUoVPvzwQ06dOkW/fv2YMWMGvXr14rnnnsvzOTw8PFiwYAEBAQHUq1fPOmhj5syZtG/fnp49e3LXXXfh5ubGsGHDiupWRDlUu3ZtlFLs3r2bq1evMn78eOrWrYuTkxOtW7e2dfWytGbNGgDuv//+LOv4wgsvoJQiJCTEunBsXgQHB3PkyBEAJk+enGWZdP+vawJ3Z3cupZQJ+ABjSP1TgCW7sqkeSn2NBj7KvFNrHQV8kvpxhCop6zEVEXnmVUBTp06lRo0at2xfvXp1luXbtWvHunXrsj1fVsfNnz8fAE9PT3x9falatSpHjx7NUKZSpUps3Lgxx7quWrUqx/1C5MXZs2cZNGgQYWFhuLq64uDgYOsqZevnn38GoEePHlnu9/Pzo1mzZpw4cYJdu3bx0EMPZVkus/QDoTIPsErToEEDTCZT2mOCB4Ds5q1MAO4E3tda/6mUapbL5Wulvl7QWqdkU+Z06qs/0Cjd5zJHWl4lXOXKla3dEkLY0vPPP0+1atX47bffiI2NJSYmplBG7RW269evW0fpNmuWfTxo2tTIjvP333/n+dzpGzMpKVnHD4vFkn6gVZYVUEr5AXOBUGBGHi+fdlK7HMqkb5DkFgxLNQleJZi3tzfe3t62roYQgDEa9scff7Q+PwJjbmJeKaUK/JUfwcHB1vfVq1fPtlzavvTlc5N+gFV2Qe/UqVPpg1d2Q9eXAe7Ai1rryDxePm1eTT2lVHYJTJume1+mh81Lt2EJ5ePjQ6VKlWxdDSGsRo4cSZUqVQp8/O0cmx+xsbHW9zkNiEpbNDYmJibP565atSotW7bk2LFjLF68mKFDh2aYvwnGMPp03DOfQynVB2PgxV7g0zxfHH4A5gOuwHhgSabz+gKjcrp2WSLBqwSqUqUKFStWtHU1hMigU6dOt3V8SEhIIdXEtmbNmsWgQYM4cuQIAwYMYO7cuTRs2JCgoCAWL17M119/jYODA0lJSZBpEIZSyg14B0gGxut8JJfVWh9WSm0DegOvK6UswFrgJtABeAtwTndIbgNASrUy3W2olGqklHpWKbVDKRWslEpSSt1QSu1TSk3OoeltE0opqlWrJoFLlEhpI1xLOjc3N+v7+Pj4bMvFxRkLx+Z3tfGBAwfyyiuvALBp0yaaN2+Oo6MjtWvXZtmyZTz00EP06tUrrXhUpsNnY4xCfFtrfTxfFzaMAv4EnIClwHUgAfgFaAFMTlc287XLlDIdvDBG+byJMVz1PLAeYz5Fa4wm9z6lVGWb1S6dtMDl7l6mW/qiFLOzy2mcQMmR/jlXUFBQtuXS9lWrlv9HQy+//DK///47I0eOpGnTptSsWZO77rqL999/n+3btxMZaX2MdS7tjVIqLQvGdWCRUqpC+i+MgJRWNm17hjWOtNbhQFfgf8CPwMXUa3yJMXn523TFz1GGlfVuwzPA/wFfa62tHdupy5psA9pgBLH/2KR2/9YHPz8/ax+8EGVR1apVC3xsfrocfXx88Pb2JiwsjJMnT2Y7XD5twEXaqMP86tSpU5ZdqSkpKRw7dizt4750u2pgjBT0BbKPqobo1NdXgVfS79BamzHmh32Q+SCl1KOpb5OBv3K5RqlWpoOX1jrLtOpa60tKqf8Be4DHlFJjU/9BFDuTyYSfn1++Mm0IURpdu3at2K517733sm7dOn788ccskwFcvXqVkydPAhT66gs7duxIa3mZMXp7ilNaSqptWuubxXztYlXWuw1zcjj11RnwskUF7OzsqFGjhgQuUS5orQv8lV9Dhw4F4IcffrhlYj/Am2++idaaatWqce+99972vaW5ceMGU6ZYMzN9pLUOTfugtd6ttVbZfQH/TVc2bfsreb22UqoHMABjoMaCXIqXeuU5eDVIfTVj5AMrVvb29vj7++Ps7Jx7YSFEvvTr148OHTpgsVjo378/+/fvByAxMZHFixezdOlSAF599VXraubppaXESpen0OratWtMnTqVgwcPkpCQAEBSUhLffvstXbp04dSpU2nz36bdcvBtUkoNUkr9Tynln5b+SSnlq5SaCnyDkWpqkdZ6f2Ffu6Qp092GuUj7h7VNa51YnBdOC1wlOb2OEKWZUor169dbl0Tp1KkTFSpUICEhgeTkZAD+97//MXbs2HyfOz4+noULF7Jw4ULrcijR0dHW87Zq1Yrt27fj5+dXFN12zYCXU9+blVLxQNrwZA0spgiCZklULoOXUmoUMBhjHZxsU7OkLivgmWnzrQkN88HBwQF/f/9b1u4SQhSuGjVqcOTIERYsWMDGjRu5dOkS7u7utG7dmqeeeopBgwYV6Lw+Pj68+uqr7Nq1i7NnzxIWFkalSpVo0aIFgwcPZvTo0UX5/3sLUAXogpG/0AW4hDFUfrnWOi/LqpQJqiD9ycVBKbUQ6FuAQ7trra/mcN7uGMNJHYCRWuvPcyj7Cv/+lZPBzp07s0zMmxMnJyf8/PwkcAlRPpTprO62VpJ/i1bHyIqcX9n2xSmlugKbAUfgmZwCV6qlwKpM22pgjFLMF2dnZ/z8/ErNXBkhhCjJSmzw0loPB4YX1vmUUmkT+NwwkmHmuux26vo4UZnOk+9ru7i4ZFj9WAghxO0pscGrMCmlOgI7MBJVztRav1Fc13Z1dcXPz69AQU8IIUTWynzwUkq1B77HCFyvaK1fK65rV6hQgWrVqkngEkKIQlamg5dS6k6MZQQ8gDla61eL69ru7u5UrVpVApcQQhSBMh28MAJXRYznVjWVUquyKfeC1jqssC5asWLFYlu7SAghyqOyHrzSVnP0JOfku68AhRK8KlWqVGqWjhBCiNKqTAev1HxhxaZy5cp4e3sX5yWFEKJcKtPBqzh5e3tTuXKJWBpMCCHKPAlehcDX1xdPT09bV0MIIcoNCV63qWrVqnh4eNi6GkIIUa5I8CogpRTVqlXD3d3d1lURQohyR/IVFVCVKlUkcAkhhI1I8CogV1dXW1dBCCHKLQleQgghSh0JXkIIIUodCV5CCCFKHQleQgghSh0JXkIIIUodCV5CCCFKHQleQgghSh0JXkIIIUodCV5CCCFKHQleQgghSh0JXkIIIUodySqff3YAgYGBtq6HEKIEq1OnTm0gUGudbOu6lEVKa23rOpQqSqmuwB5b10MIUSrU0VpfsnUlyiIJXvmklHIC2gHBQEoRXaYGRoDsBkgTzzbkZ2B7ZeFnIC2vIiLdhvmktU4E9hblNZRSaW8D5a8225Cfge3Jz0DkRAZsCCGEKHUkeAkhhCh1JHgJIYQodSR4lUxRwKupr8I2opCfga1FIT8DkQ0ZbSiEEKLUkZaXEEKIUkeClxBCiFJHglcpoJRyU0oNU0otVUr9ppSKVUpppdQ2W9etrFFKDVVK7VFK3VBKxSil/lJKPa2Ukv8rRUwp1UgpNUkp9blS6rRSypL673ygresmSh6ZpFw6NAA+t3Ulyjql1LvAeCAB2AkkAd2Bd4DuSqmBWmuLDatY1j0FTLJ1JUTpIH9Nlg7RwMcYv1g7AP+zbXXKHqXUAIzvbwjQUmvdW2vdH+MPh1NAf2CiDatYHpwA3gAGA/WBX2xbHVGSScurFNBaXwDGpH1WSjW1YXXKqumpr1O11ufSNmqtrymlngJ2A9OUUsuk9VU0tNYr039Olx5KiFtIy0uUe0qpGkBbwAysy7xfa/0LcBWoCnQs3toJIbIiwUsIaJP6elJrHZ9NmT8zlRVC2JAELyGgTurr5RzKXMlUVghhQxK8hIAKqa+xOZSJSX11L+K6CCHyQAZsFDGl1EKgbwEO7a61vlrY9RFCiLJAglfRqw40KsBxDoVdEZGttFaVWw5l0lpn0UVcFyFEHki3YRHTWg/XWqsCfF2ydd3LkUupr7VyKOOfqawQwoYkeAkBh1NfmymlXLIp0y5TWSGEDUnwEuWe1joAOAQ4AoMy71dK3Q3UwMi+sa94ayeEyIoELyEMr6e+LlBK1U/bqJTyBd5L/ThfsmsIUTLIYpSlhFLqG6Ba6kcfoC7GCrNn0hWbo7XeXsxVKzOUUu9hJIdNAH7i38S8HsAmYKDWOsVmFSzjlFJ38O8fCgBNMaYmnAMi0jZqrSXLiZDRhqVIG24dUOCJkag3jU+x1aYM0lqPV0rtBZ4G7gbsgNMYSZGXS6uryHmQ8d9zmgbFXRFR8knLSwghRKkjz7yEEEKUOhK8hBBClDoSvIQQQpQ6EryEEEKUOhK8hBBClDoSvIQQQpQ6EryEEEKUOhK8hBBClDoSvEShU0qtUkpppdTubPYrpdTF1DLvZVUmm+PWpR6zP902J6VUD6XUTKXUZqVUUGoZrZR6qBBuJ8+UUq3TXftsHsrXTlf+ntspm25f5q9EpdTl1O/dA0V1L0IUNwleothpI63LZ6kfByulHHM7RinlCfRJ/fhpul1NgB3AHIwVq6thO/9J976BUqqTDepwE7iW7gugJjAQ+EEp9UYez1MS7kWIbEnwEraSFrwqA73zUH4w4AQkAl9m2hcF7ATmAwMKqX75opSyB4amflyZ+vqfbIoXpUla66ppX4Az0Az4NnX/C0qpbjmdoATdixDZkuAlbEJrfQHYm/pxRB4OGZn6ulVrHZlu+zGgstb6fq31dK31xsKsZz70BHwx1vuaC2iMVqWTjeoDGK1crfXfGOuURaVu7pP9EUAJvRch0pPgJWwprfvvYaWUV3aFlFL1gM6ZjgFAa23RJSO7dFrLZI3W+jLwG0bW/342q1E6Wus44ELqR7dcipfoexECJHgJ2/oaiMdYwXhIDuXSWl3XMZ5vFRql1O6cBpfk8RyVMVozyRj3BLAm9bVEdLcppVyAeqkfz+dQrsTfixAgwUvYkNb6JsYij5BN16FSSgHDUz+u0VonF0PV8msIRgD+UWsdmrptHcZilj2UUlVtVjNAKdUI+Aqj9RRBptZrJiX6XoRII8FL2FraL9IOSqmGWezvirFqdPqyJU1ai2Rt2gatdThGK9EOGJaHc2xUSoVk9wX8mce6vJXp2ASMBTUfBDYCnbTWETkcXxj3IkSRk+AlbO0nICj1/cgs9qdtO6a1PlrYF9da36O1VlrrewpyvFKqMdAeiOPfVmSa/HS3VQKq5PDlnccqeWQ6Lm2QhSNQEcjp2WJh3YsQRU6Cl7AprXUKsDr14/DUbkIAlFLOGKPkoOS2ukalvm7RWsdk2rcFiAZaKKXa5HKee1ODaJZfQJ081ue/mY5zB9oAq4DuwK4cJisX1r0IUeQkeImSIC0w1QLuSre9H0ZrIZl///IvMZRSJv59Hrc2836tdTzwTerHrFqVRU5rHaO1PqK1Ho0xP84ZWKaUsktfrjTcixDpSfASNqe1PsW/z3TS/2JMe/+91voaJc/9gF/q+y1ZpWfi33sYmjr515ZWpb42Alpl2lfa7kWUcxK8REmR1voaqJRyVkpVwRhkkH5fSZOf5z++GJN/belKuvd1M+0rbfciyjkJXqKk+AIwYww4eAQjPZE9EInxvKVEUUp5AP1TP/bBGHCR3de7qeVsPdjBL937pLQ3pfReRDknwUuUCKnDt7elfhzJv11UX2mtE21TqxwNAlyAcGCH1joquy+MOVYAfVInAdvKY+neH073vjTeiyjnJHiJouSglPLO5cshXfm07sEeQOtM27KllKqU/pzpdnnkcK20YwuaYSOt5bElDxOnf8PI8J5bJpEioZSqopSaBzyRummz1jp9F2KpuRch0kjwEkWpMxCay1eXdOW/S92W9u/yrNZ6P7k7nOmcab7K4VoFppSqizF5GmBDbuW11hb+nTdV1N1tmScpRwMhwHRAAYeAMWmFS/i9CJEtCV6ixNBaJ5FxmPZn2ZW1sZEYgSAaY5J1XqQFhvapk4GLSuZJys4YgXsnMA7omJoxI01JvhchsqVKRkJuIYQQIu+k5SWEEKLUkeAlhBCi1JHgJYQQotSR4CWEEKLUkeAlhBCi1JHgJYQQotSR4CWEEKLUkeAlhBCi1JHgJYQQotSR4CWEEKLUkeAlhBCi1Pl/Y7/FjjWd9e8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r = 0.822, p = 0.003\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r = 0.474, p = 0.166\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== younger ==========\n",
      "r = 0.951, p = 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r = 0.844, p = 0.002\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r = 0.463, p = 0.178\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== ctrl ==========\n",
