all-features/5.5-transplit-DWPCs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transform and split DWPCs, assess performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "import bz2\n",
    "\n",
    "import pandas\n",
    "import numpy\n",
    "import sklearn.metrics\n",
    "from scipy.special import logit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "unperm_name = 'rephetio-v2.0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feature_df = pandas.read_table('data/matrix/features.tsv.bz2')\n",
    "feature_type_df = pandas.read_table('data/matrix/feature-type.tsv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hetnet</th>\n",
       "      <th>compound_id</th>\n",
       "      <th>disease_id</th>\n",
       "      <th>status</th>\n",
       "      <th>primary</th>\n",
       "      <th>disease_name</th>\n",
       "      <th>compound_name</th>\n",
       "      <th>prior_prob</th>\n",
       "      <th>CbG</th>\n",
       "      <th>CcSE</th>\n",
       "      <th>...</th>\n",
       "      <th>CuGuDpCpD</th>\n",
       "      <th>CuGuDpCtD</th>\n",
       "      <th>CuGuDpSpD</th>\n",
       "      <th>CuGuDrD</th>\n",
       "      <th>CuGuDrDrD</th>\n",
       "      <th>CuGuDtCpD</th>\n",
       "      <th>CuGuDtCtD</th>\n",
       "      <th>CuGuDuGaD</th>\n",
       "      <th>CuGuDuGdD</th>\n",
       "      <th>CuGuDuGuD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rephetio-v2.0_perm-5</td>\n",
       "      <td>DB00014</td>\n",
       "      <td>DOID:0060073</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>lymphatic system cancer</td>\n",
       "      <td>Goserelin</td>\n",
       "      <td>0.022675</td>\n",
       "      <td>2</td>\n",
       "      <td>249</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rephetio-v2.0</td>\n",
       "      <td>DB00091</td>\n",
       "      <td>DOID:0060073</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>lymphatic system cancer</td>\n",
       "      <td>Cyclosporine</td>\n",
       "      <td>0.047417</td>\n",
       "      <td>30</td>\n",
       "      <td>344</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.028591</td>\n",
       "      <td>0.004754</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 1230 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 hetnet compound_id    disease_id  status  primary  \\\n",
       "0  rephetio-v2.0_perm-5     DB00014  DOID:0060073       0        1   \n",
       "1         rephetio-v2.0     DB00091  DOID:0060073       0        1   \n",
       "\n",
       "              disease_name compound_name  prior_prob  CbG  CcSE    ...      \\\n",
       "0  lymphatic system cancer     Goserelin    0.022675    2   249    ...       \n",
       "1  lymphatic system cancer  Cyclosporine    0.047417   30   344    ...       \n",
       "\n",
       "   CuGuDpCpD  CuGuDpCtD  CuGuDpSpD  CuGuDrD  CuGuDrDrD  CuGuDtCpD  CuGuDtCtD  \\\n",
       "0        0.0        0.0        0.0      0.0        0.0        0.0   0.000000   \n",
       "1        0.0        0.0        0.0      0.0        0.0        0.0   0.028591   \n",
       "\n",
       "   CuGuDuGaD  CuGuDuGdD  CuGuDuGuD  \n",
       "0   0.000000        0.0        0.0  \n",
       "1   0.004754        0.0        0.0  \n",
       "\n",
       "[2 rows x 1230 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>feature_type</th>\n",
       "      <th>unperm_mean</th>\n",
       "      <th>unperm_sd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>prior_prob</td>\n",
       "      <td>prior</td>\n",
       "      <td>0.040476</td>\n",
       "      <td>0.073232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CbG</td>\n",
       "      <td>degree</td>\n",
       "      <td>11.311000</td>\n",
       "      <td>12.444000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      feature feature_type  unperm_mean  unperm_sd\n",
       "0  prior_prob        prior     0.040476   0.073232\n",
       "1         CbG       degree    11.311000  12.444000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_type_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def transform_dwpcs(x, scaler):\n",
    "    x = numpy.array(x)\n",
    "    return numpy.arcsinh(x / scaler)\n",
    "\n",
    "transformed_df = feature_df.copy()\n",
    "dwpc_features = feature_type_df.query(\"feature_type == 'dwpc'\").feature\n",
    "degree_features = feature_type_df.query(\"feature_type == 'degree'\").feature\n",
    "feature_to_scaler = dict(zip(feature_type_df.feature, feature_type_df.unperm_mean))\n",
    "for column in dwpc_features:\n",
    "    transformed_df[column] = transform_dwpcs(transformed_df[column], feature_to_scaler[column])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "column_names = list()\n",
    "columns = list()\n",
    "for metapath in dwpc_features:\n",
    "    df = pandas.pivot_table(transformed_df, values=metapath, index=['compound_id', 'disease_id'], columns='hetnet')\n",
    "    df = df[df['rephetio-v2.0'].notnull()]\n",
    "    dwpc = df.iloc[:, 0]\n",
    "    pdwpc = df.iloc[:, 1:].mean(axis='columns')\n",
    "    rdwpc = dwpc - pdwpc\n",
    "    for column in dwpc, pdwpc, rdwpc:\n",
    "        columns.append(column)\n",
    "    for feature_type in 'dwpc', 'pdwpc', 'rdwpc':\n",
    "        column_names.append('{}_{}'.format(feature_type, metapath))\n",
    "\n",
    "split_df = pandas.concat(columns, axis=1)\n",
    "split_df.columns = column_names\n",
    "split_df.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>compound_id</th>\n",
       "      <th>disease_id</th>\n",
       "      <th>dwpc_CbG&lt;rG&lt;rGaD</th>\n",
       "      <th>pdwpc_CbG&lt;rG&lt;rGaD</th>\n",
       "      <th>rdwpc_CbG&lt;rG&lt;rGaD</th>\n",
       "      <th>dwpc_CbG&lt;rG&lt;rGdD</th>\n",
       "      <th>pdwpc_CbG&lt;rG&lt;rGdD</th>\n",
       "      <th>rdwpc_CbG&lt;rG&lt;rGdD</th>\n",
       "      <th>dwpc_CbG&lt;rG&lt;rGuD</th>\n",
       "      <th>pdwpc_CbG&lt;rG&lt;rGuD</th>\n",
       "      <th>...</th>\n",
       "      <th>rdwpc_CuGuDtCtD</th>\n",
       "      <th>dwpc_CuGuDuGaD</th>\n",
       "      <th>pdwpc_CuGuDuGaD</th>\n",
       "      <th>rdwpc_CuGuDuGaD</th>\n",
       "      <th>dwpc_CuGuDuGdD</th>\n",
       "      <th>pdwpc_CuGuDuGdD</th>\n",
       "      <th>rdwpc_CuGuDuGdD</th>\n",
       "      <th>dwpc_CuGuDuGuD</th>\n",
       "      <th>pdwpc_CuGuDuGuD</th>\n",
       "      <th>rdwpc_CuGuDuGuD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DB00014</td>\n",
       "      <td>DOID:10283</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.043621</td>\n",
       "      <td>-0.043621</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.028787</td>\n",
       "      <td>-0.028787</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022707</td>\n",
       "      <td>...</td>\n",
       "      <td>0.142122</td>\n",
       "      <td>0.615617</td>\n",
       "      <td>0.508843</td>\n",
       "      <td>0.106774</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.388299</td>\n",
       "      <td>-0.388299</td>\n",
       "      <td>0.105593</td>\n",
       "      <td>0.209833</td>\n",
       "      <td>-0.10424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DB00014</td>\n",
       "      <td>DOID:10534</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.058666</td>\n",
       "      <td>-0.058666</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.431882</td>\n",
       "      <td>0.342985</td>\n",
       "      <td>0.088897</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 3620 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  compound_id  disease_id  dwpc_CbG<rG<rGaD  pdwpc_CbG<rG<rGaD  \\\n",
       "0     DB00014  DOID:10283               0.0           0.043621   \n",
       "1     DB00014  DOID:10534               0.0           0.058666   \n",
       "\n",
       "   rdwpc_CbG<rG<rGaD  dwpc_CbG<rG<rGdD  pdwpc_CbG<rG<rGdD  rdwpc_CbG<rG<rGdD  \\\n",
       "0          -0.043621               0.0           0.028787          -0.028787   \n",
       "1          -0.058666               0.0           0.000000           0.000000   \n",
       "\n",
       "   dwpc_CbG<rG<rGuD  pdwpc_CbG<rG<rGuD       ...         rdwpc_CuGuDtCtD  \\\n",
       "0               0.0           0.022707       ...                0.142122   \n",
       "1               0.0           0.000000       ...                0.000000   \n",
       "\n",
       "   dwpc_CuGuDuGaD  pdwpc_CuGuDuGaD  rdwpc_CuGuDuGaD  dwpc_CuGuDuGdD  \\\n",
       "0        0.615617         0.508843         0.106774             0.0   \n",
       "1        0.431882         0.342985         0.088897             0.0   \n",
       "\n",