      "r = 0.951, p = 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r = 0.837, p = 0.003\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r = 0.503, p = 0.138\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== yctrl ==========\n",
      "r = 0.948, p = 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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+pdQErfXSYmulEOK2sZVFJC9dusSsWbP4888/AWPm9+HDhzNs2DCcnAo8o6dAnJyc8PPzy5QuSil7tMtjcHM52efZipJmcfBSSk0AZmJMQRIK/J/W+g+lVDVujTL8MD0v13Naa9tf2EeIciI2NpbQ0NBSHbgSExP59NNP+fjjj82jHzt16sTUqVOpUaN4pxbZ29vj6+ub+zww18fg5g9I8Lr9LApe6fe57sV4mXA1MCJ9EAda6xDgQaXUCxjnd/UADiulRmit1xRrq4UQxc4WFpHcsWMHM2fO5OLFi4Ax8/sbb7xB9+7dC535PScGgwEvLy+8vb3zrFfZVUJ7TIEbswDbmv9m6yzted2HcUG1F7XWX+VUQGu9WCm1CfgGY+LIHwtxHiHEbXTt2jUiIiKs3YxcXblyhTlz5rBhwwbA2CN69tlnGTNmTKEzv+fEzs4OLy8vPD09MRgKNoJQuXRDK3uIDkzfkpBXcVFMLA0qO4DBWusLeRXSWh9XSt0FzABeK2TbhBC3QWRkJFFRUdZuRo6Sk5P55ptvWLx4cabM79OmTaN+/frFdh6llLmnVdCglel4505ox18hfr3xHlhaZLG1TeTM0gwbKmtGjQIc00FrXWbyG0qGDVFWaK0JCwvjxo0b+Re2gr179zJjxgxOnToFgI+PD6+99hp9+/Yt1kuEHh4e+Pr6Ym9ffBeItE5EKSeZsVyCLJ2kbPFd3LIUuIQoK9LS0ggNDSUuLs7aTckmKiqKd999lzVrjLfKlVIMGDCgSJnfc+Lm5oavr2+xj0wEsCB/uSikPIOXUsoDY5r+Qi3ak37p0FFr/WdhjhdCFD9TZviEhNJ1byY1NZUff/yR+fPnm3uDzZo1IzAwkObNmxfbeXIa9i5sT349r+sYh8P7Z92hlPoc8NRaP57H8T8BfgU4jxDiNiitgevIkSPMmDGDQ4eMC+16eHjw8ssv89RTTxU983u6fIe9C5tSkKCS23XbhynAUs15HC+EuI1KY+C6ceOGOfO7KZvHo48+yoQJE4ol8zsYh717e3vj5eVVrPfKhHVJj0iIciAlJYXg4OBSsxaX1ppffvmFd955p8Qyvyul8PT0xNvbu9h6b6L0kOAlRBlX2jLDnzlzhhkzZrB7927AmPl97NixDB48uNgyv7u7u+Pr61v8meRFqSHBS4gyLC4ujtDQ0FKRYPfmzZt89NFHfPnll+ZA+uCDDzJ58uRiy/zu7OxMpUqVcHZ2Lpb6ROklwUuIMqq0TD7WWrN582Zmz55NSEgIALVq1WLq1Knce++9xXIOBwcHfH19cXd3L5b6ROknwUuIMiY1NZUrV66Uijlcly9f5s0332Tbtm0AODo6MmLEiGLL/C6DMcovCV5ClCEJCQmEhIRY/f5WYmIin332GR9//LF5kMi9997LtGnTqFmzZrGco2LFivj6+spgjHKqIMHLRSk1OKftAEqpQeQ+HN6lsA0TQljmxo0bhIWFWX05kx07djBr1iwuXLgAQJUqVZg8eTIPPPBAsfSOXF1d8fPzK5HMGMJ2FCR4eWBcpys3y/LYZ1phWQhRgqKjowkLC7NqG8LCwnjrrbcyZX4fMmQIY8aMwc3Nrcj1Ozo64ufnVyx1CdtXlEnKQggr0loTFnqBUycOExYWhqOjEz6V/PGrVP223v9JTk7m22+/5f333zdnfm/bti3Tp0+nQYMGRa7fzs4OHx8fPD09i1yXKDvyDF5aa8vXBhBClCitNSeP7WH/3i1ERkQQfSMG4wUOhZ2dPU7OLjRu2p469VuWeBD777//CAwMNGd+9/b25vXXXy+WzO9FXaZElG0yYEMIG5KWlsa2jSu4eP4YUdeuEx+fMWOGJjU1mZtxyRz4bytXQi/Q4d7eGAzFP6Ahp8zvTz/9NC+99BIVK1Yscv0eHh74+PjIJGORq/yyyn8BXNdav3Kb2iOEyMOuP3/mwvljREVdIyEhKddyqakpXAk5z3//buTODj2K7fxpaWmsXLmSefPmER0dDRRv5ndXV1d8fX1lkrHIV349r2eBK4AELyGs7Eb0VU4d38u1a9F5Bi6T1NQULp4/RsMmd+JRsehJbo8ePcqMGTM4ePAgULyZ32UwhrCUXDYUwkYcPbiTGzfiiE8oeHLdtDTNqRP/0bb9A4U+740bN1i0aBHLly8v9szv9vb2+Pj4FMulRlG+SPASwgZordn9zzZiLc6akcaFc0e5o939Fg+g0Frz66+/8s477xAZGQlA/fr1mT59OnfeeaeF7chMMmOIopLgJUQpl5iYSFDQZWJiC7WgOWlpaaQkJ+Pg6FjgY86ePUtgYGCmzO9jxoxhyJAhRRpEIcuUiOIiwUuIUiwmJoYrV66QnJz/Pa7cGDMFFCxXgCnz+7Jly0hOTgaMmd8nTZpE1apVC90GkGVKRPEqSPCqoJSaVpSTaK1nFuV4IcqjqKgo8+U6e3sH7OzsSE21PGehUgoHh1u9rqCgILp168aqVavMIwRzyvxeo0YNpk6dSqdOnYr0PmQEoSgJKq88aEqpNIohvZPWusxcH1BKBQDnz58/T0BAgJVbI8oirTXh4eHmoegme//5g3NnDgO5r8215uft7D90mq6d7qDLfa0BAwF1GqPtfBg8eDB///03FStWJCoqCi8vL+zt7XPM/D58+HCGDRtWpIDj5OSEr69veR5BKDfzSlBBel5pQFBJN0QIYVzOJDQ01JxmKaMGje/gwrkj5LeupL29HTv/PkS7Oxrh7uFO/cZtOXXqvHm/nZ0dfn5+JCUl8dFHH7F06VJz5vd77rmHqVOn5vvFLC0tDa11jvetZAShuB0KknMlQmtduyiPEn8XQliR1pqrVyMJDrpMeFgYSUmFuz+VlJTEpUuXcgxcAB4VfahesyEGu7y/c9YOqIqzsxNvz/+OatXq4OVVKdP+oKAgGjZsyAMPPMCiRYtITEzE09MTBwcHdu3axRtvvMH69etp2LAhQUHG761r1qyhdevWbN++nV69etGsWTPOnj3LoUOHGDp0KO3bt6dNmzYMGjSI0NBQCVyixMmADSEssG/fPu68807uuusuNm3axP59e9m1cwcJCfEYDAa0Bq3TaN6iFR063k2lSpULVG98fDzBwcHmeVS5adehB4mJN4kIDyItl/tfSilatajH9h0HqFozc9aLiIgI5s2bB0BoaCh2dnY8/vjj/PTTT3h7e9OuXTu6d+/O22+/na3exMREPvroI2bMmIG3tzd+fn4cPnyYPn368Oabb+Lj48PHH39Mr169OHPmTJHngAmRF8l2KYQFPvvsM0aPHs3hw4eZPHECWzZvJCbmBsnJySQmJpKUlEhycjIH9v/Hx0s+5M/tW/NdXysmJoagoKB8AxeAwc6Oe7s8TrPmHXF0dMHePvPwd2UwYFAG2rW/G4D3318MGC9HAjz11FNs374dgMaNG7N27Vo8PT2pUaMGtWrVwsPDgx49etC/f/9s505NTWXq1Knccccd1K5dmwoVKnDfffcxevRoOnfuTPPmzVm8eDHOzs789ttv+f8yhSgC6XkJUUDx8fEsX76cNWtWs2vXDv7ZvYcHuncF4Pr167z/wVIee7QPe//bT0hIKL4+3oSFhXH23Dk+/fRzDh48SOvWrfnmm2+oXbs2Fy5coE6dOqxcuTJTXsAff/yRefPm8ddff+Ho6Mi2bduYM2cOISEhtGjRggEDBvDKK6+wadMmDCQRHnaRLVv+ZM3a34m6dh1PT09c3Iy9ng0bNtCxY0c++OAD83sw6du3Lw0aNODcuXM0b96c0NBQ876WLVsCMGrUKIKCglBKoZTC29sbwPxzSkoK48ePZ+vWrYSFhZGamkp8fDyXLl0q2T+GKPek5yVEAa1atYpatWpxYP9emjVpzKFDR8w9GpPt23dwd8e7GP78/+Hk7MyPK9cwc+Ysxo8fz+7du0lISODFF18kLS0NBwcH7r77blavXp2pjtWrV9O3b18cHR0JCQlh7NixdO7cmZ9//plBgwbx7rvvAsYA4l+jLgYHHz79YgUDBw2ma9eu+Pr68v777wNQtWpVpkyZwpUrVwDo2rUrS5YsAeDdd9/l7Nmzeb7nZ599ll9++YUhQ4aglGLChAm4u7sTEBCAj48Pzz77LHv27GHBggXs2rWLAwcOUL169ULf9xOioCR4CZGDiIgIdu3axZYtW9ixYwcXLlzg888/p+fDDxN/8ya1atXEwcGek6dOZzrurvZ3Ur9eXXx9fehwVzsiIiNp1/YO7AzQtGlTxo4dy9atW7l48SJxcXE88cQTrF+/3jzazxQATD2f77//nho1ajBp0iTq1KmT4yW9r7/+mg4dOjBq1Cjc3d2pWrUqdevWBSA4OBgAX19fAGbPnm1eILJOnTr8/vvv1KlThyNHjmSq89ChQwC0b9+eGjVqUKNGDRwdHdm7dy+pqanmicY7duzghRdeoGfPnjRt2hR3d/dMPTghSkp+lw1rA6n5lBGizDh58iQbN24kODgEpRQpKSnY2dlx/fp1/vrrL+7v3oWkxASUUjRv1pT9Bw7RpHEj8/GVKt8a2Wea3+RXyZfDhw6yes1ajh07RlxcHDdu3MDFxYVu3boxbdo0WrRowebNm9m+fTuNGzfmwQcfBDBf0svIFNhMzp49S5cuXQDj5ct9+/YRExMDGLNaNGrUyByMnn76afPE57NnzxIaGsrIkSP58ssviYuLo3Llyvzxxx/88MMPAEyZMoWLFy9y9epVc4C9dOkS1atXB6BBgwZ8++23tG/fnri4OF577TUcLUhDJURh5beS8kWlVB2lVB8gTmu9OeN+pdQqwDuPKl7TWu8thnYKUeI2btzE1q1bzWmRTFJSUti37z/S0tKYOm1GtuOio2+Yf7bLsOKvyrDNzs7AjRs3zHWbBmc4ODjQqVMnfvnlF1JTU1m3bh0vvvgi9vaW3Y5OSkrizTffZOvWrZm2b9iwAaUU9913HwDDhw/H19eX4cOHU79+fZKTk/H392fx4sWMGzeO9evXExISwrBhwwgMDMTLy4s5c+awa9cuZs2aRXx8fKZLgl988QXDhw/njjvuoFq1agQGBhIREWFR24UojIJcNvwe+AlonMO+u4FOQOccHp2AhUVtoBC3w44dO3IMXGAMNEePHuXee+9j+PP/x4hhQ82PypX8OHDwUJ51aw3RN2LMPZesunXrBsDPP/9MTEwM06ZN4/DhwwA4Ozvz888/s3XrVvr27Uvz5s157bXXMtStiYuL47PPPuObb74BbiW/BePlQh8fH+rUqUOnTp1YunQpY8aMoUaNGuY5XABdunShadOm1K1bl0uXLjFzpjGj24MPPkinTp0YPnw4bdq0MW9r2rQpX375JS1btuTff/8lPj6eGjVqsGvXLh588EHef/99/Pz8zPPIxowZg6enJzVr1jS3E+DChQsopVi+fDn33HMPzs7ONGrUiD/++MNcZtu2bSilzD3GjMft3Wv8bpycnMyLL75ItWrVcHJyokaNGkycODHPv4uwbXkGL6XUXcCdwGmt9Qd5FJ2dw+My0EEpVbS1E4QoYQkJCaxf/78cAxfAuXNniY+Pp0WLFvj5+VGp0q1H06ZNOHjwMHmNho+JiSExITHXLOr+/v4AfPzxx+YekknXrsbRjK+//joDBw5k/Pjx3Lhh7OmdPn2aZ599NtPIvl69ejF8+HDzZUPTMH0XFxe2b9/OAw88wLRp07h8+TJxcXHm+1Pffvstx48f58qVKwwePBh3d3eUUmzatIlz586xbt06Tp823t/78ssvGTduHM8//3y2TBzfffcd7u7u/Pvvv0ycOJGXXnqJRx55hAYNGrB3716GDBnC888/n+2+2GuvvcaLL77IgQMHuP/+++nbt6/5fl1BvP/++/z000+sWLGC06dP88MPP9CwYcMCHy9sT349r34YcxsuyauQ1npq1gfwNsYrJ08VT1OFKBl79/6X55pShw8fpkaNGri4uJCapjPN22rSuBHXo6M5d/6CedvPv6zj+xUrzSMRU1PTUAqio6PNPaq7776b1q1b07p1awYOHAgYL08+/PDDmc5tGmjh4ODAzJkz+eOPP3j55ZcBGD16NP/884+5rKenJxs3buTYsWOMHj0agOeee44RI0aQkpKCn58f3333HYsWLQLA29ubU6dOAbB7927i4+OJi4tj7dq1jB07lm+++YajR4/SpEkTPvzwQ77++msAqlWrxvDhw2ncuHG2/ItNmzYlMDCQ+vXr88orr5izyI8bN4569eoxbdo0tNbs3Lkz03GjRo3iySefpFGjRixatIgaNWqYR0UWxMWLF2nQoAH33nsvNWvWpGPHjvzf//1fgY8Xtie/C+t3pT9vKETd64CPgA6FOFaI22bnzp259roAHn30MfPPKakae7tbgc7Ly5NpU4yXp+5o08q8PS0tDTc3N8aMGg5AaloyISHGy3QLFy6gSZOm5rKnT59mzJgx+Pv706JFixzbsGLFCqpXr87mzZuZNGmS+Rw1atSgW7dufP311+zYsSPTciMrV64kJiYGb29vgoKCmDBhAk888YQ5q/yTTz7J999/j5+fH126dOHw4cPZejvPPPOMsf2pqbz99ts0b96cxx9/nMTEROLj43FxcclUPmP7lVJUqlQp04ATBwcHvLy8CA8Pz3Rchw63PiYMBgPt27fn2LFjOf4ucvLss89y//3306BBAx544AEefvhhHnroIQwGGVBdVuX3l60PpGqtT+ayP9evq1rrICAeqFvItglxW5guwxWE1pCWRp5ZM1JSU0lJTc1WRqcZXx8+uI8hQwazadMmfH19zQMgunbtar7/BdCwYUM2btwIQI8ePWjRogVjxowxt7dly5YsWLCAv/76i7S0NDp27MjEiRPNlwxjY2M5dOgQ27ZtIzo6milTpvDvv/+a6zf1tPz9/Zk/f36me3Kpqak899xz1K5dGxcXFypXrszs2bMZP348mzdv5sCBAzRq1CjTe4yJieG3337D19cXDw8P7rnnHuLj4zMFVKUUCQkJLF26FFdXV/MoyYiICB588EHc3Nxo1aoVUVFR5mN+//339N/9rXPt2LEDMI6uBKhbty73338/4eHhLF26lD59+tCoUaMCZS0Rtim/4OUJROexfwIwOo/9cel1CFFq5Ze+KavE5LT0HIbZ64mNjSM1JfPsEoNd5m95qakp1K0dwA8//MCsWbN49dVXAeOIQdP8LJM1a9YAxkuKpiBXqVIl3Nzc6NOnD6NGjTIPTR83bhz79+9n8uTJhISEcPPmTe688046duxI8+bNefjhh2ndurV5CP9PP/1EgwYNOHjwIC1atODq1avmARBpaWn4+/vz448/cvz4cWrVqkVKSgopKSm0atWKunXrcvXq1UxtTU1NpWHDhvz111/s3r2bVq1acfHixWyJhmNjY2nTpg0HDx4098qmTJnC6NGj2b9/P1WrVmXTpk00bmwcI+bu7g6Q6T7ZmTNnMtU5ZcoUTpw4wcaNGzl37hyLFy/m9OnT2cqJsiO/4BUPVMhtp9b6G631x3kc7wEkFKZhQtwurq4u+RfKIiEpjTRtXP7DmJBXGwdmmOdCXebjT7/gk8++YOnHX7Dk4y+4eOmy+fiGDetx+fJl+vfvb55TtWnTpmz3vDJm8PD09KRWrVrY29ub19mKj4/nySefxN7enrVr1zJo0CD++OMPXnrpJerVq4e/vz+Ojo6MHDmSP/74g40bN5qDSWxsLG+//Tb169fniy++AKBfv378/vvvBAUFce+99xIcHExAQACdO3fGycmJJUuWcOLECcaOHWvu9WRsX8OGDWncuDGNGjVi8eLFGAyGbAHExcWFtm3bUr9+fcaMGQMYe17JyclorXF1dSUpKYmnnjLeLq9UqRJKKQIDAzl16hR//PFHphGLAFu2bMHT0xN3d3eSk5M5efIkHh4e5vloouzJL3hFAI5KqZqWVqyUqgU4AuH5lRXCmu68s53F86oAqvnXZPTYcbRs1ZobN6JJTr6V5b1atao83f9xnn7q1sO/WlXzfjc3V2rXrsW33976EI6JiTFfNlywYAFgHAkJ0L17d3x9fbl8+TJRUVEsXbqUy5cv07BhQ5ydnc0B6quvvgIgLi6ODz74wJyTcODAgSxevJgPP/yQBx54AIAJEybQu3dvAPOHfP369Rk4cCCNGzdm8ODBvPLKK/j5+bF06VLi4uLYt28f9913H25ubtkmTycnJ7N161YaNGhAxYoVcXd3JyUlJdugjoy/a9OAlJEjRzJ//nxatmzJvn37MpWzt7fHycmJc+fO0bJlS6ZPn87zzz+fqc7u3bvz559/0qxZM5o2bcr27dv57bffcHV1LeBfU9ia/IKX6QJ5r0LU3Sf9eXchjhXitunQ4a78C2Xh6OhIly6dcXFxxaOiF/YO9tjZgZ0dKAUO9vZ4eVbEM8Pj7o7tM9XRtHFDNm7cRL169XjggQfo3r07u3fvxs3NzTwar3Jl45Iqs2bNYv369YwYMYI6derkOLCje/furF+/HoPBwPjx4wkICMDJyQkXFxc8PDwYOXIkZ86cMQ9579evn/lY02jLESNGEBERwVdffcW1a9d45ZVX+P333zl48CDjxo2jZs2ahIeHM3fuXHr27Ent2reW6/Pw8MDJySlTnsOaNWty7733ZmrnZ599xtixYzOdt3bt2uzatYuEhARzRnrT/SqDwYDBYODAgQPEx8fz999/07ZtWwBatWoFwKJFiwgPD2fZsmU8/fTTnDlzhk8//dSyP6qwKfkFr3UYL9dPVEp5FrRSpZQX8BrGYfa/Frp1QtwGHh4etGvXLtPAgrzY2dnh6+uLn58fQUFB3Ii+nuc8r9zUrFkdJydHVqxYwebNmzl8+DBvvfUWcXFxeHl5AdCoUaNMxxw8eJA6deoAxkEKp06dMvfOAPbv309aWhotW7akdu3auLu7F2rQwo4dO2jfvj1jx46lTZs21KtXL98kviWV59DPz4+bN29mGlhz4MCBbOV8fX0ZNGgQy5Yt4/PPP+err77KdWK4sH35Ba8fgbOAP7BOKVUlvwqVUlUxBj1/4AywsqiNFKKk9e3bhwYNGuQbwOzt7fH0rEivXj0zjYgzfk+zjMFgoEXzZsydO5fU1FQuX76MUor+/fuzYYNxdoppAMXly5f5+OOP+fvvvxkyZAgAvXv3xtnZmZUrV5KWlsaePXsIDAykb9++dOjQAXt7ewICAjhy5AgnT54kMjIyzykBGTVo0IB9+/bx22+/cfr0aWbNmmVeByyvY7799luOHTvGnj176N+/f7HkOWzfvj1ubm5MmjSJM2fOsHr1aj766KNMZaZNm8batWs5ffo0x48fZ82aNdSpUwcnJ6cin1+UTnkGL611GjAISMI4X+u4Umq+Uqq7UqqSUsoh/VEpfdsC4Fh62QRgUHodQpRqBoOBwYMHcf/93XF1dc32oefo6IiDgwMtWjTnkUceyTRC0a1ChTwnOedEa82RoyfYu3efuWfUtGlTfvzxR2bMmGFO7/TKK69wzz33MGjQIL7//nvmzJljvmTo4uLC559/joeHB1prxowZw7333mu+7wUwbNgwGjduTNu2bfHz88s2OTg3I0aM4Mknn2TAgAHceeedXLhwgfHjx+d5zBdffEFsbCx33HEH/fv3Z+jQodkycGRVs6bxdnpe2TC8vb357rvv2LhxI82bN+eTTz5h1qxZmco4OTnxxhtv0LJlS+6++25iYmL49Ve56FOWqYIME1ZK9QC+A7zI/yumAq4BA7TWvxe5haWMUioAOH/+/Pl8/2MK25Samsrx4yc4duwocXE3cXZ2IiAggAYNGnDt2rVsl+FSU1P54rMlJCUV7BJVREQkW7ft4ErYrbFM48aNY8SIEZlSSDVs2JBFixbRo0ePXOsy5TH08fGRCbmlj2XfaIRFCjTESmu9QSnVEpgMDAZyG8JzE/gKmJM+SVkIm2NnZ0ezZk1p1syYBUNrTWRkZLZ5TRnLN2/RigP792ZbnDKjxKQk/v13LwcPHTX33Pz8/GjWrJk5nZMlKlSogJ+fX4Hv1QlRlhR4fHB6MBqtlBoHtMOYZd60HEoUcBzYrbUu2EV1IWxASkoKISEhmQZF5KRlqzYcP3aEmzfjsu3TWnPq9Fl27PiHuPQ5Vm5urty8GY+fnx/Tp0+3qE1OTk74+fnJMHBRrhXosqG4RS4blh8xMTGEhYUVeLTetWtR/LR6BQkJiZhu9UZdu8627TsICgoBjAM+Otx1J+PGjaN5i9YWtcfe3h4fHx8qVqxo2RsR1iKXDUuQBC8LSfAq+7TWhIWFWZTz0CQ2Npa///6Tk8eP8e+efezdu98c/OrVrc2jj/bloYd74e9fo8B1KqXw8vLC29tb7mvZFgleJcjytAJClGFJSUmEhIRkWi3YEhUqVMDOzpnVPxlXJAbw9fXh2SGD6PdEf/P8rYJyc3OjUqVKcl9LiCwkeAmRztLLhFkFBQUxe/ZstmzZAhiX/xg+fDjDhw835yIsKAcHB3MCXiFEdhK8RLmntSY8PDxbDr6CSkpK4osvvmDJkiXmgR13330306ZNs/jSslIKb29vvL29LZ47JkR5IsFLlGtJSUmEhoYWOo3Q33//zYwZMzh//jxgzIA+efJkevToYXHwkaHvQhScBC9Rbl2/fp2IiAiL1/MCCA8P55133mHdunWAca7XoEGDeOGFF6hQIddVhHLk6OhIpUqVZOi7EBaQ4CXKnZSUFMLCwoiLyz4nqyDHfvfddyxatMh8fJs2bZg+fXq2JLr5sbOzMw99l0uEQlhGgpcoN7TWXLt2jaioqEINyti/fz+BgYGcOHECAC8vL1577TUeeeQRi4awy9B3IYpOgpcoFxISErhy5UqhhsBfu3aNefPmsXKlcYEEpRRPPvkkL7/8ssVD393d3fH19ZX7WkIUkQQvUaZprYmKiiIqKsrie1tpaWmsXr2a9957z7zkfZMmTQgMDKRly5YW1eXi4oKfn5/FQ+aFEDmzOHgppbak/6i11t2KWk6IkpKUlMSVK1fyzUuYkxMnThAYGMj+/fsB40jAl19+maeffjpT5vf8ODg44OfnZ/EgDiFE3grT8+qc/pzf19iClrstlFIDgFFAC8AOOAF8CSyRNcfKnujoaCIiIiy+txUbG8v777/PN998Yz62V69eTJw4ET8/vwLXYzAY8Pb2xsvLSwZjCFECChO8/qRgAamg5UqcUupDYDTGBTI3A8lAN+ADoJtSqp8EsLIhOTmZsLAwbqZnby8orTX/+9//mDNnDhEREQDUqVOH6dOnc9ddd1lUl4eHB76+vtjby1V5IUqKxf+7tNadi7NcSVNKPY4xcF0B7tNan07fXhnYCjwKvAAsslojRZFprbl+/TqRkZEW39s6d+4cM2fO5O+//waM96dGjx7Ns88+a9Ey9nJfS4jbpzx8NZyU/vy6KXABaK3DlFKjgG3ARKXUYul92aa4uDgiIiIsHkkYHx/P0qVL+fzzz0lONi5D161bN9544w38/f0LXI+9vT2+vr54eHhYdH4hROGV6eCllKoO3AEkASuz7tdab1dKBQP+wF3ArtvbQlEUSUlJREREFGqy8ZYtW3jzzTcJDg4GoHr16kydOpXOnTsXuA6lFJ6envj4+Mh8LSFuszIdvADTan9HtdbxuZTZgzF4tUaCl01IS0vj6tWrXL9+3eJLhMHBwbz55puZMr8///zzjBgxAhcXlwLX4+LiQqVKlXBycrLo/EKI4lFiwUspZQ90BNBa/1lS58lH7fTni3mUuZSlrCjFbty4QWRkJCkpKRYdl5SUxJdffslHH31kHjrfoUMHpk2bRp06dQpcj729PX5+fri7u1t0fiFE8SrJnldFjPeT0kr4PHkxTa7J67pSbPpztk8jpZQn4Jllc/Uit0pYrChztv7++29mzpzJuXPnAGPm90mTJvHQQw8VeBi7XCIUonS5HUHFlie5vARMt3Yjyrvo6GjCw8MtvkRYXJnfXV1dqVSpkkUjD4UQJaus3/My9aryWo7W9CkWk8O+hcCyLNuqA38VqVWiQAqb/T0pKYmvv/6aJUuWEBtr/CfQunVrAgMDLcr8LpcIhSi98gxeSqkHilB3aRg3fCH9uVYeZWpkKWumtb4OXM+4TbIl3B6FyZARHBzKihU/8uPKFVy/FgWAk5MTffo+ygtjx1K5csEyZEjWdyFKP5XXpRilVBpFy5KhMOY2LHgyuGKklKqBcUBGEuCZ04hDpdRljL2pe7TWOwtQZwBw/vz58xYv8S7yl5CQQHh4uEX3tpKSkvhy2XJ++XkNZ86cNG+vXac+zVu2wdXVFYMy0KpVc3r3fDDPgOTm5