       "   pdwpc_CuGuDuGdD  rdwpc_CuGuDuGdD  dwpc_CuGuDuGuD  pdwpc_CuGuDuGuD  \\\n",
       "0         0.388299        -0.388299        0.105593         0.209833   \n",
       "1         0.000000         0.000000        0.000000         0.000000   \n",
       "\n",
       "   rdwpc_CuGuDuGuD  \n",
       "0         -0.10424  \n",
       "1          0.00000  \n",
       "\n",
       "[2 rows x 3620 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "split_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>compound_id</th>\n",
       "      <th>disease_id</th>\n",
       "      <th>status</th>\n",
       "      <th>disease_name</th>\n",
       "      <th>compound_name</th>\n",
       "      <th>prior_logit</th>\n",
       "      <th>degree_CbG</th>\n",
       "      <th>degree_CcSE</th>\n",
       "      <th>degree_CdG</th>\n",
       "      <th>degree_CiPC</th>\n",
       "      <th>...</th>\n",
       "      <th>rdwpc_CuGuDtCtD</th>\n",
       "      <th>dwpc_CuGuDuGaD</th>\n",
       "      <th>pdwpc_CuGuDuGaD</th>\n",
       "      <th>rdwpc_CuGuDuGaD</th>\n",
       "      <th>dwpc_CuGuDuGdD</th>\n",
       "      <th>pdwpc_CuGuDuGdD</th>\n",
       "      <th>rdwpc_CuGuDuGdD</th>\n",
       "      <th>dwpc_CuGuDuGuD</th>\n",
       "      <th>pdwpc_CuGuDuGuD</th>\n",
       "      <th>rdwpc_CuGuDuGuD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DB00091</td>\n",
       "      <td>DOID:0060073</td>\n",
       "      <td>0</td>\n",
       "      <td>lymphatic system cancer</td>\n",
       "      <td>Cyclosporine</td>\n",
       "      <td>-3.000196</td>\n",
       "      <td>4.094622</td>\n",
       "      <td>6.533791</td>\n",
       "      <td>6.214612</td>\n",
       "      <td>1.443635</td>\n",
       "      <td>...</td>\n",
       "      <td>1.016867</td>\n",
       "      <td>1.273669</td>\n",
       "      <td>0.889808</td>\n",
       "      <td>0.383861</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>DB00206</td>\n",
       "      <td>DOID:0060073</td>\n",
       "      <td>0</td>\n",
       "      <td>lymphatic system cancer</td>\n",
       "      <td>Reserpine</td>\n",
       "      <td>-4.492476</td>\n",
       "      <td>2.776472</td>\n",
       "      <td>4.454482</td>\n",
       "      <td>5.337561</td>\n",
       "      <td>0.881374</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.232211</td>\n",
       "      <td>0.495113</td>\n",
       "      <td>0.317494</td>\n",
       "      <td>0.177618</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 3640 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  compound_id    disease_id  status             disease_name compound_name  \\\n",
       "0     DB00091  DOID:0060073       0  lymphatic system cancer  Cyclosporine   \n",
       "1     DB00206  DOID:0060073       0  lymphatic system cancer     Reserpine   \n",
       "\n",
       "   prior_logit  degree_CbG  degree_CcSE  degree_CdG  degree_CiPC  \\\n",
       "0    -3.000196    4.094622     6.533791    6.214612     1.443635   \n",
       "1    -4.492476    2.776472     4.454482    5.337561     0.881374   \n",
       "\n",
       "        ...         rdwpc_CuGuDtCtD  dwpc_CuGuDuGaD  pdwpc_CuGuDuGaD  \\\n",
       "0       ...                1.016867        1.273669         0.889808   \n",
       "1       ...               -0.232211        0.495113         0.317494   \n",
       "\n",
       "   rdwpc_CuGuDuGaD  dwpc_CuGuDuGdD  pdwpc_CuGuDuGdD  rdwpc_CuGuDuGdD  \\\n",
       "0         0.383861             0.0              0.0              0.0   \n",
       "1         0.177618             0.0              0.0              0.0   \n",
       "\n",
       "   dwpc_CuGuDuGuD  pdwpc_CuGuDuGuD  rdwpc_CuGuDuGuD  \n",
       "0             0.0              0.0              0.0  \n",
       "1             0.0              0.0              0.0  \n",
       "\n",
       "[2 rows x 3640 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_df = feature_df.query(\"hetnet == @unperm_name\").copy()\n",
    "base_df.insert(8, 'prior_logit', logit(base_df['prior_prob']))\n",
    "for metaege in degree_features:\n",
    "    base_df['degree_{}'.format(metaege)] = numpy.arcsinh(base_df[metaege])\n",
    "base_df.drop(\n",
    "    ['hetnet', 'primary', 'prior_prob'] + list(degree_features) + list(dwpc_features),\n",
    "    axis='columns', inplace=True)\n",
    "transformed_df = base_df.merge(split_df)\n",
    "transformed_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "path = 'data/matrix/rephetio-v2.0/transformed-features.tsv.bz2'\n",
    "with bz2.open(path, 'wt') as write_file:\n",
    "    transformed_df.to_csv(write_file, sep='\\t', index=False, float_format='%.5g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>feature_type</th>\n",
       "      <th>metapath</th>\n",
       "      <th>dwpc_auroc</th>\n",
       "      <th>pdwpc_auroc</th>\n",
       "      <th>rdwpc_auroc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CbG&lt;rG&lt;rGaD</td>\n",
       "      <td>0.636046</td>\n",
       "      <td>0.613955</td>\n",
       "      <td>0.574447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CbG&lt;rG&lt;rGdD</td>\n",
       "      <td>0.580136</td>\n",
       "      <td>0.570848</td>\n",
       "      <td>0.536699</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "feature_type     metapath  dwpc_auroc  pdwpc_auroc  rdwpc_auroc\n",
       "0             CbG<rG<rGaD    0.636046     0.613955     0.574447\n",
       "1             CbG<rG<rGdD    0.580136     0.570848     0.536699"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows  = list()\n",
    "for column in transformed_df.columns[transformed_df.columns.str.contains('dwpc')]:\n",
    "    feature_type, metapath = column.split('_', 1)\n",
    "    auroc = sklearn.metrics.roc_auc_score(transformed_df.status, transformed_df[column])\n",
    "    rows.append([feature_type + '_auroc', metapath, auroc])\n",
    "auroc_df = pandas.DataFrame(rows, columns=['feature_type', 'metapath', 'auroc'])\n",
    "auroc_df = auroc_df.pivot_table(values='auroc', index='metapath', columns='feature_type').reset_index()\n",
    "auroc_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>feature_type</th>\n",
       "      <th>metapath</th>\n",
       "      <th>dwpc_auroc</th>\n",
       "      <th>pdwpc_auroc</th>\n",
       "      <th>rdwpc_auroc</th>\n",
       "      <th>nonzero</th>\n",
       "      <th>pdwpc_primary_auroc</th>\n",
       "      <th>delta_auroc</th>\n",
       "      <th>pval_delta_auroc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CbG&lt;rG&lt;rGaD</td>\n",
       "      <td>0.636046</td>\n",
       "      <td>0.613955</td>\n",
       "      <td>0.574447</td>\n",
       "      <td>0.84662</td>\n",
       "      <td>0.56577</td>\n",
       "      <td>0.070275</td>\n",
       "      <td>0.000020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CbG&lt;rG&lt;rGdD</td>\n",
       "      <td>0.580136</td>\n",
       "      <td>0.570848</td>\n",
       "      <td>0.536699</td>\n",
       "      <td>0.38146</td>\n",
       "      <td>0.54954</td>\n",
       "      <td>0.030600</td>\n",
       "      <td>0.000103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "feature_type     metapath  dwpc_auroc  pdwpc_auroc  rdwpc_auroc  nonzero  \\\n",
       "0             CbG<rG<rGaD    0.636046     0.613955     0.574447  0.84662   \n",
       "1             CbG<rG<rGdD    0.580136     0.570848     0.536699  0.38146   \n",
       "\n",
       "feature_type  pdwpc_primary_auroc  delta_auroc  pval_delta_auroc  \n",
       "0                         0.56577     0.070275          0.000020  \n",
       "1                         0.54954     0.030600          0.000103  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "primary_auroc_df = pandas.read_table('data/feature-performance/primary-aurocs.tsv')\n",
    "primary_auroc_df = primary_auroc_df.rename(columns={'feature': 'metapath', 'auroc_permuted': 'pdwpc_primary_auroc', 'pval_auroc': 'pval_delta_auroc'})\n",
    "primary_auroc_df = primary_auroc_df[['metapath', 'nonzero', 'pdwpc_primary_auroc', 'delta_auroc', 'pval_delta_auroc']]\n",
    "auroc_df = auroc_df.merge(primary_auroc_df)\n",
    "auroc_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "auroc_df.to_csv('data/feature-performance/auroc.tsv', sep='\\t', index=False, float_format='%.5g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>feature_type</th>\n",
       "      <th>metapath</th>\n",
       "      <th>dwpc_auroc</th>\n",
       "      <th>pdwpc_auroc</th>\n",
       "      <th>rdwpc_auroc</th>\n",
       "      <th>nonzero</th>\n",
       "      <th>pdwpc_primary_auroc</th>\n",