oafn59cIhTFQb7plqCCXja0yT+C1vqyUmof0AZ4Avg6436lVCeMgesK8Pftb6EwSUpK4urVq8TE5HT1Nu/jxk+YyPatm0lMNAY8T09v2rS9Cx9fY08rNTWNVNI4ePAwCfGJPPlE32w9aEmgK4RtyS94hQJVgMe01mstqVgp5QuEF7JdxWkOxgnK7yildmmtzwAopSoBH6WXeVuya1hHSkoKV69e5caNGxYPyDhx4gQvv/wK586dBcDewYFmzVtTt17DHHtXSckpnDx1mv0HDtOmdQvAOIjD29sbT09PuSQshA3JL3jtAXoDbYG1FtZdKpLyaq1XKaWWYMwof1gptYlbiXk9ML6vD6zXwvIpLS2NqKgorl27ZnHQio2NZfHixXzzzTekpqYCUKNmbVq2bouLi2uexyYlJ7P9r120btUcT09PfH19LVriRAhROhQkePUB2t2GtpQYrfVopdQOYAzQiVtLonyBLIlyW2mtiY6O5urVq+bAY8mxv/32G3PmzCE83Nip96joSes27ahUuWqB67l5M540DZUrV7bo/EKI0qMgwQuM+QEtlYpxsESpCAxa6+XAcmu3ozy7ceMGV69eNSfBtcT58+eZOXMmu3YZM3g5OzvTuXNX3Ny9MRSw52RQCmdnF1xcXQkKCqVxo4YWt0MIUTrkF7y2cSs/oEXSh5kHFOZYUbbExcURGRlJYmKixccmJCSwdOlSPvvsM3PQ6969O5MnT+avnf9y5OiJAtXj6OiIs7MLBoOB1NTUQmXqEEKUHnkGL611EnDwNrVFlDHx8fFcvXrV4oUhTbZt28asWbMICgoCwN/fn6lTp9KlSxeAAq2bZWdnh4uLa6aFIe3t7HB2Kd1rbgUGBrJq1SqOHDli7aYIUSqV9QwbwgoSEhK4evVqoZYqAWPm99mzZ7N582bAOIz9ueeeY+TIkZkyv9erW4dDh4/luI6XQSmcnJ1xcjIGqciIcNau+Z5KlarQ76mBNKhft1BtE0KUDvll2NiFMT3SD1rr6NvSImGzihq0kpKSWLZsGR999BHx8cb55B06dGDq1KnUrZs92DRqVB87Q/bh7U5OTjg5OWcaLn/i+BEaN23B6VPHSUtNoVbNGtmOE0LYjvxy39wFLAFClVIrlFIPK6UkX47IJCEhgeDgYC5dulTowPXPP//Qt29f5s2bR3x8PH5+fixYsIAvv/wyx8AFxst/HTu0x9HB+B3M3t4ed3d3XFxcMwWulJQUzp45QePGzalXryHR1yOynbtr1664ublRsWJFunbtSkhICGAc4Thv3jzq16+Pk5MT1atXZ9Ik4+LcFy5cQCnF3r17M9WnlGLVqlWZyqxYsYJOnTrh4uJC69atOXToEEeOHKFjx464ublxzz33cP78+Wzv8bPPPqNmzZq4uLjwyCOPEBkZmWn/l19+SZMmTXB2dqZBgwYsWLAgU0otpRSffPIJTzzxBG5ubtSpU4dvv/02Ux0zZ86kVq1aODk5UaVKFQYPHpz7H0qIUiK/QLQaY2olZ4wZKn4FgpVS7yqlmpd040TpVhxBKzw8nPHjxzNkyBDOnTuHwWBgyJAhbNiwgYcffjjficP33duBgFq1qFjRkwoV3LGzy34x4fy501Rw96BK1ar0feQRNv7xu3nwx8GDB+nSpQv16tVj586d/PPPPzz11FPm9cImT57MrFmzmDRpEkePHmXlypXUqGF5r2369Om8/vrr7N+/H09PT55++mleeOEFZs+eze7du0lISODFF1/MdMyFCxf49ttv+fnnn9m0aROnT59m6NCh5v2ffvopkydPZubMmRw/fpx58+bxzjvv8NFHH2WqZ+bMmfTt25eDBw/y1FNPMXToUC5dMi5jt3r1at577z0++ugjTp8+zbp162jXzqZnxohyIs/chgBKqYrAU8Bg0heX5NYE5EMYLysu11pHZD+67JHchsbLe5GRkeaM7YWRmprK8uXLWbhwYabM79OnT6dx48YFqsOUQNfDw4OvvlnBgYNHSE1NzZbMd/0vq6gVUJdXxr/CY4/0ok6dOrz33nv069ePZ555hnPnzvH339mzg8XGxuLr68vChQsZOXJktv0XLlygdu3a7Nmzh7Zt22Zq18qVK+nXr5+5zNKlSxkxYgQA69ato3fv3qxevZrHHnsMgGXLljF27Fjz7yIwMJBZs2Zx/vx5atasCcCOHTu49957OXXqFPXr16dmzZrMnj2bQYMGmc+9cOFCPvnkE44dO2Zuy8SJE5kzZw5g7IV6eHjwySefMHDgQObPn8/HH3/MkSNHcHBwKNDvXRSYpGwpQfkO2Ei/1/UJ8IlSqg4wBHgGqAO0BOYDc5VSG4CvgF+11pZP5BGlXnJysjmVU1EcOnSI6dOnmz9gPT09mTBhAo899liBs7hnTaA77LnBhF4JY/OW7fy7+z+Skoz/BBMTbnLlSgh//PE7TZs2AeCZZ57h888/p1+/fuzfv59HH300x3McO3aMxMREunXrVqT3C9CiRQvzz6bJ0c2bN8+0LS4ujps3b+LqaswS4u/vbw5cAO3bt8dgMHD8+HE8PT25fPkyI0aMYNSoUeYyKSkp2TKWZDy3aZkX0yTvJ554gkWLFlG7dm0efPBBevToQZ8+fXByciryexaiJFk02lBrfQ7j4ozTlVL3YAxk/TCumtwb6AVcU0qtAL7WWu8u5vYKK0hJSSEqKoro6GiLUzlldP36debPn8+PP/5orufJJ5/klVdewcvLq0B12NnZUalSpRzX2KpapTIDBzzJwAFPkpKSglKKN954g7S0NFq2vPUBbjr35cuXC/1eAHOgzfg7yW0CdsZejelSaE7bCroEjKnc0qVL6dixY55ls/aolFLm42vUqMHJkyfZvHkzmzZtYvz48cyYMYN///0XN7e8lsETwroKPVRea70D2KGUGgv0xRjI7ge8MeYRHKWUOgV8pbV+uzgaK26v1NRUoqKiuH79epGCVlpaGj/99BPvvvsu165dA6BRo0bMmDGDVq1aFbged3d3KlWqVKBchPb29qSkpPDVV18xZ84cevXqlWn/oEGD+PLLL2ndujVbtmzJsY7GjRvj5OTE5s2bqV+/frb9fn7GrPWhoaHmbQcOHCjw+8lPcHAwly9fNt9j2717N2lpaTRu3JjKlStTrVo1zp49W+QBFs7OzvTs2ZOePXsyceJEqlSpws6dO3nggaIs5ydEySryPC+tdSLwI/Bjeqb2ARjvj7UCGgKzAQleNkRrzfXr14mKirI4/2BWJ06cYMaMGezbtw8wXu576aWXGDBgQKaJw3kxGAxUrlzZ4hWN169fT2RkJMOGDcPHxyfTvv79+7N06VLWrFlDhw4dGD58OGPGjMHZ2Zm//vqLBx54gJo1azJu3DgmTZqEk5MT9913H1evXuW///5j1KhRuLi4cNddd/HOO+9Qt25doqOjzSMRi4OLiwtDhgxh/vz5xMfHM3LkSHr27GkOpDNmzOCFF17A09OThx9+mOTkZPbt20dwcHCB27Fs2TJSUlJo3749FSpU4IcffsDBwSHHYC1EaVLck5SvAyEYl1JpjjEBrrAhRck/mFFsbCwffPABX3/9tTkA9urVi9dff51KlSoVuB5nZ2eqVq1aqMEEn3/+OV26dMkWuMB4r2fixIlERESwadMmJk+ezF133YWTkxNt27alZ8+eAMyZMwcvLy9zpo/KlStn6ul88cUXPP/889x5553UrVuXjz76iPvuu8/ituYkICCA/v3707t3byIjI3nggQf47LPPzPuff/553NzcePfdd5k0aRIuLi40bdqUsWPHFvgcnp6evPPOO7z66qskJyfTpEkT1qxZQ+3atYvlPQhRUvIdbVigSpS6F2Nvqx/GZUbAONImAuNIxJeLfJJSoqyONixK/sGMcsr8Xrt2baZPn06HDh0sqsvb2xsfHx9ZZ0vYKvmHW4IK3fNSStXDGLAGArVMmzHOC1uHceThb1rrlKI2UpSchIQEIiIizBktiuL8+fPMmjWLnTt3AsZe0+jRo/m///s/86jAgrCzs6Nq1armUXdCCJGVRcFLKeUF9AcGAe1Nm9Of92AMWN9rra8VWwtFiShqKqesdX388cd8+umn5suNXbt25Y033qB69eoW1eXs7Ey1atUKfD9MCFE+5fsJoZSyxzgEfhDwMODIrYAVAnyDcURhwdamEFZVnEELYPv27cycOTNT5vc33nijUHOjPD098fPzk8uEQoh85ZeY9wOM2TW8TZuAeGAtxl7WptxWIU4Peh0BtNZ/FlN7RSEVdXmSrEJCQnjrrbfYuHEjYJxLNHToUEaNGoWTkxOHj53m6LEzxMbF4+zsRP26NWnTslGuAy/8/PwKPNdLCCHyHLChlMoYmHZgDFg/aq1j8q1YKR+MAzbStNZl5hqQrQ3YSEpKIiIioth6Wnllfq9Tpw6bt//Lbxt3kpyUTELiraVKnJ0cQcF9He+gz8OdcXQ0BjGlFJUrV8bDwyPH8wlhw+QSQgnKL6hcxBiwvk7PrlEY8ge0gtTUVCIjI4mOLr6VbP79919mzJjB2bNnAWNvaeLEifTs2ZO0NM0nX67i8LGzOa6vZQpkW//ay4lTFxj/4mDcK7hRpUoVGZghhLBYfispy2QPG5OWlmaeYFzQVEP5iYiIYO7cufzyyy+AcdLwM888w7hx48wTh39Y8xuHj54lKTl74MooOTmZ0CsRfPbVzyycO6lA2TKEECIrWZurjDBlxTh//jyRkZHFErhSU1P57rvveOihh8yBq3Xr1qxZs4YpU6aYA9e1azfY+feBfAMXGC8T2jk4Exp+jaMnCtuZt9yqVatkIIgQZYgErzIgJiaGCxcuEB4eXuR0TiaHDh3iiSeeYObMmcTExODp6cmbb77J8uXLsy1Zsn3n3lxqycxgsMNgsOPkkb/57afPaNOyCf7+/jz00EP873//K5Z2F6eAgADee+89azdDCJGDMjOQojy6efMmkZGRJCQkFFud169fZ8GCBfzwww/mZLz9+vVj/PjxeHt753jMjr/3k5yS91x0OzsH0tJS2bV1NfYODjRs1gEv78rMe+sVduz4i5EjR5oXSBRCiPxIz8sGJSQkEBQURFBQULEFrrS0NNasWUOPHj1YsWIFWmsaNWrE999/z+zZs3MNXABxN/Nug52dA84ubhw9YJwxcU+3J6lWoz6e3r5UrVadsWPHcujQIQDmz59PixYtcHNzw9/fn+eff57r16+b61q2bBkVKlTIVP+2bdtQShEZGWne9vXXX1OrVi1cXV3p1asXYWFhmY45e/Ysffv2pUqVKri5udGmTRvWrVtn3t+5c2cuXrzIhAkTUEpluuS4a9cuOnXqhKurK/7+/owaNarIa5wJISwjwcuGJCQkEBwczKVLl4ptvhbAyZMnGThwIJMmTeLatWu4ubkxadIkVq9eTZs2bfI9Pq87SabAlZycSMSViwTUbY69/a1UUaag4OnpCRgHgyxcuJCjR4+yfPlydu/ezQsvvGDR+/n333959tlnGT58OAcOHKB3795MmzYtU5nY2FgeeughNm7cyMGDB3n88cd57LHHOHHCONd+zZo1VK9enWnTphEaGmpe9uTw4cM88MAD9OnTh4MHD7JmzRoOHDjA0KFDLWqjEKJo8puknPNCRwUja4oXk+LOimGSW+b31157zbzab0G4u7tx7Xr2noe9vSNOzq4opbgZaxyyX8Hj1kTktNQ0Knpk7kW99NJL5p8DAgKYO3cuffv25auvvirwKsuLFi2iW7duvPHGGwA0aNCAPXv28Pnnn5vLtGzZkpYtW5pfv/HGG/z666+sWrWKKVOm4O3tjZ2dHe7u7lSpUsVc7t133+Wpp55i/Pjx5m1LliyhdevWhIeHW5QxXwhRePnd8+oMaGSullWUVNDSWrNhwwbeeuutImd+B+h8b1vWbfgr0zIqDo7OODm5mF+fOmZcVPu/v39DKQMurhW4o+1dpKQk4+R0qye2ZcsW5syZw/Hjx4mOjiY1NZWkpCQmTJjA77//zquvvppve44fP07v3r0zbevQoUOm4BUXF8eMGTNYt24doaGhJCcnk5CQQIsWLbJWl8l///3HmTNn+OGHH8zbTPcGz549K8FLiNskv+D1NcbgJW6jkgpaABcvXmTmzJns2LEDACcnJ0aPHs3QoUMtyvye0T0dWrPut1sZwJycXHBwdM5UxiH9UmHdhm2oVbc50dfC2PPvNl599VWWLFliblvPnj0ZNmwYM2fOxMfHh3379vH000+be4YGgyHbqs6FWXvs1VdfZcOGDbz33nvUr18fV1dXBg8enOME64zS0tJ4/vnnefnl7Kv8+Pv7W9wOIUTh5DdJ+dnb1A5ByQathIQEPvnkEz755BPzh32XLl2YMmWKxZnfs3Kv4MZD99/Nbxt3YrB3wsEhexA02Nnh6OhM0MUT1Gvclmo16lOnekXWrl1L5cqV+eGHH3jzzTdJSkpiwYIFfPPNN4wdO5aZM2cCxkuBAEOGDAGMl+pGjRrF/PnzeffddwFo1qwZvXr1om7duvzzzz+AcYDH2LFjueeeewBwdXWle/funD17lrZt2zJx4kTCw8Pp2bMnZ8+epUGDBoCxNxUXF8ecOXOYOnUq9erV4/XXX6dNmzYcPXoUe3t7ateuzapVq1i6dCk7d+4kICCARYsWcf/99xfp9ymEyJ8M2CgFMg7EKInAtX37dnr16sWHH35IcnIy/v7+fPjhhyxdurTIgcuk071tcXR0zjFwmbh7GEcs7tj8I8GXTrNn3xESEhLYs2cPZ8+epX79+qSlpbFw4UIiIiJISUlh4cKFgHHV4IYNG3Ls2DFcXV05cOAAZ86c4fDhw+b6P/74Y3bv3k1sbCybNm1izpw5XLlyhfj4eLZsMd6+3bx5M3v37iUiIoLff/+dN998k/nz57Nq1apMIwanTJnCzZs3qVevHlu2bGHs2LGMGDGCDh06sHv3bvP9tAkTJtChQwf69OnDnXfeSf/+/YmNjS2W36kQIncSvKzINOS9pIJWaGgoY8eOZfjw4Vy+fBkHBwdGjBjBunXr6N69e7GdRynFsZOXcc4nR6GdvQP3dHsK30o1OHrgLy6cPcrNm/GcOnWKqlWr0qJFCxYtWsT8+fN54403SE5ONk8SdnNzw97ensaNG7N8+XK2bt1K8+bNCQkJMfe87r77bubOncvWrVv59NNPWbJkCdOmTSMtLY3XX38dMN77GjBgAJGRkbRu3ZqhQ4cyffp0WrRoYU4OHBcXx/z585k3bx4JCQl06dKFkSNHMmzYMP73v//x559/mpeACQ4O5qeffqJBgwa89dZbREVFceDAgWL73QohcpZnVnmRXXFklU9ISCAyMrJYh7tnlJyczLJly/jwww/Nmd/vuusupk2bRt26dYv1XEopqlSpwqiX3yL6xq0eR9Z/Vgf3bCL40kkMBnu0TiMtLY0q/rV5pN8gKnsa0zcdOXLEXN50uc/UiwkMDMxWBnIe4BEfH09wcDDVqlVj2bJljBw5MtN8uHfffZeFCxcSHBxs3vb666+zdetWdu/ezZ49e2jXrh2urq6Z5nclJycTEBDAyZMnuXDhArVr12bXrl3mQS5aawwGA6tXr+axxx4r8u9W2DwZ6FaCJMPGbVTca2rlZPfu3cyYMYMzZ84AmTO/F3duP6UU/v7+JCWncjM+Icu+7OW9favRom0XlDLg7OKGwWBHWEQ0Vb29CjUII68BHhkHXmRdlVkplW1dMaWUOR+k6fnXX3+lZs2amcplPS7ja9Pvt7gSIgshcifB6za4HUErMjKSuXPn8vPPPwM5Z34viK5du/LMM8/w3HPP5VnOYDDg7++Pi4sLN8IjsTMYyC/c2Nnb41bBM9O2lJRU/Pz8CAsLQ2ttDgBZL705Ojpmy9u4d+9e8wAPU3b6jFkyCqtJkyY4OTlx8eJFunbtWuT6hBDFT4JXCSps0Dp69Cj9+vWjZcuWrFixIs+yqamprFixggULFhATc2uN0C+//JK77rorU9nIyEiWLl3Ktm3buHLlCl5eXjRs2JBBgwbRqVMnwHj5zsXFhbzY2dlRvXp1nJycAHBzdSGlkAmBHR3s6dy5M1FRUbz11lv079+fbdu2sWrVqkzlAgICuHjxIvv27aNmzZq4u7tnGuDx2GOP8c8//5gHeBSFu7s7r776Kq+++ipaa+677z5iY2P5559/MBgMDB8+vMjnEEIUjQzYKAHx8fEEBQVx+fLlQvW2Vq5cyYABAzh9+rR54cec5JT53ZSmyDTk2yQoKIhHH32UHTt28Morr/DLL7+wbNkyOnfuzPTp083lvL298wxe9vb21KhRwxy4ACq4ueLn65XrMblRQLOm9WncuDFLlizhk08+oUWLFmzcuJHJkydnKvv444/z8MMP061bN/z8/Pj+++8zDfBo0qQJn332WbFlgZ81axaBgYG89957NG3alPvvv5/Vq1dTu7YscSdEaSADNiyU14CN4rg8mJCQwD333MN3333HV199RcWKFc0j5YKCgujWrRtvv/02CxYsyJRstl+/fgwYMCDbQIFHH32Ut99+m2HDhnHixAk2bNiAm5tbpjI3btwwj7TLetmwYcOGzJw5k507d/Lnn39SpUoVZs6cycCBA83H//vvvzwzcDDnz52lgocXDZvdxZ4d67ir0yP4Vsp9KL6zsxPTXh9B86b1C/37EqIUkwEbJUh6XsWgqD2tjDZs2EC1atVo2LAhffv2Ze3atebBC6YvGpMnTzYHLg8PDypUqMDkyZNp1KgRixcvBmD9+vXs2LGDN954g+vXr/PXX3/xzDPPZAtcpjry8uGHH9KjRw/279/PU089xdChQ83Ll8TGxtKrVy/uaNOKB3sPoXGLjhw/tCvf92kwGPDxrkizJvUK/ssRQoh0cs+rCIrS09Jac/h0CP/bcYywqFicHO1pHFCJDT/8SN++fQFo164dLi4ubN68mdq1a5svpaWlpeHq6sq4ceO4//776dq1K8ePH6dt27ZUrFgRMF7+My1jcujQIbTWhR4m369fP1588UUMBgOzZs1i0aJF/PnnnwwcOJDvvvuO1NRUli1bRlBIBJMDF5GUGM/+fzfmWp/BYMDN1YWZb4yR1Y2FEIUiwauQQkND882Dl5uN/5zgva+2cuVqDAlJyebskeu5SdKJfXg2epDeUTFU8nbnwQcfZO7cuVy5csU82q5Dhw688847VK5c2dwbi4qKyvV8Rb003LFjR3NGd3t7e/z8/MwJfU+cOEGzZs1wcXGhft2azJnxEq9OeivH4KUw5lL09fFkxhujqeSX+xphQgiRFwlehWSa/GuppSt38tGPO0hMyr7ycFL4cUCzZfkctiyfg1IKrW/NGfL39yc4OJjx48eblywpyNyiWrVqoZTi7NmzFuXdM11OzJqwN+OcqKzq1anB3Ddf4ecflxJQoxrJ2oHklFQcHR1o3qQej/XpTtPGdaXHJYQoEglet9HarYdYsjLnwIVOg+hz4NcanLzg6mF0fARgDBbt27cnMDCQHj165HkO06TZjMHF09OTe+65h2+//ZZBgwblOWDDxMvLCz8/v3zfU6NGjfjqq6+Ij483j1L8b+9eAEY89wSdO3fOtw4hhLCUDNi4TVJT05jz+UYSEnMIXACxQZCaACmJELwN0gNXnYYtee6557h8+XK2TBE58ff3RynFtm3biIqKMudMNA2Hf/zxx/ntt984d+4cZ8+eZfny5fTp0ydTHW5ubgUKXAADBgzAzs6OYcOGcezYMTZt2sRbb70FIL0rIUSJkeB1m2zbe4bklDzSBl09AsoA144Ze2H2buDfmeueHXjyyScJDg5m1678R/FVrlyZF154gYULF9KxY0dmzZoFQI0aNVizZg1333037733Hn369GHIkCFs2bLFvOwIGAdTuOaTYDcjd3d3fv31V44ePUrr1q2ZMGECgYGBADg7O+d9sBBCFJLM87KQaZ7X5s2bLVpOZNjMFfz5Xw4TjpPjIHwPxFxK32AAnybg0xwMDri5OPLZ9P60aVyjOJqfK1OCXUtSSeXm559/5tFHHyU8PBxfX99iaJ0QNkkuPZQgued1m4RG3si8QadB1DGIPAQ6/VKiaxWo3A6cPG+VUxAeVbLrQxkMBqpWrZrjHLCC+Oqrr6hTpw41atTgyJEjvPTSS/Tu3VsClxCixEjwuk0Mhgxfwm6GwZV/Iem68bWdM1RqCx61c0jHrjIfW8zs7Ozw9/cv0iW+sLAwpk+fTmhoKFWqVKFnz5688847xdhKIYTITILXbVKzihcnT1+E8P/gxrn0rQq8GoJvK7DLeQXitLQ0qvlVLJE22dvb4+/vnylPYWG89tprvPbaa8XUKiGEyJ8Er9sgNTWVqnahcP5nSE2f2OzsC1Xag7NPnsd6e7jStG6VYm+Tg4MD1atXz7Y+lRBC2AIJXiXs0KFDzJgx49YKwAZH8GsDnvVzXrExA2cne55/7K5iH3Lu5OSEv79/gYbeCyFEaSSfXiUkOjqaBQsWsGLFCnN6prs73c+eCF+S0vLv7djZGajmV5FHu7Qo1na5uLjg7+9vTvckhBC2SD7BCmnfkTMkJ2efcKy1Zu3atfTo0YPvv/8erTUNGjRg+fLlfPHJB8x68TGcnfL+zuDoYEf1yp588+ZAXJxzvhdWGG5ublSvXl0ClxDC5sk8LwuZ5nm51OmMvaMbAx/pzOBHuuDt5c7p06cJDAxkb3p6JFPm94EDB2a6RHfgRBCLvv+TPUcvYVCKxKQUDHYGXBztMRgMDHi4DcMf60gF16INpMjI3d2dKlWqSNYLIW4f+c9WgiR4WcgUvKjSAexdcHC0x83Rnq5N3fnl59WkpBh7Yw899BCTJk0yJ9DNSWhENJt3n+Z6zE0cHeyp7e9N5zvq4+BgV6xt9vT0pFKlSsVapxAiXxK8SpAELwtlCl52zsYchNGnITURgICAAKZNm8bdd99t1Xaa+Pj44OOT94hGIUSJkOBVgmTARmGlxMO1k5CYvo6WMlCnaXvWLv+4yPOmikulSpXw9PS0djOEEKLYSfAqrMiDmFeRdPYBzwYE3XQn9mai1YOXUorKlStnW+ZECCHKChl2Vmga7JyMCXR9WoC9cS2r1Rv+tmqrlFJUq1ZNApcQokyTnldhuVWDivXBcGtwRVJSCqcuhFitSQaDAX9/f/OikEIIUVZJ8Cos91qZApdJUlKyFRpjTLBbvXp1q1+yFEKI20EuGxYjZVBU9vW87ed1cHCgZs2auQauZ599ll69et3mVgkhRMmRnldhXT8DCRGZNmmgWe3HSuyUXbt25ZlnnuG5554zb5M8hUKI8kh6XkXh5AVV7zY/qt/Rl4fv75SpSFJSUomd3tnZmerVq0vgEiIXN27cYMqUKTRu3BhXV1d8fHzo1q0bq1atKnLd//zzD0899ZR5WaEqVarQp08fNm7caHFdSqlHlFLa9MinrJNSapxSaqdS6ppSKlkpdVUp9adS6gWlVPHllCvNtNbysOABBAAaZz+Ns4+meldN9a7aqe6D+sMvftTt2rXT/fv310OHDtVeXl66WbNm+uTJk/rbb7/VLVq00I6OjtrHx0cPGTJEHz58WJ88eVKfPHlSt2vXTj/99NN6xIgR2tPTU3t7e+uhQ4fq48ePm/dj7NyZH6mpqToyMlL3799f+/v7a2dnZ92kSRP9xRdf6IyGDBmie/bsqYUoTy5fvqxr165t/v9SoUIFbW9vb349atSoQtf99ttvm4KMVkppLy8vbWdnZ6578uTJWhf8M6UCcDnj/+08yvoBB7N8FlzP8no/4FPQ89vqQ3pexcDZyYFxz/am+90tAfjll1/QWvPdd98xd+5cwsLCGDZsGI0bN2bt2rXMnj2b9evXM3/+/Ez1/Prrr9jZ2bFixQqmTp3KV199xf/+9z8AFi9eTJUqVRgzZgwHDhwgJCQEg8FAQkICbdq0Yd26dRw9epRx48YxYsQINm/efNt/D0KUFlpr+vXrx/nz5wkICGDnzp3ExMQQExPD3LlzMRgMLFmyhE8//dTiun/55RcmTpyI1prnnnuOK1euEBUVxbVr15g5cyZKKd566y2UUk8XsMpZQHXg3wKUfR9oASQAwwBXrbUn4AaMBpKAVsC7Fr4t22Pt6GlrDzL2vFAag512dHLSrq6u+t5779Xt2rXTDRo0MPeoTp48qUeOHKlr1apl7kWdPHlSz5kzRzs4OOgDBw6Ye1atWrXKdFzHjh11v379zK/9/f31tGnTdH6eeuop/dxzz5lfS89LlDc//fSTBrTBYND79+/Ptv+ll17SgK5SpYpOTEy0qO5WrVppQHfo0CHH/f/3f/9n6gFdBOx13p8nbYAUYC8wlDx6XoATkJheZmouZQLT91/P67xl4SE9r0Kq5OtJoyZN2fC/9az79VdzjwqgWbNmmcqePXuWli1bZlqK5I477iA5OZmLFy+atzVs2DDzOSpV4urVq+bXBoMBd3f3TGVSU1OZPXs2LVq0wMfHhwoVKrBmzRouXbpUbO9VlG8BAQEopdi2bRvBwcGMHj2aOnXq4OTkRKtWrazdvBx99913AHTv3j3HNr766qsopbhy5QpbtmwpcL2hoaEcOHAAgJdeeinHMq+88orpx5pApxwLAUopA/AxxhyIo4C0fE7vBZjuZ+3Ppcx/6c8uqowvISHBq5BaNalNlUq+1K5dm1q1alGrVi1zBnlLJgln/PeVdeCFUsr0bQo/P78c1+F67733mDdvHhMmTGDz5s0cOHCARx55pEQHiojy6dSpU7Rq1YolS5YQFhaGg0P+i6pay9atWwF48MEHc9zv7+9P06ZNASwKXhm/FGb9smlSv379jP9X78+jurFAW+ATrfWeApw+HIhP/7l1LmXuSH8+oE0fHmWUBK/boG7duhw8eJC0tFtfrP777z/z/Kz8VKlSBS8vLxwdHUlNTc20b8eOHfTu3ZtBgwbRqlUr6taty6lTp4r9PQgxfvx4qlatys6dO4mLiyM2NrZYRu0Vt/DwcPMVC1OAykmTJk0AOHbsWIHrzvhlM+v/RZO0tDQyxI0cG6CU8gfeBCKAyQU5t9Y6Dfg8/eVkpdTzSimX9PpclVKjgEkY73u9XpA6bZkEr9tgwIABhIeHExgYyNmzZ9m2bRvz5s1j4MCB+fbSnJyczHkKAwIC+OuvvwgODiYyMhKABg0asHnzZnbs2MGJEycYO3Ys58+fL/H3JMofe3t7Nm7cSMeOHc3b6tWrV+DjlVKFflgiNDTU/HO1atVyLWfal7F8fjJ+2cwt6B0/fjxj8KqaS1WLAXfgNa31tQI3wBiUVgPOwKfATaXUdSAO+BD4E+iitd5mQZ02SSYI3QaVK1fm008/Ze7cufTt2xcPDw969eqV8dp4NgaDATc3t0yX/2bOnMmIESOoW7cuiYmJaK2ZMmUK58+f56GHHsLFxYVnn32WZ555xqJvk0IUxODBg/NcXDU/RTnWEnFxceaf8/py6OrqCkBsbGyB665SpQotWrTg0KFDzJs3jwEDBmS7nP/OO+9kfJn5JjWglOoNPArsAL4q8MkBrfVNpdRA4CzwWvrmiqaq08/na0mdtkoWo7SQaTHKzZs3U7169RI5h+QpFKVJQEAAFy9e5Pvvv6d///7Wbk6+du3aZV4M9vTp07n2Dt944w3eeustGjRowMmTJwtc/6pVq3jiiScAeOSRR3jzzTdp0KABISEhzJs3j8WLF+Pg4EBycjLACa11Y9OxSik34BhQDWijtT6cYd+zwJcAWuscu5tKqQbAOqAOsAD4AuOoxloYRyu+jPGK2mit9dICvykbJJcNSxkHBwdq1KghgUuUOn5+ftZuQoG4ubmZf46Pj8+13M2bNwGoUKGCRfX369ePwMBAANauXUuzZs1wdHQkICCAxYsX06NHD3r27Gkqfj3L4TMxjkJ8P2PgKgillB3wM1AfCNRaT9BaH9da30x/ngDMwNgDe1cpZRt/sEKS4FWKODo6UqNGDRwdy0d2F2Fb7Oyyr6JQGmW8zxUSkvsSRaZ9Vavmdlsqd9OnT2fXrl0MHjyYJk2aULNmTe677z6WLl3K+vXruXbNfBvrtOkHpVQ9YBzGUYPvKaUqZHxgnMdlKmvanvHD4EGgEcZ5XAtzaZppewWgm8VvzIbIPa9SwtnZGX9/f5v5gBDCUlWqVCn0sVeuXClwWT8/P3x9fYmMjOTo0aO5Dpc33Rc2jTq0VIcOHejQoUO27ampqRw6dMj0MuPqtNUBO6ASkN/CfzHpzzMwTjwGMF1+jNRa53ijTmsdo5SKwJhGKiCfc9i0Mh28lFINgYcxfmNpifFG5k2M15x/AJZorROt10IjV1dXqlWrluM8LiHKirCwsNt2ri5durBy5Uo2btyY48Co4OBgjh49CkC3bsXbQdmwYYOp55UEFOdcAtNcGx+llIvWOts1UaWUK7cGbMRk3V+WlPVPy83AfIyz3M9g/If0H8bcXwuAv5VS3lZrHeDu7o6/v78ELlHmFSUVkKUGDBgAwB9//MHBgwez7Z8/fz5aa6pWrUqXLl2K/N5MoqOjmTBhgunl51pr87pJWuttWmuV2wP4vwxlTdsDM1RveiMG4PlcmvA8xnteULBciTarrH9ingSeA/y01vdqrZ/WWnfF2P0+inGW+gJrNa5ixYpUrVrV4nksQoi89e3bl/bt25OWlsajjz7KP//8A0BiYiLz5s1j4cKFAMyYMSPHe8ymlFjPPvtstn1hYWG8/vrr/PfffyQkJACQnJzM//73P+6++26OHz9uGuE4sZjf1nbgePrP7yilxiulPAGUUp5KqfHA2+n7d2mt9xbz+UuVMn3ZUGud4/UArfUFpdRI4C/gSaXUMK31bc2n5O3tja9vuZiOIcRtp5Ri1apV3HfffZw/f54OHTpQoUIFEhISSElJAWDkyJEMGzbM4rrj4+OZO3cuc+fORSmFp6cnMTEx5npbtmzJ+vXr8ff3v1Gc70lrnaqU6gdsxDjU/j2MAz9iyDyf7AxQ0Iz2Nqus97zyYkps6Qz43M4Tm24oCyFKTvXq1Tlw4ACTJ0+mUaNGpKSk4O7uTpcuXfjxxx9ZsmRJoer18/NjxowZdOrUiSpVqhAbG4uXlxddu3bl448/Zu/evfj7+xfzuzHSWh8DmgFTgd1ANOCKcUj+3xgnLrfWWpf5zNzldpKyUqoVxgCWBHgUdOBGUScpV65cmYoVK+ZfUAhh6+R+QAkq05cN82G6Hr3udow4VEpRtWpViydECiGEyK5cBq/0NCxPYRw2n2tG5/SboZ5ZNlvc3TIYDFSrVs2cS00IIUTRlNrgpZSaC/QpxKHdtNbBedTbDeMCcBoYobXOK6nZS8D0QrTBzM7ODn9/f5ydnYtSjRBCiAxKbfDCOJom59Xe8pbrCnlKqXsw5gZzBF7UWn+bT10LgWVZtlXHOEoxX/b29lSvXl3SPQkhRDErtcFLaz0QGFhc9SmlOgL/A9wwrqGzuABtuE6WxJoFnZPl6OhI9erVs62OLIQQoujKxSerUuouYAPGuRBTtNbvluT5JE+hEEKUrDIfvJRS7YDfMQauQK317JI8n+QpFEKIklemg5dSqi3wB+ABzNJazyjJ81WoUEHSPQkhxG1QpoMXxsBVEeN9q5pKqWW5lHtVax1ZlBNVrFjxti1zLoQQ5V1ZD15e6c+ewJA8ygUChQ5eXl5eNrPKrBBClAVlOnilLzNQonx9ffH2tuqqKkIIUe6U6eBV0iRPoRBCWIcMiSskCVxCCGE9ErwKyc3NzdpNEEKIckuClxBCCJsjwUsIIYTNkeAlhBDC5kjwEkIIYXMkeAkhhLA5EryEEELYHAleQgghbI4ELyGEEDZHgpcQQgibI8FLCCGEzZHgJYQQwuZIVnnL2QEEBQVZux1CiFKsdu3aAUCQ1jrF2m0pi5TW2tptsClKqXuAv6zdDiGETaittb5g7UaURRK8LKSUcgLuBEKB1BI6TXWMAfJeQLp41iF/A+srC38D6XmVELlsaCGtdSKwoyTPoZR5Aegg+dZmHfI3sD75G4i8yIANIYQQNkeClxBCCJsjwUsIIYTNkeBVOl0HZqQ/C+u4jvwNrO068jcQuZDRhkIIIWyO9LyEEELYHAleQgghbI4ELxuglHJTSj2jlFqolNqplIpTSmml1Dprt62sUUoNUEr9pZSKVkrFKqX2KqXGKKXk/0oJU0o1VEqNU0p9q5Q6oZRKS/933s/abROlj0xStg31gW+t3YiyTin1ITAaSAA2A8lAN+ADoJtSqp/WOs2KTSzrRgHjrN0IYRvk26RtiAG+wPjB2h4Yad3mlD1Kqccx/n6vAC201r201o9i/OJwHHgUeMGKTSwPjgDvAk8B9YDt1m2OKM2k52UDtNZngedMr5VSTazYnLJqUvrz61rr06aNWuswpdQoYBswUSm1WHpfJUNr/VnG1xnSQwmRjfS8RLmnlKoO3AEkASuz7tdabweCgSrAXbe3dUKInEjwEgJapz8f1VrH51JmT5ayQggrkuAlBNROf76YR5lLWcoKIaxIgpcQUCH9OS6PMrHpz+4l3BYhRAHIgI0SppSaC/QpxKHdtNbBxd0eIYQoCyR4lbxqQMNCHOdQ3A0RuTL1qtzyKGPqncWUcFuEEAUglw1LmNZ6oNZaFeJxwdptL0cupD/XyqNMjSxlhRBWJMFLCNif/txUKeWSS5k7s5QVQliRBC9R7mmtLwP7AEfgiaz7lVKdgOoYs2/8fXtbJ4TIiQQvIYzmpD+/o5SqZ9qolKoEfJT+8m3JriFE6SCLUdoIpdRPQNX0l35AHYwrzJ7MUGyW1nr9bW5amaGU+ghjctgEYBO3EvN6AGuBflrrVKs1sIxTSrXh1hcFgCYYpyacBqJMG7XWkuVEyGhDG9Ka7AMKPDEm6jXxu22tKYO01qOVUjuAMUAnwA44gTEp8hLpdZU4DzL/ezapf7sbIko/6XkJIYSwOXLPSwghhM2R4CWEEMLmSPASQghhcyR4CSGEsDkSvIQQQtgcCV5CCCFsjgQvIYQQNkeClxBCCJsjwUsUO6XUMqWUVkpty2W/UkqdTy/zUU5lcjluZfox/2TY5qSUelApNUUp9bNSKiS9jFZK9SiGt1NgSqlWGc59qgDlAzKU71yUshn2ZX0kKqUupv/u7i+p9yLE7SbBS9x22pjW5ev0l08ppRzzO0Yp5Qn0Tn/5VYZdjYENwCyMK1ZXxXqGZPi5vlKqgxXacAMIy/AAqAn0A/5QSr1bwHpKw3sRIlcSvIS1mIKXN9CrAOWfApyARGBFln3Xgc3A28DjxdQ+iyil7IEB6S8/S38ekkvxkjROa13F9ACcgabA/9L3v6qUujevCkrRexEiVxK8hFVorc8CO9JfDirAIYPTn3/VWl/LsP0Q4K217q61nqS1XlOc7bTAQ0AljOt9vQlojL1KJyu1BzD2crXWxzCuU3Y9fXPv3I8ASul7ESIjCV7CmkyX/x5WSvnkVkgpVRfomOUYALTWabp0ZJc29Uy+01pfBHZizPrf12otykBrfRM4m/7SLZ/ipfq9CAESvIR1/QjEY1zBuH8e5Uy9rnCM97eKjVJqW16DSwpYhzfG3kwKxvcE8F36c6m43KaUcgHqpr88k0e5Uv9ehAAJXsKKtNY3MC7yCLlcOlRKKWBg+svvtNYpt6FpluqPMQBv1FpHpG9biXExyweVUlWs1jJAKdUQ+AFj7ymKLL3XLEr1exHCRIKXsDbTB2l7pVSDHPbfg3HV6IxlSxtTj2S5aYPW+irGXqId8EwB6lijlLqS2wPYU8C2LMpybALGBTUfANYAHbTWUXkcXxzvRYgSJ8FLWNsmICT958E57DdtO6S1PljcJ9dad9ZaK61158Icr5RqBLQDbnKrF2liyeU2L6ByHg/fAjbJI8txpkEWjkBFIK97i8X1XoQocRK8hFVprVOBb9JfDky/TAiAUsoZ4yg5KL29rmfTn3/RWsdm2fcLEAM0V0q1zqeeLulBNMcHULuA7fm/LMe5A62BZUA3YEsek5WL670IUeIkeInSwBSYagH3ZdjeF2NvIYVb3/xLDaWUgVv345Zn3a+1jgd+Sn+ZU6+yxGmtY7XWB7TWQzHOj3MGFiul7DKWs4X3IkRGEryE1Wmtj3Prnk7GD0bTz79rrcMofboD/uk//5JTeiZuvYcB6ZN/rWlZ+nNDoGWWfbb2XkQ5J8FLlBam3lc/pZSzUqoyxkEGGfeVNpbc/6mEcfKvNV3K8HOdLPts7b2Ick6ClygtvgeSMA44eARjeiJ74BrG+y2lilLKA3g0/WVvjAMucnt8mF7O2oMd/DP8nGz6wUbfiyjnJHiJUiF9+Pa69JeDuXWJ6getdaJ1WpWnJwAX4CqwQWt9PbcHxjlWAL3TJwFby5MZft6f4WdbfC+inJPgJUqSg1LKN5+HQ4bypsuDDwKtsmzLlVLKK2OdGXZ55HEu07GFzbBh6nn8UoCJ0zsxZnjPL5NIiVBKVVZKvQU8n77pZ611xkuINvNehDCR4CVKUkcgIp/H3RnK/5a+zfTv8pTW+h/ytz9LnSY/5HGuQlNK1cE4eRpgdX7ltdZp3Jo3VdKX27JOUo4BrgCTAAXsA54zFS7l70WIXEnwEqWG1jqZzMO0v86trJUNxhgIYjBOsi4IU2Bolz4ZuKRknaTsjDFwbwZGAHelZ8wwKc3vRYhcqdKRkFsIIYQoOOl5CSGEsDkSvIQQQtgcCV5CCCFsjgQvIYQQNkeClxBCCJsjwUsIIYTNkeAlhBDC5kjwEkIIYXMkeAkhhLA5EryEEELYHAleQgghbM7/A2F8GbQTHHIQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r = 0.866, p = 0.001\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r = 0.487, p = 0.154\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run replication analysis and plot\n",
    "\n",
    "fig_dir = '/Users/jacobv/Dropbox/Work/Projects/PLS_ABA/Manuscript/Figs/SubFigs/'\n",
    "sns.set_context('notebook',font_scale=2)\n",