       "      <th>delta_auroc</th>\n",
       "      <th>pval_delta_auroc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>CbGaD</td>\n",
       "      <td>0.712840</td>\n",
       "      <td>0.628067</td>\n",
       "      <td>0.669358</td>\n",
       "      <td>0.32238</td>\n",
       "      <td>0.56754</td>\n",
       "      <td>0.145300</td>\n",
       "      <td>6.114000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>CbGpPWpGaD</td>\n",
       "      <td>0.701548</td>\n",
       "      <td>0.650548</td>\n",
       "      <td>0.654061</td>\n",
       "      <td>0.97642</td>\n",
       "      <td>0.62547</td>\n",
       "      <td>0.076082</td>\n",
       "      <td>1.372500e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>CrCbGaD</td>\n",
       "      <td>0.648075</td>\n",
       "      <td>0.589791</td>\n",
       "      <td>0.642535</td>\n",
       "      <td>0.41748</td>\n",
       "      <td>0.56589</td>\n",
       "      <td>0.082185</td>\n",
       "      <td>8.079100e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>CiPCiCbGaD</td>\n",
       "      <td>0.657599</td>\n",
       "      <td>0.606607</td>\n",
       "      <td>0.639864</td>\n",
       "      <td>0.35576</td>\n",
       "      <td>0.56734</td>\n",
       "      <td>0.090258</td>\n",
       "      <td>1.038500e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>CrCrCbGaD</td>\n",
       "      <td>0.620441</td>\n",
       "      <td>0.593827</td>\n",
       "      <td>0.639633</td>\n",
       "      <td>0.51709</td>\n",
       "      <td>0.58001</td>\n",
       "      <td>0.040433</td>\n",
       "      <td>4.739400e-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "feature_type    metapath  dwpc_auroc  pdwpc_auroc  rdwpc_auroc  nonzero  \\\n",
       "27                 CbGaD    0.712840     0.628067     0.669358  0.32238   \n",
       "178           CbGpPWpGaD    0.701548     0.650548     0.654061  0.97642   \n",
       "679              CrCbGaD    0.648075     0.589791     0.642535  0.41748   \n",
       "519           CiPCiCbGaD    0.657599     0.606607     0.639864  0.35576   \n",
       "752            CrCrCbGaD    0.620441     0.593827     0.639633  0.51709   \n",
       "\n",
       "feature_type  pdwpc_primary_auroc  delta_auroc  pval_delta_auroc  \n",
       "27                        0.56754     0.145300      6.114000e-07  \n",
       "178                       0.62547     0.076082      1.372500e-08  \n",
       "679                       0.56589     0.082185      8.079100e-07  \n",
       "519                       0.56734     0.090258      1.038500e-05  \n",
       "752                       0.58001     0.040433      4.739400e-05  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#auroc_df.sort_values('rdwpc_auroc', ascending = False)\n",
    "auroc_df[-auroc_df.metapath.str.contains('CtD')].sort_values('rdwpc_auroc', ascending = False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Visualization Sandbox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6q8fSNVaG70rvq5IDK+7FsHFNTzgQ8do8lwJDYz2ddNfqZY2ycFhtBQzaQLt2\nSKxAbAgbKaMtfvhhLJAKCT9KUHPYyOGbQcqq5UKJC31hLsjuNa7VC1DH4N28j1x8v5ab8EapujBo\njMMWBcVnOVPXWxj5zZJDr7f6bZS/x9UIJGgD7fpAYoUKaZrin/7pn/Dggw/Ctm38/M//PM4555zN\nGNuOYyNltMUP3zI5RCQhhIDGsGz4pqjfViTgJ+rdyXzeC9HupEizDEGUAmCouzqadRN1x1Q12GRX\nlZYJgGvrcz2qNE93M261HE/Baia8tXiew7yvzcqtrLf6bZTrWGsFjFE/Q+xulpylfvKTn+CVr3wl\nTNPEwYMHwRjDnXfeCc45br/9dhx//PGbNc4dwagTybCV/ihiB0PTwB1V263uWst2bi3qtwkpsdCO\n8cOfzcM2dUzUTSRCqo2pUQIvyAAIuI6OtheDY3GYKwgTcHt1LSX6KSbJumsMDe+Ma8LbKXLoUa5j\ntRUwaAMtsRKWNETXXXcdXv7yl+N3f/d3e56/6667cP311+ODH/zghg5utzJspV88P0i1VUzcrqMj\nilKV2B6hc2txHj9IcXTOR5pJmKbEQidGGKWYnnQgJCCEQGEHVKJeeU9VZZsEgyVlaTDWYhjWMklS\njmL9WI33Rxto1wcSK+QcOnRokRECgBe/+MX41Kc+tWGD2s0sVTG7mHQLgQCk6DEugybuqhR5UKHQ\nbosElVvinENIoBOosjhhmAJMQtc0OFZepZupz3PGyvptAOD5EfwgLR8PWwmvl6FwHWNgIVjKUawf\nq/H+dorHOG52k1hhSafZNIevZHZLEm2z6VbM7qrFqjLg4nn1eLhqq2C5QqGuY8APEnSCBGkqYBoM\ncwsR/DCBwTlcx0DDMXH8tIOGa4Ez1Ra8WOVWz11zTEiI8rzDVsLrJRv2gwSObWCibsGx1XUMuh+U\noyC2IyRWyFnqJuyWG7TZVGuj9VfMXkq1NczLqIZJBhUK9YMEk1M1HJh2kTGBBx9ZQJZmmJ6wMdm0\nwSAxUbMxVeklVD1XECawc0FEYegYlxASiyoQFJ+bbUWlwIEztmpDMczg7JYcBYUgiZ3Ckobo+9//\nPp70pCcBQBmrVNWLJRmiDULjAATrCW/199kZFIP3/DgvAaMMmOcni/bSNGo6pGQ9hUIBoDlRQxAm\n0MBwYMoF5xoAgTDOoGmL68VVPRrL0hFGKVIhSuPY9mIAHHXXGJjjYkyi5Udo+TGarrnq/j7DDM6w\nqgyDJumdXOfVAAAgAElEQVTtPJlTCJLYKSw5AzzwwAObNQ4iZ6lEb39n1KLqNVB4N6oSQdGw7siM\nD6eiYmN5UdKiUGjdMdEJUnSCGKmQ8IJEbYa1GSBUgz9TWyybLia/wqBlQgBcgkmBth+j7UdgjKNa\ncLT6OcY4AAkhlu/vUxiKJJNqT5SuodWJ4DpG/lrX2BQbXqu5KyEl2l6Klhdjsm4NVA2utbTRuAwX\nhSB3Ngvzc7umXfhIS9E4jvGVr3wFnU4HAJBlGR555BFcfvnlGzq43chSid5qZ9QoVWG06UkHUZQi\nERKeH8G1TbC85lqcClR7PErB0Gz0trpW+38E5loh/DCBbRlgUkLTOKabdllVoIrGVQHOI7MBUpFB\n58pLluCoOTrCSAKIUXeMsmJ18bksN3A1xywl5sOqggupOqjGqUAQZbAsDZ3cwyuMbOE5cvTWtSsK\nhLb8BICEY5tLqgaHPe5nOcPVb6imp1fWzHAl7JYQ5G7Fsix89buHccEuaBc+kiF63etehyAI8PDD\nD+Pss8/Gv//7v+NpT3vaRo+N6KPaGdUPYwhRrccmwZmGThhjwlWFR8O+qgpV0UPpnQAI4xS6xlFz\nLAiRQeM69k2p9gjD8k6PH+sgzTJwDtiWkeeHJCAB2+RgTO1srW6qLbw9JbIQ3eKoQyZQz49Lw5uK\nDCKUSPMCpP1Gtt+AFAVCi0KpcZQCdWvR+1ZT2mipx/2Gqr0KIcaoXhfJpHc2e/YeGPcQNo2R1lAP\nPfQQPvaxj+H888/HK17xCnz605/GkSNHNnpsRB/d4ptqf5CuqT45UgLtIIFtcXDG4DomdI1h/7Rb\nChmCMEGSyTKsVUy4YZTCMDRoGodjaWi4NvZN2djTsMv8EtCrbuOMwbJ0GJqSevthDDCUjesm6zYa\nrirQmWYSM/MBZlohWp0IAhKuo0PjHBJLT6CZ6FbT5oxBCFGGIqv3AlhsQIoCoUyqa4zTDJ4fgfHe\nfRkrLU7KmITnJ1jwInh5u4v+MVfp79Y7CCHV9zLXjtDqRGj50UiqwsJ7HqWoLUFsZUYyRNPT02CM\n4bTTTsMPfvADHDhwAHFMlXo3EyElbEeDHyboBCkMjWPvlA2Nc/hh3p4ADAwcnaBrMJo1C4bG4NgG\nOGNIM4kjM365irYsHaamqW6lANodJd0WUiLJhk+yUZTCtHRwxiGEapZXiA4aNR2apurERXEG3dDg\nBynanVRVZWBqPIbWDUNWJ+LCi9E4yhCea+swdZV3avsRbEcrjcAgA1IUCG3UDLi2AU3TUFTert7T\nIv9UGGrPjwe2myhZputtv0Hs79Y7iH4vquX1/rYo90PsdEYKzR08eBDXXXcdXvSiF+FNb3oTjhw5\ngiRJNnpsRAXPj8Ekx/F5zoFxCQ4G7jC0fQ4B5Xk4loZMcCSZyq84toG2H6HumGCMqc2wAnCgJrgo\nSjG9pw6dMzDGMFm3sH+PCykZoigpxQ5A7yRrWToykYJZGsKYwTRyb8xV+3nSVELjHJalgVV6+lQn\n7mKCHZZ36Zb5sZRnB4nJho0szgAJcA4064PzacVnpWRoulYpFa/amOK8RXgzyyt8LyVYWK7rbX+4\nrOGamAmWXrT1GxqGwd7eVhJKEBvP/OwMAEDK48Y8ko1nJEN0zTXX4N5778Xpp5+ON7zhDfjXf/1X\n3HjjjRs9NqJC/2RVCA8AZUziTEAKCQkVitK5hkyqPErhJdVdK69irWY2KSXiLIMXRJAAaq6OpmOV\nCp2iTNCgHEThcXg+4NgsbyDXFRFoXHVGDaM0f1xMrt0LYUyFpIp9RbatIQwzZEJA5PkmKRkYU0Lz\n+VYMw4zL7QNLeQpV0UdV1m7qHI2aMpbFeZMsA+espxTSMEbtHFs+5ssbiv5j1lwNQZisqGJEEd6r\nStabLoXrtjNCrE/7kO3ASIZICIGzzz4b3/rWt9BoNPCc5zwHT3ziEzd6bLue/o2jlqX3FDktsCwd\nWRCDcw4pM2QZMNcOkKQyLxUklXYbgKmphmpSShyZ9ZEJib3TKgQWRWmPTLQaOuunu/IXZQgN6IoI\nao7qjmqZenlOxiT8UFX7NnUO29EgBSuN1rG5BI6tQeNA24uhdiYxtDoRNM7gOgZSIUqjOopKrBBW\nZFKN07b0RcYyioU6b37pSx13IwQC/ccUUKHLQozhBwnqrqp2XkjibVuD52fdz0iJdict22YUhWlp\nX9H2pRAr7HTpNrCMITp8+DBe97rX4bnPfS4uvfRSXHHFFTjppJPw6KOP4sorr8QFF1ywWePclVRX\nwHa+cbSYQBd7JxZqjonDsx0IyZBGCXRDQxCmqDk6TK46tU7U1aQ370XIpBIOJJnAkdkAlqlmYCVE\nYEtOst2qDQniTBmHajfTojuqxtGTB6rZ3WN6nQR1l5dGK80ySHC0gxQLXow4yXDygTpEvudISgGN\nsyWNwKDwVXVSB4AoURN4mkkEUYpMSPihqizOuBzYgK963es9ufcfs1ofEKgKFkTeKFAZ7bqjVV4v\nKnHkn5GUWyK2D0saone961248MIL8ZKXvAQAMDExgY9//ON44IEHcP3115MhWif6J8+i/lt/KRzH\nNnpK7RR0V9QMNcdQvYI6Efww7yUUMkhThW7qrolmTcmYGdSE5QcJ0ixDXdNKQ1cN+QzLS3h+nHtj\nahIMo3RgZ9SCReHFPEzXNVocrU6MTEhwDmRCYmYhhGMZqh+SBPZOM0w2zaHGYFCFCdfprSgx1w5h\nmQYcW4eEKuh6XB7+6t+PNMr3td65mkHhv0x0vcxMApnMUHO65lVC9DQSZAA6Yb65GEpIQso6Yquy\nbGWFm266adHzZ5xxBubn5zdsULuN/th/f+ioqGg9LGRUXVEXk5bOlVEKwqwMO1XzCkU4raiqYGjK\nM+mv0C2khJTd6t/VvEQmCs+oK2jQOR9qJPon2GbdBEc3B7V/2i29gZptgoEhzfIGf6aGMM4w1w6B\nNBua/xhUYcKGhB8kODTjQ0LCtDikFPDDBH6YwjbVZuBqpfOVfF9KULE4ZDespNByuZxhJZyyilDC\n1HhPyKZZNyGExOGZBJnMoGsctqFB9e4QaHdSqoq9zSCxQg7nvTPfpz/96aGvEaun31OIUwFbKn/B\nywuJNmr6kiGjgm5fIpXzkaYs20lUz1V3TbR8JVKwdA5ha2gHMdp+DM54XimBIQgz7N/jDOwxtNLN\noIsn2MXGZKphqU2sAGqOgShOkWUSUZzBsXXUXAML8+lQZZsaUzdEpTEVAmRQ3WuDKMPcfAyNAYbJ\nofO88KoE2kEMI/+7HmZMhJSYbYfoBCkAiYZroeYYQ3tF9VdW8PxYdb0VGfwwRduP4dUS1F0DUrLy\nvP3X1n/vFnueSu1XeHYLXoROHpZV35ugUN02YzeJFZacOvbu3Yv777+/fGwYauV7//33Y3p6ek0n\nvu+++/DSl750TcfYjvRvXhRSLprATV2p3KQE6o6JZj4hFhWsB+25KShWvdNNGyfsq2N6wladWisi\nhyK0NO9F6AQJDFPD7HyAth+DgUGIDF6Q5uPN4AfdH0ShdJtrq3MXG0RHSdwP24BZvSbb0aBzBkhl\nIPdPO6i7er5/KMbRWV+FFCuTavXzAiqs1wkSdQ/BIKUyNFGcQUjVRj2VEmEkYFsaOFP7izpBUlaB\nGLaR1PNjBKFQx5FA249UFYd8PGXoLJ/4+ysrZEK95odpeYyFToJ2J13yvP33rvA8q/eyd1HAy9Bn\n8ZhKAG0v9uw9gD17D5BY4bWvfS0uu+wyXHbZZTj77LPBGMM3v/lN3HrrrXjf+9636pPecccd+Nzn\nPodarbbqY2xXRgnr7J92ceioD8hu0dBM9NZeK55frkhntTJ30bn18WMdWJYOz08gJRCGKUxT/Sk0\nXQPznbistt5wrZ4eQwJdIyClkm03B+StBjEst1K9J0xyNGtdj2CurQqocibhhRkW2hFqloaJRkX0\nUPm8FAycMzRdA5mEKn3E1f4hy9QgohQAQ8MxYFsaGq4BSGCirnJUVQ+oWty1OIfnJzB0Bq+dIhUS\nGmfYN8WV3DoT8IIYNdssK0D0V1bQuDIKxQJC2VzR3WeF0UUG1XExJlVPqUyWqjrGLQghKx41lQAi\ntiZLGqJzzz0X73vf+/ChD30If/InfwIAOPPMM3HjjTfirLPOWvVJTznlFNxyyy14y1vesupjbDeW\n6sUzKHY/UTcWhb2qtdequaOlzldM+pqGcnNqnAlkQZpXY8g7szIGgOV5I6Uk05gaZ1XGPUjRNSrF\neDIhcHgmQCY8TDUsmKZW2We0ONwnhIRrW5CI0aiZ5YbWYWNIEgFd09RuU6Y21Ro6QztIUHcsNByl\npOM8DwkwXmmT0Xvu6rjzO4sFLyx7MHHGMd+KMD3plPc0jBLs36MWWbrGUfVv6q5qSeEFGjKZlcaB\nV4ITo3gu5aIkz6FJMEjIMq+YxMAJe+sjiROqfyumM7xlBkFsFMvuIzr77LPxZ3/2Z+t60vPPPx+P\nPvrouh5zq1M1CqMIEAYlrBc8ldvobrwUSxYM7S++2chzTFpe6cB1NBybi6EFCRgk6jUDEkC9ZpQr\n+eLcxWRVqLAKQzrKpFm0r5hphTA0jhSirEgQZwLeQoJ9U275/iL8lwlVQULXVHXvumPgwHQdXitU\npXDy9xTN+YoQRpIKMNU9sJyU903U0czzKFXvsJCpVxv39Sv+ejabOiasPOeEotVEJkrRRtHUsLg3\n/ZUVOGOYrNu9/Zl4sXl39L1Jnh+XRXBVJ18ltqhXN9MOuaZ+I1P9W0kzQX2NtggkVuij3W7jlltu\nwde//nXouo5zzz0Xr371q+E4zvIfXif27Wts2rnWg/7x8jz0NTVVy5VqAvsmXTRcc+ju+/7au7Yb\nYSrpft7SNZy4vzHw88X5CgxbLyeXqakawihBkmVwHLMsYDpZt8E56wkn6RrHRN3CghdBtwzUmy6O\nznoIUoG9Ew6O31uHri9tjX72eAu1pg2pc2RC4thsB3v31KBxVhq5A1M1pJmArnEIIZGkAodmPCws\nKA9y33QNlq0Mx8knTSAMU7iO8vCElOVjXeOYmHQQhKpZn845ao5RGrrl6hn3vy6ERJhJRGlWjtet\nWeW5ASV/rz4u7lnBsL/dtdRW5qYOburIhFqVGJauis3WzPJ+cFNHGCRoTrql8ekfW3GsKpNTNUxP\nbN5ve61st7lhVCyTgXGOvXsbmJzcmddYMJIhuuqqq3DyySfjhhtugJQSd999N/7wD/8Q73nPe9Z0\ncinl8m/KOXq0vaZzbSb79jUWjXe+E6LtqUS2xoBazcCxY20cXsFelP7VrWlqmJnxBr638BYKGJOY\nj9OelXHgZ7A4x3TTwcxMB0fDwceKg7gMyXl+Uiq+0ijF7Gxn4N6h6rU8+vgCZlshkkwgTQWSTBlR\n19YxF6UwNY64CBtChf8Oz3SwUBQglRJpbGGyZmHq+Ak89tgCoiSDrqlQIstX/nEYL/KQYmTI4gRI\ns+W+tqH3Mc0kOkGMRACPJS1MTdiYne2UHpXrGJif6/TsAzt2rI1MAAf2NxD60bp3h211op79UrrG\nkMQxZsKkvP7Aj7HQiaAx3hPCjftq31X/Vqana5if60DE20OxNei3tpVZidF06nsAADMzHpJE26gh\nbSrDrn8kQ/TTn/4UN998c/n4qquuwm/91m+teVC7QQ1SIgFV/VnlLXxfVUmobrw8bq/KKwwKD1Wf\nm6gvP2G5jrGom2tVGOD5MRjr7hECurmJQZLsQqpdKL4AObQTbL8A4/HZDhhTRVUNQ4NraTA0Ds9P\nYBkaTjixtqicUZRm3YQ+Z8gyCcdWHk8mAD/KAEgIKdGs2YiiFDwfg7VEFYrl6DcOSSbzsJsFz4/A\nDQ0659BtntfAk3jsqNcjsR811LWWVt/dgrDGkkasCMOWjwc4r9XvStcWd+QliI1mJEN02mmn4d57\n7y0FCg888ABOPfXUNZ34xBNPxCc/+ck1HWM7IWXvxs8FL1q08bKQ7fZPToOea9YsxFmGnzzaQpRk\nsAwNp57YhKmplZMfJIvqlfUfp2gdDgw2eowrebFSrkn1fs4BSLh5qZ5BnWCLMRbnqtkGWp0Yhs6h\naxpMk8OxDJiGEil4fowwzEo1oG1rudpL7cd08tChxoF5L0Qmlex6diGCF8QwNA2mqa67qJ6QZMoD\nUurAeGRvo984RLlBA9BTMBZALrkWUNGx7qbRfmm5qhG32OvpF1msRPix1ObU6v6uaoPEYUa5eqyG\na5beHFX4JjaLJQ3ReeedB8YYoijC3//93+MJT3gCOOd48MEHccopp2zWGHcE/Zs/TZ0jSjL4kdpP\nonOGpJI0Lzyl/qrUheIOgDJCaZa3DldG6Yk/NwWg91xFtQQhlHchRIZOmEFC4qT9dUw17DIUU92X\n5PtpXmi1K9U+cX8N7JjEvKdEC3VHh671TlT912obOrQmRyOfBI/N+6UBzqTA4RlV7LR4HIbASQca\niCJVc07TWL6fyESQqgOHUZY3AuRwbANBmMCxeWnc4zivqJ2LQkb1NooW40UItaxwIboFY8v7Whoh\n5PdcdAUp+fWXNeKw2OtZ6YbgUen1cFjZ7n2UUGB7DV4asb4UYoV2u4ZGo7mjI0hLGqKPf/zjmzWO\nHc+gvUIPPrKQS2VVu+0oyidNr+spAQJzC4la8ecTdRAmaGkMM60AnDM4lg6AYaYdYK6turIyLiHz\ncjWdvDqDxgVavqpBZ+dN7dpejAUvGqhAC1OBhY7qcVSVnNcdA1IwZEKAMw7X7ZYQ6i1Jo65975SD\n2YUQkMoAT0/aPfcmkxk0pvWoAS3dwL6JBk7a33sfJ+oqPyOEAOcolYDdlhVKSWha3YoC6t/uMZaa\nkIMwQctPSgkzYwx7Jxr5uXu/w2bdLPN+gPKWipYVanMwx6SllVUt+scxaiXvnv1CpcKODTUmw7yl\nUUKBhVCluhgqxkqe0eYiRArbqeGr3z2MCxpNNJsT4x7ShrGkITrxxBPx4x//GI7j4IQTTgAAfP7z\nn8cTn/hEnH766ZsywO1O/6RXze9MTzo9q2/LUpsO5z0lDChK8yRZBAaZVztQuRnHNmDqOuIsQxCl\nkFIiTVWLBaWik+U+IQmGuqM6sLb8FGkmck9CRyaBBS8sJ6g4FciEarMQR0p5BqZ6G0V5iKcIM0qp\nkvittsCeptVzbdVJ1tQ5Tj95snytX7gxWbfgWkZZlcDUB+cpCm9NAtA0hpptouYYEFKWOSJdV/mk\nIFRhs1KyXvE2BhVGLQQX8+0IQSxgGgxc46WRARZP8MV4Wl63DBOY2lRbnNfQNKSsK5SojqN6vP5u\nscVm3GZd3YdiUdH2YgBKfNBvTJbzePq9PddZfI+LjrKFZ1l4beQZbT579h6AW2vA72wfMcZqWTIY\n8NWvfhWXXHJJz56fo0eP4tJLL8XXvva1DR/cTmDQKrSgaN8wUbNQd1VLb553SS2eY4zBNjRwpqHm\nGGp1LVUI6uTj6zA1LZc7SyV2YKrsTLuj6pc1XAsGZ+XqvunqmKw7ZQ2yMEwx70Vo+cor6oQJ5jvq\ncZRmiGNl5ApDCXQn08JwaJwvurZh5XwAoOlamMivcaJu4bjpGvT8XuxpWNg/7cLzY8y0Qjx21MNM\nK0QrH5OQqr7b/ikXUZyi3YkxMx+UOaI0zXBsPlRVx6MMnh/Byz2cwnBkQk20iRDohClmWiEefGQB\nSabyYLalclk1RwfDcGUnZwyTNRs/d6CJkw/UMVm3S4NRYJqqyd2CFyEIe2Xeg/5O/CDFQieG50eq\nhJAX97QOL8oHlY/F4mP0/60VJZAOz3bQ8lMIqY5TLCyqNFyz61kOqFFIEBvBkh7RTTfdhI985CM9\nTfAuueQSPP3pT8c73vGOXSU2WC1LJaT7QzOuY5Sy3OqGy/3TLh47qqTVGgPqjqq9ZnADTzhxIk/i\nR2W+IhMSYZRhwet2Pi1Wto26iZor0fEzhFEM1zHQqJn42bwPgMO1dSUCYDFMXZXb0bnWs/m2d9wq\nZCekVCHFymq82P1fzfNM1uxKL6Ni8k3K9x+Z8fHwkTY0pqkyNUDZwrvtx9ijUmAIwgymqWGiZmGh\nEyEIM9Rdjk6YwTQ4GjVVIUJC5t5bd1VfbJhtBwkYk6jZOqJU1dRruBbafgSRT8SNetdrGCXH0p/3\nieNskWhkkGfRNSCiJ0+nvJdK1QWGsslhcb7l/taKMTuWDj9MEYQpmq7Zk+8q4LzrpW1E/ooYnfnZ\nGYRBgDDwd3yeaElDFEXRwE6sT37ykxGG4YYNaiewXCWCQZNa8bjoPVR9noFDYwK1vATL7EJYdjrd\nP+1CQJbhriSRsC0NnVB5Alak9YTGAGBPo1uuR008DEII6NzEnqYFMOT9gUIEYYZ90sFkwypzJ/2T\nlecn6E/Ke36ChU6sGttlEkeOBdAZ76kqUH1/y1f5qnYnAecJWj7DgT1uGR5jFQe+WlVCY0CSZfB8\n5Q0WleGPzgcAJOquibpjAIW3wgCNs7zlOABwaLlxcm1deZ81tfGzCBH2l9SpOebAcFX/4sK21X6e\n7rgH/70UBqxb706W19aoKcGI8tgY0vxa+yuyDxM/FM8VrUEgsaiqR+E1eUmG2dkO6jVD1egT3VwU\nsbkIkUKIDKZl7fg80ZKGKE1TxHEM0+z9I4zjGFEUDfkUAXRXoXXHRDuIcXi2g4ZjoVHTe4wQsFju\nXFCUsWl3VIO7MBbIZART0zA9qdbYfpDisaMemjUTjboJKVQIruOrDqycAbqhDZw0Na66lHb8pGwQ\n59gajs5F8IIUrq0B4HAdBo2pSak4jpAq+6S8EYFUpGjWrHKT8rwXY8GL0QljuLYJliv75r0ov96k\nNM7Ftc57EaRULUaEFAijGIBbSswbNb3MYZg6L+u91RwTx+aDcjXvBYkScjAG2zLg+Qk4GCbqKiwm\nBcP+ParSQpIJMKY2pYZhCsYZ6o6GRt3sMSpBmCBKlEKxFaRo+TGarr3IK+rPI5l670bE/sm/KkJg\nDKpLLJNljqhR6VfU6kTglQ63nPU28RsmfigMVC3/W+yECTTOy79FzhjmvRAPPdqG5RoIOhGEsLGn\n6QwsaLvRjQEJRZEjArDj80RLGqJf//Vfx7XXXou3ve1tsCz1BxnHMa677jo885nP3JQBblcKo8KY\nWse7ltETIhpkdAataBdKBR2DkBk6vgCvMdgA2n4CL29s1wkSuLaJqYaJiboBKWRPT57iuP0Vm/0o\ngWbpaLoWhMjwk0MLsE0DEqrMzoIXY7JpY8GPexrHeX6sVstMg2MzBKGAlCpvBCjvRdc0MMYQRAlc\n20CcCHSCBEICfpig7UewbQNxlMJxDESRgG5w2JaGMAJqllFKpoueOxN1C6Gv9jVVm8tNT6qeSU3X\nxKEZH1km0KhZgBQQQiBDBiF1zLWjUhm4f4+bj1eJOvbvcSGhjPt8K67I1xniTCCMBYSshs5Ej2Ee\nNDk3XBPHOBYZh+IeFt+LFAyMS5UndMyBE/wi5V9fKHSQ+KH4nhkHIBgMznFgj5JzV8OVh2dCREkK\nETIs+DH8KEGj1qtuHDRuEjIQ68GShuiyyy7DlVdeiXPPPRcHDx6ElBI//vGP8axnPQtXX331Zo1x\nW1I1Kv0bIYcZnUEr2kJB54cxhFQrYAbVr8jzEwhItWfGKrwe5X2YOkeYCsRRCtPSEYRJj/wYUOo3\nIWS+jyjLKx3oqLsGGGPwwyTv14PynEWdsuIYSS4KyDIBhAksU4ehaXAdHbatQUqJdhDB0DiYLWFb\nqsJ1x0/ghQnqtqE2jEpgesLEzEKscjyugbqj94Qoqy3KqxuEOQCudcekMdUpFhIAU5LqOBLIHOVF\nVCsvFOG34r50/KRUixVVyuuukXeJ5QgiVXKo6GibZCqkVeyrch0dqHiO1ZxLP/2LkZbXLUxbTPBF\nA8OWF6MTZHBtDQ3XhB+kEBDw/AiZBFqdWBWtzUNp1c66S7Xr6P6NZoiTDNxQU0KaDhYzDBr3eggZ\nyMva3SxpiAzDwI033oj7778fhw4dAuccT3nKU3D88cdv1vi2LT3y5b6NkIUwYVAJnv5Jq9irojaj\n8p4Kz0JKzLUChJmAGfCyUKWUahPj48c64NDBOUOSZfjZYQ+dMIZj6tBzYyGlCvkUHT21PA7m2Dra\nnQh1V4fGNVgmh5IN94Z7otxL0DSV1zK1rrSYM4bj99ZwEq+hWbPws8MeMinK9uSured7lqSqru1a\n8KMMrmUgTjK4fWWDBiXRi8eFkZ33lADDtjiOzqu9Swema8hEt+J5kYObqkzM3e9LGaGijXqhUCsq\nFBiaBkCUNe7CMAG3jbK6QnGOUSbn/sVIkQOr7uHx/ASJUN6mbXH4YQbG8pyTBAph+EInKe+hul+9\nLUJKIz0kjzRZt+D5KTSuWls0a8ZAMcNSx1gL5GUtphArAIAU62DttzAjlfj5gz/4A3zhC1/Y6LHs\nKKpGpX8jZDHpLaemUgU/AYDD0FUFgbqjJtKJuoFOFMOydCRhAgGmchhAPpmw8vieH0GAoePHAGPw\nowQ1h8EP1MTDoFomcCZh23qZk9nTtLE3z0W1/QhekOCRI23Ua8qD6fgZUpFC4xyOqUNjvNwLNShX\n0ajpaHeUfFjXOBxLA2MqH6RxjiDM0HAM1N1eJVw1DGU6psqh5FLnTChDPlFXzfR6DQAHmJIke35U\nGpVi31Fxr/rDWsUxquVxigoFQG/dv1LSznql1ctNzoWc3PO74cWiH1J1D0+cCnhhjHpu+BxLQ8O1\noHFgttobSsqePU/V7qzV8Qz7bo6brsEPUlg1E3XTwN4pG0ZfxYzqXqf+Nhpr9Wg2wsva7hRihTDo\n4L+c/QQ0Gs1xD2nDGMkQnXHGGfjsZz+LM888E7bdjRsXm1yJpRnk6Sz3w+tXae2ddBDHytAEoWpp\nnaQZarYJxzIQxQlMnQ9MUpcTFGN5jbYUaRYhTQUmc6+g6ZplyKdQVLmOoTZ5ehH8UMCyNAgJHDkW\nqEPRHtMAACAASURBVN43rgEBiSBIVP8fCGja8J399fz4jAE1x4BrqWR5mjK4jo5OoCZcoFusU0iJ\nI7MBMplByAzavI75ToQ4EjAMDp0z2Fa3U211ta7CoV2Pxg8TeH6CdqAm8CjNoHMGAYnJPB9Sd81S\nIJIhg86UIdTyhFv/tRUVKQoPqmiyN2xDblX8YFt6T3ixXlvslXlBAj9MlYiDoTRCyqAkZX0+NfF3\nLVGzboJjcW5q2Hejc7Xp2HYtHD7SHngNg1SdxbGqFbxX49FsVLmj7Ux1Q+tOlm4DIxqi++67D/fd\nd1/Pc4wxfOlLX9qQQe0EllshDvrhVT/TCVSYSUqZq8hQTrRFlWmd68hECo1rsE0DlqH1nKdY/RY5\nHlVzLYFrq1+5rumIE4lUqHCZEgMYPRNI4WUIEZXihySrtLaWQFZ4boz3dE5NhcDjMx3MexE0pqHm\naKg7Jk6w6qVIYE/dKMdsaAxpJuH5ERIBpEmWb0ZVBtcLEmim2qAqhTJUB/bUwCqFRqsr/m6lA+XR\nFDmUBT9UnkeYwrV1PHa0U+ZWlPFVnpYfKu9twesKEibrvQn87vlYmW8a5glUw0/VChYAygm+3yuT\nUqBum+q25irI4hzH7a31CBKKa5VMoO0laHeScv/WKChP21rUJqJgqcXTKFUblmLUckfEzmQkQ/Tl\nL395o8ex41gu5l388KphjsePdco6bwudBEGkVs1CCnhBiqlGbhTyHIJlMDx6LEazbsHUGPZM2D3n\nKSa26rk6QQzHNOBHahKWUoXmFnxlGBo1HakQZX8hxpUirxMkajJkQBhn0LUMjZrK7TRdFU4TMley\nyQgaVwn0hY6SZKcyw+MzMY6fVuq34v2AmuirebNMAAbnmJhUHlTTtZHlarViTxW4xEInQSY9TLhm\nufF0qarUxb6povSRkDJvadGVkB+Z8ct27O1AlQlybQOZVNW1J+u9x1zqfP30Ljw4EtEVG5gaL0sk\n9e4p03BgurcyRfH/Yed+7KiHdi5kyVKBhx5pY/9UhomK0V8NS3ktUZR2awUOqdqwFCu5j8TOY1lD\ndPfdd+PgwYM488wzAQDvfe97ccopp+Ciiy7a8MFtZ5YLvRU/PLU3JG8KV1klSwhYppa3FZCIkqQ0\nWqlQ+QWucexpWmi6Zs/KepA3VvzIDU15D7qmJkCdczAGVXRUqMnW85OytEvbi1WtOlfHkbkAkMC+\nPS4YlDGs7ufx84m7GMdCJ+mpTi0hyslKSomZ+QCGzhHGaR5eUyFHx2Y9n6k5yiBxMHDOYNo6/CCB\nxlV7bdXnafh3UdyPYtOt62jo+N2abk3XKPfTqFCXmkyFzBCEWf59cdSctTUnKyZyKSWEzDC3EMG2\nOBquVbarKMohFd9XIVMvKq/370Mb5G3HaVdiHkQZhJDIhECaqXDvICXiKCzltViWjqxSMX6Y0IEY\nnd1UWWHJSOzHP/5xfPKTn0S93l0G/uqv/iruuusu3HXXXRs+uO1Mf4x7WMy7f5VcTNQN11QSZDBE\ncQbb6jZ980PVW0jLcwbVJLXGu96YlKoI6qNHOqrTqCi8igRpXsV73gvxo4dnkYgMmZRo+TEeOeLB\n8yPIPAGuiqRqsE0drmOg5hjgueFyXR2FMl3t1QEWOhE8PwIgUHQxV/XqeLl/yI8SGAZHJ0zQ8lMc\nmfMx58UIQqX+8vwICx1VwJXluQ/X1aBrDF4nQhCl2DPhYLJm5vuzWFkdYK6t6uYVk3FxP1T7DGW9\nkjSDZaoGfZal50ZUyd5rjgktryyha8irm