    "\n",
    "# color scales for lines\n",
    "pals = ['cividis','viridis','magma'] \n",
    "\n",
    "# used to control location of text within plot\n",
    "# I havent messed with this in awhile as I ended up moving the text\n",
    "# manually after creating the figure. Like a pleb.\n",
    "scale = {1:0.02,2:0.015,3:0.008} \n",
    "\n",
    "# for each dataset\n",
    "for nm,gtC in datasets.items(): \n",
    "    print('%s %s %s'%(('='*10),nm,('='*10)))\n",
    "    # for each PLS component\n",
    "    for i in range(1,4):\n",
    "        # standardize data (because no intercept) -- pattern expression\n",
    "        vals = StandardScaler().fit_transform(gtC.loc[:,'C%s'%i\n",
    "                                                 ].values.reshape(-1,1)\n",
    "                                         )[:,0]\n",
    "        gtC.loc[:,'zC%s'%i] = vals\n",
    "        # Load Allen component data\n",
    "        allen = pandas.read_csv('data/GCorr_GTexComp_ABA_C%s_data_ROTATION.csv'%i\n",
    "                               ).sort_values('GTeX_div')\n",
    "        allen.index = allen.GTeX_div.values\n",
    "        allen.drop('Pallidum',axis=0,inplace=True)\n",
    "        # concatenate GTEx and AHBA data for seaborn plotting\n",
    "        plotr = pandas.concat([pandas.Series(allen['C%s_Expression'%i\n",
    "                                                  ].values),\n",
    "                               pandas.Series(gtC['zC%s'%i].values)],\n",
    "                              axis=1)\n",
    "        plotr.columns = ['Allen','GTEx']\n",
    "        plotr.index = allen.GTeX_div.values\n",
    "\n",
    "        # add pretty colors (...for continuity!)\n",
    "        # normalize colorscale of markers by AHBA values\n",
    "        cmap = sns.color_palette(pals[i-1],as_cmap=True)\n",
    "        sg = plotr.Allen.values\n",
    "        scaled_sg = MinMaxScaler().fit_transform(sg.reshape(-1,1))[:,0]\n",
    "        norm = Normalize(vmin=np.min(scaled_sg), \n",
    "                                           vmax=np.max(scaled_sg))\n",
    "        cscale = cmap(norm(scaled_sg))\n",
    "        \n",
    "        # plot it\n",
    "        g = sns.lmplot(x='Allen',y='GTEx',data=plotr,\n",
    "               scatter_kws={'marker':'o','s':200,'color':cscale},\n",
    "                      line_kws={'color':'k'})\n",
    "        # get the inter-dataset correlations\n",
    "        r,p = stats.pearsonr(plotr.Allen.values,plotr.GTEx.values)\n",
    "        print('r = %s, p = %s'%(round(r,3),round(p,3)))\n",
    "\n",
    "        # add text indicating which brain region each marker is\n",
    "        for it,div in enumerate(plotr.sort_values('Allen').index):\n",
    "            x = plotr.loc[div,'Allen']\n",
    "            y = plotr.loc[div,'GTEx']\n",
    "            dist = len(div)*scale[i]#.02\n",
    "            if it%2==0:\n",
    "                plt.text(x, y,div,{'size':14},va='top')\n",
    "            else:\n",
    "                plt.text(x, y,div,{'size':14},va='bottom',\n",
    "                         )\n",
    "        # decorate\n",
    "        plt.xlabel('LV%s: AHBA'%i)\n",
    "        plt.ylabel('LV%s: GTEx'%i)\n",
    "        plt.text(0.7,0.1,'r = %s'%round(r,3),transform=g.ax.transAxes)\n",
    "        #plt.savefig(os.path.join(fig_dir,'GTEx_Repl_%s_C%s_ROTATED.pdf'%(nm,i)), bbox_inches='tight')\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add brain divisions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# C1\n",
    "\n",
    "tele = ['Brain - Cortex','Caudate (basal ganglia)',\n",
    "       'Brain - Nucleus accumbens (basal ganglia)',\n",
    "        'Brain - Frontal Cortex (BA9)','Brain - Putamen (basal ganglia)',\n",
    "        'Brain - Hippocampus','Brain - Anterior cingulate cortex (BA24)',\n",
    "        'Brain - Amygdala']\n",
    "dien = ['Brain - Hypothalamus']\n",
    "met = ['Brain - Substantia nigra']\n",
    "mes = ['Brain - Cerebellum','Brain - Cerebellar Hemisphere']\n",
    "\n",
    "divs = [tele, dien, met, mes]\n",
    "names = ['Tele', 'Dien', 'Met', 'Mes',] \n",
    "brain_division = dict(zip(names,divs))\n",
    "for name, div in brain_division.items():\n",
    "    pred.loc[pred[pred.Region.isin(div)].index,'DevOrigin'] = name\n",
    "\n",
    "# C2\n",
    "cortex = ['Brain - Cortex','Brain - Cerebellum',\n",
    "         'Brain - Cerebellar Hemisphere',\n",
    "         'Brain - Frontal Cortex (BA9)','Brain - Hippocampus',\n",
    "         'Brain - Anterior cingulate cortex (BA24)',]\n",
    "noncortex = ['Caudate (basal ganglia)',\n",
    "             'Brain - Nucleus accumbens (basal ganglia)',\n",
    "            'Brain - Putamen (basal ganglia)','Brain - Hypothalamus',\n",
    "             'Brain - Amygdala','Brain - Substantia nigra']\n",
    "divs = [cortex,noncortex]\n",
    "names = ['Cortex','NonCortex'] \n",
    "brain_division = dict(zip(names,divs))\n",
    "for name, div in brain_division.items():\n",
    "    pred.loc[pred[pred.Region.isin(div)].index,'Cortex'] = name\n",
    "\n",
    "# C3\n",
    "assoc = ['Brain - Cortex','Brain - Frontal Cortex (BA9)',\n",
    "        'Brain - Anterior cingulate cortex (BA24)']\n",
    "lbf = ['Brain - Nucleus accumbens (basal ganglia)',\n",
    "      'Brain - Hippocampus','Brain - Amygdala']\n",
    "bg = ['Caudate (basal ganglia)','Brain - Putamen (basal ganglia)',\n",
    "     ]\n",
    "other = ['Brain - Cerebellum','Brain - Cerebellar Hemisphere',\n",
    "        'Brain - Hypothalamus','Brain - Substantia nigra']\n",
    "\n",
    "divs = [assoc,lbf,bg,other]\n",
    "names = ['Association','Limbic/BF','BasalGanglia','Other'] \n",
    "brain_division = dict(zip(names,divs))\n",
    "for name, div in brain_division.items():\n",
    "    pred.loc[pred[pred.Region.isin(div)].index,'CortType'] = name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2483"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Widen dataframes for individual analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can apply the same logic above to measure individual correspondence to Allen components. In other words, now that we know the Allen components replicate in GTEx, we can look at replication at an individual level. Since GTEx has a wide age range, we can look to see whether and how the manifestation of these components changes with age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred.loc[:,'SUBJID'] = df.SUBJID.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For each component, make a subject x region matrix\n",