4tyM+1qqed13LwgBmca9jQtpb6DXNTHqEBAwgsitIMY\nXhCpx/n1FL2OHjnS7rleXWcIowRekMALImgag8aV/D5OBVIh8MiRDr7xwON44KezmPWCnrJCw1iq\nfqASLRRyeGOR0IFYOf2VFdrt1riHtGEsuWz5y7/8S9x55509hugZz3gGbr/9dvze7/0eXvziF2/4\nALcro8a8q+EO19HLkEajpnb6R0mGIEpgGgxHZgPs3+OgZudCgdyo+GGicgc1AwIM8604L0Sq+g6J\nPJ/T6sSlyMDzASBDo2aqdg+tCFEkVI8fDrT8GC0/hcaUj8NtBs45pMzw08daSGWGKMrQ6tgwdA7X\nNjEzH8IyVXHWDAyNmpnv2o8QxxJ7Ji1oTOW7fD+BY+tqfFLkro1Sw4VRijSTpad0bC7E3kkHnUCg\n3nQRBQk0jSOJM0zlFRKqBhjoDYcWz6mwl7r2qYZS1AkJxFEKzVI5oqIKd921cAAMkIAQAmGcIkkE\nWp1oWU+iqi4LM4m2FyKJs3JjrszLKYWxEoCo8YpFZZt0ztHpZGVjPimBx4/5cC0jX7So3J5QkcRS\ndOHaOlzbxJG5AEkkAFfCsTV4gQDjDA8famPBV2FKyzR6Si+tFsrxrD9UWSGHc95jhAr27NlT1vMi\nBrNczHuYFHYqb2LW6kSwLB2pkLBMDWEswFlemDOvPdZqJ/DzfUEMqv14Ld+Vn0mBo/Oh2pDKmZq0\nOkmlmrJAmsfNao6J+VaANEPZHsIPE6SZxETNgmSqUytjEp0ww5G5Dmq2Acs0MNuKASaxb1KVEgoi\nAQZV7se1dUw3bZy0r4G5dlTmQwAgy5TAgTH1r66pgquMSTi2iUyIcgKOkqys9M054FiqUKtS4HVr\noS14cWmc250InSjBcdP1ssFdYTwkRDmRc6bqwe3JV/lAVwnWbYEQqXAW4xiU7+unW8w1hm7pODan\nWqlnudcVBAlsW4dtcYRRhpqjYXYhhGFwSEjMeRFafowT9taQIUORw/JDlccrvt8jsyrMxjlHnEn8\n6OF5OJaBTpii5ug49fh8EgtTeEGMOBaIU4F5L4AfJVDZMo6pprlmufRqcjy0iZUoWNKaaJqGmZmZ\nRc8fO3YMWZYN+AQxKv1SWENjPeGOTKjQm5ASjmUAUOo5CYFmrTtpCgm0gxiPz3r42REPnq9ahDOm\nQkB+mEBK9T6tEi7RuJI+A8qTmnBNFXJxlSrMtpR6rmhbEIUJWp0IR+cChHGGTqC+/yRL4QcxWp0I\ncZzBMhhcW4Wy/CAtQ0aMS7R81bahE6RqU26YwbE1mBrDVNNGFGWQYAjCBEllYtQ11TSPMVX5oBMm\n8IMYjbreEybSuMpTeUGMTpQA4LnqDj37hpp1E66j9j0hFwoMWsEXoTT1XXQ74y43aVcragAoDb7K\n02RIhBKN+GEKx+Y4aX8DpqHl1SzURuUkFcqjCpUR7gRJWU2iCB1KdBcOswsBvFAJFCQEjs4F8MM0\nb/1hQGOa2hPGVMuQJBWwLQOpyJCmEoz9/+y9e4xk6Vnm+fu+cz8nIjIib1Vd3Y3bmPVi8G24zBhr\nMLixLTASGK1EtxYLGAQyrNAKkMxgtFiAF4zBml3LEmJmjYdFq7XxWnhg1yxmAc+gBS/b46VNm4ux\n3b51t6vynhHnfr7L/vGdiIzMrszKqu5q9yWfvyqjIk58GZn5vud93+d9HstBUfP5xw740rWcg7w+\nV7vuieAsi5QLPLdwZkX0xje+kR/7sR/j53/+53npS1+KtZaHHnqId7zjHdx3331P1RmflVjWflt2\nwpzv7szKBnDSLBpYSSM2V1P83rMIoKhdBVA1jmbcdqp3cHV3GJdWM2zPNmsbxeZ6stCdG2Y+WeZR\nFJrQk9yxkZHGATt7FUWpkJ4gCSWz0lI3HUXTkReKVmmiwKNVmqrtMAbiOHQkgtCj6wxVqxgmEWni\nLwKMWyLt3O6O5wGObZclCUXVMqs6sl7Qs+mchcV4EOF7HuuTgK7VtFojJaTRfNZz/DMdpCF7s5pZ\n2VLUyrHdtMYYn6RPtPPPfFq41pSUkKbX/zO4Hp1aGcP+dVpoyzhKXu7recL3pCSvOqSwi8o09Jza\nduhLWn1EMgg85zkVhj5BT55oW80gcfJLWRIyzlwLs6rdzz3q5Xmi0Gf3sGZaSKRwBox7hzXaGnwh\nubyasjcTi7OtjmPHEsxbRmN/wRC8HUy25SpoVjaLRd3lz/gCDsvKCnVVYu3lr/CJbh/OTERveMMb\naJqGN7/5zVy9ehWAu+++mx/5kR/h/vvvf0oO+EzGydbDPMnMFxrnQ/L5Fr3SlocfOXQmb9YJkhoE\nkS8ZjyLqvoU3LZpFpVLWmrzqyJKAyUq0WAT1pGRjklDXevH13AtoGasD2NgYsr09W7QDR1nAtf2S\nohJsjBOsdcrbceQRRR5tq1nJPHwhSTJBID2i0NmJjyfRQv1h3ibTxlDULUHg0Wo3EyqqljvWsl75\nOmRadMyqlqpWRIEk8D2CQJL1DK+1Ucxh3rK2lrErC4C+0jv+GfueZJg6vT1jLU1n8aQ8RhaRQizm\nReB2b85qty3PP/YPa4LA2ba32rC1W3JlY3Dd56dJSOA5P6iydok2jgRShtglV15ws6mt3RKJwJOC\n9UlCXnYEvdgrgEntgvVYN4q1cbJg/rWdj++7BN+0hrVRjO8JlLE8upUzGblKN4p8ilpxaXXQ3wiE\n+P01l0kvSmsOcvOE2mZnWZ3AkX7h/Pu7WGI9jjlZYf7vZzNuyLG877772Nzc5OUvfzlSSlZWnp1+\nGE8G5n94MnTJwmAXjp3zHZX53CPuhTdPaps1nTN8K2pn6gYQBJJHt3LGg2jB/traLcmS0CUJq2kb\nvXBdHY+OqNedYaFuPRfRnAeUk+ed22cncUgaO5KDJz3SCAyGuhYoY8jiiM3VhOG8VdWbty173HTa\nKSIorQk8Z1hnrVlUAm1rFnfCTmpIcFh2brm2F1v1PcGV9cHivNdjIi4HNidA2mKs6VXPLXEoe9+e\n4623m5GUWa4MDvOjxV5wVOmznh8mIU3VMXbqQFR1t/gdWP6efCm5sjE4tqS6TI0HFu1bYy2zsqGt\nnOrGqPdbqhvFYd4ChkEa9jM3waxse2aes3xfnySsZI5puJwgloludasYJMcFWG+2OrqR1clcM3H+\nOVwQHI7jJFnh2UrdhnMutL7zne/kwx/+8O0+yzMeJ//wZuWRmjK4oBUtteI8KRmPwqNk1at0F5Wb\nx9StYmOcUdbKzWXqFiG8RVtpcy1FIrDGUEnNIAqxCPLCBfSqdgoIzmfNLILBPPAc5m6vZjBKOcw7\nirp38ox9BokHBCSRB8KjaRXE0DSWtXFIGnuLpDIXB10OJld3CpRxrbQo8jFl4+Y3pfPCWRtHCOkS\ntUYTxR7FXk7dGDdnimJ0r2w9pzYLaRdJSQinpjZnCM6DmrWO0j5MXcWxMoiuG0BvVVLmqIUGVa0W\nbq+nVQzqRIabM+euxy47WUHMGXzz791YFjYWLhtaOgNbe+6mZGUQcmVjQF627E2PEqYnBbOqY5C4\nxDMcBAtJozkGaYjB4kvh5mFxuJiJwa21zZZfY63lID+yCJlf+6IKugCcMxHdfffdvOUtb+FlL3vZ\nMa25N7zhDbftYM9EnPxjFSe4IKEvF604hKsy8sINoS0G35trrim3P4PXU4ydaV1Ra9J+2dMXgu29\nCm0M2wcVWeLhnA8sStPrykm292u0sWwflKyNHO05jWs84aG0M3r77CN77O6VWAGBcEFjmIVg3TBc\nCMv6aobRhqrRICxVbRA0DHuVhGXdMXAstbFx7C5w1/E9QRA4xp8nPSSC0TCiqjtabdgcp2wdVP3/\nC2cqWKilFppbaJ0Mo4W22ZxBNk/ug8RHCulmbuL6JAS4NbqxsZY48TjYaZy6deKxOcnOrBhO7h7N\nq5rr4eSNzFwId6E72OvKqX5W5gmvl0qyx+ZxaRJwdbek6Vw1GkceTfN4GaaTiW+URlzaHJEF3jHt\nODh/wrienp4QgqJyihhZryk4F9ydV3y3WnVd4NmBcyWiyWQC8Di9uYtEdBwn77KzzKOquoXVw/pq\nwtUdN9/w+h0WZSwrvTyNu7tuyRJXkRSVQhuXhFZXAvYOnT27Lx3Tbmu/wFqJFBaLZFoq6l66xxPC\nsbNqjcHQ1IainjGMQ1bHMYNEUDWuypq1GrQmjXyiKKRuOwZJsBjGzwq3QOv8jxRFqUgiyc6BxgpY\nHcSPu7MvKifv42SKArCaMHDRLI18jDbsHNbkZUfdun2pJPIZpyG+Lxml0UIIdRnzCmP+OS/bNcxb\nWXNzO9sb16V9W3JupDfIAldJLrWmzjP/yMsWYSV3rA04jBs8IZn7TJ1WMZxljHcyEXTaLmZrywQW\nY+1Cdkhbt3+UxF5vN2EWauvzc5RVx9o4OdLMa7qFGR7M1ShYrA8sW06srQ4WbdqTCtvnwXIyXfZ9\noq+ChBAs25sv44KscBwXZIUTePvb3774t1IK37+Q77ge5nfZ0Gu0FU6OZ37nV9dq4S+kLRS90vVy\n4Al9uXAFHaZ2QVBoGsXGaoyH8xGaFS1Z74OTRAlVrSl62RtHnxbM9kvi0ONgpvADQdsapAd7hxXW\nWKpWobVBtYqmVQgE1jaMsmSxp/PYdo7fS7ZoYyh6GR8hPPzAp641XeLu2OdBy/SKDEJIosDncOYG\n/J50jK661aRx6Az9pKDp7GJmNhnFdMqQJu5ao0GIPUbllrQcJf15YFuedSxajkm4mM3ppepsWT38\nZu7Ej4RILXWt6Ixh7k3kn6IkIKXoDQ5rHt2u0XbGeBD1tgvdsQpoHvi39io6peiUaz/WTUcWBwuS\nYBRKwr5EOTlHmn8uy2QMT8pjCc7tUXGUhPoKvdWGL+/kx9YKTla65/2M4Ogak2F0aiv0Vqqu5wou\nyAo9mqbhrW99K695zWt47WtfC8BP/uRPsrKywtve9jbC8GK4uIz5kHptJeFgv6BThrJxeyF51bG5\nmvV/bBKscbL9J/xnwtBj96AiDLzH0YPntONZoahbhRASYTX0QplYyyD1EUL29G83DyjrloNZQ90Z\nOqXZXE2pW6fOrI3ljs0hW7sziqqjagQrg8RRoPut/VEWYho32xHCEoe9Nl7rbBTm59c9AyovFdbY\nvg1j8XyPKPRpO83uYUNVt9x1WWLx0MaitOZLVws6Y7ljLWVzkh4bzC9XDcM0ZLdqr9tam3/+J++s\nF4SCJfVwpTV5ecQwPE9VNA+mRdUSRj620QsG29yr6HrIy5ZruzWqDyqHeYNE9KzJI8XqJA56LUFN\n02mi0Kfql1gR4OHmiHHgcblffD7N62p5PUAIR25wDq9O226e+PRclgF3hkYd3w+cO9Celz13WsI5\nrRV6ocZwOp5LZIUz70He8Y53kCQJr3zlKxePvfOd7yQMQ37913/9th/umQxtoG6P9kKUca2O+VLi\nyiDqLQycIKUn5m0mNy8p6pb9vOHqTrG4xjLteH2cIKylqF1Sslb3v6gCiyWNHTU3zxse3Z5R1N1C\nxXt7ryQOnRKzENApg8AFgvWVhDjy2d4vnDW5lFjAGMudmxlX1jJ3h40gCT0Gib9oIS0ETXvlAujn\nW1LQdYYk8kkin41JiickAsveoSNlBIFkkPhY24uRntKmMcb2jrKu8pwb4p202AAWmnVl01E1Gru0\no1O3ZlEJ0LfwboTlBddASjZXE1YG0YIFdz2du/nvQqdU74fUklcdTacfp1jdtk7OZ5zFxKHfU9Bx\nrrJIBmnE6jBaJKH578RJ/bf5OcElmzQOGKYRSewhhbcIaFHkE/ruifPfv8g/LuzqktXR9zEtm1O/\nz+XPaP5zOOmDtHzOs7TrLvDcwpkV0QMPPMAf/MEfHJPzGQwGvPWtb+X7vu/7bvvhnslwbSiJaSzG\nmIW45vIdoxCC8eD4wmReKaq67UVObe+Fc1QB7E2dxYIB4tgZ0xlraVrLeOispDvd0XXgScvV3ZKq\nMdAnJynA8yVV05FEPk0jCUOPYRqzNk7cvAG30+QYbJai6oOhmLuUFhzmjmo+IERIpxDeLrmZprFH\nUXJsJ6asFFXdsT6OsdaJie4e5m7fRVtGgwhj5+zB/vM4McB/dHvGYd4sqohlU7uTskmNVtS1JfQ9\nDmY108Jp+G2uJeTFkXBslpxP4maZlr08Wwl92dudH81pltt9nsS12SxOHgKX/EeD6HGK1Z6EOPZo\n9w2tcurm6VpG5J9fC+56i6jXayfOJaWWq5I71gd84ZG9xdcnVbSn+RET9HptzdOWYJ9sOZ+TqwcX\n8kDPbJyZiDzPu66mXBAEF3OiG2DuoCmlWAQ73xOntiimZcM0bynqBmNYiGJaC4d5h7G2H647jbRZ\n1VDXBmMNZa3RGDptFirLceQxLRV5rbBA0xq07ZhkEYHnMSvdEuydl0ZkWcSONrSNIgg9utaxsoq6\n5TB3rqtN43ZVml7FIUsD0sQJsyZxgOqcmrUnJHHoUde6H07bxd3+XLnbYvGkUxPYGGdoo5mVDY9t\nz4hDn67TZEmwGJovB5j9aY22buA+rZwwq9w86eHjZhP7O47csTtVOGZZyB1rA4S0hJ7nCAA9bmY+\nMUhDru4Ui3ZqHPnH2H1wvD01SENWV0J2Dlq01QzTkNWVeKFYvXyGQRqS7xRMVmLaRhFGPl2rWRsd\nT7YnA/ppj9+onXgycfgnEt5J9txJJuhyC/CsRHMjf66bxZN9vacjTpIVns1WEGdmk/F4zEMPPcRL\nXvKSY48/9NBDx2jcF3g8TjpontUDl0IgcXTlQdxxbb/kYNYwHsVkSYCxhi/vVCSRa6sIIRaLgEnk\ns3tQAZLSOkVrYzWe9JhVDa3SdJ1mVrek2scOIkaDAKVcS3D3sKJWhv3DitVRhGqs21CxolfBNnie\nIA6Mm2cYl6CyxLX14thnlIWQHX1veemClzYKbS2egNVRTNNqwsh3ATb0AMn6JGB7r6JpLXEUMkh8\ngsBjZ78miTzaTrM2Tpb2h1xQzSsNGLLoiHBwsqrJK5eAZmWLlEdLvNe2C+LQp2kNcShvOOO53s8r\niQOWfU8tx9/8pJLD2ihhPEiO/f9pc67FtQfHKw332V4/AJ/2+PJ7BFKyshoea+vdCCfPOMx8lrtx\np1WuJxPDzSwQnwdP9vWejlgmK8ytIF43HDEaPftEBc5MRD/1Uz/FT/zET3D//ffzspe9bKE19773\nvY/f+I3feKrO+IzF9doUy3eY2sBBUVOWike2C9pOMR46ssP2foEnBKPU72m7vaWDdfTtS6sZVe30\n1wxQNQ1lI1kTjvacRAFFoQh8D983TLLIhUoLewc1l1YT9qduyVR4HnWn2Z02rI5isjTk6n5OUWmU\n1fhCEoa6V78W/bwJpHCyRVXtTPvKuiUOfcpG9Yy1OeUYZ9tgWnTtgvYgCPqZkBNJXRvHyH4mUlaK\nNAGEhx94ND0FWAiLlIJZb32RxkteSObxJnKesGgr+iTkFkDd0qt7LIldBbeszH1enBzKjwYhkuvT\ntOH0pHOzy7anWXKfFpivp5d38prXw8kKZ+4ee73K56z3P8/3dCt4sq/3dMQyWQGe3VYQZyail7/8\n5bznPe/hve99Lx/5yEcQQvDiF7+Y9773vbzwhS98qs74rMLyH4+xlocfOSCvtNOVMwZlNMMkYZRG\nzpBNCKZ5S9m01I2h7TRKG1ZXIrIkom0V1mi0Bk+6hdOsH54PsoCm05RVS5aFSARpFjhTuCjgsOjI\nmxYOS4qiIQqc+d0gDelaA2iksBir2N5VZLHv9OUit4cDgqbRjCYRbemES7+8VyBxuyxJEjgSRE9C\naFuL52umuRt0r44S1icxZdNRN7qv+ABxJOzqSSc0Ci5BxYOIYRohhNufmhMF5nMzOCIgJHGItU6N\nuqo1Aom22p1n8fMwtxTEHp9Yzh6234yA6FnLtqdZct8oMN/sAu9pFc6tJM9bef8bYXlV4oJx98zH\nDQc9X/u1X3smQ+4XfuEXeNvb3vakHuqZDmOcpP40d9vkw8w/ZlUw/4N1lgVOlDQMJLPC2UcnUcAg\nDtmdllR1QJYGlLXBoB0lvFXYQ0EWh+RV63TeMueY2rYapS2+lFyapGgLYeChlMEaSyQ9/MwtFnba\n0LaGonQqzmXVkEQeo4EbZIeBR9spitYlv1nR0HaWzXHEC+5aRQjhqMj95rztLSsEgrzXVPOkWGzz\nR5HHzoFzA/VxLbFPfX6fYeKo53nVIoQg8CRJ5C2WfoVwUa1VBtk6Tb0k9qlqp1E3D0SH+ZGAJrgE\ntnvQODuEfqHS9yRpFCz8jXxPYLDsz5ozacUnk8xZiWW5avCjgIOixhp3zmXh21u59mmW3DcK9Der\npH1ahXPaLOjJfv8bYXlVwrTP7h2b5wKeMOPgk5/85JNxjmcVZmXLLG8xFqw1fHmnYFYoVgbBsUBk\nMY5dVoOxhmEa0nUaX7qW02SYIKBfZiwpSuUSQ+0sGNZXQpIopKwVceiqjk6AUgZlDSujiM89csju\nQY0QgjvXU7IsRHUGLEwGMUXdorSmqhWDxGeYRmSRz3gQ4/kdSvvUTYEfBEyLFqzg2r4hS3K3lNrp\nnsIr+nad6OncbndqmAZYa/Clq6GUMiAdeUF1mlZbRplLBp702ZjEjNKQsulQ2rC9VxHHPrOydm3J\nyEPg2oIrWcAgO5oRzbXr5gh8D22csZzFUPaGcb4XLpZgjT0uTDu3rFhu8RlrGQ/OPxNdriYO8ppZ\nnyDny7XzKu60IftZg/85wWG+nFpU7UL54GYC/Y3IBadVODdbKV3g1rFMVoBnN2Hhgvp2G6C0WbRP\nytolJKU1h7nhMO9YGQQMMzdbSWOnmmwshIHgzo0h2oiekq1IIrdRf3k15QtNR6ckvmcJQ0lea0ap\ns0owWMraEQuEtHzukQMOygZrBOPMxwqJlJLI89lcCRb20mkUsjrO2NrL8YVECIG2MB7FqLkVeauw\nEjobMM5C8qpjVnUL9lfTuABfNx1B4JQcsI7SbKzTpUsS16ILA0mnNXHozozoG2rGYtB4wtHajbF4\nwqlI5JUT+cxiV9EYoZkMosclESGWvICkc3KV0iOJ5s+xVLVmdPkoYO7PGmwvSaT7uZdFuJ2jXlJn\nVijGjzcqvi6MtRzm3dGibCCP2Su0yhwjOVxvyH7W4H9eeeS5U7iYq0fcLGvsRuSC0yqc5wJJZ+Uw\n3QAAIABJREFU4OmCZbICPLsJC095IrLW8ou/+It86lOfIgxDfuVXfoW77777qT7GbYXvSecL1Duj\nSiGo215zq1fB3totCXq6cxT4DAPJPXeOALi6UzIrlGsZTUsgJUud5XenXLBeSUIEsDaO2ZykzErF\nFjkg2N6v8QPJrFD4vkfdKe65YwUhYHUU9XMmF3WTyMfzBIPYKTIobdg9KAl7GrXFkkY+00pRdS3D\nyGN9kjBIgkUbLIklm1mK70kO8oYo8LGB6S3MhVMxKFxgvrKecZg3RKGbVQ0nIXXTUbeaTmkOSr+f\n9QiMMaRxSF45SwohJJfXB+zvlQzSkEe2Zs55tqfHg2A0ODK/u7pToI12bL3AI/DEwnxvDk/CYS+5\nRP++O/slYeBWF5LYR4rzR1s3tzCLJNY06pi9wnyBdPn9T+KsYH+aesTNJoQbvf5WZ0EXePJwkqwA\nz17CwlOeiP70T/+Utm15//vfzyc+8Qne/va385u/+ZtP9TFuK4ZpyHAQMs1bPOmCn+l1zuaKA3Wr\neHQ7Z2u/QgiYjCNn36Ats6plb1qTVwop3H5QfVVTtx1Wa5QVlE3H8zeynsYtqOqW/bwl9CWdMajG\nIIVwqgYG8qIlCj32pjXKOJ+eujWAwPcF6+OUptXYfpZTNwopJdOiIgx8QqUJ/YAwChin8TF7C086\nckIWh2RxyGHR8KVrMwqr6JRmMoqR0jH/ZrOOLI5YyQI211J29iqa1jBMfKIw6SWPXLVl+52pLJZU\njYC5xFHVcHU3Z3u/chp2niSJGtaGyYLdlZetM/lLY4RwkjqXVjMGmX9MsiZNAqdNZ+dtOOdAK6Rr\nl9ZNxygLj82QTiMlGGs5yBuUYbEDlCUB2hdY83hrh9OG7OcJ9k80ISy/3lqnaQjOP8lYe+r3+GST\nDi5wAXgSEtFcNuW8+PjHP863fuu3AvCyl73sWTVj+pFf+/PHPfaef/1q8rLlIG8WA3NjLY/t5nz+\nyzOaTuN7ku2DiqrSrGQRW7sFrYKqU5RVR90YDquOsnLWypNRTFErDqeOeVbULVWjiDyJS0odxggC\nX+AHEqHcz2h9JSEMfQ73C/amhpVBSBRI0iika1suryZ0pm9PWUgiyd60wvpgifCkYBB5fPXdo169\n+igYzSV3AKqqIwgEWNembBrN6jihrFqMcZXOYdGRJi1p4rOft7TaoBv3/7Jvq42GPkUBSIEQGm0E\nW3slVdmwe1jTKc3etGKYxQhConWfaemSzlyBYnM1YVg7Kvl44ILsPAB32rK1Wy4kidLEZ1Yahv3M\nQxvHQkyio5nO/PrXm63kpSOnSGGIYx9PwNpKSntCQXxZQ+8wbx93nfME+yeaEAZpuNAtLOqWJA6I\nrEVpc2ab72IWdIHbgXMnot3dXdbW1qiqiq2tLZ73vOcBHNOhOw/yPGc4PCo3fd/HGHNdBYdnAz7x\n6S/z9S+4xJUkYGu3ZFa0tJ1GSGg73VNwO3xfUjcd6ysxs6pFaXdXfzCr2Z81pLGHtpKmNShlSSKf\nVhlmZUvZdC7IWziYtYSBk/oZJD5xGLA5GdB0gHACmGkSUtQFe9MGH8F4bBZma2XdkUQeTaMJQh+l\nNKqfJzm6dcJqv5hprGVaNjyyNVsIamZJyLTsQMxZYj6qVyAoao0QtqdXW67uVL2DqNOOM8aSxD5Z\nHFA1HaujiLsvJdhrhkEcYiw0yvDFqzMEEIQe0pcIQa+jJpjmziNprkBR1d0xv6T9WbP42czFWkdp\nuPDICb0j9XMAKRsEbkm3M7B/WDFZSQikawcuB+1OW4zVvUK5ZZBGC5HWkzhrRnOeYH+959yMjM6y\nbqG2BtuTH+bnucBXHifJCnBEWHgm4TzkinMlot/93d/lQx/6EB/60IfY29vjx3/8x/nhH/5h7rvv\nPn72Z3/2pg41GAwoimLx9XmT0MbG8IbPeTri3b//j6TRP/Kyr1nlv/qOr2Ml8Nk+KKl3YTiM6DpD\nXgMI8D3CJGB1lFApi+cLGqXpjGE0iLF5jZVOhjkdhESRhxLWWWwrwzALGBKSRD53XgpoOoU1EIQB\nYSzptKExlsEgpKg7pCfYXHW/1F7gE0Q+m8OI6axhfSNBdZo4CmgMbI5TfN+xAbZmNZ4nOZw1eJ5E\nBh6pL2lqRVcqxpOEvGjoNPgejIYRq5MUZZ3OXeBJ0j4xJ7FPMggQvmT3sGaQxVzZGJAlIaNRROh7\nyMDvqdyWqtB4geeCd+PYgqNhzPPunLAyiAhiR5WfrM6DsmVjnDJMQ6QU+FHA3qxiVrTknWI8iFhd\ny1gXjo0wGcbMyhalDb4n2TADDvMGL/Ipyo5EG6LYXwR5KUCGjkSRKIMf+YydfReR7+Z6y7+7xjil\nB9sogt4iYp4s1laWaQw3j8O8wY+OJIN8T7IyOD2hydD9+YdxsNjVAri0OTzzdU83PFNjw40QhYIk\nOS5CmyRDPrPVIbb3vkKnujmUZcH3fPvXsbIyOvN550pEH/jAB/jABz4AwJ133snv//7v8/3f//3c\nd999N32wb/iGb+CjH/0o3/md38mDDz547sXY7e1n7pCubOBjf7fHx/7u/+ZrrkR8y0uuYJWHZy2H\ndcNs2pAmITt7BVVRUysFWqCsk6DpCsXhtMJYg28Edd1RTAUza0BIDssOgWWa14yziL2qowg6jNWk\nScjWbkkaBdSt21nKYrdoWhcdh16JAvb3SsIg4PJ6ysGsoSxaPE/g+R6y6bi6PSMMJL4n0a3iMK9p\nlaVTmvEgpOksUSCdYkHksbVT0hmFLz3WhhGq0UwGAdd2SqbacLVzCXY69YhCN7BIfUEWSlSryDtD\nkVdobbm2V1HUjt02GiWURYO21iVZAWVVs7dfolsnZLpbH+2VeBLaql1UJQdFzZe3C5Sx1I1GdxrV\naAap895Z3klpcclvb69EG0NRd1hr2WsUXaMoKsUoDWn795uWbd+2O2r1wfHf3YO8ZlYopmUNvazT\nKAsf9943wrwane+qZanHrLffmL+3FOJxbcFlzGdlc9YgSDbGKXXZnPm6pxM2NobPqNhwM0kzGayS\nZI9/vmVhsvu0h7ENOzsz2tYVG6d9/+dKRF3XHfMeCoLgjGefjde+9rX85V/+Jffffz9w3HTvuYDP\nPNbwmcc+x4u/KubrX3AJbIywgkEW0nWWw6IjCQTDYczOYcX6SsLGKCGvNLOyJutbKdNKY3sBTQ9B\nGPg0SiF6+nHZKjwhyELn4hlHbjbleR7GuGF0FHs0raFWhmv7JVJ6bO0XrI4imlYzGoT4QjjKtNZU\nTcdh0fLlnZw0CVkdRXRK8+WdAj/wGCUxaSLZOahJYo9AOwfT7f0CX3p9WzAgLxVeJIgALFSNJo19\nhlno3GX7JeCDvCGvHHvQIjksakbDhM21AW3nGGmrKwkbk4RRGiLF0exk2WF0rs4MTj1aGYsUMBlF\n7E3rPng7IsFJOM+fYNHu6rRhf9oyK1qaTrG5elTFCOzifYx17797WB1Th54Vrh2YxiFl3ZKXLZNh\nSNqLvJ6nrQaulTffVQPDtV13MxLHPsoYtvYKBkl05rWO5kyClYHTpVsZRGcmoSdbRfsCF4BzJqLX\nvOY1/NAP/RDf9V3fBcCf/Mmf8B3f8R239IZCCH7pl37pll77dMe/+9lv51OP7fIf/vyzfPax8szn\nfvKLNZ/84heYRPDyF62jlaVqNVXV4Y8i0jggLltUZ/ED0e/pSLSyFI0iSSRl2VL3y6kag24NdeAT\n+YKi0ohAoqwl9AVSSqQniAKP/WlDGEo8CXnV0VlLEgbMasWsdIw1IQ0H05o4DlnJAr6UO0KEABpl\noVJkcUjoe0zLjsgIKtnihyF50bA2TsEalNVUjbM2uLZXUtUKYyxZEpBEzvxvkMB4GIJgoUAwSEM3\nSK/qXrHBw9jItfY6SRC7SmqQeARLenPz2cm0aJD94qjScyo3FFXnKOXGsruVE0UBvicIQ2+xbHoa\neSBNQnYPKsajiKZVmBY+/9ghm5OEYRod05trGrVwTl2eAc3FUUVPOZeCxXlvRk1aG47tJymtydIQ\nT8Bh1S6IMWdd61aIB7eqen2RwG4e15sR3QriOFmYHz7VqMrixk/inInozW9+M3/8x3/MAw88QBAE\n/OAP/iCvec1rntABn43wpeTr79rgn//E3fyH//R3/MF//ALlDbot+w189MEdhj589T0rgKRVygVM\nAdpakjikqjStMRhlMNpitCXy3dwni3zCwKNoNUXZQBwifYE1lqJsuOOuMaNBxNUdzc5hu1BCgF7m\np3MU8cAXBH5I1SjC0CeOfNJIMC06kljSakEofKZ7FTY25GXNnZsDQNBoxdW9iu2DisCDZneGwKmF\nX1kPKGtFVSuUMUgBRe0cX1dH0YJ6vBzgpmWDEJaqcS6xSeIzSnzWVxJMqplVHQezDotA9W0qiVwE\nueWB+6xqe8UEV7UIIWgbTacNk9jvjQBLoiigK5petXvmTO+iYKFcMB/w52VDbW3voKrIS0XgeccM\n605ifp5BFrC1U7nP25NsrifH/v/k80+DJ1nsqgH4nkvIgzTqBVHl4ixPJvngVvaX5jtdtTILWnte\ndmd+Xidbj8syWc8VnFxovRXUVcG/eNE6w+HZM5rbifO897lZc3P/IWstSfLEhqrPdqRpwGu/6QX8\ni5fcxf/xn/6RP/3/dm/4mpmCT3zmkHEKX3fPhKJuWR1F7B3UvaFcSxaG7OcNSSwJPEgS1xpbG7uF\nVt93lOrH9gokkjs3Ep53aYgnfbCQxiFj7Sqv2awlCD1CKRGRZJq3C8p20ymu7hXEocf6KGKYBSht\n8YSibjWXNlJi32M8jClrxR3rKf/wuX2EJ1DaID3BbNpyeX2A7jR1p7m2W6K006wbDSK6ThGHvfoC\ncDB1NGZnLyE4mDW4EsmdVwr4qjtGrK0kNFVH4EkurabUtXazGWHZmKQc5g2HebfwCXKsvBaBWKh7\nA2yMEwaJv5DbUcYiekFRY03PfHMV2iANFnf+njxaVBY480GDPTbsh9P3fCSiN+JzcySJOPP5p2GQ\nhhjsIlBfWguQ0u0rzZl/573WzeBW9pfysqVVhqpXGdG1Qsb+mdXUydbjrFDPOer49RZabxZlMWP4\nDFBiOFci+rVf+zUefPBBvvu7vxtjDO9617v45Cc/yZve9Kbbfb5nNEZRxH/9upfx7d8y470ffJCH\nr3U3fM1BCX/19/u8+HkN6+PLbKynWCM4FK59JCTUvQhpHIWEgQtlZd3Q9eSBOPCQngee5IvXci6v\nC7T1QECrLAhBazS6dcycF9wxpsxrqs7psVlrCUKJMR4PPzZlMorYHGf4viQ/rBlmIXesZ6RxyNZe\nSdu5KieLnN6dMQbhCdLIx4YeVdlhjRNiHQ0jAk+wNhyyMjjaz1kY/pUNUnhs77th/mQUk7ROuy72\nPe6+NGJ7Z0ZedrSlq6rSyENIQVF1bqZkjNvVqTuyJMQTHnHkImZRdVS1xpdufjS3VFDKkA4CuqoD\nQS9P5FpeeQlzO3Q34+qQAsqmIwz65V8hubpTLO7yT1OHnlvFzzFfw7vZvSApBOMsXrjTLmNlcPuW\nTm9lf2n+853fBJg+CZ9VTZ1sPWpjLmjlz2KcKxF99KMf5cMf/vCiKrr//vt5wxvecJGIzokrwyH/\n3b/6Vvaqit/5g7/hk5+vb/iaT36h5JNfeJj1FP7Z11/GagAnjDrKBHXjPIGUMjw6K1CdJgglVQdd\nq1hJJaoztAi0Ujx6rUZKy/ZBhbYG1UEQWZSGz1+dYYRgEAcIz/kVdZ0FNEpZ9qctoyxmdSUh9ARX\nNkdO385aRoMQkHiemzfNPWtWByFZ7+jqJy4p5VXHPzy8QxhI7r40IkpWKMsOYwVN3aGsS6jDNEJp\nRdUY0tipE0iB8w2S4nEyOnVrGCTOkdZY4yoDLNZKJsNo4VOktHZmeJGT7imqDmsF4ywijj32D+t+\nlcCSxWHfGtRY3F7QXAD18npGmvo8ulVSVm4ZNIl92qVl0NPUoecVxZypZqxbQI4in8ATC2WIJ4Lb\nWTncyrXn+2R55WZzvuc0BM+qpk62Hj0pL+SEnsU4VyJaW1tjOp2yuroKOBbdZDK5rQd7NmI1SfiZ\n+1/JQV3zv/2fD/GxT92YdrpTwv/1wFXWBvCKl1wm8iLyqqPpDJ3WTEYJRWPo0BzmLa0CiWGQ+YAg\n8AT7RY3SsH9YU7cKhKs0JIK8aggiRyDwAo+mbSlrRa0M42FEnHg0laaoWrI44/lXVmg7izHW7QNN\nYupKMRnGeLJF+oJASuLIw5cCGXlUjeLR7YL9vMYYmAQxs6rjU5/bZ22UYHEzAYGlbgzWNnjSQwhH\nmR6mQb+k6j4TbVwLby5UmsSS4SDksGyRwhntgZMUWmaiGSRxJEnjEGtddZIl/qJCWRsneJKe2eYU\nL/anDVK6tuaRAKqrROSmYG/qLwbBnrj+zGR5UC+ERUiY9S01hHWqEpVT6H66WF4/meSCeRW1uZot\nmIzzudtZr1luPQ4z/zknJ/RkkBWeKYrd50pEKysrfO/3fi/33nsvvu/zF3/xF6ytrfGWt7wFeO5R\nsJ8oxnHMj33fN/Nd0yn/0+99nC/t3ngrYDeHD3/sKj7wX94zIPIDAl9wMGtRSpHXiizyGcSSslGU\npWJt7JEmHp99dIrSMC1rhkmARVJWLSCQnmCYuD2fvGwpGqeKYIxBK0Mc+qyPY9Io6AVSPe4cx24j\n38Bh0ZAlIVnasdEvxw7TECzcc8eIx7Zz/v4LuxzmLYdFR9cLkI5HEU2nXUXTkxg6ZQj9oxaOq0IE\ngzRilAWLQOSqCrEQXZ2rJrDpAvzcvdQKcUwJ21rDKI3Rve7fXNpnjsBzd/vLStvG2KOWkoW6admd\n1jS93XnbafzAWygtCGmPJb+1tcExppm1Ak+w0Oo7zJuFi621loP8fMH/PMP8J5JMbpUddz3cShV1\nVuvxuYIng6zwTFHsPlciet3rXsfrXve6xdcvfvGLb9uBnku4azTil37s1VzNc37n9x/knx678RKh\nAv7u8zkCuDL2uPPShCwNe/VqyyD1WV2JkEIyXgn5u0/vc1DUqF6pYT/v2Jgk5FXLZJDie4AQHMxq\nhHQJaJj5ZJFH2u8ora+lhL25nZSCzbXUyemUDXWj0NriC2edMA/sLqFYWq3Z3q/QGHwpKK1ld79i\nfSUmi0PqWpEmThS2rB21um46glCQRAF3rKVMBkdeO84uwvYEBBeA0yTkoKg5mDXOciKWDAcR+7OW\nomr6ZU2BFJbnXxlT1fTac1HvHnv6vGM0CBfJrak7rBBc3S0ByyDxWRsnCyvzub/RwkHVOG+q6zHN\njuwq3GzME1D0tOv5c57oMP+JJJOng93Dc53y/WSQFeCZodh9rkT0t3/7t7z61a/mFa94xbHF1gs8\nObg8GPBzP/gveXhvj1//dw9ynp12Czx6oKm7Hb7+ay6xMgiJI8/t9eSKKDRs7XRonNRL1bQ0nSVL\nfDdesYIggJUsxmKwQhL5gjiMaZRCYRDArGmRe4L1cYJtOoSEx7ZzNxuysDlJmZYNs7pmd6diPIwZ\npk5ZOy9bPOGRJQF53VLUHV2jaLAIa1idRPhCEkc+baOclYO1TtbEOuXwYXpkeWCs5dGtGQeztrd+\ncOoBZdUxy12ySWJnrSERNI3q5zD9J2YFbXukorAc2E4LeqM0WoicVrUmjjyKsgUBeaUYZTFJHDAZ\nugC/rGUHTvT1ekyzo90kf9GuKiu1UGOAs4N/py3TUqG1puksYXC083QabftmksnTwe7hyazKLvD0\nxrkS0Td/8zfzR3/0R/zyL/8yL3zhC3n1q1/Nt33bt7G5uXm7z/ecwlevrvJbP3cvn9vf592/8zcc\nNDd+zW4Bf/GJa9y9Ci/8qk2iJGRzNUBbePiRA6q6w0qBH3oYZYkCF7z90FF887pFKonSmmQYIfDY\nn9X4gYfSlqZSTA1kaUCuDMZazCTtA79kkAZUtSKLI7IoYpiF+FLi96yoOPYIA4+qNniex12bMUkS\ngPTwrOwdVi3KGqyFuukoaoUFNscJyjpLC4Cq7kj6gK+tqyDm7bl5a6vsF2YNFm0seaWcokPkVBuW\nk8YyTga9aeE+/FmhsBhGg5AkmVeFsrf1cKXPcpA+GcB9T16XaXa9dlXgNecO/kXVsntYMC06hBSs\nj2IYHlfOfiLJ5Olg9/B0qMou8NTgXIno9a9/Pa9//etRSvHBD36Qd7/73bz1rW/lH/7hH273+Z6T\neP5kwr/56Xt5ZDrlt973n3ls/8av+dIefGlvi3/+NQl3XlqnqEw/XwloWyd2KoDQF4AlDCRbuwVN\nrVldTUkjiTKWyIfQ95y5ny+RgaRWlsO8JQqcLYRrQwk2JjHG+hyULVXlWkPWWqpW0SpD22mC0OPy\nasreQU3daeLIJ0s9jNGUfcVSNR3aQlkriqohTQJHh9aa/VnNlfUhylgOixq/VeTTmijw8DyP4SBE\n4uYu00o5Lx0pqSoneTNKfbcD1c+RhLCPk9IBjs2S4thjZ7/uF38taewzy1t8IbE4hfCyaalqxWPb\nOSsDJ9Hjy8cnnWEa0lbnu5M/K/ifrNimZUcYBtiidTJJbdfvJ53vejfCyURpei+oZemk5UXf24Gn\nQ1X2lcSTQVaI44SqOp+6wVcS50pE73nPe3jggQf49Kc/zYte9CJ+9Ed/lFe84hW3+2zPedw1GvHf\nv+lePr29zdt/+6Fzveb//UwFn/kS3/iCIRuTjGmukEKhTchkFNF2qt/1EVicdlqjDHWr2T2sSZIQ\nrSyzuiP0O1Rn8AKPourQ2mOUHO3DNI2h8RRdq0FAGEq+tOMsx9M4QAg43G9I4sBZimuDag2q00Rx\nRBBI9qYNX94pmQwirLXMSoUysDFJ2Tqo8KUgCkLiEOrWkgbOfryoG5QGYzWTYUyS+j1rTjJIfLQF\nYS1ZGi7mSSuDwBEDTB/Yc8VB3vTVm6KotZtr7RvC0C2HVq1iVrZkScgg8fCljy8NvpCsrSQI4SjJ\nW7slVzYGjwvgUp4/SJ811D9ZsVV1Rxb7iMkAY81CMWI5WM+vd5b30Xkxf/+5dcZJlt/tmOc8Haqy\nrySeKFnhSFXhjq+ossJ5cK5E9Gd/9mc8+uijfM/3fA+veMUr+MZv/MYLdYWnEP/Fxgbv/TlXIb37\nd/8z2/mNX/Pxz86AGV+9DpcvrZOGAcoasIKydgoCnbKEkYewloNZy6xqGCm35FrXGh1IksAn7N1K\njZH4oat6hLDOctwYLq9nXN3OKaqWrd2CO9cyyrojDj2KuiOOPGfmV3XI1LGdw1Cyd1hTtQZtDMr2\nUkPG0OQtbWdQxrA2iFFGs3uoeh8gQRoF7M8046FjVCltaWvDXRuDxb7QwdS1F0PP0bVjXzLKooWB\nX1525FV7wo3UtdparUhkRNGoBWOubhS+lFxec1TvWeGWk4uqc5WTdbsy1opzz59uBifbUlkSYq0h\njjzaFrIkODVY32jWsny+0xxaj15vFiy/5cdvxzznuaakcBJPlKzwTFFVgHMmove9732UZckDDzzA\nxz72MX71V3+V0WjE+9///tt9vgss4a7RiHf85L3sVRX/y//+CR58+GxhVYCHd+DhnR2+7u6QJM1o\nOoMQgqY357PGo1EWi2UQh3i+dCZ1GAIvwOAoZXdvDGlahe6c+d5kGNK0hmu7mt3Diij2kVIShz55\nrZBBx95hxepKgsAlr2EasjFJ2NkvyKsjZ9BB4tP1UkBhIJFW0BlN6PkMVyKkAGUMqjOEY5/DaU3b\nKapGk8XOr6VVhlQAGIqmI4x86lohIrBNx0qWOfKEPAqUnTFMZy3KWqq65WvvWcOTjiQRRQHGWIra\n7f4kse/sKnp4nkvoppdGaDvHXJvT2heVgnE6a606soY4LRGc1fY62aa6vJZQ15pWGcKRZHMtxT/F\n1+tGs5blJHKaQ+v1WH7zx8/zHhe4wFk4VyKaJ6G/+qu/4q//+q8ZjUa86lWvut1nu8ApWE0S/tvv\nfwUP7+3xP/z2gxTnqN7//kst0LI+gHuurFJ3GowgzjwGaYBqIrS17E1rqqZBCkmFoNOWpJexaZUl\nDix10/LpwwIhBEnoUzcdUeWjlMH3JU3TUQWSutFMRob9aU2jNL4UxKFEW8H6OMVaS91qRmlEEHh0\nnXa7RIFH1xn8QJKFAWkcEPiS1WFEVXVsH1TUXYfne6Sxq4pCX/a6cNHibt1og+xbZ3NG2mTo2kkI\nwXTW4gUSD2gbwe5hxR2rKRuTlLbVBFnMSmYWy7PgKOllpYgiyd5BQ+BLQt8jHQULGSA4av89ujVj\nd1YvFm3LimMSP3Djthdcr00VI4fnq6puNGs5TxK5HstvuQI77T2e6xTsC5wP57aB+JZv+RZe9apX\n8aY3vWmhsHCBryy+enWVd7/5Xv5pa4v/8b2f5MbCQbCTw84/7fE1mwGXNsespKGzApeWrtEIAQiJ\n9DyCwMeTkk5bhJAMEjeMf2x7Sl51WCNoI0PbajotkJ6hLg3KaGdPIeDhxyyD1COLI7SxPLZTMhoE\nCCFYG6dUjQbr9p8Ebm6l+gSyOy3plMYSoJXh6n6FMprRIGKFEK2cNt5kGBPHHtOipaxbtLUIBG1n\niKQTO9XW+RfJUcwoi7hjw7C9X9DWruV2ZSPDk97CVnzcJ6xZocirljQNqCvF1d1yIbDqCx8wfdJo\ngKWKSfYJRrqE3ClXOY6H7vrLUj43anvB+dpUpwX9G81azkMKuNH7n/YeFxTsW8etkhXmtg/ntWB4\nOuDMRPTYY48B8Hu/93t4nmuB1HW9ePzKlSu3+XgXOA9euLnJb/7cveyUJb/9e/8Pn7p249d8Zqvj\nM1vb3LUmuLK5StFofCG5NE6Z1i15pdDGkoaSKJTEoaRVloNZzUHRYg1Iz1JUhrp1hnyeL6kaRddp\nRllIFHpsH5RMZx7Pu+IzHkQUTUsWh+RVw6NbM5IoZH0cE0cBEpASdg4a4kiwsZJigZ13vkn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wnmKaLIp1CM+wClbIv2nE2pHOD4is62BIamMVjwGCy4cbM9pWNaBl4Ql6xpzyYoOz49fWV0Q2Nr\ndwHL1AhDRVsuiZ2IU8dBoy1js09nhsFCfCMPVYSuQRRFtGYTcdOJSuCb3xHX0yuVQ1zPJ5m0Km0l\nfBZ0ZLAMbdyZyuiAUi1YWn1tLm2zfZygM96sJqycmaqW0dG1sUt99bOz6jkhNcE+kWSxzW2jkxWS\nyRSOs+ckKNSTQCR2W1syyaWfOZot7w/w3IZ3eOVPvXghlUAQVwSIiJfwCk7AW9uGeGeH4pD9Ozhk\nvzZMUyeTtFigG/QXHSxNx7ZNXD+uGj2vJRFXZCiD64dECZNQxfsr/YUyvYNlQCNh6yil6B106GpL\nxfsnClJJC71S3qZ6qDSdsPCDqNZaGw2GCh4acRJCpFwcN97TKpYDTD0u+5OrJCgEYdxxNZWM36/p\nChUN39zHWyYbr0RPdtTeUnXmM1jw6sroaLzXUySdtGoJFKOrNlSNt08kWWxzW32ywnCSwr57TIJC\nPQlE4q9i6jqH7NfB4oXtbBsssO5/32fTu4P05B00peF6PpEBfgBBoCj68NrmPt7dkefA+Tk+vLiD\nDx7SRqbfpH+ojGnopJJJfD+uwD2/IxnfiPW4yncubfHuYBHQ8aMQDQNnyCWTtNA1SCerHUP1WlCo\n3uSzaQtNN+OZQtGj6ISkkwappEnvgEOkNBKmQdI2Kwt3BroGJWf4Ll904pt7iuHyO/UzoIj4YGzR\n8cmXXEpBhFeOa9JRKWFUKMZNBUc3yIt7GPmg4uy+SIXDsyEVf3Zrdur7RJLFNrfVJyvsqUkKVRKI\nxLTQNY2FbTnOWpYjiCJe39LLy69tZ/N7Q+iuj4ris0AacUJDvuTzfr9DJp3n8EO66GpLoyJFEMVZ\ncwnboFwO0DWtEpBSlJyAbNpGKZ100oBAgalIJiz26coQhfFhVA2dXMasbZbX713EeysekYIgCik4\nilLZr+3rtKRtBotxryRQcdHQupSMUFEJEsNVGNpabDQtTu2Os+Q08iWXSEHZi+juK5JOJmhJxwGu\n2lxv9CylWoGhGjAGixHZtIWORty9dbhp3VQSAySLTcwVEojEtDN1ncMP6mRhV5b//dMONm7qZ1tf\ngThHIE5n1jWdIFRs3jbE//fM6+zTnmZeW5KO1iSRUpiGRrY9jaWDpmnomlYpsqmRS5sYms6BC1vJ\nFz0syyBh6szrzJC0jDGZa/XivaL4/2tAwXGJIkWkIJuK/3PIpmziWKHIpW2yGTMuehqBbcR16Koz\nHojPSaVTFrm0jYbOUKlceT/4lWXGKIoqsxqXlvTEqdfZdFw9YqgQ709lkjbptFlbYtyVg6uSxSbm\nCglEYkbomkZHNsUJRx/AQQvbWP/aNt78ywAq8jF0C8uKq0h7XkjZjdjaU2Jbf5l9OpJ0tWXobE9i\n1doORGiaQkejoyWFfSD09DmEecX8tjQd7UncckDvgEs2ZdT2UqqzjUipWpZZsewRKUUqYRIpcN2I\nUIWYhkEmZVF0PNIpuzYLMXWNtkwSiGcviUTcRbU/X2KwEGAY4AWKVFFnn3lZNBSapqMRoaFj2QaZ\npIXnB+RL4HoBCzqGexGNnqXomoaORi6dIFup4lByAlqz1ohAMpVEhPpgJYkLc099soKK9ux1VQlE\nYkaZus5BC1rZvyvHu915/vjnHby2ZQjXDzB0BcrAMPW4z5GlsaNfMVT0+d9N3RyysJWF87PYho6B\nQWs2nnXs6BkijCLSKZOyG9Az4JBLJrCtiKFSwGDJozVtVzLfXPryZXoHXCxLQ9N0dE3huAFlN6Sj\n1SaZsOgfKrOtt0RLxmJ+expDH44QuqbV6sRVSwENvRfEwRRwHJeyC/vMA4VGS9oim7EYKngkLIOS\nDh2tKTRNI5ey6RssY1sGtqkzf14aGBko8iWXbKVwa7WKw+jZz64mIkjiwtxTTVYoO0VOPvbgPTJJ\noUoCkZgV1YB04PwWPnF0mY2benl3R56eAQcrYRAFIWgG5UrL7+29DgN5jzff7Wfp4QtIJizCyGKw\n4PJedxE/VHheXJ/OqfQdKvT5RKpS+kZB/2CZrvY0RSfAC0KCSCOd1ACNfedlKaR8QhWfI0omTDQV\n7y9t7yvSkk6STpm1lt6jl9BMIw5KkVIkElpcBq6yf5RKmrRlkrRlksybl2VDOSSMIgxNJ9IUtm3Q\nWgkCJcevNdGrfka1qV01CE1HIoIkLsw91WSFaqKCtgfPYBsWiJ599lmefvpp1qxZ06ghiAaoLtl9\n/MiFvNudZ+Ombrr7y1itCQjB8QMGhzx0UyPUoFgOefOdIdqy8cxhoFBGKQ3H9SmVA/IlhWno5Ese\nYRi3IfeDEA0NpVTlHE7ca8gPg7gOnG3SmvFQmqJY9Mk7PtmUhaZrhFFA0QlRlNH0BO2dca240Rv/\n7S0pwjAiUnGQzaSsERlttd9XH5WAUHBHPF8/S6mqpppXf9Z0JCJI4oJoZg0JRDfccAP//d//zWGH\nHdaIjxdNoH6GVCh5uEHEm3/pY/N7efKFAFtXlUOdGoVSmdff7mVr9yC6YaAixY5+h2zaIpMwac8l\n6BksEYaKgbyHbeuU3IBsKk4qKHsBoYq7lebSCbKmHu89KWjJWChNYWgGYRSgiGvKZVImUaRGpFZX\nkwg0dDrbk5SdkCBUtOc00snhRnqjA0d90oBt6iQTw//ZVQNCfaCoZs9NtnS2q4kIk72+flnQTFi1\nYC97SY1V3SPa0/eHoEGB6JhjjuG0007jxz/+cSM+XjSR+g319g/vS1sugeN69AyWydoGmZSJ5yvC\nSJEv+qBHRGGE64WU3RDVkuCABTne7R7EMg0cL8AwLPKlMpEyeX1LH0XHQzc0DF1DFSBtGwzmy2h6\nnCGno+FHPgODcQZeJmVRKPmgFO/pBRKJyhKdonawFaAlY4wIFtUben3ju9G/Y32dufrX7Gpg2dVy\nOpO9vn5ZcLAQJ3Vk05bsJTVYFAWUikN7/P4QzHAgeuSRR3jwwQdHPLZ69WpOP/101q1bN5MfLeYg\nU9c5bNE8ujrS/N/XtjNU8DEM0HXIZZIUywGgsX2giKnrlL0QN7DYvHWQpG0RRhG2pROEUaWKt0Ip\n0HQD1wvjzLtkhBso8r1FbMukVA6JIsVg0cW2DFQQggN+pT+SpmtkggBDNxkqeWRTcV8gFSkMXR8x\nYxgvIWDBqN9xooBQfTyIInb0lugbcmvJDKY+s+to9Ut2QRTFZ5bGeU7Mro7O+Nuzp+8PwQwHouXL\nl7N8+fJp+VldXRP3bm9GMt7dt2B+C4ctnk++5NE/5PDndwfQdY3+QZdiOU6hbsulCVVI0jIpOB4H\ndWbIFwMypg46pBIWZTckFcWdWgeLHrap09qSIJWxSCmbbNqkb8Ch5AWYtkE6ZeN6IQGKzo4s6ZSF\nroPjBuzTlsSw9drR1mzaxtR1kulEbW9It+P/nIaXuhSDhbgnkT7RwaZR3tk2RKYlSTXBO1Aa+87w\n342dsgkqESdf9OjszNSCpWnotd+vWTXTd3c6pVM2uq7R2ZmjtXXP/B2r5kzWXHd3vtFDmLKurpyM\nd5qkTJ395qXY8l4BUw/ZrytDoejgewG2pWPq4HkB5XJIFAY45QBD12lNWGimTr7kUii4eG6InTbJ\nJU20MELXdEoFj0LRI++4RKHCSdtkUgkCFVIsuqgwIqpk1SX0uNJCd3+JIFS0ZRN0tafxyv6YVuCF\nUpyNZ2jQnkuyaUtPraXEePsu9Xs0W3vyZJJxpQYAFCQNbUbPAdX/7K7OLD29eXrLQe1zqr9fM2rm\n7+54diVolirXvacnj+ftGdklE/3+cyYQib2Trml0tqTpbImz5rq6crRlbLb3lhgsupi6xkELcxga\n9FUCQSKtx+d0LEXJ1WjNWOzbmSJh6QRBiG4apFJGfEDVNnA9HVdF9AyUURokTAM/9IkiA9cP0DQd\np1IGKJ1MABGppIVTDmnNxjeISCkiFPmSR77ok02bZFLxTKK65wLjn+GpX9IzNINS2as1vrMrFWRn\n8hzQiH26liSBO1GDeDGbBvp6SSSSjR7GrGhYIFq6dClLly5t1MeLOWxhZxZT00knbXRNkUpaFB0f\n07RJWuB5ETsGSrSmkxx1yPy6PZx4ppJL25ScgKLjM789QzZpxe0k0ga5pEXStrBNI261kLTpbE/h\nlH2Gin4cUOL6pSiiWlJBoeQRhvEZIDRFqRxRyThHjWqyPnrfxQ+H69alkgYlR4FixIHX+tcYuh53\nlhV7tGJxiBOPOXCPT1QAmRGJOcjUdRZ2ZdmnM15S8kNF2Q2wKkHIsnWiSOEFPr0DDrZtYGjgBxGu\nH9YOns7vSGFqOqDYtzONYeiEoULXoaMlniEUXZ+C48dtGDImLXVZc9Xab5FSDBRc8iUfiIuwOq5P\nyQkwDZ2WrI2a5AyP6waEKoqL3wFtWbvWGjxf9DF0cMo+oVKgxX2VXHdkO0Ip4bPnae/o3CsSFUAC\nkZjD6peUrMo+yra+YnzIVAPLNDBNHUPTCaOIwaJHWy5Zu5lbhlEpQKqjaVAqB5Q8H0szQNdwyj5O\nOUBVmtTlMvaIlg/V2dBQ0aXohAzkHfKlENOE9myKVIdBazZBuTS291C9RMIkdIb7FSUS5piluOpz\n9a+pJyV8xFwmgUjsEbJpm30605TdAC+IaElbJBIWlmHU9mcipTD14Zt5KhEfGs2mbUp/8VBKkbYt\nDF3hOAGRilCVmYZp6KST5rg393wxIJnQKTgBXhgSRQampdM3WAZ2fubHMoZrysHYKggQt1Qf/Zqq\n6owsqvxe9Q30hJgLJBCJPUK1dFDbocna7MAp+yNmDknLqPUdguGbua5pJGyTBQkTpaBU9gijCM9X\ncQ26ytJI2VWMRxHFBU3TFpZvoJTC1DUsc2qZTuMdZq2f4QC0ZG10xs7GIJ79aOhANKaBnpi7Bgf6\nUWr879yeRgKR2KNMVsVg/rw0JcePl7oI2d5TZsu2PAnLwE7ooDRKZY8ggsCP8IKAoqNoy9kYhkEq\nOX5gacna5AselmlgGHHduUzKwp5iQbfxZkxjg1Niwj2fMBquTze6gd7ukP2m5lBtE743kEAk9ljj\n3eCrf37zL/34YYRC0TvkMFhwSSbi+nK5lM28tiSlskGh7GEYBi1pk1x2/Jt7SzqBjoadMNm6I0++\n6OK6EYsWZomiXf8X7a4GAkMHIq22BFnfQG93yH5Tc2jv6NwrEhVAApHYS7l+CBo4bkhf3iUIItpy\nBn2DLt39/Qw5GbIpk1zSIpM0ac0mJpxl1AJe0aWrNVXrAOs4UaV1+K7Z1UAw3Z1YpWWEmG0SiMRe\nKWEZuEFIpBRhFKEiKLkBRdfHCzT68y5lN0Bv12nNarWAkE5ZteW90bOV+jbk8Z+jWumc8Uw085lK\nIJjJ5TNpGSFmm3zFxF7poP1aSJgGmoKkZTKvPVlbRkslDRKWSaQUW7YPsbW3wFDRJQgVO3pLY2Yr\nEAcGp+xTcDyKThBnsOk65iR38fFmPjD2xm/o1Nqd9+ddhoouQyV33PdOh2zaHtGe4q+dYYndI8kK\nQuzhbMPg0APb49TnYpm3tw4R+BG5lI1uaARBBEonaUHRCdmmSjjlANsemXlXDQaFUlyMtSVKkC+5\nuG5IR6dNLm3TO0GttolmPjvLoovbiXsjWlJM5/LZrraYEDNDkhWE2EvUp30PFV0GCh7vbB/CRcMw\nQGECUVxJu+Rilg0sQyOTstE0rTZzCKP4Z+XScfABaMsmJq26Xb8EplRcHaL6+OiltrHnikZOm3Z1\n+Wy2iqhK1t3uk2QFIfYyuqbRlk3Slk1y4IIc23qK9Bc8hkplNC2uspBN2bTn4koMQ46LrRskEiZD\nRRdNV6i6Q6RTCQz1M52yG5BImCilGCx4DBZ8WrNW7SY+et8mlzHRtfHPFe1MpBTbeop4YVQ7ADtd\nmXEz+bPFnksCkRCj6JrGPp0Z0mkTowfCUJFNxmeDTD2u1JAvRSQSw2d3TCNOn65vsQ0QRarWHmL0\n7GC8JbBCya20MY9GZMztyrminSmUPFw/pOQG8ezF8Znfkdn5G6f4s70gqpRRilefewcAABHmSURB\nVA/X1leEEGI8EoiEGIeuabRlkrSk4zpxAwUXjeGq1xp6HIQqxUqDSMWBZVRFg/wUU7GrM55q1p1R\n6cpafe907tuEEZS9iKiyER5EakwR1Z2ZLOPP0PX4ulAppyQpUbulXHYaPYRZI18RISZRDQD7z8/R\nmrVqy2S5jDmipbahjZ8wMDp9e6Kkgmqmmq6BoQ0HvJm4iRs6JGwdXdNBgaXrY4qo7sxkGX/plIlR\n+dm28ddVedibnfw3i/eKFhAgMyIhpmT0jCRScX+g+r2Q8YLG6PTtiQJL9edP9+HU8cSf4aPrWm3s\nprFry3xhNHwNqj2Ssmm7Nv5s2pJEhb/S3tICAiQQCbFbqvtIOwsaubRNT2XZTdMVkYL+vLvTluEz\neROf6tgnY+hQKNT3UYpqy46SmCB2lQQiIXbTVPZtdH34NUNFt7YHtLOW4TNd4+2v3XPKpm0GCi5Q\n33piukYn9jYSiISYJTsr3TOXarzF6e4JKQUkpoV8dYSYJeOV7tmVPzcbKQUkpovMiISYJfWJCOPt\nF+0sUaG6h+SHcbp1ImFWurs2JiFASgGJ6SKBSIhZUn/jnmi/aLIbezVIVc8vhZXDolK5QMx1TT75\nF2LPtDv7QcOJDMOHRaf6XiGamQQiIRpgd/aDhvdj4v9TPfrT7HtJQuyMfIWFaIDd2eivviedMrEN\nnXTlEK0kCYi5TvaIhGiA3dnol+QAsaeSQCTEHkR6AYm5SJbmhNiDTFSMVIhmJoFIiD3IXKrOIESV\nBCIh9iBzrTqDmFhLS2ujhzBr5GsqxB5Eyu6IuUiSFYTYg8x0Zp0kQ4iZIDMiIcSUSTKEmAkSiIQQ\nUybJEGImSCASQkyZJEOImSBfIyHElEkyhJgJs56sUCgUuOqqqygWi/i+z7XXXstHPvKR2R6GEGI3\nSJkhMRNmPRDdf//9fOITn+Ciiy5i8+bNrFixgscee2y2hyGEEKJJzHoguuSSS7DteDofBAGJhPzr\nSggh9mYzGogeeeQRHnzwwRGPrV69miOOOILu7m6uueYavv71r8/kEIQQQjQ5TSmlZvtD33jjDa66\n6ipWrlzJCSecMNsfL4QQTS8IQkzTaPQwZsWsL839+c9/5itf+Qr/9m//xpIlS6b8vu7u/AyOanp1\ndeVkvDNkLo0VZLwzbS6Od6r6+0szOJLGmOj3n/VAdNttt+F5HjfccANKKVpaWrjzzjtnexhCCCGa\nxKwHorvuumu2P1IIIUQTkwOtQgghGkoCkRBCiIaSQCSEEKKhJBAJIYRoKAlEQgghGkoCkRBCiIaS\nQCSEEKKhJBAJIYRoKAlEQgghGkoCkRBCiIaSQCSEEKKhJBAJIYRoKAlEQgghGkoCkRBCiIaSQCSE\nEKKhJBAJIYRoKAlEQgghGkoCkRBCiIaSQCSEEKKhJBAJIYRoKAlEQgghGkoCkRBCiIaSQCSEEKKh\nJBAJIYRoKAlEQgghGkoCkRBCiIaSQCSEEKKhJBAJIYRoKAlEQgghGkoCkRBCiIaSQCSEEKKhJBAJ\nIYRoKAlEQgghGkoCkRBCiIaSQCSEEKKhJBAJIYRoKAlEQgghGsqc7Q90HIcVK1YwNDSEbdvcdNNN\nzJ8/f7aHIYQQoknM+ozo4Ycf5ogjjuCHP/whf//3f8+9994720MQQgjRRGZ9RnTxxRejlALgvffe\no7W1dbaHIIQQoonMaCB65JFHePDBB0c8tnr1ao444gguvvhi/vSnP3HffffN5BCEEEI0OU1VpycN\n8NZbb/GFL3yBZ599tlFDEEII0WCzvkd0zz338MQTTwCQTqcxDGO2hyCEEKKJzPqMqLe3l5UrV+K6\nLkopVqxYwUc/+tHZHIIQQogm0tClOSGEEEIOtAohhGgoCURCCCEaSgKREEKIhpr1A60747ouV199\nNb29vWSzWW666Sba29tHvOaGG27g97//PZlMBoC77rqLbDY7q+NUSnH99dfzxhtvYNs2N9xwAwcc\ncEDt+f/6r//irrvuwjRNzjnnHM4999xZHd9oOxvvAw88wCOPPEJHRwcA//qv/8pBBx3UoNEO+8Mf\n/sCtt97KD37wgxGPN9v1hYnH2ozXNggCrrvuOrZu3Yrv+3zxi1/klFNOqT3fTNd3Z2NtxusbRRHf\n+MY32Lx5M7qu861vfYsPfOADteeb6fo2BdVk7r//fnX77bcrpZT62c9+platWjXmNeeff77q7++f\n7aGN8Mwzz6hrr71WKaXUhg0b1OWXX157zvd9ddppp6l8Pq88z1PnnHOO6u3tbdRQlVKTj1cppa66\n6iq1cePGRgxtQvfee68688wz1XnnnTfi8Wa8vhONVanmvLaPPvqouvHGG5VSSg0MDKiTTjqp9lyz\nXd/JxqpUc17fZ599Vl133XVKKaVeeumlpr8/NFrTLc2tX7+eZcuWAbBs2TJ+97vfjXheKcWWLVv4\n5je/yfnnn8+jjz7aiGGyfv16PvWpTwFw9NFH88c//rH23KZNm1i0aBHZbBbLsvibv/kbXn755YaM\ns2qy8QJs3LiRu+++m8997nPcc889jRjiGIsWLeLOO+8c83gzXt+JxgrNeW1PP/10rrzySiD+17tp\nDi+ONNv1nWys0JzX99RTT+Xb3/42AFu3bh1RyqzZrm8zaOjS3HglgDo7O2vLbJlMhkKhMOL5UqnE\nhRdeyCWXXEIQBFx00UUceeSRHHroobM2boBCoUAul6v92TRNoihC1/Uxz2UyGfL5/KyOb7TJxgvw\nd3/3d1xwwQVks1n+5V/+heeff54TTzyxUcMF4LTTTmPr1q1jHm/G6zvRWKE5r20qlQLia3nllVfy\n1a9+tfZcs13fycYKzXl9AXRd59prr+U///M/+d73vld7vNmubzNo6Ixo+fLl/PSnPx3xv2w2S7FY\nBKBYLI74C4P4S3nhhReSSCTIZDIcf/zxvP7667M+9vpxAiNu6tlsdkQALRaLtLS0zPoY6002XoiL\n0ba1tWGaJieeeCKvvvpqI4Y5Jc14fSfTrNf2/fff5+KLL+bss8/mjDPOqD3ejNd3orFC815fgJtu\nuolf/vKXfOMb36BcLgPNeX0bremW5o455hief/55AJ5//nmOPfbYEc9v3ryZ888/H6UUvu+zfv16\nPvzhDzd0nBs2bBgxIzvkkEPYsmULQ0NDeJ7Hyy+/zEc+8pFZH2O9ycZbKBQ488wzcRwHpRQvvvhi\nQ67pRNSoM9fNeH2rRo+1Wa9tT08Pl156KVdffTVnn332iOea7fpONtZmvb5PPPFEbZkwkUig63rt\nH37Ndn2bQdNVViiXy6xcuZLu7m5s22bNmjXMmzePBx54gEWLFnHyySdz33338fOf/xzLsjjrrLM4\n77zzZn2cqi4LDeKq4hs3bsRxHM4991yee+457rjjDpRSLF++nPPPP3/Wx7gr433yySdZu3YtiUSC\nj3/841xxxRUNHW/V1q1bWbFiBQ899BBPPfVU017fycbajNf2hhtu4Be/+AUHH3wwSik0TeMf//Ef\nm/L67myszXh9Hcfha1/7Gj09PQRBwGWXXUapVGrK69sMmi4QCSGE2Ls03dKcEEKIvYsEIiGEEA0l\ngUgIIURDSSASQgjRUBKIhBBCNJQEIiGEEA3VdNW3xZ7rjjvuAJjVcx6FQoHbbruNl19+GdM0aWlp\nYeXKlRx++OHjvv6tt97illtuqZXrOfTQQ/n6178+pgJ81XPPPcfdd9+N4ziEYcipp55aq4t24YUX\n8uUvf5njjjtuzPv6+/tZs2YN69atw7IsEokEV1xxxYiq0uP52te+xosvvkhbWxthGALwT//0T5x1\n1llTviZCNBsJRGKPpZTisssu4/jjj+eJJ55A13VeeuklLrvsMn72s5+NKEQJsGPHDi6++GJWrVpV\nq1V2991386UvfYkf/vCHY37+Cy+8wKpVq7jvvvs48MAD8TyPK6+8ku9973t8+ctfnnBcnudx0UUX\nccYZZ/DLX/4STdPYvHkzl156Kfvvv/9O6yZeeeWVtcDzzjvvcMEFF7BgwQI+/vGP7+olEqIpSCAS\n02LdunXcfvvtmKbJ+++/z9FHH823v/1t1q5dy09+8hPa29tpaWnhqKOOYtWqVXzgAx/gs5/9LA8/\n/DAPPPAAP//5zwmCgFNPPZVf/epXfPKTn+Tkk09m48aNZLNZbr31VhYuXMhvf/tbbr75ZpRSLFy4\nkDVr1tT6Uo324osv0t3dPSIofOxjH+PGG28kDEPWrVvHLbfcQhRFHHrooeyzzz6ccMIJIwpmfv7z\nn+eAAw4gDEO+//3vs2HDBrZt28YFF1zAU089xRVXXMGBBx4IgG3bXH/99bz11lu19z/00EOsXr0a\ngGuvvZalS5fyzDPPkEqluPzyy2uvW7x4Mddff31tlvOLX/yCBx54ANd1KZfLrFq1aky5K4ADDjiA\niy66iB/96EcSiMScJXtEYtq88sorXH/99Tz99NO4rsvatWv5j//4D5544gnuv/9+tm3bBsBJJ51U\na+/xu9/9jqGhIfr6+li/fj0f/ehHMQyDgYEBjj/+eJ588knOOOMMVq1ahed5XH311XznO9/hySef\nZMmSJTz++OMTjue1117jyCOPHPP4smXLak3UtmzZwtq1a1m9ejWvvfYaRx999IjX6rrOGWecgWEY\nQDybeeqppzj//PN59dVXOeqoo0a8fvTMJJPJ8Nhjj7F69WquueYafN9nw4YN4waVZcuWcdhhh6GU\n4uGHH+buu+/m8ccf5/Of/zz//u//PuHv+cEPfnBE8BNirpEZkZg2xx57LIsWLQLgM5/5DCtWrOCz\nn/0syWQSgE9/+tNEUcTSpUv55je/SRRFbN68mTPOOIN169bxyiuvcNJJJwGQTCb5h3/4BwDOOuss\n1qxZw5tvvsmCBQtYsmQJwJh2AKPpuj6mCOloixcvrs2oNE3b6evrA9VUfv7y5csBWLJkCR0dHbWA\noWla7TVr1qzhN7/5DeVymWXLlnHddddx++238+tf/5rNmzezbt26WiAcj6ZpJBKJScchRDOTGZGY\nNvUNy6ptJupv1NXnbdtmyZIlPPnkkxxyyCEsXbqUl19+md/+9re1ZbH6G7VSCsuysCxrxOcVCgW2\nb98+4XiOOOKIcVsCfPe732XdunUAI27gRxxxBK+88sqI1yql+NKXvkRfX9+Y1x955JFjGgy+/fbb\nrFy5ctxropTCNE2OPPJI1q9fX3t8xYoVPP7443zhC18gn89TKpVYvnw5W7du5bjjjuPCCy+cNOC9\n8cYbI9pQCzHXSCAS02b9+vXs2LGDKIp44oknuPzyy/n1r39NoVDAdV2effbZ2mtPPPFE7rrrLpYu\nXcrSpUv51a9+RSqVoq2tDYirFz/33HMAPProoyxbtozFixfT39/Ppk2bALj33nt56KGHJhzPscce\nS0dHB3fccQdRFAHwm9/8hscee2zcG/d5553HCy+8wAsvvFB77M4776Svr6+2lFfv0ksv5c4772TL\nli1A3FfmpptuYr/99qu95qc//SkQL1sWi0UOOuggTj/9dFzX5e677yYIAiAOqi+99BKGYfD2229j\nGAZf/OIXOf7443nhhRdq44eRrSbefvttfvSjH/G5z31uwusgRLOTpTkxbbq6uli5ciXbt2/nk5/8\nJJdeeimpVIpzzjmHtra2ETfok046iW9961t87GMfo6WlhXnz5tWW5aqefvppbrvtNhYsWMDNN9+M\nbdvccsstXHPNNQRBwIEHHsh3vvOdScf0/e9/nxtvvJEzzzwTy7Job2/n3nvvHTewdHZ2cu+993Lz\nzTdz6623EkURhx9++IQtwD/1qU/xla98ha9+9atEUUQQBHz605+upadrmkaxWOTss8/GMAzWrFmD\nYRgYhsHatWv57ne/y1lnnYVlWYRhyCmnnMI///M/k06n+dCHPsTf/u3fkk6nOe6443jvvfdqn3v7\n7bezdu1aIJ5xXXvttWP2toSYS6QNhJgW69at44477qjdIP9aH/rQhxrSeVcIMftkRiSaUv0e0WT+\n53/+h1WrVo3ZU9I0jXvuuYeurq6ZGqIQYprIjEgIIURDSbKCEEKIhpJAJIQQoqEkEAkhhGgoCURC\nCCEaSgKREEKIhpJAJIQQoqH+H5m+xMBAZk+DAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f80e7adb048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.jointplot(transformed_df['pdwpc_CrCrCbGaD'], transformed_df['rdwpc_CrCrCbGaD'], alpha = 0.1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aQSwkE6z12KH+AjpKZv18IR12p8C5FFOOS2ktdPJSMkiIuXUMRCeeeCLPfe5z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XvMKmBpG8eGwXIDY1xsrcDW5HybUvPsS7kWY4xB66wg6UizDE+psOnXLE5SohTqjSwYaQWelyeO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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f80e7b4add8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.jointplot(transformed_df['pdwpc_CbGpPWpGaD'], transformed_df['rdwpc_CbGpPWpGaD'], alpha = 0.1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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H1yCZsI45WxOikbp7epfNBcEgmZUYl4hZ09asZtNq61y16JpGVyJCV2KpRyKE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\nT4KVEEKItvf/AQ3cdA3tgfzBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f80cd4bd518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.jointplot(auroc_df['dwpc_auroc'], auroc_df['pdwpc_auroc'], alpha = 0.1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bl88h7Pr1lVzGttRxVRvBtLLsIYMFlVqP1gIABgdLDA83qDcjwla2JpQVKmRH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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f80cd27fef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.jointplot(auroc_df['delta_auroc'], auroc_df['rdwpc_auroc'], alpha = 0.1);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}