    "# and keep track of number of valid regions for each subject\n",
    "gpdfs = {}\n",
    "for i in range(1,4):\n",
    "    gpdf = pred[['SUBJID','C%s'%i,'Div']\n",
    "              ].set_index(['SUBJID'],append=True\n",
    "                         ).pivot_table(index='SUBJID',\n",
    "                                       columns='Div',\n",
    "                                       values='C%s'%i)\n",
    "    \n",
    "    for ind,row in gpdf.iterrows():\n",
    "        n_valid = len([x for x in row.values if pandas.notnull(x)])\n",
    "        gpdf.loc[ind,'n_valid'] = n_valid\n",
    "    gpdfs.update({i: gpdf})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(376, 11)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Div</th>\n",
       "      <th>Accumbens</th>\n",
       "      <th>Amygdala</th>\n",
       "      <th>AntCing</th>\n",
       "      <th>Caudate</th>\n",
       "      <th>Cerebellum</th>\n",
       "      <th>Frontal</th>\n",
       "      <th>Hippocampus</th>\n",
       "      <th>Hypothalamus</th>\n",
       "      <th>Nigra</th>\n",
       "      <th>Putamen</th>\n",
       "      <th>n_valid</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUBJID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GTEX-1117F</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.000865</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GTEX-111FC</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.433798</td>\n",
       "      <td>-0.246158</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GTEX-1128S</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.310180</td>\n",
       "      <td>-0.394155</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GTEX-117XS</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.411935</td>\n",
       "      <td>-0.263327</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GTEX-1192X</th>\n",
       "      <td>-0.060583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.340225</td>\n",
       "      <td>0.678619</td>\n",
       "      <td>-0.506628</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.079877</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.277315</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Div         Accumbens  Amygdala  AntCing   Caudate  Cerebellum   Frontal  \\\n",
       "SUBJID                                                                     \n",
       "GTEX-1117F        NaN       NaN      NaN       NaN         NaN -0.000865   \n",
       "GTEX-111FC        NaN       NaN      NaN       NaN    0.433798 -0.246158   \n",
       "GTEX-1128S        NaN       NaN      NaN       NaN    0.310180 -0.394155   \n",
       "GTEX-117XS        NaN       NaN      NaN       NaN    0.411935 -0.263327   \n",
       "GTEX-1192X  -0.060583       NaN      NaN -0.340225    0.678619 -0.506628   \n",
       "\n",
       "Div         Hippocampus  Hypothalamus  Nigra   Putamen  n_valid  \n",
       "SUBJID                                                           \n",
       "GTEX-1117F          NaN           NaN    NaN       NaN      1.0  \n",
       "GTEX-111FC          NaN           NaN    NaN       NaN      2.0  \n",
       "GTEX-1128S          NaN           NaN    NaN       NaN      2.0  \n",
       "GTEX-117XS          NaN           NaN    NaN       NaN      2.0  \n",
       "GTEX-1192X          NaN      0.079877    NaN -0.277315      6.0  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# an example\n",
    "print(gpdfs[1].shape)\n",
    "gpdfs[1].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Individual congruence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we use similar code above to define the correlation between each subject's regional component expression to the expression of that component in the AHBA. We exclude all individuals with less than 6 valid regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for each component\n",
    "for c in range(1,4):\n",
    "    cdf = gpdfs[c]\n",
    "    # load allen regional daata\n",
    "    allen = pandas.read_csv('data/GCorr_GTexComp_ABA_C%s_data.csv'%c\n",
    "                           ).sort_values('GTeX_div')\n",
    "    allen.index = allen.GTeX_div.values\n",
    "    allen.drop('Pallidum',axis=0,inplace=True)\n",
    "    avec = allen.loc[cdf.columns[:-1]]\n",
    "    # for each subjects\n",
    "    for i,row in cdf.iterrows():\n",
    "        # build a correlation df\n",
    "        forcorr = pandas.DataFrame(allen,copy=True)\n",
    "        # exclude subjects with <6 regions available\n",
    "        if row['n_valid'] > 5:\n",
    "            # calculate subject - AHBA correlation\n",
    "            forcorr.loc[:,'gvec'] = row.values[:-1]\n",
    "            forcorr.dropna(inplace=True)\n",
    "            r = stats.pearsonr(forcorr['C%s_Expression'%c].values,\n",
    "                              forcorr['gvec'].values)[0]\n",
    "            rho = stats.spearmanr(forcorr['C%s_Expression'%c].values,\n",
    "                              forcorr['gvec'].values)[0]\n",
    "            cdf.loc[i,'C%s_r'%c] = r\n",
    "            cdf.loc[i,'C%s_rho'%c] = rho"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we summarize everything into one convenient dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['SUBJID', 'RIN', 'BATCH', 'GBATCH', 'AGE', 'SEX',\n",
    "       'SeqPC1', 'SeqPC2', 'SeqPC3', 'SeqPC4', 'SeqPC5'] #+ bwcols\n",
    "subsum = pandas.DataFrame(gpdfs[1].index)\n",
    "for col in cols:\n",
    "    subsum.loc[:,col] = df.groupby('SUBJID')[col].first().values\n",
    "for c in range(1,4):\n",
    "    subsum.loc[:,'C%s_r'%c] = gpdfs[c].loc[:,'C%s_r'%c].values\n",
    "    subsum.loc[:,'C%s_rho'%c] = gpdfs[c].loc[:,'C%s_rho'%c].values\n",
    "    subsum.loc[:,'n_valid'] = gpdfs[c].loc[:,'n_valid'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we save this spreadsheet, because we do all the analysis in the C_BrainSpanValidation notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save this spreadsheet to open it \n",
    "subsum.to_csv('data/GTEx_IndivCongruence.csv')"
   ]
  }
 ],
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